From 9f8f2701bfb6030cae204e256ac8028c3fde9d1d Mon Sep 17 00:00:00 2001 From: vhirtham Date: Wed, 2 Feb 2022 09:28:54 +0100 Subject: [PATCH 01/70] Add SpatialData --- tutorials/experiment_design_01.ipynb | 8 ++++---- weldx/core.py | 25 +++++++++++++++++++++++ weldx/tests/asdf_tests/test_weldx_file.py | 2 +- weldx/welding/groove/iso_9692_1.py | 2 +- 4 files changed, 31 insertions(+), 6 deletions(-) diff --git a/tutorials/experiment_design_01.ipynb b/tutorials/experiment_design_01.ipynb index c88619463..02340238c 100644 --- a/tutorials/experiment_design_01.ipynb +++ b/tutorials/experiment_design_01.ipynb @@ -476,9 +476,9 @@ ], "metadata": { "kernelspec": { - "display_name": "", + "display_name": "weldx", "language": "python", - "name": "" + "name": "weldx" }, "language_info": { "codemirror_mode": { @@ -490,9 +490,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.11" + "version": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/weldx/core.py b/weldx/core.py index cf259322b..99add56cd 100644 --- a/weldx/core.py +++ b/weldx/core.py @@ -1546,3 +1546,28 @@ def interp_like( """ return NotImplemented + + +# -------------------------------------------------------------------------------------- +# SpatialSeries +# -------------------------------------------------------------------------------------- + + +class SpatialSeries(GenericSeries): + _allowed_variables: list[str] = ["x"] + """Allowed variable names""" + _required_variables: list[str] = ["x"] + """Required variable names""" + + # _evaluation_preprocessor: dict[str, Callable] = {} + # """Function that should be used to adjust a var. input - (f.e. convert to Time)""" + + _required_dimensions: list[str] = ["x"] + """Required dimensions""" + _required_dimension_units: dict[str, pint.Unit] = {"x": "mm"} + """Required units of a dimension""" + _required_dimension_coordinates: dict[str, list] = {"x": ["x", "y", "z"]} + """Required coordinates of a dimension.""" + + # _required_unit_dimensionality: pint.Unit = None + # """Required unit dimensionality of the evaluated expression/data""" diff --git a/weldx/tests/asdf_tests/test_weldx_file.py b/weldx/tests/asdf_tests/test_weldx_file.py index 8f44c53e7..5f447ab12 100644 --- a/weldx/tests/asdf_tests/test_weldx_file.py +++ b/weldx/tests/asdf_tests/test_weldx_file.py @@ -377,7 +377,7 @@ def get_mem_info(): diff = after - before # pytest increases memory a bit, but not as much as our large array would # occupy in memory. - assert diff <= large_array.nbytes * 1.1, diff / 1024**2 + assert diff <= large_array.nbytes * 1.1, diff / 1024 ** 2 assert np.all(WeldxFile(fn)["x"] == large_array) @staticmethod diff --git a/weldx/welding/groove/iso_9692_1.py b/weldx/welding/groove/iso_9692_1.py index db13967d8..d9fdceb09 100644 --- a/weldx/welding/groove/iso_9692_1.py +++ b/weldx/welding/groove/iso_9692_1.py @@ -548,7 +548,7 @@ def to_profile(self, width_default: pint.Quantity = None) -> geo.Profile: # calculations: x_1 = np.tan(alpha / 2) * h # Center of the circle [0, y_m] - y_circle = np.sqrt(R**2 - x_1**2) # skipcq: PTC-W0028 + y_circle = np.sqrt(R ** 2 - x_1 ** 2) # skipcq: PTC-W0028 y_m = h + y_circle # From next point to circle center is the vector (x,y) x = R * np.cos(beta) From 95b5f21bd169c023c61540015e9db2f4076ee8ab Mon Sep 17 00:00:00 2001 From: Hirthammer Date: Wed, 2 Feb 2022 16:46:22 +0100 Subject: [PATCH 02/70] Add notebooks --- tutorials/01_03_geometry.ipynb | 37 ++++- tutorials/SpatialSeries.ipynb | 146 ++++++++++++++++++++ tutorials/TraceSegmentSpS.ipynb | 153 +++++++++++++++++++++ tutorials/welding_example_02_weaving.ipynb | 6 +- weldx/core.py | 30 +++- weldx/tests/asdf_tests/test_weldx_file.py | 2 +- weldx/welding/groove/iso_9692_1.py | 2 +- 7 files changed, 363 insertions(+), 13 deletions(-) create mode 100644 tutorials/SpatialSeries.ipynb create mode 100644 tutorials/TraceSegmentSpS.ipynb diff --git a/tutorials/01_03_geometry.ipynb b/tutorials/01_03_geometry.ipynb index 92d8f98ae..2dc13da5c 100644 --- a/tutorials/01_03_geometry.ipynb +++ b/tutorials/01_03_geometry.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "markdown", + "id": "aca3e29e", "metadata": {}, "source": [ "# Groove based workpiece data and geometry\n", @@ -21,6 +22,7 @@ }, { "cell_type": "markdown", + "id": "ab9f4cae", "metadata": {}, "source": [ "## Plotting the specimen's groove\n", @@ -35,6 +37,7 @@ { "cell_type": "code", "execution_count": null, + "id": "d78202f9", "metadata": {}, "outputs": [], "source": [ @@ -46,6 +49,7 @@ { "cell_type": "code", "execution_count": null, + "id": "71f68845", "metadata": {}, "outputs": [], "source": [ @@ -54,6 +58,7 @@ }, { "cell_type": "markdown", + "id": "cab51cfe", "metadata": {}, "source": [ "The workpiece data of this particular file consists of two parts:\n", @@ -79,6 +84,7 @@ { "cell_type": "code", "execution_count": null, + "id": "f2a173ef", "metadata": {}, "outputs": [], "source": [ @@ -88,6 +94,7 @@ }, { "cell_type": "markdown", + "id": "211b939d", "metadata": {}, "source": [ "Apart from the visual representation, the plot also contains all relevant information like the groove's area and the ISO 9692-1 parameters.\n", @@ -104,6 +111,7 @@ { "cell_type": "code", "execution_count": null, + "id": "99a47972", "metadata": {}, "outputs": [], "source": [ @@ -113,6 +121,7 @@ }, { "cell_type": "markdown", + "id": "2d5dabb3", "metadata": {}, "source": [ "We can plot the content of a `Profile` the same way as we did before with the groove:" @@ -121,6 +130,7 @@ { "cell_type": "code", "execution_count": null, + "id": "78f8b2e1", "metadata": {}, "outputs": [], "source": [ @@ -129,6 +139,7 @@ }, { "cell_type": "markdown", + "id": "dedc39ee", "metadata": {}, "source": [ "The only difference here is that we don't get the additional, norm-related information." @@ -136,6 +147,7 @@ }, { "cell_type": "markdown", + "id": "7081157c", "metadata": {}, "source": [ "## 3d plot (matplotlib)\n", @@ -148,6 +160,7 @@ { "cell_type": "code", "execution_count": null, + "id": "e6a8b3ff", "metadata": {}, "outputs": [], "source": [ @@ -159,6 +172,7 @@ }, { "cell_type": "markdown", + "id": "6220c711", "metadata": {}, "source": [ "Now all that remains, you might have guessed it, is to call the plot method:\n", @@ -169,6 +183,7 @@ { "cell_type": "code", "execution_count": null, + "id": "e21f8bd7", "metadata": {}, "outputs": [], "source": [ @@ -178,6 +193,7 @@ { "cell_type": "code", "execution_count": null, + "id": "17b59101", "metadata": {}, "outputs": [], "source": [ @@ -186,6 +202,7 @@ }, { "cell_type": "markdown", + "id": "e2769729", "metadata": {}, "source": [ "By default, the `plot` method shows us the triangulatad data.\n", @@ -196,6 +213,7 @@ { "cell_type": "code", "execution_count": null, + "id": "efbaa9f3", "metadata": {}, "outputs": [], "source": [ @@ -204,6 +222,7 @@ }, { "cell_type": "markdown", + "id": "8c16b264", "metadata": {}, "source": [ "The density of the triangle mesh or the point cloud can be controlled py the parameters `profile_raster_width` and `trace_raster_width`.\n", @@ -216,6 +235,7 @@ { "cell_type": "code", "execution_count": null, + "id": "6474ba52", "metadata": {}, "outputs": [], "source": [ @@ -228,6 +248,7 @@ }, { "cell_type": "markdown", + "id": "05693b01", "metadata": {}, "source": [ "As you can see, we now got only 3 densely rendered profiles.\n", @@ -243,6 +264,7 @@ }, { "cell_type": "markdown", + "id": "9b411aef", "metadata": {}, "source": [ "## 3d plot (k3d)\n", @@ -260,6 +282,7 @@ { "cell_type": "code", "execution_count": null, + "id": "d84588a7", "metadata": {}, "outputs": [], "source": [ @@ -268,6 +291,7 @@ }, { "cell_type": "markdown", + "id": "54a42499", "metadata": {}, "source": [ "Now we got a nice 3d rendering of the geometry with a closed surface that we shift and turn as we like.\n", @@ -280,6 +304,7 @@ { "cell_type": "code", "execution_count": null, + "id": "42c3d5e6", "metadata": {}, "outputs": [], "source": [ @@ -288,6 +313,7 @@ }, { "cell_type": "markdown", + "id": "42eaedb3", "metadata": {}, "source": [ "Now lets plot the geometry again:" @@ -296,6 +322,7 @@ { "cell_type": "code", "execution_count": null, + "id": "4deada64", "metadata": {}, "outputs": [], "source": [ @@ -304,6 +331,7 @@ }, { "cell_type": "markdown", + "id": "1ecda514", "metadata": {}, "source": [ "## Export 3d geometry into a CAD file\n", @@ -316,6 +344,7 @@ { "cell_type": "code", "execution_count": null, + "id": "2ccd3dd6", "metadata": {}, "outputs": [], "source": [ @@ -326,6 +355,7 @@ }, { "cell_type": "markdown", + "id": "03370d27", "metadata": {}, "source": [ "The parameters `profile_raster_width` and `trace_raster_width` do have the exact same effect as in the `plot` method described before." @@ -333,6 +363,7 @@ }, { "cell_type": "markdown", + "id": "36cb0dd2", "metadata": {}, "source": [ "## Conclusion\n", @@ -353,9 +384,9 @@ ], "metadata": { "kernelspec": { - "display_name": "", + "display_name": "Python (weldx)", "language": "python", - "name": "" + "name": "weldx" }, "language_info": { "codemirror_mode": { @@ -367,7 +398,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.9" } }, "nbformat": 4, diff --git a/tutorials/SpatialSeries.ipynb b/tutorials/SpatialSeries.ipynb new file mode 100644 index 000000000..d919e7268 --- /dev/null +++ b/tutorials/SpatialSeries.ipynb @@ -0,0 +1,146 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "759924f6-2e78-4aba-9750-fe1870344af3", + "metadata": {}, + "outputs": [], + "source": [ + "from weldx.core import SpatialSeries2, GenericSeries\n", + "from weldx import LocalCoordinateSystem, Q_\n", + "import numpy as np\n", + "from xarray import DataArray" + ] + }, + { + "cell_type": "markdown", + "id": "3f0a0e78-40b6-4521-8e1d-a1fc0f322e9f", + "metadata": {}, + "source": [ + "## Discrete" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "06c52d39-24fe-40ff-9a5b-594ad8435869", + "metadata": {}, + "outputs": [], + "source": [ + "data = DataArray(Q_([[1,2,3], [4,5,6]], \"m\"), dims=[\"s\",\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"]))\n", + "\n", + "s = Q_([0,5], \"mm\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ccd72f65-3173-4481-bb81-07692fa2fa06", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Values:\n", + "\t[[1 2 3]\n", + " [4 5 6]]\n", + "Dimensions:\n", + "\t('s', 'c')\n", + "Coordinates:\n", + "\tc = ['x' 'y' 'z'] None\n", + "Units:\n", + "\tm" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spsd = SpatialSeries2(data, dims=[\"s\", \"c\"], coords=dict(s=s))\n", + "spsd" + ] + }, + { + "cell_type": "markdown", + "id": "b88b7d17-c2fe-4095-b160-76b69ed3714d", + "metadata": {}, + "source": [ + "## Expression" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3fbd63fa-c3ba-4c9b-9a87-779e130db08b", + "metadata": {}, + "outputs": [], + "source": [ + "exp = \"a*s + b\"\n", + "params = dict(\n", + " a=Q_([1,1,1], \"mm\"), \n", + " b=Q_([1,1,1], \"mm\"), \n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5a60c890-aac4-499b-bdbc-d54a3529b8c5", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "SpatialSeries2 requires dimensions '['s', 'c']'.", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Input \u001b[1;32mIn [10]\u001b[0m, in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[0m spse \u001b[38;5;241m=\u001b[39m \u001b[43mSpatialSeries2\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2\u001b[0m spse\n", + "File \u001b[1;32m~\\PycharmProjects\\weldx\\weldx\\weldx\\core.py:987\u001b[0m, in \u001b[0;36mGenericSeries.__init__\u001b[1;34m(self, obj, dims, coords, units, interpolation, parameters)\u001b[0m\n\u001b[0;32m 985\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dims \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dims, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m 986\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mArgument \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdims\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m must be dict, not \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdims\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 987\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_init_expression\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 988\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 989\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe data type \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(obj)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m is not supported.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[1;32m~\\PycharmProjects\\weldx\\weldx\\weldx\\core.py:1093\u001b[0m, in \u001b[0;36mGenericSeries._init_expression\u001b[1;34m(self, expr, dims, parameters, units)\u001b[0m\n\u001b[0;32m 1090\u001b[0m expr_units \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_test_expr(expr, dims, units)\n\u001b[0;32m 1092\u001b[0m \u001b[38;5;66;03m# check constraints\u001b[39;00m\n\u001b[1;32m-> 1093\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_constraints_expression\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexpr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexpr_units\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1095\u001b[0m \u001b[38;5;66;03m# save internal data\u001b[39;00m\n\u001b[0;32m 1096\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_units \u001b[38;5;241m=\u001b[39m expr_units\n", + "File \u001b[1;32m~\\PycharmProjects\\weldx\\weldx\\weldx\\core.py:1460\u001b[0m, in \u001b[0;36mGenericSeries._check_constraints_expression\u001b[1;34m(cls, expr, var_dims, var_units, expr_units)\u001b[0m\n\u001b[0;32m 1457\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_check_req_items(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_required_variables, var_names, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexpression variables\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 1459\u001b[0m \u001b[38;5;66;03m# check dimension constraints\u001b[39;00m\n\u001b[1;32m-> 1460\u001b[0m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_req_items\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1461\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_required_dimensions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1462\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_expression_dims\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexpr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvar_dims\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1463\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdimensions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1464\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1466\u001b[0m \u001b[38;5;66;03m# check dimensionality constraint\u001b[39;00m\n\u001b[0;32m 1467\u001b[0m req_dimty \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_required_unit_dimensionality\n", + "File \u001b[1;32m~\\PycharmProjects\\weldx\\weldx\\weldx\\core.py:1408\u001b[0m, in \u001b[0;36mGenericSeries._check_req_items\u001b[1;34m(cls, req, data, desc)\u001b[0m\n\u001b[0;32m 1406\u001b[0m \u001b[38;5;124;03m\"\"\"Check if all required items are contained in `data`.\"\"\"\u001b[39;00m\n\u001b[0;32m 1407\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mset\u001b[39m(req)\u001b[38;5;241m.\u001b[39missubset(data):\n\u001b[1;32m-> 1408\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdesc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mreq\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[1;31mValueError\u001b[0m: SpatialSeries2 requires dimensions '['s', 'c']'." + ] + } + ], + "source": [ + "spse = SpatialSeries2(exp, parameters=params)\n", + "spse" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90ac71ed-38a6-453d-ba36-6be5ea48a4bb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (weldx)", + "language": "python", + "name": "weldx" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/TraceSegmentSpS.ipynb b/tutorials/TraceSegmentSpS.ipynb new file mode 100644 index 000000000..996a96608 --- /dev/null +++ b/tutorials/TraceSegmentSpS.ipynb @@ -0,0 +1,153 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "228ec7cb-5828-459e-a5a8-349a093ec1b1", + "metadata": {}, + "outputs": [], + "source": [ + "from weldx import Trace, LinearHorizontalTraceSegment, Q_, GenericSeries\n", + "from weldx.core import SpatialSeries\n", + "from xarray import DataArray" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dae8950a-ca96-47bc-8a33-a604fd1fe86d", + "metadata": {}, + "outputs": [], + "source": [ + "lhts = LinearHorizontalTraceSegment(\"2mm\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c33e35c-65e7-46ab-8e9d-27d6eb277d75", + "metadata": {}, + "outputs": [], + "source": [ + "tr_1 = Trace(lhts)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae841131-305e-4b89-a8fe-2082c24129b0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "tr_1.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dbe0ede7-2696-425e-a58d-e4782877d327", + "metadata": {}, + "outputs": [], + "source": [ + "class SDTraceSegment:\n", + " def __init__(self, sd):\n", + " self._sd = sd" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ed0318d3-f74e-44e4-b09d-891d0f89a8bb", + "metadata": {}, + "outputs": [], + "source": [ + "expr = \"a*x+b*y+c*z\"\n", + "#expr = \"x+y+z\"\n", + "params = dict(\n", + " a= DataArray(Q_([1,0,0]), dims=[\"c\"]), \n", + " b= DataArray(Q_([0,1,0]),dims=[\"c\"]), \n", + " c= DataArray(Q_([0,0,1]),dims=[\"c\"])\n", + ")\n", + "sps = SpatialSeries(expr, parameters=params)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "17debdfb-0a46-4a2b-bf77-980a0ac34437", + "metadata": {}, + "outputs": [], + "source": [ + "sdts = SDTraceSegment(sps)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "539d8648-94f6-4c3c-b8e4-97b6cb569a8b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Values:\n", + "\t[[[[1]]]\n", + "\n", + "\n", + " [[[1]]]\n", + "\n", + "\n", + " [[[1]]]]\n", + "Dimensions:\n", + "\t('c', 'x', 'y', 'z')\n", + "Coordinates:\n", + "Units:\n", + "\tmm" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sps.evaluate(x=\"1mm\", y=\"1mm\", z=\"1mm\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (weldx)", + "language": "python", + "name": "weldx" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/welding_example_02_weaving.ipynb b/tutorials/welding_example_02_weaving.ipynb index c00042bdc..cc2068967 100644 --- a/tutorials/welding_example_02_weaving.ipynb +++ b/tutorials/welding_example_02_weaving.ipynb @@ -620,9 +620,9 @@ ], "metadata": { "kernelspec": { - "display_name": "", + "display_name": "Python (weldx)", "language": "python", - "name": "" + "name": "weldx" }, "language_info": { "codemirror_mode": { @@ -634,7 +634,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.11" + "version": "3.9.9" } }, "nbformat": 4, diff --git a/weldx/core.py b/weldx/core.py index 99add56cd..eb0194ceb 100644 --- a/weldx/core.py +++ b/weldx/core.py @@ -1554,19 +1554,39 @@ def interp_like( class SpatialSeries(GenericSeries): - _allowed_variables: list[str] = ["x"] + _allowed_variables: list[str] = ["x", "y", "z"] """Allowed variable names""" - _required_variables: list[str] = ["x"] + # _required_variables: list[str] = [] + # """Required variable names""" + + # _evaluation_preprocessor: dict[str, Callable] = {} + # """Function that should be used to adjust a var. input - (f.e. convert to Time)""" + + _required_dimensions: list[str] = ["x", "y", "z"] + """Required dimensions""" + # _required_dimension_units: dict[str, pint.Unit] = {"x": "mm", "y": "mm", "z": "mm"} + # """Required units of a dimension""" + # _required_dimension_coordinates: dict[str, list] = {"x": ["x", "y", "z"]} + # """Required coordinates of a dimension.""" + + # _required_unit_dimensionality: pint.Unit = None + # """Required unit dimensionality of the evaluated expression/data""" + + +class SpatialSeries2(GenericSeries): + _allowed_variables: list[str] = ["s"] + """Allowed variable names""" + _required_variables: list[str] = ["s"] """Required variable names""" # _evaluation_preprocessor: dict[str, Callable] = {} # """Function that should be used to adjust a var. input - (f.e. convert to Time)""" - _required_dimensions: list[str] = ["x"] + _required_dimensions: list[str] = ["s", "c"] """Required dimensions""" - _required_dimension_units: dict[str, pint.Unit] = {"x": "mm"} + _required_dimension_units: dict[str, pint.Unit] = {"s": ""} """Required units of a dimension""" - _required_dimension_coordinates: dict[str, list] = {"x": ["x", "y", "z"]} + _required_dimension_coordinates: dict[str, list] = {"c": ["x", "y", "z"]} """Required coordinates of a dimension.""" # _required_unit_dimensionality: pint.Unit = None diff --git a/weldx/tests/asdf_tests/test_weldx_file.py b/weldx/tests/asdf_tests/test_weldx_file.py index 5f447ab12..8f44c53e7 100644 --- a/weldx/tests/asdf_tests/test_weldx_file.py +++ b/weldx/tests/asdf_tests/test_weldx_file.py @@ -377,7 +377,7 @@ def get_mem_info(): diff = after - before # pytest increases memory a bit, but not as much as our large array would # occupy in memory. - assert diff <= large_array.nbytes * 1.1, diff / 1024 ** 2 + assert diff <= large_array.nbytes * 1.1, diff / 1024**2 assert np.all(WeldxFile(fn)["x"] == large_array) @staticmethod diff --git a/weldx/welding/groove/iso_9692_1.py b/weldx/welding/groove/iso_9692_1.py index d9fdceb09..db13967d8 100644 --- a/weldx/welding/groove/iso_9692_1.py +++ b/weldx/welding/groove/iso_9692_1.py @@ -548,7 +548,7 @@ def to_profile(self, width_default: pint.Quantity = None) -> geo.Profile: # calculations: x_1 = np.tan(alpha / 2) * h # Center of the circle [0, y_m] - y_circle = np.sqrt(R ** 2 - x_1 ** 2) # skipcq: PTC-W0028 + y_circle = np.sqrt(R**2 - x_1**2) # skipcq: PTC-W0028 y_m = h + y_circle # From next point to circle center is the vector (x,y) x = R * np.cos(beta) From b2e5a0111497abb6dd7599c250a9e21a9d9d3c57 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Fri, 4 Feb 2022 12:36:02 +0100 Subject: [PATCH 03/70] Refactor SpatialSeries --- tutorials/SpatialSeries.ipynb | 83 +++++++++++++---------- tutorials/TraceSegmentSpS.ipynb | 83 +++++++++++++---------- weldx/core.py | 22 +----- weldx/tests/asdf_tests/test_weldx_file.py | 2 +- weldx/welding/groove/iso_9692_1.py | 2 +- 5 files changed, 96 insertions(+), 96 deletions(-) diff --git a/tutorials/SpatialSeries.ipynb b/tutorials/SpatialSeries.ipynb index d919e7268..83f87fa54 100644 --- a/tutorials/SpatialSeries.ipynb +++ b/tutorials/SpatialSeries.ipynb @@ -7,7 +7,7 @@ "metadata": {}, "outputs": [], "source": [ - "from weldx.core import SpatialSeries2, GenericSeries\n", + "from weldx.core import SpatialSeries, GenericSeries\n", "from weldx import LocalCoordinateSystem, Q_\n", "import numpy as np\n", "from xarray import DataArray" @@ -24,43 +24,46 @@ { "cell_type": "code", "execution_count": 2, - "id": "06c52d39-24fe-40ff-9a5b-594ad8435869", - "metadata": {}, - "outputs": [], - "source": [ - "data = DataArray(Q_([[1,2,3], [4,5,6]], \"m\"), dims=[\"s\",\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"]))\n", - "\n", - "s = Q_([0,5], \"mm\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, "id": "ccd72f65-3173-4481-bb81-07692fa2fa06", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'s': {'dimensionality': ''}, 'c': {'values': ['x', 'y', 'z']}}\n" + ] + }, { "data": { "text/plain": [ - "\n", + "\n", "Values:\n", "\t[[1 2 3]\n", " [4 5 6]]\n", "Dimensions:\n", "\t('s', 'c')\n", "Coordinates:\n", - "\tc = ['x' 'y' 'z'] None\n", + "\ts = [0 5] \n", + "\tc = ['x' 'y' 'z'] \n", "Units:\n", "\tm" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "spsd = SpatialSeries2(data, dims=[\"s\", \"c\"], coords=dict(s=s))\n", + "spsd = SpatialSeries(\n", + " Q_([[1,2,3], [4,5,6]], \"m\"),\n", + " dims=[\"s\", \"c\"], \n", + " coords=dict(\n", + " s=Q_([0,5], \"\"),\n", + " c=[\"x\", \"y\", \"z\"], \n", + " )\n", + ")\n", "spsd" ] }, @@ -74,42 +77,48 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "id": "3fbd63fa-c3ba-4c9b-9a87-779e130db08b", "metadata": {}, "outputs": [], "source": [ "exp = \"a*s + b\"\n", "params = dict(\n", - " a=Q_([1,1,1], \"mm\"), \n", - " b=Q_([1,1,1], \"mm\"), \n", + " a=DataArray(Q_([1,1,1], \"mm\"), dims=[\"c\"]), \n", + " b=DataArray(Q_([1,1,1], \"mm\"), dims=[\"c\"]), \n", ")" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "id": "5a60c890-aac4-499b-bdbc-d54a3529b8c5", "metadata": {}, "outputs": [ { - "ename": "ValueError", - "evalue": "SpatialSeries2 requires dimensions '['s', 'c']'.", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Input \u001b[1;32mIn [10]\u001b[0m, in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[0m spse \u001b[38;5;241m=\u001b[39m \u001b[43mSpatialSeries2\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2\u001b[0m spse\n", - "File \u001b[1;32m~\\PycharmProjects\\weldx\\weldx\\weldx\\core.py:987\u001b[0m, in \u001b[0;36mGenericSeries.__init__\u001b[1;34m(self, obj, dims, coords, units, interpolation, parameters)\u001b[0m\n\u001b[0;32m 985\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dims \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dims, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m 986\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mArgument \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdims\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m must be dict, not \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdims\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 987\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_init_expression\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 988\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 989\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe data type \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(obj)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m is not supported.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[1;32m~\\PycharmProjects\\weldx\\weldx\\weldx\\core.py:1093\u001b[0m, in \u001b[0;36mGenericSeries._init_expression\u001b[1;34m(self, expr, dims, parameters, units)\u001b[0m\n\u001b[0;32m 1090\u001b[0m expr_units \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_test_expr(expr, dims, units)\n\u001b[0;32m 1092\u001b[0m \u001b[38;5;66;03m# check constraints\u001b[39;00m\n\u001b[1;32m-> 1093\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_constraints_expression\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexpr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexpr_units\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1095\u001b[0m \u001b[38;5;66;03m# save internal data\u001b[39;00m\n\u001b[0;32m 1096\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_units \u001b[38;5;241m=\u001b[39m expr_units\n", - "File \u001b[1;32m~\\PycharmProjects\\weldx\\weldx\\weldx\\core.py:1460\u001b[0m, in \u001b[0;36mGenericSeries._check_constraints_expression\u001b[1;34m(cls, expr, var_dims, var_units, expr_units)\u001b[0m\n\u001b[0;32m 1457\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_check_req_items(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_required_variables, var_names, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexpression variables\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 1459\u001b[0m \u001b[38;5;66;03m# check dimension constraints\u001b[39;00m\n\u001b[1;32m-> 1460\u001b[0m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_req_items\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1461\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_required_dimensions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1462\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_expression_dims\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexpr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvar_dims\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1463\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdimensions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1464\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1466\u001b[0m \u001b[38;5;66;03m# check dimensionality constraint\u001b[39;00m\n\u001b[0;32m 1467\u001b[0m req_dimty \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_required_unit_dimensionality\n", - "File \u001b[1;32m~\\PycharmProjects\\weldx\\weldx\\weldx\\core.py:1408\u001b[0m, in \u001b[0;36mGenericSeries._check_req_items\u001b[1;34m(cls, req, data, desc)\u001b[0m\n\u001b[0;32m 1406\u001b[0m \u001b[38;5;124;03m\"\"\"Check if all required items are contained in `data`.\"\"\"\u001b[39;00m\n\u001b[0;32m 1407\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mset\u001b[39m(req)\u001b[38;5;241m.\u001b[39missubset(data):\n\u001b[1;32m-> 1408\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdesc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mreq\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[1;31mValueError\u001b[0m: SpatialSeries2 requires dimensions '['s', 'c']'." - ] + "data": { + "text/plain": [ + "\n", + "Expression:\n", + "\ta*s + b\n", + "Parameters:\n", + "\ta = [1 1 1] mm\n", + "\tb = [1 1 1] mm\n", + "Free Dimensions:\n", + "\ts in \n", + "Other Dimensions:\n", + "\t['c']\n", + "Units:\n", + "\tmm" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "spse = SpatialSeries2(exp, parameters=params)\n", + "spse = SpatialSeries(exp, parameters=params)\n", "spse" ] }, @@ -124,7 +133,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python (weldx)", + "display_name": "weldx", "language": "python", "name": "weldx" }, @@ -138,7 +147,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.8.12" } }, "nbformat": 4, diff --git a/tutorials/TraceSegmentSpS.ipynb b/tutorials/TraceSegmentSpS.ipynb index 996a96608..efe5e34db 100644 --- a/tutorials/TraceSegmentSpS.ipynb +++ b/tutorials/TraceSegmentSpS.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "228ec7cb-5828-459e-a5a8-349a093ec1b1", "metadata": {}, "outputs": [], @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "dae8950a-ca96-47bc-8a33-a604fd1fe86d", "metadata": {}, "outputs": [], @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "9c33e35c-65e7-46ab-8e9d-27d6eb277d75", "metadata": {}, "outputs": [], @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "ae841131-305e-4b89-a8fe-2082c24129b0", "metadata": {}, "outputs": [ @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "dbe0ede7-2696-425e-a58d-e4782877d327", "metadata": {}, "outputs": [], @@ -69,24 +69,23 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "id": "ed0318d3-f74e-44e4-b09d-891d0f89a8bb", "metadata": {}, "outputs": [], "source": [ - "expr = \"a*x+b*y+c*z\"\n", + "expr = \"a*s+b\"\n", "#expr = \"x+y+z\"\n", "params = dict(\n", - " a= DataArray(Q_([1,0,0]), dims=[\"c\"]), \n", - " b= DataArray(Q_([0,1,0]),dims=[\"c\"]), \n", - " c= DataArray(Q_([0,0,1]),dims=[\"c\"])\n", + " a= DataArray(Q_([1,0,0], \"mm\"), dims=[\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"])), \n", + " b= DataArray(Q_([0,1,0], \"mm\"), dims=[\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"])),\n", ")\n", "sps = SpatialSeries(expr, parameters=params)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 7, "id": "17debdfb-0a46-4a2b-bf77-980a0ac34437", "metadata": {}, "outputs": [], @@ -96,42 +95,54 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "id": "539d8648-94f6-4c3c-b8e4-97b6cb569a8b", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "\n", - "Values:\n", - "\t[[[[1]]]\n", - "\n", - "\n", - " [[[1]]]\n", - "\n", - "\n", - " [[[1]]]]\n", - "Dimensions:\n", - "\t('c', 'x', 'y', 'z')\n", - "Coordinates:\n", - "Units:\n", - "\tmm" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "{'s': \n", + "\n", + "Dimensions without coordinates: s}\n", + "{'c': {'values': ['x', 'y', 'z']}, 's': {'dimensionality': ''}}\n" + ] + }, + { + "ename": "KeyError", + "evalue": "\"Could not find required coordinate 's'.\"", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "Input \u001b[1;32mIn [8]\u001b[0m, in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43msps\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\users\\vhirtham\\pycharmprojects\\bam\\weldx\\weldx\\core.py:1237\u001b[0m, in \u001b[0;36mGenericSeries.evaluate\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m 1234\u001b[0m coords \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_evaluate_preprocessor(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1236\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_expression:\n\u001b[1;32m-> 1237\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_evaluate_expr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcoords\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1238\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_evaluate_array(coords)\n", + "File \u001b[1;32mc:\\users\\vhirtham\\pycharmprojects\\bam\\weldx\\weldx\\core.py:1260\u001b[0m, in \u001b[0;36mGenericSeries._evaluate_expr\u001b[1;34m(self, coords)\u001b[0m\n\u001b[0;32m 1258\u001b[0m \u001b[38;5;28mprint\u001b[39m(eval_args)\n\u001b[0;32m 1259\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_obj\u001b[38;5;241m.\u001b[39mevaluate(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39meval_args)\n\u001b[1;32m-> 1260\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__class__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1262\u001b[0m \u001b[38;5;66;03m# turn passed coords into parameters of the expression\u001b[39;00m\n\u001b[0;32m 1263\u001b[0m new_series \u001b[38;5;241m=\u001b[39m deepcopy(\u001b[38;5;28mself\u001b[39m)\n", + "File \u001b[1;32mc:\\users\\vhirtham\\pycharmprojects\\bam\\weldx\\weldx\\core.py:983\u001b[0m, in \u001b[0;36mGenericSeries.__init__\u001b[1;34m(self, obj, dims, coords, units, interpolation, parameters)\u001b[0m\n\u001b[0;32m 981\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dims \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dims, \u001b[38;5;28mlist\u001b[39m):\n\u001b[0;32m 982\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mArgument \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdims\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m must be list of strings, not \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdims\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 983\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_init_discrete\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 984\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj, (MathematicalExpression, \u001b[38;5;28mstr\u001b[39m)):\n\u001b[0;32m 985\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dims \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dims, \u001b[38;5;28mdict\u001b[39m):\n", + "File \u001b[1;32mc:\\users\\vhirtham\\pycharmprojects\\bam\\weldx\\weldx\\core.py:1031\u001b[0m, in \u001b[0;36mGenericSeries._init_discrete\u001b[1;34m(self, data, dims, coords)\u001b[0m\n\u001b[0;32m 1028\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m 1030\u001b[0m \u001b[38;5;66;03m# check the constraints of derived types\u001b[39;00m\n\u001b[1;32m-> 1031\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_constraints_discrete\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1032\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_obj \u001b[38;5;241m=\u001b[39m data\n", + "File \u001b[1;32mc:\\users\\vhirtham\\pycharmprojects\\bam\\weldx\\weldx\\core.py:1435\u001b[0m, in \u001b[0;36mGenericSeries._check_constraints_discrete\u001b[1;34m(cls, data_array)\u001b[0m\n\u001b[0;32m 1433\u001b[0m ref[k][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalues\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m _vals[k]\n\u001b[0;32m 1434\u001b[0m \u001b[38;5;28mprint\u001b[39m(ref)\n\u001b[1;32m-> 1435\u001b[0m \u001b[43mut\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxr_check_coords\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_array\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mref\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\users\\vhirtham\\pycharmprojects\\bam\\weldx\\weldx\\util\\xarray.py:497\u001b[0m, in \u001b[0;36mxr_check_coords\u001b[1;34m(coords, ref)\u001b[0m\n\u001b[0;32m 493\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m 495\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m coords:\n\u001b[0;32m 496\u001b[0m \u001b[38;5;66;03m# Attributes not found in coords\u001b[39;00m\n\u001b[1;32m--> 497\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not find required coordinate \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 499\u001b[0m \u001b[38;5;66;03m# only if the key \"values\" is given do the validation\u001b[39;00m\n\u001b[0;32m 500\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalues\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m check \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39mall(coords[key]\u001b[38;5;241m.\u001b[39mvalues \u001b[38;5;241m==\u001b[39m check[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalues\u001b[39m\u001b[38;5;124m\"\u001b[39m]):\n", + "\u001b[1;31mKeyError\u001b[0m: \"Could not find required coordinate 's'.\"" + ] } ], "source": [ - "sps.evaluate(x=\"1mm\", y=\"1mm\", z=\"1mm\")" + "sps.evaluate(s=\"1\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0fc5a100-5bb3-4b4a-ac0c-e888429eb1f5", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python (weldx)", + "display_name": "weldx", "language": "python", "name": "weldx" }, @@ -145,7 +156,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.8.12" } }, "nbformat": 4, diff --git a/weldx/core.py b/weldx/core.py index eb0194ceb..3a8d593cc 100644 --- a/weldx/core.py +++ b/weldx/core.py @@ -1255,6 +1255,7 @@ def _evaluate_expr(self, coords: list[SeriesParameter]) -> GenericSeries: """Evaluate the expression at the passed coordinates.""" if len(coords) == self._obj.num_variables: eval_args = {v.symbol: v.data_array for v in coords} + print(eval_args) data = self._obj.evaluate(**eval_args) return self.__class__(data) @@ -1430,7 +1431,6 @@ def _check_constraints_discrete(cls, data_array: xr.DataArray): ref[k]["dimensionality"] = _units[k] if k in _vals: ref[k]["values"] = _vals[k] - ut.xr_check_coords(data_array, ref) @classmethod @@ -1554,26 +1554,6 @@ def interp_like( class SpatialSeries(GenericSeries): - _allowed_variables: list[str] = ["x", "y", "z"] - """Allowed variable names""" - # _required_variables: list[str] = [] - # """Required variable names""" - - # _evaluation_preprocessor: dict[str, Callable] = {} - # """Function that should be used to adjust a var. input - (f.e. convert to Time)""" - - _required_dimensions: list[str] = ["x", "y", "z"] - """Required dimensions""" - # _required_dimension_units: dict[str, pint.Unit] = {"x": "mm", "y": "mm", "z": "mm"} - # """Required units of a dimension""" - # _required_dimension_coordinates: dict[str, list] = {"x": ["x", "y", "z"]} - # """Required coordinates of a dimension.""" - - # _required_unit_dimensionality: pint.Unit = None - # """Required unit dimensionality of the evaluated expression/data""" - - -class SpatialSeries2(GenericSeries): _allowed_variables: list[str] = ["s"] """Allowed variable names""" _required_variables: list[str] = ["s"] diff --git a/weldx/tests/asdf_tests/test_weldx_file.py b/weldx/tests/asdf_tests/test_weldx_file.py index 8f44c53e7..5f447ab12 100644 --- a/weldx/tests/asdf_tests/test_weldx_file.py +++ b/weldx/tests/asdf_tests/test_weldx_file.py @@ -377,7 +377,7 @@ def get_mem_info(): diff = after - before # pytest increases memory a bit, but not as much as our large array would # occupy in memory. - assert diff <= large_array.nbytes * 1.1, diff / 1024**2 + assert diff <= large_array.nbytes * 1.1, diff / 1024 ** 2 assert np.all(WeldxFile(fn)["x"] == large_array) @staticmethod diff --git a/weldx/welding/groove/iso_9692_1.py b/weldx/welding/groove/iso_9692_1.py index db13967d8..d9fdceb09 100644 --- a/weldx/welding/groove/iso_9692_1.py +++ b/weldx/welding/groove/iso_9692_1.py @@ -548,7 +548,7 @@ def to_profile(self, width_default: pint.Quantity = None) -> geo.Profile: # calculations: x_1 = np.tan(alpha / 2) * h # Center of the circle [0, y_m] - y_circle = np.sqrt(R**2 - x_1**2) # skipcq: PTC-W0028 + y_circle = np.sqrt(R ** 2 - x_1 ** 2) # skipcq: PTC-W0028 y_m = h + y_circle # From next point to circle center is the vector (x,y) x = R * np.cos(beta) From 50d2d72bf94aba8e70429b14a66495e31ef1a225 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Fri, 4 Feb 2022 14:24:54 +0100 Subject: [PATCH 04/70] Fix constraint behavior during evaluation --- tutorials/TraceSegmentSpS.ipynb | 869 +++++++++++++++++++++++++++++++- weldx/core.py | 11 +- 2 files changed, 857 insertions(+), 23 deletions(-) diff --git a/tutorials/TraceSegmentSpS.ipynb b/tutorials/TraceSegmentSpS.ipynb index efe5e34db..b1483c388 100644 --- a/tutorials/TraceSegmentSpS.ipynb +++ b/tutorials/TraceSegmentSpS.ipynb @@ -99,42 +99,871 @@ "id": "539d8648-94f6-4c3c-b8e4-97b6cb569a8b", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\utils.py:117: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", + " index = pd.Index(np.asarray(array), **kwargs)\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "{'s': \n", - "\n", - "Dimensions without coordinates: s}\n", - "{'c': {'values': ['x', 'y', 'z']}, 's': {'dimensionality': ''}}\n" + "\n", + "array([1])\n", + "Coordinates:\n", + " * s (s) int32 1\n", + "Attributes:\n", + " units: \n" ] }, { - "ename": "KeyError", - "evalue": "\"Could not find required coordinate 's'.\"", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "Input \u001b[1;32mIn [8]\u001b[0m, in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43msps\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\users\\vhirtham\\pycharmprojects\\bam\\weldx\\weldx\\core.py:1237\u001b[0m, in \u001b[0;36mGenericSeries.evaluate\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m 1234\u001b[0m coords \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_evaluate_preprocessor(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1236\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_expression:\n\u001b[1;32m-> 1237\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_evaluate_expr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcoords\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1238\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_evaluate_array(coords)\n", - "File \u001b[1;32mc:\\users\\vhirtham\\pycharmprojects\\bam\\weldx\\weldx\\core.py:1260\u001b[0m, in \u001b[0;36mGenericSeries._evaluate_expr\u001b[1;34m(self, coords)\u001b[0m\n\u001b[0;32m 1258\u001b[0m \u001b[38;5;28mprint\u001b[39m(eval_args)\n\u001b[0;32m 1259\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_obj\u001b[38;5;241m.\u001b[39mevaluate(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39meval_args)\n\u001b[1;32m-> 1260\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__class__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1262\u001b[0m \u001b[38;5;66;03m# turn passed coords into parameters of the expression\u001b[39;00m\n\u001b[0;32m 1263\u001b[0m new_series \u001b[38;5;241m=\u001b[39m deepcopy(\u001b[38;5;28mself\u001b[39m)\n", - "File \u001b[1;32mc:\\users\\vhirtham\\pycharmprojects\\bam\\weldx\\weldx\\core.py:983\u001b[0m, in \u001b[0;36mGenericSeries.__init__\u001b[1;34m(self, obj, dims, coords, units, interpolation, parameters)\u001b[0m\n\u001b[0;32m 981\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dims \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dims, \u001b[38;5;28mlist\u001b[39m):\n\u001b[0;32m 982\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mArgument \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdims\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m must be list of strings, not \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdims\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 983\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_init_discrete\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 984\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj, (MathematicalExpression, \u001b[38;5;28mstr\u001b[39m)):\n\u001b[0;32m 985\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dims \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dims, \u001b[38;5;28mdict\u001b[39m):\n", - "File \u001b[1;32mc:\\users\\vhirtham\\pycharmprojects\\bam\\weldx\\weldx\\core.py:1031\u001b[0m, in \u001b[0;36mGenericSeries._init_discrete\u001b[1;34m(self, data, dims, coords)\u001b[0m\n\u001b[0;32m 1028\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m 1030\u001b[0m \u001b[38;5;66;03m# check the constraints of derived types\u001b[39;00m\n\u001b[1;32m-> 1031\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_constraints_discrete\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1032\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_obj \u001b[38;5;241m=\u001b[39m data\n", - "File \u001b[1;32mc:\\users\\vhirtham\\pycharmprojects\\bam\\weldx\\weldx\\core.py:1435\u001b[0m, in \u001b[0;36mGenericSeries._check_constraints_discrete\u001b[1;34m(cls, data_array)\u001b[0m\n\u001b[0;32m 1433\u001b[0m ref[k][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalues\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m _vals[k]\n\u001b[0;32m 1434\u001b[0m \u001b[38;5;28mprint\u001b[39m(ref)\n\u001b[1;32m-> 1435\u001b[0m \u001b[43mut\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxr_check_coords\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_array\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mref\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\users\\vhirtham\\pycharmprojects\\bam\\weldx\\weldx\\util\\xarray.py:497\u001b[0m, in \u001b[0;36mxr_check_coords\u001b[1;34m(coords, ref)\u001b[0m\n\u001b[0;32m 493\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m 495\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m coords:\n\u001b[0;32m 496\u001b[0m \u001b[38;5;66;03m# Attributes not found in coords\u001b[39;00m\n\u001b[1;32m--> 497\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not find required coordinate \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 499\u001b[0m \u001b[38;5;66;03m# only if the key \"values\" is given do the validation\u001b[39;00m\n\u001b[0;32m 500\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalues\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m check \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39mall(coords[key]\u001b[38;5;241m.\u001b[39mvalues \u001b[38;5;241m==\u001b[39m check[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalues\u001b[39m\u001b[38;5;124m\"\u001b[39m]):\n", - "\u001b[1;31mKeyError\u001b[0m: \"Could not find required coordinate 's'.\"" + "data": { + "text/plain": [ + "\n", + "Values:\n", + "\t[[1]\n", + " [1]\n", + " [0]]\n", + "Dimensions:\n", + "\t('c', 's')\n", + "Coordinates:\n", + "\tc = ['x' 'y' 'z'] None\n", + "\ts = [1] \n", + "Units:\n", + "\tmm" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sps.evaluate(s=DataArray(Q_([1],\"\"),dims=[\"s\"], coords=dict(s=DataArray(Q_([1],\"\"),dims=[\"s\"], coords=dict(s=DataArray(Q_([1],\"\"), dims=[\"s\"]).pint.dequantify())))))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0fc5a100-5bb3-4b4a-ac0c-e888429eb1f5", + "metadata": {}, + "outputs": [], + "source": [ + "t = DataArray([1,3,5], dims=[\"a\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8f4fd0fa-4725-48b7-b22a-722aa409bb71", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray (a: 3)>\n",
+       "array([1, 3, 5])\n",
+       "Coordinates:\n",
+       "  * a        (a) int32 1 3 4
" + ], + "text/plain": [ + "\n", + "array([1, 3, 5])\n", + "Coordinates:\n", + " * a (a) int32 1 3 4" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t.assign_coords(dict(a=[1,3,4]))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fe85a016-29a4-4838-be67-8a8dbe64a8d7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray (s: 1)>\n",
+       "<Quantity([1], 'dimensionless')>\n",
+       "Coordinates:\n",
+       "  * s        (s) int32 1
" + ], + "text/plain": [ + "\n", + "\n", + "Coordinates:\n", + " * s (s) int32 1" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DataArray(Q_([1],\"\"),dims=[\"s\"], coords=dict(s=DataArray(Q_([1],\"\"), dims=[\"s\"]).pint.dequantify()))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f23408a2-60fe-4397-b55f-865c06027829", + "metadata": {}, + "outputs": [], + "source": [ + "a = DataArray(Q_([1,2,3],\"mm\"), dims=[\"c\"]).pint.dequantify()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "3c1a153f-4111-4ee7-b3eb-1c000fb87f93", + "metadata": {}, + "outputs": [], + "source": [ + "b = DataArray(Q_([2,3,5],\"\"), dims=[\"c\"], coords=dict(c=a))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "4191d792-1c47-4062-bfa2-0f0105890150", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "array([1, 2, 3])\n", + "Coordinates:\n", + " * c (c) int32 1 2 3\n", + "Attributes:\n", + " units: mm\n" ] } ], "source": [ - "sps.evaluate(s=\"1\")" + "print(b.coords[\"c\"])" ] }, { "cell_type": "code", "execution_count": null, - "id": "0fc5a100-5bb3-4b4a-ac0c-e888429eb1f5", + "id": "469b0ca2-faf6-4205-aaa0-3adfd4e187f2", "metadata": {}, "outputs": [], "source": [] diff --git a/weldx/core.py b/weldx/core.py index 3a8d593cc..2dfcd1d13 100644 --- a/weldx/core.py +++ b/weldx/core.py @@ -276,7 +276,6 @@ def evaluate(self, **kwargs) -> Any: k: v if isinstance(v, xr.DataArray) else xr.DataArray(v) for k, v in self._parameters.items() } - return self.function(**variables, **parameters) @@ -1026,7 +1025,6 @@ def _init_discrete( else: # todo check data structure pass - # check the constraints of derived types self._check_constraints_discrete(data) self._obj = data @@ -1255,8 +1253,15 @@ def _evaluate_expr(self, coords: list[SeriesParameter]) -> GenericSeries: """Evaluate the expression at the passed coordinates.""" if len(coords) == self._obj.num_variables: eval_args = {v.symbol: v.data_array for v in coords} - print(eval_args) + # for k, v in eval_args.items(): + # v.assign_coords(dict(k=v)) data = self._obj.evaluate(**eval_args) + + # TODO: Discuss - This might be done before by assigning coords to the + # eval_args that go into the math expression. Might need tweaks in + # `SeriesParameter` + for k, v in eval_args.items(): + data = data.assign_coords({k: v.pint.dequantify()}) return self.__class__(data) # turn passed coords into parameters of the expression From ce6fa6b534f9861bdfd144340bc00f972e259d6d Mon Sep 17 00:00:00 2001 From: vhirtham Date: Fri, 4 Feb 2022 14:27:11 +0100 Subject: [PATCH 05/70] Run pre-commit --- tutorials/01_03_geometry.ipynb | 4 +- tutorials/SpatialSeries.ipynb | 87 +- tutorials/TraceSegmentSpS.ipynb | 885 +-------------------- tutorials/welding_example_02_weaving.ipynb | 4 +- 4 files changed, 65 insertions(+), 915 deletions(-) diff --git a/tutorials/01_03_geometry.ipynb b/tutorials/01_03_geometry.ipynb index 2dc13da5c..d54699e6c 100644 --- a/tutorials/01_03_geometry.ipynb +++ b/tutorials/01_03_geometry.ipynb @@ -384,9 +384,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python (weldx)", + "display_name": "", "language": "python", - "name": "weldx" + "name": "" }, "language_info": { "codemirror_mode": { diff --git a/tutorials/SpatialSeries.ipynb b/tutorials/SpatialSeries.ipynb index 83f87fa54..d018dccb3 100644 --- a/tutorials/SpatialSeries.ipynb +++ b/tutorials/SpatialSeries.ipynb @@ -2,15 +2,16 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "759924f6-2e78-4aba-9750-fe1870344af3", "metadata": {}, "outputs": [], "source": [ - "from weldx.core import SpatialSeries, GenericSeries\n", - "from weldx import LocalCoordinateSystem, Q_\n", "import numpy as np\n", - "from xarray import DataArray" + "from xarray import DataArray\n", + "\n", + "from weldx import Q_, LocalCoordinateSystem\n", + "from weldx.core import GenericSeries, SpatialSeries" ] }, { @@ -23,46 +24,18 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "ccd72f65-3173-4481-bb81-07692fa2fa06", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'s': {'dimensionality': ''}, 'c': {'values': ['x', 'y', 'z']}}\n" - ] - }, - { - "data": { - "text/plain": [ - "\n", - "Values:\n", - "\t[[1 2 3]\n", - " [4 5 6]]\n", - "Dimensions:\n", - "\t('s', 'c')\n", - "Coordinates:\n", - "\ts = [0 5] \n", - "\tc = ['x' 'y' 'z'] \n", - "Units:\n", - "\tm" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "spsd = SpatialSeries(\n", - " Q_([[1,2,3], [4,5,6]], \"m\"),\n", - " dims=[\"s\", \"c\"], \n", + " Q_([[1, 2, 3], [4, 5, 6]], \"m\"),\n", + " dims=[\"s\", \"c\"],\n", " coords=dict(\n", - " s=Q_([0,5], \"\"),\n", - " c=[\"x\", \"y\", \"z\"], \n", - " )\n", + " s=Q_([0, 5], \"\"),\n", + " c=[\"x\", \"y\", \"z\"],\n", + " ),\n", ")\n", "spsd" ] @@ -77,46 +50,24 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "3fbd63fa-c3ba-4c9b-9a87-779e130db08b", "metadata": {}, "outputs": [], "source": [ "exp = \"a*s + b\"\n", "params = dict(\n", - " a=DataArray(Q_([1,1,1], \"mm\"), dims=[\"c\"]), \n", - " b=DataArray(Q_([1,1,1], \"mm\"), dims=[\"c\"]), \n", + " a=DataArray(Q_([1, 1, 1], \"mm\"), dims=[\"c\"]),\n", + " b=DataArray(Q_([1, 1, 1], \"mm\"), dims=[\"c\"]),\n", ")" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "5a60c890-aac4-499b-bdbc-d54a3529b8c5", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Expression:\n", - "\ta*s + b\n", - "Parameters:\n", - "\ta = [1 1 1] mm\n", - "\tb = [1 1 1] mm\n", - "Free Dimensions:\n", - "\ts in \n", - "Other Dimensions:\n", - "\t['c']\n", - "Units:\n", - "\tmm" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "spse = SpatialSeries(exp, parameters=params)\n", "spse" @@ -133,9 +84,9 @@ ], "metadata": { "kernelspec": { - "display_name": "weldx", + "display_name": "", "language": "python", - "name": "weldx" + "name": "" }, "language_info": { "codemirror_mode": { diff --git a/tutorials/TraceSegmentSpS.ipynb b/tutorials/TraceSegmentSpS.ipynb index b1483c388..db22d2507 100644 --- a/tutorials/TraceSegmentSpS.ipynb +++ b/tutorials/TraceSegmentSpS.ipynb @@ -2,19 +2,20 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "228ec7cb-5828-459e-a5a8-349a093ec1b1", "metadata": {}, "outputs": [], "source": [ - "from weldx import Trace, LinearHorizontalTraceSegment, Q_, GenericSeries\n", - "from weldx.core import SpatialSeries\n", - "from xarray import DataArray" + "from xarray import DataArray\n", + "\n", + "from weldx import Q_, GenericSeries, LinearHorizontalTraceSegment, Trace\n", + "from weldx.core import SpatialSeries" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "dae8950a-ca96-47bc-8a33-a604fd1fe86d", "metadata": {}, "outputs": [], @@ -24,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "9c33e35c-65e7-46ab-8e9d-27d6eb277d75", "metadata": {}, "outputs": [], @@ -34,30 +35,17 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "ae841131-305e-4b89-a8fe-2082c24129b0", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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coords=dict(c=a))" + "b = DataArray(Q_([2, 3, 5], \"\"), dims=[\"c\"], coords=dict(c=a))" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "4191d792-1c47-4062-bfa2-0f0105890150", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "array([1, 2, 3])\n", - "Coordinates:\n", - " * c (c) int32 1 2 3\n", - "Attributes:\n", - " units: mm\n" - ] - } - ], + "outputs": [], "source": [ "print(b.coords[\"c\"])" ] @@ -971,9 +170,9 @@ ], "metadata": { "kernelspec": { - "display_name": "weldx", + "display_name": "", "language": "python", - "name": "weldx" + "name": "" }, "language_info": { "codemirror_mode": { diff --git a/tutorials/welding_example_02_weaving.ipynb b/tutorials/welding_example_02_weaving.ipynb index cc2068967..8681ca924 100644 --- a/tutorials/welding_example_02_weaving.ipynb +++ b/tutorials/welding_example_02_weaving.ipynb @@ -620,9 +620,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python (weldx)", + "display_name": "", "language": "python", - "name": "weldx" + "name": "" }, "language_info": { "codemirror_mode": { From f1962fcfcda1cc35a832d3a34a725681fbdba55d Mon Sep 17 00:00:00 2001 From: Hirthammer Date: Mon, 7 Feb 2022 17:37:46 +0100 Subject: [PATCH 06/70] Add sympy length calculation --- tutorials/TraceSegmentSpS.ipynb | 842 +++++++++++++++++++++- tutorials/sympy_diff.py | 28 + tutorials/trace_segment.py | 57 ++ weldx/geometry.py | 2 +- weldx/tests/asdf_tests/test_weldx_file.py | 2 +- weldx/welding/groove/iso_9692_1.py | 2 +- 6 files changed, 908 insertions(+), 25 deletions(-) create mode 100644 tutorials/sympy_diff.py create mode 100644 tutorials/trace_segment.py diff --git a/tutorials/TraceSegmentSpS.ipynb b/tutorials/TraceSegmentSpS.ipynb index db22d2507..114097358 100644 --- a/tutorials/TraceSegmentSpS.ipynb +++ b/tutorials/TraceSegmentSpS.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "228ec7cb-5828-459e-a5a8-349a093ec1b1", "metadata": {}, "outputs": [], @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "dae8950a-ca96-47bc-8a33-a604fd1fe86d", "metadata": {}, "outputs": [], @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "9c33e35c-65e7-46ab-8e9d-27d6eb277d75", "metadata": {}, "outputs": [], @@ -35,17 +35,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "ae841131-305e-4b89-a8fe-2082c24129b0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+       "array([1, 3, 5])\n",
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+       "  * a        (a) int32 1 3 4
" + ], + "text/plain": [ + "\n", + "array([1, 3, 5])\n", + "Coordinates:\n", + " * a (a) int32 1 3 4" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t.assign_coords(dict(a=[1, 3, 4]))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "fe85a016-29a4-4838-be67-8a8dbe64a8d7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray (s: 1)>\n",
+       "<Quantity([1], 'dimensionless')>\n",
+       "Coordinates:\n",
+       "  * s        (s) int32 1
" + ], + "text/plain": [ + "\n", + "\n", + "Coordinates:\n", + " * s (s) int32 1" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "DataArray(\n", " Q_([1], \"\"),\n", @@ -131,7 +916,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "f23408a2-60fe-4397-b55f-865c06027829", "metadata": {}, "outputs": [], @@ -141,7 +926,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "3c1a153f-4111-4ee7-b3eb-1c000fb87f93", "metadata": {}, "outputs": [], @@ -151,10 +936,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "4191d792-1c47-4062-bfa2-0f0105890150", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "array([1, 2, 3])\n", + "Coordinates:\n", + " * c (c) int32 1 2 3\n", + "Attributes:\n", + " units: mm\n" + ] + } + ], "source": [ "print(b.coords[\"c\"])" ] @@ -170,9 +968,9 @@ ], "metadata": { "kernelspec": { - "display_name": "", + "display_name": "Python (weldx)", "language": "python", - "name": "" + "name": "weldx" }, "language_info": { "codemirror_mode": { @@ -184,7 +982,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.9" } }, "nbformat": 4, diff --git a/tutorials/sympy_diff.py b/tutorials/sympy_diff.py new file mode 100644 index 000000000..1b4f5b492 --- /dev/null +++ b/tutorials/sympy_diff.py @@ -0,0 +1,28 @@ +import sympy + +s = sympy.symbols("s") +exp1 = 1 * s**2 + 0 * s + 0 +exp2 = 0 * s**2 + 1 * s + 0 +exp3 = 0 * s**2 + 0 * s + 1 + + +temp = sympy.sqrt(exp1.diff(s) ** 2 + exp2.diff(s) ** 2 + exp3.diff(s) ** 2) +print(temp) +print(sympy.integrate(temp, (s, 0, 1)).evalf()) + + +from weldx import MathematicalExpression + +params = dict(a=[1, 0, 0], b=[0, 1, 0], c=[0, 0, 1]) +me = MathematicalExpression("a * s**2 + b * s + c", parameters=params) + +der_sq = [] +for i in range(3): + ex = me.expression + subs = [(k, v[i]) for k, v in me.parameters.items()] + + der_sq.append(ex.subs(subs).diff("s") ** 2) +print(der_sq) +expr_l = sympy.sqrt(der_sq[0] + der_sq[1] + der_sq[2]) +print(expr_l) +print(sympy.integrate(expr_l, ("s", 0, 1))) diff --git a/tutorials/trace_segment.py b/tutorials/trace_segment.py new file mode 100644 index 000000000..206651eb5 --- /dev/null +++ b/tutorials/trace_segment.py @@ -0,0 +1,57 @@ +import matplotlib.pyplot as plt +import sympy +from xarray import DataArray + +from weldx import ( + Q_, + U_, + GenericSeries, + LinearHorizontalTraceSegment, + LocalCoordinateSystem, + Trace, +) +from weldx.core import SpatialSeries + + +class SDTraceSegment: + def __init__(self, series): + self._series = series + + def _get_squared_derivative(self, i): + me = self._series.data + exp = me.expression + # todo unit stripped -> how to proceed? how to cast all length units to mm? + subs = [(k, v[i].data.to_base_units().m) for k, v in me.parameters.items()] + return exp.subs(subs).diff("s") ** 2 + + @property + def length(self) -> float: + + der_sq = [self._get_squared_derivative(i) for i in range(3)] + expr = sympy.sqrt(der_sq[0] + der_sq[1] + der_sq[2]) + mag = float(sympy.integrate(expr, ("s", 0, 1)).evalf()) + print("ohoh") + return Q_(mag, Q_(1, "mm").to_base_units().u).to("mm") + + def local_coordinate_system(self, position: float) -> LocalCoordinateSystem: + coords = self._series.evaluate(s=position).data.transpose()[0] + return LocalCoordinateSystem(coordinates=coords) + + +expr = "a*s**2 + b*s + c" +params = dict( + a=DataArray(Q_([0, 0, 1], "mm"), dims=["c"], coords=dict(c=["x", "y", "z"])), + b=DataArray(Q_([1, 0, 0], "mm"), dims=["c"], coords=dict(c=["x", "y", "z"])), + c=DataArray(Q_([0, 0, 0], "mm"), dims=["c"], coords=dict(c=["x", "y", "z"])), +) +series = SpatialSeries(expr, parameters=params) + +segment = SDTraceSegment(series) +print(segment.length) +trace = Trace([segment, segment]) +print(trace.length) +trace.plot(Q_(0.1, "mm")) +plt.show() + + +# todo : check s=0 -> [0,0,0] diff --git a/weldx/geometry.py b/weldx/geometry.py index f6af94557..47141b5a6 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1805,7 +1805,7 @@ def rasterize(self, raster_width: pint.Quantity) -> pint.Quantity: raster_data = np.hstack([raster_data, data_point]) last_point = self._coordinate_system_lookup[-1].coordinates.data[:, np.newaxis] - return np.hstack([raster_data.m, last_point]) + return np.hstack([raster_data, last_point]) @UREG.check(None, "[length]", None, None, None) def plot( diff --git a/weldx/tests/asdf_tests/test_weldx_file.py b/weldx/tests/asdf_tests/test_weldx_file.py index 5f447ab12..8f44c53e7 100644 --- a/weldx/tests/asdf_tests/test_weldx_file.py +++ b/weldx/tests/asdf_tests/test_weldx_file.py @@ -377,7 +377,7 @@ def get_mem_info(): diff = after - before # pytest increases memory a bit, but not as much as our large array would # occupy in memory. - assert diff <= large_array.nbytes * 1.1, diff / 1024 ** 2 + assert diff <= large_array.nbytes * 1.1, diff / 1024**2 assert np.all(WeldxFile(fn)["x"] == large_array) @staticmethod diff --git a/weldx/welding/groove/iso_9692_1.py b/weldx/welding/groove/iso_9692_1.py index d9fdceb09..db13967d8 100644 --- a/weldx/welding/groove/iso_9692_1.py +++ b/weldx/welding/groove/iso_9692_1.py @@ -548,7 +548,7 @@ def to_profile(self, width_default: pint.Quantity = None) -> geo.Profile: # calculations: x_1 = np.tan(alpha / 2) * h # Center of the circle [0, y_m] - y_circle = np.sqrt(R ** 2 - x_1 ** 2) # skipcq: PTC-W0028 + y_circle = np.sqrt(R**2 - x_1**2) # skipcq: PTC-W0028 y_m = h + y_circle # From next point to circle center is the vector (x,y) x = R * np.cos(beta) From 6613907e326aabba5a2e818cfad3885d671cb37d Mon Sep 17 00:00:00 2001 From: Hirthammer Date: Tue, 8 Feb 2022 14:07:55 +0100 Subject: [PATCH 07/70] Fix unit error --- tutorials/TraceSegmentSpS.ipynb | 845 ++------------------------------ tutorials/trace_segment.py | 6 +- weldx/geometry.py | 35 +- 3 files changed, 72 insertions(+), 814 deletions(-) diff --git a/tutorials/TraceSegmentSpS.ipynb b/tutorials/TraceSegmentSpS.ipynb index 114097358..b1bb42572 100644 --- a/tutorials/TraceSegmentSpS.ipynb +++ b/tutorials/TraceSegmentSpS.ipynb @@ -10,7 +10,8 @@ "from xarray import DataArray\n", "\n", "from weldx import Q_, GenericSeries, LinearHorizontalTraceSegment, Trace\n", - "from weldx.core import SpatialSeries" + "from weldx.core import SpatialSeries\n", + "from weldx.geometry import DynamicTraceSegment" ] }, { @@ -40,16 +41,25 @@ "metadata": {}, "outputs": [ { - "data": { - "image/png": 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Ym6HZknZ/nqEtSdq7urqYnBBf7UDvClZWWcyftHSihKKoTQAGCSHfURS1huehWcWrBoNeNlWr1TAYDE7v39HRgYGBAc7p2Xx6KDU1NUwRHndf1JZmaPoHNjExgfDwcMhkMkRHR/vlDG2vAz19bv64qgQAqamp6OrqMnkKQK/ZZt0A0ixs8yMAV1MUdQWAIAARFEW9Swi51Y1D9o7BMF8JcWX/wcFBBAcHcxaQ8eWhGAwGVFZWIjs7m1MMhQvmkvbx8XHI5XITSbuvdmm3B7sDvdFoZM6NLZgLDQ31q9yIsrIyNDU1gaKoTAA9AG4C8GOzzT4GsH0mvrEMwCghpA/AIzMPzHgYD7rbWABeMBhcV0IMBgM6OjogkUhc6jPChk+J++joKIqKinxG4s6eodmSdrroTGRkJHQ6HfR6vd/N0CKRiJHrA4BGo2E8q9HRUTQ0NPiFpF0ikeCll17ClVde+Rmml1XfIoTUURR1DwAQQl4FUIHpJdVmTC+r3u61AcPDBoPrSohGo0FlZSUiIiI4Sa/dIXFnX8C+iKWiM3K5HGfPnvX5Lu32CAwMRHJyMqKjo9HU1IT4+Hi/kbRfccUVIITksZ+bMRT0/wmAe20dgxByCMAhd4zPHI8ZDKPRCJ1O57KxmJiYQHV1NebPnw+tVgu1Wu3SOGiJe1hYGK8Sd7rStT9AS9oDAwNRVlbGzNBzoegMRVEmkna6A70gaecHjxgMvgRkdHq2qysYtIciSNxNoWfouVh0xloH+paWFgQGBvq1XN8buN1g8CUgKy0t5ZTlqNfr8d133yEvL0+QuNvAUtEZ9gztz5J2c7k+nY5P97cVJO32cZvB4ENA1tLSgrGxMZSWlnIKzI2MjGB0dBTLli3jrUeqrxT7dTcXgqSdPjdB0m4ft3oYrq6EmAvIuMzivb29aG9vR1RUFCdjQQvI+PBQRkZG/HIWc0bS7o/nZk3S7u/9bS1BUVQ0pvM7GBvAFrVZw20Gg74FoUvqOYpOp0NlZSXi4uKQkZHh8vuzJe4FBQVobm52+VjmMRRXoT2UgIAANDU1zZkZ2l7RGX/DlqSd7m+rVquh1Wr98nujKOppAFsBtOD7zFIC4HJ7+/rUAjydnp2VlYWEhASXj8P2UEpKSqBSqVw+ljsk7nTxYjo+UF9fD71ej+joaBiNRoSFhbn8Pt7C1gw9MTEBvV4PAH45Q1vqb1tbW4vGxkYA8Ecx4I0AsgkhTtd98BmDMTExgfr6eixevJhTPgPbQ+EqcW9pacHIyAivEvfw8HBotVpQFGUyQ+v1eoyMjKCjowNDQ0NQKpV+LWlnz9Dnz59HUFAQ+vr65kwH+pCQEOTl5UEsFmN4eBg9PT2MGLC6utrh68XVehgURaUB2AUgEYARwF8JIf/r4GnUAogCMOjg9gw+YTCmpqYgl8uxdOlSXgRkfHgoarUaarWad4m7tdRliUQCmUwGlUoFsViMiIiIOSNpp/M+MjIyZknajUYjYzz8VTDHFgOOj4/jH//4B9rb27F69WrcfPPN+NnPfmZxX471MPQAfjFjPMIBfEdR1Odm+1rjWQBnKYqqBaChnySEXG1vR68bjI6ODoyPj2PRokWcjAVfAjK6CI9IJMKiRYs4eSiuStwdlbT7a9GZuSxpj4iIwDPPPINvvvkG//nPf2zGzrjWw8C0xB2EkHGKouoxLXt3xGD8HcDvANRg2jtxGK8ZDEIIE12Pj4/n5PK7Q+Le2trqsrHgW+JuTdJOZ5fORUl7d3c3ADDehz9K2u0FfXmohwEAoCgqA8ASAN86ODQ5IeRPDm5rglcMhl6vR3V1NSIiIrBgwQJObQY7OjowODiIsrIyTjOSuYfS2trq0nHYRXjcIXG/EGZoW5J2f+1Abwku9TCYFykqDMAHAB4ghIw5+NbfURT1LKaVsOxbEu8tq1pDo9Hg7NmzSEtLQ0pKisvHIYSgvr4eOp0OS5cu9QmJu1qtxrlz5ziXCXQGSzO0XC43KTojk8n8XtJu3oHe33vAApzrYYCiKCmmjcU/CCEfOvHWS2b+Xc56zveWVdkCMi7r80ajEb29vUhISMCCBQt8QuKu0+lQU1OD/Px8Jq3a0zgiaffXLu3mHegtCebCwsIumHoYM6snbwKoJ4T8wZn3JYRc5uqYPXbV0G3oCgsLOeUZaDQatLa2Ijw8HLm5uS4fh2+J+8TEBEpLSzkFXPnGkqSd3aVdq9VicnLSL2doS4K5gYEBjIyMMLeEvi6Y41gPYyWAzQBqKIqqnHnuV4SQCnvvS1FUFIAtADJgmul5v90xO3RmHOnp6UFXVxeWLl3KKaeA9lASEhI43Z+7Q+IeGRnp04pH8y7tGo0Gp0+fnhOSdlowFxQUBJ1Oh9zcXL+RtLtaD4MQ8g0sxzccoQLACfjaKgkhBE1NTZiYmEBZWRmnL4vWLBQWFmJiYsLlehh8StxpD2Xp0qWorKx0+VjeIDAwEAEBASgoKBAk7RceQYQQl9oSuNVg1NbWQiQSobi4mLOAjC1xn5iYcOk4tIfCh4CsuroaoaGhnD0UX8CSpF2pVPrFDG0Pe5J2f+5vy4F3KIr6CYBPYbpKorS3o1sNRmZmJqcZik+Ju1arRVVVFW8CMq4eii8THBzMzNC0upaeoWnBXExMjF/O0OaSdrZc/wKStGsB/B7AozAVn2XZ29GtBiMsLMxptSqN0WhEbW0tpFIpZ4n7wMAAJiYmsHLlShMB2evftKMgJQLLM2OY5060KVHTM4Z8C5MNXx6KP2FN0t7Q0DAnJO3m/W3nsqSdxc8B5BBC5M7u6JNra3xL3JVKJaKiomapTQtSIvDAv2vwzDULcfn8OHzbPowH/l2DnTcWwNhnuhzOp8S9v7+fCdL5G45K2v1thrYlaWf3t/VHwZwF6jC94uI0PmcwpqammP4efEncFy1axEiR2SzPjMHOGwtwx66zCJSIoDcS3L06A4uTI1DNKhtKx1D4kLjTS34DAwNzYoa2Jmmnu7TTs7e/YUnSzhbM6XQ6jI+PIzAw0B9jWAYAlRRFfQXTGIZvLKs6Cp2ezbfE3VY9jOWZMbg0T4Yvzg9BKqbwp69a8cqRNuREUriKtCMrSIVwMsk5hsKWuNMXmaUZmk6+8jdszdBDQ0OYmppCYmKiX87Q5mJA+voaGBhAa2urP/a3/c/Mw2l8xmAMDw9jYGCANwGZoxL3E21KnOkcwc8uzcTuU924e3UGhia02F/ViecPTCsNU6KCcGl/My7Nk2FZRjSCA5xbKWBL3AMDA6HVTtctsTRDt7S0oLe3F4ODg34taWfP0LT+RaPRMDM0nbLur4K5wMBA5OTkMKt2crmc6W/72WefQSKRwGg02vUaXa2HMfPaD2Zes7ivNQghf3fqhFn4hMEYGxuDwWDgXUBmjxNtSiZmsTwzBssyo/HAv2vw4g8XYgllRERiOlrVwTjSpMD/VfbivVPdCJCIsCwjGpfmxuKSXBnSY60bN3aZQFribs09p2fo6Oho5oc2lyTtISEhSE5OnpOCOVoMSAvmzp49i76+PhQXF+Oaa67B008/bXFfLvUwKIoSA/gLgPU29uUdrxoMWuKu1WqxcOFCTheLKwKymp4xxlgA07cnz1+Th/0n67EuNQDLCvKwDMDNZWnQ6o041TGMw41yHG5S4H/2NQL7GpERG4JLcmORDB1y9UbQsT6uEndrkvba2loQQgRJu48SEBCAO+64A7t27cLp06fR22uuJfseLvUwMJ3W3UwIsbUv73jNYLAl7rGxsZwCfq5K3H+yKsPk79HRUYgVrfjFlUU4d870cw+QiLAyOxYrs2Pxq41Ah2IKR5qmjce/TvdAozdi53fHsSIrBquzoxGjGcCCtDheJO6CpN0/Je1isdhmOwqO9TAsPW++rwkURT0CYD8h5KzdwVvBK9+AucS9vr7epeOwi/B4WuKeHhuCzbHzsHn5PKi0Bvzj4Cl06cNxpFmJrxqml7dz4w24JFePS3NjUTIvClIxP6sgtmZoiqIY78NfZ2hrknZ/70BvDsd6GDbrZFihDcAOiqKKAFQB2AfgACFk2M5+DB43GBMTE6iqqsKCBQs4S9wrKysRFhbGWeLe0dGBgYEBlyXuwQFiLEmQ4tr0BKyPGUZoUj4qB7Q43KTArhOdePNoB0IDxViZFYNLcmVYnh6OhAh+cjAszdC0InVychIRERGQyWRzRtKuVCrR3t6Oqakpxvvwx3MDONfDCLDyvFUIIf8E8E8AoChqCYAfAPhwJh5yENPex0lbx/Dop0wLyIqKijhJ3HU6Hbq7u5GTk8O5CA9fEneNRoPz58+jpKQEISEhWJIN3H5xOiY0epxoVeJIkwKHm+Q4UD8EAFiQEIrV2TFYnRODgpQISET8zJbmkna6oA4tafdnUVlgYKDJudGCufb2dkgkEkRGRkKv14MQ4hfeB8d6GEMAcu3sa5WZ25KzAJ6lKCoC08HTOwH4hsGgJe5ce6ROTEygtbUVMpmMk7HgW+KuUqmwbNmyWT/EsEAJ1i2Mx7qF08HLhoEJfHV+AF+3DOOt4114/VgXIoIkWJkVjdU5McgINEAWzs/XIhKJmBkawKyiMxqNBnK53K8l7ewu7f39/ZicnMSpU6f8QjDHpR4GIURPUdR2ACb7ujKOmdJ+H8w8bI/ZlTdwcjBobm7mVeKenp7OaUzukLhHRkbaDThSFIX5CWHIjA7AHRfPw5haj+Ntw/i6WYlvWpTYd24IFIA8WSAuX6jB6uwY5CeHQ8TTbGledObbb7/FyMjInJG0JyQkYHx8HPn5+YxgrrW1FQEBAT4raXe1HsbMaxWYNigew60Gw2g0oqamBlKplLPEne2hKJVKl+th8FUm0NxDOXPGbv3UWUQESbBhYRw2LIyDkRDU90/g0zNtONOrxqtfd+KVrzsREyLFyuxorM6OwcVZ0YgM5mcVRCQSQSKRICcnB8D0DO0vRWfsYS5ppzvMNTY2QqPR+HUHem/jVoNRWVmJ6OhoTh4BnxJ3vgRkfHkobEQUhfykcEQURmNLiQRBkTIcax3G1y1KfN2sxCc1gxBRQFFKBFbnxGB1dgzmJ/C3UsAuOmMwGJj4wFwoOjOXO9C7AkVRXwB4kV3Oj6KovxJC7rK3r1sNRkFBAafZiU+Ju0qlQmNjI2cBGVeJOy1cAqbX6emm1eZEh0hx5eJ4XLk4HgYjQW3fOL5unjYefzrUjj8dakdcWABWZU/HPpZnRCM8SIK3jndhcVI4LsqIYo51sn0EtX3j2LbCek4AG7FYPKvozFyWtNMTCd3f1l/PzQkyAfw3RVFlhJCnZp4rdWRHtxoMqVTqcj0MPiXuHR0d0Gg0WL16tdc8FEIIjEYjjEYj87nQn41er4dIJLJ6gYpFFIpSIlCUEoHtl2ZAPqHF0VYlvm4exsHzcvxf1QAkIgpL0iKQHhOMN4914Q8/XIhlmdE42T6CB/+vHi9ct9Dl874QJO10f1ta09TY2Mj0gPXH/rZ2GAGwFsCfKIr6BMCtju7ok4vXfEvcCSGIjIzkZCy4SNzZxoKiKJNxGI1GGAwG5l+NRsMoWa15H7KwAFxTmIhrChOhMxhR3TPO3LrsOdsPALhrdw0WJIShU6nC765bYOJxcMGWpN3fi85Yk7Rb6m/r51CEED2An1EUtRXANwAc6o3hcwbDHRL3+Ph4NDQ0uHQctoDMlRiKubEwv61iexbd3d0YHR1Famqqifdh69ZFKhZh6bxILJ0XiQcuy0T/mAbftCjx9olunOufrn36wJ5zKJ0XycQ+0mOCeYl92JK0s7u0GwwGzu/laez1t9Xr9RgaGkJCQoLfiQEBsFdh3qYoqgZWVmLM8SmDQdcX4FviPjXlUnEhkyI8rgjICCEwGAxMIpG1Hykd2J2cnMTSpUuZuA/b+6D/D0wbGWsGJDEiEPOigzGm1uPOi9Pwz+96cXFmNJrlU3j+81Y8/3kr0qKDsDo7BglGAwp0BgRJ+VkFsTZDKxQKjI2NIS4uzq/l+mwx4NmzZ6HRaCz2t3UViqJiAPwL08KydgA3WkrbtiZrpyjq9wCuwnTNzhYAtxNCRsz3J4S8Zvb3dwC2OTJGnzEYdNJNaWmpRyXu1iCE4LvvvmOK8Dh7gbONhS1DQxulwMDAWQlkbO+DNhr0gzZC5saDHbO4KCMKKzKjmb+TowLxTfP0ysuHlf1Q6414pfo4LsqIwursGKzKiUZqFD+rIOwZWqPRICYmBkajET09PSZyfT8qOsNA31ampqYiJycHOp0OCoUCXV1dGB8fx1tvvQWKojAyMuLsNfgwgC8IIc9RFPXwzN//bfbetmTtnwN4ZCap63cAHjHfnyteNxiEECiVSgQGBvpMj1SVSoXJyUlkZ2e71COVEAK9Xg8ANs9Hq9UyjZlsqRrp47CNB/tWh+191PSO4flr5zMxi4syovDCdQtR2zeOizKicFNpMG4qTYZaZ8B7B09hQCTDkWYljjQrgc+AzNhg5tZl6bxI3gRzEokEUVFRzAxNC+boojP+LGmXSqWMYM5oNEKtVmPHjh244oorsHjxYvz1r3919FDXAFgz8/+/AziE2T/4i2BF1k4IOcDa7gSAH7l2RtbxqsGgJe4ikQiZmZmcjEV7ezuGhoZ4K8ITFBTkkrGgbyFs3YIA04Hd6upqZGdnIy4uzqn3oD8n9q0L/dhSlgRgOoZDex8XZUTNCnoGScUokImxrSwHD5cTdChV+LpFiW9ahrH7dC92fduDkAAxlmdEYXVODFZlxyAxgp+VgrksaReJRLj00ksRGRmJY8eOQaPR2N/pexIIIX0AMKMXibewjaOy9m2Yvr3hFa99G2yJ+9jYmMuzirsk7s52MqNvQegfqa3zGRkZQX19PfLz83mJuDvifdC3LZY+H4qikBEbgozYEGy+KBVTWgNOto8wKy9fNioAAHnxoYz3UZTKr2BurkrazZdj161bh/7+fpPn6urqajHdI8QR7MraKYp6FIAewD8cHqiDeMVgjI+Po7q6mpG4j42NuXQcdhEeb0rc6boGfX19SExMtLnsOjAwgPb2dixZssQtmYWWvA/2si07cGqtXGBIgBhr8mKxJm86ualFPjWdNNaixN9PdOPNY10IDxRjRdZ0yvqq7BjIwvhZKZjrkvaDBw9aenoxAFAUNUBRVNKMd5EEYNDCttbk7pg5xm0ANgFYS9xQrt3jnzidMchV4q7X63H69GmmCI+rsD0UVyTutGcxf/58DA4Ooq6uDgaDgSlyGxkZCYqimAQypVKJkpISjwX6rHkfCoUCYrEYOp3OrveRExeKnLhQ3L4ibVqu3zbCGJAD9dPFghYlhk17HzkxWJwUDjFP3oe5pJ32PmhJuz8L5izwMYDbADw38+9HFrY5BSuy9pnVk/8GcCkhxLWlQTt41GD09PSgu7ubcxd3tVqN7u5uFBYW8iYgc8VDYa+EBAcHIyMjAxkZGSZr9vX19QgNDYVWq0VAQACKi4u9ltBEv+/AwAB6enqY1H36POhzsZX3ERYowboFMqxbIJuR608yty6vH+3Ea990IipYgpXZMViVHY2VWTGzjsFl/HQH+uzsbEYw19LSArVazazIGAwGvxPMzfAcgH9TFHUHgE4ANwAARVHJmF4+vcKOrP0lAIEAPp+5lk8QQu7hc4AeMRhsiXtpaSlniXtHRweSkpI4GQs6hkKLkpzF1koIe82eTiATiUTQaDQ4ffo0YmNjIZPJPJ6PQCehjY+Po6SkxOR7oL0P+vYFgEOxjwWJYViQGIafrJyHUZWOEcx90zKMvbWDoADkxkixOodg/WIxFiaG8SbXN+/SPjg4iNHRUZw5c8anJe3WIIQoMJ2ybf58L6ZrYtB/W5S1E0Jy3DpAeMBgsAVkfEncs7KyOGUPci0T6OhKiFqtRnV1NdLS0pCU9P3qBXvNPjw8HDKZzO35CHQVc6lUiqKiIosZp8Ds2AdtRGjjaMv7iAyWYmN+PDbmx8NICOp6x/FNyzA+r+vFWycH8ObJAcSGSrFqptLYisxoRATxWywoLCwMBQUFJpL2C7hLO++41WDodDqcOXMG8fHxnCXuzc3NGB8fR1lZGQYHB102GHQRHq4CMnvGYnx8HLW1tViwYAFTFQowXbOnVwTkcjk6OztBURSj0+AzH0Gn06G6uhpxcXGYN2+eQ/tYin04432IKAoFKREoSInA2kQNpGExqFUY8HWzEl81KvBR9QDEFFCcFjkTOI1GXjx/qyCCpN09uNVgVFdXIz09HfHxlpaTHYNPiTuXMoHOGAv6wiwsLERoaKjV7dgrAtnZ2UwB3/b2dkxMTCAyMhIymYxTPoJKpUJVVRWysrJc/h6cWXmxLteX4KpkGa4qSIDeSFDTM8YETnd+1YadX7UhPjyAqXO6PCMKoYHT58xVsm8uaae9DzrYfYFI2nnBrQZjyZIlnPbnU+I+MTEBQohbBGRsuru70dfXh5KSEqeXZ9kFfAkhGB0dhVwuR3t7O3PRy2Qyh/MRRkdHce7cOd7yPWjseR9sub6lH+C0FD8SS9Iicf9lmRgY0+Bo63TcY/+5IXxQ2Q+JiMLSGcFcTIgUD354Di/8cBEuyojiJNmnu7DRcn29Xo+RkZE5Idf3BG41GCKRyOV6GLTEPScnh7OH0tjYCKPR6JKH4oyArLm5GSqValZA0RUoimJWBAAwBXtbWlowNTWFqKgoxvuw9F6Dg4Noa2tDcXGxW4N+jngf7OcsGZCEiED8sDgJPyxOgs5gRGX3997HCwdbAQCyUCl+9q9abMyPw+EmJaOV4YpEIjGR69uStPtb0pg78MnMl5GREdTV1fEmcad/dO4SkBkMBtTV1SE4OBgFBQVuubACAwNNVgTYRW6lUilz0QcHB6OzsxNyudyj+R405t6HQqHA6OgoU3yHnQ1rTa5flh6FsvQo/HxtFnpH1fhmxnh806LEf6oGcPeqebzV92BjT9LO7m97oeJzBmNgYAAtLS0oKSnhNDOyi/CEh4e7VA9Dr9dbvbBpnBGQ8YV5kVu1Wg25XI7GxkaMjIwgICAAubm5Xr8fHxgYQFdXF5YuXcoYLktyfdo7sTTe5Mgg3Lg0GRmxIajsHsMPixPx7zN9uCh9tj6Gb6z1t62pqcHk5CS6urqQkJDgl/1tXcVnDAYhhHcBGe2hOFsPw2AwICAgAGfOnGFmbktxA1pAlpOT41J9T76ghXJDQ0NIS0tDZGSkSQFftvfhCdj5Huz6HgBMvA+24bAm1we+l+y/OBPDWJkVYyLh9wTm/W0rKysRHBxssb+tqwFqrvUwWK8/COD3AOIIIXKXBmMFnzAYhBB0dnZCIpFwFpDRHoorEnd2cLO4uBhardZq3GB8fBz19fVYvHgxp6IpfKDRaFBVVWWS70EbsKmpKcjlctTX10Or1TIp6+7KR6DzPSQSicV8Dxp7gjnauxOLxajtGzcxDuaSfW8gEokgk8mQmprKyPXlcjm6urrw2GOPMZ6nk7eoXOthgKKotJnXOnk6VRMoO/oUTuIVnU5nN+ip1+tx9OhRxMTEYPHixQ59uH19fVCr1cjMzDR5nvZQiouLTTyUqakpNDQ02Fy1oS9Y9j02G3bcYGBgAFqtFunp6UhMTERISIjXXNKJiQnU1tYiLy/P7r01XcBXLpdjeHgYISEhzMoLH/kIruR7WMLc86CxFfsAppeQm5ubUVBQ4PJ7OwpdOd7S59bd3Y1rr70WJSUlCAoKwt/+9jd7h6MAgKKoBgBrWOKzQ4SQ+SYbUtQKAE8SQjbM/P0IABBCnp35ew+ApzGtQymdUx6GWq1GZWUlQkJCkJKS4jWJu3lw09I46NZ8Y2NjCAkJQXFxMUZGRtDU1ASVSoXo6Gim6bGndAwKhQJNTU0oKCiwme9BY6mAr1wuR11dHVPAlxbMOfsZqlQqVFdXIzMzk9OqFvC99yGRSEy8DzrmYS9pzNukpKQgJCQEu3fvdnZXTvUwKIq6GkAPIaTKXROY1wwGLXFfuHAhBgYGXD4OV4m7o8umRqMR58+fBwBGQBYWFsYU7KVn7ubmZo/EDXp6etDb2+tSvgdgWsCXvSLQ19eH8+fPIzQ0lDkHe8en8z0WLVrEaVXLEuxlW7o9Aztl3RG5vi/hrnoYFEWFzByjnNsIbeMVgyGXy00k7q4aDHYRHlck7o6W0qONUnR0NDIyMizqMOhkH2B23IDtfXCdEdkFg/nI96CxtiJQXV0No9FoVTDnqXwPGltJYyqVivlOfdX7cGM9jGxMNyiivYtUAGcoirqIENI/6ygu4nGD0d3djZ6eHs5d3Gnlp6cEZPPmzXO4ZF9ISAjmzZuHefPmMXGDoaEhNDY2Ijg4mJm5nY0b2CoYzCfmKwI6nQ5KpRLd3d0YGxtjVgTUarXH63uwYXsfw8PDaGlpYbxMR+X6PobL9TBmJO7MLQxFUe3w5xgGnxL38fFx9PX1oayszOkiPK4IyBYuXOhyBXK+4gY6nQ5VVVUezfegkUqlSEhIQEJCAiOYa2howNTUFEJDQ9HV1QWZTOa1fIS+vj50dXWZVDFzRTDnA3Cth+F2PGIw6C7ugYGBvEjc+/v7kZqa6lZj4aiAzBkcjRuY6xi4FAzmG6PRiLa2NsTGxqKsrIyR63d2dmJ8fBwRERHM0rO7vQ46d2dkZAQlJSUm+Q/25Pq+6H1wrYdhtk8G3+MDPGAwtFotKisrkZCQwJvEnVZ2OoszArL+/n6XA4qOYh43mJychFwuR01NDaNjCAoKQmdnJxYvXuz1Fn10vkdqaiqSk5MBzBbM0XL9jo4OJlfBGcGcoxiNRtTX10MkEqGoqMjuD56rYE5gGrcajKmpKZw5c4YXARlb4m4eZbYHIQRqtRpTU1MIDrbeJpAtIFuyZIlHy7yxdQx03KC1tRXNzc2QSqXo6OhgvA9vtOZzJN/DXK6v0WgYvcvk5CQvcn1g+vaspqYGMTExLjWZ4kOuf6HiVoPR1NSE/Px8XgRkrhbhIYQgICAACQkJqK+vtxo38ISAzJkx9/T0YHJyEitXroREImEyCauqqgCAWbXwRNxAqVSisbERixcvduo2MDAwEMnJyUhOTobRaGTk+m1tbSYqUWcS3+h8j4yMDE6Nutk44n340q2LN3GrwSgqKnJZ3g5wl7jTS2wURSE9PZ2JG9ANg+m4QVRUFPr6+pCSkuJSfU8+MRqNaGhoYNLT6QuUbvyTlZXFFNrxRNygt7cX3d3dWLJkCadVLTrxja4+RgvmaI+OTru3lfg2NjaGuro6TkFoR8YJWPY+2MljdDzsQsMntCSW4CJxtxXcNG8YTOdLSKVS9Pb2Qq1Wm7QH8CR6vR41NTWIioqymO9BYy9uQHsfXMr82RKQ8UFQUBBTQo9OuzdPfGO3DxgaGkJLSwuKioo82lLAUn/biYkJqFQqAKYd5i4E78MnDQbdxd0VibszKyEjIyNMp7Pw8HAm36Cnpwf19fUICwvzWNzAUsFgR7AWN2hra3M5bmCvYDDfmMv1VSoVk9yn0WggkUig0+lQUlLi1SpYIpEIY2NjjOgwJCTEYn/buWw4vC4+A4D6+nokJCQgOjoaHR0dFgVkbKyJz+wJyNj09/ejs7MThYWFFhOo2ApEhWK6VaC74gbWCgZzhR03UCqVDsUN+BKQ8QEhBI2NjRgfH0doaChGR0cRFBTkcuIbV2xdM2yj0dDQgE2bNkEudzhnym+KafiMwYiPj8fAwAAMBgPy8/NtWmlLBsOZUnr02n1BQYHDsy6dbyCXy3mNGzgrIOMCHTdQKBSYmppiit/SZf74FJBxxWAwoLa2FqGhocjOzma+T7qEnlwuh06n4ySYcxRnrpkjR47gv//7vxEWFoajR486+haCwQAcNxh1dXUYHx9HXFwcsrKy7M7e5gbDGQEZvXY/f/58ly8wdtxAoVC4HDegCwYXFRV5fKmUHTdQKpUQiURQqVRYsGABb6sPrqLValFVVYXk5GSbGiGDwQClUgm5XI6RkRGEhIQw3gdfty5s0eGCBQusXjOEELz33nt488038cEHHzirbRIMBuCYwVCr1Th+/DhSUlKQl5fn0HHZBsNZAZmra/e2oAvtyOVyh+IG7HyP/Px8r7f1GxwcREtLCxISEjA6OgqNRsOrYM4ZJicnUVNTg9zcXKc0QuzEN4VCYbG/rbM4es0YjUY899xzqKysxO7du10pqCQYDMC+waAl7iEhIUhPT3e4uCptMObNm+ewgKyqqorXtXtr0A2D6QvXPG5AC8iCgoKQm5vr9XwPumBwYWGhSd1NWq4/MjLCxA3c3XbQ1XwPS+j1eubWZWxszOkANh2ETk9Pt3nNaDQa3HfffYiIiMCf/vQnVxPSBIMB2DYYdNHawsJCppiqowajt7cXKpUK8+bNs2ssPLF2bwu6YTDtfej1esTFxSEvL8+rngUhBA0NDTAYDFi4cKFNL8JS3IDvtoO0gKyoqIj3lRBark97gYQQm/1tHRUdDg8PY/Pmzbjiiivwi1/8govxFwwGYN1g0BL34uJiBAYGMqskjhgMOnfi/PnzSEpKshk3oNfuCwsLPbp2b4mpqSlUVVUhMTGRWb4NCAgw8T48hcFgQE1NDSIiIpCZmenUhc533IAQgra2NoyNjWHx4sWcUsYdxTyAze5vOzo6yogObX0nHR0duPXWW/Hwww/jhhtu4DokwWAAsw0GW+JeWFjIzLCOGgx2jgX9o5PL5RbbCnZ1dWFgYACFhYVe0V6wGRkZsVgwmM43kMvlJnGDqKgot3kflgoGu4p53ECv1zMztyNxAzoILRaLMX/+fK/cnrED2H19fdDpdEhLS0NCQoLViej06dPYvn07XnvtNaxYsYKPYQgGAzA1GGyJu/nF4YjBsLUSYh430Gq1kEqlWLRokdd7RgwMDKCjo8NqvgcNu2Hw8PCwW8r8OVMw2BXouAHdvMhWmT8630MmkzG3lt6CHYTOzc1lvgdLE9Enn3yC3/3ud/j3v/+NnJwcvoYgGAzge4NBS9wTExMtJgPZMxjOdCCrra1FYGAgwsLCmHwDe20F3QEhBB0dHVAqlSgsLHTa1aYL7cjlcl7aA9D5HnwEFB3BVtxAKpWipqbGZ/I96urqEBISYpLvAZhORG+//TYOHDgAlUqFd955BytWrODTyPmNwXD7DSNXARmdPedIBzLztXu2TsFSW0F3xQ0sFQx2FvMyf0qlEgMDA2hoaGDiBrGxsQ5lO3ItGOwK7DJ/mZmZTNygpaUFw8PDiI6OZm4tvVHeD/j+mklKSrIoOhSJRIiKikJYWBg0Gg0WLlyI9evX449//COioqKwaNEiL4zau7jVwxgcHDTpQGYNSx6GM5oQZ9buVSoVE/BSq9W8xw0cFZC5iq24QUREhIlxYhcMXrx4sdfzPYaGhtDa2oqCggLodDrmHCiKYow4F8GcMzh6zUxMTOCOO+5AUVERfvOb37grJ8VvPAy3GoyamhqkpaXZvQc3NxjOGIvh4WGcP3/epQ5k7PYAfMQNXCkYzBW6zJ9cLjeJG8TExKCpqQkBAQHIy8vzeu/Pzs5ODA0NmeR70NByfTpuQKfdx8bGumXVxNFrpr+/H7fccgvuvPNObNu2zZ2foWAwAOfFZzExMU4ZC3rt3l5A0VG4xA34KBjMFTpuQDdBlkqlzNKzpXwDT42psbEROp0OixYtsvs5Wkp8oz0oPsr8OXrNnDt3DnfccQd+//vfo7zcra0+AMFgTOOKWtVRAVlbWxtGR0edEpA5Ax03UCgUTFtBa3EDuo6DJwRk9mAXDI6KirKab+CJuAEdhA4LC3NII2QJjUbDGHEuAWxnBGSHDh3CI488gnfffdcjbRchGIxpnFWrRkZGekxA5gzs9gByudwkbjA+Pu5z+R75+fmzCgbT+Qb0sicAt8YNNBoNqqurkZKSwhQM5go7gK1UKpkAdmxsrE1D7eg1QwjBu+++i7///e/Ys2cPb+N2AMFgAM4ZjMjISMTFxdntzVFTU4PY2Fivrt3T+Qatra1Qq9WIiYlBXFycQ20F3cXAwADa29tRWFjoUPzFWtyAjzJ/7s73oGEHsK31t3W0YLDRaMQzzzyDuro6vPfeex5ZemYhGAzAMYNBu/7t7e024wbuKP7qKuyCwdnZ2SarFrbaCroDWkCmUChQUFDg0o+dEGJSaIdLewA+BWTOYCmAHRkZiYGBAWRlZdkVkP3sZz+DTCbDH//4R4+kp5shGAzAtsGwFNy0FDegYwZNTU1uafbrLPbW7tkp6+y2gnTCEp+wCwbbE5A5A13mjxbMORo3oAsGu0NA5iyDg4Oor69HcHAw0+PF0kSkVCqxZcsWbNq0Cf/v//0/b3mtgsEArBsMR1ZC6LhBe3s7BgYGTHqSurO6ki3otfucnBzIZDK727uzzB+d7xEZGem0gMwZzMv8SaVS5hzoMn/sgsEFBQU+ke/BFh1amoi6uroQHR2Nhx9+GI8++ih++MMfenPIgsEALBsMZ5ZNu7q6MDg4yDQetpRvYN5W0F1wyfeg4Stu4GrBYD6gy/zRiW+RkZGYnJxEaGgo0wjZm9Ciw6KiIoufKT0RPf/889i9ezciIyNxzTXX4KmnnvJaxikEgzGNJbWqo8umjY2N0Gq1Fut7snUKnogb2CsY7Ap03IBetXA0buCugsGuoNFocPbsWUgkEuj1ercI5hyFEIKmpiZoNBq7NWE/+ugjvPDCC3j//fcRHx+Po0ePYsOGDR4c7SwEgwGYGgxnBWTmxV/tvQ87bsBXewBXCwa7gnncwFKZP08WDLaHpYLBU1NTzDl4ssyfo9cMIQQvvfQSDhw4gPfff9+tKzhOIhgM4HuDQXeOsncLwsfaPV9xA1pARlGUx/I92O9t3h5AKpViamqKcwcyPhgdHcW5c+dsBqHZZf6Gh4dNYlB8tgdwtGCwXq/HQw89hMnJSbzxxhte/wzNEAwGMP1l6vV6h4wFvXbvbPFXR8bgbNzAnQWDnYUupUfX1lSr1Q61FXQXg4ODaGtrczjfA7Cc+MZHewBnBGS33347SktL8cQTT/Bm/Pfv348dO3bAYDDgzjvvxMMPP2zyOiEEO3bsQEVFBUJCQvD222+jpKSEed1gMKC0tBSVlZV7CSGbeBmUm3GbwTAYDGhtbUViYqLdpkKeWru3FDcwbw/gyYLB9qALBgcGBjIFg83bA3gybmBLQOYM1gRzzgSwh4eH0dDQYPea6e/vx49//GPcc889uO2223gz/gaDAXl5efj888+RmpqKsrIy7N6920TyXlFRgT//+c+oqKjAt99+ix07duDbb79lXv/DH/6A06dPY/fu3X5jMNx2Uz48PIx77rkHw8PDWLNmDcrLy7F8+fJZMQVaDOQJV5uiKERFRSEqKspiW8GQkBCMjY1h0aJFvHo5rqDValFdXY2EhASkpaUxz1trK3j+/Hm3xQ1oL0ev12PJkiWcjyuRSBAfH4/4+HgTuX5NTY1JzoS1Mn+OXjN1dXW488478eKLL2LdunWcxmzOyZMnkZOTg6ysLADATTfdhI8++sjEYHz00UfYsmULKIrC8uXLMTIygr6+PiQlJaG7uxt79+7Fo48+it27d/M6NnfiNoMhk8nwxRdfYGxsDF988QU++OADPPjgg8jLy0N5eTnWrVuHjz76CMuWLXNLs19HCAwMRHJyMpKTkzE4OIjGxkbExsaiubkZ7e3tdtsKugu2gCwuLs7mtsHBwUhLS0NaWhoTNxgaGkJTUxMvbQXpgsHh4eFuqbtJURTCwsIQFhaGjIwMm/1tpVIpIzosKSmxGYT+8ssv8etf/xr/+Mc/sHjxYl7HDEwXJWIb8tTUVBPvwdo2PT09SEpKwgMPPIDnn38e4+PjvI/Nnbg9BzYiIgLXXXcdrrvuOhiNRtTW1uKjjz7CpZdeivj4ePT390Oj0aCsrMwbKbkAvl+7v+iiixgPiM43aG5utthW0F3YEpDZQywWMwYC+L49wLlz51xqK0gXDE5NTfWYEEsqlSIhIQEJCQkmAezKykqoVCoEBQVhwYIFVr8DQgjeeecdvPPOO9i/f7/b6pJYupU3N6bWtvn0008RHx+PpUuX4tChQ24Zn7vw6C9UJBKhsLAQhw8fxkMPPYQtW7bgwIED2LVrF+6//37k5+ejvLwc69evh0wm84gOg167X7JkiclFGBQUhNTUVJMyf3K5HC0tLW5rD0ALyJYsWcLLSkJoaChCQ0NNyvz19/fj/PnzduMGnhKQ2YKiKERERCA4OBgjIyNMMaauri6L/W2NRiOefvppNDQ04MCBA25dek5NTUVXVxfzd3d39yyjam2bPXv24OOPP0ZFRQXUajUAXE5R1LuEkFvdNmCecOsqidWDziRusTEajThz5gz27t2Lzz77DIQQrFu3DuXl5SguLuZ9Vncl34PGWnsAV+MGXAsGu/J+ttoKKpVKjxYMtoW1BtHs9gByuRxPPvkkDAYD0tPT8fbbb7s9a1Ov1yMvLw9ffPEFUlJSUFZWhvfeew/5+fnMNnv37sVLL73EBD3vv/9+nDx50uQ4hw4dwmWXXeY3QU+vGAx7EEKgUCiwf/9+7Nu3D1VVVSgqKsKGDRuwdu1aREVFcfI+HF27dwSubQVpARkhxGazX3fCbitIZ85mZWUhMTHRqzU+6K519kSHCoUCW7duRXJyMnQ6HSQSCd599123j6+iogIPPPAADAYDtm3bhkcffRSvvvoqAOCee+4BIQTbt2/H/v37ERISgr/97W8oLS01OYZgMNyAXq/HqVOnsHfvXhw8eBASiQTr16/Hhg0bsHjxYqd+ZK42+3Xm+PSPT6vVIjY21mpbQXcXDHYGumDwxMQEMjIyGCMI8CeYcwa6YLC9fI/W1lZs2bIFjz/+OK699lqPjM0NCIlb7oIQgv7+fuzbtw/79u1DfX09ysrKUF5ejssuu8xmoJAPAZkz2GorSAjxeMFgaxiNRpw7dw5SqXRWwWBbbQXd5fY7mu9B5za88cYbuOiii9wyFg8hGAxPodPpcOzYMVRUVODLL79EWFgY1q9fj/LychMXn++Cwc7CjhsMDAxgYmICCQkJSE1NdaitoLugO5DFxcVZbDLFhh03sJb4xgVHCwYTQvCf//wHf/zjH7Fnzx5kZGRwel/A9azNrq4ubNmyBf39/RCJRLjrrruwY8cOZ99eMBjegBCC7u5uVFRUYN++fWhpacHy5cuZvhzbt2/32tItDb1Uu3DhQmbplk/BnDM4k+9hCa1WywQdrQnmHMXRgsFGoxF//vOf8eWXX+Lf//43L4pdLlmbfX196OvrQ0lJCcbHx7F06VL85z//cbbJkWAwfIGJiQnccMMNGBwcBCEEMpmMiX04uzLCB93d3ejr60NRUZGJUbDVVtBdZf5oAZkr+R6WYLcHUCqVEIvFDrcHcFR0qNPp8OCDD0Kr1eL111/nzbAeP34cTz75JD777DMAwLPPPgsAeOSRR5ht7r77bqxZswY333wzAGD+/Pk4dOjQrHok11xzDbZv347169c7MwS/MRjenW7dDEVRuOmmm7BlyxYAQFtbGyoqKvDII4+gp6cHK1euRHl5OVatWuVWHQa72W9JScmsJWJrbQXpfAO+4wZ0vkdxcTFv5023FaR7sqjVaqY1oq32AI7me4yPj2Pr1q1Yvnw5HnvsMV5Xk7hmbdK0t7fj7NmzWLZsGW9j8zXmtMEIDQ3FbbfdxvydlZWF7du3Y/v27VCpVDh06BD27t2Lxx9/HCkpKSgvL0d5eTmvClV2weCCggKHjiuVSpGYmIjExESTuEFnZyentoLsgsElJSVuzVUICgpCSkoKUlJSTBLf2P1tJRIJOjs77eZ79Pb24pZbbsG9996LzZs3u6X9pDmOZm3STExM4Prrr8fOnTt58dh8lTltMGwRHByMjRs3YuPGjYy4qqKiAjt27IBCocAll1yCDRs2YMWKFS67vrSALDEx0WLBYEegKAqRkZGIjIxEdnY2EzegBXOOxg2MRiMaGxthMBhcbhDtKpYEcy0tLRgaGkJQUBB6enqs9retqanBXXfdhT/+8Y+4/PLL3TI+LlmbwPSt0vXXX49bbrnF27VB3c6cjmG4yvj4OL766itUVFTg6NGjyMrKQnl5OTZs2ICkpCSHZjhnCwa7gqNxA08VDHYE84LBABjvg24PEBUVBZ1Oh87OTjz++ON477333NopnUvWJiEEt912G2JiYrBz505Xh+A3MQzBYNiBzlHYu3cv9u/fj4mJCUauf9FFF1l062kBmafyPWjouAG7rWBERAS6u7sxb948jxcMNsdWvgfN1NQU6uvrcd9996GjowM333wz7rzzTpPCM+7A1azNb775BqtXr0ZBQQHjtf32t7/FFVdc4czbCwZjrjIyMoLPP/8cFRUVOHXqFBYsWMAI5uLj41FTUwO1Wu21fA8ao9GI3t5eNDc3QyqVMiXy7LUVdBeO5nsYjUY89dRTaGlpwV//+ld8++23kEqlnmiI7E0Eg3EhYDQaUVVVxXgfQ0NDiIiIwPPPP4/S0lKv9ucwLxjsSFtBd2FNQGaOWq3G3XffjbS0NPz+97/3en8TDyIYjAuNxx9/HJ2dnVi7di0OHDiAM2fOoLCwkCkWFBMT47HYQU9PD3p7e2fle9BYaivorjJ/jhQMBqYT2jZv3owf/ehH2L59u9f7m3gYvzlZ3gwG14Ko/o5cLkdsbCxzoRsMBpw+fZoRzFEUhXXr1mHDhg0oLCx0yyoFLSCjM1sdnaHZBXpt9bd1FkcLBjc3N2Pr1q148skncfXVV7v8fmy4XI/29nUDfmMwQAix9XAIvV5PsrKySEtLC9FoNKSwsJDU1dWZbLN3717ygx/8gBiNRnL8+HFy0UUXOXp4v8doNJL+/n7y9ttvkxtvvJEsWrSIbN68mbz77rukp6eHTE5Ocn6Mj4+TEydOkLNnz5KJiQmXjzM2Nkba29vJd999Rw4ePEiOHTtGGhoaiEKhcOo49fX15MiRI2RkZMTmdgcPHiQFBQXk1KlTvH3eXK5HR/Z15P2dxN7v0GcevORhcC2IOtehKAoJCQm47bbbcNttt0Gv1+P48eOoqKjA//7v/yI4OJhJWXelqbK1gsGuIBaLERcXh7i4OJP2AHV1dQ61ByAOFgwmhODDDz/En/70J3zyySdIT0/nNG42XK7H9vZ2u/vaghACsViMyclJVFVVISEhAdnZ2bydm7fhxWDwlVp7oSCRSLB69WqsXr0ahBD09vaioqICzz33HBobG7Fs2TKUl5djzZo1ditecRWQ2YKiKKbMX3p6OtMeoK+vz6TMn0wmQ0BAAFMwOCIiwmbBYKPRiJ07d+LIkSP4/PPPmXRyvuByPTqyrzXITCW5wcFBXHzxxViyZAmqq6vx5ptvYtWqVRzPyjfgxWAQHlJrL1QoikJKSgp+8pOf4Cc/+Qm0Wi2OHj2KvXv34ne/+x2io6OZ2Edubq7JjM2lYLArmLcHoAVz1dXVMBgM0Gg0SE5OtpkcptPp8Itf/AIGgwGffvqpW5S5XK5HLtcpRVHo7e2FXC7Ho48+ittvvx1vvfUWrr32Wpw+fZoXGb634cVgcE2tFfiegIAAXHbZZbjsssuYWp8VFRV4/PHH0dHRgYsvvhgbNmxAV1cXoqOjceWVV3ol34MtmIuLi0NNTQ1SUlKgVqtx4sQJhIeHM9XGaKMwNjaGrVu3YtWqVfjVr37ltvR0LtejVqt16jo1Go3Mebz//vv49a9/jaioKJSVleHWW2/Ftm3b0NHRgbVr16Kurs6ruTm8YCfI4RA6nY5kZmaS1tZWJlBUW1trss2nn35qEmQqKytz9PACM6hUKrJv3z6ycuVKkpKSQn7wgx+QF198kdTU1HAKdHJ5dHV1kS+++IIMDAwwz01MTJC+vj5SU1NDDh06RF5++WVy1113kYKCAvLOO+8Qo9Ho1s+Jy/XoyL6W+Nvf/kYeeeQRUlVVRf785z+T+++/n+zZs4d5fdOmTeTXv/61td29Hsx09MGLwSBkOuqcm5tLsrKyyP/8z/8QQgh55ZVXyCuvvEIImV4p+NnPfkaysrLI4sWLXY6K79u3j+Tl5ZHs7Gzy7LPPznr9q6++IhEREaSoqIgUFRWRp556yqX38VWam5vJT3/6U6LRaEhjYyPZuXMn2bBhAykuLibbt28nn3zyCVEqlR4xFk1NTeTQoUN232/fvn1kxYoVZN26daSgoIAcPHjQ7Z8Tl+vR0r7mGAwG5v8TExMkODiYbNy4kRBCyPDwMHnxxRfJgw8+SD799FNHhut1Q+Dow68StxypjHTo0CG88MIL+PTTT704Us8zOTmJr776Cnv37sXRo0eRnp7OCOZSUlJ4jRcRMi0gm5iYsJvvceDAATz11FPYvXs3FixYAIPBAJ1O59euOX0bQgjBwMAAEhMTUVNTgxUrVmD37t246qqr0NfXh7/85S+YmprCQw89hISEBFAUBYPBYOnz8ptgnl/J2x1ZLrtQCQ0NxaZNm7Bp0yYYjUacP38eFRUV+OlPf4rR0VFceumlTH9bLnUw2AKywsJCq4aIEIK33noL77//Pj777DMmJVwsFvt9yrdIJMLAwABuueUWqNVqbNu2Ddu2bcOePXvwX//1X/jqq69QUlKCzZs3A4BJkWe/P3dvD8AZrC2FmXP8+HEUFRVh48aNqKur8+QQfQKRSIRFixbhwQcfxBdffIEvv/wSq1atwp49e7By5Urceuut2LVrF/r7+y2uClhDp9Ph7NmziIyMtLlsajAY8Pjjj+Pw4cMmxoIPlEol1q9fj9zcXKxfvx7Dw8MWt9u/fz/mz5+PnJwcPPfcc8zzv/zlL7FgwQIUFhbiuuuuw8jIiMPvTX9WhBC8+uqruOmmm/Cb3/wGFRUVeOONN/CDH/wAL7zwAkpLS6FQKDB//nzMnz+f0/n6Gn5lMCxd3OYXbUlJCTo6OlBVVYX77rvPn3tV8Abd3/b1119HdXU1nnjiCSiVSmzduhVr1qzBU089hRMnTkCv11s9hkqlwpkzZzBv3jybyWEqlQpbt24FRVH417/+xbs25bnnnsPatWvR1NSEtWvXmhgDGoPBgHvvvRf79u3DuXPnsHv3bpw7dw4AsH79etTW1qK6uhp5eXlM/U5b0NcdRVH44IMP8POf/xyVlZXYuHEjLr/8ctx00004fPgwPvjgA/zkJz/BH/7wB17P2aewE+TwKY4dO0bKy8uZv3/729+S3/72tzb3SU9PJ0NDQ+4emt+iVCrJ7t27yZYtW0h+fj658cYbyRtvvEE6OjqYlZeenh5y8OBB0tfXZzO42d7eTlatWkX+8pe/uG0lJC8vj/T29hJCCOnt7SV5eXmztnH0Ovnwww/Jj3/8Y4ff+6uvviKXX345ef3118maNWvItddey7z26quvkquuuopUVlYyz7EDo3bwejDT0YdfGQxHlrz6+vqYi/Xbb78laWlpbl/Gmyvo9Xpy6tQp8uSTT5IVK1aQ5cuXk1tvvZVs3LiRDA4O2jQWZ8+eJcXFxeSTTz5x6xgjIyNN/o6Kipq1zfvvv0/uuOMO5u9du3aRe++9d9Z2mzZtIu+8845D7/vxxx+TjRs3kt///veEEEJGRkbI1VdfTe677z5mm/379zt0LAt43RA4+vCrWxKJRIKXXnqJ0VzceOONyM/Px6uvvspUR9qzZw8WL16MoqIi3H///fjnP/8pZJQ6iFgsRmlpKZ544gkcPXoUV155JSorKxEZGYk1a9bgrrvuwgcffDArbnD06FFs2bIFb775JjZt4t4idN26dVi8ePGsx0cffeTQ/sSBW9dnnnkGEokEt9xyi8VjtLe3mxyrsLAQSUlJaG5uRnt7OyIjI/HnP/8Zx44dw2OPPQYA2LBhg9X3nzPYsSgXLLfffjuJi4sj+fn5Fl83Go3kvvvuI9nZ2aSgoIB89913Hh6h+/nss8+IRqMhhEx7d9988w351a9+RZYtW0ZWrVpFnnrqKfL000+TsrIy0tHR4ZEx8XFL8vbbb5Ply5eTyclJi+/x+uuvk0cffZT5m1afVlVVkc2bN5OXX36Z9Pf3E0IIOX36NNm3bx/X0/K65+DoQzAYVjh8+DD57rvvrBqMC12u39vbS15//XVSWlpKRkZGPPbeDz74IJOw9+yzz5Jf/vKXs7axdeu6b98+snDhQjI4OGj1PYaGhsiyZcvI559/TgghJre0Bw4cIFu2bCGvv/46n+ftdUPg6EMwGDZoa2uzajDuuusu8t577zF/s2c+Afchl8vJ5ZdfTnJycsjll19OFAoFIYSQnp4eJtOSEOvZmtnZ2SQ1NZXJBL777rtNjk97E3v37iVPPPEEGR8fnzWGN998kzz44INkamqKr9PyuiFw9OFXiVu+hCDX9w6xsbH44osvZj2fnJyMiooK5u8rrrjCYuXu5uZmm8enE6vS0tJw6NAhGI1GANMJaxRFgaIobNu2DXq93ut9er2BXwU9fQlCBLn+XMD8e6QNREFBASYnJ/H0008DmE6GY3+/EonE4jUw1xEMhosIcv25AW0Ejh8/DmDaMNBG48UXX0RfXx/zmrV9LyQEg+EiV199NXbt2gVCCE6cOIHIyEjhdsQFuKZ607zwwgugKApyudzpMXz22WfYtWsXgGmPgzYaAQEBuOSSS9DX1wfge+/jgsZOkOOC5aabbiKJiYlEIpGQlJQU8sYbb7hFrn+h88tf/tJk1eOhhx6atY29wrydnZ2kvLyczJs3z6GsXnrVg/63q6uL5OTkkK+//nrWtp9++ilZuXKlS+fmBF4PZjr6EAyGl7GX7zHX63vwkVdx/fXXk8rKSqdkAB0dHeTvf/87aWxsJIQQ8vLLL5Pf/e53RK/Xz0rpfv/9950+LyfxuiFw9HFB3JIQHw5Obd26Ffv377e5zerVq1FZWYnKyko8/vjjHhqZZxgYGGBu5ZKSkjA4ODhrG1sq5Y8//hgpKSkoKipy6n3lcjnOnz+PG2+8EYcPH8bk5CT6+vogFouZknv0LciPfvQjAL59HXmKObkupFQqUV1djTVr1vj88tcll1zCpCHPVdatW4f+/v5Zzz/zzDMO7W/ph0pRFKampvDMM8/gwIEDTo+ppKQEJSUlWLhwIY4cOYJvv/0W+/btw8UXX4wbbrgBAGbVHL0Qg5zm+O4viQP9/f245557UFdXB4lEgjfffBM//vGPTaTWdNWkU6dOIT09ndeaDXxD1/dITk7GCy+8gPz8fG8PySkOHjxo9bWEhASmP01fX5/F78HailRLSwva2toY76K7uxslJSU4efKkSdEaW2zevBlarRanTp1CWFgYent7AZgW9xVgYeeexS/R6/Xk6aefJo888gj59a9/TdauXUuUSqXFbS+55BJy8OBBrypabWWUjo6OMtmGe/fuJTk5OZ4cmtvhmurNxtVSBvR3f+TIEbJq1SqiUqmcPgZHvB6bcPQx50yoXq+HWCyGWq3Ga6+9hpSUFLz//vuIjo6e5dpWVFQgJiYGa9eunXUcQgiMRqPX71sjIiKYZkZXXHEFdDqdS0uHvsrDDz+Mzz//HLm5ufj888+ZPqa9vb1MpqY1lTJf0LcaMTExUCqVUCgUvB17ruFXRYAdZc+ePfjLX/6CqqoqtLa2IioqCoRMd6WiXc3Dhw/j0KFDWLp0KTZt2gSdTgepVIqJiQmL3cboAJg73NT29nZs2rQJtbW1s17r7+9nCsiePHkSP/rRj9DR0SHcT7uBY8eOYXJyEuvXr/f0W/vNlzmnYhh6vR4vvPACDhw4gKeffhrvv/8+jh8/jo0bNzI/MPoH//LLL2Pr1q24/PLLTZ5/4403UFlZiaysLERFReH2229HeHi41T6iXH+4N998Mw4dOgS5XI7U1FQ89dRT0Ol0AIB77rkHe/bswSuvvAKJRILg4GCX63t0dXVhy5Yt6O/vh0gkwl133YUdO3bMOh9rHc0vBC6++GJvD8H3sXPP4lcYjUZy4sQJcuzYMUIIITt37iQ33HADIcS0XNqZM2fI6tWrLR7jgQceIOXl5WTPnj3ksssuIy+//DJ58803yS9/+Uty9uxZQsjs7twudOv2OL29vUzNjrGxMZKbm+twR3MBt+P12ISjjznlYVAUhWXLljF/X3/99ViwYAFUKhWzQlJTU4P9+/dj27ZtAKYLxtLCoo6ODsjlctx77724+uqrkZiYiPLycvzzn/9Ef38/7r//fnz66aeIiIjARx99hMsuuwwREREmpePZx/MlkpKSmHyH8PBwLFy4ED09PQ51NBdS3gVo5lzQk01qaio2bNiA4OBgJnj5/PPPIzk5makmLhaLmde+++47REREYOHChQCAyspKrF27FldddRXuvvtu6HQ6EDLdvOa6667DM888g/Lycrz22mtMjEMsFvucsTCnvb0dZ8+eNTGugONtHAQuXOa0wWBDURR0Oh3CwsKwZcsWREVFmbwGAEeOHEFaWhqTC3D48GFceeWVAKY7eC1ZsgTBwcH4+OOPkZOTg8ceeww//elPsWvXLnz44YdYsmQJ7rzzToyPj3v8/BxlYmIC119/PXbu3Dmr4zttONn4uvET8Cz2VkkuGCiKEgO4D0ArIeRjiqLiARwFcDkhpIuiqN8CGCeEPEtR1EEAHxJCXqYo6nIA/wPgFQAfAHgNwD8JIXu9dCpWoShKCuBTAJ8RQmY1z6Ao6jUAhwghu2f+bgCwhhDS59mRCvgqF4yHYQ9CiIEQspMQ8vHMU7EATs8YiwgACwCcn3mtEMCHM/9fBOAzTP/QpgCkAvC5tFFq2lV4E0C9JWMxw8cAtlDTLAcwKhgLATZzKujJFYqiKDLjchFC6gHcPPNSCIBjAM5QFLUGQAghpJ+iqEgAiQD6CSF07nIxgBs9OW4HWQlgM4AaiqIqZ577FYB5AEAIeRVABYArADQDmAJwu+eHKeDLCAaDBWHdn1EUJSKEGGee7wfwwszzUgDbZjZbBCANQOXMa+sADBBChjw4bIcghHwDOwlCM+d/r2dGJOCPCDEMB2B7HmbPh2Dao+glhLRTFLUHwCQh5DZPj1FAwBMIBsNJrBmPmddWA+ghhLR6eFgCAh5BMBgCAgIOI6yS8AglJC0IzHEED0NAQMBhBA9DQEDAYQSDISAg4DCCwRAQEHAYwWAICAg4jGAwBAQEHOb/A+QRZBNjUVTeAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "ename": "DimensionalityError", + "evalue": "Cannot convert from 'dimensionless' (dimensionless) to 'millimeter' ([length])", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mDimensionalityError\u001b[0m Traceback (most recent call last)", + "Input \u001b[1;32mIn [4]\u001b[0m, in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mtr_1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\Anaconda3\\envs\\weldx\\lib\\site-packages\\pint\\registry_helpers.py:367\u001b[0m, in \u001b[0;36mcheck..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 365\u001b[0m val_dim \u001b[38;5;241m=\u001b[39m ureg\u001b[38;5;241m.\u001b[39mget_dimensionality(value)\n\u001b[0;32m 366\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m DimensionalityError(value, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma quantity of\u001b[39m\u001b[38;5;124m\"\u001b[39m, val_dim, dim)\n\u001b[1;32m--> 367\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32m~\\PycharmProjects\\weldx\\weldx\\weldx\\geometry.py:1861\u001b[0m, in \u001b[0;36mTrace.plot\u001b[1;34m(self, raster_width, axes, fmt, axes_equal)\u001b[0m\n\u001b[0;32m 1838\u001b[0m \u001b[38;5;129m@UREG\u001b[39m\u001b[38;5;241m.\u001b[39mcheck(\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[length]\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 1839\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\n\u001b[0;32m 1840\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1844\u001b[0m axes_equal: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 1845\u001b[0m ):\n\u001b[0;32m 1846\u001b[0m \u001b[38;5;124;03m\"\"\"Plot the trace.\u001b[39;00m\n\u001b[0;32m 1847\u001b[0m \n\u001b[0;32m 1848\u001b[0m \u001b[38;5;124;03m Parameters\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1859\u001b[0m \n\u001b[0;32m 1860\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1861\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrasterize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mraster_width\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mto(_DEFAULT_LEN_UNIT)\n\u001b[0;32m 1862\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 1863\u001b[0m fmt \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx-\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", + "File \u001b[1;32m~\\Anaconda3\\envs\\weldx\\lib\\site-packages\\pint\\registry_helpers.py:296\u001b[0m, in \u001b[0;36mwraps..decorator..wrapper\u001b[1;34m(*values, **kw)\u001b[0m\n\u001b[0;32m 293\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 294\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[1;32m--> 296\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mureg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mQuantity\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 297\u001b[0m \u001b[43m \u001b[49m\u001b[43mresult\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_replace_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mret\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues_by_name\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mret\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mret\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[0;32m 298\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\Anaconda3\\envs\\weldx\\lib\\site-packages\\pint\\quantity.py:294\u001b[0m, in \u001b[0;36mQuantity.__new__\u001b[1;34m(cls, value, units)\u001b[0m\n\u001b[0;32m 289\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m 290\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munits must be of type str, Quantity or \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 291\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnitsContainer; not \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mtype\u001b[39m(units))\n\u001b[0;32m 292\u001b[0m )\n\u001b[0;32m 293\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, \u001b[38;5;28mcls\u001b[39m):\n\u001b[1;32m--> 294\u001b[0m magnitude \u001b[38;5;241m=\u001b[39m \u001b[43mvalue\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43munits\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m_magnitude\n\u001b[0;32m 295\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 296\u001b[0m magnitude \u001b[38;5;241m=\u001b[39m _to_magnitude(\n\u001b[0;32m 297\u001b[0m value, inst\u001b[38;5;241m.\u001b[39mforce_ndarray, inst\u001b[38;5;241m.\u001b[39mforce_ndarray_like\n\u001b[0;32m 298\u001b[0m )\n", + "File \u001b[1;32m~\\Anaconda3\\envs\\weldx\\lib\\site-packages\\pint\\quantity.py:724\u001b[0m, in \u001b[0;36mQuantity.to\u001b[1;34m(self, other, *contexts, **ctx_kwargs)\u001b[0m\n\u001b[0;32m 707\u001b[0m \u001b[38;5;124;03m\"\"\"Return Quantity rescaled to different units.\u001b[39;00m\n\u001b[0;32m 708\u001b[0m \n\u001b[0;32m 709\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 720\u001b[0m \u001b[38;5;124;03mpint.Quantity\u001b[39;00m\n\u001b[0;32m 721\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 722\u001b[0m other \u001b[38;5;241m=\u001b[39m to_units_container(other, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_REGISTRY)\n\u001b[1;32m--> 724\u001b[0m magnitude \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_convert_magnitude_not_inplace(other, \u001b[38;5;241m*\u001b[39mcontexts, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mctx_kwargs)\n\u001b[0;32m 726\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m(magnitude, other)\n", + "File \u001b[1;32m~\\Anaconda3\\envs\\weldx\\lib\\site-packages\\pint\\quantity.py:673\u001b[0m, in \u001b[0;36mQuantity._convert_magnitude_not_inplace\u001b[1;34m(self, other, *contexts, **ctx_kwargs)\u001b[0m\n\u001b[0;32m 670\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_REGISTRY\u001b[38;5;241m.\u001b[39mcontext(\u001b[38;5;241m*\u001b[39mcontexts, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mctx_kwargs):\n\u001b[0;32m 671\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_REGISTRY\u001b[38;5;241m.\u001b[39mconvert(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_magnitude, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_units, other)\n\u001b[1;32m--> 673\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_REGISTRY\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_magnitude\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_units\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\Anaconda3\\envs\\weldx\\lib\\site-packages\\pint\\registry.py:1003\u001b[0m, in \u001b[0;36mBaseRegistry.convert\u001b[1;34m(self, value, src, dst, inplace)\u001b[0m\n\u001b[0;32m 1000\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m src \u001b[38;5;241m==\u001b[39m dst:\n\u001b[0;32m 1001\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m value\n\u001b[1;32m-> 1003\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\Anaconda3\\envs\\weldx\\lib\\site-packages\\pint\\registry.py:1917\u001b[0m, in \u001b[0;36mContextRegistry._convert\u001b[1;34m(self, value, src, dst, inplace)\u001b[0m\n\u001b[0;32m 1913\u001b[0m src \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_active_ctx\u001b[38;5;241m.\u001b[39mtransform(a, b, \u001b[38;5;28mself\u001b[39m, src)\n\u001b[0;32m 1915\u001b[0m value, src \u001b[38;5;241m=\u001b[39m src\u001b[38;5;241m.\u001b[39m_magnitude, src\u001b[38;5;241m.\u001b[39m_units\n\u001b[1;32m-> 1917\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\Anaconda3\\envs\\weldx\\lib\\site-packages\\pint\\registry.py:1518\u001b[0m, in \u001b[0;36mNonMultiplicativeRegistry._convert\u001b[1;34m(self, value, src, dst, inplace)\u001b[0m\n\u001b[0;32m 1513\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m DimensionalityError(\n\u001b[0;32m 1514\u001b[0m src, dst, extra_msg\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m - In destination units, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mex\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1515\u001b[0m )\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (src_offset_unit \u001b[38;5;129;01mor\u001b[39;00m dst_offset_unit):\n\u001b[1;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1520\u001b[0m src_dim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_dimensionality(src)\n\u001b[0;32m 1521\u001b[0m dst_dim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_dimensionality(dst)\n", + "File \u001b[1;32m~\\Anaconda3\\envs\\weldx\\lib\\site-packages\\pint\\registry.py:1036\u001b[0m, in \u001b[0;36mBaseRegistry._convert\u001b[1;34m(self, value, src, dst, inplace, check_dimensionality)\u001b[0m\n\u001b[0;32m 1033\u001b[0m \u001b[38;5;66;03m# If the source and destination dimensionality are different,\u001b[39;00m\n\u001b[0;32m 1034\u001b[0m \u001b[38;5;66;03m# then the conversion cannot be performed.\u001b[39;00m\n\u001b[0;32m 1035\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m src_dim \u001b[38;5;241m!=\u001b[39m dst_dim:\n\u001b[1;32m-> 1036\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m DimensionalityError(src, dst, src_dim, dst_dim)\n\u001b[0;32m 1038\u001b[0m \u001b[38;5;66;03m# Here src and dst have only multiplicative units left. Thus we can\u001b[39;00m\n\u001b[0;32m 1039\u001b[0m \u001b[38;5;66;03m# convert with a factor.\u001b[39;00m\n\u001b[0;32m 1040\u001b[0m factor, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_root_units(src \u001b[38;5;241m/\u001b[39m dst)\n", + "\u001b[1;31mDimensionalityError\u001b[0m: Cannot convert from 'dimensionless' (dimensionless) to 'millimeter' ([length])" + ] } ], "source": [ @@ -58,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "dbe0ede7-2696-425e-a58d-e4782877d327", "metadata": {}, "outputs": [], @@ -70,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "ed0318d3-f74e-44e4-b09d-891d0f89a8bb", "metadata": {}, "outputs": [], @@ -86,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "17debdfb-0a46-4a2b-bf77-980a0ac34437", "metadata": {}, "outputs": [], @@ -96,40 +106,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "539d8648-94f6-4c3c-b8e4-97b6cb569a8b", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\vhirtham\\Anaconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\utils.py:117: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " index = pd.Index(np.asarray(array), **kwargs)\n" - ] - }, - { - "data": { - "text/plain": [ - "\n", - "Values:\n", - "\t[[1]\n", - " [1]\n", - " [0]]\n", - "Dimensions:\n", - "\t('c', 's')\n", - "Coordinates:\n", - "\tc = ['x' 'y' 'z'] None\n", - "\ts = [1] \n", - "Units:\n", - "\tmm" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sps.evaluate(\n", " s=DataArray(\n", @@ -140,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "0fc5a100-5bb3-4b4a-ac0c-e888429eb1f5", "metadata": {}, "outputs": [], @@ -150,762 +130,20 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "8f4fd0fa-4725-48b7-b22a-722aa409bb71", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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""" - return self._total_length_lookup[-1].m + return self._total_length_lookup[-1] @property def segments(self) -> list[trace_segment_types]: @@ -1777,6 +1805,7 @@ def rasterize(self, raster_width: pint.Quantity) -> pint.Quantity: """ + if not raster_width > 0: raise ValueError("'raster_width' must be > 0") From 7c1caa837f74c6cb5c13e87fb75433f2c5ca44ea Mon Sep 17 00:00:00 2001 From: Hirthammer Date: Tue, 8 Feb 2022 14:11:46 +0100 Subject: [PATCH 08/70] Fix test --- weldx/tests/test_geometry.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/weldx/tests/test_geometry.py b/weldx/tests/test_geometry.py index 9dfd007cf..b7d46f7d0 100644 --- a/weldx/tests/test_geometry.py +++ b/weldx/tests/test_geometry.py @@ -2433,11 +2433,10 @@ def test_trace_rasterization(): # check with arbitrary coordinate system -------------- orientation = WXRotation.from_euler("y", np.pi / 2).as_matrix() coordinates = Q_([-3, 2.5, 5], "mm") - cs_base = tf.LocalCoordinateSystem(orientation, coordinates.m) + cs_base = tf.LocalCoordinateSystem(orientation, coordinates) trace = geo.Trace([linear_segment, radial_segment], cs_base) data = trace.rasterize("0.1mm") - print(data) raster_width_eff = trace.length / (data.shape[1] - 1) for i in range(data.shape[1]): From 51a67c19b4e648229a5d066454e6cc292dd8edda Mon Sep 17 00:00:00 2001 From: Hirthammer Date: Tue, 8 Feb 2022 14:37:07 +0100 Subject: [PATCH 09/70] Fix all tests --- weldx/geometry.py | 4 ++-- weldx/tests/_helpers.py | 5 ++++- weldx/tests/test_geometry.py | 6 +++--- 3 files changed, 9 insertions(+), 6 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index db126b35b..9f4da09d6 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1639,7 +1639,8 @@ def __init__( """ if coordinate_system is None: - coordinate_system = tf.LocalCoordinateSystem() + default_coords = Q_([0, 0, 0], _DEFAULT_LEN_UNIT) + coordinate_system = tf.LocalCoordinateSystem(coordinates=default_coords) if not isinstance(coordinate_system, tf.LocalCoordinateSystem): raise TypeError( @@ -1688,7 +1689,6 @@ def _create_lookups(self, coordinate_system_start: tf.LocalCoordinateSystem): # Fill length lookups segment_length = segment.length total_length += segment_length - self._segment_length_lookup += [segment_length] self._total_length_lookup += [total_length.copy()] diff --git a/weldx/tests/_helpers.py b/weldx/tests/_helpers.py index 22ba66acf..ae3285546 100644 --- a/weldx/tests/_helpers.py +++ b/weldx/tests/_helpers.py @@ -4,6 +4,7 @@ import numpy as np +from weldx.constants import Q_ from weldx.transformations import LocalCoordinateSystem, WXRotation @@ -46,7 +47,9 @@ def rotated_coordinate_system( rotated_orientation = np.matmul(r_tot, orientation) - return LocalCoordinateSystem(rotated_orientation, np.array(coordinates)) + if not isinstance(coordinates, Q_): + coordinates = np.array(coordinates) + return LocalCoordinateSystem(rotated_orientation, coordinates) def are_all_columns_unique(matrix, decimals=3): diff --git a/weldx/tests/test_geometry.py b/weldx/tests/test_geometry.py index b7d46f7d0..169021861 100644 --- a/weldx/tests/test_geometry.py +++ b/weldx/tests/test_geometry.py @@ -2281,7 +2281,7 @@ def test_trace_construction(): """Test the trace's construction.""" linear_segment = geo.LinearHorizontalTraceSegment("1mm") radial_segment = geo.RadialHorizontalTraceSegment("1mm", Q_(np.pi, "rad")) - cs_coordinates = np.array([2, 3, -2]) + cs_coordinates = Q_([2, 3, -2], "mm") cs_initial = helpers.rotated_coordinate_system(coordinates=cs_coordinates) # test single segment construction -------------------- @@ -2370,7 +2370,7 @@ def test_trace_local_coordinate_system(): # check with arbitrary coordinate system -------------- orientation = WXRotation.from_euler("x", np.pi / 2).as_matrix() - coordinates = np.array([-3, 2.5, 5]) + coordinates = Q_([-3, 2.5, 5], "mm") cs_base = tf.LocalCoordinateSystem(orientation, coordinates) trace = geo.Trace([radial_segment, linear_segment], cs_base) @@ -2392,7 +2392,7 @@ def test_trace_local_coordinate_system(): weight = i / 10 position_on_segment = linear_segment.length * weight position = radial_segment.length + position_on_segment - lcs_coordinates = [position_on_segment.m, 0, 0] + lcs_coordinates = Q_([position_on_segment.m, 0, 0], "mm") cs_exp = tf.LocalCoordinateSystem(coordinates=lcs_coordinates) + cs_start_seg2 cs_trace = trace.local_coordinate_system(position) From 869199323516a95cda5edb6d1c49c62a576c21dc Mon Sep 17 00:00:00 2001 From: Hirthammer Date: Tue, 8 Feb 2022 17:38:26 +0100 Subject: [PATCH 10/70] Add DynamicTraceSegment --- tutorials/SpatialSeries.ipynb | 92 +++++++++--- tutorials/TraceSegmentSpS.ipynb | 244 +++++++++++++++++++++----------- weldx/geometry.py | 50 +++++-- 3 files changed, 266 insertions(+), 120 deletions(-) diff --git a/tutorials/SpatialSeries.ipynb b/tutorials/SpatialSeries.ipynb index d018dccb3..74d734caa 100644 --- a/tutorials/SpatialSeries.ipynb +++ b/tutorials/SpatialSeries.ipynb @@ -2,16 +2,15 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "759924f6-2e78-4aba-9750-fe1870344af3", "metadata": {}, "outputs": [], "source": [ + "from weldx.core import SpatialSeries, GenericSeries\n", + "from weldx import LocalCoordinateSystem, Q_\n", "import numpy as np\n", - "from xarray import DataArray\n", - "\n", - "from weldx import Q_, LocalCoordinateSystem\n", - "from weldx.core import GenericSeries, SpatialSeries" + "from xarray import DataArray" ] }, { @@ -24,19 +23,44 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "ccd72f65-3173-4481-bb81-07692fa2fa06", + "execution_count": 2, + "id": "06c52d39-24fe-40ff-9a5b-594ad8435869", "metadata": {}, "outputs": [], "source": [ - "spsd = SpatialSeries(\n", - " Q_([[1, 2, 3], [4, 5, 6]], \"m\"),\n", - " dims=[\"s\", \"c\"],\n", - " coords=dict(\n", - " s=Q_([0, 5], \"\"),\n", - " c=[\"x\", \"y\", \"z\"],\n", - " ),\n", - ")\n", + "s = DataArray(Q_([0,5], \"\"), dims=[\"s\"]).pint.dequantify()\n", + "data = DataArray(Q_([[1,2,3], [4,5,6]], \"m\"), dims=[\"s\",\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"], s=s))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ccd72f65-3173-4481-bb81-07692fa2fa06", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Values:\n", + "\t[[1 2 3]\n", + " [4 5 6]]\n", + "Dimensions:\n", + "\t('s', 'c')\n", + "Coordinates:\n", + "\tc = ['x' 'y' 'z'] None\n", + "\ts = [0 5] \n", + "Units:\n", + "\tm" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spsd = SpatialSeries(data, dims=[\"s\", \"c\"])\n", "spsd" ] }, @@ -50,24 +74,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "3fbd63fa-c3ba-4c9b-9a87-779e130db08b", "metadata": {}, "outputs": [], "source": [ "exp = \"a*s + b\"\n", "params = dict(\n", - " a=DataArray(Q_([1, 1, 1], \"mm\"), dims=[\"c\"]),\n", - " b=DataArray(Q_([1, 1, 1], \"mm\"), dims=[\"c\"]),\n", + " a=DataArray(Q_([0, 0, 1], \"mm\"), dims=[\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"])),\n", + " b=DataArray(Q_([1, 0, 0], \"mm\"), dims=[\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"])), \n", ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "5a60c890-aac4-499b-bdbc-d54a3529b8c5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Expression:\n", + "\ta*s + b\n", + "Parameters:\n", + "\ta = [0 0 1] mm\n", + "\tb = [1 0 0] mm\n", + "Free Dimensions:\n", + "\ts in \n", + "Other Dimensions:\n", + "\t['c']\n", + "Units:\n", + "\tmm" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "spse = SpatialSeries(exp, parameters=params)\n", "spse" @@ -84,9 +130,9 @@ ], "metadata": { "kernelspec": { - "display_name": "", + "display_name": "Python (weldx)", "language": "python", - "name": "" + "name": "weldx" }, "language_info": { "codemirror_mode": { @@ -98,7 +144,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.9" } }, "nbformat": 4, diff --git a/tutorials/TraceSegmentSpS.ipynb b/tutorials/TraceSegmentSpS.ipynb index b1bb42572..e0ff1e83e 100644 --- a/tutorials/TraceSegmentSpS.ipynb +++ b/tutorials/TraceSegmentSpS.ipynb @@ -11,181 +11,253 @@ "\n", "from weldx import Q_, GenericSeries, LinearHorizontalTraceSegment, Trace\n", "from weldx.core import SpatialSeries\n", - "from weldx.geometry import DynamicTraceSegment" + "from weldx.geometry import DynamicTraceSegment\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "9ece03aa-6c2a-480d-b9a1-1a036041fece", + "metadata": {}, + "source": [ + "## Discrete" ] }, { "cell_type": "code", "execution_count": 2, - "id": "dae8950a-ca96-47bc-8a33-a604fd1fe86d", + "id": "01dabe70-4c3f-408a-99cc-3beae8bd978d", "metadata": {}, "outputs": [], "source": [ - "lhts = LinearHorizontalTraceSegment(\"2mm\")" + "data = DataArray(\n", + " Q_([[0,0,0], [0,5,0], [1,5,0], [1,9,0]], \"mm\"), \n", + " dims=[\"s\",\"c\"], \n", + " coords=dict(\n", + " c=[\"x\", \"y\", \"z\"], \n", + " s=DataArray(Q_([0, 0.5, 0.6, 1],\"\"), dims=[\"s\"]).pint.dequantify()\n", + " )\n", + ")\n", + "series_disc = SpatialSeries(data)" ] }, { "cell_type": "code", "execution_count": 3, - "id": "9c33e35c-65e7-46ab-8e9d-27d6eb277d75", + "id": "0975aed9-23c5-4ae0-aed9-93baba07661e", "metadata": {}, "outputs": [], "source": [ - "tr_1 = Trace(lhts)" + "segment_disc = DynamicTraceSegment(series_disc)" ] }, { "cell_type": "code", "execution_count": 4, - "id": "ae841131-305e-4b89-a8fe-2082c24129b0", + "id": "00c46ac2-4a1f-433d-b0cf-5e3a4b9e8986", "metadata": {}, "outputs": [ { - "ename": "DimensionalityError", - "evalue": "Cannot convert from 'dimensionless' (dimensionless) to 'millimeter' ([length])", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mDimensionalityError\u001b[0m Traceback (most recent call last)", - "Input \u001b[1;32mIn [4]\u001b[0m, in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mtr_1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\weldx\\lib\\site-packages\\pint\\registry_helpers.py:367\u001b[0m, in \u001b[0;36mcheck..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 365\u001b[0m val_dim \u001b[38;5;241m=\u001b[39m ureg\u001b[38;5;241m.\u001b[39mget_dimensionality(value)\n\u001b[0;32m 366\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m DimensionalityError(value, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma quantity of\u001b[39m\u001b[38;5;124m\"\u001b[39m, val_dim, dim)\n\u001b[1;32m--> 367\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32m~\\PycharmProjects\\weldx\\weldx\\weldx\\geometry.py:1861\u001b[0m, in \u001b[0;36mTrace.plot\u001b[1;34m(self, raster_width, axes, fmt, axes_equal)\u001b[0m\n\u001b[0;32m 1838\u001b[0m \u001b[38;5;129m@UREG\u001b[39m\u001b[38;5;241m.\u001b[39mcheck(\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[length]\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 1839\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\n\u001b[0;32m 1840\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1844\u001b[0m axes_equal: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 1845\u001b[0m ):\n\u001b[0;32m 1846\u001b[0m \u001b[38;5;124;03m\"\"\"Plot the trace.\u001b[39;00m\n\u001b[0;32m 1847\u001b[0m \n\u001b[0;32m 1848\u001b[0m \u001b[38;5;124;03m Parameters\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1859\u001b[0m \n\u001b[0;32m 1860\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1861\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrasterize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mraster_width\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mto(_DEFAULT_LEN_UNIT)\n\u001b[0;32m 1862\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 1863\u001b[0m fmt \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx-\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\weldx\\lib\\site-packages\\pint\\registry_helpers.py:296\u001b[0m, in \u001b[0;36mwraps..decorator..wrapper\u001b[1;34m(*values, **kw)\u001b[0m\n\u001b[0;32m 293\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 294\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[1;32m--> 296\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mureg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mQuantity\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 297\u001b[0m \u001b[43m \u001b[49m\u001b[43mresult\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_replace_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mret\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues_by_name\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mret\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mret\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[0;32m 298\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\weldx\\lib\\site-packages\\pint\\quantity.py:294\u001b[0m, in \u001b[0;36mQuantity.__new__\u001b[1;34m(cls, value, units)\u001b[0m\n\u001b[0;32m 289\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m 290\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munits must be of type str, Quantity or \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 291\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnitsContainer; not \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mtype\u001b[39m(units))\n\u001b[0;32m 292\u001b[0m )\n\u001b[0;32m 293\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, \u001b[38;5;28mcls\u001b[39m):\n\u001b[1;32m--> 294\u001b[0m magnitude \u001b[38;5;241m=\u001b[39m \u001b[43mvalue\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43munits\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m_magnitude\n\u001b[0;32m 295\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 296\u001b[0m magnitude \u001b[38;5;241m=\u001b[39m _to_magnitude(\n\u001b[0;32m 297\u001b[0m value, inst\u001b[38;5;241m.\u001b[39mforce_ndarray, inst\u001b[38;5;241m.\u001b[39mforce_ndarray_like\n\u001b[0;32m 298\u001b[0m )\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\weldx\\lib\\site-packages\\pint\\quantity.py:724\u001b[0m, in \u001b[0;36mQuantity.to\u001b[1;34m(self, other, *contexts, **ctx_kwargs)\u001b[0m\n\u001b[0;32m 707\u001b[0m \u001b[38;5;124;03m\"\"\"Return Quantity rescaled to different units.\u001b[39;00m\n\u001b[0;32m 708\u001b[0m \n\u001b[0;32m 709\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 720\u001b[0m \u001b[38;5;124;03mpint.Quantity\u001b[39;00m\n\u001b[0;32m 721\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 722\u001b[0m other \u001b[38;5;241m=\u001b[39m to_units_container(other, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_REGISTRY)\n\u001b[1;32m--> 724\u001b[0m magnitude \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_convert_magnitude_not_inplace(other, \u001b[38;5;241m*\u001b[39mcontexts, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mctx_kwargs)\n\u001b[0;32m 726\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m(magnitude, other)\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\weldx\\lib\\site-packages\\pint\\quantity.py:673\u001b[0m, in \u001b[0;36mQuantity._convert_magnitude_not_inplace\u001b[1;34m(self, other, *contexts, **ctx_kwargs)\u001b[0m\n\u001b[0;32m 670\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_REGISTRY\u001b[38;5;241m.\u001b[39mcontext(\u001b[38;5;241m*\u001b[39mcontexts, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mctx_kwargs):\n\u001b[0;32m 671\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_REGISTRY\u001b[38;5;241m.\u001b[39mconvert(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_magnitude, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_units, other)\n\u001b[1;32m--> 673\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_REGISTRY\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_magnitude\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_units\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\weldx\\lib\\site-packages\\pint\\registry.py:1003\u001b[0m, in \u001b[0;36mBaseRegistry.convert\u001b[1;34m(self, value, src, dst, inplace)\u001b[0m\n\u001b[0;32m 1000\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m src \u001b[38;5;241m==\u001b[39m dst:\n\u001b[0;32m 1001\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m value\n\u001b[1;32m-> 1003\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\weldx\\lib\\site-packages\\pint\\registry.py:1917\u001b[0m, in \u001b[0;36mContextRegistry._convert\u001b[1;34m(self, value, src, dst, inplace)\u001b[0m\n\u001b[0;32m 1913\u001b[0m src \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_active_ctx\u001b[38;5;241m.\u001b[39mtransform(a, b, \u001b[38;5;28mself\u001b[39m, src)\n\u001b[0;32m 1915\u001b[0m value, src \u001b[38;5;241m=\u001b[39m src\u001b[38;5;241m.\u001b[39m_magnitude, src\u001b[38;5;241m.\u001b[39m_units\n\u001b[1;32m-> 1917\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\weldx\\lib\\site-packages\\pint\\registry.py:1518\u001b[0m, in \u001b[0;36mNonMultiplicativeRegistry._convert\u001b[1;34m(self, value, src, dst, inplace)\u001b[0m\n\u001b[0;32m 1513\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m DimensionalityError(\n\u001b[0;32m 1514\u001b[0m src, dst, extra_msg\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m - In destination units, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mex\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1515\u001b[0m )\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (src_offset_unit \u001b[38;5;129;01mor\u001b[39;00m dst_offset_unit):\n\u001b[1;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1520\u001b[0m src_dim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_dimensionality(src)\n\u001b[0;32m 1521\u001b[0m dst_dim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_dimensionality(dst)\n", - "File \u001b[1;32m~\\Anaconda3\\envs\\weldx\\lib\\site-packages\\pint\\registry.py:1036\u001b[0m, in \u001b[0;36mBaseRegistry._convert\u001b[1;34m(self, value, src, dst, inplace, check_dimensionality)\u001b[0m\n\u001b[0;32m 1033\u001b[0m \u001b[38;5;66;03m# If the source and destination dimensionality are different,\u001b[39;00m\n\u001b[0;32m 1034\u001b[0m \u001b[38;5;66;03m# then the conversion cannot be performed.\u001b[39;00m\n\u001b[0;32m 1035\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m src_dim \u001b[38;5;241m!=\u001b[39m dst_dim:\n\u001b[1;32m-> 1036\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m DimensionalityError(src, dst, src_dim, dst_dim)\n\u001b[0;32m 1038\u001b[0m \u001b[38;5;66;03m# Here src and dst have only multiplicative units left. Thus we can\u001b[39;00m\n\u001b[0;32m 1039\u001b[0m \u001b[38;5;66;03m# convert with a factor.\u001b[39;00m\n\u001b[0;32m 1040\u001b[0m factor, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_root_units(src \u001b[38;5;241m/\u001b[39m dst)\n", - "\u001b[1;31mDimensionalityError\u001b[0m: Cannot convert from 'dimensionless' (dimensionless) to 'millimeter' ([length])" - ] + "data": { + "text/plain": [ + "\n", + "Dimensions: (c: 3, v: 3)\n", + "Coordinates:\n", + " * c (c) ]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "expr = \"a*s+b\"\n", - "# expr = \"x+y+z\"\n", - "params = dict(\n", - " a=DataArray(Q_([1, 0, 0], \"mm\"), dims=[\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"])),\n", - " b=DataArray(Q_([0, 1, 0], \"mm\"), dims=[\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"])),\n", - ")\n", - "sps = SpatialSeries(expr, parameters=params)" + "trace_disc.plot(\"0.5mm\")\n", + "ax = plt.gca()\n", + "ax.plot([0,10],[0,10])" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "17debdfb-0a46-4a2b-bf77-980a0ac34437", + "cell_type": "markdown", + "id": "ab8045aa-4a06-4894-8bb3-85c48b741cd9", "metadata": {}, - "outputs": [], "source": [ - "sdts = SDTraceSegment(sps)" + "## Expression" ] }, { "cell_type": "code", - "execution_count": null, - "id": "539d8648-94f6-4c3c-b8e4-97b6cb569a8b", - "metadata": {}, - "outputs": [], - "source": [ - "sps.evaluate(\n", - " s=DataArray(\n", - " Q_([1], \"\"), dims=[\"s\"], coords=dict(s=DataArray(Q_([1], \"\"), dims=[\"s\"]))\n", - " )\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0fc5a100-5bb3-4b4a-ac0c-e888429eb1f5", + "execution_count": 7, + "id": "ed0318d3-f74e-44e4-b09d-891d0f89a8bb", "metadata": {}, "outputs": [], "source": [ - "t = DataArray([1, 3, 5], dims=[\"a\"])" + "expr = \"a*sin(s)+b*cos(s)+c*s/10+d \"\n", + "# expr = \"x+y+z\"\n", + "params = dict(\n", + " a=DataArray(Q_([1, 0, 0], \"mm\"), dims=[\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"])),\n", + " b=DataArray(Q_([0, 1, 0], \"mm\"), dims=[\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"])),\n", + " c=DataArray(Q_([0, 0, 2], \"mm\"), dims=[\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"])),\n", + " d=DataArray(Q_([0, -1, 0], \"mm\"), dims=[\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"])),\n", + ")\n", + "sps = SpatialSeries(expr, parameters=params)" ] }, { "cell_type": "code", - "execution_count": null, - "id": "8f4fd0fa-4725-48b7-b22a-722aa409bb71", + "execution_count": 8, + "id": "17debdfb-0a46-4a2b-bf77-980a0ac34437", "metadata": {}, "outputs": [], "source": [ - "t.assign_coords(dict(a=[1, 3, 4]))" + "segment = DynamicTraceSegment(sps, 2*np.pi)" ] }, { "cell_type": "code", - "execution_count": null, - "id": "fe85a016-29a4-4838-be67-8a8dbe64a8d7", + "execution_count": 9, + "id": "469b0ca2-faf6-4205-aaa0-3adfd4e187f2", "metadata": {}, "outputs": [], "source": [ - "DataArray(\n", - " Q_([1], \"\"),\n", - " dims=[\"s\"],\n", - " coords=dict(s=DataArray(Q_([1], \"\"), dims=[\"s\"]).pint.dequantify()),\n", - ")" + "trace = Trace([segment, segment, segment])" ] }, { "cell_type": "code", - "execution_count": null, - "id": "f23408a2-60fe-4397-b55f-865c06027829", + "execution_count": 10, + "id": "d935f063-862c-4ac7-951c-9d83d090d4c6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "a = DataArray(Q_([1, 2, 3], \"mm\"), dims=[\"c\"]).pint.dequantify()" + "trace.plot(\"0.1mm\")\n" ] }, { "cell_type": "code", - "execution_count": null, - "id": "3c1a153f-4111-4ee7-b3eb-1c000fb87f93", + "execution_count": 11, + "id": "287c4511-0d68-4a88-922d-ffcb08a841d0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "19.222850693296916 mm" + ], + "text/latex": [ + "$\\begin{pmatrix}19.222850693296916\\end{pmatrix}\\ \\mathrm{mm}$" + ], + "text/plain": [ + "array(19.22285069) " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "b = DataArray(Q_([2, 3, 5], \"\"), dims=[\"c\"], coords=dict(c=a))" + "trace.length" ] }, { "cell_type": "code", - "execution_count": null, - "id": "4191d792-1c47-4062-bfa2-0f0105890150", + "execution_count": 12, + "id": "86808edb-7187-4169-afcd-953b475ed0ed", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "print(b.coords[\"c\"])" + "from weldx import LocalCoordinateSystem\n", + "\n", + "from weldx.visualization.matplotlib_impl import draw_coordinate_system_matplotlib, axes_equal\n", + "num_lcs = 11\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "for i in range(num_lcs):\n", + " lcs = segment.local_coordinate_system(i/(num_lcs-1))\n", + " lcs = LocalCoordinateSystem(lcs.orientation, lcs.coordinates.data.m)\n", + " draw_coordinate_system_matplotlib(lcs, ax)\n", + "axes_equal(ax)" ] }, { "cell_type": "code", "execution_count": null, - "id": "469b0ca2-faf6-4205-aaa0-3adfd4e187f2", + "id": "2a7cffc9-f7f8-4242-9274-b92637ba47fd", "metadata": {}, "outputs": [], "source": [] diff --git a/weldx/geometry.py b/weldx/geometry.py index 9f4da09d6..47e2f9bc2 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1587,28 +1587,56 @@ def local_coordinate_system( class DynamicTraceSegment: """Trace segment that can be defined by a ``SpatialSeries``.""" - def __init__(self, series): - self._series = series + def __init__(self, series, max_s=1): + from weldx.core import SpatialSeries - def _get_squared_derivative(self, i): + self._series: SpatialSeries = series + self._max_s = max_s + self._length = self._len_expr() if series.is_expression else self._len_disc() + + def _get_derivative(self, i): me = self._series.data exp = me.expression # todo unit stripped -> how to proceed? how to cast all length units to mm? subs = [(k, v[i].data.to_base_units().m) for k, v in me.parameters.items()] - return exp.subs(subs).diff("s") ** 2 + return exp.subs(subs).diff("s") - @property - def length(self) -> float: - """Get the length of the segment.""" + def _get_squared_derivative(self, i): + return self._get_derivative(i) ** 2 + + def _len_expr(self): der_sq = [self._get_squared_derivative(i) for i in range(3)] expr = sympy.sqrt(der_sq[0] + der_sq[1] + der_sq[2]) - mag = float(sympy.integrate(expr, ("s", 0, 1)).evalf()) + mag = float(sympy.integrate(expr, ("s", 0, self._max_s)).evalf()) return Q_(mag, Q_(1, "mm").to_base_units().u).to("mm") - def local_coordinate_system(self, position: float) -> LocalCoordinateSystem: - coords = self._series.evaluate(s=position).data.transpose()[0] - return LocalCoordinateSystem(coordinates=coords) + def _len_disc(self): + return Q_(10, "mm") + + def _lcs_expr(self, position: float) -> tf.LocalCoordinateSystem: + coords = self._series.evaluate(s=position * self._max_s).data.transpose()[0] + x = [ + self._get_derivative(i).subs("s", position * self._max_s).evalf() + for i in range(3) + ] + z_fake = [0, 0, 1] + y = np.cross(z_fake, x) + return tf.LocalCoordinateSystem.from_axis_vectors(x=x, y=y, coordinates=coords) + + def _lcs_disc(self, position: float) -> tf.LocalCoordinateSystem: + coords = self._series.evaluate(s=position).data[0] + return tf.LocalCoordinateSystem(coordinates=coords) + + @property + def length(self) -> float: + """Get the length of the segment.""" + return self._length + + def local_coordinate_system(self, position: float) -> tf.LocalCoordinateSystem: + if self._series.is_expression: + return self._lcs_expr(position) + return self._lcs_disc(position) # Trace class ----------------------------------------------------------------- From 825d6fec9411c9cf83b399bc80ec70197ec65a0f Mon Sep 17 00:00:00 2001 From: Hirthammer Date: Tue, 8 Feb 2022 17:45:53 +0100 Subject: [PATCH 11/70] Run pre-commit --- tutorials/SpatialSeries.ipynb | 78 ++++---------- tutorials/TraceSegmentSpS.ipynb | 153 +++++++-------------------- tutorials/experiment_design_01.ipynb | 6 +- 3 files changed, 59 insertions(+), 178 deletions(-) diff --git a/tutorials/SpatialSeries.ipynb b/tutorials/SpatialSeries.ipynb index 74d734caa..aa951bc54 100644 --- a/tutorials/SpatialSeries.ipynb +++ b/tutorials/SpatialSeries.ipynb @@ -2,15 +2,16 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "759924f6-2e78-4aba-9750-fe1870344af3", "metadata": {}, "outputs": [], "source": [ - "from weldx.core import SpatialSeries, GenericSeries\n", - "from weldx import LocalCoordinateSystem, Q_\n", "import numpy as np\n", - "from xarray import DataArray" + "from xarray import DataArray\n", + "\n", + "from weldx import Q_, LocalCoordinateSystem\n", + "from weldx.core import GenericSeries, SpatialSeries" ] }, { @@ -23,42 +24,25 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "06c52d39-24fe-40ff-9a5b-594ad8435869", "metadata": {}, "outputs": [], "source": [ - "s = DataArray(Q_([0,5], \"\"), dims=[\"s\"]).pint.dequantify()\n", - "data = DataArray(Q_([[1,2,3], [4,5,6]], \"m\"), dims=[\"s\",\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"], s=s))" + "s = DataArray(Q_([0, 5], \"\"), dims=[\"s\"]).pint.dequantify()\n", + "data = DataArray(\n", + " Q_([[1, 2, 3], [4, 5, 6]], \"m\"),\n", + " dims=[\"s\", \"c\"],\n", + " coords=dict(c=[\"x\", \"y\", \"z\"], s=s),\n", + ")" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "ccd72f65-3173-4481-bb81-07692fa2fa06", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Values:\n", - "\t[[1 2 3]\n", - " [4 5 6]]\n", - "Dimensions:\n", - "\t('s', 'c')\n", - "Coordinates:\n", - "\tc = ['x' 'y' 'z'] None\n", - "\ts = [0 5] \n", - "Units:\n", - "\tm" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "spsd = SpatialSeries(data, dims=[\"s\", \"c\"])\n", "spsd" @@ -74,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "3fbd63fa-c3ba-4c9b-9a87-779e130db08b", "metadata": {}, "outputs": [], @@ -82,38 +66,16 @@ "exp = \"a*s + b\"\n", "params = dict(\n", " a=DataArray(Q_([0, 0, 1], \"mm\"), dims=[\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"])),\n", - " b=DataArray(Q_([1, 0, 0], \"mm\"), dims=[\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"])), \n", + " b=DataArray(Q_([1, 0, 0], \"mm\"), dims=[\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"])),\n", ")" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "5a60c890-aac4-499b-bdbc-d54a3529b8c5", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Expression:\n", - "\ta*s + b\n", - "Parameters:\n", - "\ta = [0 0 1] mm\n", - "\tb = [1 0 0] mm\n", - "Free Dimensions:\n", - "\ts in \n", - "Other Dimensions:\n", - "\t['c']\n", - "Units:\n", - "\tmm" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "spse = SpatialSeries(exp, parameters=params)\n", "spse" @@ -130,9 +92,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python (weldx)", + "display_name": "", "language": "python", - "name": "weldx" + "name": "" }, "language_info": { "codemirror_mode": { diff --git a/tutorials/TraceSegmentSpS.ipynb b/tutorials/TraceSegmentSpS.ipynb index e0ff1e83e..a7e43344a 100644 --- a/tutorials/TraceSegmentSpS.ipynb +++ b/tutorials/TraceSegmentSpS.ipynb @@ -2,18 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "228ec7cb-5828-459e-a5a8-349a093ec1b1", "metadata": {}, "outputs": [], "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "from xarray import DataArray\n", "\n", "from weldx import Q_, GenericSeries, LinearHorizontalTraceSegment, Trace\n", "from weldx.core import SpatialSeries\n", - "from weldx.geometry import DynamicTraceSegment\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" + "from weldx.geometry import DynamicTraceSegment" ] }, { @@ -26,25 +26,25 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "01dabe70-4c3f-408a-99cc-3beae8bd978d", "metadata": {}, "outputs": [], "source": [ "data = DataArray(\n", - " Q_([[0,0,0], [0,5,0], [1,5,0], [1,9,0]], \"mm\"), \n", - " dims=[\"s\",\"c\"], \n", + " Q_([[0, 0, 0], [0, 5, 0], [1, 5, 0], [1, 9, 0]], \"mm\"),\n", + " dims=[\"s\", \"c\"],\n", " coords=dict(\n", - " c=[\"x\", \"y\", \"z\"], \n", - " s=DataArray(Q_([0, 0.5, 0.6, 1],\"\"), dims=[\"s\"]).pint.dequantify()\n", - " )\n", + " c=[\"x\", \"y\", \"z\"],\n", + " s=DataArray(Q_([0, 0.5, 0.6, 1], \"\"), dims=[\"s\"]).pint.dequantify(),\n", + " ),\n", ")\n", "series_disc = SpatialSeries(data)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "0975aed9-23c5-4ae0-aed9-93baba07661e", "metadata": {}, "outputs": [], @@ -54,35 +54,17 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "00c46ac2-4a1f-433d-b0cf-5e3a4b9e8986", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Dimensions: (c: 3, v: 3)\n", - "Coordinates:\n", - " * c (c) ]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "trace_disc.plot(\"0.5mm\")\n", "ax = plt.gca()\n", - "ax.plot([0,10],[0,10])" + "ax.plot([0, 10], [0, 10])" ] }, { @@ -135,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "ed0318d3-f74e-44e4-b09d-891d0f89a8bb", "metadata": {}, "outputs": [], @@ -153,17 +112,17 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "17debdfb-0a46-4a2b-bf77-980a0ac34437", "metadata": {}, "outputs": [], "source": [ - "segment = DynamicTraceSegment(sps, 2*np.pi)" + "segment = DynamicTraceSegment(sps, 2 * np.pi)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "469b0ca2-faf6-4205-aaa0-3adfd4e187f2", "metadata": {}, "outputs": [], @@ -173,82 +132,42 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "d935f063-862c-4ac7-951c-9d83d090d4c6", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from weldx import LocalCoordinateSystem\n", + "from weldx.visualization.matplotlib_impl import (\n", + " axes_equal,\n", + " draw_coordinate_system_matplotlib,\n", + ")\n", "\n", - "from weldx.visualization.matplotlib_impl import draw_coordinate_system_matplotlib, axes_equal\n", "num_lcs = 11\n", "fig = plt.figure()\n", - "ax = fig.add_subplot(111, projection='3d')\n", + "ax = fig.add_subplot(111, projection=\"3d\")\n", "for i in range(num_lcs):\n", - " lcs = segment.local_coordinate_system(i/(num_lcs-1))\n", + " lcs = segment.local_coordinate_system(i / (num_lcs - 1))\n", " lcs = LocalCoordinateSystem(lcs.orientation, lcs.coordinates.data.m)\n", " draw_coordinate_system_matplotlib(lcs, ax)\n", "axes_equal(ax)" @@ -265,9 +184,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python (weldx)", + "display_name": "", "language": "python", - "name": "weldx" + "name": "" }, "language_info": { "codemirror_mode": { diff --git a/tutorials/experiment_design_01.ipynb b/tutorials/experiment_design_01.ipynb index 02340238c..aec34ce19 100644 --- a/tutorials/experiment_design_01.ipynb +++ b/tutorials/experiment_design_01.ipynb @@ -476,9 +476,9 @@ ], "metadata": { "kernelspec": { - "display_name": "weldx", + "display_name": "", "language": "python", - "name": "weldx" + "name": "" }, "language_info": { "codemirror_mode": { @@ -495,4 +495,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} From 54b132ad419d24ca7c2518ab2262c47f109a36c1 Mon Sep 17 00:00:00 2001 From: Cagtay Fabry <43667554+CagtayFabry@users.noreply.github.com> Date: Tue, 8 Feb 2022 20:19:02 +0100 Subject: [PATCH 12/70] lint --- .pre-commit-config.yaml | 2 +- tutorials/sympy_diff.py | 4 ++-- tutorials/trace_segment.py | 9 +-------- weldx/geometry.py | 3 +-- 4 files changed, 5 insertions(+), 13 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index bf1a3d214..8d61300bc 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -20,7 +20,7 @@ repos: exclude: devtools/conda.recipe/meta.yaml # doesn't play nice with jinja # - id: no-commit-to-branch # only makes sense for local pre-commit hooks - repo: https://github.com/sondrelg/pep585-upgrade - rev: v1 + rev: v1.0.1 hooks: - id: upgrade-type-hints args: [ '--futures=true' ] diff --git a/tutorials/sympy_diff.py b/tutorials/sympy_diff.py index 1b4f5b492..802761f6e 100644 --- a/tutorials/sympy_diff.py +++ b/tutorials/sympy_diff.py @@ -1,5 +1,7 @@ import sympy +from weldx import MathematicalExpression + s = sympy.symbols("s") exp1 = 1 * s**2 + 0 * s + 0 exp2 = 0 * s**2 + 1 * s + 0 @@ -11,8 +13,6 @@ print(sympy.integrate(temp, (s, 0, 1)).evalf()) -from weldx import MathematicalExpression - params = dict(a=[1, 0, 0], b=[0, 1, 0], c=[0, 0, 1]) me = MathematicalExpression("a * s**2 + b * s + c", parameters=params) diff --git a/tutorials/trace_segment.py b/tutorials/trace_segment.py index 22ed700e1..a77c242e2 100644 --- a/tutorials/trace_segment.py +++ b/tutorials/trace_segment.py @@ -3,14 +3,7 @@ import sympy from xarray import DataArray -from weldx import ( - Q_, - U_, - GenericSeries, - LinearHorizontalTraceSegment, - LocalCoordinateSystem, - Trace, -) +from weldx import Q_, LinearHorizontalTraceSegment, LocalCoordinateSystem, Trace from weldx.core import SpatialSeries from weldx.geometry import RadialHorizontalTraceSegment diff --git a/weldx/geometry.py b/weldx/geometry.py index 47e2f9bc2..8fe763414 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1634,6 +1634,7 @@ def length(self) -> float: return self._length def local_coordinate_system(self, position: float) -> tf.LocalCoordinateSystem: + """Calculate a local coordinate system at a position of the trace segment.""" if self._series.is_expression: return self._lcs_expr(position) return self._lcs_disc(position) @@ -1831,9 +1832,7 @@ def rasterize(self, raster_width: pint.Quantity) -> pint.Quantity: pint.Quantity Raster data - """ - if not raster_width > 0: raise ValueError("'raster_width' must be > 0") From 10f5987084e68ce49c5b2b6d019b7e15cb3aa28b Mon Sep 17 00:00:00 2001 From: Cagtay Fabry <43667554+CagtayFabry@users.noreply.github.com> Date: Tue, 8 Feb 2022 20:19:27 +0100 Subject: [PATCH 13/70] add _k3d_line to Trace --- weldx/geometry.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/weldx/geometry.py b/weldx/geometry.py index 8fe763414..7c5e8f09f 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1904,6 +1904,13 @@ def plot( else: axes.plot(data[0].m, data[1].m, data[2].m, fmt) + def _k3d_line(self, raster_width: pint.Quantity = "1mm"): + """Get (or show) a k3d line from of the trace.""" + import k3d + + r = self.rasterize(raster_width).to(_DEFAULT_LEN_UNIT).magnitude + return k3d.line(r.astype("float32").T) + # Linear profile interpolation class ------------------------------------------ From 74110dccbca82f2d7e848e9c087f241d723799e6 Mon Sep 17 00:00:00 2001 From: Cagtay Fabry <43667554+CagtayFabry@users.noreply.github.com> Date: Tue, 8 Feb 2022 20:22:58 +0100 Subject: [PATCH 14/70] clean notebooks --- tutorials/01_03_geometry.ipynb | 31 ------------------------------- tutorials/SpatialSeries.ipynb | 8 -------- tutorials/TraceSegmentSpS.ipynb | 15 --------------- 3 files changed, 54 deletions(-) diff --git a/tutorials/01_03_geometry.ipynb b/tutorials/01_03_geometry.ipynb index d54699e6c..a208e0b42 100644 --- a/tutorials/01_03_geometry.ipynb +++ b/tutorials/01_03_geometry.ipynb @@ -2,7 +2,6 @@ "cells": [ { "cell_type": "markdown", - "id": "aca3e29e", "metadata": {}, "source": [ "# Groove based workpiece data and geometry\n", @@ -22,7 +21,6 @@ }, { "cell_type": "markdown", - "id": "ab9f4cae", "metadata": {}, "source": [ "## Plotting the specimen's groove\n", @@ -37,7 +35,6 @@ { "cell_type": "code", "execution_count": null, - "id": "d78202f9", "metadata": {}, "outputs": [], "source": [ @@ -49,7 +46,6 @@ { "cell_type": "code", "execution_count": null, - "id": "71f68845", "metadata": {}, "outputs": [], "source": [ @@ -58,7 +54,6 @@ }, { "cell_type": "markdown", - "id": "cab51cfe", "metadata": {}, "source": [ "The workpiece data of this particular file consists of two parts:\n", @@ -84,7 +79,6 @@ { "cell_type": "code", "execution_count": null, - "id": "f2a173ef", "metadata": {}, "outputs": [], "source": [ @@ -94,7 +88,6 @@ }, { "cell_type": "markdown", - "id": "211b939d", "metadata": {}, "source": [ "Apart from the visual representation, the plot also contains all relevant information like the groove's area and the ISO 9692-1 parameters.\n", @@ -111,7 +104,6 @@ { "cell_type": "code", "execution_count": null, - "id": "99a47972", "metadata": {}, "outputs": [], "source": [ @@ -121,7 +113,6 @@ }, { "cell_type": "markdown", - "id": "2d5dabb3", "metadata": {}, "source": [ "We can plot the content of a `Profile` the same way as we did before with the groove:" @@ -130,7 +121,6 @@ { "cell_type": "code", "execution_count": null, - "id": "78f8b2e1", "metadata": {}, "outputs": [], "source": [ @@ -139,7 +129,6 @@ }, { "cell_type": "markdown", - "id": "dedc39ee", "metadata": {}, "source": [ "The only difference here is that we don't get the additional, norm-related information." @@ -147,7 +136,6 @@ }, { "cell_type": "markdown", - "id": "7081157c", "metadata": {}, "source": [ "## 3d plot (matplotlib)\n", @@ -160,7 +148,6 @@ { "cell_type": "code", "execution_count": null, - "id": "e6a8b3ff", "metadata": {}, "outputs": [], "source": [ @@ -172,7 +159,6 @@ }, { "cell_type": "markdown", - "id": "6220c711", "metadata": {}, "source": [ "Now all that remains, you might have guessed it, is to call the plot method:\n", @@ -183,7 +169,6 @@ { "cell_type": "code", "execution_count": null, - "id": "e21f8bd7", "metadata": {}, "outputs": [], "source": [ @@ -193,7 +178,6 @@ { "cell_type": "code", "execution_count": null, - "id": "17b59101", "metadata": {}, "outputs": [], "source": [ @@ -202,7 +186,6 @@ }, { "cell_type": "markdown", - "id": "e2769729", "metadata": {}, "source": [ "By default, the `plot` method shows us the triangulatad data.\n", @@ -213,7 +196,6 @@ { "cell_type": "code", "execution_count": null, - "id": "efbaa9f3", "metadata": {}, "outputs": [], "source": [ @@ -222,7 +204,6 @@ }, { "cell_type": "markdown", - "id": "8c16b264", "metadata": {}, "source": [ "The density of the triangle mesh or the point cloud can be controlled py the parameters `profile_raster_width` and `trace_raster_width`.\n", @@ -235,7 +216,6 @@ { "cell_type": "code", "execution_count": null, - "id": "6474ba52", "metadata": {}, "outputs": [], "source": [ @@ -248,7 +228,6 @@ }, { "cell_type": "markdown", - "id": "05693b01", "metadata": {}, "source": [ "As you can see, we now got only 3 densely rendered profiles.\n", @@ -264,7 +243,6 @@ }, { "cell_type": "markdown", - "id": "9b411aef", "metadata": {}, "source": [ "## 3d plot (k3d)\n", @@ -282,7 +260,6 @@ { "cell_type": "code", "execution_count": null, - "id": "d84588a7", "metadata": {}, "outputs": [], "source": [ @@ -291,7 +268,6 @@ }, { "cell_type": "markdown", - "id": "54a42499", "metadata": {}, "source": [ "Now we got a nice 3d rendering of the geometry with a closed surface that we shift and turn as we like.\n", @@ -304,7 +280,6 @@ { "cell_type": "code", "execution_count": null, - "id": "42c3d5e6", "metadata": {}, "outputs": [], "source": [ @@ -313,7 +288,6 @@ }, { "cell_type": "markdown", - "id": "42eaedb3", "metadata": {}, "source": [ "Now lets plot the geometry again:" @@ -322,7 +296,6 @@ { "cell_type": "code", "execution_count": null, - "id": "4deada64", "metadata": {}, "outputs": [], "source": [ @@ -331,7 +304,6 @@ }, { "cell_type": "markdown", - "id": "1ecda514", "metadata": {}, "source": [ "## Export 3d geometry into a CAD file\n", @@ -344,7 +316,6 @@ { "cell_type": "code", "execution_count": null, - "id": "2ccd3dd6", "metadata": {}, "outputs": [], "source": [ @@ -355,7 +326,6 @@ }, { "cell_type": "markdown", - "id": "03370d27", "metadata": {}, "source": [ "The parameters `profile_raster_width` and `trace_raster_width` do have the exact same effect as in the `plot` method described before." @@ -363,7 +333,6 @@ }, { "cell_type": "markdown", - "id": "36cb0dd2", "metadata": {}, "source": [ "## Conclusion\n", diff --git a/tutorials/SpatialSeries.ipynb b/tutorials/SpatialSeries.ipynb index aa951bc54..0e45d58ef 100644 --- a/tutorials/SpatialSeries.ipynb +++ b/tutorials/SpatialSeries.ipynb @@ -3,7 +3,6 @@ { "cell_type": "code", "execution_count": null, - "id": "759924f6-2e78-4aba-9750-fe1870344af3", "metadata": {}, "outputs": [], "source": [ @@ -16,7 +15,6 @@ }, { "cell_type": "markdown", - "id": "3f0a0e78-40b6-4521-8e1d-a1fc0f322e9f", "metadata": {}, "source": [ "## Discrete" @@ -25,7 +23,6 @@ { "cell_type": "code", "execution_count": null, - "id": "06c52d39-24fe-40ff-9a5b-594ad8435869", "metadata": {}, "outputs": [], "source": [ @@ -40,7 +37,6 @@ { "cell_type": "code", "execution_count": null, - "id": "ccd72f65-3173-4481-bb81-07692fa2fa06", "metadata": {}, "outputs": [], "source": [ @@ -50,7 +46,6 @@ }, { "cell_type": "markdown", - "id": "b88b7d17-c2fe-4095-b160-76b69ed3714d", "metadata": {}, "source": [ "## Expression" @@ -59,7 +54,6 @@ { "cell_type": "code", "execution_count": null, - "id": "3fbd63fa-c3ba-4c9b-9a87-779e130db08b", "metadata": {}, "outputs": [], "source": [ @@ -73,7 +67,6 @@ { "cell_type": "code", "execution_count": null, - "id": "5a60c890-aac4-499b-bdbc-d54a3529b8c5", "metadata": {}, "outputs": [], "source": [ @@ -84,7 +77,6 @@ { "cell_type": "code", "execution_count": null, - "id": "90ac71ed-38a6-453d-ba36-6be5ea48a4bb", "metadata": {}, "outputs": [], "source": [] diff --git a/tutorials/TraceSegmentSpS.ipynb b/tutorials/TraceSegmentSpS.ipynb index a7e43344a..2a0cbcdfd 100644 --- a/tutorials/TraceSegmentSpS.ipynb +++ b/tutorials/TraceSegmentSpS.ipynb @@ -3,7 +3,6 @@ { "cell_type": "code", "execution_count": null, - "id": "228ec7cb-5828-459e-a5a8-349a093ec1b1", "metadata": {}, "outputs": [], "source": [ @@ -18,7 +17,6 @@ }, { "cell_type": "markdown", - "id": "9ece03aa-6c2a-480d-b9a1-1a036041fece", "metadata": {}, "source": [ "## Discrete" @@ -27,7 +25,6 @@ { "cell_type": "code", "execution_count": null, - "id": "01dabe70-4c3f-408a-99cc-3beae8bd978d", "metadata": {}, "outputs": [], "source": [ @@ -45,7 +42,6 @@ { "cell_type": "code", "execution_count": null, - "id": "0975aed9-23c5-4ae0-aed9-93baba07661e", "metadata": {}, "outputs": [], "source": [ @@ -55,7 +51,6 @@ { "cell_type": "code", "execution_count": null, - "id": "00c46ac2-4a1f-433d-b0cf-5e3a4b9e8986", "metadata": {}, "outputs": [], "source": [ @@ -65,7 +60,6 @@ { "cell_type": "code", "execution_count": null, - "id": "9ae901f9-101e-43a9-87d0-91df0c0ed9c0", "metadata": {}, "outputs": [], "source": [ @@ -75,7 +69,6 @@ { "cell_type": "code", "execution_count": null, - "id": "3ee3f94f-6d2a-480e-8d1e-97b0217bf074", "metadata": {}, "outputs": [], "source": [ @@ -86,7 +79,6 @@ }, { "cell_type": "markdown", - "id": "ab8045aa-4a06-4894-8bb3-85c48b741cd9", "metadata": {}, "source": [ "## Expression" @@ -95,7 +87,6 @@ { "cell_type": "code", "execution_count": null, - "id": "ed0318d3-f74e-44e4-b09d-891d0f89a8bb", "metadata": {}, "outputs": [], "source": [ @@ -113,7 +104,6 @@ { "cell_type": "code", "execution_count": null, - "id": "17debdfb-0a46-4a2b-bf77-980a0ac34437", "metadata": {}, "outputs": [], "source": [ @@ -123,7 +113,6 @@ { "cell_type": "code", "execution_count": null, - "id": "469b0ca2-faf6-4205-aaa0-3adfd4e187f2", "metadata": {}, "outputs": [], "source": [ @@ -133,7 +122,6 @@ { "cell_type": "code", "execution_count": null, - "id": "d935f063-862c-4ac7-951c-9d83d090d4c6", "metadata": {}, "outputs": [], "source": [ @@ -143,7 +131,6 @@ { "cell_type": "code", "execution_count": null, - "id": "287c4511-0d68-4a88-922d-ffcb08a841d0", "metadata": {}, "outputs": [], "source": [ @@ -153,7 +140,6 @@ { "cell_type": "code", "execution_count": null, - "id": "86808edb-7187-4169-afcd-953b475ed0ed", "metadata": {}, "outputs": [], "source": [ @@ -176,7 +162,6 @@ { "cell_type": "code", "execution_count": null, - "id": "2a7cffc9-f7f8-4242-9274-b92637ba47fd", "metadata": {}, "outputs": [], "source": [] From 42074f7fc35d8fc360af98e4ec4217ec2ce0ee09 Mon Sep 17 00:00:00 2001 From: Cagtay Fabry <43667554+CagtayFabry@users.noreply.github.com> Date: Tue, 8 Feb 2022 20:24:16 +0100 Subject: [PATCH 15/70] mypy --- weldx/geometry.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index 7c5e8f09f..471f0caa1 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1642,7 +1642,9 @@ def local_coordinate_system(self, position: float) -> tf.LocalCoordinateSystem: # Trace class ----------------------------------------------------------------- -trace_segment_types = Union[LinearHorizontalTraceSegment, RadialHorizontalTraceSegment] +trace_segment_types = Union[ + LinearHorizontalTraceSegment, RadialHorizontalTraceSegment, DynamicTraceSegment +] class Trace: From d6e1304dcfd4dd5294d456178e953682f91626a4 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Mon, 14 Feb 2022 16:12:05 +0100 Subject: [PATCH 16/70] Use MathExpr for derivative --- tutorials/TraceSegmentSpS.ipynb | 351 ++++++++++++++++++++-- tutorials/sympy_diff.py | 6 +- weldx/geometry.py | 17 +- weldx/tests/asdf_tests/test_weldx_file.py | 2 +- weldx/welding/groove/iso_9692_1.py | 2 +- 5 files changed, 347 insertions(+), 31 deletions(-) diff --git a/tutorials/TraceSegmentSpS.ipynb b/tutorials/TraceSegmentSpS.ipynb index 2a0cbcdfd..253485ccd 100644 --- a/tutorials/TraceSegmentSpS.ipynb +++ b/tutorials/TraceSegmentSpS.ipynb @@ -2,7 +2,8 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "id": "e998eaed", "metadata": {}, "outputs": [], "source": [ @@ -17,6 +18,7 @@ }, { "cell_type": "markdown", + "id": "661602ad", "metadata": {}, "source": [ "## Discrete" @@ -24,7 +26,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, + "id": "100b135a", "metadata": {}, "outputs": [], "source": [ @@ -41,7 +44,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, + "id": "6a8fec99", "metadata": {}, "outputs": [], "source": [ @@ -50,27 +54,262 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, + "id": "59c8a6fd", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n" + ] + }, + { + "data": { + "text/plain": [ + "\n", + "Dimensions: (c: 3, v: 3)\n", + "Coordinates:\n", + " * c (c) ]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "trace_disc.plot(\"0.5mm\")\n", "ax = plt.gca()\n", @@ -79,6 +318,7 @@ }, { "cell_type": "markdown", + "id": "e8a84704", "metadata": {}, "source": [ "## Expression" @@ -86,7 +326,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, + "id": "b9d78954", "metadata": {}, "outputs": [], "source": [ @@ -103,7 +344,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, + "id": "edcd21b3", "metadata": {}, "outputs": [], "source": [ @@ -112,36 +354,100 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, + "id": "b11e1ff9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\variable.py:259: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", + " data = np.asarray(data)\n" + ] + } + ], "source": [ "trace = Trace([segment, segment, segment])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, + "id": "be17250e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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AZzAQq/fh4+ODj48PYNXsnOyCuenCcWEwhBBUV1djsVgIDg6edAFZUoCa206P4Xfv5yC/9B9XJfDLU2LYVdo0IK06HCaTif379xMQEEBMTAxGo1Hp3TlSMFCelu4MBg4VzLW2tiqCOa1WO8D7cOJYpr3BkNOm3d39bekm0Fi0t7eTlZU1REA2OBPyfZWJ1/MLcdGo6DFZ+GlaKL88JQYYfQkCVmORl5dHQkICoaGhw8YwhgsGNjU1kZWVBaBcEJ6enie996FSqfDz88PPzxo87unpwWAwUFRURE9PD76+vopgzul9TDzT2mAMzoRMZMBwNAGZnAl57NIUfihq4r+5fagk0Kglbl8Wx9t7q9hV2jQgbjGcsZDvhLNnzyY0NHTI34dDDga6uLiQkZGhBAMrKiro6OjA29vbGQy0wdXVlYiICCIiIgbI9YuLi3FxcSEgIACTyTT2jpzYxbT8xk21gGxJnD9rf57CzW8exGgWqCVw02l49so0lsT5szjOb8xliNzbUy5SOlrkYKDsnbS1tWEwGKioqFAmhAUEBODh4eH0PgbJ9bu6umhqaqKpqYnm5mYCAwNPOrn+RDPtDIajMyFFRUV0dHSwcOHCAS6r7TKkq8/Mf3eUYzRbPZpZfir+eFGaXZkQsPb2rK+vZ8GCBRQWFk7Y8dsGA+Pj4+nt7aWpqYmysjK6uroU78PPz8/pfXBEMCcvVQAaGxspKirCzc3tpBTMHSvT6ls1WQKyuXPnDtm3vAx58KdJvLS9XMmE/DQ9lC15dUP2N9wyRAjBoUOH6O3tZcGCBQ6/i7m4uBAWFqb07pSXQGVlZWg0GuWOerJ0zxoN2fsIDAxECKHI9R0x3/ZEZtoYDEcLyFpaWpgxYwYxMTHDPmdJnD8PXTyHO97JwmKxehZyJuQVbfOYSxDb3p5paWmTvjwYKxjo6empdB872mDg4FEIbx1opLe7G7/GSm5YGsW6nZVoJAmTEEN+B2svU7DORLlhaRR7ylqU/08mwwnm5Pm2hw4dwsPDw1kjMwLTwpTKmhBHGIuuri6ys7Nxd3cfYixe2lbGrlJrHUWP0cwr28sxWwQCBmRCkgLUyhJkOHp7e9m3bx9BQUEkJCRMi1iCHAxMS0tjwYIF+Pv709fXx/79+8nMzKSqqkrJPIHVGOwpaxmwj/s3FHL/xiNLKnkSvPyYWgWvZbah6T9fjSSxdkvJsL/L2972TjYaSWJPWQtrPsonJcyLPWUtrNtZ6eB3ZGTk+baJiYksWrSI2NhY+vr6yMnJYd++fZSUlNDa2uqs0mUaeBiysXBEK72WlhZyc3NJSEigpqZmyN/lZcijP0/h9d2V7C1vAazLkO8PG+zKhHR0dJCVlcXs2bPH7O05Vcidw2Xv58XvS4js6ybScKR3Z2+PhTvXlytT3/eUtSgT086ZHUh8oDtlTV2YLILPsxuobe3lx4oW5obqePq7MrYc0pNX38EZCf48+30Ze8pb2Fvewhmz/Hnm+zIyonwwmq3G+KnvyrAIwW/OjGNTbgNf5Dfy5KXJ7ClrYUdBM7MCXfmyonLSPQ8YON9WrpmxnW8ry/UtFsukH9t0YEoNhqMFZCUlJcyfP3/EO8OSOH8evyyFm9/IpM9s/QIMV5A1Ek1NTeTn55OWlnZMmRBHIi8j0kKP9OF00+m4/9synlmdQkaUF9/mVvPGvnLOjpC4890sZgW6kt/YS2yAOw1tvdz6Ts6Q/W4vaQbgQJ21u/b+Kqv3tfWw1WP7rsj679ZC67/b+p9vxfp5rN1SgkoCnUbFa7sr2V/ZxlWpvjy4tY7HLk2ZsiWLLVqtdohcX6/X09bWRnZ2NoGBgQQGBp40NTJTsiSRg5uOMBZCCMrKyqioqGDBggXDBvxslyKF9Z2KsUgK9Ry2IGs4ampqKCwsJCMjY9oaCzgyA2VfhfU89pS18PLOSn6xOIJfv5fLta9ncc+GcvqExIYyC90myKzrQUIg+rqZH6plbri1gnJVUiB/WjkDb1cN1y2OwNtVzbVpXvi5abhlWRS+bhruPCMWH1cNVy0Mx8dNw52nx+DrpuHaRRG4a1W4aVWcPSsAtWQ1G2YB3UYL3xc102O08GZmE//vTGvNirxkmS7Icv24uDh8fX1JTEzEzc2NiooK9uzZQ35+Pg0NDXR0dEzU67lKkrRHkqRMSZJyJUm6f0J2fAxMuofhyEyIxWKhoKAAs9k86gQyeSly86mxPLL5MBqVhEYlUdXcM+wyZMeOI8uZ8fT2nAoGByYXxfpy49IofvNRIckBKnL02fi4aXhhmzVmkF3TTrCXjjMTAnDRqPg4s57L5ofyUWY995w7i/aOdu77qpKfxKnZcljPtqImHr1kFssSgvFUm3h+Rz1rlsdz3ZJIvF21rN1Sovwe4e2q/J4Y6slHmdZsU0aUD3vKWzCZBWYhMJoFLhqJXpPAbIH9NV189YN10JN8TlPpZYyEVqsdUiOj1+u57LLLqK2t5T//+Q8XXHABycnJR/sSvcDZQogOSZK0wDZJkjYJIXZN3FmMj0n9tjtaQJaZmYmvry/x8fGj7ntJnD9/v2A2v3kvBwlw0ap47qp0gFGzIbKATK1WD+ntOV2QPYr/XJyIq1bFO/tq+CKvEbOA/fUWXDQSqeHeBHnq2JjbyBULwnj/QB0xfm68vLOSJy61zkVZFu/P3e9bGwc9eVkqC2N8+OuneWw+ZK37cGmtoKUVrkl2x9i/njcJwZrl8UpWxPb3nNp2nrw0mYK6Dp753rocKqjr4MnvStGqrMbCy1VNe4+Zd7NbuOXUaADufj+Xc5OCpubNHAe2NTJbt27llFNOISQkhM8///yoDYawrqVld0Xb/zOlkddJMxgWi4XKykqlPf1EXmw9PT0cPHiQ6OhowsPDR3yeXJy1ONaPDw7UIkkgBAN0IyMVZBmNRg4ePEhQUNCI7fomm8HehNkiyK9rx8dVzc1vZSvfrEhfV/QdfZwSruFAo4WMKJ8BxmFJrN+wYxjPTQoC6cik93/9NJmL+uMKc+eG4e9fQWNjI1BLbm4758cN7N05nFeQU9vOM6tTAHh5ZyV3nxHH89vKWRrlzZ7yVuV5r+yo4LXdVWhU0pDxC8cDWq2WX/ziF8e8H0mS1MCPwEzgWSHE7mPe6TEwKQZDFpDV1tbi5eU1oZV1soAsKSlJKQkeCXkpcnF6GD8UGXDRqJCA3Jp2ZSkykjR93759xMfHExISMmHHPh4GGwewpi3vXJ/Db86Ko7ypm89z6mnrMaNTS8QHuVPc2MWqxED2VLTy+CWz8O6po8c7eljjMNwYxr9fMGvIcdj2IvX19cVisTBjxgyld2d2djbAiL07ZSOyrn+otOx5AOwqbWFWgJZCgxGTBUwWC5fPCzshmiUfLUIIMzBXkiRf4CNJklKEEEOj0JOEw4OeJpNJ6bs50S68Xq8nOzub9PT0MY0FWJcifzt/Nv/dWYFKAq1a4sVr5vLMlWn8Zn22Egi1pbW1la6uLpKSkqbMWMCRpYZcK/HNIT3PfF+Gm1bNQ18V8+6PNXQbLdx2WjRPXDqHpk4jt5wazffFTdy4NIoF0d7AEeNgGpQ5ss57Pbo4gRwMjI2NJSMjg7S0NNzd3amqqmLPnj3k5eXR0NCA0WhUtrlhaZTymotifcmpbefuM+OobjMrz1Gr4KPsumkV+JwqhBAtwFbg3Kk8Dod5GI4WkFVVVVFdXU1GRsaYHsuru6vxNhqZB3xfZEDCOiE9JXz0pYgsIHN3d1e0CFPFolhfHrk4kd+8n0uItwtFjV0AJAR5kBruxXdFsmHwVSbDL4r1ZVGM9fd4fx3eNvty5B17OLm+3D3LtkTbVjB3w9Io7t9QqGSs3LUquowWVBJsym04KT0MSZKCAKMQokWSJDfgHODfU3lMDl2SOEoTIgvIFixYYFeZc3KoJ7//sAJdQBUfHaxFq5ZQS9KoSxHb3p579+6dsOO3h3U7K9G0mVnY/7vZInhxWzkfHayjvddMe2MXyWGe3H/BLFq7Taz5KJ9bTo1m/f5aDB19A2a0yjNQDlY0s8R3Uk8DGNi7Mz4+nr6+PkXv0tnZqXTP+qSwi8KGDowWUEnQZbQwJ8SDvPpOGjp6J//ApwdhwGv9cQwVsF4I8flE7FiSJD8gChsbIITYP9Z2DjMYcuXmRFbEWSwWsrKycHFxGVZANhILY3z4zUIv7vu8AACdeuSsyGi9PSeLlDAv7v6hj/gZBipbevm/beXoO434uKpx16q4YkE4H2XWs7vUWlMx2JsYbkZrWqgrhYXtI7zi5KHT6QYI5mS5vkuHnkP1vST4qTjcbCHES0defSdnzvRnXrTPVB/2lCCEyALmTfR+JUn6J3A9UMyRrIsAzh5r2+lVRDAKJpOJyspKYmNjiY6Otnu7l7aVMSvABV9XFf3eLuckBZFd3cZNp8YOWIqM1dtzIrBYLHR0dAyoDBwc0Jwb6U2Cr4rb3rWmNdUqiZ+mhvB9kYGnVycrac/hgpfH00Q1lUrFh/ntpIQFcO2qGRg9S3l8ayUSUN/eR1qwlgNVrVy9MGyqD/VEYzUwQwjRN94NjwuD0dXVRXl5OUFBQeMyFmDNjNz9bhahblZDeu6cYD7NrOMPqxKAI8VZ9vT2PFZMJhMHDhwArKlguXN4YpAbaz7K598XJ1LX2suTW0sxdFrwd9fQ1GXixlOicNepuSgtZMzMxvE2UU0O5q69JIlDjdalhwASAt0pb+vlktkefLXvEK5t7s7enRNHDuALNIx3w2lvMGQBWXh4OK6urmNvMIglcf5ctzCMp76vJNzHld1lzfxhVQIv/lBGUpgXS+L86ejoIDMzc0hvz4mku7ubgwcPEhsby8eHOkkJ8yIyQG11x1ubSPW3cEt/7YRaJXFGpIosA0pswjYuIXO8GQdbbL2qtZck8bsP8mjrsbbSmxvmSnmrkZuXxfQvueYwO8SFpqYmRa7v5+eHv7+/s3fn0fEQcECSpBys1aQACCEuGmvDaW0w6urqKC0tZf78+ej1+nHLi+VCrYpm63tS09rDT9NDMVmEshRJ8BYj9vacKDo6OigrKyM5ORkfHx9S2hq45+MC1l6SRIhPKI9tbSGrxoybVqLbKFgYBD/WW7h/VSRnJ0cqsYnhjMbxiq1nkRTqidFsQQAxvloeXhVBtdGDNR/lc+PSKGWJJffuNJvNA3p3urq6Kt7H0dxUTkJew5ptyQbGFWSclgZDFpAZDAYWLFhw1K325UKtGQFWAdoFKSHKcmRJnD9Rum4OHz48Ym/PiaC7u5vm5mYyMjJwd3dXprc/cOEs7ng3h16T9UI5c6Y/B6vbuG5xOK/vquInsRIzPM0cPHgQrVrNPcv82V9uYGGMz7QsSR8vsmfx+w/zUEkSXUYLiSEe1LZ0k1nbzfkLIpR4zOD6ENvBRoDSPUsOVvv5+REQEICPj4+ze9bw6IUQTx3NhtPOYFgsFvLz8xFCMH/+/GP6wJfE+XPLabE8/OVhvHWwo6RJWY54m9uI8zAO6e05kcidvrP6gtHW97Akzrr23lfRyt83FNJjshr3FYmB7Kto5dGfzVGyHXevz+LcxQEsWjCD3t5eQgwGYg0G9u7de1wP8rFdiswJ80StkjB0Ggn3ceG9GzPYuK+IB7fWERgYaPeSS+7dGRUVhdlsprm5mYaGBg4fPoybmxtms3laK4qngB8lSXoI+JSBS5KpS6seDeMRkNnDS9vKqGjqQgLa+uD2pZEkhniSEaymoLGbn5260CF3a9venoGBgaRg9XQevngO3xcZeHNPFRLgplVx3eJIXt9dNSTbcVu6TnHFT6RBPvJS5K/nzuSJb0sxdBpRqyTaekzsKWshPcyN/3dm6FFnetRqtdKjQu7deejQIcrLyxUtk7N3p5KqXWLz2PGVVu3p6eHAgQPExMSMKiAbD6kR3jz/XSkCSAvW8PquCv67vZT7V0Tw0yVJE/Iagxnc2zM3N5cFUd7cdVY8t72diUWAViXholXx5KXJA+onEkM9lYskyV/NwoVDS7WPx0E+tl7Folhf7jg9ht99mA9YjaYsRlvzUT5/PC2I9DA3zp+ATJXcu9Pb2xt/f3+8vb1pbm6mtrb2pO7dKYQ462i3nRYGYzwCsvEih0ld1WAymVGrVQ7ThPT29nLw4EF+0Lty6pwAEvqHL729v46nvq9Eo5LoMwvmR3lz86kxE1I/MdIgn5KSEnQ6nXJBTGXcQ/YqHrk4kcqWHh76qkj523WLI5VzXntJEjsKqkgPc0yXc7l3Z1BQkNI9y2AwkJOTgxBiRMHciUa/kO06IJaBlZ6/HmvbKTcYer2ewsJC5s6di4eHx4Tu+5Xt5VyeEc5/d1ayp9bErxaHE+TrwSvby0fs/n20yL09Z82ahQi2Dm9+4KdJ/HdHC3trelGrrK3oblwWxdt7q4ZsPxEp0pEG+RQWFtLT04PFYqG5uXlSgoGDvYr7zk/glneyMVusojIPnYprFkWyfn8ti2J8ledFaDsdelwysmBOFs0NN99WnjB3As633Qjs4njLklRWVlJTU8OCBQsc4hL+alkMt755EID5IRrWH2xAIHj2yvQJfZ3BvT0DA+HelQnc+U4WQoBOLeGiUfPMlWksjvVjfqTXpKRJ5WBgZGQknZ2dSgs5ORjoyEE+tmnTrj4zf/28UKm01aoknrosZcBybKpTxsMJ5gwGA1VVVuN+gs23dRVC/O5oNpwSgyGE4PDhw3R1ddktIDua13hnRxHmfi1LrxkE1hZwG7LrJszDqKmp4fmtRazImKVE4neVNvHPDQWo+mdypId7cudyaypXTqtOdgm3Wq1Gp9Mxe/Zshw3yGexV/GXVDG55OwtTv6DMTasiLcKbXJvq1OlYzm4rmIuLixt2vm1vb+/xPLP1f5Ik3QR8zsAsydD+DoOYdINhsVjIzMzEzc2N9PR0h2Up8vPzMRqN6DRqNCpBrt5knWJW0Dhhr1FSUkJLSwvnLkri9x/k8cRqHbWtPfz5kzwsFnDXqbl4hitfl3UP2X4qqzQdNchH9ioe/ulsihq7eHprGSbZq1BLA0YYDJDgT/OK1eHm2+bl5ZGXl4dGozke59v2AY8Af2ag+Cx+rA0n1WBYLBZycnKIiIgYtybEXmwFZM/84hRe3VnBw18eRgVK0ZbcGfxose3tOX/+fCRJ4onVqdz6ZibdRjMqCTxc1Dx7ZTqenTWcnRypqGIXx/pNzIlOIIODgZ2dnUow0GKxDPA+bC+Itw40EuFqYuZM6+8LY3w4c6Y/t76do5S4u2tVpB4HXoW9yL07PTw8mDVrFpIkKSnu7u5ufHx8OHz48ITEiCRJigJeB0Kxxhr+Twjx5DHvGH4HzBRC6Me74aQZjM7OTpqampg9ezZRUY7pAD24t+eu0iae/KaYhEA3Duu7WRzrx4s/lAFgsghuOjV23K8h9/b8tk7DacnhygWUW9NOt9HaLWphjB+3nxnHkjh/cnJqWBDtrZSiT0eDYYu9g3wCAgJIDHbj719WEBHRgkYt8c+NhynSd+GhVdFptKBVwdPHqVdhL4NrZFpaWnjuuecoKytj1apVXHHFFfzyl7882t2bgN8LIfZLkuSFteBqsxAi7xgPOxfoOpoNJ8VgNDc3k5eXh4+Pz4SnTWWGS81mV7dxcXoY7+yrRqeCHytauGx+OE9+U8wLV88d92vIArK4uDhOD9QpXkNuTTv/+eowABemhrKt2DBkW1kVe7yN27Md5PPKjgpmSBrcu7vJysrCy2RiYbDETW9lYREgYa1a3VvewuITyKuwFzlL9cQTT3DgwAFefvlliouLj3p/QohaoLb//+2SJOUDEcCxGgwzcFCSpG8ZGMOY+rSqLCDLyMigoKDAIa+h1+s5dOjQEAFZaoQ3r2wv57dnxfLU1jJMQvD2vmr+2K8l2VXapPTFGIvW1lZycnJITk7G19eXUKxt/W57K5OuPqtncc+Kmdx4auyAqWmOkbNNDVUtPby8o5Enfj4Hk68P//mykJLmIz04T4nUsaesmbQIb0K8Xbh5WbTiVQBTPsVssomKipowb1qSpFisFZoT0TX84/6fceNQg1FSUoJer2fhwoUOG/gj9/YcTkCWXd3GE6tTSQtxZVdhDTuq+9CqoL3XZNcoRBmj0Uhubi7z5s3jzf0NpEZYWBLnj9Es6O43FokhntzYb3hsp6Yt9Z3oM554hutIftW6/fh5aHn28iPvj7AIOvvM3Px2tuJRqLAurn+SEszXBXrOjHVla2kzkgTzAwT/PDeWx78poaypW+kODtZBz0jw9/OPdCaXZ6teljq9l22TjSRJnsAHwG+EEMOP4hsHQojXjnZbhxoMOQrviCIhe3p7yp7Dd/m1ZDcaOSMhgO8OG3hzdyVv7alSWvON5mmUl5fT19fHKaecglarVRSwa1bM5J8bDyFJ1pZ/1S3DT03LyZmyjvDDMtq4AtssxmF9Fz21Fl7bVcniWD/++EkBxXrrstd2UaVSwewgD77Mb8TfXcsXRV0khnjg46rm7982EO2lp7zVGgj+oaAGFRZaesx8kdcAksR5c4LJqW1HI0m8vLOSP54WxHvZzfhXCkxCKB7JdJizOhX0Tzz7AHhTCPHhVB+PQw1GSEiIQ6ZcWywWsrOz7ertuau0iXs/OcRvFnpxzcp5nP/0Dor1Xfi7a8mI9h3R07Dt7enu7q5U+y2J8+fBnyZx+9tZCKwVi89dNRcYfWradMG2oEo2Di/vrGRBjA93rc9habwf20uaSQ715MfKNtZuKQVKh92XADQStPWa8XRRU9/eh6eLivq2Xtp7NejUEsUtZuXJr/6o59UfrYF5V41EiJeW297JJtrPjSJ9FxenheDlYr25yCMW1+2sVIyJvLQ5WYyHZP1ivwLkCyEem+rjgSkaxnws9PX1sW/fPmUY7lh57+zqNv7z09kkB2nZVdpEU5eR2AA3mrqMnLb2e+5+N3uAp/HStjLMZmsfCo1GQ2pqKpIkDRjgvCGnQbnLpkb4KN7EaMObp4J1OyuVOSa2RPq6cNf6HO5+L5fb380h0EPLzpJmuowWthwy0GO08GPlwPOI8HHhnnPicdep8dCpuGVZFB5aFSqVxFUZ4YDELadGo1WrWfuzOfzjglnoNGpuOTUaXzcNj1ySyM/nWocsL47x5pyZXnipzagQFPV7Lh9n1XP7p1W88mMTQZ5antxaytbDBtZuKSE13FoUJ2dbUsK82FPWwrqdlQ59D6eYZcC1wNmSJB3s/zn/aHcmSdKfJEk6pqbCU64lGQ+ysZg5cybBwfaNz7vp1Fi6urr4cFslz3x1xDic+/QOSvVd6NQSm/Maya9t58Ufynjk4kT27t1Lo8qPunotCQlWgyQvRS7PiODz7Dp0ahUqaeypaZPFWEuNEA8V7+R182VZNq79Mz++KTSglqBY34VlUPJGo5I4Jd6XH4qamRflzYHKNr7Ma0QlwZOXWpcuiQFq/rihjCe3lvL8FalHennIM1lt1LjyY3LLwZv7vYU1H+Zx2Rw/PsrWE+clkW2wkOCnxt/bjQNV7RzoN1zbipvYWdIMEvzmzDjrtv2e0onqcQghtmENFU0UpcDdkiSlA5nAJuArIUSzvTs4bgxGV1cXNTU1zJ8/Hx+f8bedL24xDfAkWrqMpIZ7kV3Tzht7rHep20+NRKUvos0jgge+LB+wTLEOcE7kN+9lW+MWGknRpIxnKdLd3U1NTQ3+/v4TWhk4eKmxu7SZF7ZXEOSp48Y3sxSPyF2rItBDR3tvNxJgtjEUKeGeBLjr+K6oCYHg+6Jm7umfxP76rioe2VLC5fPCFKM0P8KT02Pc8fHxHnUmq8y5c4K484zYYY1KqG8Va7eUcFasO/tqulkY2keuSnBqlBvbq3rQqlR09AeYH/+2FBeNVY8CDMjEOBkZIcQ7wDsA/Z7GucCH/XNPvga+EELsGW0fx4XBqKuro66ujujo6KMyFgAXJbgxr99Y2F7gv1mfxaZca/Pk57ZVsSTWh8zqEu4+ewZL4vx5aVsZqmYzqtImXt5WhoR1gPNYU9OGo62tjezsbEJCQpRBPt7e3gQGBuLn52d3Jmk4bwKsy4Y71+cQH+hOXl0HQkB7j0m5RcX5qFBpXZXgpWyrVBLcfloM86J8lKFIL++oIC3ck+uWRAIo/+4sG3gzun2hLzPlUs9+Bs9kleenjmRU5DjKmuXxNLW0kBDoxkv7DPzu7FgunOXBf3eU81pWB6mBanL0ZkwWgalP8Mz3ZZTpu1jb36nsRPU0HIEQ4gBwAHhIkiRvYAVwI3D8Ggy5t6derycmJmZCsi1yqlX2NHaVNrMiwZutxW14u2nZVdaKRgWPbykmwENHRVMXHx3oRZtlrbfQqiUQsL+idVxLkcbGRg4fPkx6ejparRZJkgYM8ikvLx/Qq3K0Aq+UMC9lJsl1SyLZmNvA3zcUoga6jRZyazsIcNcS6KWjqLELc/96o7TVAnSRHuHF6TMD+N+eKi7PCOfNvdVk1bTx5r6aIUOR9pS1KBf6dUsiFcMxHoa7gG2NSk5tu/K61dUS72U3s2Z5PCYhKG5X82lRL/csj+dwfRslrQZ6TQKTgAOVbUT5aJkb6TWgktTJ+OhP1X7Q/zMq09ZgWCwWCgoKMJvNZGRkUF1dPSFVknLqVPY07lnmT5y7kbmxM1i7uZhTZ/izp6yZPpOFez7Mxc9Ni9ECxn532CIEOrWau8+O5zfrs1mRFER8oMeAUvPBadq6ujpaWlqUhsZ9fdb5MSqVCl9fX2Vu6//9UEqkpY+o1hKltVxFjytP7qhn9bxw5WJdFOvLwhgfHtlSwrpdlRg6jww5ljF0GTF0WR+fF+WNTgW7y9tw1UisTAzi5Z2VA3qITuVQpMEG5bJUP2U2zDqb7MjLOyt5anUqBXUdPLm1FKNZUNlq5JwntmMSEn85OwyjsY91OyudXoaDmJZZEnngj4uLCykpKQ6p48iqauXXGR7MCdDQ6xvDy9srrN3E4/156dp5uGhURPm50tw98GK0WKxGw9NVw2OXptDQ3su/vzyMRmX173eVNnHrmwfRqKzdtlpaWmhra+OzWk/++cWRTlPrdlby+q6qAVF+V62W+7bU0e0VZZ1+bnTnvs2VxLn3sXZLCY9uzGZrfh1nPrGT74usS4PBxiLYU8uMQGtvT7UEv18ex52nx3KooYtLElzQqFVsym8YMn91oie6TxS2091tDcfzV6SyZrlVXNncCyHeLpjNZu75+BA1VVWs/TyT1tbW464Uf7oz7TwMR/T2HIzRaGSeq56gqCBiY2N5aVvZkKDlJXPDeHtfNT9ND+WzrDoliyCAXpOFv3ySj7o/BpAQ4sETW4rZVdrMvvJmLp0fYe0l2lrHLC9odQ3hi7wSBILzU0KYH+GJRpJYu6WE3y2Po6vPzHeHDbywvYKZQe7c8W4OM3ygqLWclDBPyjqNCLp59UAzrx4YGEPwcVXT2mMmys+Ve5bH4+GiUeIQb+6tZm95C+t2VvHQT2bi3VPHTxZZy7UHM93FYLLhWmczSxas9Rw9JkGRvod/bTVyx+lxvLSjgr+c7U1NTQ2HDh1SaoE8PT1Pqt6dIyFJ0hbgUSHERpvH/k8IcfOY245hgY/JPBuNxgGFWwcPHiQhIWHEVnyj9fasrKxECHFUsnjZvZ83bx5dXV0cPHiQGTNmjNjbU16u3HJaLE9/W0Jn/3LklBl+ZFe1Y7FYmB3qxf7KVmIC3FBJEmX6rlHfLBeNCo1KUuIgfWZhDaCOso1askrE+8wDnxXqpaPbaObiWW58kN9BerCWHdVGLkkL4tuilgFFWfJSY/XcQAoLC0lLS5uw4GBTUxNNTU1Dgp7HQnV1NcCY4yptYxb/3lxEYYM1kOvpolZK0HNq2/nlkkjy8/ORJInu7u4J690pt2McbnCSEIIzzjhDGYtpB5PaREOSpBKgEvhGCHF//2P7hRDzx9p22ixJ9Ho92dnZpKenO0zR2trayoEDB0hOTh61EXB2dRsrkoJICvPCx81a4Zka4U2krxvnpQRjQSKnpo3bz4ijrdvE5RkR+LpruWZxJF4uan6eoOX/nRPF0jirJiI93JMrF0by0/RQEkM96TMLUsM9uX5JBPMjrQVJp83w47GfJfKHFTNw16nRSIAEfWaBSoL0CC8kYH6UN3XtfcyL9OHTol6eXJ3GY6vT+dUCPz7JbmRlpMDfpKe5uZkF0d7TdqlxrNguUWpae5UrLsLXegHLxV2SJKHT6QgJCWH+/PmkpaVZl3tVVezZs4e8vDzq6+sxGofGgU5gWoDlQIgkSZ9JkmR36nFaGIyqqiqKi4tZsGCBw8YV9vT0KAKysVKzN50aywWpodzxdiZ1bT2oJSjTd7Exp574QA/UKrh4bhh3nz2DW06L5d9fHuaW02K5Y0kwt6aq2VIlsKhdKajv4NIkD8qbezhjpj/nzA6grq2X206LoaqllyAvV/LrO/lJShDZNe1UNXXz4rZyFkR5YxZYG+ZKcP6cILKq21mzPJ7XrpvLPcvj2VrUxMrZ1mE/7u7u3L0qlf+7Ko3Q8Ah8fX1paGhg3759uLVXcm6MWgm0nijIBm/NR/ncdmoMLhrrV/lQfSd3rs8ZsUeo3Ltzzpw5LFq0iMjISLq6usjKymL//v2UlZXR3t5+osc+JCGESQhxO9bMyDbArkrIKY1hyL09Ozs7HdbbE6xubnd3N6eddtq4OkBLSFgEhHtItJkEEhIl+k6evTJdiXeYLII/rEqgqaWVQ4dquGbFItQB9UrPDc/OGs6aE8Fd67MBeHp1Kovj/PBy1fDI5mJWZ4TzVX4jN58awxPflKDTSHxf3IxKgkvnhrAxt5G9Fa38bnnsmDURtnEI20E+BoNBeZ+Li4tPmEE+ObXtpIZ78fy2cn57dhwPfWXtPdFrsrApt2HMmIw9vTvHWyNznPCC/B8hxKuSJGUDd9iz4ZS9C2azmezsbNzc3MYUkB0tsoCsu7sbX19fu42FPDHt/gsT+d37OdR0Cn6aHoSLRkW0v/uA4OiNy2KsvT1djKSnW2X8JovghavnsjjWj8LCRrxaajktxgNXV1cW9y9TzBbBPStmYLYI7rtgFms+zMNoFvSZBS5qeP7KNBbF+rEyKYh7PipgVqC74jarVCquWRQ+Zk2Ebe/O4OBgCgoK8Pb2PmEG+dywNIrKZmu/1EP1HQAs6xfPNXT0jrbpsNj27rRYLLS3t6PX64fUyBwPE+ZGQwjx4qDffwRusGfbKTEYfX19HDx4kNDQ0Enp7RkdHU1hYaHd28qNdzRq6x04yV81bD9QubenSqVi3rx5yh37plNjEUJgNpuJj4+np6eHu4P16PV6/v72D6RF+nJJsnXp8E2hgT9/ko+xP7CZGqzjt6uSFKO0bGYQj1+qIbu6laUzArBYLFgsFoQQSJKESqVS/h0LlUo1pHenXq8f0LszMDDwuBrkE+XnxgVzgnn3QC0AOTXtXD4vjE9z6gcUnY0XlUqFj4+Psnzt7e1VBkR1d3djMploamoiJCRk2kyYmwwm3WB0d3eTmZlJQkICQUFBDnmN3t5eDhw4QFRUFBEREXR1ja99oe0QZ4DyNosyxDkpzMvaPKe/t2dgYCCxsbEDLjAhhNKCXqVS4e7uTnR0NNHR0fT6GPjdB7l09pnZWpLNrloLEtbJbJemBbDhUBvSoKD54jg/xTMBFIMhGw+LxYLZbM3kyF/esQyIbe/OkQb5yNmE6TzIJyXMixd+KFfSy6fN8OOrQ3ruPD12QovOBvfuPHDgAG1tbVRXVzt0vq0kSeuAnwANQoiUCd35UTCpBqOvr4/8/HzS09Px9vZ2yGvIqdnExEQCAgKOah/ykiQ53IvcmnZOj9CQFObFiqQgsqvbSA91U3p7hoaGDtjWbDZjsViQJAlJknhlezkp4d7KBb90RgAXp4fx7+3WATlalbWnxJ3zdMwJ6Ga2nw+/fS+bxy5NZkn88McvGwPZOAxnOMxm87i8j5EG+WRlZQHTc5CPrKm5fkkkz/9QQbCXjs9zGzl9pv9RlbDbi0qlQqvVEhsbi6ur64D5tt3d3axfvx6VSkVPT8+waddx8irwDNbu4VPOpBmMuro62tvbmTdvnsOMhcFgoKCgYEhvz/GSGuHNC9+XKsuErVUmtr2dybNXppPkr2b//v1Kb08Z2zu+bCzAKlL73Qe5PPbzZBbH+fH4lmJe212Fq0ZFj8nCTF8Vd52dwJlzwjGZTMQ1NWE2mdm4Ow/3Dh8CAwPHnE6mUqkUozCS9zGeoTv2BAOnel4rWL2Lu9/PpddorfVpaO9Dp5bYX9l6TMuR8WI739ZsNlNXV8dXX33FsmXLWLZsGU899dRR71sI8X1/P89pgcMNhhCC0tJSmpqa8PPzc8hYPrBmQiorK4ft7Xk0SEhIHCnvlJBoaW4ht7aOefPmDXA95QvU9q4uszjOj8d+nszvPsglwteVnJp2ovxcaenqY2WMlu9rBG5u1uHDGo2G4OBgVgcHc1n/sGC5PsVisRAQEEBgYOCQ+SC2DOd9mEwmamtrcXV1HRA4tdf7GG6Qj8FgoKGhAbPZrLjkkzXIx1ate0qcH18V6JGwFrnpNCpuOzVmyrqTq9VqLrroIh599FF+/PFHWltbJ/0YHIlDDYbFYiEvLw8hBPPnz1fc24nEtrfnwoULJyQAlV3dxnkpwZQ3dbO7tJmMUDU/TQ/h4/2VzE2I5JRBxsJsNiOEUC7CwcuQ9EhvdGqJnJp24gPd0Lf38usMd644az77KtoGeCAytsOC4+LiBsQY2tralGHBY2U45EyRm5sb8fFW7cXgpYtsOOwxHvIgHx8fH3x9fWlsbESr1SpyfR8fHwICAhyaipR7fzx00Wzy6qzZEYG138Yl6aHTSrV6tO0YpisONRiZmZnKF94Rdx55kppWq53Q1OxNp8by3x3lvPdjDQB768zEB7Rw0ADXLz9yUdsai5GWIcnhXvz8//ZS395Hargnh+o7uCrFi6uWZyBJkuKB5NS0DTAYgxkuxqDX68nMzARQvA/bDEdfXx9ZWVmEhIQMaHevVqvRarWK4ZDPQ/6/Wq222/tQq9UDgoGtra0YDAbKysrQaDTKksrNze2YPx9bz+KRixO5fX0uff2zGH+SEsz2kmYuSQ894eefTCUONRhz5sxxWIRdzlIEBwcTE3Nsow8Hs6u0iRd/KOOeFTN4ZHMxHlp4I7vTqmbtT3cOzoTYIhuB376fg0UI2nrMXJAcxBWxvdSYwnn4h0bOKGtRDMTgLMhY2MYY4uPj6evrU3pqyDEGLy8vqqurSUhIIDAwcNj9DBf7kIO2MH7vQ6VS4efnh5+f9Vxsg4E9PT34+voSEBCAr6/vUXmCtl3F9le2KcZiaawvD/00cYC+5HgvfZ+uONRguLi4OKRruCwgG09vz/GQXd3GIxcnsvnHQ4R6aqjtMPGTlBBMFsGu0iayKlv45dKoAV4FMGApkhLuhVatorGjj0gfHZdGdhIVFUdG/xSxsTyK8aDT6QgLCyMsLAwhBNXV1RQVFeHi4kJZWRkdHR0EBgaOGmMYLvZh633IxnE83odtMFAeIyjXMuh0OmVJJcdwxkLu0XHn+hy6+wOdS2J9KWjoVIKcJ5p3IUnS28CZQKAkSVXA34UQr0zV8Rx39a4tLS3k5uaSkpLisPXhlXMDyczMJCgokLpD1mXJt4V6ksO9+M36bB79WdIQYwFHliL/uSSJx7eU0NhhHZxk6Oyj0yNGMW7j9SjGQ0NDAzU1NSxZsgRXV9cBBUdyjCEwMBB/f/9RYwz2eB+yQbEHeYygLCzs7u7GYDBQWFhIb28vfn5+BARYC9NsjZHtMsQiBDtLmxVjcUFKMA8P8iymu0x/vAghrpzqY7DluDIYbW1tVFVVMX/+fLvvSuOlqamJ/Px8+nxjeHNbMfeunMl/vipCq5b4z1dFrDknnsVx/sPeqRfH+fHoz+dw85uZmCygU0v8Zp6O+Bnx/OmzIjw8PBxmKIQQlJeX09TUxPz58xVjMLjgqLW1Fb1eT2lpqRJjCAwMxN3dfVzeh9FopL6+Hi8vr6PKvLi5uREZGUlkZCRms5mWlhb0er0SRAVrXMY2wPlpdj0bcxsBWBzry46S5hPWs5iuHBcGQwiBXq+no6ODJUuWOCwuUlNTQ0VFBRkZGfxvX53SVOe1bUXUdZmY368ilSSJ3aXN5NS08atlA+MnmVVt9C+tOT9Oy9UrFqLVanlM5zKhyxBbLBYLhw4dQgjB3LlzR7xgh4sx6PV6Dh8+THd3N35+forYarQYg9lsJicnh8DAQCIjI5XHZO/DZDIpHspIx2LrOcg6jeJ2NXva+zCbzSRKnUQ0NCCZTFyX6sHt7+QgL27XLI/jF0uiTmjPYroy7Q2GEIL8/Hx6e3uJjo52iLEQQlBcXExbWxsLFixAo9Eo/Th3lhho7bN2ODlQ2caNy2LYXdqsZEFs4xb7ylt46ttS1BIkBWr4rkawv6pDWYI4wliYTCays7Px9fUdUqI+Fq6urspd3mKx0NzcrAQpdTrdAO9Dprt/cntcXNyA+NHg2If8r5y+Hex92AYwB49KBPjn1nrOnRNEergnz+8rVozFqZE6VsZo6O3tdXoWU8C0Nhgmk4nMzEx8fX0d1q5PFpCp1WrmzZs34ILbWWLgt+/l8NglieRW6nlml5473snGTSvx8AXxLIzxIb+undvfyeLflyTxt88OAdaZJb87NxkJadgai4mip6eHrKwsoqOjh5SojxeVSqUEIcFqGPR6PYcOHVJiDG5ublRVVZGcnDxi/Ghw7MP2B46UrC+I9ubBC2fx6/dyWRLny5ZDBi6fF0Z6mBtP7mjAZLLw4cE63j9g7QniqlGRHuFFTkMn+yraiKitxWKxEOjvz8/nBCipbSeOZdoajJ6eHg4ePEh0dDTh4eFKi76JRE7NBgUFERMTM0RAllXZwqM/n8OSOH/OSArjq5I9FDZ04qZVE6Ht5LVNO3gh28R18wL4/Qd5mMwCF43Ec1ekKwbCnhqLo6G9vZ2cnBySkpIGlKhPFG5ubkRFRREVFYXZbKa0tFTJbpSWlirex2haicHGA44sXSwWCzo1dPaZ2XLIgFYF7x6o5cdyHWUtfcqyzrofCY1a4uZTrcs/2TOZH+lJU1MTNTU1FBQU4OnpSUBAAP7+/selXP94YFoajNF6e04U3d3dHDhwYNjenvKX+lfLjhiR3aXNNHb0EeHjQnVrL7dsNNBjsvCfi2bRqNcrupNZATpm+0lKtN8RSxG9Xk9RURFpaWkj9kedSKqrq2ltbWXZsmVoNBq6urrQ6/Xk5eVhNBoVVauvr++o8RPbf1/eVoYQAi8XNSlhnuyrbAMERU0DO4N56FR09ln4zZkxyrLjxqVR/HdXJYuuSCW4P00t+kvpDQYD2dnWZkXycZ3g3bMmlWlnMPR6PYWFhccsIBuN1tZWcnJy7BaQ2cYs5kZ5c/qj29F3GlEBvX1G1m43AFblaXGzka8OlhHl0o2Hh4dyJ56oO15VVRV1dXXMnz/f4XdRIQSFhYXWLus2/T7kpjwxMTGYTCaam5upr6/n0KFDuLu7K+c8mqZHo1bxyOZi7lkxg3mR3vz6vRwaO4701ZQbJHf2WV2Np78ro0jfycxAjxEnucul9IPl+gaDgY6ODsW4TWe5/nRnWhmMqqoqqqurycjIcJhIraGhgaKionEJyHJq2pQ4xO7SZtQqFX7uEs1dJn77aQkAWrXE/11lnbV61/pszksOZk1KhFK+LYSwSzw2ErJmpru7m3nz5jm8aYvcEc3Ly4tZs2aNeLwajWbYpjyyYE5uyvN+biupNvoak0Vw1qwA1n5drIxwiPAAQ69EXKA7+XWduGpUzIv0YldZKz0mC59k1WMR8LuzY5Wu6CPpRmxL6Q8fPoyrqyudnZ1UVlYqNSFjFbM5Gcq0MBiT1duzrKyMxsZGFi5cOOAuM5yAzBY5dSp7GuckBpLuZ+KZXXrqu/o7ZYV7kVPTRkp4v3RfMEQ8ZjAYqKysVBrUyDqLse54ZrOZ3Nxc3NzcSE1NdfgXvLe3l8zMTCIjI8cVbLZtyrOlWiIpxAMvLzPV1dWoW5q5/TsjZ83w4Zw5YXySWUexvgutSsIiBIl+EnXdap6/0toj5rZ3srAIuOnUGH51iuC2d3OUZd8T35bxWVYDtW09PHlZirJUGWl8gtyq0N/ff0Ap/WQK5k4UpvzdsVgsZGdn4+Li4tDenu3t7ajVajIyMgass0cSkA1HTk0bj/4sibLych76oW1AYG5/ZRutPSae+76Mu86M4/qlR1oP2tZs2ErE9Xo9FRUVSJKkuPGDG9TIArLQ0FCl5sGRdHR0kJOTw6xZs44pfjSgD0hyMpXUYz6Yz8ZDrWw81IoELI1yJbehj3PDVWyuFPz6rBgWx/nxyvZynr8iDUAxwi4aFfOjvMitbQcBh/VdqCXo7OnDaDTyY2Ub9358iLU/mzPmsdmW0o8233a0YraTlSk1GJPV2zMvLw9JkobcnUcTkA3HdYvCycrKwsPdE0E7fWYLbloVKqDLaKG4sQsPnYrnvi8jKdRLWcLIF46MrUR8xowZA+54tt2qXV1dyc/PZ+bMmSMKyCaSpqYmDh06RGpq6rjiR4Pl/DJnzwrgrvXZBHrqKDNYm/VG+LpS3dLDeclB/HBYz68SISVYy7xIV578oYyEILchxXC/+yBX6bb+6s4KHtlcTEKQB4cbO/n1BwWcMdOPzOp2/n3RLOZFeLCz2EBefSe/OmXs79Tg+baDe3faeh8nU+/OkZgyg+FoARkc6e0ZHBxMa2vrAGMxuJXeWMgFS7tavGgyCpbPDuTznAa6jRZOifNjX0ULgZ46alqt3apvejOT2SEelDd1K192YNgK0cHisdbWVqqqqqivr8fT05OOjg5cXV0dut6uqalRyu7Hih8NNhAp4d5K3Oav58/itZ1VPL21FI3Kakg7eruZF+nN5QvC+fdXxdx6Wgz/3VHB6kQ3fnXhAlQqFcltbbjoqti87xC65jLF48oeFD96aXuF0m1dq5Z4+Ktivitq5sxZASxLCGJniYE/fHKIhy+ahdFoVGJT9mZKhiulNxgMlJaWDujdebIyJQajubmZvLw8hwrIOjo6yMzMJDExETc3N6Xz0UiZkNFobW0lLy+P5ORkMJj53Qe5LIz2BSA9wpsdpc1cnhHO3y+YzbYiA3e/l4PRbCG3tgMJ2FZsYG6UNw9uOsymvAaeXp2q7HuwAZEkiZ6eHrq6uli2bBmSJKHX6ykuLqarqwtfX19FPDYRdzwhBCUlJbS3t5ORkTFkn8N5D2qVxO3vZPHcFWksjvOjq8+EySL46GAtn2XV02Oy4KpRMTfKh6zqNq5aGMFbe6v516bDPHlpMh6d1UScEcBju1o5u8Jao+Lj48Mly3y4BKvnKetdklWdeHV30tBgJLOqc0AR3O7SZtx1arr7zGwtNHDX+hwOVrXx2KUpCGHhf/vquHJuIC0tLQQHB2M0Go9Jrt/d3U1TUxNFRUU0NzdTWlpKcHDwUcv1j0cm3WDo9XpqamocKiAb3NtT7hp+NMaioaGB0tJS5s6di5ubG4u94aZl0fxnczGuWhWFDR1c3j+M6Nw5wZw6M4C7zozjqW9LWRrvy/biJl7ZUckbe6qwWECtljD1FzHtLm3m9neyuOvMOOX4ysrK2F7USJdrEIv6i6KGk4gXFxcr5dtye7zxIndE02q1pKens25HxZjGQb7L/2xuGLe/k4Wvm5baNqtXpVNL9JgsrJoTxM/nhvGHj/MV76q508imvAYOFx3m7ORIfp4WSWRE87BFbTqdbsBdXo73zHVpQt3URJkUQFmXjj9vLOHZy1NpaO/lDx/n822hgQtSrN7q7z/M54Hz48nJyWH27Nn4+voec7MgNzc35bPIzMzEz89P+SxcXV2VorEJaPyrIEnSucCTgBp4WQjx8ITt/CiYNIMhp9x6e3tZsGCBw3Lho/X2HI+xEEJQUVGBXq9n/vz5A47XZBFoVNBjtHDbaTHcdVY8586x9rgAeGl7Bc9fab3AdpU2cetbWfSaBBqVRI/Rws1vZjErxIOq5h6uXxLFS9srSAzxwLunnhd/bGVnjZGnV/sqr2frhcgS8YSEBEUifvjwYaVBjT3isVe2l5MY4o5rawVBQUFER0ezu7SZiuZu1u2sHLIE+NUp0dy1PpvZwZ5kVrchAW/utQ5N7jb2Mj/Kh3PnBPH8D+X8KiOcd36swdtFM8AbuPfsKGLVBlpUPkrw1p6itsExBrkpzw95FdyYBN49dbRYXNGoJEwWwcacBr47bODB82IpLSul2y2YJf3B29Hk+vYI5myRJAlfX1+lJF+eMFdQUMBvf/tburu72bp1K8uWLTvq77okSWrgWWAFUAXslSTpUyFE3lHtcAKYFINhsVjIz8/HZDKRlpbmMAFZUVER7e3tioDM9m+ym29PqzghBIcOHcJsNg8oWJLd81h/N0wWOCXej//tqULf0cf9FyYqEX7bC0XC2ph2fpQXBfWdXLs4gi/yGjlU3wnA8z+UE+Sp5Za3skgPcyO3oQ+VzeEN9kJk/v5ZAUhw/08SFfHYnz7MoqenmCtnClxcXAgMDOSFH9vQaNTc/5NEZVuL2cQd7+Tw7wtiyeg3Fr/7IJeHLk4iLdybO9/NJjHEk6yaNjx0ap75rgyAHytbCfLUcV5yMN6uGv63p4qrFkTwvz1VFDZ0KN7Eolg/fvdBLuenWCtoW1paKCgo4NLT0vDy8jq6D7gfuSnPXy+1elzf5FTx9y9L+flMNe8dNmER0Gs0syO/gi8qBI9fOjRYPFKzoLEEc6Ph7u6Ou7s7UVFRfPLJJ6xYsYL33nuPN954g5dffvloT3cRUCSEKAGQJOkd4KfAiWswbAVkjprnadvbc7CATAihTO+WjZZctOPj4zPkeOSCJbnFvu2+5OCe3ERsUYwvWdVtbMpr4PyUEBbH+Q0IZsoXonwh7S5tVmas3nJqDG/vq2ZVUgBF1Xpau2Ffdbey7a/eOEikryv17X0sivXl2e/KUEkSp80MoLChg015DQCcn2x93b3lrXxbbPVwrlqWyuwQF7ZkV/JFXgNCQIJHL+mxwewub+f57dWcEufDn76o4MU9eooaO/Fy0XDrW0eaNP9Y2Yq/u5ZlM/xx16nZkFPP5RnhfHiwjlBvF17aXsETl6awOM4PfUefcjxwpEVhTk0bMW69VFRUMHfu3Al11cF6QZe3C55cbU3BflaaRZfRgskCHxw28tfTA5jpbTUCo3lcYzULMplM41q6eHt74+rqyrPPPnuspxgBVNr8XgUsPtadHgvSGNHjYyrCb29v58cffyQmJobw8HAOHjxIQkLCUa23ZfHZ4PSrrYAsNjZ24MH3p01tlyByKbNer6elpUUp35Yj35mZmURFRREWFjbscfz98wIlsOftquHJy6yFRsP1xhgcMJQNxnnJwdz/k0S+zavmD58U8svF4byxv5ELUoL5OLOOVXOCyaxqpaixCxeNil7T8G0O1SqwWLAG/oxm/Ny1SEBTlxE3rZquPjOShFJJORKh3i4sifMj2t8No0nw5t4qLs8I5/0Dtdy0LJqXtlcMWKbIHs9ItSbye19WVkZLSwupqakOLYiSDfP8YDVZDX00dllw1Uj8+4I4Wltbya5u5eJETyXzMp7Yma33YXut5OXlkZCQMOykMyEEZ5xxBgcOHLD3ZYZ1eSVJugxYJYS4sf/3a4FFQoi77D6BCcahHkZ+fj6zZ892mIBMTs0OFpCNFtwcqZT54MGDdHR0EBISgpub27By6Ve2l3N+cgj6jj6+LTTwk1Traw5nLIAhj+XUtCneRmNjI25tldy0LIoXtlcrQcXls4MUL+S202J458canrwshUg/V174vpzPc+o5bYY/GTG+dPSa2FnSRG5tB7NDPEgK9cIiBPl1HRxu6CQl3IslcX64aVVsL6xnf0035yYFck1GCHmVjTy9o5GzotRsrTZydrwHnh4e/P7DfJ68zOo5nBLvrxgH24bFz12RpsRrZGzjERaLhYKCAgDS09MdPiU+s6qFW1O1GMxubCnrAaxew4e5LWTVtPPYz1OYHeKCXq+3dlPr61O8zNEEc/J+Bsv1Ozo66O62eoNGo3FcS5dxUgXYlq1GAjUT/SLjwaEGY+7cuQ5TCo7U23M8mRC5lLm3t5e6ujoWLFhAT08P1dXV5Ofn4+npqXgfOp1OWZKY+kuUP8ms49OsugFp0tGQDUhlZSX19fXMnz+f3L21irGw5bzkYO46K16JB9y0LJrtJU2KEbmhvyjpgwO1ymN/XGkNwP1QlKs8dkq8H5WVlRzW93DrqdG8u7+WtEgfXtrbzFNXpJMR5cWW7Er+sqGEuUESd87zJMqlm95ed7uMw2DkIdj+/v5DWgY4gu7ubtJ1DbR4BvPEFxWcOyeIL/IaSQzx4PuiJlZnhPdnYmKU+bZms5mmpqYhgrmAgIAx5fptbW3k5+eTkpKCu7v7sGMqJ9Bw7AUSJEmKA6qBK4CrJmrnR4NDDYYkSQ4xGPX19RQXF49LQDYS1dXVSppXp9Ph4+Mz4uyPWqPHgEXaeJu2yJqZnp4eRUA2mhcC1ovzpmXRPL21VDEsi2L9FC/ENtA4+LGMKG/ueDsTSZJ45oo0lsT5szhuqNewal4cvr6+ZNe08ZM0f0UibrFYCPD35+fJgXadq9zQJyYmZkjLAEcg18fMmTOH93NauOvMOF7cVg5YS/VPifPj06w6nusvM5dRq9VDvEyDwUBubu6oMa66ujoqKiqYN2+eYlgGz7c1m80UFBRQWVnJsSKEMEmSdCfwJda06johRO4x7/gYcGgMw2g0DhgzMBExDIvFQmNjI3Pnzh1RQGZv2rS4uJjOzk5SUlLGLLwxGo08u6WQ4oY2att6yWsSLJ/pzRWLovmqwEC039CSZlvkPpgeHh7MmDFjXIZmuOIp2yzJcI/JArL3yjS4u7sPeN5I/UiHO+empib0ej1tbW1DPC5bHN3QZzByfUxaWhpubm7Ke7S3vIXnvi8jys+VyuYezkjw5/kr0+3er8lkUs65tbUVDw8PAgIC6OrqoqOjY8x4zPfff88f/vAHPD092b59u70ve9wIVo4bg1FRUUFdXR2urq6kpKQctYAMjrTlc3FxISEhYVwXr6xlEICrRuLiGRo2lJr40xnBLE+JGna6eV9fH5mZmYSHhxMRETGu8z4aOjo6yM7OntD4ka3HZTBY+3/Icv3e3l6Ki4tJS0sbNgg4kdjWx9im6OWAstkilDEEOrU1pW3rsY33tdra2igoKKCnpwc3N7cRWxQIIXjrrbd45ZVX+OCDD8b7OR83BmPK1ar2YDKZqKysxNXV9ZgFZEajkaysLIKDgweMD7QHWy3DI5uLCfJy4d1DPdb+DDEulJaWDpn90dvbS3Z2NgkJCZOiQZALucYrIBuLkaat5efn09nZSVBQEO3t7Wi1WocV5Y1UHwPWpdt5c4L5OKsOABeNCq1a4vbTY4+6RaLZbKa4uJiQkBClWdDg+baSJBEQEMC6des4ePAgmzdvPuZak+nMtDcYsoDM29sbHx+fYxKQdXV1kZWVxYwZMwgKChr3sciNdHJq2gjx1lHZ3MOFqSEgqajsdSOn3YdfLk1RSpnlEYGRkZG4uro6vFHt4HiMI9FqtbS3t+Pm5kZGRoaSbSovL1caCg8n1z9aRquPkZcj56eE8MHBWgBWJgXhqlUpKeHxMlw8Zrj5ths2bOChhx6iq6uL2267jdbWVqfBmCrk3p6JiYl0dXUpAdSj0YS0tLSQn59PcnIy3t7eR3U88po/v66dujZr78mthQaSQj0VCbtcytzd3Y1GoyEjI4OOjg6KioocIh6DgfGY+fPnT0o3rtzcXNzd3RWPz1auL0vEh/O4jqYeQ47HjFQfI/feSAzxxCIgPtCdz7PruWfFjKNqwmxPPEaSJMxmM2+++Sa33norv/jFL9i8ebOSbj1RmbYxjMECMjnoGRUVNW5jUV9fT1lZGenp6cdcbSgXCf1icSRPfFtKbIAb5YZu7lkxg+uXRiOEoLS0lNbW1iEBMlk8ptfraWpqGnH2x3iQ4zE6nW7UVnoThRyPCQsLs6uhj+20taamJrunrcmM1dBH9i7y69r5z+ZiANy1KhbG+pJV3T7uEQ+2DZZH+0zKy8u55ppr+OMf/8hll11m9/5HwBnDOBbk3p4TISCzHR84EWtr22VJhK8rZYZuViYFYrYIdpUY+OZgMZckeQ1bsDTcfNHBsz/kYiJ7vASj0UhmZiYhISHjjsccDZ2dneOOx4w0bU32uOSZqsN5XHI8JiUlZcR4jOxdnBLnh6q/qtUi4Pol1jqV8XgX9jZY3rdvH3feeScvvvgiS5cutWvfJwrTysOQBWQdHR2kpaUN+AJVVFRgMpmIiho6NX045PGBFouFpKSkCa/Cs82WuGhU3HlGNP/3Qzl/PjOUCxfPHved3na+aHNzsyIeG6mU+VjjMeOlubmZgoICUlJSJmyNPprH1dzcTE1NDWlpaWM29NlV2sTNb2Zhsggl2DmezIhtg+Xk5ORRjfVnn33Gv//9b9avX8/MmTPHdb6j4PQwxoutgGxwb08hBN7e3hQWFlJXVzeqeAyObXygPdhmS574poQ+k4VHt5Rx25IQLlqSOPYOhsG2lySgzP4YrpRZrjY8lnjMeKitraWysnJAwdJEMJLHtX//foxGI2FhYXR0dKDVaod8zra1Kd8c0mPqF8zMi/TmltNi7Z44N1w8ZjiEEDz//PNs2LCBzZs3n7Rdt6aFwZB7e8rpK1vk6jlPT08yMjKU1FZtbS0FBQVDZn/I0e3RBGTHiu3YgS9y6siq7cTPVY2ru9VzsrcwajTc3d2HLWWWqxFjY2MnZS6JLCCznQjvKFxcXGhpaSEoKIgZM2bQ0tJCY2OjMiZALhpzc3NTliK3nBrDm3uqUUmgU6vIqW0H7Js4Z288xmQy8ac//YmWlha++OILh43AOB6Y8iXJSL097cmEyNOu5GIio9FIb28vM2bMIDIy0uEBwC8PlvL/NpZhtIDJAj+bG8qFqaEOmacqx2MMBoNyMRkMBkwm04Bioolaesk9TFQqFbNnz3a4gGys+hi5fPvVXdVEewqWJQTxQ61g7VZrIx+dWuLF/rkw9rz/9sZjOjo6+NWvfkV6ejr/+Mc/HPU+HDdLkik1GBMhIJMxGAwUFhYSHh5OR0eHUlgju/kTfTf+bM8h/vVNLY9dal3z3vRmJmbLkS/uaE1/x8to8ZjhSpmPddqa0WgkOzt70gRk44nHyFmq6zMCeXFXHT1GgQVYPsOLtZem4uLiMuZ7bm88pq6ujquvvpobb7yRG264wZHvw3FjMKZsSVJXV0dpaemQ3p5HYyzkgqWMjAzlIhlOPCbfieUKvaNBHh+YWdnCk6tTWRJvvTv9YnEU63ZW0mcWFOs7RxwxMF7keIyPj8+QgiWwyvUHzxfV6/VkZWVZxWPjnLYmd0ePjY2dFAGZPfUxtvGKxXF+/HJpFI9tKUGrkhDAeYkBbC9tZv3Wg8zytX7OIwnm7I3H5OXl8atf/YpHHnmElStXTuAZH99Muocxc+ZMGhsbrU1dJ0hA1tXVNWZ0Wy5l1uv1A2Z/jGfW5kgCMtkwXJgawv92VyGAy+aH83VB4zEtTY41HiNPW9Pr9XZNW2trayM3N3fSBGRyfYwsIBsJ+f199OdzyKxq48lvS5XZqxelhvDwJXOU5zxycSIzvMwYDAZaW1sVwZy/vz81NTV2NfTZunUrf/rTn3jjjTdITbWvdcExctx4GJNqMA4cOKA0JElOTj4mAZnt+MCZM2eOW2Yuz5swGAyoVCrFjR9p9sdIAjJbL2JxnB+fZ9dx70f5ACyJ9WPddXMHPNfe5YlcbZiYmKjUMBwLspBKPmdgwLQ1eZTBZArIDAYDqampwxqvwQrdrwsa+O37uZgtoFFJSBKcNyeYH4qbBnQDG9z1q6Ojg8bGRkVuHhERQVBQ0LAelxCCN954g9dee433339/XGMijxGnwYCBBsNkMvHDDz8QGhpKYmLiMQnI5PGBE1WwJJcy6/X6YUuZRwuQDdeG7853sjBaBH1mwYrEQJ5cnTrEsIyGrYDsaJS99mDrcTU3NyOEICEhgaCgIIdON7e3Psb2/WruNvL3zwpo77U2qHHVqJSu7GO9r7bxmPDwcCXeY+tx+fn5odVqeeCBB8jNzeWtt96aUOGeHTgNBhwxGD09PRw8eBAhBGlpaQMugqMVkDlqfODgUmYhBH19fSQlJREYGDjqMdp+eZPDvbj85X2UGrrxcVVjFow5AQ2s1Ya1tbWkp6dPStpUbugTFRWFwWCgqanJLo/raBgtHjNcz4+nvi3h/7aVYxGgkqyGIi3Cm5zadrvey56eHjIzM4eNx9jOt33wwQfZvXs3ISEhPPPMM8yfP3+yZ6oeNwbDsbkyjjQCnjVr1pDg5niNRUtLC5mZmSQnJzts1qhcypyQkKA0HI6OjqampoZdu3ZRUFCAXq9X2rHZYluf4emi4ZPbFjEz0J3WHjMdvWby+msEZMOiTHrnyMVrMBgmRW1qNpvJyspCpVKRmpqKn58fM2fOZNGiRaSmpqLT6SgpKWHXrl3k5+fT2Ng47DnbS09PD/v37yc0NJT4+Pghn7dcV7G7tJn2HhNrPsjlhR/Kke9nOrWKZ69IY91183h6daryXGBIt3awxmMOHDjA7Nmzhw3eyoI5Pz8/6uvruf7667n99tt5/PHHJ1RA9sUXXzB79mxmzpzJww8PnUHUvwR/SpKkIkmSsiRJmj9hL+4AHOph1NTUcOjQIdLS0vD09FTSqra9EMcjICsvLyctLW3C29UPRhaQtbW1kZKSogTIBpcyy+Xb8qTvwciG4axZAXyUWYcQkBTqSam+i1+fdaTrttlsZv3Wg5R3wB8ucvzdbTwNfQZ7XPJ8UXvFYzB8PGY4j+L/tpXxzNYy68Ank4XkME+qWnpICvG026sAaGxstCseU1ZWxrXXXsuf//xnfvazn415HuPFbDYza9YsNm/eTGRkJAsXLuTtt99mzpwjE+Y3btzIBRdc8AVwPtYRAk8KIaZ0lMBoODSt2tLSQkZGxoDKuPGmTQcLyBxdbSgXLKnVatLT0wccn73iMT8/65wQ27X1yjnB3PFONvl1HagkeGprKXGB7iyN8eadb/fz/MFenlid5nBjMV4B2UjiMXunrcnxmMFLUdmjeOznycwI8uA/XxWxIacegXWy3Okz/cmuaVdmnwyOVYzUiFhusJyRkTFqLGbPnj38+te/5qWXXmLxYsdcn3v27GHmzJnEx8cDcMUVV/DJJ58MMBiffPIJwOvCeufeJUmSryRJYUKIWocc1DHi0KsvISFhQJYExqc2lQNkQgjmzp07adWGgYGBREdHj3l8bm5uREVFERUVhdlsprm5WSll3lwJfzojmPQw6zLMRa3CTatiRpAHebXt9Bgt3P52NoFu0GmyutsTWew1HE1NTRQWFo6q/hwLV1dXIiMjlWlr8oyXoqKiIYI524Y+/9tbS0p4n3KOi2J9OS85mBvfyEQgsAiYFeRBbXsv1yyM4NVdlUPGG4xW7i0v6Xp7e5k/f/6o35VPPvmEtWvX8vHHHysXsyOorq4eEJSPjIxk9+7dQ57D0GFFEcDJZzBsEULg4eFBdna28qUa3EHLFkcLyAYjFyzFxcUNKFG3F7VarZwXQGqqtZQ5Ly+PrLoeXsg28uB5cZydEsne8lbuejcblTDT2A1g4bXdlQR66tB39A07GvFYjYhtwdJEaSHkzlq2gjmDwcAjnx0kVNfLnEANs2fPRqPRKEOdH/xpElUtPby1p4ratl5Fkn7WrAAOVrXxVP9MFHm8QlKo1wCjMZyxsK2PSUlJGVVA9swzz/DVV1+xefNmh83LsX29wQyXyh1uU8cc0bEzKQZDDm7GxcURHR1NU1MTNTU1w87+gCMFS9HR0cqwW0di267etkT9WPDw8MDDw4Po6Gh+3FbG/asEEdpOdu/ejVqt5twoC5+Xq1idEcqHB2rZWmhga6EBtUrizJkBvLitXLlY/v5ZAZvyGgbMP7HXgNg29HHUks42HuHq6kpCYAOP7u7l7Jme3BBo4I0fCng934Svm5rfvm/tkq9RSZw7J4hdpc1cuWD8HoWMvfEYk8nEvffeS2dnJxs3bpwUAVlkZOSAcQNVVVVDajv6RW/TaljRaDg06NnX14fJZBpxCTJYPGaxWPDy8qKpqYk5c+ZMSMHSWDQ0NFBSUkJ6evq4RugdDbL68/uCOp7P6uOOua7M9pOoMrrzz+/09JoEAR5aDJ1GJECtklgc58eBylZUEgNmtNozrtA2HjN79vh7dAzHcMHKV3dW8PTWUp66NAmX1grK+zx58Lt6TGaBt5sGQ6cRgDg/HTrJxKEmC2fGunGwro/HLk1mSXzAuOpUZMYjIPvlL3/JggUL+Pvf/+7wpa2MyWRi1qxZbNmyhYiICBYuXMhbb71FcvIRqcCGDRv4yU9+Yhv0fEoIsWhSDvAocJiHYTabKS8vJzQ0dMShQpIk4eXlhZeXF3FxcdTV1XH48GG8vLwoKCgYs5T5WKmoqKCxsXHMANlEYDs+0OgVxpOrfVgc52cVj2VVoEKQ5CdR1WnintNDaTFpWL+/ju3FTQD4uVsnvJ8205+95S3ceWYcL22vULyQwVPe5XjMW4cFnp6e3J945P0fa6bJaI/JSwt5qNKu0iZe/KGc5bP8uePdXHzcNDR2WtPHEmDoNLI0zo/7fjKbmpYeqyR9WQSv7a7i8jkeiPpCsjo8iAoM5N8XzbK7Q1ZzczOHDh0aMx5TV1fHVVddpfTdnMz6Co1GwzPPPMOqVaswm83ccMMNJCcn88ILLwBw6623cv755wOUAEVAF/DLSTvAo8BhHoZer+fyyy+nubmZM888k5UrV7JkyZIR6wvk9mhpaWnodLoBhTUGgwFJkgaUMh/LBy8LyIxGI3PmzHH4HWe0eIztnXVRrC9b82v402dFXBCn5vNiI2fEebKltIs5oZ5k17TT1z+m0UOnJtLP2iJwabwfP5a3cs2iSN75sZqHLpyJS0s5Lbpg/vqV1SUeboL8eB7LiPHhi9wG/rGxkKVxfnxf1ESgp46alh7lSyLrO+ZH+XD27EBe3l7BlQvCeefHmmGHOsv6kJQgHXq9Hr1ej8ViGdAgabjPWY7HpKenj7q0yM3N5cYbb+TRRx/lnHPOOcZP0aEcN4VbDl2SgLWAZsuWLWzcuJGdO3cya9YsVq5cycqVKwkJCVEyISaTaVQB2UjisYCAgHGty+V29V5eXsMWEE00Y8VjRnXxL5vDDE8zW/NreGRnKwDLZ/ryTUkbS2L9aezoJbe2fcB0dq0KzBYI9dah7zRy2swAPF3UbM7XsyjWl73lLVy3OBK1SuLVXZUsm+HPtv4ZpCaz4P0DNSSFepFT286cUE/aekyUGboQYuCXQS2BWUBCoAtLgy1ERkTy/LYqrlgQzv/2VAHjX0LB6NPWtFrtiA2WB/PNN9/wl7/8hTfeeIOUlJTxfmyTjdNgDIfchm/Dhg188cUXdHZ2IoRg1apV/OEPf7B7WSCLx+RiIrm93VilzL29vWRlZRERETEpwqKjFZANp0+5a302yxN8uTHdg22FDTx3sIfVaX58kNvOeSnBfJZdz6pZPrS3tVHQpqGiuQcfVw0CaOsxjfvY3bQqov3dCfDQ0tjRx+GGTs5MCOCXS6MwdBr556ZCzp3pwecFrdxyWiyv7KxSvAfbIO2xpIptWxTo9Xq6u7txdXUlMTFxRLm+EIL//e9//O9//+ODDz6YlKD5BOA0GGPR0dHBueeeS1JSEn19fezbt4/k5GRWrlzJihUrxtRt2NLb26t8qUaa/TFWu/qJRq5NmAgB2XBeyCvbSnn2+3LuWexFrHsfhS3w7MEebloWzat7arkiw7oUeOznyZiFhTUf5PGT1BA+zarnDytnYrII1n5dzKo5QXyV18hfz5uFTiPx988PccWCCN7t3xasHazk/clLi7sXepISpKPNNYQ73s2xy3s4WmQBma+vL25ubhgMBtrb24e0KLBYLPzzn//k0KFDvPnmmw4T7jkAp8EYCyEEmZmZzJ07F7B6H/v372fDhg18+eWXCCE455xzWLlyJXPnzrV7OM9wpcxubm40NzcrJeqOZnA8xhHIRmRRrC+HDh2iq6uLLdWCNzPb+P1Cd06bHUJZl457PysCjj2GYbu0uCRBx9UZoUo8xlGFZjByfYxtjEuv13PfffdhNpuJiYnh1VdfdXgQe4JxGoxjQQiBwWDgiy++YNOmTWRmZpKens6qVatYvnw5vr6+dnsf5eXlVFZW4u7uTl9f35ilzMd63Pa2q58I5IIlT09P4uPjWbejgpRwb9LD3JR4z/P7WtDpXPjruTMUj+tosyQ9PT28++0BmiVvfnPu0XcRsxe5oc9Y9TEGg4Hrr7+e8PBwjEYjGo2GN954Y8KPp6mpicsvv5yysjJiY2NZv379sEvN2NhYvLy8UKvVaDQa9u3bN9aunQZjIjGZTOzdu5cNGzbw9ddfo9FoWLFiBatWrRoyyV1GCEFJSQnt7e2kpqaiVqsHlDLbM/tjPBxLQ5+jwd54jK1grrm5Ga1We1TT1ia6oc9YNDY2UlJSMmY3rpKSEq677jr+9re/cfHFFzv0mO699178/f354x//yMMPP0xzczP//ve/hzwvNjaWffv2jUdR7TQYjkIIQV1dHZs2bWLTpk3k5+ezcOFCVq5cyVlnnYW3tze9vb0UFhaOOT5QLmXW6/X09vYOmP0xnlTreMcHHivHEo/p7u5Wzrmnp8euaWsTGY+xB7k+Ji0tbdSlxe7du7n77rt5+eWXWbTI8bVOs2fPZuvWrYSFhVFbW8uZZ57JoUOHhjzPaTCmMUajkR07drBx40a++eYbXFxcaG5u5te//jVXX3213Re+LB6T78Tu7u5K5mU0Ob1cbeiohj6DmQgBmYw909Ymu6GPPfUxQgg+/vhjHn/8cd5//31iY2Mdelwyvr6+tLS0KL/7+fnR3Nw85HlxcXH4+fkhSRK33HILN99881i7dhqMqaC6uprzzz+fZcuWUVNTQ3FxMUuXLmXlypWcccYZdt8dhRDK5DG9Xo/JZBp22pojxgeORk1NDVVVVWMWLB0ttufc19eHJEmKzN/RQcTB8ZiRvEKLxcLTTz/NN998M2IM4Vg455xzqKurG/L4Aw88wC9+8Qu7DEZNTQ3h4eE0NDSwYsUKnn76aU4//fTRXtZpMKaCnp4eCgsLSUtLA6zr/B9++IENGzbw3XffERgYqMQ+bLt+j8Vwsz+0Wi2tra3MnTt3Uhr6DI7HOBL54gXQ6XS0tLTY7XEdDfbGY4xGI2vWrKGvr4+XXnrJ4R7PYOxdkthy33334enpyZo1a0Z7mtNgTDdk1ebGjRvZtGkT1dXVLFu2jJUrV3LqqafaHfS0WCwUFhZiMBiUEvbxzv4YDxaLhby8PLRa7ajxmIlCbrAcGhqqxGPs9biOBnvjMe3t7Vx//fUsWbKEv/71r5MmILPlnnvuISAgQAl6NjU18Z///GfAczo7OxURZWdnJytWrOBvf/sb55577mi7dhqM6U53dzdbt25lw4YN/PDDD0RERCgl6yNN+5IFZJIkKeMDB5cyT+S0NVlAFhQUpPQXdST2xmMmatqavfGYmpoarr76au644w6uvfbayW7Qq2AwGFi9ejUVFRVER0fz3nvvKfNObrzxRjZu3EhJSQmXXHIJYH2frrrqKv785z+PtWunwTieEEJw6NAhxfswGAycfvrprFq1iqVLlypDnvPy8kYdH2hbyizP/jjaaWvH2tBnvMgTyMYbjxmuRYE9Hpe98Zjs7GxuvvlmHn/8cc4+++xxn9dxgtNgHM+0t7fz7bffsnHjRrZv305YWBhlZWU8/fTTnHrqqXZf+LJgbqRS5pFwREOf0airq6OiomJCGiyP5XGNJx7z9ddf87e//Y233nprQB/MExCnwThRyMrKYvXq1Sxfvpy8vDw6OjoUuf6iRYvGJZizleuPNPujoaGB0tLSMQuWJgK5oY9cNj/R3bgGe1zyaAlPT88hk+8Gb/fqq6/y9ttv88EHH0zKjNcpxmkwThTq6+tpb29n5syZgNV137x5Mxs3bmTv3r0kJiYqgrng4OBxCeYGT1sTQtDd3T0paUzbhj6JiYmT0mD54MGDuLm5KcuY4Twui8XC/fffT3FxMW+88YbDxzZOE5wG42TAYrGQmZmpCOaMRiNnnXUWq1atIiMjw+70p5zG7OzsVPQHtuXbEx3kM5lMZGVljRqPmUiGi8cMblHQ3NzMd999x+HDh5k1axZr1651ePp4GuE0GCcbQgiampr48ssv2bRpE/v37yctLY2VK1dyzjnn4O/vP+yFOVxDn56eHsX76O7unlDBnDw+MCYmZlo1WD58+DC//e1vaW1tVRr2Xn311Q4/vmnCyW0w3nvvPe677z7y8/PZs2cPCxYsGPZ5R6HqO24wm83s27dPEcxJksQ555zDqlWrSEtLQ6VS0d3dTXZ2NpGRkSMWLA2etqbT6Y5KPAZHBGRJSUn4+vpOwFmOjr3xmKKiIq6//nruu+8+LrroIoxGIx0dHQ4Rudn73fziiy+4++67MZvN3Hjjjfzxj3+c8GOx4eQ2GPn5+ahUKm655RbWrl07qsEYp0jnuEQIQUNDA1988QUbN25UCpUOHTrEO++8o8RH7EGetiYL5mynrY0Wh5AFZGOND5wo7BWQ7dy5k9/+9resW7duxO/JRGLPd9OeEYejYTabx+sJHjcGwyFdw5OSkhyx2+MWSZIICQnhF7/4Bb/4xS/YsmULt912G8uXL+fGG2/Ezc1NKVlPSkoa9cIfbtqaPLrQ1dVV8T5s06NyQ5/JGPIs17SYTCbmzZs3aibkww8/5KmnnuKzzz4jJmbim+8Mhz3fTXtGHI6EEAK1Wk1nZyeZmZmEhIQwY8aMYz7u6cKkTT4bDkmSWLly5XhUfScEYWFh/PDDD4SEhCCEoKamho0bN/Lwww9TWFjI4sWLWblyJWeeeeaoFZCDp611dh6ZtmY0GvHz86O3txeLxcK8efMmRYOSnZ2Nt7f3qHNQLBYLTzzxBN9//z2bN2+elOXReLBnxOFw9E9ip6GhgVNOOYV58+aRlZXFK6+8wqmnnurIQ540jtpgjKbq++lPf2rXPrZv3z5A1ZeYmDiWqu+EwPZOJUkSERER3HTTTdx000309fWxfft2NmzYwL///W/8/PyU2EdCQsKo3ofttDW5R4fZbEYIQU5OjmJcHKF07e3tJTMzc9R4DFjTq7///e8xm818/vnnDvF4jvW7ac+Iw+GQJImamhr0ej1//vOf+eUvf8m6deu4+OKL2bdv36TJ8B3JURuMr7/++phfXP5iBQcHc8kll7Bnz56TwmCMhk6n46yzzuKss85SJtdv3LiRv/3tb5SXl3PKKaewatUqTjvttBFjEYMb+ggh6OzsRK/Xk5OTg9lsHnP2x3iwV0DW1tbG9ddfz6mnnsr/+3//z2G1H8f63bRnxKGMxWJRzuO9997jL3/5C76+vixcuJBrrrmGG264gfLycpYvX05ubq7Dlc2OZvIlf/10dnbS3t6u/P+rr74adX7Ee++9p1QHjpZN+eKLL5g9ezYzZ87k4YcfnvDjnkwkSSI2Npbbb7+dzz77jD179nDJJZfw7bffsmLFCn72s5/xwgsvUFJSotwV29vb2b9/P3FxcYraVJIkPD09iY2NJSMjg/nz5+Pt7U1NTQ27du0iOzub2tpa+vr6xn2MTU1N5OTkkJKSMqqxqK6u5qKLLuLaa6/lz3/+85SoTe1l4cKFHD58mNLSUvr6+njnnXe46KKLhn2ufB6vvvoqBw4c4L333uPaa69FrVbz6aefAnD//fczZ84cHnjggUk7B4chhBjt56j48MMPRUREhNDpdCI4OFisXLlSCCFEdXW1OO+884QQQhQXF4u0tDSRlpYm5syZI/71r3+Nus+8vDxRUFAgzjjjDLF3795hn2MymUR8fLwoLi4Wvb29Ii0tTeTm5h7taUxrLBaLKCwsFE888YRYtWqVmDt3rrjyyivFwoULRXl5uejs7LTrp6OjQ9TV1YmcnByxdetW8e2334qsrCxRU1MjOjo6Rt328OHDYuvWraKpqWnU5+3cuVOkpqaKb7/9dqrfNru+m0IIsWHDBpGQkCDi4+OH/W6azWbl/x0dHcLNzU3Zvrm5WTz66KNizZo14vPPP7fnsMa6DqfNz3FXuHXmmWeOmA7buXMn9913H19++SUADz30EAB/+tOfJvUYp4LXX3+dBx98kFNOOYV9+/YRExPDypUrWbVqFREREXYvO4xGo1I01t7ePux8W9EvIOvo6CAlJWXUYOpXX33F/fffz9tvv01iYuKIzzuekJchQgjq6+sJDQ0lOzubpUuX8vbbb3PhhRdSW1vLs88+S1dXF/feey8hISFIkjRSyvXkTqtOFUcb3T4RSEhIYO/evXh5eSk6kY0bN3LbbbfR2trKGWecocy3Ha0uQqvVEhoaSmho6ADBnLymDwgIoLW1FTc3N9LS0kY0REII1q1bx3vvvceXX345KRL9yUKlUlFfX8/VV19NT08PN9xwAzfccAPvv/8+l19+Od9++y3z58/n2muvBRhQUXu8l7tPK4MxVdHtE4GlS5cq/1epVMyZM4c5c+awZs0aZb7t+++/z+9///sh821Heo8kScLHxwcfHx9mzJih1Bao1Wp6enrIz89XxGO2Slez2cx9991HeXk5X375pcNVt5OF6E+bCiF44YUXuOKKK4iPj+e5557DYrFw4403Kt5vY2Mjs2fPnupDnnCmlcGYzOj2yYS3tzeXXHIJl1xyCRaLhezsbDZs2MD1119Pd3c3Z599NqtWrWLBggUjSty7u7vJyckhISGBoKCgAeKxsrIy1Go1HR0d6HQ6nn32WeLj43n33XeP+zsqHDEUkiTxwQcfsG3bNsrKyrjxxhuJiIigqamJTz75BD8/P2666SY6Ozun+pAdxxhBjmnHaEFPo9Eo4uLiRElJiRL0zMnJGXFfBoNBnHPOOWLmzJninHPOEU1NTcM+LyYmRqSkpIj09HSRkZExIecxXWhqahJvv/22uO6660RycrJYvXq1ePnll0V5ebkS9KyurhZff/21qK2tHTGw2dTUJNatWyfmzJkjIiIixK233irKysqm+vQmlG+//VacffbZ4qWXXhJnnnmmuPjii5W/vfDCC+LCCy8UBw8eVB6zDYyOwZQHM+39OW4MxkRFt2255557xEMPPSSEEOKhhx4S995777DPi4mJEY2NjRN0JtMXk8kk9u7dK+677z6xdOlSsWTJEnHNNdeI8847TzQ0NIyaCTlw4ICYO3eu+Oyzz0RfX5/YunWraGhomPBjXL9+vZgzZ46QJGnEG4cQE2/kP/30U3HeeeeJRx55RAghREtLi7jooovEXXfdpTzniy++ONrdT7khsPfnuDEYjmDWrFmipqZGCCFETU2NmDVr1rDPO1kMhi0Wi0X885//FCkpKeKKK64Qc+bMEVdffbV4/fXXRVVV1QBj8dVXX4nU1FTx448/Ovy47EmvC3Hsn1lpaakQwvo+CCFEWVmZuOGGG8Qtt9yi/K28vFxkZGSIv/zlLwO2lbcZB1NuCOz9mVYxjMmmvr6esLAwwKrvaGhoGPZ5J6PmRZIkFi1axL333otOp8NkMrF79242btzIs88+i1arZcWKFahUKj799FM+//zzSelsPhnCxpdffpmysjL+9a9/KanQmJgY7r77btauXcumTZv42c9+RnR0NC+++CKNjY0Dtj+hA+1jWJTjnuXLl4vk5OQhPx9//LHw8fEZ8FxfX99h91FdXS2EEKK+vl6kpaWJ7777ztGHPa2xWCyipqZGvPTSS2LBggWipaVl0o9hLA8jNjZWzJs3T8yfP1+8+OKL49p3Y2OjWLx4sdi8ebMQYqDH8NVXX4nrrrtOvPTSSxN53lPuOdj7c8J7GKNlXkJCQqitrVUmWY1UK+DUvAxEkiTCwsK48cYbufHGGyd8/1MpbDSbzQQGBvK3v/2Nbdu2sWTJkgGK4RUrVlBZWUl+fv6kT16bDkzfgv5J4KKLLuK1114D4LXXXhv2y2iv5mUsDYsQgl//+tfMnDmTtLQ09u/fP8Fnc+Lw9ddfk5OTM+THXmMBwxt5e5DTwFFRUXR1dWGxWABrdacQ1jqfG264gYceeuiEqS8ZF2O4ICc0er1enH322WLmzJni7LPPFgaDQQgxfs2LPRqWDRs2iHPPPVdYLBaxc+dOsWjRIsef4AnMaEuSjo4O0dbWpvx/6dKlYtOmTcM+d3CA0jYVevvtt4s1a9aMeAxHEdwciSlfatj7c1IbjIlix44dSppXCCEefPBB8eCDDw54zs033yzeeust5XfbDI0T+3GEsFEI62coIxuN7u5ucfXVVw/4m4OYckNg788JH8OYDOzRsAz3nOrqaiVL48Q+5IrVwYSHh7Nx40YA4uPjyczMtHufX375JR9//DFLly5FCIFKpcJisaDT6Tj99NOpra0FBva+OFk5uc9+ghBibA2LPc9xMjnIn4X8b3JyMl9//TXbtm1TPhOVSoVKpSIiIoLHHntMeexkx+lhTAD2aFicOpfpgyRJVFRUsHXrVpYuXUpCQgK/+93v2LFjB0uXLkWSJMU4XHDBBXR3d0/xEU8fTgqTOdzdfSKxp0PTRRddxOuvv44Qgl27duHj4zPqcmSsrMvWrVvx8fFh7ty5zJ07l3/84x8Tfl4nMnq9noKCAlavXs13331HZ2cntbW1qNVqxVjIGZJLL70UcPz36LhgjCDHcYnBYFC6OxmNxkl5zeE0LM8//7x4/vnnhRDWiPrtt98u4uPjRUpKyqhFR/ZkXb799ltxwQUXOO6EThJef/118Y9//ENccMEFQqVSifXr10/FYUx5MNPenxPSYOTm5orZs2cLk8kkhBDi5ZdfFl1dXQOeI0fC9+zZI+rr6yf9GEfDnqzL8Www1qxZI2bPni1SU1PFxRdfLJqbm4d93qZNm8SsWbPEjBkzFJGgI+jt7RXbtm0Tl19+uXjiiSeEEONSmk4EU24I7P05IZcks2fP5pprruGvf/0rf/3rX3n77bfp6ekZ8BzZ7VyzZg3Z2dnTyt0cKaMymJ07d5Kens55551Hbm7uZB7iMbFixQpycnLIyspi1qxZSitFW8xmM3fccQebNm0iLy+Pt99+m7y8PIccj1arZdmyZdxxxx28//779PT0OAOcI3DCvSsmk0npCPXiiy8SERHBe++9h5+f3xCjsHHjRvz9/Vm+fPmQ/QghBlT3TSbDvebgjMr8+fMpLy8nMzOTu+66i4svvniSju7YWblypdKoZ8mSJVRVVQ15ju30MZ1Op0wfcwTye+vv709TUxMGg8Ehr3MicMIZDI1Gw/vvv8/27dsRQnDFFVcoxkKSJCWQ9d1337F3715+9atfAVZDA9YZG4ASKZe/TBaLRdnW0diTUfH29lY0Dueffz5GoxG9Xj8pxzeRrFu3jvPOO2/I4/Z6WRNJa2srTzzxBBEREQ59neOZE8pgmEwmHn74YZ577jn++c9/cu2117Jz506AAfl1gOeee45FixaxYsWKAY+//PLLXH/99fzjH//gqaeeUnQkcl7eFkd5H/ZkXerq6pTX37NnDxaLhYCAgFH3e8MNNxAcHDzi/BchJk7vcs4555CSkjLkx9ZLeOCBB9BoNFx99dXDHstgHF23csoppyjfByfDc0LVYajVas466yzOOOMMli5dyo8//sh///tfzjvvvAFVegcOHKC2tnbAnU0WHZWXl1NbW8uFF16o9H1wcXGhoKCAq666irlz5yqt4uUv8FFM6x4VjUbDM888w6pVqzCbzdxwww0kJyfzwgsvAHDrrbfy/vvv8/zzz6PRaHBzc+Odd94Z84K6/vrrufPOO7nuuuuG/fumTZs4fPgwhw8fZvfu3dx2221H3XV9rP6sr732Gp9//jlbtmwZ9riddSvTlDGiosc1lZWV4osvvhiQIcnKyhIPPvig+O9//yuEsKYwbbsqXXPNNeKTTz4RQgixbds24e7uLj799FPxwAMPiNNOO020trYKIYT4+OOPlf/bYru/6UhpaalITk4e9m+TpXfZtGmTSEpKGrWF33j7sx7nTHn2w96fE2pJMpjIyEhWrVqFm5ub4uL+5z//ITw8XAkSqtVq5W8//vgj3t7eSlengwcPsnz5ci688EJuueUWjEYjQliH11xyySU88MADrFy5khdffFGJb9h6HscbkxU3uPPOO2lvb2fFihXMnTuXW2+9FYCamhrOP/98YKCXlZSUxOrVq0lOTp7wY3EyPk6oJcloSJKE0WjE09OT6667bsBFLf//+++/JyoqSmmk891333HBBRcA1gle8+bNw83NjfXr1zNz5kz++te/snnzZtauXUtAQAAPPPAAGRkZPP7443h5eU3+SR4jsuG0xRHGr6ioaNjHbQVkYA3mygbEyfRgrFGJJw2SJKmBu4ASIcSnkiQFA9uBs4UQlZIkPQi0CyEekiTpa+BDIcRzkiSdDfwLeB74AHgReEcIsWGKTmVUJEmKBT4XQgyJfEqS9CKwVQjxdv/vh4AzhRC1k3uUTqYrJ/SSZDwIIcxCiCeEEJ/2PxQA7Os3Ft5AIlDQ/7c04MP+/88BvsR6oXUBkcDxOhfwU+A6ycoSoNVpLJzYctIsSexBkiRJ9LtcQoh84Mr+P7kDO4D9kiSdCbgLIeokSfIBQoE6IYQc0p8LrJ7M47YXSZLeBs4EAiVJqgL+DmgBhBAvABuB84EioAv45dQcqZPpitNg2CBs1meSJKmEEJb+x+uAtf2Pa4Eb+p82B4gCDvb/7RygXggxsO/8NEEIceUYfxfAHZN0OE6OQ5wxDDuw9TwGPe6O1aOoEUKUSZL0PtAphPjFZB+jEyeTgdNgjJORjEf/304DqoUQJZN8WE6cTApOg+HEiRO7cWZJJhDpeK3YcuLETpwehhMnTuzG6WE4ceLEbpwGw4kTJ3bjNBhOnDixG6fBcOLEid04DYYTJ07s5v8Dq8/9QjMvVqoAAAAASUVORK5CYII=\n", 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ndarray.\n", + " data = np.asarray(data)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "from weldx import LocalCoordinateSystem\n", "from weldx.visualization.matplotlib_impl import (\n", @@ -162,6 +468,7 @@ { "cell_type": "code", "execution_count": null, + "id": "2098d754", "metadata": {}, "outputs": [], "source": [] @@ -169,9 +476,9 @@ ], "metadata": { "kernelspec": { - "display_name": "", + "display_name": "weldx", "language": "python", - "name": "" + "name": "weldx" }, "language_info": { "codemirror_mode": { @@ -183,7 +490,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.8.12" } }, "nbformat": 4, diff --git a/tutorials/sympy_diff.py b/tutorials/sympy_diff.py index 802761f6e..3f6eb473d 100644 --- a/tutorials/sympy_diff.py +++ b/tutorials/sympy_diff.py @@ -3,9 +3,9 @@ from weldx import MathematicalExpression s = sympy.symbols("s") -exp1 = 1 * s**2 + 0 * s + 0 -exp2 = 0 * s**2 + 1 * s + 0 -exp3 = 0 * s**2 + 0 * s + 1 +exp1 = 1 * s ** 2 + 0 * s + 0 +exp2 = 0 * s ** 2 + 1 * s + 0 +exp3 = 0 * s ** 2 + 0 * s + 1 temp = sympy.sqrt(exp1.diff(s) ** 2 + exp2.diff(s) ** 2 + exp3.diff(s) ** 2) diff --git a/weldx/geometry.py b/weldx/geometry.py index 471f0caa1..0041cf743 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -17,6 +17,7 @@ import weldx.util as ut from weldx.constants import Q_ from weldx.constants import WELDX_UNIT_REGISTRY as UREG +from weldx.core import MathematicalExpression from weldx.types import QuantityLike _DEFAULT_LEN_UNIT = UREG.millimeters @@ -1593,6 +1594,8 @@ def __init__(self, series, max_s=1): self._series: SpatialSeries = series self._max_s = max_s self._length = self._len_expr() if series.is_expression else self._len_disc() + if series.is_expression: + self._derivative = self._get_derivative_expression() def _get_derivative(self, i): me = self._series.data @@ -1601,6 +1604,15 @@ def _get_derivative(self, i): subs = [(k, v[i].data.to_base_units().m) for k, v in me.parameters.items()] return exp.subs(subs).diff("s") + def _get_derivative_expression(self): + params = self._series.data.parameters + expr = MathematicalExpression(self._series.data.expression.diff("s")) + vars = expr.get_variable_names() + for k, v in params.items(): + if k in vars: + expr.set_parameter(k, v) + return expr + def _get_squared_derivative(self, i): return self._get_derivative(i) ** 2 @@ -1616,10 +1628,7 @@ def _len_disc(self): def _lcs_expr(self, position: float) -> tf.LocalCoordinateSystem: coords = self._series.evaluate(s=position * self._max_s).data.transpose()[0] - x = [ - self._get_derivative(i).subs("s", position * self._max_s).evalf() - for i in range(3) - ] + x = self._derivative.evaluate(s=position * self._max_s) z_fake = [0, 0, 1] y = np.cross(z_fake, x) return tf.LocalCoordinateSystem.from_axis_vectors(x=x, y=y, coordinates=coords) diff --git a/weldx/tests/asdf_tests/test_weldx_file.py b/weldx/tests/asdf_tests/test_weldx_file.py index 05dd3a574..57b88c866 100644 --- a/weldx/tests/asdf_tests/test_weldx_file.py +++ b/weldx/tests/asdf_tests/test_weldx_file.py @@ -377,7 +377,7 @@ def get_mem_info(): diff = after - before # pytest increases memory a bit, but not as much as our large array would # occupy in memory. - assert diff <= large_array.nbytes * 1.1, diff / 1024**2 + assert diff <= large_array.nbytes * 1.1, diff / 1024 ** 2 assert np.all(WeldxFile(fn)["x"] == large_array) @staticmethod diff --git a/weldx/welding/groove/iso_9692_1.py b/weldx/welding/groove/iso_9692_1.py index db13967d8..d9fdceb09 100644 --- a/weldx/welding/groove/iso_9692_1.py +++ b/weldx/welding/groove/iso_9692_1.py @@ -548,7 +548,7 @@ def to_profile(self, width_default: pint.Quantity = None) -> geo.Profile: # calculations: x_1 = np.tan(alpha / 2) * h # Center of the circle [0, y_m] - y_circle = np.sqrt(R**2 - x_1**2) # skipcq: PTC-W0028 + y_circle = np.sqrt(R ** 2 - x_1 ** 2) # skipcq: PTC-W0028 y_m = h + y_circle # From next point to circle center is the vector (x,y) x = R * np.cos(beta) From 993f39e45a2535054766f16c36b2ad103f43cbed Mon Sep 17 00:00:00 2001 From: vhirtham Date: Mon, 14 Feb 2022 17:05:11 +0100 Subject: [PATCH 17/70] Add alternative length calculation --- tutorials/TraceSegmentSpS.ipynb | 94 +++++++++------------------------ weldx/geometry.py | 18 ++++++- 2 files changed, 41 insertions(+), 71 deletions(-) diff --git a/tutorials/TraceSegmentSpS.ipynb b/tutorials/TraceSegmentSpS.ipynb index 253485ccd..d033cb2f0 100644 --- a/tutorials/TraceSegmentSpS.ipynb +++ b/tutorials/TraceSegmentSpS.ipynb @@ -290,7 +290,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 6, @@ -347,107 +347,61 @@ "execution_count": 8, "id": "edcd21b3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'DynamicTraceSegment' object has no attribute '_derivative'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[1;32mIn [8]\u001b[0m, in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[0m segment \u001b[38;5;241m=\u001b[39m \u001b[43mDynamicTraceSegment\u001b[49m\u001b[43m(\u001b[49m\u001b[43msps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpi\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\users\\vhirtham\\pycharmprojects\\bam\\weldx\\weldx\\geometry.py:1596\u001b[0m, in \u001b[0;36mDynamicTraceSegment.__init__\u001b[1;34m(self, series, max_s)\u001b[0m\n\u001b[0;32m 1594\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_series: SpatialSeries \u001b[38;5;241m=\u001b[39m series\n\u001b[0;32m 1595\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_max_s \u001b[38;5;241m=\u001b[39m max_s\n\u001b[1;32m-> 1596\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_length \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_len_expr\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m series\u001b[38;5;241m.\u001b[39mis_expression \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_len_disc()\n\u001b[0;32m 1597\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m series\u001b[38;5;241m.\u001b[39mis_expression:\n\u001b[0;32m 1598\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_derivative \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_derivative_expression()\n", + "File \u001b[1;32mc:\\users\\vhirtham\\pycharmprojects\\bam\\weldx\\weldx\\geometry.py:1624\u001b[0m, in \u001b[0;36mDynamicTraceSegment._len_expr\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1621\u001b[0m expr \u001b[38;5;241m=\u001b[39m sympy\u001b[38;5;241m.\u001b[39msqrt(der_sq[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m+\u001b[39m der_sq[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m der_sq[\u001b[38;5;241m2\u001b[39m])\n\u001b[0;32m 1622\u001b[0m mag \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(sympy\u001b[38;5;241m.\u001b[39mintegrate(expr, (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ms\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_max_s))\u001b[38;5;241m.\u001b[39mevalf())\n\u001b[1;32m-> 1624\u001b[0m params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_derivative\u001b[49m\u001b[38;5;241m.\u001b[39mparameters\n\u001b[0;32m 1625\u001b[0m \u001b[38;5;28mprint\u001b[39m(params)\n\u001b[0;32m 1627\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Q_(mag, Q_(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmm\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mto_base_units()\u001b[38;5;241m.\u001b[39mu)\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmm\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[1;31mAttributeError\u001b[0m: 'DynamicTraceSegment' object has no attribute '_derivative'" + ] + } + ], "source": [ "segment = DynamicTraceSegment(sps, 2 * np.pi)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "b11e1ff9", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\variable.py:259: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " data = np.asarray(data)\n" - ] - } - ], + "outputs": [], "source": [ "trace = Trace([segment, segment, segment])" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "be17250e", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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ndarray.\n", - " data = np.asarray(data)\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from weldx import LocalCoordinateSystem\n", "from weldx.visualization.matplotlib_impl import (\n", diff --git a/weldx/geometry.py b/weldx/geometry.py index 0041cf743..afd1ff004 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1593,10 +1593,11 @@ def __init__(self, series, max_s=1): self._series: SpatialSeries = series self._max_s = max_s - self._length = self._len_expr() if series.is_expression else self._len_disc() if series.is_expression: self._derivative = self._get_derivative_expression() + self._length = self._len_expr() if series.is_expression else self._len_disc() + def _get_derivative(self, i): me = self._series.data exp = me.expression @@ -1621,6 +1622,21 @@ def _len_expr(self): expr = sympy.sqrt(der_sq[0] + der_sq[1] + der_sq[2]) mag = float(sympy.integrate(expr, ("s", 0, self._max_s)).evalf()) + # params_vec = self._derivative.parameters + # params = {} + # expressions = [self._derivative.expression for _ in range(3)] + # for k, v in params_vec.items(): + # for i in range(3): + # new_name = f"{k}{i}" + # expressions[i] = expressions[i].subs(k, sympy.symbols(new_name)) + # params[new_name] = v[i] + + # expr = sympy.sqrt( + # expressions[0] ** 2 + expressions[1] ** 2 + expressions[2] ** 2 + # ) + # expr = sympy.integrate(expr, ("s", 0, self._max_s)) + # MathematicalExpression(expr).evaluate(**params) + return Q_(mag, Q_(1, "mm").to_base_units().u).to("mm") def _len_disc(self): From ed12568ec1a2d7803075e7c21c7aee951e6e1067 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Tue, 15 Feb 2022 16:08:08 +0100 Subject: [PATCH 18/70] Add option to limit orientation to the xy plane --- tutorials/TraceSegmentSpS.ipynb | 96 +++++++++++++++++------ tutorials/sympy_diff.py | 6 +- weldx/geometry.py | 8 +- weldx/tests/asdf_tests/test_weldx_file.py | 2 +- weldx/welding/groove/iso_9692_1.py | 2 +- 5 files changed, 83 insertions(+), 31 deletions(-) diff --git a/tutorials/TraceSegmentSpS.ipynb b/tutorials/TraceSegmentSpS.ipynb index d033cb2f0..ec4f53998 100644 --- a/tutorials/TraceSegmentSpS.ipynb +++ b/tutorials/TraceSegmentSpS.ipynb @@ -290,7 +290,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 6, @@ -347,61 +347,107 @@ "execution_count": 8, "id": "edcd21b3", "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'DynamicTraceSegment' object has no attribute '_derivative'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Input \u001b[1;32mIn [8]\u001b[0m, in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[0m segment \u001b[38;5;241m=\u001b[39m \u001b[43mDynamicTraceSegment\u001b[49m\u001b[43m(\u001b[49m\u001b[43msps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpi\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\users\\vhirtham\\pycharmprojects\\bam\\weldx\\weldx\\geometry.py:1596\u001b[0m, in \u001b[0;36mDynamicTraceSegment.__init__\u001b[1;34m(self, series, max_s)\u001b[0m\n\u001b[0;32m 1594\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_series: SpatialSeries \u001b[38;5;241m=\u001b[39m series\n\u001b[0;32m 1595\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_max_s \u001b[38;5;241m=\u001b[39m max_s\n\u001b[1;32m-> 1596\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_length \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_len_expr\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m series\u001b[38;5;241m.\u001b[39mis_expression \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_len_disc()\n\u001b[0;32m 1597\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m series\u001b[38;5;241m.\u001b[39mis_expression:\n\u001b[0;32m 1598\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_derivative \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_derivative_expression()\n", - "File \u001b[1;32mc:\\users\\vhirtham\\pycharmprojects\\bam\\weldx\\weldx\\geometry.py:1624\u001b[0m, in \u001b[0;36mDynamicTraceSegment._len_expr\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1621\u001b[0m expr \u001b[38;5;241m=\u001b[39m sympy\u001b[38;5;241m.\u001b[39msqrt(der_sq[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m+\u001b[39m der_sq[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m der_sq[\u001b[38;5;241m2\u001b[39m])\n\u001b[0;32m 1622\u001b[0m mag \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(sympy\u001b[38;5;241m.\u001b[39mintegrate(expr, (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ms\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_max_s))\u001b[38;5;241m.\u001b[39mevalf())\n\u001b[1;32m-> 1624\u001b[0m params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_derivative\u001b[49m\u001b[38;5;241m.\u001b[39mparameters\n\u001b[0;32m 1625\u001b[0m \u001b[38;5;28mprint\u001b[39m(params)\n\u001b[0;32m 1627\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Q_(mag, Q_(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmm\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mto_base_units()\u001b[38;5;241m.\u001b[39mu)\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmm\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[1;31mAttributeError\u001b[0m: 'DynamicTraceSegment' object has no attribute '_derivative'" - ] - } - ], + "outputs": [], "source": [ - "segment = DynamicTraceSegment(sps, 2 * np.pi)" + "segment = DynamicTraceSegment(sps, 2 * np.pi, limit_orientation_to_xy=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "b11e1ff9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\variable.py:259: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", + " data = np.asarray(data)\n" + ] + } + ], "source": [ "trace = Trace([segment, segment, segment])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "be17250e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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ndarray.\n", + " data = np.asarray(data)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "from weldx import LocalCoordinateSystem\n", "from weldx.visualization.matplotlib_impl import (\n", diff --git a/tutorials/sympy_diff.py b/tutorials/sympy_diff.py index 3f6eb473d..802761f6e 100644 --- a/tutorials/sympy_diff.py +++ b/tutorials/sympy_diff.py @@ -3,9 +3,9 @@ from weldx import MathematicalExpression s = sympy.symbols("s") -exp1 = 1 * s ** 2 + 0 * s + 0 -exp2 = 0 * s ** 2 + 1 * s + 0 -exp3 = 0 * s ** 2 + 0 * s + 1 +exp1 = 1 * s**2 + 0 * s + 0 +exp2 = 0 * s**2 + 1 * s + 0 +exp3 = 0 * s**2 + 0 * s + 1 temp = sympy.sqrt(exp1.diff(s) ** 2 + exp2.diff(s) ** 2 + exp3.diff(s) ** 2) diff --git a/weldx/geometry.py b/weldx/geometry.py index afd1ff004..9ae37d645 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1588,11 +1588,13 @@ def local_coordinate_system( class DynamicTraceSegment: """Trace segment that can be defined by a ``SpatialSeries``.""" - def __init__(self, series, max_s=1): + def __init__(self, series, max_s=1, limit_orientation_to_xy=False): from weldx.core import SpatialSeries self._series: SpatialSeries = series self._max_s = max_s + self._limit_orientation = limit_orientation_to_xy + if series.is_expression: self._derivative = self._get_derivative_expression() @@ -1647,6 +1649,10 @@ def _lcs_expr(self, position: float) -> tf.LocalCoordinateSystem: x = self._derivative.evaluate(s=position * self._max_s) z_fake = [0, 0, 1] y = np.cross(z_fake, x) + if self._limit_orientation: + return tf.LocalCoordinateSystem.from_axis_vectors( + y=y, z=z_fake, coordinates=coords + ) return tf.LocalCoordinateSystem.from_axis_vectors(x=x, y=y, coordinates=coords) def _lcs_disc(self, position: float) -> tf.LocalCoordinateSystem: diff --git a/weldx/tests/asdf_tests/test_weldx_file.py b/weldx/tests/asdf_tests/test_weldx_file.py index 57b88c866..05dd3a574 100644 --- a/weldx/tests/asdf_tests/test_weldx_file.py +++ b/weldx/tests/asdf_tests/test_weldx_file.py @@ -377,7 +377,7 @@ def get_mem_info(): diff = after - before # pytest increases memory a bit, but not as much as our large array would # occupy in memory. - assert diff <= large_array.nbytes * 1.1, diff / 1024 ** 2 + assert diff <= large_array.nbytes * 1.1, diff / 1024**2 assert np.all(WeldxFile(fn)["x"] == large_array) @staticmethod diff --git a/weldx/welding/groove/iso_9692_1.py b/weldx/welding/groove/iso_9692_1.py index d9fdceb09..db13967d8 100644 --- a/weldx/welding/groove/iso_9692_1.py +++ b/weldx/welding/groove/iso_9692_1.py @@ -548,7 +548,7 @@ def to_profile(self, width_default: pint.Quantity = None) -> geo.Profile: # calculations: x_1 = np.tan(alpha / 2) * h # Center of the circle [0, y_m] - y_circle = np.sqrt(R ** 2 - x_1 ** 2) # skipcq: PTC-W0028 + y_circle = np.sqrt(R**2 - x_1**2) # skipcq: PTC-W0028 y_m = h + y_circle # From next point to circle center is the vector (x,y) x = R * np.cos(beta) From 34ed2ae011e5c8671c3fa259a3ac4ea2068f437a Mon Sep 17 00:00:00 2001 From: vhirtham Date: Wed, 16 Feb 2022 15:58:11 +0100 Subject: [PATCH 19/70] Add --- tutorials/TraceSegmentSpS.ipynb | 176 +++++++++++++++++++++++++++++++- weldx/geometry.py | 49 ++++++++- 2 files changed, 218 insertions(+), 7 deletions(-) diff --git a/tutorials/TraceSegmentSpS.ipynb b/tutorials/TraceSegmentSpS.ipynb index ec4f53998..effe4dbc4 100644 --- a/tutorials/TraceSegmentSpS.ipynb +++ b/tutorials/TraceSegmentSpS.ipynb @@ -62,6 +62,10 @@ "name": "stderr", "output_type": "stream", "text": [ + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", " imin = index.get_loc(minval, method=\"nearest\")\n", "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", @@ -100,6 +104,14 @@ "name": "stderr", "output_type": "stream", "text": [ + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", " imin = index.get_loc(minval, method=\"nearest\")\n", "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", @@ -284,13 +296,173 @@ "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", " imin = index.get_loc(minval, method=\"nearest\")\n", "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imax = index.get_loc(maxval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", + " imin = index.get_loc(minval, method=\"nearest\")\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", " imax = index.get_loc(maxval, method=\"nearest\")\n" ] }, { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 6, @@ -299,7 +471,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] diff --git a/weldx/geometry.py b/weldx/geometry.py index 9ae37d645..fc76ec608 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -17,7 +17,7 @@ import weldx.util as ut from weldx.constants import Q_ from weldx.constants import WELDX_UNIT_REGISTRY as UREG -from weldx.core import MathematicalExpression +from weldx.core import MathematicalExpression, SpatialSeries from weldx.types import QuantityLike _DEFAULT_LEN_UNIT = UREG.millimeters @@ -1588,8 +1588,25 @@ def local_coordinate_system( class DynamicTraceSegment: """Trace segment that can be defined by a ``SpatialSeries``.""" - def __init__(self, series, max_s=1, limit_orientation_to_xy=False): - from weldx.core import SpatialSeries + def __init__( + self, + series: SpatialSeries, + max_s: float = 1, + limit_orientation_to_xy: bool = False, + ): + """Initialize a `DynamicTraceSegment` + + Parameters + ---------- + series: + A `SpatialSeries` that describes the trajectory of the trace segment. + max_s: + [only expression based `SpatialSeries`] The maximum value of the passed + series `s` parameter. The value defines the segments length by evaluating + the expression on the interval [0, `max_s`] + limit_orientation_to_xy: + If t + """ self._series: SpatialSeries = series self._max_s = max_s @@ -1616,6 +1633,14 @@ def _get_derivative_expression(self): expr.set_parameter(k, v) return expr + def _get_tangent_vec_discrete(self, position): + coords_s = self._series.coordinates["s"].data + idx_low = np.abs(coords_s - position).argmin() + if coords_s[idx_low] > position or idx_low + 1 == len(coords_s): + idx_low -= 1 + vals = self._series.evaluate(s=[coords_s[idx_low], coords_s[idx_low + 1]]).data + return (vals[1] - vals[0]).m + def _get_squared_derivative(self, i): return self._get_derivative(i) ** 2 @@ -1642,7 +1667,20 @@ def _len_expr(self): return Q_(mag, Q_(1, "mm").to_base_units().u).to("mm") def _len_disc(self): - return Q_(10, "mm") + diff = self._series.data[1:] - self._series.data[:-1] + length = np.sum(np.linalg.norm(diff.m, axis=1)) + return Q_(length, diff.u) + + def _get_lcs_from_coords_and_tangent(self, coords, tangent): + z_fake = [0, 0, 1] + y = np.cross(z_fake, tangent) + if self._limit_orientation: + return tf.LocalCoordinateSystem.from_axis_vectors( + y=y, z=z_fake, coordinates=coords + ) + return tf.LocalCoordinateSystem.from_axis_vectors( + x=tangent, y=y, coordinates=coords + ) def _lcs_expr(self, position: float) -> tf.LocalCoordinateSystem: coords = self._series.evaluate(s=position * self._max_s).data.transpose()[0] @@ -1657,7 +1695,8 @@ def _lcs_expr(self, position: float) -> tf.LocalCoordinateSystem: def _lcs_disc(self, position: float) -> tf.LocalCoordinateSystem: coords = self._series.evaluate(s=position).data[0] - return tf.LocalCoordinateSystem(coordinates=coords) + x = self._get_tangent_vec_discrete(position) + return self._get_lcs_from_coords_and_tangent(coords, x) @property def length(self) -> float: From 43752e76abea307b8d73d16d33d441edd140906d Mon Sep 17 00:00:00 2001 From: vhirtham Date: Thu, 17 Feb 2022 08:57:23 +0100 Subject: [PATCH 20/70] Replace code with function --- weldx/geometry.py | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index fc76ec608..3ef85907f 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1685,13 +1685,7 @@ def _get_lcs_from_coords_and_tangent(self, coords, tangent): def _lcs_expr(self, position: float) -> tf.LocalCoordinateSystem: coords = self._series.evaluate(s=position * self._max_s).data.transpose()[0] x = self._derivative.evaluate(s=position * self._max_s) - z_fake = [0, 0, 1] - y = np.cross(z_fake, x) - if self._limit_orientation: - return tf.LocalCoordinateSystem.from_axis_vectors( - y=y, z=z_fake, coordinates=coords - ) - return tf.LocalCoordinateSystem.from_axis_vectors(x=x, y=y, coordinates=coords) + return self._get_lcs_from_coords_and_tangent(coords, x) def _lcs_disc(self, position: float) -> tf.LocalCoordinateSystem: coords = self._series.evaluate(s=position).data[0] From c01f180e90ed565aafef79db43dbcea8fd7b51d3 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Fri, 18 Feb 2022 11:16:37 +0100 Subject: [PATCH 21/70] Fix tests --- weldx/geometry.py | 4 +--- weldx/tests/test_geometry.py | 20 ++++++++++++++------ 2 files changed, 15 insertions(+), 9 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index 42f00ad17..8e32f5511 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1437,9 +1437,7 @@ def local_coordinate_system( """ relative_position = np.clip(relative_position, 0, 1) - coordinates = np.array([1, 0, 0]) * relative_position * self._length - if isinstance(coordinates, pint.Quantity): - coordinates = coordinates.m + coordinates = np.array([1, 0, 0]) * relative_position * self.length return tf.LocalCoordinateSystem(coordinates=coordinates) diff --git a/weldx/tests/test_geometry.py b/weldx/tests/test_geometry.py index 169021861..8ed7a8a25 100644 --- a/weldx/tests/test_geometry.py +++ b/weldx/tests/test_geometry.py @@ -2096,7 +2096,8 @@ def check_trace_segment_length(segment, tolerance=1e-9): """ lcs = segment.local_coordinate_system(1) - length_numeric_prev = np.linalg.norm(lcs.coordinates) + + length_numeric_prev = np.linalg.norm(lcs.coordinates.data.m) # calculate numerical length by linearization num_segments = 2.0 @@ -2111,7 +2112,9 @@ def check_trace_segment_length(segment, tolerance=1e-9): cs_0 = segment.local_coordinate_system(0) for rel_pos in np.arange(increment, 1.0 + increment / 2, increment): cs_1 = segment.local_coordinate_system(rel_pos) - length_numeric += np.linalg.norm(cs_1.coordinates - cs_0.coordinates) + length_numeric += np.linalg.norm( + cs_1.coordinates.data.m - cs_0.coordinates.data.m + ) cs_0 = copy.deepcopy(cs_1) relative_change = length_numeric / length_numeric_prev @@ -2150,7 +2153,9 @@ def check_trace_segment_orientation(segment): for rel_pos in np.arange(0.1, 1.01, 0.1): lcs = segment.local_coordinate_system(rel_pos) lcs_d = segment.local_coordinate_system(rel_pos - delta) - trace_direction_approx = tf.normalize(lcs.coordinates - lcs_d.coordinates) + trace_direction_approx = tf.normalize( + lcs.coordinates.data.m - lcs_d.coordinates.data.m + ) # Check if the x-axis is aligned with the approximate trace direction assert vector_is_close(lcs.orientation[:, 0], trace_direction_approx, 1e-6) @@ -2173,7 +2178,10 @@ def default_trace_segment_tests(segment, tolerance_length=1e-9): assert isinstance(lcs, tf.LocalCoordinateSystem) # check that coordinates for weight 0 are at [0, 0, 0] - assert vector_is_close(lcs.coordinates, [0, 0, 0]) + coords = lcs.coordinates.data + if isinstance(coords, Q_): + coords = coords.m + assert vector_is_close(coords, [0, 0, 0]) # length and orientation tests check_trace_segment_length(segment, tolerance_length) @@ -2235,8 +2243,8 @@ def test_radial_horizontal_trace_segment(): lcs_cw = segment_cw.local_coordinate_system(weight) lcs_ccw = segment_ccw.local_coordinate_system(weight) - assert vector_is_close(lcs_cw.coordinates, [x_exp.m, -y_exp.m, 0]) - assert vector_is_close(lcs_ccw.coordinates, [x_exp.m, y_exp.m, 0]) + assert vector_is_close(lcs_cw.coordinates.data.m, [x_exp.m, -y_exp.m, 0]) + assert vector_is_close(lcs_ccw.coordinates.data.m, [x_exp.m, y_exp.m, 0]) # invalid inputs with pytest.raises(ValueError): From f8d75b6717c68510adc8cd29bdf0069aa5f498f1 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Fri, 18 Feb 2022 12:13:38 +0100 Subject: [PATCH 22/70] Add docstrings --- weldx/geometry.py | 48 +++++++++++++++++++++++++++++++++++------------ 1 file changed, 36 insertions(+), 12 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index 8e32f5511..0c7430705 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1615,14 +1615,20 @@ def __init__( self._length = self._len_expr() if series.is_expression else self._len_disc() - def _get_derivative(self, i): + def _get_component_derivative(self, i: int): + """Get the derivative of an expression for the i-th vector component.""" me = self._series.data exp = me.expression # todo unit stripped -> how to proceed? how to cast all length units to mm? subs = [(k, v[i].data.to_base_units().m) for k, v in me.parameters.items()] return exp.subs(subs).diff("s") - def _get_derivative_expression(self): + def _get_component_derivative_squared(self, i): + """Get the squared derivative of an expression for the i-th vector component.""" + return self._get_component_derivative(i) ** 2 + + def _get_derivative_expression(self) -> MathematicalExpression: + """Get the derivative of an expression as 'MathematicalExpression'.""" params = self._series.data.parameters expr = MathematicalExpression(self._series.data.expression.diff("s")) vars = expr.get_variable_names() @@ -1631,7 +1637,8 @@ def _get_derivative_expression(self): expr.set_parameter(k, v) return expr - def _get_tangent_vec_discrete(self, position): + def _get_tangent_vec_discrete(self, position: float) -> np.array(): + """Get the segments tangent vector at the given position (discrete case).""" coords_s = self._series.coordinates["s"].data idx_low = np.abs(coords_s - position).argmin() if coords_s[idx_low] > position or idx_low + 1 == len(coords_s): @@ -1639,11 +1646,9 @@ def _get_tangent_vec_discrete(self, position): vals = self._series.evaluate(s=[coords_s[idx_low], coords_s[idx_low + 1]]).data return (vals[1] - vals[0]).m - def _get_squared_derivative(self, i): - return self._get_derivative(i) ** 2 - - def _len_expr(self): - der_sq = [self._get_squared_derivative(i) for i in range(3)] + def _len_expr(self) -> pint.Quantity: + """Get the length of an expression based segment.""" + der_sq = [self._get_component_derivative_squared(i) for i in range(3)] expr = sympy.sqrt(der_sq[0] + der_sq[1] + der_sq[2]) mag = float(sympy.integrate(expr, ("s", 0, self._max_s)).evalf()) @@ -1664,12 +1669,16 @@ def _len_expr(self): return Q_(mag, Q_(1, "mm").to_base_units().u).to("mm") - def _len_disc(self): + def _len_disc(self) -> pint.Quantity: + """Get the length of a segment based on discrete values""" diff = self._series.data[1:] - self._series.data[:-1] length = np.sum(np.linalg.norm(diff.m, axis=1)) return Q_(length, diff.u) - def _get_lcs_from_coords_and_tangent(self, coords, tangent): + def _get_lcs_from_coords_and_tangent( + self, coords: pint.Quantity, tangent: pint.Quantity + ) -> tf.LocalCoordinateSystem: + """Create a `LocalCoordinateSystem` from coordinates and tangent vector.""" z_fake = [0, 0, 1] y = np.cross(z_fake, tangent) if self._limit_orientation: @@ -1681,22 +1690,37 @@ def _get_lcs_from_coords_and_tangent(self, coords, tangent): ) def _lcs_expr(self, position: float) -> tf.LocalCoordinateSystem: + """Get a `LocalCoordinateSystem` at the passed rel. position (expression).""" coords = self._series.evaluate(s=position * self._max_s).data.transpose()[0] x = self._derivative.evaluate(s=position * self._max_s) return self._get_lcs_from_coords_and_tangent(coords, x) def _lcs_disc(self, position: float) -> tf.LocalCoordinateSystem: + """Get a `LocalCoordinateSystem` at the passed rel. position (discrete).""" coords = self._series.evaluate(s=position).data[0] x = self._get_tangent_vec_discrete(position) return self._get_lcs_from_coords_and_tangent(coords, x) @property - def length(self) -> float: + def length(self) -> pint.Quantity: """Get the length of the segment.""" return self._length def local_coordinate_system(self, position: float) -> tf.LocalCoordinateSystem: - """Calculate a local coordinate system at a position of the trace segment.""" + """Calculate a `LocalCoordinateSystem` at a position of the trace segment. + + Parameters + ---------- + position: + The relative position on the segment (interval [0, 1]). 0 is the start of + the segment and 1 its end + + Returns + ------- + LocalCoordinateSystem: + The coordinate system and the specified position. + + """ if self._series.is_expression: return self._lcs_expr(position) return self._lcs_disc(position) From fe4063962fcdcfffa333c15f9f4a03030487da2d Mon Sep 17 00:00:00 2001 From: vhirtham Date: Fri, 18 Feb 2022 12:18:27 +0100 Subject: [PATCH 23/70] Run cleanup scripts --- tutorials/TraceSegmentSpS.ipynb | 521 ++------------------------------ 1 file changed, 21 insertions(+), 500 deletions(-) diff --git a/tutorials/TraceSegmentSpS.ipynb b/tutorials/TraceSegmentSpS.ipynb index effe4dbc4..85a5ba8e3 100644 --- a/tutorials/TraceSegmentSpS.ipynb +++ b/tutorials/TraceSegmentSpS.ipynb @@ -2,8 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, - "id": "e998eaed", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -18,7 +17,6 @@ }, { "cell_type": "markdown", - "id": "661602ad", "metadata": {}, "source": [ "## Discrete" @@ -26,8 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "100b135a", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -44,8 +41,7 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "6a8fec99", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -54,434 +50,27 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "59c8a6fd", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", - " imin = index.get_loc(minval, method=\"nearest\")\n", - "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", - " imax = index.get_loc(maxval, method=\"nearest\")\n", - "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", - " imin = index.get_loc(minval, method=\"nearest\")\n", - "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n", - " imax = index.get_loc(maxval, method=\"nearest\")\n" - ] - }, - { - "data": { - "text/plain": [ - "\n", - "Dimensions: (c: 3, v: 3)\n", - "Coordinates:\n", - " * c (c) ]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "trace_disc.plot(\"0.5mm\")\n", "ax = plt.gca()\n", @@ -490,7 +79,6 @@ }, { "cell_type": "markdown", - "id": "e8a84704", "metadata": {}, "source": [ "## Expression" @@ -498,8 +86,7 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "b9d78954", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -516,8 +103,7 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "edcd21b3", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -526,100 +112,36 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "b11e1ff9", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\variable.py:259: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " data = np.asarray(data)\n" - ] - } - ], + "outputs": [], "source": [ "trace = Trace([segment, segment, segment])" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "be17250e", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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ndarray.\n", - " data = np.asarray(data)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from weldx import LocalCoordinateSystem\n", "from weldx.visualization.matplotlib_impl import (\n", @@ -640,7 +162,6 @@ { "cell_type": "code", "execution_count": null, - "id": "2098d754", "metadata": {}, "outputs": [], "source": [] @@ -648,9 +169,9 @@ ], "metadata": { "kernelspec": { - "display_name": "weldx", + "display_name": "", "language": "python", - "name": "weldx" + "name": "" }, "language_info": { "codemirror_mode": { From 896cdd0c3ce6f63967564a6eb3bb2e8c4f910e16 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Fri, 18 Feb 2022 12:27:29 +0100 Subject: [PATCH 24/70] Fix pydocstyle issues --- weldx/geometry.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index 0c7430705..936bef84d 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1592,7 +1592,7 @@ def __init__( max_s: float = 1, limit_orientation_to_xy: bool = False, ): - """Initialize a `DynamicTraceSegment` + """Initialize a `DynamicTraceSegment`. Parameters ---------- @@ -1605,7 +1605,6 @@ def __init__( limit_orientation_to_xy: If t """ - self._series: SpatialSeries = series self._max_s = max_s self._limit_orientation = limit_orientation_to_xy @@ -1670,7 +1669,7 @@ def _len_expr(self) -> pint.Quantity: return Q_(mag, Q_(1, "mm").to_base_units().u).to("mm") def _len_disc(self) -> pint.Quantity: - """Get the length of a segment based on discrete values""" + """Get the length of a segment based on discrete values.""" diff = self._series.data[1:] - self._series.data[:-1] length = np.sum(np.linalg.norm(diff.m, axis=1)) return Q_(length, diff.u) From 6ececce591ed444dfae9181d73133427ec8279dd Mon Sep 17 00:00:00 2001 From: vhirtham Date: Fri, 18 Feb 2022 12:29:24 +0100 Subject: [PATCH 25/70] Fix a deepsource issue --- weldx/geometry.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index 936bef84d..eedf74814 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1636,7 +1636,7 @@ def _get_derivative_expression(self) -> MathematicalExpression: expr.set_parameter(k, v) return expr - def _get_tangent_vec_discrete(self, position: float) -> np.array(): + def _get_tangent_vec_discrete(self, position: float) -> np.ndarray: """Get the segments tangent vector at the given position (discrete case).""" coords_s = self._series.coordinates["s"].data idx_low = np.abs(coords_s - position).argmin() From 805cbbe4025326818fb876e0085f7f884fe3c3b3 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Fri, 18 Feb 2022 12:36:49 +0100 Subject: [PATCH 26/70] Remove unused code --- weldx/geometry.py | 15 --------------- 1 file changed, 15 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index eedf74814..b956a6393 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1651,21 +1651,6 @@ def _len_expr(self) -> pint.Quantity: expr = sympy.sqrt(der_sq[0] + der_sq[1] + der_sq[2]) mag = float(sympy.integrate(expr, ("s", 0, self._max_s)).evalf()) - # params_vec = self._derivative.parameters - # params = {} - # expressions = [self._derivative.expression for _ in range(3)] - # for k, v in params_vec.items(): - # for i in range(3): - # new_name = f"{k}{i}" - # expressions[i] = expressions[i].subs(k, sympy.symbols(new_name)) - # params[new_name] = v[i] - - # expr = sympy.sqrt( - # expressions[0] ** 2 + expressions[1] ** 2 + expressions[2] ** 2 - # ) - # expr = sympy.integrate(expr, ("s", 0, self._max_s)) - # MathematicalExpression(expr).evaluate(**params) - return Q_(mag, Q_(1, "mm").to_base_units().u).to("mm") def _len_disc(self) -> pint.Quantity: From e514d3b21113252ea77792f9dca0cdc6cf2b3c7f Mon Sep 17 00:00:00 2001 From: vhirtham Date: Fri, 18 Feb 2022 13:08:40 +0100 Subject: [PATCH 27/70] Express LinearHorizontalTraceSegment as DynamicSegment --- weldx/geometry.py | 94 ++++++++++++++++------------------------------- 1 file changed, 32 insertions(+), 62 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index b956a6393..9cdec5744 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1381,67 +1381,6 @@ def shapes(self) -> list[Shape]: # Trace segment classes ------------------------------------------------------- -class LinearHorizontalTraceSegment: - """Trace segment with a linear path and constant z-component.""" - - @UREG.wraps(None, (None, _DEFAULT_LEN_UNIT), strict=True) - def __init__(self, length: pint.Quantity): - """Construct linear horizontal trace segment. - - Parameters - ---------- - length : - Length of the segment - - Returns - ------- - LinearHorizontalTraceSegment - - """ - if length <= 0: - raise ValueError("'length' must have a positive value.") - self._length = float(length) - - def __repr__(self): - """Output representation of a LinearHorizontalTraceSegment.""" - return f"LinearHorizontalTraceSegment('length': {self.length!r})" - - @property - @UREG.wraps(_DEFAULT_LEN_UNIT, (None,), strict=True) - def length(self): - """Get the length of the segment. - - Returns - ------- - pint.Quantity - Length of the segment - - """ - return self._length - - def local_coordinate_system( - self, relative_position: float - ) -> tf.LocalCoordinateSystem: - """Calculate a local coordinate system along the trace segment. - - Parameters - ---------- - relative_position : - Relative position on the trace [0 .. 1] - - Returns - ------- - weldx.transformations.LocalCoordinateSystem - Local coordinate system - - """ - relative_position = np.clip(relative_position, 0, 1) - - coordinates = np.array([1, 0, 0]) * relative_position * self.length - - return tf.LocalCoordinateSystem(coordinates=coordinates) - - class RadialHorizontalTraceSegment: """Trace segment describing an arc with constant z-component.""" @@ -1651,7 +1590,7 @@ def _len_expr(self) -> pint.Quantity: expr = sympy.sqrt(der_sq[0] + der_sq[1] + der_sq[2]) mag = float(sympy.integrate(expr, ("s", 0, self._max_s)).evalf()) - return Q_(mag, Q_(1, "mm").to_base_units().u).to("mm") + return Q_(mag, Q_(1, "mm").to_base_units().u).to(_DEFAULT_LEN_UNIT) def _len_disc(self) -> pint.Quantity: """Get the length of a segment based on discrete values.""" @@ -1710,6 +1649,37 @@ def local_coordinate_system(self, position: float) -> tf.LocalCoordinateSystem: return self._lcs_disc(position) +class LinearHorizontalTraceSegment(DynamicTraceSegment): + """Trace segment with a linear path and constant z-component.""" + + @UREG.wraps(None, (None, _DEFAULT_LEN_UNIT), strict=True) + def __init__(self, length: pint.Quantity): + """Construct linear horizontal trace segment. + + Parameters + ---------- + length : + Length of the segment + + Returns + ------- + LinearHorizontalTraceSegment + + """ + if length <= 0: + raise ValueError("'length' must have a positive value.") + data = DataArray( + Q_([[0, 0, 0], [length, 0, 0]], _DEFAULT_LEN_UNIT), + dims=["s", "c"], + coords=dict( + c=["x", "y", "z"], + s=DataArray(Q_([0, 1], ""), dims=["s"]).pint.dequantify(), + ), + ) + series_disc = SpatialSeries(data) + super().__init__(series_disc) + + # Trace class ----------------------------------------------------------------- trace_segment_types = Union[ From 5890308be2894f1bfeb8e1217b08abd6eb074ba7 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Fri, 18 Feb 2022 14:17:28 +0100 Subject: [PATCH 28/70] Express RadialHorizontalTraceSegment as DynamicSegment --- weldx/geometry.py | 93 +++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 89 insertions(+), 4 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index 9cdec5744..42e5f62d1 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1381,7 +1381,7 @@ def shapes(self) -> list[Shape]: # Trace segment classes ------------------------------------------------------- -class RadialHorizontalTraceSegment: +class RadialHorizontalTraceSegmentOld: """Trace segment describing an arc with constant z-component.""" @UREG.wraps(None, (None, _DEFAULT_LEN_UNIT, _DEFAULT_ANG_UNIT, None), strict=True) @@ -1558,7 +1558,15 @@ def _get_component_derivative(self, i: int): me = self._series.data exp = me.expression # todo unit stripped -> how to proceed? how to cast all length units to mm? - subs = [(k, v[i].data.to_base_units().m) for k, v in me.parameters.items()] + def _get_component(v, i): + if isinstance(v, Q_): + v = v.to_base_units().m + if v.size == 3: + return v[i] + return float(v) + + subs = [(k, _get_component(v.data, i)) for k, v in me.parameters.items()] + print(subs) return exp.subs(subs).diff("s") def _get_component_derivative_squared(self, i): @@ -1599,7 +1607,7 @@ def _len_disc(self) -> pint.Quantity: return Q_(length, diff.u) def _get_lcs_from_coords_and_tangent( - self, coords: pint.Quantity, tangent: pint.Quantity + self, coords: pint.Quantity, tangent: np.ndarray ) -> tf.LocalCoordinateSystem: """Create a `LocalCoordinateSystem` from coordinates and tangent vector.""" z_fake = [0, 0, 1] @@ -1615,7 +1623,7 @@ def _get_lcs_from_coords_and_tangent( def _lcs_expr(self, position: float) -> tf.LocalCoordinateSystem: """Get a `LocalCoordinateSystem` at the passed rel. position (expression).""" coords = self._series.evaluate(s=position * self._max_s).data.transpose()[0] - x = self._derivative.evaluate(s=position * self._max_s) + x = self._derivative.evaluate(s=position * self._max_s).data.m return self._get_lcs_from_coords_and_tangent(coords, x) def _lcs_disc(self, position: float) -> tf.LocalCoordinateSystem: @@ -1680,6 +1688,83 @@ def __init__(self, length: pint.Quantity): super().__init__(series_disc) +class RadialHorizontalTraceSegment(DynamicTraceSegment): + """Trace segment describing an arc with constant z-component.""" + + @UREG.wraps(None, (None, _DEFAULT_LEN_UNIT, _DEFAULT_ANG_UNIT, None), strict=True) + def __init__( + self, radius: pint.Quantity, angle: pint.Quantity, clockwise: bool = False + ): + """Construct radial horizontal trace segment. + + Parameters + ---------- + radius : + Radius of the arc + angle : + Angle of the arc + clockwise : + If True, the rotation is clockwise. Otherwise it is counter-clockwise. + + Returns + ------- + RadialHorizontalTraceSegment + + """ + if radius <= 0: + raise ValueError("'radius' must have a positive value.") + if angle <= 0: + raise ValueError("'angle' must have a positive value.") + self._radius = float(radius) + self._angle = float(angle) + + if clockwise: + self._sign_winding = -1 + else: + self._sign_winding = 1 + + # todo change sign sign back to + and correct winding signs + expr = "(x*sin(s)-w*y*(cos(s)-1))*r " + params = dict( + x=DataArray( + Q_([1, 0, 0], "mm"), dims=["c"], coords=dict(c=["x", "y", "z"]) + ), + y=DataArray( + Q_([0, 1, 0], "mm"), dims=["c"], coords=dict(c=["x", "y", "z"]) + ), + r=self._radius, + w=self._sign_winding, + ) + sps = SpatialSeries(expr, parameters=params) + super().__init__(sps, max_s=self._angle) + + def __repr__(self): + """Output representation of a RadialHorizontalTraceSegment.""" + return ( + f"RadialHorizontalTraceSegment('radius': {self._radius!r}, " + f"'angle': {self._angle!r}, " + f"'length': {self._length!r}, " + f"'sign_winding': {self._sign_winding!r})" + ) + + @property + @UREG.wraps(_DEFAULT_ANG_UNIT, (None,), strict=True) + def angle(self) -> pint.Quantity: + """Get the angle of the segment.""" + return self._angle + + @property + @UREG.wraps(_DEFAULT_LEN_UNIT, (None,), strict=True) + def radius(self) -> pint.Quantity: + """Get the radius of the segment.""" + return self._radius + + @property + def is_clockwise(self) -> bool: + """Get True, if the segments winding is clockwise, False otherwise.""" + return self._sign_winding < 0 + + # Trace class ----------------------------------------------------------------- trace_segment_types = Union[ From b2cff2d239b81fda7f15e59bf96bfec22cbedea3 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Fri, 18 Feb 2022 14:17:56 +0100 Subject: [PATCH 29/70] Remove legacy code --- weldx/geometry.py | 141 ---------------------------------------------- 1 file changed, 141 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index 42e5f62d1..5d4e1bd0e 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1381,147 +1381,6 @@ def shapes(self) -> list[Shape]: # Trace segment classes ------------------------------------------------------- -class RadialHorizontalTraceSegmentOld: - """Trace segment describing an arc with constant z-component.""" - - @UREG.wraps(None, (None, _DEFAULT_LEN_UNIT, _DEFAULT_ANG_UNIT, None), strict=True) - def __init__( - self, radius: pint.Quantity, angle: pint.Quantity, clockwise: bool = False - ): - """Construct radial horizontal trace segment. - - Parameters - ---------- - radius : - Radius of the arc - angle : - Angle of the arc - clockwise : - If True, the rotation is clockwise. Otherwise it is counter-clockwise. - - Returns - ------- - RadialHorizontalTraceSegment - - """ - if radius <= 0: - raise ValueError("'radius' must have a positive value.") - if angle <= 0: - raise ValueError("'angle' must have a positive value.") - self._radius = float(radius) - self._angle = float(angle) - self._length = self._arc_length(self._radius, self._angle) - if clockwise: - self._sign_winding = -1 - else: - self._sign_winding = 1 - - def __repr__(self): - """Output representation of a RadialHorizontalTraceSegment.""" - return ( - f"RadialHorizontalTraceSegment('radius': {self._radius!r}, " - f"'angle': {self._angle!r}, " - f"'length': {self._length!r}, " - f"'sign_winding': {self._sign_winding!r})" - ) - - @staticmethod - def _arc_length(radius, angle) -> float: - """Calculate the arc length. - - Parameters - ---------- - radius : - Radius - angle : - Angle (rad) - - Returns - ------- - float - Arc length - - """ - return angle * radius - - @property - @UREG.wraps(_DEFAULT_ANG_UNIT, (None,), strict=True) - def angle(self) -> pint.Quantity: - """Get the angle of the segment. - - Returns - ------- - pint.Quantity - Angle of the segment (rad) - - """ - return self._angle - - @property - @UREG.wraps(_DEFAULT_LEN_UNIT, (None,), strict=True) - def length(self) -> pint.Quantity: - """Get the length of the segment. - - Returns - ------- - pint.Quantity - Length of the segment - - """ - return self._length - - @property - @UREG.wraps(_DEFAULT_LEN_UNIT, (None,), strict=True) - def radius(self) -> pint.Quantity: - """Get the radius of the segment. - - Returns - ------- - pint.Quantity - Radius of the segment - - """ - return self._radius - - @property - def is_clockwise(self) -> bool: - """Get True, if the segments winding is clockwise, False otherwise. - - Returns - ------- - bool - True or False - - """ - return self._sign_winding < 0 - - def local_coordinate_system( - self, relative_position: float - ) -> tf.LocalCoordinateSystem: - """Calculate a local coordinate system along the trace segment. - - Parameters - ---------- - relative_position : - Relative position on the trace [0 .. 1] - - Returns - ------- - weldx.transformations.LocalCoordinateSystem - Local coordinate system - - """ - relative_position = np.clip(relative_position, 0, 1) - - orientation = tf.WXRotation.from_euler( - "z", self._angle * relative_position * self._sign_winding - ).as_matrix() - translation = np.array([0, -1, 0]) * self.radius * self._sign_winding - - coordinates = np.matmul(orientation, translation) - translation - return tf.LocalCoordinateSystem(orientation, coordinates) - - class DynamicTraceSegment: """Trace segment that can be defined by a ``SpatialSeries``.""" From 7b4596dfca82d5c39a28a726298818cd59a903de Mon Sep 17 00:00:00 2001 From: vhirtham Date: Mon, 21 Feb 2022 09:58:48 +0100 Subject: [PATCH 30/70] Fix notebook --- tutorials/welding_example_01_basics.ipynb | 264 +++++++++++++++++----- 1 file changed, 211 insertions(+), 53 deletions(-) diff --git a/tutorials/welding_example_01_basics.ipynb b/tutorials/welding_example_01_basics.ipynb index a6946ef6d..bdcfd9591 100644 --- a/tutorials/welding_example_01_basics.ipynb +++ b/tutorials/welding_example_01_basics.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "nbsphinx": "hidden" }, @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -91,9 +91,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "groove.plot(raster_width=\"0.2mm\")" ] @@ -113,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -151,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -169,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "nbsphinx": "hidden" }, @@ -197,9 +210,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ax = geometry.plot(\n", " profile_raster_width,\n", @@ -223,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -242,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -263,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -292,7 +318,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -309,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -337,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -353,11 +379,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ - "coords = [tcp_start_point.magnitude, tcp_end_point.magnitude]\n", + "coords = np.stack([tcp_start_point, tcp_end_point])\n", "\n", "tcp_wire = LocalCoordinateSystem(\n", " coordinates=coords, orientation=rot, time=[t_start, t_end]\n", @@ -373,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -394,16 +420,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ - "tcp_contact = LocalCoordinateSystem(coordinates=[0, 0, -10])" + "tcp_contact = LocalCoordinateSystem(coordinates=Q_([0, 0, -10], \"mm\"))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -424,9 +450,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "csm" ] @@ -441,9 +478,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ax = csm.plot(\n", " coordinate_systems=[\"tcp_contact\", \"tcp_wire\"],\n", @@ -464,21 +514,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# add the workpiece coordinate system\n", - "csm.add_cs(\"T1\", \"workpiece\", LocalCoordinateSystem(coordinates=[200, 3, 5]))\n", - "csm.add_cs(\"T2\", \"T1\", LocalCoordinateSystem(coordinates=[0, 1, 0]))\n", - "csm.add_cs(\"T3\", \"T2\", LocalCoordinateSystem(coordinates=[0, 1, 0]))" + "csm.add_cs(\"T1\", \"workpiece\", LocalCoordinateSystem(coordinates=Q_([200, 3, 5], \"mm\")))\n", + "csm.add_cs(\"T2\", \"T1\", LocalCoordinateSystem(coordinates=Q_([0, 1, 0], \"mm\")))\n", + "csm.add_cs(\"T3\", \"T2\", LocalCoordinateSystem(coordinates=Q_([0, 1, 0], \"mm\")))" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ax = csm.plot(\n", " coordinate_systems=[\"tcp_contact\", \"tcp_wire\", \"T1\", \"T2\", \"T3\"],\n", @@ -492,9 +555,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "csm" ] @@ -510,11 +584,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": { "nbsphinx": "hidden" }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\traittypes\\traittypes.py:97: UserWarning: Given trait value dtype \"float64\" does not match required type \"float32\". A coerced copy has been created.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "262b5801a27243a4ace2ab091e999e0f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3fe17f32227e4bd78004306a0f9b78e8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HBox(children=(IntSlider(value=0, description='Time:', max=1), Play(value=0, max=1), Dropdown(d…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "csm.plot(\n", " backend=\"k3d\",\n", @@ -539,7 +650,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -548,18 +659,65 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\vhirtham\\pycharmprojects\\bam\\libo\\libo\\__init__.py:29: UserWarning: Using local libo package files without version information.\n", + "Consider running 'python setup.py --version' or 'pip install -e .' in the libo root repository\n", + " warnings.warn(\n", + "c:\\users\\vhirtham\\pycharmprojects\\bam\\libo\\libo\\__init__.py:29: UserWarning: Using local libo package files without version information.\n", + "Consider running 'python setup.py --version' or 'pip install -e .' in the libo root repository\n", + " warnings.warn(\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\variable.py:259: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", + " data = np.asarray(data)\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\variable.py:259: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", + " data = np.asarray(data)\n" + ] + } + ], "source": [ "file = WeldxFile(tree=tree, mode=\"rw\")" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\vhirtham\\AppData\\Local\\Temp\\ipykernel_17892\\361942109.py:1: WeldxDeprecationWarning: Call to deprecated function show_asdf_header.\n", + "Deprecated since: 0.6\n", + "Removed in: 0.7\n", + "Please use file.header() instead.\n", + " file.show_asdf_header()\n", + "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\variable.py:259: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", + " data = np.asarray(data)\n" + ] + }, + { + "data": { + "application/json": {}, + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": { + "application/json": { + "expanded": false, + "root": "/" + } + }, + "output_type": "execute_result" + } + ], "source": [ "file.show_asdf_header()" ] @@ -567,9 +725,9 @@ ], "metadata": { "kernelspec": { - "display_name": "", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -581,7 +739,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.8.12" } }, "nbformat": 4, From 2b4ce61ddd9a43d0a70d8862710adc960a6cc640 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Mon, 21 Feb 2022 10:08:22 +0100 Subject: [PATCH 31/70] run cleanup --- tutorials/welding_example_01_basics.ipynb | 252 ++++------------------ weldx/geometry.py | 3 +- 2 files changed, 48 insertions(+), 207 deletions(-) diff --git a/tutorials/welding_example_01_basics.ipynb b/tutorials/welding_example_01_basics.ipynb index bdcfd9591..adc89def8 100644 --- a/tutorials/welding_example_01_basics.ipynb +++ b/tutorials/welding_example_01_basics.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "nbsphinx": "hidden" }, @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -91,22 +91,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "groove.plot(raster_width=\"0.2mm\")" ] @@ -126,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -147,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -164,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -182,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "nbsphinx": "hidden" }, @@ -210,22 +197,9 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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"execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -379,7 +353,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -399,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -420,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -429,7 +403,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -450,20 +424,9 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "ax = csm.plot(\n", " coordinate_systems=[\"tcp_contact\", \"tcp_wire\"],\n", @@ -514,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -526,22 +476,9 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "ax = csm.plot(\n", " coordinate_systems=[\"tcp_contact\", \"tcp_wire\", \"T1\", \"T2\", \"T3\"],\n", @@ -555,20 +492,9 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "csm" ] @@ -584,48 +510,11 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": { "nbsphinx": "hidden" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\traittypes\\traittypes.py:97: UserWarning: Given trait value dtype \"float64\" does not match required type \"float32\". A coerced copy has been created.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "262b5801a27243a4ace2ab091e999e0f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output()" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3fe17f32227e4bd78004306a0f9b78e8", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(HBox(children=(IntSlider(value=0, description='Time:', max=1), Play(value=0, max=1), Dropdown(d…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "csm.plot(\n", " backend=\"k3d\",\n", @@ -650,7 +539,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -659,65 +548,18 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\vhirtham\\pycharmprojects\\bam\\libo\\libo\\__init__.py:29: UserWarning: Using local libo package files without version information.\n", - "Consider running 'python setup.py --version' or 'pip install -e .' in the libo root repository\n", - " warnings.warn(\n", - "c:\\users\\vhirtham\\pycharmprojects\\bam\\libo\\libo\\__init__.py:29: UserWarning: Using local libo package files without version information.\n", - "Consider running 'python setup.py --version' or 'pip install -e .' in the libo root repository\n", - " warnings.warn(\n", - "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\variable.py:259: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " data = np.asarray(data)\n", - "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\variable.py:259: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " data = np.asarray(data)\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "file = WeldxFile(tree=tree, mode=\"rw\")" ] }, { "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\vhirtham\\AppData\\Local\\Temp\\ipykernel_17892\\361942109.py:1: WeldxDeprecationWarning: Call to deprecated function show_asdf_header.\n", - "Deprecated since: 0.6\n", - "Removed in: 0.7\n", - "Please use file.header() instead.\n", - " file.show_asdf_header()\n", - "C:\\Users\\vhirtham\\Miniconda3\\envs\\weldx\\lib\\site-packages\\xarray\\core\\variable.py:259: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " data = np.asarray(data)\n" - ] - }, - { - "data": { - "application/json": {}, - "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": { - "application/json": { - "expanded": false, - "root": "/" - } - }, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "file.show_asdf_header()" ] @@ -725,9 +567,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "", "language": "python", - "name": "python3" + "name": "" }, "language_info": { "codemirror_mode": { diff --git a/weldx/geometry.py b/weldx/geometry.py index 5d4e1bd0e..8fb2f7bf4 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1416,7 +1416,7 @@ def _get_component_derivative(self, i: int): """Get the derivative of an expression for the i-th vector component.""" me = self._series.data exp = me.expression - # todo unit stripped -> how to proceed? how to cast all length units to mm? + def _get_component(v, i): if isinstance(v, Q_): v = v.to_base_units().m @@ -1425,7 +1425,6 @@ def _get_component(v, i): return float(v) subs = [(k, _get_component(v.data, i)) for k, v in me.parameters.items()] - print(subs) return exp.subs(subs).diff("s") def _get_component_derivative_squared(self, i): From 43c5c526ab674f3033dc9e05d09175c2103d2af2 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Mon, 21 Feb 2022 11:44:17 +0100 Subject: [PATCH 32/70] Fix deepsource issues --- tutorials/sympy_diff.py | 28 -------------------- tutorials/trace_segment.py | 54 -------------------------------------- weldx/geometry.py | 4 +-- 3 files changed, 2 insertions(+), 84 deletions(-) delete mode 100644 tutorials/sympy_diff.py delete mode 100644 tutorials/trace_segment.py diff --git a/tutorials/sympy_diff.py b/tutorials/sympy_diff.py deleted file mode 100644 index 802761f6e..000000000 --- a/tutorials/sympy_diff.py +++ /dev/null @@ -1,28 +0,0 @@ -import sympy - -from weldx import MathematicalExpression - -s = sympy.symbols("s") -exp1 = 1 * s**2 + 0 * s + 0 -exp2 = 0 * s**2 + 1 * s + 0 -exp3 = 0 * s**2 + 0 * s + 1 - - -temp = sympy.sqrt(exp1.diff(s) ** 2 + exp2.diff(s) ** 2 + exp3.diff(s) ** 2) -print(temp) -print(sympy.integrate(temp, (s, 0, 1)).evalf()) - - -params = dict(a=[1, 0, 0], b=[0, 1, 0], c=[0, 0, 1]) -me = MathematicalExpression("a * s**2 + b * s + c", parameters=params) - -der_sq = [] -for i in range(3): - ex = me.expression - subs = [(k, v[i]) for k, v in me.parameters.items()] - - der_sq.append(ex.subs(subs).diff("s") ** 2) -print(der_sq) -expr_l = sympy.sqrt(der_sq[0] + der_sq[1] + der_sq[2]) -print(expr_l) -print(sympy.integrate(expr_l, ("s", 0, 1))) diff --git a/tutorials/trace_segment.py b/tutorials/trace_segment.py deleted file mode 100644 index a77c242e2..000000000 --- a/tutorials/trace_segment.py +++ /dev/null @@ -1,54 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -import sympy -from xarray import DataArray - -from weldx import Q_, LinearHorizontalTraceSegment, LocalCoordinateSystem, Trace -from weldx.core import SpatialSeries -from weldx.geometry import RadialHorizontalTraceSegment - - -class SDTraceSegment: - def __init__(self, series): - self._series = series - - def _get_squared_derivative(self, i): - me = self._series.data - exp = me.expression - # todo unit stripped -> how to proceed? how to cast all length units to mm? - subs = [(k, v[i].data.to_base_units().m) for k, v in me.parameters.items()] - return exp.subs(subs).diff("s") ** 2 - - @property - def length(self) -> float: - - der_sq = [self._get_squared_derivative(i) for i in range(3)] - expr = sympy.sqrt(der_sq[0] + der_sq[1] + der_sq[2]) - mag = float(sympy.integrate(expr, ("s", 0, 1)).evalf()) - print("ohoh") - return Q_(mag, Q_(1, "mm").to_base_units().u).to("mm") - - def local_coordinate_system(self, position: float) -> LocalCoordinateSystem: - coords = self._series.evaluate(s=position).data.transpose()[0] - return LocalCoordinateSystem(coordinates=coords) - - -expr = "a*s**2 + b*s + c" -params = dict( - a=DataArray(Q_([0, 0, 1], "mm"), dims=["c"], coords=dict(c=["x", "y", "z"])), - b=DataArray(Q_([1, 0, 0], "mm"), dims=["c"], coords=dict(c=["x", "y", "z"])), - c=DataArray(Q_([0, 0, 0], "mm"), dims=["c"], coords=dict(c=["x", "y", "z"])), -) -series = SpatialSeries(expr, parameters=params) - -segment = SDTraceSegment(series) -segment = LinearHorizontalTraceSegment("10mm") -segment2 = RadialHorizontalTraceSegment("1mm", Q_(np.pi, "rad")) -print(segment.length) -trace = Trace([segment, segment2]) -print(trace.length) -trace.plot(Q_(0.1, "mm")) -plt.show() - - -# todo : check s=0 -> [0,0,0] diff --git a/weldx/geometry.py b/weldx/geometry.py index 8fb2f7bf4..1dfd1f51d 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1435,9 +1435,9 @@ def _get_derivative_expression(self) -> MathematicalExpression: """Get the derivative of an expression as 'MathematicalExpression'.""" params = self._series.data.parameters expr = MathematicalExpression(self._series.data.expression.diff("s")) - vars = expr.get_variable_names() + var_names = expr.get_variable_names() for k, v in params.items(): - if k in vars: + if k in var_names: expr.set_parameter(k, v) return expr From e596c8673009ba5e0b16fe269b2a3193e8dc8d00 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Mon, 21 Feb 2022 12:11:36 +0100 Subject: [PATCH 33/70] Fix deepsource issues --- weldx/core.py | 8 -------- 1 file changed, 8 deletions(-) diff --git a/weldx/core.py b/weldx/core.py index 2dfcd1d13..a164c387e 100644 --- a/weldx/core.py +++ b/weldx/core.py @@ -1253,8 +1253,6 @@ def _evaluate_expr(self, coords: list[SeriesParameter]) -> GenericSeries: """Evaluate the expression at the passed coordinates.""" if len(coords) == self._obj.num_variables: eval_args = {v.symbol: v.data_array for v in coords} - # for k, v in eval_args.items(): - # v.assign_coords(dict(k=v)) data = self._obj.evaluate(**eval_args) # TODO: Discuss - This might be done before by assigning coords to the @@ -1564,15 +1562,9 @@ class SpatialSeries(GenericSeries): _required_variables: list[str] = ["s"] """Required variable names""" - # _evaluation_preprocessor: dict[str, Callable] = {} - # """Function that should be used to adjust a var. input - (f.e. convert to Time)""" - _required_dimensions: list[str] = ["s", "c"] """Required dimensions""" _required_dimension_units: dict[str, pint.Unit] = {"s": ""} """Required units of a dimension""" _required_dimension_coordinates: dict[str, list] = {"c": ["x", "y", "z"]} """Required coordinates of a dimension.""" - - # _required_unit_dimensionality: pint.Unit = None - # """Required unit dimensionality of the evaluated expression/data""" From e9bac0264a64baa1a811ee1391e3dff350c0becf Mon Sep 17 00:00:00 2001 From: vhirtham Date: Mon, 21 Feb 2022 14:25:35 +0100 Subject: [PATCH 34/70] add some doc fixes --- tutorials/SpatialSeries.ipynb | 106 ------------------ tutorials/TraceSegmentSpS.ipynb | 191 -------------------------------- weldx/geometry.py | 6 +- 3 files changed, 3 insertions(+), 300 deletions(-) delete mode 100644 tutorials/SpatialSeries.ipynb delete mode 100644 tutorials/TraceSegmentSpS.ipynb diff --git a/tutorials/SpatialSeries.ipynb b/tutorials/SpatialSeries.ipynb deleted file mode 100644 index 0e45d58ef..000000000 --- a/tutorials/SpatialSeries.ipynb +++ /dev/null @@ -1,106 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from xarray import DataArray\n", - "\n", - "from weldx import Q_, LocalCoordinateSystem\n", - "from weldx.core import GenericSeries, SpatialSeries" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Discrete" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s = DataArray(Q_([0, 5], \"\"), dims=[\"s\"]).pint.dequantify()\n", - "data = DataArray(\n", - " Q_([[1, 2, 3], [4, 5, 6]], \"m\"),\n", - " dims=[\"s\", \"c\"],\n", - " coords=dict(c=[\"x\", \"y\", \"z\"], s=s),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "spsd = SpatialSeries(data, dims=[\"s\", \"c\"])\n", - "spsd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Expression" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "exp = \"a*s + b\"\n", - "params = dict(\n", - " a=DataArray(Q_([0, 0, 1], \"mm\"), dims=[\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"])),\n", - " b=DataArray(Q_([1, 0, 0], \"mm\"), dims=[\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"])),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "spse = SpatialSeries(exp, parameters=params)\n", - "spse" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "", - "language": "python", - "name": "" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials/TraceSegmentSpS.ipynb b/tutorials/TraceSegmentSpS.ipynb deleted file mode 100644 index 85a5ba8e3..000000000 --- a/tutorials/TraceSegmentSpS.ipynb +++ /dev/null @@ -1,191 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from xarray import DataArray\n", - "\n", - "from weldx import Q_, GenericSeries, LinearHorizontalTraceSegment, Trace\n", - "from weldx.core import SpatialSeries\n", - "from weldx.geometry import DynamicTraceSegment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Discrete" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = DataArray(\n", - " Q_([[0, 0, 0], [0, 5, 0], [1, 5, 0], [1, 9, 0]], \"mm\"),\n", - " dims=[\"s\", \"c\"],\n", - " coords=dict(\n", - " c=[\"x\", \"y\", \"z\"],\n", - " s=DataArray(Q_([0, 0.5, 0.6, 1], \"\"), dims=[\"s\"]).pint.dequantify(),\n", - " ),\n", - ")\n", - "series_disc = SpatialSeries(data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "segment_disc = DynamicTraceSegment(series_disc)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "segment_disc.local_coordinate_system(0.55)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trace_disc = Trace([segment_disc, segment_disc])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trace_disc.plot(\"0.5mm\")\n", - "ax = plt.gca()\n", - "ax.plot([0, 10], [0, 10])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Expression" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "expr = \"a*sin(s)+b*cos(s)+c*s/10+d \"\n", - "# expr = \"x+y+z\"\n", - "params = dict(\n", - " a=DataArray(Q_([1, 0, 0], \"mm\"), dims=[\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"])),\n", - " b=DataArray(Q_([0, 1, 0], \"mm\"), dims=[\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"])),\n", - " c=DataArray(Q_([0, 0, 2], \"mm\"), dims=[\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"])),\n", - " d=DataArray(Q_([0, -1, 0], \"mm\"), dims=[\"c\"], coords=dict(c=[\"x\", \"y\", \"z\"])),\n", - ")\n", - "sps = SpatialSeries(expr, parameters=params)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "segment = DynamicTraceSegment(sps, 2 * np.pi, limit_orientation_to_xy=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trace = Trace([segment, segment, segment])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trace.plot(\"0.1mm\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trace.length" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from weldx import LocalCoordinateSystem\n", - "from weldx.visualization.matplotlib_impl import (\n", - " axes_equal,\n", - " draw_coordinate_system_matplotlib,\n", - ")\n", - "\n", - "num_lcs = 11\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot(111, projection=\"3d\")\n", - "for i in range(num_lcs):\n", - " lcs = segment.local_coordinate_system(i / (num_lcs - 1))\n", - " lcs = LocalCoordinateSystem(lcs.orientation, lcs.coordinates.data.m)\n", - " draw_coordinate_system_matplotlib(lcs, ax)\n", - "axes_equal(ax)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "", - "language": "python", - "name": "" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/weldx/geometry.py b/weldx/geometry.py index 1dfd1f51d..d75eddf01 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1467,7 +1467,7 @@ def _len_disc(self) -> pint.Quantity: def _get_lcs_from_coords_and_tangent( self, coords: pint.Quantity, tangent: np.ndarray ) -> tf.LocalCoordinateSystem: - """Create a `LocalCoordinateSystem` from coordinates and tangent vector.""" + """Create a ``LocalCoordinateSystem`` from coordinates and tangent vector.""" z_fake = [0, 0, 1] y = np.cross(z_fake, tangent) if self._limit_orientation: @@ -1479,13 +1479,13 @@ def _get_lcs_from_coords_and_tangent( ) def _lcs_expr(self, position: float) -> tf.LocalCoordinateSystem: - """Get a `LocalCoordinateSystem` at the passed rel. position (expression).""" + """Get a ``LocalCoordinateSystem`` at the passed rel. position (expression).""" coords = self._series.evaluate(s=position * self._max_s).data.transpose()[0] x = self._derivative.evaluate(s=position * self._max_s).data.m return self._get_lcs_from_coords_and_tangent(coords, x) def _lcs_disc(self, position: float) -> tf.LocalCoordinateSystem: - """Get a `LocalCoordinateSystem` at the passed rel. position (discrete).""" + """Get a ``LocalCoordinateSystem`` at the passed rel. position (discrete).""" coords = self._series.evaluate(s=position).data[0] x = self._get_tangent_vec_discrete(position) return self._get_lcs_from_coords_and_tangent(coords, x) From 3a90ef9657c2b3f84c9d19efbeea06441521caec Mon Sep 17 00:00:00 2001 From: vhirtham Date: Mon, 21 Feb 2022 15:03:46 +0100 Subject: [PATCH 35/70] Test docfix --- weldx/geometry.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index d75eddf01..dc6e7c829 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1401,7 +1401,8 @@ def __init__( series `s` parameter. The value defines the segments length by evaluating the expression on the interval [0, `max_s`] limit_orientation_to_xy: - If t + If `True`, the orientation vectors of the coordinate systems along the trace + are confined to the xy-plane. """ self._series: SpatialSeries = series self._max_s = max_s @@ -1496,7 +1497,7 @@ def length(self) -> pint.Quantity: return self._length def local_coordinate_system(self, position: float) -> tf.LocalCoordinateSystem: - """Calculate a `LocalCoordinateSystem` at a position of the trace segment. + """Calculate a `tf.LocalCoordinateSystem` at a position of the trace segment. Parameters ---------- @@ -1506,7 +1507,7 @@ def local_coordinate_system(self, position: float) -> tf.LocalCoordinateSystem: Returns ------- - LocalCoordinateSystem: + tf.LocalCoordinateSystem: The coordinate system and the specified position. """ From 952e4010713f500a263b527da57c802c3af77d42 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Mon, 21 Feb 2022 17:03:22 +0100 Subject: [PATCH 36/70] Add some quality of life functions to SpatialSeries --- weldx/core.py | 46 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) diff --git a/weldx/core.py b/weldx/core.py index a164c387e..c0eec38e6 100644 --- a/weldx/core.py +++ b/weldx/core.py @@ -1568,3 +1568,49 @@ class SpatialSeries(GenericSeries): """Required units of a dimension""" _required_dimension_coordinates: dict[str, list] = {"c": ["x", "y", "z"]} """Required coordinates of a dimension.""" + + def __init__( + self, + obj: Union[pint.Quantity, xr.DataArray, str, MathematicalExpression], + dims: Union[list[str], dict[str, str]] = None, + coords: dict[str, pint.Quantity] = None, + units: dict[str, Union[str, pint.Unit]] = None, + interpolation: str = None, + parameters: dict[str, Union[str, pint.Quantity, xr.DataArray]] = None, + ): + if isinstance(obj, Q_): + obj = self._process_quantity(obj, dims, coords) + dims = None + coords = None + if parameters is not None: + parameters = self._process_parameters(parameters) + super().__init__(obj, dims, coords, units, interpolation, parameters) + + def _process_quantity( + self, + obj: Union[pint.Quantity, xr.DataArray, str, MathematicalExpression], + dims: Union[list[str], dict[str, str]], + coords: dict[str, pint.Quantity], + ) -> xr.DataArray: + """Turn a quantity into a a correctly formatted data array.""" + s = coords["s"] + if not isinstance(s, xr.DataArray): + if not isinstance(s, Q_): + s = Q_(s, "") + s = xr.DataArray(s, dims=["s"]).pint.dequantify() + coords["s"] = s + + if "c" not in coords: + coords["c"] = ["x", "y", "z"] + + if dims is None: + dims = ["s", "c"] + + return xr.DataArray(obj, dims=dims, coords=coords) + + def _process_parameters(self, params): + """Turn quantity parameters into the correctly formatted data arrays.""" + for k, v in params.items(): + if isinstance(v, Q_) and v.size == 3: + params[k] = xr.DataArray(v, dims=["c"], coords=dict(c=["x", "y", "z"])) + return params From c0ce76c3a05c840082a993fc11c286ebd2925562 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Mon, 21 Feb 2022 17:13:36 +0100 Subject: [PATCH 37/70] Fix linter issues --- weldx/core.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/weldx/core.py b/weldx/core.py index c0eec38e6..10191081d 100644 --- a/weldx/core.py +++ b/weldx/core.py @@ -1586,8 +1586,8 @@ def __init__( parameters = self._process_parameters(parameters) super().__init__(obj, dims, coords, units, interpolation, parameters) + @staticmethod def _process_quantity( - self, obj: Union[pint.Quantity, xr.DataArray, str, MathematicalExpression], dims: Union[list[str], dict[str, str]], coords: dict[str, pint.Quantity], @@ -1608,7 +1608,8 @@ def _process_quantity( return xr.DataArray(obj, dims=dims, coords=coords) - def _process_parameters(self, params): + @staticmethod + def _process_parameters(params): """Turn quantity parameters into the correctly formatted data arrays.""" for k, v in params.items(): if isinstance(v, Q_) and v.size == 3: From 93c516d45a63a01f769d70db2bce550589d43868 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Tue, 22 Feb 2022 10:30:34 +0100 Subject: [PATCH 38/70] Add more quality of life functionality --- weldx/core.py | 7 ++++++- weldx/geometry.py | 4 ++++ 2 files changed, 10 insertions(+), 1 deletion(-) diff --git a/weldx/core.py b/weldx/core.py index 10191081d..8b0aa9174 100644 --- a/weldx/core.py +++ b/weldx/core.py @@ -1593,7 +1593,12 @@ def _process_quantity( coords: dict[str, pint.Quantity], ) -> xr.DataArray: """Turn a quantity into a a correctly formatted data array.""" - s = coords["s"] + if isinstance(coords, dict): + s = coords["s"] + else: + s = coords + coords = dict(s=s) + if not isinstance(s, xr.DataArray): if not isinstance(s, Q_): s = Q_(s, "") diff --git a/weldx/geometry.py b/weldx/geometry.py index dc6e7c829..5841fd5b0 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1389,6 +1389,7 @@ def __init__( series: SpatialSeries, max_s: float = 1, limit_orientation_to_xy: bool = False, + **kwargs, ): """Initialize a `DynamicTraceSegment`. @@ -1404,6 +1405,9 @@ def __init__( If `True`, the orientation vectors of the coordinate systems along the trace are confined to the xy-plane. """ + if not isinstance(series, SpatialSeries): + series = SpatialSeries(series, **kwargs) + self._series: SpatialSeries = series self._max_s = max_s self._limit_orientation = limit_orientation_to_xy From 6080323b57d277359f4c969a799d1df2d40930a2 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Tue, 22 Feb 2022 11:08:24 +0100 Subject: [PATCH 39/70] Simplify implementation of 'old' trace segments --- weldx/geometry.py | 32 ++++++++++++++------------------ 1 file changed, 14 insertions(+), 18 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index 5841fd5b0..fbb314251 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1397,6 +1397,8 @@ def __init__( ---------- series: A `SpatialSeries` that describes the trajectory of the trace segment. + Alternatively, one can pass every other object that is valid as first + argument to `SpatialSeries.__init__`. max_s: [only expression based `SpatialSeries`] The maximum value of the passed series `s` parameter. The value defines the segments length by evaluating @@ -1404,6 +1406,10 @@ def __init__( limit_orientation_to_xy: If `True`, the orientation vectors of the coordinate systems along the trace are confined to the xy-plane. + kwargs: + A set of keyword arguments that will be forwarded to the ``__init__`` method + of the `SpatialSeries` in case the ``series`` parameter isn't already a + `SpatialSeries`. """ if not isinstance(series, SpatialSeries): series = SpatialSeries(series, **kwargs) @@ -1538,17 +1544,11 @@ def __init__(self, length: pint.Quantity): """ if length <= 0: - raise ValueError("'length' must have a positive value.") - data = DataArray( - Q_([[0, 0, 0], [length, 0, 0]], _DEFAULT_LEN_UNIT), - dims=["s", "c"], - coords=dict( - c=["x", "y", "z"], - s=DataArray(Q_([0, 1], ""), dims=["s"]).pint.dequantify(), - ), + raise ValueError("'length' must be a positive value.") + + super().__init__( + Q_([[0, 0, 0], [length, 0, 0]], _DEFAULT_LEN_UNIT), coords=[0, 1] ) - series_disc = SpatialSeries(data) - super().__init__(series_disc) class RadialHorizontalTraceSegment(DynamicTraceSegment): @@ -1578,6 +1578,7 @@ def __init__( raise ValueError("'radius' must have a positive value.") if angle <= 0: raise ValueError("'angle' must have a positive value.") + self._radius = float(radius) self._angle = float(angle) @@ -1589,17 +1590,12 @@ def __init__( # todo change sign sign back to + and correct winding signs expr = "(x*sin(s)-w*y*(cos(s)-1))*r " params = dict( - x=DataArray( - Q_([1, 0, 0], "mm"), dims=["c"], coords=dict(c=["x", "y", "z"]) - ), - y=DataArray( - Q_([0, 1, 0], "mm"), dims=["c"], coords=dict(c=["x", "y", "z"]) - ), + x=Q_([1, 0, 0], "mm"), + y=Q_([0, 1, 0], "mm"), r=self._radius, w=self._sign_winding, ) - sps = SpatialSeries(expr, parameters=params) - super().__init__(sps, max_s=self._angle) + super().__init__(expr, max_s=self._angle, parameters=params) def __repr__(self): """Output representation of a RadialHorizontalTraceSegment.""" From 6734a99abd6e7f99abae71a00cad7487ff8cc623 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Tue, 22 Feb 2022 16:02:41 +0100 Subject: [PATCH 40/70] Do some refactoring --- weldx/core.py | 2 ++ weldx/geometry.py | 59 ++++++++++++++++++++++++++--------------------- 2 files changed, 35 insertions(+), 26 deletions(-) diff --git a/weldx/core.py b/weldx/core.py index 8b0aa9174..da6a39bff 100644 --- a/weldx/core.py +++ b/weldx/core.py @@ -1557,6 +1557,8 @@ def interp_like( class SpatialSeries(GenericSeries): + """Describes a line in 3d space depending on the positional coordinate ``s``.""" + _allowed_variables: list[str] = ["s"] """Allowed variable names""" _required_variables: list[str] = ["s"] diff --git a/weldx/geometry.py b/weldx/geometry.py index fbb314251..e58a8d401 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1386,7 +1386,9 @@ class DynamicTraceSegment: def __init__( self, - series: SpatialSeries, + series: Union[ + SpatialSeries, pint.Quantity, DataArray, str, MathematicalExpression + ], max_s: float = 1, limit_orientation_to_xy: bool = False, **kwargs, @@ -1414,19 +1416,19 @@ def __init__( if not isinstance(series, SpatialSeries): series = SpatialSeries(series, **kwargs) - self._series: SpatialSeries = series + self._series = series self._max_s = max_s self._limit_orientation = limit_orientation_to_xy if series.is_expression: self._derivative = self._get_derivative_expression() + self._length = self._len_expr() + else: + self._derivative = None + self._length = self._len_disc() - self._length = self._len_expr() if series.is_expression else self._len_disc() - - def _get_component_derivative(self, i: int): + def _get_component_derivative_squared(self, i: int): """Get the derivative of an expression for the i-th vector component.""" - me = self._series.data - exp = me.expression def _get_component(v, i): if isinstance(v, Q_): @@ -1435,21 +1437,22 @@ def _get_component(v, i): return v[i] return float(v) + me = self._series.data subs = [(k, _get_component(v.data, i)) for k, v in me.parameters.items()] - return exp.subs(subs).diff("s") - - def _get_component_derivative_squared(self, i): - """Get the squared derivative of an expression for the i-th vector component.""" - return self._get_component_derivative(i) ** 2 + return me.expression.subs(subs).diff("s") ** 2 def _get_derivative_expression(self) -> MathematicalExpression: """Get the derivative of an expression as 'MathematicalExpression'.""" - params = self._series.data.parameters expr = MathematicalExpression(self._series.data.expression.diff("s")) - var_names = expr.get_variable_names() - for k, v in params.items(): - if k in var_names: - expr.set_parameter(k, v) + + # parameters might not be present anymore in the derived expression + params = { + k: v + for k, v in self._series.data.parameters.items() + if k in expr.get_variable_names() + } + expr.set_parameters(params) + return expr def _get_tangent_vec_discrete(self, position: float) -> np.ndarray: @@ -1479,15 +1482,16 @@ def _get_lcs_from_coords_and_tangent( self, coords: pint.Quantity, tangent: np.ndarray ) -> tf.LocalCoordinateSystem: """Create a ``LocalCoordinateSystem`` from coordinates and tangent vector.""" - z_fake = [0, 0, 1] - y = np.cross(z_fake, tangent) + x = tangent + z = [0, 0, 1] + y = np.cross(z, x) + if self._limit_orientation: - return tf.LocalCoordinateSystem.from_axis_vectors( - y=y, z=z_fake, coordinates=coords - ) - return tf.LocalCoordinateSystem.from_axis_vectors( - x=tangent, y=y, coordinates=coords - ) + x = None + else: + z = None + + return tf.LocalCoordinateSystem.from_axis_vectors(x, y, z, coords) def _lcs_expr(self, position: float) -> tf.LocalCoordinateSystem: """Get a ``LocalCoordinateSystem`` at the passed rel. position (expression).""" @@ -1521,6 +1525,9 @@ def local_coordinate_system(self, position: float) -> tf.LocalCoordinateSystem: The coordinate system and the specified position. """ + if not isinstance(position, (float, int, Q_)): + position = np.array(position) + if self._series.is_expression: return self._lcs_expr(position) return self._lcs_disc(position) @@ -1587,7 +1594,7 @@ def __init__( else: self._sign_winding = 1 - # todo change sign sign back to + and correct winding signs + # todo change sign sign back to + and correct winding signs? expr = "(x*sin(s)-w*y*(cos(s)-1))*r " params = dict( x=Q_([1, 0, 0], "mm"), From 959429dd510f971dd574776a1fc6f6dbe5440329 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Tue, 22 Feb 2022 16:58:59 +0100 Subject: [PATCH 41/70] Take advantage of xarray --- weldx/geometry.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index e58a8d401..bb21486d1 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1495,8 +1495,13 @@ def _get_lcs_from_coords_and_tangent( def _lcs_expr(self, position: float) -> tf.LocalCoordinateSystem: """Get a ``LocalCoordinateSystem`` at the passed rel. position (expression).""" - coords = self._series.evaluate(s=position * self._max_s).data.transpose()[0] - x = self._derivative.evaluate(s=position * self._max_s).data.m + coords = self._series.evaluate(s=position * self._max_s).data_array + x = self._derivative.evaluate(s=position * self._max_s).pint.dequantify() + if "dim_0" in x.dims: + coords = coords.rename({"s": "n"}) + x = x.rename({"dim_0": "n"}) + else: + coords = coords.isel(s=0) return self._get_lcs_from_coords_and_tangent(coords, x) def _lcs_disc(self, position: float) -> tf.LocalCoordinateSystem: From c5f1f0021468cc81254b2438e0d168de693f64d4 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Tue, 22 Feb 2022 17:53:31 +0100 Subject: [PATCH 42/70] Prepare array evaluation --- weldx/geometry.py | 25 ++++++++++++++----------- 1 file changed, 14 insertions(+), 11 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index bb21486d1..2db160173 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1482,32 +1482,35 @@ def _get_lcs_from_coords_and_tangent( self, coords: pint.Quantity, tangent: np.ndarray ) -> tf.LocalCoordinateSystem: """Create a ``LocalCoordinateSystem`` from coordinates and tangent vector.""" + coords = coords.rename({"s": "n"}) + if coords.coords["n"].size == 1: + coords = coords.isel(n=0) + x = tangent - z = [0, 0, 1] + z = np.array([0, 0, 1]) y = np.cross(z, x) if self._limit_orientation: - x = None + x = np.cross(y, z) else: - z = None + z = np.cross(x, y) + + orient = np.array([x, y, z]).transpose() - return tf.LocalCoordinateSystem.from_axis_vectors(x, y, z, coords) + return tf.LocalCoordinateSystem(orient, coords) def _lcs_expr(self, position: float) -> tf.LocalCoordinateSystem: """Get a ``LocalCoordinateSystem`` at the passed rel. position (expression).""" coords = self._series.evaluate(s=position * self._max_s).data_array - x = self._derivative.evaluate(s=position * self._max_s).pint.dequantify() - if "dim_0" in x.dims: - coords = coords.rename({"s": "n"}) - x = x.rename({"dim_0": "n"}) - else: - coords = coords.isel(s=0) + x = self._derivative.evaluate(s=position * self._max_s).data.m + return self._get_lcs_from_coords_and_tangent(coords, x) def _lcs_disc(self, position: float) -> tf.LocalCoordinateSystem: """Get a ``LocalCoordinateSystem`` at the passed rel. position (discrete).""" - coords = self._series.evaluate(s=position).data[0] + coords = self._series.evaluate(s=position).data_array x = self._get_tangent_vec_discrete(position) + return self._get_lcs_from_coords_and_tangent(coords, x) @property From be8c99f52a212f25da7851e9a2818077cf7c55ab Mon Sep 17 00:00:00 2001 From: vhirtham Date: Wed, 23 Feb 2022 11:41:06 +0100 Subject: [PATCH 43/70] Segment array evaluation works for expressions --- weldx/geometry.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index 2db160173..7e834ad3d 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1487,7 +1487,7 @@ def _get_lcs_from_coords_and_tangent( coords = coords.isel(n=0) x = tangent - z = np.array([0, 0, 1]) + z = [0, 0, 1] if x.size == 3 else [[0, 0, 1] for _ in range(x.shape[0])] y = np.cross(z, x) if self._limit_orientation: @@ -1495,14 +1495,21 @@ def _get_lcs_from_coords_and_tangent( else: z = np.cross(x, y) - orient = np.array([x, y, z]).transpose() + if x.size == 3: + orient = np.array([x, y, z]).transpose() + else: + orient = DataArray( + np.array([x, y, z]), + dims=["v", "n", "c"], + coords={"c": ["x", "y", "z"], "v": [0, 1, 2], "n": coords.coords["n"]}, + ) return tf.LocalCoordinateSystem(orient, coords) def _lcs_expr(self, position: float) -> tf.LocalCoordinateSystem: """Get a ``LocalCoordinateSystem`` at the passed rel. position (expression).""" coords = self._series.evaluate(s=position * self._max_s).data_array - x = self._derivative.evaluate(s=position * self._max_s).data.m + x = self._derivative.evaluate(s=position * self._max_s).data.m.transpose() return self._get_lcs_from_coords_and_tangent(coords, x) From f3da1ab55c0777e79978de72d7d5a1e4bf899290 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Wed, 23 Feb 2022 17:02:30 +0100 Subject: [PATCH 44/70] Add full array support for segment lcs --- weldx/core.py | 22 ++++++++++++---------- weldx/geometry.py | 15 ++++++++------- 2 files changed, 20 insertions(+), 17 deletions(-) diff --git a/weldx/core.py b/weldx/core.py index da6a39bff..a81405afa 100644 --- a/weldx/core.py +++ b/weldx/core.py @@ -1559,14 +1559,16 @@ def interp_like( class SpatialSeries(GenericSeries): """Describes a line in 3d space depending on the positional coordinate ``s``.""" - _allowed_variables: list[str] = ["s"] - """Allowed variable names""" - _required_variables: list[str] = ["s"] + _parameter_name = "s" + + # _allowed_variables: list[str] = ["s"] + # """Allowed variable names""" + _required_variables: list[str] = [_parameter_name] """Required variable names""" - _required_dimensions: list[str] = ["s", "c"] + _required_dimensions: list[str] = [_parameter_name, "c"] """Required dimensions""" - _required_dimension_units: dict[str, pint.Unit] = {"s": ""} + _required_dimension_units: dict[str, pint.Unit] = {_parameter_name: ""} """Required units of a dimension""" _required_dimension_coordinates: dict[str, list] = {"c": ["x", "y", "z"]} """Required coordinates of a dimension.""" @@ -1596,22 +1598,22 @@ def _process_quantity( ) -> xr.DataArray: """Turn a quantity into a a correctly formatted data array.""" if isinstance(coords, dict): - s = coords["s"] + s = coords[SpatialSeries._parameter_name] else: s = coords - coords = dict(s=s) + coords = {SpatialSeries._parameter_name: s} if not isinstance(s, xr.DataArray): if not isinstance(s, Q_): s = Q_(s, "") - s = xr.DataArray(s, dims=["s"]).pint.dequantify() - coords["s"] = s + s = xr.DataArray(s, dims=[SpatialSeries._parameter_name]).pint.dequantify() + coords[SpatialSeries._parameter_name] = s if "c" not in coords: coords["c"] = ["x", "y", "z"] if dims is None: - dims = ["s", "c"] + dims = [SpatialSeries._parameter_name, "c"] return xr.DataArray(obj, dims=dims, coords=coords) diff --git a/weldx/geometry.py b/weldx/geometry.py index 7e834ad3d..998dd499e 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1482,9 +1482,8 @@ def _get_lcs_from_coords_and_tangent( self, coords: pint.Quantity, tangent: np.ndarray ) -> tf.LocalCoordinateSystem: """Create a ``LocalCoordinateSystem`` from coordinates and tangent vector.""" - coords = coords.rename({"s": "n"}) - if coords.coords["n"].size == 1: - coords = coords.isel(n=0) + if coords.coords["s"].size == 1: + coords = coords.isel(s=0) x = tangent z = [0, 0, 1] if x.size == 3 else [[0, 0, 1] for _ in range(x.shape[0])] @@ -1500,8 +1499,8 @@ def _get_lcs_from_coords_and_tangent( else: orient = DataArray( np.array([x, y, z]), - dims=["v", "n", "c"], - coords={"c": ["x", "y", "z"], "v": [0, 1, 2], "n": coords.coords["n"]}, + dims=["v", "s", "c"], + coords={"c": ["x", "y", "z"], "v": [0, 1, 2], "s": coords.coords["s"]}, ) return tf.LocalCoordinateSystem(orient, coords) @@ -1516,8 +1515,10 @@ def _lcs_expr(self, position: float) -> tf.LocalCoordinateSystem: def _lcs_disc(self, position: float) -> tf.LocalCoordinateSystem: """Get a ``LocalCoordinateSystem`` at the passed rel. position (discrete).""" coords = self._series.evaluate(s=position).data_array - x = self._get_tangent_vec_discrete(position) - + if coords.coords["s"].size == 1: + x = self._get_tangent_vec_discrete(position) + else: + x = np.array([self._get_tangent_vec_discrete(p) for p in position]) return self._get_lcs_from_coords_and_tangent(coords, x) @property From 64712ecc7b1b389c0c3ab7702dde7e9b620bc521 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Wed, 23 Feb 2022 17:07:08 +0100 Subject: [PATCH 45/70] Fix deepsource issues --- weldx/core.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/weldx/core.py b/weldx/core.py index a81405afa..7c56ae8e3 100644 --- a/weldx/core.py +++ b/weldx/core.py @@ -1561,8 +1561,6 @@ class SpatialSeries(GenericSeries): _parameter_name = "s" - # _allowed_variables: list[str] = ["s"] - # """Allowed variable names""" _required_variables: list[str] = [_parameter_name] """Required variable names""" From 1fdce4629cb20795fed3530add77c7ef59cee116 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Wed, 23 Feb 2022 17:09:18 +0100 Subject: [PATCH 46/70] Update changelog --- CHANGELOG.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index c542b6a8f..0a1bd00e0 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -9,6 +9,8 @@ added ===== +- `SpatialSeries` and `DynamicTraceSegment` [:pull:`696`] + - first draft of the ``multi_pass_weld`` schema for WelDX files [:pull:`667`] - add `GenericSeries` as base class supporting arrays and equations [:pull:`618`] From ae9db90086f7d0eb351e48031b51e18222175bdb Mon Sep 17 00:00:00 2001 From: vhirtham Date: Wed, 23 Feb 2022 17:10:12 +0100 Subject: [PATCH 47/70] Fix error in changelog --- CHANGELOG.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 0a1bd00e0..77fa389f0 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -9,7 +9,7 @@ added ===== -- `SpatialSeries` and `DynamicTraceSegment` [:pull:`696`] +- `SpatialSeries` and `DynamicTraceSegment` [:pull:`699`] - first draft of the ``multi_pass_weld`` schema for WelDX files [:pull:`667`] From 88e7de9d1230c2b59b3b5d94895f75626f051911 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Thu, 24 Feb 2022 11:42:19 +0100 Subject: [PATCH 48/70] Add function to calculate the length along the segment --- weldx/geometry.py | 33 ++++++++++++++++++++++++++++----- 1 file changed, 28 insertions(+), 5 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index 998dd499e..cfc433128 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1422,9 +1422,11 @@ def __init__( if series.is_expression: self._derivative = self._get_derivative_expression() - self._length = self._len_expr() + self._primitive = self._get_primitive() + self._length = self.get_section_length(self._max_s) else: self._derivative = None + self._primitive = None self._length = self._len_disc() def _get_component_derivative_squared(self, i: int): @@ -1464,13 +1466,34 @@ def _get_tangent_vec_discrete(self, position: float) -> np.ndarray: vals = self._series.evaluate(s=[coords_s[idx_low], coords_s[idx_low + 1]]).data return (vals[1] - vals[0]).m - def _len_expr(self) -> pint.Quantity: - """Get the length of an expression based segment.""" + def _get_primitive(self) -> MathematicalExpression: + """Get the primitive of a the trace function if it is expression based.""" der_sq = [self._get_component_derivative_squared(i) for i in range(3)] expr = sympy.sqrt(der_sq[0] + der_sq[1] + der_sq[2]) - mag = float(sympy.integrate(expr, ("s", 0, self._max_s)).evalf()) + smax, unit = sympy.symbols("smax, unit") + primitive = sympy.integrate(expr, ("s", 0, smax)) * unit + params = dict(unit=Q_(1, Q_("1mm").to_base_units().u).to(_DEFAULT_LEN_UNIT)) - return Q_(mag, Q_(1, "mm").to_base_units().u).to(_DEFAULT_LEN_UNIT) + return MathematicalExpression(primitive, params) + + def get_section_length(self, s: float) -> pint.Quantity: + """Get the length from the start of the segment to the passed value of ``s``. + + Parameters + ---------- + s: + The value of the relative coordinate `s`. + + Returns + ------- + pint.Quantity: + The length at the specified value. + + """ + if self._series.is_expression: + return self._primitive.evaluate(smax=s).data + else: + raise NotImplementedError def _len_disc(self) -> pint.Quantity: """Get the length of a segment based on discrete values.""" From 57314892eb273abbd62e4d03dd0499ab396706bd Mon Sep 17 00:00:00 2001 From: vhirtham Date: Thu, 24 Feb 2022 13:13:36 +0100 Subject: [PATCH 49/70] Rename variables and functions --- weldx/geometry.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index cfc433128..7d77c072b 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1422,11 +1422,11 @@ def __init__( if series.is_expression: self._derivative = self._get_derivative_expression() - self._primitive = self._get_primitive() + self._length_expr = self._get_length_expr() self._length = self.get_section_length(self._max_s) else: self._derivative = None - self._primitive = None + self._length_expr = None self._length = self._len_disc() def _get_component_derivative_squared(self, i: int): @@ -1466,7 +1466,7 @@ def _get_tangent_vec_discrete(self, position: float) -> np.ndarray: vals = self._series.evaluate(s=[coords_s[idx_low], coords_s[idx_low + 1]]).data return (vals[1] - vals[0]).m - def _get_primitive(self) -> MathematicalExpression: + def _get_length_expr(self) -> MathematicalExpression: """Get the primitive of a the trace function if it is expression based.""" der_sq = [self._get_component_derivative_squared(i) for i in range(3)] expr = sympy.sqrt(der_sq[0] + der_sq[1] + der_sq[2]) @@ -1491,7 +1491,7 @@ def get_section_length(self, s: float) -> pint.Quantity: """ if self._series.is_expression: - return self._primitive.evaluate(smax=s).data + return self._length_expr.evaluate(smax=s).data else: raise NotImplementedError From a5d1089fbb9c5fe1e60f987244dcfcc8b991220f Mon Sep 17 00:00:00 2001 From: vhirtham Date: Thu, 24 Feb 2022 15:49:58 +0100 Subject: [PATCH 50/70] Replace obsolete function --- weldx/geometry.py | 24 +++++++++++++++++++++--- 1 file changed, 21 insertions(+), 3 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index 7d77c072b..808184d97 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1423,11 +1423,11 @@ def __init__( if series.is_expression: self._derivative = self._get_derivative_expression() self._length_expr = self._get_length_expr() - self._length = self.get_section_length(self._max_s) else: self._derivative = None self._length_expr = None - self._length = self._len_disc() + + self._length = self.get_section_length(self._max_s) def _get_component_derivative_squared(self, i: int): """Get the derivative of an expression for the i-th vector component.""" @@ -1493,7 +1493,7 @@ def get_section_length(self, s: float) -> pint.Quantity: if self._series.is_expression: return self._length_expr.evaluate(smax=s).data else: - raise NotImplementedError + return self._len_section_disc(s=s) def _len_disc(self) -> pint.Quantity: """Get the length of a segment based on discrete values.""" @@ -1501,6 +1501,24 @@ def _len_disc(self) -> pint.Quantity: length = np.sum(np.linalg.norm(diff.m, axis=1)) return Q_(length, diff.u) + def _len_section_disc(self, s: float) -> pint.Quantity: + """Get the length until a specific value of ``s`` (discrete version).""" + if s >= self._max_s: + diff = self._series.data[1:] - self._series.data[:-1] + else: + coords = self._series.coordinates["s"].data + idx_s_upper = np.abs(coords - s).argmin() + if coords[idx_s_upper] < s: + idx_s_upper = idx_s_upper + 1 + + s_eval = np.append(coords[:idx_s_upper], s) + vecs = self._series.evaluate(s=s_eval).data + + diff = vecs[1:] - vecs[:-1] + + length = np.sum(np.linalg.norm(diff.m, axis=1)) + return Q_(length, diff.u) + def _get_lcs_from_coords_and_tangent( self, coords: pint.Quantity, tangent: np.ndarray ) -> tf.LocalCoordinateSystem: From 7cb530dec9a3ac9b5f96cd5a3870e056063f4a11 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Thu, 24 Feb 2022 16:35:12 +0100 Subject: [PATCH 51/70] Try fix doc --- weldx/geometry.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index 808184d97..965559081 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1568,7 +1568,7 @@ def length(self) -> pint.Quantity: return self._length def local_coordinate_system(self, position: float) -> tf.LocalCoordinateSystem: - """Calculate a `tf.LocalCoordinateSystem` at a position of the trace segment. + """Calculate a local coordinate system at a position of the trace segment. Parameters ---------- @@ -1578,7 +1578,7 @@ def local_coordinate_system(self, position: float) -> tf.LocalCoordinateSystem: Returns ------- - tf.LocalCoordinateSystem: + weldx.transformations.LocalCoordinateSystem: The coordinate system and the specified position. """ From ecc712b32c3572d30538aff4326e281851e2db00 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Thu, 24 Feb 2022 17:10:26 +0100 Subject: [PATCH 52/70] Try fix docs --- weldx/__init__.py | 4 +++- weldx/core.py | 2 +- weldx/geometry.py | 4 ++-- 3 files changed, 6 insertions(+), 4 deletions(-) diff --git a/weldx/__init__.py b/weldx/__init__.py index a1839b856..18169db99 100644 --- a/weldx/__init__.py +++ b/weldx/__init__.py @@ -140,7 +140,7 @@ # class imports to weldx namespace from weldx.config import Config -from weldx.core import GenericSeries, MathematicalExpression, TimeSeries +from weldx.core import GenericSeries, MathematicalExpression, TimeSeries, SpatialSeries from weldx.geometry import ( ArcSegment, Geometry, @@ -150,6 +150,7 @@ Shape, Trace, SpatialData, + DynamicTraceSegment, ) from weldx.transformations import ( CoordinateSystemManager, @@ -190,6 +191,7 @@ "util", "welding", "TimeSeries", + "DynamicTraceSegment", "LinearHorizontalTraceSegment", "Config", "Time", diff --git a/weldx/core.py b/weldx/core.py index 7c56ae8e3..10f0c62df 100644 --- a/weldx/core.py +++ b/weldx/core.py @@ -24,7 +24,7 @@ from weldx.types import UnitLike -__all__ = ["GenericSeries", "MathematicalExpression", "TimeSeries"] +__all__ = ["GenericSeries", "MathematicalExpression", "TimeSeries", "SpatialSeries"] _me_parameter_types = Union[pint.Quantity, str, Tuple[pint.Quantity, str], xr.DataArray] diff --git a/weldx/geometry.py b/weldx/geometry.py index 965559081..866859f3c 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1549,9 +1549,9 @@ def _get_lcs_from_coords_and_tangent( def _lcs_expr(self, position: float) -> tf.LocalCoordinateSystem: """Get a ``LocalCoordinateSystem`` at the passed rel. position (expression).""" coords = self._series.evaluate(s=position * self._max_s).data_array - x = self._derivative.evaluate(s=position * self._max_s).data.m.transpose() + x = self._derivative.evaluate(s=position * self._max_s).transpose(..., "c") - return self._get_lcs_from_coords_and_tangent(coords, x) + return self._get_lcs_from_coords_and_tangent(coords, x.data.m) def _lcs_disc(self, position: float) -> tf.LocalCoordinateSystem: """Get a ``LocalCoordinateSystem`` at the passed rel. position (discrete).""" From 149b70528987499227a8dcbdd194f726c3191bb4 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Thu, 24 Feb 2022 17:25:16 +0100 Subject: [PATCH 53/70] Fix a doc error --- weldx/geometry.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index 866859f3c..40e1c7a2e 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1502,7 +1502,7 @@ def _len_disc(self) -> pint.Quantity: return Q_(length, diff.u) def _len_section_disc(self, s: float) -> pint.Quantity: - """Get the length until a specific value of ``s`` (discrete version).""" + """Get the length until a specific position on the trace (discrete version).""" if s >= self._max_s: diff = self._series.data[1:] - self._series.data[:-1] else: From edc826aabc3244461aff7524096f080d27230fbc Mon Sep 17 00:00:00 2001 From: vhirtham Date: Fri, 25 Feb 2022 10:25:24 +0100 Subject: [PATCH 54/70] Try more fixes --- weldx/geometry.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index 40e1c7a2e..83a261acf 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1398,20 +1398,21 @@ def __init__( Parameters ---------- series: - A `SpatialSeries` that describes the trajectory of the trace segment. - Alternatively, one can pass every other object that is valid as first - argument to `SpatialSeries.__init__`. + A `~weldx.core.SpatialSeries` that describes the trajectory of the trace + segment. Alternatively, one can pass every other object that is valid as + first argument to of the ``__init__`` method of the + `~weldx.core.SpatialSeries`. max_s: [only expression based `SpatialSeries`] The maximum value of the passed - series `s` parameter. The value defines the segments length by evaluating - the expression on the interval [0, `max_s`] + series ``s`` parameter. The value defines the segments length by evaluating + the expression on the interval [0, ``max_s``] limit_orientation_to_xy: If `True`, the orientation vectors of the coordinate systems along the trace are confined to the xy-plane. kwargs: A set of keyword arguments that will be forwarded to the ``__init__`` method - of the `SpatialSeries` in case the ``series`` parameter isn't already a - `SpatialSeries`. + of the `~weldx.core.SpatialSeries` in case the ``series`` parameter isn't + already a `~weldx.core.SpatialSeries`. """ if not isinstance(series, SpatialSeries): series = SpatialSeries(series, **kwargs) @@ -1444,7 +1445,7 @@ def _get_component(v, i): return me.expression.subs(subs).diff("s") ** 2 def _get_derivative_expression(self) -> MathematicalExpression: - """Get the derivative of an expression as 'MathematicalExpression'.""" + """Get the derivative of an expression as `MathematicalExpression`.""" expr = MathematicalExpression(self._series.data.expression.diff("s")) # parameters might not be present anymore in the derived expression @@ -1482,7 +1483,7 @@ def get_section_length(self, s: float) -> pint.Quantity: Parameters ---------- s: - The value of the relative coordinate `s`. + The value of the relative coordinate ``s``. Returns ------- From 6fc83682e4866b09fb776cfc97408bcad84da2d4 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Fri, 25 Feb 2022 10:50:35 +0100 Subject: [PATCH 55/70] Fix doc errors --- CHANGELOG.rst | 2 +- weldx/geometry.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index a07ce1882..a5ac7178b 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -9,7 +9,7 @@ added ===== -- `SpatialSeries` and `DynamicTraceSegment` [:pull:`699`] +- ``SpatialSeries`` and ``DynamicTraceSegment`` [:pull:`699`] - first draft of the ``multi_pass_weld`` schema for WelDX files [:pull:`667`] diff --git a/weldx/geometry.py b/weldx/geometry.py index 83a261acf..385814c24 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1403,9 +1403,9 @@ def __init__( first argument to of the ``__init__`` method of the `~weldx.core.SpatialSeries`. max_s: - [only expression based `SpatialSeries`] The maximum value of the passed - series ``s`` parameter. The value defines the segments length by evaluating - the expression on the interval [0, ``max_s``] + [only expression based `~weldx.core.SpatialSeries`] The maximum value of + the passed series ``s`` parameter. The value defines the segments length by + evaluating the expression on the interval [0, ``max_s``] limit_orientation_to_xy: If `True`, the orientation vectors of the coordinate systems along the trace are confined to the xy-plane. From 2309d166c5f25d23b2796902dcb2f2207da33497 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Fri, 25 Feb 2022 11:43:31 +0100 Subject: [PATCH 56/70] Silence deepsource issues --- weldx/__init__.py | 20 ++++++++++++-------- weldx/geometry.py | 3 +-- 2 files changed, 13 insertions(+), 10 deletions(-) diff --git a/weldx/__init__.py b/weldx/__init__.py index 18169db99..f7ac14619 100644 --- a/weldx/__init__.py +++ b/weldx/__init__.py @@ -130,16 +130,20 @@ from weldx.constants import Q_, U_ # main modules -import weldx.time +import weldx.time # skipcq: PY-W2000 + +# skipcq: PY-W2000 import weldx.util # import this second to avoid circular dependencies -import weldx.core -import weldx.transformations +import weldx.core # skipcq: PY-W2000 +import weldx.transformations # skipcq: PY-W2000 import weldx.config -import weldx.geometry -import weldx.welding +import weldx.geometry # skipcq: PY-W2000 +import weldx.welding # skipcq: PY-W2000 # class imports to weldx namespace from weldx.config import Config + +# skipcq: PY-W2000 from weldx.core import GenericSeries, MathematicalExpression, TimeSeries, SpatialSeries from weldx.geometry import ( ArcSegment, @@ -152,7 +156,7 @@ SpatialData, DynamicTraceSegment, ) -from weldx.transformations import ( +from weldx.transformations import ( # skipcq: PY-W2000 CoordinateSystemManager, LocalCoordinateSystem, WXRotation, @@ -162,10 +166,10 @@ from weldx.time import Time # tags (this will partially import weldx.asdf but not the extension) -from weldx import tags +from weldx import tags # skipcq: PY-W2000 # asdf extensions -import weldx.asdf +import weldx.asdf # skipcq: PY-W2000 from weldx.asdf.file import WeldxFile __all__ = ( diff --git a/weldx/geometry.py b/weldx/geometry.py index 385814c24..6108f879b 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1493,8 +1493,7 @@ def get_section_length(self, s: float) -> pint.Quantity: """ if self._series.is_expression: return self._length_expr.evaluate(smax=s).data - else: - return self._len_section_disc(s=s) + return self._len_section_disc(s=s) def _len_disc(self) -> pint.Quantity: """Get the length of a segment based on discrete values.""" From 3a0b3e77e788f9926c80ecf6e280193931da1917 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Fri, 25 Feb 2022 11:55:57 +0100 Subject: [PATCH 57/70] Test Sphinx fix --- CHANGELOG.rst | 2 +- weldx/__init__.py | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index a5ac7178b..a07ce1882 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -9,7 +9,7 @@ added ===== -- ``SpatialSeries`` and ``DynamicTraceSegment`` [:pull:`699`] +- `SpatialSeries` and `DynamicTraceSegment` [:pull:`699`] - first draft of the ``multi_pass_weld`` schema for WelDX files [:pull:`667`] diff --git a/weldx/__init__.py b/weldx/__init__.py index f7ac14619..103881086 100644 --- a/weldx/__init__.py +++ b/weldx/__init__.py @@ -50,6 +50,7 @@ Time TimeSeries GenericSeries + SpatialSeries MathematicalExpression CoordinateSystemManager LocalCoordinateSystem @@ -72,6 +73,7 @@ Shape Trace SpatialData + DynamicTraceSegment **Full API Reference** From 7296d570e1b7d0d2fa3425844d3f17e52aa69dc2 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Fri, 25 Feb 2022 12:13:33 +0100 Subject: [PATCH 58/70] Test undoing some previous fixes --- weldx/geometry.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index 6108f879b..e1e9ce6c0 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1398,12 +1398,11 @@ def __init__( Parameters ---------- series: - A `~weldx.core.SpatialSeries` that describes the trajectory of the trace - segment. Alternatively, one can pass every other object that is valid as - first argument to of the ``__init__`` method of the - `~weldx.core.SpatialSeries`. + A `SpatialSeries` that describes the trajectory of the trace segment. + Alternatively, one can pass every other object that is valid as + first argument to of the ``__init__`` method of the `SpatialSeries`. max_s: - [only expression based `~weldx.core.SpatialSeries`] The maximum value of + [only expression based `SpatialSeries`] The maximum value of the passed series ``s`` parameter. The value defines the segments length by evaluating the expression on the interval [0, ``max_s``] limit_orientation_to_xy: @@ -1411,8 +1410,8 @@ def __init__( are confined to the xy-plane. kwargs: A set of keyword arguments that will be forwarded to the ``__init__`` method - of the `~weldx.core.SpatialSeries` in case the ``series`` parameter isn't - already a `~weldx.core.SpatialSeries`. + of the `SpatialSeries` in case the ``series`` parameter isn't + already a `SpatialSeries`. """ if not isinstance(series, SpatialSeries): series = SpatialSeries(series, **kwargs) From de3828e99254e05a0b7e79b1b5a0887bf6e480d5 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Fri, 25 Feb 2022 12:26:43 +0100 Subject: [PATCH 59/70] Undo changes --- weldx/geometry.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index e1e9ce6c0..6108f879b 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1398,11 +1398,12 @@ def __init__( Parameters ---------- series: - A `SpatialSeries` that describes the trajectory of the trace segment. - Alternatively, one can pass every other object that is valid as - first argument to of the ``__init__`` method of the `SpatialSeries`. + A `~weldx.core.SpatialSeries` that describes the trajectory of the trace + segment. Alternatively, one can pass every other object that is valid as + first argument to of the ``__init__`` method of the + `~weldx.core.SpatialSeries`. max_s: - [only expression based `SpatialSeries`] The maximum value of + [only expression based `~weldx.core.SpatialSeries`] The maximum value of the passed series ``s`` parameter. The value defines the segments length by evaluating the expression on the interval [0, ``max_s``] limit_orientation_to_xy: @@ -1410,8 +1411,8 @@ def __init__( are confined to the xy-plane. kwargs: A set of keyword arguments that will be forwarded to the ``__init__`` method - of the `SpatialSeries` in case the ``series`` parameter isn't - already a `SpatialSeries`. + of the `~weldx.core.SpatialSeries` in case the ``series`` parameter isn't + already a `~weldx.core.SpatialSeries`. """ if not isinstance(series, SpatialSeries): series = SpatialSeries(series, **kwargs) From 305733035752dd79d2621de8b392abe910b1773b Mon Sep 17 00:00:00 2001 From: Cagtay Fabry <43667554+CagtayFabry@users.noreply.github.com> Date: Fri, 25 Feb 2022 13:04:53 +0100 Subject: [PATCH 60/70] assign array coords before calling evaluate --- weldx/core.py | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/weldx/core.py b/weldx/core.py index 10f0c62df..e868ce212 100644 --- a/weldx/core.py +++ b/weldx/core.py @@ -1252,15 +1252,14 @@ def _evaluate_preprocessor(self, **kwargs) -> list[SeriesParameter]: def _evaluate_expr(self, coords: list[SeriesParameter]) -> GenericSeries: """Evaluate the expression at the passed coordinates.""" if len(coords) == self._obj.num_variables: - eval_args = {v.symbol: v.data_array for v in coords} - data = self._obj.evaluate(**eval_args) - - # TODO: Discuss - This might be done before by assigning coords to the - # eval_args that go into the math expression. Might need tweaks in - # `SeriesParameter` - for k, v in eval_args.items(): - data = data.assign_coords({k: v.pint.dequantify()}) - return self.__class__(data) + eval_args = { + v.symbol: v.data_array.assign_coords( + {v.dim: v.data_array.pint.dequantify()} + ) + for v in coords + } + da = self._obj.evaluate(**eval_args) + return self.__class__(da) # turn passed coords into parameters of the expression new_series = deepcopy(self) From 6f577ec33998529b8ff470f0eeac41626c18cb7c Mon Sep 17 00:00:00 2001 From: vhirtham Date: Mon, 28 Feb 2022 09:05:58 +0100 Subject: [PATCH 61/70] Add type hint --- weldx/geometry.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index 6108f879b..ef8755f8e 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1430,7 +1430,7 @@ def __init__( self._length = self.get_section_length(self._max_s) - def _get_component_derivative_squared(self, i: int): + def _get_component_derivative_squared(self, i: int) -> sympy.Expr: """Get the derivative of an expression for the i-th vector component.""" def _get_component(v, i): From 8c5708955286f5802680d935601cae2057fe91d0 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Mon, 28 Feb 2022 09:41:27 +0100 Subject: [PATCH 62/70] Update weldx/geometry.py Co-authored-by: Cagtay Fabry <43667554+CagtayFabry@users.noreply.github.com> --- weldx/geometry.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index ef8755f8e..7786dee0f 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1595,7 +1595,9 @@ class LinearHorizontalTraceSegment(DynamicTraceSegment): @UREG.wraps(None, (None, _DEFAULT_LEN_UNIT), strict=True) def __init__(self, length: pint.Quantity): - """Construct linear horizontal trace segment. + """Construct linear horizontal trace segment of length `length` in `x`-direction. + + The trace will run between the points `[0, 0, 0]` and `[length, 0, 0]` Parameters ---------- From e83e3b0de8adfe28aaaa05e8ed017f0341119a4e Mon Sep 17 00:00:00 2001 From: vhirtham Date: Mon, 28 Feb 2022 09:57:18 +0100 Subject: [PATCH 63/70] Update weldx/core.py Co-authored-by: Cagtay Fabry <43667554+CagtayFabry@users.noreply.github.com> --- weldx/core.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/weldx/core.py b/weldx/core.py index e868ce212..b36a62fea 100644 --- a/weldx/core.py +++ b/weldx/core.py @@ -1587,18 +1587,19 @@ def __init__( parameters = self._process_parameters(parameters) super().__init__(obj, dims, coords, units, interpolation, parameters) - @staticmethod + @classmethod def _process_quantity( + cls, obj: Union[pint.Quantity, xr.DataArray, str, MathematicalExpression], dims: Union[list[str], dict[str, str]], coords: dict[str, pint.Quantity], ) -> xr.DataArray: """Turn a quantity into a a correctly formatted data array.""" if isinstance(coords, dict): - s = coords[SpatialSeries._parameter_name] + s = coords[cls._parameter_name] else: s = coords - coords = {SpatialSeries._parameter_name: s} + coords = {cls._parameter_name: s} if not isinstance(s, xr.DataArray): if not isinstance(s, Q_): From 2ee8051d4e07c243ffa7474759bce94ae1a2486d Mon Sep 17 00:00:00 2001 From: vhirtham Date: Mon, 28 Feb 2022 10:19:36 +0100 Subject: [PATCH 64/70] Run pre-commit --- weldx/geometry.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index 7786dee0f..8faf5d28e 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1596,7 +1596,7 @@ class LinearHorizontalTraceSegment(DynamicTraceSegment): @UREG.wraps(None, (None, _DEFAULT_LEN_UNIT), strict=True) def __init__(self, length: pint.Quantity): """Construct linear horizontal trace segment of length `length` in `x`-direction. - + The trace will run between the points `[0, 0, 0]` and `[length, 0, 0]` Parameters From a92fc5ad36fa0ebac77d70b2e331a1c4d5ef3911 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Mon, 28 Feb 2022 10:37:09 +0100 Subject: [PATCH 65/70] Fix docs --- weldx/geometry.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index 8faf5d28e..c49573807 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1595,9 +1595,9 @@ class LinearHorizontalTraceSegment(DynamicTraceSegment): @UREG.wraps(None, (None, _DEFAULT_LEN_UNIT), strict=True) def __init__(self, length: pint.Quantity): - """Construct linear horizontal trace segment of length `length` in `x`-direction. + """Construct linear trace segment of length ``length`` in ``x``-direction. - The trace will run between the points `[0, 0, 0]` and `[length, 0, 0]` + The trace will run between the points ``[0, 0, 0]`` and ``[length, 0, 0]`` Parameters ---------- From 2c99112bc551eac1b5e104cd747f1c9e93aca61e Mon Sep 17 00:00:00 2001 From: vhirtham Date: Mon, 28 Feb 2022 11:32:26 +0100 Subject: [PATCH 66/70] Rename internal variable and add property --- weldx/core.py | 23 ++++++++++++++--------- 1 file changed, 14 insertions(+), 9 deletions(-) diff --git a/weldx/core.py b/weldx/core.py index b36a62fea..1d6f26822 100644 --- a/weldx/core.py +++ b/weldx/core.py @@ -1558,14 +1558,14 @@ def interp_like( class SpatialSeries(GenericSeries): """Describes a line in 3d space depending on the positional coordinate ``s``.""" - _parameter_name = "s" + _position_dim_name = "s" - _required_variables: list[str] = [_parameter_name] + _required_variables: list[str] = [_position_dim_name] """Required variable names""" - _required_dimensions: list[str] = [_parameter_name, "c"] + _required_dimensions: list[str] = [_position_dim_name, "c"] """Required dimensions""" - _required_dimension_units: dict[str, pint.Unit] = {_parameter_name: ""} + _required_dimension_units: dict[str, pint.Unit] = {_position_dim_name: ""} """Required units of a dimension""" _required_dimension_coordinates: dict[str, list] = {"c": ["x", "y", "z"]} """Required coordinates of a dimension.""" @@ -1596,22 +1596,22 @@ def _process_quantity( ) -> xr.DataArray: """Turn a quantity into a a correctly formatted data array.""" if isinstance(coords, dict): - s = coords[cls._parameter_name] + s = coords[cls._position_dim_name] else: s = coords - coords = {cls._parameter_name: s} + coords = {cls._position_dim_name: s} if not isinstance(s, xr.DataArray): if not isinstance(s, Q_): s = Q_(s, "") - s = xr.DataArray(s, dims=[SpatialSeries._parameter_name]).pint.dequantify() - coords[SpatialSeries._parameter_name] = s + s = xr.DataArray(s, dims=[cls._position_dim_name]).pint.dequantify() + coords[cls._position_dim_name] = s if "c" not in coords: coords["c"] = ["x", "y", "z"] if dims is None: - dims = [SpatialSeries._parameter_name, "c"] + dims = [cls._position_dim_name, "c"] return xr.DataArray(obj, dims=dims, coords=coords) @@ -1622,3 +1622,8 @@ def _process_parameters(params): if isinstance(v, Q_) and v.size == 3: params[k] = xr.DataArray(v, dims=["c"], coords=dict(c=["x", "y", "z"])) return params + + @property + def position_dim_name(self): + """Return the name of the dimension that determines the position on the line.""" + return self._position_dim_name From 92644b4d4cb86fb95377de51f9689a7df894f569 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Mon, 28 Feb 2022 11:49:25 +0100 Subject: [PATCH 67/70] Generalize variable name in DynamicTraceSegment --- weldx/geometry.py | 28 +++++++++++++++++----------- 1 file changed, 17 insertions(+), 11 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index c49573807..f291a7923 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1442,11 +1442,13 @@ def _get_component(v, i): me = self._series.data subs = [(k, _get_component(v.data, i)) for k, v in me.parameters.items()] - return me.expression.subs(subs).diff("s") ** 2 + return me.expression.subs(subs).diff(self._series.position_dim_name) ** 2 def _get_derivative_expression(self) -> MathematicalExpression: """Get the derivative of an expression as `MathematicalExpression`.""" - expr = MathematicalExpression(self._series.data.expression.diff("s")) + expr = MathematicalExpression( + self._series.data.expression.diff(self._series.position_dim_name) + ) # parameters might not be present anymore in the derived expression params = { @@ -1460,7 +1462,7 @@ def _get_derivative_expression(self) -> MathematicalExpression: def _get_tangent_vec_discrete(self, position: float) -> np.ndarray: """Get the segments tangent vector at the given position (discrete case).""" - coords_s = self._series.coordinates["s"].data + coords_s = self._series.coordinates[self._series.position_dim_name].data idx_low = np.abs(coords_s - position).argmin() if coords_s[idx_low] > position or idx_low + 1 == len(coords_s): idx_low -= 1 @@ -1471,8 +1473,8 @@ def _get_length_expr(self) -> MathematicalExpression: """Get the primitive of a the trace function if it is expression based.""" der_sq = [self._get_component_derivative_squared(i) for i in range(3)] expr = sympy.sqrt(der_sq[0] + der_sq[1] + der_sq[2]) - smax, unit = sympy.symbols("smax, unit") - primitive = sympy.integrate(expr, ("s", 0, smax)) * unit + s, u = sympy.symbols("smax, unit") + primitive = sympy.integrate(expr, (self._series.position_dim_name, 0, s)) * u params = dict(unit=Q_(1, Q_("1mm").to_base_units().u).to(_DEFAULT_LEN_UNIT)) return MathematicalExpression(primitive, params) @@ -1506,7 +1508,7 @@ def _len_section_disc(self, s: float) -> pint.Quantity: if s >= self._max_s: diff = self._series.data[1:] - self._series.data[:-1] else: - coords = self._series.coordinates["s"].data + coords = self._series.coordinates[self._series.position_dim_name].data idx_s_upper = np.abs(coords - s).argmin() if coords[idx_s_upper] < s: idx_s_upper = idx_s_upper + 1 @@ -1523,7 +1525,9 @@ def _get_lcs_from_coords_and_tangent( self, coords: pint.Quantity, tangent: np.ndarray ) -> tf.LocalCoordinateSystem: """Create a ``LocalCoordinateSystem`` from coordinates and tangent vector.""" - if coords.coords["s"].size == 1: + pdn = self._series.position_dim_name + + if coords.coords[pdn].size == 1: coords = coords.isel(s=0) x = tangent @@ -1540,8 +1544,8 @@ def _get_lcs_from_coords_and_tangent( else: orient = DataArray( np.array([x, y, z]), - dims=["v", "s", "c"], - coords={"c": ["x", "y", "z"], "v": [0, 1, 2], "s": coords.coords["s"]}, + dims=["v", pdn, "c"], + coords={"c": ["x", "y", "z"], "v": [0, 1, 2], pdn: coords.coords[pdn]}, ) return tf.LocalCoordinateSystem(orient, coords) @@ -1555,8 +1559,10 @@ def _lcs_expr(self, position: float) -> tf.LocalCoordinateSystem: def _lcs_disc(self, position: float) -> tf.LocalCoordinateSystem: """Get a ``LocalCoordinateSystem`` at the passed rel. position (discrete).""" - coords = self._series.evaluate(s=position).data_array - if coords.coords["s"].size == 1: + pdn = self._series.position_dim_name + + coords = self._series.evaluate(**{pdn: position}).data_array + if coords.coords[pdn].size == 1: x = self._get_tangent_vec_discrete(position) else: x = np.array([self._get_tangent_vec_discrete(p) for p in position]) From e314d7979da90c8c57afb79f20aa4afc7de331fb Mon Sep 17 00:00:00 2001 From: vhirtham Date: Mon, 28 Feb 2022 13:14:33 +0100 Subject: [PATCH 68/70] Rename all variables related to 's' --- weldx/geometry.py | 66 ++++++++++++++++++++++++++--------------------- 1 file changed, 36 insertions(+), 30 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index f291a7923..eba912045 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1389,7 +1389,7 @@ def __init__( series: Union[ SpatialSeries, pint.Quantity, DataArray, str, MathematicalExpression ], - max_s: float = 1, + max_coord: float = 1, limit_orientation_to_xy: bool = False, **kwargs, ): @@ -1402,10 +1402,11 @@ def __init__( segment. Alternatively, one can pass every other object that is valid as first argument to of the ``__init__`` method of the `~weldx.core.SpatialSeries`. - max_s: - [only expression based `~weldx.core.SpatialSeries`] The maximum value of - the passed series ``s`` parameter. The value defines the segments length by - evaluating the expression on the interval [0, ``max_s``] + max_coord: + [only expression based `~weldx.core.SpatialSeries`] The maximum coordinate + value of the passed series dimension that specifies the position on the 3d + line. The value defines the segments length by evaluating the expression on + the interval [0, ``max_coord``] limit_orientation_to_xy: If `True`, the orientation vectors of the coordinate systems along the trace are confined to the xy-plane. @@ -1418,7 +1419,7 @@ def __init__( series = SpatialSeries(series, **kwargs) self._series = series - self._max_s = max_s + self._max_coord = max_coord self._limit_orientation = limit_orientation_to_xy if series.is_expression: @@ -1428,7 +1429,7 @@ def __init__( self._derivative = None self._length_expr = None - self._length = self.get_section_length(self._max_s) + self._length = self.get_section_length(self._max_coord) def _get_component_derivative_squared(self, i: int) -> sympy.Expr: """Get the derivative of an expression for the i-th vector component.""" @@ -1462,30 +1463,30 @@ def _get_derivative_expression(self) -> MathematicalExpression: def _get_tangent_vec_discrete(self, position: float) -> np.ndarray: """Get the segments tangent vector at the given position (discrete case).""" - coords_s = self._series.coordinates[self._series.position_dim_name].data - idx_low = np.abs(coords_s - position).argmin() - if coords_s[idx_low] > position or idx_low + 1 == len(coords_s): + pos_data = self._series.coordinates[self._series.position_dim_name].data + idx_low = np.abs(pos_data - position).argmin() + if pos_data[idx_low] > position or idx_low + 1 == len(pos_data): idx_low -= 1 - vals = self._series.evaluate(s=[coords_s[idx_low], coords_s[idx_low + 1]]).data + vals = self._series.evaluate(s=[pos_data[idx_low], pos_data[idx_low + 1]]).data return (vals[1] - vals[0]).m def _get_length_expr(self) -> MathematicalExpression: """Get the primitive of a the trace function if it is expression based.""" der_sq = [self._get_component_derivative_squared(i) for i in range(3)] expr = sympy.sqrt(der_sq[0] + der_sq[1] + der_sq[2]) - s, u = sympy.symbols("smax, unit") - primitive = sympy.integrate(expr, (self._series.position_dim_name, 0, s)) * u + mc, u = sympy.symbols("max_coord, unit") + primitive = sympy.integrate(expr, (self._series.position_dim_name, 0, mc)) * u params = dict(unit=Q_(1, Q_("1mm").to_base_units().u).to(_DEFAULT_LEN_UNIT)) return MathematicalExpression(primitive, params) - def get_section_length(self, s: float) -> pint.Quantity: - """Get the length from the start of the segment to the passed value of ``s``. + def get_section_length(self, position: float) -> pint.Quantity: + """Get the length from the start of the segment to the passed relative position. Parameters ---------- - s: - The value of the relative coordinate ``s``. + position: + The value of the relative position coordinate. Returns ------- @@ -1494,27 +1495,29 @@ def get_section_length(self, s: float) -> pint.Quantity: """ if self._series.is_expression: - return self._length_expr.evaluate(smax=s).data - return self._len_section_disc(s=s) + return self._length_expr.evaluate(max_coord=position).data + return self._len_section_disc(position=position) + # todo: remove def _len_disc(self) -> pint.Quantity: """Get the length of a segment based on discrete values.""" diff = self._series.data[1:] - self._series.data[:-1] length = np.sum(np.linalg.norm(diff.m, axis=1)) return Q_(length, diff.u) - def _len_section_disc(self, s: float) -> pint.Quantity: + def _len_section_disc(self, position: float) -> pint.Quantity: """Get the length until a specific position on the trace (discrete version).""" - if s >= self._max_s: + if position >= self._max_coord: diff = self._series.data[1:] - self._series.data[:-1] else: - coords = self._series.coordinates[self._series.position_dim_name].data - idx_s_upper = np.abs(coords - s).argmin() - if coords[idx_s_upper] < s: - idx_s_upper = idx_s_upper + 1 + pdn = self._series.position_dim_name + coords = self._series.coordinates[pdn].data + idx_coord_upper = np.abs(coords - position).argmin() + if coords[idx_coord_upper] < position: + idx_coord_upper = idx_coord_upper + 1 - s_eval = np.append(coords[:idx_s_upper], s) - vecs = self._series.evaluate(s=s_eval).data + coords_eval = np.append(coords[:idx_coord_upper], position) + vecs = self._series.evaluate(**{pdn: coords_eval}).data diff = vecs[1:] - vecs[:-1] @@ -1552,8 +1555,11 @@ def _get_lcs_from_coords_and_tangent( def _lcs_expr(self, position: float) -> tf.LocalCoordinateSystem: """Get a ``LocalCoordinateSystem`` at the passed rel. position (expression).""" - coords = self._series.evaluate(s=position * self._max_s).data_array - x = self._derivative.evaluate(s=position * self._max_s).transpose(..., "c") + pdn = self._series.position_dim_name + eval_pos = {pdn: position * self._max_coord} + + coords = self._series.evaluate(**eval_pos).data_array + x = self._derivative.evaluate(**eval_pos).transpose(..., "c") return self._get_lcs_from_coords_and_tangent(coords, x.data.m) @@ -1667,7 +1673,7 @@ def __init__( r=self._radius, w=self._sign_winding, ) - super().__init__(expr, max_s=self._angle, parameters=params) + super().__init__(expr, max_coord=self._angle, parameters=params) def __repr__(self): """Output representation of a RadialHorizontalTraceSegment.""" From 684b59258d2bd8897adc3f65b084c42a1660be62 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Mon, 28 Feb 2022 13:15:07 +0100 Subject: [PATCH 69/70] Remove obsolete function --- weldx/geometry.py | 7 ------- 1 file changed, 7 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index eba912045..8dc5764ab 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1498,13 +1498,6 @@ def get_section_length(self, position: float) -> pint.Quantity: return self._length_expr.evaluate(max_coord=position).data return self._len_section_disc(position=position) - # todo: remove - def _len_disc(self) -> pint.Quantity: - """Get the length of a segment based on discrete values.""" - diff = self._series.data[1:] - self._series.data[:-1] - length = np.sum(np.linalg.norm(diff.m, axis=1)) - return Q_(length, diff.u) - def _len_section_disc(self, position: float) -> pint.Quantity: """Get the length until a specific position on the trace (discrete version).""" if position >= self._max_coord: From 7c3123ce86fad885bbb3927b085b78c6cce287e3 Mon Sep 17 00:00:00 2001 From: vhirtham Date: Mon, 28 Feb 2022 17:11:39 +0100 Subject: [PATCH 70/70] Fix todo --- weldx/geometry.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/weldx/geometry.py b/weldx/geometry.py index 8dc5764ab..4126893fc 100644 --- a/weldx/geometry.py +++ b/weldx/geometry.py @@ -1654,12 +1654,11 @@ def __init__( self._angle = float(angle) if clockwise: - self._sign_winding = -1 - else: self._sign_winding = 1 + else: + self._sign_winding = -1 - # todo change sign sign back to + and correct winding signs? - expr = "(x*sin(s)-w*y*(cos(s)-1))*r " + expr = "(x*sin(s)+w*y*(cos(s)-1))*r " params = dict( x=Q_([1, 0, 0], "mm"), y=Q_([0, 1, 0], "mm"), @@ -1692,7 +1691,7 @@ def radius(self) -> pint.Quantity: @property def is_clockwise(self) -> bool: """Get True, if the segments winding is clockwise, False otherwise.""" - return self._sign_winding < 0 + return self._sign_winding > 0 # Trace class -----------------------------------------------------------------