diff --git a/colour_hdri/examples/examples_advanced_processing_with_the_nodegraph.ipynb b/colour_hdri/examples/examples_advanced_processing_with_the_nodegraph.ipynb index a677370..2235d46 100644 --- a/colour_hdri/examples/examples_advanced_processing_with_the_nodegraph.ipynb +++ b/colour_hdri/examples/examples_advanced_processing_with_the_nodegraph.ipynb @@ -41,9 +41,9 @@ "* python : 3.12.6 (main, Sep 9 2024, 21:36:32) [Clang 18.1.8 ] *\n", "* *\n", "* colour-science.org : *\n", - "* colour : v0.2.5-55-g0fc43df *\n", - "* colour-datasets : v0.2.5-55-g0fc43df *\n", - "* colour-hdri : v0.2.5-55-g0fc43df *\n", + "* colour : v0.4.6-99-gc5589bb6e *\n", + "* colour-datasets : v0.2.5-58-geca82d7 *\n", + "* colour-hdri : v0.2.5-58-geca82d7 *\n", "* *\n", "* Runtime : *\n", "* imageio : 2.36.1 *\n", @@ -182,22 +182,22 @@ "\n", "\n", "ConvertRawFileToDNGFile (#2)\n", - "\n", - "ConvertRawFileToDNGFile (#2)\n", - "\n", - "execution_input\n", - "\n", - "raw_file_path\n", - "\n", - "output_directory\n", - "\n", - "dng_converter\n", - "\n", - "dng_converter_arguments\n", - "\n", - "execution_output\n", - "\n", - "dng_file_path\n", + "\n", + "ConvertRawFileToDNGFile (#2)\n", + "\n", + "execution_input\n", + "\n", + "raw_file_path\n", + "\n", + "output_directory\n", + "\n", + "dng_converter\n", + "\n", + "dng_converter_arguments\n", + "\n", + "execution_output\n", + "\n", + "dng_file_path\n", "\n", "\n", "\n", @@ -214,16 +214,16 @@ "metadata\n", "\n", "\n", - "\n", + "\n", "ConvertRawFileToDNGFile (#2):execution_output->ReadFileMetadataDNG (#3):execution_input\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "ConvertRawFileToDNGFile (#2):dng_file_path->ReadFileMetadataDNG (#3):dng_file_path\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -242,9 +242,9 @@ "image\n", "\n", "\n", - "\n", + "\n", "ConvertRawFileToDNGFile (#2):dng_file_path->ProcessRawFileRawpy (#5):raw_file_path\n", - "\n", + "\n", "\n", "\n", "\n", @@ -262,10 +262,10 @@ "execution_output\n", "\n", "\n", - "\n", + "\n", "ConvertRawFileToDNGFile (#2):dng_file_path->RemoveDNGFile (#6):path\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -294,22 +294,28 @@ "output_metadata\n", "\n", "\n", - "\n", + "\n", "ConvertRawFileToDNGFile (#2):dng_file_path->ProcessingMetadata (#10):sources\n", - "\n", + "\n", "\n", "\n", "\n", "\n", "GraphRawProcessingDNG (#1)\n", - "\n", - "GraphRawProcessingDNG (#1)\n", + "\n", + "GraphRawProcessingDNG (#1)\n", "\n", "\n", "\n", + "GraphRawProcessingDNG (#1):execution_input->ConvertRawFileToDNGFile (#2):execution_input\n", + "\n", + "\n", + "\n", + "\n", + "\n", "GraphRawProcessingDNG (#1):raw_file_path->ConvertRawFileToDNGFile (#2):raw_file_path\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -330,16 +336,16 @@ "input_transform\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingDNG (#1):CCT_D_uv->ComputeInputTransformDNG (#4):CCT_D_uv\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingDNG (#1):bypass_input_transform->ComputeInputTransformDNG (#4):bypass\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -368,27 +374,27 @@ "output_image\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingDNG (#1):correct_vignette->CorrectLensAberrationLensFun (#7):correct_vignette\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingDNG (#1):correct_chromatic_aberration->CorrectLensAberrationLensFun (#7):correct_chromatic_aberration\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingDNG (#1):correct_distortion->CorrectLensAberrationLensFun (#7):correct_distortion\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingDNG (#1):bypass_correct_lens_aberration->CorrectLensAberrationLensFun (#7):bypass\n", - "\n", + "\n", "\n", "\n", "\n", @@ -410,9 +416,9 @@ "output_image\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingDNG (#1):downsample->Downsample (#8):factor\n", - "\n", + "\n", "\n", "\n", "\n", @@ -436,27 +442,27 @@ "output_image\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingDNG (#1):output_colourspace->ApplyInputTransformDNG (#9):output_colourspace\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingDNG (#1):bypass_input_transform->ApplyInputTransformDNG (#9):bypass\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingDNG (#1):output_colourspace->ProcessingMetadata (#10):output_colourspace\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingDNG (#1):orientation->ProcessingMetadata (#10):orientation\n", - "\n", + "\n", "\n", "\n", "\n", @@ -480,9 +486,9 @@ "output_image\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingDNG (#1):bypass_watermark->Watermark (#11):bypass\n", - "\n", + "\n", "\n", "\n", "\n", @@ -504,15 +510,15 @@ "output_image\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingDNG (#1):orientation->Orient (#12):orientation\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingDNG (#1):bypass_orient->Orient (#12):bypass\n", - "\n", + "\n", "\n", "\n", "\n", @@ -538,157 +544,157 @@ "execution_output\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingDNG (#1):output_file_path->WriteImage (#13):path\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingDNG (#1):output_colourspace->WriteImage (#13):output_colourspace\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "ReadFileMetadataDNG (#3):execution_output->ComputeInputTransformDNG (#4):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ReadFileMetadataDNG (#3):metadata->ComputeInputTransformDNG (#4):metadata\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ReadFileMetadataDNG (#3):metadata->CorrectLensAberrationLensFun (#7):metadata\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ReadFileMetadataDNG (#3):metadata->ProcessingMetadata (#10):input_metadata\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ComputeInputTransformDNG (#4):execution_output->ProcessRawFileRawpy (#5):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ComputeInputTransformDNG (#4):input_transform->ProcessRawFileRawpy (#5):input_transform\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ComputeInputTransformDNG (#4):input_transform->ApplyInputTransformDNG (#9):input_transform\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ComputeInputTransformDNG (#4):input_transform->ProcessingMetadata (#10):input_transform\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ProcessRawFileRawpy (#5):execution_output->RemoveDNGFile (#6):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ProcessRawFileRawpy (#5):image->CorrectLensAberrationLensFun (#7):input_image\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "RemoveDNGFile (#6):execution_output->CorrectLensAberrationLensFun (#7):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "CorrectLensAberrationLensFun (#7):execution_output->Downsample (#8):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "CorrectLensAberrationLensFun (#7):output_image->Downsample (#8):input_image\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Downsample (#8):execution_output->ApplyInputTransformDNG (#9):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Downsample (#8):output_image->ApplyInputTransformDNG (#9):input_image\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ApplyInputTransformDNG (#9):execution_output->ProcessingMetadata (#10):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ApplyInputTransformDNG (#9):output_image->Watermark (#11):input_image\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ProcessingMetadata (#10):execution_output->Watermark (#11):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ProcessingMetadata (#10):output_metadata->Watermark (#11):metadata\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ProcessingMetadata (#10):output_metadata->WriteImage (#13):metadata\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Watermark (#11):execution_output->Orient (#12):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Watermark (#11):output_image->Orient (#12):input_image\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Orient (#12):execution_output->WriteImage (#13):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Orient (#12):output_image->WriteImage (#13):image\n", "\n", "\n", @@ -719,40 +725,40 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-12-26 11:16:17,030 - 55254 - INFO - GraphRawProcessingDNG: Processing \"NodeConvertRawFileToDNGFile#2(None)\" node...\n", - "2024-12-26 11:16:17,031 - 55254 - INFO - Converting \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.CR2\" file to \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.dng\" file.\n", + "2024-12-31 12:19:51,956 - 9204 - INFO - GraphRawProcessingDNG: Processing \"NodeConvertRawFileToDNGFile#2(None)\" node...\n", + "2024-12-31 12:19:51,959 - 9204 - INFO - Converting \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.CR2\" file to \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.dng\" file.\n", "*** GPU Warning: GPU3 disabled via cr_config at init time. ***\n", - "2024-12-26 11:16:17,660 - 55254 - INFO - GraphRawProcessingDNG: Processing \"NodeReadFileMetadataDNG#3(None)\" node...\n", - "2024-12-26 11:16:17,662 - 55254 - INFO - Reading \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.dng\" image EXIF data.\n", - "2024-12-26 11:16:17,821 - 55254 - INFO - GraphRawProcessingDNG: Processing \"NodeComputeInputTransformDNG#4(None)\" node...\n", - "2024-12-26 11:16:17,822 - 55254 - INFO - ComputeInputTransformDNG: As Shot Neutral (EXIF): [ 0.41307 1. 0.646465]\n", - "2024-12-26 11:16:17,822 - 55254 - INFO - ComputeInputTransformDNG: Camera Neutral: [ 6500. 0.]\n", - "2024-12-26 11:16:17,829 - 55254 - INFO - ComputeInputTransformDNG: Camera Neutral \"CIE xy\" chromaticity coordinates: [ 0.31352687 0.32363006]\n", - "2024-12-26 11:16:17,869 - 55254 - INFO - ComputeInputTransformDNG: Camera Neutral \"CCT\": [ 6.50000684e+03 4.08085121e-08]\n", - "2024-12-26 11:16:17,907 - 55254 - INFO - ComputeInputTransformDNG: \"CIE XYZ D50\" to \"Camera Space\" matrix \"M\": [[ 0.45288218 0.05789952 -0.079701 ]\n", + "2024-12-31 12:19:52,748 - 9204 - INFO - GraphRawProcessingDNG: Processing \"NodeReadFileMetadataDNG#3(None)\" node...\n", + "2024-12-31 12:19:52,750 - 9204 - INFO - Reading \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.dng\" image EXIF data.\n", + "2024-12-31 12:19:53,020 - 9204 - INFO - GraphRawProcessingDNG: Processing \"NodeComputeInputTransformDNG#4(None)\" node...\n", + "2024-12-31 12:19:53,022 - 9204 - INFO - ComputeInputTransformDNG: As Shot Neutral (EXIF): [ 0.41307 1. 0.646465]\n", + "2024-12-31 12:19:53,023 - 9204 - INFO - ComputeInputTransformDNG: Camera Neutral: [ 6500. 0.]\n", + "2024-12-31 12:19:53,035 - 9204 - INFO - ComputeInputTransformDNG: Camera Neutral \"CIE xy\" chromaticity coordinates: [ 0.31352687 0.32363006]\n", + "2024-12-31 12:19:53,084 - 9204 - INFO - ComputeInputTransformDNG: Camera Neutral \"CCT\": [ 6.50000684e+03 4.08085121e-08]\n", + "2024-12-31 12:19:53,160 - 9204 - INFO - ComputeInputTransformDNG: \"CIE XYZ D50\" to \"Camera Space\" matrix \"M\": [[ 0.45288218 0.05789952 -0.079701 ]\n", " [-0.77978718 1.54738188 0.24800704]\n", " [-0.14456804 0.18718594 0.64275275]]\n", - "2024-12-26 11:16:17,929 - 55254 - INFO - ComputeInputTransformDNG: White balance multipliers \"RGB\": [ 2.6271393 1. 1.393652 ]\n", - "2024-12-26 11:16:17,955 - 55254 - INFO - GraphRawProcessingDNG: Processing \"NodeProcessRawFileRawpy#5(None)\" node...\n", - "2024-12-26 11:16:17,963 - 55254 - INFO - ProcessRawFileRawpy: Processing \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.dng\" file...\n", - "2024-12-26 11:16:24,935 - 55254 - INFO - GraphRawProcessingDNG: Processing \"NodeRemoveFile#6(None)\" node...\n", - "2024-12-26 11:16:24,942 - 55254 - INFO - GraphRawProcessingDNG: Processing \"NodeCorrectLensAberrationLensFun#7(None)\" node...\n", - "2024-12-26 11:16:25,012 - 55254 - INFO - CorrectLensAberrationLensFun: Searching for \"Canon\" \"Canon EOS 5D Mark II\" camera model.\n", - "2024-12-26 11:16:25,013 - 55254 - INFO - CorrectLensAberrationLensFun: Using \"Camera(Maker: Canon; Model: Canon EOS 5D Mark II; Mount: Canon EF; Crop Factor: 1.0; Score: 200)\" camera for lens aberrations correction.\n", - "2024-12-26 11:16:25,013 - 55254 - INFO - CorrectLensAberrationLensFun: Searching for \"EF16-35mm f/2.8L II USM\" lens model.\n", - "2024-12-26 11:16:25,015 - 55254 - INFO - CorrectLensAberrationLensFun: Using \"Lens(Maker: Canon; Model: Canon EF 16-35mm f/2.8L II USM; Type: RECTILINEAR; Focal: 16.0-35.0; Aperture: 2.799999952316284-22.0; Crop factor: 1.0; Score: 52)\" lens for lens aberrations correction.\n", - "2024-12-26 11:16:25,015 - 55254 - INFO - CorrectLensAberrationLensFun: Correcting lens vignette...\n", - "2024-12-26 11:16:25,101 - 55254 - INFO - CorrectLensAberrationLensFun: Lens vignette was successfully corrected!\n", - "2024-12-26 11:16:25,101 - 55254 - INFO - CorrectLensAberrationLensFun: Correcting lens chromatic aberration...\n", - "2024-12-26 11:16:25,640 - 55254 - INFO - CorrectLensAberrationLensFun: Lens chromatic aberration was successfully corrected!\n", - "2024-12-26 11:16:25,641 - 55254 - INFO - CorrectLensAberrationLensFun: Correcting lens distortion...\n", - "2024-12-26 11:16:25,816 - 55254 - INFO - CorrectLensAberrationLensFun: Lens distortion was successfully corrected!\n", - "2024-12-26 11:16:25,822 - 55254 - INFO - GraphRawProcessingDNG: Processing \"NodeDownsample#8(None)\" node...\n", - "2024-12-26 11:16:25,824 - 55254 - INFO - GraphRawProcessingDNG: Processing \"NodeApplyInputTransformDNG#9(None)\" node...\n", - "2024-12-26 11:16:27,426 - 55254 - INFO - GraphRawProcessingDNG: Processing \"NodeProcessingMetadata#10(None)\" node...\n", - "2024-12-26 11:16:27,427 - 55254 - INFO - GraphRawProcessingDNG: Processing \"NodeWatermark#11(None)\" node...\n", - "2024-12-26 11:16:27,482 - 55254 - INFO - GraphRawProcessingDNG: Processing \"NodeOrient#12(None)\" node...\n", - "2024-12-26 11:16:27,482 - 55254 - INFO - GraphRawProcessingDNG: Processing \"NodeWriteImage#13(None)\" node...\n" + "2024-12-31 12:19:53,164 - 9204 - INFO - ComputeInputTransformDNG: White balance multipliers \"RGB\": [ 2.6271393 1. 1.393652 ]\n", + "2024-12-31 12:19:53,177 - 9204 - INFO - GraphRawProcessingDNG: Processing \"NodeProcessRawFileRawpy#5(None)\" node...\n", + "2024-12-31 12:19:53,181 - 9204 - INFO - ProcessRawFileRawpy: Processing \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.dng\" file...\n", + "2024-12-31 12:20:05,020 - 9204 - INFO - GraphRawProcessingDNG: Processing \"NodeRemoveFile#6(None)\" node...\n", + "2024-12-31 12:20:05,022 - 9204 - INFO - GraphRawProcessingDNG: Processing \"NodeCorrectLensAberrationLensFun#7(None)\" node...\n", + "2024-12-31 12:20:05,148 - 9204 - INFO - CorrectLensAberrationLensFun: Searching for \"Canon\" \"Canon EOS 5D Mark II\" camera model.\n", + "2024-12-31 12:20:05,190 - 9204 - INFO - CorrectLensAberrationLensFun: Using \"Camera(Maker: Canon; Model: Canon EOS 5D Mark II; Mount: Canon EF; Crop Factor: 1.0; Score: 200)\" camera for lens aberrations correction.\n", + "2024-12-31 12:20:05,202 - 9204 - INFO - CorrectLensAberrationLensFun: Searching for \"EF16-35mm f/2.8L II USM\" lens model.\n", + "2024-12-31 12:20:05,207 - 9204 - INFO - CorrectLensAberrationLensFun: Using \"Lens(Maker: Canon; Model: Canon EF 16-35mm f/2.8L II USM; Type: RECTILINEAR; Focal: 16.0-35.0; Aperture: 2.799999952316284-22.0; Crop factor: 1.0; Score: 52)\" lens for lens aberrations correction.\n", + "2024-12-31 12:20:05,218 - 9204 - INFO - CorrectLensAberrationLensFun: Correcting lens vignette...\n", + "2024-12-31 12:20:05,433 - 9204 - INFO - CorrectLensAberrationLensFun: Lens vignette was successfully corrected!\n", + "2024-12-31 12:20:05,438 - 9204 - INFO - CorrectLensAberrationLensFun: Correcting lens chromatic aberration...\n", + "2024-12-31 12:20:06,503 - 9204 - INFO - CorrectLensAberrationLensFun: Lens chromatic aberration was successfully corrected!\n", + "2024-12-31 12:20:06,509 - 9204 - INFO - CorrectLensAberrationLensFun: Correcting lens distortion...\n", + "2024-12-31 12:20:06,866 - 9204 - INFO - CorrectLensAberrationLensFun: Lens distortion was successfully corrected!\n", + "2024-12-31 12:20:06,878 - 9204 - INFO - GraphRawProcessingDNG: Processing \"NodeDownsample#8(None)\" node...\n", + "2024-12-31 12:20:06,881 - 9204 - INFO - GraphRawProcessingDNG: Processing \"NodeApplyInputTransformDNG#9(None)\" node...\n", + "2024-12-31 12:20:09,834 - 9204 - INFO - GraphRawProcessingDNG: Processing \"NodeProcessingMetadata#10(None)\" node...\n", + "2024-12-31 12:20:09,843 - 9204 - INFO - GraphRawProcessingDNG: Processing \"NodeWatermark#11(None)\" node...\n", + "2024-12-31 12:20:09,909 - 9204 - INFO - GraphRawProcessingDNG: Processing \"NodeOrient#12(None)\" node...\n", + "2024-12-31 12:20:09,911 - 9204 - INFO - GraphRawProcessingDNG: Processing \"NodeWriteImage#13(None)\" node...\n" ] }, { @@ -866,97 +872,97 @@ { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "digraph\n", - "\n", + "\n", "\n", "\n", "ConvertRawFileToDNGFile (#15)\n", - "\n", - "ConvertRawFileToDNGFile (#15)\n", - "\n", - "execution_input\n", - "\n", - "raw_file_path\n", - "\n", - "output_directory\n", - "\n", - "dng_converter\n", - "\n", - "dng_converter_arguments\n", - "\n", - "execution_output\n", - "\n", - "dng_file_path\n", + "\n", + "ConvertRawFileToDNGFile (#15)\n", + "\n", + "execution_input\n", + "\n", + "raw_file_path\n", + "\n", + "output_directory\n", + "\n", + "dng_converter\n", + "\n", + "dng_converter_arguments\n", + "\n", + "execution_output\n", + "\n", + "dng_file_path\n", "\n", "\n", "\n", "ReadFileMetadataDNG (#16)\n", - "\n", - "ReadFileMetadataDNG (#16)\n", - "\n", - "execution_input\n", - "\n", - "dng_file_path\n", - "\n", - "execution_output\n", - "\n", - "metadata\n", + "\n", + "ReadFileMetadataDNG (#16)\n", + "\n", + "execution_input\n", + "\n", + "dng_file_path\n", + "\n", + "execution_output\n", + "\n", + "metadata\n", "\n", "\n", - "\n", + "\n", "ConvertRawFileToDNGFile (#15):execution_output->ReadFileMetadataDNG (#16):execution_input\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "ConvertRawFileToDNGFile (#15):dng_file_path->ReadFileMetadataDNG (#16):dng_file_path\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "ProcessRawFileRawpy (#18)\n", - "\n", - "ProcessRawFileRawpy (#18)\n", - "\n", - "execution_input\n", - "\n", - "raw_file_path\n", - "\n", - "input_transform\n", - "\n", - "execution_output\n", - "\n", - "image\n", + "\n", + "ProcessRawFileRawpy (#18)\n", + "\n", + "execution_input\n", + "\n", + "raw_file_path\n", + "\n", + "input_transform\n", + "\n", + "execution_output\n", + "\n", + "image\n", "\n", "\n", - "\n", + "\n", "ConvertRawFileToDNGFile (#15):dng_file_path->ProcessRawFileRawpy (#18):raw_file_path\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "RemoveDNGFile (#19)\n", - "\n", - "RemoveDNGFile (#19)\n", - "\n", - "execution_input\n", - "\n", - "path\n", - "\n", - "bypass\n", - "\n", - "execution_output\n", + "\n", + "RemoveDNGFile (#19)\n", + "\n", + "execution_input\n", + "\n", + "path\n", + "\n", + "bypass\n", + "\n", + "execution_output\n", "\n", "\n", - "\n", + "\n", "ConvertRawFileToDNGFile (#15):dng_file_path->RemoveDNGFile (#19):path\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -985,60 +991,66 @@ "output_metadata\n", "\n", "\n", - "\n", + "\n", "ConvertRawFileToDNGFile (#15):dng_file_path->ProcessingMetadata (#23):sources\n", - "\n", + "\n", "\n", "\n", "\n", "\n", "GraphRawProcessingCameraSensitivities (#14)\n", - "\n", - "GraphRawProcessingCameraSensitivities (#14)\n", + "\n", + "GraphRawProcessingCameraSensitivities (#14)\n", "\n", "\n", "\n", + "GraphRawProcessingCameraSensitivities (#14):execution_input->ConvertRawFileToDNGFile (#15):execution_input\n", + "\n", + "\n", + "\n", + "\n", + "\n", "GraphRawProcessingCameraSensitivities (#14):raw_file_path->ConvertRawFileToDNGFile (#15):raw_file_path\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "ComputeInputTransformCameraSensitivities (#17)\n", - "\n", - "ComputeInputTransformCameraSensitivities (#17)\n", - "\n", - "execution_input\n", - "\n", - "metadata\n", - "\n", - "CCT_D_uv\n", - "\n", - "camera_sensitivities\n", - "\n", - "bypass\n", - "\n", - "execution_output\n", - "\n", - "input_transform\n", + "\n", + "ComputeInputTransformCameraSensitivities (#17)\n", + "\n", + "execution_input\n", + "\n", + "metadata\n", + "\n", + "CCT_D_uv\n", + "\n", + "camera_sensitivities\n", + "\n", + "bypass\n", + "\n", + "execution_output\n", + "\n", + "input_transform\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingCameraSensitivities (#14):CCT_D_uv->ComputeInputTransformCameraSensitivities (#17):CCT_D_uv\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingCameraSensitivities (#14):camera_sensitivities->ComputeInputTransformCameraSensitivities (#17):camera_sensitivities\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingCameraSensitivities (#14):bypass_input_transform->ComputeInputTransformCameraSensitivities (#17):bypass\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -1067,27 +1079,27 @@ "output_image\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingCameraSensitivities (#14):correct_vignette->CorrectLensAberrationLensFun (#20):correct_vignette\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingCameraSensitivities (#14):correct_chromatic_aberration->CorrectLensAberrationLensFun (#20):correct_chromatic_aberration\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingCameraSensitivities (#14):correct_distortion->CorrectLensAberrationLensFun (#20):correct_distortion\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingCameraSensitivities (#14):bypass_correct_lens_aberration->CorrectLensAberrationLensFun (#20):bypass\n", - "\n", + "\n", "\n", "\n", "\n", @@ -1109,9 +1121,9 @@ "output_image\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingCameraSensitivities (#14):downsample->Downsample (#21):factor\n", - "\n", + "\n", "\n", "\n", "\n", @@ -1135,27 +1147,27 @@ "output_image\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingCameraSensitivities (#14):output_colourspace->ApplyInputTransformCameraSensitivities (#22):output_colourspace\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingCameraSensitivities (#14):bypass_input_transform->ApplyInputTransformCameraSensitivities (#22):bypass\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingCameraSensitivities (#14):output_colourspace->ProcessingMetadata (#23):output_colourspace\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingCameraSensitivities (#14):orientation->ProcessingMetadata (#23):orientation\n", - "\n", + "\n", "\n", "\n", "\n", @@ -1179,9 +1191,9 @@ "output_image\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingCameraSensitivities (#14):bypass_watermark->Watermark (#24):bypass\n", - "\n", + "\n", "\n", "\n", "\n", @@ -1203,15 +1215,15 @@ "output_image\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingCameraSensitivities (#14):orientation->Orient (#25):orientation\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingCameraSensitivities (#14):bypass_orient->Orient (#25):bypass\n", - "\n", + "\n", "\n", "\n", "\n", @@ -1237,157 +1249,157 @@ "execution_output\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingCameraSensitivities (#14):output_file_path->WriteImage (#26):path\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphRawProcessingCameraSensitivities (#14):output_colourspace->WriteImage (#26):output_colourspace\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "ReadFileMetadataDNG (#16):execution_output->ComputeInputTransformCameraSensitivities (#17):execution_input\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "ReadFileMetadataDNG (#16):metadata->ComputeInputTransformCameraSensitivities (#17):metadata\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "ReadFileMetadataDNG (#16):metadata->CorrectLensAberrationLensFun (#20):metadata\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "ReadFileMetadataDNG (#16):metadata->ProcessingMetadata (#23):input_metadata\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "ComputeInputTransformCameraSensitivities (#17):execution_output->ProcessRawFileRawpy (#18):execution_input\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "ComputeInputTransformCameraSensitivities (#17):input_transform->ProcessRawFileRawpy (#18):input_transform\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "ComputeInputTransformCameraSensitivities (#17):input_transform->ApplyInputTransformCameraSensitivities (#22):input_transform\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "ComputeInputTransformCameraSensitivities (#17):input_transform->ProcessingMetadata (#23):input_transform\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "ProcessRawFileRawpy (#18):execution_output->RemoveDNGFile (#19):execution_input\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "ProcessRawFileRawpy (#18):image->CorrectLensAberrationLensFun (#20):input_image\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "RemoveDNGFile (#19):execution_output->CorrectLensAberrationLensFun (#20):execution_input\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "CorrectLensAberrationLensFun (#20):execution_output->Downsample (#21):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "CorrectLensAberrationLensFun (#20):output_image->Downsample (#21):input_image\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Downsample (#21):execution_output->ApplyInputTransformCameraSensitivities (#22):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Downsample (#21):output_image->ApplyInputTransformCameraSensitivities (#22):input_image\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ApplyInputTransformCameraSensitivities (#22):execution_output->ProcessingMetadata (#23):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ApplyInputTransformCameraSensitivities (#22):output_image->Watermark (#24):input_image\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ProcessingMetadata (#23):execution_output->Watermark (#24):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ProcessingMetadata (#23):output_metadata->Watermark (#24):metadata\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ProcessingMetadata (#23):output_metadata->WriteImage (#26):metadata\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Watermark (#24):execution_output->Orient (#25):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Watermark (#24):output_image->Orient (#25):input_image\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Orient (#25):execution_output->WriteImage (#26):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Orient (#25):output_image->WriteImage (#26):image\n", "\n", "\n", @@ -1423,38 +1435,38 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-12-26 11:16:33,947 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeConvertRawFileToDNGFile#15(None)\" node...\n", - "2024-12-26 11:16:33,947 - 55254 - INFO - Converting \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.CR2\" file to \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.dng\" file.\n", + "2024-12-31 12:20:19,761 - 9204 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeConvertRawFileToDNGFile#15(None)\" node...\n", + "2024-12-31 12:20:19,763 - 9204 - INFO - Converting \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.CR2\" file to \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.dng\" file.\n", "*** GPU Warning: GPU3 disabled via cr_config at init time. ***\n", - "2024-12-26 11:16:34,616 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeReadFileMetadataDNG#16(None)\" node...\n", - "2024-12-26 11:16:34,616 - 55254 - INFO - Reading \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.dng\" image EXIF data.\n", - "2024-12-26 11:16:34,770 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeComputeInputTransformCameraSensitivities#17(None)\" node...\n", - "2024-12-26 11:16:34,771 - 55254 - INFO - ComputeInputTransformCameraSensitivities: Using \"Canon EOS 5D Mark II\" camera model sensitivities.\n", - "2024-12-26 11:16:34,771 - 55254 - INFO - ComputeInputTransformCameraSensitivities: Using \"Daylight\" illuminant!\n", - "2024-12-26 11:16:34,932 - 55254 - INFO - ComputeInputTransformCameraSensitivities: Input Transform Matrix: [[ 0.86124796 -0.01402326 0.15277529]\n", + "2024-12-31 12:20:20,426 - 9204 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeReadFileMetadataDNG#16(None)\" node...\n", + "2024-12-31 12:20:20,427 - 9204 - INFO - Reading \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.dng\" image EXIF data.\n", + "2024-12-31 12:20:20,616 - 9204 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeComputeInputTransformCameraSensitivities#17(None)\" node...\n", + "2024-12-31 12:20:20,617 - 9204 - INFO - ComputeInputTransformCameraSensitivities: Using \"Canon EOS 5D Mark II\" camera model sensitivities.\n", + "2024-12-31 12:20:20,618 - 9204 - INFO - ComputeInputTransformCameraSensitivities: Using \"Daylight\" illuminant!\n", + "2024-12-31 12:20:20,972 - 9204 - INFO - ComputeInputTransformCameraSensitivities: Input Transform Matrix: [[ 0.86124796 -0.01402326 0.15277529]\n", " [ 0.059413 1.1495537 -0.20896661]\n", " [ 0.02251894 -0.19229327 1.1697743 ]]\n", - "2024-12-26 11:16:34,933 - 55254 - INFO - ComputeInputTransformCameraSensitivities: White balance multipliers \"RGB\": [ 2.5270655 1. 1.3662276]\n", - "2024-12-26 11:16:34,934 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeProcessRawFileRawpy#18(None)\" node...\n", - "2024-12-26 11:16:34,935 - 55254 - INFO - ProcessRawFileRawpy: Processing \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.dng\" file...\n", - "2024-12-26 11:16:41,995 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeRemoveFile#19(None)\" node...\n", - "2024-12-26 11:16:41,998 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeCorrectLensAberrationLensFun#20(None)\" node...\n", - "2024-12-26 11:16:42,063 - 55254 - INFO - CorrectLensAberrationLensFun: Searching for \"Canon\" \"Canon EOS 5D Mark II\" camera model.\n", - "2024-12-26 11:16:42,064 - 55254 - INFO - CorrectLensAberrationLensFun: Using \"Camera(Maker: Canon; Model: Canon EOS 5D Mark II; Mount: Canon EF; Crop Factor: 1.0; Score: 200)\" camera for lens aberrations correction.\n", - "2024-12-26 11:16:42,064 - 55254 - INFO - CorrectLensAberrationLensFun: Searching for \"EF16-35mm f/2.8L II USM\" lens model.\n", - "2024-12-26 11:16:42,065 - 55254 - INFO - CorrectLensAberrationLensFun: Using \"Lens(Maker: Canon; Model: Canon EF 16-35mm f/2.8L II USM; Type: RECTILINEAR; Focal: 16.0-35.0; Aperture: 2.799999952316284-22.0; Crop factor: 1.0; Score: 52)\" lens for lens aberrations correction.\n", - "2024-12-26 11:16:42,065 - 55254 - INFO - CorrectLensAberrationLensFun: Correcting lens vignette...\n", - "2024-12-26 11:16:42,148 - 55254 - INFO - CorrectLensAberrationLensFun: Lens vignette was successfully corrected!\n", - "2024-12-26 11:16:42,148 - 55254 - INFO - CorrectLensAberrationLensFun: Correcting lens chromatic aberration...\n", - "2024-12-26 11:16:42,679 - 55254 - INFO - CorrectLensAberrationLensFun: Lens chromatic aberration was successfully corrected!\n", - "2024-12-26 11:16:42,679 - 55254 - INFO - CorrectLensAberrationLensFun: Correcting lens distortion...\n", - "2024-12-26 11:16:42,860 - 55254 - INFO - CorrectLensAberrationLensFun: Lens distortion was successfully corrected!\n", - "2024-12-26 11:16:42,866 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeDownsample#21(None)\" node...\n", - "2024-12-26 11:16:42,867 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeApplyInputTransformCameraSensitivities#22(None)\" node...\n", - "2024-12-26 11:16:43,958 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeProcessingMetadata#23(None)\" node...\n", - "2024-12-26 11:16:43,960 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeWatermark#24(None)\" node...\n", - "2024-12-26 11:16:44,010 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeOrient#25(None)\" node...\n", - "2024-12-26 11:16:44,011 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeWriteImage#26(None)\" node...\n" + "2024-12-31 12:20:20,983 - 9204 - INFO - ComputeInputTransformCameraSensitivities: White balance multipliers \"RGB\": [ 2.5270655 1. 1.3662276]\n", + "2024-12-31 12:20:20,994 - 9204 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeProcessRawFileRawpy#18(None)\" node...\n", + "2024-12-31 12:20:21,000 - 9204 - INFO - ProcessRawFileRawpy: Processing \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.dng\" file...\n", + "2024-12-31 12:20:32,891 - 9204 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeRemoveFile#19(None)\" node...\n", + "2024-12-31 12:20:32,893 - 9204 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeCorrectLensAberrationLensFun#20(None)\" node...\n", + "2024-12-31 12:20:32,979 - 9204 - INFO - CorrectLensAberrationLensFun: Searching for \"Canon\" \"Canon EOS 5D Mark II\" camera model.\n", + "2024-12-31 12:20:32,985 - 9204 - INFO - CorrectLensAberrationLensFun: Using \"Camera(Maker: Canon; Model: Canon EOS 5D Mark II; Mount: Canon EF; Crop Factor: 1.0; Score: 200)\" camera for lens aberrations correction.\n", + "2024-12-31 12:20:32,994 - 9204 - INFO - CorrectLensAberrationLensFun: Searching for \"EF16-35mm f/2.8L II USM\" lens model.\n", + "2024-12-31 12:20:33,001 - 9204 - INFO - CorrectLensAberrationLensFun: Using \"Lens(Maker: Canon; Model: Canon EF 16-35mm f/2.8L II USM; Type: RECTILINEAR; Focal: 16.0-35.0; Aperture: 2.799999952316284-22.0; Crop factor: 1.0; Score: 52)\" lens for lens aberrations correction.\n", + "2024-12-31 12:20:33,003 - 9204 - INFO - CorrectLensAberrationLensFun: Correcting lens vignette...\n", + "2024-12-31 12:20:33,171 - 9204 - INFO - CorrectLensAberrationLensFun: Lens vignette was successfully corrected!\n", + "2024-12-31 12:20:33,177 - 9204 - INFO - CorrectLensAberrationLensFun: Correcting lens chromatic aberration...\n", + "2024-12-31 12:20:34,547 - 9204 - INFO - CorrectLensAberrationLensFun: Lens chromatic aberration was successfully corrected!\n", + "2024-12-31 12:20:34,555 - 9204 - INFO - CorrectLensAberrationLensFun: Correcting lens distortion...\n", + "2024-12-31 12:20:34,873 - 9204 - INFO - CorrectLensAberrationLensFun: Lens distortion was successfully corrected!\n", + "2024-12-31 12:20:34,883 - 9204 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeDownsample#21(None)\" node...\n", + "2024-12-31 12:20:34,893 - 9204 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeApplyInputTransformCameraSensitivities#22(None)\" node...\n", + "2024-12-31 12:20:36,896 - 9204 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeProcessingMetadata#23(None)\" node...\n", + "2024-12-31 12:20:36,904 - 9204 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeWatermark#24(None)\" node...\n", + "2024-12-31 12:20:36,989 - 9204 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeOrient#25(None)\" node...\n", + "2024-12-31 12:20:36,991 - 9204 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeWriteImage#26(None)\" node...\n" ] }, { @@ -1569,13 +1581,13 @@ "output\n", "\n", "\n", - "\n", + "\n", "ParallelForMultiprocess (#28):execution_output->GraphBatchMergeHDRI (#42):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ParallelForMultiprocess (#28):results->GraphBatchMergeHDRI (#42):array\n", "\n", "\n", @@ -1625,19 +1637,19 @@ "output\n", "\n", "\n", - "\n", + "\n", "ParallelForMultiprocess (#28):loop_output->GraphRawProcessingCameraSensitivities (#29):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ParallelForMultiprocess (#28):index->GraphRawProcessingCameraSensitivities (#29):index\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ParallelForMultiprocess (#28):element->GraphRawProcessingCameraSensitivities (#29):element\n", "\n", "\n", @@ -1645,133 +1657,139 @@ "\n", "\n", "GraphHDRI (#27)\n", - "\n", - "GraphHDRI (#27)\n", + "\n", + "GraphHDRI (#27)\n", "\n", "\n", "\n", - "GraphHDRI (#27):array->ParallelForMultiprocess (#28):array\n", - "\n", - "\n", + "GraphHDRI (#27):execution_input->ParallelForMultiprocess (#28):execution_input\n", + "\n", + "\n", "\n", "\n", "\n", + "GraphHDRI (#27):array->ParallelForMultiprocess (#28):array\n", + "\n", + "\n", + "\n", + "\n", + "\n", "GraphHDRI (#27):processes->ParallelForMultiprocess (#28):processes\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "GraphHDRI (#27):batch_size->GraphBatchMergeHDRI (#42):batch_size\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphHDRI (#27):weighting_function->GraphBatchMergeHDRI (#42):weighting_function\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphHDRI (#27):exposure_normalisation_factor->GraphBatchMergeHDRI (#42):exposure_normalisation_factor\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphHDRI (#27):bypass_watermark->GraphBatchMergeHDRI (#42):bypass_watermark\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphHDRI (#27):bypass_exposure_normalisation->GraphBatchMergeHDRI (#42):bypass_exposure_normalisation\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphHDRI (#27):bypass_preview_image->GraphBatchMergeHDRI (#42):bypass_preview_image\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphHDRI (#27):processes->GraphBatchMergeHDRI (#42):processes\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphHDRI (#27):output_colourspace->GraphRawProcessingCameraSensitivities (#29):output_colourspace\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphHDRI (#27):CCT_D_uv->GraphRawProcessingCameraSensitivities (#29):CCT_D_uv\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphHDRI (#27):camera_sensitivities->GraphRawProcessingCameraSensitivities (#29):camera_sensitivities\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphHDRI (#27):correct_vignette->GraphRawProcessingCameraSensitivities (#29):correct_vignette\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphHDRI (#27):correct_chromatic_aberration->GraphRawProcessingCameraSensitivities (#29):correct_chromatic_aberration\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphHDRI (#27):correct_distortion->GraphRawProcessingCameraSensitivities (#29):correct_distortion\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphHDRI (#27):downsample->GraphRawProcessingCameraSensitivities (#29):downsample\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphHDRI (#27):orientation->GraphRawProcessingCameraSensitivities (#29):orientation\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphHDRI (#27):bypass_input_transform->GraphRawProcessingCameraSensitivities (#29):bypass_input_transform\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphHDRI (#27):bypass_correct_lens_aberration->GraphRawProcessingCameraSensitivities (#29):bypass_correct_lens_aberration\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphHDRI (#27):bypass_watermark->GraphRawProcessingCameraSensitivities (#29):bypass_watermark\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphHDRI (#27):bypass_orient->GraphRawProcessingCameraSensitivities (#29):bypass_orient\n", - "\n", + "\n", "\n", "\n", "\n", @@ -1843,13 +1861,13 @@ "loop_output\n", "\n", "\n", - "\n", + "\n", "CreateBatches (#57):execution_output->ParallelForMultiprocess (#58):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "CreateBatches (#57):batches->ParallelForMultiprocess (#58):array\n", "\n", "\n", @@ -1857,25 +1875,31 @@ "\n", "\n", "GraphBatchMergeHDRI (#56)\n", - "\n", - "GraphBatchMergeHDRI (#56)\n", + "\n", + "GraphBatchMergeHDRI (#56)\n", "\n", "\n", "\n", - "GraphBatchMergeHDRI (#56):array->CreateBatches (#57):array\n", - "\n", - "\n", + "GraphBatchMergeHDRI (#56):execution_input->CreateBatches (#57):execution_input\n", + "\n", + "\n", "\n", "\n", "\n", + "GraphBatchMergeHDRI (#56):array->CreateBatches (#57):array\n", + "\n", + "\n", + "\n", + "\n", + "\n", "GraphBatchMergeHDRI (#56):batch_size->CreateBatches (#57):batch_size\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "GraphBatchMergeHDRI (#56):processes->ParallelForMultiprocess (#58):processes\n", - "\n", + "\n", "\n", "\n", "\n", @@ -1901,27 +1925,27 @@ "output\n", "\n", "\n", - "\n", + "\n", "GraphBatchMergeHDRI (#56):exposure_normalisation_factor->GraphPostMergeHDRI (#66):exposure_normalisation_factor\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphBatchMergeHDRI (#56):processes->GraphPostMergeHDRI (#66):processes\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphBatchMergeHDRI (#56):bypass_exposure_normalisation->GraphPostMergeHDRI (#66):bypass_exposure_normalisation\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphBatchMergeHDRI (#56):bypass_preview_image->GraphPostMergeHDRI (#66):bypass_preview_image\n", - "\n", + "\n", "\n", "\n", "\n", @@ -1955,43 +1979,43 @@ "output\n", "\n", "\n", - "\n", + "\n", "GraphBatchMergeHDRI (#56):weighting_function->GraphMergeHDRI (#59):weighting_function\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphBatchMergeHDRI (#56):bypass_watermark->GraphMergeHDRI (#59):bypass_watermark\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "ParallelForMultiprocess (#58):execution_output->GraphPostMergeHDRI (#66):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ParallelForMultiprocess (#58):results->GraphPostMergeHDRI (#66):array\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ParallelForMultiprocess (#58):loop_output->GraphMergeHDRI (#59):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ParallelForMultiprocess (#58):index->GraphMergeHDRI (#59):index\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ParallelForMultiprocess (#58):element->GraphMergeHDRI (#59):element\n", "\n", "\n", @@ -2057,13 +2081,13 @@ "image\n", "\n", "\n", - "\n", + "\n", "CreateImageStack (#71):execution_output->MergeImageStack (#72):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "CreateImageStack (#71):image_stack->MergeImageStack (#72):image_stack\n", "\n", "\n", @@ -2071,19 +2095,25 @@ "\n", "\n", "GraphMergeHDRI (#70)\n", - "\n", - "GraphMergeHDRI (#70)\n", + "\n", + "GraphMergeHDRI (#70)\n", "\n", "\n", "\n", + "GraphMergeHDRI (#70):execution_input->CreateImageStack (#71):execution_input\n", + "\n", + "\n", + "\n", + "\n", + "\n", "GraphMergeHDRI (#70):exr_file_paths->CreateImageStack (#71):paths\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "GraphMergeHDRI (#70):weighting_function->MergeImageStack (#72):weighting_function\n", - "\n", + "\n", "\n", "\n", "\n", @@ -2113,28 +2143,28 @@ "output_metadata\n", "\n", "\n", - "\n", + "\n", "GraphMergeHDRI (#70):metadata->ProcessingMetadata (#74):input_metadata\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphMergeHDRI (#70):input_transform->ProcessingMetadata (#74):input_transform\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "GraphMergeHDRI (#70):output_colourspace->ProcessingMetadata (#74):output_colourspace\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "GraphMergeHDRI (#70):exr_file_paths->ProcessingMetadata (#74):sources\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -2157,9 +2187,9 @@ "output_image\n", "\n", "\n", - "\n", + "\n", "GraphMergeHDRI (#70):bypass_watermark->Watermark (#75):bypass\n", - "\n", + "\n", "\n", "\n", "\n", @@ -2185,15 +2215,15 @@ "execution_output\n", "\n", "\n", - "\n", + "\n", "GraphMergeHDRI (#70):output_file_path->WriteImage (#76):path\n", - "\n", + "\n", "\n", "\n", "\n", - "\n", + "\n", "GraphMergeHDRI (#70):output_colourspace->WriteImage (#76):output_colourspace\n", - "\n", + "\n", "\n", "\n", "\n", @@ -2215,55 +2245,55 @@ "output_image\n", "\n", "\n", - "\n", + "\n", "MergeImageStack (#72):execution_output->Downsample (#73):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "MergeImageStack (#72):image->Downsample (#73):input_image\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Downsample (#73):execution_output->ProcessingMetadata (#74):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Downsample (#73):output_image->Watermark (#75):input_image\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ProcessingMetadata (#74):execution_output->Watermark (#75):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ProcessingMetadata (#74):output_metadata->Watermark (#75):metadata\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "ProcessingMetadata (#74):output_metadata->WriteImage (#76):metadata\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Watermark (#75):execution_output->WriteImage (#76):execution_input\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "Watermark (#75):output_image->WriteImage (#76):image\n", "\n", "\n", @@ -2292,123 +2322,135 @@ { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "digraph\n", - "\n", + "\n", "\n", "\n", "NormaliseExposure (#78)\n", - "\n", - "NormaliseExposure (#78)\n", - "\n", - "execution_input\n", - "\n", - "image_paths\n", - "\n", - "normalisation_factor\n", - "\n", - "scaling_factor\n", - "\n", - "bypass\n", - "\n", - "execution_output\n", + "\n", + "NormaliseExposure (#78)\n", + "\n", + "execution_input\n", + "\n", + "image_paths\n", + "\n", + "normalisation_factor\n", + "\n", + "scaling_factor\n", + "\n", + "bypass\n", + "\n", + "execution_output\n", + "\n", + "\n", + "\n", + "ParallelForMultiprocess (#79)\n", + "\n", + "ParallelForMultiprocess (#79)\n", + "\n", + "execution_input\n", + "\n", + "array\n", + "\n", + "task\n", + "\n", + "processes\n", + "\n", + "execution_output\n", + "\n", + "index\n", + "\n", + "element\n", + "\n", + "results\n", + "\n", + "loop_output\n", + "\n", + "\n", + "\n", + "NormaliseExposure (#78):execution_output->ParallelForMultiprocess (#79):execution_input\n", + "\n", + "\n", "\n", "\n", "\n", "GraphPostMergeHDRI (#77)\n", - "\n", - "GraphPostMergeHDRI (#77)\n", + "\n", + "GraphPostMergeHDRI (#77)\n", "\n", "\n", "\n", - "GraphPostMergeHDRI (#77):array->NormaliseExposure (#78):image_paths\n", - "\n", - "\n", + "GraphPostMergeHDRI (#77):execution_input->NormaliseExposure (#78):execution_input\n", + "\n", + "\n", "\n", "\n", "\n", - "GraphPostMergeHDRI (#77):exposure_normalisation_factor->NormaliseExposure (#78):normalisation_factor\n", - "\n", - "\n", + "GraphPostMergeHDRI (#77):array->NormaliseExposure (#78):image_paths\n", + "\n", + "\n", "\n", "\n", "\n", - "GraphPostMergeHDRI (#77):bypass_exposure_normalisation->NormaliseExposure (#78):bypass\n", - "\n", - "\n", + "GraphPostMergeHDRI (#77):exposure_normalisation_factor->NormaliseExposure (#78):normalisation_factor\n", + "\n", + "\n", "\n", - "\n", - "\n", - "ParallelForMultiprocess (#79)\n", - "\n", - "ParallelForMultiprocess (#79)\n", - "\n", - "execution_input\n", - "\n", - "array\n", - "\n", - "task\n", - "\n", - "processes\n", - "\n", - "execution_output\n", - "\n", - "index\n", - "\n", - "element\n", - "\n", - "results\n", - "\n", - "loop_output\n", + "\n", + "\n", + "GraphPostMergeHDRI (#77):bypass_exposure_normalisation->NormaliseExposure (#78):bypass\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "GraphPostMergeHDRI (#77):array->ParallelForMultiprocess (#79):array\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "GraphPostMergeHDRI (#77):processes->ParallelForMultiprocess (#79):processes\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "WritePreviewImage (#80)\n", - "\n", - "WritePreviewImage (#80)\n", - "\n", - "execution_input\n", - "\n", - "path\n", - "\n", - "cctf_encoding\n", - "\n", - "bypass\n", - "\n", - "execution_output\n", - "\n", - "preview_path\n", + "\n", + "WritePreviewImage (#80)\n", + "\n", + "execution_input\n", + "\n", + "path\n", + "\n", + "cctf_encoding\n", + "\n", + "bypass\n", + "\n", + "execution_output\n", + "\n", + "preview_path\n", "\n", "\n", - "\n", + "\n", "GraphPostMergeHDRI (#77):bypass_preview_image->WritePreviewImage (#80):bypass\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "ParallelForMultiprocess (#79):loop_output->WritePreviewImage (#80):execution_input\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "ParallelForMultiprocess (#79):element->WritePreviewImage (#80):path\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "" @@ -2435,177 +2477,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-12-26 11:16:47,435 - 55254 - INFO - GraphHDRI: Processing \"ParallelForMultiprocess#82(None)\" node...\n", - "2024-12-26 11:16:47,436 - 55254 - INFO - ParallelForMultiprocess: Processing \"GraphRawProcessingCameraSensitivities(12)\" node...\n", - "2024-12-26 11:16:47,437 - 55254 - INFO - ParallelForMultiprocess: Index 0, Element /Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2598.CR2\n", - "2024-12-26 11:16:47,437 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeConvertRawFileToDNGFile#84(None)\" node...\n", - "2024-12-26 11:16:47,438 - 55254 - INFO - Converting \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2598.CR2\" file to \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2598.dng\" file.\n", - "*** GPU Warning: GPU3 disabled via cr_config at init time. ***\n", - "2024-12-26 11:16:48,016 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeReadFileMetadataDNG#85(None)\" node...\n", - "2024-12-26 11:16:48,017 - 55254 - INFO - Reading \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2598.dng\" image EXIF data.\n", - "2024-12-26 11:16:48,167 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeComputeInputTransformCameraSensitivities#86(None)\" node...\n", - "2024-12-26 11:16:48,167 - 55254 - INFO - ComputeInputTransformCameraSensitivities: Using \"Canon EOS 5D Mark II\" camera model sensitivities.\n", - "2024-12-26 11:16:48,168 - 55254 - INFO - ComputeInputTransformCameraSensitivities: Using \"Daylight\" illuminant!\n", - "2024-12-26 11:16:48,317 - 55254 - INFO - ComputeInputTransformCameraSensitivities: Input Transform Matrix: [[ 0.86124796 -0.01402326 0.15277529]\n", - " [ 0.059413 1.1495537 -0.20896661]\n", - " [ 0.02251894 -0.19229327 1.1697743 ]]\n", - "2024-12-26 11:16:48,326 - 55254 - INFO - ComputeInputTransformCameraSensitivities: White balance multipliers \"RGB\": [ 2.5270655 1. 1.3662276]\n", - "2024-12-26 11:16:48,330 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeProcessRawFileRawpy#87(None)\" node...\n", - "2024-12-26 11:16:48,337 - 55254 - INFO - ProcessRawFileRawpy: Processing \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2598.dng\" file...\n", - "2024-12-26 11:16:54,763 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeRemoveFile#88(None)\" node...\n", - "2024-12-26 11:16:54,764 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeCorrectLensAberrationLensFun#89(None)\" node...\n", - "2024-12-26 11:16:54,830 - 55254 - INFO - CorrectLensAberrationLensFun: Searching for \"Canon\" \"Canon EOS 5D Mark II\" camera model.\n", - "2024-12-26 11:16:54,831 - 55254 - INFO - CorrectLensAberrationLensFun: Using \"Camera(Maker: Canon; Model: Canon EOS 5D Mark II; Mount: Canon EF; Crop Factor: 1.0; Score: 200)\" camera for lens aberrations correction.\n", - "2024-12-26 11:16:54,831 - 55254 - INFO - CorrectLensAberrationLensFun: Searching for \"EF16-35mm f/2.8L II USM\" lens model.\n", - "2024-12-26 11:16:54,832 - 55254 - INFO - CorrectLensAberrationLensFun: Using \"Lens(Maker: Canon; Model: Canon EF 16-35mm f/2.8L II USM; Type: RECTILINEAR; Focal: 16.0-35.0; Aperture: 2.799999952316284-22.0; Crop factor: 1.0; Score: 52)\" lens for lens aberrations correction.\n", - "2024-12-26 11:16:54,832 - 55254 - INFO - CorrectLensAberrationLensFun: Correcting lens vignette...\n", - "2024-12-26 11:16:54,910 - 55254 - INFO - CorrectLensAberrationLensFun: Lens vignette was successfully corrected!\n", - "2024-12-26 11:16:54,911 - 55254 - INFO - CorrectLensAberrationLensFun: Correcting lens chromatic aberration...\n", - "2024-12-26 11:16:55,438 - 55254 - INFO - CorrectLensAberrationLensFun: Lens chromatic aberration was successfully corrected!\n", - "2024-12-26 11:16:55,438 - 55254 - INFO - CorrectLensAberrationLensFun: Correcting lens distortion...\n", - "2024-12-26 11:16:55,623 - 55254 - INFO - CorrectLensAberrationLensFun: Lens distortion was successfully corrected!\n", - "2024-12-26 11:16:55,628 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeDownsample#90(None)\" node...\n", - "2024-12-26 11:16:55,630 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeApplyInputTransformCameraSensitivities#91(None)\" node...\n", - "2024-12-26 11:16:56,699 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeProcessingMetadata#92(None)\" node...\n", - "2024-12-26 11:16:56,702 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeWatermark#93(None)\" node...\n", - "2024-12-26 11:16:56,757 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeOrient#94(None)\" node...\n", - "2024-12-26 11:16:56,758 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeWriteImage#95(None)\" node...\n", - "2024-12-26 11:16:57,413 - 55254 - INFO - ParallelForMultiprocess: Index 1, Element /Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2599.CR2\n", - "2024-12-26 11:16:57,414 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeConvertRawFileToDNGFile#84(None)\" node...\n", - "2024-12-26 11:16:57,415 - 55254 - INFO - Converting \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2599.CR2\" file to \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2599.dng\" file.\n", - "*** GPU Warning: GPU3 disabled via cr_config at init time. ***\n", - "2024-12-26 11:16:57,958 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeReadFileMetadataDNG#85(None)\" node...\n", - "2024-12-26 11:16:57,958 - 55254 - INFO - Reading \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2599.dng\" image EXIF data.\n", - "2024-12-26 11:16:58,099 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeComputeInputTransformCameraSensitivities#86(None)\" node...\n", - "2024-12-26 11:16:58,100 - 55254 - INFO - ComputeInputTransformCameraSensitivities: Using \"Canon EOS 5D Mark II\" camera model sensitivities.\n", - "2024-12-26 11:16:58,100 - 55254 - INFO - ComputeInputTransformCameraSensitivities: Using \"Daylight\" illuminant!\n", - "2024-12-26 11:16:58,268 - 55254 - INFO - ComputeInputTransformCameraSensitivities: Input Transform Matrix: [[ 0.86124796 -0.01402326 0.15277529]\n", - " [ 0.059413 1.1495537 -0.20896661]\n", - " [ 0.02251894 -0.19229327 1.1697743 ]]\n", - "2024-12-26 11:16:58,288 - 55254 - INFO - ComputeInputTransformCameraSensitivities: White balance multipliers \"RGB\": [ 2.5270655 1. 1.3662276]\n", - "2024-12-26 11:16:58,297 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeProcessRawFileRawpy#87(None)\" node...\n", - "2024-12-26 11:16:58,299 - 55254 - INFO - ProcessRawFileRawpy: Processing \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2599.dng\" file...\n", - "2024-12-26 11:17:04,830 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeRemoveFile#88(None)\" node...\n", - "2024-12-26 11:17:04,832 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeCorrectLensAberrationLensFun#89(None)\" node...\n", - "2024-12-26 11:17:04,895 - 55254 - INFO - CorrectLensAberrationLensFun: Searching for \"Canon\" \"Canon EOS 5D Mark II\" camera model.\n", - "2024-12-26 11:17:04,896 - 55254 - INFO - CorrectLensAberrationLensFun: Using \"Camera(Maker: Canon; Model: Canon EOS 5D Mark II; Mount: Canon EF; Crop Factor: 1.0; Score: 200)\" camera for lens aberrations correction.\n", - "2024-12-26 11:17:04,897 - 55254 - INFO - CorrectLensAberrationLensFun: Searching for \"EF16-35mm f/2.8L II USM\" lens model.\n", - "2024-12-26 11:17:04,898 - 55254 - INFO - CorrectLensAberrationLensFun: Using \"Lens(Maker: Canon; Model: Canon EF 16-35mm f/2.8L II USM; Type: RECTILINEAR; Focal: 16.0-35.0; Aperture: 2.799999952316284-22.0; Crop factor: 1.0; Score: 52)\" lens for lens aberrations correction.\n", - "2024-12-26 11:17:04,899 - 55254 - INFO - CorrectLensAberrationLensFun: Correcting lens vignette...\n", - "2024-12-26 11:17:04,985 - 55254 - INFO - CorrectLensAberrationLensFun: Lens vignette was successfully corrected!\n", - "2024-12-26 11:17:04,986 - 55254 - INFO - CorrectLensAberrationLensFun: Correcting lens chromatic aberration...\n", - "2024-12-26 11:17:05,579 - 55254 - INFO - CorrectLensAberrationLensFun: Lens chromatic aberration was successfully corrected!\n", - "2024-12-26 11:17:05,580 - 55254 - INFO - CorrectLensAberrationLensFun: Correcting lens distortion...\n", - "2024-12-26 11:17:05,727 - 55254 - INFO - CorrectLensAberrationLensFun: Lens distortion was successfully corrected!\n", - "2024-12-26 11:17:05,731 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeDownsample#90(None)\" node...\n", - "2024-12-26 11:17:05,736 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeApplyInputTransformCameraSensitivities#91(None)\" node...\n", - "2024-12-26 11:17:07,069 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeProcessingMetadata#92(None)\" node...\n", - "2024-12-26 11:17:07,070 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeWatermark#93(None)\" node...\n", - "2024-12-26 11:17:07,123 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeOrient#94(None)\" node...\n", - "2024-12-26 11:17:07,128 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeWriteImage#95(None)\" node...\n", - "2024-12-26 11:17:07,853 - 55254 - INFO - ParallelForMultiprocess: Index 2, Element /Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2600.CR2\n", - "2024-12-26 11:17:07,854 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeConvertRawFileToDNGFile#84(None)\" node...\n", - "2024-12-26 11:17:07,855 - 55254 - INFO - Converting \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2600.CR2\" file to \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2600.dng\" file.\n", + "2024-12-31 12:20:43,049 - 9204 - INFO - GraphHDRI: Processing \"ParallelForMultiprocess#82(None)\" node...\n", + "2024-12-31 12:20:43,051 - 9204 - INFO - ParallelForMultiprocess: Processing \"GraphRawProcessingCameraSensitivities(12)\" node...\n", + "*** GPU Warning: Special file 'TempDisableGPU2' found -- skipping GPU2 ***\n", + "*** GPU Warning: Special file 'TempDisableGPU2' found -- skipping GPU2 ***\n", "*** GPU Warning: GPU3 disabled via cr_config at init time. ***\n", - "2024-12-26 11:17:08,560 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeReadFileMetadataDNG#85(None)\" node...\n", - "2024-12-26 11:17:08,561 - 55254 - INFO - Reading \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2600.dng\" image EXIF data.\n", - "2024-12-26 11:17:08,716 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeComputeInputTransformCameraSensitivities#86(None)\" node...\n", - "2024-12-26 11:17:08,717 - 55254 - INFO - ComputeInputTransformCameraSensitivities: Using \"Canon EOS 5D Mark II\" camera model sensitivities.\n", - "2024-12-26 11:17:08,717 - 55254 - INFO - ComputeInputTransformCameraSensitivities: Using \"Daylight\" illuminant!\n", - "2024-12-26 11:17:08,816 - 55254 - INFO - ComputeInputTransformCameraSensitivities: Input Transform Matrix: [[ 0.86124796 -0.01402326 0.15277529]\n", - " [ 0.059413 1.1495537 -0.20896661]\n", - " [ 0.02251894 -0.19229327 1.1697743 ]]\n", - "2024-12-26 11:17:08,827 - 55254 - INFO - ComputeInputTransformCameraSensitivities: White balance multipliers \"RGB\": [ 2.5270655 1. 1.3662276]\n", - "2024-12-26 11:17:08,841 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeProcessRawFileRawpy#87(None)\" node...\n", - "2024-12-26 11:17:08,853 - 55254 - INFO - ProcessRawFileRawpy: Processing \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2600.dng\" file...\n", - "2024-12-26 11:17:15,784 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeRemoveFile#88(None)\" node...\n", - "2024-12-26 11:17:15,786 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeCorrectLensAberrationLensFun#89(None)\" node...\n", - "2024-12-26 11:17:15,848 - 55254 - INFO - CorrectLensAberrationLensFun: Searching for \"Canon\" \"Canon EOS 5D Mark II\" camera model.\n", - "2024-12-26 11:17:15,849 - 55254 - INFO - CorrectLensAberrationLensFun: Using \"Camera(Maker: Canon; Model: Canon EOS 5D Mark II; Mount: Canon EF; Crop Factor: 1.0; Score: 200)\" camera for lens aberrations correction.\n", - "2024-12-26 11:17:15,849 - 55254 - INFO - CorrectLensAberrationLensFun: Searching for \"EF16-35mm f/2.8L II USM\" lens model.\n", - "2024-12-26 11:17:15,850 - 55254 - INFO - CorrectLensAberrationLensFun: Using \"Lens(Maker: Canon; Model: Canon EF 16-35mm f/2.8L II USM; Type: RECTILINEAR; Focal: 16.0-35.0; Aperture: 2.799999952316284-22.0; Crop factor: 1.0; Score: 52)\" lens for lens aberrations correction.\n", - "2024-12-26 11:17:15,850 - 55254 - INFO - CorrectLensAberrationLensFun: Correcting lens vignette...\n", - "2024-12-26 11:17:15,934 - 55254 - INFO - CorrectLensAberrationLensFun: Lens vignette was successfully corrected!\n", - "2024-12-26 11:17:15,934 - 55254 - INFO - CorrectLensAberrationLensFun: Correcting lens chromatic aberration...\n", - "2024-12-26 11:17:16,463 - 55254 - INFO - CorrectLensAberrationLensFun: Lens chromatic aberration was successfully corrected!\n", - "2024-12-26 11:17:16,463 - 55254 - INFO - CorrectLensAberrationLensFun: Correcting lens distortion...\n", - "2024-12-26 11:17:16,643 - 55254 - INFO - CorrectLensAberrationLensFun: Lens distortion was successfully corrected!\n", - "2024-12-26 11:17:16,647 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeDownsample#90(None)\" node...\n", - "2024-12-26 11:17:16,657 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeApplyInputTransformCameraSensitivities#91(None)\" node...\n", - "2024-12-26 11:17:17,704 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeProcessingMetadata#92(None)\" node...\n", - "2024-12-26 11:17:17,706 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeWatermark#93(None)\" node...\n", - "2024-12-26 11:17:17,758 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeOrient#94(None)\" node...\n", - "2024-12-26 11:17:17,763 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeWriteImage#95(None)\" node...\n", - "2024-12-26 11:17:18,376 - 55254 - INFO - ParallelForMultiprocess: Index 3, Element /Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.CR2\n", - "2024-12-26 11:17:18,377 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeConvertRawFileToDNGFile#84(None)\" node...\n", - "2024-12-26 11:17:18,378 - 55254 - INFO - Converting \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.CR2\" file to \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.dng\" file.\n", - "*** GPU Warning: GPU3 disabled via cr_config at init time. ***\n", - "2024-12-26 11:17:18,919 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeReadFileMetadataDNG#85(None)\" node...\n", - "2024-12-26 11:17:18,920 - 55254 - INFO - Reading \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.dng\" image EXIF data.\n", - "2024-12-26 11:17:19,059 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeComputeInputTransformCameraSensitivities#86(None)\" node...\n", - "2024-12-26 11:17:19,060 - 55254 - INFO - ComputeInputTransformCameraSensitivities: Using \"Canon EOS 5D Mark II\" camera model sensitivities.\n", - "2024-12-26 11:17:19,060 - 55254 - INFO - ComputeInputTransformCameraSensitivities: Using \"Daylight\" illuminant!\n", - "2024-12-26 11:17:19,188 - 55254 - INFO - ComputeInputTransformCameraSensitivities: Input Transform Matrix: [[ 0.86124796 -0.01402326 0.15277529]\n", - " [ 0.059413 1.1495537 -0.20896661]\n", - " [ 0.02251894 -0.19229327 1.1697743 ]]\n", - "2024-12-26 11:17:19,189 - 55254 - INFO - ComputeInputTransformCameraSensitivities: White balance multipliers \"RGB\": [ 2.5270655 1. 1.3662276]\n", - "2024-12-26 11:17:19,191 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeProcessRawFileRawpy#87(None)\" node...\n", - "2024-12-26 11:17:19,193 - 55254 - INFO - ProcessRawFileRawpy: Processing \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.dng\" file...\n", - "2024-12-26 11:17:26,898 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeRemoveFile#88(None)\" node...\n", - "2024-12-26 11:17:26,901 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeCorrectLensAberrationLensFun#89(None)\" node...\n", - "2024-12-26 11:17:26,966 - 55254 - INFO - CorrectLensAberrationLensFun: Searching for \"Canon\" \"Canon EOS 5D Mark II\" camera model.\n", - "2024-12-26 11:17:26,967 - 55254 - INFO - CorrectLensAberrationLensFun: Using \"Camera(Maker: Canon; Model: Canon EOS 5D Mark II; Mount: Canon EF; Crop Factor: 1.0; Score: 200)\" camera for lens aberrations correction.\n", - "2024-12-26 11:17:26,967 - 55254 - INFO - CorrectLensAberrationLensFun: Searching for \"EF16-35mm f/2.8L II USM\" lens model.\n", - "2024-12-26 11:17:26,968 - 55254 - INFO - CorrectLensAberrationLensFun: Using \"Lens(Maker: Canon; Model: Canon EF 16-35mm f/2.8L II USM; Type: RECTILINEAR; Focal: 16.0-35.0; Aperture: 2.799999952316284-22.0; Crop factor: 1.0; Score: 52)\" lens for lens aberrations correction.\n", - "2024-12-26 11:17:26,968 - 55254 - INFO - CorrectLensAberrationLensFun: Correcting lens vignette...\n", - "2024-12-26 11:17:27,044 - 55254 - INFO - CorrectLensAberrationLensFun: Lens vignette was successfully corrected!\n", - "2024-12-26 11:17:27,045 - 55254 - INFO - CorrectLensAberrationLensFun: Correcting lens chromatic aberration...\n", - "2024-12-26 11:17:27,579 - 55254 - INFO - CorrectLensAberrationLensFun: Lens chromatic aberration was successfully corrected!\n", - "2024-12-26 11:17:27,579 - 55254 - INFO - CorrectLensAberrationLensFun: Correcting lens distortion...\n", - "2024-12-26 11:17:27,723 - 55254 - INFO - CorrectLensAberrationLensFun: Lens distortion was successfully corrected!\n", - "2024-12-26 11:17:27,727 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeDownsample#90(None)\" node...\n", - "2024-12-26 11:17:27,740 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeApplyInputTransformCameraSensitivities#91(None)\" node...\n", - "2024-12-26 11:17:28,718 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeProcessingMetadata#92(None)\" node...\n", - "2024-12-26 11:17:28,720 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeWatermark#93(None)\" node...\n", - "2024-12-26 11:17:28,774 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeOrient#94(None)\" node...\n", - "2024-12-26 11:17:28,779 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeWriteImage#95(None)\" node...\n", - "2024-12-26 11:17:29,395 - 55254 - INFO - ParallelForMultiprocess: Index 4, Element /Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2602.CR2\n", - "2024-12-26 11:17:29,396 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeConvertRawFileToDNGFile#84(None)\" node...\n", - "2024-12-26 11:17:29,397 - 55254 - INFO - Converting \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2602.CR2\" file to \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2602.dng\" file.\n", + "*** GPU Warning: Special file 'TempDisableGPU2' found -- skipping GPU2 ***\n", "*** GPU Warning: GPU3 disabled via cr_config at init time. ***\n", - "2024-12-26 11:17:30,063 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeReadFileMetadataDNG#85(None)\" node...\n", - "2024-12-26 11:17:30,064 - 55254 - INFO - Reading \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2602.dng\" image EXIF data.\n", - "2024-12-26 11:17:30,218 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeComputeInputTransformCameraSensitivities#86(None)\" node...\n", - "2024-12-26 11:17:30,219 - 55254 - INFO - ComputeInputTransformCameraSensitivities: Using \"Canon EOS 5D Mark II\" camera model sensitivities.\n", - "2024-12-26 11:17:30,219 - 55254 - INFO - ComputeInputTransformCameraSensitivities: Using \"Daylight\" illuminant!\n", - "2024-12-26 11:17:30,375 - 55254 - INFO - ComputeInputTransformCameraSensitivities: Input Transform Matrix: [[ 0.86124796 -0.01402326 0.15277529]\n", - " [ 0.059413 1.1495537 -0.20896661]\n", - " [ 0.02251894 -0.19229327 1.1697743 ]]\n", - "2024-12-26 11:17:30,378 - 55254 - INFO - ComputeInputTransformCameraSensitivities: White balance multipliers \"RGB\": [ 2.5270655 1. 1.3662276]\n", - "2024-12-26 11:17:30,379 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeProcessRawFileRawpy#87(None)\" node...\n", - "2024-12-26 11:17:30,382 - 55254 - INFO - ProcessRawFileRawpy: Processing \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2602.dng\" file...\n", - "2024-12-26 11:17:36,992 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeRemoveFile#88(None)\" node...\n", - "2024-12-26 11:17:36,995 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeCorrectLensAberrationLensFun#89(None)\" node...\n", - "2024-12-26 11:17:37,128 - 55254 - INFO - CorrectLensAberrationLensFun: Searching for \"Canon\" \"Canon EOS 5D Mark II\" camera model.\n", - "2024-12-26 11:17:37,135 - 55254 - INFO - CorrectLensAberrationLensFun: Using \"Camera(Maker: Canon; Model: Canon EOS 5D Mark II; Mount: Canon EF; Crop Factor: 1.0; Score: 200)\" camera for lens aberrations correction.\n", - "2024-12-26 11:17:37,138 - 55254 - INFO - CorrectLensAberrationLensFun: Searching for \"EF16-35mm f/2.8L II USM\" lens model.\n", - "2024-12-26 11:17:37,147 - 55254 - INFO - CorrectLensAberrationLensFun: Using \"Lens(Maker: Canon; Model: Canon EF 16-35mm f/2.8L II USM; Type: RECTILINEAR; Focal: 16.0-35.0; Aperture: 2.799999952316284-22.0; Crop factor: 1.0; Score: 52)\" lens for lens aberrations correction.\n", - "2024-12-26 11:17:37,148 - 55254 - INFO - CorrectLensAberrationLensFun: Correcting lens vignette...\n", - "2024-12-26 11:17:37,323 - 55254 - INFO - CorrectLensAberrationLensFun: Lens vignette was successfully corrected!\n", - "2024-12-26 11:17:37,324 - 55254 - INFO - CorrectLensAberrationLensFun: Correcting lens chromatic aberration...\n", - "2024-12-26 11:17:38,190 - 55254 - INFO - CorrectLensAberrationLensFun: Lens chromatic aberration was successfully corrected!\n", - "2024-12-26 11:17:38,191 - 55254 - INFO - CorrectLensAberrationLensFun: Correcting lens distortion...\n", - "2024-12-26 11:17:38,397 - 55254 - INFO - CorrectLensAberrationLensFun: Lens distortion was successfully corrected!\n", - "2024-12-26 11:17:38,403 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeDownsample#90(None)\" node...\n", - "2024-12-26 11:17:38,430 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeApplyInputTransformCameraSensitivities#91(None)\" node...\n", - "2024-12-26 11:17:39,560 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeProcessingMetadata#92(None)\" node...\n", - "2024-12-26 11:17:39,562 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeWatermark#93(None)\" node...\n", - "2024-12-26 11:17:39,617 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeOrient#94(None)\" node...\n", - "2024-12-26 11:17:39,635 - 55254 - INFO - GraphRawProcessingCameraSensitivities: Processing \"NodeWriteImage#95(None)\" node...\n", - "2024-12-26 11:17:40,317 - 55254 - INFO - GraphBatchMergeHDRI: Processing \"NodeCreateBatches#97(None)\" node...\n", - "2024-12-26 11:17:40,319 - 55254 - INFO - GraphBatchMergeHDRI: Processing \"ParallelForMultiprocess#98(None)\" node...\n", - "2024-12-26 11:17:40,320 - 55254 - INFO - ParallelForMultiprocess: Processing \"GraphMergeHDRI(6)\" node...\n", - "2024-12-26 11:17:40,323 - 55254 - INFO - ParallelForMultiprocess: Index 0, Element [('/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2598.exr', CanonicalMapping({'EXIF': CanonicalMapping({'Make': 'Canon', 'Camera Model Name': 'Canon EOS 5D Mark II', 'Camera Serial Number': '330229410', 'Lens Model': 'EF16-35mm f/2.8L II USM', 'DNG Lens Info': '16 35 undef undef', 'Focal Length': 16.0, 'Exposure Time': 0.0020000001, 'F Number': 8.0, 'ISO': 100.0, 'CFA Pattern 2': array([0, 1, 1, 2]), 'CFA Plane Color': array([0, 1, 2]), 'Black Level Repeat Dim': array([2, 2]), 'Black Level': array([1023, 1023, 1023, 1023]), 'White Level': array([15600]), 'Samples Per Pixel': 3, 'Active Area': array([ 51, 158, 3804, 5792]), 'Orientation': 1, 'Camera Calibration Sig': 'com.adobe', 'Profile Calibration Sig': 'com.adobe', 'Calibration Illuminant 1': 17, 'Calibration Illuminant 2': 21, 'Color Matrix 1': array([[ 0.5309, -0.0229, -0.0336],\n", + "2024-12-31 12:21:17,690 - 9204 - INFO - ParallelForMultiprocess: Processed \"('/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2599.exr', CanonicalMapping({'EXIF': CanonicalMapping({'Make': 'Canon', 'Camera Model Name': 'Canon EOS 5D Mark II', 'Camera Serial Number': '330229410', 'Lens Model': 'EF16-35mm f/2.8L II USM', 'DNG Lens Info': '16 35 undef undef', 'Focal Length': 16.0, 'Exposure Time': 0.01666666667, 'F Number': 8.0, 'ISO': 100.0, 'CFA Pattern 2': array([0, 1, 1, 2]), 'CFA Plane Color': array([0, 1, 2]), 'Black Level Repeat Dim': array([2, 2]), 'Black Level': array([1023, 1023, 1023, 1023]), 'White Level': array([15600]), 'Samples Per Pixel': 3, 'Active Area': array([ 51, 158, 3804, 5792]), 'Orientation': 1, 'Camera Calibration Sig': 'com.adobe', 'Profile Calibration Sig': 'com.adobe', 'Calibration Illuminant 1': 17, 'Calibration Illuminant 2': 21, 'Color Matrix 1': array([[ 0.5309, -0.0229, -0.0336],\n", " [-0.6241, 1.3265, 0.3337],\n", " [-0.0817, 0.1215, 0.6664]]), 'Color Matrix 2': array([[ 0.4716, 0.0603, -0.083 ],\n", " [-0.7798, 1.5474, 0.248 ],\n", @@ -2621,9 +2500,10 @@ " [ 0.4351, 0.6621, -0.0972],\n", " [ 0.0505, -0.1562, 0.9308]]), 'Forward Matrix 2': array([[ 0.8924, -0.1041, 0.176 ],\n", " [ 0.4351, 0.6621, -0.0972],\n", - " [ 0.0505, -0.1562, 0.9308]]), 'As Shot Neutral': array([ 0.41307 , 1. , 0.646465]), 'Baseline Exposure': 0.40000000000000002, 'Baseline Noise': 0.80000000000000004})}), InputTransform(M=array([[ 0.86124796, -0.01402326, 0.15277529],\n", - " [ 0.059413 , 1.1495537 , -0.20896661],\n", - " [ 0.02251894, -0.19229327, 1.1697743 ]], dtype=float32), RGB_w=array([ 2.5270655, 1. , 1.3662276], dtype=float32)), 'sRGB'), ('/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2599.exr', CanonicalMapping({'EXIF': CanonicalMapping({'Make': 'Canon', 'Camera Model Name': 'Canon EOS 5D Mark II', 'Camera Serial Number': '330229410', 'Lens Model': 'EF16-35mm f/2.8L II USM', 'DNG Lens Info': '16 35 undef undef', 'Focal Length': 16.0, 'Exposure Time': 0.016666668, 'F Number': 8.0, 'ISO': 100.0, 'CFA Pattern 2': array([0, 1, 1, 2]), 'CFA Plane Color': array([0, 1, 2]), 'Black Level Repeat Dim': array([2, 2]), 'Black Level': array([1023, 1023, 1023, 1023]), 'White Level': array([15600]), 'Samples Per Pixel': 3, 'Active Area': array([ 51, 158, 3804, 5792]), 'Orientation': 1, 'Camera Calibration Sig': 'com.adobe', 'Profile Calibration Sig': 'com.adobe', 'Calibration Illuminant 1': 17, 'Calibration Illuminant 2': 21, 'Color Matrix 1': array([[ 0.5309, -0.0229, -0.0336],\n", + " [ 0.0505, -0.1562, 0.9308]]), 'As Shot Neutral': array([ 0.41307 , 1. , 0.646465]), 'Baseline Exposure': 0.40000000000000002, 'Baseline Noise': 0.80000000000000004})}), InputTransform(M=array([[ 0.86482609, -0.0187031 , 0.15387701],\n", + " [ 0.06217639, 1.14592312, -0.20809951],\n", + " [ 0.02503111, -0.19675131, 1.1717202 ]]), RGB_w=array([ 2.52706552, 1. , 1.36622751])), 'sRGB')\" element with index \"1\".\n", + "2024-12-31 12:21:19,294 - 9204 - INFO - ParallelForMultiprocess: Processed \"('/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2602.exr', CanonicalMapping({'EXIF': CanonicalMapping({'Make': 'Canon', 'Camera Model Name': 'Canon EOS 5D Mark II', 'Camera Serial Number': '330229410', 'Lens Model': 'EF16-35mm f/2.8L II USM', 'DNG Lens Info': '16 35 undef undef', 'Focal Length': 16.0, 'Exposure Time': 8.0, 'F Number': 8.0, 'ISO': 100.0, 'CFA Pattern 2': array([0, 1, 1, 2]), 'CFA Plane Color': array([0, 1, 2]), 'Black Level Repeat Dim': array([2, 2]), 'Black Level': array([1023, 1023, 1023, 1023]), 'White Level': array([15600]), 'Samples Per Pixel': 3, 'Active Area': array([ 51, 158, 3804, 5792]), 'Orientation': 1, 'Camera Calibration Sig': 'com.adobe', 'Profile Calibration Sig': 'com.adobe', 'Calibration Illuminant 1': 17, 'Calibration Illuminant 2': 21, 'Color Matrix 1': array([[ 0.5309, -0.0229, -0.0336],\n", " [-0.6241, 1.3265, 0.3337],\n", " [-0.0817, 0.1215, 0.6664]]), 'Color Matrix 2': array([[ 0.4716, 0.0603, -0.083 ],\n", " [-0.7798, 1.5474, 0.248 ],\n", @@ -2639,9 +2519,10 @@ " [ 0.4351, 0.6621, -0.0972],\n", " [ 0.0505, -0.1562, 0.9308]]), 'Forward Matrix 2': array([[ 0.8924, -0.1041, 0.176 ],\n", " [ 0.4351, 0.6621, -0.0972],\n", - " [ 0.0505, -0.1562, 0.9308]]), 'As Shot Neutral': array([ 0.41307 , 1. , 0.646465]), 'Baseline Exposure': 0.40000000000000002, 'Baseline Noise': 0.80000000000000004})}), InputTransform(M=array([[ 0.86124796, -0.01402326, 0.15277529],\n", - " [ 0.059413 , 1.1495537 , -0.20896661],\n", - " [ 0.02251894, -0.19229327, 1.1697743 ]], dtype=float32), RGB_w=array([ 2.5270655, 1. , 1.3662276], dtype=float32)), 'sRGB'), ('/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2600.exr', CanonicalMapping({'EXIF': CanonicalMapping({'Make': 'Canon', 'Camera Model Name': 'Canon EOS 5D Mark II', 'Camera Serial Number': '330229410', 'Lens Model': 'EF16-35mm f/2.8L II USM', 'DNG Lens Info': '16 35 undef undef', 'Focal Length': 16.0, 'Exposure Time': 0.125, 'F Number': 8.0, 'ISO': 100.0, 'CFA Pattern 2': array([0, 1, 1, 2]), 'CFA Plane Color': array([0, 1, 2]), 'Black Level Repeat Dim': array([2, 2]), 'Black Level': array([1024, 1024, 1024, 1024]), 'White Level': array([15600]), 'Samples Per Pixel': 3, 'Active Area': array([ 51, 158, 3804, 5792]), 'Orientation': 1, 'Camera Calibration Sig': 'com.adobe', 'Profile Calibration Sig': 'com.adobe', 'Calibration Illuminant 1': 17, 'Calibration Illuminant 2': 21, 'Color Matrix 1': array([[ 0.5309, -0.0229, -0.0336],\n", + " [ 0.0505, -0.1562, 0.9308]]), 'As Shot Neutral': array([ 0.41307 , 1. , 0.646465]), 'Baseline Exposure': 0.40000000000000002, 'Baseline Noise': 0.80000000000000004})}), InputTransform(M=array([[ 0.86482609, -0.0187031 , 0.15387701],\n", + " [ 0.06217639, 1.14592312, -0.20809951],\n", + " [ 0.02503111, -0.19675131, 1.1717202 ]]), RGB_w=array([ 2.52706552, 1. , 1.36622751])), 'sRGB')\" element with index \"4\".\n", + "2024-12-31 12:21:19,364 - 9204 - INFO - ParallelForMultiprocess: Processed \"('/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.exr', CanonicalMapping({'EXIF': CanonicalMapping({'Make': 'Canon', 'Camera Model Name': 'Canon EOS 5D Mark II', 'Camera Serial Number': '330229410', 'Lens Model': 'EF16-35mm f/2.8L II USM', 'DNG Lens Info': '16 35 undef undef', 'Focal Length': 16.0, 'Exposure Time': 1.0, 'F Number': 8.0, 'ISO': 100.0, 'CFA Pattern 2': array([0, 1, 1, 2]), 'CFA Plane Color': array([0, 1, 2]), 'Black Level Repeat Dim': array([2, 2]), 'Black Level': array([1023, 1023, 1023, 1023]), 'White Level': array([15600]), 'Samples Per Pixel': 3, 'Active Area': array([ 51, 158, 3804, 5792]), 'Orientation': 1, 'Camera Calibration Sig': 'com.adobe', 'Profile Calibration Sig': 'com.adobe', 'Calibration Illuminant 1': 17, 'Calibration Illuminant 2': 21, 'Color Matrix 1': array([[ 0.5309, -0.0229, -0.0336],\n", " [-0.6241, 1.3265, 0.3337],\n", " [-0.0817, 0.1215, 0.6664]]), 'Color Matrix 2': array([[ 0.4716, 0.0603, -0.083 ],\n", " [-0.7798, 1.5474, 0.248 ],\n", @@ -2657,9 +2538,10 @@ " [ 0.4351, 0.6621, -0.0972],\n", " [ 0.0505, -0.1562, 0.9308]]), 'Forward Matrix 2': array([[ 0.8924, -0.1041, 0.176 ],\n", " [ 0.4351, 0.6621, -0.0972],\n", - " [ 0.0505, -0.1562, 0.9308]]), 'As Shot Neutral': array([ 0.41307 , 1. , 0.646465]), 'Baseline Exposure': 0.40000000000000002, 'Baseline Noise': 0.80000000000000004})}), InputTransform(M=array([[ 0.86124796, -0.01402326, 0.15277529],\n", - " [ 0.059413 , 1.1495537 , -0.20896661],\n", - " [ 0.02251894, -0.19229327, 1.1697743 ]], dtype=float32), RGB_w=array([ 2.5270655, 1. , 1.3662276], dtype=float32)), 'sRGB'), ('/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.exr', CanonicalMapping({'EXIF': CanonicalMapping({'Make': 'Canon', 'Camera Model Name': 'Canon EOS 5D Mark II', 'Camera Serial Number': '330229410', 'Lens Model': 'EF16-35mm f/2.8L II USM', 'DNG Lens Info': '16 35 undef undef', 'Focal Length': 16.0, 'Exposure Time': 1.0, 'F Number': 8.0, 'ISO': 100.0, 'CFA Pattern 2': array([0, 1, 1, 2]), 'CFA Plane Color': array([0, 1, 2]), 'Black Level Repeat Dim': array([2, 2]), 'Black Level': array([1023, 1023, 1023, 1023]), 'White Level': array([15600]), 'Samples Per Pixel': 3, 'Active Area': array([ 51, 158, 3804, 5792]), 'Orientation': 1, 'Camera Calibration Sig': 'com.adobe', 'Profile Calibration Sig': 'com.adobe', 'Calibration Illuminant 1': 17, 'Calibration Illuminant 2': 21, 'Color Matrix 1': array([[ 0.5309, -0.0229, -0.0336],\n", + " [ 0.0505, -0.1562, 0.9308]]), 'As Shot Neutral': array([ 0.41307 , 1. , 0.646465]), 'Baseline Exposure': 0.40000000000000002, 'Baseline Noise': 0.80000000000000004})}), InputTransform(M=array([[ 0.86482609, -0.0187031 , 0.15387701],\n", + " [ 0.06217639, 1.14592312, -0.20809951],\n", + " [ 0.02503111, -0.19675131, 1.1717202 ]]), RGB_w=array([ 2.52706552, 1. , 1.36622751])), 'sRGB')\" element with index \"3\".\n", + "2024-12-31 12:21:19,687 - 9204 - INFO - ParallelForMultiprocess: Processed \"('/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2598.exr', CanonicalMapping({'EXIF': CanonicalMapping({'Make': 'Canon', 'Camera Model Name': 'Canon EOS 5D Mark II', 'Camera Serial Number': '330229410', 'Lens Model': 'EF16-35mm f/2.8L II USM', 'DNG Lens Info': '16 35 undef undef', 'Focal Length': 16.0, 'Exposure Time': 0.002, 'F Number': 8.0, 'ISO': 100.0, 'CFA Pattern 2': array([0, 1, 1, 2]), 'CFA Plane Color': array([0, 1, 2]), 'Black Level Repeat Dim': array([2, 2]), 'Black Level': array([1023, 1023, 1023, 1023]), 'White Level': array([15600]), 'Samples Per Pixel': 3, 'Active Area': array([ 51, 158, 3804, 5792]), 'Orientation': 1, 'Camera Calibration Sig': 'com.adobe', 'Profile Calibration Sig': 'com.adobe', 'Calibration Illuminant 1': 17, 'Calibration Illuminant 2': 21, 'Color Matrix 1': array([[ 0.5309, -0.0229, -0.0336],\n", " [-0.6241, 1.3265, 0.3337],\n", " [-0.0817, 0.1215, 0.6664]]), 'Color Matrix 2': array([[ 0.4716, 0.0603, -0.083 ],\n", " [-0.7798, 1.5474, 0.248 ],\n", @@ -2675,9 +2557,10 @@ " [ 0.4351, 0.6621, -0.0972],\n", " [ 0.0505, -0.1562, 0.9308]]), 'Forward Matrix 2': array([[ 0.8924, -0.1041, 0.176 ],\n", " [ 0.4351, 0.6621, -0.0972],\n", - " [ 0.0505, -0.1562, 0.9308]]), 'As Shot Neutral': array([ 0.41307 , 1. , 0.646465]), 'Baseline Exposure': 0.40000000000000002, 'Baseline Noise': 0.80000000000000004})}), InputTransform(M=array([[ 0.86124796, -0.01402326, 0.15277529],\n", - " [ 0.059413 , 1.1495537 , -0.20896661],\n", - " [ 0.02251894, -0.19229327, 1.1697743 ]], dtype=float32), RGB_w=array([ 2.5270655, 1. , 1.3662276], dtype=float32)), 'sRGB'), ('/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2602.exr', CanonicalMapping({'EXIF': CanonicalMapping({'Make': 'Canon', 'Camera Model Name': 'Canon EOS 5D Mark II', 'Camera Serial Number': '330229410', 'Lens Model': 'EF16-35mm f/2.8L II USM', 'DNG Lens Info': '16 35 undef undef', 'Focal Length': 16.0, 'Exposure Time': 8.0, 'F Number': 8.0, 'ISO': 100.0, 'CFA Pattern 2': array([0, 1, 1, 2]), 'CFA Plane Color': array([0, 1, 2]), 'Black Level Repeat Dim': array([2, 2]), 'Black Level': array([1023, 1023, 1023, 1023]), 'White Level': array([15600]), 'Samples Per Pixel': 3, 'Active Area': array([ 51, 158, 3804, 5792]), 'Orientation': 1, 'Camera Calibration Sig': 'com.adobe', 'Profile Calibration Sig': 'com.adobe', 'Calibration Illuminant 1': 17, 'Calibration Illuminant 2': 21, 'Color Matrix 1': array([[ 0.5309, -0.0229, -0.0336],\n", + " [ 0.0505, -0.1562, 0.9308]]), 'As Shot Neutral': array([ 0.41307 , 1. , 0.646465]), 'Baseline Exposure': 0.40000000000000002, 'Baseline Noise': 0.80000000000000004})}), InputTransform(M=array([[ 0.86482609, -0.0187031 , 0.15387701],\n", + " [ 0.06217639, 1.14592312, -0.20809951],\n", + " [ 0.02503111, -0.19675131, 1.1717202 ]]), RGB_w=array([ 2.52706552, 1. , 1.36622751])), 'sRGB')\" element with index \"0\".\n", + "2024-12-31 12:21:22,421 - 9204 - INFO - ParallelForMultiprocess: Processed \"('/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2600.exr', CanonicalMapping({'EXIF': CanonicalMapping({'Make': 'Canon', 'Camera Model Name': 'Canon EOS 5D Mark II', 'Camera Serial Number': '330229410', 'Lens Model': 'EF16-35mm f/2.8L II USM', 'DNG Lens Info': '16 35 undef undef', 'Focal Length': 16.0, 'Exposure Time': 0.125, 'F Number': 8.0, 'ISO': 100.0, 'CFA Pattern 2': array([0, 1, 1, 2]), 'CFA Plane Color': array([0, 1, 2]), 'Black Level Repeat Dim': array([2, 2]), 'Black Level': array([1024, 1024, 1024, 1024]), 'White Level': array([15600]), 'Samples Per Pixel': 3, 'Active Area': array([ 51, 158, 3804, 5792]), 'Orientation': 1, 'Camera Calibration Sig': 'com.adobe', 'Profile Calibration Sig': 'com.adobe', 'Calibration Illuminant 1': 17, 'Calibration Illuminant 2': 21, 'Color Matrix 1': array([[ 0.5309, -0.0229, -0.0336],\n", " [-0.6241, 1.3265, 0.3337],\n", " [-0.0817, 0.1215, 0.6664]]), 'Color Matrix 2': array([[ 0.4716, 0.0603, -0.083 ],\n", " [-0.7798, 1.5474, 0.248 ],\n", @@ -2693,80 +2576,36 @@ " [ 0.4351, 0.6621, -0.0972],\n", " [ 0.0505, -0.1562, 0.9308]]), 'Forward Matrix 2': array([[ 0.8924, -0.1041, 0.176 ],\n", " [ 0.4351, 0.6621, -0.0972],\n", - " [ 0.0505, -0.1562, 0.9308]]), 'As Shot Neutral': array([ 0.41307 , 1. , 0.646465]), 'Baseline Exposure': 0.40000000000000002, 'Baseline Noise': 0.80000000000000004})}), InputTransform(M=array([[ 0.86124796, -0.01402326, 0.15277529],\n", - " [ 0.059413 , 1.1495537 , -0.20896661],\n", - " [ 0.02251894, -0.19229327, 1.1697743 ]], dtype=float32), RGB_w=array([ 2.5270655, 1. , 1.3662276], dtype=float32)), 'sRGB')]\n", - "2024-12-26 11:17:40,326 - 55254 - INFO - GraphMergeHDRI: Processing \"NodeCreateImageStack#100(None)\" node...\n", - "2024-12-26 11:17:40,327 - 55254 - INFO - Reading \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2598.exr\" image metadata.\n", - "2024-12-26 11:17:40,583 - 55254 - INFO - Reading \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2599.exr\" image metadata.\n", - "2024-12-26 11:17:40,778 - 55254 - INFO - Reading \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2600.exr\" image metadata.\n", - "2024-12-26 11:17:40,981 - 55254 - INFO - Reading \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.exr\" image metadata.\n", - "2024-12-26 11:17:41,166 - 55254 - INFO - Reading \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2602.exr\" image metadata.\n", - "2024-12-26 11:17:41,305 - 55254 - INFO - GraphMergeHDRI: Processing \"NodeMergeImageStack#101(None)\" node...\n", - "2024-12-26 11:17:41,306 - 55254 - INFO - Reading \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2598.exr\" image.\n", - " File \"\", line 198, in _run_module_as_main\n", - " File \"\", line 88, in _run_code\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel_launcher.py\", line 18, in \n", - " app.launch_new_instance()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/traitlets/config/application.py\", line 1075, in launch_instance\n", - " app.start()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelapp.py\", line 739, in start\n", - " self.io_loop.start()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/tornado/platform/asyncio.py\", line 205, in start\n", - " self.asyncio_loop.run_forever()\n", - " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/asyncio/base_events.py\", line 641, in run_forever\n", - " self._run_once()\n", - " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/asyncio/base_events.py\", line 1986, in _run_once\n", - " handle._run()\n", - " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/asyncio/events.py\", line 88, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 545, in dispatch_queue\n", - " await self.process_one()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 534, in process_one\n", - " await dispatch(*args)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 437, in dispatch_shell\n", - " await result\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/ipkernel.py\", line 362, in execute_request\n", - " await super().execute_request(stream, ident, parent)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 778, in execute_request\n", - " reply_content = await reply_content\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/ipkernel.py\", line 449, in do_execute\n", - " res = shell.run_cell(\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/zmqshell.py\", line 549, in run_cell\n", - " return super().run_cell(*args, **kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3075, in run_cell\n", - " result = self._run_cell(\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3130, in _run_cell\n", - " result = runner(coro)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/async_helpers.py\", line 128, in _pseudo_sync_runner\n", - " coro.send(None)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3334, in run_cell_async\n", - " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3517, in run_ast_nodes\n", - " if await self.run_code(code, result, async_=asy):\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3577, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"/var/folders/xr/sf4r3m2s761fl25h8zsl3k4w0000gn/T/ipykernel_55254/2474524247.py\", line 5, in \n", - " graph.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 1282, in process\n", - " super().process(**kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1928, in process\n", - " node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2467, in process\n", - " execution_output_node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 1051, in process\n", - " super().process(**kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1928, in process\n", - " node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2435, in process\n", - " index, element = self.get_input(\"task\")([i, element, node, self])\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2315, in _task_multiprocess\n", + " [ 0.0505, -0.1562, 0.9308]]), 'As Shot Neutral': array([ 0.41307 , 1. , 0.646465]), 'Baseline Exposure': 0.40000000000000002, 'Baseline Noise': 0.80000000000000004})}), InputTransform(M=array([[ 0.86482609, -0.0187031 , 0.15387701],\n", + " [ 0.06217639, 1.14592312, -0.20809951],\n", + " [ 0.02503111, -0.19675131, 1.1717202 ]]), RGB_w=array([ 2.52706552, 1. , 1.36622751])), 'sRGB')\" element with index \"2\".\n", + "2024-12-31 12:21:22,442 - 9204 - INFO - GraphBatchMergeHDRI: Processing \"NodeCreateBatches#97(None)\" node...\n", + "2024-12-31 12:21:22,464 - 9204 - INFO - GraphBatchMergeHDRI: Processing \"ParallelForMultiprocess#98(None)\" node...\n", + "2024-12-31 12:21:22,466 - 9204 - INFO - ParallelForMultiprocess: Processing \"GraphMergeHDRI(6)\" node...\n", + " File \"\", line 1, in \n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/multiprocessing/spawn.py\", line 122, in spawn_main\n", + " exitcode = _main(fd, parent_sentinel)\n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/multiprocessing/spawn.py\", line 135, in _main\n", + " return self._bootstrap(parent_sentinel)\n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n", + " self.run()\n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/multiprocessing/process.py\", line 108, in run\n", + " self._target(*self._args, **self._kwargs)\n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/concurrent/futures/process.py\", line 263, in _process_worker\n", + " r = call_item.fn(*call_item.args, **call_item.kwargs)\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2364, in _task_multiprocess\n", " sub_graph.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 817, in process\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 951, in wrapper\n", + " result = function(*args, **kwargs)\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 836, in process\n", " super().process(**kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1928, in process\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 951, in wrapper\n", + " result = function(*args, **kwargs)\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1975, in process\n", " node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/nodes.py\", line 1472, in process\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 951, in wrapper\n", + " result = function(*args, **kwargs)\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/nodes.py\", line 1493, in process\n", " image_stack_to_HDRI(image_stack, self.get_input(\"weighting_function\")),\n", " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/generation/hdri.py\", line 118, in image_stack_to_HDRI\n", " warning(\n", @@ -2774,70 +2613,30 @@ " warn(*args, **kwargs) # noqa: B028\n", "/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/verbose.py:325: ColourWarning: \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2598.exr\" image channels contain negative or equal to zero values, unpredictable results may occur! Please consider encoding your images in a wider gamut RGB colourspace.\n", " warn(*args, **kwargs) # noqa: B028\n", - "2024-12-26 11:17:42,473 - 55254 - INFO - Reading \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2599.exr\" image.\n", - " File \"\", line 198, in _run_module_as_main\n", - " File \"\", line 88, in _run_code\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel_launcher.py\", line 18, in \n", - " app.launch_new_instance()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/traitlets/config/application.py\", line 1075, in launch_instance\n", - " app.start()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelapp.py\", line 739, in start\n", - " self.io_loop.start()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/tornado/platform/asyncio.py\", line 205, in start\n", - " self.asyncio_loop.run_forever()\n", - " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/asyncio/base_events.py\", line 641, in run_forever\n", - " self._run_once()\n", - " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/asyncio/base_events.py\", line 1986, in _run_once\n", - " handle._run()\n", - " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/asyncio/events.py\", line 88, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 545, in dispatch_queue\n", - " await self.process_one()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 534, in process_one\n", - " await dispatch(*args)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 437, in dispatch_shell\n", - " await result\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/ipkernel.py\", line 362, in execute_request\n", - " await super().execute_request(stream, ident, parent)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 778, in execute_request\n", - " reply_content = await reply_content\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/ipkernel.py\", line 449, in do_execute\n", - " res = shell.run_cell(\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/zmqshell.py\", line 549, in run_cell\n", - " return super().run_cell(*args, **kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3075, in run_cell\n", - " result = self._run_cell(\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3130, in _run_cell\n", - " result = runner(coro)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/async_helpers.py\", line 128, in _pseudo_sync_runner\n", - " coro.send(None)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3334, in run_cell_async\n", - " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3517, in run_ast_nodes\n", - " if await self.run_code(code, result, async_=asy):\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3577, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"/var/folders/xr/sf4r3m2s761fl25h8zsl3k4w0000gn/T/ipykernel_55254/2474524247.py\", line 5, in \n", - " graph.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 1282, in process\n", - " super().process(**kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1928, in process\n", - " node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2467, in process\n", - " execution_output_node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 1051, in process\n", - " super().process(**kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1928, in process\n", - " node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2435, in process\n", - " index, element = self.get_input(\"task\")([i, element, node, self])\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2315, in _task_multiprocess\n", + " File \"\", line 1, in \n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/multiprocessing/spawn.py\", line 122, in spawn_main\n", + " exitcode = _main(fd, parent_sentinel)\n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/multiprocessing/spawn.py\", line 135, in _main\n", + " return self._bootstrap(parent_sentinel)\n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n", + " self.run()\n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/multiprocessing/process.py\", line 108, in run\n", + " self._target(*self._args, **self._kwargs)\n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/concurrent/futures/process.py\", line 263, in _process_worker\n", + " r = call_item.fn(*call_item.args, **call_item.kwargs)\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2364, in _task_multiprocess\n", " sub_graph.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 817, in process\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 951, in wrapper\n", + " result = function(*args, **kwargs)\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 836, in process\n", " super().process(**kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1928, in process\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 951, in wrapper\n", + " result = function(*args, **kwargs)\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1975, in process\n", " node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/nodes.py\", line 1472, in process\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 951, in wrapper\n", + " result = function(*args, **kwargs)\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/nodes.py\", line 1493, in process\n", " image_stack_to_HDRI(image_stack, self.get_input(\"weighting_function\")),\n", " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/generation/hdri.py\", line 118, in image_stack_to_HDRI\n", " warning(\n", @@ -2845,70 +2644,30 @@ " warn(*args, **kwargs) # noqa: B028\n", "/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/verbose.py:325: ColourWarning: \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2599.exr\" image channels contain negative or equal to zero values, unpredictable results may occur! Please consider encoding your images in a wider gamut RGB colourspace.\n", " warn(*args, **kwargs) # noqa: B028\n", - "2024-12-26 11:17:43,132 - 55254 - INFO - Reading \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2600.exr\" image.\n", - " File \"\", line 198, in _run_module_as_main\n", - " File \"\", line 88, in _run_code\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel_launcher.py\", line 18, in \n", - " app.launch_new_instance()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/traitlets/config/application.py\", line 1075, in launch_instance\n", - " app.start()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelapp.py\", line 739, in start\n", - " self.io_loop.start()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/tornado/platform/asyncio.py\", line 205, in start\n", - " self.asyncio_loop.run_forever()\n", - " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/asyncio/base_events.py\", line 641, in run_forever\n", - " self._run_once()\n", - " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/asyncio/base_events.py\", line 1986, in _run_once\n", - " handle._run()\n", - " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/asyncio/events.py\", line 88, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 545, in dispatch_queue\n", - " await self.process_one()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 534, in process_one\n", - " await dispatch(*args)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 437, in dispatch_shell\n", - " await result\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/ipkernel.py\", line 362, in execute_request\n", - " await super().execute_request(stream, ident, parent)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 778, in execute_request\n", - " reply_content = await reply_content\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/ipkernel.py\", line 449, in do_execute\n", - " res = shell.run_cell(\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/zmqshell.py\", line 549, in run_cell\n", - " return super().run_cell(*args, **kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3075, in run_cell\n", - " result = self._run_cell(\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3130, in _run_cell\n", - " result = runner(coro)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/async_helpers.py\", line 128, in _pseudo_sync_runner\n", - " coro.send(None)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3334, in run_cell_async\n", - " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3517, in run_ast_nodes\n", - " if await self.run_code(code, result, async_=asy):\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3577, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"/var/folders/xr/sf4r3m2s761fl25h8zsl3k4w0000gn/T/ipykernel_55254/2474524247.py\", line 5, in \n", - " graph.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 1282, in process\n", - " super().process(**kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1928, in process\n", - " node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2467, in process\n", - " execution_output_node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 1051, in process\n", - " super().process(**kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1928, in process\n", - " node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2435, in process\n", - " index, element = self.get_input(\"task\")([i, element, node, self])\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2315, in _task_multiprocess\n", + " File \"\", line 1, in \n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/multiprocessing/spawn.py\", line 122, in spawn_main\n", + " exitcode = _main(fd, parent_sentinel)\n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/multiprocessing/spawn.py\", line 135, in _main\n", + " return self._bootstrap(parent_sentinel)\n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n", + " self.run()\n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/multiprocessing/process.py\", line 108, in run\n", + " self._target(*self._args, **self._kwargs)\n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/concurrent/futures/process.py\", line 263, in _process_worker\n", + " r = call_item.fn(*call_item.args, **call_item.kwargs)\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2364, in _task_multiprocess\n", " sub_graph.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 817, in process\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 951, in wrapper\n", + " result = function(*args, **kwargs)\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 836, in process\n", " super().process(**kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1928, in process\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 951, in wrapper\n", + " result = function(*args, **kwargs)\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1975, in process\n", " node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/nodes.py\", line 1472, in process\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 951, in wrapper\n", + " result = function(*args, **kwargs)\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/nodes.py\", line 1493, in process\n", " image_stack_to_HDRI(image_stack, self.get_input(\"weighting_function\")),\n", " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/generation/hdri.py\", line 118, in image_stack_to_HDRI\n", " warning(\n", @@ -2916,142 +2675,30 @@ " warn(*args, **kwargs) # noqa: B028\n", "/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/verbose.py:325: ColourWarning: \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2600.exr\" image channels contain negative or equal to zero values, unpredictable results may occur! Please consider encoding your images in a wider gamut RGB colourspace.\n", " warn(*args, **kwargs) # noqa: B028\n", - " File \"\", line 198, in _run_module_as_main\n", - " File \"\", line 88, in _run_code\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel_launcher.py\", line 18, in \n", - " app.launch_new_instance()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/traitlets/config/application.py\", line 1075, in launch_instance\n", - " app.start()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelapp.py\", line 739, in start\n", - " self.io_loop.start()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/tornado/platform/asyncio.py\", line 205, in start\n", - " self.asyncio_loop.run_forever()\n", - " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/asyncio/base_events.py\", line 641, in run_forever\n", - " self._run_once()\n", - " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/asyncio/base_events.py\", line 1986, in _run_once\n", - " handle._run()\n", - " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/asyncio/events.py\", line 88, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 545, in dispatch_queue\n", - " await self.process_one()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 534, in process_one\n", - " await dispatch(*args)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 437, in dispatch_shell\n", - " await result\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/ipkernel.py\", line 362, in execute_request\n", - " await super().execute_request(stream, ident, parent)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 778, in execute_request\n", - " reply_content = await reply_content\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/ipkernel.py\", line 449, in do_execute\n", - " res = shell.run_cell(\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/zmqshell.py\", line 549, in run_cell\n", - " return super().run_cell(*args, **kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3075, in run_cell\n", - " result = self._run_cell(\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3130, in _run_cell\n", - " result = runner(coro)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/async_helpers.py\", line 128, in _pseudo_sync_runner\n", - " coro.send(None)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3334, in run_cell_async\n", - " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3517, in run_ast_nodes\n", - " if await self.run_code(code, result, async_=asy):\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3577, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"/var/folders/xr/sf4r3m2s761fl25h8zsl3k4w0000gn/T/ipykernel_55254/2474524247.py\", line 5, in \n", - " graph.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 1282, in process\n", - " super().process(**kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1928, in process\n", - " node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2467, in process\n", - " execution_output_node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 1051, in process\n", - " super().process(**kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1928, in process\n", - " node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2435, in process\n", - " index, element = self.get_input(\"task\")([i, element, node, self])\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2315, in _task_multiprocess\n", + " File \"\", line 1, in \n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/multiprocessing/spawn.py\", line 122, in spawn_main\n", + " exitcode = _main(fd, parent_sentinel)\n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/multiprocessing/spawn.py\", line 135, in _main\n", + " return self._bootstrap(parent_sentinel)\n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n", + " self.run()\n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/multiprocessing/process.py\", line 108, in run\n", + " self._target(*self._args, **self._kwargs)\n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/concurrent/futures/process.py\", line 263, in _process_worker\n", + " r = call_item.fn(*call_item.args, **call_item.kwargs)\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2364, in _task_multiprocess\n", " sub_graph.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 817, in process\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 951, in wrapper\n", + " result = function(*args, **kwargs)\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 836, in process\n", " super().process(**kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1928, in process\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 951, in wrapper\n", + " result = function(*args, **kwargs)\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1975, in process\n", " node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/nodes.py\", line 1472, in process\n", - " image_stack_to_HDRI(image_stack, self.get_input(\"weighting_function\")),\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/generation/hdri.py\", line 126, in image_stack_to_HDRI\n", - " weights = np.clip(weighting_function(data), EPSILON, 1)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/generation/weighting_functions.py\", line 218, in double_sigmoid_anchored_function\n", - " w[mask] = 1 - anchored_sigmoid_function(a[mask], domain_h_in, domain_h_out, k)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/generation/weighting_functions.py\", line 206, in anchored_sigmoid_function\n", - " return 1 / (1 + np.power(1 / ((x - c) / (d - c)) - 1, k))\n", - "/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/generation/weighting_functions.py:206: RuntimeWarning: divide by zero encountered in divide\n", - " return 1 / (1 + np.power(1 / ((x - c) / (d - c)) - 1, k))\n", - "2024-12-26 11:17:43,881 - 55254 - INFO - Reading \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.exr\" image.\n", - " File \"\", line 198, in _run_module_as_main\n", - " File \"\", line 88, in _run_code\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel_launcher.py\", line 18, in \n", - " app.launch_new_instance()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/traitlets/config/application.py\", line 1075, in launch_instance\n", - " app.start()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelapp.py\", line 739, in start\n", - " self.io_loop.start()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/tornado/platform/asyncio.py\", line 205, in start\n", - " self.asyncio_loop.run_forever()\n", - " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/asyncio/base_events.py\", line 641, in run_forever\n", - " self._run_once()\n", - " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/asyncio/base_events.py\", line 1986, in _run_once\n", - " handle._run()\n", - " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/asyncio/events.py\", line 88, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 545, in dispatch_queue\n", - " await self.process_one()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 534, in process_one\n", - " await dispatch(*args)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 437, in dispatch_shell\n", - " await result\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/ipkernel.py\", line 362, in execute_request\n", - " await super().execute_request(stream, ident, parent)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 778, in execute_request\n", - " reply_content = await reply_content\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/ipkernel.py\", line 449, in do_execute\n", - " res = shell.run_cell(\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/zmqshell.py\", line 549, in run_cell\n", - " return super().run_cell(*args, **kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3075, in run_cell\n", - " result = self._run_cell(\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3130, in _run_cell\n", - " result = runner(coro)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/async_helpers.py\", line 128, in _pseudo_sync_runner\n", - " coro.send(None)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3334, in run_cell_async\n", - " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3517, in run_ast_nodes\n", - " if await self.run_code(code, result, async_=asy):\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3577, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"/var/folders/xr/sf4r3m2s761fl25h8zsl3k4w0000gn/T/ipykernel_55254/2474524247.py\", line 5, in \n", - " graph.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 1282, in process\n", - " super().process(**kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1928, in process\n", - " node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2467, in process\n", - " execution_output_node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 1051, in process\n", - " super().process(**kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1928, in process\n", - " node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2435, in process\n", - " index, element = self.get_input(\"task\")([i, element, node, self])\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2315, in _task_multiprocess\n", - " sub_graph.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 817, in process\n", - " super().process(**kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1928, in process\n", - " node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/nodes.py\", line 1472, in process\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 951, in wrapper\n", + " result = function(*args, **kwargs)\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/nodes.py\", line 1493, in process\n", " image_stack_to_HDRI(image_stack, self.get_input(\"weighting_function\")),\n", " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/generation/hdri.py\", line 118, in image_stack_to_HDRI\n", " warning(\n", @@ -3059,70 +2706,30 @@ " warn(*args, **kwargs) # noqa: B028\n", "/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/verbose.py:325: ColourWarning: \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2601.exr\" image channels contain negative or equal to zero values, unpredictable results may occur! Please consider encoding your images in a wider gamut RGB colourspace.\n", " warn(*args, **kwargs) # noqa: B028\n", - "2024-12-26 11:17:44,985 - 55254 - INFO - Reading \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2602.exr\" image.\n", - " File \"\", line 198, in _run_module_as_main\n", - " File \"\", line 88, in _run_code\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel_launcher.py\", line 18, in \n", - " app.launch_new_instance()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/traitlets/config/application.py\", line 1075, in launch_instance\n", - " app.start()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelapp.py\", line 739, in start\n", - " self.io_loop.start()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/tornado/platform/asyncio.py\", line 205, in start\n", - " self.asyncio_loop.run_forever()\n", - " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/asyncio/base_events.py\", line 641, in run_forever\n", - " self._run_once()\n", - " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/asyncio/base_events.py\", line 1986, in _run_once\n", - " handle._run()\n", - " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/asyncio/events.py\", line 88, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 545, in dispatch_queue\n", - " await self.process_one()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 534, in process_one\n", - " await dispatch(*args)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 437, in dispatch_shell\n", - " await result\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/ipkernel.py\", line 362, in execute_request\n", - " await super().execute_request(stream, ident, parent)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/kernelbase.py\", line 778, in execute_request\n", - " reply_content = await reply_content\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/ipkernel.py\", line 449, in do_execute\n", - " res = shell.run_cell(\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/ipykernel/zmqshell.py\", line 549, in run_cell\n", - " return super().run_cell(*args, **kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3075, in run_cell\n", - " result = self._run_cell(\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3130, in _run_cell\n", - " result = runner(coro)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/async_helpers.py\", line 128, in _pseudo_sync_runner\n", - " coro.send(None)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3334, in run_cell_async\n", - " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3517, in run_ast_nodes\n", - " if await self.run_code(code, result, async_=asy):\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py\", line 3577, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"/var/folders/xr/sf4r3m2s761fl25h8zsl3k4w0000gn/T/ipykernel_55254/2474524247.py\", line 5, in \n", - " graph.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 1282, in process\n", - " super().process(**kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1928, in process\n", - " node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2467, in process\n", - " execution_output_node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 1051, in process\n", - " super().process(**kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1928, in process\n", - " node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2435, in process\n", - " index, element = self.get_input(\"task\")([i, element, node, self])\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2315, in _task_multiprocess\n", + " File \"\", line 1, in \n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/multiprocessing/spawn.py\", line 122, in spawn_main\n", + " exitcode = _main(fd, parent_sentinel)\n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/multiprocessing/spawn.py\", line 135, in _main\n", + " return self._bootstrap(parent_sentinel)\n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n", + " self.run()\n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/multiprocessing/process.py\", line 108, in run\n", + " self._target(*self._args, **self._kwargs)\n", + " File \"/Users/kelsolaar/.local/share/uv/python/cpython-3.12.6-macos-aarch64-none/lib/python3.12/concurrent/futures/process.py\", line 263, in _process_worker\n", + " r = call_item.fn(*call_item.args, **call_item.kwargs)\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 2364, in _task_multiprocess\n", " sub_graph.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 817, in process\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 951, in wrapper\n", + " result = function(*args, **kwargs)\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/graphs.py\", line 836, in process\n", " super().process(**kwargs)\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1928, in process\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 951, in wrapper\n", + " result = function(*args, **kwargs)\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 1975, in process\n", " node.process()\n", - " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/nodes.py\", line 1472, in process\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/network.py\", line 951, in wrapper\n", + " result = function(*args, **kwargs)\n", + " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/network/nodes.py\", line 1493, in process\n", " image_stack_to_HDRI(image_stack, self.get_input(\"weighting_function\")),\n", " File \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/generation/hdri.py\", line 118, in image_stack_to_HDRI\n", " warning(\n", @@ -3130,20 +2737,17 @@ " warn(*args, **kwargs) # noqa: B028\n", "/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/.venv/lib/python3.12/site-packages/colour/utilities/verbose.py:325: ColourWarning: \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2602.exr\" image channels contain negative or equal to zero values, unpredictable results may occur! Please consider encoding your images in a wider gamut RGB colourspace.\n", " warn(*args, **kwargs) # noqa: B028\n", - "2024-12-26 11:17:45,979 - 55254 - INFO - GraphMergeHDRI: Processing \"NodeDownsample#102(None)\" node...\n", - "2024-12-26 11:17:45,980 - 55254 - INFO - GraphMergeHDRI: Processing \"NodeProcessingMetadata#103(None)\" node...\n", - "2024-12-26 11:17:45,982 - 55254 - INFO - GraphMergeHDRI: Processing \"NodeWatermark#104(None)\" node...\n", - "2024-12-26 11:17:46,038 - 55254 - INFO - GraphMergeHDRI: Processing \"NodeWriteImage#105(None)\" node...\n", - "2024-12-26 11:17:46,626 - 55254 - INFO - GraphPostMergeHDRI: Processing \"NodeNormaliseExposure#107(None)\" node...\n", - "2024-12-26 11:17:47,061 - 55254 - INFO - NormaliseExposure: Normalisation factor: 2.315559317621998\n", - "2024-12-26 11:17:47,765 - 55254 - INFO - GraphPostMergeHDRI: Processing \"ParallelForMultiprocess#108(None)\" node...\n", - "2024-12-26 11:17:47,765 - 55254 - INFO - ParallelForMultiprocess: Processing \"NodeWritePreviewImage#109(None)\" node...\n", - "2024-12-26 11:17:47,765 - 55254 - INFO - ParallelForMultiprocess: Index 0, Element /Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2598_5_HDR.exr\n" + "2024-12-31 12:21:37,961 - 9204 - INFO - ParallelForMultiprocess: Processed \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2598_5_HDR.exr\" element with index \"0\".\n", + "2024-12-31 12:21:37,981 - 9204 - INFO - GraphPostMergeHDRI: Processing \"NodeNormaliseExposure#107(None)\" node...\n", + "2024-12-31 12:21:39,321 - 9204 - INFO - NormaliseExposure: Normalisation factor: 2.3141094514022926\n", + "2024-12-31 12:21:41,288 - 9204 - INFO - GraphPostMergeHDRI: Processing \"ParallelForMultiprocess#108(None)\" node...\n", + "2024-12-31 12:21:41,296 - 9204 - INFO - ParallelForMultiprocess: Processing \"NodeWritePreviewImage#109(None)\" node...\n", + "2024-12-31 12:21:46,352 - 9204 - INFO - ParallelForMultiprocess: Processed \"/Users/kelsolaar/Documents/Development/colour-science/colour-hdri/colour_hdri/resources/colour-hdri-examples-datasets/frobisher_001/IMG_2598_5_HDR.jpg\" element with index \"0\".\n" ] }, { "data": { - "image/png": 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2UwITMF6c48n9D3Dx4T3cGkb8zmdu4cuffRsv37mJlSo/QcCgu0G17tLsoC1m900WMLVb2GYT1RnIynQzAYHrWNgYZJ2XzUzVF79jaLPUxiKAC3P75MJtgG4jnLyOzXoHBCFfpxvltncZOe0Q4hbr4R52F+8gjx/hxukpGIzt9gnSMIG2W10YDAjTiJSyCBEsFn5MhJwZcYjIWRQ7Zp0wU0ZBFgGBCCknTOlCBKMgI7xerXDr5k18+tNv4M1Pn2g7uC4g9rS0wV9CuxhRpKUi1rjYe5h/870nJf77/HHPh0sC3bz2FKD51tENoSlt/xYFvxM+QwhYxYCEgMQBtD5Fzg9wfu8jvPm5V5GHU0yPHuLJ+x/iMZ3gdg6gmzfx+MkTDNOAizxgy4QPcQe3N2LR82iMeJIDbuQdzmhTlLlpGrF78gTj+RNsYsKbdzZ47e4ruHGyll1wrbstpHx7e4Gnova8o20cCp67b/4XJZycIsR/n3NAn+91hcyfB/zb/73/uShgckba7UTQ41yUdeCE7W7EBz/9Lr7/g+/gW9/+1zg7O8N6vQIB2E075JyBSazlKARQMgsBQPhr/Qtl0ecWEkDhxWCjoUIjOZvCrtLrGCM26zXu3rmNm7dXTT4eyzw99uB58WKEjwGuTO3LHKWlz1gO7SO21hzNl0IrKnaY1UkMhAxC5gDENTJfYHxyjjsv3wTHFdLFBbaPz7HDAGICrdfY7naIKWDCGhMD59hgHcUi5SIRRiasOGHEALPFzikh7XZI4w5DyLhzMuDW6Q2sBlF0FZzq6FlDw67UldTgqOfRppBr8bz+O+/Ttt/knWuduI3O0EV6ZqTEiImRpoxEWRR3IFlQESFnIERNkxkTZ4BI6kmhyJjZuJDVXetiqj5byHmlCqlc9L/6X/yHolQlBk8JGcA4jki7hLPtDmnKuNiN+N73foRv/uWP8Y3vfB+73ShKRwBTTmAK4JyqQlNxnLRT/YaW9H2pcdc/BLZd0KIPZDAneGv0EERJcnqywWYTfAZN2uvAs2H7x0crDir1rpR2HqvNs90slPlJQCAwEtJuwsnpCqCAPCWk7YSJCAMDiAFTyiAmJEQ1pRgwBMlvYiAxIXJWpV5VCuVpAueEAMbJKmA9DKLoclXuJJaF7x8f0MKLl/F9HL9eMJC5zapYI1BmcGJkZFCINRKhWKKDBM+ZAJCuKxaFQEu4TPjK2lOVfgGEv/cHv1eV7mpRm1JGThnjmJBzxpQS7n3wEd679xHeff9DpCkhaF1zzpU42qKFGUxuq5St3pU+t/js5OWZ/OuV/caDVM4cBgyD5vfJF51eOOxB8+V4/qHMH+FHUaxfhBdkp4QlGTVviZcRysak6W+piyNlVIVepKq4PiynvBgcv6QKs3dt7pXhl1CxdwiuwgR9nD6+p6IRVZk3ubhmxedFtAFi4QeE1Q2EQNisZJeF4qpmXRb8VHdmbJvGVnEzob9bNNg0N0K4lMTV8GBzfRmFfvbLdCqMYY69yjCoT1OZeVHqKYOHCc72XBg/FWGfzKw8VMXeJhKGzRpP8gmeXGTweoO7tzI+euU2zu8/QgTh4S4ANwaEXcZ6iBingA/TDTzZvILzjx5h95kv40s3PsRHuI3zCfjGhSgPiDNGBOzGCRdPHuPs/gcYH3+EV28S/uZX7+LtT7+Cm6cnxTpPdgVEAG4tI1z79Z3dO8q3noHryJJY9gnDrIsEGxUmIHI73l5x6BWFJX+LD/rE35FN3o47ni5EAGI4AcCIJ6/h9OSz2Nx8B7snP8L2yQPQcIpNzIhxhZwzAgHbizOklDFNEzhnhCA7RDmLqGg7m8yMlAAElp0lzkBW9ahWK3BA4gRCwGolO6erYZBFwI1buHlj5axidJBsW9jh69PI6iWJ0wJeRce3D/oFJpU6y1svaM5L6Z4bodTPeaphpWu81a/QF6MDQwDiahDl/chAHHC6YZzf2GA824JA2CUA6wCaGEMISJlwllfYDjcwnm+R7ryKV9fnOMcGYwbevSBVlMmyL+WMabfF7skTpN05bq4Jb71+ilfu3MB6NTSWyR6v2z5ZOCrg+l9+TXiWDRk/JQzPS3JdiRqehzJ16mgKfouIY78NnlstP9EKfMKN269dKeYbb/0afuv3/h7+5g++jj/7k3+Kb337X+Pxo0cIYYVAjBB055eAaRr1WL4cu68W63r8IxCYWwUf0OKBKQ9AAZnFGjDEAQSxaFqvBmxONtis4+VdfEVEvDza7ODh9YDmv9eaHUsTfBY0D/eWf1XJX2mB8ffEEVNmIASsBsa4GpDHCQRgygQMBNKNlcyEkSNSWCONE/LpTdyII0aIJdjDyYQU2SjNkEV+GnfI04j1ANy9tcKNkzWiHatv+sRkl31dQc0m2sEu65730dNS7oKm361xXRDN5gNBZnS0pMyYcgZNBFACcyjyVowEKI6ARJGVIa+BgEQAUVbJl5WuVOVeVku6osCkYD1elHsm533us2+I24vSaEbKsrDb7WTDLeeM3/qtL+LvnW/xp3/2HfzTf/Hn+OZ3f4jz8wuQWplnx/PE2L6eTCkDZsp6kFFRoY/Kf7lsigOyQS7pxBqRy3E92USOGIZBF6OYI+hT8O8XCsuT7NCHy/PwQQthLRmglrcp7mbt8xiAHAk5Cf9KtrPNOn5MSCBkCuCUwas11iEhIyIzY8rUoIsopzPylMA5YQjAeh2xXsXqGqhp0tK3Tz5YewPqlM/MoAxMgq0AZRDE2omz4GoxQBHiULDDcmXFc6AqDSWECmkKftBN6aYLjbt3bxWlmGKbWGuxKvj09Msbb7yKXx8n/PSnH+B7P/gp3vvgQ4zTpHK/jiRZNUWpW/DclJFOhva4rQ+FFrU46qmnfiG17IpB++jFwCeZbPTQs/YrAVf+FAjIZGM451tgm2oaJwQE1NNonsQXpZ6z7gyAWKwS9hutXFfe+QWBXzHFHjAfxn4JuxRnCYL7NeKY9H3SvzXEWu9TAO4BYIRwIpMsDiIImQU6CcOy1V0xheZ65E1PDTXWe71gZ9/8bmVdRsqzXwqYYHYdkmKLbnmWSpX/bFHesslOQVdDalZUv1AbX4zzgvpikF8K1W9ECAETB+STWzgbA37wZ9/DV//mlxCHgM/9xlv4i3/5Hfzop4+BuEL49Ffw0bjBBz+5h93jjEdnI1597U3cffAuhhsj7l3cxAOIoCEMK+Nk9wg/u9ji4f0fALsneOvVDX791z+NN1+7i/VqhRh0IaJ1DkDnc1D62PhDYSOm8NPekPKq8tSPHtzoNcpQ2PhTszgqibttJiK0lgH6XkfjlwFsjt1CWH0FJy+9hWHzDsaL90HpAcIwYrc7w3qQ40bj7kKFh4w0iZJ+0qNaIUQk9dMH0iN7OSNyQKaEnDKIIlKacOeVT+PB/Z+BIcpDsnk7DIirFeLKLHlcPQs22rMOnuJwwXOgw+Z9ULG5jbecamlNstSf9m+7Q1wndJ2P5N7rPG0jwifQfA3vvTKPUI6GaFgGgQfCLhE+/Ok9vP7ZV0Eg3H39Jfzsh+/jowdbIETQ7ddwkQY8eXCGtGVsdwk3b93B6flDhHXCk3GNCxUGBBcZq2mLR+OEi7P7QNrhpZsDXv/UHdy5daq+nrwyj5pm1Dnn2l6ETo/BNU5DIal71Aikc6BRHjZJVHhlnwU1gm1DK2p1fuGBiEBxhbe+9Nfxmbd/B3/73k/w9T/+J/jmt/4E9z+8hynLUfxBd1vSNIHUL27OcqBQDDMJpFYdmbN8U998piAFiy/NzBknN27j/OyxhOvmDakialhFhGgqlPpTz3X13HYZxy9vvEVul2F7ox3MaOEb7YlD/ZceD5bz9+FzRV7FfQAqCwEcBiQGzh+c4dbdmyACTm+f4vGHj3F+LvSYNrcw5oDdxQ7DxJgmxnqzwWrcgmLGLkWIt9Xa/zFP2OZcfDaeriNu3d3gZL0ui7ge3+Y4s2TRMu+HJVrs+X2fxV4aT/Pfvm4+ismOlmf5I5VcmZASgyiDWXieyFJJaB0TIup4BDJFnlpVkPIxO2XCdbHOTCCvGA2y6WX++EyOLXVXhZ8pJRADNusBU8rY7iYMDGw2K/ze7/4mfus3v4If//hn+Jd/+Gf4k298Fx8+fAzKcpTPjhWKz7SsNFAVfsjKUqnU0XrFzj1UBYY8D6sNpnGr7DhUmkzVP9lsABfG/iBopGvh/hXh6cj85akOKq0XCEEj7zf9UycugzBejNicykbJajNgezZinIROk/rPTaP42MsZGIaIkBKIGCkHtdpzlIkzJmbkSdwkraL41V0NoZVTukF7mn7zG/JLeN0I3PZhhuzz01bXAbZstYig79k087phZQr6EAgpiy9CU5jYJnZLt7jiLJtSReIU10LaDjEUsFQ1E49t1mTxYynH8lMShS5WEZ/93Ot4443X8NGDR/jBD9/BT977AOcX28KfSVvnNy59Xf06xpT30r1atq2NlW7FOCAlMcjxCiAzJHmRAtPHQQc+DtjP7xfiFTyX0c9FoQw5bpvEtRFAQAjKI6QXyjpZmE1z80Epw45Zl5MWptBbWNHO5JdfTvgVVOwZ7BvaXvzaJ3r5fAbUPVG/N0ru+WUA586PQVBFjqjwzHKjL7NVzzk1AKHTWPv6uDeNWMhps3sxXxDMBcqKmuQF3RLkJLSGwmOmzPNRegVg44fPCU9EoQhRwZR7IajVXkAkUn9/A+7cWuHmJuDdkwHf/+YP8JW/8WXs+AR3v/w2zoZbiB99iPfvvAWegLha4eTmCR58ANxcMZ6c3sXF/Xt4mF8GTpUFTSMe3f8Z7j28h1vDDr/1mZfwpc+9jVfu3MZqiNV3njokD/aO2vZemAlERYBl7VP5v46yqYwzfH/peFsnmjDIKOVz7frGaq8IHY7pk8u7txD6ZQLBtRsYTr+E4fRtcHqEvPsREH6GSBPW/ARDGDCt5HjGdifqnhAjpmkUJ+I5FaExh1x2GgMTJk5iSZkDGAEnt1/F7vwhYnyicyMgBsIqrjAMcaGCqMZ6TsCbSX1+UJe4f50gaAe+jbg3WVep/VNhH130cxkFD5bD/Dt1dKL7K+bzFi/gZBOxHgIergLuv/chXnvrNSQEnL76KnZhg3Bxhsebu4IbMWBYr8BPCJvA2K1OMZ6d4YJvACsVYHPC9uwxzs6fYB0TPv3SCV69+wpunGzUJ1lb/2bRT237ml5qNX3lcYmj7O3vBQnKv5nw3ONumUYLwu8vn1hDoBDx8qc+j7/7P/if4W/9W/8B3nvvh/jDf/IP8b0ffk+U9bxDWAXkKMfup2kCQSxxcpbjdswZSEH36uwok1jo5qw9SfK72txEGi9kcQ8AughYRcH3K1b7gBQvgTyLu4j8h78dLMfHX/hy+Sp+cTa1FnmY4Xzz6xR6hXYQYTWIBdlFJJw9OsPNl24ig7C6eQMpRNA4Yjuc6PwPiDFiBDAQMMWVXHKDtVhZ6/im3Ra7aYeBMu6crnDj5CbWq6HSINf4HusOdgVafi5Qviz202K/OWIwC2c/jHtVgOWrl7UCQy4dIFRH5AykxLpcGhEC68UgJCbBE9TZfQCiyFsMFifosAV/XSCbn2YiQiLx58iMao1chJO5+oLLm+RFISAwsBpMMRvByNicDPjC25/B5z73afy9//5jfO+HP8V/9V//S3z/J+/pJlsCR+G74lpDOHfgqK405h1VLH+ol4gJYViD0+RkVek781W5F66EcxUui3oou8uKOkSN9nGWp5EFe6XeXoWeV57AlKQSf3cxYXO6AoMQ1wMyApASUlypeJPFijSJ1SiHgJSSxCM3F9OElBICAZuVWFMP3eUQ1uSrNLUooKnH6iar5ncxcF/BXo5ASwd6sW7faFeyIwm8Pz5m28wCxkJzCUSqkA9ZL0GsaxT7pyj2nFKvFJVR+YAKR+S1i765XKUR2SyTeoYga+GcZGyZM4ZVwCuvvIS7d+/gqxdv496HD/Gdv/wB7j94pEc1s2wwWF3U8r5uj9fytTfkX10IVWs+iRPCAGbnJ9PGY0k59IsIV5znV8zq0syWZAfjAxmi3DOLZwrGrWTDZXF2C1rPw9SiGhCFdQjUWo+6uv5SjOMV4VdYsWewb7gPLrcWwI7l2uJ9BTt+K3mdAbgFRlLBQLp+0KO4gap/k8pB2J3Uq8drbTFuuytGoGcMYJHjmzDO3ojABZMSYG6TkAtvGGN999+rgNt+hRJ/+5WfdnFvDl5FKRJUaFcFH5mCL4j1nqY5P9vi/ruPsH77dbzxpbfw4b0H+P4HAQ92AWfTq6Cwwet4BCCCh4DNG5/FI4oYcsL9R+/jxuYtnOMUtD7B9sljnD24h+nxR3jtNuGrv/kyvvCZ13Dr9ESc6xYLPVILOzhFnrS4bvRUn3jWU63fvFYcMCUcIDPJm7A39Ipqd7IO5Mw/lzLTvvvRhdUa/PKSPhmbCBruguJLOF0/wDS+B+AdIJ1jHbfImIrgtmbGuNshcUYaRwQEDANjHCdVBOhtbhGYJvHxk3YX2J0/xrA+LScbZK5GrDcnIJqE6ZSjDKYsqBzLK9qWlW59uyzOfmFvJhd2Yf3HBps97rt0Dr0drte0bX5u4lHNtz9ib8qRXqFnYQRg3E04e7RFfOUWbr/6Es6fXOD+E+BiIuzyDYAG3MaFSZkYbt/FlgghZ5xtH2M1vCSL/TjIIv/8DHl3jpsb4PVP38DLd25isxrKcbIqrNb+rk0jdE0ri9MZJi30oS0QlrFuzn+8gGqyM3lTvUWgkhW5efXLCkSEzY2X8Pkv/g4++7mv4t0ffxt//mf/HN/5iz/DoyePkfOEbJdbkbguSJMsBnIiUMwILP797Lh+TgBRkEt1oIvGcYsQV7AVGClvWq9XkONOKDp4K8uOldjypr52mOheF1k09uDyIkJf0l/XibcPx7vXuVLPK+w0huF/CW83+lLKGLcTTm6ssbl5inE34mxHGDOQeA3kgI26QGEihJMTTCAEZuymHWI8QUIE1CF7GndgPW770u01bpysMaibBT9GM1lloY8Kb+1C5n25v3f7cfV8uSmnS9TUz4Bpzut9MqWngC6OMhdlpyisRUkyjgm7OAFEiEz6ndTLOUAry8MWYFyPqpbvunRnBkdggNxqy0rbTYHXKKz1gZhrnsoSYwjAYLwiqXyYkRLw2mt38NLd2/jNX/si/vK7P8C/+qOv41vf+T6enF+AOHdKOiBn6dGc9eAWya3ahdKacs+Oc7FcrhCo3upr/CoOuiHfjOJC318x9ClQ95qwiLUtgbKnA0RhX1hr6VTzXFLo+X8zi5/HsIoY1gNSythN0JuqxShiMNcIRGK5BwDMmHJCoEFlKFEI5SnX47YnEeshFl7u23wZy/T4WdLuoQcfCzT9Vr/NlIoNj6GZstnEg8wABQbljJyAKRAGqr6+keVIewCBYyiTMWsmDc7a+pAlDamvYTt6K+POB2RXLhPeLt4JREAMoKzHr4lBJAYvN2+e4OR0gzc+9Qre/+A+fvjj9/Cz9+9jO46wI/Vl9UMoSj6z1jMZmaE3KRe/9a4OOReZUVsAIrsp+Fkx83rY/Wy04OOdnV4O2B++EIHVTYOu77lsllYqGlwudR7XzaOGUup42u22IXR48SJx9RMIR8XeDJZE6aVwAx+vd1JGkCO5AdLVAWnMMmnNMWiotltVyPOOfHV31fksqLcTdUo92H4FWupALqShtkviJfusQS5WeSBj0u0i3X79JRf9wli/lnybxX2xzKsWekGVeyHKwskr9OoNfwG7iyd47ycf4ubLt3D/YoW/3L2O3fty1fvp6QonMeDeR2dINwDSm25zYlykhNObr+EJ38L27AnOP/opwvgYn3v9FL/+W2/hrdfuYrMexHLLt8230fpGH8pVK6bAhPpraPqg0j+/hGe0fb5fyNJ5YeTOKuHPZdsxGbSztPXRpXmUMf3lByIChrsY4kvAydtAfojp7EcYL97DMASID6EsRwWYsYsDck4Yx1GO5k5yAydhxJTEqiSlhLi5ibC7QAgDRtXpl6O4a+10mCJYBsl2McldkFOEj6IAOPyvluSU+7RXIzhP14Y1L9TiKdx8rtGoCsKO6xuD94JAgzPdzbteoReUthRlnzF6AqYp49GDM6xvbHA2RXyQbmF6LALYahWxCoQn5yN4BUBvujXn76vNTex4g2m3w3jxAJS2uHtrhU99+iW8dPMUK1vod203+tl2ksbjVvB2VHGx8zuyXGN2K/zFG3S59nehGb0gD4DMTw6h0gNf918RPA/DGp95+6/hzS/8Fv7uH7yP7//lH+Nb3/pTfOcvvyWLCFXAkWpkU7DbdJMsVvTGbEKSI0sIyIkR4ho0jSAK4o5M86AYEFYDwHWDoBnxDu2WsLD9toynVw0+DJUvNPymIHMfs3vzP+RDa/rWAk9Dqfte6Ef9Boj1xvZ8h7iK2KWAJ3mDvGXYJl8kwm5M4Cg01qxKEjPisMbEgypgz0E54XQTcev2CU43q3J82hpA7mEJMyrKU/NtHxbt8SuvgYvdiFoLdnGoHCXtJLRZBftx8K9GW2sVTBrkeiyXGTFn7EbdfFqJUoUQwIHUPyUVumi300qGrIoY6KJalIKyYA6IOWJYoRy9A0yJV1tTWm/V1EVgJvFza5CZQbDj70AIjCGs8Tu/8xV89Te+hA9+dg9/9q+/ja9/+3v47vffcet9lXEYyidlMR8oliPCcs5LZXFiEA0gZIhPTdevgRA4ApjfhDsbAN4zZs3g8WE8fiY8f36wzDZ65V3/rQvvkIwTYxrFUisxsOMIMZyiopATH5uox1z0J4SIDD3iPe0AZqwGwmYzyDFdapFsL05anfcFfYJgVh9HiEwv5VAIZMdPlcfZpXtJLR4RQ1GKyPF68b1nmXvLOqAq3G3lQQBIL5xjVp90jq73DE8+UUmPkrMe9w/CZ5mzbI67tgUa8Jk3P4XXX38NTx4/wU/feR/vvn8P9+4/0EuQ7EIfZ+KguG7tVwFJ3AXY5ZMUFN87i93Ct54H8l0diT8BqL4IBxX++5gnoGNiPo2hJyGojQBCdrJq0wdUdSPFMpOAob8M4zI8vyb0svqS5uQq0K/vXwQcFXt74apDt4Sw5ndvB+ni+wBuASCMkx4NiNL1MUb41WGZqGU2VMNic1BsSgHbeVBNgZDhYn9tizvuaKukMWfxbTvqToeS+jZd3zeFIVoftDZojbRpAr0KZEXAP6TMa55FgVf86pF9k+d0cgcXbwz4zvuEn1wMYAZWq4BhNWCzWSExgJxwcrrB+cRybDYnjDkjP9zi7N63cHO1xe984TV85fO/hldfuoVVjOJ/olfoWbMWFv9iidcqNKqCg4qJvV9QMZmga9+WGF/t4aIYMqHR0dRMYrWQtR5LO/klDdcSWOv3qwSCRxsgvobh1muIp/cwXryDfPFTEI9YseBEjAMSpzLG2wtGYLnhmnY7pDSCQsTD++9gtbmBEAbxXwRxtE2Qy3JyPmv62PDTK/fMN2ahKgXFuwXAPELbNgvu6ZPSBrRfHX73c4D2P83my4IgTZXp9wq8maVesZLzVnr2LOl5dYLxdsD7jwkPRjnCESMhRrmtUI6DZQyrAaNenIDMSMzg84Td2c+wjhPefPkmXnv5U7h5slGlvc0Ha1anlHTttR41OtbQOAtb4h8dLjYjQ+3H5tZqN+a+7EKpudITPy6+zstj/asBRAG37r6B3/69fw+/9bt/Dz/4zh/jG3/+z/AXf/F1bHdbRNFIyBEvzhDDXcI4sV6GRAASpiwWQ9uzhwjDCkQRelmoKIsArIeIi53emFtrgKJOr//UkL3f+obUb/PgpQTX7ajukfoPLUufJ694KslbxZ53TbGk0PO/HFdIm4DHW+Ai1/zMml+GjBFDRGLFc2ZZHIwZ0+4RhpDx0o01bp7acVu/oeZxvWsHUYtPXRdVfrvcEQex60Cg5dmcYqM6d/qx73+XKyvzv/Sr0orMEAf6IJnj2neT3lRpC/XMWRWhhCkl2UzVG0TNAi9nSZvNWk/lUA7eQo+ByBj02HPdfKKmTwhc7q7J4MIrLJ/IAUMEpqnegxgCAWPCZhXx6U+/hk+9/ir+4N/+fXz969/BH/7x1/Ht7/4Yu3EU6yRiRLUMNI/YSU/KMIk1HydRTk7jBShEmbdOgQ+QWur7WdJLbd0Y7BujedRnhqtS9wOcfR53FvD0Cr2COwTxqTUQtkkvw7EgR0fkaLZcrlHnsIxVTjsEYmxWEZuV3W67H7d9I6/LBztWfNBK1idqjOr3DPJBPL5y/ahpU8vzFZfUGjZnRgIXAxIzHMkAKHCRx6zKgtPVj2bhD9Y4vcSGAcDcJWkntW1yixYn5xpNILvHgwkDCEl93cr6Sy6wGyJw+/ZN/Pqtm/jylz6Hd999Hz/8ybt4/4OP5FIgVOUPmJDdZVi+HrYJnpJs0slglQ6TZqkfwhcJz4sO+AyfReK7Cs/fm66rROYFtUNDf5Yu0ahr49lx22dsm6/GEs8nF0hLA9Mn7DPh9rWP9jzwfgmOir3nBvtGvXX3aLsHq7gCwIgQXyD+GKd38qkP9RiPcTaYIqCW7df5Qsq55FlQ44CJtMWr+7m+HR68FwPLOaD4MSCn3OpnulvYm9LO/ObJDURVmVd96anSj/xzte4hCjjbJeziLXxw70PQjVPEELHerLA53SDEAGTgpRuEh4kBztg9eYJH9z9A2m3x6tl7+P1/47N4+7Ofxq0bp1IXUis7YzhO0PALFt87xWrRWu12E0p8Hw5hbaUM9qbicKbtqEfuNGFgAqvZnc/Pbsu1o9a+/MbvHlShiLoL9yum13Og4zu8hs3NV8E3vgxOHyDv3kXePUSaHgO7C5yc3gSnhCFGTCnh4uIC0xQBmmRBkDPAQUzM1UHiKkYwxEFvv9RvpMNGSed+u1VBmR/gRq7qMX0W2EwAm3lzn0L+R2CZay0xq+W5TnPrPPceqA2r/vS8gg/l+25ipLDBkydnwGqFGER5uloPxa/SyYqw1Uk97bbYnj0BTxM240N87s27eOXlO9isVgWng3U9Kn7aP9Q0srad/FB147q0CPKPVATapaVFlSDIRtSOezrFj0SdCzdzSo2Sl8f9X0WQOTXgi1/9fbz9a7+Lv/PBj/Htb/wrfOubf4QP7t/DxfYcSCNWq7Xw6SBHcqdxVOfOuRw3BMzJs+Qdgig7YozlkqRGaesXMj0xRhOxe71CvJ8TNHjifhs+WfBqrtArceSh+U2ZkWnAtNsBMValnt1SDjEqm7Qf8jRh2u3kYqO0xd2XTnHjdIMhxlLPqkTs2rHA+AiOPLctnuHP88anlko7zG/JyOIvgLIQKnEJGJxVo0pp8hCsJEJOknbHGYkTpiFgPRAoJN0IJXFFMlX5zdhMzoobpY9UuaeKgII3JPQ6BElo/v4kEYM4gJGQbTFHqjSPBDDAOp4hBHG6T4ygVhzTlItbEl6t8Df+xm/hd/7aV/HjH7+LP/6zb+Hr3/oe7j98hHEcgZwRoxwJG0isRczFBghAtmPF1Ph2CkqbA8Vm3rRPy8q9/eBlgP1R9oc/HT1YWsheaTJ7ubYiVxn5/Qq9Vr6QbhZumFKCCsJORpC5FYMczSVALkZJSb7nhNOTQfxdu4tM5m1axtsrNHP+rXm2ti3RZwOe57MvX6cEeBrqbv3mXbNYvtXfnipIM2MCI0wZOQTEANjRV4kfRMnmZDAwYEzPDEO4yDO6aZJzKSwgFNczzWYIA7YOFbWdpidCDCTWhME2YQQ3mWs/2hFOYgZTxFuf/TTefPN1fPTgEX7y0/fwznv3cH6xRUoJDFbfoNI+Zi5Keukmp8lrxsP40/Ok7z8nvr2I6FeDfWvCqyv05EOhoewqYzLDYhV1nrGQheiP2+7D8yvCrLyr5LfIaBe+XVE4mLvBasHPyqeZMUfF3nMFT5YJQAD4ARi3YQwv6y1PiIPEC+q022VRBTkd0uJ8uFUAlGN3jnKXIw6ky0ZW1ZurWrugd4LYYmtojgGWkZWp3+WQRMFXjZbLrmd73Nb85gXEIIKevZNa6EUKxaeeKfTKzbhOsQcKYFrhbDthiAE5RqxWK2xONjg5WSOPE06GiNfXwIcXj3Dv3XcRp8f4wqdv4jd/9zfx5qdewma9knIAzRdu0W9Evi4M+gVK9ZmHIvj4uC0hccd1fVdZOtJFP9XZVF6UkfoyfN4EMbf308fq189OvwPh6/ErDUQgugGiz4GGtxBPzxAvfoAtfgietojrBHDCdrdFThnTbgIj4N33L3DrxgniSuZJ0lM6YYgAA8OwEmsSV05Z6IOw5JNkrsTzZH7P83U4QR93YQL0VGIWj3xMKhOrUdyFivt7lXn2HVTS2y209g6IwLmb1MlyEIf5q1XEMAyidA0BtwfgfNziyaNHCHmLV26v8cZn38CdW6eNX61y7BadwOC/of3WKONCjzctku1XlDe9uRjW5mrCsCcIKEJ1AdMFuzhkldfh+fmrgz4JQHLZxutfwL/5qc/jb/7tfx/3Pvgx/tU//of49l9+CxfbC4SQMQTGmCYwZ6SUwSA8fDxhsx5AeieW+iMvVmTDEOsRRSnKoVmPp44GlCBPF1x9jT8v4ezCoM4/HyYMV5f9FwRzMt6oPHFBoVfiGf4sKfS84EABKavbEuX9IZJeyMCIRNgEYEwTdhdbEE+4cRJx6+5tnGxWlcZolhWrqFPWuaZoPRa/+/fFjlr6uLxoXI6znBtpFM/nL8Vh16dChyqNNXnQjqLZIttWsMzAlDJyZkwpIqeAYSDEIFZTTFkscpw8ZvW0xXY2KplkcZY5ICpOjJi0kqE4ylcTHfGHZTdzeFKqR2UpEKJ6BRsoyNF5ZkzKbMWiSBoR9UpQWkV8/gufwVufexP/zh/8m/jxO+/hv/lHX8P3fvAT7KZJ+gCsiMzgnAAQLnZZeEWwOljXSq1CJPB4DWpKuER3d/nI7o2xL2APP59zmEugIkfljQ4R5sq9jp86ejGXI8QKrMq8VU6w49gDAckuPLLjtqfr4jrDl1tqup/5LjfxEl59SeqnDFuK6hRyHtjh/965pL0boApyO2q6UB02Zbz62Yyk1lASPyEXus76284z5UeC4upHU3+DuFKQDG0sKvUyhW2tksk1XPA85HpsPyj+y+Uf2aKKzzYAIEaMAS+/fAd3797BV770eXz08DG++70f4oP7D4SPlPKzykHSgWPKxbrQK/DrPJ9bkPX1vx5ciYIvx7pa0ucCz0uh18BsrUFlDUoke0wJ1U8rETBEp1heKuMScNO94d1Xz8fn4L/xQtg1BugSkaDUlZ9u2I+KvecOdQg4PwRRBvJ7AK0A+iIGWivzsiO4AcxydMcs9whCsMqxWpj/LWOARuWpXvVsR/o0PeyyDUmlSp6q9JtVeZFYNZx6DuT+tG76Ty03RDQ320a1xov2HPXXHcUlPWpbLspwCkGiasVHJlgSxpwRYsTZdsTNu2tsNmucbFaglBED4+EH7+K79x5hevhN/I2338BX3/4N3L1zC2vLX+senaBQF/5C3GMRPlwLncIN8H706rfK18h1t/xbGDVo5gjfnbKeYbaMsuXt5hz0GJ/Ts2Qow3VMvty4a8N19ZXdrwYQgRABuoVw+ts4PfkCePce0vbH2F18hCECacgIw4R33vsI4xjwgx89xGbzBJ/57FtYhRUIshAFGMNqhfFc8dyKgD+KawKDKflUCHL/uqq5hUYd12LVOxdFFr5dsRvQTosqpHd2arZwJDhlnjsub++NMg9FIdAo+QLpsSnUb1pmZvGzNU4J65OIYZA/s3a9ePIIHzzZIsf38NYrt/H6y2/g9GRdfWRqdUv+1okOyoU4voWNQEB9kq7HMMOliueHYVEVUHDTWWM2cwPOerDNoC4GuMQ7QgUiwvrkNt586zfwP/qffgX33vsBvvnn/xzf/Pp/i/sf3UdkIAdGiBkPH58jJcKHH24xDDvcuXsXUTV8FMRifRhEIQV0uONQcK+oeBB12+24+XtXiDRueQXiKnWIvZfv5OI6nCm/HpfJ5BSv3KsltQpAzbfLH6h4nnJGXNkGYJWBxt0WeTchh0d46eYGt27cwnoYGhwvuXZ4V3n3cstp78uhnqqhlZXuj+sks4PhNZvO6rYb6kNobQvWYOkQkMztg/rHsg1ZE9+YGMgJuzEj54gYSd2SMCioo/2Qxa+ku5zANq+ynUJhFtrMAeZzjwGsUTd7mbNcDud7jkKZu+J0PWAgEo/Vas0TQhRLeY9QGodtZ40l/gBg9fIt3LlzE196+3P48Y/ewR/9yTfxjb/4Hh48flLbTIyLixE5E852E0JIOD09QSjO+qRsUzA3QEZfl7bJ61DuHSvCJcq/Z4dDs3eJpy0q7QqOS5hPd6lCr2OP5SKDBNBADQ0BAWmawInBeYfT1YD1Orqb6tuKXsZbax2x0BFXTf1xw556ULWFLf4igXayMJcYnuaKFk+0b0RqfkF+DDJSkkuIMptRg5Vhx+G50m4/uxVlRTnLyIHAWY7xS1A9um9Wd6WW3iWU/eg4ZlUwSjqlHQhyNWUxhCG3Wc7Fmvb0xgYnp2u8+spdPPjoIX78k3fx7s/u4Xy3E60jCb0ZJ7Eu3O0SKGSsVqsZRxT61B0Z/kWDa9Z5kQ70THRf9rQUpr3XMEdHN0jmhl3UEgOJBekC7z5Yb599V5f9eVyLaux53/e8Dy6h8AvZXVduPyr2ngssDWYGaAPgNggXkGk74fTkFoiAGGJR5OWUlYB1AqHdglslhpa6+KMMXNR2tg6oaWHCjzzXSVKJ9HWFiSrKVQIoyNoK+X6nPUSxsCmWetEs9cTRbfSWeSSCvFnvmU89U+xViz0VuEJAHAjr9Rqr9RqrIWK8OMOTj+7h4tGH+NSdgN/8vS/jC2++ipunJ3r8tz/qJ4v6QsQ6YWF2bA9dHCe02Nd6UVTNSZhltdkzU3ZQHQfq8jMwhYPt2nlbQE/U2nq7/FTB6y/y8O07Qg/KgOg2sLmFYfMFDDcfYNr+FbY/+yt89PARNusTMAY8enSBcWK8d+8cRGuAgJVa8MitmlUYmzHDBZ8jJvT2ij9g2YKvOgOuQpIXSg618TphhDqfCA7fvUVe8Eq9env1zFoPLi3IxZMGV8WfFMiUhZ7EiDgILUnTiN35E4wXZ7h1EvDGZ1/FKy/dlIV+p9AzXPRtqFSyNrDBjwU8NAnV8jikF7dd3/5qJfgylS6T+97QctR6SgSql+AAhT84FG/q7OnRdQW9XwkgQogrfOozX8Frb34Zf+vv/gd45yd/iX/1z/4LfOc7f4EHF1sMcQDWAdvthJSBx09GkCw1ENViL4ZmOVWzh6G4m2n9e/et5+VtnHnAUnaH4eCsbZ49rhR+Wfim/9Yp9yy946teMVAXDH0Zmqb436Vyu22aRmxWwO27N3HjZK1+TK1qy/gq6Lq8+Kc+8h6aV3rsEvy5CnodjNPxh8LzNWErb1S89wnaKSDKtQQggkBB/e8y5AISCrLgD1SUf0YhxYIv6YLfxkJ5QGDEnIoPZNayGCh+YVn9ZWVmhExIQW6rFTcrGUOMSq9YL+TQCrDcSl3mkzYw6kauHTkkdXqfyDztTxgiYTeK1ZDNcHHTAIyTxP/iFz+Hz3/+Lfy7Hz7A9370E/yzf/7H+P6P38X5NImcSiy33TNwsctl0K2dszkwG9AFRKQ6tnvVfo7fLwU/ldbvIIrvn4m94q5X6DVxXFb7lHsulWNM5gu8rh2Y9bhtzhgCsDldYaW3287zv6Td3I/VszG/S7qsgOFA845nUdrS8rMXGrSADHYbowCYMOUMJkYsMqXSWK0Ts1yAE2BrSpN7BceC9iP1QoprHAOgjKKwY+jR9Sz4bv7e7SZtS2xH3j0EzyMIZcM7m/yS9MKVJHSjbniIlCW++QivvHoXd19+CV85P8e9Dx/gr77/E9z/6CFSznI5T0DxljVNxdzBzfnl4TYZ++ng2WbCxwXLfI32zvlDaEVLH8s301nYKSVZzwRdk2NPny/XDrU7D6Z7Nrx/vrCvLgfmBO0bn2U4KvaeO1jvBxDdBHAKhBGELYAVhpunMkJxDXAGUQSzOCuuXEiFNXbCnJpgmfAEKKPt/JT4G3PLnj5B/fU1qV0+aBQCPriioeeklZO0grzG14W9WeEVZd6CYk/iRERTBpgy0Hy42JFcp9Trb9BkEIaYsFlHbO7excX5Qzx890MM6TG+9Jlb+OpffxtvvHoHJ0N0ygfUxYn1MamSrRE6UDCq+q3zigHr5yr4OHHGCcl+hkihZpEZmMqir923dmobxwtMScCEJm4PfihLHOHr1WLPxT4q9g6D9M8KGF7DEF/BK299GfH023jj8Q/w4cOEi/Nv4Px8wsnJGquTFQAgrgbkzAgxImUbsyocVUsFwAbHvpUhpxrvkELP1RQNk7iKHHGZhGwSlsN/r4gzXAyhKt7rhTfdJRkmODo/KgUvi0BZBX3A8ByIFDBEwnB6imnc4vHDMwTe4tU7G7z+mVdw+8ZJPW5bqtviY2lBaXM1/e87wr5XPKMmUNC2oZDzfi10tIWCxy4zJ6MATTh0wV6VQm116wZP/e59ocpbaNIcYQmICKe3XsGXvvq38IUv/3W888Nv4w//xX+JP/3Tf4mH5xnj7j2MU5abF1dRhVLZqAsxtAPm+Wz3pCS/UeSh+9bVrPCHBqevgt/LLW1+7JmaX3LfqfJJh59zSz0qaQ2/0f1Sl2eZv4bvUCv91QopTRi3I4gn3DwZcOulGzhZD3VTzuHO4aZxW9ZCbwgf389V6eDTMvT8vOe7s0V/l6Hh9TIslE5o5RhG8QeZbZ6RzLhhGECQhXDQG2ADq8WMmi4zAylncIAe3VO6nMVfFyIXJRtA9RINyqAUgMAIHIrsMXIGhREEOVItddZL0HIW315aJqCO+MH1ojl1NyM+8lB8/WUO4KEq2qckzm4zy8U4ACEEBlECETAMhFc/9TJefu0l/LXf/DL+6ns/xj/7V3+GP//Wd3ExMnJ6hJS4yLFSTYJtyC+OyWwgn0K5dwCZnxrVfQZLn/s51ynx9in3PA9qlXjdZhralwYvSdzwMGekKYGQsRoCNicrDDGoz9LltH27ZmHX5HVGep+VRfaiBHW/ABp5/llKKv3uFG29qyXzAZsZIObGD6wZZQCMrDpsodvyD5H0SbmcuuFv7ZNz2SmuakisZdvLDqhau5qij83YgVVpX9e7hc4zUHzbFgPaXI7f1xNJIhMTGIkyQgBu3ryB05unePON13Dv3kf4qx/+FO+8dw9TYnDeImcuMqhmXDbH9/Y8/WIo964yl+fNvL5Cjw6WZv3KMGUu0F6GcZXl5944z4qwnwg41IjrzZWjYu9jAz9IAcAGAOEij0KK1ZkxQOCU9PgkN8jBqlhqfOmhIlC9xrse0ZJYdixX0bMh9JZDO1Ha3Lu45B+rIF4Ed/gFfiiKuegVefoboxy9HfzNt9RdouEsffwR3CBnQYpywJQFAOF0HXH+8EM8uv8ebq8v8Hu/9ia++oXP46XiV0sZE0GJvv5WOu5+qSjOjNF39/02bZfX2kmqHizM0ysWdC+t5GvZBh1vuwwDyjQsblN82Z3CfNVgj8oITXlQR1YSkAsny+yo2LsyEAXQ+hXcfeO/A7z+63jwp/8ZYghYr1cY4gDW29liDOKUOwbkSZ1zl5W5vSwp5+AmCTfPRaDwqKrhvdVel9nC72LrZq9+HvrFPHmF+8LxWzsub/7zWss9AM17VRSYQGlpjPashozx4hzbs0fYxAmf/dQdvP7yyzjdrIrDbXJ1lt/syBiVNsx6hUyUtLDqzqDiesGgEivYzXLuqxVY7kDpRsH3co911W1CN1JUyy3fua2zEQWrtc/beMEviRT0AoAQhw0++6XfwVtf/G3c3Pxv8P/7x39W/cHGACPyFORYIxGJpVHpa8mnEm3M0c7jeZfEj/0SujpV3zO10/Ly7yW0w8VW8e6UezbnUePD8NnyASo/7OQJz35CIKRph2m3xRAy7t7c4NaN21gN0dXHFqYondPUXHFhrsZr+7nFyQVm6inHU6AO9c8dXfa9PqfMnSK/G+rDKiCjFeyOyEIVUyhjaFYTOQEIWY7ZgorMYzmZTy5P9zMIE8stscJzxDIoMKkSMYuCMGRwIAQEMESBM1EAhaTKuTo3Mme9iArgnDFlsdK2fsoZemFGqJZ7ulEsC3JGHAKGnAGW21NBcnw2JTkuP0bxJSgWP4TVcIrf/u2v4Mtf+QL+D//n/we+/b2fgoCyUcVeOa0L/oJ3i3h5iS72kHLPyWbPB/ZN2vn3fYq8qqSv6Rrlnn72yj1qH1y8Wm4gIOeEnBIiMU7XEZv1ph7vpj21pznGXglogU50X5bzu6yURhC7Yl3mOT+9okiU9v4CGyEm7arODERMwT+TPVAlDwah7HEQIUFvyyWb+dxsTBOJf29yvocpCT1JlIsvPiOk9VIdaThT1qkRtC8YamgLO/VgtCfqUf0Q1KctV1+NwoMZIWSEJHQry/4CYljh059+Da+99jL+yb/4Y7x/74HU3c23Oib8zGPyYmFhjl4BOdplHy1825NV01eHE8hGEMNmZwzUyuuLFdtT7lPBktA1f+tl83050ULcy1ZVzwZL2Lofjoq9jx0IoNsAdgAAvtgq8RmAnEBxhZRHKG1rkplEXxbrAMpOgsaxm8ZE6OPiLyWzmbi3068SbldQ0RJgLlHQ/HEm4HsfeDG6Y7ehVea5sEEv1IgxiEVerMq9ehzXX7YhZUb17ioEXo+UEOErbwx4/90zfO6rn8Xbn76L05OV+oSBXN8OE2bhdqQqGpryQFlK7VP4uBW92C4nKWzULabJfNaY41m3zC7O71QB6BzEEEubuKStY9EOiTJdPzaqQDCG67cdSwnM2l/Whtqe4vD6CNcCmTcvAwjYnNxADqPc0Co+uBFDRFZ/cNtxhxnpd6+yMBKp3lvoYcFazxYYVc1U559nMXYct+aP2XPTnuaJ6oLd5iyZcNXdTN0r5EvY/BiuP/7eWvKh4HY5iuuFeyK8djvg8cMdXn79Jbx8+wbWajFVcbpbCFtbS9sYzQIERTezKORSE8Aujsd/uwmc57kUOuJozayUqmGvpLhXypVi2wdXwVICt2klS6UMhwSpI+wFIsJrr72MGAjDsAJTRox6cyBkzmdmDDFgSumwcGfEF/1x2/6bTIg+zmGrvUtES+rntEvvIphyrsF5+4N9v0Sxt1ehN1fs2XeDWxtgt004vXWKGycr9ZFpNeRS3zmO+kfjtf33xW5xfL2mNto6j81dfvNYNWY3Kgcq0t+W15dSV9rz0BKi7N9gylkdzysF5EoryzFXTZyzuodBQLnanVSOUr/NgdTnVdD7vZmQOdQeIJJ7M0CIws6QmRF1ER+YMAVCCAkYbQEfTKoBm4KC5SKOnBkRYtFl1lucgcQJtIoi75Io7mIgJL3RFtEW/SiyXxwYIZIo96YJ0yhHg3NiuY0zArdunoolSYzgrMqM0jTS+pL26QK2aX8exMTLAvdp9i5B70PQ8BOHa70Sb59yr1HkOXm44acF3Xq8W8bD9UBIE2O1kdtt547yuUkwR50lnnu47ctweOG/d1G0+G1faX1d5wNpfV50ciYTXAHk8hihCcULneUBT8mW1n+uaoziq73o9QCEDKRGfrARUnmNoS7suCjwMmWkLIHMQA6u/UV+ha5/dUPMLk0CxF8fCc4aXpjMyOxSM5qLG0NA2YBLKYv1r9IhVuu8zXpV5NVsmx2Fn5LSDWdduATPgI+XJX6mrK9S+myaVr7c12Ppheahe3ltFDZx2DrvCnz6+QHP5vFVy6bud1/aj3v8DsFRsfexAwFYAXobWJpIBd8BzAlEAZySY2JUhHmbGlXRA9gxAFbpjfRb9bWnz0GdD3vPwmRCXcdc/OIfzdJi3hahqSrQV4u7csutXojRWutVBd8Qo8Ql+UZquRe9As8pBsTvHrrjfVYHFmsZCrh7e4V//+9+BUROmWe8gNxuowkhhYhQJ7BIy+UUSrebrmmN0blsmvTFZx7qZSgwdks1R2GIQRQ3xkia3pa6ZaiJu42KtUGVuuVbESapjHfxwaVgR/v8UVzS/Jd8gB3hKkDYrE5wcrLClBLWqxU4RxAIw2qFKTFOw4BxlwpmOaxsxqxgHqHuhtrk65R7Nv+KMqe31nPau1YpuCQstlzVcMy+Ncq5Tpm3V7FH1WKvUeyFinMmyFuYL6suLCoTPt1E/MYXX2toQIOLHicdMhWL2wZn+3hz1fZe4Qe1PKPJhYY30ArzzdHYMvhUlY/k07lcvAIHVUivfnyqGsI796mlV/6wKFQd4VJYrTdYDQNWqyiKiqhX4wJ6w6fczld85i7ksfT94LdeubeQYqbWv6pE2aB8xY1Wqdcp76jibqOkR7fZh8p3L1foaT3cJuRqRXjjlZstPdpbdyy+HJzmtPg45+f21hBur6qrlJy6HEyYaOhFk2rB1rKrdONaWSO0I+/WohazjKOmzVlvhqQmro2p0B2xPC5jxmrTQ7LZaB1ARLLCByFkVSCQxA/U1odZFW4mh+pCngHQlLHjCTklkVUpSdmMMo9ksS8L8Skn9ckn8mVSJUDOufhZFus9VRgi6AkY4UtZ/XpRZsRVwHoF5LzCbhyRUkZOLDdkToQ4DCLD6q29dgwXpb/0uGC+HMk8bi4EHrDM259yGb2vivRdqoJf5N6XlHpduNXfIeJcuVcL2IenMQK3bq5QOVqrZFrG4T3IizlWPjWQ59Q9VG2ZnYxZCN0zRofeLXVPF/vQUsW2DLOAW8iV4C60cZkVAwXVANaTSVUVaPllTVNlHZM9ReEXpBClEUqpsqTkrHNIhds6fywfVncBrJsHVE61EaNYwwMoLgJMmjJ5OZIYvZhVGMWAGAlDzJhSLrQkMSPk3Lp1Kn1T14qMdmN8CZ4O655vDoey3jeDr63Uo1nMxYiHylsNy+meGVcPwN68Z/z80hQVLhmuXp7o6dGLUPYdFXsvBDKANQBg2JiwFMF2u48paBpzGhSOYTul4JbU1gU83EYulZkkP06U6yYyu++F5noitiDxFgHeH4k1xZ1Z7JVjt9WCr3wLothbhaCE1268s6O4ojCMqqErigT1jRyCXcMOp/lXM+5GIKlm30KonWAyE05qA/0CW+9yEgUcqmLPMxRjAr6zPNH0QcW0vbzp3DCBmNQPTPG5J0kj9hADFYRLWEnjzPGphnuFDpVBt7n0MVPYX3KQRUXG5uQEw2rAei3nEVarAdM4iWLafGY2gynIW8ZRJgLK4HuS0DwrJVChRLJVQrDH8gd9/ovQTgKzxCn4NlPq2a3VXvHu3ksae5f6BHOCru9VeWA4w917KwJ5PC+19rjcT+d9gkkjXDZY7PpweYex4LYNV8Hzju7W5Ww5CoOSp6+rGxOmZoQWy2d3I6QrsTHXwcJO9BHRrw8MxBgKnx6GQfjaIFzCLAMo1hudG62Ow7mKgh0e0kK88qR0vZAGr/TvK9sMOFoFQT/2lfvJAq8wTKfI26/Ys2Pz4rvRxbN5PeO1br673/JDs6rN48zC9yHnpZ9Q+6oWsBc7ZgFLMVtl3xLili+8WPNGOpu1uyUrV1AeAJzRWOw1LMYpbjJkcQwSGSTpXBBbPP1u7kJCkNuiOagMykikcx9y5Jchm6vMJBuT7theIgYjq0+vXC5IAaALbq27We4xFyf3GCo9zolBUY7lmjxNpa5uI0m0hEBgvWWXwDkWpV+aEsKYCv8SuYlbvgaAAhWrxoNUtMHLA/yWcOCyDOEXB9H7OlB4po15/+6/Lyv3Ki7uU+75hrn3JfxtwvZTqH1Iua//r8zdXJ3mysDLcmFHv5ZTds06oPC7rNw9fdN1iynG+pSNOxB9rq6a/LJQbruGey/ygl244XiJoZTFFwojcXKWNRsz3M3VijfOd3lRNJpSr4pHxcUJQGib5Ygg2TqNalVBADv/wiS+1VmP5VPKYnlItVPKhYyOp1jzl2SvZhgIXf2uC0+L0E9Z2gIT7XnvdfnslXBuX/7PEa7Eop8q8hWi7SfxB98/jpE/KvZeCEyA3p6X9NrwQAPs2u5xlHN75Ygt0ArwXqnnrj0vtwmxkVtjwFVLUNd4tkRwS07TMpBnAEp42V0IUSmmUjp3K23xqeeVeq0/vZVZ6YXqXy/YxRnR+dpzF2YEVQLINevsjtWiW2hA62bKN2qUAWXBgdoEYwK2yGiYemEURm5rfvV4rYRXKyBdtPfD7oQen5f0tS3SqgJGvVhofUOboz1mlk7wgn8jPNSdRi7vNt6E7I4Fk/YX3Dw4wtNBjIwpTWDaYBUHJFU0DzHqbmPA+UW12CsMcUGo90PaLstrSKPcsznoFvy200hUhRx/xLcvTVC7E1B14vsF/l6lXqByAU6J6y33zP+e0hBvqQcnPMmlNgwU3EazoCDXC766/qh8L/D2H+jAd3LPhjlGQzylaN/axAW3gCpAz3CsYqbRcI+3rcWALvVnE8Ur9rmpi58/gudW/IsTIH+ZQMYyg4jlWA+JVU9WM2mx6hFH+7spl0TtCO4X4Bu6sF/X141lRyc8MTGlQJfWYYn7WPGgVeA5ZXv/DSXA3WC4oNizumhZ/a+V2dSq40O0EEizCIfYl4vQ6vCWCzyQzV51TrNq5+azJaY+2p6syAXW+HUQm67wQ47l2ZVzVj9z3QaScovSLrKjtoTsxlL9iSAQkDQsQuQJcNZbdWUcE8zyRgu3y2oZ5Tuz4BIHEp9cVlawDSQufrpswZ9UTpo0X9kQlrbFEGEXnVVeyN3clHIYhKh5UgRWNIgPP8WOlHKhkcXNTahydNDGEAFTvprMtB/rrxDnqlqDqxSymD11v5JZlaEdbut7xceFzfE9uLyEr8uvy8h8RQzdm2jGr7tIT5V/AY+Be+rAmNO5BdlvGfbVrlPiMc8Ue4T2dE4RQ3So/AmP+is9xXD+uYt1bpVImdF0alWEEar1teRExmfALg4K3oLbPDMBQFZ5Uo/Pqt/qarTApfx6wkrLDaFueBMQWY/banh21rbVO1LLB02myhkvAJYRePb1Gni+NGuupdRzD7O8LuW5aHnVc4ZS52Wkfprcnh2uhqZ7g56ShC/CUbH3wkBuyVwT5OgARdjxXFIFXzk+BxPuqkBXlHps5IY7ZDQLMobdtkrQVaVEKPmZwATqdDrdzCL3S1qG0T47ZmdKvdgo9dqjuHasIcSoRymq0k+Ue6Lkq8oBYS5kyjxCtdhTQQswh8vWVqrEuXbjPNzaRLVl9aif3cJUO8PaXsbF9w3VfjcPMz3dLNZ+lq9feKHWt/a7sWFJKcyq1ALqM7bUp2yqdWNmrSmszJiXBdq7apOzK+MI14dxSgATTk5PcHJjo8NIiEPElBJAhGmaUJHOi1qERulGcDdiGw57f3so71yO4NovGqVetQLGohIRQNHb+/eqKLPFvB6ptYtsKLTHb8slGaLYM8tbf5FGqyDUOpHiuiJU9a1XBa2Kq1XEaIRkh4f13fF8hyBdr8MLql586TGBiuBYwfDasLURiDxNbavgymf/4tqIMs6VCs3zAJrtnDq23LwttOGI508DzIxJlXar1YBhPXQ+9oR2p5zmiRd466Hbb7XExbj+vbXiQ3Np0rz8+fxuvqhCruI8OoUelQX+/LvkURV7KHhccbjFz4YfWfruva9vP3Xb+veNXkhyaOo3dOOaODLvUH11qjx29EvDl5U57rGQb1qO79wU+Pg+nhxBUxmTyNEtoR+Z9RguS3iGO2IdZCMzkPjHinorZbZLMdR/Vij+9lAX6HqJRtQ8iEnKKno3O3InG7eUCByA2F9wYPNbzP8wpaTzSbR/KYtCvRy3Le1T38HKb7kbG38DKDseaFZGgtciwxrP9rw1X6Zwa1B7Cc9dEA7x5/lxwF7fdyB3X0QjHx+01PPy7hI+O3xrn7s29RVoHxciLr4ebtTC51YWp0PRnyNcQcHXfSuf3eBdd3FfaAnbnOj4hoXpN5s7Xkasc6CuGRl1NZJdWTbvyvwzVyRFgy9ZMNe8Zd7UUxilLN8OqusZPcGLoBd1CP2QCnhcqHjeyTyODnqDma7H3CkYrb/J5o5h7beMn9f92WC5kMtwe29W/acFRtix2f38uIuwF5f2s+FrQV+vwxGvmuNz0QJeH3yRvD/oecJRsfdCoHovG01A0YVAAOQSDQJs2VV3Z2Uy+qO4noI01jmogpkp9JpbHnupRmmYd65qC0ImaqUMJ7wTqPjMIm+tF1Shp0dti4IvBHcUtx7RjVGOQZjPPXF2ymqh563zTJlXj972i4rSVXC7zLXqKATK6m/MDqYy89So35HsRAQdk97/Xq84RBdmu2A+jjdjtwdqApStst0mp0J2OzSFcdkOcxlfjRRh8rcbbKrTKZP42Ostto5wRWAuN9+GGEEUcD6NICIMw4BxHEXpliYb4JrWo1qRxFpBwhR47Xf/7pV7rcBW5rGz2isFQ+vimY3jqA2eecVc6JR65XbrevFN8DSiUeyh+5M5awKn0Rg4PG4Ue7PnWu1+gVHxygkxXeutvaHj+b2SzxbCLa2oj015i9KNLjjLpgs80W1qZkvT2WaBjmcVIOt4g5w/TUJZZPtcTelzRPOnAem4lO3YjvD0UZUMIcilGQTo1aMGysPRLLe64C5OQc39cf17Ve518Zfa0NXMfg3/7KXyrV6pt8SDAfRhmnGv0APct8JjXWUMt5tvvs5z5HOUbClJbVBVkS/G6vvlskjGUos84YeliVx5f7/4l0/UxG3StkEL+c/nSOkHTWsO4zNVGsYqK0oad7zOZEClPSETOBASCDHLEVoKkmfIhBzE1i0lKdWO4JYxCQCp2V7UCTZlQs6i8Ms63+xEBkjLsOZQvTHX5qgdLZbjw4xpAnJkuXXR08NAiCxlB/I9JW0bp0nzJLVqVCf7JO2rPIiQ7JivU+gRt0rVQ3AZZlqkQ5tvT6M4oNkDmskxw8WCy44Po37br9xry9n73pW/WNcrBFymeH8uLK42r6Etnm0vD8lTKBCazOunw0PelpG9xV6hTz29dMoszL04Muy4PQC/rtTYdimj+NvjSvuKvGIXB+pxfEaziQuyk1XcTIPCf8q8dD73tCdSFj+eYEclSYwSyskIm59Os5eSum8C2qP91v6C56i4DadMvyreXQnJny/sm2X9d1p469GwxdkOw5bwuAt/Fpx7Npn0OlT45wwL/Hwp+Fmn0VGx98JAh2yaQBRAtELQXdQpKeUz+c8UL6wExvwbUF20AyhSpR3JbX0nVOWeK72rUfXRVDZqiFRgkcwavxpOmPdHZ81PHtmNt+XobbXW6y35hqiWPdEr9UzAI6fYoyrcEbTvpF/Mcs8f/THhxBOpskte+qJGYLijdl7+hvE2Kv3jOqLsevv8a6gLM+1ZFye7cCrK2ZY3SN1MgjAlnx3V7aG1JzIlX9BpYjfHSf6t3Y6pncOzUddfWeB0jlfu3sT3fxgxxBXGxOBpBIEwrAawWuptx3YOAH45T80b0M4FedE4ZMJlu6ifX56B1i+Kzce+/u654EbVslWcp/aWW7PI80q9qvCzMJlpppw3ZV57pA/uHXV+O4WfPxJffacsCCz9N0cIlmmg9r7dRu1kco/LVPIw/5f+6Cv5SLO8ARuHToApNMhaxG7gnPBltLnQsTpiZr1r00Oqxs17U1hLio5wZWA8/OgJTjdrAGLJkxnIWdxoxBgAXUCMCwZ7ADqEnr3Ov3mct++G6w6nlxR67ed2Fns+1mjZ4TbL/Df/n6MH7XFdx4vr9J0p9hr8dgFuehdcaxqDva+lIDoUfoUQ31WFxloq9vhU+3OOz3VhWPp8YX3Y+8H1Id4Pb6/k823kZqL0rXQ0Isui2HwEl3bYb2zdj5Bb/DPU+0fpn6y0TBzbc1LPWqoEsMV4cIQ0QedFkFuki12dHvEliDWgKQAY1bInqHwbgm5oZoAj6dHirGNlN+7a5olUOBPAMUrdOh9aUkfCdpyw0ktwxAdXxsX5Dqsh1nYxisUjhSAXBABI5eaQAzBH6MPx98aaq2Hmyr6FlAsMx+NancPu1/4l/4uKY/13l9/ed1eXvT02CzDa95wYFs17yL9bKUtqudn7UvtceuPjeygz5jVpCzL8vtqMsYJFgW/rwEQyZ9o6e7raKvCE9ni3LRLfZp1RNsNz1bCVLMsn/4566Ubpf0fEPB3ybIfderdc2gEG28UZhQBKXf3la8UXuqMlKYkfT1kvy0Ua0zipCw2jeigIRa7jr6pQv9ZYPfdcrkCH3MNsKjQPc6XeIZy9LnZebZn5nHD+kwx9E3l/0HXhqNh7IVC7OQ4byG5/ADCWHUgRLDSSSpX+mI3he8PelbMXc2rUeKyUqSgEixjpfewFBMqys6LMgIjdsYTKZYpgr4v2mcWe3n4b1I+eWe/1Sr1h0Jtxo/jbClH8k5niLhY/K7RopQdyx0SoCiOB2rrWZ3kgL61ruqLUs75Ei1DFQqcOyYyQOW936ii6hpejhDXDwgtruaqA1TrK4qlyy1zmAYM4gJCR1f+eiT09FEUBW7OUbauz7kydQmiWwxGuBzuEEHFjs9LriwNWYQUiwmoYkNSCx3xvlaPUXgHDbn65I7l+YY/yVHG9XqIhcf2xXHNW7jcCTDhruYggvPdTV/4a/PO+NanciFv96IVCH4rCz99iXawyet9HSl80rAp7VH4revtn1AfuFtk+rKRcBnLRaOFqaAsveA7AbiEvmErWj24xr7hPRWM4X0SU6rNLADhpstKQsiBnL6476xbL3/rQJG0311pg34IjHATCk8fnoEBYD7HQ7Uhy+3UIEXZFStJjfG5JhYq59b0N3hPqXur4Ao1yD8rh7Di/IyYzGcLnS91rg2NzJV/Bz4OKvfqHLs8yvbtvtQ7kwpaGoP16pVnbpPHbKPPUlVQsxKC+vPmifUmCWPixqqAOSjc4hQbUOixcoeCUVLMpBD/AGdUSrVfYBCJQAnLZdWFEI18sR1mDkglbkItfPL01l+VZjsnW2mbNgDIBEEsbo00gEqs7YoCCHLfLQY7NarWLny4yCyGxBcoEUJYFfk65Ht3jPMMTWdDLTdUUMmLwloMiaw7BeK1IVDEE7LZibS/xJc+AUBWNsCPM1lzPnxdAK9TPl0U4FJdM8bInKVW2scgKO15YT2jMlXvtXEHB/5ls3c/tHk8c/vX41H9dxucDWH5J0D7O1n+77N0h5GXF1vASySFOyWPGsRdLK2uOK84duY6Ga0TmIqdA0LBWjlwfOau4ec+ZEt4UXm0tChmDHbt1vUTcKMUs57r+tEZymW+iNKy8xBSIhueU+vKtTGpOlQBcZMxAYnVc6qF4PE1iYW/ryRIIwY2q0LyGou1KSP6MmVxWBu17dRxqgd3M1sBLfOvQ96WqHIx0lRyeHyx122U18BhxWV7PDD0TeYZCjoq9FwJVyDo92YjAECKmkRBBCHnnSL2Mpl/kA0bAlNgwqjKv+5VMSH2n6cywPEhy8ohbLWFsp8ft2EEFLLQCvL8d06xz6pFb/aXqe28YYrHkMwVfiPVCjGK1R1R+SShuvY68CHvuHU4Z0Sj2jDALyEI8NEo4I2G6adwIhcqT6lEV7Q/qMM6r1qpSRPoxdPEKUwsaVvivFGaKOF87ZpSbpQJDFmxgseIrV3nUmcM2T6oGoApldVZpPziGrzPsRRPaXxYgZozbC4ACTtYbrE/WiCu5iWtYDbgYNeJuixlHbQi3fiCUVZoIN/V742MTfoZxO324zqvmtm0AbvLBxKlmIe7xDNS+Fz96ZgFht93aRRmdUs+UeZBfO1YFcgIV2UJD6Y5GKPKWCaclrDbTC2RLs7csfrEn3A1BpdLAUmRaeJ7rASutLb3tKkouVhWKUQTiXglYywyuomyNQvELQ0UE7/L0A+sCG5irJ47Qg/RzUuvbIQ4YVhEhykCHGDBmHftJfOcudnUZ+Mo7vLKuxWtLQ2Wy9PEbxZ0vpDvT16TrqlOnpynyClo63ivhwdOChi873kmeH6KirZXh6jFT7i2sBBZnZMu8Zni8DHRJeFeiy3QfXfCvzbKY9y8KLKHgar9s7AkclPR7i/4uhauuT2OQwSo7GI0QbhHYlFQElgNtcpyOqPjrkiNzooArljMlb5UniBFMHtGxZ5a2ZYhyL4cATrksvgECItWbL9Uyp7gKIIiykMQSjxNBNBOyuRkyAXrTpZWl2TRzzsvHKYgn4RwDhmEAwBiGILd2apo4RIDtFmEgqi9ZUnNCCoSiW1jwpXlovOs4XrJa25sJ0Gjv5q9tHnuSL7+7DTPHh60P0fDmJbz2ONLi5fxl/ybbZe2Y43n/hZu4DiMXMm8VWIsFHqrMIob3cWq/1mP7sibjxfgL1MCTI3bsoIte5H/MwxtuU9gDlXq1qr2qvPZCCrnK+GeL4/352YkxkK5U3JxoLRFt41lLZLHGlYtxgBhR+CJndGVIFezZKKTNXw6qnAc3CnkA5QZuk439OtOYX7FavsYZeKrVfUaYz+WrZjmbwfMpOX/s5WdazudKeNvEuzKmPxNctZRL43UdTd3vgajPD2jh+ZrL86Ni74VAXf6l9QYhRIACovrtyHL3dkO1SSlE8QHAVSDYp9RrLHMc8S67LP5cbqW7hWlL0SLEUcjl7KYxdqN6trgP6mvIBB+73ZaCWetFDOpzb4gRYQgYIiFExhChCoBWsYdyFNcTWlLlgTzPwmB1c4jYCxf63izEvZTTE63m3caF3Hfr606qoXqsRVJ6pUXlkxxQbm2qnLsy2pnAZGMG6HgKlxNRVbR/FPzc4do3WkxAdYLbgpnfLwYe4TLIjFsv3cVrr0ecnZ1hFQNSjAgkC4gwyXjtdtbFRqVN3DJrmyp8Nf/2tABY8LlnJKQuHZpFBFE734pVaN0htYnWMPui4EOhBf7PK/PKbbmdUs8uvinH6Es+FTfLr8OvQ/hdmrFn0lLz4JfFcxDRz8TsdoRKHoa4jYLMeviwGLPk1NlVbVaeCYfVP6rRZTIC4I4Mt1ZbNYO6hPdzhMu8O8J1gXPGyckpbt4O2O12iCGIjzES3kdZlBDjtD8PG9t+HvBCoJ9pDZ43ySsdMGu9RaWSw3/bDvJ4XvANxu9tQ8/iV0V/v9FXcFqr4488AtSElW/ufaYwKHVa7r99QHsi9J+uJSMfjNiMUIO/np2WLLj5KYGe4vAsAmZ9YxcrKfUuT30aL4U0fu9YxsgW0sIDdANGNxLZ5hAzsjqxNz+9Ng+UFKmjez3qVhiUnD8Q334MvZIDpMdqTVHGWY7hFkWSkjhmmcc5o2xoZugxOpVkhkGkuaxnhYnEig8uL0AUmEErahtd5j6i9EWWduWcwWCsVitsNsCUEiKJsrPwMB2klJ+Ojl5VubfX3x72LyiLUhVuvlAfpwmtygy0PKTwXeOzHR6383wBh32Ffd4LYX3bDnHUw1+WOfAyXJZO+8qPQ0tUL8l/IS8FdpdU7K/TfJSp62OfhcypnrpVukIq69nppYr/qPICMZYue/bNXtp8KScOvNauzBvWPqxl13So4QyYOxnf1hhdXFdeFclUCtPLcMw1ledTvjvqxgPkQsdB8N5oki+7L/cqoOTvY9L6+EKum2DPTJ0RiIXxv2yKN2meji5ayiX+/NQ8mxe+XSd9X4lLon5sQ37NLj0q9l4I1FEJAOKwEiEHsuOXgqwEGPWYV3ubZRXYMVPmGXEkYCkNXLgJoEWpJ2nYC/GBxCkKquDuF/hBv5X3UK327GhupKCKPyoXCoQYMISgR29Jf/VW3FCtAEFA1HxruShKBHTlV+GlFVRM6O0FlcZyh2pfeHGrWrzZqDisckGl20sdTLHSjXwv8Li6eNHcEyEnazkmwc0wA4yADBCDyWwEHXlllx7+vl3L3x/QfhZy/CsOUS7PSOOIKSeMGYhghDhgGCKmPAJgjDyVAfDzRkAmY3skd59AWedanRPtcxGQPJ6XyctuUqnin+riCh63OnwDPC1olXv+GG6j1FuwvAVQaIngUVeeNrL59X3RzHSHowtCiUvkfouYuSjEzvIgVFwpkltfj6YatWr6VsiwDzKKVKTUJhB97F6ytwtTjI74grwoV3W3PuPsCtpX8JEqGIhj/gxOCZkzEgu/Drp5NeUEgJH89kmHt+VT/aeNthC/JqzzxCefJTH+r4SmHt8jN1up5ll+CM2irSJkXSAVpR8cPqPBa4/Lljd1iGF4XZvmnhfbPn952pn5PGa0E6fAe3NcGHhf/kzRVy3sjTD5jRqfD8HkgJ6uzcvOzPUyCM3XLtpRig4wIwexriMOJUvLJSMj6vyQU731CFxZ00N9JCdPb0TxJvMlK/shBA7IWedPZoSgG5XaJlMqVr+sACd1G6P+9Bh6zJbk+Hv1DchSJskmcVLBmkhxODFiykAMpX8ALhtSANcyzJJQxyWQ3r7LevwXrYR4cGHn5swzKfdaIbB/beM1hddfr8yr2ElVXtBIZX4YX3W/JWY/r1348ndf5kK193xfjLiXVqIOTMc6u89N1H15+TB34vypQbqnlur0W30sX/JyPhbKptqrLSublq7Sts70p0IsvfAAiV8NSZQyMWSiiVNy2PqBuvKtP0PJ13WYTtaG62UUxbnxt5TtKC0BULwk6OUd7Xz3R4C94QZnRqAMCkH7RXul8DLWuth/1mu6caG7G9dV7LmWPSO0uezPs+fZ7rWfQgvsvfnczfXZe5/dMnIfhMv4+1L4U/Ps58HsL8uL51E+NgXfFeGo2HshIEcIAGDICTESQAGJGBFA2FUnwSB3F6IS/sb/kj92S0DxLaUWWvVzXbyX41qACouVnfhbdE1wL4SdTCAzYkgFk1vLHbPeqwt987MXYr1cQ5R5otiLUW5FE7981VeXt9jDgiKxr0s98kNOHlYG5TQo1edqu7Th2qR67NnkWkZRspadJ5fWyd9FhGl8UCxICx7xe598lXJ3i70SLL5mPDUm5Twi1KqjaFeoFx2ojnoVsJVpN2uEI1wLeBJfP0/OPgKHDVYnayCNOs9jWejTWGZlmTOznT3/sYvQHLdHkZHcc5nIFffRWvrZQNedXsV9Vzy5Agq+LOBf3RGlqrwrPjKXlHoVn60+/li9/1bL97/yz3ya1i8zQca6dPZuivCq8lgCn7btm8XoM7A9gtauD2V4W8mKSpjbfXH1NH5AdR6VOeByo27JqGQQrAJ6qUhGteH1tQtt4gK/ygSCkUehvtvdOUAD4hDBKemlT6Es+GmyeWjWuFhA9APQL9wPpezohbfsr/k4emJpjIGh/Sy/XonvvjtFXlXuVfmgiYMuf4+7JU8/v13bu/pcCtTG9fgq7+T+vWbel5Tb5+XYN6y97di1/Ls95uWkA+svqvR53xHcMsLsvjlTnsxZeJDR+SIkmlMOiDIvizwYSPzZUfB1kpsw9b4KJ0NoeQRRIDOpD185hZEB8SHNcottUEGMOSNluTQtkNySK0pobQ+pm5Qsm0Qxah8kxpSr4pwZGIaAtoekfWINWBUB5UAMAdOU1HF+HQO7tI31hO00jYDKtlZ/z4PJH39QXHMjuAxuQlxVuXfdVeIiD1zkix5xOuWe470lWvNb53CTdUdT2pf9yrx9AZfi7AFE9ieSlpLQwrerwD4ZoNSzil9XAFuVoPTlfkUSNWnaUuU5cytnZEKzwW84mxnwvryN1te1Jjc0BBWz1DKOCw8AaaiepTc8y9l4hcQP7OVOzc3qp3ykuGcxH5woGcsac6kHufZh7Q15yplBgf2nwrdUL18uwCLjl1Tb1NLza8KlxOAZ4ToMsovuuayfzz3e7iuClhBrX8nPhdl+gqEb537Z7ySwFwZHxd4LgYTidS0OCLQCEBBUg5Ty4BazcDNBXxzBL8EWXixzVLAotMnSViVgEcS0rDIBCy3n6mOlMMW6uG8W9c2zLtqdb71ivafPMcgGqVn4xRAQYj22F4tSoFUQ2KI/mG8WAkit08pNSDCrFWuMCazV+XGPWI6dqa6Dyy1QhcFR5yifW+WhlTTPdfl1VkMniNt3GQcdn3J0SkKtHkam+2li88wunJdyzHcNFafqJrQVfYDOmwNi1xEOQQgYxx22eYN1JEwpgcOAQAOGYUCaEgiMbV65RA7RvaUe2rnarfFRJuBs8Q6H+xXNq5xWD+jWuS5Q9IGFC1MjtJVnVNwvVnoOb01xFwxvySn1gqMbfvFAlq8VVRG24La9H5ifPfmche99X86zGYM9MatfmrngZGKwpzOe8beRuwWhInVZpFNfH6MLVKwzS71Vedf0g/la7MYdmACMKGNealE3ogTs+VdZyceYmJFTQg5rEDISizVAgFwcpRoMjGw37fVQR6XS7e5bidrieUuv66xbohmG46YfOrTIJP9PN9F7C91CFwxvqcmlweeSXaOUr7yrUTQsKAD31vUKsIzrcxy9OsxVC22XNj2/VwzgGeI7eodWHmP/xS7zMLmgV/DNHlq5ImeWWyHJZBnCxFwX9gwEZDATBlCdX7rrGbQOTG78YXSpHuGTylkcRmaZJJkZlE3ZJoo/kfs0TghFieB5l8l8lKVO9p6ZwSkjlT5lRLXKoSDHewOJYiAzN3Pf8s16bL7KPVwsGzPL7bgcolq7mhLD8ECW/Kn3sHoAz/bHuzzRUox+k+9pQdl7eWnRmuZzy/Hv+rl/b/PbixDzkMVIT8tp/IH1FwFLfXV15V5NWJXz+7KgxWcGlzlRZAa2GMYP5C3YJWsu/+C6i2l/5dnHdQpAUe6xyBts6zc1LCGSW6o7xaO0t66rELi+s8TPtpnA2Z3aavvHfG2qrr6oSzMzYteMEh+2xjK7xtJZ8MYwz6LXez4YynvelmNfOXBJ2+/iLoVeRaF3KNtfarC5w4ufm177uCnSUbH3giECCHEAKIBsy2+dYHuOIjDpzqUSSMAWkVR3RqsIpLu6krj4DDHLPpiw2B3FK99VaAMEIwsh5irIFO5fFXom4Nlv6xfP+9rS47aRQZH04owgz8UnXxXaglfuablVkdD+FSFDqyetsl0dE4pr3++jN71wIvk5waI7xlIUgE0uXPpoP0dWgbmUIf3fWPPMTPjqoyzk62cbP2J1jk0E24ZiEsVxZvWtV6eL8ePaZOu7X1WC/MyQEVdrTOcPcPelNzFAdxdjwGq1Qt5NAGVQzI3iCkD1q2c3Wi6sxqvfHK7jxS4bH04QQatYfPl8TWqr5ZsDfo/ffl6YUt/w0Cz19NMC7rNcehOcoj70uItWwWe/Vl4tvI2DfvKj/+qDnhmMbva61Fq29oMTiG3oCm11YA7sK67bd0drikjaC3NUBQbSxX3BYTu6ZkV6+l5LLMq9knOGKPeM8/iFKqHW1MJ8nLmy45cbWI4Gxojp4glu3n4JkYXXwfgY6y2dgWfzsaJzxxgWXnFZ1I4+NO+G+nuizYZLP1aeROXXzwCPn+XZ6EH32+ZjySt/LfSkq8NS9Zam11VnXD9DezrRvFcdV4NnzUOnJyhoy+0B931iO7n8/cK7K6XS30J0PP1GlQP2Kfi6D5kzOCdkGzRrBhEigExUjpuOUFqOgMBUFvmkCrleFpP5yGpUQ0jgwhNYmYkdaZX+FX4gSogAZFMK6kUammfQyRuC5RNQLgwjwkSMIbBY/k0EjhA/z4BYGmrX5CxKy6RWzqaICASklDCYAy9U5QEAUAjI4w7rzYle6CF9R3rMgnUg2z6vk6ND3Tk4flHG95K488/dccnFOO65n8/uufxLmOFmpQ3th7mCz2dJC9/2YvnHBNT9Pg+42pJ8qasv18O2FGomd1xSGmeZvyYrZHPFhFYRL3URvCPYhoH4sCxzkqrS20sipA3JNO/VQpMKnWRwIHfxn9ADK5dhc6qG2eZHsa51J9SYgRykFo6USZEaP9tV1UQI0DSZy2UZvqctUc4ThmFV1nW1pXyVzj8IhQeXQp86l2eqx6Gc7WHxuXw7tK3esPAjzBDjxVfhqNh7IWDCA2EIEetNBBCQKIiib2oXVM62phXwyy+hHMkVyQtkOzDFFNqOkFoSfWo0OFz9yzHq7gq4EE8TpGa78dT+OklfLfC8ck9uOSr+9IrFXlX8UZCdYa8EANlNuVXJF+Sj1su1ZUaMaOmjA4bjLPWb+2myUqGwvDfKkcUK2EaQi7dQGae8Lc3xC4vC4aACb3WOXBcjNmaSRFi7mq9r4UXW5aros9pU68EjZX4qmBjj7gKbkw2QGWmU43mrlfiXfKLX6MUUu4T7GXZR0DWCu39vF3ezcNj0aH1t+bLLoswENi+QeyG+MHXDtbmSXazyMDt66635SJ2k2yU4rXLP0RT0vwahoYmNcvqZgVtcta51C+G9Bdl335flO9esQO1wE8+KNCVBWTRr4l5AlPd6pL6pn5sHXAJ8XIuY0Cr2SkPcr/+zxXX0hS10xC8hcAKS3Io7rAZZ3IizMcQoR3G3O1lhRA7LeXToXucy7V/1dWGtTOCW9ooP85iVn1dW2SntLPri+FFR5jfHbOFZvlfqtYuuFoc7fDYcd+U/zxnU53Xw3SvdL8Xz+feWbFBnge39N7XxrZ8Wh9/CVMZrgiyjRZGlyyyJhVvxY6W0n012cDTeXKCQ+u0NQLG8AwNkR21VFtMhFz7DctyOAkOYgdSd2PzYQfJVucSu9JDNHxRrQpDKJ4GQWS6rSGoNaxcwyTzjwjNylnJIT4WYopIgRkS2QWNr9RTMf54qGZSmppTF51dOCKr0q0d1ZY5PqjwI1i/9wFzVkq4hoR1x6KMuZXs4SVuGy2cPmlcc1Yi0ELaM15ZsAX99/Evq57vjunRgiXVbnk0Yt93doFHVCbUb6A2O9TW7+qq9F6+vMk0c+5Y0fYNcHswMVos5a5RJH2YdzrTQghn94iJTkJZR6JSrF6Pd5iNfnqYhvYNR8Isd/amN8E2S47u5yD+sFgnmY88s7YTEOFnJNg9cjrksnWaOUKSfGKo4DCgk1ngXSV0kt2fjSmX4roKvHxNQ/3zltR4djHqQX165hPm8Wgr7hYUFXF349FzhqNh7IVA1MQGEQLIwCkkuzUjRlHRUGY4t3HurPVPYURVGYISTnCjPVUkH2HFcI6TOmXax5FPzaZgpdDWJLgTYFuhK6uqzCoMwYU8VdU6pFyKVo7eDHtG1o7oheh97c0f79WhukSIrYdpLdUpHtsShWNM5waQITZ6Tk4tvHUuOAjlStM/2tn8noFw7t1DV/mWRALvqSNHchEmb9MgdidjKqhAhmL+nXkSRMp/vsupXCKIoPLYXW2xuZLx0J2IdgEBRnHtPI4CMFBd4IJlAVHHQhKOqrOvfbZG09F6/NxdoFIG2zuumLm5RZRXrww3vijjWK/VIFz+h4m25YMMr+Kw8xe+afVXg9wsHv8PrqjiD6wkCHs/htN9t/lU9MheA2/cl/KG6gGjqPF+sS/vqKsILhCZgAzVZ+aZ47hV8xdl1KbLStkpSkv7NxD797f9MscfwFjRtC2YN/aUARsKwFr49jROGFWM1BEQCSJ17c05gAKkZbKOtlg+wOEsLUW9pM+8J6xenTYjhuw8vc6ZhRvDjVKvtJljHZqsyDw7/Ld+K01QypCY7y8TCZzyH2sfl5Z+Hy7F9trCn9h0NnW2rUXP3VKCvT1uH2jZLSguxuzTkZbe2/rUvuZVT9j7WTV0AYM6qhO43HerC2Gh7BoEDECiocq+qgWVc1VeWKQlY5NZggklgIBNYF+fZLMJdmzMTiIMq37Q3cgCH0sw6Z1i9oUYGUkAMWesJpEktcgahfSGI9aFtIMnivvZ2gB2/I2BkbImwIdlkNougcZwQBmlTTgk5DljZxrL2gWoKF/dp+3G7Ei9SfJXe2J9iOb99peyv3DykIij5cKrj4OfOXKnXcJgmr8vn6r76eQy8vCf7/JY4Wluvhfr494UMFjldc5x1n3piT51pjuvLNG5/3XzsotxyvKLVyNVTXHYajNSfpjalLY5NxqknhfqaFeUZ+xZzQ8/sll15n5mtmAdqtN7Ba4xyFFcuw650yWlfW9/TXPo1JSlhGISmWZkpJ70FF7CbtoOTbeUobtY+eHYorb0yUdibw8L3/SkMlmZVg+dLz453z/K+hPZdXjsfQXlEI69eI49nhLlc/wzDdAgWCMj1qNzV4ajYeyFQh2vKE2JYNZgRQuqELqWURXBqn4tSrwhn1Ul+c8yjLB4lMjkhx++U2/GvcpzT4quw1yzA4YV3ZfC9Es79mVIvqhKvXJgRQ+NnL1Iou62wfACQOk2g6rjLKq1d1XIjBoppeYkLx1iUWdUstDdKNu1yvSpE3NA0Q7pEdiobWzjX40AXyP74rVPALubqfCepVF7qae2361fsPjdhzCo1q8LXC3CFqHzcFPSXEhgYx4IPm80GIUaMuuM/DNHEJrn5b9b35CbZHKqyznBecbUIamiUe552+CnILqyMNbtCukJN0C2LdK0zWbjhT0MjUHHfH8X19IA8zXB5u++A2uY5aaNR6O2FVji8GpM0SumVER4q/obZMr/WfxFtS1KefS+KXKBQ79IGhlPEOALkcb9UX+lVoSHmZIYXyYgtxAX85Rm+jnTgL6Lerx1RlXv+bym/X3DIE86e7ADIuA/DgBACEqfiT1bIq/NdVsDjJ5quaV79BDT8Rh1+2Dg36dq8m8sz9kEZqsobG57q/nGcsPLaji7oB4f/KLJByaVF55qzj7NU1SU65XFtdgx2ubn1pd/QaLPen5ZmX+pYUfkp8sVSGc0rdYv66qmznLoAZhTH+nFR+dd8sCOsKP7kwNWKxVIRofjuhT2rP14KQcc0IECUfkHzFjolNZQw6QDxcyVWfTCqpnyrnMTISnFD7YeMXHxOW2NyAigk2MYkgwGVG0FASoTMWa187GImx1vIqqr9YHXUi0QwTgAYpydr6WFmjOOIcZfKWES9FI6Z3eYyCv9dnLYdjl8JGkTfJwxgJiosK4f6+bwwd3ty3SF/M8upfG5xmTqsXUjbz/3r84IlvLsKPPdleQN9t9nYtXT58np4vVvF6yU1TFeuS1/ldi6TodzkzD3dsLUgyvFYPYSr9bcBszq4FnFdG7GL4pV6Fd240GbPDzNz8dlZas0EcSFESoMkjxCo9IlZIxachuczrUW6bX5ksO4zZEwTsF7puogZaUpIybWbHP92/culL54dHMtymT9dPotJZ5Njf9BigOfRWFbqXabQq3jxFHh+3SQA9nbiNfp2X7ctVee5UZVuDjwNyzgER8XeC4Yxjzi5uYZMZHGmP6R13aE0EcYzcXsmlBX5ItKBSroMc35cqW/vy8kTYxEka96tHzk31Y3DG7NQSmv/oVHsobktM6hSLwa9KZeq5V4IrWVeuR3JKLlJbUApb0mCIKA9d+rbbExN1V4+ndduVUUnqiLEslIhle0YlCrX5rTfue41RuqragPbaAT0uRP4ewXe3JyeXd1dU0rrdG9chW7muntdq+PVFEe4FqxkGDPp1MuEcZqwXg9YxYhIawCMmDZ7e9jGQKz2/JKeiqBkAo05/V58RlUU+V/LF2imOgzHDa3bBbfDN7JfTwuqcs4vqEL5C+UYPflnzPObfUet23Kn9dIRNV86eaWilEmkVVsCJipHSpr+8SUZDjpG3NRmpsxBEQi99ZwJutY+hh2F9xs7XNHe07lCz13LrT5aYfO9V/qVOzx3ZFSs9ezyjKbi3R/QWufZssEr+Mg990dR9y9UflGAeUIYbDwrz0w5YxiUh2EQC6M8XEuu7edanZpU+U+JTO1K3niSj1O+K/67OLMpOvvWYJ4WSU0wue+tEqVV6vVKAF9Un6cveQZL03NWn8vF4oJP+uJdkTbIdF3oSCYxXSnPirMuE302y12bZ1bpwuFncsJilSRVzrKItaY7QaSOlx3Tk7w5BBDLhnNQCxn7y1w3a7ykUWRKtcizzrUN53IMV4++BZU59TJNAFnwqCgFNT3n4ufOLgiIHBCGIEf1VJleLP7cXIyRpD7aX6bgRCSxPMwsN+RG2WDd7iZQ8KpZodGZucqodgJi35F7h40zvDwEZON9rVQ1eUcaDhd1gEBRrUvByQV8Jh+/z7HgxH58nvHYa4JvasETzysvTfX8oCdnFfV7OWVPepfPZcq9fcCZ5Uh6JXG17LJocL1uRZBbYlDd5CxYqBk2s70RIitf7IOtdZVc1dNiQetT5S3d0GAUpaOd4rJyyhFf42tGwwKcaGS0Q2gCMwBmpJwLHZiUHvZ1luQm12o/PGeLh4bHP8N0vFqtGuZ6MJ9+vJo4lxQ2X4+/KNhT6LMqY/ck99j8jMNXM+zEuUuqcCU4KvZeCCRYV8cMnKxuQpDHkI6rkOQYU0MuyQQYwA+/CE3kLLlEWi1illcEOi5qfmCsBNn5MZ8llav3QrtVpQiE+l59s/TCvv/TBVD0x3D115R65fZbLSzUfM1vSyl0hmYG1CL2jLdyfeklC6pxTIBprF8KM2EVGDWTGX0xZkrNyd6mxtQJcVYXr0VccIpBJY6917nD7K02a6Vtr83a5Jl3OfLdN+EIV4MwIAbCNI4AEiIxIjPWqxOEGIvVKavFniIWAK46Jg86fcvRfKAl/uQt9mjmW8SEwzK/yhHfJTF6Pnf9VG+YvXsiVzVPFyRRu9j3CnuvACRti6X1PrpsPpNrV1936t5r7bj70s7zkjG55pOP7xeqezoHQDtyc+wxhV4Vipzilc0X5kJytoU9VTwva3kqRRMceXCKivIBVK1xSt6+zglub99VxEn85c/H67/Zr56VKcd0gbmS7xeTyuQ8ivUtEVKSfgskyokYV0XRIQL7JSKZDVyjPuiCy4vyn1lsb81HdVwL7fD5tKn3Vcmy8t88ftZ4C7KAw2mQow9eoWe80+qJrrCmPmV7zQX2fN4vXdvNtaXWkg8huHo0FdwDjhdzH9J96PJk7tuwENehu68tUFjFnNq46nI74E0Odssr+bFwE8UUeiYRMBik/iNBejxXx5htnjuZzM4FBEJxJWE3cMtdFHWjWmQT22istL5Qd8rIWfw62MI6uwU/6ZFZZpbtBArIBAwR4MRlkV/knSy7vLKgrxthzEDOGSkTxjEhquJuu90V69usfv0CpOxgt8CTjvmh6XI5yh1MauNwtXz3DLy97qlnT+EXkLB9MZwlFzRT8Nkj9UkXMr6u+5c5X2/eeyK6mANVfOtyll403r80fFcf0Dl+LlVoWT1Q+PwViyu9qXhRTwB4GsLlSGzhHoROHuAiL9opEXJ5ZS1M8qi9JUnru4hZsibtr9oCBJ+D1cE1oio0NZ4a1gbrE5Zbb81CUPpY170at1SHpc2mx8+yCye4TcA0TYVnCX2utfGuJJ4ShS+FyrtxjUKegai4HA4Trjm9OKTUo0OBP1e4rF7tmulg8gO89bmAyVJz0aCpwnXgqNh7IeC6mSeEwQ7nydCFOKJdLFMZ5bLE5HoEt9JkJeHmh8/SmMDEKmAVwUnFfHe8V3NvLfa0qo4t1F+vdFNhryj1gKKUM79b4nNLdmltcR+JitWeHWOyxT/0mIV1RlU0hnJpxoxrljo6ZJ0dgfWSMde4zTefhabzFnXN+RgnrRTmuLDQJ5g065SrOiItX3OLOSvzaQi5tL0K0c6qyywNWZ1Cd4LYJ5ZGf5KBAYyM3XbEaoiYkhwxymkCBSDGoIcdGeshd4ltfOuYleX6gjDc2BEoDrS+RXS2sL1wQYUiRLg51VqRUhHaSzw3hV2h9VtZ3BV0VYsGqgo9p9TrfWfaQsErAfwR/7b0PSLJIopY3F6V6Y7cGji/elx6v5bZkpG+oLnaoUAhr1WRUOK7cSmKD0eHyk60htexQ3Wn0AkDRU/UaDfmC5TiT6vUuFfYdY0o381ir1fcuTlcjvX6I7r+t3di6OETTnzyDpyAaUqC07pK4pyVPVFxEh6jw/MyOerMqgL9vv7u+YFk3KTV6A6b3Xx3qTu2OIOGl+pcpKX50CrzCtKj4nvFYRQc7hUB5Moj6stYqFqDr+R7sEnbZnVok4oaNGln4yFeS303uRDqhP+5SqZ0qRtzL5OB+nhdbbR4b73X1IDmc6PELEdxNbzXFOp329j18ibZd7NCVIuerDQ82A3QpKpBUrcfnMUliFbYVP5U5Fgq9bAjd3ZJh9CopOJecNU0ZV0GMyFlLqc9gIAYxbpvqKNSyq+3ZWoXcCjtzznLHUIxYDfuANabNG3RTwCzKTorjw3OqnIOSuAdji7yiT1JJeIic2v0+BZ9QfK8gjx3BZprcr3Fdry6ycFwvavn4XKuS/Mvi3+5rLyvSo7jL3639dHV6UWXv5s3S6W2eVLLHq5YTKE6ZqVPVdqx+WIGIG56NvOSyoPM/eqLz8LtwhtGc+mf5iA4V+lLtgypKvFtaUMa1vMBO3Vgz5yqsQigF+2Ai8VfTePkYbJiawtNmR9C0M05lOO9lX47gw6Fj2td1PDz6y7znrXgvd/24XAb+Vn6pJ/tPx+gPc89uHX6Aj72KZ+pPUv4bvN4oaxDcFTsvRBIsJtxMyes4iAE15x30lAxqBG66r5I8fNhgpkJSKgWWsYJzJrMJoXtnhpXbpa3tsPTCXaE6hPFE2JbFxZC63bsWms9n64u7IOzwAvlGG5QpR7BrPKKQs/71ms5QgueORTFRu2rVjI+wDGbhTHX8fCcttGI+PzafBvBxkcp5lQt4ykQbDHc1bVpV9d2FdzFP6xjdjCiwCj7aTLYCAjzfI5wfRgI292IaTcixAGrOGAkca4/hID1MABgJCO3s7Grz1W5Z9AuSGZHbQhlISav1c6sP4rreRSAakWxIM7WqVuRvsd7i1mYjlPyaeSixGt87zklQND6FzqhZVg9ZtjRd431yx7hqHzmmi+VxO1i2F9iXZWhS0smV3yR1luxv5JfW2ACZWQb+uXoi8Wv5L/maYtvUEFhq0CRZUu25iKg+tiszGCpFUvivZuUTZw+Hu0J26fYC11cXw/fJ58sSHkERcKYMvKUQCEihoBELH5jKWAVZAYx+1uD51B6tyfxMF7VxQMKH6vfugm/Z/7XWWjPc25lL7Mal4g0+1blgDaeKfVMTqlBNY/eCvA64922wANXFol+Ad7GI9+OhdzbxJd6LGwz0swrXcBi/wEHaL3G80a75bNj/z0v2GclxRq3yIGebsKVwXbqzWRLiCIO1f9y2ZRRWSSrLMNUN26iVpyp+vNMsK0Akjxt04FILYkIIYvCLzhfywh146EcwyNR6Mm3DNKLPgqrcjzGXI9MeuFFIKOvDOaMSIPchQFG5ozddgeQKABEaa8+9giNiwnNbT4PuhFp+K7NOd6DqvPU0o+Xxd6L+7PM9ocZPs9kgRq+iC3Gv91HamM8d8XI/uPGe+jCU0LDpTt6IVOmebtafo7ELOHwvPecbNMwjgVgFEW00SBeoCn95kg5l2C+Nfdk7rlInRKsCnytgKMr1j75rQq8QCg3a/fH+dE9USGEKPhtecD4TPlFUSpW3Kll11MscvweUEu+zMBgq15UXNBEH+dZpqug7sdV7mUBc7w9rNB7ml66appeGn1xsIDje1i2BfUS9FMXaS9XZRgOjoq9FwJV6gt5QDw5AQD1IwAMgzkCqPGMaBZBlZ2wBcB2VLygy34CeAZru62ouyyF5Fs8tlKVOFM7v4IvySn07L0u+OF+4ZR69uyUearcoxDkZtHm5lutgFP2Ncq9Ip0Wit7zB/frWmISrPbLnLLwQtwF8kPZ5dFjnVZkr68U970oHl0pnqE0zJxdcitjoXo2oHVE5XMZcEadSQcp+RGuAlmEhXh6gvVmABMwUEQYNghDQNbV1ECtxZ6JIOazseCeDasKLf6orQkmNQ8VzMjvVsr8cVOriTurhHuuON0GLoj7Bc8NN21RL7ShVfIXP5tOMWjCkxVVNhVc2W1dPG4CppdmzKpbogCoC2QXxfoiWJ8o/SWX4Yx/c/0maF+F6IptXR+VIXGlq8DdKzAtfyZx+mxUoaC7xSRPEpTWgqtw7YO4qnktoPaip29+Xnja4HvN4tg89oSWUK34MqrfPUtvln4Wzx/S6ZV9Xoz7ZEAaz5CmDDAjrFeIanUfEEBxEAVCkDGIJEf4DCq5rv3czCMfuxuKpkcqidB3xfMmV6Mh3j2Hnz9U/p1BxwoaNurTd/zVs+aK9xrioy8p91zbyGdqrfbIXayn5jNlVlbTsDrHiy9Llw93ObWJ9/QVUFaKnh63ebgW8ayUGY2p7lc0pm/MQvXqniV3E8hyMHqjeG75+41fDbfxKK5dwEI7yOhjHbN6DJfB5tyK9MIMqsf/7b0WKfRdLtuQRXtQustgJJCY+DAABCCEcnih8BqlZwxVqKvf1hipXMxGBERVDLLSWjnCx4XnBArISvZt+HJiXJxv7TJMUIzl5kyZ13b1uySotoSX0CnyD5XHA918WErXy34WRMDitNtTlf01pIOPDU7alCk8m9qmocOWZn4/D1quVO4KWVWRiVqa2eX1tOBpYt1Iv3q+Mxy+JC77AXGGAT638qpyAFMzeiWIjBaQPRut8PKVHV93bItqHkXJzkByso3geXsuooRqRt5xR21kNSaxRNafxlMkmuG8crLgymHA34hr/Kgu+bT1zJimqfa7Hs/1adpp/Gxz5TIo43uNYpZQYB9a7EWXBX65P4+eWS2U8fxQfC/PO1TMkiT7/KAvldvPS/z5udVJ+/4a/XtU7L0QcAv6NGK1tsMCRtpGmBKHdGVZFnoqnNiC3hhqIcVOsUQqrIms5+x2HGfoj+AaobOJ0xzDscV6o7BbsuYhza8u6M0Sp1rqBN3tNMGKis8SUwzqtbgub/0NwZVlZe/x3VSoeKjPds15L/wWpOR5PtKhjiH08f3CdAmFO27YV3VPkM+y4eH+SPCSQEmYhZXFfum2OgdknPWadxDS3soc4TAwMERsdzuk7QVIb+mLeSq3QROr9EZbGKIZnnslXqPccyW0fvQqTaiCrj+q34loKjCZE3MvjJZ4nmk0i2/97T5UbEf3RwVNi6GtiXgLyj6ru6F5jeNKbfrCCXGu7WUcnLBrbgr2oV/77juA9+v7SR7qBsi8ZhWtucPxusgsAqdUtK0YeyHKjXmZJ9WSxsau+mPUurFrAwFkSjWutV/uCf+d9jz3neJbbb8BYqMjNKbm0VvxWX7+eG87Fofr+eKA0xarTcCUEvI0glh8FgVOdU5nPb5U2t7VvPDhuZhXvx4KvyRWmQtL0PGKwkt7xVU31n5xWFCy/a9utpEbvn3KPVfmwsLTP5eNrcJ/tRc83hVBCS2LR9+nvk6ae0PfyKFszeWgYO5omI+8rOhzcdqHeYXLPNF6WvW4piSfRn+rhU7Xr4c0IU6eO+QviXU1X/14MjgQiMV3nzm7B6FcsmHMwHKtfijr3MigorwLIHH+Hxicsx7dFdmxKO50kzcQIQ5BFXkiH8qRXsk7xADigIQskloAMoc677S6u3GCWeUBhPPtFnGgarFnPcqVL1Ppi961xmIH4+BYXyH51Qw29mXY4f0iLe1pg5br5tESjvo5OKMiswnq486hO0G5By7jA15GmidzqG9Y5cp8hqV3R0eWj8tfnv5QEk8ahFRVQ4+lbCwz6hp/YJuilM9FlnK8xt4V9+smMfXJZ7SpH1MzVGmaWsQE2wTWuIRiBFLdt6AEUiAQi+WwXTjU0nmBlO30gsA4TkXZz+zwuJCxKwzKc4RC8i8t6qqEo8KhpeY8b32i7vueDPaheRvjIBfdV4WrVboTn/tkfduvUZNLoPLnpdelOj1dmb4FVxtF4KjYe0GwBbABwBjzA9wwoqVDnXI9AmshgNFWZ3lDXlSlqhCAE7b0manY8gBOKDEBtoqw1Xef1cd2Y31damoV1pTDGAEol9YSFWenCChKvH5RX33nkSruOuWdfw/+uxdC9NnPfduCNWVejSzeWEM0KVX7ol2g175xbe+P3jqmV7de9wlVLv+lOE35Fp+LYF9CbOh1F12iuvxM4q6mGfXHS00uT7H2kLkhS+qrE44jCNh0GWLA9uwC5+c73LwFnI9nOIm2kMkAMTJiJyT7MTM838MC/JzD/ilFZUovKfkAUxx5616pU7/7bjTI4R4q/suP1r+J0y72i8LO4ndh3rqnvLsG7puRXpnp6wticIZeWFJW6g78AeX9892TkyW5pknLVdC2RKacY0dnSjs9jekX/MVPKkoejaKO0fSPWexIGJU8qReclQ51VAXtbbf2jRa+7Qv3pXh/fb3bbJuJ5OJ5RZ7R5LhQ5s8fUhqFfQTCtJswjhPWG7nlfqWsynq8HNEr83LJt15dNBnUOAf4RQ+K8G2Ojk6gHYHlPFwozYNoIVrBCWrjVWWffC3KM6+MW1AWLtVOZ7jLvX2uhxxqJeZ46vPwfLz3j9nJSF2tBL/6udxRak8jXWgjhfj2cp0zs3Cy4lyLygZuW2Tthrrp4J312yZq17Rmc8WDOKxvB5hcocWCLwPc+yhl8b9nfKFsRBA1PvYCxErGHOkDgF0ywpzBWbzy5RAQOMslHCQ34Q5DRAgB67XIjatISCobBfVra9aJBEKMQM5lK0W7gSorQsY4yVzdbneisCe5JCeljDgAiRMiGZ57XGvnyjLGLmK5lO9FuMW0Fu8KR3IvgcMUdSGUll4qj1mKuKTQO6RXXorTR6+b25fmsvBtSZZaohPuS7M+KtTmssJrXo7YL1CJperUovdPogWO3AonfsO0r9Dsqx+nPYFzgxKrZNNFfgHayLDFF5/rB1K6BzJ8b8stCnSi4qIpRuEpdqweKmv1Ih4FAmfbZCg5OiRjJNXhTdNU18+ZwZl173PJOYJJx8+Gfx8rHMAxOvB2WVrPwxeT7J9YC988X9yHq9fs53115+XgviZ7kl0DutkyI17zmNcvY19/7YejYu+FQB3KaUoYNreb7zTkQjirxR65ea5Ckj+uWyitOTbmMq/YLezad0WaOeUq8YrwCFPAwf2i8IFinQNjJhLBLtIA2Q4tUBb25WZMDe/954VOmVfegaYCDXTSkVNCzFDJfNdJY5eHqk9bVvbGzSw9A348Sr+6xcOS/7yDq6zLCN6h+nKRCoz5idNsa4MwMMCaTkhTRsoMZCBlEWqPcF3IAGdM04hAhGEYsJsmjCljWJ+qUJIAsDjVn01hEX54n3LPLdxB9VhUmxaOH87T2vy1o9jskL4cz7b8UJP2gmShUS6mV8YVa7uGRCnOK33wx229L86atir2DrEyht7E7epnwqatYbkxvWsRzwuYFb1UODVhseuAGRYWklCVhFb3GfcmEUrFBWbNyaxsqn5P0pqiSGdFraY7amINFlLkFt6dwJwziy8Zlr2NaZo0JKAq0prKLjz7ieutpXsF3lxYr+Dj+Hz9Joy99+muJ9g8b0jTBQBGTmLbHEJEykI/w7BWvif9HENWPPFt3K/c89BwrI599cmWUL6wqu5bXf0st8/XdMYDG8V+H3mB3cLhMzBTIFU60mfQAevcp33P3UKg/VlunaMHDuUrPSjpF8TvBWVGPVK5IMGXOHtyJID4gPKgaRq39av/QGg61PpEy2KURWzQI6s+92b2mXypbZyNm9LuWblG51y3cGmv8h4357z4ZK21UyuMekt4ViVW9HSNavfHSBiGiCGKNd96RRjiCpmzOMMP0vagzMArSwE5cjtNGcyMlFVxoGVdXOwAFn97wo7kohxmBkXzha2yNnV1W5gyy9AhsXbAtbIAWmXXvsQ9bhGaMblSVZdw31Wine7kgy6pzGXATT5NfnzVvurLvEIq144ydTq/1VctVmjM1S7d2Ke4XLIArta7Yt0KoMhUl1bNjxM1I9byHGfcUV77/A3HvWGBGpgEyHfusp3LYj1lEhC6FeRYPQhDFL4r/jFFZspZ/YIaN4lWRwJnlvVNIVe1FuZjz5+CM/pZN4tt/l0HK58eDogFzy3/gyh4RfRczocWnuagErZ79s3tCHWJhVnMq1Xy+tm08/S6sFDoM4xni/a8SAf2wVGx90LAupmAiTDcGJSG2i7q1BxdaIQGI4xmqdVb7Rgt1fzN8s77Reh1Ul6pVRaUBJilCe2VUpw1jeZVFuUwpZ4Q0KqzU0en5qtEBcdQlIF6DMIr+Wa345rktE8i8fVdCmeU1bLFaVbv3EYt+XV5kY9ErvPY5WMMkzFTHnrLv2Z13vd13/867mYdJVI8Li5GvP/+Q7z73kf48MMnePz4DE/Otri4GLHbTZhSwjgm5JTBANKUMAwBMUaMU8KTJ1tkMNKUsduNeO+D84W+O8IhEFxjUFghxoBxSghEWCVC3AhORFpp3HPYHC4K/Ibh6Lj3HN7hve1Aziz14DCAOmu9Mt1rrGaGzfhp/dA8OaVcwU2XxATGQkOKwg/12SvzGqWet+pzv+gFANcPfbV7lC2JuEnR9DkWcLdAe+xvtu4uwt/8tteeGrGj6aUmKi231gLsSIgscMcp4dHjCzx8dI6z8x2221Eua5kypikhMyMlWdQyAM65CMUpZ+y2sjudM4sVCq+AcBstPd0nCRUR3cW3v16p10NPr31ZXnnny7D33H37+cK4vRDBKojfrcRyG27MgKz3CRF20+ZYE5YmdjS9x/FF8LzEfZudzZN85lzDH5c6wM9LUVS+LqB3+bA0oiU9dR+tCU1m5Iqb51nwnFwNyWfrfAsXulYzmflE6th77S1L4wQloG52LHSbt+googI6nO6Wr02/eQW8bxcLfl5sR1xsR4y7hCklTFNCylyUS4LjMv7l5tpAittZF/pA5ozTk9t4+VOfgRHnHsOt3EL5yvD5DVkqdL0oF/3GhY0RtTkXXuAmEqGldaL/E+vd0NHnOl2q9aG3SAoE9alHRXEZEECDL9N8Ksqx4SEQcmKMlJHzhJQzmDNyFuUBZ8LFxU7HWOTSzIxBp4aJpKHMsDTv0KddlGvavVlQ5QcHkl+pGMPvDm3QDeLit0oh0OFkx5OpT7Ene2DGeks7FjffTSban12bSf+6j89dAh2el3XWFdN6XGMWA49xykgpl40324hntPKdv3gCQKEFVo87L0XceflqzWiq1dNmp/iaD1gdx0a28XiOItI0On9zI9SwBu9cthsSkwnLM6G4cCryovrF42hl1EqFoEo9kmO2WWklYGGEcUqOAFc3Ne28Njr3tEj984NeeXsw7nXDaP5yMB7PP/nnwxi5lGJ/7L2wLxs///bU85ol1fgsiuesCuNiCwRUXnpJ+qbsMO2LOoOjYu+FwKo8TeNjbDayw1JFRLEC8MfQqjGYWeRhptwjoFjjGZuxYAlDTQsAdjzLzdr2vg1Hto2QFuJQqZ7VtazwjWJrGluztQv46ji/ONAPpLfhuqO4jZWes+DzC8GlXaPSIyqFNeGeHVnH5iL8NZhdopqk5TjWjK21y6jad7ZQKBXQOmiZ7MppsqQWm0u2Nr7Aw4/O8J3v/BTf+OaP8fVv/gjv/ewj7HaTMHslFtks87RvbFFvTmc5y450mjJWq1gUAOfjJRzgCDNgTDqdGJvNBjlnxGEApx1WMel8j0rY1TqqkYZlbngh0XC3HNEF5nqB2VRx1nvovnsBdGnadr++erCFXcfMjVZ4y12rV0FfVDpRFHolDpVcejrhhZIic6Krt6vQ4WO1FXdabK+9zdoiE+LMjUFTBgPzG6x9uaEs+tsyqKRlUGPhI2PYShVW8vn5Du++/wDvvvcA77z3IR49PseUcmuZl7uFBVu+VOosyoCMGN1CdX0TJzetTG7K3y/m+YkSMVf2cRfH8ltwiTD766WrpeefL1ycPxJPucwYhgGc5WZOzglEaqFHditu2FPrS3CxiSnQBxeK4C30F1Pz7HGxLxdwrYnrPvqn5li9x2X7z4sGPg8n41CbaVfOgXH3FjDu+KnJyoZD84pXiaulJ9SGekvArhqzDQWjGc1HcmPT83NyQ8PYjaq0f3yOh4/Osd2OunCvFVzaqC+WJe6dWU9BaGvikMpzv9tPs3/1qXQQFd9WoLZPXRPLe79pZLSc0Y6/tcqO4DbWyKgjwSyXVYLJHdW1X0JSa5wwWD3lu8gzjnqbfEvAECN4yABNmJJc+sFc9pwBABfn51YDuXmXWfCaxd+etN3OAC4oS2d8eR8sYO4BevC0sIS9++L5v4WJX781AgLK/ChfXfg+HN9TycXsDWZH02vI1fPU6LwYskeeX4CiuPYL9QOQUsZ2N+FiO2G7nZSXo8Hzq0Kr+AZS8ukXvWy6ivevRr/hxridNf1+s/fSZLTc8to/1K0cZp+YGFnxKFJtn7lIF/mF5NbacrmiWO0W+mJ5aZ1CCHLBzZSQA8lFZL7PCBjH0S27lF6TbYhXOma06EWB7++Pswz/a2+O9NcY5GMs57QPXw8kXAYnsvhPl2DiciZXBdrzvJDVVYaFmZGS8KaU9KTMU1TLkjR9cRWXrgpHxd4LgWp5sJvOsYpyK24OwVsOowjKtkPjiJWNsplD112dXrrX4wwwog2AuRy/FCLINabmSaR+nHqkJtQFuf8GqYfo8aqgL+9V6CzHbt3iHot/oVrt9dZ6nhQ1QqJxHFpYA9qLCefuvXTqHopjnMLCm7wtXVdgowDcUwf2i2HuIzvoyoaw62/8+Y/xv/vf/7/w5HxXhFdWp7AM6E1vjPVmJbv3pgBWZZ5ZZYZBBNMxiOC/GydsNivQeKhOR1iEzADL7j8F3ctPGY/HHYawEeuelfgLAuSoc784ZD+NnTIPJE/ex8mSkFEXE4rMXL8thRVBzC2EXW4LjXQMnOqfR01SfO0X/E0Sr+AzZYBFNRpi+aBNuyw9mACmS0SNWOiib2tFpD5D2CUzRHWhWVL4o2C+7CZ7u0XS95NT5DXPkthUPJVCUBF0f/LTj/Bf/qM/x25MmFl2mrjJLArknFuM1TKYGTGIsjHrTZMpZ8QYXTsyoMfEa97UPXta5+gZVPpu4vWXX8CltzL8JRlLYpsvYw99/jnANO6QcxLn/rYQyoxdmhBpEKVpNL+PqbIYg76Z5bvyroO8oEsCoxAu7OoahUOcbzGudynRKulcHJ+x4oS35CoLYY1Xf7uaLPRTrzBvL7chx9Kr+olq4ibzZsZ1G4DKHl2dqGThZ2Uzb327uFfwQWkC99UAGPjo4Tm+8913MaVcxYcGFWRMQ5RVrt+/JLchIAvSesESMzsFH5CYi9W+ZQtQ2agotNmIoNavuWVdyxVXAnWDtt9UycqrykUZLt8iri7Qcma10bWyoR/AyMhIpkBOASFkBCLkqCt2VfRFtVw0dzDFDxfg2i4++larjDTV+gIidu7GVJSkflCmnGGXdFR+7VdadeJeDRUXIlyWZgE3lsnK1bBbWa3DWYfn/nOXbYPDaHH4sFLPY7GHy5fvbd59/85lgsMZ6U+H2H4md1Rkf74NnvtY9en8YsS9D8/El/re+nNRMC2XVH3V7TsaahZChd5Q2wLyxNOmNlDxfjaOpDhPetOtjCqXuaJUnvrxXKp9z9md9KOdYXUHAZTlMgzOBA4qP3CQDQGyk2laxbL5UAsgyAU6kRk5o5GfiMS6WeQq15esmwlKP8qtwi/oKO714Rnko6dJukC3Fz49PSygG+0J2k8trkkLLqvPAr3dl/OUMrbbNJfHr5DvvuKfFo6KvY8dGLJ4GgCIgLUaNgCAqL4Rck7wMhWoWuyJsFkvuKg79eZXz6SIlqXUcBSGUQVMY94qCFL5pxRqRLxHrcXvFt/+UWJb1+nVEoeoCn0lkXys1nomlJJbKLYSiIKzBmmwwC9Uuftmdb5M+nIoXCSUfgHKdX3brDZ64mL13EcWLFzHxxYDmZFzxrs/uY+vfe0v8Q/+4R/i+z98DxQCTk43WA0RRCQ70cxInBGHoP4jRJkHQlmMpilhc7IGMTDlLBYnaoGSMzBO19gSOIICAyHjyZMzjKMc45nGHWgSnzxEhDWrP4/sTKnLVPAqHp1rnXN91p3EZWZgQqElIcD57CuxXPiiLycvnlMrrNdI7Z5sRd9KG6yslh5UhX6j1AsmGDoaoTl5WbFuEpQeny3YCyiaMfS4v1tst0sKU7Y1K00NZzcUbaGk/dv1mHVaLcm8yhspK4WrWMtVuSfWKYwHD87w/R99gD//xg9x/8PHgturQW5WJhR8BRgUArLyj0rGNO/MGAaxDuVcb88LFFT490fIskvb96n9mWI6LDwD6thGn3tLvqXjtP4Ybj/PeE/Y0oLwxcF2OyIzY7fbIaUEBiPlJEbYISAAGJCVFVv/dgtFxe0ZBu4R9C6N40mG/+RY+SEBcnEEZto6x/66KE2EkqxV2Et8Jzu4/L3ir2lil79X8pffQg674/IWi5WbzvKCQ0tPDSyvrm0teZj5JJUoJoOQq5uXS6qswQycX+xw/8MneOe9D3F2vgMIiuNUZCc9k1c25fo6SV5qVeb6ociKGg5ALfSthVyUjbV6VC69COxoNIuVDNjGM8jFGK4agWoeDEL0tFHHOATjHSi02SsqzFdq3aiWSBkM5IwJBJ4kcaB6uiVMBAoZgJw8MDHY/IkmMHKoOBj0MjeCWO/FEETmYUbKoiw0PE9p0k1TOc5nYyEUzeS0K3tcW4AF5FzA53mqjno0kS9B+FlhC5/8nytzjvLGkx3vpC7UCxT+31nRhrNUfy5ZHS/hqa175ikW0jf1m1eryb7ITfvzawm9jNA0Jpydj3j4+AJTqvjWKrYr5d6/HPEXc/XpXCz2awigGnRoCkMto2kdLqOxAhY5x/y3+7FrZUBPz2tHzHhSX1n7yCin0TIg2j2yw+76l6DKtSyblUYeVc7NquUrNSfLU9eaURT+YFZFPmGaRIGfi9kk1w109Hi+Z1g+Jmh4+P4Ysy9PXdi+oF4G2Pf01IUfgAX87oOWojRouLdi1xhQn8WeZDkzxiljnHKDw3u75VDxdIU4V4CjYu9jh254xx1ON6fwjIL5ogq9xtz11xxGe6LTXpRh/wCFtZX4Qt38Tq8V0JAGxYgqcytV7DndEiOkGmj/WZg/gust9qrVnlPk9RdpKGktVLqpgK+9LTBLb7aNwoKyqq442viL1HQPhW2O23qpYB9L68pybWEAu4sd3vvpffzgB+/j/Q8e4PGjM+y2Ix4/vsAPf/QBHj+5AIeAV165g3v3JDzGiBgDVquh9DNGVQBkRoyhCM6ZGYGA87MtxnESi54YsF4PqvhjcEpYdqZ/hP2QAE64cXODjz4ExkkXHmnCycosriIAuWADgBOO+mN5KrZ3U06mWLXIKDhMthgyotGls/BOiV2iKZp78NOY+ndHH6iJLGV4H0jWxorOVNrhLYApqA9OOOX/TFis6Zob2rhajBQBrFO6+V34HpMbZ9CkGSjNrVUwa74l4rfQb7zUt/rCwG7KePDgDPc+fIRHjy9wsR0xTQnb3Yj7Hz3BdjsBRLhxY4MnZxfYbncIIQgN1eO00v5cNmy84tNUxOM4IU2yexgCIcYoMjO54wEA/KZCaQCA9oZbR48Xfyf3HF1Y/9f0FJaVfnvFoZ8rjBdn4JyxXg84PxfrR2KAckYcss5vsaxKadmSx3+bK/daHLWUVU5o8btw8e7SLMujlrBgxdbDDJnbGhRsclEaFup5f197NzdbCz7qYqLQiEWOy/ZTXROQS10vodDesvJ4IZuZqw7Xn7OAjocvpp1vluTMOL/Y4exsi4vdJL4wU8aUMs7Ot5h0E221itiNCdOUC50Mnl4anUelaZWuyzy0I/kElJsjCXU6lcszbCFdlHzWRJ0fpDfagorbDsp+4yU38hsRgZsL0ajU2/o/GB9QXhDkSgswRB5hVRoKobJ6W8t100PO5CKkhClI/WgUH1lJrXEYcjN9yoyV+sezoba+C0YrtQ8Cid+9lEweZuy2cnlGjBEjpkIriRkUVIGgA2AbKxXqXFlA5wVYmOnLrPxgksU4VyKjl0Sy9YiPPdOo1ahw87OmmfPKy6ozJ0cVj/eqUrti2tuGqTw9FVi9Sh3qIDAzxjFjN06Ypqx+G4GcM3ZjKm5xAunhjjIfu/o0N7nOoViRagRqwvSz4SE7GVKVz1Zm8Xdn89o2E1B/ZclleNyHW1ltTSv1VxpOhsfV5chs6mgd6561KPcoZ6FD5i0JAcx6dDnK5gNT9dhpYlyRWKhaP1oxsgayIrnKRkrzigUgc7l8zebddS4t+IWF2aSjhVfa93pJdvuJ1pV7tpnwh6Ncnue+iu9PaXwoma9bm7es/m9dzr0kfa1acJvuCs1ehKNi72MHhjjTFiu93e4J4oncisthA8AUKgYqPBaHwVyOh5gJdL0Ft1UE2MJX0lXBrTKmQgbrJUYa127NpZ7BqJBQhGiiStSp250vi/sS7IQ+YyLU/To/eqbkE1JcK+AVfaVB5Gq5xDFyF25kn92vf7bohySyJQK1kH5vuC9f3sftiP/vf/41fO1r38a9+4/x4OFjpMw4Wa9UWEjlmMmdl27hU6/dBYHw4YPH2G53SClgHBNiFG4U9A74qMeZOaPsTA2D3PA2DLHcSGyEShJfVQI7QgtJbtobEyhnhGHA+bjFrfUdmdZxDQAYd6LYa1RNC1dYCk4vzMOF6bes3HP56m243GXQZ0Xu30owqMN/TxMckyf3HYrnRhc0AwJQFXeVLnhxsHz3+Ozy7oXJpu660ufFGPqVgSqdU90PAUDg8o1cmtagbylnFfMboZtUuJZeJhDGMeNff/OH+P6P3seTsy3OL3bIzFjFiCllZF0QEAEnJxvcunkKAuHsYitKASKZW0VRor+GsypkMMSpvFnz2FiU3Xz2aG634no6mNGKJp5W1kZKXZcUfZN7NiWf+eQL7nsPn2zak6Y6RjllXegHjHnCBhuA7MY+ICWxzHUYgmaVMX/UD8u8p4l3KP0VoKjOCtukLrziYs9aK9pXnPbUrGHVLu9Di8A+f0tAnYRbLU9IyVwNNPJX8tRwbjJ1ZXCXmlyYh0IgyMXu+X2tAEEUbe+89xHuf/gYu3HSTTQgRrGwtYU+iLAaIjYb4dvjpMe8GUhU21f91Gldi3IBRSFmcpaROMNzT4/F2he6QDYL8Pmi39JxqtaLrQuF/p2q7yvdgLBNRju6WnpP6WskQjYVX2UIGqcqMj0VympVR4nAlDElmWxy6aUsrjbriGGIZdNSNjTsyLD4IhuGOvaTXjiUTXbOGbupWvGwjgcRIeWMIQzaHzJmea+cWIb4Csq9pYSorHwx+PC1DR3aXqGwp4TCi2o+rfhLe7Mni+yUzAfpmcd/wOHBJat8oNlgeyb1gtWBRUn38NEFzi92mKZcFvdLYx4Cyc2uORc/rdz1TauIbJ8vHaGCpz2tracSPJ6X8i2G4pDQmurSpKzpgHL8vinIjvIXzbynk3ZbrVjfFaOVroH+WK9WTf4yA4GVXmYwB7BaxA8DARScjKP1tL53MhuXPLnOFYZYUDorauvHzCyXYFk7gZ+LYk/6HNdn8Jfmet2QPXEXEuzNwxI0SM5Nmms1c6kgPhx8IHqXcRvCDOzGXHjFpfVUtLhKez7OWXVU7L0QqIq7cWKs1+JjL7As9LNuJfS72NVpqZpDswqW9qyRvCKgKvTq93K5BtXjX4ZkItCpsG3chYyB+n+9wNAK8yaqe4GiVd7VdzPMaxR5nmFYLo2EQKjWHaH77pHRv/fWIHPmMwsr7Xd58qE0e/KZhTmJoHifFSLxf/0//Rf4J3/4F5hSwo3TE5ydbRFiwHq9wnqzxmrNWA0RMcbSvJdfuYNhNeCjjx7h/HyHaZqQkjmRlsV/ooxhiMXkPISAlGTHPWfzuZNklztl8U+T+v49wqXAE8ATnjw5Q0qya7u92Ip11LBSwUh8643T/luNekZuwtTM0q7EcSzRfWvtdLj5sk96bHB5Jos3WF2FefvihHvq3u3BLwrbyH0xVBRXtTyzPK5N9fhp9kiFhHimyijCZ3nVByZ31AJAe5Re6SX8DYgurPnQtRdwu+J1vP7pP/8Wvvuj95AyY70aMO4mIMgx+mGIYMjRMLtZnJlweuMEIQacn4uVbU6yk118SGm9g972CBWg1e1msRwAzJJH+EMoXD/CXERUhV6v2PPt9OF+qWBHc72VX4DwPf/rFX1Wbm9xfR0R88XBbnehNwzvkHNGSoxpmsAZcuQeQAhirZvT3Eq8EWAdIs9Eyb3KPbeYbxJ1kqSl7zP2C6uFLqaFl4KiXWCD200aLzN4nKiKwoLLPm2TfeWhRZYB9Mg8HP/sLnNwi4XiT9h/Q0MmXRrvd6+JNe+nsmCklgyUo2rAX33/Z/jgw0eqXIpivUliLRZCUFfCbpEMYL0eEAJhHBNS4qKg92MuCnmqFmQAvDVylSFRrZq1goFIrPcZRalXj6AtLDBsIcx14W9WMCYnNkq+bPQ9qwVfEKf1gUEsCrZABEJAYCCxuVEWq5t66yf8pJmxCIYq+DiDOJTFv/RXRspAnsQJf8oBQwjIOWAYAigQhhjq0KryOAMgtWbMIIy7HZi5Oapn/gnFhy6BdNOYZxZ7dWSW35ZgT4xDCXthYW+aq9LSnjDQPNjz/U4GWC5njt8tGTE61ckTjbx9YCFdolzew77gpg5lGUT+0wLM+/n+vSd4crFr8ZLId1WD4yDFf3Y+35xGibs6HmxRL4L4OU0B5RI1EtziQmt5MR+jAp5tEGx4qsFHycfJdKYQ9K6WAMjRfgDNhWP22DWwnylGezIzArs1EwMMucWaKdcpFAgBVclnmz+wsXDjbKfdchaLPdmsUxqS63joqJT6/LLCDJMJ3ZerUZHlOLSckRcqCqud9/G1en2BVz9d9JbjXGwTJi/PMS7vEKr486zwtFkcFXsfOzCAdXnLE+N0lUoIAOS8Rb2RDYWQOeMS2OrVLOtEnhdK3MrxRnzF/0BR7ulqtuzIeKGWUP31qfA2P4pbBXIjzsUyB1CT7VZoDxrdxCHR4ckNuOTjm0TqFXsA5se3aOG5x7T++9LidN875mGeM1g/wITiqwpOLq4r8tEHD/Bf/7OvI4NxcrLGOE44PV2DM+Ph/5+9/wq6JkkT87Dnzaw65rO/bT/TY3ZmZ9bOLhaeCxAgCZIIkgCkoIDABS9ESReKUISkIG90I11JwVCEQtINIySKZDAQdBAYoFlSi8CSBBa72NnF7MzuTI/tad/9e/O5Y6oqM3WRturU+czf3TsGk93ff8pkpX9tvu+bT09pO0NVV0ynNbvzGbrWOON3o/cPdphMKhaLFY8eHbNcrplMahyOyWzKetUym02YzSacnS7pOoOuFKbzhMwGJV/nQrBPFxQCP0lXS7YNC90rY50InXUo45hWxosB1isuTLv23wyZuPBPtr71iyQyUaULrn/TX8NCxgO5UPrLOy7ncNuLw1UK/SNrOhL/Esf0mf0+3Pbctfpv6LnhMrQAKeoUKX4z7LhUrYx0sw+TZWD3LDzmse2Ffe9R4pIRLeLLpA2SXEMuIbvKJCub8O/p2ZLvvnUH57zrnTGWqtbgYLVc+/hOQcE3qasUS08pYTqboCtF23Scna1o2w6pNOIcVV3Rdoa6rqjmFc26xYZDXFzBsCL5pGwo4bwiswCRxRf6CjpX/MbDNmxg2Mt1OLSQdkU5kRqUFnulsi/+DdfeZfHrx5+6Zp3WfBU2Waxzfh60DXDuFQSmjGF4HonZlmXrN5svRkB89Nl4WcPbIR0tfgfve19Hhc/I29I1P+INGf1+vG1S5AGS0g5I7mSuLD/hONfPW5YXvyubBrkHhZRdRD/dGJ7SzdO7cXY8eHSMI1voRYVaDH8hym/AVcGt3gvPEqzoFZ0xtI230o+shgoxopQWaqWT9UAUbJ1LqCrTCsnzriuNVjr1J7MzeWXEzxw5tq9XbMWtE589CsYxll86gCMK9tYr+JTywrdSXsknSqHFYgPvp2yw7qOw+IlW1843KLOIxcA7j8us+AM1xAr+DA2h60DEn4SpHTjtsZsNba0rr+RLQ6QUKuB3ay04Rdd2ab3FA0hsWGtKRUE/xjC0/QU0lop1cuV0yW/H4L73/hnRaB9yxp+M11Mq8guqPGzIWMMGG3s9eN1ap/ReXjryYcn2ZLBh8Kh46p90neV02aT6o3ILSjpLXr9S4CXxB7pE2CoAL29kSNhQHNEMnCd1iOR4lq7XgQLOi4sI6/28ZDiHxCgm/CIeFqKST8S7xarwi/hYvoQxEVcqOyXJtOViHa6VhIZdHCeHijjZSRpjUXgcEyiKc34TRasckiACkSLjFqvohcuIc5dFvP6Gm7v8ivpI0yVYh5zx46i8d30+7PcyX6U9Bc4YrteL8Nrlyr1cIX0ID9eWvlJvW8Yt/R2qDrbV+3GsrZ8o9j72JJRWUM6cUunaX4dTUk3nBf7swtYXEkvNXelCO9ydjzsovvCYMdvuJOuVRB1KNzJJ9/GOWEdixgdCeXiNSE8Fp2O7U38ygfMuG0KInE+gBH0AL4X5JAaMxWgakrkSTIYHVgzzlL92JN/gGxnmjZToPFI7Uk7x82v/+W+hlDCfTXDGcXS6YDaboGvFrpqyWrcslw3L5Yr1as3O7tyffBvi52mtmc2n3L59jfv3n9A0LZNJxenpEmMM16t9bx1UV0ymFfPZlJOTJSLQNP4k1/l8Gtx3hQ4bD279SbpscgCGrjNUlUZQ7O5do+vWVGFde9ogrNZNRuIDxuZc5C99Ji/yvkOGzKODEau9iCcoITzWO1KzZFgvnzk2jz2Q4qLQ+Semrxd7KS7/Xn4pvou7wLmPPZhx0Y00IjyQ3uj14TC+iUKoHwDPQfeg1kW3vShIRGbdl5LzFtY9G3Af+5cZ9bgR89WvvomIMKm9m9hq3fi4mEqYSEXbWbquY9m2dG3FZFKnOfcxCBV1VbG3N+f0ZEFn/CE5PtC7RalpwAcKXfm863WLiBdCnHNUkypZQyod2x0t50orvW0KNhfydUTlXv5maO0Xx2aImyMe1yO/enC/fV5/EGm1tsmVUmkPHbP5DtZ2iTKZQJezcoD8O4T1AdBv8IsjSCFTaDd4XzIFZf6ACbYO5RaruV5ymdoPptODxyaeSEJcCe8FnCeGoITLkZQE3ZFxAvKphaWyqsgwNFDOYy/F+xGlQW8ypPdtD2fG/jg/ku+9/wjEx3rzVl/Wu4IGKzYTLOVb02GNh9WoNotDopVCJsK66fxaUxJOuPeWQUoJygpUPm+M1ReFXa3TBOQzyMQr1pyzGd+GUB3lmvK2aA6sd3t1xuZDN4rfHO8rxh/N7xM/aK3nVVQ4LV5sUvApUaig9FMICpUVAKG9cU1nxV9eR86CFYcTi7aCs37D0kqkOfjwJcZiggLVj6NiYnUKVQCSYp9FaFm3XoyPuBeEuqrD2BXLAu/aO1yTo2kcPAcL7kOId5f+/GJoL+k3g+s+/Ev/2RjJCPl6p89uqfPc5ic4J9PqsULSpfQKHIHwc79Pj9xQJ+Cvjo4WYZ0Gqmcz/IIk2LC2VE7Tg3OQZOOeeJIEQ5ubnMkit8DjxRD73+AS70XCkN8D+2iXnXM4FZV7eYFm5X/eGEw7wQnPRtgMXgJFRx0edmR4EI8XUnvz0x/2jAMi3o2We+Iszuk8DuSQVNZ5nBjHzQbrXBV5qFCRDZMpTrJltItzIiitPU2iv1o2Di/6o0wfAWq4yvPz3o7igXj7rLsH2yoIMDSGXq48HGUhJS7qA3cvqwNWTRkibfBt8V0EkdFhOwf3S/E+Phiz8tuGYs9LP1HsfezJ4mPszQBHs17CZI5ftN5aynTtFkRXLohiVQ5WSrSwcy6yJ5EI5Fh8pQVgWVMUYNPOUuK+A4HZaJMMfvJ9iqMQ/qLFHhG5S475xOivKiqTkb+xERo+O+/aDa4jpA5dweK7kiAOLVJgAzMUsRH7oDxES/6b777xAbu7M5xznC3XNE2H1orpdIKIotKanfmEpjWYzvLw/lPWTQviXWvimDpnMcafyrNar0Bgd2caBFHL7u4MGxjynfmE45MFzjmadct0UiEV1FWFHhvan6Rzk2s7RHVocTSrhumBZbFc0nQtTk+AaNUA6/V6k1EeLLuSh+ql3jN/k/OWa9Dfp9icJbEYbiGlb/1Nb/p7jCU9RjbjBxKDGZmxAplslBuFNCnfB+qWmCsp1JJFGyJTPMQDvk2DneDYx9jf+M3A9ScJqz3lRF8Dkog2ZArsKBQSRTyrYnB81d5q6P7jY6aTKljuGIyxKOVd5REff0eqChUUR6enSzrjrT2j1UhU9FnnrWm6lQHBW4mGqNzTYLHrHEzqitW6ARzGGCqrcErl0zcBqPHW5NESL+K8+L5U8rnim46+Mi9elwo/N/gu/sa6SsVeack3/BsipcsiqQukySum9bL1oQxwmNZQTR3rdeN3dFXl6V3Y6T/P5b5Pgrdwcj1ksPkGRhR2PTpUlDuGSzYLTJ+dl4aUuC99knBBflXCfs6YythWoQxmTiDbjWXBqxc7SyCp4CJARnwVBYQCliN6SPa4Lj7tD2lqxwBnFtCfxvjkbOVPqA9CvXMW6xQ6IEolDtEKCXFtm3VxOEOoLHEfQeiMFgNRGeVPsVcJd2mt6DovgHhFYJiKIsSJ1pqq1l4B5izKBZwXcG7k8xyCxoEG5RzOFG5qLsa7CgHErQ2yV1byhVv/Kw4rDrGCUvHQDYWoQsFnQxy84MXht8W8EiApAPJsQVoDIRaWdX58xGBsceqtgBgfl1hpf5KmrnxdXWexUoa2STIknbF0TYh1iHepV5WjM+EQBInKUP8+W+ZeDs+cD4pb3gqjgqEH8W2MQs4zdv0sSQZXJVzLIOcGzJ9T9xh2H3Li/QySQwuxqYQZK9jDfqDnxb8XJun3M7IV66ZLsW2dLeWIxM4kmQvYCK7fS1FRmfBA2VXpXWflelFPkZTyB2xFS7fYLiknIjeQqGBzKp/mmaz8QxnJQpfBqLlsxSf+H+KmgYuKvMDnRZiMlnUqrpEejPcH3ped6/DhRXysvdiNFBLA+tOv48aHFh+axMmmEtg5cNZiOpe8OKyN+C2MQ49/vTiW5g8mPQNAP/MnQ2D6UEVeoeJQ+mD8hxzp1cvd8tz1b81FCt2CTYMtrNwV23QRu3bZ9BPF3see+sHCu1aY1PF0TI9Y0+laG1ROMhaPvKpL6BpwhUyfX5bvsl4vH75RLp94mpxPLqDegrGK0ntqExnoCiE5IvX4PiHugSAvZRmiyYrEsp7LnpY4xhZsS2OC3jBe3zaWIgq8RRllvDw3KDcpUIaWgPl7h3C2XqGV4uR06YPpW8t63TKp/cEZy+WaxWLN6dmKxXJNs/ZKvemkTgI/eLPyGDOnqjWz6YSd+SwwtorppMYYi+kMVBo5ISgCKpzzjLqyBqXTHuJP0iWTVBqMj6mnqwoQtDhsJyjVgHiLBLB0TakcprgeWXcjQv+YHiDykD293maO/sc9PNKHvN5KlmxBx9j79CQycDlDiRMyDijKJT7PzEP6fniCmvgYVdvAO41BvC/j5Y18FwW5obDjimnIqLLAjmnXucCiLipuizqTss9nXLctIkLTtLRdR4zhpEN8y7btaFvDet3ShNNsEb/rLErShEblPPh4PXWlk+WfiHcRjYo/lEDw/I7ugHHnu047+BP8hlPsbPyL63QsTmk5sFHBZ4v7odIvieLl6BfvYpk6fDtmzVeFPGPt2ZY+WpbTKwX8QQiiVVrv3vunS3DucMmSZwzON+BzhJPr5dzK6RU0vMhSsAojn53H1Y5cj2kDJD4f0HOSDX+/JIn8xKD4Aarb1p7NluV/y8DrEngglwp0afgSze3hiL4gXbrtRquNXDoJZ/baG3gtFxpgrEHEH7QSreyscSjxcGuMPzG5MzYox7Lg3dvEGCBcrcqNvHjC7SA2HQQXv9i/3LeqqphWdSrPWTDOw18chTxXcYoFV/vy/EmfWclnjE2u/umgiSj8h3ti6c5hwkEXSixilbckVAqnBCMKExR9yU1XFMrFeKvRYs+BJcXrI8bYw1viqHhib4znFw4sUFrQ2isBcT7uYVw7cflENq4zFof4+FsOb9UYxsZ3yab2OAJ7N5q2AvUFaUumS3377GnIA6SnUuYoYX17vvE8ZSkXv9vEmsPMoT3xgMHLqOsiX+JGsfCVknXRfTvTY28ZJgkGonIt8hqxDefVGuEu3UsBz4PrWGw5Vko0VXK5L9vah/MkP0bcjM6wjOu3v4DvxNfQ74d/FccBynh80VU3WvXFe8ghmobbqjHEVNpwsA4l/vAMSz6II4YC8K79QbkX1oQN8muMKx83WSXwQf5RNoYpxybLwbnPHy03cfmU6NyHhP8e+R2jxRd9eFG+S9T/zF1IC35TsolpO89zQaOGv72FXWCK+LwEOvcxoOWRTpwj+mxNP1HsfeypT/1Nt0LXnsmKhAk7PEVvyKBnC5zBC4jvQnmu2N5L3xYKQBmunMjMDZjL9Gxwk1FgiSSGwV8lFZj+Gyr5ZMApp8rLDpZ/ju2C3RAaCsjbGNn4fPtob8+7AfmkQXHldVluyVGUL4V/5V/7U7z3+n3++9/+Bk3TYq3z7nX2jEePTjhbhhMxrRcUcN41xwd0dt6NJDC3k7qiqn2Mrum0pp5U1HVFXfl4UFrn06mqusK6FmscbdsxVTE+1E+UeldOxoJ0gUnwVlht2+G6NsXiihxdd44lDyFbFkYZKOwCNBcMXv+zXuYsrcRlWXwzyihHGB9tVgGHxc5srCou+5g379RutjNDRP63VPp5YVX1GJAeDqKEozKAfrx3vfaAbORi5KsM5rEh9A4bkkFpktpKUO5REPqg7AsZv/Tzn+LRgxO+8+adFOMyBmc/W6xpmy6cTp1PzYwoJbmBBO5Ca43WglaKqqq8+61SXikv4WCNsDeutY/ZhQNrDFq0Vwj08OJluLttKW5YlVxPdNeNf/EZ5AM2xlx3o/VLPIijIyv22nAdYwIKfSvrYfoQHOiW5A/LsElZoVSIV2pNXq+xByOHZ2xPfZoySmmG5Kx83yMpA+QwyLRtVGTsZuuykFRVvh3aXQxpa7wqnveE1MHXIzhue7szfe0LYIONzKGybqBJ6w9jWYp/n4Zx2ACXh/nlF29wdrrm/uPjIPSDc5auw1vdFwdjJIuX3ITBGAQrFyXpL50+Sx+3phAsLuIkyfUAFZpaV0RtlkhwG3deUHYJBvPcJnFbhFqnUfOn0DpLZx3WGq/0C4owbJ6A0mW3tPpzzuY4XSEen5Oo2JMUg1Arhbig6HMxFiHe7S4QNG9t4119kyWy5MNJlIon4yofp9AolLaZlBSbzc65cDp5tkzExcOIbLpOwx43UK6SCpJ81TTGmY49u7CQ+DuGDGTwx5Z88dXGu+0i6OjTEbx2mW838GJct4FGnltc4g0ync/qnAuSgDjh8GBOs+58nD2Xi7C4AB/nMVujxRI3O4f4VMpJHk74YLiVeOvXHIM9Ul6XoLykA3EcPCwoSpWgV9S7tJlonQWbcVcsIdLDOAapnoFFX1T0uajYKxR+OQZhgXddwIGhL9E4JRuQZPykhAJ3iI8/qgqFYcEPOkgwnq0aI81xG4i4398fz9Rba/SW1KW+G15twwLbyr306G7did/CNz1LKgqYTLTn+0yEopDlGXH4H2X6iWLvY0/9xdh2CxIKVV7ZMhokNTIBxUJLcZcC51ueeJsFpHhbcp2RsZccFDW0K1ddcNqBsEvB+Ax5gYxcE3lIzGhkHiOhEomH3wq9DAnxxmfDcYsuWmNjue26TGOnLTr61nfbqOaQ6xhS1Nhuiusyb9GnkSoE+NV/8U9x9xNv81v/5DtMJjU78ymr5YrVqkFXir29eYjPky2butawbjrvkuvwyrxJzWQSgu9XXsFXVRoRH58nBopVyq+b2bRmOqk5Oj4DXLIuMGbM9e0n6dxUa+icj22E35U0XUdjojJCsMpb8qRArCNDnJZGT6odW5OlEDYU4iMsDmLs0VfmlIr+MbAbd48YNGPjOjNdku8yAxb6VuKUokJ/oll4nvV8fY4sYrHYRjcoJ+++QsR3owF+ho96Erav3xJiuODSybiZ8ckWeZmhiMxrGOcCgf/cF1/l3rXHfP/d++hKMa8nyUpPiTCZ1hl3hqYY6zCdP5HLORcsTyofOyYo87RWAT+Asy4EdghLCB+Qv9Ka5arxDLcNe95JIC2x+tigbEvD9THErfHE2/Iv5omuuPG6ISsA41+0dC7dduPpuqXCTxX5hrRiW1ufLcWTM6MVkRcQrLfSirRUCdZtceHo0eHB6D4zN5o/3Cyi/0TSP8X9lhI3rofEv/d+E1tIovUQ5z7WX556PbROgYz6xvYYzpevSmVXFipLHiMz5rmgEufKYBCzAkGKjRHfnzLOni9TeOH5G5zOFzx6euqVU1phjcVYr0zSOguqcUy8hW20fCMopEgCrypO0RWCkqyYcxAfx89JcslN8T/Lflo/KlHYd5CtanrKftKziGdj4HkVhGULTEM7jPPWfMY6tPhNGeugM8ZvFDronMN0XbDyIwnU3oLYj6nEeGTKx+UzEnBbiMMXFQcisT3+11mFKIWRHMS/VPBFXFlVCmt1cAvur71YeGedD8jvivdCVmyEdjjJ8b+unCJZOC/D2NuxxwVO6T07p+Rtm3fnoskCecjmi/59AjcZz1WSitipbXVvYYO2UqkCrvzLrDzfOt6xb0M56Jz8B/tzVnXL2apNsk1e11vwWtGeftUDJR6RfrPR59TUEkcNxJKokMtKvUwhxoa5QNH0ZLgC5sD5DQCX3Vcl1G0KZVj0FkiuvKHmsu+eJerH5Yvw2N+CzS2OBitRsRfbmuZbIt70ynynMg7Iaz4DnYkydJgrG9tZtLmckB9NvZ70fi7MV95vpfVjn2++uSrHdRH4b/+wBK58sRU/XKVBDqa1plM2hHsoyv2Q60E2LsbfD0D7Suknir2PPZXuSGCMIsToDMGNHbYzRZ5N6h2tP3pEKij1XGRKiaJsQFIFzcyuuG5T+I+EIinaIiNeUJm4qqNirlDK+U8Lgp7eRKSd85cxFzznxaC8ROLKnhb3uX35eZln+Kx8PhRct6GTYR3lruxYGUEYTWNIHtRINXufRCLj5+rJo2NE4PbtQwTFdFqjjs+YTie0waWnsxZjDAf7u0mYb4Jyz1v4eUu+ycQH3tdhdz/mddb5GDMiGOvY2ZlyerbGWovS0lOq/CRdMXUtVBPvpge0qwY3bTBW0MrPfaUqnLW0rTkXQY+uXMnWGPmFbKCJCOcbzHCv0OHaDuzbKG2X3v0Yve/t8PXA12d2AcQT80YB6wUqSdBdMJel+U3q7qBdEbxyTL5NuO+FGpAcaiCynCUzSXrqcVq21BN6J3BmDOfLiAymK0uJDfZM9uLMn6q6tzsHEXSlEWmoKp0EextOo5zNJkmI7owJ8TO9hZ8SCYdk6CDEBqVoGLK4EeCsY1JXrJt4GicpLlBOjv5Cuiz7MJZviEHG2BPwCrm6uJ+TFX0tWdFXWvMNLftKxV6ZoqKvPIBjLF0d050tO6z4k04dhLAGnbfWDHRMK2+RaC5hsdeHdb9OgPOnYDCkkeb3NAVDJHJOMX0GM9PzFAOJ4awOxq23sUWC6fJhUfQmGbzgOn/ltrzbJo/HegtXuHKIC0EvlV4iM5ehvIc648UIyoxYtWm8VfZ06i1LrRLo8imNpRWb33zzuCZakRjrlWFeySUJDyThN+HEMF8uxtmzyaptOE4GS+cMrmi1b2/uZy9oiJOwsRFZGX8qpQn3UeFWVV7t5wScccRNFGMcrvJWfNb5PnW1BWcxLsSsMwZnTN+iDxBrQ3wuG5R2/vTbchJU6KQSoRNBR0sesrsygBZBgmLPGD9GEVeWChWHS8qZprE4lLdUwlvfxhhkEvybVVg/58feOgcQpbfMtnwbZ+lq6SqYLcH21o9k9HJcOXiO4miD7SiA8bwWRwJ/Dn48d6Qk0+0L0WKqooSR7V+YoERPMXBxWA8N+fuIp0XSoV8uIKTe3CfeSjaGVooOlpa4Y6PmbeEvsFjsV5vhvtwEKMKLRP6iSvgmz5u1zsfkDDjNOofV3rrV4je+3EDRV/Kyybo4jNGw8z3lW+h/3jDOKy7iRWcFq7xLbmntN+yzI7vdl67UMXTSJm6//Jj+MKSrczdXL2crrH+E6dJYMGXchPJnxqSRRdiySTvmAdXDpVvYOdm42F73h0k/Uez9USYHpnMoFWLsOW855oOrDpBa/GcolEeGpLDYiwH0IzKOJwGlU+McaccjrsiAthPDUoaJKpnd2I6o6iuZAc8f+edRNVTq/jaE9vIiFqJSiWXp/T6PXjOSN/ds83qYbyyVHMiwXhl578WgTB4LQTkxJVvaF5Ssr3/nfZTWaK1YrVoWizXGwf7hLutliyj44M7jgvl0Pp6bVsxnU3Z3Zun0LcSflBeZjUprRIk/Qc46OmeRcMqe4JjPp4AXMOqqwqyiEP2TdNnkjEHsEqU0EOIV6hnOgGDACRUVznR07cjYDpB4AOXBs00XeyQI9S6v8RT/LQR3j1hlwK9uKgYyu5QzSmS2ijopLO7K5wX/1Fe8ZWYtM2PxUb++ZIks8ZpUT2l5V26+xrHqqyFyF/MYlHBbCrXx36DEczEIP738gfPMTwql46ZwFvE0vTm8e/8pSnzMvK41tE2HdTCbT+hafxDG8dEi1e+n1bt8KvFK+7KuqMCDbNHjgsDgrA3MsJ/d2vvSJZddpaMLbYdXpg3xbjmWYzh2LG0TN877JqbIhkSF3IrsshuVfi5cx4EtYak8MT3WV1r0lQd0DCbmkm20naULsRK9YOYQXfs5CSEMKtFgszv1RjrXVGdz3SRLwDFp9pxyeha5ZblFmbnLMvh6+/O+7FVAr2T+YOPd4FmGx221BEE8aPMHDquD681u9Ki2y/wLkHBowntxKIbuV4U03ZuyXrYxxA0np0viRoa1wRoeqOsq0F1YrtvQx1BswHc6WOWW5ZbWPyrhR5KCACThBa09PxmtV+K3rTW0tks8pipwWW8misnwnIxLyg4DSIh755wEa0RNDJxvnX9vgzBkAL+v5RVumoyrpRK64Hrrgptrgpmo+BQQsWALZUfgNU2YVCuAKH8vgojHldGizyD+8A5Rwb3X5HEJDJMCCFaRLrbdmLzKHIjodOOxiTfr+7CnZZ4LxiM54ubN5Qobx9sbmO4y6HmQuVdyCWDli6hlhgFpiNR4XBGYPt9o4LCOvmTdq7rX5KLv7gIFTR+1nJvW6xw+ySu1/POowId86ELP5ygwLVsOqs3NiPPpBsNXXA+VgNZZb0G7JX+E8eFSydKLBzwhKvU9TLko1ojHqQRVV7QeLnk75RROHNqBKLxSPrqyu2L9ush/lfPYx6cuGaGQcCoEpZ2LuDFfWyRY/krClWWfIeO+odLGN0f1BkYFgPtht9j7cDB95dI/RK6PpgXnslAIaYJHyrjqVHbFyedb0etYoZHt+ggG5lmK+Ili72NP2WLPAV3T4FyIai4anMXaddoN3JqEwaZVZuBlgHSzMI1HTELPnLlAZ4FZKEltgRAhMFMFcZWIKCUxWgmxF7Q3/QWhczO+XvGHEMweir+i4737Uogb/jrG3W+HZTDI44pnw3zCprKrzOdZvcGRw5v1JE1DOfqW7755B+scZ8cLjDHM5hMODnZRSpjPZlQa3n77Prv78xAk2g9Z2xhvHSAxpk6INyPxJE0JQfO9ciAy/Nb60+IQYTqtfRB/JVS1RtZmpP0/Secm1eG6Jc6WO7iCNR1N6y2PRFchVkM7XkZkIOjxvxtyI0RFXCHUxp1UoG+mG8sICGJwwsZIyMdR/FOq8zNDOWC4IsMfQTngh6TYl8E16SZbovSGo7+h0HtGDIjcH5fhXR6VImcc44IBSDH5En7M45zE38ArbBUMkgKi/6z8vff4xFuErFqstVSTimlQ2tVVhVLCkyenTKZ1r9XWWB8nL4xBtHCJ4waFoB6UTtFiz5+MSbIKjFa8mdno8BZyY7g0/pXKsbHeR5w7HO/LpuEiVPRP6o341xV/dvDd2OmUw7aXJ+9uozXDduVkMN6dcOBuZYM1NXg868inZW6W3AfqDNPnp75tUb+oSLmjZeg5PCaJtve08mVjLjd3hb6/B6MJT5TP4nW6HdTRL6j/fnhPoVwflJOsY/xdvunh0BB3zw3j7ZUFbXw2uO+PsOtdOE7OVgAphqbWijrQBK+kh8WyRVc5bhziD9lwblBLRj/Ece2luAYCzo1Khcxv+WzWRhwi2Yq6LFPiSZURn+Q2+BZFZYFX6lkEDP7gmBTXytOT5AqIV3ZYl+N7+T9fkvaaTFASNiYtWItz/RMxgd64DFPfpU9SHEKRbL0nCMr6PiqJp4Jn2hM3T5yLoQpMUb4fZ2ezhZ6o4Ir/YSX+QIfjWG/NdAlxdGuuUZAet2YafiA55wZ6GK7FTVQiwwe9fBdhmvPeu2GOwSCeNxYS+AC25WGElo80YN16xZ4Nip+SHkf8aEyQr4rWJou7ssk9OB/VS/TxUW8eMpz7GL1eVrEynJNII0peYYNNIUNoaGOQKTyOKGTMVG7uUxnXM3qHKQGnVHbjDb8UcE6Avf5A5VYl7iCNb4T30LOg6CMq/MhwHxsZn9kCR5UxxbMOusTDYYx+wJo9gY3N/o+q3Mtk2ID9kazncn7bXmz76IJ+XjgUZYPHgOkKKYZV2VbC1rbE5TyC3y/HZQ3yyfDB+ekjVOy53JmPQk35Y5UyK2XMGjExppDHMnFX9/w9LPpIr8fF++XliYj04sEMrfpSvgEDm3dFCyJQFr/JzWcCHhFsvO0p8eLPULGncrm5kqKTw3sG79jyrvy2vC+dTSJGGYLk8H15PSyjTKXQWdwXlj39vP63PTnj4fGCqqqYzTxBUdrH5sHh48uEeDtRgOw6vzNvnY/7ooJCtG3b3mldkN0EfFNyIOjoyid4M/u2dRjb0LVlG12vydsExo9kS+JHOTmLcy3GmBBXSVPXNZ3pMMbgnELXE++K222zhuyzqtGCbGOJpiXZ437SKze4TuUMP9/amcws9Z8OLyInWmAsyVZ/SakX8UB4XyoUhsq8sqzYhHInOPVKyIcOXcAUZKuAMAoRF46WlWE+WarIiBt0WUXpxiHDDPm71XLNYtWgtKJyXrEUBcpILyMadTYHZS+t8OI4mrD7neqlT2+jkBEVe8nNXiTE0nToGFLbeUE9CzomtNkSd+JFNEiFSGnxVuLXc0wPrpwklFfjlY6+LRkfSXEd38Vn8XoICVKUW7ZfD56XSsBhmzxuNtYka0gRD+vWmiDgC7rWYMtTcT/EMGyRTENrLsg3VtQQWLbA+NabAT2VOJKDcnrku1TQ5zKyQFb06pJk5CrUJtZd8vYlzYqeDbDlXM0Nprwfs0t6Lx1ta2g6k2A7ViXloBRIOJ3KGMl0eW2zAsCGDpSkNsG569fhnzmMKayIJONBXHCZ85I4paJNaW/Z5l3xMt4ulTsSBHyLA2f9ARfOn0DrY9yR5t3YGFfWu7K2RrAYFF45qLXCe63bsLHiRzaxtgmPlsK/yxPrB4qk1CtpTu8XSCfuhjh8gX5pwgEbShG8oLHhBOAU51BUT+BX4aTyj0Tgz6ToXLqcoDij6+Gr/rMLqsxX45nHnkrvTf7d0OENcp7brGH7S+DaMiAFCPWfDL0atuXbli6jBHCeBnfGJVzSx3FjVQ2svhy9U5nTs/Nr7qWoMCwtyjKeCQq8qGgrFG7xxOj8H8mStmx65OHi9wEyvaKOjB+EqMC3qQ85hG8pR4WWFTswfTgvBibyPLGCkC+7Bxd8T6IlksslKvbyu4S7JGyOxrYUNCBbiYei1IeIpflDly5HOa9CX4eY5MqFbXt/Sb6Gi7M9eyrp86Cec7s1wkIM818JzmMZJdt7iXQlxZ4zS2x3jDUdznZYs8aYlma1oFktadYrVivDcy9+hv2br4Tj4v9pT5ZymK2zyWXCiUKcxYoaiBN9UjQkTKMn5JIpfkbIQbQPAm145J8LSWj1zIJHmD32fSBs93bvQkUZKaZMmYjEMuJfzLAh7Pdr6V+fd8/gevhbUM1zWJXz85TPy9nYBmnFs14TN/Muj045PVtio0Ivjk1AKloUxhoODnborPWB2Y0JQrpXJPn4XEVcp8Dcxjg4gHcPc16J1xmDbQ2rtT98Q2vPtBrjaK0C5jy5923uzk9Yr5Z0XctysWC9WnF6esbZYsm6sbSd8OnPfIEv/cqfpZ7ORsbhn44kSuOMPyGwqrSPsyXaK2nxyhJRGtcZQgzWSxHPbcq9yIj5Z4N1Xr7sMchDFR+99xcy4CMN3tg1jvkKZqvMK+XrpMiS3n0SxkL/tzak9xMYwWG/hsMRGcRid9fLiwEv9hhJz3XGgPYxQ2RyIzeamNLQ1MwAlmPtWC4b1k1bWNwq4qc45wPGOx9bz0RXNBdOwA4KOhdPcuu5f0lW3hf1ebcUizMuBdSPVn3OOabUzIHf/vv/X773tX2WyzVd17JYrlivGhbLFct1aK+a8Lmf/iJ/+V/9n3B4/XmiVeAmXvwwqVzk5cFGQv/kXVu864cG7yvtYt5o8cdInvJvaM0Xr3Uqv9KSYiHmcAeqaL0/ndOGAwIu1dWNR8PFe7lC/N3mtz004cZnqU/viye9IZLxb0rlj/Q/LTNLkb3HL7ANzgdtLDRf22T+tCyFwqok1uYo2KPNjrj+b4Lzsh7XX/G9d+GqaztvqcdgLENxSnn4qyuVV290SXN5w7UUJl0YV0md9C8jvnTOBk/wTadlEzaSfvfLX2M6UTRNR2e8pX/bdeE3WppXvPzSc/yZP/UL3Lixn8fZ5bp1UoqpXnucc4WTfKYFOmxSKgHjBNWasAHpCaHfiPSHE1lv2oNzJvGlvT6FhkSFXIJy55KyPY14oeTzt5Joi4+/pZJlnxWFtn6DxVmXBf9CsZfmJ7RI4rh/VFLlAD6fRfAbPhu7vmx5m9/0afnY9TC7FNcg57dj+HJIVrYMyGbfY8XuwkHceH2FQbfGZt5gMGAJ9hO8F88HDX+W5VOwI+k+sgRvv/0BzrbhdGebDs4z4QAf8DLB4cEen371RXZ2pp5uuLiufePGLN5yXfHwxvxcKe9sH/FXrM+fuh2/jbJpxhmFfd9GD6VYN9HKOsfDC80qFHyxhI2YfVGpl7CWdy2OXEakj7G+PneVK/uwHM7Hlj6ihiV61ZvyTYIu5dUYvf+I23WZtJVVSgx6cXuZFDLa3hrdzFKiKTfy/Mr1bm9KXquXTFdS7H3/a7/GkzfmnC5WNOuGtmlZLBuWy8YH+g/+7Z989RX++K/8aV74wp+/SvE/pqk/Jc7qpHDxiLAbcXLKS2XTBNcV30ZEGRV90VrPFQixKLPwv0tMWGJ4s1Ag5YqNHLsU9z1lXL7MwnohqMfapCx7YK23FTOU9Qzvxz686D72U0auy8Eaez/GPrktf2OciQx+4fU/fIemaXFA5TRSaSqlaLoOQViuVoAwmdS0qzVKCatl593zOh8M2+/O+yDU3s3Ou/b5WHzhRB8HVe1345MlgdZ0bZtYZ6VAo8DC3/oP/t/UGparlrYztK2hbTua1mCMS8T9xo1r/Oqf+OP8z//3/ydE/un06reuxbkurXNjHVrPcM7PhbMWrTStWY1IljFF2CyfDeLjFcAcaXApdPaz9ZHG8C4JKGX5vbZkBiw9TTC+aVmXf+K/oQwyU5WE/0E9sey0q5oejLSpbKsr8rjcqziWLj335SUGLlovxz6lEyHzuAz4gZGd777izjOErhjosizh7p0nScGG9ps4SqukjDfBrUdrjWk7RAltY1CF9Z5zeVdcBVd6b9kbAlcHhZLWEf/68AAa5fFDMY9xBv+D/+jvYDoP42YgCESLHkTY2fl7/Pp//d/x//h3/z2u3X6ZwUIdpBKHbmNzxvDyGJ6NyrWueBYPmhpjpco6y60yV3xb5hta8LniN/75mGc2uDnFI1WsdVR64jfE8BtnlSiMa5+JkxtlTsthchfk7b3ow8IGWduoY5wL71HbRMKHp1H3oH6DWudMGeik/HKk6n6T+jWlYpJgOxiYWFUv5EhoYdwQLdMA7W67jvi4LHGIYY+PlwFexbM4Aa/FGLjRklOUeLMWEWx01yvd2ILSoHS1j8+zUJvnIzastCAT8pD/g9/6Q9brpT/UwpUH9fg1Hj+bTGq+8pVv87/5X/8Nnrt1DSe256MQcYJIYSnrw+Cl06JT/UEppoKrLqL8YRZ1RdN6fsUEeNRKISisWCRaGrocdL/XJ5XHW1x0P5aEm1341hb99+umaI924PzpmU4cKIfYaKdsCyUeYYMlWvdKmhdbuPGNpysigWJxjbvUb4X6C949QwP6RH3k/TkljGj7Rr+8uLjNvCNdHO+5bBvE8ZQIvlzqk9Wqzdby6fTogiKNFZJFr361XH0oxiQRgDfevMNiuQibAxmuo3wXv9PqAe+9e49f/bO/FA7z6qsvXP5o45mEse2paiXH+QUPH1p5a1wplHtKJB32GF18h8q6VOQAHhDIsaNDf1xkUfJIllZ9MQRRxNy2+NdvHmQ5OMvScTzjwR4/iTke0xg62Fi7V1nMl63sHKAccn795EauLlm3g647HwtvK/PDYuKPIl1JGv/y732L/Z1pEiSsc4lZ8KfD+eC/+v17/Av/6ksfV5t/RFJkf2t6C8x2EAMkOwWu9QGEN7716VxyLhEhBoY3Ca3hK+czZVfczDTboppMkPNf/jci2RLpFYg3CuQFJo4nYUbuMgmTsQ7lPIcmQt8Nqux1tpYo27WJOUYYkjT2bvBsLA8jeYfvt1Dqre0oywRai3n0FHZnqL05kTx94vMveSEaL9R3nWG1anny9DTtRDdtx3LVYDrDyfEiuN/6cdBaMZn4oNxKqeT6CR047zIQ484oUZjghtu2xivytCbGvqi0woiCDr73xj2U2LRz7YWBKDhIKlOeHnP7Woc7ex/Ze3VkjH78k+ADi1f1hM644KZjcM4gYXfQOUUXTi/urZqLlhjFKhrCde/TvNbKlbyxKSDl9+XXY3BVCo0D2JNBHjZfpW8lM75p7RSlJUVzbJ+UgY/740DBePX0eul5DIwc8VYhCPd6G5n4jBCTRaMrxjwq/QocGq39kkVPVFQ4AWNpz5bIpEJN61Tv7duHhXu8FyC7td8Uiy5epgsn31rLat0kixH/jXhYtTlOpgjeKg+8FZ+QhNfsdu8Sg5vcyIK1CsCDx8e0TeP7kYSA0hIxzNdyhbML/of/6j/lr/2b/1Z/YnpWdP2R7v+W6SLWJ9LNmCp83L1Yn8Ur+wxZWTeWSuu/+G3Zxt5KDHmHIo4lammsA60rv4GpAtMfTtKLX9kuuuZePUUhI9e98bK4Pcei90o15hRHqrDVSLlKNFAqhzfKS5YSRfERpgfVjln8lLzGuc3NwB/6vDleG3FE08Oxwvv4s8AmAV1KzuYsZt0iWiO1TuXt7c2SUK0D7rDWpZNyHT5Mhg2hV7yyv+D1JLuKCZne2pinYGEynGfYjXHiYlkRf5wuVqxWa3IxjrLIiFvbtsPahn/wP/4+f/1f/0tYOlTATzFKnilmXgWlNs4r9wjXMfnzZTNOj+3RCpwRKlHBINd7DVjlcNoHCpCogDTGuyUPLOSEGGt1k1AkK8NiXDM58m3sx6b2Jwf7U60DdQjutplmlopdyad9bk1Xg8Thp5c5Z+eiMp6h2s2bMdZA8oUMXgzL6PEHQxZ7S7Elp+6GGUb6PY4xtwzi2JwVOCeF5iDQQmNAFKLz8+m0KhrpF5OlOAAmVJMoYBS20jf9cRpKJhelchjKb5rObIR7GVsm3vK84/XX3+OXvvTT+PN8I4S7omyXulhyTUPGMqnORDLtiPxIiLMnEmErKORiGJLkoWCLjYsxXB4ZviJ8giMp8kL1aUxiGJNh2JYYYiShqmAMk2lRv26Pe7bZbf1oplE4v0ze+GTbN8+Acy6VLoHvLosOt5H+dGBfwZRUWli3G1l7hQy5xR+WdCXF3pOjM7q2K1igjCdjXJGq8izydHf6kTb0RzNtol9jythBBMYlx00CoqFErwRfSmbkCxpR1CbZ5bZkTIeUIMuzHqkGGpjk6bDAxxjvooiCQJUYNQvdfYud0AMp7nsVSPE3POWwfDZsyTauY9tzN7jeloS+IDgm9I2X7Rw8fnDEO2/foW1a9r/8bT792vdZ7M1xv/I53L/wKxzeOOD2Z1+kaTqckA66aNsulV1PKqbTCbPZxLvRdgZrbRDULYKPi9O2+eRIpXLQ6OVijdI+sHXTtKybLuxw59gB0aqgbdpw2lzNYrlGFa7DbtBPJSEeD8JsWiM7t84Zxx/v5HAo51BUKGX8KcTOK1WdFqxtEalomi64Q7hhAaOllqnPBAbYKa0zJDwvtVjFgTgbCoCSyR4oBTaT9K56d2mH1t9H5q5k/P0jSfgkwz7FDi8kJdIApNOlKphhF5jGxKb1LXEk9StYJA9WcM/2ZgDSUTDffBn7HATu0PaT4yX3Hx9jO8Pkzfvs3X3IelqjP3Gb6Rc+ye7OlBu3DrwwLyBeAg4HLvgqtNZUVUVtrFfYBZfPUlD3ivu8fmKcHIA2WPk5B10X3PLTGORJF/HrMloBNq3xJ/L2h6DI70IMT6grxYvP32AzxYUU8WV5P8waCc/YLnj5zfD7MfxfKvZKBcmGJFf8waY7bjV4pgb54l0kugoRG7kfr8RTIcSGaNr2IoF/pNtFs/vQtsV9/pzUA/FL5B8Wfz5v3gfMBO/xkWzm3VASRlZhwFyU1lXbah3rSiovWd72P9w2Dhvy4wiol3OyWrecnq19jNrHJ8yOz+gqjVzbo3r+GpO6Zmd3lpS6NliRRWHfEehmJbjoBho2zSRp5zyM2h5uz+NhjSVq8pLF3UjfEg4JdRvjcqiOzRHMAjt+E+Hawa4Xxp0Kp2z6/2KsvhTLN6NyjCPh4TgPNmDaUtGWXOLivIVjO0DQAlZUao9xDpvc263fwCiE7H50k6wUiGMd3fISvxI2NeIBGv5wE50ULyLKj0OA80gpEgYRcFiU6HDYyZYh/SiScInDcD6+us97MbTWSs+GKHtY0OhtyYzE5z1/gn5/z0EG/XFxm9nctrw5tZ2haTvvrrlYU61ajFLIvEbvz/xhOJNqqP4ZrAXxdgsBpr1F3PhWyLBbwMa6isWM4aieAtFtfrutIiXCfDbxPFk4zTqNd1CY27hxUFQet7Fc0a5k4er6sxj5vSEliHH6PIsWy4pySXa57SFpyeslwrlnKwO9LgxMyr+YvMwUvivmIm4Q5MGOP4Gz/FgB7QefLgD1j6Cg7XBOenIJxWkPQK7YtpFkrfMHYziQziLGegOBSqFqTx98yLQLmjWAy80ejmsKhs8/6nQlxd563TGpYsybkjgLiKCV79YUBXr3o23pj2xqydYH3sJRosWe+J0/a+32pe02LjLRTFu74bEUjH2PKQhKvsg/DpbXGOFMiK9g3oZ0u0/mpfcsIvZksVNY8CXpXcqSx/4cm7WPYZDhs2FrKXo4LGeIcEoBscfWDcpXgzzhuYPXfu+7/Cf/2f/AnbsPWZ4tODSGf3c2Re49Zf3mXf7tv/0PuPVTL/Ov/81/DtsaHp8tuHnjgLrSQUknQBDQtbfW0VqlmFtaa6wxRKnGOeiMQYmHTWMsq1VDs/Zuvs66FHQ/ttSacAR9sN6Z1prpjmbZQWdsT7FX9luEYCElVMD+7hRkZyTvPx1J6Qmtg6ZZIHpC3LO0Fiql/DhXiq5p+/HRNiTLy6W02q7C3Zc4gv6qjQx5AsnhdxTPZBOK+hn7ivxN453wnD7zFb8pf5MpSnjvmci8Gxzr9f3ImxApFEG4d5B2bV1uQmBGM8yPCgBBeYijF6c0tu37bz7gt7/yOkfHp7RNy8xa/s264vh4RffohP/sa9/n4NYhf+KPfQ5rLYt1w+7OLCjpco3WOn9IjoqWLQ7lyDH2wrirwIDHdlvraLsO0wX64YJSwPXL9uPhv6m0ogo03BovPJ/Hw1kcGmE6qfi5P/kvklcPxXWpDIsWdfF9qcxuySzHGAsUU1wxpcItfhP/koMPfTw9FkdvWG78Dra3oXznUFrjAGNaRDQ59lawlLIW0UK7NpuKPb8I8/Ww+LFmjMH2OQKep/eF5elGPy6JLM4bjm2ZBsPcG/mR8obx0DYFe/pTXuChrWhTMlwk3Uy4Ge/94EmyzCqqDoD36PEJb777kNWqwRhD5Rx/SilW6w57tuar7z9itjfjk6/cxllH2xkmkyopj1LbXTyQxuMr5YlpQCe5/4qoPJPQJIc1hessZFoel1WB3xEvPGud+YPtAqpL9QtQVYovfelnUAJh69C7vIqjVtp7BDjr3WhdaI9zPbzT2WD9FvjTOC8by7agBRkxx3ylpSAgClFBaReVoWljyMf/iyTDHxCQDw1SwehgMqmptE5CvgnxEG0IeeDIJ1s7axLxinMXrSkRCfh7yBd+3OnycHwpMD43/wUwPvxgo4BCkTVSlOu92CxMer8jdsxb8GWv7OGij8BRyFTliJ6drXl8tKDr/OaMcvBJAYzBtYb3T1bUk4prh3Mg0u0xhZ0jBscsYUsGTcpBQIoubMCqG/vx34SbjTC756VQoVLCyy+/4Psf+apAp7T4A3QULricu7TSXY+3sCl2L5AU6XkMcoUZ17NBpVx/UPLKCdepfeSwBGkMJHDcEZcoodLKw3/A32lzJSoOw9fRUnDrAhUGMY1/cMnPEx8xunkGLFHQ4qvXsw3L9CnEhV287DgkhUjOLEDTWlZNwac5OBB/2JNrDGetQSlhOvF8cmlotZFKgIy8w6B9Qw7nWadwaxtG0pUUe50xKdhuEt7incouQJ4hUVdfNz9WaXP1OWd93AE1DTMtuDKeEVnETLs0fVSZ3AcyjfKCYPwm7v5Gyzj/jUtMKkAv1kwpEQcGKypqXUnNy7mM9ZLbIUW2zKdJsghLSDhJ+/Hr7UCf3w/zbh/jflll/hFpaON6mzR1HgNXlqOw1vAf/a2/x9PjJccnC9rVGsGxONhnerZmfz5l5/4D/snvfY/f/EffwBlDYxzrdcvNmwfYztJ0HW3TJSJkbd5l6ozxp+IGE3FR4oPkG3+iZYRRHwia4C4f4kokIuVnVmnh2o1dPv/5F/ni51/h0aOOX/sfXg8uo3bDiiIdxCIgge2/CrL58Uwtpmt9oGQRlNJUdYWzjqquMJ0Ptr9sGn8C4BYJKyP+LeusZKqDEF8qCyKDFl2GohVuSdJ67NSQ2lwylUtCigfDdVDuiUZ4Fxl8WxYaEUdCIKRvsvpuvO0RTToYdtq3RDIDvalzGY53nwlwrqhbMj41Dn7zd7/DYtWyWrc+Nh4OO5syoWO/qpicnPLWOw/43ht38KeqOrrOsrs7w4X4lzacVBvjZcauW5st9sq2xzh70fKu3Ntxg5hZsS9KCfOdCbdvH/D87Ws4ptx77NI35ZqIkJ4H2KV5nu5dY3x9lgspTqbBW9V1ROWeo0Eo4lX51hXljB2cMVygUVFYMuRl/qooZ9uO69WRlsNgrPG0NtA0b2XtLS4JtLdtTM/6IX49CmvnwV8A4p6oNAJj21nhPm28uMdjtPWiT4YCeKTv+fMSR2zcbNCXCMPbmhE3KIVzrSLPI+dbskfxMwZqj88tju+//cDHmg2u8uBwVYU2lqlW6Kbl8ZMzHj469UoufOzVSV0lpVcJZ6VlS+SZXXoR+DeXBWmX1tP4Kd1lqmrF3t6U/b0Z8/leGobzuJ9YbeQx5ntTGtum9eUCv9hZGyAyuwwbFwLlG5Pa3XYdTivSugj/ZPwv6UcQH58g3EdW2DmXN1LIoCLire8qpZFwWm0lCq0UWkWFOz4skOSFqLTy1j3O+VM7s7lRVgQKOGwRGicoDVRsb6CvKnjanLu+PgIJfBt7+1HlLz5LFxs4ZpzzHv2+eCBbXmZ88SzjU/Ch5WM3dnkBfnCDpjl49PTMx5C2fYWycp6XFutYrloWq5aoWHc2Kpv6ivakpMgs2ka405i/L/ldLokS6loxqTXzWZXqOA/Oh8/qqcY4m/mHAH/RIpcIn47gamwTjIM3FnBSHCBVTLoMBjcq5yIWi+3NA5SE09xOiRudHs4VWUkfagEZKPuCJ0OCzWJAcszSwWgl/JQP3vDcg4+ZvZ3G/gglSd1M9+dkzRn6P+d9MFrGVVZ1ZoeuRsDPzRU7Hdasg6zUK9iy+KvCTWcc3TLHdu7R7IsasaVBz7qClPJrvNLnzkIvXUmxV5r0ywAA/fHaoMQwrTRIP7bcszDTP9op9r202PMMgacGHc5V4BqP8EcQ0fjDMkkPEBIYCTm+TMxZUJTEP5KJTUCHPcudjKeH2CAj6UFz+t8WzOJGGamiIetQgs82q4oy77Z3MSWQHWvxOWlIJsd6vFn3+997n+W6pW07jAlx6rTwza7lFxVopfnZSc231tafStf44PXrdcN65eNdReU5pVDgsvtsIjQlj+9cj9H08SWKlgVXX8EjihdfPuTnf/5VvvhTn2RvbwcReHp0H/DKA7WhIJBk1CDKl10rxWxawcl7ML0OkznpYJTR8fnxS44OrRRN2zGdzbyrs61DfCBH5xqU1nTNuj9fgyShtHNejj5KgnBaG+U6zYggk9gIu2N1yWhdbpClzLQhuEUrhwTmkuqM9/0q8tpKmwAlg0hZTn5aHoBRMssM8J5yWQXkYv4CrQpBVRSEyTxmRaGOZH0Tcdn9u09pOuPjWsYdYRHetJZXBGYivFBp7nYuKPBMUOwZuq4DJDDKJGHdBguYsanpgaJzvXkvkxQdFAUHh3NeevE6z926znRaIwhHp959tef20uPM+4UqEepK8+7v/31uf/qn2bn5AqqeQXSDSZsApbVcVOxFN9l4X1rzDfF4GW91+BtPrI142RZ/rshTeBSMpvK523I/GFQHLsQ2NcZSVV4IUKr2ihvrMHQ+XEI4CGWcno80Z9iEYVPHtulHie/g3Tl5hlTz0li6YPI3vxnDHX02PaIHit+UU8a+2N6IEo7HVu2w+wnnpXnZHKA0FZIVaKfHK69EjzGgQmmPnOWaQCXCgQgnYR3YkKexDmPbsHbKGujR6W3JDTs06GHsf8S/s1nFwf6Mvb0ZldZhrIuRcZt9jXMmkDYVlFK88c3v8MIrt9m/duA3qUI5HaDocKICbyFeuLeFm6zzCgCTNqrz+Efrmnj4j0gWyuOGb3SXjXgpWQIWFoEiQbEX4jBHQd5E62QAK5iS1oy4IkcFRgZBX0c8HEOFONAiOjcBH7d4u2vz6MQ9cxpaeZ2X5zy0cF7ahoLGbNHG3w6hVgY/46VfCvEkQpeLzJbJPcaneM/FA5EYF8/LrtYtG6HOgTPnCFwtM4G2pL+QrVUvUeVWUnDehwN2TmthWmsmtQ5KrHJ0hjJLAef0h1uJ8OjuAw6u7TGdT1Fapzq6gs+J1Uc4zIddRJmkPJKiqKPclJXB2gwGHrGOVJ6/yF2P+AB/EFiy67LRcMUXaiXH3WPEwi7Nluvzi1AYI0mBL+MCEpIC6MchncdqcMG7ywDrGJyfj0PG82aqtYWZeZb5CLxUlMmHKfqSRA50DLv/US6DSBtF9aSgS39/JcWedRZjTCGcRQKd3QaMEnZ358hG0cNhuXwjf7RT0W8JQcxDkF4RwZlghVEspfMA0EGy2ttIUhI8SNY7IwQQ3wIfgNiNEAA/tQVzKH2OPrxzoRwpvw7vJH1TWuoRfocHZtAvPP2pwfPh71BAvCidN7LD6wFVTffbJadf/6//MdZ5K5lJXXF0tmAiNb+7avhjVYVUmr9cTfhvsEwmtY+b5zpcZzg7W6G1Qgd3OY2glVCJoJ2jVopJcNeLrnpirQ9CHXa4PSH0zfM8kbe8jDCqxI/o9Z093BtP+eZbx2gtTCYVD1ZZ8LDOJsJejorgwCqcOGbTmqqqkPf/CZyewOwADl6Ew9uwexOqCqhBolB+2Tn6UUmBsQk7uha/m6j0LDAqPi6a0tC0bTzqIH/LJUdjmDFRvkJJt4UGUj6KmtnALKdvB60YEuO+EN63vEu5C14+YYPCMqOXN5VbCnf9DpZl9r6T/pM8FK7X//i2DLDusxRuPRL4SBebGpV3EuAn4MahFgL46tffShbSWivapsFUmu91HZ9RilopflFpXqscrdY01uGcwTmv0PdEW8KJkAGmxSsidbA2UaHNUVmnRJIVS0SlEPC75O4nS2lgp57gHi2593iV3FRsPQe9nzcL0gLZjEYIUFfefe33/95/gTlbc3DjJjdeeoGdWzeZ3LpNtbvP/PAm+wc32dm7htI15Ul1Pin6brjxfaR7dWp9rnsoLrhQTsQnkW5uwy1lPWUZQ6w2/KZ/7TDencflmEMiwmy2l8rzm5qw7i6y5LliKprdA+3z4Hzw1FtaRzo9gnG2IaEBrfekOy68CIhjbOdQ4BxCa1GFjD/P7/rJlQNQHp4Q1u+QYmds238S5zHiw9GxE3jvzuPE4ygldK3FKuGBddwST5tfUsJdPO+LdelkMmMKdkfy6IuKnE2G0zReLvd7iPLd8Gnxba0U7qzhZNEGBZ0w34N6dr3HsQxG0/8f5lArH+7jta/8Ad/4HcPu3g571/eZ7e8y3d+jnk3Z3d9ld3+XvZ0ZuqoSj0mEC4J1XCy+UNCJc0mBEOfd4l1rHRIOB7Kg/MnhIgot2c23VAL6EALhHFsXYgCW45PWexwrSWsk/lt6x/jxjSd4ZkWBAHUV44VLwpfd8CCsVOrHnQoC50aep2tGrsdLOwcEN1+IbH0Vn51b4/nN2YLDxooYNy4Y4sfLlAfC0+NlvwLr+YMzYDfwKAfOcSokK/6kL9oyJBkvyuawFXB+0ZgUSxqF4FrLurUJL08n5x0gFSojw2eUxe6+9wF33rZMJzXTnRnVdEI1m6Krisl0wmQ6YTqps8Vq+LYMKZCr8BgmxuVTZaOhtymaNxNze4R8SFBZbvBZSkr10nIqV1FSF+l9mXtfXLlokRkL898oVVg+hld2RFH4w5QuWjqXz7u5iDfyb4H3Z0qXAMxyPreiussVlTKuG9O7jx+2QB1w15R+1OZLFv1M7879xjmcySvZnruZ1E9XUuw565K2HAJiCgDtYd9irWJ+MMdb7J035MPnz7pCflhT7LtHx/5JMGcWFaRKgc4fRtI7Sa+HvS65vBI+6zPymxYZfbY3GcWLyzss8a9nJdO/KpGpSyXFL7NgnomaRA6XnlS6MVabNfbvy9+xZzEN2fthnqEWZGz9nad62XznnONbb76Pc/BzX/w033jtdZbLJSerht9aN/zszpxf2JnzzemMnZmjcw5dVUzXDdZaKqW4NpvwU9Oan9eKFy1cE2FPhLlzTIBaomLPIcEkXQHYcC8gJsTOS0QVvwsuIJ1DKgWN9Ycyuy6AcMtvLA1fDh+4krrFcXJEoMc5QU9rZvMp/PS/Cs0JnN2DJ+/AG9/1a7yawc1PwsEtmB+C2iUr+cp5GEuX5Xp+8CkiXqW0Z/rbFc75+Cu2A1VBu+qCoBO/GStnS2+3DUHEw2OB4+P7MSlXSBa94jaFbjcoYuwuzU5YH6mMANsRlxTYZNQSb2sq8xaMhguVluixD/XjTPSGwiooy6RYz9HND6JAGnFk2Vlf2AcPj3DASy/e4IMPHtK2mnXb8UZn+F5d8YVJzf2qZoI/UVqUoqq8lZwSYV5pnq80n1DCNQc7CBOBygW1VVDsgUuKT0dUxPkxjqfkxvG1+HdOHM56BaExMYZWZJsN79Sar+7nEespS+I0xjERkMq7l/9zf/Nf4+jhY+6/d5eH73zA2998Dayjmlbs3L7J9NohO7ef5/lPfZ6D2y+xu3+Iric4G60UO4QK7xpdecFda3S1gw+CHRtSWu6NLWJX5NkGNRbPnpWbZuVqHIvfR3HtyOxdS7lxHwWdZr1I8+GMh0DT2OL7ML5bWnj5FHD5eRrDDByMxm4q85Xd3ihkkHVIqgdN6uOBwYiW344UkpV6UXW2LW+xBnqXfUDfGCIh8TzJqklyngzdrldtunNwfLYEBwcHOxwfnWGMpbOWR9byQGue03CqFFoF1bQBpyLsCLUS9pVwDZgDNUIlhAOXyJvkpaAPG/NXUsOIA4GET2zABc7alH/dGgp1xSjNiXUjfl0rpfj8H/s5lmcLTp8e8+TJMet374IDXSkme7tU8xk7h/s89+JzXLt+wO7uHF1pb6lnLMYaKuWt3JT2Vm+VUtSTuie0S9jciL0rObXQu3Bgh7dcUEmBH2DNGa/ac9lNMJabfosN5hJ7CNlCOlkb2uxOXAwxXdekwUqWe1cQsj6OVGLFczNdkLbhpm3PRlGHbL25dHtGcVV8cW5HIz3v/TB+tb3mVTi5ejatfWiN4H6+dI4zEfYEmqSI2pQdlHiFwEygdqQN9BC5YZMnkdzfMTgfts9FWc0VkVSdfzMxF66Efoni+YvnXnmBpmlYL9csFiu6p8d+DJWimtaoumYyn7J/sMdsPgtKvnCCdTicR4dDu7wVrsdBVaXpIVzowX2/ZySruXSCNv1YfRHGXcg7tOqLnZJeyUUIkziCQaYZt8ITrDXpa8r6fgzT+eT/w3Erl670UkhsS+YrfRuSI8W21kqFwwwDp+e8RboI4RDJ8fIj51geuQb9ro2N3sVw3n83ZoE9uUJ/r2ixF3fPItwWFNAJiI/9tb8Tuw397oylIZIsv/lxSN7tCsBi6ToDwbQYoDk9LdzAspLgvIWwMVIbq0ryyiAulILQJRONoPgT+sqBIdN+znzE3d5UrST5Ilnq9JR7PVAY3g/zlGks32jn+30b9Lefd9va2zbibss3Pi2entJ0Fq2ET336ZZ5//oDXXnuDN9/8gCePT/i/HJ1w/fiUY638KZ9ac3iwy/7kGq8K/ArwJaV4yUJtrN/9FyGYdaZdROccNuxAJxEyWIHiHBKYaRUtCBRpp40qmAtoBVr7eDJhDmcKvIseBfEc76t1jtlEUdcTr7maXYfZNbjx02AbWB3ByR14/A68+w2gghc/5a369m/CdBfEgHRhrZbxsMTnZwZMGJ5Q+UOFH9zSuwEZyzy4OxvTBqZHY02Drh0P7p4EPqw/lm7k6sqpWLLRwkxcwdjkf7Z+31O6nU/1e611xau+pUnpajGA6SGnW75NDFpkCIcs9KBxEd8MwLLAdpT97kF3gSZdL+t5AoGwOPPueUqE27eusbc74+7dRzx6dMxiueK/XK35+6s1CxVduYT5fMJUa24DPyXwaYQbCMpaT1eRRF8jTHvBUxXPJBmeWBeUf6H8aE0mSEYbCE6BEuUtXMLQzcKGQI9H9txs/CFa+ToHdTjA5/DmNQ5vXuOVn3qVrmk5PTrl8f2H3H/nDvffvcPDtz+gkm/z8PY3mOztML15E72/x5Nly2LZIGJDfJwwt0rY3Znzyic/z0uf/DyznWvU9QSlQUQhMnSr3UYXxniMUvFn8XuyUdFXdrp0+S0P4ojlOcBgTEt0u63DQBrj46CKEpw1WOc4O1uXS6hX0lasde7LQVcHpGrbyZll3QkJSPmg/+95TbtUuxhaA26WK8OrEhBHPxlp23kNSnxpv4T+JyNQPfgo5mibLsHS3u6c6aTm+OSMxdmKtu34etsx6QxtLEO8dWulhD3glgg3gR3CPDmXVuNwhUU8VrasdEWK+3RxyISsyIeA80XCM/99T0DpwXlvyIheHioo9nZ35uzuzLlx6zqmM6xWDYuTM06eHnP65JjTJ0ccf3Cfp+/eRU9rJnu76NmERWNZtx2QNyM8bRFm05qXXrzNSy/dZmdnTl1XXvkhkrwMylNsNcpbwEvkHz2uQ/wzY5w/sdd5nGKdozUdnel87NGBpU1S8qm+pZEfGg9E1uUTw9MGCnhladwECq6HzXpoJfUh6PdFaWPJnkPLt32/Bc7HXo1Droxm2JQNxvjs8eq3YfONnpUZh6A7kn+Tnp+fjAkxt0SYTCuqSrFat7StD59xzzkeGkc5496bXDEFdgR2CYp9RzJ8cdA7cbUP2/0Wxxhy27FxWItES8VssXjx2Z251tJqbzKpmUxqdnd30kFc7aphtVzRLFesFyvWx6csn5ygKo2eTlCVpjE+vAjkjUXfRKGqFIcH+xwe7lLXNVr7QyyS0UiRP8J11P9lBXy2ZLYu8EFBaehjFVuM8/JRjDlcDBFRmR95pTTWUcaOyr0wEV70iN43kDZwga77wSrwP0w6j7KXUDrO728v4BxM8myN+QjSZUA9WnaDoLUgSvlDqcJmzoIcuqcsF0L0ZvEwHhX2PRmoSGPtKEnxGLz25JKRX7Z8ty1d2WJveMperNz4o7xwznHzuWsjX48RgOHzCIbDLnzMq+JjTQVijzEKXQ3il4+qZ0GYin2/ItGOVQyJc+RVZVjeJufbCylV7IAQEG+k62NkZyArBKIR6zkHM8jwQfnrd7xjDAfnvAWKtX78TGfZO9gPiF549823/c5T09Gs16zXa1bLNevVCsTyz/9LfwFd1WymbWM9Mma9YO1lJ/LzP/ydb2KdMJtq/ptf+4esVmtefvEmv/wLP4VrDe98+w2++/4jmtMGC+zPav78c9f4y7szPt1a6s5AZ6EzdG1Hax2LzrAWx6mDBY6Vg5V1NORYPm04ncsjHmGihMpB5RxahEp5M3cLaKLFgLBb+9M5tUClhCctgMq7YlLEFQt9Ti4vDvb354OduLBW9BR2n4Pd2/D8z4NZwdlDOLkH99+Et/8ARMP1F/zf3g7UGgi+S8nVbhnGVpEVfNPwt7GIfiDJWzyGgOqifMBd4wUEXWmMsdTiqGfRned82L6sjL+R0g7psIr84CKmub+rvMmc955EBmykuLRiJKGRjVQKqGk31gLWJAWKtZa93Vlq/Qd3n9B2HV1nabuOpulYNx1tECb/+C//FJXWI4ZNoYqySR0AAQAASURBVOeJeoaLoOzsnyZazoBLjGgJB69//x4OodaKP/z692nbjsODXT7x0i2csTy5/4gHR2d0TQvAtNL8zGTOn5jUvGAsEuhoYw1LY2mcY2ktLbDCq6Aa5+gcdETrlODOFiz5lPj4XsqBCkKBlmDVh8fj8eiJSYhZ5YNPw6mMuMSWJwdDklBEYDrzsfnQwUJYKSZVxY35jBvP3+QzX/ws69Wa46fHPPzgAfffu8ejD+7TvvkeEyXMD/e5du2QZjJhiXDa+gMmtFacnRzz8P59XvuDf0w9nTGf7zKd7/H8cy/yyc/+CrsHt/L6i3Rla9omKgo59l4Zm68rnkWVSxmnL6YuxUP0Q+XXhOm6YJmrfJBt50+837Qd6KdnhnGysnWzj5vaLIFz9fkXVHWJhwO8MORBBrldgjmbLZ/wtH06zazo0fHSW39ZhzHGW8mZcEo8jk++csvHP9to3oC3iY1w5YCMvI7fQvpegIcPj3F4y9cP7jzEGstsNuHa4S5Yx+J0wemyTe5alRKen1a8Wmn2rXc9jYJpa72Ffuu8wq1zYPDKAkMODeUP3sgCc4R1gWT9o6TfjTgSlUhaW4J3F91II1oQF8quw2nZKlikKjRaaeq6Zn9/h1sv3KJrO5bLFafHp5w+PeHs6ITTR0/RItTzKdP5HKM1LbAOVqxKhNVyxdOjE777vbeo6orZdMp0OuHWjQM+8+lPcnCwSxVOm45uz3EjI8KTUoJWPuC/chZxCod/hoK5TInWRMYYOutphbHeK8G6oPDzJkto0Zggbyjte+wSH55t4G20ghTJwmHYHDnXivYjSs8Kws9W04cvIytS/D+etnshQ1cZbn1cOxfmJluBRevy64c7m5ZeW5mYZ+B9Qjo9WxNX2fHxEgfUlWI2q8FB17Q0nUsrQuG9aK4pmEWaEGQo41zPTnwYCbZUBkSngHxMYW7rNkrm6+8/kysuDhUQSmnZKsrT4+l0wu7BLtZY2rZlvVqzXq5pVmvcYunxUV1R1TVOKQzQ2sxztC2slmvuP3iE1pqq0lRVxe7OjFs3rjOdTbwiP9DyKF9m2u6SEt86PybpqIzQzrqqwvh5HtEr5F2CUz+wlhgyI57cm3Cj6h9ClJSk5WZ4FoivNrgfY7oqHjiPz5BL5rtUJeGiXN/lgSadyZsl0esER1KuTqd6RJLYbO+28+3Z+rSf2tYQ4bxpTWqnDvTOx8jt11nhrXATR+gyp1jCdA+uL2jXpmQ1nob5rqJivrrFnvWKjF4MHXEoq7Dh2XTv4BKlbbJ9fWXJmPuMbPn2hzn1p8ckcwpvHaAqnZg5gGgC7OWqAvnIVXsdEKYLgqtkd7M+qYtCeEJpQWAf4QAH/451cQigG4u4hM300jNXZc6v/e43+ce/8xWataFtDU3T0DYdTdtiTMdf/av/HJ/49Ku8+fq7/O2//d95BB9Ogo2VrNcd1nb80i9+huc+8blBxeP92wTLsXxle3OozS//zjfpjOHJkxVHR6cszxY8uPsQsZabk5p/+ade5Ntv3MEZw5+6scu/8dxNvqhqWDQs24431w13jeG765Z7IjxRwpFzrJViCayMxSlhMpukwWwDo72/s0uzbv0x3XUF1ivstCgvHHWtD5IbHFqcsUwqzdnyjGldUYviNMS/sYQT6cKSGCL+iGB3dma07QWoSQSqHTj8JBx8Al7+ZeiWXsn3+B34/tega2B+4GPzXb8BewegDV75Hcd7Fca5wiv54h6pxSv6huxOMYcOogI22CaEbB8ej0TmYjKZJoFEqPIOv/GOBbOdSbGaEkcRmvpRse3Fuh4Tcst855n69MrLEN9fB7JlbQywg9t8WGLz77x+j29//106Y+k6R9d1XojvDMZa/uwf/zzPP3ed9+885Te//K2wuxZP/vV9NJ233PjUJ27y4gu3kt4uC+1QhiiID0tlXmI4Ej6MP0X/w3h+7827WGtZLDtWyzVN03J6coY4x45W/MytQ+4/OgZr+ezOlH9+b4dPiaZtOs6s5U5reOosdzrDkQhLgaWDTgktIX6TgK4zebbW0rYd0xCXUwlUWkMYC++O53FgXVdEpauz1h/usm6odIjbGZUirlwirpgj6cVxndRVCCweaLNETjwI3Foxn9TM9nZ57uXn+fyXvuAVfY+PePDeXe69/QGP3/sAs27Z2Znxwv4esjfHVHOOjaUJh4qs12uOjo6xzvHOW6/zja9/hU99+vPceO5FuqbjlVd/htnudbT2YT5K8HXOYkxLs14SLQPqyazIGzNHxZ3D45FyoRYT31vD3nLDOktVeSbUCyRVXjPWf6cr3VMLDKmJjFxfLQ1o1yivW0LrOUB+iQb06f04tKdId6Od8g/uPzjh/qOnXgFqXTjVOQhjzvGpT9xmf3+Ho+Mlb7x9rydkxRSF/pvXdtnf3x1tb7ZWzn1PV65U12wOg5/KjDcfPD7BWUdjLF04DGu99idiTpTi+d0pJ4sGcNyqNJ+f1twQv7HTWMextSwd/hehFR98PzmJO8/XRUUaEKy/HZUO7rWE944Umy96eKjS+sx5K7muM+hgvbcnwo2yb2PTUwyyP+U5wrV/IRI38QVRDq0nTKc1h4f7mJefo207VosVJ0+POXlyzOLpEa4z1JOag+kEmU4wumJtHV0Yy7btWCxWOAd37z7ku6+/yysvP8eNG4e4zvLJT7zI3u4OWucDAgDECA0dnTE0YdMEgfl0yqSukcrzO36nssK5Sdj8tXTWYaxJrsp+QYmXWZy3rm+7VbLeUKqEc5XRY3BJUUolZc4fSboAlLd98nGnIYyenq05PVsR47dGeh3vb1zbYTqtWa1aHj05S2X0ygwDuzurmc4mXC7lASpJ2jDHWH1nC79NHo0HnHOsgzJCCezVmqbzm0A7ItxSwg4efo2DlXNekc2mXXj5N8Sc/VaPS7eOzKaWODbu4QpwKHDt3LHpJ6WKU2PLOuMmpoASja40s9kUe+iyom+5Zr1Y0i5XnrfQmmmlkUrjRAf85gfGWpvg9OTkjAcPn3LtcI+dnRnOOq5fO2Q6qb0Vv/R5S4PneToT7CRFqLX2OEHwm5jiXfxzV1xSEPdcaF1eGSIKa3NojRzKpNimjpa64f0fGYxfNV0A4Oe9Ltfj1nznMClNa/0p8YUoE3UXOJhNNVorOmNZrc0o7oq0uKrE87KXSs+ACENqO5trDfMfD3kS/MZc1MNUwFz8ryPAOv0ALyVs5z6dD+fPipMzt325dDWLvaAZRxziCmzjAOVQYVDm+5dR7A1T2ewy2L6DZARdxtT5UQrG7wcpBv/0KSouK4wJZsApd8TeRRHRXvmZqw/CfNGenvA7yF/KuMOXW5sRGecAOTlEaR/JjssJ8cY/+L//3/4WVeV4aW+XlTiu7S5ZrSfcfv6M9x9c4+/+9m9x7be+RnftJXYPryMv/AL1B3/I5w4+w9tP3+DP1bf4evOEL7//+mAgxgZneL/BagwaPdJ153j73iOM9TsUSikmkwlaKXbnU8y65b/83gfc0vC/+uQL/DO7+7jlmm8dP+V3jOEblebbx2c0SjhdrL0STinapmO2M6Fr/cm5s/nUIx/jMM7RrFs6a6mMYdk0VHWFTCd+zMVbODZNi9bgam+16KzDime6lkpRBQLZqWA1F5iwUvmTozHmdbS3O0O4AqyLABrqPbixC9c/A87A+hhO7sKDt+DOG6A17BzCzRfh2i2YKq/o67nUrfAKvXjqdI0X1DW4Ogh2LdgjsAtQHYgG0+DUDNQU3A5eQVgqskMb0yI/D+YsOIuzhmbdMN/XWGcRsTgnWCt01oRzYiabDMczpy3ErYRz139Wxt2KpGwzMt/5+KVfq0tKpWJPIr7Z+HCsZAf8V3/vn6AVHE4ntDgOph3rTnPjoOH+yYzf/NabzL/1PnZ2QD3fQfZfQh19wO3ZTZ4sH/LTeo93zYK3jh7S63TQTJUuyWP1b02xM7LZ78cnZ94Sx/oTEqtKY0WYTDSus3z9wRF7Av/SzQN+cTJl1Xa8tlryPWt5XyvurRuMCOu284oipTCdoa4rr8AL13XgPp1zdMZgrUWF90or7xsQknUe1sVrAoBg8RwsS5rgwqaVMFWKGSWGLhQzoY8uWXoL02nNRO+QTQwiCxwUpfGZAtDUVUU9m7J3uM+Lr75E98d/gcXZgif3H3HvXa/oO3nzfWq8i+Ps+gF6b5dTVFL0tdZgF6d8+1tfZfrm1zGN4bWv/Sb7h9c5vH6bnb1r3Lr9Mjt71zk6esh3vvVVHj18wOLszFvQGcP+3gE3bj7Hrdsv8Mqnfoa6qlmvFoi06GrO7t4NqnoWYmOW/MZwKXQpDlfXGuqpjzOkJC/4GHbfn6Dptpd13pq7ZEphFfpPuZyy/lnSRSzpOe/D49e+8y4iMK80BphVls4KOzPL6Vrzxr3H1PeOcPUMXdcwPUStjtitdlm2ZzynpjyxDU9WZ3gBLddc9jkKqKXk2huSc8ZnSOHP1o3nX1y2InEKtK5w1nLnbM1U4IuzCS9VmtZY7rYdD5zjSISTzq+KzjqC7OpDNGgVlJsOrYVsLZuFUuP8Ka/ZpTWIpo4QT88LIgnOw4CYwJ8pgWmPtxrvfo/rrjRa1YNcgTZGOiIk2qi1oq4q5vMp164f0H3S8xqL0zNOnpxw8uSI9aOn3t5+UrO7M0NNJ6wRVtbzL8Y5Vk3DG2+9xzvv3cEay2vf/j67u3MODveYz+fcvHHI7t6c4+Mz3njzfb9pump8DFtr2Z3PuX5tn5vXD3n11ZeoK8163eKcYTKZcrjv3QJrrXFKJ4tw7ybpwsa6xZouCKcuWNx7HBh5g7gWKMbghzldWpAcZLyKADrMd+/BMeColXcLr5Q/kHhSO9ad4tHJEn2ywqkKUQqpZtCtmKoJjW3YR7N0loVpz6/0Uniuv+6HHF1823SmUP5k9BEV6ieNQQO3teJQ/MEtx9ZxhudCm/BhsBEjMUXBF9TR93DLdftWRbJaiGUbbSZwa6Wbb3x3VWdRpQQl2xUpZUxKT+sdWinqumI+n2GvH9B1hnbdeM+oxZJusfLKEa3RdYVUFT6qrsdlXrHb8ejxU5489Zaxd+89ZDKdMJtNqOua3Z05k2nNatXw6NERq9WatuuIsQUnk5r5fMrufM6N6wcorXxoK2fRlbcC1lqhg9Id8onF4NKGSHS398/iFlBfRk3s348AnG9L58FxT/m0TbUgm3ljWqw8fEaNjD880O+pGIRlY7x6NvJVSoMzaFFYZ6kROrJi7Y8iDU+9ThxuuOistw6di5ckLbB2hHW8qcTblsbybOcI++3ZVsZl647pioq9CKCFO0Bgeqz1p4UJsHvzxgUlXSaVaLjssiHHrYsn5A2/+WFJQ9CKbhs6PRcqnPEuAvkocQqLyEsWvTWFVSuBRSs54REC6QZfjlY8suoKA6/0TAj8aKlkOBdRxszC0dERn3rxBv/289f4dbvkX/krf59f/8d/gr/8v3yb//P/6yVe/Ou/zPv/yft88lf/Gl//z/9D3Kv/Mrfe/Q5fuPazfPDgLb44ucnr7piq0kym0XWz7OHwPv6WbllDcNrOUZw9PWHZdJguuG3hUFpRTWa89MkXsZ3l803D31iu2F+3fPXhE/5/OP7QOU4B11ker9YopeicRdqWyWSCE2jWrXfvDMK7NX6nzLUhnoyDZt15i8V4gpUOlnrGB5iuVR2ECg3isM67OFWVxhiDrhQu07u0FrPKK95DdBy4eWOHyeRWMX15/hLCvCipCuY3YH4dbn8xxOd7inv0Bu6D15HvfwXqOfyJ/ynOrRDVINKAi87FS3De5Ro3AbvGGoOzHbZrENt5Sxul6MSlU0Wr2SRYeAZrP135djuD6B18fL8pPlpStpDsJy9gmWgFEIhY2z3EOecVPs6BM1TVJCLQQhgdAs1HlSLM9wXcVO0g30Vm8KPfuvInr40htr6olavlihsHO/yN/Tm/6Vr+jZ97k//0rZf46//iE/6dX3+Vgy+9wtHvP+X6Z3+BD776u3D9C+w+vc/z8xc4On3EK9UOd90KrZS32kjrNXIlQbknxfKEPBeFNcEGdBcKyfju7HRN09kUd8lXIVSTisPrBzjreLnr+Ettx6SzfPVswR8A7zvHGgHjWLRdcusy1qKDO4pX3oV2hThRXnjNwaO7zmRr0FB3dmHy8B0ZLQiuY9aigjLBKTcyj8WzWK7LC3NnPuH6YaTplq6xPkaoSDikRxJ4RGu5tB6co64qDudTDm9c49XPf9rH5zs549G9h3z/m9/nztsf0Jy+y+605uf/5T9Ds2w4Wax4fLZmbQwW4WzdcrrqeHpyzLvvvU9nOtrO4ZxitVrRhThJOrndOO4/eMC7777lbXonv+4VMs4rWZyDg8Nr7O9d5xOf/Cyf+uwXObz2HLqaBNrriK661niXTOsKRSbQrI+DAKiCwsWiVJX5pMuBQZ6HSwKOi/NUKJ/Pr+zSknCvkou/kOLfkQKKN23bsTOr+aVpzRsYfuXFE776eIdf+sKK3/j2IbOXD1m+t2Ln5kscv/8Obud5pstT9usDVs2CQ2qOab2LebG7H6fjfPnA9fmeS6Rm3SX36rjRFdf7fGeKc45D6/iCMWjreH/d8j5wFAQBHLQ2W4n7+v142AGvFwX/cMC6X2tRCkkIVTKuivmjQCbiw+DgvHGZI8c67s1Hb9TSU3GAgkmt2ZnthHZ53JTjY5HphOTrHOPPn3hb1xW7O3Nu3r6JMYb1as3ZyRkP7z7k+MkxpnnKRGte+uKn/GZl27FoOrpAQ5vOsO4sZ6sV9x8dYa1J1sJNm13iJfXPcXR8xr2Hj3AOvvz7r4WYexEHwu7unN2dHT750m0+8YkXuHawj1QBNzqb4nSt14sk05SjZcw68DMS5sZb/uRMH79wGvSql2JHr6KL6EOpv7v48+05rLVUWvGyVjzC8YmDjvcWmlees3zvvqY6nNI+7ZjsHrA6eoqd7FG1a6Z6Smsa5kqzctavNyVXwFyuj7MuqTAwgZ72v/FrXofQEzPneM75A2+eWssxXqGXJYayrgB8xAwFozQiuW+08twOZ83fh2EZtRbqahLq95aKWZG3uQKSshKShZtWiumkZmdvB2ev0XUdzbrh9OSM1WKFPVuilXDw/E2s8ZZ3TZBhHN74oTOWpus4PVsSwy0hJFlnCOfLdcPJ6QLHY95+7+5A6eaYTPwpvtcO97h+7YD5bAoqboJlnNt1bcLpJa9obUdxS9a4/mimj6rlo6MQ5mZPhCVwWDuODRzswKMFqInCrh2qqnFNg1M1YgwKhcVSISEMwuUwTr81bMDImHRfZrE2u9IPv4vrW+OIfgArBw39eLg/SunqMfYC9NkSX4m3BCAwP5PdvY+4meXEV8V9/G3IyrJtLnk/iFRKkoKgsNYgzBOyUtOZd/MpdhHiv0P0OgZg6dm2l8OUIMAv/cRvFjJCJBw92pi5yEEdbuO27HWvYVl69H8bHEhWDVjbUdcVv20c9vYN7jb/BmeV4b23/wy/8JmbzN+b8Zkv/Qxf/Y3/jL/2l/4Ynf4u1V/908wOdpk3N/lPTt7j+/ffR1eaqp4xDvJu5M8O3vV6x7b063/nN73lJV4QEFFUtVDXmgrhnxXHX1o3PDk65d9t1vxDFEsLi1XjhftgWWOMycyy84J9FxS/ghcilVZorTAxOrYQTp30N957RDCdb6+Opu4i1JWm7QwiIa6MI50OVHjokeMhbQr9Eanv70zZvfbKYHye4M//y/PpnIA9BeX3QZwFUXUwqtrJeYUQn+95qE9YTdZo9xwTVdE8/g5nd9+m2rlNtXuTevcAPfWfmOYU0/kTKtugWLVOmGhPMqy1rOIhIqGV086y6jpqvfQ7fHiBsesMdX2KKO2VpNNdvJJvH2+dWPdAwJoVxnTBTUpQStOsj3DOMp1Ng2LbMj/YCTEcRiBjALsXsxPnkJoNwN3+RQkF/rPNneXckGy5EL8o+dYLWjXaIQc4Z9Fa8YfW4fZ3+Ib7JZba8LW7n+KTz+2iTiqee/UF3v7uV/nFn/8EVh4g116l2p0w+fouv7U64uHJEaKEKsRe6RP1IdOed26HIN6TmUIhibEM97/3lTcyjgvEL8KjQvgSjl/tLO8uG/6B6XgdoXWOtvOHK8TYI3EdxLZEBV2yCFEh0LtSiHWpEdZmeIzCDwl+VXqutMIaLyChBGV9TK9SKOhTp/gwEoJMY6cTzauvvpTuH753n9fevc98Z+LLVn6X/MbuLmfNmrrWKSj+dFKj1w0vf+aldGBQXVVcn0+5/twNXnvzPZY3DrAHu6yU4ve//RZP7j7h5o1Dbt884MVr+zTW8ajpeHi04OlynayinQtxQ4PywTkfyyyRFucZOu8O0oYT/Ly7vDWOk9MzRO7w+ve+xd7v/gbP3brNZz77BT79uS9xcP0Wzq0x7QprF3Rd55X+aX0I62aFw1EFl2nnHJNpndfWD0169sac35UIbQVwy8ZFyOlp2QcO3HTCXfcCrRjeP77B9b0aWWn2ru/z5MF7vPTiNZycwvw6eqLRdya82a44W6+85Vxwv9qKIwvYivB+1fTOe496HY8KtKhseAX4jLE8bQ2vW8vDIKiYsNEWN2ZLl2xfhPSUvhIEae/2WWCtAW4q0Xp0jY3tIbjuS8ARGwLMRv97mC5dVZXixvXDgGqE46fH3H166l37wwFcSoT5ZEprOrRSwTrQ007VdVy/eeiFJCVoVVNXFXv7u9x5fES7O8PNJrQivHf/CYuTBbs7M/Z2ZhzsTOkAbSynq5ZF24WNDxd1GUlR5yAHJYy6DufjXHZd5p/i5sly1fDo8THvvn+X2de/zfXDPV55+Tk+9erL7B/uYU1H07RY02ADXpE86UkRoJVOo+VpzUdlgf/RpqEFzh+dFJRllCPAVZpjN8GK4Wg1Zz4VpFNMd6YsTo84OJjhaJBrO4hWqGPNI9vR2M7T1Uu7QQ6R1OZXPbRUvH7ydNHHD5mlRoBD4KaDpXU8xLGgwIkjSCg/GvLO4/WPKSjOaf15mS6N5pWId4cNha2WK46Xa6/IDHKCAJOqoguHhEXlvlIKsZb5zsyXpQREpfh8x2dLzKTCVT5e5tPTBc2qYRoO6pjVGut8DOGm9cq+hA9d1if4/rgemIM/NDC62+Y59Ti16wyLhY/l+d4H99mZTzk83OPGjUPvUmxNCNvSpROxJRTucUWOuxaHUyk1Lgr+CKTzaOTFMkZOY/ni92vAKWElCotj1Qm1FrCga0XXtd6TBQO68uEjGmHl4nkNV2hI4uWv3t7V8LCjQaYpXmptnY/sbi6u5kOlbSD8UaVniLE3MF0Nko8Vi0MhSiNqPP7JR5dKN1yHd8OLw9QV+S5zqt7HndbpytguCGZBo+Iser5D15neaVwO1ztdKQtdcukuZGI1tnwiF7gZjLsU3csiHDKqMMj1udT2dPhCELwC57WlPTL4I1hBCGpS8cHzN/jVf/aP8erP/QpffefX+PRP/zU+9XlH2xwzmR7yvT/4Nn/uz/5JrHMsmgXTep+vfPX3+fbb79CGE9OqurTqvAichH58x/PyZwHiN3/3NR/0mqCcU+CM4/rhHn/FGP7001O++fgp/54W3tI1j58co5RiufLrQ4UTNGMMGGsdpjPoyseUsOLjvlSV9sRVwolSoYnG2b7QkJRI4pV4cRwk70YqJ6w7P0ZKKTpD6k9p4RAZgHzvFZez2YSd688VQxe0IDKnnxzO3kFkFwin4Joa9HNsS1K/yOygw7X3Pc5Z3mV+qFkfv83Z0+/TtobJbB81PWR67TZqchO0Q1crTHOC7Vo6l60lWmOotdB2fpxak60fXNOEXXgvNFbKYDtBpju4xVPfa/UAVU+R6jq4W4hMgCXOeIWNNcYLWc6xWLZhDWuchWa94vZL17PiZjC2km8TfF+F8PZnJ9wN3HHT+7TtX0L4ObUVEqYL986VSr1SiB6BFunvxyWhNL9GKsW9g12+8PlX2PvUJ6gff5Nrr/wCf/Jlx+lqRT2dcfed+3z+p1/1rltdQzWZ8t4b73HvyfuYoDCrQmDuQjzut8O6HmRbXNmFza4PhwH4ztt3k+DnbHB9tbAzm/JnreWXlmu+tljyP4rwWCkWizWiJBzwEfof4JygjIqWttEIS0QFoTkw1JIn0MVt7WL4e/xRUDRGfOqtyRydM8niqEcaohKklGjCu9BU6rriU595KY2DA06On6J3btKu4smhwkRP+Idf/jo7O1PPQOOYVxW//JmXePnTLyYrPwfEXYQXbh/SmI679498rL0HR4g43vngHm+8cwdnHbu7Mw73d7l544AXr+3QGMfxsuHRyYLlqkuxfaMro1IQQ/PEAwQcYYPEZYWoshrrBD2tOT465ejolO+/+TbXfvcf8unPfJZr1+Y0ywWIDlYEfvzjKcRt68MWRGurtunYP9xJeOWidFUYj2k7VfqI2cXYwLhOth2GVVZf/g4fKcXJbMJzz11jeuMaenGP+bUXefUQ1l2LrmpOnpxy+7nrHmfbDlXVPH30lJPFItAdb3EybMmIcfJGZ66ig7n/+LiwqssfTqqKTzt4ue14v+l4XWAhQtMab71tXGoPkWeKsMvg0LkoREeUWJhkuYG1yFC29Aq1eOOVg/E7Ke57BQzTYJ60Uty4dZja6hBWqxVS72BamzYyRCreePsO9UQn4axWilduHnDt5mHAPIKT3N+DvRnGGo5PV3QO7OkSwfH46ISHT47BBVe76YTdnRkH8znGOlatYbFqvJV00Q1ng3Wij4eODTHRrH/ZU4Iq5TycV5rlcs1yuebOvce89s03eOnFW0ymFet1S13FE8hL+HVp45NgJWk6w3ReP5PC+KNLl4D1SyIYB72Yqs/amkRFRFhXmv29OdXOHGlPqOcHXJ+DsQZRmtVizd7+Ds55N2hRiuVCs16uU7Mv4wZZ4tlzRZ4t704XzWgepX18ymvWcWQtD/GSZTDCTThh3LGqP5jn92ITzke/C9MdbRjdaCYuBedKCbu7gUcP8kLbtYieYE22CBZRPHpyhAqxfB1+E+3a7swr9iTDeaxkOq29gmfdenq5bkFgsVpztljhgCq48E8mFdO6Dvy5t94zdiCPBj4kyTKJZyt4t0T//b8iirbtOGo7jk8W3L37iMPDXR/zrTNoLYmtdXHNOgpFU7D6tzbJjj9QUA9pow1XhNkPCeK9JOFfo7zxiqo1YlpUPWFWeRdbEcF0a+pJPuxEROhak72c6PO329t+OX4q/g77OXq6ccg4A2aEgynZpLUfR9pafoLzC/JdkK6k2AuSRd+n3+F3Gq139akmMdzgx5lkcF2uCkW/fkc+uXRMwfdxKvtc0R5BdOQ+dapaBZfA6FKZPiu65ELeDZu9cFsStH5v3OhlP21CVGqGc4WQkoWnsr4EsGVjwnMbP9tWd2AU+3PjX1S1j8Hx/Tc/4Od/+Yt862tf5+577/H6N7/lXdZoMBaWp8d8+7Vv0llDJ4pa1bz7xjucnp36nRelQhD68zBHOfBu8Oy8a3/fLpecrhrP9IVT/JTyjPffqGv+zOMj/uHjp/yt+Yy7izVniyVN609UXK8bqkqh9ISq0rTOpdOnTXAdFVEosdSTism0Tkq9uKtpg1QjQbHmT14Ga4JrrkiKPWFjecq79UFw93MFA5s1e+SZdb11obTfoal2B0r8cOBFVj0sENFQHSCyB7SImoCbcW50ENlBpgfI9BTYQ+YvUE9v0d39MtqsmVuFaxva1QPaxw/DYpugpgdMd6/j6gNQjtasaM2Cru08NCpN11nqakJdT+i6lrZdYTpHXSnm04rWGJSqODs9AgiMgIAsqKZnTOf3cWoPQeOcd5+04aQvEeiMHyOt/RqutHD9uespkPS2FZUUfDJ4vkGpzlnLbpBLcpw5/2yTRCbd3QC1lDdu4+FYna5/nX6jqBfYgSDQOuctyxDhwcMjPvmJ53jnrTs8fXLEB2/fozOW1vl5a9cr7r5zF2MtnfiTEJ88fMJ63SRrmqpSBUHsM/wuc3JpbcdODbFf2VdX5F2vWtZtOGHR2mCB57v357TmS4sVv7lY8Dt1zXHjrUCMMTjjLWqVUuhKh+DVMTZWxLFhjMQr3XXY8SZqZbN2wOvEnHcjc1YSwxQV00pUz/rSBuVCHHM3dlpm6Hey+pFwH9xMn3v+0OMYHM5YfxKvKEQU1nQslg239g6ZTWfszKe0raGeaA7nNRLxio2urJnAPXfzGseLNfeOFswmU2YivPzcAV/+xhuI6Xwsls5w7/FTHjw+AhyTquJgf4eXD/aoblYsO8fRYs3xYuVdlcNqsxZU5QNuW2sxxm+saSXUSmGs39RYLr3lnRI/L3cfNjw++gN2dzR7O1NqrcBGywKV1m/Q4aSNlkkN8715iT77IHBRekbO+7LqvGTJUyqNevWVDeg3JoO1I/l5jgmVI8K4X0f++dnZkrbd58mjY5bLJcdPTvzptyEOhOlaTp6c+M0SASWKxdky0Crf4OGJuEPc5EauzkWXYWD8Xoi3/Mgn+blswQV8VgkvNy1vNC1vacXauOQG7hW/Lgm8ydrX0RN+CTjZw2vEjSUnG/6NIJ9wUGgn5I29MCZCuWnRP/Vxo78xXwJ4ScLs/v4s57Ue3iOvYa2lbVr2pjOq2gvmxniL61mtc9iVQvERa9zbmbNqOk6WLVVVUcuUw70Zb995hFjrYzNZx8liyenCi1daa2bTmsPZFKUVrXUsW8Oqab3gaElWeR4GFTibDmTxFoaB+xdo2zaMj//3+HTJ2RvvM5kopnUVvI6yYiHOTJb3gxuiFibTenxBfdzpssBepEujlYKF3wLFvWaM5YipafxhM8vFirZtWS3XAUYC/2Mt68U60EBfYrtuC8+ljDO2V+QG9xs3o0/jENro8knm/COpvSXCobE8dI4n+Lh6MedQoTuckjxSOS51IuHnfTnW9KF4O8w2Dub9NMLblaeQZwvVXFfXGSa68sYEWqXNx0oXLuiu5J7873RS0xnLujWe3wFm05onx2eggkmLc6zalnWAR78pq5lXlfc0cl7RF8OO9PG5ShtrzuWNOm/d6cPKmHTghv/ENJbm4RFaex5RBcTqUqb++EROVYmkw7B+5FNmuZ6Bx9iqVcBYR+V87GFr/W/mt0mWlHGdivRDUaTSzx3k7S8vFEnwdGxUMSgwR5jiWLhgfXheMz7udAE6uErbrhhjb8SVzDkIigHrHPV8hvSs5v6o0lBZ50aeu+Iv3DsH8nEQaYdnKZpUp5JgfioanPJWPyI+cK8LJ7YEAdQV1nmlLJTjOsVqZAM3bSyAhPz7b/rrqLzb3DXpGxq5HsJ1IQ8pX+5H/EtrJWWmeF90EAmx3/yhIl/42U/zsz//c0x3b/K13/4Kn/3CT9Os10ymFkTzB7/ze3zx536W5aphNp0hUvE7t/4hi5NjTs+O0EpQajIyKiNrIf2NxdeDzbb669/8b38vPWmW3hVtcXLGL1aaP/v0lH/w6An/n/mMh8cLTs8WrJsmES0V3O20VggS3GRzWwS8m4IS6qoKpwd5Rtszmr4dKggJktaIUE8qH3PLOerKfxcFFT+vDhX8ca1xYddbBj2PeeMa8deqrug6i5oMUUhcRw44Bhpw1wYiosXvjZyPfkRNgEMcc6TeQeQ6Kwf1fM7h9c9zcuebHL70KZTewVlD9+C7WHsGZo05OsVahal2UWpOjWbZrUC3WKtp2iOc00ynE9Zrw+5coxWcLRsqDU0HM60RLXSNQOUVplXX0DYT5vMVup6AE3+S66qhaw3THb9+nYO60lSq4vqLL3ry4Qq4iMAk/ZEZKvc2lt4WKljCq4wwn7mYrCAqv0t03wXcE54OGdaEA6JlwwZuKYh20VU30qnOhJhwFp57+Qaf/+SL7Ozs8vp33+WnXr7Ncm2wtR+jt19/l1c/+QLLlUGmFYLirde+T7Oa0TTRRW/IhPUPGci4KeOoKACnXI6gbMutjSv6D/7wnXTXtR1aC+264WWl+JXlmv/xbMFv1RVnqzbBeClkRle2KCQPpShRgrjg1ibZrRbyMimV+rFrWqtkmRfjAuUxz99BYHKcLfL0k0cdIe6i87jHWsu1/SkxCFhnQmB5Z2mN4cnRGV3bJVdoj7T82LaNQQyBnm26c+/tzJhUFbO5D5x9cz7nky8/xz/6xhtQ13zupef43jt3ef65G+zUE7RY3r/zgKPFktV6zWrVoJSink25VtesUDxetqDAOQXrFeDdtLvOMqkVSqBpDUoZjIFKKUSBdQIqHERiO1pTsVx2zKcaS4gj3HmBVdeRQfWKPS2KazcP0Lrqj2wEmDGYLkYjvRrNN5yk9E9ZlJ+vkTkd/XxYb/Fym2He1nL75HCjUGuzC/re/i63rx0wmdQ8ePCUm4d7dJ3FBlfPxw+fcP36vres1t7C/PHdh5i2ojOdX1qlwD9oVBm7LuGeHs/aP/Cn+DAV9cEHT3LbgyW26QzXRPhEa3i9aXlTK5rOx4naFFQkwZsEfBLbKgVA9061zR+HeRwROIv8+TTcwSAU81BuIo3NnUvtg2jtvzOtEo2yzm9Qgt/0W64arPECe78cFzYTSyuauLp9g6aTygfgn9RordmtK64f7vPGnUdIpblxsMeDJyfs7+1Qa43C8fT4lGXT+kD9IS6privmWtMhLFoTeGLB2RaH39iw1lEFXsgYh1gbYu6VFsMuyDMW45TfiKhU2nCJSh8V+ClH/F6xszv1SsQBrXP0yPkPT7oMTjk37+bDURbF5XU5nU7Y25mhteb0dMnubOLdqoPyaHG2ZD6fenkoKLcXJ4twsrsdtzodadVGAwbt27buAY6Ol8WnES84pgjXrOOhczwmxmIcckMxbZW68nthc7BS47b0MYH/JSbuIoQf8xSKu0mlErxGK9+IM5rWHxYl880NlHiI1LYlVWmvOFPab2BOtGZnPuXR8RkoYWc25XS5ZlZ7fCA4lqs1bTgczB8AJohWfvNNvJIvjoZzJsBi3ryMyqIoHyfyEGRj5yxOBOskxMGNykkX+KHI++axlyA/iWxbRf8Up2LytRYmYbO6aQ2TGFc8vO9aQ12FA8UCbmibLtEbgQtxk9u4uNqMrBuz+dB5k6uZ/HAo9T5qsvEMh2dAEuUiMbOgcDhrmc3n+AgLP2gKN1b/GJBe9UyhqyQ3KL/yVlEuHPoRLSqSxV7JILrcYomIJ+/8lESU3hf5bpN5zRebI+GgYEAj07LxV5YTFb3pPUlQJq4VF4uLxKDfv7G0OF2hKx+L4exkydd+7+vM969x58EDvvLl3+PsdEk9dVy/fshkNuG1P/wqD+8dcXjzgNl0n5PTp3z6M5/hm699HbRLsae2pzhP8Xeo2BsbSE8lnXP82t/7MiJCPdU8fdzSdi1VrfgXVmveWDf8+/MZD04WHB+f0bRtmmsgCfvR2k5kIFgFazslQl1XQZAMxMo571JWxLeMCFMpn18pb0mrtEquwnll+R8vr8f1Enf7R9iYYt7resLRyarwB4ovo9JuSVbeKRx16Ji3npR07tC2JHgYmSNuB9QcEEzXMd3dReQ61XSOouHo6Ihrt36V6toCZjVSfZbJ8hvY1YoHR2dgTlmvG5xoTBdPDXQ4OrrW+pMJcXSdZbVs2dutqLXCKX9inyCI1XRt562tWLNeVcxne2gctuu8wKNrRAldcPOtqgpVTb3FInXAn9EEvVB2F9PhwuhsSvzbUx/Oh3uog1GVvhDa+66HU/pMckYdXjTuy5N9erBRuQNxkgx9Io0/W7XJNbRZtbz+5h3mO3OOTs/41vfe5WzVQuXY3ZmhK807b73PydGS+cGMaT2jbVe88vJN3nzrDqKC1UYcgV79xb2LgtoAp5EFuOjFXPAiOOBr33obgKrWLBc+qLvSil9qO77RGX67rjldN6yCABzjuAQ9fOFyR74IilrBu8gI0ZU24IIErVIwmZIYUaVAax0U+hn++1Ob8bojl1kQ9KJhEvI5nHgl43LdgRLa1iHO0lmvlD05W/L0aMV61VJNFKKUj2czr7GdD0ewN60R5QKSYQNfVLqi1pp6UjOZTtjf22V3d+4PvNETrh/sUE9qVq3h6eKYv/jLX2DRtCxR/OKnnudr33sP27Y8XjS4Zec3TCQfBBQ3SYzpCiUFtK1hOlHeSkccbXDVEKewxoAITdPRVBpnJ0zFhUOurA83IjHeoY+9pXWFEuWVy2GNJTpKmuY81AMguewRNmOg68p/hoTd9e/LNR0zuHjaacjqz/zd3hqX/t2izUu3/lnT2qSI7jrDw8dH1HXNqllz78FT79KsSfTqyeMj1quWelZT6QpjOq4d7vLo8XGApYwfB6ivNyCR7xjyQrb3YX4RYef9e09AghVIUNqJCC9bx52g1Ft3hUVJ2YgxIX6QJIxN6of0WkGMtZdGN8xZxm8RN5SVbnBzl0pxqpTym4pOgvu6cxjnrWdW65bVqqVtvUJfRJjvzKhr7QV9galWoV95vWdZIcfhVFqja810OmUyqf1KE8V8VqMrRWssi6blc688T2MMLcJLN/Z5/8ERzhgWTQetDd4FMlBCuxweQeJJ4Y6qihsqRTzhsLki4l30jRKcrajFJbwYT8pOaFx5a1EhhjjYHO++cuDjTdtVDhkeoxXTRWkAwQWqGr7JdQ9TdEN3eIv207NVilt8crpMij2tvdXVMlhY6xBexlnDfDZhsVynSjb6OOxwIu1u4/WQR8k98aUena78nUi2Jhfh0PlTb7NSb1yld2GS3s/lPxtqh5+p8i1lI94NMlqcR37OecvjNlheRcVqPalDbFo/d5VSPVqxoa8UlSyRlVJUugqHHXl4qisff9dYR2tabh3uY6zFIBzsTP2cOEvTWcB4/JtoSUkvs+zgHEkJX3opJF+jQq52VtA6+NElwJZUju9D3DxlSCx/oGkM3seh8xIpMrrbaPg5qWfc5QjGKJ4f8gcckXhehGTNJ2GzBefjuXbtxbqXSy/9RHSKZ+G+abNiOGlxBabOH5Lxg1LqbYz2R9iIZzoVNyYbMKe4SEQ0e7tzmqOn3Hn/d9G1N+VVuvYafF0hgQkW5ZlgH6TeCwUiOkx+vI/CTEAmhYXSRQC3PT7D4Pk5x35fPg2Fo/H6lNQBKCYgfVNoa00SOuNn4mSwXiPjPtjp3aDKA0KUdm6TZEe+GLQ37eLkT7Nir3wfzVsLyx1X5C//C98mi73YjtRmS3ZZdjy4/xitNWfLNd/89ht867tvMak1iOLv/O3/0h+XXWv+rf/d3+TTf+UvsOoMn//C53h07w63Xvx5/uJfuMvP/fH/Gf+H/+3/AiPLxKD154rBM0dfqVeehzOGTgEUtms5Xq0wFs6eLDg+PqHrDH/hc5/gZ+895f9YKx6cLjk5PWPdNH6eycy6p1nhkIdCWom7/Coo7mLMLQoFXKkUdOKPmo8IVsVg18ofL+4cSUixBXOb29J30izlnuFThze5f/h0yW/8d3+XejKhrmqqekJdQ1VPqaoKXSmqaoKujlDSUtVRAAalK6p6jlLW44iAA5SoEKBcQBTCPkiFSAXOsXf4aeq5wrHDZPdlqNc4lsAE2fsphAXIIcxfRc9aps0brJsVdVUhRlh3xu/i1wqtPDxZa1itLRNdM5tpxAlVrVmvWkznGQvtHM75HX0lPvh+XDY2zKGuZ3TNMllJfuITL3P9hVthbQeVUwwGHqc6orNSSOgJdlvwmOv9pIdu+NANc4yU4bKVXh/W418G7gS+CZSLvIN2lSrGpCoIcO+cD1ytlGLddNy594i79x+HAM7CP/onX8c6b4H2F//8L/PzP/dTVNrx6su3WJ4d88KLL1KZY/7kn/wS/87/9T/F0mVrDBfXskvrN+KsxKgPNitKS5+N4RMw4bAV52C5WLFerTHW8vnb13i+s/wXWnG6blmvG4zp+gJ/ZEwD/ephZwfJ+jbCcxzXNPeSGCVvlSJBUeIb50mjIirKfR9cdrmNeCM2ZbB23MidhHhfldacLVv+n//+rzGdTqgrhTHQdGuq6YJg7kNdV3ztO+9ytlhSn1ZBeINj3WL2drBvPqCuFJN6QqU9/GulOFl0rF3FtcPrGLHIZM7CaJ57/nk6B9P5Dteu3aCzLadNw/7BAS+9+CJ3z8546aUXePPpAtaWk+4BTdt5IcJ5Ad7ayKT7IbDO0nXejbuuvOul0sofbGI8LtDignASrKCBKmhRXBhLpWqsadPO/3O3D9k/2PXzqEohYVPJLhs3l0gjOPjKyVEgmU3eIUJoVMhk/UaE74C/Yp74uSNvPKYVW4ZvEBarJmx4WE5Ozjg5XSTL1TffueNzKeFzn32FF1+8jVKOG4e7tM2a/YMDlF3x6qsv8xv//VdxeKXrOBXPfE7Jq2ZeZpy+xSFxzsNMazwlbjuvKLbOcXt3xv665Q+VeEu98LyEpQunM7Gu2zF7ANWUL+KCxO+KpM2gpDjrsVOFJcSlk98UaVrDP/jyN1McX2ehsx26apLyV3WK9+4/pWlatDZJYbZSgptOcI9O0UpRae0FbeXp+bpxdE4xn8/9PFU1rVPs7+1j8BuF89kOBoM1htlsxuHBAcdNw+HBPo+XDXSOtT3DGIMKfLs/JNdlRVpYwMb4UAFauzSu1rqkiIz8Ewg6fKujwiLhZK9sinRlf3fOdD4h79IU+TeHNOCLZxS8N4vbdrE1v1zwpP+8UAFuwxXFNyUeiL9NZxIcrdYtq3Wb1vqjpye+NIFbtw45ONhFxJ+4brouHDS2w/XrB3zvex8kXLKtl8XSTxOQh2ZwP/wu4IcYUy0bVcC8UkyN486HVeqNVXyOSPpHpj4Svyn3/XfuhbAdEhRjBlGFM78IR6dLOmOy4QHQicFVGs7WSb4QybiqMw6LP3XXiQOtME6YTmdYfHiaup7gsHStpaorZrMZq84wn828Fa51dM7HvUYLKtIhNsX+0uU+wn/isSUu474rtNAfby9X2VT2dFJT1ZpC2/BDkT6Sdcg5S3H4ouANypEwKW6xx7NLY9P75Tp7a85nFdPg0VVVGue8J57gmM8mPH66AHfJtT/gWfON28hT3m/KIf65Fi+VLUY++3FIV1TsxThwmQJGwccrCxzTqeb1r32HBw+X3sXUdlijsM4HzfebgQIuHDMeFHcqKu3EoaTKlkiigwJQIShUpREUiEJXFUpVQWmoUWqCrgVd1Wg1RVWaajLxygVdoaoapWuvJFH+G69EzMrGqHD0hxl4RQNhpzS6QEmPi0ijgz9LRRXvFTAhHvYhVP6AAsH3IaR4KEnc5S9JbZlijKS0M1rSXpfjOaTmlEu6J7iGehJglO9K4ldY4g2EYRzJRN2GMu1GvvLeRkkhVBKkh4FSDyxvv3kPrTU3bxxQBZeXtvFBbm2IZ9M6x6/92m/jUCHGyoT1akk9+TJPHt/lq3/wDovlCTt7E8aVt+6Kf+V3cdAtf/CPvg2iaNYrmtWK5dmSP1Zp/s33HvAfm4637IzFYkUT4oekksL6KRmjoaWNCi520TWvdMETCbtqKpu+a629UGNtUEK5BJfOglWEd6EfEkzaxYzAdW7LJuPq0JXwznv3ee+9/5C489WF2H4QlP6RyEa4VmU/HFpP0CrCrkZXFVrXXiioKrSu/I5fXVHXU6pKU1UKXU+YTGrq9Bxm8/+CehKfv4WuvfBunMPaGq00VT31rsfK4ZyB1jKZ7aIq7/4sVU2toXItrp5SqRUTVXshZ32GadZMZhOWZ6dMxUFrcPUUqWboyZyd/UOElsmZ3w09Oj5hunMDj2azJU9WiGfGoycEpCUmiUEpl5/ky97aLOcpKfHJTFGZvYTzvAKLNZDyJBVZWqMlA90jmVGhX6yfCPKRVnhrGUHhuP/wBBHF7u4s7eIa4xkDGwJ0ms7y2jffAgLzpjTGtEzqtzk5PeHt957QtGvqqU7MZ3lwSN7MKDcfchttfBdxYtkfSMzd9753D0To2g7TdbRNyyeU8KeenvGb1vDI1TRtixnGhZF+eX1s4sdewgm4kYGOcF6mUvEXXeH6VtQuPUtKiqIJyZJvwNX2QLukPWE3VynhydMT/tv/+tdTP1KQ6dyFHi2KFi1xWWmt/cnBSnk6rD1Oq4KruxLvyiFa+Q2CSnvcpBTf+Mp3PG4LJ/T9x/+tt462olietDxZrKkcLFuNtQqcUE0rqtBJay1Yy2Q2xYW4iFVQOipnsKqi6lpmlfZWuusG07bUk4q2aVDOoazFqQrRFaJrJrMZgqFqfPytk9M1P/W5HfyhJ1mp7AZjVI73BpyXtyP4dltyrp8n0/Qh7GbmvLQWL5pGRkilyJ5fDddtpENpARQKN3GxRsfp6RpBmAbXJlvSpQin1nH37mO8UBnaZi26esx6tebJ0yXGdOgYR3Mo9feRXzEe+dcV7zawXkDADx6ceH7W2OQadk3gU6uGN5xjgUonMpf1b1N/lMnDYST9hTQfGxMaJEnx1H/d62eYqw3a3CcOl04isFw2fPO176R6yqU1VlzfLdglXsTH8M2/MQZl2sBQXrmugjQuorj73oO0oW+d4yvffDttWLYrw7I1KOdojeCcF1s83x+GLeBCHw7A8zg6jLHgcKJQxlBphRbBdR3OWHSlfPxTwpqN/L7S6LpGsKgQs2vVdNwKir10Qu9F41zg1GfVEgxp95bbDVw+Asa9fBKFt4125Y9GPs8ySMQXQbPfNN5VPoWDSLQ1F+uAk+gCG2iF9zBZ0nUdy2UbXMA3aeB5a3qMJxn9LhR5droO2bOcMgVudJbHQBtg63JgtAnno8M6TNL7GR3rbZ8V5+xcKQnQtpZ79x72eSTychm2Yrh2Uwggybg6//nMEcf1wg2IcHK0TN9YB+/fe5roxn1zSmMs4sBYlelUkr9JeNfzQC6tRb8e440P/aAEf0hZsBaz1hZoViDoEWI4IgnyWWcMu7X3ELrMAS4/0JTB7+KMI7nKp+k6KxlGK8oHRG1hJENBTeuVHT5XiH3ceCvMrlsnpeyFrXe9n3DjxrL0Ulzrw0yKYK3Hx+uv+WHTM4I4cFXFXmLEPHBlawcQ5Qnobq147Zuvc3jtAGNs2DkPiisy4pDIwGlNPdFUVU1Ve6bfWx/4cMASFGX+zuHEYG0DzmE6hcOEQgNDbTWI8UpImwUfEUGc9/+O8Tic9bv48XvrbEAIPk6OE9CqRgL3oJNVYVQ+euGl1hNU2OWsqgm6rqmqibdcqhWTyV2qyQQwwc3B9tZyUuxRCMS92Y27YxEQHK6Q9iMJLr/Mgn1maMv5KwVcRn/9vFprPfJLgnBfuZeEyxCroKe/21AGDv4IbslFTDkQHj54jIjidNHQtk2yDI0BtNumY2d3zj/+3ddYr7ugQA6nogXFVbP+OhZ4eXqTPuqiqKu8HvsLVns9RNfHYH/37/4jjLGs12ueq7xV2Z8D3l+s+I29HY5OTr2lnutbyaUShARDJc+ulaKOcSgCg+pN5MP8Fs3ygq/ujfmYa7ezhECiJAYorqne2gnzH9dcrK+MizDVwt27D9ndmwW3N5ctGBIh9/8oEUSBrryizisrhc61GOsZbE+4VSD4xQm/ISaGtQav0O8LPs7ZcJJXITipKuAcg5AVz0EM8YykVmjlLYq19nBbVzUo5V1olUKUQ/CMvzEtlRJUVSECdVUxnc3Z2dthd28XpXZ488177O/tc3b8FOtc2LE+SOvPFsJlmusg4Uu4KU/CTgyXS4NZfJOXY5/g5e/iXMYJdOUCi7NfgiN5/WR4TVlzuek+0oDIBMeCijqJ68wvdEnPheOTBSLe5bEzOcBugmVjqSc1b71zt3B7K9YoYLo7OAeHeiePUVryLrczDU6mWb2NiMFAxvXkz3qHf/KHbwSGpGMvMIKfQfGkbfneZMJqvfZB9BOFK6dsOGEZ2pRkV/sIN9a5YtsnlBCY4Z5Sj9z2hJ0Sfs99L8vo8UNu2OfYLknKpUrByclZcJvLc7rJjGXLOKWUz6E83HfWINbSEHGgyvWHRkUmz1EevpPLjfP09WJIRXSOGSgU7rdxvbnsPqe8kkEH1yBRcSy9ICCEGHrGoPDu0AkPBzfhejJB1ISnRyfs7e1guyUOR9MZJtNZFmwKeOqRmRE+WHp35bshfR+k3tQN4HqIEOKVy+Pj7zM/keIR0f/rVxgWUPwtXHkcFArJSC/9ylytG6LLYxkcPwfS97E2Hz89SaebDvkg++gYgJmakBo60ssC3fVxRYKLsZEJ1+J494NHHjcYyzQM1E2EhbE8CNadl7biKTIJkmPkpfEvBm2YdwSV4RxuKEwN+cVLNWwzKYHVau0tK1I5EdIHCuBwEZV3iYewNkC459MinU6w7SIlEEoc0mOJAh25k/gIb0Gb6FH6tmyMGygbwiFCQTnkA+sXYWwCzhC80sARrfs8H6W1BvEn6E6ntZcXnI8jqKsqlzMCJR91ynOx8XTw43r3pZ5j+GWWF8L4F5ObNgAKBFXiqrLMVE/4pg1WC8OYk65YnCLCYrnuPevB4dJv4J/njtpb8SUs9q6HvY1j5GHr6fEitS1aas4RWuAstfkZ53abMmikuHGpYsx92m3ke9ZkjE1eduOpT4H8AWz5QfQUyPmkvzhciTcy373ZJcdx+X2/+tG+9jc9CmWiDEYs4puw0NLmQ8DD0UMING1nfOzyILx6j6diA2lra34M0hgrN/KqvE6uuG4IvfT4na47Z8xi3MRzsmxd8eesj2G2FF8vdECcP7rUEKnUDy6dM/QferVd+fCMcuczMUwCKnFgDffuPUFpfzpN3PU0QdGQFUO+EBGhnvgTQau68oRTFe4eMR+SXBUl1r/RfdnAqZHYI3nnMAI3we3PC1V+J08UiJiirBYlpSJOgYoe/hU4izOCdBWWLjARGvAWikoqf0JqOMnwT//5f4an975LPbvtY/Uo3VfEEDuX0KHvZxiQyGQlpDoU1iiAMc2V27h3xfzFOenHy4sKO3DisOKZITeWJ35rPRqPzyTOc3yPixx3/pOhxZ7j9e++B6JZrBoWp2ehfV5hZ2Mg5KrCmpbVqkvj5+MX+r4uF0tUVXN0smCUamxcM3he/sU2xndhVpxw5+kTVqs1TdNyXCn+4nzKk7MVv6taHivFcrXGWO+iIOGQmZ7IlpQVXokVYxFMJpMkgIJnRlH5RKE47xIUA0K2oHR4c2lrXTjRNc9/muOwDkTEb1zZHFMm9jPhaEcqIwftNpycLPAH0blwAIxLisOyTt9TSbFUoiXiUHghjezYlBRocAPGyYS9UH6k3UQi/AfGH0FZQVRHVkiE0xaFAGFB4ewcSlUgjkppqmqCPwk3bA4Yi8N66wQRlPIHkXzxZz7D9777Ni+/+hp1vYPWdZrjHt6KRCrWm7ho6b3rMVtjA7Qh4I0o9CPMkxV6sRFZII71ZlyU1HMJZ8Tyygl08f8shBLm3xVrsMBX9+4/BXzQ3aZpUtlQ0BalcM7QtX7cys0agLbxLpHLdbspDPcGOYthvedpYEhwVaaIb48XS9q2w3SGlRY+V1csm5Z3rLAQoW27zPCI9NZ9npJiDAPt01U4KEP6CpJSfRZLUsnNM/cgwhw6nwZfzm2ZN7sAFm0aY8wks+POWVZNG1BVv/0l3UmK0WKX3lvtPZs7SwHt/SS5/dliKL3ISpH4zgkiFkzEE00xDi5953kRr7D1ytYq0BzPdbhgQZo2JMN4PP/8LR7cf8w7b99lOptkxUiChxG4PUdoHRMk3Eg+N8wzfNdjCvrXcVMwWTkkpV4IqxKtcCKZHpTkhiVGxU3uYMLXApyEWFbG2EKBn9dqaowLJ7MnPJJrMcYg4hVr24nEZm97/S6uNgUW/++qbX18TGtplXBbCa1xPBFosJhSabGtHcXiTfMfw0uMZCrk4TyOBQ6BCHPbxP0iCSGe6TkDVNRZPuiM7fUp4fyNCv3HG8L1R52knBnJoLrxjoL+h3szbFOe8dLSMAbhTzNV4sv4j4P9/R1OTxc8eXxCVeuEiy+bIl6/DDJ06Z/RNwyauZl3UFlPiZoKF7Ysx5GySOtgaKFbvl+vu0QnSqvusnkq5LUbCCv2y4EM3o+lkXXZx5OFLFUkz2OJd7cPdMw6YVe8d8exAzuAvauky1K7EnUMp3LbNDxLGivPOufl9RLOL0wfE4yXNYzCa/+9nxbpZdverIwzctih8dEOEh2TSUXTdCyX62xp/MOStk1TJuH+tsDtQzC9bDWy7cZJEUvziqtyDOQvoFMXFnRBKk+9jko9B7RXKuWPLn1UbbqSYq+HkF1WJAgOa72g3qwX3Ln7iHfvPMqGO4HoRiVFyZB7xYTfJatqTVV5CxpRIbB1zB/LKhBMz/Q3MPOZsc95JQhOMe5YdqnN93E3Ne4eJHguyizrjTuDWYaI3423ScVrEb78m38XrTQuMBZeODPZmioMcGL2/J1/WriZZeKwCbJe8CqZ59KKIwvnNlg2RoVRaZGXlHHW787aYB69EaeKwW8QCCyByMdOxZ3SnlYhXBdWe02zRIlBnGckJJjma60x1qErTbNegQhd1/oDDZxDB8VZpTXVwR7WWY6entDHThdIBcjIeMZvpPfMtIbVqmGxWGLalj99eI0/c7biu8uGr2BpepZ6/gSoqJxIDLwjjakNSp9pcCmNhKVnaZUs9grmqWBURUjuhSmWHpIRnIuKwXD6bSgjti+ul6HSp8f0CnRdw/HJwu98JtgkwR3pmgxryWpGZSV7gseSSe8zSH1Y3LzOpv4FfigUfelX+vl7RHus3aUw0as7jmq/j/En4pDf+ke/zZd/53dT7MBoIRbnvDduru+Ou0FcwztXXOd3WYk3dp0UbQk2C2Wyi4J+sT4o84VXo8qcARgX3HWZN9YR8VlkQoxpPXxjU59V2Gixzruim64FEW99KQEThr4rJejZBOccq+W6jxcLkEkD61yC7ggZvcxS4tM8A11naTtD03ih/9PzOZ9tOu62He9CsiaMC0DiCLg0G0QlaMaR3m0sxsWUYp6dZBG+V0ZqcuTayGtpgNN68Nq7HMNvcf7ytZ8XcNbjuOWqSV/3gTXDfbxLinJVwDcDeOsVMwLrZW0bzZVxnJDKHIfjzZL7je/3Y3v9w368+eZbvPP2O0VfMz2MqZwdKdbKBsM9RpoKktUrsfdsQOuBkhZHRsLDYYHnUxsKwOw1RhKJHj6PdURa1luXBfKKcWUjF+Mgrfl4Imt0k0ox0FwYdxfyVj6cRtd2G2N0ngyeXw3txPKz+MQab5VlgtB/U1fcVJYTa3hKCA/g+hhipLthyPLsey+QvhWe5H/6fRgolsquOefCxvFIJ0Pm/tcjcF5U0RcAvUdEPoEyp9FShrS9x6OfX8BW5ccQpwwq29aOrSVeWM14oZsowD95/OgxTx4/6fX1KvqfTIv6uGaU1m9+me/c8I3bWIsu4PB04F7qzwbGyfVncB9BA/2nEQOUsfZc8H5ycellNJfLtiMdLN5L0EJYMz6wCdcNgUMoI3BsqcBfZ0MG/3hH8/9n789+Lluy/DDst2Lvfc75psybmXeoW7fmoUd2d5FUky1DTVs2YRiQ/CJYMOAXA4LfDPjv8Jv9ZsCyDFiwYVgvtClBgkCRalIkm5R6KLJZ3VVd1VV15yHnbzjD3jti+WHFilgRe5/zfV/mvVW3mozEl+ecvWOONceKFTj2EkT/Erjdopq5t2u6B/Qmvap7F6ucr8A+v0EXZ+lT5E3+pu5KaTw8nZbr0fhWiQsvwlLm0UdUv/s0UkUG1usNNuttWtBSH/r5pNs3PwNE++AKJS+4tsYXnQyL51rhTFXzUtNcRdckw+cA8cztGBjx+T6C+2mkWxn2Qrw9UxlH9s6BBKkl4NnTc7kV0FpiKwOYGsnyzjtA5NB24g7fReOexmpIiZFYSalkqjJrIGdC9XPbdb9yFptP65e6rDBfGCMqwSYF64YxFlbxk1TxScZBB4R+V45VhVrE43lm3lUgVi5MltGRMlmzsxznTZV1UdJjnpDzJm8YpWQxn40ByIFjnLbqWC44By1NjWblVvMmwqB/yaAXy4Dxq7/6LXDYYrFY4Qu0RiCH7/zq6yAG/n8/GHB0/0u4/OAnOL77MZjP8Nrx76ChDj/84Lu4+z/7TWDj0f2rH+MHf/4unj05NzBBKG+83UNZ0gJQRfHKz//uv/pjAIyhH/BgGLGAxOlYB4914+BHuTbeB/EA1VsvdT7UCJfWAnIBxqLrSuFRDYHMyWvVGlEzcuV1BQjj6NF1ISl23odk4BMjS0hjYVg/Bl0LZJgB5H3s1ma9wzCM1a10earI5M3v5EHjXDoqk4LvpnlN0FOkw7yk4lhTybwQutKzQpqmcqm1vzF7OR7zPdGAKf5ncMk0gn0OLpsYT+RyyuzsYaUsWFE5KQm1rTSbjXrFczXKaZ7E8FRiqY1wmQZwUdbkMf1R2FVcrw2ITJSeUbTuMQhfeONVhDCibVucYQAT4RtvnIIY+INPPLrjV7A7f4LF6gLAEifd1+CowcPn7+Hol94CBg/34RM8fPgU6/VWIJiiIKb90R8Wrljh38JD7Q+Tv/+r770PgBG8x3EIaCAe6j0zhmiUsPQ2xw0pFyx7qiPFzkxkCcrbdP3MMut6AuaiqkyapP0m42mwK5aPwzCK/zL5s3jAiJMjT/p+LGOD2tkxSDVFOUq31OVNM52W6wWzG4htRXP2qN1tElWFMv5b3M8vM0pTla3MzynEx7RPacrjAlaSyjRZnmorgXlekoG5KiLcqyHfjNUa3iHfec69iKtnhWXINFvF6btzdgKwh3MNljHWzqtnSwDAu5cBTXeEcbtG04kMtGzuAyBcbZ+je+0u4APofI3LS/GanYzL0K968Aob2DvL+fuHHz0DIPi0CAwHORLvmeENXUljNWg+wY+IW9nAXXSoQMM8nRW9QP1eyxneXDCRSYniIxc1tCEuocgGuW47NbMzZ2Cyluu5yH0bTL5JOoDg9atDYkHxcB8Nq97M1FVvqtzUw6de//2zVL6ZyEE880mTr9OXM9VMpb8sHVp8ntTCAIjl2HKUa7sImSdLUTGf7Riu7eCHAa4RHG7pCCBCP2zRnK5Ezt0OkedMZ8TSuvk0Jz8WW8QAgPOLbaxQzlYRyxurGbxQMmu/h/Tf+L3kmeLvTTs3BybCl16sogKnlSfuqeLFMf76Cj81amJxeRakPm269eJpjlfMZroB6YmoChXf99MHg+tVxqZxOXxSfNY1gmdbr6ETgsgaABziRUccQAu5cAw+xI2yQz19uZSO4WKetn1e0mfRn9t57EWiKp5YSEpfYn8EPH96AR8YxyedlkhJWULpOaPPSALjO0LT5D9VcWvibFnOXDdrLl0a9OKz+nddRoVcGLZmJBsVhBPz50oxUyWMgGRCSWM2LZFc/+ysspCER/ki+lAhNRWiX5KxlRBHKTAzQ/W+yca2fEtqVuattx4FjkGCGYFCPAarRy5DCkZuPfeCaSOYdtRIQOlF7JxaKs2iPXr0FPfvv4LtuscbRy3GtsV3vvUGAODvPw6484Vvo3/0Ie6+DhAafOvBb6Fplvhw+Ale/dt/Df7hBov3PsZy0VVrelsOSdUnoEdymYG/+1/+U3SLBo1z+D90Lf7v44B7fsQzZoAcRt/LnGlsvHizKzlCGD0oXgOvBtNa0QfyMWm9LVPWxhxXikq33nAlxSnSzpA9MeNx+AJ6uBREOQTkSx5SrsxY1JOIgO1mC2agWzTVVPIUv+IUKp6IQU+9s1Acvd+3m394tSrqQOWzuT7M1zHTitVk0qOSIVlhm4s2spCpXWpMUOi66lzW+L+kd0bCsnqGEdR0naxRz3rtWsOSpQ9qCBYSVtIB1L9h6jNisBVC89EmpTkw0oQY+giMi8sNTo6PMAweZ51DcA7fePUMBOBP1ozV2WvwV+dYnQHEDq+dfBHkWpz7xzj5pS8hXPZon13iadMYuOHq09LotIB5zeyyGyJv8//R934ab3kl/E8ah9/3HkfssWEGnEsX+qixngydtt6x6bcJBp37GOeNlOflNbLriFi/whMDhgYDzCEpRURm/c146mNOSdlPfCtvZI2D3ATctNNNtuuUZj1+qCQtbVbsw0/T0WtlVCo+5jPsfWkX/HCe8qQIm3U8ICzaNdKXVVPpUbWbMEfnJpIO2w/L56W+Enama5099igZ3gnKnynLHyWZS+w643SG28xHqsEysNsNWHQdvA9YNgQmwoNTMex92APt6hSh36FbymhPFndA1GDHayxeuwvuPdxmZ44/mja47mE9c7GzIkCZ78XkggC899GT5GX6LQf8lBkdM4ZUeznXVLVi+8ETeXJPF8HFECaGG5vTbB5YWk7atz1N6bpNO6D0Bkk2oArNbdqHLeWR+FKmuAEmf3bphk3P3rFc45j5sF/rNU6b1zXDeaFUNlj3sjTS7im+x0Ov7JXkmWbfQyczKy/GP44BbeMQAqON+vvJUpT6cw807QJhHNE0UueiXYFAGLkXw94YQMM4xZsDQyyzGR7Jdb/z9+cXm8iLGPcZeAqR6l8k5tYMad/7fm4Y+t7SkH0wd5O+XJf9RY8YA5jKrb/IaQaXD6Ub2ut/ruk6WLxVJXsr46jPQtkHAKCLfINCjDvmOemLLt6ZEAKDOomLTP4m0Fp267apNxdnrCCXZeSANZ+P9Fn15faXZ0CFu3wLKhkt5epqK94Ic4oAbAwjKr/HKvQSjvyKDwIrTbiM+TR8qfYkskJNbqfSFhLgmueWmVUCjY5DmqCsW0wEntqoWAlnpg3mVCGQPKzyjIhHH5d5k7BolTXJwEbwn/WuCdYQF+T6EmvwCzmeXaPGPXtBCsMY96bGQ3H7rG7IrQSJJ0+e4c7pAt2wxhe/8AiPdnfxf/snxyAwfunZn+P06hwfXDzG5Z/fBxrg4vW/C0KDk/WH+OI/+H/g+bMWP/5Ajuyk48SpfiusWLTaw35JSUE579vLLZ6cX4IRwMOAbzNwRYQPhgEfc/bgUQMIIR43St5dZOaf4b1H27bipRrXA2maQvLwTIZCmDVDhA057516G4LENCIAw+hjnKLsMar9AWTNxD1foxAYwSK2QelYB8sNaJTjfmWGIPMqcD/F3ITnWsjk2cs8K3xIOFUx5xpV5wgHUZG7bCSVNBpTnXPyzOB5lafA86KpiK9qrNUiiquEdKukJVd1V1UALOX7jPBZt+e8pMlgF0uyeutkWmHpgRr42dUGv9hU/IMpo/RFXyQvIEZh+FyvN1guWzS+x5fOrvBkXOG/+ckCBMYbm4c42m3xfLdG/8kx4IDt2fdAICyHc7z+wz/A5cbh4/MRnGchNlkqvYITeQLnFOl0hNJ6lgHYbkastzt5FwIeMNATcO4DLpiLqdUWbZ2F13ScH/ViK5RtQyeYFPd1hctOW9lLX+mxez1GKO1rf7LhRb20FXIKgyxMiuMf08U2KNMcbs38Vi+einwW1NbiwMGY3oebrx7ORSQ7UHpP1nm81p/765m8mWE1Ge9LelNXOOe5kSCjoAslLbB8P8FXNDozABsvS2mA8qQsQ9UHWDn1SS5Zkbylca9MfT/IxiV7vNL1WIcOP3jcgACcDZfoxgGfjD3Gyw4gYFx+BIDQjlucPXofu55wsR1N+zOzRABPvNZ05lS64jyMYp0Iw+AxjEJLEBgnkCM7G2ZUZylmGp/pVpyyZNS3VpACH0t6lFaW5w2DugmjNNo0Ne2CpS/75i2+CWGCoj/HZHnx4bSPz6dqXnBAB+u9Jlkc3C9vXFtD8XN+9eZKmU1BfZ46lLmhbaXQiUBFdyftsqEbpl2CxMF0TmjCygUMTPjkSvTApe/RhIAheIReZMzQXgIAXPBYXl5g9MBuzMf2D/ZjNlnZrSaykrxneD2Dy8AC4nQxxr8XT/vX97q+z76f0Pp5nnmT+ufKvmj6fNCHTzHNyCSfp2RpyfTNAZizeJoFjbIMA/NH7A/VHmXa+LIjiVG5HoXmNBxAgTGAgRB5XOu1JJphBAfxgt873tlWa86+Px+QnYo0qTVKvXL/sqdbe+ylXV69BTVCXuMADgF9H7A6WhSxQNJRUasOG+VX/8+0OAphHBKTKomZHq7QJyrJ7iF1htYrohi5dQrznPOqVwoVCIJKmZ8a6qx3E5n8XIzdip2x3fTG1m5A1CBVUgKNXlB40dRKpb41Cnr5hyQwBuYUaDcb9bIhj0MQLzDv4ZsGPnj44BC83Ejq45FdNUSpoRDB/LG5OMMIvpvNBquFwxeaNb7z1if4wZMz/MMnvwZHwP+m+SHOaMTvj4yT9ZugE4+/+j//M4CAs5++j//pF/8F3g1fxj9/+BvJszBffmHJAyGftJ8TfC2ATI17//i//mNcrTdYX23x1aHHv/ABaO7gODA+DgE+yJEjG7wTebTQI4Mggo8GtnEcEbgBWqsoRY/VlEdvnCsD/CavZg5pHglygzBzvlEYCn8GGLMRJ3ojKrxZmAAEpkUPxOgDuraZCsDmwT5DXVEmwrPymH1KykTQmaHOUpbSGMB1H6jKPZdK9pFnck+iUoy+1c4eZ7y2t+FK10nqTnNvmqx7mtA8AUFh1FM6ltc159U8OutUwINegCP0PrgYC5L1aD1NaQgU1zOdybhtBQ3GMAxoW8Jd6vEbdy/wZ5sl/vTqCyACfoce4pQCfuQZi/4OaBHwpV/+GCDg9MkF/sd3P8FP+Q7+zuVrRfuOJGJfPspMQndQrWwhWGQinTA8dvd733sffT9g6MUb9/3AgFuhY8YFR+/ZSCPTGgDJgJIUe0Kaz+A9iJ2QFUdI8RVTeZLbQSeSPJuvhhlRNugFVVpARqlU+q91TCOMpPYLnJF1p8bVquKk/KGU5EfDqOaEtLQkswSgqpOAiWHN8uei5htUmPLV36YPbqLCTbkJTbNR2cN5FlTWaWWgjPd1vhKaFP8pCeXmNAJHY3bEZwEAxSe9WZDMb6RniGWTVyrPGFMJ8MGjCcCKPN5c9fh4aPFRfwcE4Kt0iSUxHjGjHY6AhnH39QsAwHLd41ur53jMHR71qyi77J18M4E1s9DPGVNsfPDRR88x+gA/ehxzwHMGQC0aBrac57RofgJv8XFiZkKFHCScCrGpI+JZiDEF01KTvuPKUCkNKX5nY428m04LXzdZ007fkHlNezVX182bPtCpqtVpP1K2mjy9RPsTmeYlUt7kOThjmvvGT+36T9QeRtJrpss6nZxEn62Kw3OTyGZyuPpE8uTpKOBuF3DhG1x48cy9Tz1aMK6Y4UILOGB1JnFb24HxarfFGoQPxvICtyRLpuHN0/K9oF4N4eJim0JVdBAvHkBCa/jZmq9PitWUdy3nM034+bR/03rnf79IP/9NqhIVH5/PdJuF3sePMAW34vvNST+ShMqAA2NFEpuyZ/HMPUaAAzCq0EtA24ksy1vgmBg9AVfXMlLVv2cGgykVqEv3vU/FHUqj/b8OuHPLW3GzsibKHeKayPEFDgyfGNj89M0DkIUsjjJJQGC9cMAo60XtVRvVDqatU3aXlcFaS0ImxrkcJkFe9wJDFNiSd41Wn26Xi/Nj+j9lQiZYetUiAeYECUV5mlN9VlTlqpccsiEmtRsV7XmjXlbO62MfYsxz6ZIH9dpzPsgtco2D940Y+kaCb3Mgas0ffAAaMbaRGvdAQHHrsATKH0aPd7Z38J/+ye/gyvdoX/tHQHD484/OsXRXuHvf48HpD3G+XeGdv/8a0AA/2Wzxez/6Dp58eAaGKNLeRwOiqV9VncOJAK4NevrH+G/+wR/g8mKN7XaL/9A5/ING2rrLjCesinbeH1D4E0VejuOyD3CNE7zxHmgc2Mt858DzDo1zCBAD6TCOaIpLNVSBpxyu0MDvMPoUkJxUGa7kD137Il6xGoSs8YljbMlo6AE1FlTtzF3PLaNgWsC2TnvCzQNFq0Qz7zLsqxcWF7hXO/baykS4jd6P9jxeLbwx4mUHUvCwKqLKWn5ZKCf6QbEm7RtRcSRwwus4u8bLb5Rf1MoX8X9i1DOwlI/fGnrAYpTnIMfoApHQghC9tkNACHIjMNfefUFu1AZHI2WCK0JgoQ9PxhX+8w+/hqvg4U7/AsTA+7sdur7H0UnAK8tHuBpaPP3hKeCAJ4PHHzz8Ap5cLPLwNJyAKjRQxSbHJrUzp5siDua2dcUjs8zf++E76Hc9hmHAd5zDD53QshUDa+gt1XY1KLEzXUsEOX6rhje57CeAPYOCjb1KsqMY6WURqy/iiBhTlK9k4A3e3rYcsWkGzwE1DkyfZ3pPaeOh5kc3ThawlRdaRnso3aSpiHdlc1SUZdOHeXWwxj9OxIvn3tdywlyNk4L6uO6r6SDt6duMnMD5q/kw/7N5v3cezZoY457gqP62xr0sO8inepvXHnt1kxpOQOTFtW/xvfN76DnALR8BTHi+G9GQR7cAjpo1+uCwebgECFh7xjuXDdY7l8ZjvV5vlAwxqG0r8kpw5aOHTzGOHsEHvEWEh3H8HfINeofsZKkZQp6vOL9B8begL5ziGxfQxEq30pO8ZkCBk1S9q9NtMNaA943ypvYrXjjLiF8m1QzSPCya4Kppiwe2mhsMMq3DjZXdG9SZSMstJ+YQDl9Xbo54FSUj34vkAJxh01BUzEKczk/dDhOG4PDRtsUIBpo1AGAbL8dpGqBrRowBGC5FfhyCx7Ndg37MveK6aiiOzY87r/1+Sk9gXFxtkwxyB8BVfOvw8gH102ay7VHqjk5yylx+Tur6nKUJ7/tLkOJgPq9jelkYmDXY6UaeaYOK/Cob5AqUldVySgDhMsSLGZ0g78gARVnAOdEPwiD1BWZsA2DvZ6rxPGNvJaOizpBzFvgW0zDmuNAdsmHv87jOn0W6pWGvUvhifGjHgGvipQBBAGcMQUUYI4RFL7DiCFoWWJJnFQEgqZxcRajTFliqsuwj8pJn74lYgABnJLwkaCkNTl6Gc6jOSTgUmVgUQ/k8BDBTRpO6nvCnZFdUMNnUdFLoLD8NbLyKCl6iCrw9DpaNeFl5h1HmszLOjKiE5nWfGPVCQAge3juM3sE1ctxTDHwBoyc0IcTvAU0T4u69k3OfLu7OBYonQBWGAsZhwEe7t/DjR7+BL53+Ae7+zrugweHX/uwcHYCn/87b+B/9r/4M3/vnv41f+b3/HRjA//n+B3i/+XX8xvYHuH/036O/2MIHMbBNRcGas+qMGmW60IxL0vPR48dijAsebwJ45+4xxnjc9XkcQymEZyJZA4JrROn3nkEU43UFvWyDEVz0OIoVBoS8oWoUBlVTihHG9SUCSBazGFJxfFIQxJRVI4zCJYOkiiQEBVuf1on98qvFBgbg1Jmy6HRue5+YVic2X4pju1YgKdC69Hic9lJKle3T9Fk0ugmv4XkDXDFBWZhOLVCVrZIBqaIPE5kR2bsjz63SVV3bRBEQ5c9Md5KiPGfQE0N8IDmqF1z0OovGvBAI3nGkBSRefSHIhUGBwBTEOBBC3Mvj6LYaN4h8wMV4F4+v3sTdxXs4+tpTkCe89vEWDYBvvPUMf+uvPcZ3f/Im7v7wb4IB/MPjc/xj9wW8OXyC4+4djH6IHoQxRmScnET2UCbrLc6IJ9iViqa5FWL//Gotx1yZcYcZT1eLaMwL2KY5rOiK8dKzrES92NWzQek/xflXozvbRSo4kiH+ScrKRCY1ZXhjRifTx5DrrI16WtB6z8Pkm2N0s7yPlV8ZpvlpqiuGRwJmliwumUXY1/IE/8ycl4IsFYah6yjHFOhsm/qI6wdFgbIKLoUbmDFzlQ9mxMnKlOmh5R26KZEMc1bGqzz3AmughpiHcl6pg4p+JDks4vo2HOGqv4Oj9hm6+xsgEE4vRjgADx7s8M0v7/D+42OsHn4ZDOAvFlv8hO7gzF9g0TxFiMJ63uqdDHtiZCdwktcKFh6Tiz3d9j2UYS/BWHcu9Xuwa1RWX35PfDL3pbyEzXbULLuh7zfldZkTzb3V9m+Jb7fUehRVdLNceexNj9PfvJFaDJsf16HRRq47Me4ebFrJyac0njRf+mBPvdruXprF898V66n43+aTJ1kWjt8mOoY591BPQGwwbfAa719EWjByi96v0LoNmuMBYGCxExn8+Djg1VcYz9cO7eUdAMC7zYDntEIbdmjcNh6V3TN/+1KBXpQfVTQ1nVxhoCVgiLScMefH/mJJKbCC6oTeoxALDtZjRjLbyvT7Z5QUuSu4+oVPSo73DOjnOs69y2q5BU3ezD2ZGPmMaHbwOO6McU/LMhNC3BqnRvrU+Mj3W+B4CexGgHoxM105xsAOxAFEYZY/3QiSE72qFaecbBxpwuGzeX8Z0+2O4qrAZw1E8bkjUVg8yW0o3jNAsuzW44UQhZ34I7Oa6AEYghzvAIEd4CYYd/2yzBFoNX4EAuxNtir45f6pAMjT8mrEqwVjY+SblzinkJeEoGpcKnjH5gzDRQJm6wyYlQzOebU2jkJ4fFcaZvNaFka99BcVcqZ8+3ERT0+O4TqSm029bxC8GLbkk8RbT4/rehefNXDOA45APhpx9diZS+IEQvD41W+egV2Ppw8vcfroLob1iB/eOUUbAs63S/zkB2/hxz8+w/HwDA0TyAcsr67Qrq/w6oNX8OT8Q/gY2L4khvUfzBoZK1MBB1SsY2BG4yQg6EfjiOdMGDc7bJjRw8y7XX9Vsoikleh5R7rw0fNBvXXUm0BuwoyCFGoFagpjqT6Fgai0oT72A87x+sBgcoXRJTZmgDQG9jfGjBRI1U7XHDfUdivNNADisLlHU7s9EeY4xTcRm+a6WRkCEvJJmcILME6k4uFeIz/XP8g467CdjjRP5dnc+fq4+GnryfguD3Qttb+cnmWPEUOJK1ogcfYCgqNkcA5MYHYxJieSwY9DADuKnnpBYDcEgfVEV1yKCccc8NYXVnCNx7MnO6yujjD2AY9WHo4Zm6HFTz8+w4ePlhj9RvhJYLT9Dm7Y4eR4hfW2jzd1V5Nj4LQw7ZBe6jCjuEDNAXF1otHNgXE+emy5RRhGDMwx2Latv/SslLUsAUKbY7suSvOphIUplotElRWzadJ6GSiO61shqrzj1nAgTtOT2svGUa6zz+lSxRuq+KNFjqLs7FjnRkbFzyJERcG2axVqpoFUrMY/o0oVnUsrvJ/GzfSb6hflEMpqJnTClCmRfQbnkWmRRQH9Ux7AOkaFC0p4kzYH7BF7a9yDsjCTR4/uZ6YSu1xG8mJm3D1rQS5gux7R9h3CyLjqWhAzhuDw5GKB86sGngfZgGWG8yPIeywWHfpxm+VOKnG6/B77MIPbEXCK53k2CISAHYvnQfCC46EurxjIFd+qmqqPQJYGvrJGW0eh7+xBjDS3ZPp/Q2Y5B3fKbg41vbc+pV0Q/j53ofLLJq6+TKq/UXsGStjg+YGyc/M6Q+pulexY9tG9vUu5Z6GtLpaIV6S3EzhNuA7YGOYMJB2m5Gcl75T2GPZIf15/YLWSk1bDEND4BiEw+ijqB2asd8C2JyzZXFcRAogDmsbJhrkSm2KGpjgOkyORtgPJ1jAy5PQB8/75folU13nd7/11KHW6MYrvr+xl8FIAJH7dH8H2FzLp3EzFjCL9rMZ83Z6M4UKYdNj+rHA/LWE1kJRPMyQ6oNIU2eUHALSN5PFedVTAJ37EGDww+rTVbho/fIJiTtaczXjDlIx6nwWSf07TLS/PkEkPjPinxy9E2RtHDxdP50ncMClVeMYlRpO0m9xAAFiduEJ8QFRQtP20yZLAaSqPQZReepQzGOXKHskyzCsxy7njILl3lapcvGGjKKT+xTbZPqgkvrT7PEd5omCt12uooJ9aSgK5fpo/1EY9lN9jVSEwgmeERrzwnMvHbL2XGHtjUONeIzFrfIAP8VZWPbbrHBoXAOcjJfAS4CLOZ4hl/qP/6D/A8ujL+H//J9/Hf/i7/2u8+/aHCF9/ihAY7/63/xRXf/Qq/uJHH+J74V8CzDj77z/Cnfaf4+hkha//jd/C93/0AUbvwcFGz2AUwKRzl77UKifqTGAGjk9O0D27xLEDHg0jPnn4FABwOfqJKKIEmHV9mRPcpaOxuhPq1DtCbg6SQPvR0y7iju5G6C2TeuzWohSRIZ6soFAKCPa4NYC065L7rqZJA72E6JGlc2Hfmsp1XGYiMoZS9Wnn+5biy2QNzXPLqGrGVHRYv3EyoOtYU5wjJUMpqHlWMLOyz2acMJwSpo3YucgIJ/Y7VgE1Vp5JjhkaT6dP3yhoG4NOwmelOaxry8X8aHmL/8nIzJyO1YYQEMjl4/nx4hyncfgCR9pAcBSS8h+UmKjHWpBn/8G/95u4e+8V/D//X5/gd3/tr+LDT87RP1iDA+OTH/0Ul++c4NGjc7wdPgAALN85x9K9jW7Z4sFX3sInj87lBmgfwI0dX4T7bJ1Kcy24ku/IUq+jxAdiVxfLBZrNDouBcMUel1cbgIFd2L+/X4AYIQk9FuytgZzZco1qnU09Fof3gX1RPsGAUp/yeR77tKyuT1L4TZ7UH/u7gkNEuaA27lG5FLbEDVJNn3IHiPcb86b9ywWn+BfnxMgLdqBpHovG53pFM7+mDwrauS/x3NdMaJSnWNzOwD+tKnnYpO9AjpE3LyfkOHswhr/cbhEPzsAGp2O7jN/89S/i6OgIf/CHl/jGG2/h/HILfzIAzLh49AS7pwtcXe3whM8BZrRPd2jpKZq2wcm9O7i8kpvYObAcE0m0rpqcatglvM7MNgNNIxuOzgM9A7tejA6e9yshdTqIi8gQMe9ptwdOr2uYyyz7jJ37+lOwyKq9csP7mm4wMl+Mkz03b5bzY+Z9nWjPj0m5mj7traQsIGOvicDhZNu+jQfgdXXdJHOxvjXNNd8SrBl5BbDhWxR5yrADaqBP20dRNrCyJMD5dBMjyaUyl4wvvnEXXdfh3fd2eHB2F9vdiLDwADN2VxuMmwb9bsQ6RrhzmwBHWxARFsdLiY8FC1Nm3CWwm1REV96bZFOS4ViO52lYik/LW++zTLeClUOVvCCsluUFz1+mqs9d2sM/Uqplns+4Gzd/Wy7qdIkV90sancOGZX1DyxarmxhEeYrg9GQB1zicn29wtOwwegY72Tb2g8d2EMeXAfHEXOAYdYvgGkLwbLsH7dm+dFOeUZdhXL+0f9nSC1yeUSqKKvAR5FYkT1GQi9dseSI4CzgGuKZLJQH8iSUOUYgxnURwzznnkErFpbrWJL6SKu4xUDQZYda4yVEUchOIWaAzjIbItibzkAhedKnLngpRkSPAEavjVBaykwaiebMQqsxWj2vUXkIpV9boMyAfMOyFqKjbZ6oMpGPWetQG0dnLxMoLroydF5r46c3RW++MUc8jBIcxBLjg4TxFjzWP6EYJeMLH7z/H6D18CPjRD3+CxeoZ+qHH++99jHfe+Qh3ToGGHFzbIbDDbvBoWoAcYex3wNCjPT3COEqsvhACxs25xPTjAA4e7Ecwx8/gxXDmJJ6dfge5eHxV+jaMjGfPLvHuTz/E2z9+DydHhFfvn+F0fYVnux4B4r105UoiQpA6CsXRLlckoDnuAZljkfLnQg5OLqBGeZ0UDGLMnowQRmCjDK8EkmOSjtA04jF4dSW3fh4fL7AeMuywgZkkCJIY9jT6JVt4jcAqH1zapg2s1l5i5SeXP03Zg1TZ4KnGT0tV1USj7HCp+BmjqWV4BdtMiBh/kgamR9Lz9VjbXDeVTlhFTp7XglJ+ydonmEnlnM82VRj1qnqQ6Lb9btbAHK8Tww5nQ7N68QYCO4mBGjjkuFwcb82ORj2i6BFKIW44RK8fx3j8bAMfAlp2ePu9xzh+soH3Ix49ucDjx+foOjm6Sq4BM2H0Ac7JvAY/At6jWXZyB0+8hXq92WLhzc3dPuTPeLN0/Sf4FleYZEPparPD48fn+PjRMyw7wunxCouhx2bk6E0uAYDrZZrziqh5kl3gwqMuUfwKXlT4CrmNwk5pcMNuNNkxNZGG9TGI0XJh2H5mG5OkHln6XWFb+WI6OmzKZDoXR0XWuJeg/gbpGhHM4gobgVDZsOlYTYPyV06TOB9OoyIcuhb26d5YoFwVTb0qO1XXN9ODyROu3hujnr7MpEFxPZOtUsBHwl+h25Qu0qi9+SnF21OjXlSSkeFYSEj2pVxvhhh3E3j67ApXVwNCCLha77C+2qJp9PSEAzNlD3CiuCFHoLYRD2ERYjAMo+A9OG0OcDwez2lwJV4nGm5kuX4YcXW1w+XVBm1DWHQtWu8x+ACVJsfZNbh9KunAfI03a6fiEQZulaaNMYhR2zQ3bKPs2T4YBfbDKVDC1b62JqzrmmSp4k2NaJO6GcXa7y1xIKaOFUdm25rlAZ9isjRuTycKA3exiJTe1f2Lb/P3YsMxe+KkfHEi7UYYs3jnSQ8J602PZpCTMn0/ou9HOKfyhfQlcO6LyrXUkCVXMYwOkrxiN8KSB7LCRrXASaZjiXfdDx67fhS5lx0ch+Jmzs+7Ya/EecOTMaEIn31K8HXYiHr79DkwFO5DdJjn+2SeTyEZFn44D2q5wfyqBYp5MWb6nSv5wMo78bv3nF4Mo4eLp/m8lxjzFQYeoPc5ZwrnZHFdO1Rkp/kaYp+ZETf49RI93i+e/SVPt748IxFYFfaiEKZHNH2QjEGd7SjabYBoNFOwiMcMYRQUzlqLLZ/ZO82Sklp5sjmkbhMLLyorBAvARccKAx6quqVB44KOjAV5p7ISbIw2Fnimf+AcByZ+r2MSMUTRFSE+j1hb1lyKEJlAmGdm7cxiJkE9mHXl9C5WEw19gatbcZ3E2mu8x+gd2uDhgxzHFa89RhsCfHDwIaAJ2SDYBA8OBPJeGD4Rnnz0BMyM5xdr/Cf/8X+Mq/UazMA/+8MfgJnRNA6OCHdPlwB9gL4fselHgIHVciHHgjngD777J+iHAavlAk9+/Kc4PT0pFRUonEZhAVlHE2XApSOHRA7/8J/8Obbc461X7+KvfvsN/I1f+/fx/NklHv+9P8Q/+q9/H84RfuevfQsX/8P3cXJ2BILHX/n2V/DuB4+x3g5JSHEEOVbUj3KxBZcM2oegIbCgyomLxm1HDo4kJp9Tjz8SA92ia8XgFi/dcM6hbZx8tg5NE/+cg4ufTSOGzOfna3zy8Dk2fRZvaixLKBIyrMjzeueuYnkqK6vwiWyktEaKzD1iuQR49v1Mx0xTgnsljVCDOpLiqv+hOD6Cas6zwV/6JeOvlZ/M+WRcUrei7j4GmueiIDsCm1EZZVMgM95y8FPDYTbw2WO4STg2Y1WYL7h2XEtGSeM5ZK87NfLpjdMhOLjgxIM7sFygEd+7aAiU7xyNgA7MARfnawCMzW7Af/Ff/j76YQAz8NN3P0m0joiwWrYAnsP7gGEU5aFtW1Bk3O++/yF88Gi7Bu+++zGWy0WlCJj5qEBKFZNsxHX4ix9/gh4er5yu8Pr9M7z52q9hs+lx9YN38Rff/ymICF/70mvYvvMxFssOQMCbr93D0+dXGEafPAAUL/0o8UjzmmShPP1k7UeCQMF7Rwmz9HfTNEmpUkU+43z8IyfGe6L0CSJstz0ur7ZJ8dflTzwIWbQu4cjCuVHI4gN73NhgMGDr1Y2GKizHHDrX/Hw2mbWdGvmm7dTSeOmkM9NaGnJlkstTAW1mb18LYKtkgkqJqeFyWidPJkt5mH1S831GxnU5dhzXytBtPSatdDLJSUZeKI7fMpA2dOPmZZalKLUGALvdAAYwjh7f+7O34Ufxnn/67CLCkvShbR2ALTiEFGPLuSbJf8+ePU8Xzzx7do62bUtZJ02RXWQzoRmTAAIePb6EZ8bRssXp8RJ3vvYF9MOI/pNnePzJMxAB9+6eYHx6hbaVbaw7J0dYb3v4uCGqVbsYkiPF9TEwobQ0dQOZj+iz/FP5/DQPKM8VRVpRfyoeDGNA32dadChl9pthY4LDli3X7+r6WMd4bdO3SBXusMGdG7ZziJ4U/HyPce/6mYz5dPy37N/hSiu4rjo017dC9lDcVCpg+JB1LEi0AFTE1pqsuspGyHLSMMrx2cCMjz95lmBvs31a9MVpuB02eEEZ7rbbPslqm80WzuWd8kNzkBKl/wAAV+seAUDXOCy6Bg9eOYEPAf5qh6u13Mq7WnbwuyEaA4DlosE4hnzCQKsuxFK+dg0m3Zrp3wQ8KvArjJdVAd0IuSlsfqrpOkLwgpWWfPBTrfymXbh5nr0yz0s0/SktZoH/iLxfkSzy+cLmYfOkcjm8mMhwnPCambFeD2nF1pu+aD/pTnO0C0gyMQAMg8/8rtZxrh2plBtj/F3xyyEsF43oKGPAGD0DmxgiSPlyE7135/SCnwVO0d4f0zRR9a5Jt/TYy4KKVZQIFC8L4OhBkb3SCGVsfEARNgqeKqywCpjajgCVEv3cutaRn7Gpr+bg1kF7P6GQ8kK4xdMkT6RBD1UgeJ6YUhyP9F0epN1rI/Qm4wOZurNMiKnBE7Cu71Ycmybte56PQtGt/5C/qwqQDGAoLd7pOB5nrxjx2NNbcfXobcg34nogeCo9aOLFGy4EkA8AOSAErJ9t8PSnP8ZvfPEUzy/X6FqHzVGH3eDRjyP6weNqs8MYGO3RPZAjtC3wwXsPMQaWC1uIcHGxkjaaBvfuLdG0DfphgKrJPsgNknqEVS+6KNc1KwJMDb7z61/C1WbAMAQ8e7zGh5+8i6ePnuKNsyU+XBLur5b4W7/zG/jP/uUP8bf/3b+OLW/xnV/5Fi4utxhDNEg6Qts0aNsG222PTb9D3w/YbXvs+gHD4MWTYb3DZtdjGDwCBzTOoR/GJAzp0Uftt/XCc0TJcOdim865FAsxeerF744cTk+P8Nab9/H2++d4+8NthiRjFNKDwxlOIs5xPiJu4bQ8gldhEhsPRDIwmlWhRCMKMD9E3NjiKZLwX1w4E5mN7jzXsQqTF4s9bhvrVmMfYL/numRuAD3KP9flhM9Kb5D7oO+KUGTV8GHm8ZCRz3rspRpUkE5G+/Q44nikb1GxtzRCvPU4efAFkhtyg8vHcTkg34Ybb8ZVL7/Co48Z26sBl08e4827S2x3A5wDhqGBD+LpOwbxyvGB4bojMZI54PnlZTIqAoTdbhfDQTgcH7Xiteu9mQddz/yZDZ52TpPKjS+++Qr6wcN7xvqqx/nlGuvLNe4sW5y3wHHX4ptf/QK++8FD/NK3v4SRR3zx9Vex6wfZjFIcjHg3jh7DOGL0HuOglwsFMSJE476PvJOI4i3eJZ/VGKfp2L4a9KLhLn23in66VTs/Xy473L1zgt1QcWSuf1CihQkLOfM/Rdss2E7jyyY2rNOtF1YZnJtH54qfTxIVeIDYt+LorK2c9Kv1FSTTL+0nJ+/Eog4j9KY1MDLGIdJUUJdacpx7XnYtv9on7Cqez9Uz6ZBdTTJjijRRjXMQmpSMfROZgRK9Tpwg0qz6OO7Ye+zWV7i7ajF4DyLAO4o3acst7BoD1zUddG03uz7BPyAhXtQTb9E1AFFUDIwkmXgICvorP2pRnXD37EiODjGj7z22uzWGfsCyddg6QuccXr13hvefr/Haq2fwHHD37CT2BSWuxY1tb08wRD6tpxd04zsvXTYOFvQJhhopPJq20u84xzaUjBoB2rbB0bJDtyrF+0PsE7qWungo+1I/KMjnXF3XNWbSdHWuqTytNN0unt+BvHk8ugg3rfS6+l6+kjmD1h6KUD6qCRkSGZ/LlGQBADmWuObJjCBVYHmWH3osO5c8bkO8ZC1vDmhNKpdBPGNN15myacE1uY30fsKnZuaCM60GZGNQvXv9GOIFdwEtASPJZYpHqxbn/YCjVQcGY7loM64mdiP4VTg/hCxTBKP72mljzn29DifmjPnl74o/OaBpCO3UMfdgug3K3Kiyl017cTJh+afQyGeQ9vH0F6nmRvM4zTSDwXMvKllmJkv9fia/bN6HdAkcqvL7aHhUGXNXjFEh2/OumYC9r+VFY7x9AwPso0wNpPBkjSMMgYW2sPx2BxYun6bJ7WdZ40X6GtMNYcVmi3vzN063MuypjU2F+3w8RwQYjsJJYAZCvvRU0bPY3lblOdahTCXNCSMdcTOyIszrvb9SM6bemkga8xrqN8VvPUJE8cKDiTAcy1CGYGt95jRsPYiWCVYWpMwYEjMocxxSdYruwiJJNlBOBfSa8drn1vCXU/LCiQJsCp4fhVj5lGO3ow9ovUfwJMdx7Z85yts4OR5LgbB+doH33/kQ908XuH+2ylKIEYJ9iN596ZIOj6/ffQvBe/SjxzAMGDxjGL0YALslHn34GCenx9nQ1TQCxy4qKZyNldmTJgtCgHjCHR8t0DcB260cDf6zP/sJvtT3uH+ywPMw4p/8d3+AeyuHP/7n38VAhB997x0MwwiQQ9M2aLsWXddhuVqga0VBabVPsb0QAloHrLoGDRjjyBi9XCSgl5GIZ2zIu3WJypbKhij+4rVHhGhcdMbA18g7R2icmxzdsUoTiCO8x5sJDf4DyDs7nONuJvUgzqdlBol2cGbfKL7dVmLIHnkUkY5tv4ByFzrib1HDJB4N0m9V5hJOURZQ0/xg6mk3xwCToIeqD4locVnOcmJTp/S5HkMWIrUp+c5p3eyzRKMi4dGjk4pzXHxXo3+OsSq4ExBCk4Td4ABnDYEcDYPxb7vZ4dnTcxwvWhwvu8QDoH0CxOhOiJdiCF25v7oL5hDd/j08s9AWZji3xNXFGovFAuQUB1z0ZNG6kceUaGOEFWYAcjS96xo4Ehry/NkFPv74Me56j+NFiy0H/OTH7+KoI7z39vsIIDz86KkY5KJBr3ENXNOg7Ro0TqKGW8ObrpMjCUAsG2PxIhouwyQEnq5p2sWP62ZxPv2pxx4BpJf0pHd7jugxskOX/rbPDaxM+arh7emJUUQUdiucm0+ZDsy+S3XVhjwDy/a5kXWlXuW1VQtJFqn6nUUQpE2OuWHU1e17FSeP6+dFIaoflE1OhE42a8Y5D5cFLR3MhDDSPph11g3OOFsZd9Sj18gOoLwxizxvwzBis96iax26tt7e1S7mvor8yDjuVolWiCectiNG/H47oGmbwtgFs+4qh+qAc+RhMwtEURkQw8R2s8PFxRpHIaBrCAMCHj9+hq4Bnj0R78Kri40o/LFd5a+NE3wDYLzq8hK4OMfKH9lMYKbTdv20l4a/qnBn2rD8Pj2PtIEIWFbQZfF6TukzAJIeTGB0guP78PTmqUYlNl8K/jdTMsWKOpivqvxAvlQVX9v4jVKWIW5b0EoJk1dF/cUnzFJWbctvzaG6S64xkQQgx0S1xNPG2eIcZ8/7gKEf0TqAncVznv8aO7twTcZVy58hsOxHCZOTYF0rMLhlG5jK7ZJBQlTrkeERu96jY6CBeP2v1zs4ANuteBfvdqOtODmXOAMPe9dUxImCLlucrufDdLmQ5QxpNjKrOXUCXQ4VcH6B0zX4m3ju5zzdlhby5Mtn127GXEPXjT6U5aj6GSN5cgdOhvvcHhUfdZtgoKGIFKiGGhsJIdtMimoMebrJFJFRuDzniyH1sZ5U0dh+E4/2/UO5WTrQyXKO92Q6ID/eNt3aY0+Emtgs50X33kdPjbjLm4TFSkW31jaVL1kqVuDLk5CP7NUDnZt8snls+wTjOUclgUxbU/F5LCddzURZjvTJeLOHklLx6pnOEyKxpuwJU6QJwGammwdUWp3nx0vmd1ThDdWYGu/Ms5CfqeFtn2HPBs9nNjH2gjmeG+JuNctNuCG0crtc3LVmH8CNS+018cZMhICTsyW+/ktv4Ud/9h6uNmuEwOi6Bl1DYl1PQizBB0Jgh6YhHDcdyJG5aVOZpBgR/+if/RnaxomQ3chx1rYVzznXNFgsWjStQ7fo0LQtuk6McE3byvHVtkPTdGA04JHRBI8GAWdHHXrf487JEq+MHldPn+PsaImLYQRzwHa9joaHuF4kvphytEfWKx+zFcRiBrxZL/XGI0AMcERoADTEoCbjTkA0qBOBOUg5UIwtKLg1jpFUF4qBtOsIuLqaIXQhQyYQiaEqwFZLSPhsPdeMt54VWlT5sGB6ILB+nRTuS0Fv6iko+nfpTo5ET+bizqhCm8dlheNCULZGsJidSWNFZOaZ6i3w2uBrTdRsG1qiXpYZQdD+TuuSjFhQqRmqPKtRT8esDWePPUsz5jx31PBkjfqE4Lgw6qkSm8sELJYtXn39Lh5+/By7YQQzy9HwhONxMlgFcjmG3kRvHTX6ZWVY6n7vpx+n3USKdakRm+LRdIpH0F2jR9EbifNF0cOVGnCMl+k4gMBYdg08e6wWLY5CQL/eYtk22IWAgRljP4gxLoMNgHhENq1ZVvjVcCRKfV4jpQUE0gvCo1Eg8y5O4KCMSeMgZXhlAPBAusZH8S3+bFpCd2zhicv4nED0Zp4RGRXJgELcVjArleycD/X7uXQDwabIojhYFcoGpnnBoegrVaOcG181DVyMqO7wFM9nupDqrMc4V2IyDweNevkzYneSB8QwpnSSk4ciIr1OZZUOTOLsRQkjkY4sy1kDi/a3bR1OT1e4uNimjd/Cs9QMWMiEzHfTuEwbCxlMaNKzpxcFXin/TLzMHE3P3qtCB0D5aHqi71HGahvho11L6ALghwGtI4wst+PqbZ06T97gleVFBtrNOpnNGkP/SyUJhYxYUfdsb+Npnbm5jA/j0mOSpsA0+UUGB7JcPlNkHr0+1ZT7sz9HwvaqX7N1FV8Ot6t4MZ8Mnl9TWbIR2qJ7G9Wve9q9Zv0mtCrKE+pdVz+3BWw/s0MCIkxnqke5MMByPH6x7LDbDUkGL7xKbfcMjVTjeCnH5NybdT7el43XkicZtMnyVsFrm2dOeXRRLnQkHnsak0v1Ha6nVAhRFIWVtk+Xkesvhv1O8lKRZW9SPC9/27KcTh7dJt0ABX726UCnDPT97Ppzm6S8b6Z718LJLRooyu4VQuimWQr6ui+/PqfoITp6Lvs+A8sWTlWm3UcHx5ETv6nbt7rQYY9WKqeTUeRVvCYcmPZKfvk0042g9rqGb9GxWxv2kvJByISOjcIPaxQiqInB7elVOuSnwrROfrb4TVJkCwcZeObMSFzKeqVECTYZIDLiECYSqhoD0xGVCEjJm0/zan1SLAeMN/0oKi5GMyNilaPOzLUWKNgAr65BrkeVa61V8839IX1KngBh5M65pJiHdBPmjHGvqk+89dSNl+GZ0QQ2HilyOQS7gKO7x/i3/vbv4O0f/+c4f3qF3//un6PrGtw5arFqHc7uHOF41eJk0SVPtIYIgMSYG0dE77Mo5HM8qhoIfpRbmsMuu/9bI3I/jFHQyIxcFAMxKoA0Tl00BGw2ePPuAs2pQ+OP0Q0j+jFgOa7Q7AasRwnOu9kN8AEpeD+A5IUgR4ECAhM8Z9al3jzJSBbnD56AhlJ8PcRlZUM0ieXYohg1BHbVOB7DX0qsw7ieGvcyCWLxf0cx/gAhNk4JJpJigdyJhI8ZgZNkKLhiIDbiZv3Mxo6cYxY1ZlgmR5VUXxr0Yj+skGcVF4WHiUCsNImycMuSkVImpHoiSalZTBwRl5Vr/y0ZM/TFpnp3OBniUlYj+BXGvfwse4rkPiRmZ48Pp6rL47NkPp1q9uYvGwJzHK55OkNYHnX45i99DU8efQ+79Q4/ff8hmsZh2Tl0DWG57LDoGiwauS1DY0syxMPV681aav3SzhPH+7cYPJbzosn7EJVpPQqeDW7pMg1zfJ2GHndWDdxyCRc6NJ5jrNAWbgzoo8egxssMRoHSeUjHd9K6WK8HRA4YgUeHkhQUGUctnADIHnlW49d8iAb+uJGm8CdtZSuebmSl8BMZ1ZHBIXutJxSPSMsWr3LxikVx8T49LhLNLddslqLsHuPeTCdmOpBrK+o1P/ZVNeXg+n2K53NdmPX6oz31WkI3SfUznrZt3zInemxpGEc6jcj/Mh0zI1LjnjYR5RFBPTb0THredg0evH4fV1cfYTeMePx8LeEonPCotpWQEW00uAkeSn0ubRBlA4HSlGKTNIQpTADZsy5PeJrTtLGSjIwEeI9V50CtGDUpHrNz8buPuOz1+F3i1hkvFX9qaFd8yfCV8ZRQw4/8r+EGrAG0xjIufiEDC6ksMAMDiZDkOc3tmvoqwJ/1mZmZ+Bp+P7U0t8hVhgynGWbmqgFwjbFwpkDdlcLgd4hw0TQHzzdd8/K5DEWre7IeGhpHGKFaqJgpm0GAy/VXGhh5XdMQTk6P0PejhNroPdR2Lnw6yqO1Yo680WCq3dPv+fmuNzdTPbatCAxEghOtbrg5ZH4GJENmgc8z86NfrmNXmU5OZbg0x2bw14HjTUH2QFemeP6C9X1m6UCnMiXS9Lnr/Ty9qLt5LeC8WLM3oc+3qsTygBgWresaeD+KI4m5BVSLzcI5l6u2t21Mp8baMeYnzlKrsn3Lc+fPC9Ty1V+edEvDXhZBEruKUOt9MIFROcZg4jShXJWXn1QdxUNB8QpF3HQhMUCewqHNJ0BFiYtHWSm2qcqMvtfKc3wvIcjyLG9OZ+G3AKo0FkocQr9TAux5wZwAucGlNmaq8AWAEGa89rjOmhTIYpcnKfucOBaDs8IZzHfzl+NtqLIu8bQCqSEvX5piPfaK23b1yC5X3j3pEg4CXAACgVxs0Hu8ee8YX3rtDL/yzS/DhyBxqkaPYehx1QcMfoD38Zn3cAS88/Eai4ZwvGjQtQ7L1qFrWzROfi86/d5MgsyHwGiIEUjGJHFzsrsukVzokuAC4j3nmw7cLXC8BE6il8CJGuacizf7AcF7MIAx3t4XvMduDBgGOVK43Y3ofYjxAUZ4D/Q+YDcEMDk50suMtm3QOaAfc5DxMWTlwTlxfXYkfQCRGBUjbMvtxoAxy0ehC6DAALoE/ap0WLjLrthmpzXCPlRhjJqBer+pYUA9jrKCY+HbwnOWmhi5gDVtl4nKutL3KvZXnKSk/xjFxbxOdVoDhnofJkHXGDkkez6iN4/lZXfTR5E/H9ivjYxlUbZqjMlnJy0roElY1X+FkSkNL9NFzcvI9CB64KGmE1p3MurlMkpn6o2DHKg/4M7xAq+cLvH6q/fMBUwBPnj0Xj7tc0fA04sejaPoySvGQDHEidef3PYs3rwOpVeqhBNAal+9WRM9p2oeo0ISqAGaFl0LLKLSsmAkHEs0NnosWHo6+pAC7g8RbznmDSybH6MXmApeDBWNIzSk9ALZQBhhwEUcFxzNUKH4qsZW5TqKFxTX8LokBiCkCvJR3Nq4h6TgZSg2sFr8wCQP0usbSFdzNCS+mBgesmhS1V13JNOwKW1SSYPK51U1tvZ9o6gdf2Y9NubyzKwVV1+UnukGRqIOagnjeoaUQLKJO4q8aRmfgbPMwCav0g5OXyxtNBzSyGrLrsXRssHZ8Srhm2xwhci/8qagGMeB9dbDkW5UGQ88UvjPhrlsAMv0q5C74lyFOFlz0pgYPEliVzkkPtcozzHyWcJ3/R7xMxgDYL5MIxv4fWSmHGUJ7bvG0WRTt0Ikpf6lZUswrWtvkZUs03zBZPmoPtlr3NP0ck0e7sstS2QeP19yOr79bc8+n3mRlmDa0sF6s7ZxuCNc/U7YlcFstj2l1VOvvZy3OKILzMtrZjz2eeIFLF6vjSMsO1fQJYF/gzdpPJzk2MhKQcibWpPfyA0rDtezy4AiOmDkdk0RS0AOcIyk9Wu8rTnaYA0Miayaz7l3tlVLead1TtNcHz6LdHvc+vknNt9y3z/Ho3jphawFg/25aO83xdN5Ol7mM579e3qkhnsbb6/oWBY90oNDrIL2vcB+PLe1JjmXZ8pVstaknj2yXDWcmbb3vPgcpFsa9qKQBxjhXqi6Kl1JsSOk469KSOfYmXxN0grICImqXLBlzmblUg3VSomAGQGuOmYLhvEGrEFGFeuC/MY6yTBOxL6iPOZb1Gdrmc5AmTMzub1JMx+gXzr3tt/6NSvYMZ/xmLMxnUovHTOfsX4NiB9C6bmXDXcBgUM0FoYsrNpAs3qrnGmPkH97H+DAWHYOd4+XUblljKNH4KOksI7Re8Yzg0PAq/fEEDDGgPTee4zjiO044mozIFxt4pHxAOKQCFOjR1yd3JzVuAaLVuJjEbkUDw9QLyHZUR/TYhqSESmeeD+EeLQPABrxCGzjDiYIq1khRhZMhCC50UcNGwzAe7kBuB9G9DEQMBOw7SUgv94cyt7DOYoGBYllAnLox4AxiIcgAXKzMSC7l9XWEte/opGwoaxWcNI0slKRlMXCuCcAJPgsc0WmEWt/mwK2fkyxJHkOwbSZa4c17qU+xPZrwaq0q+87lk8R983mAPRYcQ4boP2cHVMl9Am8ZIG3JpVWmE3YPSEVuVIVpDN35fSXBGqDf0p3cxval/IIbsJXZAWWzbNUOtJDTsif+8jQNckxMJrGYdU1CNwAYHgf64vlssIsdZ4ch2QUSJ8+YAgB/eARek6x+ZKnGpXGgDZ65Okt24h0QIksqZYRMp6HQklUPAdSfEUAQAOlE3qsuMvLbFc0cRpLE9XTXQ0dPhoFQ/RMGj3HYPxxJqPRPitKAqMaAiBE1LSGg314Vgp3KGA5Z8ovDhn3tL402kMNm7zlDO1vv6guwvqs5x4Arh4m+cDSHFNv0WcAGg9P3xTvNHNRcKbb9dhUwaXqeVVoViKo8ElxPuUv6AsXz5QOZiNtLBfHoBs2spEZTykUTZcLmfFZabh5x1aOYzRE6Nom36geGIzG5Ff6JH1adFYmked6+6xeOGM9dhIXjuuaL5UxR3URlRFLVKtpLQ0lKJAi47nifjw2T2IfmKyhyomxBT3hnmmsvLQ0Dshxidl2xoqtOvccPfGFaiQ6sP8I6S1ThWiWtx7OOwPzL9uNa3DsJn2qX4FL/voyqahP04RY7Cl3TYY6z0FDYMLFw/WRXVc9vTQpNTUEKP2YMgihrY7iDZS279omTDiJOITWcfHM0rJgNqFrgDJSd8Jtss+zsFkPXz65ejD/M4n1hWwAo8PVMK/0zVRm2yxkG81uy5hxwZT71ybdAs9fhCT8YiWe+WYeXDfwa+jfYfPd/moKHEudK4WZSo1BYQLch3MzcF6j6RyPnUOPutxNUShRu0Oymda4p+0a7+de1/Nzq05ek25l2Jtze2YwGhfPXZuu6jFOB3O8LBLHZDpTqmillRoUohKdnhRCRpZwEpHVGigbFFM8mciMUmDcqtkEm1z1RsuWDeDQsmUTYSHZmRzmUVKOyveUXmair0cnU58tAlXrQygFyGy0K3/XBj17bC6jf2S2XN2IO4mzp8a9mN94nMAqr8ZL0BoZHr3zEZquQdctcHa8wvJohdYHjN6LgQ3ah5CEXx+l5VUIcjNVCHJDLjgeg80KAXO8FTcaBMX45+OlHyMuR48QRni/k74FL1AUFdfGRaOAI7SuQRuP57Zti9bJJRkNUQxY72J8PJKdeucwgkBeFG7nshep4AnFNmQLkcihbbNhTBUV9TxKR3chsfs4+GTQ8d6DIDEGmVkMnp4Rgsc4yvxse7mx04doCCEPXMzjeUIDPcUX55LABQ4nfCXAHvFLx7wSXNu9FoMiFQWt0bROWb4s2ZQeOQNlQSofkdR+ly1JXVmQ1d/6TqqLcB2VYp3viQef9q8ezNy8shXT7UaIKVNUNL8+hTEP1vCGjIMJ12RelKZwHKd6XsYaIlxUMfYCA87grjZrfiK1La1IXqWjhOfPL+AaQtN0WC1atF0XNxgCnOOijnxJh47LRQWZk0et8htrBChvrcuhAkII2LHE+wz9mDsfJ7E2Atpbbpt4EYV+JiO+wgwAMYBT6n82eOmayhyrhzuBIPdZWOWEkpeFxXP5EhJHUOMoR8Mfp42O7CE9+BBvH5V58eRQRt8yiwYZM3P2000GnsRrtCd1DErj5WeAXWu/jWJeQ3hRZFaplOcT415dWaJBJb1I9QLYb+Cb/JjWP7EGmQxKf+aqA25xZCfLA+a/9M7KAmyfofJgjhmyUQ85vIh636qXbpJYrCRrfpPWVXpUbzc7waHGxYuiYvy8wEBjx2E3BKQGp2NgztOqv9P3TN+VzmSaxxiZo9yRccawi4RnBBTef4U3oDDfBNZK501EhMlayVxSlNdUEnTJ0yFlSnRjCjfWAGANfDpf6sGbZDcj4wBy5PBl0j6+OzX1VIXKYRSP63Qdf79B1deUOdBXU++Nj+beqM2cXsgYua8yfcTz30tiUrapJJNVB7JIq3Te0nKUc8Km0vK5zK9cEhdxR0PhxBxW9tNqkvzFKGJ9TmJMsuF76beOKffK0gH9KKitIXuK6wAS7uv32YViO/6qf+nTQlmu27aL6v1kaU2/C9wHEk6nsaZ3DNp3zvCG6VME/Z9bugme/6VMNZ7DQtcNBa1D2dJ7+aIXTwCZVijaWIqiZcuqTc9q4Wem3X1v5/DmUJoM75ppObhpUmA5VU+m2fY1VeD5gTxFlbcA71t77CVBIlJQUgVlVMEvenVBdjATUTLCY1AFsoAMs+urxDwdY0WeIbaTmYceYIDK1EFgczzQeNZZzpWYnTK+3GfiouNgFfrSkmhjqdG8aoULkPY4H3ZLzG6v1GPLc2R8WThP/S1amFsxoFDwQzBK/9xfrFcZTTR2wJSpj9/OxdkLakRIR1jLdqDKuC6FD/h7/5//Ak/XHs41ePP+CXaXz7FctGjIwTtCgAS8d46ycdAYDL3cOS+ebNH454NPEy2GQBfzMoAuGiL1SJ54yTHMLTohJA+BMdbbe4+eA8JuBPOIwBsgGhMlKG88TheNAW2jnn8tusbBuQbLRYcAUXY8KApGBB+ciUdCsQ8MivGJFNIbexNfhEkVqrhpAVAS9JMIR3EOfMhzhwAOgG+u8INPLiqYzADtiFDeF2bgN+EwZUWx9tjTnluCn467RUJpcYEs/E4BPHnlJVKieJhhv/BEM0aWGu9sPD1Vw7K90nj5ILbFFv9h4j6Zo2Alm9uL5yrYJhKnnTMk5fqUpUDBM6XT+o5hBcOE3EnijzOWNkC0L+o9RvEYq8Ffww+KTqrCGavX1/oXOODP/vh72PSyHneOFhj7LdpGfF4CiaGaYoxLMLKXj9INg89JyU0XPqDYvJBQIE1xwQVDYn8yYC7dycfnlI75wBgRwCMDGFP7kUPFo75IN1s7NcyTS8f927YRuHKU6CgRwXsyzDpCTVyOIh5RMpbk/HpDX9MIPeB4ORAgHoKE8jiwVXw2vsVzn4YxSQXbSRBkjHsJzQQpa4VQcT+Py9aErOxikmVvmqttXpRXDr+n0pr9A9NjsQn+5w18fJ1QWpS05NESHIUg82i2yvl22PxnYbp8ZhAv9YJT7wqvPeQ5yZ58nCeHc/dz3QIoYvhHwtVMO4CP3/sIg5dnq0WD4AcJgRHHz4hH4PSGejNPibboJ1v6lSciDZMz7bV9TUqxPjPGMW3DehHmvJxERMGyaAQ0xj+YZ865BHsGa5IxpbidN9WbB0NpArP4qG3rDx0bEZKvY5LXzHwBQLu4nXg/xTGDJ1TnPYhlJdgewG+uPm9ACgoYu1H2KDwcUowyPuFgvtsmS0YPVjsvvJd1pB9TmmDhfXbTYq56Ha9tvy40+9siuXy9eHYJH8fYOuGfGo+dzbzKcXdLhEvyZPUzrvrFcRKTPGsWSuowsl38xtUcJlFFX1SXRgH51EXCQeW+SgdqALE01/LzSHzSpi9m1sPyniyqxnkqspjxsRkkof0UAPaWKPWzSbfF8z0SwS9u4um3w2LH3lqo/rFnbrOsAoui8o6BXT+mZ3Vxqj6nPCM+5j0ZUj4ucGG2k7j5VEzy7Sm4Dzf3FeYi03RCD0GixfO5F5mCm1e3AO1bcf7A8ZiUKguwxDf9gnrLJPOf8fwKkLh76eY1LZ4IPOX6EiSQuc/CHufTh5TLA0WsOzLQafullSQlPhFm47XCSIY0u2Ot4lkSIxmgrO2k+nLKBkY1FMB0v07WA2KSUp+mxRnTxU8CJmcBVr7biy4wMe6J0KvraerTeooYe1VsvXQMNxvzQvQAUAbNWi8boYQZm6tLrK9GjIFxsmyxOX+Gk2WDpnF4vg0YuYFzDl+4t0TbiGh7uSOgaVPAfQahaVqoB4keWxNjpJcjLtHoJ0YB2fISA16D0UVFvxUY0COCYnTLHo9AjtkTGOnIsHoQeo0/iICt9+BdAIc+xuEK4gUY8UGMchL/r3EE17RwrkHTtiA4uKZB0zQYnUPgeEuwc8ngpUeOoLBpjEUcfydBJa5vAwY1AJjADuiipyAzslG2BGNkqKOET9OYklmgy8a9mIe0T5mCZfzn+aZKYDY/2eCKjl/xMQtBatRXGMvCmh0LkqKl35Vu5ONobIpRyi/jUhrFxpODzWDmE+mEzwz3RskwQKvAFh4eCcc1Uzb0xF+psrThkoTVHCNz71/MmeiILr61HFU0ZOh36HcSU3LROgy7DRatGLQ3o+AkOcKdo1YMZAzsPEEvsWnjUR/FgRY65oifcfNCjX4y5gwDgQlBjbkG7gEZfjIMFnOrZePMsKy/GtA8MwZmwAcwezCH5G2u0EiQy3jUEKievS55AMZbfEmMBC56BCpIZnt/KRIke2w0DiQbAasHIjL+GWUmKQmHgC8x6fRl8sy8SfNV7srmt5MnNwH8GociH80mz7IuNobFCfol9h7XJuJzcSyWTWWEsp16sDPpEOnK08LzefbUbUXIjJ/ItIbLahPvr9oo9WoBikTyIn9gpjSHBXiYuvWvXn1DVeG9hx8lPjA7gh8Qb6EGBi/zSgCWiyZ5x41xPRRHAMQNhwLCDJ2jxKuKsSpoG3lOSpYTbOWsPJ/5OZt8iW6mykOa+wIfwYUHYLqYh8h8R/TmU95cUOJc1wyiqHEj8yMu6QP2gNGN8Pym77h6vKfwBPhn+lGTlWu6CTv+GyU2NOlwIcsHPs10kMzxtFeT/Irn+vO6Civv3MTak6HTYK+lCQV9QJ5wVPXF91nml4dO5bB4OYVeokkgtI1qQ/lOtowfQGqAShqsIY/0O7jEYkujCnisxp9/UyqnsGHrSnVyzlMLA7Yq0t/JE9BiMhd1GtHS8Kty9Ys8RbJe8THvpwynn6s0XdRrstu1+UWemJsIRSbrDF4aQecgfbU4P1Nk2qcCFYwOZnI6IAEmmzVU2J0dndLdCWZX36j42FvPNY+uz3NNIaozVTuz88UPw6SVol4Gem99FDcJkOlInvxXMBnO6MWk77IRLRvn8nG2JNxUwoI1AAIwOmdG3XIHAxGAItPK3EyEqCgRUXQ9yMcFKT5P5iyUvTZ94bgLQyVCHIprkbwGcR1DRh5LURmhBvK5trLwEvMQpoY7TJVzVL81T0YyVvk55pGLLySeXkix9epLOIp4OmyOilRLlpvwctFE4HQN9hoex4sGQz/Co4H3AZvFgK4RgeDiYsDFjnH3qEXbEMABu+CwaFssFi1ADierFowGHkBwQNOJF416HwL5YgjvRVgfx2jgSx498qdH3XRuPVu4RBJadHw+mLgiMWk8QgmsH+JR4IAde7QU0A8B3u+AsBFkY46x+WRN1BOQGfGm3hauaQByaJsGcgy4QRONJXrLIAMp9pfgHoHitqUPBqmNUpPkq6TM2CXLgE/pezauzQkySZy0DIENOTeE/yZCvhLYbNxDwnuj95h3qVVpw7ju5H0FKyIkjpONfGpcV0Eu0oT4qsTPA/heM6vi6xyOmKTGG0sV7NgKw1T6p8uoa2kIl2HqxcxW+OxS/TC0o+yzBRNLUbVWUhrC+YKaHoxFQ4J/TAgjMDQ+wet2F7AbGUedGL/AjJEjLsQLMxadg+5269F2hiv6aL1bGBJn0j7XsYVoodWpUT5m180aEzRet4EsmXM9FqtzGEMVOFLP4jGuhx75zzCQjjWxGDFdNAQCcrwRoOQtlAwFCq5ke5F5VT3Oss95HWEVv/SIy2PnBd5XVWS0mQg9Wm/+ZRueSzOUYKaP5Tv5UrZTSaEG3tOlA5XgSXPtHELMa7o+nQkczqCPqXzPs3nV2McmKxfjzURYBqrel2mYidFrCA62Nc10DKUyUBG/jG/yxHvxNhdvcfEEDiMnD7VxYIyB0TUuGYcDqyFcMrVNNngzEJ9nXlKOXb3xEwHI41AaboA82w/nobI2IOZ2s9E/GxpF4g3xghA7EVo7xaVIl5Ykg2DkJzGcB2IeKkrnShQnFbPqE3o3AtkZVE6gM0Gy/H6i5Ozj3Dz5En8mYjXF5f1dvfURWuWPlLq4v7CVpX8W6br14YM55t6WMtF1ZXO2WyxCUY1u8gmMhoAUdx2IMq/yC5IoNxKSJjcl4J+PvVvPdZD5qIwGdinnHCLm1jIPsQR4nmZADeGK74UckOQxNgKQwXXb//jNGu2tJ+CcsaTsSTm2TyMVLOLzlm6L5/H/AqZ/YRIf+HWgyI2HyLCa/cumhFumo2p8B+bhs+4uVfhl30y/2Vpmfs0M7OBY9/TvujQrM9+gVNGXA7Lhy6D1LQ17SAKSevQgKU+cZUUlaARo3BQgHsFFMpOleosoI4SCyU8ot/40u8lWiKVYXmN95aNDWUmzxofU3owLXDYEarupcSPsWvEs/lbvPSstmjm8jtHOCVITrxpMult0rWAMjIkhj3Xa4jiywU3z5MJWoSBQep+89aKhiwOn8srYFEwUbjLJnR938BLvjgDxunEEB4Z4wDAIXuLVsQcHOYrWQG6ZDV6MbMF7+EC43G7RNqLYL84aDB7oRxn7neMWz66iFx4ITdOIh1zjkkfN0cIhgMDBpTn1gaORTAYmAeqjIQtIN3xZJZ8B+CjkKDR4lhs7AzcpyL2WkePJlAQUrT9dThIvBumZwd6LF+I4IoQewfsIH7IOZ6+e4cuvHoGI8cEnW9w9a7EbSG7W9Q1aR1B73uDloG1SXOxaRu0jQaHCGRDjKqnXW4T9iLt69CIfqa+MbVAYN8zGSBgT0m3eqTduVkQqhpUtD3E+cvtFrZam2OO2OgfIShfHTpOhMYD9Xgl/c8mSBn1kmeK+cgdSpru6fvlHQjuD74qb2p4aL9J3Q7fk8ti8AZBoxKSnkbZXmu9kxhlQj+FE4imHbkCCeyQgI4hRm2McTa0jMMGPepKP0AQHz4iXxsjxv20vyMsM4yFHSYHoWsr4Fhe/6BuMQTTCbKjX2sylnb18BFfyaBxAyVspC7pWkOPKuukQwEAIGJkRxhBjaWZvQGZgebrEvZMOIMb5xYjVysF7QkOMMYj3IwelUaWHYtnzuV8o4bVmMAm/5pXIDFOmjj3tVfJhicuW/sxUPhFW60GQriRV5e3XRKjK51U7+1Ppr1/Qs7k0oXFzA6BJPRb3VJFkkyd9Z0waqEgPlIiy4i+pOQpVvZl2FH97piPJE+k3Rzwn2I7Z/9Vcp5uHWo5ZwkCot6mL/EZ5V9uQHPlFXIGJp5y5wc8AqYZnqI3dOqzaay92smAtaWrU2IC8iVbNcp5uOyfQTRNEuVRlqewNqAXbhdwuTAjY7ILQrqA0ScYdYucanb+6/1WfbqXg7Skwwcs55MuzuqfiXMNN+2VhPssSNy+XNxjnC97UaFKw9FtN6A1T1RH7M32fLgLqmSxIdz30mJUnj65fjZLecZbPZvpQNhlpT2S2Vu4FAHKZ9wJiBPShrCltYKVP227Mo+ts6KPtkZ1PMl9qvlSMwmyipbwzAJDRVw2fmNBV5iInmobQNVLP4BmNy+slF2KZtg8ymL9EaVYwuFmRGcnhc5oOrOUtlnkC37jtyG9e4lAuy+kLswiq4czEIZnjIPNPsuywb5IOcZ65AUwezRR8EayreTGxejjni+6KzLbTt2jw1kE4uPgnD1XoTDYwNfipUFQY1ZAu1bAiXaKH6kEHlIGelQCqMGXotJYtjuvW8YBUAQcMhGmTbMpSFtiUQaU6kLz2UpcKeSWPM62BgWzNFkoeM8tczZBT0t3ryfpmCyWS4YIQPQrl+dyR28RozGcIsqMemEEUxBsEcefMQY5SM8ABCDCx9Wa89VRRlk8VxLPAnrqfMD0ekQshMc0hME5cI8dVOWDwwkFF4QV28fKLfhgRVg6OCY0DtoOP8ycKS/AsykCMDeiHgPVmhPcBfUC8EANoXIO2kaO8pysHwKH3wKqLsfA8g6mNSxqP0ZBcaOEc0KJWmuUCD2YxOmpycW6cIjNzggvPMs/yS7wiAfHKgYG9wPlWLYYaM0rPIB8C3vtELtJ4dhnw+Pkm1hfgmgarRbzggxhPLnukfX6u4SybIdgAK4Oi0Y5LgUqFNYuLpl4b6y4bsK35Wl+iRBQqH5nmMh0qyAZHtMjSrOJtpqGU1izH2kOispkWkGlDj+jLAHJcUMykSiJRfOWaNe1Lh5hVJUQaC1MWkAValamogUrhpEkdIUPGysnXOHv5OJq2kWu2zSWvlT09zwK8fHoQFmYN0+U7kSYFcPJqRSvEzZEIvUr3JL9uCmQFuR/kSLxnvRQDII2J5xyWrQDWyITOiSFuDIg0SAFO5lm9C5ziORL0xOO75WolwyAncEqCeZjkjfBG0oKmkOZUZi7bVw3d5oBnl2Lx3O4YV9shGQ3IRSUhGjuGEPJVvQZCMjKaZ2a9Cps9shktjy3DwNwNuQVO70kVplQv8yQW1ST6cM1OdEVnEnYb+pK/loRE54diO4dSaXS7Jln+PtdVW2M9MC7bmmst4zwniFKP1uR9aemZhlJQILN1JdKSN2eVDxY9Nv1kLWg8bFKw90QnMuyrwV+NUzrnge2cUtE3NfxJmJgcb1kNewSAnEPrADW6ufiOlW6n5rL8J3VMocl0LQ2Xzfyr/GVl1NxbMvCjrZi5quZZ8V5xi8HY7ETyGEagH0IuSTBxdwEayytybpz24Og1qHtNHq4+r6/gJu1NWkj053Zl5m+EvWXbVR9umwo9YE/9N+2HrUqN1zaTymuqKxkUwJT63GxmCnyweB77IF+krXwRFhKSqJzF0PUrvbpVj0gXZsX/JWsONeOMDCcG/hjdkufnUcvZMdh3dlx1hsrZd3Y+nM1s8yU8r2hAJL2DxCRA4GjMTMw1GwMADV3w6aWXw4SfUSoW5TbZfyFG92JpdmiGWX1Kw55UdUN5bv7BTBkVFxJ+zOAX1dThcOVUfc62e6hTMwVvPJ3VOOoUeP47UIqat0HzW16eYYSO9GnFFiPiRaFRBbu0MGRiDqi0agxt+l10KS5eoVAuOHoAanupCxGO81G52ktQBdhUa8oXKzAMoCxtfhlIS4pOkjdnoNJ0z37W83oQ8PZhSSHR5AZFgdXxlAqhXKAgR8H60WO3G7HdjVhvB2y2PYbB4+hogeNVh6NVi2Unx92axqFhAjsC4BB8QHAhxa0LnL3YQlQ4xfskc3kx8AVDb1TA5RT3jpww8KZxstYM9J4xBlHmA8eLLACACItG1sYRELwIvj4A7KJiTJDLT4KPgfEbjCr4MmP0HgtyAAP9wGhDwBaE1gHrnsC+wXHHGHtRGwIL/J0sW2xHoGsbeO/gWYwCbTweCHJonAgYHSgLLCxCeGA1duZbLB1Tit8FIowhHiWM42NImdYJPoQocXhI/C41SAduE0wyM46XR0mQGr2MOXkAhhAlsb4AqRSfqILedMzWWizAyRCSGCgbPE7jV2EzzkUJtqmliSClVSYZ0YhElWVvcpxoVrhFwhlrpECqSn4URsnYZzVmCvnKRkwVUOuUh1dSgNvJZDa3mdtinJJPDUw5/o3g+zgGDKPHMHr0g4cPjEXboOvkcpe2IRP/TfrsoMfUGWxuw83eJjCG/FpILY0S+jYEY+wyi80sBmoP8cbRi5g0NU7yEcl7pQ3ZwEg68GSE1MsxwOIJR45A7JIhfAChIYn5hcahc7IRoN4vDMKidRi9HIcN0SAgxwMhXoCQ76qdZdjmiJOANYoyZy9WHZ9PSIeMt2AJkm+81eIF29kYAoKG0mcwFm2sHwQfDfxlaITKqmdAawqVSqSnCJphbyoup02xOQUxM9eDSfMXdRf0Zq5A3niYzWXa5rrnk69WEMjYe41d7+apIqtltWYDxeSjKgdQ4bwawtPFUfG4d+Q3TaPxWdVj1UxIioOaPfZK49KcDJLpJhVPKcscMZc1AIOzYTsfX83lNU5XPi1R01BK/En7lIx/qU2BngCAQkghM8YQ54Iyf9N+to7SBoBeCqLtWu8/HbnKhSAYr26zHrPrRokuGs4ZN7szCRXjSJIuYazqAIBmkeuwlwVJWYYE0H2xVONz8fwa/N1X9sYtM/bj+E1rsXz8xvkPUo7PIJV0ls0j5YtzUoKFK/X0VF6hOKYxlyMbSohrw6PYXlD1Q/Wn6TrsWV2e5tCWUn9N/9OoI56XHmi5pnQShOdmoqw0jjB5vgRGChuic6nzlvbISfERSU4p6ZiZQ0W+tEnBKc/hzpWJ6i+GzBXvdDMxkUAjoybq/9mkl8Phn2G6ZUczz8ftCr5UKvH8ZvnMrzn5wIyb6ufx4WexhlMcrd7n5ueT4V+3qX8yFsMm5mh94rvVPBxaCSq+zZd6IZzTddpHeD8DRL71rbiRm6CeamEqMpPMusPPpu/l/wSG3sBoaaYqQ4mwKS0FskRhhScUsp9UxHoEMMcCSkcHySx6rkFbLVNdqHDdM8w39tWaD7OAcP2qcf39YJEKGgoin+4hBhANPoHgQ4AfA/rBY7MbsdkN2GwH7HYD+sFHI5u4e7eNQ7dwWK06bHcDPnmyhfcM5xxWyxYnxti36BosAoPJwTUBYyseNSFeGiFGhRC95CzSGu1JfyYilRfLNU6O5VKTjuX5qOwrw1YFoHVI6x7ijAQQfAB8kGO7gAOz9G0YPXajlx3utJaMcRzFu8Uz0DqMo8QCccwx1B3H2CGMbc9YuoBhABZwuNoxPBzWvdxOe7ZqMLLD6ZKwWjQR5gi7UYwC3jlw9Ppq1CoQgtx8G4PpE4BOrAdp/nxgcHasg4vPu1Y8HBXD9JoXZjnO15LCKuAcY0GEwA7MDoGBk2WAGPZ0YSaQacHLALoqHkC+JINgL2JIcZyKOHjIHnJavgKPCYOoupQMbak+fQFMb5utjHu2nth4Ot4bC5KpS6uG0i4gG/hyL0pa9LJMoepoFvDyeuichWTACxjUgDd4iePoQzLEEVFS8ruOMI4el+tBDGVE6FonuN016Fo16AMgAgUHFzjjNyMplFbJKIURnhk857UiPfqqMC5wkoy/Zt3sTnWmfZSMVmx4CLNcYDN6iU+pU0cQw0cT6SOiMs9BdeFIP0P0jPFASwHeAw059L2sfz8KvK06MegvW/GMUzPxGC8H0A0uZhMziEO6GEMBtGGA4+YCoIaKLMwrf1HyRHlIcc4MF4hzS2CgIWhIDMkisUbTNB6S1oxgVLBAnSYj+0zC7GmBQk4wfZ5F8DJNSI35VdQ10yZX+Yu8nMem/0uf8iDsrKiXvjWwvnS6ltkXSBS/EqyRNm3Q6UVNgYt5UU9yIkLwjF0fsvLvSC6iKYx9sRUy+A1BavXPSx74mFFSq0TIhvkkTqW14cT/6uI2PlydV3HJ5pBNgLoeoUcuAW+mTZkn5BZCiJvRQSbOe8E4H10A24hHrZO502FnY71VDTKTI2DKdwwc6ZzYARNYTkhY44ENP4NCbBJ5iEy9TGjqNm8DuAdw07D9g3mw//WN23+peuK63NhGmPi+NfAVVAC3m8TbpLLe0ngn3/PFd4yaZOsY1QjkQ9a9FP7Um003nXSSJ2qO6dHkIo06Jc06wykVv8oxTYofmA6L56lolDHKDUFTV9xhyCpq7Dzn7DKP0XMXEjMbyN4yeuzVRXzKTWtn5mFgguL1kKpiaX3q+Z3jP1U7NmyNe3EsO5huwJ4/H+mWRCJntwV/PnhePJmy+2l2qj45e6jb5yZsOJKBHTMwCSQ97fB678Pqm6UXKXOTFUhiWfV7UhFhSkvikKb8gavPnGbIzo37Oik0Mylz8/QikHi7W3GDKEjBMJZk0LNbmyoCcmnAU4VLfuYjo+U+meUknIGdkI6+UXXkphbyMqEsGaC2qy2mOtMlGihWLR9VyQq1Klc5cLP2ZEok8pFkeaxCYO32fVMhKQ8mM5g8PPFhVGmAGRhDwDsfPMOzS/HA6zrCatlguXBYLhqcnR6hcTEQNUnQemJg9HIU9v4rhLZxGL3Hrh+x2Yy42u7w5HwNDkDXNTg5WuDO6RHu3vG4cyb+Ja5p0bYthnFE28hlF94a9xiAUWBFC82rL4a7OI/M2A0BbRNd8KNxT4/hNSTPxsAYxoCudTEoN+IcqFcio3EB/Riid06Qd9HzDRB394YIo2e0TuYyBNm5lOO0sssPkosmGnC8GVj60vceAQ4NCP3A2LmA7UhouMGCAjZ9wOmqgQuM0RO2nnCyIAwjwK14EH33hx/hd3/jS9gOAcPIIHIAiQdV4+QijNZpvCDCGAQeQ8iekgpgKtB5ArLRiTD6gMZR9I6U48nOIc3DhABGqShCNbKqZgx6SiFZ8UqZRX04bsa4BxRKi95+pjHfFDbm2G+WPhX0qcDfBF46AxOJ2CphKLyQCsUgblbsqztJ4QUpMBNZk4h9iYFylNVjJSLI6xEC8PR8jc3OC1w6QtuKwt62DstlFxX8GIMpMEgNYSC4IxK6HrJHXz+I9y4DaJwY+lbLDkcrxmoJoYvOwbkA5wghuHz8NYlMZOZmuoqqdOu6jCGkS16SAA8xYrlorpdjKTH4PtSoB3gAHIQ2EImxwxHHW6wZwSMZ8UPkEZ4h9AMuxrSSMABMEtczIHsMKovz8TiwUijvGQMxxiAxMxtyGMaAZefEmzYAAxMWTTxOE7103394jm+++QqGwPAeSfFXnHUEuIQnOW5WcRQ6IUDkRwZsEh2N660Mj0gMiJJpRmRg84UN5ho8swCsfHav9negHOyr8vVsMuJFkXdSe9HmbE9nC076NNNG2oi8TSrJ38G09zULtV1vBgw+JFnMuWyUa1sxFBOprBPLRSJLnSrxYgAco8f+MEQYIoo0Q25nb1vpvB5noyAey9mQVQ0yTW6J6bIcypf0SHymXwrMVjYSLzvIhkIG9tguqzg5wU+l11aUyxtI5fF/xHFZY5+NP2yNpBxkozBEnHAkBr823g7ALDGkGzIxsAA8v9zh1Tsr8ZxlAwqkRwezTJz7BcNXyyQstoQS5lxP5qd5Lq+DuX1pDp1/dnVY5vtyfUlwSjesgw1szvFz81O7aPtXs/vbzj9DTlQo/FFsQzd6k1HZyFB1UvzP+BLD7MT3avB3UQx30SfApfpyWI5yb+bADHIhfsVwPtM5qOeq1onYvkPpLGFZ19x8T9gPx01B3XDOL2LbmSYgthc489KGBG91v4+hoaS0PcHz7RhwvHCJDqXZovK7UsZCip5bv/SfHczM/FHZxr/WaYZv3yz7nKzw6XXnYB7l0Ziu+SwM1IlKvNCGJ22nUBvynqjSwyZN7W+4nmaLv4fKfJZgqn1Ico9t2H5WneL4uc+zr57bwLKxPlfldHy59EFY2IPn9evbzN+tb8VVIYYN0U6x1MDpFiT1gMsxXAw5E0kvFrbecmZalRbXnjFprgzJJxTCeL7pFklhyoudDX/Ktaz+XXyxRr0IMTXg1L1XQMlYmSuvmdIE5hioShXf7KPsfaGPzO56EAV0vR3w4SfnuFgPIBJD3J3TJY6WRzg7OcbpcQfnRJkPIWAzikEvBDkKu+jirbIAzk4YuCc3xgZm9IOXo7ubAY+eXuDjxxfouhanJ8e4f+8Ur92/g/v3TvHKnQCGGAGa1qH1hDYawzwTWnt0j8XzwAeGa+W5j9JwH8/RqPIvF3YEOWrLAneeAYnfF+ExPqdGvNcIYszquix9NE7KDaPHUdekI9py46ys9+gZwxCwauXZ6KVcYDHoDRKQC6MPICfeeOPo4VuR8n0QQWHTj1h2MTbYGLAbABcAJgcixtUOaFsZT0cBHtKv3jNWHWFkYNE1YlAQSI43d8pfQ4SmIzEGxr4HVqOAGDkIjCa6L40eeS6BFKwXCc/z0Q6Ozw1qRDS2UhmbI+0ZzdkyFUQ6YHETpfCY8KlCDLaIEZUfUnywVI/1PbIhADDtT3GJDIfj2HgSwhQvi42GnJ/m+ocbfp9L9bjtGCzOR+PcMAScX+6w60Vsbxrxru3aDqtFJzDj5PZXZpZbYKMHDxGi147AxHIhjWkcm9EHjNHb92qzw8XVDk3jsFx0OD5e4fR4ieOjJY5W0YTrKN7eGm/Fc2KA5mpYenQsKW5RuM6750prc8kEi0rnOE8IAfHocPTaZU4KEANR4RYc6BpKxmPXZLou3o5AF5Uda4AQz1/pi/QtKvNBYg+CAwI3ADMGH+KtnVJu8AG6eUEQWuYa8adtEBAAeC+eA526JDZRdWCL5zJSIg0zSFBjnYTgEQDkGFDIacw+lIq/D7GAASgr1OwDSTI/pl578qUW8hR2KeWp662Mh3Oprg8oPOgm7/bVZb2Dq/HYglxknxKN69B3km5UgIt8PHklxuptP2KMwVpd9LBvnOBv27go/2Rv+USdWAzbents2wCLDsmQpd5+3gtv3/UjiBza1iU5YNG16DrhUSJEZ0WWI0zUcKTvbLwtwFwUNR1oqkN5jrzJi0RmSZjVs0Zy6eZtYDX+S2piXWIAj7fsqtxq6Ip6HHuVr0y8rkiZwCwEQi7Syv0IcvYv9S2SWcUMEDREiWwgMrJBRalYpu/RCF/Lkch8tOBLyKEBNKezc35roEWJ59NXFf4frCb2/EX6YHjEy9QTK9g3nkNlZr+bn7oOt2Hxe5th5UPGeYIErIiQvGotfhT9oBw/MpPGrLQoy9QNsjGGnJD40k6O6jcOjQuAcxJPW3W4WBVq+rlnvIoX0/clz5E+TWuo5bTa6KFjyR7qZTm2hWfWjhkIpMbTuqOxQaVtsWKOvhMpSjgZgz2bfsUxqTdd6cxhdDeCMfjtSQcAn+Yff2rpBuj9+Uu37PSLkMaXTgpuFc+3TkD7EKwAh1o8qcausIh4AZ7IYoLPZBrTOwiUVFxnqKtfzxmnZ/PX4z1crGzjQPuzmcxPnYdr8awiHoYdJ/zeV25aP9+oz5Op25fxFpN1e8Oe+YxP8y4nVNnQo2q6k1mxArt7aiYsW5MpMaksvWhzeXRkLHKWHltrtMYagjH0WYEoHZ8oIqva1c2Sm3Qvx/+bX5GSae3DkkN4UDAkW6cZpBpXVSDUo6p6PKcfRgRmfPOrD/D0+QZPn6+x3g54/PgSDx9foHUOR0cd7t45wv1XjnH3zgqnp0cgMHaNeJ2EPoB3OwyNQ7NssGobwInn2NlpIwp845KgMI4Bm+2IYRzw9vuf4KfvPcTRaolXXjnD66/exeuvvoIH905x984xTlmMWa5huCagMZHkQ5DgeHq0sKGsIKuI1xAwROWd49i3vcfpUr1aAnyQHXRndt10KRoCukZmd4z1MjOGIM+lbTHbBlblneONXOJFuBuBk4V40MmOPoDA6H1UwgaPhgi7QS79cFFKcE69DwWeNLA5QYwo296jI8Y4MjZ9kNiCXqT0YwbOVg2WjbTXjxIfjwD0Pu8jSnzCJikqTePS8aYcYDt6FbHxArLwVxNJNjkULCmSLxtbT2PcGfBNQdmBhF7F5kCkjIUsiiJ72Z/IBa1xD0D2uNP4Y6nJPEDFGcsU2dCZ8jgumzy5AGVkzAKvMaTcLOUJmtAD21eD52rQUvz3MX7aq/dOsN722GzkeP3V1Q6XV1s4InRdg6NVh+OjhcTLXC5AYDjyAodjAI8BgQjUunTRAohwsuji5SpxPlkUgmH08N7jyfNLPHl2JW0cLXF6coSzkyMcHy9xtIr0kvQYEMDOzFsk8mpYdiTxr9S4R/GZxPfLeD4ExqJ14PgvhBgPkHRHjZOwrB6uQFS04xhCpCHMCnt2o0T6RKwev4xF06RNKzUsaliAwctmw+gZvlGzkfY540zijxAj6+jV61c2CHxghIibCwDLltCSnBbOR6mjYS7Chng9mkMlVqFTryBzbJ8pQxubvwL09kkjCc1LzOTiGzCnPSurnyLHnPCTcUJxcb9UZOWDoheT7humHH9W+FoRHDb/7zPy3S6V/HxOWE+/K14v3juMk6MuetTKBlg/ePS9B1E8Wt86dHqEvm2Ff/kgfCCo1x2BnXqfi5dfG735rAaqhnBmxmbTY73p0TQOXddiueiwXHRYLNro3ZdxlJP8Z8YWcUbhQPmSYSOiIKersdVQlg2Tkr+Ur+xSuNR1s3LMyXuO7VvOYTu0rcDigcec+5tiATLHmJ7ZOJf8Pw1O1Qgj9DKI8SHksmkDEXJhT4qdC050sVD6GMk4LzKubYni+DOsTvjJDdIhNLu2YO7Gy2abKag4XuL3i/bVkPkXR+WXSLznh7A3edC2FGNllob3EZGXRm9dF41yiGUVdlVmUH6g+Kh7t8WGiOIsGMMoG4UUPYHF87+Jxr5ot3YAzcxcRd2K+mczVnlvCrNza7bXGBHbYpOPEQ2lplE27/KmIgqDYcq+B1HyUd1cX4wCDgdKmwAAkudzAY83TBVL/jdpLu0VAn5Oac9C1V63mVci0XmglJ1oH9IYhpjQvHqglhqVY5UGqnxORJEHR+lrVvmqxoB5ObLGkxfmL7dI1+HDi9CbunwlRspv/V7xlJ8Xrt7uKC7nWCZR9AA4Kh3IcJUGOkcUVTtAGe8qC3nT4ZchnjmH3quzKvQzZox22dBXFjDSZSa72TPPcqoJ15pjY2awKUAF30DomR5ZBMr+JsOqfuf8PQn/zBhHj/V2wHKxxMnxCq/ev4uh32G76/Hk6SUePVvjat3j8mqLy6sdPvjwGZq2wcnxEvdeOcaDe8d45ewYy9ahCQG70WPnGeebEc45PLi3xNnZEbquRdd1aNsWXSfHb13TRo8wiv3Y4eJyi6fnF/jo0TM05HBycoRX79/FG6/fxesPzvDg3hHOTldogGiIE49LEbRl7OnoECjtXqoArMdA20ZnEvFSigA0onCL8VF22MWAkI9QJy9TEobfOcIQBaouGjVcvFEPzOgcsNkx+pElRh2kb0qI9aKBcQyg1mE3jtCYHgRGP0oQf0dA41qwGhIiRG17j6ZTgUuMdsEHeGb0xNg5D2oJTBLQf/RyHPJiO8oNp06MeAsClo0Q6pZEK/HRgOCZEHzG5SQQGnjK0CbzZG8mVBG7htryaf1eYZoSQ8kKP5scVHrg7sEZijimUFC1YogvTQS0wtCnXsExPydjHpk8pnecas+TwaXSefPE1TfG5IOzd7KNaxcCox892qbFYtHg5HgF70cMw4j1psfVpkffe/T9iL4f8fx8A9eIB87xkRr6FnK0m+U4+xgY215uwz4+7rBadmIUbpq4oy8GfYoxIAE5vjoMoxzX3+5wcbkBEWG56HByssLZ6RFOT5Y4PpL65JbtvGaKe0kYDnkzyHpIk2wzxuOzSGWVLqbb8jiXtTCrx2vjnpN468bwAE28GTp7p4ryNAwyJ+o5GNJWvTFSBAa5GMLAGIvUKCJ9icEFOEPOMAZQo7gnfo0cxz6OHg0IevY/XSRCwG7gtNtKBCDe+CnCWZwLzsKbzqfMjTl4wtVnylObKeZFskLAKQQ4pYU0ya/sdh8/rDEoUZAZIbGoZo/QMRERioIVzdlbeZXzJSS0OflGq7bvkqAf4duHAOcatC1huWjThUfDIEY+8awVnrPZjZBjtQ5d51KsTInpyMmINcZgVYuFQ9s28cIcgiOXbo1Ww5/0RXjHOHoM44hdPwAA2rbBohPcXi7aaFjkhIOFoGt+ZDkSVabMS9PkoAJXNmubaISRx5S/kCjUdmMv0ZVY3BEwBMETjpfzWE9CXRc92s7xPI72yh7dq7kQoPTBxrhkIJYJkIuCENvNCpecDJAxyGeim85KqhlfCx6ODEM3SXvxfB71ixHSDfLOlZvvw6GCdpLnY13eSnFUGKIMObcqf8vE6b+Z58hyl4ZiaNpIr43epXTde5HlQOrZScnYRyQopBepqpysF+eApA3Bg7hplwYuyJTjd8pmMyAXRzWNS3F328aZuJyp9N7xASU/35e/WAjzveA1Fb5R+Tg/r/om4TXsJgDmF57Fm6+xGG0qn+u3rTfL0vGdSozaronJHqr50LWx7dQ07za4/bJphh3+YqWfxwCuWR8FuZuuc5KdCAUckXmXcCJmmvJdwtwkKP/QEykUlAZxkgOu8+C7STpYRS3fHZi/62i1vr8pPb813d/DJgGUMaird1NhdH+7s2VugfO3jrE3xJ0dEUrEyOKTJ1VWmnR29fhsPrKqZjqq3udy6smTvO7AJohr7RZeCRYR+oVBRtHLHM2VPmZBNUlOsQKFfzbPSlCyhoSCZaV+w9SWRl0TlwgAdgXr36YiJE8dWEGOE+NXRd8Hxm43YLsbsN4G/OTDh7h7usJr947w2v1jfOsbd/HN4LHebPHoyQWePt/geTzGd3GxweXlFu9/8AyLRYs7d47x2oNTvP7qGd584wQnxwuslkusjhbo2k48Aije/gqJzQMOaJoWXUNwTQvXiAcBOULwcnz34nKL84s1fvLuR/jzv/gAXdfg7p1jfOGVI7npNkqlHBVXVZB7H+SGSQfshiBrGgIaB/SDx2ohBi2KxrC065DmUy7jGL2T2EI+oG0cfDL4yQIMXuLu+cAIRgNoG/GsIXAMSA70Y6bCx53D1RAADvAsBgjmgNET+jFAvHwkPheigi9HA2P9LhPYxjk0FBI8jSHEG3dZbu1tWjgw2Pu4swuwBzwkTiI7wrOrAfeORamDY7REciyZCDz65DWxHeXCDooRWNTLIE1hxAtORg0o+hq8jWIKZxpgY+2l3xmgSwGdLcNSdd5wOCBLhSWSxb4YA4C+SvShkuBRVasGczXwJWbKKT8ZyyCbTJQfFDSp6NykRfNq5rESqpLJG6MexAA0eo9x9OgHxpPzS6yWHU6PO5weL/Da8RFe5YB+GHG13mKzHbDZjRhHoQ+7fsCz8w3aRmLnnZ4scXq6xJ2zZTQEtGi7Bk3TJCOei4Y8F8+POXLxeF9837i4RNHzdDdit+vx+OkFPnn0HE3jcLRa4OyoSx5IukIUh83RwKieBnJUSMatvKZrKOF3xm0qvidlCDF2ZrwdO9oqIu4hKf2OszDhHBDt4CZvhsWuIQkNwHqsDmnjYAzqRZgRQcbG6eIMvfiHWWiOo5CFEdZYXmJ4cS7yEGOoK4/IE4be42hBcUOF086rKDEhwdEQxEuRuE0zVRow9ghNmV3n9/m/CaRPKpx7XPPDg0kRcioJFdWweZLy0SQ/mWy2sMHoaR5T1S3kq/2JbZv5mf5WuiuG2XjDrfe42np0rcTIXXQNTo47nEBuiO/7MV6aE2K8TKEP260Y+rrOYblosVy0WC3UC0e87h25rBAoHSSl5/HGZyfGaScABj3WP3oxMF5ttri8EmWga1usunzhkw45Xe/F2TMNlI3ySnJDxNkSDi3tj3geHzM4Gd6SHIdYlxrkMiuK48twqLxHvYcBmDi0ue9ANrDYdbSbOgnPFV7Y0JIoW2p8U/WY12Pz1utKN9d0REMQ2uegmxCWD+Z+KumZN32V63GTtAeND1d64wIvkKYCw8s1b/HOFH6ZIfDky573AGAMs5YHSlxlwbdOboeLG9bqSZvhZQQDgaJsmi/HaltVzCnibeSUaYxUjDfBlFXmYzs+5NNA3CN6DrocwmXPOO2aWEcFQg53YUWhffCmdaXNYFuRKTc35ZYeqKyhSWWAui3FwfTbvLSx7WybBKBQHU2/9KS+lYEty1IeG4JcZEhmAzxXznmTruztv0nXpT38/DOp+wZZEyxnNgiNB0kGNmzVbJ4n/IxedkoOC1eLqcikj20FZf9080AYXWyCyovrZsZz3ZgT7t0g/03qmkuHlvRQ2wcN5YcamytnOljg91ybB9bnZebpdkdxA8MjJAjiqESopTf1CGpEo0J4SvVox82xueQREwVJNczpJRvpmC44K+yJcFacmNnUkR6Z7xlYEYVCVZJSB1Vw0GGRLXcAECzUGePULKOfcIvyIZtCeQffKPeR4Uq8ubyb3g+jxHhjYLcb8bC/xCePL9A2DnfPjvDGgxO8/uAEX/vKKb7ZiJB5frHBo8eXOL/c4mrdox89nj27wvPna/zk7Uc4Olrh9dfv4ctvvYavfvkYX3j9Do6POjRECITouu/AIcB1DVZdi9bFGD0LMfDJRR2tHK8JYqzqezE8PH9+hcunzwDoESA5EivB6iVQ9cKJ6UluqxQC1jWEwCGtt86fjwq2CA/yzEUKqp5JRMBuFMWki/OgcfkYYlSQGyhFcN8NOUbX6DPs63HgQPHocOMwjqJQd02D3Riw7gNOFw7DyACFeMTYoR88WnLRyy72b/RxbcWdyDmH4OX4Y+caeAe5DKUBtiNw1Dlseo9V57Dp4w2+Psf9E+OLwDOxHJnuR9kC3gyMix1j0RB2IycYCxb8I+5MPfZQINY++meNblY354jPWr5kWDNH9DISiGKZniMKRdlTMElJVGBUeaQO5ftk4APymBKNsfQNqZ5CuJqlxgfIc+LcuaAlAaUxP/8ORqEWAV8MaZc+Hr91hKNlh7OTJU6PF7h/726C6e12wNV6h+1uwG7w8D5gs+2x2Q54/PRK4mSeHuPeK6e498oJzlYrLLp4DDXSfReVemoIXdPE40BybEcVCBe9dtXg5b14Dm62PXbrDZSmiQKfPQSYxUvXXtAEUDLM5fWS/+2tkSrw6u5k4Ii7xHLEN8S4W4xoVJN6RH7JXjcS30jw2sfdS2sckL7HI/VeeF/jCH6UeJuLllJsUDXeeR8kbAGywi/GcwdV7BxJjEHvAxpySZlqSIx8XUMYRv2UAF4cDfB6U7ceSU4XJMQ57b14CjcOGPXoFrJHYSFhzIJsiVP7XpePlJ/ndbTZAdzIwJezVARkpvmCJtjSlP6boSv2AU/y5EYO07n5HpW1zZEH+7YwhHG+EEkvhArM2MUYeM6JV54Y6xocHy9Ti8OYDX0aF3cYPIYh4GotR2qXyw5HqyWOjxq0yxZN4wpbiYZVgZP4rerdo5fwiKEqi+uCz4zRx9u4hyGNI8fpygO2spmmJCvFz0QiizmTubXllV/5SDcUtzNcCHfgVKk5DmzAujb+sfbJghFL3Nwm3VqrsqZ41gqvNbFmDd8SnqcGSZkXpZHJszDSgWRYNHUwI3mUJ/hh9STmGAsUUTZJUJVkoeugd9YHfx/OH3p9TZkb13NtgRqjShy/ZRdyvahg7lNKBSUw7ajCrhDLkXfpZVDWyNe2Dl2sRePxFd58URegUWBSjug36DqH1jnj8VnSUjEG5yP5+rs2FijshniRlFdAQyEOZnluMvaKVl87V7lui5dTWo/Ec2sctvzhunYAlXezF96ERum44hQq7UiNFHTc0JH4TOtVI3ytT6r+mwYFZKM/ct+0nz+L9AIo/flOP6N5u65Jy+MoAhYhbhAlXLI2gPzJ8UtE2Rg2B4Y3zyf7SuXl/GiK62prsM9fBBY+6yl/WRidtevsq9TIBFxltbTpOnvRnEg7t6Fwm7m7lWHPgyXgMGTHUKlVCHqckSuQAFT5pWrI4oUXCZYG1wdnYZIAzta8/BH/S5NpKDsp9UTsi9ZBnAlv7JM11GWKWxsT5g7XlWyqXvPEzMxDVQbJzkLsUMlspktnBTqg3KULBuE8M/woO+fKbD3Lbr+OdQgBj55c4NGTS/zgJw3OTld449UzfOOrX8Av/9KX8NtnR3COcHl5hUePz/HRw2d49Pgcz87X2A0j3nv/Id5//yH+4I8anN05xVtvvoqvf+0L+MZXXsVrr53h+GiRAuc3rkEAo21d3NVzSnnQNQ6tI7imAYHRdA/AzOgvL/GDf/TfwrMo4o44KcGDzwY3QMcunm1eA9pG4bhx+bieCDkhGggdAkuAelV8OWRPGm++jwGQe0NyH+TiDlFeWid5VPjeDgHLVjwUyDUAxPOQg5cyPmAYgbZpQMRYtYTtKB6HDAc/BjTOGQ/FGCsMDMc+whQlA1tD4i/bD16OGDJj1ckRYUIQw6NrsOnFG4/1VmEw1r2PdWdi7r2PN48aYg5pMxGtiFP7KEy6gYwjTiu8FxJYScISY0qPbQzMKl/KgSS91VVnDFaaQAWlrfE5GfoMbKX6NSel/6qLdgxlr37WguSBaTNtldQ8Ge9jHsV1jX0VQgCHIMfBDbf3nnG53uFy3aNxhGU08j24f4bXXr+Lryw7EBF2ux5X6y3OLzfRq6/H6AOeP7/E8/NLvPOew2q5xN07J3hw/wz3753i9FQ8+lLcPBeNVVHZb1Ik+6yIyDFUBt2RKy7HXY9PfvzDZLhwnKfQc46PJ8NRI5qOkZLi7hzBBfFWUMNgCLpJEwNXm3lT3JZj7/o9BtaHGAAJBK+8hjlduuFIaMfoheYIXMRV5YwbgeWYlIsXebQNYiiA6JWRvBGzp3XyrlE+EQGaWbz7CATvM69oHdBDhEDFOaGPAewyTvSeY3mFP463qZaQmGA50pja83xvMniu9cwWuw7/Uenne9pOckSF03VdNZ5lEcEAWmUAKPPNJPPy5gLWPkJpPyx/R/qecD9wghOuMobA6INHP3hcruMN2IsWJ8dLnJ4eoWsbEMmx7l0/YLcb0Q+DyAhB4uZtNj2ePSO0XYvVaoGT4yWOjxZYLjs0DWXFXjc6SJV/RVIywfolz4oWABhh9Lh4/CjxExvrTgxxEVZNMFYbA1mn3W4Os/3TL/YCCs6gkRTt6rud58LzGlkU9AGCJxEn1PiuDehpCYULvVVce62xnVNfdCyopUwpn+hRPNbPHEMFKLzH+fORfliB1ofSQzHN96SV/P+1sStvmWarKhjqS9RzXYH6gZUlzJtPaagvlGrebr+y/bIHTpORDwIfjZMjsYuugVuJQV6M6mps47S5rUf0t7sBLm62d514/Iqhjwz8K07G38jMOBn99JfEvkEIAf16m4fGuXw9/2x/hDLPREaKoJ+KUJ4rLrMZeSFvnNlns32I7wrHC60vgVFp6p4tb8rtHa95puXSuClvTBRGm8Tm5GHgeRz7WcL1p0gy/rVLNWzPTaTyMOX1RHqqg9LFV0rfk7GNDb/zLHEdYzk9SqtxODP6lo1T8RIFvJsgnvBjwIsmw6qnY8bt4Gpu+vYaz/bkv1W6udAHVG2xHeAM/Zo0tYde3ab/tzLsKQDJTbFK7FTgy5biaTf02IGK5nGaNU4QI3vvIR/pE0EPibqngPzg5D1SbF4SksVbJkcKaEys3Ldo/dZdVa5ErSi88gRS5ki1tqXKkOFolaagghWbx3MLzOa/TOg5fapCnI17osyP3mP0PsXfEcE3VDs6MoZhHPHk2RWePFvjh28/w927d/H1r76J3/iVL+GXv/E1/NZvHqNxhPVmg4ePn+PDDx/j7fc+xrsfPML55QYXF1f4/sUVvv/Dt7HoFnhw/w6++uU38O1vvYVvfu01vPbqCstVK0JD9OZrnRx77aKxLwDoGhcv4WjAXjwMCCbIL2chdzeK8t02eR42vdyCu4vHSeWiiZAU/dHLfHvPCE0+ltNobBCSd21D6Ecxjg3x1ltA4uf4wBg8sFrkAP6jOq6Ck3eArBeD/QgH3VETb50xLthm8DhdOgAODgHBifeNMzDnGfHYMWHRANtRbsX1sW0x1rZixGwcdiOLtw7yTX1qcO/HAUcd4EMTjxdLPL5lQxhiQH4xVmaPpykHynCtIF4SmX1cKkt42SiYDYaTrECh4au3rmm+7JHixAwtyGhovPS0m4wCTZHy6LNygMQ257QN4aP5hz0gWva5pgVlBjZflLErw87MXA1YweC/4IDCZp5bEe7XMfD9w6cbrFYrPLh/B2++fg+vP7iPL36xg3OEoR9wud7i/PwKT55d4NnzK2z7Httdj+3DHT55+BRN63B8dIT7987w2qt38eD+KU5POvHUi4EvVQgJDDEMREVBjnqLJx9FxQMwMWni+ByJIQyUb44EIJ6uENx2DWEMlGKKCj1UOEOiGXAKZ1kwbkmU9tYhK8lQWirvljEYlxpRtYtySYU2ADDLpoke8VXDIpxsRCxb8Vokkhu5BW8p4bm2R3F+Bq+bDnk+El91FOOCZjqhc8Msx7K7pknGBp1LDSkAzptbKQTFDVKJpvvEo/jcKCP126SA7LHgFVRmPotpKTZ0Q3Ftvu7pHExNIXv69IJpQkHY/jK8HorrZvPOFJ0bNbN65HlcrQe0XYuT4xXunB7h9OQYd+9EI58P6PsB222P9XaHzWYX4/ONuLwccXm5lo24rsXxsVyGc3K8xHLRyA3ScUIIlPAhGarBMY6fGPPBsnGGmf7qBpV+13lIF1REA5fKOWzmRcYrMqMa8OtNIOW/1ismlYvvOhM3185ppt4aJsJsDJPikDzwrJfzROFU38VHiX4L04Mj4e96fN+uu8xFvAgNeZxqZRBxLoCdg4ap0YacnU/k+XqxNIUww8oPltL2b/bilvXcNO3B8Tn8fill79oucP2g/loYcvSdvZhrrl4OiCFlAnZ9jKO5aCXO5Uo4hISACRIPc/DpeL4PAb6PJ2VI5PKua1JszHR0F0iTkzd7zHkI0hh9gMaLJJj5jCBkNaZCI4wPo896wpP6fVVduV6Z7dRiXSE3pduhGTfHCcrliSU8Rn0Uca6uiOaJXpSblEhzkuSGPRXGi+2l/2aMZAdo+vizTC+Nn/8aptk1usHCqVGfAJBDOh1jw2EkOSHEOOhsygWGj/xaNgRieaDUcQBkV1Sqnhe/iu7fFgbqIX8asDuLG7hd324zltRnQ9wsnbtJO5ZHz91HoTJRQRNv2D/gtoa9WHsS3gvlOD+YRfxYbvqc0yjyiTRVuHRXtJZoKRv4bIVJqYlxChpET0DAxtgryb8+MsDMqWbbfdS+h5Op0Vh++t5WD+SbdGuEQs3IKuNfEu4FY0XYzQhtg9z6ZNQKoAB0rkHTAJvBJ0FSZNA8mmEY8Ojxczx+tsEffu8DvPLKK/j2N97E7/zVr+Gv/fqb+NY3vgZqgKv1Fh98+Anee/8h3n7nI7zz/kM8enqO7XbAoyfP8ejxM/zhd3+Ao6MV3nj9Pr75jS/iV779JXzz66/jwf0TdIsmBZlgYixjbD4CwZPDxYXs+okAEkEDASFQOp7SknjKAXqZg9wo62OZYYyfXo6hNTF4FgFJ6bBL4BCD1QckZRsA2ihgdyTH8NQb0HP21NNd8Z0PYvjrxHCmsDR4DwbhqBOjNiHeOtYSuiYeoY6x+BaLBiEasgiMq+2IV09a7Ia45oFA7DF4hwYxgH4HLBvGlRej5jCOOOqAnQdC8CByGEYxlIyesWxlfo5bEVbGSDwcGD0jXQxgYX+vQD8jMFuFKNMJrt8kmAZQGPgMeCYEKmX0koBwrMdFpYph8liCaJCxMPKhzmNGtI+gzhO+6sfNSHBBY5KANmfML/HdhxBvVc6Kc0PCuMeRkWJXQUmren15XK23WG8GvPvROY6OjvDagzv46lv38eUv3MHXXr0PR0A/DHh2folnzy6jke8SV+sthtHjar3B1XqDd9//BF3X4uz0GK8+uIvXX7uLVx+c4eR4ITE3Ka8/aWwuSHyofjemWVKDWu6l4GPDSDfKJVgBEu3TuJsa98epJA1GugYW2XBHsR4fvV905p3ODzkwB6ihIh91ySbb0cebMeMGgSKG3OLdxFhDMgq90VuP8TBL7LG2obTzTgB2o8fpwonhPw1BbvR20UDYNWKQ7IMoKT4EMUxynpsQ8dcHiQ86BjFcKHYyxKgw8rwgcRhOEzTNviyMedfVXbyfSgo15tij8MkIC6UrqBlnLYlOmq4fTEXVafH92Fy3cwO8Z/th1lxfRz6fDLBJUM+el2qssq3ZnjCzHMMd1nh2vkXXdTg9WeHe3WO8cmeF05MjEBFG77GNxr31Zof1doe+H8UDZxjQPxvw7NklmqbBchkvwjk5wsnJEotFI8qFjoNkqwqqRADwoy+GrWNQI4CCQmGMN0ehlT5o2VqJzdy8mmLO5XWNtR3ntPZMw21/AHNEV/tsNqeAeJuwQmHk4ekES0zJmBDT6MWj38bHoijfMkUvfFAO70HSXlOsdd7QTUbGFI8rKwMU801w41CivT8+vTRF9/k8NPn68m3qj2tx20LmVFOYx/g981zLGPXrihbM4fOhFRTlPcDvWC5ccwO6rsXRssVq2eF4ET35OETj3piM/z6E6F0uOsN2O0T+JEbC5UIu5GoaV3jjAqgMXJRu7rWGqznw4/TfdJ70cTKmXTN2lROKus13V+W1tMN6E87BmB1HFmcrwIzJniIChAe3tnGzxonWUfkdnPWQug/2dy2HXzdHs+kzQu1/k/anW6/Rnjo4iDcehXg6pkE8+Zb5k8qdekFW4RDkIaFjCOl0jYuxOJOT055AekJHZngt9oAUH3iHT2dO9lX8MnXv7XNNkA2/na1nBl+V/VzXvxudlrkm3d6wZzsAFUL3UGvYY3WWXMeS5nrb5JmntFOfJUWX0smnxATTZOfDQAUDjkKyGKIL0df0HwLUZgyzE8+QODERcqa3bEZWEw1mtRGzYGqmgfyo4PIxPxef1v02xd8JZawLPZ7nvcdr90/xyZMeXdeB/YCmdWCSnbw+GgCYCOAARgAFDx4HPHzyHA+fb/DP/uR93L93F7/5y2/hb/321/HXf/NN/NXf/iJ+69/yuLq4wsXT5/jwo0f4yU8/wI/f/ggfP3yCp8+v0Pc93n3vI7zz7of4vX/0Rzg+PsJbX3wNv/orX8Wv/eqX8dWvvY47d1cYR4+PP3qO73//PfzLf/VjDA/fQ8sahF5ix/lAJnaWPF+2DkP00Mu71AJXXbzgIjoEQW9y9tE4Jzsbsl4+cDLosY2GC6QAxGo0cCRxrkYvMa5cNFpI7CsHgkfvjSdrVOj15jLP4sEUejHKtJ14Ba63HoumzQosi6FtjMf55Egxx7h74p01ksQGXLQOXROVhoawGwmrBrjceRCHZNDaxKO3jhz0OMW294Br0JC0MfpgbvxEQlMLv7WwoXgwjX1VCkHpuF6dDRbPS+V+zvigODIbs1Pnb+bKSjLIuFcQLx5VyDvJt++YU93wvhd1Ga6EW8OM42v1zNXj45kGME6Pl7i88nJ5glOXfU5HR6UKjjAp8MTRyHe1GfD2B89xfLzCF19/Bd/4yn185c07+NpX7oK+ErDd9bi62uDZ+RWePDnH46fnuLhcY73t4b3Hs2cXePrsHD/68XtYdB3u3j3FG6/fw5tv3MP9+6c4XrXwzDg/3+DjT57hgw8fY7x8Lrv0rJfHRC8V5RUQ/FUP0+zdkvG8ccAwmrh7nP8yvUSah6QcKy+JXwW38/FTFWBCyDfl6mZKEwV2b/EEwltsrC3nIEIUi5HNkVyy00ShiQwcBr2BDNKON2sfghzrbeNOaxiE5o1ejHZ9H8Q4QBICYPAh1kXJwDL4AJBL9EqO4u6H2YJ/1i8OXpZh3s0JNba9yfu6JzTzZtpanZIiZt9eIyXt4/U3kq32Wv+va6PEd8DivMpUht/HzN2iwa7P4UqIEE9plre4ZtmLAYiR7skzjyfnGyy6DndOj/DqPTHy3b+3Au5xOq673fbR+L/FdjdgGMTQt9nssNls8ejRczRNg6OjBc5Oj3Hn7AjHx0t08aKM7WbAxeUGz8+vEHbbpMAGCP5MlH3FFx2PnV4gesLmcpZGKsnQY69WeZ9AFGn4lVx3MrgTwQYsV0/+wJqz7K/ykSg6IXkqkvB716AwOVKSYWPdIfddaVVgynJGbCNEQ/445jUF9Oit/I5djXQje1FpTMYXSzPM95alNe1T+op0oKm9eP6i6dpJYUtIJtn3Fr8N/tdwyuVaOZdjtBZh1nhfMyJY+RDg48V5TdNjuWhxtOpwtGpxfNwBMJfdjB7DMKLvxxirV05vDKOPG3g7OKLkzbdcdmLocw4MxjgEbPsB2+0IFgA9nDjzvbn1Iyo3KyyvyDrhnvms6iGTV8tpNVYkI/N+0l022mqa+xhnF5nmYE8dWjaxI8q/E47GNgKiPSVmUHG1jp1H5llR/88pXYvn/yYdXp9r9IO9dSZdABijB1/bOImlKbu5yYNPN75t2C6tY/QMeACD6rtSh4syKyM62eix/lsEc3w5DnJ92sdXtN1Dsu2N6ledAhXd2DMwrr5rFitz7Etab3HqoMLz26bbG/aMzsuJstTNC3XKx2tNbDmOgg5UaSJzqoZTfZbwGStdurkSQLGbnzuV1bZgvtdjyMSfYA5Y7QHGOW5Rz4kVBDKHysf69MH8UnH1Q8ekyumcUU8R11eGPT0S1jQOX/vSGX78ziWAgOAJpydLvPnGCXa7Ac8udvjw8UbacmIQlDWIxsFxwMPHz/EP/tkVfu8PfooH9+/iO7/6Jfytv/k1/PXf+hLe+vZr+NK3v4nv/I0d1hfnGLc7fPjRQ3z08RP86C/exY/f/ggffvIMjx89wSefPMJ3/8X30S0WePDgFXzj61/E1cUl3n33Q1xdrRHY45df63DPybHCfHtUvhXXEWE9BKxajs9DjvEVp7lxuruYZjAhjB7d2wyMZYwPol4EIchxXGapYwyMRTR8qrFAglJT8tbrGomTt2p1paXFzgE7H2+2ZKAjFEa/JipjHLJ3YTPK3Os69z5gO3h0DWHhGN5HYszAMlo6xmTclKOJBFVwcnw7hh73Y/RjwKIhsGfsPOA4oCWAQ0DnCD6ohwXNIwLVkGskFit8kcX4CM8HjXsxvzk2qcfxUz5L9MouiWBstmHrrrM98j+tbjLGBDszFDUJg/teFA1Pf+8j0onhyo/8Gcuom73idoq7FfHi/itLPH66kzKBsFy0uHO2wOg9NtsR5+shuqDJ8TWFNYDBwePyaos/f/sT/OjdJzg5XuGLb9zFt7/6AF9/6y7eeOMUb37hVYzjiKvNFuMw4vnzK5xfrPHw8VM8fnqB5xcbXF2tcXF5ifff/xhN2+D4+AivPriL3W6HZ0/P0fc9mBmvnzY4ctJ2OtKDKIwgGqSCeKoJbgIUb47W+FJ63Lzg5lYgZjk+36YbdFWIZjniy0aZKEEv/dZnDcllOW2TGBUAweMx0mpVzL2uMavXQKTX0Ph7XLDLMTAGH2JcUiBEPA9A2j3VzYUASkd3C8CLsCPLy8mjGAg5VmkcWENRaWz2AOJN0l5GmV9cJ9iVvH2unrkydKDMnlJxbmaLHDD63UiYuk2m8iO9myigyShdGvUUJo+PGlxtfCwrHqCrZSvhIsaAXR+tztYiEOkvAmPXD3j01OPRszUWixZ3z8TI9+DuEc7OFrhz5wTBizEwxIt11Lh/tRZjX9/32O16PH9+CXIOi0WHk+MV/DhivdnCjx4M4GxJKci/FY6twOw5e9ckmEApGO9duzhEPXJbzLN9FvmtXe9I+YRXqdxI2ZhmAdgq0frY6jhJvEtGOoD0LJ3KlqyxPZHohPYzGW2YizkykFFIsZzGHeOGssq66hGp8QxvBKB5Qq7RxPYZ7F+y2hsw5cPZb9TGbdItpu1WVVQ0oOD1VaE2XtCmiSAbw0ob6s2lVD7KOt57rDcB680QL8ppcbxa4Piow3LZYrUSmWL0smE8DBLGp+/HePlejEu9k9h8dLFF0zg5WeIllI/GlNSDOIfmwa7RnGx1UB4z85boyEzFSb+sKkvlkn6YjwDvaWpvVxKeq1xW5VHjpKV3sP3iKbxSncf02z4v2ouDuK7Pk/SpI8tnWu0vdNq7LpZ3ECpab2CAr19bCcMlMdKJJPZm2zq0DaFpHdqYR4/oqpHPF3qEyKXee7lwB2LcY8TQMpz7VcPtof4lcnTT9JLsqsCNl6XhVfnJhuRM+5MqOHflJrjxIvx1X3phjz1OQoQS1ExpU/yPdAw2vkm7sur5lo1/rIp3NAqo0Q3pYg0kyl0wiKTPU+G1o0RYj7+q515hnUiZ6nU0y2HyHJ4PLZlmJo4yzAfcn6nAvs8GPSMAcD6Op4q+jzdj+jEb9Tga5jjIrtrJEWPwLa4ue2yaBttdwJ3TIzy4d4InTz+Eaxu8cneJp5eM7egRQgOQ7ODFgB7w44iHj8/x937/h/i9P3wbb73xCr7xlVfxu7/zTfz2b30Zb7z5BpoGeO0bPX59d4U3/+hPcO/kT/FH/Z/i/e0l1ruA9c6j7wds1mt88P5HaJ1IJ+o1s2zl9lcC0DYuGfTA2bNOvTjzUbyA3RDnguXGwHHkBCcq8Kqg3ziH3eDRNXITrB7h9RywbPVSgHh0Mcb50Z10Zo5efFkJWLVRYI87eQ3l435EcoQ4CQLMyU2fIiy3jtGPHotWPHFIlRBmnG88Hpx08TZfIcqDZ6CTkPjWE0mUhujp1DgEH2MVOjmCN3hG2zTw3gMgnK0c1jsv3j3gGHvsEGyqRCJ4wRY9Yia7h2myTnMoTJvykp9N/gpb6hspCcX75HEb6UuRTXFpRmgqU6YN++hr7al7TYWTDHO7N4rr9n321ss4P2fUk3wioC86wLNDvxsxjIRxDFgtO5wcLbBen4Nah6NVi00f4ShGecmbLIwQPC6utvjznw740btPo+J/il/+2gN86yuv4I3Xz8RLdJRjfD/+6Yc4WnwMjB/h+Sg3rw5elYQBz59fpItsxFhGxTF2MVhFY2OE6Uyrc0B69mIAV+9aORabpWQ28KLB/MULIXrecj6e5uIugPUIFz6VN5T0CK0aBdTob4OLZ3TR+DuRb3E+iqfjEC9cue02pDWW2d8OASdLuUhI6/AMoJFxcbxYKis4EvOHHMkuKiF55ImhhOKlPMCqdejHkMbqCBhSr+fA9LB0UWJ5ejjBW92wOySnKNgXeQ4KNhFKmMombyAMzaKnQcZrq7iFwDWH42VfSnmi5vV2E6+QC1iPWouHlwjjiDfISzD8YdgBRFh0Dv0YN7OSYKW0m0EI6HcjHvaXePR0jdWqw+nREq/dP8Fr945wdroUQ3OQY3yPn56jay7wLJxjGzzGyDcpeGy8x3azTWCjU5U8YVErMEauqebMI95sG5+FMH8ESJGUILhC8aItH6cKKI/haxFVTpQv1IqVGvXSWKjsZ1bulWbYF1HfDuK1V58SGTywaPIJBKHrYmPX8ZZ8TWXpHO+ZknxMWVZAjqWZ14CvgcOKOU/e7H9/25R49E0z3rBp3vN9Ln1WRodr2b7JNOlvYdWPH4b/Z70JCV+UtylfdCkOZWKE1WBFR5A4uwPaC6ETJ0di5FstlwJHK823hdsQtqFP8al1g2EcPcbRF7CePq+Rm+r5YCqXe6/oyZBbfDnHyoyqZcLBwkA3NwWmHf08FMR/7lnBa5RuQHW9soQPlI/jVmvPuYo0Pq23pp9RRSg2HVPM0Lpfn4N0Yzz/S56uJbswMHog88TD/Lp2WU5vjT7EuPbiyde1YuxTeEqGfORY19oVxfVQbSrcepy3yFM0xPvx96ZVvEyq8Rw4QE9m5IdJnlu2n9b7mnqvS7c27DHKyyhUsUyCCHROoiCSBMks6Gdilo9QkEr4pm4VAFPcFUaqx3QoTnpskWAMgWycAE07KBfLmgqrvdHUjD6dJV6GWrMpwOaW0SIrzy146SVUKPb6GZEyx9WL8bZ8iIpuSMq+GgG6lvDKnQ6Pn3ncvdOCwXh+vkXjgIdrj6OjBV45XeBXv/0quuUKzy8D3v54jQ+fjNiMGuiO41oEgAO8H/G//V98G//X/++f4I//7EOcnp3hG19/Hf/Ob38Dv/0bX8Xq6hH+7n/2d7DZBQxDQNutgGEHZjkS2o4j2Dk0ywYnDji+e4KnFwOCz7vs3odIjAitI1zsPHZjiIYoOXa2asUosR18vMxCb74M8WKI7GmjN8fpPvaiJdAOybjArMcC5fZN59T7LxrHGkpH8QIHMMQo3TiCj8eaJdg/Re83gDnIZSExUI66Q/voabloCTvP8Ii3Wkb/Z13D3SAKS+MIyxbYjXIMGRGOddfWew92DotWxtkQYWBG6+RoXmC5EOSYQxw70LWE87UCshraLdwrntAU1mdgP6EpGWHEoEbOZ0Sh2vBv6qf0X9lwrq+UggxJKSrhmUwZ22ujQsnkZtNLEFvAEmuunnHxnit8t/Ez1CW+8NpzhKNVg6tNwGolquJmN8IRYTcEdIsWR8sGb7x6gqbtsNkFPL0YcL4OGNRtzdA92Rwg/I1ffQ2//y8/xHsfP8c//u4Sr796hl/52gP8ytfuYXtxhX/53T+JcS0ZrmlBfgSzeLNREEu1ax0WBHSrBTY7nwxk0o567YlxbBvrUk9rMVoInIxejxxmpV2N0RqQPtj5ZVF4dwYK1Ejaxlt2k4cviWcbUaZBebPKeOOx0BgPOb6gAlBgvSAkUn3Oce8aR2icPPcGN2DoAUFoTsuEMQS00aJAUE9GErrDQg+ZJbbkGPujeOcDsGiExrUuK34ZoTKeHwJla2TQvFMV5gAuxfkX/D4sbnH1o8TJufyWFhgNkTDf0Ruka9H6pfB+3siSDHqxfuulx5zf65IxxHjVdYR+YLRt5IWDFwNXz3HHnnDnZAFqGgwjY7Pz2PacLl9SWSjLX4yvvH6Kn354jmcXW/zk/RanJ0u8/uAEb9w7wrDb4YP3Pshxb8mBkILgwgXZNCEitAQ0bRNDfRj4MHCvm2Jyg7U8DBEXGeborVF2Fa/VO69eZme1X9umsEvEu31S+2TqtrxFY13pd82f6DKmYYh0rXRTUKfZIocYKRnUikwh7QivBrJcIhcRxHmNY0pxgSkbRDUmH6DGnnyEF7gFCtwSX66x+1/blE0Hq+GbZHrxtj/zxLNf5bchBjyTgQFZf5QXv4QI/ApbjoBF65KMN0adoKxdab7wiLsnCzy72GK7G/H03GHRtTg5XuD0uIMfRzx/fplojQJ+DSKkfy4bGPcp5Um0SGM38h7n34pjWQKZX/oZNC9x1ZRLeF5M7HydSqvmsqQ+xTxEFC+QnMeHfO4g68Kqt1gWZceu78JMfdqHNJYZ3vx5SrfC879E6YVkCJraA2qDkcBclqNUTigrLme5a53E0vTi7KKefIvWIYSA3TBO5aZp1w6+v25oN3n3oqmmR8WzfXh+i77cxKBqZ/02dV+X99Pw2ru1YW/WtZKRelrEKUkvox8bRzElWQCE6BGyYiJfM+AKgFP+jhILCEjH8KwXoO0LkmhkihIk1poqmNBFMisaf+aFIDOaYlYSJOj7WWZdfa9jEwqTtwa+WuhXLz3ON1xFj70QL83QOdV4cqvlArxocXW1Rj8iWut38L7Fth9w/+4CxyuHfvBYrQjf+uoDfOfXv4LdCPz0gwv86U+e44MnIwbO3nMhMHa7Ef/H//2/jd/7Hz7Cm6/fwd//7of4P/2n/xQnJ/8Cf+PNLU5dg+/+8BO4xsnttNF1f7XqcNp1OD3u0DQtcHGJTXBY74D1zuNoIQp84wiLOAMuxuZxAIZouBIlXt2KZZfCURsvzJB10htgdf2cE4PWLsasUWKphmmdX1WmxVgRFY8gce9WHWHw8XY7D3iJF45FvFWXA7BDyMd2ibEbxdV5GBmptWiM8QHYBcaiEVhQQwORKPyBGYvWYTt4GQvHG0BZ43aJN+MYGB0TutalG3gJLsbQAzgEEJzEM4vxEuTIXpCxWCS2QErV75nEiHTBuugxxHOWEWNT5jqyTS7iTMSx+qiU4l5KZN4pRrJ5QVU3eR6np7g482ImXU+8b/CWi1/yrRB8MywyoziCm13q87yp8a9rG3Dj0Pc91B7v/YjADqMPOF41WHRyQ3PbAa/dP8GX3lxi9MDj8y0+frzF87VPHnOACJnjEPDv/+7X8KN3znHndIUfvn+O/+qf/QT/4I8+wFfuDFiSw08fnov3GOfNhbZrsHQNlgsHcg2w3WFgEo++MWDRcrqVVsyQGjtM5iFE454a3QPyEQKCS7dnSj81TpbMqxrjfVD+Uinncc4Uz50RptXQ1jUSF1PzhOgi0ETPGJlfNUzmGJyBYXY7M/cRxQtoneCIc1FY46z8NE42IvLtg5JXPXECi+GucXI82DsA0YHCQeP0RDoSlT0Hc0yvAuAbyQ9Weknfhc8qvlWPplVEBM/vDrdckJ6pzFrlLWlW6ksqVhX+TLQMTm3eIGdBCFSZTd55xrhn62RkGGEQ3OiN52f2Xu+67KHaAjg9XuCVsxaegfV2xMV6wLaPG1OpZuEJv/6N+3j0dIvVssPD51t8/6eP8cP3WtxberREeHK1ywp43AhonENLciRY3EF9PDLOCVYDWwNWlMZYYZrRwm4SmxhWRBG38zjLXaCsZMv3Ukmy9FZjERa3YANprqxhQfFJPaFyRLusfIuhzoJT9NrnHDNLx6qwr2voSC4CU+9frdyeLsiGzTg3ISv2VrbWmF8Jtbncmn65tAf5rsHJ29SO66qqB/KZ4O+nkPjgz/zM6AkJNmcyJrjhSteIMJljwwo8dK2Ty+hYNr37IVRGPikcmPHa/WOsNyPa1mG9HfHo6RWePHdYtgI722F6xlNhL4XACNbInL1O7fLY78W7atyGbc1krgwMe1IhVhleU2wQzCyKbbs+3ZzoSL0AiUZlXKRqsOUQyo4X8h4yXSmaqURSW7/Czst69fys0o3w/Bc03Wj6r8tU8S0tk2DI4gIBLl4Ol0JyzQEBA6tFm7z3vGdsdyN2veiuhBwOoupK+cXg6k3W79NgDS/aDuMwTrwIqkzKzHTuJvz2unlJdOBTQpIXOorLEIWBOQtMWZjSY7DzM8BRA5Cg30aBiatSGwaFMLMhwDQhcirY6WUBquzHD5RSd4ZYVdry1RtzKUUEBBvSbxcqeyFlI9G+lIGu9NCrfxdCgCKwMebJ7lxIv/tBjuIeL5to9JP3eqPVq68e40/+9BOsjjp0iw7PL3qcHDfw44jl4hgXlzu8//EFvvNXjnF0tMK9O6f40hdfxb/7Nzu899El/vD7D/Gn75zj2VaEg//L3/lX+Le/8yW8ebaCHwK++uoZ/pe/+3X88+8/xD/8h9/Dv/dLLt32loJ7M6HtWnTHCyyPOtBuwEAO7z/cggNj6VrocVWNQwcOABq0DaGLt0QuorGtbQjrXrwVO7ONHjj/ESMF/hxj2a4RRUQu78zGPB+VFaCM+5OYOecjwS6K+yFIP5og6wOPNP9dQ9gOYgykaJjpWofdKIY8F8fpENCPhJMWMRAqYRjF8He+CThdyiUZNMSeOGBkxsJJLDId6+A5eR2Z0AiiPDXitbdoJX7gZuR4VDEeNQbPGpoVMzjCYUbriPzVedsc38cKchnXteh0l1P+r4Uh26MikHRVLr+nIs9EJkMmxFS/uCYdxGvTteksloL85K2Zd3vc3t5wpR57egS9M/F2QoznREQ4OVngw48v0XYOTdNgs/VYLGSXrm06bPsRzy92eOvNBbou4Gi1xDfunuDbX2nw7GKH9z65xEdPt9gMQsn+yZ98iK+99QruLFuEwLh3usRf+cZ9vP3JFX74Fx/h114j0++4IcME5xzcokHTNaDRwxPh+dUotzjGyx7UFiDzFvHOEZoglLaNLzWAvHj1lHOnAjaSwSHGy3QE7zgdRYeZ53RsCShg0m7vqMBkFQSJr5m9BJWuNYR41J0Sj2viJRdy4ZjGDJXNgUUnipsGSScA2zFg0Tgx+HmFz2ici0Y6BRMNEUA671GTUU9jH4CmlWO6g1fPKFUgaglyCo+VfX6PQKL4f/hR8Q6K9wVWH+wLMI/315XJ33k2g8XTmh4w5vD49mmK8zx5Zz1zFZatwN4kL26k+QWA5aLB+eUgga4dyWVSJkbsMAZsdjvcbRo0DWPRtThaLfDaPYfNbsSzix0uNiOGGNPxJx+e4/7dI6xa8WQ9XrT4woNjPL3c4dGjc7xxqngOg3hxE6ohODm3CybCZiexWp0gQ+XhJrOrR8oAQwMox8G1XizJ+Gl+a18cAYGy4UFaUBk0M5pKT8m90cq1ZIyVmbxM46JoVeoZrNyJKG4AsOB5NtYz2miUUw9FIqSNRz02rIqb1m+nN7CNNZwXX/Mm70UGvOHfaiS9Fs9f8OF+mvBi6RbofRgpP81O3aLtm9AJXRtLEzi9m96uqsk5uQBJDX3W0K2/x5GxdATHDOccjtoOxysJxbHtPfoxX4z2/GKH1aqLXt8StuX0eIHNbsR6vcWxBsWc6YwamZWWKx+cylOmjH1v8fwAeFr6qzRR8UrxZULoybzbU+lMkYR76bnBQ1uV7Q9gYmEi64+2W4qbBEw2G2xdiZ5ZQQRZVi7mNpKAsm+H8XzfXPw8rGy3wvPPYbq1PDAjeuwdewXTdV6hG+oEJXxHHEcobuqpji35h9HDO5cuZSEClp2EtxrHUo6e64vll7c1Hr/w+t6inb1t7KnjReW5GlfrupKI8ZLCYrHWnwJ+3s6wp8obhLRpPBIVImpALOPNATb4dXqvsUIA2Z2iUnizXhxK3ZKBT9tShlEwTK5WczbTNK/lXKYCNsuro4rs1hD8aUTbgiibp5U8WbUj3EaQSjJqPD09Qz/EmHrr7YgWHhcbL7fOjYRu0aBrHTa7HruB0fz/2fvz+GuWqy4U/q6q7t57/4ZnOENO5iCRQVBBGUQGc3lfQAY1CBEkiEmI8irqBQRBriiCel/1In5U7vXqxyQMCRCITAIJwwW8Eq6AyhAIBsg8nJxznvMMv2EP3V213j/WWlXVvXv/huc5JwRf6pzfs/furq6qrlpr1Vqr1uA89hc1Qgw4XTIOfIPj017MdfsW602DyD026w1u31lib2+O+bxH3wfM6gYf9Jyn4AOf9TTcvLPGr7/1cfzSm2/inTfXeMcjx9hsIh456fCb776Dj/igB/GJf+jp+LHXHiOvEIFZYnmBHKqqQl1VEkPv6Ai4eg1Pr4FZTTiYR/SbFYjMskXd3aK5lYmQXnv9TWp9RqJcq1TobvsIh5yFL7IkrwjK3RDypmux6RyJIA5dYdJYWMbkSxB6FeIjY93LkhnzDojiTNYxU007XWWW5yoPLDfiRtwQofJAozG0ggoQ1ieRZL89mDkQSZDiNohZv4CFul7EiBAd2El8D68CkgXdb1XxWdD9ZLUHTcwhcQqHcDv+VgoXO6DcXjpxR1nQH27r6SR11IyhHQ36tduF5V+J24UMNuaaB+0r8clM1nmsxnkUdmqM2xvh4OfELmmbtj3LkRE4J8FhZrRdhEPEpotqwUHw3qlQ30t8RkeY1R6RI9qWMZtX2LQR3hNC6NH1HswRfd9juWpRNxWqqhKlX+Xx4H0HuP/aIT5o3eO9jy/x7sdPcfu0x63jDfqecdwGPHZnjWc8eIDnPv0KfuM3NtPTR5phy3k4cuD1EpgvcMUBlQdmNSP2HUCFlZlaH/gE+8J8WLZJWytPpO6lnJRidkoXIUqtMiZXZt7ZhobAQKMzT3BiuUNCr6JuBSaMd4oU5u7GsJPODOO2hk6/hAg4LwlwKi8WTY6EZhlNHzMKcugg370q59LpI6Gw2AQ8S4w1yyxsyvQyEUHCBua8CzPQBySLdi5q7oby7bv5yuU5kLyvD8d53gjKL6nmPTA/PPF96tqF29t6YLoFLuClVIbbOhpMi+WmPOAU5kNUAZ1EIGdoOAYSl3DnSPcCDXwdJJaO9x7OSaIpOOBgb4a9xRxdF3C0bHHntMOqlSQ7sfbY9IyTdYcrBw0euLLAI++9MT1/JEp3pyEruA9AVWGu1ysvFuxAqdxT/snwXPdH+2147uzQl4cxpcxqr1R0E4aCrn2XI8FhGTDqirTWBxigQuCxA78sxmcpyZ7xhBT+wsbhUh/ZHdkUjyHm7NhlvKzUOttzQwzj4nucQjsutl3GYC+/mzLsYoIGcInDT0wZD/nSzd/jOz9ZXYyVerbPiwxRxIJO1qSZM3F2DZxjixIyzdD6IR3qGr0nNHWFuq4QAqPtgsSfDpL5lp14drRdwLypsDevcXq63Pm+to+mwTqC58xLp/119Kw9Z0q5kcFtUnwDQ3xOYtB4DFPzW9zbCTOKRFNt7VrTKbwr32En/Nt82KFCyauOR54E3kL0LPB46wCch9/fByD/hJd7xvP3QbnneS32+XL5B5S07GTHJIzrD8ZFhn9yuCZ032SGHMJFvLzUut6TxMm86DvQELfOwxWauDZ+xV1tXKbuznLWIM8p5Vqd9R5PQteTg7k8d53LXSfPGLoFCPNpVgRCrHOcJPnf4o+otV5yrzVozQSOURA03QFlU6QC0ArLuII5Kh4EuFD+DSqlGplxKzmrQY1xsU2ap2vw9nUe3OKyWlGP07ubgjCd5LMw9X0f0YWAru0Rug6bLqLvAoID9hvhSEOMiD3QdozjGCWRAznE2CAigpiwXC5RNzPcOWlx/9U9dBpkv3WEtm1xutxgPm8xm3W40zMOFoz5bB/3XdnDx334Hj7yg56Bdz5yhDe87Rbe8q5bqBdzPOup1/Gz//0xvP2REzgOALy8S8zK3boiHCxqHC4azD3gvANfmeO6c7h1+wjeV2hZFGSVY40hIkz8plP3VkeY1+Jm10AUX2DWdN0S7Fsy/kSAXILPLoiwZDH0bM7bXoRl59UNls3qheGVK6kdYd1HVM5h08dkGec9oQ0R3okFUq1+rxsN9N8FoPZOspsZEXaieGvVwqJyhKUqcRqVLsTNAklBxxDrpabK8flAwlxFSLKO5CQeGYEouSizE/dLrhyCWu+IhZ5lT5RoP1EFrKlSMhfGiCVGD9iKracjgSGuGfZtKflGcfJGqDDBOA1xPl8urAHHjNDWj3wlE/GsaBlvxACPyca5ZdLFflhh657RBlPmicVtAAeJFxkjIyo+ABYUX9xtNhtJpiA3qsRUtpsWvqqw3gB7i0asSCuPECRrXtsG1FVAVQWsA6OJQF3V2Js3eM7TGjzjwSuSOfvmEo/fXsLXFa5dWeAtj5zg5vEGGmkujd9eyztCU3vMay9x54iAeYU9cliu13DOIbAqwPT0URM9a4IMWXuxQJIV6YvrptDKbgg5JmuIFteuwHM2S0eNb6VcsyMRrJzuP85J8GFXuZScw+ont3zW0052KSOuuRyaa4MxWw7ZddY5cdU36x8gu/Ca4h9AVgCyWewI4piAYTBjlnqWfNMp7EDjHsm7DkN7m9KgjJp5Fm8peD6BUMV2Cd62xp3G3VEzPKQhZ7OMRcdlzYlHaOePuyzFPj1NB6bHfdYYTeFTWuJnl3uZOztRtzWLDPQaI9XatTGFGOFIrPSa2iUmPpLsg6GPCC7COQZzAHvAOwmif9/hAlf3F1htOhwtO5yuO7H6mdd4/LjFatNnWj56JRmnQ+WdKNCIgNqhBqHte1jsL9musms9OMfRkz0xT3CyOqC8lxi8AwaP4hpfWodbMYVfCbvl2bD8FhwhPTRM+OgoKesApNiVQQeR+ivw3P6i0SYCulQ/17NxpDEg472MV3ei4v3TOxXrnZQGWjfh0QjQztqvdqLFmfjCWxWeDOXeuMdd5cnqdjee301bnNeNc5ulom+QY0Gvx+LwtMD4RLvtwBkF/KcwMsxqMRoBBxA5eO8wdw6zpkLXR7RdQNcHEDnUlcdy04vL3sQ7ZNgl5RuLG2pxbrxfuUfZcwa70gby/jGC8/TseOKNFlhbmBDTyuoFTKZ1zGzA4Hr5WXZX8rFb7SPj/Pidy/cezwOn98wxM6fGnsaldF8UM9vtl89cujyJOHs35Xc7nm81PPpawhhPV50McZZ5rKKBEeSOFcheLfmSYk8NX4gIfYjnwwsN53xq3FNl1/2Lzu/27nJ39XfShUu0fdFnLzLeS78/X3zOzyqXV+yRCGreFy6xyp0RuaRMMUGLLBYXpAscAADQ60lEQVSPCWBGrMo4e6TCNQMW886y6tkbihBQbJRGHgvLIKlSWkNwYoasrzF7Ojl5O6FmfJ0HDUy1ONhYtjZ6faLYjAaBdTlr3vteTOq7LuDmnTUOZ/L+VS3mtn3PGrdJGPy92iFwhHeETR8RuogH7p/jdBmw2QSsTk/Qriv0XY/7ru2jrglXDvexXLW4dXsF7xvM5zMAHRpf4eY734WHnvV0cHCoqwrPfMpVPHTtCk6ON/jt9x7hV978CO6gxtFqgz3uwPBgFve0a3uSoOPq3lyt9QieA+AYfe3gfYUrh3vwrs3EVglR7eVk0Wlg+hBYs74GfVcRrCsvRK0L2V3RkQqdKmAEjXHlNeaAxPIDOCphTDMvloHOSSB7SeAhsUtKRQaRxM3qg7jGuSiwThHJFc9iaVVkCTUA7wklR0yQdiRTKSHGCK+K8QjCpmfUrggArvNqyhGA0QVC7aHWONJ+Ug4yJ6snC/4PIM8p08A0O41MLUWyXSoS/sOE+cREmSs5pX2HaahEyNfyBcMFQnq51GhJpAdot4WbpcJvl9pCHxwO54zPkopg66GzHO63cH5iKCW7bjShdLcNQaxxZ5UJ0Dnuolm4hKAuucyowOgiEGOH/f0KbScm913bIvQOIUTsLRp4D8xnDbouYLlq4ZxHVVcAJMDu8vYSh9evAJHgnce1gzkOFzNsNgE3jtZ4940TrOGx7no0LPhgyTvmtSTomDc1vBNrQsdRrEK9uOfOZzUclSeGImR7VUY73TeiWsiGKPCXcMfZ9ZxURKxsKDHMkUXR5cji46l1b4YC/aJWNkTJVcncYzWYEMDD+HYgSops1a2Lco1MaVgE3zdNeSFQ9GwuTJZUSr9D3fQGUoYoEyOywkMUukM4tp/jfcT20nIYEpbPdtA8rrGAPtjby98myYz3wbHQv3Vlu5R4sltBsJMVnqw3eLMnhGvniW+juxfoJ4UbQYnrAp9dF1MWRVco9EyANGUTQ1wzJYlL1JiZoiQTC1+BqabykmSjErzv+gByDt6rxToRulWL2WIOcZsnLGY15nWNvg84Wfc4Ot2gh0MXghzRFVnJ65pQe/EKIHIpIzPAiLo/1N6DqAxCZ7HsVBlNwqsJBaGkwOCY4dTePfFEaa+hARgSMq9UCmulQrKsB5DidA5qj+L+WMVhNMJc81NbNoYx7qBQMBbjtLW3A4N0gy2JSbEHqcIcxXPmZTDEiMIVsKQDmC5b12n6xvTzE3jO2+//vihPCGo/CW2XeJ7a0kvmWm1ley2HHIfxU0kBXLQfmRKtMEE+BPM+URmIGKHrZH9Xt/m6cso/M9oQ0bY9oim6yxchixGdFdwZlfOi51iS2y+SwHvifctr43lIN2C0Eql/+z1u96wyRaMHbRbIaZZyg2EUdXe2Xzywa5xAodQb8IfbLSeLvYn+aFTvUuV3AFfvpby/4vlZDY5XdDeenz+OQV3axW8ITSD15yet68kOf9RgYEcfNAHb1sZFcWz87L3Ore2tZ/Vx0XZ20pgnoFyU/zuvlPzBji35UuVyij3VBg8Ee6A49ZShmcutKFVKNmss6KviRflGY6xMCWEbYhlkOBMyHkwq2SmmQjanGcrYsHNxbYwRwxkui7VndWzzmmyQB9fz5p7fKZnb87aKgIr7rIK+mdK3XUDtpC0v3L4I+87mRoJnd8HcXYC9Wlw+r8wd9mYOzITlusPxcY+T0xVOTzc4PNzD4cEcfVhjb2+D42XEfD5H4wEExtX79tGul4i316j29uHrOQI57C1qfNjTr+PZezO89T238aY24DavAczwYc+5hmsHczSVR2TGzTsr7M1q3FlucGVRoXnus0FRrGTaNsItNLNXZIQg6OgcJQuWto8gYvR9BIix7qIoLSOnYNd15TBnYNlGVCQKA6fz2QfGvJY2GaJMC5xdCSRJhyyuJ7F+c57QqrVQ5UUpx31U6x6x+FtU0kfHEbVm+hBFgrrWqoLQAvBXThizruckxDSOYDHsvAOiKhgiM1YbRjWvQCSxwxga9FvH5d0A4FN8QoLGVFCKHuXoRtx5SZUjJBLIIHOl4nnCH4VKsxAwoBaczViVmdLxtlWIWMb30nStaTZntMFMIUxRczed5e1nzyxnV55kGIvdPU3VOcdkZZwtiZ8pcSO9Tq2sBeeTegCC50hZ6YgIjVeX0prQVA4MoOsiNpuAtu3Qtj3m6xqzWY0QOzRNj03LqKpKFLsRmO81CF0HXnVwdQPytbRdezz16gLXmwqP31nh0RCx4g5AhYfuW2DR1OIeyIzlukNdeazbHvPawd9/HR4ae7JnUGMxMDOdNMs4s4Cz+JUAowus+CPwFhXmayZ0fYDjwlWWxZqHkC1iktWQrZdajoPNWkiUg+LCaGOBBrjXg5UoynXpnxMNMeWjwK3GWGU5a7e4SSFmuNb45vmdzcIOklTE1S6N3fCQVUsgfeYXMSW/zYm48lNSrqSkG7pn2eluCY008c2EkhFgb3FIJU6PY23uwvNdZSDETHE520+c1doFerx8OYuJs738Is+zwrbF0Mwx5ijxAyU5NgtVmwtTbtcuH8gEtUgPAViFHlUvWfAiR1RtxLyX+FtmMVfVHjEEcBfhvAc5sbD33uHKosZe5bBcdTiOQIcAkMPhokJd+YSnXRfhKznwqh3B7c8La9rs/m20DchCBCO7i5rrvCk67V0Nv9hJ5nkq50VhHZRjjplCfUrYTbsY5T5KmkHIdMITUuzoMpultW8vkGlXDqZf4jYjg3CpMAhRDuYMnUBU3M8KeXvPcm8ucdNoziD5h9GG7SkYzUTRYMHPnl+2KybcvRdJ5HdJuQieD+vmNcuclEz4JN9ffLc5pSJckckWaZ9M9JVBUUNJONI+onjFRAnXInKbEAxyhBlJmIiuD+hg1oGEpqKUdA5Q+cL2PSJQNbLSpzxumxpTzBsOlPdKXClBrxSvUHwysnt7en7HnJe4P5hLymtgz6c2GcX8Fm1hGFwpH5rlClPWdNZmRM5Yb/Gfc//57dP6lXvw1NyOPu9qn7swnv9euVThwQeA3aqESTw/o/423yH8q6N8IJS2jNFJi4CfUgPTKeg9O7suttRBn+VB0a4yBUrbcPr+U8q5P6vOVBmv7VQ794paZbv3Mn+XVOwNf9omt7XZMWC2TUwYnFLJbY0XQchx9RJTkinZwIqNsgvvkIngrX/LmSnFmMmJGq9QeXwzJoKqmGHW8U40yMN/Bsx82tjYmEsT6CXIbdeLa93evEJTi3tnjBITY9P2yYz+aBVwCCduWhCBkIixN/NitcXCbB7sL1B5xq07G8RemMmGRVC+dlDjvisNli0QA+H2cYdHbvTwVYVNR+g64OrBHB/83AfBoUfXd3CoUF3dA6paDu2OViBXgyKjmdV46kGD5uQYbwhrEK7iqfcdABCFlVjPSJba+cyLS2wfcbqJWG8CTpeA29cYWSrwAGKRRJC4eZHFgq8NjHntsNLEGebeysyYNw5gwrW9Stxhe0YPSVoRFY5CiAguu70RAZtOMtsm6xwWQWNREygwggKCWUy0fYQnBwJnNzxWyyoHCWSsm3VU5VrlM6jFGAFPaVs3CywHpIDoQUwzsOkYe43GWqKIyhFmFaElBrrMgFQk7+mgbhNO3Jq9uhjOKk2sEiKocuhjxH7jsO62AXkqCH7aFkpUL3aVjJLb7nkTjUkpGTHDj4FV30iI2Bppvkg76g1aKHfP0Q5bMt/l8LbVjcNRjDfeki6asGbMeN9roNvIaGqXlPPMSNmdQ5A1WrcR88bURVmJJ4y3dORAmM1qOGIs1z04yrp7iCJwMXOS9ToQOAKrTUR7uhF32CgWP/NZjac8sA/miBgDIhzcvAG8F4Z03YmvOjN85XFl5lG1G7yHexCAK3tzfXGBBcP3upJDBkRRgPchYtMhKfZM6UZQ5TREgSZJA7IA3AVzUcxW4pWXWVk0DiEiZar1LrvKmcc6Qd3rSGLM1c7Enhy3qCaSAPQFPjKQMveKcEDJItyUArbHkNI5UeplsWZwwKStly6+JoiD5VCjYQuMzIk+BSBpFZih8UNdHpPeNgvFSq0fWZV+gSVpUI4helaZQEy9WhgDT2LEZLvTzZ3dMw+/b5Xxnj3G6bstYxy+SHUecB6JPNoeZjDpXIaH0lovRoHJyiPBlsGvKfCsVJUHkSjUTAA2RVpdEepKXUshh0abNiRr1BhbVJXHwX4D5ghmidFHtU/CPvqYFtc5h1nlQKHHkQbKmzdVelfiHGvYMrkLDbODOQDKi5RzmS3x7NAL6TMFAC9wy3lK2jY7DACQD3tV8W1Lb6gWY7aYNYUXMwYWsZzGYnTI2la4Y52aYn8p0HXIWZYeJjCBSJWOKlyZctNonwn8RGpZD0jolGJspmCxP3P5Nct7HoxCLZ9xkXIJxLxga2Pd/v8opcTzrXs8nMmBgsjghHPdBEAYkqvyk2x/ijyoJPNbKoF1D0XmU7gXt/xZo3lqTb4qg8TapsuyB0mc6IiNdubdcBHT+xlPx9sHczwcavpiynsj2UkBPX5vGrolp/Z2wJPhecKj0Zzbodq4H8Ofi5SpFZ8G8fIl8+LwqLLBh/AMw/Aa5dwmJeQAz4veJq79XnmSy3iyJ8QB3r60VQb4PhY2dje9PRRmcKRBTFjBF0ptDEiW4Uhx8Sw8KPe51PbE97spY/btbstZ43hfbkVPRF/WxhSY3c1cXdoVl4uVNkYmFhIx6w6fo34J90J2wmqPU3bT1eaUiedkCTRQPnM2L08x8YqSJ6Bwc5BfZ3McPJ68UcPl8UzqgYe3xk1yrgP9njYrzsx9iIyTZYu2DehDQOUk1Xy/AQ6vHmgdiaO36QJqxyCOuL4vMaC8d+hClOyuDDzloftx5XCBvUWNN/3mO/ERf+i5qAh47OYR3v7OR7BctfCOMK9FiUBRMs0eXpnjytUF5rMZjpcdvKuwt1fh9tEab3/3CX7/Bz6IxeEc1EYwRyAExC4gckRYb7B89AjrThVTlU8uAoAosJzGoHv01inuv7qPeVMjHp9gDYc2eoTYK0yYkJwZ7CTMkqzlTJUalQM2PaOLkh2WSISjVRuxbAPmlQgmN/oezATWmHcGU5ElJl63zu6MXZDEHMSMHg6NL1wfmTXGnroJ6viaSkYsbroi6HS9SQViHTerRXrpg/QxqwjHG1FI1s5h1fYqxIiq1jvC7Q2j0dhcUZ9rPKHRJCG1FyLuHYNClCQFCco5JRYRFwu1YiLN6qfKzNrJXJrF4gBDGNmKVoF6vHEltDIOrJDIadAG6/e86SRMm2K0SiZxUrLfgXc7rw8IQnFj+/q4jTFd4O0b+nNIEwYKPRYhuw9R8UGE+RiA+byBxceJUZRfnsRdcm/m1BpXhFeb4oPDfcxnNera47HHbuPpT3sADsDpco2bt4/Rdr0w7C4Lwp6A2aLCfC4JbDZtgHMOTe2wWne4eafFg/cfoJ7VQFClUGRwEHUXdz3ak7WMO6rwQbZODNZrYKFp+/MGlffgTYsOhMDiYm68b8Lx9I8Uk6PN9ViS2kgMSAtQ75zgWGfJKRxw2qqQEVkt6AwTREm47jXpDCwmnoxVsu8i4Qd0vvvAOVg2cswzCZepSTfMf1et4yq1kjTFbeUIm6AxBJ0ksQGya5UjYNUzzDNfFCOAdwwoXnolNC5ov6UEg7wGhr+2FScLRULKwlnui2eVUiAp9+ABum89hAJXJwTDuxSoJodbylGjSmeRi13M02XKpKsdMr73fVbo2TqwwobU46QAM7fzugLYTkIL79XZrEZdSYysk5M1rl5ZgABs2h6rVZvCRJhyjHSN64pQVQ7eOfRq1iOZ1gOW64CDvQauciC1iJUEXbJncWSETa+u71mpnd7VJpGATRfQ1GLBhz4gQBRTQ9fZUZzH/DiAodWAKbySex9BD8XU8p6AVjNGi4s9Ddq1+HbGfsQ0fuE9zU0eqW8JZ5GWk8pnizEX381a1/gSGa8oz0nx0QINGG46AJtQxLBlJOtrV8SldoS0JluoYng+2ofKsZllRnn/rLKFxyXej+9tPYmtGmeQgN91ZYznw3t57YFMU2ytrQ6Q6TEPaHd+DhArWYEFQtdFzGtJ+RKi7HFs2qiC+No+yIwUGxIKi5FZ4m02VXLPS2ujeI4IxCBW8OfBSoyMSmP7mhJ9/EyJ04PrNNxDCMNYtGn+OCvdrI7RGwZyjDqjq8iwZor7QZ+jcY0VhVN4MvjOQ+UgF+1AcXasQZdxc+ZpuHh3KuaIcifjOTO++axVKWly2dyFy9nI/XulLDyEpfSdt6qdy1sk61qS/b5MjLNryUtYGgBtyfcl69pcaQjLZ3kxDd/BYP4M8nch0DlrLsavMnX9Mm1cZIyX5fnOa/+89u6Vz7zXcsmsuIAJ+HbCaTFR0v0t9j9HETKWZIv9sFNKyoTNrHZYqXFBC6UNOgNABjOad4ipyS435R34M2KlpMTBpXxviBScNpRSoWcn9V3fo48BtY84mHus2oDQElZ9QH+8SUqmECS2XnBAU5MGZhc31HnjsNoEMAMf8JynYm/RoKlrXL92FbWvQA548KH7ceXqAd772E08+vANiavBBHKMmgAXexw2hGvXPB64WuNozXjO0+Z405tv48atJeqKMP/gp+H6lUOEDuBNRDw+BaMBk4c/nCM8cgfdJoKc01OD4YkUADz1/j21ehGBtnOMLkgMMFszsWyjtDaBGR45qDQRYaFpu5dtTDC57iKu7VU4aXsNUh8TA5aUIwxVrrBa6YlglDdxWRexehTLG3u+UeVfYHFVCOTQajKKECOYJVPuopE+KtVQrAOjbpXZCmLpt7fwcCQuxE0FLGonrtOmrCNgb+aSUo6IcLIJ6TT++sKrVZYDs1wnUmtWgrouCryrvjFZHoXImFdOnldJw05LTZjJgl62ykg84QDSx5gyfTdfZN2Qyo0IyIl0RtWHqLWF7BdVFJy9PWwPepfCfvvpvPvZIykwNuuGzUAIYm3qnSh5OxX+uxARN32yImG1pmSXLUkJYsklgrm0d9/1K2hqD+8d9hZzsd4i4OBwD/PFDEcnpzg+OpW2lBH2AIgjZhVhsXA4WHisO8b1KzUefXyF01UH74DqwatYzGdgkdLBmxZi/0dwsxrxeIXQC7eaGBUervfhfpNodhsl6Ydk9KSBwsMnwVeVC5RdtiVOl9RvQ947+ggsyGETI/oIWOQQEQzySXaMRnNFSWejy7CcXZ0ZgtsxZgVeZFX4QYV/zkrSLkrGT1HOyEt0keEoJguKPjLqmkDB3lXeJytFKSlhmJEs1jd9duFd1KrcBIE5JgWAvIcoEyIzKJZujVmYMoVeaYBBNtcTsJz22wuWMY5vs5VDhN2F53dbpoZ63vDvhtmaEvLHzG+21uSksAkR4Kgu0r0J9vKQJc5wRrvJrMwsbiqwtzdD5WU/resquXXN5g3qusK67bBZt8mSl1lgRQ6hJBZeU3v0AVjMPU5OO7RtgCPgwEu8W7DQHW4D2Nz0Kgded+rCTdgm5FLmjQeUlktYB7NaRdqvlX3byasBpFa2mhRKKwRm1CTZ6GOxEdg+lA79Yr5urvPDbcr2HB2Djs2ltaBk+WbKEeMxIwvdNFdXACnWZs7OKxnng7YvcclYraiyd0lOBCS/Q+Tk2ltbvGomYBThz2hluZ+k1zL6SUhKXdiSbW2eNrVnhKS5dNnGc9bfvzut9/hM+sejL6WuTcAsK5tLuUGmgpDnZyh31JXGqSRKB1qAZYD3yYo/J4OSzg0GvJNM6YGBugK6jtFG8TGhmcS7BZBNU20vdAT0McHyruUqLfhMiZbkmvHcWOsT6z+2ogOQMlcnBeOInyqLNblriabGP8WhDsbMeQzjQ6xYXGNG8pKw+bdYhuWoXHF6xpR5BkDWE4abI6We7b0lzKQxJj4J+fnRvPxeeRJKJte7bg3gcQrGyvuuYHxKSztT1Jb0fauzYhtmyt4ZkQEXAVaeMtUvT2dLYnMGBhGG1cfvetEy6G5HuUib7wu4HpOpqW1rfH98rbye7ime3u07XPQQfle5pMVedrtNBz8FJI43vvyDy/0EaQNNHJ9ufbppAHlSEgOH4eZT7nHjMp4Tm+hdOGOrOcYHY0zP0ncPNyMejS0r80wwjOpqZtZU63WPvZnHatMDIFSONVNVKxnmvDAMBwuX0lh3FlfOAU95yv24erjAarXC9Sv76NqIdhPhfAWnChzngHjrFp6xt4enffAz8e5HbuPG7WMAEpdn3QbM5w3aVYvFosHc97h9vMbVg4iHnn4dXTzFb73lHfjg5z4XVw4WQCT4B64gHLdAB3RHS1SHe5g1PcLxOhMtRjYNJsKsnuGxW6cgAvbnM/gewKbHZtODvEftvGaTZcwqdXuNjOjyCbfNZR/VHVbjjFWNJAlpexH+JdumQ+006QVTEnQBe5bEclGZqirFv5JKgVlcciEJLOa1bObmsRSZ0QbGAempfYwIIcKRWAVZRtzIokDc9BGLHuiDTFBkRlMR+iDZcY9a2eyXm4jaixtSF4C+k1PbnoB1J++/1xM85ThrkpBG3mPVxRRsnYDENMIEn7RhiZlIF9T1uQB+w3MCpXhDY9waEkFO+AJAYl4qfg/cehXft569iBQwZmh2oGU5xin6eCbNPOMGT9xPc6E3E74jwyqznqBXDp0GPJSsyg5dF4CKVLnHmNUWRJ2TaxoRcHCwh8W8Rtf1WMwbxBAl7pTzko1cGU23OsbVpsGVB2vcOV7hZLWBJEgRhXNde4SuR11XqFzAatNh0TAOry4QucVjj9/CUx64H/NZLUzt/gy0CUAAwrqFmzWofETcdAm3E53W2a18hZPVBgBhVldiwhIjes3E50jdZxmYFQKzUxAkZHiTBDEW4weoSWOJRVNqURJuQq8McBJySRTcRIisojblDLxmQWqWPQxN1uEVvy02XRRF3awyFykZn43Tq8VEOpwIjNoxoiblMdfiygkurdWit03WeqIgsMy/kUV5yAw0HiB1y+W0h8l4uz7DcmImSiHA4FPxuhyj3cv4ug361tcAgW2vvBDKTrX4/i7tZ8ZiJ7+AIb7bb1HijzIkkyh/+xgLoT3HSoUy5wwAZFZ6HiFEiU1rcK6am6RMiC0W3mN+MMdq3aHtQhpfiKJs4iAxthxFdH1EXQGzeY3IPU5OlzjY30ddeREsZhWoj+BIiF0PV3kBxH6USqL44cih7YRv8ea2H6MqGrN7bmSgsnHbtpBAQfcNLpTSyCEvjOex6hL70uYWGRY1yY65vw4NnDKdsvUzpSrS3ia1TZmXBG3Ow0zMttEmVexnGNDf6kHRR6unY3eUDnVNQWJxRKNmtidsY0go5qUUvrBVt8Bz7C5n3bu7so3nbMo94P0U5c/G84mag/pjOC7NF0rDBUqa5qEU4bxLCm37zAculAX5GFARoaop7XvlmIQWIFl8SsgKkSEYjLbtMGtqODMN9S5p0TiKtY9Ta/pdhYgSLGflRKZ99oolfCaMmtgCStlNjZS3ZK8hnzp4fFJ5MuiiqGRyA43uW99cNLqN53KtPCxL4y4GYYl/spuvKvG5rCMdp/mbYELPcqGfwvMnHo9/r6RSrPVFyNcYRssb41BkWw8qEBIAcnnfOfORhHt2kJ2TZ6brBfwRkEOenTGcs/q1sd4LF3de85dt66xnLoIfu2hLuj/C+4v0w8XFKVnzfYG3Z8LcuGShlfNGxBnQMuOUISrVtesjoE2ToO0aVA9dDhLnlPtPvdmGWPzG9m8bb45tV/xZG5zbK/uxTadU0k0/j+G9mLNc9iGi72P+1O8Okgxi3lRYd4y9hnB1r8L1vQqLWlxPJXA+p/htJuxev3qA61f2MasW+PAPfS48SSbKg8MG167OsTevQJuISDXcaYe9eISZW+HZT38qnvO0h0BwqD1hf+bQ9z1i7BFjwEP3H2Kz7LE6OUXfddib1bjv6gF+9VffLtZ+3oGIsbgyw8HVOfaecgVEEb6uUM1qeDN7oSFg970oMQ/njSSZCFEYITUl7kOX3ES7kE/QTUDv7QgNnILOO+XiG++wbmNikiwA9cHcp81UEm2Y0C5KtcqpFQ1ZZlphxOtKYlh1KmxbjCTvSIWU/GZOLRUWlSoJIGS6cuI624Vsol8yKyEwlhtx0dwYPJC4FC83IZ/GUnbpYogLRdszAouACEi92ktcvnUbE0PSK7VyRGjNTQwCkwSSOI0sLsXktslkCdslZo9/GdyXFUpWlsePDeiF1jb82RrFZCvn1BpSoou1MqIZA9qB0bsh0auStqQ/FXDtkyDWeJV36ILM96J2WDSifLZ1NKbRNmZmxt58hr35DJWr8dSn3J8SGM3mFRbzCk3tQT0D5OA2AU1co3Idrl89xH1XDwCIENFUhBjEnZ454nBvjr6NaNsWIUTUlcfeosF73nMLfR/1FBCoZxVm8xrNwRwEFmWBKgTyZmgMBRBiQO0d5rUXazNFZFJBOsaQmO0YMQCEyDleJXNOgGGCek4MNAzVMKtcwvM+mDJEcKZyZUKZfNJpQpWMg9P4AcEXk4UEmykpDlIc02K9LPs0K70qreIiy2GMuRVaNk5PFh8V6S/vMzL2XhUbKZM1ISkS+xCTcjHFHoPQFcPzcu5MwXjPFnM8+iyun42/UqnYas/p4DKDuWzZjee73sF4lbz3j/Z7VtGe1fKOZa0qL7TZUxYIE94UUnFTe01O4XB4uFCrTnGrrWsvluyRJX5pz/DcwyFgbzHD3rxJir/KFAXa8KypEAMj9AHMklW+qSscHa00y7KMyVceVe3gZ5Xs3Y5ybK6JYjxJ5fXAbCQVmJUpsD2nYxgoGWhAXQpjIajo9arYo0qXZ0bOFl4y4rY+LuGAPqw/CEjWs1ZsScpM8Qyk2LVmWGiKgPK9LNanKdFJ65lCpnSXLdhaWGDzLJjlcRhtGvRFesBQzGfZrqNy1u6unImiF3ia89czerjMaO5uHJnhyJ/n8xqj9y94FrtgtFU+zTqaksI5PTdaC8s2CwjOS3WJw+m9xs81LU+ExmqWkDF14c2SSYeMymvIjgzz0t560yHFQiY5MHaeQAM+fTeeWxIZb+9W7DX2s3zlqXlME0fDOZ8gGzpH+bt5R6W+Rh0lPEe+Z+t7kb2OijFlfiav/1QTif4X4zfeQr5TSdqL9oZ0IM0hMn+yta1y0c7o5r1u5b9XRmWC6I1ho/w7q5R1jPeV7/lvipdK98qGiipjmDZaEOxEp+gn3XwiSrnvbQ/7XAo9dX9XW+N7u547q8/LvvU0np/f75OBg1M08TLlUoq93KtQLBMeQIUgw0O12mAT5WK7T/ssD4jW8Pv4WaRnUVzXbs75K4TzwQCL10JuC8VzMCFJa/D4nUrBvrDIC+paN/gz5V4Ieo0RQ4/jZY+q9ljMa6zWPUiVeX2ImDceIUJcdUPE3rzCrHa47/pVzJsaJ3dO8Na3Pob2pMWcCPO5mN93JxusqwYbVyE885nAfYeYP+XpuPq063jmA/fjo/7AcwHyePyoRQiMPgA9zfDIYxs8+uhNwDncun2Mo6MT3LlzhKc9bYZfecO75P1IBUvHuHbfHp7yjPuwOJjBebHYSWtcUKX5rJFYcEFddh2h8g4zjSnS6+YtAbZLYZWBwRqqpYvu/lfmDn2UBBvi8qKxuJQROZh7MLNauzFq7xAZWDQSR89RtqAUgUz6s5N2UwQClKzs8im/jMEUj12Q8c28wK/EQZFxW5bKPopCros5Ftamz7E5eo3Hs9wEVBbLTLknc+ECS5yxEIFZ7bCo5Z1unvYJliNzcuGIMaKL2QrMpRhpFoOoJFAFYhQ4NnDWKDYkxYotJmaM1Fsox4OPjJRJQZKZoaGif1S/+DMlP++4P/kMxoL6qMr4iUKQNwWR4f7kH5uVRsSmE+VzXfmU9IYBBI6S4CWyJosQ67rKEfb25qi9w2bd4ubNE4RNQE2ESjNUhk2Pznn05BGvXwPvz1AdXMH8yh6u7e/jWQ/dD4CwXIc0lkAVjk97nJwsxcpztcZ6vcF6tcGVKxXe8/Bt2IGMxFBlLPZqHFzbQz2rNMGL21oTZqCuxG3QLAKIBN4qFSTshJsKuLFZHm5oPGByZrXgX4iMQiZJwnBTyb7UK4Pj1AK3rlyy1jWlHgoBPRZ95v1EM2OP9ojIIlCbu6QpGuykFcgWVpbxNFnKKW7LA2aNSZqIx1h9Q8Q8L2adaAoiZqEN5ZiTGyE0PpEpPZSoGLWSvncIcJNXhys0xuJJBoR3t1W2wRjh+bibspMRUt4NnttzY3fRKV5gMNJEF4ZK/rzvl/flM4SshLXDJGvLmeupPm/CfF1X8I7Q9z2Wpy1iHyVRincgEGIfEckhgsCLBdBUcLM56nmNxazBtYMFAIEnG3OEw6aN2Gwky1Lb9ej0bz5zuHO0KqZZJqFuPGbzRqz2iAYuRGURFz9DJFNI5YyaNqWTDPPos1R4Vbp3li6w2rjel0/DQVOuWLIRUwAY/Ke+xvClbZsnwGB8bJ6Lcj1ZwBdCFuvLmeKwtIgde7KY4JWf58EnOPPTzuXEH22f7w9wpNh7SjwvpunuJI4JPDgbl89uLO3dU7i2hZ6MAWIOkHTyge2/EeM/+In8ef7Ih11OkwgeKHl5NEw5+MqH/7ZPOXWbi5HRqjeG7ZEAgQNExBItPeBJvG8qh8o7zDVzeiw2LkZOXAPkEDFymEhYb/otPPCeUNU+W/PtKNmyVd/1HPg6a35LZYcvcGnclPDvebxlnUldRVE3/aThWKbIGI++WJ9TCgfD4wSWo/7y/G7HUxz2KQAivI3RsryTle2ma+MXK8Z4V3qbu0fq/zHLGTwAzr6VFbMYwXK5VygQD5aq3CAVx+wwj/RgearzEv6gPDqzWrMXcGRVnMrKpXLxyS67urlo9xcFz7Pam2rjrHYHtGL0e/zcZaaxpHkX7v8e1+lyFnsot+o8DIm1V2wyrLU5ZmYXGQCnXNUMQAcbqf6zZTkzGkvZL09OHQ8+t0/ph3+DGiVvUTII+qwxD5nJ4sGmmj71L8YobnTBguUHMEum1m7T4XQdsLdXY9WJAilG4HTVw5PE2QoBuHL9Gu574D44ACenLeCA2HV417sew9GNE3Q317h23zV87Cd/LHB0B3G5wdzXuPbAQ7gyX2BWVVjct4frV6/ikz/2I/D0B6/iZN3h+tU5qhjw6CM3wZFxZ9nivY/dxnsevYVHbtzGjcdv4Q2//lZ0XSdjZ0ZFET1H+Mrj6U87hPMOVe1lpokyk63TH2MEOULgmAPwj8z8Y8yCqiuEYUO2yISu14y4gLjIsVjXeQL2GznZdCQZZWvvdK4ZR2uxhOsDg1iUBUBBTIGUZXPVRbS6To7EugYs7rBmGeFJrGpMiUjqVm4nq5FFuKu9WF/2OhfmmhfVCs8YGcMHs94bxsdCsvowwaHyhL1anGrvrAIqKpROGt8psvSv8Y/VgkwtEpjBZnmVNiLK+FpiDhl+lERozPkOOeeM+zyouvU4j/oq8KusgwGu5k0u0Qv7LO+N6oz/dlHbTAmQGkzWxyN854k/U+xxhLq6UMpK3PaiuJOsxaKs2XQhxediBuaLBfYO9kTxa652IeD27WOsTzYIyw6L/QWe/UHPBjYrcNujIoe9/UPMqxqVc6j3GuwtFvj9z346rhzMsekiFosanhknx0uAGas24OhkhaPjFY5Plzg9XeLh996UuHiqgHDECBBL2StXZmJNmkxaKM+7fjFhhY3jgHxXbEvrWwrgouTOxFZgmBO8VhqkxxIPNN6UCBpgP8XqAta9uL+bq5u52RsznZgxFkVqiJkpM+aoN4GLJA6axfASBkxe1eIEWnB+R5KQyA5PDYfT3oYi1hCLAtcS8RhyGC0grQNkGsIAVl3MtAJGE3nUX45n6MwnTp+Z4hl4x2eukNfE8GL87LjBcny7S4HnJUyUezOK+bOqeUjb+I5pPOezBzIefrFm5bgyjeZB30N6mZLhRN23Y6blIXBhRQlUdYW6qQSONR4kc8RqtUHf9ohtQN3UuP7gdaDrxcWWCHUzQ+3Fws/VHk1d48Hrh5jPao3x6EDMotSDxPTcbDqsNx02bYe27XB0tESMMdE5idUqiurFrJJxTlhyDxZZTQ8GMX4AlHRhTJ/HgrMp3GWPy3un7VOWBMNc62zu+zC0cikF85I5Zra9sIDxKHBWuvYazSqZcblHui5SxyGPY9xf+fqsndu77NBHpGcszhpY+BlXzFs5l5R4g9wiUdqpzsG5cwpjq5F7ag9GL4Z8d74z5OO3OuRL/E0P/xLjHNKa8VDGvEq5VyVr0aRk4i0YMoVttsKUMB0xMDgwfOWwOJiLZW7UxEveJSt95+X3YlbB6wG1WZ33GovWwjpYbL4+RGzaDtkLaajwtsQ+Z8qSXOLTGNDTq2Q4xghu7c/gt+jP5sxoIiHjvj0XR2sxJbgm2ouRlV/ZD4Z0YddeWL7HWMifsphKmDcBdANaVxCHpNQbwUnZRp6nAs9zb/dW7gVR/kcpZ7z/WdMyJk+D9aNtGAEyjyC4RKhqN2iMiocEF4qYmzzCF31uzItEjgO8m9ob77WM29+6jxG+nNXWOb/Pe+4s8L3s6+7am3f1e1aZun/eM0/Q8gC4rMUeM8B2CpWZWpBaOaEEOhV6WTd1zjcNGFEKyfZMufFPCfCc44ENmOr0A4M2xm50Y3ebUvjYEgJsvFvtFf0U10rXu6k/ETqH1j3MwLoNqBxj7gH0PW7e6bA3r9FHj/vu25dg2U2NZtbg8NqBWHl0AesuoF236AG0yzXQB9xansIf7uOZz/5AeD/H9YMDPJUID147RF018KFH03gcPHAdiweuYX54gD/6YX8At49bcL2Hd7z7ccybCrMKODlZ4vbRCd7yzhv47bc/ire961GEsMRvvflhcLcGccCmj6gpoqlZ3fcAX9d5XRLjw6hrj1ldJYuFnM1HgTFUsAzLIQnqFr8uL2IYzSmg1mkq0EdW17jI6PqAtg+ILFZx605doxloVUByBJQ01vjlHJsjC5jJpYYkVlblhPh6J8kNao1ryBDXBbO4q1RZI0pCUUxY3BILTmwJNjadxhTTd6x9HqOdclaaYKEima/jVUjZNc0CoY+i3PMqdVReMuEajjoyt25VoJQckCHmqJSWCEO8LYke53tFM1nBl+sPeuCEclsb57nlIpXOqZPHNFYqFu9T0KChgjG74ZZ0TzZb6bjroyjoCUCMWK4D6tojMmFvbyYWO97DVx6z+UzwIIhA3ndBkjK0PRAZy24DN5vh2vX74ajGXjPDIQEHizmcq+A4wlcOs/0Fqv0F6tkcz3zoIYnl6Rrcun2Kyjt4B2zaFuv1Bo/fPsWNWye4eecEMba4ceMICD2IJaanJ4b3QNSgT7R1pCjv7r2DryTovwk8pQBLnJPliPUokrCfLc8o4cvAok6XI7nIU8arEDOO9iErC4K69Jk70RgOqGjcYLd0be1UcS8ZpKmIZwKw0g+vHLhZCdqpfflO9ozRql5M9kQQiZysaF0xX6aoc5pMaqNKPSALkhazy/rzJMqQtD0TqfURVEgqkGwXTvDEja1LuZ2dTV2gq4uWM4e788dd9FEwBInX0ZsDZWPJU6T7sh6m+AEz2k6s8iKAunZwmhDDDsEASOIczVTJYLBkaEAXAqiqsNjbA0FcdecAZnUFIlHcOUcSAmNWw1cVrh3uS2Z28litu5RRtu8juj7gdNXidLnBcr0Gc8Dp6VqBKMrYySzQGGZFMMWBO7POKxQaQzxXEZQyfKbHefh9i+7rGiR3W4N15gKOc/ZMa8Nc4EwYSqUY23itiS2xlCnoKB00JNdKlMq0kRBWvgeK9zR44KzoYObk4l96OTsyTwUplvyqFBJtT7E6SRGZxkFb8Ht+uTiyPBH4O/493uen/s66t6v+PY1vF83iYR2bXgG1HA8XMFovC5SscIrnMu2Qz6CZc+q6ScrdCnJwa886kth88keY115pDyUFdwlnfRCloRxIM9q2T8CRlNf2wDnWPGOl9RQuYeIelzd5e33SrcFcju4hx+EbjGk8Dp64PqYrMDqRG0m0iYrnKd8f95fGOpqTIWs8wtNRu/bbQveMXyM1ZcsyIME0mJsLofnvle1yxrxN3TLY1K0uXSxBZRfcZRhh5RNp8IwrYQQFPJKGekrPD8eY9z7Ti9ihAac6CW7LF3kyygXgcBf9uODjlypTNOkyr75rPLuu047vd9PWvZZLJc9IoMLiTiUEhxJ0GfCYG+UAoCxNllFV5YCIWWK8WIDuCepdBuEdbBTj6vq8JOGQ/kpGc7xzJwLPGQHG7ZcIkn5zEeiU86bOrJY6SdFngqlei4WFD3OyPAEzup7Rai9EjFt3AvbmFTYd4/r918ExoG0l6H3fixLtdNMi+AqLKAolX3m0qED7VwAA88UMYRMw25vDnfZwV+cIPbB3ZQ998Hj08RUefu/juPOeh9G1PR599DaOlj14QVg0HpuuR1UBXUd4/NYp9htxs/svv/SbePYznwKKETMHbFSBdHXRgAnw3kOYhwzezCKsGPExJp28g6sAbgN6FpdgEUxJT7LUEsXmBpK1dmXZBkHYdHJKYYkkusBoVLHgSNrrNVh/pQKLMehOASBqqi8CDVwFvROrH+clzt+idirc6SlpzO1UzoRwY7EJPfOAeY8s7jWNz7Bx0DgsN0DXSwbQTR8xq2SO2j6iriQOIhGSEsCyhnoHLDsJ1j+vCetOYFUCj0fUXmKrka6HxeVrU5yyMqHGYMtKsC0wCRTR+/U2D5PcGE5RcU9vbGXJ4wFGJe6F0oXyeoGXBZkocXs3wRYYycGtM1uUVvkMzqgkGTYXtpGy/ihdogaWCPqMXSNoRsXUM2O1ihosn7HYWwhdCAHeuWT52/YBtXOolOF3ziGwB2YzgIGqqRB7RtNUoDaA5jViAGYHDQITlqcdjo5PsbpzhBAijk9WWHcRcwCVd+j7CO8le+/pUmDTe4d3vusxXL92IH0gIrBDjBGLugITiwsPG+nONheskjfD3h+A02DqISIiKJyIskwEIlsnpLlyTpIRmPASVIo3eA3M8JyF+BRwn4ex+TIYGU2yvUjvUVaGmxLMFOAGW8YrDZgwEqWA4VvJmEUWpWwKjK5z7dR9PkSxsDBXXjvIaCpNOoA8HrNYMovkygN9QBpfZL2uAzNmzxT3REgb1ljBMgRyDPFtokrCT8p7fTmXg0Q5E+2XOP1k8ZeXKRNbPVDAssGx4f9YIE/4PqKDBW8NgNF1nPaSpqmQrDUVPiIDiBFMJMkbSNY8ggDNXusqB44i3FPPQO1l36s8mAmbPmC9btGt1+Aolnp9EMTwntRaHuAItF0Q11VHuHX7FIvFDIDss1H358o7fadtoODiL18UJLBETYyclTsroIZEPN3jDO+moEe6bvdkH1GWKbOSI/jd2g+K+0nhp/WSO2DBv5Z1s75DHjIlXXobFt6htN7zBATFDDlY4OLwLrtoEwEch3SISF12oUlYCryF8i2li6LNX0yECk9sOXtzfZ+U3bvzE9z+mE6dNQ6aum6WmZIkyanlZeIbim8AJViWKwTzvSNyQIQkoYtIJt6ukgOxPgj/H9TlPoRsySr4kJN2ROXDQMB606GufSGKyReLH707L+70JCS4K37vmrsxnqNQVtgznHiJ3IbRXpqc74sV2wunXsPGZd/LZ2wvtzHE0d42fKdi7Jzvlwqb8XNJRi5Z0PLZ4hJxUW9MqJ6I8n6A5++zcs7c7bo9hlcgiS3nlkEVk4l2PVicDEVDDOT9duqpdJ3Eas8VNluDsd5FmdoXL1KyLDhxb+r3PcLgkwXCeTWGnZT9XaTfKbTd9dy9KusvpdiLjEkBXRjBHFg1ZXEDEg8nVWWlSx2fZWqx7c9OXMEsSoGJoxwBlolp4uKDMRA2d5UpZM3fGRMf+V2QNypTVJWKO1PcMGOg8LO6m7bHahOxP3fgKLEwuigKoroCKPY4PV7idB3xjIeuCFNPhEgsAXAJWG7WQFXDNXs4eMbTcf8z78fNY1EavuVNb8Hb3/0YPuDpT8Vi4TBfM3w9Q9d6vPXt78bb3vIIjk+WqEMPP5vjHQ8f4YH9CovGqXuOzOHh3IE54PhkiYO9OR67scbD734vnv7Uh9CxxH0iCrh1zGCOqKtqYNmVM07GrORwhMo59BzAEv4HHec4IMbAVp6Sq4xcz4vhQGlT9Z7AvVi3dUHam3lRYAW1frDTEu8IM2WSWIViJsK6i/COk5Bvr2BMk3NOiRppfDvhoje9uFmaa2tt1jzqhmmxf7wq0uxk3lwmQjTXXACqWDB3vXUXsWhqVI3UrT3QkyXLAE5aUQrPawlijt7irAj0zyu53lSCL5FFmRLkgBiRpV/JsJkRewtvFFcT/1EIQkQFM4khQbfsuMacpLbHzJTRgLRTKqM5oKZs/2OI3YSsr+HECGYQLBnq8bttU4jBlWJTNSY9MXLpO2fOFAXs6GcfoiqbKcFbYA2i7QFCwGYjCVGuHszR6lwSm5JYYmPBe5CvcXDtCvav7mO5Ftr7+KM3cOvOCe67eijZbntG5T36QLh56w5u3jjGpm3hY4SrKtw6WmO/cWg0pp+VWUVgRGw2LWZNjZOTUxzdPsaVKwcI7BQ3IpYbtcxzTmaWixNFICnvAcF97y0rrVwMHAtqbwwwqZCQV9dKVhRk6xkRetWSwUnW0WztJ0WU7RYPMyvYQwTcKO5ZgjIaMvpmYS3PyWZma+JN6tcsuXZdTlgzPkQurOp0XEkBoTEWuyCxAE35L4cU6t7rJPFNULdOoQ1mVSqAb9e9s7kSGpuSiEBiBoaMFPmdd1wwIaVkXtNt28MHa0WJTiBf2S7Fs2fWexJLAWrllcG9ktbJJ0afmTYwstWoKXGMBhGZkMYImtF+PqtQOkyYG2XQTE7kPKrFHM28QafZnpcnp1iuN9ifzwDv4YNkfo+RsFytsDzdoO8DHMuB2XId0FSUYMJ4sUqTv/R9QOWdWOyuNpjPZ2BYTC1OGbwtVth4lbjA86TQMx84DK27Dc9LS7dEI/IkYPCV82EYFRM6DgdBCvesi2OtJH51vJ7FeMqhcHrOaFNJNwYbytbz5ZykDPXgQSXbL7xz8FRYHCPTwF4tlFOSGwK4UNaZBaFzlA4VCNntnpHj/V2mFMs4fZOG9XbW/V1WeOvLFEcwvp95jHGRJWNwJDApLVCCOOBBCsJa1WKlb3txu2nR9gFN5QEHON2YmIG2DWg7iZUryh6BGU+FiGT0Wz8tlExkRt/1qKsqHawxM2IoBn9OyYI6JVzjEV6kL7tgkPJ8EAbgdf6jE9cYBZ6Pr9NuPJ/qY6pvKj51u5/Eg/I9mJHChAz4YQAWf9euW5+DNrWzcbTSPGc8OXf3VJ7Qxt5PyyXp4lkl8UHpx47+dO3Nnd7QxULdWGb1kp6YDqHkUxLJ2NUVZ1gyr5LyiXtVFl20jOnQ5D1M41pZb1zOA88nC3TPmrYp2nVenanyRI/9Uoq9yokyBSq0EERo816z+wHgGGEZSTOVG0oKnKX+DAFkP9O2kbhiA+7SKqjYXgYbhFwrxI2R1EKmEODUbX6Opxcxb/6Fyw0yozZQ7EUefnJW+Jmgb+48fYjoWFwvD2YOfYwAUxJQAzMaYsTNGo8+ChxeOcTVqzMs1z3atsOzn34fNm2PdrlBW1/DM597Pw4OD3F0RwTSX/zZ/4r3vvdxLE9bPHT/VTz40P04PjnGo28+we3TU9w5XovbGiIefMqDuP3YDVzda9AHxp31BvcdzHG0bFF5RlM7VI7Rdy3atscb/vvb8bQHr4N9jeUGaOoaezNCRNTYW5kbTa7TQHJDYhRZfHQuTeHVq2LKsr5JHDqzbqEkQIm7g8TPCshJLwRmKGUJM6sVk2/Mdc5G2Aag8jKQLuR6vRJXR6pcjIxYaRZNFqat7SMCk2Q5jpTcg8hc7cAaR81hEyQZRmBGTS6BZhfEKq8NBq6qAHKCFLeXkhDDe0LtRIF4MHMI7LBqe8wreSYwoQ/yvY+MRi2AnMuMf4gRVHvNgipWfxabxU5wibJrobEW5nKVAoAz6wllGZh/BwkzQSydVOWqW/tiwaWVavlStLS7maHigQBXdHmpUuJ5Zp+QhULO8zSl2Nu6p89EhcGg2XBT2AJAYEqtsmLX4fgEmM1nmM8rdH1ECAHXr+5JvJy2R+8XuHr/PmazGaq19PfOt7wLR8enaNseh/sLHBzuY7NZ4/jxDVabFutNL1bDYBwcHGB1coqFJuTZ9D32ZjXWbQ/yAqeOgBAC+hDwnkdv4srBAlDlr/deFJSAxt4aTXShuE+Ks5EViYFWYIYvgMAEhKT4YrF6tnU2gTVZQ5FAgVd8jeCkRElMfSGQmjIb2jcxwEZvtX6pvDEm3kIBiIKO0xjkxJ2y4o/Fhb5X2pQE9NQ/p4OKEs8lHABj1UZ9T9JkI2J5zCyuxxoKCdH6JKGFpcu/vYCNjdXFV94hlksE0LYlQxI0MjLIrjmF2gUOl6Jrud/vZGrGYHNBjuheGKACrScHU7AVQ7zHEC7KZ8qvpYuohV6w+3bwQMRqTddLbL3aSYKjGLE3rwVP+4joasz3G1RVhb4T+n3r8dvYrDuEPmLW1JjNGvShx+a0Rxd6dF1MAmLT1Og2XUq20kVG451mlM6KI/MyODpZYj6rAXLo9QBRQmjmDPNTpQzILe8b02wO+LWiCaOXSXHFGd+kTaOldkHwfBxvrlTAl0JWKSQlN1h7lqdgSAZBtoZK0+UZPRigwiJZ3q5Qmg+L8RixtA6GWUSJR4GN3yx6Km88Uc7Gy8hJ6RJtM5pW8KLJ+ocEz/sLbH5PhCCxE7d/FxTe8WNy5ni7fjn3U22bQkf2HDlM5Sj7SeXN6wMAOVTqnh/VCnu13Ajv2QVJcOE9YozoW05haAAo/Sb14NBxcT4km8KnTRtQeclor2cIYhmI3ULpWXNY3h8oJ4o5G8ptxbjGfRQ3xjyD3R6fC2y1UYyj3MfK5xI9GI1z/E5bn0W7Zf/jvSsfIGY8NxplbpXlXGW/pGLO0rvl61QMgiae+b1yTrnARF1mLsttzfijtM0ZnKT9gUB2WKOdmMVt8n5UWYrVqKYEhgTLxfPjXbkEw8h2IKDMB+W+L0uzp/a3yUONgtcej2dXW09k2bUfXbbPi+5rY9pDo3tGS85r68nYQy+l2EtxIpKwL4JqVZFkF/V5U0v/MsQtluUpNghVixRA7gtiKCkrZ8oERWAE/vYstonzgMsrmgIwUBYUz20BbzEQLi4O3XGyEG/x8pKmfaDEyxtrebpVVwTfOQRmHK8DDucVIktMjFntEZlxug4SPLfv8Ohjt3B8MsN9913Fh33Is/CMB+Z4x3tPEOY19h+8H66qMasqXD3weO/DD2O9anH1KYdYzBus3Ql++bdvI65b0KJCAGO+V8P7Gkd3TrG58ziuLmo8dmeN03WHZj4D2nYYI9CJe2fXdXjPw4/i9tEprhxeQV1FMAccnQpnkgTkRHlkDqrKy/wLJ5rWTYQLAIEG8aUqL5Yuba8CdIxgJsxqh+N1r3GMJCFF6d4ClqQZM+WQS4s/ib8lJ5gnG3FxjJERoIo8ZAHaCJVzpC4xwliJCyyh7+QlQ4gJtja9rH0pWMTIaENUt59szWW/+17cGlm5mcg5GUDlxAWv7RltTzgFcDhzOG2B03Wnm4coCzhoIgCWdapUqWduzKzj6QML58QePTMcJOagYS/pLlAyhua2ZdaHW8iS1txOcQupy9pR6S3hcbEuAyufCUq8G8/zEHaVcriDzdieG+2Y6V/O37dwvhCsSjgedKQ/vSc4ZerFzVos3UKU2FvM4nLtiEAh4ORkic2mxt7eHA895Rqu7le4fbRBrDyagz04J9nxaOZwdHSEru+xOJijrh162uDdj63AfS+uemDUjQO5CuvVBv1qiXntcbLuxR2vroDQK13L6lM5yY84OjrBct1iPpvDe4HtdSfhqafwHIBmy1UOonA9NhoIjXlHpshz2WXN5hgslqabLiYFXeQsDNuimjurCeXWUZl0o+05uzwaLVPFBRXDNzdcW29yQo86FTIsQQWD0ccMVXZ2HhmaSEdhQmHArHeCKkyyBZjClcVjipqxGIzYA/PaKd7nDdFcM7KCkZNSr2SsmJXupW2QQcQabzDvqWURwQPJQnKKebP2afRjyMCkjT19nMu87EJgPvPndJmS9rYa4J2XDJQT3G4xCQU9KJo2ZQ6zuEqbZVzpGt4bPLC6yvYOTV3h8HCBeeOwWgewc/CzBkQOjhzqirDerOUwZuZFCUA97px2cpDqhK+yuJZdFxE7Ueq1XVRFs2gaSzxPFjvMWK836Poge4ZKJHLgU1islTPGGFrfDWYjT04pfBi9SG6jiufO5ZhyBXs4tGBgTkk1BgpTznhvSazs4LbY0gpBGXmvGvUVi2vWxTi7rbUZsY0fVDxDg/2ksM7RSbDDgQBCpe8fwnAWEYcHRzbO8X5W6kC4oF+7Sp4HxfPdVYcdDR7Ol58MweTJLLzjx+SUTV0sgHqgoCm/F8+L4l4OtZvKCV5qIjZnIWMg+03f97BDXXICy+uuR+kRkRJwqFKeSC3CS2K8Y2X6EBBihHcelujNErhNvvqFCO729AznQCbGUQ5dMxaCC3IwLSBz5vdKpWWi1RfZY7bGla/tsnA9C4+294VtNOGinllNjvvKh8D52al34YnvF9Dhp3IveP67vtwFHF+22VJvYZbVybK2sJ4TXlWxdbTHDbB2ROhVXLzQqyTLcQZM63ghWL6LMsAnGu7547Lr3tT1rXpn4PlFh/8kgcGg7USznsS+ziuXUuwhJhIEIs0KqjJcXVdJwBor9eyquW0Vv2CxIJLCz0pxFJoN/JRRGnM247JzRnn0awhO+mZpbHaRi7ulci4JWVy43KpSr/wNuw6JXVXXDnXtxQrn0WNEdvCesQkRBzOHWcUAR8xrh8iEyISGGPOZQ+173HjsBo7uHIE+5Fk4Ommxahk3lw8jHt/Ab6wCbtzeYL08xWHV4h2PLXFlRriz7EEEPOP+q0Af4eczrI5WOL5zA5UDYgBWfYtN16FDhSYGbNoezK54HxEKJE7XEo899hgO9vaw7AmzmtTiIOYseum9zX3GYIf0ZNzmyOLeiTBvTK9YylFStJkSoe0te6BdF8F95gW2QmRlivJzdUUIrWS6XXfi9syRQT5bAMUIeCcEUeLcCCcdNah4H4FFnUEmqOXglcrhdCOMOSlGmaWRKSEsoL93YhU4r3WcgdB4RuVLYYbVdU8YIu9Ecd5pLJXTTUDlombtJHFJJNa4W4yuFzdkoy7eDU3/A4tCvlWFoSgq1AULBrN547EEI2A5bbYT3iFOFRwtFV94dMs4mITew7h620qCYTeDe5fG8xK3h476A9aWi9+G/3ptoJwvlPbpEMCYOJcz2MXIiMebRO/6yJhVpJ54qnSC/FUkbnTeBZyenmKzXoOecg2rNoi7dHsE3pzikY5xsurRty1mvset4w7zGli3AnjX9ubgGOHqCu2qw2Z9kpOq9GKNF+DgY0QfYhL2zUJFmA9xyz09PcGsadD1hMpF1BpnMlnycDGDNg9ptosVURpCEHiKKmAb6Xcjy6AQS4abdCUp7TOseG7tM5sLqyj8uiLGXbbUFrpg1j8pQQAyPYoMsM/gYMz4rAI0dwkqhYWoYyKFqMgak5OMUbNMvVkpPmTmIK6TJIk9yHEK4t92MSk9U12yOI1qAVgE4injfEHfx3uL5yd1IiNp5sfMXFIaMJLF5aBMIma+Pn07z7ndfHJkhhGhmby/4yqPaozwP1+TL2M2xZkQzgzexEQ3zWLT2nAuN0kQ/skRo2079H0ADuboQpT1D2ugb3EcGG0nlrsVRaw2UWMsSqOLxgtcOImV2fddwruUqAuksfN4sFa2HzA0c+ZmA++9hgkwN8LR/PKEEoCQLWwGdJoSTJXPjC14kpUchrBh8e/KMdtPR2aln63VUhGQGyhaDM9tMPb+4iWQaTezKGQ1esdAQi8eH4w3cs6OHRkSI1EFuUHbyguLIifHZu1jtjIuEUlc/nL8tHK+bTKMbmVr5kx/zy2MgcVlMdVnPjOl3CuH9v5aeOePs0mG4TNgutBspV3SvdKCND1u2zxJDFuxkvNpnwldAGLEhgVfRVGnvGTMtMaSIRmuxZF2S1XwSbYarFPCH0GM0Ae4xkHiRFs8vol9ezwpA0Tm4rLCOQ2fp1FNHjU5nrcxaI3r8rjCqJ30zAR8brXN+UDxPKAfzEnx/PjwYfzAgD3C0HrYSsn7no972Whi6M3yJJRzB/O7pFyIEOaq6ZVHazdVxvBNxXMR4r2R9ho98I1Akh+M7x0Mk0Z9XoRO7XqfvMEPxrbV3pl4vl3GVrPl97Q/XmKcT3Y553VSmQL5s9Ag0S9sz8NUvbuZk7udx8sp9iDWPURAXXvUlcsWFEZ0tF4KnG+QYwBm2uPUpECdZVDLTN/QlSNZ++182+FGY6SPgN2KwBT5v7xvb8DjSwkhjbCakJ+t9JAUe/k7knJv1lQ4PJyh6wKWqw7LVYsuALNKBOtIDssuIvQs5vp9hAfQ1A5dH7Ffe1QOeOjaDFVd4dFHH4V3wHvedYQYIh55O3C66kAc0Ufgcc9YNA53TsQV52QVUDUzXL12gJP33EAIPSrnwKpQ7HpCGwg1VeAQcLIJaGpR5HUhilJH16vvAx6+cRMPPPAUXD3w6PqATcvoQ0hZLgFGiEE3IxEe2q7H3mI2miuZn0hdYlShjM+67VFXogzp1XrvZBNViLY2XHIRldh8oowLURSFbYhY1F6UWCxtVOpraoJNybAbs56VsjleHSAJLpwp26DWcuB0ih/tN1NSYljSiq48Xgc01g6hrrIQZEx+HwFSZZ1zQE0A9+oWCD3VJVNmmqBPCC4iMqH2gm+WLCREFgVnBFA5BFXqAMIsGsZkNROplV4xV4aLhj4muBXCVRaodGYZA+bNUM++DDC33OgmKeqYpRkSgxEvW/zCoO5YkTf8yoNN3WA5waxVsTp6zehE5R1mswohRLRdQNcFSdzilPYRoY0S20YYdnFDNyVM4wWGD+dinXN8fALngKM7EhT/5Baw6SQ8e4wWb5Gw3gicbboI5yssFjNs7pxKkH5dvKaSE/qeAQ+J7dn2UZN1ACEGuLpKNDPEiKOTJfb3DzGfUUr6YG5oNmsWCNqsl0OIqOtqe94AMIXB0kaIO7oooLP13qaL6XkonkQITFOCR50DIgQwakfoddsp3doMdo02MWXXXlvHtH7mrhsteYW57aIYTw4nll2IKVkW5XMHuwdVtDlQZ8pAaUjeV+oSkNy27CDFLKiMtpigL3oXStmwDe+iWvuKkoHSwZOAama7KH0i7+PIAklZEj7pl4S+owrD6yO8G1nwbbWxVXbVOItVOpsV4q0vxc9Ee4b4n7/z4DnnCJXGqQxBszJzjptmMJiu6d5iMRIlO6ocihERNpsNiID1SizfN0ukbJcWQ98T0KtyuVfFdl1VaNddUsiD1RVYrYTl/ICzRZvyJc7neY3MWLctZs0MlcbINb6mxPOSgkYGOFrojYIepqmKWytnCcOsbmQkS7WptSh5wGQ9yKrADzZ2UaZZ/ZL2D77zcAkN3qPuaY4yHS/fZbgPFLhW3LOaploRz4OMX8P54e13K3GvGIPQORmXK16qhAmkXsrM9tgq47XYZZF7bjkDcXn68u9o2ZqKKdw/oxhtLGFjsh/O8De+aHhPJGFbAKRkUJYgatAnhhak0H0gWhpoBb5CJ5doblYW5IUq16QPET5GjZErB9yMEY9WDEfeWxUVeoEH97cnpFSqWxu7rOLGzwHFfk2iwLc2xqLcDhYRxVC3aIFup4Pqg+BBo3GWeJ5mlPK9s95lrNzbeofhcg7WYTwG+8bFGj+p5fwN+v27XHKOBnDB5z++834hE5UVzSKvfH68h9DUxQuWbdiykGnbPM0uPmjntUuWu3qFCZy1fe59AYI2ZhpdK+9NlfOW7C6X856KO79KLpUT65+6EvcQEy4AAJSDF/MU1BRAniy4DNDsGgpFD4YMVnkvx6srP8v6nIhkan9wz/rKm1b6K+8xciZbNmuOIsNtHCr17F4s++PcT+UdTk9bnJ62SbknLpjAzDOI5Wy9mXlExJxAoQ04WXW4ddrheB2w2vQIfQ/HEft7c3zws67gOQ/tA2BcmQPzmlA7xmLmcbKWZA7MwP7MY7M8xc1HHwVxQAwRc084mFfoA/DOx5e4slejpqDKQZnHTRcwqz36Pqb3CSHg1u0jvPfxO7iz7AGOWDSEPvTJMiHGHuvNBpu2Q9v36PqAOyfrNC9ZMZfnTUDJYkLJ+3u1Wqs1623bBcxqh9oLK23JK1JcHOR4OYvGK5HNzJIlLnAuC0AGrU77Go4H6PoMU44kpqS5myaBDTn+H+l4KocET7UXq8J0cqdwHlQ5UeYxiszJ4sLmIbIoNbwD2KzodHxRYayLkgSh8pRi6Fkclhz9KL9L32e1puotk3LUqcsxUVY6Zul+XEZZztImknF5i1ncaipjLQ+QdoDAxd+owfLyRNXys+yupAko8HVIY5DhK9EKrVP2z6L0bdsemy4gREbbh8SseQcQq+VZZYqqvBabLmLVRmw6ccnnKErspq7w4LU5rh82YADzSuIyeSfWqJs+C8yzyiG0LZbHJyAWs7faScKYGIFbpy3mtYcjUeAafPVRDhRSTFAIvi1XaxydrsQakMUVPcaYxi3hA3qxAgwRIUas2q6gozy5jKWyOMNdVrhZTDqzVjA3nhIMbOpry/yskGj0hcjoyfAZs0S1FkivGc1TkVqELxUH0rPI72H9O5fbTnSFVQmeYAOF8Kfzm+CNEw6DTdk2ZGeMfjFEYVR50gziw5hpCbyL8YXISaGW6QZNK/WsswTXhpPnlCn83qqQ8Slf3fVfSUOGV6Zp0O4RlnRhNx0yGlD0UtIxHvZAJIdeIQi8BQ2eyEjGuFlRizylgcWCsuvVBdMUaJAYlgcLj8VMVFW1l+cdZI37mNeocoTYR7RtKwdubFmTBeZWXURtXhQFvJo1fCzWgRnouh7rVjPpslq76ksYHocQEGNMfE8X4tasj+QZmavi+uAAi81aPytPYrEfD9pmDbeB0fpwbnNLeYVtZYvgEe/8nZ4bvY+N3RpL/O8IJKdgvwS9UVeFNVjWFuT3y3NjdCzdL9Yu9TEeK3bMyxmS0rl4fk7FCz//JJcp+BkPbudYizlNdDf9DWHH6m4p9SBCqR3GJCrKsp61xie3dsZCZCFawUJj2KgSXPO2xSoXX5JKr9wLWLPqivmQ0v4h8CYPnWIv3ZqvicmbEmLtmh1IJSXWGc9bvO2yq/GcTJUpvBu3zaObacw8rFt+nxLweaLuThyfGOvgOZsbo2MTD/CO70+61uP9BaEvWqYI8AUeKZ+97CsP+Ygxn4C0RmfheXruDLp6maUWnBur98/YpHZ3faFyWTCcep/faVDb9Q675v5ex/tkoe6lY+yZYs98yAnihiJ7A4PJMgFRUgSwzUqWauTnwOTHrmlnShmTq245jqKegW2yueLiO86b+F13R6deBQM1FO4xUPqNLfSy8pATZMQQExO5t2gkQ+XCibIJDHAAosfhosKyY1Bk1KqEAiLaTYdjJqw6Ru0DNm3Aet1i03bwiLh9GrDfECqvVn4NoY3AtT2Hk1XA1YVHFwlt1+P6foO2B06XPU7XG0QC7pwGLKpC38ucBUY97V/M5eyu7zs8fvMmrhxex2odsddEzabLaWcS5VTErKngHeH+q/sDQcFiRUmcveyim90DKMWtIlK3MuVvvMaf63qA67yWyY03ipUUqWLL1qdSkPMkSqvAQMVmKcfpBD9t4Kq5M2sD6xtqkVB5Qtub6C8V+gQ/EncJADYhw5JTV2HvLJul/lGRoAKAVxfZEEyhwircscTn8TIffRArPWZGG4C9xqztxK2qZ6RgyU7XpYvZXTBERo6iTCmAPxXMBqggRIa6CYUV5okGdaS9jKOWJXdQhQshqqB0Uwzi1j3Gdn9p3UaEc8CR8aCN4eNDgdd+l7jMerNssmT4LWEMwGhqj+WqR9NYEhNrgCSOWpBrzhmDHlXhKu7XzkV1swuS9RIRq5bRVDmxQ6OWmHuNw7qLmNekCTsiFk2FPkrcybaXzNOrNko26+LNCQXuAagsUUwMWC6XWMz20HWMpmKwZrZNuSNZYjtWlYMHYX/ewFRtaW50PrgwvSaTUsngk9JWESMAdVFntUitk2lO3k6i4rTRJ1sFw9UUGYALeGET5imPx/YgWyNoPCPVNHjn1E22gJQEABY7jxJ9qpHdWQ2PxspLQrYMNEWHU2ski5Ni9c3tkDkH2q89Jcsv0qHafBBUqIx5nOI2mLHCxmXfDWPOZDgKLiczjdv3Ju+Xd87anEse9AyacKGy4yEu/ilpAW9X2NlMogmQWJpdH1OG1tJ6zNw7ARVaFQhjBHpIYhNHBB9FgJf4kJp4iTIOVOo+VntRDtbelAe6P7BY+Vnc1y5wkQhpSAJtWi2LL0dxC66qBiGIpWpyGypxiyVmHwFoKjk4M7dRswhl3SB2zZnNjeyPQ2u08plS+GFkt8ayUhKU9D1HW0zmP4t3H8+B4WPZTtkHFw/Z12SBVMxvqWC072MldrZGEivjfN/iHiL/aZ/eNCLadiyGl+aygOWtva8oJZ7fc9mB4Lvx/skvWzB3Fv6fcWPXfcOFrUMQlC7V5S09+EeOecUxr7GtWQFGGS6RLTNZF7ZMwFDKI+fNdankCzHC655tITFSx8X7277iz6GDu/vcXZ/PuJfEuSk8LxsYvfSu/iaWarryxMM74aD8PtqvEjkzWjfV7ojWlQcU5fDK6+M5m5iCJ6f8TiL0RctdMQd399guOFP0lu8TAssuPC/3ubPGWNY5d9xjOlYmFdjxAk/E8j7hMPk+A/LdJU3XjnGUPMrUvYuUi6z/RculFHsWzD+fIGJwyjg5IM7MihG44WkEwyLjm+suAQMXv7KkU7Gzdm+T4FQTMXDvS9sVEvMtQ+BhMg3td3gaKjtNKeRH2bEzU2ubkW24BeM2ZvKIRJnTBsb+zGmSBs2RFBh9T2IpFwMQxG1zMfNovDD+q04UOlcXDqsVY7XucWXhcLSK8MS4duCxbiMO5h63T3rcf+jx+LG41x7OPU5WPVatWNp1fcC8rtB24v7rKM9WpXHCmCOqyuHpD+2Bo0zc8fExTpdLzJt93NlEteSJIOfBQRiIeVMDLMGDT05XqDR5QI5NKHPk2WJ3yVyZi3cXANJTQ8fiDm7ZIJchquucxN2rvcCoCUYmZAPqnqaKMROGTFlnKejB0MyWqmTjMqg3p0DDpeIvqMRmlhKAZd3NCkWCWNf0rEq1wMjZy6TevHYSaw9mzaDWVGqx0er7BVNQeqSAxJa100EcoEyhB7gkNMkJMcGzzFUMjMYDp/0QkxwRuEgskOWAQjos02SOd5sziHCJu+MmgYyrgzKqM+5wm6W9IBM6pgsD/Ne2efgbivfDuueTXYuN1lSUrGUAqNUvUmxJqLtcXZG6aUd1vWMsakLXMbpeFHfrnuEQsZiJNe2sdlhtIvZnDqebCO9FcbjpArqQLe1q7xECJwVDdh8jVahJcO2rh01Sem02G7Rdi8o3WG3MFZdBzgG9KRbENMk5wro3a+Rs3WhMqkPO0gwApNrhoMTT5pdIs7qC0MWYXFIF9524zunUlxY3JgQlJZjSZBO2raZDtnYo3YTksEZGV0ZilGyE4kKZYFg3NfvP8NyUcBL/0hhyWffKqfusvTvkPaPLbvXeARxJFQBDJp+VBjJMqQfb6gAgxfIz93vpU7J/lxhhVhElo3l2uSCHVaLDBIm4cFdT3++x8OALj79u4T8Gv7cezayGFtvjswVlfsiUtgkeSZSsTulMULirPWn8VmhiBWm3qpCudQFoNOajI6EfvSZO8qTKMpetwwdCqI1Hf8ybnF6pDx1CCPDOi5X6iLaxKvUAwZs+RBB5A/DBnGytt14oXQwt5AND97KkoDCesdxpjQdDgvdy+pOiZErYH60v6QNT6pDEr9H29wFop2t6WFFqcmHJvDjVTf3r3KQQGXZtgOdcTtnwgECvlRY+ZQzd0uJxXC6Kg3eNrzR9eVeZEobuRoab7OeMznfeugStsfkeK/Om5s7isNpzpsyO1k7RRnLbp6Je2r8oWeFQ0dcU3I9fK8XpgyWRiiBy4GLzK3HKFdeMruTW8n40XsXyPdN86Auld9UvklUekjGUR+8zMSZO7e14z9H1MY0YT0rJgw4emK4+ectNzHu5HOP1BabpSRoKjYZEBf0o6MJFXJuf8LIDz39Hy/toHs4AjUGdVEZzVeLsuO4Yn8fXp5qdKtvPD/fTcpyXmrYLbAgJL897gdHtux7T+1kZry8mfp9VLrPO55VLueI6b1Y8Fizc4nEVL2DKLfAWkbXtIJt4I9dNz5pglxU+pRNQUqqNKX26bq1Rujh27EHxO9Uvny+Vd6Z8Si65hctt5CIWEhfjsvfJXSZFYeqLxRWXhLldtxGrDWPTAesu4mQT4RGw2fSoK48ODlQ5LHux1HLO4WBRgRxwtOqx6WOyCri65zGfeaxbxl6jlnp7DkcrRlMDBzOP0w1j3cn1ddvj9obR9mLZN6+BVRuy0EeE5Vrcact5iiFis1nj6PgYIQT0UTJusUbFiFFP9tX6C8w42F+kuTJLElN+RXQpDlCIIkA7dcEF5em0OEHeaSZNzlY3IWqWV3Vx6jRrZoii2HOOkrIO0BhGRClTLkPco7qQocgYZXHtIzRqyWZxlURBK32wzldQJYC5cJgSgrR9Zk7KnE7TnjsnljZmlSTuG6IEMCHdnhNDIoFBc4fqVAKkot8x9sUoCp0uSKbeyPIejc/Mg7nikv7B4KBg6BIybn0d4n1i/rh8kjMOjpB5G1NLpC/xvbzGoz9c6M96RKIzGbbL9yh/Dy328kDHgmP5Wq1ldoUoVCX5iyjl2z4KvKqlT4BoZdqgaiKSLNAEoQt95BSofVGL9XTXS+zEVi311p0kY5lVhLYTXFg0Dn2IWKn736IRJbLBDCDr3/XiTjtcT0bXd1ivN5BEMlGEgYI+JzhR5r1p6kS3E01M9UMR802FdxrGiyooeGFNQ8U6Cd4FbTfEXM+syU04AgTuSXHI2g2crajKxWOY0kV/F0k8TPlp01O+B1hGbnge9aIJakH9sYjM5b+AFRitzW2a4G5zbDHc+sTRb++xVqxK4Pxc5OzKmGbahAj9x2j+YEoG+DwC9KLe5FDGtGBXvSe5DEiQ4TOG78dl5eLi1hxPvAADyRXX6H6IpuDNyrmUGRkACEghs0jCJxBpDMuY9USVuuMGPWzqo1zrglz3mlU1MlCrMs8stGtn1p550IYHA/dTHUcMkoAju+LlFzZc1VZ0bL5sYkT7OSkk9HVT/2UprV0Mz1MbXMStZduDM46OrWHMcmlyDxqtl+Fcyj9T4mF+7XSvfLa8b/dsXOZ2adcGFsP2vpTrGJ6D85zluRg+uwt3bG6sjnMT8zz858kpl0Twqap8F3+XGcPOW3xOnTS/PHUZQI4HJdcKr4+iThnbESP8MNhNSj37DYN11j2RBq+ZDn/KjlL97fHKnhmL+yWmbyuYzMNkmvyPe9A2zhXohwnuSgXIuL00L+XztF2Pd3zf2tfK/iYe2glXyLhfPmfrMyXJl7Sm7H/w7mUXvPu9xrTsScTks8sl8fz9dQyXeXwXbO1sj4YXx3hePjPeI8vrZ8HxuWUEp5ct97rEl31+Urk1gedPZLlI2wO6+AS3fZFy2X4vp9hTBY0rjxBGJeFasQsMlGdWjzNxM6EobdTFPbtRKs9kI9n+r6TKk3GdBn+89RfBg3spBo3VieNnMKiXvqNoX78LlhIy88Y4WoUceBrCoM8qEcj3GofKi6tl2/WgGMCRsL83x2wxB1wFeIdr+zWu7M9EmQPCtYMGN08jCBGOWFxzZ4STDQAWN9tbyx5AxPX9CiergNtrxqyuUXug8ZaxS+JtGce5N6+wUGE9KTgjo+s6nJ6cYtO2Gn8nJFciBmMx02dGmzgjK6bMEq6LMbmJ2Zw5IFm4cGSxatD5csRqGcfDDJEkQk3byzhNgSauh4yu5yTgS3Dy4Q5ByKembS+WTqIANNhT5ZczBQAl5V4aO3L2SrHAkLY7VUD0QRSTqzaoezEn5sxr4B5TcJLiXuWEFSKOqQ878bWsvQxxCWsDg0kVeL0pKeU7qZAYgrjtShyf4kSHsgvuuIz2qjxrg7oZI1HUS8qu8fXBl9xqUr6n20NM37Wvj8c2rp/7zAq9vAHyYJyl5e5YqZdGx0V/Jfep1zZdFMU2bK4F1ysLa+BUcA9RYuKxKMZ8UyUgW8w85o1YxkQQ5o3HaSsjIGJxzfWkVlkRlSOs2ggmUeJtuohVz6i8TzErbeiOsrKnrjzqyg/mQZT4AZt2g74P+WCDY5qLuhDwpeT2x3Q3sFnlUsJzU6Q5KuPOaUuU428x5wyUNt2WDVoy4CLNda/WkMBQsWErk4UlpIOEpIhg67tIapOUl0NIM3pkCSsApMy2Fs/TrJ/scEDWLb+fKSwIqlBMm2KGP9vXSiWjHSZYhm57SztwsHkPOnkDRtEEt7uVDiaQbxInJ5B1fGkKj++1pD4GxIfTvRHLkH6XysstqNk1UDalMaX2Spg2d1oHs/wEALGgdt5OVQi1d6iqbANUe5Js2JC/LojFXnLrJaSETLUnPZRSXo0yzJpSzpZawj4MXddZcbrvQwqpUfIxAKc9Lk9aQacTzcy4nvaLhE9ZCZGmvLhH2t6IjALICjPbm0sFSdn3WSUl+eHhGAZ96WSP9z+DEaNF1rd9EpCU6Fzgc4nn6V3LPnk87uEeVCr7hTdDGkCyzitpw8Rc3BOe302ZJATvg/7O6HPn7UuO9SwYGwroGc/H/JTt+VbHEaE8YzKlXkwtjA+Xxjgy9Q1pny1HI98z723vVNLilAht8BxvtWPPDmtgC9YGzwwAf1uZUcL9GDft93nLleqV9L1oq1yLSTyf6BfIeG17/uQ4CvpQWtjuHOjEc+Nb5Xu8r1HrzPJ+iOe/k2WLzhLOxIUpeJ6Cv3spujvvHMNl2nki6ry/lUk8R16Xi7zT+3JrvUi5lCtu5Z1m2swZCcEYZqvd5vuKS1ofFnso7ybqjTs4VSwJfG6et4j17qKMNplio7A5mng4b3LFBpYYrGydkpV/xkyVlhOFQIxijmAbjW3IhPsOakRmrNcBi0aUSPPaqdm7COpRT9yF0YxYLdfYtA2e/pQD3H9tjqNlh6PbS/i6wowD3n1TrGraIGZudeOwiiJRzCvCnXXEwcLBAXj3rQ0O9xosanG/m1UOp5seIfQikDoH7wheP0lP7ThClZwREQGr9Qrr1Rqz2ULjgG3APJPA/+RAcOj6gMq7gcCZEkTEbAFpAnitvmomrHYBSZkYAqMnQuMdiCJYM3VWTpiephL3QxOzak9Yk7jodurKsNwIy9SGCGK1nLC1VmYocg587IjQBxnPrHHogsGoMNO9BtUzoc3coYBsJRSiJDFghrpGugSGlihAlD5iB+s0+6coB6Rm0MygIFYrQYuTJ//1QbL1BmbNdCuuPhEMrtRVkhy60ItVo7ZtsQYBtepRH7+06RgXuYuCFXQgK0ZY3OtHjyWGdEQrSpJAY44vtYmik+3L42s8uDBiUQd0JOOpjNFogdUxJb/9yg0N+7DB5S1hb+YhVm8xudmZspmglqRcCIAQhXkfKlw9aLC3qLFuA9arDs47ODCOlj3A4urJqiBodRIr57DuWbJtMuHOssesqVA7Ti6zbS+Wd8nCTS2xnUrdjIwPYjWjY+p6SfoRI2LowVzB4m8CGivMqXuPTkOmkcUhir27wlSMAiuWRdSCjhMPFebmpmqK7t58coiS65unHDeoFY1WUrQlq5aE57q2yG5ChvNVoZA35sjw2+DCXKyBrDSIpmSEvJf3WUgyt1iz2gsKI6bcK5WWNjqzREzCl9Kr2lyrWMYfdX8yQDLL4WxJnPux+7aXD8C3AGeD5KHnvY7ZAGWCJlhbW7cmbmzh6hllzGqc+cw2Ym5d2v49pAOTzQ3Gkq/WPsc+9KQJkQpFWFIIFXge1DpvPvNoaoc+MLoupLjF606g01zDnVMlLUkm2D7mLM5iqUtwjpXOZ8s8my8T9JN1ib4CF58hygGdc17xVeJBJDSH0QRVzBcwm3gkDI5aB1Zrhl+JR9K/0nItsikAZR8sFRzmnkuE5JIXFKgH+K2fpWVfprH5UMySZZULbXtnqQgYKw9tzDYfORlPnp9iuYt75TxmmEhxDIv2TYHJsD0pQ10eW06EUqIXF/fPNJ/aUXbi8N00Mi5TyHvRji5CKC76yMSNXXVLWObiQrmewPDV7Nhe9jRSfhpq0ZsfDCNZajw9ZWZICR+j2ZLZFGAjoqW4s/NFVH7LrvaGkJQroPzO9iYla7M1W1Rc5tHdEp4Sr0cFXeJpnUg6oMI2fg82jwG/ug3yNKq+PXpMw2XxXuPtbkxTtnDmDFgdj6F06R4o5VNjeSKm8Px3tLyf4Pllynl4fl6ZWm9G5pXKa+O2d9HWEr7GsHaZsW09xSQ5De6toUuVAQ5cchPZGt4Iz5+scq9NnzWtu9aTJu7d6/JcTrHn5ERZKK34ijDnuCicdqYh58JECdhtewDn2EPTk5ldfIWQKUNTEu90tYyENJ6gTAW3tNbl7pz6KRkrzvudfi9PTtPGWGxypfBfjsGYt1xPBb5S8HSEto8gVdh0wdzNsrXZ3tyhmUW06xXe9o5T7B1egfMzLPY9wBFPnbd47KgDQyyyCMCmZTSVw9G6x8HcYb2O2DDhYG+GdRvQVEDsNesxAT0c2lUP50NOwKDKPYsPFiJj2QYsmgar9QrL9QreN4gxgDmg73sVdCNuHW+wv2iwXHfYtB3uv7af5tCyyFrcrD6wxqpjcBHnytzWIgMgsUban7mUKbANGQaTgACZu6YgsklIBtB4oIvCIBmzJO7VhAgGmeWPutx6dQ3uiswYDFJhShomUuWeRvk2VXLgHOOvC5LkwASXEMVdcqYwYsoVIAdJ7qK40JpVnsXnCwqH5hbfhYjIObmNc0DfxyTkgy2WkyYDCHpy6zIzQVQKpBnH4ph5hGFezrw4KcVYo2eU8SaYNshxc1p3eFp9dhnj/RQ9GMh1Y6WeCsb2T8nT8viZYnxGCwYZ65QTDEHgK8as+I3Q2FwA6sqhqhh93+PWrQ71bAZyFeqZMHeHVcDJOiizqwrgIDC27hmzSuLx9QzMmgp9iEmBaOBFjhA6iYkJcnBelXuFkBlZ3NJrB3R9j7bvJH6mJs9IyXIYWG16NLVH2/UIIWBv0aQ5yUpCwMEhxlDE/JFCUAPFkOls1+ckIZFFEU0a/oAoz23GAWtLdwnDO+2p8GJNgoK1EZW+O5C6Pg7hxnAKLFa+Cc2Rd6DI6rakSldkY6yk1LDRyf5XZAZmUcATRIEP8Mi9j1NbckiQx25KHLJ35Gz56JzEDI0cYa7S9kxSTGCCET2XiRps6lt1z330wpWnH+NdN0YXdtXb3v636dvu9iduFZy+0adSGROKe6YckPAQEtZi2Qd4X4HIqTKYMXPZ4po5x4V0ROjUSj2oq7jXQwNb02RpCqkrigBSJZIJzUXyG1WaR81uTcgBAUua2PUR3jvNjhvR1JXey7QArLufwtwYlsbbgfVNyEp5o6hjhjcl0CimXPZE6xeK7xm4y+VLik6tspUR1+Ynb/Nb658UKqx0u5DkbK1yg9vPF+ceRZXhTmW4nGgMGevNSk+M9un0Uu53asw7y7l4fqEqlytTY7voeJ+ArnbduOwQ+Jw20voSJXpgFrVmdACY9fW4HZl1xtDSWtxwWdst6DYVfBFjkMShED0S7MuBAaeYt2m8WsHCWxj/4wuecAzjpSK7LCV+Gl9khgpnkdtd7ZTvYjhTKjCn5t5I8NSWs4WaYzzf0Td2XNvRzFbdKfJQ4liJy2V9Lh+w554kvHlCyvsQzy9TnoghTOL5rj7K/WoCxuxze0/YauLMsY/7H8hlOx4s4W6Szg/w/FxRbtjORL9T73XeOz3ZIFNO0d3scxcZ31T7T9a7XUqx1zQejT4hWftUqFTllG0IKRxyzoCh+r6Ss7PTJhoAfckcEUifk/YnGSxsX5ycqMEM5ho8upatInjwaYo8uVRY7enItk5zeUR0y+v63eK7MUT5Mm88al9Y6IGSss+aON0EdIHgyWHeeDzy2E0s1wGOIxYVsKiB9Upi4UkyCUZTO3D0WHYRHVdo6gp7HmCOOJw7EDM6EnfTZRfUkkeUsSFEtBChsK4rNJVHjMBq02LdEThuUFcbbDYbzJoOXdejch5d1xVWnYybd04xm1W4dmVPMjfG4dwwMxDFlahSAYVZ41CRCcSqqOwiQmQcziWmkFmlUCXKjV5NGjY9Y6Fx9aJyPCGqIKTmMZtOLWJikblQ+2sUFrvA+psQA0BOEnD0MQJcuukincyC1QrPEaIKVFFRIgtKEj8wAuAobZqbRRRdS2JIYkR2YSRK8QUtZpYjEewoiBWI16yJ84rQU45ZFlnuAznhhllJ2HJU3sPiJDLHAU6kl6Qi5krSwG0LcLkUdSbuX2b/z45h02UnsRwwShnf07WCu7UehpvwDqXeGeM15tk2WMlkqq44xlkCIEcKt9JO24s13YxEcXxyupRYfRCFTu2BXn3wRCg2V1Snyl0P7x0akjHMKgexEBLY7jQ7dxZQNZMmM7x3yeKs64XesOvhfY++71F5EfwdEWIIxdszlusWVeWwmNdZkVUyocj7hodZmlCaiySkQuiXCQxeM8VGxXOz3gXE5bZ2bsAMR1Ve25qYqzszq+ItD9uSYQQbixN8s0MA6UfGSrpJZfgxJTkVe0UWN6wbs8SzsAM5++VQMEnzpVoHo02OxPLWcJ1hQr9Yc1kWXEMvCSsh7Ub1G0oWdlostIa9R1qj3Yh3Btc3xP2dzNEu5NzV54XLdAMTW/7EtaFCZasl3r7GwJawWB42GF4RRowwZfdJAGmfM0vTTdvJIR+La7gnJHrNun+YniwwwLGM1yh7JzEna7Sei8EXdC3qc6JbsMM1uecoSkgN55GTy8RCxQB0XYDzhLryBW+UF9f2hkRfUIjgAqCpRE0+VkEtSFlVXAlHtB4jZ/jlPP8lvJaKe6LBqyfFnz1jeG8KEXuHgZLDvmgdcwMeVkhLO3DTLYWgsUCU0NyepaJOATvls5ElBm9io8loTa43hQXFNjMo94xy7+flzPfjM3+e2+5YAD/3mULBT5RdtoGstBsrckiZhoT3aacY7i4J17Y7Tc/ZlpL6SXDJ4OTRZPtRfrmgsT4dFR2N5mL4O+9nY+VUTM/bMXQmEFvvjuG8jvuJo7oDXMqvn/G+nCfD79HCjds4CyZ2jm20R+7a6lL7hu82tl37JyNbXO0Y2P+/4vllypM9F2n5i0U+i05M8hXYhuWpumf1zxO/x3hyVr9PeClpzpPVx++Cct787lqfy5ZLKfbqqkLlRSMTmAYMaskMkf0u/Hdo9FsuUn42UWMCKGdKG1jtlHStbIZ1Q5mcEdtAxu3wYKxpi+R0G9n9jot3NKWePZfbLu8D2/etV3vWOckq55xD4yO6PqLtAe8dKlX4EEkCDGPgzKXtzrKDo4CnXZ/jxp0NXOwAyOm+Cf61l0y7qw1jue7hyWG1kc/5vAJFwnIT0PYRgRkVMeY10PWlBSTrOxF6CWYF13jUVYXTTQ+4Cl3XSdbMWYu+79HMxVXPeRUYOOLq4QJ1VYEg79KHqJZwuuRE6LkDsbqvkQrsXq09TQkBYNPLOAtPBgDZ0s1iWMljIiR4RyogidBjCj5wRFN5HG0CPHQcQSyevFNFozPLQHEVrvXE1DtCp0KXxbhLVhKqFTABy+IsMWx8ynQ4gKMpARO25Gy9oJTtl+0ZmDuGJCYh1Yo4dfszZQC5rJhw6g6YYjIRa8B1c//NLoXek7h+scRtTHBgjCFGqDxmZjgLVUmQG3E7hsPbKMsFV3t3W8AA1be4rqL/ohIXVYwWDAS8HXUGNGnUedosSRVSjlCpdWgfc5xGZoCiWNUODwsY6zaAiHC4qHBKAcSCgxaj0REwr0Ug7nqxppGYbhEEQj1zAMvhgCVfIdI4XcV62ngZSEG1a+/gnUfbB8B5SZDT9wiVKP+rimCZHY0uzWc1vMsa6eRur50QgMhBP/OzPq23/BEy/pcKE2uEUmvlAQypAJxWRw4RtGlRVnIxDlWwQmPhqfVUsoJ1OTOnCWZm1ZeGotbodsmshbfcipLyIAsaNgZxK5SkPp6yy4TNkbnSJZwisbIYMHCc3RdtHEHxPFkoqYDW6WApWfzJu9n+ddFS1pxAcYwubT90z2W6sSmU3/qZ8HaIsOMWx0rOrR45P5eUM8gKoMCyXsk6k4vslMUYuiDPzWuHts8AlVxSKcfCtIMpsfYGyKn1P+shl40Hlp09v0e5HpEZpJ4C2S3cLPIjXIzgGOF85tFsP61qp66nmYdLB54YYGaalBL27eVLOjDaRrZhqsAzay/tSQVNNgtVG51Y1BWCDQl3aYp/V1xPyr087N3rT6UwnZWexi+NFXnQfTEO5mEwRUBWecivYo+ksu6IhpR9pLpctEH52cuWcn940gS/J7ic+ZqXnYMRKzLmTAbzTrvrGVwwVGE/Gk+5pnZ5oEQms4obxsa0h5LiDsN1Glu15RuM7HWUgb1s17KumzzGo/uDdzQ8H/TPRT2eDOo+BVND5WNuKcP+8J2ouFeOya4zstJ9yxpuAh7Gl3bBvdGPASt8DqKU+Lxznxw/Q8N5OG9cv1fuotylyLHzMYWDXRmcS5g5qzwxLNPZQDmJg3fX1D2X85ofH5I9keV3Gp+eqP4vlTyjrj3m8xpNU6FyYknivctMg/6TmCwGvvRvfiO+57W/gle/9lfw3T/6K/juH/1lfPeP/jL+9jf+76iqCv/mVT+FP/2ClxTIwamN53/+X8K3fPtPwPkKJu5mbpixv38Ff/Vv/iP821f/J/yb7/pPeOnf+Pto5nvDzYdze3lsXPwuNrZUj9OmZwK9ZThMDKz9l941308/B5KCuWkCMUaEGEAE7C1qPPDAIfqQJy+EiHUnGTMJjGUb0QVg1VoihSjBrz3wyK019uce16/McbphnGwiDmYOMQJ3lhHLtbjaLmpRGlxZEO7bc+A+4tZJCwZjf+Yw83KK1vZZSDfLEwADiItqelZ5Rtu26IPE2eu6Ti317HkRRg735vDk1e02pjh5IUSxCGwDuj5Ixk9HieEGZ7fZymXBoNdMtwBhVolAGtQKsOs5JTlpVCnQ95yeM6bKhNzIkqxE5pXyiSWJq2ynzwaWmFtmZSnzicRcSLZSEcwrB7C65SYGXgW7MRPhPakCl5LSBcyFK2COkZXcBtky/CK5MfchW99ZHJeUNRiqaNAdJkbJvgzO8SGHgdUd6srDp0QLovTIQkwu9jr/8J/+K/z6mx9Lf7/224/h13/7MfyfL381qrrG//3zv4GXfsnf2HqYGfiSL/2b+Omf+3VUVXHOMMLbcpf54A/9cPyTb/43+PH/+Mv4hV99B37gta/HF/7FLxkq3O3ZNMrc3pA2jC0eRniMYZ2M0jxE73IySHFI14YANLXH/v6ssChhVXDnRC5tz+qCL20ROLlLHq96NBVhb16hDVLX4HvVRrS9WGlWXujTrAb2GgJHxkoyaqBRZTVBBN9SkTPeTKlw9/EOCH2PGCO6rkMIASGExNGw1p/VdUrCkuiHWiZwFGV+iFHhvVijNO9IzxutNPypLE6dPtObaRsLLWBGynwbzV0dJZ4bzSgEfP2VFK/IVkFBcbtJOUGktikhLDmBWbqZJZ/9jZkiV5gPlXNdWgCZ5VIJt2U8W5lPJKHHkvfkcQwBUuArgVsh+FAak3OW6d4USNORmQwFv+Vf/zvcPGq3/r7n+/4D6rrGb77l3fiyr/iqrecA4Cv/1tfijb/1jiGeX7Bcv34ffvWNb8aNOxtcuXoFBUYOOAMeXxi9Q5IOdf8ub47X7GyOetS/Lpr3hKbxW4JjjJmHsDiS5pqboIbEGt07ccUPelhkiW66wMny1OK5VV4O8cTFT/c3l/EmTr1XUUzwBZDwQKx3zXI/EaxUp/KiVh7yShnGcnIdJGX0eAwlY166w9tBh9Hp0uLXpSzSOQFGiUtGU4Z77vB+coHlvP+TzlmekYwroKHChYbVtudzpM2j4rPcZ8rPNB+G58U9gwv7zjyeSSiflOl42d54uKWi8Kzyspe9HF0ft/7+w4/8KOq6xnve+yj+1ld/zeSz/8vf+Tq8890PXxrPv+gvvgj/9b/9Mo5OlnjXe96Lf/Evv+VSz1u5INperL7tjzz8TcjWzoPaXKzvVHP6SLaEywozay0WcJ3WnlQBzTmb9aD3s19iWIUy7pS4Ni6ugJUkfxX4ONyrCvq6a0ycR13ShTF9oLL66PqQR5lsOuN98Tn1TNnHGC9o9DkuY2vg8XPAEL+HDxfPjWiAveOuZ7f6vAgyn1Fe/opXFPQ6//3oa1+Luq7x6GOP4Wu+ZhrPv+7rvg4Pv/e9l8Lzj/7oj8ZP/ORP4uatW3j85k289nWvwx/+w3/43l7iguUCKDJZ57wpPpfeGG0vftqt8ve4Px79vmiZRLsz+IC7av+sl57gu6ZwZUf188dzRoWLrPFlmrxH9Lrncrfvc0mLPXHF5boBYkBgAndBrC0mmAgb1C/94s/iX//zvyf3VFAJXYfQd/jZn/lRPO9T/gz+w2teMXwYjD/x//rT+Nmf+mGE0Ke2kzBKwJd+1T/CtesP4h//3b8CX9X4ki/7Brz0r/1d/B/f9LWD/nnE9AzbKgF1KNAPhE7O10qru8EzxbNpLhJzSWjbgBAj6sbjYL5AXXls2oCTk426mxFql2MsRYYmhJA2vReBft0FXD+ssLeosGkl++2jtzeYOQYjYrmReTycW2wliclTu4jriwrrThSH91+psWkZJ+semzYAHFFXDss2Yt7UefwqUMID3vlkYVZrcO0YI9abNTZti67vE8MQIuQ5AEhRrgBzXxVBn7HetAAoCbaOHLzLSo3aE9pes/wVbnRiVSQWBpZJdt2J1YNT5WLkHECcSBJoGDNPJAk4LHaIxBc0MGS9Ly8TQ0RwDvNarSQow5RYHEFipkGEaVI3KlMySHxCUdqYQs47QgyFYi5amwpvBZMYI4sApzhCKN24xJU7MhAtsydlob+pNXYgO5DLStvIQAyMjhmVy9uI9w7QOE8dS9QvDsMYKgwGqau94c9/+o8/ia/76v85DxBA127Qdx1++Ideg+d/7hfgZf/2X2Fcnv85fx4/9APfg15hpxSCBkU3pw/78I/Azcdv4Gu/6kvx3offjY/8ox+Dv/cP/hlCCPiuV75s+PT0V4wVemUHeRMqfg+Y9W16QkCKPBBCBEeGrxxmlYd3Dn2I2LQCXeY2DWQm3qxyGGaRKcqrxQyY1Q5dEHw4XvaoiNFB3HWJCPNacYflRNwTY692kiGzj9ibefRBlAZ9kMMC5witZslN409rSklw52hB4UXx3PedZs0MQgchysM0C7FUmNAgOU7f9wBTogk5np98F/zjpGAqmQFTgJniSdxvKXHBgodZkvegkuKgctmCxgT6cv28ZpzmyIgkrq2ReRBni1mFKyrc5SmPLQXQV5qZlfWS39BoUd6PhvAmewmpRZesiCkjDEaDKVEMbgvBzxKy5PXM9cp4e/ZSKVkKCOAgsXDtIAC7y0/++Ovw17/0Lxf1CJt2g67r8L2v/i688AtfhH/xz79p8AwD+PNf+BfxPd/9KoGDPANptDuDvQP4F9/yb/Drv/4GPP0Zzxy0udXJROGJ++NrF2E480hlP4/RDq8AV/lkKd1rphYTzE1Qs+XKPEFe27rKyVoIQNvFpLC1a3a4xbo3OWY0TpIexShtmOW47SU2Jj/CJ/BIME6wrfCi8fMSbm/RwSENzDDGiGHQk86WHZhpnMACvsfzC+SxSQKS4RyWQm/p+iq/yzib22soWz3nd6bxe+W1sVLupwWlS+tHZnWtHTKLlfBQ+Vw+Oeq7mMNcP+N3upzo1zDGWrkGCT64HKzRdIGn3RvssLzuda/FX3rpF6ffBGCzETz/zle9Ci960Yvxv/3Tf7L13Bf9xRfhla/8jgLPzy9f9uVfgS//ir+Jr/2ar8Yv/MLPY39/H895zgdc+HngAq80UeH8aRgn8csxUbcUs+c0aHBVWqfagRIXj9r9cvlcUSftN8hjoKL9yU4Hb5SvmWdAgosd47Yv5Z6ScKYE0MLaPDFFO/il8jPhz1S/2AbZMp7uFDyXTVnbW1MxWNeCPm83N3jWaJG1PZ638aWpNRlTSB7VvYiFbUm30sMXgMNxee1rX4svfslLBtcMz1/1ylfixS95Cf7JP9nG8xe9+MX4jm//9gvj+f7+Pl77utfhh37oh/DXvvRLUVUV/v43fANe92M/hmc/61mXoheXLReajgKX0he+1FSmZjL854tjPB//Lvtm3NVSXmBwu/F8UG3ry6ic0cYUHbpAl+eXC+5b91ImaegZ15+Ict5r3U3fl8yKK1Z6IIdY13CRxVXDkW5QNgQj7jKcvmtx59bjSalnb0MAfubHvh+f8fwvxId82EfiTW/85UTI/sAf+ig89LRn4ad/4gcycSuYr2c88/fhIz7qE/F1X/FCvPW33wgA+LZ/84/xt77+W/CdL/9m3Lr52PAhGJEcbTJcMF2Jec0KPLu/dco6HhOXPykxsCARpuraY76osX8wBzOwXHU4Ot6g7QL6LqBXwX3ViQtcZImX0/Uaa8eJO4mvHfoIHC87rNqA64dzLNcBB3OPzYbRqNXKumOctoz9mcTj6wLjgcMGxytxoXvwSoNbJz3WbY/KAQcLBw4Sl21WEaIXhUSlR9mRRRF3a93i+v48M/4sgn3bbrBer9F3XVZ+hoLZtblhoO0D1puIrlerPWZwiIjRCcTo/Hu1RDBmyhR95QYMmPAiHDZDsoH2kbBsNYQ2FYwAiTukrdmiEYvTqnBL846SUi4qVHuvAjdnxYDpFcxSCCrsRY6oSVyVOktOQeJ6UQoeQcyCVCgSiwxwdq0xSwZjvLxz+j6ucG+UuaocoQ2c3JEtRlpm/PNCBC6ECgKgSiTrV2R9EQZ85dXqKsBFSlmLM7Tn92nbFjduPJqEiNQtgO/7nlfhi178/8FHftTH4pf+y88nOvDRH/sJeNazPwDf/72v2nnSPf79/a/5zsHvd73z7fiIj/wY/L8/7bPwXa/8d4MHBs+WuD64meEh/Zo4DOCJukbHAI115R2qyqOZVWAmdF3AetMLnGsWWiKg69U6BTlwNpEqswGQKmnaNqDvGYt5hVYz3fa9WMsSHLoo7vdNJe64kRn7My+xIx3hYF5h2Ub0vSgKZrVZtwnMsBNXbq8LwhB4XnUBi1mGPbC8cx8C+r5DCHEwn/Z0SRZDiOj7MvM1AKUZaRILyzfW/WHMXCehhYweCCJ6T6hYrXSLdTB5wheSQl0JnnuihOclPWGmpPjSM4IkwJHKK1uCN8wdXg8xoJYzirfSDKUYfRpcIWfa1cv58MPgKGNWHPVr1sOly21Z8ipmHi6tCatFls/wmoRVNZVKCoxyIalsG9i0LR599BG9Nrz5ym9/Bf7Kl/4N/LGP+3j8/H/+uTSuT/jEP4Hf9/s+EK/89m/VZodJEQAarXd+6S9+6Zfg6tWr+KZ/+o/wqZ/2GcMX3sERjfF+a98vfkw1cZZAZQdM3hOqyimsM7qek8WbwY0pf4E8r/bulp2WgXSwY5Z6Fmc2ud+qIlkU0DJXTUUp/ENTyV5jIRwsU67tU0y2X1Hac5g5xbRNM67/2P4eOcf33TXXMXI6WDCFg+zhQ9El8QIjeBp/L+efdb7ZZRwbPMRDXsByu5XWRradle9g11J/5TgM5u1dDBvTvMn9zFsMx2yPlO8gByU82C8Glnij9080b1zX+i1fhofvaq7X5TuN5/ciQutms8EjjzySqpflW1/xMvzPX/Zl+PhP+AT83Otfn67/iec9D8997nPxrS9/2RktD8u1a9fwDd/4D/BnP/vP4Kd/6qfS9Te84Q1nPnfW2C9S8bznlTQmeAIstIk9n1vYUvKVi1f2yaz0exzmIa956aqtu13KimtJpKz9SYWi4XnRpx0GpKppf+NhtszR0CeVzRNdllfK15+Ygsm+7D0zjczKzOK18vcCd3b2Z7RhB57xxLWy4rjvwVQXD561V1yk3R2gkiuU/RZlShl6Rrc7S4nn4/Kyl70MX/blX45P+IRPwOsLPH+e4vnLXnZxPP/QD/1Q3H///fj6v/f38K53vQsA8I3f8A341Te8Ac95znPw5je/+cJtXaZcCM/1S/rOl1jXXX0aXR61U+6KJUyN4fgeup8cz7Yfye66U78vAGrn9J/buZt3OxdX7rLsojF3W+517baeLfiNi5ZLueLO5zPM5w2axqNSSyfvXbJSKAdFAywBksRgYKuQ/Y63/zZ++02/hud9yvPTBgQAz/uU5+NNb/xlPPyutw3ezxb2gz70D+P05Ahv+a03phu/9ks/D+aI537wH0zXTJmXlE1AOm1K/Q2+j6zyUCr1eNAWuNjaCwbAkTDms5nH1WsLPPTQVTz4wFV4X+H0tMOt2yssly1CnzlVc3Vc1JnJboPEaNv0EctNxLpjrNuIxgGVY2y6iKNlC44Bd45bbFpx7z3dRFQeuL7vwVHiax3MCMfriFlDOJxXuHEkLnVX5h61d1i1EScbxrqPWHUqAEIZCufQVBVmdY0bJwE3jlZpHc01oO97rFYrtG0HcFYSMReMJovSq+8ZXScuuG0XwIHR9v2AibV5ZWR3UxGgxY03MTgkSixjCCzDl7Pr+jspugZj4TRGEaI59WnKarMo8pTbCMyqSDOYoKJdsTC0TLclgZfkBjo3akGRZH59T1NCyhgyPJXCCoEGp/3CzJsChtMcmnuRQVS0YPrWr208GArZVV2hrn3CbbHscZNMBO3+kheQgd/6zd/AG37lv+FzPveF5WV89ud+AX7pv/4C3vqW30IpqFy2HBwe4s6dW4lxsj9pz3B1yJWy/Vds4mz1B8/b9/y8rIfEpqoqh/miweHhAvv7czjn0G4CVqsWbdsnXLLnAKD0XghKR3pV0nVB3Me9rnkfJdYex4j1JhTJYURZt2gcWONrzSpJGlN5wqx2ON2IS928FsV0FxhtL4qELpbvTWo17OC9x0kbcbrqBDK4wJkY0Ko7blnK+c51RdEQ+qiKQHHFz6AyzGZuigEQUiy7pGQq8IAz4Mq1wqLHLP1KfEwhFbSrWIABEdRt3nAvu0eZgBWi4XmmTcyiJIxslrYJSGCJMUwYy/ND6T3T+3PeGsfCCoqDsGE7nIULpQX2U8aWY4QZ3JZ4bt07L+ttrrikuH5x5qaUOmSOf+ONv4b/9l9/EV/4F140wOMXfuFfxM//55/Db/3WmwZMbH6rrbcDAHzIh/wBfOVX/x381b/yxQhxpGSaoBNbl1Plqes7SI0tj/0Z/fWEunaYzSo0jQcRyV7WxpR4ZlyS+yiylWhkUc6a25Mp88XaT+i4KAml/173hVozR4hbOVKcTjvUYRYrVonXqJZ7nGNpDsZFsse1QRLpsFp4JdrJ2R33LAnHcNbwwHAtJQzCcEcYrLkJusUeaHzU1LOE0fUhOc9rqshRQhWVX/QvpSJgc3NEgr1SwMuJbnJH5Vh2KTuG13nypaj4d/AshsX6G+CO4XlZfwJ5s1X0xRQBFym/9mu/hl/8xV/Ai1/8xYPrL3rRi/FzP/d6vOlNb7pwW5/yKZ8K5xye8fRn4Fff8Ot4y9vege/8ru/GM5/5zMn6O1B/uuyoOHW5xHfn7DBXoET2o92oMFASF3BY8hYDPEi0gAf7FTC02CuVenZwN+iPUdD5XWOzA6JpopcO72wMo3Hnipk28ESPU6A1rlfy9lN4Xn6WZKEYwlavW8NMHWw/W87z+JmMV2Xrw713q48d46OifxN7p+dnoowI5q61HbdXkLbpzu6i/Nqv/Rp+4Rd+AV/8xUM8f/FLXoLXv/5yeP6mN70JN27cwEtf+lLUdY35fI6XvvSleOMb34i3ve1tT8yAR+VMPEdemxGDdTciyKD9qXGM+R6auD6GtSdoGYeN3mP9y46JdsD+ZcvdrslFnitB4G7LZds4n7bdfbmUYo+I0NSVCPwqAFbebzEdidjqSP/oH/sT+LZ//3P41te8Hq/Qv+d/3kvTi/zMj38//tgnfipm8wXAwHy+wMd+wqfgZ9Raz4Szcl+6eu0B3Ll9M/1mACEGnBwf4eq1+2FCe76fBXgzP0fxG2U/2LbQK2Of2LuVG5QxynXtcHAww4NPuYKnPvU69vcWWK0Cbjx+ilt3Vti0fW5L2+1DRBsYm56x6qRVD6AmoHbAwcxhrgk0Vl3E0TrAEXA4d2gqwrqL2J97MMRK7YFDCRV9tAzY9AH7c8KdlQj3s8rhsaMWjWfszyssu4jlRgLy185c1lyeK3XrMbdPxIA7yw4n6z4JgnJiH7Far7BpWxXAzD2OkxKNWQSXvheXwL6P6LuA1abFZtOBYUoLs4oTF0PLClh5l04dzf3FazZBgigtiNS6gc2SwBINiBWexOcpswAiKfD6IDHyTjaMto8aNy8rcKJqKB1ZjDJJgsAKA30Ut6nAOYZZr1yZWRKQ9mPsCEdxQSstAL1HIWBnYdw2IhNwvSofGUixtwbwqt+FKVVrOxKrTHFxlvuNzxYehufem8Cvcbhs3ocEIRMjBp73yZ+GX/zVt+EXf+Vt+AX9+8t/9ctT9e/73lfh0z7jz2Cxtw8wsLe3j0/99D+1ZYE3wU+eWT7ij3w0/uRnfjZe8+pvn2DyMq7la5nh3GZeM80ovwiN0/nQ+WlmHgcHcxweLtDUNbou4nTZYrXu0Ic4bDspuoAQkJKuEFRpTKKUqypRdnWRsekEnmeVuJD3URJsAPL8wVzCNq+7iD5GNBVh1XGyrD5dB3gCZpVDGzgF5fckVsAwJlvpngkvirhYdQGbLiQ8N7pp7rilImNAp8EJ95JyLzK6LqDvIxgxWXgTKCn+DR/9wHJP+i0DVItrImVhS2E/Mudswyy70ECRzhaDTz43vRwSmGWs4VdyZyUkgah03TXrw4jsam8K+qR8Jy5clJH75szoJIEbNl6dCMW1gTvlSPAqN6g0LptHbUwNgpPlYuWyEtCK4bhZjyWBcfyJ/P5/8tM/E+94z0284z2P4+369xVfmePwvPLbX4Hnf/bnYn9/D2DGwf4B/vTzPweveuW3Ddsa4N42ljdNg3/7su/A3/+7X4t3v+udW/fHT281p50M7hU/dtIVmweSfcbrId18VqGqxFq6bSPaLibBvGzMcKG07iEIs2XWdCa8S0xN298M5jgdygQWa1yANI6qWvKHDOttL/hRqVtuiquKoYJqi3fRf1Kcz4IBZxZ+qjyUGEwvZ8vE/Jl5he14kQOQlfEVAxzscVo/WUYV62X9jJVUU8oNa0/25Pw79WffacQjjtocfwLDeLPjvWV8zQ7rdjH9NsNbrrnFLwNbmyek32NX0XwYWIygmCvaqWQYl8/6rD+FW7ePcOv2EW7q31f/7a9N91/x8pfjc1/wAuzv7wMADg4O8Dmf+wJ86ytecYHWc/l9H/iBcM7ha/721+Irv/Ir8Oc//8/h+n334bWv+3HUdb01BxcuZ1Sm8Z/iuvMWNsWSyAwPqXe1X94j/aeEUaMH5f0Uz1mbcjRsi1Aq9ZCU5Ql2y/ZG45rG2cH2Mrw+UX/8qmPaMan8y8OaVGhaKa/xxPf0fhMPJzwoLhAuFk90ss8Rzl4Kxib6mNg6B3g7fqWt/nbA2dZcn4fHF0FyAH/qT/0pHB0fD/6+9msznr/8ZS/DC/7cnxvg+Qte8AK84uUvv1gHWk5OTvDJ/9P/hC/8C38By9UKxycn+JOf/un4zM/4jK2D4ieqbOH5+KKVi/AFFyxpXyl+7/pu9abqDGD8CSgXoZ+89eXiZQquLwiCFyu78OBJLHfT1a5nztg+zi53MYmXcsWdzRs0M4eKGX0rwmunQdRNOACEISGmFF7h13/lv+Df/e//KI2RAZye3Ent/tx/fB2+6C9/Ff74J30afuYnfhAf90l/Ehwj/p//9GNILNeAmpcMDwNsNk5FGRF5Vs5siojmjcgY0TFDmpn31AKZ6bi4sjWNx2LeYLE/h3eE9Trg1u0V1ps+Mb/WvyjMGF2frXpCjKBKkkFElf44Mta9xJFbNA4Hc4/AjPUm4ngZ0NQRTc24st9gs+kQGbi2V6HrGW0fcDATbdLJmrGYOdSVw+PHHQ4XDjEAd5Y9FjVALLHfuiABuCMcHItgqs6sMPehg5nDpheBZt0GcRuG1G03aziqEKMHix8tmDnFFpw1NfqesWk7dG1A23aIEtoPxA4gPb0kmSPvxM0OmsHT2uvVai+SuNiZ9UIIDKpJYtzBmClRnJpLsWXAjJERK7Fw6FSo8QpZIjA5EGWmLjJALFlio7pINRVpQhNR2EhiQklMQSqtOyKwWlBYpkznsrWPMH/SSWCXLMHSBoQcYyiyWHFaDLKKGNFJtl9bJTlhFpxIir3IKqgR9hpC2Ig7SGCCgwiKpSBQVx7EDOcZsQ8IyuAa3Kc9UXHSUOUX/vPP4h/8va9O9QDgzu1bqd0f/eHvw1f/nX+AT//M5+P7X/Od+IzP+rPgyHjtj/zA1oZiY//XL/tufNRHfxwA4D3veSf+7Gd8klaRf3//B/0B/Mv/8zvwr//V/4af+08/MxBYx6W8N65iyv2tUjBPRICvJLFI3UiiiL6PWK16dKrJ3WqXRRHbdb3GQRRrz1phHCTw36kwX3tNCAOB/XXLqDzDe8asqRD6AGax0hOBPGJWyQg3PaPWBBmnm5ASy6w7seAlXXNTtLG6tCbX2DTxhFmlSmkdh8WhZGb03IPgENmBCsLcB30/78X6qI9qrRdy0ocBzMhzjhxCjIoTSvFZcMri3PlK8CAGVcx5IMYsLIvFUE40YMtgwoe5z5uQJHRG3XKNWDgnbudK7VhIjyS46fM+ZLjShm3m3RR30PbHoQOkjvSZLO3I4Cxb/pqixYRyO0CxN3OldFbAEUM+G09odT8T4VAy/pZxMr334OCEuke1SrIxYXf52f/7Z/CVf7NMhEO4devxtJ7f95pX4x/+f78Jz/+zL8B3vvLb8Nmf8wJwjPiB7/seDBan4Fxf/e9/CB/3xz8RAPCud74Dn/DHPhJ/9+v/IX7zN/87vvfV35mf0c+dTBEPf/DEvbMYKqO7llSkqkSlHFUxHcbS4wjWQsiHWA7ZYg9QxWu0Q0CgUmV51FAIjvW6zwc2tcuxLr1qt+2gykFg0A5lusjZpZ6HLplpPW2NFRckWZX0FWKO3wXIYZ0ojijBTXlI5x0lWDUl/pjpZuTOSZUZsD70r1QQJKvcxGvRaLrF8txvKSeEjpV4XvY/xFONv8ms8TKR9l0LNZEUIMpDDLQhNASBRFNY9/LRHIxpRPkljbXAu9Kafix/2hzYGpGjtHdZt270RLbGRUp8Nq2uHZaf+Zmfxl//a186uHbz5s30/dXf/V34pn/2zXjBn/s8fNu3vgJ/7vM+HzFGfO/3vHpnmz/0wz+CT/zETwIAvOPtb8dHfsQfgnMOTdPgK77iy/CTP/ETAIAv+sIX4p3vfhjP++RPxk/8+I+fO9atcv7rZV5G58fg0uD7zOYLWWJKoE1iAmXQSXCuRN6StVkb9lyqjzy+MUwTMh7z6Lr9GISnGG/xxWeOFz1UPg/Gw8U7n0V/07uMQy1kGmnwnt9ZOuFR7amoqyZbXkrrUfAcJa6m8Y5+n1V27ouU52mq/8nmbf53tF0qHNM2Uo77nHFedHp++qd/Gl/6V//q4FqJ59/1Xd+Fb/7n/xyf93mfh1e84hX4/M8XPH/1q3fj+Y/86I/ikz5J8Pztb387/tAf/IOYz+f4dy97GV7/+tfjhV/wBfDe4yu/6qvwwz/yI/jYj/kYrNfrC474LsvUhF10ku6iTNGHqe98zvcneogDOoGJcfL2vSey4ydxyp/wMjX/Z63JubzlJZ8567mzyqUUe3Kq71E5oKo8nPfoQsxCPkqgyUzZZr3Ee9/zDpQxZUpmZrU6xc+//ifxvE95Pn76J34Qz/vU5+M//+yPY7NaIVFFpb62Dd6+dQNXrt2Xdh8G4JzHweEV3Lr1eNowBp82Lqb0DBIDub3hcfEjb0YyaCKxrpkvGuzvzzBrGnR9xHLZJYsdU0SlnlmCUi9P19i0AW0n1ns5Y57F2hIlkXOiHI0Alm1E5Skp+cSiJ2LTdThd91jUBE+M4xXjdBOx12j8HRAWjTDmjx91uLrnsOkYbZDsues2YtUymMXCqHLAyarFnpuhrnw6hbeIUdcP5mh7UeidbgJWmw7zukJTEQJ3qInB7BI83DlZI2r076ryWJ4ucfvmLWGeqEYMG3h3BRxaxEhDYRdm6WZrL0qryKJsaDRpgFmueS/1y6x8wkiQKvhU8WWCjGbXtfkOLLEJ95oMaxbPxDtCF8QqqmT+TMkG5CDIRBa3iDQbqDzfeFNQkr6bBbuXPkRBZ4rDgrmkoeItMgCFkxA5ZQEWCyhGjISqQooJSDqmGACwQ+0YG50jsR4hDZqeSbwoJRycFzHBgjyXgpYpEDMeL/GOt78VZSkZytOTE/zE6/4DPvtzvwDf95rvxGd/7hfgx177g1gtTzNu5lkEAHz9//JlYskLSPzGApM/8Pd/MF72Hf8er/nub8e//T++eYBrZcmXefQ7XdkpjJHiYVV7NE2NqvKIgdF2AV0XBsqW3KYIuV3bS4KYELMwZpsbA2axJtanqsxTN3NT8kVWK9cY0HYRtVr1rDuNreezJUCjMHe6Dlg0ouAOUSx+uj6mrNCS5AHYdD2ortSSFYPx781q9Kpsa/uILkTUTjIlhxjgqVQIMlabTizUSLIqd22L1XIlwgB5cOzhaI5AYulnim97PiuqhoJOiDnrrQlCTmnC2MIGKF14SYURdanVtbR2+wDUPhP6CMBzjmFXFddNuKH0dN7dUmZEzko9r+MYKMkKYc1gphTWrOKA4SrXhLN1FJX3Jc+NPE6QwwcWauRsjhlwNLQOKTsVoV8SsUSOQ85lzAUCOF0u8da3vHnUSi7Hx8f4Dz/4fXjhX3gRXvXKb8ML/8KL8APf/xqcnpwOGyoG8uV//a9gvhA877oOAPBJf+KT8WEf/gfxZ57/Ofp+0stvvfVhfPM3/WP84//1G3e0N32At6ukvV1h11cuJWkKvcTGHLux5enRQ6Ydlj0myBPUrY4MroUmi/Ivh2iIIYeaIBJ3+XRoBACaXAWQQ55arfSYOR0WWQxHmbNsoWrHn2VCmFr3INtLkuW4Hiw6nRyBO6Ej9vaOPEII6NoAE8TlhX1agzFTSgkZBhwZLMGXPJPvJyUfb9Pt4kwhXyvW0nDfF8pVU16bi+NgnZAVMcW2nPFS+yqVkKnPNFYbUA59wcW/KNsr3oWKfYHT7OX7ZrFcKkZKa1Abr8V1nirlnpbXanc5PT3dintV7s7Hx8f4vn//GrzoxS/Gt37rK/CiF78Yr/ne78Xp6elUcwCAv/IlfxmLEZ4//PDDAIDfeOMbU70bN27gxo0bePaznn3mGCfLWa9V8JcWY9Toqh2MTDbJuibbrMJkF4k+cOaBSgUfINa4pRLP1paLdoAdSj2cu3yDsQ73Li6uFTBVlKTcJFEUG09dtj1hTpEKpX+HNUp1nVnjG74OxjxFPHKVTLMTvkxXH3CToz15e3Rn9DX6XSoDB7RiRxvjNT2rr7FFpZUp2DirEFDmLtlZpvC8LMfHx3jNa16DF7/kJXjFK16BF7/kJfje7/meM/H8L/+lv7SF5y984QvxAR/wAfj4P/7H0376hS98IW7euoXnP//5ZyoK77oU612wBzvLefBwN4XP+T7G7fL7kzGespS4dh4c33OZAtoz8Hz86N2O627w/Ly6T0S5KA7fTbmkYi+CY1QhQBQTXiUmOfXJEMI0ZCDlkwdU2TY9ZuCnf+z78fX/9OX4ox/zSfiQD/tIvOpl/xwGZRFQyxCrz3jTf/8VHBxcwQc890Px1t/+DQDAh//hjwGRw2//918thPxsJTcaCUxZNBAIRsRaXzxtRs4Bs1mFvf0Z9vbmIBBW6x6P31xKvDjbEAtloriDRqxWHdbrDbo+KIPIiXEEIzEWvbrNebXesVP1ro/oe4LzhMYTDuYOyy5i00eEIH1GAAczqVN7wvEqYFZ53DoNOJiJOw8IWNQOd5YBzBGL2mHT22k3wTvGZtOCY8TB/kLccdnmR+YrCdwRWLUtwE6s3AAA82SN1ndi1rKYNbh98w6O7txBG21+REioG2A2dyBIllyLdxNCFEWdHWEbQw0RarxjoBL2wCxcHEmSCkNmE24tVFA2PBOFCsgyBqoFHcSaDWQx+jI7ZC4ujIJZgyhjSgaqUncOs3gyF2CxwkRyiY1qHZQa0rEGtdYoBRIgCyKmuDClT4AJMNml18QBc430qnyUGGeaYZcBjpZ1sXTXMQY2KqNhcyVK0a2NaMjvpdcZ3QIB+Pff+534tu/8QTzvkz8Vf+SjPhb/7B///VSTlWiU4tCjj7wXU+W5H/QhePl3fD9+8PtejX/5z/7XNI8DprDE6/FgyjLAc/1KYq1TNxWaugKI0HdRYmOqm+2YrjHEOq9XpV+OCTZUNNizUeffOUoKVrE8i2KNpvR1VkssrD4yYo9EW2ZVTiqz6QUfl61Y6plwX3vCuhXFogXkt5hRjoC+Fzf8pqkVVijRXPmU8XEksc5Gtsw2QRMkCgkQo64qrE5XWK/XsOSYLIOGr4CKCgFVcSkOLIVsPQyvRSllFDiqZD5grLmAN840XNYRSaFnCvihjia7/SaXuGKtSsGgTBxDyIk2Bi5WgnZJWMSE8GN4thUvKb1+Acvau8VJE5o2VNDYHFhW3h6m6M+JE5JyhErUMLqaBcysALoc+zLG8+/49lfgh1/7f+HTPv0z8cc+7uPx9V/3t7fqlq/98MPv2WrzRV/0+VioUh8A/sgf/Sh8y7/+d/isT/9kvPUtbxnhczlnZwyu7JfyOvrKSTZwCCy3bcQg9tSoOYlBN1To7SqG7ya02R4S2faqwpU2clLUWV9mkedI9m/vJaaeZXhP99Sa0FNWBhjmRGGiJBs3T8wTZ/oTYnYFHiv8bVzeO3Rtj04tiO0+oBbmAzjLbRGNGtV5EeWijlYt5ZMyCwVdHs8p51bKOoYreV/BYH9L6zGeBxThCFKdIb0ZPzOkW8V70fB+Gf+zrF5sU1t4lN6x6HTQhq6zo0yzLSP3VBnPY7nlX0SKKMdMAF7+ipfjp37qZ/CZn/VZ+PiP/wT87a/56q26ZbP/v/a+Nda37arrN+Zca/0f+3Ee91yuMQU0qZaixIDXR8THFySthUa+kCgmgh8k8EGjBoNGgURoeQREsUGDbUFFE00UDB9UCKVFWsCLhQjtvb32cUtve1t6zz1n7/1/rTXnHH4Yc8w51/qv/9773PetayTn7L3XY77WHGOO+Zvj8elPf7p3nwC8//0SlP8PveEN+NTTTwMAbt26hTt37uCpTz51daOGFZZE/V8prrNA1DED7417r8ioOAwPkK5qBhV1l6Cx/izXrtS+QqfUdbUEBvVeud5w8XOv7gMNVvlegmzDR/Nc14RwIwNA/bk8tDQca4CwVjYUGJvfqXPxhf5t2p+7g1qvSwWb96o9OBfKGi6bDFc0gzFed1nBELQtxNz1J+L1m3Qlveud78Qvvfe9eMtb3oKv/uqvxt//ju+49PmSz5WWy2VMuJg7oH8bY/aef0E0mJusvxyg5zGkL5hG+Xbk3ktKA8XmWnU+j8l0JZ+/hPSg4/jgkuT51fNS0gMBe6cnC9w4aXB+f4XF0RF8t8WKPTSWkW5shpbSdd3gxs2H0mgRhKHPz+4lJv/Qb/8GPvP0U/j2v/e9ePqTH8NHPvybAIoFsNgQAsCnP/kx/OZj/xN/8299D/71v/gnsLbCN3/bP8D73/vfcPfZMiMujyyi5UY/3xwqVaIMxgD5tcFi0WB5NEPT1OjagPNziQ0nllbZoiJt3AOjbR02W3mucz5tBGwxFjoGnQ9YwKJpCK2T67tONteziiRjXrSQ3DmJt3U0M6gMY14b7LqAeSUuOjsX8NyFw50Ti3ubgKMZoTIGq9ajMsC9bcC8NiA2kjUzWmSpy6U1BOcd7p+vcONkCQsD57LSqO1oqkpcitsdThZLuM7FjY+MgQfDAti1DufnZ8kNVphHvo/3G6w2LepHljHYt7zjgwCYHqLdMIK4tFJWJFTRCYwEZogCRzGWWLRjU6DRcwLaOFDKWqhxuWwEKzmwWFJBYh91LoMDtY1gHnOyirNG3KLUupE5u1FZA3QxlpK2sfXAPLpQBgbqpL0JoKMZwRTwYCqsKXRmx7EwAByQrYdiOzWOpGfAgPdAyWTRGP/WOTmf1bDGYrttUc8aBO+AdsSXUhk+MnbTNLhz52HtBQDAeYd7GguTGY/92vvx1Cc+hrf/0Dvw0f/7EXzwg/8ryo9YVpQfPT7UvsbaXv+Hvwzv+nf/Bb/yvvfgp97543jozhcBEKu25559Nr4g/xUs3yNVLgl5AIg0QL5F01SobAXvA3Y7HzewnNrC8Rf9Dt77FENOLXx6p/X6zaCupAaVjRaXLFadCr6ZwpIzWXtWBOMFqHNqaQqJ8biJ2a83nVjnGUMSd4sQY+4BxAIGKACeAQEBI7e7FvNZDSAH2lYAkMCoyMBTQOccZk2D4EPqD3EEACHut7vdNlnOlRRCh847GGoS0KTyz5jCDQ4a9y6PnT6n7+n30k0tFZs1DTKeAuLzwA2XCyBR+wkB+F1sVDokiM+kuH2EdNCh65FYvDKq+LwPeXwlw2nsPyRuqo4KQQ/EMispCEfFAqrjlCRE+iVukMq5HttajpECy6T3jQBKVWVBXME5B2MtmAPgOFes3xd9mjUNvujhR3o3nHO4e/fZ9M77f+WX8dGPPokf/1fvwkeeeBy//uu/iiENFaGhMvuJj3+sd//2Qw8BAJ54/HGc3b8PnS3XpnI+GTkwE+s8I4dvLvTcSYcWwPkgZN9CbwxHUXmdZDjyXCyvg3MsLbUoV5BNXWsDy2HfrJJ1pIrgmfJzF7L81nhy5ejoOsleLP9TbDaWuwmUI3FTlYM7k9x0h92T8fKjgAdzEKvDmclCL85DOb7LwpzRL7zU8cq2M+kY096zav1buu7pXBqLbJc9APRvlRNZhxVLx/56qTxJFONrFm3NB2paf2Z0KuTTYMk8uIEo6x0+pKBPbmweLO1z+c2slQzOwYvrbk+IXkKz2QyPPPJI75pzDs8+m/n8l9/3Pjz55JN497t/Ch/+8IfxgQ98YLQvh4gBPPnkk/jZn/0Z/MiP/Ci+/du+FWdnZ/je73sbHn/8cfzSe95zZTsTjQymynwyRdiGkscPtYujRB2Z32N9SGtUca38/spr+l2HegEV7+lcKuVKKrdYG3p1DdqT1oFiHpYtHAP3DvWOx5g83St4LDGU9Oiqw6Gx9hPF12OZOjZcXOk5+aYujfD5SNlZ5uUyewdzg/qGbUztGBZeVnr9y3uy9VCx17mfYiFfg7dLuorPAeB9kc9/6t/8m4N8fhX9/M//PH7wh34I73jHO/BjP/ZjMa7md8I5h/c8CJ8fooFMLXnwED3YSD1/GuPz/bn98rXnBVG5MD/gaw/4SqarGOMF0Bifv9g01vcHretBxu6BgL2qqiVZBpG4YbCI7/J0MIndwkXvK//En8VP/Idf7JX19O9+HH/3W/9yr3e/9D9+Bn/lW/42fuY/vjMtTmkZYu5t+AHgn//Ad+JvfPs/xD9620+AOeDXfuUX8O4ffzt6y0BPhxmz3InPUiHYo+SWoNkVjo7mWC5nCAxs1h3OztboOrd3mqcbTx8CttsO602LrnMpWxyrFwszPBcZD6Mw9gHYuADrZXNeRzdRkFjfdZ5RVwYMwDAL6NCJq62PmXOPGot7KwEhbiwt1jvGsgGYCRctw7BkxTxdVthsPTatPFtXBEuEdZetN0xcZe+dXeBoMcd8PsN2G9JizpCNhSHCzFYIXuJpyXfjJFmbuoExVmL2hb6LaxsAy110Xe0rMgIyGklEgBxqX5JZyDeKB66yCULc4Jicxdb7kLKbafBxYWQBOuoqu8bpMwzZHHXRdTdwdJdCjM3H/VDY2WpHnl93AbMqZpGNyoQhAfoufIgx9PruiMZkpU/dDX3yissxzkJ8yIcAjeGVwD6Om43YLt0AgjkptsoPyZoKYhFSmRxk31grrnsEiX0Yx6xUGsYWoj/3F74G7/u1D6Gkj330SXz91/6Z4knGf/5PP42/8x3/GD/xL/9Zb4OViEd/Tb9/7Zveioceehhv/YZvxFu/4RvT/ac/9Un8xT//lUh8PiygoJycRIDUqjKYNRXqpgYz0LUeF9u2d8LY2wxwzAbdebTOixVfKKyDe3KnT2JpGeBDdgMX+SOxo3worDXjfGg7ibUZQnTBnUl8R0BAfXHLFR7ZOYaByJNZTeicxM4EKyAoMf16G1YwNtsWs7pCVVfounLsNB6kHA5wCNkFuZDL1lqkxDtItwEIXmTYpzmm41gq1r6oT2NVpc0Yc5QBcj2EbBkk/McgMgkQETA8yxHNSA2UgJ26tQuwGMAJ2NA+lPO8nP+GkKymdB7pdsYaYOeQ+L9U3voAUB9kBbJLsm7eQ1pjASLOVvDFGPuQwTwyKKyYFHyiWHaO1yl9MAjlGhSQ+rLHhEW7v+Zr34THP9pPZvGRjzyBP/3oV/Su/fS//Sl81/d8L370h38Q16EHV6qu+Yau7ZTBPGtF4Pog2dmzSz16GyPVFzQbu65bQ/lyGa/HJvSAnxQLrpwPzHCcLbx9kOzZcogkyY5clA0MSutDYFlbfMgHRwp6+6JdWpdzErZCMr9rH3TecZqnKXxA7iYYmoW+n3VW/1Dvij0rVH2YsFcmFf/Ke9o37Yv8zSkGLRd1SwgLSX7AxZiXcijoGLC66nOvHeU4lW3zUT6U5ekY7Y0v75dR6p96Yw+w2RuL3EO1OBzOl1Qd9/uQgCPl7Uugmx5rD/j8TW96Mz719Gd6zz/++OP4ij/65b1rP/mT78b3fd/b8AM/8P0HarmavuWb/zp++If/KX72v/4cQgh43/vei697y5vhnHte5cm6kHVs0Xn6AzXGsgrwXvbMIRp7VqdZCeRpPTpH9PfysI3Rn0NK5SHBVZTXDirmYF7RxubEcB6hAOj26jzQiME0yuWNXNdn9w9H5E6Ph6HtieVcUb+KtaHnWNmeq9rdk0uHmGjwPA/+3mvTJUUx0LPEH7vfK5tyWZc177Lmv/nNb8Znnul7xjz++OP48je+sXft3e96F9729rfj+9/+9ktqOkxPPPEE3vr1X4/v+u7vxvs/8AGEEPDBD34Qb37Tm/DMM+OeOVfSkK/QX48O0YPrGi+MeOT3Ui8cm5svZRt7ZdPItf/P6NAa+KKX/zIRXQfdf/TRR78KwG/8tb/0Rjx8q8Hvfe4etm3A2bN3cb5e43N3N9h0HqaySQkD5cDkRPkMUYWREBfSd1Bp2a6R2/tULD5JkA8X8rEVMS4XREkZqGqD+aLByckCdVVht/NYrXfYbh2CutcVm30NKO19wGbbYbtt0bUeLohr6XBhSFZ7UUFXcIOZMasJs9pE5U9e8PHkftkQOi8KqiEF3sQKbV4LANM6xjZa7rnAOF0YeDbYtAEnM4PzXcDJ3OJsLfH96pgxoo3WZJsuKrGFwFTLm8pWOD1ZYtcCbRtS/0P0E6uMgXcBX/XH7sBYwYx3XQdiafdnP/10snTrPKNjdWcjNJXBn/ySOT5/5lDXEtC9qQjLRtrOsY0MwrbzOJ0bnMwNjhqD5zYB9zci0m8fS7w/S4T7mxjPC4T5rMKu82idfI/OM+Y1YTmz6FzAqs1jUBmgqcSiyhJhMTMIDLQuYNEYnMwtdh3jYufROklkUBmg8zLmXTQrtAZoXYzfRbIh2rRiCXhrabGsBYTYtIxFI0DgvbVkOF7vQgRcxb1ZgdetC8ntypAkznBBMiPWVvpqjCQ/YBDqmHikihaFi8agcwFn25hAw0ucNmsIz5wDv/M5i9fdBmZVwMXFBp0L2K3X2LYdLtadxNSMqFNWLCjh+H3+3qdyAb7s/mESRuI+Yw+e2LuQCs9tFOC+qi3msxrGWjgX0LaS5EKTKKQiVKZwjJ/nPFzn4D3Dc3ZRKxcGBVFLhZXjjcpKpltV4IF8Ui8AXlbu1YU0BCQA3MdEN3WM2zivJch958W6d+cYs8pg24mlpVqW+iAO1ppBOjUtahIMwBiDxayBc5yyVIPjRiHybPCM1/3+I1B0ofDRTdcH4Pz+/bSJ8vEQw8SBr4zBl9yqsNoK4A7EZCLx4AJAdCkkdEHk2LwWa+VNy9h28l2OZiZlL9x2OQNxXZmU5RMQkLS2wj8hxEy4cWxN5EljJI5QHeNnuiAg6jwm4tm5IOVUFC0ci1hmFF2ao0GryuUuhjxYNga1pSTzaivr4qYNaGK2YrACuSIz9H0FIMV6iJNVcgaC83xQ90s9EKij2/U2WnxzBIQMAc4s0c0exvb8GbCPVudBE2J57Nq4bpUWwOm/4u/BtSvp+SpNo6x+ud6S2xcPP6xBFWPnBS/zWkD7fkXl3xzlo1rV9lxuB3x+qOH5EGr/VYkHW7S5KM8Uv3vO37e28r6uUy6IC68eSOl76cBLTQHHxoiA2hqxMA39kaWofzAzbpzUSRdJ2ZkDsN22aSPFPADhCLgd4/mql5Xym/JmadVaG6QQFpLES+41keeIJMGXClRrsmWs6igaGkb5pLRI1zIISO7IkkVbwiDIYYt8fxv1H30/gbPUB2qJspdAXeWDMT1IAOLhWuRF+a5yXQF2zVyt+iGnOrmYNzmuYtoMUg6/wUFiMYrI5ZTUbX50Azcf+RJ86qkn0e62sq6xgNmaoX2PJV/uHciLSPpNVH9lVqvLfb1/yLIloHf1jmi87oP3irW1lKM93aC4r3zM8XoJDg6ty3jw81DbMrg34PP0hOia6V5RoC+Cdo7VM7di6a5tIkK2zC3KYiBlBtdENdofW4CwIbWziIGY2qXxRzOvMHJfyn2LjmFelzM/J90K+RmtQ6+V46cjYCl79wTkmJaaeKiUFfqu8nVJ2sUh/w0B4DRulNeCPDry7/TmLbzuS/8gPvLEh7HZbHq66qF6XvNE+2PU/2Wcng9vv5RUzrUk2/HStnN46KZ0FRRkBvP44OEdyr1h5q/L6h5rCw0+cI8nLylnKBsfhMpvUZbzQsp8EBp++7LuxWKBL3vjlwPAH3/sscf+92XlPJDF3p07N/C6R46xPl+h7baomxqzlvY/MOLWm4EUS4GK7Tj3o/hQ8dW4N7R9pac/8QsWGBWa5TmTPEdSdW8BksUjWufNaxwdzbFYNPCOsd05PPfcRXQ56VvmISoCzoeU3bbrXIqzJ1nipJ1EsnnUjbWJyphs5Hs9QdsxmANqa7CcRcUvELadgIfHcyvx+jrZGBzNJI7aKgJ31gInVhw5Z2Sw2gKt63BzabALwMnC4P7aoakIVdxgti6ACdi2HOMMFQFui40Js8fde+c4PVliubC4WIk1IjR+HcWYCSgsu+JC6rqQ4u5tOyAQJcs1QJJh6ELkvATtl/GKm1UPMAugVVG2VFNgkBDggihylekv/CauxNIkjaklYGlWeoJsnqDKQ3+hrgzBx4Fglk2IvisKvmTrTVYRXpOiSP0+zgVDUhYQrzvAGtk81tHKjyDggmYJNSQbqS6CO7YSvlIFRWP2aYD1UhEkQgSpKM1hjWmm7teB1S1ZOnt8NMdyTmh3O3gfYKxFZbqDmsGeIOqzcE9aXroAX1k+Z97n4f0Bn5dFqrJEEiuwrsTVtq6rmL3Vo13vUkw8Kb6vlIYgwH3nHLwmxIgyQOVCGm/03WlK4axKYrmJrCLwA5KkDgrwh5CzIDeVuHu2EbgzBphFK5KKgLYDfIhJM5gwqyW2no2gVedlfgLCXwraZJlKSPENOWC12WI+a9AYg10b42hxjj8lSTFivyJfKcivVttdkK9RAhted8voW5opKCBWzZQtc+IH1OQXyseaAbPP5/HzcQY5VNkvp5YCZmnTr3MKEeSLH4yRy1TGDtx3MQ4BKUB16gsUTKBefSbys6WsmGiGbW2L8nMcUgkOnsaXU3kc5/NQY+9tdlQ5CvmeYiMAMGtqEBO8EzluDMGUGQUuocRtXPBYT/Ma/D188bqV7P15WLVKxeohXbTEtcaktbfrfLKsLUvWRDAKqoRonVeIhLxBKrS+9N0ONEstTU0hn4E4byI4x8jW2Qra6mEMQ/gb8acLaqEnPysj2XDTelW6gXP2pih1p3JAWyeuuZXN4GAWr3keDoarzwOchj0/EmWE3tc5ovOFi+fK8RRezRoeD8sdac6QynnJsZJsoS0XqWwEinh+lOdBD+RF/3nlLyCDqeW6q/KoBCvKb7DHo0X55dql/U/97g2eFMDY/74lUFFVFhxsXMOyReYXAiX5biivt9x3ty2pvKRA1953fh5UfrOh6Eu6mNapfxfzpQQqyvmRvj3t89FYm0f5HCVQTBnwSc8edpq9jvXi2EHqMHRS2UAdoz0+L9p9FZ/rvf5yxanUg2UMPk7Jx+W1sb9p5FqPL4u/e3WrfByUvTeeg8YO2z/K5+XrhtLB1VCGfEEQ9X5cvpco6NXc/+F8eMW+1/Op9NBEBC5l3kOvXFXVy0WX8vnLTM+3zgcC9r7i0T+C150QnHM4u9jhdz/xDO7evVcIRlGQaSB9hoKTqL8U5M3vYJoTwIXEzINcDvfwNyRlp3yjXDQVECIi1I3F8miGo+Uc1lrsdg737m2x2xUutLHyBIywbBA22xbr1Ta51mmG23IxNmRSOzQGWnYNEyBMwSGNLdE6lkyUgbCoDZo6K97bTpayo0ZOgTe7gOXcoLbAtvMgAIuZwXMrASIWFeN4brBqgVvHhLsXDifRAu1s7WCIsZwZ3Fv5mCwij5yeXOs3ZZZ4XRerNeqqxunJHGdnHVrvQSA4ViWBC8CD0HYBITi4AKxasdQhICZikMEKiKfczACbeNpH8XQZUZkRIFWCiMtmSK181NLJM7CcW2x2AU1t0HUhgaq6yUqb7qhcqTvxzsWKqO9q5ANgbATBQgbxjCGEmLTAeUIIAVVlQFQEH48bOB1Uz4wqAggSKJ3TOJdWGswC5ISIEpDN8ZQUDNDkHMl9EMWpJ2Tzp2UqYJROMSHAgOcAE7+bnga+8fW/D/AdQgjY7hzu3T3Her0ZrKSlJsOgAjFPSsWIhrTP50qU3x3uUIoj73Gla5zP0yaRJNh701RoZhUMGTgXsNm4DNqn6rj4BtE6r3NoW5fnULxXVmaKJZmIYOKGQedeGdBc/5d5JHOituomKN9X53VTiYukZoG2jGRFN6sI61b4oy6SbCwauT6rZKS3bQCRWMDK7wPltwDQtO+GgLZtYYzFfFZhu/XR0oZyhmTOspih8QJDtG7lXlwjFM8LbwWkOF/IslXcZ4UvNOg/cz6pJkLK/tnUFl1MMuR9djnqKbupAQLeEyHxjW4yFAoOLPk8S7BQD2KcY1gyyTrTRNRQcb8UuF5qyvEwKYMturkrZ0EvVYz2OY1JbkvqS69/Mvpq7SBjqD0quKzoT7nZeuTOKbrdFoEDGhewWW/Rtl3/g+nYAaNB/PvftlBMi5t7G63ywWtoeQdW+d7cUt6SRBgU3cJFJnddGID2RdkJfBLQQ61T0jwq20iDOuN//e80VAFjmA0mBNJs9/IvIINiNq1RmgkeCVQ0RqzV5FsLmOcZycpVgb9OwUEj62LJfcNWlX3w0Xq3qmSe69pbqllc+LOFOJkCNLtvv8w0a4boWSwny9oC8IrjHUVMDwjJ/A3IoXCez7kfhUzjvK73ChuMReInnbtEMbauypGcXXtsOSrLVFnWA7qHA1L8qVl50wiVfI68Rg8bne5l1uwPcfGcXj85mmNrQtI3uxbYqUnkkA5NlFcJFeK857Uisjry84gs6fN85vvhvUN1XvVMWdbe/MKIDNf1oChcwTsU7+taXLphl/NkeA5zGZ+rfBrGmOQDL/c8Fi7pM42OUHEowP06NERG2TagiCtYXh2u5yMyLeQRP9TAzE8q2hjIO7P9tfWycRz7fYx66yH2R+jQvKLi3qFxH1s657WFbw06lt1ACA8eb+9VR9T7AWAwXy+hV2vPL/vur9Y2X0nDxr/AdWT46ks1Nq/0cndVv55v+x4I2JvdehS333CEP3XjBrbrLX5h96v40OMfy7GlkLYA6X9ZwCgLzULzL/fGAPaD5hfKS+7ggcUm6Z1ZAS21ACJZ0Gw8xZ8vGhwdzTFfiMvZet1is95IxlogbzQjsMdBXBjWmxabTQvXRVfbuBEIXLYwWukBkoCBNeNqIeRZLfmi0qpxa+KYBGbsWnm6C5RAL0aOpTOvCMbKRr+O8bWOZ4SLrQTOuXVksO0YFy3jxtLgfBOwqEUBOt86LBtx37u39umkWjf46kqi10owwBiC8x3unzscHy9gNoz1ygOGwS7Eby0bpc4FbLcb3H3uLIJ6pvwsqewQwQ2QWJLNahmjzufVXqYRxc25gB9NTOKhzdVsrx6QmGPBoPMB1simV+MXJXCRNLuzuMzIhijCNHFj0LkAA4OdYywjoNo5BWOi1SD357P8rvG+OLkNmjixy+DlDNm8yYYuW++YOD/AOXaS6q2aLVjL7rxacJTWjNJBzfYrAEPfSoqhwGUew9e//ovx8A3C8YcWWO86/NbvPIXPfvbZBIBRbAj3ACLR2Mpv2xdY43xL448UO5fB7yOPDkmBKmsIVS1gXl1VEp+u8+jaXc5IrHIpzlnl+bbzEh/Th8JyZ/9kWzdxqoyOxedSngoRuMluMSIRRORQBJGQFH9tS2U09l48EAAwr4BdJ3WJiz5j54B5Q9h1IVn07Log7vYQ69xhZB21KstjV34TQgge211AM6vRdQFdK9tRjjuQBIB6Rtd1WK+3aH0+xCi/iY6KNSrj1CpJgA/9zjK/xLoycAxFEGQ90VLSwQhkzsumlWGs9kvGUV3eVM4Ib+QDGOkDp/EnI4cGGntTXV/VetDopgx5Y6WbJtZ60U+K0ZvvypcolqhiqMQiMq6esf0hMqqhbHFnTJbJOr8ScKeuxsjzSNhTkhdov+/cuYmZ7TD7bIXWBXz6mbs4P18PWpzpkJIx1OeudV9l5QFGPiQ7lKTLsf8GsMak2HkcYgxL7/v8WIqUyPgC5nEG7Meb068X+5uuDO7pFy83mnJN5muOZcjFP8N9N1pDOrcLd08rYJ+L7uXO54Oa5KINPewa8F/5+8hHZOYYe49AQXQS3QBrhxkKgAa0XRgF9co/y4Oq0rUwgRrFS6W8K8tRPmZI/7zJ/Fc+p+1I8pjVQnZfc9Ts3yHFK0SSB7p2MACka+XcyYfXlynl5dfXZ/JYqU1z/3ll/T7Yk8PX9ICdqMeo0txrBwPlqdrR0QKLuei9zjPOzjeHgb1XKZVjUMbOE8vXYl0efJD8zYEHBfP0uauePURjmEq5dpSVUfH8EPxLcwL9uT9mXD0mn/fbwL06huhTXNllzPasm0fqLHi0N+fH+p/+9fm8fK/Pb8PK+r8yAcRqczjghcQn+23sS+ZxOrTeXdKk0TJGrw3HnfbnhN4rwfrLDOqbpgJzhd3Wx/2jl1xYrzWi3g8AI+N4Lb3h1UdXzbdXhK6h91z1/tilwfS+6pU+DV+4bOCu16RLn72Kz68jB15N9EDAnkTm/lIcffECR/B43W8/IWBBAVKkD1D8TMoaIVov9VcweYx6s6BcgmMc9ST0y6/WA/JSOzkr/VEJqKxBPatwcrzA0fEchgw2W4e7z67FOo9L17rskhJYMttuY+y8bevSYqfWdqFQRiU7pCpj/ekgAIG6qkknSVcZ5nSSZYiS286uDbBzk1zg1BrMBwHsDMXA2THG8K6TjeCNpSTRMAZ46NhitWM0NsB5YN06nMwIbQc8t3EwxqTTK0uU3FGThZEqUhTdNVndORjnqzVmdYPT0wqdY2zXIZ6UM7rW4f79c3z+/hocvwEhxuhicfsN0RSnskbGM46dAhghMDgw6lpdhBWMyDGodG6EwNjsPHYzg9UmRPdeirGx5OsYIlgryQYYYgkl7nVAtnPhBKKEOOlkTPRUWGJb7TrZFLaOk3udupUbiNVRCoYfv7/OSR9YvlsEe+o4EXJckEL5j7uIXFLM4mkhp3LKCSyWeU0FeE9oIjhBWoRuoBIfady2vtvQtiPMTh7C699QwxjGU7/7ezEpB6V6elM7ScbCLahQQPeouNHjc+S+XG8hKJXD7H5nK4PZrMasqUBk0HUe63UbYwxxb7wQ2wrObvWu83A+Z3zUgPLFHjF3mQAatFYPBbJmHsef+/XKpi3GdfNBrPM4A7tidRJd6hBd+ZI7rYCIizom0SBgOTPoYjbcwAI+zyopY92GXgB4taYppO/eRjuNLQG7tkVlK8zmNoJ4ORGN8x7bzQ6rGHMru6ZJHWXw7hLULHlZwSuNu6d16zgrCK3j27kAV0noAuVJtXyV8c6HHqBoTYi8FmnJyWW6uKqfjiEbf+eRYuqJLMxrTj/kBHrzVy0wDBXf3FCcq9yrNGWrRPEiAeJyrHbQeb2UGGti92jjVa0zASXI806zFpd86wLhaHmEhx62MIbx3L2L2P/DWlTi6xdB0xkqfZfxeH4uykLKrraVlbgI3nNKhHEI0APUrT5nt92r+4CmN9ZlVkWhWM/ztfJNeVuyNfd3cTraapVqkC13dN5LJnZ5uLH9mE5q5ccsrvtlMrM9/XhwYTg2LiaMqqoY6sHrAYis2a7z0bI9l1XKDR0KkZdF4So3OYvFYRuTjNXRY6RDTO/HXX4VYCvHS98vu1aCJKpfpme4dM8XS5e99S1pmnkBKA+iVY72nhoZaypuiEbSt3anXpnSHqNIYyGnCNg7CE/SnKLspTwmtmpwdCxSarPZ9cdm2NQhY76ClHQXQz2gWA8pEw3mcV4r+vNwXMbs1/lC6YAI6beB8zNZ989zOC0Jka/024L3AZ4HbbNayibAED3tbX98L6FRq7BBe8s2EvI1fTOAYZh663zZp+EacZ1DmKIpvbJK3n/QcWMA5dKddB3st+dQ2UPe1TJ7enEpOqnf3vGVRcP+WDQzudJp3Iaynkt79wrTcD3ByDc+8NGvOxdeaerJ75HfX3Z6AD667P0hLjPyGIBX+fx7BWg4ZC/WPHggYK/bfBzPPvVZUDXD6t6zqGqDXeviJnhEQQZAhTgqlTYR7mUMl7GVudwsobfh7O2yCUkyxm0TyIgbrK0MlkcNTk8WaBqxzjs/32Gz6eCdz5s9jopR3JSHwNjuOqzXO2x3HUKQjT+YC7N4SptAEdbcs3TjgmvKoK9qyaVCWWPjALpQ5Lg8bMSVrqkMmuhz09QCkLkAbFoP53PcptYzTuaEzS7geJ7BJ0Cyua62DqfLChcbj/NdQGWkLGuzG5u4mpn+Zj9+CD0FpwikEAHOdQBJltk7D5/Ah4DVxQaff+4M284na7WkAFA8dQVgIoJpSFx5GTHgvgeaxiSXRN2sJZe2aM0mcaHk2wfIZs15yQq66+TIvalNCkofGKjL+aPgQ6wDpOOQExuEAPikBeUT+6REMyPAJHAQyKBlNCASBV6BUq0vggWGOLpacdoU6VxQ/lH3WwVeJZ6fWBq6IHOKEbM2wsCazB+lpYAPYpkoboKc3JNLV4/PfPYuPv3MXdR1hc16DWsJzvkUbD6xa95v9IgHP/eE+YEbpXvr6IJeyInE6WkuAHVTYT6rUVUWPgDbnUPXSWbb3gLGGeRhjjH2Og/nXOL9nnUiCsUqXjTFDU5a2SDekc55Ij1rKOLk9OcSGOgcUNksQ9SqNLAoaYFyrDkfOLneNjFRDofUSrQuZsvtOCVcAZBAIpXHKQxA8Ul0Xg8BuBA81GL46HgGDoxt22K13sW4feKSrOOkMlLj92n9MucyoK4At84B7YUOE5FueHQ9yPHPCNk1uYqJKVAAaWMTjWMHh/sRBR30w/b4vHhGXXa1Px59l1wBa7lXboj86sOgUpULrLKHk1UKo+/Cm8ZI/yM5QBjTiNVdmGJ9gZFiiCqfn52t8PlnO1hr0LVtlHV9UGxIhSjZ5/3nqbWN11byOZJcJAJspYkwTORfcbUdsy7J4D33AD29d7Cn2rnBBmyclPdpJKNh4qh0JTB6IGsCLBDXmkIWC2Cdk2VobEh93seDGR80ZiqlWoddGW15UU8CDRKoLGtwKSOT/Bp2UX/QvvxHbLOGJdD69liUCpkJ1UXyUq0grMbQ1TZSUa6+mJeKPgPvA6/pmBW6eo+1by+eJY9vzspvE0Ixf9NYSyFpesVyy/Evx00LL8e8BFTTewRArZ4JPR7e7jpsNhuRVd7vyb49Hj60qD9P/n5QSmuRGbHOY11lB20rf2XV5UcfuZIe5Nmryul9t5Hyy29dLAP9eaBl0ci8vKS+y2j4nlrYq5x8QWNQMsahgjIjpucU306GEkS9sbi8bwfucn/cx6Z3+feh6T8oMv8+tvzS1d+hHKJhGw5V2huy+EJZhnMe63WX1vGysS8T647TsLPl9f1fL1+Tr770mqBRGfAyN+AF13sdPh+rt9A5gLyevVhU8vlVdG0+v+LZl4yeZ8UPBOx94Bffi1ndgJmx27a4cdxhte3QzE9QUUBdW6xXFzi9eROBA9YXFzg5vQFrLdbrC3gncbuausGu3cFaixAC5vM5XNeJouocFosluq5DVdeYzebYbTcJbNpu1miaGTrXwtoK3jncuHkL280KbbvD0fEJdtsLLI8WuHPnISyXM2w2K+x2Hs/e3WC7bQFm1LM56maOruuS8rXZbtB1Httth/VmJxkyo6tnYKT4K1RIS0OEujbyXFRMBYSR+y5a0MimPidXIM7AGEApiYYowP2g8WBxqXOOsIygnbWSMdaSWJ91njGPQbBbJyf8F1vGjYXB/bUE1V9tA24d1Xhu7bDehZihEclySKeygm6ALiZ9EECt+zT+lLgNMSwxTo8N7t49w72LtVjaVTn6WB6baJVkBKgCA7PG9jLzIYJwJm76GQqKRMU4blxDVN5TLD5rYnwxg1ktWSGR6hflsIvZb3NMQfkumrUyQBNNyHjqLkFdcxmISTBkLCojWcF0w6CbaNG183wQIIMR2MAQJ7Az6AyMPnRO50+aDxqXTCy7NGGIql+a7RIRjFDQWDcMClQKsCAu5Z3Pws/ErY3GAvyt3/5EnPMB3nnMG3G9Ojq5gcDiTn1xfoZbt24jcMD52Rlu3rwFW1mszs/RuQ7BezSzGbbbLay18CFguVjEGF4M5xyOlsdo2x3qpsF8vsBms058vl6vMJvN0bU72KqCcw63b9/BenWO7XaL09NTXFycY7GY4dbtW2iaGufn9+FdwHrt0HUODGA+n8NWVQQuDIgI6/UqJsLw6CKPC/Ci4yVaFKmCGecumbzpTfGY0qYj/65zLQmJPKmz7Ih1pOQM8Y7OzaYisb4zEkuLKv120TXXiIWSNZIBd1ETNp3wdOsClo3FupUs0AoU5g1g3oQPF9WEdxfNF7lFqZXGMOYNYb3ZYr3rAOZoadenpIgSYMmAwais2QfU4viqxUneeFNUaHO8OOEhsVLrvIxFZdWdWd5RK+phbDul0qU5cn+S1+VhjH4vS+ICq5aQ/XYjKyspvmruB+k4FINbxnXrbfyRXaTULVv4WJ7RdSRBs0wpcYcOmboGBwaIswt/8TnTIf4zn7uPEBSw5xTH9fT0JnyQNebs7D4eeughhBBw//593L59G9ZanJ+fo+taeO8xn82x2WxkPfcBy6Ml2l0LgNE5h+PjY+x2OzRNg8Viic16jcABxhisVivM53O0bYuqqtB1He7ceRiri3NsNhuc3jjF+fkZZrMGt27dhLUW9+49J2BTtKwFJGNYVVVo2y6uU4T1eg3m0LPOG272hwogAUABFA03euVGDsj8w6UJxp7Gm90p9bnAYqllisy41iCBVGpJmuJOkWbAlZ+GcsbnLmj8VUq1D7/5Ho30owcqxDZVliQsQQyxsaeEc7+36jpaGqD22nBISeVSOsrL+rom0ErWTIWMUkt6sIxdSZqAh8s6DtVPopOpLjA2T4YXhhuH/lxRXUGEw3B26BNpDRk0bBgHrax7b1OYvlfxfFHcat1K7ETvABBsVYN5gxs3byIEDyKDs/v38dCdO5HP7+H27duoqgpnZ2fo2g7ee8zmM2w2G1S2gg8ey+USbdsCDHSuw8nxMba7HWbNDIvlQvgvSPKti4uLPT5/+OGHcXF+jvVmgxunpzg7O0PTNLhx8xSGCM89dxei23HMug4sF0tUdS3ruSEQGaxXq8KVnvc+zEE+L67t8flw3J8nDcsHCh4btkfnFRf8iP7fY3049PeDkOrG17XSG+WjsYEef7v4mR9Wmad9xthTIzys0Hz5/Xo8Buzx31hT9/j8QMv33lOVblAvDZ4ZtvtB6k98fsmzgQ1sNUMIXsJSVIy2W+HmrZuSBI8I9+/fx507d+BDwP1793D7oT6fO+8xn8+wWW9gqwrB+2I9B7quw/HJCXa7bebz1Vo8sIzBxSry+a5FVWc+Pz8/x2Yt6/nZ/TPUTY0bN26AANy9exd6qBmihctyuRQ50XaZz9era7mGT3SY0r7wxSjsBRYyXGf3yn4hAu1FoGuJslcpXRfYmwPAe3/5/+D4aAHnHVwX4N0Wdy88TLPDfF5j51o4T7h/voGxFdoOwGoLAoFshW3bARCgxnUSVyYEIOwCgkdcvAnUCiDmAmO728BYi+AdiBg7T3A7j6peoItBXlabDqAGDg62rnA0u4Hjk2NsW+C5sxXOzs5StkZm2fo0HKBhooP38N7j4mKH1XqbTvYBA+acM1FTp0nGuqysewbIRhuOuPGKRnJyiGoRXU6LTVy0qApqWWFycoSkGsbFwIMTwLXaAfMZwTPhogUsVQjEWM5NAqfAjJ2XpBh3NwGLpsZqxziaW3zuwsN5C1tXeaGJyi+leD6cYu4FQNzEeooFA1bcyYzJsZ0qA3zm82uJWWfrZMaiFjUmooJU2eSW6GP5HgYXHWPtYlytzmDrdfNroafMm85jGwiNNai2UkFgYO3EVallgoNBEwhbB2wdg1qDdUdonYCgEreK0AZCU0nCjdYxtk4soBjy0xChidH0KwtsWmAXCDNnYEwEWz2jg1gqXbSELkgsPusJnY+gZIjALYmL7JwpupYQtjGrdFMBs9rgbCsn6q0DKsoZlXVTI3OCk9UR4iaw9TLZjJG+GEvYdBytvRhVBfhAybpjI/gatp30tbbAKsZs+/gnP4v5rIHrxIKtbbfo2MI4RlVZdC6AqgabXYeqsiBTo+08yHnUsyVavwLZCgwLY2qQtTDE8DCAEWXB2AagCpKGs8J218FWM3jvpc+2ATNhcXRDnq/EYraZH8F5RtXUOL5xguPjYwQ2uFg7nF+o+6wBUxW1sAreE4yVsoNnOG+xaz1CMGBTSww11ZwSgwIcokXpIVIwNz5LBikDcfFQ4ucEAirwOli51P1dwF6gqcW2kwHYWoDGeQT9lU9DYCwXRsC8hYCBy8Zg0wbAVphX2bolAQza0wKw22+NXowgOvWz6W1bdfuwvQ1IWZA1JioRcbtOkn08GAZsDLFgxeKNKU1nUAXAMYKJsoGMPGMAWAHYAxE8CR/BirANJFa/IVo2BQJggA6SUTRAZHEK7aDXFIkAshwGARQto8Fgw3AQ+cVG6mIDMJH8LcuF8CZiogRD8hwAT1m2k81ZUL1qWpQPejj2NYUdLD5aiM8TxRAYlL+nR7SKKt00006E4UnaG0iW/bPzDeq6lsMtMthuN7D1HEwGVW3hg8dstkDnPKqqQt3MBCBjj+XRMVarFaqaYW2FZsawlQB71taoG4LzHo2tUdUNAku8y845zBZLeO9ARJj5AGMr3Lh5BO88ZrOo2B8fSwKgpsHxyTGOj44BougWLe03lUVtZT2v6xkAyWzvoxt9Vc/RdU74u5z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4g92k3l/bczy5/wEuPryHW8OI3/78LXztC2/hxTs3sYqheCYOpkCVtstnB/1ids88ozQBOGgam4GsTDcTENgMdWZQITVBd8a70rVUFL3g3kPLbz2uPo1wG6DbCCevYbPeAmEFADjdKLe9y8hphxC3WA/3sLt4B3n8CDdOT8FgbLdPkNIE2m5VMRgQphEpZREimEXBJzH2xSEi5yx9xQBztt5uhFbOjECElBOmdCGCkTKi9WqFWzdv4nOfex1vfO5Ev4MbarQE7bihVUYUaamINS71HubfPJ+Tknozv9zz4JKXbl57CtA86+iG0BSyWxg+eCOEKaerGJA4ICGA1qfI+QHO732EN774MvJwiunRQzx5/0M8phPczgF08yYeP3mCYRpwkQdsOeBD3MHtjXj0PBojnuSAG3mHM9qI0gogTyN2T55gPH+CTUx4484Gr9x9CTdO1rIKrt8THO6Ub1joU/emPjRaDofn9VXzi/KenCHEP59zQF/udYXMnwf8t/97/3MxwOSMtNuJoMe5GOvACdvdiA9++j384Iffxbe/82c4OzvDer0CAdhNYsjHJN5yFAIoZfUQAIS/1j8qSp9TJGDjqEqFMkgiETbFYGdGbiDGiM16jbt3buPm7VVTjscyApzXRvd8nuA5wWwW7k3jL5c8x/1dz9bbhEtGv3JReL29Nq+TGAgZhMwBiGtkvsD45Bx3XrwJjiukiwtsH59jhwHEBFqvsd3tEFPAhDUmBs6xwWYQj5SLRBiZsOKEEQPMFzunhLTbIY07DCHjzsmAW6c3sBoGt7iBIn/4b7sO1yz8Q+8LflMru8G9X5wL5H6ollbpa5vNZpTtxEiJERMjTRkTZTHcgUShIkLOQIiaJzMmzgCRtJMCWOsqno7Wdm2LzepApozXr5WF2ID/1f/iPyqGc04JGcA4jki7hLPtDmlKuNiN+P733sa3vvc2vvndH2C3G8XoCGDKCUwBnJPZ58tiG2mn+gUt6ftc+WbTPwS2VVD7YQZzKm0GZA4NQ8TpyQabTfAFNHmvA88b258WDhr1njJv/9zL9TI/CQgERkLaTTg5XQEUkKeEtJ0wEWFgADFgShnEhISorhQDBrE/Y2JCYkLkrEa9ahTK0wTOCQGMk1XAepAFeM9LO4ll4fnzA1q4afDapTH+40HmNqthDaDM4MTIyKAQayJSndMKzoxMAEj1ik6uaK+WeYHRBWYqBv2//3u/U/RZ8fRlWVBPGeOYkHPGlBLuffAR3rv3Ed59/0OkKSFoW3POlTia8M0MJrdUytbuSp9bfHby8kz+9cb+yoNCCFgNA4ZBy/s0IunPGXr582A6f1EUX+FHUbxfwMTqdW3pZdS8J14ucpd54ELHllo66gx6kVAdFi5p5CeB45c0YXavn3tl+Awa9p4VPJvtWa6nohHVmDfps4DqrefLGCAefkBY3UAIhM1KVlkorjSZ1GVkpazM2DKNaXEzob+qfV7RZyOEJfVcfOiJ3H4Jg8qsar1OSOultmv8dCQUN1r/rvEEKlXY11t5vVGvupgXr0F1Md9EwrBZ40k+wZOLDF5vcPdWxkcv3cb5/UeIIDzcBeDGgLDLWA8Ruyngo3QDTzYv4fyjR9h9/mv46o0P8RFu4zwxvvkgiuGAM0YE7MYJF08e4+z+B9g9/ggv3yT8jW/cxVufewk3T0+Kd56sCogAbASoIqaNswkz1PYYLSnnyn6IQKpchjLifugIEe0UKfQTraGwlM9uyD/lZ2ST9+OONxYSADGcAGDEk1dwevIFbG6+g92TH2P75AFoOMUmZsS40i20wO7iDCllTCbsBVHycxY8jjGq4p+REoDAiMxInIGs5lFtVuCAxAmEgNVqDSLCahhECbhxCzdvrJxXjA6SLQv7gbiSpN8mKFmcFfAqNr5DpXs+QqXN/n4fE5zl7IRSP+epvitd47wCiQquEBGGAMTVIMb7kYE44HTDOL+xwXi2BYGwSwDWATQxhhCQMuEsr7AdbmA83yLdeRkvr89xjg3GDLx7TmDttwxCyhnTdovd2ROk3TlurglvvnaKl+7cwHo1uC0tnuFS1ycmarSdVKl1lVEtdyPzsHtXBkzN+AQEdoKrG7PQ/fZ4bp7On14g3Lj9ypVSvv7mr+A3/ubfx9/44Z/hT/7oX+Db3/kzPH70CIFWCDEjqEJHBEzTKJ57SUwR1WNdt38EAnNr4ANcH3IdF1BAZvEGDHEAQTya1sOAzckGm3W8vIuviIiXJ5ttPFxMc6WK5pdXzLecmQ6893jRe+US1LM9BCSOmDIDIWA1MMbVgDxOIABTJmAgUJb0mQkjR6SwRhon5JObuDGMGCGeYA8nE1JkoTRDlPw07pCnEesBuHtrhRsna6X7PZ0z2WVfV1AV1Q504gJ13EtPS729hZ8r/WATDeE8LLsyMkRiJYgyP+UMmgigBOYA86aLkdT9XAoNalgFi0yTCCDKKvlyy2tYPHRkR58a/yhYjxfjni3UfPELrwseFaGHkbIodrudLLjlnPEbv/EV/P3zHf74T76Lf/6v/hTf+t6PcH5+AVIv8+x4Xs5mkPNWu/peZCaTlVkWziG0lFjlKWLFc10wB5ftemJsjhiGKN755eO7Dv/EjABPUdnyJDv04PIy/CuaJ2rJgDOUa4asYS9Asn00R0JOIh0nW9lmHT8mJBAyBXDK4NUa65CQId64Uw4Nuhidz1MC54QhAOt1xHoVa2igvrWzZ59+8OTBprwZ9iZk0TUogyDeTpzF6xXmg1vwxM8plWMYajjhguzeiO8XNovRTXHr7t3bxShmOWRTDCuOi3fe66+/jF8dJ/z0px/g+z/8Kd774EOM06Ryv1r1yGiYGHULnpsx0snQHrf1otCiFm1qzxV6rLpmiEH76JOBXyTbYc/arwRcddZAQC40lubfXtiLpgnBuErB65rUnFZYeSMjAOKxqrrDvo/4RcLxq8IvoWGvH8ZehV1KswTB/RJEbLK4epP+bSDeeq8CuAeAEcKJTLI4iCBkHugkDMsU/uIKzdUjq65KFhWwE+ysLMCvVloiExK9KmAE+zokxZRuuTaCaAp4NdiV9Jq83donb8g+uQjNKty7tCbkSywGZ9BTo14IARMH5M1NnI0BP/yT7+Mbf+OriEPAF3/tTfzFv/4ufvzTx0BcIXzu6/ho3OCDn9zDnccZj85GvPzKG7j74F0MN0Z8cHETDyEeAhlApIyT3SP87OICD+//ENg9wedf3uBXf/Vz+Pwrd2UbS0CzDTkAXcxBU7arAsP6reR6ShjkXECn/l8zlJKOJjvvRu+rX5hwO3aNZ4De2xh+NsDm2C2E1ddx8sKbGDbvYLx4H5QeIAwjdrszrIcAooBxd6HCQ0aaJgCESbdqhRCQMssWAUoS44kzIhMyshoII1KacOelz+HB/Z+BIcZDIsi8HQbE1QpxZZ48vqW9PU+fKA7b273pZlDftCmW0y/pJEv9af963PfSb52P5O7rPK1FNRJ9STvDe09LKBTmnEHggbBLhA9/eg+vfeFlEAh3X3sBP/vR+/jowRYIEXT7FVykAU8enCFtGdtdws1bd3B6/hBhnfBkXOPCBAQIHq2mLR6NEy7O7gNphxduDnjt1du4c+sGonrhVlom7Sf3Lb2yQtx+c9MN/r7rdHIJbJudleDzeHw1HtB0LXf39q2fETQnIlBc4c2v/nV8/q2/hr997yf48z/8Z/jWt/8I9z+8hynLVvxBtxulaQJpnNyse2psaw2pEVDib6LE5guFX4pClDnj5MZtnJ89lvdUt7ZSlJhlIZpMUH/geTaW8fJaXLgk9gt6+5L5kjt5p58MfrK0D5tL6h7M8PzA+7khr+K+NElX4sOAxMD5gzPcunsTRMDp7VM8/vAxzs8ngAJocwtjDthd7DBMjGlirDcbrMYtaMjYpagBU2r/xzxh62I2nq4jbt3d4GS9Lkpcg98L3eTxfan7/P1cVbaWtO+BnsbPurH57dvmk5jsaGWWP1LJlQkpMYgymGVhOhDKNr3AhAgCEMpCZVZDGJNseWXVrsgWRdiMagSK3BhGgy6Oud3v2nYqsgqzti0GbNYDxpSw2wEDA5vNCn/zd34dv/HrX8fbb/8M//r3/wR/9M3v4cOHj0FZtvLZNkKJmZaVBorRIot5EqyKpMjQJUJywSKThRiMYbXBNG6VHYdKk9Vr3GKSzSfAnjE8ANfC/WuU+TxyHTRaLxCCXt53d6VKBmG8GLE5lYWS1WbA9mzEOMn4kcbPTaPE2MsZGIaIkBKIGCkHpL5ezpiYkScJk7SKEld3NYRWTukG7Wn6zS/IL2mUjcBtI72A7HO57epgIj/I9A25Fx4nPAyoBvoQCClLLMKyENgtDEtb2HnBKh1RfTL4MVWZg8lyuf6B0aDK6yiI0T9RRkpi0MUQ8YUvvobXX38FHz14hB/+6B385L0PcH6xLfxZqqtew31bG7kIrcFefpRj6rfGOCAloYHeAGTGvU9SYHoedOB5wH5+v5Cu4LlKUsWgLLSUk4Q2EkYRlEe4OUji0ALwPBYuIFt4gTJvicygt6DRzuSXzyb8Ehr2DPYNbS9+9emWVGHz3oto10bhrl8EcFbcQUFBDTlCUoV29HWbiGHXziev0t+uVS1pMEJdyGmzejFXCPYJlKT1+14pTLz5rQl6Y55bHJsZAJfi6ElfhbL9LqhBT+IEiWAViTTe34A7twfc3ES8ezLgB9/6Ib7+17+GHW9w92tv4Wy4hfjRh3j/zpvgCYirFU5unuDBB8DNFePJ6V1c3L+Hh/lF4ES3nUwjHtz/Ge49vIdbww6/8fkX8NUvvoWX7tzGagit5w5pXBnSfqLKJLx3QlClJRRBQAWMopm38S288M6mlesgEZyQ3Bj7zCYkCYvQwVW/pFK2eQJ+ovzrEwPBtRsYTr+K4fQtcHqEvPsxEH6GSBPW/ARDiJhWspq73Z2LwBIjpmmUuZaz+uEScsi60phkFX9K0n85ICPg5PbL2J0/RIxPZEw0zskqDhiG2LcO5h3ARSrS573U58ZzkfvXCVJf9kTiULauXfunwj666OayFkx73/l7j/cLf859Xp4FnGwi1kPAw1XA/fc+xCtvvoKEgNOXX8YubBAuzvB4c1dwIwYM6xX4CWETGLvVKcazM1zwDWClJo+csD17jLPzJ1jHhM+9cIKX776EGyebGpPMtb8q0hWvlzzzZkZMeLxrx2Rvfy+88I9MmO9xt0yjXvitLz5DQKAQ8eKrX8Lf/R/8z/C3/t5/iPfe+xF+/5/9I3z/R98XY/20Q1gF5CiG+WmaQBBPnKyx+JgzkFTLIdvKxCAWRdJwlEFYbW4ijeei3AOAKgGrGHRb45WafUCKl5c8S7uI/IefNa/3jf0SPh9IDpRJt5SkxQ3McL75dQa9wjEDYTWIB9lFJJw9OsPNF24ig7C6eQMpDKBxxHY4UR4oMYBHAAMBU1wh73YYeQ1E03Yz0m6L3bTDQBl3Tle4cXIT61V0Cwr146n/ogN9YV1cv9tLba4fujz9cx8vb1Yd24w4OHFK3kbWYkjsWEINRM5ASqzq0oQQoFsSSVyCJ2iw+wDEIMY5sARBhyn8tT1mQCAiJJJ4jhZeppEVuWzY859WZVQWvIwMrAaLZB3ByNicDPjyW5/HF7/4Ofz9//5jfP9HP8V/9f/71/jBT95DThmUEziKnAiuMbECRw2l4SEo/yXBc+olYkIY1uA0OVlV+i7qoQMHB+AqGrqmuyzpoeIuq+ogCu+5expZsDfq7TXoeeMJ7BAHSb+7mLA5XYFBiOsBGQFICSmuVLzJEqYhaYiUEOSwJB1LE2hympBSQiBgswpYrwYM3eEQ9slX+VQzaoEcnvdput/Fl/6G9j9q4mY2KL9/pEtx5gFFNRtzXcwaC80lEKlBPmj8M3VSqFVxNew5o16pytVc9RBTNJoOa5xIiiGG1EkDcjBSiOKFOawCXnrpBdy9ewffuHgL9z58iO/+5Q9x/8EjPcxOvAJrvL0ahKga97Qf/L8qBFVvPskewqDb7rvxWDIO/SLCFef5FYu6tLBG/rVnuj07Q4x7sv0WIFOcIQsuizNc0Hr+jrnQbFuYCj3x2sdPP8PwS2zYM9g33AfVrYUyCNWoBwAriLce9PkZgFtgXVeKQbo+6lbcQDW+SSWIXA0xXLfXmjJeYuspgV7wol7ABHlIgHcicK9VCPSFtFLiAmN0gro+NAG9ZYFWVv2Vn1a5twCv3ogXoxr31KBHZtQjEbDOzy5w/93HWL/1Gl7/6pv48N4D/OCDgAe7gLPpZVDY4DU8AhDBQ8Dm9S/gEUUMOeH+o/dxY/MmznEKrE+wO3uCswf3MD3+CK/cJnzj11/Elz//Mm6dnkpwXeehZ0ywN+TVhZ4aE896IxBc3LzCQcoPw3W5080aekW171s3d7d6pcy0735074wJfkZY2CLI2ETQcBcUX8Dp+gGm8T0A7wDpHOu4RcZU5JA1M8bdDokz0jgiIGAYGOM4IWfB4TQlIERMeupu3l1gd/4Yw/q09HUIERQi1ptTEE3CdMq2oN6gV3G7v97/XZZmOaGjJlcyB9Q5suBt5/KVdA7/a/Jl+tDf91vszTjSGPVQ3xGAcTfh7NEW8aVbuP3yCzh/coH7T4CLibDLNwAacBsXWhFhuH0XWxBCzniyfYz18AJGrIE4iJJ/foa8O8fNDfDa527gxTs3sVkNGiNRFRJH2xoFpdA9Nxa+72adtjQulrbnN71p32h+FZa5bBmbj1FTkxOsQe03fNaAiLC58QK+9JXfxhe++A28+/Z38Kd/8i/x3b/4Ezx68hg5TzAzPUhsPmmaYMG8KWYEFmNfzlk8lZLyWj0xKacJadwixBVAZwCohIdYr1eQ7U4oNniry7aVVPXGbjtMdLeLLBpXxOWr9NcVU3haMMPx7nZu1HOLTkWhRFWayOGaZkwpY9xOOLmxxubmKcbdiLMdYcxA4jWQAzaYrFCEkxNMIARm7KYdYjxBQgSCBGRP4w6s221fuL3CjZMNBg0g78doJqss9FHhrd2bQ3c99OPq+XJTT5dp0UzIC7zeZ1OZClDlKDMQhN+LwVqMJOOYsI0TQITIpM9Jo5wDtLIyTAHTLe3Q2LMuphGYwREYEFTeMcNpFwvJdT4x1zKVJcYQgMF4RdJvyUgT8Mord/DC3dv49V95C3/5vR/h3/zbP8e3v/sDPDm/AHHujHRAztKjOWsLSE7VZqjHkhoaSrD+nJBzksV3MhlMviMOuiDfjOJC31/x7dPi79VhEWuX+fuBqbvvXevpVAtfMuj5fzNLnEdaRQzrASll7CYgsRhzAcKA4lYtnnsAwIwpJwQaNOZWcNtts2y3PYlYD7Hwcv/Nl7FMj58l7x568Fyg6bf6bCalNzyGZsZm8xfIDFBgUM7ICZgCYaAa61vi65F4qMdQJmPWQqrhHSgLDCx5zDBn29etzcyXd1g5pZQIiAGUdfs1MYjEKeHmzROcnG7w+qsv4f0P7uNHb7+Ln73/IbbjCNtSX3bQdSGonK1XKI+1y/RlVnqWM4qwBxR+JF65z4qZ18PuZ6MFz3d2ejlg//uFBKxhGpQXcVksrVQ0uFLqPOZKl0sNXOagnW4rNvuOeR5u6mcajoa9GSyJ0kvvDXy6fqV+Azs0Q7o6II0aU0QDg3Ko0ZCqkNcG8rX7cvJpZ8Gryr0JTmipA7k3Td4l8ZKb1+RSeZnShPCZJ05RzFF+i2CPmsfKbZR755lnWx2CGvfqabxBtuMSlVVTooDdRcJ7P/kQN1+8hfsXK/zl7jXs3pej3k9PVziJAfc+OkO6AdDpCjkzcgIuUsLpzVfwhG9he/YE5x/9FGF8jC++dopf/Y038eYrd7FZD4jNSaH121RNcR1F9agVSwA9iafpg9pn/UjM+nyP8u5GuxJU41YyUQqB9LO0xuhyo78gFHxWgYiA4S6G+AJw8haQH2I6+zF2F+9hGAKIJLAyhQBixi4OyDlhHEeEEPWkXDHujSkjgpBSQtzcRNheIIQBo4bdLFtx19rp0NFRA76tYpI7IKcKRWYA2Pdv81XOuF8Iwvzb9+S2d81NMbzbXJ5LxD3+N2K7U+it30lpETX45Ax6ZcXNn9BGBZemKePRgzOsb2xwNkV8kG5heqyBjlcRq0B4cj6CVwD0pFtmCf6+2tzEjjeYdjuMFw9AaYu7t1Z49XMv4IWbp1iZou+/XdvfC/f6eUIuqb4r9GBP53dkuabshOB9ZvZiBqTa9x5EmK2x+OZ7dK3hn30Iwxqff+u38MaXfwN/9/fexw/+8g/x7W//Mb77l98WJUINcLYdMAU7TVe8cUNOYgTghMQS3yUnRohr0DSCKEg4Mi2DYkBYDQBLPN2Zyt6h3RIWts+W8XTv60uSL0HPb5bmFC3d+R+PEy5/Y7gzeYG654V+1GeAeG9sz3eIq4hdCniSN8hbidkmsWwJuzGBo9BY8ypJzIjDGhMPaoA9B+WE003ErdsnON2syvZp+wByF8s4ZxfUPNuHRbynHHm52I2u99ilId2CNJPQZg3sx8HfGm31TTCFuGzLZUbMGeM4IRIBq6jtCeBAGp+SCl2002mlQEYyz4kSL0lPr80BMUcMq9bLjTqvPRuDutgsSmAm1Fh2UA9amBc1EAJjCBv89m9/Hd/4ta/ig5/dw5/82Xfw59/5Pr73g3ecSC50kHRxxJT5QLFsEZZ9Xtp+YlAYQDkDFNrTMAMhsMXXPgDGH5bGzCdqAh3vL+f5wuU8YZlt9Ma7pWceb1rk4MSYRvHCSwzsOEIcp6gY5CTGJup+Uf0JISLDDHo7gOV0281GdkcEapFs7xdam/e9+hTBrD2OENVFSIG6EGiyCpVD95J6PCKGYhSR7fVcFrAA7W6uFKOSp+o7TDnrFndCiCxhU0pDvMyr7fSGNdj0FoeHHICgIS/Mi9PaH2jA5994Fa+99gqePH6Cn77zPt59/x7u3X+ghyDpYkVBFtZ6uPB4s/bJ4R62xTgIvlPnsVv41seBfFdH4ueO6k8JBw3+BxIQJIyDhWGQnRDUZ0Z2smrTB+QWgswzk4ChPwzjMjy/JvSyei9qXRWWLC3PG46Gvb1w1aFbQliLsbODeO7dB3ALAGGcNJ5JNI+9CNUOpTSbqC4QSTXFsVMk1RBgyhuccUCfecNPba0SVVCn/Av6AZVke6fYOdur0iS56/rvQvJiFLM/cvFKFox5+tyuicxjj0psPTEKBKST27h4/Uv47vuEn1wMYAZWq4BhNWCzWSExgJxwcrrB2cSIBHCeMOaM/GCLs/vfxs3VFr/95Zfx9S/9Cl5+4RZWMUr8id6gZx+5oPyLJ15r0KiGzfrcK1S9qZXqaFv2wpPaWBiazzHzTITAdh5zewKfH2nhrfWtGfd+mUDwaAPEVzDcegXx9AOMF+8iX/wUxDsMLGJCjANSTmWMtxeMwDondyMu0g4UIh7efwerzQ2EMEj8IlDZ2rRZrZDzWdPH1WBfjXsWG7NQlSL3dwqADSKwqBi0KlNvTVgy6pFL0Je052o2XxYE6UIa5ga8made8ZLzXnp2LePFwwnG2wHvPyY8GCWcQdS4UMMQNTRaxrAaMGYNcZBV8TyfsDv7GdZxwhsv3sQrL76KmycbNdrbfLDP6oyS7nsLrlJNC/etDN+fbfdQPxTzzgXg8RzNmHs6USg1t8KDb6fnIbWqXzY8D7h193X85t/89/Ebv/P38cPv/iG++af/An/xF3+O7W4rOzXBssWLs56FRZiUTxARkBKmLB5D27OHCMMKRBF6WKgYiwCsh4iL3ehovpRVzOn1n/pm77P+Q+qz+eulDAd75eAjP3f8O+rStdkrnkr21rDXeOtZWrg07pfjCmkT8HgLXORannnzm8IZQ0RixXNmUQ7GjGn3CEPIeOHGGjdPb2K9Gkp7PM4usTxhrfsV/cpvlzviIHYdeGllep3HCAABs7Hvf5cbi7IoY6TfQm4FlQOjygKZGZOeVMkQRT3nXOSsKSUxzml4gqBtzVnyZqO3KptyqDFMGQxExjAMZeGkLDI304yLvSyDC6+wciIHDBGYJuWTJId1YMzYrCI+97lX8OqrL+P3/t7v4s+/+R38/h9+E9/53tvYjSOMAEc1Qibtoqw7ZZjkGzhJXLdpvACFWI0CsDlNiJEwTVaCtJzsO5fGYN8YzZM+M1yVuh/g7FcqdO6l5w16nXHPjXPBHYLE1BoI26SH4dgrR0dka7YcrmFzmMG6OL9DIMZmFbFZDXUreWnT/m+5Lh/sWPFBL1mfya+t7Rvkg3h85fZR8029dkeK58wy5xN0OytQHEcyAApcFwPKsLHSV1SvXHIfp4fYMABo+BkzJnrZhAoyOJ6oRYEIJIoLAsu3JI11K/qXnOY9RMLt2zfxq7du4mtf/SLeffd9/Ogn7+L9Dz6SQ4FQjT/QWNhLPS7hNwkpySKdDFbtMAJAGofwk4SPiw74Ap9F4rsKz9+br2tE5gWzQ0N/lg7RqEbk2XbbZ/w234x9unLlo9gvk1lGTyD0nrrkQIvvH/t442jY+xhh36i34R5tC+0qrgAwIiQWSBuPTVXDMklsdYMqZ4MZAux53YrrAwDLvRn4XLrFL6jxCerOdfuOFmbKpTMjiQGKlhmrU+zFgEdly62cQFQNeyZEmhEvqBEvuFNxhQEFnO0SdvEWPrj3IejGCWIYsN4M2JxuEGIEMvDCDcLDxCDO2D15gkf3P0DabfHy+Xv43b/2Bbz1hc/h1umpbPsl+7M2V0GjMe45VuoZIft0JWkRaRxCax2AetqYEQf1IAx95zMGJrC63fnyzKRcBA8dLMdL/YA7QemAIPSZBx3f4VVsbr4CvvE1cPoAw+5d5N0DpPEJMF7g5PQmOCUMUbbfXlxcYJpsRX8Sd36W1X2N2Y1VHMBgxLhuhK7KMBRhuW1PDYjYagUiS+kscXKVmQSqx273spkA+1gKNT8CPdeiWZJGaLOvLLhAc+88d99svbU/tO9BKM93iZHCBk+enAGrlRr1IlbrQWJ1MHCyImx1Uk+7LbZnT8DThM34CF984y5eevEONqtVwelgXW545waImo+s305uiHyP2Ls5eEGk4vnc9N5SXTP+tv1sSefCTV81679Ubz4WQegXEWRODfjKN34Xb/3K7+DvfPA2vvPNf4Nvf+vf4oP793CxPQfSiNVqDWY19OWMaRw1uHMu2w2BULYMAuIxSpCFOqPnjdHWKzKLxLgk7G6vkO7nBA2euN+GTxa8mhv0Shq5aH5TZmQaMO12QIzVqKdetQxxKpu0H/I0YdrtwDkjpi3uvnCKG6cbDDGWdlYjYvcdCwg746H1i2f483Hj05xKW/D3eZ3tr86Jjp0QEQbn1VikNIZ6kcvX5gSAGTvOSJww5YD1QKCgISSylFPlN6HRYigwDz0bXzXuqSFA/sQKHqNsj5RQM57GMogDGAlZjW+2cEtRaDvHCGIgrANSykjECCztn6Zctv8xrfDX//pv4rd/69fw9tvv4g//5Nv4829/H/cfPsI4jkDOiFG2hMmJylxCbIAAZNtWTE1sp6DaXaDYGrZmo7cwoIdG/OfgtbekyO6bzM1jL9dW5CrYsd+g18oX0s3CDVNKsCDVVUYQESYG2ZpLgByMkpI8zwmnJwNWQ5Qt20viS1Pf9TB10ejfX+/V9lGez8rZVy73Oa8H1m8+NIuVW+PtkTsplxGmrPoWYFtfJX017NlWdLEAshpaDH9NntFFk5xLZQGhhJ5p5CQGTA8Vs53mJzEIppTVW1O+KdsWfwKAIMZ3QIyUFPHmm5/DG2+8ho8ePMRPfvozvPPePZxfbJFSAoM1Nqh8H5eVDWWtxTuxHRRvHP746PvPiW8vIvrVYJ9OeHWDnjwoNNQ5kxTZYbGJOs9YyEL022334fkVYVbfVcrrGe7Su6X3+/qok8X7ZH5WPs2MORr2PlbwZJkABIAfgHEbxvCynvKEOEi6QOiDdlZBzgQ1Y5ZLhrxCifWxEl0yZa4a+KxpTv0vbd53kIbj2B24Ot1XB2tSeZjLqme73Va21HjvPLsvRr/ioVeNe+VkXBMudTU60wpn2wlDDEhxwGq1wuZkg5PNCnlKOBkiXlsDH148wr1330WcHuPLn7uJX/+dX8cbr76AzWol231h5cIp/Ubkq2LgFRPt5rrrQ9NV4YdmhCTU3q1d5fKxE5CKoUbnhMWyMKbjyyaI1x7AzWoiL4xTXTX34/1LDkQgugGiL4KGNxFPzxAvfojtkx+BpwvEdQY4Ybu7QE4Z024CI+Dd9y9w68YGcQWAgKQoHYYIMDAMK/EGcvUURR+E6m1bBfy5Ec+T+T3X1+EEfdqFCdBTiVk68int5MDOcBe8sL7HmGfPQSW/xbG0ewBgCtipMMpBAuavVhHDECXocgi4PQDn4xZPHj1CyFu8dHuN17/wOu7cOm3iapVtt+gEBv8M7bPGk6celFg7w9PZvQhFs38XOtQ9oRbJK1tAs75ptmCXhqzxmv/nbw76NADJYRuvfRn/7qtfwt/42/8B7n3wNv7NP/1H+M53v4WL3RYhZMTAmNIE5oyUMhiEh48nbNYDKEL4jgXnV6+iYQh1i6JUtYyShAVjX3nR5fEGe8xxdmFQrysQqq51BSawIJiT8UaVBxYMeiWd4c+SQc8zSApIWcOWOKOeHcgQibAJwJgm7C62IJ5w4yTi1t3bONmsKo2x4ny/eGOSew5tx+Jzf7/YRz0nPkRYLxfbfXtNfOODOVwu+17Fe09jYYY2e20Dr8IEMzCljJwZU4rIKWAYCDFk8ZyjLB45Rs9DjRRsBsOsBjEJlMbIHBCZAUSM0BgVELmOWV101GOI7GQOZYUAl62yFAhRJaeBAlKYtL3CbOXUTPmIqEeC0iriS1/+PN784hv47/7ev4u333kP//U/+QN8/4c/wW6aIFsVWY8OZXAWL7yLXRZeoUJd2Tao8yNEAo/XwLCWte8Zu8Pl7U3RyXFNhg56WfFK4PCy8EaHCHPjXsdP+3nZ0A/xApOp2coJth17ICDZgUe23fZ0XUJn+Hprl1z569w3HPz4Q7mfIW+f3BnkPLDD/71zSXs3QA3kttV0oUlsxniNsxlJvaEkfQLU0EdgjW3ZTkA7AAPF+MKEsp2WUlb3KhsLx8266Vqax9ZeQkAo8yrotn9bXNOkErMNAAIjUsCLL76Au3dfwNe/+iV89PAxvvf9H+GD+w+Ej5TeyyoHSQeOKZcD0rwBv87zuQfZPnS7GlyNM++XGZ623uvBx2XQa2DGEqmYL4hkjSmhxmklAoZYF/cX67gEPDf2vPua1GGh8Zdz4ys1bAnYtZWfrpajYe9jhzoEnB+CKAP5PYBWAH0FA62VedkW3ABm3SagnnsE1mC+Ngurxx45ogSietSzbenT/GDb0iETsAhJbrZ4pyAT6lroJOJ+NpL9GYMntFY9UqOe22qrwnmM9TrEqBb5asQjUsNeMfRVr73ixadGAyBgyowQI862I27cXeNks8bJZgXKjBgYDz94F9+79wjTw2/hr7/1Or7x5V/D3RduYW3l63dEJyh4xZ8BRKJicCtfWK0AAFfvO99n5PoYhcXouKAa3XrFiuowu2klAyejbCKTm3OwQNB1+DKU8Tomn/2Q0rzuX3ogAiECdAvh9DdxevJl8O49pO3b2F18hCECaWCEYcI7732EcQz44Y8fYbM5w+e/8CZWtAKB9IRMxrBaYTzXYO9WBfxWXBMYnJFvpuCXpjlFo45r8epdNNE/HQPy2Gxtrv3jUpniSHDGvFCNeXZPgB144w0CjZEviIEd9t4p/3J6YcA4JaxPxKA3qPE0EHDx5BE+eLJFju/hzZdu47UXX8fpybrGyNTmlvKtEx00J1a7Prf+KDi9t8d8p83x/BC0hlRnjVNazqUxPtpipeWlfEe3pG4u6Y5QgYiwPrmNN978NfyP/qdfx733fohv/em/xLf+/L/B/Y/uiQIRGCFmPHx0jpQIH364xTDscOfuC4iksciCeKwPQ0TKdkJfNx7sR2xBVDyIuu1y3Py+Sbxw626oTVJ+FyYnuX8awbgxyDlcdrJANe5VXOq351ZcoqYuw/MpywmJ5t2vWTHutsi7CTk8wgs3N7h14xbWw9DgeCm1w7vKu5c/mvbe7Okk966y+sPpuMkxh2ZcvcfufAjn6ft3XOUCEc8CkmnkWbzmzNPfCmdiICfsxoycox5eJtt3KWig/QBQdNv2IMq2/Zpxhlg98Vhi7jGANepiL3PWbYBVCJGtcbWsQAEDESa4g8vCIN47HqE0DdvKGjNCIAwAVndv4c6dm/jqW1/E2z9+B//2j76Fb/7F9/Hg8ZP6zcS4uBiRM+FsNyGEhNPTE4QSnF9GzAzMDZgctYCZLsn+sSJcYvx7djg4K5fw33DE4dAsZEVDT2rCRYNexx7LQQYJoIEaGgLSg40Sg/MOp6sB63V0J9W3Db2Mt9Y2YqEjrpr7ecOedlDlG25I2snCRidamitWPBGQSHWHOhSsC1cEJkJmc2pQHRG2Hd50U5nBzr4HkB7OQYwcCJQJdQtuAEUxxvdyKpWQUFKGGfWolKf4YrQDWXe42QI5FXoDcPGmPb2xwcnJGi+/dBcPPnqIt3/yLt792T2c73YAB4CE3oyTeBfudgkUMlarleuzyq88nl/TXvzpgGu2eZEO9Ex0X/G09E6pXmWOaOgGQQy3AGRRhsSDdIF3H2y3L75ry/4yrkU1Fu6vOyGuQN0XhJXryu1Hw97HAkuDmwHaALgNwgVk2k44PbkFIiCGWAx5OeVK+Hx5RRlzVLxYeiRDWX3mxmzncEkJMdWHJb0zDF5XmCgr744A2q8X8oMefGFbbWOM1VNPt2TI9iVNQxY7r27Tta23treeQuexBxLvnYGwXq2xXq+xGiLGizM8+egeLh59iFfvBPz63/wavvzGy7h5eiKrv6GuBphC0AZ+7YQW6rbt9dehPCp9w7Uo/xRk7j4lvQo4qM96IUiGTfKW+B7+6HaXtqENvg3KpPxBHv77jtCDzgG6DWxuYdh8GcPNB5i2f4Xtz/4KHz18hM16A0bEo0cXGCfGe/fOQWENELBSDx6iAHNGKCsytYpG6fdGPhvf5kSvxqjjVETiRngyocSn2veN1+sRL1w6fPceecEb9UKlB96AV/JTE1+PCDXGns7JgueUhZ7EiKhbcNI0Ynf+BOPFGW6dBLz+hZfx0gs3RdEPtZ6K1tR8g/ZeYwNp8GMBD6WTaxn77eKqvGJ+tBLQmuTJzQszDhRaXlppCaiNk+mUf3YFU5eTCfsa+ssNRAhxhVc//3W88sbX8Lf+7n+Id37yl/g3/+K/xHe/+xd4cLHFMAwABWy3E1IGHj+ZQJBDsKJ67MXQqFO1eBiKO2zs77tnPS9v08xfzIu7HPMPdMj8zvHE3jBfeGhjsKsyQoODzjDQ8FpfUTHe2KIgldNt0zRiswJu372JGydrxG677RK+CrouK//UJ15I1eDlJfhzFfQ6mKbjD+zvyL9rx94/bkddjGsJQASJRw9IlNpxB6YgCn+oC5NGIZmBaUqq8NtYqAwTZDtk1DFiratGgCDRo1kU8JAJKchptWKcyeJpDYgsa4dYgADIqdTF0KPtMuOuePsBRJImkUXanzBEwm6UA1gscD6pS9E4ZRCt8ZWvfBFf+tKb+Pc+fIDv//gn+Bf/8g/xg7ffxfk0iZxKjGmSLZ8Xu1wG3bY6zubAbEAXcI/q2O41+7mFvqXXT4XO+ybbfOK3rx2+yz01z5s0rqh9xj2XyzEmO+ip0g5m3W6rp9tuTldY6em28/Iv+e7ZWD0b8zvYZU4IUNF89urpjbZ7KvVCg1aQwWVHDilTmHIGEyOWnV0ochBDDCuJgQDTKU3uBYgYwQxtvZDiPo4BUEYx2DFUbsuC76wTm9kHeqpb3j0EzyMIuuANZKs/6YEriZFzrvNBG53UMvjSy3dx98UX8PWzc9z76AH+6gc/wf2PHiLlLFvw7QwcloPZ2nltPGqBBVNRpZ4Cnm0mPC9Y5mu0d84fQitaeliemc3CHJZYeb3o5C2Pu6TN9o+JwYdTfgrgUDsOzAm6XO7wcDTsfexgvR9AdBPAKRBGELYAVhhunsoIxTXAGUQRzAlMngtVg57IOexWAlHfK3UpJyQC8CfmljV9I+QKnQroDAGOWDkhpCG6DZa3K2zNFjddYTcDXvHQawx7bhsudfH0Qrv11hv1+hM0GYQhJmzWEZu7d3Fx/hAP3/0QQ3qMr37+Fr7x77yF11++g5MhOuMDnHEBBXGk/VXxKl9O2pva1+S6okQmLAZXahlD56pjgokZboJ5V/aLGqioXqdFNRJkV+YS+KFsvkNXR6hLfTTsHQbpnxUwvIIhvoSX3vwa4ul38PrjH+L+w4SL82/i/HzCyckaq5MVACCuVhKAPEYkDT/it8bX+JeAIWox5KHq761xD/Avy2m4taVomMSlckRvaezLMuRwyALFgw4XbXtWs/VWcRbesAcz5qHkF8O5CZStkSCocSxSwBAJw+kppnGLx+dnCLzFy3c2eO3zL+H2jZO63dbjttFEo1X1M/SeXbru66nFyTp42rXeULjUfYWOznrdHfRXBRpflzsIsCze+LpBVPITal124+P4MbiG1DrCXiAinN56CV/9xt/Cl7/27+CdH30Hv/+v/jH++I//NR6eZ4y79zBOYpQIq6hCqSzUhRjaAfN8truSuVP+qTTfPetahkIt/OtL8XvvlzY/dk3Nr5/rOkd743wvA5hBz8kD6H6pK7PMX8N3qJf+aoWUJozbEcQTbp4MuPXCDZysh7oo53Dn8KdxW9dCb4i+e8hMP7+7DJ16ft7z3ZnS3xVoeD0f4j01q0xCrlINjSUHUhRFnOQwC0hMwwCJFxtYt9Op6zIDSFMGBwZzQM463llPN4y2oCQFZ7bg+hmUAhAYQY67BDEwcgaFEQQuhjqQbL9LnPV7WT1fZcGXmBGHilsStzaAOag3EZA5gIdqaJ9SgmwFlniZACEEBlECETAMwMuvvogXX3kBv/XrX8P3v/82/uW/+RP86be/h4uRkdMjpMRFjpVmOj6yhHOzgXwK494BZH5qVPcFLD3u51xnxNtn3PN8tDXieRx3lS/hJZGOZUaaEggZqyFgc7LCEIMam5fz9t81ezd7wPtelKdX8azfm9lf9vd9rdz8PCVQ7XdnaKt6oryzGLCZBf99HFgzhgGMrDZsodsoNJwDUA6n5hnFkkehhuxkAjhlwTNUQ53R63qithr6dPEbYDWem8yCSudZ3jNLewCAKJft99XgLjIxgZFITte9eesGTm+d4o3XX8G9ex/hr370U7zz3j1MicF5KwfohMrHQGbA3z8TflGMe1eZy/PPvL5B7zA3tH5lmDEXaA/DuIr6uTfNUyHspw0OfcT15srRsPfcwA9SALABQLjIo5BiDWYMEDgl3T7JDXKwul/7Lbn1LZXVyKLwlRUY25ar6NkQ+v2t7dWP8sYxVmruTaH1Cr47zdYb8vTXrgd/8i3ZQRlUVn+LQcBtwQ2yF8TFwSPjPrixjjh/+CEe3X8Pt9cX+Ju/8gZ+9ctfwt0SV0sZE0GJPlDdzeEElzpiprDX+Hmeinll3jEEmIeO25bbeHFQYcXs+jXYvbOxGPsstXZDY9trmwx6Id6Y1XhQsjM3bS/ZS18e4SpAFEDrl3D39f8W8Nqv4sEf/+eIIWC9XmGIA1hPZ4tRAvBTDMiTBucumrndLBnnUKXMognIddke4OeDvu+99rrCFn59W7B8T+089Mo8eYN756E3O+TG0Yk+fp5/Zlv6iSptMdqzGjLGi3Nszx5hEyd84dU7eO3FF3G6WZWA2w6t9JdRt7LU/jbhpeKAiZL22e3BFWXxBH6FmRDIqKs3GJgwanjejoLv5Vnvs9XSjRT5Nhg+t222whpjX6VSe2o8wjIQ4rDBF77623jzK7+JG5v/Df7xP/2TwttCDEVDokBg9TDKORe+a+WUmyV53uN5l8WP/RL73vP42t8p/y7Pj5l3bYe3hR44GcHSw/DZygEK/2nliZoOEGE/TTtMuy2GkHH35ga3btzGaoilnGJwJ4+zs89qsFKg7ec5Ti4PEtUEsz46BNRfd3TZ93pLmeft7Jt1WKKr9KpukYXQQjeG5jWRE4CQZZstqMg8VlLO4lXn6X4GMDEhRqFDDDlpM+jWWoLE5kOQu4AAhhhwJgqgIF5xFiwfRMicwXp8ImfGlHOh7dB2BK5GADB0ITkCa6GJcQgYshjbsx6nGtXDZxgCxghMiUSxZ8JqOMVv/ebX8fWvfxn/h//z/wPf+f5PQUBZqGJvnFaFvyyyXBcve9xeeP/xbsndN1fnz3tDHjqctfflX3LY5fhTRWuH320lADQmW07IKSES43QdsVlvdD6Qz+6Am4fLZvrLPpm7XLRwtfx+GRpB7Fpt8SU/vaFIjPYWP1MKI3idUVG/pK0Uom2SSR4MqiF6MiGBi4wm8ic3C9PE4mxgZ20AQE6MTGJgs1h8RkjroTry4SWWn1IeZvGq42D4hkJ7IsmhkyFEaQvXWI3CgxkhZIQksQSzGh1jWOFzn3sFr7zyIv7Zv/pDvH/vgbTdzbc6JvzMY/LJwsIcvQJqtGofLTzbU1TTV4czEEHCPui8i4FaeX2xYXvqfSpYErrmd/s0or6kHsvnvPvjhiVs3Q9Hw95zBwLoNoAdAIAvtkp8BiAnUFwh5RFK25psJtETcSXKtpKgabyhxrbmBqCsmlalshdZ3RT21qFeoqD55UzA9zHwYtRtt1SMejFGhBgwlHcBgx6oEaPG0ou69dbF4yvGAWc0iBrdVQi8xMpiInz99QHvv3OGL37jC3jrc3dxerLSmDCQ49thwiyKgOIF9brlzz6yFbjdgpP2a1XjqTDCWlaAGe/MiKACiO2FU0XIH4hALN9kJocSSxH9kHBhcuWZGhCcbW9GGIsh2M2DIrR3ZokjXA3E6PQigIDNyQ3kMMoJrRKDGzFEZI0Htx13mJF+dyuX1AhMpEg824qrlrs6s+q1ZzE+Rog39u1bbWzFW6rKuM1ZMuHK4l0uG/bqu/k2XL/9vXryWV9aGhQcKfIWEV65FfD44Q4vvvYCXrx9A+tVhJ3mJllqZzpU9ugAUDvX6/azeV9Q88IrA1aG0VlCmAn3huftQM9Fig7PtVLqUtUEvSGvq6HVe7QlSvUPCVJH2AtEhFdfeRExyEE4TBkxhnLCXggBmcWLaErpsHBXGPPydtt2pvRpHK67chYL2lN3O6ddm1wCM841OE+OHlzFsHcFg16P3wa3NsBum3B66xQ3TlYqG1gL6wSf4+jC/N6D330S6852X4Rh4lw46/F4qddbaryvgS49+yS+TKMnLY2Z47lc+MWEKcu2VivV5EfSQzPINTJnDQ9TYlnJ4Eh8PlnoCKQxr0jP92ZC5lDbQiTnZoAQtcmZGYEZkYHAhCkQQpiAnUhOoSx067eowp+RkTMQEcBTKp4/nBmJE7CKZfdCjAERsg2PmYEYitHAFofiwAiRxLg3TZjGDEZGTpDTOCNw6+apeJLECM5qzCh8U0/SDaR9uoBtPUouDvQlL/dZ9i5B70PQ8BMvO1L7rDfu7ffS6/C5XDcXe+4F1gMhTYzVRk63nQfK7414S8CYJVp4dBgOK/57laLFZ/tq841aHkTr86rvXd2wxFwPrrGZWb1zZYxaytY1xTXNYrV7HSFkIDXyg40QFccECgBlLga8TBkpy4BIvFrXN84Iydp+AgA7NAkoJ/gGJ6SZzFiNmIIrJU4oySEgtgCXUpKtuWr84yxhBDbrVZFXJSao7wvpNC8/L8Iz4ONlmZ+p6KvUvsAol2QD2nND87d7eW0UNnHYO+8KfPrjA57N46vWTd3vvrzPe/wOwdGw99yBAKwAPQ0sTSb4DmBOIArglBwTE0o+F9hsuybX1ULb8um341rcPQs+7C09ZEJdV7pX/tGoEoufQ+WvetyVU22jrJy23nqh3A8xFg+9GIOutNZtu7ZCaoaB6AwHdXsfijIfiAEKuHt7wH/w976uzyDGPHLtBRrDnhdMqsJAhQn2AfQJKH1MNXtDEgrCE1AOxyieQgSJu1iZISCGD4mXRTOF3BSkDHVxBzROhRfGaxwuCz/hD0gJnXxo5futuKTlL8UAO8JVgLBZneDkZMCUEtarFThHEAjDaoUpMU7DgHGXZjKnM8Ghia9DqKuhNvk64x6c8LXoreesd1cx6umnlDTkJnRjnOuMeXsNe1Q99hrDXlHSq5GvBGAnNAYD+2ZryulJxK995RVHg2zbl+Kcx0mHTP2J1UvfLJc0f7UouzghiK0dflTtl5oCmlRO058ZHzv624RIQDU1lBh73mjo2lvLrIs/i0LVES6F1XqD1bDCahXViBcLNwgaTH+IocbMXShj6Xk7eu7KBnMWJ7O/68z615QoC38yPglv1Fsw7Nkc9EZ6dIt9wIJBb8mwVxqAarAjrFaE11+62dKjeaMXn9VeufSjZ9mW+HnzXGnS/E2720IetU96mo/GlLXQGKAYlPplRN9GblJUGljqy1lPhvSHdqCMawZ0LLmOGatPD0E87kwuJRINH7KQkbO656h3jZ96zGpwY7U0MiAB8Ak0EXYM5JhL+UW2okoPWRXxKSdQIAwqX8qWXUJIGazbZGULHyGEBCAUQ4Ep7gyWw9RWAesVkPMKu3FEShk5sZyQORHiMIgMq6f22jZc401iiCTxKrwEPG4uvDzgmbc/5zJ6XxPpLVfBL3L3S0a97r213yFiT0d8BfvwNEbg1s2Ve8xNmktZVcfMCl98Vh7XLf61UIRx7xTXvN0/RkvX87Ibukj929LEtg7zgFsoleAOtHGF1Rjg0ur+jGs/B7PmqYu9Wj+LwS9AjWYkXnYBrJmyxJc2pG7mj5WjW2xZPX5VpjQd1jyFAZQQAUbzTF6OJE4v5hVGMSBGYIgBk8bblAUBiffZhHUqfVN1wrqY/jyNb89ewqGi982yaxv1aJZyMeGh+lbDcr5nRdVDsLfsOsWxzM8PwCXD1WM5d88+CWPf0bD3iUAGsAYADBv1OAsRbKf7cGtca7U89d5RKt4I8WQKPPQABa55gxnxnCjXTWR2zwvN9URsxodU+KKWKBbDnXnsddtuo4uzF0NECAGrIKcllUM1ylZcMUpGtdAVQ0IAiFhjpEANeFX594dUVKFV/xpBpX5Xo1zYv9ZNVI0BzYqUoqr9a3X5zvJE0ws+xbUdflwyTOFh0jgwXcy9iI4YUP1tmHvJY6zN6vUmSld/Y/h5zhT2Mw4WF2RzssGwGrBei8KzWkVM4ySGaYuZ2Q9mZ9Cbe+E0Q1WMexLzkdS4U42Dc2+ghlDs0fyp+7W6DOexx6hXT61uT8ENnSEwlDKgxj3AeeYVA0IVNNt76w6dy53Q2wuKKLg6/6z+uRdSexQojHlBUyhor2NmSZYMdKXdxRBHDd2Y+ftw+2RevYVbqOPG9VWbzg+3J4BHuDowECMVPj0Mg/C1QTyVxDMgg2I90bngMNDgXEXxDg9pIV25UrpeSIM3+veNbQYcrYGgx4nK/YQfVgY688hbMOwZ3wiat6QFyn3BOMPJDqcdq1qQORbSzN4vzOcDj1r8bPuqoSGXwlJKX9YSRXFPeNl04KWz2XcvGBHaFPMSOaPx2Kt8odI1M/BFo89MmPR9sIVHNf6BhabnQAi2lY4YiXTuQzxtGKzhQkgWJrmevptITrnMqvDbAhEIKlta27l4IkmQewaGSmvFwzBDDHl2GrW11S0ksRoIgpQXiMBZDmTILAdlhDEV/mVyk985QoB8d3YG0H2wiJfL6fYflkHlcLunKnspH/yY9/f++bJxr+LiIeNevfH82P0stmvPrXtIl6c78Hxf4sLjr1tKWVRczmkswGC/we+yehep94xWmWGsz9mEAzFjnjPqVbVQTruGu/ffZroPu7LZkans0iCLcY9Zt/BDGiI8o/ZbkavMqGeFm2FQx739rJZeBy9P2XxkFzCFBG9Zt+VTyuJ5SLVTyoGMjqdYjL3LjMReNn86eFqEfsraFphoj17X5bNXwrl95X+MMCv3UEVP24h9+fYMYZ+8l0Oex8gfDXufCEyAnp6X9NjwQAPs2O5xlH17ZYst0Arw3qhnB2mQnXhJQhSddY6claBuyTAVwZn6zMpALQMgQjFC2Pa4wkWIANteV7zsQt2G2xj2ZAvuKgaEEMWoFwOGsmWXilHPn35XDtEgaGBkdttq0SkaKG0rxjcngJTToWoy9EJLw9QLo6hguk7w/acMZCboNBnr08JAu7723hgSxUL7Vzfzci1eoD/1oqMMPv4Xd/cEiXtj1wQUT8FPkrF8FiFGxjRN4LDBKg5IJB4FQxw09lbA+UX12CsMcUGo90PaquX1TWPcA5WCyhOjD1SFnD4mii9TULuf9zLxvVK/bNSzQ27ICUid557z2gNR46kHIzGAeo0A1SjQ4nOZt2gFFL/FvBd4+wet0OKEO4fDfgxCk6nDZ//YCUDlvROkG0MPqvFdaHituwjB7ZN5zMQ67EApDYWeN21xtOYI1wcZS+FFKWeAxKvHTumzrYQxEHZTLpnaEdwvxjV0Yb+tr7mf0QlPTAgzG36doh0yFOt5z1uXjHr6zJDWcBlo09v81EIbvHS/VmfTqm6S0uwZ9bdL2ZYT8EJ9V8UKOpBnJqU35rnS5ibZnmrJvazp6yA22dohX5xdOWeNMzdfQGoODSKRBwIRshvLOs5A0ncRJMHsOeupupIswTxvpHEczBgYynMJz5XBJKdSlnkTbAGJa5wuVfaT0q6JASDJojHJVt0YIszIUnkhz+Zm+U41IlAEVjRIDD+llClllA2LFuYmVDlayhMeNWXeO4bNeKLnuUtp9hv3rmQ12E9aDmc7YNSj+qA11lt96PDaTc4el5fwdc+tNezyNJdBR+YY+8u5Ni1owGPgnoJ7usNtzsOw1Pp5TotZ1+f0KkMRQ3So/A6P+it1MWpscQJrfM46m2Wbe21e0R3VeFfbUGU6w53q3c0Vz1GnelZPP5EnxdOOLEi6fZkRM/I7rBT0wB1A2hVZt9tqv2XnbVvWvDs+aDKV7rh/zrCMwLOn18DzpZl8LaOeu5iVdSnPxZxXfYxQ2nwIqa9f4vMpYs949UP5lCR8EY6GvU8MxN18TZDtlxTBuj2X1MBXts/BhLsq0HmjHhcKXEuvRiAJBixIZZI9StqiTipCNPqmCnteDbFf0jqM9tk2OzPqxQWjnvfSM2++IYbiwRciVS+/QMU4gFCFO9sFoqH1igcQ0AZcLorETMG3vrB0+k2OmlV6bqcwtSNX6Zs3kaGUKwzQTCwthazvtFyveKG2t1UI5Px1KqZEKoVpzNg6nk5gQFe/tcjGuCG0dq/zKINmwtQRrg7jJMb5k5MTnNzYqDRFiEOU0/mIME0jKtI1ohbkSfXC2bcNt3py1W25sgXXfuGMek4ncHJQD2QI7u4rHnVKfTG6h3b7bTkkQwx7Pl6mN+xVA6G2iUSQrIa9iq/VsFj7q7TKk7WCh3O8NLyvfVx7vRVoWszpMYFoLkPYibVN3SgPmvJb1GJXlii96L+RqSl7XkZfTuuxtxTxa7aIcYRrATNjUqPdajVgWA+gDAC6FVe9olJO88yd1FbRekmca2lDn9bft158qN4Yeytsa2ieKB+tOI8Fox4V3Fz24sOiEWAJXxt+ZPn7BjZ4j9nkpf5uYXLT3pt5woZuNBkuwZx5h+qt13zrF86p/3Ibq4d/oRYOePbJVW50qZh1bqIcCCZpZe5k1m24KltmeO85UmOfxMeKCDDnOQpZ9tpCt6Yqb7PtcCCJp4dAYMogJqmr2N1YF2tlR4bt54t9wH+b34GAzJiSnGqLIDEuUxaDum239d1YTgGGFVTBnwCaCw/k4mUEyGnXsZx0TY63UjmQZC80KLiE5+4VDvPnvqr+2YHSfRWNAe+gp14jRy/gs5t47XX3TX0D2suFhIu3hz9q4fEcWxeMEx87XMHA192Xx27wlsdxqYBWT/MG9ZKC3Dt9ZnPH5rGPvW2Lv1aKGQXNtlX0S7j5x50Hq6qdzLVssrI9L+j2MHsDo9r1EDS+vB0GWOe9ScG1fU0POTrYqcq+x9wuGMHtIps7hrXfM37e9ucBl+H23kz9owVGSF3avfy4S7AXl5ZZ8LWhb9fhhM9Uy/MHX01Px59TlUfD3icCNXrZaAJKqFsHwMmtELjVPVvhcFtxPQWZeefABDehrD4QdBNNWTJLcTMDH+u2Cy9ltCv1FjOr9dhTQ55utY3lPrituHWLboyyDWJQbz4JdioeEdR55xXhb1HZsPbpR5E7Wt26EK1AQv773b372iLkOfLnyqt55a2q9TQnhGR1WEc7wufd2O2CmhfKrlhPkyOpycfNqzbeatA1sDQRJn+7waY6nWQjSxXwjnBNYEbOCaRbz4kCzqcRRIRhiBjHUYxuKZV5VYBQ8RvATEt3Bjw0Akabbtm4h4Lg5Lz2SsXQtnhm45ChxbPg8L4z6jkPvuDuG0NgqPha/2wl13AEhcZU2crhdtO8atyzR72CcUhI6WWcftdca+YzlOGmDl9O/1sb1yqaZXW4EF1UPNdU9rhdLKiSeRUgSeeGKqGoU4NLwkqjzOhzxPOnAdEqUrZtO8LTRzUyhBAkDhigR48aKA6jUbe6112aMnX2p/X31bjXpV/6hq5l9ktANfCT51u9UW8PD+7facG9QQ9wzzre7FvV8+SFt/3PYdNbk/bw/F9+S7Nbw+ciE/hhaRK38tysJWXc5LfJ66udlT+nMY69l9dZDXtZZcMIjUVVVissTms1uDFkRoZM4EBIIMQsW2gp6GEYmZCD7G9ISWq1LbilZQEAdLFHJ9iUCTnL4o8Z1GxHBkjrsM+huoBLACiKQS7bCdRgTBOQI8upi8SF7IVAiLpoEsioae2xcZp03pP0T84SZJ/k+2DKPgjJtvk6gx5dwwP6Msy0RMvGvcrXrws0u2gfzHDR4WThw5qg/Z3zW3+x977czHvuMtztHx7q+4+Fw7m2e9qyH88NDlKig3XNyP2VMglk77FX6BN1yZ0xq8GImsjM47IQyN4xDuakKvH26oIsg6sDg8osel5Fs4gLyCIBu9jI9tPIeWSevxmskk3KekquM+yBxCmh7jbrlkAYSCmV5+LVmBWH3UK6fkjBbbjQWFfFuysh+cdbwL5Z1j+nhbseDVuc7fBrCY+798+Cc/v4/dVzPwXO/TxggZ8vvX5WG/HRsPeJgQ7ZNImSTCsEXUWdJqV8pvQ5BY5gnnqkz9zqigofZeUF0FXY1rjnau9axPW9EkYQqcAiDamCO4qgU5V5d2iGKft6+q1tsTVvvd6wN8R6Km4x6gUT8MgZ9qgKd0a4TYkIRqC6VUYTOvTDTTFuvfkq+a8BYy29dm8RdDBT/I2J1fTk3rmFH7OeufeFMVIVDr03TRU+WbcBKodEgJ79bjx6mTDD1AONDcgoJ8cRTPCuhDtoWd4geoSrA6dzvHT3Jn7wo4ghrjAmBk8jCHJ4Bk8TAMJ2bOcA4NV5au6Adi7Ijc1jfd4p9b1xT+aXjTjqfOzb766LsF6tbNWIT+0pt/UE62rUqwY/t7UetoUXxZjnvX9M6Ou33No8l++1hQUUU9dcmFCatQcpejz1fVxOo3Y4Rf596R0fGsEKoRku1rI1p9ftu7bVrIbnVGmZ8gV2Gf1Kt3l5N7OoWeTpKmtJ0RGuDIyHHz3G6WYNQE/Sy0DO4qkbYwBSBhEwLjjsAegQenY7f+Zx3p4brjucXjLotY87wdeQpyJe4VHFc8E/8/953HWGPe/9U7LC/cJVrzfNM4fMi3O2+7YZuLn+NNO776pCY608Uxy7XBUtuXlZMbIW3NLZNg4uYDSE6rZ9dKPq6hc8961vv8Xn5MxyUqzSRh8wn0BAtBiJNivMK0fmmCnz8hlZeYp8DyeNrKVGAPMECo6Q2uEcFAiUgxoOyDRxAKy7Ndh3g8h9JMaJEHRBM6PEuQOlwveiGjWM/oFE3slRTlplJ0sCBIuztx0nrGKUZ5mRc8bF+Q6rIdbvYhSPRwoByFJPMiHuEMwR+nD6vanmZhgiPwf25FxgOK046ox57veqRj1qi95/79qyt8dmL4z2fXwMy+N1raGtfslEQN3NIVoj6Cul7KHMBxroUvGlqbuKZcuqLeAl0wOtaE9wAVSLHdVt543+KOlNl/C/xbjmOqs8oko7gHrohvUbu84j9+vZDhd9V7xuhbzK1n4n6Ghmbg5fq7HQVdch2WIfdPuX0ahpnPRgnErPitOMI7BXNahfa6w+9lKuQIfcxWwqNBdzo94hnL0udl5dzbxOyR8fjfjEoG8y7391XTga9j4RqN0chw1ktT8AGIWIqYBQt81RJWSG5/rbsHfl7N5jz54ZcS3x+KqKWRkZBwTKsrKiWzFICWOD+UoozZjWbMsr8fXaa/Pe6416wyDbcUNUT54o8cnMcBdLnBVyW3FbhaMPuA/UWCqtsEHlm8lL68Y8QM6XUhhEqwpXbxkzvPWETAxn8jBQZcY2FEUIsu5UXlhP6azPrNsr4aMqVBOrETCjRIShajjwULx8mhU1gFQhzGYI0H9+AUnipwx2CCHixmalxxcHrMIKRITVMIgXAFBib5Ut98UAU2UsmYYL23C1pkYIVQFon3EPXD16rWIuuXvx1hmx/Z9T3KmJrVkDnpuxz2LtVaNecAZ5KD4brihm2jur3/Cl+61ta9vpP8e8KxqYCS/z2e5z0cLR0PbeeiygKsdFrm2qdnSm0GAdgnmzVEZ1km8RqlGelXkCoD1Yw3m3WPlG90zSpvrTAvsvOMJBIDx5fAEKhPUQy6BHkpNxQ4gwTwfB90alQmOc6/HP4fnsrbup4ws0xj0oh7MYmo6YzGQIX26HGy2OzY181PDdfYa9+oeuzDK9u2e1DeTeLQ1B+/RKs3aBHMxnfEcZzMu5S9LX147iEh+lpR+BYrxzLfKIrDSk0v0FU08l6/0Ugh/gjKxxIee0NRCBEiRoPgVAt8daN2SwHnxRFfKs41u3senJti5Cf9bvk15RTxsEgGT/K2dofEr14MkBk31T4TdcaKiegSt1q/d5TlxCTmTOMzwhAigxYgygkBE1nkvUjouBMITWayeGgN12LNc2AAH18CfD8+KYy3P+3A4UPBk+DIfSmuFlX1Y/Jnve21tB5TrheuNexVVn1LN5U5PM53aPJw5ne3zqny7j8yVYvuf1Mp4vZ7ns/potcv2ig9HslupbtYDX9kszFn4QGFyMYPLAjHRaaNkYUMdX9AKu4Z1mPWdGeDN4ta0oZAwu/It9RedlaiVX/dM+kpv2mJdhnc+2C0Xwuf9m04O8bgpwkTHFsO/mmeLxNImHvcmk5SVQ9T0s0969cCUkf8ZCLqtjgef5K+rSVFTs+eBicfufLzXlYKKrlPDxwVK3XdYCjxGXlfXM0DORZ6jkaNj7RKAKWScnGxEYQsQ0ShDikHdOppPR9Eo+UMhr3alnxrzuFyWfXaDjL32MvcqwWYWm2lollGgF+BpPq56GGUPdYhtDQKQae28YYvHkEwOfGPRCECEyRmckCO602wPbb0Uw7YIju0+WvlLCrtfFy859n1s0Lr2PLn8RhGajKhmrImOD470ArT91hTuo7aewCxGmzRDnwdzYGZJHtmuynBYlLKlpMds8ca4Fjo+j9+JrCfxRwX9aIGaM2wuAAk7WG6xPVoirCAqEYTXgYtSEuy1mHLUZ8jp/9m3DLSfrNkaAbjXf8puHRtXANEFrOKox7haUd8Wzasw3Q5557oVCEwItGPVsixXkt8bFRMFlKD5XvLbmOqWiNLcXU/TCp/E9SvbFhoFtKj8EDY4vFEYL1/Xk7PZ9RUFq5PqGvnihl0zItkTclUlVQXCBtMhldFldmb0UtiQp+GdHGrAMQp+Tet8OccCwkjixICDEgDErr54kdu5iVzteY6PjjXUtXlseKhpUn74x3PlKuj19Tb6uOZUPVK8HRSnH9+v7JaNe0Y0IbXp75utw7Vg07nXtXJyRDfOq2L03fZ/1Kilcv+6jCw3Oc4vH+5QCy0iaifsX/SVX+WFx81zH6y2PQQaXUA+Myi0Cm5HKys7ldGOL1yVefgxQKJ43RbdXISIQI1g8YB37smVPjdwIAZxyUb4BAvwJ0sxid9A+BEHj+6kXYGJY0BBCRsgksftYjQfOu8bPOds6G4iQQgYBSDFgNQwAGMMQiiEeBMRBXLezeuhFjSVLup+QAqHYFhZiaR4a7zqOl2hrewsBGuvd/LYtY0/25Xu3YOb4sPUh3LNSfIPXPY5Q+6xLcGUus4Bv18PzPkePlQsI06Rbgt4Adng8GwM8VRm9Uvq+7O4bPDlyNKZPXuR/zN833KawByrt8vlsjnLJz+473LhW4UrS++8EFyGoD/PTeiLawrPVx8hZw2dmIEYUvsgZXR3SELumsn1evyGocR5cDfLaHyHYt6sW5viZMb+yE/cae+CpNvcZoS3kOkXOZm3/YIm39vIzLZdzJbydpbsytj81XLWGS9N1HU3d74GkHx/QwvU11fOjYe8TgeoGktcbcf+lgKhxO3KiyqGVapNSiLKaaNtqF4x5/hdKUC2GgY8B0+zLJTdXlBAWRZCCBkbWdya1UxVu7OAMcoKPnW5bDH0hYtCYe0OMCEPAoEa9IUINANDtuGqkK1txnSeAXtcTNM2YgEqQutWG+eq/cLPQMGTlcHDlOFDSX95btvqc4CytJZkvx2q050W8pnpqU21oZTal/Y6BmT2mrJpxlkMvICdqkB4LTDbwnQAVUIPgto20uIo4wtNAZtx64S5eeS3i7OwMqxiRYkSggGEYECZRSna7bu4Uccu8beYiKLBACwDMY+4ZCamqQ6NEELXzrViQbNsVykSrzB7OwNfd65835pXTcjujnh18Q+70xNYY0JWvlRcjY+mEboo6nOqxl7qLmruf5C6GXSmnRrYruN24QlRkdGRhoRV1FJpH1jTqU9Yq2vioRpctIc0MgP24gasK7+cIL/bBEa4CnBknJ6e4eTtgt9shhiAxxkh4H2XxIBqn/WXY2PbCIzcX9TkW3rXJKh0wb71Fo5LDfzXXu/mifL2wIeW5qEqSLe4Fd90b/loa4b7D4TdKC+r9nFdbQcv9t++pR02feI7x18CAgwmbEZoZ2KhLZfy7L8LzBKdHz6oRdK/UyijVbOJ09QstcF5l3jhgSjPrAkwQfsCsy4emaBOLhx3XBR+rWQLd61Y36NxRwwXr9kCRVETmCXpQmnkeZ7YFpNouZvV01rh+0HpqzK2MYRC5OqsVkYiQU67fr/JwYBL5RpwWEZlFVlXlPZAdpCFbfBmM1WqFzQaYUkIkyV94mA5Syk9HR69q3Nt7mAb2K5S2ddjSybM+TfO2GjPguIjjzaCKoy3eunQdvvZ8uil74V3/bbQvweKTy4AWrvtSlu+LStU0sC/jai3y9IFL7KN9bV0e4YbGoC3CImO27al0xbbY2+4lhmuDkxdmB1Ggjou0gZqya373qOhKdSI3i6s1W31fRK2Wnsbo0lrNrr4SC7B8Y8uz5vNfcYQhBzoOJDE7jX65uvt6rwJlijwXq4+v5LoZ9szUWQctjP9lU7zJ83R00XIu8edr2rXmaHSdzPvSLvT3UjufC1yzS4+GvU8E6qgEAHFYi5ADWfFLQTQBhnmXOWJCaAR2zIx5RhzLBexkI9uGZ/ETigDKpjhSMeoUIT4QpFm9Yc0UeTiBvm7JMwNfIDHoBZLDMexAgRADhhB0661twQ2IbksfBaEOUcuFq6ca9jqPgdJNlbLYc7/92JiM9JDfgNvGIZx7su0jeoTS7Y4ZcLe1dSYIaUJPl52O3tQhDKKVLGyYjVETMojEULgkOvl6srsvTNDaw7MvPcJVIcrhGWkcMeWEMQMRjBAHDEPElEcAjJGnMgBFsSrQ4m3B+2Vtvsw1gBevmzh7hudl8ppA5wQoqspVwTHDG4dvgKcFrXHPvDFmRr1AzYqoKRa2zd7wqKkP1iYUJOpR0vBw5od7cCK774YJm338K49Hhag4wRZGQH2KxbZVq0yxyRUoJrm2cpTEDTgC4NKVbdc+ixn/XMJK43zB2eXaV/GRKhiI91AGp4TMGYmFXwddvJpyApiR4Dx5Orwtj+o/bTJvRO6hWmMa5WFWhacdatBrTXpWI8p8Mpz0nhUF+RzuVkUfDT4XWaEkoaYK6pCFaqWaZoZx804DGiRa8sDdX8CVX18JnDjlTGYG+8avq5ubn1qSjXORJzpvbFRZBf14LrQhg/UwCJQFAfPWMzkDDOTAoEwlLmshX5DtvBEy3rKrt26BMz7D0BjJqZI9ghjeiBlyMBoBTAgc1KAHUGaEIO44WTOKVxPKAWpEAKeMnFWpV2NljLJYlLJ4FppsxFnqC0RIzOAQNGg/gVNGTOoWRCjbhm1BCuBaBztDh/K6zBnMuv0XrdJ5ULFzc+aZjHsdjdhLMhpG2M4Tb8yr2Gm4XRGX7K3NXfdbUu7lg7TnedOSebMXPuWpwfe58cz2cbneW2+Htx+H0Ua6p9bq7Fs+RQfzSpt2sZn2arpyZIzbJm96pt8VYvllzCV94RqK51K4Mr3CY9zc56pzMepODXZ1V7nFNTzDxQOWFynbVloCEIo+mC18jRN2qkGweuIBQgMCZVAI2i/aZ4WXMWzRg12vmVGQlZhc17AHLLL9p4C2lP1lzpCq3vbTaIG9N497HtXf98XtQ+5DeS55uPT+qWnCx0lM9pXF8yTPPvbPBkfD3icCdu4oMOSEGAmggESMCCDsNA4AiyRTkUzNN34rnd92S0DjoWXGPmicHdT0nosV4xXMqMe1HFJF0Xn9eUOeSQSt544/BVO31EY18MV6uIYY8+xPjXrOW6/32ANV5X++9YdgCod+VmFA1cndJMK6pdXLM8rWqsBiXaYU1LY5zY19tRyLj1b07YbTusTdpTA+Gy/foMoqK8OsglV2iiBITqACm1Ab1VBS8+sMqv0Bv2WPSr80OsIRrgWcRNB/cvYROGywOlkD04gYCTFGJDWg0IgyO/386axKncZeEzTb7VGFmXptk5BLvqJAGIM2xanhQG2ctpbJOyNbo8j7FdGK93Ji6D6jXsVna8+S5x45ZPLP0V83YiWVfuhh3sUeIzxuHM7b981VoMbi69R/dnX2ZTVSvhO2DZu5GiObcbei3CmX9gFmiKjGXUD4Uuq/DN7DfD45f1mBkUdRIra7czANiEMEp6SHPgU5lRAATeYlYLwY19MGe8X9UM6OXrSLAVaOoyeWxxgmegxCwWvh8+65478VVys9aNKgK5+sZKCZgl0bGmPfvu/tb6h93LNdUzV7XH5mWEBfwbv2G9qxa7WAVml0RNn6y7zk0CrsQO2qMsJODvE0IXMWHmSLjkV2lG1qBJk3pmDLwRdyqJmfFRli+DPjQRM7lCAGZCaN4Sv5MyAxpDXcgx3UxZwhtjVR4LMej2v9RaTyZ5ZFohj1HTMmrgsSzMAwBN9z9d+sxkrt6GwGAAKmKSGs5aZux1M5MUkR0zQCujMFnAsPKzy3bH9AwbW2HQvgJsRVjXvX1RIXeeAiX2wQsUqNhb9rxoLP/ncZ92b43N3s47OHHj8tzpbp2WXs6d11y14q0+oD4MWvK7fS9+9+QxI1edpa5bqe6iogMSnlHROVRUTBhVZ+kimsz8jt5HB1MGBhMpv5wABsL73RoezTkfOc7WigNUPogz6wGJwoBdcwLn0P2u6khp7KRc4MCuweVZ1W7fLlACyyAqh+E9eirg+XEoNnhCtP2jlOeg7lSMAMb/dVQT7TZTV/LMz2UwzdOHus8vfPaxoswdGw94lAQom6FgcEWgEICCzPUo5OmYWbCXqjjLSc+gVFLDYCjCpYOOOeaXRmBBShDgUrq4EHSmD1yHKi6pBiCrymXTKwVeNeja1nJ+TGsk0XiMG233qjnhj2ohoHivJvhkKt0wyHJnQA1WAA2LNWBCiCCZYRi/zz0tXacy6AtikqrMzQqwiN7NvV4px6GmDYoRutwMXW31yFVnkkSr55WVY6Ym21xMqgoKe8QQx/9Th4VRKo/+4aB/AITwEUMI47bPMG60iYUgLHAYEGDMOANCUQGNvsya1D9Eu24TaCHpkU1Svv9dLTiCqn1Q26JtxYZd7jtMxYxXtDAMOi3luPHN4a7nqv3jBLg1Ju3SbhjQSVDlajPRUa2HU8DJt68jkbor33y3O+pSZO+C4w3wTnKY03JjRGPDi8Kw86hVCReqa4N+0xnqDKv73jji7oQzEwcXcq8qR/5P4AvxAl4CMJ+h76ZQLGxOKZm8MahIzE4g0QUJV/gDGynbTXQx3FSre7ZyVpi/gtva5za4lmGE0x+9AhJZP8P+QfVj7rvXW9gb/ley0+l+IcvruUraFhwQA4h7o5/jJYxnVafNfXUVMcFserUWbW87NKSokzxJ/zf6MX7J+YLKIIvTcqHy08gyi3KWXZckqyV2RSDzplAOUQsMFoClSxJyrKtoTryEUuK8Y8VBrEtlWXGJllkmRmkG6X5SyGP5H7NE0I1eMcdcGiLBZnAthkQPHakfB2WftUttYyIHH5smz3zVkOhqvGbSrl5pyLPEeaRLzwWP8yOET9Pi78sMqZjLQ00FfR3lpEvzTTUop+ke9pwfiu3bRoTfO5tYjPtZCmR0qyPQgxf9MlqnyuQq827we60mB8fLDUV1c37tWMNsX2UyBavJYYcnLMTCaTD5ysZ3ohy9Z6211UamdX96yLubkKBaWqAZAN13TnichzXBaCMqFtj9WrfIMhBZd7lvRZz8WRbfNkbKhsuZXv1nACobaRlPbEpgNdekBxSBY4irOMtskbap4Grjz81yjlsjIPYsa84w+mXXrb6Aj7sl4NPT97YHOHFx/PqNjzhKNh7xOGCCDEAaBQFbK1nXNqgng1xNghGCUCghOmCvlyQXVEWHHbdWHCYrcVrzxXoQ0QjCyEmKsgY9y/GPQAE/Dtt8bFMyOfCW9BY+8xKJJsnYgEivacSmyuasCrwUyFiHeGhKJYVDOX0OFejGip0z56sywYOxLq5etaY33hjSyz9Pvqq56UjajSW/tKYSgCaCNUCR8VRlysQxlMAYEJmWXjcaamqLpKpgVxI9Ed4XqQEVdrTOcPcPfOGxggHjwUA1arFfJuEnyKFgcTZdrIDxVhiMtDJ9g6Ib6MF8PdU1FwROChOimachfEAhdzBfB4hSLEWx1meKs4t4T7LIfeBGq35jq8nRn47Nfqq5W7NAbWcdoGppkB/emncYvPpB1tdLSVv6u64cIVVvqtfesbxJ7uuvKY/NeZSNoLc2bwl29nRziKMlwEck/fa43FuFdKzlg27NlvcPfB/c4645cAdDthjJgunuDm7RcQWXgddBGLOIMzQKFVXjp0bqF7bvkKP77EuDerwFB/T7J9w1VHvvLThiU4/CzXHpfdr+Sv5ZRyPTpQrcsnnDWvdEiX/wrQz9DeJNDyXSheofD5WYfN2L0hdm9CWGb8pivuO1ij6etSjKSs9FvxuCFIXX670QeZMzgnPfmWio8ukywBZqKy3XSE0nKI/CBKvhjXMrWyWZG51MMGpF59yg9YeYptaYWmD8GU6wBkMwqqp6DysqAxaUKwcgLAocy9ROKJwzkjTQSOkAVkQLf7SifkLIeNJdPZ1RARKCClhCHKIqgYIgHWQzMoBORxh/XmRA0eUrGU7cNc9J3O3dUecPyijO8laeePu+2Si2ncNS0+bf8lSTibiyV/j8cLeNvxR59gD5bvgaV01+E31+VNHiv9r39/zZo9+bg0eyvcl2G/NJ/k4Qxw5rKLKJvRGs4Qr4XZgiDBFgyUzhQ8QXnf0iiZaHk291FpUuk+BgdyB/8JPfDlUiF08i6zbh9mm9PVWYUZyEHaUPRQq1LTZzuqmqrzRFZDf9/TlinnCcOw6jDB+uTqY74EhQeXSp+6lGdqx6GS7WLxujw7vKzWs8pfapghxiffhKNh7xMBjf4JwhAi1psIICCRGvqmltHOfGt6SVC5OBUFT4ilj61VPfcsi145RQ9gJTqkwguXdF7BboxpKlk13nMqDNifeOB54x7LNlyLp2fGvmiGPzMQqEGklOtP3pU6AkiDN/hVf1omRuVTFzCNl95z89NJXws83nPfhQYE1HFryvXto3p+h6tSHphxRoVJ6FYRe69le4MBQbbriqgeYZtUrClgFEOfigOOkR4p81PBxBh3F9icbABmpFG2561WMsefJBEQYoptvgP8uhjoGuMeunvu0s+LrtPTDEM1hTyXVBUN2utW0Dehs9KA5i/wbOut9+Yj3UpRY2VW5YBQaUl91uO1iVv6nCGHSu8bl2uB4Zp+Z8FB9+X7Kir0wfVlec5WOsiXq+l71cGMBMWYqJlFQKw0XO7NI5Da9rl541fea1p7mHDYsEcLf1H/lojaZ5h+cAInIE0ThmEQ5UaCjSFG4VvbneI5hwMFOZxeuJonbxHbsxszyhf2w76UlgK0hrW6iNjgeS/dW2rFxWabLTzLnxv1eoNeLbY3GpSENf2+brsm9FnqvWEjz15eDc/nz1vRgDoPbB+/qU1fsGhRNrDyuEVkGFmq9KTN3hWWWMJFOBmOIUWWxV7qabPE7Q1A8bwDA2RbbYnqaZc6jsyMwNBYyQRQgG3ZLVsESXYQRO0Vhi3+oMR6Buk2vkDILIdVJPWGJQQ5JAxqIBikXySOH3RbfDVUElC89mxbM5iR9ETdrLhhBo2UM5AZnBOCRu33W3WJCJMaD8LSaug+C9sSNCT0AA3YV+zhLG0drpzF+V3mJJWEtPBuP16Xp4v1N2XvaZ/vjuui+z4THPXvuO2yBo24fGWly0pXJWvfqutp7L14vTxN2i8p9Mja0H+QK6MY0ItVrEof5h1ejWku34x+cZEpSOuodKPlWH6Zj3x9qp+Sbqk1L+C+HJszhVNlIKtXMLvyLMaeedoZnfG6bpWHlSawxQH1gVC0RHUDlHaGIv6BKo7k7Fv49FCG7yr4+pyA+utr6HqH9MKD/PKq5WOOr58pWMDVhUcfKxwNe58IVEtMACGQKEYhyaEZKRpxojKziyKvHjeSmwuDt/eFMTGccQ+FsBrJrAdotOS5eP+Z+zTMFbq6RBe2bAo6qiIuNFCFQeipearEF6NeAEIk+QuEoXjxuS25ZDH2+kD79WCNuh0ILWE6RKTIGIdZwSrTK19WhCaTQpxJxI1JS4G4qcPdoDHEYuGXXdqipS80vZGcLK8z6BZjjbVVq4fFvxGxtZzMC4v3VJmzb9SzMrBfWogBQMD2YovNjYwXbg9YByBQRIwRNMnhGSn2w2l4Z0Kji4kHE+br5Liqsc/SGT1otvraXO23RDilytrWvze8a3C+GO0MV+Gu3QEb3sBn9VGd43ZNrg40ioT2TtvEGVxPMOjwvFi/27I96pN70sfNmwv+WhDXfI0Q3SnrInRXTd0LhCZg+2zyjAueewMfo6NxpTnsyFVCjbHn2lt++z/rIBPp+y2nM5XpMwOMhNVa+PY0TRgyYzUERDJjA4FzAgNIzQS1mYuOz/RSno2Xp80OZ7t3vhjq3xgL8u/LnNnHlDxeuQlG3SOqfxX/a7ktjmuNNC/f3s94DrWXcyV/xkQvhZli7+iLFwv213KorjnFqd9mhXTK60KZRO79UlssHEfTun2X7RY85qxG6H7RoS4iGG3PIHAAgu4oKcY21HEOLLKqsBaJ6RtM+5cDa3U9MusBGVX+tf5gDmp8M0WbwEFC/fswBBIeBrr3LyCEjJDFcJcmPdxiCBDvPvE+lAUk6emsXs7SRlXymYAxYUvAhuREa/MIGscJYQhgEHJKyHHAyhaWoTJo1jIvIXFX5kWKr8ZTnrm8y6tbfmLTwLerXHvKQbN3s3LL+8vm6qH2Xf2L+/KWOFrbroX0+9rX0y84PGzo7/VG5zo24KZt7trTtsa4VbZ0kGtw1X24bJc1uZ+a+Wx4WHaKdcUZlLiV7L++6igEFBogbfVuK5JWfWRBnQRlcqycVA0JCZpR6ZLbvsS2mwEm90oZSbfsD4MuWiikcgqu0EdGUE9lbQsRxAXy48O5Jdb/PKGfr3txhPZcA3uNele1C16azOPWkrz6CUDfN72q/7HBnIA0csbHWefRsPeJQB2uKU+IYWWSMQAghARTdAFUSlkEJ3/tXjuabQTTrYOirvR6I5CfyMagVVH1QRasPc7ABq3H7lvlHtUI5/6CrqDGIAJU8dSLbZw9i7HXGPeAcgR5WQW2/rH2dBTG3NDt8wrzNcGZnYeD7z1nqKvshZr0ZWjaIV0A14gZlTL0db8tn1vI40aYTJiwfFIO6bWIv7oir6IzAbLtpQytW40D6nz5pCnqZwIYGEeYuXuz2SAMEaOu+A9D1FVNPflv1veYTwlH4quRznBecVUnYW/s87TDymyMeubF67lIz6W94t0I9JVuFANbQyPgDH3VGN8enkG1XLLZiaJcGj6Hrl3F6HcJzPq2QIdYzZZ3F7JgEfdQ29Skr30z20HvszqCRHDCfNkq46ulGg9Tcb4SLC5GAv/BfltuKWmpPUBRxAUy2pNxXaF7/yLq+doR1bjn//zHuzb9IkOecPZkB0BGZRgGhBBUQdBYsoAa1heJODRz0zXNbTd+xonYv3OLMv2Vte1KAfkVb2uNhSG6Zvi5RgU3e7qAIgd0eN2U1xqVqHnZ1tQ3dfaAK524iuLV4ku/oKE/B4br0vlrC68+7MFSHV2Z/Xba8r4j4b7tMxrS5a0PbAureLOZJ09u6ILyFUfvRWHWLftlMaYGiikGPFDZwif6NslY6PbZrGmqPYELD0BWGVU97+yQi6Bx+OxjctLttrowyZmBGFQWDEhJYmjlnFAWlTwPotJUKU/bUuTiUZ6fnqxl/JgxjiPGXSpjFENAWTALFQfK2C1NjdnAXQEaRN+T2ViAf0TLsfYu3X3Rk+sO+RtcpfK44PUMp5fy9sV2V1eDp+Udz9d60nebjd189A63o9Xh3Ly6Bj2scjsXwlBOcnZ7xu3eh3oqOKo6QDN6hREYp3Hx9FDIUTXqOTyH4pioJfV7MnM9IMNazQQ9Yad8D5HG2ITE4SRmPena+szzGe+RXo16GbIekCljmoD1KopexIw0JaSUC54TOf7tuleG9eOZS2WqNHPmOrmXOf7S5WWvFl+Qv12Wt69CVq6U8LK2HIRGWdqf5CmrpT3Pr1ns1Sp1ZoKPk2IdDXufMIx5xMnNNQSLVgCAIa2rzUqHuNfz6wq8PPXCWUX4uoqakUXwctS3jeVktVSGYnVTobtuZdt+C7FXZqGUtjCFxrAH9dwTwhkClQM1qseebdn1p2pWA0CRKkKodTtBtEVDJfB+32mx6EEMm0UYUTQqHnn2a6yO6mET1j8qVTWCtzOeGHDTGr9S1Q+ZK8hyNpbDyiCL0MSsW/DIpUH5hvrUzHX1WyV2jcawceoHoRoqjvAUsBJcyjb1MmGcJqzXA1YxItIaACOmzUIP20zRfzsPPqDySBNozCtv8Rp+3tVfKxdwM8vmFBaYmcetxsjvaEDBe0PV1oAn2+hta30oW+qLkc7Ktbz664UKbyBY6jd/b3O9F4sbkci7OSuYs165L3kcVnSyBHWpyRnvasVVZIbve5eWYbFgHE7r4BSq7oUAV7y3CxRZWp+VhRj2eO5ICQDx1hsx72Dq/uyZjy2gy+cg1K259nyfJ98vLn1hnhAGG89K+xNnDIMeAoVBPIzy0Mm1/Vxtwb/1rMHma5N7Pu39lEE/2LIYVNP0QzB/pvPGPWyMBG7+tFvxgd6o1xsBfFV9mb7mS6FDbs/jDkHD44ytFrnqGcTqjlZZ3M/LyqQGz9uWFvrXG3Ks8UVOONgkSZ2zKLH26W4y1fGiGoOLCBzUcEcS+47UUMikcbDM2OcmUKGZTCAOpXNtO64dPBYoADmXMjIgJ8wGeRaMnylBY84lzp3F5osM8azLiiPExSAANxdjJFioGvEU1A6I6nmYWU7IjQFgxnY3qTGx9hFDDRJlwVl3QOzdct9wnKsrbGR05Vq5anbaN+Vm3L3B78Xk5Kn/Mj6TT9+X6HFiX3MOP7oUGrwwPCm8cl+JH6f67OrvyFnlzZ55H8jvyrnMuLeUDwA4s2xJrySu1u3DjDTCCNAE3CbbHi+PizMBWhmpFSIrX7R/2ta3cqhhRsXzSjuYjWywpiFQqDhhC6iFr1lbqX6GlQlmcEDB+ZRliy8YmJQe+lGpZFF5ms5e2juXng4aHv/sbOdqqS5B9zbJ3Kh3JYPe0xjzngrmLZ4neUY835PdY/MzDl8tkNvbS5pwJTga9j4RSLCujhk4Wd2EIE+V3pptpvKwXdElE2DknYEITWaEMuY799gjP4FIlf9G2RRiSoVKWnNaod2aUgRCvW89cdoVUzHwyTs7UCPE3rgHMeqV02+1Ml2GJV+YVYruozz42xIIYoGMN9mVa1SVukgMpd9L9f45N/WVS5U0aQFxS58z5lSiccf0c8JUE2rSUB0sXZlHK3hVtloIcHXA1zw068EjXBXCgBgCpnEEkBCJEZmxXp0gxAgKuglIPfYUUQDwTHED9DUD7AfFzV2/Bbddsa+4Xnacl624s0hPVnDHJ6m9dY3yzzv7XvdL6I18zam4RXBCNUCRXTuhopnDrnNmLerbyd0Tm+dw9KN0V5V3tT/qd/Ylcd8pwJ7eKiV5GlGqJI0371aHyZWjSrBVWdpJS3H3FJOdoaLQMpDbdqjPGok7wa3tu+8oEr/7A6p3nxn4LK+PaGVefEZ0w0K5v3iQ8yjet0RISfotkHgExLgqXqkisF8iksnAwWP+0gjIjSCz50qzK6/RV5a1mOdQk6wo/8zjZ023IAs4nDaDPbmM5P6prHQ/zy540syZfv541bXls0tfS/6Np1/9hy+CU465f9M96MpkbkdjMa0maUKCNO/bjf/UJVuqomAtSzxI8mOBmt4Men4ZkDKg1jTZnqtjzDbPncwq2C8GOdtVYq1ldZMz2iRKewYigYuxsdI1WZIO5buFulSFn3TLLDMjgkEUkQkYIsoilxjmlL9k4ynSuZUvAjlnpEwYx4SohrvtdieLUZBDRwDxSCRm3Z5MahBY4gXtuD6tZmajtOh5u1junoG32z3t9JS9mxkL5VAzrz3fooX01N50Je/D58tgztf31rGn2DLLZzhsvVhlf16o86pg+FnKXTQOLZct84uuXHXpTcWL2qt+DLisac75hj2poVtY5Q1yNDhrkYpRaCgf13sGik5aVC/Ho7LiFHdzpPdgzrqlP7im2rk45RuL3tt2lzliqKOybMlPuSw0T9NUeJZt4608kJrxex5QxwDXqOQZiIor4TKcu45Rr7dHfHrgUJt8H+7ny03ywyT22UAL68XGZUnnanA07H0i4LqZJYYHUElJiLqVjzzisWMM5plDhQ4Ls7AtlOy881C27onQVsXjIua77b2lNq7EumH2DeISWuMaiuIOYwJ2TbLSIrsmWm+8si03Bj0RNxTl3+2nKOWXe92WW4169k09mtW+KByjICbXfI0SbGktv0vvOaenyF75XgTjgiI9k8tL3qJAXnHT9OQDfl0VdLbY9FGhvXh1WQxGFo+aKmuYkeWa1R1Bunxk7LY7rIaIKckWo5wmUABiCBpDBFgPuctso65zgA37eFEYbvwIdO7sPUCD6rxvV7PrTGuMioq/VdonNLe1Uth8qQb3gq51Cz250xNnW3JR83m61xkHah+1PbbYhQsP/SsviFKfVKH6OLrXPE/noTc7+BdF8HTfI0NSx6UYPqqE3BxoZCvodVooJS/CANV7bgrS9P57qCWdYLSGvf4j/cd7Lzz7avvz9wnVuAdUbz4z9u2jaZ9y4pN34ARMU0KMQU7fI4BzVmMCQZ2HEKPD89Jl3ZwkADNlj5tsHjcb415h7g02OwrhcrdIMIeevwPFANRmaI15ng8b3684jEJLyjN0ebvrJaDyXfXJPtW+LWo52qUvY5l1H+K31HeTe0Od8D83yZRvbdETzThZuqW2aR9Wct8qJ0RuXnWvuWzFRUvfrTC1dpmxzjxmihHLFoZMhoDMdVuoNTrHMEVZvHklFp/UUfx7ixxL5WPtxEo7jMJkWRH3gpPYdHEyZzATUmYEYoQo9CVGAnMu0nbtL25Oy5TPIEA97nLOcoZQDNiNO4D1JE1T+gnltwwhQ07xxWw61No7o8Min9iTVRIuMreWz6OW32e/XJ4j9+++JHUBuMHlBSNxb6gn//JA/VeHpfTXk5H3NamidvcN8Hzb13a1ev1YNOrHUiN6+n/lCaPJCj4bLajG+kJTnDA4j3an13rBRge40peyE8R0zuYrlEfZvGU5pE+QwPSSKoOQvuv5QAktotec6iKxhGmSbw0un30JFzw3nat+IesCRwi2OIeyvbfSb4uHW3vleelFlZ/jymP8sVW899k+HG4TP0uf+Kyf5Gcvt2Dp3qDlzUsEfD8GP0OzemLeNeMqcDTsfSJgyg6QOWE1DEJwLXgnDRWDOsnOyK8PjF8EfarbHGbbdN3qMpdteKSCSfXy0GqqAOdWabznnRFiU8ILoW2Edur+LF9V7IPzwAsUigdfNeqFkqcY87xxr2oLbRfPkILqD/trj5XesrGEPY20XsdmRoiXSJUKdvBtdnUqpyyMx3PY0G9l03K9x4B/bd/F1NgDvZpnsReNyYKB0B8p+pwY2GceBsJ2N2HajQhxwCoOGEmC6w8xYD0MABhpidz6uURwxj2DViGZbbVRgdqEukInAPRbcW3qlpL1Me0VZ9EgfcV7NIK9/S0Z482I18TeQ00TCtmrxgFvWOin+qxrPNNd4Kq+zyz2y2y1WQsLrr4q/y6pTK56b1j17wqqmkJu49OndDTF0rO2ztFWMADbksJGikzptO/rBXurwdXHS1/h6Ryh7Uzq0vgOtz8zDvoz8kJXZnB/vhzfDt8nny5IeQQiYUwZeUqgEBEpIBFLrFgirMwzl+PBskrvzmi5G0y0ozAz7vUTfs/8r7PQrtsR9MlmvV4Z++xZlQPadGbUMxrRFkHN9eWK//LXzKGd971xvk/rUbCne7W4a6rwTaea96CXG7p06Gn9XFvwMazKKycq9Lxgn5cUwyvpKIS/zkMqhj8hy85X0i0ee3puMkwuRsG6cBO14Uw1nmdiFEWcWI0FQNnaSyA5QINJDlyzVeFQFx7MC8cMigDAeoJms0MkVF5ioUcmPfAimOESAHNGoKEa/zhjt90BJMY+MdprjD1CDS+hxkZuNycuQMd33bheZU4Ztl4pZuZlBR5CMcVh+ellgf157Zv2zbsW3z8+aOf9ocKfzWzQcOmOXlSPuqvX4efAMg4vUiO3aGfVNZJGk6cYoL0c4QssemAVEoovsMXW9IkLf6oew+bdK9Wo3OQ9xrvvI22X0f1AKCdrl51qjhH6K7K2wvMaUhWJZryk7GRBva8HyhU2imkSw14Wdz5gMK23foPRuUPLRM8KV0Hd51XvZS/meHvYoPc0vXTVPL00+snAAm8+8NheNaj6LNXajQujdFU4GvY+EahSX8gD4uYEADSOADAMFgigpjcmUE8lMkNeZRCFhhsZrBa+lsEyNE5CXWUBuLF3NZqGCUiuRcHX1Bn04Im0CX5WhhLf6q1jMfVCOTmXQpCTRVXxbxrQG/ZmjMAxqRk26UNP5QuFR9W+fVpfQLNP1l4ZA7O+BpaVf9/Xvv7Oa8V9SvUOkjZ6T50GfN2NYuibYCqOZXfEgTK6ddha7xGuD1mEh3h6gvU6ggkYKCIMG4QYkEWbwkDqyePwTvWjusrpp7Miut9qK2m98t8a/uWhzB+b6n3aBqi9drJTl6xLCMPzIgUVpV5oQ2vUM4/cUOiEGfEsj6ZFFdbKv0vTkoGZXtWjW/e4YoSnyB1ae3zsy2KY6OfoMLX3fZOcwbUWpuvLnQHTymcS7xXNqaTK7qB8AQ0tI7ixtjlEQDn13PVRnQFG39zL8mw+3jVt7p57mmzvvYceuWfk3sG97z0Cffk/f0jjGfKUAWaE9QpxkEWRgAAKcpAGB9n6E6n1zDVW4z1wypf2JL6j902PVBLRjH9bqtEQH54DLa/vsHlWGVpW4HGwwUf3rJICt6hneF3KrIU2+FCTto1oFMvaL/NHtSFd09qMEDpbcMj35azTqb3tK5bCSjkz8DTMERju0tSyqXvP7ccszInFtrsGFrw3OcaU1EIc6nfU8eDCiySeb411Z2nYxlcNejb4WX9t+7/do4hdyguYJAwBEYJuQWEwEkhcfBhAZIADiEPhI/bB8jVqUFcjXoxUDmMjAqLKjWKvZISAsuBli8rZukW7LyfGxfnWDsMExVhOzpR5LTKqnNArcYr7Pl8E8heVx/cjt5iPm4v6as+82yfC7W9hj+jtZcFJnc+klVT0bghLS1n8/H5mWu5knj1FNXTB+q2nmR+TWYD8P81YXq38ShP30I8uLfsBYf+lHt/9hfP8n5Uo+F9iahpd6HUfcvqDq5LLa9FxkvOKK96+XbvINbBfBrSOMGcSq98vT9tzcw4pi8DB1VPojMlcQNnl5j8tM6Zpqv2u23NLnnbi4tAy0ccBZXyvUc0SCuzDsL2Yt8Av95dBBwqascynB09SF3jeoWpsnn38o9XJJFd8bG15tjY5ZeQa/Xs07H0i4AT9NGK1lm6nQtpGFbiMgHFV9JQwGcEyxgolrBYTAYBuYdCp4AgjiJwgwQULzUxYYqyZAKWta1Zoy+/coGfGtvIfqT0OraeOrHaaYNUp+ZbJvNUIRWCUAHzWQKvbswU34wsVD/W6MAuq1N9n28MohUv1GMuV0zVM1iuw+6QrV2+pcgHtPVNtxhk+oyVuy3aUkcBu63Z9zyW5HvMOQtrX5iNcAgwMEdvdDml7AQIjZyDmCSGqEFLm3RZFAFI8t23xM+Oeq6GNo+eEFVNryW/V77d1K5532+9LYP2eaSwo3x7f7d5WbNu/6jVheF5Xdo02+JVaS2MkZG7Y2ydPsP92Gweli0BV2pcy16/gxXteqlOIKjwm+Zf2tCZTOuH6s8S/aQTmjr4Uw0OnyKmhTz7RxWYkcte28u6+gQAyoxr7lvrO8bTDjygWrn06XrinLp2Pvxe6X18ud+8sv6/35wectlhtAqY0IU8jiOWE0cCpeghl0jGeSpubli+Q+vmr5URKLQ6nojrtlmuwRHC8tJ/PNZ3hbXla0hvu6xW5sSSXrk7Ajry0ZS/VDvtez/PInrpURSZCZc2uLI/htUp2126kDGVdKc1M75tbaFhtCzpaXSugWuDSCM1Qqz6w7G6n3SJtqx46y3S7aQPVPi2LHT29NygiT42VB0I5rIIIJdi9PG8Nftblgcwrps6NDCrbcwNIgv+HDM5BjH+sp06b4U53dAQixCGoIU/kw+C8weUwjGAR+4AAZA613/SbduME88oDgPPtFnGg6rFnPeoWUMyywdSH1liCPUh/gBb06a7m4bevQOoul2hpl0b//CKYz9pRiCZtKaZOvllVSy1wDl+X0K/90ASdoOaF+6l4rjVjccHzOtCVz/tw/JL8OJDNc0j5BHP48DUfKLq08ZJ+dDTEb9UthienbjRqk2t+oVzGako2uTJHleZTi5hQF4GtnhAstmXLS2Dp2ORfp/t07Uo5N18/jlMx9jM7PJ7pS/bw+ULTx5envFbZC8N0oGy9ou75ngL2oXmbopcXr9SEqzW6V8+7bEva8vV70INn1Fd//Gx19qVdDkfD3icCWwAbAIwxP8ANtUkZI0q5boG1N4ART+d5Q45EeoMfUJTJqlMS7BQyMoHMmuN8u6u3T22PENBeqTLmocKachgjAMGSqIBGxECJrSWnn1WlXldIygpoqNcquNVfcoY96wNrkl57Y534XmMeo47F5ZoiKoeiNq/lb8BOd0PN44OWNe9KT6BBZRu3UrR719ffZzUmS/ZtOkrB2ur7AFUh8D9eaiqzTNoj8b7MvHd1wnEEARv6GAO2Zxc4P9/h5i3gfDzDSRSPVEYGiJERVdBxOOW8bBvjXg+FCNRbYD5dqUzpJSMfdMzNm8cELXnWMulKg7xBvSjy5OhEY3Rvlf1isLP03btZbC67RyfEzbqjGjN9e0EMzigHlrSc3cTLEimmL9Wlafu3Ghvb91Ysa5uqB520q4ivbN9vpLUpuA6S0l3vWQRPv7mWDXjDvZUhYzJXBtk12tP0fts/de/7ryX319HX0tme9no+Yl57fs3el+UP3Ph00aKURuQsW/um3YhxnLDeyCn3K2VPNtasYTfcANbeKsbn+Txq0gALvGgBdM61M9PRCbQjsFyGe7vAimghWTHYkE9X/yu0wN7vWzBo6msrL3yqmSM1DXNb7hK9qGVYJnna94ktelDz1H/7DKH6ksu4ObP9LE2zaNErt7UIoR3s0gKFNnibf9MrxVPGvO4E/PbRVnyi5re0l3SHhrWVfI9JXzErlrtPtYPcsuMJZZcJURNjL0BkQFlatDarTMoSEy8gI4eAwFkO4aCAMAQ5xCYErNfi+bsaCIkBsMTPkpNwrX8IIQKcbSFb5anCfwAgY5ykjdvtTk/9lThcKWXEAUicEAkduZ7P12WMXcRyI9WuX/cAAe3hV08HhynqwtvFDAWhFxMuGfQW7PfzUls0bsBvtLmklIVnS7LUHMPbxQL7p7g+XKvnRVb3lOdwbt/qQ8adeY8bnlm9NMNla9DsaT9OC3S4dSjRhi2xJgbKoid1MixJ39Z+oNbJpGtUMaATFYNe1G2ytq0esO34TTXlO7mZSx7JGClLG6ZpKvSQM0tsvgAwcjsepZQDA/NpgAM4tsRVr5oXWMbfBTQ/UBh1v7wnnT2/Rj9fgvLL8sD+T756zXsqnk+eWY7rz6J9/bUfjoa9TwTqUE5TwrC53TynIVcZSo1AJYYDoVBY8oacIiBaYGMu80q2QrCz9VC7JWtBmrB0RXiEGeHQ/BlTLIIRbCXFjHgozCaYAVC/y07GLcp+v83We+YFdw/ULbn14+t1I+l6rt0RkuD6b/GUKhhXrml8lX2sPq9Vc65j0inpVWl1ysEhPG3w+BJCZ69LMlX2mXU1qtZpgWXNRT9NE1IGkIGUge12d6BRR1iGDHBGmkYEIgzDgN00YUwZw/oUMRAICQBLUH3FhwpqgN9n3HOKO6hui2rztuX1eQ1HLM4mO6QvsTetPNSshPquNNvhf0nX0YSWRCkdATXXxYhHno6gCGeLwqgDhp7E3Xw1o8SZBJyXq+Xw6anFG6ppFpztagm+GBsKTyZ6Q51rIDNXZdj6j5Xal2xU5PV2M4q1yY234TXDeQkxeo+hnFliybCcNDdNk74JsNivs8bOrqm7tvw9LNEqT4e9B58vtyG0XRuuL9h83JCmCwAZOUkYgxAiUs4SwH9Y6wFS0s8xZJ0T7TcVzCy8ZN5Xvqd6SbDP1pZXC/AROZqEM9ozr5f8TUHkOsFplniB3XbpegNSpSN9AR2wzn1y1/6zCjIWTdH/LHydb7NLbG1p8i8g8EI7bav9jAR7WrOURj6giyKyoAl4Jdjhu2+pXFHl95omqSNK0C2rvvRm9rkdArbAIpc2XqS8p6l8EVXNuFeIo5tzLa2sfkFWbFaP46ze5LJhQhYDylkbAGIkDEPEEGWReL0mDHFAZlHQKTBSIj3cQheRh9relBlpymBmpCyGQFIaenGxA1ji7Qk7koNymBkULRa2ytre63EZnfdAh8TaOdcqAmiNXfsy93O2J7dXaarO525al0a0+E/+1SWN2QctXtf52Ca5Wl/1dV4hl+fP+u9+Y9ThakWs6DdzLpcxN1xKP8w8gF1aBoOzILrJVJc2rSHOzYi1dTd43fZI0xBjTDX+h2o8QlP7HRB+15j821MmAQvZFNV7b4jCd1llHCKRZ+Sk3HZBGAA4C65Xcbgim8XY87vghH5qOrKvNjx//nBALPjYyj+Igovv5g+Xy1meRT00NB892eoEi5IKs5QHYakB18z6dEPgcys+PH1hHT2Yy/WH4GjY+0TAndM1EYYbg9Ifc92b9MQxlRDYEROLhcDcHBkOOBmj0LLqeecDrjYOZlpmU4Y94hqDBIvEtgp/VpZX6svpuETVDkf1OVC99mxbhqVvTrx1h2jMrIqLWOvb2xMFe8dOMnCd0n+r15S8YaRhdNaxNgBA6wloWRYQnVEHrbzrBXo/zprXC8kMgBkXFyPef/8h3n3vI3z44RM8fnyGJ2dbXFyM2O0mTClhHBNyymAAaUoYhoAYI8Yp4cmTLTJEyN3tRrz3wflC3x7hEAiuZVBYIcaAcUoSSD8R4kYUqkgriOhxjoItZsBv+JabZ4savGLgzJjXTpmZt16ZerWgZtbP+GmH76XeapTzAr8RAxMYq7HfJ6MOnXujnvfqc7/oBQDuWubR0jHTArWDC8Y1pMLoQqehFqHOPeu8a8nyL9Aka6tXXIuC4htBrmVGGyydemmMU8Kjxxd4+OgcZ+c7bLcjtrsR05QxTQmZGSllWXkGwHryWwgBKWfstrI6nTOLFwqvgHAbc3q6JFD576M9f/sEjn3lE+Zbbr2RkFG9+z4dMG4vlBVIbNjEchpuzIDo+4So252BsWYsn9j1U4/ji1AmQousM3dMKWfOtZvNVIvltyhP5anh6sLr5RnTTxErxfI0hXkjATXZAYfnHjdIaG2blpT9tg1rFMXus2tvNMjY5vfCEvucLTawfkvTJkeY65Bpmo5otzKZ4OfFdsTFdsS4S5hSwjQlpMzFuCQ4LuNvHssUSHE7q6IPZM44PbmNF1/9fGlDT6XMc7inYM0iizP0SfKuX2yMusLNO9vKkaRd7K0SL6Gepm7PvfHBknmPpEASR4803EUIAQEMGrTONZq8TIQhEnJijFMG5wkpZzBn5CzGA86Ei4sdZEFFvIUyMwZFuRCMcpW9Nt1HYxnNrgKad28RVPnBgexXqsbwe47Pcz7WP/No3gy7x1V42kFN+kXoJmA9mGIpg+ebB2AB7317FhMdgtJXfk5e0czncFxFd0xTwjhlpJTLwpucXi0Y6UlFjw9GC6wdd16IuPPi1T+lNKunzc7wtW8udKhfab2jEeawW2itWziodNLoSCtTWTNmC8e9vBjkmiOVYkyWC0FOx00k22yzW/AIiufjlBoC3Hhsl7lFWvDTIvXPD3rj7cG0131H85uD6Xj+yF/3edveXsqxP/VeuArt2dPOa9YEo8bMYni2+Vd8gYDCww9BQwMAIEz7ks7gaNj7RGBVrqbxMTYbWWGpQc0TinILQFYPULgB7THumcBinnzi+eE99VDzAihbP9xEbgm1kV4lclREAKO2NZU3tlXrnhrp6nWwVyVwfj1IAwGyZc4Med5LLwB1e66IU/Bt8RjZaOoqhQGOefivtHx5Xk7TIdYPjmM14PNy+6ynRdzVad59s+zUYnP3jAE8/PAM3/3Ln+Kb33obf/6tH+O9n32E3W4SZq/EIptnnikAkGcWdJazrEinKWO1isUAcD5ewgGOMAPGJGOUGZvNBjlnxGEApx1WMYlAQ1EJu9+ipxe6yumFxDplZXLMpjCWpsqS954rb/HGtcX9+ubBFLuOmbv10Uoa7LqQgyKVVeHM6Jxdw4S2VngrAoNHw5kxA6iK6cLcdXk8qvUrxCW/Kbo0L29B/gTgTo80+ouqcLd5qxd2oc2atqG5urp/fr7Du+8/wLvvPcA7732IR4/PMaXcrNqZIc81BxZaweoVY0BGjE5RXd/EyU3rFW4LaPrWd6SfKBFtyIGeBvb3Vjbt+eulq6Xrny9cnD8qkXKHYQBnlm32OYFIPfTITsUNe1rd4vkhaGd3+7wegDBHZ5+qv1zsyx7X+hZ4Q7bL32yrL7js/rPXJW3NR1quq3rh25d6sPLi+knOj49txtCeT64SV2v/JyvWvex5+bxVVDK5UXLywmysZ7IEYxzVaP/4HA8fnWO7HVVxrwmXaPqcxuj22BJMnhGHanji3Moljnq7T6wKuijIoVmgafyHK7lUJb5OtErT6zbcpu2oW3Bbuuy/T0+s5BqJuihFTOJ1lxnDIDG4jKiuVhGBvMxbxbkhRvCQAZowJQmxwmyn9Urii/NzvRL8BrPgNUu8PZmztvtiwVg648v7YAFzl5H5mYAWrval83/LBfU0gcrPPqPePhzf08hZVR6Yl17s6bClMl1yXko0E4z2D4bJKFf15EspY7ubcLGdsN1OysvR4PlVoZdFUmp5eMWlvcTV3Rr9huv4dtb0680+SpPRcitrXuNc8vJjwaQRkgBEI4+MEiJd5BcCDSixbBnitUtYxvMQguzOmhJykMN6mj4jYBxHp3YFpWO2IN7Ojms4Sj0z+P5+nnX439lzz/P24ZG7o+WX+zLtByey+EeXYOJyIVeFGUPfX9RVhoWZkZLwppR0p8xTNMuyNH1xlZCuCkfD3icC1fNgN51jFeVU3BwCIuAUZyqSgSmyhZfpKIsdQB7UVQq0Sj9sw5NJZNyekGSGIreSTIRmu0NBakIRxsk/A4ogKCePawqS08Iaz5tQD80oBsHmT2PsmZtf0Lh23nhY2uTJknEcWtYBi1UA7qXv1CUSYj/uflY2LT2cU+XGKgGALSiEPxHSw4KY64y23/zTt/G/+9//v/DkfFe21rAGhWVAT3pjrDcrWb03A7Aa88wrMwzSjlFXAHbjhM1mBRp7Rf8Il0JmgGX1n4KqKinj8bjDEDYIgZBXNu6y1bn3NmM/jZ0xDyRXdbvAspBRlQkjEPVZPXijviuCmFOEXWkLH+kYONU/j5rNITj2stANFfrI/9kzdtdGX+aGw2XpoW29CduFLvpvRV3wsGeGZaQLHqagOkpRO5y6fmKN0mckFoCdgl3Q3R2EYbFOK4lp4/yZ0Y/B+Mk7H+Ef/5M/xW5MmHl2WnpmMSDn3GKsfhQzI4YABiPrSZMpZ8QY3XdkQLeJt31L3d9MxEClY33a0OUhlx6oJ+L6NL1C5T7kUwLTuEPOSYL7G13PjF2aEGkQo2m0uI+pshiD/jPLe7KJduW2LBV1DYvCjPNdltbvPyeHjH62NAXrdzUee9pG++Tm17fGfVydHdzOFvI5DG+s/E5lb7rEGZIWtlcpe/QURYtoD8dp5q19K1vKpuPmz903Pnh4ju9+711MKTuaMU8oB0G03t1GawCLkVy37DGzM/BBPf4qnShGCXcYEJENcyVozSnrOlgBeuotyULtzJtIeZUt3paFZ6phJGr62kZm9dHVsBQMfQBGRkYyfpIIIWQEIuSo4SVYPBajei6GqByHIMYAwH27xOhbDRkp1fYCYhTdjUk9LEpHAcyYcoYd0lH5dUd7Xf9ejooLCS7Ls4D4i7Tgitjd8FayBhhGdh5WrtgWd1u+eDWjXk/jr0i3XLaao6/gkrI8qfEshhd4f3d3qDzbfr6U8vxixL0PzySWupW4gOdE1H2bB7eIuGdrqHkIlXZQ2zvkiadNbaDgJ2bjSPq/EAfFKtUlrYdM5iulLXLurqtRl0FrZ1jbQQBldVLJBA66vV9P0xa6UwucHaqh3RNiQGRGzqFZECUSQ6vIVa4vhaXDZNeiH39CW3GvD1fl4lfJenlZjW2gv3qGpiw2gdtH/av9VGOhkGdpzwK93VfylDK22zSXx69Q7r7qnxaOhr3nDgxRngYAjJwyVsMGABA1NkLOqawomABcdHGgrM7KMxcry2IadO7CJq6xU/IxYzxVCGYzjKFWakS8n17z5ypwobZdHlWlt1HmqT0Nt/wVbz24bblOcWwlkKY15bLpAXvI3bPa5sPSl0PhIqH0CihX/bZfypqV5RXbdrx8XRYnDJCA0Tkx3v3pffzBH/wl/uE/+n384EfvgULAyekGqyGCiGQlmhmJM+IQavwIXQozZTRNCZuTNYiBKWeJV8GMYSDkDIzTNZYEjqDAQMh48uQM40628UzjDjRJTB4iYM1iPOHsXKnLkDe+XjpV6nyRW1XauK22FlQXAqBCWH8Ih3+/GMvJi+fU3LlEcxNgsb27T3L2uZKAGrrglEk9SMdvsyoeP6Uey+s+fUlTtudKzoLiOC8kK/3rFbiSRGis9b2vtKiJNO+hRenDXjHDH+hjAalt201mxoMHZ/jBjz/An37zR7j/4WPB7dWAGIKud5iAyqAQkJV/MLeVcs4YBmHtrLgv/RFU+PdbyHLfme5j7M8OvLD227W9jy4dd/n7Lbbk8vad1nfcgQ79hGG7HZGZsdvtkJL0X8pJnLCDeOgNyMqKrX87RVFxe+7JhcUhOJxG+Q23rxrycYkAuTgCPTLbJc2TNAWUbNVjrxRV8LpeFxrT1VP6qSmeuns4Bbj1XCup+t0JXd6ZR24pq00vqO7oQ1MoaZIqI9S2ec2kyhrMjPOLEfc/fIJ33vsQZ+c7gKA4Xmlk2ZVBVGLjdo1VA15dJGwXc9AY+sxzoImBV5pH5dCLwG7xhamcOmm0OlPbjGBfrTQmOhpuY2xGRkKoXUHVUGEHdTQbL1i8+pAyJhBYWads2U2lXAoSJHi1ikUMtniiCSgRXQDbDCJjZAdwTCkDzEg5I5DguXhdTLpoKtv5bB4I9dLRbeTHS5BtBgvpjbUcKGlGPZrE12nDAi2l7q973LcD6HCDurfufuHWXXge23/BwvfQQuvZ6T2zDPMnPU3ry2zKbw7C2N+mInjoCE1jwtn5iIePLzClim+tgaRO+v3qiKNxB4bXZIMiHxWHDq1JdcEFO16Zz+SJu+0mIa4jSHXs5UlHz8u/y1qYy1jbqKOWAbHukfny6l+CGNcoy2Jl+QaRc7MumJaWU21+IAJFMfhD6SCBME2p0kXrN67f2eL5/j5/HtDw8P0pZk+eurIrvKJDV09d+QFY4MP9q6Uk1CTf17BrDKgvYk+2nCW8wzjlBof3dsuh6ukKaa4AR8Pec4dueMcdTjengGNAzBcoQq8xd/01rxBPdNqDMuC1BqBJL9StKPmOm1FPtKgyHMvXNn3O+WpxStCprtLb95ghz4x5i157gaq3XtEIhLTKx/TSRv3Wkq4wAgN7v2CsqhqHy2OaQo9ReyisGVXtOOA9zHr5WvNq3zEYu4sd3vvpffzwhz/D+x88xONHZ9htRzx+fIEf/fgDPH5yAQ4BL710B/fuPcDjR2eIMSLGgNVqKP2MUQ0AWT12iIsnXyDg/GyLcZzEoycGrNeDGv4YnBKWg+kfYT8kgBNu3Nzgow+BcUoyK9OEk1WSVX5I/MVp0thbRQjqjU4qtndTTqaJD6Is+GnCWiUaXT4yJcouXFW1mAb8NKb+Xudr8afrXnrjnH1jXawwuuINe6I4Bm1sMfA1wmL9GB870BJUsVA/yh3+I49a/5vS49x/gxZQOsbVwPXbu16a95svgvvBAHZTxoMHZ7j34SM8enyBi+2IaUrY7kbc/+gJtlvZ2n3jxgZPzi6w3e4kfhQRgm6nlaZk2Ap/9Y5WxR3AOE5Ik6weykmRUWRmctsDALTGOutEoB5yYV/naa392vXknsXufejS+r5biqG3Vxz6ucJ4cQbOGev1gPNz8X4UXSMjDlnHRTyrzPAnsMQ/lox7LY5aziontPgtl4rAM1lgznn879WB2itXQMNCC02gWV4/NxsPvs6YbhdWyozjFqW/hiYgl9u2sJd+NTzhhWJ6oseuP2cvup7cm7eFnBnnFzucnW1xsZskFmbKmFLG2fkWky6irVYRuzFhmnJZCLEDyAoFo2q00E8rC78p19iaBJSTI/10KodnqIjk43NJeUozidRwRyVsB2VPl3PZDlfosdJwL9PV8bFQLNXYG/QgDIbII6xGQ/sA0mc21gwLLcIIKWMKSXqCzLgu38GQ4PopM1YaHw9c0EPaYrRS+yDqgmxK1SN0t5XDM2KMGDEVWkksh3IUPsZcFlYq1JmwgM4LsDBzlln5wSyLaa6E7JckKuU4TJtZvjWpT+fpRMPEr9Yc6h+679275dVV0+O985O7YkP2tKvQIFc2M8YxYzdOmKascRuBnLN4f6pRPpBu7ijzcVbSnu+v9TTiX/NOH5v8xU6G1AUOq7PEu3MLB2VOG64S9ABGAEQFl5s+7hopU8XmCTk8rgfCzaaOEgou3yDGPcpZ6JBFS0IAs25djpCttVQjdpqIWyQWqt6PVk0gKlt+GVxlI6V5ZTMFczl8zebedQ4t+IWF2aTrJlknAPTywOHi9hOtK/dsM+EPJ7m8zH0N359TcFq22Pp4eXaC+x4t//qt4DbfFT57EY6GvecODAmmLV56u90TxBM5FZfDBoAZVAxUeCzbumwLWXWBrqfgmiFAKFPddociDDou4lZq/ZYUJcq2cuIE14K85Ag5UWXi1K3OF+W+5vGGvGZFmkiMHj6Onhn5hBTXBnhDn/+gInkscYzcpTGyz+7XX7vkezFoIe3eZ0t5XP3a+eN2xP/3v/h9/MEffBf37j/Gg4ePkTLjZL1SYUGCZ8dAuPPCLbz6yl0QCB8+eITtdkRKAeOYENXbMegZ8HbPGWVlahjkhDdZrZZ+NUIFwAebOcK1YEJOjGlMoJwRhojzcYtb6zsyraNE8h53/VZcj+st3vUH5dQX3aOSTF8WpObyW2Pt1wL6osj9WwmGEYfujeK6bwM12VTMm9GJaujzcfb8No/GsGfZazGukvYDSAmZpw4V9Gn3sv1+6a9qMFA6yc2nzsqtArNvL6lwLY0kCI7+2bd+hB/8+H08Odvi/GKHzIxVjJhSlq1yWtfJyQa3bp6CQDi72IpRgEjmVjGU6K/hrAoZDAkqb948NhZlNZ89mpvBzTq0N+51ioejo9JWb+Sz3yUjn8XkC+55D59u2pO80payKvoBY56wwUZpbwQDxbDnMASNljG/3PNg4fGh/Pt4TUNbHEIXxKqtLXhYb9p31JbkqVnDqn2+jieSy7vwo/SilXCL54nJRe6lkb/SHhV8Clnseqg9pfKAQl22u5JLvcDvuX5NyhnvvPcR7n/4GLtx0kU0IEbxsDVFH0RYDRGbjeDfOOk2bwYS1e+rceq0rc64YAYxk7OMxBmee3os3r6oCjLXdF7pt3ycqvdiE0Ihd/fesKcLEHVXhvrzWfcpS4pEyGLiczzG6Fg1ZHoqlNWrjhKBKWNKMtnk0EtRrjbriGFAWbQMgRChW4YhsciGoY79lFjjDcucZM7YTdWLh3U8iAgpZwxB1CVSL8nc8+cO9qDz5WDjuif/wrLALPvVqemhlJeU4oz2La67/HuKIEtcPEmv1oxCbQoeXKLlA84Wv+TNd2ntszJJvcQePrrA+cUO05SLcr8ktoVAcrJrzrq7XJcnqCva4bZv2aVjWfC0p7U1BIjXduouBl0YVRwSWlO31hedDijb7+UbqeKt3XeNYdhpteJ9Ry79nK47aqy0iTMDgZVeZjAHMCS80BADoLsXgu5mML3UFi/ITRMzwLDr4ClxnXuOZWZmOQTLOhT4uRj2Clv5WKveP5OuI33tS7u3DJuTzbdUPaTePWUDFtSky2C5vnmHMwO7URbkZjGt9xQxO9vsWm34eOBo2PtEoBruxomxXkuMvcDiwWMrf/0qdnW0E0NeOdnSrjVRa9zTCeOel8M1yvYHbuvROopRj6oCUP9tBXIvzNsbL1B4Ylu+jeoBGvDMoSZCEUtmzMIU0D5oO1BRJHTXHgxpl9DeOlrf+1V5viQP+nYui2LykwHW+IE6Cv/X/9N/iX/2+3+BKSXcOD3B2dkWIQas1yusN2us1ozVEBFjLJ/84kt3MKwGfPTRI5yf7zBNExJZsGtR/hNlDEMsLuchBKQkniU5s56UmzBEeR4DQXbofXpOovyFAJ4ATnjy5AwpTRinhO3FTryjhpUKRmLQG6e0t5iK7zKHTJha9LQrd0CvAdRZ6b3ZluY1ZiUV1Ju1jOo1tXmoe+U9dOzXb8WvD7uKHX3oqy+r/qaDd4J+UeB7ZYhRcRoOWxniyerStivKQlyXDtHwyrVviBckzULnbbb//F9+G9/78XtImbFeDRh3ExBkG/0wRDAioirGIGnP6Y0ThBhwfi5etjnJSnY5fRwAWLxrchKaIvgNLaPylhBqPKxQuH6EHOwkwnP7C2CRltl7ryrYoklPpxOqsS+iNfTZKeJm7HPS9acQdrsL8cLY7pBzRkqMaZrkHKQ4yJcH8dbNae4l3nCqHs99qj1SYcO3m0xdHqMZPSvyitVCF9PCDS0nrXKKx1WHy4WLd88aXKY5ZtUylYKR+6zqllLeNzPG0ckST1iQEUCdqdb/hHpT3zWp5h9f6A91ZKD6BP3VD36GDz58pMalKLGcSLzFQggaSpgaWW+9HhACYRwTUuJioPdjLgZ5qh5kALw3shkMKaB6NWsDAxFiNMMedVvQenopv8UrQRV/84IxOdGHVhGvPgJRBgcx6OVACIGLUTFAngdmJLaoK+J1U+P0wU8aN/9Y26QGPs4gDkX5l/5KSBnIkwThTzlgCAE5BwyDnGQ9xFCHVo3HufQZIf//2fuzYOmS5DwQ+zzinJPLXf+19q6u3tANoIHGRoLEgBwuQw5pwyE5YyNS88AHUZKZHmQmyagXvUhPko3pQcsLzaTRzNgYbbiJNHI4IAUaMSR2NIBGN7q7qrfq2qv+fblbZp4lwvXg4RFxTp7Me+9fVb2ho+r+mXlO7BHu4e7hiyNxp8HcM9UTWgnB5J5A4QKE1zT20sro1/OZvA004yZSMr4bybD26KK4dAti0Md6rmPwfWO5dfju4xjFU4O6cikLtjDSEaFum6j1hnt94EQrYWst6/P88MEZzlZNHy4DTuvxPFkZYwyIGT5omeZj5UEft45ouKfyPU0GMYgaCWwlOoZH64lC/qxtgi5PUvhQLT4V8iVBYHZpq2NngRLK6K9Y8aAraacorgvCdmYYDkRUACRGiDLqfdhCcsFmIJZJFPDKEIVHfBrq8V409uRCNeAQn9YjrErszw9qWoNkwuDJBqxAW3+mpxsrygnm/DTvPb5YGmv84ihhkJ16T1e1E3cNecbz0Col+Hm/6Umr+KFg70NPDKCKv3zHmJYuvgEA75vky0kP7ehLLzu7SG9fKBANQmn26fjA6lJgVqEIWRBkvJHJiVpCRHggFdHxACgTQa7IOWrmQJB9nodAMaiGCQeCBtAAqT8Zyvzp6amQyLm+EG/bZ542BaaIk7vl90henayccVIq88JCMC1sek2e3D/Cv/utl+GZMZ1VaNsOs1kF7xnHj0/Rdg5FaTGZVNiZTWFLC3ZyG723P0dVFVgsVnjw4BjLZY2qKsFgVNMJ6lWL6bTCdFLh7GyJrnMwhYHv5CDzQcjXcXD2yUEg8MN0ueTbGMG5qkqAjPgvdIxJIb6AvBfBhWtrKTMk4sI/QzNcJaJzE9z1yLd9eI9wO+BRhyDd88OVM/0jMKWHvxRReI0djP/FnBHOxwT+6bJAmESsEYVaSSKSE25koEd89Bl2Rk4h95zeM2d1ZY6oU3MZo0uZ/CCbbMWZFH/0COZozkHJLBAgnJ4t8c03boFZTO+c8yhKCzCwWtbi3ykI+KqyiL70jCFMphVsYdA2Hc7OVmjbDlRYEDOKskDbOZRlgWJWoKlb+BDEhTOCFZQYfyCHc4tEAjDSZslnJn+uwTY8iIbIgkfy5ua3KuRTjT39zP+Ge+88Cuo7l7qmjrBShEsWzyzrYH2Ac9FkdLkPw21HzGiWxMScl3P4ZAvYb6hr+HOMCs9hPT3vncCU44B+PblpvtbVqy6WH+8bZXmABHVARh9l78HJQIuzMcWecaol9z8l7yhDyFp3JnwdTE9u5slgNHWHew+OwUgaeipQU/cXZETQVQSzemGeKfP55tA2oqWvOMjYFBSiNBZdOMNVNtLTuosagWndbWFhjY3jSeRM2hl6pijO8N5HX3Vx2JzoRPX7TIFGijjci4DPGAMOn95I4AlrPHyg/YwnYcShQk5l4BGFHomkVA0aBKac4UkCapAnWAaYDbpOAtd4E55ZwW4+9LUsRMgXZcTGwBCDEDSD2KBru7jf1DegB0TIYBTPq/mg72+gsbQNnM9LFyw7Bve995dGo7T2L9a+b2snF+Rnp8napd1IfX0iQMpt0eqLZ3rvmL7gbOdkD6+PbhOm7TqP02UT21fhFpCfs0j7N1wE6ByYIHz30T86IquheMYgp/v6fdq0ChT3bOaaZDCS/BGDs6B6/Ua4h1wTKSSsaRLyqTm+CZ+gcPEY4Jq4T8f13JBnbUrRdEmr5JgK143i5BAoB2HO2QBGrQhIfGFaY5LrgYBbcj+e3mjwjDRv+dyoT8FEDV14R32g6QKkQ8r4YTTe+76ODTYWvEx/croi25w59XnheRit92KV5O3FYh59od6mjBvGOxQdbGr3w9hbPxTsfeiJkAuA2J2gtKV8Z3nuuhqCqFKJhGQocYwRsfYJ3PhBmXZOhomjc37K3wEKQByp1oha+zeoGTGuuSLDHv7JRXBW+x0RPLJbHcr86YUSJkcGqVyqMZ/DHqmQfQ5TrgWSpyEZROib7Y7Vmc1drp0SJ++SmDVk/6V/9JswhjCbVmDHODpdYDqtUJYGxkxQ1y2WywbLZY16VWO+MxN/eV6Ee9ZaTKcT3LhxiLt3H6FpWlRVgdPTJZxzuFLsoaoKtE2BalJgNp3g5GQJIqBpJJLrbDYJ5ruEDl4Dt/4wXTQxADh0nUNRWBAMdnavoOtqFGFf69mwqpu0IzMgX0fu/SeKAvSd3KKuFQr5ch29dKOaC61STRsIFlIY7z9jDMMeJAJewZay58kMXzrX0+aL+Sl9Qm+Bc3PRPlLsWYtTNsY872AWlQmVCRAKuge1apqixCorsa6zlKPOIBTkYUtIRD5Cw2Hev/jF10FEqEoJVrOqG/GLaQgVFWg7j67rsGxbdG2BqioR/QkaYYDLosDu7gynJwt0ToLkSEAHD2MmAR8Y2ELy1nULIkQhQFEVURtSokYCcvyr5lwUA6zNY0oeon3n0NfsG9P20+f5HCketyOfw++9U3BDf75zaVX7YErpYazs1elsDu+7eDK5cC63UTiA9DmE9QHQr9GLIxSfPOqf+fHNiARgtC1az7M9cTrtc6Y7Ht/reCLSMRnoQnGCvsholk0pMroj8wRQJpjjREBn9Q1l8gT0hX1Z5Zwer83tOl+QFlNhnUB4590HAAGFNUHry8Na9VdnJDqt82hdB+8EVuMFQ5g3awyoItRNJ8I8QyHCvTDM6i8PIa/66lOBgrVpAUxcBwKpxq7i2+CEXqikRAsxGPBi9srOp6Ab2Wfy95W7IsjoRiIY74VWMeqXT7T4SP2FkgjVDBEMTBIAQPeE7GntL7JP9oAnBpOH9QB7A9eJNrPSms4zrPNwQYAq80iofBFdFegc+Ux7tm6FjVfcCxDKogSzX4NRlwVE2JrGwbOf4f2wdxcufj60U/aHwff8KB4A93rV8TllUVc3t7m1+0o35HA+Vkn8Sr1MGYRvaWPkEStURK4IgAS5CttcTj2f4BeRNuDgu7qH7nr5NCRVpEkiDFFvutTKRveRdiWb4qxcuCwIZ4QqUowlZgabINzLJjYJ/1XzLzCePTyrsBmsBNLAehp98Y/TJQCQBHxDii3tt+R7Ty7QPJhtmgcAKXiGXBTovHkj5roxandoKN15UNKM5oQzjLUArzlbWA9e9J1MHwBquMzzbW+HeKD38/K3B9sbCDA0hl4uPR3Dc31ID22gw1bNwMJqKHZQeGHEPTbW9ibcT9l7fTCm5bcJxW5LPxTsfejJQ3zsTQEwmnoFVDPIphVtKde10AARNNgJGXuffg02qJZh1kNIDwHKNPHQI3S1ImVgkxwunRhJuybvwxgqRnaIcKzDINWZkHzuv4AGn1kIsx6JsZHU6PXkYs/0eS7MGwbf0M8x4aC2P4IZehpDPCiD7LmU+eZr72FnZwpmxtmyRtN0sNZgMqlgyKCwFvNZhabt4DqP+3cfo25agMS0RueT2cM5icqzqlcAATvzCbx38N5jZ2cq6uvMmM8qHJ8swMxo6haTqgAVQFkUsJum64dpY+K2BZkOliQAymTfY7FcoulasK2AQPwAQF3X64TyYBsRsH6DupYyLT2MF4j3AflhsXaFlC/4OoOe19Xb9f1igzHlyKRfc66hl5BMeBOKmOxyIqcVlChewz/6LI6R+wVypiJ+V+FcIPl7wom+BCQnkuNz1jFwzNPDxdqvcOLffXiMSVUEzR0H5zyMEVN5kPjfoaKACT64Tk+X6FyK/Cj1JRM67z26lQMIoqUbvHJPgsYuM1CVBVZ1A0CCORTegI1J0TcByPE/QdLEY/RFt7nGnc5LCRHu5cI8/Z4L/HJ8CqQLEW1rqL031ODL3/VWHBdLORX2/lO9bMWVAQDXOhQTRl036JwT22ZC0P4Buq7bWE8PSvsS+/wF1oV36U2sp/c+P4cobcqtEgXqTdF5tPnwJB4K+vLqcm3++FxhXf/dcjwPcVG6rkgj7fnOogCn3K9EmXqSj978CWMe4Iv1aR/HxX70SC/K6knrdHK2kgj1galn9vBsQqRYkv1hDSj4tW3qLDhD6K+OTZlO1RhQYZREsTcRd1lr0AUXDyIIDEuhl6cArLUoygLsxYzVMIKWDRJdFmeJAQuYoNUP1eBTUzVmuGAKF4V7+dwp+iWGJwb5YLERtPbIeBHwkRETXUOwwdeyXItR0JyMutbZKiSDLc8M61l8ZZGY4SYzZwY58UtsrGgNFoWBCdqOnjK6WHse5rprgq9DiEm9KRidC0EQQohdCjOVNHMvhme2H+sb3m4AYQHxsTLUyzP2/UkSDb5R9mtwIvfendf48E0+ovXRBRgJuK2nTbulYoH9tJsuLBrI8Vm2V+qmi75t2es5l80LhfZCoaFz/V7SS8WIB7LmcxoqE/xtEiQYG3xpAlCfun06KkfE2QWl0WjunMpywEGZCWxv1kJeYfc4wKu0lQv2Eu8Xol8HGA+DQtwvEaFnUJ+1Ie5FxNeeDiOSmp7F9D8I8C2JaxImXltpZoC9h+s4aj17n7SUo5ZfNvfn+dL87qQnAOgnLjIEpvdV5WUali+D+e/RQE9a7xBxDH4Lfj+nhYxMAzaQcpfp00i3njT9ULD3oae+s/CuJZSlRscUyjNG11o75Shh8UinJ99ZQK5GnSiAuNH0EVEmc8oJJY7R5LQDMdYQ5bf0g50X/yh7RClrPD/6CD4i8YiRNXBGXvHYROizzUTM5nKbGLyh0C6vfwvE5xS/njo9u8asuTVtvv7BdVavYI3ByelSnOl7j7puUZUSOGO5rLFY1Dg9W2GxrNHUItSbVGVk+AFRK1efOUVpMZ1UmM+mopJuDSZVCec8XOeAwoJOEAQBBZgl0o/xDsbGO8QfpgsmKgrA1aJFWRQACJYYviMY0wAkkQABRtec45Mn/z5k+jcR+BjszjWCPzt9skdZbI3NkKc3wHkfBm2lPZ9p5yFHETkO0Epyjb5EPET8sRZBTSPnYjQpHa2/En2oX/pwqIzcUFuI8/mJxTOYzW5/A8kcZIXZmoUWIoHIQN22ICI0TYu266A+nGzwb9m2HdrWoa7bIMR3AIlPKDIU102F84D46ykLGzX/iMREVAV/MAQEy281B9Sb7zLe4E8gF046WP3TfTomVMvJKhXwKYPbYV3oF1nxQTt5HsJ6cI0xQd9lhHwfLMlpDIlPPQbImrDfAfYEIolkLJ5+OGryDKAFmehk+Hit5+OaeXkOxBdjGGScQNw0J8N+6teR/HELpLNf/xm7BhRYyhBH/jkEm5E+9HvWf56bi2lvEh7g/vPh0Hgwdzku46Qp0MN9SoNlTeQXqc47EIVAK04DrTAMCdw6x3AhMq7z6tMpXWrEagfkhjX5RZ5GuB34pgOCiZ+OL/WzKApMijLWxx5wLPCns5DWKuFtLqU+512mqSdjUFN/NYsTWQBHAQG0dmY4JoAJhjzIG9EkNAZMBGcMXNDki2a6ZGCYMpO+MNk+RPglAtTHHsQflwlRwsmoIJGCYI9grRd3GCx+D/W80Gr1HOycB4PgnRM4Dxp7FIeUfGEzcDlNnnGAvFimC5V98kTZX+8p5Tn6ILIp33ievJbz361jzWHm0J9BIJ2tU6R0CY9i4fNTVsQH/KACIUBpA9UyC/AQhXapjm2tKtzF35TB8+C7VpvPlSErbiKyJqWvfThX2iXiZtgovBdYzvof+Iq+EL8/Dnml8yD00NBUV7X69Dcggr54ZgSgXPfpKXBmSIJneKRAHOoKgAzBqHAv7Akf+FemdKmjcO4DTx019rL9rTspwYPm+e4kxVXvF/57x+/YWXxewfPyXaD9yw9BgTadyyNv5dWTtLGFJtH24iN9ngPdCD/2vtPIIIZwfpH0Q8Heh576zLzrVijKEF1LqVAvN/yKSPR72qxyC5lrhuQUaT9yLkdM0MsaftBw5wRkNyQuBSlniECRKbSO/BAfOn+lWGFE3YqIKb1LAJU3PsQk+uexHk1xE0RmkDeKvTaBytrkZr/zOvNP6mePVOCg3Z4AEAAM/qP/+Ofxzqt38T/+1lfRNC28ZzGv82d48OAEZ8sQEdMLowAW0xxx6MxiRhKIy6osUJTio2syKVFWBcqyQFmIPyhrU3SqoizguYV3jLbtMDHqH+qHQr1LJ+cB6gIhJFpYbdOBuzb44gp7h3NNnu1oOu60HMYBqJP24c3QmjBPAb6nrZuTLcMGFbDH+0U5HGY3s7GvUbsC6Z3C+sjYEhmVEbOKRzg5aUfehvazd5rmDvQ5/MtIRalfHrQ2di2VUALFF3mwIa2phyGY49wPu0ZZxs999qN4cO8E33j9FlwwjVXn7GeLGm3ThejUKWqmrnNkHgN1Ya2FtQRrDIqiEPPbIMAHqX8buRO3Vnx2gQHvHCxZEQj0kNVFqLtNSfFxjhPVXFf/9BmQAmpw9jz/rn75hsI+FewV4U/fbSKr3gcFuiFJsAwfhRXGiPkfeyeaSFneseAZm1P/7Bo90UaIvQw19Dfd4DIgz3ShWRluh9FCNDiuB9q+G1rq5RowqT0oHQxjc1dzBDFkwKh3kUlZhZw3zAkrpGnMawGiVs1wWKTlBaaee+Yqzk5r3H14HJh+ORO6DmgGgTGixov2aQ2nBy0Xk8xvY/TZfP5D3mg+F/BnbAdAAYvSFlBpFlEwG2dhlDnCIGLdSreBCKUpELWPdEye4b0Tk13voiaMXvbkJru5UJDZ9/10eZ8Yf2OiD0JLRuCKhFnnQKN6Dho/pNo2ogUYNZEpBSeJGoHWwFgvGnjW9c6ouIzMITp5EnCAZW59iHYQz6WwYH6jYG8EYMPjJ/W3N1bjhla2V6Kfm5ABDd5vQRrrx/tmFnQDGknFtqRhWV57EfBboHG2VjegDTDEC+eVZcLB/gxN3YmfPV1PBjw4wEdW04XwGBINNcCnlC/ycMEH021ItF+TD3Y9eTlCeX4O5DQNjEEuElRBvWfVPGbAJ9ylNeh5qHMQ2xlo9OWBN9Qfe0+zj/NRa7lEB3KY+x7tGeoxPgjyAp5kL8K+/HzIt5r3nAllOdalQsw89cf7g516NP1F8m/4to27HksXnt0hwsnWZZRuepKUVVBVVug+p1AUsjwhDv9Oph8K9j701N+MbbeACdPORoQtOYLRnZW07dKrCDSB8s0j3krmAWOPQA0FrBsFf1m/krAwKxv+KCN8hrSAyivy247oGyIQj3pQxQAaUYA3qDFVNpg3wnZhHrAZjWzKk38foqEcXIfPRk7UtQkZIb3WzHMDEQLCL/7Fn8ftF97Eb/7+N1BVJeazCVbLFVarBrYw2N2dBf88SbOpax3qphOTXBYH+lVVoqqC8/1CBHxFYUEk/nnUUawxsm+mkxKTqsTR8RkAjtoFzm3zr/XDNJpKC3Qsvo0gt5LOdWhcAzVl9EY0eaIj1pEpjiAL6hFO40cIr79LFSi5le3uvjCnF6RjBOyGbPpoZ0e+J6IrE/3nQr9M2Jc8V0kHjFFDDSXoYqE4ZIUc7XMfcofEoWoej+CH4aMBCiAK0RKDEMxobyPhkzTyEiZR4jVUplQtE378My/izuFDfPvtu7CFwaysopaeIUI1KRPuDF1xnuE6icjFzEHzpBDfMUGYZ60J+AFiZheJVBl9UcgN/nJVC+vuw513jJ6RY/WxSdmUhvsjr0MFcDz40zy5hp+HOPUcCgBzs11CMt11Ie/QTFfbHfMl9MHgM/VRplpEwiD4YI4XzlJD8LzBhGPkHB97t7kDY3nSw/XX/ScU/8l+X/Q7DX/k39axhV7irbVHKnjWfLT2fqiVkqft/FWm2YUEfjmNkQjzjJnPGh4e4UmAkNFZYS9zvpgQXPH0U1dxOlvgweNTEU5ZA+88nBcTbmvzCwuZNx8ERDFQBYlz/aSdlwRVhCAky9YcIPHjxxRNcln7mo/TC9OtzD4DSasmaP/FsYRnepFkggmqCcyyBzAJ/XDswSwaiJYYRAaegc45uShkoGOxFlDzXaV3RYNYZpoIoBD4xxsPRwG3hUi0uiBEKtiTT/YGZAwcJSf+uYBPcWVRGHhvYYxP+7O30IzOc4iQm70nBG2m4KA/zBvjCfl9JQm3ZRh7u4G8XFPn2YLuhjhg7eW2d3G3rb3o/47gNsQTgx9jpPYwbZik8VOqD1fysk8HjVeW9tbWRrNm9ndnWJUtzlbixzaQ1VGgN9S6i7Uyrw+7Rx+lTx50JS5PH6z7ezCwgDmcp2rGqbo+vqeMDskDcLBcAHAyX6XQtsuEYWotEE15M1ycBH3c0+CD8jZBiJdTvpG3DC6lVLAn/2frTYo3gymySThA5zh0CID0Ofl7JnjtZ9bnfEG+r+V655I/I5dyI2U2VjNyUF+W4rok+K+3PQCWdEpfoI5NHWJgUlp0RjTse9PyPvfDCCk1+n4ocbhM+qFg70NPfYfizhkEH53BuTHDd7mTxvXTm9TPQSAwhV6liCSTymhCjb0AmZSQ4xrzrwfFQOCWbjvCM4JS7L28UpQikFGOKJSAighYEXnIb7BWXyw4OHLkT7U08nwY+T5MOajn83yRMnneYRl9Fg4NPUAidTjo2oCYJDAe3T8GEXDjxgEIBpNJCXN8hsmkQhtMejrv4ZzD/t5OZOabINwTDT/R5KsqEfLZcLuvedkzbCHOZJ1nzOcTnJ7V0RG8EOyXR8g/TAC6FigqMdMD0K4a8KSB8wbWyB6wpgB7h7Z1WxG07J4hM560MeJ+i5TfoGy+ZRPg5xwq+ns4mZYOK+sd9xkK6GWj9DpJ8hWcKTpnj8QbMljPUEmEflL8kY0T2XAH/RL0GT3yoe9ENCMK4yM1segbp2Tsb5wVaVPNQpLGT5+lDrkVv3Fei3ZY6licSVTV3Z0ZQARbWBA1KAobGXu5SfaYTqvIRHfOBf+ZLgqWJEiGDUxsEIoGPK4XAewZVVmgboL5qEH0C5QSo7+RLko+jOUbw9ljeS3ET5+mGZKgr0US9OXafG7wfZP/PfUJqO83YbTLY7qzZQtPVhgXILg16MAswheQaFECvhdxb1PqE51DOD2n25zXMSh7AWp27fTMkABDTaTSq42zlZ9nCtaDzKOat8OiG75va07TOj+ewWYeZChDD5wxenG6cmTGCcpTtdmMcH8s+dQ3jbhZmUxEs9QbAjrEKI25FptcvgHqHN+HoBWK+lQDRS9MesL/jOkUP3s+arUN58nBo2MHznqtJnNhmiTwRHwpzC7F+fEwTHChfmMAMgaFJRBJEA52DL1EcY7BhWjxeZYxdYVo5Hae4bwTc9dg8hrnBCzCPSYQeRHakc8iJsmniYI7g44IVjV5kMyVAcCSCAbF5YHMkeJKFagoVlfKsmmCOS4LnHvnwcan+SIVTvM5vre2AOK54L4Jd56fzsds/fNz9Pwfq20T8J7Tdqy/RzpT3Cvnd3XAoo/QPSOPYz/XTO63tNW7COiLeNbyusCzRR+4YHgQTE5NKJ4mCls4BdbobZ0Ie7SOP7MB5pq4YzMnEHaOxmK/2Xh11rsE4MBzItEXRcQ3Cdt5z7BgocECrHsr2q0ecvHFA0EfZ21F7eIwR8PB94RvYfy5JnEU/JFebhK84eDDM+UbjpmRzO5zU2oE4f06lXTxOf1eSDl1+uG2cf6zD6KNc+dekUw87NdpnEuvn5IIGy5ph4G58m7kD4elae3L5rbfT/qhYO9DTxnzxIDrGMYEH3ss5IQyC3nqHYi9F4EV5UzQRwAyZKy+oNSXgcjzON7Caq8IyUdU7j8mJ3a1H4pGc2JA6CMKrBRBkWJOhOYHFqWXqZJhmMsRtJre+ez9MG8OUWPfh/k2oSZtb1PZnNDYdMQq5T84vUeg9dVvvgtjLaw1WK1aLBY1HAN7Bzuolw3IEN679RAMDjeDDO+FmJ9NJ9iZT2P0LVAK9Q4AhbUgQ/DkwZ7RsZPbbedBYMxmEwDCYJRFAbdi/NDH3uUSOwfyS5CR6KLOM4ydgh1A6AAmlCjAzqFrR+Z2sC2iVu2WZ+kcIySVWyEu5ZEyLZmPiHybclZv/Jbr3iLCfH97Zxp3KRvybLmIeO17xBnpbeyS3qJS6LfWH/uRCM2A/iKOS/WkbwETDiCUsnd5PhWepj4mLlkzJR3DXGjPObqIbeQSA0m37z6GITE161qHtungGZjOKnStBMI4PlpE30+yrGLyaUiE9pyttQrwgKTRoz6uxCSOoi+gsrShTNDys6oF3UGEaWN4FyPPt1Ejm3DhRcg9JUNUILdCEuap0C//btHHU3nEdMry5II+k/3eSG5tTL7z6IKvRGHMGGRLWafgwqAgC/Z+s4neVoItoxYz+JSfI9zslnp6Grn5fszqTEOmQenNz8eERkP6YNiTYc3JP/CmVgJGCtJ8HoHW9H19GDm1oHhQz+LeZWeGtDaa6QbhFvdejvQViHWfnK6gFxneB214AGVZxOAry7pF3oxqwVhLsLZvoZBr/6hAKynJKL0n760NMW6Jevih9Q6t76CCPQ1QJHKqbOKytoS6Uf9UBAeAmAINIpfT3luo43zP8l6DbDgAhnVfGFhZTpQm+Ks0Bp4QA3Korz4VEkj8jMBoRwVj6a0Li+pJfJG6eGEcIu5CfjuQBO9wEqjDGpfmJRBMBgCCViQHRk7WKexXBoisLlTAJrII233sncOZBXDfnqsP6Hp5c4Gs2IS31zDdRdDzIHOvZsqe9A/0bIPn75Q2Gdcgi8XXOjhso89Z95rudTkbO58joMlQy3l++Oo6uU8SoZY8VwE+kIIu5NdPFJDFhkC1qRvaPA+mL/s+xMWePVwQ4G9cfVrfKspV6QUygZIpLBlEgzISnCo51a2AXg4HPpANmBiWATIQ2FRT9hxvcU6hhZRv7tgWUv1hYohS/6B9RfDh5wP+y3nMhOoD7gtdyOBXumN6E2MCwH2va+y9P5g+r96LVfYBNXmhureRULJx1nP1qYaLpy6LfL4RvY5VqrTGBzAxT1LFDwV7H3rqE41d04A5eDUnC7CH900UzGxMPa0RSv8GAiFHulHTBRDEROipM+c9oqxcn+in1G7qBJTZzZF5ZNrDGHQo+jyPvqZ19v4gUdHykllHBp9myztG3/dS/m4TeshBdhNzisH7kaOW9PTTOvuK8KloaovB+Obrt+CZcXa8gHMO01mF/f05jDGYTacoLPDmm3exszcNTqJlytrGoWk6qJNZZrkhU6IeoOA0X4QDSvB7L9HiQITJpBQn/oZQlBZUD8J7/zCdn0wH7paAz29wCd51aBqJEkq2CL4a2vE6lIBAj/7tHRjpYEovFAahT/qcK3oIYmDTv+bycQxESMnwXPCHCMfUy5PBf3iWQDwJ+Xv+8/Q5DQhRrTOvG4mwiw6R17pMI9+ypHOcEQDRJ99AuzHdgVCkFTYyBlEA0X8WqXIAdx6egBloVi289yiqApMgtCuLAsYQHj06RTUpe/vAOy9+8sLcqYaLzhuQiFUVOqnGnkTGRNQKVH9TidjoIBpyY7hU/3Lh2HD0+js3ib0sGTLchAZAhWSyq7g0x69+UM6N1JUL8oaBOfJxbepv/7mDhwuBT4A09947OCdCSMGzKVrmes19oE4wvT1RlpPTwwzUZbNuEhT0MEaAu7UO0vDBeE+ADD5jlf190zelT7DcH0uvyti33vvhb2TC9UE9UTtGfqUf2YQpOlQBTcyZN5kd13kX+1Qc1n8FeD85WwFA9KFprUEZzgQR0gOLZQtbUE/2kTviz6pDmrp1bZ64BwKeVaFCClikdSccwkSZoIHiZWuupal3rRHi1C8eJGiHhwccwB6ZXyuZ26h5BxF2eE7+veRPBmWJAFuINpxnEeKpqS76uJY9b9yafZM+in4I1T+fnjfGyxgNaVTwdPbo5QmzuipwWf0yz+yThh6FyKNPrLGXZRnbb5euZ1uu0XnLBd7b68z/enCyCYxjXho+6OW7GKYZTzzMsXYhsnkuKMt34bN8JE/dimDPB8FPfh4rreNc4K+y3kaNu7wPPTgflUv08VFvHRKci4/eoCwyzBfPiJxW6HM+qa1g2cAAAk8R+cCB6XdAp2Ak2JVsQmAaAtiYZMargrIMzkHq3w6jkxCpgzi/Cu9hZPG8SUK+nj9MSs98hqNyn+JJBp3j4TBH32XJXjq3Pvh6L5JhDfZHsm6l/Da92FTofLS5PUve4TFgukRStyqbatjYl7i/s27k3btAouGPixbEByrY4zSYD0JM+QOVEinlXA1y6lNIsIze6l4I0Hjku5L3JER+UhNNfvh6fH1Abr3qKH2JhwCyl/EGJLSnyDS7GaGYlTLkm5BsP3CGQSzQGyANvq+RDCOfY9+Hszk8NcZAcvj+IokHf0if7NcpoCxPe3KG+8cLFEWB6VQOFGNFqwYsBKT625Hbf4euk5t5z+L3xQSBaNu2vWhdQDITAKswQBxBqykfQbR42pbhfIOuzfvIvS5vYhg/kCuJ7+fEHswtnHNBK8CiLEt0rgvRBA1sWYG9R9tt0obsk6pRg2y4ReP2XN+7Y6hhY/GNg0nE0rDu/hetPSeekvAvCvUG73OwHQrz8guEnlCPBr0hpKBD610dbNs4k1lZDH5r/xLMizYLBfQ8nK3MvE8Jz0yDr8fph++rZY3FqoGxBgWLJpkylHpe6vRwMMdFeK5aeHqmunD7HdtF/7xVJkMFe9F3IVHwpcmw6lKbhVGP9cGFPnuoZhWRBagAUS4oG/59UIlCGyWS3z0VKOr65DCUC/j0u/526Pdx2P+hgC//PeyT4GbnXdSGJDKwVkxzfYhuaksL+Dwq7vuYhg2c6XA/X5wIHq7TBhgfy7r2MMHlUFCX0xGJRhiUVnpBQW8NyDeny+w2bTun7fMzK0a5hmpxjHPTObzneJWyPACjbR2azkXY1qYSfGbjDGdyT7Ep/+6TAMCHtvOjNsI599uggLOcy7SIclzLweyf5XsuaDNWNNvEFC8uTsTVikcMVKjlQWxALBFomVSrMGjWefUrK0x/6wgeLoTHkUsG50UoIBfPMrMRR+t6RHyndIyuZGQ4Eg7NaM/8OWLE3eCHTwYGixBgwxgRrkI0n2IQJ6IQkTMx/Oqr+ANh+MMwtoNyBsW0vk1Hs5/7ehP+puzfsW80eN4XONNIzq3dGg48B64N41w7akcmMW+rn6//pJcuIgRgOYM719dUG6NT8kK9NWP0ojLHZ9tb7iUVGOYaZQnPBMonC3ahdWvE6PQfotA+77rScFpeV9RQqJ3SGnuWywN1fZBc+OYDDT3LbmD6cJ7BMxJ/2gdzyRddCOS8Z46vMBD2Kf4KsJ8Ee9wbW9ISD1WZ9+FL83suXezkvMz5Oo4lLlHZ5RobLXqZpbnUMubn86Ds1m6PkBDD/JeCc61DSeALpksJ9tgt4btjeNeBfQfvajjXolkt0KyWaOoVViuHm898DHvXngeZLfrGf2SSRz7Nnn00mWAyIPbwZAI7sfHISr/GbuYjBZsd/nk1QbgHJD97kkcqEmJBEGafAB/exQ9IgQx55i/iIaJ1RMaVIAI9iuUT9h7WPnw29h6D78PP88Bx/PhfLz+Wjwd/mjJzYRqWy8sCy6NTnJ4t4VWgF4hNRSqWDJx32N+bo2MvjtmdAxGiIEn8c2V+nQJxq1H0AAK8lDVGomT61mFVtwCLkJAhzGjrDYAZHt35Om7PTlCvlui6FsvFAvVqhdPTM5wtlqgbj7YjvPSxT+NzP/sLKCfTkbn9o5HIWLCTCIFFISbVTFaEtBBhCRkL7hyCD9YLnWebhHtKiPXCUOfU7LpzzXHGISOgzyXARzpM2UD6YJz9CHkofs2Zr/Xfyojp+DdSyrT2ZFTYIVOguE+eZN4IMmJxSEhKpp5GBmcCPVYWH705TNnzO3/GctmgbtpM49akPjOLw3gW33pOTdE4RMAOAjrWSG498y9KwvusPXEI7cGOo0N91epjZkxQYgbgt/7N/xff+tIelssaXddisVyhXjVYLFdY1qG/psInf+Qz+Mt/5T/BwZWnAkpTcmUMFz9Jyndn7kdVNey0zVwLuo9L+0I7hgj2ctcNmmeYV9vJ/fbpdxvrLyxFX4jJ3UES3jCC4/+gdfSko9/+UF+g91KyrheITzKUsKk2YHBcxelZRxA0LEADfDDIHHdJTk/EvCMIaNhHBcwtpnSU4bMUIVxxCWvx8YGMEONJ2J2eDol0Hnzr2k409fI+aw4W33TMjLIwaScrXcZqPYEeM8mKQ9NECM4Ia8PsgyX4utGyCxdJn//8lzCtDJqmQ+dE07/tuvCpmuYWzz37FP7kz/8Erl7dS7IVpjB/QfhHhGDsGvvDzEGMnmlUktAW1oqWomOCaRzarpVAFCS0sNbmRbUHzC7SpTqqHOWoma3uec8+CtvjsmZCPv2tZ4tEzzRRs8+TgfVywcKeE+OfCfbi+mh/iCLT94GkAXxeivEb2ddDML5sV9bL5HOL0e/D7JR9D1C+vdGx3+dMSB/jKULJzuUtae31JSbdOx+D3QwnLMJ+hPfs+aDjT7J94qmX1askwZtvvgf2bYju7GPgPBcC+ACy9w/2d/HSi89gPp9Ag44l9ydJCSPXeEttcRSA6XMJWmHjXtT2FM5zeokynNHH5oOzK9s3qmWd/OFpnlRnYnUHPvvipYRiLTEt1oBoej5qe2tcoPLU56zLdy19QB2jAczR+o/BVxp7/cH0ayPtc8nsT4qgQzG/tkf7WfIp45Hng+reT1fWpAznpUsJ9r79pV/Co9dmOF2s0NQN2qbFYtlguWzE0X+wb//Ii8/j5372T+DpT//py1T/A5YiaYQeCehtdGIuiLAbNXKK+cd2LSG7DMkIXlJtveRjL5k0ZIUiG6AEEvfrzncs5dCryDI+SGWgeFSJKmQEGGK5SPDGunMqYAgum/4Gk7Hx9xh2GZK/Y+A5loaH0DD/GOhledaoIMarX34LTdOCARRsQYVFYQyargOBsFytAEjUzHZVwxjCatmJeV4nvjTkdl6cUFsrBjVlKf64ogkOA0Upt/FRk8BadG0b2QFjAAsDeODv/df/b5QWWK5atJ0EfWjbDk3r4BzHw/3q1UP84h/7OfzP/nf/JxD90bTq99yCuYvw5DyjsFMwi4YAew9rLFq32nLQjO29gX+8DBEoCHHKin62Ick7jIqbGKZYf94XxSv5U0UDA2F/n3Cn3vNcg29dQJARBpTqpfhgrE9ZX3MOkhMm0+wBDcb60m+OMlEh3JInPp0XzatJzWeSYGGdIB2QpVldhNu3HkUBG6xc4hhrojDeBbMeay1c24EMoW0cTKa9xyyMsDAMMp82mNgzIwqUrNWLFAMQw8IIftCeZWv7X/+3/wSuExh3A0ZANXpAhPn8X+OX/8W/wv/j7/6XOLzx3Npo+ynts81kzhhezhdWkwrXuuxZHkhjWF/eZu6Hj7Oy+afuOBXkcfZdzX8lwq9nYZaCdzJ4zyhsFQQfcs5aMnDcbp+eDcdMfipvnZLNVWTDJ0QtiGFgjbEyG87MdNom+OsJb2K+COUjZfVBAvZeCyN9WutSXkK55ujraT0bBRw4ZPqJRoQxAwZ7M6u5Pks8+HV8vAzwSuJdJOA19YGrmpxkSNRaiODVXC8TWqpZWm5qr88TU5vWQzuWa5AR0pT/2m9+GXW9lKAWnAfqETjSYlX1DXzhC1/H//p/9Tdx88YhmHyIDi7vFSeo7zuAAS8+7yz1Vzxqu5ng78oYCepVFmhaoVcUK4lWsYEnD1JNQ05O93tjMmm+iTOfZpo1CAD6pojU7w8YYImeycSAYZBXPeWgrRfgXC5YVJuX4rp4zjWHx9Il2blsc42b1J9Hn77fNI4DLl/DJcpeNOsGPKivhmfvxsybHscDny40w6tVm7TlM7+8CX7H2xjyctvQ8qY0tgu0/Guv38Jiucg00vL+JPxizT288/Yd/OIv/FQI5jUUsa1vQIUtCmdKordk8DEaNQQ+rBFewrOPp7AhisEe1cR3KKyLYxrAg5B5+UzpBSiigE/gnOOnuiBSdRWf/StjTHwwZ/g2YNsgJPyj5HN8+04cO5LXSjwZ6tje2BagHFJ+m9KTYM6u246FN9X5YWLpi6ZLceOf/72vYW8uxKz3cmuhxAIzhUicBey7d/Af/JVnP6w+f5+lEvlSs+8AK8wC2ADcgrdE0etv2GxL6WESCPkUUCMrpe+47zgaFNBbpP0pYc+MSI9Ao4UiDZkR58qQZ5hYb1W1kigMUOJvNBpur0UkRmtTnuEs5Z9ajgfPxvLggnmH7Q3XbNivrM62g3twBOxMYXZn8VB84VPPChMNYeq7zmG1avHo8Qk8A+w9mrbDctXAdQ4nx4tgfivzYK1BVYlTbmMCA28M0HUAi8mA+p0xZOCCGW7bOhHkWQv1fVFYA0cG6IBvvXYHhny8ueZguqPaBdGXzeNj3DjswGfvgnZfHFmXH/xEkGiBRVmhcxzMdByYHSjcDjIbdCF6cW8nbZJx9OoPu28I18h3Z8IS+U5euxSgvHxeegSuKGsy1rzpNB++UlMnRDygWKVn1gUkQbP2j0IEuYEgPOGPQf+AFCQISYAn2TNGOPwTNfOQTUPQVtaycdpU6BfQY0KTFCnRGLyDAXiP9mwJqgqYSRnbvXHjIDOPFwayq+VSTE1CXBci33qPVd1EjREpI0712Sc/mUQQrTwg+qBS5jWZ3XMkcKMZWQjgAQD3Hh6jbRoZR2QCMsFmqI+WK7Bf4N/+9/8Af/1v/53BwqsALF+v88if80gfQj9ybhF+K971EGGfRs/dlHLtvx4gZM+Hmn7aPy3roVIaz4C1hVxgmkD0h0h6WtJ37hzfW9tHPeq0feQY2qrRe6kWUxqetnkuyr7kwuFhfUlTIn9MUTiVNzum8RNpjdHuZud1BGggv7bI21gXkIw+DKmPP/NecN62ABxc3YKsXMZp2t2dRqbaBtzhPYsvXCiKYIm0CgRhf+pMbipGSOdtDK2WdSzBeYJd9ROndSn+OF2ssFrVSNUw8ioVt7ZtB+8b/Oqv/gH+xn/2F+C5gyGOOApgCUgRumGCULtHd2b1MjjSgjnjbw3AjlCQCQq5YjXgDYOtDcLEIIB0LkbW5N4+Tz6j47kWxpFMrNO8puNI+9t3t+Dgw0VJmBNjIg5EJgTQ1mO0zw8jbdum4X3/5Raccblm+z96yGAdftexAPVhOscTOco9p/0c/s4b5lg5eZjTOnmGTRUEGiITHjEz2DmADMim55NJkXVS2pAosBmnljWtGrWpTH/ehvjmvDSGowCg6dyau5exPSSa5x1effUd/NTnfgQSzzf4wVReEelUU791rM8iYalP+uasOp1EFAPuUHAZpAJHVjck0UIhh/H1vaywmtOvAp5pU+U6KOrGhLJbXMVJSpsqXg/xf0ZnjIPv0w8J0r8raQ3OL5pXn2wq8wQ450LpHKLmImsziiOyCqKLB6VdWKw06nYta6+SNfTyPZIuJdh7dHSGru0SCdTDVeJXpCiEJJ7sTD7grn4/pnX061wXfof7A+fAnPwmAVBFiV4NsRZCYjiRIySKiFLKRTZ33Uk+Z3QxK8OameKGDd7zDTPoB2ue4XtCdKtA2X/a0ahlqEC0RkEQ1qMcEoZ9SGnM3Hss/xA7bMMWWtbjYqCbow0hzB/eO8Jbb95C27TY+/zX8dIr38Zidwb+mU+C/4OfxcHVfdz4+DNomg5MiIEu2jYxAWVVoJpUmE4rMaPtHLz3gVEPZixBUKd9MyY5jV4uahgrjq2bpkXddOGGO/kOUK2CtmlDtLkSi2UNk8Xz5sE4DQV/PCBMJwVofn3DvPzgJ2FeGAYFjHHhNl+EqmwI3rcgKtA0XTCHGDBpqlkzqDVPfSJQD56cIRwcOBFAMy9zIzhgCAKjh3j22YcqioS7MuOknRlUlDs7TrCP7IYXEd/Ew3XYJ5MRwxybTyPMxxHHRWFu8h0csSLWPlgJ8rg46xNGgeEOfTw5XuLug2N451C9fhe7tx+gnpSwL1zH5NMfwc58gqvX94WZJ4C8lHPBmT1YBOxFUaB0XgR2weQzZ9SNUQ2/gPdNMpVpg5YfM9B1wSw/zkEaA5HsS9UCbFonEXmz1Jsp4uDDEygLg2eeuor1pBspnWlpzoZZ9eAZE8blu2uIv/MzQVMH0bzTv+Ga5f3j7PnQDLcYPBua6+ovJXYMiHx4E8y1TXCxQRZtO8Lwb2NoR2EwnScXYWyHTcWsF8g/rH7tPN+SOcK7PqL1DJlBVy/P0A9zTseM9QsYH0rEPbw2WxmOwAAWRnj8YeWZxIwArOoWp2e1+Kh9eILp8QJdYUCHuyieOkRVlpjvTKNQ1wfflcrsM8K5WRBYzUDDpRnFtgRGfQ+3p/nwzsd+RY27kTWOOCS07RwnVx3rM5gYdsglwuH+jjDjkEurINqTABuhfwTAJ1QOF7R5cvzpFaFmgoxoEqf7IIbtIFgCfPBnp3X6aN7uI5Mde94bdxIKcJg3dT8Q6ZVwqaEBNCS4iY2ClyiUCHAeTtuEQQhgeBiy8O48U9xLAt9I8fOD4XxI6Rzw70E0Zc+GKHuNEBj7OSRGlGJJo+yNdwsy6M8L9/jTi6a2c2jaTsw1FzWKVQtvDDArYfemEgynKtZPmQGiFoV5pRmiOGljyt+tHR8jtJoW6gkQeb3spoYMEWbTSmgypujHU7MQEpxzIqig11gc+8m91aJsoZTe03XWb+qnT0g0rUv5kgzGcyRNab9ou0IqhvM6UzDJ/2JvYhhi7p1JekEQU2w64bMf5LRxT16ABLho/nU41xRn+Xw47Rdbe3XZZfKeJTAGA9R5kPOyzwsDU8r5IC7TzunWoPHzuLktw/hA06UEe3XdoYo3lPnhTAARrJFhTWAAu/PB9vT7NrVI2gei4Ug2aKOR3Px57zdvbV77svFwiww+hkTBGjvbq2Ts4IyILyPehuc29YpQzBv/pVRBP3BGKE3Dmre1lAv7huCzCasMwYhGvg/ry5nAsWeaxrRBZAFe/r1v4u//w3+LW7fvY3m2wIFz+LvTCej2EZrX7+Dv/ONfxfVPPIf/7D//c/Ctw8OzBa5d3UdZ2CCkIxCEQbeWgq8aG31uWWvhnUMUljLQOReZcOc8VqsGTS1mvuw5Ot3XnnoXQtAH7Z1JaTGZWyw7oHO+J9jL55NID3pCAWBvZwrQfMP8/+AnYyu0DDTNAmQrqE8e74HCGpnnwqBr2oF/NGBcGLEd3cfdtnaabTnesldjZHQOijQsN4CtPmQMKfXMKC/AfB+iw3PFFUqAZXmGggCtM2km95uVcSRNAb3YyC9G9NZW503ri9XQgAmIg6R0UZJ9V+b626/fw2994VUcHZ+ibVpMvcffLgscnyzRPTjGP/zSt7F//QB/7Gc+Ce89FnWDnfk0COnSWknEWgSmMwjUGMnHXsDrJhDgOkPeM9qug+vC+cFBKMD9upVpBWRPFuEM944DQ745eTAsCJOqwI//8b8IrOHKHDeLW/wkuFNBpJrutUgkxxgJFBckqzc/Dzj7iwY+SLuZsS64y8vmwrphdPVN/ZCyxlowAOdaENkI58xBU8p7kCW0tRvR5FlnHDY0s51K3cLgyRbeRCRfgvTdStCfS73Hr3HmR4r0rgMpL4X159k2iaPYMIcRc/Ron00ighHiKRSMTQfAe/DwBK+/fR+rVQPnHApm/Lw1WNUt/KLGF999gOnuFB95/gbYM9rOoaqKKDyKLbMGpBF8ZeQwDegkjV+DU0RtODC8y0xngXSWKyOa4XeQMM/WJvpgM4PKsX0CUBQGn/vcj8IQ4IMnKgqXDKWxYDYAezgNixvqz/FO5z2soXTmKJ0yXLJ4FmSHT1w71ZTRSgzIBFzGGT0Z6pegGIjwSFnQIBOUDqqqRGFtuBgFXPCH6IPLAwbHgEbsnXYwrp1qU4Io4O8xuvDDTBeH461g/CQ1yGSmn8MG1hrMzvGRqrj3Yv2Tep+DKNZj7Q3om9G5UuDgTBCV5To7q/HwaIGuk8sZw8BHSGhrbh3ePVmhrAocHswA6Lk9JrBjqHPMHLayu4LQnXW/mFiDVR77kDLhx5qb3W0pNGgM4bnnnpbx5wJtEpcSzGKy7pH4BgZ6Z5v3PgrPAUQtuT4gRyozwevglOL+pKSdE77H/iG5JYhzQJToSRDIEAprBP4D/o6XKyo4DKVVU3DjBiWM0OzfnXSJE/yStV4yf3YWX76ddaQxhPP07znVXWQy1jQepGjTeqyajE5jYJ8k2BM3DmetgzGESSV0cq5otZZygFTaYdC/HNdcaHxbhnPRdCnBXudcdLYbmTf9ZZIJkBAk5oM4Xb6P0/ruY/biQN9Mw0oTOPdnhEi2Zcg9J1ERLWYpUPO9DaMET1CnjurTUb1aNrpkiVRXqjgQWIowOZHna0NLBnz5PsjOf0W4QLoJ1VuUlCmrfywaIbI8w9/jc9wvmyceeT/8HEBpfDYk4GiQNz333uO//Xv/Go+Plzg+WaBd1SAwFgf7mC4aVNUE87t38fu/9y38+m98FewcGseo6xbXru3Ddx5N16FtungIeZ9umTrnJCpuuL0mQ+IkP/jSUxgVR9AI5vKBAPdpTATAWMLh1R186lPP4DOfeh4PHnT4pX/7ajAZ9WtaFOKfTA9RHw/UP9qphetacZRM4kS4LAuwZxRlAdeJs/1lU0MUrsbReg7Hoyl7qUx87ihaCbRItBJGhfs9wvjcRgdN08j38M9wHyTijOIfUV42H1CeD70TjGLeEQFUTmTwOCaQvmXk5iBT32veGKOWtU0JnzoGfu13v4HlqsWqbsU3Hhh+NkXVdNgrC1Qnp3jjrXv41mu3xNzLM7rOY2dnCvZenFyHSLXqz0mH7n3S2Mv7rn72VPNOBZTCjAbGf3B2GEOYzSvcuLGPp24cgjHBnYccy+R7grIx98cOTHYPR+YH6G8kxc+qSafmsgxGDbkO2Ibb8+c2e6ZJBYdjbVokDTzGuDZ33s7FE8OFwEUy4Xqjyywalwhnb9t0Pe2HrILNx9LG4yhdyIWfg1GsX9dtSuePeJhjW4kE25R/ItOSyD9648qJhX4b8VSlwcPhT9qIRvtltuUZIbxjnzJhnAfj22/eE1+zwVQeYHBZwHYeE2tg6xYPH53h/oNTqH83zy0q9XXL3IOz/FJBaWaOL6R91cTpm6cFDL5lXEVpsLs7wd7uFLPZbhzqGFaLU8EJzxKA2e4EjW/j/tLA2Z33KXY0SURtx+Ion52L/W67DmxFiD56RkQGX2AGWQRhJYWZOQt6lMCEiGAsoTAWRCJ6KMjChoj0UQvcUM/vn7FGtHaYJWqnqkEFgZ0IAgCGD2sc+gzKfGKH89Wopc3mdfhA2PBN5O0HlT8rRr0v6+8ugA3GH4zBL/p4bXviAeocGSSPfd1Sf9hoNCj44PGZ+JD2fYGyYaGlyTOWqxaLVYso7PIqbOoL2iNFkUi0cQ3M+Oxy5p5kCGVpUJUWs2kR29gG58Nn5cTCsU/0Q4A/1dSLdBoLHvSqHBAG6ryPl/yE/Hwa0oMchXOKxbS/aYIyflRrIb3oFC1egySkh7ZKA2FfsGSIsJnj+ZDPKMEY11j7nAJvCPUgPrMvesZ+6OkJ4VvLrp/Fm5vpH+Dn0A8jL7WOy+zqBOcXOcD75bb3jXSzgYEk1OOcytVzDYGPZnTL5Nu5d2af14kNHXrSpTNG9nhht65CL11KsJer9K9FoWK9vXaYFBagvm+5JyGmv7+Tjr1B7i/Iu3DTaRyYLcCNIPye+vGwjk1bon9bFMGIkOJk6LvMyV6kH5HyUWAiklAuY6SV4R60y/1H8TMnFGJR6vd0u9aejnmoVTH2OdxXw98RZIc9Hvk9fDfcv+cdm4R3v/UulnWLtu3gXPBTZwmvtC1+0giB+WNVia/VXqLSNeK8vq4b1Cvxd6XCc+RMASfz2XjQ5IQCc4/QFP8SWc+CqS9BEMUzzx3gs599EZ/5xAvY3d0BEfD46C4AER4YDAUEFJUayEjdpTGYTgrg5B1gcgWoZjLP2QH/g54YLawxaNoOk+lUTJ19EfwDMTpuYKxF1zT99RokCrVtebn2iAc/5CN/kxDBMHzG+IE7hHMd4+a+5MSC4o4I1vosw1N58I1Uu2rwIWqzpJ5m3yh9j2OhBA+9ez/Ohp+mqH9rHuYsusBR5BlqjMQqI2rfyDPC3duPJeiEc1FgzkR43Tk8T4SpMXi6sLjdcRDguSDYc+g6cccghDIisy4meOObpAeKnPD8MC9lAyQD7B/M8OwzV3Dz+hVMJiUIhKNTEbr1zF56lHm/UkOEsrB4+w/+DW689COYX3sappwC0bzFi8ALmZP5KNRTM1kV9GWO92PD+qcRaSn71O8asTbXBhwK+fIouhfBPYmU7P/mtWzMHsYQnPMoCmECjClFcOMZDp24SwimzWOMxaUpu3wDrj3fVgaDjb6eZXAaXzqtl8lwxwD/907riBf6NfTlgef3aM1/Jrac1hE35rA1fp4rraJ5T49XMcJjru3xwDMOCShB2CfCSdgHPuRpPMP5NsMrqYfbBUJZzrV86YEur+Lf6bTA/t4Uu7tTFNaGuc5mZtBoxHBh2fRSwRiD1175Op5+/ib2DvdRlEX0YdohRHonE2gLEubei5ksAj7x3sPFi+o02/Gy1wBEJmktal+Jorms4qWoCZhpBBIZEewFP8zKyDvVTgYAT3CUnSBDU2QWYUUEl2yeNDiGkY6CyKYuQPwWj5s2b8Af7yNtAeO1PE/a8hALxnq3wmH+dgi1fc16GsxyXuZcUOchjxNXPK1eNvD49TzSnlJuYjG19yNLesaMQNViSkCbn79AFNyf1+TW99sKDo5KawmT0qIqbRBi5VM45FlySqY/1YYID27fw/7hLiazCYxNvkI7pItMbV7hMAlEEk8iUIF+G1EBIOAYZvH/Hd/1tfjyAGWpioAP1Cxe83pVXJHOeXUhBUohgntzkPB+ohclf4p+neFL3UCEON4fhLQJzjVtB8ULnMmjVMHFqQsa7NNxvVo82XoEWkp58mFSWxKlQDdh9+9U0rORTJ8Lumi6lGDPsw+31tkSkGhEqGTfGcLOzgy0VvU6EfVHLlFwYp7dALILWhjZ/PQ9Mqwj6uQvdACqSqTpM+6z+UP4EDVnzm6SMsCSpc2IQ32QldcbDiRV80gs5oibVFNv8Le2B3osQPY39j4f0GU2/qY853Fg2hc9PMcZg1/+F78Nz6IlU5UFjs4WqKjE764a/ExRgGyBv1xO8D/AoapK8ZvHHbhzODtbBbNbMZezIFhDKIhgmVFagyrcYqmpHnkvTqjDDbcxJkWRAgKTQnHpbPi8Ot8Fv/YYr7xxDGsJVVXg3ioxHhKqvo+WpVoGvAETYzopURQF6N3fB05PgOk+sP8McHAD2LkGFMHpPSlTftE1+n5JSugE7UjIbaKxs0CoiF80Y4GmbTXUQSqLC87GJpo4mphyHxQ2yQEi4aJQO96L4WHc58E3aOVk//a09cLeG6PjFb+YNVzQZwx6PRq0GWcg3lTIYONQKeVSP1wRs1KYioxBjlrPQE+Q158B4ItfeSPmtdagbRq4wuJbXYePW4OSDH7SFHi58GitRRMdwItAXw5tiR5HSKZkhgEbtE2SaIsQNUsic5uPPvRd8XB8D8yrCvxgiTsPVyEIh4EvZ4DdS5cFcYP0vRHqRioLMV/7g3/9T+HOauxfvYarzz6N+fVrqK7fQLGzh9nBNeztX8N89xDGlsgj1Uky6Jvh6mIpCaXBMXI8GwJX9KLbGvTJL82Tt4Us/7AfwzTE4evnAMOJOU88TwUGptPdOA491uvuPE2eDV3Y1L2MDNh2lm94hHDKRzgYJbTPa1vP8/xcz2B8/fQdMpxDkj1rYgjYo+9S4nwCKKOTwv7N5yDhPS2X3hJluTcJRQh459bDSOMYQ+haD28I97zHdZLz+VlDuA2hfeE53hQ4l8EipdmncPelPuuGlM4YNZP1vPdQy5XGgM8anCzaIKAjzHaBcnqlB22D2ZT/wxpaY2AM4eUvfBlf/R2Hnd05dq/sYbq3g8neLsrpBDt7O9jZ28HufAobgsBF3Bn2mrFiBgtGT0BHLCbtSdOfBHo5aCz7EHHXmBBfTfCgj2ds2EVEwYVAiGPLyc9XOntGzqJM4iNzkuid4NY/xszMz5eyUH/hFPFlNwyElc/ph5r0gBs2Ndz5GPk+XhttzTZ4QbTxlT4br2qEANiQ67wZzNdmI/beNE2jfSA8Pl7233uhD84A7JAw2fvMOCWA9bIlVMwbpiTBMY1O2xbUN9pfxRncetStj3h5UrmtxdOeVz6NACLcfuc93HrTY1KVmMynKCYViukEthDf3tWkwqQqk8YqEIkF8cmedTzQlF4vCAbrLXyrAE9uLRbXMhEhvXqDzZLAKycLhf685KcLZeXy0Ydvkc4ZEH4AjMk0H8MrPyIo/F5K522dJ8q7CVQ3wPsTpQsAen89N5e9CM7QjHXjer+1YAugDLhrgr7X5gtW/UTvtpZhBru0l/2WIKvDdCnBHnuO0nJAYVwAWmDfw3uD2f4UorG3bcrXhSI/WEnHLuhYngR1ZrKBqySgk2AkvUh6ggW31p5Y86w5oa6QE/IaWS2p8PXJ3qj9Qpwxi+EvQ7DD1cl5+5xw7BGplBP4ob5IyQ6PwA3Siwt9rh2Va+NczzsmBRm+25bW8zAzvvb6u2Bm/PhnXsJXXn4Vy+USJ6sGv1k3+LH5DD8xn+KVyQTzitExwxYFJnUD7z0KY3A4rfCJSYnPWotnGDgkwi4RZsyoAJQk7qYNGBQ4BBNOIjFrIZDz8V3sqQlCWOclkl/rg+p9YEQXHX5l4fB5GUgQeOQHXzgUwwHMTLCTEtPZBPiRvwI0J8DZHeDRW8Br35J8xRS49hFg/zowOwDMDpKQL5/rsTS2pt+LKZltGGOF6G9XYBb/K74DTAG0qy4wOlpqrJ4No900BQqDvEEDbx3c4/e+pm6/kTGIGP7KiVYgMWpJY6+HSRJB2Su/ZW0z6m2Ia/Jnyrkn8w5CL1KaMq9r9VOCmcAW9gTiUa15oLkcKnvv/hEYwLNPX8V7t+6jbS3qtsNrncM3yxKfLkvcLQpUkIjSZAyKQrTkDBFmhcVThcULxuCQgTkBFQgFOOimUTQLyE1NvPaZCMij4WbvmOSstkZ8QekImT1ADm+1Bb64pzVyX1iiyxhgnAigwqIoC/y5//w/xtH9h7j7zm3cf+s9vPnKy4BnFJMC8xvXMDk8wPzGTTz10R/B/o1nsbN3AFtWYK9aii0IIvRjFCAS36G2mAu+iquUa+4Nk542mmcINZx9qoagz8robhzz3zdsQ8t3SWGIEBmdpl5E4S47gUDX+Kx8mN8NI7l4UoDdniWHhbzNNaI4H/Y5VQ7u8np19E5e6r2SdvOyI+1FnBGhelPeDHn1vlIvCwHIySh5IAWiVhOlPOHNWvvxFwPHZ0uAgf39OY6PzuCcR+c9HniPe9biJgOnxsCaIJp2ABuFHUJpCHuGcEjAjOX8LiBntkF/foe7mMIkqW++3hj1bA/4xIeI2UlYz6hbh+WgzrGZRbavjTH41M/8OJZnC5w+PsajR8eo374NMGALg2p3B8VsivnBHm4+cxOHV/axszODLaxo6jkP5x0KY6Vv1gJEKIxBWZX9MyBcbuSnQU5HAkFrAaK5YKIAP8AaOxHtsY+C97gzAl6MwgNKgqAYNTzc6kRtQ5/MiUMRAEDXNXGyoubeJZisDyPlx/nWTOekTbhp07NR1EEbf1y4P6O4Sl9sHWjI0P/A+LfNLa9C5OrppBTXGkEjeMmMMyLsEtAoTRPpi7TfDIlAYEqCB3Ld9eGcjcJ5b/evJ1X2UFzAWhiMyp27E/ojJoGrm88/jaZpUC9rLBYrdI+PZQ6NQTEpYcoS1WyCvf1dTGfTIOQLEayDX0ob/FUaI2cUEYkP3xzhok/7rc9D5sIr8DecvRMY5wi7Q60+HVSGWZCfu0oZKk/DwZ3JMHnvYmnk7f0AptHV2HZYfxiNXgiJXQyKL5QY0be1NSYEMwyUHot9CZEEhcoaXuuyXlEPKUhg88xdlAaMe5bXR1xdYviX1NgLaseRQMpOQCaAxPfX3rzE+i36pl4NkWRe5gchaRRcwMOj6xzEXFHG25yeCqEQo6fpZ3/mhodB7/nYzsoKDQnexMymfD3hwJBoH21APvMIuFqdRu4URRyKt/2p3NiRR4P3PPJuWMcw9QfPgXBLDMQwLw8+1UxsmDef8eF+TRO7eHyKphPH0R996Tk89dQ+Xn75Nbz++nt49PAE/5ejE1w5PsWxNSKAsxYH+zvYqw7xIgE/S4TPkcGzDJTOB8adAO/kOwMcCI4Yl1HPOVVzZ4BMUNNXDYJA5LL3QCC0YQ1gTBT8EgFTCygTmxPJY7DrmTGtDMqyEsnV9AowPQSu/gjgG2B1BJzcAh6+Bbz9VQAF8MxHRatv7yow2QWog3BCSgDk610AmAKosM7kfw/hB15JFFznMQvmzs61EuTEGHjXwJaMe7dPksAoL/5B9CHbwiKwE82uuFPTPxvL5/A5pikzTHH3Z8uWM/ehpmxds0qp12CvTKIvKGXN2qOUIRVc26rZbf5g3LkRj44jKxaYgyFR2O/p4kzM8wwRbtw4wO7uFLdvP8CDB8dYLFf456sV/s2qxsJQxH+zWYWJtbgB4BNEeIkIVxmwXgJYMIUokIxExIbuR1ERA+JvBlEr2FAwVGGOfqJ8+C6Ce9E0Eg0XwRFTM8AdcbpZP6ACUmagtARrLQ6uHeLg2gGe/8SL6JoWp0eneHj3Pu6+dQt3376F+2++h4K+jvs3Xka1O8fk2jXYvV08WrZYLGsQcfCPE9bWEHbmMzz/wqfw7IufwnR+iLKsYKyMk2hoVrsJ54+dlEqCMWQGGyRBHQ/y6Kd+H5oLd3CujQxNGfaucx2in1Pv4JlxdlZnTNfIWf3ESYF7+6M8cZZvTMqW/7sp6T68SP/G6YQtT4YEzFqGfF3Xv66lSJf2a+gXGYHqQSHNof4SiYDdnRkmVYnjkzMszlZo2w5faTtUnUOrdZBotxZGBAHXQbgGYA5EdyscBEqClrg3v/25zpj3AOM5rxzl+qFd1SL1SCZrjkYqHsxxoJQinjDGYGc+w858hqvXr8B1DqtVg8XJGU4eH+P00TFOHx3h+L27ePz2bdhJiWp3B3ZaYdF41G0HgJMWdqA9ppMSzz5zA88+ewPz+QxlWYiGMqm5XaBRIM8sjHjxJdVsDJrdJEIU5xiOJVquMQaeGa3r0LkO3nt414c8nafkVD8zpxSEB88pYnhPcztoW0s50R5q6qGW1Adyko+ntS275SzfVH4DnI+9Ohdy86N8lFYewQMjzW/C5msjyzNeYNjD8/y85FzwuUWEalKgKCQwTtuK+4w7zLjvGPmKy9FuMCGB7x0EwT6PwXP+73AYFH+P6VOnIWi+RNvo5/mxO1OrudZeVZWoqhI7O/MYiKtdNVgtV2iWK9SLFerjUywfncAUFnZSwRQWjRP3IkAWSRoAiFAUBgf7uzg42EVZlrDWRP5P6cGcPpTLCGTv9Xk4iTkovVASxoupv/BHPhfSUepHjk/iXAcczwEX60v1KBIFfpRmveu+uwL895O2new9SmlTxg3Pt2CSJ+vMOWkjTth6zq8n1ewGCNYSyBgJShUucxaQC7d8xbXbloQjVcnWEM7zNNaPMQp1bDhDSjfPe3E4fwKNvWGUPW3cSSgvMDOu3TwcKT12AAyfKxhuCqTw/ZjSMrP6KOQSINk+ppxmqP0iW2RDE70pSkxZQlLZ1ueYa2CKm4gZZchTsAsMG4l1QF9RIjqlnbFjav3r+jZmMJtousbMYB9CVDsP13ns7u+FQ4Hw9utvys1T06Gpa9R1jdWyRr1aAeTx5//DPwNbqM/HIdHBG/oxfD70IkGD58CXf+cVeCZMJxb/wy/9GlarGs89cw0//ROfALcOb339NXzz3QdoTht4AHvTEn/65hX85Z0ZXuo8ytYBnQecQ9d2aB1j4RxqYpwysGDGKvw1Yc0a79EForMgQkmECQU38p5hSaJEeRYzE6saAwB2CgvnPSwIhSE8Eg4l3YpFbYE0D2JgKtOztzcb3MSFvWInwM5NYOcG8NRnAbcCzu4Dp3eAu68Db/4hQBa48rT87c6B0gIItkvRV9YyzLFBEvBNwl/clN/VxNwBwZSeyYjDXScMgi0KOOdRkkcxVXOe7TD95AKAsGe3MAHnEc3UA8912B1CztgKSBs5UzeSCYnEJGSCLPE6D/XT5D1jd2cae//e7YdoO4eu82i7Dk3ToW46tIGZ/Lmf/gQKazdq7aydnlAGL2k5reFKSn3UYq9++w4YhNIafPkrr6FtOxzsz/HCs9fBzuPR3Qe4d3SGLqj9TwqLH2/GS9MAAQAASURBVK1m+GNViac9g5yMrfWMU+fQeGDpPVoCamY0EDFUx4wudKMLl2oUGFsDQkFBHMUSudYE31Sqra1aA1UQ4BsI03xKQ5NYRFydhEEMjdg5mYpvPlg5l42xqIoCV2cTXH3qGj72mY+jXtU4fnyM++/dw9137uDBe3fRvvEOKiLMDvZweHiApqqwBHDaSsAoaw3OTo5x/+5dvPzl30Y5mWI228Fktounbj6Dj3z8Z7Gzfz0u2PgFzXD3jT0jALPwO/fN1w2eKd6xWXkGUETH4TJVsidc1wXNXCNOtplBtuhByhjMvR8Yz33L9ce4TuVehNhdr2fr19H844wBY4hDRIAivVLNMoac7ZNJIkWPjpfCvHmGc0605JwPfs0YH3n+eowA3+8OYW1ycvjfMALOB8EJV96/fwyGmMa/d+s+vPOYTiscHuwAnrE4XeB02UZzrcIQnpoUeLGw2GMGeRmjY4H1DkDLPnmfZMCBo7gZgalQIYOefEZJsMAA970FU6SWC4N4008k5qIjy7I2ARzqLkO0bBME/wYW1liUZYm9vTmuP30dXdthuVzh9PgUp49PcHZ0gtMHj2GJUM4mmMxmcNaiBVAHLVZDhNVyhcdHJ/jmt95AURaYTiaYTCpcv7qPj730Eezv76AI0abV7Dm5EKHYL2sMHDMMexAbMAjWWICA2aQCQ+lEh9ZLxHDn1ComBMYIRGphTBDWGBgrnxzd4iTBn0TMRU/LL12kfogCvbREH6bYcNDSk7zr50uCFPlHznYhWG2R4Fb82oWLalYtsEDzA7hyMB/V9Br2aszXXv62/209nZ7V0F12fLwEAygLg+m0BBjomhZNl1lmQKxoDg0w1eMz0OMOElgrP2Xy2PC5MIAD4A1pqU2UrT4zg2fnGHetJWOUNlP8LRqx1hpMJhV29nfgnUfbtqhXNepljWZVgxdLwUdlgaIswQF+2mCWTkRoW2C1rHH33kNYa1EUFkVRYGc+xfWrVzCZViLID2d5FLj36FAR4otOAkd7NyKCNQAVRZi/QCcGjV2F0xitO6tLKR0CgvVSxpcP6FBCBtYXueX+DqXL4oFtdAZdMN+FGglfcglGbmXSafBGTnOM8B0AJhM7wm+s95e3zMBF5qVtHRTOm+AL2ZC40wBCdPQBGVVAtHAjRcgJlnPbjB5cn9OvIR+1KQ3zXUbEfHmNPS+32T0fOsQw3sCHZ5PdvY119Luapwh2SNOG7NkQ9X2/pP7yOL0CZtEOMIUNUdT0hEiEVk6P9i7cL9l60r7Q254h5xsaTLnz4znmSytAw1cjX9dzrvU/PhiiFsKXPv8KfvvzX0BTO7StQ9M0aJsOTdvCuQ5/7a/9Obzw0ot4/dW38Y//0b+KyN1Qqq2uO3jf4ad+8mO4+cInN85RvzNDsMxBNc+jx2vyAfX533kFnXN49GiFo6NTLM8WuHf7Psh7XKtK/KVPPIOvv3YL7B1+/sou/tbNa/iMLYFlg2Xb4fVVjdvO45t1gztEeGQIR8yojcESwMp5sCFUkyp0hdAGQntvbwdN3YYw3QXgGJYAS0aYo67DZDaNJn7ceVSFxdnJCpOyQEkGp0Ldyq2/Uiq0vjqKYOfzKdr2HNREBBRz4OAjwP4LwLM/DXRL4OSOaPN9+0tA1wCzfeDwBnB4FdjdB2zX25Mi5GMIip0g3ZH68HtI7mRryIBq4QTdhJDt/eMRJS6qaiJwYwiEIjJncKKNMJtX2U5KsC5d/QDI9uGZN8bk5pnPM+2LlcqfVk+9VzSyN/R3NsZBJq2RAHzj1Tv4+rffRuc8uo7RdV347uC9xy/83Kfw1M0rePfWY/z6578Wbtcy33fMcJ0Qdx994Rqeefp6vMtITHv2O2P005QJ0c4xc5ojCohXCT8A+Nbrt+G9x2LZYbWs0TQtTk/OQMyYW4MfvX6Auw+OAc/4+LzCn9/dwUeNRds6nDmPW12Hx97jVudwRIQlAUsGOkNoEfw3EWCLdDx779G2HSZVCdc5AStroabCYo7n4ZlRFkWcXzXJbeoWhbUitopCjHyLcG9xM2tmVGURHIuHszne3gRBgDWYVSWmu3PcfO4pfOpznxZB38Mj3HvnNu68+R4evvMeXN1iPp/i6b1d0O4Mrpjh2Hk0IahIXdc4OjqCZ+CtN17FV7/yBXz0pU/h6s1n0DUdnn/xRzHduQJrxc1HDr7MHs61aOolVDOgrKZZXs2sgjuGXBbkOzdf//yVCJk8M4pCfYQRQEXaM8F+wxZWd1OvVv1OI98vlwaAPkrrZtC6Dc4v0IHtWaSNHEsPz3/9vHvvBHcfPBYBqOcQ1TkwY8z46As3sLc3x9HxEq+9eafHZGlSpv/a4Q729nbGe5RpK+c9SYT3+mT0Tv4Mb957eAL2jMZ5dCEYVl1LRMzKGDy1M8HJogHAuF5afKoqcZXkYqfxjGPPWHL4BNCSON/3FHRHA42ngjRAmnaOUdhgXovwnhF98ymDZEy2r1m05DrnYINwbJcIV/OxYWQ9s0mWKM+K84NAhBDaIJBhWFthMilxcLAH99xNtG2H1WKFk8fHOHl0jMXjI3DnUFYl9icVaFLB2QK1Z3RhLtu2w2KxAjNw+/Z9fPPVt/H8czdx9eoBuPP4yAvPYHdnDmtTgAAAIEdy4eEcmqaNA5pNJqhKMfO1REG9osCUK2ggDOcZzktU47gDPAXvXR7WFGi7FdSi15gczk1CjyF0qFyifCAn98XSKJyfX+SDS+MYa/j09KzG6dkqCu71vNbfVw/nmExKrFYtHjw6i3X06gwTuzMtMZlW/Zcb5yE/zWNFaznG2jtbNNALBh8uaOogjDAE7JYWTSeXQHMiXDeEOQR+HQMrZhFkQ3x15Xrh+d+QThoOhwafmi/qWsQHSAJ8AAcEHI5NyYakezdPwk5QpJEMWdjCYjqdwB9wEvQta9SLJdrlCuw9rLWYFBZUWDBZwW+QifHeRzg9OTnDvfuPcXiwi/l8CvaMK4cHmFSlaPFT/+xwEJqnc0FPkgiltYITCFF7tzCmJ6JTAXHPhJbTziAy8L6NsJtcxmT6kqqpG95/x2D8sukcAN/2Ot+PG/NtIVKa1kuU+IzMj1rQDEwnFtYadM5jVbtRmNWzuChIaNkLpSdAhCG1nU+thvXXIE8EuZhTOUwBYEbyyQiwDoHrMfhOY9oO50+KkxXWL5oup7GnviyU4dEWGYBhmDAps72Dy1SLVJGm3Nk+A1EJOvep8/3kjF8miZFHZFHBZQHngt1/TNz7kO98SUFE5EYTR9u72qHIFI7xBT0ed2Q0o0kRaPgnuSgdO0WwYafLg//7/+3voSgYz+7uYEWMw50lVnWFG0+d4t17h/hnv/WbOPzNL6E7fBY7h1dAT/8Eyve+jE/ufwxvPn4Nf6q8jq80j/D5d19FH7yyzo6OaBPi2EQWhKfMePPOAzgvNxTGGFRVBWsMdmYTuLrFP//We7hugf/lR57Bv7e7B17U+Nqjx/idrsNXC4uvn5yhIcLpopZIVcagbTpM5xW6ViLnTmcTQT5OTPiaukXnPQrnsGwaFGUBogpsBE6dk8PVWoBL0eFhz/CFEF1LY1CEA7IzQWuOVciRH5kq7E37aHdnCsJFhPg6hUEYUO4CV3eAKx8D2AH1MXByG7j3BnDrdbkNnx8C154GDq8DEwPYHJ06ACuIQE/jGVUQQV8BcBnENi3gjwC/AEwHkAVcAzZTwEwAViOKXJAd+hhhbRvMeYA92Ds0dYPZngWzB5EHM4E9ofOdqPpTtU5wPDGq37BHKQBuTwUXyKPiZiwbaA0yx/rCvbe9/Io/OP4MJQbjonXI07r++3/9+7AGOJhUaMHYn3SoO4ur+w3unkzx6197HbOvvQs/3Uc5mwN7z8IevYcb02t4tLyPH7G7eNst8MbRffQGHSRTySR52PtNkYGzXup2Rx8SHp6ciYmIlwiJRWHhiVBVFtx5fOXeEXYJ+A+v7eEnJ1Osmg4vL5f4lvd41xDurBo4ItRtJ4IiY+A6h7IUzbA2fC8D9cnM6JwIOk14b6wBijSrngXWSSQBMj5muT+C+AcyJNpHE2MwRY6hKTENuoKZpvdkUqKy87DAlBiAyO2GZ0GLpSwKlNMJdg/28MyLz6L7uZ/A4myBR3cf4M7bIug7ef1dlBATx+mVfdjdHZzCBEGfR+sZfnGKr3/ti5i8/hW4xuHlL/06dg+u4PDKDcx3D3D9xvOY717B0dF9fONrX8SD+/ewODsTDTrnsLe7j6vXbuL6jafx/Ed/FGVRol4tQNTCFjPs7F5FUU6Db8yc3hjsBO6iH66udSgn4mfIUNzwIQAWoBE0N9a1ZcddNKkvtcHT7UK870AaxWTh4cvfeBtEwKywcACmhUfnCfOpx2lt8dqdhyjvHIHLKWxZApMDmNURdoodLNsz3DQTPPINHq3OIAya1Ds4piKDmnOufZx1fv81y1ndCP0SNWUJbABrC7D3uHVWY0LAZ2YVnrUFWudwu+1wj4EjAk46L3qhPgjhIIIDa00QbjKsJSRt2cSUOpYor8mkNbCmjOBPTxiRCOdhQhyC+SoBk7ggG+YC/TWzhYU1ZZaLwr+JhpTuyHNrCWVRYDab4PDKPrqPCK2xOD3DyaMTnDw6Qv3gsejbVyV25lOYSYUahJUXH8OOGaumwWtvvIO33rkF7zxe/vq3sbMzw/7BLmazGa5dPcDOzgzHJ2d47fV35dJ01YgPW++xM5vhyuEerl05wIsvPouysKjrFswOVTXBwd4OyrJEaS3YWKF/mMHBasazCP+86wJzGvyHFYLlkv9WJLgb0xj9HksXpi5iRvkyXm68puHTO/eOATBKI4FRCiMBiauSUXeEBydL2JMV2BRiFl1MgW6FianQ+AZ7sFiyx8K1Fx7nRdKYIEN3edO5TPiT0IcK1E8aBwvghjU4IHFrcewZZxAqtA1lPZIvzMivscD1UPMw7SiKx6oSGpzlyYVdDI5GUPm7yxqLiguPzYKU3J+nnPXiVqYsC8xmU/gr++g6h7ZuxDJqsUS3WInvMWthywJUFOggEXZdoGGYOzx4+BiPHosFwe0791FNKkynFcqyxM58hmpSYrVq8ODBEVarGm3XQX0LVlWJ2WyCndkMV6/sw1gjrq1YrGOmk4kEHwx+/wDOFGaSzzxRTgqcaeSBM8zI6fg4T2P0ezltg/8ePb5JtEDreTUtVgKfKpGR4IFyp+IALBsn4lnFk8YC7GDJwLNHCQpa6985gmUY9TpSuOFL50U7dEbCTXoANSPs43Uh3qY0loe3vMv7s6mOi7at6ZKCPQXQsKABgZFqCwTiZefa1XNqukjK0XA+ZDVkUOGeHSnzvZKGS6FmG2okBRAKsPMSlCQXf583lGGejWXCrqXAyudMx4h8YJO4a2s/AiLsdSFUHzW/YqXbatXtSzg6OsJHn7mK//1Th/hlv8R/9Ff/DX75t/8Y/vL/4g38n/9fv4Bn/sZP492//y5e+sW/ji/9o/8G/OJfwvW3v4FPH/4Y3rv3Bj4zuYZX+RhFYVFNplgf7HDy9N1Qcf5i4HT2+ATLpoPrunhwGGtQVFM8+5Fn4DuPTzUN/uZqhb26wxfvPcT/D8CXvccpAO48Hi5rGGPQsQe1LaqqAhPQ1K2Ydwbm3Tu5KeO2CxoPkscHBgIAyAZNPSe+n0ojWjfWin9Hz2LiVBQWzjnYwiDKlgOcCz+Rk7U6iwKT167MUVU3smlM6xcR5nnJFMDsKjC7Atz4TPDP9xj84DXwe6+Cvv0HQDkF/th/CuYVyDQgagB2EOPipSAi8gBXgK/hnQP7Dq5rYHwHzxKYpCOOUUWLSQXPDEMVAAvYQvrNDmTnEP9+ExB2ASQNyX5iEIn2CQPxEGu7+2BmFNaKajc7FEWlCLTPjKrg/cNII1p7/V+6mjmpO+xM+t1jD5UZzQZD8W/7gHLMvlqucHV/jr+5N8Ovc4u/9eOv4x+88Sz+xl98iP/ilz+K/c89h6M/OMK1j/8E3vni7wJXPo2dR3fx1OxpHJ0+wPPFHLd5BWuMaG1ENixRJSrvWJv3gTbBGjqkfl8ZwNlpjabz0e8SIMRfURU4uLIP9oznOoe/0LaonMcXTxf4QzDe9YyaATjGou2iWZdzXrRMCEF4F/rl5UwQ5jU5j+46FwUN2nYyYWLBC6yWYsGfl/cwQZjAZiDODJMSn2m9nPbCfFbhyoGe6R5d48VHKAU/npQzAxQpJQqC1bIocDCb4ODqIV781Evin+/kDA/u3Me3X/k2br35HtrTtzGflPjsX/qTaJYNThYrPDyrUXcOHoSzusXpqsPjk2O888476JxD24nLhtVqJfMCcT0QSHbcvXcPb7/9huj0Vr/cmxtmYP/gEHu7V/DCRz6Oj378Mzg4vAlbVMEaQe9mAe/EJDP6Rgvz0tTHob7g99B7GFP0TU4umi7MhYcly2F75Bx/34kvXuV4t/sDatsO82mJn5qWeI09fvaZE3zx4Rw/9ekVfuXrB5g+d4DlOyvsXHsWR+++BZ4/hcnyFHvlPlbNAgeocIxWtLKy231dju38wfpF1XmpqbtoXq0XXbrfZ/MJmBkHnvFp52A9492mxbvMOGJhZsGE1ictcWlf5iO/4OGM8Wef6Cj19ZbIX0q4SvMrQ0YkLgzAolwW0PNmzN/H6cQADFCVFvPpPPRLzrXkHyvD6wrnSFqzTBIooywL7MxnuHbjqmg4rmqcnZzh/u37OH50DNc8RmUtnv3MR+Wysu2waNoYZbbpHOrO4Wy1wt0HR/DewXlprwn0DjjKTAAwjo7PcOf+AzADn/+Dl4PPPUAVIXd2ZtiZz/HCszfwkReexuH+HqgQYR5z8tNV14vI/Oez5Vwd6BmKeIB0otfm9sNJQa66vowjTV+GnNh+4l++Bu89CmvwnDV4AMYL+x3eWVg8f9PjW3dLFAcTtI87VDv7WB09hq92UbQ1JnaC1jWYGYsViwdp1dZcG+LoQ+7jrAsKDFw4T/tlZF/b4HpiyoybLC4vHnuPY4hAL3EMeVuJNuoRHGP82oXHNnj5PklGEchXoX3RoNbLugTbeauJEFINN2sMJlWJ+e4c7A/RdR2ausHpyRlWixX82RLWEPafugbvRPOuCTwMQ5QfOufRdB1Oz5ZQd0tCB/motZnD+bJucHK6AOMh3nzn9kDoxqgqieJ7eLCLK4f7mE0ngLonUSYVQNe1PZ414eUO2U8kiev3Z/qgej46C2FtdomwBHBQMo4dsD8HHiwIpjLwNcMWJVzTgE0Jcg4GBh4eBQguzv5lMRbWYIT6b9fAyPtkRTEsp/vbgqF2ACsWdzgeW8Dxezhd3sdegD71CaIMvFVzASJUO7sfcDfzhS+y3/rZIAnLNpnkfTdS/xQmGHjvQJhFZGUmUzHz8b5XZgyYLvpstHl9MICASG9mPIJm6ysXadmxHvSzRYS5pp2Ugx1GJD9JNOB9h7Is8FuO4W9cxe3mb+GscHjnzT+Bn/jYdczemeJjn/tRfOlX/iH++l/4GXT2myj+2s9jur+DWXMNf//kHXz7zruwhUVRTjEO8jzy5wfvLpZ++Z/8umhehhODiFCUBcrSogDh3yfgL9QtHj0+w99tVvg1GCw9Y7FqxM9d0HZxzsUDS81tuiD4JQgTaayBtQZOvWMTQlAW+SHWIwQXHHSJME8Y8bKwaDsHouBXhhGjA0Unsgis/jDaaqYmQQTs7Uywc/hcNgsM4BGSPytZS2YC/Clg5B6EPUCmDEpV85SXEPzzPQWUJ1hVNSzfRGUKNA+/jrPbb6GY30Cxcw3lzj5scFvnm1O4TiJUtl0bfCUQKqtaEh4rzxKzJvRy0nmsug6FXQZ1fmEYu86hLE9BxoqQdLIDEfLtgbAPoOyBgHc1nOsi82OMRVMfgdljMpvIGNljtj8P/UrzGWF3AMTnkxNb9uUGwnD9Ud9cUOF9FCRDrqFRAmf/bvq2Xk+/PLOHtQZf9gzem+Or/FNYWocv3f4oPnJzB+akxM0Xn8Zb3/wifvKzL8DTPdDhiyh2KlRf2cFvro5w/+QIZAhFUQhzG3o7PgMcBS8ZZbfW62jOq4Rl+P17X3gtFuNw+Ck8GhA+B8Yvdg5vrxr8atfhVRBaBtrOBUFyICI5zV6M0qgMP0EEZ0a04Mhz7IT3CSKV+UGEXxP7bqyBdyHEjiEYL/5/hkfAACMnnJ1prE4qixdffDb+vv/OXbz89l3M5pXUbQysIVzd2cVZU6MsbXSKP6lK2LrBcx97NgYMKosCV2YTXLl5FS+//g6WV/fh93dgjMEffP0NPLr9CNeuHuDGtX08c7iHxjMeNB3uHy3weFkHrWin8YTE9JBkHlsNFhR66z0Hc5AW1gguMiFa8MnpGYhu4dVvvYLd3/0V3Lx+Ax/7+Kfx0ic/h/0r18Fcw7UreL9A13Ui9I/7g1A3KzAYRWnD1DGqSRmZkh+EdLGh0MjXPvwx5Cx7zwM8KXGbn0ZLDu8eX8WV3RK0sti9sodH997Bs88cgukUmF2BrSzsrQqvt0uc1SvRnAuC1M10D2cfSah3mSV5650HvYGrAE0Z2+cBfMx7PG4dXvUe9yHmYy5ctKmrmiGOjZFrs3rVDIwyiwrOgRN9eqwXCTIEyEoGGSOayGN0YK9mSUVhcPXKQWS3jh8f4/bjU5RlIWbAQWtxVlVonYMNwXpMELaarsOVawfCJJER/3xFgd29Hdx6eIR2ZwqeVmiJ8M7dR1icLLAzn2J3PsX+vEIHwDqP01WLRduK+ZMGDUMS1DGAnjMkAMzi57LrEv2klyfLVYMHD4/x9ru38Qdf+TquHOzi+edu4qMvPoe9g11416FpWnjXwDuRrlJa9CAIYFhj42wVhY24+3stDWHjg+eCNtWetP2OAHBhccwVPDkcrWaYTQjUGUzmEyxOj7C/PwWjAR3OQdbAHFs88B0a38m5emEzyCGSWi/VQ0vZ60ePF302JZHUIAAHAK4xsPSM+2AskPM3WJvc9EgailXnLOuQLRo0v6X32zJd+MwxRGIOGypbLWscL+vAJyThXVUU6EKQsETfGpD3mM2nUpchgEz0z3d8toSrCnAh/jIfny7QrBpMQqCOaSmX3Y1nNJ0I+5LfNaHeIunByedZ4IjgSQNoZBRewKld57BYiC/Pd967i/lsgoODXVy9coDprBK/rZ0Hcxd9ZSpLK7gi+V3T6TTGXJYV/J5J287I83mMlMbyafkaABvCigw8GKuOUFoCPGBLg65rxZIFDrCFaK43hBVrvIZLdKQntLhcf1fDYEeDTBMI19qyOH1y5zfzvtImEP6g0hP42BuorgbOx5MHw4SInPOt9bz/lJvhMsQET6epy/JdJKreh53q+M35LjBm6uPHw87m0Z8UoIdGFtAiY1LOhcaMS+tp5sWk85UqHTrj5uwzr4JBm3diYJS17/ntiM/y9LdzviaEfGAq0DJVgfeeuopf/Pd/Bi/++M/ii2/9El76kb+Oj36K0TbHqCb7+NYffh1/6hf+ODwzFs0ZJuUevvDFP8DX33wLbYiYpozX+nyMJcp7PSg3JiyQcf/6774sN7kIwjlDYOdx5WAXf9U7/ImjU7zy8DH+SwO8YUo8fHwMYwyWK9kf4meCog8Y7xmuc7CF+JTwJEKjorByuAYmQ29xHfs+0xCFSCRCPJ0HSreRhgl152M0PJUNcly+3FQ7fRONAYPptML86lPZ1IWNRzP0E4P9eyDaBSDmLnAlYG9uXoXyGUz3O3B7V3DO8g5mBxb18Zs4e/wq2tajmu7BTA4wObwBU10DLMMWK7jmBL5r0XHSlmidQ2kJbSfz1Dof92vTNMG8SZj+wjj4jkCTOXjxWEZt7sGUE1BxBeDrIKoALMFBy8p7J4IV9lgs26DJY8EeaOoVbjx7JQluIlWY7aoBWFzm4O2tTkQa3Kuz/36YtrSWcZjKyLBqcfIgG0YYHerXPDRtIAKoMLizv4NPf+p57H70BZQPX8Hh8z+BP/4s47ReoZxMcfutu/jUj7wopltdg6Ka4J3X3sGdR++KM3WSiGzKXKRxpYbIc89za7yN24ASclyoP77xxm1E8w0fTF89MJ9O8Ave46dWNb50tsS/M4SHxmCxqEGGQoCPMP4A5wjCKNW0jeBDJjDNgaCmtICs19pZv3r0URA0Kk41IXBGxy5qHPWOhihgzDia8C50FWVZ4KMfE8Ge4peT48ew82toVxo5lFDZCX7t81/BfD4RAhqMWVHgpz/2LJ576Zmo5ScmvHKGP33jAI3rcPvukfjau3cEIsZb793Ba2/dAnuPnZ0ZDvbmuHb1AM8cztE4xvGywYOTBZarLvr2VVNGE8y/gBRAgBEuSBhRIGq9MBl2UuH46BRHR6f49utv4PDzv4aXPv5xHB7O0CwXANmgMS3zr1E121bcFhCJtnPbdNg7mCcNgXPSZWFc02ai8AMmF7WDuk+iyhCNd35kMD3KyxicTCvcvHmIydVD2MVtzA6fxYsHQN21sEWJk0enuHHziuBs38HYAo8fPMbJYhHOHblEXmvqIkO/xNTcfXicadWlglVZ4CUGnmsd3m1avErAgghNK34vXYjGSsox6oZTui7Dfz3YJiBXydJo9XnXe5QcURR0CY6lWI6y31vHTv2v1hhcvX4Q+8oAVqsVqJzDtT4qsBAVeO3NWygrG5mz0hg8f20fh9f2ob7p8vHu707hvMPx6UoCAZ0uQWA8PDrB/UfHAAdTu0mFnfkU+7M5nGesWofFqhEt6WwYHMyRtU8+ALwoHCSXN3Kmy0WfLSyWyxrLZY1bdx7i5Vdew7PPXEc1KVDXLcrCxMiJCX45XnwiaEm6zmEyrS6qFPYhpQts+AsiGAZ6PlUvUbTXm3iKEKEuLPZ2ZyjmM1B7gnK2jyszwHkHMharRY3d3bnQuuxBxmC5sKiXdWz7ImaQOZ4dZXnWC/TS6aIZzWOs+Kc89Iwj73EfwlkGJdyIE8YDV/Rnb/so1uF8tFxY7jUv6WMsyTAN4NwYws7ONOSXutquBdkK3iWNYCKDB4+OhJ4I5KQlwuHOVAR7Ac9wtnkmk1IEPLUI5rluAQIWqxpnixUYQGENyqJAVRWYlHIR1jqPpnNBSzobTqBDIi+jY4/CQMRFiCc9GbRth6O2w/HJArdvP8DBwY74fOtcuOBDwsmhcBI0Ba1/79Ol3ci0fqfTWh8uCaRPSm+MJQr/OkNykVtakGthygqTIsA0GbiuRlmlYCdEhK51ycoJffp2c98vRk/p53Cco9GNQ8YpgCkYK06auB/2em+sP8L5OfnOSZcS7AXOoi/2YBEOwYupT1FdssonSjT4nu+Kfsww+e6zfGPlP6zEWX8IZJUSSYImM60yJ9KhTL4zOePVh1s2o7E19YQEPc57Ux/XISoCCHPGpCTmKa+eocgx60xAul6LbWo7DmdduFeU4oPj26+/h8/+9GfwtS99BbffeQevvvI1dM7BoYb3hOXpMb728itw3qEjg9KUePu1t3B6dio3L8bAlqrlua0j+smDZ5vep9QulzhdNUL0hSh+xsgN898sK/zJh8f4tQeP8PdmU9xe1DhbLNG0ElGxrhsUhYGxFYrComUO0ac9HBlQYBwNeZRVgWpSRqGe3mr6QOFSELJL5GXAu2CaSxR9T6gZiTGq5cfBdCUjYJNkL6xs3xcjs5gZezCK+cCJufo2i/O1AJEFioMg2GtBpgJ4smU9ANAcNNkHTU4B7IJmT6OcXEd3+/OwrsbMG3DboF3dQ/vwfthsFcxkH5OdK+ByHzCM1q3QugW6ViLjGWPRdR5lUaIsK3Rdi7ZdwXWMsjCYTcRXkjEFzk6PAED8E7IBsEAxPcNkdgdsdiFBMkQTy4dIX0SEzskcifNvg8ISrty8Eh1Jb9pdEXZp8HztpLoYAy+8Yt+Z/FjJKLsboJb8h6ImGsswSlEPW0nOiSPjx6JZBiLcu3+Ej7xwE2+9cQuPHx3hvTfvoHMeLcu6tfUKt9+6Dec9OpJIiI/uP0JdN1GbpihMhjb7BD8nSg7Iv4+wBvmlSPzOQL1qUXcdmBEDVQQ3V/hT1uJzyxV+/WyJ3ykLHDeiBeKcAzug6zrRbCtscF6tvrEUx0pFKnS34ca7f2ZJn8lIJ5k92FMkmFQwbcjEZ0AgisPeZIaY3qwlzmhkIdYZAAV/NTefOoBqsrDzMOAQ3c7Auw6LZYPruweYTqaYzyZoW4eysjiYlSDFK15NWRMevXntEMeLGneOFphWE0yJ8NzNfXz+q6+BXCe+WDqHOw+PcO+h+G+qigL7e3M8t7+L4lqBZcc4WtQ4XqhJruwx7wFTiMNt7yW4hvciGCqNgfNy8bNciuadIVmX202Lh8d/iJ25xe58gtIawKtmQYLzIMOJFy1VCcx2Zzn6TNvqImTGZSjvrP6LivOUSYrSEGDQ3oDwGGM4R3BFvzitvZN9JA/PzpZo2z08enCM5XKF40cnEtgg+IFwXYuTRycBpwKGDBZny55Geh5sIvZtlNQ5n/7hSG9RuAsRzY8UyY+jIB8APm4Iz7UtXmtavGEMaif+Lzlol0QXFgGWI0lH/TmXCLcUfO+lwGSyluFfBfl0ayL9BNLFXqiQkF9aUBrX2nhTogjwFJnZvb1pyuvl9Fdaw3uPtumwO2EUpTDmzonG9bS0QTudeoIPbXF3PsOq6XCybFEUBUoCDnanePPWA5D34pvJM04WS5wuhL2y1mI6KXEwncBYg9Yzlq3DqmnhwpqoVp7AoFysJdxM0fcTEdC2rU4/AMLx6RJnr72LqjKYlEWwOko4X9cj8fvBDNEC1aQc31AfdroosGfpwmglI+HXPQj2a9kE/pqaRoLNLBcrtG2L1bIOMBLOWO9FiBf4SSJCW7eZ5VLCGZsb4sHvtR+jT3UKfTAN1fc5O3KdCAeecd97PIL41dOcI3eXa5RPn9NMZq5bS451fcjeDrONg3k/jdB2k2z/RvwWN7xoxFe2EGUCa+LlYxGEfKntPpxPqhKd86hbJ/QOgOmkxKPjM8AElRZmrNoWdYBHuZS1mBWFWBox0IYAan2zeNmZerHGnC7qjF4kgOBiwA0p4hqP5v4RrBUa0QTEyjETeny04mNDFIJh/QAk3QOXoTFiGjlvQ3KeUbD4HvZePjnuCx81KbNjds3XOA0rHe385d704HGDGS4ImIEwAWPBQftwWzc+7HQOOrhM3y7pY2/ElCwQG57kXTmbQmL7fafTUFjHI885+wu/mQH6MA5p2dhiJixtGgrqp2QBNqL1Q8FxL4eILYES5CwMbs7wJ79O2swgH0Y2QET+fSSco7dhgeGtSV/RiBPCDc8jrxz/8rnmtFdi5mFPFeNQ8P0mQUU+/WMv4cc+++OY7FzDl37rC/j4p38ETV2jmniALP7wd34PP/rjP4blqsF0MgVRgd+5/mtYnBzj9OwI1hCMqUZmZWQvxL9N/vXyvGm2f/1f/l580izFFG1xcobPFRa/cHSKX73/EP+f2RT3jxc4PVugbpp4aJlgbmetAYGCmWxqhwAxUzDiqFqiBwmhnd/MG2US4h4hlFUR/EoxyiIEzsiFS8wwwR7XOw633jQYecqrY2aWkPdd52HWBPkKewzgGEAD8OGARfQQNLod/ZCpAByAMQOVcxBdwYoZ5WyGgyufwsmtV3Dw7Edh7BzsHbp734T3Z4Cr4Y5O4b2BK3ZgzAwlLJbdCrAtvLdomhoMi0lVoW4cdmYW1gBnywaFBZoOmFoLsoSuIaAIPgldg7apMJvVsGUFMEkk11WDrnWYzGX/MksQgcIUuPLMM3J8xLnPgCmj+OIMjx2+PXS2DrM5vNII8ZmqSQKivFw89zngngxu8y70cACSlu5YnfnjGCU4DRedCz7hPHDzuav41EeewXy+g1e/+TY+8dwNLGsHX0q5N199Cy9+5GksVx1oUoJg8MbL30azmqJp1ERPiTDtCKW+AbGvnPVJGeA4Ts2DtCoqC/nDL78V56JrO1hLaOsGzxmDn101+HenC/xmWeBs1UYY16ifUfCWMcmRmtRpMQTiYNYWNNp0e8Spy4T6Os/WmqiZp36BhqsXzftDZNHeeuZrLIha8AoDZAy89zjcm0CdgHUuOJZnj9Y5PDo6Q9d2wRQ6dJpkFtvGgZyeZ0NzbsLufIqqKDCdiePsa7MpPvLcU/iNr74GlCU++exNfOut23jq5lXMywqWPN69dQ9HiyXqug5O9A3K6QSHZYkVDB4uW8BAhPH1CoCYaXcdoypFMNe0DsY4OAcUxoCMONFnI/vC+Q6tK7BcdphNLDyCH+FOGFZbKoEqgj1LBodX90Mk42xm80NyhLFbo1JG8w0XKf6zltb4/pFs/RVYf8kb2t+AUoActkf6730yQd/d28GNw31UVYl79x7j2sEuus7DB1PPh/cf4cqVPdGstqJh/vD2fbi2QOc62VomEzsMOqUa0Tlc50xEvKQa2fj66L33HqW+B+171zkcEuGF1uHVusXr1qDpxE/UOqNCEd6IEYR60teoyUfoR7VNhcOxMCDqEPCD1qtCveFeyIjA/BJpbO049i/Ux4z5pIhnlAjMJKfzHstVA++EYe/Xw+EyMdei0d0tHZpUhTjgr0pYa7FTFrhysIfXbj0AFRZX93dx79EJ9nbnKK2FAePx8SmWTYuu69C2onFsywIza9GBsGhdoIkJ7FswREPZe0YRaCHnGOR98LmXCUPDhYpnD8dGLiIKE2lXFfoYSlYQJmgxzXemIkREtu4K4pdmmr8D6SI4ZWve4UMVoQxycTpnJpMKu/MprLU4PV1iZ1qJNlY4yxZnS8xmE+GHwu3Y4mQhODZowp/ne2ttTw940/PKHR0vs6KKFxgTEA6DUO8hgp/aSOcM00auK70jrM9pRNQbxhiLXmDhtg02z5MJCavCRHhVLV/FGU0nwaJoNoRzxCBSm7ZUYUVwZqxcYFbWYj6b4MHxGWAI8+kEp8sa0yBIJwDLVY02BAeTAGDCE5XGwJEI+XQ2mF2AxXR5qcIi5Y+jPDheMngwkQTJCZd5kYYNZ3iPrA57r6yKsJ8vMsHfZ2ltAS+BJLKs1hKqcFndtA5VYeNFMgAJNlbYHv3fNl08bzL2Z2tz/S+XW5G6cesPWS4vpvS9IdT7oI+NJwieAfQZJemU8Qz2HtPZDMLQf7dPuLH2x4D0sjGFLpN4UH8hWlEcgn6oRkXU2MsJRO71mAIBo4CQH6LoleBYap14TV+TUC+bEwokoCI9HvnL6pHbtoDoh4ef7pVYnbYWC2/cIovTJWwhZlJnJ0t86fe+gtneIW7du4cvfP73cHa6RDlhXLlygGpa4eUvfxH37xzh4No+ppM9nJw+xksf+xheefkrgOU+IzCadJ30cyjY40HeNIvMwC/968+DiFBOLB4/bNF2LYrS4M/XDV67/xD/1WyKeycLHB+foWnbuNYAIrOv2nZEA8YqaNsZIpRlERhJpNsqQzF6V7zFDwLDshSfBuyDzy3ONe8S0SH8uu4Xve0fIWPidDDKssLRyQrZKRo+PUSheRk+CwAGjDIMTLQnqWc+P5YIAiMzEM8BMwMgDNZkZwqiKygmMxg0ODo6wuH1X0RxuACmJaj4OKrlV+FXK9w7OgPcKeq6AZOF6zRqIIPRoes8xBc7o+08VssWuzsFSmvAhrGqG5lXb9G1XbhFrlGvCsymO7Bg+K4ThseWIEPonMBDURQwxSQwAepUP2nmKmOV4E+hl3pExnmpD+fDO9TBrFKfCe2VG8EpWk86XJN+7pCp7OfrV6AMFUNxF3C2aqNpaLNq8errtzCbz3B0eoavfettnK1aoGDszKewRYG33ngHJ0crzPanmJRTtO0Szz93Da+/cQtkgtYGeL1vA1BWgV4PpwHxOw36qVV86WtvQoREBsuFE40wa/BTrcPLp2f4rbLEad1gFRhg9eOigrmogBcJ+kRREgLjaNSUNuCCCK2UEZkUhRPGQDRD9XygkYipA+Yz4WhO79JJE/IxONxYL+sOMIS2ZRB7dF6EsidnSzw+WqFetSgqAzIGV68eYDor4TsHMsDupAwahqJFLPgi9a+wFqW1KKsS1aTC3u4udnZmEvDGVriyP0dZlVi1Do8Xx/izP/1pLJoWSxj85Eefwpe+9Q582+LhogEvO7kwoRQIiMKZ5lwXUBXDe6BtHSaV+AYECewDALGBdw4gQtN0aAoL9hUmxCHIlRd3I6T+DiVioA3+Y4xJwoF4jmKdcRgCyRijvJa0ygFk9yBwjMTJCeJsT+tL1minIavE/B3rTX6Cx1GtswSDB03roYLornO4//AIZVli1dS4c++xmDRbhPPK4NHDI9SrFuW0RGELONfh8GAHDx4eB1hK+JGH7Q5/BhjMQcL38qYXCjvv3nkkgjeiGNSMiPCcZ9xuRKhXdw5dpzRb1ugYEz9IKtyL46BeL+LFXjxZw5oJfkv04La07WQd5iOIkLHtHJiC+TozHIv2zKpusVq1aFsR6BMRZvMpytIGlwKEiTWhTxkO5dSC+OE0MNbClhaTyQRVVcpOI4PZtIQtDFrnsWhafPL5p9A4hxaEZ6/u4d17R2DnsGg6oPXBuoAGQmhO7hFINCidYxQFRc3q6E84XK6Ihr2HMwT2okmoeFEjZUc0HrShCRQ1mYfznaPTD5sD2ixyyGFTMcsalA5K9DGQwjjFltbbHiY1Q2eIRvvp2Sr6LT45XYq/RJKLKCLCMmhY2+Behr3DbFphsaxjI2tjHMFtOU7cSLPmeDjUenS6kl9ESZucCAcsUW+TUG9cpHduot7HxYsNgfuJGt9QN0jMIFXjXOk5Fs3jNmheqWC1rMpguiprVxiTnRXrpxaRSZYDxqCwRQh2JPBUFuJ/13lG6xyuH+zBeQ8Hwv58ImvCHk3nAe8E/45MYG43yIxoPZFbKejFQk+ZwROsDXZ0EbAp1iNj0MtTnI9ov4Np09H+5D18stI95S5GUEYRekgCHCHSvCBEbT4Kly1g8efatefLXi689XPCXVP43bRJMBzlDgRMWKLefreEemsz/wF24omi4mrSBSaNkAiL3Z0ZmqPHuPXu78KWosprbCkSfFuAjIW1YmZpjA1O6oUpILJh8fW3MjMBmVAy5zoP4Db7Zxg83xL2++JpyByNt2eoDHNWAZSmXvwsuch0ajGNSBo0j5EI98FNbw8+CT2hTCSuwtHG/Wdj1HDA96FvyAR7iHXnGnmJSc7y5/+F8usae5o8ksky497dR7DW4mxZ45Wvv4avffMNVKUFyOCf/ON/LuGyS4u/87/9n+Klv/pnsOocPvXpT+LBnVu4/sxn8Wf/zG38+M/9T/B/+N/8z+FoGQm00fHGZ4wk1BsT7I1hDgPftThereA8cPZogePjE3Sdw5/55Av4sTtH+D+WhHunS5ycnqFuGllnJGJdaPwQ5CFnwANxb4LgTn1uIRPA5UJBJmGSFcEadXZtbIj+hsik+Iy4TX3pO93O98/wKUNU7u8/XuJX/tU/Q1lVKIsSRVmhLIGinKAoCtjCoCgq2OIIhloUpcC+IcDYAkU5gzFecETAAYZMiIZGABkQdgEqQVQAzNg9eAnlzIAxR7XzHFDWYCwBVKDdT4CwAOgAmL0IO20xaV5D3axQFgXIEerOyS1+aWCNwJP3HquaUdkS06mYFBWlRb1q4TohLCwzmA2Ig/mzY2joQR+A1JZTdM0yakm+8MJzuPL0dYjpfbjhV59futQ58Ref5YzdBjzGvY/4kIcPeZhjpA5WLb0hrOtfBOxYfya36F/4KCOdegPFEXGMAZ89eryAMQZ10+HWnQe4ffdhDPTyG7//FXgWDbQ/+6d/Gp/98U+gsIwXn7uB5dkxnn7mGRTuGH/8j38O/8X/9R/Ao8u0WLK9zPkwOBHqg8uKpMXa3++qOe1CsBVmYLlcoV7VcN7jUzeu4KluiX9qCad1i7pu4FzXZ/h1CcP51WMTwt5R/3cm4AIluGMiFfRRNK9XLVo5GhNu0guWaHKreCNfg2wv8MgvClqbhbU4W7b4f/5Xv4RJVaEsDZwD6q5GWS1ix8qywJe+8TbOFkuUp0Vg3oBj28LtzuFfv4eyMKjKCoUV+LfG4GThUHOBw4MrcORB1QwLZ3Hz5lPowJjM5jg8vIrOtzhtGuzt7+PZZ57B7bMzPPvs03j98QKoPU66e2jaTpgIFgZeZA7qp1DgvOsoOPUX2LXWSGATJ7jAUmDoSfwJGQBFEMAoVBpTwrs23vzfvHGIvf0dwVdGhQFDCMi2wnBfnJd4uEYXTfk+4z6NkGehBKEq16D8LGdERCXZ097sC7+TICA/LRfLJgpQTk7OcHK6CJqrwOtv3ZJyhvDJjz+PZ565AWMYVw920DY19vb3YfwKL774HH7lf/wiGCJ0HT/FE52T06qJlhmcZHklFMbqxd8TA2g7ERR7ZtzYmWKvbvFlQ6Kp1wUzvgyWzl3OSLpuxuyJ1gt7dzjJRDGyu+LcjGSEMrKXY9fkUqRpHX71869EP77sgc53sEUThb+mM3jn7mM0TQtrXVzHlSHwpAQ/OIU1BoW1wmgHuqVuGB0bzGYzWaeiRMsGe7t7cJCLwtl0DgcH7xym0ykO9vdx3DQ42N/Dw2UDdIzan8E5BxPo9hhMKNJSMjHOiVaPtRzn0vuAE0NGdXVgQ1mr+zjiZBE26bmytzPDZFZFfB1nbwNJ2evX+0y8+cvG/BdrN+WM3xgY8f8xWir/bDoX4WhVt1jVbZynB49PAMg2vn79APv7OyCSiOuu6zCZTkA8x5Ur+/jWt96LuGTTKCO5AcQF4P4/G3CE/MucfKolpQpgVhhMHOPW+xXqjTW8hSX9jomPSC7lvv3WHQnEFy5cPDuQyYz5iXB0ukTnXFI8ANCRAxcWOKuhgYyIEq7qHMNDou4yMWAMHBMmkyk8GNYWKMsKDI+u9SjKAtPpFKvOYTadihauZ3TcRG1OFcIFsOwlvXQxGfyzBtwhRNciETcgynXSlBAB7GPdk6pEUVpk0obvifSB7ENs2YrDFxltkM+Ei36LGc4xls7H98s6xTiYTQtMgkVXUVgwiyUegTGbVnj4eAHwBff+gGZNP3gtT/57nQ+R55aEI1uMFPtBSJcU7Km2SToBA28RhAWMycTi1S99A/fuL8XE1HfwLkScYfVDQgCHMONBcGdUaEcMQ0XSRCIbBIDiQ8NYC4IByMAWBYwpgtDQwpgKtiTYooQ1E5jCoqgqES7YAqYoYWwpQhIjZUSImISNKnCUYAYiaEC4KVUTKOpREXF2ILFUTPbeQAJ7qFlVIQEKCDKGkDQoSTIjUeK5n5JWhrTXYxyZMnIa/cMOA8Z1QBj2GULd/jnzCwyZYTCiirr6lvGMKDwa+0sSAE7cw0CoB3i8+fodWGtx7eo+imDy0jbi5NYHfzatZ/zSL/02GCaYjFSoV0uU1efx6OFtfPEP38JieYL5bgXQmMYeb/kbe4/BdwLg8Ye/8XWADJp6hWa1wvJsiZ8pLP72u/fx33Ut3vBTLBYrNMF/SFzTsH9ywmi44iaY2KlpXm6CRxRu1Yyqvks+Yeg9mE0mxGOwB7xBeBfGQkGlndwIXGdM0BrmY9iC8NY7d/HOO/9NyOvRuUhpwasavA8jJDH7TeNgWFvBGoVdC1sUsLYUpqAoYK2YHtuyRFlOUBQWRWFgyxJVVaEsi/AcmM7+KcqqQlWVKMs3YEth3h0zvC9hjUVRTgSuDYPZA61HNd2BKcT8mYoSpQUKbsHlBIVZoTJyOcH1GVxdo5pVWJ6dYkIMtA5cTkDlFLaaYb53AEKL6kxuQ4+OTzCZX4WgWduDI13tFMkQicCLW4wigZJvW0pfU2ZGb/dw9qBHnGYHZCqfk9CU1yD7Kcvfr2sgClb80cMxQTimuAIAmGDAuHP/BEQGOzvTeIvrnBAGPmqNeLz8yhsAAvFmLJxrUZVv4uT0BG++8whNW6OcZD7pssAhnHVqDI95fZf1P40+EYHf+tYdgAhd28F1HdqmxQuW8PNHp/gN7/CASzRtCzf0CxOFHsN5T3NPJhPqxdviHukZn5tATKe57Qtz9WY974OeXawbbiDXGSxhOE7kmzGER49P8C//xS/HeYlOptMQemcRIfQxbCtrreAn1WwLuKoIpu4mXOgZa+SCoLCCm4zBV7/wTdHyCRH6/rt/KdrRngyWJy0eLWoUDCxbC+8NwISiKkQYh0CXeI9qOgEH31tFiP5n2cGbAkXXYlpY0dKtG7i2RVkVaJsGhhnGe7ApQLYAFSWq6RSAQ9EQPAMnpzU+8ck5iAxsdkHCgznKn63Bef5zBN9uSsz9PJxthuEJFiGb095mykj2iJBoUCLggYHqhDBV2QbIBG7Eyg4wTgMTOAmmTZ7T5VLSpGDcvv0QstfDfvUetniIelXj0eMlnOtg1Y/mkOuP0jt9zv36I5yMYz3Fq/funQg963w0DTsk4KOrBq8xYwETgrD0Ee4YczI8zwUO9ejPuHntTOgQRcFT//XaOAeBz3od2bxlRhMRsFw2eOXlb8R28rkZqy43CwY40iLiwzd9qg/K/ALDatR5AogMbr9zL17oe2Z84ZU35fgjQrtyWLYdDAOtIzAL2yJ0f5i2gAvFHYDQODbMMTGDjYFxDoUNMNp18M6L5kjXwSDkU3rfCN1B8DDBZ9eq6XA9CPZihN7z5jnDqU8qJRie3Rt+ruHyETDu5SNl3tb6lQqNFE9yZsUXgdZrGjGVj+4g4tmaqmUAJ2oCG84KsTBZous6LJctPPvgR3NDx0dGn5+pW7LFKs9O69DHRBdMAFztPB4CaANsXQyM1uH8QsI66n2MzvXWspeEcS3Wth537tzv00i9KmmtTP6IIg+ccHX6k8yK43ruBohwcrSMZTwD7955HM+Nu+4UjfMgBpw36ZxS/lv7yiHYIDgK9mQ/ho1Jyb8mew8EbTHvfVYPAUGOoO6IKPBnnXPYKcVC6CIBXL6rKYHf+RlHcvXoAv2eXySNNJQCRG0gJENFTSvCDskVfB83ooXZdXVcu3N7z72P8GMT/Keke32YyUC09Vb4cO01v5vpcoI9KCEhwJW0HQC55GbslAYvv/IqDg734ZwPN+ccb9YUcZAScNairCyKokRRCtEvGlbiDphUUCatg8nBO/Fb5zoDhguVBoLaW4CcCCF9YnyISMxsGNEfB3uJ3iKDMnKgEMGQ+MlhAqwpQKYQRiBqFarwURiE0lYw4ZazKCrYskRRVKK5VBpU1R0UVQnABzMH39vLUbCHJFCJ/wr1nc7owBGkfa2A02cnOPsnLRn3Dttc4Nb/TAeeEOAGxigj3Bfu9YV/Y38jgr0o4AtmyaTBTQQj3L/3EEQGp4sGbdsE58UcHWi3TYf5zgy//bsvo667IEAOUdGC4KqpvwIP4LnqGpIrYM4+h9/H/oLmXmCWR7gJ/LN/9htwzqGua9wM/pX+FAjvLlb4lZ0Zjk5ORVOP+1pysQZChKGcZrfGoCyLpKlHCCryYX21W4TA+PYFR2Om3ewRHIlmewCcNJ3037Ce8SCFtpW0ASaWcPv2fezsToPZGycNhniQyz8mHLy2LFDYIggrCR23cL4Du3DgBqGfzyP8Bp8Y3rseMaGMD7MPkbwyxskUKVItguAZmTYJcxA0FFFwWhSidQhjxITWGJBhEITwd65FYQimKEDEKIsSk+kM8905dnZ3YMwcr79+B3u7ezg7fgzPHG6s93UiQsQ9xPnU9VcyQ85OilMXCS6Ok5mV0QLrB15vh8aGsr0QkUMfbtPaJ2I3gUMSfulfxCFaj8IKsjahmJsiOkNg+k9OFiASk8fOJQe7EZadR1mVeOOt28mR8oBRd90tMAMHdp7mKJ/krMtxvOGVXkj04SRVQQAk1jvw+19+LRAkHXYDIfgxNnjUdPhmVWJV1+JEP55w+ZJRv+YMxxtKpvYKN555zXm5EsM9oR44fkbiTHF+Nva8jt4wB5xLOkEoCpesAU5OzoLZnK6dzk6vh9AhGGMkhyGQATrvQN6jgeJAk9oPnVIiL7hSz2pPfkOZGV/JppTIJp+BhMz8NkAUczKfMyJksME0iIzOpTAChOBDzzkYiJAx4uFgJlxWFYgqPD46we7uDL5bgsFoOodqMhWaxvSWuH/MjNDB1PuFwS/K/h2k3tIN4JoHGfUbp/npd4mh6g1qwMQY9iqrJAjqc+KfgUwgqe+kPnVn0Ll0TgNJQ529+Np8+PgkRjcd0kH+wTEAYGoq6fWgc2vTzejjiggXYzMTfhPw9nsPBDc4j0kY3zUQFs7jXtDuvLAWT5ZJ9kZm4hX6GCdtmHcElYE5ng/xZW+eRgZ1wWQIWK1q0ayI9SikD8K2hS8qvIs0hPcBwoVO03NaYDvhpqS1TFm+1H8G41akI0LU6XhIDa+8NU8ubBDYVs1mcawfRkEUcQZBhAYMDtp9QkdZawGSCLqTSSn8AosfQVsUEbfnHM2HldJarD0dfHDvdy7nGJZM/EKY/2xxVRyfI6gcV+V1xnZCmTZoLQx9TnK2OYkIi2Xde9Y7kpZygZ/DST7WtenIYbH3fX208kVg6/HxIvZNNTXnEO/wZ7HPT7i2m4RBI9XRyKu4BiOFP4jd5pyPVnbjKaNhgBCALT1QS4GUj/qbg3O8kZlzrw2Jg9Ou8bUeG2v/0iMTJtJgxgL/oBstd2MSLxuMuMRqOye+y4MsQCyesgukjb35AUhjpNzIq/x7z7Jsy0HcdVvmTP0mbsmyccdv2R/DbNG/XhgAsdhPOegp9d1LW6b+fe+2SwfPyDUEIsFEgNGe+Bp37jyGsRKdRm89XRA0JMGQVEJEKCuJCFqUhRycJgmy9B9hsiQl5nAs0QCvJg0IvTlUIgvB7E+YKrnJk68uq6eD6Wk5iMaPCJsKgD3YEagr4CF+fBgFgA5gEQAauQQEmPEn/vS/h0e3v4lqdiP46rF9QQx0cgcMfpgQJbLi6EZubROTN7ilzn7nwjhdEx8e9LT0PMCkGoU+Ist1zRe5GVF/DcxieqsEafQ3FTlrL2OkdY29V7/5DkAWi1WDxelZ6J8I7Lw6Qi4KeNditeri/In/QhnrcrGEKUocnS4wemqsfcfgef437KPOL+HW40dYrRo0TYvjwuDPTqd4tFji9wh4SITlqobzYqJAhqKfu7hSUaggQiz1RVBVVWRAAYjmm0kRhZRQpiAYICQNSoaoS3vPsDb53OqtcdgHRCQXVz75lNE5iDiaEeuIByQ7EcwY6ZsEgOEoOMzblJFS9KWimohD5iXMyPiqRGICa9hQDuD8ljD5KMsJfiX0CQTjCURdLKcCAMFJkPUO9mjGFAAxCiMCQGYHHy4HhBGV0O6iTSWBSD7zox/Dt775Jp578WWU5RzWlnGNe5gr0ZsRr8XOZ+96xNbYBK0xeGMCfcUBCfZ1T8ezOpc0hxpVlJPq0vryBeQE3pza5yAIyBl1rfrO3ccAxOlu0zQRbwDZ2WIMmB26VuYtv6wBIFpVxmJZt+nSoz+1aYzZiOJznZjQ5pAmV3x7vFiibTu4zmFlCZ8sCyybDm95hwUBbdslgoeot+/TkugcBWEcBZggE5kBBmJ0Tt3xWpNGA82ZJoU52BQNPqNne3mTCWAukBlsptCBiKXYY9W0AVX1+98XkgTEk93Si9bek5mzbCR6KPU/aQzFF0koou+YQOQBp3iijnguiivCviESga0IW4tw5vhwXMk+iReSYT6eeuo67t19iLfevI3JREx40hme5rcHt2Pcdja+4QseyTd4sy6w6hEF/e+qeRe1HFS7Gsrkh2fZcZ3XxMMaWYWycYARXxOAk+DLyjmfCfDTXo2d4RCZnRVPpFaccyASwdrwkOjv4E10YR/+1xkW+XfVtuIf03u0hnDDEFrHeERAAy8BACKcrzU+rDStv7qXGMmU8cNpHjMcAijMbWL3s0Sy53ld7bNfjgfbkCUoTj4mheleTYrrexdsQ5r7A0qUrwwlUI0Ps/mI53/47db5AB1ArmmoTvjjSuX4Uv9hYG9vjtPTBR49PBYFhE0RWzekCFt5V7bkHd/E4/C+ljcBcviZ7fJYOWHDdhypC3EfqIZu1LbJ3tfBDI+5r9Wdd09drPo1hKXjYoAG78fSyL7s48mMl8oSQXBb63w8xzwTdojQMWPJgB/A3mXSRU+7HHUMl3LTMjxJGqvPMwu/nsP5uelDgvG8hVF47b+XZaFets3dSjgjuR0an23ls6uqQNN0WC7rpGn8vZI2LVM6wuVnhtvj2YJz0U6vGdr0gynzpXnJXTkG8uecU+dWdE7Ko16rUI8BtJeq5TuXPqg+XUqw10PInAQJBIb3IdpcvcSt2w/w9q0HcXOp/b0KKXKCXAQTcktWlBZFYUVQYYJja80feXrq1xuJC0SmXb6nvBQYJ/U7lkxqs9/Rv1FABrqZszrzdvVmMPEQWm5zn7Tc7/7GP4M1FhwIC2HOXNKmChMciT3oggeCbY2u7INsBLvIJGe/A1EdNfKCZqMKjJIQzcNzYBa93M76oB695qcKKlAaCAyBJEwCRPDHPvna094yIwbuAKFpljDkxGqMxGG7aFtYOM+whUVdSyTMrmslQAQzbBCcWWuxt78Lzx5Hj0+QBHP9WRtPY+hPy1Avj2sdVqsGi8USrm3xJw6u4E8uanxzWeP34dH0NPXUsb8w4pGA5zRHPgh9JsGkVG6NMiIjow57xFNGqBIhmhcm7QiCRMLMhK4UNDNCHdo/3S+crU9crkxw0XUNjk8WcvMZYRMR7hC/I8Fa1JoxScg+duOW15GGN8iTvm9i7PO2c8K/b0KUrTIN26S1MeQDzbJka4CIQ37zN34Ln/+d342+A1VDTNe8N2/cN8ddO1zDO86+p3dJ8Db2PQraImxmwuS41mlfcSyXBIIYFeZkZTm2lv7LBUHhpjxBOeBcK/AdfVoGn5IkZhrGGriuBYhE+zIIlXWZjCHYqUQbXi3rPl7MQCanoCmsnM+Zm4xRT/g0rUDXebSdQ9MI0//SbIaPNx1utw5vg6M2oW6AQEJGnIiwvroiOv9FiHLd86UVcHysJa8j7g+l2pD20gCn9eC195WQ7axegRwly7oA7AXHLVdNLJ02faoxBw4961TAtwneUjUjsJ63ttZdGscJsc4B/hn0d9Bd9Hqxra3YZP/B66+/gbfefCsbazoXNeWrQ9leWSO4x46m7MjiPNPIs3jWA8jPYiUkBA4zPB/7wEhOiXLkQvGI7neSYht6lvX2ZYa81K8skPxm6Z7XiKxqJhV9oHFYdw55C/Gp1rXd+hz1fvfPd86e9/XE0jNN3olWlgtM/zVb4JrxOPEOj5HcAwxbWcPVpFhQkliBZFp4ivpz4UusLO9xf2jMIUrrYPixA9wf7yicZ030GUDBYykCZUqjtQzP9h6NPp7//8/en/3clmR3Ythvxd77nPMN99689+ZQWVmsStbAsTn0zDbULctuGAYkvwgWDPjJ0INfDPjv8Jv9ZsCyDFgwbFgvkluCBEHdrWa3utmUSHZ1s4tksciqzMo57/xNZ9g7YvlhrRWxYu99zvd9NzOrkmTHxf3OHmLHuOZYsaJczrdrQlNGle0vd0+J11YzX+iUBMmTp0+e4tnTZ1Vfb2P/qeirq2iW189/OanTcH8Mi6w0PB+4l/szoTil/oLuU5o0a5UsWC98rZy2XlXp2CmnmQ6692YsTHF+YDOtGyOHkq75r3zmEjtP12pw3ADHUYLoXwC3m1Q39vP8Y38i9ztDXueqqknztS2bK076Hm/qrpRxmqfDcj0a3yrVoR5qmcce0fjd55FGZODqao311SZPaK0P/XTS7aufAaJ9cIWaF1xb4ssOhsdzK3CmqHmpaa6ga5Ljc4B45nYMDPjzuwXX0q0Me0lPzzTGUbxzIEFqCXj+7ExOBfSW2JEBzIxkZeUdIApoO3GH79S4Z7EacmJjJcj12guVZ61C1BOfpZAaeOeMCTmfETIpq1b6KTN3Ggk2OVg3KG+9GMdP8kY+eQ+k3XZ2zAl68hcX4moCsXFhmiGC1fYyNsYLZI87y5NK3uwNk8+OkHzJGf5YTz8eG/ig74pwWRv/LC+JNRGZWmaDXvn/i7/4bXDaYLFY4St0hUQBv/6Lr4MY+P99v8fRg6/h4sMf4fjeJ2C+g9eOfwMNdfjBh9/Fvf/5rwLrAd2//hG+/8fv4fnTsxoOqtNvK6AZ5dP/FcWrf//Jf/0vADD6XY+H/YAFEpYMXHHCVSAMemx8TOIBGnTVXlYJORvhsrEFcgDGouuK8KjCkG1NTWV0i+ElI1eZV4AwDBFdl7JiF/NJnSb0OcUiY5YlrcdgBpD32qz11QZ9P0xOrMrG7xlh3HDIDHt2MEg2bDi8Hc/Kfl4yI3BOJfOR0FXoTimD6ql27fW0oRRRGwrLgkDB/wIuhUZwLMFlM+NRLmfMzm9Wgh9PPwYZtb00W+hg9dzwMF/bY5NY6mcVTjtDX66FFVa8cRBwBuCC3gZXBqsm5DMIX3njVaQ0oG1b3EEPJsI33zgFMfA7n0Z0x69ge/YUi9U5gCVOurcRqMGjF+/j6OfeAvqI8NFTPHr0DFdXG6lJPTBye5ircbP22aJJgYexP0y5/tff+wAAI8WI45TyUSg7TujVKOHpbYkbUk8Yu7G3LeCZ1CBDIaqp8/MJuIOqCmmS+puCp8nPWNkOw6j+FJLr8YCBbIGFxE2qYoP60XFINZEJifIpdeRxPDOvw4LZDcS2qi3SDZppyDXf1oTB4b/H/fKyoDSNs+XMIh+lWdIEuCHXCRxJKtPkeGpVCNzzmgxgnLHAPaPy2NN2VIZ3Ro6xWLdDnzm6UixDrtpRnL67d04AjgihwVJj7bx6ZwkAeO8ioemOMGyu0HQiAy2bBwAIl5sX6F67B8QEOrvCxYV4zc71juc7X2EhV0+n1x99/ByA4NMiMQJkS3xkINKIvtmnhLrvfggYzsA9GsppU4u8Zn3C+L1953hzxUQmX4w7ng2/1nebwhhTXhAsnZh8PiEVAGccMrm+HufbYPJN0gEEH78agfDsl/toWI3eGAszBXWMiOrz67Rj17QZdN6Xs9ztmeLqlyaX05czxUylvyId+reTUvTBctnBvN47hcyTpaiYz7eM0HaIfY/QCA63dAQQYddv0JyuRM7d9MpzpiPiad18mpcfaXR1dr7RAhkNkMPOjo/Mu3Wim472XtYwyjPF35s2bg5MhC+9XEEVThsq7Cni5TH++gI/N2ricXkWpD5vuvXyaY5XzGa6AelhFPZ8mD7Itc9vqWlCCZ+kz7pGZKNNNBk8iawBIEAPOuIEWsiBY4hJF8oOtfSzpbwNF/O07cuSvoj23M5jT4mqeGIhK32ZMRLw4tk5YmIcn3T2RU62NpxlYreUQ0QSGD8Qmqb8NxV3TJxdi2abOebStUFPn83du4diSKsPOPDGABFiXAyBypAGZC8NAlKuZirsEMnxz6EsUct3hlVs+lAlNVWiX26bNUGlwMIczPumeJKUU1JrI1w2PCWJOYiQkJLGIMpbLlMORl6UfxeMnosgWurVCD4W19BejgjS48fP8ODBK9hc7fDGUYuhbfHr334DAPAPniTc/cp3sHv8Ee69DhACvv3w19A0S3zU/wgP/+5fQXx0heX7n2K56Ea8dp9UtC/R6NeuRSD+e//VP0O3aNCEgP/josP/Y+hxP/Z4nhLQdIhxJ2NmsfH0ZFciieFDegy8zcVY0QfKNmk7LTNve7bhU6XbTriSz0lpZyqemOqJ4CVzEeqdwJYS5JAHZ+gxCFJYNOV5o3FSukUzGkae4pcOm+GJGPSKd5bfer9vNX//TNH0jqbPx22YL2emFq/J+EdOwvbCNld1FCHTmtToduC5osu3zv8lv3MSllc6HNe3S2/Uq7fAupX9TB8sDwE0ogPeuJcNV5z7XMmbnvaZwQ9Gc1AMBWz9ZJxfrHFyfIS+j7jTBaQQ8M1X74AA/P4VY3XnNcTLM6zuAMQBr518FRRanMUnOPm5ryFd7NA+v8CzpnFww6NfT6ORBy7PmZ92d+3x4ve+946e8kr4nzYNfitGHKWINQMIlA/0MWM9OTrtvWPzfT6MyY2fjSEZzytzVI2vlm/wxIBbYAGYU1aKxHjpYEPTeJtTVvYz3yoLWUMvJwE37XSRzUt8sxilCxlG0vxixV4ptHTzcKLqZz7D3pe1VHIoT71TxOPPAWHRz5G9HFWTH+WCKD8fp8kb9j9u3rS8MexU9TJqjz1mPUDDFt4IthV3guP2TSHWua6CL6POMrDd9lh0HWJMWDYEJsLDUzHsfbQD2tUp0m6Lbim9PVncBVGDLV9h8do98C4irLdu+6Org8ctHI+cwxvrzxjn9ef9j5/mhaZvB+AdZnTM6LU+Ho01TWvJ7eCxzLk31Z4wE8ONz+kWDzyPJnjI9CWX33mF3uiNyAoA5s8ZQ6ln9rnxM6rvD3/1E0g3rHr2jOUKx/RqioJTgy3zDHl72TGoKxy3sjbS7vl8j4de3aoC31RF4p/mzBXTdCF0GBLaJiAlRqv6+8lSlPqzCDTtAmkY0DRS5qKVuKQD78SwNyRQP0zx5kAX62w1rk/ldkkvztfKixgPGXgKWah7mZhbM6R97/u5bth7T0Mqjn3DvlvW67K/7BZjAFO59c9ymsHlQ+mG9vqfaroOFm9VyN7CWPVZGPsAAHTKNyhp3LHIWV8MemZCSgzqJC4yxZtAa92s26adOzhjBTksowSs+XKkL6ottz88A0UgtK1l5LSUy8uNeCPMKQLwMYyovtYi7BCOLMweks/hmDJbG7j8ulEbexJ5oUZeOI8NR4mrFX0CzDDjCx33Q6qg0vaJwDM2KppHRdbiykJ4xaDMPFhGRDz6eJS3CIBFWZMMWQG0f1kh1OtknndqEAIQuGzhFO89iWfXmHEv/5eykq8rGw81Pp/31jPNIWtP8vv06XPcPV2g66/w1a88wePtXfzf/+kxCIyfe/7HOL08w4fnT3Dxxw+ABjh//e+BEHBy9THe+of/T7x43uKHH8qWnRzbL5dv//ex29G9RUMfjfvmYo0nZxcAErjv8R0mXBLhw37AJ1w8eGysCbrdiAhEQeMRFoNJjBFt24qXqs4HzP6ph0lAvYNSMq9Z74mVNIBJMUSnJDGNCEA/RI1TVDxGrT2ATIu45zf1ONhcAhKvSh/udkP2Rsj5CFnrE7ifYm7Gc/vI5dnLPF22GTtwVX1BVZ6vn6rcoxJmGjvOOXnm8HyUp8LzqirFV6UTuTVO8bRTJT25GjfVBMBavi8InxXzbERHxksoXMq9bs9jqmAyG6sSg8PY4KdVGazCw2MxCkLvyQwFeawIV1drLJctmrjD1+5c4umwwt//0QIA4431IxxtN3ixvcLu02MgAJs7/xqEgGV/htd/8Du4WAd8cjZoUxwNzEqvOzDFq2heqDNdwDxevWcZgM16wNVmK+9SwkMGdgScxYRz5mpobc7NUGpetnladHzMi61Sth2dYDLcdyK+a7SXveyVbbu3bYTSp9IeH2PGDpkwHlCBTekGAGDIB9ugTlPtdQLrGdeNdNI071iOPBjT+0Bd04dzEckOfL0n6zxe2+2+j3j6xg/uiGRmBN5THE8uCu7XdKGmBZ7vZ/hSo3MWl+w7pQGCu47o8HgDq1yzQWtW8utdDT7tdr0sXHLEK90OV6nD9580IAB3+gt0Q49Phy2Giw4gYFh+DIDQDhvcefwBtjvC+WZw9U+TGLFp9J5Hd64bFR0n9H3EbtA6EuMEsmVnw4z5vRS+8plm6ZBlo75ZQYwuucXbWtc2OjZvGDTaUtHdfU2oAGfPuOmblCYo+lNM1tbrWzNDhupiXrJDB8u9Jnkc3C9vXFtCdTs/e6NsxvdHlKkYAAs39LVMPJ1dcyf1zohVlj3GKIZxTlgFRs+ETy9FD1zGHZqU0KeItJMF7dReAABCilhenGOIwHYo2/YPtmM2eSygmrFoipGzERsMyJGGguu1L/Bt0/75va7ts+8ntH6eZ96k/LlvXzZ9OejD55hmZJIvU6pl+/GbAzDHAE1c80bfMDA2/I/lsfmyi6zSkcSovBqE5jScQEkXw5LyuFYPUwKj6QdwAuIeA/M+WJ5y9v35gHJgoiWzRpWgP3++06099tiMPImdUMJoAsApYbdLWB0tqlgglIXWOSWe8t+yq4PBemLqRPDXUucEmSLJzExdLVcV2x/NwLzLq6ZM0BhBKl5Y+lAEx2LcsOe5H/lD3xeuZPvaR5HLKrlDqqwEmhDh2ivD4ZQ2LmqcV77r/8gCY2LOgXYl5p5uOVMjHqekXmARsWkQU0JMESnKiaRRt+ya0U/akESC9P/D6IREMNbrNVaLgDebK/z6W5/i+8/u4Def/BIIwP+2+QHuUMRvDYyTqzdBJxG//r/4AxABd975EP+zt/4l3ks/g99+9CsaP1AONxDUriHpMGv0AOK1AXn+3/8338XV1RpXlxt8o9/hX0YGmjs45oRPdCwYyDH2rIZsJtQVTxAhqoGtHwYkboDWK0q6kqp57GTgymgAI2SAnLhc4LHfyeEiw+Dckks3slImc64nbhqWeZgABKZJ7oeY0LXNVAB2D/YZ6qpvFJ6Nx+xTUuYEnbkslejqjfCl5mtK8VnKNqO9iWox+lYre475mEEj69N6U3mZwOXBlOlz/rA26hkdK/Na8loeG3WDy0IDioE+BY0Fyba1HtOFASu3uq8JLutk932PtiXcox1+5d4F/nC9xB9cfgUExt+ixzilhD+JjMXuLmiR8NbPfwIi4PTpOf7te5/iHb6L/+zitYqGBSIkOD9vUrpTD1WFO3mgESbK7fe+9z52ux79rsf9OOCDBCCs0DHjnBOSEuaiZEsfvfeKzZ+NZ4oRxEHWDAIhx1e076ExhiaSPLtLx7CoGPSSBQ4COaXSaL6VMY0wkutnVPyJE4OaALot7ri8YgBy39FoLlzd9v66KswoPukEjR/csMCcb3w1fXBQhXMyTP35SKDWgnwLr+0zfE8K3y84PK67SAPZaKwTnHcjsBqz1bgnAGD4ZCcLkrtHfgb9NnulmkG77jhiimgSsKKIN1c7fNK3+Hh3FwTgG3SJJTEeM9D2K6Bh3Hv9HACwvNrh26sXeMIdHu9WWfw5PEKjgTTkm2tbHnjg449fSHy9IeKYGS+YAWrRMLDxdHpuQsatyMxMqFAAafhCV4biWdKYgpS/s3c8MlRKRYbfxVgj76bDwtcN1rTRN2Re01bNlXXzqg80alTrtB0528uSp5l0Hcu/TSoLRwdHzHLf+Kmf/8k0M7JeM53WKdAauNYqzhxwsxscHv0ie/J0xLjXJZzHBudRPHMf0A4tGJfMCKkFArC6I3Fb257xarfBFQgfDvUBbtM2zNPyyRjQ6FfT+flGNgyB0UG8eAA5/DHOlnx9MqyuvR1nMk34+bR903Ln7z8jiP+bBHjx6cubbidqzfMjTMGtut5L+ucLNHoRwFiRxKbcsTiEHCMhABiM7xLQdrJqxBvgmBg7Ai6vZaSmL8xUjnkq4EvY7WL+PKA22v9FwJ1bnopbFDZR7qBzot4JiRFH3lfjtBeA/FYOrUdOEFUFbSQqz5Y+WsH0ZZpyUVuxHQesvsMkyOteYMgCmwlv+v2Is/oeTOUt71lS10iAi7GHIkiXXpVhG50ww6kYYnK9bILhnFGv9rgpnjhm1AvZY8+89kJMcopcExBjI4a+gRDbEog6fxMT0IjBj1KCmO4JIL9FVALl98OAdzd38Z/8/t/EZdyhfe0fAyngjz85wzJc4t6DiIenP8DZZoX3/sHrQAP8aL3FP/rBr+PpR3fAMC80rceVfzPUJoC9tx6q37//D38HF+dX2Gw2+A+aBv+w6RFjwj0GnrIp2kWBzsJTkAmhQOCYEJogeBMjmiYgRpmHEng+oAkBCTKO/TCg0YMnimeQtMmdP5Lhtx9iDkhOpgyP5A+b+ypesRmEuOCdeBGoQpcYoKZ0rh65fRJ/eaGCaYHtYgDJK0J7pmnuccH08mGBfTOuc4V7M469ubCMj5lmWAUjaUDxboq/8yyRR99Xyon9WP029mSGkSpruTcDpMdzf2FWPrYyufRb6VPxBGHJU/0Xwz4n2UaXiJQWUN6Wn5KcCMzZuw/FIKinC5ONFStNYaEPT4cV/ouPvoHLFBFO/wTEhA/ON+h6wtFJwivLx7jsWzz/wSkQgKd9xO88+gqeni9K9yycABXQIRubPBRl5GxRJKAcpJHxSKeZGPjeD97DbrtD3/f49RDwgyAnZq4AXMFOqfazoZBgsEcA9KRrZskvh/0kcGRQ8rFX9RgRpZdVrD7FETGmsONfUmsyT73cB57Fc8CMA9Pnnt5nA+9eqLsmjRVtvzJ8XVE3qWrE7wz//LfskGsfOanxr4wfz70fywlzJU4+tMfjtlpeGjd7mm+uFE8v/F9270cwXzWy2nLLGubDPGsFxmpjHpQuAMWzd+yxVw9Bprsqy13FFt87u48dJ4TlY4AJL7Y9GiJ0C+CoucIuBawfLQECriLjxxcNrrYh98d7vd4qmWzmBlZgQnDl40fPMAwRKSa8RYRHyodalBP0xnLbWDJgFLz3xr1k+FvRF87xjStoMtY3mTvDzTkQm0eY22Cs78tN8ub6J3j+EpUfSmMG6R5WVfCeqnlUzA06mefhliB2sMxMWm45MHuzX1NONaH17I5puqlB4AKbjqJiFuJsfMb1MKFPAR9vWgxgoLkCAGz0cJymAbpmwJCA/kLkxz5FPN822A2lVTwqusztfL+vBzvB8/PLTZZp7wK41LcBnz2gfu2cUREZZJpbMh9s8OeFPp9bmvC+PwfJeCh9Ofv0WWFg1mCXF/KQ5Y58fBYj79zxQrQX33ybEggXqgMgCPIODJDKAiGIjSj1Ul5ixiYB/nymOTz3Ms1kLHIGp+e5v5b6ocSF7lAMe1/Gef4i0i0NeyMDkMaHDgyERg8FSAI4Q0omwjghTL3AsqGriNAiyKhnFQEgKZxCmUCXUdKMtM75sRkEi0AFUp8MZa5Z0DIanL0M58gXZ+FQZGI1ORLdmjB4JkQqQI4NElnAU9dBUzbB7HFOjZ++VN//Irjb2JWtdLWHHnycPdtSmxghQO4VMRM7o15KSCkixoAhBoRGtnuKgS9hiIQmJb1OaJqkq/dB9n2at14iPYfaYChh6Ad8vH0LP3z0K/jand/Bvd94D9QH/PIfnqEF4dm/9Q7+J//rP8T3fvuv4Rd+838PZsb/5cGHeL/5Zfzq5vt4cPQ/YHe+QUxiYCsCypij+l9y/00i9KfpltH9+MkTxBiBFPEmCD++d4xhiIhDxAvY1uOpEFyBqxYZGgn4HyODSON1JTtsg5GCehxpgQmpGA2cwmBqiqUsTyihpRAy/Fj9fjsvC4K4b824p9mdx57R5uTLc33cJ7/6TZEMIPgDi6tGe1y+PrG7qLbteoGkQuva43HcSvuqrt/omSNDyiSF1/C8Aa5C+CJM5xqozjaWAYlnxFnyY1O8O8rYGl3d57FX3iEryor3ZoRz9CCRxIlMQb3OksXdJMTASgtIvPpSkvhqiRDIjIJJ1/JY3VZ1gSgmnA/38OTyTdxbvI+jt58DkfDaJxs0AL75zef4O3/lCb77ozdx7wd/EwzgN49f4J+EN/HV/lMcdz/GEPt8mjfQSC/dHI1FBO8tztAd7Hk6jZoKsX9xeaUex4y7zHi2WgiPSwmbPIZclQ/npVdYiRwmwSieDcY3SMffjO5cNdxzJHaTTqgm0/J4GHf0p4L1VMocG/XGYzRZjJphdLO8j6EGSMc0b4TJN0xOEQXcKHlccpOwr+YJ/rkxr+k1VYah6yjHJMOMTpd50uzYjr2wpgSR/fNRPvaZuMxhZXDWORKxgossYrg/8txLDDRaNuu3llfKoKodlKuXcjbpCJe7uzhqn6N7sAYS4fR8QCDg4YMtvvUzW3zw5BirRz8DAPjtxQY/pLu4G8+xaJ4hqbBeJLu629ZHP4YWwLvypnEfB23pZreDMewlGFddyO3u/aT50n0jvMhQ8R8z/NeQWk07e7p9fSrTN4d5pYT9/G1PuqXWY6hii+VGf266nf7mlVRsG/tG6VBvlevOH+667xsjJ59Tf/J4+YczZVu9e2kWz1xr4W4mCuvJ+eRJkYX1qtI3WOkql3dWiqONeYHXef9CacHALXZxhTas0Rz3AAOLrcjgx8cJr77CeHEV0F7cBQC81/R4QSu0aYsmbBDnTs49MB5wTfTAN527suMFDLQE9ErLGXN+7C+XjC4bqBaQ4yrPdWBV8hzG88+Vrx5szBSu/lykAzTrp9rPvdPqkZ4mb+aezBn5JvLiqAx5z1me9WdiicM+IenSODXSpiYq32+B4yWwHQDaiZnpMjB6DiBOIEqz/OlGkJzp1VhxKsnHkSbUR2X+BLDlp55utxXXBL78vzwPJApLJDkNJUYGSKbde7wQkIWtbATQd4kBpCReaSBwkPhuk0Zck2o/Ka1XqXwiwJ9km7e55vaZAFjXU3aSUYUMDF3lpmtcsWd6IcJw3VYTvMWjRDKoDpOBOetK0uBKwM/CdH7kPHQqYx6ylwu49tKrvHRsdT7Bba3VOHsxIhAhhIAYA1IUw578knjr2XbdGPRZgxAiEAgU1YhrhzoEEznkJNlf/NYdcNji2aMLnD6+h/5qwB/fvYM2JZxtlvjR99/CD394F8e7Z2iYgJiwvLxAe3WBVx/ew9OzNaIGtp8yQv8fZUK9lamCA0IZXVmlaIIEBP049njBSwzrLdbM2MGNu4dwG3giqSXIFjeyiVfPB/PWMW8COwnThASexYmxQOO+UaUN420/4BKvDwymUBldtDI3dCVumKkiOZCqH645HLB6M2eQxwkQh83qG565umliHeKbiE2zzazfZglbW2+KMXRBgAstEzow0/0K9JTR5jErY5HLAeAy7G1+JdP7crIinx84cDd6UGiCh2vS/J4OJCZQSkiBssE5MYE5aExOZIMfpwQOpIsBYuwDyZZ7ynQl5JhwzAlvfWWF0EQ8f7rB6vIIwy7h8SoiMGPdt3jnkzv46PESQ1zLwkxitLstwm6Lk+MVrjY7PanbDbbvK5B/bYAzNlOeBaf3mwhethkGMM5ixIZbpH5AzxZs25dfe1ZKPTVAmLLBfl6M5jsPvDk0soWaopjNgkWGydpbu+Svz7h1uKaX3qhXbIw8zl7jyoy2Wjw0XcgIFybAF3eYy8/kYpk1j6eFXE8932crsZwV/rn2VdVaXTQrDM+322Wb70JdDO+5IXdbTcOo7aMFgIrLGQ/Ic2A0gjLe5MWBLCuMjHuuLL+N1zxla6taHcmLmXHvTgsKCZurAe2uQxoYl10LYkafAp6eL3B22SByL3jOjBAHUIxYLDrshk2RO6mGaZ6Z8fkwClyQcDK8BELClsXzIMWEiLEnT5m1ibI0qmrM02oDX12iL2M0jLOZ89iSa/8NmeUc3DkRY2/Ve8sz2gXh74fw42UTjy4mxd+oPgcl7PD8wLdz4zpD6m6VqjHm+Xd7p3LPRFfjY8RLhZGpUm+4Lg3wtNgOz8nuAr4+N1jjLf1l/oHVKoDA6PuEECVY/k5F/cSMqy2w2RGW7I6rSAnESXetxMy/6wGepep1+6+ZD1/CwJDdByO6+XmlcZnX3e8vw6jTjVF8f2GfBS8FQPRyfwTbP5PJxmY0Rnv5+RfdnGsm2nEhTBrsb0e4n6dw1JGczzIY7UCRjt30AwDaRvLEWHTUmPkRo4/AEGWpva788A6KOVlzNuMNUzbqfRFI/iVNtzw8QwY9MfS/KroQZW8YIoLuzpO4YfJV5RmXGU3WbpAxSsLqAZDwa4Bq/A4Kapj02FiezKV6G0TtpUclQ6Vc2XYtkVnJAb4JyhVLqVpnW4j9JtycmyrwlfxsX7kHMwLFJLbQSOKx4zVM0IfVlIV1J7Tbf4yNeqivtaiUGCkyUiNeeCGUGHoxijfekMy412CI9jzlU1lTjEiB0IQAUNT5jaIl63imKGX+h//hv4/l0c/g//Mffx//wd/+3+C9dz9C+tlnSInx3n/3z3D5e6/iT//kI3wv/SuAGXf/h49xr/1tHJ2s8LN/49fwR3/yEYYYwclHz2BUwGQDm1NA/RKjTCIIHJ+coHt+geMAPNlFfProOQDgQg+r8LNiBJhtfpkz3OWtsTpXsmVPt6FHC7QvW5Gyt5zlVdywbbcepYgc8WQDhVpA8NutAeRVl9L24u2V4ZqgHlmah2vYLpVSVjpsIIqQQoV5TETZW4ovs3NYqvGcrGJME+ncDOhFs5LLWgAuQc2Lglnib7Lrp+sL1TVloRuY2u/YBFQjNJgYCzlnwmionCHGGfcyPhvNYZtbrsbHvvf4byc6F8899cyjMNqezwgWhy8lMf6Zx16ScU1GTMxjLQmc//v/7q/i3iuv4P/1//4Uf/uX/jI++vQMu4dX4MT49E/ewcWPT/D48RneTR8CAFY/PsMq/BjdssXDr7+FTx+fZRrDje+fTiWXfuexHks7cJ7PlKcSi+UCzXqLRU+4TAkXl2uAge0erwJXTdaDyE+yg5lCewvXGM1mVY7H4X1gX33P5dd7ORWc5yqfTzY/WeF3eXJ7/P0IDqFyQW3cK3RuXOXNMH5Mn0oDiGeMeTO4VSeawT9tn5MXZjtaVb6vgj1bet2DinbuSzx3yXVTuMbtAvzTorKHTb5GXrAxGjeWEUqcPf++0P8qHpwT2Qz2mBm/+stfxdHREX7ndy/wzTfewtnFBvGkB5hx/vgpts8WuLzc4imfAcxon23R0jM0bYOT+3dxcbmR8hLLNhHr05iFjLpdw+vMaDPQNLLgGCKwY2C7E6ND5P1KyDgdxMX83mPizPe3RQyus9QGzsM0SsXxmi96WJvwrQPNYBS+qIM9N261lHx99yZset93Y/q0t5D6A+n7mAgcTr7u23gAXlfWTTJX8zumue4qw5qTV0x2BZyzBdc0ynvvyWvNbzU42dPoTd5+DqHvX33jHrquw3vvb/Hwzj1stgPSIgLM2F6uMawb7LYDrjTCXVgnBNqAiLA4Xkp8LHiYcv2ugd0lcvi1P8miJCOwbM+zsBSfl7feF5luBSuHCnlJWK2/N/32z1Hawz9yGss8X1QzDk703Mt6UqdTbLhf0+gSNqzoG/ZtNbuZQdS7CE5POoSmwdnZGkfLDkNkcJBl49hHbHpxfOmhO+YSa9QtQmhI4kiPGnydif2W2mGRQ9xI/EVIL3F4Rq0omsBHkFORIpmHl3hhRSIEDzgOuKbsXQL4E3NWDJmKojX+yn9r4tK41Cy+kgqxpIGiyQmzxU1O4/A5FuGBzjEa27o2fTkSmjOjlDYEKtsXs5CdNRCeCERjD72xl5D7ye3IgHzAsJdmDHkmieftuLbVBursZR57MSE5o16KCanR3+i23saAGDl77aUUMOgW3hDVay8R1I0SiIRPPniBIUbExPiTH/wIi9Vz7PodPnj/E/z43Y9x9xRoQkBoOyQO2PYRTQtQIAy7LdDv0J4eYRgkVl9KCcP6LMf04xTBcZDf1Eu8vxCkLRTyNSjo9lVpWz8wnj+/wHvvfIR3f/g+To4Irz64g9OrKzzb9hp3jHFJHppVeA6OASqcZaUbpvRShtWyLVL+h+SCkzOy8akyqmnMngIHTmCjAq8EAgUxxDeNxOq7vJRTP4+PF7jqC+ywg5ksCJIY9gIszpKDV5UM5Ydr27SDVS6ZXYMdCWaMkaAM6L7kUNEfKuN40rSqQh0K0/aCq6NbFdvMiKi3ZIHpkZVa29Y2bneptmzVKM/HglJ5ydYmuEHlks8LA5VRb1QOMt32146Wue11hQbYQRq2rVYYeEoBiVOJy6WGQDHuMUiNgETqhaqCAQfGk+drxJTQcsC77z/B8dM1Yhzw+Ok5njw5Q9cBgQgUGjAThpgQgoxrigMQI5plB9klK279V+sNFtGd3B3LokNiO5G6/i/4ZjhESAm4XG/x5MkZPnn8HMuOcHq8wqLfYR0TLOrrzk+UoUZNiEWpKiOrmUoOfwBOZcTzpZjwlUodFVg53PBeqtIn6ZjF69xpEKPlwrF9K2sGt8wjy64Nttnq0wdjLC7lGr/19OCmIvE1IpjHFXYCoc2la9iYBpVLzoM472w/Ihw2F/7pzAIc/Lv8aW5V3ahxeTMtmDwZ05PKcM31d5mvF7JVC/jI+Ct0m/JBGiVkh/zPJ1srT8jGPjhaR8hhSgDgaj0ovQCePb/E5WWPlBIur7a4utygaWz3RIDE8tWFYCJdkCNQ24iHsAgx6PtB8B6cFwdk94HFmdSxprK9PdNwJ67t+gGXl1tcXK7RNoRF16KNEX0sdHOYnYPbp5oOzJd4s3pGPMLBrdG0QYMYtU0z/vhAzTS6wgRGgf1wCtRwta+uCeu6JnmqOPaGPvxN/cDP/d4vDuy4GUsrk7pmecDnmDyN29OIysBdTSLld+P26dtyXS04Fk+cnM/xA+OZzOKdJy0kXK13aHrZKbPbDdjtBoRg8oW0JZWislxLDXlypWF0kOUVvxCWPZANNkYTbLfMEu9610dsd4PIvRwQOFUnc37ZDXs1zjuejAlF+OJThq/rzDC3TV8CQ+E+RId7vk/m+RzSTcZzFs393Vhs4Rrv/evqmkfygZd39DpGzi/6ISFoOLYYJab+CAMP0PuSM4dz8rhuDaqy03wJ2mZm6AK/HaLHB8WzP8/p1odnZAJrwp4KYbZFMybJmMzZjtRuA6jRzMBCtxkiy9bQjdsAoN8ziOwAA+gX8yAyBlb465HHQDai2EdcNewgIkiFzgXd3hqyszFAlFVsp40lnmkfOMfts+uyXFqYbjCDTv6YipeWlaMTxflT98zNnZvMLKhXxj7/DSDelFRi7OVTcYMY6poYMcSANkU9HTciRTHktSkgJjlmvnEn5TYpgWMCqeceE+Hpx0/BzHhxfoX/+D/6j3B5dQVm4J//7vfBzGiagECEe6dLgD7EbjdgvRsABlbLhWwL5oTf+e7vY9f3WC0XePrDP8Dp6UmtqGjfs4LgAIdUAAkhyDZECvjNf/rH2PAOb716F3/5O2/gb/zSv4cXzy/w5L/9Xfzj/+a3EELAb/yVb+P8f/w+Tu4cgRDxl77zdbz34RNcbfospASCbCvaDXKwBdcMOqaE4gwk8xvU8hAoIJDE5Avm8UdioFt0bfaUIm172wT5bQOaRv+HgKC/TRNAgfDibI1PH73Aeje3iV3vDYxTgRV5bsbFOeyjonhweWtGL2+kKNxDv8uA59/PNMxVJWhX04j5Le2mZHOFt37Mi8Ff2sVscOHpQeF8DNvqVgyq+xjoZCzsHXP2yGU3FIXx1p2fxqgoBj6/DTcLxyh9LTiOwrVZBHNGTePZwiOY8Y5tYUCMeyEF8eBOLAdo6PvA9bUYAQOYE87PrgAw1tse/+V/9VvY9T2YgXfe+1TwJMg8rJYdADmxsh9EeWjbFqSM+70PPkKMEW3b4L33PsFyuRgpAqjo4Qg6hZZnI27An/7wU+wQ8crpCq8/uIM3X/slrNc7XH7/PfzpH70DIsLbX3sNmx9/gsWyA5Dw5mv38ezFJfohZg8AAtC0DeIgHstlTopQnm/Z2pEhUPA+5I36+b5pmqxUmSJfcF7/k+C1GEZJ6Qdhs9nh4nKTFX+b/syDUPC4hiMP504h0wd+u7EDW8CXa3gxCssxh85jfj6bPB+cGPmm9Yyl8dpJZ6a23OWRSa4MBayavW2tgI1Hj2olZgyX0zJ5MlgG2/7JmO8zCn+TbceUZR5bk7e4WkYns5zk5IVq+y0DeUGXgXIyrjWztH677cEAhiHie3/wrmyzA+PZ83OFJWlD2wYAG3BKOcZW0EOiAMbz5y/ywTPPn5+hbdsat+HrHg0q+fGW/j9+coHIjKNli9PjJe6+/RXs+gG7T5/jyafPQUS4f+8Ew7NLtK0sY909OcLVZodoIUy06KAhOXJcHwcTRktzM7TPfq7LrfJL4/l18/NYkdKK8a/hQT8k7HaFFh1KhVcV2JjgsGfL43fj8tj6eG3Vt0gzuHNdQ2ZK2Nvm/M7hx2wLblCP9d8efB7jMIbrUYPm2lbJHmzzoVTA8SHvWJBpAaiKrTWZdZONUOSkfpDts4kZn3z6PMPeevOsakuwcDvs8IIK3G02uyyrrdcbBN2tIq04PAaGJ76tl1c7JABdE7DoGjx85QQxJcTLLS6v5FTe1bJD3PZqDACWiwbDkMoOAyuxEkv52jnwqW4WTZ+7B/5ZZbwcfWALITeFzc813RL/blpozQc/18Jv2oSb59kr83yGqj+nyZyVH7zyMrZ5QOWDkeRGjidIbG2FfmZcXfV5xq7Wu6q2rDvN0S4gy8QA0Pex8MSxjnNtT+W7QePvil8OYbloREcZEgb1DGw0RJDx5SaUXURz8tcXnWjvzTRNVL1r0i099lAJe0WA0VNTzVDCbOG8RPAZleG3p5qRzVb1PREnpmz0saeljCI6sStvDNHeQXvOH8axNCXc4mFSD6ROdTYIjkDVXkPkAtb+gFBWr/WltYNh3LYIg6VNY4Mn4F3fS708A4HWdq/M6lxVQnoxcJUtmeUe+s5XkbfjcfGKEY89OxW3bL2Va9266z1n3MEbwYx6iYCUcPV8jWfv/Cl+5auneHFxha4NWB912PYRu2HAro+4XG8xJEZ7dB8UCG0LfPj+IwyJ5cAWIpyfr6SOpsH9+0s0TYNd3+fZF2bIuoWVy8mW1bwWRYCpwa//8tdwue7R9wnPn1zho0/fw7PHz/DGnSU+WhIerJb4O7/xl/Cf/qsf4O/+O38VG97g13/h2zi/2GBIapAMhLZp0LYNNpsd1rstdrse280O212Pvo/iyXC1xXq7Q99HJE5oQsCuHwT3RuNvBifKSj1lw13QOkMIORZiCKTPQzYCnJ4e4603H+DdD87w7kebAknOKGQbhwucKM6xVw4LnI634BkoW7nZWykbqws+I9dX40YF62PO5Q3hGR+L5560kfSalVlRVWb2YvHbbbVs+ZTLdVbWOEtc0h1jfPNNLhXVAecN1114C3nqtxi6cTxk5PMee7lmE6TzWOfHiuNK31Sx9zRCvPU4e/AlSuBESKFsx+UEPQ2X88m45uUnB3GULeabyx4XT5/gzXtLbLY7hEDo+wYxiafvkMQrJyZG6ASmmgC8uLjIRkWAsN1uNRxEwPFxK167Me4dh2yU8JNRSdyEr775CnZ9RIyMq8sdzi4ucXWxxt1li7MWOO5afOsbX8F3P3yEn/vO1zDwgK++/iq2u14WowwHFe+GQU74HmLE0NvhQkmMCGrcj8o7iUhP8VYsYDee1lQq3oZmuMvXXtEPJZ89Xy473Lt7gm0/4sg8vjGcdEIOF/7HBeT1i2l82dxeA7V8aAxVeaZpxM8niSo8gLat2jrrC88oPvIVdJ4pdKgMJ/SarONpzxxpci313Rq9cIWPPpqUuU/YNbiea8CMXFBmk6o+wck8RpOysW8iM1CWKTKlzjSjpsHDLmJ7dYl7qxZ9jCACYiRZ3ExyCrvFwA1NB5vb9XaX4R+QEC/GKxZdAxBlxaDAp4OYSiyym0pdxr07R7J1iBm7XcRme4V+12PZBmwCoQsBr96/gw9eXOG1V+8gcsK9OyfaFoyMapTlmryDQXm07V6whe8ydezokrTTjAbW0nyQj6sr32uXfCgZMwK0bYOjZYduVYv38/jk39dG7DGLnYP7ffrIbRSRsSJFk4u5lkqGW8XzO5C39Mcm4aaFXlfeZy9kzqC1hyLUj8aEDJmMz2RSKS/Ta7j4epWglQvwPCv2Oyy7kD1uk5zT5vRFK8nkRaCPqYITpiLrhKbUkd8Xwjc7Ftbcwj0Iq2WbF+/jkOSAu5jQEjCQHKZ4tGpxtutxtOrAYCwXbcHVzG4Evyrnh1QWP5LTff2wMZf2XocTY4N/rccij3t+HICmIbRTx9yD6TYoc6PCPmvai5MZyz+HSr6ANEu0XrKYG43jNFM9lyMCTdOMNcZPSUQlt43yy+J9QnB46r/fR8NVZSxN8eHOrLnXIcfe1/Kicd6+iQGOKlOjhCdrAqFPLLSF5T4cmLiym6bUn20pL9VWTTeElUpioduxpFsZ9qxwE+7L9hwRYEw4SRJNPR96auhZLW/nmHVcAMgPGCNvcTOKOIbTGRFY8jnebOWOiaQzr2H8pronZC+/oHXlGCLeAOjcj7z1uWyjNe++QrCKO7nrQynG5fSm0AMpz00Zl2xgmAjoY8ZrzGp074qXFfOUD9HIwfOZnWFXtt0OMaKNESkSYjJDFJdtvEG2xzUcZDtsirh6fo4PfvwxHpwu8ODOqkghTgiOSb378iEdET977y2kGLEbIvq+Rx8Z/RDFANgt8fjjJzg5PS6GrkYPrQhWNrIgXjxpvGAhnnDHRwvsmoTNRrYG/+Ef/ghf2/V4cLLAizTgn/6T38X9VcC/+O3voifCn3zvx+j7AaCApm3Qdi26rsNytUDXioLSWpvU2JMSow3AqmvQgDEMjCHKQQJ2GElS78e8Wudg0CsbovgH9XyAeuip0U+9f0IQ754mhMnWnQxzikQC73oyocN/AGVlh0vczawe6Hh6ZpBpBxcYR3V1A07H9Y0JjGWVabrilNuh+FuVMIlHg3xvylzGKSoCah4flO24dY/GClKhXlUbMtFi910Z48JcfFHjPhQh0vop15znDYbz+jLH7VQ6R5qxeOfUtAOM7KEnBryElJos7Kag3npmCGQ1DLLAz2a9xfNnZzhetDhedpkHgB1t1JUhdnTlweoemJO6/UdEZqEtzAhhicvzKywWC1AoOECZYUH7P1aoDW7leGYioGsbBBIa8uL5BT755AnuxYjjRYsNJ/zoh+/hqCO8/+4HSCA8+viZ8D816DWhQWgatF2jsURRGd5sngJJAGJZGNODaNhvLZQFsvGc5lV8nTeP8/l/EA9fIoDU86m827NFj1EcuuzeP3ewMuWrjrdX4Oq844Fs4Ducpvy+epfLGhvyHCz7507WLeVm5B7lHZk39HvHUUH+gU8zxc2+ymXObEzm8Rc8fQVUNCT3Xa+LgcuAftxF46tWlV+gQd6KSzoWVkT22oP+ZjpCbnGgjFvfD1hfbdC1AV07Xt61Jpa2SrmM426V8VQ84Qq9IgrYbXo0bVMZu+DmnZ3c42lpNQpEqgyIYWKz3uL8/ApHKaFrCD0Snjx5jq4Bnj8V78LL87Uo/Fqv8dcmqCwBZEObd0AIRsPJxtDmphg8cjsLeOfxlO5RuR4Z88YGQChNWI6gy+P1nNLnACR/Mc46h+O30Ddm03h+2F0Yqdv3pTTXMdybVHYgX0GJayu/USoyxG0/tLbMQm9VfvULN5WjuuXecqiR3n2VDzUEkA8EnAEUg1dbxI0xod8NaAPAIYzyz1xqYxehKbjq+TMEluMQhXcBWd6SxvnxKRXUcnupNATAFhb7fsB2F9Ex0EC8/q+utggANhvxLt5uh6pkkyGCg4e9c0ooeqxD6Mks+vlyMkH1OoOgG3OgLJTnjz8rBv6UU82YZ15PAll8KdNtaSFPLr64egvmUrl3+lCRo8bPOAuFnDgb7kt9+2mvwXBDlHkKjzOQHcA4M8OOPN1kiMgpXKYXGHYwkHeqWGy/iUf7LdjIbDrQyHqM92Q6ID/eNt3aY0+EGq2Wy6THGMG6VUIem7A4Eqy8tc3kS5aCDfjKILgtHqOOzg0++TyEUj/Bec5RTSDz0pQ+1++kqYUoF++fUqbXgqpnNk5QYk1lVXuceALp0w4ROFud5/tL7t4U9lLmxHjH7lkqz2w7yT7Dnmy9M+9MF2PPjEx2zaLsS1y9Fkn334t3j69PtuNK4MGAkztL/OzPvYU/+cP3cbm+QkqMrmvQNSTW9SzEEmIiJA5oGsJx04ECuZM2jUmKIfH3/vkfom2CCNmNbGdtW/GcC02DxaJF0wZ0iw5N26DrWrRdi6ZtZftq26FpOjAa8MBoUkSDhDtHHXZph3snS7wyRFw+e4E7R0uc94MYMK6ulMDofJH4YsrWHpmvss1WEIsZiG6+zABJgBjgiNAAaIhBTcGdBN0eSwRmiWdGgMYWFNwaBoGTXaUYSL2BgMvLGfhLNWSmpHif8VZxx3vsmQDpvfW80GLKhwdTjcE1gwWTZHBfCXooxgZ/QrUY91ALR5Y/z0tu2QHj3khQ9kYwzW4H7Zi1r8IdT50cE6vw3/B9pDhNyMKMIOjv87yYEmllOOWZc0YT8Gs6VdOMOc8dKcsbxFMiNeqhitGJqixgsWzw6uv38OiTF9j2A5hZtoZnHNfBYBPIxUjVqLeOGf3g+sHMeP+dT/JqImlZ5plKujWddAt6aGwreiNxvkg9XKkBQxYsAicQGMuuQeKI1aLFUUrYXW2wbBtsU0LPjGHXC90sYANAt8jmOSsKv4mpotSzoVNWIDJdgBkFCu/iDA7GmCwOUoFXBoAI5GN8DN/0tmkJ3bGHJ67jcwLZm3lW2shCWumfgdl0i1xtvjpo17uBYFNlMRwcfZS9A+3NCMeqttKol3P9Gw3DrEEu1zPF85km5DLHfZz7YjIO1xn1FAYUu92zoozbYqUVlJV6xoE4e1pXfo4sP0l3KNcNyPba09MVzs83iDGBwbVnqeuwkCcZ76YJhTZWMpjQkefPziu8Mv6ZeZnbml68V4UOSF4N1mn0XeXBthE+2rWELgGx79EGwsCMCOTTOm2cosMrz4sctLt5ckYTR/9rJQmVjDii7sWQztMyS3UFH4ZlxCRNgWlyVwz2SlNQt7F0ZFrCLF58hlS3Z18ehb4J7Zkpq7o4XK/hxXxyeH6oUiDT92uzsr/cU+818zehVSpPmHfd+Ln/wLezOCRAYdqZV0ZCUQiExbLDdttnGbzyKvXNczTSjOO1HFNyr6/K9r5ivJY82aBNnrcK/mdbP5FrZ6khqFwYSDz2LCaXyUU8HlIhRCoKG22fTiOPLxz7neSlKsveVOQ0f++/5Xx43m3SDVDgJ58ONGoPJfryJHY/oyZeCye3qKD6dq8QQjfNUtHXffntOamH6BC5bvsMLHs4FVyhvXRwGDjLXOP6vS502KOV9g6nUWqrf++wc/XzuaYbQe11Fd+iYbc27IVMTFEIHTuFH17xI5iJIexpVd7k57awEQB/OuU45cnJJtrMjmDoXzgzMpfyXikqwWYDREEcwsTCYMbAvEVFAckCgpcGVeCV8pNDEEVWK2rQHme2knxBJW8BXpuDMsLsuBW7fHP/kX8lT4Iw8hCCCvQaNN9vxXX/x+WJt17xiIvMaBI7jxQ5HIJDwtG9Y/y1v/sbePeH/wXOnl3it777x+i6BnePWqzagDt3j3C8anGy6LInWkMEQGLMDQPU+0yFfNatqokQBzmlOW2L+783Iu/6QQWNwsgtzpcoAhanTg0B6zXevLdAcxrQxGO0/YB+SFgMKzTbHleDBOddb3vEhBy8H0D2QpCtQAmJCZGL4GTePNlIlsRDCpGAhnJ8Pei0siOaxLJtUYwagqtmHNfwlxLrMIi3gsW9zIKY/g2k8QcIWjllmPDKmzWCTdIpCJwlQ8EVB7GKm+Nn+4xf48Sj31oAz+JO5a0HL9hmbmbN90Kj0hBXJms5ZLhkY1VxKjKSMmExjirl8crt9wJiTYpyGq8Oe2PdiN2PjHvlWfEUKYVkZueUZ5sCgamCo+R+g9IJ/78YAp0RsKq7tGF5tMC3fu5tPH38PWyvtnjng0domoBlF9A1hOWyw6JrsGhEAbfYkgzxcI12slZwtFLnNGkHeKjHxVKMSZXpshWcUHAAIN3WrnEs+x3uHjUIyyVC6tBEixXaIgwJO10MsXiZycGZ9TdV44ECDcy5LXnyrStOQanpe4GXYAYSr/FbPqiBXxfSDP6krmLFs4Ws7H2eUb1wIzOOmxHNK4VyXQA0c+MKHbl6nx9XieamazZL9e0e495MI2YaUEqrynXfV8Y9V5Rvas2Rp3g+14RZrz/aU275M5P2PN8jO9likd+CrGKRCsuKv5mOuR4putsDdv8nHnsA2q7Bw9cf4PLyY2z7AU9eXEk4iiA8qm3Fe7xVa77godFiyrBFNjYZzd0iaUpTmACKZ5011PMaV0f26o0Rqy6AWjFqkm6zC3odFZejbb/L3LrgpY3NGNoNXwp8FTwljOFH/lq4AW8AHWMZV3cocEImC8zAQCYkZUxLva68EQ5NA9nU41r3eFrmZ05zkzzK4OGU6gGrigFwrbFw8sG4KROD3z78pOlbnq96zMvnMvi3e1D84FCxwgiNhYqZbwsIcD3/RgOV1zUN4eT0CLvdIKE2dhFmUxM+rfLoWDFHWWhwxe5pd9XzPc9dOb4uRSAiwYk2L5SjElnNkFnh88z42MV17MrTyfFw5zF2nb8OHG8KsgeaMsXzlyzvC0sHGlUokU9fuh5MAGPi0HMt4LxclTehz7cqxPMAFj7ZdQ1iHMSRJC+wl89m4ZynszZbN6ZD4+0Y8wPnqVVdv+e58/sFrhGt/gynWxr2igiS2ZVCbYzJBUZljcHEeUB59L3c0mgrnq9mpIi7d2WuubRj9LcIdZS5uMpKWqcpM1QouhnwsjqjgqYKkiLwkApEIxaYvV2KtGRGQjoI2LYPnHNd7pVTZtKM1x6PsxYFfob5meFOGLXzzpvz1GPnQWXCPks8rURmyKPaqJemXnmct+yacU+31qmBkFMSX/nEoKAVxog37x/ja6/dwS9862cQU5I4VUNE3+9wuUvoY48YB8QhYhcjAgE//uQSiybgeNGgawOWbUDXtmiC3C86u24mQeZTYjTESCR9krg5xV2XSA50yXABMSBGdEC3wPES2UvgxAxzIejJfkCKEQxg0NP7UozYDgl9L1sKN9sBu5g0PsCAGIFdTNj2CUwBQz+AwWibBl0AdkMJMj6kojyEALQksB1EUxKjokrxcrpxgRqGCV0AJQbQZeg3pcPDXXHFdiutCvswhVE1A/N+M8OAeRwVBcfDdw2zY+MXdMznvWWoLitfj2J/6SBl/adqiL02GlVvxTXvwyzoOiOHNKFs0ZvjE9Uzcj+j/Ll218k5+8Dcin7Z1qY5WPJlYdX+VUam3L1CFy0vw+Ex66LGiE5Y2dmo52jQDE3JxisCwAl3jxd45XSJ11+97w5gSogpYhfl1z8PBDw736EJpJ68YgwUQ5x4/clpz+LNG1B7pcpBHqV+82bN9JxG40i6qh8aoGnRtcBClZYFI+NYprHqseDp6RBTDrjfK96y5k0six9DFJhKUQwVTSA0ZPQCxUCoMBCIEMhw1LCnxBQxY6vBk+EF6Rxel4qhZwQfE+MesoKXv/WwWt1gkgf59Q2kqzkaoi8mhocimozKHjek0LA52jQpe6Y/vvR9vRjbAWY9NubyzMyVl3/yveK6/bK95/Ku9q80uUbwkVx9ZDdcZAZ2eY12MJc6/IFkeYiyrMZYdi2Olg3uHK8yvskCV1L+VRYFk7bhahMRyBaqnAceGfwXw1wxgBX6VeSu0qikgzWSRHUuSPoYAF1TED5vPMfLdm7sbbGrHCpUtgLZPBn+RmWmrLKEtd3iaLIr2yCScvvytGWYtrn3yEqeab5k8nzUnuw17ln6bFUebsutv5E5OWjAukHB+/B5n810/wDtk9yR4fW6hvDoPmNXATPMjZbR6qnXXsnrDZVlbOZpLbIcRlX5YPF6bQJh2YWKLgn8O7zJ/eEsxyorBcF73Y3u8x8nK02HyhAdoCK3W7LFLAQgMLLWb/G25miDNzBksup+5975Wj3lnZY5TXNt+CLSy+DWTzvVuLNHwPgypc88kWPBYH8u2ntleDpPx+t8zrN/T4vMcO/j7VUNK6JHfnCIVdC+F9iP577ULOfyzHcjWWtSzh5ZbtSdmbr3vPgSpFsa9lTIA5xwL1TdlK6s2BHy9lcVNzDHzuQySyswnzcp38cUyFrEtITRTBUBkwr3zgIrnPHNgYwJtRhvHnaA491ptGgTvapvzBg4aqkfAQ+sBQEOQIllPkC/bOx9u+2FCYx5G67zmPMxnWovHTeeWr4FxBdl2zz3XFkaRDobCzGtO6nHXlH8EwhB7+WQjQDGsgu4d7zEoHn7ISLxUVY6B/WeiSwGxNfuJwwpSoBci/U3DNgMAy7XPdLlWreMJwnqr4SpsS2uQU7OakKDRSvxsYhCjocHmJeQrKgPGopjyMISZYonwkgCUaMz0Yjhr9UVTBBWpMJEJcTI3EmX5USfvNUZQIxyAvCuH7DTQMBMwGYnAfnt5FCOESGQGhQklgkoYDckDEk8BAmQk40BIKAKbD+FRsHzxAkNFbWiOvkZxbMHxkSycQ+KXwWfyVXi7W9TwLafKZZkzyG4Okvp8Ma93AatfyxY1Xb1fdvySXHfLQ7AthWXsAHWztk+jYQ+gZci8I5JpWd6GbsnpKIUaoJ04a6c/2eB2insUFqdSWxuS70FN+MrigJbvbevcxwu3zTWklnnRON1QLberboGiRsAjBiVauj3RWGWMk+OUzYK5N+Y0KeEXR+Rdpxj82VPNaqNAa165Nkp21A6YESWTMtIjMHaUSmJhudAjq8IAGhgdMK2FXdlmqsZNa5hRg7v6W6GjqhGwaSeSUNkDcav46lG+6IoCYxaCICkqOkNB/vwbCoEzmUqL3yesdKYp93KPFSxy1uP0P76q+IUvmY99wDw6CHpw4rmuHKrNgOwrab2pnpnmasPZ5o97pspuDR6PvpoViLg0aXifM5f0ReunhkNKUZa/c7IuMESGZ7WZtEi/ZU6SnkFsq2LRY5jNETo2qacqJ4YjBLvMcsm2qZF52USeW7hQuzAGe+xk7mwtqMcKuO26kKVES9Tjoa1NpSgQoqC54b7BCMbYVIiKjnR+HqeL+0zuKZxQIlLzL4xXmy1sWf1xBeqkenA/i2kt0wjRPO89XDeGZj/rM24Bsdu0qbxKyiMfuYGjsuzVBOp/d9dU/A4z0FDoOLzIXlKYMSVxjRP71GZ8ADTz2bHVWhrID2B0rfd6oQLJ6FdaANXzzwtE6PcKB4MkFU2vUTx9K3pwFz8owl+j4ZyMrJkY+VkAzgdbgzzRt9cYZ62VLKNZfffuH6N2/wXIt0Cz1+GJPzZSjxz5R5c1/Fr6N9NtzePc1U4lhtXCzMjNQaVCXAfzs3A+RhN53jsHHqMv7spCmX+dkg2sxL31D3G+7nX4/G5VSOvSbcy7M25PTMYTdB9166pto0zoGxfNQ6at1IYVfTSyhgUyBi6ilOVkKGFWOBn3wIyxWnkSWfeRf4DLT7DJo9aY5YJ/53PmJtsN8YMtY1VMLFSbvUZamAvTXNjCuStk7nNHoFG8+PNjbXRrr4fG/TKfRmkzGx5dCLueCuueaSxnu6o3mTMANK4HWYwkLYzMx7/+BM0XYOuW+DO8QrLoxXamOQwDj3+ibMBUbf3qrQsB0oAnJKckAvWbbA+fiDU8CgGQTH+RT30Y8DFEJHSgBi30rYUAYghglT5bzVuVxsatLo9t21btEEOyWiINGB90Ph4BEGPgAEEiqJwWzxKUQrEW4DUwChgF9C2xTBmiop5HhV/VYndxylmg06MEQSJMcjMiCmqkTBiGGR8tjs5bThqfESmCJzP43lGA/P207EkZzgv+KoEz23xs60xBa79WotDkREFHaPpOBXjguGIeRSxq1MKL1sklcnQuCxnqMtla7+0EUURpqxIAcWosJdhzfQtP2YvpvuFkNE3h7hfrtsG0hveHA5mA52Mi9EU1n6a56WWoHAxirGXGAjF2Ofr98JyMVQpLTGix4wXL84RGkLTdFgtWrRdpwsMCcE8d43eOLol/QqqILMzcJmQ7PpWnVpXQgWklLDlBI4JaTdYRbkTYyOgP+W20YMo7NeM+CYOGL9IzuOpGLxsTgXCcsB9EOQ8C6+cUPay8HguFylDuxlHWQ1/nBc6SvzDPiY9fVTGJVJAHX3LTRqkz3KYSIEDcoaw0qJxDErzbHJ9daXfRjEfQ3j1ia909Hxi3BsXlmmQ/+vKBbDfwDe5mZY/sQa5DEZ/5oqzy1l8n+8Q13/yOy8L1H9HHsz6qhj1UMKLmPeteelm5KVRnY6oZ3mktGez3goONUEPitL4eYmBxvfDLwhIA4P1gd0igd3n60Lf5VtnCGTGwAyoTFI4ROmK4RkBlfdf5Q0ozDeDtdF5G7fJFNhw6Lfi/cAgPZynWgccccMabziX6w18Nl7mwZvlKSfjALLl8LOkfXzXm3pmP6q7UT0ep+v4+w2KvuabA2115d54a+6N6izppYyR+wqzRzx/PSEmVOczflIWEuvRr2g56jGZ9Ck/l/GVQ+IUdywUjubwsp8VluUvRhXrcxJjkh3fy/fWp0I/PB2wn4raOrJnuA4g475dz05UrpPHj92vh7JStq8Xo/eTqXXtrnAfyDid+5rfMWjfPsMbps8R9H9q6SZ4/ucyjfEcHroOjMcU/a8pX5DTHzhRecLVYkD+ti7atWws/MzUu+/tHN4cSpPuXQMmBxdNnJxv8DZb3Iz6Vtfh8PxAnqrIW4D3rT32siChFFRiKBDErUGaYqeRBDii5ITHRCTb/irIMG8flC0UeRsrygjpTU0ay+ESltfK8HHUQO7Ocy6nfJkHS1bkvdQrO7c0xpkzTHjDpQ0D+QaVDjizwVRGHqfKhYgVt4pwTtbeqoa5GQMqBT8lp/Tv/w9VKs3YAfeNGfd4zrindSUrw5Rv1EY9cDEyEgOICf/t//e/xLOriBAavPngBNuLF1guWjQUEAMhQQLeh0CqvCZ3kq9uT2XxdGE1/sUU80CLITCosYsBdHrgh23JEy85BjTgN4oXIou3DKeEXYzYsRgGOA1IvAbUmChBeXU7nRoD2sY8/1p0TUAIDZaLDgmi7ESQCkaEmIKLRyK/KTFI4xMZYDX+JD6FSROquGkBUDaIZGJEOgYxFU9NyBjG5hLf//R8BJMFoAMR6vPCHPxmHKaiKI499qzlnuDnQPVWhINi8vBbAHxMWCs9fyx0Fi5SGVnGeJc9/7gY5Muag/Pysbzs8R8u7pPbClYTgL14boJt7oI1zpGW61ORAgXPjE7bO8NBV77dEJAP/MgLINYW8x4j3cbq6IPjB1UjTeHM7fAtELrwh//ie1jvZD7uHi0w7DZoG/F5SSSGatIYlzBakusqRj6L7WqwXHiQo1UMEJrqgAuGxP40/LayrR6jYzExBiTwwACGXL9yKN3qi3yydTDDPIW83b9tG4GrQJmOgggpei8JHX+F5SoekcEfSn47oa9phB6wHg4EiIcgod4OnBUDBtaxxYuYuzFJFdvJEOSMe8anFdnGCqHhfumXLwlF2cUky940V9q8KG8cfk+hY/aPQnLyFxn+5w18fJ1QWn3paZsnOAZB1aMR3R3Rj1E37I+H6fpZQbyaHpnXDecFilwjIx+eUaxXWgaP6lb6IYZ/ZFwttAP45P2P0Ud5tlo0SLHPpzWbP20AWUR71wcrvsb7Qr/KQORucqG9XJWhY2LPnHHM6vBehCUvZxFRsEyNgM74B/cshJBhz2FNNqZUR6DlcktnShgIOGNjfqnzVmiE+Toarc/jpKld3E68n+KYwxMa59V2HsIzSwfwm0e/NyAFFYzdKLsKD4cUo4JPOJjvtsmj88Fi54X3uox8M6UJHt5nFy3mirf++vrHH83eeySXy/PnF4jaxzYI/7R47OzGlUB5EdHT4nJdHvKoXayDWESWEW9hJ9vpFY/GMIsq9mJ0aBRQdl0U/DM5255NkAEZIzw/d/RxRBpL8ryHynsZpyqL6x+7ThLazwFgb4lSP5l0WzzfIxH82U08vTosduwthWZvDuStUVTfMba7IT8bF0Oj3ynP0Me8J4Orx+PCbCNx86GY5Nvz4T7cHH9c86x9hPMw6Ho8n3tRKLh7dQvQvhXnT6zbpExZgCe++Q7mLZPNf87zK0Hi7uWT1+zzTOCplOe4PTmiTeBauM5AqATYGaQqVcC1SzkD/GZh+dh5rTCyIc2vWJt4lsVIroXasmGjPLQ2s5Xpmj9O3gNiknKbpp8zppOfBUwuAqxcTw+6qJRAZxTwzcjlzMbY0+/N4Gb/2BRtm9bawFCYLmN9eYGrywFDYpwsW6zPnuNk2aBpAl5sEgZuEELAV+4v0TYi2l5sCWjaHHCfQWiaFuZBYl49YoyM+QRfMwIyi0AuBrwGQ1BFvxUYsC2CYnQrxkigxOxJbPGwxh6EjISETYzgbQKnncbhSuIFqPggRjmJ/9cEQmhahNCgaVsQAkLToGkaDCEgsZ4SHEI2eNmWIxhsOmMR630WVFRJasCgBgATOACdegrKfNnYeMAsEAVVWGQ1lnVe5TrHZ2E4457lQVbqDEAr/Pfkcg7A3WN2Qpg/zVbqdfTCWR7EuDcqF7VHnl0bCpftaFyeK+epPPjG8Jxpyri+8ZD6nt8yOQboFdjKwyPjvGXSe0fVMp2yscp9kbiacsLtnv+a0+plk7596IIRDel3W+y2ElNy0Qb02zUWrRi01wMjsXi73j1qxUDGwDYS7BCbVrf6GA60sD4rfurihRn9pM8FBhITkhmVHdwD0v0qHpB7lz2eYQbD4lEYmdEzAzGBOYI55QD2wi/kOlBAsPh/6tkbsgegnuJLQpOCegQaSHpF34NVtscqI8r8iM0DEQqncDE2FUYLEMynzEYdPx09qzmttcVP/EgJ809uAvhjHBLELjx/VBY7w+KkdZlP69woPldrCuwK87KF+/5Q2kO66jJGubjKMH3vRciCn8g81aayRjmuStFhc0K7AEUmecofmIrMU4GHK9v+j8fX09IYI+Ig8YE5EGIPPYUa6KOMKwFYLprsHTfofJixHIAuOFQQ5ugcZV5V9dVA28lz8mU9eV7OKuNZ47/ly3QzF57y2Ff4CK48APPBPETuGurNZ7y5zFXl/zMDD2bcKPyIa/qAPSB6Izy/+TuuRnPPxxPgn2nHmKxc00z4/t8oOTn9mo88H/g800Eyx9NWTfIbntvtdQWOvHMza8+GToe9niZU9AFlwDEqT98XmV8eBpPD9HAKO0STQGibQpHtTLaCH0CugGoabKGd7BpcY7GnURU8jvpf7ot8aLDhy8plcsnjCEFdDxl/RwYaGvEfX6YTLR2/qme/ylMl7xWveT9nOP1SpemkXpPdz82f5YG5iVDkss7gpRN0DtJXj/Mzn8zW53litairKQAZMNnNocHubO+yuDXG7NEVVT97y7nm0fV5rvmIxplGK9bznx+GSS8lfxbovfVW3CxA5i158qdiMlzQi6m8U5UjCwN+O1sWbsAVUJnCnEukXKUrE5OHuZbCzUSIUomI1PWgbBckfV4MWlKUby90m4quwlCNEIfiWpQtxagQYz4zSucOZJl77WwYkkeHoFbE9xjyZvIUJGOTn2HecZxIPebUuMdpcghHFU+Hi2GxzGlNkQhRDppInI/BvkLE8aJBvxsQ0SDGhPWiR9eIQHB+3uN8y7h31KJtCOCEbZI4eYtFC1DAyaoFo0EEkALQdOJFU7wHy8EQ4qnHGAY18KUS4yMx8lY3G9vINVya0GLTGJOLK6LJ4hFKYP2kW4ETthzRUsKuT4hxC6Q1bD9zE1RhQPEEZIae1NsiNA1AAW3TQLYBN2jUWJKDnLIJUoojTCBdtozJIbU3auQLxrgj7ACf3LV5xzn0c0Y4zesZAjty7gj/tUJ+Ltdw2Z7LC08ffP0+ZSFsfEBGboGjK2bk84fiGN0YG1PLIO1vv+vz5PIaOmHGG8/6OE+W0ev8Bt6gXgw6jnA5pl6LBzU+h1y+jrPREGvzCEw8RbVSyWgIlwNqdmAsGhL8Y0IagL6JOVbdZpuwHRhHnRi/wIyBKXvEggIWXYCtdtvWds7xO5HHxY9Piql6bn1LbByrDNWYnXtjgu1SKKXo4pZti7Ux1EMzApln8aDzYVv+CwyYcYNZjJhBDYGAbG8EKHsLZUOBgSv5VhReNe5n3eYyj/CKX37E9bbzEd5XRTgePeemx9Wdr3guzVCC3MYiP9Tv5GLMz6tqHLznQwdGgifNjMVhBn646dORwOEM9pjq9zyb14x9vtdc9TffK9My78synMboyZXFMCoybRgOKANOZtRPYxRvc/EWF0/gNLA5pWHoGUNidE3ItD2xbo1Xj/W2oYLnjLyt3XhJ3Xfzxs8EoBo/iwnr5Tg3WH7I8+fjUbBxMqN/MTSKxJv0gBBfv5VOQMZdmxeL+wkgh/OA5qHq61KI4WReQJhr43VpBpUz6MxPcMUpqgbtKX+2Nc49aU818/XeNLN9o5NOuYn7P/Yyy08iXTc/s7h38G0tE133bcl2i0moirFFAIHRlJDjrgMq8xq/IIlyIyFpSlUC/mXbu/dcB7mfkdHAT+WcQ8TcXJYu1gDP0wwYQ7jheyUHZHmMnQDkcN23X6+80d57As4ZS+pv6759HqliEV+2dFs8178VTP+ZSXzg7sAnN+4iT+Wlz1Bgxi3XUDO+A/PwOS59vAfTv5le+VJm7maafbAne9p3XZqVmW/wVdWWA7LhZ0HrWxr2kAUk8+hBVp64yIpG0AiwuCmAebplM1kut5JXCRWTryl3eVcRPieokX5vsb7K1iGpk6wMMzhSuR+PpDcEVlJ17qQHTyr3tlfJS4tuDK9jtHOClB8tcv0dp4kRRf/MetmUPyU2S2Xsk4+rroPy++ytp4YufyAGclk13FjBPDc2kNNjU0ryJiVwIASYEYBBiHKyHEdwkq1oDSR4fopiZEsxIibCxYbRNqLYL+406CPQD7Ih5u5xixdX0oYIQtM04iHXyOmaIRCOFuL9l1LIYxoTq6ItfZEA9WqsBPIJX17JZwBRhRzrcWTx2Enc5CD39o0Ew6YsoFj5ZgxMejDIjhmcxBCahgEp7ZBiVPiQebjz6h38zKtHIGJ8+OkG9+60GHpCFxi72KINBLPn9XHIMJoFF7tR7SPDpsEZoB6o5vWmsK+4W+IL6dSPjW0wGHfC5UjIL8nhKNXMwK4qQbZYHvRdqb8q1UiMtj3TERsDFKWLtdEl5hhX9Mba438nyZMGe+SZ4r7vDqRCd23+yo3TywvdYpff6reDRwohlvcJ2WOv0Ic5Jd/RdsfNJ+SO4fC5TFNWRDPcIwMZAUIHNI6mlZGYEAdgIMGZJgVEhh4aI9v/NjtBXjEAmIccZQWia6ngm05+1TY4g6jSATNGct3kSjiWb2wLruSxOICSd6Qs2FwBsmDC4mU8gIGUMDAjDUljaaYK3pYnS9w/6QBinJ33WK0axEhoiDEk8X7kZDSq9lCsWz53hxpexwwm49e8EllgypWxp76RfOiyc01/Ju2aEVbHnaBCZabf26XRGKqfu04cXkst/vp2fRChZ1+PeSNZF10Orq8zzS4lFJyfL716YvHtVH6xbbLjcnMB/j/tKVWBvtAlVjwn+Ib5v+afaouHVg4zYwCyt2lQfmO8q21ItvxCR33iKedO8HNAWhag3Hhy6c3Ya08bWbGWPD5mbNC2TamjG25XP2CLJlC5lPXAsOINaB+2CzldmJCw3iahXYrXiaXfSRvX2PiN2z9q061U3j0fTPByDvkmcH24hJs2xz4tssTNvysLjPMf3tRoUrH023XhZmnUEH+br2eHsJ6winSPu65ZefLoeiipqs48e1SwS+WN0h4TG4AKFykU3guIETCmuqS8gJV/fb2ax+bZ0UffIj+e5C7GfKnqhVtEy3lnAKCgrxk+S1/tD3OVE01D6Bopp4+yqO/lO7+Ad5jB/DlKs4LBzT6ZkRy+pOnAXN5imifwjS+u54fK9Zy+Motg1J2Zw57mOMj8kyI77BukQ5xnrgNzOssNHl2bxryY2Dycy0F3VWbf6FtUeOsgHFz9k4cm1xnTyN4yJhSZUU0bZodqwJVQCC/lLQlVoGcDBioK15iZZscbzMQD4sKifOA+2yJbvqUisBmDYirbX2lkWKvkFcrjkefAQbZl8zHPLMscDxzR+uKZNJkXBwFutYu07SY4jv9nRuN+U5IV9cQMOdWVEKArZwGylVpkTiS42Hoz3nq1oZCycapeUXOeG6xb5FLKTLNPjJPQIIBBnNBH4aCyxRXY6uEXu34ArwjMhCYAmz7q+InCkiKLIU1jA8Y+4fJKDo7YRdYDMYAmNGgb2cp7ugoAAnYRWHUaCy8ymFqdUvWfIznQIgSgxVhplgM8pO9lOoNK/sGQmUssvMgyznInXpEAqYdegb3E5VQthhkzas+gmBLe/1QO0nh+kfDkxVrLSwhNg9VCD/ggxtOLHbJ5hcdwVswQ7ICVUTxqK4HKPPc8Lrpy662yI5h3NGWCKF6ZcpLomFTUgqMT9ly/ChGlPGfmvWdGQHvrPQClPtuiLx3w3nvTNJJIuLR7LILOp0PMaiREOgtTwTOBVmMqpigbnDS5IeTIWD34FmfPjF+c6ygl++rk/T4fAzUcGE1mIAJYuMEwL1qjSQmcvVrRCnELJEKvDavkTwgo2+I5MXa9bImPbIdiAGQx8ULAshXAGpjQBTHExQSNHWYAJ40z7wLxlvOwxhm/p0oXVwpVUnj09EDyKrwRwfvbJDemDLdY4uk2Jzy/EIvnZgtcbvpsNKCgSoIaO/qUylG9DkIKMrpnbr4qmz2KGa30rcDA3Am5FU7vSSNMGb0sg1gVkxH6mpVo14jyfa29lUtj2KW+gibXYSvPXu9tU6m++o6qO0wHhMflT+sqOM8ZoszTLXtfenpmMXwNyHxZmbQUA77xwSq5W6MD3sMmB3u39nOBfTP4m3FKxpzzoleuoDLEiXwhYWJQQqUQsvGeQkAbADO6BX3HRrdzdUX+kzKm0OSalrtb+FGR57yMWlrrhM1cixur0ThnWmp4D8Z6K5JHPwC7PpUvCS7uLkBDfUTOjdMeHL0Gda/Jw6PffQUIrPgRumnKNAyet9/sm3HYntumqnfXdHFfqvSAUeE3LXKOfprxui7PLbpmWa3wsjFc3mRkKnzweK5tkAupqxyEhYwkZfeWzV/t1S2yB1WLauQm3HA+OBlODPwa3XLPONp3vg/+XQWLowwjZ9/Z8Qg+s8+X8XxEA5T09hKTQHYJ5eNzpTJ/Ls7wkvC2L302TPgJpVsSiIqvf/l793JptmuOWX1O3Z4UdUN5bv7BzDcmLmT8mMEvGlOHw4XT6He23kONmvnwxsM56sc4edkmjRrxsg4ftzw8wwkd+deLLcWoZUKjCXZFN3IxB0xazaZc5GvJz9UrVMoFI4FcfbkJCsfOEOcEUanC4umwlskop+l5Yaq0lrR3+c5BWlF0vGg3P5v7xBtHs/emWs4ez7gVUCoUBdb6YwqhGX0kXmKMCbshYrsdsNkOuNrssN706PuIo6MFjlcdjlYtlp3EtmqagIYJHET5TDEhhZTj1iUuXmzlfwK4cHlm9XiZoTcWDJ+CMPCmCVmw30XGkESZT6wHWQAAERaNjHkgIEURfKNWm2xgE8TDLSUwNxhM8NWyFsqldz2jTQkbENoAXO0IHBscd4xhJ2pDYoG/k2WLzQB0bYMYAyIThgQ5IVe3BzbBFAQqAguLEJ50LGxeYmIEphy/C0QYkm4lRFm5S0lOvSMGksJyhHolZOGpKcoLM46XR1mQGiJjUO9IMwCKJLarQCrHJxpBL5HFZjEqDG2jKUbeGFAMXyUrV8LbWK7Ot5VkN2qFJ+7FcohqS22GOZ4aG5yE5U/MRS5KRUd3n2mY4pSQL0dPeK9s7oTm8ns7mcznJu3qKNaMTpwJyDn+jZ4ePQwJ/RDRDxG7PiImxqJt0HVyuEvblNhWZEYs2DZ1BofR4sCIrvg4WEb7jcy7H4V5Y+CFkzOLgToB+URr3z+xp+k2+IRMG7xnuDSrLC7Y4Rhg8YSTbbwhG8J7EBqSmF9oArpGaCMpnjMIizZgiLIdNpEtUpTtgQQqwjY5PAcjUHBjVObFFA37LGakQ/bAZLAEyXfKboIYGLMxBAQLpc9gLFotH4SoBv46NMLIqudAawqVRqSnCFpgbyou50WxOQWxMNeDyfJXZXt6M/tBMYnN5nJ187jlc5fFUumr+PzSSFTwDc2Q78gvjXKUJhY8TMnixQrOy9Z2Pcypsfis5rHqBsTioDqPvdq4NJZBXFtGbbNujY3clAGgGLbL9tWS1+J0Zc0ZZU6yWdbhFFAO2LGiTDpMACilHDJjSDoWVPib4WUbKC8A2KEgVm/Nw/UZUHhe9up28wFMDAoWIqbmrKyL3YWEinHESZc+ZAKAZlHK8IcFybcMCaD7cmmMz9Xza/B337c3r/wAjt+iiMzHb5z/IOX4AlJNZ9k9MsV2TkowuDL4t/jVJuObdypZ/43HAkVuG7WCRjemP00XUvbMLteXlHs09V71NMHoQe2BVkrKO0F4biRcXSZnKF0h0m2+jngwyriFUGAkL1nPwIzhuBvIMiY2pjNjcCjR+MKRueqdLSZmEujaman/F5M+Mw7/pNItG1p4Pm734WdKNZ7fLJ+7m5MPXL9p/FwffhFzuI//z1Q/nxz/uk35k74Yb6V5vDU8H7ORQzNB1VXNnee+v3GyedpHeL8ARL71qbjKTTAeamEqMpLMptSza3v9VzzlZFY8zTRlKBM2o6VAkSi88IRK9pOCVKnxsYAMAHJeqyKLgDOEcvxRcf8rQKOP4L368sjcbNZ4fH3wE565LEzNv0gkf2JKiEPCro9Ybwestz3Wmx7bbY9dH9VDToxGbRPQLQJWqw6bbY9Pn24QIyOEgNWyxYkz9i26BovEYAoITcLQikdN0kMjxKiQ1LAwg7Reacom+jJZoQmyLZeavC3PlH1j2KYAtAF53pOOSIJ43sQk23aBAFbPwn6I2A5RVrjdXA7DIN4tkYE2YBgAjuphp6fvSuwQxmbHWIaEvgcWCLjcMiICrnZyOu2dVYOBA06XhNWiUZgjbAcxCsQQ1CAhigaDgJTk5FsNpk8AOrHQ5uGKibOdlFm8/gCga4Fe3XtsS5PkYSQwWjJYBUJgLIiQOIA5IDFwskwQw55N1AQynXQEB+hemWbFCYI/iCHHcari4KF4yCknKkLUDRiT4R5qPIdrgytuxrhXaqgMEUZfaNSfPAxcymaPfYWiINf8GZnCqMMFz8t82JgljeUWY0JvBrw+ShzHmLIhjoiykt91hGGIuLjqIY6yhK4Ngttdg641gz4AIlAKCLrtfs4L2B+SU/V50nkuc0W29dVgXDx3s/HXzZtfqS4CLmWjFTseIoZyOeU6FHclAGL4aJQ+QpV5TqoLqyYiRkVGH4GWEmIEGgrY7WT+d4PA26oTg/6yJXRNyFA16OEAtsDF7GIGccoHY1ibGgaYQh6r7LWUm6QmPCVPVHWpKPVyOJWOMRhoKBs+JYvEGi3jeAAynYBo3h3ehu5ZziTMnvHMSk5wbb4WwWdIjburypqp00sfniP7AjnfWJtKJ/yomJe+N7C+fHLtugmvr1g+VUbavEBnBzUlrsbFPMmJCCkytrtUlP9AchBNZezTWqgYxsXoxjD/PDPgm+SUK6uSSXqOGmZZjgru5nLq5OPDZRjgwn5qkFVjJo/LEXoUHAgYbSJXZpaXki5GJxm4GAXjoroAtopHbTB+jcx3OPfJeu+ZHLLRzg+G71flZ8oK3aEYK7386+fXvgrWqbyYTmgOaTXXpQO46dj+wTzY//rG9X+mchRebmwjzHx/32b7m8nzL5fqcmvjnVyXg+8YY5JtfTQjUEyF+glY2Hb0sqBqgzxRc1yLrnVkzJp1gVOq7uo+TT7fV67h+kSO4LJlzbWtwCRXeJ47xSVPMrojKIaolZi3jC2eB8WnUrU1Zh4GxnA27tv4szw/o3Gf5T+jerJnOs/Qls8p3YA9fznSLYlEyb4Pl39yeF49mbL7aXYa/XLxUPfPXdhwZAM7RkPEKLsZZwz++9LLwMTLfHOTGchi2eh+nCeTKV+uySOTxvHot6QR2blVWycfzQzK3Di9DCTe7lTcJApScowlG/T80qaJgFwb8EzYNpHP+Eu9TuY4ulvqFABU4BttuRkLeYVQ1gxQmmG1uzLzIRqoZq1sVSkKtbW/iK3WEk9dVABTiDLmaELg2O37sJDkfAHHM8zsHqkio9IAMzCkhB9/+BzPL3bo+4iuI6yWDZaLgOWiwZ3TIzRBA1GTBK0nBoYoW2EfvEJom4AhRmx3A9brAZebLZ6eXYETo+vE0Hf39Aj37kbcvSP+JaFp0bYt+mFA28hhF9Eb95gACihL0763ZrizPjO2fULbUPbms3h1fWQ0cpoJhsToh4SuDRqUW+Z6SOaVyGhCwm5I6p2T5J16vgHiIdQQYYiMNiQhmkmonxgUZZUfpIYBMFISRaCPjN0uIiGgAWHXM7YhYTMQGm6woIT1LuF01SAkxhAJm0g4WRD6AeBWPIi++4OP8bd/5WvY9An9wCAKAIlnUBPkIIw2WLwg6R/BjDqqCDiDFhEhElCMToQhJjSB9FRgAite2zhMCKDCFVfw6K5Z2YY34mVmMd4cN2PcAyqlxbaPF8G+9Gci7HD9rVfMSygAyS34N0GiYuxj7VHuntEbKK7tK9suRmW7hYgJidiXGJiU4x8bEUGZj5SAZ2dXWG8jYpK5bVtR2Ns2YLnsVMHXGEyJQWYIAyEckdB159G36wdcbXowgCaIoW+17HC0YqyWELoYAkJghJAkDqUZ+7LIpMhSMV9PH5G9CiSuVMoHRWQBHmLECiqdy7YUDb4PM+rJVl5OQhuIxNgRiPUUa0aKyEb8pHMcGUI/EDQ+qIQBYJK4ngnFY9BYXNTtwEahYmT0xBiSxMxsSOjQsgviTZuAngmLRrfTqJfuB4/O8K03X0GfGDEiw6DhbCAgZDwpcbO8119BAIFuD9qZjup8G8MjEgNinoBZQNMLnotFVzOqSmTcJ/HMuPjkJxOE3p/2NGGKVlWd/nckvI4+NHyy0xfn6sgLkbdJNfkbX1RprxCntOlqvROPdaXZIRSjXNuKodjWyQyPzD5MnSnxGrtRPfb7XmGISGmGnM7ettJ4285GiWGHoWnRdSfz4NaYLtNR+IF4xJXxNmD2spF42UEWFEZwza5PY/w0eu1FOSm34E/+1X55Y1/Z3o5sJM38FSRx7CD8NyblmdqeBJEPbKs9A3hxscWrd1fiOcsOFMi2DpLjOdYuOL5aJ2GxNZQwl3IKP3UKy16gOpzm0PknV4abzM/YlgyndMMy2A1ZxbfrgSTXRN++Mbu/7fgzZEeFhz/BaYU9t90amCfjhv8FXzTMjr43g3/QdaQQpOKQyythOWrj3oER5Er80nA+0zEYj9WcTsT5v9sF5t/z/HhP2A+rfGfeyLmSoiP4XSkMW1SU7xsSvG2sf7BQUlaf4PlmSDhehEyH8mhRfW2UsZKi5+Yv//GdmRk/quv4C52mYsZLZn9JgvkSpRjvGbHO0q7r+kI1XljFk7pzqA15P7GhXFuVk9t9+1Dj7770efCSg+Wz0cgRPo3FwFGjjB7M0XBgOraJZWF9rshp/6ZjNpv24Pn49W3G79an4poQw45ol7hLnE9BsvXyEsNlQs70x3vLuXdGi8eeMXmsnHhjzEevy0m3QLXFTssuXnsEi4/lWuRm1Rn1FGLKVMnVuPWZcVD1pEI25hpgxvVOwWE00074LJ+WeQGLAnq16fHRp2c4v+pBBHRdg7unSxwtj3Dn5Binxx1CEGU+pYT1IFGYU5KtsIuuxWLRAFjgzgkD9+XE2MSMXR9l6+66x+Nn5/jkyTm6rsXpyTEe3D/Faw/u4sH9U7xyN8kohYCmDWgj0CZReCMTWt8HFs+DmBihlXmLKg3vdB+NKf9iXJatuKLAiqIuAbcVHjnJduRG/PgIYszquiJ9NEG+64eIo64BKzBYPDsiMXT2PbCSID0YNJhtYjXoDdLHISZQEAPgMETEVqT8mGSlf70bsOw0NtiQsO2BkACmACLG5RZoWym3o4gIadcuMlYdYWBg0TUgqCFDFQ0V/dAQoelIjIHa9sRmFBAjB4HRqPvSEJHHEkAO1ouM52VrBzu4y+iAorBluKQRs2C4+E0G3yPcRC08ZnwaIUaFBWTYhyxQwmf3hNroxciDz8Od9+TLirCWkFeFqoWGQt2zSHiIQO+7nktz/bZ6R/iS1KB9drHFdidie9OId23XdlgtOoGZIKe/MrOcAqsePERQrx2BieVCKrM4NkNMGNTb93K9xfnlFk0TsFx0OD5e4fR4ieOjJY5WqoQE0tNb9VQ89UTlUbfMCN1k+ixjHXXs7YRqH+ohw6INiCOCBOjW4bKF1uJNMaAKt+BA11A23oSGHK6IYbBTZccbIMTzV9oiCosq80liD4ITEjcAM/qY9NRO+a6XgH0wZWkXGaERz+IGSbYdR/Ec6MwlsQm5jwXPpadEFmaQYMY6cdRV5V4DCgUu8O4V/5jsgzJ+XqjZB5LkbqZeewVXvJBnU0U5z7jckfFwLo3LAyoPusm7fWWNDr+Y1DtLfwh15uvRd5Ju9AFX+XjySozVm13EoHElg3rY26nQcjI0siIvp7mXBb9G40cCQNsAiw7ZkGXefjEKb9/uBhAFtG1QOaDFomvRda0YEAkgKoosK0woFOQBs3c+3hbgDoqadjR/bTynjIe8ITclzOZZI+9t8dbw34pvtCwxgOspuya3OrpiHscRZjwsspaUlcAcVMbiciAW6yJgKG1TMpvHhGAhSmQBkVEMKkbFCn1XI/xYjkThoxVfgoUGKDmDH/NbAy1qPJ++GuH/wWK05S/ThkJQPlM5WsC+/hz6Zvba3do83IbF761G+aLFiwSkzQHIXrbecO1lMpNbLH5kIY2ZaWZYtgWyIYrsI/Glg2zVbwKakIAQJJ626XBalI3gWOQaJ8OL6fua50ibuHqbUXr80Jet5RcP9fKurpNn544ZSBbPHeOGaoVG27RglvX+zD5BzmDPrl3aJ/Omqw2XzuGE4Ax+e9IBwJ+M0+ecboDeX750y0a/DGn8zMnAbcTzvRPQPgSrwGEsnoz6brAIPQBPZDHBZ3KV2RkERirmYsv6NNWhDmaflfFuC1u+ir3f7aPRN6nPZxiTA0Xs2e8LqRi95xu1eTJ0+zLeYrBub9hzv/q0rHLClA1Tom0lc8QKDKCYiwUZcNZkykyqSC9WXemdXw3y9JicB57FGoIz9HmBiNm1qRphI9FFcpM2uvhak1QEsMws2DfUj9r+NGVxNu7luRnxTCC0raq2PWfXD0ic8K1vPMSzF2s8e3GFq02PJ08u8OjJOdoQcHTU4d7dIzx45Rj37q5wenoEAmPb9OiHhLRL4G1E3wQ0ywartgGCeI7dOW1UgRdfmsTAMCSsNwP6oce7H3yKd95/hKPVEq+8cgevv3oPr7/6Ch7eP8W9u4RTbkAkBziEhtG4GNApSXA821rYUFGQTcRrSA7WCF3x8NnsIk6X5tWSEJOsoAe36mZT0RCw0BA0g5bLzOgT0DXmASdm23zgB0uMP0C8CLcDcLIQDzrWuGBIchhHjIxNH9EQYdvLoR9BpYQQzPsQsK1VoiCIEWWzG9ARYxgY613S8mScjwHcWTZYNlLfbpD4eARgF8s6osQnbLKi0jQhb28qAbbVq8iM9WP4GxNJdjkcerCTmEmvvdeePJp6wlSLA0oZK1kUVfZRY4AqWL4RYttGY/HHfJWVklikKZv78pgKTSF2ecoHVJCxCLzOkLI/zXAPTIa6ZiwOz01JN/yPGj/t1fvHuNr0WK9le/3l5RYXlxsEInRdg6NVh+OjBVarFsvlAgRGoChwOCTwkJCIQG3IBy2ACCeLTg9X0fFkUQj6ISLGiKcvLvD0+QW6rsXRaonT0yPcOTnC8fESRyuZ17INSIWLTM8UVrV/gZC9aG0cA5mRr+B5nxiLNoD1X0oaD5BsRa3AlXm4Aqpoax+S0hBmg73iuWNtItb2RMaiafKiFSv9t7AAfZTFhiEyYpMj9qA+bZMLf4QYWYdoXr+yQBATIyluLgAsW0JLslu4bKVWw5w2VbYaum2pXqEzryC3bZ8NX1GEnTHsVex9JFxWcS1R2lGugDntmQ23J8gxJ/xQGa95iWnaWFf4XmEql2P9H+HriOBU1KIg+Sw/v1mq+fmcsJ7vR7xevHcYJ0edetTKAtiuj9jtIoh0a30b0NkW+lYWgVJMwgeSed0ROJj3uXj5terN5zVQM4QzM9brHa7WOzRNQNe1WC46LBcdFotWvfsKjnpekvumOGNwYHzJsRFRkI2FwQxltrXUvvXyVT2/wWROP3PM2XvOnubnoKygm8d7GyjTAeNrFr8u5dNnTZnXVjicGiOM0Es50IdT+TYvIEIO7DGDnGwTlHIrpY+RjfMGgk4S1v4XWJ3wkxukQ2h27YelGZ8128yHhuM1fr9sW/PYTMnUTyTxnhthb/KgbUljZdaG9wHKS9VbN6hRDvpt5mFc+I2s/0hPbe22WhBR+GYw+oHR9wmknsDi+d+I/BjUQB4Amhm5EXWryp/NOMp7U5idmzNn/52ti10+hhpKXaXs3pmnofHLcXG+/RW2OyumladRwBFAeREAUD7MJe9t0ogl/5s0l/YKAT+ltGeixl63hVeiEjW87ET7kMYxxIzmowdmqTE+bTTQ5HMiUh6s0tdEOJrpA+blyDFDeWn+cos0bsOYHr0MvRmXPxIj5d6uRzzlp4Wrt9uKyyWWiYoeAKvSgQJX0tEppc3iVrHgFULHWW2dpDrEM5fQe3OzaAaCidGuGPrqD5x06UUl26LhIWNyPcfGXGdze/gGQs90yyJQtzcbVu2ay3UW/pkxDBFXmx7LxRInxx1efXAP/W6LzXaHp88u8Pj5FS6vdri43ODicosPP3qOpm1wcrzE/VeO8fD+MV65c4xlS2gSYztEbCPjbD0ghICH95e4c+cIXdei6zq0ra3kt2KoCw0IhGGQwzjOLzZ4dnaOjx89RxMCTk6O8OqDe3jjtbt4/dW7eHj/CHdOV2gANcSJx6UI2tL3vHUIlFcvTQC2baBdYyOpHkEpAY0o3LJKKQY0MyDYhGYvUxKG3wVCrwKVCN2sWxYkYxeA9ZaxG1hi1EHaZoTYDhoYYgI1AdthQIklxNgNGsSfgCa0YOj2SG3RZhfRdCZwidEuxoTIjB0xthRBLYFJAvoPUQ4EON8McsJpECPegoBlI4S6DaLCRDUgRCakWHC5GDUKPBVok3FKDhZNxB5D7fhpTcwNpikzlKLws8tBtQfuPMro+oAzANS1OOJLEwEtG91RhF7Lz9mYVypn10Y7CKQaDK6VzsNpPkf1dcXoy4aUPDdmwB8i2qbFYtHh5PgIMQ7o+wFX6x0u1zvsdhG73YDdbsCLszVCIx44x0di6DtaLWRrN8t29iExNjs5Dfv4uMNq2YlQ3zS6oi8GfdIYkIBsX+37Qbbrb7Y4v1iDiLBcdDg5WeHO6RFOT5Y4PpLy5JTtMmfO/qt0rCwGedd6kmVGMdzZmHGhi/m0PC7fepi17bWq/4u3roYHaPRk6OKdKspT33M+YRcQ/mczk40UiUFBQxg4Y5EZRaQtGlyAC573QwI1hnsa2EH7PgwRDQi29z+lonBse86rrUQA9MRPEc4K3zDjiY2nPHeRZXj0m/OMzRTzIlkl4FQCnNHCGf6f8X1SnGuK43lGSyZ1+PZZG8a8fUbQqz4c0Zy9hY9yTgSPm6d5CQfKyz2vL79mwA+hQdsSlos2H3jU92LkE89azotrFCSMRteFHCtTYjpyNmINGqxqsQho2yYfBBMoZGM+qeFP2mIH8ET0w4DtrgcAtG2DRSe4vVy0aljkjIOVoOtuihyJUabCS/PgYASu7ObW0Qi4ZyKiiULtF/YyXdGsgSQ2bXJnfHm6YfNiW9tZ9+NYq/zWvTEXAsoOCDNsKpPJin8E1NNPZFZWWB5iETNJs2i43UL/UPC14uGYGZMDaS+ez6N+1UO6Qd657+bbcOhDP8jzEbJupTgaDBms3Pb7WybOf2aeo8hdFoqhaZVeO73LFmFjFFkOZJ6dlI19RIJCdpCqycl2cA4IyKdF20JQWQ0C2MfvlMVmQA6OapqQ4+62TXBxOfPXe/sH1Px8X/5qItx1xWtG+Eb14/J81DYJr1Fi9eY5mQqxSMTCg+2RK9w3z5Ivt8jS+s4kRquXy/61NBoPm5tJPexhBT+xNMMO/2yln0YHrpkfA7mbznOWnQgVHJF7l3FCM83z3ZHrPIquZztSKBkNYpUHahx/2XSwiLF8d2D8rqPV9v6mKHJrur+HTQJwu1On76bC6P56Z7+5Bc7fOsZerys7IpSIkSVmT6qiNNnoZl8TW22EmemovLcO6oiYJ0/2ugMXJz+M3cJHgoVCvzBIFb3c1lxrizznIjlpAYY07J6VJJ2yXoxHemzL9Bt1JzqHAoCfwVFp1WX21IEX5DgzflP0Y2Jstz022x5Xm4QfffQI905XeO3+EV57cIxvf/MevpUirtYbPH56jmcv1nih2/jOz9e4uNjggw+fYbHocPfuMV57eIrXX72DN984wcnxAqvlEqujBbq2Q9O2QgAaMeS1bQNwQtM06JqA0LQIjXgQUCCkKNt3zy82ODu/wo/e/wR//MMP0XUt7t05xlfur7IRzoRcM5QxM3YxyQmTAdj2SeY0JTQB2PURq4UYtEiNYXnVIc+GHMYxRDnNV7bMBUTWeEUKvH1ktI1s203OG6ZtxLOGYAHJgd1QqPBxF3DZJ4ATIhMaFs/BIZJs11UFjaFbZQHdGqjlB84EtgkBDaUMT0NKeuIuy6m9TYsABseoK7sEjowIiZPIgfD8ssf9Y1HqAD1FV+Mp8hCz4r8Z5MAO0ggs5mWQh1DxgrNRA4a+TthSMYULDZh67TkFIn9sHhmeYXFddq6T5pBM2+IMAPYq04eRBD8u1gzmZuDLzJRz/r1GvvKgoklV4yY1ulczj41Q1UzeGfUgBqAhRgxDxK5nPD27wGrZ4fS4w+nRAq89PMKrnLDrB1xebbDe9FhvBwyD0IftrsfzszXaRmLnnZ4scXq6xN07SzUEtGi7Bk3TiBEPpLF9ZKstIKe9yva+oHQg6BSp5+l2wHa7w5Nn5/j08Qs0TcDRaoE7R232QHKjm5WQQT30iKBbhaTfxmv8lvEMTw7PQU4ZgsbO1NOx1RFRcQ9Z6Q9chIkQZIs84PMWPO8aktAAajxrFSyTtl28CAsiSN84H5wRUBTxQIRAqQgjbLG8xPASgvKQbNAFigIopfe7iKMF6RZ7Xagwz0ANVwAWD+eGAOI2j5pXKjxIzoFx9b78mUB6/d30TcUPbyRRGeJNJaGqmBqpZyuYpyvygN2DSR5X1C3kq/3JeNzomd0XHohywm2MuNxEdK3EyF10DU6OO5xAFpF2u0EPzUkaL1Pow2YzgIjQdQHLRYvlosVq0eiWu4DQBDHmZYVAf8nouZ74HILyPqEDDM6ep30fcbne4OJSlIGubbHqyoFP1mVzyGMUzzRQMcobyU2KszUcetrPMJprNDFlHCuGf4IzyBVWJO+ogInxHvMeBuDi0Ja2A8XA4ufRL+pkPC9kShV2Y2HltG3zmLdt88wpG+hM4bIe9UloX0CJo1iNDReclqk8DKk3heNZ3L6u0Bt/8BJpKjB8tuo93rmPP0sXeHKx5z0AOMOs54ESV1nwzWQ4WbA2T9rCCwYwkIRXWNzNppG4m0EN9MGMek4Oqq+LbmQGwzw+atS33UC8g3oOhsKP9/TTz4l3VCCUcBdeFNoHb1ZWXgz2Bbnv5obc0wOTNSyZDDCuy3Aw37uXPradr5MAjFVHa5ft1CdjLlzqsY8ZMr9NgOxw8SxPG57q4vf0+N+kSdrDz7+Qsm+QNcNyYYOweJDkYMMXze55scVTWewx/LY0FZnssf4ZG/oKTYnC6LQKqg6um+vPdX3OuHeD/Dcpay4dmtJDdR80lB+qbO4718AKv+fqPDA/n2WcbrcVN4nhoCjkGtssOfJtQrJtS7OGc+ltJsBu2xw7YkdUDHPMJYir8HIuCnsmnPaySFLkBDmrvlwXYIUKhUXgozIbeXRrb7/xqlM9SCgzxc6USaM8/nfPw1JHMUpWyr0y3MSsgeNZFf1BYrwxsN0OeLS7wKdPztE2AffuHOGNhyd4/eEJ3v76Kb7ViJB5dr7G4ycXOLvY4PJqh90Q8fz5JV68uMKP3n2Mo6MVXn/9Pn7mrdfwjZ85xldev4vjo05iV+lKfxMCOCWErsGqa9EGjdGzEAOfHNTRyvaaJMaq3U4MD89fXOLi2XMAdrKnbImVYPUSqHoRxPRUvNsIXUNInPJ82/hFVbBFeLB5k9kwzyQiYDuIYrJoLG5f8eAbom0DFsF925cYXUMssB90+04i3TrcBAyDKNRd02A7JFztEk4XAf3AACXdYhyw6yNaCuplp+0bos6tuBOFEJCibH/sQoMYIIehNITNABx1Aetd1F+FO/UesoMySCyesr1wkENEkBjrnnG+ZSwawnbgDGPJg7/iztRjDxVijULTO8guRjevm7Pis30/ZlgePQoNyR9aqUWAInui7XA/VmIxsBepLMtV3qA2Z+Tzn3HpcW7tLDU+QJ4z5y4fsvvjGbvhPFg9p1Whlq24MqcXUbffBsLRssOdkyVOjxd4cP9e3uq12fS4vNpis+2x7SNiTFhvdlhvejx5dilxMk+Pcf+VU9x/5QR3ViuJ0WdcGaRKMoMaQtc0uh1Itu2YAiHGP8qwFKN4Dq43W2yvNjCaJgp88RBgFi9df0ATIIpKArv5kr/+1MiUc8vfxIL7RLKF3gx8pkAFBR6RX4rXTYycY3RFXb201fhicNEt9VHoTBsIcZCtTIu2xAY1412MCUFPzDUBKSUNw618IhAhsnhJNBSyMtWQGPm6htAP9isBvFgN8HZSt21Jho7noGO6i6wHCBEG27qF4lFYSRizIFvj1L7X9SPj5xnDquwAbmTgK1kUGWaWj7NMYUXx6Osiyc7QlemD+SGgkuXaVAkC1dNxNv+2MoRxORDJDoRKzNhqDLygXnlirGtwfLzMNfZDMfRZXNy+j+j7hMsr2VK7XHY4Wi1xfNSgXbZomlDZSiysCoLEbzUPPjuER4xLbk7ZPOsj+n7A0A+5H1UIE8ZYXMspy2GUs43GyD6g6nvjV1HpRnDzSHolYS/KJOTtwA6sx8Y/Azdro0FQTKRxduVL+U48aylIIYXPFb4lrMsMkjIuzGU3gTgDCh3IhkVXBjOyR7kNki1iSLvKgkVMGaqyLHQd9M764O/D+UOvr/nmxuVc+8EYo2ocv2UTSrkYwdznlCpK4Ooxhd0glpV32WFQ3sjXtgGdlmLx+CpvPtUFaBCYlC36DbouoA3BeXzWWqXoasWDz+7HxgKD3aQHScVYVnwrPUv/zLGVilZfO1albI+XU1qPzHPHOOz5w3X1ACbvYsT3y7tsXNQhNNqRK6nouKMjWqGVa0b4sT5p+m/uFJB1P6C0zdr5k0gvgdJf7vQTGrfrqvQ8jhSwCLpAlHHJ2wDKL+uFoqx62qPC3bnkRahioLaHU1w3W4N//jKw8EUP+WeF0Vm7zr5CnUzAo6yeNl1nL5qL+zq3oHCbsbuVYS+CJeAwxAPOqFVKZgzhEUgApvzWz9UNOcfBKhZqclSTizWv/OifPJiOstcBIIvXnx1f7xVFb6jzXKBetwe4mrZcYX5W+kbla64RpyBfGYscyLIqfTp1XqAD6lW65BAuMiMOsnJuzDayKI/W1z4lPH56jsdPL/D9HzW4c7rCG6/ewTe/8RX8/M99DX/99AihIVxcXOLxkzN8/Og5Hj85w/OzK2z7Ae9/8AgffPAIv/N7De7cPcVbb76Kn337DXzz66/htdfu4PhooUo9oQkNEhhtG3RVLxjlQdcQWvUUIABN9xDMjN3FBb7/j/87RBZFPBBng0QfyzVgfRcjWrSAtiocN4H0tEsTcpIaCAMSS4B683BkXRllWDBsmbghAYtWxtwU8cHgPDHaIHlM+N70chImM0ChASAGF05RGHhM6AegbRoQMVYtYTOIxyEjIA4JTRCPJ1uNlS2HjKAGPlOmxCgo/rK7PsoWQ2asOuBqywhIYngMDdY78cbjhrLwf7WLWnYh5TFGBDWNZGIOwOITGSzSGB0qWIXz1nPwXklgNQnLuOHxxS0KzGFGKWqqhhT8NZpAFaUd41qOneXekXXGclH+Mzpox1H20e1YkDwwbK6umppn473mMVy32FcpJYntyAyvPMbIuLja4uJqhyYQlmrke/jgDl57/R6+vuxARNhud7i82uDsYq1efTsMMeHFiwu8eHGBH78fsFouce/uCR4+uIMH909xerpUQ5/F+lFjVQBIPfgkhayIiPLfIdyVIy6H7Q6PfviDbLgIXIYwKizkIeViRGOdDFPcQyCEJN4KZhjMh25Acdl5NhhuJy6HpdgWPaDUm43azPnQDTtkRg7OCQoXBuwFN2RuLJ4Oo22gXsjqlZG9EctCQvau4dIOo/VBITzGwonaAOwg/M4OfhH6mMDuwIBdZP2+wF9KCSHUkJhhmRXfR/i4Nzk8t3JmP7sO/zHSz/fUPSlmVByPfqviMmOu38zSlbnkXt5cwNpHKP2P5+/I1xn3E2cc4FHGlBi7FLHrIy6u9ATsRYuTY4l12bVikB+GiO2ux3Y7YNf3IiMkiZu3Xu/w/Dmh7VqsVgucHC9xfLTActmhaago9rbQQab8G5IqrLs8K1oAYKQh4vzJ48xPPC8RHFP+QGVCfQxkG3Z1Ysijlf+PiKuf4vyeypiO4Yodc8oqjfKBqAsOhhNmfC/zpYvXChd2qrg1yGI757ZYX1DLmNYra4Ft62fWUAGCZJn/R6UfJtASbOGxGoqRLF7GLvM9Y3g3wfMbpNmiKob6Gcq57oPxg4omlJvPqasvlca83V+yv9gDp9nIB4GPJsiW2EXXIKxEjhajuhnbOC9u2xb9zbZH0MX2rhOPXzH0efg3nNR7FGacjX521wrnTClhd7UpXePy/Zi+sr9JdZ6JjKSgnz+hMlZcZ3PyQlk4889m26DvvBEwl5fBqJYxZ7933+3tr3tm3+V+k5c53PssusrD5OrKwzJmaV9w+hxJxl+4NIbtuYE0Hma8nsh2dVA++Mroeza2seN3kfMCeAmtUeJwFvStK6fq5QjOShBPxCHhZROjeOxP+ozbwdXc8O01nu3Jf6t0c6EPGNU1llOuK24fvbpN+29l2DMAkpNijdiZwDfeHlB1TfKrdJXXUU2ockTM8lZKjguET/q8eO2Venxgb1MCi1trHQiSlXuRZq4GWoVXM6ZdOyy+rVmS9ENQiYTwxc6Vzu5PIfReoLT4SeU3RlkpH2LM8XeYATmtcdQ5MPphwNPnl3j6/Ao/ePc57t27h5/9xlfwK7/wM/j5n30bv/Yrx2gawtV6jUdPXuCjj57g3fc/wXsfPsbZxRrn55f4o/NL/NEP3sWiW+DBg7t4+2fewHe+/Ra+9fZreO3VFZarVoQGC8QbGj2VVox9CawePwGhbcBxQD+IKJa9+7kIudtBvG/apozDeiunbmx1O+kuinBjiv4QZbxjZKSmKOmNW9kYdOvtbhDjWB/1UEpI/JyYGH0EVguIMK+GR4PXEEpDmRkcIwJsRU28dQb1al33EafLACAgICEF8b4JGeYYkaHbjgmLBtgMcipuVOotxtoWgeRUxO3A4q2DclKfGdx3Q4+jDoipEa8i9TBcNoReA/LLAQXF42lecnHYTmMis49LFQmvGAWLwXCSNVdgsO6MdpWwro8qWkCzxgFZQLAPs6RUoSl8Hqgy4Jkc+5zTOoSPlhu/QbRuM4/u6wzsLoyxG8OuPHTNqMdm4BYcYM1cxlaE+ysNfP/o2Rqr1QoPH9zFm6/fx+sPH+CrX+0QAqHf9bi42uDs7BJPn5/j+YsLbHY9NtsdNo+2+PTRMzRtwPHRCg/u38Vrr97DwwenOD3p0LZBAmqrABLUw62xbd+AbvUWTz6KqZzs6edVYXGIAgx2ciQA8XSF4HZoCEOivEVf6KDBGTLNAAzOZMJt22xMYhyLbqxsLGMitBqMy4yo1kRZALAKAGZZNLEtvmZYRJBtr8tWvBaJ5ERuwVvKxrPE5UCPNohxrgvFsGHGEFuJHZynso+DxyyG+a5psrHBxtJw3owAWRi8oZRQo+k+8UifV3y8fpsVkD0WvEp4mc/iatKKbiiu7S+bR/nmy5p895JpQkHY3zleD+NvbvHOfTov1JpHXsTlVY+2a3FyvMLd0yOcnhzj3l018qnn7Gazw9Vmi/V6q/H5BlxcDLi4uJKFuK7F8fESpydHODleYrlo5ARpHRACZXzIMA3WOH4EO5mdbSvPaBzMCA7UinU+oCLJll7ffxsX6S/AZB72dQwgBjL/9V4x+Tt91+W4ufWYejnU6HBWuslwSB5EtsN5VDh1tMfE2JyfJG5RZOSDe/y8y1joQWhejlUrg4hzCRwCSpgamwvkQP2GGS8PslMIc6z84FdW/81e3LKcm6Y9OD6H359J2bu2CTx+ML6sDDn27lAMT4EBaEiZhO1O42guWolzuRIOISFgksTD7GPenh9TQtzpThmSXTZd1+TYmHnrLpAHpyz2uP0QSgNMPzOWMh5Py4/RO+ui+qxnPBm/9/2elF/Yzlisc3KTOx2ab4ETVL4nlvAY462Ic2WVsSk4k9ukDVTx3B0qOS1QD7aX9rs+Gg4mT4tu2qfPKX1m/PwLmGbn6AYTZ0Z9AkAaLsqcZ0xGzXYAXeQ3HhSZQYkRDVeDOKKEICHUKh3H/hgAj59j9AFqfLtpGnf584DdMW5Yobdp2236ktvsiJunczepx/PomWg1mbdXNPGG7QNua9jT0rPwXinH5cEs4ut3wl79h5x7UXakmcJlq6JjiZbcI66pua74yqmgAHFZFeZqRuqWV8DMWm7d/LqauaGxWH723hcPZC+9CUJhzMhGxr8s3ArGmjHPBH8f5DZmo1YCJaALDZoGWPexGCrJjQsYfd/j8ZMXePJ8jd/93kd45ZVX8J1vvonf+Mtv46/88pv49jffBjXA5dUGH370Kd7/4BHe/fHH+PEHj/D42Rk2mx5Pnr7AkyfP8bvf/T6OjlZ44/UH+NY3v4pf+M5b+NbPvoGHD07QLZp8HBUTY9mo4gtCpIDzsw0YFitQGSMSElPentIS8sm0SbX4APPMEwOAnTI5JNZVd42Lp0qHnwKCBqtPyAQSAFoVsDv1rDFvwMjFU89WxbdDEsNfRxKbToGzjxEMwlEnRm1i2ao3tIS2MaOEeBcuFg2SGrIIjMvNgFdPWmx7nfNEII7oY0ADDaDfAYuGcRnFqNkPA446YBuBlCKIAvpBDCVDZCxb2aJ13IqwMijxCGDsGPlgAA/7ewlepUiRe1SkrWJor95kmAZQGfgceGYEYvcy0w1HPpgZQZUqhsvjCSKVQiojH+byjPLVRe0jfKObm5HgisZkAW3OmF/je0xJT1UuinNDwriHwbz3dE5I6hGvr4jLqw2u1j3e+/gMR0dHeO3hXXzjrQf42lfu4u1XHyAQsOt7PD+7wPPnF9nId3m1QT/I95dXG7z3wafouhZ3To/x6sN7eP21e3j14R2cHC/QaHAsm3+y2FyQ+FC77ZBHSfpko0EZHxsuimqGFSDTPjuYwuL+hKx5M/IxsCiGO4KNX20UCzY+uvXVDBVlq0sx2Q5JT8bUBQLjGXKKd6OxhqQXdqK3beNhZj3QoFaCtkPE6SKo4d+6ICd6hyB0rGvEILlLoqTElNQwWcYmKv7GJNuChySGC8NOhhgVBp4XJA7DaYam2ZeVMe+6sqv3Hk9o8gRAxeezERZGVzBmnGNJdFL1+MFUVK2aM//dXKabil3sf9yc22vl89kAmwX14nlpxqrp6FkZLNtw+0s8P9ug6zqcnqxw/94xXrm7wunJsRr5EjZq3Ltab3G12WK3G8QDp++xe97j+fMLNE2D5VIPwjk5wsnJEotFowtIqpaTLFXBlAgAcYhVt60PBv8GCpUx3h2ixCr/2bdjJbZw83H/7fvctFxPXoRzNDy3S7/PW3StrZZV6WpDpQzj4XkHi6ZsTNA0xIRlG6r4WGT9I/XCB5XwHiT1NdVclwNxspExycIiVWOqcswBA9Ek0d6bzy9Zc25IH25Jpg7XaTd04DWAGjKnmsK4PVy9PVT3NJc35o2kgTkNZVo8A5ET4jbJgWuhl9Pply1Wyw7HC/Xk46TGvSEb/2NKGiJDdIbNps8H7iw0DudC43Ca4d5SGMGKndzrDVdzBifOf6YvsljGji4c6LvJCVXZ7jqM8vr2eG/CORjz/Sji7AgwNfldRIDw4NZX7uY40zqqr8G6EDHTBn8/NqxfN0az6QtC7X+T9qdbz9GeMjiJNx4lwcemkYXbIF4oyIv++bfIh+JwAiRdNCd1bgkaizM7OU1D7gEw2Xvak700mg+8w+czJvsK/ixl723zmCAzsvFttpwZfPXs5yBt+xxw9PaGPd8AmPC1h1rDnRZWaRPGyrLEVDzznBDltyQBtjqkz+CLq/1l8isVksWFeyqE5+KIqlWy2UFnSJwYhZzpKZvKakyozAqlK5P9TX053YYn4+J/vfttjr+T6lgXsj0vIcaI1x6c4tOnO3RdB449mlbOhRxS0sMctJ2cwEigFMFDj0dPX+DRizX++e9/gAf37+FXf/4t/J2//rP4q7/6Jv7yX/8qfu2vRVyeX+L82Qt89PFj/OidD/HDdz/GJ4+e4tmLS+x2O7z3/sf48Xsf4R/949/D8fER3vrqa/jFX/g6fukXv45vvP067t5bYRgiPvn4Bf7oj97Hv/rXP0T/6H20bEHoJXZcTJSNbRYra9kG9OqhV5R+UagXDXAZZVUcMMOnrF6YgY6TzFdMnA16Ipw74UW9DM1oEEgU+yGyHAyiRguJfRVAiNhF58mqCr2dXBYZaBtC2kkg9FUnXoGXm4hF0xYFlsXQNkRRDmRLsXhjSjsTBhJj4qIN6HSr37IBtgOwaoCLbQRxyob0tW69DaSHIBCw3kUgNGhI6hhicid+IlM4D79jYcPwgMpF/XHOMx9jD/B4Tqi+nqGwGbdnBOY8fpOYOyhevRUhvkbqBk3qL/n2bXOaFnGIgldKAZd7b9gzumHbNm37eKEBjNPjJS4uoxyeEMxln/PWUS1VYVLgidXId7nu8e6HL3B8vMKbr7+Cb339Ab7+5l28/fV7oK8nbLY7XF6u8fzsEk+fnuHJszOcX1zharNDjBHPn5/j2fMz/MkP38ei63Dv3ineeP0+3nzjFTx4cIrjVYfIjLOzNT759Dk+/OgJhosXskrPdniM4rjhOQR/xdssVZ57ZihuAtAPKgwrrmWFl52iAcN7B8MongeC2+aRoEY5pRl2Uq4tppjDUvR4otDgY22FABGiWIxsgZDj2xlcaG4xFBJpfE2uYmqlJKcftrrSmnqheUMUo92uT+I9TxICoI9Jy6IsvEksPqFXAWoEzTH29sHkDOiPkHKKnu7dnFDj6zMBh2bfuuS3yk5rm+a2F+7tNVLSPvScISPzmazb1+L56MlIAK2M+PB8XnGXgW7RYLvjvDWUCLpLsyxIlRpN9hIj3dPnEU/P1lh0He6eHuHV+2Lke3B/BdznvF13s9nh8mqNq/UWm+0OfS87ANbrLdbrDR4/foGmaXB0tMCd02PcvXOE4+MlOg1DsVn3OL9Y48XZJdJ2kxXYhBLfsmqp4Yv1xw8vjO+W77K8ZDiex44q5X085ub5muVRG2u2ra0+HIxkmItdlQ1qmo+TzI95DcbICA2cydEMeNYOSHgAa6P2IzEVOUPrSGrIHwYtQ3HAYv4aHSNYHGG9J+SYjC+XZpjvLb+2tE/pq9KBqvbi+cumaweFPSGZZN/7+W3wfwynXM9VMC9rcnSI6u/mKokpIerBeU2zw3LR4mjV4WjV4vi4A+AOuxki+t0g8bhzrF6J4ywLeFsEouzNt1x2YugLcvTT0Cfx6N8M4GHY33nXvuJhOk1E9WKF5xVOTazf7SmHXF77zorxpJrc+0lzueSlPPYlbI9ttceeMuzbzI6o3GccnanDZGRgSn/IPavK/ymla/H836TD83ON3LC3zKwLAIN68LVNkFiaspqb4/LawrcP22VlDJGBCKA3fVfKCCqzMvSgHtvWf4tgjp+Ng1yf9vGVQ+LYbYba8HNCN/Z0jEfXlsXLHPvaYeVWuw5GeH7bdHvDXtFN8krrtHqhTvlwjCyGFIIoxMzy6efuuFtP+JyVLhvMANRee64ZlsPiuNQkFVlnL0oej3NMOz3hFuMxMUHAuIATDMiND89PFY9urE+mnM4Z9Qxx48iwZ1vCmibg7a/dwQ9/fAEgIUXC6ckSb75xgu22x/PzLT56spa6ghgEZQ7kehh6PHryAv/gn1/iH/3OO3j44B5+7Re/hn/7b76Nv/prX8Nb33kNX/vOt/Drf2OLq/MzDJstPvr4ET765Cn+9E/fww/f/RgfffocTx4/xaefPsZ3/+UfoVss8PDhK/jmz34Vl+cXeO+9j3B5eYmUEn7+9Q73QwKBzbEPALJ3TiDCVZ+walmfJw3SX1YRS0yvIiyZ4ci27q17xlLjg9hqfUqcvWka3fa2UA3VjAUSoJ5UQWE9vIKxasvMA6J0b6MF71dvmyju/ATbhmOeiSSnVQ4y9jbPu5iw0QMyFkEOE2EWj5ul7uMZYsqehYPFX4EYKQIISeHKYrPsBjkkJEbGNgINJznRMyU5mCSZh8UeoxaNIddJLF74Io/xCs8HjXua3+GJbZHK+TzRq5skgrFbhh033Yx95qU6Km7SRylmXorOwuC+F1XF0/t9RJo9vfAGff3G3OwNt3PcLcWLB68s8eTZVr5JhOWixd07CwwxYr0ZcHbVK0EUrzSDNYDBKeLicoMfvPMp/vS9pzg+XuGtN+7hO994iLffuoc33jjFm195FcMw4HK9wbAb8OLsEmfnl3j05DmePDvHi/M1Li+vcH5xgQ8++ARN2+D4+AivPryH7XaL58/OsNvtwMx4/bTBUZC6zSNOhNbiddkn8VQz3CwxvgR4SmDhEfcFsrdKH8WYbtkI4gXYUDlYKXvneBKt9/asIcrb9T2wN6qE21Zg8+g1Rp39BllOshMPCa6UNNnmn8SIr9vhLb8RNVtcSKC8dbcAS5EcZHo5exSDU4lVquU2pEpjswcQb5L2Msry4jrBztj9YelvRG24LJpcnzt/hL2fHDD63UiY4tHvoUz1T343UUCzUbo26hlMHh81uFxH/VY8QFfLVuBoSNju7DhnR+mU/iIxtrsej59FPH5+hcWixb07YuR7eO8Id+4scPfuCVK8h13fI+nBOmbcv7zaYLPtsdvtsN3u8OLFBSgELBYdTo5XiMOAq/UGcYhgAHeWlIP8e+HYC8yxwhMnEKNczLIiZwW0Lbfjcc7OB8pv/Xwr5ZPyTW6kYkwbiYtZPrTHXsfJ4l820gFke+lMtmTkbf1GJ6zPRWTkaoxKGuOB9VvjhrIddmUekRbP8EZQXKq4RhPbZ7D/jMWW7t2w7HH2G9Vxm3SLYbtVESMaUPH60UetHtBmiSChLYw2jBeX8vdKVGOMuFonXK17PSinxfFqgeOjDstli9VKZApbMO77mLfpy+F7Gpd6K7H56HyDpgmysyRKKB+LKWkbcQ6Ng58j9i9c/2aT58twdGSmYHs+htH8ndMPbQvwnqr2NiXjucllozxmnPT0Dr5d9n6mw+O2j7+t6tNOXNfmaSX4nJHlCy32z3TaOy+edxBGtN7BAF8/txKGS0KxEEnszbYNaBtC0wa0MPlUCjMjX6z0CJFLY4xy4A7EuGe756wRfls5cD3sZXJ00/QZ2VWFG5+Vho++nzPOXQfvHm9vghsvw1/3pZf22LP1w7zq4Citxf8oREy65ldlzcvHjH8Sgw/ZA8iMbrKSSlmHARcDSn7mvgdQDAcm0I7aUUsG5X1JToHzyuPB8bAv88g4BYxdnj0Fcf2+GPScAMBlO54p+jGKB1gcilGP1TDHSVbVTo4YfWxxebHDummw2SbcPT3Cw/snePrsI4S2wSv3lnh2wdgMESk1IpmylEUpIQ4DHj05w9//rR/gN3/3XXz1jVfwza+/ir/zG9/CX/u1n8FX3nwDTQO89s0dfnl7ia/+3u/j/skf4Pd2f4APNue42iZcbSN2ux7rqyt8+MHHaINIJ+Y1s2zl9FeCxI6L2mdw8awzL868FY8TNr1dy4mBg2jb2Vgg8KQHazQB211E18hJsCmJd1DkhCXZoQC6dTGxxjUQYd88cMTLRpSAVVu27DHE+y26YNdBvadMaTM3fVJYbgNj17Ma3IzQS/vP1hEPTzoEBeyUGH1koJOQ+CkxUoAqE9JP8QwMSDGJMBhkC54ZOWKMAAh3VgFX2yjePSin/9bQ7GHTkMhw2aGHZvJrmC7rNIfBtPte8vPYauGEXnnhFT7/PnvcZvR2+TNdKP2ax8JCG/bR17Gn7jUFTl6OGUR5xtX74q1XcH7OqGfbR4cYseiAyAG77YB+IAxDwmrZ4eRogaurM1AbcLRqcbUzTz6J8iLxR0VsTGrk++N3evzJe89U8T/Bz7/9Kr799Vfwxut3xEt0iNhstvjhOx/haPEJMHyMFwNjN8gWdFESerx4cZ4PshFjGVXb2C02lJn0Ett2WBkHO/CFo2xLNe9a8TAtUjQ7eAlaj3ghqOctl+1pEp+kMFEba+/Vk0/CU3zMW2ipBBcvyr3F31G+xWUrnp1sJ164ctptynMso7/tE46XDVRtE0GLATTSL9mOWHgQQxcJAskqKiF7EIuhhPRQHmDVBuyGlOlmIKDPrZ4D1cPSRY3l+eEEb23B7pCcYmBf5Tko2Og8MdVV3kAYmkVPh4zXFnELgWsOx+u21PLEmNf7RbxKLmDbai0eXiKMy8JN10gw/L7fAkRYdAG7QXgGZ8HKyWFI2G0HPNpd4PGzK6xWHU6Olnj9wQleu3+EO6dLOfU1yTa+J0/P0DXneJ7OsEkRg4a+oBSxjhGb9SaDjQ1V9oTFWIFxcs1ozCL0ZFt9ltKeLUCKpATBFdKDtqIOFZhzsPHMY7goJ8YXxoqVGfVyX6hu51jpyveu72ULvgca4cGLhsp2XC6H91h/a75WdhKU0BXS1xxjT59bLM0yB3wNHI6Y8+TN/ve3TZlH3zTjDavmPddz6YsyOlxXr880hXee5nH8v/Cn8j9oLFszqolcanAwp0WKjiBxdnu050InTo7EyLdaLgWOVpZvg7AmbNIux6e2BYZhiBiGWMF6/r1GbhqPB1M93fucgZghp/hyiZVJQPaPYIwMdAcUaV/foSD+c88qXmN0A8WL1n8RE5XtuKO5t7bxaL69POLH1RsEARczdNyuL0G6MZ7/OU/Xkl04GN0HdFTzJtqTtfqMJeTDEFOJa9+ooa8NGZ6yIR9yyI6v1nA9jRYVbt3PW+SpKmLsxd+bFvFZ0hjPgQP0ZEZ+mMtzm5Tn+5pyr0u3NuwVobQIFiVmERfCpVeZOAEldn2mAOZhpIJEnljKiojwKy2dpcTxrlpydYPgDIHsnABdPagny5sK62grNXCzezLindNLp2yOs9bhWOoRKwzeKfb2q0hZ4uppvC1F5iqYvBoBupbwyt0OT55H3LvbgsF4cbZBE4BHVwOOjhZ45XSBX/zOq+iWK7y4SHj3kyt89HTA2vbA6HY05gRwRIwD/nf/y+/g//af/z6++4cf4fTOHXzzZ1/Hv/XXv4m//ivfwOryMf7ef/qfYb1N6PuEtjsC+i2YZUtoOwzgENAsG5wE4PjeCZ6d90ixxKyIMQkxIkIbCOfbiO2Q1BBlMWvEKLHpI/oErGAnXyY9GKJ42tjJcaTjuWgJtJOxrrYFthJrqwnIB2xYLAJj1mJ8EKN0EwhR45o1QZ53gTRGYMpbDUHFHTqqp+WiJWzVk6+PanDgglPbXoT4piEsW9lqu2yDwnxZtY0xgkPAopU4hA0Rema0QbbmJRajyDEn7TvQtYSzdQFWMbRPReWJGj8nDVGBa5ATRmAYRS6fN3jXJ7B5+Kf8Z9weY4i1FORISsmfhcc6017if63h79CLmyWv3NbPuHrPI3z38TPMJb7y2guEo1WDy3XCaiWq4no7IBBh2yeJvbNq8MarJ2jaDuttwrPzHmdXCb25rTm6J4sDhL/xi6/ht/7VR3j/kzP8999d4vVX7+AX3n6IX3j7Pjbnl/hX3/199IPAc2haUBzALN5slMRSHdqABQHdaoH1NmYDmdRjXntiHNtoWeZpHVNCp54KQ7Ith8WDz4zRFpA++fFlUXi3DgrMSNqGgufFC1CUqaCaf1msct54LDQmQryDTQBKbAeEqLcdQ0Ml6CndQZ5HhxtG0+1QjBCAlglDEk9c421GbxILTbMQBAGEQdtjeBeTxN1kyBZeU/wKQhU8PwTK3shgeacqjOeh4/dchLRrliF5dFPlnvnUG3GpIHlh0i8h3V2L1p8J7+eNLNmgp+V7Lz3m8t6mjCEGo64j7HpG20pH+z6KgWvHumJPuHuyADUN+oGx3kZsdpwPX8pe6Fn+Ynz99VO889EZXpxv8KMPWpyeLPH6wxO8cf8I/XaLDz/4sMS9JfUHV8EuJBbZjggtAU3bYDdwlvtQqgWAvCjmPXETCy9lyDuDv2zg5/Lrg8lbEhyoBKtsFGBoaF9XP7myM6/gEuvKvrH8XhE3+uDrMZpmMZYr/NH3KTGoFZlC6hFeDQg/l+lWIx5pn1DvcLG6EiQmn/Rd2ji4Om+MArfEFz+Pt01jFDhYDN8k08vX/YUnnr2Ue0cMeCYDAzL/qA9+Ea+bsqgaCFiojJyUj8TotQ6bXPkOBNw7WeD5+Qab7YBnZwGLrsXJ8QKnxx3iMODFi4tMawzwxyBi4E+hGBj3KeVZtMh9d/Iel3vDsSKBzE+9N3L5XmZcdd9lPK8Gdr7MrKOO37lnloeIsh4xhw9e9zXNzvQWz6J83+1dminPmp7zjk7d+InD9jXpVnj+5yi9lAwxZVsVfGiWvCgOFDlh+kVJXRsklmYUZxfz5Fu0ASklbPthKjdNm3bw/XVdu8m7l01jelQ924fnt2jLTQyqftRvU/Z1eT8Pr71bG/b2ulZyeZ87TPZC/dhYxRTnOZcVfxOeSHNbeQTYyqetkvhRIcDFs1PGl7feWdYS8YTdh5wck7HmTgr3E1Gu6mGon9OojeP8hVFw9USYvDfwjYV+89KzbZwpe+wlPTTDxtSMf6vlArxocXl5hd0AtdZvEWOLza7Hg3sLHK8Cdn3EakX49jce4td/+evYDsA7H57jD370Ah8+HdBr3cSiTG83A/5P/4e/hX/0P36MN1+/i3/w3Y/wf/5P/hlOTv4l/sabG5yGBt/9wacITZDTadV1f7XqcNp1OD3u0DQtcH6BdQq42gJX24ijhcSMawJhoSPQ6EmRAXLaZEN6CpyNh54QFqjVAzNktmWrrounFcSgtRtkUI1J28qbGU8IGpMvcT4J0A7kWHWEXg2QMQJR4oVjoafqMgPbmBBt2y7Jab4xysEZ5hkFNcbEBGwTo2sEBuwkUSIZ58SMRROw6aP0hfUEUJa4XW2QbbhDYnRM6Nog3ljMIASNoQdwSiAEiWem8RKGCDQhyUl94BpKx4L1AUrEULrgXfQY4o1bZMpchj8BW0Fe6QVVZRru5eRwNWMkuxc0aiaPcLr6fm/Be9P1xPsGb7m6k6tK8DWBUZU4twW3uNSXcTPjX9c24CZgt9vlmFQxDkgcMMSE46MGi05OaG474LUHJ/jam0sMEXhytsEnTzZ4cRWzxxwgQubQJ/x7f/tt/MmPz3D3dIUffHCG//qf/wj/8Pc+xNfv9lhSwDuPzsR7jO20XkbbNViGBstFAIUG2GzRs+BePzAWrRrwiNRjxWKHyTgkNe6Z51yC0TyBazs9U9ppcbJ0nkiMaTEZf5kq53kLPjMCl618zIL7nRrvLE9SF4FGPWNkfM0wWWJwJoZb7VQYpbJdvg2CI+J5geyJwGrYGKKL46PcxDxxbGt/A+0fA1AHigC4A0Rk+3BijavHwpPGStGN5AcvveTrgtQzj6ZFKIKXd4drrkiPr382b02zclvyZ6OPvxAtg3OdN8hZEQJTZrN3njPu+TJZ8wpvI4QhOs9P9VhPCV1XPFRbAKfHC7xyp0Vk4Goz4Pyqx2aXFL6KmpkS45e/+QCPn22wWnZ49GKDP3rnCX7wXov7q4iWCE8vt0UB14WAJgS0JFuCxR006pZxWXhq1NO9GLBUGmODaUYLt0gMF8OKSHG79LNeBSpKdim7HjPPc3I8PRhNQR4r+87gxowqvk1WvhkrPA2C0i8rV52Cs2yby2eRC+xE7txGQsbTTOJVmSMnqxotszqNVuR+u7589rQH+a7ByduUjuuKGnfkC8HfzyHxwdvyzHi3yzTJyw5umOv3CpMlNqzAQ9cGOYyOJdbqrk8jI598nJjx2oNjXK0HtG3A1WbA42eXePoiYNkK7Gz66R5Pg71gAJiK1633OvXT468nIMP1/FeGilFmmlxMUyVWOV5TLRDMTIqve7y72ejBnBHFnvvFAp+l7kLd8Erew5Ru+U/yGDkebLDzWb16flLpRnj+ZzTdaPivy0SYGPcqGPK4QEAgd1hG5sXTOleLBkMUz/UYGZvtgO1OdnARSjiIUVPqC0ejbjJ/nwdreNl6GIdx4mVQZfLNTONuwm+vG5dMBz4nJHmprbgMURh0ASkro/KuxNWbY8qsGgCB9TCKIsSCqA5gDCPM7AgwVZ2X145ZqjHBiG8l6SM/gIm13q15QlhzOVza6V6V73wswTk/Pddex9E9c8+le2afFXiLqafbbmPS1bmUjXu7XrbiHi+brADHlGAnWr366jF+/w8+xeqoQ7fo8OJ8h5PjBnEYsFwc4/xiiw8+Ocev/6VjHB2tcP/uKb721Vfx7/zNDu9/fIHf/aNH+IMfn+H5RoSD/+t//q/xt37ta3jz7gqxT/jGq3fwv/rbb+O3/+gxfvM3v4d/9+eCMn3O7QcT2q5Fd7zA8qgDbXv0FPDBow04MZahhW1XbWzC1dDQNeIJ10eJEwfI1tKrXdIDLApQJC7/zRDJupq5aAjbRhQRObyTs2Af3QSYq36ZL/OuUcEKYqRLSdrRJBG+kY2NGoOvZ6y6AFLDTNcEbAYx5AXtZ0DCbiCctCKstQ2hH8Twd7ZOOF0GdKHAXQjAwIxFkFhk1tc+cvY6cqERRHlqIFuBWokfuB5YtyoiB9YfM4gC487Y5JQKv72rwLdtt3eeflxwfVRwVRcyHlmiCpeqQNKj78p7qvKU94UejfH3piT/IF67pk1HcYzr/m3N0f12e3/CVYnJaHBUFOCk8ZyICCcnC3z0yQXaLqBpGqw3EYuFrNK1TYfNbsCL8y3eenOBrks4Wi3xzXsn+M7XGzw/3+L9Ty/w8bMN1r3g3T/9/Y/w9luv4O6yRUqM+6dL/KVvPsC7n17iB3/6MX7pNXLt1gUZJoQQEBYNmq4BDRGRCC8uB3AqMe7MFiDjpngXCE0S77ZWX1oAefHqqUc24ygZuZBxagMhBs5b0bWRua1js082piicmMCUhW4u8TXzARcoh2r06hlsi0+NHnIhB45ZzFAxpi86UdwsSDoB2AwJyybIVsBoY0JaPqm3oHQ4ex/buKsmw2yn5gJNK9t0ez3Ew07NnFt0GkPryD6/RyAx/D/8qHoH4+sVVh9sC4B6MfEawYcn1zybwePplJ/fTFi7Lk1xvtwUI3PxzDVY9gJ7k724kccXAJaLBmcXvQS6DnJIio8R2w8J6+0W90KDpmEsuhZHqwVeux+w3g54fr7F+XpAr7Fbf/ThGR68coSVeqwfL1p85eExnl1s8fjxGd44pdLu/F8XoRo9mS8lMBHWWwHgoIb6UM2ZjK5tKZN8Og9U4uB6LxZfpVfEjXclKoYHqUH5iNO8Z2U8Hf9KBtNYmdnLVCfFijLPYOOLpLhJLHhejPWMVo1y5qFIhLzwaNuGTXGz8n1fE/tYw2XyLW/2XmQgOv5tRtJr8fwlH+6nCS+XZtj69Znn0ufZqFvUfRM6YXPjaQLnd9PTVS0FlXnN0OcN3XY/DIxlIARmhBBw1HY4Xkkojs0uYjeUg9FenG+xWnXq9S1hW06PF1hvB1xdbXBsQTFnGmNGZqPlxgen8pT7xr93OG1esbNj5b4xmmhwbvgyIfTk3u0pdOaTjHv5ucNDX5RvD+AXAoqk65tluEmojTbjvmV6NhKO3RSXtigJqNt2GM/3jcUXjit7qsVPp+rPJd1aHpgRPfb2fQTT47xCN8wJSsM8EaksrTHzCoCiHyJiCLrYKzC97IIcGDnUcvRcWzy/vK3x+KXn9xb17K1jTxkvK8+NcXVcVhYxPqOwWM3154CftzPsmfIGIW0Wj8SEiDEgjuPN+eDX+b3FCgH0hL9aePNeHEbdsoHP6qLqdRHoqwG3TDVXneStJEDyOSa9ssMQCj5NjkusymP3YiRPuhr0Iy7GFKigGHXLrZxqJYa8q82AFhHn6yiHOgyEbtGgawPW2x22PaMJDU6OOsQUcXnFOG26/z97fx5/XXLVhcLfVbX3Puf8hmfo7qQzBwmToIIyiAzmcl9ABjUIESSISYjyKuoFBEHuRRGnV72IH5V7vfoxCUMCBGIYBBKGF/BK8gqoDIFgGDInne5++hl+wxn23lXr/WOtVVV7n31+w/N0J8FLdf+ec87etatqV621aq1Va8DxaSfmun2L9aZB5B6b9Qa37yyxtzfHfN6j7wNmdYMPffaT8cHPfCpu3l7j19/2GH7pd27inTfXeMcjx9i0EQ+fdPjNd9/BR3/ok/Apf/Bp+PHXHue1A4FZbcfJoaoq1FUlMfSOjoCr1/C0GpjVhIN5RL9ZgUgsWxjqSqYn/5r0E7VXNzMTBCBKgEqF7raPcMhZ+CJL8opeNCAg5E3XYtM5EkEcusKksbCMyZcg9CrER8ZaE4FVLiuau2CQkKmmna4yy3OVB5abiHlFaIhQeaDRGFpBBQjrk0iy3x7MxN2i8Yw2ZPd1hmUrigjRgR0jRAdP2RXbkbx77aiAUySrPWhijk03zKzIE99K4WI3oNtPqTu2zku/SmasbB7l9SG1TCzUmIgWMtiYax4wQEp8MpN1Hqtx0fvDK+ONcDCXExuAbdqG7xwl5mMZU6/tIhwiNl1UCw6C906F+h59AMgRZrVH5Ii2ZczmFTZthPeEEHp0vQdzRN/3WK5a1E2FqqpE6Vd5POm+A9x/7RAfuu7x3seWePdjp7h92uPW8QZ9H3HcBjx6e42nP3CA5zztCn7jNzbT00OaYct5OHLg9RKYL3DFAZUHZjUj9h1AhZVZoewzlzbB+awsA0TJZfEuk4JLiXCEKLXKmFyZeWcbWnGYo9Z/MGGd0j0Txrto/WaGPx8ADKALDuIOFyLgvCTAqbxYNDknm21a4+JpglgpN5osxKtyLllHEQqLTcCzxFizEAJmiRRUEVBCnSlcra0+IFm0c1FzNx+xfTdfuTwHkvf14TjPG0H5Y1DzLhkgnvg+de3C7W09MN1CUkohr42eXyUG3eCBFDecwk+IKqCTCORC/wGQuIQ7R7oXaODrIFkufeXhnCaacsDB3gx7izm6LuBo2eLOaYdVK0l2Yu2x6Rkn6w5XDho8cGWBh997Y3r+SPdkw9E+AFWFuV6vnNAyoFTuKf9keK77IxX4JfVzGJfSMs72jdLFjzDERPsesZ0jZsCoK9IajwAGqBB4siVfyRxmWmVK/aCHFTaOdCiI7I5siscQc3bsMl5Wap3tuSGGcfE9u9YPFyQpJRiDvfxuyjZ1G9EALnH48Sk7tvW7b+AJKHfTxVipZzyCyBCFgUGyJuUBHshjnGOL2r6Q2pJDaznUNf8TQlNXqOsKITDaLkj8aaUJ7Bz6KNfnTYW9eY3T0+XO97V9NA3WEby+gyv319Gz9lw6WKJRu1RcK3k2fbjkWc/iwuicOoZEU23tWtMpvCvfYSf823zYoYI1NDXyJPDmeiUej/sYKyHfByD/uJd7xvP3QbnneS32+XL5B5S07GTHJIzrD8ZFhn9yuCZ032SGHMJFvLzUut6TxMm86DvQELfOwxWauDZ+xV1tXKbuznLWIM8p5Vqd9R5PQNeTg7k8d53LXSfPGLoFCPNJrmBiLAumjtBccDMDY0TPoDUTOEZB0HQHlE2RCkArLOMK5mhARblQ/g0qpccy4zbGwFRjXGyT5ukaPLrO41tcVhu2khA3ap3M9IcY0fcBXYjo2h6h67DpIvouIDhgvxGONMSI2ANtxziOURI5kEOMDSIiiAnL5Qp1M8Odkxb3X91Dp0H2W0do2xanyw3m8xazWYc7PeNgwZjP9nHf1T184kft4WM+9Ol458NHeOPbbuEt77qFejHHM59yHT/33x/F2x8+geMAwMu7xKzcrSvCwaLG4aLB3Esii3hljuvO4dbtI3hfoWWxLBPFncShiyyKpxBFiJjX4mbXQBRfYNZ03UhWTTFGgFyCzy6IsFT7HIgaYLS9CMvOa0w9ZhWiJKEEIJaC6z6icg6bPibLOO8IbeB0+lmr3+tGA/13Aai9S0IaQS2SSN1ovTy7VCVOo9KFuFlozLCkuBRrO4vPRyTMVWRJ1mEOORwjIlFyUWYn7pdcOYQg7ZmVIACVZGIK+D1VSubCGLHE6AFbsfW2NiNl7CwxTkYIa2DU4ZgGFDemMHLKUq9oavQjX8lEvKBL2CYDl8wvWMDXxBikwtY9ow2mzBPL3AAOEi8yRkYkgQHAguKLu81mI3Ea5UaVmMp208JXFdYbYG/RaAxJjxAkiUrbBtRVQFUFrAOjiUBd1dibN3j2Uxs8/UlXJHP2zSUeu72Crytcu7LAWx45wc3jDTTSXBq/vZZ3hKb2mNcelVoU8bzCHjks12s45yRhTbS4dhKbi9kSZMjaVwlXxUXVrptCK7shGEAKjZCDAJfXgc3S0SxopDNHIlg5hR/v1Oq1kv5iNDdb0hiC8px3ANhJRlxkl0NzbTBmywHpniNCq5Nka02EhOs2VEeC68aYOUWgUsBghTFJgSDw7xR24MzqSd+1gD5TGpRRM8/iLeX5CYQqcZy3rXGncXfUDA9pyNksY9HxuCZP197+cZdlB4O+cwDjqxO3TeGTLfG5cLmXubMTdVuzyECvMVKtXRtTiBGOxEqvqV1i4iPJPhj6iOAinBMLbfaAdxJE/77DBa7uL7DadDhadjhdd2L1M6/x2HGL1aZPe8v4lWScDpV3okAjAmqHGoS272Eu6GI5n13rwTmOnuyJeYKT1QHlvcTg3boAxDXeFNrlFJvCr4TdUmEovwVHJEZYpr3kKCnrAIvbp3it62XuttAZsb9otImALtXP9fLY8zXDexmv7kQlots7FeudlAZaN+HRCNDO2q92osWZ+MJbFZ4I5d64x13liep2N57fTVuc141zm6Wib5BjQa/H4vC0wPhEu+3AGQX8W5gOYlaL0Qg4gMjBe4e5c5g1Fbo+ou0Cuj6AyKGuPJabXgLuT7xDhl1Sz47ihlqcG+9noFs+Z7ArbSDvHyM4T8+OJ77Y3s1sgvJUbZUSJtM6ZjZgcL38LLsr+djBRBQ018ZQvnP53uN54PSeOWbm1NjTuJTui2Jmu/3ymUuXJxBn76b8bsfzrYZHX0sY4+mqkyHOMo9VNDCC3LEC2aslX1LsRU7xb/sQz4cXGs751Linyq77F53f7d3l7urvpAuXaPuiz15kvJdfzG/IAADQ3ElEQVTuly8+52eVyyv2SNwvvC9cYpU7I3LpvjFgZLF4lBlJIFnG2VMBZMAocJEkw162yHALI4+U25AqpTUEJ2bI+hqzp1uTN6bI43s0urCFyBMMlm0sWxu9PlG89yCwLmfNe9+LSX3XBdy8s8bhTN6/qsXctu85Zczq+oi92iFwhHeETR8RuogH7p/jdBmw2QSsTk/Qriv0XY/7ru2jrglXDvexXLW4dXsF7xvM5zMAHRpf4eY734UHn/k0cHCoqwrPePJVPHjtCk6ON/jt9x7hV37nYdxBjaPVBnvcgSHWQZV3uLYnCTqu7s3VWo/gOQCO0dcO3le4crgH79pMbEkSZNTeqfuKCjGBNetr0HcVwbryQtQ6dVcWSzkVOlXACBrjymvMAYnlB3Ck0ZJTyoTbR7HLrJzELkHKI4vkWtMFUWq7KLDuNGB+HzSLmQM8W0INiaNXwhFBXHzF4o8QY4RXxXgEpVh6KQA4S0INU44AMobaA0zmmkdZOcjmHmvWO4KHXoP9B6aBaXYamlqKZKxFwm+YMJ+YKHMlp8wE0VCJkHE9XzBcIKSXS42WRHqAdiPcHOAdA6U6f1iG885nfuYNlCceOkvVt4XzE0Mp2XWjCdndVhR2y3WPWWUCdI67aBYuoVBUV2B0EYixw/5+hbYTk/uubRF6hxAi9hYNvAfmswZdF7BctXDOo6orABJgd3l7icPrV4BI8M7j2sEch4sZNpuAG0drvPvGCdbwWHc9GhZ8sOQd87rCYuYxb2p4J9aEjiNAjODFPXc+q+GoPDFURZQqoy1gfFQL2RBlPRPuOLuu8QZhrkmUGObIoujKcTjVujdDgXLnamWj9AFAUuoXGTSSlZ9ZR9nWpbp1Ua7pnme/GdCM2HmxCWbRl5MWlJDaq9I983CiTLR3Atv6j+iHodxoHzELkHIY4ulrO2ge11hAH+zt5ZyZJDPeB8dC/9aV7VLiyW4FwU5WeLLe4M0eF66dJ76N7l6gn9LlPuO6wGfXxZSYyRUKPRMgTdnEEItMccuOGjNTlGRi4Ssw1VReLNsrwfuuDyDn4D0D0H1z1WK2mEPc5gmLWY15XaPvA07WPY5ON+jh0IUgR3RFVvK6JtRevAKIXMrIDDCi7g+19yAqg9BZvDxVRpPwarajmgKDY4ZTe/fEEyVYHCq6DfZtzqZYuFxP+VAyxTkN6iTX9vGzVGT4xQgVxriDQsFYjNPW3g4M8hZTWuHb+nOKHW3PmZfBECMKV8CSDmC6bF2n6RvTz0/gOW+///uiPC6o/QS0vRVaQ/8RGl5aVE+tZZZb8rM5ziwX7UemRCtMkA/BvE9UBiJG6DrZ39Vtvq6c8s+MNkS0bS8HwXEE9ZRjRNt+k1E5L3qOJbn9IpmD2n7f8tp4HtINGK1E6t9+78LzqTJFowdtFshplnKDYQzJz3T7xQO7xgkUSr0Bf7jdcrLYm+iPRvUuVd4PuHov5QMVz89qcLyiu/H8/HEM6tIufkNoAqk/v/FqFvImJd7b0ceInUxjTv3hcmAzic+XLLa3ntXHRdvZSWMeh3JR/u8y7e3Yki9VLqfYU23wQLAHilNPHZwRNIIq8GSYY2uBdNqvfGMpnJRKsDLIcCZkPJjUYZy9TLhLbNi5uDbGMtvQuKK1Z3V4uloa29bmnsdrjL2dFI/byAwmJDZczKb0bRdQO3nOC7cvwp6zuZHg2Z1ZiRGwV4vL55W5w97MgZmwXHc4Pu5xcrrC6ekGh4d7ODyYow9r7O1tcLyMmM/naDyAwLh63z7a9RLx9hrV3j58PUMgj71FjY982nU8a2+Gt77nNt7cBtzmNYAZPvLZ13HtYI6m8ojMuHlnhb1ZjTvLDa4sKjTPeRYoigKvbSPcQjN7RUYIgo6WjVbcaSOIGH0QZcG6i9j0Ys1kwa5r7zCvgWUbUZEm2tD57DX5hSXX6KMISeZK4FyGEk8So8p5scpjVuWhl9gljsQCsO0Zi0r67ziiduIiJYoEc60l9IgpAL+8jyglTYhpHMFi2HkHRD0NjcxYbhiH80qsmHTs3on7lSg7BgCf4hMSNKZCNPAWgGgDp2D/pBmPB5krFc+zNC+fJjwZUAvOZmTITOl42ypsPozvpYlazCPF3wiHija2EGZQdxel5e1nzyxjNnTi7phhLAhDevqcY7IyzpbEz4yidNaptYyPSd8EQPAcKSuduGqry2hNaCqJcdl1EZtNQNt2aNse83WN2axGiB2apsOmZVRVJYrdCMz3GoSuA686uLoB+Vrarj2ecnWB602Fx+6s8EiIWHEHoMKD9y2waGpxD2TGct2hrjzWbY957eDvvw4PtabtGdRYDMzscmaWcYyoii+NHQjB2xCjusKoVY0j1ExY9iG56JPiceTsbi8HI4X7rQBa6tep+Z8oTbOrk8TAygH++8ga51IVkIqHpnwU+NYYq6pwtLhJIWb2XeOb535ctsrt+ggnxD0FKXdKh5D6zC9iSn6bk8CClHaYUSbdSHA4snAaok7G4J0gO9r7yjkdtLYDz3eVgRBT7sE7Hz6PHX78y1lMnO3xF3meFbYthmYZY874hJIcm4WqzYUpt2uXD2SCWqSHAKxCj6qXLHiRI6o2Yt5L/C2zmKtqjxgCuItw3oOcWNh773BlUWOvcliuOhxHoEMAyOFwUaGuvOKpKCR9JQdetSO4/XlhTZvjIhttA7IQwchwaa7zpui0dzX8YieZ56mcF6MdZIp9Tgr1kke0SUu7GOU+Ms1Q1o6zEnWs+DPWz2AaxV6YraaKfYoKPEfuBxB6UHmlUYDiq923vTgh7WBvLnHTaE46uNM92biY3WWIp2lBLiRJbFdMuHsvksjvkmJrdKG6+o+tWeakZMIn+f7iu80pFeGKTLYwS/EslDMoaigJR9pHhPNOLYKjym1CMMgRZiRhIro+oINZBxKailLSOUDlC+SDN6pGVvqUx22waYp5w4HyXokrJeiV4hWKT0Z2b0/P75hzGoF2mssCz+351CajmN+iLQyDK+VDs1xhyprO2ozIh3DmVZL7z2+f1q9AoMm5HX3e1T53YTz/vXKpwoMPALtVCZN4fkb9bZIjmhRH+UAobRn6I+EVCdTJ/sYD3LGz62JLHfRZHhTtKlOgtA2nHzilnPuz6kyV8dpOtXOvqFW2ey/zd0nF3vCnMbNbTC0jncQyDU+pwNBsmRp/y4T5xJRkSjawYiMVm4o4fcUoxnCfPgeuSLveqbzBU5LLsAFmbGexKasUmJg396zsM8Y3C/QS5LbrxVJnb16hqZ3UixITY9P2yYz+aBUQ4cRNCyIQEjH2Zl6stliYzYP9BSrPuHVng9gLM9kwowuMawc17rvSYNkCMRBuH3d4+EYPX1XYdISuA67uz/FhH/IkcOjR9R0cKlRX94CqFmbjaAVyNSgymlmNpxw0aE6O8cawBuEqnnLfAQCCxZmLLFlq5zOP2jtwH3G6iVhvAk6XgNvXGFmFNU6tDHCrMQVr79D2kpBi1caU9Ue85BjzxgFMuLZXqTusCuWVuCiBxCTZlAGVWjluOkmsYQx+YBE0FjWBQnZ1NIuJtudk8SYZKgUgzLLKkSbYgJyG9mpVaDATYwQ8pW3dLLAckAKiB42Yv+kY+4256Ir77qwitMRAlxmQiuQ9nVkPOXFr9k6Up7NKE6uECKoc+hix3zisu21InlKwpW2hRPViV8koue2eN9GYlDEjZvQhXR0JEVsjzRcnUHW7hXL3HO2wJfNdDm/7VHU4ivHGW9LFpLxXGtD35irOaGqXlPPMSNmdQ5A1WrcR88bURVmJJ4y3dORAmM1qOBILP46y7h6iCFzMnGS9DgSOwGoT0Z5uxB02EkJYYj6r8eQH9sEcEaPSlnkDeC8M6boTX3Vm+Mrjysyjajd4D/cgAFf25vriAguG73UlSnREUYD3IaLtkBR7pQLAq8ahV2tbS1JReTmkECEmW4lXXmZl0TiEiJSp1jJoA+YCKCvnlYnpgyhEBGdy3CLLgpvWnSCxDmO2cC0zcJpSwPYYs8CTAxmHlG4pgU7GkdLF1wRxsCjaG7bAyJzoUwCSVoEZGj/U5THpbU+i3JOMuGqdC1HwNVUZQ3SqTEE+Ep4wkC0VCZMYMdnuBJ7vKqkqD79PDnUXB30vnNUYhy9SvTxQ1C+Gq6ZA8krPEx3gbK0Xo8Bk5ZG8DQx+TYFnpao8iEShZgKwKdLqilBX6loKOTTatGKdJ0rEFlXlcbDfgDmCWQ6gqPZJ2Ecf0+I65zCrHCj0ONJAefOmSu9KnGMNy56nVkPpYA6A8iLlXGZLPFWuqdu5WcrGAsbAEPd31bbZYQCAfNirim/brwzVYswWs6bwYs5utuk9kK16jVdMSjbWqSn2lwJdt3jOMvysCESqdFThypSbRvsMjh3pYV6EhE4pxmYKFvszl19mTgrWPATFfZxVduD5PZaSPvyPVko837rHw5kcKIgMTjjXTQCEIbkqP8n2p8iDSjK/pRJY91BkPoV7ccufNZqn1uSrpMXGILCjI9IkLxEb7cy74SLmPTHHOM4Hc8N5SE/qF1PeG8lOCujxe9PQLTm1twOeDM9tTktek4F0qDbux/DnImVqxadBvHzJvDg8qmzwASDxHVNzm5SQAzwvepu49nvlCS7jyZ4QB3j70lYZ4PuYFO9uensozOBIg5iwgi9jgSo3SvpcunQGHpT7XGp74vvdlDH7drflA2W7eTzGUbADg3K3c3VpV1wuVtoYmVhIxKw7fI76RfI7nbDas9lNV5tTJp6TJVDSGWjDArjKTk4gg22XgwNPu7lr6nk8eaOGy+OZVIOHt8ZNcq4D/c6AupkU1jmRcbJs0bYBfQionKSa7zfA4dUDrSNx9DZdQO0YxBHX9yVum/cOXYjipsrAkx+8H1cOF9hb1Hjzb74TH/0Hn4OKgEdvHuHt73wYy1UL7wjzWpQIFAm1Aw6vzHHl6gLz2QzHyw7eVdjbq3D7eI23v/sEH/LBT8LicA5qI5gjEAJiFxA5Iqw3WD5yhHWniqnKJxcBQBRYzomS8pFbp7j/6j7mTY14fII1HNroEaJkojCX7KTusoVhs34RhZYE5gY2PaOLkh2WSISjdRtx2gbMK49ZRbhx2oOZwBrzLm+YEhOvW0uGWlaFZ60CVg+HxmfXx8CsMfbUTRAaoLgSOLfEGQSg6+UdzDpuVov00quicVYRjjdigVM7h1XbqxAjqtrKEW5vGI0GJhe3CVE8Nl4sGGsvRNw7BoUIS4FuxNupcB/N+klPfQNn5WPtKCk4S7fABLdU4AbzADMGjLxxYIVEToM2WL+T3uP0rzEyQwQqiOWkZL8D7y5yffASY0Kx3caYLvD2Df05pAkDhR6LkN2HqPggwnwMwHzewOLjxCjKL09iLbY3c2qNK8KrTfHB4T7msxp17fHoo7fxtKc+AAfgdLnGzdvHaLteGHaXBWFPwGxRYT6XBDabNsA5h6Z2WK073LzT4kn3H6Ce1UBQpVBkcAhCs7oe7claxh1V+CCklWS9Bhaatj9vUHkP3rToQAgsLuY27UmITv9IMTnaktI4VcRHzgHqnRMc6yw5hQNOWxUyIqsFnQnIoiRc92J1YJYOZuUq2Xeh+MGAKgT6wEn+IeSYZzmOXn4fqHVcpVaSpritHGETWPFUktgA2bXKEbDqOSfmYIvJx4DLAj+I4ILSxlKCQV6DFLeJCwWAzq1l4Sz3xemyLZBMMXSTjEzewLfu7sTzM0ex/X18MdOmfOsscrGLebpMmXS1Q8b3vs8KPVsHVtiQepwUYE6JZF0JbwC1mrVpms1q1JXEyDo5WePqlQUIwKbtsVq1KUyEKcdI17iuCFXl4J1T63bLtB6wXAcc7DVwlQOpRawk6JL9iyMjbHp1fc9K7fSuNokEbLqAphYLPvQBAUix7wCkTWAsqBhOAUOrAVN4Jfc+gh6KqeU9AW2QNkQZV/BknOPbGfsR0/iF9zQ3eaS+Ndu0XaDy2WLMxXez1oWO1foNqkQnSkmtE246AJtQxLBlJOtr5/J8OIe0JluoYng+2ofKsZllRnl/uuQ2xnv+FN7ven4Kz3eQgN91ZYznw3t57YGCTcUoZh4yPeYB7c7PAWIlK7BA6LqIeS0pX0KUPY5NG1UQX4MXZqTYkGAkvq/rI5qmSu55aW1MYx2BGMQK/jx6GCOj0ti+HIdWP+W7TC15qaSyeqWyL80fZ6UbARkv9TPFqDO6igxrprgf9Dka11hROIUng+88VA5y0Q4UZ8cadBn3UHZJ706jOdLGxnNmfPNZq1LS5PJdLlzORu7fK2XhISyl77xV7VzeIlnXkuz3ZWKcXUtewtKYETO+zw4CykpDWD4rcNDwHQzmzyB/FwKds+Zi/CpT1y/TxkXKZXm+89o/r7175TPvtVwyKy5gAr6dcFpMlHR/gv0fAn1pcmyUmdKHETaz2mGlxukJ7WNsNTAmylvjxrQrbrkp78CfESslJQeFHV4fIgWnDaV0tzMhvusD+hhQ+4iDuceqDQgtYdUH9MebpGQKQWLrBQc0NWlgdnFDnTcOq00AM/BBz34K9hYNmrrG9WtXUXtx33zSg/fhytUDvPfRm3jkoRsSV4MJ5Bg1AS72OGwI1655PHC1xtGa8eynzvHm37mNG7eWqCtg/mFPw/UrhwgdwJuIeLwEowaThz+cIzx8B10bQc4hZcMdzc5T7t9TqxcRaDvH6ILEALMNWyzb1N0UaoEyUAIRFrVDYMayjQkm113Etb0KJ22PtmeAAxjZisCpJU+viQgCQTKMah0haKK0E6tHtbzR5xuNZRZYlHmBHFq1Lgwxglky5S4a6aNSDcU6MOpWma0glod7Cw9HUS1pgEXtxHValXWOgL2ZS0o5IsLJJqTT+OsLr1ZZDsxynSgr78xKkXqNC8ZIFk8hMuaqHLWTWVGgjFz2VLrnAm+GeDWFKdN380XWDWkYPrh0x5zCpVRGyH5RRcHuMr0771LYbz+ddz97pFSwmBI/hIjIEd6JkrdT4b8LEXHTJysSVmtKdmZJajihlmu9tHff9Stoag/vHfYWc7HeIuDgcA/zxQxHJ6c4PjqVthSvPADiiFlFWCwcDhYe645x/UqNRx5b4XTVwTugetJVLOYzcIAo+DYtxP6P4GY14vEKoRduNTEqPFzvw/0m0exOcU0yetJA4eFJaL6DKheocNkm0viBohAzetpHYEEOmxjRR8Aih4hgkE+yYzSaK0o6G12G5ezqLHRCcL9SnIisCj+o8M9ZSdpFOQxI8e5YrjmKyYKij4y6JlCwd5X3yUpRSkoYZiSL9U2fXXgXtSo3QWCOSdkh7yFWwpEZFEu3xixMmUKvNMAgm+sxLHN257toGeP4Nls5RNhdeH63ZWqo5w3/bpitKSF/cp9nYdFNYRMiNMM1g/vsMorEB0Ctq1kzH+fnAGBvb4bKy35a11Vy65rNG9R1hXXbYbNukyUvs8AKsYR7qGtxoe8DsJh7nJx2aNsAR8CBl3i3YKE73AawHQ5VDrzuNAMvYZuQS5k3HlBuLkROSZsKPSGUfdvJqwEaGoMFn6xCYEZNko0+FhuB7UOmpC4TEQTOfGFeKNtzdAw6NpfWgpLlmylHjMeMLHTTXF0BpFibRq8iS8Z5s/aVuGSsVlSUcDYnApKJCDG7Dtdew9swAAyTGBitLPeT9FpkfSIpdWFLtrV52tSeEZLm0mUbz1l//+603uMz6R+PvpS6NgGzrGwu5QaZCkKen6HcIV4W0AOgbJUuGeB9suLPyaCkc4MB7yRTemCgroCuY7QxCOzNKninuaHN19z2QonHkmB513KVFnymREtyzXhurPWJ9bd9rdSHRRiODfm9XbT9LNidGv8UhzoYM+cxlApIG2fCM0bykrD5t1iG5agsRjAg8xQLgPIKMKZgHeO5zWkJJ6w3Gao0Trg9nJffK09AyeR6160BPE7BWHnfFHkABpZ2pqgt6ftWZ8U2zEleUx4iAqw8ZarPxWhKYnMGBhGG1cfvetEy6G5HuUib7wu4HpOpqW1rfH98rXwuPa94erfvcP4h/NnlkhZ72e02HfwUkFhaqY1LHMyYuSgZx6dbn24aQJ6UxMBhuPnw7q62ANMmelhX+uT8dQsfjDE9S9893Ix4NLaC4WdzvWGkYJaRsV732Jt5rDY9AELlWDNVtUBN6ooacbBwKY1118cUV+/JT74fVw8XWK1WuH5lH10b0W4inK/gKicWWRGIt27h6Xt7eOqHPQPvfvg2btw+BiBxedZtwHzeoF21WCwazH2H28drXD2IePBp19HFJX7rLe/Ahz3nObhysAAiwT9wiHDcAh3QHS1RHe5htukRjteZaDESsSEizOoZHr11CiJgfz6D7wFsemw2Pch7VM4n99lZpUxtZERHKZi+CQ29KskszljVSJKQthfh37Jt1o4k6QVTEnQBe5bEclGZqsrZUkqlwCwuuSBseonP54iTx1KMYkl3QHr6HyNCiHAkVkGNZsSNLArETR+x6IE+yARFNhc5QuUIR61s9stNRO3FDakLQN/JqW1PwLoTGNjrCZ5ynDUGpfdYdTEFWycgC/cj4Z3UYb7TDKAl8BueE0gZuTELMyaCnPAFgLgsKn4P3HoV37eevYgUMGZodqBlOcYp+ngmzTzjBk/cT3OhN7OAn/GeWU/QK4dOAx5KVmWHrgtARarcY8xqC6LOyTWNCDg42MNiXqPreizmDWKIEnfKuaQocsRwq2NcbRpceVKNO8crnKw2kAQpmoym9ghdj7quULmA1abDomEcXl0gcotHH7uFJz9wP+azWpja/RloE4AAhHULN2tQ+Yi46RJuJzqts1v5CierDQDCrK7gAgDN6i3B9tV9loGZPhUZcAqChAxvkiBGre0YKbalxZ+kSEm4Cb0ywEnIJY0jSYisojaZ2z6SBalZ9gBZiUqU9yyOcn1WmYtUGUNMXPFjMfY+MGrHiJqUx1yLKye4tO5lV2mTtZ4oCCzzb2Sg07i1jQdI3XI57WEy3q6fYCZKIcDgU/E6oXhxb4Ank7A9QmCbkwuh7BiZLojn79eSGYspUjCF7/ZblPijDMkkyt8+xkJo5xQrFcqcMwCQWel5hBAlNq3BuWpukjIhtlh4j/nBHKt1h7YLaXwhirKJg8TYchTR9RF1BczmNSL3ODld4mB/H3XlRbCYVaA+giMhdj1c5QUQ+1G2zOKHI4e2E77Fm9t+jKpozO65kYGKMozRAH503+BCKY0c8sJ4Hqsu1nE2t8WaaJIdU1APDZwynbL1M6Uq0t4mtU2ZlwRtzsNMzLbRJlXslzAgMQGFf+1VUdmrFaZzZkVv9AcpjmhpHTzGkFDMSyl8YatugefYXc66d3dlG8/ZlHvAByjKn43nEzUH9cdwnL0QhoYLWWE7lCKcd0mhbZ/5wIWyIB8DKiJUNaV9rxyT0ALkw9koNKeuPBiMtu0xayRxFQjq8604F8XaxxXW9FOFiBIsZ+VEpn32iiV8Joya2AJK2U2NlLdkryGfOnh8Unky6KKoZJaxNLpvfXPR6DaeqwzCw/EYj2E/LPFPdvNVJT6XdaTjNH8TTOhZLvRTeP744/HvlVSKtb4I+RrDaHnD4YyS4EBjyLq875z5SMI9O8jOyTPT9QL+CMghz84Yzln92ljvhYs7r/nLtnXWMxfBj120Jd0f4f1Z/Qw+C9o1QQKf8HImzI1LFlo5b0ScAY0TscmvmOra9RHQpknQdg2qhy4HmXNK/aetxDbE4je2f9t4h7Htcl8Dd5RRP7bplEq6VKf4w6COuLWIRZe42PV9zJ/63YHR9xHzpsK6Y+w1hKt7Fa7vVVjUDjPliKMyAOSysHv96gGuX9nHrJrjoz7iOfAkmSgPDhtcuzrH3rwCbSIi1XCnHfbiEWZuhWc97Sl49lMfBMGh9oT9mUPf94ixR4wBD95/iM2yx+rkFH3XYW9W476rB/jVX327WPt5ByLG4soMB1fn2HvyFRBF+LpCNavhzW+NhoDd96LEPJw3qNU0gZFNifsgVkNmdVYSuD5qEggWNsA5iV3nlItvvMO6jYlJsgDUB3OfNtNNH5PlWlRBvSqUhpKZVk7k60piWNk4LEaSd4SKUOTG1VhZzFhUhcAPUSQ2qpwzE317HwcRApcbcdHcGDyQuBQvNyGfxlJ26WKIC0Xbs7gHq7mXd3LyHwJj3cbEkPTGtJBk140K15aZ0yl6NRXJCe4Y55Fhu8Ts8a+MT8Nnt2tm1OTB9bzeuwnfxUjiBOW4YCsjmjGgHRi9GxK9KmlL+lMB1z4JYo1XeYcuyHwvGodF41C7bClmTKNtzMyMvfkMe/MZKlfjKU++H5bAaDavsJjXaGoP6iNADm4T0MQ1Ktfh+tVD3Hf1AIAIEU1FiEHc6ZkjDvfm6NuItm0RQkRdeewtGrznPbfQ91FPAYF6VmE2r9EczEFgURaoQmC8GRIBIQZNYuNFIaemMKSCdIwhMdsxYgAIyahAYcGU+cmKwVlioGGohpnGlgSgCjJTYrNmzJbvgheclFy+iGfEyO05okIgUDc7ZdhTHNNivSz7tMg/hXWOvlOnSX56VdQ4iIJR4qMi/eV9RvC9V8WGvZu454rw3oeYlIsp9hgkJEJMsFsoOZjhfX7Huy5jTqa4fjb+SqViqz2ng8sM5jJlgj8o8HzXOxivkvf80X7PKtqzWt4pfFVeaLOnLBAmvCmk4qb2mpzC4fBwoVad4lZb115ofWSJX9ozPPdwCNhbzLA3b5LirzJFgTY8ayrEwAh9EHxwDk1d4ehopRmpZUy+8qhqB6+nauQox+aaKMaTVJ7SwdFgvjgrBcdzajyVXSwZaEBdCmMhqOj1qtijSpdnRs4WXjLitj4u4YA+rD8ISNazVmxJykzxDNnzS8t2UwSUe5jF+iwV/QQkhUzpLluwtbDA5lkwKyycCkvG1BepVXIxv2W7jspZu7tyJope4GnOX8/o4TKjufwYBn+cP8/nNUbvX/AsdsFoq3yadbTCUqH4wWgtLNssIDgv1SUOp/caP9e0PCnsi8Sbrn3Gh0w6ZFReQ3aUMO89Yb3pkGIhkxwYO0+gAZ++G8857Vf6bsVeYz/LV56axzRxNJzzCbKhc5S/m3dU6mvUUcJz5Hu2vhfZ66gYU8nP8Oj3oE+DIQzfP+/DVJL2or0hHUhziMyfbG2rXLQzunmvW/nvlVGZIHpj2Cj/ziplHeN95Xv+m+Kl0r2yoaLKGKaNFgQ70Sn6STcfj1Lue9vDPpdCT93f1db43tRzU7hy0ecvWn9Mm0racLf9XKRM0cTLlEsp9nKv8mdKAlAhyPBQrZY3USOEBZOi1LckWsPvJfM8sclynoBBvcm/Qjgv9/rRa1lbJQMPLlV8xbukcRWCfbLIixpEOg7/TLkXgl5jxNDjeNmjqj0W8xqrdQ9xrRSF4LzxCBHiqhsi9uYVZrXDfdevYt7UOLlzire+9VG0Jy3mRJjPa7EGOtlgXTXYuArhGc8A7jvE/MlPw9WnXsczHrgfH/v7nwOQx2NHLUJg9AHoaYaHH23xyCM3Aedw6/Yxjo5OcOfOEZ761Bl+5Y3vkvcjFSwd49p9e3jy0+/D4mAG58ViJ61xQZXmswaOHDjEZGlUeYeZxhTpdYOUANtcxLPivB66QF4FD0Cy/faR0fZRXV5EYWVM1sHcg5nV2k0YpMjAvJE4emJhx+nUwxJhhJjpolkhmJWdbOCZdJvisVOfnFklcOGKExjLUtlHEYi6ZJElFoHG//Uaj2e5CSnWGCv3YC5cgMQZCwGY1Q6L2iEycPO0T7AcmZMLR+QobpHapytjpFEWkIaYkPFMhkbYqpJwqMDjAodLpN7aM3nwgRKxB8JZiXsYNzD8MyU/77g/+QzGgvqoyviJQpCPjAHuT/6xWWlEbDpRPteVT0lvGEDgKAleIqsFpVjXVY6wtzdH7R026xY3b54gbHrURKg0Q2XY9OhchZ484vVr4P0ZqoMrmF/Zw7X9fTzzwfsBEJbrkMYSqMLxaY+Tk6VYea7WWK83WK82uHKlwnseup3wTWKoMhZ7NQ6u7aGeVZrgxRVrIrDBDNSVuA2aRQCRwFulgkQcPjFY/6GwxQMmZ1YL3gXFH2vDhOGmFoBOyjDFvVqtI0r3BTssAabjaWVLigHAieIxZndJUzRQqpstrCzjaWkM2yeilnG/7aPGD1RWhYbzYtaJpiBiFtpQjjkpQ6HxiWIWPJWCJkXETgFu8upwhcZYPMmA8O62yjZsf09XysYGXW4j5d3guT035g22CdNopKnuUMmfFfrlffkMISthLXulteXM9VSfN2G+rit4R+j7HsvTFrGPkijFOxAIsY+I5BBB4MUCaCq42Rz1vMZi1uDawQKAwJONOcJh00ZsNpJlqe16dH2PrusxnzncOVoV0yyTUDces3kjVntEAxeisninqTBK9z7KGTVtSs9imHOdvLtIkinDo+Ih/WGHWYaDplyxZCOlgnwgtI/BWNt2hCHsIeNUGaOUkceTQEafHeA5xp4sWfDKil0efIIzP+00ti5DknTZfYOfXH/4nnkN0qRevkzgwdm4fHZjae8ebqA70HOElIPvmHpg+28kMIx+jvaXC0xD0f32c5kXGyiotLIcfCnNQLYKd2phHyOjVW8M2yMBAgeIdbZo6QFP4n1TOVTeYV67dBhVDtMS1wAWIsYOEwnrTb+FB94Tqtpna74dJVu26rueA19nzW+p7PAFLo2bIhSHIDysM6mrKOqmnzQcyxQZ49EX63NK4WB4XMLQ1JZlh4q7inHKwtsYLTN6sKPdATHNvM+u9zq33D1S/49ZzuABcPatrJjFCJbLvUKBZrBU5Zoqjtlhnuyj050nsqg/El9BQziyKk5l5VK5+ESXXd1ctPuL0uiz2ptq46x2B7Ri9Hv83GWmsaR5F+7/HtfpchZ7sK16yJBIrL1ikzFKxDEzu/Y8IwmMJcNsADrYSPWfLcuZwVjKfoe/hyPPn4xxm8O/suUx85/fpVD6sZ0em1Ivb6rmnml/MUZxowsWLD+AmSSO3qbD6Tpgb6/GqlMFUgROVz08iXVWCMCV69dw3wP3wQE4OW0BB8Suw7ve9SiObpygu7nGtfuu4RM+7ROAozuIyw3mvsa1Bx7ElfkCs6rC4r49XL96FZ/2CR+Npz3pKk7WHa5fnaOKAY88fBMcGXeWLd776G2855FbePjGbdx47Bbe+OtvRdd1MnZmVBTRc4SvPJ721EM471DVXmaaKDE8trYxRpAjBI45AP/IzD9aXB1lmE0YtrYii3ttr1lkzUWuC6Lw22/MikAyytbe6VwzjtZiCdcHBrEoxYCCmIJSls1VF9HqOjkS6xqwuMOaZYQnsaoxJSKp/0ltFlicrelmFaHXuTDXPFMEE8zyT+DYrPfKLH4EJKsPY+oqT9hrxKn2ziqgokLppPGdIotLssY/VgsytUhghlnaZqGFCjwvMIcMP0oiNOZ8h5yzrP2QuR9g6AivBlg64MgLelAI2GOm3fpEeW9UZ1KwnyglvbMGs3IZA3zniT9T7HGEurpQykrc9qK4k6zFsu6bLqT4XMzAfLHA3sEeCHIPADgE3L59gvXJBmHZYbG/wLM+5FnAZgVue1TksLd/iHlVo3IO9V6DvcUCH/Ksp+HKwRybLmKxqOGZcXK8BJixagOOTlY4Ol7h+HSJ09MlHnrvTYmLpwoIR4wAsZS9cmUm1qTJpCXDun0xYYWN4wASnKeIIsUaEuW4raXdt80hQZVobAKyxL0UJUIOsM8sbvDrXtzfzdXN3OyNmU7MGHPKumv3jTnqQ85eawctg9NWmMCfGStHkpDIDk/NDT7tbSiCcivsaH6MhBwm1JDWAQRGao09uupiDktg86RwmfvL8Qyd+cTpM5OKlh2fuYJiQ7H/jZ8dN1iOb3cp8BzIMFHuzSjmL1cd4PkA3zGN53z2QMbDL9asgNXUdobdRAuKd03JcKLu2zELjUHhyvazqq5QN5XAscaDZI5YrTbo2x6xDaibGtefdB3oenGxJULdzFB7sfBztUdT13jS9UPMZ7XGeHQgZlHqQWJ6bjYd1psOm7ZD23Y4OloiRrPvLA55HLCYSYzeKUvuwSKr6cEgxg+Aki6M6fNYcM6ZaTUWFZB/O8NZi0uY17MPQysXNazfEv6ZbS8sYFyTmZWuvUazSmZc7qnSRV/XIY/D3nTq9Vk7jzyNd2OG3+KsgYWfsazH5XsYnRovi/Ee5+PcOYWx1cg9tQejFwVfXVCaMR+/1WFJlM77mx7+JcY5pDXjoYx5lUQNOSt9spKJt2DIMjVnK0wJ0xEDgwPDVw6Lg7lY5kZNvORdstJ3Xn4vZhW8l8NcszrvQz6I6wOn2Hx9iNi0XZKPTEFt72CJfc6UJbnEpzGgp1fJcIwR3NqfwW/Rn82Z0URCxn17Lo7WYkpwTbQXIyu/sh8M6cJZe6HdHwv5NLqQf/LWnIz7yHOSY6KFEZyM2x6PcewXd9flXhDlf5RyxvufNS1j8jRYIxrCWKpXyA5EhKp2g8bKjLaCC0XMTR7hiz435kUixwHebe2Nj0MZt791H9s4tLOtc36f99xZ4HvZ193Gs7P7PatM3T/vmcdpeQBc1mKPGWA7hSoWmNTKCXZNbqbTbCN4RZ2xMs8YoFLhloV4FH/qdqTXkQA7U/5tpR3yJl22h+ITGPVTjHfQlo1tfN2E0IhdljsidI6vAes2oHKMuQfQ97h5p8PevEYfPe67b1+CZTc1mlmDw2sHIBDaLmDdBbTrFj2AdrkG+oBby1P4w30841kfDO/nuH5wgKcQ4UnXDlFXDXzo0TQeBw9cw+KBa5gfHuCPfOTvx+3jFlzv4R3vfgzzpsKsAk5Olrh9dIK3vPMGfvvtj+Bt73oEISzxW7/zELhbgzhg00fUFNHUrO57gK+rvC6J8WHUtZeYW+Yu64YBe12oYBmWzSpHBGZSYVvWI0RGDHlOk8JEBfrI6hoXGX0f0PUBkSUw/bpT12gG2hiTIJxpbLacK13xjCgnlxqSWFmVE+LrnSQ3qDWuIcNi4Jl7FBKMWJ8Wt8SsjyJHdAHYdJrVVhXEtbo61S4r+ipnf9LG8Sqk7JpmgdBHUe55lToqLwpHQxVH5tatsFtyQIaYo1JaIgzxtiR6nO8VzWQFX64/6KGgKeON89xykUoXqMPIeD0mEpm08UCRX9Ilu250TzZb6bjroyjoCUCMWK4D6tojMmFvbyYWO97DVx6z+UzwIIhA3ndBkjK0PRAZy24DN5vh2rX74VyNvWaGQwIOFnM4V8FxhK8cZvsLVPsL1LM5nvHggxLL0zW4dfsUlXfwDti0LdbrDR67fYobt05w884JYmxx48YREHoQi/WxJ3HljBr0ibaOFOXdvXfwlQT9N4GnFGCJ3UDIT9n9UFihAKoQHVlF6DNdyIosw6sQM472ISsLzP3e3InGJV1J+1LpUmOJMcQt0WiRhQwQiybDMVFCyBiz8sZIl3OmwBRa1avyM8JipFDCXxuT0Qq7tlGlHpAFSYvZZf35chyAWg9mZWMskWwXTvDEja1L5Z67o6kLdHXRcuZwd/64iz4KhiDxOnpzoGwseYp0X9bDFD9gRtuJVV4EUNcOThNi2CEYAEmco5kqGQyWDA3oQgBVFRZ7eyCIq+4cwKyuQCSKO+dIQmDMaviqwrXDfXQ9A+SxWncpo2zfR3R9wOmqxelyg+V6DeaA09O1AlHUsBRmgcYwK4IpDtyZdV6h0BjiuYqglOEzPc7D71t0X9cgudsarDMXcJyzZ1ob5gI3Fr7LsY3XmlgUKNlCP8fYTK6VGCrTBkJY+R4o3tPggbOig5lTJtzSy9mReSpI6WPmQ6yO7SlWJyki0zhoC37PLxdHlscDf8e/x/v81N9Z93bVv6fx7aJZPKxj0yugluPhAkbrZYGSFU7xXLn/AZJcCs6hrpuk3K0AjfdKCV6cd/pHmNdeaQ8lBXcJZ30QpaEcSEu8vSyDmRs6hpvHjjJWWk/hEibucXmTt9cn3RrM5egeptd2S+nFE9fHdAVGJ3IjiTZRfj4j2XZ/aayjORmyxiM8teeLPoQ32M6MO3hXGo1HLw7m5l4R8/+p5Yx5m7qV+E8M4acElV1wl2FEgM8U/PaMK2EEBTySGnyk54djzHuf6UXs0IBTnQS35Ys8EeUCcLiLflzw8UuVKZp0mVffNZ5d12nH97tp617LpZJnJFBhcacSgkMJuqioacxMAihLk2VUVTkgYpYYLxage4J6l0F4RTFQ9jSsC4Ym4ZD+SGsxSubUNjfkzbYc63g3H1yy9y9/mjuOWuqYcB85BVqPeq9UCiTXDGZ0PaPVXogYt+4E7M0rbDrG9fuvg2NA20rQ+z6IEu100yL4CosoCiVfebSoQPtXAADzxQxhEzDbn8Od9nBX5wg9sHe4hz56PPLYCg899BjuPPQQurbHI4/cxtGyBy8Ii8Zj0/WoKqDrCI/dOsV+I252/+WXfhPPesaTQTFi5oCNKpCuLhowAd5XMudsi6JzE2IiPsakk3dwFcBtQA9xCRbBlHJcOjCczQ0ka+3Ksg2CsOkkK7AlkugCo6nslFPa6zVYf5UYdlk7RwavpDBMA1dB7zQGn5cMfQtLbqCwo7kQJDaey1Y75vzWMw+Y98iSuKPxlNw0DxqH5QboeskAuukleylD6taVxEEslQBEYsHjHbDsJFj/oiasOoFVCTweUXuJrSaxn2ROK0doU5yyMqHGYMtKsC0wCRTR+zNWGe4UwozhaU6GM5Elr9ihMphk5nd4vcDLgkyMutxRBEZycOvMFqVVPoMzKkmGzYVtpKw/Eu7rZ+pXn7FrBM2omHpmrFZRg+UzFnsLoQt9kNP4KBmX2z6gdg6VMvzOOQT2wExST1R1hdgzmqYCtQE0rxEjMJs1CExYnnY4Oj7F6s4RQog4Pllh3UXMAVTeoe8jvJfsvadLRuPFGu+d73oU168dAAxUiAjsEGPEoq7AxOLCw0a6CxsMlbwZ9v4AnMJAiIgIykxLzFARiGydkObKOSAEixem2W05C/kDBRsXAfd5OzZf2gcspV+xH5TKcFOCmQLcYMssiI0Js+cMdgJjMPbIopRNgdF1rl3PahEsFhaVy1lFLQ6ixS608ZRxRz2Jq2IfkMYXWa8rd2jMninuiZA2rLGCZQjkGOLbRJWEn2Qfw7kcJMqZaD99nar3figTWz1QwLLBseH/WCAfHijmuS14awCMruO0lzRNpfxBTIdckQHECCaS5A0k+BFBgGavdZUDRxHuqWeg9rLvVR7MhE0fsF636NZrcBRLvT4IYnhPai0PcATaLojrqiPcun2KxUJoiUu8jMCrvNM2UHDxly8KEpAiKCNn5c4wNyTids9op2VuN9pve21SlHMWrrn4nkqCy+lFTgo/rZfcAROvMayb9R3yULa84/RcZBpY73kCgmJG5IzbaYqUvyECOA7pEJGG7IAmYTG81YdNyV9Md1JCosDzx62cvbm+T8ru3flxbn+KTu0aB01dN7lHkiQ5tbxMfEPxzXiizMMQzPeOyAESMlfWU9txlRyIGf8f1OU+hGzJKviQk3ZE5cNAwHrToa59IYrJF4sfvTsv7ngSlH8xuLMp2bF32HSVeI5CWWHPcOIlchupu8n5vlixvXDrNYpx2ffyGcJQJoyjPWv4TsXYOd8vFTbj50ypl/nvPDBCtrphDMXnSTn5XssHAJ6/z8o5c7fr9hhegSS2nFsGVRTgduJbIUwZ32l97lqmdJ3EQMQVNluDsd5FmdoXL1JsrzuXlmIazy9bnigQzqsx7KTs7yL9TqHtrufuVVl/KcVeZEwK6MII5lhgaWMBBgy8EfSSSFmmFtv+7MQVzBJjYuIox4TArWni4oOHwuYudJ1C1vydMfGR3wV5ozJFVXLNM8WeCfyxtPKRupu2x2oTsT934CixMLooCqK6Aij2OD1e4nQd8fQHrwhTT4RILAFwCVhu1kBVwzV7OHj603D/M+7HzWNRGr7lzW/B29/9KD7oaU/BYuEwXzN8PUPXebz17e/G297yMI5PlqhDDz+b4x0PHeGB/QqLxql7jszh4dyBOeD4ZImDvTkevbHGQ+9+L572lAfRsbiZEgXcOmYwR9SVWt7pGpmgGmJM8+YcoXIOPQewhP9BF3McEGNgK5+z4Mn1vBiWKZNZBBfuxbqtC9Je4wmboO5NOvcgYZhnyiQxqwsfEdZdhHechHwDPWOazDoB0Ph2mklso3FwzLW1VkGc1Q3TYv+Yq6CdzEcWV60QzTUXgCoWnMZeW3eMRVOjauQ9ag/0ZO69wEkrSuF5LYGX0VucFYH+eSVuyU0laxFZYjcEOSBGZOlXMmxmxN7CFsXVxH8UgpDhva0zCoKecN2+W9tjZspoQNopldEcUFO2/7fxsezQ+ufceInPw3fbpguDK8Wmakx6qdhLjGq5ARvs6GcfoiqbNQseCUw7IklmgIDNRhKiXD2Yo9W5JDYlscTGgvcgX+Pg2hXsX93Hci2097FHbuDWnRPcd/VQst32jMp59IFw89Yd3LxxjE3bwscIV1W4dbTGfuPQVG5ggSnK5IjNpsWsqXFycoqj28e4cuUAgZ3iRsRyo5Z5zsEOWso5M+U9IEK+95aVVi4GjgW1NwaYVEhghQs7KDIhRRV/CjAi9Kolg5Oso6ZMKWmIJRKQduR6iIAbxT2znpO1j47L4tvJc7KZ2ZqkYOJR1txii8kJa94DI5dWdRIiIOGE4nkfkKx9Y7RDCkpWUa3itSUqyFalghd23auQxgyQo+Q+zBA6F7hcqQlmqLhgQkrJvKbbJV6XrKMNKV/ZWcqRvK9ligLUyiuDeyWtk0+MPjNtYGSr0RT/kTJza9Eog2a0n88qlA4T5kYZJGMEyHlUizmaeYNOsz0vT06xXG+wP58B3sMHBpFDjITlaoXl6QZ9H+BYDsyW64CmMtdOTrxYpclf+j6g8k4sdlcbzOczMCymVkTX29iMkxquEhd4nhR65gOHoXV3YoBLqYCH90qJISnrKVu82YSOw0GYoox1cayVxK+O1xPbwratYR43JbgmQ4ACXqi4P56TlKEePOiEIYeRtXPw6vLsKI9HsidrZt5kEQwUpDNZEDqnh+I2UrYdMsf7u0wplnH6Jg3r7az7u6zw1pcpjmB8P/MY4yJLxuBIYFJawFQ0kXkEI6xVLVb65p7bblq0fUBTecABTjcmZqBtA9pOYuWS7icShqIQkYx+66d5iURm9F2PuqrSXsTMiKEY/DlF9hIDhpwZvnz/9GUXDFKeD8IAvM5/dOIao8Dz8XXajedTfUz1TcWnbveTeFC+BzNSmJBSwUEALP6uXbc+B21qZ+NopXnOeHLu7qk8ro19gJZL0sWzSuKD0o8d/enamzu9oYuFurEQTCU9MR1CyackkrGrK86wZPHiyyfuVVl00TKmQ5P3MI1rZb1xOQ88nyjQPWvapmjXeXWmyuM99ksp9iqHlA1UzMNFaPNes/sB4BhhGUkHFM+4yqSwQxLC7Z78VGYKSJBqwJ1OhApoL4nbmPxtA5fZ7nB6aPAcTy9i3vwLlxsAWbgvFHuRh5+cFX4m6Jvysw8RHYvr5cHMoY8RYEoCamBGQ4y4WeORR4DDK4e4enWG5bpH23Z41tPuw6bt0S43aOtreMZz7sf+4SGO74hA+l9+7r/ive99DMvTFg/efxVPevB+HJ8c45HfOcbtkyXuHK/FbQ0RT3ryA7j96GO4utegD4w76w3uO5jjaNmi8hFN7VE5Rt+1aNseb/zvb8dTn3Qd7GssN0BT19ibESKixt7KR8fJdRpIbkgsNzLRQlZ49aqYsqxvkrGSNVB9dlEVdweJnxWQk17IaSClLGGBLVYWEjNtsbEAsZKrxCMKXcj1eiWujkiUi5ERNZ6fZQSU7LSQpCiRknsQRXsPRhcl0PgmSDKMwIyaXMpI2wWxymuDgasqgJxYHN5eioTlvcTK8w44mDkEdli1PeaVPBOY0Af53kdGoxZAzmXGP3AEwWsWVLH6s9gsdoJLVMT2o6w8SdYECY+z+DNk1cYIpMxsOqnKVbf2xYJLK9XypWg5fkYe4fwjd3mpUuL54JqB6QjfSyXfgCbYPX0mRoURzYabwhYAAlNqlRW7DscnwGw+w3xeoesjQgi4fnVP4uW0PXq/wNX79zGbzVCtpb93vuVdODo+Rdv2ONxf4OBwH5vNGsePbbDatFhverEaBuPg4ACrk1MsNCHPpuuxN6uxbnuQFzh1BIQQ0IeA9zxyE1cOFoAqf733oqAENPbWcKKTdaJZpEAF06IYgxyY4QsgSEp/rWAKMZOrTWBN1lAqZHjF1whOShQlAwMrAlNmQ/smBtjordYvlTfGxJv7rYyH0xjkxD0/HyKrZS8npbl3GWZDVFoVaYDnZlm8aqO+P2myEVMGi7WehkJChCbvIaGFnigl2bEXiLbXqYuvvEMcgjdNxelK23TCAMaExa1NaiGw5IU0yfIMpmaEnzzVNrYfvhcGiMcEZNRZwVYM8R5DuBgOMH8tXUQt9ILdZ2OJiNWarpfYerWTBEcxYm9eC572EdHVmO83qKoKfScwcOux29isO4Q+YtbUmM0a9KHH5tQSYsQkIDZNjW7TpWQrXWQ03mlG6aw4Mi+Do5Ml5rMaIIdeDxC9blCJn5uYszIgt7xvTLM54NeKJoxeJsUVZ3yTNo2W2gXB83G8uVIBXwpZpZCU3GDtWZ6CIRkE2RpyydtZe4VFMvI6T1kkGI8RbdGNZ9aeO4sZTNmip/J2+Jmz8TJyUrpE24ymFbxosv4hwfP+Apvf4yFI7MTt3wWFd/yYnDnerl/O/VTbptCRPUcOUznKflJ5lw//yKFS9/yoVtir5UZ4zy5IggvvEWNE3+ZQPgCUfpN6cOi4OB+STeHTpg2ovGS01zMEsQzEbqH0rDks7w+UE8WcDeW2YlzjPoobY57Bbo/PBbbaKMZR7mPlc4kejMY5fqetz6Ldsv/x3pUPEDOeG40yt8pyriymddlXdsvP16kYBE0883vlnHKBibrMXJbbmvFHaZszOEn7g3inlFupWdwm70eVpViNakoeKMFy8fx4Vy7BMLIdCCjzQbnvy9Lsqf1t8lCj4LXH49nV1uNZdu1Hl+3zovvamPbQ6J7RkvPaeiL20Esp9lKcCJiwL4JqVZFkF/V5U0v/JqZWBQ2DULVIAQAmMxdXUlbOlEJ4BuLhSanxTgPiPODyhk0NlAXFc1vAWwyEi4tDd5wsxFu8vKRpHynxMpOYe6orgu8cAjOO1wGH80rirPURs9ojMuN0LW55ru/wyKO3cHwyw333XcVHfvgz8fQHZnjHe08R5jX2n3Q/nK8xryq4A4/3PvQQVqsWV598iMW8wcad4Jd/+zbiugUtKgQw5ns1vK9xdOcUmzs3cXVR49E7a5yuOzTzGdC22Z04MtiJe2fXdXjPQ4/g9tEprhxeQV1FMAccnQpnkgTkYv2YgaryMv/CiaZ1E+ECQKBBfKnKi2Df9ipAxwhmwqx2OF73KfNtr4y0L4TbTceYKYdcWvxJ/C05wTzZiItjjIwAVeRBLe4yOMI5UpeYHPPOO0LfScMhxARbm94sVuVZUwa0FFVZlq25TKjue3FrZOVmzJLPYvfFwGh7RtsTTgEczhxOW+B03enmIYpHDpoIgGWdKlXqmRszQ+ZTLBgjwB49i5tzn3YSzU+YcB1JEHSqVBwrA4x6sSK8LEEhdVk7Kr2lbYyHTVjdydPXnXhePL+jlMMdE9qM55nCDPFdvmzhfBKshnA8aFh/ek9wytSLm7VYupkFFrO4XDsiUAg4OVlis6mxtzfHg0++hqv7FW4fbRArj+ZgD85JdjyaORwdHaHreywO5qhrh542ePejK3Dfi6seGHXjQK7CerVBv1piXnucrHtxx6srIPRK17L6VE7yI46OTrBct5jP5vBeYHvdSXjqKTwHoNlylYMoXI+NBkJj3pky1pRgKaMXy6ZRecKmi8oYUxKkTfEHDN1Zk5WK4pwl3Wh7zi6Pep1VcUHF8M0N19ZbYuoBnQoZlqBC1i5DkXUbGZpIp1h/NuGdVZAqGHIT7iw5gFrvBjBiD8xrp3ifN0RzzcgKRk5KvZKxYla6V2yDRKzxBm0nHRYRPJAsJKeYt9TW6McQr9LGnj7OZV52ITCf+XO6TEl7Ww3wzksGynmdtiuOzhEAZGUOs1hgmmVc6RreKwxbUou+d2jqCoeHC8wbh9U6gJ2DnzUgcnDkUFWE9WYthzEzL0oA6nHntJODVCd8lcW17LqI2IlSr+2iKppF05gtbHMohsiM9XqDrg+yZ6hEIgc+BgcjqskYWt8NZiNPTil8GL1IbqOK587lmHIFezi0YGBO8TEHClPGQLGe8IvzOppglJojFPQ89xWLaxmnC9pVwEbENn5Q8czQ+r2wztFJsMOBAEKl7x/CcBYRhwdHNs7x/lXqQLigX7tKngfF891Vhx0NHs6XnwjB5IksvOPH5JRNXSyAeqCgKb8Xz4viXg61m8oJXmoiNqc46yD7Td/3kDi1lGB53fUovaBSAg5VyhOpRXhJjHesTB8CQozwzus+oAncdr36hQju9vQM50AmxlEOXTMWggtyMC0gc+b3SqVlotUX2GPKbXk4wN0Wrmfh0fa+sD3rXNQzq8lxX/kQOI9zsr+J7xfQ4adyL3j+u77cBRxfttlSb2GW1cmytrCeE15VsXW0xw3KCJBMrXGRV0mW4wyY1vFCsHwXZYBPNNzzx2XXvanrW/XOwPOLDv8JAoNB21t05v1QLqXYQ0wkCESaFVRluLqukoAF6BozRGmnV42pLH7BYkEkhZ+VQmosDfwAHp6Q7NyRpgqPfg3BSd8sjc0ultteqZxLQhYXLreqyCt/w65DYlfVtUNde7HCeeQYkR28Z2xCxMHMYVYxwBHz2iEyITKhIcZ85lD7HjcevYGjO0egD38mjk5arFrGzeVDiMc38BurgBu3N1gvT3FYtXjHo0tcmRHuLHsQAU+//yrQR/j5DKujFY7v3EDlgBiAdd9i3XXoUKGJAZu2B7Mr3keEf4nTtcSjjz6K/b09dD1hVpNaHMScRS+9t7nPGOzQKC4Zkkly5XOgd7GUo2SpYoxTa+61aU1EcJ/5HK+KXFb/MosSNbSS6XbdRTk1jQyqsgWQuMAxIkgFDeGkowYVlxh7GWSCWg5eqRxON8KYk2KUWRqZEsIC+nsnVoHzWscZJKZZ5UthhtV1T164cgR4OemPDJxuAioXNfg+IURx7+uCPNP1wCyl69Q4gZyFi8CikG9VYcjgZKVh84li43HKlIHltNlOeIc4VXC0VHzh0S3jYChBwzlKgmE3g3uXxvMSt8e4P+TOuOiQi2sD5XxS7CPRLhOsnMsZ7GJkxONNond9ZMwqgplsVs5ywRIqEjc67wJOT0+xWa9BT76GVRvEXbo9Am9O8XDHOFn16NsWM9/j1nGHeQ2sWwG8a3tzcIxwdYV21WGzPslJVXqxxgtw8DGiDzEp9Zg17iIkVsdm0+L09ASzpkHXEyoXUdeuEDTyBA3mYTTDnOZL8MqRxKwRmMxJcso1MSu54UpS2mdY8dzaZzYXVlH4dUWMu2ypLXTBrH9SggBkehQZYG99Kv4zMKsAzV2S3tss4wTNxdU9Rotvx6ig1nUxK8VL2JaT1WxtA8cpiH/bxaT0tKklsjiN2QLQJqCM82Xv432O5wd9N9PMb/GRlFHXLC4HZRIx8/Xp23nOk+J+ool7LyNCM3l/x1Ue1Rjhf74mX8ZsinOSWIWZwZuY6KZZbFobZi1qI62c0O227dD3ATiYowtR1j+sgb7FcWC0nVjuVhSx2kSNsSiNLhovMOQkVmbfdwnvzI08gjR2HmdeCxmeGJo5c7OB917DBJgb4Wh+eUIJQMgWNgM6TQmmymfGFjzJSg5D2EhW4wV82U9HZqWfrdVs3u0dS0WL4bkNxuiFWMZk2s2s4T/Udb5kaIvHB+ONnD0AIkNiJI6soEQxobSCqIhlqHtvmqOMSOLyl+OnlfNtk2F4nq2ZM/09tyjtLSd9F4qfVWGwthfp9/1UeOePs0lGgiuYWEJZCC/oXmlBmh63bZ7kAFgOd3zaZ0IXgBixYcFXUdQpL0mZ1lgyJMO1ONJuqQo+yVaDdUr4I4gR+gDXODDLrmjK5qLqtKA/QGQuLquxRTEX4+7ts2x2PG9j0BrX5XGFUTvpmQn4HL8Ocz5QPA/oy3cqnx8fPozrD9gjDK2HrSRW+fxhwA7buNyfznvmbsu5g/ldUi5ECHPV9MqjtZsqY/im4rkIFu9G22v0wDcCSX4o8YzHje4Y/yVeJ8mPtndtAV/R/9aLndHR2Gq2/J72x0uM84ku57xOKlMgfxYaJDzH9jxM1bubObnbebycYg9i3UME1LVHXblsQWFER2tKUgy7UACYaY9TkwJ1lkEtM312IqoKCVBW6G297fDCaD/brQhMkf/L+7bB8fhSQkgjrCbkZys9JMVe/o6k3Js1FQ4PZ+i6gOWqw3LVogvArBLBOpLDsosIPYviqY/wAJraoesj9muPygEPXpuhqis88sjD8I7wnncdIYaAh99OOF11II7oI/CYZywahzsn4opzsgqomhmuXjvAyXtuIIQelXNgVSh2PWETCDVV4BBwsgloalHkdUGUYWZ11fc9HrpxEw888GRcPfDo+oBNy+hDSFkuAUaIQTcjER7arsfeYjaaK5mfSF1iVKGMz7rtUVeiDOmjxLM7WUcVoq0Nl1xEJTafCOMhiqKwCxHz2osSi8WCp/Ky8KRrXTLsxqxnpWyOVweIYtE5sVLtoNZy4HSKH+03U1JiWNKKrjxeB1CRKFPqKgtBxuT3ESBV1nm1zede3QJhrrryXB9N0CcEFxGZUHvBt6iMf4gsCs4IoHKqbFT3vOLEN6uZSK30hHwZU5EYN+ET05rZiXIWqHRmGQPmzVDPvgxRknK9SYo6ZmnOxv1hrwVOl89tfeXBpm6wnGBWL5ZKPS7qVd5jNqsQQkTbBXRdkMQtTmkfEdoosW2EYRc3dFPCNF5g+HAu1jnHxydwDji6I0HxT24Bm07Cs1s8ttoT1huBs00X4XyFxWKGzZ1TCdKvizerKGWF9pDYnm0f4Z1DjECIAa6uEs0MMeLoZIn9/UPMZ5SURBa7zWbNAkGb9XIIEXVdbc8bAKYwIrlZCZcsWSJr7EoVbhRPIgSmDe6F/kq8uwBW13JZl9KtzWDXaBNTVoTZOhqdN2WZWQB5p7BewIEJMaWiV+iOxbIbQpT15T2BOlMGSkPyvlKXgOS2ZQcpZkGFJLznGKTmipxwk0Tos7n0oHTwVMKytGifyPs4skBSYkUSGot3t2ex8/oI70YWfFttbJVdNc5id85mhXjrS/HThjnC//ydB885R6gqtfgOmpWZc9w0g8F0TWmnxUiU7KhyKEZE2Gw2IALWK7F83yyRsl1aDH1PQN9n61ByhLqq0K67pJAHqyuwWgnL+QGrNSklvsT5PK+RGeu2xayZodIYucbXlHheUtDIAEcLvZHxKE9V3Fo5SxhmdSMjWapNrUWpBEzWg6wK/GBjz0rAtDcVbaXvPFxCg3cRuCnFtS1pQjGUrDQf7YPlyE21Yla6NiZQ2SZvv1uJe8UYRBEo43LFS5UwUb55HA+6KOO12GWRe245A3F5+vL7tWxNxRTun1GMNpawMdkPJzFn66LhPRGhV7O1oIctIeQ9JPWJ4YEOdB+IlgZaga/QySWam5UFeaHKNelDhI9RY+RKuAbGNo9Wzo/Ehs4VyuFO2fuVSnVrY5dVXPnOYxwzBb61MRbldrCIKIa6RQsMH8vqg+BBo3Fu43l+t7P6T3MwoiXjdyiWc7AO4zHYNy7W+Akt52/QH9jlknM0gAs+//Gd9wuZqKxoVnrl82PYoqmLFyxbsMUWMm2bpzmz+ccBtu7qFSZw1va59wUIjnmHYkhnvs95S3aXy3lPxZ1fJZfKifVPXTnU6rKRGAvKwYsT0RlQ/wzkyYLLAM2uoVD0YMhglfdyvDrO/fG4fv4b3iv+4+12B/eYR5lskYQk1tO1Uqln94bWaFkBUHmH09MWp6dtUu6JCyYw8wxiOVtvZh4RMTET6zbgZNXh1mmH43XAatMj9D0cM/b35viwZ17Bsx88AMC4MgfmNUmW1JnHyVpcS5mB/ZnHZnmKm488AuKAGCLmnnAwr9AH4J2PLXFlr0ZNQZWDQog2XcCs9uj7mN4nhIhbt4/w3sfu4M6yBzhi0RBC6JNlQow91psNNm2Htu/R9QF3TtZpXvI85XkTULKYUPL+nkS5VGvW27YPmNXi0sAgbHpB/eTyjBwvZ9H4BEt2zRIXOJcFIAUTjS1Eo/FIsgxOdSSmpLmbJoENlFyXScdTOSR4qr1YFaaTOx1vUOVEmccoMieLi6ACWmRh8r0D2KzoUCgiIW6DM02YYTH0LA6LbSv2jcHo+6zWVL1lUo46dTk2JcqYIRqWUZaztIlkXC4rlLg54oTzCHnqr0TuUYPl5Ymq5Wc5iJImbCnsChozjqmZ6pT9swj8bdtj0wUEhVdj1kRJpJZnlSmq5PG+F6Xcqo3YdOKSz1GU2E1d4UnX5rh+2IABzCuJy+SdWKNu+iwwzyqH0LZYHp+AOAKRUTuS6xG4ddpiXns4EgWu4Vkf5UAhxQSF4NtytcbR6UqsAVmUZzHGNG4JH9CLFWCICDFi1XZDOjpaRoMZU4RnuMux9EKI6R1lfAVsFevIkDlIgaphNAXJ/a1UtgAmrOX8yKTX+iR9iOotBIaJA/kOlN7n/p3LbSe6wkYHbNxcCH86vwmWhkK5jGeIbCWeh6jrTwJTZcy0BN7F+ELklL24VODZew+UetZZAdd5xGeUKfzeqpDxqWx5+r+ShgyvnDuAqZ5LgjAeJ+eRGO0ARnSMhz0QyaFXCAJvQVMjM5IxblbUIk9pYHHV7Xp1wTQFGiSG5cHCYzGTfav28ryDrHEf8xpVjhD7iLZt5cCNLWuywNyqi6hdVsjanJs1fCzWgRnouh7rtkvJpiQhBWelFDNCCIgxJr6nC3Fr1kfyjMxV8Xt8gGUHT4ajsdiPB22zhtvAaH0MX4o5LvsdK1sIQ7en8e/03Oh9bOzWWOJ/S0I0NW4bp90b3czWYHlDz++X58boWHleXo5v3HapfNialzMkpXPx/JyKF37+CS5T8DMe3M6xFnOa6G76G8KO1d1S6sH4tky3jb44En7Q+9zOWIgsRCtYaAwbVYJrzharW+/EhUqv3AtYs+qK+ZDS/iHwJg+dYi/dmq+JyZtUgsHwPFusT+EBF3VzjLnBsAZzMlW28G5iHDy6mcY8gTt2fzyW8nNqnFs4PjHWwbTb3BR78rjwju9PuNbjAwWhL1rOYxN2PFI+e9lXHvIRYz4BaY3OwvP03Bl09TJLbXtrhtMCO3fM0b0s9WXBcOp93t+gtusdds39vY73iULdS8fYM8We+ZATxA1FiBSDyQSs7MrDiWJz2v0IAA9NfjC0yuPU5xZDbc+iDCCqQMvFd5w38bvujk69CuK7Jdxz3vjGFnql8tAgI4aYmMi9RSMZKhdOlE1ggAMQPQ4XFZYdgyKjViUUENFuOhwzYdUxah+waQPW6xabtoNHxO3TgP2GUHm18msIbQSu7TmcrAKuLjy6SGi7Htf3G7Q9cLrscbreIAK4cxqwqAp9L3MWGPW0fzGXs7u+7/DYzZu4cngdq3XEXiNCvQgDspCinIqYNRW8I9x/dT+bk3OOFSVK1Oyim90DKJ3yE5lbGYDK3FrF9ZTrvJZJcIliJUWm2NL1qRTkJKulWNdUbIIGpxP8tIGr5GDjACz7pFgkVJ7Q9ib6S/y0PsGPKOQAYBMU3iHKH9Z2XCGceyoSVADw6iIbgilU1LqHWOLzeBH2+iBWesyMNgB7jSnmhGHoGRpfS92AoiT1EMIvc5z9h3I2TnMlJhSf+mKW0doUBnJt5FoLGav81LhqE0xojreJrXtWppgroyVTDzAPnxlyZDxoY/j4UOC13yUus94smywZfksYAzCa2mO56tE0lsTEGiCJoxZU4eKMQY+qcBX4di6qm12QrJeIWLWMpsqJHRq1xNxrHNZdxLwmTdgRsWgq9FHiTra9ZJ5etVGyWRdvTihwD0BliWJiwHK5xGK2h65jNBWDNbOt4QmzxHasKgcPwv68gbm/prnR+WAuFWqUaaPF3tN5jxGAuqizWqTW5iJLeTuJitNGn2wVDFfN4s0UO8ZQW/bLFA+SsxBnI3SOkqbBO6dusgWkJACwQPeUFJA18h5IlBVopfKSkC0Dnfbt1BrJ4qSU9UXgyoH2a0/J8ot0qDYf9i6WkRcw66bMWtq4bIyGMWcyHAWXk5nG7XuT98s7Z23OJQ96Bk24UNnxEBf/lLSAtyvsbKYU9L0ndH1MGVpN0Jc9wqwyVWhVIIwR6CGJTRwRfBQBXuJDSniFZJEZRWEcWRR+fZBPUR7o/sBi5WdxX7sgyr7xepXLZFl8OYpbcFU1CIHgXVbwU4lbLDH7CEBTeYEzGL/D9g3jbNnWacEGJgV5EvhHYFEKP4zs1lhWStBMw/bLNZoC6PKy4VfZTtkHFxNnX4szgCy4Uf5JxTvlPrM7nSOxMs73Le4h8p/26Uc+/FwMr5xLg+Wtva8oJZ7fc9mB4Lvx/okvW3h6Fv6fcWPXfcOFrUMQlC7V5S09+EeOecUxr7GtWQFGGS6RLTNZF7ZMwFDKI+fNdankCzHC655tITFSx8X7277iz6GDu/vcXZ/PuJfEuSk8LxsYvfSu/iaWaroyjz7PGGPZVqnwL+OAJVo30ciY1lndcqw8uj6esx2k7fEv70+Evmi5K+bg7h7bBWeK3vJ9QmDZheflPnfWGMs65457TMeKpAK7YPLxWN7HHSbfZ0C+u6T13jGOkkeZuneRcpH1v2i5lGLPO9K4MMXpwpk2yUiCYknghqcRjNJFx4Sr0sWvLMPTyjGJS50iTRMViJb+NZGTEkG2BB4JgbTfUsAfWPTob4mfVDC1thnZhlswbmMmj0iUOW1g7M+cJmnQHEmB0feSKKKPEQiAJ7HCa7ww/qtOFDpXFw6rFWO17nFl4XC0ivDEuHbgsW4jDuYet0963H/o8dixuNcezj1OVj1WrVjadX3AvKnQduL+6ynPVqVxwpgjqsrhaQ/ugaNM3PHxMU6XS8ybfdzZBLXkiSDnwUEYiHlTAyzBg09OV6g0eUCOTShz5Nlid8lcmYt3GwBS5akDJ8WUJ8IyRHWdk7h7jRcYjRo8w4RsQN3T1KXXhKGsNKQUg8iRCOfiUqfucQoTFmi4VPwFldjEUkL69USSvTJZY4h1Ta99doGRs5dJvXntJNZe6k8tbdRio1XlXjAFpUcKSGzxyBzEAco7VRTDwelaNnpC7FndKaPE99Oku6k4IrAbbT6lMJB2r/I3tr9PlBJtx00CGVcHZVRn3OGQpR02cCZBHNOFAf5r2zz8jUQH8rtcwJZJXaEltqFZywBQq1+k2JJQd7m6IngSnBPXO8aiJnQdo+tFcbfuGQ4Ri5lY085qh9UmYn/mcLqJ8F4Uh5suoAvZ0q72HiFwUjBk9zFShZoE17562CSl12azQdu1qHyD1cZccRnkHNCbYkFMk5wjrHuzRraT/8ykOuQszQBg4RmCElabXyKSoOMgdDEmBZ3gvsC1tVNa3JgQlJRgjCT4lFYVpkgrrQZlrTkp46lYXaMprpDe7ADC/jM8t9hfIRbZgSHrXjl1ny3enVmyIxOyCzFHiz2Yx2jv4rQ35zIe2XyaMt9CAEifQBsKoEe2iigZzbPLBTmsEh0mSMSFu5r6fo+FB194/HUL/zH4vfXoFnNse3y2oMwPMReu4Ap3Xg9amG2txMpa4rdCEytIu1WFdK0LQKMxHx0J/eg1cZInUxBn63AajTHRLwLmTU6v1IcOIfTwrhIr9dELsir1AOHr+hBB5A3A85zwxHrrhdLF0EI+MGy+igyBSgOGinQuWbuy26womRL2R+tL+sCUOiTxazT9PbVLmQ9kpkxctEnLam91U/8FzUquhjwS3k2hWvRln3at9JgpY+iWFo/jclEcvGt8penLu8qUMHQ3MtxkP2d0vvPWJWiNzfdYmTc1dxaH1Z4zZXa0doo2kts+FfUMj5V3sD7S5zkLxiji9MGSSEUQSazpPM6MU664ZnQlt5b38/Eqlu+Z5kNfKL2rfklJsSzcRfk+E2PK7e14zyl6jN3wUfKggwemq0/e2op/iSEMjNcXKGnH9njLvdzqZiOZTBfOc21+QsoOPH+/lvfRPJwBGoM6qYzmqsTZcd0xPo+vTzU7VbafH+6nd10usCEkvDzvBUa3rVxkfj+Qy3h9MfH7rHKZdT6vXMoV13mx4jGrCosTZIyjMC0ME3YzIPHg32zijVw3PWuCXVb4lIJzUqoZlS3+TOgZbHuF0FVuDeOxDJ4vlXcD5VPORJhi0MTxM9ZnMTZkgpz7YnHFJWFu123EasPYdMC6izjZRHgEbDYBdeXRwYEqh2UvllrOORwsKpADjlY9Nn1MVgFX9zzmM491y9hr1FJvz+FoxWhq4GDmcbphrDu5vm573N4w2l4s++Y1sGxDFvqIsFyLO205TzFEbNZrHB0fI4SAPprFnkTFiFFP9tX6C8w42F+kuTJLElN+RXQpDlCIIkA7dcFNxJGRMlx6p5k0OVvd9FEUFubi1AVxWQpRFHvOZWUdoDGMiFKmXEYOvG9EMaqroigBCI0TYd3iKhHlPljnK6gSwITxUgnR6/uaMqfTtOfOiaVNbQk4VPDwJqRrY5XXeCvqCm7uUJ1KgFT0m4uMzVx+u8Bo+5gsPBqfmQdzxSX9g8HBAI8x6KDE9bEQnHB1wJplPC+ReRtTS6Qv8b28xjvqnP1nPSLRmQzb5XuUv8dKvfH7c/HbXqtNmV1F+dz2kkilDxLfzkFcY7wjBIhWpg128CDKfYLQhT5yCtS+qMV6uusldmLbRSxqwrpjVF5csttOYmstGoc+RKzU/W/RiBLZYAaQ9e96cacdriej6zus1xtIIpkowkBBnxOcKPPeNHWmsjqxmZ6HQcw320/KeFGZgufA8MacsK6JCD7ZXd3qmTW5CUeA4AopDtleFThn4i0Xj2FKF/0dS3fhPAYAg/cAy8gNz6NeNEEtqD8Wkbn8D2GFgPROQFboGUxaDLfsLjzEtbJYFUvwY8KkuTKmmTYhQv8xmj+YksEUjQC9qDc5lOIGn1XvCS4DEmT4jOH7cVm5uLg1xxMvwEByxTW6H2J2wzM0C7FwAScghcwiCZ9AZIcuWU9UqTtuiBpjL8q1Lsh1r1lVIwO1KvN6Flpfu6zgtWJ4MHA/1XHEENH3feGKl1/YcFVb0bH51ECa2uKKKST0dVP/ZSmtXQzPUxsJz5FgOOnPCvwY1N8ax/Ya2toTsoLb8DrhwA76zqP7ds/GZW6Xds3oWlkPlOsYnoPznOW52O5//B52v3SXdG5inof/PDHlkkg+VY3v4m+ygUv0Ob6xk5ahoIETdS0elFwrvD6KOmVsR4zww+AwKfXsNwzWpfdktVe0OYZ7pPrb45X9JBb3S0zfVjCZh8k0+Z8+2hzD33gMg/lJSDW9fFM4XeLQsN3t71v7WtnfxEM74QoZ98vnbH22Oi7aGvc/ePfyUd79XmNa9gRi8tnlknj+hI7hHpu4m7q7nhtcH8HDGM/LZ8Z7ZHn9LDg+t4zg9LLlXpf3ss9PKrcm8PzxLBdpe0AXH+e2L1Iu2+/lFHtk7nm0k2oLrnGmUDAixgOCNZbL7R6P7tmNUnkmrW3/V1LlqbhOwz/e+htntk392fc4fgZbz8X0ToWlSuIYCZl5YxytQg48DWHQZ5UI5HuNQ+XF1bLtelAM4EjY35tjtpgDrgJ5h2v7Na7sz0SZA8K1gwY3TyMIEY5YXHNnhJMNAA5Y1A63lj2AiOv7FU5WAbfXjFldo/ZA4y1jl8TbMo5zb15hocJ6UnBGEfhPT06xaVuNvxOSKxGDsZjpM6NNnJEVU2YJ18WY3MRszhyQLFxiZLVqgDI3ouSI4GGGSBLBqO3VWkUVaOJ6yOh6TgK+BCfPsGOE04TxtreA/EUyl4QLpgAQDii5NCkltdhlYoEhQ+tUAdEHUUyu2iDuxWY5CHOVywpO0v7MMpHYLCkonfha1l6GuIS1gcFkCjxTdYsyj1RIDCFn0jWFhUzC0EWvLGPehcc3UOJneSXjHsbXB19yq4bTmYcYYvqufX08tnH93GdW6OUNkAfjLC13x0q9Ia3LbZZzwYAksyhgw1yvKwtr4FRwD1Fi4rEoxnxTJQltMfOYN2IZE0GYNx6nrYyAiNU1l7AJABBROcKqjQCJEm/TRax6SexhMStt6I6ysqeuPOrKD+ZBlPgBm3aDvg/5YINjmos6CfhWcvtjuhvYrHIp4bkp0hyVcee0Jcrxt5hzBkpAlSYpwYxauOlc92oNCQwVG7YyWVhCOkiIw+VDGV/SskZnvJA2I4x+U6FQU0UOy3NdyHTTsgmXVjimsBD8R7EpZvizfa1UMtphQlLgwfovMuixjAOclZq2RPck508g3y6cHN8Y4+/kM/dYUh8D4sPp3ohlSL9L5eUW1OwaKJvSmFJ7BreOsjutg1l+AgDBe4LzdqpCqL1DVWUboNqTZMOG/MlBVeHWS0gJmWovSaa6oPsTZZg1pZwttYR9GLqus+J034vlve31pYLP9rg8aXm+Ms3MuJ72C71nc1LuI+U90vZGZBQAin1fnikVJGXfZ5WU5IextZelopM93v8MRowWWd/2SUBSonOBzyWep3dF8X48HvdwDyqV/cKb5UEn67ySNkzMxT3h+d2WJwKpz+prJ/EZVrn4jR3tnFF3KKBnPB/zU7bnWx1HlJM3ICv1YmphfLg0xpGpb0j7bDka+Z55b3unkhanRGiD53irHXt2WANbsDZ4ZgD428qMEu4Z2/fHyo6pkuqV9L1oq1yLSTyf6BfIeG17/uQ4CvpQWtjuHOjEc+Nb5XtcElyf+PIBhufvz7JFZwln4sIUPE/B370UmS7eunY37TwedT7QyiSeI6/LRd7pfb21nlcu5YpbeaeZNnNGQjCG2WoLyN4GUK0Piz2Ud5PSDdf+KQl8bp63iPXuoow2cbFQNOh3UDttcsUGlhisbJ2SlX/GTJWWE4VAjGKOrP+0IRPuO6gRmbFeBywaUSLNa6dm7+JWGvXEXRjNiNVyjU3b4GlPPsD91+Y4WnY4ur2EryvMOODdN8Wqpg1yDl03DqsoEsW8ItxZRRwsHByAd9/a4HCvwaIW97tZ5XC66RFCLwKpc/CO4PWT7JQwQpWcEREBq/UK69Uas9lC44BtwNxI4H9yIDh0fUDl3UDgTAkiYraADKqwatRXzYTVLuR4USEweiI0lQNRBGumzkot/JpK3A9NzKo9YU3iotupK8NyIyxTG8QFW+OFC/OkzJBZW5gVUB9kPLPGoQsGozK+XoPqmdBm7lBAthIKUZIYMENdI10CwxCzJZ7EaCJNbkCqHJCaQTODiiLRrEKyyqsPkq03MGumW3H1iWBwpa6S5NDFXqwatW2LNQioVY/6+KVNx7jIXRSsoAOG2gBLZuzRY4khHcmIJUmgMceX2kTRyfbl8TUeXBixqImOZIKSGTijBblO1gvwoN38vaR4eUvYm3mI1VtMbnambCaoJSkXAiAYXdehDxWuHjTYW9RYtwHrVQfnHRwYR8seYHH1ZFUQtGouUDmHdc+SbZMJd5Y9Zk2F2nFymW17sbxLFm6K306lbtbXSQI66Zi6XpJ+xIgYejBXMleqmAgxiguuSboDGlkeoui7K0zFKLBiWUQt6DjxUGFubqqm6O7NJ4coub550rhBEPwGOCnaklVLwnNdW2Q3IaMzVZUV8sYcGX7bUpuLNZCVBtGUjJD38qqJZL1XqcWvxdjS4YtF3YC5l9GZJWISvpRe1eZaxTL+qPuTAZJZDmdL4tyP3be9fIgd+bdB8tDzXsdsgDJBE0pMOO/GFq6eUcasxpnP8NaXrUvbv/N+vbO5wVjyVUnmZJZ1mhCpUIQlhVCB50Gt8+Yzj6Z2Yi3ehRS3eN0JdMYUm1WVtAR4qPWeHmiJpS7BOVY6ny3zbL5M0DerMXsFLj5DlAM657ziawTYJaEWMJqgivkCZhOvhMFR68BqzfbUxCPpX2m5FtkUgLIPlgoOc88lQnLJM8vbAX7rZ2nZl2lsdoO0ZFnlQtvYSkXAWHloY7b5MCVqOT/Fchf3ynnMMJHiGBbtmwKTrU3kY8g8NpL9vYDbEjfojIP4s8pOHL6bRsZlCnkv2tFFCMVFH5m4satuCctcXCjXExi+mh3by55Gyk9DLXrzg2EkS42np8wMKeFjNFsymwJsRLQUd3a+iMpv2dXegJ5yBZTf2d6kZG22ZouKyzy6W8JT4vWooEs8rRNJB1TYxu/B5jHgV7dBnkbVt0ePabgs3mu83ZUHDAOcG7/0RBmPoXTpHijlU2N5Iqbw/P1aPkDw/DLlPDw/r0ytNyPzSuW1cdu7aGsJX2NYu8zYtp5i0rwE99TQpcoABy65iWwNb4TnT1S516bPmtZd60kT9+51eS6n2HNyoiyUVnxFmCVumAlIWUOXORcmSsBu2wM4xx6aZP4pu/gKIVM2uhDGkJ61iHlTE5SpIBf/ytBG08dSvxTmM5Mn38uT07QxFrt8KfyXYxj2JM95C8YEFTwdqUWVCJSdWokFztZme3OHZhbRrld42ztOsXd4Bc7PsNj3AEc8Zd7i0aMODFGAEYBNy2gqh6N1j4O5w3odsWHCwd4M6zagqYDYa9ZjAno4tKsezgc4ZUScc2JdoIqxEBnLNmDRNFitV1iuV/C+QYwBzCFZ9hAibh1vsL9osFx32LQd7r+2n+Yqxqzc8ySCTe2N8c+ra25rUTmZVRuxP/MpU2Ab7CQ+J9oAZO6agsiKkCzMceNzlkFjlsS9WhhlMssfdbn1mhW6KzNjKLPmVPomUuWey2OXceQYf12QJAcmuJjF3kwB2pQr9jQz0EVzMYYKFCLIB4VDc4vvQkTknNzGOaDvYxLywTkLr3dOBUsGu8xMEJUCaY7REFHC+hDzaHi5APWi0TPKeBNMG+S4Oa07PK0+u4yxL9ODkhaUQ+bBtWSxwqOnCgarPAyw8RktGGSsU/gNQeArxqz4jdDYXADqyqGqGH3f49atDvVsBnIV6pkwd4dVwMk6KLOrCuAgMLbuGbNK4vH1DMyaCn2ISYFo4EWOEDqJiQlycF6Ve4WQGVlgqnYVur5H23cSP1OTZ5hlDxhYbXo0tUfb9QghYG/RpDnJSkLAwSHGUMT8kUJQA8WQ6WzX5yQhkUURTRr+gCjPbcYBa4tSh94p7KJwb9O1i8X6RKXvDqSuj0O4MZwCi5WvWVVIf5zac6q8D0XyDxtOUqrBBDIqAqOLAp4gCnyAR+59nNqSQ4I8dlPiJMUdZ+WJcxIzNHKEuUrbM0kxgQlG9FwmarCpb9U999ELV55+jHfd2Nptp3/w1m/eHtd0k9O3Ck7f6JNZVgNIByelskb2DglrsewDvK9AJHstwJi5bHHNbHEhZa/q1ErdXH69z4mmAGRLU0hdUQSQKpEMBovkN6o0j5rdmpADApY0sesjvHeaHTei0dgRRhMS3QOBFebGsDTeDqxvAlIwf6OohPwsAzmBRjHlsidav1B8zw+Vy2eWf8aTbmXEpaGSYQwnaX0NpwH4QpKztcoNbj9fnHsUVXgAS8QY0hijI/qw8UdGVwfKx4kx7yzn4vmFqlyuTI3touN9HLradeOyQxiLEOM20voSJXpgFrVmdACY9fW4HZn1Uraxukn+KXhbpoIvYgySOFBuNMG+HBhwinmbxqsVLLyF8T++4AnHMF4qsstS4qfxRWaocBa53dVO+S6GM6UCc2rujQRPbTlbqDnG8x19T1Tdem7q99Yei+05Aoa4vNVOuc9gut4HTHkf4vllyuMxhEk839VHuV9NwJh9bu8JW02cOfZx/wO5bMeDJdxN0vkBnp8ryg3bmeh36r3Oe6cnGmTKKbqbfe4i45tq/4l6t0sp9prGo7H4X0GZGmaQKqdsQ0jnikXGW5FTSs7OTptoAPQlc5Sy5CqnOMlgbf27Y6IGM5hrMG//Zv1SfpoiTy4VVns6suFpbj49HZwsZVkTQI7vxhDly7zxqH1hoQdKyj5r4nQT0AWCJ4d54/HwozexXAc4jlhUwKIG1iuJhSdKMkZTO3D0WHYRHVdo6gp7HmCOOJw7EDM6EnfTVRcQomRYJRb3wBYiFNZ1habyiBFYbTZYdw4cN6irDTabDWZNh67rUTmPrusKq07GzTunmM0qXDvck8yNcTg3zAzEbM0kLi2UgpCXAvGmiwiRcTgXCzizSqFKlBu9mjRsesaeKt2icjwxmoseA17ikTm9njIXan+NwmIXWH8TQgDgJAFHHyOYSzddpJNZsAU/J0QVqKKiRBaUHGqn1nRR2jQ3iyi6lizYR2QXRqIUX9Cb9Q9EsKMgViBe3YfnFaGnHLMsstwHcsINs5Kw5ai8h8VJZI4DnBjAcvpebEU7KWNRZ+L+Zfb/7Bg2XXYSy3KDKjijzACOr5WWuIOnhs+dMV5jnm2DlUym6opjnCUAcqRwK+1Y/MMZSeytk9OlxOqDKHRqD/TqgydCsbmiOlXuenjv0JCMYVY5iIWQwHan2bmzQKiZNJnhvUsWZ10v9IZdD+979H2Pyovg74gQQyjenrFct6gqh8W8zoosznNhMBOZ4aGBwDlnXzZYIwj9MoHBa6bYqHguVnXScB8ZtXMDZlhchfOamKs7MyfFmw3bkmEEG4sTfDNLuahmvJENz4ts7NqPd5LxmWH7Ag3g0CzxLOxAyvbOJrbZuFHgFCXa5Ci7ZZlSSIR+seaqCrQyxYbBc1S/oWRhl9qhgYt4WsXdiHcG1zfE/Z3M0S7k3NXnhct0AxNb/sS1oep/qyXevsbAlrBYHjYYXhFGjDBl90kAaZ8zS9NN28khH4truCckes26f9ieEBiSXIYsXqPsncScrNF6LgZf0LWoz4luwQ7X5J6jKCE1nFeYJ1EKI8Nq1wU4T6grP+CNBvPDeR7MKjbxeMUcRE0+VkEtSFkpBeUMsWBVoJXSeMKpoq3iXYkGr54Uf8avGd6bQsSUsAMlh33ROkkJP6iQlnbgplsKQWOBKKG5PUtFHR7CjV2PLDF4ExtNRj9yvSksKLaZQblnlPsAL2e+H5/589x2xwL4uc8UCn47SDZZwdoaKMpg688Z75MKa7i7JFzb7jQ9Z1tK6ifBFoNLj6aCVwE0pARl3Bn3sk0Xs6JtrJyK6Xk7hs4EYuvdMZzXcT9xVHeAS/n1M96X82T4PVq4cRtnwcTOsY32yF1bXWq/wHejReP207sMs0BOjun/iXh+mfJEz0Va/mKRz6ITk3wFtmF5qu5Z/fPE7zGenNXv415KmvNE9fG7oJw3v7vW57LlUoq9uqpQedHIBKYBgzqS+XXzKDaz0W+5SPnZRI0JoCJTWgkFJV0rm1FmbZcmeWs/0gvlWLl4ibxR2ukzF+9oSj17LmNueR/Yvm+92rPOSVY55xwaH9H1EW0PeO9QqcKHSOLtGQMngnDEnWUHRwFPvT7HjTsbuNgBkNN9E/xrL5l2VxvGct3Dk8NqI5/zeQWKhOUmoO0jAjMqYsxqoOvLXJCs70ToJZgVXONRVzVONz3gKnRdJ1kzZy36vkczF1c951Vg4IirhwvUlVgjMDP6ENUSTpecCD13AKv7Gol1Wu1tCJyYjk3PmNc5LpUVs3SzGFZpviBKj6AJMSI0xlwEwBFN5XG0CfDQcQSxePJOYhZVzqUA5YEZtZ6YeiexswBTGGZhziz4TMCyOEvZDV1G6BzAarVVpUy0peUFqYI2u+A4mIWhS+50IIJTtz9RBqhFmM6n00wAKSYTsQZcN/ff7FLoPYnrF0vcxgQHxhhihMpjZoazUJUEuRG3Yzi8jbJccLV3twUMUH2L6yr6LypxUcWowEDA21FnQJNGnafNsliPigQnelXWWJZMimJVOzwsYKzbACLC4aLCKQUQCw5ajEZHwLwWGOh6saZxROj6CAKhnjmA5XBAhAlL9pID9w8xHSmodu0J3nm0fQCclwQ5fY9QSQyuqiJYZkdztZ3Panin4egZycU+6oITGIGDKplzTD0/WG+NfxkV7nkEJ5TxxPAcpkyjoQVOjGIBBDYXd05PinWd4pPSY+dY56XM5m7Zrs09P7dPao1eChGTbkVJeWCxlXR8DBDMFV7mwRh4GSNnVzpj+kmsLAYzxnk+gcJFmApXTBXQOh2/HRzYpNr+ddFS1pxAcYwubT90z2W6sSmU3/qZ8HaIsOMWx0rOrR45P5eUM8gKoMCyXmadKa7lQ56EIVY8BGBeO7Q9p46TSyrlWJghKj0BqaW/uXvrIZeNBxoSoniPcj0iM0g9BbJbuFnkR7gYwTHC+cyjWbD9qnbqepp5uMwTDTAzTYrtg+VMGoxbMoABzowGzFysR6E0YOR+AU4WqjY6NZzNgg0JHTLFvyuuJ+VeHvbu9adSmC4V9KasHT2n+2IczMNgimA0stw7xkJeqSRMvHbRR6rLRRuUn71sKfeHJ0zwe5zLma952TkYsSJjzmQw77S7nsEFQxX2o/GUa2qXB0pkMqu4YWxMeygp7jBcp7FVW74he7Ydvk8J3pZ1vXQBH9N9LtoDjfvnop7sW+MyBVND5WNuKcP+8J2ouJfGZHNS7M0lnR70Ne7/AmO0ekVuP1umMxGlxOed++T4mRG9O29cv1fuotylyLHzMYWDXRmcS5g5qzw+LNPZQDmJg3fX1D2X85ofH5I9nuX9jU+PV/+XSp5R1x7zeY2mqVA5sSTx3hWKCvknK8qAr/gbfw/f99pfwate+yv43h/7FXzvj/0yvvfHfhl/6+/9H6iqCv/mlT+NP/X8FxfIwamN533RX8S3fedPwvkKJu5mbpixv3+Iv/I3/iH+7av+E/7N9/wnvOSv/100873h5sN5THlsXPwuNjZYPU6bngn0luGQ7Rn7L71rvp9+DiQFc9MEYowIIYAI2FvUeOCBQ/QhT14IEesuou3F5WvZRnQBWLWWSEHdQj3w8K019uce16/McbphnGwCDmYOMQJ3lhHLtbjaLmpRGlxZEO7bc+A+4tZJCwZjf+Yw83KK1vZZSB9YkxQQF9X0rPKMtm3RB4mz13WdWurZ3Ikwcrg3hyevyTIksUYI8p4hRLRtQNcH9DEHoLc5te+VWhA5snh6wm7Mquz2FiPQ9UhJThpVCvQ9p+eMqTI3tMiSrETmlfKJJYmrbKfPmju0WVnKfCIxF5KtVATzyskY0kmnzh/DBIc8pd47iZ9ifSuHkl0Bc4ys5DbIolT0HsmNuQ/Z+s7iuKSswVBFA+X123Q2v9ly1obqnUNdefiUaEGUHspSDmiC/foH//Rf4dd/59H092u//Sh+/bcfxf/1slehqmv83z//G3jJl/91jB9mBr78K/4GfuYNv46qKs4ZONOBMTf5YR/xUfgn3/pv8BP/8ZfxC7/6Dvzga1+PL/kLXz4ULjNK5wZK2pT6GFs8jPAYwzoZpXmI3uVkkOKQrg0BaGqP/f1ZYVHCquDOiVzantUF3xRDnOD0eNWjqQh78wptkLoG36tWaIX3YnXKLAr6vYbAkbFqRYRoVFlNEMG3VOSMN1OzJBZ4AELfI8aIrusQQkAIIXE0rPVndZ2SsCT6oUo9jqLMDzEqvBdrlOa9cKejbKkGAJUfKhB6M21joQXMSJlvLYs1ISuzLLadLk/xvpQUr+LmqEJXFNxuUk4QqW2KTEtOYDHGzJIPk1ii1lHF3kDpeoZTs1wq4baMZ5stxaUbS96TxjECSIGv3GcWfCiNyTmnme5NgTQdmclQ8Nv+9b/DzaN26+/7XvMfUNc1fvMt78ZXfvXXbj0HAF/zN78Bb/qtdwzx/ILl+vX78Ktv+h3cuLPBlatXUGDkgDPg8YXROyTpEDxoYwuX0wO7yqh/XTTvCU3jtwTHGDMPYXEkzTU3QQ2JNbp34oof9LDIEt10gZPlqcVzq7wcfomLn+5vLuNNnHqvopSKIcMDsd4NGoMzEaxUp/KiVh7yShnGcnIdpAOe8RhKt73ygM4OOoxOlxa/phC3PXqkj0g0pXRFLumaKdXsWVf89okTznTG9JalwqVsa3I+R9o8Kj7Lfab8TO0Znhf3DC7s+1b4GOQ5R9EHlYMdjfsiwsNLX/oydH3c+vsPP/pjqOsa73nvI/ibX/f1k8/+r//bN+Kd737o0nj+pX/hhfiv/+2XcXSyxLve8178i3/5bZd63soF0fZi9W1/5OFvQrZ2HtTmYn2nmtNHzM21zG5rrSVXcRRrT6qA5pzNetD72S8xrGJ7bzHeqcddAStJ/irwcbhXFfR16oVH90q6MKYPtF09XR/yKKOmB7x2sc/T9DNlH2O8oNHnuIytgcfPAUP8Hj5cPDeaE3vHXc9u9XkRZD6jvOzlLy/odf77sde+FnVd45FHH8XXf/00nn/jN34jHnrvey+F5x/3cR+Hn/ypn8LNW7fw2M2beO3rXoc/9If+0L29xAXLBVBkss55U3wuvTHaXvy0W+XvcX88+n3RMh6Pwdk54Hi59s966Qm+awpXdlQ/fzxnVLjIGl+myXtEr3sud/s+l7TYE1dcrmsgBgQmcBdSVlArxsyYWfEv/eLP4V//878j91RQCV2H0Hf4uZ/9MTz30/80/sOrX148LA388f/5T+HnfvpHEEKf2k7CKAFf8bX/CNeuPwn/+G//Zfiqxpd/5TfjJX/1b+P//JZv0Bbk310b7eB6qdhDsWmxbmqcr5X3Bu0Xz6a5SJsMoW3FzbVuPA7mC9SVx6YNODnZqELGoXY5xlJksawRvl4E97ZnrLuA64cV9hYVNq1kv33k9gYzJ8LKciNC9+HcYivJiXjtIq4vKqw7URzef6XGpmWcrHts2gBwRF05LNuIeVPn8TOD9djbO58szGoNrh1jxHqzxqZt0fV9Yhj6CHkOgEW9Ef2lKMhE0GesNy0ASpl0HTl4l5UatSe0vQjepRudWRVZsHqAse4i5rWDU+Vi5BxAnEgSaKhnHYgIjbodAhZf0MCQ9b6saAwRvXOY1WoloSY5rOPwBLGQAyfLPcnkCxW2RBHQBSSlnXME0gzCfSgC8xu8FUxijBJ70BgYgrkqCj716roZXRaUTFhvakquz+SQlASRgRgYHTMql7cR7x2gcZ46lqhfHIYxVBgMUld7Q8n/9B9/Ct/4df9LHiCArt2g7zr8yA+/Gs/7gi/GS//tv8K4PO/z/xx++Ae/D73CTikEDYpuTh/5UR+Nm4/dwDd87VfgvQ+9Gx/zRz4ef+fv/zOEEPA9r3jp8OnJr5zaAkZMe3m9/D2gIeW/NmtIkQdCiODI8JXDrPLwzqEPEZtWoMsRNIZWZuLNKoeBHEsxMhYzYFY7dEHw4XjZoyJGB3HXJSLM0/oKjnhi7NVOMmT2EXszjz6I0qAP4lLnnOBU5cUklgZrSklw52hB4UXx3PedZs0MMNVIxnGRlAt7k0FynL7vAaZUP1khKS4K/mV385IZMAWYKZ7E/ZYSF8yAWpnKMDxyXD2QJBRJygPaVniYKyNHRiRxbY3M6bTVaJoDwFS4yyeBS+df98KcXMCE/ZhoUXZ7HcKb7CXmVqmOjyaEaaWUddPgthD8LCFLgsdi3KbAF6tm1mdMOCWAg8TCjaVj8HT5qZ94Hf7aV/yl1A9A2LQbdF2H73/V9+AFX/JC/It//i2DZxjAn/uSv4Dv+95XChzkGUij3RnsHcC/+LZ/g1//9TfiaU9/xqDNrU4mCk/cH1+7CMOZRyr7edS1cAS4yie36V4ztZhgboKaLVepcIq6tnUFDUEh19suJoWtXatM6aV7k2NG4yTpUYzShlmO215iY/IjfAKPBOME2wovGj+PY56gIR0c0sAMY4wYBj3pbOVYeYNsn6Mlp9F1SUAynMNS6C1dX+X3WVBkSj/O70zj98prY6XcT8c0P6Y9FcnInBkpeU8uXDw16ttqjODT8DtdNvpVnAYMFAzGMxgdT10aTUc6iNiFK2V53etei7/4ki8bvO9mI3j+3a98JV74whfhf/+n/2TruS/9Cy/EK17xXQWen1++8qu+Gl/11X8D3/D1X4df+IWfx/7+Pp797A+68PPABV5posL50zBO4kdbNDnfO7tBg6ty3e3gz+AOxf1y+VxRx5a/xAUq2p/sdPBG+Zp5BiS42DFu+1LuKQlnSgAtrM13tlPcT9M2okdT9Ye/CzyfgOeyKWt7ayoG61rQ5+3mBs8aLbK2xy87vjS1JmMKyaO6F7GwLelWevgCcDgur33ta/FlL37x4Jrh+Stf8Qq86MUvxj/5J9t4/sIXvQjf9Z3feWE839/fx2tf9zr88A//MP7qV3wFqqrC3/3mb8brfvzH8axnPvNS9OKy5ULTUeBS+sKXmsrUTMKxYoHHeD7+XfbNuKulvMDgduP5oNrWl1E5o40pOnSBLs8vF9y37qVM0tAzrj8e5bzXupu+L5kVV6z0QB6xruEii6uGI92gbAisjK0Mp+9a3Ln1WFLq2dsQgJ/98R/AZz/vS/DhH/kxePObfjkRst//Bz8WDz71mfiZn/zBCaYSePozfh8++mM/Bd/41S/AW3/7TQCA7/g3/xh/85u+Dd/9sm/FrZuPpnEnBCoaGW5WY+Y1K/Ds/tYp63hMxdhsg3P6ks6RWDsuauwfzMEMLFcdjo43aLuAvgvog1jirTrJWhlZ4uV0vcbaceJO4muHPgLHyw6rNuD64RzLdcDB3GOzYTRqtbLuGKctY38m8fi6wHjgsMHxSlzonnSlwa2THuu2R+WAg4UDB6APYsEWvSgkKj3KjiyKuFvrFtf355nxZ7EKatsN1us1+q7Lys9QMLvI89X2AetNRNer1R4zOETEqIb6Ov/eY8BUmaKv3IABE16Ew2ZINtA+EpZtTIxQYgRI3CGNAV80IvBXDposQ63hVCCKyth5LwJ3edJvJvgWtB4m7MUIX4mrUmfJKUiUmr4QPIKaC4hQJBYZ4BwnyCz0zHLUO8miS+RSfDwobFaO0AZO7sgWI20ojEj7gQvmkgBY3EHO82nCgK+8WkAGuEiatbhor2Cw2rbFjRuPJCEiIR+A13zfK/GlL/p/42M+9hPwS//l5xMd+LhP+GQ881kfhB/4/lfuVsCPfv/Aq7978Ptd73w7PvpjPh7/r8/8XHzPK/7d4IHBsyWuD24qTSj7H1lAFLUGdY2OARrryjtUlUMzm4OZ0HUB600vcK5ZaInEstQ5Y9SzstgpE0+qpGnbgL5nLOYVWs102/diLUtw6KK43zeVuONGZuzPPDadwMLBvMKyjeh7URTMarNuUytYJxmQvS4IQ3Bp1QUsZpalMeNLHwL6vkMIccD02NMloxpCRN+Xma8BKM1Ik1hYvrEp+TDc7JLQovWgQf29J1QsVrXjQOFMSO8EBuqKVNmuruvgZLkrtImS4qu0GjShwFx6SliwQwgiscBgqOWM4q2N22L0QXMbpky7ejkrRg2OFC6QM3OX9wx/7fdgjkYwOuAtWS2yfH42CatqKkXjyS+4CpvfTdvikUce1mvDm6/4zpfjL3/FX8cf/cRPws//5zekcX/yp/xx/L7f98F4xXd+uzZbKhClk+F655f+spd8Oa5evYpv+af/EJ/xmZ89nKwdHNEY77f2/eLHVBNnCVRO93TvCVXlFNYZXc/J4s3gxpS/QIYne3fLTmvPx8jJUs/izCb3W7aER4C5xTUVpUOkppK9xkI4WKZcc/tkyvug7TnMYgFY+WLG9R/b3yPn+L675los8ZHe2+gnpwybnN4fjMk4UmPGtaTFjgB2GccGD/GQF7CYvKW1kW1n5TvYtdRfOQ6D+UTfdJ9L8yb3jSZZM+OtsYxbmSa9HNcIdydebbsuldbAeZLKdzXX6/KdxvN7EaF1s9ng4YcfTtXL8u0vfyn+l6/8SnzSJ38y3vD616frf/y5z8VznvMcfPvLXnpGy8Ny7do1fPPf+/v4M5/3p/EzP/3T6fob3/jGM587a+wXqXje80oaEzwBFnPVni9WomiMxotX9sms9Hsc5iGveemqrbtdyoprSaSs/UmFouF50acdBqSqaX/jYbbM0dAnlc0TXZZXytefmILJvuw9M43MyszitfL3And29me0YQee8cS1suK478FUFw+etVdcpN0doJIrlP0WZUoZeka3O0uJ5+Py0pe+FF/5VV+FT/7kT8brCzx/ruL5S196cTz/iI/4CNx///34pr/zd/Cud70LAPD3vvmb8atvfCOe/exn43d+53cu3NZlyoXwXL+k73yJdd3Vp9HlUTvlrljC1BiO76H7yfFs+5Hsrjv1+wKgdk7/uZ27ebdzceUuyy4ac7flXtdu69mC37houZQr7nw+w3zeoGk8KrV08t4lK4VyUDTAEiAdeRrYKmS/4+2/jd9+86/huZ/+vLQBAcBzP/15ePObfhkPvetthYzBaWE/9CP+EE5PjvCW33pTauvXfunnwRzxnA/7A2kXSkomzs/baVPqb/Cd0/fyWcAYtVLpV2ztBQPgSNxVZjOPq9cWePDBq3jSA1fhfYXT0w63bq+wXLYIfeZUzdVxUWcmuw0So23TRyw3EeuOsW4jGgdUjrHpIo6WLTgG3DlusWnFvfd0E1F54Pq+B0eJr3UwIxyvI2YN4XBe4caRuNRdmXvU3mHVRpxsGOs+YtWpAAhlKJxDU1WY1TVunATcOFqldTTXgL7vsVqt0LYdwDEJo8wFo8kSb6/vGV0nLrhtF8CB0fb9UAAq1twUAyZAB1VgWTD7ylGyjrEMX46Q3A4jF4quwVg4jdEsMAyGTVltwq+n3EZgcevrgsEEFe1moayPWVgimJuxKjj0JZPMj6wgNMHNEiyYUY0JKwQanPpmZj7vRkTZvcggKqpEFZFdnIzVK1G1qivUtU+4LZY9bpKJoN1fbAEBBn7rN38Db/yV/4bP/4IXlJfxeV/wxfil//oLeOtbfguloHLZcnB4iDt3biXGyf6kPcPVIVfKKPC87Hqs1EvMWH7erM0ciXA/XzQ4PFxgf38O5zzaTcBq1aJt+4RL9hwAlN4LQelIr0q6Loj7uCme+iix9jhGrDehSA4jyrpF48AaX2tWSdKYyhNmtcPpRlzq5rUoprvAaHux9uxi+d6yzt45eO9x0kacrjqBDC5wJga06o5blnK+c13B3dBHVQRK7K4MKjRYE1MMgJBi2Rmjn+CflDpqI44gseJgjG4Rq6ug87YXAFlRltCQswBgwhMjC1ghIo3TaBOzKAkthmDJsRstMmEsDyXTzLKebY1jYQXFQdiwHc7CRUELEsPIOUaYwW2J5zYPzjv4whXX4nVenLkppQ6Z499406/hv/3XX8SX/PkXDvD4BV/yF/Dz//kN+K3fevNgrMMyeDsAwId/+O/H13zd/4a/8pe/DCGOlEwTdGLrcqo8dX0HqbF1RAEXBEkWUTvMZhWaxoOIZC9rY0o8My7JfRTZSjSyKGfN7cnoulj7Cf8hSkK53uu+UGvmCHErR4rTaYc6zGLFKvEa1XKPcyzNwbhI9rg2SCIdVguvRDs5u+OeJeEYzhoeGK6lhEEY7ghcfjNBt9gDjY+aepYwuj4k53lNFTm4uEflF/1LqQi4sLo1PC+HScMkHhmTy/7G7ze+zpMvRcW/g2cxLFTUSrhjeF7Wn0Beo5sljbnX8mu/9mv4xV/8BbzoRV82uP7CF74Ib3jD6/HmN7/5wm19+qd/BpxzePrTno5ffeOv4y1vewe++3u+F894xjMm6+9A/emyo+LU5RLfnbPDXIESO2DZhQoDJXEBhyVvMcCDRAuyTJPoMuV3LJV6dnA36I9R0PldY9PwNgMmpxxLYUWe9syJdzX6MOalrJ+Jvsf1St5+Cs/Lz5IsFEPY6nVrmKmD7WfLeR4/k/GqbH249271sWN8VPRvYu/0/EyUEcHctbbj9grSNt3ZXZRf+7Vfwy/8wi/gy75siOcvevGL8frXXw7P3/zmN+PGjRt4yUtegrquMZ/P8ZKXvARvetOb8La3ve3xGfConInnyGszYrAuTl92tD81jjHfQxPXx7D2OC3jsNF7rH/ZMdEO2L9suds1uchzJQjcbblsG+fTtrsvl1LsEREaE/gdwXuPyvv0QluAqSP9I3/0j+M7/v0b8O2vfj1ern/P+8KXpBf52Z/4AfzRT/kMzOYLgIH5fIFP+ORPx8/+5A8mIVH+MgN09doDuHP7pv6W7SPEgJPjI1y9dv9APGCrkzbBoRXK0PWxUP4NPvN1e7dygzJGua4dDg5meNKTr+ApT7mG/cUCq1XAjcdOcevOCpu2z21pu32IaANj04vFHiDWeTUBtQMOZg5zTaCx6iKO1gGOgMO5Q1MR1l3E/tyDIVZqDxxKqOijZcCmD9ifE+6sRLifVQ6PHrVoPGN/XmHZRXHdZUm6IUoyl+dK3Xosex9iwJ1lh5N1nwRBi523Wq+wadsUGy4xLqrQYxbBpe/FJbDvI/ouYLVpsdl0YBalhQjX4loXo2UFJIntkzbLrMATwUhiDBGpdQObJYEo2MwKT+LzlFkAkRR4fZAYeScbRttHjZuXFThRNZRmYRVZ3G5ZYaCP4jYVOMcw65UrM0sCInkfU2lwFBc0UyIQxFIxC9hZGLeNyARcUxRaOwMFVcH9ClOq1nYEtQ7VxCQAGp8tPGxuDcfNXc9iOg2IUWk9wMBzP+0z8Yu/+jb84q+8Db+gf3/pr3xVqv6a738lPvOz/zQWe/sAA3t7+/iMz/qTWxZ4E/zkmeWj//DH4U98zufh1a/6zgkmL+NavpYZzm3mlYd9Fkw5qfLLLPOamcfBwRyHhws0dY2uizhdtlitO/QhDttOii4gBKSkKwRVGpMo5apKIKOLjE0n8DyrxIW8j5JgA5DnD+ZiM7ruIvoY0VSEVcfJsvp0HeAJmFUObeAUlN+TWAHDmGyle8k6TBAXqy5g04WE50Y3zR23VGRkIUYor+FeUu5FRtcF9H0EIyYLbwIlxb/hox9Y7km/ZYBqi3+XhC3Ftcicsw3ri5WKdIthaYcEm56TpZQp1Imy4i+57DInl3qAkvVhhLnaZ/f+LERzdmNUOJS2Ob+b1dU1IJtIxbWBO+VI8EqAWQiGaR61scAGX9JeZdaIRTEct3dPAuP4M/eIP/FZn4N3vOcm3vGex/B2/fvqr8lxeF7xnS/H8z7vC7C/vwcw42D/AH/qeZ+PV77iO4ZtDXBvG8ubpsG/fel34e/+7W/Au9/1zq3746e3mtNOBveKHzvpis0DAaQC/mzmMZ9VqCqxlm7biLaLif6WjRkulNY9BGG2zJrOhHeJqSkPVx4JRuxQJrDQBYA0jqpa8ocM6xamolK33BRXFUMF1Rbvov+kOJ8FA84s/FR5KDGY3sSTofjMvMJ2vMgxT0VZqEIWuMsxJ8uoYr2sn7GSakq5Ye2FmJUyJViX/JtxLGU79n38CdBgnFNgXF5LtGViHoG8F2255ha/Shgu5yPFOkSej3QYOBpDevMLClqf+7l/ErduH+HW7SPc1L+v+1vfkO6//GUvwxc8//nY398HABwcHODzv+D5+PaXv/wCrefy+z74g+Gcw9f/rW/A13zNV+PPfdGfxfX77sNrX/cTqOs61dvC8fPKGZVp/Ke47ryFTbEkMtmTYUrRlb4W30n/KWHU6EF5P8Vz1qYcDdsymm7jM2V5sb3l9kbjmsbZwfYyvD5Rf/yqY9oxqfzDGE6n4b68xhPf0/tNPGyXS5pOuFg80ck+Rzh7KRib6GNi60z0bXx9cnw74Gxrrs/D44sgOYA/+Sf/JI6Ojwd/3/ANGc9f9tKX4vl/9s8O8Pz5z38+Xv6yl12sAy0nJyf4tP/pf8KX/Pk/j+VqheOTE/yJz/osfM5nf/bWQfHjVbbwfHzRykX4gguWtK8Uv3d9t3pTdQYw/jiUi9BP3vpy8TIGtx3oe/dlFx48geVuurroMxdu+y4m8VKuuLN5g2bmUDGjb0V47TSIugkHgC4oA+aJ8eu/8l/w7/6Pf5jGyABOT+6kdt/wH1+HL/1LX4s/9qmfiZ/9yR/CJ37qnwDHiP/ff/pxJJZrQM1Lhkc62jq3GRF5Vs5sazKLa8liZIshzcx7qk1mOk4gJwGzF/MGi/05vCOs1wG3bq+x3vSJ+bW+RGHG6Pps1RNiBFWSDCKqvx5HxrqXOHKLxuFg7hGYsd5EHC8DmjqiqRlX9htsNh0iA9f2KnS9tL0/E23SyZqxmDnUlcNjxx0OFw4xAHeWPRa1rFWnyoYuMCIcHItgGhU9zX3oYOaw6UWgWbchuQ0zGO1mDUcVYvRg9aNl5hRbcNbU6HvGpu3QtQFt2yFKaD8QO4CyJQqzCEBtz4Bm8LT2+iguvEziYmfWCyEwqCH0FtefVfnWM6pGls0yYMbIiJVYOHQq1FgCXhGYHIgyUxcZIJYssVFdpJqKNKGJKGwkMaEkpiCV1h0RWC0oLFMmuayUMyUAAAR2yRIsbUA6HlM4tMGsCMWlOBIliy8C6wkz65gJHhYQHYiRUDWEsBF3kMAEBxEUS0GgrjyIGc4zYh8Sg2twn/ZExUlDlV/4zz+Hv/93vi7VA4A7t2+ldn/sR16Dr/vf/j4+63Oehx949Xfjsz/3z4Aj47U/+oNbG4rh379+6ffiYz/uEwEA73nPO/FnPvtTtYr8+yEf+hH4l//Xd+Ff/6v/HW/4Tz87EFinSsLvURVT7m+VgnkiAnwliUXqRhJF9H3EatWjU03uVrssitiu6zUOolh71grjIIH/ToX52mtCGIiL6bplVJ7hPWPWVAh9ALNY6YlAHlXwF0VVrQkyTjchJZZZd2LBS7rmpmhjdWlNrrFpggizSpXSOg6LQ8nM6LkHwSGyAxWEuQ/6ft6L9VEf1VovJOXbEGbkOU8OfYwSB0yXwWKLWZw7XwkexKCKOS/wnJQAbK6KGSATreWspDMhiYGksEzEwkn8P3VcSu723hNiz+n9DVfaYLCR38b6gLY/Dh0gdaRPp0hu8TptBxWYye79Zk1nij5gO7sgAzp2+Ww8odX9TIRDyfhbxsn03oODE+oede4pgf3O8nP/98/ia/5GmQiHcOvWY2k9X/PqV+Ef/H++Bc/7M8/Hd7/iO/B5n/98cIz4wdd8HwaLU3Cur/r3P4xP/GOfAgB41zvfgU/+ox+Dv/1N/wC/+Zv/Hd//qu/Oz+jnTqaIhz94x71dzxvdlQzJYo0LpfldF4QODhiL3A+zutTqvEtM2Fw1QtZGDgGBSpXlUUMhONbrnpJCrXZqbRNV2Ud6AOUIDgKDdijTRc4u9Tx0ySyFRuPLCMPDIQvobzQyxqiKI0pwY5buzGrBzjmBk+0zW1NuMKXKDNvfTdIoFQTJKjfxWjSabrE891vKCaFjJZ6P+89rrPE3mTVeJtK+G1R5Ygl+wEOrem1gMKZEUxhIqUlH98ffDQ3TWAu8K63pp2XQzKeSo7R3Wbdj2pAscQkp8dm0unZYfvZnfwZ/7a9+xeDazZs30/dXfe/34Fv+2bfi+X/2C/Ed3/5y/Nkv/CLEGPH93/eqnW3+8I/8KD7lUz4VAPCOt78dH/PRfxDOOTRNg6/+6q/ET/3kTwIAvvRLXoB3vvshPPfTPg0/+RM/ce5Yt8r5r5d5GaWvBpcG32c2X8gNUwJtEhMor3WCcyXylqzN2rDnUn3k8Y1hmpDxmEfX7ccgPMV4iy8+jf8cRzkdjIeLdz6L/qZ32Y7VajTS4D2/s3TCo9pTUVcZObbzhdbYOqbhfA4UsaPfZ5Wd+yLleZocwtT1c2EsV0vbSDnuM569zPT8zM/8DL7ir/yVwbUSz7/ne74H3/rP/zm+8Au/EC9/+cvxRV8keP6qV+3G8x/9sR/Dp36q4Pnb3/52/ME/8Acwn8/x7176Urz+9a/HC774i+G9x9d87dfiR370R/EJH//xWK/XFxzxXZapCbvoJN1FmaIPU9/5nO+P9xDH29nWOC/AJ91Lx0/glD/uZWr+z1qTu3m38545C893lUsp9uRU36NyQFV5OO/RhZiFfJRAk5myzXqJ977nHShjypTMzGp1ip9//U/huZ/+PPzMT/4QnvsZz8N//rmfwGa1QtpFlPraNnj71g1cuXbfgLF2zuPg8Apu3XpsyOwMFHqZs82blG5sow2Pix95M5JBOyL4ijBfNNjfn2HWNOj6iOWySxY7poiy1kS5ErE8XWPTBrSdWO/ljHkWa0sztzoCkzCZyzai8pSUfGLRE7HpOpyueyxqgifG8YpxuonYazT+DgiLRhi7x446XN1z2HSMNkQczBzWbcSqZbBa1FUOOFm12HMz1JVPp/AWMer6wRxtLwq9003AatNhXldoKkLgDjUxmF2Chzsna0SN/l1VHsvTJW7fvCXvTTVi2MC7K+DQIsasYDI4EUs3W3tRWkUWJeSsEkHcq9us95qwgrP1kTESEiNQFV9JkGG1UNRMmCyxCfeaDGsWz8Q7QhfEKkqEJk4KA4vZZQhPZHGLZHzWb+NNQUmpzeRcxiIUMkxxWDCXNFS8RQagcBIipyzA2qMo8FwWvEjHFAIAlgQtGxXQxHqENGh6JvGilHBwXsQEH41RK2NhDbnH1WqJd7z9rShLyVCenpzgJ1/3H/B5X/DFeM2rvxuf9wVfjB9/7Q9htTzNuJlnEQDwTf/rV4olLyDxGwtM/uAP+TC89Lteg1d/73fi3/6f3wrewZ3lyzz6na7sFMZI8bCqPZqmRlV5xMBou4CuCwNlS25ThNyu7SVBTIhZGLPNjWX+CKTB11WZF4ThNyVfZLVyjQFtF1GrVc+609h6PlsCNApzp+uAhSq4Q5T4e10fU1ZoU05tuh5UV2rJisH492Z1Ura1mhGx9pIpOcQAT6VCkLHadGKhRpIEqGtbrJYrEQbIg2MPR3MEEks/U3xbf2YRWrrClTgquCaCkFOaMLawAUoXXlJhpIiHWcgDfQBqnwl9BOA5C/dVcd2EmzEzZElIUhtaz+s4BkqyQlgzmIk8UluM+yjXRF90YOWhHL/hGBHk8IGFGjmbY5b4rSXDWHYqQr8kYokch5zLmAsEcLpc4q1v+Z1RK7kcHx/jP/zQa/CCP/9CvPIV34EX/PkX4gd/4NU4PTkdNlQM5Kv+2l/GfCF43nUdAOBT//in4SM/6g/gTz/v8/X9pJffeutD+NZv+cf4x//o7+1ob+oAb/JrHrsKZt45iY9qSqteYmOO3diKXT1ZfU5Z9pggT1C3OjK4FpqcE0LlZEZBaQCRuMunQyMASMlV5JCnVis9SeBkVkbFu1G2ULUjnzIhjCkObS9JluN6sGjKZ3uRro/p7R15hBDQadbtzC/5tAZjpjQrlgccGSzBF6c6nMZvCpGkiNC7xZlCaoeLtTTc94Vy1ZTXpYtjuWbJirBcZ1L+R6+XSkjOVbXPNLoU+iLtMaX2rihpTexd0+xh8CQh8zOgoTUoa0U30b6Vck/jtKa7y+np6Vbcq3J3Pj4+xmv+/avxwhe9CN/+7S/HC1/0Irz6+78fp6enU80BAP7yl/8lLEZ4/tBDDwEAfuNNb0r1bty4gRs3buBZz3zWmWOcLGe9VkErLXETK/7Ywchkk4yhTHFOF4k+8JA+2zVArHFLJZ6tLRftADuUejh3+QZjpaLlvMdSHuPo0aTcJIv1mkPXWNsT5hSp0GDE5WNZXWfW+EkpVo55ingULSaanfBluvqAmxztyeVc7yrjdhMuFuhj490JO9he07P6GltUWpmCjbMKAUn5e1aZwvOyHB8f49WvfjVe9OIX4+Uvfzle9OIX4/u/7/vOxPO/9Bf/4haev+AFL8AHfdAH4ZP+2B9L++mXvOAFuHnrFp73vOedqSi861Ksd8Ee7CznwcPdFD7n+xi3y+9PxHjKUuLaeXB8z2UKaM/A8/Gjdzuuu8Hz8+o+HuWiOHw35ZKKvQiOUYUAURx4lZjk1CdDCNOQgZRPHlBl2/SYGT/z4z+Ab/qnL8Mf+fhPxYd/5MfglS/95zAoi4BahuT6b/7vv4KDgyv4oOd8BN76278BAPioP/TxIHL47f/+qwNhPm+Cg5HAlEUDgWBErPXF02bkHDCbVdjbn2Fvbw4CYbXu8djNpcSLsw2xUCZK1tOI1arDer1B1wdlELOFGhiJsejVbc6r9Y6dqnd9RN8TnCc0nnAwd1h2EZs+qtJGYvIdzKRO7QnHq4BZ5XHrNOBgJu48IGBRO9xZBjBHLGqHTW+n3QTvGJtNC44RB/sLccdlmx+ZryRwR2DVtgA7sXIDAMyTNVrfiVnLYtbgzs07uHPnDtpo8yNCQt0As7kDqYuexbsJIcI7E4QzxXEQSz5PQFNl19p06i7QBQaScGuhgrLhmbkCqYuuMi8EsWYDWYy+zA6Zi0tJBAiijCkZqErdOcQNl5MLsFhhIrnEpphE1hBB+5f2SoEEQMokaIJYZE16gSzAmGCWRs1if+RVWJIYZ5phlwGOlnWxSGyTGNiojIYon0CiFN3aiIb8Xnqd0S0QgH///d+N7/juH8JzP+0z8Ic/9hPwz/7x3001WYlGKQ498vB7MVWe86Efjpd91w/gh17zKvzLf/aPErM6YApHNOBMzksHmIUfsdapmwpNXQFE6LsosTHVzXZM1xhinder0i/HBBsqGuxZs8RxjpKCVSzPolijKX2d1RILq4+M2CPRlpkmhHAk1nqVIyxbsdQz4b72hHUrisXak8brym7sfS9u+E1TK6xQornyKeNjkFhnI1tmm6AJEoUEiFFXFVanK6zXa1hyTJZBw1dARfk5U97HgaWQrQfBLGGdSr0EU7TTkLHmUkAfWluYxYTB8RaNR1aaJzwv1qrE88DD65ZoY+BixbDcHrD4m2Owy9Y6PPid4KOEZQVmc/0VmjZU0NgcWFbeXt+RkBMnJOUIlahhdDULmFkBdDn2ZYzn3/WdL8ePvPb/i8/8rM/BH/3ET8I3fePf2qpbvvZDD71nq80XfukXYaFKfQD4w3/kY/Ft//rf4XM/69Pw1re8ZYTP5ZydMbiyX8rr6Csn2cAhsNy2EYPYU6PmJAbdUKG3qxi+m9Bme0hkpL0qudJGToo668ss8hzJ/u29xNRLGd7tnloTesrKAMOcaHuRagi3hsyZ/oSYXYGHuJLnw3uHru3RqQVxOUdmYT5mqJOSY6SRINieZqPlrIxHnjuUvxkjHNh2j81jpqHyANNKRysxP6xtDunN+Lkh3Srei4b3B4faRfVim9rCo/SORaeDNgo8N5ptGbmnCu34PHNCilKOmQC87OUvw0//9M/icz73c/FJn/TJ+Ftf/3Vbdctm3/Oe9wzuE4A3vEGC8n/oh3843vXudwMArl+/jgceeABvf8fbzx/UuMOy0PAr6T4LKI8ZeWveB00q4zA+QDpvGFT0PVAaA1t7Vxqf3bfrVOAMhvesj/Jzq+8dAzb6XirZxlUzrFvoiYkJoCEsjy0NpwYgqJUNBabgO72oPjC8TduwO+r1oqVA80G3O2Gh7OEsYNgiENP9TtHgMcxsNXHWAC8xpP9/e98ea9121fWbj7XWfpxzvuflGlMQk0YoSgx4fUR8/IOktdDIPySKieAfEvhDowaDRoFEaHkERLFBg20pWk00UTD8oUIovUgLeLEQob231z6h7e2l93ucc/bea6815xz+MeaYc6619z7nO993n3WN5PvO3mvNPd9jzDHGHI+rwjvf8Q780vvehze/+c34mq/5GvyD7/zOC8uXeC6wWCxiwsU8APmutd4p/0gw2pskHw7AQ0zpI8NevN3z7iWFEWPzQG0+xGa6FM9fQrjqPF6dkjxcOy8lXEmxd3I8x7XjGmf3V5gvl/B9ixV5PiZE4Kex8ABUVY1r12+l2VJghD47vZeQ/EO//Rv47Kc/ie/4+9+HT3/qY/jIh38TQHEAFgIhAHzmUx/Dbz71P/G3/vb34t/8y38KYyy+5dv/Id7/vv+GOy9IRtx4jOwcoqWgn1+OmSpmBmOA/EpjPq+xWDao6wp953F2xrHh2NKK8s0fISqRCF3nsGm5XO98EgRMMRcyB70PmMOgrhU6x8+3PQvXjVWcMS9aSG4dx9taNhpWE2aVxrYPmFl20dm6gLvnDrePDe5tApaNgtUaq87DauBeGzCrNBRpzpoZLbLE5dJoBecd7p+tcO14AQMN5zLTKP2orWWX4m6L4/kCrndR8OGZ9SAYANvO4fTsNLnBMvLw+ni/wWrToXp8EV1E+Tc+sALTg7kbQmCXVmGQoxJIrPN85Gw1ONsnxxJj1lwYJI6jF9cpqGTNIHG5jFbslhaILanAsY96J0oChcpEZR5RypRrNLtFsXKQ26ui1YepsquzKFw6D8yiC2UgoEoWS9FaLu5MUXiQKqwpImtENBR+kvUQgJxNN2bRBGWlpMrCVMmQyZ6cNRWMNmjbDlVTI3gHdHt8KQXhI2LXdY3btx+L+5qfOe9wT2JhEuGpX3s/PvmJj+FtP/x2fPT/fgQf/OD/ivQj1hXpxwAPkQUYAvD6P/LleOe/+y/4lSffi3e/4ydw6/YXAWCrtrsvvDBA9gLlByDMpULmnJWSAPkGdW1hjYX3AdutjwIspaopfpC97r1PMeTEwqdkyqXvBHEl1bAmWlwSgXX90S06Cez83Gh2+9aeFXUuCvUKHONxE7Nfb3q2ztNacdwthRhzD1DEygBRgGeFACsj222HWVMByIG2U3IHEKzS8Cqgdw5NXSP4MJjbAFZOOBew3bbJcq6EEHr03kGrOtFIoX9a54zL7G43nDspJ7+T9RKhVhXCmgQZTwHxaeiGCyoUicUiGc3xDykqAbRCUrLoePgohXTRIecRW7wSbCzvQ55fznAaxw+OmyqzwgoDyvs87v2sABls5SzQFJJNQsF0pkU8L5SGolhW8l6zQslaA0UWzjloY0AUADcUOfYxOU1d44see3zwwjmHO3deSL95/6/8Mj760WfxE//6nfjIM0/j13/9VzGGMSM0ZmY/8fGPDd7fvHULAPDM00/j9P59jDD8cij3k+YLM7bO03z55sLAnXRsAZwvQnYt9PbpUYReJxqOvBfL54w/3C+xKBclm7jWBuLLvsbyOWKj8kzwuQ+ZfvsCR/Jei/Tes+V/is1G/Fa4H63YTZUv7iTMxu4e4PnyexUeRIGtDhudiV7ch3x9h7TYVKxLeryn74KTpcpAyor1L2GcWblUAxRtRM1KKlvQAsEntnQcno+Ck0rF+JpFX/OFmrSfEV34htT7YkIPCRBlu+NCovTJnc2TJWMu18wYzuAcPLvuDojoBdA0DR5//PHBM+ccXngh4/kvP/kknn32WbzrXe/Ghz/8YXzgAx/YO5ZDQACeffZZ/OzP/gx+9Ed/DN/x7d+G09NTfN/3vxVPP/00fum97720nwn2TKbQfKWLsA0ljh/qF0WKumd/7xtDOqOKZ+X6C67Juo75AlX8TvZSSVdSvfFZ6fG9g3+xIPN7xYFT9HCfcu/Q6Ggfkqd3BY4lhOIRPci8jfsvY9vB8+LJwMk3DWkPnu+pO9O8XOfgYm7U3riPqR/jystGH/zxDm09VO2DvE+xkB8At0u4DM8B4MmI5+/+6Z8+iOeXwc///M/jh374h/H2t78dP/7jPx7jan4XnHN471Xw/BCMaGqJg4fgajP18LAPz3f39svXn0eC8pC94s+u+JMMlyHGI8A+PH+xYd/YrzofVyl7JcWetRUny1CK3TCIyXd5O5jIbuGi91V/8s/hJ//DLw7q+vTvfhx/79v+ymAmf+l//Az+6rf+HfzMf3xHOpzSMUQ0EPgB4F/84Hfhb37HP8I/futPgijg137lF/Cun3gbBsfAgIfZZ7kTy6qCsEfKzUGzLZbLGRaLBoGAzbrH6ekafe92bvNE8PQhoG17rDcd+t4lyywSkwsieCoyHkZi7AOwcQHGs3Be6WxVVsWA2ZXVIACaOM7ctmdXWx8z5y5rg3sr7tu1hcF6S1jUAJHCeUfQxFkxTxYWm9Zj07HCorIKRims+2y9oeMpe+/0HMv5DLNZg7YN6TAnsGChlUJjLILneFq8bpQoa13V0NpwzL4wdHHtAmCoZ9fVguGRuqE0JyJADrXPySx4jeRQDkQwUCzgaBZyrFZwPsTsZux+JwGICazoqGx2jWNlJvfdE8+3iUoWHa0TWaAbhsLOVjssiK1dQGN1cuvh2D/sNnXuQ4qhJwIJwFYOwvSJu6FPXnE5xlmIhXwIkLg5WmUhJztKZQEQRJmxjfjgKDM1VvM/ESK1Mey6p8CxD+OclUzDvoPoz//Fr8WTv/YhlPCxjz6Lb/i6P1uUJPzn//Qe/N3v/Cf4yX/1zwcC1gAl99Qvn7/ujW/BrVuP4S3f+E14yzd+U3r/6d/7FP7SX/gqJDwfV1BATk7ClnHWajS1RVVXIAL6zuO87QY3jANhgGI26J4zO/sQ2CVayl0gO7F7bYAP2Q2c6Q9bj/pQWGvG/dD1bHEXQnTBbTi+I8BKfXbLZRzZOoIG05OmUhxzM0r7Nu6DnoZWWgBh03ZoKgtbWfR9OXcSD5IvByiEnEFatHPgmG0p8Y5Mf/zgCNDk0x6TeSwZa1+0p6LkIkw0KxhyPKkQsmWQVuJ+q5NChJXhmY4YXWajReKs2K2dkjKV90Wm6QOmu9j/WiFZTalYqShBjQa28SJAzhWpa2hJM1SySr1p3lR212VhkLIVPMkuZ4WoKDGVRmHFJMon7gHHdsuUSyuNUJ5BAQkndpCw6PbXft0b8fRHh8ksPvKRZ/BnnvjKwbP3/Nt347u/9/vwYz/yQ3gQuDpT9YC/kLNdZWWeMUxwfeDs7KFYhEFSmPifD5Ti56WjrejCRbgeuzBQ/KRYcIPtQHCEdPHkA2fP5kskTnbkIm0gqHQ+BOJLJB/yxZEovQdWprEt5zhsBWd+lzEIvyS0nvfXWAFCiMpHNcw6K1/Eu2LXChVpH43nShX/yvZkbDIWJf1T2ZJe6uUQFpz8oFSEl3Qox80TV33aGZvMU/qMyOMMrPeR5mhnfmm3jpL/lBc7CpuducgjFIvD8X5JzZX7MbYttAexrkNCwQC1R3j+xje+Cb/36c8Oyj/99NP4yj/2FYNnP/VT78L3f/9b8YM/+AMHWrkcvvVb/gZ+5Ef+GX72v/4cQgh48sn34evf/CY45x6qPj4XMo9NFD0lDvAW6Rntwf8HhH1lZZuVijxpR/aIfC4v2wjDPSRQXhJcBvnsUMUezJzbvj0x3kdAVvrvtHmgE+Nzs2x5/FzK7l6O8JsBDkP6g8TLXtS+kLWx51jZHxRl9/V7QJcOIdGoPI2+7/TpgqoIGFji73s/qFsNx3HR7w69f9Ob3oTPPjf0jHn66afxFW94w+DZu975Trz1bW/DD7ztbRe0dBieeeYZvOUbvgHf/T3fg/d/4AMIIeCDH/wg3vTGN+K55/Z75lwKY7zC8Dw6BFfnNR4NaM/nki/ctzdfyj4O6lZ7nv1/BofOwBe9/hG8VHOuHkS7/8QTT3w1gN/463/5DXjsRo3ff/4e2i7g9IU7OFuv8fydDdreQ1mTmDCoHJhchCUh1INZTNR31GjZrz2vd6E4fBIhHx/k+07EeFwkJQlb583mNY6P56isxXbrsVpv0bYOQdzrCmFfAkp7H7Bpe7Rth77zcCHwjboMJw4iWe1FBl2UG0SEplJoKh2ZP/6Bjzf3i1qh98ygaiWKN7ZCm1WsgOkcoY2Wey4QTuYanjQ2XcBxo3G2DTieGZyuOb5fFTNGdNGabNNHJrYgmGJ5Y43FyfEC2w7oupDGHzzPq9Ua3gV89R+/DW0sAOI4XsT9/txnPp0s3XpP6Enc2RRqq/GnvmSGz5861BXbq9VWYVFz3yn2kaDQ9h4nM43jmcay1ri7Cbi/YZJ+88iisVzn/U2M5wWFWWOx7T06x+vRe8KsUlg0Br0LWHV5DqwGassWVUYpzBsTLSYC5rXG8cxg2xPOtx6d40QGVgO95znvo1mh0UDnYvwuxQLRpmNLwBsLg0WlEYhjHM5rVgTeW3OG4/U2RIUruzdLUoS2Z8WgD2I5yVZbneO1VOA5lQQMVUw8YqNF4bzW6F3AaRsTaHiO02a0wnNnwO88b/C6m0BjA87PN+hdwHa9Rtv1OF/3HFMzap0yY6GSHn+I37tQHsAXvT8MjEg0ROzB+4MM6KCPrLi3lcGsqaCNgXMBXcdJLlhJVzD4QlMoxs9zHq538D66To8lUghtGDKsFF9Yw5luhYEH8k09K/Aycy8upCEgxm7kdes9r3kgVuBRVEY3lpV7jdVoe7a0FMtSH9jBWjJIp65FToIAaK0xb2o4x3EppeOBmOnUmq0/XvcHl1DRhcJHN10fgLP795MQ5eMlRjSchdUaX3LDYtWywh2IyUQsKyABRJdChT4wHZtVbK286Qhtz+uybHTKXtj2OQNxZXXK8gmwkrQyjD8hxEy4cW51xEmtOY5QFeNnusBK1FnFcQq3LnA9VkULxyKWmYouzdGgVeiyzO+81qiMSjSvMnwubrqAOmYrBokil2mG/F4sBtl6iJJVclYE5/0g7pdyIVAZphFttPimqBDSCnB6gb55DO3ZcyAfrc6DJMTy2Hbx3CotgNN/xffRs0vhYZmmvah+Md+S+8drpo2GjbHzgud9zUr7YUPld4r0UaxqBy63Izw/1PF8CbX7UwmdUHQ11VcqeD3l9a0M/17OKRfkAqsMPZGtsUMZSG48RwqojGYL0zCcWRX5DyLCteMq8SIpO3MA2rZLghTRSAmngJsxnq94WQm+CW6WVq2VRgphwUm8+F0dcU4ptnoXgmp0towVHkVCwwiehIL+Sh0KSO7InEWbwyAEksQnorjMv0/KWTVU1CqFpEitbL4Yk4sEIF6uRVzkdVWJZijkzNXCH1Jqk4p9k+MqJmEwluULR47FKMp+Seo2W17D9ce/BL/3yWfRbVs+14i9ZUQJvIOSL4FQ83KBrInwrzzW/Xz/GGVLhd7DCFsXTZsofNNcF7ideIPiveCxyDKlcnBsXUajv4f6lpV7IzxPJZjXTO+KCn0RtHNfOzOj4CgrCpWKcflEJitYI8kMLrFsZTymUMKG1M8iBmLql8QfzbhCyGMp5RaZw3wuZ3xOvBVymYJN3FGgyQwYlb17AnJMS0k8VMbkld8KXpcgQxzj31gBnOZN5bMgzw7/O7l+A6/7Q38YH3nmw9hsNgNe9VA7r3lQu3M0/LAfHga3X0oo91qi7Xhp+zm+dBO4TBWkR/t45/KufBf/K/Hrorb39WV0Bz7EyQvqGdPGq0C5FmU9j1LnVWC89mXb8/kcX/6GrwCAP/HUU0/974vquZLF3u3b1/C6x4+wOVuh61tUdYWmGyXESJ0pzbQpCv5CaIZRfFSxamOH2JLpGW78AgX2Es3ynonLKW56cADx4RGt82YVlssZ5vMa3hHarcPdu+fR5WRomYfICDgfUnbbvncpzh5nieN+KsXCowjWwoyxID8YCbqeQBRQGY1FExm/oND2rDw8mhmO19ezYLBsNIgIq6i4MwY4NqwYa5TGqgU61+P6QmMbgOO5xv21Q20VbBQwO8cZZtuOYpyhIsCtKmOSeNy5d4aT4wUWc4PzFVsjQuLXqRgzAYV1TDxIXR9S3L22B4JS0XKNy/WO0kHUJ3fZeGBGFzkQK86sCGsqKwYVAlxAyoxZHvw6nsTcJRUPdFaWZqYnsPAEYR6yMIWofPCR0yawECK/ZQafYvwy3lssOIqwrSITzfXaWA+Pi2Ma+hCVeHF/uiDB0CNBDZz1MATOEAqoxKBIzD6jomOFQrZsUIhKKpX2sMQ0E/frQNktGQCOljMsZgrddgvvA7QxsLo/yBnsEKKSOpYFVFF2H4W8tH7KuE/j92M2dVf5ILECK8uutlVlY/ZWj269TTHxuPohUxoCu5f1zsFLQoxIA4QuiKUEYehOU/ZFmEQXrXCMVrBR8QPFVqWi4A8hZ0GuLbt7dlFxpzXQRCsSq4CuB3yISTNIoak4tp6JSqve8/4E2FpQ63zzHVlopPiGFLDatJg1NWqtse1iHC3K8ac4KUYcV8QrUfKL1XYfeMVKxYYXaRli7Yu0J5MRDUVX2sTsq5T8QvCYrWDHeB6Xj0TJwfSFcSofmdnFVoSQfJ5oDeg4MYRcpyB2oKGLcQhIAarTWDCkm9KejvhsVGZMJMN2uRYSYzMuSaJbpbUgxf085tgHwo4wRyG/E90IADR1BUUK3jEd11pBF4kXLoLUXyqE1eHRvR/HR/29tJGdr4dZq4zvKgn41moYrdPZ2/c+WdaWNUsiGFGqhGidV5CELCAVXJ/M8SGmWCxN5TyRYiEwTbfRUlus70RpK5cxBMZvxL8uiIVetrbuQ47/KmcOIMH5D9DEOJ7OsWuuNVk5mMlr3oej6RriAKVpz0UijZDP0gnZL1SUK+ezxFVCMd+56Uu3T7kvKTaSLbT5oSo7AaYBvhiE4FHZ7vjMkfe66Hu5H8SiWOYorYHgZzn+ov7y+JTxp3EX/aZYwZCO53dSj7UGFAxCtDothrkLV8HPVwEwDwYorfJ5S0N32xLKR6Lo2lnnh4ByzcakT9ZY9qXsv3K/lIoKKTdYe5X30FhRtI/sjpcwK4pVVviksnsuQ1Pf85uDZfYQ6nHopLKDMkc7eF70+7JtKO+GxxWlWg/WMVqcEo/LZ/u+qz3PBniJId1Ro8I7+2Jf30bvx23u4Hn5c63SxdWYhnxBgBr8uViWKODVPP7xfnjF1uthGj20EYELkffQTy5r6uWCC/H8ZYaHbfNKir2vfOKP4nXHCs453Drf4nc/8Rzu3LlXEEZmkNWI+owJp1LDoyALv+OCRTwFKie5nO7xJyRmp+SAxjc5OrqTVLXBYtFguZzBGIPt1uHevRbbbeFCGxtPihFiAWHTdliv2uRaJxluy8NYK536ITHQStewELICRmJLdI6tw/qgMK806ioz3m3PR9my5lvgzTZgMdOoDNhqEsC80bi7YgvCuSUczTRWHXDjSOHOucNxoxEIOF07aEVYNBr3Vj4mi8gzVyrIRFlkDXC+WqOyFU6OZzg97dF5DwUV3Ttj5I6k8FDo+oAQHFwAVh1b6iggJmLgyQqIt9zEXEGIO8KHkKwOQlSkchBxtpzpI6Mqlk6egMXMYLMNqCuNvg9JqSpClgh9iMyVuBNvXWxI8d4QpsEHQJuoBAuIVnLRfS8mLXBeIYQAazWUKoKPRwFOJpUzbqpoWcPrL/NcWmkQsSInRC2BMjmekigDXAxWxDeFeWxAjOelc50xR0K+xQQrBjwFaGKclNvAN7z+DwC+RwgB7dbh3p0zrNeb0Umqio8EVWjME1Oxh0Pai+cJMwsBqpRQipNgP9OV6UHJHCchUXGw97q2qBsLrTScC9hsXFbap+aoWINondc7dJ3Leyi+KxvTxZHMMRIpCXdSfhBnCSoqg3lPVEbcBHl9ZV/XMfNz7wh1pWAIyYqusQrrjvGjKpJszGt+LlabbRegFFvA8ufcD9kHokCTOdBKoes6aG0wayza1kdLmxiXMq1HXgG2SgkIxBakZVwjFOXZjS8gxfkCMLg4AVtBStB/onxTrRRS9s+60uhjkiHvs8tReSoMlQ0xoY/gowgf8f9AnM+zVBbKRYxzBKN0ss4U+iF6vxS4Hry/RbHL+LrrUpj3u8y5fMvhBcZKo9LyQ8ZHKKwdKF9EZEqOJAgmoSO+ePz2Cfpti0ABtQvYrFt0XT9csFhBRPMBpzE8c0eMafFyR9AqCz4AJ3vglB/sLdnTnAhDRbdwpsl9H0ZK+6LuOBlscU/JOiXto3LMatRm/G+4TmMWMIbZIIWgJNs9/wvISjGTzijJBI+kVNSardV4rVmZ5wkpPIco/npRDmq+HCuxb9yrcgw+Wpday/tczt6SzaLCny3EzRQg2X2HdaZdEzdlue6kSlpbKLwirxBJzEARkvEb4EvhvJ/zOAqBnvK5PqhsNBcJn2TvKomtK3QkZ9ce1z2eUKFlA0X3eEKKr2VWXop9Ka2xaPwzGYq8y6g5KEhFOXl+vJyh1dlar++ArZhEvsZAFR9KrxWm1RGf99CTIc5nvB+/O9TmZWXKunb2F/bQcDkPisrHeyApiNXQDZuKMuN7mIvwPJ0xseICvff++EHnSO2doeJSgIZtkBpygNKksP/l+Tim1zs0Tcklg8L+kSORspI8EwFZMts9Wy+ax32f98H4eBvP0KF9pYp3h+a97J+UmVUGvtPoiaWBEK4eb+9VB2rwB8Bov14Ar9aRX7Tur0ifX4xGx50/hEBXqG7wveCDX0x4xG4+Mly25g/bvysp9pobT+Dmly3wp6+doF1v8QvbX8WHnv5Yji2FJAKk//kAU5loFpx/KRsD2A2aXzAveYAHDpvEd2YGdMCQK+6Hibf4s3mN5XKG2ZxdztbrDpv1hjPWAlnQjIo9ChyPZ73psNl0cH10tY2CQD5cMkOoAU7AQDnjamZqsyWfj0GNc7B6rm/bcek+qKT0IuRYOjOroA0L+lWMr3XUKJy3HDjnxlKj7QnnHeHaQuNsEzCvmAE6ax0WNbvv3Vt7kAj7kSsVVxJ5Vt54aw043+P+mcPR0Rx6Q1ivPKAJ5EJca4oWTgFtu8Gdu6dRqafzmhfrEqJyA4otyZqK56j3+bTnbaSicE7ofUDtNRSy24pke/UAxxwLGr0PMJqVzhK/SJRpsieMZpcZFoiimiZyU70L0NDYOsIiKlR7J8oYdl3UFAb7mT9LvC9KboM6buwkCMS9qrUIdDnRh1Z8AwfKsZPkN8ZE66ioGOxFcadLa0ZePNl7kt1s7JIgbgQyh69//RfjsWsKRx+aY73t8Vu/80l87nMvJAWYih2hYk+w8lsN1vYQW7iPqdt5UVLxSxj1MYiiymgFW7Eyr7KW49P1Hn23jbEeKdOluGcF57vec3xMHwrLnd2bbRHihBndF59LcEoSq2S3GCaGTHJUVCIhMf7SF6sl9l68EAAws8C257bYRZ+wdcCsVtj2IVn0bPvA7vZg69xxZB2xKstzVzLPCiF4tNuAuqnQ9wF9x+IoRQkkKUA9oe97rNctOp8vMco1kVkxWmicWCWx4kPWnPcXW1cGiqEIAp8n0jcThe8AtoBloZWgjYwLCa9EgFJKcCNfwPAYKM2/0nxpUFkkd1ujcrymFMsOWbASoYmkXQyTYgz2u+AliiOqmCq2iIynZ+w/x0GLrrlAei40ebi/kLKrl8lDGD05eYGM+/bt62hMj+ZzFp0L+Mxzd3B2th71OMMhJmPMzz3Qe6GVBxD5MMVg4CHH8WvAaJ1i51GIMSy9H+LjgKTwgrEyj7LCfn93hu1iV+jKyj1Z8VLQ5Ge8X3MsQyr+aRq60Ypbl9WFu6fhveCie7nzOSFSctEG4qXACP/Kz3sWkYhi7D0FFViRJgKwDJggCtCArg97lXrl1/KiqnQtlOkZnBXCa8X5U2UdcuYpwOuMf2V75cUtFDhkQDz7xpyjZP8OQcKhZIvhZOUOAOlZuXfy5fVFTHm5+omNTQMuLqxHMFAcouC7VEmvZM74zC3bSF+KW7Xlco75jPle5wmnZ+vXnGKvnIMydh5bvhbn8mhB8poDV1XmSbnLyh6CfYJoeXaUjami/Fj5l/YEhnt/n3H1/l017gMN2hhrn+LJzmPfsW7e02aBo4M9f4Bvyzb9tIPHhDG+jRsbfuQ2RbIa4ULCk90+qlEd++DQeXdBl/bWsffZeN7V7p6Qd6Wy/iKD+rq2ILLYtj7Kjx7uYTfvKwlq8AfAnnl8IL7h1QeX7bdXCh6p7QN4PtreV2vvQRDval26sOxlzT1id152uJJij7mZL8XyixdYwuN1v/0MWyOgyLCmdv8OMvSJaYM8SCRZDXZBeQSXrgtqhBkDRV7qJyVCKIyaNQpVY3F8NMfyaAatNDatw50X1mydR6VrXXZJkcy2bYyd13YuHXYhJjAQtxQmvFF4SuPLEAhJoSDjVXLKEKVg71qp5Laz7QJME4OcE5I1mA+ssNMqBs6OMYa3PbdxbWFwb+WhNXDryGC1JdQmwHlg3TkcNwpdD9zdOFZMQZhnldxRk2WPMFIqumsKc6IIZ6s1mqrGyYlF7wjtWqwjFPrO4f79M3z+/hqkABuVehyont1+QzTFsUbzfMa5M1qDb51YeVVV4iIsygiJQZWVyiEQNluPbaOx2oTo3qtibCxeHa0UjNHxHdC7EN3rALFzUaCkRAkxQF6UrZMLnzUK256Fws6F5F6n4r7RYKuj5MYMJMFbKVYiVFrcX1XMiquKuCAF8x8rzjXFbLs64l58HgggD9SWXXzraP3Fc5YZJsEjFRVW1ooFK9fU9grN8S28/ssqaE345O/+fkzKEfGVRls7UcbCLahgQHegeDHA82Islx8E+UncpUlpYqxB01RoagulFPo+YL3uYoyhbGMmzCPTruxW73oP53PGRwkoX8iIecgKUKPeyqVA5sxlvoftstAW47r5wNZ5lBW7bHUSXeoQXfmSOy0rEedVTKKhgEXD+5pv+Fn53FiuY92FQQD4bE2TqO+OoJ3mVgHbroM1Fs3MRCUepf3svEe72WIVY25l17TIvCdFJgZKzRKXha5I3D1pu9xP5br1LsBZha6XGGjZwpjnO196gGKMK+SzSGpOLtPFU1k6wTFJusOKjIwnTAflgqoUKnICghRuQcavVdyrNGg0ZatE8UMFEBU0QfAPEnKA7R5NfCptysRlPJdzaYi3LigsF0vcesxAa8Lde+eDdvZBwusXgdMZM30HWkyfBM+TgB8v6azhuAjeU0qEcUihByC5JIrF9E7bBzi9fUMmYRSK8zw/K3/Jv+ZszUMpTmZbrFI1suWOXL5wJnYuXJsiphNlKz8idt0vE7SM+zxWxI3nxsWEUdbGUA9eLkD4zHa9j5btua6SbshUMM4WlQvdjOPJ0bxGQjYV60FIl5jej1x+h0fjYL7k9+XQBgozld9LOzKXbKWtds+3xGnmA6C8iBY6Oig1QpR8bqadjIChtbsa1Mn90aJpLOiUAnYuwhM1V5H2qjwnxtZYHjGV2my2IPSDdgbbYoyYryAITRNXWwDxEmfoDj4mHom00nAf7qcxu20+KhwgIcM+UC6Tef+8h9ORMKLnoF0Fz1X7LJaySWGIAfe2O78XwF6rsFF/yz4q/lFZlM8nyuFlyvLl58G6PlDvdteixP2rzhsBg4zEidfBbn8O1T3GXalzwBeXpFMN+7v/ZJGwPwZ1w096idtQtnPh6F5hGJ8n2LPGBxb9QffCKw0D+r3n88sOV8Cji34/1sscKPrq3n+vAIyn7MXaB1dS7PWbj+OFTz4PZRus7r0AW2lsOxeF4D0MMoAyelASpJGJOyF9Gf4wv8hvCtlnIGUrJMoYxSaoGITcWI3FssbJ8Rx1zdZ5Z2dbbDY9vPNZ2KPIGEWhPARCu+2xXm/RbnsOUMy2/oVZvEpCIBNrGli6UYE1ZdBXseQSoiyxcQA5KHJcHtJA2wfUpFFHn5u6YgWZC8Cm83A+x23qPOF4prDZBhzNsvKJ519h1TqcLCzONx5n2wCruS5jsgUZu5rpobAfF0JuwRXE4g1wrgcUZ5m9/dgxfCCsztf4/N1TtL1P1mqJAVAS/wrQUYOpFbvyEmLAfR9Q1zq5JIqwllzaojUbx4XKTLUPHBhdg7Dt+cq9rnQKSh8IqGTNRUKEuNly5aLkE4IVAuCFWaZ8Y5+YaCIE6AFTIkrLaEDEDLwoSqU9YldMrSi6Wg0TGrC3XxxbdNkWxauT75rrkFtLZsY0jM74kSwQ4vxYI8kMxFJFsp5yu5/93B185rk7qCqLzXoNYxSc8ynYfELXLG8MgEZ/d4j5gRele+veA72gE2UMSBHyq9pi1lSw1nDigK1H33O/BwcYZSUPUYyx13s45xLuD6wTUTBW8aEuXlDiykrWGEhZVJWKwejLODlCE7MyqneANZmGiMt6oKiAVjnWnA+UXG/rmCiHlX7cg87FbLk9oXWhiPcmwme2pi73XO670NqsgAvBQyyGl0cNKBDarsNqvYXzgWmu7NiCRkr8PmmfXeIpKSUqzYG0ZQ/IKLh8XmOVrFMk/hljqUuXAyoKtPms2QcUBzguI8o8WdgBnhdlxGVXxuMxdMllZS0N6pWQCz6MO1Zk6Ex/ZZ1KF95ijuI/o/gCYR9HLIkBVGwvEFIMUcHz09MVPv9CzxcdXRdp3VApNobUBA3nZdz+VWB/a0M8F2WRUoCxkghDR/wNg6zUg1qK87dU6Mm7gyOVwY0FsL39FdxXezIaJoxKTwJhoGRNCgvEs4byO1EuS7IMuViS8j5wvFcfJGaqSq2O+70PynaS0iAplYG61gMamejXeIjyR+X5KVAJRDksgewdGtehCpoJ4UUyKyhK2JyJOvZbjyorz6hxbxNdy72UWSvt6MZ7Y188y3IvyOdybUIYtq6KgzNtr1hvOf/lvEnlu/XENuV3CoBYPSsMcLjd9thsNkyrvN+hfTs4vG/DXLSJXmRIZ5HeY51HcsqO+lZ+JOHl9xa5FA7j+dVgPGUlL7HTr5L8j/eB1KX27MsL2rsIxr8TC3uhk4809nLyDlUkvHTRCdFvJ0MJ4U32nTV7G90DNJz3Q9t73PWL2hqsy77jV12+DuP9den4iv1R/qCswzmP9bpP53jZ2ZcJdffDIWRSez9efCZf/ug1AXtpwMvcgUdu90HwfPw64jmQ9/L4svFR4Sr0+4Hx/JKyLxk8ZMNXUux94BefRFPVICJs2w7Xjnqs2h717BhWBVSVwXp1jpPr1xEoYH1+juOTazDGYL0+h3cct6uuamy7LYwxCCFgNpvB9T0zqs5hPl+g73tYW6GZzbBtN0nZ1G7WqOsGvetgjIV3Dteu30C7WaHrtlgeHWPbnmOxnOP27VtYLBpsNitstx4v3NmgbTuACFUzQ1XP0Pd9YoY37QZ979G2PdabLWfI9CEJ1xJ/RRXUUiuFqtJcLjKmHAON37toQcNCfRHrj7JiDFApiQYzwMOg8SB2qXNOYRGVdsZwxlijNDrHMblmMQh25/iG/7wlXJtr3F9zUP3zNuDGssLdtcN6G2KGRiTLIdnKonQD5DAZKgGEj5b4UxLjzSjCyZHGnTunuHe+Zks7m11vKc1NtErSrKgCAU1tBpn5RMGmo9Avc5MUXVFwDZF550xvnBVz3QU0lUbjOCtknMKkpOtj9luTYoDF2Fsxg12AJJrg+RQpQZRmBMQkGDwXVnNWMBEYRIhWgzEjKgUIgTS0oqTsDLIDo9Woi+WFcdccBA+EnPggK2hytkugUNLFQYuygl2YOYmHDwG9z8RPR9FGYgH+1m9/Iu75AO88ZrVH1wcsj68hELtTn5+d4saNmwgUcHZ6iuvXb8BYg9XZGXrXI3iPumnQti2MMfAhYDGfxxheBOcclosjdN0WVV1jNptjs1knPF+vV2iaGfpuC2MtnHO4efM21qsztG2Lk5MTnJ+fYT5vcOPmDdR1hbOz+/AuYL126HsHAjCbzWCsjYoLDaUU1usVfLTO6yOOs+JF5ovnWwmDibj3dRZ6UzymJHTkz7LXEpHImzrTjthGSs4Q38jerGOWWK05lpaysnbRNTe68BvNGXDnlcKmZ5zuXMCiNlh3nAVaFIVZAMxC+PhQTfruovtMt1TqpdaEWa2w3rRYb3uAKFraDWrK1nkKMIotcK3RSWEo9Ibi/IrFSRa8VaI7QhlYGchWar3nubBG3Jn5N2JFPY5tJ62WLs38L2ftLS9jZL2MYhdYsYTMI8xCGBTYxTLWkemVPMuTm+O6YWRdRWlaxC2b8ZgfJiUnxGVcpcQdMmXiGhwIUJRd+IvlTJf4zz1/HyGIwp5SHNeTk+vwgc+Y09P7uHXrFkIIuH//Pm7evAljDM7OztD3Hbz3mDUzbDYbPs99wGK5QLftAHCymaOjI2y3W9R1jfl8gc16jUABWmusVivMZjN0XQdrLfq+x+3bj2F1fobNZoOTayc4OztF09S4ceM6jDG4d+8u08loWQtwxjBrLbquj+eUwnq9BlEYWOeNhf0xA6gAQBRQxft0ho0YrawYG4vwQwlM8E3KBWJLLV1kxjUaMXRHdsdNcaeUZMBFvAzKGZ/7IPFXVWp9vOY7sGccA6VC7JM1isMSxBAbO0w4DUcrrqOlAeqgD4eYVCqpoxoI/d7HuIRKXNWRaJRY0icFXwGSgIeKhT4oRCjEi4N8DhyYqr1CtQwtl424HiWZ8e6QEukMGXVsHAetbHsscOT1KsoX1a3WHcdO9A6AgrEViDa4dv06QvBQSuP0/n3cun074vk93Lx5E9ZanJ6eou96eO/RzBpsNhtYY+GDx2KxQNd1AAG963F8dIR2u0VTN5gv5ox/gZNvnZ+f7+D5Y489hvOzM6w3G1w7OcHp6Snqusa16yfQSuHu3TsAYuiZiOeL+QK2qvg81wpKaaxXq8KVnnYW5iCeF8928Hw87w8J4/qBAsfG/ZF9RQU+Yvh93xgOfb8KCG/8oFZ6e/FoB1EO/rr4m9sTmidj3gt7cFhU82XzAxwbdatcaxp9vqz7BQuV2xeWbtSuGpUZ93unngvaT3h+QdlAGsY2CMFzWApL6PoVrt+4zknwlML9+/dx+/Zt+BBw/9493Lw1xHPnPWazBpv1BsZaBO+L8xzo+x5Hx8fYbtuM56s1e2BpjfNVxPNtB1tlPD87O8Nmzef56f1TVHWFa9euQQG4c+cOyzfEmeoBYLFYMJ3o+ozn69UDuYZPcBiSXPhKdwTYOWcH8MC05KWDMX6+luBBFXszAHjfL/8fHC3ncN7B9QHetbhz7qHrLWazClvXwXmF+2cbaGPR9QBWLRQUlLFoux4AK2pcz3FlQgDCNiCkwNUKqmOFmAuEtttAG4PgHZQibL2C23rYas4WdEphtekBVcPBwVQWy+Yajo6P0HbA3dMVTk9PU7ZGIm6jpgAJEx28h/ce5+dbrNZtkT1MgyjnTORrdslYl5l1T4Ay0YYjCl7RSI4vUWM8tHKnqmhRlSwrdE6OkFjDeBh4UFJwrbbArFHwpHDeAUZZBEVYzDijah+v9Leek2Lc2QTM6wqrLWE5M3j+3MN5A1PZfNBEwVmleD6UYu4FgN3EBowFAYbdybTOcQGtBj77+TXHrDNVMmMRixodtYLKmuSW6GP9HhrnPWHtYlytXqP14r4aA6ETsOk92qBRG8C23EAgYOXYVakjBUcadVBoHdA6guo01r1C51gJynGrFLqgUFsFTwadI2ycgvYs6Hee+1vHaPrWAJsO2HqgcRpaA13PCrIebKl03in0gWPxGa/Q+6iUDFFxq9hFdkYqZbBqt6wsrS3QVBqnLd+odw6wipILSnYVApRYJBW3/dvocmg0Jy7QRmHTZ5dMaznZiFh3bFi/hrbnsVYGWMWYbR//1Ocwa2q4ni3Yuq5FTwbaEaw16F2AsjU22x7WGihdoes9lPOomgU6v4IyFgQDrSsoY6AVwUMDmpkFbWpAWXAaTot228PYBt57sBtlDSKF+fIagnfQtmF31dmSFbB1haNrxzg6OkIgjfO1w9m5uM9qkLKRC7MsGBquO3iC8wbbziMEDdIVWz4J55RRgrMH7yisChBlrih1NFIG4qIQ5IgQwTspXkcnl7i/s7IXqCtxDAdMxYrGWVT6C56GQFjMNSvz5qwMXNQamy4AxmJmxY2e2xsILoXCbrc3eSLE2rhMMtJ24vZhBgJIWZHROjIRUVxXnH08aAJMDLFg2OKNVNrOUBaAIwQdaYPSnGhAAzBsmRiUgleMRzBMbINScOD6PYHDF2jGT6uYlgWDHNohPiPRRACZDkMBii8lPAikCQ5Mv0hzW6TBgcA1K/OhI24iJkrQissB8CrTdmVyFlQOr8h7RBVzwHWPFEkypqh8CFD59jPWZWJMvuSmmSQRglfc36D42D89W6Oqar7cUhptu4GpZiClYSsDHzyaZo7eeVhrUdUNK8jIY7E8wmq1gq0IxljUDcFYVuwZU6GqFZz3qE0FW9UIxPEue+fQzBfw3kEphcYHaGNx7foS3nk0TWTsj444AVBd4+j4CEfLI0Cp6BbN/dfWoDJcvqoaAJzZ3kc3elvN0PcuWjYW+77c3tgvdI2Byg/xRzTAJxEuM07RGCGwUwkADn+R+hMfqyIOpfxkZljJVWtxxWalXkWEWh1WHu3rwRjGwm4JWgGm2vNC3idrnzwGrYCqYt6HUCgpR+1FAzK+KIu0lgDAsmJea54frfmo8ETpQi5ljI4kQBVtBAJUodTOSnHpcxFbNtJUFQAd+TEAUD5nilZq6OYragS+0FSI3uAclw+8fkYBKoay8xT3YOxDVvaXdeb5EQ+OSHKKS2DE+IFDhY/4ZkqsRgUwDwbmpW3VoCcHbTQ2m0268DKw8TJ9juA9jLWYzWbJSvP4+ATn5+cgEKxlvDPaIpBHVdVQSsN7F2PZzqCUhq0qeB+wWCySheAiBBhjcLQ8gvcenjiMzdHJCQCgqisslwssj5bQijXctppBKQVLwrcDzXwOBaCyFXzgjDLzxRLOucGe3IvnuBwPXkpBbl/bCfULYVuN1nas1Hsp5d2LhO19NFKBE3dJLLd03GBIg8oLw5LeyaU9H8EqtR+IUrny96LYkzYE18v+jC/ywui51JcvZYbjLkhuHgMNaUg5RZIIByg9Mh5sr40vP8o53rdfy5AD0hdbN9wPY7Fs5uj7DkYbrDcbzOKFl7WAi3juQ4C1FrP5DADgvcfJ8QnOV+eoKeI5ISn26qqG1hqud/y7poFSClVVxYu8ZTzPOZySsRZHR8fw3sVwVRonxycAAVVVYbGYY7lcQmm2up/N5uliVxR7sxnjfl3VjOcAlDrawfMJXh5IZ9Zov47xfPCbci/T4ecDulIehKPNP6ZLF/Fs+/iYQzDG83TOjuq4Sp0vFpR0s5nN5PHs8C8Y1INkzXniiSf+GoD3PEoHJ5hgggkmmGCCCSaYYIIJJphgggkmmGCCB4Zvfuqpp/79RQUe1GLvvwP4ZgCfANA+YqcmmGCCVyd8EYCvB/BzAJ5/hfsywQQTvDQw4fkEE3zhw4TnE0zwhQ8Tnk8wwRc+zAB8KVgfdyE8kMXeBBNMMMEEE0wwwQQTTDDBBBNMMMEEE0zw6gJ9eZEJJphgggkmmGCCCSaYYIIJJphgggkmmODVBpNib4IJJphgggkmmGCCCSaYYIIJJphggglegzAp9iaYYIIJJphgggkmmGCCCSaYYIIJJpjgNQiTYm+CCSaYYIIJJphgggkmmGCCCSaYYIIJXoMwKfYmmGCCCSaYYIIJJphgggkmmGCCCSaY4DUIk2JvggkmmGCCCSaYYIIJJphgggkmmGCCCV6DMCn2JphgggkmmGCCCSaYYIIJJphgggkmmOA1CP8PaUPs6R2svWEAAAAASUVORK5CYII=", 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", 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", 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" ] diff --git a/colour_hdri/network/graph_editor.py b/colour_hdri/network/graph_editor.py new file mode 100644 index 0000000..7b31eed --- /dev/null +++ b/colour_hdri/network/graph_editor.py @@ -0,0 +1,942 @@ +import os + +os.environ["OPENBLAS_NUM_THREADS"] = "1" + +import json +import logging +import re +import traceback +from pathlib import Path + +import colour.utilities.network +from colour.hints import Any, List, Tuple +from colour.utilities import ( + ExecutionNode, + ExecutionPort, + For, + NodePassthrough, + NodeSetGraphOutputPort, + ParallelForThread, + PortGraph, + PortNode, + attest, + optional, +) +from PySide6.QtCore import ( + QObject, + QRunnable, + QSettings, + Qt, + QThreadPool, + QUrl, + Signal, + Slot, +) +from PySide6.QtGui import QAction, QKeySequence +from PySide6.QtWebChannel import QWebChannel +from PySide6.QtWebEngineCore import QWebEngineSettings +from PySide6.QtWebEngineWidgets import QWebEngineView +from PySide6.QtWidgets import ( + QApplication, + QFileDialog, + QMainWindow, + QMessageBox, + QPushButton, + QSplitter, + QTabWidget, + QVBoxLayout, + QWidget, +) + +import colour_hdri.network.nodes + +LOGGER = logging.getLogger(__name__) + +HTML_INDEX = Path(__file__).parent / "resources" / "index.html" + + +def collect_colourscience_nodes() -> dict[str, ExecutionNode | PortNode]: + nodes = { + "For": For, + "ParallelForThread": ParallelForThread, + "NodePassthrough": NodePassthrough, + } + + for module in (colour.utilities.network, colour_hdri.network): + for name in module.__all__: + if not name.startswith("Node"): + continue + + object_ = getattr(module, name) + + try: + if issubclass(object_, (ExecutionNode, PortNode)): + nodes[name] = object_ + except TypeError: + continue + + return nodes + + +COLOUR_SCIENCE_NODES: dict[str, ExecutionNode | PortNode] = ( + collect_colourscience_nodes() +) + + +def exception_dialog(exception_info: Tuple) -> QMessageBox.Ok: + exception_type, value, message = exception_info + + LOGGER.critical(message) + + message_box = QMessageBox() + message_box.setIcon(QMessageBox.Critical) + message_box.setWindowTitle(f"{exception_type.__name__} : {value}") + message_box.setText(f"{exception_type.__name__} : {value}") + message_box.setDetailedText(message) + + return message_box.exec() + + +def confirmation_dialog(message: str) -> QMessageBox.Yes | QMessageBox.No: + message_box = QMessageBox() + message_box.setWindowTitle("Unsaved Changes") + message_box.setText(message) + message_box.setStandardButtons(QMessageBox.Yes | QMessageBox.No) + message_box.setDefaultButton(QMessageBox.No) + message_box.setIcon(QMessageBox.Icon.Warning) + + return message_box.exec() + + +class WorkerSignals(QObject): + started = Signal() + ended = Signal() + exception = Signal(tuple) + result = Signal(object) + progress = Signal(int) + + +class Worker(QRunnable): + def __init__(self, fn, *args: Any, **kwargs: Any) -> None: + super(Worker, self).__init__() + + self._function = fn + self._args = args + self._kwargs = kwargs + self.signals = WorkerSignals() + + self._kwargs["progress_callback"] = self.signals.progress + + def run(self): + try: + self.signals.started.emit() + result = self._function(*self._args, **self._kwargs) + except Exception: + message = traceback.format_exc() + + LOGGER.critical(message) + + exception_type, value = sys.exc_info()[:2] + self.signals.exception.emit((exception_type, value, message)) + else: + self.signals.result.emit(result) + finally: + self.signals.ended.emit() + + +class LiteGraphWidget(QWidget): + graph_changed = Signal() + graph_loaded = Signal(bool) + graph_saved = Signal(bool) + graph_process_started = Signal() + graph_process_ended = Signal() + graph_process_exception = Signal() + node_process_started = Signal(list) + node_process_ended = Signal(list) + node_process_exception = Signal(list) + + def __init__( + self, parent: QWidget, file_path: str = None, developer_mode: bool = False + ) -> None: + super().__init__() + + self._parent = parent + self._file_path = None + self.file_path = file_path + self._developer_mode = developer_mode + + self._dirty = False + + self._web_channel = QWebChannel() + self._web_channel.registerObject("backend", self) + + self._webview_QWebEngineView = QWebEngineView() + self._process_graph_QPushButton = QPushButton("Process Graph") + + self._developer_tools_QWebEngineView = None + if self._developer_mode: + self._developer_tools_QWebEngineView = QWebEngineView() + self._webview_QWebEngineView.page().setDevToolsPage( + self._developer_tools_QWebEngineView.page() + ) + + self._setup_layout() + self._setup_views() + self._setup_signals() + + @property + def file_path(self) -> str | None: + """ + Getter and setter property for the file path. + + Parameters + ---------- + value + Value to set the file path with. + + Returns + ------- + :class:`str` + File path. + """ + + return self._file_path + + @file_path.setter + def file_path(self, value: str | None) -> None: + """Setter for the **self.file_path** property.""" + + if value is None: + return + + attest( + isinstance(value, str), + f'"file_path" property: "{value}" type is not "str"!', + ) + + self._file_path = value + + @property + def dirty(self) -> bool: + """ + Getter and setter property for the dirty state. + + Parameters + ---------- + value + Value to set the dirty state. + + Returns + ------- + :class:`bool` + Dirty state. + """ + + return self._dirty + + def _setup_layout(self) -> None: + layout_QVBoxLayout = QVBoxLayout() + if self._developer_tools_QWebEngineView is not None: + splitter = QSplitter(Qt.Vertical) + splitter.addWidget(self._webview_QWebEngineView) + splitter.addWidget(self._developer_tools_QWebEngineView) + layout_QVBoxLayout.addWidget(splitter) + else: + layout_QVBoxLayout.addWidget(self._webview_QWebEngineView) + + layout_QVBoxLayout.addWidget(self._process_graph_QPushButton) + + self.setLayout(layout_QVBoxLayout) + + def _setup_views(self) -> None: + self._webview_QWebEngineView.settings().setAttribute( + QWebEngineSettings.LocalContentCanAccessRemoteUrls, True + ) + self._webview_QWebEngineView.load(QUrl.fromLocalFile(HTML_INDEX.resolve())) + + def _setup_signals(self) -> None: + self._webview_QWebEngineView.page().setWebChannel(self._web_channel) + + self._webview_QWebEngineView.loadFinished.connect( + self._webview_QWebEngineView_on_page_loaded + ) + + self._process_graph_QPushButton.clicked.connect( + self._process_graph_QPushButton_clicked + ) + + self.node_process_started.connect(self._on_node_process_started) + self.node_process_ended.connect(self._on_node_process_ended) + self.node_process_exception.connect(self._on_node_process_exception) + + def _webview_QWebEngineView_on_page_loaded(self) -> None: + self._register_colourscience_nodes() + + if self._file_path is not None: + self.load(self._file_path) + + @Slot() + def canvas_changed(self): + self._dirty = True + self.graph_changed.emit() + + def _on_node_process_started(self, path: List[int]) -> None: + self._webview_QWebEngineView.page().runJavaScript( + f"onNodeProcessStarted({path});" + ) + + def _on_node_process_ended(self, path: List[int]) -> None: + self._webview_QWebEngineView.page().runJavaScript( + f"onNodeProcessEnded({path});" + ) + + def _on_node_process_exception(self, path: List[int]) -> None: + self._webview_QWebEngineView.page().runJavaScript( + f"onNodeProcessException({path});" + ) + + def _process_graph_QPushButton_clicked(self) -> None: + self._webview_QWebEngineView.page().runJavaScript( + "exportGraph();", self._process_graph_callback + ) + + def _process_graph_callback(self, result: str) -> None: + if not hasattr(self._parent, "threadpool"): + LOGGER.critical("Parent widget does not define a threadpool attribute!") + return + + try: + LOGGER.debug("Processing graph: %s", result) + graph = self._build_graph(json.loads(result)) + + worker = Worker(graph.process) + + worker.signals.started.connect(self._graph_process_started) + worker.signals.ended.connect(self._graph_process_ended) + worker.signals.exception.connect(self._graph_process_exception) + + self._parent.threadpool.start(worker) + except Exception: + exception_type, value = sys.exc_info()[:2] + + message = f"{traceback.format_exc()}" + + exception_dialog(exception_type, value, message) + + def _graph_process_started(self) -> None: + self.graph_process_started.emit() + self._webview_QWebEngineView.page().runJavaScript("onGraphProcessStarted();") + + def _graph_process_ended(self) -> None: + self.graph_process_ended.emit() + + def _graph_process_exception(self, exception_info: Tuple) -> None: + self.graph_process_exception.emit() + self._webview_QWebEngineView.page().runJavaScript("onGraphProcessException();") + + exception_dialog(exception_info) + + def _register_colourscience_nodes(self) -> None: + registry = "" + for node_class in COLOUR_SCIENCE_NODES.values(): + node = node_class() + class_name = node.__class__.__name__ + node_name = re.sub("^Node", "", class_name) + title = " " if node_name == "Passthrough" else node_name + description = node.description.replace('"', "'") + category = node.category + + LOGGER.info('Registering "%s" node...', class_name) + registry += f"function colourscience_{node_name}() {{" + for name, port in node.input_ports.items(): + if isinstance(port, ExecutionPort): + registry += f'\tthis.addInput("{name}", "executionPort");' + else: + registry += f'\tthis.addInput("{name}");' + for name, _port in node.output_ports.items(): + registry += f'\tthis.addOutput("{name}");' + registry += "\n" + registry += "\tthis.properties = {" + registry += f'\t\tpythonClassName : "{class_name}"' + registry += "\t};" + registry += "\n" + if node_name == "Passthrough": + registry += "\tthis.flags = {" + registry += "\t\tcollapsed : true" + registry += "\t};" + registry += "\n" + registry += "};" + registry += "\n" + registry += f'colourscience_{node_name}.title = "{title}";' + registry += f'colourscience_{node_name}.desc = "{description}";' + registry += "\n" + registry += ( + f'LiteGraph.registerNodeType("{category}/{node_name}", ' + f"colourscience_{node_name});" + ) + registry += "\n" + + registry += "sortRegisteredNodeTypes();" + + self._webview_QWebEngineView.page().runJavaScript(registry) + + def _listener_on_process_started(self, *args): + _node, path = args + self.node_process_started.emit(path) + + def _listener_on_process_ended(self, *args): + _node, path = args + self.node_process_ended.emit(path) + + def _listener_on_process_exception(self, *args): + _node, path = args + self.node_process_exception.emit(path) + + def _build_graph( + self, lg_graph: dict, path: list = [], graph: PortGraph | None = None + ) -> PortGraph: + def _lg_node_by_id(id_: int) -> dict: + """Return the *Litegraph* node with given id.""" + + return next( + iter([node for node in lg_graph["nodes"] if node["id"] == id_]), None + ) + + graph = optional(graph, PortGraph()) + + lg_node_to_node, lg_node_to_type, constants = {}, {}, {} + + # Nodes + for lg_node in lg_graph["nodes"]: + lg_node_to_type[lg_node["id"]] = lg_node["type"] + + node = None + if (name := lg_node["properties"].get("pythonClassName")) is not None: + node_class = COLOUR_SCIENCE_NODES[name] + node = node_class(f'{lg_node.get("title", name)} ({lg_node["id"]})') + elif lg_node["type"] == "graph/subgraph": + sub_graph = PortGraph(name=lg_node.get("title")) + + for input_ in lg_node.get("inputs", []): + name = input_["name"] + if name == "execution_input": + sub_graph.add_input_port(name, "executionPort") + else: + sub_graph.add_input_port(name) + + for output in lg_node.get("outputs", []): + name = output["name"] + if name == "execution_output": + sub_graph.add_output_port(name, "executionPort") + else: + sub_graph.add_output_port(name) + + node = self._build_graph( + lg_node["subgraph"], [lg_node["id"]], sub_graph + ) + elif lg_node["type"].startswith("basic/"): + value = lg_node["properties"]["value"] + + if lg_node["type"] == "basic/array": + value = eval(value) + + constants[lg_node["id"]] = value + + if node is not None: + lg_node_to_node[lg_node["id"]] = node + node.on_process_started.add_listener( + lambda x, + y=path + [lg_node["id"]]: self._listener_on_process_started(x, y) + ) + node.on_process_ended.add_listener( + lambda x, y=path + [lg_node["id"]]: self._listener_on_process_ended( + x, y + ) + ) + node.on_process_exception.add_listener( + lambda x, + y=path + [lg_node["id"]]: self._listener_on_process_exception(x, y) + ) + + graph.add_node(node) + + # Edges + for link in lg_graph["links"]: + _id, origin_id, origin_port_index, target_id, target_port_index, _type = ( + link + ) + + origin_node = lg_node_to_node.get(origin_id) + target_node = lg_node_to_node.get(target_id) + + if lg_node_to_type[origin_id] == "graph/input": + origin_node = graph + elif lg_node_to_type[target_id] == "graph/output": + # NOTE: Connections with outputs are skipped as they create DAG + # cycles: A sub-graph is a node, connecting its inputs to the + # sub-graph nodes and their outputs to the sub-graph outputs + # create the cycles. + # A special node that can set the sub-graph relevant output is + # thus inserted instead of the connection. + + # TODO: Handle case where constant is connected. + + if origin_node is None: + continue + + node_set_graph_output_port = NodeSetGraphOutputPort() + node_set_graph_output_port.set_input( + "name", _lg_node_by_id(target_id)["properties"]["name"] + ) + origin_node.connect( + list(origin_node.output_ports)[origin_port_index], + node_set_graph_output_port, + "value", + ) + graph.add_node(node_set_graph_output_port) + continue + + if origin_id in constants: + target_node.set_input( + list(target_node.input_ports)[target_port_index], + constants[origin_id], + ) + elif all([origin_node is not None, target_node is not None]): + if origin_node == graph: + origin_port = _lg_node_by_id(origin_id)["properties"]["name"] + else: + origin_port = list(origin_node.output_ports)[origin_port_index] + + target_port = list(target_node.input_ports)[target_port_index] + origin_node.connect(origin_port, target_node, target_port) + + if self._developer_mode: + graph.to_graphviz().write_svg(f"graph_{graph.name}.svg") + + return graph + + def load(self, file_path: str | None = None) -> None: + if file_path is None: + file_path = self._file_path + + self._file_path = file_path + + try: + with open(self._file_path) as lgson_file: + LOGGER.info('Opening "%s" file...', self._file_path) + + self._webview_QWebEngineView.page().runJavaScript( + f"importGraph('{lgson_file.read()}');" + ) + + self._file_path = file_path + self._dirty = False + + self.graph_loaded.emit(True) + except Exception as exception: + message = f'Error reading "lgson" file: {exception}' + LOGGER.critical(message) + + QMessageBox.critical(None, "Error", message) + self.graph_loaded.emit(False) + + def save(self, file_path: str | None = None) -> None: + if file_path is None: + file_path = self._file_path + + self._file_path = file_path + self._webview_QWebEngineView.page().runJavaScript( + "exportGraph();", + lambda result: self._save_callback(result), + ) + + def _save_callback(self, result: str) -> None: + try: + with open(self._file_path, "w") as lgson_file: + LOGGER.info('Saving "%s" file...', self._file_path) + + result = result.replace("'", "\\'") + lgson_file.write(result) + self._dirty = False + + self.graph_saved.emit(True) + except Exception as exception: + message = f'Error saving "lgson" file: {exception}' + LOGGER.critical(message) + + QMessageBox.critical(None, "Error", message) + + self.graph_saved.emit(False) + + def undo(self) -> None: + self._webview_QWebEngineView.page().runJavaScript("undo();") + + def redo(self) -> None: + self._webview_QWebEngineView.page().runJavaScript("redo();") + + def stash(self) -> None: + if not hasattr(self._parent, "settings"): + LOGGER.critical("Parent widget does not define a settings attribute!") + return + + self._webview_QWebEngineView.page().runJavaScript( + "exportGraph();", self._stash_callback + ) + + def _stash_callback(self, result: str) -> None: + LOGGER.debug("Stash: %s", result) + + self._parent.settings.setValue("graph_stash", result) + + def unstash(self) -> None: + if not hasattr(self._parent, "settings"): + LOGGER.critical("Parent widget does not define a settings attribute!") + return + + if (graph_stash := self._parent.settings.value("graph_stash")) is not None: + LOGGER.debug("Stash: %s", graph_stash) + + graph_stash = graph_stash.replace("'", "\\'") + self._webview_QWebEngineView.page().runJavaScript( + f"importGraph('{graph_stash}');" + ) + + def align_selected_nodes_to_grid(self) -> None: + self._webview_QWebEngineView.page().runJavaScript("alignSelectedNodesToGrid();") + + +class GraphEditor(QMainWindow): + def __init__(self, developer_mode: bool = False) -> None: + super().__init__() + + self._developer_mode = developer_mode + + self._settings = QSettings("colour-science", "GraphEditor") + self._recent_files_count = 5 + + LOGGER.info("Settings location: %s", self._settings.fileName()) + + self._threadpool = QThreadPool() + + self.setWindowTitle("Graph Editor") + self.resize(1280, 720) + + self._tab_widget_QTabWidget = QTabWidget() + + self._setup_menu() + self._setup_layout() + self._setup_views() + self._setup_signals() + + @property + def settings(self) -> QSettings: + """ + Getter and setter property for the settings. + + Returns + ------- + :class:`QSettings` + """ + + return self._settings + + @property + def threadpool(self) -> QThreadPool: + """ + Getter and setter property for the threadpool. + + Returns + ------- + :class:`QThreadpool` + """ + + return self._threadpool + + def _setup_menu(self) -> None: + # "File" menu + file_menu = self.menuBar().addMenu("&File") + + new_action = QAction("New", self) + new_action.triggered.connect(self._new_triggered) + new_action.setShortcut(QKeySequence("Ctrl+N")) + file_menu.addAction(new_action) + + open_action = QAction("Open...", self) + open_action.triggered.connect(self._open_triggered) + open_action.setShortcut(QKeySequence("Ctrl+O")) + file_menu.addAction(open_action) + + file_menu.addSeparator() + + self._recent_files_menu = file_menu.addMenu("Recent Files") + self._update_recent_files_menu() + + file_menu.addSeparator() + + save_action = QAction("Save", self) + save_action.triggered.connect(self._save_triggered) + save_action.setShortcut(QKeySequence("Ctrl+S")) + file_menu.addAction(save_action) + + save_as_action = QAction("Save as...", self) + save_as_action.triggered.connect(self._save_as_triggered) + save_as_action.setShortcut(QKeySequence("Ctrl+Shift+S")) + file_menu.addAction(save_as_action) + + # "Edit" menu + edit_menu = self.menuBar().addMenu("&Edit") + + undo_action = QAction("Undo", self) + undo_action.triggered.connect(self._undo_triggered) + undo_action.setShortcut(QKeySequence("Ctrl+Z")) + edit_menu.addAction(undo_action) + + redo_action = QAction("Redo", self) + redo_action.triggered.connect(self._redo_triggered) + redo_action.setShortcut(QKeySequence("Ctrl+Shift+Z")) + edit_menu.addAction(redo_action) + + edit_menu.addSeparator() + + stash_action = QAction("Stash", self) + stash_action.triggered.connect(self._stash_triggered) + edit_menu.addAction(stash_action) + + unstash_action = QAction("Pop Stash", self) + unstash_action.triggered.connect(self._unstash_triggered) + edit_menu.addAction(unstash_action) + + edit_menu.addSeparator() + + align_selected_nodes_to_grid_action = QAction( + "Align Selected Nodes to Grid", self + ) + align_selected_nodes_to_grid_action.triggered.connect( + self._align_selected_nodes_to_grid_action + ) + edit_menu.addAction(align_selected_nodes_to_grid_action) + + def _setup_layout(self) -> None: + self.setCentralWidget(self._tab_widget_QTabWidget) + + def _setup_views(self) -> None: + self._tab_widget_QTabWidget.setTabsClosable(True) + self._tab_widget_QTabWidget.setMovable(True) + + self._add_new_graph() + + def _setup_signals(self) -> None: + self._tab_widget_QTabWidget.tabCloseRequested.connect( + self._tab_widget_QTabWidget_tabCloseRequested + ) + + def _set_window_title(self) -> None: + title = "Graph Editor" + + if self._file_path is not None: + title = f"{title} - {self._file_path}" + + self.setWindowTitle(title) + + def _tab_widget_QTabWidget_tabCloseRequested(self, index) -> None: + if self._tab_widget_QTabWidget.widget(index).dirty: + if ( + confirmation_dialog( + "The graph contains unsaved changes.\n\n" + "Do you want to close without saving?" + ) + != QMessageBox.Yes + ): + return + + self._tab_widget_QTabWidget.removeTab(index) + + if self._tab_widget_QTabWidget.count() == 0: + self._add_new_graph() + + def _new_triggered(self) -> None: + self._add_new_graph() + + def _open_triggered(self) -> None: + file_path, _ = QFileDialog.getOpenFileName( + self, "Open Graph", "", "Litegraph (*.lgson)" + ) + + if not file_path: + return + + self._add_new_graph(file_path) + + def _save_triggered(self) -> None: + self._tab_widget_QTabWidget.currentWidget().save() + + def _save_as_triggered(self) -> None: + file_path, _ = QFileDialog.getSaveFileName( + self, "Save Graph", "", "Litegraph (*.lgson)" + ) + + if file_path: + self._tab_widget_QTabWidget.currentWidget().save(file_path) + + def _undo_triggered(self) -> None: + self._tab_widget_QTabWidget.currentWidget().undo() + + def _redo_triggered(self) -> None: + self._tab_widget_QTabWidget.currentWidget().redo() + + def _stash_triggered(self) -> None: + self._tab_widget_QTabWidget.currentWidget().stash() + + def _unstash_triggered(self) -> None: + if self._tab_widget_QTabWidget.currentWidget().dirty: + if ( + confirmation_dialog( + "The graph contains unsaved changes.\n\n" + "Do you want to pop the stash without saving?" + ) + != QMessageBox.Yes + ): + return + + self._tab_widget_QTabWidget.currentWidget().unstash() + + def _align_selected_nodes_to_grid_action(self) -> None: + self._tab_widget_QTabWidget.currentWidget().align_selected_nodes_to_grid() + + def _add_new_graph( + self, file_path: str | None = None, graph_name: str = "Untitled" + ): + if file_path is not None: + # Selecting existing graph if found + for index in range(self._tab_widget_QTabWidget.count()): + if self._tab_widget_QTabWidget.widget(index).file_path == file_path: + self._tab_widget_QTabWidget.setCurrentIndex(index) + return + + # Closing initial graph if not dirty + if ( + self._tab_widget_QTabWidget.count() == 1 + and self._tab_widget_QTabWidget.currentWidget().file_path is None + and not self._tab_widget_QTabWidget.currentWidget().dirty + ): + self._tab_widget_QTabWidget.removeTab(0) + + litegraph_widget = LiteGraphWidget(self, file_path, self._developer_mode) + + litegraph_widget.graph_changed.connect(self._graph_changed) + litegraph_widget.graph_loaded.connect(self._graph_loaded) + litegraph_widget.graph_saved.connect(self._graph_saved) + + index = self._tab_widget_QTabWidget.addTab(litegraph_widget, graph_name) + self._tab_widget_QTabWidget.setCurrentIndex(index) + + def _set_current_tab_text(self) -> None: + current_index = self._tab_widget_QTabWidget.currentIndex() + + if current_index == -1: + return + + tab_name = "Untitled" + + file_path = self._tab_widget_QTabWidget.currentWidget().file_path + + if file_path is not None: + tab_name = Path(file_path).name + + if self._tab_widget_QTabWidget.currentWidget().dirty: + tab_name = f"{tab_name} *" + + self._tab_widget_QTabWidget.setTabText(current_index, tab_name) + + def _graph_changed(self) -> None: + self._set_current_tab_text() + + def _graph_loaded(self, status: bool) -> None: + current_index = self._tab_widget_QTabWidget.currentIndex() + + if current_index == -1: + return + + file_path = self._tab_widget_QTabWidget.currentWidget().file_path + + if status: + self._set_current_tab_text() + self._tab_widget_QTabWidget.setTabToolTip(current_index, file_path) + self._add_to_recent_files(file_path) + else: + self._tab_widget_QTabWidget.removeTab(current_index) + self._remove_from_recent_files(file_path) + + def _graph_saved(self, status: bool) -> None: + current_index = self._tab_widget_QTabWidget.currentIndex() + + if current_index == -1: + return + + file_path = self._tab_widget_QTabWidget.currentWidget().file_path + + if status: + self._set_current_tab_text() + self._add_to_recent_files(file_path) + + def _update_recent_files_menu(self): + self._recent_files_menu.clear() + + recent_files = self._settings.value("recent_files", [], type=list) + + if not recent_files: + no_recent_action = QAction("No Recent Files", self) + no_recent_action.setEnabled(False) + self._recent_files_menu.addAction(no_recent_action) + return + + for file_path in recent_files: + action = QAction(file_path, self) + action.triggered.connect( + lambda checked, path=file_path: self._open_recent_file(path) + ) + self._recent_files_menu.addAction(action) + + def _add_to_recent_files(self, file_path): + recent_files = self._settings.value("recent_files", [], type=list) + + if file_path in recent_files: + recent_files.remove(file_path) + + recent_files.insert(0, file_path) + + recent_files = recent_files[: self._recent_files_count] + + self._settings.setValue("recent_files", recent_files) + + self._update_recent_files_menu() + + def _remove_from_recent_files(self, file_path): + recent_files = self._settings.value("recent_files", [], type=list) + + if file_path in recent_files: + recent_files.remove(file_path) + + recent_files = recent_files[: self._recent_files_count] + + self._settings.setValue("recent_files", recent_files) + + self._update_recent_files_menu() + + def _open_recent_file(self, file_path): + self._add_new_graph(file_path) + + +if __name__ == "__main__": + import sys + + logging.basicConfig( + level=logging.DEBUG, + format="%(asctime)s [%(levelname)8s] [Thread ID: %(thread)d] %(name)s: %(message)s", + ) + + application = QApplication(sys.argv) + graph_editor = GraphEditor(True) + graph_editor.show() + + sys.exit(application.exec()) diff --git a/colour_hdri/network/graphs.py b/colour_hdri/network/graphs.py index 6e310d8..c4973c4 100644 --- a/colour_hdri/network/graphs.py +++ b/colour_hdri/network/graphs.py @@ -19,6 +19,7 @@ ExecutionNode, ParallelForMultiprocess, PortGraph, + notify_process_state, ) from colour_hdri.generation import double_sigmoid_anchored_function @@ -244,6 +245,11 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: output_port, ) + self.connect( + "execution_input", + self.nodes["ConvertRawFileToDNGFile"], + "execution_input", + ) self.connect( "raw_file_path", self.nodes["ConvertRawFileToDNGFile"], @@ -330,6 +336,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: "path", ) + @notify_process_state def process(self, **kwargs: Any) -> None: """ Process the node-graph. @@ -536,6 +543,11 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: output_port, ) + self.connect( + "execution_input", + self.nodes["ConvertRawFileToDNGFile"], + "execution_input", + ) self.connect( "raw_file_path", self.nodes["ConvertRawFileToDNGFile"], @@ -627,6 +639,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: "path", ) + @notify_process_state def process(self, **kwargs: Any) -> None: """ Process the node-graph. @@ -736,6 +749,11 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: output_port, ) + self.connect( + "execution_input", + self.nodes["CreateImageStack"], + "execution_input", + ) self.connect( "exr_file_paths", self.nodes["CreateImageStack"], @@ -782,6 +800,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: "path", ) + @notify_process_state def process(self, **kwargs: Any) -> None: """ Process the node-graph. @@ -866,6 +885,10 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: self.add_node(node) for connection in [ + ( + ("NormaliseExposure", "execution_output"), + ("ParallelForMultiprocess", "execution_input"), + ), ( ("ParallelForMultiprocess", "loop_output"), ("WritePreviewImage", "execution_input"), @@ -882,6 +905,11 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: output_port, ) + self.connect( + "execution_input", + self.nodes["NormaliseExposure"], + "execution_input", + ) self.connect( "array", self.nodes["NormaliseExposure"], @@ -916,6 +944,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: "task", _task_multiprocess_post_merge_hdr ) + @notify_process_state def process(self, **kwargs: Any) -> None: """ Process the node-graph. @@ -991,6 +1020,11 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: output_port, ) + self.connect( + "execution_input", + self.nodes["CreateBatches"], + "execution_input", + ) self.connect( "array", self.nodes["CreateBatches"], @@ -1043,6 +1077,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: "results", self.nodes["GraphPostMergeHDRI"], "array" ) + @notify_process_state def process(self, **kwargs: Any) -> None: """ Process the node-graph. @@ -1162,6 +1197,11 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: output_port, ) + self.connect( + "execution_input", + self.nodes["ParallelForMultiprocess"], + "execution_input", + ) self.connect( "array", self.nodes["ParallelForMultiprocess"], @@ -1274,6 +1314,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: "Watermark" ].set_input("include_exposure_information", False) + @notify_process_state def process(self, **kwargs: Any) -> None: """ Process the node-graph. diff --git a/colour_hdri/network/nodes.py b/colour_hdri/network/nodes.py index 714a2b9..1776590 100644 --- a/colour_hdri/network/nodes.py +++ b/colour_hdri/network/nodes.py @@ -48,6 +48,7 @@ ExecutionNode, as_float_array, batch, + notify_process_state, ones, orient, required, @@ -190,7 +191,7 @@ class NodeConvertRawFileToDNGFile(ExecutionNode): """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, **{"category": "RawProcessing", **kwargs}) self.description = 'Convert given raw file, e.g., "CR2", "CR3", "NEF", to "DNG"' @@ -200,6 +201,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: self.add_input_port("dng_converter_arguments") self.add_output_port("dng_file_path") + @notify_process_state def process(self, **kwargs: Any) -> None: # noqa: ARG002 """ Process the node. @@ -259,7 +261,7 @@ class NodeReadImage(ExecutionNode): """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, **{"category": "ImageIO", **kwargs}) self.description = ( "Read the image from input path and return its data and metadata" @@ -272,6 +274,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: self.add_output_port("exif_tags") @required("OpenImageIO") + @notify_process_state def process(self, **kwargs: Any) -> None: # noqa: ARG002 """ Process the node. @@ -285,7 +288,7 @@ def process(self, **kwargs: Any) -> None: # noqa: ARG002 self.log(f'"{path}" image does not exist!') return - image, metadata = read_image_OpenImageIO(path, attributes=True) + image, metadata = read_image_OpenImageIO(path, additional_data=True) input_colourspace = self.get_input("input_colourspace") if isinstance(input_colourspace, str): @@ -316,7 +319,7 @@ class NodeWriteImage(ExecutionNode): """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, **{"category": "ImageIO", **kwargs}) self.description = ( "Write the input image to input path using the input metadata" @@ -330,6 +333,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: self.add_input_port("bypass", False) @required("OpenImageIO") + @notify_process_state def process(self, **kwargs: Any) -> None: # noqa: ARG002 """ Process the node. @@ -391,7 +395,7 @@ class NodeWritePreviewImage(ExecutionNode): """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, **{"category": "ImageIO", **kwargs}) self.description = "Write the image at input image path as a preview image" @@ -401,6 +405,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: self.add_output_port("preview_path") @required("OpenImageIO") + @notify_process_state def process(self, **kwargs: Any) -> None: # noqa: ARG002 """ Process the node. @@ -443,13 +448,14 @@ class NodeRemoveFile(ExecutionNode): """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, **{"category": "Os", **kwargs}) self.description = "Remove the file at input path" self.add_input_port("path") self.add_input_port("bypass", False) + @notify_process_state def process(self, **kwargs: Any) -> None: # noqa: ARG002 """ Process the node. @@ -479,7 +485,7 @@ class NodeOrient(ExecutionNode): """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, **{"category": "ImageTransform", **kwargs}) self.description = "Orient the input image" @@ -488,6 +494,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: self.add_input_port("bypass", False) self.add_output_port("output_image") + @notify_process_state def process(self, **kwargs: Any) -> None: # noqa: ARG002 """ Process the node. @@ -524,7 +531,7 @@ class NodeWatermark(ExecutionNode): """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, **{"category": "ImageFilter", **kwargs}) self.description = "Watermark the input image using given input metadata" @@ -535,6 +542,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: self.add_output_port("output_image") @required("OpenCV") # pyright: ignore + @notify_process_state def process(self, **kwargs: Any) -> None: # noqa: ARG002 """ Process the node. @@ -608,7 +616,7 @@ class NodeProcessingMetadata(ExecutionNode): """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, **{"category": "Metadata", **kwargs}) self.description = "Add processing metadata to the input metadata" @@ -621,6 +629,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: self.add_input_port("sources") self.add_output_port("output_metadata") + @notify_process_state def process(self, **kwargs: Any) -> None: # noqa: ARG002 """ Process the node. @@ -675,13 +684,14 @@ class NodeReadFileExifData(ExecutionNode): """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, **{"category": "Metadata", **kwargs}) self.description = "Return the EXIF tags from the input image." self.add_input_port("file_path") self.add_output_port("exif_tags") + @notify_process_state def process(self, **kwargs: Any) -> None: # noqa: ARG002 """ Process the node. @@ -711,13 +721,14 @@ class NodeReadFileMetadataDNG(ExecutionNode): """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, **{"category": "Metadata", **kwargs}) self.description = 'Return the metadata from the input "DNG" image' self.add_input_port("dng_file_path") self.add_output_port("metadata") + @notify_process_state def process(self, **kwargs: Any) -> None: # noqa: ARG002 """ Process the node. @@ -749,7 +760,7 @@ class NodeComputeInputTransformDNG(ExecutionNode): """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, **{"category": "RawProcessing", **kwargs}) self.description = ( "Compute the input transform from the input metadata using the " @@ -761,6 +772,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: self.add_input_port("bypass", False) self.add_output_port("input_transform", InputTransform()) + @notify_process_state def process(self, **kwargs: Any) -> None: # noqa: ARG002 """ Process the node. @@ -871,7 +883,7 @@ class NodeComputeInputTransformCameraSensitivities(ExecutionNode): """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, **{"category": "RawProcessing", **kwargs}) self.description = ( "Compute the input transform from the input metadata using the " @@ -884,6 +896,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: self.add_input_port("bypass", False) self.add_output_port("input_transform", InputTransform()) + @notify_process_state def process(self, **kwargs: Any) -> None: # noqa: ARG002 """ Process the node. @@ -977,7 +990,7 @@ class NodeProcessRawFileRawpy(ExecutionNode): """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, **{"category": "RawProcessing", **kwargs}) self.description = ( 'Process given raw file, e.g., "CR2", "CR3", "NEF", using "Rawpy"' @@ -988,6 +1001,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: self.add_output_port("image") @required("rawpy") # pyright: ignore + @notify_process_state def process(self, **kwargs: Any) -> None: # noqa: ARG002 """ Process the node. @@ -1043,7 +1057,7 @@ class NodeCorrectLensAberrationLensFun(ExecutionNode): """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, **{"category": "RawProcessing", **kwargs}) self.description = ( "Correct the input image lens aberrations, i.e., vignette, " @@ -1060,6 +1074,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: self.add_output_port("output_image") @required("lensfunpy", "OpenCV") # pyright: ignore + @notify_process_state def process(self, **kwargs: Any) -> None: # noqa: ARG002 """ Process the node. @@ -1218,7 +1233,7 @@ class NodeDownsample(ExecutionNode): """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, **{"category": "ImageTransform", **kwargs}) self.description = "Downsample the input image by the input downsampling factor" @@ -1227,6 +1242,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: self.add_input_port("bypass", False) self.add_output_port("output_image") + @notify_process_state def process(self, **kwargs: Any) -> None: # noqa: ARG002 """ Process the node. @@ -1260,7 +1276,7 @@ class NodeApplyInputTransformDNG(ExecutionNode): """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, **{"category": "RawProcessing", **kwargs}) self.description = ( 'Apply the input transform to the input image using the "DNG" method' @@ -1272,6 +1288,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: self.add_input_port("bypass", False) self.add_output_port("output_image") + @notify_process_state def process(self, **kwargs: Any) -> None: # noqa: ARG002 """ Process the node. @@ -1318,7 +1335,7 @@ class NodeApplyInputTransformCameraSensitivities(ExecutionNode): """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, **{"category": "RawProcessing", **kwargs}) self.description = ( "Apply the input transform to the input image using the " @@ -1331,6 +1348,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: self.add_input_port("bypass", False) self.add_output_port("output_image") + @notify_process_state def process(self, **kwargs: Any) -> None: # noqa: ARG002 """ Process the node. @@ -1374,7 +1392,7 @@ class NodeCreateBatches(ExecutionNode): """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, **{"category": "HDRI", **kwargs}) self.description = "Create batches from the input array" @@ -1382,6 +1400,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: self.add_input_port("batch_size", 3) self.add_output_port("batches", []) + @notify_process_state def process(self, **kwargs: Any) -> None: # noqa: ARG002 """ Process the node. @@ -1407,7 +1426,7 @@ class NodeCreateImageStack(ExecutionNode): """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, **{"category": "HDRI", **kwargs}) self.description = "Create an image stack from the input files" @@ -1415,6 +1434,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: self.add_input_port("cctf_decoding", linear_function) self.add_output_port("image_stack") + @notify_process_state def process(self, **kwargs: Any) -> None: # noqa: ARG002 """ Process the node. @@ -1450,7 +1470,7 @@ class NodeMergeImageStack(ExecutionNode): """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, **{"category": "HDRI", **kwargs}) self.description = "Merge to HDRI the input image stack" @@ -1458,6 +1478,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: self.add_input_port("weighting_function", double_sigmoid_anchored_function) self.add_output_port("image") + @notify_process_state def process(self, **kwargs: Any) -> None: # noqa: ARG002 """ Process the node. @@ -1490,7 +1511,7 @@ class NodeNormaliseExposure(ExecutionNode): """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, **{"category": "Exposure", **kwargs}) self.description = ( "Normalise the exposure of the input images by dividing them by given " @@ -1504,6 +1525,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: self.add_input_port("bypass", False) @required("OpenImageIO") + @notify_process_state def process(self, **kwargs: Any) -> None: # noqa: ARG002 """ Process the node. @@ -1532,7 +1554,7 @@ def process(self, **kwargs: Any) -> None: # noqa: ARG002 self.log(f"Normalisation factor: {normalisation_factor}") for image_path in image_paths: - image, attributes = read_image_OpenImageIO(image_path, attributes=True) + image, attributes = read_image_OpenImageIO(image_path, additional_data=True) image *= normalisation_factor image *= self.get_input("scaling_factor") diff --git a/colour_hdri/network/resources/assets/anime.js/js/anime.js b/colour_hdri/network/resources/assets/anime.js/js/anime.js new file mode 100644 index 0000000..eac67fe --- /dev/null +++ b/colour_hdri/network/resources/assets/anime.js/js/anime.js @@ -0,0 +1,1313 @@ +/* + * anime.js v3.2.2 + * (c) 2023 Julian Garnier + * Released under the MIT license + * animejs.com + */ + +'use strict'; + +// Defaults + +var defaultInstanceSettings = { + update: null, + begin: null, + loopBegin: null, + changeBegin: null, + change: null, + changeComplete: null, + loopComplete: null, + complete: null, + loop: 1, + direction: 'normal', + autoplay: true, + timelineOffset: 0 +}; + +var defaultTweenSettings = { + duration: 1000, + delay: 0, + endDelay: 0, + easing: 'easeOutElastic(1, .5)', + round: 0 +}; + +var validTransforms = ['translateX', 'translateY', 'translateZ', 'rotate', 'rotateX', 'rotateY', 'rotateZ', 'scale', 'scaleX', 'scaleY', 'scaleZ', 'skew', 'skewX', 'skewY', 'perspective', 'matrix', 'matrix3d']; + +// Caching + +var cache = { + CSS: {}, + springs: {} +}; + +// Utils + +function minMax(val, min, max) { + return Math.min(Math.max(val, min), max); +} + +function stringContains(str, text) { + return str.indexOf(text) > -1; +} + +function applyArguments(func, args) { + return func.apply(null, args); +} + +var is = { + arr: function (a) { return Array.isArray(a); }, + obj: function (a) { return stringContains(Object.prototype.toString.call(a), 'Object'); }, + pth: function (a) { return is.obj(a) && a.hasOwnProperty('totalLength'); }, + svg: function (a) { return a instanceof SVGElement; }, + inp: function (a) { return a instanceof HTMLInputElement; }, + dom: function (a) { return a.nodeType || is.svg(a); }, + str: function (a) { return typeof a === 'string'; }, + fnc: function (a) { return typeof a === 'function'; }, + und: function (a) { return typeof a === 'undefined'; }, + nil: function (a) { return is.und(a) || a === null; }, + hex: function (a) { return /(^#[0-9A-F]{6}$)|(^#[0-9A-F]{3}$)/i.test(a); }, + rgb: function (a) { return /^rgb/.test(a); }, + hsl: function (a) { return /^hsl/.test(a); }, + col: function (a) { return (is.hex(a) || is.rgb(a) || is.hsl(a)); }, + key: function (a) { return !defaultInstanceSettings.hasOwnProperty(a) && !defaultTweenSettings.hasOwnProperty(a) && a !== 'targets' && a !== 'keyframes'; }, +}; + +// Easings + +function parseEasingParameters(string) { + var match = /\(([^)]+)\)/.exec(string); + return match ? match[1].split(',').map(function (p) { return parseFloat(p); }) : []; +} + +// Spring solver inspired by Webkit Copyright © 2016 Apple Inc. All rights reserved. https://webkit.org/demos/spring/spring.js + +function spring(string, duration) { + + var params = parseEasingParameters(string); + var mass = minMax(is.und(params[0]) ? 1 : params[0], .1, 100); + var stiffness = minMax(is.und(params[1]) ? 100 : params[1], .1, 100); + var damping = minMax(is.und(params[2]) ? 10 : params[2], .1, 100); + var velocity = minMax(is.und(params[3]) ? 0 : params[3], .1, 100); + var w0 = Math.sqrt(stiffness / mass); + var zeta = damping / (2 * Math.sqrt(stiffness * mass)); + var wd = zeta < 1 ? w0 * Math.sqrt(1 - zeta * zeta) : 0; + var a = 1; + var b = zeta < 1 ? (zeta * w0 + -velocity) / wd : -velocity + w0; + + function solver(t) { + var progress = duration ? (duration * t) / 1000 : t; + if (zeta < 1) { + progress = Math.exp(-progress * zeta * w0) * (a * Math.cos(wd * progress) + b * Math.sin(wd * progress)); + } else { + progress = (a + b * progress) * Math.exp(-progress * w0); + } + if (t === 0 || t === 1) { return t; } + return 1 - progress; + } + + function getDuration() { + var cached = cache.springs[string]; + if (cached) { return cached; } + var frame = 1/6; + var elapsed = 0; + var rest = 0; + while(true) { + elapsed += frame; + if (solver(elapsed) === 1) { + rest++; + if (rest >= 16) { break; } + } else { + rest = 0; + } + } + var duration = elapsed * frame * 1000; + cache.springs[string] = duration; + return duration; + } + + return duration ? solver : getDuration; + +} + +// Basic steps easing implementation https://developer.mozilla.org/fr/docs/Web/CSS/transition-timing-function + +function steps(steps) { + if ( steps === void 0 ) steps = 10; + + return function (t) { return Math.ceil((minMax(t, 0.000001, 1)) * steps) * (1 / steps); }; +} + +// BezierEasing https://github.com/gre/bezier-easing + +var bezier = (function () { + + var kSplineTableSize = 11; + var kSampleStepSize = 1.0 / (kSplineTableSize - 1.0); + + function A(aA1, aA2) { return 1.0 - 3.0 * aA2 + 3.0 * aA1 } + function B(aA1, aA2) { return 3.0 * aA2 - 6.0 * aA1 } + function C(aA1) { return 3.0 * aA1 } + + function calcBezier(aT, aA1, aA2) { return ((A(aA1, aA2) * aT + B(aA1, aA2)) * aT + C(aA1)) * aT } + function getSlope(aT, aA1, aA2) { return 3.0 * A(aA1, aA2) * aT * aT + 2.0 * B(aA1, aA2) * aT + C(aA1) } + + function binarySubdivide(aX, aA, aB, mX1, mX2) { + var currentX, currentT, i = 0; + do { + currentT = aA + (aB - aA) / 2.0; + currentX = calcBezier(currentT, mX1, mX2) - aX; + if (currentX > 0.0) { aB = currentT; } else { aA = currentT; } + } while (Math.abs(currentX) > 0.0000001 && ++i < 10); + return currentT; + } + + function newtonRaphsonIterate(aX, aGuessT, mX1, mX2) { + for (var i = 0; i < 4; ++i) { + var currentSlope = getSlope(aGuessT, mX1, mX2); + if (currentSlope === 0.0) { return aGuessT; } + var currentX = calcBezier(aGuessT, mX1, mX2) - aX; + aGuessT -= currentX / currentSlope; + } + return aGuessT; + } + + function bezier(mX1, mY1, mX2, mY2) { + + if (!(0 <= mX1 && mX1 <= 1 && 0 <= mX2 && mX2 <= 1)) { return; } + var sampleValues = new Float32Array(kSplineTableSize); + + if (mX1 !== mY1 || mX2 !== mY2) { + for (var i = 0; i < kSplineTableSize; ++i) { + sampleValues[i] = calcBezier(i * kSampleStepSize, mX1, mX2); + } + } + + function getTForX(aX) { + + var intervalStart = 0; + var currentSample = 1; + var lastSample = kSplineTableSize - 1; + + for (; currentSample !== lastSample && sampleValues[currentSample] <= aX; ++currentSample) { + intervalStart += kSampleStepSize; + } + + --currentSample; + + var dist = (aX - sampleValues[currentSample]) / (sampleValues[currentSample + 1] - sampleValues[currentSample]); + var guessForT = intervalStart + dist * kSampleStepSize; + var initialSlope = getSlope(guessForT, mX1, mX2); + + if (initialSlope >= 0.001) { + return newtonRaphsonIterate(aX, guessForT, mX1, mX2); + } else if (initialSlope === 0.0) { + return guessForT; + } else { + return binarySubdivide(aX, intervalStart, intervalStart + kSampleStepSize, mX1, mX2); + } + + } + + return function (x) { + if (mX1 === mY1 && mX2 === mY2) { return x; } + if (x === 0 || x === 1) { return x; } + return calcBezier(getTForX(x), mY1, mY2); + } + + } + + return bezier; + +})(); + +var penner = (function () { + + // Based on jQuery UI's implemenation of easing equations from Robert Penner (http://www.robertpenner.com/easing) + + var eases = { linear: function () { return function (t) { return t; }; } }; + + var functionEasings = { + Sine: function () { return function (t) { return 1 - Math.cos(t * Math.PI / 2); }; }, + Expo: function () { return function (t) { return t ? Math.pow(2, 10 * t - 10) : 0; }; }, + Circ: function () { return function (t) { return 1 - Math.sqrt(1 - t * t); }; }, + Back: function () { return function (t) { return t * t * (3 * t - 2); }; }, + Bounce: function () { return function (t) { + var pow2, b = 4; + while (t < (( pow2 = Math.pow(2, --b)) - 1) / 11) {} + return 1 / Math.pow(4, 3 - b) - 7.5625 * Math.pow(( pow2 * 3 - 2 ) / 22 - t, 2) + }; }, + Elastic: function (amplitude, period) { + if ( amplitude === void 0 ) amplitude = 1; + if ( period === void 0 ) period = .5; + + var a = minMax(amplitude, 1, 10); + var p = minMax(period, .1, 2); + return function (t) { + return (t === 0 || t === 1) ? t : + -a * Math.pow(2, 10 * (t - 1)) * Math.sin((((t - 1) - (p / (Math.PI * 2) * Math.asin(1 / a))) * (Math.PI * 2)) / p); + } + } + }; + + var baseEasings = ['Quad', 'Cubic', 'Quart', 'Quint']; + + baseEasings.forEach(function (name, i) { + functionEasings[name] = function () { return function (t) { return Math.pow(t, i + 2); }; }; + }); + + Object.keys(functionEasings).forEach(function (name) { + var easeIn = functionEasings[name]; + eases['easeIn' + name] = easeIn; + eases['easeOut' + name] = function (a, b) { return function (t) { return 1 - easeIn(a, b)(1 - t); }; }; + eases['easeInOut' + name] = function (a, b) { return function (t) { return t < 0.5 ? easeIn(a, b)(t * 2) / 2 : + 1 - easeIn(a, b)(t * -2 + 2) / 2; }; }; + eases['easeOutIn' + name] = function (a, b) { return function (t) { return t < 0.5 ? (1 - easeIn(a, b)(1 - t * 2)) / 2 : + (easeIn(a, b)(t * 2 - 1) + 1) / 2; }; }; + }); + + return eases; + +})(); + +function parseEasings(easing, duration) { + if (is.fnc(easing)) { return easing; } + var name = easing.split('(')[0]; + var ease = penner[name]; + var args = parseEasingParameters(easing); + switch (name) { + case 'spring' : return spring(easing, duration); + case 'cubicBezier' : return applyArguments(bezier, args); + case 'steps' : return applyArguments(steps, args); + default : return applyArguments(ease, args); + } +} + +// Strings + +function selectString(str) { + try { + var nodes = document.querySelectorAll(str); + return nodes; + } catch(e) { + return; + } +} + +// Arrays + +function filterArray(arr, callback) { + var len = arr.length; + var thisArg = arguments.length >= 2 ? arguments[1] : void 0; + var result = []; + for (var i = 0; i < len; i++) { + if (i in arr) { + var val = arr[i]; + if (callback.call(thisArg, val, i, arr)) { + result.push(val); + } + } + } + return result; +} + +function flattenArray(arr) { + return arr.reduce(function (a, b) { return a.concat(is.arr(b) ? flattenArray(b) : b); }, []); +} + +function toArray(o) { + if (is.arr(o)) { return o; } + if (is.str(o)) { o = selectString(o) || o; } + if (o instanceof NodeList || o instanceof HTMLCollection) { return [].slice.call(o); } + return [o]; +} + +function arrayContains(arr, val) { + return arr.some(function (a) { return a === val; }); +} + +// Objects + +function cloneObject(o) { + var clone = {}; + for (var p in o) { clone[p] = o[p]; } + return clone; +} + +function replaceObjectProps(o1, o2) { + var o = cloneObject(o1); + for (var p in o1) { o[p] = o2.hasOwnProperty(p) ? o2[p] : o1[p]; } + return o; +} + +function mergeObjects(o1, o2) { + var o = cloneObject(o1); + for (var p in o2) { o[p] = is.und(o1[p]) ? o2[p] : o1[p]; } + return o; +} + +// Colors + +function rgbToRgba(rgbValue) { + var rgb = /rgb\((\d+,\s*[\d]+,\s*[\d]+)\)/g.exec(rgbValue); + return rgb ? ("rgba(" + (rgb[1]) + ",1)") : rgbValue; +} + +function hexToRgba(hexValue) { + var rgx = /^#?([a-f\d])([a-f\d])([a-f\d])$/i; + var hex = hexValue.replace(rgx, function (m, r, g, b) { return r + r + g + g + b + b; } ); + var rgb = /^#?([a-f\d]{2})([a-f\d]{2})([a-f\d]{2})$/i.exec(hex); + var r = parseInt(rgb[1], 16); + var g = parseInt(rgb[2], 16); + var b = parseInt(rgb[3], 16); + return ("rgba(" + r + "," + g + "," + b + ",1)"); +} + +function hslToRgba(hslValue) { + var hsl = /hsl\((\d+),\s*([\d.]+)%,\s*([\d.]+)%\)/g.exec(hslValue) || /hsla\((\d+),\s*([\d.]+)%,\s*([\d.]+)%,\s*([\d.]+)\)/g.exec(hslValue); + var h = parseInt(hsl[1], 10) / 360; + var s = parseInt(hsl[2], 10) / 100; + var l = parseInt(hsl[3], 10) / 100; + var a = hsl[4] || 1; + function hue2rgb(p, q, t) { + if (t < 0) { t += 1; } + if (t > 1) { t -= 1; } + if (t < 1/6) { return p + (q - p) * 6 * t; } + if (t < 1/2) { return q; } + if (t < 2/3) { return p + (q - p) * (2/3 - t) * 6; } + return p; + } + var r, g, b; + if (s == 0) { + r = g = b = l; + } else { + var q = l < 0.5 ? l * (1 + s) : l + s - l * s; + var p = 2 * l - q; + r = hue2rgb(p, q, h + 1/3); + g = hue2rgb(p, q, h); + b = hue2rgb(p, q, h - 1/3); + } + return ("rgba(" + (r * 255) + "," + (g * 255) + "," + (b * 255) + "," + a + ")"); +} + +function colorToRgb(val) { + if (is.rgb(val)) { return rgbToRgba(val); } + if (is.hex(val)) { return hexToRgba(val); } + if (is.hsl(val)) { return hslToRgba(val); } +} + +// Units + +function getUnit(val) { + var split = /[+-]?\d*\.?\d+(?:\.\d+)?(?:[eE][+-]?\d+)?(%|px|pt|em|rem|in|cm|mm|ex|ch|pc|vw|vh|vmin|vmax|deg|rad|turn)?$/.exec(val); + if (split) { return split[1]; } +} + +function getTransformUnit(propName) { + if (stringContains(propName, 'translate') || propName === 'perspective') { return 'px'; } + if (stringContains(propName, 'rotate') || stringContains(propName, 'skew')) { return 'deg'; } +} + +// Values + +function getFunctionValue(val, animatable) { + if (!is.fnc(val)) { return val; } + return val(animatable.target, animatable.id, animatable.total); +} + +function getAttribute(el, prop) { + return el.getAttribute(prop); +} + +function convertPxToUnit(el, value, unit) { + var valueUnit = getUnit(value); + if (arrayContains([unit, 'deg', 'rad', 'turn'], valueUnit)) { return value; } + var cached = cache.CSS[value + unit]; + if (!is.und(cached)) { return cached; } + var baseline = 100; + var tempEl = document.createElement(el.tagName); + var parentEl = (el.parentNode && (el.parentNode !== document)) ? el.parentNode : document.body; + parentEl.appendChild(tempEl); + tempEl.style.position = 'absolute'; + tempEl.style.width = baseline + unit; + var factor = baseline / tempEl.offsetWidth; + parentEl.removeChild(tempEl); + var convertedUnit = factor * parseFloat(value); + cache.CSS[value + unit] = convertedUnit; + return convertedUnit; +} + +function getCSSValue(el, prop, unit) { + if (prop in el.style) { + var uppercasePropName = prop.replace(/([a-z])([A-Z])/g, '$1-$2').toLowerCase(); + var value = el.style[prop] || getComputedStyle(el).getPropertyValue(uppercasePropName) || '0'; + return unit ? convertPxToUnit(el, value, unit) : value; + } +} + +function getAnimationType(el, prop) { + if (is.dom(el) && !is.inp(el) && (!is.nil(getAttribute(el, prop)) || (is.svg(el) && el[prop]))) { return 'attribute'; } + if (is.dom(el) && arrayContains(validTransforms, prop)) { return 'transform'; } + if (is.dom(el) && (prop !== 'transform' && getCSSValue(el, prop))) { return 'css'; } + if (el[prop] != null) { return 'object'; } +} + +function getElementTransforms(el) { + if (!is.dom(el)) { return; } + var str = el.style.transform || ''; + var reg = /(\w+)\(([^)]*)\)/g; + var transforms = new Map(); + var m; while (m = reg.exec(str)) { transforms.set(m[1], m[2]); } + return transforms; +} + +function getTransformValue(el, propName, animatable, unit) { + var defaultVal = stringContains(propName, 'scale') ? 1 : 0 + getTransformUnit(propName); + var value = getElementTransforms(el).get(propName) || defaultVal; + if (animatable) { + animatable.transforms.list.set(propName, value); + animatable.transforms['last'] = propName; + } + return unit ? convertPxToUnit(el, value, unit) : value; +} + +function getOriginalTargetValue(target, propName, unit, animatable) { + switch (getAnimationType(target, propName)) { + case 'transform': return getTransformValue(target, propName, animatable, unit); + case 'css': return getCSSValue(target, propName, unit); + case 'attribute': return getAttribute(target, propName); + default: return target[propName] || 0; + } +} + +function getRelativeValue(to, from) { + var operator = /^(\*=|\+=|-=)/.exec(to); + if (!operator) { return to; } + var u = getUnit(to) || 0; + var x = parseFloat(from); + var y = parseFloat(to.replace(operator[0], '')); + switch (operator[0][0]) { + case '+': return x + y + u; + case '-': return x - y + u; + case '*': return x * y + u; + } +} + +function validateValue(val, unit) { + if (is.col(val)) { return colorToRgb(val); } + if (/\s/g.test(val)) { return val; } + var originalUnit = getUnit(val); + var unitLess = originalUnit ? val.substr(0, val.length - originalUnit.length) : val; + if (unit) { return unitLess + unit; } + return unitLess; +} + +// getTotalLength() equivalent for circle, rect, polyline, polygon and line shapes +// adapted from https://gist.github.com/SebLambla/3e0550c496c236709744 + +function getDistance(p1, p2) { + return Math.sqrt(Math.pow(p2.x - p1.x, 2) + Math.pow(p2.y - p1.y, 2)); +} + +function getCircleLength(el) { + return Math.PI * 2 * getAttribute(el, 'r'); +} + +function getRectLength(el) { + return (getAttribute(el, 'width') * 2) + (getAttribute(el, 'height') * 2); +} + +function getLineLength(el) { + return getDistance( + {x: getAttribute(el, 'x1'), y: getAttribute(el, 'y1')}, + {x: getAttribute(el, 'x2'), y: getAttribute(el, 'y2')} + ); +} + +function getPolylineLength(el) { + var points = el.points; + var totalLength = 0; + var previousPos; + for (var i = 0 ; i < points.numberOfItems; i++) { + var currentPos = points.getItem(i); + if (i > 0) { totalLength += getDistance(previousPos, currentPos); } + previousPos = currentPos; + } + return totalLength; +} + +function getPolygonLength(el) { + var points = el.points; + return getPolylineLength(el) + getDistance(points.getItem(points.numberOfItems - 1), points.getItem(0)); +} + +// Path animation + +function getTotalLength(el) { + if (el.getTotalLength) { return el.getTotalLength(); } + switch(el.tagName.toLowerCase()) { + case 'circle': return getCircleLength(el); + case 'rect': return getRectLength(el); + case 'line': return getLineLength(el); + case 'polyline': return getPolylineLength(el); + case 'polygon': return getPolygonLength(el); + } +} + +function setDashoffset(el) { + var pathLength = getTotalLength(el); + el.setAttribute('stroke-dasharray', pathLength); + return pathLength; +} + +// Motion path + +function getParentSvgEl(el) { + var parentEl = el.parentNode; + while (is.svg(parentEl)) { + if (!is.svg(parentEl.parentNode)) { break; } + parentEl = parentEl.parentNode; + } + return parentEl; +} + +function getParentSvg(pathEl, svgData) { + var svg = svgData || {}; + var parentSvgEl = svg.el || getParentSvgEl(pathEl); + var rect = parentSvgEl.getBoundingClientRect(); + var viewBoxAttr = getAttribute(parentSvgEl, 'viewBox'); + var width = rect.width; + var height = rect.height; + var viewBox = svg.viewBox || (viewBoxAttr ? viewBoxAttr.split(' ') : [0, 0, width, height]); + return { + el: parentSvgEl, + viewBox: viewBox, + x: viewBox[0] / 1, + y: viewBox[1] / 1, + w: width, + h: height, + vW: viewBox[2], + vH: viewBox[3] + } +} + +function getPath(path, percent) { + var pathEl = is.str(path) ? selectString(path)[0] : path; + var p = percent || 100; + return function(property) { + return { + property: property, + el: pathEl, + svg: getParentSvg(pathEl), + totalLength: getTotalLength(pathEl) * (p / 100) + } + } +} + +function getPathProgress(path, progress, isPathTargetInsideSVG) { + function point(offset) { + if ( offset === void 0 ) offset = 0; + + var l = progress + offset >= 1 ? progress + offset : 0; + return path.el.getPointAtLength(l); + } + var svg = getParentSvg(path.el, path.svg); + var p = point(); + var p0 = point(-1); + var p1 = point(+1); + var scaleX = isPathTargetInsideSVG ? 1 : svg.w / svg.vW; + var scaleY = isPathTargetInsideSVG ? 1 : svg.h / svg.vH; + switch (path.property) { + case 'x': return (p.x - svg.x) * scaleX; + case 'y': return (p.y - svg.y) * scaleY; + case 'angle': return Math.atan2(p1.y - p0.y, p1.x - p0.x) * 180 / Math.PI; + } +} + +// Decompose value + +function decomposeValue(val, unit) { + // const rgx = /-?\d*\.?\d+/g; // handles basic numbers + // const rgx = /[+-]?\d+(?:\.\d+)?(?:[eE][+-]?\d+)?/g; // handles exponents notation + var rgx = /[+-]?\d*\.?\d+(?:\.\d+)?(?:[eE][+-]?\d+)?/g; // handles exponents notation + var value = validateValue((is.pth(val) ? val.totalLength : val), unit) + ''; + return { + original: value, + numbers: value.match(rgx) ? value.match(rgx).map(Number) : [0], + strings: (is.str(val) || unit) ? value.split(rgx) : [] + } +} + +// Animatables + +function parseTargets(targets) { + var targetsArray = targets ? (flattenArray(is.arr(targets) ? targets.map(toArray) : toArray(targets))) : []; + return filterArray(targetsArray, function (item, pos, self) { return self.indexOf(item) === pos; }); +} + +function getAnimatables(targets) { + var parsed = parseTargets(targets); + return parsed.map(function (t, i) { + return {target: t, id: i, total: parsed.length, transforms: { list: getElementTransforms(t) } }; + }); +} + +// Properties + +function normalizePropertyTweens(prop, tweenSettings) { + var settings = cloneObject(tweenSettings); + // Override duration if easing is a spring + if (/^spring/.test(settings.easing)) { settings.duration = spring(settings.easing); } + if (is.arr(prop)) { + var l = prop.length; + var isFromTo = (l === 2 && !is.obj(prop[0])); + if (!isFromTo) { + // Duration divided by the number of tweens + if (!is.fnc(tweenSettings.duration)) { settings.duration = tweenSettings.duration / l; } + } else { + // Transform [from, to] values shorthand to a valid tween value + prop = {value: prop}; + } + } + var propArray = is.arr(prop) ? prop : [prop]; + return propArray.map(function (v, i) { + var obj = (is.obj(v) && !is.pth(v)) ? v : {value: v}; + // Default delay value should only be applied to the first tween + if (is.und(obj.delay)) { obj.delay = !i ? tweenSettings.delay : 0; } + // Default endDelay value should only be applied to the last tween + if (is.und(obj.endDelay)) { obj.endDelay = i === propArray.length - 1 ? tweenSettings.endDelay : 0; } + return obj; + }).map(function (k) { return mergeObjects(k, settings); }); +} + + +function flattenKeyframes(keyframes) { + var propertyNames = filterArray(flattenArray(keyframes.map(function (key) { return Object.keys(key); })), function (p) { return is.key(p); }) + .reduce(function (a,b) { if (a.indexOf(b) < 0) { a.push(b); } return a; }, []); + var properties = {}; + var loop = function ( i ) { + var propName = propertyNames[i]; + properties[propName] = keyframes.map(function (key) { + var newKey = {}; + for (var p in key) { + if (is.key(p)) { + if (p == propName) { newKey.value = key[p]; } + } else { + newKey[p] = key[p]; + } + } + return newKey; + }); + }; + + for (var i = 0; i < propertyNames.length; i++) loop( i ); + return properties; +} + +function getProperties(tweenSettings, params) { + var properties = []; + var keyframes = params.keyframes; + if (keyframes) { params = mergeObjects(flattenKeyframes(keyframes), params); } + for (var p in params) { + if (is.key(p)) { + properties.push({ + name: p, + tweens: normalizePropertyTweens(params[p], tweenSettings) + }); + } + } + return properties; +} + +// Tweens + +function normalizeTweenValues(tween, animatable) { + var t = {}; + for (var p in tween) { + var value = getFunctionValue(tween[p], animatable); + if (is.arr(value)) { + value = value.map(function (v) { return getFunctionValue(v, animatable); }); + if (value.length === 1) { value = value[0]; } + } + t[p] = value; + } + t.duration = parseFloat(t.duration); + t.delay = parseFloat(t.delay); + return t; +} + +function normalizeTweens(prop, animatable) { + var previousTween; + return prop.tweens.map(function (t) { + var tween = normalizeTweenValues(t, animatable); + var tweenValue = tween.value; + var to = is.arr(tweenValue) ? tweenValue[1] : tweenValue; + var toUnit = getUnit(to); + var originalValue = getOriginalTargetValue(animatable.target, prop.name, toUnit, animatable); + var previousValue = previousTween ? previousTween.to.original : originalValue; + var from = is.arr(tweenValue) ? tweenValue[0] : previousValue; + var fromUnit = getUnit(from) || getUnit(originalValue); + var unit = toUnit || fromUnit; + if (is.und(to)) { to = previousValue; } + tween.from = decomposeValue(from, unit); + tween.to = decomposeValue(getRelativeValue(to, from), unit); + tween.start = previousTween ? previousTween.end : 0; + tween.end = tween.start + tween.delay + tween.duration + tween.endDelay; + tween.easing = parseEasings(tween.easing, tween.duration); + tween.isPath = is.pth(tweenValue); + tween.isPathTargetInsideSVG = tween.isPath && is.svg(animatable.target); + tween.isColor = is.col(tween.from.original); + if (tween.isColor) { tween.round = 1; } + previousTween = tween; + return tween; + }); +} + +// Tween progress + +var setProgressValue = { + css: function (t, p, v) { return t.style[p] = v; }, + attribute: function (t, p, v) { return t.setAttribute(p, v); }, + object: function (t, p, v) { return t[p] = v; }, + transform: function (t, p, v, transforms, manual) { + transforms.list.set(p, v); + if (p === transforms.last || manual) { + var str = ''; + transforms.list.forEach(function (value, prop) { str += prop + "(" + value + ") "; }); + t.style.transform = str; + } + } +}; + +// Set Value helper + +function setTargetsValue(targets, properties) { + var animatables = getAnimatables(targets); + animatables.forEach(function (animatable) { + for (var property in properties) { + var value = getFunctionValue(properties[property], animatable); + var target = animatable.target; + var valueUnit = getUnit(value); + var originalValue = getOriginalTargetValue(target, property, valueUnit, animatable); + var unit = valueUnit || getUnit(originalValue); + var to = getRelativeValue(validateValue(value, unit), originalValue); + var animType = getAnimationType(target, property); + setProgressValue[animType](target, property, to, animatable.transforms, true); + } + }); +} + +// Animations + +function createAnimation(animatable, prop) { + var animType = getAnimationType(animatable.target, prop.name); + if (animType) { + var tweens = normalizeTweens(prop, animatable); + var lastTween = tweens[tweens.length - 1]; + return { + type: animType, + property: prop.name, + animatable: animatable, + tweens: tweens, + duration: lastTween.end, + delay: tweens[0].delay, + endDelay: lastTween.endDelay + } + } +} + +function getAnimations(animatables, properties) { + return filterArray(flattenArray(animatables.map(function (animatable) { + return properties.map(function (prop) { + return createAnimation(animatable, prop); + }); + })), function (a) { return !is.und(a); }); +} + +// Create Instance + +function getInstanceTimings(animations, tweenSettings) { + var animLength = animations.length; + var getTlOffset = function (anim) { return anim.timelineOffset ? anim.timelineOffset : 0; }; + var timings = {}; + timings.duration = animLength ? Math.max.apply(Math, animations.map(function (anim) { return getTlOffset(anim) + anim.duration; })) : tweenSettings.duration; + timings.delay = animLength ? Math.min.apply(Math, animations.map(function (anim) { return getTlOffset(anim) + anim.delay; })) : tweenSettings.delay; + timings.endDelay = animLength ? timings.duration - Math.max.apply(Math, animations.map(function (anim) { return getTlOffset(anim) + anim.duration - anim.endDelay; })) : tweenSettings.endDelay; + return timings; +} + +var instanceID = 0; + +function createNewInstance(params) { + var instanceSettings = replaceObjectProps(defaultInstanceSettings, params); + var tweenSettings = replaceObjectProps(defaultTweenSettings, params); + var properties = getProperties(tweenSettings, params); + var animatables = getAnimatables(params.targets); + var animations = getAnimations(animatables, properties); + var timings = getInstanceTimings(animations, tweenSettings); + var id = instanceID; + instanceID++; + return mergeObjects(instanceSettings, { + id: id, + children: [], + animatables: animatables, + animations: animations, + duration: timings.duration, + delay: timings.delay, + endDelay: timings.endDelay + }); +} + +// Core + +var activeInstances = []; + +var engine = (function () { + var raf; + + function play() { + if (!raf && (!isDocumentHidden() || !anime.suspendWhenDocumentHidden) && activeInstances.length > 0) { + raf = requestAnimationFrame(step); + } + } + function step(t) { + // memo on algorithm issue: + // dangerous iteration over mutable `activeInstances` + // (that collection may be updated from within callbacks of `tick`-ed animation instances) + var activeInstancesLength = activeInstances.length; + var i = 0; + while (i < activeInstancesLength) { + var activeInstance = activeInstances[i]; + if (!activeInstance.paused) { + activeInstance.tick(t); + i++; + } else { + activeInstances.splice(i, 1); + activeInstancesLength--; + } + } + raf = i > 0 ? requestAnimationFrame(step) : undefined; + } + + function handleVisibilityChange() { + if (!anime.suspendWhenDocumentHidden) { return; } + + if (isDocumentHidden()) { + // suspend ticks + raf = cancelAnimationFrame(raf); + } else { // is back to active tab + // first adjust animations to consider the time that ticks were suspended + activeInstances.forEach( + function (instance) { return instance ._onDocumentVisibility(); } + ); + engine(); + } + } + if (typeof document !== 'undefined') { + document.addEventListener('visibilitychange', handleVisibilityChange); + } + + return play; +})(); + +function isDocumentHidden() { + return !!document && document.hidden; +} + +// Public Instance + +function anime(params) { + if ( params === void 0 ) params = {}; + + + var startTime = 0, lastTime = 0, now = 0; + var children, childrenLength = 0; + var resolve = null; + + function makePromise(instance) { + var promise = window.Promise && new Promise(function (_resolve) { return resolve = _resolve; }); + instance.finished = promise; + return promise; + } + + var instance = createNewInstance(params); + var promise = makePromise(instance); + + function toggleInstanceDirection() { + var direction = instance.direction; + if (direction !== 'alternate') { + instance.direction = direction !== 'normal' ? 'normal' : 'reverse'; + } + instance.reversed = !instance.reversed; + children.forEach(function (child) { return child.reversed = instance.reversed; }); + } + + function adjustTime(time) { + return instance.reversed ? instance.duration - time : time; + } + + function resetTime() { + startTime = 0; + lastTime = adjustTime(instance.currentTime) * (1 / anime.speed); + } + + function seekChild(time, child) { + if (child) { child.seek(time - child.timelineOffset); } + } + + function syncInstanceChildren(time) { + if (!instance.reversePlayback) { + for (var i = 0; i < childrenLength; i++) { seekChild(time, children[i]); } + } else { + for (var i$1 = childrenLength; i$1--;) { seekChild(time, children[i$1]); } + } + } + + function setAnimationsProgress(insTime) { + var i = 0; + var animations = instance.animations; + var animationsLength = animations.length; + while (i < animationsLength) { + var anim = animations[i]; + var animatable = anim.animatable; + var tweens = anim.tweens; + var tweenLength = tweens.length - 1; + var tween = tweens[tweenLength]; + // Only check for keyframes if there is more than one tween + if (tweenLength) { tween = filterArray(tweens, function (t) { return (insTime < t.end); })[0] || tween; } + var elapsed = minMax(insTime - tween.start - tween.delay, 0, tween.duration) / tween.duration; + var eased = isNaN(elapsed) ? 1 : tween.easing(elapsed); + var strings = tween.to.strings; + var round = tween.round; + var numbers = []; + var toNumbersLength = tween.to.numbers.length; + var progress = (void 0); + for (var n = 0; n < toNumbersLength; n++) { + var value = (void 0); + var toNumber = tween.to.numbers[n]; + var fromNumber = tween.from.numbers[n] || 0; + if (!tween.isPath) { + value = fromNumber + (eased * (toNumber - fromNumber)); + } else { + value = getPathProgress(tween.value, eased * toNumber, tween.isPathTargetInsideSVG); + } + if (round) { + if (!(tween.isColor && n > 2)) { + value = Math.round(value * round) / round; + } + } + numbers.push(value); + } + // Manual Array.reduce for better performances + var stringsLength = strings.length; + if (!stringsLength) { + progress = numbers[0]; + } else { + progress = strings[0]; + for (var s = 0; s < stringsLength; s++) { + var a = strings[s]; + var b = strings[s + 1]; + var n$1 = numbers[s]; + if (!isNaN(n$1)) { + if (!b) { + progress += n$1 + ' '; + } else { + progress += n$1 + b; + } + } + } + } + setProgressValue[anim.type](animatable.target, anim.property, progress, animatable.transforms); + anim.currentValue = progress; + i++; + } + } + + function setCallback(cb) { + if (instance[cb] && !instance.passThrough) { instance[cb](instance); } + } + + function countIteration() { + if (instance.remaining && instance.remaining !== true) { + instance.remaining--; + } + } + + function setInstanceProgress(engineTime) { + var insDuration = instance.duration; + var insDelay = instance.delay; + var insEndDelay = insDuration - instance.endDelay; + var insTime = adjustTime(engineTime); + instance.progress = minMax((insTime / insDuration) * 100, 0, 100); + instance.reversePlayback = insTime < instance.currentTime; + if (children) { syncInstanceChildren(insTime); } + if (!instance.began && instance.currentTime > 0) { + instance.began = true; + setCallback('begin'); + } + if (!instance.loopBegan && instance.currentTime > 0) { + instance.loopBegan = true; + setCallback('loopBegin'); + } + if (insTime <= insDelay && instance.currentTime !== 0) { + setAnimationsProgress(0); + } + if ((insTime >= insEndDelay && instance.currentTime !== insDuration) || !insDuration) { + setAnimationsProgress(insDuration); + } + if (insTime > insDelay && insTime < insEndDelay) { + if (!instance.changeBegan) { + instance.changeBegan = true; + instance.changeCompleted = false; + setCallback('changeBegin'); + } + setCallback('change'); + setAnimationsProgress(insTime); + } else { + if (instance.changeBegan) { + instance.changeCompleted = true; + instance.changeBegan = false; + setCallback('changeComplete'); + } + } + instance.currentTime = minMax(insTime, 0, insDuration); + if (instance.began) { setCallback('update'); } + if (engineTime >= insDuration) { + lastTime = 0; + countIteration(); + if (!instance.remaining) { + instance.paused = true; + if (!instance.completed) { + instance.completed = true; + setCallback('loopComplete'); + setCallback('complete'); + if (!instance.passThrough && 'Promise' in window) { + resolve(); + promise = makePromise(instance); + } + } + } else { + startTime = now; + setCallback('loopComplete'); + instance.loopBegan = false; + if (instance.direction === 'alternate') { + toggleInstanceDirection(); + } + } + } + } + + instance.reset = function() { + var direction = instance.direction; + instance.passThrough = false; + instance.currentTime = 0; + instance.progress = 0; + instance.paused = true; + instance.began = false; + instance.loopBegan = false; + instance.changeBegan = false; + instance.completed = false; + instance.changeCompleted = false; + instance.reversePlayback = false; + instance.reversed = direction === 'reverse'; + instance.remaining = instance.loop; + children = instance.children; + childrenLength = children.length; + for (var i = childrenLength; i--;) { instance.children[i].reset(); } + if (instance.reversed && instance.loop !== true || (direction === 'alternate' && instance.loop === 1)) { instance.remaining++; } + setAnimationsProgress(instance.reversed ? instance.duration : 0); + }; + + // internal method (for engine) to adjust animation timings before restoring engine ticks (rAF) + instance._onDocumentVisibility = resetTime; + + // Set Value helper + + instance.set = function(targets, properties) { + setTargetsValue(targets, properties); + return instance; + }; + + instance.tick = function(t) { + now = t; + if (!startTime) { startTime = now; } + setInstanceProgress((now + (lastTime - startTime)) * anime.speed); + }; + + instance.seek = function(time) { + setInstanceProgress(adjustTime(time)); + }; + + instance.pause = function() { + instance.paused = true; + resetTime(); + }; + + instance.play = function() { + if (!instance.paused) { return; } + if (instance.completed) { instance.reset(); } + instance.paused = false; + activeInstances.push(instance); + resetTime(); + engine(); + }; + + instance.reverse = function() { + toggleInstanceDirection(); + instance.completed = instance.reversed ? false : true; + resetTime(); + }; + + instance.restart = function() { + instance.reset(); + instance.play(); + }; + + instance.remove = function(targets) { + var targetsArray = parseTargets(targets); + removeTargetsFromInstance(targetsArray, instance); + }; + + instance.reset(); + + if (instance.autoplay) { instance.play(); } + + return instance; + +} + +// Remove targets from animation + +function removeTargetsFromAnimations(targetsArray, animations) { + for (var a = animations.length; a--;) { + if (arrayContains(targetsArray, animations[a].animatable.target)) { + animations.splice(a, 1); + } + } +} + +function removeTargetsFromInstance(targetsArray, instance) { + var animations = instance.animations; + var children = instance.children; + removeTargetsFromAnimations(targetsArray, animations); + for (var c = children.length; c--;) { + var child = children[c]; + var childAnimations = child.animations; + removeTargetsFromAnimations(targetsArray, childAnimations); + if (!childAnimations.length && !child.children.length) { children.splice(c, 1); } + } + if (!animations.length && !children.length) { instance.pause(); } +} + +function removeTargetsFromActiveInstances(targets) { + var targetsArray = parseTargets(targets); + for (var i = activeInstances.length; i--;) { + var instance = activeInstances[i]; + removeTargetsFromInstance(targetsArray, instance); + } +} + +// Stagger helpers + +function stagger(val, params) { + if ( params === void 0 ) params = {}; + + var direction = params.direction || 'normal'; + var easing = params.easing ? parseEasings(params.easing) : null; + var grid = params.grid; + var axis = params.axis; + var fromIndex = params.from || 0; + var fromFirst = fromIndex === 'first'; + var fromCenter = fromIndex === 'center'; + var fromLast = fromIndex === 'last'; + var isRange = is.arr(val); + var val1 = isRange ? parseFloat(val[0]) : parseFloat(val); + var val2 = isRange ? parseFloat(val[1]) : 0; + var unit = getUnit(isRange ? val[1] : val) || 0; + var start = params.start || 0 + (isRange ? val1 : 0); + var values = []; + var maxValue = 0; + return function (el, i, t) { + if (fromFirst) { fromIndex = 0; } + if (fromCenter) { fromIndex = (t - 1) / 2; } + if (fromLast) { fromIndex = t - 1; } + if (!values.length) { + for (var index = 0; index < t; index++) { + if (!grid) { + values.push(Math.abs(fromIndex - index)); + } else { + var fromX = !fromCenter ? fromIndex%grid[0] : (grid[0]-1)/2; + var fromY = !fromCenter ? Math.floor(fromIndex/grid[0]) : (grid[1]-1)/2; + var toX = index%grid[0]; + var toY = Math.floor(index/grid[0]); + var distanceX = fromX - toX; + var distanceY = fromY - toY; + var value = Math.sqrt(distanceX * distanceX + distanceY * distanceY); + if (axis === 'x') { value = -distanceX; } + if (axis === 'y') { value = -distanceY; } + values.push(value); + } + maxValue = Math.max.apply(Math, values); + } + if (easing) { values = values.map(function (val) { return easing(val / maxValue) * maxValue; }); } + if (direction === 'reverse') { values = values.map(function (val) { return axis ? (val < 0) ? val * -1 : -val : Math.abs(maxValue - val); }); } + } + var spacing = isRange ? (val2 - val1) / maxValue : val1; + return start + (spacing * (Math.round(values[i] * 100) / 100)) + unit; + } +} + +// Timeline + +function timeline(params) { + if ( params === void 0 ) params = {}; + + var tl = anime(params); + tl.duration = 0; + tl.add = function(instanceParams, timelineOffset) { + var tlIndex = activeInstances.indexOf(tl); + var children = tl.children; + if (tlIndex > -1) { activeInstances.splice(tlIndex, 1); } + function passThrough(ins) { ins.passThrough = true; } + for (var i = 0; i < children.length; i++) { passThrough(children[i]); } + var insParams = mergeObjects(instanceParams, replaceObjectProps(defaultTweenSettings, params)); + insParams.targets = insParams.targets || params.targets; + var tlDuration = tl.duration; + insParams.autoplay = false; + insParams.direction = tl.direction; + insParams.timelineOffset = is.und(timelineOffset) ? tlDuration : getRelativeValue(timelineOffset, tlDuration); + passThrough(tl); + tl.seek(insParams.timelineOffset); + var ins = anime(insParams); + passThrough(ins); + children.push(ins); + var timings = getInstanceTimings(children, params); + tl.delay = timings.delay; + tl.endDelay = timings.endDelay; + tl.duration = timings.duration; + tl.seek(0); + tl.reset(); + if (tl.autoplay) { tl.play(); } + return tl; + }; + return tl; +} + +anime.version = '3.2.2'; +anime.speed = 1; +// TODO:#review: naming, documentation +anime.suspendWhenDocumentHidden = true; +anime.running = activeInstances; +anime.remove = removeTargetsFromActiveInstances; +anime.get = getOriginalTargetValue; +anime.set = setTargetsValue; +anime.convertPx = convertPxToUnit; +anime.path = getPath; +anime.setDashoffset = setDashoffset; +anime.stagger = stagger; +anime.timeline = timeline; +anime.easing = parseEasings; +anime.penner = penner; +anime.random = function (min, max) { return Math.floor(Math.random() * (max - min + 1)) + min; }; + +module.exports = anime; diff --git a/colour_hdri/network/resources/assets/litegraph.js/css/litegraph.css b/colour_hdri/network/resources/assets/litegraph.js/css/litegraph.css new file mode 100644 index 0000000..954d4ce --- /dev/null +++ b/colour_hdri/network/resources/assets/litegraph.js/css/litegraph.css @@ -0,0 +1,686 @@ +/* this CSS contains only the basic CSS needed to run the app and use it */ + +.lgraphcanvas { + /*cursor: crosshair;*/ + user-select: none; + -moz-user-select: none; + -webkit-user-select: none; + outline: none; + font-family: Tahoma, sans-serif; +} + +.lgraphcanvas * { + box-sizing: border-box; +} + +.litegraph.litecontextmenu { + font-family: Tahoma, sans-serif; + position: fixed; + top: 100px; + left: 100px; + min-width: 100px; + color: #aaf; + padding: 0; + box-shadow: 0 0 10px black !important; + background-color: #2e2e2e !important; + z-index: 10; +} + +.litegraph.litecontextmenu.dark { + background-color: #000 !important; +} + +.litegraph.litecontextmenu .litemenu-title img { + margin-top: 2px; + margin-left: 2px; + margin-right: 4px; +} + +.litegraph.litecontextmenu .litemenu-entry { + margin: 2px; + padding: 2px; +} + +.litegraph.litecontextmenu .litemenu-entry.submenu { + background-color: #2e2e2e !important; +} + +.litegraph.litecontextmenu.dark .litemenu-entry.submenu { + background-color: #000 !important; +} + +.litegraph .litemenubar ul { + font-family: Tahoma, sans-serif; + margin: 0; + padding: 0; +} + +.litegraph .litemenubar li { + font-size: 14px; + color: #999; + display: inline-block; + min-width: 50px; + padding-left: 10px; + padding-right: 10px; + user-select: none; + -moz-user-select: none; + -webkit-user-select: none; + cursor: pointer; +} + +.litegraph .litemenubar li:hover { + background-color: #777; + color: #eee; +} + +.litegraph .litegraph .litemenubar-panel { + position: absolute; + top: 5px; + left: 5px; + min-width: 100px; + background-color: #444; + box-shadow: 0 0 3px black; + padding: 4px; + border-bottom: 2px solid #aaf; + z-index: 10; +} + +.litegraph .litemenu-entry, +.litemenu-title { + font-size: 12px; + color: #aaa; + padding: 0 0 0 4px; + margin: 2px; + padding-left: 2px; + -moz-user-select: none; + -webkit-user-select: none; + user-select: none; + cursor: pointer; +} + +.litegraph .litemenu-entry .icon { + display: inline-block; + width: 12px; + height: 12px; + margin: 2px; + vertical-align: top; +} + +.litegraph .litemenu-entry.checked .icon { + background-color: #aaf; +} + +.litegraph .litemenu-entry .more { + float: right; + padding-right: 5px; +} + +.litegraph .litemenu-entry.disabled { + opacity: 0.5; + cursor: default; +} + +.litegraph .litemenu-entry.separator { + display: block; + border-top: 1px solid #333; + border-bottom: 1px solid #666; + width: 100%; + height: 0px; + margin: 3px 0 2px 0; + background-color: transparent; + padding: 0 !important; + cursor: default !important; +} + +.litegraph .litemenu-entry.has_submenu { + border-right: 2px solid cyan; +} + +.litegraph .litemenu-title { + color: #dde; + background-color: #111; + margin: 0; + padding: 2px; + cursor: default; +} + +.litegraph .litemenu-entry:hover:not(.disabled):not(.separator) { + background-color: #444 !important; + color: #eee; + transition: all 0.2s; +} + +.litegraph .litemenu-entry .property_name { + display: inline-block; + text-align: left; + min-width: 80px; + min-height: 1.2em; +} + +.litegraph .litemenu-entry .property_value { + display: inline-block; + background-color: rgba(0, 0, 0, 0.5); + text-align: right; + min-width: 80px; + min-height: 1.2em; + vertical-align: middle; + padding-right: 10px; +} + +.litegraph.litesearchbox { + font-family: Tahoma, sans-serif; + position: absolute; + background-color: rgba(0, 0, 0, 0.5); + padding-top: 4px; +} + +.litegraph.litesearchbox input, +.litegraph.litesearchbox select { + margin-top: 3px; + min-width: 60px; + min-height: 1.5em; + background-color: black; + border: 0; + color: white; + padding-left: 10px; + margin-right: 5px; +} + +.litegraph.litesearchbox .name { + display: inline-block; + min-width: 60px; + min-height: 1.5em; + padding-left: 10px; +} + +.litegraph.litesearchbox .helper { + overflow: auto; + max-height: 200px; + margin-top: 2px; +} + +.litegraph.lite-search-item { + font-family: Tahoma, sans-serif; + background-color: rgba(0, 0, 0, 0.5); + color: white; + padding-top: 2px; +} + +.litegraph.lite-search-item.not_in_filter { + /*background-color: rgba(50, 50, 50, 0.5);*/ + /*color: #999;*/ + color: #b99; + font-style: italic; +} + +.litegraph.lite-search-item.generic_type { + /*background-color: rgba(50, 50, 50, 0.5);*/ + /*color: #DD9;*/ + color: #999; + font-style: italic; +} + +.litegraph.lite-search-item:hover, +.litegraph.lite-search-item.selected { + cursor: pointer; + background-color: white; + color: black; +} + +/* DIALOGs ******/ + +.litegraph .dialog { + position: absolute; + top: 50%; + left: 50%; + margin-top: -150px; + margin-left: -200px; + + background-color: #2a2a2a; + + min-width: 400px; + min-height: 200px; + box-shadow: 0 0 4px #111; + border-radius: 6px; +} + +.litegraph .dialog.settings { + left: 10px; + top: 10px; + height: calc(100% - 20px); + margin: auto; + max-width: 50%; +} + +.litegraph .dialog.centered { + top: 50px; + left: 50%; + position: absolute; + transform: translateX(-50%); + min-width: 600px; + min-height: 300px; + height: calc(100% - 100px); + margin: auto; +} + +.litegraph .dialog .close { + float: right; + margin: 4px; + margin-right: 10px; + cursor: pointer; + font-size: 1.4em; +} + +.litegraph .dialog .close:hover { + color: white; +} + +.litegraph .dialog .dialog-header { + color: #aaa; + border-bottom: 1px solid #161616; +} + +.litegraph .dialog .dialog-header { + height: 40px; +} +.litegraph .dialog .dialog-footer { + height: 50px; + padding: 10px; + border-top: 1px solid #1a1a1a; +} + +.litegraph .dialog .dialog-header .dialog-title { + font: 20px "Arial"; + margin: 4px; + padding: 4px 10px; + display: inline-block; +} + +.litegraph .dialog .dialog-content, +.litegraph .dialog .dialog-alt-content { + height: calc(100% - 90px); + width: 100%; + min-height: 100px; + display: inline-block; + color: #aaa; + /*background-color: black;*/ + overflow: auto; +} + +.litegraph .dialog .dialog-content h3 { + margin: 10px; +} + +.litegraph .dialog .dialog-content .connections { + flex-direction: row; +} + +.litegraph .dialog .dialog-content .connections .connections_side { + width: calc(50% - 5px); + min-height: 100px; + background-color: black; + display: flex; +} + +.litegraph .dialog .node_type { + font-size: 1.2em; + display: block; + margin: 10px; +} + +.litegraph .dialog .node_desc { + opacity: 0.5; + display: block; + margin: 10px; +} + +.litegraph .dialog .separator { + display: block; + width: calc(100% - 4px); + height: 1px; + border-top: 1px solid #000; + border-bottom: 1px solid #333; + margin: 10px 2px; + padding: 0; +} + +.litegraph .dialog .property { + margin-bottom: 2px; + padding: 4px; +} + +.litegraph .dialog .property:hover { + background: #545454; +} + +.litegraph .dialog .property_name { + color: #737373; + display: inline-block; + text-align: left; + vertical-align: top; + width: 160px; + padding-left: 4px; + overflow: hidden; + margin-right: 6px; +} + +.litegraph .dialog .property:hover .property_name { + color: white; +} + +.litegraph .dialog .property_value { + display: inline-block; + text-align: right; + color: #aaa; + background-color: #1a1a1a; + /*width: calc( 100% - 122px );*/ + max-width: calc(100% - 162px); + min-width: 200px; + max-height: 300px; + min-height: 20px; + padding: 4px; + padding-right: 12px; + overflow: hidden; + cursor: pointer; + border-radius: 3px; +} + +.litegraph .dialog .property_value:hover { + color: white; +} + +.litegraph .dialog .property.boolean .property_value { + padding-right: 30px; + color: #a88; + /*width: auto; + float: right;*/ +} + +.litegraph .dialog .property.boolean.bool-on .property_name { + color: #8a8; +} +.litegraph .dialog .property.boolean.bool-on .property_value { + color: #8a8; +} + +.litegraph .dialog .btn { + border: 0; + border-radius: 4px; + padding: 4px 20px; + margin-left: 0px; + background-color: #060606; + color: #8e8e8e; +} + +.litegraph .dialog .btn:hover { + background-color: #111; + color: #fff; +} + +.litegraph .dialog .btn.delete:hover { + background-color: #f33; + color: black; +} + +.litegraph .subgraph_property { + padding: 4px; +} + +.litegraph .subgraph_property:hover { + background-color: #333; +} + +.litegraph .subgraph_property.extra { + margin-top: 8px; +} + +.litegraph .subgraph_property span.name { + font-size: 1.3em; + padding-left: 4px; +} + +.litegraph .subgraph_property span.type { + opacity: 0.5; + margin-right: 20px; + padding-left: 4px; +} + +.litegraph .subgraph_property span.label { + display: inline-block; + width: 60px; + padding: 0px 10px; +} + +.litegraph .subgraph_property input { + width: 140px; + color: #999; + background-color: #1a1a1a; + border-radius: 4px; + border: 0; + margin-right: 10px; + padding: 4px; + padding-left: 10px; +} + +.litegraph .subgraph_property button { + background-color: #1c1c1c; + color: #aaa; + border: 0; + border-radius: 2px; + padding: 4px 10px; + cursor: pointer; +} + +.litegraph .subgraph_property.extra { + color: #ccc; +} + +.litegraph .subgraph_property.extra input { + background-color: #111; +} + +.litegraph .bullet_icon { + margin-left: 10px; + border-radius: 10px; + width: 12px; + height: 12px; + background-color: #666; + display: inline-block; + margin-top: 2px; + margin-right: 4px; + transition: background-color 0.1s ease 0s; + -moz-transition: background-color 0.1s ease 0s; +} + +.litegraph .bullet_icon:hover { + background-color: #698; + cursor: pointer; +} + +/* OLD */ + +.graphcontextmenu { + padding: 4px; + min-width: 100px; +} + +.graphcontextmenu-title { + color: #dde; + background-color: #222; + margin: 0; + padding: 2px; + cursor: default; +} + +.graphmenu-entry { + box-sizing: border-box; + margin: 2px; + padding-left: 20px; + user-select: none; + -moz-user-select: none; + -webkit-user-select: none; + transition: all linear 0.3s; +} + +.graphmenu-entry.event, +.litemenu-entry.event { + border-left: 8px solid orange; + padding-left: 12px; +} + +.graphmenu-entry.disabled { + opacity: 0.3; +} + +.graphmenu-entry.submenu { + border-right: 2px solid #eee; +} + +.graphmenu-entry:hover { + background-color: #555; +} + +.graphmenu-entry.separator { + background-color: #111; + border-bottom: 1px solid #666; + height: 1px; + width: calc(100% - 20px); + -moz-width: calc(100% - 20px); + -webkit-width: calc(100% - 20px); +} + +.graphmenu-entry .property_name { + display: inline-block; + text-align: left; + min-width: 80px; + min-height: 1.2em; +} + +.graphmenu-entry .property_value, +.litemenu-entry .property_value { + display: inline-block; + background-color: rgba(0, 0, 0, 0.5); + text-align: right; + min-width: 80px; + min-height: 1.2em; + vertical-align: middle; + padding-right: 10px; +} + +.graphdialog { + position: absolute; + top: 10px; + left: 10px; + min-height: 2em; + background-color: #333; + font-size: 1.2em; + box-shadow: 0 0 10px black !important; + z-index: 10; +} + +.graphdialog.rounded { + border-radius: 12px; + padding-right: 2px; +} + +.graphdialog .name { + display: inline-block; + min-width: 60px; + min-height: 1.5em; + padding-left: 10px; +} + +.graphdialog input, +.graphdialog textarea, +.graphdialog select { + margin: 3px; + min-width: 60px; + min-height: 1.5em; + background-color: black; + border: 0; + color: white; + padding-left: 10px; + outline: none; +} + +.graphdialog textarea { + min-height: 150px; +} + +.graphdialog button { + margin-top: 3px; + vertical-align: top; + background-color: #999; + border: 0; +} + +.graphdialog button.rounded, +.graphdialog input.rounded { + border-radius: 0 12px 12px 0; +} + +.graphdialog .helper { + overflow: auto; + max-height: 200px; +} + +.graphdialog .help-item { + padding-left: 10px; +} + +.graphdialog .help-item:hover, +.graphdialog .help-item.selected { + cursor: pointer; + background-color: white; + color: black; +} + +.litegraph .dialog { + min-height: 0; +} +.litegraph .dialog .dialog-content { + display: block; +} +.litegraph .dialog .dialog-content .subgraph_property { + padding: 5px; +} +.litegraph .dialog .dialog-footer { + margin: 0; +} +.litegraph .dialog .dialog-footer .subgraph_property { + margin-top: 0; + display: flex; + align-items: center; + padding: 5px; +} +.litegraph .dialog .dialog-footer .subgraph_property .name { + flex: 1; +} +.litegraph .graphdialog { + display: flex; + align-items: center; + border-radius: 20px; + padding: 4px 10px; + position: fixed; +} +.litegraph .graphdialog .name { + padding: 0; + min-height: 0; + font-size: 16px; + vertical-align: middle; +} +.litegraph .graphdialog .value { + font-size: 16px; + min-height: 0; + margin: 0 10px; + padding: 2px 5px; +} +.litegraph .graphdialog input[type="checkbox"] { + width: 16px; + height: 16px; +} +.litegraph .graphdialog button { + padding: 4px 18px; + border-radius: 20px; + cursor: pointer; +} diff --git a/colour_hdri/network/resources/assets/litegraph.js/js/litegraph.js b/colour_hdri/network/resources/assets/litegraph.js/js/litegraph.js new file mode 100644 index 0000000..4016a73 --- /dev/null +++ b/colour_hdri/network/resources/assets/litegraph.js/js/litegraph.js @@ -0,0 +1,35285 @@ +//packer version + +(function (global) { + // ************************************************************* + // LiteGraph CLASS ******* + // ************************************************************* + + /** + * The Global Scope. It contains all the registered node classes. + * + * @class LiteGraph + * @constructor + */ + + var LiteGraph = (global.LiteGraph = { + VERSION: 0.4, + + CANVAS_GRID_SIZE: 10, + + NODE_TITLE_HEIGHT: 30, + NODE_TITLE_TEXT_Y: 20, + NODE_SLOT_HEIGHT: 20, + NODE_WIDGET_HEIGHT: 20, + NODE_WIDTH: 140, + NODE_MIN_WIDTH: 50, + NODE_COLLAPSED_RADIUS: 10, + NODE_COLLAPSED_WIDTH: 80, + NODE_TITLE_COLOR: "#999", + NODE_SELECTED_TITLE_COLOR: "#FFF", + NODE_TEXT_SIZE: 14, + NODE_TEXT_COLOR: "#AAA", + NODE_SUBTEXT_SIZE: 12, + NODE_DEFAULT_COLOR: "#333", + NODE_DEFAULT_BGCOLOR: "#353535", + NODE_DEFAULT_BOXCOLOR: "#666", + NODE_DEFAULT_SHAPE: "box", + NODE_BOX_OUTLINE_COLOR: "#FFF", + DEFAULT_SHADOW_COLOR: "rgba(0,0,0,0.5)", + DEFAULT_GROUP_FONT: 24, + + WIDGET_BGCOLOR: "#222", + WIDGET_OUTLINE_COLOR: "#666", + WIDGET_TEXT_COLOR: "#DDD", + WIDGET_SECONDARY_TEXT_COLOR: "#999", + + LINK_COLOR: "#9A9", + EVENT_LINK_COLOR: "#A86", + CONNECTING_LINK_COLOR: "#AFA", + + MAX_NUMBER_OF_NODES: 1000, //avoid infinite loops + DEFAULT_POSITION: [100, 100], //default node position + VALID_SHAPES: ["default", "box", "round", "card"], //,"circle" + + //shapes are used for nodes but also for slots + BOX_SHAPE: 1, + ROUND_SHAPE: 2, + CIRCLE_SHAPE: 3, + CARD_SHAPE: 4, + ARROW_SHAPE: 5, + GRID_SHAPE: 6, // intended for slot arrays + + //enums + INPUT: 1, + OUTPUT: 2, + + EVENT: -1, //for outputs + ACTION: -1, //for inputs + + NODE_MODES: ["Always", "On Event", "Never", "On Trigger"], // helper, will add "On Request" and more in the future + NODE_MODES_COLORS: ["#666", "#422", "#333", "#224", "#626"], // use with node_box_coloured_by_mode + ALWAYS: 0, + ON_EVENT: 1, + NEVER: 2, + ON_TRIGGER: 3, + + UP: 1, + DOWN: 2, + LEFT: 3, + RIGHT: 4, + CENTER: 5, + + LINK_RENDER_MODES: ["Straight", "Linear", "Spline"], // helper + STRAIGHT_LINK: 0, + LINEAR_LINK: 1, + SPLINE_LINK: 2, + + NORMAL_TITLE: 0, + NO_TITLE: 1, + TRANSPARENT_TITLE: 2, + AUTOHIDE_TITLE: 3, + VERTICAL_LAYOUT: "vertical", // arrange nodes vertically + + proxy: null, //used to redirect calls + node_images_path: "", + + debug: false, + catch_exceptions: true, + throw_errors: true, + allow_scripts: false, //if set to true some nodes like Formula would be allowed to evaluate code that comes from unsafe sources (like node configuration), which could lead to exploits + use_deferred_actions: true, //executes actions during the graph execution flow + registered_node_types: {}, //nodetypes by string + node_types_by_file_extension: {}, //used for dropping files in the canvas + Nodes: {}, //node types by classname + Globals: {}, //used to store vars between graphs + + searchbox_extras: {}, //used to add extra features to the search box + auto_sort_node_types: false, // [true!] If set to true, will automatically sort node types / categories in the context menus + + node_box_coloured_when_on: false, // [true!] this make the nodes box (top left circle) coloured when triggered (execute/action), visual feedback + node_box_coloured_by_mode: false, // [true!] nodebox based on node mode, visual feedback + + dialog_close_on_mouse_leave: true, // [false on mobile] better true if not touch device, TODO add an helper/listener to close if false + dialog_close_on_mouse_leave_delay: 500, + + shift_click_do_break_link_from: false, // [false!] prefer false if results too easy to break links - implement with ALT or TODO custom keys + click_do_break_link_to: false, // [false!]prefer false, way too easy to break links + + search_hide_on_mouse_leave: true, // [false on mobile] better true if not touch device, TODO add an helper/listener to close if false + search_filter_enabled: false, // [true!] enable filtering slots type in the search widget, !requires auto_load_slot_types or manual set registered_slot_[in/out]_types and slot_types_[in/out] + search_show_all_on_open: true, // [true!] opens the results list when opening the search widget + + auto_load_slot_types: false, // [if want false, use true, run, get vars values to be statically set, than disable] nodes types and nodeclass association with node types need to be calculated, if dont want this, calculate once and set registered_slot_[in/out]_types and slot_types_[in/out] + + // set these values if not using auto_load_slot_types + registered_slot_in_types: {}, // slot types for nodeclass + registered_slot_out_types: {}, // slot types for nodeclass + slot_types_in: [], // slot types IN + slot_types_out: [], // slot types OUT + slot_types_default_in: [], // specify for each IN slot type a(/many) default node(s), use single string, array, or object (with node, title, parameters, ..) like for search + slot_types_default_out: [], // specify for each OUT slot type a(/many) default node(s), use single string, array, or object (with node, title, parameters, ..) like for search + + alt_drag_do_clone_nodes: false, // [true!] very handy, ALT click to clone and drag the new node + + do_add_triggers_slots: false, // [true!] will create and connect event slots when using action/events connections, !WILL CHANGE node mode when using onTrigger (enable mode colors), onExecuted does not need this + + allow_multi_output_for_events: true, // [false!] being events, it is strongly reccomended to use them sequentially, one by one + + middle_click_slot_add_default_node: false, //[true!] allows to create and connect a ndoe clicking with the third button (wheel) + + release_link_on_empty_shows_menu: false, //[true!] dragging a link to empty space will open a menu, add from list, search or defaults + + pointerevents_method: "mouse", // "mouse"|"pointer" use mouse for retrocompatibility issues? (none found @ now) + // TODO implement pointercancel, gotpointercapture, lostpointercapture, (pointerover, pointerout if necessary) + + ctrl_shift_v_paste_connect_unselected_outputs: false, //[true!] allows ctrl + shift + v to paste nodes with the outputs of the unselected nodes connected with the inputs of the newly pasted nodes + + // if true, all newly created nodes/links will use string UUIDs for their id fields instead of integers. + // use this if you must have node IDs that are unique across all graphs and subgraphs. + use_uuids: false, + + /** + * Register a node class so it can be listed when the user wants to create a new one + * @method registerNodeType + * @param {String} type name of the node and path + * @param {Class} base_class class containing the structure of a node + */ + + registerNodeType: function (type, base_class) { + if (!base_class.prototype) { + throw "Cannot register a simple object, it must be a class with a prototype"; + } + base_class.type = type; + + if (LiteGraph.debug) { + console.log("Node registered: " + type); + } + + const classname = base_class.name; + + const pos = type.lastIndexOf("/"); + base_class.category = type.substring(0, pos); + + if (!base_class.title) { + base_class.title = classname; + } + + //extend class + for (var i in LGraphNode.prototype) { + if (!base_class.prototype[i]) { + base_class.prototype[i] = LGraphNode.prototype[i]; + } + } + + const prev = this.registered_node_types[type]; + if (prev) { + console.log("replacing node type: " + type); + } + if ( + !Object.prototype.hasOwnProperty.call(base_class.prototype, "shape") + ) { + Object.defineProperty(base_class.prototype, "shape", { + set: function (v) { + switch (v) { + case "default": + delete this._shape; + break; + case "box": + this._shape = LiteGraph.BOX_SHAPE; + break; + case "round": + this._shape = LiteGraph.ROUND_SHAPE; + break; + case "circle": + this._shape = LiteGraph.CIRCLE_SHAPE; + break; + case "card": + this._shape = LiteGraph.CARD_SHAPE; + break; + default: + this._shape = v; + } + }, + get: function () { + return this._shape; + }, + enumerable: true, + configurable: true, + }); + + //used to know which nodes to create when dragging files to the canvas + if (base_class.supported_extensions) { + for (let i in base_class.supported_extensions) { + const ext = base_class.supported_extensions[i]; + if (ext && ext.constructor === String) { + this.node_types_by_file_extension[ext.toLowerCase()] = base_class; + } + } + } + } + + this.registered_node_types[type] = base_class; + if (base_class.constructor.name) { + this.Nodes[classname] = base_class; + } + if (LiteGraph.onNodeTypeRegistered) { + LiteGraph.onNodeTypeRegistered(type, base_class); + } + if (prev && LiteGraph.onNodeTypeReplaced) { + LiteGraph.onNodeTypeReplaced(type, base_class, prev); + } + + //warnings + if (base_class.prototype.onPropertyChange) { + console.warn( + "LiteGraph node class " + + type + + " has onPropertyChange method, it must be called onPropertyChanged with d at the end", + ); + } + + // TODO one would want to know input and ouput :: this would allow through registerNodeAndSlotType to get all the slots types + if (this.auto_load_slot_types) { + new base_class(base_class.title || "tmpnode"); + } + }, + + /** + * removes a node type from the system + * @method unregisterNodeType + * @param {String|Object} type name of the node or the node constructor itself + */ + unregisterNodeType: function (type) { + const base_class = + type.constructor === String ? this.registered_node_types[type] : type; + if (!base_class) { + throw "node type not found: " + type; + } + delete this.registered_node_types[base_class.type]; + if (base_class.constructor.name) { + delete this.Nodes[base_class.constructor.name]; + } + }, + + /** + * Save a slot type and his node + * @method registerSlotType + * @param {String|Object} type name of the node or the node constructor itself + * @param {String} slot_type name of the slot type (variable type), eg. string, number, array, boolean, .. + */ + registerNodeAndSlotType: function (type, slot_type, out) { + out = out || false; + const base_class = + type.constructor === String && + this.registered_node_types[type] !== "anonymous" + ? this.registered_node_types[type] + : type; + + const class_type = base_class.constructor.type; + + let allTypes = []; + if (typeof slot_type === "string") { + allTypes = slot_type.split(","); + } else if (slot_type == this.EVENT || slot_type == this.ACTION) { + allTypes = ["_event_"]; + } else { + allTypes = ["*"]; + } + + for (let i = 0; i < allTypes.length; ++i) { + let slotType = allTypes[i]; + if (slotType === "") { + slotType = "*"; + } + const registerTo = out + ? "registered_slot_out_types" + : "registered_slot_in_types"; + if (this[registerTo][slotType] === undefined) { + this[registerTo][slotType] = { nodes: [] }; + } + if (!this[registerTo][slotType].nodes.includes(class_type)) { + this[registerTo][slotType].nodes.push(class_type); + } + + // check if is a new type + if (!out) { + if (!this.slot_types_in.includes(slotType.toLowerCase())) { + this.slot_types_in.push(slotType.toLowerCase()); + this.slot_types_in.sort(); + } + } else { + if (!this.slot_types_out.includes(slotType.toLowerCase())) { + this.slot_types_out.push(slotType.toLowerCase()); + this.slot_types_out.sort(); + } + } + } + }, + + /** + * Create a new nodetype by passing an object with some properties + * like onCreate, inputs:Array, outputs:Array, properties, onExecute + * @method buildNodeClassFromObject + * @param {String} name node name with namespace (p.e.: 'math/sum') + * @param {Object} object methods expected onCreate, inputs, outputs, properties, onExecute + */ + buildNodeClassFromObject: function (name, object) { + var ctor_code = ""; + if (object.inputs) + for (var i = 0; i < object.inputs.length; ++i) { + var _name = object.inputs[i][0]; + var _type = object.inputs[i][1]; + if (_type && _type.constructor === String) _type = '"' + _type + '"'; + ctor_code += "this.addInput('" + _name + "'," + _type + ");\n"; + } + if (object.outputs) + for (var i = 0; i < object.outputs.length; ++i) { + var _name = object.outputs[i][0]; + var _type = object.outputs[i][1]; + if (_type && _type.constructor === String) _type = '"' + _type + '"'; + ctor_code += "this.addOutput('" + _name + "'," + _type + ");\n"; + } + if (object.properties) + for (var i in object.properties) { + var prop = object.properties[i]; + if (prop && prop.constructor === String) prop = '"' + prop + '"'; + ctor_code += "this.addProperty('" + i + "'," + prop + ");\n"; + } + ctor_code += "if(this.onCreate)this.onCreate()"; + var classobj = Function(ctor_code); + for (var i in object) + if (i != "inputs" && i != "outputs" && i != "properties") + classobj.prototype[i] = object[i]; + classobj.title = object.title || name.split("/").pop(); + classobj.desc = object.desc || "Generated from object"; + this.registerNodeType(name, classobj); + return classobj; + }, + + /** + * Create a new nodetype by passing a function, it wraps it with a proper class and generates inputs according to the parameters of the function. + * Useful to wrap simple methods that do not require properties, and that only process some input to generate an output. + * @method wrapFunctionAsNode + * @param {String} name node name with namespace (p.e.: 'math/sum') + * @param {Function} func + * @param {Array} param_types [optional] an array containing the type of every parameter, otherwise parameters will accept any type + * @param {String} return_type [optional] string with the return type, otherwise it will be generic + * @param {Object} properties [optional] properties to be configurable + */ + wrapFunctionAsNode: function ( + name, + func, + param_types, + return_type, + properties, + ) { + var params = Array(func.length); + var code = ""; + if (param_types !== null) { + //null means no inputs + var names = LiteGraph.getParameterNames(func); + for (var i = 0; i < names.length; ++i) { + var type = 0; + if (param_types) { + //type = param_types[i] != null ? "'" + param_types[i] + "'" : "0"; + if (param_types[i] != null && param_types[i].constructor === String) + type = "'" + param_types[i] + "'"; + else if (param_types[i] != null) type = param_types[i]; + } + code += "this.addInput('" + names[i] + "'," + type + ");\n"; + } + } + if (return_type !== null) + //null means no output + code += + "this.addOutput('out'," + + (return_type != null + ? return_type.constructor === String + ? "'" + return_type + "'" + : return_type + : 0) + + ");\n"; + if (properties) { + code += "this.properties = " + JSON.stringify(properties) + ";\n"; + } + var classobj = Function(code); + classobj.title = name.split("/").pop(); + classobj.desc = "Generated from " + func.name; + classobj.prototype.onExecute = function onExecute() { + for (var i = 0; i < params.length; ++i) { + params[i] = this.getInputData(i); + } + var r = func.apply(this, params); + this.setOutputData(0, r); + }; + this.registerNodeType(name, classobj); + return classobj; + }, + + /** + * Removes all previously registered node's types + */ + clearRegisteredTypes: function () { + this.registered_node_types = {}; + this.node_types_by_file_extension = {}; + this.Nodes = {}; + this.searchbox_extras = {}; + }, + + /** + * Adds this method to all nodetypes, existing and to be created + * (You can add it to LGraphNode.prototype but then existing node types wont have it) + * @method addNodeMethod + * @param {Function} func + */ + addNodeMethod: function (name, func) { + LGraphNode.prototype[name] = func; + for (var i in this.registered_node_types) { + var type = this.registered_node_types[i]; + if (type.prototype[name]) { + type.prototype["_" + name] = type.prototype[name]; + } //keep old in case of replacing + type.prototype[name] = func; + } + }, + + /** + * Create a node of a given type with a name. The node is not attached to any graph yet. + * @method createNode + * @param {String} type full name of the node class. p.e. "math/sin" + * @param {String} name a name to distinguish from other nodes + * @param {Object} options to set options + */ + + createNode: function (type, title, options) { + var base_class = this.registered_node_types[type]; + if (!base_class) { + if (LiteGraph.debug) { + console.log('GraphNode type "' + type + '" not registered.'); + } + return null; + } + + var prototype = base_class.prototype || base_class; + + title = title || base_class.title || type; + + var node = null; + + if (LiteGraph.catch_exceptions) { + try { + node = new base_class(title); + } catch (err) { + console.error(err); + return null; + } + } else { + node = new base_class(title); + } + + node.type = type; + + if (!node.title && title) { + node.title = title; + } + if (!node.properties) { + node.properties = {}; + } + if (!node.properties_info) { + node.properties_info = []; + } + if (!node.flags) { + node.flags = {}; + } + if (!node.size) { + node.size = node.computeSize(); + //call onresize? + } + if (!node.pos) { + node.pos = LiteGraph.DEFAULT_POSITION.concat(); + } + if (!node.mode) { + node.mode = LiteGraph.ALWAYS; + } + + //extra options + if (options) { + for (var i in options) { + node[i] = options[i]; + } + } + + // callback + if (node.onNodeCreated) { + node.onNodeCreated(); + } + + return node; + }, + + /** + * Returns a registered node type with a given name + * @method getNodeType + * @param {String} type full name of the node class. p.e. "math/sin" + * @return {Class} the node class + */ + getNodeType: function (type) { + return this.registered_node_types[type]; + }, + + /** + * Returns a list of node types matching one category + * @method getNodeType + * @param {String} category category name + * @return {Array} array with all the node classes + */ + + getNodeTypesInCategory: function (category, filter) { + var r = []; + for (var i in this.registered_node_types) { + var type = this.registered_node_types[i]; + if (type.filter != filter) { + continue; + } + + if (category == "") { + if (type.category == null) { + r.push(type); + } + } else if (type.category == category) { + r.push(type); + } + } + + if (this.auto_sort_node_types) { + r.sort(function (a, b) { + return a.title.localeCompare(b.title); + }); + } + + return r; + }, + + /** + * Returns a list with all the node type categories + * @method getNodeTypesCategories + * @param {String} filter only nodes with ctor.filter equal can be shown + * @return {Array} array with all the names of the categories + */ + getNodeTypesCategories: function (filter) { + var categories = { "": 1 }; + for (var i in this.registered_node_types) { + var type = this.registered_node_types[i]; + if (type.category && !type.skip_list) { + if (type.filter != filter) continue; + categories[type.category] = 1; + } + } + var result = []; + for (var i in categories) { + result.push(i); + } + return this.auto_sort_node_types ? result.sort() : result; + }, + + //debug purposes: reloads all the js scripts that matches a wildcard + reloadNodes: function (folder_wildcard) { + var tmp = document.getElementsByTagName("script"); + //weird, this array changes by its own, so we use a copy + var script_files = []; + for (var i = 0; i < tmp.length; i++) { + script_files.push(tmp[i]); + } + + var docHeadObj = document.getElementsByTagName("head")[0]; + folder_wildcard = document.location.href + folder_wildcard; + + for (var i = 0; i < script_files.length; i++) { + var src = script_files[i].src; + if (!src || src.substr(0, folder_wildcard.length) != folder_wildcard) { + continue; + } + + try { + if (LiteGraph.debug) { + console.log("Reloading: " + src); + } + var dynamicScript = document.createElement("script"); + dynamicScript.type = "text/javascript"; + dynamicScript.src = src; + docHeadObj.appendChild(dynamicScript); + docHeadObj.removeChild(script_files[i]); + } catch (err) { + if (LiteGraph.throw_errors) { + throw err; + } + if (LiteGraph.debug) { + console.log("Error while reloading " + src); + } + } + } + + if (LiteGraph.debug) { + console.log("Nodes reloaded"); + } + }, + + //separated just to improve if it doesn't work + cloneObject: function (obj, target) { + if (obj == null) { + return null; + } + var r = JSON.parse(JSON.stringify(obj)); + if (!target) { + return r; + } + + for (var i in r) { + target[i] = r[i]; + } + return target; + }, + + /* + * https://gist.github.com/jed/982883?permalink_comment_id=852670#gistcomment-852670 + */ + uuidv4: function () { + return ([1e7] + -1e3 + -4e3 + -8e3 + -1e11).replace(/[018]/g, (a) => + (a ^ ((Math.random() * 16) >> (a / 4))).toString(16), + ); + }, + + /** + * Returns if the types of two slots are compatible (taking into account wildcards, etc) + * @method isValidConnection + * @param {String} type_a + * @param {String} type_b + * @return {Boolean} true if they can be connected + */ + isValidConnection: function (type_a, type_b) { + if (type_a == "" || type_a === "*") type_a = 0; + if (type_b == "" || type_b === "*") type_b = 0; + if ( + !type_a || //generic output + !type_b || // generic input + type_a == type_b || //same type (is valid for triggers) + (type_a == LiteGraph.EVENT && type_b == LiteGraph.ACTION) + ) { + return true; + } + + // Enforce string type to handle toLowerCase call (-1 number not ok) + type_a = String(type_a); + type_b = String(type_b); + type_a = type_a.toLowerCase(); + type_b = type_b.toLowerCase(); + + // For nodes supporting multiple connection types + if (type_a.indexOf(",") == -1 && type_b.indexOf(",") == -1) { + return type_a == type_b; + } + + // Check all permutations to see if one is valid + var supported_types_a = type_a.split(","); + var supported_types_b = type_b.split(","); + for (var i = 0; i < supported_types_a.length; ++i) { + for (var j = 0; j < supported_types_b.length; ++j) { + if ( + this.isValidConnection(supported_types_a[i], supported_types_b[j]) + ) { + //if (supported_types_a[i] == supported_types_b[j]) { + return true; + } + } + } + + return false; + }, + + /** + * Register a string in the search box so when the user types it it will recommend this node + * @method registerSearchboxExtra + * @param {String} node_type the node recommended + * @param {String} description text to show next to it + * @param {Object} data it could contain info of how the node should be configured + * @return {Boolean} true if they can be connected + */ + registerSearchboxExtra: function (node_type, description, data) { + this.searchbox_extras[description.toLowerCase()] = { + type: node_type, + desc: description, + data: data, + }; + }, + + /** + * Wrapper to load files (from url using fetch or from file using FileReader) + * @method fetchFile + * @param {String|File|Blob} url the url of the file (or the file itself) + * @param {String} type an string to know how to fetch it: "text","arraybuffer","json","blob" + * @param {Function} on_complete callback(data) + * @param {Function} on_error in case of an error + * @return {FileReader|Promise} returns the object used to + */ + fetchFile: function (url, type, on_complete, on_error) { + var that = this; + if (!url) return null; + + type = type || "text"; + if (url.constructor === String) { + if (url.substr(0, 4) == "http" && LiteGraph.proxy) { + url = LiteGraph.proxy + url.substr(url.indexOf(":") + 3); + } + return fetch(url) + .then(function (response) { + if (!response.ok) throw new Error("File not found"); //it will be catch below + if (type == "arraybuffer") return response.arrayBuffer(); + else if (type == "text" || type == "string") return response.text(); + else if (type == "json") return response.json(); + else if (type == "blob") return response.blob(); + }) + .then(function (data) { + if (on_complete) on_complete(data); + }) + .catch(function (error) { + console.error("error fetching file:", url); + if (on_error) on_error(error); + }); + } else if (url.constructor === File || url.constructor === Blob) { + var reader = new FileReader(); + reader.onload = function (e) { + var v = e.target.result; + if (type == "json") v = JSON.parse(v); + if (on_complete) on_complete(v); + }; + if (type == "arraybuffer") return reader.readAsArrayBuffer(url); + else if (type == "text" || type == "json") + return reader.readAsText(url); + else if (type == "blob") return reader.readAsBinaryString(url); + } + return null; + }, + }); + + //timer that works everywhere + if (typeof performance != "undefined") { + LiteGraph.getTime = performance.now.bind(performance); + } else if (typeof Date != "undefined" && Date.now) { + LiteGraph.getTime = Date.now.bind(Date); + } else if (typeof process != "undefined") { + LiteGraph.getTime = function () { + var t = process.hrtime(); + return t[0] * 0.001 + t[1] * 1e-6; + }; + } else { + LiteGraph.getTime = function getTime() { + return new Date().getTime(); + }; + } + + //********************************************************************************* + // LGraph CLASS + //********************************************************************************* + + /** + * LGraph is the class that contain a full graph. We instantiate one and add nodes to it, and then we can run the execution loop. + * supported callbacks: + + onNodeAdded: when a new node is added to the graph + + onNodeRemoved: when a node inside this graph is removed + + onNodeConnectionChange: some connection has changed in the graph (connected or disconnected) + * + * @class LGraph + * @constructor + * @param {Object} o data from previous serialization [optional] + */ + + function LGraph(o) { + if (LiteGraph.debug) { + console.log("Graph created"); + } + this.list_of_graphcanvas = null; + this.clear(); + + if (o) { + this.configure(o); + } + } + + global.LGraph = LiteGraph.LGraph = LGraph; + + //default supported types + LGraph.supported_types = ["number", "string", "boolean"]; + + //used to know which types of connections support this graph (some graphs do not allow certain types) + LGraph.prototype.getSupportedTypes = function () { + return this.supported_types || LGraph.supported_types; + }; + + LGraph.STATUS_STOPPED = 1; + LGraph.STATUS_RUNNING = 2; + + /** + * Removes all nodes from this graph + * @method clear + */ + + LGraph.prototype.clear = function () { + this.stop(); + this.status = LGraph.STATUS_STOPPED; + + this.last_node_id = 0; + this.last_link_id = 0; + + this._version = -1; //used to detect changes + + //safe clear + if (this._nodes) { + for (var i = 0; i < this._nodes.length; ++i) { + var node = this._nodes[i]; + if (node.onRemoved) { + node.onRemoved(); + } + } + } + + //nodes + this._nodes = []; + this._nodes_by_id = {}; + this._nodes_in_order = []; //nodes sorted in execution order + this._nodes_executable = null; //nodes that contain onExecute sorted in execution order + + //other scene stuff + this._groups = []; + + //links + this.links = {}; //container with all the links + + //iterations + this.iteration = 0; + + //custom data + this.config = {}; + this.vars = {}; + this.extra = {}; //to store custom data + + //timing + this.globaltime = 0; + this.runningtime = 0; + this.fixedtime = 0; + this.fixedtime_lapse = 0.01; + this.elapsed_time = 0.01; + this.last_update_time = 0; + this.starttime = 0; + + this.catch_errors = true; + + this.nodes_executing = []; + this.nodes_actioning = []; + this.nodes_executedAction = []; + + //subgraph_data + this.inputs = {}; + this.outputs = {}; + + //notify canvas to redraw + this.change(); + + this.sendActionToCanvas("clear"); + }; + + /** + * Attach Canvas to this graph + * @method attachCanvas + * @param {GraphCanvas} graph_canvas + */ + + LGraph.prototype.attachCanvas = function (graphcanvas) { + if (graphcanvas.constructor != LGraphCanvas) { + throw "attachCanvas expects a LGraphCanvas instance"; + } + if (graphcanvas.graph && graphcanvas.graph != this) { + graphcanvas.graph.detachCanvas(graphcanvas); + } + + graphcanvas.graph = this; + + if (!this.list_of_graphcanvas) { + this.list_of_graphcanvas = []; + } + this.list_of_graphcanvas.push(graphcanvas); + }; + + /** + * Detach Canvas from this graph + * @method detachCanvas + * @param {GraphCanvas} graph_canvas + */ + LGraph.prototype.detachCanvas = function (graphcanvas) { + if (!this.list_of_graphcanvas) { + return; + } + + var pos = this.list_of_graphcanvas.indexOf(graphcanvas); + if (pos == -1) { + return; + } + graphcanvas.graph = null; + this.list_of_graphcanvas.splice(pos, 1); + }; + + /** + * Starts running this graph every interval milliseconds. + * @method start + * @param {number} interval amount of milliseconds between executions, if 0 then it renders to the monitor refresh rate + */ + + LGraph.prototype.start = function (interval) { + if (this.status == LGraph.STATUS_RUNNING) { + return; + } + this.status = LGraph.STATUS_RUNNING; + + if (this.onPlayEvent) { + this.onPlayEvent(); + } + + this.sendEventToAllNodes("onStart"); + + //launch + this.starttime = LiteGraph.getTime(); + this.last_update_time = this.starttime; + interval = interval || 0; + var that = this; + + //execute once per frame + if ( + interval == 0 && + typeof window != "undefined" && + window.requestAnimationFrame + ) { + function on_frame() { + if (that.execution_timer_id != -1) { + return; + } + window.requestAnimationFrame(on_frame); + if (that.onBeforeStep) that.onBeforeStep(); + that.runStep(1, !that.catch_errors); + if (that.onAfterStep) that.onAfterStep(); + } + this.execution_timer_id = -1; + on_frame(); + } else { + //execute every 'interval' ms + this.execution_timer_id = setInterval(function () { + //execute + if (that.onBeforeStep) that.onBeforeStep(); + that.runStep(1, !that.catch_errors); + if (that.onAfterStep) that.onAfterStep(); + }, interval); + } + }; + + /** + * Stops the execution loop of the graph + * @method stop execution + */ + + LGraph.prototype.stop = function () { + if (this.status == LGraph.STATUS_STOPPED) { + return; + } + + this.status = LGraph.STATUS_STOPPED; + + if (this.onStopEvent) { + this.onStopEvent(); + } + + if (this.execution_timer_id != null) { + if (this.execution_timer_id != -1) { + clearInterval(this.execution_timer_id); + } + this.execution_timer_id = null; + } + + this.sendEventToAllNodes("onStop"); + }; + + /** + * Run N steps (cycles) of the graph + * @method runStep + * @param {number} num number of steps to run, default is 1 + * @param {Boolean} do_not_catch_errors [optional] if you want to try/catch errors + * @param {number} limit max number of nodes to execute (used to execute from start to a node) + */ + + LGraph.prototype.runStep = function (num, do_not_catch_errors, limit) { + num = num || 1; + + var start = LiteGraph.getTime(); + this.globaltime = 0.001 * (start - this.starttime); + + //not optimal: executes possible pending actions in node, problem is it is not optimized + //it is done here as if it was done in the later loop it wont be called in the node missed the onExecute + + //from now on it will iterate only on executable nodes which is faster + var nodes = this._nodes_executable ? this._nodes_executable : this._nodes; + if (!nodes) { + return; + } + + limit = limit || nodes.length; + + if (do_not_catch_errors) { + //iterations + for (var i = 0; i < num; i++) { + for (var j = 0; j < limit; ++j) { + var node = nodes[j]; + if ( + LiteGraph.use_deferred_actions && + node._waiting_actions && + node._waiting_actions.length + ) + node.executePendingActions(); + if (node.mode == LiteGraph.ALWAYS && node.onExecute) { + //wrap node.onExecute(); + node.doExecute(); + } + } + + this.fixedtime += this.fixedtime_lapse; + if (this.onExecuteStep) { + this.onExecuteStep(); + } + } + + if (this.onAfterExecute) { + this.onAfterExecute(); + } + } else { + //catch errors + try { + //iterations + for (var i = 0; i < num; i++) { + for (var j = 0; j < limit; ++j) { + var node = nodes[j]; + if ( + LiteGraph.use_deferred_actions && + node._waiting_actions && + node._waiting_actions.length + ) + node.executePendingActions(); + if (node.mode == LiteGraph.ALWAYS && node.onExecute) { + node.onExecute(); + } + } + + this.fixedtime += this.fixedtime_lapse; + if (this.onExecuteStep) { + this.onExecuteStep(); + } + } + + if (this.onAfterExecute) { + this.onAfterExecute(); + } + this.errors_in_execution = false; + } catch (err) { + this.errors_in_execution = true; + if (LiteGraph.throw_errors) { + throw err; + } + if (LiteGraph.debug) { + console.log("Error during execution: " + err); + } + this.stop(); + } + } + + var now = LiteGraph.getTime(); + var elapsed = now - start; + if (elapsed == 0) { + elapsed = 1; + } + this.execution_time = 0.001 * elapsed; + this.globaltime += 0.001 * elapsed; + this.iteration += 1; + this.elapsed_time = (now - this.last_update_time) * 0.001; + this.last_update_time = now; + this.nodes_executing = []; + this.nodes_actioning = []; + this.nodes_executedAction = []; + }; + + /** + * Updates the graph execution order according to relevance of the nodes (nodes with only outputs have more relevance than + * nodes with only inputs. + * @method updateExecutionOrder + */ + LGraph.prototype.updateExecutionOrder = function () { + this._nodes_in_order = this.computeExecutionOrder(false); + this._nodes_executable = []; + for (var i = 0; i < this._nodes_in_order.length; ++i) { + if (this._nodes_in_order[i].onExecute) { + this._nodes_executable.push(this._nodes_in_order[i]); + } + } + }; + + //This is more internal, it computes the executable nodes in order and returns it + LGraph.prototype.computeExecutionOrder = function ( + only_onExecute, + set_level, + ) { + var L = []; + var S = []; + var M = {}; + var visited_links = {}; //to avoid repeating links + var remaining_links = {}; //to a + + //search for the nodes without inputs (starting nodes) + for (var i = 0, l = this._nodes.length; i < l; ++i) { + var node = this._nodes[i]; + if (only_onExecute && !node.onExecute) { + continue; + } + + M[node.id] = node; //add to pending nodes + + var num = 0; //num of input connections + if (node.inputs) { + for (var j = 0, l2 = node.inputs.length; j < l2; j++) { + if (node.inputs[j] && node.inputs[j].link != null) { + num += 1; + } + } + } + + if (num == 0) { + //is a starting node + S.push(node); + if (set_level) { + node._level = 1; + } + } //num of input links + else { + if (set_level) { + node._level = 0; + } + remaining_links[node.id] = num; + } + } + + while (true) { + if (S.length == 0) { + break; + } + + //get an starting node + var node = S.shift(); + L.push(node); //add to ordered list + delete M[node.id]; //remove from the pending nodes + + if (!node.outputs) { + continue; + } + + //for every output + for (var i = 0; i < node.outputs.length; i++) { + var output = node.outputs[i]; + //not connected + if ( + output == null || + output.links == null || + output.links.length == 0 + ) { + continue; + } + + //for every connection + for (var j = 0; j < output.links.length; j++) { + var link_id = output.links[j]; + var link = this.links[link_id]; + if (!link) { + continue; + } + + //already visited link (ignore it) + if (visited_links[link.id]) { + continue; + } + + var target_node = this.getNodeById(link.target_id); + if (target_node == null) { + visited_links[link.id] = true; + continue; + } + + if ( + set_level && + (!target_node._level || target_node._level <= node._level) + ) { + target_node._level = node._level + 1; + } + + visited_links[link.id] = true; //mark as visited + remaining_links[target_node.id] -= 1; //reduce the number of links remaining + if (remaining_links[target_node.id] == 0) { + S.push(target_node); + } //if no more links, then add to starters array + } + } + } + + //the remaining ones (loops) + for (var i in M) { + L.push(M[i]); + } + + if (L.length != this._nodes.length && LiteGraph.debug) { + console.warn("something went wrong, nodes missing"); + } + + var l = L.length; + + //save order number in the node + for (var i = 0; i < l; ++i) { + L[i].order = i; + } + + //sort now by priority + L = L.sort(function (A, B) { + var Ap = A.constructor.priority || A.priority || 0; + var Bp = B.constructor.priority || B.priority || 0; + if (Ap == Bp) { + //if same priority, sort by order + return A.order - B.order; + } + return Ap - Bp; //sort by priority + }); + + //save order number in the node, again... + for (var i = 0; i < l; ++i) { + L[i].order = i; + } + + return L; + }; + + /** + * Returns all the nodes that could affect this one (ancestors) by crawling all the inputs recursively. + * It doesn't include the node itself + * @method getAncestors + * @return {Array} an array with all the LGraphNodes that affect this node, in order of execution + */ + LGraph.prototype.getAncestors = function (node) { + var ancestors = []; + var pending = [node]; + var visited = {}; + + while (pending.length) { + var current = pending.shift(); + if (!current.inputs) { + continue; + } + if (!visited[current.id] && current != node) { + visited[current.id] = true; + ancestors.push(current); + } + + for (var i = 0; i < current.inputs.length; ++i) { + var input = current.getInputNode(i); + if (input && ancestors.indexOf(input) == -1) { + pending.push(input); + } + } + } + + ancestors.sort(function (a, b) { + return a.order - b.order; + }); + return ancestors; + }; + + /** + * Positions every node in a more readable manner + * @method arrange + */ + LGraph.prototype.arrange = function (margin, layout) { + margin = margin || 100; + + const nodes = this.computeExecutionOrder(false, true); + const columns = []; + for (let i = 0; i < nodes.length; ++i) { + const node = nodes[i]; + const col = node._level || 1; + if (!columns[col]) { + columns[col] = []; + } + columns[col].push(node); + } + + let x = margin; + + for (let i = 0; i < columns.length; ++i) { + const column = columns[i]; + if (!column) { + continue; + } + let max_size = 100; + let y = margin + LiteGraph.NODE_TITLE_HEIGHT; + for (let j = 0; j < column.length; ++j) { + const node = column[j]; + node.pos[0] = layout == LiteGraph.VERTICAL_LAYOUT ? y : x; + node.pos[1] = layout == LiteGraph.VERTICAL_LAYOUT ? x : y; + const max_size_index = layout == LiteGraph.VERTICAL_LAYOUT ? 1 : 0; + if (node.size[max_size_index] > max_size) { + max_size = node.size[max_size_index]; + } + const node_size_index = layout == LiteGraph.VERTICAL_LAYOUT ? 0 : 1; + y += node.size[node_size_index] + margin + LiteGraph.NODE_TITLE_HEIGHT; + } + x += max_size + margin; + } + + this.setDirtyCanvas(true, true); + }; + + /** + * Returns the amount of time the graph has been running in milliseconds + * @method getTime + * @return {number} number of milliseconds the graph has been running + */ + LGraph.prototype.getTime = function () { + return this.globaltime; + }; + + /** + * Returns the amount of time accumulated using the fixedtime_lapse var. This is used in context where the time increments should be constant + * @method getFixedTime + * @return {number} number of milliseconds the graph has been running + */ + + LGraph.prototype.getFixedTime = function () { + return this.fixedtime; + }; + + /** + * Returns the amount of time it took to compute the latest iteration. Take into account that this number could be not correct + * if the nodes are using graphical actions + * @method getElapsedTime + * @return {number} number of milliseconds it took the last cycle + */ + + LGraph.prototype.getElapsedTime = function () { + return this.elapsed_time; + }; + + /** + * Sends an event to all the nodes, useful to trigger stuff + * @method sendEventToAllNodes + * @param {String} eventname the name of the event (function to be called) + * @param {Array} params parameters in array format + */ + LGraph.prototype.sendEventToAllNodes = function (eventname, params, mode) { + mode = mode || LiteGraph.ALWAYS; + + var nodes = this._nodes_in_order ? this._nodes_in_order : this._nodes; + if (!nodes) { + return; + } + + for (var j = 0, l = nodes.length; j < l; ++j) { + var node = nodes[j]; + + if (node.constructor === LiteGraph.Subgraph && eventname != "onExecute") { + if (node.mode == mode) { + node.sendEventToAllNodes(eventname, params, mode); + } + continue; + } + + if (!node[eventname] || node.mode != mode) { + continue; + } + if (params === undefined) { + node[eventname](); + } else if (params && params.constructor === Array) { + node[eventname].apply(node, params); + } else { + node[eventname](params); + } + } + }; + + LGraph.prototype.sendActionToCanvas = function (action, params) { + if (!this.list_of_graphcanvas) { + return; + } + + for (var i = 0; i < this.list_of_graphcanvas.length; ++i) { + var c = this.list_of_graphcanvas[i]; + if (c[action]) { + c[action].apply(c, params); + } + } + }; + + /** + * Adds a new node instance to this graph + * @method add + * @param {LGraphNode} node the instance of the node + */ + + LGraph.prototype.add = function (node, skip_compute_order) { + if (!node) { + return; + } + + //groups + if (node.constructor === LGraphGroup) { + this._groups.push(node); + this.setDirtyCanvas(true); + this.change(); + node.graph = this; + this._version++; + return; + } + + //nodes + if (node.id != -1 && this._nodes_by_id[node.id] != null) { + console.warn( + "LiteGraph: there is already a node with this ID, changing it", + ); + if (LiteGraph.use_uuids) { + node.id = LiteGraph.uuidv4(); + } else { + node.id = ++this.last_node_id; + } + } + + if (this._nodes.length >= LiteGraph.MAX_NUMBER_OF_NODES) { + throw "LiteGraph: max number of nodes in a graph reached"; + } + + //give him an id + if (LiteGraph.use_uuids) { + if (node.id == null || node.id == -1) node.id = LiteGraph.uuidv4(); + } else { + if (node.id == null || node.id == -1) { + node.id = ++this.last_node_id; + } else if (this.last_node_id < node.id) { + this.last_node_id = node.id; + } + } + + node.graph = this; + this._version++; + + this._nodes.push(node); + this._nodes_by_id[node.id] = node; + + if (node.onAdded) { + node.onAdded(this); + } + + if (this.config.align_to_grid) { + node.alignToGrid(); + } + + if (!skip_compute_order) { + this.updateExecutionOrder(); + } + + if (this.onNodeAdded) { + this.onNodeAdded(node); + } + + this.setDirtyCanvas(true); + this.change(); + + return node; //to chain actions + }; + + /** + * Removes a node from the graph + * @method remove + * @param {LGraphNode} node the instance of the node + */ + + LGraph.prototype.remove = function (node) { + if (node.constructor === LiteGraph.LGraphGroup) { + var index = this._groups.indexOf(node); + if (index != -1) { + this._groups.splice(index, 1); + } + node.graph = null; + this._version++; + this.setDirtyCanvas(true, true); + this.change(); + return; + } + + if (this._nodes_by_id[node.id] == null) { + return; + } //not found + + if (node.ignore_remove) { + return; + } //cannot be removed + + this.beforeChange(); //sure? - almost sure is wrong + + //disconnect inputs + if (node.inputs) { + for (var i = 0; i < node.inputs.length; i++) { + var slot = node.inputs[i]; + if (slot.link != null) { + node.disconnectInput(i); + } + } + } + + //disconnect outputs + if (node.outputs) { + for (var i = 0; i < node.outputs.length; i++) { + var slot = node.outputs[i]; + if (slot.links != null && slot.links.length) { + node.disconnectOutput(i); + } + } + } + + //node.id = -1; //why? + + //callback + if (node.onRemoved) { + node.onRemoved(); + } + + node.graph = null; + this._version++; + + //remove from canvas render + if (this.list_of_graphcanvas) { + for (var i = 0; i < this.list_of_graphcanvas.length; ++i) { + var canvas = this.list_of_graphcanvas[i]; + if (canvas.selected_nodes[node.id]) { + delete canvas.selected_nodes[node.id]; + } + if (canvas.node_dragged == node) { + canvas.node_dragged = null; + } + } + } + + //remove from containers + var pos = this._nodes.indexOf(node); + if (pos != -1) { + this._nodes.splice(pos, 1); + } + delete this._nodes_by_id[node.id]; + + if (this.onNodeRemoved) { + this.onNodeRemoved(node); + } + + //close panels + this.sendActionToCanvas("checkPanels"); + + this.setDirtyCanvas(true, true); + this.afterChange(); //sure? - almost sure is wrong + this.change(); + + this.updateExecutionOrder(); + }; + + /** + * Returns a node by its id. + * @method getNodeById + * @param {Number} id + */ + + LGraph.prototype.getNodeById = function (id) { + if (id == null) { + return null; + } + return this._nodes_by_id[id]; + }; + + /** + * Returns a list of nodes that matches a class + * @method findNodesByClass + * @param {Class} classObject the class itself (not an string) + * @return {Array} a list with all the nodes of this type + */ + LGraph.prototype.findNodesByClass = function (classObject, result) { + result = result || []; + result.length = 0; + for (var i = 0, l = this._nodes.length; i < l; ++i) { + if (this._nodes[i].constructor === classObject) { + result.push(this._nodes[i]); + } + } + return result; + }; + + /** + * Returns a list of nodes that matches a type + * @method findNodesByType + * @param {String} type the name of the node type + * @return {Array} a list with all the nodes of this type + */ + LGraph.prototype.findNodesByType = function (type, result) { + var type = type.toLowerCase(); + result = result || []; + result.length = 0; + for (var i = 0, l = this._nodes.length; i < l; ++i) { + if (this._nodes[i].type.toLowerCase() == type) { + result.push(this._nodes[i]); + } + } + return result; + }; + + /** + * Returns the first node that matches a name in its title + * @method findNodeByTitle + * @param {String} name the name of the node to search + * @return {Node} the node or null + */ + LGraph.prototype.findNodeByTitle = function (title) { + for (var i = 0, l = this._nodes.length; i < l; ++i) { + if (this._nodes[i].title == title) { + return this._nodes[i]; + } + } + return null; + }; + + /** + * Returns a list of nodes that matches a name + * @method findNodesByTitle + * @param {String} name the name of the node to search + * @return {Array} a list with all the nodes with this name + */ + LGraph.prototype.findNodesByTitle = function (title) { + var result = []; + for (var i = 0, l = this._nodes.length; i < l; ++i) { + if (this._nodes[i].title == title) { + result.push(this._nodes[i]); + } + } + return result; + }; + + /** + * Returns the top-most node in this position of the canvas + * @method getNodeOnPos + * @param {number} x the x coordinate in canvas space + * @param {number} y the y coordinate in canvas space + * @param {Array} nodes_list a list with all the nodes to search from, by default is all the nodes in the graph + * @return {LGraphNode} the node at this position or null + */ + LGraph.prototype.getNodeOnPos = function (x, y, nodes_list, margin) { + nodes_list = nodes_list || this._nodes; + var nRet = null; + for (var i = nodes_list.length - 1; i >= 0; i--) { + var n = nodes_list[i]; + if (n.isPointInside(x, y, margin)) { + // check for lesser interest nodes (TODO check for overlapping, use the top) + /*if (typeof n == "LGraphGroup"){ + nRet = n; + }else{*/ + return n; + /*}*/ + } + } + return nRet; + }; + + /** + * Returns the top-most group in that position + * @method getGroupOnPos + * @param {number} x the x coordinate in canvas space + * @param {number} y the y coordinate in canvas space + * @return {LGraphGroup} the group or null + */ + LGraph.prototype.getGroupOnPos = function (x, y) { + for (var i = this._groups.length - 1; i >= 0; i--) { + var g = this._groups[i]; + if (g.isPointInside(x, y, 2, true)) { + return g; + } + } + return null; + }; + + /** + * Checks that the node type matches the node type registered, used when replacing a nodetype by a newer version during execution + * this replaces the ones using the old version with the new version + * @method checkNodeTypes + */ + LGraph.prototype.checkNodeTypes = function () { + var changes = false; + for (var i = 0; i < this._nodes.length; i++) { + var node = this._nodes[i]; + var ctor = LiteGraph.registered_node_types[node.type]; + if (node.constructor == ctor) { + continue; + } + console.log("node being replaced by newer version: " + node.type); + var newnode = LiteGraph.createNode(node.type); + changes = true; + this._nodes[i] = newnode; + newnode.configure(node.serialize()); + newnode.graph = this; + this._nodes_by_id[newnode.id] = newnode; + if (node.inputs) { + newnode.inputs = node.inputs.concat(); + } + if (node.outputs) { + newnode.outputs = node.outputs.concat(); + } + } + this.updateExecutionOrder(); + }; + + // ********** GLOBALS ***************** + + LGraph.prototype.onAction = function (action, param, options) { + this._input_nodes = this.findNodesByClass( + LiteGraph.GraphInput, + this._input_nodes, + ); + for (var i = 0; i < this._input_nodes.length; ++i) { + var node = this._input_nodes[i]; + if (node.properties.name != action) { + continue; + } + //wrap node.onAction(action, param); + node.actionDo(action, param, options); + break; + } + }; + + LGraph.prototype.trigger = function (action, param) { + if (this.onTrigger) { + this.onTrigger(action, param); + } + }; + + /** + * Tell this graph it has a global graph input of this type + * @method addGlobalInput + * @param {String} name + * @param {String} type + * @param {*} value [optional] + */ + LGraph.prototype.addInput = function (name, type, value) { + var input = this.inputs[name]; + if (input) { + //already exist + return; + } + + this.beforeChange(); + this.inputs[name] = { name: name, type: type, value: value }; + this._version++; + this.afterChange(); + + if (this.onInputAdded) { + this.onInputAdded(name, type); + } + + if (this.onInputsOutputsChange) { + this.onInputsOutputsChange(); + } + }; + + /** + * Assign a data to the global graph input + * @method setGlobalInputData + * @param {String} name + * @param {*} data + */ + LGraph.prototype.setInputData = function (name, data) { + var input = this.inputs[name]; + if (!input) { + return; + } + input.value = data; + }; + + /** + * Returns the current value of a global graph input + * @method getInputData + * @param {String} name + * @return {*} the data + */ + LGraph.prototype.getInputData = function (name) { + var input = this.inputs[name]; + if (!input) { + return null; + } + return input.value; + }; + + /** + * Changes the name of a global graph input + * @method renameInput + * @param {String} old_name + * @param {String} new_name + */ + LGraph.prototype.renameInput = function (old_name, name) { + if (name == old_name) { + return; + } + + if (!this.inputs[old_name]) { + return false; + } + + if (this.inputs[name]) { + console.error("there is already one input with that name"); + return false; + } + + this.inputs[name] = this.inputs[old_name]; + delete this.inputs[old_name]; + this._version++; + + if (this.onInputRenamed) { + this.onInputRenamed(old_name, name); + } + + if (this.onInputsOutputsChange) { + this.onInputsOutputsChange(); + } + }; + + /** + * Changes the type of a global graph input + * @method changeInputType + * @param {String} name + * @param {String} type + */ + LGraph.prototype.changeInputType = function (name, type) { + if (!this.inputs[name]) { + return false; + } + + if ( + this.inputs[name].type && + String(this.inputs[name].type).toLowerCase() == String(type).toLowerCase() + ) { + return; + } + + this.inputs[name].type = type; + this._version++; + if (this.onInputTypeChanged) { + this.onInputTypeChanged(name, type); + } + }; + + /** + * Removes a global graph input + * @method removeInput + * @param {String} name + * @param {String} type + */ + LGraph.prototype.removeInput = function (name) { + if (!this.inputs[name]) { + return false; + } + + delete this.inputs[name]; + this._version++; + + if (this.onInputRemoved) { + this.onInputRemoved(name); + } + + if (this.onInputsOutputsChange) { + this.onInputsOutputsChange(); + } + return true; + }; + + /** + * Creates a global graph output + * @method addOutput + * @param {String} name + * @param {String} type + * @param {*} value + */ + LGraph.prototype.addOutput = function (name, type, value) { + this.outputs[name] = { name: name, type: type, value: value }; + this._version++; + + if (this.onOutputAdded) { + this.onOutputAdded(name, type); + } + + if (this.onInputsOutputsChange) { + this.onInputsOutputsChange(); + } + }; + + /** + * Assign a data to the global output + * @method setOutputData + * @param {String} name + * @param {String} value + */ + LGraph.prototype.setOutputData = function (name, value) { + var output = this.outputs[name]; + if (!output) { + return; + } + output.value = value; + }; + + /** + * Returns the current value of a global graph output + * @method getOutputData + * @param {String} name + * @return {*} the data + */ + LGraph.prototype.getOutputData = function (name) { + var output = this.outputs[name]; + if (!output) { + return null; + } + return output.value; + }; + + /** + * Renames a global graph output + * @method renameOutput + * @param {String} old_name + * @param {String} new_name + */ + LGraph.prototype.renameOutput = function (old_name, name) { + if (!this.outputs[old_name]) { + return false; + } + + if (this.outputs[name]) { + console.error("there is already one output with that name"); + return false; + } + + this.outputs[name] = this.outputs[old_name]; + delete this.outputs[old_name]; + this._version++; + + if (this.onOutputRenamed) { + this.onOutputRenamed(old_name, name); + } + + if (this.onInputsOutputsChange) { + this.onInputsOutputsChange(); + } + }; + + /** + * Changes the type of a global graph output + * @method changeOutputType + * @param {String} name + * @param {String} type + */ + LGraph.prototype.changeOutputType = function (name, type) { + if (!this.outputs[name]) { + return false; + } + + if ( + this.outputs[name].type && + String(this.outputs[name].type).toLowerCase() == + String(type).toLowerCase() + ) { + return; + } + + this.outputs[name].type = type; + this._version++; + if (this.onOutputTypeChanged) { + this.onOutputTypeChanged(name, type); + } + }; + + /** + * Removes a global graph output + * @method removeOutput + * @param {String} name + */ + LGraph.prototype.removeOutput = function (name) { + if (!this.outputs[name]) { + return false; + } + delete this.outputs[name]; + this._version++; + + if (this.onOutputRemoved) { + this.onOutputRemoved(name); + } + + if (this.onInputsOutputsChange) { + this.onInputsOutputsChange(); + } + return true; + }; + + LGraph.prototype.triggerInput = function (name, value) { + var nodes = this.findNodesByTitle(name); + for (var i = 0; i < nodes.length; ++i) { + nodes[i].onTrigger(value); + } + }; + + LGraph.prototype.setCallback = function (name, func) { + var nodes = this.findNodesByTitle(name); + for (var i = 0; i < nodes.length; ++i) { + nodes[i].setTrigger(func); + } + }; + + //used for undo, called before any change is made to the graph + LGraph.prototype.beforeChange = function (info) { + if (this.onBeforeChange) { + this.onBeforeChange(this, info); + } + this.sendActionToCanvas("onBeforeChange", this); + }; + + //used to resend actions, called after any change is made to the graph + LGraph.prototype.afterChange = function (info) { + if (this.onAfterChange) { + this.onAfterChange(this, info); + } + this.sendActionToCanvas("onAfterChange", this); + }; + + LGraph.prototype.connectionChange = function (node, link_info) { + this.updateExecutionOrder(); + if (this.onConnectionChange) { + this.onConnectionChange(node); + } + this._version++; + this.sendActionToCanvas("onConnectionChange"); + }; + + /** + * returns if the graph is in live mode + * @method isLive + */ + + LGraph.prototype.isLive = function () { + if (!this.list_of_graphcanvas) { + return false; + } + + for (var i = 0; i < this.list_of_graphcanvas.length; ++i) { + var c = this.list_of_graphcanvas[i]; + if (c.live_mode) { + return true; + } + } + return false; + }; + + /** + * clears the triggered slot animation in all links (stop visual animation) + * @method clearTriggeredSlots + */ + LGraph.prototype.clearTriggeredSlots = function () { + for (var i in this.links) { + var link_info = this.links[i]; + if (!link_info) { + continue; + } + if (link_info._last_time) { + link_info._last_time = 0; + } + } + }; + + /* Called when something visually changed (not the graph!) */ + LGraph.prototype.change = function () { + if (LiteGraph.debug) { + console.log("Graph changed"); + } + this.sendActionToCanvas("setDirty", [true, true]); + if (this.on_change) { + this.on_change(this); + } + }; + + LGraph.prototype.setDirtyCanvas = function (fg, bg) { + this.sendActionToCanvas("setDirty", [fg, bg]); + }; + + /** + * Destroys a link + * @method removeLink + * @param {Number} link_id + */ + LGraph.prototype.removeLink = function (link_id) { + var link = this.links[link_id]; + if (!link) { + return; + } + var node = this.getNodeById(link.target_id); + if (node) { + node.disconnectInput(link.target_slot); + } + }; + + //save and recover app state *************************************** + /** + * Creates a Object containing all the info about this graph, it can be serialized + * @method serialize + * @return {Object} value of the node + */ + LGraph.prototype.serialize = function () { + var nodes_info = []; + for (var i = 0, l = this._nodes.length; i < l; ++i) { + nodes_info.push(this._nodes[i].serialize()); + } + + //pack link info into a non-verbose format + var links = []; + for (var i in this.links) { + //links is an OBJECT + var link = this.links[i]; + if (!link.serialize) { + //weird bug I havent solved yet + console.warn( + "weird LLink bug, link info is not a LLink but a regular object", + ); + var link2 = new LLink(); + for (var j in link) { + link2[j] = link[j]; + } + this.links[i] = link2; + link = link2; + } + + links.push(link.serialize()); + } + + var groups_info = []; + for (var i = 0; i < this._groups.length; ++i) { + groups_info.push(this._groups[i].serialize()); + } + + var data = { + last_node_id: this.last_node_id, + last_link_id: this.last_link_id, + nodes: nodes_info, + links: links, + groups: groups_info, + config: this.config, + extra: this.extra, + version: LiteGraph.VERSION, + }; + + if (this.onSerialize) this.onSerialize(data); + + return data; + }; + + /** + * Configure a graph from a JSON string + * @method configure + * @param {String} str configure a graph from a JSON string + * @param {Boolean} returns if there was any error parsing + */ + LGraph.prototype.configure = function (data, keep_old) { + if (!data) { + return; + } + + if (!keep_old) { + this.clear(); + } + + var nodes = data.nodes; + + //decode links info (they are very verbose) + if (data.links && data.links.constructor === Array) { + var links = []; + for (var i = 0; i < data.links.length; ++i) { + var link_data = data.links[i]; + if (!link_data) { + //weird bug + console.warn("serialized graph link data contains errors, skipping."); + continue; + } + var link = new LLink(); + link.configure(link_data); + links[link.id] = link; + } + data.links = links; + } + + //copy all stored fields + for (var i in data) { + if (i == "nodes" || i == "groups") + //links must be accepted + continue; + this[i] = data[i]; + } + + var error = false; + + //create nodes + this._nodes = []; + if (nodes) { + for (var i = 0, l = nodes.length; i < l; ++i) { + var n_info = nodes[i]; //stored info + var node = LiteGraph.createNode(n_info.type, n_info.title); + if (!node) { + if (LiteGraph.debug) { + console.log("Node not found or has errors: " + n_info.type); + } + + //in case of error we create a replacement node to avoid losing info + node = new LGraphNode(); + node.last_serialization = n_info; + node.has_errors = true; + error = true; + //continue; + } + + node.id = n_info.id; //id it or it will create a new id + this.add(node, true); //add before configure, otherwise configure cannot create links + } + + //configure nodes afterwards so they can reach each other + for (var i = 0, l = nodes.length; i < l; ++i) { + var n_info = nodes[i]; + var node = this.getNodeById(n_info.id); + if (node) { + node.configure(n_info); + } + } + } + + //groups + this._groups.length = 0; + if (data.groups) { + for (var i = 0; i < data.groups.length; ++i) { + var group = new LiteGraph.LGraphGroup(); + group.configure(data.groups[i]); + this.add(group); + } + } + + this.updateExecutionOrder(); + + this.extra = data.extra || {}; + + if (this.onConfigure) this.onConfigure(data); + + this._version++; + this.setDirtyCanvas(true, true); + return error; + }; + + LGraph.prototype.load = function (url, callback) { + var that = this; + + //from file + if (url.constructor === File || url.constructor === Blob) { + var reader = new FileReader(); + reader.addEventListener("load", function (event) { + var data = JSON.parse(event.target.result); + that.configure(data); + if (callback) callback(); + }); + + reader.readAsText(url); + return; + } + + //is a string, then an URL + var req = new XMLHttpRequest(); + req.open("GET", url, true); + req.send(null); + req.onload = function (oEvent) { + if (req.status !== 200) { + console.error("Error loading graph:", req.status, req.response); + return; + } + var data = JSON.parse(req.response); + that.configure(data); + if (callback) callback(); + }; + req.onerror = function (err) { + console.error("Error loading graph:", err); + }; + }; + + LGraph.prototype.onNodeTrace = function (node, msg, color) { + //TODO + }; + + //this is the class in charge of storing link information + function LLink(id, type, origin_id, origin_slot, target_id, target_slot) { + this.id = id; + this.type = type; + this.origin_id = origin_id; + this.origin_slot = origin_slot; + this.target_id = target_id; + this.target_slot = target_slot; + + this._data = null; + this._pos = new Float32Array(2); //center + } + + LLink.prototype.configure = function (o) { + if (o.constructor === Array) { + this.id = o[0]; + this.origin_id = o[1]; + this.origin_slot = o[2]; + this.target_id = o[3]; + this.target_slot = o[4]; + this.type = o[5]; + } else { + this.id = o.id; + this.type = o.type; + this.origin_id = o.origin_id; + this.origin_slot = o.origin_slot; + this.target_id = o.target_id; + this.target_slot = o.target_slot; + } + }; + + LLink.prototype.serialize = function () { + return [ + this.id, + this.origin_id, + this.origin_slot, + this.target_id, + this.target_slot, + this.type, + ]; + }; + + LiteGraph.LLink = LLink; + + // ************************************************************* + // Node CLASS ******* + // ************************************************************* + + /* + title: string + pos: [x,y] + size: [x,y] + + input|output: every connection + + { name:string, type:string, pos: [x,y]=Optional, direction: "input"|"output", links: Array }); + + general properties: + + clip_area: if you render outside the node, it will be clipped + + unsafe_execution: not allowed for safe execution + + skip_repeated_outputs: when adding new outputs, it wont show if there is one already connected + + resizable: if set to false it wont be resizable with the mouse + + horizontal: slots are distributed horizontally + + widgets_start_y: widgets start at y distance from the top of the node + + flags object: + + collapsed: if it is collapsed + + supported callbacks: + + onAdded: when added to graph (warning: this is called BEFORE the node is configured when loading) + + onRemoved: when removed from graph + + onStart: when the graph starts playing + + onStop: when the graph stops playing + + onDrawForeground: render the inside widgets inside the node + + onDrawBackground: render the background area inside the node (only in edit mode) + + onMouseDown + + onMouseMove + + onMouseUp + + onMouseEnter + + onMouseLeave + + onExecute: execute the node + + onPropertyChanged: when a property is changed in the panel (return true to skip default behaviour) + + onGetInputs: returns an array of possible inputs + + onGetOutputs: returns an array of possible outputs + + onBounding: in case this node has a bigger bounding than the node itself (the callback receives the bounding as [x,y,w,h]) + + onDblClick: double clicked in the node + + onInputDblClick: input slot double clicked (can be used to automatically create a node connected) + + onOutputDblClick: output slot double clicked (can be used to automatically create a node connected) + + onConfigure: called after the node has been configured + + onSerialize: to add extra info when serializing (the callback receives the object that should be filled with the data) + + onSelected + + onDeselected + + onDropItem : DOM item dropped over the node + + onDropFile : file dropped over the node + + onConnectInput : if returns false the incoming connection will be canceled + + onConnectionsChange : a connection changed (new one or removed) (LiteGraph.INPUT or LiteGraph.OUTPUT, slot, true if connected, link_info, input_info ) + + onAction: action slot triggered + + getExtraMenuOptions: to add option to context menu +*/ + + /** + * Base Class for all the node type classes + * @class LGraphNode + * @param {String} name a name for the node + */ + + function LGraphNode(title) { + this._ctor(title); + } + + global.LGraphNode = LiteGraph.LGraphNode = LGraphNode; + + LGraphNode.prototype._ctor = function (title) { + this.title = title || "Unnamed"; + this.size = [LiteGraph.NODE_WIDTH, 60]; + this.graph = null; + + this._pos = new Float32Array(10, 10); + + Object.defineProperty(this, "pos", { + set: function (v) { + if (!v || v.length < 2) { + return; + } + this._pos[0] = v[0]; + this._pos[1] = v[1]; + }, + get: function () { + return this._pos; + }, + enumerable: true, + }); + + if (LiteGraph.use_uuids) { + this.id = LiteGraph.uuidv4(); + } else { + this.id = -1; //not know till not added + } + this.type = null; + + //inputs available: array of inputs + this.inputs = []; + this.outputs = []; + this.connections = []; + + //local data + this.properties = {}; //for the values + this.properties_info = []; //for the info + + this.flags = {}; + }; + + /** + * configure a node from an object containing the serialized info + * @method configure + */ + LGraphNode.prototype.configure = function (info) { + if (this.graph) { + this.graph._version++; + } + for (var j in info) { + if (j == "properties") { + //i don't want to clone properties, I want to reuse the old container + for (var k in info.properties) { + this.properties[k] = info.properties[k]; + if (this.onPropertyChanged) { + this.onPropertyChanged(k, info.properties[k]); + } + } + continue; + } + + if (info[j] == null) { + continue; + } else if (typeof info[j] == "object") { + //object + if (this[j] && this[j].configure) { + this[j].configure(info[j]); + } else { + this[j] = LiteGraph.cloneObject(info[j], this[j]); + } + } //value + else { + this[j] = info[j]; + } + } + + if (!info.title) { + this.title = this.constructor.title; + } + + if (this.inputs) { + for (var i = 0; i < this.inputs.length; ++i) { + var input = this.inputs[i]; + var link_info = this.graph ? this.graph.links[input.link] : null; + if (this.onConnectionsChange) + this.onConnectionsChange(LiteGraph.INPUT, i, true, link_info, input); //link_info has been created now, so its updated + + if (this.onInputAdded) this.onInputAdded(input); + } + } + + if (this.outputs) { + for (var i = 0; i < this.outputs.length; ++i) { + var output = this.outputs[i]; + if (!output.links) { + continue; + } + for (var j = 0; j < output.links.length; ++j) { + var link_info = this.graph ? this.graph.links[output.links[j]] : null; + if (this.onConnectionsChange) + this.onConnectionsChange( + LiteGraph.OUTPUT, + i, + true, + link_info, + output, + ); //link_info has been created now, so its updated + } + + if (this.onOutputAdded) this.onOutputAdded(output); + } + } + + if (this.widgets) { + for (var i = 0; i < this.widgets.length; ++i) { + var w = this.widgets[i]; + if (!w) continue; + if ( + w.options && + w.options.property && + this.properties[w.options.property] != undefined + ) + w.value = JSON.parse( + JSON.stringify(this.properties[w.options.property]), + ); + } + if (info.widgets_values) { + for (var i = 0; i < info.widgets_values.length; ++i) { + if (this.widgets[i]) { + this.widgets[i].value = info.widgets_values[i]; + } + } + } + } + + if (this.onConfigure) { + this.onConfigure(info); + } + }; + + /** + * serialize the content + * @method serialize + */ + + LGraphNode.prototype.serialize = function () { + //create serialization object + var o = { + id: this.id, + type: this.type, + pos: this.pos, + size: this.size, + flags: LiteGraph.cloneObject(this.flags), + order: this.order, + mode: this.mode, + }; + + //special case for when there were errors + if (this.constructor === LGraphNode && this.last_serialization) { + return this.last_serialization; + } + + if (this.inputs) { + o.inputs = this.inputs; + } + + if (this.outputs) { + //clear outputs last data (because data in connections is never serialized but stored inside the outputs info) + for (var i = 0; i < this.outputs.length; i++) { + delete this.outputs[i]._data; + } + o.outputs = this.outputs; + } + + if (this.title && this.title != this.constructor.title) { + o.title = this.title; + } + + if (this.properties) { + o.properties = LiteGraph.cloneObject(this.properties); + } + + if (this.widgets && this.serialize_widgets) { + o.widgets_values = []; + for (var i = 0; i < this.widgets.length; ++i) { + if (this.widgets[i]) o.widgets_values[i] = this.widgets[i].value; + else o.widgets_values[i] = null; + } + } + + if (!o.type) { + o.type = this.constructor.type; + } + + if (this.color) { + o.color = this.color; + } + if (this.bgcolor) { + o.bgcolor = this.bgcolor; + } + if (this.boxcolor) { + o.boxcolor = this.boxcolor; + } + if (this.shape) { + o.shape = this.shape; + } + + if (this.onSerialize) { + if (this.onSerialize(o)) { + console.warn( + "node onSerialize shouldnt return anything, data should be stored in the object pass in the first parameter", + ); + } + } + + return o; + }; + + /* Creates a clone of this node */ + LGraphNode.prototype.clone = function () { + var node = LiteGraph.createNode(this.type); + if (!node) { + return null; + } + + //we clone it because serialize returns shared containers + var data = LiteGraph.cloneObject(this.serialize()); + + //remove links + if (data.inputs) { + for (var i = 0; i < data.inputs.length; ++i) { + data.inputs[i].link = null; + } + } + + if (data.outputs) { + for (var i = 0; i < data.outputs.length; ++i) { + if (data.outputs[i].links) { + data.outputs[i].links.length = 0; + } + } + } + + delete data["id"]; + + if (LiteGraph.use_uuids) { + data["id"] = LiteGraph.uuidv4(); + } + + //remove links + node.configure(data); + + return node; + }; + + /** + * serialize and stringify + * @method toString + */ + + LGraphNode.prototype.toString = function () { + return JSON.stringify(this.serialize()); + }; + //LGraphNode.prototype.deserialize = function(info) {} //this cannot be done from within, must be done in LiteGraph + + /** + * get the title string + * @method getTitle + */ + + LGraphNode.prototype.getTitle = function () { + return this.title || this.constructor.title; + }; + + /** + * sets the value of a property + * @method setProperty + * @param {String} name + * @param {*} value + */ + LGraphNode.prototype.setProperty = function (name, value) { + if (!this.properties) { + this.properties = {}; + } + if (value === this.properties[name]) return; + var prev_value = this.properties[name]; + this.properties[name] = value; + if (this.onPropertyChanged) { + if (this.onPropertyChanged(name, value, prev_value) === false) + //abort change + this.properties[name] = prev_value; + } + if (this.widgets) + //widgets could be linked to properties + for (var i = 0; i < this.widgets.length; ++i) { + var w = this.widgets[i]; + if (!w) continue; + if (w.options.property == name) { + w.value = value; + break; + } + } + }; + + // Execution ************************* + /** + * sets the output data + * @method setOutputData + * @param {number} slot + * @param {*} data + */ + LGraphNode.prototype.setOutputData = function (slot, data) { + if (!this.outputs) { + return; + } + + //this maybe slow and a niche case + //if(slot && slot.constructor === String) + // slot = this.findOutputSlot(slot); + + if (slot == -1 || slot >= this.outputs.length) { + return; + } + + var output_info = this.outputs[slot]; + if (!output_info) { + return; + } + + //store data in the output itself in case we want to debug + output_info._data = data; + + //if there are connections, pass the data to the connections + if (this.outputs[slot].links) { + for (var i = 0; i < this.outputs[slot].links.length; i++) { + var link_id = this.outputs[slot].links[i]; + var link = this.graph.links[link_id]; + if (link) link.data = data; + } + } + }; + + /** + * sets the output data type, useful when you want to be able to overwrite the data type + * @method setOutputDataType + * @param {number} slot + * @param {String} datatype + */ + LGraphNode.prototype.setOutputDataType = function (slot, type) { + if (!this.outputs) { + return; + } + if (slot == -1 || slot >= this.outputs.length) { + return; + } + var output_info = this.outputs[slot]; + if (!output_info) { + return; + } + //store data in the output itself in case we want to debug + output_info.type = type; + + //if there are connections, pass the data to the connections + if (this.outputs[slot].links) { + for (var i = 0; i < this.outputs[slot].links.length; i++) { + var link_id = this.outputs[slot].links[i]; + this.graph.links[link_id].type = type; + } + } + }; + + /** + * Retrieves the input data (data traveling through the connection) from one slot + * @method getInputData + * @param {number} slot + * @param {boolean} force_update if set to true it will force the connected node of this slot to output data into this link + * @return {*} data or if it is not connected returns undefined + */ + LGraphNode.prototype.getInputData = function (slot, force_update) { + if (!this.inputs) { + return; + } //undefined; + + if (slot >= this.inputs.length || this.inputs[slot].link == null) { + return; + } + + var link_id = this.inputs[slot].link; + var link = this.graph.links[link_id]; + if (!link) { + //bug: weird case but it happens sometimes + return null; + } + + if (!force_update) { + return link.data; + } + + //special case: used to extract data from the incoming connection before the graph has been executed + var node = this.graph.getNodeById(link.origin_id); + if (!node) { + return link.data; + } + + if (node.updateOutputData) { + node.updateOutputData(link.origin_slot); + } else if (node.onExecute) { + node.onExecute(); + } + + return link.data; + }; + + /** + * Retrieves the input data type (in case this supports multiple input types) + * @method getInputDataType + * @param {number} slot + * @return {String} datatype in string format + */ + LGraphNode.prototype.getInputDataType = function (slot) { + if (!this.inputs) { + return null; + } //undefined; + + if (slot >= this.inputs.length || this.inputs[slot].link == null) { + return null; + } + var link_id = this.inputs[slot].link; + var link = this.graph.links[link_id]; + if (!link) { + //bug: weird case but it happens sometimes + return null; + } + var node = this.graph.getNodeById(link.origin_id); + if (!node) { + return link.type; + } + var output_info = node.outputs[link.origin_slot]; + if (output_info) { + return output_info.type; + } + return null; + }; + + /** + * Retrieves the input data from one slot using its name instead of slot number + * @method getInputDataByName + * @param {String} slot_name + * @param {boolean} force_update if set to true it will force the connected node of this slot to output data into this link + * @return {*} data or if it is not connected returns null + */ + LGraphNode.prototype.getInputDataByName = function (slot_name, force_update) { + var slot = this.findInputSlot(slot_name); + if (slot == -1) { + return null; + } + return this.getInputData(slot, force_update); + }; + + /** + * tells you if there is a connection in one input slot + * @method isInputConnected + * @param {number} slot + * @return {boolean} + */ + LGraphNode.prototype.isInputConnected = function (slot) { + if (!this.inputs) { + return false; + } + return slot < this.inputs.length && this.inputs[slot].link != null; + }; + + /** + * tells you info about an input connection (which node, type, etc) + * @method getInputInfo + * @param {number} slot + * @return {Object} object or null { link: id, name: string, type: string or 0 } + */ + LGraphNode.prototype.getInputInfo = function (slot) { + if (!this.inputs) { + return null; + } + if (slot < this.inputs.length) { + return this.inputs[slot]; + } + return null; + }; + + /** + * Returns the link info in the connection of an input slot + * @method getInputLink + * @param {number} slot + * @return {LLink} object or null + */ + LGraphNode.prototype.getInputLink = function (slot) { + if (!this.inputs) { + return null; + } + if (slot < this.inputs.length) { + var slot_info = this.inputs[slot]; + return this.graph.links[slot_info.link]; + } + return null; + }; + + /** + * returns the node connected in the input slot + * @method getInputNode + * @param {number} slot + * @return {LGraphNode} node or null + */ + LGraphNode.prototype.getInputNode = function (slot) { + if (!this.inputs) { + return null; + } + if (slot >= this.inputs.length) { + return null; + } + var input = this.inputs[slot]; + if (!input || input.link === null) { + return null; + } + var link_info = this.graph.links[input.link]; + if (!link_info) { + return null; + } + return this.graph.getNodeById(link_info.origin_id); + }; + + /** + * returns the value of an input with this name, otherwise checks if there is a property with that name + * @method getInputOrProperty + * @param {string} name + * @return {*} value + */ + LGraphNode.prototype.getInputOrProperty = function (name) { + if (!this.inputs || !this.inputs.length) { + return this.properties ? this.properties[name] : null; + } + + for (var i = 0, l = this.inputs.length; i < l; ++i) { + var input_info = this.inputs[i]; + if (name == input_info.name && input_info.link != null) { + var link = this.graph.links[input_info.link]; + if (link) { + return link.data; + } + } + } + return this.properties[name]; + }; + + /** + * tells you the last output data that went in that slot + * @method getOutputData + * @param {number} slot + * @return {Object} object or null + */ + LGraphNode.prototype.getOutputData = function (slot) { + if (!this.outputs) { + return null; + } + if (slot >= this.outputs.length) { + return null; + } + + var info = this.outputs[slot]; + return info._data; + }; + + /** + * tells you info about an output connection (which node, type, etc) + * @method getOutputInfo + * @param {number} slot + * @return {Object} object or null { name: string, type: string, links: [ ids of links in number ] } + */ + LGraphNode.prototype.getOutputInfo = function (slot) { + if (!this.outputs) { + return null; + } + if (slot < this.outputs.length) { + return this.outputs[slot]; + } + return null; + }; + + /** + * tells you if there is a connection in one output slot + * @method isOutputConnected + * @param {number} slot + * @return {boolean} + */ + LGraphNode.prototype.isOutputConnected = function (slot) { + if (!this.outputs) { + return false; + } + return ( + slot < this.outputs.length && + this.outputs[slot].links && + this.outputs[slot].links.length + ); + }; + + /** + * tells you if there is any connection in the output slots + * @method isAnyOutputConnected + * @return {boolean} + */ + LGraphNode.prototype.isAnyOutputConnected = function () { + if (!this.outputs) { + return false; + } + for (var i = 0; i < this.outputs.length; ++i) { + if (this.outputs[i].links && this.outputs[i].links.length) { + return true; + } + } + return false; + }; + + /** + * retrieves all the nodes connected to this output slot + * @method getOutputNodes + * @param {number} slot + * @return {array} + */ + LGraphNode.prototype.getOutputNodes = function (slot) { + if (!this.outputs || this.outputs.length == 0) { + return null; + } + + if (slot >= this.outputs.length) { + return null; + } + + var output = this.outputs[slot]; + if (!output.links || output.links.length == 0) { + return null; + } + + var r = []; + for (var i = 0; i < output.links.length; i++) { + var link_id = output.links[i]; + var link = this.graph.links[link_id]; + if (link) { + var target_node = this.graph.getNodeById(link.target_id); + if (target_node) { + r.push(target_node); + } + } + } + return r; + }; + + LGraphNode.prototype.addOnTriggerInput = function () { + var trigS = this.findInputSlot("onTrigger"); + if (trigS == -1) { + //!trigS || + var input = this.addInput("onTrigger", LiteGraph.EVENT, { + optional: true, + nameLocked: true, + }); + return this.findInputSlot("onTrigger"); + } + return trigS; + }; + + LGraphNode.prototype.addOnExecutedOutput = function () { + var trigS = this.findOutputSlot("onExecuted"); + if (trigS == -1) { + //!trigS || + var output = this.addOutput("onExecuted", LiteGraph.ACTION, { + optional: true, + nameLocked: true, + }); + return this.findOutputSlot("onExecuted"); + } + return trigS; + }; + + LGraphNode.prototype.onAfterExecuteNode = function (param, options) { + var trigS = this.findOutputSlot("onExecuted"); + if (trigS != -1) { + //console.debug(this.id+":"+this.order+" triggering slot onAfterExecute"); + //console.debug(param); + //console.debug(options); + this.triggerSlot(trigS, param, null, options); + } + }; + + LGraphNode.prototype.changeMode = function (modeTo) { + switch (modeTo) { + case LiteGraph.ON_EVENT: + // this.addOnExecutedOutput(); + break; + + case LiteGraph.ON_TRIGGER: + this.addOnTriggerInput(); + this.addOnExecutedOutput(); + break; + + case LiteGraph.NEVER: + break; + + case LiteGraph.ALWAYS: + break; + + case LiteGraph.ON_REQUEST: + break; + + default: + return false; + break; + } + this.mode = modeTo; + return true; + }; + + /** + * Triggers the execution of actions that were deferred when the action was triggered + * @method executePendingActions + */ + LGraphNode.prototype.executePendingActions = function () { + if (!this._waiting_actions || !this._waiting_actions.length) return; + for (var i = 0; i < this._waiting_actions.length; ++i) { + var p = this._waiting_actions[i]; + this.onAction(p[0], p[1], p[2], p[3], p[4]); + } + this._waiting_actions.length = 0; + }; + + /** + * Triggers the node code execution, place a boolean/counter to mark the node as being executed + * @method doExecute + * @param {*} param + * @param {*} options + */ + LGraphNode.prototype.doExecute = function (param, options) { + options = options || {}; + if (this.onExecute) { + // enable this to give the event an ID + if (!options.action_call) + options.action_call = + this.id + "_exec_" + Math.floor(Math.random() * 9999); + + this.graph.nodes_executing[this.id] = true; //.push(this.id); + + this.onExecute(param, options); + + this.graph.nodes_executing[this.id] = false; //.pop(); + + // save execution/action ref + this.exec_version = this.graph.iteration; + if (options && options.action_call) { + this.action_call = options.action_call; // if (param) + this.graph.nodes_executedAction[this.id] = options.action_call; + } + } else { + } + this.execute_triggered = 2; // the nFrames it will be used (-- each step), means "how old" is the event + if (this.onAfterExecuteNode) this.onAfterExecuteNode(param, options); // callback + }; + + /** + * Triggers an action, wrapped by logics to control execution flow + * @method actionDo + * @param {String} action name + * @param {*} param + */ + LGraphNode.prototype.actionDo = function ( + action, + param, + options, + action_slot, + ) { + options = options || {}; + if (this.onAction) { + // enable this to give the event an ID + if (!options.action_call) + options.action_call = + this.id + + "_" + + (action ? action : "action") + + "_" + + Math.floor(Math.random() * 9999); + + this.graph.nodes_actioning[this.id] = action ? action : "actioning"; //.push(this.id); + + this.onAction(action, param, options, action_slot); + + this.graph.nodes_actioning[this.id] = false; //.pop(); + + // save execution/action ref + if (options && options.action_call) { + this.action_call = options.action_call; // if (param) + this.graph.nodes_executedAction[this.id] = options.action_call; + } + } + this.action_triggered = 2; // the nFrames it will be used (-- each step), means "how old" is the event + if (this.onAfterExecuteNode) this.onAfterExecuteNode(param, options); + }; + + /** + * Triggers an event in this node, this will trigger any output with the same name + * @method trigger + * @param {String} event name ( "on_play", ... ) if action is equivalent to false then the event is send to all + * @param {*} param + */ + LGraphNode.prototype.trigger = function (action, param, options) { + if (!this.outputs || !this.outputs.length) { + return; + } + + if (this.graph) this.graph._last_trigger_time = LiteGraph.getTime(); + + for (var i = 0; i < this.outputs.length; ++i) { + var output = this.outputs[i]; + if ( + !output || + output.type !== LiteGraph.EVENT || + (action && output.name != action) + ) + continue; + this.triggerSlot(i, param, null, options); + } + }; + + /** + * Triggers a slot event in this node: cycle output slots and launch execute/action on connected nodes + * @method triggerSlot + * @param {Number} slot the index of the output slot + * @param {*} param + * @param {Number} link_id [optional] in case you want to trigger and specific output link in a slot + */ + LGraphNode.prototype.triggerSlot = function (slot, param, link_id, options) { + options = options || {}; + if (!this.outputs) { + return; + } + + if (slot == null) { + console.error("slot must be a number"); + return; + } + + if (slot.constructor !== Number) + console.warn( + "slot must be a number, use node.trigger('name') if you want to use a string", + ); + + var output = this.outputs[slot]; + if (!output) { + return; + } + + var links = output.links; + if (!links || !links.length) { + return; + } + + if (this.graph) { + this.graph._last_trigger_time = LiteGraph.getTime(); + } + + //for every link attached here + for (var k = 0; k < links.length; ++k) { + var id = links[k]; + if (link_id != null && link_id != id) { + //to skip links + continue; + } + var link_info = this.graph.links[links[k]]; + if (!link_info) { + //not connected + continue; + } + link_info._last_time = LiteGraph.getTime(); + var node = this.graph.getNodeById(link_info.target_id); + if (!node) { + //node not found? + continue; + } + + //used to mark events in graph + var target_connection = node.inputs[link_info.target_slot]; + + if (node.mode === LiteGraph.ON_TRIGGER) { + // generate unique trigger ID if not present + if (!options.action_call) + options.action_call = + this.id + "_trigg_" + Math.floor(Math.random() * 9999); + if (node.onExecute) { + // -- wrapping node.onExecute(param); -- + node.doExecute(param, options); + } + } else if (node.onAction) { + // generate unique action ID if not present + if (!options.action_call) + options.action_call = + this.id + "_act_" + Math.floor(Math.random() * 9999); + //pass the action name + var target_connection = node.inputs[link_info.target_slot]; + + //instead of executing them now, it will be executed in the next graph loop, to ensure data flow + if (LiteGraph.use_deferred_actions && node.onExecute) { + if (!node._waiting_actions) node._waiting_actions = []; + node._waiting_actions.push([ + target_connection.name, + param, + options, + link_info.target_slot, + ]); + } else { + // wrap node.onAction(target_connection.name, param); + node.actionDo( + target_connection.name, + param, + options, + link_info.target_slot, + ); + } + } + } + }; + + /** + * clears the trigger slot animation + * @method clearTriggeredSlot + * @param {Number} slot the index of the output slot + * @param {Number} link_id [optional] in case you want to trigger and specific output link in a slot + */ + LGraphNode.prototype.clearTriggeredSlot = function (slot, link_id) { + if (!this.outputs) { + return; + } + + var output = this.outputs[slot]; + if (!output) { + return; + } + + var links = output.links; + if (!links || !links.length) { + return; + } + + //for every link attached here + for (var k = 0; k < links.length; ++k) { + var id = links[k]; + if (link_id != null && link_id != id) { + //to skip links + continue; + } + var link_info = this.graph.links[links[k]]; + if (!link_info) { + //not connected + continue; + } + link_info._last_time = 0; + } + }; + + /** + * changes node size and triggers callback + * @method setSize + * @param {vec2} size + */ + LGraphNode.prototype.setSize = function (size) { + this.size = size; + if (this.onResize) this.onResize(this.size); + }; + + /** + * add a new property to this node + * @method addProperty + * @param {string} name + * @param {*} default_value + * @param {string} type string defining the output type ("vec3","number",...) + * @param {Object} extra_info this can be used to have special properties of the property (like values, etc) + */ + LGraphNode.prototype.addProperty = function ( + name, + default_value, + type, + extra_info, + ) { + var o = { name: name, type: type, default_value: default_value }; + if (extra_info) { + for (var i in extra_info) { + o[i] = extra_info[i]; + } + } + if (!this.properties_info) { + this.properties_info = []; + } + this.properties_info.push(o); + if (!this.properties) { + this.properties = {}; + } + this.properties[name] = default_value; + return o; + }; + + //connections + + /** + * add a new output slot to use in this node + * @method addOutput + * @param {string} name + * @param {string} type string defining the output type ("vec3","number",...) + * @param {Object} extra_info this can be used to have special properties of an output (label, special color, position, etc) + */ + LGraphNode.prototype.addOutput = function (name, type, extra_info) { + var output = { name: name, type: type, links: null }; + if (extra_info) { + for (var i in extra_info) { + output[i] = extra_info[i]; + } + } + + if (!this.outputs) { + this.outputs = []; + } + this.outputs.push(output); + if (this.onOutputAdded) { + this.onOutputAdded(output); + } + + if (LiteGraph.auto_load_slot_types) + LiteGraph.registerNodeAndSlotType(this, type, true); + + this.setSize(this.computeSize()); + this.setDirtyCanvas(true, true); + return output; + }; + + /** + * add a new output slot to use in this node + * @method addOutputs + * @param {Array} array of triplets like [[name,type,extra_info],[...]] + */ + LGraphNode.prototype.addOutputs = function (array) { + for (var i = 0; i < array.length; ++i) { + var info = array[i]; + var o = { name: info[0], type: info[1], link: null }; + if (array[2]) { + for (var j in info[2]) { + o[j] = info[2][j]; + } + } + + if (!this.outputs) { + this.outputs = []; + } + this.outputs.push(o); + if (this.onOutputAdded) { + this.onOutputAdded(o); + } + + if (LiteGraph.auto_load_slot_types) + LiteGraph.registerNodeAndSlotType(this, info[1], true); + } + + this.setSize(this.computeSize()); + this.setDirtyCanvas(true, true); + }; + + /** + * remove an existing output slot + * @method removeOutput + * @param {number} slot + */ + LGraphNode.prototype.removeOutput = function (slot) { + this.disconnectOutput(slot); + this.outputs.splice(slot, 1); + for (var i = slot; i < this.outputs.length; ++i) { + if (!this.outputs[i] || !this.outputs[i].links) { + continue; + } + var links = this.outputs[i].links; + for (var j = 0; j < links.length; ++j) { + var link = this.graph.links[links[j]]; + if (!link) { + continue; + } + link.origin_slot -= 1; + } + } + + this.setSize(this.computeSize()); + if (this.onOutputRemoved) { + this.onOutputRemoved(slot); + } + this.setDirtyCanvas(true, true); + }; + + /** + * add a new input slot to use in this node + * @method addInput + * @param {string} name + * @param {string} type string defining the input type ("vec3","number",...), it its a generic one use 0 + * @param {Object} extra_info this can be used to have special properties of an input (label, color, position, etc) + */ + LGraphNode.prototype.addInput = function (name, type, extra_info) { + type = type || 0; + var input = { name: name, type: type, link: null }; + if (extra_info) { + for (var i in extra_info) { + input[i] = extra_info[i]; + } + } + + if (!this.inputs) { + this.inputs = []; + } + + this.inputs.push(input); + this.setSize(this.computeSize()); + + if (this.onInputAdded) { + this.onInputAdded(input); + } + + LiteGraph.registerNodeAndSlotType(this, type); + + this.setDirtyCanvas(true, true); + return input; + }; + + /** + * add several new input slots in this node + * @method addInputs + * @param {Array} array of triplets like [[name,type,extra_info],[...]] + */ + LGraphNode.prototype.addInputs = function (array) { + for (var i = 0; i < array.length; ++i) { + var info = array[i]; + var o = { name: info[0], type: info[1], link: null }; + if (array[2]) { + for (var j in info[2]) { + o[j] = info[2][j]; + } + } + + if (!this.inputs) { + this.inputs = []; + } + this.inputs.push(o); + if (this.onInputAdded) { + this.onInputAdded(o); + } + + LiteGraph.registerNodeAndSlotType(this, info[1]); + } + + this.setSize(this.computeSize()); + this.setDirtyCanvas(true, true); + }; + + /** + * remove an existing input slot + * @method removeInput + * @param {number} slot + */ + LGraphNode.prototype.removeInput = function (slot) { + this.disconnectInput(slot); + var slot_info = this.inputs.splice(slot, 1); + for (var i = slot; i < this.inputs.length; ++i) { + if (!this.inputs[i]) { + continue; + } + var link = this.graph.links[this.inputs[i].link]; + if (!link) { + continue; + } + link.target_slot -= 1; + } + this.setSize(this.computeSize()); + if (this.onInputRemoved) { + this.onInputRemoved(slot, slot_info[0]); + } + this.setDirtyCanvas(true, true); + }; + + /** + * add an special connection to this node (used for special kinds of graphs) + * @method addConnection + * @param {string} name + * @param {string} type string defining the input type ("vec3","number",...) + * @param {[x,y]} pos position of the connection inside the node + * @param {string} direction if is input or output + */ + LGraphNode.prototype.addConnection = function (name, type, pos, direction) { + var o = { + name: name, + type: type, + pos: pos, + direction: direction, + links: null, + }; + this.connections.push(o); + return o; + }; + + /** + * computes the minimum size of a node according to its inputs and output slots + * @method computeSize + * @param {vec2} minHeight + * @return {vec2} the total size + */ + LGraphNode.prototype.computeSize = function (out) { + if (this.constructor.size) { + return this.constructor.size.concat(); + } + + var rows = Math.max( + this.inputs ? this.inputs.length : 1, + this.outputs ? this.outputs.length : 1, + ); + var size = out || new Float32Array([0, 0]); + rows = Math.max(rows, 1); + var font_size = LiteGraph.NODE_TEXT_SIZE; //although it should be graphcanvas.inner_text_font size + + var title_width = compute_text_size(this.title); + var input_width = 0; + var output_width = 0; + + if (this.inputs) { + for (var i = 0, l = this.inputs.length; i < l; ++i) { + var input = this.inputs[i]; + var text = input.label || input.name || ""; + var text_width = compute_text_size(text); + if (input_width < text_width) { + input_width = text_width; + } + } + } + + if (this.outputs) { + for (var i = 0, l = this.outputs.length; i < l; ++i) { + var output = this.outputs[i]; + var text = output.label || output.name || ""; + var text_width = compute_text_size(text); + if (output_width < text_width) { + output_width = text_width; + } + } + } + + size[0] = Math.max(input_width + output_width + 10, title_width); + size[0] = Math.max(size[0], LiteGraph.NODE_WIDTH); + if (this.widgets && this.widgets.length) { + size[0] = Math.max(size[0], LiteGraph.NODE_WIDTH * 1.5); + } + + size[1] = + (this.constructor.slot_start_y || 0) + rows * LiteGraph.NODE_SLOT_HEIGHT; + + var widgets_height = 0; + if (this.widgets && this.widgets.length) { + for (var i = 0, l = this.widgets.length; i < l; ++i) { + if (this.widgets[i].computeSize) + widgets_height += this.widgets[i].computeSize(size[0])[1] + 4; + else widgets_height += LiteGraph.NODE_WIDGET_HEIGHT + 4; + } + widgets_height += 8; + } + + //compute height using widgets height + if (this.widgets_up) size[1] = Math.max(size[1], widgets_height); + else if (this.widgets_start_y != null) + size[1] = Math.max(size[1], widgets_height + this.widgets_start_y); + else size[1] += widgets_height; + + function compute_text_size(text) { + if (!text) { + return 0; + } + return font_size * text.length * 0.6; + } + + if (this.constructor.min_height && size[1] < this.constructor.min_height) { + size[1] = this.constructor.min_height; + } + + size[1] += 6; //margin + + return size; + }; + + /** + * returns all the info available about a property of this node. + * + * @method getPropertyInfo + * @param {String} property name of the property + * @return {Object} the object with all the available info + */ + LGraphNode.prototype.getPropertyInfo = function (property) { + var info = null; + + //there are several ways to define info about a property + //legacy mode + if (this.properties_info) { + for (var i = 0; i < this.properties_info.length; ++i) { + if (this.properties_info[i].name == property) { + info = this.properties_info[i]; + break; + } + } + } + //litescene mode using the constructor + if (this.constructor["@" + property]) + info = this.constructor["@" + property]; + + if ( + this.constructor.widgets_info && + this.constructor.widgets_info[property] + ) + info = this.constructor.widgets_info[property]; + + //litescene mode using the constructor + if (!info && this.onGetPropertyInfo) { + info = this.onGetPropertyInfo(property); + } + + if (!info) info = {}; + if (!info.type) info.type = typeof this.properties[property]; + if (info.widget == "combo") info.type = "enum"; + + return info; + }; + + /** + * Defines a widget inside the node, it will be rendered on top of the node, you can control lots of properties + * + * @method addWidget + * @param {String} type the widget type (could be "number","string","combo" + * @param {String} name the text to show on the widget + * @param {String} value the default value + * @param {Function|String} callback function to call when it changes (optionally, it can be the name of the property to modify) + * @param {Object} options the object that contains special properties of this widget + * @return {Object} the created widget object + */ + LGraphNode.prototype.addWidget = function ( + type, + name, + value, + callback, + options, + ) { + if (!this.widgets) { + this.widgets = []; + } + + if (!options && callback && callback.constructor === Object) { + options = callback; + callback = null; + } + + if (options && options.constructor === String) + //options can be the property name + options = { property: options }; + + if (callback && callback.constructor === String) { + //callback can be the property name + if (!options) options = {}; + options.property = callback; + callback = null; + } + + if (callback && callback.constructor !== Function) { + console.warn("addWidget: callback must be a function"); + callback = null; + } + + var w = { + type: type.toLowerCase(), + name: name, + value: value, + callback: callback, + options: options || {}, + }; + + if (w.options.y !== undefined) { + w.y = w.options.y; + } + + if (!callback && !w.options.callback && !w.options.property) { + console.warn( + "LiteGraph addWidget(...) without a callback or property assigned", + ); + } + if (type == "combo" && !w.options.values) { + throw "LiteGraph addWidget('combo',...) requires to pass values in options: { values:['red','blue'] }"; + } + this.widgets.push(w); + this.setSize(this.computeSize()); + return w; + }; + + LGraphNode.prototype.addCustomWidget = function (custom_widget) { + if (!this.widgets) { + this.widgets = []; + } + this.widgets.push(custom_widget); + return custom_widget; + }; + + /** + * returns the bounding of the object, used for rendering purposes + * @method getBounding + * @param out {Float32Array[4]?} [optional] a place to store the output, to free garbage + * @param compute_outer {boolean?} [optional] set to true to include the shadow and connection points in the bounding calculation + * @return {Float32Array[4]} the bounding box in format of [topleft_cornerx, topleft_cornery, width, height] + */ + LGraphNode.prototype.getBounding = function (out, compute_outer) { + out = out || new Float32Array(4); + const nodePos = this.pos; + const isCollapsed = this.flags.collapsed; + const nodeSize = this.size; + + let left_offset = 0; + // 1 offset due to how nodes are rendered + let right_offset = 1; + let top_offset = 0; + let bottom_offset = 0; + + if (compute_outer) { + // 4 offset for collapsed node connection points + left_offset = 4; + // 6 offset for right shadow and collapsed node connection points + right_offset = 6 + left_offset; + // 4 offset for collapsed nodes top connection points + top_offset = 4; + // 5 offset for bottom shadow and collapsed node connection points + bottom_offset = 5 + top_offset; + } + + out[0] = nodePos[0] - left_offset; + out[1] = nodePos[1] - LiteGraph.NODE_TITLE_HEIGHT - top_offset; + out[2] = isCollapsed + ? (this._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH) + right_offset + : nodeSize[0] + right_offset; + out[3] = isCollapsed + ? LiteGraph.NODE_TITLE_HEIGHT + bottom_offset + : nodeSize[1] + LiteGraph.NODE_TITLE_HEIGHT + bottom_offset; + + if (this.onBounding) { + this.onBounding(out); + } + return out; + }; + + /** + * checks if a point is inside the shape of a node + * @method isPointInside + * @param {number} x + * @param {number} y + * @return {boolean} + */ + LGraphNode.prototype.isPointInside = function (x, y, margin, skip_title) { + margin = margin || 0; + + var margin_top = + this.graph && this.graph.isLive() ? 0 : LiteGraph.NODE_TITLE_HEIGHT; + if (skip_title) { + margin_top = 0; + } + if (this.flags && this.flags.collapsed) { + //if ( distance([x,y], [this.pos[0] + this.size[0]*0.5, this.pos[1] + this.size[1]*0.5]) < LiteGraph.NODE_COLLAPSED_RADIUS) + if ( + isInsideRectangle( + x, + y, + this.pos[0] - margin, + this.pos[1] - LiteGraph.NODE_TITLE_HEIGHT - margin, + (this._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH) + + 2 * margin, + LiteGraph.NODE_TITLE_HEIGHT + 2 * margin, + ) + ) { + return true; + } + } else if ( + this.pos[0] - 4 - margin < x && + this.pos[0] + this.size[0] + 4 + margin > x && + this.pos[1] - margin_top - margin < y && + this.pos[1] + this.size[1] + margin > y + ) { + return true; + } + return false; + }; + + /** + * checks if a point is inside a node slot, and returns info about which slot + * @method getSlotInPosition + * @param {number} x + * @param {number} y + * @return {Object} if found the object contains { input|output: slot object, slot: number, link_pos: [x,y] } + */ + LGraphNode.prototype.getSlotInPosition = function (x, y) { + //search for inputs + var link_pos = new Float32Array(2); + if (this.inputs) { + for (var i = 0, l = this.inputs.length; i < l; ++i) { + var input = this.inputs[i]; + this.getConnectionPos(true, i, link_pos); + if ( + isInsideRectangle(x, y, link_pos[0] - 10, link_pos[1] - 5, 20, 10) + ) { + return { input: input, slot: i, link_pos: link_pos }; + } + } + } + + if (this.outputs) { + for (var i = 0, l = this.outputs.length; i < l; ++i) { + var output = this.outputs[i]; + this.getConnectionPos(false, i, link_pos); + if ( + isInsideRectangle(x, y, link_pos[0] - 10, link_pos[1] - 5, 20, 10) + ) { + return { output: output, slot: i, link_pos: link_pos }; + } + } + } + + return null; + }; + + /** + * returns the input slot with a given name (used for dynamic slots), -1 if not found + * @method findInputSlot + * @param {string} name the name of the slot + * @param {boolean} returnObj if the obj itself wanted + * @return {number_or_object} the slot (-1 if not found) + */ + LGraphNode.prototype.findInputSlot = function (name, returnObj) { + if (!this.inputs) { + return -1; + } + for (var i = 0, l = this.inputs.length; i < l; ++i) { + if (name == this.inputs[i].name) { + return !returnObj ? i : this.inputs[i]; + } + } + return -1; + }; + + /** + * returns the output slot with a given name (used for dynamic slots), -1 if not found + * @method findOutputSlot + * @param {string} name the name of the slot + * @param {boolean} returnObj if the obj itself wanted + * @return {number_or_object} the slot (-1 if not found) + */ + LGraphNode.prototype.findOutputSlot = function (name, returnObj) { + returnObj = returnObj || false; + if (!this.outputs) { + return -1; + } + for (var i = 0, l = this.outputs.length; i < l; ++i) { + if (name == this.outputs[i].name) { + return !returnObj ? i : this.outputs[i]; + } + } + return -1; + }; + + // TODO refactor: USE SINGLE findInput/findOutput functions! :: merge options + + /** + * returns the first free input slot + * @method findInputSlotFree + * @param {object} options + * @return {number_or_object} the slot (-1 if not found) + */ + LGraphNode.prototype.findInputSlotFree = function (optsIn) { + var optsIn = optsIn || {}; + var optsDef = { returnObj: false, typesNotAccepted: [] }; + var opts = Object.assign(optsDef, optsIn); + if (!this.inputs) { + return -1; + } + for (var i = 0, l = this.inputs.length; i < l; ++i) { + if (this.inputs[i].link && this.inputs[i].link != null) { + continue; + } + if ( + opts.typesNotAccepted && + opts.typesNotAccepted.includes && + opts.typesNotAccepted.includes(this.inputs[i].type) + ) { + continue; + } + return !opts.returnObj ? i : this.inputs[i]; + } + return -1; + }; + + /** + * returns the first output slot free + * @method findOutputSlotFree + * @param {object} options + * @return {number_or_object} the slot (-1 if not found) + */ + LGraphNode.prototype.findOutputSlotFree = function (optsIn) { + var optsIn = optsIn || {}; + var optsDef = { returnObj: false, typesNotAccepted: [] }; + var opts = Object.assign(optsDef, optsIn); + if (!this.outputs) { + return -1; + } + for (var i = 0, l = this.outputs.length; i < l; ++i) { + if (this.outputs[i].links && this.outputs[i].links != null) { + continue; + } + if ( + opts.typesNotAccepted && + opts.typesNotAccepted.includes && + opts.typesNotAccepted.includes(this.outputs[i].type) + ) { + continue; + } + return !opts.returnObj ? i : this.outputs[i]; + } + return -1; + }; + + /** + * findSlotByType for INPUTS + */ + LGraphNode.prototype.findInputSlotByType = function ( + type, + returnObj, + preferFreeSlot, + doNotUseOccupied, + ) { + return this.findSlotByType( + true, + type, + returnObj, + preferFreeSlot, + doNotUseOccupied, + ); + }; + + /** + * findSlotByType for OUTPUTS + */ + LGraphNode.prototype.findOutputSlotByType = function ( + type, + returnObj, + preferFreeSlot, + doNotUseOccupied, + ) { + return this.findSlotByType( + false, + type, + returnObj, + preferFreeSlot, + doNotUseOccupied, + ); + }; + + /** + * returns the output (or input) slot with a given type, -1 if not found + * @method findSlotByType + * @param {boolean} input uise inputs instead of outputs + * @param {string} type the type of the slot + * @param {boolean} returnObj if the obj itself wanted + * @param {boolean} preferFreeSlot if we want a free slot (if not found, will return the first of the type anyway) + * @return {number_or_object} the slot (-1 if not found) + */ + LGraphNode.prototype.findSlotByType = function ( + input, + type, + returnObj, + preferFreeSlot, + doNotUseOccupied, + ) { + input = input || false; + returnObj = returnObj || false; + preferFreeSlot = preferFreeSlot || false; + doNotUseOccupied = doNotUseOccupied || false; + var aSlots = input ? this.inputs : this.outputs; + if (!aSlots) { + return -1; + } + // !! empty string type is considered 0, * !! + if (type == "" || type == "*") type = 0; + for (var i = 0, l = aSlots.length; i < l; ++i) { + var tFound = false; + var aSource = (type + "").toLowerCase().split(","); + var aDest = + aSlots[i].type == "0" || aSlots[i].type == "*" ? "0" : aSlots[i].type; + aDest = (aDest + "").toLowerCase().split(","); + for (var sI = 0; sI < aSource.length; sI++) { + for (var dI = 0; dI < aDest.length; dI++) { + if (aSource[sI] == "_event_") aSource[sI] = LiteGraph.EVENT; + if (aDest[sI] == "_event_") aDest[sI] = LiteGraph.EVENT; + if (aSource[sI] == "*") aSource[sI] = 0; + if (aDest[sI] == "*") aDest[sI] = 0; + if (aSource[sI] == aDest[dI]) { + if (preferFreeSlot && aSlots[i].links && aSlots[i].links !== null) + continue; + return !returnObj ? i : aSlots[i]; + } + } + } + } + // if didnt find some, stop checking for free slots + if (preferFreeSlot && !doNotUseOccupied) { + for (var i = 0, l = aSlots.length; i < l; ++i) { + var tFound = false; + var aSource = (type + "").toLowerCase().split(","); + var aDest = + aSlots[i].type == "0" || aSlots[i].type == "*" ? "0" : aSlots[i].type; + aDest = (aDest + "").toLowerCase().split(","); + for (var sI = 0; sI < aSource.length; sI++) { + for (var dI = 0; dI < aDest.length; dI++) { + if (aSource[sI] == "*") aSource[sI] = 0; + if (aDest[sI] == "*") aDest[sI] = 0; + if (aSource[sI] == aDest[dI]) { + return !returnObj ? i : aSlots[i]; + } + } + } + } + } + return -1; + }; + + /** + * connect this node output to the input of another node BY TYPE + * @method connectByType + * @param {number_or_string} slot (could be the number of the slot or the string with the name of the slot) + * @param {LGraphNode} node the target node + * @param {string} target_type the input slot type of the target node + * @return {Object} the link_info is created, otherwise null + */ + LGraphNode.prototype.connectByType = function ( + slot, + target_node, + target_slotType, + optsIn, + ) { + var optsIn = optsIn || {}; + var optsDef = { + createEventInCase: true, + firstFreeIfOutputGeneralInCase: true, + generalTypeInCase: true, + }; + var opts = Object.assign(optsDef, optsIn); + if (target_node && target_node.constructor === Number) { + target_node = this.graph.getNodeById(target_node); + } + var target_slot = target_node.findInputSlotByType( + target_slotType, + false, + true, + ); + if (target_slot >= 0 && target_slot !== null) { + //console.debug("CONNbyTYPE type "+target_slotType+" for "+target_slot) + return this.connect(slot, target_node, target_slot); + } else { + //console.log("type "+target_slotType+" not found or not free?") + if (opts.createEventInCase && target_slotType == LiteGraph.EVENT) { + // WILL CREATE THE onTrigger IN SLOT + //console.debug("connect WILL CREATE THE onTrigger "+target_slotType+" to "+target_node); + return this.connect(slot, target_node, -1); + } + // connect to the first general output slot if not found a specific type and + if (opts.generalTypeInCase) { + var target_slot = target_node.findInputSlotByType(0, false, true, true); + //console.debug("connect TO a general type (*, 0), if not found the specific type ",target_slotType," to ",target_node,"RES_SLOT:",target_slot); + if (target_slot >= 0) { + return this.connect(slot, target_node, target_slot); + } + } + // connect to the first free input slot if not found a specific type and this output is general + if ( + opts.firstFreeIfOutputGeneralInCase && + (target_slotType == 0 || + target_slotType == "*" || + target_slotType == "") + ) { + var target_slot = target_node.findInputSlotFree({ + typesNotAccepted: [LiteGraph.EVENT], + }); + //console.debug("connect TO TheFirstFREE ",target_slotType," to ",target_node,"RES_SLOT:",target_slot); + if (target_slot >= 0) { + return this.connect(slot, target_node, target_slot); + } + } + + console.debug( + "no way to connect type: ", + target_slotType, + " to targetNODE ", + target_node, + ); + //TODO filter + + return null; + } + }; + + /** + * connect this node input to the output of another node BY TYPE + * @method connectByType + * @param {number_or_string} slot (could be the number of the slot or the string with the name of the slot) + * @param {LGraphNode} node the target node + * @param {string} target_type the output slot type of the target node + * @return {Object} the link_info is created, otherwise null + */ + LGraphNode.prototype.connectByTypeOutput = function ( + slot, + source_node, + source_slotType, + optsIn, + ) { + var optsIn = optsIn || {}; + var optsDef = { + createEventInCase: true, + firstFreeIfInputGeneralInCase: true, + generalTypeInCase: true, + }; + var opts = Object.assign(optsDef, optsIn); + if (source_node && source_node.constructor === Number) { + source_node = this.graph.getNodeById(source_node); + } + var source_slot = source_node.findOutputSlotByType( + source_slotType, + false, + true, + ); + if (source_slot >= 0 && source_slot !== null) { + //console.debug("CONNbyTYPE OUT! type "+source_slotType+" for "+source_slot) + return source_node.connect(source_slot, this, slot); + } else { + // connect to the first general output slot if not found a specific type and + if (opts.generalTypeInCase) { + var source_slot = source_node.findOutputSlotByType( + 0, + false, + true, + true, + ); + if (source_slot >= 0) { + return source_node.connect(source_slot, this, slot); + } + } + + if (opts.createEventInCase && source_slotType == LiteGraph.EVENT) { + // WILL CREATE THE onExecuted OUT SLOT + if (LiteGraph.do_add_triggers_slots) { + var source_slot = source_node.addOnExecutedOutput(); + return source_node.connect(source_slot, this, slot); + } + } + // connect to the first free output slot if not found a specific type and this input is general + if ( + opts.firstFreeIfInputGeneralInCase && + (source_slotType == 0 || + source_slotType == "*" || + source_slotType == "") + ) { + var source_slot = source_node.findOutputSlotFree({ + typesNotAccepted: [LiteGraph.EVENT], + }); + if (source_slot >= 0) { + return source_node.connect(source_slot, this, slot); + } + } + + console.debug( + "no way to connect byOUT type: ", + source_slotType, + " to sourceNODE ", + source_node, + ); + //TODO filter + + //console.log("type OUT! "+source_slotType+" not found or not free?") + return null; + } + }; + + /** + * connect this node output to the input of another node + * @method connect + * @param {number_or_string} slot (could be the number of the slot or the string with the name of the slot) + * @param {LGraphNode} node the target node + * @param {number_or_string} target_slot the input slot of the target node (could be the number of the slot or the string with the name of the slot, or -1 to connect a trigger) + * @return {Object} the link_info is created, otherwise null + */ + LGraphNode.prototype.connect = function (slot, target_node, target_slot) { + target_slot = target_slot || 0; + + if (!this.graph) { + //could be connected before adding it to a graph + console.log( + "Connect: Error, node doesn't belong to any graph. Nodes must be added first to a graph before connecting them.", + ); //due to link ids being associated with graphs + return null; + } + + //seek for the output slot + if (slot.constructor === String) { + slot = this.findOutputSlot(slot); + if (slot == -1) { + if (LiteGraph.debug) { + console.log("Connect: Error, no slot of name " + slot); + } + return null; + } + } else if (!this.outputs || slot >= this.outputs.length) { + if (LiteGraph.debug) { + console.log("Connect: Error, slot number not found"); + } + return null; + } + + if (target_node && target_node.constructor === Number) { + target_node = this.graph.getNodeById(target_node); + } + if (!target_node) { + throw "target node is null"; + } + + //avoid loopback + if (target_node == this) { + return null; + } + + //you can specify the slot by name + if (target_slot.constructor === String) { + target_slot = target_node.findInputSlot(target_slot); + if (target_slot == -1) { + if (LiteGraph.debug) { + console.log("Connect: Error, no slot of name " + target_slot); + } + return null; + } + } else if (target_slot === LiteGraph.EVENT) { + if (LiteGraph.do_add_triggers_slots) { + //search for first slot with event? :: NO this is done outside + //console.log("Connect: Creating triggerEvent"); + // force mode + target_node.changeMode(LiteGraph.ON_TRIGGER); + target_slot = target_node.findInputSlot("onTrigger"); + } else { + return null; // -- break -- + } + } else if ( + !target_node.inputs || + target_slot >= target_node.inputs.length + ) { + if (LiteGraph.debug) { + console.log("Connect: Error, slot number not found"); + } + return null; + } + + var changed = false; + + var input = target_node.inputs[target_slot]; + var link_info = null; + var output = this.outputs[slot]; + + if (!this.outputs[slot]) { + /*console.debug("Invalid slot passed: "+slot); + console.debug(this.outputs);*/ + return null; + } + + // allow target node to change slot + if (target_node.onBeforeConnectInput) { + // This way node can choose another slot (or make a new one?) + target_slot = target_node.onBeforeConnectInput(target_slot); //callback + } + + //check target_slot and check connection types + if ( + target_slot === false || + target_slot === null || + !LiteGraph.isValidConnection(output.type, input.type) + ) { + this.setDirtyCanvas(false, true); + if (changed) this.graph.connectionChange(this, link_info); + return null; + } else { + //console.debug("valid connection",output.type, input.type); + } + + //allows nodes to block connection, callback + if (target_node.onConnectInput) { + if ( + target_node.onConnectInput( + target_slot, + output.type, + output, + this, + slot, + ) === false + ) { + return null; + } + } + if (this.onConnectOutput) { + // callback + if ( + this.onConnectOutput( + slot, + input.type, + input, + target_node, + target_slot, + ) === false + ) { + return null; + } + } + + //if there is something already plugged there, disconnect + if ( + target_node.inputs[target_slot] && + target_node.inputs[target_slot].link != null + ) { + this.graph.beforeChange(); + target_node.disconnectInput(target_slot, { doProcessChange: false }); + changed = true; + } + if (output.links !== null && output.links.length) { + switch (output.type) { + case LiteGraph.EVENT: + if (!LiteGraph.allow_multi_output_for_events) { + this.graph.beforeChange(); + this.disconnectOutput(slot, false, { doProcessChange: false }); // Input(target_slot, {doProcessChange: false}); + changed = true; + } + break; + default: + break; + } + } + + var nextId; + if (LiteGraph.use_uuids) nextId = LiteGraph.uuidv4(); + else nextId = ++this.graph.last_link_id; + + //create link class + link_info = new LLink( + nextId, + input.type || output.type, + this.id, + slot, + target_node.id, + target_slot, + ); + + //add to graph links list + this.graph.links[link_info.id] = link_info; + + //connect in output + if (output.links == null) { + output.links = []; + } + output.links.push(link_info.id); + //connect in input + target_node.inputs[target_slot].link = link_info.id; + if (this.graph) { + this.graph._version++; + } + if (this.onConnectionsChange) { + this.onConnectionsChange(LiteGraph.OUTPUT, slot, true, link_info, output); + } //link_info has been created now, so its updated + if (target_node.onConnectionsChange) { + target_node.onConnectionsChange( + LiteGraph.INPUT, + target_slot, + true, + link_info, + input, + ); + } + if (this.graph && this.graph.onNodeConnectionChange) { + this.graph.onNodeConnectionChange( + LiteGraph.INPUT, + target_node, + target_slot, + this, + slot, + ); + this.graph.onNodeConnectionChange( + LiteGraph.OUTPUT, + this, + slot, + target_node, + target_slot, + ); + } + + this.setDirtyCanvas(false, true); + this.graph.afterChange(); + this.graph.connectionChange(this, link_info); + + return link_info; + }; + + /** + * disconnect one output to an specific node + * @method disconnectOutput + * @param {number_or_string} slot (could be the number of the slot or the string with the name of the slot) + * @param {LGraphNode} target_node the target node to which this slot is connected [Optional, if not target_node is specified all nodes will be disconnected] + * @return {boolean} if it was disconnected successfully + */ + LGraphNode.prototype.disconnectOutput = function (slot, target_node) { + if (slot.constructor === String) { + slot = this.findOutputSlot(slot); + if (slot == -1) { + if (LiteGraph.debug) { + console.log("Connect: Error, no slot of name " + slot); + } + return false; + } + } else if (!this.outputs || slot >= this.outputs.length) { + if (LiteGraph.debug) { + console.log("Connect: Error, slot number not found"); + } + return false; + } + + //get output slot + var output = this.outputs[slot]; + if (!output || !output.links || output.links.length == 0) { + return false; + } + + //one of the output links in this slot + if (target_node) { + if (target_node.constructor === Number) { + target_node = this.graph.getNodeById(target_node); + } + if (!target_node) { + throw "Target Node not found"; + } + + for (var i = 0, l = output.links.length; i < l; i++) { + var link_id = output.links[i]; + var link_info = this.graph.links[link_id]; + + //is the link we are searching for... + if (link_info.target_id == target_node.id) { + output.links.splice(i, 1); //remove here + var input = target_node.inputs[link_info.target_slot]; + input.link = null; //remove there + delete this.graph.links[link_id]; //remove the link from the links pool + if (this.graph) { + this.graph._version++; + } + if (target_node.onConnectionsChange) { + target_node.onConnectionsChange( + LiteGraph.INPUT, + link_info.target_slot, + false, + link_info, + input, + ); + } //link_info hasn't been modified so its ok + if (this.onConnectionsChange) { + this.onConnectionsChange( + LiteGraph.OUTPUT, + slot, + false, + link_info, + output, + ); + } + if (this.graph && this.graph.onNodeConnectionChange) { + this.graph.onNodeConnectionChange(LiteGraph.OUTPUT, this, slot); + } + if (this.graph && this.graph.onNodeConnectionChange) { + this.graph.onNodeConnectionChange(LiteGraph.OUTPUT, this, slot); + this.graph.onNodeConnectionChange( + LiteGraph.INPUT, + target_node, + link_info.target_slot, + ); + } + break; + } + } + } //all the links in this output slot + else { + for (var i = 0, l = output.links.length; i < l; i++) { + var link_id = output.links[i]; + var link_info = this.graph.links[link_id]; + if (!link_info) { + //bug: it happens sometimes + continue; + } + + var target_node = this.graph.getNodeById(link_info.target_id); + var input = null; + if (this.graph) { + this.graph._version++; + } + if (target_node) { + input = target_node.inputs[link_info.target_slot]; + input.link = null; //remove other side link + if (target_node.onConnectionsChange) { + target_node.onConnectionsChange( + LiteGraph.INPUT, + link_info.target_slot, + false, + link_info, + input, + ); + } //link_info hasn't been modified so its ok + if (this.graph && this.graph.onNodeConnectionChange) { + this.graph.onNodeConnectionChange( + LiteGraph.INPUT, + target_node, + link_info.target_slot, + ); + } + } + delete this.graph.links[link_id]; //remove the link from the links pool + if (this.onConnectionsChange) { + this.onConnectionsChange( + LiteGraph.OUTPUT, + slot, + false, + link_info, + output, + ); + } + if (this.graph && this.graph.onNodeConnectionChange) { + this.graph.onNodeConnectionChange(LiteGraph.OUTPUT, this, slot); + this.graph.onNodeConnectionChange( + LiteGraph.INPUT, + target_node, + link_info.target_slot, + ); + } + } + output.links = null; + } + + this.setDirtyCanvas(false, true); + this.graph.connectionChange(this); + return true; + }; + + /** + * disconnect one input + * @method disconnectInput + * @param {number_or_string} slot (could be the number of the slot or the string with the name of the slot) + * @return {boolean} if it was disconnected successfully + */ + LGraphNode.prototype.disconnectInput = function (slot) { + //seek for the output slot + if (slot.constructor === String) { + slot = this.findInputSlot(slot); + if (slot == -1) { + if (LiteGraph.debug) { + console.log("Connect: Error, no slot of name " + slot); + } + return false; + } + } else if (!this.inputs || slot >= this.inputs.length) { + if (LiteGraph.debug) { + console.log("Connect: Error, slot number not found"); + } + return false; + } + + var input = this.inputs[slot]; + if (!input) { + return false; + } + + var link_id = this.inputs[slot].link; + if (link_id != null) { + this.inputs[slot].link = null; + + //remove other side + var link_info = this.graph.links[link_id]; + if (link_info) { + var target_node = this.graph.getNodeById(link_info.origin_id); + if (!target_node) { + return false; + } + + var output = target_node.outputs[link_info.origin_slot]; + if (!output || !output.links || output.links.length == 0) { + return false; + } + + //search in the inputs list for this link + for (var i = 0, l = output.links.length; i < l; i++) { + if (output.links[i] == link_id) { + output.links.splice(i, 1); + break; + } + } + + delete this.graph.links[link_id]; //remove from the pool + if (this.graph) { + this.graph._version++; + } + if (this.onConnectionsChange) { + this.onConnectionsChange( + LiteGraph.INPUT, + slot, + false, + link_info, + input, + ); + } + if (target_node.onConnectionsChange) { + target_node.onConnectionsChange( + LiteGraph.OUTPUT, + i, + false, + link_info, + output, + ); + } + if (this.graph && this.graph.onNodeConnectionChange) { + this.graph.onNodeConnectionChange(LiteGraph.OUTPUT, target_node, i); + this.graph.onNodeConnectionChange(LiteGraph.INPUT, this, slot); + } + } + } //link != null + + this.setDirtyCanvas(false, true); + if (this.graph) this.graph.connectionChange(this); + return true; + }; + + /** + * returns the center of a connection point in canvas coords + * @method getConnectionPos + * @param {boolean} is_input true if if a input slot, false if it is an output + * @param {number_or_string} slot (could be the number of the slot or the string with the name of the slot) + * @param {vec2} out [optional] a place to store the output, to free garbage + * @return {[x,y]} the position + **/ + LGraphNode.prototype.getConnectionPos = function ( + is_input, + slot_number, + out, + ) { + out = out || new Float32Array(2); + var num_slots = 0; + if (is_input && this.inputs) { + num_slots = this.inputs.length; + } + if (!is_input && this.outputs) { + num_slots = this.outputs.length; + } + + var offset = LiteGraph.NODE_SLOT_HEIGHT * 0.5; + + if (this.flags.collapsed) { + var w = this._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH; + if (this.horizontal) { + out[0] = this.pos[0] + w * 0.5; + if (is_input) { + out[1] = this.pos[1] - LiteGraph.NODE_TITLE_HEIGHT; + } else { + out[1] = this.pos[1]; + } + } else { + if (is_input) { + out[0] = this.pos[0]; + } else { + out[0] = this.pos[0] + w; + } + out[1] = this.pos[1] - LiteGraph.NODE_TITLE_HEIGHT * 0.5; + } + return out; + } + + //weird feature that never got finished + if (is_input && slot_number == -1) { + out[0] = this.pos[0] + LiteGraph.NODE_TITLE_HEIGHT * 0.5; + out[1] = this.pos[1] + LiteGraph.NODE_TITLE_HEIGHT * 0.5; + return out; + } + + //hard-coded pos + if (is_input && num_slots > slot_number && this.inputs[slot_number].pos) { + out[0] = this.pos[0] + this.inputs[slot_number].pos[0]; + out[1] = this.pos[1] + this.inputs[slot_number].pos[1]; + return out; + } else if ( + !is_input && + num_slots > slot_number && + this.outputs[slot_number].pos + ) { + out[0] = this.pos[0] + this.outputs[slot_number].pos[0]; + out[1] = this.pos[1] + this.outputs[slot_number].pos[1]; + return out; + } + + //horizontal distributed slots + if (this.horizontal) { + out[0] = this.pos[0] + (slot_number + 0.5) * (this.size[0] / num_slots); + if (is_input) { + out[1] = this.pos[1] - LiteGraph.NODE_TITLE_HEIGHT; + } else { + out[1] = this.pos[1] + this.size[1]; + } + return out; + } + + //default vertical slots + if (is_input) { + out[0] = this.pos[0] + offset; + } else { + out[0] = this.pos[0] + this.size[0] + 1 - offset; + } + out[1] = + this.pos[1] + + (slot_number + 0.7) * LiteGraph.NODE_SLOT_HEIGHT + + (this.constructor.slot_start_y || 0); + return out; + }; + + /* Force align to grid */ + LGraphNode.prototype.alignToGrid = function () { + this.pos[0] = + LiteGraph.CANVAS_GRID_SIZE * + Math.round(this.pos[0] / LiteGraph.CANVAS_GRID_SIZE); + this.pos[1] = + LiteGraph.CANVAS_GRID_SIZE * + Math.round(this.pos[1] / LiteGraph.CANVAS_GRID_SIZE); + }; + + /* Console output */ + LGraphNode.prototype.trace = function (msg) { + if (!this.console) { + this.console = []; + } + + this.console.push(msg); + if (this.console.length > LGraphNode.MAX_CONSOLE) { + this.console.shift(); + } + + if (this.graph.onNodeTrace) this.graph.onNodeTrace(this, msg); + }; + + /* Forces to redraw or the main canvas (LGraphNode) or the bg canvas (links) */ + LGraphNode.prototype.setDirtyCanvas = function ( + dirty_foreground, + dirty_background, + ) { + if (!this.graph) { + return; + } + this.graph.sendActionToCanvas("setDirty", [ + dirty_foreground, + dirty_background, + ]); + }; + + LGraphNode.prototype.loadImage = function (url) { + var img = new Image(); + img.src = LiteGraph.node_images_path + url; + img.ready = false; + + var that = this; + img.onload = function () { + this.ready = true; + that.setDirtyCanvas(true); + }; + return img; + }; + + //safe LGraphNode action execution (not sure if safe) + /* +LGraphNode.prototype.executeAction = function(action) +{ + if(action == "") return false; + + if( action.indexOf(";") != -1 || action.indexOf("}") != -1) + { + this.trace("Error: Action contains unsafe characters"); + return false; + } + + var tokens = action.split("("); + var func_name = tokens[0]; + if( typeof(this[func_name]) != "function") + { + this.trace("Error: Action not found on node: " + func_name); + return false; + } + + var code = action; + + try + { + var _foo = eval; + eval = null; + (new Function("with(this) { " + code + "}")).call(this); + eval = _foo; + } + catch (err) + { + this.trace("Error executing action {" + action + "} :" + err); + return false; + } + + return true; +} +*/ + + /* Allows to get onMouseMove and onMouseUp events even if the mouse is out of focus */ + LGraphNode.prototype.captureInput = function (v) { + if (!this.graph || !this.graph.list_of_graphcanvas) { + return; + } + + var list = this.graph.list_of_graphcanvas; + + for (var i = 0; i < list.length; ++i) { + var c = list[i]; + //releasing somebody elses capture?! + if (!v && c.node_capturing_input != this) { + continue; + } + + //change + c.node_capturing_input = v ? this : null; + } + }; + + /** + * Collapse the node to make it smaller on the canvas + * @method collapse + **/ + LGraphNode.prototype.collapse = function (force) { + this.graph._version++; + if (this.constructor.collapsable === false && !force) { + return; + } + if (!this.flags.collapsed) { + this.flags.collapsed = true; + } else { + this.flags.collapsed = false; + } + this.setDirtyCanvas(true, true); + }; + + /** + * Forces the node to do not move or realign on Z + * @method pin + **/ + + LGraphNode.prototype.pin = function (v) { + this.graph._version++; + if (v === undefined) { + this.flags.pinned = !this.flags.pinned; + } else { + this.flags.pinned = v; + } + }; + + LGraphNode.prototype.localToScreen = function (x, y, graphcanvas) { + return [ + (x + this.pos[0]) * graphcanvas.scale + graphcanvas.offset[0], + (y + this.pos[1]) * graphcanvas.scale + graphcanvas.offset[1], + ]; + }; + + function LGraphGroup(title) { + this._ctor(title); + } + + global.LGraphGroup = LiteGraph.LGraphGroup = LGraphGroup; + + LGraphGroup.prototype._ctor = function (title) { + this.title = title || "Group"; + this.font_size = 24; + this.color = LGraphCanvas.node_colors.pale_blue + ? LGraphCanvas.node_colors.pale_blue.groupcolor + : "#AAA"; + this._bounding = new Float32Array([10, 10, 140, 80]); + this._pos = this._bounding.subarray(0, 2); + this._size = this._bounding.subarray(2, 4); + this._nodes = []; + this.graph = null; + + Object.defineProperty(this, "pos", { + set: function (v) { + if (!v || v.length < 2) { + return; + } + this._pos[0] = v[0]; + this._pos[1] = v[1]; + }, + get: function () { + return this._pos; + }, + enumerable: true, + }); + + Object.defineProperty(this, "size", { + set: function (v) { + if (!v || v.length < 2) { + return; + } + this._size[0] = Math.max(140, v[0]); + this._size[1] = Math.max(80, v[1]); + }, + get: function () { + return this._size; + }, + enumerable: true, + }); + }; + + LGraphGroup.prototype.configure = function (o) { + this.title = o.title; + this._bounding.set(o.bounding); + this.color = o.color; + this.font_size = o.font_size; + }; + + LGraphGroup.prototype.serialize = function () { + var b = this._bounding; + return { + title: this.title, + bounding: [ + Math.round(b[0]), + Math.round(b[1]), + Math.round(b[2]), + Math.round(b[3]), + ], + color: this.color, + font_size: this.font_size, + }; + }; + + LGraphGroup.prototype.move = function (deltax, deltay, ignore_nodes) { + this._pos[0] += deltax; + this._pos[1] += deltay; + if (ignore_nodes) { + return; + } + for (var i = 0; i < this._nodes.length; ++i) { + var node = this._nodes[i]; + node.pos[0] += deltax; + node.pos[1] += deltay; + } + }; + + LGraphGroup.prototype.recomputeInsideNodes = function () { + this._nodes.length = 0; + var nodes = this.graph._nodes; + var node_bounding = new Float32Array(4); + + for (var i = 0; i < nodes.length; ++i) { + var node = nodes[i]; + node.getBounding(node_bounding); + if (!overlapBounding(this._bounding, node_bounding)) { + continue; + } //out of the visible area + this._nodes.push(node); + } + }; + + LGraphGroup.prototype.isPointInside = LGraphNode.prototype.isPointInside; + LGraphGroup.prototype.setDirtyCanvas = LGraphNode.prototype.setDirtyCanvas; + + //**************************************** + + //Scale and Offset + function DragAndScale(element, skip_events) { + this.offset = new Float32Array([0, 0]); + this.scale = 1; + this.max_scale = 10; + this.min_scale = 0.1; + this.onredraw = null; + this.enabled = true; + this.last_mouse = [0, 0]; + this.element = null; + this.visible_area = new Float32Array(4); + + if (element) { + this.element = element; + if (!skip_events) { + this.bindEvents(element); + } + } + } + + LiteGraph.DragAndScale = DragAndScale; + + DragAndScale.prototype.bindEvents = function (element) { + this.last_mouse = new Float32Array(2); + + this._binded_mouse_callback = this.onMouse.bind(this); + + LiteGraph.pointerListenerAdd(element, "down", this._binded_mouse_callback); + LiteGraph.pointerListenerAdd(element, "move", this._binded_mouse_callback); + LiteGraph.pointerListenerAdd(element, "up", this._binded_mouse_callback); + + element.addEventListener("mousewheel", this._binded_mouse_callback, false); + element.addEventListener("wheel", this._binded_mouse_callback, false); + }; + + DragAndScale.prototype.computeVisibleArea = function (viewport) { + if (!this.element) { + this.visible_area[0] = + this.visible_area[1] = + this.visible_area[2] = + this.visible_area[3] = + 0; + return; + } + var width = this.element.width; + var height = this.element.height; + var startx = -this.offset[0]; + var starty = -this.offset[1]; + if (viewport) { + startx += viewport[0] / this.scale; + starty += viewport[1] / this.scale; + width = viewport[2]; + height = viewport[3]; + } + var endx = startx + width / this.scale; + var endy = starty + height / this.scale; + this.visible_area[0] = startx; + this.visible_area[1] = starty; + this.visible_area[2] = endx - startx; + this.visible_area[3] = endy - starty; + }; + + DragAndScale.prototype.onMouse = function (e) { + if (!this.enabled) { + return; + } + + var canvas = this.element; + var rect = canvas.getBoundingClientRect(); + var x = e.clientX - rect.left; + var y = e.clientY - rect.top; + e.canvasx = x; + e.canvasy = y; + e.dragging = this.dragging; + + var is_inside = + !this.viewport || + (this.viewport && + x >= this.viewport[0] && + x < this.viewport[0] + this.viewport[2] && + y >= this.viewport[1] && + y < this.viewport[1] + this.viewport[3]); + + //console.log("pointerevents: DragAndScale onMouse "+e.type+" "+is_inside); + + var ignore = false; + if (this.onmouse) { + ignore = this.onmouse(e); + } + + if (e.type == LiteGraph.pointerevents_method + "down" && is_inside) { + this.dragging = true; + LiteGraph.pointerListenerRemove( + canvas, + "move", + this._binded_mouse_callback, + ); + LiteGraph.pointerListenerAdd( + document, + "move", + this._binded_mouse_callback, + ); + LiteGraph.pointerListenerAdd(document, "up", this._binded_mouse_callback); + } else if (e.type == LiteGraph.pointerevents_method + "move") { + if (!ignore) { + var deltax = x - this.last_mouse[0]; + var deltay = y - this.last_mouse[1]; + if (this.dragging) { + this.mouseDrag(deltax, deltay); + } + } + } else if (e.type == LiteGraph.pointerevents_method + "up") { + this.dragging = false; + LiteGraph.pointerListenerRemove( + document, + "move", + this._binded_mouse_callback, + ); + LiteGraph.pointerListenerRemove( + document, + "up", + this._binded_mouse_callback, + ); + LiteGraph.pointerListenerAdd(canvas, "move", this._binded_mouse_callback); + } else if ( + is_inside && + (e.type == "mousewheel" || + e.type == "wheel" || + e.type == "DOMMouseScroll") + ) { + e.eventType = "mousewheel"; + if (e.type == "wheel") { + e.wheel = -e.deltaY; + } else { + e.wheel = e.wheelDeltaY != null ? e.wheelDeltaY : e.detail * -60; + } + + //from stack overflow + e.delta = e.wheelDelta ? e.wheelDelta / 40 : e.deltaY ? -e.deltaY / 3 : 0; + this.changeDeltaScale(1.0 + e.delta * 0.05); + } + + this.last_mouse[0] = x; + this.last_mouse[1] = y; + + if (is_inside) { + e.preventDefault(); + e.stopPropagation(); + return false; + } + }; + + DragAndScale.prototype.toCanvasContext = function (ctx) { + ctx.scale(this.scale, this.scale); + ctx.translate(this.offset[0], this.offset[1]); + }; + + DragAndScale.prototype.convertOffsetToCanvas = function (pos) { + //return [pos[0] / this.scale - this.offset[0], pos[1] / this.scale - this.offset[1]]; + return [ + (pos[0] + this.offset[0]) * this.scale, + (pos[1] + this.offset[1]) * this.scale, + ]; + }; + + DragAndScale.prototype.convertCanvasToOffset = function (pos, out) { + out = out || [0, 0]; + out[0] = pos[0] / this.scale - this.offset[0]; + out[1] = pos[1] / this.scale - this.offset[1]; + return out; + }; + + DragAndScale.prototype.mouseDrag = function (x, y) { + this.offset[0] += x / this.scale; + this.offset[1] += y / this.scale; + + if (this.onredraw) { + this.onredraw(this); + } + }; + + DragAndScale.prototype.changeScale = function (value, zooming_center) { + if (value < this.min_scale) { + value = this.min_scale; + } else if (value > this.max_scale) { + value = this.max_scale; + } + + if (value == this.scale) { + return; + } + + if (!this.element) { + return; + } + + var rect = this.element.getBoundingClientRect(); + if (!rect) { + return; + } + + zooming_center = zooming_center || [rect.width * 0.5, rect.height * 0.5]; + var center = this.convertCanvasToOffset(zooming_center); + this.scale = value; + if (Math.abs(this.scale - 1) < 0.01) { + this.scale = 1; + } + + var new_center = this.convertCanvasToOffset(zooming_center); + var delta_offset = [new_center[0] - center[0], new_center[1] - center[1]]; + + this.offset[0] += delta_offset[0]; + this.offset[1] += delta_offset[1]; + + if (this.onredraw) { + this.onredraw(this); + } + }; + + DragAndScale.prototype.changeDeltaScale = function (value, zooming_center) { + this.changeScale(this.scale * value, zooming_center); + }; + + DragAndScale.prototype.reset = function () { + this.scale = 1; + this.offset[0] = 0; + this.offset[1] = 0; + }; + + //********************************************************************************* + // LGraphCanvas: LGraph renderer CLASS + //********************************************************************************* + + /** + * This class is in charge of rendering one graph inside a canvas. And provides all the interaction required. + * Valid callbacks are: onNodeSelected, onNodeDeselected, onShowNodePanel, onNodeDblClicked + * + * @class LGraphCanvas + * @constructor + * @param {HTMLCanvas} canvas the canvas where you want to render (it accepts a selector in string format or the canvas element itself) + * @param {LGraph} graph [optional] + * @param {Object} options [optional] { skip_rendering, autoresize, viewport } + */ + function LGraphCanvas(canvas, graph, options) { + this.options = options = options || {}; + + //if(graph === undefined) + // throw ("No graph assigned"); + this.background_image = LGraphCanvas.DEFAULT_BACKGROUND_IMAGE; + + if (canvas && canvas.constructor === String) { + canvas = document.querySelector(canvas); + } + + this.ds = new DragAndScale(); + this.zoom_modify_alpha = true; //otherwise it generates ugly patterns when scaling down too much + + this.title_text_font = "" + LiteGraph.NODE_TEXT_SIZE + "px Arial"; + this.inner_text_font = "normal " + LiteGraph.NODE_SUBTEXT_SIZE + "px Arial"; + this.node_title_color = LiteGraph.NODE_TITLE_COLOR; + this.default_link_color = LiteGraph.LINK_COLOR; + this.default_connection_color = { + input_off: "#778", + input_on: "#7F7", //"#BBD" + output_off: "#778", + output_on: "#7F7", //"#BBD" + }; + this.default_connection_color_byType = { + /*number: "#7F7", + string: "#77F", + boolean: "#F77",*/ + }; + this.default_connection_color_byTypeOff = { + /*number: "#474", + string: "#447", + boolean: "#744",*/ + }; + + this.highquality_render = true; + this.use_gradients = false; //set to true to render titlebar with gradients + this.editor_alpha = 1; //used for transition + this.pause_rendering = false; + this.clear_background = true; + this.clear_background_color = "#222"; + + this.read_only = false; //if set to true users cannot modify the graph + this.render_only_selected = true; + this.live_mode = false; + this.show_info = true; + this.allow_dragcanvas = true; + this.allow_dragnodes = true; + this.allow_interaction = true; //allow to control widgets, buttons, collapse, etc + this.multi_select = false; //allow selecting multi nodes without pressing extra keys + this.allow_searchbox = true; + this.allow_reconnect_links = true; //allows to change a connection with having to redo it again + this.align_to_grid = false; //snap to grid + + this.drag_mode = false; + this.dragging_rectangle = null; + + this.filter = null; //allows to filter to only accept some type of nodes in a graph + + this.set_canvas_dirty_on_mouse_event = true; //forces to redraw the canvas if the mouse does anything + this.always_render_background = false; + this.render_shadows = true; + this.render_canvas_border = true; + this.render_connections_shadows = false; //too much cpu + this.render_connections_border = true; + this.render_curved_connections = false; + this.render_connection_arrows = false; + this.render_collapsed_slots = true; + this.render_execution_order = false; + this.render_title_colored = true; + this.render_link_tooltip = true; + + this.links_render_mode = LiteGraph.SPLINE_LINK; + + this.mouse = [0, 0]; //mouse in canvas coordinates, where 0,0 is the top-left corner of the blue rectangle + this.graph_mouse = [0, 0]; //mouse in graph coordinates, where 0,0 is the top-left corner of the blue rectangle + this.canvas_mouse = this.graph_mouse; //LEGACY: REMOVE THIS, USE GRAPH_MOUSE INSTEAD + + //to personalize the search box + this.onSearchBox = null; + this.onSearchBoxSelection = null; + + //callbacks + this.onMouse = null; + this.onDrawBackground = null; //to render background objects (behind nodes and connections) in the canvas affected by transform + this.onDrawForeground = null; //to render foreground objects (above nodes and connections) in the canvas affected by transform + this.onDrawOverlay = null; //to render foreground objects not affected by transform (for GUIs) + this.onDrawLinkTooltip = null; //called when rendering a tooltip + this.onNodeMoved = null; //called after moving a node + this.onSelectionChange = null; //called if the selection changes + this.onConnectingChange = null; //called before any link changes + this.onBeforeChange = null; //called before modifying the graph + this.onAfterChange = null; //called after modifying the graph + + this.connections_width = 3; + this.round_radius = 8; + + this.current_node = null; + this.node_widget = null; //used for widgets + this.over_link_center = null; + this.last_mouse_position = [0, 0]; + this.visible_area = this.ds.visible_area; + this.visible_links = []; + + this.viewport = options.viewport || null; //to constraint render area to a portion of the canvas + + //link canvas and graph + if (graph) { + graph.attachCanvas(this); + } + + this.setCanvas(canvas, options.skip_events); + this.clear(); + + if (!options.skip_render) { + this.startRendering(); + } + + this.autoresize = options.autoresize; + } + + global.LGraphCanvas = LiteGraph.LGraphCanvas = LGraphCanvas; + + LGraphCanvas.DEFAULT_BACKGROUND_IMAGE = + "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGQAAABkCAIAAAD/gAIDAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAQBJREFUeNrs1rEKwjAUhlETUkj3vP9rdmr1Ysammk2w5wdxuLgcMHyptfawuZX4pJSWZTnfnu/lnIe/jNNxHHGNn//HNbbv+4dr6V+11uF527arU7+u63qfa/bnmh8sWLBgwYJlqRf8MEptXPBXJXa37BSl3ixYsGDBMliwFLyCV/DeLIMFCxYsWLBMwSt4Be/NggXLYMGCBUvBK3iNruC9WbBgwYJlsGApeAWv4L1ZBgsWLFiwYJmCV/AK3psFC5bBggULloJX8BpdwXuzYMGCBctgwVLwCl7Be7MMFixYsGDBsu8FH1FaSmExVfAxBa/gvVmwYMGCZbBg/W4vAQYA5tRF9QYlv/QAAAAASUVORK5CYII="; + + LGraphCanvas.link_type_colors = { + "-1": LiteGraph.EVENT_LINK_COLOR, + number: "#AAA", + node: "#DCA", + }; + LGraphCanvas.gradients = {}; //cache of gradients + + /** + * clears all the data inside + * + * @method clear + */ + LGraphCanvas.prototype.clear = function () { + this.frame = 0; + this.last_draw_time = 0; + this.render_time = 0; + this.fps = 0; + + //this.scale = 1; + //this.offset = [0,0]; + + this.dragging_rectangle = null; + + this.selected_nodes = {}; + this.selected_group = null; + + this.visible_nodes = []; + this.node_dragged = null; + this.node_over = null; + this.node_capturing_input = null; + this.connecting_node = null; + this.highlighted_links = {}; + + this.dragging_canvas = false; + + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + this.dirty_area = null; + + this.node_in_panel = null; + this.node_widget = null; + + this.last_mouse = [0, 0]; + this.last_mouseclick = 0; + this.pointer_is_down = false; + this.pointer_is_double = false; + this.visible_area.set([0, 0, 0, 0]); + + if (this.onClear) { + this.onClear(); + } + }; + + /** + * assigns a graph, you can reassign graphs to the same canvas + * + * @method setGraph + * @param {LGraph} graph + */ + LGraphCanvas.prototype.setGraph = function (graph, skip_clear) { + if (this.graph == graph) { + return; + } + + if (!skip_clear) { + this.clear(); + } + + if (!graph && this.graph) { + this.graph.detachCanvas(this); + return; + } + + graph.attachCanvas(this); + + //remove the graph stack in case a subgraph was open + if (this._graph_stack) this._graph_stack = null; + + this.setDirty(true, true); + }; + + /** + * returns the top level graph (in case there are subgraphs open on the canvas) + * + * @method getTopGraph + * @return {LGraph} graph + */ + LGraphCanvas.prototype.getTopGraph = function () { + if (this._graph_stack.length) return this._graph_stack[0]; + return this.graph; + }; + + /** + * opens a graph contained inside a node in the current graph + * + * @method openSubgraph + * @param {LGraph} graph + */ + LGraphCanvas.prototype.openSubgraph = function (graph) { + if (!graph) { + throw "graph cannot be null"; + } + + if (this.graph == graph) { + throw "graph cannot be the same"; + } + + this.clear(); + + if (this.graph) { + if (!this._graph_stack) { + this._graph_stack = []; + } + this._graph_stack.push(this.graph); + } + + graph.attachCanvas(this); + this.checkPanels(); + this.setDirty(true, true); + }; + + /** + * closes a subgraph contained inside a node + * + * @method closeSubgraph + * @param {LGraph} assigns a graph + */ + LGraphCanvas.prototype.closeSubgraph = function () { + if (!this._graph_stack || this._graph_stack.length == 0) { + return; + } + var subgraph_node = this.graph._subgraph_node; + var graph = this._graph_stack.pop(); + this.selected_nodes = {}; + this.highlighted_links = {}; + graph.attachCanvas(this); + this.setDirty(true, true); + if (subgraph_node) { + this.centerOnNode(subgraph_node); + this.selectNodes([subgraph_node]); + } + // when close sub graph back to offset [0, 0] scale 1 + this.ds.offset = [0, 0]; + this.ds.scale = 1; + }; + + /** + * returns the visually active graph (in case there are more in the stack) + * @method getCurrentGraph + * @return {LGraph} the active graph + */ + LGraphCanvas.prototype.getCurrentGraph = function () { + return this.graph; + }; + + /** + * assigns a canvas + * + * @method setCanvas + * @param {Canvas} assigns a canvas (also accepts the ID of the element (not a selector) + */ + LGraphCanvas.prototype.setCanvas = function (canvas, skip_events) { + var that = this; + + if (canvas) { + if (canvas.constructor === String) { + canvas = document.getElementById(canvas); + if (!canvas) { + throw "Error creating LiteGraph canvas: Canvas not found"; + } + } + } + + if (canvas === this.canvas) { + return; + } + + if (!canvas && this.canvas) { + //maybe detach events from old_canvas + if (!skip_events) { + this.unbindEvents(); + } + } + + this.canvas = canvas; + this.ds.element = canvas; + + if (!canvas) { + return; + } + + //this.canvas.tabindex = "1000"; + canvas.className += " lgraphcanvas"; + canvas.data = this; + canvas.tabindex = "1"; //to allow key events + + //bg canvas: used for non changing stuff + this.bgcanvas = null; + if (!this.bgcanvas) { + this.bgcanvas = document.createElement("canvas"); + this.bgcanvas.width = this.canvas.width; + this.bgcanvas.height = this.canvas.height; + } + + if (canvas.getContext == null) { + if (canvas.localName != "canvas") { + throw ( + "Element supplied for LGraphCanvas must be a element, you passed a " + + canvas.localName + ); + } + throw "This browser doesn't support Canvas"; + } + + var ctx = (this.ctx = canvas.getContext("2d")); + if (ctx == null) { + if (!canvas.webgl_enabled) { + console.warn("This canvas seems to be WebGL, enabling WebGL renderer"); + } + this.enableWebGL(); + } + + //input: (move and up could be unbinded) + // why here? this._mousemove_callback = this.processMouseMove.bind(this); + // why here? this._mouseup_callback = this.processMouseUp.bind(this); + + if (!skip_events) { + this.bindEvents(); + } + }; + + //used in some events to capture them + LGraphCanvas.prototype._doNothing = function doNothing(e) { + //console.log("pointerevents: _doNothing "+e.type); + e.preventDefault(); + return false; + }; + LGraphCanvas.prototype._doReturnTrue = function doNothing(e) { + e.preventDefault(); + return true; + }; + + /** + * binds mouse, keyboard, touch and drag events to the canvas + * @method bindEvents + **/ + LGraphCanvas.prototype.bindEvents = function () { + if (this._events_binded) { + console.warn("LGraphCanvas: events already binded"); + return; + } + + //console.log("pointerevents: bindEvents"); + + var canvas = this.canvas; + + var ref_window = this.getCanvasWindow(); + var document = ref_window.document; //hack used when moving canvas between windows + + this._mousedown_callback = this.processMouseDown.bind(this); + this._mousewheel_callback = this.processMouseWheel.bind(this); + // why mousemove and mouseup were not binded here? + this._mousemove_callback = this.processMouseMove.bind(this); + this._mouseup_callback = this.processMouseUp.bind(this); + + //touch events -- TODO IMPLEMENT + //this._touch_callback = this.touchHandler.bind(this); + + LiteGraph.pointerListenerAdd( + canvas, + "down", + this._mousedown_callback, + true, + ); //down do not need to store the binded + canvas.addEventListener("mousewheel", this._mousewheel_callback, false); + + LiteGraph.pointerListenerAdd(canvas, "up", this._mouseup_callback, true); // CHECK: ??? binded or not + LiteGraph.pointerListenerAdd(canvas, "move", this._mousemove_callback); + + canvas.addEventListener("contextmenu", this._doNothing); + canvas.addEventListener("DOMMouseScroll", this._mousewheel_callback, false); + + //touch events -- THIS WAY DOES NOT WORK, finish implementing pointerevents, than clean the touchevents + /*if( 'touchstart' in document.documentElement ) + { + canvas.addEventListener("touchstart", this._touch_callback, true); + canvas.addEventListener("touchmove", this._touch_callback, true); + canvas.addEventListener("touchend", this._touch_callback, true); + canvas.addEventListener("touchcancel", this._touch_callback, true); + }*/ + + //Keyboard ****************** + this._key_callback = this.processKey.bind(this); + canvas.setAttribute("tabindex", 1); //otherwise key events are ignored + canvas.addEventListener("keydown", this._key_callback, true); + document.addEventListener("keyup", this._key_callback, true); //in document, otherwise it doesn't fire keyup + + //Dropping Stuff over nodes ************************************ + this._ondrop_callback = this.processDrop.bind(this); + + canvas.addEventListener("dragover", this._doNothing, false); + canvas.addEventListener("dragend", this._doNothing, false); + canvas.addEventListener("drop", this._ondrop_callback, false); + canvas.addEventListener("dragenter", this._doReturnTrue, false); + + this._events_binded = true; + }; + + /** + * unbinds mouse events from the canvas + * @method unbindEvents + **/ + LGraphCanvas.prototype.unbindEvents = function () { + if (!this._events_binded) { + console.warn("LGraphCanvas: no events binded"); + return; + } + + //console.log("pointerevents: unbindEvents"); + + var ref_window = this.getCanvasWindow(); + var document = ref_window.document; + + LiteGraph.pointerListenerRemove( + this.canvas, + "move", + this._mousedown_callback, + ); + LiteGraph.pointerListenerRemove( + this.canvas, + "up", + this._mousedown_callback, + ); + LiteGraph.pointerListenerRemove( + this.canvas, + "down", + this._mousedown_callback, + ); + this.canvas.removeEventListener("mousewheel", this._mousewheel_callback); + this.canvas.removeEventListener( + "DOMMouseScroll", + this._mousewheel_callback, + ); + this.canvas.removeEventListener("keydown", this._key_callback); + document.removeEventListener("keyup", this._key_callback); + this.canvas.removeEventListener("contextmenu", this._doNothing); + this.canvas.removeEventListener("drop", this._ondrop_callback); + this.canvas.removeEventListener("dragenter", this._doReturnTrue); + + //touch events -- THIS WAY DOES NOT WORK, finish implementing pointerevents, than clean the touchevents + /*this.canvas.removeEventListener("touchstart", this._touch_callback ); + this.canvas.removeEventListener("touchmove", this._touch_callback ); + this.canvas.removeEventListener("touchend", this._touch_callback ); + this.canvas.removeEventListener("touchcancel", this._touch_callback );*/ + + this._mousedown_callback = null; + this._mousewheel_callback = null; + this._key_callback = null; + this._ondrop_callback = null; + + this._events_binded = false; + }; + + LGraphCanvas.getFileExtension = function (url) { + var question = url.indexOf("?"); + if (question != -1) { + url = url.substr(0, question); + } + var point = url.lastIndexOf("."); + if (point == -1) { + return ""; + } + return url.substr(point + 1).toLowerCase(); + }; + + /** + * this function allows to render the canvas using WebGL instead of Canvas2D + * this is useful if you plant to render 3D objects inside your nodes, it uses litegl.js for webgl and canvas2DtoWebGL to emulate the Canvas2D calls in webGL + * @method enableWebGL + **/ + LGraphCanvas.prototype.enableWebGL = function () { + if (typeof GL === "undefined") { + throw "litegl.js must be included to use a WebGL canvas"; + } + if (typeof enableWebGLCanvas === "undefined") { + throw "webglCanvas.js must be included to use this feature"; + } + + this.gl = this.ctx = enableWebGLCanvas(this.canvas); + this.ctx.webgl = true; + this.bgcanvas = this.canvas; + this.bgctx = this.gl; + this.canvas.webgl_enabled = true; + + /* + GL.create({ canvas: this.bgcanvas }); + this.bgctx = enableWebGLCanvas( this.bgcanvas ); + window.gl = this.gl; + */ + }; + + /** + * marks as dirty the canvas, this way it will be rendered again + * + * @class LGraphCanvas + * @method setDirty + * @param {bool} fgcanvas if the foreground canvas is dirty (the one containing the nodes) + * @param {bool} bgcanvas if the background canvas is dirty (the one containing the wires) + */ + LGraphCanvas.prototype.setDirty = function (fgcanvas, bgcanvas) { + if (fgcanvas) { + this.dirty_canvas = true; + } + if (bgcanvas) { + this.dirty_bgcanvas = true; + } + }; + + /** + * Used to attach the canvas in a popup + * + * @method getCanvasWindow + * @return {window} returns the window where the canvas is attached (the DOM root node) + */ + LGraphCanvas.prototype.getCanvasWindow = function () { + if (!this.canvas) { + return window; + } + var doc = this.canvas.ownerDocument; + return doc.defaultView || doc.parentWindow; + }; + + /** + * starts rendering the content of the canvas when needed + * + * @method startRendering + */ + LGraphCanvas.prototype.startRendering = function () { + if (this.is_rendering) { + return; + } //already rendering + + this.is_rendering = true; + renderFrame.call(this); + + function renderFrame() { + if (!this.pause_rendering) { + this.draw(); + } + + var window = this.getCanvasWindow(); + if (this.is_rendering) { + window.requestAnimationFrame(renderFrame.bind(this)); + } + } + }; + + /** + * stops rendering the content of the canvas (to save resources) + * + * @method stopRendering + */ + LGraphCanvas.prototype.stopRendering = function () { + this.is_rendering = false; + /* + if(this.rendering_timer_id) + { + clearInterval(this.rendering_timer_id); + this.rendering_timer_id = null; + } + */ + }; + + /* LiteGraphCanvas input */ + + //used to block future mouse events (because of im gui) + LGraphCanvas.prototype.blockClick = function () { + this.block_click = true; + this.last_mouseclick = 0; + }; + + LGraphCanvas.prototype.processMouseDown = function (e) { + if (this.set_canvas_dirty_on_mouse_event) this.dirty_canvas = true; + + if (!this.graph) { + return; + } + + this.adjustMouseEvent(e); + + var ref_window = this.getCanvasWindow(); + var document = ref_window.document; + LGraphCanvas.active_canvas = this; + var that = this; + + var x = e.clientX; + var y = e.clientY; + //console.log(y,this.viewport); + //console.log("pointerevents: processMouseDown pointerId:"+e.pointerId+" which:"+e.which+" isPrimary:"+e.isPrimary+" :: x y "+x+" "+y); + + this.ds.viewport = this.viewport; + var is_inside = + !this.viewport || + (this.viewport && + x >= this.viewport[0] && + x < this.viewport[0] + this.viewport[2] && + y >= this.viewport[1] && + y < this.viewport[1] + this.viewport[3]); + + //move mouse move event to the window in case it drags outside of the canvas + if (!this.options.skip_events) { + LiteGraph.pointerListenerRemove( + this.canvas, + "move", + this._mousemove_callback, + ); + LiteGraph.pointerListenerAdd( + ref_window.document, + "move", + this._mousemove_callback, + true, + ); //catch for the entire window + LiteGraph.pointerListenerAdd( + ref_window.document, + "up", + this._mouseup_callback, + true, + ); + } + + if (!is_inside) { + return; + } + + var node = this.graph.getNodeOnPos( + e.canvasX, + e.canvasY, + this.visible_nodes, + 5, + ); + var skip_dragging = false; + var skip_action = false; + var now = LiteGraph.getTime(); + var is_primary = e.isPrimary === undefined || !e.isPrimary; + var is_double_click = now - this.last_mouseclick < 300 && is_primary; + this.mouse[0] = e.clientX; + this.mouse[1] = e.clientY; + this.graph_mouse[0] = e.canvasX; + this.graph_mouse[1] = e.canvasY; + this.last_click_position = [this.mouse[0], this.mouse[1]]; + + if (this.pointer_is_down && is_primary) { + this.pointer_is_double = true; + //console.log("pointerevents: pointer_is_double start"); + } else { + this.pointer_is_double = false; + } + this.pointer_is_down = true; + + this.canvas.focus(); + + LiteGraph.closeAllContextMenus(ref_window); + + if (this.onMouse) { + if (this.onMouse(e) == true) return; + } + + //left button mouse / single finger + if (e.which == 1 && !this.pointer_is_double) { + if (e.ctrlKey) { + this.dragging_rectangle = new Float32Array(4); + this.dragging_rectangle[0] = e.canvasX; + this.dragging_rectangle[1] = e.canvasY; + this.dragging_rectangle[2] = 1; + this.dragging_rectangle[3] = 1; + skip_action = true; + } + + // clone node ALT dragging + if ( + LiteGraph.alt_drag_do_clone_nodes && + e.altKey && + node && + this.allow_interaction && + !skip_action && + !this.read_only + ) { + if ((cloned = node.clone())) { + cloned.pos[0] += 5; + cloned.pos[1] += 5; + this.graph.add(cloned, false, { doCalcSize: false }); + node = cloned; + skip_action = true; + if (!block_drag_node) { + if (this.allow_dragnodes) { + this.graph.beforeChange(); + this.node_dragged = node; + } + if (!this.selected_nodes[node.id]) { + this.processNodeSelected(node, e); + } + } + } + } + + var clicking_canvas_bg = false; + + //when clicked on top of a node + //and it is not interactive + if ( + node && + (this.allow_interaction || node.flags.allow_interaction) && + !skip_action && + !this.read_only + ) { + if (!this.live_mode && !node.flags.pinned) { + this.bringToFront(node); + } //if it wasn't selected? + + //not dragging mouse to connect two slots + if ( + this.allow_interaction && + !this.connecting_node && + !node.flags.collapsed && + !this.live_mode + ) { + //Search for corner for resize + if ( + !skip_action && + node.resizable !== false && + isInsideRectangle( + e.canvasX, + e.canvasY, + node.pos[0] + node.size[0] - 5, + node.pos[1] + node.size[1] - 5, + 10, + 10, + ) + ) { + this.graph.beforeChange(); + this.resizing_node = node; + this.canvas.style.cursor = "se-resize"; + skip_action = true; + } else { + //search for outputs + if (node.outputs) { + for (var i = 0, l = node.outputs.length; i < l; ++i) { + var output = node.outputs[i]; + var link_pos = node.getConnectionPos(false, i); + if ( + isInsideRectangle( + e.canvasX, + e.canvasY, + link_pos[0] - 15, + link_pos[1] - 10, + 30, + 20, + ) + ) { + this.connecting_node = node; + this.connecting_output = output; + this.connecting_output.slot_index = i; + this.connecting_pos = node.getConnectionPos(false, i); + this.connecting_slot = i; + + if (LiteGraph.shift_click_do_break_link_from) { + if (e.shiftKey) { + node.disconnectOutput(i); + } + } + + if (is_double_click) { + if (node.onOutputDblClick) { + node.onOutputDblClick(i, e); + } + } else { + if (node.onOutputClick) { + node.onOutputClick(i, e); + } + } + + skip_action = true; + break; + } + } + } + + //search for inputs + if (node.inputs) { + for (var i = 0, l = node.inputs.length; i < l; ++i) { + var input = node.inputs[i]; + var link_pos = node.getConnectionPos(true, i); + if ( + isInsideRectangle( + e.canvasX, + e.canvasY, + link_pos[0] - 15, + link_pos[1] - 10, + 30, + 20, + ) + ) { + if (is_double_click) { + if (node.onInputDblClick) { + node.onInputDblClick(i, e); + } + } else { + if (node.onInputClick) { + node.onInputClick(i, e); + } + } + + if (input.link !== null) { + var link_info = this.graph.links[input.link]; //before disconnecting + if (LiteGraph.click_do_break_link_to) { + node.disconnectInput(i); + this.dirty_bgcanvas = true; + skip_action = true; + } else { + // do same action as has not node ? + } + + if ( + this.allow_reconnect_links || + //this.move_destination_link_without_shift || + e.shiftKey + ) { + if (!LiteGraph.click_do_break_link_to) { + node.disconnectInput(i); + } + this.connecting_node = + this.graph._nodes_by_id[link_info.origin_id]; + this.connecting_slot = link_info.origin_slot; + this.connecting_output = + this.connecting_node.outputs[this.connecting_slot]; + this.connecting_pos = + this.connecting_node.getConnectionPos( + false, + this.connecting_slot, + ); + + this.dirty_bgcanvas = true; + skip_action = true; + } + } else { + // has not node + } + + if (!skip_action) { + // connect from in to out, from to to from + this.connecting_node = node; + this.connecting_input = input; + this.connecting_input.slot_index = i; + this.connecting_pos = node.getConnectionPos(true, i); + this.connecting_slot = i; + + this.dirty_bgcanvas = true; + skip_action = true; + } + } + } + } + } //not resizing + } + + //it wasn't clicked on the links boxes + if (!skip_action) { + var block_drag_node = false; + var pos = [e.canvasX - node.pos[0], e.canvasY - node.pos[1]]; + + //widgets + var widget = this.processNodeWidgets(node, this.graph_mouse, e); + if (widget) { + block_drag_node = true; + this.node_widget = [node, widget]; + } + + //double clicking + if ( + this.allow_interaction && + is_double_click && + this.selected_nodes[node.id] + ) { + //double click node + if (node.onDblClick) { + node.onDblClick(e, pos, this); + } + this.processNodeDblClicked(node); + block_drag_node = true; + } + + //if do not capture mouse + if (node.onMouseDown && node.onMouseDown(e, pos, this)) { + block_drag_node = true; + } else { + //open subgraph button + if (node.subgraph && !node.skip_subgraph_button) { + if ( + !node.flags.collapsed && + pos[0] > node.size[0] - LiteGraph.NODE_TITLE_HEIGHT && + pos[1] < 0 + ) { + var that = this; + setTimeout(function () { + that.openSubgraph(node.subgraph); + }, 10); + } + } + + if (this.live_mode) { + clicking_canvas_bg = true; + block_drag_node = true; + } + } + + if (!block_drag_node) { + if (this.allow_dragnodes) { + this.graph.beforeChange(); + this.node_dragged = node; + } + this.processNodeSelected(node, e); + } else { + // double-click + /** + * Don't call the function if the block is already selected. + * Otherwise, it could cause the block to be unselected while its panel is open. + */ + if (!node.is_selected) this.processNodeSelected(node, e); + } + + this.dirty_canvas = true; + } + } //clicked outside of nodes + else { + if (!skip_action) { + //search for link connector + if (!this.read_only) { + for (var i = 0; i < this.visible_links.length; ++i) { + var link = this.visible_links[i]; + var center = link._pos; + if ( + !center || + e.canvasX < center[0] - 4 || + e.canvasX > center[0] + 4 || + e.canvasY < center[1] - 4 || + e.canvasY > center[1] + 4 + ) { + continue; + } + //link clicked + this.showLinkMenu(link, e); + this.over_link_center = null; //clear tooltip + break; + } + } + + this.selected_group = this.graph.getGroupOnPos(e.canvasX, e.canvasY); + this.selected_group_resizing = false; + if (this.selected_group && !this.read_only) { + if (e.ctrlKey) { + this.dragging_rectangle = null; + } + + var dist = distance( + [e.canvasX, e.canvasY], + [ + this.selected_group.pos[0] + this.selected_group.size[0], + this.selected_group.pos[1] + this.selected_group.size[1], + ], + ); + if (dist * this.ds.scale < 10) { + this.selected_group_resizing = true; + } else { + this.selected_group.recomputeInsideNodes(); + } + } + + if (is_double_click && !this.read_only && this.allow_searchbox) { + this.showSearchBox(e); + e.preventDefault(); + e.stopPropagation(); + } + + clicking_canvas_bg = true; + } + } + + if (!skip_action && clicking_canvas_bg && this.allow_dragcanvas) { + //console.log("pointerevents: dragging_canvas start"); + this.dragging_canvas = true; + } + } else if (e.which == 2) { + //middle button + + if (LiteGraph.middle_click_slot_add_default_node) { + if (node && this.allow_interaction && !skip_action && !this.read_only) { + //not dragging mouse to connect two slots + if ( + !this.connecting_node && + !node.flags.collapsed && + !this.live_mode + ) { + var mClikSlot = false; + var mClikSlot_index = false; + var mClikSlot_isOut = false; + //search for outputs + if (node.outputs) { + for (var i = 0, l = node.outputs.length; i < l; ++i) { + var output = node.outputs[i]; + var link_pos = node.getConnectionPos(false, i); + if ( + isInsideRectangle( + e.canvasX, + e.canvasY, + link_pos[0] - 15, + link_pos[1] - 10, + 30, + 20, + ) + ) { + mClikSlot = output; + mClikSlot_index = i; + mClikSlot_isOut = true; + break; + } + } + } + + //search for inputs + if (node.inputs) { + for (var i = 0, l = node.inputs.length; i < l; ++i) { + var input = node.inputs[i]; + var link_pos = node.getConnectionPos(true, i); + if ( + isInsideRectangle( + e.canvasX, + e.canvasY, + link_pos[0] - 15, + link_pos[1] - 10, + 30, + 20, + ) + ) { + mClikSlot = input; + mClikSlot_index = i; + mClikSlot_isOut = false; + break; + } + } + } + //console.log("middleClickSlots? "+mClikSlot+" & "+(mClikSlot_index!==false)); + if (mClikSlot && mClikSlot_index !== false) { + var alphaPosY = + 0.5 - + (mClikSlot_index + 1) / + (mClikSlot_isOut ? node.outputs.length : node.inputs.length); + var node_bounding = node.getBounding(); + // estimate a position: this is a bad semi-bad-working mess .. REFACTOR with a correct autoplacement that knows about the others slots and nodes + var posRef = [ + !mClikSlot_isOut + ? node_bounding[0] + : node_bounding[0] + node_bounding[2], // + node_bounding[0]/this.canvas.width*150 + e.canvasY - 80, // + node_bounding[0]/this.canvas.width*66 // vertical "derive" + ]; + var nodeCreated = this.createDefaultNodeForSlot({ + nodeFrom: !mClikSlot_isOut ? null : node, + slotFrom: !mClikSlot_isOut ? null : mClikSlot_index, + nodeTo: !mClikSlot_isOut ? node : null, + slotTo: !mClikSlot_isOut ? mClikSlot_index : null, + position: posRef, //,e: e + nodeType: "AUTO", //nodeNewType + posAdd: [!mClikSlot_isOut ? -30 : 30, -alphaPosY * 130], //-alphaPosY*30] + posSizeFix: [!mClikSlot_isOut ? -1 : 0, 0], //-alphaPosY*2*/ + }); + } + } + } + } else if (!skip_action && this.allow_dragcanvas) { + //console.log("pointerevents: dragging_canvas start from middle button"); + this.dragging_canvas = true; + } + } else if (e.which == 3 || this.pointer_is_double) { + //right button + if (this.allow_interaction && !skip_action && !this.read_only) { + // is it hover a node ? + if (node) { + if ( + Object.keys(this.selected_nodes).length && + (this.selected_nodes[node.id] || + e.shiftKey || + e.ctrlKey || + e.metaKey) + ) { + // is multiselected or using shift to include the now node + if (!this.selected_nodes[node.id]) this.selectNodes([node], true); // add this if not present + } else { + // update selection + this.selectNodes([node]); + } + } + + // show menu on this node + this.processContextMenu(node, e); + } + } + + //TODO + //if(this.node_selected != prev_selected) + // this.onNodeSelectionChange(this.node_selected); + + this.last_mouse[0] = e.clientX; + this.last_mouse[1] = e.clientY; + this.last_mouseclick = LiteGraph.getTime(); + this.last_mouse_dragging = true; + + /* + if( (this.dirty_canvas || this.dirty_bgcanvas) && this.rendering_timer_id == null) + this.draw(); + */ + + this.graph.change(); + + //this is to ensure to defocus(blur) if a text input element is on focus + if ( + !ref_window.document.activeElement || + (ref_window.document.activeElement.nodeName.toLowerCase() != "input" && + ref_window.document.activeElement.nodeName.toLowerCase() != "textarea") + ) { + e.preventDefault(); + } + e.stopPropagation(); + + if (this.onMouseDown) { + this.onMouseDown(e); + } + + return false; + }; + + /** + * Called when a mouse move event has to be processed + * @method processMouseMove + **/ + LGraphCanvas.prototype.processMouseMove = function (e) { + if (this.autoresize) { + this.resize(); + } + + if (this.set_canvas_dirty_on_mouse_event) this.dirty_canvas = true; + + if (!this.graph) { + return; + } + + LGraphCanvas.active_canvas = this; + this.adjustMouseEvent(e); + var mouse = [e.clientX, e.clientY]; + this.mouse[0] = mouse[0]; + this.mouse[1] = mouse[1]; + var delta = [mouse[0] - this.last_mouse[0], mouse[1] - this.last_mouse[1]]; + this.last_mouse = mouse; + this.graph_mouse[0] = e.canvasX; + this.graph_mouse[1] = e.canvasY; + + //console.log("pointerevents: processMouseMove "+e.pointerId+" "+e.isPrimary); + + if (this.block_click) { + //console.log("pointerevents: processMouseMove block_click"); + e.preventDefault(); + return false; + } + + e.dragging = this.last_mouse_dragging; + + if (this.node_widget) { + this.processNodeWidgets( + this.node_widget[0], + this.graph_mouse, + e, + this.node_widget[1], + ); + this.dirty_canvas = true; + } + + //get node over + var node = this.graph.getNodeOnPos( + e.canvasX, + e.canvasY, + this.visible_nodes, + ); + + if (this.dragging_rectangle) { + this.dragging_rectangle[2] = e.canvasX - this.dragging_rectangle[0]; + this.dragging_rectangle[3] = e.canvasY - this.dragging_rectangle[1]; + this.dirty_canvas = true; + } else if (this.selected_group && !this.read_only) { + //moving/resizing a group + if (this.selected_group_resizing) { + this.selected_group.size = [ + e.canvasX - this.selected_group.pos[0], + e.canvasY - this.selected_group.pos[1], + ]; + } else { + var deltax = delta[0] / this.ds.scale; + var deltay = delta[1] / this.ds.scale; + this.selected_group.move(deltax, deltay, e.ctrlKey); + if (this.selected_group._nodes.length) { + this.dirty_canvas = true; + } + } + this.dirty_bgcanvas = true; + } else if (this.dragging_canvas) { + ////console.log("pointerevents: processMouseMove is dragging_canvas"); + this.ds.offset[0] += delta[0] / this.ds.scale; + this.ds.offset[1] += delta[1] / this.ds.scale; + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + } else if ( + (this.allow_interaction || (node && node.flags.allow_interaction)) && + !this.read_only + ) { + if (this.connecting_node) { + this.dirty_canvas = true; + } + + //remove mouseover flag + for (var i = 0, l = this.graph._nodes.length; i < l; ++i) { + if (this.graph._nodes[i].mouseOver && node != this.graph._nodes[i]) { + //mouse leave + this.graph._nodes[i].mouseOver = false; + if (this.node_over && this.node_over.onMouseLeave) { + this.node_over.onMouseLeave(e); + } + this.node_over = null; + this.dirty_canvas = true; + } + } + + //mouse over a node + if (node) { + if (node.redraw_on_mouse) this.dirty_canvas = true; + + //this.canvas.style.cursor = "move"; + if (!node.mouseOver) { + //mouse enter + node.mouseOver = true; + this.node_over = node; + this.dirty_canvas = true; + + if (node.onMouseEnter) { + node.onMouseEnter(e); + } + } + + //in case the node wants to do something + if (node.onMouseMove) { + node.onMouseMove( + e, + [e.canvasX - node.pos[0], e.canvasY - node.pos[1]], + this, + ); + } + + //if dragging a link + if (this.connecting_node) { + if (this.connecting_output) { + var pos = this._highlight_input || [0, 0]; //to store the output of isOverNodeInput + + //on top of input + if (this.isOverNodeBox(node, e.canvasX, e.canvasY)) { + //mouse on top of the corner box, don't know what to do + } else { + //check if I have a slot below de mouse + var slot = this.isOverNodeInput(node, e.canvasX, e.canvasY, pos); + if (slot != -1 && node.inputs[slot]) { + var slot_type = node.inputs[slot].type; + if ( + LiteGraph.isValidConnection( + this.connecting_output.type, + slot_type, + ) + ) { + this._highlight_input = pos; + this._highlight_input_slot = node.inputs[slot]; // XXX CHECK THIS + } + } else { + this._highlight_input = null; + this._highlight_input_slot = null; // XXX CHECK THIS + } + } + } else if (this.connecting_input) { + var pos = this._highlight_output || [0, 0]; //to store the output of isOverNodeOutput + + //on top of output + if (this.isOverNodeBox(node, e.canvasX, e.canvasY)) { + //mouse on top of the corner box, don't know what to do + } else { + //check if I have a slot below de mouse + var slot = this.isOverNodeOutput(node, e.canvasX, e.canvasY, pos); + if (slot != -1 && node.outputs[slot]) { + var slot_type = node.outputs[slot].type; + if ( + LiteGraph.isValidConnection( + this.connecting_input.type, + slot_type, + ) + ) { + this._highlight_output = pos; + } + } else { + this._highlight_output = null; + } + } + } + } + + //Search for corner + if (this.canvas) { + if ( + isInsideRectangle( + e.canvasX, + e.canvasY, + node.pos[0] + node.size[0] - 5, + node.pos[1] + node.size[1] - 5, + 5, + 5, + ) + ) { + this.canvas.style.cursor = "se-resize"; + } else { + this.canvas.style.cursor = "crosshair"; + } + } + } else { + //not over a node + + //search for link connector + var over_link = null; + for (var i = 0; i < this.visible_links.length; ++i) { + var link = this.visible_links[i]; + var center = link._pos; + if ( + !center || + e.canvasX < center[0] - 4 || + e.canvasX > center[0] + 4 || + e.canvasY < center[1] - 4 || + e.canvasY > center[1] + 4 + ) { + continue; + } + over_link = link; + break; + } + if (over_link != this.over_link_center) { + this.over_link_center = over_link; + this.dirty_canvas = true; + } + + if (this.canvas) { + this.canvas.style.cursor = ""; + } + } //end + + //send event to node if capturing input (used with widgets that allow drag outside of the area of the node) + if ( + this.node_capturing_input && + this.node_capturing_input != node && + this.node_capturing_input.onMouseMove + ) { + this.node_capturing_input.onMouseMove( + e, + [ + e.canvasX - this.node_capturing_input.pos[0], + e.canvasY - this.node_capturing_input.pos[1], + ], + this, + ); + } + + //node being dragged + if (this.node_dragged && !this.live_mode) { + //console.log("draggin!",this.selected_nodes); + for (var i in this.selected_nodes) { + var n = this.selected_nodes[i]; + n.pos[0] += delta[0] / this.ds.scale; + n.pos[1] += delta[1] / this.ds.scale; + if (!n.is_selected) this.processNodeSelected(n, e); /* + * Don't call the function if the block is already selected. + * Otherwise, it could cause the block to be unselected while dragging. + */ + } + + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + } + + if (this.resizing_node && !this.live_mode) { + //convert mouse to node space + var desired_size = [ + e.canvasX - this.resizing_node.pos[0], + e.canvasY - this.resizing_node.pos[1], + ]; + var min_size = this.resizing_node.computeSize(); + desired_size[0] = Math.max(min_size[0], desired_size[0]); + desired_size[1] = Math.max(min_size[1], desired_size[1]); + this.resizing_node.setSize(desired_size); + + this.canvas.style.cursor = "se-resize"; + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + } + } + + e.preventDefault(); + return false; + }; + + /** + * Called when a mouse up event has to be processed + * @method processMouseUp + **/ + LGraphCanvas.prototype.processMouseUp = function (e) { + var is_primary = e.isPrimary === undefined || e.isPrimary; + + //early exit for extra pointer + if (!is_primary) { + /*e.stopPropagation(); + e.preventDefault();*/ + //console.log("pointerevents: processMouseUp pointerN_stop "+e.pointerId+" "+e.isPrimary); + return false; + } + + //console.log("pointerevents: processMouseUp "+e.pointerId+" "+e.isPrimary+" :: "+e.clientX+" "+e.clientY); + + if (this.set_canvas_dirty_on_mouse_event) this.dirty_canvas = true; + + if (!this.graph) return; + + var window = this.getCanvasWindow(); + var document = window.document; + LGraphCanvas.active_canvas = this; + + //restore the mousemove event back to the canvas + if (!this.options.skip_events) { + //console.log("pointerevents: processMouseUp adjustEventListener"); + LiteGraph.pointerListenerRemove( + document, + "move", + this._mousemove_callback, + true, + ); + LiteGraph.pointerListenerAdd( + this.canvas, + "move", + this._mousemove_callback, + true, + ); + LiteGraph.pointerListenerRemove( + document, + "up", + this._mouseup_callback, + true, + ); + } + + this.adjustMouseEvent(e); + var now = LiteGraph.getTime(); + e.click_time = now - this.last_mouseclick; + this.last_mouse_dragging = false; + this.last_click_position = null; + + if (this.block_click) { + //console.log("pointerevents: processMouseUp block_clicks"); + this.block_click = false; //used to avoid sending twice a click in a immediate button + } + + //console.log("pointerevents: processMouseUp which: "+e.which); + + if (e.which == 1) { + if (this.node_widget) { + this.processNodeWidgets(this.node_widget[0], this.graph_mouse, e); + } + + //left button + this.node_widget = null; + + if (this.selected_group) { + var diffx = + this.selected_group.pos[0] - Math.round(this.selected_group.pos[0]); + var diffy = + this.selected_group.pos[1] - Math.round(this.selected_group.pos[1]); + this.selected_group.move(diffx, diffy, e.ctrlKey); + this.selected_group.pos[0] = Math.round(this.selected_group.pos[0]); + this.selected_group.pos[1] = Math.round(this.selected_group.pos[1]); + if (this.selected_group._nodes.length) { + this.dirty_canvas = true; + } + this.selected_group = null; + } + this.selected_group_resizing = false; + + var node = this.graph.getNodeOnPos( + e.canvasX, + e.canvasY, + this.visible_nodes, + ); + + if (this.dragging_rectangle) { + if (this.graph) { + var nodes = this.graph._nodes; + var node_bounding = new Float32Array(4); + + //compute bounding and flip if left to right + var w = Math.abs(this.dragging_rectangle[2]); + var h = Math.abs(this.dragging_rectangle[3]); + var startx = + this.dragging_rectangle[2] < 0 + ? this.dragging_rectangle[0] - w + : this.dragging_rectangle[0]; + var starty = + this.dragging_rectangle[3] < 0 + ? this.dragging_rectangle[1] - h + : this.dragging_rectangle[1]; + this.dragging_rectangle[0] = startx; + this.dragging_rectangle[1] = starty; + this.dragging_rectangle[2] = w; + this.dragging_rectangle[3] = h; + + // test dragging rect size, if minimun simulate a click + if (!node || (w > 10 && h > 10)) { + //test against all nodes (not visible because the rectangle maybe start outside + var to_select = []; + for (var i = 0; i < nodes.length; ++i) { + var nodeX = nodes[i]; + nodeX.getBounding(node_bounding); + if (!overlapBounding(this.dragging_rectangle, node_bounding)) { + continue; + } //out of the visible area + to_select.push(nodeX); + } + if (to_select.length) { + this.selectNodes(to_select, e.shiftKey); // add to selection with shift + } + } else { + // will select of update selection + this.selectNodes([node], e.shiftKey || e.ctrlKey); // add to selection add to selection with ctrlKey or shiftKey + } + } + this.dragging_rectangle = null; + } else if (this.connecting_node) { + //dragging a connection + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + + var connInOrOut = this.connecting_output || this.connecting_input; + var connType = connInOrOut.type; + + //node below mouse + if (node) { + /* no need to condition on event type.. just another type + if ( + connType == LiteGraph.EVENT && + this.isOverNodeBox(node, e.canvasX, e.canvasY) + ) { + + this.connecting_node.connect( + this.connecting_slot, + node, + LiteGraph.EVENT + ); + + } else {*/ + + //slot below mouse? connect + + if (this.connecting_output) { + var slot = this.isOverNodeInput(node, e.canvasX, e.canvasY); + if (slot != -1) { + this.connecting_node.connect(this.connecting_slot, node, slot); + } else { + //not on top of an input + // look for a good slot + this.connecting_node.connectByType( + this.connecting_slot, + node, + connType, + ); + } + } else if (this.connecting_input) { + var slot = this.isOverNodeOutput(node, e.canvasX, e.canvasY); + + if (slot != -1) { + node.connect(slot, this.connecting_node, this.connecting_slot); // this is inverted has output-input nature like + } else { + //not on top of an input + // look for a good slot + this.connecting_node.connectByTypeOutput( + this.connecting_slot, + node, + connType, + ); + } + } + + //} + } else { + // add menu when releasing link in empty space + if (LiteGraph.release_link_on_empty_shows_menu) { + if (e.shiftKey && this.allow_searchbox) { + if (this.connecting_output) { + this.showSearchBox(e, { + node_from: this.connecting_node, + slot_from: this.connecting_output, + type_filter_in: this.connecting_output.type, + }); + } else if (this.connecting_input) { + this.showSearchBox(e, { + node_to: this.connecting_node, + slot_from: this.connecting_input, + type_filter_out: this.connecting_input.type, + }); + } + } else { + if (this.connecting_output) { + this.showConnectionMenu({ + nodeFrom: this.connecting_node, + slotFrom: this.connecting_output, + e: e, + }); + } else if (this.connecting_input) { + this.showConnectionMenu({ + nodeTo: this.connecting_node, + slotTo: this.connecting_input, + e: e, + }); + } + } + } + } + + this.connecting_output = null; + this.connecting_input = null; + this.connecting_pos = null; + this.connecting_node = null; + this.connecting_slot = -1; + } //not dragging connection + else if (this.resizing_node) { + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + this.graph.afterChange(this.resizing_node); + this.resizing_node = null; + } else if (this.node_dragged) { + //node being dragged? + var node = this.node_dragged; + if ( + node && + e.click_time < 300 && + isInsideRectangle( + e.canvasX, + e.canvasY, + node.pos[0], + node.pos[1] - LiteGraph.NODE_TITLE_HEIGHT, + LiteGraph.NODE_TITLE_HEIGHT, + LiteGraph.NODE_TITLE_HEIGHT, + ) + ) { + node.collapse(); + } + + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + this.node_dragged.pos[0] = Math.round(this.node_dragged.pos[0]); + this.node_dragged.pos[1] = Math.round(this.node_dragged.pos[1]); + if (this.graph.config.align_to_grid || this.align_to_grid) { + this.node_dragged.alignToGrid(); + } + if (this.onNodeMoved) this.onNodeMoved(this.node_dragged); + this.graph.afterChange(this.node_dragged); + this.node_dragged = null; + } //no node being dragged + else { + //get node over + var node = this.graph.getNodeOnPos( + e.canvasX, + e.canvasY, + this.visible_nodes, + ); + + if (!node && e.click_time < 300) { + this.deselectAllNodes(); + } + + this.dirty_canvas = true; + this.dragging_canvas = false; + + if (this.node_over && this.node_over.onMouseUp) { + this.node_over.onMouseUp( + e, + [ + e.canvasX - this.node_over.pos[0], + e.canvasY - this.node_over.pos[1], + ], + this, + ); + } + if (this.node_capturing_input && this.node_capturing_input.onMouseUp) { + this.node_capturing_input.onMouseUp(e, [ + e.canvasX - this.node_capturing_input.pos[0], + e.canvasY - this.node_capturing_input.pos[1], + ]); + } + } + } else if (e.which == 2) { + //middle button + //trace("middle"); + this.dirty_canvas = true; + this.dragging_canvas = false; + } else if (e.which == 3) { + //right button + //trace("right"); + this.dirty_canvas = true; + this.dragging_canvas = false; + } + + /* + if((this.dirty_canvas || this.dirty_bgcanvas) && this.rendering_timer_id == null) + this.draw(); + */ + + if (is_primary) { + this.pointer_is_down = false; + this.pointer_is_double = false; + } + + this.graph.change(); + + //console.log("pointerevents: processMouseUp stopPropagation"); + e.stopPropagation(); + e.preventDefault(); + return false; + }; + + /** + * Called when a mouse wheel event has to be processed + * @method processMouseWheel + **/ + LGraphCanvas.prototype.processMouseWheel = function (e) { + if (!this.graph || !this.allow_dragcanvas) { + return; + } + + var delta = e.wheelDeltaY != null ? e.wheelDeltaY : e.detail * -60; + + this.adjustMouseEvent(e); + + var x = e.clientX; + var y = e.clientY; + var is_inside = + !this.viewport || + (this.viewport && + x >= this.viewport[0] && + x < this.viewport[0] + this.viewport[2] && + y >= this.viewport[1] && + y < this.viewport[1] + this.viewport[3]); + if (!is_inside) return; + + var scale = this.ds.scale; + + if (delta > 0) { + scale *= 1.1; + } else if (delta < 0) { + scale *= 1 / 1.1; + } + + //this.setZoom( scale, [ e.clientX, e.clientY ] ); + this.ds.changeScale(scale, [e.clientX, e.clientY]); + + this.graph.change(); + + e.preventDefault(); + return false; // prevent default + }; + + /** + * returns true if a position (in graph space) is on top of a node little corner box + * @method isOverNodeBox + **/ + LGraphCanvas.prototype.isOverNodeBox = function (node, canvasx, canvasy) { + var title_height = LiteGraph.NODE_TITLE_HEIGHT; + if ( + isInsideRectangle( + canvasx, + canvasy, + node.pos[0] + 2, + node.pos[1] + 2 - title_height, + title_height - 4, + title_height - 4, + ) + ) { + return true; + } + return false; + }; + + /** + * returns the INDEX if a position (in graph space) is on top of a node input slot + * @method isOverNodeInput + **/ + LGraphCanvas.prototype.isOverNodeInput = function ( + node, + canvasx, + canvasy, + slot_pos, + ) { + if (node.inputs) { + for (var i = 0, l = node.inputs.length; i < l; ++i) { + var input = node.inputs[i]; + var link_pos = node.getConnectionPos(true, i); + var is_inside = false; + if (node.horizontal) { + is_inside = isInsideRectangle( + canvasx, + canvasy, + link_pos[0] - 5, + link_pos[1] - 10, + 10, + 20, + ); + } else { + is_inside = isInsideRectangle( + canvasx, + canvasy, + link_pos[0] - 10, + link_pos[1] - 5, + 40, + 10, + ); + } + if (is_inside) { + if (slot_pos) { + slot_pos[0] = link_pos[0]; + slot_pos[1] = link_pos[1]; + } + return i; + } + } + } + return -1; + }; + + /** + * returns the INDEX if a position (in graph space) is on top of a node output slot + * @method isOverNodeOuput + **/ + LGraphCanvas.prototype.isOverNodeOutput = function ( + node, + canvasx, + canvasy, + slot_pos, + ) { + if (node.outputs) { + for (var i = 0, l = node.outputs.length; i < l; ++i) { + var output = node.outputs[i]; + var link_pos = node.getConnectionPos(false, i); + var is_inside = false; + if (node.horizontal) { + is_inside = isInsideRectangle( + canvasx, + canvasy, + link_pos[0] - 5, + link_pos[1] - 10, + 10, + 20, + ); + } else { + is_inside = isInsideRectangle( + canvasx, + canvasy, + link_pos[0] - 10, + link_pos[1] - 5, + 40, + 10, + ); + } + if (is_inside) { + if (slot_pos) { + slot_pos[0] = link_pos[0]; + slot_pos[1] = link_pos[1]; + } + return i; + } + } + } + return -1; + }; + + /** + * process a key event + * @method processKey + **/ + LGraphCanvas.prototype.processKey = function (e) { + if (!this.graph) { + return; + } + + var block_default = false; + //console.log(e); //debug + + if (e.target.localName == "input") { + return; + } + + if (e.type == "keydown") { + if (e.keyCode == 32) { + //space + this.dragging_canvas = true; + block_default = true; + } + + if (e.keyCode == 27) { + //esc + if (this.node_panel) this.node_panel.close(); + if (this.options_panel) this.options_panel.close(); + block_default = true; + } + + //select all Control A + if (e.keyCode == 65 && e.ctrlKey) { + this.selectNodes(); + block_default = true; + } + + if (e.keyCode === 67 && (e.metaKey || e.ctrlKey) && !e.shiftKey) { + //copy + if (this.selected_nodes) { + this.copyToClipboard(); + block_default = true; + } + } + + if (e.keyCode === 86 && (e.metaKey || e.ctrlKey)) { + //paste + this.pasteFromClipboard(e.shiftKey); + } + + //delete or backspace + if (e.keyCode == 46 || e.keyCode == 8) { + if (e.target.localName != "input" && e.target.localName != "textarea") { + this.deleteSelectedNodes(); + block_default = true; + } + } + + //collapse + //... + + //TODO + if (this.selected_nodes) { + for (var i in this.selected_nodes) { + if (this.selected_nodes[i].onKeyDown) { + this.selected_nodes[i].onKeyDown(e); + } + } + } + } else if (e.type == "keyup") { + if (e.keyCode == 32) { + // space + this.dragging_canvas = false; + } + + if (this.selected_nodes) { + for (var i in this.selected_nodes) { + if (this.selected_nodes[i].onKeyUp) { + this.selected_nodes[i].onKeyUp(e); + } + } + } + } + + this.graph.change(); + + if (block_default) { + e.preventDefault(); + e.stopImmediatePropagation(); + return false; + } + }; + + LGraphCanvas.prototype.copyToClipboard = function () { + var clipboard_info = { + nodes: [], + links: [], + }; + var index = 0; + var selected_nodes_array = []; + for (var i in this.selected_nodes) { + var node = this.selected_nodes[i]; + if (node.clonable === false) continue; + node._relative_id = index; + selected_nodes_array.push(node); + index += 1; + } + + for (var i = 0; i < selected_nodes_array.length; ++i) { + var node = selected_nodes_array[i]; + if (node.clonable === false) continue; + var cloned = node.clone(); + if (!cloned) { + console.warn("node type not found: " + node.type); + continue; + } + clipboard_info.nodes.push(cloned.serialize()); + if (node.inputs && node.inputs.length) { + for (var j = 0; j < node.inputs.length; ++j) { + var input = node.inputs[j]; + if (!input || input.link == null) { + continue; + } + var link_info = this.graph.links[input.link]; + if (!link_info) { + continue; + } + var target_node = this.graph.getNodeById(link_info.origin_id); + if (!target_node) { + continue; + } + clipboard_info.links.push([ + target_node._relative_id, + link_info.origin_slot, //j, + node._relative_id, + link_info.target_slot, + target_node.id, + ]); + } + } + } + localStorage.setItem( + "litegrapheditor_clipboard", + JSON.stringify(clipboard_info), + ); + }; + + LGraphCanvas.prototype.pasteFromClipboard = function ( + isConnectUnselected = false, + ) { + // if ctrl + shift + v is off, return when isConnectUnselected is true (shift is pressed) to maintain old behavior + if ( + !LiteGraph.ctrl_shift_v_paste_connect_unselected_outputs && + isConnectUnselected + ) { + return; + } + var data = localStorage.getItem("litegrapheditor_clipboard"); + if (!data) { + return; + } + + this.graph.beforeChange(); + + //create nodes + var clipboard_info = JSON.parse(data); + // calculate top-left node, could work without this processing but using diff with last node pos :: clipboard_info.nodes[clipboard_info.nodes.length-1].pos + var posMin = false; + var posMinIndexes = false; + for (var i = 0; i < clipboard_info.nodes.length; ++i) { + if (posMin) { + if (posMin[0] > clipboard_info.nodes[i].pos[0]) { + posMin[0] = clipboard_info.nodes[i].pos[0]; + posMinIndexes[0] = i; + } + if (posMin[1] > clipboard_info.nodes[i].pos[1]) { + posMin[1] = clipboard_info.nodes[i].pos[1]; + posMinIndexes[1] = i; + } + } else { + posMin = [ + clipboard_info.nodes[i].pos[0], + clipboard_info.nodes[i].pos[1], + ]; + posMinIndexes = [i, i]; + } + } + var nodes = []; + for (var i = 0; i < clipboard_info.nodes.length; ++i) { + var node_data = clipboard_info.nodes[i]; + var node = LiteGraph.createNode(node_data.type); + if (node) { + node.configure(node_data); + + //paste in last known mouse position + node.pos[0] += this.graph_mouse[0] - posMin[0]; //+= 5; + node.pos[1] += this.graph_mouse[1] - posMin[1]; //+= 5; + + this.graph.add(node, { doProcessChange: false }); + + nodes.push(node); + } + } + + //create links + for (var i = 0; i < clipboard_info.links.length; ++i) { + var link_info = clipboard_info.links[i]; + var origin_node; + var origin_node_relative_id = link_info[0]; + if (origin_node_relative_id != null) { + origin_node = nodes[origin_node_relative_id]; + } else if ( + LiteGraph.ctrl_shift_v_paste_connect_unselected_outputs && + isConnectUnselected + ) { + var origin_node_id = link_info[4]; + if (origin_node_id) { + origin_node = this.graph.getNodeById(origin_node_id); + } + } + var target_node = nodes[link_info[2]]; + if (origin_node && target_node) + origin_node.connect(link_info[1], target_node, link_info[3]); + else console.warn("Warning, nodes missing on pasting"); + } + + this.selectNodes(nodes); + + this.graph.afterChange(); + }; + + /** + * process a item drop event on top the canvas + * @method processDrop + **/ + LGraphCanvas.prototype.processDrop = function (e) { + e.preventDefault(); + this.adjustMouseEvent(e); + var x = e.clientX; + var y = e.clientY; + var is_inside = + !this.viewport || + (this.viewport && + x >= this.viewport[0] && + x < this.viewport[0] + this.viewport[2] && + y >= this.viewport[1] && + y < this.viewport[1] + this.viewport[3]); + if (!is_inside) { + return; + // --- BREAK --- + } + + var pos = [e.canvasX, e.canvasY]; + + var node = this.graph ? this.graph.getNodeOnPos(pos[0], pos[1]) : null; + + if (!node) { + var r = null; + if (this.onDropItem) { + r = this.onDropItem(event); + } + if (!r) { + this.checkDropItem(e); + } + return; + } + + if (node.onDropFile || node.onDropData) { + var files = e.dataTransfer.files; + if (files && files.length) { + for (var i = 0; i < files.length; i++) { + var file = e.dataTransfer.files[0]; + var filename = file.name; + var ext = LGraphCanvas.getFileExtension(filename); + //console.log(file); + + if (node.onDropFile) { + node.onDropFile(file); + } + + if (node.onDropData) { + //prepare reader + var reader = new FileReader(); + reader.onload = function (event) { + //console.log(event.target); + var data = event.target.result; + node.onDropData(data, filename, file); + }; + + //read data + var type = file.type.split("/")[0]; + if (type == "text" || type == "") { + reader.readAsText(file); + } else if (type == "image") { + reader.readAsDataURL(file); + } else { + reader.readAsArrayBuffer(file); + } + } + } + } + } + + if (node.onDropItem) { + if (node.onDropItem(event)) { + return true; + } + } + + if (this.onDropItem) { + return this.onDropItem(event); + } + + return false; + }; + + //called if the graph doesn't have a default drop item behaviour + LGraphCanvas.prototype.checkDropItem = function (e) { + if (e.dataTransfer.files.length) { + var file = e.dataTransfer.files[0]; + var ext = LGraphCanvas.getFileExtension(file.name).toLowerCase(); + var nodetype = LiteGraph.node_types_by_file_extension[ext]; + if (nodetype) { + this.graph.beforeChange(); + var node = LiteGraph.createNode(nodetype.type); + node.pos = [e.canvasX, e.canvasY]; + this.graph.add(node); + if (node.onDropFile) { + node.onDropFile(file); + } + this.graph.afterChange(); + } + } + }; + + LGraphCanvas.prototype.processNodeDblClicked = function (n) { + if (this.onShowNodePanel) { + this.onShowNodePanel(n); + } else { + this.showShowNodePanel(n); + } + + if (this.onNodeDblClicked) { + this.onNodeDblClicked(n); + } + + this.setDirty(true); + }; + + LGraphCanvas.prototype.processNodeSelected = function (node, e) { + this.selectNode(node, e && (e.shiftKey || e.ctrlKey || this.multi_select)); + if (this.onNodeSelected) { + this.onNodeSelected(node); + } + }; + + /** + * selects a given node (or adds it to the current selection) + * @method selectNode + **/ + LGraphCanvas.prototype.selectNode = function ( + node, + add_to_current_selection, + ) { + if (node == null) { + this.deselectAllNodes(); + } else { + this.selectNodes([node], add_to_current_selection); + } + }; + + /** + * selects several nodes (or adds them to the current selection) + * @method selectNodes + **/ + LGraphCanvas.prototype.selectNodes = function ( + nodes, + add_to_current_selection, + ) { + if (!add_to_current_selection) { + this.deselectAllNodes(); + } + + nodes = nodes || this.graph._nodes; + if (typeof nodes == "string") nodes = [nodes]; + for (var i in nodes) { + var node = nodes[i]; + if (node.is_selected) { + this.deselectNode(node); + continue; + } + + if (!node.is_selected && node.onSelected) { + node.onSelected(); + } + node.is_selected = true; + this.selected_nodes[node.id] = node; + + if (node.inputs) { + for (var j = 0; j < node.inputs.length; ++j) { + this.highlighted_links[node.inputs[j].link] = true; + } + } + if (node.outputs) { + for (var j = 0; j < node.outputs.length; ++j) { + var out = node.outputs[j]; + if (out.links) { + for (var k = 0; k < out.links.length; ++k) { + this.highlighted_links[out.links[k]] = true; + } + } + } + } + } + + if (this.onSelectionChange) this.onSelectionChange(this.selected_nodes); + + this.setDirty(true); + }; + + /** + * removes a node from the current selection + * @method deselectNode + **/ + LGraphCanvas.prototype.deselectNode = function (node) { + if (!node.is_selected) { + return; + } + if (node.onDeselected) { + node.onDeselected(); + } + node.is_selected = false; + + if (this.onNodeDeselected) { + this.onNodeDeselected(node); + } + + //remove highlighted + if (node.inputs) { + for (var i = 0; i < node.inputs.length; ++i) { + delete this.highlighted_links[node.inputs[i].link]; + } + } + if (node.outputs) { + for (var i = 0; i < node.outputs.length; ++i) { + var out = node.outputs[i]; + if (out.links) { + for (var j = 0; j < out.links.length; ++j) { + delete this.highlighted_links[out.links[j]]; + } + } + } + } + }; + + /** + * removes all nodes from the current selection + * @method deselectAllNodes + **/ + LGraphCanvas.prototype.deselectAllNodes = function () { + if (!this.graph) { + return; + } + var nodes = this.graph._nodes; + for (var i = 0, l = nodes.length; i < l; ++i) { + var node = nodes[i]; + if (!node.is_selected) { + continue; + } + if (node.onDeselected) { + node.onDeselected(); + } + node.is_selected = false; + if (this.onNodeDeselected) { + this.onNodeDeselected(node); + } + } + this.selected_nodes = {}; + this.current_node = null; + this.highlighted_links = {}; + if (this.onSelectionChange) this.onSelectionChange(this.selected_nodes); + this.setDirty(true); + }; + + /** + * deletes all nodes in the current selection from the graph + * @method deleteSelectedNodes + **/ + LGraphCanvas.prototype.deleteSelectedNodes = function () { + this.graph.beforeChange(); + + for (var i in this.selected_nodes) { + var node = this.selected_nodes[i]; + + if (node.block_delete) continue; + + //autoconnect when possible (very basic, only takes into account first input-output) + if ( + node.inputs && + node.inputs.length && + node.outputs && + node.outputs.length && + LiteGraph.isValidConnection( + node.inputs[0].type, + node.outputs[0].type, + ) && + node.inputs[0].link && + node.outputs[0].links && + node.outputs[0].links.length + ) { + var input_link = node.graph.links[node.inputs[0].link]; + var output_link = node.graph.links[node.outputs[0].links[0]]; + var input_node = node.getInputNode(0); + var output_node = node.getOutputNodes(0)[0]; + if (input_node && output_node) + input_node.connect( + input_link.origin_slot, + output_node, + output_link.target_slot, + ); + } + this.graph.remove(node); + if (this.onNodeDeselected) { + this.onNodeDeselected(node); + } + } + this.selected_nodes = {}; + this.current_node = null; + this.highlighted_links = {}; + this.setDirty(true); + this.graph.afterChange(); + }; + + /** + * centers the camera on a given node + * @method centerOnNode + **/ + LGraphCanvas.prototype.centerOnNode = function (node) { + this.ds.offset[0] = + -node.pos[0] - + node.size[0] * 0.5 + + (this.canvas.width * 0.5) / this.ds.scale; + this.ds.offset[1] = + -node.pos[1] - + node.size[1] * 0.5 + + (this.canvas.height * 0.5) / this.ds.scale; + this.setDirty(true, true); + }; + + /** + * adds some useful properties to a mouse event, like the position in graph coordinates + * @method adjustMouseEvent + **/ + LGraphCanvas.prototype.adjustMouseEvent = function (e) { + var clientX_rel = 0; + var clientY_rel = 0; + + if (this.canvas) { + var b = this.canvas.getBoundingClientRect(); + clientX_rel = e.clientX - b.left; + clientY_rel = e.clientY - b.top; + } else { + clientX_rel = e.clientX; + clientY_rel = e.clientY; + } + + // e.deltaX = clientX_rel - this.last_mouse_position[0]; + // e.deltaY = clientY_rel- this.last_mouse_position[1]; + + this.last_mouse_position[0] = clientX_rel; + this.last_mouse_position[1] = clientY_rel; + + e.canvasX = clientX_rel / this.ds.scale - this.ds.offset[0]; + e.canvasY = clientY_rel / this.ds.scale - this.ds.offset[1]; + + //console.log("pointerevents: adjustMouseEvent "+e.clientX+":"+e.clientY+" "+clientX_rel+":"+clientY_rel+" "+e.canvasX+":"+e.canvasY); + }; + + /** + * changes the zoom level of the graph (default is 1), you can pass also a place used to pivot the zoom + * @method setZoom + **/ + LGraphCanvas.prototype.setZoom = function (value, zooming_center) { + this.ds.changeScale(value, zooming_center); + /* + if(!zooming_center && this.canvas) + zooming_center = [this.canvas.width * 0.5,this.canvas.height * 0.5]; + + var center = this.convertOffsetToCanvas( zooming_center ); + + this.ds.scale = value; + + if(this.scale > this.max_zoom) + this.scale = this.max_zoom; + else if(this.scale < this.min_zoom) + this.scale = this.min_zoom; + + var new_center = this.convertOffsetToCanvas( zooming_center ); + var delta_offset = [new_center[0] - center[0], new_center[1] - center[1]]; + + this.offset[0] += delta_offset[0]; + this.offset[1] += delta_offset[1]; + */ + + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + }; + + /** + * converts a coordinate from graph coordinates to canvas2D coordinates + * @method convertOffsetToCanvas + **/ + LGraphCanvas.prototype.convertOffsetToCanvas = function (pos, out) { + return this.ds.convertOffsetToCanvas(pos, out); + }; + + /** + * converts a coordinate from Canvas2D coordinates to graph space + * @method convertCanvasToOffset + **/ + LGraphCanvas.prototype.convertCanvasToOffset = function (pos, out) { + return this.ds.convertCanvasToOffset(pos, out); + }; + + //converts event coordinates from canvas2D to graph coordinates + LGraphCanvas.prototype.convertEventToCanvasOffset = function (e) { + var rect = this.canvas.getBoundingClientRect(); + return this.convertCanvasToOffset([ + e.clientX - rect.left, + e.clientY - rect.top, + ]); + }; + + /** + * brings a node to front (above all other nodes) + * @method bringToFront + **/ + LGraphCanvas.prototype.bringToFront = function (node) { + var i = this.graph._nodes.indexOf(node); + if (i == -1) { + return; + } + + this.graph._nodes.splice(i, 1); + this.graph._nodes.push(node); + }; + + /** + * sends a node to the back (below all other nodes) + * @method sendToBack + **/ + LGraphCanvas.prototype.sendToBack = function (node) { + var i = this.graph._nodes.indexOf(node); + if (i == -1) { + return; + } + + this.graph._nodes.splice(i, 1); + this.graph._nodes.unshift(node); + }; + + /* Interaction */ + + /* LGraphCanvas render */ + var temp = new Float32Array(4); + + /** + * checks which nodes are visible (inside the camera area) + * @method computeVisibleNodes + **/ + LGraphCanvas.prototype.computeVisibleNodes = function (nodes, out) { + var visible_nodes = out || []; + visible_nodes.length = 0; + nodes = nodes || this.graph._nodes; + for (var i = 0, l = nodes.length; i < l; ++i) { + var n = nodes[i]; + + //skip rendering nodes in live mode + if (this.live_mode && !n.onDrawBackground && !n.onDrawForeground) { + continue; + } + + if (!overlapBounding(this.visible_area, n.getBounding(temp, true))) { + continue; + } //out of the visible area + + visible_nodes.push(n); + } + return visible_nodes; + }; + + /** + * renders the whole canvas content, by rendering in two separated canvas, one containing the background grid and the connections, and one containing the nodes) + * @method draw + **/ + LGraphCanvas.prototype.draw = function (force_canvas, force_bgcanvas) { + if (!this.canvas || this.canvas.width == 0 || this.canvas.height == 0) { + return; + } + + //fps counting + var now = LiteGraph.getTime(); + this.render_time = (now - this.last_draw_time) * 0.001; + this.last_draw_time = now; + + if (this.graph) { + this.ds.computeVisibleArea(this.viewport); + } + + if ( + this.dirty_bgcanvas || + force_bgcanvas || + this.always_render_background || + (this.graph && + this.graph._last_trigger_time && + now - this.graph._last_trigger_time < 1000) + ) { + this.drawBackCanvas(); + } + + if (this.dirty_canvas || force_canvas) { + this.drawFrontCanvas(); + } + + this.fps = this.render_time ? 1.0 / this.render_time : 0; + this.frame += 1; + }; + + /** + * draws the front canvas (the one containing all the nodes) + * @method drawFrontCanvas + **/ + LGraphCanvas.prototype.drawFrontCanvas = function () { + this.dirty_canvas = false; + + if (!this.ctx) { + this.ctx = this.bgcanvas.getContext("2d"); + } + var ctx = this.ctx; + if (!ctx) { + //maybe is using webgl... + return; + } + + var canvas = this.canvas; + if (ctx.start2D && !this.viewport) { + ctx.start2D(); + ctx.restore(); + ctx.setTransform(1, 0, 0, 1, 0, 0); + } + + //clip dirty area if there is one, otherwise work in full canvas + var area = this.viewport || this.dirty_area; + if (area) { + ctx.save(); + ctx.beginPath(); + ctx.rect(area[0], area[1], area[2], area[3]); + ctx.clip(); + } + + //clear + //canvas.width = canvas.width; + if (this.clear_background) { + if (area) ctx.clearRect(area[0], area[1], area[2], area[3]); + else ctx.clearRect(0, 0, canvas.width, canvas.height); + } + + //draw bg canvas + if (this.bgcanvas == this.canvas) { + this.drawBackCanvas(); + } else { + ctx.drawImage(this.bgcanvas, 0, 0); + } + + //rendering + if (this.onRender) { + this.onRender(canvas, ctx); + } + + //info widget + if (this.show_info) { + this.renderInfo(ctx, area ? area[0] : 0, area ? area[1] : 0); + } + + if (this.graph) { + //apply transformations + ctx.save(); + this.ds.toCanvasContext(ctx); + + //draw nodes + var drawn_nodes = 0; + var visible_nodes = this.computeVisibleNodes(null, this.visible_nodes); + + for (var i = 0; i < visible_nodes.length; ++i) { + var node = visible_nodes[i]; + + //transform coords system + ctx.save(); + ctx.translate(node.pos[0], node.pos[1]); + + //Draw + this.drawNode(node, ctx); + drawn_nodes += 1; + + //Restore + ctx.restore(); + } + + //on top (debug) + if (this.render_execution_order) { + this.drawExecutionOrder(ctx); + } + + //connections ontop? + if (this.graph.config.links_ontop) { + if (!this.live_mode) { + this.drawConnections(ctx); + } + } + + //current connection (the one being dragged by the mouse) + if (this.connecting_pos != null) { + ctx.lineWidth = this.connections_width; + var link_color = null; + + var connInOrOut = this.connecting_output || this.connecting_input; + + var connType = connInOrOut.type; + var connDir = connInOrOut.dir; + if (connDir == null) { + if (this.connecting_output) + connDir = this.connecting_node.horizontal + ? LiteGraph.DOWN + : LiteGraph.RIGHT; + else + connDir = this.connecting_node.horizontal + ? LiteGraph.UP + : LiteGraph.LEFT; + } + var connShape = connInOrOut.shape; + + switch (connType) { + case LiteGraph.EVENT: + link_color = LiteGraph.EVENT_LINK_COLOR; + break; + default: + link_color = LiteGraph.CONNECTING_LINK_COLOR; + } + + //the connection being dragged by the mouse + this.renderLink( + ctx, + this.connecting_pos, + [this.graph_mouse[0], this.graph_mouse[1]], + null, + false, + null, + link_color, + connDir, + LiteGraph.CENTER, + ); + + ctx.beginPath(); + if (connType === LiteGraph.EVENT || connShape === LiteGraph.BOX_SHAPE) { + ctx.rect( + this.connecting_pos[0] - 6 + 0.5, + this.connecting_pos[1] - 5 + 0.5, + 14, + 10, + ); + ctx.fill(); + ctx.beginPath(); + ctx.rect( + this.graph_mouse[0] - 6 + 0.5, + this.graph_mouse[1] - 5 + 0.5, + 14, + 10, + ); + } else if (connShape === LiteGraph.ARROW_SHAPE) { + ctx.moveTo(this.connecting_pos[0] + 8, this.connecting_pos[1] + 0.5); + ctx.lineTo( + this.connecting_pos[0] - 4, + this.connecting_pos[1] + 6 + 0.5, + ); + ctx.lineTo( + this.connecting_pos[0] - 4, + this.connecting_pos[1] - 6 + 0.5, + ); + ctx.closePath(); + } else { + ctx.arc( + this.connecting_pos[0], + this.connecting_pos[1], + 4, + 0, + Math.PI * 2, + ); + ctx.fill(); + ctx.beginPath(); + ctx.arc(this.graph_mouse[0], this.graph_mouse[1], 4, 0, Math.PI * 2); + } + ctx.fill(); + + ctx.fillStyle = "#ffcc00"; + if (this._highlight_input) { + ctx.beginPath(); + var shape = this._highlight_input_slot.shape; + if (shape === LiteGraph.ARROW_SHAPE) { + ctx.moveTo( + this._highlight_input[0] + 8, + this._highlight_input[1] + 0.5, + ); + ctx.lineTo( + this._highlight_input[0] - 4, + this._highlight_input[1] + 6 + 0.5, + ); + ctx.lineTo( + this._highlight_input[0] - 4, + this._highlight_input[1] - 6 + 0.5, + ); + ctx.closePath(); + } else { + ctx.arc( + this._highlight_input[0], + this._highlight_input[1], + 6, + 0, + Math.PI * 2, + ); + } + ctx.fill(); + } + if (this._highlight_output) { + ctx.beginPath(); + if (shape === LiteGraph.ARROW_SHAPE) { + ctx.moveTo( + this._highlight_output[0] + 8, + this._highlight_output[1] + 0.5, + ); + ctx.lineTo( + this._highlight_output[0] - 4, + this._highlight_output[1] + 6 + 0.5, + ); + ctx.lineTo( + this._highlight_output[0] - 4, + this._highlight_output[1] - 6 + 0.5, + ); + ctx.closePath(); + } else { + ctx.arc( + this._highlight_output[0], + this._highlight_output[1], + 6, + 0, + Math.PI * 2, + ); + } + ctx.fill(); + } + } + + //the selection rectangle + if (this.dragging_rectangle) { + ctx.strokeStyle = "#FFF"; + ctx.strokeRect( + this.dragging_rectangle[0], + this.dragging_rectangle[1], + this.dragging_rectangle[2], + this.dragging_rectangle[3], + ); + } + + //on top of link center + if (this.over_link_center && this.render_link_tooltip) + this.drawLinkTooltip(ctx, this.over_link_center); + else if (this.onDrawLinkTooltip) + //to remove + this.onDrawLinkTooltip(ctx, null); + + //custom info + if (this.onDrawForeground) { + this.onDrawForeground(ctx, this.visible_rect); + } + + ctx.restore(); + } + + //draws panel in the corner + if (this._graph_stack && this._graph_stack.length) { + this.drawSubgraphPanel(ctx); + } + + if (this.onDrawOverlay) { + this.onDrawOverlay(ctx); + } + + if (area) { + ctx.restore(); + } + + if (ctx.finish2D) { + //this is a function I use in webgl renderer + ctx.finish2D(); + } + }; + + /** + * draws the panel in the corner that shows subgraph properties + * @method drawSubgraphPanel + **/ + LGraphCanvas.prototype.drawSubgraphPanel = function (ctx) { + var subgraph = this.graph; + var subnode = subgraph._subgraph_node; + if (!subnode) { + console.warn("subgraph without subnode"); + return; + } + this.drawSubgraphPanelLeft(subgraph, subnode, ctx); + this.drawSubgraphPanelRight(subgraph, subnode, ctx); + }; + + LGraphCanvas.prototype.drawSubgraphPanelLeft = function ( + subgraph, + subnode, + ctx, + ) { + var num = subnode.inputs ? subnode.inputs.length : 0; + var w = 200; + var h = Math.floor(LiteGraph.NODE_SLOT_HEIGHT * 1.6); + + ctx.fillStyle = "#111"; + ctx.globalAlpha = 0.8; + ctx.beginPath(); + ctx.roundRect(10, 10, w, (num + 1) * h + 50, [8]); + ctx.fill(); + ctx.globalAlpha = 1; + + ctx.fillStyle = "#888"; + ctx.font = "14px Arial"; + ctx.textAlign = "left"; + ctx.fillText("Graph Inputs", 20, 34); + // var pos = this.mouse; + + if (this.drawButton(w - 20, 20, 20, 20, "X", "#151515")) { + this.closeSubgraph(); + return; + } + + var y = 50; + ctx.font = "14px Arial"; + if (subnode.inputs) + for (var i = 0; i < subnode.inputs.length; ++i) { + var input = subnode.inputs[i]; + if (input.not_subgraph_input) continue; + + //input button clicked + if (this.drawButton(20, y + 2, w - 20, h - 2)) { + var type = subnode.constructor.input_node_type || "graph/input"; + this.graph.beforeChange(); + var newnode = LiteGraph.createNode(type); + if (newnode) { + subgraph.add(newnode); + this.block_click = false; + this.last_click_position = null; + this.selectNodes([newnode]); + this.node_dragged = newnode; + this.dragging_canvas = false; + newnode.setProperty("name", input.name); + newnode.setProperty("type", input.type); + this.node_dragged.pos[0] = this.graph_mouse[0] - 5; + this.node_dragged.pos[1] = this.graph_mouse[1] - 5; + this.graph.afterChange(); + } else console.error("graph input node not found:", type); + } + ctx.fillStyle = "#9C9"; + ctx.beginPath(); + ctx.arc(w - 16, y + h * 0.5, 5, 0, 2 * Math.PI); + ctx.fill(); + ctx.fillStyle = "#AAA"; + ctx.fillText(input.name, 30, y + h * 0.75); + // var tw = ctx.measureText(input.name); + ctx.fillStyle = "#777"; + ctx.fillText(input.type, 130, y + h * 0.75); + y += h; + } + //add + button + if (this.drawButton(20, y + 2, w - 20, h - 2, "+", "#151515", "#222")) { + this.showSubgraphPropertiesDialog(subnode); + } + }; + LGraphCanvas.prototype.drawSubgraphPanelRight = function ( + subgraph, + subnode, + ctx, + ) { + var num = subnode.outputs ? subnode.outputs.length : 0; + var canvas_w = this.bgcanvas.width; + var w = 200; + var h = Math.floor(LiteGraph.NODE_SLOT_HEIGHT * 1.6); + + ctx.fillStyle = "#111"; + ctx.globalAlpha = 0.8; + ctx.beginPath(); + ctx.roundRect(canvas_w - w - 10, 10, w, (num + 1) * h + 50, [8]); + ctx.fill(); + ctx.globalAlpha = 1; + + ctx.fillStyle = "#888"; + ctx.font = "14px Arial"; + ctx.textAlign = "left"; + var title_text = "Graph Outputs"; + var tw = ctx.measureText(title_text).width; + ctx.fillText(title_text, canvas_w - tw - 20, 34); + // var pos = this.mouse; + if (this.drawButton(canvas_w - w, 20, 20, 20, "X", "#151515")) { + this.closeSubgraph(); + return; + } + + var y = 50; + ctx.font = "14px Arial"; + if (subnode.outputs) + for (var i = 0; i < subnode.outputs.length; ++i) { + var output = subnode.outputs[i]; + if (output.not_subgraph_input) continue; + + //output button clicked + if (this.drawButton(canvas_w - w, y + 2, w - 20, h - 2)) { + var type = subnode.constructor.output_node_type || "graph/output"; + this.graph.beforeChange(); + var newnode = LiteGraph.createNode(type); + if (newnode) { + subgraph.add(newnode); + this.block_click = false; + this.last_click_position = null; + this.selectNodes([newnode]); + this.node_dragged = newnode; + this.dragging_canvas = false; + newnode.setProperty("name", output.name); + newnode.setProperty("type", output.type); + this.node_dragged.pos[0] = this.graph_mouse[0] - 5; + this.node_dragged.pos[1] = this.graph_mouse[1] - 5; + this.graph.afterChange(); + } else console.error("graph input node not found:", type); + } + ctx.fillStyle = "#9C9"; + ctx.beginPath(); + ctx.arc(canvas_w - w + 16, y + h * 0.5, 5, 0, 2 * Math.PI); + ctx.fill(); + ctx.fillStyle = "#AAA"; + ctx.fillText(output.name, canvas_w - w + 30, y + h * 0.75); + // var tw = ctx.measureText(input.name); + ctx.fillStyle = "#777"; + ctx.fillText(output.type, canvas_w - w + 130, y + h * 0.75); + y += h; + } + //add + button + if ( + this.drawButton( + canvas_w - w, + y + 2, + w - 20, + h - 2, + "+", + "#151515", + "#222", + ) + ) { + this.showSubgraphPropertiesDialogRight(subnode); + } + }; + //Draws a button into the canvas overlay and computes if it was clicked using the immediate gui paradigm + LGraphCanvas.prototype.drawButton = function ( + x, + y, + w, + h, + text, + bgcolor, + hovercolor, + textcolor, + ) { + var ctx = this.ctx; + bgcolor = bgcolor || LiteGraph.NODE_DEFAULT_COLOR; + hovercolor = hovercolor || "#555"; + textcolor = textcolor || LiteGraph.NODE_TEXT_COLOR; + var pos = this.ds.convertOffsetToCanvas(this.graph_mouse); + var hover = LiteGraph.isInsideRectangle(pos[0], pos[1], x, y, w, h); + pos = this.last_click_position + ? [this.last_click_position[0], this.last_click_position[1]] + : null; + if (pos) { + var rect = this.canvas.getBoundingClientRect(); + pos[0] -= rect.left; + pos[1] -= rect.top; + } + var clicked = + pos && LiteGraph.isInsideRectangle(pos[0], pos[1], x, y, w, h); + + ctx.fillStyle = hover ? hovercolor : bgcolor; + if (clicked) ctx.fillStyle = "#AAA"; + ctx.beginPath(); + ctx.roundRect(x, y, w, h, [4]); + ctx.fill(); + + if (text != null) { + if (text.constructor == String) { + ctx.fillStyle = textcolor; + ctx.textAlign = "center"; + ctx.font = ((h * 0.65) | 0) + "px Arial"; + ctx.fillText(text, x + w * 0.5, y + h * 0.75); + ctx.textAlign = "left"; + } + } + + var was_clicked = clicked && !this.block_click; + if (clicked) this.blockClick(); + return was_clicked; + }; + + LGraphCanvas.prototype.isAreaClicked = function (x, y, w, h, hold_click) { + var pos = this.mouse; + var hover = LiteGraph.isInsideRectangle(pos[0], pos[1], x, y, w, h); + pos = this.last_click_position; + var clicked = + pos && LiteGraph.isInsideRectangle(pos[0], pos[1], x, y, w, h); + var was_clicked = clicked && !this.block_click; + if (clicked && hold_click) this.blockClick(); + return was_clicked; + }; + + /** + * draws some useful stats in the corner of the canvas + * @method renderInfo + **/ + LGraphCanvas.prototype.renderInfo = function (ctx, x, y) { + x = x || 10; + y = y || this.canvas.height - 80; + + ctx.save(); + ctx.translate(x, y); + + ctx.font = "10px Arial"; + ctx.fillStyle = "#888"; + ctx.textAlign = "left"; + if (this.graph) { + ctx.fillText("T: " + this.graph.globaltime.toFixed(2) + "s", 5, 13 * 1); + ctx.fillText("I: " + this.graph.iteration, 5, 13 * 2); + ctx.fillText( + "N: " + + this.graph._nodes.length + + " [" + + this.visible_nodes.length + + "]", + 5, + 13 * 3, + ); + ctx.fillText("V: " + this.graph._version, 5, 13 * 4); + ctx.fillText("FPS:" + this.fps.toFixed(2), 5, 13 * 5); + } else { + ctx.fillText("No graph selected", 5, 13 * 1); + } + ctx.restore(); + }; + + /** + * draws the back canvas (the one containing the background and the connections) + * @method drawBackCanvas + **/ + LGraphCanvas.prototype.drawBackCanvas = function () { + var canvas = this.bgcanvas; + if ( + canvas.width != this.canvas.width || + canvas.height != this.canvas.height + ) { + canvas.width = this.canvas.width; + canvas.height = this.canvas.height; + } + + if (!this.bgctx) { + this.bgctx = this.bgcanvas.getContext("2d"); + } + var ctx = this.bgctx; + if (ctx.start) { + ctx.start(); + } + + var viewport = this.viewport || [0, 0, ctx.canvas.width, ctx.canvas.height]; + + //clear + if (this.clear_background) { + ctx.clearRect(viewport[0], viewport[1], viewport[2], viewport[3]); + } + + //show subgraph stack header + if (this._graph_stack && this._graph_stack.length) { + ctx.save(); + var parent_graph = this._graph_stack[this._graph_stack.length - 1]; + var subgraph_node = this.graph._subgraph_node; + ctx.strokeStyle = subgraph_node.bgcolor; + ctx.lineWidth = 10; + ctx.strokeRect(1, 1, canvas.width - 2, canvas.height - 2); + ctx.lineWidth = 1; + ctx.font = "40px Arial"; + ctx.textAlign = "center"; + ctx.fillStyle = subgraph_node.bgcolor || "#AAA"; + var title = ""; + for (var i = 1; i < this._graph_stack.length; ++i) { + title += this._graph_stack[i]._subgraph_node.getTitle() + " >> "; + } + ctx.fillText(title + subgraph_node.getTitle(), canvas.width * 0.5, 40); + ctx.restore(); + } + + var bg_already_painted = false; + if (this.onRenderBackground) { + bg_already_painted = this.onRenderBackground(canvas, ctx); + } + + //reset in case of error + if (!this.viewport) { + ctx.restore(); + ctx.setTransform(1, 0, 0, 1, 0, 0); + } + this.visible_links.length = 0; + + if (this.graph) { + //apply transformations + ctx.save(); + this.ds.toCanvasContext(ctx); + + //render BG + if ( + this.ds.scale < 1.5 && + !bg_already_painted && + this.clear_background_color + ) { + ctx.fillStyle = this.clear_background_color; + ctx.fillRect( + this.visible_area[0], + this.visible_area[1], + this.visible_area[2], + this.visible_area[3], + ); + } + + if (this.background_image && this.ds.scale > 0.5 && !bg_already_painted) { + if (this.zoom_modify_alpha) { + ctx.globalAlpha = (1.0 - 0.5 / this.ds.scale) * this.editor_alpha; + } else { + ctx.globalAlpha = this.editor_alpha; + } + ctx.imageSmoothingEnabled = ctx.imageSmoothingEnabled = false; // ctx.mozImageSmoothingEnabled = + if (!this._bg_img || this._bg_img.name != this.background_image) { + this._bg_img = new Image(); + this._bg_img.name = this.background_image; + this._bg_img.src = this.background_image; + var that = this; + this._bg_img.onload = function () { + that.draw(true, true); + }; + } + + var pattern = null; + if (this._pattern == null && this._bg_img.width > 0) { + pattern = ctx.createPattern(this._bg_img, "repeat"); + this._pattern_img = this._bg_img; + this._pattern = pattern; + } else { + pattern = this._pattern; + } + if (pattern) { + ctx.fillStyle = pattern; + ctx.fillRect( + this.visible_area[0], + this.visible_area[1], + this.visible_area[2], + this.visible_area[3], + ); + ctx.fillStyle = "transparent"; + } + + ctx.globalAlpha = 1.0; + ctx.imageSmoothingEnabled = ctx.imageSmoothingEnabled = true; //= ctx.mozImageSmoothingEnabled + } + + //groups + if (this.graph._groups.length && !this.live_mode) { + this.drawGroups(canvas, ctx); + } + + if (this.onDrawBackground) { + this.onDrawBackground(ctx, this.visible_area); + } + if (this.onBackgroundRender) { + //LEGACY + console.error( + "WARNING! onBackgroundRender deprecated, now is named onDrawBackground ", + ); + this.onBackgroundRender = null; + } + + //DEBUG: show clipping area + //ctx.fillStyle = "red"; + //ctx.fillRect( this.visible_area[0] + 10, this.visible_area[1] + 10, this.visible_area[2] - 20, this.visible_area[3] - 20); + + //bg + if (this.render_canvas_border) { + ctx.strokeStyle = "#235"; + ctx.strokeRect(0, 0, canvas.width, canvas.height); + } + + if (this.render_connections_shadows) { + ctx.shadowColor = "#000"; + ctx.shadowOffsetX = 0; + ctx.shadowOffsetY = 0; + ctx.shadowBlur = 6; + } else { + ctx.shadowColor = "rgba(0,0,0,0)"; + } + + //draw connections + if (!this.live_mode) { + this.drawConnections(ctx); + } + + ctx.shadowColor = "rgba(0,0,0,0)"; + + //restore state + ctx.restore(); + } + + if (ctx.finish) { + ctx.finish(); + } + + this.dirty_bgcanvas = false; + this.dirty_canvas = true; //to force to repaint the front canvas with the bgcanvas + }; + + var temp_vec2 = new Float32Array(2); + + /** + * draws the given node inside the canvas + * @method drawNode + **/ + LGraphCanvas.prototype.drawNode = function (node, ctx) { + var glow = false; + this.current_node = node; + + var color = + node.color || node.constructor.color || LiteGraph.NODE_DEFAULT_COLOR; + var bgcolor = + node.bgcolor || + node.constructor.bgcolor || + LiteGraph.NODE_DEFAULT_BGCOLOR; + + //shadow and glow + if (node.mouseOver) { + glow = true; + } + + var low_quality = this.ds.scale < 0.6; //zoomed out + + //only render if it forces it to do it + if (this.live_mode) { + if (!node.flags.collapsed) { + ctx.shadowColor = "transparent"; + if (node.onDrawForeground) { + node.onDrawForeground(ctx, this, this.canvas); + } + } + return; + } + + var editor_alpha = this.editor_alpha; + ctx.globalAlpha = editor_alpha; + + if (this.render_shadows && !low_quality) { + ctx.shadowColor = LiteGraph.DEFAULT_SHADOW_COLOR; + ctx.shadowOffsetX = 2 * this.ds.scale; + ctx.shadowOffsetY = 2 * this.ds.scale; + ctx.shadowBlur = 3 * this.ds.scale; + } else { + ctx.shadowColor = "transparent"; + } + + //custom draw collapsed method (draw after shadows because they are affected) + if ( + node.flags.collapsed && + node.onDrawCollapsed && + node.onDrawCollapsed(ctx, this) == true + ) { + return; + } + + //clip if required (mask) + var shape = node._shape || LiteGraph.BOX_SHAPE; + var size = temp_vec2; + temp_vec2.set(node.size); + var horizontal = node.horizontal; // || node.flags.horizontal; + + if (node.flags.collapsed) { + ctx.font = this.inner_text_font; + var title = node.getTitle ? node.getTitle() : node.title; + if (title != null) { + node._collapsed_width = Math.min( + node.size[0], + ctx.measureText(title).width + LiteGraph.NODE_TITLE_HEIGHT * 2, + ); //LiteGraph.NODE_COLLAPSED_WIDTH; + size[0] = node._collapsed_width; + size[1] = 0; + } + } + + if (node.clip_area) { + //Start clipping + ctx.save(); + ctx.beginPath(); + if (shape == LiteGraph.BOX_SHAPE) { + ctx.rect(0, 0, size[0], size[1]); + } else if (shape == LiteGraph.ROUND_SHAPE) { + ctx.roundRect(0, 0, size[0], size[1], [10]); + } else if (shape == LiteGraph.CIRCLE_SHAPE) { + ctx.arc(size[0] * 0.5, size[1] * 0.5, size[0] * 0.5, 0, Math.PI * 2); + } + ctx.clip(); + } + + //draw shape + if (node.has_errors) { + bgcolor = "red"; + } + this.drawNodeShape( + node, + ctx, + size, + color, + bgcolor, + node.is_selected, + node.mouseOver, + ); + ctx.shadowColor = "transparent"; + + //draw foreground + if (node.onDrawForeground) { + node.onDrawForeground(ctx, this, this.canvas); + } + + //connection slots + ctx.textAlign = horizontal ? "center" : "left"; + ctx.font = this.inner_text_font; + + var render_text = !low_quality; + + var out_slot = this.connecting_output; + var in_slot = this.connecting_input; + ctx.lineWidth = 1; + + var max_y = 0; + var slot_pos = new Float32Array(2); //to reuse + + //render inputs and outputs + if (!node.flags.collapsed) { + //input connection slots + if (node.inputs) { + for (var i = 0; i < node.inputs.length; i++) { + var slot = node.inputs[i]; + + var slot_type = slot.type; + var slot_shape = slot.shape; + + ctx.globalAlpha = editor_alpha; + //change opacity of incompatible slots when dragging a connection + if ( + this.connecting_output && + !LiteGraph.isValidConnection(slot.type, out_slot.type) + ) { + ctx.globalAlpha = 0.4 * editor_alpha; + } + + ctx.fillStyle = + slot.link != null + ? slot.color_on || + this.default_connection_color_byType[slot_type] || + this.default_connection_color.input_on + : slot.color_off || + this.default_connection_color_byTypeOff[slot_type] || + this.default_connection_color_byType[slot_type] || + this.default_connection_color.input_off; + + var pos = node.getConnectionPos(true, i, slot_pos); + pos[0] -= node.pos[0]; + pos[1] -= node.pos[1]; + if (max_y < pos[1] + LiteGraph.NODE_SLOT_HEIGHT * 0.5) { + max_y = pos[1] + LiteGraph.NODE_SLOT_HEIGHT * 0.5; + } + + ctx.beginPath(); + + if (slot_type == "array") { + slot_shape = LiteGraph.GRID_SHAPE; // place in addInput? addOutput instead? + } + + var doStroke = true; + + if ( + slot.type === LiteGraph.EVENT || + slot.shape === LiteGraph.BOX_SHAPE + ) { + if (horizontal) { + ctx.rect(pos[0] - 5 + 0.5, pos[1] - 8 + 0.5, 10, 14); + } else { + ctx.rect(pos[0] - 6 + 0.5, pos[1] - 5 + 0.5, 14, 10); + } + } else if (slot_shape === LiteGraph.ARROW_SHAPE) { + ctx.moveTo(pos[0] + 8, pos[1] + 0.5); + ctx.lineTo(pos[0] - 4, pos[1] + 6 + 0.5); + ctx.lineTo(pos[0] - 4, pos[1] - 6 + 0.5); + ctx.closePath(); + } else if (slot_shape === LiteGraph.GRID_SHAPE) { + ctx.rect(pos[0] - 4, pos[1] - 4, 2, 2); + ctx.rect(pos[0] - 1, pos[1] - 4, 2, 2); + ctx.rect(pos[0] + 2, pos[1] - 4, 2, 2); + ctx.rect(pos[0] - 4, pos[1] - 1, 2, 2); + ctx.rect(pos[0] - 1, pos[1] - 1, 2, 2); + ctx.rect(pos[0] + 2, pos[1] - 1, 2, 2); + ctx.rect(pos[0] - 4, pos[1] + 2, 2, 2); + ctx.rect(pos[0] - 1, pos[1] + 2, 2, 2); + ctx.rect(pos[0] + 2, pos[1] + 2, 2, 2); + doStroke = false; + } else { + if (low_quality) + ctx.rect(pos[0] - 4, pos[1] - 4, 8, 8); //faster + else ctx.arc(pos[0], pos[1], 4, 0, Math.PI * 2); + } + ctx.fill(); + + //render name + if (render_text) { + var text = slot.label != null ? slot.label : slot.name; + if (text) { + ctx.fillStyle = LiteGraph.NODE_TEXT_COLOR; + if (horizontal || slot.dir == LiteGraph.UP) { + ctx.fillText(text, pos[0], pos[1] - 10); + } else { + ctx.fillText(text, pos[0] + 10, pos[1] + 5); + } + } + } + } + } + + //output connection slots + + ctx.textAlign = horizontal ? "center" : "right"; + ctx.strokeStyle = "black"; + if (node.outputs) { + for (var i = 0; i < node.outputs.length; i++) { + var slot = node.outputs[i]; + + var slot_type = slot.type; + var slot_shape = slot.shape; + + //change opacity of incompatible slots when dragging a connection + if ( + this.connecting_input && + !LiteGraph.isValidConnection(slot_type, in_slot.type) + ) { + ctx.globalAlpha = 0.4 * editor_alpha; + } + + var pos = node.getConnectionPos(false, i, slot_pos); + pos[0] -= node.pos[0]; + pos[1] -= node.pos[1]; + if (max_y < pos[1] + LiteGraph.NODE_SLOT_HEIGHT * 0.5) { + max_y = pos[1] + LiteGraph.NODE_SLOT_HEIGHT * 0.5; + } + + ctx.fillStyle = + slot.links && slot.links.length + ? slot.color_on || + this.default_connection_color_byType[slot_type] || + this.default_connection_color.output_on + : slot.color_off || + this.default_connection_color_byTypeOff[slot_type] || + this.default_connection_color_byType[slot_type] || + this.default_connection_color.output_off; + ctx.beginPath(); + //ctx.rect( node.size[0] - 14,i*14,10,10); + + if (slot_type == "array") { + slot_shape = LiteGraph.GRID_SHAPE; + } + + var doStroke = true; + + if ( + slot_type === LiteGraph.EVENT || + slot_shape === LiteGraph.BOX_SHAPE + ) { + if (horizontal) { + ctx.rect(pos[0] - 5 + 0.5, pos[1] - 8 + 0.5, 10, 14); + } else { + ctx.rect(pos[0] - 6 + 0.5, pos[1] - 5 + 0.5, 14, 10); + } + } else if (slot_shape === LiteGraph.ARROW_SHAPE) { + ctx.moveTo(pos[0] + 8, pos[1] + 0.5); + ctx.lineTo(pos[0] - 4, pos[1] + 6 + 0.5); + ctx.lineTo(pos[0] - 4, pos[1] - 6 + 0.5); + ctx.closePath(); + } else if (slot_shape === LiteGraph.GRID_SHAPE) { + ctx.rect(pos[0] - 4, pos[1] - 4, 2, 2); + ctx.rect(pos[0] - 1, pos[1] - 4, 2, 2); + ctx.rect(pos[0] + 2, pos[1] - 4, 2, 2); + ctx.rect(pos[0] - 4, pos[1] - 1, 2, 2); + ctx.rect(pos[0] - 1, pos[1] - 1, 2, 2); + ctx.rect(pos[0] + 2, pos[1] - 1, 2, 2); + ctx.rect(pos[0] - 4, pos[1] + 2, 2, 2); + ctx.rect(pos[0] - 1, pos[1] + 2, 2, 2); + ctx.rect(pos[0] + 2, pos[1] + 2, 2, 2); + doStroke = false; + } else { + if (low_quality) ctx.rect(pos[0] - 4, pos[1] - 4, 8, 8); + else ctx.arc(pos[0], pos[1], 4, 0, Math.PI * 2); + } + + //trigger + //if(slot.node_id != null && slot.slot == -1) + // ctx.fillStyle = "#F85"; + + //if(slot.links != null && slot.links.length) + ctx.fill(); + if (!low_quality && doStroke) ctx.stroke(); + + //render output name + if (render_text) { + var text = slot.label != null ? slot.label : slot.name; + if (text) { + ctx.fillStyle = LiteGraph.NODE_TEXT_COLOR; + if (horizontal || slot.dir == LiteGraph.DOWN) { + ctx.fillText(text, pos[0], pos[1] - 8); + } else { + ctx.fillText(text, pos[0] - 10, pos[1] + 5); + } + } + } + } + } + + ctx.textAlign = "left"; + ctx.globalAlpha = 1; + + if (node.widgets) { + var widgets_y = max_y; + if (horizontal || node.widgets_up) { + widgets_y = 2; + } + if (node.widgets_start_y != null) widgets_y = node.widgets_start_y; + this.drawNodeWidgets( + node, + widgets_y, + ctx, + this.node_widget && this.node_widget[0] == node + ? this.node_widget[1] + : null, + ); + } + } else if (this.render_collapsed_slots) { + //if collapsed + var input_slot = null; + var output_slot = null; + + //get first connected slot to render + if (node.inputs) { + for (var i = 0; i < node.inputs.length; i++) { + var slot = node.inputs[i]; + if (slot.link == null) { + continue; + } + input_slot = slot; + break; + } + } + if (node.outputs) { + for (var i = 0; i < node.outputs.length; i++) { + var slot = node.outputs[i]; + if (!slot.links || !slot.links.length) { + continue; + } + output_slot = slot; + } + } + + if (input_slot) { + var x = 0; + var y = LiteGraph.NODE_TITLE_HEIGHT * -0.5; //center + if (horizontal) { + x = node._collapsed_width * 0.5; + y = -LiteGraph.NODE_TITLE_HEIGHT; + } + ctx.fillStyle = "#686"; + ctx.beginPath(); + if ( + slot.type === LiteGraph.EVENT || + slot.shape === LiteGraph.BOX_SHAPE + ) { + ctx.rect(x - 7 + 0.5, y - 4, 14, 8); + } else if (slot.shape === LiteGraph.ARROW_SHAPE) { + ctx.moveTo(x + 8, y); + ctx.lineTo(x + -4, y - 4); + ctx.lineTo(x + -4, y + 4); + ctx.closePath(); + } else { + ctx.arc(x, y, 4, 0, Math.PI * 2); + } + ctx.fill(); + } + + if (output_slot) { + var x = node._collapsed_width; + var y = LiteGraph.NODE_TITLE_HEIGHT * -0.5; //center + if (horizontal) { + x = node._collapsed_width * 0.5; + y = 0; + } + ctx.fillStyle = "#686"; + ctx.strokeStyle = "black"; + ctx.beginPath(); + if ( + slot.type === LiteGraph.EVENT || + slot.shape === LiteGraph.BOX_SHAPE + ) { + ctx.rect(x - 7 + 0.5, y - 4, 14, 8); + } else if (slot.shape === LiteGraph.ARROW_SHAPE) { + ctx.moveTo(x + 6, y); + ctx.lineTo(x - 6, y - 4); + ctx.lineTo(x - 6, y + 4); + ctx.closePath(); + } else { + ctx.arc(x, y, 4, 0, Math.PI * 2); + } + ctx.fill(); + //ctx.stroke(); + } + } + + if (node.clip_area) { + ctx.restore(); + } + + ctx.globalAlpha = 1.0; + }; + + //used by this.over_link_center + LGraphCanvas.prototype.drawLinkTooltip = function (ctx, link) { + var pos = link._pos; + ctx.fillStyle = "black"; + ctx.beginPath(); + ctx.arc(pos[0], pos[1], 3, 0, Math.PI * 2); + ctx.fill(); + + if (link.data == null) return; + + if (this.onDrawLinkTooltip) + if (this.onDrawLinkTooltip(ctx, link, this) == true) return; + + var data = link.data; + var text = null; + + if (data.constructor === Number) text = data.toFixed(2); + else if (data.constructor === String) text = '"' + data + '"'; + else if (data.constructor === Boolean) text = String(data); + else if (data.toToolTip) text = data.toToolTip(); + else text = "[" + data.constructor.name + "]"; + + if (text == null) return; + text = text.substr(0, 30); //avoid weird + + ctx.font = "14px Courier New"; + var info = ctx.measureText(text); + var w = info.width + 20; + var h = 24; + ctx.shadowColor = "black"; + ctx.shadowOffsetX = 2; + ctx.shadowOffsetY = 2; + ctx.shadowBlur = 3; + ctx.fillStyle = "#454"; + ctx.beginPath(); + ctx.roundRect(pos[0] - w * 0.5, pos[1] - 15 - h, w, h, [3]); + ctx.moveTo(pos[0] - 10, pos[1] - 15); + ctx.lineTo(pos[0] + 10, pos[1] - 15); + ctx.lineTo(pos[0], pos[1] - 5); + ctx.fill(); + ctx.shadowColor = "transparent"; + ctx.textAlign = "center"; + ctx.fillStyle = "#CEC"; + ctx.fillText(text, pos[0], pos[1] - 15 - h * 0.3); + }; + + /** + * draws the shape of the given node in the canvas + * @method drawNodeShape + **/ + var tmp_area = new Float32Array(4); + + LGraphCanvas.prototype.drawNodeShape = function ( + node, + ctx, + size, + fgcolor, + bgcolor, + selected, + mouse_over, + ) { + //bg rect + ctx.strokeStyle = fgcolor; + ctx.fillStyle = bgcolor; + + var title_height = LiteGraph.NODE_TITLE_HEIGHT; + var low_quality = this.ds.scale < 0.5; + + //render node area depending on shape + var shape = node._shape || node.constructor.shape || LiteGraph.ROUND_SHAPE; + + var title_mode = node.constructor.title_mode; + + var render_title = true; + if ( + title_mode == LiteGraph.TRANSPARENT_TITLE || + title_mode == LiteGraph.NO_TITLE + ) { + render_title = false; + } else if (title_mode == LiteGraph.AUTOHIDE_TITLE && mouse_over) { + render_title = true; + } + + var area = tmp_area; + area[0] = 0; //x + area[1] = render_title ? -title_height : 0; //y + area[2] = size[0] + 1; //w + area[3] = render_title ? size[1] + title_height : size[1]; //h + + var old_alpha = ctx.globalAlpha; + + //full node shape + //if(node.flags.collapsed) + { + ctx.beginPath(); + if (shape == LiteGraph.BOX_SHAPE || low_quality) { + ctx.fillRect(area[0], area[1], area[2], area[3]); + } else if ( + shape == LiteGraph.ROUND_SHAPE || + shape == LiteGraph.CARD_SHAPE + ) { + ctx.roundRect( + area[0], + area[1], + area[2], + area[3], + shape == LiteGraph.CARD_SHAPE + ? [this.round_radius, this.round_radius, 0, 0] + : [this.round_radius], + ); + } else if (shape == LiteGraph.CIRCLE_SHAPE) { + ctx.arc(size[0] * 0.5, size[1] * 0.5, size[0] * 0.5, 0, Math.PI * 2); + } + ctx.fill(); + + //separator + if (!node.flags.collapsed && render_title) { + ctx.shadowColor = "transparent"; + ctx.fillStyle = "rgba(0,0,0,0.2)"; + ctx.fillRect(0, -1, area[2], 2); + } + } + ctx.shadowColor = "transparent"; + + if (node.onDrawBackground) { + node.onDrawBackground(ctx, this, this.canvas, this.graph_mouse); + } + + //title bg (remember, it is rendered ABOVE the node) + if (render_title || title_mode == LiteGraph.TRANSPARENT_TITLE) { + //title bar + if (node.onDrawTitleBar) { + node.onDrawTitleBar(ctx, title_height, size, this.ds.scale, fgcolor); + } else if ( + title_mode != LiteGraph.TRANSPARENT_TITLE && + (node.constructor.title_color || this.render_title_colored) + ) { + var title_color = node.constructor.title_color || fgcolor; + + if (node.flags.collapsed) { + ctx.shadowColor = LiteGraph.DEFAULT_SHADOW_COLOR; + } + + //* gradient test + if (this.use_gradients) { + var grad = LGraphCanvas.gradients[title_color]; + if (!grad) { + grad = LGraphCanvas.gradients[title_color] = + ctx.createLinearGradient(0, 0, 400, 0); + grad.addColorStop(0, title_color); // TODO refactor: validate color !! prevent DOMException + grad.addColorStop(1, "#000"); + } + ctx.fillStyle = grad; + } else { + ctx.fillStyle = title_color; + } + + //ctx.globalAlpha = 0.5 * old_alpha; + ctx.beginPath(); + if (shape == LiteGraph.BOX_SHAPE || low_quality) { + ctx.rect(0, -title_height, size[0] + 1, title_height); + } else if ( + shape == LiteGraph.ROUND_SHAPE || + shape == LiteGraph.CARD_SHAPE + ) { + ctx.roundRect( + 0, + -title_height, + size[0] + 1, + title_height, + node.flags.collapsed + ? [this.round_radius] + : [this.round_radius, this.round_radius, 0, 0], + ); + } + ctx.fill(); + ctx.shadowColor = "transparent"; + } + + var colState = false; + if (LiteGraph.node_box_coloured_by_mode) { + if (LiteGraph.NODE_MODES_COLORS[node.mode]) { + colState = LiteGraph.NODE_MODES_COLORS[node.mode]; + } + } + if (LiteGraph.node_box_coloured_when_on) { + colState = node.action_triggered + ? "#FFF" + : node.execute_triggered + ? "#AAA" + : colState; + } + + //title box + var box_size = 10; + if (node.onDrawTitleBox) { + node.onDrawTitleBox(ctx, title_height, size, this.ds.scale); + } else if ( + shape == LiteGraph.ROUND_SHAPE || + shape == LiteGraph.CIRCLE_SHAPE || + shape == LiteGraph.CARD_SHAPE + ) { + if (low_quality) { + ctx.fillStyle = "black"; + ctx.beginPath(); + ctx.arc( + title_height * 0.5, + title_height * -0.5, + box_size * 0.5 + 1, + 0, + Math.PI * 2, + ); + ctx.fill(); + } + + ctx.fillStyle = + node.boxcolor || colState || LiteGraph.NODE_DEFAULT_BOXCOLOR; + if (low_quality) + ctx.fillRect( + title_height * 0.5 - box_size * 0.5, + title_height * -0.5 - box_size * 0.5, + box_size, + box_size, + ); + else { + ctx.beginPath(); + ctx.arc( + title_height * 0.5, + title_height * -0.5, + box_size * 0.5, + 0, + Math.PI * 2, + ); + ctx.fill(); + } + } else { + if (low_quality) { + ctx.fillStyle = "black"; + ctx.fillRect( + (title_height - box_size) * 0.5 - 1, + (title_height + box_size) * -0.5 - 1, + box_size + 2, + box_size + 2, + ); + } + ctx.fillStyle = + node.boxcolor || colState || LiteGraph.NODE_DEFAULT_BOXCOLOR; + ctx.fillRect( + (title_height - box_size) * 0.5, + (title_height + box_size) * -0.5, + box_size, + box_size, + ); + } + ctx.globalAlpha = old_alpha; + + //title text + if (node.onDrawTitleText) { + node.onDrawTitleText( + ctx, + title_height, + size, + this.ds.scale, + this.title_text_font, + selected, + ); + } + if (!low_quality) { + ctx.font = this.title_text_font; + var title = String(node.getTitle()); + if (title) { + if (selected) { + ctx.fillStyle = LiteGraph.NODE_SELECTED_TITLE_COLOR; + } else { + ctx.fillStyle = + node.constructor.title_text_color || this.node_title_color; + } + if (node.flags.collapsed) { + ctx.textAlign = "left"; + var measure = ctx.measureText(title); + ctx.fillText( + title.substr(0, 20), //avoid urls too long + title_height, // + measure.width * 0.5, + LiteGraph.NODE_TITLE_TEXT_Y - title_height, + ); + ctx.textAlign = "left"; + } else { + ctx.textAlign = "left"; + ctx.fillText( + title, + title_height, + LiteGraph.NODE_TITLE_TEXT_Y - title_height, + ); + } + } + } + + //subgraph box + if ( + !node.flags.collapsed && + node.subgraph && + !node.skip_subgraph_button + ) { + var w = LiteGraph.NODE_TITLE_HEIGHT; + var x = node.size[0] - w; + var over = LiteGraph.isInsideRectangle( + this.graph_mouse[0] - node.pos[0], + this.graph_mouse[1] - node.pos[1], + x + 2, + -w + 2, + w - 4, + w - 4, + ); + ctx.fillStyle = over ? "#888" : "#555"; + if (shape == LiteGraph.BOX_SHAPE || low_quality) + ctx.fillRect(x + 2, -w + 2, w - 4, w - 4); + else { + ctx.beginPath(); + ctx.roundRect(x + 2, -w + 2, w - 4, w - 4, [4]); + ctx.fill(); + } + ctx.fillStyle = "#333"; + ctx.beginPath(); + ctx.moveTo(x + w * 0.2, -w * 0.6); + ctx.lineTo(x + w * 0.8, -w * 0.6); + ctx.lineTo(x + w * 0.5, -w * 0.3); + ctx.fill(); + } + + //custom title render + if (node.onDrawTitle) { + node.onDrawTitle(ctx); + } + } + + //render selection marker + if (selected) { + if (node.onBounding) { + node.onBounding(area); + } + + if (title_mode == LiteGraph.TRANSPARENT_TITLE) { + area[1] -= title_height; + area[3] += title_height; + } + ctx.lineWidth = 1; + ctx.globalAlpha = 0.8; + ctx.beginPath(); + if (shape == LiteGraph.BOX_SHAPE) { + ctx.rect(-6 + area[0], -6 + area[1], 12 + area[2], 12 + area[3]); + } else if ( + shape == LiteGraph.ROUND_SHAPE || + (shape == LiteGraph.CARD_SHAPE && node.flags.collapsed) + ) { + ctx.roundRect(-6 + area[0], -6 + area[1], 12 + area[2], 12 + area[3], [ + this.round_radius * 2, + ]); + } else if (shape == LiteGraph.CARD_SHAPE) { + ctx.roundRect(-6 + area[0], -6 + area[1], 12 + area[2], 12 + area[3], [ + this.round_radius * 2, + 2, + this.round_radius * 2, + 2, + ]); + } else if (shape == LiteGraph.CIRCLE_SHAPE) { + ctx.arc( + size[0] * 0.5, + size[1] * 0.5, + size[0] * 0.5 + 6, + 0, + Math.PI * 2, + ); + } + ctx.strokeStyle = LiteGraph.NODE_BOX_OUTLINE_COLOR; + ctx.stroke(); + ctx.strokeStyle = fgcolor; + ctx.globalAlpha = 1; + } + + // these counter helps in conditioning drawing based on if the node has been executed or an action occurred + if (node.execute_triggered > 0) node.execute_triggered--; + if (node.action_triggered > 0) node.action_triggered--; + }; + + var margin_area = new Float32Array(4); + var link_bounding = new Float32Array(4); + var tempA = new Float32Array(2); + var tempB = new Float32Array(2); + + /** + * draws every connection visible in the canvas + * OPTIMIZE THIS: pre-catch connections position instead of recomputing them every time + * @method drawConnections + **/ + LGraphCanvas.prototype.drawConnections = function (ctx) { + var now = LiteGraph.getTime(); + var visible_area = this.visible_area; + margin_area[0] = visible_area[0] - 20; + margin_area[1] = visible_area[1] - 20; + margin_area[2] = visible_area[2] + 40; + margin_area[3] = visible_area[3] + 40; + + //draw connections + ctx.lineWidth = this.connections_width; + + ctx.fillStyle = "#AAA"; + ctx.strokeStyle = "#AAA"; + ctx.globalAlpha = this.editor_alpha; + //for every node + var nodes = this.graph._nodes; + for (var n = 0, l = nodes.length; n < l; ++n) { + var node = nodes[n]; + //for every input (we render just inputs because it is easier as every slot can only have one input) + if (!node.inputs || !node.inputs.length) { + continue; + } + + for (var i = 0; i < node.inputs.length; ++i) { + var input = node.inputs[i]; + if (!input || input.link == null) { + continue; + } + var link_id = input.link; + var link = this.graph.links[link_id]; + if (!link) { + continue; + } + + //find link info + var start_node = this.graph.getNodeById(link.origin_id); + if (start_node == null) { + continue; + } + var start_node_slot = link.origin_slot; + var start_node_slotpos = null; + if (start_node_slot == -1) { + start_node_slotpos = [start_node.pos[0] + 10, start_node.pos[1] + 10]; + } else { + start_node_slotpos = start_node.getConnectionPos( + false, + start_node_slot, + tempA, + ); + } + var end_node_slotpos = node.getConnectionPos(true, i, tempB); + + //compute link bounding + link_bounding[0] = start_node_slotpos[0]; + link_bounding[1] = start_node_slotpos[1]; + link_bounding[2] = end_node_slotpos[0] - start_node_slotpos[0]; + link_bounding[3] = end_node_slotpos[1] - start_node_slotpos[1]; + if (link_bounding[2] < 0) { + link_bounding[0] += link_bounding[2]; + link_bounding[2] = Math.abs(link_bounding[2]); + } + if (link_bounding[3] < 0) { + link_bounding[1] += link_bounding[3]; + link_bounding[3] = Math.abs(link_bounding[3]); + } + + //skip links outside of the visible area of the canvas + if (!overlapBounding(link_bounding, margin_area)) { + continue; + } + + var start_slot = start_node.outputs[start_node_slot]; + var end_slot = node.inputs[i]; + if (!start_slot || !end_slot) { + continue; + } + var start_dir = + start_slot.dir || + (start_node.horizontal ? LiteGraph.DOWN : LiteGraph.RIGHT); + var end_dir = + end_slot.dir || (node.horizontal ? LiteGraph.UP : LiteGraph.LEFT); + + this.renderLink( + ctx, + start_node_slotpos, + end_node_slotpos, + link, + false, + 0, + null, + start_dir, + end_dir, + ); + + //event triggered rendered on top + if (link && link._last_time && now - link._last_time < 1000) { + var f = 2.0 - (now - link._last_time) * 0.002; + var tmp = ctx.globalAlpha; + ctx.globalAlpha = tmp * f; + this.renderLink( + ctx, + start_node_slotpos, + end_node_slotpos, + link, + true, + f, + "white", + start_dir, + end_dir, + ); + ctx.globalAlpha = tmp; + } + } + } + ctx.globalAlpha = 1; + }; + + /** + * draws a link between two points + * @method renderLink + * @param {vec2} a start pos + * @param {vec2} b end pos + * @param {Object} link the link object with all the link info + * @param {boolean} skip_border ignore the shadow of the link + * @param {boolean} flow show flow animation (for events) + * @param {string} color the color for the link + * @param {number} start_dir the direction enum + * @param {number} end_dir the direction enum + * @param {number} num_sublines number of sublines (useful to represent vec3 or rgb) + **/ + LGraphCanvas.prototype.renderLink = function ( + ctx, + a, + b, + link, + skip_border, + flow, + color, + start_dir, + end_dir, + num_sublines, + ) { + if (link) { + this.visible_links.push(link); + } + + //choose color + if (!color && link) { + color = link.color || LGraphCanvas.link_type_colors[link.type]; + } + if (!color) { + color = this.default_link_color; + } + if (link != null && this.highlighted_links[link.id]) { + color = "#FFF"; + } + + start_dir = start_dir || LiteGraph.RIGHT; + end_dir = end_dir || LiteGraph.LEFT; + + var dist = distance(a, b); + + if (this.render_connections_border && this.ds.scale > 0.6) { + ctx.lineWidth = this.connections_width + 4; + } + ctx.lineJoin = "round"; + num_sublines = num_sublines || 1; + if (num_sublines > 1) { + ctx.lineWidth = 0.5; + } + + //begin line shape + ctx.beginPath(); + for (var i = 0; i < num_sublines; i += 1) { + var offsety = (i - (num_sublines - 1) * 0.5) * 5; + + if (this.links_render_mode == LiteGraph.SPLINE_LINK) { + ctx.moveTo(a[0], a[1] + offsety); + var start_offset_x = 0; + var start_offset_y = 0; + var end_offset_x = 0; + var end_offset_y = 0; + switch (start_dir) { + case LiteGraph.LEFT: + start_offset_x = dist * -0.25; + break; + case LiteGraph.RIGHT: + start_offset_x = dist * 0.25; + break; + case LiteGraph.UP: + start_offset_y = dist * -0.25; + break; + case LiteGraph.DOWN: + start_offset_y = dist * 0.25; + break; + } + switch (end_dir) { + case LiteGraph.LEFT: + end_offset_x = dist * -0.25; + break; + case LiteGraph.RIGHT: + end_offset_x = dist * 0.25; + break; + case LiteGraph.UP: + end_offset_y = dist * -0.25; + break; + case LiteGraph.DOWN: + end_offset_y = dist * 0.25; + break; + } + ctx.bezierCurveTo( + a[0] + start_offset_x, + a[1] + start_offset_y + offsety, + b[0] + end_offset_x, + b[1] + end_offset_y + offsety, + b[0], + b[1] + offsety, + ); + } else if (this.links_render_mode == LiteGraph.LINEAR_LINK) { + ctx.moveTo(a[0], a[1] + offsety); + var start_offset_x = 0; + var start_offset_y = 0; + var end_offset_x = 0; + var end_offset_y = 0; + switch (start_dir) { + case LiteGraph.LEFT: + start_offset_x = -1; + break; + case LiteGraph.RIGHT: + start_offset_x = 1; + break; + case LiteGraph.UP: + start_offset_y = -1; + break; + case LiteGraph.DOWN: + start_offset_y = 1; + break; + } + switch (end_dir) { + case LiteGraph.LEFT: + end_offset_x = -1; + break; + case LiteGraph.RIGHT: + end_offset_x = 1; + break; + case LiteGraph.UP: + end_offset_y = -1; + break; + case LiteGraph.DOWN: + end_offset_y = 1; + break; + } + var l = 15; + ctx.lineTo( + a[0] + start_offset_x * l, + a[1] + start_offset_y * l + offsety, + ); + ctx.lineTo(b[0] + end_offset_x * l, b[1] + end_offset_y * l + offsety); + ctx.lineTo(b[0], b[1] + offsety); + } else if (this.links_render_mode == LiteGraph.STRAIGHT_LINK) { + ctx.moveTo(a[0], a[1]); + var start_x = a[0]; + var start_y = a[1]; + var end_x = b[0]; + var end_y = b[1]; + if (start_dir == LiteGraph.RIGHT) { + start_x += 10; + } else { + start_y += 10; + } + if (end_dir == LiteGraph.LEFT) { + end_x -= 10; + } else { + end_y -= 10; + } + ctx.lineTo(start_x, start_y); + ctx.lineTo((start_x + end_x) * 0.5, start_y); + ctx.lineTo((start_x + end_x) * 0.5, end_y); + ctx.lineTo(end_x, end_y); + ctx.lineTo(b[0], b[1]); + } else { + return; + } //unknown + } + + //rendering the outline of the connection can be a little bit slow + if (this.render_connections_border && this.ds.scale > 0.6 && !skip_border) { + ctx.strokeStyle = "rgba(0,0,0,0.5)"; + ctx.stroke(); + } + + ctx.lineWidth = this.connections_width; + ctx.fillStyle = ctx.strokeStyle = color; + ctx.stroke(); + //end line shape + + var pos = this.computeConnectionPoint(a, b, 0.5, start_dir, end_dir); + if (link && link._pos) { + link._pos[0] = pos[0]; + link._pos[1] = pos[1]; + } + + //render arrow in the middle + if ( + this.ds.scale >= 0.6 && + this.highquality_render && + end_dir != LiteGraph.CENTER + ) { + //render arrow + if (this.render_connection_arrows) { + //compute two points in the connection + var posA = this.computeConnectionPoint(a, b, 0.25, start_dir, end_dir); + var posB = this.computeConnectionPoint(a, b, 0.26, start_dir, end_dir); + var posC = this.computeConnectionPoint(a, b, 0.75, start_dir, end_dir); + var posD = this.computeConnectionPoint(a, b, 0.76, start_dir, end_dir); + + //compute the angle between them so the arrow points in the right direction + var angleA = 0; + var angleB = 0; + if (this.render_curved_connections) { + angleA = -Math.atan2(posB[0] - posA[0], posB[1] - posA[1]); + angleB = -Math.atan2(posD[0] - posC[0], posD[1] - posC[1]); + } else { + angleB = angleA = b[1] > a[1] ? 0 : Math.PI; + } + + //render arrow + ctx.save(); + ctx.translate(posA[0], posA[1]); + ctx.rotate(angleA); + ctx.beginPath(); + ctx.moveTo(-5, -3); + ctx.lineTo(0, +7); + ctx.lineTo(+5, -3); + ctx.fill(); + ctx.restore(); + ctx.save(); + ctx.translate(posC[0], posC[1]); + ctx.rotate(angleB); + ctx.beginPath(); + ctx.moveTo(-5, -3); + ctx.lineTo(0, +7); + ctx.lineTo(+5, -3); + ctx.fill(); + ctx.restore(); + } + + //circle + ctx.beginPath(); + ctx.arc(pos[0], pos[1], 5, 0, Math.PI * 2); + ctx.fill(); + } + + //render flowing points + if (flow) { + ctx.fillStyle = color; + for (var i = 0; i < 5; ++i) { + var f = (LiteGraph.getTime() * 0.001 + i * 0.2) % 1; + var pos = this.computeConnectionPoint(a, b, f, start_dir, end_dir); + ctx.beginPath(); + ctx.arc(pos[0], pos[1], 5, 0, 2 * Math.PI); + ctx.fill(); + } + } + }; + + //returns the link center point based on curvature + LGraphCanvas.prototype.computeConnectionPoint = function ( + a, + b, + t, + start_dir, + end_dir, + ) { + start_dir = start_dir || LiteGraph.RIGHT; + end_dir = end_dir || LiteGraph.LEFT; + + var dist = distance(a, b); + var p0 = a; + var p1 = [a[0], a[1]]; + var p2 = [b[0], b[1]]; + var p3 = b; + + switch (start_dir) { + case LiteGraph.LEFT: + p1[0] += dist * -0.25; + break; + case LiteGraph.RIGHT: + p1[0] += dist * 0.25; + break; + case LiteGraph.UP: + p1[1] += dist * -0.25; + break; + case LiteGraph.DOWN: + p1[1] += dist * 0.25; + break; + } + switch (end_dir) { + case LiteGraph.LEFT: + p2[0] += dist * -0.25; + break; + case LiteGraph.RIGHT: + p2[0] += dist * 0.25; + break; + case LiteGraph.UP: + p2[1] += dist * -0.25; + break; + case LiteGraph.DOWN: + p2[1] += dist * 0.25; + break; + } + + var c1 = (1 - t) * (1 - t) * (1 - t); + var c2 = 3 * ((1 - t) * (1 - t)) * t; + var c3 = 3 * (1 - t) * (t * t); + var c4 = t * t * t; + + var x = c1 * p0[0] + c2 * p1[0] + c3 * p2[0] + c4 * p3[0]; + var y = c1 * p0[1] + c2 * p1[1] + c3 * p2[1] + c4 * p3[1]; + return [x, y]; + }; + + LGraphCanvas.prototype.drawExecutionOrder = function (ctx) { + ctx.shadowColor = "transparent"; + ctx.globalAlpha = 0.25; + + ctx.textAlign = "center"; + ctx.strokeStyle = "white"; + ctx.globalAlpha = 0.75; + + var visible_nodes = this.visible_nodes; + for (var i = 0; i < visible_nodes.length; ++i) { + var node = visible_nodes[i]; + ctx.fillStyle = "black"; + ctx.fillRect( + node.pos[0] - LiteGraph.NODE_TITLE_HEIGHT, + node.pos[1] - LiteGraph.NODE_TITLE_HEIGHT, + LiteGraph.NODE_TITLE_HEIGHT, + LiteGraph.NODE_TITLE_HEIGHT, + ); + if (node.order == 0) { + ctx.strokeRect( + node.pos[0] - LiteGraph.NODE_TITLE_HEIGHT + 0.5, + node.pos[1] - LiteGraph.NODE_TITLE_HEIGHT + 0.5, + LiteGraph.NODE_TITLE_HEIGHT, + LiteGraph.NODE_TITLE_HEIGHT, + ); + } + ctx.fillStyle = "#FFF"; + ctx.fillText( + node.order, + node.pos[0] + LiteGraph.NODE_TITLE_HEIGHT * -0.5, + node.pos[1] - 6, + ); + } + ctx.globalAlpha = 1; + }; + + /** + * draws the widgets stored inside a node + * @method drawNodeWidgets + **/ + LGraphCanvas.prototype.drawNodeWidgets = function ( + node, + posY, + ctx, + active_widget, + ) { + if (!node.widgets || !node.widgets.length) { + return 0; + } + var width = node.size[0]; + var widgets = node.widgets; + posY += 2; + var H = LiteGraph.NODE_WIDGET_HEIGHT; + var show_text = this.ds.scale > 0.5; + ctx.save(); + ctx.globalAlpha = this.editor_alpha; + var outline_color = LiteGraph.WIDGET_OUTLINE_COLOR; + var background_color = LiteGraph.WIDGET_BGCOLOR; + var text_color = LiteGraph.WIDGET_TEXT_COLOR; + var secondary_text_color = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR; + var margin = 15; + + for (var i = 0; i < widgets.length; ++i) { + var w = widgets[i]; + var y = posY; + if (w.y) { + y = w.y; + } + w.last_y = y; + ctx.strokeStyle = outline_color; + ctx.fillStyle = "#222"; + ctx.textAlign = "left"; + //ctx.lineWidth = 2; + if (w.disabled) ctx.globalAlpha *= 0.5; + var widget_width = w.width || width; + + switch (w.type) { + case "button": + if (w.clicked) { + ctx.fillStyle = "#AAA"; + w.clicked = false; + this.dirty_canvas = true; + } + ctx.fillRect(margin, y, widget_width - margin * 2, H); + if (show_text && !w.disabled) + ctx.strokeRect(margin, y, widget_width - margin * 2, H); + if (show_text) { + ctx.textAlign = "center"; + ctx.fillStyle = text_color; + ctx.fillText(w.label || w.name, widget_width * 0.5, y + H * 0.7); + } + break; + case "toggle": + ctx.textAlign = "left"; + ctx.strokeStyle = outline_color; + ctx.fillStyle = background_color; + ctx.beginPath(); + if (show_text) + ctx.roundRect(margin, y, widget_width - margin * 2, H, [H * 0.5]); + else ctx.rect(margin, y, widget_width - margin * 2, H); + ctx.fill(); + if (show_text && !w.disabled) ctx.stroke(); + ctx.fillStyle = w.value ? "#89A" : "#333"; + ctx.beginPath(); + ctx.arc( + widget_width - margin * 2, + y + H * 0.5, + H * 0.36, + 0, + Math.PI * 2, + ); + ctx.fill(); + if (show_text) { + ctx.fillStyle = secondary_text_color; + const label = w.label || w.name; + if (label != null) { + ctx.fillText(label, margin * 2, y + H * 0.7); + } + ctx.fillStyle = w.value ? text_color : secondary_text_color; + ctx.textAlign = "right"; + ctx.fillText( + w.value ? w.options.on || "true" : w.options.off || "false", + widget_width - 40, + y + H * 0.7, + ); + } + break; + case "slider": + ctx.fillStyle = background_color; + ctx.fillRect(margin, y, widget_width - margin * 2, H); + var range = w.options.max - w.options.min; + var nvalue = (w.value - w.options.min) / range; + if (nvalue < 0.0) nvalue = 0.0; + if (nvalue > 1.0) nvalue = 1.0; + ctx.fillStyle = w.options.hasOwnProperty("slider_color") + ? w.options.slider_color + : active_widget == w + ? "#89A" + : "#678"; + ctx.fillRect(margin, y, nvalue * (widget_width - margin * 2), H); + if (show_text && !w.disabled) + ctx.strokeRect(margin, y, widget_width - margin * 2, H); + if (w.marker) { + var marker_nvalue = (w.marker - w.options.min) / range; + if (marker_nvalue < 0.0) marker_nvalue = 0.0; + if (marker_nvalue > 1.0) marker_nvalue = 1.0; + ctx.fillStyle = w.options.hasOwnProperty("marker_color") + ? w.options.marker_color + : "#AA9"; + ctx.fillRect( + margin + marker_nvalue * (widget_width - margin * 2), + y, + 2, + H, + ); + } + if (show_text) { + ctx.textAlign = "center"; + ctx.fillStyle = text_color; + ctx.fillText( + w.label || + w.name + + " " + + Number(w.value).toFixed( + w.options.precision != null ? w.options.precision : 3, + ), + widget_width * 0.5, + y + H * 0.7, + ); + } + break; + case "number": + case "combo": + ctx.textAlign = "left"; + ctx.strokeStyle = outline_color; + ctx.fillStyle = background_color; + ctx.beginPath(); + if (show_text) + ctx.roundRect(margin, y, widget_width - margin * 2, H, [H * 0.5]); + else ctx.rect(margin, y, widget_width - margin * 2, H); + ctx.fill(); + if (show_text) { + if (!w.disabled) ctx.stroke(); + ctx.fillStyle = text_color; + if (!w.disabled) { + ctx.beginPath(); + ctx.moveTo(margin + 16, y + 5); + ctx.lineTo(margin + 6, y + H * 0.5); + ctx.lineTo(margin + 16, y + H - 5); + ctx.fill(); + ctx.beginPath(); + ctx.moveTo(widget_width - margin - 16, y + 5); + ctx.lineTo(widget_width - margin - 6, y + H * 0.5); + ctx.lineTo(widget_width - margin - 16, y + H - 5); + ctx.fill(); + } + ctx.fillStyle = secondary_text_color; + ctx.fillText(w.label || w.name, margin * 2 + 5, y + H * 0.7); + ctx.fillStyle = text_color; + ctx.textAlign = "right"; + if (w.type == "number") { + ctx.fillText( + Number(w.value).toFixed( + w.options.precision !== undefined ? w.options.precision : 3, + ), + widget_width - margin * 2 - 20, + y + H * 0.7, + ); + } else { + var v = w.value; + if (w.options.values) { + var values = w.options.values; + if (values.constructor === Function) values = values(); + if (values && values.constructor !== Array) v = values[w.value]; + } + ctx.fillText(v, widget_width - margin * 2 - 20, y + H * 0.7); + } + } + break; + case "string": + case "text": + ctx.textAlign = "left"; + ctx.strokeStyle = outline_color; + ctx.fillStyle = background_color; + ctx.beginPath(); + if (show_text) + ctx.roundRect(margin, y, widget_width - margin * 2, H, [H * 0.5]); + else ctx.rect(margin, y, widget_width - margin * 2, H); + ctx.fill(); + if (show_text) { + if (!w.disabled) ctx.stroke(); + ctx.save(); + ctx.beginPath(); + ctx.rect(margin, y, widget_width - margin * 2, H); + ctx.clip(); + + //ctx.stroke(); + ctx.fillStyle = secondary_text_color; + const label = w.label || w.name; + if (label != null) { + ctx.fillText(label, margin * 2, y + H * 0.7); + } + ctx.fillStyle = text_color; + ctx.textAlign = "right"; + ctx.fillText( + String(w.value).substr(0, 30), + widget_width - margin * 2, + y + H * 0.7, + ); //30 chars max + ctx.restore(); + } + break; + default: + if (w.draw) { + w.draw(ctx, node, widget_width, y, H); + } + break; + } + posY += (w.computeSize ? w.computeSize(widget_width)[1] : H) + 4; + ctx.globalAlpha = this.editor_alpha; + } + ctx.restore(); + ctx.textAlign = "left"; + }; + + /** + * process an event on widgets + * @method processNodeWidgets + **/ + LGraphCanvas.prototype.processNodeWidgets = function ( + node, + pos, + event, + active_widget, + ) { + if ( + !node.widgets || + !node.widgets.length || + (!this.allow_interaction && !node.flags.allow_interaction) + ) { + return null; + } + + var x = pos[0] - node.pos[0]; + var y = pos[1] - node.pos[1]; + var width = node.size[0]; + var deltaX = event.deltaX || event.deltax || 0; + var that = this; + var ref_window = this.getCanvasWindow(); + + for (var i = 0; i < node.widgets.length; ++i) { + var w = node.widgets[i]; + if (!w || w.disabled) continue; + var widget_height = w.computeSize + ? w.computeSize(width)[1] + : LiteGraph.NODE_WIDGET_HEIGHT; + var widget_width = w.width || width; + //outside + if ( + w != active_widget && + (x < 6 || + x > widget_width - 12 || + y < w.last_y || + y > w.last_y + widget_height || + w.last_y === undefined) + ) + continue; + + var old_value = w.value; + + //if ( w == active_widget || (x > 6 && x < widget_width - 12 && y > w.last_y && y < w.last_y + widget_height) ) { + //inside widget + switch (w.type) { + case "button": + if (event.type === LiteGraph.pointerevents_method + "down") { + if (w.callback) { + setTimeout(function () { + w.callback(w, that, node, pos, event); + }, 20); + } + w.clicked = true; + this.dirty_canvas = true; + } + break; + case "slider": + var old_value = w.value; + var nvalue = clamp((x - 15) / (widget_width - 30), 0, 1); + if (w.options.read_only) break; + w.value = w.options.min + (w.options.max - w.options.min) * nvalue; + if (old_value != w.value) { + setTimeout(function () { + inner_value_change(w, w.value); + }, 20); + } + this.dirty_canvas = true; + break; + case "number": + case "combo": + var old_value = w.value; + if ( + event.type == LiteGraph.pointerevents_method + "move" && + w.type == "number" + ) { + if (deltaX) w.value += deltaX * 0.1 * (w.options.step || 1); + if (w.options.min != null && w.value < w.options.min) { + w.value = w.options.min; + } + if (w.options.max != null && w.value > w.options.max) { + w.value = w.options.max; + } + } else if (event.type == LiteGraph.pointerevents_method + "down") { + var values = w.options.values; + if (values && values.constructor === Function) { + values = w.options.values(w, node); + } + var values_list = null; + + if (w.type != "number") + values_list = + values.constructor === Array ? values : Object.keys(values); + + var delta = x < 40 ? -1 : x > widget_width - 40 ? 1 : 0; + if (w.type == "number") { + w.value += delta * 0.1 * (w.options.step || 1); + if (w.options.min != null && w.value < w.options.min) { + w.value = w.options.min; + } + if (w.options.max != null && w.value > w.options.max) { + w.value = w.options.max; + } + } else if (delta) { + //clicked in arrow, used for combos + var index = -1; + this.last_mouseclick = 0; //avoids dobl click event + if (values.constructor === Object) + index = values_list.indexOf(String(w.value)) + delta; + else index = values_list.indexOf(w.value) + delta; + if (index >= values_list.length) { + index = values_list.length - 1; + } + if (index < 0) { + index = 0; + } + if (values.constructor === Array) w.value = values[index]; + else w.value = index; + } else { + //combo clicked + var text_values = + values != values_list ? Object.values(values) : values; + var menu = new LiteGraph.ContextMenu( + text_values, + { + scale: Math.max(1, this.ds.scale), + event: event, + className: "dark", + callback: inner_clicked.bind(w), + }, + ref_window, + ); + function inner_clicked(v, option, event) { + if (values != values_list) v = text_values.indexOf(v); + this.value = v; + inner_value_change(this, v); + that.dirty_canvas = true; + return false; + } + } + } //end mousedown + else if ( + event.type == LiteGraph.pointerevents_method + "up" && + w.type == "number" + ) { + var delta = x < 40 ? -1 : x > widget_width - 40 ? 1 : 0; + if (event.click_time < 200 && delta == 0) { + this.prompt( + "Value", + w.value, + function (v) { + // check if v is a valid equation or a number + if (/^[0-9+\-*/()\s]+|\d+\.\d+$/.test(v)) { + try { + //solve the equation if possible + v = eval(v); + } catch (e) {} + } + this.value = Number(v); + inner_value_change(this, this.value); + }.bind(w), + event, + ); + } + } + + if (old_value != w.value) + setTimeout( + function () { + inner_value_change(this, this.value); + }.bind(w), + 20, + ); + this.dirty_canvas = true; + break; + case "toggle": + if (event.type == LiteGraph.pointerevents_method + "down") { + w.value = !w.value; + setTimeout(function () { + inner_value_change(w, w.value); + }, 20); + } + break; + case "string": + case "text": + if (event.type == LiteGraph.pointerevents_method + "down") { + this.prompt( + "Value", + w.value, + function (v) { + inner_value_change(this, v); + }.bind(w), + event, + w.options ? w.options.multiline : false, + ); + } + break; + default: + if (w.mouse) { + this.dirty_canvas = w.mouse(event, [x, y], node); + } + break; + } //end switch + + //value changed + if (old_value != w.value) { + if (node.onWidgetChanged) + node.onWidgetChanged(w.name, w.value, old_value, w); + node.graph._version++; + } + + return w; + } //end for + + function inner_value_change(widget, value) { + if (widget.type == "number") { + value = Number(value); + } + widget.value = value; + if ( + widget.options && + widget.options.property && + node.properties[widget.options.property] !== undefined + ) { + node.setProperty(widget.options.property, value); + } + if (widget.callback) { + widget.callback(widget.value, that, node, pos, event); + } + } + + return null; + }; + + /** + * draws every group area in the background + * @method drawGroups + **/ + LGraphCanvas.prototype.drawGroups = function (canvas, ctx) { + if (!this.graph) { + return; + } + + var groups = this.graph._groups; + + ctx.save(); + ctx.globalAlpha = 0.5 * this.editor_alpha; + + for (var i = 0; i < groups.length; ++i) { + var group = groups[i]; + + if (!overlapBounding(this.visible_area, group._bounding)) { + continue; + } //out of the visible area + + ctx.fillStyle = group.color || "#335"; + ctx.strokeStyle = group.color || "#335"; + var pos = group._pos; + var size = group._size; + ctx.globalAlpha = 0.25 * this.editor_alpha; + ctx.beginPath(); + ctx.rect(pos[0] + 0.5, pos[1] + 0.5, size[0], size[1]); + ctx.fill(); + ctx.globalAlpha = this.editor_alpha; + ctx.stroke(); + + ctx.beginPath(); + ctx.moveTo(pos[0] + size[0], pos[1] + size[1]); + ctx.lineTo(pos[0] + size[0] - 10, pos[1] + size[1]); + ctx.lineTo(pos[0] + size[0], pos[1] + size[1] - 10); + ctx.fill(); + + var font_size = group.font_size || LiteGraph.DEFAULT_GROUP_FONT_SIZE; + ctx.font = font_size + "px Arial"; + ctx.textAlign = "left"; + ctx.fillText(group.title, pos[0] + 4, pos[1] + font_size); + } + + ctx.restore(); + }; + + LGraphCanvas.prototype.adjustNodesSize = function () { + var nodes = this.graph._nodes; + for (var i = 0; i < nodes.length; ++i) { + nodes[i].size = nodes[i].computeSize(); + } + this.setDirty(true, true); + }; + + /** + * resizes the canvas to a given size, if no size is passed, then it tries to fill the parentNode + * @method resize + **/ + LGraphCanvas.prototype.resize = function (width, height) { + if (!width && !height) { + var parent = this.canvas.parentNode; + width = parent.offsetWidth; + height = parent.offsetHeight; + } + + if (this.canvas.width == width && this.canvas.height == height) { + return; + } + + this.canvas.width = width; + this.canvas.height = height; + this.bgcanvas.width = this.canvas.width; + this.bgcanvas.height = this.canvas.height; + this.setDirty(true, true); + }; + + /** + * switches to live mode (node shapes are not rendered, only the content) + * this feature was designed when graphs where meant to create user interfaces + * @method switchLiveMode + **/ + LGraphCanvas.prototype.switchLiveMode = function (transition) { + if (!transition) { + this.live_mode = !this.live_mode; + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + return; + } + + var self = this; + var delta = this.live_mode ? 1.1 : 0.9; + if (this.live_mode) { + this.live_mode = false; + this.editor_alpha = 0.1; + } + + var t = setInterval(function () { + self.editor_alpha *= delta; + self.dirty_canvas = true; + self.dirty_bgcanvas = true; + + if (delta < 1 && self.editor_alpha < 0.01) { + clearInterval(t); + if (delta < 1) { + self.live_mode = true; + } + } + if (delta > 1 && self.editor_alpha > 0.99) { + clearInterval(t); + self.editor_alpha = 1; + } + }, 1); + }; + + LGraphCanvas.prototype.onNodeSelectionChange = function (node) { + return; //disabled + }; + + /* this is an implementation for touch not in production and not ready + */ + /*LGraphCanvas.prototype.touchHandler = function(event) { + //alert("foo"); + var touches = event.changedTouches, + first = touches[0], + type = ""; + + switch (event.type) { + case "touchstart": + type = "mousedown"; + break; + case "touchmove": + type = "mousemove"; + break; + case "touchend": + type = "mouseup"; + break; + default: + return; + } + + //initMouseEvent(type, canBubble, cancelable, view, clickCount, + // screenX, screenY, clientX, clientY, ctrlKey, + // altKey, shiftKey, metaKey, button, relatedTarget); + + // this is eventually a Dom object, get the LGraphCanvas back + if(typeof this.getCanvasWindow == "undefined"){ + var window = this.lgraphcanvas.getCanvasWindow(); + }else{ + var window = this.getCanvasWindow(); + } + + var document = window.document; + + var simulatedEvent = document.createEvent("MouseEvent"); + simulatedEvent.initMouseEvent( + type, + true, + true, + window, + 1, + first.screenX, + first.screenY, + first.clientX, + first.clientY, + false, + false, + false, + false, + 0, //left + null + ); + first.target.dispatchEvent(simulatedEvent); + event.preventDefault(); + };*/ + + /* CONTEXT MENU ********************/ + + LGraphCanvas.onGroupAdd = function (info, entry, mouse_event) { + var canvas = LGraphCanvas.active_canvas; + var ref_window = canvas.getCanvasWindow(); + + var group = new LiteGraph.LGraphGroup(); + group.pos = canvas.convertEventToCanvasOffset(mouse_event); + canvas.graph.add(group); + }; + + /** + * Determines the furthest nodes in each direction + * @param nodes {LGraphNode[]} the nodes to from which boundary nodes will be extracted + * @return {{left: LGraphNode, top: LGraphNode, right: LGraphNode, bottom: LGraphNode}} + */ + LGraphCanvas.getBoundaryNodes = function (nodes) { + let top = null; + let right = null; + let bottom = null; + let left = null; + for (const nID in nodes) { + const node = nodes[nID]; + const [x, y] = node.pos; + const [width, height] = node.size; + + if (top === null || y < top.pos[1]) { + top = node; + } + if (right === null || x + width > right.pos[0] + right.size[0]) { + right = node; + } + if (bottom === null || y + height > bottom.pos[1] + bottom.size[1]) { + bottom = node; + } + if (left === null || x < left.pos[0]) { + left = node; + } + } + + return { + top: top, + right: right, + bottom: bottom, + left: left, + }; + }; + /** + * Determines the furthest nodes in each direction for the currently selected nodes + * @return {{left: LGraphNode, top: LGraphNode, right: LGraphNode, bottom: LGraphNode}} + */ + LGraphCanvas.prototype.boundaryNodesForSelection = function () { + return LGraphCanvas.getBoundaryNodes(Object.values(this.selected_nodes)); + }; + + /** + * + * @param {LGraphNode[]} nodes a list of nodes + * @param {"top"|"bottom"|"left"|"right"} direction Direction to align the nodes + * @param {LGraphNode?} align_to Node to align to (if null, align to the furthest node in the given direction) + */ + LGraphCanvas.alignNodes = function (nodes, direction, align_to) { + if (!nodes) { + return; + } + + const canvas = LGraphCanvas.active_canvas; + let boundaryNodes = []; + if (align_to === undefined) { + boundaryNodes = LGraphCanvas.getBoundaryNodes(nodes); + } else { + boundaryNodes = { + top: align_to, + right: align_to, + bottom: align_to, + left: align_to, + }; + } + + for (const [_, node] of Object.entries(canvas.selected_nodes)) { + switch (direction) { + case "right": + node.pos[0] = + boundaryNodes["right"].pos[0] + + boundaryNodes["right"].size[0] - + node.size[0]; + break; + case "left": + node.pos[0] = boundaryNodes["left"].pos[0]; + break; + case "top": + node.pos[1] = boundaryNodes["top"].pos[1]; + break; + case "bottom": + node.pos[1] = + boundaryNodes["bottom"].pos[1] + + boundaryNodes["bottom"].size[1] - + node.size[1]; + break; + } + } + + canvas.dirty_canvas = true; + canvas.dirty_bgcanvas = true; + }; + + LGraphCanvas.onNodeAlign = function (value, options, event, prev_menu, node) { + new LiteGraph.ContextMenu(["Top", "Bottom", "Left", "Right"], { + event: event, + callback: inner_clicked, + parentMenu: prev_menu, + }); + + function inner_clicked(value) { + LGraphCanvas.alignNodes( + LGraphCanvas.active_canvas.selected_nodes, + value.toLowerCase(), + node, + ); + } + }; + + LGraphCanvas.onGroupAlign = function (value, options, event, prev_menu) { + new LiteGraph.ContextMenu(["Top", "Bottom", "Left", "Right"], { + event: event, + callback: inner_clicked, + parentMenu: prev_menu, + }); + + function inner_clicked(value) { + LGraphCanvas.alignNodes( + LGraphCanvas.active_canvas.selected_nodes, + value.toLowerCase(), + ); + } + }; + + LGraphCanvas.onMenuAdd = function (node, options, e, prev_menu, callback) { + var canvas = LGraphCanvas.active_canvas; + var ref_window = canvas.getCanvasWindow(); + var graph = canvas.graph; + if (!graph) return; + + function inner_onMenuAdded(base_category, prev_menu) { + var categories = LiteGraph.getNodeTypesCategories( + canvas.filter || graph.filter, + ).filter(function (category) { + return category.startsWith(base_category); + }); + var entries = []; + + categories.map(function (category) { + if (!category) return; + + var base_category_regex = new RegExp("^(" + base_category + ")"); + var category_name = category + .replace(base_category_regex, "") + .split("/")[0]; + var category_path = + base_category === "" + ? category_name + "/" + : base_category + category_name + "/"; + + var name = category_name; + if (name.indexOf("::") != -1) + //in case it has a namespace like "shader::math/rand" it hides the namespace + name = name.split("::")[1]; + + var index = entries.findIndex(function (entry) { + return entry.value === category_path; + }); + if (index === -1) { + entries.push({ + value: category_path, + content: name, + has_submenu: true, + callback: function (value, event, mouseEvent, contextMenu) { + inner_onMenuAdded(value.value, contextMenu); + }, + }); + } + }); + + var nodes = LiteGraph.getNodeTypesInCategory( + base_category.slice(0, -1), + canvas.filter || graph.filter, + ); + nodes.map(function (node) { + if (node.skip_list) return; + + var entry = { + value: node.type, + content: node.title, + has_submenu: false, + callback: function (value, event, mouseEvent, contextMenu) { + var first_event = contextMenu.getFirstEvent(); + canvas.graph.beforeChange(); + var node = LiteGraph.createNode(value.value); + if (node) { + node.pos = canvas.convertEventToCanvasOffset(first_event); + canvas.graph.add(node); + } + if (callback) callback(node); + canvas.graph.afterChange(); + }, + }; + + entries.push(entry); + }); + + new LiteGraph.ContextMenu( + entries, + { event: e, parentMenu: prev_menu }, + ref_window, + ); + } + + inner_onMenuAdded("", prev_menu); + return false; + }; + + LGraphCanvas.onMenuCollapseAll = function () {}; + + LGraphCanvas.onMenuNodeEdit = function () {}; + + LGraphCanvas.showMenuNodeOptionalInputs = function ( + v, + options, + e, + prev_menu, + node, + ) { + if (!node) { + return; + } + + var that = this; + var canvas = LGraphCanvas.active_canvas; + var ref_window = canvas.getCanvasWindow(); + + var options = node.optional_inputs; + if (node.onGetInputs) { + options = node.onGetInputs(); + } + + var entries = []; + if (options) { + for (var i = 0; i < options.length; i++) { + var entry = options[i]; + if (!entry) { + entries.push(null); + continue; + } + var label = entry[0]; + if (!entry[2]) entry[2] = {}; + + if (entry[2].label) { + label = entry[2].label; + } + + entry[2].removable = true; + var data = { content: label, value: entry }; + if (entry[1] == LiteGraph.ACTION) { + data.className = "event"; + } + entries.push(data); + } + } + + if (node.onMenuNodeInputs) { + var retEntries = node.onMenuNodeInputs(entries); + if (retEntries) entries = retEntries; + } + + if (!entries.length) { + console.log("no input entries"); + return; + } + + var menu = new LiteGraph.ContextMenu( + entries, + { + event: e, + callback: inner_clicked, + parentMenu: prev_menu, + node: node, + }, + ref_window, + ); + + function inner_clicked(v, e, prev) { + if (!node) { + return; + } + + if (v.callback) { + v.callback.call(that, node, v, e, prev); + } + + if (v.value) { + node.graph.beforeChange(); + node.addInput(v.value[0], v.value[1], v.value[2]); + + if (node.onNodeInputAdd) { + // callback to the node when adding a slot + node.onNodeInputAdd(v.value); + } + node.setDirtyCanvas(true, true); + node.graph.afterChange(); + } + } + + return false; + }; + + LGraphCanvas.showMenuNodeOptionalOutputs = function ( + v, + options, + e, + prev_menu, + node, + ) { + if (!node) { + return; + } + + var that = this; + var canvas = LGraphCanvas.active_canvas; + var ref_window = canvas.getCanvasWindow(); + + var options = node.optional_outputs; + if (node.onGetOutputs) { + options = node.onGetOutputs(); + } + + var entries = []; + if (options) { + for (var i = 0; i < options.length; i++) { + var entry = options[i]; + if (!entry) { + //separator? + entries.push(null); + continue; + } + + if ( + node.flags && + node.flags.skip_repeated_outputs && + node.findOutputSlot(entry[0]) != -1 + ) { + continue; + } //skip the ones already on + var label = entry[0]; + if (!entry[2]) entry[2] = {}; + if (entry[2].label) { + label = entry[2].label; + } + entry[2].removable = true; + var data = { content: label, value: entry }; + if (entry[1] == LiteGraph.EVENT) { + data.className = "event"; + } + entries.push(data); + } + } + + if (this.onMenuNodeOutputs) { + entries = this.onMenuNodeOutputs(entries); + } + if (LiteGraph.do_add_triggers_slots) { + //canvas.allow_addOutSlot_onExecuted + if (node.findOutputSlot("onExecuted") == -1) { + entries.push({ + content: "On Executed", + value: ["onExecuted", LiteGraph.EVENT, { nameLocked: true }], + className: "event", + }); //, opts: {} + } + } + // add callback for modifing the menu elements onMenuNodeOutputs + if (node.onMenuNodeOutputs) { + var retEntries = node.onMenuNodeOutputs(entries); + if (retEntries) entries = retEntries; + } + + if (!entries.length) { + return; + } + + var menu = new LiteGraph.ContextMenu( + entries, + { + event: e, + callback: inner_clicked, + parentMenu: prev_menu, + node: node, + }, + ref_window, + ); + + function inner_clicked(v, e, prev) { + if (!node) { + return; + } + + if (v.callback) { + v.callback.call(that, node, v, e, prev); + } + + if (!v.value) { + return; + } + + var value = v.value[1]; + + if ( + value && + (value.constructor === Object || value.constructor === Array) + ) { + //submenu why? + var entries = []; + for (var i in value) { + entries.push({ content: i, value: value[i] }); + } + new LiteGraph.ContextMenu(entries, { + event: e, + callback: inner_clicked, + parentMenu: prev_menu, + node: node, + }); + return false; + } else { + node.graph.beforeChange(); + node.addOutput(v.value[0], v.value[1], v.value[2]); + + if (node.onNodeOutputAdd) { + // a callback to the node when adding a slot + node.onNodeOutputAdd(v.value); + } + node.setDirtyCanvas(true, true); + node.graph.afterChange(); + } + } + + return false; + }; + + LGraphCanvas.onShowMenuNodeProperties = function ( + value, + options, + e, + prev_menu, + node, + ) { + if (!node || !node.properties) { + return; + } + + var that = this; + var canvas = LGraphCanvas.active_canvas; + var ref_window = canvas.getCanvasWindow(); + + var entries = []; + for (var i in node.properties) { + var value = node.properties[i] !== undefined ? node.properties[i] : " "; + if (typeof value == "object") value = JSON.stringify(value); + var info = node.getPropertyInfo(i); + if (info.type == "enum" || info.type == "combo") + value = LGraphCanvas.getPropertyPrintableValue(value, info.values); + + //value could contain invalid html characters, clean that + value = LGraphCanvas.decodeHTML(value); + entries.push({ + content: + "
" + + (info.label ? info.label : i) + + "" + + "" + + value + + "", + value: i, + }); + } + if (!entries.length) { + return; + } + + var menu = new LiteGraph.ContextMenu( + entries, + { + event: e, + callback: inner_clicked, + parentMenu: prev_menu, + allow_html: true, + node: node, + }, + ref_window, + ); + + function inner_clicked(v, options, e, prev) { + if (!node) { + return; + } + var rect = this.getBoundingClientRect(); + canvas.showEditPropertyValue(node, v.value, { + position: [rect.left, rect.top], + }); + } + + return false; + }; + + LGraphCanvas.decodeHTML = function (str) { + var e = document.createElement("div"); + e.innerText = str; + return e.innerHTML; + }; + + LGraphCanvas.onMenuResizeNode = function (value, options, e, menu, node) { + if (!node) { + return; + } + + var fApplyMultiNode = function (node) { + node.size = node.computeSize(); + if (node.onResize) node.onResize(node.size); + }; + + var graphcanvas = LGraphCanvas.active_canvas; + if ( + !graphcanvas.selected_nodes || + Object.keys(graphcanvas.selected_nodes).length <= 1 + ) { + fApplyMultiNode(node); + } else { + for (var i in graphcanvas.selected_nodes) { + fApplyMultiNode(graphcanvas.selected_nodes[i]); + } + } + + node.setDirtyCanvas(true, true); + }; + + LGraphCanvas.prototype.showLinkMenu = function (link, e) { + var that = this; + // console.log(link); + var node_left = that.graph.getNodeById(link.origin_id); + var node_right = that.graph.getNodeById(link.target_id); + var fromType = false; + if (node_left && node_left.outputs && node_left.outputs[link.origin_slot]) + fromType = node_left.outputs[link.origin_slot].type; + var destType = false; + if ( + node_right && + node_right.outputs && + node_right.outputs[link.target_slot] + ) + destType = node_right.inputs[link.target_slot].type; + + var options = ["Add Node", null, "Delete", null]; + + var menu = new LiteGraph.ContextMenu(options, { + event: e, + title: link.data != null ? link.data.constructor.name : null, + callback: inner_clicked, + }); + + function inner_clicked(v, options, e) { + switch (v) { + case "Add Node": + LGraphCanvas.onMenuAdd(null, null, e, menu, function (node) { + // console.debug("node autoconnect"); + if ( + !node.inputs || + !node.inputs.length || + !node.outputs || + !node.outputs.length + ) { + return; + } + // leave the connection type checking inside connectByType + if (node_left.connectByType(link.origin_slot, node, fromType)) { + node.connectByType(link.target_slot, node_right, destType); + node.pos[0] -= node.size[0] * 0.5; + } + }); + break; + + case "Delete": + that.graph.removeLink(link.id); + break; + default: + /*var nodeCreated = createDefaultNodeForSlot({ nodeFrom: node_left + ,slotFrom: link.origin_slot + ,nodeTo: node + ,slotTo: link.target_slot + ,e: e + ,nodeType: "AUTO" + }); + if(nodeCreated) console.log("new node in beetween "+v+" created");*/ + } + } + + return false; + }; + + LGraphCanvas.prototype.createDefaultNodeForSlot = function (optPass) { + // addNodeMenu for connection + var optPass = optPass || {}; + var opts = Object.assign( + { + nodeFrom: null, // input + slotFrom: null, // input + nodeTo: null, // output + slotTo: null, // output + position: [], // pass the event coords + nodeType: null, // choose a nodetype to add, AUTO to set at first good + posAdd: [0, 0], // adjust x,y + posSizeFix: [0, 0], // alpha, adjust the position x,y based on the new node size w,h + }, + optPass, + ); + var that = this; + + var isFrom = opts.nodeFrom && opts.slotFrom !== null; + var isTo = !isFrom && opts.nodeTo && opts.slotTo !== null; + + if (!isFrom && !isTo) { + console.warn( + "No data passed to createDefaultNodeForSlot " + + opts.nodeFrom + + " " + + opts.slotFrom + + " " + + opts.nodeTo + + " " + + opts.slotTo, + ); + return false; + } + if (!opts.nodeType) { + console.warn("No type to createDefaultNodeForSlot"); + return false; + } + + var nodeX = isFrom ? opts.nodeFrom : opts.nodeTo; + var slotX = isFrom ? opts.slotFrom : opts.slotTo; + + var iSlotConn = false; + switch (typeof slotX) { + case "string": + iSlotConn = isFrom + ? nodeX.findOutputSlot(slotX, false) + : nodeX.findInputSlot(slotX, false); + slotX = isFrom ? nodeX.outputs[slotX] : nodeX.inputs[slotX]; + break; + case "object": + // ok slotX + iSlotConn = isFrom + ? nodeX.findOutputSlot(slotX.name) + : nodeX.findInputSlot(slotX.name); + break; + case "number": + iSlotConn = slotX; + slotX = isFrom ? nodeX.outputs[slotX] : nodeX.inputs[slotX]; + break; + case "undefined": + default: + // bad ? + //iSlotConn = 0; + console.warn("Cant get slot information " + slotX); + return false; + } + + if (slotX === false || iSlotConn === false) { + console.warn( + "createDefaultNodeForSlot bad slotX " + slotX + " " + iSlotConn, + ); + } + + // check for defaults nodes for this slottype + var fromSlotType = slotX.type == LiteGraph.EVENT ? "_event_" : slotX.type; + var slotTypesDefault = isFrom + ? LiteGraph.slot_types_default_out + : LiteGraph.slot_types_default_in; + if (slotTypesDefault && slotTypesDefault[fromSlotType]) { + if (slotX.link !== null) { + // is connected + } else { + // is not not connected + } + nodeNewType = false; + if ( + typeof slotTypesDefault[fromSlotType] == "object" || + typeof slotTypesDefault[fromSlotType] == "array" + ) { + for (var typeX in slotTypesDefault[fromSlotType]) { + if ( + opts.nodeType == slotTypesDefault[fromSlotType][typeX] || + opts.nodeType == "AUTO" + ) { + nodeNewType = slotTypesDefault[fromSlotType][typeX]; + // console.log("opts.nodeType == slotTypesDefault[fromSlotType][typeX] :: "+opts.nodeType); + break; // -------- + } + } + } else { + if ( + opts.nodeType == slotTypesDefault[fromSlotType] || + opts.nodeType == "AUTO" + ) + nodeNewType = slotTypesDefault[fromSlotType]; + } + if (nodeNewType) { + var nodeNewOpts = false; + if (typeof nodeNewType == "object" && nodeNewType.node) { + nodeNewOpts = nodeNewType; + nodeNewType = nodeNewType.node; + } + + //that.graph.beforeChange(); + + var newNode = LiteGraph.createNode(nodeNewType); + if (newNode) { + // if is object pass options + if (nodeNewOpts) { + if (nodeNewOpts.properties) { + for (var i in nodeNewOpts.properties) { + newNode.addProperty(i, nodeNewOpts.properties[i]); + } + } + if (nodeNewOpts.inputs) { + newNode.inputs = []; + for (var i in nodeNewOpts.inputs) { + newNode.addOutput( + nodeNewOpts.inputs[i][0], + nodeNewOpts.inputs[i][1], + ); + } + } + if (nodeNewOpts.outputs) { + newNode.outputs = []; + for (var i in nodeNewOpts.outputs) { + newNode.addOutput( + nodeNewOpts.outputs[i][0], + nodeNewOpts.outputs[i][1], + ); + } + } + if (nodeNewOpts.title) { + newNode.title = nodeNewOpts.title; + } + if (nodeNewOpts.json) { + newNode.configure(nodeNewOpts.json); + } + } + + // add the node + that.graph.add(newNode); + newNode.pos = [ + opts.position[0] + + opts.posAdd[0] + + (opts.posSizeFix[0] ? opts.posSizeFix[0] * newNode.size[0] : 0), + opts.position[1] + + opts.posAdd[1] + + (opts.posSizeFix[1] ? opts.posSizeFix[1] * newNode.size[1] : 0), + ]; //that.last_click_position; //[e.canvasX+30, e.canvasX+5];*/ + + //that.graph.afterChange(); + + // connect the two! + if (isFrom) { + opts.nodeFrom.connectByType(iSlotConn, newNode, fromSlotType); + } else { + opts.nodeTo.connectByTypeOutput(iSlotConn, newNode, fromSlotType); + } + + // if connecting in between + if (isFrom && isTo) { + // TODO + } + + return true; + } else { + console.log("failed creating " + nodeNewType); + } + } + } + return false; + }; + + LGraphCanvas.prototype.showConnectionMenu = function (optPass) { + // addNodeMenu for connection + var optPass = optPass || {}; + var opts = Object.assign( + { + nodeFrom: null, // input + slotFrom: null, // input + nodeTo: null, // output + slotTo: null, // output + e: null, + }, + optPass, + ); + var that = this; + + var isFrom = opts.nodeFrom && opts.slotFrom; + var isTo = !isFrom && opts.nodeTo && opts.slotTo; + + if (!isFrom && !isTo) { + console.warn("No data passed to showConnectionMenu"); + return false; + } + + var nodeX = isFrom ? opts.nodeFrom : opts.nodeTo; + var slotX = isFrom ? opts.slotFrom : opts.slotTo; + + var iSlotConn = false; + switch (typeof slotX) { + case "string": + iSlotConn = isFrom + ? nodeX.findOutputSlot(slotX, false) + : nodeX.findInputSlot(slotX, false); + slotX = isFrom ? nodeX.outputs[slotX] : nodeX.inputs[slotX]; + break; + case "object": + // ok slotX + iSlotConn = isFrom + ? nodeX.findOutputSlot(slotX.name) + : nodeX.findInputSlot(slotX.name); + break; + case "number": + iSlotConn = slotX; + slotX = isFrom ? nodeX.outputs[slotX] : nodeX.inputs[slotX]; + break; + default: + // bad ? + //iSlotConn = 0; + console.warn("Cant get slot information " + slotX); + return false; + } + + var options = ["Add Node", null]; + + if (that.allow_searchbox) { + options.push("Search"); + options.push(null); + } + + // get defaults nodes for this slottype + var fromSlotType = slotX.type == LiteGraph.EVENT ? "_event_" : slotX.type; + var slotTypesDefault = isFrom + ? LiteGraph.slot_types_default_out + : LiteGraph.slot_types_default_in; + if (slotTypesDefault && slotTypesDefault[fromSlotType]) { + if ( + typeof slotTypesDefault[fromSlotType] == "object" || + typeof slotTypesDefault[fromSlotType] == "array" + ) { + for (var typeX in slotTypesDefault[fromSlotType]) { + options.push(slotTypesDefault[fromSlotType][typeX]); + } + } else { + options.push(slotTypesDefault[fromSlotType]); + } + } + + // build menu + var menu = new LiteGraph.ContextMenu(options, { + event: opts.e, + title: + (slotX && slotX.name != "" + ? slotX.name + (fromSlotType ? " | " : "") + : "") + (slotX && fromSlotType ? fromSlotType : ""), + callback: inner_clicked, + }); + + // callback + function inner_clicked(v, options, e) { + //console.log("Process showConnectionMenu selection"); + switch (v) { + case "Add Node": + LGraphCanvas.onMenuAdd(null, null, e, menu, function (node) { + if (isFrom) { + opts.nodeFrom.connectByType(iSlotConn, node, fromSlotType); + } else { + opts.nodeTo.connectByTypeOutput(iSlotConn, node, fromSlotType); + } + }); + break; + case "Search": + if (isFrom) { + that.showSearchBox(e, { + node_from: opts.nodeFrom, + slot_from: slotX, + type_filter_in: fromSlotType, + }); + } else { + that.showSearchBox(e, { + node_to: opts.nodeTo, + slot_from: slotX, + type_filter_out: fromSlotType, + }); + } + break; + default: + // check for defaults nodes for this slottype + var nodeCreated = that.createDefaultNodeForSlot( + Object.assign(opts, { + position: [opts.e.canvasX, opts.e.canvasY], + nodeType: v, + }), + ); + if (nodeCreated) { + // new node created + //console.log("node "+v+" created") + } else { + // failed or v is not in defaults + } + break; + } + } + + return false; + }; + + // TODO refactor :: this is used fot title but not for properties! + LGraphCanvas.onShowPropertyEditor = function (item, options, e, menu, node) { + var input_html = ""; + var property = item.property || "title"; + var value = node[property]; + + // TODO refactor :: use createDialog ? + + var dialog = document.createElement("div"); + dialog.is_modified = false; + dialog.className = "graphdialog"; + dialog.innerHTML = + ""; + dialog.close = function () { + if (dialog.parentNode) { + dialog.parentNode.removeChild(dialog); + } + }; + var title = dialog.querySelector(".name"); + title.innerText = property; + var input = dialog.querySelector(".value"); + if (input) { + input.value = value; + input.addEventListener("blur", function (e) { + this.focus(); + }); + input.addEventListener("keydown", function (e) { + dialog.is_modified = true; + if (e.keyCode == 27) { + //ESC + dialog.close(); + } else if (e.keyCode == 13) { + inner(); // save + } else if (e.keyCode != 13 && e.target.localName != "textarea") { + return; + } + e.preventDefault(); + e.stopPropagation(); + }); + } + + var graphcanvas = LGraphCanvas.active_canvas; + var canvas = graphcanvas.canvas; + + var rect = canvas.getBoundingClientRect(); + var offsetx = -20; + var offsety = -20; + if (rect) { + offsetx -= rect.left; + offsety -= rect.top; + } + + if (event) { + dialog.style.left = event.clientX + offsetx + "px"; + dialog.style.top = event.clientY + offsety + "px"; + } else { + dialog.style.left = canvas.width * 0.5 + offsetx + "px"; + dialog.style.top = canvas.height * 0.5 + offsety + "px"; + } + + var button = dialog.querySelector("button"); + button.addEventListener("click", inner); + canvas.parentNode.appendChild(dialog); + + if (input) input.focus(); + + var dialogCloseTimer = null; + dialog.addEventListener("mouseleave", function (e) { + if (LiteGraph.dialog_close_on_mouse_leave) + if (!dialog.is_modified && LiteGraph.dialog_close_on_mouse_leave) + dialogCloseTimer = setTimeout( + dialog.close, + LiteGraph.dialog_close_on_mouse_leave_delay, + ); //dialog.close(); + }); + dialog.addEventListener("mouseenter", function (e) { + if (LiteGraph.dialog_close_on_mouse_leave) + if (dialogCloseTimer) clearTimeout(dialogCloseTimer); + }); + + function inner() { + if (input) setValue(input.value); + } + + function setValue(value) { + if (item.type == "Number") { + value = Number(value); + } else if (item.type == "Boolean") { + value = Boolean(value); + } + node[property] = value; + if (dialog.parentNode) { + dialog.parentNode.removeChild(dialog); + } + node.setDirtyCanvas(true, true); + } + }; + + // refactor: there are different dialogs, some uses createDialog some dont + LGraphCanvas.prototype.prompt = function ( + title, + value, + callback, + event, + multiline, + ) { + var that = this; + var input_html = ""; + title = title || ""; + + var dialog = document.createElement("div"); + dialog.is_modified = false; + dialog.className = "graphdialog rounded"; + if (multiline) + dialog.innerHTML = + " "; + else + dialog.innerHTML = + " "; + dialog.close = function () { + that.prompt_box = null; + if (dialog.parentNode) { + dialog.parentNode.removeChild(dialog); + } + }; + + var graphcanvas = LGraphCanvas.active_canvas; + var canvas = graphcanvas.canvas; + canvas.parentNode.appendChild(dialog); + + if (this.ds.scale > 1) { + dialog.style.transform = "scale(" + this.ds.scale + ")"; + } + + var dialogCloseTimer = null; + var prevent_timeout = false; + LiteGraph.pointerListenerAdd(dialog, "leave", function (e) { + if (prevent_timeout) return; + if (LiteGraph.dialog_close_on_mouse_leave) + if (!dialog.is_modified && LiteGraph.dialog_close_on_mouse_leave) + dialogCloseTimer = setTimeout( + dialog.close, + LiteGraph.dialog_close_on_mouse_leave_delay, + ); //dialog.close(); + }); + LiteGraph.pointerListenerAdd(dialog, "enter", function (e) { + if (LiteGraph.dialog_close_on_mouse_leave) + if (dialogCloseTimer) clearTimeout(dialogCloseTimer); + }); + var selInDia = dialog.querySelectorAll("select"); + if (selInDia) { + // if filtering, check focus changed to comboboxes and prevent closing + selInDia.forEach(function (selIn) { + selIn.addEventListener("click", function (e) { + prevent_timeout++; + }); + selIn.addEventListener("blur", function (e) { + prevent_timeout = 0; + }); + selIn.addEventListener("change", function (e) { + prevent_timeout = -1; + }); + }); + } + + if (that.prompt_box) { + that.prompt_box.close(); + } + that.prompt_box = dialog; + + var first = null; + var timeout = null; + var selected = null; + + var name_element = dialog.querySelector(".name"); + name_element.innerText = title; + var value_element = dialog.querySelector(".value"); + value_element.value = value; + + var input = value_element; + input.addEventListener("keydown", function (e) { + dialog.is_modified = true; + if (e.keyCode == 27) { + //ESC + dialog.close(); + } else if (e.keyCode == 13 && e.target.localName != "textarea") { + if (callback) { + callback(this.value); + } + dialog.close(); + } else { + return; + } + e.preventDefault(); + e.stopPropagation(); + }); + + var button = dialog.querySelector("button"); + button.addEventListener("click", function (e) { + if (callback) { + callback(input.value); + } + that.setDirty(true); + dialog.close(); + }); + + var rect = canvas.getBoundingClientRect(); + var offsetx = -20; + var offsety = -20; + if (rect) { + offsetx -= rect.left; + offsety -= rect.top; + } + + if (event) { + dialog.style.left = event.clientX + offsetx + "px"; + dialog.style.top = event.clientY + offsety + "px"; + } else { + dialog.style.left = canvas.width * 0.5 + offsetx + "px"; + dialog.style.top = canvas.height * 0.5 + offsety + "px"; + } + + setTimeout(function () { + input.focus(); + }, 10); + + return dialog; + }; + + LGraphCanvas.search_limit = -1; + LGraphCanvas.prototype.showSearchBox = function (event, options) { + // proposed defaults + var def_options = { + slot_from: null, + node_from: null, + node_to: null, + do_type_filter: LiteGraph.search_filter_enabled, // TODO check for registered_slot_[in/out]_types not empty // this will be checked for functionality enabled : filter on slot type, in and out + type_filter_in: false, // these are default: pass to set initially set values + type_filter_out: false, + show_general_if_none_on_typefilter: true, + show_general_after_typefiltered: true, + hide_on_mouse_leave: LiteGraph.search_hide_on_mouse_leave, + show_all_if_empty: true, + show_all_on_open: LiteGraph.search_show_all_on_open, + }; + options = Object.assign(def_options, options || {}); + + //console.log(options); + + var that = this; + var input_html = ""; + var graphcanvas = LGraphCanvas.active_canvas; + var canvas = graphcanvas.canvas; + var root_document = canvas.ownerDocument || document; + + var dialog = document.createElement("div"); + dialog.className = "litegraph litesearchbox graphdialog rounded"; + dialog.innerHTML = + "Search "; + if (options.do_type_filter) { + dialog.innerHTML += + ""; + dialog.innerHTML += + ""; + } + dialog.innerHTML += "
"; + + if (root_document.fullscreenElement) + root_document.fullscreenElement.appendChild(dialog); + else { + root_document.body.appendChild(dialog); + root_document.body.style.overflow = "hidden"; + } + // dialog element has been appended + + if (options.do_type_filter) { + var selIn = dialog.querySelector(".slot_in_type_filter"); + var selOut = dialog.querySelector(".slot_out_type_filter"); + } + + dialog.close = function () { + that.search_box = null; + this.blur(); + canvas.focus(); + root_document.body.style.overflow = ""; + + setTimeout(function () { + that.canvas.focus(); + }, 20); //important, if canvas loses focus keys wont be captured + if (dialog.parentNode) { + dialog.parentNode.removeChild(dialog); + } + }; + + if (this.ds.scale > 1) { + dialog.style.transform = "scale(" + this.ds.scale + ")"; + } + + // hide on mouse leave + if (options.hide_on_mouse_leave) { + var prevent_timeout = false; + var timeout_close = null; + LiteGraph.pointerListenerAdd(dialog, "enter", function (e) { + if (timeout_close) { + clearTimeout(timeout_close); + timeout_close = null; + } + }); + LiteGraph.pointerListenerAdd(dialog, "leave", function (e) { + if (prevent_timeout) { + return; + } + timeout_close = setTimeout(function () { + dialog.close(); + }, 500); + }); + // if filtering, check focus changed to comboboxes and prevent closing + if (options.do_type_filter) { + selIn.addEventListener("click", function (e) { + prevent_timeout++; + }); + selIn.addEventListener("blur", function (e) { + prevent_timeout = 0; + }); + selIn.addEventListener("change", function (e) { + prevent_timeout = -1; + }); + selOut.addEventListener("click", function (e) { + prevent_timeout++; + }); + selOut.addEventListener("blur", function (e) { + prevent_timeout = 0; + }); + selOut.addEventListener("change", function (e) { + prevent_timeout = -1; + }); + } + } + + if (that.search_box) { + that.search_box.close(); + } + that.search_box = dialog; + + var helper = dialog.querySelector(".helper"); + + var first = null; + var timeout = null; + var selected = null; + + var input = dialog.querySelector("input"); + if (input) { + input.addEventListener("blur", function (e) { + if (that.search_box) this.focus(); + }); + input.addEventListener("keydown", function (e) { + if (e.keyCode == 38) { + //UP + changeSelection(false); + } else if (e.keyCode == 40) { + //DOWN + changeSelection(true); + } else if (e.keyCode == 27) { + //ESC + dialog.close(); + } else if (e.keyCode == 13) { + refreshHelper(); + if (selected) { + select(selected.innerHTML); + } else if (first) { + select(first); + } else { + dialog.close(); + } + } else { + if (timeout) { + clearInterval(timeout); + } + timeout = setTimeout(refreshHelper, 250); + return; + } + e.preventDefault(); + e.stopPropagation(); + e.stopImmediatePropagation(); + return true; + }); + } + + // if should filter on type, load and fill selected and choose elements if passed + if (options.do_type_filter) { + if (selIn) { + var aSlots = LiteGraph.slot_types_in; + var nSlots = aSlots.length; // this for object :: Object.keys(aSlots).length; + + if ( + options.type_filter_in == LiteGraph.EVENT || + options.type_filter_in == LiteGraph.ACTION + ) + options.type_filter_in = "_event_"; + /* this will filter on * .. but better do it manually in case + else if(options.type_filter_in === "" || options.type_filter_in === 0) + options.type_filter_in = "*";*/ + + for (var iK = 0; iK < nSlots; iK++) { + var opt = document.createElement("option"); + opt.value = aSlots[iK]; + opt.innerHTML = aSlots[iK]; + selIn.appendChild(opt); + if ( + options.type_filter_in !== false && + (options.type_filter_in + "").toLowerCase() == + (aSlots[iK] + "").toLowerCase() + ) { + //selIn.selectedIndex .. + opt.selected = true; + //console.log("comparing IN "+options.type_filter_in+" :: "+aSlots[iK]); + } else { + //console.log("comparing OUT "+options.type_filter_in+" :: "+aSlots[iK]); + } + } + selIn.addEventListener("change", function () { + refreshHelper(); + }); + } + if (selOut) { + var aSlots = LiteGraph.slot_types_out; + var nSlots = aSlots.length; // this for object :: Object.keys(aSlots).length; + + if ( + options.type_filter_out == LiteGraph.EVENT || + options.type_filter_out == LiteGraph.ACTION + ) + options.type_filter_out = "_event_"; + /* this will filter on * .. but better do it manually in case + else if(options.type_filter_out === "" || options.type_filter_out === 0) + options.type_filter_out = "*";*/ + + for (var iK = 0; iK < nSlots; iK++) { + var opt = document.createElement("option"); + opt.value = aSlots[iK]; + opt.innerHTML = aSlots[iK]; + selOut.appendChild(opt); + if ( + options.type_filter_out !== false && + (options.type_filter_out + "").toLowerCase() == + (aSlots[iK] + "").toLowerCase() + ) { + //selOut.selectedIndex .. + opt.selected = true; + } + } + selOut.addEventListener("change", function () { + refreshHelper(); + }); + } + } + + //compute best position + var rect = canvas.getBoundingClientRect(); + + var left = (event ? event.clientX : rect.left + rect.width * 0.5) - 80; + var top = (event ? event.clientY : rect.top + rect.height * 0.5) - 20; + dialog.style.left = left + "px"; + dialog.style.top = top + "px"; + + //To avoid out of screen problems + if (event.layerY > rect.height - 200) + helper.style.maxHeight = rect.height - event.layerY - 20 + "px"; + + /* + var offsetx = -20; + var offsety = -20; + if (rect) { + offsetx -= rect.left; + offsety -= rect.top; + } + + if (event) { + dialog.style.left = event.clientX + offsetx + "px"; + dialog.style.top = event.clientY + offsety + "px"; + } else { + dialog.style.left = canvas.width * 0.5 + offsetx + "px"; + dialog.style.top = canvas.height * 0.5 + offsety + "px"; + } + canvas.parentNode.appendChild(dialog); + */ + + input.focus(); + if (options.show_all_on_open) refreshHelper(); + + function select(name) { + if (name) { + if (that.onSearchBoxSelection) { + that.onSearchBoxSelection(name, event, graphcanvas); + } else { + var extra = LiteGraph.searchbox_extras[name.toLowerCase()]; + if (extra) { + name = extra.type; + } + + graphcanvas.graph.beforeChange(); + var node = LiteGraph.createNode(name); + if (node) { + node.pos = graphcanvas.convertEventToCanvasOffset(event); + graphcanvas.graph.add(node, false); + } + + if (extra && extra.data) { + if (extra.data.properties) { + for (var i in extra.data.properties) { + node.addProperty(i, extra.data.properties[i]); + } + } + if (extra.data.inputs) { + node.inputs = []; + for (var i in extra.data.inputs) { + node.addOutput( + extra.data.inputs[i][0], + extra.data.inputs[i][1], + ); + } + } + if (extra.data.outputs) { + node.outputs = []; + for (var i in extra.data.outputs) { + node.addOutput( + extra.data.outputs[i][0], + extra.data.outputs[i][1], + ); + } + } + if (extra.data.title) { + node.title = extra.data.title; + } + if (extra.data.json) { + node.configure(extra.data.json); + } + } + + // join node after inserting + if (options.node_from) { + var iS = false; + switch (typeof options.slot_from) { + case "string": + iS = options.node_from.findOutputSlot(options.slot_from); + break; + case "object": + if (options.slot_from.name) { + iS = options.node_from.findOutputSlot(options.slot_from.name); + } else { + iS = -1; + } + if ( + iS == -1 && + typeof options.slot_from.slot_index !== "undefined" + ) + iS = options.slot_from.slot_index; + break; + case "number": + iS = options.slot_from; + break; + default: + iS = 0; // try with first if no name set + } + if (typeof options.node_from.outputs[iS] !== "undefined") { + if (iS !== false && iS > -1) { + options.node_from.connectByType( + iS, + node, + options.node_from.outputs[iS].type, + ); + } + } else { + // console.warn("cant find slot " + options.slot_from); + } + } + if (options.node_to) { + var iS = false; + switch (typeof options.slot_from) { + case "string": + iS = options.node_to.findInputSlot(options.slot_from); + break; + case "object": + if (options.slot_from.name) { + iS = options.node_to.findInputSlot(options.slot_from.name); + } else { + iS = -1; + } + if ( + iS == -1 && + typeof options.slot_from.slot_index !== "undefined" + ) + iS = options.slot_from.slot_index; + break; + case "number": + iS = options.slot_from; + break; + default: + iS = 0; // try with first if no name set + } + if (typeof options.node_to.inputs[iS] !== "undefined") { + if (iS !== false && iS > -1) { + // try connection + options.node_to.connectByTypeOutput( + iS, + node, + options.node_to.inputs[iS].type, + ); + } + } else { + // console.warn("cant find slot_nodeTO " + options.slot_from); + } + } + + graphcanvas.graph.afterChange(); + } + } + + dialog.close(); + } + + function changeSelection(forward) { + var prev = selected; + if (selected) { + selected.classList.remove("selected"); + } + if (!selected) { + selected = forward + ? helper.childNodes[0] + : helper.childNodes[helper.childNodes.length]; + } else { + selected = forward ? selected.nextSibling : selected.previousSibling; + if (!selected) { + selected = prev; + } + } + if (!selected) { + return; + } + selected.classList.add("selected"); + selected.scrollIntoView({ block: "end", behavior: "smooth" }); + } + + function refreshHelper() { + timeout = null; + var str = input.value; + first = null; + helper.innerHTML = ""; + if (!str && !options.show_all_if_empty) { + return; + } + + if (that.onSearchBox) { + var list = that.onSearchBox(helper, str, graphcanvas); + if (list) { + for (var i = 0; i < list.length; ++i) { + addResult(list[i]); + } + } + } else { + var c = 0; + str = str.toLowerCase(); + var filter = graphcanvas.filter || graphcanvas.graph.filter; + + // filter by type preprocess + if (options.do_type_filter && that.search_box) { + var sIn = that.search_box.querySelector(".slot_in_type_filter"); + var sOut = that.search_box.querySelector(".slot_out_type_filter"); + } else { + var sIn = false; + var sOut = false; + } + + //extras + for (var i in LiteGraph.searchbox_extras) { + var extra = LiteGraph.searchbox_extras[i]; + if ( + (!options.show_all_if_empty || str) && + extra.desc.toLowerCase().indexOf(str) === -1 + ) { + continue; + } + var ctor = LiteGraph.registered_node_types[extra.type]; + if (ctor && ctor.filter != filter) continue; + if (!inner_test_filter(extra.type)) continue; + addResult(extra.desc, "searchbox_extra"); + if ( + LGraphCanvas.search_limit !== -1 && + c++ > LGraphCanvas.search_limit + ) { + break; + } + } + + var filtered = null; + if (Array.prototype.filter) { + //filter supported + var keys = Object.keys(LiteGraph.registered_node_types); //types + var filtered = keys.filter(inner_test_filter); + } else { + filtered = []; + for (var i in LiteGraph.registered_node_types) { + if (inner_test_filter(i)) filtered.push(i); + } + } + + for (var i = 0; i < filtered.length; i++) { + addResult(filtered[i]); + if ( + LGraphCanvas.search_limit !== -1 && + c++ > LGraphCanvas.search_limit + ) { + break; + } + } + + // add general type if filtering + if ( + options.show_general_after_typefiltered && + (sIn.value || sOut.value) + ) { + filtered_extra = []; + for (var i in LiteGraph.registered_node_types) { + if ( + inner_test_filter(i, { + inTypeOverride: sIn && sIn.value ? "*" : false, + outTypeOverride: sOut && sOut.value ? "*" : false, + }) + ) + filtered_extra.push(i); + } + for (var i = 0; i < filtered_extra.length; i++) { + addResult(filtered_extra[i], "generic_type"); + if ( + LGraphCanvas.search_limit !== -1 && + c++ > LGraphCanvas.search_limit + ) { + break; + } + } + } + + // check il filtering gave no results + if ( + (sIn.value || sOut.value) && + helper.childNodes.length == 0 && + options.show_general_if_none_on_typefilter + ) { + filtered_extra = []; + for (var i in LiteGraph.registered_node_types) { + if (inner_test_filter(i, { skipFilter: true })) + filtered_extra.push(i); + } + for (var i = 0; i < filtered_extra.length; i++) { + addResult(filtered_extra[i], "not_in_filter"); + if ( + LGraphCanvas.search_limit !== -1 && + c++ > LGraphCanvas.search_limit + ) { + break; + } + } + } + + function inner_test_filter(type, optsIn) { + var optsIn = optsIn || {}; + var optsDef = { + skipFilter: false, + inTypeOverride: false, + outTypeOverride: false, + }; + var opts = Object.assign(optsDef, optsIn); + var ctor = LiteGraph.registered_node_types[type]; + if (filter && ctor.filter != filter) return false; + if ( + (!options.show_all_if_empty || str) && + type.toLowerCase().indexOf(str) === -1 + ) + return false; + + // filter by slot IN, OUT types + if (options.do_type_filter && !opts.skipFilter) { + var sType = type; + + var sV = sIn.value; + if (opts.inTypeOverride !== false) sV = opts.inTypeOverride; + //if (sV.toLowerCase() == "_event_") sV = LiteGraph.EVENT; // -1 + + if (sIn && sV) { + //console.log("will check filter against "+sV); + if ( + LiteGraph.registered_slot_in_types[sV] && + LiteGraph.registered_slot_in_types[sV].nodes + ) { + // type is stored + //console.debug("check "+sType+" in "+LiteGraph.registered_slot_in_types[sV].nodes); + var doesInc = + LiteGraph.registered_slot_in_types[sV].nodes.includes(sType); + if (doesInc !== false) { + //console.log(sType+" HAS "+sV); + } else { + /*console.debug(LiteGraph.registered_slot_in_types[sV]); + console.log(+" DONT includes "+type);*/ + return false; + } + } + } + + var sV = sOut.value; + if (opts.outTypeOverride !== false) sV = opts.outTypeOverride; + //if (sV.toLowerCase() == "_event_") sV = LiteGraph.EVENT; // -1 + + if (sOut && sV) { + //console.log("search will check filter against "+sV); + if ( + LiteGraph.registered_slot_out_types[sV] && + LiteGraph.registered_slot_out_types[sV].nodes + ) { + // type is stored + //console.debug("check "+sType+" in "+LiteGraph.registered_slot_out_types[sV].nodes); + var doesInc = + LiteGraph.registered_slot_out_types[sV].nodes.includes(sType); + if (doesInc !== false) { + //console.log(sType+" HAS "+sV); + } else { + /*console.debug(LiteGraph.registered_slot_out_types[sV]); + console.log(+" DONT includes "+type);*/ + return false; + } + } + } + } + return true; + } + } + + function addResult(type, className) { + var help = document.createElement("div"); + if (!first) { + first = type; + } + help.innerText = type; + help.dataset["type"] = escape(type); + help.className = "litegraph lite-search-item"; + if (className) { + help.className += " " + className; + } + help.addEventListener("click", function (e) { + select(unescape(this.dataset["type"])); + }); + helper.appendChild(help); + } + } + + return dialog; + }; + + LGraphCanvas.prototype.showEditPropertyValue = function ( + node, + property, + options, + ) { + if (!node || node.properties[property] === undefined) { + return; + } + + options = options || {}; + var that = this; + + var info = node.getPropertyInfo(property); + var type = info.type; + + var input_html = ""; + + if ( + type == "string" || + type == "number" || + type == "array" || + type == "object" + ) { + input_html = ""; + } else if ((type == "enum" || type == "combo") && info.values) { + input_html = ""; + } else if (type == "boolean" || type == "toggle") { + input_html = + ""; + } else { + console.warn("unknown type: " + type); + return; + } + + var dialog = this.createDialog( + "" + + (info.label ? info.label : property) + + "" + + input_html + + "", + options, + ); + + var input = false; + if ((type == "enum" || type == "combo") && info.values) { + input = dialog.querySelector("select"); + input.addEventListener("change", function (e) { + dialog.modified(); + setValue(e.target.value); + //var index = e.target.value; + //setValue( e.options[e.selectedIndex].value ); + }); + } else if (type == "boolean" || type == "toggle") { + input = dialog.querySelector("input"); + if (input) { + input.addEventListener("click", function (e) { + dialog.modified(); + setValue(!!input.checked); + }); + } + } else { + input = dialog.querySelector("input"); + if (input) { + input.addEventListener("blur", function (e) { + this.focus(); + }); + + var v = + node.properties[property] !== undefined + ? node.properties[property] + : ""; + if (type !== "string") { + v = JSON.stringify(v); + } + + input.value = v; + input.addEventListener("keydown", function (e) { + if (e.keyCode == 27) { + //ESC + dialog.close(); + } else if (e.keyCode == 13) { + // ENTER + inner(); // save + } else if (e.keyCode != 13) { + dialog.modified(); + return; + } + e.preventDefault(); + e.stopPropagation(); + }); + } + } + if (input) input.focus(); + + var button = dialog.querySelector("button"); + button.addEventListener("click", inner); + + function inner() { + setValue(input.value); + } + + function setValue(value) { + if ( + info && + info.values && + info.values.constructor === Object && + info.values[value] != undefined + ) + value = info.values[value]; + + if (typeof node.properties[property] == "number") { + value = Number(value); + } + if (type == "array" || type == "object") { + value = JSON.parse(value); + } + node.properties[property] = value; + if (node.graph) { + node.graph._version++; + } + if (node.onPropertyChanged) { + node.onPropertyChanged(property, value); + } + if (options.onclose) options.onclose(); + dialog.close(); + node.setDirtyCanvas(true, true); + } + + return dialog; + }; + + // TODO refactor, theer are different dialog, some uses createDialog, some dont + LGraphCanvas.prototype.createDialog = function (html, options) { + var def_options = { + checkForInput: false, + closeOnLeave: true, + closeOnLeave_checkModified: true, + }; + options = Object.assign(def_options, options || {}); + + var dialog = document.createElement("div"); + dialog.className = "graphdialog"; + dialog.innerHTML = html; + dialog.is_modified = false; + + var rect = this.canvas.getBoundingClientRect(); + var offsetx = -20; + var offsety = -20; + if (rect) { + offsetx -= rect.left; + offsety -= rect.top; + } + + if (options.position) { + offsetx += options.position[0]; + offsety += options.position[1]; + } else if (options.event) { + offsetx += options.event.clientX; + offsety += options.event.clientY; + } //centered + else { + offsetx += this.canvas.width * 0.5; + offsety += this.canvas.height * 0.5; + } + + dialog.style.left = offsetx + "px"; + dialog.style.top = offsety + "px"; + + this.canvas.parentNode.appendChild(dialog); + + // acheck for input and use default behaviour: save on enter, close on esc + if (options.checkForInput) { + var aI = []; + var focused = false; + if ((aI = dialog.querySelectorAll("input"))) { + aI.forEach(function (iX) { + iX.addEventListener("keydown", function (e) { + dialog.modified(); + if (e.keyCode == 27) { + dialog.close(); + } else if (e.keyCode != 13) { + return; + } + // set value ? + e.preventDefault(); + e.stopPropagation(); + }); + if (!focused) iX.focus(); + }); + } + } + + dialog.modified = function () { + dialog.is_modified = true; + }; + dialog.close = function () { + if (dialog.parentNode) { + dialog.parentNode.removeChild(dialog); + } + }; + + var dialogCloseTimer = null; + var prevent_timeout = false; + dialog.addEventListener("mouseleave", function (e) { + if (prevent_timeout) return; + if (options.closeOnLeave || LiteGraph.dialog_close_on_mouse_leave) + if (!dialog.is_modified && LiteGraph.dialog_close_on_mouse_leave) + dialogCloseTimer = setTimeout( + dialog.close, + LiteGraph.dialog_close_on_mouse_leave_delay, + ); //dialog.close(); + }); + dialog.addEventListener("mouseenter", function (e) { + if (options.closeOnLeave || LiteGraph.dialog_close_on_mouse_leave) + if (dialogCloseTimer) clearTimeout(dialogCloseTimer); + }); + var selInDia = dialog.querySelectorAll("select"); + if (selInDia) { + // if filtering, check focus changed to comboboxes and prevent closing + selInDia.forEach(function (selIn) { + selIn.addEventListener("click", function (e) { + prevent_timeout++; + }); + selIn.addEventListener("blur", function (e) { + prevent_timeout = 0; + }); + selIn.addEventListener("change", function (e) { + prevent_timeout = -1; + }); + }); + } + + return dialog; + }; + + LGraphCanvas.prototype.createPanel = function (title, options) { + options = options || {}; + + var ref_window = options.window || window; + var root = document.createElement("div"); + root.className = "litegraph dialog"; + root.innerHTML = + "
"; + root.header = root.querySelector(".dialog-header"); + + if (options.width) + root.style.width = + options.width + (options.width.constructor === Number ? "px" : ""); + if (options.height) + root.style.height = + options.height + (options.height.constructor === Number ? "px" : ""); + if (options.closable) { + var close = document.createElement("span"); + close.innerHTML = "✕"; + close.classList.add("close"); + close.addEventListener("click", function () { + root.close(); + }); + root.header.appendChild(close); + } + root.title_element = root.querySelector(".dialog-title"); + root.title_element.innerText = title; + root.content = root.querySelector(".dialog-content"); + root.alt_content = root.querySelector(".dialog-alt-content"); + root.footer = root.querySelector(".dialog-footer"); + + root.close = function () { + if (root.onClose && typeof root.onClose == "function") { + root.onClose(); + } + if (root.parentNode) root.parentNode.removeChild(root); + /* XXX CHECK THIS */ + if (this.parentNode) { + this.parentNode.removeChild(this); + } + /* XXX this was not working, was fixed with an IF, check this */ + }; + + // function to swap panel content + root.toggleAltContent = function (force) { + if (typeof force != "undefined") { + var vTo = force ? "block" : "none"; + var vAlt = force ? "none" : "block"; + } else { + var vTo = root.alt_content.style.display != "block" ? "block" : "none"; + var vAlt = root.alt_content.style.display != "block" ? "none" : "block"; + } + root.alt_content.style.display = vTo; + root.content.style.display = vAlt; + }; + + root.toggleFooterVisibility = function (force) { + if (typeof force != "undefined") { + var vTo = force ? "block" : "none"; + } else { + var vTo = root.footer.style.display != "block" ? "block" : "none"; + } + root.footer.style.display = vTo; + }; + + root.clear = function () { + this.content.innerHTML = ""; + }; + + root.addHTML = function (code, classname, on_footer) { + var elem = document.createElement("div"); + if (classname) elem.className = classname; + elem.innerHTML = code; + if (on_footer) root.footer.appendChild(elem); + else root.content.appendChild(elem); + return elem; + }; + + root.addButton = function (name, callback, options) { + var elem = document.createElement("button"); + elem.innerText = name; + elem.options = options; + elem.classList.add("btn"); + elem.addEventListener("click", callback); + root.footer.appendChild(elem); + return elem; + }; + + root.addSeparator = function () { + var elem = document.createElement("div"); + elem.className = "separator"; + root.content.appendChild(elem); + }; + + root.addWidget = function (type, name, value, options, callback) { + options = options || {}; + var str_value = String(value); + type = type.toLowerCase(); + if (type == "number") str_value = value.toFixed(3); + + var elem = document.createElement("div"); + elem.className = "property"; + elem.innerHTML = + ""; + elem.querySelector(".property_name").innerText = options.label || name; + var value_element = elem.querySelector(".property_value"); + value_element.innerText = str_value; + elem.dataset["property"] = name; + elem.dataset["type"] = options.type || type; + elem.options = options; + elem.value = value; + + if (type == "code") + elem.addEventListener("click", function (e) { + root.inner_showCodePad(this.dataset["property"]); + }); + else if (type == "boolean") { + elem.classList.add("boolean"); + if (value) elem.classList.add("bool-on"); + elem.addEventListener("click", function () { + //var v = node.properties[this.dataset["property"]]; + //node.setProperty(this.dataset["property"],!v); this.innerText = v ? "true" : "false"; + var propname = this.dataset["property"]; + this.value = !this.value; + this.classList.toggle("bool-on"); + this.querySelector(".property_value").innerText = this.value + ? "true" + : "false"; + innerChange(propname, this.value); + }); + } else if (type == "string" || type == "number") { + value_element.setAttribute("contenteditable", true); + value_element.addEventListener("keydown", function (e) { + if (e.code == "Enter" && (type != "string" || !e.shiftKey)) { + // allow for multiline + e.preventDefault(); + this.blur(); + } + }); + value_element.addEventListener("blur", function () { + var v = this.innerText; + var propname = this.parentNode.dataset["property"]; + var proptype = this.parentNode.dataset["type"]; + if (proptype == "number") v = Number(v); + innerChange(propname, v); + }); + } else if (type == "enum" || type == "combo") { + var str_value = LGraphCanvas.getPropertyPrintableValue( + value, + options.values, + ); + value_element.innerText = str_value; + + value_element.addEventListener("click", function (event) { + var values = options.values || []; + var propname = this.parentNode.dataset["property"]; + var elem_that = this; + var menu = new LiteGraph.ContextMenu( + values, + { + event: event, + className: "dark", + callback: inner_clicked, + }, + ref_window, + ); + function inner_clicked(v, option, event) { + //node.setProperty(propname,v); + //graphcanvas.dirty_canvas = true; + elem_that.innerText = v; + innerChange(propname, v); + return false; + } + }); + } + + root.content.appendChild(elem); + + function innerChange(name, value) { + //console.log("change",name,value); + //that.dirty_canvas = true; + if (options.callback) options.callback(name, value, options); + if (callback) callback(name, value, options); + } + + return elem; + }; + + if (root.onOpen && typeof root.onOpen == "function") root.onOpen(); + + return root; + }; + + LGraphCanvas.getPropertyPrintableValue = function (value, values) { + if (!values) return String(value); + + if (values.constructor === Array) { + return String(value); + } + + if (values.constructor === Object) { + var desc_value = ""; + for (var k in values) { + if (values[k] != value) continue; + desc_value = k; + break; + } + return String(value) + " (" + desc_value + ")"; + } + }; + + LGraphCanvas.prototype.closePanels = function () { + var panel = document.querySelector("#node-panel"); + if (panel) panel.close(); + var panel = document.querySelector("#option-panel"); + if (panel) panel.close(); + }; + + LGraphCanvas.prototype.showShowGraphOptionsPanel = function ( + refOpts, + obEv, + refMenu, + refMenu2, + ) { + if (this.constructor && this.constructor.name == "HTMLDivElement") { + // assume coming from the menu event click + if ( + !obEv || + !obEv.event || + !obEv.event.target || + !obEv.event.target.lgraphcanvas + ) { + console.warn("Canvas not found"); // need a ref to canvas obj + /*console.debug(event); + console.debug(event.target);*/ + return; + } + var graphcanvas = obEv.event.target.lgraphcanvas; + } else { + // assume called internally + var graphcanvas = this; + } + graphcanvas.closePanels(); + var ref_window = graphcanvas.getCanvasWindow(); + panel = graphcanvas.createPanel("Options", { + closable: true, + window: ref_window, + onOpen: function () { + graphcanvas.OPTIONPANEL_IS_OPEN = true; + }, + onClose: function () { + graphcanvas.OPTIONPANEL_IS_OPEN = false; + graphcanvas.options_panel = null; + }, + }); + graphcanvas.options_panel = panel; + panel.id = "option-panel"; + panel.classList.add("settings"); + + function inner_refresh() { + panel.content.innerHTML = ""; //clear + + var fUpdate = function (name, value, options) { + switch (name) { + /*case "Render mode": + // Case "".. + if (options.values && options.key){ + var kV = Object.values(options.values).indexOf(value); + if (kV>=0 && options.values[kV]){ + console.debug("update graph options: "+options.key+": "+kV); + graphcanvas[options.key] = kV; + //console.debug(graphcanvas); + break; + } + } + console.warn("unexpected options"); + console.debug(options); + break;*/ + default: + //console.debug("want to update graph options: "+name+": "+value); + if (options && options.key) { + name = options.key; + } + if (options.values) { + value = Object.values(options.values).indexOf(value); + } + //console.debug("update graph option: "+name+": "+value); + graphcanvas[name] = value; + break; + } + }; + + // panel.addWidget( "string", "Graph name", "", {}, fUpdate); // implement + + var aProps = LiteGraph.availableCanvasOptions; + aProps.sort(); + for (var pI in aProps) { + var pX = aProps[pI]; + panel.addWidget( + "boolean", + pX, + graphcanvas[pX], + { key: pX, on: "True", off: "False" }, + fUpdate, + ); + } + + var aLinks = [graphcanvas.links_render_mode]; + panel.addWidget( + "combo", + "Render mode", + LiteGraph.LINK_RENDER_MODES[graphcanvas.links_render_mode], + { key: "links_render_mode", values: LiteGraph.LINK_RENDER_MODES }, + fUpdate, + ); + + panel.addSeparator(); + + panel.footer.innerHTML = ""; // clear + } + inner_refresh(); + + graphcanvas.canvas.parentNode.appendChild(panel); + }; + + LGraphCanvas.prototype.showShowNodePanel = function (node) { + this.SELECTED_NODE = node; + this.closePanels(); + var ref_window = this.getCanvasWindow(); + var that = this; + var graphcanvas = this; + var panel = this.createPanel(node.title || "", { + closable: true, + window: ref_window, + onOpen: function () { + graphcanvas.NODEPANEL_IS_OPEN = true; + }, + onClose: function () { + graphcanvas.NODEPANEL_IS_OPEN = false; + graphcanvas.node_panel = null; + }, + }); + graphcanvas.node_panel = panel; + panel.id = "node-panel"; + panel.node = node; + panel.classList.add("settings"); + + function inner_refresh() { + panel.content.innerHTML = ""; //clear + panel.addHTML( + "" + + node.type + + "" + + (node.constructor.desc || "") + + "", + ); + + panel.addHTML("

Properties

"); + + var fUpdate = function (name, value) { + graphcanvas.graph.beforeChange(node); + switch (name) { + case "Title": + node.title = value; + break; + case "Mode": + var kV = Object.values(LiteGraph.NODE_MODES).indexOf(value); + if (kV >= 0 && LiteGraph.NODE_MODES[kV]) { + node.changeMode(kV); + } else { + console.warn("unexpected mode: " + value); + } + break; + case "Color": + if (LGraphCanvas.node_colors[value]) { + node.color = LGraphCanvas.node_colors[value].color; + node.bgcolor = LGraphCanvas.node_colors[value].bgcolor; + } else { + console.warn("unexpected color: " + value); + } + break; + default: + node.setProperty(name, value); + break; + } + graphcanvas.graph.afterChange(); + graphcanvas.dirty_canvas = true; + }; + + panel.addWidget("string", "Title", node.title, {}, fUpdate); + + panel.addWidget( + "combo", + "Mode", + LiteGraph.NODE_MODES[node.mode], + { values: LiteGraph.NODE_MODES }, + fUpdate, + ); + + var nodeCol = ""; + if (node.color !== undefined) { + nodeCol = Object.keys(LGraphCanvas.node_colors).filter(function (nK) { + return LGraphCanvas.node_colors[nK].color == node.color; + }); + } + + panel.addWidget( + "combo", + "Color", + nodeCol, + { values: Object.keys(LGraphCanvas.node_colors) }, + fUpdate, + ); + + for (var pName in node.properties) { + var value = node.properties[pName]; + var info = node.getPropertyInfo(pName); + var type = info.type || "string"; + + //in case the user wants control over the side panel widget + if ( + node.onAddPropertyToPanel && + node.onAddPropertyToPanel(pName, panel) + ) + continue; + + panel.addWidget(info.widget || info.type, pName, value, info, fUpdate); + } + + panel.addSeparator(); + + if (node.onShowCustomPanelInfo) node.onShowCustomPanelInfo(panel); + + panel.footer.innerHTML = ""; // clear + panel + .addButton("Delete", function () { + if (node.block_delete) return; + node.graph.remove(node); + panel.close(); + }) + .classList.add("delete"); + } + + panel.inner_showCodePad = function (propname) { + panel.classList.remove("settings"); + panel.classList.add("centered"); + + /*if(window.CodeFlask) //disabled for now + { + panel.content.innerHTML = "
"; + var flask = new CodeFlask( "div.code", { language: 'js' }); + flask.updateCode(node.properties[propname]); + flask.onUpdate( function(code) { + node.setProperty(propname, code); + }); + } + else + {*/ + panel.alt_content.innerHTML = ""; + var textarea = panel.alt_content.querySelector("textarea"); + var fDoneWith = function () { + panel.toggleAltContent(false); //if(node_prop_div) node_prop_div.style.display = "block"; // panel.close(); + panel.toggleFooterVisibility(true); + textarea.parentNode.removeChild(textarea); + panel.classList.add("settings"); + panel.classList.remove("centered"); + inner_refresh(); + }; + textarea.value = node.properties[propname]; + textarea.addEventListener("keydown", function (e) { + if (e.code == "Enter" && e.ctrlKey) { + node.setProperty(propname, textarea.value); + fDoneWith(); + } + }); + panel.toggleAltContent(true); + panel.toggleFooterVisibility(false); + textarea.style.height = "calc(100% - 40px)"; + /*}*/ + var assign = panel.addButton("Assign", function () { + node.setProperty(propname, textarea.value); + fDoneWith(); + }); + panel.alt_content.appendChild(assign); //panel.content.appendChild(assign); + var button = panel.addButton("Close", fDoneWith); + button.style.float = "right"; + panel.alt_content.appendChild(button); // panel.content.appendChild(button); + }; + + inner_refresh(); + + this.canvas.parentNode.appendChild(panel); + }; + + LGraphCanvas.prototype.showSubgraphPropertiesDialog = function (node) { + console.log("showing subgraph properties dialog"); + + var old_panel = this.canvas.parentNode.querySelector(".subgraph_dialog"); + if (old_panel) old_panel.close(); + + var panel = this.createPanel("Subgraph Inputs", { + closable: true, + width: 500, + }); + panel.node = node; + panel.classList.add("subgraph_dialog"); + + function inner_refresh() { + panel.clear(); + + //show currents + if (node.inputs) + for (var i = 0; i < node.inputs.length; ++i) { + var input = node.inputs[i]; + if (input.not_subgraph_input) continue; + var html = + " "; + var elem = panel.addHTML(html, "subgraph_property"); + elem.dataset["name"] = input.name; + elem.dataset["slot"] = i; + elem.querySelector(".name").innerText = input.name; + elem.querySelector(".type").innerText = input.type; + elem.querySelector("button").addEventListener("click", function (e) { + node.removeInput(Number(this.parentNode.dataset["slot"])); + inner_refresh(); + }); + } + } + + //add extra + var html = + " + NameType"; + var elem = panel.addHTML(html, "subgraph_property extra", true); + elem.querySelector("button").addEventListener("click", function (e) { + var elem = this.parentNode; + var name = elem.querySelector(".name").value; + var type = elem.querySelector(".type").value; + if (!name || node.findInputSlot(name) != -1) return; + node.addInput(name, type); + elem.querySelector(".name").value = ""; + elem.querySelector(".type").value = ""; + inner_refresh(); + }); + + inner_refresh(); + this.canvas.parentNode.appendChild(panel); + return panel; + }; + LGraphCanvas.prototype.showSubgraphPropertiesDialogRight = function (node) { + // console.log("showing subgraph properties dialog"); + var that = this; + // old_panel if old_panel is exist close it + var old_panel = this.canvas.parentNode.querySelector(".subgraph_dialog"); + if (old_panel) old_panel.close(); + // new panel + var panel = this.createPanel("Subgraph Outputs", { + closable: true, + width: 500, + }); + panel.node = node; + panel.classList.add("subgraph_dialog"); + + function inner_refresh() { + panel.clear(); + //show currents + if (node.outputs) + for (var i = 0; i < node.outputs.length; ++i) { + var input = node.outputs[i]; + if (input.not_subgraph_output) continue; + var html = + " "; + var elem = panel.addHTML(html, "subgraph_property"); + elem.dataset["name"] = input.name; + elem.dataset["slot"] = i; + elem.querySelector(".name").innerText = input.name; + elem.querySelector(".type").innerText = input.type; + elem.querySelector("button").addEventListener("click", function (e) { + node.removeOutput(Number(this.parentNode.dataset["slot"])); + inner_refresh(); + }); + } + } + + //add extra + var html = + " + NameType"; + var elem = panel.addHTML(html, "subgraph_property extra", true); + elem.querySelector(".name").addEventListener("keydown", function (e) { + if (e.keyCode == 13) { + addOutput.apply(this); + } + }); + elem.querySelector("button").addEventListener("click", function (e) { + addOutput.apply(this); + }); + function addOutput() { + var elem = this.parentNode; + var name = elem.querySelector(".name").value; + var type = elem.querySelector(".type").value; + if (!name || node.findOutputSlot(name) != -1) return; + node.addOutput(name, type); + elem.querySelector(".name").value = ""; + elem.querySelector(".type").value = ""; + inner_refresh(); + } + + inner_refresh(); + this.canvas.parentNode.appendChild(panel); + return panel; + }; + LGraphCanvas.prototype.checkPanels = function () { + if (!this.canvas) return; + var panels = this.canvas.parentNode.querySelectorAll(".litegraph.dialog"); + for (var i = 0; i < panels.length; ++i) { + var panel = panels[i]; + if (!panel.node) continue; + if (!panel.node.graph || panel.graph != this.graph) panel.close(); + } + }; + + LGraphCanvas.onMenuNodeCollapse = function (value, options, e, menu, node) { + node.graph.beforeChange(/*?*/); + + var fApplyMultiNode = function (node) { + node.collapse(); + }; + + var graphcanvas = LGraphCanvas.active_canvas; + if ( + !graphcanvas.selected_nodes || + Object.keys(graphcanvas.selected_nodes).length <= 1 + ) { + fApplyMultiNode(node); + } else { + for (var i in graphcanvas.selected_nodes) { + fApplyMultiNode(graphcanvas.selected_nodes[i]); + } + } + + node.graph.afterChange(/*?*/); + }; + + LGraphCanvas.onMenuNodePin = function (value, options, e, menu, node) { + node.pin(); + }; + + LGraphCanvas.onMenuNodeMode = function (value, options, e, menu, node) { + new LiteGraph.ContextMenu(LiteGraph.NODE_MODES, { + event: e, + callback: inner_clicked, + parentMenu: menu, + node: node, + }); + + function inner_clicked(v) { + if (!node) { + return; + } + var kV = Object.values(LiteGraph.NODE_MODES).indexOf(v); + var fApplyMultiNode = function (node) { + if (kV >= 0 && LiteGraph.NODE_MODES[kV]) node.changeMode(kV); + else { + console.warn("unexpected mode: " + v); + node.changeMode(LiteGraph.ALWAYS); + } + }; + + var graphcanvas = LGraphCanvas.active_canvas; + if ( + !graphcanvas.selected_nodes || + Object.keys(graphcanvas.selected_nodes).length <= 1 + ) { + fApplyMultiNode(node); + } else { + for (var i in graphcanvas.selected_nodes) { + fApplyMultiNode(graphcanvas.selected_nodes[i]); + } + } + } + + return false; + }; + + LGraphCanvas.onMenuNodeColors = function (value, options, e, menu, node) { + if (!node) { + throw "no node for color"; + } + + var values = []; + values.push({ + value: null, + content: + "No color", + }); + + for (var i in LGraphCanvas.node_colors) { + var color = LGraphCanvas.node_colors[i]; + var value = { + value: i, + content: + "" + + i + + "", + }; + values.push(value); + } + new LiteGraph.ContextMenu(values, { + event: e, + callback: inner_clicked, + parentMenu: menu, + node: node, + }); + + function inner_clicked(v) { + if (!node) { + return; + } + + var color = v.value ? LGraphCanvas.node_colors[v.value] : null; + + var fApplyColor = function (node) { + if (color) { + if (node.constructor === LiteGraph.LGraphGroup) { + node.color = color.groupcolor; + } else { + node.color = color.color; + node.bgcolor = color.bgcolor; + } + } else { + delete node.color; + delete node.bgcolor; + } + }; + + var graphcanvas = LGraphCanvas.active_canvas; + if ( + !graphcanvas.selected_nodes || + Object.keys(graphcanvas.selected_nodes).length <= 1 + ) { + fApplyColor(node); + } else { + for (var i in graphcanvas.selected_nodes) { + fApplyColor(graphcanvas.selected_nodes[i]); + } + } + node.setDirtyCanvas(true, true); + } + + return false; + }; + + LGraphCanvas.onMenuNodeShapes = function (value, options, e, menu, node) { + if (!node) { + throw "no node passed"; + } + + new LiteGraph.ContextMenu(LiteGraph.VALID_SHAPES, { + event: e, + callback: inner_clicked, + parentMenu: menu, + node: node, + }); + + function inner_clicked(v) { + if (!node) { + return; + } + node.graph.beforeChange(/*?*/); //node + + var fApplyMultiNode = function (node) { + node.shape = v; + }; + + var graphcanvas = LGraphCanvas.active_canvas; + if ( + !graphcanvas.selected_nodes || + Object.keys(graphcanvas.selected_nodes).length <= 1 + ) { + fApplyMultiNode(node); + } else { + for (var i in graphcanvas.selected_nodes) { + fApplyMultiNode(graphcanvas.selected_nodes[i]); + } + } + + node.graph.afterChange(/*?*/); //node + node.setDirtyCanvas(true); + } + + return false; + }; + + LGraphCanvas.onMenuNodeRemove = function (value, options, e, menu, node) { + if (!node) { + throw "no node passed"; + } + + var graph = node.graph; + graph.beforeChange(); + + var fApplyMultiNode = function (node) { + if (node.removable === false) { + return; + } + graph.remove(node); + }; + + var graphcanvas = LGraphCanvas.active_canvas; + if ( + !graphcanvas.selected_nodes || + Object.keys(graphcanvas.selected_nodes).length <= 1 + ) { + fApplyMultiNode(node); + } else { + for (var i in graphcanvas.selected_nodes) { + fApplyMultiNode(graphcanvas.selected_nodes[i]); + } + } + + graph.afterChange(); + node.setDirtyCanvas(true, true); + }; + + LGraphCanvas.onMenuNodeToSubgraph = function (value, options, e, menu, node) { + var graph = node.graph; + var graphcanvas = LGraphCanvas.active_canvas; + if (!graphcanvas) + //?? + return; + + var nodes_list = Object.values(graphcanvas.selected_nodes || {}); + if (!nodes_list.length) nodes_list = [node]; + + var subgraph_node = LiteGraph.createNode("graph/subgraph"); + subgraph_node.pos = node.pos.concat(); + graph.add(subgraph_node); + + subgraph_node.buildFromNodes(nodes_list); + + graphcanvas.deselectAllNodes(); + node.setDirtyCanvas(true, true); + }; + + LGraphCanvas.onMenuNodeClone = function (value, options, e, menu, node) { + node.graph.beforeChange(); + + var newSelected = {}; + + var fApplyMultiNode = function (node) { + if (node.clonable === false) { + return; + } + var newnode = node.clone(); + if (!newnode) { + return; + } + newnode.pos = [node.pos[0] + 5, node.pos[1] + 5]; + node.graph.add(newnode); + newSelected[newnode.id] = newnode; + }; + + var graphcanvas = LGraphCanvas.active_canvas; + if ( + !graphcanvas.selected_nodes || + Object.keys(graphcanvas.selected_nodes).length <= 1 + ) { + fApplyMultiNode(node); + } else { + for (var i in graphcanvas.selected_nodes) { + fApplyMultiNode(graphcanvas.selected_nodes[i]); + } + } + + if (Object.keys(newSelected).length) { + graphcanvas.selectNodes(newSelected); + } + + node.graph.afterChange(); + + node.setDirtyCanvas(true, true); + }; + + LGraphCanvas.node_colors = { + red: { color: "#322", bgcolor: "#533", groupcolor: "#A88" }, + brown: { color: "#332922", bgcolor: "#593930", groupcolor: "#b06634" }, + green: { color: "#232", bgcolor: "#353", groupcolor: "#8A8" }, + blue: { color: "#223", bgcolor: "#335", groupcolor: "#88A" }, + pale_blue: { + color: "#2a363b", + bgcolor: "#3f5159", + groupcolor: "#3f789e", + }, + cyan: { color: "#233", bgcolor: "#355", groupcolor: "#8AA" }, + purple: { color: "#323", bgcolor: "#535", groupcolor: "#a1309b" }, + yellow: { color: "#432", bgcolor: "#653", groupcolor: "#b58b2a" }, + black: { color: "#222", bgcolor: "#000", groupcolor: "#444" }, + }; + + LGraphCanvas.prototype.getCanvasMenuOptions = function () { + var options = null; + var that = this; + if (this.getMenuOptions) { + options = this.getMenuOptions(); + } else { + options = [ + { + content: "Add Node", + has_submenu: true, + callback: LGraphCanvas.onMenuAdd, + }, + { content: "Add Group", callback: LGraphCanvas.onGroupAdd }, + //{ content: "Arrange", callback: that.graph.arrange }, + //{content:"Collapse All", callback: LGraphCanvas.onMenuCollapseAll } + ]; + /*if (LiteGraph.showCanvasOptions){ + options.push({ content: "Options", callback: that.showShowGraphOptionsPanel }); + }*/ + + if (Object.keys(this.selected_nodes).length > 1) { + options.push({ + content: "Align", + has_submenu: true, + callback: LGraphCanvas.onGroupAlign, + }); + } + + if (this._graph_stack && this._graph_stack.length > 0) { + options.push(null, { + content: "Close subgraph", + callback: this.closeSubgraph.bind(this), + }); + } + } + + if (this.getExtraMenuOptions) { + var extra = this.getExtraMenuOptions(this, options); + if (extra) { + options = options.concat(extra); + } + } + + return options; + }; + + //called by processContextMenu to extract the menu list + LGraphCanvas.prototype.getNodeMenuOptions = function (node) { + var options = null; + + if (node.getMenuOptions) { + options = node.getMenuOptions(this); + } else { + options = [ + { + content: "Inputs", + has_submenu: true, + disabled: true, + callback: LGraphCanvas.showMenuNodeOptionalInputs, + }, + { + content: "Outputs", + has_submenu: true, + disabled: true, + callback: LGraphCanvas.showMenuNodeOptionalOutputs, + }, + null, + { + content: "Properties", + has_submenu: true, + callback: LGraphCanvas.onShowMenuNodeProperties, + }, + null, + { + content: "Title", + callback: LGraphCanvas.onShowPropertyEditor, + }, + { + content: "Mode", + has_submenu: true, + callback: LGraphCanvas.onMenuNodeMode, + }, + ]; + if (node.resizable !== false) { + options.push({ + content: "Resize", + callback: LGraphCanvas.onMenuResizeNode, + }); + } + options.push( + { + content: "Collapse", + callback: LGraphCanvas.onMenuNodeCollapse, + }, + { content: "Pin", callback: LGraphCanvas.onMenuNodePin }, + { + content: "Colors", + has_submenu: true, + callback: LGraphCanvas.onMenuNodeColors, + }, + { + content: "Shapes", + has_submenu: true, + callback: LGraphCanvas.onMenuNodeShapes, + }, + null, + ); + } + + if (node.onGetInputs) { + var inputs = node.onGetInputs(); + if (inputs && inputs.length) { + options[0].disabled = false; + } + } + + if (node.onGetOutputs) { + var outputs = node.onGetOutputs(); + if (outputs && outputs.length) { + options[1].disabled = false; + } + } + + if (node.getExtraMenuOptions) { + var extra = node.getExtraMenuOptions(this, options); + if (extra) { + extra.push(null); + options = extra.concat(options); + } + } + + if (node.clonable !== false) { + options.push({ + content: "Clone", + callback: LGraphCanvas.onMenuNodeClone, + }); + } + + if (0) + //TODO + options.push({ + content: "To Subgraph", + callback: LGraphCanvas.onMenuNodeToSubgraph, + }); + + if (Object.keys(this.selected_nodes).length > 1) { + options.push({ + content: "Align Selected To", + has_submenu: true, + callback: LGraphCanvas.onNodeAlign, + }); + } + + options.push(null, { + content: "Remove", + disabled: !(node.removable !== false && !node.block_delete), + callback: LGraphCanvas.onMenuNodeRemove, + }); + + if (node.graph && node.graph.onGetNodeMenuOptions) { + node.graph.onGetNodeMenuOptions(options, node); + } + + return options; + }; + + LGraphCanvas.prototype.getGroupMenuOptions = function (node) { + var o = [ + { content: "Title", callback: LGraphCanvas.onShowPropertyEditor }, + { + content: "Color", + has_submenu: true, + callback: LGraphCanvas.onMenuNodeColors, + }, + { + content: "Font size", + property: "font_size", + type: "Number", + callback: LGraphCanvas.onShowPropertyEditor, + }, + null, + { content: "Remove", callback: LGraphCanvas.onMenuNodeRemove }, + ]; + + return o; + }; + + LGraphCanvas.prototype.processContextMenu = function (node, event) { + var that = this; + var canvas = LGraphCanvas.active_canvas; + var ref_window = canvas.getCanvasWindow(); + + var menu_info = null; + var options = { + event: event, + callback: inner_option_clicked, + extra: node, + }; + + if (node) options.title = node.type; + + //check if mouse is in input + var slot = null; + if (node) { + slot = node.getSlotInPosition(event.canvasX, event.canvasY); + LGraphCanvas.active_node = node; + } + + if (slot) { + //on slot + menu_info = []; + if (node.getSlotMenuOptions) { + menu_info = node.getSlotMenuOptions(slot); + } else { + if ( + slot && + slot.output && + slot.output.links && + slot.output.links.length + ) { + menu_info.push({ content: "Disconnect Links", slot: slot }); + } + var _slot = slot.input || slot.output; + if (_slot.removable) { + menu_info.push( + _slot.locked + ? "Cannot remove" + : { content: "Remove Slot", slot: slot }, + ); + } + if (!_slot.nameLocked) { + menu_info.push({ content: "Rename Slot", slot: slot }); + } + } + options.title = (slot.input ? slot.input.type : slot.output.type) || "*"; + if (slot.input && slot.input.type == LiteGraph.ACTION) { + options.title = "Action"; + } + if (slot.output && slot.output.type == LiteGraph.EVENT) { + options.title = "Event"; + } + } else { + if (node) { + //on node + menu_info = this.getNodeMenuOptions(node); + } else { + menu_info = this.getCanvasMenuOptions(); + var group = this.graph.getGroupOnPos(event.canvasX, event.canvasY); + if (group) { + //on group + menu_info.push(null, { + content: "Edit Group", + has_submenu: true, + submenu: { + title: "Group", + extra: group, + options: this.getGroupMenuOptions(group), + }, + }); + } + } + } + + //show menu + if (!menu_info) { + return; + } + + var menu = new LiteGraph.ContextMenu(menu_info, options, ref_window); + + function inner_option_clicked(v, options, e) { + if (!v) { + return; + } + + if (v.content == "Remove Slot") { + var info = v.slot; + node.graph.beforeChange(); + if (info.input) { + node.removeInput(info.slot); + } else if (info.output) { + node.removeOutput(info.slot); + } + node.graph.afterChange(); + return; + } else if (v.content == "Disconnect Links") { + var info = v.slot; + node.graph.beforeChange(); + if (info.output) { + node.disconnectOutput(info.slot); + } else if (info.input) { + node.disconnectInput(info.slot); + } + node.graph.afterChange(); + return; + } else if (v.content == "Rename Slot") { + var info = v.slot; + var slot_info = info.input + ? node.getInputInfo(info.slot) + : node.getOutputInfo(info.slot); + var dialog = that.createDialog( + "Name", + options, + ); + var input = dialog.querySelector("input"); + if (input && slot_info) { + input.value = slot_info.label || ""; + } + var inner = function () { + node.graph.beforeChange(); + if (input.value) { + if (slot_info) { + slot_info.label = input.value; + } + that.setDirty(true); + } + dialog.close(); + node.graph.afterChange(); + }; + dialog.querySelector("button").addEventListener("click", inner); + input.addEventListener("keydown", function (e) { + dialog.is_modified = true; + if (e.keyCode == 27) { + //ESC + dialog.close(); + } else if (e.keyCode == 13) { + inner(); // save + } else if (e.keyCode != 13 && e.target.localName != "textarea") { + return; + } + e.preventDefault(); + e.stopPropagation(); + }); + input.focus(); + } + + //if(v.callback) + // return v.callback.call(that, node, options, e, menu, that, event ); + } + }; + + //API ************************************************* + function compareObjects(a, b) { + for (var i in a) { + if (a[i] != b[i]) { + return false; + } + } + return true; + } + LiteGraph.compareObjects = compareObjects; + + function distance(a, b) { + return Math.sqrt( + (b[0] - a[0]) * (b[0] - a[0]) + (b[1] - a[1]) * (b[1] - a[1]), + ); + } + LiteGraph.distance = distance; + + function colorToString(c) { + return ( + "rgba(" + + Math.round(c[0] * 255).toFixed() + + "," + + Math.round(c[1] * 255).toFixed() + + "," + + Math.round(c[2] * 255).toFixed() + + "," + + (c.length == 4 ? c[3].toFixed(2) : "1.0") + + ")" + ); + } + LiteGraph.colorToString = colorToString; + + function isInsideRectangle(x, y, left, top, width, height) { + if (left < x && left + width > x && top < y && top + height > y) { + return true; + } + return false; + } + LiteGraph.isInsideRectangle = isInsideRectangle; + + //[minx,miny,maxx,maxy] + function growBounding(bounding, x, y) { + if (x < bounding[0]) { + bounding[0] = x; + } else if (x > bounding[2]) { + bounding[2] = x; + } + + if (y < bounding[1]) { + bounding[1] = y; + } else if (y > bounding[3]) { + bounding[3] = y; + } + } + LiteGraph.growBounding = growBounding; + + //point inside bounding box + function isInsideBounding(p, bb) { + if ( + p[0] < bb[0][0] || + p[1] < bb[0][1] || + p[0] > bb[1][0] || + p[1] > bb[1][1] + ) { + return false; + } + return true; + } + LiteGraph.isInsideBounding = isInsideBounding; + + //bounding overlap, format: [ startx, starty, width, height ] + function overlapBounding(a, b) { + var A_end_x = a[0] + a[2]; + var A_end_y = a[1] + a[3]; + var B_end_x = b[0] + b[2]; + var B_end_y = b[1] + b[3]; + + if (a[0] > B_end_x || a[1] > B_end_y || A_end_x < b[0] || A_end_y < b[1]) { + return false; + } + return true; + } + LiteGraph.overlapBounding = overlapBounding; + + //Convert a hex value to its decimal value - the inputted hex must be in the + // format of a hex triplet - the kind we use for HTML colours. The function + // will return an array with three values. + function hex2num(hex) { + if (hex.charAt(0) == "#") { + hex = hex.slice(1); + } //Remove the '#' char - if there is one. + hex = hex.toUpperCase(); + var hex_alphabets = "0123456789ABCDEF"; + var value = new Array(3); + var k = 0; + var int1, int2; + for (var i = 0; i < 6; i += 2) { + int1 = hex_alphabets.indexOf(hex.charAt(i)); + int2 = hex_alphabets.indexOf(hex.charAt(i + 1)); + value[k] = int1 * 16 + int2; + k++; + } + return value; + } + + LiteGraph.hex2num = hex2num; + + //Give a array with three values as the argument and the function will return + // the corresponding hex triplet. + function num2hex(triplet) { + var hex_alphabets = "0123456789ABCDEF"; + var hex = "#"; + var int1, int2; + for (var i = 0; i < 3; i++) { + int1 = triplet[i] / 16; + int2 = triplet[i] % 16; + + hex += hex_alphabets.charAt(int1) + hex_alphabets.charAt(int2); + } + return hex; + } + + LiteGraph.num2hex = num2hex; + + /* LiteGraph GUI elements used for canvas editing *************************************/ + + /** + * ContextMenu from LiteGUI + * + * @class ContextMenu + * @constructor + * @param {Array} values (allows object { title: "Nice text", callback: function ... }) + * @param {Object} options [optional] Some options:\ + * - title: title to show on top of the menu + * - callback: function to call when an option is clicked, it receives the item information + * - ignore_item_callbacks: ignores the callback inside the item, it just calls the options.callback + * - event: you can pass a MouseEvent, this way the ContextMenu appears in that position + */ + function ContextMenu(values, options) { + options = options || {}; + this.options = options; + var that = this; + + //to link a menu with its parent + if (options.parentMenu) { + if (options.parentMenu.constructor !== this.constructor) { + console.error("parentMenu must be of class ContextMenu, ignoring it"); + options.parentMenu = null; + } else { + this.parentMenu = options.parentMenu; + this.parentMenu.lock = true; + this.parentMenu.current_submenu = this; + } + } + + var eventClass = null; + if (options.event) + //use strings because comparing classes between windows doesnt work + eventClass = options.event.constructor.name; + if ( + eventClass !== "MouseEvent" && + eventClass !== "CustomEvent" && + eventClass !== "PointerEvent" + ) { + console.error( + "Event passed to ContextMenu is not of type MouseEvent or CustomEvent. Ignoring it. (" + + eventClass + + ")", + ); + options.event = null; + } + + var root = document.createElement("div"); + root.className = "litegraph litecontextmenu litemenubar-panel"; + if (options.className) { + root.className += " " + options.className; + } + root.style.minWidth = 100; + root.style.minHeight = 100; + root.style.pointerEvents = "none"; + setTimeout(function () { + root.style.pointerEvents = "auto"; + }, 100); //delay so the mouse up event is not caught by this element + + //this prevents the default context browser menu to open in case this menu was created when pressing right button + LiteGraph.pointerListenerAdd( + root, + "up", + function (e) { + //console.log("pointerevents: ContextMenu up root prevent"); + e.preventDefault(); + return true; + }, + true, + ); + root.addEventListener( + "contextmenu", + function (e) { + if (e.button != 2) { + //right button + return false; + } + e.preventDefault(); + return false; + }, + true, + ); + + LiteGraph.pointerListenerAdd( + root, + "down", + function (e) { + //console.log("pointerevents: ContextMenu down"); + if (e.button == 2) { + that.close(); + e.preventDefault(); + return true; + } + }, + true, + ); + + function on_mouse_wheel(e) { + var pos = parseInt(root.style.top); + root.style.top = (pos + e.deltaY * options.scroll_speed).toFixed() + "px"; + e.preventDefault(); + return true; + } + + if (!options.scroll_speed) { + options.scroll_speed = 0.1; + } + + root.addEventListener("wheel", on_mouse_wheel, true); + root.addEventListener("mousewheel", on_mouse_wheel, true); + + this.root = root; + + //title + if (options.title) { + var element = document.createElement("div"); + element.className = "litemenu-title"; + element.innerHTML = options.title; + root.appendChild(element); + } + + //entries + var num = 0; + for (var i = 0; i < values.length; i++) { + var name = values.constructor == Array ? values[i] : i; + if (name != null && name.constructor !== String) { + name = name.content === undefined ? String(name) : name.content; + } + var value = values[i]; + this.addItem(name, value, options); + num++; + } + + //close on leave? touch enabled devices won't work TODO use a global device detector and condition on that + /*LiteGraph.pointerListenerAdd(root,"leave", function(e) { + console.log("pointerevents: ContextMenu leave"); + if (that.lock) { + return; + } + if (root.closing_timer) { + clearTimeout(root.closing_timer); + } + root.closing_timer = setTimeout(that.close.bind(that, e), 500); + //that.close(e); + });*/ + + LiteGraph.pointerListenerAdd(root, "enter", function (e) { + //console.log("pointerevents: ContextMenu enter"); + if (root.closing_timer) { + clearTimeout(root.closing_timer); + } + }); + + //insert before checking position + var root_document = document; + if (options.event) { + root_document = options.event.target.ownerDocument; + } + + if (!root_document) { + root_document = document; + } + + if (root_document.fullscreenElement) + root_document.fullscreenElement.appendChild(root); + else root_document.body.appendChild(root); + + //compute best position + var left = options.left || 0; + var top = options.top || 0; + if (options.event) { + left = options.event.clientX - 10; + top = options.event.clientY - 10; + if (options.title) { + top -= 20; + } + + if (options.parentMenu) { + var rect = options.parentMenu.root.getBoundingClientRect(); + left = rect.left + rect.width; + } + + var body_rect = document.body.getBoundingClientRect(); + var root_rect = root.getBoundingClientRect(); + if (body_rect.height == 0) + console.error( + "document.body height is 0. That is dangerous, set html,body { height: 100%; }", + ); + + if (body_rect.width && left > body_rect.width - root_rect.width - 10) { + left = body_rect.width - root_rect.width - 10; + } + if (body_rect.height && top > body_rect.height - root_rect.height - 10) { + top = body_rect.height - root_rect.height - 10; + } + } + + root.style.left = left + "px"; + root.style.top = top + "px"; + + if (options.scale) { + root.style.transform = "scale(" + options.scale + ")"; + } + } + + ContextMenu.prototype.addItem = function (name, value, options) { + var that = this; + options = options || {}; + + var element = document.createElement("div"); + element.className = "litemenu-entry submenu"; + + var disabled = false; + + if (value === null) { + element.classList.add("separator"); + //element.innerHTML = "
" + //continue; + } else { + element.innerHTML = value && value.title ? value.title : name; + element.value = value; + + if (value) { + if (value.disabled) { + disabled = true; + element.classList.add("disabled"); + } + if (value.submenu || value.has_submenu) { + element.classList.add("has_submenu"); + } + } + + if (typeof value == "function") { + element.dataset["value"] = name; + element.onclick_callback = value; + } else { + element.dataset["value"] = value; + } + + if (value.className) { + element.className += " " + value.className; + } + } + + this.root.appendChild(element); + if (!disabled) { + element.addEventListener("click", inner_onclick); + } + if (!disabled && options.autoopen) { + LiteGraph.pointerListenerAdd(element, "enter", inner_over); + } + + function inner_over(e) { + var value = this.value; + if (!value || !value.has_submenu) { + return; + } + //if it is a submenu, autoopen like the item was clicked + inner_onclick.call(this, e); + } + + //menu option clicked + function inner_onclick(e) { + var value = this.value; + var close_parent = true; + + if (that.current_submenu) { + that.current_submenu.close(e); + } + + //global callback + if (options.callback) { + var r = options.callback.call( + this, + value, + options, + e, + that, + options.node, + ); + if (r === true) { + close_parent = false; + } + } + + //special cases + if (value) { + if ( + value.callback && + !options.ignore_item_callbacks && + value.disabled !== true + ) { + //item callback + var r = value.callback.call( + this, + value, + options, + e, + that, + options.extra, + ); + if (r === true) { + close_parent = false; + } + } + if (value.submenu) { + if (!value.submenu.options) { + throw "ContextMenu submenu needs options"; + } + var submenu = new that.constructor(value.submenu.options, { + callback: value.submenu.callback, + event: e, + parentMenu: that, + ignore_item_callbacks: value.submenu.ignore_item_callbacks, + title: value.submenu.title, + extra: value.submenu.extra, + autoopen: options.autoopen, + }); + close_parent = false; + } + } + + if (close_parent && !that.lock) { + that.close(); + } + } + + return element; + }; + + ContextMenu.prototype.close = function (e, ignore_parent_menu) { + if (this.root.parentNode) { + this.root.parentNode.removeChild(this.root); + } + if (this.parentMenu && !ignore_parent_menu) { + this.parentMenu.lock = false; + this.parentMenu.current_submenu = null; + if (e === undefined) { + this.parentMenu.close(); + } else if ( + e && + !ContextMenu.isCursorOverElement(e, this.parentMenu.root) + ) { + ContextMenu.trigger( + this.parentMenu.root, + LiteGraph.pointerevents_method + "leave", + e, + ); + } + } + if (this.current_submenu) { + this.current_submenu.close(e, true); + } + + if (this.root.closing_timer) { + clearTimeout(this.root.closing_timer); + } + + // TODO implement : LiteGraph.contextMenuClosed(); :: keep track of opened / closed / current ContextMenu + // on key press, allow filtering/selecting the context menu elements + }; + + //this code is used to trigger events easily (used in the context menu mouseleave + ContextMenu.trigger = function (element, event_name, params, origin) { + var evt = document.createEvent("CustomEvent"); + evt.initCustomEvent(event_name, true, true, params); //canBubble, cancelable, detail + evt.srcElement = origin; + if (element.dispatchEvent) { + element.dispatchEvent(evt); + } else if (element.__events) { + element.__events.dispatchEvent(evt); + } + //else nothing seems binded here so nothing to do + return evt; + }; + + //returns the top most menu + ContextMenu.prototype.getTopMenu = function () { + if (this.options.parentMenu) { + return this.options.parentMenu.getTopMenu(); + } + return this; + }; + + ContextMenu.prototype.getFirstEvent = function () { + if (this.options.parentMenu) { + return this.options.parentMenu.getFirstEvent(); + } + return this.options.event; + }; + + ContextMenu.isCursorOverElement = function (event, element) { + var left = event.clientX; + var top = event.clientY; + var rect = element.getBoundingClientRect(); + if (!rect) { + return false; + } + if ( + top > rect.top && + top < rect.top + rect.height && + left > rect.left && + left < rect.left + rect.width + ) { + return true; + } + return false; + }; + + LiteGraph.ContextMenu = ContextMenu; + + LiteGraph.closeAllContextMenus = function (ref_window) { + ref_window = ref_window || window; + + var elements = ref_window.document.querySelectorAll(".litecontextmenu"); + if (!elements.length) { + return; + } + + var result = []; + for (var i = 0; i < elements.length; i++) { + result.push(elements[i]); + } + + for (var i = 0; i < result.length; i++) { + if (result[i].close) { + result[i].close(); + } else if (result[i].parentNode) { + result[i].parentNode.removeChild(result[i]); + } + } + }; + + LiteGraph.extendClass = function (target, origin) { + for (var i in origin) { + //copy class properties + if (target.hasOwnProperty(i)) { + continue; + } + target[i] = origin[i]; + } + + if (origin.prototype) { + //copy prototype properties + for (var i in origin.prototype) { + //only enumerable + if (!origin.prototype.hasOwnProperty(i)) { + continue; + } + + if (target.prototype.hasOwnProperty(i)) { + //avoid overwriting existing ones + continue; + } + + //copy getters + if (origin.prototype.__lookupGetter__(i)) { + target.prototype.__defineGetter__( + i, + origin.prototype.__lookupGetter__(i), + ); + } else { + target.prototype[i] = origin.prototype[i]; + } + + //and setters + if (origin.prototype.__lookupSetter__(i)) { + target.prototype.__defineSetter__( + i, + origin.prototype.__lookupSetter__(i), + ); + } + } + } + }; + + //used by some widgets to render a curve editor + function CurveEditor(points) { + this.points = points; + this.selected = -1; + this.nearest = -1; + this.size = null; //stores last size used + this.must_update = true; + this.margin = 5; + } + + CurveEditor.sampleCurve = function (f, points) { + if (!points) return; + for (var i = 0; i < points.length - 1; ++i) { + var p = points[i]; + var pn = points[i + 1]; + if (pn[0] < f) continue; + var r = pn[0] - p[0]; + if (Math.abs(r) < 0.00001) return p[1]; + var local_f = (f - p[0]) / r; + return p[1] * (1.0 - local_f) + pn[1] * local_f; + } + return 0; + }; + + CurveEditor.prototype.draw = function ( + ctx, + size, + graphcanvas, + background_color, + line_color, + inactive, + ) { + var points = this.points; + if (!points) return; + this.size = size; + var w = size[0] - this.margin * 2; + var h = size[1] - this.margin * 2; + + line_color = line_color || "#666"; + + ctx.save(); + ctx.translate(this.margin, this.margin); + + if (background_color) { + ctx.fillStyle = "#111"; + ctx.fillRect(0, 0, w, h); + ctx.fillStyle = "#222"; + ctx.fillRect(w * 0.5, 0, 1, h); + ctx.strokeStyle = "#333"; + ctx.strokeRect(0, 0, w, h); + } + ctx.strokeStyle = line_color; + if (inactive) ctx.globalAlpha = 0.5; + ctx.beginPath(); + for (var i = 0; i < points.length; ++i) { + var p = points[i]; + ctx.lineTo(p[0] * w, (1.0 - p[1]) * h); + } + ctx.stroke(); + ctx.globalAlpha = 1; + if (!inactive) + for (var i = 0; i < points.length; ++i) { + var p = points[i]; + ctx.fillStyle = + this.selected == i ? "#FFF" : this.nearest == i ? "#DDD" : "#AAA"; + ctx.beginPath(); + ctx.arc(p[0] * w, (1.0 - p[1]) * h, 2, 0, Math.PI * 2); + ctx.fill(); + } + ctx.restore(); + }; + + //localpos is mouse in curve editor space + CurveEditor.prototype.onMouseDown = function (localpos, graphcanvas) { + var points = this.points; + if (!points) return; + if (localpos[1] < 0) return; + + //this.captureInput(true); + var w = this.size[0] - this.margin * 2; + var h = this.size[1] - this.margin * 2; + var x = localpos[0] - this.margin; + var y = localpos[1] - this.margin; + var pos = [x, y]; + var max_dist = 30 / graphcanvas.ds.scale; + //search closer one + this.selected = this.getCloserPoint(pos, max_dist); + //create one + if (this.selected == -1) { + var point = [x / w, 1 - y / h]; + points.push(point); + points.sort(function (a, b) { + return a[0] - b[0]; + }); + this.selected = points.indexOf(point); + this.must_update = true; + } + if (this.selected != -1) return true; + }; + + CurveEditor.prototype.onMouseMove = function (localpos, graphcanvas) { + var points = this.points; + if (!points) return; + var s = this.selected; + if (s < 0) return; + var x = (localpos[0] - this.margin) / (this.size[0] - this.margin * 2); + var y = (localpos[1] - this.margin) / (this.size[1] - this.margin * 2); + var curvepos = [localpos[0] - this.margin, localpos[1] - this.margin]; + var max_dist = 30 / graphcanvas.ds.scale; + this._nearest = this.getCloserPoint(curvepos, max_dist); + var point = points[s]; + if (point) { + var is_edge_point = s == 0 || s == points.length - 1; + if ( + !is_edge_point && + (localpos[0] < -10 || + localpos[0] > this.size[0] + 10 || + localpos[1] < -10 || + localpos[1] > this.size[1] + 10) + ) { + points.splice(s, 1); + this.selected = -1; + return; + } + if (!is_edge_point) + //not edges + point[0] = clamp(x, 0, 1); + else point[0] = s == 0 ? 0 : 1; + point[1] = 1.0 - clamp(y, 0, 1); + points.sort(function (a, b) { + return a[0] - b[0]; + }); + this.selected = points.indexOf(point); + this.must_update = true; + } + }; + + CurveEditor.prototype.onMouseUp = function (localpos, graphcanvas) { + this.selected = -1; + return false; + }; + + CurveEditor.prototype.getCloserPoint = function (pos, max_dist) { + var points = this.points; + if (!points) return -1; + max_dist = max_dist || 30; + var w = this.size[0] - this.margin * 2; + var h = this.size[1] - this.margin * 2; + var num = points.length; + var p2 = [0, 0]; + var min_dist = 1000000; + var closest = -1; + var last_valid = -1; + for (var i = 0; i < num; ++i) { + var p = points[i]; + p2[0] = p[0] * w; + p2[1] = (1.0 - p[1]) * h; + if (p2[0] < pos[0]) last_valid = i; + var dist = vec2.distance(pos, p2); + if (dist > min_dist || dist > max_dist) continue; + closest = i; + min_dist = dist; + } + return closest; + }; + + LiteGraph.CurveEditor = CurveEditor; + + //used to create nodes from wrapping functions + LiteGraph.getParameterNames = function (func) { + return (func + "") + .replace(/[/][/].*$/gm, "") // strip single-line comments + .replace(/\s+/g, "") // strip white space + .replace(/[/][*][^/*]*[*][/]/g, "") // strip multi-line comments /**/ + .split("){", 1)[0] + .replace(/^[^(]*[(]/, "") // extract the parameters + .replace(/=[^,]+/g, "") // strip any ES6 defaults + .split(",") + .filter(Boolean); // split & filter [""] + }; + + /* helper for interaction: pointer, touch, mouse Listeners + used by LGraphCanvas DragAndScale ContextMenu*/ + LiteGraph.pointerListenerAdd = function ( + oDOM, + sEvIn, + fCall, + capture = false, + ) { + if ( + !oDOM || + !oDOM.addEventListener || + !sEvIn || + typeof fCall !== "function" + ) { + //console.log("cant pointerListenerAdd "+oDOM+", "+sEvent+", "+fCall); + return; // -- break -- + } + + var sMethod = LiteGraph.pointerevents_method; + var sEvent = sEvIn; + + // UNDER CONSTRUCTION + // convert pointerevents to touch event when not available + if (sMethod == "pointer" && !window.PointerEvent) { + console.warn("sMethod=='pointer' && !window.PointerEvent"); + console.log( + "Converting pointer[" + + sEvent + + "] : down move up cancel enter TO touchstart touchmove touchend, etc ..", + ); + switch (sEvent) { + case "down": { + sMethod = "touch"; + sEvent = "start"; + break; + } + case "move": { + sMethod = "touch"; + //sEvent = "move"; + break; + } + case "up": { + sMethod = "touch"; + sEvent = "end"; + break; + } + case "cancel": { + sMethod = "touch"; + //sEvent = "cancel"; + break; + } + case "enter": { + console.log("debug: Should I send a move event?"); // ??? + break; + } + // case "over": case "out": not used at now + default: { + console.warn( + "PointerEvent not available in this browser ? The event " + + sEvent + + " would not be called", + ); + } + } + } + + switch (sEvent) { + //both pointer and move events + case "down": + case "up": + case "move": + case "over": + case "out": + case "enter": { + oDOM.addEventListener(sMethod + sEvent, fCall, capture); + } + // only pointerevents + case "leave": + case "cancel": + case "gotpointercapture": + case "lostpointercapture": { + if (sMethod != "mouse") { + return oDOM.addEventListener(sMethod + sEvent, fCall, capture); + } + } + // not "pointer" || "mouse" + default: + return oDOM.addEventListener(sEvent, fCall, capture); + } + }; + LiteGraph.pointerListenerRemove = function ( + oDOM, + sEvent, + fCall, + capture = false, + ) { + if ( + !oDOM || + !oDOM.removeEventListener || + !sEvent || + typeof fCall !== "function" + ) { + //console.log("cant pointerListenerRemove "+oDOM+", "+sEvent+", "+fCall); + return; // -- break -- + } + switch (sEvent) { + //both pointer and move events + case "down": + case "up": + case "move": + case "over": + case "out": + case "enter": { + if ( + LiteGraph.pointerevents_method == "pointer" || + LiteGraph.pointerevents_method == "mouse" + ) { + oDOM.removeEventListener( + LiteGraph.pointerevents_method + sEvent, + fCall, + capture, + ); + } + } + // only pointerevents + case "leave": + case "cancel": + case "gotpointercapture": + case "lostpointercapture": { + if (LiteGraph.pointerevents_method == "pointer") { + return oDOM.removeEventListener( + LiteGraph.pointerevents_method + sEvent, + fCall, + capture, + ); + } + } + // not "pointer" || "mouse" + default: + return oDOM.removeEventListener(sEvent, fCall, capture); + } + }; + + function clamp(v, a, b) { + return a > v ? a : b < v ? b : v; + } + global.clamp = clamp; + + if (typeof window != "undefined" && !window["requestAnimationFrame"]) { + window.requestAnimationFrame = + window.webkitRequestAnimationFrame || + window.mozRequestAnimationFrame || + function (callback) { + window.setTimeout(callback, 1000 / 60); + }; + } +})(this); + +if (typeof exports != "undefined") { + exports.LiteGraph = this.LiteGraph; + exports.LGraph = this.LGraph; + exports.LLink = this.LLink; + exports.LGraphNode = this.LGraphNode; + exports.LGraphGroup = this.LGraphGroup; + exports.DragAndScale = this.DragAndScale; + exports.LGraphCanvas = this.LGraphCanvas; + exports.ContextMenu = this.ContextMenu; +} + +//basic nodes +(function (global) { + var LiteGraph = global.LiteGraph; + + //Constant + function Time() { + this.addOutput("in ms", "number"); + this.addOutput("in sec", "number"); + } + + Time.title = "Time"; + Time.desc = "Time"; + + Time.prototype.onExecute = function () { + this.setOutputData(0, this.graph.globaltime * 1000); + this.setOutputData(1, this.graph.globaltime); + }; + + LiteGraph.registerNodeType("basic/time", Time); + + //Subgraph: a node that contains a graph + function Subgraph() { + var that = this; + this.size = [140, 80]; + this.properties = { enabled: true }; + this.enabled = true; + + //create inner graph + this.subgraph = new LiteGraph.LGraph(); + this.subgraph._subgraph_node = this; + this.subgraph._is_subgraph = true; + + this.subgraph.onTrigger = this.onSubgraphTrigger.bind(this); + + //nodes input node added inside + this.subgraph.onInputAdded = this.onSubgraphNewInput.bind(this); + this.subgraph.onInputRenamed = this.onSubgraphRenamedInput.bind(this); + this.subgraph.onInputTypeChanged = + this.onSubgraphTypeChangeInput.bind(this); + this.subgraph.onInputRemoved = this.onSubgraphRemovedInput.bind(this); + + this.subgraph.onOutputAdded = this.onSubgraphNewOutput.bind(this); + this.subgraph.onOutputRenamed = this.onSubgraphRenamedOutput.bind(this); + this.subgraph.onOutputTypeChanged = + this.onSubgraphTypeChangeOutput.bind(this); + this.subgraph.onOutputRemoved = this.onSubgraphRemovedOutput.bind(this); + } + + Subgraph.title = "Subgraph"; + Subgraph.desc = "Graph inside a node"; + Subgraph.title_color = "#334"; + + Subgraph.prototype.onGetInputs = function () { + return [["enabled", "boolean"]]; + }; + + /* + Subgraph.prototype.onDrawTitle = function(ctx) { + if (this.flags.collapsed) { + return; + } + + ctx.fillStyle = "#555"; + var w = LiteGraph.NODE_TITLE_HEIGHT; + var x = this.size[0] - w; + ctx.fillRect(x, -w, w, w); + ctx.fillStyle = "#333"; + ctx.beginPath(); + ctx.moveTo(x + w * 0.2, -w * 0.6); + ctx.lineTo(x + w * 0.8, -w * 0.6); + ctx.lineTo(x + w * 0.5, -w * 0.3); + ctx.fill(); + }; + */ + + Subgraph.prototype.onDblClick = function (e, pos, graphcanvas) { + var that = this; + setTimeout(function () { + graphcanvas.openSubgraph(that.subgraph); + }, 10); + }; + + /* + Subgraph.prototype.onMouseDown = function(e, pos, graphcanvas) { + if ( + !this.flags.collapsed && + pos[0] > this.size[0] - LiteGraph.NODE_TITLE_HEIGHT && + pos[1] < 0 + ) { + var that = this; + setTimeout(function() { + graphcanvas.openSubgraph(that.subgraph); + }, 10); + } + }; + */ + + Subgraph.prototype.onAction = function (action, param) { + this.subgraph.onAction(action, param); + }; + + Subgraph.prototype.onExecute = function () { + this.enabled = this.getInputOrProperty("enabled"); + if (!this.enabled) { + return; + } + + //send inputs to subgraph global inputs + if (this.inputs) { + for (var i = 0; i < this.inputs.length; i++) { + var input = this.inputs[i]; + var value = this.getInputData(i); + this.subgraph.setInputData(input.name, value); + } + } + + //execute + this.subgraph.runStep(); + + //send subgraph global outputs to outputs + if (this.outputs) { + for (var i = 0; i < this.outputs.length; i++) { + var output = this.outputs[i]; + var value = this.subgraph.getOutputData(output.name); + this.setOutputData(i, value); + } + } + }; + + Subgraph.prototype.sendEventToAllNodes = function (eventname, param, mode) { + if (this.enabled) { + this.subgraph.sendEventToAllNodes(eventname, param, mode); + } + }; + + Subgraph.prototype.onDrawBackground = function ( + ctx, + graphcanvas, + canvas, + pos, + ) { + if (this.flags.collapsed) return; + var y = this.size[1] - LiteGraph.NODE_TITLE_HEIGHT + 0.5; + // button + var over = LiteGraph.isInsideRectangle( + pos[0], + pos[1], + this.pos[0], + this.pos[1] + y, + this.size[0], + LiteGraph.NODE_TITLE_HEIGHT, + ); + let overleft = LiteGraph.isInsideRectangle( + pos[0], + pos[1], + this.pos[0], + this.pos[1] + y, + this.size[0] / 2, + LiteGraph.NODE_TITLE_HEIGHT, + ); + ctx.fillStyle = over ? "#555" : "#222"; + ctx.beginPath(); + if (this._shape == LiteGraph.BOX_SHAPE) { + if (overleft) { + ctx.rect(0, y, this.size[0] / 2 + 1, LiteGraph.NODE_TITLE_HEIGHT); + } else { + ctx.rect( + this.size[0] / 2, + y, + this.size[0] / 2 + 1, + LiteGraph.NODE_TITLE_HEIGHT, + ); + } + } else { + if (overleft) { + ctx.roundRect( + 0, + y, + this.size[0] / 2 + 1, + LiteGraph.NODE_TITLE_HEIGHT, + [0, 0, 8, 8], + ); + } else { + ctx.roundRect( + this.size[0] / 2, + y, + this.size[0] / 2 + 1, + LiteGraph.NODE_TITLE_HEIGHT, + [0, 0, 8, 8], + ); + } + } + if (over) { + ctx.fill(); + } else { + ctx.fillRect(0, y, this.size[0] + 1, LiteGraph.NODE_TITLE_HEIGHT); + } + // button + ctx.textAlign = "center"; + ctx.font = "24px Arial"; + ctx.fillStyle = over ? "#DDD" : "#999"; + ctx.fillText("+", this.size[0] * 0.25, y + 24); + ctx.fillText("+", this.size[0] * 0.75, y + 24); + }; + + // Subgraph.prototype.onMouseDown = function(e, localpos, graphcanvas) + // { + // var y = this.size[1] - LiteGraph.NODE_TITLE_HEIGHT + 0.5; + // if(localpos[1] > y) + // { + // graphcanvas.showSubgraphPropertiesDialog(this); + // } + // } + Subgraph.prototype.onMouseDown = function (e, localpos, graphcanvas) { + var y = this.size[1] - LiteGraph.NODE_TITLE_HEIGHT + 0.5; + console.log(0); + if (localpos[1] > y) { + if (localpos[0] < this.size[0] / 2) { + console.log(1); + graphcanvas.showSubgraphPropertiesDialog(this); + } else { + console.log(2); + graphcanvas.showSubgraphPropertiesDialogRight(this); + } + } + }; + Subgraph.prototype.computeSize = function () { + var num_inputs = this.inputs ? this.inputs.length : 0; + var num_outputs = this.outputs ? this.outputs.length : 0; + return [ + 200, + Math.max(num_inputs, num_outputs) * LiteGraph.NODE_SLOT_HEIGHT + + LiteGraph.NODE_TITLE_HEIGHT, + ]; + }; + + //**** INPUTS *********************************** + Subgraph.prototype.onSubgraphTrigger = function (event, param) { + var slot = this.findOutputSlot(event); + if (slot != -1) { + this.triggerSlot(slot); + } + }; + + Subgraph.prototype.onSubgraphNewInput = function (name, type) { + var slot = this.findInputSlot(name); + if (slot == -1) { + //add input to the node + this.addInput(name, type); + } + }; + + Subgraph.prototype.onSubgraphRenamedInput = function (oldname, name) { + var slot = this.findInputSlot(oldname); + if (slot == -1) { + return; + } + var info = this.getInputInfo(slot); + info.name = name; + }; + + Subgraph.prototype.onSubgraphTypeChangeInput = function (name, type) { + var slot = this.findInputSlot(name); + if (slot == -1) { + return; + } + var info = this.getInputInfo(slot); + info.type = type; + }; + + Subgraph.prototype.onSubgraphRemovedInput = function (name) { + var slot = this.findInputSlot(name); + if (slot == -1) { + return; + } + this.removeInput(slot); + }; + + //**** OUTPUTS *********************************** + Subgraph.prototype.onSubgraphNewOutput = function (name, type) { + var slot = this.findOutputSlot(name); + if (slot == -1) { + this.addOutput(name, type); + } + }; + + Subgraph.prototype.onSubgraphRenamedOutput = function (oldname, name) { + var slot = this.findOutputSlot(oldname); + if (slot == -1) { + return; + } + var info = this.getOutputInfo(slot); + info.name = name; + }; + + Subgraph.prototype.onSubgraphTypeChangeOutput = function (name, type) { + var slot = this.findOutputSlot(name); + if (slot == -1) { + return; + } + var info = this.getOutputInfo(slot); + info.type = type; + }; + + Subgraph.prototype.onSubgraphRemovedOutput = function (name) { + var slot = this.findOutputSlot(name); + if (slot == -1) { + return; + } + this.removeOutput(slot); + }; + // ***************************************************** + + Subgraph.prototype.getExtraMenuOptions = function (graphcanvas) { + var that = this; + return [ + { + content: "Open", + callback: function () { + graphcanvas.openSubgraph(that.subgraph); + }, + }, + ]; + }; + + Subgraph.prototype.onResize = function (size) { + size[1] += 20; + }; + + Subgraph.prototype.serialize = function () { + var data = LiteGraph.LGraphNode.prototype.serialize.call(this); + data.subgraph = this.subgraph.serialize(); + return data; + }; + //no need to define node.configure, the default method detects node.subgraph and passes the object to node.subgraph.configure() + + Subgraph.prototype.reassignSubgraphUUIDs = function (graph) { + const idMap = { nodeIDs: {}, linkIDs: {} }; + + for (const node of graph.nodes) { + const oldID = node.id; + const newID = LiteGraph.uuidv4(); + node.id = newID; + + if (idMap.nodeIDs[oldID] || idMap.nodeIDs[newID]) { + throw new Error( + `New/old node UUID wasn't unique in changed map! ${oldID} ${newID}`, + ); + } + + idMap.nodeIDs[oldID] = newID; + idMap.nodeIDs[newID] = oldID; + } + + for (const link of graph.links) { + const oldID = link[0]; + const newID = LiteGraph.uuidv4(); + link[0] = newID; + + if (idMap.linkIDs[oldID] || idMap.linkIDs[newID]) { + throw new Error( + `New/old link UUID wasn't unique in changed map! ${oldID} ${newID}`, + ); + } + + idMap.linkIDs[oldID] = newID; + idMap.linkIDs[newID] = oldID; + + const nodeFrom = link[1]; + const nodeTo = link[3]; + + if (!idMap.nodeIDs[nodeFrom]) { + throw new Error(`Old node UUID not found in mapping! ${nodeFrom}`); + } + + link[1] = idMap.nodeIDs[nodeFrom]; + + if (!idMap.nodeIDs[nodeTo]) { + throw new Error(`Old node UUID not found in mapping! ${nodeTo}`); + } + + link[3] = idMap.nodeIDs[nodeTo]; + } + + // Reconnect links + for (const node of graph.nodes) { + if (node.inputs) { + for (const input of node.inputs) { + if (input.link) { + input.link = idMap.linkIDs[input.link]; + } + } + } + if (node.outputs) { + for (const output of node.outputs) { + if (output.links) { + output.links = output.links.map((l) => idMap.linkIDs[l]); + } + } + } + } + + // Recurse! + for (const node of graph.nodes) { + if (node.type === "graph/subgraph") { + const merge = reassignGraphUUIDs(node.subgraph); + idMap.nodeIDs.assign(merge.nodeIDs); + idMap.linkIDs.assign(merge.linkIDs); + } + } + }; + + Subgraph.prototype.clone = function () { + var node = LiteGraph.createNode(this.type); + var data = this.serialize(); + + if (LiteGraph.use_uuids) { + // LGraph.serialize() seems to reuse objects in the original graph. But we + // need to change node IDs here, so clone it first. + const subgraph = LiteGraph.cloneObject(data.subgraph); + + this.reassignSubgraphUUIDs(subgraph); + + data.subgraph = subgraph; + } + + delete data["id"]; + delete data["inputs"]; + delete data["outputs"]; + node.configure(data); + return node; + }; + + Subgraph.prototype.buildFromNodes = function (nodes) { + //clear all? + //TODO + + //nodes that connect data between parent graph and subgraph + var subgraph_inputs = []; + var subgraph_outputs = []; + + //mark inner nodes + var ids = {}; + var min_x = 0; + var max_x = 0; + for (var i = 0; i < nodes.length; ++i) { + var node = nodes[i]; + ids[node.id] = node; + min_x = Math.min(node.pos[0], min_x); + max_x = Math.max(node.pos[0], min_x); + } + + var last_input_y = 0; + var last_output_y = 0; + + for (var i = 0; i < nodes.length; ++i) { + var node = nodes[i]; + //check inputs + if (node.inputs) + for (var j = 0; j < node.inputs.length; ++j) { + var input = node.inputs[j]; + if (!input || !input.link) continue; + var link = node.graph.links[input.link]; + if (!link) continue; + if (ids[link.origin_id]) continue; + //this.addInput(input.name,link.type); + this.subgraph.addInput(input.name, link.type); + /* + var input_node = LiteGraph.createNode("graph/input"); + this.subgraph.add( input_node ); + input_node.pos = [min_x - 200, last_input_y ]; + last_input_y += 100; + */ + } + + //check outputs + if (node.outputs) + for (var j = 0; j < node.outputs.length; ++j) { + var output = node.outputs[j]; + if (!output || !output.links || !output.links.length) continue; + var is_external = false; + for (var k = 0; k < output.links.length; ++k) { + var link = node.graph.links[output.links[k]]; + if (!link) continue; + if (ids[link.target_id]) continue; + is_external = true; + break; + } + if (!is_external) continue; + //this.addOutput(output.name,output.type); + /* + var output_node = LiteGraph.createNode("graph/output"); + this.subgraph.add( output_node ); + output_node.pos = [max_x + 50, last_output_y ]; + last_output_y += 100; + */ + } + } + + //detect inputs and outputs + //split every connection in two data_connection nodes + //keep track of internal connections + //connect external connections + + //clone nodes inside subgraph and try to reconnect them + + //connect edge subgraph nodes to extarnal connections nodes + }; + + LiteGraph.Subgraph = Subgraph; + LiteGraph.registerNodeType("graph/subgraph", Subgraph); + + //Input for a subgraph + function GraphInput() { + this.addOutput("", "number"); + + this.name_in_graph = ""; + this.properties = { + name: "", + type: "number", + value: 0, + }; + + var that = this; + + this.name_widget = this.addWidget( + "text", + "Name", + this.properties.name, + function (v) { + if (!v) { + return; + } + that.setProperty("name", v); + }, + ); + this.type_widget = this.addWidget( + "text", + "Type", + this.properties.type, + function (v) { + that.setProperty("type", v); + }, + ); + + this.value_widget = this.addWidget( + "number", + "Value", + this.properties.value, + function (v) { + that.setProperty("value", v); + }, + ); + + this.widgets_up = true; + this.size = [180, 90]; + } + + GraphInput.title = "Input"; + GraphInput.desc = "Input of the graph"; + + GraphInput.prototype.onConfigure = function () { + this.updateType(); + }; + + //ensures the type in the node output and the type in the associated graph input are the same + GraphInput.prototype.updateType = function () { + var type = this.properties.type; + this.type_widget.value = type; + + //update output + if (this.outputs[0].type != type) { + if (!LiteGraph.isValidConnection(this.outputs[0].type, type)) + this.disconnectOutput(0); + this.outputs[0].type = type; + } + + //update widget + if (type == "number") { + this.value_widget.type = "number"; + this.value_widget.value = 0; + } else if (type == "boolean") { + this.value_widget.type = "toggle"; + this.value_widget.value = true; + } else if (type == "string") { + this.value_widget.type = "text"; + this.value_widget.value = ""; + } else { + this.value_widget.type = null; + this.value_widget.value = null; + } + this.properties.value = this.value_widget.value; + + //update graph + if (this.graph && this.name_in_graph) { + this.graph.changeInputType(this.name_in_graph, type); + } + }; + + //this is executed AFTER the property has changed + GraphInput.prototype.onPropertyChanged = function (name, v) { + if (name == "name") { + if (v == "" || v == this.name_in_graph || v == "enabled") { + return false; + } + if (this.graph) { + if (this.name_in_graph) { + //already added + this.graph.renameInput(this.name_in_graph, v); + } else { + this.graph.addInput(v, this.properties.type); + } + } //what if not?! + this.name_widget.value = v; + this.name_in_graph = v; + } else if (name == "type") { + this.updateType(); + } else if (name == "value") { + } + }; + + GraphInput.prototype.getTitle = function () { + if (this.flags.collapsed) { + return this.properties.name; + } + return this.title; + }; + + GraphInput.prototype.onAction = function (action, param) { + if (this.properties.type == LiteGraph.EVENT) { + this.triggerSlot(0, param); + } + }; + + GraphInput.prototype.onExecute = function () { + var name = this.properties.name; + //read from global input + var data = this.graph.inputs[name]; + if (!data) { + this.setOutputData(0, this.properties.value); + return; + } + + this.setOutputData( + 0, + data.value !== undefined ? data.value : this.properties.value, + ); + }; + + GraphInput.prototype.onRemoved = function () { + if (this.name_in_graph) { + this.graph.removeInput(this.name_in_graph); + } + }; + + LiteGraph.GraphInput = GraphInput; + LiteGraph.registerNodeType("graph/input", GraphInput); + + //Output for a subgraph + function GraphOutput() { + this.addInput("", ""); + + this.name_in_graph = ""; + this.properties = { name: "", type: "" }; + var that = this; + + // Object.defineProperty(this.properties, "name", { + // get: function() { + // return that.name_in_graph; + // }, + // set: function(v) { + // if (v == "" || v == that.name_in_graph) { + // return; + // } + // if (that.name_in_graph) { + // //already added + // that.graph.renameOutput(that.name_in_graph, v); + // } else { + // that.graph.addOutput(v, that.properties.type); + // } + // that.name_widget.value = v; + // that.name_in_graph = v; + // }, + // enumerable: true + // }); + + // Object.defineProperty(this.properties, "type", { + // get: function() { + // return that.inputs[0].type; + // }, + // set: function(v) { + // if (v == "action" || v == "event") { + // v = LiteGraph.ACTION; + // } + // if (!LiteGraph.isValidConnection(that.inputs[0].type,v)) + // that.disconnectInput(0); + // that.inputs[0].type = v; + // if (that.name_in_graph) { + // //already added + // that.graph.changeOutputType( + // that.name_in_graph, + // that.inputs[0].type + // ); + // } + // that.type_widget.value = v || ""; + // }, + // enumerable: true + // }); + + this.name_widget = this.addWidget( + "text", + "Name", + this.properties.name, + "name", + ); + this.type_widget = this.addWidget( + "text", + "Type", + this.properties.type, + "type", + ); + this.widgets_up = true; + this.size = [180, 60]; + } + + GraphOutput.title = "Output"; + GraphOutput.desc = "Output of the graph"; + + GraphOutput.prototype.onPropertyChanged = function (name, v) { + if (name == "name") { + if (v == "" || v == this.name_in_graph || v == "enabled") { + return false; + } + if (this.graph) { + if (this.name_in_graph) { + //already added + this.graph.renameOutput(this.name_in_graph, v); + } else { + this.graph.addOutput(v, this.properties.type); + } + } //what if not?! + this.name_widget.value = v; + this.name_in_graph = v; + } else if (name == "type") { + this.updateType(); + } else if (name == "value") { + } + }; + + GraphOutput.prototype.updateType = function () { + var type = this.properties.type; + if (this.type_widget) this.type_widget.value = type; + + //update output + if (this.inputs[0].type != type) { + if (type == "action" || type == "event") type = LiteGraph.EVENT; + if (!LiteGraph.isValidConnection(this.inputs[0].type, type)) + this.disconnectInput(0); + this.inputs[0].type = type; + } + + //update graph + if (this.graph && this.name_in_graph) { + this.graph.changeOutputType(this.name_in_graph, type); + } + }; + + GraphOutput.prototype.onExecute = function () { + this._value = this.getInputData(0); + this.graph.setOutputData(this.properties.name, this._value); + }; + + GraphOutput.prototype.onAction = function (action, param) { + if (this.properties.type == LiteGraph.ACTION) { + this.graph.trigger(this.properties.name, param); + } + }; + + GraphOutput.prototype.onRemoved = function () { + if (this.name_in_graph) { + this.graph.removeOutput(this.name_in_graph); + } + }; + + GraphOutput.prototype.getTitle = function () { + if (this.flags.collapsed) { + return this.properties.name; + } + return this.title; + }; + + LiteGraph.GraphOutput = GraphOutput; + LiteGraph.registerNodeType("graph/output", GraphOutput); + + //Constant + function ConstantNumber() { + this.addOutput("value", "number"); + this.addProperty("value", 1.0); + this.widget = this.addWidget("number", "value", 1, "value"); + this.widgets_up = true; + this.size = [180, 30]; + } + + ConstantNumber.title = "Const Number"; + ConstantNumber.desc = "Constant number"; + + ConstantNumber.prototype.onExecute = function () { + this.setOutputData(0, parseFloat(this.properties["value"])); + }; + + ConstantNumber.prototype.getTitle = function () { + if (this.flags.collapsed) { + return this.properties.value; + } + return this.title; + }; + + ConstantNumber.prototype.setValue = function (v) { + this.setProperty("value", v); + }; + + ConstantNumber.prototype.onDrawBackground = function (ctx) { + //show the current value + this.outputs[0].label = this.properties["value"].toFixed(3); + }; + + LiteGraph.registerNodeType("basic/const", ConstantNumber); + + function ConstantBoolean() { + this.addOutput("bool", "boolean"); + this.addProperty("value", true); + this.widget = this.addWidget("toggle", "value", true, "value"); + this.serialize_widgets = true; + this.widgets_up = true; + this.size = [140, 30]; + } + + ConstantBoolean.title = "Const Boolean"; + ConstantBoolean.desc = "Constant boolean"; + ConstantBoolean.prototype.getTitle = ConstantNumber.prototype.getTitle; + + ConstantBoolean.prototype.onExecute = function () { + this.setOutputData(0, this.properties["value"]); + }; + + ConstantBoolean.prototype.setValue = ConstantNumber.prototype.setValue; + + ConstantBoolean.prototype.onGetInputs = function () { + return [["toggle", LiteGraph.ACTION]]; + }; + + ConstantBoolean.prototype.onAction = function (action) { + this.setValue(!this.properties.value); + }; + + LiteGraph.registerNodeType("basic/boolean", ConstantBoolean); + + function ConstantString() { + this.addOutput("string", "string"); + this.addProperty("value", ""); + this.widget = this.addWidget("text", "value", "", "value"); //link to property value + this.widgets_up = true; + this.size = [180, 30]; + } + + ConstantString.title = "Const String"; + ConstantString.desc = "Constant string"; + + ConstantString.prototype.getTitle = ConstantNumber.prototype.getTitle; + + ConstantString.prototype.onExecute = function () { + this.setOutputData(0, this.properties["value"]); + }; + + ConstantString.prototype.setValue = ConstantNumber.prototype.setValue; + + ConstantString.prototype.onDropFile = function (file) { + var that = this; + var reader = new FileReader(); + reader.onload = function (e) { + that.setProperty("value", e.target.result); + }; + reader.readAsText(file); + }; + + LiteGraph.registerNodeType("basic/string", ConstantString); + + function ConstantObject() { + this.addOutput("obj", "object"); + this.size = [120, 30]; + this._object = {}; + } + + ConstantObject.title = "Const Object"; + ConstantObject.desc = "Constant Object"; + + ConstantObject.prototype.onExecute = function () { + this.setOutputData(0, this._object); + }; + + LiteGraph.registerNodeType("basic/object", ConstantObject); + + function ConstantFile() { + this.addInput("url", "string"); + this.addOutput("file", "string"); + this.addProperty("url", ""); + this.addProperty("type", "text"); + this.widget = this.addWidget("text", "url", "", "url"); + this._data = null; + } + + ConstantFile.title = "Const File"; + ConstantFile.desc = "Fetches a file from an url"; + ConstantFile["@type"] = { + type: "enum", + values: ["text", "arraybuffer", "blob", "json"], + }; + + ConstantFile.prototype.onPropertyChanged = function (name, value) { + if (name == "url") { + if (value == null || value == "") this._data = null; + else { + this.fetchFile(value); + } + } + }; + + ConstantFile.prototype.onExecute = function () { + var url = this.getInputData(0) || this.properties.url; + if (url && (url != this._url || this._type != this.properties.type)) + this.fetchFile(url); + this.setOutputData(0, this._data); + }; + + ConstantFile.prototype.setValue = ConstantNumber.prototype.setValue; + + ConstantFile.prototype.fetchFile = function (url) { + var that = this; + if (!url || url.constructor !== String) { + that._data = null; + that.boxcolor = null; + return; + } + + this._url = url; + this._type = this.properties.type; + if (url.substr(0, 4) == "http" && LiteGraph.proxy) { + url = LiteGraph.proxy + url.substr(url.indexOf(":") + 3); + } + fetch(url) + .then(function (response) { + if (!response.ok) throw new Error("File not found"); + + if (that.properties.type == "arraybuffer") + return response.arrayBuffer(); + else if (that.properties.type == "text") return response.text(); + else if (that.properties.type == "json") return response.json(); + else if (that.properties.type == "blob") return response.blob(); + }) + .then(function (data) { + that._data = data; + that.boxcolor = "#AEA"; + }) + .catch(function (error) { + that._data = null; + that.boxcolor = "red"; + console.error("error fetching file:", url); + }); + }; + + ConstantFile.prototype.onDropFile = function (file) { + var that = this; + this._url = file.name; + this._type = this.properties.type; + this.properties.url = file.name; + var reader = new FileReader(); + reader.onload = function (e) { + that.boxcolor = "#AEA"; + var v = e.target.result; + if (that.properties.type == "json") v = JSON.parse(v); + that._data = v; + }; + if (that.properties.type == "arraybuffer") reader.readAsArrayBuffer(file); + else if (that.properties.type == "text" || that.properties.type == "json") + reader.readAsText(file); + else if (that.properties.type == "blob") + return reader.readAsBinaryString(file); + }; + + LiteGraph.registerNodeType("basic/file", ConstantFile); + + //to store json objects + function JSONParse() { + this.addInput("parse", LiteGraph.ACTION); + this.addInput("json", "string"); + this.addOutput("done", LiteGraph.EVENT); + this.addOutput("object", "object"); + this.widget = this.addWidget("button", "parse", "", this.parse.bind(this)); + this._str = null; + this._obj = null; + } + + JSONParse.title = "JSON Parse"; + JSONParse.desc = "Parses JSON String into object"; + + JSONParse.prototype.parse = function () { + if (!this._str) return; + + try { + this._str = this.getInputData(1); + this._obj = JSON.parse(this._str); + this.boxcolor = "#AEA"; + this.triggerSlot(0); + } catch (err) { + this.boxcolor = "red"; + } + }; + + JSONParse.prototype.onExecute = function () { + this._str = this.getInputData(1); + this.setOutputData(1, this._obj); + }; + + JSONParse.prototype.onAction = function (name) { + if (name == "parse") this.parse(); + }; + + LiteGraph.registerNodeType("basic/jsonparse", JSONParse); + + //to store json objects + function ConstantData() { + this.addOutput("data", "object"); + this.addProperty("value", ""); + this.widget = this.addWidget("text", "json", "", "value"); + this.widgets_up = true; + this.size = [140, 30]; + this._value = null; + } + + ConstantData.title = "Const Data"; + ConstantData.desc = "Constant Data"; + + ConstantData.prototype.onPropertyChanged = function (name, value) { + this.widget.value = value; + if (value == null || value == "") { + return; + } + + try { + this._value = JSON.parse(value); + this.boxcolor = "#AEA"; + } catch (err) { + this.boxcolor = "red"; + } + }; + + ConstantData.prototype.onExecute = function () { + this.setOutputData(0, this._value); + }; + + ConstantData.prototype.setValue = ConstantNumber.prototype.setValue; + + LiteGraph.registerNodeType("basic/data", ConstantData); + + //to store json objects + function ConstantArray() { + this._value = []; + this.addInput("json", ""); + this.addOutput("arrayOut", "array"); + this.addOutput("length", "number"); + this.addProperty("value", "[]"); + this.widget = this.addWidget( + "text", + "array", + this.properties.value, + "value", + ); + this.widgets_up = true; + this.size = [140, 50]; + } + + ConstantArray.title = "Const Array"; + ConstantArray.desc = "Constant Array"; + + ConstantArray.prototype.onPropertyChanged = function (name, value) { + this.widget.value = value; + if (value == null || value == "") { + return; + } + + try { + if (value[0] != "[") this._value = JSON.parse("[" + value + "]"); + else this._value = JSON.parse(value); + this.boxcolor = "#AEA"; + } catch (err) { + this.boxcolor = "red"; + } + }; + + ConstantArray.prototype.onExecute = function () { + var v = this.getInputData(0); + if (v && v.length) { + //clone + if (!this._value) this._value = new Array(); + this._value.length = v.length; + for (var i = 0; i < v.length; ++i) this._value[i] = v[i]; + } + this.setOutputData(0, this._value); + this.setOutputData(1, this._value ? this._value.length || 0 : 0); + }; + + ConstantArray.prototype.setValue = ConstantNumber.prototype.setValue; + + LiteGraph.registerNodeType("basic/array", ConstantArray); + + function SetArray() { + this.addInput("arr", "array"); + this.addInput("value", ""); + this.addOutput("arr", "array"); + this.properties = { index: 0 }; + this.widget = this.addWidget( + "number", + "i", + this.properties.index, + "index", + { precision: 0, step: 10, min: 0 }, + ); + } + + SetArray.title = "Set Array"; + SetArray.desc = "Sets index of array"; + + SetArray.prototype.onExecute = function () { + var arr = this.getInputData(0); + if (!arr) return; + var v = this.getInputData(1); + if (v === undefined) return; + if (this.properties.index) arr[Math.floor(this.properties.index)] = v; + this.setOutputData(0, arr); + }; + + LiteGraph.registerNodeType("basic/set_array", SetArray); + + function ArrayElement() { + this.addInput("array", "array,table,string"); + this.addInput("index", "number"); + this.addOutput("value", ""); + this.addProperty("index", 0); + } + + ArrayElement.title = "Array[i]"; + ArrayElement.desc = "Returns an element from an array"; + + ArrayElement.prototype.onExecute = function () { + var array = this.getInputData(0); + var index = this.getInputData(1); + if (index == null) index = this.properties.index; + if (array == null || index == null) return; + this.setOutputData(0, array[Math.floor(Number(index))]); + }; + + LiteGraph.registerNodeType("basic/array[]", ArrayElement); + + function TableElement() { + this.addInput("table", "table"); + this.addInput("row", "number"); + this.addInput("col", "number"); + this.addOutput("value", ""); + this.addProperty("row", 0); + this.addProperty("column", 0); + } + + TableElement.title = "Table[row][col]"; + TableElement.desc = "Returns an element from a table"; + + TableElement.prototype.onExecute = function () { + var table = this.getInputData(0); + var row = this.getInputData(1); + var col = this.getInputData(2); + if (row == null) row = this.properties.row; + if (col == null) col = this.properties.column; + if (table == null || row == null || col == null) return; + var row = table[Math.floor(Number(row))]; + if (row) this.setOutputData(0, row[Math.floor(Number(col))]); + else this.setOutputData(0, null); + }; + + LiteGraph.registerNodeType("basic/table[][]", TableElement); + + function ObjectProperty() { + this.addInput("obj", "object"); + this.addOutput("property", 0); + this.addProperty("value", 0); + this.widget = this.addWidget("text", "prop.", "", this.setValue.bind(this)); + this.widgets_up = true; + this.size = [140, 30]; + this._value = null; + } + + ObjectProperty.title = "Object property"; + ObjectProperty.desc = "Outputs the property of an object"; + + ObjectProperty.prototype.setValue = function (v) { + this.properties.value = v; + this.widget.value = v; + }; + + ObjectProperty.prototype.getTitle = function () { + if (this.flags.collapsed) { + return "in." + this.properties.value; + } + return this.title; + }; + + ObjectProperty.prototype.onPropertyChanged = function (name, value) { + this.widget.value = value; + }; + + ObjectProperty.prototype.onExecute = function () { + var data = this.getInputData(0); + if (data != null) { + this.setOutputData(0, data[this.properties.value]); + } + }; + + LiteGraph.registerNodeType("basic/object_property", ObjectProperty); + + function ObjectKeys() { + this.addInput("obj", ""); + this.addOutput("keys", "array"); + this.size = [140, 30]; + } + + ObjectKeys.title = "Object keys"; + ObjectKeys.desc = "Outputs an array with the keys of an object"; + + ObjectKeys.prototype.onExecute = function () { + var data = this.getInputData(0); + if (data != null) { + this.setOutputData(0, Object.keys(data)); + } + }; + + LiteGraph.registerNodeType("basic/object_keys", ObjectKeys); + + function SetObject() { + this.addInput("obj", ""); + this.addInput("value", ""); + this.addOutput("obj", ""); + this.properties = { property: "" }; + this.name_widget = this.addWidget( + "text", + "prop.", + this.properties.property, + "property", + ); + } + + SetObject.title = "Set Object"; + SetObject.desc = "Adds propertiesrty to object"; + + SetObject.prototype.onExecute = function () { + var obj = this.getInputData(0); + if (!obj) return; + var v = this.getInputData(1); + if (v === undefined) return; + if (this.properties.property) obj[this.properties.property] = v; + this.setOutputData(0, obj); + }; + + LiteGraph.registerNodeType("basic/set_object", SetObject); + + function MergeObjects() { + this.addInput("A", "object"); + this.addInput("B", "object"); + this.addOutput("out", "object"); + this._result = {}; + var that = this; + this.addWidget("button", "clear", "", function () { + that._result = {}; + }); + this.size = this.computeSize(); + } + + MergeObjects.title = "Merge Objects"; + MergeObjects.desc = "Creates an object copying properties from others"; + + MergeObjects.prototype.onExecute = function () { + var A = this.getInputData(0); + var B = this.getInputData(1); + var C = this._result; + if (A) for (var i in A) C[i] = A[i]; + if (B) for (var i in B) C[i] = B[i]; + this.setOutputData(0, C); + }; + + LiteGraph.registerNodeType("basic/merge_objects", MergeObjects); + + //Store as variable + function Variable() { + this.size = [60, 30]; + this.addInput("in"); + this.addOutput("out"); + this.properties = { varname: "myname", container: Variable.LITEGRAPH }; + this.value = null; + } + + Variable.title = "Variable"; + Variable.desc = "store/read variable value"; + + Variable.LITEGRAPH = 0; //between all graphs + Variable.GRAPH = 1; //only inside this graph + Variable.GLOBALSCOPE = 2; //attached to Window + + Variable["@container"] = { + type: "enum", + values: { + litegraph: Variable.LITEGRAPH, + graph: Variable.GRAPH, + global: Variable.GLOBALSCOPE, + }, + }; + + Variable.prototype.onExecute = function () { + var container = this.getContainer(); + + if (this.isInputConnected(0)) { + this.value = this.getInputData(0); + container[this.properties.varname] = this.value; + this.setOutputData(0, this.value); + return; + } + + this.setOutputData(0, container[this.properties.varname]); + }; + + Variable.prototype.getContainer = function () { + switch (this.properties.container) { + case Variable.GRAPH: + if (this.graph) return this.graph.vars; + return {}; + break; + case Variable.GLOBALSCOPE: + return global; + break; + case Variable.LITEGRAPH: + default: + return LiteGraph.Globals; + break; + } + }; + + Variable.prototype.getTitle = function () { + return this.properties.varname; + }; + + LiteGraph.registerNodeType("basic/variable", Variable); + + function length(v) { + if (v && v.length != null) return Number(v.length); + return 0; + } + + LiteGraph.wrapFunctionAsNode("basic/length", length, [""], "number"); + + function length(v) { + if (v && v.length != null) return Number(v.length); + return 0; + } + + LiteGraph.wrapFunctionAsNode( + "basic/not", + function (a) { + return !a; + }, + [""], + "boolean", + ); + + function DownloadData() { + this.size = [60, 30]; + this.addInput("data", 0); + this.addInput("download", LiteGraph.ACTION); + this.properties = { filename: "data.json" }; + this.value = null; + var that = this; + this.addWidget("button", "Download", "", function (v) { + if (!that.value) return; + that.downloadAsFile(); + }); + } + + DownloadData.title = "Download"; + DownloadData.desc = "Download some data"; + + DownloadData.prototype.downloadAsFile = function () { + if (this.value == null) return; + + var str = null; + if (this.value.constructor === String) str = this.value; + else str = JSON.stringify(this.value); + + var file = new Blob([str]); + var url = URL.createObjectURL(file); + var element = document.createElement("a"); + element.setAttribute("href", url); + element.setAttribute("download", this.properties.filename); + element.style.display = "none"; + document.body.appendChild(element); + element.click(); + document.body.removeChild(element); + setTimeout(function () { + URL.revokeObjectURL(url); + }, 1000 * 60); //wait one minute to revoke url + }; + + DownloadData.prototype.onAction = function (action, param) { + var that = this; + setTimeout(function () { + that.downloadAsFile(); + }, 100); //deferred to avoid blocking the renderer with the popup + }; + + DownloadData.prototype.onExecute = function () { + if (this.inputs[0]) { + this.value = this.getInputData(0); + } + }; + + DownloadData.prototype.getTitle = function () { + if (this.flags.collapsed) { + return this.properties.filename; + } + return this.title; + }; + + LiteGraph.registerNodeType("basic/download", DownloadData); + + //Watch a value in the editor + function Watch() { + this.size = [60, 30]; + this.addInput("value", 0, { label: "" }); + this.value = 0; + } + + Watch.title = "Watch"; + Watch.desc = "Show value of input"; + + Watch.prototype.onExecute = function () { + if (this.inputs[0]) { + this.value = this.getInputData(0); + } + }; + + Watch.prototype.getTitle = function () { + if (this.flags.collapsed) { + return this.inputs[0].label; + } + return this.title; + }; + + Watch.toString = function (o) { + if (o == null) { + return "null"; + } else if (o.constructor === Number) { + return o.toFixed(3); + } else if (o.constructor === Array) { + var str = "["; + for (var i = 0; i < o.length; ++i) { + str += Watch.toString(o[i]) + (i + 1 != o.length ? "," : ""); + } + str += "]"; + return str; + } else { + return String(o); + } + }; + + Watch.prototype.onDrawBackground = function (ctx) { + //show the current value + this.inputs[0].label = Watch.toString(this.value); + }; + + LiteGraph.registerNodeType("basic/watch", Watch); + + //in case one type doesnt match other type but you want to connect them anyway + function Cast() { + this.addInput("in", 0); + this.addOutput("out", 0); + this.size = [40, 30]; + } + + Cast.title = "Cast"; + Cast.desc = "Allows to connect different types"; + + Cast.prototype.onExecute = function () { + this.setOutputData(0, this.getInputData(0)); + }; + + LiteGraph.registerNodeType("basic/cast", Cast); + + //Show value inside the debug console + function Console() { + this.mode = LiteGraph.ON_EVENT; + this.size = [80, 30]; + this.addProperty("msg", ""); + this.addInput("log", LiteGraph.EVENT); + this.addInput("msg", 0); + } + + Console.title = "Console"; + Console.desc = "Show value inside the console"; + + Console.prototype.onAction = function (action, param) { + // param is the action + var msg = this.getInputData(1); //getInputDataByName("msg"); + //if (msg == null || typeof msg == "undefined") return; + if (!msg) msg = this.properties.msg; + if (!msg) msg = "Event: " + param; // msg is undefined if the slot is lost? + if (action == "log") { + console.log(msg); + } else if (action == "warn") { + console.warn(msg); + } else if (action == "error") { + console.error(msg); + } + }; + + Console.prototype.onExecute = function () { + var msg = this.getInputData(1); //getInputDataByName("msg"); + if (!msg) msg = this.properties.msg; + if (msg != null && typeof msg != "undefined") { + this.properties.msg = msg; + console.log(msg); + } + }; + + Console.prototype.onGetInputs = function () { + return [ + ["log", LiteGraph.ACTION], + ["warn", LiteGraph.ACTION], + ["error", LiteGraph.ACTION], + ]; + }; + + LiteGraph.registerNodeType("basic/console", Console); + + //Show value inside the debug console + function Alert() { + this.mode = LiteGraph.ON_EVENT; + this.addProperty("msg", ""); + this.addInput("", LiteGraph.EVENT); + var that = this; + this.widget = this.addWidget("text", "Text", "", "msg"); + this.widgets_up = true; + this.size = [200, 30]; + } + + Alert.title = "Alert"; + Alert.desc = "Show an alert window"; + Alert.color = "#510"; + + Alert.prototype.onConfigure = function (o) { + this.widget.value = o.properties.msg; + }; + + Alert.prototype.onAction = function (action, param) { + var msg = this.properties.msg; + setTimeout(function () { + alert(msg); + }, 10); + }; + + LiteGraph.registerNodeType("basic/alert", Alert); + + //Execites simple code + function NodeScript() { + this.size = [60, 30]; + this.addProperty("onExecute", "return A;"); + this.addInput("A", 0); + this.addInput("B", 0); + this.addOutput("out", 0); + + this._func = null; + this.data = {}; + } + + NodeScript.prototype.onConfigure = function (o) { + if (o.properties.onExecute && LiteGraph.allow_scripts) + this.compileCode(o.properties.onExecute); + else console.warn("Script not compiled, LiteGraph.allow_scripts is false"); + }; + + NodeScript.title = "Script"; + NodeScript.desc = "executes a code (max 256 characters)"; + + NodeScript.widgets_info = { + onExecute: { type: "code" }, + }; + + NodeScript.prototype.onPropertyChanged = function (name, value) { + if (name == "onExecute" && LiteGraph.allow_scripts) this.compileCode(value); + else console.warn("Script not compiled, LiteGraph.allow_scripts is false"); + }; + + NodeScript.prototype.compileCode = function (code) { + this._func = null; + if (code.length > 256) { + console.warn("Script too long, max 256 chars"); + } else { + var code_low = code.toLowerCase(); + var forbidden_words = [ + "script", + "body", + "document", + "eval", + "nodescript", + "function", + ]; //bad security solution + for (var i = 0; i < forbidden_words.length; ++i) { + if (code_low.indexOf(forbidden_words[i]) != -1) { + console.warn("invalid script"); + return; + } + } + try { + this._func = new Function("A", "B", "C", "DATA", "node", code); + } catch (err) { + console.error("Error parsing script"); + console.error(err); + } + } + }; + + NodeScript.prototype.onExecute = function () { + if (!this._func) { + return; + } + + try { + var A = this.getInputData(0); + var B = this.getInputData(1); + var C = this.getInputData(2); + this.setOutputData(0, this._func(A, B, C, this.data, this)); + } catch (err) { + console.error("Error in script"); + console.error(err); + } + }; + + NodeScript.prototype.onGetOutputs = function () { + return [["C", ""]]; + }; + + LiteGraph.registerNodeType("basic/script", NodeScript); + + function GenericCompare() { + this.addInput("A", 0); + this.addInput("B", 0); + this.addOutput("true", "boolean"); + this.addOutput("false", "boolean"); + this.addProperty("A", 1); + this.addProperty("B", 1); + this.addProperty("OP", "==", "enum", { values: GenericCompare.values }); + this.addWidget("combo", "Op.", this.properties.OP, { + property: "OP", + values: GenericCompare.values, + }); + + this.size = [80, 60]; + } + + GenericCompare.values = ["==", "!="]; //[">", "<", "==", "!=", "<=", ">=", "||", "&&" ]; + GenericCompare["@OP"] = { + type: "enum", + title: "operation", + values: GenericCompare.values, + }; + + GenericCompare.title = "Compare *"; + GenericCompare.desc = "evaluates condition between A and B"; + + GenericCompare.prototype.getTitle = function () { + return "*A " + this.properties.OP + " *B"; + }; + + GenericCompare.prototype.onExecute = function () { + var A = this.getInputData(0); + if (A === undefined) { + A = this.properties.A; + } else { + this.properties.A = A; + } + + var B = this.getInputData(1); + if (B === undefined) { + B = this.properties.B; + } else { + this.properties.B = B; + } + + var result = false; + if (typeof A == typeof B) { + switch (this.properties.OP) { + case "==": + case "!=": + // traverse both objects.. consider that this is not a true deep check! consider underscore or other library for thath :: _isEqual() + result = true; + switch (typeof A) { + case "object": + var aProps = Object.getOwnPropertyNames(A); + var bProps = Object.getOwnPropertyNames(B); + if (aProps.length != bProps.length) { + result = false; + break; + } + for (var i = 0; i < aProps.length; i++) { + var propName = aProps[i]; + if (A[propName] !== B[propName]) { + result = false; + break; + } + } + break; + default: + result = A == B; + } + if (this.properties.OP == "!=") result = !result; + break; + /*case ">": + result = A > B; + break; + case "<": + result = A < B; + break; + case "<=": + result = A <= B; + break; + case ">=": + result = A >= B; + break; + case "||": + result = A || B; + break; + case "&&": + result = A && B; + break;*/ + } + } + this.setOutputData(0, result); + this.setOutputData(1, !result); + }; + + LiteGraph.registerNodeType("basic/CompareValues", GenericCompare); +})(this); + +//event related nodes +(function (global) { + var LiteGraph = global.LiteGraph; + + //Show value inside the debug console + function LogEvent() { + this.size = [60, 30]; + this.addInput("event", LiteGraph.ACTION); + } + + LogEvent.title = "Log Event"; + LogEvent.desc = "Log event in console"; + + LogEvent.prototype.onAction = function (action, param, options) { + console.log(action, param); + }; + + LiteGraph.registerNodeType("events/log", LogEvent); + + //convert to Event if the value is true + function TriggerEvent() { + this.size = [60, 30]; + this.addInput("if", ""); + this.addOutput("true", LiteGraph.EVENT); + this.addOutput("change", LiteGraph.EVENT); + this.addOutput("false", LiteGraph.EVENT); + this.properties = { only_on_change: true }; + this.prev = 0; + } + + TriggerEvent.title = "TriggerEvent"; + TriggerEvent.desc = "Triggers event if input evaluates to true"; + + TriggerEvent.prototype.onExecute = function (param, options) { + var v = this.getInputData(0); + var changed = v != this.prev; + if (this.prev === 0) changed = false; + var must_resend = + (changed && this.properties.only_on_change) || + (!changed && !this.properties.only_on_change); + if (v && must_resend) this.triggerSlot(0, param, null, options); + if (!v && must_resend) this.triggerSlot(2, param, null, options); + if (changed) this.triggerSlot(1, param, null, options); + this.prev = v; + }; + + LiteGraph.registerNodeType("events/trigger", TriggerEvent); + + //Sequence of events + function Sequence() { + var that = this; + this.addInput("", LiteGraph.ACTION); + this.addInput("", LiteGraph.ACTION); + this.addInput("", LiteGraph.ACTION); + this.addOutput("", LiteGraph.EVENT); + this.addOutput("", LiteGraph.EVENT); + this.addOutput("", LiteGraph.EVENT); + this.addWidget("button", "+", null, function () { + that.addInput("", LiteGraph.ACTION); + that.addOutput("", LiteGraph.EVENT); + }); + this.size = [90, 70]; + this.flags = { horizontal: true, render_box: false }; + } + + Sequence.title = "Sequence"; + Sequence.desc = "Triggers a sequence of events when an event arrives"; + + Sequence.prototype.getTitle = function () { + return ""; + }; + + Sequence.prototype.onAction = function (action, param, options) { + if (this.outputs) { + options = options || {}; + for (var i = 0; i < this.outputs.length; ++i) { + var output = this.outputs[i]; + //needs more info about this... + if (options.action_call) + // CREATE A NEW ID FOR THE ACTION + options.action_call = options.action_call + "_seq_" + i; + else + options.action_call = + this.id + + "_" + + (action ? action : "action") + + "_seq_" + + i + + "_" + + Math.floor(Math.random() * 9999); + this.triggerSlot(i, param, null, options); + } + } + }; + + LiteGraph.registerNodeType("events/sequence", Sequence); + + //Sequence of events + function WaitAll() { + var that = this; + this.addInput("", LiteGraph.ACTION); + this.addInput("", LiteGraph.ACTION); + this.addOutput("", LiteGraph.EVENT); + this.addWidget("button", "+", null, function () { + that.addInput("", LiteGraph.ACTION); + that.size[0] = 90; + }); + this.size = [90, 70]; + this.ready = []; + } + + WaitAll.title = "WaitAll"; + WaitAll.desc = "Wait until all input events arrive then triggers output"; + + WaitAll.prototype.getTitle = function () { + return ""; + }; + + WaitAll.prototype.onDrawBackground = function (ctx) { + if (this.flags.collapsed) { + return; + } + for (var i = 0; i < this.inputs.length; ++i) { + var y = i * LiteGraph.NODE_SLOT_HEIGHT + 10; + ctx.fillStyle = this.ready[i] ? "#AFB" : "#000"; + ctx.fillRect(20, y, 10, 10); + } + }; + + WaitAll.prototype.onAction = function (action, param, options, slot_index) { + if (slot_index == null) return; + + //check all + this.ready.length = this.outputs.length; + this.ready[slot_index] = true; + for (var i = 0; i < this.ready.length; ++i) if (!this.ready[i]) return; + //pass + this.reset(); + this.triggerSlot(0); + }; + + WaitAll.prototype.reset = function () { + this.ready.length = 0; + }; + + LiteGraph.registerNodeType("events/waitAll", WaitAll); + + //Sequencer for events + function Stepper() { + var that = this; + this.properties = { index: 0 }; + this.addInput("index", "number"); + this.addInput("step", LiteGraph.ACTION); + this.addInput("reset", LiteGraph.ACTION); + this.addOutput("index", "number"); + this.addOutput("", LiteGraph.EVENT); + this.addOutput("", LiteGraph.EVENT); + this.addOutput("", LiteGraph.EVENT, { removable: true }); + this.addWidget("button", "+", null, function () { + that.addOutput("", LiteGraph.EVENT, { removable: true }); + }); + this.size = [120, 120]; + this.flags = { render_box: false }; + } + + Stepper.title = "Stepper"; + Stepper.desc = "Trigger events sequentially when an tick arrives"; + + Stepper.prototype.onDrawBackground = function (ctx) { + if (this.flags.collapsed) { + return; + } + var index = this.properties.index || 0; + ctx.fillStyle = "#AFB"; + var w = this.size[0]; + var y = (index + 1) * LiteGraph.NODE_SLOT_HEIGHT + 4; + ctx.beginPath(); + ctx.moveTo(w - 30, y); + ctx.lineTo(w - 30, y + LiteGraph.NODE_SLOT_HEIGHT); + ctx.lineTo(w - 15, y + LiteGraph.NODE_SLOT_HEIGHT * 0.5); + ctx.fill(); + }; + + Stepper.prototype.onExecute = function () { + var index = this.getInputData(0); + if (index != null) { + index = Math.floor(index); + index = clamp(index, 0, this.outputs ? this.outputs.length - 2 : 0); + if (index != this.properties.index) { + this.properties.index = index; + this.triggerSlot(index + 1); + } + } + + this.setOutputData(0, this.properties.index); + }; + + Stepper.prototype.onAction = function (action, param) { + if (action == "reset") this.properties.index = 0; + else if (action == "step") { + this.triggerSlot(this.properties.index + 1, param); + var n = this.outputs ? this.outputs.length - 1 : 0; + this.properties.index = (this.properties.index + 1) % n; + } + }; + + LiteGraph.registerNodeType("events/stepper", Stepper); + + //Filter events + function FilterEvent() { + this.size = [60, 30]; + this.addInput("event", LiteGraph.ACTION); + this.addOutput("event", LiteGraph.EVENT); + this.properties = { + equal_to: "", + has_property: "", + property_equal_to: "", + }; + } + + FilterEvent.title = "Filter Event"; + FilterEvent.desc = "Blocks events that do not match the filter"; + + FilterEvent.prototype.onAction = function (action, param, options) { + if (param == null) { + return; + } + + if (this.properties.equal_to && this.properties.equal_to != param) { + return; + } + + if (this.properties.has_property) { + var prop = param[this.properties.has_property]; + if (prop == null) { + return; + } + + if ( + this.properties.property_equal_to && + this.properties.property_equal_to != prop + ) { + return; + } + } + + this.triggerSlot(0, param, null, options); + }; + + LiteGraph.registerNodeType("events/filter", FilterEvent); + + function EventBranch() { + this.addInput("in", LiteGraph.ACTION); + this.addInput("cond", "boolean"); + this.addOutput("true", LiteGraph.EVENT); + this.addOutput("false", LiteGraph.EVENT); + this.size = [120, 60]; + this._value = false; + } + + EventBranch.title = "Branch"; + EventBranch.desc = + "If condition is true, outputs triggers true, otherwise false"; + + EventBranch.prototype.onExecute = function () { + this._value = this.getInputData(1); + }; + + EventBranch.prototype.onAction = function (action, param, options) { + this._value = this.getInputData(1); + this.triggerSlot(this._value ? 0 : 1, param, null, options); + }; + + LiteGraph.registerNodeType("events/branch", EventBranch); + + //Show value inside the debug console + function EventCounter() { + this.addInput("inc", LiteGraph.ACTION); + this.addInput("dec", LiteGraph.ACTION); + this.addInput("reset", LiteGraph.ACTION); + this.addOutput("change", LiteGraph.EVENT); + this.addOutput("num", "number"); + this.addProperty("doCountExecution", false, "boolean", { + name: "Count Executions", + }); + this.addWidget( + "toggle", + "Count Exec.", + this.properties.doCountExecution, + "doCountExecution", + ); + this.num = 0; + } + + EventCounter.title = "Counter"; + EventCounter.desc = "Counts events"; + + EventCounter.prototype.getTitle = function () { + if (this.flags.collapsed) { + return String(this.num); + } + return this.title; + }; + + EventCounter.prototype.onAction = function (action, param, options) { + var v = this.num; + if (action == "inc") { + this.num += 1; + } else if (action == "dec") { + this.num -= 1; + } else if (action == "reset") { + this.num = 0; + } + if (this.num != v) { + this.trigger("change", this.num); + } + }; + + EventCounter.prototype.onDrawBackground = function (ctx) { + if (this.flags.collapsed) { + return; + } + ctx.fillStyle = "#AAA"; + ctx.font = "20px Arial"; + ctx.textAlign = "center"; + ctx.fillText(this.num, this.size[0] * 0.5, this.size[1] * 0.5); + }; + + EventCounter.prototype.onExecute = function () { + if (this.properties.doCountExecution) { + this.num += 1; + } + this.setOutputData(1, this.num); + }; + + LiteGraph.registerNodeType("events/counter", EventCounter); + + //Show value inside the debug console + function DelayEvent() { + this.size = [60, 30]; + this.addProperty("time_in_ms", 1000); + this.addInput("event", LiteGraph.ACTION); + this.addOutput("on_time", LiteGraph.EVENT); + + this._pending = []; + } + + DelayEvent.title = "Delay"; + DelayEvent.desc = "Delays one event"; + + DelayEvent.prototype.onAction = function (action, param, options) { + var time = this.properties.time_in_ms; + if (time <= 0) { + this.trigger(null, param, options); + } else { + this._pending.push([time, param]); + } + }; + + DelayEvent.prototype.onExecute = function (param, options) { + var dt = this.graph.elapsed_time * 1000; //in ms + + if (this.isInputConnected(1)) { + this.properties.time_in_ms = this.getInputData(1); + } + + for (var i = 0; i < this._pending.length; ++i) { + var actionPass = this._pending[i]; + actionPass[0] -= dt; + if (actionPass[0] > 0) { + continue; + } + + //remove + this._pending.splice(i, 1); + --i; + + //trigger + this.trigger(null, actionPass[1], options); + } + }; + + DelayEvent.prototype.onGetInputs = function () { + return [ + ["event", LiteGraph.ACTION], + ["time_in_ms", "number"], + ]; + }; + + LiteGraph.registerNodeType("events/delay", DelayEvent); + + //Show value inside the debug console + function TimerEvent() { + this.addProperty("interval", 1000); + this.addProperty("event", "tick"); + this.addOutput("on_tick", LiteGraph.EVENT); + this.time = 0; + this.last_interval = 1000; + this.triggered = false; + } + + TimerEvent.title = "Timer"; + TimerEvent.desc = "Sends an event every N milliseconds"; + + TimerEvent.prototype.onStart = function () { + this.time = 0; + }; + + TimerEvent.prototype.getTitle = function () { + return "Timer: " + this.last_interval.toString() + "ms"; + }; + + TimerEvent.on_color = "#AAA"; + TimerEvent.off_color = "#222"; + + TimerEvent.prototype.onDrawBackground = function () { + this.boxcolor = this.triggered ? TimerEvent.on_color : TimerEvent.off_color; + this.triggered = false; + }; + + TimerEvent.prototype.onExecute = function () { + var dt = this.graph.elapsed_time * 1000; //in ms + + var trigger = this.time == 0; + + this.time += dt; + this.last_interval = Math.max(1, this.getInputOrProperty("interval") | 0); + + if ( + !trigger && + (this.time < this.last_interval || isNaN(this.last_interval)) + ) { + if (this.inputs && this.inputs.length > 1 && this.inputs[1]) { + this.setOutputData(1, false); + } + return; + } + + this.triggered = true; + this.time = this.time % this.last_interval; + this.trigger("on_tick", this.properties.event); + if (this.inputs && this.inputs.length > 1 && this.inputs[1]) { + this.setOutputData(1, true); + } + }; + + TimerEvent.prototype.onGetInputs = function () { + return [["interval", "number"]]; + }; + + TimerEvent.prototype.onGetOutputs = function () { + return [["tick", "boolean"]]; + }; + + LiteGraph.registerNodeType("events/timer", TimerEvent); + + function SemaphoreEvent() { + this.addInput("go", LiteGraph.ACTION); + this.addInput("green", LiteGraph.ACTION); + this.addInput("red", LiteGraph.ACTION); + this.addOutput("continue", LiteGraph.EVENT); + this.addOutput("blocked", LiteGraph.EVENT); + this.addOutput("is_green", "boolean"); + this._ready = false; + this.properties = {}; + var that = this; + this.addWidget("button", "reset", "", function () { + that._ready = false; + }); + } + + SemaphoreEvent.title = "Semaphore Event"; + SemaphoreEvent.desc = + "Until both events are not triggered, it doesnt continue."; + + SemaphoreEvent.prototype.onExecute = function () { + this.setOutputData(1, this._ready); + this.boxcolor = this._ready ? "#9F9" : "#FA5"; + }; + + SemaphoreEvent.prototype.onAction = function (action, param) { + if (action == "go") this.triggerSlot(this._ready ? 0 : 1); + else if (action == "green") this._ready = true; + else if (action == "red") this._ready = false; + }; + + LiteGraph.registerNodeType("events/semaphore", SemaphoreEvent); + + function OnceEvent() { + this.addInput("in", LiteGraph.ACTION); + this.addInput("reset", LiteGraph.ACTION); + this.addOutput("out", LiteGraph.EVENT); + this._once = false; + this.properties = {}; + var that = this; + this.addWidget("button", "reset", "", function () { + that._once = false; + }); + } + + OnceEvent.title = "Once"; + OnceEvent.desc = "Only passes an event once, then gets locked"; + + OnceEvent.prototype.onAction = function (action, param) { + if (action == "in" && !this._once) { + this._once = true; + this.triggerSlot(0, param); + } else if (action == "reset") this._once = false; + }; + + LiteGraph.registerNodeType("events/once", OnceEvent); + + function DataStore() { + this.addInput("data", 0); + this.addInput("assign", LiteGraph.ACTION); + this.addOutput("data", 0); + this._last_value = null; + this.properties = { data: null, serialize: true }; + var that = this; + this.addWidget("button", "store", "", function () { + that.properties.data = that._last_value; + }); + } + + DataStore.title = "Data Store"; + DataStore.desc = "Stores data and only changes when event is received"; + + DataStore.prototype.onExecute = function () { + this._last_value = this.getInputData(0); + this.setOutputData(0, this.properties.data); + }; + + DataStore.prototype.onAction = function (action, param, options) { + this.properties.data = this._last_value; + }; + + DataStore.prototype.onSerialize = function (o) { + if (o.data == null) return; + if ( + this.properties.serialize == false || + (o.data.constructor !== String && + o.data.constructor !== Number && + o.data.constructor !== Boolean && + o.data.constructor !== Array && + o.data.constructor !== Object) + ) + o.data = null; + }; + + LiteGraph.registerNodeType("basic/data_store", DataStore); +})(this); + +//widgets +(function (global) { + var LiteGraph = global.LiteGraph; + + /* Button ****************/ + + function WidgetButton() { + this.addOutput("", LiteGraph.EVENT); + this.addOutput("", "boolean"); + this.addProperty("text", "click me"); + this.addProperty("font_size", 30); + this.addProperty("message", ""); + this.size = [164, 84]; + this.clicked = false; + } + + WidgetButton.title = "Button"; + WidgetButton.desc = "Triggers an event"; + + WidgetButton.font = "Arial"; + WidgetButton.prototype.onDrawForeground = function (ctx) { + if (this.flags.collapsed) { + return; + } + var margin = 10; + ctx.fillStyle = "black"; + ctx.fillRect( + margin + 1, + margin + 1, + this.size[0] - margin * 2, + this.size[1] - margin * 2, + ); + ctx.fillStyle = "#AAF"; + ctx.fillRect( + margin - 1, + margin - 1, + this.size[0] - margin * 2, + this.size[1] - margin * 2, + ); + ctx.fillStyle = this.clicked ? "white" : this.mouseOver ? "#668" : "#334"; + ctx.fillRect( + margin, + margin, + this.size[0] - margin * 2, + this.size[1] - margin * 2, + ); + + if (this.properties.text || this.properties.text === 0) { + var font_size = this.properties.font_size || 30; + ctx.textAlign = "center"; + ctx.fillStyle = this.clicked ? "black" : "white"; + ctx.font = font_size + "px " + WidgetButton.font; + ctx.fillText( + this.properties.text, + this.size[0] * 0.5, + this.size[1] * 0.5 + font_size * 0.3, + ); + ctx.textAlign = "left"; + } + }; + + WidgetButton.prototype.onMouseDown = function (e, local_pos) { + if ( + local_pos[0] > 1 && + local_pos[1] > 1 && + local_pos[0] < this.size[0] - 2 && + local_pos[1] < this.size[1] - 2 + ) { + this.clicked = true; + this.setOutputData(1, this.clicked); + this.triggerSlot(0, this.properties.message); + return true; + } + }; + + WidgetButton.prototype.onExecute = function () { + this.setOutputData(1, this.clicked); + }; + + WidgetButton.prototype.onMouseUp = function (e) { + this.clicked = false; + }; + + LiteGraph.registerNodeType("widget/button", WidgetButton); + + function WidgetToggle() { + this.addInput("", "boolean"); + this.addInput("e", LiteGraph.ACTION); + this.addOutput("v", "boolean"); + this.addOutput("e", LiteGraph.EVENT); + this.properties = { font: "", value: false }; + this.size = [160, 44]; + } + + WidgetToggle.title = "Toggle"; + WidgetToggle.desc = "Toggles between true or false"; + + WidgetToggle.prototype.onDrawForeground = function (ctx) { + if (this.flags.collapsed) { + return; + } + + var size = this.size[1] * 0.5; + var margin = 0.25; + var h = this.size[1] * 0.8; + ctx.font = this.properties.font || (size * 0.8).toFixed(0) + "px Arial"; + var w = ctx.measureText(this.title).width; + var x = (this.size[0] - (w + size)) * 0.5; + + ctx.fillStyle = "#AAA"; + ctx.fillRect(x, h - size, size, size); + + ctx.fillStyle = this.properties.value ? "#AEF" : "#000"; + ctx.fillRect( + x + size * margin, + h - size + size * margin, + size * (1 - margin * 2), + size * (1 - margin * 2), + ); + + ctx.textAlign = "left"; + ctx.fillStyle = "#AAA"; + ctx.fillText(this.title, size * 1.2 + x, h * 0.85); + ctx.textAlign = "left"; + }; + + WidgetToggle.prototype.onAction = function (action) { + this.properties.value = !this.properties.value; + this.trigger("e", this.properties.value); + }; + + WidgetToggle.prototype.onExecute = function () { + var v = this.getInputData(0); + if (v != null) { + this.properties.value = v; + } + this.setOutputData(0, this.properties.value); + }; + + WidgetToggle.prototype.onMouseDown = function (e, local_pos) { + if ( + local_pos[0] > 1 && + local_pos[1] > 1 && + local_pos[0] < this.size[0] - 2 && + local_pos[1] < this.size[1] - 2 + ) { + this.properties.value = !this.properties.value; + this.graph._version++; + this.trigger("e", this.properties.value); + return true; + } + }; + + LiteGraph.registerNodeType("widget/toggle", WidgetToggle); + + /* Number ****************/ + + function WidgetNumber() { + this.addOutput("", "number"); + this.size = [80, 60]; + this.properties = { min: -1000, max: 1000, value: 1, step: 1 }; + this.old_y = -1; + this._remainder = 0; + this._precision = 0; + this.mouse_captured = false; + } + + WidgetNumber.title = "Number"; + WidgetNumber.desc = "Widget to select number value"; + + WidgetNumber.pixels_threshold = 10; + WidgetNumber.markers_color = "#666"; + + WidgetNumber.prototype.onDrawForeground = function (ctx) { + var x = this.size[0] * 0.5; + var h = this.size[1]; + if (h > 30) { + ctx.fillStyle = WidgetNumber.markers_color; + ctx.beginPath(); + ctx.moveTo(x, h * 0.1); + ctx.lineTo(x + h * 0.1, h * 0.2); + ctx.lineTo(x + h * -0.1, h * 0.2); + ctx.fill(); + ctx.beginPath(); + ctx.moveTo(x, h * 0.9); + ctx.lineTo(x + h * 0.1, h * 0.8); + ctx.lineTo(x + h * -0.1, h * 0.8); + ctx.fill(); + ctx.font = (h * 0.7).toFixed(1) + "px Arial"; + } else { + ctx.font = (h * 0.8).toFixed(1) + "px Arial"; + } + + ctx.textAlign = "center"; + ctx.font = (h * 0.7).toFixed(1) + "px Arial"; + ctx.fillStyle = "#EEE"; + ctx.fillText(this.properties.value.toFixed(this._precision), x, h * 0.75); + }; + + WidgetNumber.prototype.onExecute = function () { + this.setOutputData(0, this.properties.value); + }; + + WidgetNumber.prototype.onPropertyChanged = function (name, value) { + var t = (this.properties.step + "").split("."); + this._precision = t.length > 1 ? t[1].length : 0; + }; + + WidgetNumber.prototype.onMouseDown = function (e, pos) { + if (pos[1] < 0) { + return; + } + + this.old_y = e.canvasY; + this.captureInput(true); + this.mouse_captured = true; + + return true; + }; + + WidgetNumber.prototype.onMouseMove = function (e) { + if (!this.mouse_captured) { + return; + } + + var delta = this.old_y - e.canvasY; + if (e.shiftKey) { + delta *= 10; + } + if (e.metaKey || e.altKey) { + delta *= 0.1; + } + this.old_y = e.canvasY; + + var steps = this._remainder + delta / WidgetNumber.pixels_threshold; + this._remainder = steps % 1; + steps = steps | 0; + + var v = clamp( + this.properties.value + steps * this.properties.step, + this.properties.min, + this.properties.max, + ); + this.properties.value = v; + this.graph._version++; + this.setDirtyCanvas(true); + }; + + WidgetNumber.prototype.onMouseUp = function (e, pos) { + if (e.click_time < 200) { + var steps = pos[1] > this.size[1] * 0.5 ? -1 : 1; + this.properties.value = clamp( + this.properties.value + steps * this.properties.step, + this.properties.min, + this.properties.max, + ); + this.graph._version++; + this.setDirtyCanvas(true); + } + + if (this.mouse_captured) { + this.mouse_captured = false; + this.captureInput(false); + } + }; + + LiteGraph.registerNodeType("widget/number", WidgetNumber); + + /* Combo ****************/ + + function WidgetCombo() { + this.addOutput("", "string"); + this.addOutput("change", LiteGraph.EVENT); + this.size = [80, 60]; + this.properties = { value: "A", values: "A;B;C" }; + this.old_y = -1; + this.mouse_captured = false; + this._values = this.properties.values.split(";"); + var that = this; + this.widgets_up = true; + this.widget = this.addWidget( + "combo", + "", + this.properties.value, + function (v) { + that.properties.value = v; + that.triggerSlot(1, v); + }, + { property: "value", values: this._values }, + ); + } + + WidgetCombo.title = "Combo"; + WidgetCombo.desc = "Widget to select from a list"; + + WidgetCombo.prototype.onExecute = function () { + this.setOutputData(0, this.properties.value); + }; + + WidgetCombo.prototype.onPropertyChanged = function (name, value) { + if (name == "values") { + this._values = value.split(";"); + this.widget.options.values = this._values; + } else if (name == "value") { + this.widget.value = value; + } + }; + + LiteGraph.registerNodeType("widget/combo", WidgetCombo); + + /* Knob ****************/ + + function WidgetKnob() { + this.addOutput("", "number"); + this.size = [64, 84]; + this.properties = { + min: 0, + max: 1, + value: 0.5, + color: "#7AF", + precision: 2, + }; + this.value = -1; + } + + WidgetKnob.title = "Knob"; + WidgetKnob.desc = "Circular controller"; + WidgetKnob.size = [80, 100]; + + WidgetKnob.prototype.onDrawForeground = function (ctx) { + if (this.flags.collapsed) { + return; + } + + if (this.value == -1) { + this.value = + (this.properties.value - this.properties.min) / + (this.properties.max - this.properties.min); + } + + var center_x = this.size[0] * 0.5; + var center_y = this.size[1] * 0.5; + var radius = Math.min(this.size[0], this.size[1]) * 0.5 - 5; + var w = Math.floor(radius * 0.05); + + ctx.globalAlpha = 1; + ctx.save(); + ctx.translate(center_x, center_y); + ctx.rotate(Math.PI * 0.75); + + //bg + ctx.fillStyle = "rgba(0,0,0,0.5)"; + ctx.beginPath(); + ctx.moveTo(0, 0); + ctx.arc(0, 0, radius, 0, Math.PI * 1.5); + ctx.fill(); + + //value + ctx.strokeStyle = "black"; + ctx.fillStyle = this.properties.color; + ctx.lineWidth = 2; + ctx.beginPath(); + ctx.moveTo(0, 0); + ctx.arc(0, 0, radius - 4, 0, Math.PI * 1.5 * Math.max(0.01, this.value)); + ctx.closePath(); + ctx.fill(); + //ctx.stroke(); + ctx.lineWidth = 1; + ctx.globalAlpha = 1; + ctx.restore(); + + //inner + ctx.fillStyle = "black"; + ctx.beginPath(); + ctx.arc(center_x, center_y, radius * 0.75, 0, Math.PI * 2, true); + ctx.fill(); + + //miniball + ctx.fillStyle = this.mouseOver ? "white" : this.properties.color; + ctx.beginPath(); + var angle = this.value * Math.PI * 1.5 + Math.PI * 0.75; + ctx.arc( + center_x + Math.cos(angle) * radius * 0.65, + center_y + Math.sin(angle) * radius * 0.65, + radius * 0.05, + 0, + Math.PI * 2, + true, + ); + ctx.fill(); + + //text + ctx.fillStyle = this.mouseOver ? "white" : "#AAA"; + ctx.font = Math.floor(radius * 0.5) + "px Arial"; + ctx.textAlign = "center"; + ctx.fillText( + this.properties.value.toFixed(this.properties.precision), + center_x, + center_y + radius * 0.15, + ); + }; + + WidgetKnob.prototype.onExecute = function () { + this.setOutputData(0, this.properties.value); + this.boxcolor = LiteGraph.colorToString([ + this.value, + this.value, + this.value, + ]); + }; + + WidgetKnob.prototype.onMouseDown = function (e) { + this.center = [this.size[0] * 0.5, this.size[1] * 0.5 + 20]; + this.radius = this.size[0] * 0.5; + if ( + e.canvasY - this.pos[1] < 20 || + LiteGraph.distance( + [e.canvasX, e.canvasY], + [this.pos[0] + this.center[0], this.pos[1] + this.center[1]], + ) > this.radius + ) { + return false; + } + this.oldmouse = [e.canvasX - this.pos[0], e.canvasY - this.pos[1]]; + this.captureInput(true); + return true; + }; + + WidgetKnob.prototype.onMouseMove = function (e) { + if (!this.oldmouse) { + return; + } + + var m = [e.canvasX - this.pos[0], e.canvasY - this.pos[1]]; + + var v = this.value; + v -= (m[1] - this.oldmouse[1]) * 0.01; + if (v > 1.0) { + v = 1.0; + } else if (v < 0.0) { + v = 0.0; + } + this.value = v; + this.properties.value = + this.properties.min + + (this.properties.max - this.properties.min) * this.value; + this.oldmouse = m; + this.setDirtyCanvas(true); + }; + + WidgetKnob.prototype.onMouseUp = function (e) { + if (this.oldmouse) { + this.oldmouse = null; + this.captureInput(false); + } + }; + + WidgetKnob.prototype.onPropertyChanged = function (name, value) { + if (name == "min" || name == "max" || name == "value") { + this.properties[name] = parseFloat(value); + return true; //block + } + }; + + LiteGraph.registerNodeType("widget/knob", WidgetKnob); + + //Show value inside the debug console + function WidgetSliderGUI() { + this.addOutput("", "number"); + this.properties = { + value: 0.5, + min: 0, + max: 1, + text: "V", + }; + var that = this; + this.size = [140, 40]; + this.slider = this.addWidget( + "slider", + "V", + this.properties.value, + function (v) { + that.properties.value = v; + }, + this.properties, + ); + this.widgets_up = true; + } + + WidgetSliderGUI.title = "Inner Slider"; + + WidgetSliderGUI.prototype.onPropertyChanged = function (name, value) { + if (name == "value") { + this.slider.value = value; + } + }; + + WidgetSliderGUI.prototype.onExecute = function () { + this.setOutputData(0, this.properties.value); + }; + + LiteGraph.registerNodeType("widget/internal_slider", WidgetSliderGUI); + + //Widget H SLIDER + function WidgetHSlider() { + this.size = [160, 26]; + this.addOutput("", "number"); + this.properties = { color: "#7AF", min: 0, max: 1, value: 0.5 }; + this.value = -1; + } + + WidgetHSlider.title = "H.Slider"; + WidgetHSlider.desc = "Linear slider controller"; + + WidgetHSlider.prototype.onDrawForeground = function (ctx) { + if (this.value == -1) { + this.value = + (this.properties.value - this.properties.min) / + (this.properties.max - this.properties.min); + } + + //border + ctx.globalAlpha = 1; + ctx.lineWidth = 1; + ctx.fillStyle = "#000"; + ctx.fillRect(2, 2, this.size[0] - 4, this.size[1] - 4); + + ctx.fillStyle = this.properties.color; + ctx.beginPath(); + ctx.rect(4, 4, (this.size[0] - 8) * this.value, this.size[1] - 8); + ctx.fill(); + }; + + WidgetHSlider.prototype.onExecute = function () { + this.properties.value = + this.properties.min + + (this.properties.max - this.properties.min) * this.value; + this.setOutputData(0, this.properties.value); + this.boxcolor = LiteGraph.colorToString([ + this.value, + this.value, + this.value, + ]); + }; + + WidgetHSlider.prototype.onMouseDown = function (e) { + if (e.canvasY - this.pos[1] < 0) { + return false; + } + + this.oldmouse = [e.canvasX - this.pos[0], e.canvasY - this.pos[1]]; + this.captureInput(true); + return true; + }; + + WidgetHSlider.prototype.onMouseMove = function (e) { + if (!this.oldmouse) { + return; + } + + var m = [e.canvasX - this.pos[0], e.canvasY - this.pos[1]]; + + var v = this.value; + var delta = m[0] - this.oldmouse[0]; + v += delta / this.size[0]; + if (v > 1.0) { + v = 1.0; + } else if (v < 0.0) { + v = 0.0; + } + + this.value = v; + + this.oldmouse = m; + this.setDirtyCanvas(true); + }; + + WidgetHSlider.prototype.onMouseUp = function (e) { + this.oldmouse = null; + this.captureInput(false); + }; + + WidgetHSlider.prototype.onMouseLeave = function (e) { + //this.oldmouse = null; + }; + + LiteGraph.registerNodeType("widget/hslider", WidgetHSlider); + + function WidgetProgress() { + this.size = [160, 26]; + this.addInput("", "number"); + this.properties = { min: 0, max: 1, value: 0, color: "#AAF" }; + } + + WidgetProgress.title = "Progress"; + WidgetProgress.desc = "Shows data in linear progress"; + + WidgetProgress.prototype.onExecute = function () { + var v = this.getInputData(0); + if (v != undefined) { + this.properties["value"] = v; + } + }; + + WidgetProgress.prototype.onDrawForeground = function (ctx) { + //border + ctx.lineWidth = 1; + ctx.fillStyle = this.properties.color; + var v = + (this.properties.value - this.properties.min) / + (this.properties.max - this.properties.min); + v = Math.min(1, v); + v = Math.max(0, v); + ctx.fillRect(2, 2, (this.size[0] - 4) * v, this.size[1] - 4); + }; + + LiteGraph.registerNodeType("widget/progress", WidgetProgress); + + function WidgetText() { + this.addInputs("", 0); + this.properties = { + value: "...", + font: "Arial", + fontsize: 18, + color: "#AAA", + align: "left", + glowSize: 0, + decimals: 1, + }; + } + + WidgetText.title = "Text"; + WidgetText.desc = "Shows the input value"; + WidgetText.widgets = [ + { name: "resize", text: "Resize box", type: "button" }, + { name: "led_text", text: "LED", type: "minibutton" }, + { name: "normal_text", text: "Normal", type: "minibutton" }, + ]; + + WidgetText.prototype.onDrawForeground = function (ctx) { + //ctx.fillStyle="#000"; + //ctx.fillRect(0,0,100,60); + ctx.fillStyle = this.properties["color"]; + var v = this.properties["value"]; + + if (this.properties["glowSize"]) { + ctx.shadowColor = this.properties.color; + ctx.shadowOffsetX = 0; + ctx.shadowOffsetY = 0; + ctx.shadowBlur = this.properties["glowSize"]; + } else { + ctx.shadowColor = "transparent"; + } + + var fontsize = this.properties["fontsize"]; + + ctx.textAlign = this.properties["align"]; + ctx.font = fontsize.toString() + "px " + this.properties["font"]; + this.str = + typeof v == "number" ? v.toFixed(this.properties["decimals"]) : v; + + if (typeof this.str == "string") { + var lines = this.str.replace(/[\r\n]/g, "\\n").split("\\n"); + for (var i = 0; i < lines.length; i++) { + ctx.fillText( + lines[i], + this.properties["align"] == "left" ? 15 : this.size[0] - 15, + fontsize * -0.15 + fontsize * (parseInt(i) + 1), + ); + } + } + + ctx.shadowColor = "transparent"; + this.last_ctx = ctx; + ctx.textAlign = "left"; + }; + + WidgetText.prototype.onExecute = function () { + var v = this.getInputData(0); + if (v != null) { + this.properties["value"] = v; + } + //this.setDirtyCanvas(true); + }; + + WidgetText.prototype.resize = function () { + if (!this.last_ctx) { + return; + } + + var lines = this.str.split("\\n"); + this.last_ctx.font = + this.properties["fontsize"] + "px " + this.properties["font"]; + var max = 0; + for (var i = 0; i < lines.length; i++) { + var w = this.last_ctx.measureText(lines[i]).width; + if (max < w) { + max = w; + } + } + this.size[0] = max + 20; + this.size[1] = 4 + lines.length * this.properties["fontsize"]; + + this.setDirtyCanvas(true); + }; + + WidgetText.prototype.onPropertyChanged = function (name, value) { + this.properties[name] = value; + this.str = typeof value == "number" ? value.toFixed(3) : value; + //this.resize(); + return true; + }; + + LiteGraph.registerNodeType("widget/text", WidgetText); + + function WidgetPanel() { + this.size = [200, 100]; + this.properties = { + borderColor: "#ffffff", + bgcolorTop: "#f0f0f0", + bgcolorBottom: "#e0e0e0", + shadowSize: 2, + borderRadius: 3, + }; + } + + WidgetPanel.title = "Panel"; + WidgetPanel.desc = "Non interactive panel"; + WidgetPanel.widgets = [{ name: "update", text: "Update", type: "button" }]; + + WidgetPanel.prototype.createGradient = function (ctx) { + if ( + this.properties["bgcolorTop"] == "" || + this.properties["bgcolorBottom"] == "" + ) { + this.lineargradient = 0; + return; + } + + this.lineargradient = ctx.createLinearGradient(0, 0, 0, this.size[1]); + this.lineargradient.addColorStop(0, this.properties["bgcolorTop"]); + this.lineargradient.addColorStop(1, this.properties["bgcolorBottom"]); + }; + + WidgetPanel.prototype.onDrawForeground = function (ctx) { + if (this.flags.collapsed) { + return; + } + + if (this.lineargradient == null) { + this.createGradient(ctx); + } + + if (!this.lineargradient) { + return; + } + + ctx.lineWidth = 1; + ctx.strokeStyle = this.properties["borderColor"]; + //ctx.fillStyle = "#ebebeb"; + ctx.fillStyle = this.lineargradient; + + if (this.properties["shadowSize"]) { + ctx.shadowColor = "#000"; + ctx.shadowOffsetX = 0; + ctx.shadowOffsetY = 0; + ctx.shadowBlur = this.properties["shadowSize"]; + } else { + ctx.shadowColor = "transparent"; + } + + ctx.roundRect( + 0, + 0, + this.size[0] - 1, + this.size[1] - 1, + this.properties["shadowSize"], + ); + ctx.fill(); + ctx.shadowColor = "transparent"; + ctx.stroke(); + }; + + LiteGraph.registerNodeType("widget/panel", WidgetPanel); +})(this); + +(function (global) { + var LiteGraph = global.LiteGraph; + + function GamepadInput() { + this.addOutput("left_x_axis", "number"); + this.addOutput("left_y_axis", "number"); + this.addOutput("button_pressed", LiteGraph.EVENT); + this.properties = { gamepad_index: 0, threshold: 0.1 }; + + this._left_axis = new Float32Array(2); + this._right_axis = new Float32Array(2); + this._triggers = new Float32Array(2); + this._previous_buttons = new Uint8Array(17); + this._current_buttons = new Uint8Array(17); + } + + GamepadInput.title = "Gamepad"; + GamepadInput.desc = "gets the input of the gamepad"; + + GamepadInput.CENTER = 0; + GamepadInput.LEFT = 1; + GamepadInput.RIGHT = 2; + GamepadInput.UP = 4; + GamepadInput.DOWN = 8; + + GamepadInput.zero = new Float32Array(2); + GamepadInput.buttons = [ + "a", + "b", + "x", + "y", + "lb", + "rb", + "lt", + "rt", + "back", + "start", + "ls", + "rs", + "home", + ]; + + GamepadInput.prototype.onExecute = function () { + //get gamepad + var gamepad = this.getGamepad(); + var threshold = this.properties.threshold || 0.0; + + if (gamepad) { + this._left_axis[0] = + Math.abs(gamepad.xbox.axes["lx"]) > threshold + ? gamepad.xbox.axes["lx"] + : 0; + this._left_axis[1] = + Math.abs(gamepad.xbox.axes["ly"]) > threshold + ? gamepad.xbox.axes["ly"] + : 0; + this._right_axis[0] = + Math.abs(gamepad.xbox.axes["rx"]) > threshold + ? gamepad.xbox.axes["rx"] + : 0; + this._right_axis[1] = + Math.abs(gamepad.xbox.axes["ry"]) > threshold + ? gamepad.xbox.axes["ry"] + : 0; + this._triggers[0] = + Math.abs(gamepad.xbox.axes["ltrigger"]) > threshold + ? gamepad.xbox.axes["ltrigger"] + : 0; + this._triggers[1] = + Math.abs(gamepad.xbox.axes["rtrigger"]) > threshold + ? gamepad.xbox.axes["rtrigger"] + : 0; + } + + if (this.outputs) { + for (var i = 0; i < this.outputs.length; i++) { + var output = this.outputs[i]; + if (!output.links || !output.links.length) { + continue; + } + var v = null; + + if (gamepad) { + switch (output.name) { + case "left_axis": + v = this._left_axis; + break; + case "right_axis": + v = this._right_axis; + break; + case "left_x_axis": + v = this._left_axis[0]; + break; + case "left_y_axis": + v = this._left_axis[1]; + break; + case "right_x_axis": + v = this._right_axis[0]; + break; + case "right_y_axis": + v = this._right_axis[1]; + break; + case "trigger_left": + v = this._triggers[0]; + break; + case "trigger_right": + v = this._triggers[1]; + break; + case "a_button": + v = gamepad.xbox.buttons["a"] ? 1 : 0; + break; + case "b_button": + v = gamepad.xbox.buttons["b"] ? 1 : 0; + break; + case "x_button": + v = gamepad.xbox.buttons["x"] ? 1 : 0; + break; + case "y_button": + v = gamepad.xbox.buttons["y"] ? 1 : 0; + break; + case "lb_button": + v = gamepad.xbox.buttons["lb"] ? 1 : 0; + break; + case "rb_button": + v = gamepad.xbox.buttons["rb"] ? 1 : 0; + break; + case "ls_button": + v = gamepad.xbox.buttons["ls"] ? 1 : 0; + break; + case "rs_button": + v = gamepad.xbox.buttons["rs"] ? 1 : 0; + break; + case "hat_left": + v = gamepad.xbox.hatmap & GamepadInput.LEFT; + break; + case "hat_right": + v = gamepad.xbox.hatmap & GamepadInput.RIGHT; + break; + case "hat_up": + v = gamepad.xbox.hatmap & GamepadInput.UP; + break; + case "hat_down": + v = gamepad.xbox.hatmap & GamepadInput.DOWN; + break; + case "hat": + v = gamepad.xbox.hatmap; + break; + case "start_button": + v = gamepad.xbox.buttons["start"] ? 1 : 0; + break; + case "back_button": + v = gamepad.xbox.buttons["back"] ? 1 : 0; + break; + case "button_pressed": + for (var j = 0; j < this._current_buttons.length; ++j) { + if (this._current_buttons[j] && !this._previous_buttons[j]) { + this.triggerSlot(i, GamepadInput.buttons[j]); + } + } + break; + default: + break; + } + } else { + //if no gamepad is connected, output 0 + switch (output.name) { + case "button_pressed": + break; + case "left_axis": + case "right_axis": + v = GamepadInput.zero; + break; + default: + v = 0; + } + } + this.setOutputData(i, v); + } + } + }; + + GamepadInput.mapping = { + a: 0, + b: 1, + x: 2, + y: 3, + lb: 4, + rb: 5, + lt: 6, + rt: 7, + back: 8, + start: 9, + ls: 10, + rs: 11, + }; + GamepadInput.mapping_array = [ + "a", + "b", + "x", + "y", + "lb", + "rb", + "lt", + "rt", + "back", + "start", + "ls", + "rs", + ]; + + GamepadInput.prototype.getGamepad = function () { + var getGamepads = + navigator.getGamepads || + navigator.webkitGetGamepads || + navigator.mozGetGamepads; + if (!getGamepads) { + return null; + } + var gamepads = getGamepads.call(navigator); + var gamepad = null; + + this._previous_buttons.set(this._current_buttons); + + //pick the first connected + for (var i = this.properties.gamepad_index; i < 4; i++) { + if (!gamepads[i]) { + continue; + } + gamepad = gamepads[i]; + + //xbox controller mapping + var xbox = this.xbox_mapping; + if (!xbox) { + xbox = this.xbox_mapping = { + axes: [], + buttons: {}, + hat: "", + hatmap: GamepadInput.CENTER, + }; + } + + xbox.axes["lx"] = gamepad.axes[0]; + xbox.axes["ly"] = gamepad.axes[1]; + xbox.axes["rx"] = gamepad.axes[2]; + xbox.axes["ry"] = gamepad.axes[3]; + xbox.axes["ltrigger"] = gamepad.buttons[6].value; + xbox.axes["rtrigger"] = gamepad.buttons[7].value; + xbox.hat = ""; + xbox.hatmap = GamepadInput.CENTER; + + for (var j = 0; j < gamepad.buttons.length; j++) { + this._current_buttons[j] = gamepad.buttons[j].pressed; + + if (j < 12) { + xbox.buttons[GamepadInput.mapping_array[j]] = + gamepad.buttons[j].pressed; + if (gamepad.buttons[j].was_pressed) + this.trigger(GamepadInput.mapping_array[j] + "_button_event"); + } //mapping of XBOX + else + switch ( + j //I use a switch to ensure that a player with another gamepad could play + ) { + case 12: + if (gamepad.buttons[j].pressed) { + xbox.hat += "up"; + xbox.hatmap |= GamepadInput.UP; + } + break; + case 13: + if (gamepad.buttons[j].pressed) { + xbox.hat += "down"; + xbox.hatmap |= GamepadInput.DOWN; + } + break; + case 14: + if (gamepad.buttons[j].pressed) { + xbox.hat += "left"; + xbox.hatmap |= GamepadInput.LEFT; + } + break; + case 15: + if (gamepad.buttons[j].pressed) { + xbox.hat += "right"; + xbox.hatmap |= GamepadInput.RIGHT; + } + break; + case 16: + xbox.buttons["home"] = gamepad.buttons[j].pressed; + break; + default: + } + } + gamepad.xbox = xbox; + return gamepad; + } + }; + + GamepadInput.prototype.onDrawBackground = function (ctx) { + if (this.flags.collapsed) { + return; + } + + //render gamepad state? + var la = this._left_axis; + var ra = this._right_axis; + ctx.strokeStyle = "#88A"; + ctx.strokeRect( + (la[0] + 1) * 0.5 * this.size[0] - 4, + (la[1] + 1) * 0.5 * this.size[1] - 4, + 8, + 8, + ); + ctx.strokeStyle = "#8A8"; + ctx.strokeRect( + (ra[0] + 1) * 0.5 * this.size[0] - 4, + (ra[1] + 1) * 0.5 * this.size[1] - 4, + 8, + 8, + ); + var h = this.size[1] / this._current_buttons.length; + ctx.fillStyle = "#AEB"; + for (var i = 0; i < this._current_buttons.length; ++i) { + if (this._current_buttons[i]) { + ctx.fillRect(0, h * i, 6, h); + } + } + }; + + GamepadInput.prototype.onGetOutputs = function () { + return [ + ["left_axis", "vec2"], + ["right_axis", "vec2"], + ["left_x_axis", "number"], + ["left_y_axis", "number"], + ["right_x_axis", "number"], + ["right_y_axis", "number"], + ["trigger_left", "number"], + ["trigger_right", "number"], + ["a_button", "number"], + ["b_button", "number"], + ["x_button", "number"], + ["y_button", "number"], + ["lb_button", "number"], + ["rb_button", "number"], + ["ls_button", "number"], + ["rs_button", "number"], + ["start_button", "number"], + ["back_button", "number"], + ["a_button_event", LiteGraph.EVENT], + ["b_button_event", LiteGraph.EVENT], + ["x_button_event", LiteGraph.EVENT], + ["y_button_event", LiteGraph.EVENT], + ["lb_button_event", LiteGraph.EVENT], + ["rb_button_event", LiteGraph.EVENT], + ["ls_button_event", LiteGraph.EVENT], + ["rs_button_event", LiteGraph.EVENT], + ["start_button_event", LiteGraph.EVENT], + ["back_button_event", LiteGraph.EVENT], + ["hat_left", "number"], + ["hat_right", "number"], + ["hat_up", "number"], + ["hat_down", "number"], + ["hat", "number"], + ["button_pressed", LiteGraph.EVENT], + ]; + }; + + LiteGraph.registerNodeType("input/gamepad", GamepadInput); +})(this); + +(function (global) { + var LiteGraph = global.LiteGraph; + + //Converter + function Converter() { + this.addInput("in", 0); + this.addOutput("out", 0); + this.size = [80, 30]; + } + + Converter.title = "Converter"; + Converter.desc = "type A to type B"; + + Converter.prototype.onExecute = function () { + var v = this.getInputData(0); + if (v == null) { + return; + } + + if (this.outputs) { + for (var i = 0; i < this.outputs.length; i++) { + var output = this.outputs[i]; + if (!output.links || !output.links.length) { + continue; + } + + var result = null; + switch (output.name) { + case "number": + result = v.length ? v[0] : parseFloat(v); + break; + case "vec2": + case "vec3": + case "vec4": + var result = null; + var count = 1; + switch (output.name) { + case "vec2": + count = 2; + break; + case "vec3": + count = 3; + break; + case "vec4": + count = 4; + break; + } + + var result = new Float32Array(count); + if (v.length) { + for (var j = 0; j < v.length && j < result.length; j++) { + result[j] = v[j]; + } + } else { + result[0] = parseFloat(v); + } + break; + } + this.setOutputData(i, result); + } + } + }; + + Converter.prototype.onGetOutputs = function () { + return [ + ["number", "number"], + ["vec2", "vec2"], + ["vec3", "vec3"], + ["vec4", "vec4"], + ]; + }; + + LiteGraph.registerNodeType("math/converter", Converter); + + //Bypass + function Bypass() { + this.addInput("in"); + this.addOutput("out"); + this.size = [80, 30]; + } + + Bypass.title = "Bypass"; + Bypass.desc = "removes the type"; + + Bypass.prototype.onExecute = function () { + var v = this.getInputData(0); + this.setOutputData(0, v); + }; + + LiteGraph.registerNodeType("math/bypass", Bypass); + + function ToNumber() { + this.addInput("in"); + this.addOutput("out"); + } + + ToNumber.title = "to Number"; + ToNumber.desc = "Cast to number"; + + ToNumber.prototype.onExecute = function () { + var v = this.getInputData(0); + this.setOutputData(0, Number(v)); + }; + + LiteGraph.registerNodeType("math/to_number", ToNumber); + + function MathRange() { + this.addInput("in", "number", { locked: true }); + this.addOutput("out", "number", { locked: true }); + this.addOutput("clamped", "number", { locked: true }); + + this.addProperty("in", 0); + this.addProperty("in_min", 0); + this.addProperty("in_max", 1); + this.addProperty("out_min", 0); + this.addProperty("out_max", 1); + + this.size = [120, 50]; + } + + MathRange.title = "Range"; + MathRange.desc = "Convert a number from one range to another"; + + MathRange.prototype.getTitle = function () { + if (this.flags.collapsed) { + return (this._last_v || 0).toFixed(2); + } + return this.title; + }; + + MathRange.prototype.onExecute = function () { + if (this.inputs) { + for (var i = 0; i < this.inputs.length; i++) { + var input = this.inputs[i]; + var v = this.getInputData(i); + if (v === undefined) { + continue; + } + this.properties[input.name] = v; + } + } + + var v = this.properties["in"]; + if (v === undefined || v === null || v.constructor !== Number) { + v = 0; + } + + var in_min = this.properties.in_min; + var in_max = this.properties.in_max; + var out_min = this.properties.out_min; + var out_max = this.properties.out_max; + /* + if( in_min > in_max ) + { + in_min = in_max; + in_max = this.properties.in_min; + } + if( out_min > out_max ) + { + out_min = out_max; + out_max = this.properties.out_min; + } + */ + + this._last_v = + ((v - in_min) / (in_max - in_min)) * (out_max - out_min) + out_min; + this.setOutputData(0, this._last_v); + this.setOutputData(1, clamp(this._last_v, out_min, out_max)); + }; + + MathRange.prototype.onDrawBackground = function (ctx) { + //show the current value + if (this._last_v) { + this.outputs[0].label = this._last_v.toFixed(3); + } else { + this.outputs[0].label = "?"; + } + }; + + MathRange.prototype.onGetInputs = function () { + return [ + ["in_min", "number"], + ["in_max", "number"], + ["out_min", "number"], + ["out_max", "number"], + ]; + }; + + LiteGraph.registerNodeType("math/range", MathRange); + + function MathRand() { + this.addOutput("value", "number"); + this.addProperty("min", 0); + this.addProperty("max", 1); + this.size = [80, 30]; + } + + MathRand.title = "Rand"; + MathRand.desc = "Random number"; + + MathRand.prototype.onExecute = function () { + if (this.inputs) { + for (var i = 0; i < this.inputs.length; i++) { + var input = this.inputs[i]; + var v = this.getInputData(i); + if (v === undefined) { + continue; + } + this.properties[input.name] = v; + } + } + + var min = this.properties.min; + var max = this.properties.max; + this._last_v = Math.random() * (max - min) + min; + this.setOutputData(0, this._last_v); + }; + + MathRand.prototype.onDrawBackground = function (ctx) { + //show the current value + this.outputs[0].label = (this._last_v || 0).toFixed(3); + }; + + MathRand.prototype.onGetInputs = function () { + return [ + ["min", "number"], + ["max", "number"], + ]; + }; + + LiteGraph.registerNodeType("math/rand", MathRand); + + //basic continuous noise + function MathNoise() { + this.addInput("in", "number"); + this.addOutput("out", "number"); + this.addProperty("min", 0); + this.addProperty("max", 1); + this.addProperty("smooth", true); + this.addProperty("seed", 0); + this.addProperty("octaves", 1); + this.addProperty("persistence", 0.8); + this.addProperty("speed", 1); + this.size = [90, 30]; + } + + MathNoise.title = "Noise"; + MathNoise.desc = "Random number with temporal continuity"; + MathNoise.data = null; + + MathNoise.getValue = function (f, smooth) { + if (!MathNoise.data) { + MathNoise.data = new Float32Array(1024); + for (var i = 0; i < MathNoise.data.length; ++i) { + MathNoise.data[i] = Math.random(); + } + } + f = f % 1024; + if (f < 0) { + f += 1024; + } + var f_min = Math.floor(f); + var f = f - f_min; + var r1 = MathNoise.data[f_min]; + var r2 = MathNoise.data[f_min == 1023 ? 0 : f_min + 1]; + if (smooth) { + f = f * f * f * (f * (f * 6.0 - 15.0) + 10.0); + } + return r1 * (1 - f) + r2 * f; + }; + + MathNoise.prototype.onExecute = function () { + var f = this.getInputData(0) || 0; + var iterations = this.properties.octaves || 1; + var r = 0; + var amp = 1; + var seed = this.properties.seed || 0; + f += seed; + var speed = this.properties.speed || 1; + var total_amp = 0; + for (var i = 0; i < iterations; ++i) { + r += + MathNoise.getValue(f * (1 + i) * speed, this.properties.smooth) * amp; + total_amp += amp; + amp *= this.properties.persistence; + if (amp < 0.001) break; + } + r /= total_amp; + var min = this.properties.min; + var max = this.properties.max; + this._last_v = r * (max - min) + min; + this.setOutputData(0, this._last_v); + }; + + MathNoise.prototype.onDrawBackground = function (ctx) { + //show the current value + this.outputs[0].label = (this._last_v || 0).toFixed(3); + }; + + LiteGraph.registerNodeType("math/noise", MathNoise); + + //generates spikes every random time + function MathSpikes() { + this.addOutput("out", "number"); + this.addProperty("min_time", 1); + this.addProperty("max_time", 2); + this.addProperty("duration", 0.2); + this.size = [90, 30]; + this._remaining_time = 0; + this._blink_time = 0; + } + + MathSpikes.title = "Spikes"; + MathSpikes.desc = "spike every random time"; + + MathSpikes.prototype.onExecute = function () { + var dt = this.graph.elapsed_time; //in secs + + this._remaining_time -= dt; + this._blink_time -= dt; + + var v = 0; + if (this._blink_time > 0) { + var f = this._blink_time / this.properties.duration; + v = 1 / (Math.pow(f * 8 - 4, 4) + 1); + } + + if (this._remaining_time < 0) { + this._remaining_time = + Math.random() * (this.properties.max_time - this.properties.min_time) + + this.properties.min_time; + this._blink_time = this.properties.duration; + this.boxcolor = "#FFF"; + } else { + this.boxcolor = "#000"; + } + this.setOutputData(0, v); + }; + + LiteGraph.registerNodeType("math/spikes", MathSpikes); + + //Math clamp + function MathClamp() { + this.addInput("in", "number"); + this.addOutput("out", "number"); + this.size = [80, 30]; + this.addProperty("min", 0); + this.addProperty("max", 1); + } + + MathClamp.title = "Clamp"; + MathClamp.desc = "Clamp number between min and max"; + //MathClamp.filter = "shader"; + + MathClamp.prototype.onExecute = function () { + var v = this.getInputData(0); + if (v == null) { + return; + } + v = Math.max(this.properties.min, v); + v = Math.min(this.properties.max, v); + this.setOutputData(0, v); + }; + + MathClamp.prototype.getCode = function (lang) { + var code = ""; + if (this.isInputConnected(0)) { + code += + "clamp({{0}}," + this.properties.min + "," + this.properties.max + ")"; + } + return code; + }; + + LiteGraph.registerNodeType("math/clamp", MathClamp); + + //Math ABS + function MathLerp() { + this.properties = { f: 0.5 }; + this.addInput("A", "number"); + this.addInput("B", "number"); + + this.addOutput("out", "number"); + } + + MathLerp.title = "Lerp"; + MathLerp.desc = "Linear Interpolation"; + + MathLerp.prototype.onExecute = function () { + var v1 = this.getInputData(0); + if (v1 == null) { + v1 = 0; + } + var v2 = this.getInputData(1); + if (v2 == null) { + v2 = 0; + } + + var f = this.properties.f; + + var _f = this.getInputData(2); + if (_f !== undefined) { + f = _f; + } + + this.setOutputData(0, v1 * (1 - f) + v2 * f); + }; + + MathLerp.prototype.onGetInputs = function () { + return [["f", "number"]]; + }; + + LiteGraph.registerNodeType("math/lerp", MathLerp); + + //Math ABS + function MathAbs() { + this.addInput("in", "number"); + this.addOutput("out", "number"); + this.size = [80, 30]; + } + + MathAbs.title = "Abs"; + MathAbs.desc = "Absolute"; + + MathAbs.prototype.onExecute = function () { + var v = this.getInputData(0); + if (v == null) { + return; + } + this.setOutputData(0, Math.abs(v)); + }; + + LiteGraph.registerNodeType("math/abs", MathAbs); + + //Math Floor + function MathFloor() { + this.addInput("in", "number"); + this.addOutput("out", "number"); + this.size = [80, 30]; + } + + MathFloor.title = "Floor"; + MathFloor.desc = "Floor number to remove fractional part"; + + MathFloor.prototype.onExecute = function () { + var v = this.getInputData(0); + if (v == null) { + return; + } + this.setOutputData(0, Math.floor(v)); + }; + + LiteGraph.registerNodeType("math/floor", MathFloor); + + //Math frac + function MathFrac() { + this.addInput("in", "number"); + this.addOutput("out", "number"); + this.size = [80, 30]; + } + + MathFrac.title = "Frac"; + MathFrac.desc = "Returns fractional part"; + + MathFrac.prototype.onExecute = function () { + var v = this.getInputData(0); + if (v == null) { + return; + } + this.setOutputData(0, v % 1); + }; + + LiteGraph.registerNodeType("math/frac", MathFrac); + + //Math Floor + function MathSmoothStep() { + this.addInput("in", "number"); + this.addOutput("out", "number"); + this.size = [80, 30]; + this.properties = { A: 0, B: 1 }; + } + + MathSmoothStep.title = "Smoothstep"; + MathSmoothStep.desc = "Smoothstep"; + + MathSmoothStep.prototype.onExecute = function () { + var v = this.getInputData(0); + if (v === undefined) { + return; + } + + var edge0 = this.properties.A; + var edge1 = this.properties.B; + + // Scale, bias and saturate x to 0..1 range + v = clamp((v - edge0) / (edge1 - edge0), 0.0, 1.0); + // Evaluate polynomial + v = v * v * (3 - 2 * v); + + this.setOutputData(0, v); + }; + + LiteGraph.registerNodeType("math/smoothstep", MathSmoothStep); + + //Math scale + function MathScale() { + this.addInput("in", "number", { label: "" }); + this.addOutput("out", "number", { label: "" }); + this.size = [80, 30]; + this.addProperty("factor", 1); + } + + MathScale.title = "Scale"; + MathScale.desc = "v * factor"; + + MathScale.prototype.onExecute = function () { + var value = this.getInputData(0); + if (value != null) { + this.setOutputData(0, value * this.properties.factor); + } + }; + + LiteGraph.registerNodeType("math/scale", MathScale); + + //Gate + function Gate() { + this.addInput("v", "boolean"); + this.addInput("A"); + this.addInput("B"); + this.addOutput("out"); + } + + Gate.title = "Gate"; + Gate.desc = "if v is true, then outputs A, otherwise B"; + + Gate.prototype.onExecute = function () { + var v = this.getInputData(0); + this.setOutputData(0, this.getInputData(v ? 1 : 2)); + }; + + LiteGraph.registerNodeType("math/gate", Gate); + + //Math Average + function MathAverageFilter() { + this.addInput("in", "number"); + this.addOutput("out", "number"); + this.size = [80, 30]; + this.addProperty("samples", 10); + this._values = new Float32Array(10); + this._current = 0; + } + + MathAverageFilter.title = "Average"; + MathAverageFilter.desc = "Average Filter"; + + MathAverageFilter.prototype.onExecute = function () { + var v = this.getInputData(0); + if (v == null) { + v = 0; + } + + var num_samples = this._values.length; + + this._values[this._current % num_samples] = v; + this._current += 1; + if (this._current > num_samples) { + this._current = 0; + } + + var avr = 0; + for (var i = 0; i < num_samples; ++i) { + avr += this._values[i]; + } + + this.setOutputData(0, avr / num_samples); + }; + + MathAverageFilter.prototype.onPropertyChanged = function (name, value) { + if (value < 1) { + value = 1; + } + this.properties.samples = Math.round(value); + var old = this._values; + + this._values = new Float32Array(this.properties.samples); + if (old.length <= this._values.length) { + this._values.set(old); + } else { + this._values.set(old.subarray(0, this._values.length)); + } + }; + + LiteGraph.registerNodeType("math/average", MathAverageFilter); + + //Math + function MathTendTo() { + this.addInput("in", "number"); + this.addOutput("out", "number"); + this.addProperty("factor", 0.1); + this.size = [80, 30]; + this._value = null; + } + + MathTendTo.title = "TendTo"; + MathTendTo.desc = "moves the output value always closer to the input"; + + MathTendTo.prototype.onExecute = function () { + var v = this.getInputData(0); + if (v == null) { + v = 0; + } + var f = this.properties.factor; + if (this._value == null) { + this._value = v; + } else { + this._value = this._value * (1 - f) + v * f; + } + this.setOutputData(0, this._value); + }; + + LiteGraph.registerNodeType("math/tendTo", MathTendTo); + + //Math operation + function MathOperation() { + this.addInput("A", "number,array,object"); + this.addInput("B", "number"); + this.addOutput("=", "number"); + this.addProperty("A", 1); + this.addProperty("B", 1); + this.addProperty("OP", "+", "enum", { values: MathOperation.values }); + this._func = MathOperation.funcs[this.properties.OP]; + this._result = []; //only used for arrays + } + + MathOperation.values = ["+", "-", "*", "/", "%", "^", "max", "min"]; + MathOperation.funcs = { + "+": function (A, B) { + return A + B; + }, + "-": function (A, B) { + return A - B; + }, + x: function (A, B) { + return A * B; + }, + X: function (A, B) { + return A * B; + }, + "*": function (A, B) { + return A * B; + }, + "/": function (A, B) { + return A / B; + }, + "%": function (A, B) { + return A % B; + }, + "^": function (A, B) { + return Math.pow(A, B); + }, + max: function (A, B) { + return Math.max(A, B); + }, + min: function (A, B) { + return Math.min(A, B); + }, + }; + + MathOperation.title = "Operation"; + MathOperation.desc = "Easy math operators"; + MathOperation["@OP"] = { + type: "enum", + title: "operation", + values: MathOperation.values, + }; + MathOperation.size = [100, 60]; + + MathOperation.prototype.getTitle = function () { + if (this.properties.OP == "max" || this.properties.OP == "min") + return this.properties.OP + "(A,B)"; + return "A " + this.properties.OP + " B"; + }; + + MathOperation.prototype.setValue = function (v) { + if (typeof v == "string") { + v = parseFloat(v); + } + this.properties["value"] = v; + }; + + MathOperation.prototype.onPropertyChanged = function (name, value) { + if (name != "OP") return; + this._func = MathOperation.funcs[this.properties.OP]; + if (!this._func) { + console.warn("Unknown operation: " + this.properties.OP); + this._func = function (A) { + return A; + }; + } + }; + + MathOperation.prototype.onExecute = function () { + var A = this.getInputData(0); + var B = this.getInputData(1); + if (A != null) { + if (A.constructor === Number) this.properties["A"] = A; + } else { + A = this.properties["A"]; + } + + if (B != null) { + this.properties["B"] = B; + } else { + B = this.properties["B"]; + } + + var func = MathOperation.funcs[this.properties.OP]; + + var result; + if (A.constructor === Number) { + result = 0; + result = func(A, B); + } else if (A.constructor === Array) { + result = this._result; + result.length = A.length; + for (var i = 0; i < A.length; ++i) result[i] = func(A[i], B); + } else { + result = {}; + for (var i in A) result[i] = func(A[i], B); + } + this.setOutputData(0, result); + }; + + MathOperation.prototype.onDrawBackground = function (ctx) { + if (this.flags.collapsed) { + return; + } + + ctx.font = "40px Arial"; + ctx.fillStyle = "#666"; + ctx.textAlign = "center"; + ctx.fillText( + this.properties.OP, + this.size[0] * 0.5, + (this.size[1] + LiteGraph.NODE_TITLE_HEIGHT) * 0.5, + ); + ctx.textAlign = "left"; + }; + + LiteGraph.registerNodeType("math/operation", MathOperation); + + LiteGraph.registerSearchboxExtra("math/operation", "MAX", { + properties: { OP: "max" }, + title: "MAX()", + }); + + LiteGraph.registerSearchboxExtra("math/operation", "MIN", { + properties: { OP: "min" }, + title: "MIN()", + }); + + //Math compare + function MathCompare() { + this.addInput("A", "number"); + this.addInput("B", "number"); + this.addOutput("A==B", "boolean"); + this.addOutput("A!=B", "boolean"); + this.addProperty("A", 0); + this.addProperty("B", 0); + } + + MathCompare.title = "Compare"; + MathCompare.desc = "compares between two values"; + + MathCompare.prototype.onExecute = function () { + var A = this.getInputData(0); + var B = this.getInputData(1); + if (A !== undefined) { + this.properties["A"] = A; + } else { + A = this.properties["A"]; + } + + if (B !== undefined) { + this.properties["B"] = B; + } else { + B = this.properties["B"]; + } + + for (var i = 0, l = this.outputs.length; i < l; ++i) { + var output = this.outputs[i]; + if (!output.links || !output.links.length) { + continue; + } + var value; + switch (output.name) { + case "A==B": + value = A == B; + break; + case "A!=B": + value = A != B; + break; + case "A>B": + value = A > B; + break; + case "A=B": + value = A >= B; + break; + } + this.setOutputData(i, value); + } + }; + + MathCompare.prototype.onGetOutputs = function () { + return [ + ["A==B", "boolean"], + ["A!=B", "boolean"], + ["A>B", "boolean"], + ["A=B", "boolean"], + ["A<=B", "boolean"], + ]; + }; + + LiteGraph.registerNodeType("math/compare", MathCompare); + + LiteGraph.registerSearchboxExtra("math/compare", "==", { + outputs: [["A==B", "boolean"]], + title: "A==B", + }); + LiteGraph.registerSearchboxExtra("math/compare", "!=", { + outputs: [["A!=B", "boolean"]], + title: "A!=B", + }); + LiteGraph.registerSearchboxExtra("math/compare", ">", { + outputs: [["A>B", "boolean"]], + title: "A>B", + }); + LiteGraph.registerSearchboxExtra("math/compare", "<", { + outputs: [["A=", { + outputs: [["A>=B", "boolean"]], + title: "A>=B", + }); + LiteGraph.registerSearchboxExtra("math/compare", "<=", { + outputs: [["A<=B", "boolean"]], + title: "A<=B", + }); + + function MathCondition() { + this.addInput("A", "number"); + this.addInput("B", "number"); + this.addOutput("true", "boolean"); + this.addOutput("false", "boolean"); + this.addProperty("A", 1); + this.addProperty("B", 1); + this.addProperty("OP", ">", "enum", { values: MathCondition.values }); + this.addWidget("combo", "Cond.", this.properties.OP, { + property: "OP", + values: MathCondition.values, + }); + + this.size = [80, 60]; + } + + MathCondition.values = [">", "<", "==", "!=", "<=", ">=", "||", "&&"]; + MathCondition["@OP"] = { + type: "enum", + title: "operation", + values: MathCondition.values, + }; + + MathCondition.title = "Condition"; + MathCondition.desc = "evaluates condition between A and B"; + + MathCondition.prototype.getTitle = function () { + return "A " + this.properties.OP + " B"; + }; + + MathCondition.prototype.onExecute = function () { + var A = this.getInputData(0); + if (A === undefined) { + A = this.properties.A; + } else { + this.properties.A = A; + } + + var B = this.getInputData(1); + if (B === undefined) { + B = this.properties.B; + } else { + this.properties.B = B; + } + + var result = true; + switch (this.properties.OP) { + case ">": + result = A > B; + break; + case "<": + result = A < B; + break; + case "==": + result = A == B; + break; + case "!=": + result = A != B; + break; + case "<=": + result = A <= B; + break; + case ">=": + result = A >= B; + break; + case "||": + result = A || B; + break; + case "&&": + result = A && B; + break; + } + + this.setOutputData(0, result); + this.setOutputData(1, !result); + }; + + LiteGraph.registerNodeType("math/condition", MathCondition); + + function MathBranch() { + this.addInput("in", 0); + this.addInput("cond", "boolean"); + this.addOutput("true", 0); + this.addOutput("false", 0); + this.size = [80, 60]; + } + + MathBranch.title = "Branch"; + MathBranch.desc = + "If condition is true, outputs IN in true, otherwise in false"; + + MathBranch.prototype.onExecute = function () { + var V = this.getInputData(0); + var cond = this.getInputData(1); + + if (cond) { + this.setOutputData(0, V); + this.setOutputData(1, null); + } else { + this.setOutputData(0, null); + this.setOutputData(1, V); + } + }; + + LiteGraph.registerNodeType("math/branch", MathBranch); + + function MathAccumulate() { + this.addInput("inc", "number"); + this.addOutput("total", "number"); + this.addProperty("increment", 1); + this.addProperty("value", 0); + } + + MathAccumulate.title = "Accumulate"; + MathAccumulate.desc = "Increments a value every time"; + + MathAccumulate.prototype.onExecute = function () { + if (this.properties.value === null) { + this.properties.value = 0; + } + + var inc = this.getInputData(0); + if (inc !== null) { + this.properties.value += inc; + } else { + this.properties.value += this.properties.increment; + } + this.setOutputData(0, this.properties.value); + }; + + LiteGraph.registerNodeType("math/accumulate", MathAccumulate); + + //Math Trigonometry + function MathTrigonometry() { + this.addInput("v", "number"); + this.addOutput("sin", "number"); + + this.addProperty("amplitude", 1); + this.addProperty("offset", 0); + this.bgImageUrl = "nodes/imgs/icon-sin.png"; + } + + MathTrigonometry.title = "Trigonometry"; + MathTrigonometry.desc = "Sin Cos Tan"; + //MathTrigonometry.filter = "shader"; + + MathTrigonometry.prototype.onExecute = function () { + var v = this.getInputData(0); + if (v == null) { + v = 0; + } + var amplitude = this.properties["amplitude"]; + var slot = this.findInputSlot("amplitude"); + if (slot != -1) { + amplitude = this.getInputData(slot); + } + var offset = this.properties["offset"]; + slot = this.findInputSlot("offset"); + if (slot != -1) { + offset = this.getInputData(slot); + } + + for (var i = 0, l = this.outputs.length; i < l; ++i) { + var output = this.outputs[i]; + var value; + switch (output.name) { + case "sin": + value = Math.sin(v); + break; + case "cos": + value = Math.cos(v); + break; + case "tan": + value = Math.tan(v); + break; + case "asin": + value = Math.asin(v); + break; + case "acos": + value = Math.acos(v); + break; + case "atan": + value = Math.atan(v); + break; + } + this.setOutputData(i, amplitude * value + offset); + } + }; + + MathTrigonometry.prototype.onGetInputs = function () { + return [ + ["v", "number"], + ["amplitude", "number"], + ["offset", "number"], + ]; + }; + + MathTrigonometry.prototype.onGetOutputs = function () { + return [ + ["sin", "number"], + ["cos", "number"], + ["tan", "number"], + ["asin", "number"], + ["acos", "number"], + ["atan", "number"], + ]; + }; + + LiteGraph.registerNodeType("math/trigonometry", MathTrigonometry); + + LiteGraph.registerSearchboxExtra("math/trigonometry", "SIN()", { + outputs: [["sin", "number"]], + title: "SIN()", + }); + LiteGraph.registerSearchboxExtra("math/trigonometry", "COS()", { + outputs: [["cos", "number"]], + title: "COS()", + }); + LiteGraph.registerSearchboxExtra("math/trigonometry", "TAN()", { + outputs: [["tan", "number"]], + title: "TAN()", + }); + + //math library for safe math operations without eval + function MathFormula() { + this.addInput("x", "number"); + this.addInput("y", "number"); + this.addOutput("", "number"); + this.properties = { x: 1.0, y: 1.0, formula: "x+y" }; + this.code_widget = this.addWidget( + "text", + "F(x,y)", + this.properties.formula, + function (v, canvas, node) { + node.properties.formula = v; + }, + ); + this.addWidget("toggle", "allow", LiteGraph.allow_scripts, function (v) { + LiteGraph.allow_scripts = v; + }); + this._func = null; + } + + MathFormula.title = "Formula"; + MathFormula.desc = "Compute formula"; + MathFormula.size = [160, 100]; + + MathAverageFilter.prototype.onPropertyChanged = function (name, value) { + if (name == "formula") { + this.code_widget.value = value; + } + }; + + MathFormula.prototype.onExecute = function () { + if (!LiteGraph.allow_scripts) { + return; + } + + var x = this.getInputData(0); + var y = this.getInputData(1); + if (x != null) { + this.properties["x"] = x; + } else { + x = this.properties["x"]; + } + + if (y != null) { + this.properties["y"] = y; + } else { + y = this.properties["y"]; + } + + var f = this.properties["formula"]; + + var value; + try { + if (!this._func || this._func_code != this.properties.formula) { + this._func = new Function( + "x", + "y", + "TIME", + "return " + this.properties.formula, + ); + this._func_code = this.properties.formula; + } + value = this._func(x, y, this.graph.globaltime); + this.boxcolor = null; + } catch (err) { + this.boxcolor = "red"; + } + this.setOutputData(0, value); + }; + + MathFormula.prototype.getTitle = function () { + return this._func_code || "Formula"; + }; + + MathFormula.prototype.onDrawBackground = function () { + var f = this.properties["formula"]; + if (this.outputs && this.outputs.length) { + this.outputs[0].label = f; + } + }; + + LiteGraph.registerNodeType("math/formula", MathFormula); + + function Math3DVec2ToXY() { + this.addInput("vec2", "vec2"); + this.addOutput("x", "number"); + this.addOutput("y", "number"); + } + + Math3DVec2ToXY.title = "Vec2->XY"; + Math3DVec2ToXY.desc = "vector 2 to components"; + + Math3DVec2ToXY.prototype.onExecute = function () { + var v = this.getInputData(0); + if (v == null) { + return; + } + + this.setOutputData(0, v[0]); + this.setOutputData(1, v[1]); + }; + + LiteGraph.registerNodeType("math3d/vec2-to-xy", Math3DVec2ToXY); + + function Math3DXYToVec2() { + this.addInputs([ + ["x", "number"], + ["y", "number"], + ]); + this.addOutput("vec2", "vec2"); + this.properties = { x: 0, y: 0 }; + this._data = new Float32Array(2); + } + + Math3DXYToVec2.title = "XY->Vec2"; + Math3DXYToVec2.desc = "components to vector2"; + + Math3DXYToVec2.prototype.onExecute = function () { + var x = this.getInputData(0); + if (x == null) { + x = this.properties.x; + } + var y = this.getInputData(1); + if (y == null) { + y = this.properties.y; + } + + var data = this._data; + data[0] = x; + data[1] = y; + + this.setOutputData(0, data); + }; + + LiteGraph.registerNodeType("math3d/xy-to-vec2", Math3DXYToVec2); + + function Math3DVec3ToXYZ() { + this.addInput("vec3", "vec3"); + this.addOutput("x", "number"); + this.addOutput("y", "number"); + this.addOutput("z", "number"); + } + + Math3DVec3ToXYZ.title = "Vec3->XYZ"; + Math3DVec3ToXYZ.desc = "vector 3 to components"; + + Math3DVec3ToXYZ.prototype.onExecute = function () { + var v = this.getInputData(0); + if (v == null) { + return; + } + + this.setOutputData(0, v[0]); + this.setOutputData(1, v[1]); + this.setOutputData(2, v[2]); + }; + + LiteGraph.registerNodeType("math3d/vec3-to-xyz", Math3DVec3ToXYZ); + + function Math3DXYZToVec3() { + this.addInputs([ + ["x", "number"], + ["y", "number"], + ["z", "number"], + ]); + this.addOutput("vec3", "vec3"); + this.properties = { x: 0, y: 0, z: 0 }; + this._data = new Float32Array(3); + } + + Math3DXYZToVec3.title = "XYZ->Vec3"; + Math3DXYZToVec3.desc = "components to vector3"; + + Math3DXYZToVec3.prototype.onExecute = function () { + var x = this.getInputData(0); + if (x == null) { + x = this.properties.x; + } + var y = this.getInputData(1); + if (y == null) { + y = this.properties.y; + } + var z = this.getInputData(2); + if (z == null) { + z = this.properties.z; + } + + var data = this._data; + data[0] = x; + data[1] = y; + data[2] = z; + + this.setOutputData(0, data); + }; + + LiteGraph.registerNodeType("math3d/xyz-to-vec3", Math3DXYZToVec3); + + function Math3DVec4ToXYZW() { + this.addInput("vec4", "vec4"); + this.addOutput("x", "number"); + this.addOutput("y", "number"); + this.addOutput("z", "number"); + this.addOutput("w", "number"); + } + + Math3DVec4ToXYZW.title = "Vec4->XYZW"; + Math3DVec4ToXYZW.desc = "vector 4 to components"; + + Math3DVec4ToXYZW.prototype.onExecute = function () { + var v = this.getInputData(0); + if (v == null) { + return; + } + + this.setOutputData(0, v[0]); + this.setOutputData(1, v[1]); + this.setOutputData(2, v[2]); + this.setOutputData(3, v[3]); + }; + + LiteGraph.registerNodeType("math3d/vec4-to-xyzw", Math3DVec4ToXYZW); + + function Math3DXYZWToVec4() { + this.addInputs([ + ["x", "number"], + ["y", "number"], + ["z", "number"], + ["w", "number"], + ]); + this.addOutput("vec4", "vec4"); + this.properties = { x: 0, y: 0, z: 0, w: 0 }; + this._data = new Float32Array(4); + } + + Math3DXYZWToVec4.title = "XYZW->Vec4"; + Math3DXYZWToVec4.desc = "components to vector4"; + + Math3DXYZWToVec4.prototype.onExecute = function () { + var x = this.getInputData(0); + if (x == null) { + x = this.properties.x; + } + var y = this.getInputData(1); + if (y == null) { + y = this.properties.y; + } + var z = this.getInputData(2); + if (z == null) { + z = this.properties.z; + } + var w = this.getInputData(3); + if (w == null) { + w = this.properties.w; + } + + var data = this._data; + data[0] = x; + data[1] = y; + data[2] = z; + data[3] = w; + + this.setOutputData(0, data); + }; + + LiteGraph.registerNodeType("math3d/xyzw-to-vec4", Math3DXYZWToVec4); +})(this); + +(function (global) { + var LiteGraph = global.LiteGraph; + + function Math3DMat4() { + this.addInput("T", "vec3"); + this.addInput("R", "vec3"); + this.addInput("S", "vec3"); + this.addOutput("mat4", "mat4"); + this.properties = { + T: [0, 0, 0], + R: [0, 0, 0], + S: [1, 1, 1], + R_in_degrees: true, + }; + this._result = mat4.create(); + this._must_update = true; + } + + Math3DMat4.title = "mat4"; + Math3DMat4.temp_quat = new Float32Array([0, 0, 0, 1]); + Math3DMat4.temp_mat4 = new Float32Array(16); + Math3DMat4.temp_vec3 = new Float32Array(3); + + Math3DMat4.prototype.onPropertyChanged = function (name, value) { + this._must_update = true; + }; + + Math3DMat4.prototype.onExecute = function () { + var M = this._result; + var Q = Math3DMat4.temp_quat; + var temp_mat4 = Math3DMat4.temp_mat4; + var temp_vec3 = Math3DMat4.temp_vec3; + + var T = this.getInputData(0); + var R = this.getInputData(1); + var S = this.getInputData(2); + + if (this._must_update || T || R || S) { + T = T || this.properties.T; + R = R || this.properties.R; + S = S || this.properties.S; + mat4.identity(M); + mat4.translate(M, M, T); + if (this.properties.R_in_degrees) { + temp_vec3.set(R); + vec3.scale(temp_vec3, temp_vec3, DEG2RAD); + quat.fromEuler(Q, temp_vec3); + } else quat.fromEuler(Q, R); + mat4.fromQuat(temp_mat4, Q); + mat4.multiply(M, M, temp_mat4); + mat4.scale(M, M, S); + } + + this.setOutputData(0, M); + }; + + LiteGraph.registerNodeType("math3d/mat4", Math3DMat4); + + //Math 3D operation + function Math3DOperation() { + this.addInput("A", "number,vec3"); + this.addInput("B", "number,vec3"); + this.addOutput("=", "number,vec3"); + this.addProperty("OP", "+", "enum", { values: Math3DOperation.values }); + this._result = vec3.create(); + } + + Math3DOperation.values = [ + "+", + "-", + "*", + "/", + "%", + "^", + "max", + "min", + "dot", + "cross", + ]; + + LiteGraph.registerSearchboxExtra("math3d/operation", "CROSS()", { + properties: { OP: "cross" }, + title: "CROSS()", + }); + + LiteGraph.registerSearchboxExtra("math3d/operation", "DOT()", { + properties: { OP: "dot" }, + title: "DOT()", + }); + + Math3DOperation.title = "Operation"; + Math3DOperation.desc = "Easy math 3D operators"; + Math3DOperation["@OP"] = { + type: "enum", + title: "operation", + values: Math3DOperation.values, + }; + Math3DOperation.size = [100, 60]; + + Math3DOperation.prototype.getTitle = function () { + if (this.properties.OP == "max" || this.properties.OP == "min") + return this.properties.OP + "(A,B)"; + return "A " + this.properties.OP + " B"; + }; + + Math3DOperation.prototype.onExecute = function () { + var A = this.getInputData(0); + var B = this.getInputData(1); + if (A == null || B == null) return; + if (A.constructor === Number) A = [A, A, A]; + if (B.constructor === Number) B = [B, B, B]; + + var result = this._result; + switch (this.properties.OP) { + case "+": + result = vec3.add(result, A, B); + break; + case "-": + result = vec3.sub(result, A, B); + break; + case "x": + case "X": + case "*": + result = vec3.mul(result, A, B); + break; + case "/": + result = vec3.div(result, A, B); + break; + case "%": + result[0] = A[0] % B[0]; + result[1] = A[1] % B[1]; + result[2] = A[2] % B[2]; + break; + case "^": + result[0] = Math.pow(A[0], B[0]); + result[1] = Math.pow(A[1], B[1]); + result[2] = Math.pow(A[2], B[2]); + break; + case "max": + result[0] = Math.max(A[0], B[0]); + result[1] = Math.max(A[1], B[1]); + result[2] = Math.max(A[2], B[2]); + break; + case "min": + result[0] = Math.min(A[0], B[0]); + result[1] = Math.min(A[1], B[1]); + result[2] = Math.min(A[2], B[2]); + case "dot": + result = vec3.dot(A, B); + break; + case "cross": + vec3.cross(result, A, B); + break; + default: + console.warn("Unknown operation: " + this.properties.OP); + } + this.setOutputData(0, result); + }; + + Math3DOperation.prototype.onDrawBackground = function (ctx) { + if (this.flags.collapsed) { + return; + } + + ctx.font = "40px Arial"; + ctx.fillStyle = "#666"; + ctx.textAlign = "center"; + ctx.fillText( + this.properties.OP, + this.size[0] * 0.5, + (this.size[1] + LiteGraph.NODE_TITLE_HEIGHT) * 0.5, + ); + ctx.textAlign = "left"; + }; + + LiteGraph.registerNodeType("math3d/operation", Math3DOperation); + + function Math3DVec3Scale() { + this.addInput("in", "vec3"); + this.addInput("f", "number"); + this.addOutput("out", "vec3"); + this.properties = { f: 1 }; + this._data = new Float32Array(3); + } + + Math3DVec3Scale.title = "vec3_scale"; + Math3DVec3Scale.desc = "scales the components of a vec3"; + + Math3DVec3Scale.prototype.onExecute = function () { + var v = this.getInputData(0); + if (v == null) { + return; + } + var f = this.getInputData(1); + if (f == null) { + f = this.properties.f; + } + + var data = this._data; + data[0] = v[0] * f; + data[1] = v[1] * f; + data[2] = v[2] * f; + this.setOutputData(0, data); + }; + + LiteGraph.registerNodeType("math3d/vec3-scale", Math3DVec3Scale); + + function Math3DVec3Length() { + this.addInput("in", "vec3"); + this.addOutput("out", "number"); + } + + Math3DVec3Length.title = "vec3_length"; + Math3DVec3Length.desc = "returns the module of a vector"; + + Math3DVec3Length.prototype.onExecute = function () { + var v = this.getInputData(0); + if (v == null) { + return; + } + var dist = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]); + this.setOutputData(0, dist); + }; + + LiteGraph.registerNodeType("math3d/vec3-length", Math3DVec3Length); + + function Math3DVec3Normalize() { + this.addInput("in", "vec3"); + this.addOutput("out", "vec3"); + this._data = new Float32Array(3); + } + + Math3DVec3Normalize.title = "vec3_normalize"; + Math3DVec3Normalize.desc = "returns the vector normalized"; + + Math3DVec3Normalize.prototype.onExecute = function () { + var v = this.getInputData(0); + if (v == null) { + return; + } + var dist = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]); + var data = this._data; + data[0] = v[0] / dist; + data[1] = v[1] / dist; + data[2] = v[2] / dist; + + this.setOutputData(0, data); + }; + + LiteGraph.registerNodeType("math3d/vec3-normalize", Math3DVec3Normalize); + + function Math3DVec3Lerp() { + this.addInput("A", "vec3"); + this.addInput("B", "vec3"); + this.addInput("f", "vec3"); + this.addOutput("out", "vec3"); + this.properties = { f: 0.5 }; + this._data = new Float32Array(3); + } + + Math3DVec3Lerp.title = "vec3_lerp"; + Math3DVec3Lerp.desc = "returns the interpolated vector"; + + Math3DVec3Lerp.prototype.onExecute = function () { + var A = this.getInputData(0); + if (A == null) { + return; + } + var B = this.getInputData(1); + if (B == null) { + return; + } + var f = this.getInputOrProperty("f"); + + var data = this._data; + data[0] = A[0] * (1 - f) + B[0] * f; + data[1] = A[1] * (1 - f) + B[1] * f; + data[2] = A[2] * (1 - f) + B[2] * f; + + this.setOutputData(0, data); + }; + + LiteGraph.registerNodeType("math3d/vec3-lerp", Math3DVec3Lerp); + + function Math3DVec3Dot() { + this.addInput("A", "vec3"); + this.addInput("B", "vec3"); + this.addOutput("out", "number"); + } + + Math3DVec3Dot.title = "vec3_dot"; + Math3DVec3Dot.desc = "returns the dot product"; + + Math3DVec3Dot.prototype.onExecute = function () { + var A = this.getInputData(0); + if (A == null) { + return; + } + var B = this.getInputData(1); + if (B == null) { + return; + } + + var dot = A[0] * B[0] + A[1] * B[1] + A[2] * B[2]; + this.setOutputData(0, dot); + }; + + LiteGraph.registerNodeType("math3d/vec3-dot", Math3DVec3Dot); + + //if glMatrix is installed... + if (global.glMatrix) { + function Math3DQuaternion() { + this.addOutput("quat", "quat"); + this.properties = { x: 0, y: 0, z: 0, w: 1, normalize: false }; + this._value = quat.create(); + } + + Math3DQuaternion.title = "Quaternion"; + Math3DQuaternion.desc = "quaternion"; + + Math3DQuaternion.prototype.onExecute = function () { + this._value[0] = this.getInputOrProperty("x"); + this._value[1] = this.getInputOrProperty("y"); + this._value[2] = this.getInputOrProperty("z"); + this._value[3] = this.getInputOrProperty("w"); + if (this.properties.normalize) { + quat.normalize(this._value, this._value); + } + this.setOutputData(0, this._value); + }; + + Math3DQuaternion.prototype.onGetInputs = function () { + return [ + ["x", "number"], + ["y", "number"], + ["z", "number"], + ["w", "number"], + ]; + }; + + LiteGraph.registerNodeType("math3d/quaternion", Math3DQuaternion); + + function Math3DRotation() { + this.addInputs([ + ["degrees", "number"], + ["axis", "vec3"], + ]); + this.addOutput("quat", "quat"); + this.properties = { angle: 90.0, axis: vec3.fromValues(0, 1, 0) }; + + this._value = quat.create(); + } + + Math3DRotation.title = "Rotation"; + Math3DRotation.desc = "quaternion rotation"; + + Math3DRotation.prototype.onExecute = function () { + var angle = this.getInputData(0); + if (angle == null) { + angle = this.properties.angle; + } + var axis = this.getInputData(1); + if (axis == null) { + axis = this.properties.axis; + } + + var R = quat.setAxisAngle(this._value, axis, angle * 0.0174532925); + this.setOutputData(0, R); + }; + + LiteGraph.registerNodeType("math3d/rotation", Math3DRotation); + + function MathEulerToQuat() { + this.addInput("euler", "vec3"); + this.addOutput("quat", "quat"); + this.properties = { euler: [0, 0, 0], use_yaw_pitch_roll: false }; + this._degs = vec3.create(); + this._value = quat.create(); + } + + MathEulerToQuat.title = "Euler->Quat"; + MathEulerToQuat.desc = "Converts euler angles (in degrees) to quaternion"; + + MathEulerToQuat.prototype.onExecute = function () { + var euler = this.getInputData(0); + if (euler == null) { + euler = this.properties.euler; + } + vec3.scale(this._degs, euler, DEG2RAD); + if (this.properties.use_yaw_pitch_roll) + this._degs = [this._degs[2], this._degs[0], this._degs[1]]; + var R = quat.fromEuler(this._value, this._degs); + this.setOutputData(0, R); + }; + + LiteGraph.registerNodeType("math3d/euler_to_quat", MathEulerToQuat); + + function MathQuatToEuler() { + this.addInput(["quat", "quat"]); + this.addOutput("euler", "vec3"); + this._value = vec3.create(); + } + + MathQuatToEuler.title = "Euler->Quat"; + MathQuatToEuler.desc = "Converts rotX,rotY,rotZ in degrees to quat"; + + MathQuatToEuler.prototype.onExecute = function () { + var q = this.getInputData(0); + if (!q) return; + var R = quat.toEuler(this._value, q); + vec3.scale(this._value, this._value, DEG2RAD); + this.setOutputData(0, this._value); + }; + + LiteGraph.registerNodeType("math3d/quat_to_euler", MathQuatToEuler); + + //Math3D rotate vec3 + function Math3DRotateVec3() { + this.addInputs([ + ["vec3", "vec3"], + ["quat", "quat"], + ]); + this.addOutput("result", "vec3"); + this.properties = { vec: [0, 0, 1] }; + } + + Math3DRotateVec3.title = "Rot. Vec3"; + Math3DRotateVec3.desc = "rotate a point"; + + Math3DRotateVec3.prototype.onExecute = function () { + var vec = this.getInputData(0); + if (vec == null) { + vec = this.properties.vec; + } + var quat = this.getInputData(1); + if (quat == null) { + this.setOutputData(vec); + } else { + this.setOutputData(0, vec3.transformQuat(vec3.create(), vec, quat)); + } + }; + + LiteGraph.registerNodeType("math3d/rotate_vec3", Math3DRotateVec3); + + function Math3DMultQuat() { + this.addInputs([ + ["A", "quat"], + ["B", "quat"], + ]); + this.addOutput("A*B", "quat"); + + this._value = quat.create(); + } + + Math3DMultQuat.title = "Mult. Quat"; + Math3DMultQuat.desc = "rotate quaternion"; + + Math3DMultQuat.prototype.onExecute = function () { + var A = this.getInputData(0); + if (A == null) { + return; + } + var B = this.getInputData(1); + if (B == null) { + return; + } + + var R = quat.multiply(this._value, A, B); + this.setOutputData(0, R); + }; + + LiteGraph.registerNodeType("math3d/mult-quat", Math3DMultQuat); + + function Math3DQuatSlerp() { + this.addInputs([ + ["A", "quat"], + ["B", "quat"], + ["factor", "number"], + ]); + this.addOutput("slerp", "quat"); + this.addProperty("factor", 0.5); + + this._value = quat.create(); + } + + Math3DQuatSlerp.title = "Quat Slerp"; + Math3DQuatSlerp.desc = "quaternion spherical interpolation"; + + Math3DQuatSlerp.prototype.onExecute = function () { + var A = this.getInputData(0); + if (A == null) { + return; + } + var B = this.getInputData(1); + if (B == null) { + return; + } + var factor = this.properties.factor; + if (this.getInputData(2) != null) { + factor = this.getInputData(2); + } + + var R = quat.slerp(this._value, A, B, factor); + this.setOutputData(0, R); + }; + + LiteGraph.registerNodeType("math3d/quat-slerp", Math3DQuatSlerp); + + //Math3D rotate vec3 + function Math3DRemapRange() { + this.addInput("vec3", "vec3"); + this.addOutput("remap", "vec3"); + this.addOutput("clamped", "vec3"); + this.properties = { + clamp: true, + range_min: [-1, -1, 0], + range_max: [1, 1, 0], + target_min: [-1, -1, 0], + target_max: [1, 1, 0], + }; + this._value = vec3.create(); + this._clamped = vec3.create(); + } + + Math3DRemapRange.title = "Remap Range"; + Math3DRemapRange.desc = "remap a 3D range"; + + Math3DRemapRange.prototype.onExecute = function () { + var vec = this.getInputData(0); + if (vec) this._value.set(vec); + var range_min = this.properties.range_min; + var range_max = this.properties.range_max; + var target_min = this.properties.target_min; + var target_max = this.properties.target_max; + + //swap to avoid errors + /* + if(range_min > range_max) + { + range_min = range_max; + range_max = this.properties.range_min; + } + + if(target_min > target_max) + { + target_min = target_max; + target_max = this.properties.target_min; + } + */ + + for (var i = 0; i < 3; ++i) { + var r = range_max[i] - range_min[i]; + this._clamped[i] = clamp(this._value[i], range_min[i], range_max[i]); + if (r == 0) { + this._value[i] = (target_min[i] + target_max[i]) * 0.5; + continue; + } + + var n = (this._value[i] - range_min[i]) / r; + if (this.properties.clamp) n = clamp(n, 0, 1); + var t = target_max[i] - target_min[i]; + this._value[i] = target_min[i] + n * t; + } + + this.setOutputData(0, this._value); + this.setOutputData(1, this._clamped); + }; + + LiteGraph.registerNodeType("math3d/remap_range", Math3DRemapRange); + } //glMatrix + else if (LiteGraph.debug) + console.warn("No glmatrix found, some Math3D nodes may not work"); +})(this); + +//basic nodes +(function (global) { + var LiteGraph = global.LiteGraph; + + function toString(a) { + if (a && a.constructor === Object) { + try { + return JSON.stringify(a); + } catch (err) { + return String(a); + } + } + return String(a); + } + + LiteGraph.wrapFunctionAsNode("string/toString", toString, [""], "string"); + + function compare(a, b) { + return a == b; + } + + LiteGraph.wrapFunctionAsNode( + "string/compare", + compare, + ["string", "string"], + "boolean", + ); + + function concatenate(a, b) { + if (a === undefined) { + return b; + } + if (b === undefined) { + return a; + } + return a + b; + } + + LiteGraph.wrapFunctionAsNode( + "string/concatenate", + concatenate, + ["string", "string"], + "string", + ); + + function contains(a, b) { + if (a === undefined || b === undefined) { + return false; + } + return a.indexOf(b) != -1; + } + + LiteGraph.wrapFunctionAsNode( + "string/contains", + contains, + ["string", "string"], + "boolean", + ); + + function toUpperCase(a) { + if (a != null && a.constructor === String) { + return a.toUpperCase(); + } + return a; + } + + LiteGraph.wrapFunctionAsNode( + "string/toUpperCase", + toUpperCase, + ["string"], + "string", + ); + + function split(str, separator) { + if (separator == null) separator = this.properties.separator; + if (str == null) return []; + if (str.constructor === String) return str.split(separator || " "); + else if (str.constructor === Array) { + var r = []; + for (var i = 0; i < str.length; ++i) { + if (typeof str[i] == "string") r[i] = str[i].split(separator || " "); + } + return r; + } + return null; + } + + LiteGraph.wrapFunctionAsNode( + "string/split", + split, + ["string,array", "string"], + "array", + { separator: "," }, + ); + + function toFixed(a) { + if (a != null && a.constructor === Number) { + return a.toFixed(this.properties.precision); + } + return a; + } + + LiteGraph.wrapFunctionAsNode( + "string/toFixed", + toFixed, + ["number"], + "string", + { precision: 0 }, + ); + + function StringToTable() { + this.addInput("", "string"); + this.addOutput("table", "table"); + this.addOutput("rows", "number"); + this.addProperty("value", ""); + this.addProperty("separator", ","); + this._table = null; + } + + StringToTable.title = "toTable"; + StringToTable.desc = "Splits a string to table"; + + StringToTable.prototype.onExecute = function () { + var input = this.getInputData(0); + if (!input) return; + var separator = this.properties.separator || ","; + if (input != this._str || separator != this._last_separator) { + this._last_separator = separator; + this._str = input; + this._table = input.split("\n").map(function (a) { + return a.trim().split(separator); + }); + } + this.setOutputData(0, this._table); + this.setOutputData(1, this._table ? this._table.length : 0); + }; + + LiteGraph.registerNodeType("string/toTable", StringToTable); +})(this); + +(function (global) { + var LiteGraph = global.LiteGraph; + + function Selector() { + this.addInput("sel", "number"); + this.addInput("A"); + this.addInput("B"); + this.addInput("C"); + this.addInput("D"); + this.addOutput("out"); + + this.selected = 0; + } + + Selector.title = "Selector"; + Selector.desc = "selects an output"; + + Selector.prototype.onDrawBackground = function (ctx) { + if (this.flags.collapsed) { + return; + } + ctx.fillStyle = "#AFB"; + var y = (this.selected + 1) * LiteGraph.NODE_SLOT_HEIGHT + 6; + ctx.beginPath(); + ctx.moveTo(50, y); + ctx.lineTo(50, y + LiteGraph.NODE_SLOT_HEIGHT); + ctx.lineTo(34, y + LiteGraph.NODE_SLOT_HEIGHT * 0.5); + ctx.fill(); + }; + + Selector.prototype.onExecute = function () { + var sel = this.getInputData(0); + if (sel == null || sel.constructor !== Number) sel = 0; + this.selected = sel = Math.round(sel) % (this.inputs.length - 1); + var v = this.getInputData(sel + 1); + if (v !== undefined) { + this.setOutputData(0, v); + } + }; + + Selector.prototype.onGetInputs = function () { + return [ + ["E", 0], + ["F", 0], + ["G", 0], + ["H", 0], + ]; + }; + + LiteGraph.registerNodeType("logic/selector", Selector); + + function Sequence() { + this.properties = { + sequence: "A,B,C", + }; + this.addInput("index", "number"); + this.addInput("seq"); + this.addOutput("out"); + + this.index = 0; + this.values = this.properties.sequence.split(","); + } + + Sequence.title = "Sequence"; + Sequence.desc = "select one element from a sequence from a string"; + + Sequence.prototype.onPropertyChanged = function (name, value) { + if (name == "sequence") { + this.values = value.split(","); + } + }; + + Sequence.prototype.onExecute = function () { + var seq = this.getInputData(1); + if (seq && seq != this.current_sequence) { + this.values = seq.split(","); + this.current_sequence = seq; + } + var index = this.getInputData(0); + if (index == null) { + index = 0; + } + this.index = index = Math.round(index) % this.values.length; + + this.setOutputData(0, this.values[index]); + }; + + LiteGraph.registerNodeType("logic/sequence", Sequence); + + function logicAnd() { + this.properties = {}; + this.addInput("a", "boolean"); + this.addInput("b", "boolean"); + this.addOutput("out", "boolean"); + } + logicAnd.title = "AND"; + logicAnd.desc = "Return true if all inputs are true"; + logicAnd.prototype.onExecute = function () { + var ret = true; + for (var inX in this.inputs) { + if (!this.getInputData(inX)) { + var ret = false; + break; + } + } + this.setOutputData(0, ret); + }; + logicAnd.prototype.onGetInputs = function () { + return [["and", "boolean"]]; + }; + LiteGraph.registerNodeType("logic/AND", logicAnd); + + function logicOr() { + this.properties = {}; + this.addInput("a", "boolean"); + this.addInput("b", "boolean"); + this.addOutput("out", "boolean"); + } + logicOr.title = "OR"; + logicOr.desc = "Return true if at least one input is true"; + logicOr.prototype.onExecute = function () { + var ret = false; + for (var inX in this.inputs) { + if (this.getInputData(inX)) { + ret = true; + break; + } + } + this.setOutputData(0, ret); + }; + logicOr.prototype.onGetInputs = function () { + return [["or", "boolean"]]; + }; + LiteGraph.registerNodeType("logic/OR", logicOr); + + function logicNot() { + this.properties = {}; + this.addInput("in", "boolean"); + this.addOutput("out", "boolean"); + } + logicNot.title = "NOT"; + logicNot.desc = "Return the logical negation"; + logicNot.prototype.onExecute = function () { + var ret = !this.getInputData(0); + this.setOutputData(0, ret); + }; + LiteGraph.registerNodeType("logic/NOT", logicNot); + + function logicCompare() { + this.properties = {}; + this.addInput("a", "boolean"); + this.addInput("b", "boolean"); + this.addOutput("out", "boolean"); + } + logicCompare.title = "bool == bool"; + logicCompare.desc = "Compare for logical equality"; + logicCompare.prototype.onExecute = function () { + var last = null; + var ret = true; + for (var inX in this.inputs) { + if (last === null) last = this.getInputData(inX); + else if (last != this.getInputData(inX)) { + ret = false; + break; + } + } + this.setOutputData(0, ret); + }; + logicCompare.prototype.onGetInputs = function () { + return [["bool", "boolean"]]; + }; + LiteGraph.registerNodeType("logic/CompareBool", logicCompare); + + function logicBranch() { + this.properties = {}; + this.addInput("onTrigger", LiteGraph.ACTION); + this.addInput("condition", "boolean"); + this.addOutput("true", LiteGraph.EVENT); + this.addOutput("false", LiteGraph.EVENT); + this.mode = LiteGraph.ON_TRIGGER; + } + logicBranch.title = "Branch"; + logicBranch.desc = "Branch execution on condition"; + logicBranch.prototype.onExecute = function (param, options) { + var condtition = this.getInputData(1); + if (condtition) { + this.triggerSlot(0); + } else { + this.triggerSlot(1); + } + }; + LiteGraph.registerNodeType("logic/IF", logicBranch); +})(this); + +(function (global) { + var LiteGraph = global.LiteGraph; + + function GraphicsPlot() { + this.addInput("A", "Number"); + this.addInput("B", "Number"); + this.addInput("C", "Number"); + this.addInput("D", "Number"); + + this.values = [[], [], [], []]; + this.properties = { scale: 2 }; + } + + GraphicsPlot.title = "Plot"; + GraphicsPlot.desc = "Plots data over time"; + GraphicsPlot.colors = ["#FFF", "#F99", "#9F9", "#99F"]; + + GraphicsPlot.prototype.onExecute = function (ctx) { + if (this.flags.collapsed) { + return; + } + + var size = this.size; + + for (var i = 0; i < 4; ++i) { + var v = this.getInputData(i); + if (v == null) { + continue; + } + var values = this.values[i]; + values.push(v); + if (values.length > size[0]) { + values.shift(); + } + } + }; + + GraphicsPlot.prototype.onDrawBackground = function (ctx) { + if (this.flags.collapsed) { + return; + } + + var size = this.size; + + var scale = (0.5 * size[1]) / this.properties.scale; + var colors = GraphicsPlot.colors; + var offset = size[1] * 0.5; + + ctx.fillStyle = "#000"; + ctx.fillRect(0, 0, size[0], size[1]); + ctx.strokeStyle = "#555"; + ctx.beginPath(); + ctx.moveTo(0, offset); + ctx.lineTo(size[0], offset); + ctx.stroke(); + + if (this.inputs) { + for (var i = 0; i < 4; ++i) { + var values = this.values[i]; + if (!this.inputs[i] || !this.inputs[i].link) { + continue; + } + ctx.strokeStyle = colors[i]; + ctx.beginPath(); + var v = values[0] * scale * -1 + offset; + ctx.moveTo(0, clamp(v, 0, size[1])); + for (var j = 1; j < values.length && j < size[0]; ++j) { + var v = values[j] * scale * -1 + offset; + ctx.lineTo(j, clamp(v, 0, size[1])); + } + ctx.stroke(); + } + } + }; + + LiteGraph.registerNodeType("graphics/plot", GraphicsPlot); + + function GraphicsImage() { + this.addOutput("frame", "image"); + this.properties = { url: "" }; + } + + GraphicsImage.title = "Image"; + GraphicsImage.desc = "Image loader"; + GraphicsImage.widgets = [{ name: "load", text: "Load", type: "button" }]; + + GraphicsImage.supported_extensions = ["jpg", "jpeg", "png", "gif"]; + + GraphicsImage.prototype.onAdded = function () { + if (this.properties["url"] != "" && this.img == null) { + this.loadImage(this.properties["url"]); + } + }; + + GraphicsImage.prototype.onDrawBackground = function (ctx) { + if (this.flags.collapsed) { + return; + } + if (this.img && this.size[0] > 5 && this.size[1] > 5 && this.img.width) { + ctx.drawImage(this.img, 0, 0, this.size[0], this.size[1]); + } + }; + + GraphicsImage.prototype.onExecute = function () { + if (!this.img) { + this.boxcolor = "#000"; + } + if (this.img && this.img.width) { + this.setOutputData(0, this.img); + } else { + this.setOutputData(0, null); + } + if (this.img && this.img.dirty) { + this.img.dirty = false; + } + }; + + GraphicsImage.prototype.onPropertyChanged = function (name, value) { + this.properties[name] = value; + if (name == "url" && value != "") { + this.loadImage(value); + } + + return true; + }; + + GraphicsImage.prototype.loadImage = function (url, callback) { + if (url == "") { + this.img = null; + return; + } + + this.img = document.createElement("img"); + + if (url.substr(0, 4) == "http" && LiteGraph.proxy) { + url = LiteGraph.proxy + url.substr(url.indexOf(":") + 3); + } + + this.img.src = url; + this.boxcolor = "#F95"; + var that = this; + this.img.onload = function () { + if (callback) { + callback(this); + } + console.log( + "Image loaded, size: " + that.img.width + "x" + that.img.height, + ); + this.dirty = true; + that.boxcolor = "#9F9"; + that.setDirtyCanvas(true); + }; + this.img.onerror = function () { + console.log("error loading the image:" + url); + }; + }; + + GraphicsImage.prototype.onWidget = function (e, widget) { + if (widget.name == "load") { + this.loadImage(this.properties["url"]); + } + }; + + GraphicsImage.prototype.onDropFile = function (file) { + var that = this; + if (this._url) { + URL.revokeObjectURL(this._url); + } + this._url = URL.createObjectURL(file); + this.properties.url = this._url; + this.loadImage(this._url, function (img) { + that.size[1] = (img.height / img.width) * that.size[0]; + }); + }; + + LiteGraph.registerNodeType("graphics/image", GraphicsImage); + + function ColorPalette() { + this.addInput("f", "number"); + this.addOutput("Color", "color"); + this.properties = { + colorA: "#444444", + colorB: "#44AAFF", + colorC: "#44FFAA", + colorD: "#FFFFFF", + }; + } + + ColorPalette.title = "Palette"; + ColorPalette.desc = "Generates a color"; + + ColorPalette.prototype.onExecute = function () { + var c = []; + + if (this.properties.colorA != null) { + c.push(hex2num(this.properties.colorA)); + } + if (this.properties.colorB != null) { + c.push(hex2num(this.properties.colorB)); + } + if (this.properties.colorC != null) { + c.push(hex2num(this.properties.colorC)); + } + if (this.properties.colorD != null) { + c.push(hex2num(this.properties.colorD)); + } + + var f = this.getInputData(0); + if (f == null) { + f = 0.5; + } + if (f > 1.0) { + f = 1.0; + } else if (f < 0.0) { + f = 0.0; + } + + if (c.length == 0) { + return; + } + + var result = [0, 0, 0]; + if (f == 0) { + result = c[0]; + } else if (f == 1) { + result = c[c.length - 1]; + } else { + var pos = (c.length - 1) * f; + var c1 = c[Math.floor(pos)]; + var c2 = c[Math.floor(pos) + 1]; + var t = pos - Math.floor(pos); + result[0] = c1[0] * (1 - t) + c2[0] * t; + result[1] = c1[1] * (1 - t) + c2[1] * t; + result[2] = c1[2] * (1 - t) + c2[2] * t; + } + + /* + c[0] = 1.0 - Math.abs( Math.sin( 0.1 * reModular.getTime() * Math.PI) ); + c[1] = Math.abs( Math.sin( 0.07 * reModular.getTime() * Math.PI) ); + c[2] = Math.abs( Math.sin( 0.01 * reModular.getTime() * Math.PI) ); + */ + + for (var i = 0; i < result.length; i++) { + result[i] /= 255; + } + + this.boxcolor = colorToString(result); + this.setOutputData(0, result); + }; + + LiteGraph.registerNodeType("color/palette", ColorPalette); + + function ImageFrame() { + this.addInput("", "image,canvas"); + this.size = [200, 200]; + } + + ImageFrame.title = "Frame"; + ImageFrame.desc = "Frame viewerew"; + ImageFrame.widgets = [ + { name: "resize", text: "Resize box", type: "button" }, + { name: "view", text: "View Image", type: "button" }, + ]; + + ImageFrame.prototype.onDrawBackground = function (ctx) { + if (this.frame && !this.flags.collapsed) { + ctx.drawImage(this.frame, 0, 0, this.size[0], this.size[1]); + } + }; + + ImageFrame.prototype.onExecute = function () { + this.frame = this.getInputData(0); + this.setDirtyCanvas(true); + }; + + ImageFrame.prototype.onWidget = function (e, widget) { + if (widget.name == "resize" && this.frame) { + var width = this.frame.width; + var height = this.frame.height; + + if (!width && this.frame.videoWidth != null) { + width = this.frame.videoWidth; + height = this.frame.videoHeight; + } + + if (width && height) { + this.size = [width, height]; + } + this.setDirtyCanvas(true, true); + } else if (widget.name == "view") { + this.show(); + } + }; + + ImageFrame.prototype.show = function () { + //var str = this.canvas.toDataURL("image/png"); + if (showElement && this.frame) { + showElement(this.frame); + } + }; + + LiteGraph.registerNodeType("graphics/frame", ImageFrame); + + function ImageFade() { + this.addInputs([ + ["img1", "image"], + ["img2", "image"], + ["fade", "number"], + ]); + this.addOutput("", "image"); + this.properties = { fade: 0.5, width: 512, height: 512 }; + } + + ImageFade.title = "Image fade"; + ImageFade.desc = "Fades between images"; + ImageFade.widgets = [ + { name: "resizeA", text: "Resize to A", type: "button" }, + { name: "resizeB", text: "Resize to B", type: "button" }, + ]; + + ImageFade.prototype.onAdded = function () { + this.createCanvas(); + var ctx = this.canvas.getContext("2d"); + ctx.fillStyle = "#000"; + ctx.fillRect(0, 0, this.properties["width"], this.properties["height"]); + }; + + ImageFade.prototype.createCanvas = function () { + this.canvas = document.createElement("canvas"); + this.canvas.width = this.properties["width"]; + this.canvas.height = this.properties["height"]; + }; + + ImageFade.prototype.onExecute = function () { + var ctx = this.canvas.getContext("2d"); + this.canvas.width = this.canvas.width; + + var A = this.getInputData(0); + if (A != null) { + ctx.drawImage(A, 0, 0, this.canvas.width, this.canvas.height); + } + + var fade = this.getInputData(2); + if (fade == null) { + fade = this.properties["fade"]; + } else { + this.properties["fade"] = fade; + } + + ctx.globalAlpha = fade; + var B = this.getInputData(1); + if (B != null) { + ctx.drawImage(B, 0, 0, this.canvas.width, this.canvas.height); + } + ctx.globalAlpha = 1.0; + + this.setOutputData(0, this.canvas); + this.setDirtyCanvas(true); + }; + + LiteGraph.registerNodeType("graphics/imagefade", ImageFade); + + function ImageCrop() { + this.addInput("", "image"); + this.addOutput("", "image"); + this.properties = { width: 256, height: 256, x: 0, y: 0, scale: 1.0 }; + this.size = [50, 20]; + } + + ImageCrop.title = "Crop"; + ImageCrop.desc = "Crop Image"; + + ImageCrop.prototype.onAdded = function () { + this.createCanvas(); + }; + + ImageCrop.prototype.createCanvas = function () { + this.canvas = document.createElement("canvas"); + this.canvas.width = this.properties["width"]; + this.canvas.height = this.properties["height"]; + }; + + ImageCrop.prototype.onExecute = function () { + var input = this.getInputData(0); + if (!input) { + return; + } + + if (input.width) { + var ctx = this.canvas.getContext("2d"); + + ctx.drawImage( + input, + -this.properties["x"], + -this.properties["y"], + input.width * this.properties["scale"], + input.height * this.properties["scale"], + ); + this.setOutputData(0, this.canvas); + } else { + this.setOutputData(0, null); + } + }; + + ImageCrop.prototype.onDrawBackground = function (ctx) { + if (this.flags.collapsed) { + return; + } + if (this.canvas) { + ctx.drawImage( + this.canvas, + 0, + 0, + this.canvas.width, + this.canvas.height, + 0, + 0, + this.size[0], + this.size[1], + ); + } + }; + + ImageCrop.prototype.onPropertyChanged = function (name, value) { + this.properties[name] = value; + + if (name == "scale") { + this.properties[name] = parseFloat(value); + if (this.properties[name] == 0) { + console.error("Error in scale"); + this.properties[name] = 1.0; + } + } else { + this.properties[name] = parseInt(value); + } + + this.createCanvas(); + + return true; + }; + + LiteGraph.registerNodeType("graphics/cropImage", ImageCrop); + + //CANVAS stuff + + function CanvasNode() { + this.addInput("clear", LiteGraph.ACTION); + this.addOutput("", "canvas"); + this.properties = { width: 512, height: 512, autoclear: true }; + + this.canvas = document.createElement("canvas"); + this.ctx = this.canvas.getContext("2d"); + } + + CanvasNode.title = "Canvas"; + CanvasNode.desc = "Canvas to render stuff"; + + CanvasNode.prototype.onExecute = function () { + var canvas = this.canvas; + var w = this.properties.width | 0; + var h = this.properties.height | 0; + if (canvas.width != w) { + canvas.width = w; + } + if (canvas.height != h) { + canvas.height = h; + } + + if (this.properties.autoclear) { + this.ctx.clearRect(0, 0, canvas.width, canvas.height); + } + this.setOutputData(0, canvas); + }; + + CanvasNode.prototype.onAction = function (action, param) { + if (action == "clear") { + this.ctx.clearRect(0, 0, this.canvas.width, this.canvas.height); + } + }; + + LiteGraph.registerNodeType("graphics/canvas", CanvasNode); + + function DrawImageNode() { + this.addInput("canvas", "canvas"); + this.addInput("img", "image,canvas"); + this.addInput("x", "number"); + this.addInput("y", "number"); + this.properties = { x: 0, y: 0, opacity: 1 }; + } + + DrawImageNode.title = "DrawImage"; + DrawImageNode.desc = "Draws image into a canvas"; + + DrawImageNode.prototype.onExecute = function () { + var canvas = this.getInputData(0); + if (!canvas) { + return; + } + + var img = this.getInputOrProperty("img"); + if (!img) { + return; + } + + var x = this.getInputOrProperty("x"); + var y = this.getInputOrProperty("y"); + var ctx = canvas.getContext("2d"); + ctx.drawImage(img, x, y); + }; + + LiteGraph.registerNodeType("graphics/drawImage", DrawImageNode); + + function DrawRectangleNode() { + this.addInput("canvas", "canvas"); + this.addInput("x", "number"); + this.addInput("y", "number"); + this.addInput("w", "number"); + this.addInput("h", "number"); + this.properties = { + x: 0, + y: 0, + w: 10, + h: 10, + color: "white", + opacity: 1, + }; + } + + DrawRectangleNode.title = "DrawRectangle"; + DrawRectangleNode.desc = "Draws rectangle in canvas"; + + DrawRectangleNode.prototype.onExecute = function () { + var canvas = this.getInputData(0); + if (!canvas) { + return; + } + + var x = this.getInputOrProperty("x"); + var y = this.getInputOrProperty("y"); + var w = this.getInputOrProperty("w"); + var h = this.getInputOrProperty("h"); + var ctx = canvas.getContext("2d"); + ctx.fillRect(x, y, w, h); + }; + + LiteGraph.registerNodeType("graphics/drawRectangle", DrawRectangleNode); + + function ImageVideo() { + this.addInput("t", "number"); + this.addOutputs([ + ["frame", "image"], + ["t", "number"], + ["d", "number"], + ]); + this.properties = { url: "", use_proxy: true }; + } + + ImageVideo.title = "Video"; + ImageVideo.desc = "Video playback"; + ImageVideo.widgets = [ + { name: "play", text: "PLAY", type: "minibutton" }, + { name: "stop", text: "STOP", type: "minibutton" }, + { name: "demo", text: "Demo video", type: "button" }, + { name: "mute", text: "Mute video", type: "button" }, + ]; + + ImageVideo.prototype.onExecute = function () { + if (!this.properties.url) { + return; + } + + if (this.properties.url != this._video_url) { + this.loadVideo(this.properties.url); + } + + if (!this._video || this._video.width == 0) { + return; + } + + var t = this.getInputData(0); + if (t && t >= 0 && t <= 1.0) { + this._video.currentTime = t * this._video.duration; + this._video.pause(); + } + + this._video.dirty = true; + this.setOutputData(0, this._video); + this.setOutputData(1, this._video.currentTime); + this.setOutputData(2, this._video.duration); + this.setDirtyCanvas(true); + }; + + ImageVideo.prototype.onStart = function () { + this.play(); + }; + + ImageVideo.prototype.onStop = function () { + this.stop(); + }; + + ImageVideo.prototype.loadVideo = function (url) { + this._video_url = url; + + var pos = url.substr(0, 10).indexOf(":"); + var protocol = ""; + if (pos != -1) protocol = url.substr(0, pos); + + var host = ""; + if (protocol) { + host = url.substr(0, url.indexOf("/", protocol.length + 3)); + host = host.substr(protocol.length + 3); + } + + if ( + this.properties.use_proxy && + protocol && + LiteGraph.proxy && + host != location.host + ) { + url = LiteGraph.proxy + url.substr(url.indexOf(":") + 3); + } + + this._video = document.createElement("video"); + this._video.src = url; + this._video.type = "type=video/mp4"; + + this._video.muted = true; + this._video.autoplay = true; + + var that = this; + this._video.addEventListener("loadedmetadata", function (e) { + //onload + console.log("Duration: " + this.duration + " seconds"); + console.log("Size: " + this.videoWidth + "," + this.videoHeight); + that.setDirtyCanvas(true); + this.width = this.videoWidth; + this.height = this.videoHeight; + }); + this._video.addEventListener("progress", function (e) { + //onload + console.log("video loading..."); + }); + this._video.addEventListener("error", function (e) { + console.error("Error loading video: " + this.src); + if (this.error) { + switch (this.error.code) { + case this.error.MEDIA_ERR_ABORTED: + console.error("You stopped the video."); + break; + case this.error.MEDIA_ERR_NETWORK: + console.error("Network error - please try again later."); + break; + case this.error.MEDIA_ERR_DECODE: + console.error("Video is broken.."); + break; + case this.error.MEDIA_ERR_SRC_NOT_SUPPORTED: + console.error("Sorry, your browser can't play this video."); + break; + } + } + }); + + this._video.addEventListener("ended", function (e) { + console.log("Video Ended."); + this.play(); //loop + }); + + //document.body.appendChild(this.video); + }; + + ImageVideo.prototype.onPropertyChanged = function (name, value) { + this.properties[name] = value; + if (name == "url" && value != "") { + this.loadVideo(value); + } + + return true; + }; + + ImageVideo.prototype.play = function () { + if (this._video && this._video.videoWidth) { + //is loaded + this._video.play(); + } + }; + + ImageVideo.prototype.playPause = function () { + if (!this._video) { + return; + } + if (this._video.paused) { + this.play(); + } else { + this.pause(); + } + }; + + ImageVideo.prototype.stop = function () { + if (!this._video) { + return; + } + this._video.pause(); + this._video.currentTime = 0; + }; + + ImageVideo.prototype.pause = function () { + if (!this._video) { + return; + } + console.log("Video paused"); + this._video.pause(); + }; + + ImageVideo.prototype.onWidget = function (e, widget) { + /* + if(widget.name == "demo") + { + this.loadVideo(); + } + else if(widget.name == "play") + { + if(this._video) + this.playPause(); + } + if(widget.name == "stop") + { + this.stop(); + } + else if(widget.name == "mute") + { + if(this._video) + this._video.muted = !this._video.muted; + } + */ + }; + + LiteGraph.registerNodeType("graphics/video", ImageVideo); + + // Texture Webcam ***************************************** + function ImageWebcam() { + this.addOutput("Webcam", "image"); + this.properties = { filterFacingMode: false, facingMode: "user" }; + this.boxcolor = "black"; + this.frame = 0; + } + + ImageWebcam.title = "Webcam"; + ImageWebcam.desc = "Webcam image"; + ImageWebcam.is_webcam_open = false; + + ImageWebcam.prototype.openStream = function () { + if (!navigator.mediaDevices.getUserMedia) { + console.log( + "getUserMedia() is not supported in your browser, use chrome and enable WebRTC from about://flags", + ); + return; + } + + this._waiting_confirmation = true; + + // Not showing vendor prefixes. + var constraints = { + audio: false, + video: !this.properties.filterFacingMode + ? true + : { facingMode: this.properties.facingMode }, + }; + navigator.mediaDevices + .getUserMedia(constraints) + .then(this.streamReady.bind(this)) + .catch(onFailSoHard); + + var that = this; + function onFailSoHard(e) { + console.log("Webcam rejected", e); + that._webcam_stream = false; + ImageWebcam.is_webcam_open = false; + that.boxcolor = "red"; + that.trigger("stream_error"); + } + }; + + ImageWebcam.prototype.closeStream = function () { + if (this._webcam_stream) { + var tracks = this._webcam_stream.getTracks(); + if (tracks.length) { + for (var i = 0; i < tracks.length; ++i) { + tracks[i].stop(); + } + } + ImageWebcam.is_webcam_open = false; + this._webcam_stream = null; + this._video = null; + this.boxcolor = "black"; + this.trigger("stream_closed"); + } + }; + + ImageWebcam.prototype.onPropertyChanged = function (name, value) { + if (name == "facingMode") { + this.properties.facingMode = value; + this.closeStream(); + this.openStream(); + } + }; + + ImageWebcam.prototype.onRemoved = function () { + this.closeStream(); + }; + + ImageWebcam.prototype.streamReady = function (localMediaStream) { + this._webcam_stream = localMediaStream; + //this._waiting_confirmation = false; + this.boxcolor = "green"; + + var video = this._video; + if (!video) { + video = document.createElement("video"); + video.autoplay = true; + video.srcObject = localMediaStream; + this._video = video; + //document.body.appendChild( video ); //debug + //when video info is loaded (size and so) + video.onloadedmetadata = function (e) { + // Ready to go. Do some stuff. + console.log(e); + ImageWebcam.is_webcam_open = true; + }; + } + + this.trigger("stream_ready", video); + }; + + ImageWebcam.prototype.onExecute = function () { + if (this._webcam_stream == null && !this._waiting_confirmation) { + this.openStream(); + } + + if (!this._video || !this._video.videoWidth) { + return; + } + + this._video.frame = ++this.frame; + this._video.width = this._video.videoWidth; + this._video.height = this._video.videoHeight; + this.setOutputData(0, this._video); + for (var i = 1; i < this.outputs.length; ++i) { + if (!this.outputs[i]) { + continue; + } + switch (this.outputs[i].name) { + case "width": + this.setOutputData(i, this._video.videoWidth); + break; + case "height": + this.setOutputData(i, this._video.videoHeight); + break; + } + } + }; + + ImageWebcam.prototype.getExtraMenuOptions = function (graphcanvas) { + var that = this; + var txt = !that.properties.show ? "Show Frame" : "Hide Frame"; + return [ + { + content: txt, + callback: function () { + that.properties.show = !that.properties.show; + }, + }, + ]; + }; + + ImageWebcam.prototype.onDrawBackground = function (ctx) { + if (this.flags.collapsed || this.size[1] <= 20 || !this.properties.show) { + return; + } + + if (!this._video) { + return; + } + + //render to graph canvas + ctx.save(); + ctx.drawImage(this._video, 0, 0, this.size[0], this.size[1]); + ctx.restore(); + }; + + ImageWebcam.prototype.onGetOutputs = function () { + return [ + ["width", "number"], + ["height", "number"], + ["stream_ready", LiteGraph.EVENT], + ["stream_closed", LiteGraph.EVENT], + ["stream_error", LiteGraph.EVENT], + ]; + }; + + LiteGraph.registerNodeType("graphics/webcam", ImageWebcam); +})(this); + +(function (global) { + var LiteGraph = global.LiteGraph; + var LGraphCanvas = global.LGraphCanvas; + + //Works with Litegl.js to create WebGL nodes + global.LGraphTexture = null; + + if (typeof GL == "undefined") return; + + LGraphCanvas.link_type_colors["Texture"] = "#987"; + + function LGraphTexture() { + this.addOutput("tex", "Texture"); + this.addOutput("name", "string"); + this.properties = { name: "", filter: true }; + this.size = [ + LGraphTexture.image_preview_size, + LGraphTexture.image_preview_size, + ]; + } + + global.LGraphTexture = LGraphTexture; + + LGraphTexture.title = "Texture"; + LGraphTexture.desc = "Texture"; + LGraphTexture.widgets_info = { + name: { widget: "texture" }, + filter: { widget: "checkbox" }, + }; + + //REPLACE THIS TO INTEGRATE WITH YOUR FRAMEWORK + LGraphTexture.loadTextureCallback = null; //function in charge of loading textures when not present in the container + LGraphTexture.image_preview_size = 256; + + //flags to choose output texture type + LGraphTexture.UNDEFINED = 0; //not specified + LGraphTexture.PASS_THROUGH = 1; //do not apply FX (like disable but passing the in to the out) + LGraphTexture.COPY = 2; //create new texture with the same properties as the origin texture + LGraphTexture.LOW = 3; //create new texture with low precision (byte) + LGraphTexture.HIGH = 4; //create new texture with high precision (half-float) + LGraphTexture.REUSE = 5; //reuse input texture + LGraphTexture.DEFAULT = 2; //use the default + + LGraphTexture.MODE_VALUES = { + undefined: LGraphTexture.UNDEFINED, + "pass through": LGraphTexture.PASS_THROUGH, + copy: LGraphTexture.COPY, + low: LGraphTexture.LOW, + high: LGraphTexture.HIGH, + reuse: LGraphTexture.REUSE, + default: LGraphTexture.DEFAULT, + }; + + //returns the container where all the loaded textures are stored (overwrite if you have a Resources Manager) + LGraphTexture.getTexturesContainer = function () { + return gl.textures; + }; + + //process the loading of a texture (overwrite it if you have a Resources Manager) + LGraphTexture.loadTexture = function (name, options) { + options = options || {}; + var url = name; + if (url.substr(0, 7) == "http://") { + if (LiteGraph.proxy) { + //proxy external files + url = LiteGraph.proxy + url.substr(7); + } + } + + var container = LGraphTexture.getTexturesContainer(); + var tex = (container[name] = GL.Texture.fromURL(url, options)); + return tex; + }; + + LGraphTexture.getTexture = function (name) { + var container = this.getTexturesContainer(); + + if (!container) { + throw "Cannot load texture, container of textures not found"; + } + + var tex = container[name]; + if (!tex && name && name[0] != ":") { + return this.loadTexture(name); + } + + return tex; + }; + + //used to compute the appropiate output texture + LGraphTexture.getTargetTexture = function (origin, target, mode) { + if (!origin) { + throw "LGraphTexture.getTargetTexture expects a reference texture"; + } + + var tex_type = null; + + switch (mode) { + case LGraphTexture.LOW: + tex_type = gl.UNSIGNED_BYTE; + break; + case LGraphTexture.HIGH: + tex_type = gl.HIGH_PRECISION_FORMAT; + break; + case LGraphTexture.REUSE: + return origin; + break; + case LGraphTexture.COPY: + default: + tex_type = origin ? origin.type : gl.UNSIGNED_BYTE; + break; + } + + if ( + !target || + target.width != origin.width || + target.height != origin.height || + target.type != tex_type || + target.format != origin.format + ) { + target = new GL.Texture(origin.width, origin.height, { + type: tex_type, + format: origin.format, + filter: gl.LINEAR, + }); + } + + return target; + }; + + LGraphTexture.getTextureType = function (precision, ref_texture) { + var type = ref_texture ? ref_texture.type : gl.UNSIGNED_BYTE; + switch (precision) { + case LGraphTexture.HIGH: + type = gl.HIGH_PRECISION_FORMAT; + break; + case LGraphTexture.LOW: + type = gl.UNSIGNED_BYTE; + break; + //no default + } + return type; + }; + + LGraphTexture.getWhiteTexture = function () { + if (this._white_texture) { + return this._white_texture; + } + var texture = (this._white_texture = GL.Texture.fromMemory( + 1, + 1, + [255, 255, 255, 255], + { format: gl.RGBA, wrap: gl.REPEAT, filter: gl.NEAREST }, + )); + return texture; + }; + + LGraphTexture.getNoiseTexture = function () { + if (this._noise_texture) { + return this._noise_texture; + } + + var noise = new Uint8Array(512 * 512 * 4); + for (var i = 0; i < 512 * 512 * 4; ++i) { + noise[i] = Math.random() * 255; + } + + var texture = GL.Texture.fromMemory(512, 512, noise, { + format: gl.RGBA, + wrap: gl.REPEAT, + filter: gl.NEAREST, + }); + this._noise_texture = texture; + return texture; + }; + + LGraphTexture.prototype.onDropFile = function (data, filename, file) { + if (!data) { + this._drop_texture = null; + this.properties.name = ""; + } else { + var texture = null; + if (typeof data == "string") { + texture = GL.Texture.fromURL(data); + } else if (filename.toLowerCase().indexOf(".dds") != -1) { + texture = GL.Texture.fromDDSInMemory(data); + } else { + var blob = new Blob([file]); + var url = URL.createObjectURL(blob); + texture = GL.Texture.fromURL(url); + } + + this._drop_texture = texture; + this.properties.name = filename; + } + }; + + LGraphTexture.prototype.getExtraMenuOptions = function (graphcanvas) { + var that = this; + if (!this._drop_texture) { + return; + } + return [ + { + content: "Clear", + callback: function () { + that._drop_texture = null; + that.properties.name = ""; + }, + }, + ]; + }; + + LGraphTexture.prototype.onExecute = function () { + var tex = null; + if (this.isOutputConnected(1)) { + tex = this.getInputData(0); + } + + if (!tex && this._drop_texture) { + tex = this._drop_texture; + } + + if (!tex && this.properties.name) { + tex = LGraphTexture.getTexture(this.properties.name); + } + + if (!tex) { + this.setOutputData(0, null); + this.setOutputData(1, ""); + return; + } + + this._last_tex = tex; + + if (this.properties.filter === false) { + tex.setParameter(gl.TEXTURE_MAG_FILTER, gl.NEAREST); + } else { + tex.setParameter(gl.TEXTURE_MAG_FILTER, gl.LINEAR); + } + + this.setOutputData(0, tex); + this.setOutputData(1, tex.fullpath || tex.filename); + + for (var i = 2; i < this.outputs.length; i++) { + var output = this.outputs[i]; + if (!output) { + continue; + } + var v = null; + if (output.name == "width") { + v = tex.width; + } else if (output.name == "height") { + v = tex.height; + } else if (output.name == "aspect") { + v = tex.width / tex.height; + } + this.setOutputData(i, v); + } + }; + + LGraphTexture.prototype.onResourceRenamed = function (old_name, new_name) { + if (this.properties.name == old_name) { + this.properties.name = new_name; + } + }; + + LGraphTexture.prototype.onDrawBackground = function (ctx) { + if (this.flags.collapsed || this.size[1] <= 20) { + return; + } + + if (this._drop_texture && ctx.webgl) { + ctx.drawImage(this._drop_texture, 0, 0, this.size[0], this.size[1]); + //this._drop_texture.renderQuad(this.pos[0],this.pos[1],this.size[0],this.size[1]); + return; + } + + //Different texture? then get it from the GPU + if (this._last_preview_tex != this._last_tex) { + if (ctx.webgl) { + this._canvas = this._last_tex; + } else { + var tex_canvas = LGraphTexture.generateLowResTexturePreview( + this._last_tex, + ); + if (!tex_canvas) { + return; + } + + this._last_preview_tex = this._last_tex; + this._canvas = cloneCanvas(tex_canvas); + } + } + + if (!this._canvas) { + return; + } + + //render to graph canvas + ctx.save(); + if (!ctx.webgl) { + //reverse image + ctx.translate(0, this.size[1]); + ctx.scale(1, -1); + } + ctx.drawImage(this._canvas, 0, 0, this.size[0], this.size[1]); + ctx.restore(); + }; + + //very slow, used at your own risk + LGraphTexture.generateLowResTexturePreview = function (tex) { + if (!tex) { + return null; + } + + var size = LGraphTexture.image_preview_size; + var temp_tex = tex; + + if (tex.format == gl.DEPTH_COMPONENT) { + return null; + } //cannot generate from depth + + //Generate low-level version in the GPU to speed up + if (tex.width > size || tex.height > size) { + temp_tex = this._preview_temp_tex; + if (!this._preview_temp_tex) { + temp_tex = new GL.Texture(size, size, { + minFilter: gl.NEAREST, + }); + this._preview_temp_tex = temp_tex; + } + + //copy + tex.copyTo(temp_tex); + tex = temp_tex; + } + + //create intermediate canvas with lowquality version + var tex_canvas = this._preview_canvas; + if (!tex_canvas) { + tex_canvas = createCanvas(size, size); + this._preview_canvas = tex_canvas; + } + + if (temp_tex) { + temp_tex.toCanvas(tex_canvas); + } + return tex_canvas; + }; + + LGraphTexture.prototype.getResources = function (res) { + if (this.properties.name) res[this.properties.name] = GL.Texture; + return res; + }; + + LGraphTexture.prototype.onGetInputs = function () { + return [["in", "Texture"]]; + }; + + LGraphTexture.prototype.onGetOutputs = function () { + return [ + ["width", "number"], + ["height", "number"], + ["aspect", "number"], + ]; + }; + + //used to replace shader code + LGraphTexture.replaceCode = function (code, context) { + return code.replace(/\{\{[a-zA-Z0-9_]*\}\}/g, function (v) { + v = v.replace(/[\{\}]/g, ""); + return context[v] || ""; + }); + }; + + LiteGraph.registerNodeType("texture/texture", LGraphTexture); + + //************************** + function LGraphTexturePreview() { + this.addInput("Texture", "Texture"); + this.properties = { flipY: false }; + this.size = [ + LGraphTexture.image_preview_size, + LGraphTexture.image_preview_size, + ]; + } + + LGraphTexturePreview.title = "Preview"; + LGraphTexturePreview.desc = "Show a texture in the graph canvas"; + LGraphTexturePreview.allow_preview = false; + + LGraphTexturePreview.prototype.onDrawBackground = function (ctx) { + if (this.flags.collapsed) { + return; + } + + if (!ctx.webgl && !LGraphTexturePreview.allow_preview) { + return; + } //not working well + + var tex = this.getInputData(0); + if (!tex) { + return; + } + + var tex_canvas = null; + + if (!tex.handle && ctx.webgl) { + tex_canvas = tex; + } else { + tex_canvas = LGraphTexture.generateLowResTexturePreview(tex); + } + + //render to graph canvas + ctx.save(); + if (this.properties.flipY) { + ctx.translate(0, this.size[1]); + ctx.scale(1, -1); + } + ctx.drawImage(tex_canvas, 0, 0, this.size[0], this.size[1]); + ctx.restore(); + }; + + LiteGraph.registerNodeType("texture/preview", LGraphTexturePreview); + + //************************************** + + function LGraphTextureSave() { + this.addInput("Texture", "Texture"); + this.addOutput("tex", "Texture"); + this.addOutput("name", "string"); + this.properties = { name: "", generate_mipmaps: false }; + } + + LGraphTextureSave.title = "Save"; + LGraphTextureSave.desc = "Save a texture in the repository"; + + LGraphTextureSave.prototype.getPreviewTexture = function () { + return this._texture; + }; + + LGraphTextureSave.prototype.onExecute = function () { + var tex = this.getInputData(0); + if (!tex) { + return; + } + + if (this.properties.generate_mipmaps) { + tex.bind(0); + tex.setParameter(gl.TEXTURE_MIN_FILTER, gl.LINEAR_MIPMAP_LINEAR); + gl.generateMipmap(tex.texture_type); + tex.unbind(0); + } + + if (this.properties.name) { + //for cases where we want to perform something when storing it + if (LGraphTexture.storeTexture) { + LGraphTexture.storeTexture(this.properties.name, tex); + } else { + var container = LGraphTexture.getTexturesContainer(); + container[this.properties.name] = tex; + } + } + + this._texture = tex; + this.setOutputData(0, tex); + this.setOutputData(1, this.properties.name); + }; + + LiteGraph.registerNodeType("texture/save", LGraphTextureSave); + + //**************************************************** + + function LGraphTextureOperation() { + this.addInput("Texture", "Texture"); + this.addInput("TextureB", "Texture"); + this.addInput("value", "number"); + this.addOutput("Texture", "Texture"); + this.help = + "

pixelcode must be vec3, uvcode must be vec2, is optional

\ +

uv: tex. coords

color: texture colorB: textureB

time: scene time value: input value

For multiline you must type: result = ...

"; + + this.properties = { + value: 1, + pixelcode: "color + colorB * value", + uvcode: "", + precision: LGraphTexture.DEFAULT, + }; + + this.has_error = false; + } + + LGraphTextureOperation.widgets_info = { + uvcode: { widget: "code" }, + pixelcode: { widget: "code" }, + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + + LGraphTextureOperation.title = "Operation"; + LGraphTextureOperation.desc = "Texture shader operation"; + + LGraphTextureOperation.presets = {}; + + LGraphTextureOperation.prototype.getExtraMenuOptions = function ( + graphcanvas, + ) { + var that = this; + var txt = !that.properties.show ? "Show Texture" : "Hide Texture"; + return [ + { + content: txt, + callback: function () { + that.properties.show = !that.properties.show; + }, + }, + ]; + }; + + LGraphTextureOperation.prototype.onPropertyChanged = function () { + this.has_error = false; + }; + + LGraphTextureOperation.prototype.onDrawBackground = function (ctx) { + if (this.flags.collapsed || this.size[1] <= 20 || !this.properties.show) { + return; + } + + if (!this._tex) { + return; + } + + //only works if using a webgl renderer + if (this._tex.gl != ctx) { + return; + } + + //render to graph canvas + ctx.save(); + ctx.drawImage(this._tex, 0, 0, this.size[0], this.size[1]); + ctx.restore(); + }; + + LGraphTextureOperation.prototype.onExecute = function () { + var tex = this.getInputData(0); + + if (!this.isOutputConnected(0)) { + return; + } //saves work + + if (this.properties.precision === LGraphTexture.PASS_THROUGH) { + this.setOutputData(0, tex); + return; + } + + var texB = this.getInputData(1); + + if (!this.properties.uvcode && !this.properties.pixelcode) { + return; + } + + var width = 512; + var height = 512; + if (tex) { + width = tex.width; + height = tex.height; + } else if (texB) { + width = texB.width; + height = texB.height; + } + + if (!texB) texB = GL.Texture.getWhiteTexture(); + + var type = LGraphTexture.getTextureType(this.properties.precision, tex); + + if (!tex && !this._tex) { + this._tex = new GL.Texture(width, height, { + type: type, + format: gl.RGBA, + filter: gl.LINEAR, + }); + } else { + this._tex = LGraphTexture.getTargetTexture( + tex || this._tex, + this._tex, + this.properties.precision, + ); + } + + var uvcode = ""; + if (this.properties.uvcode) { + uvcode = "uv = " + this.properties.uvcode; + if (this.properties.uvcode.indexOf(";") != -1) { + //there are line breaks, means multiline code + uvcode = this.properties.uvcode; + } + } + + var pixelcode = ""; + if (this.properties.pixelcode) { + pixelcode = "result = " + this.properties.pixelcode; + if (this.properties.pixelcode.indexOf(";") != -1) { + //there are line breaks, means multiline code + pixelcode = this.properties.pixelcode; + } + } + + var shader = this._shader; + + if ( + !this.has_error && + (!shader || this._shader_code != uvcode + "|" + pixelcode) + ) { + var final_pixel_code = LGraphTexture.replaceCode( + LGraphTextureOperation.pixel_shader, + { UV_CODE: uvcode, PIXEL_CODE: pixelcode }, + ); + + try { + shader = new GL.Shader(Shader.SCREEN_VERTEX_SHADER, final_pixel_code); + this.boxcolor = "#00FF00"; + } catch (err) { + //console.log("Error compiling shader: ", err, final_pixel_code ); + GL.Shader.dumpErrorToConsole( + err, + Shader.SCREEN_VERTEX_SHADER, + final_pixel_code, + ); + this.boxcolor = "#FF0000"; + this.has_error = true; + return; + } + this._shader = shader; + this._shader_code = uvcode + "|" + pixelcode; + } + + if (!this._shader) return; + + var value = this.getInputData(2); + if (value != null) { + this.properties.value = value; + } else { + value = parseFloat(this.properties.value); + } + + var time = this.graph.getTime(); + + this._tex.drawTo(function () { + gl.disable(gl.DEPTH_TEST); + gl.disable(gl.CULL_FACE); + gl.disable(gl.BLEND); + if (tex) { + tex.bind(0); + } + if (texB) { + texB.bind(1); + } + var mesh = Mesh.getScreenQuad(); + shader + .uniforms({ + u_texture: 0, + u_textureB: 1, + value: value, + texSize: [width, height, 1 / width, 1 / height], + time: time, + }) + .draw(mesh); + }); + + this.setOutputData(0, this._tex); + }; + + LGraphTextureOperation.pixel_shader = + "precision highp float;\n\ + \n\ + uniform sampler2D u_texture;\n\ + uniform sampler2D u_textureB;\n\ + varying vec2 v_coord;\n\ + uniform vec4 texSize;\n\ + uniform float time;\n\ + uniform float value;\n\ + \n\ + void main() {\n\ + vec2 uv = v_coord;\n\ + {{UV_CODE}};\n\ + vec4 color4 = texture2D(u_texture, uv);\n\ + vec3 color = color4.rgb;\n\ + vec4 color4B = texture2D(u_textureB, uv);\n\ + vec3 colorB = color4B.rgb;\n\ + vec3 result = color;\n\ + float alpha = 1.0;\n\ + {{PIXEL_CODE}};\n\ + gl_FragColor = vec4(result, alpha);\n\ + }\n\ + "; + + LGraphTextureOperation.registerPreset = function (name, code) { + LGraphTextureOperation.presets[name] = code; + }; + + LGraphTextureOperation.registerPreset("", ""); + LGraphTextureOperation.registerPreset("bypass", "color"); + LGraphTextureOperation.registerPreset("add", "color + colorB * value"); + LGraphTextureOperation.registerPreset( + "substract", + "(color - colorB) * value", + ); + LGraphTextureOperation.registerPreset( + "mate", + "mix( color, colorB, color4B.a * value)", + ); + LGraphTextureOperation.registerPreset("invert", "vec3(1.0) - color"); + LGraphTextureOperation.registerPreset("multiply", "color * colorB * value"); + LGraphTextureOperation.registerPreset("divide", "(color / colorB) / value"); + LGraphTextureOperation.registerPreset( + "difference", + "abs(color - colorB) * value", + ); + LGraphTextureOperation.registerPreset("max", "max(color, colorB) * value"); + LGraphTextureOperation.registerPreset("min", "min(color, colorB) * value"); + LGraphTextureOperation.registerPreset( + "displace", + "texture2D(u_texture, uv + (colorB.xy - vec2(0.5)) * value).xyz", + ); + LGraphTextureOperation.registerPreset( + "grayscale", + "vec3(color.x + color.y + color.z) * value / 3.0", + ); + LGraphTextureOperation.registerPreset( + "saturation", + "mix( vec3(color.x + color.y + color.z) / 3.0, color, value )", + ); + LGraphTextureOperation.registerPreset( + "normalmap", + "\n\ + float z0 = texture2D(u_texture, uv + vec2(-texSize.z, -texSize.w) ).x;\n\ + float z1 = texture2D(u_texture, uv + vec2(0.0, -texSize.w) ).x;\n\ + float z2 = texture2D(u_texture, uv + vec2(texSize.z, -texSize.w) ).x;\n\ + float z3 = texture2D(u_texture, uv + vec2(-texSize.z, 0.0) ).x;\n\ + float z4 = color.x;\n\ + float z5 = texture2D(u_texture, uv + vec2(texSize.z, 0.0) ).x;\n\ + float z6 = texture2D(u_texture, uv + vec2(-texSize.z, texSize.w) ).x;\n\ + float z7 = texture2D(u_texture, uv + vec2(0.0, texSize.w) ).x;\n\ + float z8 = texture2D(u_texture, uv + vec2(texSize.z, texSize.w) ).x;\n\ + vec3 normal = vec3( z2 + 2.0*z4 + z7 - z0 - 2.0*z3 - z5, z5 + 2.0*z6 + z7 -z0 - 2.0*z1 - z2, 1.0 );\n\ + normal.xy *= value;\n\ + result.xyz = normalize(normal) * 0.5 + vec3(0.5);\n\ + ", + ); + LGraphTextureOperation.registerPreset( + "threshold", + "vec3(color.x > colorB.x * value ? 1.0 : 0.0,color.y > colorB.y * value ? 1.0 : 0.0,color.z > colorB.z * value ? 1.0 : 0.0)", + ); + + //webglstudio stuff... + LGraphTextureOperation.prototype.onInspect = function (widgets) { + var that = this; + widgets.addCombo("Presets", "", { + values: Object.keys(LGraphTextureOperation.presets), + callback: function (v) { + var code = LGraphTextureOperation.presets[v]; + if (!code) return; + that.setProperty("pixelcode", code); + that.title = v; + widgets.refresh(); + }, + }); + }; + + LiteGraph.registerNodeType("texture/operation", LGraphTextureOperation); + + //**************************************************** + + function LGraphTextureShader() { + this.addOutput("out", "Texture"); + this.properties = { + code: "", + u_value: 1, + u_color: [1, 1, 1, 1], + width: 512, + height: 512, + precision: LGraphTexture.DEFAULT, + }; + + this.properties.code = LGraphTextureShader.pixel_shader; + this._uniforms = { + u_value: 1, + u_color: vec4.create(), + in_texture: 0, + texSize: vec4.create(), + time: 0, + }; + } + + LGraphTextureShader.title = "Shader"; + LGraphTextureShader.desc = "Texture shader"; + LGraphTextureShader.widgets_info = { + code: { type: "code", lang: "glsl" }, + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + + LGraphTextureShader.prototype.onPropertyChanged = function (name, value) { + if (name != "code") { + return; + } + + var shader = this.getShader(); + if (!shader) { + return; + } + + //update connections + var uniforms = shader.uniformInfo; + + //remove deprecated slots + if (this.inputs) { + var already = {}; + for (var i = 0; i < this.inputs.length; ++i) { + var info = this.getInputInfo(i); + if (!info) { + continue; + } + + if (uniforms[info.name] && !already[info.name]) { + already[info.name] = true; + continue; + } + this.removeInput(i); + i--; + } + } + + //update existing ones + for (var i in uniforms) { + var info = shader.uniformInfo[i]; + if (info.loc === null) { + continue; + } //is an attribute, not a uniform + if (i == "time") { + //default one + continue; + } + + var type = "number"; + if (this._shader.samplers[i]) { + type = "texture"; + } else { + switch (info.size) { + case 1: + type = "number"; + break; + case 2: + type = "vec2"; + break; + case 3: + type = "vec3"; + break; + case 4: + type = "vec4"; + break; + case 9: + type = "mat3"; + break; + case 16: + type = "mat4"; + break; + default: + continue; + } + } + + var slot = this.findInputSlot(i); + if (slot == -1) { + this.addInput(i, type); + continue; + } + + var input_info = this.getInputInfo(slot); + if (!input_info) { + this.addInput(i, type); + } else { + if (input_info.type == type) { + continue; + } + this.removeInput(slot, type); + this.addInput(i, type); + } + } + }; + + LGraphTextureShader.prototype.getShader = function () { + //replug + if (this._shader && this._shader_code == this.properties.code) { + return this._shader; + } + + this._shader_code = this.properties.code; + this._shader = new GL.Shader( + Shader.SCREEN_VERTEX_SHADER, + this.properties.code, + ); + if (!this._shader) { + this.boxcolor = "red"; + return null; + } else { + this.boxcolor = "green"; + } + return this._shader; + }; + + LGraphTextureShader.prototype.onExecute = function () { + if (!this.isOutputConnected(0)) { + return; + } //saves work + + var shader = this.getShader(); + if (!shader) { + return; + } + + var tex_slot = 0; + var in_tex = null; + + //set uniforms + if (this.inputs) + for (var i = 0; i < this.inputs.length; ++i) { + var info = this.getInputInfo(i); + var data = this.getInputData(i); + if (data == null) { + continue; + } + + if (data.constructor === GL.Texture) { + data.bind(tex_slot); + if (!in_tex) { + in_tex = data; + } + data = tex_slot; + tex_slot++; + } + shader.setUniform(info.name, data); //data is tex_slot + } + + var uniforms = this._uniforms; + var type = LGraphTexture.getTextureType(this.properties.precision, in_tex); + + //render to texture + var w = this.properties.width | 0; + var h = this.properties.height | 0; + if (w == 0) { + w = in_tex ? in_tex.width : gl.canvas.width; + } + if (h == 0) { + h = in_tex ? in_tex.height : gl.canvas.height; + } + uniforms.texSize[0] = w; + uniforms.texSize[1] = h; + uniforms.texSize[2] = 1 / w; + uniforms.texSize[3] = 1 / h; + uniforms.time = this.graph.getTime(); + uniforms.u_value = this.properties.u_value; + uniforms.u_color.set(this.properties.u_color); + + if ( + !this._tex || + this._tex.type != type || + this._tex.width != w || + this._tex.height != h + ) { + this._tex = new GL.Texture(w, h, { + type: type, + format: gl.RGBA, + filter: gl.LINEAR, + }); + } + var tex = this._tex; + tex.drawTo(function () { + shader.uniforms(uniforms).draw(GL.Mesh.getScreenQuad()); + }); + + this.setOutputData(0, this._tex); + }; + + LGraphTextureShader.pixel_shader = + "precision highp float;\n\ +\n\ +varying vec2 v_coord;\n\ +uniform float time; //time in seconds\n\ +uniform vec4 texSize; //tex resolution\n\ +uniform float u_value;\n\ +uniform vec4 u_color;\n\n\ +void main() {\n\ + vec2 uv = v_coord;\n\ + vec3 color = vec3(0.0);\n\ + //your code here\n\ + color.xy=uv;\n\n\ + gl_FragColor = vec4(color, 1.0);\n\ +}\n\ +"; + + LiteGraph.registerNodeType("texture/shader", LGraphTextureShader); + + // Texture Scale Offset + + function LGraphTextureScaleOffset() { + this.addInput("in", "Texture"); + this.addInput("scale", "vec2"); + this.addInput("offset", "vec2"); + this.addOutput("out", "Texture"); + this.properties = { + offset: vec2.fromValues(0, 0), + scale: vec2.fromValues(1, 1), + precision: LGraphTexture.DEFAULT, + }; + } + + LGraphTextureScaleOffset.widgets_info = { + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + + LGraphTextureScaleOffset.title = "Scale/Offset"; + LGraphTextureScaleOffset.desc = "Applies an scaling and offseting"; + + LGraphTextureScaleOffset.prototype.onExecute = function () { + var tex = this.getInputData(0); + + if (!this.isOutputConnected(0) || !tex) { + return; + } //saves work + + if (this.properties.precision === LGraphTexture.PASS_THROUGH) { + this.setOutputData(0, tex); + return; + } + + var width = tex.width; + var height = tex.height; + var type = + this.precision === LGraphTexture.LOW + ? gl.UNSIGNED_BYTE + : gl.HIGH_PRECISION_FORMAT; + if (this.precision === LGraphTexture.DEFAULT) { + type = tex.type; + } + + if ( + !this._tex || + this._tex.width != width || + this._tex.height != height || + this._tex.type != type + ) { + this._tex = new GL.Texture(width, height, { + type: type, + format: gl.RGBA, + filter: gl.LINEAR, + }); + } + + var shader = this._shader; + + if (!shader) { + shader = new GL.Shader( + GL.Shader.SCREEN_VERTEX_SHADER, + LGraphTextureScaleOffset.pixel_shader, + ); + } + + var scale = this.getInputData(1); + if (scale) { + this.properties.scale[0] = scale[0]; + this.properties.scale[1] = scale[1]; + } else { + scale = this.properties.scale; + } + + var offset = this.getInputData(2); + if (offset) { + this.properties.offset[0] = offset[0]; + this.properties.offset[1] = offset[1]; + } else { + offset = this.properties.offset; + } + + this._tex.drawTo(function () { + gl.disable(gl.DEPTH_TEST); + gl.disable(gl.CULL_FACE); + gl.disable(gl.BLEND); + tex.bind(0); + var mesh = Mesh.getScreenQuad(); + shader + .uniforms({ + u_texture: 0, + u_scale: scale, + u_offset: offset, + }) + .draw(mesh); + }); + + this.setOutputData(0, this._tex); + }; + + LGraphTextureScaleOffset.pixel_shader = + "precision highp float;\n\ + \n\ + uniform sampler2D u_texture;\n\ + uniform sampler2D u_textureB;\n\ + varying vec2 v_coord;\n\ + uniform vec2 u_scale;\n\ + uniform vec2 u_offset;\n\ + \n\ + void main() {\n\ + vec2 uv = v_coord;\n\ + uv = uv / u_scale - u_offset;\n\ + gl_FragColor = texture2D(u_texture, uv);\n\ + }\n\ + "; + + LiteGraph.registerNodeType("texture/scaleOffset", LGraphTextureScaleOffset); + + // Warp (distort a texture) ************************* + + function LGraphTextureWarp() { + this.addInput("in", "Texture"); + this.addInput("warp", "Texture"); + this.addInput("factor", "number"); + this.addOutput("out", "Texture"); + this.properties = { + factor: 0.01, + scale: [1, 1], + offset: [0, 0], + precision: LGraphTexture.DEFAULT, + }; + + this._uniforms = { + u_texture: 0, + u_textureB: 1, + u_factor: 1, + u_scale: vec2.create(), + u_offset: vec2.create(), + }; + } + + LGraphTextureWarp.widgets_info = { + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + + LGraphTextureWarp.title = "Warp"; + LGraphTextureWarp.desc = "Texture warp operation"; + + LGraphTextureWarp.prototype.onExecute = function () { + var tex = this.getInputData(0); + + if (!this.isOutputConnected(0)) { + return; + } //saves work + + if (this.properties.precision === LGraphTexture.PASS_THROUGH) { + this.setOutputData(0, tex); + return; + } + + var texB = this.getInputData(1); + + var width = 512; + var height = 512; + var type = gl.UNSIGNED_BYTE; + if (tex) { + width = tex.width; + height = tex.height; + type = tex.type; + } else if (texB) { + width = texB.width; + height = texB.height; + type = texB.type; + } + + if (!tex && !this._tex) { + this._tex = new GL.Texture(width, height, { + type: + this.precision === LGraphTexture.LOW + ? gl.UNSIGNED_BYTE + : gl.HIGH_PRECISION_FORMAT, + format: gl.RGBA, + filter: gl.LINEAR, + }); + } else { + this._tex = LGraphTexture.getTargetTexture( + tex || this._tex, + this._tex, + this.properties.precision, + ); + } + + var shader = this._shader; + + if (!shader) { + shader = new GL.Shader( + GL.Shader.SCREEN_VERTEX_SHADER, + LGraphTextureWarp.pixel_shader, + ); + } + + var factor = this.getInputData(2); + if (factor != null) { + this.properties.factor = factor; + } else { + factor = parseFloat(this.properties.factor); + } + var uniforms = this._uniforms; + uniforms.u_factor = factor; + uniforms.u_scale.set(this.properties.scale); + uniforms.u_offset.set(this.properties.offset); + + this._tex.drawTo(function () { + gl.disable(gl.DEPTH_TEST); + gl.disable(gl.CULL_FACE); + gl.disable(gl.BLEND); + if (tex) { + tex.bind(0); + } + if (texB) { + texB.bind(1); + } + var mesh = Mesh.getScreenQuad(); + shader.uniforms(uniforms).draw(mesh); + }); + + this.setOutputData(0, this._tex); + }; + + LGraphTextureWarp.pixel_shader = + "precision highp float;\n\ + \n\ + uniform sampler2D u_texture;\n\ + uniform sampler2D u_textureB;\n\ + varying vec2 v_coord;\n\ + uniform float u_factor;\n\ + uniform vec2 u_scale;\n\ + uniform vec2 u_offset;\n\ + \n\ + void main() {\n\ + vec2 uv = v_coord;\n\ + uv += ( texture2D(u_textureB, uv).rg - vec2(0.5)) * u_factor * u_scale + u_offset;\n\ + gl_FragColor = texture2D(u_texture, uv);\n\ + }\n\ + "; + + LiteGraph.registerNodeType("texture/warp", LGraphTextureWarp); + + //**************************************************** + + // Texture to Viewport ***************************************** + function LGraphTextureToViewport() { + this.addInput("Texture", "Texture"); + this.properties = { + additive: false, + antialiasing: false, + filter: true, + disable_alpha: false, + gamma: 1.0, + viewport: [0, 0, 1, 1], + }; + this.size[0] = 130; + } + + LGraphTextureToViewport.title = "to Viewport"; + LGraphTextureToViewport.desc = "Texture to viewport"; + + LGraphTextureToViewport._prev_viewport = new Float32Array(4); + + LGraphTextureToViewport.prototype.onDrawBackground = function (ctx) { + if (this.flags.collapsed || this.size[1] <= 40) return; + + var tex = this.getInputData(0); + if (!tex) { + return; + } + + ctx.drawImage( + ctx == gl ? tex : gl.canvas, + 10, + 30, + this.size[0] - 20, + this.size[1] - 40, + ); + }; + + LGraphTextureToViewport.prototype.onExecute = function () { + var tex = this.getInputData(0); + if (!tex) { + return; + } + + if (this.properties.disable_alpha) { + gl.disable(gl.BLEND); + } else { + gl.enable(gl.BLEND); + if (this.properties.additive) { + gl.blendFunc(gl.SRC_ALPHA, gl.ONE); + } else { + gl.blendFunc(gl.SRC_ALPHA, gl.ONE_MINUS_SRC_ALPHA); + } + } + + gl.disable(gl.DEPTH_TEST); + var gamma = this.properties.gamma || 1.0; + if (this.isInputConnected(1)) { + gamma = this.getInputData(1); + } + + tex.setParameter( + gl.TEXTURE_MAG_FILTER, + this.properties.filter ? gl.LINEAR : gl.NEAREST, + ); + + var old_viewport = LGraphTextureToViewport._prev_viewport; + old_viewport.set(gl.viewport_data); + var new_view = this.properties.viewport; + gl.viewport( + old_viewport[0] + old_viewport[2] * new_view[0], + old_viewport[1] + old_viewport[3] * new_view[1], + old_viewport[2] * new_view[2], + old_viewport[3] * new_view[3], + ); + var viewport = gl.getViewport(); //gl.getParameter(gl.VIEWPORT); + + if (this.properties.antialiasing) { + if (!LGraphTextureToViewport._shader) { + LGraphTextureToViewport._shader = new GL.Shader( + GL.Shader.SCREEN_VERTEX_SHADER, + LGraphTextureToViewport.aa_pixel_shader, + ); + } + + var mesh = Mesh.getScreenQuad(); + tex.bind(0); + LGraphTextureToViewport._shader + .uniforms({ + u_texture: 0, + uViewportSize: [tex.width, tex.height], + u_igamma: 1 / gamma, + inverseVP: [1 / tex.width, 1 / tex.height], + }) + .draw(mesh); + } else { + if (gamma != 1.0) { + if (!LGraphTextureToViewport._gamma_shader) { + LGraphTextureToViewport._gamma_shader = new GL.Shader( + Shader.SCREEN_VERTEX_SHADER, + LGraphTextureToViewport.gamma_pixel_shader, + ); + } + tex.toViewport(LGraphTextureToViewport._gamma_shader, { + u_texture: 0, + u_igamma: 1 / gamma, + }); + } else { + tex.toViewport(); + } + } + + gl.viewport( + old_viewport[0], + old_viewport[1], + old_viewport[2], + old_viewport[3], + ); + }; + + LGraphTextureToViewport.prototype.onGetInputs = function () { + return [["gamma", "number"]]; + }; + + LGraphTextureToViewport.aa_pixel_shader = + "precision highp float;\n\ + precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform vec2 uViewportSize;\n\ + uniform vec2 inverseVP;\n\ + uniform float u_igamma;\n\ + #define FXAA_REDUCE_MIN (1.0/ 128.0)\n\ + #define FXAA_REDUCE_MUL (1.0 / 8.0)\n\ + #define FXAA_SPAN_MAX 8.0\n\ + \n\ + /* from mitsuhiko/webgl-meincraft based on the code on geeks3d.com */\n\ + vec4 applyFXAA(sampler2D tex, vec2 fragCoord)\n\ + {\n\ + vec4 color = vec4(0.0);\n\ + /*vec2 inverseVP = vec2(1.0 / uViewportSize.x, 1.0 / uViewportSize.y);*/\n\ + vec3 rgbNW = texture2D(tex, (fragCoord + vec2(-1.0, -1.0)) * inverseVP).xyz;\n\ + vec3 rgbNE = texture2D(tex, (fragCoord + vec2(1.0, -1.0)) * inverseVP).xyz;\n\ + vec3 rgbSW = texture2D(tex, (fragCoord + vec2(-1.0, 1.0)) * inverseVP).xyz;\n\ + vec3 rgbSE = texture2D(tex, (fragCoord + vec2(1.0, 1.0)) * inverseVP).xyz;\n\ + vec3 rgbM = texture2D(tex, fragCoord * inverseVP).xyz;\n\ + vec3 luma = vec3(0.299, 0.587, 0.114);\n\ + float lumaNW = dot(rgbNW, luma);\n\ + float lumaNE = dot(rgbNE, luma);\n\ + float lumaSW = dot(rgbSW, luma);\n\ + float lumaSE = dot(rgbSE, luma);\n\ + float lumaM = dot(rgbM, luma);\n\ + float lumaMin = min(lumaM, min(min(lumaNW, lumaNE), min(lumaSW, lumaSE)));\n\ + float lumaMax = max(lumaM, max(max(lumaNW, lumaNE), max(lumaSW, lumaSE)));\n\ + \n\ + vec2 dir;\n\ + dir.x = -((lumaNW + lumaNE) - (lumaSW + lumaSE));\n\ + dir.y = ((lumaNW + lumaSW) - (lumaNE + lumaSE));\n\ + \n\ + float dirReduce = max((lumaNW + lumaNE + lumaSW + lumaSE) * (0.25 * FXAA_REDUCE_MUL), FXAA_REDUCE_MIN);\n\ + \n\ + float rcpDirMin = 1.0 / (min(abs(dir.x), abs(dir.y)) + dirReduce);\n\ + dir = min(vec2(FXAA_SPAN_MAX, FXAA_SPAN_MAX), max(vec2(-FXAA_SPAN_MAX, -FXAA_SPAN_MAX), dir * rcpDirMin)) * inverseVP;\n\ + \n\ + vec3 rgbA = 0.5 * (texture2D(tex, fragCoord * inverseVP + dir * (1.0 / 3.0 - 0.5)).xyz + \n\ + texture2D(tex, fragCoord * inverseVP + dir * (2.0 / 3.0 - 0.5)).xyz);\n\ + vec3 rgbB = rgbA * 0.5 + 0.25 * (texture2D(tex, fragCoord * inverseVP + dir * -0.5).xyz + \n\ + texture2D(tex, fragCoord * inverseVP + dir * 0.5).xyz);\n\ + \n\ + //return vec4(rgbA,1.0);\n\ + float lumaB = dot(rgbB, luma);\n\ + if ((lumaB < lumaMin) || (lumaB > lumaMax))\n\ + color = vec4(rgbA, 1.0);\n\ + else\n\ + color = vec4(rgbB, 1.0);\n\ + if(u_igamma != 1.0)\n\ + color.xyz = pow( color.xyz, vec3(u_igamma) );\n\ + return color;\n\ + }\n\ + \n\ + void main() {\n\ + gl_FragColor = applyFXAA( u_texture, v_coord * uViewportSize) ;\n\ + }\n\ + "; + + LGraphTextureToViewport.gamma_pixel_shader = + "precision highp float;\n\ + precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform float u_igamma;\n\ + void main() {\n\ + vec4 color = texture2D( u_texture, v_coord);\n\ + color.xyz = pow(color.xyz, vec3(u_igamma) );\n\ + gl_FragColor = color;\n\ + }\n\ + "; + + LiteGraph.registerNodeType("texture/toviewport", LGraphTextureToViewport); + + // Texture Copy ***************************************** + function LGraphTextureCopy() { + this.addInput("Texture", "Texture"); + this.addOutput("", "Texture"); + this.properties = { + size: 0, + generate_mipmaps: false, + precision: LGraphTexture.DEFAULT, + }; + } + + LGraphTextureCopy.title = "Copy"; + LGraphTextureCopy.desc = "Copy Texture"; + LGraphTextureCopy.widgets_info = { + size: { + widget: "combo", + values: [0, 32, 64, 128, 256, 512, 1024, 2048], + }, + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + + LGraphTextureCopy.prototype.onExecute = function () { + var tex = this.getInputData(0); + if (!tex && !this._temp_texture) { + return; + } + + if (!this.isOutputConnected(0)) { + return; + } //saves work + + //copy the texture + if (tex) { + var width = tex.width; + var height = tex.height; + + if (this.properties.size != 0) { + width = this.properties.size; + height = this.properties.size; + } + + var temp = this._temp_texture; + + var type = tex.type; + if (this.properties.precision === LGraphTexture.LOW) { + type = gl.UNSIGNED_BYTE; + } else if (this.properties.precision === LGraphTexture.HIGH) { + type = gl.HIGH_PRECISION_FORMAT; + } + + if ( + !temp || + temp.width != width || + temp.height != height || + temp.type != type + ) { + var minFilter = gl.LINEAR; + if ( + this.properties.generate_mipmaps && + isPowerOfTwo(width) && + isPowerOfTwo(height) + ) { + minFilter = gl.LINEAR_MIPMAP_LINEAR; + } + this._temp_texture = new GL.Texture(width, height, { + type: type, + format: gl.RGBA, + minFilter: minFilter, + magFilter: gl.LINEAR, + }); + } + tex.copyTo(this._temp_texture); + + if (this.properties.generate_mipmaps) { + this._temp_texture.bind(0); + gl.generateMipmap(this._temp_texture.texture_type); + this._temp_texture.unbind(0); + } + } + + this.setOutputData(0, this._temp_texture); + }; + + LiteGraph.registerNodeType("texture/copy", LGraphTextureCopy); + + // Texture Downsample ***************************************** + function LGraphTextureDownsample() { + this.addInput("Texture", "Texture"); + this.addOutput("", "Texture"); + this.properties = { + iterations: 1, + generate_mipmaps: false, + precision: LGraphTexture.DEFAULT, + }; + } + + LGraphTextureDownsample.title = "Downsample"; + LGraphTextureDownsample.desc = "Downsample Texture"; + LGraphTextureDownsample.widgets_info = { + iterations: { type: "number", step: 1, precision: 0, min: 0 }, + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + + LGraphTextureDownsample.prototype.onExecute = function () { + var tex = this.getInputData(0); + if (!tex && !this._temp_texture) { + return; + } + + if (!this.isOutputConnected(0)) { + return; + } //saves work + + //we do not allow any texture different than texture 2D + if (!tex || tex.texture_type !== GL.TEXTURE_2D) { + return; + } + + if (this.properties.iterations < 1) { + this.setOutputData(0, tex); + return; + } + + var shader = LGraphTextureDownsample._shader; + if (!shader) { + LGraphTextureDownsample._shader = shader = new GL.Shader( + GL.Shader.SCREEN_VERTEX_SHADER, + LGraphTextureDownsample.pixel_shader, + ); + } + + var width = tex.width | 0; + var height = tex.height | 0; + var type = tex.type; + if (this.properties.precision === LGraphTexture.LOW) { + type = gl.UNSIGNED_BYTE; + } else if (this.properties.precision === LGraphTexture.HIGH) { + type = gl.HIGH_PRECISION_FORMAT; + } + var iterations = this.properties.iterations || 1; + + var origin = tex; + var target = null; + + var temp = []; + var options = { + type: type, + format: tex.format, + }; + + var offset = vec2.create(); + var uniforms = { + u_offset: offset, + }; + + if (this._texture) { + GL.Texture.releaseTemporary(this._texture); + } + + for (var i = 0; i < iterations; ++i) { + offset[0] = 1 / width; + offset[1] = 1 / height; + width = width >> 1 || 0; + height = height >> 1 || 0; + target = GL.Texture.getTemporary(width, height, options); + temp.push(target); + origin.setParameter(GL.TEXTURE_MAG_FILTER, GL.NEAREST); + origin.copyTo(target, shader, uniforms); + if (width == 1 && height == 1) { + break; + } //nothing else to do + origin = target; + } + + //keep the last texture used + this._texture = temp.pop(); + + //free the rest + for (var i = 0; i < temp.length; ++i) { + GL.Texture.releaseTemporary(temp[i]); + } + + if (this.properties.generate_mipmaps) { + this._texture.bind(0); + gl.generateMipmap(this._texture.texture_type); + this._texture.unbind(0); + } + + this.setOutputData(0, this._texture); + }; + + LGraphTextureDownsample.pixel_shader = + "precision highp float;\n\ + precision highp float;\n\ + uniform sampler2D u_texture;\n\ + uniform vec2 u_offset;\n\ + varying vec2 v_coord;\n\ + \n\ + void main() {\n\ + vec4 color = texture2D(u_texture, v_coord );\n\ + color += texture2D(u_texture, v_coord + vec2( u_offset.x, 0.0 ) );\n\ + color += texture2D(u_texture, v_coord + vec2( 0.0, u_offset.y ) );\n\ + color += texture2D(u_texture, v_coord + vec2( u_offset.x, u_offset.y ) );\n\ + gl_FragColor = color * 0.25;\n\ + }\n\ + "; + + LiteGraph.registerNodeType("texture/downsample", LGraphTextureDownsample); + + function LGraphTextureResize() { + this.addInput("Texture", "Texture"); + this.addOutput("", "Texture"); + this.properties = { + size: [512, 512], + generate_mipmaps: false, + precision: LGraphTexture.DEFAULT, + }; + } + + LGraphTextureResize.title = "Resize"; + LGraphTextureResize.desc = "Resize Texture"; + LGraphTextureResize.widgets_info = { + iterations: { type: "number", step: 1, precision: 0, min: 0 }, + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + + LGraphTextureResize.prototype.onExecute = function () { + var tex = this.getInputData(0); + if (!tex && !this._temp_texture) { + return; + } + + if (!this.isOutputConnected(0)) { + return; + } //saves work + + //we do not allow any texture different than texture 2D + if (!tex || tex.texture_type !== GL.TEXTURE_2D) { + return; + } + + var width = this.properties.size[0] | 0; + var height = this.properties.size[1] | 0; + if (width == 0) width = tex.width; + if (height == 0) height = tex.height; + var type = tex.type; + if (this.properties.precision === LGraphTexture.LOW) { + type = gl.UNSIGNED_BYTE; + } else if (this.properties.precision === LGraphTexture.HIGH) { + type = gl.HIGH_PRECISION_FORMAT; + } + + if ( + !this._texture || + this._texture.width != width || + this._texture.height != height || + this._texture.type != type + ) + this._texture = new GL.Texture(width, height, { type: type }); + + tex.copyTo(this._texture); + + if (this.properties.generate_mipmaps) { + this._texture.bind(0); + gl.generateMipmap(this._texture.texture_type); + this._texture.unbind(0); + } + + this.setOutputData(0, this._texture); + }; + + LiteGraph.registerNodeType("texture/resize", LGraphTextureResize); + + // Texture Average ***************************************** + function LGraphTextureAverage() { + this.addInput("Texture", "Texture"); + this.addOutput("tex", "Texture"); + this.addOutput("avg", "vec4"); + this.addOutput("lum", "number"); + this.properties = { + use_previous_frame: true, //to avoid stalls + high_quality: false, //to use as much pixels as possible + }; + + this._uniforms = { + u_texture: 0, + u_mipmap_offset: 0, + }; + this._luminance = new Float32Array(4); + } + + LGraphTextureAverage.title = "Average"; + LGraphTextureAverage.desc = + "Compute a partial average (32 random samples) of a texture and stores it as a 1x1 pixel texture.\n If high_quality is true, then it generates the mipmaps first and reads from the lower one."; + + LGraphTextureAverage.prototype.onExecute = function () { + if (!this.properties.use_previous_frame) { + this.updateAverage(); + } + + var v = this._luminance; + this.setOutputData(0, this._temp_texture); + this.setOutputData(1, v); + this.setOutputData(2, (v[0] + v[1] + v[2]) / 3); + }; + + //executed before rendering the frame + LGraphTextureAverage.prototype.onPreRenderExecute = function () { + this.updateAverage(); + }; + + LGraphTextureAverage.prototype.updateAverage = function () { + var tex = this.getInputData(0); + if (!tex) { + return; + } + + if ( + !this.isOutputConnected(0) && + !this.isOutputConnected(1) && + !this.isOutputConnected(2) + ) { + return; + } //saves work + + if (!LGraphTextureAverage._shader) { + LGraphTextureAverage._shader = new GL.Shader( + GL.Shader.SCREEN_VERTEX_SHADER, + LGraphTextureAverage.pixel_shader, + ); + //creates 256 random numbers and stores them in two mat4 + var samples = new Float32Array(16); + for (var i = 0; i < samples.length; ++i) { + samples[i] = Math.random(); //poorly distributed samples + } + //upload only once + LGraphTextureAverage._shader.uniforms({ + u_samples_a: samples.subarray(0, 16), + u_samples_b: samples.subarray(16, 32), + }); + } + + var temp = this._temp_texture; + var type = gl.UNSIGNED_BYTE; + if (tex.type != type) { + //force floats, half floats cannot be read with gl.readPixels + type = gl.FLOAT; + } + + if (!temp || temp.type != type) { + this._temp_texture = new GL.Texture(1, 1, { + type: type, + format: gl.RGBA, + filter: gl.NEAREST, + }); + } + + this._uniforms.u_mipmap_offset = 0; + + if (this.properties.high_quality) { + if (!this._temp_pot2_texture || this._temp_pot2_texture.type != type) + this._temp_pot2_texture = new GL.Texture(512, 512, { + type: type, + format: gl.RGBA, + minFilter: gl.LINEAR_MIPMAP_LINEAR, + magFilter: gl.LINEAR, + }); + + tex.copyTo(this._temp_pot2_texture); + tex = this._temp_pot2_texture; + tex.bind(0); + gl.generateMipmap(GL.TEXTURE_2D); + this._uniforms.u_mipmap_offset = 9; + } + + var shader = LGraphTextureAverage._shader; + var uniforms = this._uniforms; + uniforms.u_mipmap_offset = this.properties.mipmap_offset; + gl.disable(gl.DEPTH_TEST); + gl.disable(gl.BLEND); + this._temp_texture.drawTo(function () { + tex.toViewport(shader, uniforms); + }); + + if (this.isOutputConnected(1) || this.isOutputConnected(2)) { + var pixel = this._temp_texture.getPixels(); + if (pixel) { + var v = this._luminance; + var type = this._temp_texture.type; + v.set(pixel); + if (type == gl.UNSIGNED_BYTE) { + vec4.scale(v, v, 1 / 255); + } else if (type == GL.HALF_FLOAT || type == GL.HALF_FLOAT_OES) { + //no half floats possible, hard to read back unless copyed to a FLOAT texture, so temp_texture is always forced to FLOAT + } + } + } + }; + + LGraphTextureAverage.pixel_shader = + "precision highp float;\n\ + precision highp float;\n\ + uniform mat4 u_samples_a;\n\ + uniform mat4 u_samples_b;\n\ + uniform sampler2D u_texture;\n\ + uniform float u_mipmap_offset;\n\ + varying vec2 v_coord;\n\ + \n\ + void main() {\n\ + vec4 color = vec4(0.0);\n\ + //random average\n\ + for(int i = 0; i < 4; ++i)\n\ + for(int j = 0; j < 4; ++j)\n\ + {\n\ + color += texture2D(u_texture, vec2( u_samples_a[i][j], u_samples_b[i][j] ), u_mipmap_offset );\n\ + color += texture2D(u_texture, vec2( 1.0 - u_samples_a[i][j], 1.0 - u_samples_b[i][j] ), u_mipmap_offset );\n\ + }\n\ + gl_FragColor = color * 0.03125;\n\ + }\n\ + "; + + LiteGraph.registerNodeType("texture/average", LGraphTextureAverage); + + // Computes operation between pixels (max, min) ***************************************** + function LGraphTextureMinMax() { + this.addInput("Texture", "Texture"); + this.addOutput("min_t", "Texture"); + this.addOutput("max_t", "Texture"); + this.addOutput("min", "vec4"); + this.addOutput("max", "vec4"); + this.properties = { + mode: "max", + use_previous_frame: true, //to avoid stalls + }; + + this._uniforms = { + u_texture: 0, + }; + + this._max = new Float32Array(4); + this._min = new Float32Array(4); + + this._textures_chain = []; + } + + LGraphTextureMinMax.widgets_info = { + mode: { widget: "combo", values: ["min", "max", "avg"] }, + }; + + LGraphTextureMinMax.title = "MinMax"; + LGraphTextureMinMax.desc = "Compute the scene min max"; + + LGraphTextureMinMax.prototype.onExecute = function () { + if (!this.properties.use_previous_frame) { + this.update(); + } + + this.setOutputData(0, this._temp_texture); + this.setOutputData(1, this._luminance); + }; + + //executed before rendering the frame + LGraphTextureMinMax.prototype.onPreRenderExecute = function () { + this.update(); + }; + + LGraphTextureMinMax.prototype.update = function () { + var tex = this.getInputData(0); + if (!tex) { + return; + } + + if (!this.isOutputConnected(0) && !this.isOutputConnected(1)) { + return; + } //saves work + + if (!LGraphTextureMinMax._shader) { + LGraphTextureMinMax._shader = new GL.Shader( + GL.Shader.SCREEN_VERTEX_SHADER, + LGraphTextureMinMax.pixel_shader, + ); + } + + var temp = this._temp_texture; + var type = gl.UNSIGNED_BYTE; + if (tex.type != type) { + //force floats, half floats cannot be read with gl.readPixels + type = gl.FLOAT; + } + + var size = 512; + + if (!this._textures_chain.length || this._textures_chain[0].type != type) { + var index = 0; + while (i) { + this._textures_chain[i] = new GL.Texture(size, size, { + type: type, + format: gl.RGBA, + filter: gl.NEAREST, + }); + size = size >> 2; + i++; + if (size == 1) break; + } + } + + tex.copyTo(this._textures_chain[0]); + var prev = this._textures_chain[0]; + for (var i = 1; i <= this._textures_chain.length; ++i) { + var tex = this._textures_chain[i]; + + prev = tex; + } + + var shader = LGraphTextureMinMax._shader; + var uniforms = this._uniforms; + uniforms.u_mipmap_offset = this.properties.mipmap_offset; + gl.disable(gl.DEPTH_TEST); + gl.disable(gl.BLEND); + this._temp_texture.drawTo(function () { + tex.toViewport(shader, uniforms); + }); + }; + + LGraphTextureMinMax.pixel_shader = + "precision highp float;\n\ + precision highp float;\n\ + uniform mat4 u_samples_a;\n\ + uniform mat4 u_samples_b;\n\ + uniform sampler2D u_texture;\n\ + uniform float u_mipmap_offset;\n\ + varying vec2 v_coord;\n\ + \n\ + void main() {\n\ + vec4 color = vec4(0.0);\n\ + //random average\n\ + for(int i = 0; i < 4; ++i)\n\ + for(int j = 0; j < 4; ++j)\n\ + {\n\ + color += texture2D(u_texture, vec2( u_samples_a[i][j], u_samples_b[i][j] ), u_mipmap_offset );\n\ + color += texture2D(u_texture, vec2( 1.0 - u_samples_a[i][j], 1.0 - u_samples_b[i][j] ), u_mipmap_offset );\n\ + }\n\ + gl_FragColor = color * 0.03125;\n\ + }\n\ + "; + + //LiteGraph.registerNodeType("texture/clustered_operation", LGraphTextureClusteredOperation); + + function LGraphTextureTemporalSmooth() { + this.addInput("in", "Texture"); + this.addInput("factor", "Number"); + this.addOutput("out", "Texture"); + this.properties = { factor: 0.5 }; + this._uniforms = { + u_texture: 0, + u_textureB: 1, + u_factor: this.properties.factor, + }; + } + + LGraphTextureTemporalSmooth.title = "Smooth"; + LGraphTextureTemporalSmooth.desc = "Smooth texture over time"; + + LGraphTextureTemporalSmooth.prototype.onExecute = function () { + var tex = this.getInputData(0); + if (!tex || !this.isOutputConnected(0)) { + return; + } + + if (!LGraphTextureTemporalSmooth._shader) { + LGraphTextureTemporalSmooth._shader = new GL.Shader( + GL.Shader.SCREEN_VERTEX_SHADER, + LGraphTextureTemporalSmooth.pixel_shader, + ); + } + + var temp = this._temp_texture; + if ( + !temp || + temp.type != tex.type || + temp.width != tex.width || + temp.height != tex.height + ) { + var options = { + type: tex.type, + format: gl.RGBA, + filter: gl.NEAREST, + }; + this._temp_texture = new GL.Texture(tex.width, tex.height, options); + this._temp_texture2 = new GL.Texture(tex.width, tex.height, options); + tex.copyTo(this._temp_texture2); + } + + var tempA = this._temp_texture; + var tempB = this._temp_texture2; + + var shader = LGraphTextureTemporalSmooth._shader; + var uniforms = this._uniforms; + uniforms.u_factor = 1.0 - this.getInputOrProperty("factor"); + + gl.disable(gl.BLEND); + gl.disable(gl.DEPTH_TEST); + tempA.drawTo(function () { + tempB.bind(1); + tex.toViewport(shader, uniforms); + }); + + this.setOutputData(0, tempA); + + //swap + this._temp_texture = tempB; + this._temp_texture2 = tempA; + }; + + LGraphTextureTemporalSmooth.pixel_shader = + "precision highp float;\n\ + precision highp float;\n\ + uniform sampler2D u_texture;\n\ + uniform sampler2D u_textureB;\n\ + uniform float u_factor;\n\ + varying vec2 v_coord;\n\ + \n\ + void main() {\n\ + gl_FragColor = mix( texture2D( u_texture, v_coord ), texture2D( u_textureB, v_coord ), u_factor );\n\ + }\n\ + "; + + LiteGraph.registerNodeType( + "texture/temporal_smooth", + LGraphTextureTemporalSmooth, + ); + + function LGraphTextureLinearAvgSmooth() { + this.addInput("in", "Texture"); + this.addOutput("avg", "Texture"); + this.addOutput("array", "Texture"); + this.properties = { samples: 64, frames_interval: 1 }; + this._uniforms = { + u_texture: 0, + u_textureB: 1, + u_samples: this.properties.samples, + u_isamples: 1 / this.properties.samples, + }; + this.frame = 0; + } + + LGraphTextureLinearAvgSmooth.title = "Lineal Avg Smooth"; + LGraphTextureLinearAvgSmooth.desc = "Smooth texture linearly over time"; + + LGraphTextureLinearAvgSmooth["@samples"] = { + type: "number", + min: 1, + max: 64, + step: 1, + precision: 1, + }; + + LGraphTextureLinearAvgSmooth.prototype.getPreviewTexture = function () { + return this._temp_texture2; + }; + + LGraphTextureLinearAvgSmooth.prototype.onExecute = function () { + var tex = this.getInputData(0); + if (!tex || !this.isOutputConnected(0)) { + return; + } + + if (!LGraphTextureLinearAvgSmooth._shader) { + LGraphTextureLinearAvgSmooth._shader_copy = new GL.Shader( + GL.Shader.SCREEN_VERTEX_SHADER, + LGraphTextureLinearAvgSmooth.pixel_shader_copy, + ); + LGraphTextureLinearAvgSmooth._shader_avg = new GL.Shader( + GL.Shader.SCREEN_VERTEX_SHADER, + LGraphTextureLinearAvgSmooth.pixel_shader_avg, + ); + } + + var samples = clamp(this.properties.samples, 0, 64); + var frame = this.frame; + var interval = this.properties.frames_interval; + + if (interval == 0 || frame % interval == 0) { + var temp = this._temp_texture; + if (!temp || temp.type != tex.type || temp.width != samples) { + var options = { + type: tex.type, + format: gl.RGBA, + filter: gl.NEAREST, + }; + this._temp_texture = new GL.Texture(samples, 1, options); + this._temp_texture2 = new GL.Texture(samples, 1, options); + this._temp_texture_out = new GL.Texture(1, 1, options); + } + + var tempA = this._temp_texture; + var tempB = this._temp_texture2; + + var shader_copy = LGraphTextureLinearAvgSmooth._shader_copy; + var shader_avg = LGraphTextureLinearAvgSmooth._shader_avg; + var uniforms = this._uniforms; + uniforms.u_samples = samples; + uniforms.u_isamples = 1.0 / samples; + + gl.disable(gl.BLEND); + gl.disable(gl.DEPTH_TEST); + tempA.drawTo(function () { + tempB.bind(1); + tex.toViewport(shader_copy, uniforms); + }); + + this._temp_texture_out.drawTo(function () { + tempA.toViewport(shader_avg, uniforms); + }); + + this.setOutputData(0, this._temp_texture_out); + + //swap + this._temp_texture = tempB; + this._temp_texture2 = tempA; + } else this.setOutputData(0, this._temp_texture_out); + this.setOutputData(1, this._temp_texture2); + this.frame++; + }; + + LGraphTextureLinearAvgSmooth.pixel_shader_copy = + "precision highp float;\n\ + precision highp float;\n\ + uniform sampler2D u_texture;\n\ + uniform sampler2D u_textureB;\n\ + uniform float u_isamples;\n\ + varying vec2 v_coord;\n\ + \n\ + void main() {\n\ + if( v_coord.x <= u_isamples )\n\ + gl_FragColor = texture2D( u_texture, vec2(0.5) );\n\ + else\n\ + gl_FragColor = texture2D( u_textureB, v_coord - vec2(u_isamples,0.0) );\n\ + }\n\ + "; + + LGraphTextureLinearAvgSmooth.pixel_shader_avg = + "precision highp float;\n\ + precision highp float;\n\ + uniform sampler2D u_texture;\n\ + uniform int u_samples;\n\ + uniform float u_isamples;\n\ + varying vec2 v_coord;\n\ + \n\ + void main() {\n\ + vec4 color = vec4(0.0);\n\ + for(int i = 0; i < 64; ++i)\n\ + {\n\ + color += texture2D( u_texture, vec2( float(i)*u_isamples,0.0) );\n\ + if(i == (u_samples - 1))\n\ + break;\n\ + }\n\ + gl_FragColor = color * u_isamples;\n\ + }\n\ + "; + + LiteGraph.registerNodeType( + "texture/linear_avg_smooth", + LGraphTextureLinearAvgSmooth, + ); + + // Image To Texture ***************************************** + function LGraphImageToTexture() { + this.addInput("Image", "image"); + this.addOutput("", "Texture"); + this.properties = {}; + } + + LGraphImageToTexture.title = "Image to Texture"; + LGraphImageToTexture.desc = "Uploads an image to the GPU"; + //LGraphImageToTexture.widgets_info = { size: { widget:"combo", values:[0,32,64,128,256,512,1024,2048]} }; + + LGraphImageToTexture.prototype.onExecute = function () { + var img = this.getInputData(0); + if (!img) { + return; + } + + var width = img.videoWidth || img.width; + var height = img.videoHeight || img.height; + + //this is in case we are using a webgl canvas already, no need to reupload it + if (img.gltexture) { + this.setOutputData(0, img.gltexture); + return; + } + + var temp = this._temp_texture; + if (!temp || temp.width != width || temp.height != height) { + this._temp_texture = new GL.Texture(width, height, { + format: gl.RGBA, + filter: gl.LINEAR, + }); + } + + try { + this._temp_texture.uploadImage(img); + } catch (err) { + console.error( + "image comes from an unsafe location, cannot be uploaded to webgl: " + + err, + ); + return; + } + + this.setOutputData(0, this._temp_texture); + }; + + LiteGraph.registerNodeType("texture/imageToTexture", LGraphImageToTexture); + + // Texture LUT ***************************************** + function LGraphTextureLUT() { + this.addInput("Texture", "Texture"); + this.addInput("LUT", "Texture"); + this.addInput("Intensity", "number"); + this.addOutput("", "Texture"); + this.properties = { + enabled: true, + intensity: 1, + precision: LGraphTexture.DEFAULT, + texture: null, + }; + + if (!LGraphTextureLUT._shader) { + LGraphTextureLUT._shader = new GL.Shader( + Shader.SCREEN_VERTEX_SHADER, + LGraphTextureLUT.pixel_shader, + ); + } + } + + LGraphTextureLUT.widgets_info = { + texture: { widget: "texture" }, + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + + LGraphTextureLUT.title = "LUT"; + LGraphTextureLUT.desc = "Apply LUT to Texture"; + + LGraphTextureLUT.prototype.onExecute = function () { + if (!this.isOutputConnected(0)) { + return; + } //saves work + + var tex = this.getInputData(0); + + if ( + this.properties.precision === LGraphTexture.PASS_THROUGH || + this.properties.enabled === false + ) { + this.setOutputData(0, tex); + return; + } + + if (!tex) { + return; + } + + var lut_tex = this.getInputData(1); + + if (!lut_tex) { + lut_tex = LGraphTexture.getTexture(this.properties.texture); + } + + if (!lut_tex) { + this.setOutputData(0, tex); + return; + } + + lut_tex.bind(0); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); + gl.bindTexture(gl.TEXTURE_2D, null); + + var intensity = this.properties.intensity; + if (this.isInputConnected(2)) { + this.properties.intensity = intensity = this.getInputData(2); + } + + this._tex = LGraphTexture.getTargetTexture( + tex, + this._tex, + this.properties.precision, + ); + + //var mesh = Mesh.getScreenQuad(); + + this._tex.drawTo(function () { + lut_tex.bind(1); + tex.toViewport(LGraphTextureLUT._shader, { + u_texture: 0, + u_textureB: 1, + u_amount: intensity, + }); + }); + + this.setOutputData(0, this._tex); + }; + + LGraphTextureLUT.pixel_shader = + "precision highp float;\n\ + precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform sampler2D u_textureB;\n\ + uniform float u_amount;\n\ + \n\ + void main() {\n\ + lowp vec4 textureColor = clamp( texture2D(u_texture, v_coord), vec4(0.0), vec4(1.0) );\n\ + mediump float blueColor = textureColor.b * 63.0;\n\ + mediump vec2 quad1;\n\ + quad1.y = floor(floor(blueColor) / 8.0);\n\ + quad1.x = floor(blueColor) - (quad1.y * 8.0);\n\ + mediump vec2 quad2;\n\ + quad2.y = floor(ceil(blueColor) / 8.0);\n\ + quad2.x = ceil(blueColor) - (quad2.y * 8.0);\n\ + highp vec2 texPos1;\n\ + texPos1.x = (quad1.x * 0.125) + 0.5/512.0 + ((0.125 - 1.0/512.0) * textureColor.r);\n\ + texPos1.y = 1.0 - ((quad1.y * 0.125) + 0.5/512.0 + ((0.125 - 1.0/512.0) * textureColor.g));\n\ + highp vec2 texPos2;\n\ + texPos2.x = (quad2.x * 0.125) + 0.5/512.0 + ((0.125 - 1.0/512.0) * textureColor.r);\n\ + texPos2.y = 1.0 - ((quad2.y * 0.125) + 0.5/512.0 + ((0.125 - 1.0/512.0) * textureColor.g));\n\ + lowp vec4 newColor1 = texture2D(u_textureB, texPos1);\n\ + lowp vec4 newColor2 = texture2D(u_textureB, texPos2);\n\ + lowp vec4 newColor = mix(newColor1, newColor2, fract(blueColor));\n\ + gl_FragColor = vec4( mix( textureColor.rgb, newColor.rgb, u_amount), textureColor.w);\n\ + }\n\ + "; + + LiteGraph.registerNodeType("texture/LUT", LGraphTextureLUT); + + // Texture LUT ***************************************** + function LGraphTextureEncode() { + this.addInput("Texture", "Texture"); + this.addInput("Atlas", "Texture"); + this.addOutput("", "Texture"); + this.properties = { + enabled: true, + num_row_symbols: 4, + symbol_size: 16, + brightness: 1, + colorize: false, + filter: false, + invert: false, + precision: LGraphTexture.DEFAULT, + generate_mipmaps: false, + texture: null, + }; + + if (!LGraphTextureEncode._shader) { + LGraphTextureEncode._shader = new GL.Shader( + Shader.SCREEN_VERTEX_SHADER, + LGraphTextureEncode.pixel_shader, + ); + } + + this._uniforms = { + u_texture: 0, + u_textureB: 1, + u_row_simbols: 4, + u_simbol_size: 16, + u_res: vec2.create(), + }; + } + + LGraphTextureEncode.widgets_info = { + texture: { widget: "texture" }, + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + + LGraphTextureEncode.title = "Encode"; + LGraphTextureEncode.desc = "Apply a texture atlas to encode a texture"; + + LGraphTextureEncode.prototype.onExecute = function () { + if (!this.isOutputConnected(0)) { + return; + } //saves work + + var tex = this.getInputData(0); + + if ( + this.properties.precision === LGraphTexture.PASS_THROUGH || + this.properties.enabled === false + ) { + this.setOutputData(0, tex); + return; + } + + if (!tex) { + return; + } + + var symbols_tex = this.getInputData(1); + + if (!symbols_tex) { + symbols_tex = LGraphTexture.getTexture(this.properties.texture); + } + + if (!symbols_tex) { + this.setOutputData(0, tex); + return; + } + + symbols_tex.bind(0); + gl.texParameteri( + gl.TEXTURE_2D, + gl.TEXTURE_MAG_FILTER, + this.properties.filter ? gl.LINEAR : gl.NEAREST, + ); + gl.texParameteri( + gl.TEXTURE_2D, + gl.TEXTURE_MIN_FILTER, + this.properties.filter ? gl.LINEAR : gl.NEAREST, + ); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); + gl.bindTexture(gl.TEXTURE_2D, null); + + var uniforms = this._uniforms; + uniforms.u_row_simbols = Math.floor(this.properties.num_row_symbols); + uniforms.u_symbol_size = this.properties.symbol_size; + uniforms.u_brightness = this.properties.brightness; + uniforms.u_invert = this.properties.invert ? 1 : 0; + uniforms.u_colorize = this.properties.colorize ? 1 : 0; + + this._tex = LGraphTexture.getTargetTexture( + tex, + this._tex, + this.properties.precision, + ); + uniforms.u_res[0] = this._tex.width; + uniforms.u_res[1] = this._tex.height; + this._tex.bind(0); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST); + + this._tex.drawTo(function () { + symbols_tex.bind(1); + tex.toViewport(LGraphTextureEncode._shader, uniforms); + }); + + if (this.properties.generate_mipmaps) { + this._tex.bind(0); + gl.generateMipmap(this._tex.texture_type); + this._tex.unbind(0); + } + + this.setOutputData(0, this._tex); + }; + + LGraphTextureEncode.pixel_shader = + "precision highp float;\n\ + precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform sampler2D u_textureB;\n\ + uniform float u_row_simbols;\n\ + uniform float u_symbol_size;\n\ + uniform float u_brightness;\n\ + uniform float u_invert;\n\ + uniform float u_colorize;\n\ + uniform vec2 u_res;\n\ + \n\ + void main() {\n\ + vec2 total_symbols = u_res / u_symbol_size;\n\ + vec2 uv = floor(v_coord * total_symbols) / total_symbols; //pixelate \n\ + vec2 local_uv = mod(v_coord * u_res, u_symbol_size) / u_symbol_size;\n\ + lowp vec4 textureColor = texture2D(u_texture, uv );\n\ + float lum = clamp(u_brightness * (textureColor.x + textureColor.y + textureColor.z)/3.0,0.0,1.0);\n\ + if( u_invert == 1.0 ) lum = 1.0 - lum;\n\ + float index = floor( lum * (u_row_simbols * u_row_simbols - 1.0));\n\ + float col = mod( index, u_row_simbols );\n\ + float row = u_row_simbols - floor( index / u_row_simbols ) - 1.0;\n\ + vec2 simbol_uv = ( vec2( col, row ) + local_uv ) / u_row_simbols;\n\ + vec4 color = texture2D( u_textureB, simbol_uv );\n\ + if(u_colorize == 1.0)\n\ + color *= textureColor;\n\ + gl_FragColor = color;\n\ + }\n\ + "; + + LiteGraph.registerNodeType("texture/encode", LGraphTextureEncode); + + // Texture Channels ***************************************** + function LGraphTextureChannels() { + this.addInput("Texture", "Texture"); + + this.addOutput("R", "Texture"); + this.addOutput("G", "Texture"); + this.addOutput("B", "Texture"); + this.addOutput("A", "Texture"); + + //this.properties = { use_single_channel: true }; + if (!LGraphTextureChannels._shader) { + LGraphTextureChannels._shader = new GL.Shader( + Shader.SCREEN_VERTEX_SHADER, + LGraphTextureChannels.pixel_shader, + ); + } + } + + LGraphTextureChannels.title = "Texture to Channels"; + LGraphTextureChannels.desc = "Split texture channels"; + + LGraphTextureChannels.prototype.onExecute = function () { + var texA = this.getInputData(0); + if (!texA) { + return; + } + + if (!this._channels) { + this._channels = Array(4); + } + + //var format = this.properties.use_single_channel ? gl.LUMINANCE : gl.RGBA; //not supported by WebGL1 + var format = gl.RGB; + var connections = 0; + for (var i = 0; i < 4; i++) { + if (this.isOutputConnected(i)) { + if ( + !this._channels[i] || + this._channels[i].width != texA.width || + this._channels[i].height != texA.height || + this._channels[i].type != texA.type || + this._channels[i].format != format + ) { + this._channels[i] = new GL.Texture(texA.width, texA.height, { + type: texA.type, + format: format, + filter: gl.LINEAR, + }); + } + connections++; + } else { + this._channels[i] = null; + } + } + + if (!connections) { + return; + } + + gl.disable(gl.BLEND); + gl.disable(gl.DEPTH_TEST); + + var mesh = Mesh.getScreenQuad(); + var shader = LGraphTextureChannels._shader; + var masks = [ + [1, 0, 0, 0], + [0, 1, 0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1], + ]; + + for (var i = 0; i < 4; i++) { + if (!this._channels[i]) { + continue; + } + + this._channels[i].drawTo(function () { + texA.bind(0); + shader.uniforms({ u_texture: 0, u_mask: masks[i] }).draw(mesh); + }); + this.setOutputData(i, this._channels[i]); + } + }; + + LGraphTextureChannels.pixel_shader = + "precision highp float;\n\ + precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform vec4 u_mask;\n\ + \n\ + void main() {\n\ + gl_FragColor = vec4( vec3( length( texture2D(u_texture, v_coord) * u_mask )), 1.0 );\n\ + }\n\ + "; + + LiteGraph.registerNodeType("texture/textureChannels", LGraphTextureChannels); + + // Texture Channels to Texture ***************************************** + function LGraphChannelsTexture() { + this.addInput("R", "Texture"); + this.addInput("G", "Texture"); + this.addInput("B", "Texture"); + this.addInput("A", "Texture"); + + this.addOutput("Texture", "Texture"); + + this.properties = { + precision: LGraphTexture.DEFAULT, + R: 1, + G: 1, + B: 1, + A: 1, + }; + this._color = vec4.create(); + this._uniforms = { + u_textureR: 0, + u_textureG: 1, + u_textureB: 2, + u_textureA: 3, + u_color: this._color, + }; + } + + LGraphChannelsTexture.title = "Channels to Texture"; + LGraphChannelsTexture.desc = "Split texture channels"; + LGraphChannelsTexture.widgets_info = { + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + + LGraphChannelsTexture.prototype.onExecute = function () { + var white = LGraphTexture.getWhiteTexture(); + var texR = this.getInputData(0) || white; + var texG = this.getInputData(1) || white; + var texB = this.getInputData(2) || white; + var texA = this.getInputData(3) || white; + + gl.disable(gl.BLEND); + gl.disable(gl.DEPTH_TEST); + + var mesh = Mesh.getScreenQuad(); + if (!LGraphChannelsTexture._shader) { + LGraphChannelsTexture._shader = new GL.Shader( + Shader.SCREEN_VERTEX_SHADER, + LGraphChannelsTexture.pixel_shader, + ); + } + var shader = LGraphChannelsTexture._shader; + + var w = Math.max(texR.width, texG.width, texB.width, texA.width); + var h = Math.max(texR.height, texG.height, texB.height, texA.height); + var type = + this.properties.precision == LGraphTexture.HIGH + ? LGraphTexture.HIGH_PRECISION_FORMAT + : gl.UNSIGNED_BYTE; + + if ( + !this._texture || + this._texture.width != w || + this._texture.height != h || + this._texture.type != type + ) { + this._texture = new GL.Texture(w, h, { + type: type, + format: gl.RGBA, + filter: gl.LINEAR, + }); + } + + var color = this._color; + color[0] = this.properties.R; + color[1] = this.properties.G; + color[2] = this.properties.B; + color[3] = this.properties.A; + var uniforms = this._uniforms; + + this._texture.drawTo(function () { + texR.bind(0); + texG.bind(1); + texB.bind(2); + texA.bind(3); + shader.uniforms(uniforms).draw(mesh); + }); + this.setOutputData(0, this._texture); + }; + + LGraphChannelsTexture.pixel_shader = + "precision highp float;\n\ + precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_textureR;\n\ + uniform sampler2D u_textureG;\n\ + uniform sampler2D u_textureB;\n\ + uniform sampler2D u_textureA;\n\ + uniform vec4 u_color;\n\ + \n\ + void main() {\n\ + gl_FragColor = u_color * vec4( \ + texture2D(u_textureR, v_coord).r,\ + texture2D(u_textureG, v_coord).r,\ + texture2D(u_textureB, v_coord).r,\ + texture2D(u_textureA, v_coord).r);\n\ + }\n\ + "; + + LiteGraph.registerNodeType("texture/channelsTexture", LGraphChannelsTexture); + + // Texture Color ***************************************** + function LGraphTextureColor() { + this.addOutput("Texture", "Texture"); + + this._tex_color = vec4.create(); + this.properties = { + color: vec4.create(), + precision: LGraphTexture.DEFAULT, + }; + } + + LGraphTextureColor.title = "Color"; + LGraphTextureColor.desc = "Generates a 1x1 texture with a constant color"; + + LGraphTextureColor.widgets_info = { + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + + LGraphTextureColor.prototype.onDrawBackground = function (ctx) { + var c = this.properties.color; + ctx.fillStyle = + "rgb(" + + Math.floor(clamp(c[0], 0, 1) * 255) + + "," + + Math.floor(clamp(c[1], 0, 1) * 255) + + "," + + Math.floor(clamp(c[2], 0, 1) * 255) + + ")"; + if (this.flags.collapsed) { + this.boxcolor = ctx.fillStyle; + } else { + ctx.fillRect(0, 0, this.size[0], this.size[1]); + } + }; + + LGraphTextureColor.prototype.onExecute = function () { + var type = + this.properties.precision == LGraphTexture.HIGH + ? LGraphTexture.HIGH_PRECISION_FORMAT + : gl.UNSIGNED_BYTE; + + if (!this._tex || this._tex.type != type) { + this._tex = new GL.Texture(1, 1, { + format: gl.RGBA, + type: type, + minFilter: gl.NEAREST, + }); + } + var color = this.properties.color; + + if (this.inputs) { + for (var i = 0; i < this.inputs.length; i++) { + var input = this.inputs[i]; + var v = this.getInputData(i); + if (v === undefined) { + continue; + } + switch (input.name) { + case "RGB": + case "RGBA": + color.set(v); + break; + case "R": + color[0] = v; + break; + case "G": + color[1] = v; + break; + case "B": + color[2] = v; + break; + case "A": + color[3] = v; + break; + } + } + } + + if (vec4.sqrDist(this._tex_color, color) > 0.001) { + this._tex_color.set(color); + this._tex.fill(color); + } + this.setOutputData(0, this._tex); + }; + + LGraphTextureColor.prototype.onGetInputs = function () { + return [ + ["RGB", "vec3"], + ["RGBA", "vec4"], + ["R", "number"], + ["G", "number"], + ["B", "number"], + ["A", "number"], + ]; + }; + + LiteGraph.registerNodeType("texture/color", LGraphTextureColor); + + // Texture Channels to Texture ***************************************** + function LGraphTextureGradient() { + this.addInput("A", "color"); + this.addInput("B", "color"); + this.addOutput("Texture", "Texture"); + + this.properties = { + angle: 0, + scale: 1, + A: [0, 0, 0], + B: [1, 1, 1], + texture_size: 32, + }; + if (!LGraphTextureGradient._shader) { + LGraphTextureGradient._shader = new GL.Shader( + Shader.SCREEN_VERTEX_SHADER, + LGraphTextureGradient.pixel_shader, + ); + } + + this._uniforms = { + u_angle: 0, + u_colorA: vec3.create(), + u_colorB: vec3.create(), + }; + } + + LGraphTextureGradient.title = "Gradient"; + LGraphTextureGradient.desc = "Generates a gradient"; + LGraphTextureGradient["@A"] = { type: "color" }; + LGraphTextureGradient["@B"] = { type: "color" }; + LGraphTextureGradient["@texture_size"] = { + type: "enum", + values: [32, 64, 128, 256, 512], + }; + + LGraphTextureGradient.prototype.onExecute = function () { + gl.disable(gl.BLEND); + gl.disable(gl.DEPTH_TEST); + + var mesh = GL.Mesh.getScreenQuad(); + var shader = LGraphTextureGradient._shader; + + var A = this.getInputData(0); + if (!A) { + A = this.properties.A; + } + var B = this.getInputData(1); + if (!B) { + B = this.properties.B; + } + + //angle and scale + for (var i = 2; i < this.inputs.length; i++) { + var input = this.inputs[i]; + var v = this.getInputData(i); + if (v === undefined) { + continue; + } + this.properties[input.name] = v; + } + + var uniforms = this._uniforms; + this._uniforms.u_angle = this.properties.angle * DEG2RAD; + this._uniforms.u_scale = this.properties.scale; + vec3.copy(uniforms.u_colorA, A); + vec3.copy(uniforms.u_colorB, B); + + var size = parseInt(this.properties.texture_size); + if (!this._tex || this._tex.width != size) { + this._tex = new GL.Texture(size, size, { + format: gl.RGB, + filter: gl.LINEAR, + }); + } + + this._tex.drawTo(function () { + shader.uniforms(uniforms).draw(mesh); + }); + this.setOutputData(0, this._tex); + }; + + LGraphTextureGradient.prototype.onGetInputs = function () { + return [ + ["angle", "number"], + ["scale", "number"], + ]; + }; + + LGraphTextureGradient.pixel_shader = + "precision highp float;\n\ + precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform float u_angle;\n\ + uniform float u_scale;\n\ + uniform vec3 u_colorA;\n\ + uniform vec3 u_colorB;\n\ + \n\ + vec2 rotate(vec2 v, float angle)\n\ + {\n\ + vec2 result;\n\ + float _cos = cos(angle);\n\ + float _sin = sin(angle);\n\ + result.x = v.x * _cos - v.y * _sin;\n\ + result.y = v.x * _sin + v.y * _cos;\n\ + return result;\n\ + }\n\ + void main() {\n\ + float f = (rotate(u_scale * (v_coord - vec2(0.5)), u_angle) + vec2(0.5)).x;\n\ + vec3 color = mix(u_colorA,u_colorB,clamp(f,0.0,1.0));\n\ + gl_FragColor = vec4(color,1.0);\n\ + }\n\ + "; + + LiteGraph.registerNodeType("texture/gradient", LGraphTextureGradient); + + // Texture Mix ***************************************** + function LGraphTextureMix() { + this.addInput("A", "Texture"); + this.addInput("B", "Texture"); + this.addInput("Mixer", "Texture"); + + this.addOutput("Texture", "Texture"); + this.properties = { + factor: 0.5, + size_from_biggest: true, + invert: false, + precision: LGraphTexture.DEFAULT, + }; + this._uniforms = { + u_textureA: 0, + u_textureB: 1, + u_textureMix: 2, + u_mix: vec4.create(), + }; + } + + LGraphTextureMix.title = "Mix"; + LGraphTextureMix.desc = "Generates a texture mixing two textures"; + + LGraphTextureMix.widgets_info = { + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + + LGraphTextureMix.prototype.onExecute = function () { + var texA = this.getInputData(0); + + if (!this.isOutputConnected(0)) { + return; + } //saves work + + if (this.properties.precision === LGraphTexture.PASS_THROUGH) { + this.setOutputData(0, texA); + return; + } + + var texB = this.getInputData(1); + if (!texA || !texB) { + return; + } + + var texMix = this.getInputData(2); + + var factor = this.getInputData(3); + + this._tex = LGraphTexture.getTargetTexture( + this.properties.size_from_biggest && texB.width > texA.width + ? texB + : texA, + this._tex, + this.properties.precision, + ); + + gl.disable(gl.BLEND); + gl.disable(gl.DEPTH_TEST); + + var mesh = Mesh.getScreenQuad(); + var shader = null; + var uniforms = this._uniforms; + if (texMix) { + shader = LGraphTextureMix._shader_tex; + if (!shader) { + shader = LGraphTextureMix._shader_tex = new GL.Shader( + Shader.SCREEN_VERTEX_SHADER, + LGraphTextureMix.pixel_shader, + { MIX_TEX: "" }, + ); + } + } else { + shader = LGraphTextureMix._shader_factor; + if (!shader) { + shader = LGraphTextureMix._shader_factor = new GL.Shader( + Shader.SCREEN_VERTEX_SHADER, + LGraphTextureMix.pixel_shader, + ); + } + var f = factor == null ? this.properties.factor : factor; + uniforms.u_mix.set([f, f, f, f]); + } + + var invert = this.properties.invert; + + this._tex.drawTo(function () { + texA.bind(invert ? 1 : 0); + texB.bind(invert ? 0 : 1); + if (texMix) { + texMix.bind(2); + } + shader.uniforms(uniforms).draw(mesh); + }); + + this.setOutputData(0, this._tex); + }; + + LGraphTextureMix.prototype.onGetInputs = function () { + return [["factor", "number"]]; + }; + + LGraphTextureMix.pixel_shader = + "precision highp float;\n\ + precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_textureA;\n\ + uniform sampler2D u_textureB;\n\ + #ifdef MIX_TEX\n\ + uniform sampler2D u_textureMix;\n\ + #else\n\ + uniform vec4 u_mix;\n\ + #endif\n\ + \n\ + void main() {\n\ + #ifdef MIX_TEX\n\ + vec4 f = texture2D(u_textureMix, v_coord);\n\ + #else\n\ + vec4 f = u_mix;\n\ + #endif\n\ + gl_FragColor = mix( texture2D(u_textureA, v_coord), texture2D(u_textureB, v_coord), f );\n\ + }\n\ + "; + + LiteGraph.registerNodeType("texture/mix", LGraphTextureMix); + + // Texture Edges detection ***************************************** + function LGraphTextureEdges() { + this.addInput("Tex.", "Texture"); + + this.addOutput("Edges", "Texture"); + this.properties = { + invert: true, + threshold: false, + factor: 1, + precision: LGraphTexture.DEFAULT, + }; + + if (!LGraphTextureEdges._shader) { + LGraphTextureEdges._shader = new GL.Shader( + Shader.SCREEN_VERTEX_SHADER, + LGraphTextureEdges.pixel_shader, + ); + } + } + + LGraphTextureEdges.title = "Edges"; + LGraphTextureEdges.desc = "Detects edges"; + + LGraphTextureEdges.widgets_info = { + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + + LGraphTextureEdges.prototype.onExecute = function () { + if (!this.isOutputConnected(0)) { + return; + } //saves work + + var tex = this.getInputData(0); + + if (this.properties.precision === LGraphTexture.PASS_THROUGH) { + this.setOutputData(0, tex); + return; + } + + if (!tex) { + return; + } + + this._tex = LGraphTexture.getTargetTexture( + tex, + this._tex, + this.properties.precision, + ); + + gl.disable(gl.BLEND); + gl.disable(gl.DEPTH_TEST); + + var mesh = Mesh.getScreenQuad(); + var shader = LGraphTextureEdges._shader; + var invert = this.properties.invert; + var factor = this.properties.factor; + var threshold = this.properties.threshold ? 1 : 0; + + this._tex.drawTo(function () { + tex.bind(0); + shader + .uniforms({ + u_texture: 0, + u_isize: [1 / tex.width, 1 / tex.height], + u_factor: factor, + u_threshold: threshold, + u_invert: invert ? 1 : 0, + }) + .draw(mesh); + }); + + this.setOutputData(0, this._tex); + }; + + LGraphTextureEdges.pixel_shader = + "precision highp float;\n\ + precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform vec2 u_isize;\n\ + uniform int u_invert;\n\ + uniform float u_factor;\n\ + uniform float u_threshold;\n\ + \n\ + void main() {\n\ + vec4 center = texture2D(u_texture, v_coord);\n\ + vec4 up = texture2D(u_texture, v_coord + u_isize * vec2(0.0,1.0) );\n\ + vec4 down = texture2D(u_texture, v_coord + u_isize * vec2(0.0,-1.0) );\n\ + vec4 left = texture2D(u_texture, v_coord + u_isize * vec2(1.0,0.0) );\n\ + vec4 right = texture2D(u_texture, v_coord + u_isize * vec2(-1.0,0.0) );\n\ + vec4 diff = abs(center - up) + abs(center - down) + abs(center - left) + abs(center - right);\n\ + diff *= u_factor;\n\ + if(u_invert == 1)\n\ + diff.xyz = vec3(1.0) - diff.xyz;\n\ + if( u_threshold == 0.0 )\n\ + gl_FragColor = vec4( diff.xyz, center.a );\n\ + else\n\ + gl_FragColor = vec4( diff.x > 0.5 ? 1.0 : 0.0, diff.y > 0.5 ? 1.0 : 0.0, diff.z > 0.5 ? 1.0 : 0.0, center.a );\n\ + }\n\ + "; + + LiteGraph.registerNodeType("texture/edges", LGraphTextureEdges); + + // Texture Depth ***************************************** + function LGraphTextureDepthRange() { + this.addInput("Texture", "Texture"); + this.addInput("Distance", "number"); + this.addInput("Range", "number"); + this.addOutput("Texture", "Texture"); + this.properties = { + distance: 100, + range: 50, + only_depth: false, + high_precision: false, + }; + this._uniforms = { + u_texture: 0, + u_distance: 100, + u_range: 50, + u_camera_planes: null, + }; + } + + LGraphTextureDepthRange.title = "Depth Range"; + LGraphTextureDepthRange.desc = "Generates a texture with a depth range"; + + LGraphTextureDepthRange.prototype.onExecute = function () { + if (!this.isOutputConnected(0)) { + return; + } //saves work + + var tex = this.getInputData(0); + if (!tex) { + return; + } + + var precision = gl.UNSIGNED_BYTE; + if (this.properties.high_precision) { + precision = gl.half_float_ext ? gl.HALF_FLOAT_OES : gl.FLOAT; + } + + if ( + !this._temp_texture || + this._temp_texture.type != precision || + this._temp_texture.width != tex.width || + this._temp_texture.height != tex.height + ) { + this._temp_texture = new GL.Texture(tex.width, tex.height, { + type: precision, + format: gl.RGBA, + filter: gl.LINEAR, + }); + } + + var uniforms = this._uniforms; + + //iterations + var distance = this.properties.distance; + if (this.isInputConnected(1)) { + distance = this.getInputData(1); + this.properties.distance = distance; + } + + var range = this.properties.range; + if (this.isInputConnected(2)) { + range = this.getInputData(2); + this.properties.range = range; + } + + uniforms.u_distance = distance; + uniforms.u_range = range; + + gl.disable(gl.BLEND); + gl.disable(gl.DEPTH_TEST); + var mesh = Mesh.getScreenQuad(); + if (!LGraphTextureDepthRange._shader) { + LGraphTextureDepthRange._shader = new GL.Shader( + Shader.SCREEN_VERTEX_SHADER, + LGraphTextureDepthRange.pixel_shader, + ); + LGraphTextureDepthRange._shader_onlydepth = new GL.Shader( + Shader.SCREEN_VERTEX_SHADER, + LGraphTextureDepthRange.pixel_shader, + { ONLY_DEPTH: "" }, + ); + } + var shader = this.properties.only_depth + ? LGraphTextureDepthRange._shader_onlydepth + : LGraphTextureDepthRange._shader; + + //NEAR AND FAR PLANES + var planes = null; + if (tex.near_far_planes) { + planes = tex.near_far_planes; + } else if (window.LS && LS.Renderer._main_camera) { + planes = LS.Renderer._main_camera._uniforms.u_camera_planes; + } else { + planes = [0.1, 1000]; + } //hardcoded + uniforms.u_camera_planes = planes; + + this._temp_texture.drawTo(function () { + tex.bind(0); + shader.uniforms(uniforms).draw(mesh); + }); + + this._temp_texture.near_far_planes = planes; + this.setOutputData(0, this._temp_texture); + }; + + LGraphTextureDepthRange.pixel_shader = + "precision highp float;\n\ + precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform vec2 u_camera_planes;\n\ + uniform float u_distance;\n\ + uniform float u_range;\n\ + \n\ + float LinearDepth()\n\ + {\n\ + float zNear = u_camera_planes.x;\n\ + float zFar = u_camera_planes.y;\n\ + float depth = texture2D(u_texture, v_coord).x;\n\ + depth = depth * 2.0 - 1.0;\n\ + return zNear * (depth + 1.0) / (zFar + zNear - depth * (zFar - zNear));\n\ + }\n\ + \n\ + void main() {\n\ + float depth = LinearDepth();\n\ + #ifdef ONLY_DEPTH\n\ + gl_FragColor = vec4(depth);\n\ + #else\n\ + float diff = abs(depth * u_camera_planes.y - u_distance);\n\ + float dof = 1.0;\n\ + if(diff <= u_range)\n\ + dof = diff / u_range;\n\ + gl_FragColor = vec4(dof);\n\ + #endif\n\ + }\n\ + "; + + LiteGraph.registerNodeType("texture/depth_range", LGraphTextureDepthRange); + + // Texture Depth ***************************************** + function LGraphTextureLinearDepth() { + this.addInput("Texture", "Texture"); + this.addOutput("Texture", "Texture"); + this.properties = { + precision: LGraphTexture.DEFAULT, + invert: false, + }; + this._uniforms = { + u_texture: 0, + u_camera_planes: null, //filled later + u_ires: vec2.create(), + }; + } + + LGraphTextureLinearDepth.widgets_info = { + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + + LGraphTextureLinearDepth.title = "Linear Depth"; + LGraphTextureLinearDepth.desc = "Creates a color texture with linear depth"; + + LGraphTextureLinearDepth.prototype.onExecute = function () { + if (!this.isOutputConnected(0)) { + return; + } //saves work + + var tex = this.getInputData(0); + if ( + !tex || + (tex.format != gl.DEPTH_COMPONENT && tex.format != gl.DEPTH_STENCIL) + ) { + return; + } + + var precision = + this.properties.precision == LGraphTexture.HIGH + ? gl.HIGH_PRECISION_FORMAT + : gl.UNSIGNED_BYTE; + + if ( + !this._temp_texture || + this._temp_texture.type != precision || + this._temp_texture.width != tex.width || + this._temp_texture.height != tex.height + ) { + this._temp_texture = new GL.Texture(tex.width, tex.height, { + type: precision, + format: gl.RGB, + filter: gl.LINEAR, + }); + } + + var uniforms = this._uniforms; + uniforms.u_invert = this.properties.invert ? 1 : 0; + + gl.disable(gl.BLEND); + gl.disable(gl.DEPTH_TEST); + var mesh = Mesh.getScreenQuad(); + if (!LGraphTextureLinearDepth._shader) + LGraphTextureLinearDepth._shader = new GL.Shader( + GL.Shader.SCREEN_VERTEX_SHADER, + LGraphTextureLinearDepth.pixel_shader, + ); + var shader = LGraphTextureLinearDepth._shader; + + //NEAR AND FAR PLANES + var planes = null; + if (tex.near_far_planes) { + planes = tex.near_far_planes; + } else if (window.LS && LS.Renderer._main_camera) { + planes = LS.Renderer._main_camera._uniforms.u_camera_planes; + } else { + planes = [0.1, 1000]; + } //hardcoded + uniforms.u_camera_planes = planes; + //uniforms.u_ires.set([1/tex.width, 1/tex.height]); + uniforms.u_ires.set([0, 0]); + + this._temp_texture.drawTo(function () { + tex.bind(0); + shader.uniforms(uniforms).draw(mesh); + }); + + this._temp_texture.near_far_planes = planes; + this.setOutputData(0, this._temp_texture); + }; + + LGraphTextureLinearDepth.pixel_shader = + "precision highp float;\n\ + precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform vec2 u_camera_planes;\n\ + uniform int u_invert;\n\ + uniform vec2 u_ires;\n\ + \n\ + void main() {\n\ + float zNear = u_camera_planes.x;\n\ + float zFar = u_camera_planes.y;\n\ + float depth = texture2D(u_texture, v_coord + u_ires*0.5).x * 2.0 - 1.0;\n\ + float f = zNear * (depth + 1.0) / (zFar + zNear - depth * (zFar - zNear));\n\ + if( u_invert == 1 )\n\ + f = 1.0 - f;\n\ + gl_FragColor = vec4(vec3(f),1.0);\n\ + }\n\ + "; + + LiteGraph.registerNodeType("texture/linear_depth", LGraphTextureLinearDepth); + + // Texture Blur ***************************************** + function LGraphTextureBlur() { + this.addInput("Texture", "Texture"); + this.addInput("Iterations", "number"); + this.addInput("Intensity", "number"); + this.addOutput("Blurred", "Texture"); + this.properties = { + intensity: 1, + iterations: 1, + preserve_aspect: false, + scale: [1, 1], + precision: LGraphTexture.DEFAULT, + }; + } + + LGraphTextureBlur.title = "Blur"; + LGraphTextureBlur.desc = "Blur a texture"; + + LGraphTextureBlur.widgets_info = { + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + + LGraphTextureBlur.max_iterations = 20; + + LGraphTextureBlur.prototype.onExecute = function () { + var tex = this.getInputData(0); + if (!tex) { + return; + } + + if (!this.isOutputConnected(0)) { + return; + } //saves work + + var temp = this._final_texture; + + if ( + !temp || + temp.width != tex.width || + temp.height != tex.height || + temp.type != tex.type + ) { + //we need two textures to do the blurring + //this._temp_texture = new GL.Texture( tex.width, tex.height, { type: tex.type, format: gl.RGBA, filter: gl.LINEAR }); + temp = this._final_texture = new GL.Texture(tex.width, tex.height, { + type: tex.type, + format: gl.RGBA, + filter: gl.LINEAR, + }); + } + + //iterations + var iterations = this.properties.iterations; + if (this.isInputConnected(1)) { + iterations = this.getInputData(1); + this.properties.iterations = iterations; + } + iterations = Math.min( + Math.floor(iterations), + LGraphTextureBlur.max_iterations, + ); + if (iterations == 0) { + //skip blurring + this.setOutputData(0, tex); + return; + } + + var intensity = this.properties.intensity; + if (this.isInputConnected(2)) { + intensity = this.getInputData(2); + this.properties.intensity = intensity; + } + + //blur sometimes needs an aspect correction + var aspect = LiteGraph.camera_aspect; + if (!aspect && window.gl !== undefined) { + aspect = gl.canvas.height / gl.canvas.width; + } + if (!aspect) { + aspect = 1; + } + aspect = this.properties.preserve_aspect ? aspect : 1; + + var scale = this.properties.scale || [1, 1]; + tex.applyBlur(aspect * scale[0], scale[1], intensity, temp); + for (var i = 1; i < iterations; ++i) { + temp.applyBlur( + aspect * scale[0] * (i + 1), + scale[1] * (i + 1), + intensity, + ); + } + + this.setOutputData(0, temp); + }; + + /* +LGraphTextureBlur.pixel_shader = "precision highp float;\n\ + precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform vec2 u_offset;\n\ + uniform float u_intensity;\n\ + void main() {\n\ + vec4 sum = vec4(0.0);\n\ + vec4 center = texture2D(u_texture, v_coord);\n\ + sum += texture2D(u_texture, v_coord + u_offset * -4.0) * 0.05/0.98;\n\ + sum += texture2D(u_texture, v_coord + u_offset * -3.0) * 0.09/0.98;\n\ + sum += texture2D(u_texture, v_coord + u_offset * -2.0) * 0.12/0.98;\n\ + sum += texture2D(u_texture, v_coord + u_offset * -1.0) * 0.15/0.98;\n\ + sum += center * 0.16/0.98;\n\ + sum += texture2D(u_texture, v_coord + u_offset * 4.0) * 0.05/0.98;\n\ + sum += texture2D(u_texture, v_coord + u_offset * 3.0) * 0.09/0.98;\n\ + sum += texture2D(u_texture, v_coord + u_offset * 2.0) * 0.12/0.98;\n\ + sum += texture2D(u_texture, v_coord + u_offset * 1.0) * 0.15/0.98;\n\ + gl_FragColor = u_intensity * sum;\n\ + }\n\ + "; +*/ + + LiteGraph.registerNodeType("texture/blur", LGraphTextureBlur); + + //Independent glow FX + //based on https://catlikecoding.com/unity/tutorials/advanced-rendering/bloom/ + function FXGlow() { + this.intensity = 0.5; + this.persistence = 0.6; + this.iterations = 8; + this.threshold = 0.8; + this.scale = 1; + + this.dirt_texture = null; + this.dirt_factor = 0.5; + + this._textures = []; + this._uniforms = { + u_intensity: 1, + u_texture: 0, + u_glow_texture: 1, + u_threshold: 0, + u_texel_size: vec2.create(), + }; + } + + FXGlow.prototype.applyFX = function ( + tex, + output_texture, + glow_texture, + average_texture, + ) { + var width = tex.width; + var height = tex.height; + + var texture_info = { + format: tex.format, + type: tex.type, + minFilter: GL.LINEAR, + magFilter: GL.LINEAR, + wrap: gl.CLAMP_TO_EDGE, + }; + + var uniforms = this._uniforms; + var textures = this._textures; + + //cut + var shader = FXGlow._cut_shader; + if (!shader) { + shader = FXGlow._cut_shader = new GL.Shader( + GL.Shader.SCREEN_VERTEX_SHADER, + FXGlow.cut_pixel_shader, + ); + } + + gl.disable(gl.DEPTH_TEST); + gl.disable(gl.BLEND); + + uniforms.u_threshold = this.threshold; + var currentDestination = (textures[0] = GL.Texture.getTemporary( + width, + height, + texture_info, + )); + tex.blit(currentDestination, shader.uniforms(uniforms)); + var currentSource = currentDestination; + + var iterations = this.iterations; + iterations = clamp(iterations, 1, 16) | 0; + var texel_size = uniforms.u_texel_size; + var intensity = this.intensity; + + uniforms.u_intensity = 1; + uniforms.u_delta = this.scale; //1 + + //downscale/upscale shader + var shader = FXGlow._shader; + if (!shader) { + shader = FXGlow._shader = new GL.Shader( + GL.Shader.SCREEN_VERTEX_SHADER, + FXGlow.scale_pixel_shader, + ); + } + + var i = 1; + //downscale + for (; i < iterations; i++) { + width = width >> 1; + if ((height | 0) > 1) { + height = height >> 1; + } + if (width < 2) { + break; + } + currentDestination = textures[i] = GL.Texture.getTemporary( + width, + height, + texture_info, + ); + texel_size[0] = 1 / currentSource.width; + texel_size[1] = 1 / currentSource.height; + currentSource.blit(currentDestination, shader.uniforms(uniforms)); + currentSource = currentDestination; + } + + //average + if (average_texture) { + texel_size[0] = 1 / currentSource.width; + texel_size[1] = 1 / currentSource.height; + uniforms.u_intensity = intensity; + uniforms.u_delta = 1; + currentSource.blit(average_texture, shader.uniforms(uniforms)); + } + + //upscale and blend + gl.enable(gl.BLEND); + gl.blendFunc(gl.ONE, gl.ONE); + uniforms.u_intensity = this.persistence; + uniforms.u_delta = 0.5; + + // i-=2 => -1 to point to last element in array, -1 to go to texture above + for (i -= 2; i >= 0; i--) { + currentDestination = textures[i]; + textures[i] = null; + texel_size[0] = 1 / currentSource.width; + texel_size[1] = 1 / currentSource.height; + currentSource.blit(currentDestination, shader.uniforms(uniforms)); + GL.Texture.releaseTemporary(currentSource); + currentSource = currentDestination; + } + gl.disable(gl.BLEND); + + //glow + if (glow_texture) { + currentSource.blit(glow_texture); + } + + //final composition + if (output_texture) { + var final_texture = output_texture; + var dirt_texture = this.dirt_texture; + var dirt_factor = this.dirt_factor; + uniforms.u_intensity = intensity; + + shader = dirt_texture ? FXGlow._dirt_final_shader : FXGlow._final_shader; + if (!shader) { + if (dirt_texture) { + shader = FXGlow._dirt_final_shader = new GL.Shader( + GL.Shader.SCREEN_VERTEX_SHADER, + FXGlow.final_pixel_shader, + { USE_DIRT: "" }, + ); + } else { + shader = FXGlow._final_shader = new GL.Shader( + GL.Shader.SCREEN_VERTEX_SHADER, + FXGlow.final_pixel_shader, + ); + } + } + + final_texture.drawTo(function () { + tex.bind(0); + currentSource.bind(1); + if (dirt_texture) { + shader.setUniform("u_dirt_factor", dirt_factor); + shader.setUniform("u_dirt_texture", dirt_texture.bind(2)); + } + shader.toViewport(uniforms); + }); + } + + GL.Texture.releaseTemporary(currentSource); + }; + + FXGlow.cut_pixel_shader = + "precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform float u_threshold;\n\ + void main() {\n\ + gl_FragColor = max( texture2D( u_texture, v_coord ) - vec4( u_threshold ), vec4(0.0) );\n\ + }"; + + FXGlow.scale_pixel_shader = + "precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform vec2 u_texel_size;\n\ + uniform float u_delta;\n\ + uniform float u_intensity;\n\ + \n\ + vec4 sampleBox(vec2 uv) {\n\ + vec4 o = u_texel_size.xyxy * vec2(-u_delta, u_delta).xxyy;\n\ + vec4 s = texture2D( u_texture, uv + o.xy ) + texture2D( u_texture, uv + o.zy) + texture2D( u_texture, uv + o.xw) + texture2D( u_texture, uv + o.zw);\n\ + return s * 0.25;\n\ + }\n\ + void main() {\n\ + gl_FragColor = u_intensity * sampleBox( v_coord );\n\ + }"; + + FXGlow.final_pixel_shader = + "precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform sampler2D u_glow_texture;\n\ + #ifdef USE_DIRT\n\ + uniform sampler2D u_dirt_texture;\n\ + #endif\n\ + uniform vec2 u_texel_size;\n\ + uniform float u_delta;\n\ + uniform float u_intensity;\n\ + uniform float u_dirt_factor;\n\ + \n\ + vec4 sampleBox(vec2 uv) {\n\ + vec4 o = u_texel_size.xyxy * vec2(-u_delta, u_delta).xxyy;\n\ + vec4 s = texture2D( u_glow_texture, uv + o.xy ) + texture2D( u_glow_texture, uv + o.zy) + texture2D( u_glow_texture, uv + o.xw) + texture2D( u_glow_texture, uv + o.zw);\n\ + return s * 0.25;\n\ + }\n\ + void main() {\n\ + vec4 glow = sampleBox( v_coord );\n\ + #ifdef USE_DIRT\n\ + glow = mix( glow, glow * texture2D( u_dirt_texture, v_coord ), u_dirt_factor );\n\ + #endif\n\ + gl_FragColor = texture2D( u_texture, v_coord ) + u_intensity * glow;\n\ + }"; + + // Texture Glow ***************************************** + function LGraphTextureGlow() { + this.addInput("in", "Texture"); + this.addInput("dirt", "Texture"); + this.addOutput("out", "Texture"); + this.addOutput("glow", "Texture"); + this.properties = { + enabled: true, + intensity: 1, + persistence: 0.99, + iterations: 16, + threshold: 0, + scale: 1, + dirt_factor: 0.5, + precision: LGraphTexture.DEFAULT, + }; + + this.fx = new FXGlow(); + } + + LGraphTextureGlow.title = "Glow"; + LGraphTextureGlow.desc = "Filters a texture giving it a glow effect"; + + LGraphTextureGlow.widgets_info = { + iterations: { + type: "number", + min: 0, + max: 16, + step: 1, + precision: 0, + }, + threshold: { + type: "number", + min: 0, + max: 10, + step: 0.01, + precision: 2, + }, + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + + LGraphTextureGlow.prototype.onGetInputs = function () { + return [ + ["enabled", "boolean"], + ["threshold", "number"], + ["intensity", "number"], + ["persistence", "number"], + ["iterations", "number"], + ["dirt_factor", "number"], + ]; + }; + + LGraphTextureGlow.prototype.onGetOutputs = function () { + return [["average", "Texture"]]; + }; + + LGraphTextureGlow.prototype.onExecute = function () { + var tex = this.getInputData(0); + if (!tex) { + return; + } + + if (!this.isAnyOutputConnected()) { + return; + } //saves work + + if ( + this.properties.precision === LGraphTexture.PASS_THROUGH || + this.getInputOrProperty("enabled") === false + ) { + this.setOutputData(0, tex); + return; + } + + var width = tex.width; + var height = tex.height; + + var fx = this.fx; + fx.threshold = this.getInputOrProperty("threshold"); + fx.iterations = this.getInputOrProperty("iterations"); + fx.intensity = this.getInputOrProperty("intensity"); + fx.persistence = this.getInputOrProperty("persistence"); + fx.dirt_texture = this.getInputData(1); + fx.dirt_factor = this.getInputOrProperty("dirt_factor"); + fx.scale = this.properties.scale; + + var type = LGraphTexture.getTextureType(this.properties.precision, tex); + + var average_texture = null; + if (this.isOutputConnected(2)) { + average_texture = this._average_texture; + if ( + !average_texture || + average_texture.type != tex.type || + average_texture.format != tex.format + ) { + average_texture = this._average_texture = new GL.Texture(1, 1, { + type: tex.type, + format: tex.format, + filter: gl.LINEAR, + }); + } + } + + var glow_texture = null; + if (this.isOutputConnected(1)) { + glow_texture = this._glow_texture; + if ( + !glow_texture || + glow_texture.width != tex.width || + glow_texture.height != tex.height || + glow_texture.type != type || + glow_texture.format != tex.format + ) { + glow_texture = this._glow_texture = new GL.Texture( + tex.width, + tex.height, + { type: type, format: tex.format, filter: gl.LINEAR }, + ); + } + } + + var final_texture = null; + if (this.isOutputConnected(0)) { + final_texture = this._final_texture; + if ( + !final_texture || + final_texture.width != tex.width || + final_texture.height != tex.height || + final_texture.type != type || + final_texture.format != tex.format + ) { + final_texture = this._final_texture = new GL.Texture( + tex.width, + tex.height, + { type: type, format: tex.format, filter: gl.LINEAR }, + ); + } + } + + //apply FX + fx.applyFX(tex, final_texture, glow_texture, average_texture); + + if (this.isOutputConnected(0)) this.setOutputData(0, final_texture); + + if (this.isOutputConnected(1)) this.setOutputData(1, average_texture); + + if (this.isOutputConnected(2)) this.setOutputData(2, glow_texture); + }; + + LiteGraph.registerNodeType("texture/glow", LGraphTextureGlow); + + // Texture Filter ***************************************** + function LGraphTextureKuwaharaFilter() { + this.addInput("Texture", "Texture"); + this.addOutput("Filtered", "Texture"); + this.properties = { intensity: 1, radius: 5 }; + } + + LGraphTextureKuwaharaFilter.title = "Kuwahara Filter"; + LGraphTextureKuwaharaFilter.desc = + "Filters a texture giving an artistic oil canvas painting"; + + LGraphTextureKuwaharaFilter.max_radius = 10; + LGraphTextureKuwaharaFilter._shaders = []; + + LGraphTextureKuwaharaFilter.prototype.onExecute = function () { + var tex = this.getInputData(0); + if (!tex) { + return; + } + + if (!this.isOutputConnected(0)) { + return; + } //saves work + + var temp = this._temp_texture; + + if ( + !temp || + temp.width != tex.width || + temp.height != tex.height || + temp.type != tex.type + ) { + this._temp_texture = new GL.Texture(tex.width, tex.height, { + type: tex.type, + format: gl.RGBA, + filter: gl.LINEAR, + }); + } + + //iterations + var radius = this.properties.radius; + radius = Math.min( + Math.floor(radius), + LGraphTextureKuwaharaFilter.max_radius, + ); + if (radius == 0) { + //skip blurring + this.setOutputData(0, tex); + return; + } + + var intensity = this.properties.intensity; + + //blur sometimes needs an aspect correction + var aspect = LiteGraph.camera_aspect; + if (!aspect && window.gl !== undefined) { + aspect = gl.canvas.height / gl.canvas.width; + } + if (!aspect) { + aspect = 1; + } + aspect = this.properties.preserve_aspect ? aspect : 1; + + if (!LGraphTextureKuwaharaFilter._shaders[radius]) { + LGraphTextureKuwaharaFilter._shaders[radius] = new GL.Shader( + Shader.SCREEN_VERTEX_SHADER, + LGraphTextureKuwaharaFilter.pixel_shader, + { RADIUS: radius.toFixed(0) }, + ); + } + + var shader = LGraphTextureKuwaharaFilter._shaders[radius]; + var mesh = GL.Mesh.getScreenQuad(); + tex.bind(0); + + this._temp_texture.drawTo(function () { + shader + .uniforms({ + u_texture: 0, + u_intensity: intensity, + u_resolution: [tex.width, tex.height], + u_iResolution: [1 / tex.width, 1 / tex.height], + }) + .draw(mesh); + }); + + this.setOutputData(0, this._temp_texture); + }; + + //from https://www.shadertoy.com/view/MsXSz4 + LGraphTextureKuwaharaFilter.pixel_shader = + "\n\ +precision highp float;\n\ +varying vec2 v_coord;\n\ +uniform sampler2D u_texture;\n\ +uniform float u_intensity;\n\ +uniform vec2 u_resolution;\n\ +uniform vec2 u_iResolution;\n\ +#ifndef RADIUS\n\ + #define RADIUS 7\n\ +#endif\n\ +void main() {\n\ +\n\ + const int radius = RADIUS;\n\ + vec2 fragCoord = v_coord;\n\ + vec2 src_size = u_iResolution;\n\ + vec2 uv = v_coord;\n\ + float n = float((radius + 1) * (radius + 1));\n\ + int i;\n\ + int j;\n\ + vec3 m0 = vec3(0.0); vec3 m1 = vec3(0.0); vec3 m2 = vec3(0.0); vec3 m3 = vec3(0.0);\n\ + vec3 s0 = vec3(0.0); vec3 s1 = vec3(0.0); vec3 s2 = vec3(0.0); vec3 s3 = vec3(0.0);\n\ + vec3 c;\n\ + \n\ + for (int j = -radius; j <= 0; ++j) {\n\ + for (int i = -radius; i <= 0; ++i) {\n\ + c = texture2D(u_texture, uv + vec2(i,j) * src_size).rgb;\n\ + m0 += c;\n\ + s0 += c * c;\n\ + }\n\ + }\n\ + \n\ + for (int j = -radius; j <= 0; ++j) {\n\ + for (int i = 0; i <= radius; ++i) {\n\ + c = texture2D(u_texture, uv + vec2(i,j) * src_size).rgb;\n\ + m1 += c;\n\ + s1 += c * c;\n\ + }\n\ + }\n\ + \n\ + for (int j = 0; j <= radius; ++j) {\n\ + for (int i = 0; i <= radius; ++i) {\n\ + c = texture2D(u_texture, uv + vec2(i,j) * src_size).rgb;\n\ + m2 += c;\n\ + s2 += c * c;\n\ + }\n\ + }\n\ + \n\ + for (int j = 0; j <= radius; ++j) {\n\ + for (int i = -radius; i <= 0; ++i) {\n\ + c = texture2D(u_texture, uv + vec2(i,j) * src_size).rgb;\n\ + m3 += c;\n\ + s3 += c * c;\n\ + }\n\ + }\n\ + \n\ + float min_sigma2 = 1e+2;\n\ + m0 /= n;\n\ + s0 = abs(s0 / n - m0 * m0);\n\ + \n\ + float sigma2 = s0.r + s0.g + s0.b;\n\ + if (sigma2 < min_sigma2) {\n\ + min_sigma2 = sigma2;\n\ + gl_FragColor = vec4(m0, 1.0);\n\ + }\n\ + \n\ + m1 /= n;\n\ + s1 = abs(s1 / n - m1 * m1);\n\ + \n\ + sigma2 = s1.r + s1.g + s1.b;\n\ + if (sigma2 < min_sigma2) {\n\ + min_sigma2 = sigma2;\n\ + gl_FragColor = vec4(m1, 1.0);\n\ + }\n\ + \n\ + m2 /= n;\n\ + s2 = abs(s2 / n - m2 * m2);\n\ + \n\ + sigma2 = s2.r + s2.g + s2.b;\n\ + if (sigma2 < min_sigma2) {\n\ + min_sigma2 = sigma2;\n\ + gl_FragColor = vec4(m2, 1.0);\n\ + }\n\ + \n\ + m3 /= n;\n\ + s3 = abs(s3 / n - m3 * m3);\n\ + \n\ + sigma2 = s3.r + s3.g + s3.b;\n\ + if (sigma2 < min_sigma2) {\n\ + min_sigma2 = sigma2;\n\ + gl_FragColor = vec4(m3, 1.0);\n\ + }\n\ +}\n\ +"; + + LiteGraph.registerNodeType("texture/kuwahara", LGraphTextureKuwaharaFilter); + + // Texture ***************************************** + function LGraphTextureXDoGFilter() { + this.addInput("Texture", "Texture"); + this.addOutput("Filtered", "Texture"); + this.properties = { + sigma: 1.4, + k: 1.6, + p: 21.7, + epsilon: 79, + phi: 0.017, + }; + } + + LGraphTextureXDoGFilter.title = "XDoG Filter"; + LGraphTextureXDoGFilter.desc = + "Filters a texture giving an artistic ink style"; + + LGraphTextureXDoGFilter.max_radius = 10; + LGraphTextureXDoGFilter._shaders = []; + + LGraphTextureXDoGFilter.prototype.onExecute = function () { + var tex = this.getInputData(0); + if (!tex) { + return; + } + + if (!this.isOutputConnected(0)) { + return; + } //saves work + + var temp = this._temp_texture; + if ( + !temp || + temp.width != tex.width || + temp.height != tex.height || + temp.type != tex.type + ) { + this._temp_texture = new GL.Texture(tex.width, tex.height, { + type: tex.type, + format: gl.RGBA, + filter: gl.LINEAR, + }); + } + + if (!LGraphTextureXDoGFilter._xdog_shader) { + LGraphTextureXDoGFilter._xdog_shader = new GL.Shader( + Shader.SCREEN_VERTEX_SHADER, + LGraphTextureXDoGFilter.xdog_pixel_shader, + ); + } + var shader = LGraphTextureXDoGFilter._xdog_shader; + var mesh = GL.Mesh.getScreenQuad(); + + var sigma = this.properties.sigma; + var k = this.properties.k; + var p = this.properties.p; + var epsilon = this.properties.epsilon; + var phi = this.properties.phi; + tex.bind(0); + this._temp_texture.drawTo(function () { + shader + .uniforms({ + src: 0, + sigma: sigma, + k: k, + p: p, + epsilon: epsilon, + phi: phi, + cvsWidth: tex.width, + cvsHeight: tex.height, + }) + .draw(mesh); + }); + + this.setOutputData(0, this._temp_texture); + }; + + //from https://github.com/RaymondMcGuire/GPU-Based-Image-Processing-Tools/blob/master/lib_webgl/scripts/main.js + LGraphTextureXDoGFilter.xdog_pixel_shader = + "\n\ +precision highp float;\n\ +uniform sampler2D src;\n\n\ +uniform float cvsHeight;\n\ +uniform float cvsWidth;\n\n\ +uniform float sigma;\n\ +uniform float k;\n\ +uniform float p;\n\ +uniform float epsilon;\n\ +uniform float phi;\n\ +varying vec2 v_coord;\n\n\ +float cosh(float val)\n\ +{\n\ + float tmp = exp(val);\n\ + float cosH = (tmp + 1.0 / tmp) / 2.0;\n\ + return cosH;\n\ +}\n\n\ +float tanh(float val)\n\ +{\n\ + float tmp = exp(val);\n\ + float tanH = (tmp - 1.0 / tmp) / (tmp + 1.0 / tmp);\n\ + return tanH;\n\ +}\n\n\ +float sinh(float val)\n\ +{\n\ + float tmp = exp(val);\n\ + float sinH = (tmp - 1.0 / tmp) / 2.0;\n\ + return sinH;\n\ +}\n\n\ +void main(void){\n\ + vec3 destColor = vec3(0.0);\n\ + float tFrag = 1.0 / cvsHeight;\n\ + float sFrag = 1.0 / cvsWidth;\n\ + vec2 Frag = vec2(sFrag,tFrag);\n\ + vec2 uv = gl_FragCoord.st;\n\ + float twoSigmaESquared = 2.0 * sigma * sigma;\n\ + float twoSigmaRSquared = twoSigmaESquared * k * k;\n\ + int halfWidth = int(ceil( 1.0 * sigma * k ));\n\n\ + const int MAX_NUM_ITERATION = 99999;\n\ + vec2 sum = vec2(0.0);\n\ + vec2 norm = vec2(0.0);\n\n\ + for(int cnt=0;cnt (2*halfWidth+1)*(2*halfWidth+1)){break;}\n\ + int i = int(cnt / (2*halfWidth+1)) - halfWidth;\n\ + int j = cnt - halfWidth - int(cnt / (2*halfWidth+1)) * (2*halfWidth+1);\n\n\ + float d = length(vec2(i,j));\n\ + vec2 kernel = vec2( exp( -d * d / twoSigmaESquared ), \n\ + exp( -d * d / twoSigmaRSquared ));\n\n\ + vec2 L = texture2D(src, (uv + vec2(i,j)) * Frag).xx;\n\n\ + norm += kernel;\n\ + sum += kernel * L;\n\ + }\n\n\ + sum /= norm;\n\n\ + float H = 100.0 * ((1.0 + p) * sum.x - p * sum.y);\n\ + float edge = ( H > epsilon )? 1.0 : 1.0 + tanh( phi * (H - epsilon));\n\ + destColor = vec3(edge);\n\ + gl_FragColor = vec4(destColor, 1.0);\n\ +}"; + + LiteGraph.registerNodeType("texture/xDoG", LGraphTextureXDoGFilter); + + // Texture Webcam ***************************************** + function LGraphTextureWebcam() { + this.addOutput("Webcam", "Texture"); + this.properties = { texture_name: "", facingMode: "user" }; + this.boxcolor = "black"; + this.version = 0; + } + + LGraphTextureWebcam.title = "Webcam"; + LGraphTextureWebcam.desc = "Webcam texture"; + + LGraphTextureWebcam.is_webcam_open = false; + + LGraphTextureWebcam.prototype.openStream = function () { + if (!navigator.getUserMedia) { + //console.log('getUserMedia() is not supported in your browser, use chrome and enable WebRTC from about://flags'); + return; + } + + this._waiting_confirmation = true; + + // Not showing vendor prefixes. + var constraints = { + audio: false, + video: { facingMode: this.properties.facingMode }, + }; + navigator.mediaDevices + .getUserMedia(constraints) + .then(this.streamReady.bind(this)) + .catch(onFailSoHard); + + var that = this; + function onFailSoHard(e) { + LGraphTextureWebcam.is_webcam_open = false; + console.log("Webcam rejected", e); + that._webcam_stream = false; + that.boxcolor = "red"; + that.trigger("stream_error"); + } + }; + + LGraphTextureWebcam.prototype.closeStream = function () { + if (this._webcam_stream) { + var tracks = this._webcam_stream.getTracks(); + if (tracks.length) { + for (var i = 0; i < tracks.length; ++i) { + tracks[i].stop(); + } + } + LGraphTextureWebcam.is_webcam_open = false; + this._webcam_stream = null; + this._video = null; + this.boxcolor = "black"; + this.trigger("stream_closed"); + } + }; + + LGraphTextureWebcam.prototype.streamReady = function (localMediaStream) { + this._webcam_stream = localMediaStream; + //this._waiting_confirmation = false; + this.boxcolor = "green"; + var video = this._video; + if (!video) { + video = document.createElement("video"); + video.autoplay = true; + video.srcObject = localMediaStream; + this._video = video; + //document.body.appendChild( video ); //debug + //when video info is loaded (size and so) + video.onloadedmetadata = function (e) { + // Ready to go. Do some stuff. + LGraphTextureWebcam.is_webcam_open = true; + console.log(e); + }; + } + this.trigger("stream_ready", video); + }; + + LGraphTextureWebcam.prototype.onPropertyChanged = function (name, value) { + if (name == "facingMode") { + this.properties.facingMode = value; + this.closeStream(); + this.openStream(); + } + }; + + LGraphTextureWebcam.prototype.onRemoved = function () { + if (!this._webcam_stream) { + return; + } + + var tracks = this._webcam_stream.getTracks(); + if (tracks.length) { + for (var i = 0; i < tracks.length; ++i) { + tracks[i].stop(); + } + } + + this._webcam_stream = null; + this._video = null; + }; + + LGraphTextureWebcam.prototype.onDrawBackground = function (ctx) { + if (this.flags.collapsed || this.size[1] <= 20) { + return; + } + + if (!this._video) { + return; + } + + //render to graph canvas + ctx.save(); + if (!ctx.webgl) { + //reverse image + ctx.drawImage(this._video, 0, 0, this.size[0], this.size[1]); + } else { + if (this._video_texture) { + ctx.drawImage(this._video_texture, 0, 0, this.size[0], this.size[1]); + } + } + ctx.restore(); + }; + + LGraphTextureWebcam.prototype.onExecute = function () { + if (this._webcam_stream == null && !this._waiting_confirmation) { + this.openStream(); + } + + if (!this._video || !this._video.videoWidth) { + return; + } + + var width = this._video.videoWidth; + var height = this._video.videoHeight; + + var temp = this._video_texture; + if (!temp || temp.width != width || temp.height != height) { + this._video_texture = new GL.Texture(width, height, { + format: gl.RGB, + filter: gl.LINEAR, + }); + } + + this._video_texture.uploadImage(this._video); + this._video_texture.version = ++this.version; + + if (this.properties.texture_name) { + var container = LGraphTexture.getTexturesContainer(); + container[this.properties.texture_name] = this._video_texture; + } + + this.setOutputData(0, this._video_texture); + for (var i = 1; i < this.outputs.length; ++i) { + if (!this.outputs[i]) { + continue; + } + switch (this.outputs[i].name) { + case "width": + this.setOutputData(i, this._video.videoWidth); + break; + case "height": + this.setOutputData(i, this._video.videoHeight); + break; + } + } + }; + + LGraphTextureWebcam.prototype.onGetOutputs = function () { + return [ + ["width", "number"], + ["height", "number"], + ["stream_ready", LiteGraph.EVENT], + ["stream_closed", LiteGraph.EVENT], + ["stream_error", LiteGraph.EVENT], + ]; + }; + + LiteGraph.registerNodeType("texture/webcam", LGraphTextureWebcam); + + //from https://github.com/spite/Wagner + function LGraphLensFX() { + this.addInput("in", "Texture"); + this.addInput("f", "number"); + this.addOutput("out", "Texture"); + this.properties = { + enabled: true, + factor: 1, + precision: LGraphTexture.LOW, + }; + + this._uniforms = { u_texture: 0, u_factor: 1 }; + } + + LGraphLensFX.title = "Lens FX"; + LGraphLensFX.desc = "distortion and chromatic aberration"; + + LGraphLensFX.widgets_info = { + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + + LGraphLensFX.prototype.onGetInputs = function () { + return [["enabled", "boolean"]]; + }; + + LGraphLensFX.prototype.onExecute = function () { + var tex = this.getInputData(0); + if (!tex) { + return; + } + + if (!this.isOutputConnected(0)) { + return; + } //saves work + + if ( + this.properties.precision === LGraphTexture.PASS_THROUGH || + this.getInputOrProperty("enabled") === false + ) { + this.setOutputData(0, tex); + return; + } + + var temp = this._temp_texture; + if ( + !temp || + temp.width != tex.width || + temp.height != tex.height || + temp.type != tex.type + ) { + temp = this._temp_texture = new GL.Texture(tex.width, tex.height, { + type: tex.type, + format: gl.RGBA, + filter: gl.LINEAR, + }); + } + + var shader = LGraphLensFX._shader; + if (!shader) { + shader = LGraphLensFX._shader = new GL.Shader( + GL.Shader.SCREEN_VERTEX_SHADER, + LGraphLensFX.pixel_shader, + ); + } + + var factor = this.getInputData(1); + if (factor == null) { + factor = this.properties.factor; + } + + var uniforms = this._uniforms; + uniforms.u_factor = factor; + + //apply shader + gl.disable(gl.DEPTH_TEST); + temp.drawTo(function () { + tex.bind(0); + shader.uniforms(uniforms).draw(GL.Mesh.getScreenQuad()); + }); + + this.setOutputData(0, temp); + }; + + LGraphLensFX.pixel_shader = + "precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform float u_factor;\n\ + vec2 barrelDistortion(vec2 coord, float amt) {\n\ + vec2 cc = coord - 0.5;\n\ + float dist = dot(cc, cc);\n\ + return coord + cc * dist * amt;\n\ + }\n\ + \n\ + float sat( float t )\n\ + {\n\ + return clamp( t, 0.0, 1.0 );\n\ + }\n\ + \n\ + float linterp( float t ) {\n\ + return sat( 1.0 - abs( 2.0*t - 1.0 ) );\n\ + }\n\ + \n\ + float remap( float t, float a, float b ) {\n\ + return sat( (t - a) / (b - a) );\n\ + }\n\ + \n\ + vec4 spectrum_offset( float t ) {\n\ + vec4 ret;\n\ + float lo = step(t,0.5);\n\ + float hi = 1.0-lo;\n\ + float w = linterp( remap( t, 1.0/6.0, 5.0/6.0 ) );\n\ + ret = vec4(lo,1.0,hi, 1.) * vec4(1.0-w, w, 1.0-w, 1.);\n\ + \n\ + return pow( ret, vec4(1.0/2.2) );\n\ + }\n\ + \n\ + const float max_distort = 2.2;\n\ + const int num_iter = 12;\n\ + const float reci_num_iter_f = 1.0 / float(num_iter);\n\ + \n\ + void main()\n\ + { \n\ + vec2 uv=v_coord;\n\ + vec4 sumcol = vec4(0.0);\n\ + vec4 sumw = vec4(0.0); \n\ + for ( int i=0; i= res)\n\ + break;\n\ + iCount++;\n\ + }\n\ + float nf = n/normK;\n\ + return nf*nf*nf*nf;\n\ + }\n\ + void main() {\n\ + vec2 uv = v_coord * u_scale * u_viewport + u_offset * u_scale;\n\ + vec4 color = vec4( pNoise( uv, u_octaves ) * u_amplitude );\n\ + gl_FragColor = color;\n\ + }"; + + LiteGraph.registerNodeType("texture/perlin", LGraphTexturePerlin); + + function LGraphTextureCanvas2D() { + this.addInput("v"); + this.addOutput("out", "Texture"); + this.properties = { + code: LGraphTextureCanvas2D.default_code, + width: 512, + height: 512, + clear: true, + precision: LGraphTexture.DEFAULT, + use_html_canvas: false, + }; + this._func = null; + this._temp_texture = null; + this.compileCode(); + } + + LGraphTextureCanvas2D.title = "Canvas2D"; + LGraphTextureCanvas2D.desc = + "Executes Canvas2D code inside a texture or the viewport."; + LGraphTextureCanvas2D.help = + "Set width and height to 0 to match viewport size."; + + LGraphTextureCanvas2D.default_code = + "//vars: canvas,ctx,time\nctx.fillStyle='red';\nctx.fillRect(0,0,50,50);\n"; + + LGraphTextureCanvas2D.widgets_info = { + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + code: { type: "code" }, + width: { type: "number", precision: 0, step: 1 }, + height: { type: "number", precision: 0, step: 1 }, + }; + + LGraphTextureCanvas2D.prototype.onPropertyChanged = function (name, value) { + if (name == "code") this.compileCode(value); + }; + + LGraphTextureCanvas2D.prototype.compileCode = function (code) { + this._func = null; + if (!LiteGraph.allow_scripts) return; + + try { + this._func = new Function("canvas", "ctx", "time", "script", "v", code); + this.boxcolor = "#00FF00"; + } catch (err) { + this.boxcolor = "#FF0000"; + console.error("Error parsing script"); + console.error(err); + } + }; + + LGraphTextureCanvas2D.prototype.onExecute = function () { + var func = this._func; + if (!func || !this.isOutputConnected(0)) { + return; + } + this.executeDraw(func); + }; + + LGraphTextureCanvas2D.prototype.executeDraw = function (func_context) { + var width = this.properties.width || gl.canvas.width; + var height = this.properties.height || gl.canvas.height; + var temp = this._temp_texture; + var type = LGraphTexture.getTextureType(this.properties.precision); + if ( + !temp || + temp.width != width || + temp.height != height || + temp.type != type + ) { + temp = this._temp_texture = new GL.Texture(width, height, { + format: gl.RGBA, + filter: gl.LINEAR, + type: type, + }); + } + + var v = this.getInputData(0); + + var properties = this.properties; + var that = this; + var time = this.graph.getTime(); + var ctx = gl; + var canvas = gl.canvas; + if (this.properties.use_html_canvas || !global.enableWebGLCanvas) { + if (!this._canvas) { + canvas = this._canvas = createCanvas(width.height); + ctx = this._ctx = canvas.getContext("2d"); + } else { + canvas = this._canvas; + ctx = this._ctx; + } + canvas.width = width; + canvas.height = height; + } + + if (ctx == gl) + //using Canvas2DtoWebGL + temp.drawTo(function () { + gl.start2D(); + if (properties.clear) { + gl.clearColor(0, 0, 0, 0); + gl.clear(gl.COLOR_BUFFER_BIT); + } + + try { + if (func_context.draw) { + func_context.draw.call(that, canvas, ctx, time, func_context, v); + } else { + func_context.call(that, canvas, ctx, time, func_context, v); + } + that.boxcolor = "#00FF00"; + } catch (err) { + that.boxcolor = "#FF0000"; + console.error("Error executing script"); + console.error(err); + } + gl.finish2D(); + }); + //rendering to offscreen canvas and uploading to texture + else { + if (properties.clear) ctx.clearRect(0, 0, canvas.width, canvas.height); + + try { + if (func_context.draw) { + func_context.draw.call(this, canvas, ctx, time, func_context, v); + } else { + func_context.call(this, canvas, ctx, time, func_context, v); + } + this.boxcolor = "#00FF00"; + } catch (err) { + this.boxcolor = "#FF0000"; + console.error("Error executing script"); + console.error(err); + } + temp.uploadImage(canvas); + } + + this.setOutputData(0, temp); + }; + + LiteGraph.registerNodeType("texture/canvas2D", LGraphTextureCanvas2D); + + // To do chroma keying ***************** + + function LGraphTextureMatte() { + this.addInput("in", "Texture"); + + this.addOutput("out", "Texture"); + this.properties = { + key_color: vec3.fromValues(0, 1, 0), + threshold: 0.8, + slope: 0.2, + precision: LGraphTexture.DEFAULT, + }; + } + + LGraphTextureMatte.title = "Matte"; + LGraphTextureMatte.desc = "Extracts background"; + + LGraphTextureMatte.widgets_info = { + key_color: { widget: "color" }, + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + + LGraphTextureMatte.prototype.onExecute = function () { + if (!this.isOutputConnected(0)) { + return; + } //saves work + + var tex = this.getInputData(0); + + if (this.properties.precision === LGraphTexture.PASS_THROUGH) { + this.setOutputData(0, tex); + return; + } + + if (!tex) { + return; + } + + this._tex = LGraphTexture.getTargetTexture( + tex, + this._tex, + this.properties.precision, + ); + + gl.disable(gl.BLEND); + gl.disable(gl.DEPTH_TEST); + + if (!this._uniforms) { + this._uniforms = { + u_texture: 0, + u_key_color: this.properties.key_color, + u_threshold: 1, + u_slope: 1, + }; + } + var uniforms = this._uniforms; + + var mesh = Mesh.getScreenQuad(); + var shader = LGraphTextureMatte._shader; + if (!shader) { + shader = LGraphTextureMatte._shader = new GL.Shader( + GL.Shader.SCREEN_VERTEX_SHADER, + LGraphTextureMatte.pixel_shader, + ); + } + + uniforms.u_key_color = this.properties.key_color; + uniforms.u_threshold = this.properties.threshold; + uniforms.u_slope = this.properties.slope; + + this._tex.drawTo(function () { + tex.bind(0); + shader.uniforms(uniforms).draw(mesh); + }); + + this.setOutputData(0, this._tex); + }; + + LGraphTextureMatte.pixel_shader = + "precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform vec3 u_key_color;\n\ + uniform float u_threshold;\n\ + uniform float u_slope;\n\ + \n\ + void main() {\n\ + vec3 color = texture2D( u_texture, v_coord ).xyz;\n\ + float diff = length( normalize(color) - normalize(u_key_color) );\n\ + float edge = u_threshold * (1.0 - u_slope);\n\ + float alpha = smoothstep( edge, u_threshold, diff);\n\ + gl_FragColor = vec4( color, alpha );\n\ + }"; + + LiteGraph.registerNodeType("texture/matte", LGraphTextureMatte); + + //*********************************** + function LGraphCubemapToTexture2D() { + this.addInput("in", "texture"); + this.addInput("yaw", "number"); + this.addOutput("out", "texture"); + this.properties = { yaw: 0 }; + } + + LGraphCubemapToTexture2D.title = "CubemapToTexture2D"; + LGraphCubemapToTexture2D.desc = + "Transforms a CUBEMAP texture into a TEXTURE2D in Polar Representation"; + + LGraphCubemapToTexture2D.prototype.onExecute = function () { + if (!this.isOutputConnected(0)) return; + + var tex = this.getInputData(0); + if (!tex || tex.texture_type != GL.TEXTURE_CUBE_MAP) return; + if ( + this._last_tex && + (this._last_tex.height != tex.height || this._last_tex.type != tex.type) + ) + this._last_tex = null; + var yaw = this.getInputOrProperty("yaw"); + this._last_tex = GL.Texture.cubemapToTexture2D( + tex, + tex.height, + this._last_tex, + true, + yaw, + ); + this.setOutputData(0, this._last_tex); + }; + + LiteGraph.registerNodeType( + "texture/cubemapToTexture2D", + LGraphCubemapToTexture2D, + ); +})(this); + +(function (global) { + if (typeof GL == "undefined") return; + + var LiteGraph = global.LiteGraph; + var LGraphCanvas = global.LGraphCanvas; + + var SHADERNODES_COLOR = "#345"; + + var LGShaders = (LiteGraph.Shaders = {}); + + var GLSL_types = (LGShaders.GLSL_types = [ + "float", + "vec2", + "vec3", + "vec4", + "mat3", + "mat4", + "sampler2D", + "samplerCube", + ]); + var GLSL_types_const = (LGShaders.GLSL_types_const = [ + "float", + "vec2", + "vec3", + "vec4", + ]); + + var GLSL_functions_desc = { + radians: "T radians(T degrees)", + degrees: "T degrees(T radians)", + sin: "T sin(T angle)", + cos: "T cos(T angle)", + tan: "T tan(T angle)", + asin: "T asin(T x)", + acos: "T acos(T x)", + atan: "T atan(T x)", + atan2: "T atan(T x,T y)", + pow: "T pow(T x,T y)", + exp: "T exp(T x)", + log: "T log(T x)", + exp2: "T exp2(T x)", + log2: "T log2(T x)", + sqrt: "T sqrt(T x)", + inversesqrt: "T inversesqrt(T x)", + abs: "T abs(T x)", + sign: "T sign(T x)", + floor: "T floor(T x)", + round: "T round(T x)", + ceil: "T ceil(T x)", + fract: "T fract(T x)", + mod: "T mod(T x,T y)", //"T mod(T x,float y)" + min: "T min(T x,T y)", + max: "T max(T x,T y)", + clamp: "T clamp(T x,T minVal = 0.0,T maxVal = 1.0)", + mix: "T mix(T x,T y,T a)", //"T mix(T x,T y,float a)" + step: "T step(T edge, T edge2, T x)", //"T step(float edge, T x)" + smoothstep: "T smoothstep(T edge, T edge2, T x)", //"T smoothstep(float edge, T x)" + length: "float length(T x)", + distance: "float distance(T p0, T p1)", + normalize: "T normalize(T x)", + dot: "float dot(T x,T y)", + cross: "vec3 cross(vec3 x,vec3 y)", + reflect: "vec3 reflect(vec3 V,vec3 N)", + refract: "vec3 refract(vec3 V,vec3 N, float IOR)", + }; + + //parse them + var GLSL_functions = {}; + var GLSL_functions_name = []; + parseGLSLDescriptions(); + + LGShaders.ALL_TYPES = "float,vec2,vec3,vec4"; + + function parseGLSLDescriptions() { + GLSL_functions_name.length = 0; + + for (var i in GLSL_functions_desc) { + var op = GLSL_functions_desc[i]; + var index = op.indexOf(" "); + var return_type = op.substr(0, index); + var index2 = op.indexOf("(", index); + var func_name = op.substr(index, index2 - index).trim(); + var params = op.substr(index2 + 1, op.length - index2 - 2).split(","); + for (var j in params) { + var p = params[j].split(" ").filter(function (a) { + return a; + }); + params[j] = { type: p[0].trim(), name: p[1].trim() }; + if (p[2] == "=") params[j].value = p[3].trim(); + } + GLSL_functions[i] = { + return_type: return_type, + func: func_name, + params: params, + }; + GLSL_functions_name.push(func_name); + //console.log( GLSL_functions[i] ); + } + } + + //common actions to all shader node classes + function registerShaderNode(type, node_ctor) { + //static attributes + node_ctor.color = SHADERNODES_COLOR; + node_ctor.filter = "shader"; + + //common methods + node_ctor.prototype.clearDestination = function () { + this.shader_destination = {}; + }; + node_ctor.prototype.propagateDestination = function propagateDestination( + dest_name, + ) { + this.shader_destination[dest_name] = true; + if (this.inputs) + for (var i = 0; i < this.inputs.length; ++i) { + var origin_node = this.getInputNode(i); + if (origin_node) origin_node.propagateDestination(dest_name); + } + }; + if (!node_ctor.prototype.onPropertyChanged) + node_ctor.prototype.onPropertyChanged = function () { + if (this.graph) this.graph._version++; + }; + + /* + if(!node_ctor.prototype.onGetCode) + node_ctor.prototype.onGetCode = function() + { + //check destination to avoid lonely nodes + if(!this.shader_destination) + return; + //grab inputs with types + var inputs = []; + if(this.inputs) + for(var i = 0; i < this.inputs.length; ++i) + inputs.push({ type: this.getInputData(i), name: getInputLinkID(this,i) }); + var outputs = []; + if(this.outputs) + for(var i = 0; i < this.outputs.length; ++i) + outputs.push({ name: getOutputLinkID(this,i) }); + //pass to code func + var results = this.extractCode(inputs); + //grab output, pass to next + if(results) + for(var i = 0; i < results.length; ++i) + { + var r = results[i]; + if(!r) + continue; + this.setOutputData(i,r.value); + } + } + */ + + LiteGraph.registerNodeType("shader::" + type, node_ctor); + } + + function getShaderNodeVarName(node, name) { + return "VAR_" + (name || "TEMP") + "_" + node.id; + } + + function getInputLinkID(node, slot) { + if (!node.inputs) return null; + var link = node.getInputLink(slot); + if (!link) return null; + var origin_node = node.graph.getNodeById(link.origin_id); + if (!origin_node) return null; + if (origin_node.getOutputVarName) + return origin_node.getOutputVarName(link.origin_slot); + //generate + return "link_" + origin_node.id + "_" + link.origin_slot; + } + + function getOutputLinkID(node, slot) { + if (!node.isOutputConnected(slot)) return null; + return "link_" + node.id + "_" + slot; + } + + LGShaders.registerShaderNode = registerShaderNode; + LGShaders.getInputLinkID = getInputLinkID; + LGShaders.getOutputLinkID = getOutputLinkID; + LGShaders.getShaderNodeVarName = getShaderNodeVarName; + LGShaders.parseGLSLDescriptions = parseGLSLDescriptions; + + //given a const number, it transform it to a string that matches a type + var valueToGLSL = (LiteGraph.valueToGLSL = function valueToGLSL( + v, + type, + precision, + ) { + var n = 5; //num decimals + if (precision != null) n = precision; + if (!type) { + if (v.constructor === Number) type = "float"; + else if (v.length) { + switch (v.length) { + case 2: + type = "vec2"; + break; + case 3: + type = "vec3"; + break; + case 4: + type = "vec4"; + break; + case 9: + type = "mat3"; + break; + case 16: + type = "mat4"; + break; + default: + throw "unknown type for glsl value size"; + } + } else throw "unknown type for glsl value: " + v.constructor; + } + switch (type) { + case "float": + return v.toFixed(n); + break; + case "vec2": + return "vec2(" + v[0].toFixed(n) + "," + v[1].toFixed(n) + ")"; + break; + case "color3": + case "vec3": + return ( + "vec3(" + + v[0].toFixed(n) + + "," + + v[1].toFixed(n) + + "," + + v[2].toFixed(n) + + ")" + ); + break; + case "color4": + case "vec4": + return ( + "vec4(" + + v[0].toFixed(n) + + "," + + v[1].toFixed(n) + + "," + + v[2].toFixed(n) + + "," + + v[3].toFixed(n) + + ")" + ); + break; + case "mat3": + return "mat3(1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0)"; + break; //not fully supported yet + case "mat4": + return "mat4(1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0)"; + break; //not fully supported yet + default: + throw ("unknown glsl type in valueToGLSL:", type); + } + + return ""; + }); + + //makes sure that a var is of a type, and if not, it converts it + var varToTypeGLSL = (LiteGraph.varToTypeGLSL = function varToTypeGLSL( + v, + input_type, + output_type, + ) { + if (input_type == output_type) return v; + if (v == null) + switch (output_type) { + case "float": + return "0.0"; + case "vec2": + return "vec2(0.0)"; + case "vec3": + return "vec3(0.0)"; + case "vec4": + return "vec4(0.0,0.0,0.0,1.0)"; + default: //null + return null; + } + + if (!output_type) throw "error: no output type specified"; + if (output_type == "float") { + switch (input_type) { + //case "float": + case "vec2": + case "vec3": + case "vec4": + return v + ".x"; + break; + default: //null + return "0.0"; + break; + } + } else if (output_type == "vec2") { + switch (input_type) { + case "float": + return "vec2(" + v + ")"; + //case "vec2": + case "vec3": + case "vec4": + return v + ".xy"; + default: //null + return "vec2(0.0)"; + } + } else if (output_type == "vec3") { + switch (input_type) { + case "float": + return "vec3(" + v + ")"; + case "vec2": + return "vec3(" + v + ",0.0)"; + //case "vec3": + case "vec4": + return v + ".xyz"; + default: //null + return "vec3(0.0)"; + } + } else if (output_type == "vec4") { + switch (input_type) { + case "float": + return "vec4(" + v + ")"; + case "vec2": + return "vec4(" + v + ",0.0,1.0)"; + case "vec3": + return "vec4(" + v + ",1.0)"; + default: //null + return "vec4(0.0,0.0,0.0,1.0)"; + } + } + throw "type cannot be converted"; + }); + + //used to plug incompatible stuff + var convertVarToGLSLType = (LiteGraph.convertVarToGLSLType = + function convertVarToGLSLType(varname, type, target_type) { + if (type == target_type) return varname; + if (type == "float") return target_type + "(" + varname + ")"; + if (target_type == "vec2") + //works for vec2,vec3 and vec4 + return "vec2(" + varname + ".xy)"; + if (target_type == "vec3") { + //works for vec2,vec3 and vec4 + if (type == "vec2") return "vec3(" + varname + ",0.0)"; + if (type == "vec4") return "vec4(" + varname + ".xyz)"; + } + if (target_type == "vec4") { + if (type == "vec2") return "vec4(" + varname + ",0.0,0.0)"; + if (target_type == "vec3") return "vec4(" + varname + ",1.0)"; + } + return null; + }); + + //used to host a shader body ************************************** + function LGShaderContext() { + //to store the code template + this.vs_template = ""; + this.fs_template = ""; + + //required so nodes now where to fetch the input data + this.buffer_names = { + uvs: "v_coord", + }; + + this.extra = {}; //to store custom info from the nodes (like if this shader supports a feature, etc) + + this._functions = {}; + this._uniforms = {}; + this._codeparts = {}; + this._uniform_value = null; + } + + LGShaderContext.prototype.clear = function () { + this._uniforms = {}; + this._functions = {}; + this._codeparts = {}; + this._uniform_value = null; + + this.extra = {}; + }; + + LGShaderContext.prototype.addUniform = function (name, type, value) { + this._uniforms[name] = type; + if (value != null) { + if (!this._uniform_value) this._uniform_value = {}; + this._uniform_value[name] = value; + } + }; + + LGShaderContext.prototype.addFunction = function (name, code) { + this._functions[name] = code; + }; + + LGShaderContext.prototype.addCode = function (hook, code, destinations) { + destinations = destinations || { "": "" }; + for (var i in destinations) { + var h = i ? i + "_" + hook : hook; + if (!this._codeparts[h]) this._codeparts[h] = code + "\n"; + else this._codeparts[h] += code + "\n"; + } + }; + + //the system works by grabbing code fragments from every node and concatenating them in blocks depending on where must they be attached + LGShaderContext.prototype.computeCodeBlocks = function ( + graph, + extra_uniforms, + ) { + //prepare context + this.clear(); + + //grab output nodes + var vertexout = graph.findNodesByType("shader::output/vertex"); + vertexout = vertexout && vertexout.length ? vertexout[0] : null; + var fragmentout = graph.findNodesByType("shader::output/fragcolor"); + fragmentout = fragmentout && fragmentout.length ? fragmentout[0] : null; + if (!fragmentout) + //?? + return null; + + //propagate back destinations + graph.sendEventToAllNodes("clearDestination"); + if (vertexout) vertexout.propagateDestination("vs"); + if (fragmentout) fragmentout.propagateDestination("fs"); + + //gets code from graph + graph.sendEventToAllNodes("onGetCode", this); + + var uniforms = ""; + for (var i in this._uniforms) + uniforms += "uniform " + this._uniforms[i] + " " + i + ";\n"; + if (extra_uniforms) + for (var i in extra_uniforms) + uniforms += "uniform " + extra_uniforms[i] + " " + i + ";\n"; + + var functions = ""; + for (var i in this._functions) + functions += "//" + i + "\n" + this._functions[i] + "\n"; + + var blocks = this._codeparts; + blocks.uniforms = uniforms; + blocks.functions = functions; + return blocks; + }; + + //replaces blocks using the vs and fs template and returns the final codes + LGShaderContext.prototype.computeShaderCode = function (graph) { + var blocks = this.computeCodeBlocks(graph); + var vs_code = GL.Shader.replaceCodeUsingContext(this.vs_template, blocks); + var fs_code = GL.Shader.replaceCodeUsingContext(this.fs_template, blocks); + return { + vs_code: vs_code, + fs_code: fs_code, + }; + }; + + //generates the shader code from the template and the + LGShaderContext.prototype.computeShader = function (graph, shader) { + var finalcode = this.computeShaderCode(graph); + console.log(finalcode.vs_code, finalcode.fs_code); + + if (!LiteGraph.catch_exceptions) { + this._shader_error = true; + if (shader) shader.updateShader(finalcode.vs_code, finalcode.fs_code); + else shader = new GL.Shader(finalcode.vs_code, finalcode.fs_code); + this._shader_error = false; + return shader; + } + + try { + if (shader) shader.updateShader(finalcode.vs_code, finalcode.fs_code); + else shader = new GL.Shader(finalcode.vs_code, finalcode.fs_code); + this._shader_error = false; + return shader; + } catch (err) { + if (!this._shader_error) { + console.error(err); + if (err.indexOf("Fragment shader") != -1) + console.log( + finalcode.fs_code + .split("\n") + .map(function (v, i) { + return i + ".- " + v; + }) + .join("\n"), + ); + else console.log(finalcode.vs_code); + } + this._shader_error = true; + return null; + } + + return null; //never here + }; + + LGShaderContext.prototype.getShader = function (graph) { + //if graph not changed? + if (this._shader && this._shader._version == graph._version) + return this._shader; + + //compile shader + var shader = this.computeShader(graph, this._shader); + if (!shader) return null; + + this._shader = shader; + shader._version = graph._version; + return shader; + }; + + //some shader nodes could require to fill the box with some uniforms + LGShaderContext.prototype.fillUniforms = function (uniforms, param) { + if (!this._uniform_value) return; + + for (var i in this._uniform_value) { + var v = this._uniform_value[i]; + if (v == null) continue; + if (v.constructor === Function) uniforms[i] = v.call(this, param); + else if (v.constructor === GL.Texture) { + //todo... + } else uniforms[i] = v; + } + }; + + LiteGraph.ShaderContext = LiteGraph.Shaders.Context = LGShaderContext; + + // LGraphShaderGraph ***************************** + // applies a shader graph to texture, it can be uses as an example + + function LGraphShaderGraph() { + //before inputs + this.subgraph = new LiteGraph.LGraph(); + this.subgraph._subgraph_node = this; + this.subgraph._is_subgraph = true; + this.subgraph.filter = "shader"; + + this.addInput("in", "texture"); + this.addOutput("out", "texture"); + this.properties = { + width: 0, + height: 0, + alpha: false, + precision: + typeof LGraphTexture != "undefined" ? LGraphTexture.DEFAULT : 2, + }; + + var inputNode = this.subgraph.findNodesByType("shader::input/uniform")[0]; + inputNode.pos = [200, 300]; + + var sampler = LiteGraph.createNode("shader::texture/sampler2D"); + sampler.pos = [400, 300]; + this.subgraph.add(sampler); + + var outnode = LiteGraph.createNode("shader::output/fragcolor"); + outnode.pos = [600, 300]; + this.subgraph.add(outnode); + + inputNode.connect(0, sampler); + sampler.connect(0, outnode); + + this.size = [180, 60]; + this.redraw_on_mouse = true; //force redraw + + this._uniforms = {}; + this._shader = null; + this._context = new LGShaderContext(); + this._context.vs_template = + "#define VERTEX\n" + GL.Shader.SCREEN_VERTEX_SHADER; + this._context.fs_template = LGraphShaderGraph.template; + } + + LGraphShaderGraph.template = + "\n\ +#define FRAGMENT\n\ +precision highp float;\n\ +varying vec2 v_coord;\n\ +{{varying}}\n\ +{{uniforms}}\n\ +{{functions}}\n\ +{{fs_functions}}\n\ +void main() {\n\n\ +vec2 uv = v_coord;\n\ +vec4 fragcolor = vec4(0.0);\n\ +vec4 fragcolor1 = vec4(0.0);\n\ +{{fs_code}}\n\ +gl_FragColor = fragcolor;\n\ +}\n\ + "; + + LGraphShaderGraph.widgets_info = { + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + + LGraphShaderGraph.title = "ShaderGraph"; + LGraphShaderGraph.desc = "Builds a shader using a graph"; + LGraphShaderGraph.input_node_type = "input/uniform"; + LGraphShaderGraph.output_node_type = "output/fragcolor"; + LGraphShaderGraph.title_color = SHADERNODES_COLOR; + + LGraphShaderGraph.prototype.onSerialize = function (o) { + o.subgraph = this.subgraph.serialize(); + }; + + LGraphShaderGraph.prototype.onConfigure = function (o) { + this.subgraph.configure(o.subgraph); + }; + + LGraphShaderGraph.prototype.onExecute = function () { + if (!this.isOutputConnected(0)) return; + + //read input texture + var intex = this.getInputData(0); + if (intex && intex.constructor != GL.Texture) intex = null; + + var w = this.properties.width | 0; + var h = this.properties.height | 0; + if (w == 0) { + w = intex ? intex.width : gl.viewport_data[2]; + } //0 means default + if (h == 0) { + h = intex ? intex.height : gl.viewport_data[3]; + } //0 means default + + var type = LGraphTexture.getTextureType(this.properties.precision, intex); + + var texture = this._texture; + if ( + !texture || + texture.width != w || + texture.height != h || + texture.type != type + ) { + texture = this._texture = new GL.Texture(w, h, { + type: type, + format: this.alpha ? gl.RGBA : gl.RGB, + filter: gl.LINEAR, + }); + } + + var shader = this.getShader(this.subgraph); + if (!shader) return; + + var uniforms = this._uniforms; + this._context.fillUniforms(uniforms); + + var tex_slot = 0; + if (this.inputs) + for (var i = 0; i < this.inputs.length; ++i) { + var input = this.inputs[i]; + var data = this.getInputData(i); + if (input.type == "texture") { + if (!data) data = GL.Texture.getWhiteTexture(); + data = data.bind(tex_slot++); + } + + if (data != null) uniforms["u_" + input.name] = data; + } + + var mesh = GL.Mesh.getScreenQuad(); + + gl.disable(gl.DEPTH_TEST); + gl.disable(gl.BLEND); + + texture.drawTo(function () { + shader.uniforms(uniforms); + shader.draw(mesh); + }); + + //use subgraph output + this.setOutputData(0, texture); + }; + + //add input node inside subgraph + LGraphShaderGraph.prototype.onInputAdded = function (slot_info) { + var subnode = LiteGraph.createNode("shader::input/uniform"); + subnode.setProperty("name", slot_info.name); + subnode.setProperty("type", slot_info.type); + this.subgraph.add(subnode); + }; + + //remove all + LGraphShaderGraph.prototype.onInputRemoved = function (slot, slot_info) { + var nodes = this.subgraph.findNodesByType("shader::input/uniform"); + for (var i = 0; i < nodes.length; ++i) { + var node = nodes[i]; + if (node.properties.name == slot_info.name) this.subgraph.remove(node); + } + }; + + LGraphShaderGraph.prototype.computeSize = function () { + var num_inputs = this.inputs ? this.inputs.length : 0; + var num_outputs = this.outputs ? this.outputs.length : 0; + return [ + 200, + Math.max(num_inputs, num_outputs) * LiteGraph.NODE_SLOT_HEIGHT + + LiteGraph.NODE_TITLE_HEIGHT + + 10, + ]; + }; + + LGraphShaderGraph.prototype.getShader = function () { + var shader = this._context.getShader(this.subgraph); + if (!shader) this.boxcolor = "red"; + else this.boxcolor = null; + return shader; + }; + + LGraphShaderGraph.prototype.onDrawBackground = function ( + ctx, + graphcanvas, + canvas, + pos, + ) { + if (this.flags.collapsed) return; + + //allows to preview the node if the canvas is a webgl canvas + var tex = this.getOutputData(0); + var inputs_y = this.inputs + ? this.inputs.length * LiteGraph.NODE_SLOT_HEIGHT + : 0; + if ( + tex && + ctx == tex.gl && + this.size[1] > inputs_y + LiteGraph.NODE_TITLE_HEIGHT + ) { + ctx.drawImage( + tex, + 10, + y, + this.size[0] - 20, + this.size[1] - inputs_y - LiteGraph.NODE_TITLE_HEIGHT, + ); + } + + var y = this.size[1] - LiteGraph.NODE_TITLE_HEIGHT + 0.5; + + //button + var over = LiteGraph.isInsideRectangle( + pos[0], + pos[1], + this.pos[0], + this.pos[1] + y, + this.size[0], + LiteGraph.NODE_TITLE_HEIGHT, + ); + ctx.fillStyle = over ? "#555" : "#222"; + ctx.beginPath(); + if (this._shape == LiteGraph.BOX_SHAPE) + ctx.rect(0, y, this.size[0] + 1, LiteGraph.NODE_TITLE_HEIGHT); + else + ctx.roundRect(0, y, this.size[0] + 1, LiteGraph.NODE_TITLE_HEIGHT, 0, 8); + ctx.fill(); + + //button + ctx.textAlign = "center"; + ctx.font = "24px Arial"; + ctx.fillStyle = over ? "#DDD" : "#999"; + ctx.fillText("+", this.size[0] * 0.5, y + 24); + }; + + LGraphShaderGraph.prototype.onMouseDown = function ( + e, + localpos, + graphcanvas, + ) { + var y = this.size[1] - LiteGraph.NODE_TITLE_HEIGHT + 0.5; + if (localpos[1] > y) { + graphcanvas.showSubgraphPropertiesDialog(this); + } + }; + + LGraphShaderGraph.prototype.onDrawSubgraphBackground = function ( + graphcanvas, + ) { + //TODO + }; + + LGraphShaderGraph.prototype.getExtraMenuOptions = function (graphcanvas) { + var that = this; + var options = [ + { + content: "Print Code", + callback: function () { + var code = that._context.computeShaderCode(); + console.log(code.vs_code, code.fs_code); + }, + }, + ]; + + return options; + }; + + LiteGraph.registerNodeType("texture/shaderGraph", LGraphShaderGraph); + + function shaderNodeFromFunction(classname, params, return_type, code) { + //TODO + } + + //Shader Nodes *********************************************************** + + //applies a shader graph to a code + function LGraphShaderUniform() { + this.addOutput("out", ""); + this.properties = { name: "", type: "" }; + } + + LGraphShaderUniform.title = "Uniform"; + LGraphShaderUniform.desc = "Input data for the shader"; + + LGraphShaderUniform.prototype.getTitle = function () { + if (this.properties.name && this.flags.collapsed) + return this.properties.type + " " + this.properties.name; + return "Uniform"; + }; + + LGraphShaderUniform.prototype.onPropertyChanged = function (name, value) { + this.outputs[0].name = this.properties.type + " " + this.properties.name; + }; + + LGraphShaderUniform.prototype.onGetCode = function (context) { + if (!this.shader_destination) return; + + var type = this.properties.type; + if (!type) { + if (!context.onGetPropertyInfo) return; + var info = context.onGetPropertyInfo(this.property.name); + if (!info) return; + type = info.type; + } + if (type == "number") type = "float"; + else if (type == "texture") type = "sampler2D"; + if (LGShaders.GLSL_types.indexOf(type) == -1) return; + + context.addUniform("u_" + this.properties.name, type); + this.setOutputData(0, type); + }; + + LGraphShaderUniform.prototype.getOutputVarName = function (slot) { + return "u_" + this.properties.name; + }; + + registerShaderNode("input/uniform", LGraphShaderUniform); + + function LGraphShaderAttribute() { + this.addOutput("out", "vec2"); + this.properties = { name: "coord", type: "vec2" }; + } + + LGraphShaderAttribute.title = "Attribute"; + LGraphShaderAttribute.desc = "Input data from mesh attribute"; + + LGraphShaderAttribute.prototype.getTitle = function () { + return "att. " + this.properties.name; + }; + + LGraphShaderAttribute.prototype.onGetCode = function (context) { + if (!this.shader_destination) return; + + var type = this.properties.type; + if (!type || LGShaders.GLSL_types.indexOf(type) == -1) return; + if (type == "number") type = "float"; + if (this.properties.name != "coord") { + context.addCode( + "varying", + " varying " + type + " v_" + this.properties.name + ";", + ); + //if( !context.varyings[ this.properties.name ] ) + //context.addCode( "vs_code", "v_" + this.properties.name + " = " + input_name + ";" ); + } + this.setOutputData(0, type); + }; + + LGraphShaderAttribute.prototype.getOutputVarName = function (slot) { + return "v_" + this.properties.name; + }; + + registerShaderNode("input/attribute", LGraphShaderAttribute); + + function LGraphShaderSampler2D() { + this.addInput("tex", "sampler2D"); + this.addInput("uv", "vec2"); + this.addOutput("rgba", "vec4"); + this.addOutput("rgb", "vec3"); + } + + LGraphShaderSampler2D.title = "Sampler2D"; + LGraphShaderSampler2D.desc = "Reads a pixel from a texture"; + + LGraphShaderSampler2D.prototype.onGetCode = function (context) { + if (!this.shader_destination) return; + + var texname = getInputLinkID(this, 0); + var varname = getShaderNodeVarName(this); + var code = "vec4 " + varname + " = vec4(0.0);\n"; + if (texname) { + var uvname = getInputLinkID(this, 1) || context.buffer_names.uvs; + code += varname + " = texture2D(" + texname + "," + uvname + ");\n"; + } + + var link0 = getOutputLinkID(this, 0); + if (link0) + code += "vec4 " + getOutputLinkID(this, 0) + " = " + varname + ";\n"; + + var link1 = getOutputLinkID(this, 1); + if (link1) + code += "vec3 " + getOutputLinkID(this, 1) + " = " + varname + ".xyz;\n"; + + context.addCode("code", code, this.shader_destination); + this.setOutputData(0, "vec4"); + this.setOutputData(1, "vec3"); + }; + + registerShaderNode("texture/sampler2D", LGraphShaderSampler2D); + + //********************************* + + function LGraphShaderConstant() { + this.addOutput("", "float"); + + this.properties = { + type: "float", + value: 0, + }; + + this.addWidget("combo", "type", "float", null, { + values: GLSL_types_const, + property: "type", + }); + this.updateWidgets(); + } + + LGraphShaderConstant.title = "const"; + + LGraphShaderConstant.prototype.getTitle = function () { + if (this.flags.collapsed) + return valueToGLSL(this.properties.value, this.properties.type, 2); + return "Const"; + }; + + LGraphShaderConstant.prototype.onPropertyChanged = function (name, value) { + var that = this; + if (name == "type") { + if (this.outputs[0].type != value) { + this.disconnectOutput(0); + this.outputs[0].type = value; + } + this.widgets.length = 1; //remove extra widgets + this.updateWidgets(); + } + if (name == "value") { + if (!value.length) this.widgets[1].value = value; + else { + this.widgets[1].value = value[1]; + if (value.length > 2) this.widgets[2].value = value[2]; + if (value.length > 3) this.widgets[3].value = value[3]; + } + } + }; + + LGraphShaderConstant.prototype.updateWidgets = function (old_value) { + var that = this; + var old_value = this.properties.value; + var options = { step: 0.01 }; + switch (this.properties.type) { + case "float": + this.properties.value = 0; + this.addWidget("number", "v", 0, { step: 0.01, property: "value" }); + break; + case "vec2": + this.properties.value = + old_value && old_value.length == 2 + ? [old_value[0], old_value[1]] + : [0, 0, 0]; + this.addWidget( + "number", + "x", + this.properties.value[0], + function (v) { + that.properties.value[0] = v; + }, + options, + ); + this.addWidget( + "number", + "y", + this.properties.value[1], + function (v) { + that.properties.value[1] = v; + }, + options, + ); + break; + case "vec3": + this.properties.value = + old_value && old_value.length == 3 + ? [old_value[0], old_value[1], old_value[2]] + : [0, 0, 0]; + this.addWidget( + "number", + "x", + this.properties.value[0], + function (v) { + that.properties.value[0] = v; + }, + options, + ); + this.addWidget( + "number", + "y", + this.properties.value[1], + function (v) { + that.properties.value[1] = v; + }, + options, + ); + this.addWidget( + "number", + "z", + this.properties.value[2], + function (v) { + that.properties.value[2] = v; + }, + options, + ); + break; + case "vec4": + this.properties.value = + old_value && old_value.length == 4 + ? [old_value[0], old_value[1], old_value[2], old_value[3]] + : [0, 0, 0, 0]; + this.addWidget( + "number", + "x", + this.properties.value[0], + function (v) { + that.properties.value[0] = v; + }, + options, + ); + this.addWidget( + "number", + "y", + this.properties.value[1], + function (v) { + that.properties.value[1] = v; + }, + options, + ); + this.addWidget( + "number", + "z", + this.properties.value[2], + function (v) { + that.properties.value[2] = v; + }, + options, + ); + this.addWidget( + "number", + "w", + this.properties.value[3], + function (v) { + that.properties.value[3] = v; + }, + options, + ); + break; + default: + console.error("unknown type for constant"); + } + }; + + LGraphShaderConstant.prototype.onGetCode = function (context) { + if (!this.shader_destination) return; + + var value = valueToGLSL(this.properties.value, this.properties.type); + var link_name = getOutputLinkID(this, 0); + if (!link_name) + //not connected + return; + + var code = + " " + this.properties.type + " " + link_name + " = " + value + ";"; + context.addCode("code", code, this.shader_destination); + + this.setOutputData(0, this.properties.type); + }; + + registerShaderNode("const/const", LGraphShaderConstant); + + function LGraphShaderVec2() { + this.addInput("xy", "vec2"); + this.addInput("x", "float"); + this.addInput("y", "float"); + this.addOutput("xy", "vec2"); + this.addOutput("x", "float"); + this.addOutput("y", "float"); + + this.properties = { x: 0, y: 0 }; + } + + LGraphShaderVec2.title = "vec2"; + LGraphShaderVec2.varmodes = ["xy", "x", "y"]; + + LGraphShaderVec2.prototype.onPropertyChanged = function () { + if (this.graph) this.graph._version++; + }; + + LGraphShaderVec2.prototype.onGetCode = function (context) { + if (!this.shader_destination) return; + + var props = this.properties; + + var varname = getShaderNodeVarName(this); + var code = + " vec2 " + varname + " = " + valueToGLSL([props.x, props.y]) + ";\n"; + + for (var i = 0; i < LGraphShaderVec2.varmodes.length; ++i) { + var varmode = LGraphShaderVec2.varmodes[i]; + var inlink = getInputLinkID(this, i); + if (!inlink) continue; + code += " " + varname + "." + varmode + " = " + inlink + ";\n"; + } + + for (var i = 0; i < LGraphShaderVec2.varmodes.length; ++i) { + var varmode = LGraphShaderVec2.varmodes[i]; + var outlink = getOutputLinkID(this, i); + if (!outlink) continue; + var type = GLSL_types_const[varmode.length - 1]; + code += + " " + type + " " + outlink + " = " + varname + "." + varmode + ";\n"; + this.setOutputData(i, type); + } + + context.addCode("code", code, this.shader_destination); + }; + + registerShaderNode("const/vec2", LGraphShaderVec2); + + function LGraphShaderVec3() { + this.addInput("xyz", "vec3"); + this.addInput("x", "float"); + this.addInput("y", "float"); + this.addInput("z", "float"); + this.addInput("xy", "vec2"); + this.addInput("xz", "vec2"); + this.addInput("yz", "vec2"); + this.addOutput("xyz", "vec3"); + this.addOutput("x", "float"); + this.addOutput("y", "float"); + this.addOutput("z", "float"); + this.addOutput("xy", "vec2"); + this.addOutput("xz", "vec2"); + this.addOutput("yz", "vec2"); + + this.properties = { x: 0, y: 0, z: 0 }; + } + + LGraphShaderVec3.title = "vec3"; + LGraphShaderVec3.varmodes = ["xyz", "x", "y", "z", "xy", "xz", "yz"]; + + LGraphShaderVec3.prototype.onPropertyChanged = function () { + if (this.graph) this.graph._version++; + }; + + LGraphShaderVec3.prototype.onGetCode = function (context) { + if (!this.shader_destination) return; + + var props = this.properties; + + var varname = getShaderNodeVarName(this); + var code = + "vec3 " + + varname + + " = " + + valueToGLSL([props.x, props.y, props.z]) + + ";\n"; + + for (var i = 0; i < LGraphShaderVec3.varmodes.length; ++i) { + var varmode = LGraphShaderVec3.varmodes[i]; + var inlink = getInputLinkID(this, i); + if (!inlink) continue; + code += " " + varname + "." + varmode + " = " + inlink + ";\n"; + } + + for (var i = 0; i < LGraphShaderVec3.varmodes.length; ++i) { + var varmode = LGraphShaderVec3.varmodes[i]; + var outlink = getOutputLinkID(this, i); + if (!outlink) continue; + var type = GLSL_types_const[varmode.length - 1]; + code += + " " + type + " " + outlink + " = " + varname + "." + varmode + ";\n"; + this.setOutputData(i, type); + } + + context.addCode("code", code, this.shader_destination); + }; + + registerShaderNode("const/vec3", LGraphShaderVec3); + + function LGraphShaderVec4() { + this.addInput("xyzw", "vec4"); + this.addInput("xyz", "vec3"); + this.addInput("x", "float"); + this.addInput("y", "float"); + this.addInput("z", "float"); + this.addInput("w", "float"); + this.addInput("xy", "vec2"); + this.addInput("yz", "vec2"); + this.addInput("zw", "vec2"); + this.addOutput("xyzw", "vec4"); + this.addOutput("xyz", "vec3"); + this.addOutput("x", "float"); + this.addOutput("y", "float"); + this.addOutput("z", "float"); + this.addOutput("xy", "vec2"); + this.addOutput("yz", "vec2"); + this.addOutput("zw", "vec2"); + + this.properties = { x: 0, y: 0, z: 0, w: 0 }; + } + + LGraphShaderVec4.title = "vec4"; + LGraphShaderVec4.varmodes = [ + "xyzw", + "xyz", + "x", + "y", + "z", + "w", + "xy", + "yz", + "zw", + ]; + + LGraphShaderVec4.prototype.onPropertyChanged = function () { + if (this.graph) this.graph._version++; + }; + + LGraphShaderVec4.prototype.onGetCode = function (context) { + if (!this.shader_destination) return; + + var props = this.properties; + + var varname = getShaderNodeVarName(this); + var code = + "vec4 " + + varname + + " = " + + valueToGLSL([props.x, props.y, props.z, props.w]) + + ";\n"; + + for (var i = 0; i < LGraphShaderVec4.varmodes.length; ++i) { + var varmode = LGraphShaderVec4.varmodes[i]; + var inlink = getInputLinkID(this, i); + if (!inlink) continue; + code += " " + varname + "." + varmode + " = " + inlink + ";\n"; + } + + for (var i = 0; i < LGraphShaderVec4.varmodes.length; ++i) { + var varmode = LGraphShaderVec4.varmodes[i]; + var outlink = getOutputLinkID(this, i); + if (!outlink) continue; + var type = GLSL_types_const[varmode.length - 1]; + code += + " " + type + " " + outlink + " = " + varname + "." + varmode + ";\n"; + this.setOutputData(i, type); + } + + context.addCode("code", code, this.shader_destination); + }; + + registerShaderNode("const/vec4", LGraphShaderVec4); + + //********************************* + + function LGraphShaderFragColor() { + this.addInput("color", LGShaders.ALL_TYPES); + this.block_delete = true; + } + + LGraphShaderFragColor.title = "FragColor"; + LGraphShaderFragColor.desc = "Pixel final color"; + + LGraphShaderFragColor.prototype.onGetCode = function (context) { + var link_name = getInputLinkID(this, 0); + if (!link_name) return; + var type = this.getInputData(0); + var code = varToTypeGLSL(link_name, type, "vec4"); + context.addCode("fs_code", "fragcolor = " + code + ";"); + }; + + registerShaderNode("output/fragcolor", LGraphShaderFragColor); + + /* + function LGraphShaderDiscard() + { + this.addInput("v","T"); + this.addInput("min","T"); + this.properties = { min_value: 0.0 }; + this.addWidget("number","min",0,{ step: 0.01, property: "min_value" }); + } + + LGraphShaderDiscard.title = "Discard"; + + LGraphShaderDiscard.prototype.onGetCode = function( context ) + { + if(!this.isOutputConnected(0)) + return; + + var inlink = getInputLinkID(this,0); + var inlink1 = getInputLinkID(this,1); + + if(!inlink && !inlink1) //not connected + return; + context.addCode("code", return_type + " " + outlink + " = ( (" + inlink + " - "+minv+") / ("+ maxv+" - "+minv+") ) * ("+ maxv2+" - "+minv2+") + " + minv2 + ";", this.shader_destination ); + this.setOutputData( 0, return_type ); + } + + registerShaderNode( "output/discard", LGraphShaderDiscard ); + */ + + // ************************************************* + + function LGraphShaderOperation() { + this.addInput("A", LGShaders.ALL_TYPES); + this.addInput("B", LGShaders.ALL_TYPES); + this.addOutput("out", ""); + this.properties = { + operation: "*", + }; + this.addWidget("combo", "op.", this.properties.operation, { + property: "operation", + values: LGraphShaderOperation.operations, + }); + } + + LGraphShaderOperation.title = "Operation"; + LGraphShaderOperation.operations = ["+", "-", "*", "/"]; + + LGraphShaderOperation.prototype.getTitle = function () { + if (this.flags.collapsed) return "A" + this.properties.operation + "B"; + else return "Operation"; + }; + + LGraphShaderOperation.prototype.onGetCode = function (context) { + if (!this.shader_destination) return; + + if (!this.isOutputConnected(0)) return; + + var inlinks = []; + for (var i = 0; i < 3; ++i) + inlinks.push({ + name: getInputLinkID(this, i), + type: this.getInputData(i) || "float", + }); + + var outlink = getOutputLinkID(this, 0); + if (!outlink) + //not connected + return; + + //func_desc + var base_type = inlinks[0].type; + var return_type = base_type; + var op = this.properties.operation; + + var params = []; + for (var i = 0; i < 2; ++i) { + var param_code = inlinks[i].name; + if (param_code == null) { + //not plugged + param_code = p.value != null ? p.value : "(1.0)"; + inlinks[i].type = "float"; + } + + //convert + if (inlinks[i].type != base_type) { + if (inlinks[i].type == "float" && (op == "*" || op == "/")) { + //I find hard to create the opposite condition now, so I prefeer an else + } else + param_code = convertVarToGLSLType( + param_code, + inlinks[i].type, + base_type, + ); + } + params.push(param_code); + } + + context.addCode( + "code", + return_type + " " + outlink + " = " + params[0] + op + params[1] + ";", + this.shader_destination, + ); + this.setOutputData(0, return_type); + }; + + registerShaderNode("math/operation", LGraphShaderOperation); + + function LGraphShaderFunc() { + this.addInput("A", LGShaders.ALL_TYPES); + this.addInput("B", LGShaders.ALL_TYPES); + this.addOutput("out", ""); + this.properties = { + func: "floor", + }; + this._current = "floor"; + this.addWidget("combo", "func", this.properties.func, { + property: "func", + values: GLSL_functions_name, + }); + } + + LGraphShaderFunc.title = "Func"; + + LGraphShaderFunc.prototype.onPropertyChanged = function (name, value) { + if (this.graph) this.graph._version++; + + if (name == "func") { + var func_desc = GLSL_functions[value]; + if (!func_desc) return; + + //remove extra inputs + for (var i = func_desc.params.length; i < this.inputs.length; ++i) + this.removeInput(i); + + //add and update inputs + for (var i = 0; i < func_desc.params.length; ++i) { + var p = func_desc.params[i]; + if (this.inputs[i]) + this.inputs[i].name = p.name + (p.value ? " (" + p.value + ")" : ""); + else this.addInput(p.name, LGShaders.ALL_TYPES); + } + } + }; + + LGraphShaderFunc.prototype.getTitle = function () { + if (this.flags.collapsed) return this.properties.func; + else return "Func"; + }; + + LGraphShaderFunc.prototype.onGetCode = function (context) { + if (!this.shader_destination) return; + + if (!this.isOutputConnected(0)) return; + + var inlinks = []; + for (var i = 0; i < 3; ++i) + inlinks.push({ + name: getInputLinkID(this, i), + type: this.getInputData(i) || "float", + }); + + var outlink = getOutputLinkID(this, 0); + if (!outlink) + //not connected + return; + + var func_desc = GLSL_functions[this.properties.func]; + if (!func_desc) return; + + //func_desc + var base_type = inlinks[0].type; + var return_type = func_desc.return_type; + if (return_type == "T") return_type = base_type; + + var params = []; + for (var i = 0; i < func_desc.params.length; ++i) { + var p = func_desc.params[i]; + var param_code = inlinks[i].name; + if (param_code == null) { + //not plugged + param_code = p.value != null ? p.value : "(1.0)"; + inlinks[i].type = "float"; + } + if ( + (p.type == "T" && inlinks[i].type != base_type) || + (p.type != "T" && inlinks[i].type != base_type) + ) + param_code = convertVarToGLSLType( + param_code, + inlinks[i].type, + base_type, + ); + params.push(param_code); + } + + context.addFunction( + "round", + "float round(float v){ return floor(v+0.5); }\nvec2 round(vec2 v){ return floor(v+vec2(0.5));}\nvec3 round(vec3 v){ return floor(v+vec3(0.5));}\nvec4 round(vec4 v){ return floor(v+vec4(0.5)); }\n", + ); + context.addCode( + "code", + return_type + + " " + + outlink + + " = " + + func_desc.func + + "(" + + params.join(",") + + ");", + this.shader_destination, + ); + + this.setOutputData(0, return_type); + }; + + registerShaderNode("math/func", LGraphShaderFunc); + + function LGraphShaderSnippet() { + this.addInput("A", LGShaders.ALL_TYPES); + this.addInput("B", LGShaders.ALL_TYPES); + this.addOutput("C", "vec4"); + this.properties = { + code: "C = A+B", + type: "vec4", + }; + this.addWidget("text", "code", this.properties.code, { property: "code" }); + this.addWidget("combo", "type", this.properties.type, { + values: ["float", "vec2", "vec3", "vec4"], + property: "type", + }); + } + + LGraphShaderSnippet.title = "Snippet"; + + LGraphShaderSnippet.prototype.onPropertyChanged = function (name, value) { + if (this.graph) this.graph._version++; + + if (name == "type" && this.outputs[0].type != value) { + this.disconnectOutput(0); + this.outputs[0].type = value; + } + }; + + LGraphShaderSnippet.prototype.getTitle = function () { + if (this.flags.collapsed) return this.properties.code; + else return "Snippet"; + }; + + LGraphShaderSnippet.prototype.onGetCode = function (context) { + if (!this.shader_destination || !this.isOutputConnected(0)) return; + + var inlinkA = getInputLinkID(this, 0); + if (!inlinkA) inlinkA = "1.0"; + var inlinkB = getInputLinkID(this, 1); + if (!inlinkB) inlinkB = "1.0"; + var outlink = getOutputLinkID(this, 0); + if (!outlink) + //not connected + return; + + var inA_type = this.getInputData(0) || "float"; + var inB_type = this.getInputData(1) || "float"; + var return_type = this.properties.type; + + //cannot resolve input + if (inA_type == "T" || inB_type == "T") { + return null; + } + + var funcname = "funcSnippet" + this.id; + + var func_code = + "\n" + + return_type + + " " + + funcname + + "( " + + inA_type + + " A, " + + inB_type + + " B) {\n"; + func_code += " " + return_type + " C = " + return_type + "(0.0);\n"; + func_code += " " + this.properties.code + ";\n"; + func_code += " return C;\n}\n"; + + context.addCode("functions", func_code, this.shader_destination); + context.addCode( + "code", + return_type + + " " + + outlink + + " = " + + funcname + + "(" + + inlinkA + + "," + + inlinkB + + ");", + this.shader_destination, + ); + + this.setOutputData(0, return_type); + }; + + registerShaderNode("utils/snippet", LGraphShaderSnippet); + + //************************************ + + function LGraphShaderRand() { + this.addOutput("out", "float"); + } + + LGraphShaderRand.title = "Rand"; + + LGraphShaderRand.prototype.onGetCode = function (context) { + if (!this.shader_destination || !this.isOutputConnected(0)) return; + + var outlink = getOutputLinkID(this, 0); + + context.addUniform("u_rand" + this.id, "float", function () { + return Math.random(); + }); + context.addCode( + "code", + "float " + outlink + " = u_rand" + this.id + ";", + this.shader_destination, + ); + this.setOutputData(0, "float"); + }; + + registerShaderNode("input/rand", LGraphShaderRand); + + //noise + //https://gist.github.com/patriciogonzalezvivo/670c22f3966e662d2f83 + function LGraphShaderNoise() { + this.addInput("out", LGShaders.ALL_TYPES); + this.addInput("scale", "float"); + this.addOutput("out", "float"); + this.properties = { + type: "noise", + scale: 1, + }; + this.addWidget("combo", "type", this.properties.type, { + property: "type", + values: LGraphShaderNoise.NOISE_TYPES, + }); + this.addWidget("number", "scale", this.properties.scale, { + property: "scale", + }); + } + + LGraphShaderNoise.NOISE_TYPES = ["noise", "rand"]; + + LGraphShaderNoise.title = "noise"; + + LGraphShaderNoise.prototype.onGetCode = function (context) { + if (!this.shader_destination || !this.isOutputConnected(0)) return; + + var inlink = getInputLinkID(this, 0); + var outlink = getOutputLinkID(this, 0); + + var intype = this.getInputData(0); + if (!inlink) { + intype = "vec2"; + inlink = context.buffer_names.uvs; + } + + context.addFunction("noise", LGraphShaderNoise.shader_functions); + context.addUniform( + "u_noise_scale" + this.id, + "float", + this.properties.scale, + ); + if (intype == "float") + context.addCode( + "code", + "float " + + outlink + + " = snoise( vec2(" + + inlink + + ") * u_noise_scale" + + this.id + + ");", + this.shader_destination, + ); + else if (intype == "vec2" || intype == "vec3") + context.addCode( + "code", + "float " + + outlink + + " = snoise(" + + inlink + + " * u_noise_scale" + + this.id + + ");", + this.shader_destination, + ); + else if (intype == "vec4") + context.addCode( + "code", + "float " + + outlink + + " = snoise(" + + inlink + + ".xyz * u_noise_scale" + + this.id + + ");", + this.shader_destination, + ); + this.setOutputData(0, "float"); + }; + + registerShaderNode("math/noise", LGraphShaderNoise); + + LGraphShaderNoise.shader_functions = + "\n\ +vec3 permute(vec3 x) { return mod(((x*34.0)+1.0)*x, 289.0); }\n\ +\n\ +float snoise(vec2 v){\n\ + const vec4 C = vec4(0.211324865405187, 0.366025403784439,-0.577350269189626, 0.024390243902439);\n\ + vec2 i = floor(v + dot(v, C.yy) );\n\ + vec2 x0 = v - i + dot(i, C.xx);\n\ + vec2 i1;\n\ + i1 = (x0.x > x0.y) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);\n\ + vec4 x12 = x0.xyxy + C.xxzz;\n\ + x12.xy -= i1;\n\ + i = mod(i, 289.0);\n\ + vec3 p = permute( permute( i.y + vec3(0.0, i1.y, 1.0 ))\n\ + + i.x + vec3(0.0, i1.x, 1.0 ));\n\ + vec3 m = max(0.5 - vec3(dot(x0,x0), dot(x12.xy,x12.xy),dot(x12.zw,x12.zw)), 0.0);\n\ + m = m*m ;\n\ + m = m*m ;\n\ + vec3 x = 2.0 * fract(p * C.www) - 1.0;\n\ + vec3 h = abs(x) - 0.5;\n\ + vec3 ox = floor(x + 0.5);\n\ + vec3 a0 = x - ox;\n\ + m *= 1.79284291400159 - 0.85373472095314 * ( a0*a0 + h*h );\n\ + vec3 g;\n\ + g.x = a0.x * x0.x + h.x * x0.y;\n\ + g.yz = a0.yz * x12.xz + h.yz * x12.yw;\n\ + return 130.0 * dot(m, g);\n\ +}\n\ +vec4 permute(vec4 x){return mod(((x*34.0)+1.0)*x, 289.0);}\n\ +vec4 taylorInvSqrt(vec4 r){return 1.79284291400159 - 0.85373472095314 * r;}\n\ +\n\ +float snoise(vec3 v){ \n\ + const vec2 C = vec2(1.0/6.0, 1.0/3.0) ;\n\ + const vec4 D = vec4(0.0, 0.5, 1.0, 2.0);\n\ +\n\ +// First corner\n\ + vec3 i = floor(v + dot(v, C.yyy) );\n\ + vec3 x0 = v - i + dot(i, C.xxx) ;\n\ +\n\ +// Other corners\n\ + vec3 g = step(x0.yzx, x0.xyz);\n\ + vec3 l = 1.0 - g;\n\ + vec3 i1 = min( g.xyz, l.zxy );\n\ + vec3 i2 = max( g.xyz, l.zxy );\n\ +\n\ + // x0 = x0 - 0. + 0.0 * C \n\ + vec3 x1 = x0 - i1 + 1.0 * C.xxx;\n\ + vec3 x2 = x0 - i2 + 2.0 * C.xxx;\n\ + vec3 x3 = x0 - 1. + 3.0 * C.xxx;\n\ +\n\ +// Permutations\n\ + i = mod(i, 289.0 ); \n\ + vec4 p = permute( permute( permute( \n\ + i.z + vec4(0.0, i1.z, i2.z, 1.0 ))\n\ + + i.y + vec4(0.0, i1.y, i2.y, 1.0 )) \n\ + + i.x + vec4(0.0, i1.x, i2.x, 1.0 ));\n\ +\n\ +// Gradients\n\ +// ( N*N points uniformly over a square, mapped onto an octahedron.)\n\ + float n_ = 1.0/7.0; // N=7\n\ + vec3 ns = n_ * D.wyz - D.xzx;\n\ +\n\ + vec4 j = p - 49.0 * floor(p * ns.z *ns.z); // mod(p,N*N)\n\ +\n\ + vec4 x_ = floor(j * ns.z);\n\ + vec4 y_ = floor(j - 7.0 * x_ ); // mod(j,N)\n\ +\n\ + vec4 x = x_ *ns.x + ns.yyyy;\n\ + vec4 y = y_ *ns.x + ns.yyyy;\n\ + vec4 h = 1.0 - abs(x) - abs(y);\n\ +\n\ + vec4 b0 = vec4( x.xy, y.xy );\n\ + vec4 b1 = vec4( x.zw, y.zw );\n\ +\n\ + vec4 s0 = floor(b0)*2.0 + 1.0;\n\ + vec4 s1 = floor(b1)*2.0 + 1.0;\n\ + vec4 sh = -step(h, vec4(0.0));\n\ +\n\ + vec4 a0 = b0.xzyw + s0.xzyw*sh.xxyy ;\n\ + vec4 a1 = b1.xzyw + s1.xzyw*sh.zzww ;\n\ +\n\ + vec3 p0 = vec3(a0.xy,h.x);\n\ + vec3 p1 = vec3(a0.zw,h.y);\n\ + vec3 p2 = vec3(a1.xy,h.z);\n\ + vec3 p3 = vec3(a1.zw,h.w);\n\ +\n\ +//Normalise gradients\n\ + vec4 norm = taylorInvSqrt(vec4(dot(p0,p0), dot(p1,p1), dot(p2, p2), dot(p3,p3)));\n\ + p0 *= norm.x;\n\ + p1 *= norm.y;\n\ + p2 *= norm.z;\n\ + p3 *= norm.w;\n\ +\n\ +// Mix final noise value\n\ + vec4 m = max(0.6 - vec4(dot(x0,x0), dot(x1,x1), dot(x2,x2), dot(x3,x3)), 0.0);\n\ + m = m * m;\n\ + return 42.0 * dot( m*m, vec4( dot(p0,x0), dot(p1,x1),dot(p2,x2), dot(p3,x3) ) );\n\ +}\n\ +\n\ +vec3 hash3( vec2 p ){\n\ + vec3 q = vec3( dot(p,vec2(127.1,311.7)), \n\ + dot(p,vec2(269.5,183.3)), \n\ + dot(p,vec2(419.2,371.9)) );\n\ + return fract(sin(q)*43758.5453);\n\ +}\n\ +vec4 hash4( vec3 p ){\n\ + vec4 q = vec4( dot(p,vec3(127.1,311.7,257.3)), \n\ + dot(p,vec3(269.5,183.3,335.1)), \n\ + dot(p,vec3(314.5,235.1,467.3)), \n\ + dot(p,vec3(419.2,371.9,114.9)) );\n\ + return fract(sin(q)*43758.5453);\n\ +}\n\ +\n\ +float iqnoise( in vec2 x, float u, float v ){\n\ + vec2 p = floor(x);\n\ + vec2 f = fract(x);\n\ + \n\ + float k = 1.0+63.0*pow(1.0-v,4.0);\n\ + \n\ + float va = 0.0;\n\ + float wt = 0.0;\n\ + for( int j=-2; j<=2; j++ )\n\ + for( int i=-2; i<=2; i++ )\n\ + {\n\ + vec2 g = vec2( float(i),float(j) );\n\ + vec3 o = hash3( p + g )*vec3(u,u,1.0);\n\ + vec2 r = g - f + o.xy;\n\ + float d = dot(r,r);\n\ + float ww = pow( 1.0-smoothstep(0.0,1.414,sqrt(d)), k );\n\ + va += o.z*ww;\n\ + wt += ww;\n\ + }\n\ + \n\ + return va/wt;\n\ +}\n\ +"; + + function LGraphShaderTime() { + this.addOutput("out", "float"); + } + + LGraphShaderTime.title = "Time"; + + LGraphShaderTime.prototype.onGetCode = function (context) { + if (!this.shader_destination || !this.isOutputConnected(0)) return; + + var outlink = getOutputLinkID(this, 0); + + context.addUniform("u_time" + this.id, "float", function () { + return getTime() * 0.001; + }); + context.addCode( + "code", + "float " + outlink + " = u_time" + this.id + ";", + this.shader_destination, + ); + this.setOutputData(0, "float"); + }; + + registerShaderNode("input/time", LGraphShaderTime); + + function LGraphShaderDither() { + this.addInput("in", "T"); + this.addOutput("out", "float"); + } + + LGraphShaderDither.title = "Dither"; + + LGraphShaderDither.prototype.onGetCode = function (context) { + if (!this.shader_destination || !this.isOutputConnected(0)) return; + + var inlink = getInputLinkID(this, 0); + var return_type = "float"; + var outlink = getOutputLinkID(this, 0); + var intype = this.getInputData(0); + inlink = varToTypeGLSL(inlink, intype, "float"); + context.addFunction("dither8x8", LGraphShaderDither.dither_func); + context.addCode( + "code", + return_type + " " + outlink + " = dither8x8(" + inlink + ");", + this.shader_destination, + ); + this.setOutputData(0, return_type); + }; + + LGraphShaderDither.dither_values = [ + 0.515625, 0.140625, 0.640625, 0.046875, 0.546875, 0.171875, 0.671875, + 0.765625, 0.265625, 0.890625, 0.390625, 0.796875, 0.296875, 0.921875, + 0.421875, 0.203125, 0.703125, 0.078125, 0.578125, 0.234375, 0.734375, + 0.109375, 0.609375, 0.953125, 0.453125, 0.828125, 0.328125, 0.984375, + 0.484375, 0.859375, 0.359375, 0.0625, 0.5625, 0.1875, 0.6875, 0.03125, + 0.53125, 0.15625, 0.65625, 0.8125, 0.3125, 0.9375, 0.4375, 0.78125, 0.28125, + 0.90625, 0.40625, 0.25, 0.75, 0.125, 0.625, 0.21875, 0.71875, 0.09375, + 0.59375, 1.0001, 0.5, 0.875, 0.375, 0.96875, 0.46875, 0.84375, 0.34375, + ]; + + (LGraphShaderDither.dither_func = + "\n\ + float dither8x8(float brightness) {\n\ + vec2 position = vec2(0.0);\n\ + #ifdef FRAGMENT\n\ + position = gl_FragCoord.xy;\n\ + #endif\n\ + int x = int(mod(position.x, 8.0));\n\ + int y = int(mod(position.y, 8.0));\n\ + int index = x + y * 8;\n\ + float limit = 0.0;\n\ + if (x < 8) {\n\ + if(index==0) limit = 0.015625;\n\ + " + + LGraphShaderDither.dither_values + .map(function (v, i) { + return "else if(index== " + (i + 1) + ") limit = " + v + ";"; + }) + .join("\n") + + "\n\ + }\n\ + return brightness < limit ? 0.0 : 1.0;\n\ + }\n"), + registerShaderNode("math/dither", LGraphShaderDither); + + function LGraphShaderRemap() { + this.addInput("", LGShaders.ALL_TYPES); + this.addOutput("", ""); + this.properties = { + min_value: 0, + max_value: 1, + min_value2: 0, + max_value2: 1, + }; + this.addWidget("number", "min", 0, { step: 0.1, property: "min_value" }); + this.addWidget("number", "max", 1, { step: 0.1, property: "max_value" }); + this.addWidget("number", "min2", 0, { step: 0.1, property: "min_value2" }); + this.addWidget("number", "max2", 1, { step: 0.1, property: "max_value2" }); + } + + LGraphShaderRemap.title = "Remap"; + + LGraphShaderRemap.prototype.onPropertyChanged = function () { + if (this.graph) this.graph._version++; + }; + + LGraphShaderRemap.prototype.onConnectionsChange = function () { + var return_type = this.getInputDataType(0); + this.outputs[0].type = return_type || "T"; + }; + + LGraphShaderRemap.prototype.onGetCode = function (context) { + if (!this.shader_destination || !this.isOutputConnected(0)) return; + + var inlink = getInputLinkID(this, 0); + var outlink = getOutputLinkID(this, 0); + if (!inlink && !outlink) + //not connected + return; + + var return_type = this.getInputDataType(0); + this.outputs[0].type = return_type; + if (return_type == "T") { + console.warn("node type is T and cannot be resolved"); + return; + } + + if (!inlink) { + context.addCode( + "code", + " " + return_type + " " + outlink + " = " + return_type + "(0.0);\n", + ); + return; + } + + var minv = valueToGLSL(this.properties.min_value); + var maxv = valueToGLSL(this.properties.max_value); + var minv2 = valueToGLSL(this.properties.min_value2); + var maxv2 = valueToGLSL(this.properties.max_value2); + + context.addCode( + "code", + return_type + + " " + + outlink + + " = ( (" + + inlink + + " - " + + minv + + ") / (" + + maxv + + " - " + + minv + + ") ) * (" + + maxv2 + + " - " + + minv2 + + ") + " + + minv2 + + ";", + this.shader_destination, + ); + this.setOutputData(0, return_type); + }; + + registerShaderNode("math/remap", LGraphShaderRemap); +})(this); + +(function (global) { + var LiteGraph = global.LiteGraph; + + var view_matrix = new Float32Array(16); + var projection_matrix = new Float32Array(16); + var viewprojection_matrix = new Float32Array(16); + var model_matrix = new Float32Array(16); + var global_uniforms = { + u_view: view_matrix, + u_projection: projection_matrix, + u_viewprojection: viewprojection_matrix, + u_model: model_matrix, + }; + + LiteGraph.LGraphRender = { + onRequestCameraMatrices: null, //overwrite with your 3D engine specifics, it will receive (view_matrix, projection_matrix,viewprojection_matrix) and must be filled + }; + + function generateGeometryId() { + return (Math.random() * 100000) | 0; + } + + function LGraphPoints3D() { + this.addInput("obj", ""); + this.addInput("radius", "number"); + + this.addOutput("out", "geometry"); + this.addOutput("points", "[vec3]"); + this.properties = { + radius: 1, + num_points: 4096, + generate_normals: true, + regular: false, + mode: LGraphPoints3D.SPHERE, + force_update: false, + }; + + this.points = new Float32Array(this.properties.num_points * 3); + this.normals = new Float32Array(this.properties.num_points * 3); + this.must_update = true; + this.version = 0; + + var that = this; + this.addWidget("button", "update", null, function () { + that.must_update = true; + }); + + this.geometry = { + vertices: null, + _id: generateGeometryId(), + }; + + this._old_obj = null; + this._last_radius = null; + } + + global.LGraphPoints3D = LGraphPoints3D; + + LGraphPoints3D.RECTANGLE = 1; + LGraphPoints3D.CIRCLE = 2; + + LGraphPoints3D.CUBE = 10; + LGraphPoints3D.SPHERE = 11; + LGraphPoints3D.HEMISPHERE = 12; + LGraphPoints3D.INSIDE_SPHERE = 13; + + LGraphPoints3D.OBJECT = 20; + LGraphPoints3D.OBJECT_UNIFORMLY = 21; + LGraphPoints3D.OBJECT_INSIDE = 22; + + LGraphPoints3D.MODE_VALUES = { + rectangle: LGraphPoints3D.RECTANGLE, + circle: LGraphPoints3D.CIRCLE, + cube: LGraphPoints3D.CUBE, + sphere: LGraphPoints3D.SPHERE, + hemisphere: LGraphPoints3D.HEMISPHERE, + inside_sphere: LGraphPoints3D.INSIDE_SPHERE, + object: LGraphPoints3D.OBJECT, + object_uniformly: LGraphPoints3D.OBJECT_UNIFORMLY, + object_inside: LGraphPoints3D.OBJECT_INSIDE, + }; + + LGraphPoints3D.widgets_info = { + mode: { widget: "combo", values: LGraphPoints3D.MODE_VALUES }, + }; + + LGraphPoints3D.title = "list of points"; + LGraphPoints3D.desc = "returns an array of points"; + + LGraphPoints3D.prototype.onPropertyChanged = function (name, value) { + this.must_update = true; + }; + + LGraphPoints3D.prototype.onExecute = function () { + var obj = this.getInputData(0); + if ( + obj != this._old_obj || + (obj && obj._version != this._old_obj_version) + ) { + this._old_obj = obj; + this.must_update = true; + } + + var radius = this.getInputData(1); + if (radius == null) radius = this.properties.radius; + if (this._last_radius != radius) { + this._last_radius = radius; + this.must_update = true; + } + + if (this.must_update || this.properties.force_update) { + this.must_update = false; + this.updatePoints(); + } + + this.geometry.vertices = this.points; + this.geometry.normals = this.normals; + this.geometry._version = this.version; + + this.setOutputData(0, this.geometry); + }; + + LGraphPoints3D.prototype.updatePoints = function () { + var num_points = this.properties.num_points | 0; + if (num_points < 1) num_points = 1; + + if (!this.points || this.points.length != num_points * 3) + this.points = new Float32Array(num_points * 3); + + if (this.properties.generate_normals) { + if (!this.normals || this.normals.length != this.points.length) + this.normals = new Float32Array(this.points.length); + } else this.normals = null; + + var radius = this._last_radius || this.properties.radius; + var mode = this.properties.mode; + + var obj = this.getInputData(0); + this._old_obj_version = obj ? obj._version : null; + + this.points = LGraphPoints3D.generatePoints( + radius, + num_points, + mode, + this.points, + this.normals, + this.properties.regular, + obj, + ); + + this.version++; + }; + + //global + LGraphPoints3D.generatePoints = function ( + radius, + num_points, + mode, + points, + normals, + regular, + obj, + ) { + var size = num_points * 3; + if (!points || points.length != size) points = new Float32Array(size); + var temp = new Float32Array(3); + var UP = new Float32Array([0, 1, 0]); + + if (regular) { + if (mode == LGraphPoints3D.RECTANGLE) { + var side = Math.floor(Math.sqrt(num_points)); + for (var i = 0; i < side; ++i) + for (var j = 0; j < side; ++j) { + var pos = i * 3 + j * 3 * side; + points[pos] = (i / side - 0.5) * radius * 2; + points[pos + 1] = 0; + points[pos + 2] = (j / side - 0.5) * radius * 2; + } + points = new Float32Array(points.subarray(0, side * side * 3)); + if (normals) { + for (var i = 0; i < normals.length; i += 3) normals.set(UP, i); + } + } else if (mode == LGraphPoints3D.SPHERE) { + var side = Math.floor(Math.sqrt(num_points)); + for (var i = 0; i < side; ++i) + for (var j = 0; j < side; ++j) { + var pos = i * 3 + j * 3 * side; + polarToCartesian( + temp, + (i / side) * 2 * Math.PI, + (j / side - 0.5) * 2 * Math.PI, + radius, + ); + points[pos] = temp[0]; + points[pos + 1] = temp[1]; + points[pos + 2] = temp[2]; + } + points = new Float32Array(points.subarray(0, side * side * 3)); + if (normals) LGraphPoints3D.generateSphericalNormals(points, normals); + } else if (mode == LGraphPoints3D.CIRCLE) { + for (var i = 0; i < size; i += 3) { + var angle = 2 * Math.PI * (i / size); + points[i] = Math.cos(angle) * radius; + points[i + 1] = 0; + points[i + 2] = Math.sin(angle) * radius; + } + if (normals) { + for (var i = 0; i < normals.length; i += 3) normals.set(UP, i); + } + } + } //non regular + else { + if (mode == LGraphPoints3D.RECTANGLE) { + for (var i = 0; i < size; i += 3) { + points[i] = (Math.random() - 0.5) * radius * 2; + points[i + 1] = 0; + points[i + 2] = (Math.random() - 0.5) * radius * 2; + } + if (normals) { + for (var i = 0; i < normals.length; i += 3) normals.set(UP, i); + } + } else if (mode == LGraphPoints3D.CUBE) { + for (var i = 0; i < size; i += 3) { + points[i] = (Math.random() - 0.5) * radius * 2; + points[i + 1] = (Math.random() - 0.5) * radius * 2; + points[i + 2] = (Math.random() - 0.5) * radius * 2; + } + if (normals) { + for (var i = 0; i < normals.length; i += 3) normals.set(UP, i); + } + } else if (mode == LGraphPoints3D.SPHERE) { + LGraphPoints3D.generateSphere(points, size, radius); + if (normals) LGraphPoints3D.generateSphericalNormals(points, normals); + } else if (mode == LGraphPoints3D.HEMISPHERE) { + LGraphPoints3D.generateHemisphere(points, size, radius); + if (normals) LGraphPoints3D.generateSphericalNormals(points, normals); + } else if (mode == LGraphPoints3D.CIRCLE) { + LGraphPoints3D.generateInsideCircle(points, size, radius); + if (normals) LGraphPoints3D.generateSphericalNormals(points, normals); + } else if (mode == LGraphPoints3D.INSIDE_SPHERE) { + LGraphPoints3D.generateInsideSphere(points, size, radius); + if (normals) LGraphPoints3D.generateSphericalNormals(points, normals); + } else if (mode == LGraphPoints3D.OBJECT) { + LGraphPoints3D.generateFromObject(points, normals, size, obj, false); + } else if (mode == LGraphPoints3D.OBJECT_UNIFORMLY) { + LGraphPoints3D.generateFromObject(points, normals, size, obj, true); + } else if (mode == LGraphPoints3D.OBJECT_INSIDE) { + LGraphPoints3D.generateFromInsideObject(points, size, obj); + //if(normals) + // LGraphPoints3D.generateSphericalNormals( points, normals ); + } else console.warn("wrong mode in LGraphPoints3D"); + } + + return points; + }; + + LGraphPoints3D.generateSphericalNormals = function (points, normals) { + var temp = new Float32Array(3); + for (var i = 0; i < normals.length; i += 3) { + temp[0] = points[i]; + temp[1] = points[i + 1]; + temp[2] = points[i + 2]; + vec3.normalize(temp, temp); + normals.set(temp, i); + } + }; + + LGraphPoints3D.generateSphere = function (points, size, radius) { + for (var i = 0; i < size; i += 3) { + var r1 = Math.random(); + var r2 = Math.random(); + var x = 2 * Math.cos(2 * Math.PI * r1) * Math.sqrt(r2 * (1 - r2)); + var y = 1 - 2 * r2; + var z = 2 * Math.sin(2 * Math.PI * r1) * Math.sqrt(r2 * (1 - r2)); + points[i] = x * radius; + points[i + 1] = y * radius; + points[i + 2] = z * radius; + } + }; + + LGraphPoints3D.generateHemisphere = function (points, size, radius) { + for (var i = 0; i < size; i += 3) { + var r1 = Math.random(); + var r2 = Math.random(); + var x = Math.cos(2 * Math.PI * r1) * Math.sqrt(1 - r2 * r2); + var y = r2; + var z = Math.sin(2 * Math.PI * r1) * Math.sqrt(1 - r2 * r2); + points[i] = x * radius; + points[i + 1] = y * radius; + points[i + 2] = z * radius; + } + }; + + LGraphPoints3D.generateInsideCircle = function (points, size, radius) { + for (var i = 0; i < size; i += 3) { + var r1 = Math.random(); + var r2 = Math.random(); + var x = Math.cos(2 * Math.PI * r1) * Math.sqrt(1 - r2 * r2); + var y = r2; + var z = Math.sin(2 * Math.PI * r1) * Math.sqrt(1 - r2 * r2); + points[i] = x * radius; + points[i + 1] = 0; + points[i + 2] = z * radius; + } + }; + + LGraphPoints3D.generateInsideSphere = function (points, size, radius) { + for (var i = 0; i < size; i += 3) { + var u = Math.random(); + var v = Math.random(); + var theta = u * 2.0 * Math.PI; + var phi = Math.acos(2.0 * v - 1.0); + var r = Math.cbrt(Math.random()) * radius; + var sinTheta = Math.sin(theta); + var cosTheta = Math.cos(theta); + var sinPhi = Math.sin(phi); + var cosPhi = Math.cos(phi); + points[i] = r * sinPhi * cosTheta; + points[i + 1] = r * sinPhi * sinTheta; + points[i + 2] = r * cosPhi; + } + }; + + function findRandomTriangle(areas, f) { + var l = areas.length; + var imin = 0; + var imid = 0; + var imax = l; + + if (l == 0) return -1; + if (l == 1) return 0; + //dichotomic search + while (imax >= imin) { + imid = ((imax + imin) * 0.5) | 0; + var t = areas[imid]; + if (t == f) return imid; + if (imin == imax - 1) return imin; + if (t < f) imin = imid; + else imax = imid; + } + return imid; + } + + LGraphPoints3D.generateFromObject = function ( + points, + normals, + size, + obj, + evenly, + ) { + if (!obj) return; + + var vertices = null; + var mesh_normals = null; + var indices = null; + var areas = null; + if (obj.constructor === GL.Mesh) { + vertices = obj.vertexBuffers.vertices.data; + mesh_normals = obj.vertexBuffers.normals + ? obj.vertexBuffers.normals.data + : null; + indices = obj.indexBuffers.indices ? obj.indexBuffers.indices.data : null; + if (!indices) + indices = obj.indexBuffers.triangles + ? obj.indexBuffers.triangles.data + : null; + } + if (!vertices) return null; + var num_triangles = indices + ? indices.length / 3 + : vertices.length / (3 * 3); + var total_area = 0; //sum of areas of all triangles + + if (evenly) { + areas = new Float32Array(num_triangles); //accum + for (var i = 0; i < num_triangles; ++i) { + if (indices) { + a = indices[i * 3] * 3; + b = indices[i * 3 + 1] * 3; + c = indices[i * 3 + 2] * 3; + } else { + a = i * 9; + b = i * 9 + 3; + c = i * 9 + 6; + } + var P1 = vertices.subarray(a, a + 3); + var P2 = vertices.subarray(b, b + 3); + var P3 = vertices.subarray(c, c + 3); + var aL = vec3.distance(P1, P2); + var bL = vec3.distance(P2, P3); + var cL = vec3.distance(P3, P1); + var s = (aL + bL + cL) / 2; + total_area += Math.sqrt(s * (s - aL) * (s - bL) * (s - cL)); + areas[i] = total_area; + } + for ( + var i = 0; + i < num_triangles; + ++i //normalize + ) + areas[i] /= total_area; + } + + for (var i = 0; i < size; i += 3) { + var r = Math.random(); + var index = evenly + ? findRandomTriangle(areas, r) + : Math.floor(r * num_triangles); + //get random triangle + var a = 0; + var b = 0; + var c = 0; + if (indices) { + a = indices[index * 3] * 3; + b = indices[index * 3 + 1] * 3; + c = indices[index * 3 + 2] * 3; + } else { + a = index * 9; + b = index * 9 + 3; + c = index * 9 + 6; + } + var s = Math.random(); + var t = Math.random(); + var sqrt_s = Math.sqrt(s); + var af = 1 - sqrt_s; + var bf = sqrt_s * (1 - t); + var cf = t * sqrt_s; + points[i] = af * vertices[a] + bf * vertices[b] + cf * vertices[c]; + points[i + 1] = + af * vertices[a + 1] + bf * vertices[b + 1] + cf * vertices[c + 1]; + points[i + 2] = + af * vertices[a + 2] + bf * vertices[b + 2] + cf * vertices[c + 2]; + if (normals && mesh_normals) { + normals[i] = + af * mesh_normals[a] + bf * mesh_normals[b] + cf * mesh_normals[c]; + normals[i + 1] = + af * mesh_normals[a + 1] + + bf * mesh_normals[b + 1] + + cf * mesh_normals[c + 1]; + normals[i + 2] = + af * mesh_normals[a + 2] + + bf * mesh_normals[b + 2] + + cf * mesh_normals[c + 2]; + var N = normals.subarray(i, i + 3); + vec3.normalize(N, N); + } + } + }; + + LGraphPoints3D.generateFromInsideObject = function (points, size, mesh) { + if (!mesh || mesh.constructor !== GL.Mesh) return; + + var aabb = mesh.getBoundingBox(); + if (!mesh.octree) mesh.octree = new GL.Octree(mesh); + var octree = mesh.octree; + var origin = vec3.create(); + var direction = vec3.fromValues(1, 0, 0); + var temp = vec3.create(); + var i = 0; + var tries = 0; + while (i < size && tries < points.length * 10) { + //limit to avoid problems + tries += 1; + var r = vec3.random(temp); //random point inside the aabb + r[0] = (r[0] * 2 - 1) * aabb[3] + aabb[0]; + r[1] = (r[1] * 2 - 1) * aabb[4] + aabb[1]; + r[2] = (r[2] * 2 - 1) * aabb[5] + aabb[2]; + origin.set(r); + var hit = octree.testRay( + origin, + direction, + 0, + 10000, + true, + GL.Octree.ALL, + ); + if (!hit || hit.length % 2 == 0) + //not inside + continue; + points.set(r, i); + i += 3; + } + }; + + LiteGraph.registerNodeType("geometry/points3D", LGraphPoints3D); + + function LGraphPointsToInstances() { + this.addInput("points", "geometry"); + this.addOutput("instances", "[mat4]"); + this.properties = { + mode: 1, + autoupdate: true, + }; + + this.must_update = true; + this.matrices = []; + this.first_time = true; + } + + LGraphPointsToInstances.NORMAL = 0; + LGraphPointsToInstances.VERTICAL = 1; + LGraphPointsToInstances.SPHERICAL = 2; + LGraphPointsToInstances.RANDOM = 3; + LGraphPointsToInstances.RANDOM_VERTICAL = 4; + + LGraphPointsToInstances.modes = { + normal: 0, + vertical: 1, + spherical: 2, + random: 3, + random_vertical: 4, + }; + LGraphPointsToInstances.widgets_info = { + mode: { widget: "combo", values: LGraphPointsToInstances.modes }, + }; + + LGraphPointsToInstances.title = "points to inst"; + + LGraphPointsToInstances.prototype.onExecute = function () { + var geo = this.getInputData(0); + if (!geo) { + this.setOutputData(0, null); + return; + } + + if (!this.isOutputConnected(0)) return; + + var has_changed = + geo._version != this._version || geo._id != this._geometry_id; + + if ((has_changed && this.properties.autoupdate) || this.first_time) { + this.first_time = false; + this.updateInstances(geo); + } + + this.setOutputData(0, this.matrices); + }; + + LGraphPointsToInstances.prototype.updateInstances = function (geometry) { + var vertices = geometry.vertices; + if (!vertices) return null; + var normals = geometry.normals; + + var matrices = this.matrices; + var num_points = vertices.length / 3; + if (matrices.length != num_points) matrices.length = num_points; + var identity = mat4.create(); + var temp = vec3.create(); + var zero = vec3.create(); + var UP = vec3.fromValues(0, 1, 0); + var FRONT = vec3.fromValues(0, 0, -1); + var RIGHT = vec3.fromValues(1, 0, 0); + var R = quat.create(); + + var front = vec3.create(); + var right = vec3.create(); + var top = vec3.create(); + + for (var i = 0; i < vertices.length; i += 3) { + var index = i / 3; + var m = matrices[index]; + if (!m) m = matrices[index] = mat4.create(); + m.set(identity); + var point = vertices.subarray(i, i + 3); + + switch (this.properties.mode) { + case LGraphPointsToInstances.NORMAL: + mat4.setTranslation(m, point); + if (normals) { + var normal = normals.subarray(i, i + 3); + top.set(normal); + vec3.normalize(top, top); + vec3.cross(right, FRONT, top); + vec3.normalize(right, right); + vec3.cross(front, right, top); + vec3.normalize(front, front); + m.set(right, 0); + m.set(top, 4); + m.set(front, 8); + mat4.setTranslation(m, point); + } + break; + case LGraphPointsToInstances.VERTICAL: + mat4.setTranslation(m, point); + break; + case LGraphPointsToInstances.SPHERICAL: + front.set(point); + vec3.normalize(front, front); + vec3.cross(right, UP, front); + vec3.normalize(right, right); + vec3.cross(top, front, right); + vec3.normalize(top, top); + m.set(right, 0); + m.set(top, 4); + m.set(front, 8); + mat4.setTranslation(m, point); + break; + case LGraphPointsToInstances.RANDOM: + temp[0] = Math.random() * 2 - 1; + temp[1] = Math.random() * 2 - 1; + temp[2] = Math.random() * 2 - 1; + vec3.normalize(temp, temp); + quat.setAxisAngle(R, temp, Math.random() * 2 * Math.PI); + mat4.fromQuat(m, R); + mat4.setTranslation(m, point); + break; + case LGraphPointsToInstances.RANDOM_VERTICAL: + quat.setAxisAngle(R, UP, Math.random() * 2 * Math.PI); + mat4.fromQuat(m, R); + mat4.setTranslation(m, point); + break; + } + } + + this._version = geometry._version; + this._geometry_id = geometry._id; + }; + + LiteGraph.registerNodeType( + "geometry/points_to_instances", + LGraphPointsToInstances, + ); + + function LGraphGeometryTransform() { + this.addInput("in", "geometry,[mat4]"); + this.addInput("mat4", "mat4"); + this.addOutput("out", "geometry"); + this.properties = {}; + + this.geometry = { + type: "triangles", + vertices: null, + _id: generateGeometryId(), + _version: 0, + }; + + this._last_geometry_id = -1; + this._last_version = -1; + this._last_key = ""; + + this.must_update = true; + } + + LGraphGeometryTransform.title = "Transform"; + + LGraphGeometryTransform.prototype.onExecute = function () { + var input = this.getInputData(0); + var model = this.getInputData(1); + + if (!input) return; + + //array of matrices + if (input.constructor === Array) { + if (input.length == 0) return; + this.outputs[0].type = "[mat4]"; + if (!this.isOutputConnected(0)) return; + + if (!model) { + this.setOutputData(0, input); + return; + } + + if (!this._output) this._output = new Array(); + if (this._output.length != input.length) + this._output.length = input.length; + for (var i = 0; i < input.length; ++i) { + var m = this._output[i]; + if (!m) m = this._output[i] = mat4.create(); + mat4.multiply(m, input[i], model); + } + this.setOutputData(0, this._output); + return; + } + + //geometry + if (!input.vertices || !input.vertices.length) return; + var geo = input; + this.outputs[0].type = "geometry"; + if (!this.isOutputConnected(0)) return; + if (!model) { + this.setOutputData(0, geo); + return; + } + + var key = typedArrayToArray(model).join(","); + + if ( + this.must_update || + geo._id != this._last_geometry_id || + geo._version != this._last_version || + key != this._last_key + ) { + this.updateGeometry(geo, model); + this._last_key = key; + this._last_version = geo._version; + this._last_geometry_id = geo._id; + this.must_update = false; + } + + this.setOutputData(0, this.geometry); + }; + + LGraphGeometryTransform.prototype.updateGeometry = function ( + geometry, + model, + ) { + var old_vertices = geometry.vertices; + var vertices = this.geometry.vertices; + if (!vertices || vertices.length != old_vertices.length) + vertices = this.geometry.vertices = new Float32Array(old_vertices.length); + var temp = vec3.create(); + + for (var i = 0, l = vertices.length; i < l; i += 3) { + temp[0] = old_vertices[i]; + temp[1] = old_vertices[i + 1]; + temp[2] = old_vertices[i + 2]; + mat4.multiplyVec3(temp, model, temp); + vertices[i] = temp[0]; + vertices[i + 1] = temp[1]; + vertices[i + 2] = temp[2]; + } + + if (geometry.normals) { + if ( + !this.geometry.normals || + this.geometry.normals.length != geometry.normals.length + ) + this.geometry.normals = new Float32Array(geometry.normals.length); + var normals = this.geometry.normals; + var normal_model = mat4.invert(mat4.create(), model); + if (normal_model) mat4.transpose(normal_model, normal_model); + var old_normals = geometry.normals; + for (var i = 0, l = normals.length; i < l; i += 3) { + temp[0] = old_normals[i]; + temp[1] = old_normals[i + 1]; + temp[2] = old_normals[i + 2]; + mat4.multiplyVec3(temp, normal_model, temp); + normals[i] = temp[0]; + normals[i + 1] = temp[1]; + normals[i + 2] = temp[2]; + } + } + + this.geometry.type = geometry.type; + this.geometry._version++; + }; + + LiteGraph.registerNodeType("geometry/transform", LGraphGeometryTransform); + + function LGraphGeometryPolygon() { + this.addInput("sides", "number"); + this.addInput("radius", "number"); + this.addOutput("out", "geometry"); + this.properties = { sides: 6, radius: 1, uvs: false }; + + this.geometry = { + type: "line_loop", + vertices: null, + _id: generateGeometryId(), + }; + this.geometry_id = -1; + this.version = -1; + this.must_update = true; + + this.last_info = { sides: -1, radius: -1 }; + } + + LGraphGeometryPolygon.title = "Polygon"; + + LGraphGeometryPolygon.prototype.onExecute = function () { + if (!this.isOutputConnected(0)) return; + + var sides = this.getInputOrProperty("sides"); + var radius = this.getInputOrProperty("radius"); + sides = Math.max(3, sides) | 0; + + //update + if (this.last_info.sides != sides || this.last_info.radius != radius) + this.updateGeometry(sides, radius); + + this.setOutputData(0, this.geometry); + }; + + LGraphGeometryPolygon.prototype.updateGeometry = function (sides, radius) { + var num = 3 * sides; + var vertices = this.geometry.vertices; + if (!vertices || vertices.length != num) + vertices = this.geometry.vertices = new Float32Array(3 * sides); + var delta = (Math.PI * 2) / sides; + var gen_uvs = this.properties.uvs; + if (gen_uvs) { + uvs = this.geometry.coords = new Float32Array(3 * sides); + } + + for (var i = 0; i < sides; ++i) { + var angle = delta * -i; + var x = Math.cos(angle) * radius; + var y = 0; + var z = Math.sin(angle) * radius; + vertices[i * 3] = x; + vertices[i * 3 + 1] = y; + vertices[i * 3 + 2] = z; + + if (gen_uvs) { + } + } + this.geometry._id = ++this.geometry_id; + this.geometry._version = ++this.version; + this.last_info.sides = sides; + this.last_info.radius = radius; + }; + + LiteGraph.registerNodeType("geometry/polygon", LGraphGeometryPolygon); + + function LGraphGeometryExtrude() { + this.addInput("", "geometry"); + this.addOutput("", "geometry"); + this.properties = { top_cap: true, bottom_cap: true, offset: [0, 100, 0] }; + this.version = -1; + + this._last_geo_version = -1; + this._must_update = true; + } + + LGraphGeometryExtrude.title = "extrude"; + + LGraphGeometryExtrude.prototype.onPropertyChanged = function (name, value) { + this._must_update = true; + }; + + LGraphGeometryExtrude.prototype.onExecute = function () { + var geo = this.getInputData(0); + if (!geo || !this.isOutputConnected(0)) return; + + if (geo.version != this._last_geo_version || this._must_update) { + this._geo = this.extrudeGeometry(geo, this._geo); + if (this._geo) this._geo.version = this.version++; + this._must_update = false; + } + + this.setOutputData(0, this._geo); + }; + + LGraphGeometryExtrude.prototype.extrudeGeometry = function (geo) { + //for every pair of vertices + var vertices = geo.vertices; + var num_points = vertices.length / 3; + + var tempA = vec3.create(); + var tempB = vec3.create(); + var tempC = vec3.create(); + var tempD = vec3.create(); + var offset = new Float32Array(this.properties.offset); + + if (geo.type == "line_loop") { + var new_vertices = new Float32Array(num_points * 6 * 3); //every points become 6 ( caps not included ) + var npos = 0; + for (var i = 0, l = vertices.length; i < l; i += 3) { + tempA[0] = vertices[i]; + tempA[1] = vertices[i + 1]; + tempA[2] = vertices[i + 2]; + + if (i + 3 < l) { + //loop + tempB[0] = vertices[i + 3]; + tempB[1] = vertices[i + 4]; + tempB[2] = vertices[i + 5]; + } else { + tempB[0] = vertices[0]; + tempB[1] = vertices[1]; + tempB[2] = vertices[2]; + } + + vec3.add(tempC, tempA, offset); + vec3.add(tempD, tempB, offset); + + new_vertices.set(tempA, npos); + npos += 3; + new_vertices.set(tempB, npos); + npos += 3; + new_vertices.set(tempC, npos); + npos += 3; + + new_vertices.set(tempB, npos); + npos += 3; + new_vertices.set(tempD, npos); + npos += 3; + new_vertices.set(tempC, npos); + npos += 3; + } + } + + var out_geo = { + _id: generateGeometryId(), + type: "triangles", + vertices: new_vertices, + }; + + return out_geo; + }; + + LiteGraph.registerNodeType("geometry/extrude", LGraphGeometryExtrude); + + function LGraphGeometryEval() { + this.addInput("in", "geometry"); + this.addOutput("out", "geometry"); + + this.properties = { + code: "V[1] += 0.01 * Math.sin(I + T*0.001);", + execute_every_frame: false, + }; + + this.geometry = null; + this.geometry_id = -1; + this.version = -1; + this.must_update = true; + + this.vertices = null; + this.func = null; + } + + LGraphGeometryEval.title = "geoeval"; + LGraphGeometryEval.desc = "eval code"; + + LGraphGeometryEval.widgets_info = { + code: { widget: "code" }, + }; + + LGraphGeometryEval.prototype.onConfigure = function (o) { + this.compileCode(); + }; + + LGraphGeometryEval.prototype.compileCode = function () { + if (!this.properties.code) return; + + try { + this.func = new Function("V", "I", "T", this.properties.code); + this.boxcolor = "#AFA"; + this.must_update = true; + } catch (err) { + this.boxcolor = "red"; + } + }; + + LGraphGeometryEval.prototype.onPropertyChanged = function (name, value) { + if (name == "code") { + this.properties.code = value; + this.compileCode(); + } + }; + + LGraphGeometryEval.prototype.onExecute = function () { + var geometry = this.getInputData(0); + if (!geometry) return; + + if (!this.func) { + this.setOutputData(0, geometry); + return; + } + + if ( + this.geometry_id != geometry._id || + this.version != geometry._version || + this.must_update || + this.properties.execute_every_frame + ) { + this.must_update = false; + this.geometry_id = geometry._id; + if (this.properties.execute_every_frame) this.version++; + else this.version = geometry._version; + var func = this.func; + var T = getTime(); + + //clone + if (!this.geometry) this.geometry = {}; + for (var i in geometry) { + if (geometry[i] == null) continue; + if (geometry[i].constructor == Float32Array) + this.geometry[i] = new Float32Array(geometry[i]); + else this.geometry[i] = geometry[i]; + } + this.geometry._id = geometry._id; + if (this.properties.execute_every_frame) + this.geometry._version = this.version; + else this.geometry._version = geometry._version + 1; + + var V = vec3.create(); + var vertices = this.vertices; + if (!vertices || this.vertices.length != geometry.vertices.length) + vertices = this.vertices = new Float32Array(geometry.vertices); + else vertices.set(geometry.vertices); + for (var i = 0; i < vertices.length; i += 3) { + V[0] = vertices[i]; + V[1] = vertices[i + 1]; + V[2] = vertices[i + 2]; + func(V, i / 3, T); + vertices[i] = V[0]; + vertices[i + 1] = V[1]; + vertices[i + 2] = V[2]; + } + this.geometry.vertices = vertices; + } + + this.setOutputData(0, this.geometry); + }; + + LiteGraph.registerNodeType("geometry/eval", LGraphGeometryEval); + + /* +function LGraphGeometryDisplace() { + this.addInput("in", "geometry"); + this.addInput("img", "image"); + this.addOutput("out", "geometry"); + + this.properties = { + grid_size: 1 + }; + + this.geometry = null; + this.geometry_id = -1; + this.version = -1; + this.must_update = true; + + this.vertices = null; + } + + LGraphGeometryDisplace.title = "displace"; + LGraphGeometryDisplace.desc = "displace points"; + + LGraphGeometryDisplace.prototype.onExecute = function() { + var geometry = this.getInputData(0); + var image = this.getInputData(1); + if(!geometry) + return; + + if(!image) + { + this.setOutputData(0,geometry); + return; + } + + if( this.geometry_id != geometry._id || this.version != geometry._version || this.must_update ) + { + this.must_update = false; + this.geometry_id = geometry._id; + this.version = geometry._version; + + //copy + this.geometry = {}; + for(var i in geometry) + this.geometry[i] = geometry[i]; + this.geometry._id = geometry._id; + this.geometry._version = geometry._version + 1; + + var grid_size = this.properties.grid_size; + if(grid_size != 0) + { + var vertices = this.vertices; + if(!vertices || this.vertices.length != this.geometry.vertices.length) + vertices = this.vertices = new Float32Array( this.geometry.vertices ); + for(var i = 0; i < vertices.length; i+=3) + { + vertices[i] = Math.round(vertices[i]/grid_size) * grid_size; + vertices[i+1] = Math.round(vertices[i+1]/grid_size) * grid_size; + vertices[i+2] = Math.round(vertices[i+2]/grid_size) * grid_size; + } + this.geometry.vertices = vertices; + } + } + + this.setOutputData(0,this.geometry); + } + + LiteGraph.registerNodeType( "geometry/displace", LGraphGeometryDisplace ); +*/ + + function LGraphConnectPoints() { + this.addInput("in", "geometry"); + this.addOutput("out", "geometry"); + + this.properties = { + min_dist: 0.4, + max_dist: 0.5, + max_connections: 0, + probability: 1, + }; + + this.geometry_id = -1; + this.version = -1; + this.my_version = 1; + this.must_update = true; + } + + LGraphConnectPoints.title = "connect points"; + LGraphConnectPoints.desc = "adds indices between near points"; + + LGraphConnectPoints.prototype.onPropertyChanged = function (name, value) { + this.must_update = true; + }; + + LGraphConnectPoints.prototype.onExecute = function () { + var geometry = this.getInputData(0); + if (!geometry) return; + + if ( + this.geometry_id != geometry._id || + this.version != geometry._version || + this.must_update + ) { + this.must_update = false; + this.geometry_id = geometry._id; + this.version = geometry._version; + + //copy + this.geometry = {}; + for (var i in geometry) this.geometry[i] = geometry[i]; + this.geometry._id = generateGeometryId(); + this.geometry._version = this.my_version++; + + var vertices = geometry.vertices; + var l = vertices.length; + var min_dist = this.properties.min_dist; + var max_dist = this.properties.max_dist; + var probability = this.properties.probability; + var max_connections = this.properties.max_connections; + var indices = []; + + for (var i = 0; i < l; i += 3) { + var x = vertices[i]; + var y = vertices[i + 1]; + var z = vertices[i + 2]; + var connections = 0; + for (var j = i + 3; j < l; j += 3) { + var x2 = vertices[j]; + var y2 = vertices[j + 1]; + var z2 = vertices[j + 2]; + var dist = Math.sqrt( + (x - x2) * (x - x2) + (y - y2) * (y - y2) + (z - z2) * (z - z2), + ); + if ( + dist > max_dist || + dist < min_dist || + (probability < 1 && probability < Math.random()) + ) + continue; + indices.push(i / 3, j / 3); + connections += 1; + if (max_connections && connections > max_connections) break; + } + } + this.geometry.indices = this.indices = new Uint32Array(indices); + } + + if (this.indices && this.indices.length) { + this.geometry.indices = this.indices; + this.setOutputData(0, this.geometry); + } else this.setOutputData(0, null); + }; + + LiteGraph.registerNodeType("geometry/connectPoints", LGraphConnectPoints); + + //Works with Litegl.js to create WebGL nodes + if (typeof GL == "undefined") + //LiteGL RELATED ********************************************** + return; + + function LGraphToGeometry() { + this.addInput("mesh", "mesh"); + this.addOutput("out", "geometry"); + + this.geometry = {}; + this.last_mesh = null; + } + + LGraphToGeometry.title = "to geometry"; + LGraphToGeometry.desc = "converts a mesh to geometry"; + + LGraphToGeometry.prototype.onExecute = function () { + var mesh = this.getInputData(0); + if (!mesh) return; + + if (mesh != this.last_mesh) { + this.last_mesh = mesh; + for (i in mesh.vertexBuffers) { + var buffer = mesh.vertexBuffers[i]; + this.geometry[i] = buffer.data; + } + if (mesh.indexBuffers["triangles"]) + this.geometry.indices = mesh.indexBuffers["triangles"].data; + + this.geometry._id = generateGeometryId(); + this.geometry._version = 0; + } + + this.setOutputData(0, this.geometry); + if (this.geometry) this.setOutputData(1, this.geometry.vertices); + }; + + LiteGraph.registerNodeType("geometry/toGeometry", LGraphToGeometry); + + function LGraphGeometryToMesh() { + this.addInput("in", "geometry"); + this.addOutput("mesh", "mesh"); + this.properties = {}; + this.version = -1; + this.mesh = null; + } + + LGraphGeometryToMesh.title = "Geo to Mesh"; + + LGraphGeometryToMesh.prototype.updateMesh = function (geometry) { + if (!this.mesh) this.mesh = new GL.Mesh(); + + for (var i in geometry) { + if (i[0] == "_") continue; + + var buffer_data = geometry[i]; + + var info = GL.Mesh.common_buffers[i]; + if (!info && i != "indices") + //unknown buffer + continue; + var spacing = info ? info.spacing : 3; + var mesh_buffer = this.mesh.vertexBuffers[i]; + + if (!mesh_buffer || mesh_buffer.data.length != buffer_data.length) { + mesh_buffer = new GL.Buffer( + i == "indices" ? GL.ELEMENT_ARRAY_BUFFER : GL.ARRAY_BUFFER, + buffer_data, + spacing, + GL.DYNAMIC_DRAW, + ); + } else { + mesh_buffer.data.set(buffer_data); + mesh_buffer.upload(GL.DYNAMIC_DRAW); + } + + this.mesh.addBuffer(i, mesh_buffer); + } + + if ( + this.mesh.vertexBuffers.normals && + this.mesh.vertexBuffers.normals.data.length != + this.mesh.vertexBuffers.vertices.data.length + ) { + var n = new Float32Array([0, 1, 0]); + var normals = new Float32Array( + this.mesh.vertexBuffers.vertices.data.length, + ); + for (var i = 0; i < normals.length; i += 3) normals.set(n, i); + mesh_buffer = new GL.Buffer(GL.ARRAY_BUFFER, normals, 3); + this.mesh.addBuffer("normals", mesh_buffer); + } + + this.mesh.updateBoundingBox(); + this.geometry_id = this.mesh.id = geometry._id; + this.version = this.mesh.version = geometry._version; + return this.mesh; + }; + + LGraphGeometryToMesh.prototype.onExecute = function () { + var geometry = this.getInputData(0); + if (!geometry) return; + if (this.version != geometry._version || this.geometry_id != geometry._id) + this.updateMesh(geometry); + this.setOutputData(0, this.mesh); + }; + + LiteGraph.registerNodeType("geometry/toMesh", LGraphGeometryToMesh); + + function LGraphRenderMesh() { + this.addInput("mesh", "mesh"); + this.addInput("mat4", "mat4"); + this.addInput("tex", "texture"); + + this.properties = { + enabled: true, + primitive: GL.TRIANGLES, + additive: false, + color: [1, 1, 1], + opacity: 1, + }; + + this.color = vec4.create([1, 1, 1, 1]); + this.model_matrix = mat4.create(); + this.uniforms = { + u_color: this.color, + u_model: this.model_matrix, + }; + } + + LGraphRenderMesh.title = "Render Mesh"; + LGraphRenderMesh.desc = "renders a mesh flat"; + + LGraphRenderMesh.PRIMITIVE_VALUES = { + points: GL.POINTS, + lines: GL.LINES, + line_loop: GL.LINE_LOOP, + line_strip: GL.LINE_STRIP, + triangles: GL.TRIANGLES, + triangle_fan: GL.TRIANGLE_FAN, + triangle_strip: GL.TRIANGLE_STRIP, + }; + + LGraphRenderMesh.widgets_info = { + primitive: { widget: "combo", values: LGraphRenderMesh.PRIMITIVE_VALUES }, + color: { widget: "color" }, + }; + + LGraphRenderMesh.prototype.onExecute = function () { + if (!this.properties.enabled) return; + + var mesh = this.getInputData(0); + if (!mesh) return; + + if (!LiteGraph.LGraphRender.onRequestCameraMatrices) { + console.warn( + "cannot render geometry, LiteGraph.onRequestCameraMatrices is null, remember to fill this with a callback(view_matrix, projection_matrix,viewprojection_matrix) to use 3D rendering from the graph", + ); + return; + } + + LiteGraph.LGraphRender.onRequestCameraMatrices( + view_matrix, + projection_matrix, + viewprojection_matrix, + ); + var shader = null; + var texture = this.getInputData(2); + if (texture) { + shader = gl.shaders["textured"]; + if (!shader) + shader = gl.shaders["textured"] = new GL.Shader( + LGraphRenderPoints.vertex_shader_code, + LGraphRenderPoints.fragment_shader_code, + { USE_TEXTURE: "" }, + ); + } else { + shader = gl.shaders["flat"]; + if (!shader) + shader = gl.shaders["flat"] = new GL.Shader( + LGraphRenderPoints.vertex_shader_code, + LGraphRenderPoints.fragment_shader_code, + ); + } + + this.color.set(this.properties.color); + this.color[3] = this.properties.opacity; + + var model_matrix = this.model_matrix; + var m = this.getInputData(1); + if (m) model_matrix.set(m); + else mat4.identity(model_matrix); + + this.uniforms.u_point_size = 1; + var primitive = this.properties.primitive; + + shader.uniforms(global_uniforms); + shader.uniforms(this.uniforms); + + if (this.properties.opacity >= 1) gl.disable(gl.BLEND); + else gl.enable(gl.BLEND); + gl.enable(gl.DEPTH_TEST); + if (this.properties.additive) { + gl.blendFunc(gl.SRC_ALPHA, gl.ONE); + gl.depthMask(false); + } else gl.blendFunc(gl.SRC_ALPHA, gl.ONE_MINUS_SRC_ALPHA); + + var indices = "indices"; + if (mesh.indexBuffers.triangles) indices = "triangles"; + shader.draw(mesh, primitive, indices); + gl.disable(gl.BLEND); + gl.depthMask(true); + }; + + LiteGraph.registerNodeType("geometry/render_mesh", LGraphRenderMesh); + + //************************** + + function LGraphGeometryPrimitive() { + this.addInput("size", "number"); + this.addOutput("out", "mesh"); + this.properties = { type: 1, size: 1, subdivisions: 32 }; + + this.version = (Math.random() * 100000) | 0; + this.last_info = { type: -1, size: -1, subdivisions: -1 }; + } + + LGraphGeometryPrimitive.title = "Primitive"; + + LGraphGeometryPrimitive.VALID = { + CUBE: 1, + PLANE: 2, + CYLINDER: 3, + SPHERE: 4, + CIRCLE: 5, + HEMISPHERE: 6, + ICOSAHEDRON: 7, + CONE: 8, + QUAD: 9, + }; + LGraphGeometryPrimitive.widgets_info = { + type: { widget: "combo", values: LGraphGeometryPrimitive.VALID }, + }; + + LGraphGeometryPrimitive.prototype.onExecute = function () { + if (!this.isOutputConnected(0)) return; + + var size = this.getInputOrProperty("size"); + + //update + if ( + this.last_info.type != this.properties.type || + this.last_info.size != size || + this.last_info.subdivisions != this.properties.subdivisions + ) + this.updateMesh(this.properties.type, size, this.properties.subdivisions); + + this.setOutputData(0, this._mesh); + }; + + LGraphGeometryPrimitive.prototype.updateMesh = function ( + type, + size, + subdivisions, + ) { + subdivisions = Math.max(0, subdivisions) | 0; + + switch (type) { + case 1: //CUBE: + this._mesh = GL.Mesh.cube({ size: size, normals: true, coords: true }); + break; + case 2: //PLANE: + this._mesh = GL.Mesh.plane({ + size: size, + xz: true, + detail: subdivisions, + normals: true, + coords: true, + }); + break; + case 3: //CYLINDER: + this._mesh = GL.Mesh.cylinder({ + size: size, + subdivisions: subdivisions, + normals: true, + coords: true, + }); + break; + case 4: //SPHERE: + this._mesh = GL.Mesh.sphere({ + size: size, + long: subdivisions, + lat: subdivisions, + normals: true, + coords: true, + }); + break; + case 5: //CIRCLE: + this._mesh = GL.Mesh.circle({ + size: size, + slices: subdivisions, + normals: true, + coords: true, + }); + break; + case 6: //HEMISPHERE: + this._mesh = GL.Mesh.sphere({ + size: size, + long: subdivisions, + lat: subdivisions, + normals: true, + coords: true, + hemi: true, + }); + break; + case 7: //ICOSAHEDRON: + this._mesh = GL.Mesh.icosahedron({ + size: size, + subdivisions: subdivisions, + }); + break; + case 8: //CONE: + this._mesh = GL.Mesh.cone({ + radius: size, + height: size, + subdivisions: subdivisions, + }); + break; + case 9: //QUAD: + this._mesh = GL.Mesh.plane({ + size: size, + xz: false, + detail: subdivisions, + normals: true, + coords: true, + }); + break; + } + + this.last_info.type = type; + this.last_info.size = size; + this.last_info.subdivisions = subdivisions; + this._mesh.version = this.version++; + }; + + LiteGraph.registerNodeType( + "geometry/mesh_primitive", + LGraphGeometryPrimitive, + ); + + function LGraphRenderPoints() { + this.addInput("in", "geometry"); + this.addInput("mat4", "mat4"); + this.addInput("tex", "texture"); + this.properties = { + enabled: true, + point_size: 0.1, + fixed_size: false, + additive: true, + color: [1, 1, 1], + opacity: 1, + }; + + this.color = vec4.create([1, 1, 1, 1]); + + this.uniforms = { + u_point_size: 1, + u_perspective: 1, + u_point_perspective: 1, + u_color: this.color, + }; + + this.geometry_id = -1; + this.version = -1; + this.mesh = null; + } + + LGraphRenderPoints.title = "renderPoints"; + LGraphRenderPoints.desc = "render points with a texture"; + + LGraphRenderPoints.widgets_info = { + color: { widget: "color" }, + }; + + LGraphRenderPoints.prototype.updateMesh = function (geometry) { + var buffer = this.buffer; + if ( + !this.buffer || + !this.buffer.data || + this.buffer.data.length != geometry.vertices.length + ) + this.buffer = new GL.Buffer( + GL.ARRAY_BUFFER, + geometry.vertices, + 3, + GL.DYNAMIC_DRAW, + ); + else { + this.buffer.data.set(geometry.vertices); + this.buffer.upload(GL.DYNAMIC_DRAW); + } + + if (!this.mesh) this.mesh = new GL.Mesh(); + + this.mesh.addBuffer("vertices", this.buffer); + this.geometry_id = this.mesh.id = geometry._id; + this.version = this.mesh.version = geometry._version; + }; + + LGraphRenderPoints.prototype.onExecute = function () { + if (!this.properties.enabled) return; + + var geometry = this.getInputData(0); + if (!geometry) return; + if (this.version != geometry._version || this.geometry_id != geometry._id) + this.updateMesh(geometry); + + if (!LiteGraph.LGraphRender.onRequestCameraMatrices) { + console.warn( + "cannot render geometry, LiteGraph.onRequestCameraMatrices is null, remember to fill this with a callback(view_matrix, projection_matrix,viewprojection_matrix) to use 3D rendering from the graph", + ); + return; + } + + LiteGraph.LGraphRender.onRequestCameraMatrices( + view_matrix, + projection_matrix, + viewprojection_matrix, + ); + var shader = null; + + var texture = this.getInputData(2); + + if (texture) { + shader = gl.shaders["textured_points"]; + if (!shader) + shader = gl.shaders["textured_points"] = new GL.Shader( + LGraphRenderPoints.vertex_shader_code, + LGraphRenderPoints.fragment_shader_code, + { USE_TEXTURED_POINTS: "" }, + ); + } else { + shader = gl.shaders["points"]; + if (!shader) + shader = gl.shaders["points"] = new GL.Shader( + LGraphRenderPoints.vertex_shader_code, + LGraphRenderPoints.fragment_shader_code, + { USE_POINTS: "" }, + ); + } + + this.color.set(this.properties.color); + this.color[3] = this.properties.opacity; + + var m = this.getInputData(1); + if (m) model_matrix.set(m); + else mat4.identity(model_matrix); + + this.uniforms.u_point_size = this.properties.point_size; + this.uniforms.u_point_perspective = this.properties.fixed_size ? 0 : 1; + this.uniforms.u_perspective = gl.viewport_data[3] * projection_matrix[5]; + + shader.uniforms(global_uniforms); + shader.uniforms(this.uniforms); + + if (this.properties.opacity >= 1) gl.disable(gl.BLEND); + else gl.enable(gl.BLEND); + + gl.enable(gl.DEPTH_TEST); + if (this.properties.additive) { + gl.blendFunc(gl.SRC_ALPHA, gl.ONE); + gl.depthMask(false); + } else gl.blendFunc(gl.SRC_ALPHA, gl.ONE_MINUS_SRC_ALPHA); + + shader.draw(this.mesh, GL.POINTS); + + gl.disable(gl.BLEND); + gl.depthMask(true); + }; + + LiteGraph.registerNodeType("geometry/render_points", LGraphRenderPoints); + + LGraphRenderPoints.vertex_shader_code = + "\ + precision mediump float;\n\ + attribute vec3 a_vertex;\n\ + varying vec3 v_vertex;\n\ + attribute vec3 a_normal;\n\ + varying vec3 v_normal;\n\ + #ifdef USE_COLOR\n\ + attribute vec4 a_color;\n\ + varying vec4 v_color;\n\ + #endif\n\ + attribute vec2 a_coord;\n\ + varying vec2 v_coord;\n\ + #ifdef USE_SIZE\n\ + attribute float a_extra;\n\ + #endif\n\ + #ifdef USE_INSTANCING\n\ + attribute mat4 u_model;\n\ + #else\n\ + uniform mat4 u_model;\n\ + #endif\n\ + uniform mat4 u_viewprojection;\n\ + uniform float u_point_size;\n\ + uniform float u_perspective;\n\ + uniform float u_point_perspective;\n\ + float computePointSize(float radius, float w)\n\ + {\n\ + if(radius < 0.0)\n\ + return -radius;\n\ + return u_perspective * radius / w;\n\ + }\n\ + void main() {\n\ + v_coord = a_coord;\n\ + #ifdef USE_COLOR\n\ + v_color = a_color;\n\ + #endif\n\ + v_vertex = ( u_model * vec4( a_vertex, 1.0 )).xyz;\n\ + v_normal = ( u_model * vec4( a_normal, 0.0 )).xyz;\n\ + gl_Position = u_viewprojection * vec4(v_vertex,1.0);\n\ + gl_PointSize = u_point_size;\n\ + #ifdef USE_SIZE\n\ + gl_PointSize = a_extra;\n\ + #endif\n\ + if(u_point_perspective != 0.0)\n\ + gl_PointSize = computePointSize( gl_PointSize, gl_Position.w );\n\ + }\ + "; + + LGraphRenderPoints.fragment_shader_code = + "\ + precision mediump float;\n\ + uniform vec4 u_color;\n\ + #ifdef USE_COLOR\n\ + varying vec4 v_color;\n\ + #endif\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + void main() {\n\ + vec4 color = u_color;\n\ + #ifdef USE_TEXTURED_POINTS\n\ + color *= texture2D(u_texture, gl_PointCoord.xy);\n\ + #else\n\ + #ifdef USE_TEXTURE\n\ + color *= texture2D(u_texture, v_coord);\n\ + if(color.a < 0.1)\n\ + discard;\n\ + #endif\n\ + #ifdef USE_POINTS\n\ + float dist = length( gl_PointCoord.xy - vec2(0.5) );\n\ + if( dist > 0.45 )\n\ + discard;\n\ + #endif\n\ + #endif\n\ + #ifdef USE_COLOR\n\ + color *= v_color;\n\ + #endif\n\ + gl_FragColor = color;\n\ + }\ + "; + + //based on https://inconvergent.net/2019/depth-of-field/ + /* + function LGraphRenderGeometryDOF() { + this.addInput("in", "geometry"); + this.addInput("mat4", "mat4"); + this.addInput("tex", "texture"); + this.properties = { + enabled: true, + lines: true, + point_size: 0.1, + fixed_size: false, + additive: true, + color: [1,1,1], + opacity: 1 + }; + + this.color = vec4.create([1,1,1,1]); + + this.uniforms = { + u_point_size: 1, + u_perspective: 1, + u_point_perspective: 1, + u_color: this.color + }; + + this.geometry_id = -1; + this.version = -1; + this.mesh = null; + } + + LGraphRenderGeometryDOF.widgets_info = { + color: { widget: "color" } + }; + + LGraphRenderGeometryDOF.prototype.updateMesh = function(geometry) + { + var buffer = this.buffer; + if(!this.buffer || this.buffer.data.length != geometry.vertices.length) + this.buffer = new GL.Buffer( GL.ARRAY_BUFFER, geometry.vertices,3,GL.DYNAMIC_DRAW); + else + { + this.buffer.data.set( geometry.vertices ); + this.buffer.upload(GL.DYNAMIC_DRAW); + } + + if(!this.mesh) + this.mesh = new GL.Mesh(); + + this.mesh.addBuffer("vertices",this.buffer); + this.geometry_id = this.mesh.id = geometry._id; + this.version = this.mesh.version = geometry._version; + } + + LGraphRenderGeometryDOF.prototype.onExecute = function() { + + if(!this.properties.enabled) + return; + + var geometry = this.getInputData(0); + if(!geometry) + return; + if(this.version != geometry._version || this.geometry_id != geometry._id ) + this.updateMesh( geometry ); + + if(!LiteGraph.LGraphRender.onRequestCameraMatrices) + { + console.warn("cannot render geometry, LiteGraph.onRequestCameraMatrices is null, remember to fill this with a callback(view_matrix, projection_matrix,viewprojection_matrix) to use 3D rendering from the graph"); + return; + } + + LiteGraph.LGraphRender.onRequestCameraMatrices( view_matrix, projection_matrix,viewprojection_matrix ); + var shader = null; + + var texture = this.getInputData(2); + + if(texture) + { + shader = gl.shaders["textured_points"]; + if(!shader) + shader = gl.shaders["textured_points"] = new GL.Shader( LGraphRenderGeometryDOF.vertex_shader_code, LGraphRenderGeometryDOF.fragment_shader_code, { USE_TEXTURED_POINTS:"" }); + } + else + { + shader = gl.shaders["points"]; + if(!shader) + shader = gl.shaders["points"] = new GL.Shader( LGraphRenderGeometryDOF.vertex_shader_code, LGraphRenderGeometryDOF.fragment_shader_code, { USE_POINTS: "" }); + } + + this.color.set( this.properties.color ); + this.color[3] = this.properties.opacity; + + var m = this.getInputData(1); + if(m) + model_matrix.set(m); + else + mat4.identity( model_matrix ); + + this.uniforms.u_point_size = this.properties.point_size; + this.uniforms.u_point_perspective = this.properties.fixed_size ? 0 : 1; + this.uniforms.u_perspective = gl.viewport_data[3] * projection_matrix[5]; + + shader.uniforms( global_uniforms ); + shader.uniforms( this.uniforms ); + + if(this.properties.opacity >= 1) + gl.disable( gl.BLEND ); + else + gl.enable( gl.BLEND ); + + gl.enable( gl.DEPTH_TEST ); + if( this.properties.additive ) + { + gl.blendFunc( gl.SRC_ALPHA, gl.ONE ); + gl.depthMask( false ); + } + else + gl.blendFunc( gl.SRC_ALPHA, gl.ONE_MINUS_SRC_ALPHA ); + + shader.draw( this.mesh, GL.POINTS ); + + gl.disable( gl.BLEND ); + gl.depthMask( true ); + } + + LiteGraph.registerNodeType( "geometry/render_dof", LGraphRenderGeometryDOF ); + + LGraphRenderGeometryDOF.vertex_shader_code = '\ + precision mediump float;\n\ + attribute vec3 a_vertex;\n\ + varying vec3 v_vertex;\n\ + attribute vec3 a_normal;\n\ + varying vec3 v_normal;\n\ + #ifdef USE_COLOR\n\ + attribute vec4 a_color;\n\ + varying vec4 v_color;\n\ + #endif\n\ + attribute vec2 a_coord;\n\ + varying vec2 v_coord;\n\ + #ifdef USE_SIZE\n\ + attribute float a_extra;\n\ + #endif\n\ + #ifdef USE_INSTANCING\n\ + attribute mat4 u_model;\n\ + #else\n\ + uniform mat4 u_model;\n\ + #endif\n\ + uniform mat4 u_viewprojection;\n\ + uniform float u_point_size;\n\ + uniform float u_perspective;\n\ + uniform float u_point_perspective;\n\ + float computePointSize(float radius, float w)\n\ + {\n\ + if(radius < 0.0)\n\ + return -radius;\n\ + return u_perspective * radius / w;\n\ + }\n\ + void main() {\n\ + v_coord = a_coord;\n\ + #ifdef USE_COLOR\n\ + v_color = a_color;\n\ + #endif\n\ + v_vertex = ( u_model * vec4( a_vertex, 1.0 )).xyz;\n\ + v_normal = ( u_model * vec4( a_normal, 0.0 )).xyz;\n\ + gl_Position = u_viewprojection * vec4(v_vertex,1.0);\n\ + gl_PointSize = u_point_size;\n\ + #ifdef USE_SIZE\n\ + gl_PointSize = a_extra;\n\ + #endif\n\ + if(u_point_perspective != 0.0)\n\ + gl_PointSize = computePointSize( gl_PointSize, gl_Position.w );\n\ + }\ + '; + + LGraphRenderGeometryDOF.fragment_shader_code = '\ + precision mediump float;\n\ + uniform vec4 u_color;\n\ + #ifdef USE_COLOR\n\ + varying vec4 v_color;\n\ + #endif\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + void main() {\n\ + vec4 color = u_color;\n\ + #ifdef USE_TEXTURED_POINTS\n\ + color *= texture2D(u_texture, gl_PointCoord.xy);\n\ + #else\n\ + #ifdef USE_TEXTURE\n\ + color *= texture2D(u_texture, v_coord);\n\ + if(color.a < 0.1)\n\ + discard;\n\ + #endif\n\ + #ifdef USE_POINTS\n\ + float dist = length( gl_PointCoord.xy - vec2(0.5) );\n\ + if( dist > 0.45 )\n\ + discard;\n\ + #endif\n\ + #endif\n\ + #ifdef USE_COLOR\n\ + color *= v_color;\n\ + #endif\n\ + gl_FragColor = color;\n\ + }\ + '; + */ +})(this); +(function (global) { + var LiteGraph = global.LiteGraph; + var LGraphTexture = global.LGraphTexture; + + //Works with Litegl.js to create WebGL nodes + if (typeof GL != "undefined") { + // Texture Lens ***************************************** + function LGraphFXLens() { + this.addInput("Texture", "Texture"); + this.addInput("Aberration", "number"); + this.addInput("Distortion", "number"); + this.addInput("Blur", "number"); + this.addOutput("Texture", "Texture"); + this.properties = { + aberration: 1.0, + distortion: 1.0, + blur: 1.0, + precision: LGraphTexture.DEFAULT, + }; + + if (!LGraphFXLens._shader) { + LGraphFXLens._shader = new GL.Shader( + GL.Shader.SCREEN_VERTEX_SHADER, + LGraphFXLens.pixel_shader, + ); + LGraphFXLens._texture = new GL.Texture(3, 1, { + format: gl.RGB, + wrap: gl.CLAMP_TO_EDGE, + magFilter: gl.LINEAR, + minFilter: gl.LINEAR, + pixel_data: [255, 0, 0, 0, 255, 0, 0, 0, 255], + }); + } + } + + LGraphFXLens.title = "Lens"; + LGraphFXLens.desc = "Camera Lens distortion"; + LGraphFXLens.widgets_info = { + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + + LGraphFXLens.prototype.onExecute = function () { + var tex = this.getInputData(0); + if (this.properties.precision === LGraphTexture.PASS_THROUGH) { + this.setOutputData(0, tex); + return; + } + + if (!tex) { + return; + } + + this._tex = LGraphTexture.getTargetTexture( + tex, + this._tex, + this.properties.precision, + ); + + var aberration = this.properties.aberration; + if (this.isInputConnected(1)) { + aberration = this.getInputData(1); + this.properties.aberration = aberration; + } + + var distortion = this.properties.distortion; + if (this.isInputConnected(2)) { + distortion = this.getInputData(2); + this.properties.distortion = distortion; + } + + var blur = this.properties.blur; + if (this.isInputConnected(3)) { + blur = this.getInputData(3); + this.properties.blur = blur; + } + + gl.disable(gl.BLEND); + gl.disable(gl.DEPTH_TEST); + var mesh = Mesh.getScreenQuad(); + var shader = LGraphFXLens._shader; + //var camera = LS.Renderer._current_camera; + + this._tex.drawTo(function () { + tex.bind(0); + shader + .uniforms({ + u_texture: 0, + u_aberration: aberration, + u_distortion: distortion, + u_blur: blur, + }) + .draw(mesh); + }); + + this.setOutputData(0, this._tex); + }; + + LGraphFXLens.pixel_shader = + "precision highp float;\n\ + precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform vec2 u_camera_planes;\n\ + uniform float u_aberration;\n\ + uniform float u_distortion;\n\ + uniform float u_blur;\n\ + \n\ + void main() {\n\ + vec2 coord = v_coord;\n\ + float dist = distance(vec2(0.5), coord);\n\ + vec2 dist_coord = coord - vec2(0.5);\n\ + float percent = 1.0 + ((0.5 - dist) / 0.5) * u_distortion;\n\ + dist_coord *= percent;\n\ + coord = dist_coord + vec2(0.5);\n\ + vec4 color = texture2D(u_texture,coord, u_blur * dist);\n\ + color.r = texture2D(u_texture,vec2(0.5) + dist_coord * (1.0+0.01*u_aberration), u_blur * dist ).r;\n\ + color.b = texture2D(u_texture,vec2(0.5) + dist_coord * (1.0-0.01*u_aberration), u_blur * dist ).b;\n\ + gl_FragColor = color;\n\ + }\n\ + "; + /* + float normalized_tunable_sigmoid(float xs, float k)\n\ + {\n\ + xs = xs * 2.0 - 1.0;\n\ + float signx = sign(xs);\n\ + float absx = abs(xs);\n\ + return signx * ((-k - 1.0)*absx)/(2.0*(-2.0*k*absx+k-1.0)) + 0.5;\n\ + }\n\ + */ + + LiteGraph.registerNodeType("fx/lens", LGraphFXLens); + global.LGraphFXLens = LGraphFXLens; + + /* not working yet + function LGraphDepthOfField() + { + this.addInput("Color","Texture"); + this.addInput("Linear Depth","Texture"); + this.addInput("Camera","camera"); + this.addOutput("Texture","Texture"); + this.properties = { high_precision: false }; + } + + LGraphDepthOfField.title = "Depth Of Field"; + LGraphDepthOfField.desc = "Applies a depth of field effect"; + + LGraphDepthOfField.prototype.onExecute = function() + { + var tex = this.getInputData(0); + var depth = this.getInputData(1); + var camera = this.getInputData(2); + + if(!tex || !depth || !camera) + { + this.setOutputData(0, tex); + return; + } + + var precision = gl.UNSIGNED_BYTE; + if(this.properties.high_precision) + precision = gl.half_float_ext ? gl.HALF_FLOAT_OES : gl.FLOAT; + if(!this._temp_texture || this._temp_texture.type != precision || + this._temp_texture.width != tex.width || this._temp_texture.height != tex.height) + this._temp_texture = new GL.Texture( tex.width, tex.height, { type: precision, format: gl.RGBA, filter: gl.LINEAR }); + + var shader = LGraphDepthOfField._shader = new GL.Shader( GL.Shader.SCREEN_VERTEX_SHADER, LGraphDepthOfField._pixel_shader ); + + var screen_mesh = Mesh.getScreenQuad(); + + gl.disable( gl.DEPTH_TEST ); + gl.disable( gl.BLEND ); + + var camera_position = camera.getEye(); + var focus_point = camera.getCenter(); + var distance = vec3.distance( camera_position, focus_point ); + var far = camera.far; + var focus_range = distance * 0.5; + + this._temp_texture.drawTo( function() { + tex.bind(0); + depth.bind(1); + shader.uniforms({u_texture:0, u_depth_texture:1, u_resolution: [1/tex.width, 1/tex.height], u_far: far, u_focus_point: distance, u_focus_scale: focus_range }).draw(screen_mesh); + }); + + this.setOutputData(0, this._temp_texture); + } + + //from http://tuxedolabs.blogspot.com.es/2018/05/bokeh-depth-of-field-in-single-pass.html + LGraphDepthOfField._pixel_shader = "\n\ + precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture; //Image to be processed\n\ + uniform sampler2D u_depth_texture; //Linear depth, where 1.0 == far plane\n\ + uniform vec2 u_iresolution; //The size of a pixel: vec2(1.0/width, 1.0/height)\n\ + uniform float u_far; // Far plane\n\ + uniform float u_focus_point;\n\ + uniform float u_focus_scale;\n\ + \n\ + const float GOLDEN_ANGLE = 2.39996323;\n\ + const float MAX_BLUR_SIZE = 20.0;\n\ + const float RAD_SCALE = 0.5; // Smaller = nicer blur, larger = faster\n\ + \n\ + float getBlurSize(float depth, float focusPoint, float focusScale)\n\ + {\n\ + float coc = clamp((1.0 / focusPoint - 1.0 / depth)*focusScale, -1.0, 1.0);\n\ + return abs(coc) * MAX_BLUR_SIZE;\n\ + }\n\ + \n\ + vec3 depthOfField(vec2 texCoord, float focusPoint, float focusScale)\n\ + {\n\ + float centerDepth = texture2D(u_depth_texture, texCoord).r * u_far;\n\ + float centerSize = getBlurSize(centerDepth, focusPoint, focusScale);\n\ + vec3 color = texture2D(u_texture, v_coord).rgb;\n\ + float tot = 1.0;\n\ + \n\ + float radius = RAD_SCALE;\n\ + for (float ang = 0.0; ang < 100.0; ang += GOLDEN_ANGLE)\n\ + {\n\ + vec2 tc = texCoord + vec2(cos(ang), sin(ang)) * u_iresolution * radius;\n\ + \n\ + vec3 sampleColor = texture2D(u_texture, tc).rgb;\n\ + float sampleDepth = texture2D(u_depth_texture, tc).r * u_far;\n\ + float sampleSize = getBlurSize( sampleDepth, focusPoint, focusScale );\n\ + if (sampleDepth > centerDepth)\n\ + sampleSize = clamp(sampleSize, 0.0, centerSize*2.0);\n\ + \n\ + float m = smoothstep(radius-0.5, radius+0.5, sampleSize);\n\ + color += mix(color/tot, sampleColor, m);\n\ + tot += 1.0;\n\ + radius += RAD_SCALE/radius;\n\ + if(radius>=MAX_BLUR_SIZE)\n\ + return color / tot;\n\ + }\n\ + return color / tot;\n\ + }\n\ + void main()\n\ + {\n\ + gl_FragColor = vec4( depthOfField( v_coord, u_focus_point, u_focus_scale ), 1.0 );\n\ + //gl_FragColor = vec4( texture2D(u_depth_texture, v_coord).r );\n\ + }\n\ + "; + + LiteGraph.registerNodeType("fx/DOF", LGraphDepthOfField ); + global.LGraphDepthOfField = LGraphDepthOfField; + */ + + //******************************************************* + + function LGraphFXBokeh() { + this.addInput("Texture", "Texture"); + this.addInput("Blurred", "Texture"); + this.addInput("Mask", "Texture"); + this.addInput("Threshold", "number"); + this.addOutput("Texture", "Texture"); + this.properties = { + shape: "", + size: 10, + alpha: 1.0, + threshold: 1.0, + high_precision: false, + }; + } + + LGraphFXBokeh.title = "Bokeh"; + LGraphFXBokeh.desc = "applies an Bokeh effect"; + + LGraphFXBokeh.widgets_info = { shape: { widget: "texture" } }; + + LGraphFXBokeh.prototype.onExecute = function () { + var tex = this.getInputData(0); + var blurred_tex = this.getInputData(1); + var mask_tex = this.getInputData(2); + if (!tex || !mask_tex || !this.properties.shape) { + this.setOutputData(0, tex); + return; + } + + if (!blurred_tex) { + blurred_tex = tex; + } + + var shape_tex = LGraphTexture.getTexture(this.properties.shape); + if (!shape_tex) { + return; + } + + var threshold = this.properties.threshold; + if (this.isInputConnected(3)) { + threshold = this.getInputData(3); + this.properties.threshold = threshold; + } + + var precision = gl.UNSIGNED_BYTE; + if (this.properties.high_precision) { + precision = gl.half_float_ext ? gl.HALF_FLOAT_OES : gl.FLOAT; + } + if ( + !this._temp_texture || + this._temp_texture.type != precision || + this._temp_texture.width != tex.width || + this._temp_texture.height != tex.height + ) { + this._temp_texture = new GL.Texture(tex.width, tex.height, { + type: precision, + format: gl.RGBA, + filter: gl.LINEAR, + }); + } + + //iterations + var size = this.properties.size; + + var first_shader = LGraphFXBokeh._first_shader; + if (!first_shader) { + first_shader = LGraphFXBokeh._first_shader = new GL.Shader( + Shader.SCREEN_VERTEX_SHADER, + LGraphFXBokeh._first_pixel_shader, + ); + } + + var second_shader = LGraphFXBokeh._second_shader; + if (!second_shader) { + second_shader = LGraphFXBokeh._second_shader = new GL.Shader( + LGraphFXBokeh._second_vertex_shader, + LGraphFXBokeh._second_pixel_shader, + ); + } + + var points_mesh = this._points_mesh; + if ( + !points_mesh || + points_mesh._width != tex.width || + points_mesh._height != tex.height || + points_mesh._spacing != 2 + ) { + points_mesh = this.createPointsMesh(tex.width, tex.height, 2); + } + + var screen_mesh = Mesh.getScreenQuad(); + + var point_size = this.properties.size; + var min_light = this.properties.min_light; + var alpha = this.properties.alpha; + + gl.disable(gl.DEPTH_TEST); + gl.disable(gl.BLEND); + + this._temp_texture.drawTo(function () { + tex.bind(0); + blurred_tex.bind(1); + mask_tex.bind(2); + first_shader + .uniforms({ + u_texture: 0, + u_texture_blur: 1, + u_mask: 2, + u_texsize: [tex.width, tex.height], + }) + .draw(screen_mesh); + }); + + this._temp_texture.drawTo(function () { + //clear because we use blending + //gl.clearColor(0.0,0.0,0.0,1.0); + //gl.clear( gl.COLOR_BUFFER_BIT ); + gl.enable(gl.BLEND); + gl.blendFunc(gl.ONE, gl.ONE); + + tex.bind(0); + shape_tex.bind(3); + second_shader + .uniforms({ + u_texture: 0, + u_mask: 2, + u_shape: 3, + u_alpha: alpha, + u_threshold: threshold, + u_pointSize: point_size, + u_itexsize: [1.0 / tex.width, 1.0 / tex.height], + }) + .draw(points_mesh, gl.POINTS); + }); + + this.setOutputData(0, this._temp_texture); + }; + + LGraphFXBokeh.prototype.createPointsMesh = function ( + width, + height, + spacing, + ) { + var nwidth = Math.round(width / spacing); + var nheight = Math.round(height / spacing); + + var vertices = new Float32Array(nwidth * nheight * 2); + + var ny = -1; + var dx = (2 / width) * spacing; + var dy = (2 / height) * spacing; + for (var y = 0; y < nheight; ++y) { + var nx = -1; + for (var x = 0; x < nwidth; ++x) { + var pos = y * nwidth * 2 + x * 2; + vertices[pos] = nx; + vertices[pos + 1] = ny; + nx += dx; + } + ny += dy; + } + + this._points_mesh = GL.Mesh.load({ vertices2D: vertices }); + this._points_mesh._width = width; + this._points_mesh._height = height; + this._points_mesh._spacing = spacing; + + return this._points_mesh; + }; + + /* + LGraphTextureBokeh._pixel_shader = "precision highp float;\n\ + varying vec2 a_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform sampler2D u_shape;\n\ + \n\ + void main() {\n\ + vec4 color = texture2D( u_texture, gl_PointCoord );\n\ + color *= v_color * u_alpha;\n\ + gl_FragColor = color;\n\ + }\n"; + */ + + LGraphFXBokeh._first_pixel_shader = + "precision highp float;\n\ + precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform sampler2D u_texture_blur;\n\ + uniform sampler2D u_mask;\n\ + \n\ + void main() {\n\ + vec4 color = texture2D(u_texture, v_coord);\n\ + vec4 blurred_color = texture2D(u_texture_blur, v_coord);\n\ + float mask = texture2D(u_mask, v_coord).x;\n\ + gl_FragColor = mix(color, blurred_color, mask);\n\ + }\n\ + "; + + LGraphFXBokeh._second_vertex_shader = + "precision highp float;\n\ + attribute vec2 a_vertex2D;\n\ + varying vec4 v_color;\n\ + uniform sampler2D u_texture;\n\ + uniform sampler2D u_mask;\n\ + uniform vec2 u_itexsize;\n\ + uniform float u_pointSize;\n\ + uniform float u_threshold;\n\ + void main() {\n\ + vec2 coord = a_vertex2D * 0.5 + 0.5;\n\ + v_color = texture2D( u_texture, coord );\n\ + v_color += texture2D( u_texture, coord + vec2(u_itexsize.x, 0.0) );\n\ + v_color += texture2D( u_texture, coord + vec2(0.0, u_itexsize.y));\n\ + v_color += texture2D( u_texture, coord + u_itexsize);\n\ + v_color *= 0.25;\n\ + float mask = texture2D(u_mask, coord).x;\n\ + float luminance = length(v_color) * mask;\n\ + /*luminance /= (u_pointSize*u_pointSize)*0.01 */;\n\ + luminance -= u_threshold;\n\ + if(luminance < 0.0)\n\ + {\n\ + gl_Position.x = -100.0;\n\ + return;\n\ + }\n\ + gl_PointSize = u_pointSize;\n\ + gl_Position = vec4(a_vertex2D,0.0,1.0);\n\ + }\n\ + "; + + LGraphFXBokeh._second_pixel_shader = + "precision highp float;\n\ + varying vec4 v_color;\n\ + uniform sampler2D u_shape;\n\ + uniform float u_alpha;\n\ + \n\ + void main() {\n\ + vec4 color = texture2D( u_shape, gl_PointCoord );\n\ + color *= v_color * u_alpha;\n\ + gl_FragColor = color;\n\ + }\n"; + + LiteGraph.registerNodeType("fx/bokeh", LGraphFXBokeh); + global.LGraphFXBokeh = LGraphFXBokeh; + + //************************************************ + + function LGraphFXGeneric() { + this.addInput("Texture", "Texture"); + this.addInput("value1", "number"); + this.addInput("value2", "number"); + this.addOutput("Texture", "Texture"); + this.properties = { + fx: "halftone", + value1: 1, + value2: 1, + precision: LGraphTexture.DEFAULT, + }; + } + + LGraphFXGeneric.title = "FX"; + LGraphFXGeneric.desc = "applies an FX from a list"; + + LGraphFXGeneric.widgets_info = { + fx: { + widget: "combo", + values: ["halftone", "pixelate", "lowpalette", "noise", "gamma"], + }, + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + LGraphFXGeneric.shaders = {}; + + LGraphFXGeneric.prototype.onExecute = function () { + if (!this.isOutputConnected(0)) { + return; + } //saves work + + var tex = this.getInputData(0); + if (this.properties.precision === LGraphTexture.PASS_THROUGH) { + this.setOutputData(0, tex); + return; + } + + if (!tex) { + return; + } + + this._tex = LGraphTexture.getTargetTexture( + tex, + this._tex, + this.properties.precision, + ); + + //iterations + var value1 = this.properties.value1; + if (this.isInputConnected(1)) { + value1 = this.getInputData(1); + this.properties.value1 = value1; + } + + var value2 = this.properties.value2; + if (this.isInputConnected(2)) { + value2 = this.getInputData(2); + this.properties.value2 = value2; + } + + var fx = this.properties.fx; + var shader = LGraphFXGeneric.shaders[fx]; + if (!shader) { + var pixel_shader_code = LGraphFXGeneric["pixel_shader_" + fx]; + if (!pixel_shader_code) { + return; + } + + shader = LGraphFXGeneric.shaders[fx] = new GL.Shader( + Shader.SCREEN_VERTEX_SHADER, + pixel_shader_code, + ); + } + + gl.disable(gl.BLEND); + gl.disable(gl.DEPTH_TEST); + var mesh = Mesh.getScreenQuad(); + var camera = global.LS ? LS.Renderer._current_camera : null; + var camera_planes; + if (camera) { + camera_planes = [ + LS.Renderer._current_camera.near, + LS.Renderer._current_camera.far, + ]; + } else { + camera_planes = [1, 100]; + } + + var noise = null; + if (fx == "noise") { + noise = LGraphTexture.getNoiseTexture(); + } + + this._tex.drawTo(function () { + tex.bind(0); + if (fx == "noise") { + noise.bind(1); + } + + shader + .uniforms({ + u_texture: 0, + u_noise: 1, + u_size: [tex.width, tex.height], + u_rand: [Math.random(), Math.random()], + u_value1: value1, + u_value2: value2, + u_camera_planes: camera_planes, + }) + .draw(mesh); + }); + + this.setOutputData(0, this._tex); + }; + + LGraphFXGeneric.pixel_shader_halftone = + "precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform vec2 u_camera_planes;\n\ + uniform vec2 u_size;\n\ + uniform float u_value1;\n\ + uniform float u_value2;\n\ + \n\ + float pattern() {\n\ + float s = sin(u_value1 * 3.1415), c = cos(u_value1 * 3.1415);\n\ + vec2 tex = v_coord * u_size.xy;\n\ + vec2 point = vec2(\n\ + c * tex.x - s * tex.y ,\n\ + s * tex.x + c * tex.y \n\ + ) * u_value2;\n\ + return (sin(point.x) * sin(point.y)) * 4.0;\n\ + }\n\ + void main() {\n\ + vec4 color = texture2D(u_texture, v_coord);\n\ + float average = (color.r + color.g + color.b) / 3.0;\n\ + gl_FragColor = vec4(vec3(average * 10.0 - 5.0 + pattern()), color.a);\n\ + }\n"; + + LGraphFXGeneric.pixel_shader_pixelate = + "precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform vec2 u_camera_planes;\n\ + uniform vec2 u_size;\n\ + uniform float u_value1;\n\ + uniform float u_value2;\n\ + \n\ + void main() {\n\ + vec2 coord = vec2( floor(v_coord.x * u_value1) / u_value1, floor(v_coord.y * u_value2) / u_value2 );\n\ + vec4 color = texture2D(u_texture, coord);\n\ + gl_FragColor = color;\n\ + }\n"; + + LGraphFXGeneric.pixel_shader_lowpalette = + "precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform vec2 u_camera_planes;\n\ + uniform vec2 u_size;\n\ + uniform float u_value1;\n\ + uniform float u_value2;\n\ + \n\ + void main() {\n\ + vec4 color = texture2D(u_texture, v_coord);\n\ + gl_FragColor = floor(color * u_value1) / u_value1;\n\ + }\n"; + + LGraphFXGeneric.pixel_shader_noise = + "precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform sampler2D u_noise;\n\ + uniform vec2 u_size;\n\ + uniform float u_value1;\n\ + uniform float u_value2;\n\ + uniform vec2 u_rand;\n\ + \n\ + void main() {\n\ + vec4 color = texture2D(u_texture, v_coord);\n\ + vec3 noise = texture2D(u_noise, v_coord * vec2(u_size.x / 512.0, u_size.y / 512.0) + u_rand).xyz - vec3(0.5);\n\ + gl_FragColor = vec4( color.xyz + noise * u_value1, color.a );\n\ + }\n"; + + LGraphFXGeneric.pixel_shader_gamma = + "precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform float u_value1;\n\ + \n\ + void main() {\n\ + vec4 color = texture2D(u_texture, v_coord);\n\ + float gamma = 1.0 / u_value1;\n\ + gl_FragColor = vec4( pow( color.xyz, vec3(gamma) ), color.a );\n\ + }\n"; + + LiteGraph.registerNodeType("fx/generic", LGraphFXGeneric); + global.LGraphFXGeneric = LGraphFXGeneric; + + // Vigneting ************************************ + + function LGraphFXVigneting() { + this.addInput("Tex.", "Texture"); + this.addInput("intensity", "number"); + + this.addOutput("Texture", "Texture"); + this.properties = { + intensity: 1, + invert: false, + precision: LGraphTexture.DEFAULT, + }; + + if (!LGraphFXVigneting._shader) { + LGraphFXVigneting._shader = new GL.Shader( + Shader.SCREEN_VERTEX_SHADER, + LGraphFXVigneting.pixel_shader, + ); + } + } + + LGraphFXVigneting.title = "Vigneting"; + LGraphFXVigneting.desc = "Vigneting"; + + LGraphFXVigneting.widgets_info = { + precision: { widget: "combo", values: LGraphTexture.MODE_VALUES }, + }; + + LGraphFXVigneting.prototype.onExecute = function () { + var tex = this.getInputData(0); + + if (this.properties.precision === LGraphTexture.PASS_THROUGH) { + this.setOutputData(0, tex); + return; + } + + if (!tex) { + return; + } + + this._tex = LGraphTexture.getTargetTexture( + tex, + this._tex, + this.properties.precision, + ); + + var intensity = this.properties.intensity; + if (this.isInputConnected(1)) { + intensity = this.getInputData(1); + this.properties.intensity = intensity; + } + + gl.disable(gl.BLEND); + gl.disable(gl.DEPTH_TEST); + + var mesh = Mesh.getScreenQuad(); + var shader = LGraphFXVigneting._shader; + var invert = this.properties.invert; + + this._tex.drawTo(function () { + tex.bind(0); + shader + .uniforms({ + u_texture: 0, + u_intensity: intensity, + u_isize: [1 / tex.width, 1 / tex.height], + u_invert: invert ? 1 : 0, + }) + .draw(mesh); + }); + + this.setOutputData(0, this._tex); + }; + + LGraphFXVigneting.pixel_shader = + "precision highp float;\n\ + precision highp float;\n\ + varying vec2 v_coord;\n\ + uniform sampler2D u_texture;\n\ + uniform float u_intensity;\n\ + uniform int u_invert;\n\ + \n\ + void main() {\n\ + float luminance = 1.0 - length( v_coord - vec2(0.5) ) * 1.414;\n\ + vec4 color = texture2D(u_texture, v_coord);\n\ + if(u_invert == 1)\n\ + luminance = 1.0 - luminance;\n\ + luminance = mix(1.0, luminance, u_intensity);\n\ + gl_FragColor = vec4( luminance * color.xyz, color.a);\n\ + }\n\ + "; + + LiteGraph.registerNodeType("fx/vigneting", LGraphFXVigneting); + global.LGraphFXVigneting = LGraphFXVigneting; + } +})(this); + +(function (global) { + var LiteGraph = global.LiteGraph; + var MIDI_COLOR = "#243"; + + function MIDIEvent(data) { + this.channel = 0; + this.cmd = 0; + this.data = new Uint32Array(3); + + if (data) { + this.setup(data); + } + } + + LiteGraph.MIDIEvent = MIDIEvent; + + MIDIEvent.prototype.fromJSON = function (o) { + this.setup(o.data); + }; + + MIDIEvent.prototype.setup = function (data) { + var raw_data = data; + if (data.constructor === Object) { + raw_data = data.data; + } + + this.data.set(raw_data); + + var midiStatus = raw_data[0]; + this.status = midiStatus; + + var midiCommand = midiStatus & 0xf0; + + if (midiStatus >= 0xf0) { + this.cmd = midiStatus; + } else { + this.cmd = midiCommand; + } + + if (this.cmd == MIDIEvent.NOTEON && this.velocity == 0) { + this.cmd = MIDIEvent.NOTEOFF; + } + + this.cmd_str = MIDIEvent.commands[this.cmd] || ""; + + if (midiCommand >= MIDIEvent.NOTEON || midiCommand <= MIDIEvent.NOTEOFF) { + this.channel = midiStatus & 0x0f; + } + }; + + Object.defineProperty(MIDIEvent.prototype, "velocity", { + get: function () { + if (this.cmd == MIDIEvent.NOTEON) { + return this.data[2]; + } + return -1; + }, + set: function (v) { + this.data[2] = v; // v / 127; + }, + enumerable: true, + }); + + MIDIEvent.notes = [ + "A", + "A#", + "B", + "C", + "C#", + "D", + "D#", + "E", + "F", + "F#", + "G", + "G#", + ]; + MIDIEvent.note_to_index = { + A: 0, + "A#": 1, + B: 2, + C: 3, + "C#": 4, + D: 5, + "D#": 6, + E: 7, + F: 8, + "F#": 9, + G: 10, + "G#": 11, + }; + + Object.defineProperty(MIDIEvent.prototype, "note", { + get: function () { + if (this.cmd != MIDIEvent.NOTEON) { + return -1; + } + return MIDIEvent.toNoteString(this.data[1], true); + }, + set: function (v) { + throw "notes cannot be assigned this way, must modify the data[1]"; + }, + enumerable: true, + }); + + Object.defineProperty(MIDIEvent.prototype, "octave", { + get: function () { + if (this.cmd != MIDIEvent.NOTEON) { + return -1; + } + var octave = this.data[1] - 24; + return Math.floor(octave / 12 + 1); + }, + set: function (v) { + throw "octave cannot be assigned this way, must modify the data[1]"; + }, + enumerable: true, + }); + + //returns HZs + MIDIEvent.prototype.getPitch = function () { + return Math.pow(2, (this.data[1] - 69) / 12) * 440; + }; + + MIDIEvent.computePitch = function (note) { + return Math.pow(2, (note - 69) / 12) * 440; + }; + + MIDIEvent.prototype.getCC = function () { + return this.data[1]; + }; + + MIDIEvent.prototype.getCCValue = function () { + return this.data[2]; + }; + + //not tested, there is a formula missing here + MIDIEvent.prototype.getPitchBend = function () { + return this.data[1] + (this.data[2] << 7) - 8192; + }; + + MIDIEvent.computePitchBend = function (v1, v2) { + return v1 + (v2 << 7) - 8192; + }; + + MIDIEvent.prototype.setCommandFromString = function (str) { + this.cmd = MIDIEvent.computeCommandFromString(str); + }; + + MIDIEvent.computeCommandFromString = function (str) { + if (!str) { + return 0; + } + + if (str && str.constructor === Number) { + return str; + } + + str = str.toUpperCase(); + switch (str) { + case "NOTE ON": + case "NOTEON": + return MIDIEvent.NOTEON; + break; + case "NOTE OFF": + case "NOTEOFF": + return MIDIEvent.NOTEON; + break; + case "KEY PRESSURE": + case "KEYPRESSURE": + return MIDIEvent.KEYPRESSURE; + break; + case "CONTROLLER CHANGE": + case "CONTROLLERCHANGE": + case "CC": + return MIDIEvent.CONTROLLERCHANGE; + break; + case "PROGRAM CHANGE": + case "PROGRAMCHANGE": + case "PC": + return MIDIEvent.PROGRAMCHANGE; + break; + case "CHANNEL PRESSURE": + case "CHANNELPRESSURE": + return MIDIEvent.CHANNELPRESSURE; + break; + case "PITCH BEND": + case "PITCHBEND": + return MIDIEvent.PITCHBEND; + break; + case "TIME TICK": + case "TIMETICK": + return MIDIEvent.TIMETICK; + break; + default: + return Number(str); //assume its a hex code + } + }; + + //transform from a pitch number to string like "C4" + MIDIEvent.toNoteString = function (d, skip_octave) { + d = Math.round(d); //in case it has decimals + var note = d - 21; + var octave = Math.floor((d - 24) / 12 + 1); + note = note % 12; + if (note < 0) { + note = 12 + note; + } + return MIDIEvent.notes[note] + (skip_octave ? "" : octave); + }; + + MIDIEvent.NoteStringToPitch = function (str) { + str = str.toUpperCase(); + var note = str[0]; + var octave = 4; + + if (str[1] == "#") { + note += "#"; + if (str.length > 2) { + octave = Number(str[2]); + } + } else { + if (str.length > 1) { + octave = Number(str[1]); + } + } + var pitch = MIDIEvent.note_to_index[note]; + if (pitch == null) { + return null; + } + return (octave - 1) * 12 + pitch + 21; + }; + + MIDIEvent.prototype.toString = function () { + var str = "" + this.channel + ". "; + switch (this.cmd) { + case MIDIEvent.NOTEON: + str += "NOTEON " + MIDIEvent.toNoteString(this.data[1]); + break; + case MIDIEvent.NOTEOFF: + str += "NOTEOFF " + MIDIEvent.toNoteString(this.data[1]); + break; + case MIDIEvent.CONTROLLERCHANGE: + str += "CC " + this.data[1] + " " + this.data[2]; + break; + case MIDIEvent.PROGRAMCHANGE: + str += "PC " + this.data[1]; + break; + case MIDIEvent.PITCHBEND: + str += "PITCHBEND " + this.getPitchBend(); + break; + case MIDIEvent.KEYPRESSURE: + str += "KEYPRESS " + this.data[1]; + break; + } + + return str; + }; + + MIDIEvent.prototype.toHexString = function () { + var str = ""; + for (var i = 0; i < this.data.length; i++) { + str += this.data[i].toString(16) + " "; + } + }; + + MIDIEvent.prototype.toJSON = function () { + return { + data: [this.data[0], this.data[1], this.data[2]], + object_class: "MIDIEvent", + }; + }; + + MIDIEvent.NOTEOFF = 0x80; + MIDIEvent.NOTEON = 0x90; + MIDIEvent.KEYPRESSURE = 0xa0; + MIDIEvent.CONTROLLERCHANGE = 0xb0; + MIDIEvent.PROGRAMCHANGE = 0xc0; + MIDIEvent.CHANNELPRESSURE = 0xd0; + MIDIEvent.PITCHBEND = 0xe0; + MIDIEvent.TIMETICK = 0xf8; + + MIDIEvent.commands = { + 0x80: "note off", + 0x90: "note on", + 0xa0: "key pressure", + 0xb0: "controller change", + 0xc0: "program change", + 0xd0: "channel pressure", + 0xe0: "pitch bend", + 0xf0: "system", + 0xf2: "Song pos", + 0xf3: "Song select", + 0xf6: "Tune request", + 0xf8: "time tick", + 0xfa: "Start Song", + 0xfb: "Continue Song", + 0xfc: "Stop Song", + 0xfe: "Sensing", + 0xff: "Reset", + }; + + MIDIEvent.commands_short = { + 0x80: "NOTEOFF", + 0x90: "NOTEOFF", + 0xa0: "KEYP", + 0xb0: "CC", + 0xc0: "PC", + 0xd0: "CP", + 0xe0: "PB", + 0xf0: "SYS", + 0xf2: "POS", + 0xf3: "SELECT", + 0xf6: "TUNEREQ", + 0xf8: "TT", + 0xfa: "START", + 0xfb: "CONTINUE", + 0xfc: "STOP", + 0xfe: "SENS", + 0xff: "RESET", + }; + + MIDIEvent.commands_reversed = {}; + for (var i in MIDIEvent.commands) { + MIDIEvent.commands_reversed[MIDIEvent.commands[i]] = i; + } + + //MIDI wrapper, instantiate by MIDIIn and MIDIOut + function MIDIInterface(on_ready, on_error) { + if (!navigator.requestMIDIAccess) { + this.error = "not suppoorted"; + if (on_error) { + on_error("Not supported"); + } else { + console.error("MIDI NOT SUPPORTED, enable by chrome://flags"); + } + return; + } + + this.on_ready = on_ready; + + this.state = { + note: [], + cc: [], + }; + + this.input_ports = null; + this.input_ports_info = []; + this.output_ports = null; + this.output_ports_info = []; + + navigator + .requestMIDIAccess() + .then(this.onMIDISuccess.bind(this), this.onMIDIFailure.bind(this)); + } + + MIDIInterface.input = null; + + MIDIInterface.MIDIEvent = MIDIEvent; + + MIDIInterface.prototype.onMIDISuccess = function (midiAccess) { + console.log("MIDI ready!"); + console.log(midiAccess); + this.midi = midiAccess; // store in the global (in real usage, would probably keep in an object instance) + this.updatePorts(); + + if (this.on_ready) { + this.on_ready(this); + } + }; + + MIDIInterface.prototype.updatePorts = function () { + var midi = this.midi; + this.input_ports = midi.inputs; + this.input_ports_info = []; + this.output_ports = midi.outputs; + this.output_ports_info = []; + + var num = 0; + + var it = this.input_ports.values(); + var it_value = it.next(); + while (it_value && it_value.done === false) { + var port_info = it_value.value; + this.input_ports_info.push(port_info); + console.log( + "Input port [type:'" + + port_info.type + + "'] id:'" + + port_info.id + + "' manufacturer:'" + + port_info.manufacturer + + "' name:'" + + port_info.name + + "' version:'" + + port_info.version + + "'", + ); + num++; + it_value = it.next(); + } + this.num_input_ports = num; + + num = 0; + var it = this.output_ports.values(); + var it_value = it.next(); + while (it_value && it_value.done === false) { + var port_info = it_value.value; + this.output_ports_info.push(port_info); + console.log( + "Output port [type:'" + + port_info.type + + "'] id:'" + + port_info.id + + "' manufacturer:'" + + port_info.manufacturer + + "' name:'" + + port_info.name + + "' version:'" + + port_info.version + + "'", + ); + num++; + it_value = it.next(); + } + this.num_output_ports = num; + }; + + MIDIInterface.prototype.onMIDIFailure = function (msg) { + console.error("Failed to get MIDI access - " + msg); + }; + + MIDIInterface.prototype.openInputPort = function (port, callback) { + var input_port = this.input_ports.get("input-" + port); + if (!input_port) { + return false; + } + MIDIInterface.input = this; + var that = this; + + input_port.onmidimessage = function (a) { + var midi_event = new MIDIEvent(a.data); + that.updateState(midi_event); + if (callback) { + callback(a.data, midi_event); + } + if (MIDIInterface.on_message) { + MIDIInterface.on_message(a.data, midi_event); + } + }; + console.log("port open: ", input_port); + return true; + }; + + MIDIInterface.parseMsg = function (data) {}; + + MIDIInterface.prototype.updateState = function (midi_event) { + switch (midi_event.cmd) { + case MIDIEvent.NOTEON: + this.state.note[midi_event.value1 | 0] = midi_event.value2; + break; + case MIDIEvent.NOTEOFF: + this.state.note[midi_event.value1 | 0] = 0; + break; + case MIDIEvent.CONTROLLERCHANGE: + this.state.cc[midi_event.getCC()] = midi_event.getCCValue(); + break; + } + }; + + MIDIInterface.prototype.sendMIDI = function (port, midi_data) { + if (!midi_data) { + return; + } + + var output_port = this.output_ports_info[port]; //this.output_ports.get("output-" + port); + if (!output_port) { + return; + } + + MIDIInterface.output = this; + + if (midi_data.constructor === MIDIEvent) { + output_port.send(midi_data.data); + } else { + output_port.send(midi_data); + } + }; + + function LGMIDIIn() { + this.addOutput("on_midi", LiteGraph.EVENT); + this.addOutput("out", "midi"); + this.properties = { port: 0 }; + this._last_midi_event = null; + this._current_midi_event = null; + this.boxcolor = "#AAA"; + this._last_time = 0; + + var that = this; + new MIDIInterface(function (midi) { + //open + that._midi = midi; + if (that._waiting) { + that.onStart(); + } + that._waiting = false; + }); + } + + LGMIDIIn.MIDIInterface = MIDIInterface; + + LGMIDIIn.title = "MIDI Input"; + LGMIDIIn.desc = "Reads MIDI from a input port"; + LGMIDIIn.color = MIDI_COLOR; + + LGMIDIIn.prototype.getPropertyInfo = function (name) { + if (!this._midi) { + return; + } + + if (name == "port") { + var values = {}; + for (var i = 0; i < this._midi.input_ports_info.length; ++i) { + var input = this._midi.input_ports_info[i]; + values[i] = i + ".- " + input.name + " version:" + input.version; + } + return { type: "enum", values: values }; + } + }; + + LGMIDIIn.prototype.onStart = function () { + if (this._midi) { + this._midi.openInputPort( + this.properties.port, + this.onMIDIEvent.bind(this), + ); + } else { + this._waiting = true; + } + }; + + LGMIDIIn.prototype.onMIDIEvent = function (data, midi_event) { + this._last_midi_event = midi_event; + this.boxcolor = "#AFA"; + this._last_time = LiteGraph.getTime(); + this.trigger("on_midi", midi_event); + if (midi_event.cmd == MIDIEvent.NOTEON) { + this.trigger("on_noteon", midi_event); + } else if (midi_event.cmd == MIDIEvent.NOTEOFF) { + this.trigger("on_noteoff", midi_event); + } else if (midi_event.cmd == MIDIEvent.CONTROLLERCHANGE) { + this.trigger("on_cc", midi_event); + } else if (midi_event.cmd == MIDIEvent.PROGRAMCHANGE) { + this.trigger("on_pc", midi_event); + } else if (midi_event.cmd == MIDIEvent.PITCHBEND) { + this.trigger("on_pitchbend", midi_event); + } + }; + + LGMIDIIn.prototype.onDrawBackground = function (ctx) { + this.boxcolor = "#AAA"; + if (!this.flags.collapsed && this._last_midi_event) { + ctx.fillStyle = "white"; + var now = LiteGraph.getTime(); + var f = 1.0 - Math.max(0, (now - this._last_time) * 0.001); + if (f > 0) { + var t = ctx.globalAlpha; + ctx.globalAlpha *= f; + ctx.font = "12px Tahoma"; + ctx.fillText( + this._last_midi_event.toString(), + 2, + this.size[1] * 0.5 + 3, + ); + //ctx.fillRect(0,0,this.size[0],this.size[1]); + ctx.globalAlpha = t; + } + } + }; + + LGMIDIIn.prototype.onExecute = function () { + if (this.outputs) { + var last = this._last_midi_event; + for (var i = 0; i < this.outputs.length; ++i) { + var output = this.outputs[i]; + var v = null; + switch (output.name) { + case "midi": + v = this._midi; + break; + case "last_midi": + v = last; + break; + default: + continue; + } + this.setOutputData(i, v); + } + } + }; + + LGMIDIIn.prototype.onGetOutputs = function () { + return [ + ["last_midi", "midi"], + ["on_midi", LiteGraph.EVENT], + ["on_noteon", LiteGraph.EVENT], + ["on_noteoff", LiteGraph.EVENT], + ["on_cc", LiteGraph.EVENT], + ["on_pc", LiteGraph.EVENT], + ["on_pitchbend", LiteGraph.EVENT], + ]; + }; + + LiteGraph.registerNodeType("midi/input", LGMIDIIn); + + function LGMIDIOut() { + this.addInput("send", LiteGraph.EVENT); + this.properties = { port: 0 }; + + var that = this; + new MIDIInterface(function (midi) { + that._midi = midi; + that.widget.options.values = that.getMIDIOutputs(); + }); + this.widget = this.addWidget("combo", "Device", this.properties.port, { + property: "port", + values: this.getMIDIOutputs.bind(this), + }); + this.size = [340, 60]; + } + + LGMIDIOut.MIDIInterface = MIDIInterface; + + LGMIDIOut.title = "MIDI Output"; + LGMIDIOut.desc = "Sends MIDI to output channel"; + LGMIDIOut.color = MIDI_COLOR; + + LGMIDIOut.prototype.onGetPropertyInfo = function (name) { + if (!this._midi) { + return; + } + + if (name == "port") { + var values = this.getMIDIOutputs(); + return { type: "enum", values: values }; + } + }; + LGMIDIOut.default_ports = { 0: "unknown" }; + + LGMIDIOut.prototype.getMIDIOutputs = function () { + var values = {}; + if (!this._midi) return LGMIDIOut.default_ports; + if (this._midi.output_ports_info) + for (var i = 0; i < this._midi.output_ports_info.length; ++i) { + var output = this._midi.output_ports_info[i]; + if (!output) continue; + var name = i + ".- " + output.name + " version:" + output.version; + values[i] = name; + } + return values; + }; + + LGMIDIOut.prototype.onAction = function (event, midi_event) { + //console.log(midi_event); + if (!this._midi) { + return; + } + if (event == "send") { + this._midi.sendMIDI(this.properties.port, midi_event); + } + this.trigger("midi", midi_event); + }; + + LGMIDIOut.prototype.onGetInputs = function () { + return [["send", LiteGraph.ACTION]]; + }; + + LGMIDIOut.prototype.onGetOutputs = function () { + return [["on_midi", LiteGraph.EVENT]]; + }; + + LiteGraph.registerNodeType("midi/output", LGMIDIOut); + + function LGMIDIShow() { + this.addInput("on_midi", LiteGraph.EVENT); + this._str = ""; + this.size = [200, 40]; + } + + LGMIDIShow.title = "MIDI Show"; + LGMIDIShow.desc = "Shows MIDI in the graph"; + LGMIDIShow.color = MIDI_COLOR; + + LGMIDIShow.prototype.getTitle = function () { + if (this.flags.collapsed) { + return this._str; + } + return this.title; + }; + + LGMIDIShow.prototype.onAction = function (event, midi_event) { + if (!midi_event) { + return; + } + if (midi_event.constructor === MIDIEvent) { + this._str = midi_event.toString(); + } else { + this._str = "???"; + } + }; + + LGMIDIShow.prototype.onDrawForeground = function (ctx) { + if (!this._str || this.flags.collapsed) { + return; + } + + ctx.font = "30px Arial"; + ctx.fillText(this._str, 10, this.size[1] * 0.8); + }; + + LGMIDIShow.prototype.onGetInputs = function () { + return [["in", LiteGraph.ACTION]]; + }; + + LGMIDIShow.prototype.onGetOutputs = function () { + return [["on_midi", LiteGraph.EVENT]]; + }; + + LiteGraph.registerNodeType("midi/show", LGMIDIShow); + + function LGMIDIFilter() { + this.properties = { + channel: -1, + cmd: -1, + min_value: -1, + max_value: -1, + }; + + var that = this; + this._learning = false; + this.addWidget("button", "Learn", "", function () { + that._learning = true; + that.boxcolor = "#FA3"; + }); + + this.addInput("in", LiteGraph.EVENT); + this.addOutput("on_midi", LiteGraph.EVENT); + this.boxcolor = "#AAA"; + } + + LGMIDIFilter.title = "MIDI Filter"; + LGMIDIFilter.desc = "Filters MIDI messages"; + LGMIDIFilter.color = MIDI_COLOR; + + LGMIDIFilter["@cmd"] = { + type: "enum", + title: "Command", + values: MIDIEvent.commands_reversed, + }; + + LGMIDIFilter.prototype.getTitle = function () { + var str = null; + if (this.properties.cmd == -1) { + str = "Nothing"; + } else { + str = MIDIEvent.commands_short[this.properties.cmd] || "Unknown"; + } + + if (this.properties.min_value != -1 && this.properties.max_value != -1) { + str += + " " + + (this.properties.min_value == this.properties.max_value + ? this.properties.max_value + : this.properties.min_value + ".." + this.properties.max_value); + } + + return "Filter: " + str; + }; + + LGMIDIFilter.prototype.onPropertyChanged = function (name, value) { + if (name == "cmd") { + var num = Number(value); + if (isNaN(num)) { + num = MIDIEvent.commands[value] || 0; + } + this.properties.cmd = num; + } + }; + + LGMIDIFilter.prototype.onAction = function (event, midi_event) { + if (!midi_event || midi_event.constructor !== MIDIEvent) { + return; + } + + if (this._learning) { + this._learning = false; + this.boxcolor = "#AAA"; + this.properties.channel = midi_event.channel; + this.properties.cmd = midi_event.cmd; + this.properties.min_value = this.properties.max_value = + midi_event.data[1]; + } else { + if ( + this.properties.channel != -1 && + midi_event.channel != this.properties.channel + ) { + return; + } + if (this.properties.cmd != -1 && midi_event.cmd != this.properties.cmd) { + return; + } + if ( + this.properties.min_value != -1 && + midi_event.data[1] < this.properties.min_value + ) { + return; + } + if ( + this.properties.max_value != -1 && + midi_event.data[1] > this.properties.max_value + ) { + return; + } + } + + this.trigger("on_midi", midi_event); + }; + + LiteGraph.registerNodeType("midi/filter", LGMIDIFilter); + + function LGMIDIEvent() { + this.properties = { + channel: 0, + cmd: 144, //0x90 + value1: 1, + value2: 1, + }; + + this.addInput("send", LiteGraph.EVENT); + this.addInput("assign", LiteGraph.EVENT); + this.addOutput("on_midi", LiteGraph.EVENT); + + this.midi_event = new MIDIEvent(); + this.gate = false; + } + + LGMIDIEvent.title = "MIDIEvent"; + LGMIDIEvent.desc = "Create a MIDI Event"; + LGMIDIEvent.color = MIDI_COLOR; + + LGMIDIEvent.prototype.onAction = function (event, midi_event) { + if (event == "assign") { + this.properties.channel = midi_event.channel; + this.properties.cmd = midi_event.cmd; + this.properties.value1 = midi_event.data[1]; + this.properties.value2 = midi_event.data[2]; + if (midi_event.cmd == MIDIEvent.NOTEON) { + this.gate = true; + } else if (midi_event.cmd == MIDIEvent.NOTEOFF) { + this.gate = false; + } + return; + } + + //send + var midi_event = this.midi_event; + midi_event.channel = this.properties.channel; + if (this.properties.cmd && this.properties.cmd.constructor === String) { + midi_event.setCommandFromString(this.properties.cmd); + } else { + midi_event.cmd = this.properties.cmd; + } + midi_event.data[0] = midi_event.cmd | midi_event.channel; + midi_event.data[1] = Number(this.properties.value1); + midi_event.data[2] = Number(this.properties.value2); + + this.trigger("on_midi", midi_event); + }; + + LGMIDIEvent.prototype.onExecute = function () { + var props = this.properties; + + if (this.inputs) { + for (var i = 0; i < this.inputs.length; ++i) { + var input = this.inputs[i]; + if (input.link == -1) { + continue; + } + switch (input.name) { + case "note": + var v = this.getInputData(i); + if (v != null) { + if (v.constructor === String) { + v = MIDIEvent.NoteStringToPitch(v); + } + this.properties.value1 = (v | 0) % 255; + } + break; + case "cmd": + var v = this.getInputData(i); + if (v != null) { + this.properties.cmd = v; + } + break; + case "value1": + var v = this.getInputData(i); + if (v != null) { + this.properties.value1 = clamp(v | 0, 0, 127); + } + break; + case "value2": + var v = this.getInputData(i); + if (v != null) { + this.properties.value2 = clamp(v | 0, 0, 127); + } + break; + } + } + } + + if (this.outputs) { + for (var i = 0; i < this.outputs.length; ++i) { + var output = this.outputs[i]; + var v = null; + switch (output.name) { + case "midi": + v = new MIDIEvent(); + v.setup([props.cmd, props.value1, props.value2]); + v.channel = props.channel; + break; + case "command": + v = props.cmd; + break; + case "cc": + v = props.value1; + break; + case "cc_value": + v = props.value2; + break; + case "note": + v = + props.cmd == MIDIEvent.NOTEON || props.cmd == MIDIEvent.NOTEOFF + ? props.value1 + : null; + break; + case "velocity": + v = props.cmd == MIDIEvent.NOTEON ? props.value2 : null; + break; + case "pitch": + v = + props.cmd == MIDIEvent.NOTEON + ? MIDIEvent.computePitch(props.value1) + : null; + break; + case "pitchbend": + v = + props.cmd == MIDIEvent.PITCHBEND + ? MIDIEvent.computePitchBend(props.value1, props.value2) + : null; + break; + case "gate": + v = this.gate; + break; + default: + continue; + } + if (v !== null) { + this.setOutputData(i, v); + } + } + } + }; + + LGMIDIEvent.prototype.onPropertyChanged = function (name, value) { + if (name == "cmd") { + this.properties.cmd = MIDIEvent.computeCommandFromString(value); + } + }; + + LGMIDIEvent.prototype.onGetInputs = function () { + return [ + ["cmd", "number"], + ["note", "number"], + ["value1", "number"], + ["value2", "number"], + ]; + }; + + LGMIDIEvent.prototype.onGetOutputs = function () { + return [ + ["midi", "midi"], + ["on_midi", LiteGraph.EVENT], + ["command", "number"], + ["note", "number"], + ["velocity", "number"], + ["cc", "number"], + ["cc_value", "number"], + ["pitch", "number"], + ["gate", "bool"], + ["pitchbend", "number"], + ]; + }; + + LiteGraph.registerNodeType("midi/event", LGMIDIEvent); + + function LGMIDICC() { + this.properties = { + // channel: 0, + cc: 1, + value: 0, + }; + + this.addOutput("value", "number"); + } + + LGMIDICC.title = "MIDICC"; + LGMIDICC.desc = "gets a Controller Change"; + LGMIDICC.color = MIDI_COLOR; + + LGMIDICC.prototype.onExecute = function () { + var props = this.properties; + if (MIDIInterface.input) { + this.properties.value = MIDIInterface.input.state.cc[this.properties.cc]; + } + this.setOutputData(0, this.properties.value); + }; + + LiteGraph.registerNodeType("midi/cc", LGMIDICC); + + function LGMIDIGenerator() { + this.addInput("generate", LiteGraph.ACTION); + this.addInput("scale", "string"); + this.addInput("octave", "number"); + this.addOutput("note", LiteGraph.EVENT); + this.properties = { + notes: "A,A#,B,C,C#,D,D#,E,F,F#,G,G#", + octave: 2, + duration: 0.5, + mode: "sequence", + }; + + this.notes_pitches = LGMIDIGenerator.processScale(this.properties.notes); + this.sequence_index = 0; + } + + LGMIDIGenerator.title = "MIDI Generator"; + LGMIDIGenerator.desc = "Generates a random MIDI note"; + LGMIDIGenerator.color = MIDI_COLOR; + + LGMIDIGenerator.processScale = function (scale) { + var notes = scale.split(","); + for (var i = 0; i < notes.length; ++i) { + var n = notes[i]; + if ((n.length == 2 && n[1] != "#") || n.length > 2) { + notes[i] = -LiteGraph.MIDIEvent.NoteStringToPitch(n); + } else { + notes[i] = MIDIEvent.note_to_index[n] || 0; + } + } + return notes; + }; + + LGMIDIGenerator.prototype.onPropertyChanged = function (name, value) { + if (name == "notes") { + this.notes_pitches = LGMIDIGenerator.processScale(value); + } + }; + + LGMIDIGenerator.prototype.onExecute = function () { + var octave = this.getInputData(2); + if (octave != null) { + this.properties.octave = octave; + } + + var scale = this.getInputData(1); + if (scale) { + this.notes_pitches = LGMIDIGenerator.processScale(scale); + } + }; + + LGMIDIGenerator.prototype.onAction = function (event, midi_event) { + //var range = this.properties.max - this.properties.min; + //var pitch = this.properties.min + ((Math.random() * range)|0); + var pitch = 0; + var range = this.notes_pitches.length; + var index = 0; + + if (this.properties.mode == "sequence") { + index = this.sequence_index = (this.sequence_index + 1) % range; + } else if (this.properties.mode == "random") { + index = Math.floor(Math.random() * range); + } + + var note = this.notes_pitches[index]; + if (note >= 0) { + pitch = note + (this.properties.octave - 1) * 12 + 33; + } else { + pitch = -note; + } + + var midi_event = new MIDIEvent(); + midi_event.setup([MIDIEvent.NOTEON, pitch, 10]); + var duration = this.properties.duration || 1; + this.trigger("note", midi_event); + + //noteoff + setTimeout( + function () { + var midi_event = new MIDIEvent(); + midi_event.setup([MIDIEvent.NOTEOFF, pitch, 0]); + this.trigger("note", midi_event); + }.bind(this), + duration * 1000, + ); + }; + + LiteGraph.registerNodeType("midi/generator", LGMIDIGenerator); + + function LGMIDITranspose() { + this.properties = { + amount: 0, + }; + this.addInput("in", LiteGraph.ACTION); + this.addInput("amount", "number"); + this.addOutput("out", LiteGraph.EVENT); + + this.midi_event = new MIDIEvent(); + } + + LGMIDITranspose.title = "MIDI Transpose"; + LGMIDITranspose.desc = "Transpose a MIDI note"; + LGMIDITranspose.color = MIDI_COLOR; + + LGMIDITranspose.prototype.onAction = function (event, midi_event) { + if (!midi_event || midi_event.constructor !== MIDIEvent) { + return; + } + + if ( + midi_event.data[0] == MIDIEvent.NOTEON || + midi_event.data[0] == MIDIEvent.NOTEOFF + ) { + this.midi_event = new MIDIEvent(); + this.midi_event.setup(midi_event.data); + this.midi_event.data[1] = Math.round( + this.midi_event.data[1] + this.properties.amount, + ); + this.trigger("out", this.midi_event); + } else { + this.trigger("out", midi_event); + } + }; + + LGMIDITranspose.prototype.onExecute = function () { + var amount = this.getInputData(1); + if (amount != null) { + this.properties.amount = amount; + } + }; + + LiteGraph.registerNodeType("midi/transpose", LGMIDITranspose); + + function LGMIDIQuantize() { + this.properties = { + scale: "A,A#,B,C,C#,D,D#,E,F,F#,G,G#", + }; + this.addInput("note", LiteGraph.ACTION); + this.addInput("scale", "string"); + this.addOutput("out", LiteGraph.EVENT); + + this.valid_notes = new Array(12); + this.offset_notes = new Array(12); + this.processScale(this.properties.scale); + } + + LGMIDIQuantize.title = "MIDI Quantize Pitch"; + LGMIDIQuantize.desc = "Transpose a MIDI note tp fit an scale"; + LGMIDIQuantize.color = MIDI_COLOR; + + LGMIDIQuantize.prototype.onPropertyChanged = function (name, value) { + if (name == "scale") { + this.processScale(value); + } + }; + + LGMIDIQuantize.prototype.processScale = function (scale) { + this._current_scale = scale; + this.notes_pitches = LGMIDIGenerator.processScale(scale); + for (var i = 0; i < 12; ++i) { + this.valid_notes[i] = this.notes_pitches.indexOf(i) != -1; + } + for (var i = 0; i < 12; ++i) { + if (this.valid_notes[i]) { + this.offset_notes[i] = 0; + continue; + } + for (var j = 1; j < 12; ++j) { + if (this.valid_notes[(i - j) % 12]) { + this.offset_notes[i] = -j; + break; + } + if (this.valid_notes[(i + j) % 12]) { + this.offset_notes[i] = j; + break; + } + } + } + }; + + LGMIDIQuantize.prototype.onAction = function (event, midi_event) { + if (!midi_event || midi_event.constructor !== MIDIEvent) { + return; + } + + if ( + midi_event.data[0] == MIDIEvent.NOTEON || + midi_event.data[0] == MIDIEvent.NOTEOFF + ) { + this.midi_event = new MIDIEvent(); + this.midi_event.setup(midi_event.data); + var note = midi_event.note; + var index = MIDIEvent.note_to_index[note]; + var offset = this.offset_notes[index]; + this.midi_event.data[1] += offset; + this.trigger("out", this.midi_event); + } else { + this.trigger("out", midi_event); + } + }; + + LGMIDIQuantize.prototype.onExecute = function () { + var scale = this.getInputData(1); + if (scale != null && scale != this._current_scale) { + this.processScale(scale); + } + }; + + LiteGraph.registerNodeType("midi/quantize", LGMIDIQuantize); + + function LGMIDIFromFile() { + this.properties = { + url: "", + autoplay: true, + }; + + this.addInput("play", LiteGraph.ACTION); + this.addInput("pause", LiteGraph.ACTION); + this.addOutput("note", LiteGraph.EVENT); + this._midi = null; + this._current_time = 0; + this._playing = false; + + if (typeof MidiParser == "undefined") { + console.error( + "midi-parser.js not included, LGMidiPlay requires that library: https://raw.githubusercontent.com/colxi/midi-parser-js/master/src/main.js", + ); + this.boxcolor = "red"; + } + } + + LGMIDIFromFile.title = "MIDI fromFile"; + LGMIDIFromFile.desc = "Plays a MIDI file"; + LGMIDIFromFile.color = MIDI_COLOR; + + LGMIDIFromFile.prototype.onAction = function (name) { + if (name == "play") this.play(); + else if (name == "pause") this._playing = !this._playing; + }; + + LGMIDIFromFile.prototype.onPropertyChanged = function (name, value) { + if (name == "url") this.loadMIDIFile(value); + }; + + LGMIDIFromFile.prototype.onExecute = function () { + if (!this._midi) return; + + if (!this._playing) return; + + this._current_time += this.graph.elapsed_time; + var current_time = this._current_time * 100; + + for (var i = 0; i < this._midi.tracks; ++i) { + var track = this._midi.track[i]; + if (!track._last_pos) { + track._last_pos = 0; + track._time = 0; + } + + var elem = track.event[track._last_pos]; + if (elem && track._time + elem.deltaTime <= current_time) { + track._last_pos++; + track._time += elem.deltaTime; + + if (elem.data) { + var midi_cmd = elem.type << (4 + elem.channel); + var midi_event = new MIDIEvent(); + midi_event.setup([midi_cmd, elem.data[0], elem.data[1]]); + this.trigger("note", midi_event); + } + } + } + }; + + LGMIDIFromFile.prototype.play = function () { + this._playing = true; + this._current_time = 0; + if (!this._midi) return; + + for (var i = 0; i < this._midi.tracks; ++i) { + var track = this._midi.track[i]; + track._last_pos = 0; + track._time = 0; + } + }; + + LGMIDIFromFile.prototype.loadMIDIFile = function (url) { + var that = this; + LiteGraph.fetchFile( + url, + "arraybuffer", + function (data) { + that.boxcolor = "#AFA"; + that._midi = MidiParser.parse(new Uint8Array(data)); + if (that.properties.autoplay) that.play(); + }, + function (err) { + that.boxcolor = "#FAA"; + that._midi = null; + }, + ); + }; + + LGMIDIFromFile.prototype.onDropFile = function (file) { + this.properties.url = ""; + this.loadMIDIFile(file); + }; + + LiteGraph.registerNodeType("midi/fromFile", LGMIDIFromFile); + + function LGMIDIPlay() { + this.properties = { + volume: 0.5, + duration: 1, + }; + this.addInput("note", LiteGraph.ACTION); + this.addInput("volume", "number"); + this.addInput("duration", "number"); + this.addOutput("note", LiteGraph.EVENT); + + if (typeof AudioSynth == "undefined") { + console.error( + "Audiosynth.js not included, LGMidiPlay requires that library", + ); + this.boxcolor = "red"; + } else { + var Synth = (this.synth = new AudioSynth()); + this.instrument = Synth.createInstrument("piano"); + } + } + + LGMIDIPlay.title = "MIDI Play"; + LGMIDIPlay.desc = "Plays a MIDI note"; + LGMIDIPlay.color = MIDI_COLOR; + + LGMIDIPlay.prototype.onAction = function (event, midi_event) { + if (!midi_event || midi_event.constructor !== MIDIEvent) { + return; + } + + if (this.instrument && midi_event.data[0] == MIDIEvent.NOTEON) { + var note = midi_event.note; //C# + if (!note || note == "undefined" || note.constructor !== String) { + return; + } + this.instrument.play( + note, + midi_event.octave, + this.properties.duration, + this.properties.volume, + ); + } + this.trigger("note", midi_event); + }; + + LGMIDIPlay.prototype.onExecute = function () { + var volume = this.getInputData(1); + if (volume != null) { + this.properties.volume = volume; + } + + var duration = this.getInputData(2); + if (duration != null) { + this.properties.duration = duration; + } + }; + + LiteGraph.registerNodeType("midi/play", LGMIDIPlay); + + function LGMIDIKeys() { + this.properties = { + num_octaves: 2, + start_octave: 2, + }; + this.addInput("note", LiteGraph.ACTION); + this.addInput("reset", LiteGraph.ACTION); + this.addOutput("note", LiteGraph.EVENT); + this.size = [400, 100]; + this.keys = []; + this._last_key = -1; + } + + LGMIDIKeys.title = "MIDI Keys"; + LGMIDIKeys.desc = "Keyboard to play notes"; + LGMIDIKeys.color = MIDI_COLOR; + + LGMIDIKeys.keys = [ + { x: 0, w: 1, h: 1, t: 0 }, + { x: 0.75, w: 0.5, h: 0.6, t: 1 }, + { x: 1, w: 1, h: 1, t: 0 }, + { x: 1.75, w: 0.5, h: 0.6, t: 1 }, + { x: 2, w: 1, h: 1, t: 0 }, + { x: 2.75, w: 0.5, h: 0.6, t: 1 }, + { x: 3, w: 1, h: 1, t: 0 }, + { x: 4, w: 1, h: 1, t: 0 }, + { x: 4.75, w: 0.5, h: 0.6, t: 1 }, + { x: 5, w: 1, h: 1, t: 0 }, + { x: 5.75, w: 0.5, h: 0.6, t: 1 }, + { x: 6, w: 1, h: 1, t: 0 }, + ]; + + LGMIDIKeys.prototype.onDrawForeground = function (ctx) { + if (this.flags.collapsed) { + return; + } + + var num_keys = this.properties.num_octaves * 12; + this.keys.length = num_keys; + var key_width = this.size[0] / (this.properties.num_octaves * 7); + var key_height = this.size[1]; + + ctx.globalAlpha = 1; + + for ( + var k = 0; + k < 2; + k++ //draw first whites (0) then blacks (1) + ) { + for (var i = 0; i < num_keys; ++i) { + var key_info = LGMIDIKeys.keys[i % 12]; + if (key_info.t != k) { + continue; + } + var octave = Math.floor(i / 12); + var x = octave * 7 * key_width + key_info.x * key_width; + if (k == 0) { + ctx.fillStyle = this.keys[i] ? "#CCC" : "white"; + } else { + ctx.fillStyle = this.keys[i] ? "#333" : "black"; + } + ctx.fillRect( + x + 1, + 0, + key_width * key_info.w - 2, + key_height * key_info.h, + ); + } + } + }; + + LGMIDIKeys.prototype.getKeyIndex = function (pos) { + var num_keys = this.properties.num_octaves * 12; + var key_width = this.size[0] / (this.properties.num_octaves * 7); + var key_height = this.size[1]; + + for ( + var k = 1; + k >= 0; + k-- //test blacks first (1) then whites (0) + ) { + for (var i = 0; i < this.keys.length; ++i) { + var key_info = LGMIDIKeys.keys[i % 12]; + if (key_info.t != k) { + continue; + } + var octave = Math.floor(i / 12); + var x = octave * 7 * key_width + key_info.x * key_width; + var w = key_width * key_info.w; + var h = key_height * key_info.h; + if (pos[0] < x || pos[0] > x + w || pos[1] > h) { + continue; + } + return i; + } + } + return -1; + }; + + LGMIDIKeys.prototype.onAction = function (event, params) { + if (event == "reset") { + for (var i = 0; i < this.keys.length; ++i) { + this.keys[i] = false; + } + return; + } + + if (!params || params.constructor !== MIDIEvent) { + return; + } + var midi_event = params; + var start_note = (this.properties.start_octave - 1) * 12 + 29; + var index = midi_event.data[1] - start_note; + if (index >= 0 && index < this.keys.length) { + if (midi_event.data[0] == MIDIEvent.NOTEON) { + this.keys[index] = true; + } else if (midi_event.data[0] == MIDIEvent.NOTEOFF) { + this.keys[index] = false; + } + } + + this.trigger("note", midi_event); + }; + + LGMIDIKeys.prototype.onMouseDown = function (e, pos) { + if (pos[1] < 0) { + return; + } + var index = this.getKeyIndex(pos); + this.keys[index] = true; + this._last_key = index; + var pitch = (this.properties.start_octave - 1) * 12 + 29 + index; + var midi_event = new MIDIEvent(); + midi_event.setup([MIDIEvent.NOTEON, pitch, 100]); + this.trigger("note", midi_event); + return true; + }; + + LGMIDIKeys.prototype.onMouseMove = function (e, pos) { + if (pos[1] < 0 || this._last_key == -1) { + return; + } + this.setDirtyCanvas(true); + var index = this.getKeyIndex(pos); + if (this._last_key == index) { + return true; + } + this.keys[this._last_key] = false; + var pitch = (this.properties.start_octave - 1) * 12 + 29 + this._last_key; + var midi_event = new MIDIEvent(); + midi_event.setup([MIDIEvent.NOTEOFF, pitch, 100]); + this.trigger("note", midi_event); + + this.keys[index] = true; + var pitch = (this.properties.start_octave - 1) * 12 + 29 + index; + var midi_event = new MIDIEvent(); + midi_event.setup([MIDIEvent.NOTEON, pitch, 100]); + this.trigger("note", midi_event); + + this._last_key = index; + return true; + }; + + LGMIDIKeys.prototype.onMouseUp = function (e, pos) { + if (pos[1] < 0) { + return; + } + var index = this.getKeyIndex(pos); + this.keys[index] = false; + this._last_key = -1; + var pitch = (this.properties.start_octave - 1) * 12 + 29 + index; + var midi_event = new MIDIEvent(); + midi_event.setup([MIDIEvent.NOTEOFF, pitch, 100]); + this.trigger("note", midi_event); + return true; + }; + + LiteGraph.registerNodeType("midi/keys", LGMIDIKeys); + + function now() { + return window.performance.now(); + } +})(this); + +(function (global) { + var LiteGraph = global.LiteGraph; + + var LGAudio = {}; + global.LGAudio = LGAudio; + + LGAudio.getAudioContext = function () { + if (!this._audio_context) { + window.AudioContext = window.AudioContext || window.webkitAudioContext; + if (!window.AudioContext) { + console.error("AudioContext not supported by browser"); + return null; + } + this._audio_context = new AudioContext(); + this._audio_context.onmessage = function (msg) { + console.log("msg", msg); + }; + this._audio_context.onended = function (msg) { + console.log("ended", msg); + }; + this._audio_context.oncomplete = function (msg) { + console.log("complete", msg); + }; + } + + //in case it crashes + //if(this._audio_context.state == "suspended") + // this._audio_context.resume(); + return this._audio_context; + }; + + LGAudio.connect = function (audionodeA, audionodeB) { + try { + audionodeA.connect(audionodeB); + } catch (err) { + console.warn("LGraphAudio:", err); + } + }; + + LGAudio.disconnect = function (audionodeA, audionodeB) { + try { + audionodeA.disconnect(audionodeB); + } catch (err) { + console.warn("LGraphAudio:", err); + } + }; + + LGAudio.changeAllAudiosConnections = function (node, connect) { + if (node.inputs) { + for (var i = 0; i < node.inputs.length; ++i) { + var input = node.inputs[i]; + var link_info = node.graph.links[input.link]; + if (!link_info) { + continue; + } + + var origin_node = node.graph.getNodeById(link_info.origin_id); + var origin_audionode = null; + if (origin_node.getAudioNodeInOutputSlot) { + origin_audionode = origin_node.getAudioNodeInOutputSlot( + link_info.origin_slot, + ); + } else { + origin_audionode = origin_node.audionode; + } + + var target_audionode = null; + if (node.getAudioNodeInInputSlot) { + target_audionode = node.getAudioNodeInInputSlot(i); + } else { + target_audionode = node.audionode; + } + + if (connect) { + LGAudio.connect(origin_audionode, target_audionode); + } else { + LGAudio.disconnect(origin_audionode, target_audionode); + } + } + } + + if (node.outputs) { + for (var i = 0; i < node.outputs.length; ++i) { + var output = node.outputs[i]; + for (var j = 0; j < output.links.length; ++j) { + var link_info = node.graph.links[output.links[j]]; + if (!link_info) { + continue; + } + + var origin_audionode = null; + if (node.getAudioNodeInOutputSlot) { + origin_audionode = node.getAudioNodeInOutputSlot(i); + } else { + origin_audionode = node.audionode; + } + + var target_node = node.graph.getNodeById(link_info.target_id); + var target_audionode = null; + if (target_node.getAudioNodeInInputSlot) { + target_audionode = target_node.getAudioNodeInInputSlot( + link_info.target_slot, + ); + } else { + target_audionode = target_node.audionode; + } + + if (connect) { + LGAudio.connect(origin_audionode, target_audionode); + } else { + LGAudio.disconnect(origin_audionode, target_audionode); + } + } + } + } + }; + + //used by many nodes + LGAudio.onConnectionsChange = function ( + connection, + slot, + connected, + link_info, + ) { + //only process the outputs events + if (connection != LiteGraph.OUTPUT) { + return; + } + + var target_node = null; + if (link_info) { + target_node = this.graph.getNodeById(link_info.target_id); + } + + if (!target_node) { + return; + } + + //get origin audionode + var local_audionode = null; + if (this.getAudioNodeInOutputSlot) { + local_audionode = this.getAudioNodeInOutputSlot(slot); + } else { + local_audionode = this.audionode; + } + + //get target audionode + var target_audionode = null; + if (target_node.getAudioNodeInInputSlot) { + target_audionode = target_node.getAudioNodeInInputSlot( + link_info.target_slot, + ); + } else { + target_audionode = target_node.audionode; + } + + //do the connection/disconnection + if (connected) { + LGAudio.connect(local_audionode, target_audionode); + } else { + LGAudio.disconnect(local_audionode, target_audionode); + } + }; + + //this function helps creating wrappers to existing classes + LGAudio.createAudioNodeWrapper = function (class_object) { + var old_func = class_object.prototype.onPropertyChanged; + + class_object.prototype.onPropertyChanged = function (name, value) { + if (old_func) { + old_func.call(this, name, value); + } + + if (!this.audionode) { + return; + } + + if (this.audionode[name] === undefined) { + return; + } + + if (this.audionode[name].value !== undefined) { + this.audionode[name].value = value; + } else { + this.audionode[name] = value; + } + }; + + class_object.prototype.onConnectionsChange = LGAudio.onConnectionsChange; + }; + + //contains the samples decoded of the loaded audios in AudioBuffer format + LGAudio.cached_audios = {}; + + LGAudio.loadSound = function (url, on_complete, on_error) { + if (LGAudio.cached_audios[url] && url.indexOf("blob:") == -1) { + if (on_complete) { + on_complete(LGAudio.cached_audios[url]); + } + return; + } + + if (LGAudio.onProcessAudioURL) { + url = LGAudio.onProcessAudioURL(url); + } + + //load new sample + var request = new XMLHttpRequest(); + request.open("GET", url, true); + request.responseType = "arraybuffer"; + + var context = LGAudio.getAudioContext(); + + // Decode asynchronously + request.onload = function () { + console.log("AudioSource loaded"); + context.decodeAudioData( + request.response, + function (buffer) { + console.log("AudioSource decoded"); + LGAudio.cached_audios[url] = buffer; + if (on_complete) { + on_complete(buffer); + } + }, + onError, + ); + }; + request.send(); + + function onError(err) { + console.log("Audio loading sample error:", err); + if (on_error) { + on_error(err); + } + } + + return request; + }; + + //**************************************************** + + function LGAudioSource() { + this.properties = { + src: "", + gain: 0.5, + loop: true, + autoplay: true, + playbackRate: 1, + }; + + this._loading_audio = false; + this._audiobuffer = null; //points to AudioBuffer with the audio samples decoded + this._audionodes = []; + this._last_sourcenode = null; //the last AudioBufferSourceNode (there could be more if there are several sounds playing) + + this.addOutput("out", "audio"); + this.addInput("gain", "number"); + + //init context + var context = LGAudio.getAudioContext(); + + //create gain node to control volume + this.audionode = context.createGain(); + this.audionode.graphnode = this; + this.audionode.gain.value = this.properties.gain; + + //debug + if (this.properties.src) { + this.loadSound(this.properties.src); + } + } + + LGAudioSource.desc = "Plays an audio file"; + LGAudioSource["@src"] = { widget: "resource" }; + LGAudioSource.supported_extensions = ["wav", "ogg", "mp3"]; + + LGAudioSource.prototype.onAdded = function (graph) { + if (graph.status === LGraph.STATUS_RUNNING) { + this.onStart(); + } + }; + + LGAudioSource.prototype.onStart = function () { + if (!this._audiobuffer) { + return; + } + + if (this.properties.autoplay) { + this.playBuffer(this._audiobuffer); + } + }; + + LGAudioSource.prototype.onStop = function () { + this.stopAllSounds(); + }; + + LGAudioSource.prototype.onPause = function () { + this.pauseAllSounds(); + }; + + LGAudioSource.prototype.onUnpause = function () { + this.unpauseAllSounds(); + //this.onStart(); + }; + + LGAudioSource.prototype.onRemoved = function () { + this.stopAllSounds(); + if (this._dropped_url) { + URL.revokeObjectURL(this._url); + } + }; + + LGAudioSource.prototype.stopAllSounds = function () { + //iterate and stop + for (var i = 0; i < this._audionodes.length; ++i) { + if (this._audionodes[i].started) { + this._audionodes[i].started = false; + this._audionodes[i].stop(); + } + //this._audionodes[i].disconnect( this.audionode ); + } + this._audionodes.length = 0; + }; + + LGAudioSource.prototype.pauseAllSounds = function () { + LGAudio.getAudioContext().suspend(); + }; + + LGAudioSource.prototype.unpauseAllSounds = function () { + LGAudio.getAudioContext().resume(); + }; + + LGAudioSource.prototype.onExecute = function () { + if (this.inputs) { + for (var i = 0; i < this.inputs.length; ++i) { + var input = this.inputs[i]; + if (input.link == null) { + continue; + } + var v = this.getInputData(i); + if (v === undefined) { + continue; + } + if (input.name == "gain") this.audionode.gain.value = v; + else if (input.name == "src") { + this.setProperty("src", v); + } else if (input.name == "playbackRate") { + this.properties.playbackRate = v; + for (var j = 0; j < this._audionodes.length; ++j) { + this._audionodes[j].playbackRate.value = v; + } + } + } + } + + if (this.outputs) { + for (var i = 0; i < this.outputs.length; ++i) { + var output = this.outputs[i]; + if (output.name == "buffer" && this._audiobuffer) { + this.setOutputData(i, this._audiobuffer); + } + } + } + }; + + LGAudioSource.prototype.onAction = function (event) { + if (this._audiobuffer) { + if (event == "Play") { + this.playBuffer(this._audiobuffer); + } else if (event == "Stop") { + this.stopAllSounds(); + } + } + }; + + LGAudioSource.prototype.onPropertyChanged = function (name, value) { + if (name == "src") { + this.loadSound(value); + } else if (name == "gain") { + this.audionode.gain.value = value; + } else if (name == "playbackRate") { + for (var j = 0; j < this._audionodes.length; ++j) { + this._audionodes[j].playbackRate.value = value; + } + } + }; + + LGAudioSource.prototype.playBuffer = function (buffer) { + var that = this; + var context = LGAudio.getAudioContext(); + + //create a new audionode (this is mandatory, AudioAPI doesnt like to reuse old ones) + var audionode = context.createBufferSource(); //create a AudioBufferSourceNode + this._last_sourcenode = audionode; + audionode.graphnode = this; + audionode.buffer = buffer; + audionode.loop = this.properties.loop; + audionode.playbackRate.value = this.properties.playbackRate; + this._audionodes.push(audionode); + audionode.connect(this.audionode); //connect to gain + + this._audionodes.push(audionode); + + this.trigger("start"); + + audionode.onended = function () { + //console.log("ended!"); + that.trigger("ended"); + //remove + var index = that._audionodes.indexOf(audionode); + if (index != -1) { + that._audionodes.splice(index, 1); + } + }; + + if (!audionode.started) { + audionode.started = true; + audionode.start(); + } + return audionode; + }; + + LGAudioSource.prototype.loadSound = function (url) { + var that = this; + + //kill previous load + if (this._request) { + this._request.abort(); + this._request = null; + } + + this._audiobuffer = null; //points to the audiobuffer once the audio is loaded + this._loading_audio = false; + + if (!url) { + return; + } + + this._request = LGAudio.loadSound(url, inner); + + this._loading_audio = true; + this.boxcolor = "#AA4"; + + function inner(buffer) { + this.boxcolor = LiteGraph.NODE_DEFAULT_BOXCOLOR; + that._audiobuffer = buffer; + that._loading_audio = false; + //if is playing, then play it + if (that.graph && that.graph.status === LGraph.STATUS_RUNNING) { + that.onStart(); + } //this controls the autoplay already + } + }; + + //Helps connect/disconnect AudioNodes when new connections are made in the node + LGAudioSource.prototype.onConnectionsChange = LGAudio.onConnectionsChange; + + LGAudioSource.prototype.onGetInputs = function () { + return [ + ["playbackRate", "number"], + ["src", "string"], + ["Play", LiteGraph.ACTION], + ["Stop", LiteGraph.ACTION], + ]; + }; + + LGAudioSource.prototype.onGetOutputs = function () { + return [ + ["buffer", "audiobuffer"], + ["start", LiteGraph.EVENT], + ["ended", LiteGraph.EVENT], + ]; + }; + + LGAudioSource.prototype.onDropFile = function (file) { + if (this._dropped_url) { + URL.revokeObjectURL(this._dropped_url); + } + var url = URL.createObjectURL(file); + this.properties.src = url; + this.loadSound(url); + this._dropped_url = url; + }; + + LGAudioSource.title = "Source"; + LGAudioSource.desc = "Plays audio"; + LiteGraph.registerNodeType("audio/source", LGAudioSource); + + //**************************************************** + + function LGAudioMediaSource() { + this.properties = { + gain: 0.5, + }; + + this._audionodes = []; + this._media_stream = null; + + this.addOutput("out", "audio"); + this.addInput("gain", "number"); + + //create gain node to control volume + var context = LGAudio.getAudioContext(); + this.audionode = context.createGain(); + this.audionode.graphnode = this; + this.audionode.gain.value = this.properties.gain; + } + + LGAudioMediaSource.prototype.onAdded = function (graph) { + if (graph.status === LGraph.STATUS_RUNNING) { + this.onStart(); + } + }; + + LGAudioMediaSource.prototype.onStart = function () { + if (this._media_stream == null && !this._waiting_confirmation) { + this.openStream(); + } + }; + + LGAudioMediaSource.prototype.onStop = function () { + this.audionode.gain.value = 0; + }; + + LGAudioMediaSource.prototype.onPause = function () { + this.audionode.gain.value = 0; + }; + + LGAudioMediaSource.prototype.onUnpause = function () { + this.audionode.gain.value = this.properties.gain; + }; + + LGAudioMediaSource.prototype.onRemoved = function () { + this.audionode.gain.value = 0; + if (this.audiosource_node) { + this.audiosource_node.disconnect(this.audionode); + this.audiosource_node = null; + } + if (this._media_stream) { + var tracks = this._media_stream.getTracks(); + if (tracks.length) { + tracks[0].stop(); + } + } + }; + + LGAudioMediaSource.prototype.openStream = function () { + if (!navigator.mediaDevices) { + console.log( + "getUserMedia() is not supported in your browser, use chrome and enable WebRTC from about://flags", + ); + return; + } + + this._waiting_confirmation = true; + + // Not showing vendor prefixes. + navigator.mediaDevices + .getUserMedia({ audio: true, video: false }) + .then(this.streamReady.bind(this)) + .catch(onFailSoHard); + + var that = this; + function onFailSoHard(err) { + console.log("Media rejected", err); + that._media_stream = false; + that.boxcolor = "red"; + } + }; + + LGAudioMediaSource.prototype.streamReady = function (localMediaStream) { + this._media_stream = localMediaStream; + //this._waiting_confirmation = false; + + //init context + if (this.audiosource_node) { + this.audiosource_node.disconnect(this.audionode); + } + var context = LGAudio.getAudioContext(); + this.audiosource_node = context.createMediaStreamSource(localMediaStream); + this.audiosource_node.graphnode = this; + this.audiosource_node.connect(this.audionode); + this.boxcolor = "white"; + }; + + LGAudioMediaSource.prototype.onExecute = function () { + if (this._media_stream == null && !this._waiting_confirmation) { + this.openStream(); + } + + if (this.inputs) { + for (var i = 0; i < this.inputs.length; ++i) { + var input = this.inputs[i]; + if (input.link == null) { + continue; + } + var v = this.getInputData(i); + if (v === undefined) { + continue; + } + if (input.name == "gain") { + this.audionode.gain.value = this.properties.gain = v; + } + } + } + }; + + LGAudioMediaSource.prototype.onAction = function (event) { + if (event == "Play") { + this.audionode.gain.value = this.properties.gain; + } else if (event == "Stop") { + this.audionode.gain.value = 0; + } + }; + + LGAudioMediaSource.prototype.onPropertyChanged = function (name, value) { + if (name == "gain") { + this.audionode.gain.value = value; + } + }; + + //Helps connect/disconnect AudioNodes when new connections are made in the node + LGAudioMediaSource.prototype.onConnectionsChange = + LGAudio.onConnectionsChange; + + LGAudioMediaSource.prototype.onGetInputs = function () { + return [ + ["playbackRate", "number"], + ["Play", LiteGraph.ACTION], + ["Stop", LiteGraph.ACTION], + ]; + }; + + LGAudioMediaSource.title = "MediaSource"; + LGAudioMediaSource.desc = "Plays microphone"; + LiteGraph.registerNodeType("audio/media_source", LGAudioMediaSource); + + //***************************************************** + + function LGAudioAnalyser() { + this.properties = { + fftSize: 2048, + minDecibels: -100, + maxDecibels: -10, + smoothingTimeConstant: 0.5, + }; + + var context = LGAudio.getAudioContext(); + + this.audionode = context.createAnalyser(); + this.audionode.graphnode = this; + this.audionode.fftSize = this.properties.fftSize; + this.audionode.minDecibels = this.properties.minDecibels; + this.audionode.maxDecibels = this.properties.maxDecibels; + this.audionode.smoothingTimeConstant = + this.properties.smoothingTimeConstant; + + this.addInput("in", "audio"); + this.addOutput("freqs", "array"); + this.addOutput("samples", "array"); + + this._freq_bin = null; + this._time_bin = null; + } + + LGAudioAnalyser.prototype.onPropertyChanged = function (name, value) { + this.audionode[name] = value; + }; + + LGAudioAnalyser.prototype.onExecute = function () { + if (this.isOutputConnected(0)) { + //send FFT + var bufferLength = this.audionode.frequencyBinCount; + if (!this._freq_bin || this._freq_bin.length != bufferLength) { + this._freq_bin = new Uint8Array(bufferLength); + } + this.audionode.getByteFrequencyData(this._freq_bin); + this.setOutputData(0, this._freq_bin); + } + + //send analyzer + if (this.isOutputConnected(1)) { + //send Samples + var bufferLength = this.audionode.frequencyBinCount; + if (!this._time_bin || this._time_bin.length != bufferLength) { + this._time_bin = new Uint8Array(bufferLength); + } + this.audionode.getByteTimeDomainData(this._time_bin); + this.setOutputData(1, this._time_bin); + } + + //properties + for (var i = 1; i < this.inputs.length; ++i) { + var input = this.inputs[i]; + if (input.link == null) { + continue; + } + var v = this.getInputData(i); + if (v !== undefined) { + this.audionode[input.name].value = v; + } + } + + //time domain + //this.audionode.getFloatTimeDomainData( dataArray ); + }; + + LGAudioAnalyser.prototype.onGetInputs = function () { + return [ + ["minDecibels", "number"], + ["maxDecibels", "number"], + ["smoothingTimeConstant", "number"], + ]; + }; + + LGAudioAnalyser.prototype.onGetOutputs = function () { + return [ + ["freqs", "array"], + ["samples", "array"], + ]; + }; + + LGAudioAnalyser.title = "Analyser"; + LGAudioAnalyser.desc = "Audio Analyser"; + LiteGraph.registerNodeType("audio/analyser", LGAudioAnalyser); + + //***************************************************** + + function LGAudioGain() { + //default + this.properties = { + gain: 1, + }; + + this.audionode = LGAudio.getAudioContext().createGain(); + this.addInput("in", "audio"); + this.addInput("gain", "number"); + this.addOutput("out", "audio"); + } + + LGAudioGain.prototype.onExecute = function () { + if (!this.inputs || !this.inputs.length) { + return; + } + + for (var i = 1; i < this.inputs.length; ++i) { + var input = this.inputs[i]; + var v = this.getInputData(i); + if (v !== undefined) { + this.audionode[input.name].value = v; + } + } + }; + + LGAudio.createAudioNodeWrapper(LGAudioGain); + + LGAudioGain.title = "Gain"; + LGAudioGain.desc = "Audio gain"; + LiteGraph.registerNodeType("audio/gain", LGAudioGain); + + function LGAudioConvolver() { + //default + this.properties = { + impulse_src: "", + normalize: true, + }; + + this.audionode = LGAudio.getAudioContext().createConvolver(); + this.addInput("in", "audio"); + this.addOutput("out", "audio"); + } + + LGAudio.createAudioNodeWrapper(LGAudioConvolver); + + LGAudioConvolver.prototype.onRemove = function () { + if (this._dropped_url) { + URL.revokeObjectURL(this._dropped_url); + } + }; + + LGAudioConvolver.prototype.onPropertyChanged = function (name, value) { + if (name == "impulse_src") { + this.loadImpulse(value); + } else if (name == "normalize") { + this.audionode.normalize = value; + } + }; + + LGAudioConvolver.prototype.onDropFile = function (file) { + if (this._dropped_url) { + URL.revokeObjectURL(this._dropped_url); + } + this._dropped_url = URL.createObjectURL(file); + this.properties.impulse_src = this._dropped_url; + this.loadImpulse(this._dropped_url); + }; + + LGAudioConvolver.prototype.loadImpulse = function (url) { + var that = this; + + //kill previous load + if (this._request) { + this._request.abort(); + this._request = null; + } + + this._impulse_buffer = null; + this._loading_impulse = false; + + if (!url) { + return; + } + + //load new sample + this._request = LGAudio.loadSound(url, inner); + this._loading_impulse = true; + + // Decode asynchronously + function inner(buffer) { + that._impulse_buffer = buffer; + that.audionode.buffer = buffer; + console.log("Impulse signal set"); + that._loading_impulse = false; + } + }; + + LGAudioConvolver.title = "Convolver"; + LGAudioConvolver.desc = "Convolves the signal (used for reverb)"; + LiteGraph.registerNodeType("audio/convolver", LGAudioConvolver); + + function LGAudioDynamicsCompressor() { + //default + this.properties = { + threshold: -50, + knee: 40, + ratio: 12, + reduction: -20, + attack: 0, + release: 0.25, + }; + + this.audionode = LGAudio.getAudioContext().createDynamicsCompressor(); + this.addInput("in", "audio"); + this.addOutput("out", "audio"); + } + + LGAudio.createAudioNodeWrapper(LGAudioDynamicsCompressor); + + LGAudioDynamicsCompressor.prototype.onExecute = function () { + if (!this.inputs || !this.inputs.length) { + return; + } + for (var i = 1; i < this.inputs.length; ++i) { + var input = this.inputs[i]; + if (input.link == null) { + continue; + } + var v = this.getInputData(i); + if (v !== undefined) { + this.audionode[input.name].value = v; + } + } + }; + + LGAudioDynamicsCompressor.prototype.onGetInputs = function () { + return [ + ["threshold", "number"], + ["knee", "number"], + ["ratio", "number"], + ["reduction", "number"], + ["attack", "number"], + ["release", "number"], + ]; + }; + + LGAudioDynamicsCompressor.title = "DynamicsCompressor"; + LGAudioDynamicsCompressor.desc = "Dynamics Compressor"; + LiteGraph.registerNodeType( + "audio/dynamicsCompressor", + LGAudioDynamicsCompressor, + ); + + function LGAudioWaveShaper() { + //default + this.properties = {}; + + this.audionode = LGAudio.getAudioContext().createWaveShaper(); + this.addInput("in", "audio"); + this.addInput("shape", "waveshape"); + this.addOutput("out", "audio"); + } + + LGAudioWaveShaper.prototype.onExecute = function () { + if (!this.inputs || !this.inputs.length) { + return; + } + var v = this.getInputData(1); + if (v === undefined) { + return; + } + this.audionode.curve = v; + }; + + LGAudioWaveShaper.prototype.setWaveShape = function (shape) { + this.audionode.curve = shape; + }; + + LGAudio.createAudioNodeWrapper(LGAudioWaveShaper); + + /* disabled till I dont find a way to do a wave shape +LGAudioWaveShaper.title = "WaveShaper"; +LGAudioWaveShaper.desc = "Distortion using wave shape"; +LiteGraph.registerNodeType("audio/waveShaper", LGAudioWaveShaper); +*/ + + function LGAudioMixer() { + //default + this.properties = { + gain1: 0.5, + gain2: 0.5, + }; + + this.audionode = LGAudio.getAudioContext().createGain(); + + this.audionode1 = LGAudio.getAudioContext().createGain(); + this.audionode1.gain.value = this.properties.gain1; + this.audionode2 = LGAudio.getAudioContext().createGain(); + this.audionode2.gain.value = this.properties.gain2; + + this.audionode1.connect(this.audionode); + this.audionode2.connect(this.audionode); + + this.addInput("in1", "audio"); + this.addInput("in1 gain", "number"); + this.addInput("in2", "audio"); + this.addInput("in2 gain", "number"); + + this.addOutput("out", "audio"); + } + + LGAudioMixer.prototype.getAudioNodeInInputSlot = function (slot) { + if (slot == 0) { + return this.audionode1; + } else if (slot == 2) { + return this.audionode2; + } + }; + + LGAudioMixer.prototype.onPropertyChanged = function (name, value) { + if (name == "gain1") { + this.audionode1.gain.value = value; + } else if (name == "gain2") { + this.audionode2.gain.value = value; + } + }; + + LGAudioMixer.prototype.onExecute = function () { + if (!this.inputs || !this.inputs.length) { + return; + } + + for (var i = 1; i < this.inputs.length; ++i) { + var input = this.inputs[i]; + + if (input.link == null || input.type == "audio") { + continue; + } + + var v = this.getInputData(i); + if (v === undefined) { + continue; + } + + if (i == 1) { + this.audionode1.gain.value = v; + } else if (i == 3) { + this.audionode2.gain.value = v; + } + } + }; + + LGAudio.createAudioNodeWrapper(LGAudioMixer); + + LGAudioMixer.title = "Mixer"; + LGAudioMixer.desc = "Audio mixer"; + LiteGraph.registerNodeType("audio/mixer", LGAudioMixer); + + function LGAudioADSR() { + //default + this.properties = { + A: 0.1, + D: 0.1, + S: 0.1, + R: 0.1, + }; + + this.audionode = LGAudio.getAudioContext().createGain(); + this.audionode.gain.value = 0; + this.addInput("in", "audio"); + this.addInput("gate", "boolean"); + this.addOutput("out", "audio"); + this.gate = false; + } + + LGAudioADSR.prototype.onExecute = function () { + var audioContext = LGAudio.getAudioContext(); + var now = audioContext.currentTime; + var node = this.audionode; + var gain = node.gain; + var current_gate = this.getInputData(1); + + var A = this.getInputOrProperty("A"); + var D = this.getInputOrProperty("D"); + var S = this.getInputOrProperty("S"); + var R = this.getInputOrProperty("R"); + + if (!this.gate && current_gate) { + gain.cancelScheduledValues(0); + gain.setValueAtTime(0, now); + gain.linearRampToValueAtTime(1, now + A); + gain.linearRampToValueAtTime(S, now + A + D); + } else if (this.gate && !current_gate) { + gain.cancelScheduledValues(0); + gain.setValueAtTime(gain.value, now); + gain.linearRampToValueAtTime(0, now + R); + } + + this.gate = current_gate; + }; + + LGAudioADSR.prototype.onGetInputs = function () { + return [ + ["A", "number"], + ["D", "number"], + ["S", "number"], + ["R", "number"], + ]; + }; + + LGAudio.createAudioNodeWrapper(LGAudioADSR); + + LGAudioADSR.title = "ADSR"; + LGAudioADSR.desc = "Audio envelope"; + LiteGraph.registerNodeType("audio/adsr", LGAudioADSR); + + function LGAudioDelay() { + //default + this.properties = { + delayTime: 0.5, + }; + + this.audionode = LGAudio.getAudioContext().createDelay(10); + this.audionode.delayTime.value = this.properties.delayTime; + this.addInput("in", "audio"); + this.addInput("time", "number"); + this.addOutput("out", "audio"); + } + + LGAudio.createAudioNodeWrapper(LGAudioDelay); + + LGAudioDelay.prototype.onExecute = function () { + var v = this.getInputData(1); + if (v !== undefined) { + this.audionode.delayTime.value = v; + } + }; + + LGAudioDelay.title = "Delay"; + LGAudioDelay.desc = "Audio delay"; + LiteGraph.registerNodeType("audio/delay", LGAudioDelay); + + function LGAudioBiquadFilter() { + //default + this.properties = { + frequency: 350, + detune: 0, + Q: 1, + }; + this.addProperty("type", "lowpass", "enum", { + values: [ + "lowpass", + "highpass", + "bandpass", + "lowshelf", + "highshelf", + "peaking", + "notch", + "allpass", + ], + }); + + //create node + this.audionode = LGAudio.getAudioContext().createBiquadFilter(); + + //slots + this.addInput("in", "audio"); + this.addOutput("out", "audio"); + } + + LGAudioBiquadFilter.prototype.onExecute = function () { + if (!this.inputs || !this.inputs.length) { + return; + } + + for (var i = 1; i < this.inputs.length; ++i) { + var input = this.inputs[i]; + if (input.link == null) { + continue; + } + var v = this.getInputData(i); + if (v !== undefined) { + this.audionode[input.name].value = v; + } + } + }; + + LGAudioBiquadFilter.prototype.onGetInputs = function () { + return [ + ["frequency", "number"], + ["detune", "number"], + ["Q", "number"], + ]; + }; + + LGAudio.createAudioNodeWrapper(LGAudioBiquadFilter); + + LGAudioBiquadFilter.title = "BiquadFilter"; + LGAudioBiquadFilter.desc = "Audio filter"; + LiteGraph.registerNodeType("audio/biquadfilter", LGAudioBiquadFilter); + + function LGAudioOscillatorNode() { + //default + this.properties = { + frequency: 440, + detune: 0, + type: "sine", + }; + this.addProperty("type", "sine", "enum", { + values: ["sine", "square", "sawtooth", "triangle", "custom"], + }); + + //create node + this.audionode = LGAudio.getAudioContext().createOscillator(); + + //slots + this.addOutput("out", "audio"); + } + + LGAudioOscillatorNode.prototype.onStart = function () { + if (!this.audionode.started) { + this.audionode.started = true; + try { + this.audionode.start(); + } catch (err) {} + } + }; + + LGAudioOscillatorNode.prototype.onStop = function () { + if (this.audionode.started) { + this.audionode.started = false; + this.audionode.stop(); + } + }; + + LGAudioOscillatorNode.prototype.onPause = function () { + this.onStop(); + }; + + LGAudioOscillatorNode.prototype.onUnpause = function () { + this.onStart(); + }; + + LGAudioOscillatorNode.prototype.onExecute = function () { + if (!this.inputs || !this.inputs.length) { + return; + } + + for (var i = 0; i < this.inputs.length; ++i) { + var input = this.inputs[i]; + if (input.link == null) { + continue; + } + var v = this.getInputData(i); + if (v !== undefined) { + this.audionode[input.name].value = v; + } + } + }; + + LGAudioOscillatorNode.prototype.onGetInputs = function () { + return [ + ["frequency", "number"], + ["detune", "number"], + ["type", "string"], + ]; + }; + + LGAudio.createAudioNodeWrapper(LGAudioOscillatorNode); + + LGAudioOscillatorNode.title = "Oscillator"; + LGAudioOscillatorNode.desc = "Oscillator"; + LiteGraph.registerNodeType("audio/oscillator", LGAudioOscillatorNode); + + //***************************************************** + + //EXTRA + + function LGAudioVisualization() { + this.properties = { + continuous: true, + mark: -1, + }; + + this.addInput("data", "array"); + this.addInput("mark", "number"); + this.size = [300, 200]; + this._last_buffer = null; + } + + LGAudioVisualization.prototype.onExecute = function () { + this._last_buffer = this.getInputData(0); + var v = this.getInputData(1); + if (v !== undefined) { + this.properties.mark = v; + } + this.setDirtyCanvas(true, false); + }; + + LGAudioVisualization.prototype.onDrawForeground = function (ctx) { + if (!this._last_buffer) { + return; + } + + var buffer = this._last_buffer; + + //delta represents how many samples we advance per pixel + var delta = buffer.length / this.size[0]; + var h = this.size[1]; + + ctx.fillStyle = "black"; + ctx.fillRect(0, 0, this.size[0], this.size[1]); + ctx.strokeStyle = "white"; + ctx.beginPath(); + var x = 0; + + if (this.properties.continuous) { + ctx.moveTo(x, h); + for (var i = 0; i < buffer.length; i += delta) { + ctx.lineTo(x, h - (buffer[i | 0] / 255) * h); + x++; + } + } else { + for (var i = 0; i < buffer.length; i += delta) { + ctx.moveTo(x + 0.5, h); + ctx.lineTo(x + 0.5, h - (buffer[i | 0] / 255) * h); + x++; + } + } + ctx.stroke(); + + if (this.properties.mark >= 0) { + var samplerate = LGAudio.getAudioContext().sampleRate; + var binfreq = samplerate / buffer.length; + var x = (2 * (this.properties.mark / binfreq)) / delta; + if (x >= this.size[0]) { + x = this.size[0] - 1; + } + ctx.strokeStyle = "red"; + ctx.beginPath(); + ctx.moveTo(x, h); + ctx.lineTo(x, 0); + ctx.stroke(); + } + }; + + LGAudioVisualization.title = "Visualization"; + LGAudioVisualization.desc = "Audio Visualization"; + LiteGraph.registerNodeType("audio/visualization", LGAudioVisualization); + + function LGAudioBandSignal() { + //default + this.properties = { + band: 440, + amplitude: 1, + }; + + this.addInput("freqs", "array"); + this.addOutput("signal", "number"); + } + + LGAudioBandSignal.prototype.onExecute = function () { + this._freqs = this.getInputData(0); + if (!this._freqs) { + return; + } + + var band = this.properties.band; + var v = this.getInputData(1); + if (v !== undefined) { + band = v; + } + + var samplerate = LGAudio.getAudioContext().sampleRate; + var binfreq = samplerate / this._freqs.length; + var index = 2 * (band / binfreq); + var v = 0; + if (index < 0) { + v = this._freqs[0]; + } + if (index >= this._freqs.length) { + v = this._freqs[this._freqs.length - 1]; + } else { + var pos = index | 0; + var v0 = this._freqs[pos]; + var v1 = this._freqs[pos + 1]; + var f = index - pos; + v = v0 * (1 - f) + v1 * f; + } + + this.setOutputData(0, (v / 255) * this.properties.amplitude); + }; + + LGAudioBandSignal.prototype.onGetInputs = function () { + return [["band", "number"]]; + }; + + LGAudioBandSignal.title = "Signal"; + LGAudioBandSignal.desc = "extract the signal of some frequency"; + LiteGraph.registerNodeType("audio/signal", LGAudioBandSignal); + + function LGAudioScript() { + if (!LGAudioScript.default_code) { + var code = LGAudioScript.default_function.toString(); + var index = code.indexOf("{") + 1; + var index2 = code.lastIndexOf("}"); + LGAudioScript.default_code = code.substr(index, index2 - index); + } + + //default + this.properties = { + code: LGAudioScript.default_code, + }; + + //create node + var ctx = LGAudio.getAudioContext(); + if (ctx.createScriptProcessor) { + this.audionode = ctx.createScriptProcessor(4096, 1, 1); + } + //buffer size, input channels, output channels + else { + console.warn("ScriptProcessorNode deprecated"); + this.audionode = ctx.createGain(); //bypass audio + } + + this.processCode(); + if (!LGAudioScript._bypass_function) { + LGAudioScript._bypass_function = this.audionode.onaudioprocess; + } + + //slots + this.addInput("in", "audio"); + this.addOutput("out", "audio"); + } + + LGAudioScript.prototype.onAdded = function (graph) { + if (graph.status == LGraph.STATUS_RUNNING) { + this.audionode.onaudioprocess = this._callback; + } + }; + + LGAudioScript["@code"] = { widget: "code", type: "code" }; + + LGAudioScript.prototype.onStart = function () { + this.audionode.onaudioprocess = this._callback; + }; + + LGAudioScript.prototype.onStop = function () { + this.audionode.onaudioprocess = LGAudioScript._bypass_function; + }; + + LGAudioScript.prototype.onPause = function () { + this.audionode.onaudioprocess = LGAudioScript._bypass_function; + }; + + LGAudioScript.prototype.onUnpause = function () { + this.audionode.onaudioprocess = this._callback; + }; + + LGAudioScript.prototype.onExecute = function () { + //nothing! because we need an onExecute to receive onStart... fix that + }; + + LGAudioScript.prototype.onRemoved = function () { + this.audionode.onaudioprocess = LGAudioScript._bypass_function; + }; + + LGAudioScript.prototype.processCode = function () { + try { + var func = new Function("properties", this.properties.code); + this._script = new func(this.properties); + this._old_code = this.properties.code; + this._callback = this._script.onaudioprocess; + } catch (err) { + console.error("Error in onaudioprocess code", err); + this._callback = LGAudioScript._bypass_function; + this.audionode.onaudioprocess = this._callback; + } + }; + + LGAudioScript.prototype.onPropertyChanged = function (name, value) { + if (name == "code") { + this.properties.code = value; + this.processCode(); + if (this.graph && this.graph.status == LGraph.STATUS_RUNNING) { + this.audionode.onaudioprocess = this._callback; + } + } + }; + + LGAudioScript.default_function = function () { + this.onaudioprocess = function (audioProcessingEvent) { + // The input buffer is the song we loaded earlier + var inputBuffer = audioProcessingEvent.inputBuffer; + + // The output buffer contains the samples that will be modified and played + var outputBuffer = audioProcessingEvent.outputBuffer; + + // Loop through the output channels (in this case there is only one) + for ( + var channel = 0; + channel < outputBuffer.numberOfChannels; + channel++ + ) { + var inputData = inputBuffer.getChannelData(channel); + var outputData = outputBuffer.getChannelData(channel); + + // Loop through the 4096 samples + for (var sample = 0; sample < inputBuffer.length; sample++) { + // make output equal to the same as the input + outputData[sample] = inputData[sample]; + } + } + }; + }; + + LGAudio.createAudioNodeWrapper(LGAudioScript); + + LGAudioScript.title = "Script"; + LGAudioScript.desc = "apply script to signal"; + LiteGraph.registerNodeType("audio/script", LGAudioScript); + + function LGAudioDestination() { + this.audionode = LGAudio.getAudioContext().destination; + this.addInput("in", "audio"); + } + + LGAudioDestination.title = "Destination"; + LGAudioDestination.desc = "Audio output"; + LiteGraph.registerNodeType("audio/destination", LGAudioDestination); +})(this); + +//event related nodes +(function (global) { + var LiteGraph = global.LiteGraph; + + function LGWebSocket() { + this.size = [60, 20]; + this.addInput("send", LiteGraph.ACTION); + this.addOutput("received", LiteGraph.EVENT); + this.addInput("in", 0); + this.addOutput("out", 0); + this.properties = { + url: "", + room: "lgraph", //allows to filter messages, + only_send_changes: true, + }; + this._ws = null; + this._last_sent_data = []; + this._last_received_data = []; + } + + LGWebSocket.title = "WebSocket"; + LGWebSocket.desc = "Send data through a websocket"; + + LGWebSocket.prototype.onPropertyChanged = function (name, value) { + if (name == "url") { + this.connectSocket(); + } + }; + + LGWebSocket.prototype.onExecute = function () { + if (!this._ws && this.properties.url) { + this.connectSocket(); + } + + if (!this._ws || this._ws.readyState != WebSocket.OPEN) { + return; + } + + var room = this.properties.room; + var only_changes = this.properties.only_send_changes; + + for (var i = 1; i < this.inputs.length; ++i) { + var data = this.getInputData(i); + if (data == null) { + continue; + } + var json; + try { + json = JSON.stringify({ + type: 0, + room: room, + channel: i, + data: data, + }); + } catch (err) { + continue; + } + if (only_changes && this._last_sent_data[i] == json) { + continue; + } + + this._last_sent_data[i] = json; + this._ws.send(json); + } + + for (var i = 1; i < this.outputs.length; ++i) { + this.setOutputData(i, this._last_received_data[i]); + } + + if (this.boxcolor == "#AFA") { + this.boxcolor = "#6C6"; + } + }; + + LGWebSocket.prototype.connectSocket = function () { + var that = this; + var url = this.properties.url; + if (url.substr(0, 2) != "ws") { + url = "ws://" + url; + } + this._ws = new WebSocket(url); + this._ws.onopen = function () { + console.log("ready"); + that.boxcolor = "#6C6"; + }; + this._ws.onmessage = function (e) { + that.boxcolor = "#AFA"; + var data = JSON.parse(e.data); + if (data.room && data.room != that.properties.room) { + return; + } + if (data.type == 1) { + if (data.data.object_class && LiteGraph[data.data.object_class]) { + var obj = null; + try { + obj = new LiteGraph[data.data.object_class](data.data); + that.triggerSlot(0, obj); + } catch (err) { + return; + } + } else { + that.triggerSlot(0, data.data); + } + } else { + that._last_received_data[data.channel || 0] = data.data; + } + }; + this._ws.onerror = function (e) { + console.log("couldnt connect to websocket"); + that.boxcolor = "#E88"; + }; + this._ws.onclose = function (e) { + console.log("connection closed"); + that.boxcolor = "#000"; + }; + }; + + LGWebSocket.prototype.send = function (data) { + if (!this._ws || this._ws.readyState != WebSocket.OPEN) { + return; + } + this._ws.send(JSON.stringify({ type: 1, msg: data })); + }; + + LGWebSocket.prototype.onAction = function (action, param) { + if (!this._ws || this._ws.readyState != WebSocket.OPEN) { + return; + } + this._ws.send({ + type: 1, + room: this.properties.room, + action: action, + data: param, + }); + }; + + LGWebSocket.prototype.onGetInputs = function () { + return [["in", 0]]; + }; + + LGWebSocket.prototype.onGetOutputs = function () { + return [["out", 0]]; + }; + + LiteGraph.registerNodeType("network/websocket", LGWebSocket); + + //It is like a websocket but using the SillyServer.js server that bounces packets back to all clients connected: + //For more information: https://github.com/jagenjo/SillyServer.js + + function LGSillyClient() { + //this.size = [60,20]; + this.room_widget = this.addWidget( + "text", + "Room", + "lgraph", + this.setRoom.bind(this), + ); + this.addWidget("button", "Reconnect", null, this.connectSocket.bind(this)); + + this.addInput("send", LiteGraph.ACTION); + this.addOutput("received", LiteGraph.EVENT); + this.addInput("in", 0); + this.addOutput("out", 0); + this.properties = { + url: "tamats.com:55000", + room: "lgraph", + only_send_changes: true, + }; + + this._server = null; + this.connectSocket(); + this._last_sent_data = []; + this._last_received_data = []; + + if (typeof SillyClient == "undefined") + console.warn( + "remember to add SillyClient.js to your project: https://tamats.com/projects/sillyserver/src/sillyclient.js", + ); + } + + LGSillyClient.title = "SillyClient"; + LGSillyClient.desc = "Connects to SillyServer to broadcast messages"; + + LGSillyClient.prototype.onPropertyChanged = function (name, value) { + if (name == "room") { + this.room_widget.value = value; + } + this.connectSocket(); + }; + + LGSillyClient.prototype.setRoom = function (room_name) { + this.properties.room = room_name; + this.room_widget.value = room_name; + this.connectSocket(); + }; + + //force label names + LGSillyClient.prototype.onDrawForeground = function () { + for (var i = 1; i < this.inputs.length; ++i) { + var slot = this.inputs[i]; + slot.label = "in_" + i; + } + for (var i = 1; i < this.outputs.length; ++i) { + var slot = this.outputs[i]; + slot.label = "out_" + i; + } + }; + + LGSillyClient.prototype.onExecute = function () { + if (!this._server || !this._server.is_connected) { + return; + } + + var only_send_changes = this.properties.only_send_changes; + + for (var i = 1; i < this.inputs.length; ++i) { + var data = this.getInputData(i); + var prev_data = this._last_sent_data[i]; + if (data != null) { + if (only_send_changes) { + var is_equal = true; + if ( + data && + data.length && + prev_data && + prev_data.length == data.length && + data.constructor !== String + ) { + for (var j = 0; j < data.length; ++j) + if (prev_data[j] != data[j]) { + is_equal = false; + break; + } + } else if (this._last_sent_data[i] != data) is_equal = false; + if (is_equal) continue; + } + this._server.sendMessage({ type: 0, channel: i, data: data }); + if (data.length && data.constructor !== String) { + if (this._last_sent_data[i]) { + this._last_sent_data[i].length = data.length; + for (var j = 0; j < data.length; ++j) + this._last_sent_data[i][j] = data[j]; + } //create + else { + if (data.constructor === Array) + this._last_sent_data[i] = data.concat(); + else this._last_sent_data[i] = new data.constructor(data); + } + } else this._last_sent_data[i] = data; //should be cloned + } + } + + for (var i = 1; i < this.outputs.length; ++i) { + this.setOutputData(i, this._last_received_data[i]); + } + + if (this.boxcolor == "#AFA") { + this.boxcolor = "#6C6"; + } + }; + + LGSillyClient.prototype.connectSocket = function () { + var that = this; + if (typeof SillyClient == "undefined") { + if (!this._error) { + console.error( + "SillyClient node cannot be used, you must include SillyServer.js", + ); + } + this._error = true; + return; + } + + this._server = new SillyClient(); + this._server.on_ready = function () { + console.log("ready"); + that.boxcolor = "#6C6"; + }; + this._server.on_message = function (id, msg) { + var data = null; + try { + data = JSON.parse(msg); + } catch (err) { + return; + } + + if (data.type == 1) { + //EVENT slot + if (data.data.object_class && LiteGraph[data.data.object_class]) { + var obj = null; + try { + obj = new LiteGraph[data.data.object_class](data.data); + that.triggerSlot(0, obj); + } catch (err) { + return; + } + } else { + that.triggerSlot(0, data.data); + } + } //for FLOW slots + else { + that._last_received_data[data.channel || 0] = data.data; + } + that.boxcolor = "#AFA"; + }; + this._server.on_error = function (e) { + console.log("couldnt connect to websocket"); + that.boxcolor = "#E88"; + }; + this._server.on_close = function (e) { + console.log("connection closed"); + that.boxcolor = "#000"; + }; + + if (this.properties.url && this.properties.room) { + try { + this._server.connect(this.properties.url, this.properties.room); + } catch (err) { + console.error("SillyServer error: " + err); + this._server = null; + return; + } + this._final_url = this.properties.url + "/" + this.properties.room; + } + }; + + LGSillyClient.prototype.send = function (data) { + if (!this._server || !this._server.is_connected) { + return; + } + this._server.sendMessage({ type: 1, data: data }); + }; + + LGSillyClient.prototype.onAction = function (action, param) { + if (!this._server || !this._server.is_connected) { + return; + } + this._server.sendMessage({ type: 1, action: action, data: param }); + }; + + LGSillyClient.prototype.onGetInputs = function () { + return [["in", 0]]; + }; + + LGSillyClient.prototype.onGetOutputs = function () { + return [["out", 0]]; + }; + + LiteGraph.registerNodeType("network/sillyclient", LGSillyClient); + + //HTTP Request + function HTTPRequestNode() { + var that = this; + this.addInput("request", LiteGraph.ACTION); + this.addInput("url", "string"); + this.addProperty("url", ""); + this.addOutput("ready", LiteGraph.EVENT); + this.addOutput("data", "string"); + this.addWidget("button", "Fetch", null, this.fetch.bind(this)); + this._data = null; + this._fetching = null; + } + + HTTPRequestNode.title = "HTTP Request"; + HTTPRequestNode.desc = "Fetch data through HTTP"; + + HTTPRequestNode.prototype.fetch = function () { + var url = this.properties.url; + if (!url) return; + + this.boxcolor = "#FF0"; + var that = this; + this._fetching = fetch(url) + .then((resp) => { + if (!resp.ok) { + this.boxcolor = "#F00"; + that.trigger("error"); + } else { + this.boxcolor = "#0F0"; + return resp.text(); + } + }) + .then((data) => { + that._data = data; + that._fetching = null; + that.trigger("ready"); + }); + }; + + HTTPRequestNode.prototype.onAction = function (evt) { + if (evt == "request") this.fetch(); + }; + + HTTPRequestNode.prototype.onExecute = function () { + this.setOutputData(1, this._data); + }; + + HTTPRequestNode.prototype.onGetOutputs = function () { + return [["error", LiteGraph.EVENT]]; + }; + + LiteGraph.registerNodeType("network/httprequest", HTTPRequestNode); +})(this); diff --git a/colour_hdri/network/resources/index.html b/colour_hdri/network/resources/index.html new file mode 100644 index 0000000..6dd8294 --- /dev/null +++ b/colour_hdri/network/resources/index.html @@ -0,0 +1,337 @@ + + + + + + + + + + + +
+ + + diff --git a/colour_hdri/utilities/image.py b/colour_hdri/utilities/image.py index 839ab97..5ce0d8a 100644 --- a/colour_hdri/utilities/image.py +++ b/colour_hdri/utilities/image.py @@ -295,7 +295,7 @@ def read_metadata(self) -> Metadata: # NOTE: When read from an EXR file, the EXIF data has been written # after having been parsed once usually from DNG data. is_exif_data_parsed = True - _data, attributes = read_image_OpenImageIO(self._path, attributes=True) + _data, attributes = read_image_OpenImageIO(self._path, additional_data=True) for attribute in attributes: if attribute.name == "EXIF": diff --git a/pyproject.toml b/pyproject.toml index ce45465..bba68db 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -66,6 +66,7 @@ optional = [ "matplotlib>=3.7", "networkx >=3,<4", "opencv-python>=4,<5", + "pyside6>=6,<7", "pydot>=3,<4", "rawpy<1", ]