diff --git a/calstis/calstis_2d_ccd.html b/calstis/calstis_2d_ccd.html index 441fde6..10b9a1e 100644 --- a/calstis/calstis_2d_ccd.html +++ b/calstis/calstis_2d_ccd.html @@ -13125,8 +13125,6 @@

0.1 Import Necessary PackagesMAST archive
  • os, shutil, & pathlib for managing system paths
  • matplotlib for plotting data
  • -
  • numpy to handle array functions
  • -
  • pandas to make basic tables and dataframes
  • stistools for operations on STIS Data
  • @@ -13141,23 +13139,17 @@

    0.1 Import Necessary Packages
    # Import for: Reading in fits file
     from astropy.io import fits
     
    -# Import for: Downloading necessary files. (Not necessary if you choose to collect data from MAST)
    +# Import for: Downloading necessary files. 
    +# (Not necessary if you choose to collect data from MAST)
     from astroquery.mast import Observations
     
     # Import for: Managing system variables and paths
    -import os, shutil
    +import os
    +import shutil
     from pathlib import Path
     
     # Import for: Plotting and specifying plotting parameters
     import matplotlib.pyplot as plt
    -import matplotlib
    -import matplotlib.cm as cm
    -from IPython.display import display
    -from matplotlib import gridspec
    -
    -# Import for: Quick Calculation and Data Analysis
    -import numpy as np
    -import pandas as pd
     
     # Import for: Operations on STIS Data
     import stistools
    @@ -13176,11 +13168,9 @@ 

    0.1 Import Necessary Packages -
    -
    /Users/kding/miniconda3/envs/stis/lib/python3.7/site-packages/stsci/tools/nmpfit.py:8: UserWarning: NMPFIT is deprecated - stsci.tools v 3.5 is the last version to contain it.
    -  warnings.warn("NMPFIT is deprecated - stsci.tools v 3.5 is the last version to contain it.")
    -/Users/kding/miniconda3/envs/stis/lib/python3.7/site-packages/stsci/tools/gfit.py:18: UserWarning: GFIT is deprecated - stsci.tools v 3.4.12 is the last version to contain it.Use astropy.modeling instead.
    -  warnings.warn("GFIT is deprecated - stsci.tools v 3.4.12 is the last version to contain it."
    +
    +
    The following tasks in the stistools package can be run with TEAL:
    +   basic2d      calstis     ocrreject     wavecal        x1d          x2d
     
    @@ -13190,9 +13180,11 @@

    0.1 Import Necessary Packages -
    -
    The following tasks in the stistools package can be run with TEAL:
    -   basic2d      calstis     ocrreject     wavecal        x1d          x2d
    +
    +
    /Users/kding/miniconda3/envs/stis/lib/python3.7/site-packages/stsci/tools/nmpfit.py:10: UserWarning: NMPFIT is deprecated - stsci.tools v 3.5 is the last version to contain it.
    +  warnings.warn("NMPFIT is deprecated - stsci.tools v 3.5 is the last version to contain it.")
    +/Users/kding/miniconda3/envs/stis/lib/python3.7/site-packages/stsci/tools/gfit.py:20: UserWarning: GFIT is deprecated - stsci.tools v 3.4.12 is the last version to contain it.Use astropy.modeling instead.
    +  warnings.warn("GFIT is deprecated - stsci.tools v 3.4.12 is the last version to contain it."
     
    @@ -13219,7 +13211,7 @@

    0.2 Collect # get a list of files assiciated with that target CCD_list = Observations.get_product_list(target) # Download FITS files -Observations.download_products(CCD_list, extension='fits') +Observations.download_products(CCD_list, extension=['_raw.fits', '_wav.fits']) shutil.move('./mastDownload/HST/o5f301010/o5f301010_raw.fits', '.') shutil.move('./mastDownload/HST/o5f301010/o5f301010_wav.fits', '.')

    @@ -13238,20 +13230,8 @@

    0.2 Collect
    -
    Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_crj.fits to ./mastDownload/HST/o5f301010/o5f301010_crj.fits ... [Done]
    -Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_jif.fits to ./mastDownload/HST/o5f301010/o5f301010_jif.fits ... [Done]
    -Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_jit.fits to ./mastDownload/HST/o5f301010/o5f301010_jit.fits ... [Done]
    -Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_jwf.fits to ./mastDownload/HST/o5f301010/o5f301010_jwf.fits ... [Done]
    -Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_jwt.fits to ./mastDownload/HST/o5f301010/o5f301010_jwt.fits ... [Done]
    -Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_spt.fits to ./mastDownload/HST/o5f301010/o5f301010_spt.fits ... [Done]
    -Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_trl.fits to ./mastDownload/HST/o5f301010/o5f301010_trl.fits ... [Done]
    -Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_wav.fits to ./mastDownload/HST/o5f301010/o5f301010_wav.fits ... [Done]
    -Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_wsp.fits to ./mastDownload/HST/o5f301010/o5f301010_wsp.fits ... [Done]
    -Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_asn.fits to ./mastDownload/HST/o5f301010/o5f301010_asn.fits ... [Done]
    +
    Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_wav.fits to ./mastDownload/HST/o5f301010/o5f301010_wav.fits ... [Done]
     Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_raw.fits to ./mastDownload/HST/o5f301010/o5f301010_raw.fits ... [Done]
    -Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_flt.fits to ./mastDownload/HST/o5f301010/o5f301010_flt.fits ... [Done]
    -Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_sx1.fits to ./mastDownload/HST/o5f301010/o5f301010_sx1.fits ... [Done]
    -Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_sx2.fits to ./mastDownload/HST/o5f301010/o5f301010_sx2.fits ... [Done]
     
    @@ -13422,8 +13402,8 @@

    2 Define Functions
    def calibrate(switch):
         '''Set the specified calibration step to 'PERFORM' and run calstis.
     
    -    We assume that the order this function is called turns on the prior calibration
    -    switches appropriately.
    +    We assume that the order this function is called turns on the prior
    +    calibration switches appropriately.
     
         PARAMETERS
         ----------
    @@ -13480,12 +13460,12 @@ 

    2 Define Functions ex4 = hdu[4].data ex4_flat = ex4.ravel() - plt.subplot(1,2,1) + plt.subplot(1, 2, 1) img = plt.imshow(ex1, origin='lower', cmap=color, vmax=ran[1], vmin=ran[0]) plt.colorbar(img, fraction=0.046, pad=0.04) plt.title('extension 1') - plt.subplot(1,2,2) + plt.subplot(1, 2, 2) # adjust bins plt.hist(ex1_flat, bins=50, range=ran, histtype='step', label='extension 1') plt.hist(ex4_flat, bins=50, range=ran, histtype='step', label='extension 4') @@ -13495,10 +13475,10 @@

    2 Define Functions with fits.open(file) as hdu: ex1 = hdu[1].data ex1_flat = ex1.ravel() - plt.subplot(1,2,1) + plt.subplot(1, 2, 1) img = plt.imshow(ex1, origin='lower', cmap=color, vmax=ran[1], vmin=ran[0]) plt.colorbar(img, fraction=0.046, pad=0.04) - plt.subplot(1,2,2) + plt.subplot(1, 2, 2) plt.hist(ex1_flat, bins=100, range=ran, histtype='step') else: @@ -13526,7 +13506,7 @@

    2 Define Functions
    In [7]:
    -
    img_hist(raw,[1200,1500])
    +
    img_hist(raw, [1200, 1500])
     
    @@ -13589,14 +13569,14 @@

    3 DQICORR: Initialize Data Qual
     *** CALSTIS-0 -- Version 3.4.2 (19-Jan-2018) ***
    -Begin    30-Dec-2022 16:22:05 EST
    +Begin    02-May-2023 14:56:22 EDT
     
     Input    o5f301010_raw.fits
     Outroot  ./DQICORR/o5f301010_raw.fits
     Warning  WAVECAL was specified, but WAVECORR is not PERFORM.
     
     *** CALSTIS-1 -- Version 3.4.2 (19-Jan-2018) ***
    -Begin    30-Dec-2022 16:22:16 EST
    +Begin    02-May-2023 14:56:22 EDT
     Input    o5f301010_raw.fits
     Output   ./DQICORR/o5f301010_flt.fits
     OBSMODE  ACCUM
    @@ -13604,7 +13584,7 @@ 

    3 DQICORR: Initialize Data Qual OPT_ELEM G430L DETECTOR CCD -Imset 1 Begin 16:22:17 EST +Imset 1 Begin 14:56:23 EDT CCDTAB oref$0841734eo_ccd.fits CCDTAB PEDIGREE=INFLIGHT 01/05/1999 01/05/1999 @@ -13634,9 +13614,9 @@

    3 DQICORR: Initialize Data Qual STATFLAG PERFORM STATFLAG COMPLETE -Imset 1 End 16:22:17 EST +Imset 1 End 14:56:23 EDT -Imset 2 Begin 16:22:17 EST +Imset 2 Begin 14:56:23 EDT DQICORR PERFORM DQICORR COMPLETE @@ -13656,13 +13636,13 @@

    3 DQICORR: Initialize Data Qual STATFLAG PERFORM STATFLAG COMPLETE -Imset 2 End 16:22:17 EST +Imset 2 End 14:56:23 EDT -End 30-Dec-2022 16:22:17 EST +End 02-May-2023 14:56:23 EDT *** CALSTIS-1 complete *** -End 30-Dec-2022 16:22:17 EST +End 02-May-2023 14:56:23 EDT *** CALSTIS-0 complete ***

    @@ -13678,7 +13658,7 @@

    3 DQICORR: Initialize Data Qual
    In [9]:
    -
    img_hist("./DQICORR/o5f301010_flt.fits",[1200,1600])
    +
    img_hist("./DQICORR/o5f301010_flt.fits", [1200, 1600])
     
    @@ -13712,7 +13692,7 @@

    3 DQICORR: Initialize Data Qual

    4 BLEVCORR: Large Scale Bias & Overscan Subtraction

    The BLEVCORR step is part of basic 2-D image reduction for CCD data only. This step subtracts the electronic bias level for each line of the CCD image and trims the overscan regions off of the input image, leaving only the exposed portions of the image.

    -

    Because the electronic bias level can vary with time and temperature, its value is determined from the overscan region in the particular exposure being processed. This bias is applied equally to real pixels (main detector and physical overscan) and the virtual overscan region (pixels that don't actually exist, but are recorded when the detector clocks out extra times after reading out all the parallel rows). A raw STIS CCD image in full frame unbinned mode has 19 leading and trailing columns of serial physical overscan in the AXIS1 (x direction), and 20 rows of virtual overscan in the AXIS2 (y direction); therefore the size of the uncalibrated and unbinned full framge CCD image is 1062(serial) $\times$ 1044(parallel) pixels, with 1024 $\times$ 1024 exposed science pixels.

    +

    Because the electronic bias level can vary with time and temperature, its value is determined from the overscan region in the particular exposure being processed. This bias is applied equally to real pixels (main detector and physical overscan) and the virtual overscan region (pixels that don't actually exist, but are recorded when the detector clocks out extra times after reading out all the parallel rows). A raw STIS CCD image in full frame unbinned mode has 19 leading and trailing columns of serial physical overscan in the AXIS1 (x direction), and 20 rows of virtual overscan in the AXIS2 (y direction); therefore the size of the uncalibrated and unbinned full framge CCD image is 1062(serial) $\times$ 1044(parallel) pixels, with 1024 * 1024 exposed science pixels.

    @@ -13755,14 +13735,14 @@

    4 BLEVCORR: Lar
     *** CALSTIS-0 -- Version 3.4.2 (19-Jan-2018) ***
    -Begin    30-Dec-2022 16:22:17 EST
    +Begin    02-May-2023 14:56:23 EDT
     
     Input    o5f301010_raw.fits
     Outroot  ./BLEVCORR/o5f301010_raw.fits
     Warning  WAVECAL was specified, but WAVECORR is not PERFORM.
     
     *** CALSTIS-1 -- Version 3.4.2 (19-Jan-2018) ***
    -Begin    30-Dec-2022 16:22:17 EST
    +Begin    02-May-2023 14:56:23 EDT
     Input    o5f301010_raw.fits
     Output   ./BLEVCORR/o5f301010_flt.fits
     OBSMODE  ACCUM
    @@ -13770,7 +13750,7 @@ 

    4 BLEVCORR: Lar OPT_ELEM G430L DETECTOR CCD -Imset 1 Begin 16:22:18 EST +Imset 1 Begin 14:56:24 EDT CCDTAB oref$0841734eo_ccd.fits CCDTAB PEDIGREE=INFLIGHT 01/05/1999 01/05/1999 @@ -13803,9 +13783,9 @@

    4 BLEVCORR: Lar STATFLAG PERFORM STATFLAG COMPLETE -Imset 1 End 16:22:18 EST +Imset 1 End 14:56:24 EDT -Imset 2 Begin 16:22:18 EST +Imset 2 Begin 14:56:24 EDT DQICORR PERFORM DQICORR COMPLETE @@ -13828,13 +13808,13 @@

    4 BLEVCORR: Lar STATFLAG PERFORM STATFLAG COMPLETE -Imset 2 End 16:22:18 EST +Imset 2 End 14:56:24 EDT -End 30-Dec-2022 16:22:18 EST +End 02-May-2023 14:56:24 EDT *** CALSTIS-1 complete *** -End 30-Dec-2022 16:22:18 EST +End 02-May-2023 14:56:24 EDT *** CALSTIS-0 complete ***

    @@ -13858,7 +13838,7 @@

    4 BLEVCORR: Lar
    In [11]:
    -
    img_hist("./BLEVCORR/o5f301010_flt.fits",[-200,1600])
    +
    img_hist("./BLEVCORR/o5f301010_flt.fits", [-200, 1600])
     
    @@ -13901,7 +13881,7 @@

    4 BLEVCORR: Lar
    In [12]:
    -
    img_hist("./BLEVCORR/o5f301010_flt.fits",[-200,200])
    +
    img_hist("./BLEVCORR/o5f301010_flt.fits", [-200, 200])
     
    @@ -13944,7 +13924,7 @@

    4 BLEVCORR: Lar
    In [13]:
    -
    img_hist("./BLEVCORR/o5f301010_flt.fits",[-200,200],color="RdBu_r")
    +
    img_hist("./BLEVCORR/o5f301010_flt.fits", [-200, 200], color="RdBu_r")
     
    @@ -14007,14 +13987,14 @@

    5 BIASCORR: Small Scale Bias S
     *** CALSTIS-0 -- Version 3.4.2 (19-Jan-2018) ***
    -Begin    30-Dec-2022 16:22:19 EST
    +Begin    02-May-2023 14:56:25 EDT
     
     Input    o5f301010_raw.fits
     Outroot  ./BIASCORR/o5f301010_raw.fits
     Warning  WAVECAL was specified, but WAVECORR is not PERFORM.
     
     *** CALSTIS-1 -- Version 3.4.2 (19-Jan-2018) ***
    -Begin    30-Dec-2022 16:22:19 EST
    +Begin    02-May-2023 14:56:25 EDT
     Input    o5f301010_raw.fits
     Output   ./BIASCORR/o5f301010_flt.fits
     OBSMODE  ACCUM
    @@ -14022,7 +14002,7 @@ 

    5 BIASCORR: Small Scale Bias S OPT_ELEM G430L DETECTOR CCD -Imset 1 Begin 16:22:19 EST +Imset 1 Begin 14:56:25 EDT CCDTAB oref$0841734eo_ccd.fits CCDTAB PEDIGREE=INFLIGHT 01/05/1999 01/05/1999 @@ -14059,9 +14039,9 @@

    5 BIASCORR: Small Scale Bias S STATFLAG PERFORM STATFLAG COMPLETE -Imset 1 End 16:22:22 EST +Imset 1 End 14:56:26 EDT -Imset 2 Begin 16:22:22 EST +Imset 2 Begin 14:56:26 EDT DQICORR PERFORM DQICORR COMPLETE @@ -14085,13 +14065,13 @@

    5 BIASCORR: Small Scale Bias S STATFLAG PERFORM STATFLAG COMPLETE -Imset 2 End 16:22:22 EST +Imset 2 End 14:56:26 EDT -End 30-Dec-2022 16:22:22 EST +End 02-May-2023 14:56:26 EDT *** CALSTIS-1 complete *** -End 30-Dec-2022 16:22:22 EST +End 02-May-2023 14:56:26 EDT *** CALSTIS-0 complete ***

    @@ -14107,7 +14087,7 @@

    5 BIASCORR: Small Scale Bias S
    In [15]:
    -
    img_hist("./CRCORR/o5f301010_crj.fits",[-200,200])
    +
    img_hist("./CRCORR/o5f301010_crj.fits", [-200, 200])
     
    @@ -14394,14 +14374,14 @@

    7 DARKCORR: Dark Signal Subtraction
     *** CALSTIS-0 -- Version 3.4.2 (19-Jan-2018) ***
    -Begin    30-Dec-2022 16:22:24 EST
    +Begin    02-May-2023 14:56:28 EDT
     
     Input    o5f301010_raw.fits
     Outroot  ./DARKCORR/o5f301010_raw.fits
     Warning  WAVECAL was specified, but WAVECORR is not PERFORM.
     
     *** CALSTIS-1 -- Version 3.4.2 (19-Jan-2018) ***
    -Begin    30-Dec-2022 16:22:24 EST
    +Begin    02-May-2023 14:56:28 EDT
     Input    o5f301010_raw.fits
     Output   ./DARKCORR/o5f301010_blv_tmp.fits
     OBSMODE  ACCUM
    @@ -14409,7 +14389,7 @@ 

    7 DARKCORR: Dark Signal Subtraction OPT_ELEM G430L DETECTOR CCD -Imset 1 Begin 16:22:24 EST +Imset 1 Begin 14:56:28 EDT CCDTAB oref$0841734eo_ccd.fits CCDTAB PEDIGREE=INFLIGHT 01/05/1999 01/05/1999 @@ -14443,9 +14423,9 @@

    7 DARKCORR: Dark Signal Subtraction SHADCORR OMIT PHOTCORR OMIT -Imset 1 End 16:22:25 EST +Imset 1 End 14:56:29 EDT -Imset 2 Begin 16:22:25 EST +Imset 2 Begin 14:56:29 EDT DQICORR PERFORM DQICORR COMPLETE @@ -14466,14 +14446,14 @@

    7 DARKCORR: Dark Signal Subtraction FLATCORR OMIT SHADCORR OMIT -Imset 2 End 16:22:25 EST +Imset 2 End 14:56:29 EDT -End 30-Dec-2022 16:22:25 EST +End 02-May-2023 14:56:29 EDT *** CALSTIS-1 complete *** *** CALSTIS-2 -- Version 3.4.2 (19-Jan-2018) *** -Begin 30-Dec-2022 16:22:25 EST +Begin 02-May-2023 14:56:29 EDT Input ./DARKCORR/o5f301010_blv_tmp.fits Output ./DARKCORR/o5f301010_crj_tmp.fits @@ -14482,12 +14462,12 @@

    7 DARKCORR: Dark Signal Subtraction CRREJTAB oref$j3m1403io_crr.fits CRCORR COMPLETE -End 30-Dec-2022 16:22:25 EST +End 02-May-2023 14:56:29 EDT *** CALSTIS-2 complete *** *** CALSTIS-1 -- Version 3.4.2 (19-Jan-2018) *** -Begin 30-Dec-2022 16:22:25 EST +Begin 02-May-2023 14:56:29 EDT Input ./DARKCORR/o5f301010_crj_tmp.fits Output ./DARKCORR/o5f301010_crj.fits OBSMODE ACCUM @@ -14495,7 +14475,7 @@

    7 DARKCORR: Dark Signal Subtraction OPT_ELEM G430L DETECTOR CCD -Imset 1 Begin 16:22:25 EST +Imset 1 Begin 14:56:29 EDT CCDTAB oref$0841734eo_ccd.fits CCDTAB PEDIGREE=INFLIGHT 01/05/1999 01/05/1999 @@ -14524,13 +14504,13 @@

    7 DARKCORR: Dark Signal Subtraction STATFLAG PERFORM STATFLAG COMPLETE -Imset 1 End 16:22:28 EST +Imset 1 End 14:56:30 EDT -End 30-Dec-2022 16:22:28 EST +End 02-May-2023 14:56:30 EDT *** CALSTIS-1 complete *** -End 30-Dec-2022 16:22:28 EST +End 02-May-2023 14:56:30 EDT *** CALSTIS-0 complete ***

    @@ -14546,7 +14526,7 @@

    7 DARKCORR: Dark Signal Subtraction
    In [20]:
    -
    img_hist("./FLATCORR/o5f301010_crj.fits",[-200,200])
    +
    img_hist("./FLATCORR/o5f301010_crj.fits", [-200, 200])
     
    @@ -14817,47 +14797,47 @@

    9 Summary<
    In [23]:
    -
    plt.figure(figsize=[20,10])
    -plt.subplot(2,3,1)
    +
    plt.figure(figsize=[20, 10])
    +plt.subplot(2, 3, 1)
     with fits.open("./DQICORR/o5f301010_flt.fits") as hdu:
         ex1 = hdu[1].data
    -    img = plt.imshow(ex1,origin='lower',cmap="plasma",vmax=1600,vmin=1200)
    -    plt.colorbar(img,fraction=0.046, pad=0.04)
    +    img = plt.imshow(ex1, origin='lower', cmap="plasma", vmax=1600, vmin=1200)
    +    plt.colorbar(img, fraction=0.046, pad=0.04)
         plt.title("DQICORR")
     
    -plt.subplot(2,3,2)
    +plt.subplot(2, 3, 2)
     with fits.open("./BLEVCORR/o5f301010_flt.fits") as hdu:
         ex1 = hdu[1].data
    -    img = plt.imshow(ex1,origin='lower',cmap="plasma",vmax=-200,vmin=200)
    -    plt.colorbar(img,fraction=0.046, pad=0.04)
    +    img = plt.imshow(ex1, origin='lower', cmap="plasma", vmax=-200, vmin=200)
    +    plt.colorbar(img, fraction=0.046, pad=0.04)
         plt.title("BLEVCORR")
    -    
    -plt.subplot(2,3,3)
    +
    +plt.subplot(2, 3, 3)
     with fits.open("./BIASCORR/o5f301010_flt.fits") as hdu:
         ex1 = hdu[1].data
    -    img = plt.imshow(ex1,origin='lower',cmap="plasma",vmax=-200,vmin=200)
    -    plt.colorbar(img,fraction=0.046, pad=0.04)
    +    img = plt.imshow(ex1, origin='lower', cmap="plasma", vmax=-200, vmin=200)
    +    plt.colorbar(img, fraction=0.046, pad=0.04)
         plt.title("BIASCORR")
    -    
    -plt.subplot(2,3,4)
    +
    +plt.subplot(2, 3, 4)
     with fits.open("./CRCORR/o5f301010_crj.fits") as hdu:
         ex1 = hdu[1].data
    -    img = plt.imshow(ex1,origin='lower',cmap="plasma",vmax=-200,vmin=200)
    -    plt.colorbar(img,fraction=0.046, pad=0.04)
    +    img = plt.imshow(ex1, origin='lower', cmap="plasma", vmax=-200, vmin=200)
    +    plt.colorbar(img, fraction=0.046, pad=0.04)
         plt.title("CRCORR")
    -    
    -plt.subplot(2,3,5)
    +
    +plt.subplot(2, 3, 5)
     with fits.open("./DARKCORR/o5f301010_crj.fits") as hdu:
         ex1 = hdu[1].data
    -    img = plt.imshow(ex1,origin='lower',cmap="plasma",vmax=-200,vmin=200)
    -    plt.colorbar(img,fraction=0.046, pad=0.04)
    +    img = plt.imshow(ex1, origin='lower', cmap="plasma", vmax=-200, vmin=200)
    +    plt.colorbar(img, fraction=0.046, pad=0.04)
         plt.title("DARKCORR")
    -    
    -plt.subplot(2,3,6)
    +
    +plt.subplot(2, 3, 6)
     with fits.open("./FLATCORR/o5f301010_crj.fits") as hdu:
         ex1 = hdu[1].data
    -    img = plt.imshow(ex1,origin='lower',cmap="plasma",vmax=-200,vmin=200)
    -    plt.colorbar(img,fraction=0.046, pad=0.04)
    +    img = plt.imshow(ex1, origin='lower', cmap="plasma", vmax=-200, vmin=200)
    +    plt.colorbar(img, fraction=0.046, pad=0.04)
         plt.title("FLATCORR")
     
    @@ -14893,15 +14873,15 @@

    9 Summary<

    About this Notebook

    -

    Author: Keyi Ding

    -

    Updated On: 2023-01-05

    +

    Author: Keyi Ding

    +

    Updated On: 2023-05-18

    This tutorial was generated to be in compliance with the STScI style guides and would like to cite the Jupyter guide in particular.

    -

    Citations

    +

    Citations

    If you use astropy, matplotlib, astroquery, or numpy for published research, please cite the authors. Follow these links for more information about citations:


    diff --git a/calstis/calstis_2d_ccd.ipynb b/calstis/calstis_2d_ccd.ipynb index 5175eff..cee638d 100644 --- a/calstis/calstis_2d_ccd.ipynb +++ b/calstis/calstis_2d_ccd.ipynb @@ -45,8 +45,6 @@ "- `astroquery.mast.Observations` for finding and downloading data from the [MAST](https://mast.stsci.edu/portal/Mashup/Clients/Mast/Portal.html) archive\n", "- `os`, `shutil`, & `pathlib` for managing system paths\n", "- `matplotlib` for plotting data\n", - "- `numpy` to handle array functions\n", - "- `pandas` to make basic tables and dataframes\n", "- `stistools` for operations on STIS Data" ] }, @@ -57,21 +55,21 @@ "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/Users/kding/miniconda3/envs/stis/lib/python3.7/site-packages/stsci/tools/nmpfit.py:8: UserWarning: NMPFIT is deprecated - stsci.tools v 3.5 is the last version to contain it.\n", - " warnings.warn(\"NMPFIT is deprecated - stsci.tools v 3.5 is the last version to contain it.\")\n", - "/Users/kding/miniconda3/envs/stis/lib/python3.7/site-packages/stsci/tools/gfit.py:18: UserWarning: GFIT is deprecated - stsci.tools v 3.4.12 is the last version to contain it.Use astropy.modeling instead.\n", - " warnings.warn(\"GFIT is deprecated - stsci.tools v 3.4.12 is the last version to contain it.\"\n" + "The following tasks in the stistools package can be run with TEAL:\n", + " basic2d calstis ocrreject wavecal x1d x2d\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "The following tasks in the stistools package can be run with TEAL:\n", - " basic2d calstis ocrreject wavecal x1d x2d\n" + "/Users/kding/miniconda3/envs/stis/lib/python3.7/site-packages/stsci/tools/nmpfit.py:10: UserWarning: NMPFIT is deprecated - stsci.tools v 3.5 is the last version to contain it.\n", + " warnings.warn(\"NMPFIT is deprecated - stsci.tools v 3.5 is the last version to contain it.\")\n", + "/Users/kding/miniconda3/envs/stis/lib/python3.7/site-packages/stsci/tools/gfit.py:20: UserWarning: GFIT is deprecated - stsci.tools v 3.4.12 is the last version to contain it.Use astropy.modeling instead.\n", + " warnings.warn(\"GFIT is deprecated - stsci.tools v 3.4.12 is the last version to contain it.\"\n" ] } ], @@ -79,23 +77,17 @@ "# Import for: Reading in fits file\n", "from astropy.io import fits\n", "\n", - "# Import for: Downloading necessary files. (Not necessary if you choose to collect data from MAST)\n", + "# Import for: Downloading necessary files. \n", + "# (Not necessary if you choose to collect data from MAST)\n", "from astroquery.mast import Observations\n", "\n", "# Import for: Managing system variables and paths\n", - "import os, shutil\n", + "import os\n", + "import shutil\n", "from pathlib import Path\n", "\n", "# Import for: Plotting and specifying plotting parameters\n", "import matplotlib.pyplot as plt\n", - "import matplotlib\n", - "import matplotlib.cm as cm\n", - "from IPython.display import display\n", - "from matplotlib import gridspec\n", - "\n", - "# Import for: Quick Calculation and Data Analysis\n", - "import numpy as np\n", - "import pandas as pd\n", "\n", "# Import for: Operations on STIS Data\n", "import stistools" @@ -120,20 +112,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_crj.fits to ./mastDownload/HST/o5f301010/o5f301010_crj.fits ... [Done]\n", - "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_jif.fits to ./mastDownload/HST/o5f301010/o5f301010_jif.fits ... [Done]\n", - "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_jit.fits to ./mastDownload/HST/o5f301010/o5f301010_jit.fits ... [Done]\n", - "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_jwf.fits to ./mastDownload/HST/o5f301010/o5f301010_jwf.fits ... [Done]\n", - "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_jwt.fits to ./mastDownload/HST/o5f301010/o5f301010_jwt.fits ... [Done]\n", - "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_spt.fits to ./mastDownload/HST/o5f301010/o5f301010_spt.fits ... [Done]\n", - "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_trl.fits to ./mastDownload/HST/o5f301010/o5f301010_trl.fits ... [Done]\n", "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_wav.fits to ./mastDownload/HST/o5f301010/o5f301010_wav.fits ... [Done]\n", - "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_wsp.fits to ./mastDownload/HST/o5f301010/o5f301010_wsp.fits ... [Done]\n", - "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_asn.fits to ./mastDownload/HST/o5f301010/o5f301010_asn.fits ... [Done]\n", - "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_raw.fits to ./mastDownload/HST/o5f301010/o5f301010_raw.fits ... [Done]\n", - "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_flt.fits to ./mastDownload/HST/o5f301010/o5f301010_flt.fits ... [Done]\n", - "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_sx1.fits to ./mastDownload/HST/o5f301010/o5f301010_sx1.fits ... [Done]\n", - "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_sx2.fits to ./mastDownload/HST/o5f301010/o5f301010_sx2.fits ... [Done]\n" + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/o5f301010_raw.fits to ./mastDownload/HST/o5f301010/o5f301010_raw.fits ... [Done]\n" ] }, { @@ -153,7 +133,7 @@ "# get a list of files assiciated with that target\n", "CCD_list = Observations.get_product_list(target)\n", "# Download FITS files\n", - "Observations.download_products(CCD_list, extension='fits')\n", + "Observations.download_products(CCD_list, extension=['_raw.fits', '_wav.fits'])\n", "shutil.move('./mastDownload/HST/o5f301010/o5f301010_raw.fits', '.')\n", "shutil.move('./mastDownload/HST/o5f301010/o5f301010_wav.fits', '.')" ] @@ -273,8 +253,8 @@ "def calibrate(switch):\n", " '''Set the specified calibration step to 'PERFORM' and run calstis.\n", "\n", - " We assume that the order this function is called turns on the prior calibration\n", - " switches appropriately.\n", + " We assume that the order this function is called turns on the prior\n", + " calibration switches appropriately.\n", "\n", " PARAMETERS\n", " ----------\n", @@ -328,12 +308,12 @@ " ex4 = hdu[4].data\n", " ex4_flat = ex4.ravel()\n", "\n", - " plt.subplot(1,2,1)\n", + " plt.subplot(1, 2, 1)\n", " img = plt.imshow(ex1, origin='lower', cmap=color, vmax=ran[1], vmin=ran[0])\n", " plt.colorbar(img, fraction=0.046, pad=0.04)\n", " plt.title('extension 1')\n", "\n", - " plt.subplot(1,2,2)\n", + " plt.subplot(1, 2, 2)\n", " # adjust bins\n", " plt.hist(ex1_flat, bins=50, range=ran, histtype='step', label='extension 1')\n", " plt.hist(ex4_flat, bins=50, range=ran, histtype='step', label='extension 4')\n", @@ -343,10 +323,10 @@ " with fits.open(file) as hdu:\n", " ex1 = hdu[1].data\n", " ex1_flat = ex1.ravel()\n", - " plt.subplot(1,2,1)\n", + " plt.subplot(1, 2, 1)\n", " img = plt.imshow(ex1, origin='lower', cmap=color, vmax=ran[1], vmin=ran[0])\n", " plt.colorbar(img, fraction=0.046, pad=0.04)\n", - " plt.subplot(1,2,2)\n", + " plt.subplot(1, 2, 2)\n", " plt.hist(ex1_flat, bins=100, range=ran, histtype='step')\n", "\n", " else:\n", @@ -384,7 +364,7 @@ } ], "source": [ - "img_hist(raw,[1200,1500])" + "img_hist(raw, [1200, 1500])" ] }, { @@ -408,14 +388,14 @@ "text": [ "\n", "*** CALSTIS-0 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 30-Dec-2022 16:22:05 EST\n", + "Begin 02-May-2023 14:56:22 EDT\n", "\n", "Input o5f301010_raw.fits\n", "Outroot ./DQICORR/o5f301010_raw.fits\n", "Warning WAVECAL was specified, but WAVECORR is not PERFORM.\n", "\n", "*** CALSTIS-1 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 30-Dec-2022 16:22:16 EST\n", + "Begin 02-May-2023 14:56:22 EDT\n", "Input o5f301010_raw.fits\n", "Output ./DQICORR/o5f301010_flt.fits\n", "OBSMODE ACCUM\n", @@ -423,7 +403,7 @@ "OPT_ELEM G430L\n", "DETECTOR CCD\n", "\n", - "Imset 1 Begin 16:22:17 EST\n", + "Imset 1 Begin 14:56:23 EDT\n", "\n", "CCDTAB oref$0841734eo_ccd.fits\n", "CCDTAB PEDIGREE=INFLIGHT 01/05/1999 01/05/1999\n", @@ -453,9 +433,9 @@ "\n", "STATFLAG PERFORM\n", "STATFLAG COMPLETE\n", - "Imset 1 End 16:22:17 EST\n", + "Imset 1 End 14:56:23 EDT\n", "\n", - "Imset 2 Begin 16:22:17 EST\n", + "Imset 2 Begin 14:56:23 EDT\n", "\n", "DQICORR PERFORM\n", "DQICORR COMPLETE\n", @@ -475,13 +455,13 @@ "\n", "STATFLAG PERFORM\n", "STATFLAG COMPLETE\n", - "Imset 2 End 16:22:17 EST\n", + "Imset 2 End 14:56:23 EDT\n", "\n", - "End 30-Dec-2022 16:22:17 EST\n", + "End 02-May-2023 14:56:23 EDT\n", "\n", "*** CALSTIS-1 complete ***\n", "\n", - "End 30-Dec-2022 16:22:17 EST\n", + "End 02-May-2023 14:56:23 EDT\n", "\n", "*** CALSTIS-0 complete ***\n" ] @@ -512,7 +492,7 @@ } ], "source": [ - "img_hist(\"./DQICORR/o5f301010_flt.fits\",[1200,1600])" + "img_hist(\"./DQICORR/o5f301010_flt.fits\", [1200, 1600])" ] }, { @@ -554,14 +534,14 @@ "text": [ "\n", "*** CALSTIS-0 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 30-Dec-2022 16:22:17 EST\n", + "Begin 02-May-2023 14:56:23 EDT\n", "\n", "Input o5f301010_raw.fits\n", "Outroot ./BLEVCORR/o5f301010_raw.fits\n", "Warning WAVECAL was specified, but WAVECORR is not PERFORM.\n", "\n", "*** CALSTIS-1 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 30-Dec-2022 16:22:17 EST\n", + "Begin 02-May-2023 14:56:23 EDT\n", "Input o5f301010_raw.fits\n", "Output ./BLEVCORR/o5f301010_flt.fits\n", "OBSMODE ACCUM\n", @@ -569,7 +549,7 @@ "OPT_ELEM G430L\n", "DETECTOR CCD\n", "\n", - "Imset 1 Begin 16:22:18 EST\n", + "Imset 1 Begin 14:56:24 EDT\n", "\n", "CCDTAB oref$0841734eo_ccd.fits\n", "CCDTAB PEDIGREE=INFLIGHT 01/05/1999 01/05/1999\n", @@ -602,9 +582,9 @@ "\n", "STATFLAG PERFORM\n", "STATFLAG COMPLETE\n", - "Imset 1 End 16:22:18 EST\n", + "Imset 1 End 14:56:24 EDT\n", "\n", - "Imset 2 Begin 16:22:18 EST\n", + "Imset 2 Begin 14:56:24 EDT\n", "\n", "DQICORR PERFORM\n", "DQICORR COMPLETE\n", @@ -627,13 +607,13 @@ "\n", "STATFLAG PERFORM\n", "STATFLAG COMPLETE\n", - "Imset 2 End 16:22:18 EST\n", + "Imset 2 End 14:56:24 EDT\n", "\n", - "End 30-Dec-2022 16:22:18 EST\n", + "End 02-May-2023 14:56:24 EDT\n", "\n", "*** CALSTIS-1 complete ***\n", "\n", - "End 30-Dec-2022 16:22:18 EST\n", + "End 02-May-2023 14:56:24 EDT\n", "\n", "*** CALSTIS-0 complete ***\n" ] @@ -672,7 +652,7 @@ } ], "source": [ - "img_hist(\"./BLEVCORR/o5f301010_flt.fits\",[-200,1600])" + "img_hist(\"./BLEVCORR/o5f301010_flt.fits\", [-200, 1600])" ] }, { @@ -703,7 +683,7 @@ } ], "source": [ - "img_hist(\"./BLEVCORR/o5f301010_flt.fits\",[-200,200])" + "img_hist(\"./BLEVCORR/o5f301010_flt.fits\", [-200, 200])" ] }, { @@ -734,7 +714,7 @@ } ], "source": [ - "img_hist(\"./BLEVCORR/o5f301010_flt.fits\",[-200,200],color=\"RdBu_r\")" + "img_hist(\"./BLEVCORR/o5f301010_flt.fits\", [-200, 200], color=\"RdBu_r\")" ] }, { @@ -758,14 +738,14 @@ "text": [ "\n", "*** CALSTIS-0 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 30-Dec-2022 16:22:19 EST\n", + "Begin 02-May-2023 14:56:25 EDT\n", "\n", "Input o5f301010_raw.fits\n", "Outroot ./BIASCORR/o5f301010_raw.fits\n", "Warning WAVECAL was specified, but WAVECORR is not PERFORM.\n", "\n", "*** CALSTIS-1 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 30-Dec-2022 16:22:19 EST\n", + "Begin 02-May-2023 14:56:25 EDT\n", "Input o5f301010_raw.fits\n", "Output ./BIASCORR/o5f301010_flt.fits\n", "OBSMODE ACCUM\n", @@ -773,7 +753,7 @@ "OPT_ELEM G430L\n", "DETECTOR CCD\n", "\n", - "Imset 1 Begin 16:22:19 EST\n", + "Imset 1 Begin 14:56:25 EDT\n", "\n", "CCDTAB oref$0841734eo_ccd.fits\n", "CCDTAB PEDIGREE=INFLIGHT 01/05/1999 01/05/1999\n", @@ -810,9 +790,9 @@ "\n", "STATFLAG PERFORM\n", "STATFLAG COMPLETE\n", - "Imset 1 End 16:22:22 EST\n", + "Imset 1 End 14:56:26 EDT\n", "\n", - "Imset 2 Begin 16:22:22 EST\n", + "Imset 2 Begin 14:56:26 EDT\n", "\n", "DQICORR PERFORM\n", "DQICORR COMPLETE\n", @@ -836,13 +816,13 @@ "\n", "STATFLAG PERFORM\n", "STATFLAG COMPLETE\n", - "Imset 2 End 16:22:22 EST\n", + "Imset 2 End 14:56:26 EDT\n", "\n", - "End 30-Dec-2022 16:22:22 EST\n", + "End 02-May-2023 14:56:26 EDT\n", "\n", "*** CALSTIS-1 complete ***\n", "\n", - "End 30-Dec-2022 16:22:22 EST\n", + "End 02-May-2023 14:56:26 EDT\n", "\n", "*** CALSTIS-0 complete ***\n" ] @@ -873,7 +853,7 @@ } ], "source": [ - "img_hist(\"./BIASCORR/o5f301010_flt.fits\",[-200,200])" + "img_hist(\"./BIASCORR/o5f301010_flt.fits\", [-200, 200])" ] }, { @@ -903,14 +883,14 @@ "text": [ "\n", "*** CALSTIS-0 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 30-Dec-2022 16:22:23 EST\n", + "Begin 02-May-2023 14:56:26 EDT\n", "\n", "Input o5f301010_raw.fits\n", "Outroot ./CRCORR/o5f301010_raw.fits\n", "Warning WAVECAL was specified, but WAVECORR is not PERFORM.\n", "\n", "*** CALSTIS-1 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 30-Dec-2022 16:22:23 EST\n", + "Begin 02-May-2023 14:56:27 EDT\n", "Input o5f301010_raw.fits\n", "Output ./CRCORR/o5f301010_blv_tmp.fits\n", "OBSMODE ACCUM\n", @@ -918,7 +898,7 @@ "OPT_ELEM G430L\n", "DETECTOR CCD\n", "\n", - "Imset 1 Begin 16:22:23 EST\n", + "Imset 1 Begin 14:56:27 EDT\n", "\n", "CCDTAB oref$0841734eo_ccd.fits\n", "CCDTAB PEDIGREE=INFLIGHT 01/05/1999 01/05/1999\n", @@ -952,9 +932,9 @@ "SHADCORR OMIT\n", "\n", "PHOTCORR OMIT\n", - "Imset 1 End 16:22:23 EST\n", + "Imset 1 End 14:56:27 EDT\n", "\n", - "Imset 2 Begin 16:22:23 EST\n", + "Imset 2 Begin 14:56:27 EDT\n", "\n", "DQICORR PERFORM\n", "DQICORR COMPLETE\n", @@ -975,14 +955,14 @@ "FLATCORR OMIT\n", "\n", "SHADCORR OMIT\n", - "Imset 2 End 16:22:23 EST\n", + "Imset 2 End 14:56:27 EDT\n", "\n", - "End 30-Dec-2022 16:22:23 EST\n", + "End 02-May-2023 14:56:27 EDT\n", "\n", "*** CALSTIS-1 complete ***\n", "\n", "*** CALSTIS-2 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 30-Dec-2022 16:22:23 EST\n", + "Begin 02-May-2023 14:56:27 EDT\n", "Input ./CRCORR/o5f301010_blv_tmp.fits\n", "Output ./CRCORR/o5f301010_crj.fits\n", "\n", @@ -991,11 +971,11 @@ "CRREJTAB oref$j3m1403io_crr.fits\n", "CRCORR COMPLETE\n", "\n", - "End 30-Dec-2022 16:22:24 EST\n", + "End 02-May-2023 14:56:28 EDT\n", "\n", "*** CALSTIS-2 complete ***\n", "\n", - "End 30-Dec-2022 16:22:24 EST\n", + "End 02-May-2023 14:56:28 EDT\n", "\n", "*** CALSTIS-0 complete ***\n" ] @@ -1027,7 +1007,7 @@ "Filename: ./CRCORR/o5f301010_crj.fits\n", "No. Name Ver Type Cards Dimensions Format\n", " 0 PRIMARY 1 PrimaryHDU 246 () \n", - " 1 SCI 1 ImageHDU 118 (1024, 1024) float32 \n", + " 1 SCI 1 ImageHDU 119 (1024, 1024) float32 \n", " 2 ERR 1 ImageHDU 61 (1024, 1024) float32 \n", " 3 DQ 1 ImageHDU 44 (1024, 1024) int16 \n" ] @@ -1057,7 +1037,7 @@ } ], "source": [ - "img_hist(\"./CRCORR/o5f301010_crj.fits\",[-200,200])" + "img_hist(\"./CRCORR/o5f301010_crj.fits\", [-200, 200])" ] }, { @@ -1083,14 +1063,14 @@ "text": [ "\n", "*** CALSTIS-0 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 30-Dec-2022 16:22:24 EST\n", + "Begin 02-May-2023 14:56:28 EDT\n", "\n", "Input o5f301010_raw.fits\n", "Outroot ./DARKCORR/o5f301010_raw.fits\n", "Warning WAVECAL was specified, but WAVECORR is not PERFORM.\n", "\n", "*** CALSTIS-1 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 30-Dec-2022 16:22:24 EST\n", + "Begin 02-May-2023 14:56:28 EDT\n", "Input o5f301010_raw.fits\n", "Output ./DARKCORR/o5f301010_blv_tmp.fits\n", "OBSMODE ACCUM\n", @@ -1098,7 +1078,7 @@ "OPT_ELEM G430L\n", "DETECTOR CCD\n", "\n", - "Imset 1 Begin 16:22:24 EST\n", + "Imset 1 Begin 14:56:28 EDT\n", "\n", "CCDTAB oref$0841734eo_ccd.fits\n", "CCDTAB PEDIGREE=INFLIGHT 01/05/1999 01/05/1999\n", @@ -1132,9 +1112,9 @@ "SHADCORR OMIT\n", "\n", "PHOTCORR OMIT\n", - "Imset 1 End 16:22:25 EST\n", + "Imset 1 End 14:56:29 EDT\n", "\n", - "Imset 2 Begin 16:22:25 EST\n", + "Imset 2 Begin 14:56:29 EDT\n", "\n", "DQICORR PERFORM\n", "DQICORR COMPLETE\n", @@ -1155,14 +1135,14 @@ "FLATCORR OMIT\n", "\n", "SHADCORR OMIT\n", - "Imset 2 End 16:22:25 EST\n", + "Imset 2 End 14:56:29 EDT\n", "\n", - "End 30-Dec-2022 16:22:25 EST\n", + "End 02-May-2023 14:56:29 EDT\n", "\n", "*** CALSTIS-1 complete ***\n", "\n", "*** CALSTIS-2 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 30-Dec-2022 16:22:25 EST\n", + "Begin 02-May-2023 14:56:29 EDT\n", "Input ./DARKCORR/o5f301010_blv_tmp.fits\n", "Output ./DARKCORR/o5f301010_crj_tmp.fits\n", "\n", @@ -1171,12 +1151,12 @@ "CRREJTAB oref$j3m1403io_crr.fits\n", "CRCORR COMPLETE\n", "\n", - "End 30-Dec-2022 16:22:25 EST\n", + "End 02-May-2023 14:56:29 EDT\n", "\n", "*** CALSTIS-2 complete ***\n", "\n", "*** CALSTIS-1 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 30-Dec-2022 16:22:25 EST\n", + "Begin 02-May-2023 14:56:29 EDT\n", "Input ./DARKCORR/o5f301010_crj_tmp.fits\n", "Output ./DARKCORR/o5f301010_crj.fits\n", "OBSMODE ACCUM\n", @@ -1184,7 +1164,7 @@ "OPT_ELEM G430L\n", "DETECTOR CCD\n", "\n", - "Imset 1 Begin 16:22:25 EST\n", + "Imset 1 Begin 14:56:29 EDT\n", "\n", "CCDTAB oref$0841734eo_ccd.fits\n", "CCDTAB PEDIGREE=INFLIGHT 01/05/1999 01/05/1999\n", @@ -1213,13 +1193,13 @@ "\n", "STATFLAG PERFORM\n", "STATFLAG COMPLETE\n", - "Imset 1 End 16:22:28 EST\n", + "Imset 1 End 14:56:30 EDT\n", "\n", - "End 30-Dec-2022 16:22:28 EST\n", + "End 02-May-2023 14:56:30 EDT\n", "\n", "*** CALSTIS-1 complete ***\n", "\n", - "End 30-Dec-2022 16:22:28 EST\n", + "End 02-May-2023 14:56:30 EDT\n", "\n", "*** CALSTIS-0 complete ***\n" ] @@ -1250,7 +1230,7 @@ } ], "source": [ - "img_hist(\"./DARKCORR/o5f301010_crj.fits\",[-200,200])" + "img_hist(\"./DARKCORR/o5f301010_crj.fits\", [-200, 200])" ] }, { @@ -1279,14 +1259,14 @@ "text": [ "\n", "*** CALSTIS-0 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 30-Dec-2022 16:22:29 EST\n", + "Begin 02-May-2023 14:56:30 EDT\n", "\n", "Input o5f301010_raw.fits\n", "Outroot ./FLATCORR/o5f301010_raw.fits\n", "Warning WAVECAL was specified, but WAVECORR is not PERFORM.\n", "\n", "*** CALSTIS-1 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 30-Dec-2022 16:22:29 EST\n", + "Begin 02-May-2023 14:56:31 EDT\n", "Input o5f301010_raw.fits\n", "Output ./FLATCORR/o5f301010_blv_tmp.fits\n", "OBSMODE ACCUM\n", @@ -1294,7 +1274,7 @@ "OPT_ELEM G430L\n", "DETECTOR CCD\n", "\n", - "Imset 1 Begin 16:22:29 EST\n", + "Imset 1 Begin 14:56:31 EDT\n", "\n", "CCDTAB oref$0841734eo_ccd.fits\n", "CCDTAB PEDIGREE=INFLIGHT 01/05/1999 01/05/1999\n", @@ -1328,9 +1308,9 @@ "SHADCORR OMIT\n", "\n", "PHOTCORR OMIT\n", - "Imset 1 End 16:22:30 EST\n", + "Imset 1 End 14:56:31 EDT\n", "\n", - "Imset 2 Begin 16:22:30 EST\n", + "Imset 2 Begin 14:56:31 EDT\n", "\n", "DQICORR PERFORM\n", "DQICORR COMPLETE\n", @@ -1351,14 +1331,14 @@ "FLATCORR OMIT\n", "\n", "SHADCORR OMIT\n", - "Imset 2 End 16:22:30 EST\n", + "Imset 2 End 14:56:31 EDT\n", "\n", - "End 30-Dec-2022 16:22:30 EST\n", + "End 02-May-2023 14:56:31 EDT\n", "\n", "*** CALSTIS-1 complete ***\n", "\n", "*** CALSTIS-2 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 30-Dec-2022 16:22:30 EST\n", + "Begin 02-May-2023 14:56:31 EDT\n", "Input ./FLATCORR/o5f301010_blv_tmp.fits\n", "Output ./FLATCORR/o5f301010_crj_tmp.fits\n", "\n", @@ -1367,12 +1347,12 @@ "CRREJTAB oref$j3m1403io_crr.fits\n", "CRCORR COMPLETE\n", "\n", - "End 30-Dec-2022 16:22:30 EST\n", + "End 02-May-2023 14:56:32 EDT\n", "\n", "*** CALSTIS-2 complete ***\n", "\n", "*** CALSTIS-1 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 30-Dec-2022 16:22:30 EST\n", + "Begin 02-May-2023 14:56:32 EDT\n", "Input ./FLATCORR/o5f301010_crj_tmp.fits\n", "Output ./FLATCORR/o5f301010_crj.fits\n", "OBSMODE ACCUM\n", @@ -1380,7 +1360,7 @@ "OPT_ELEM G430L\n", "DETECTOR CCD\n", "\n", - "Imset 1 Begin 16:22:30 EST\n", + "Imset 1 Begin 14:56:32 EDT\n", "\n", "CCDTAB oref$0841734eo_ccd.fits\n", "CCDTAB PEDIGREE=INFLIGHT 01/05/1999 01/05/1999\n", @@ -1416,13 +1396,13 @@ "\n", "STATFLAG PERFORM\n", "STATFLAG COMPLETE\n", - "Imset 1 End 16:22:37 EST\n", + "Imset 1 End 14:56:33 EDT\n", "\n", - "End 30-Dec-2022 16:22:37 EST\n", + "End 02-May-2023 14:56:33 EDT\n", "\n", "*** CALSTIS-1 complete ***\n", "\n", - "End 30-Dec-2022 16:22:37 EST\n", + "End 02-May-2023 14:56:33 EDT\n", "\n", "*** CALSTIS-0 complete ***\n" ] @@ -1453,7 +1433,7 @@ } ], "source": [ - "img_hist(\"./FLATCORR/o5f301010_crj.fits\",[-200,200])" + "img_hist(\"./FLATCORR/o5f301010_crj.fits\", [-200, 200])" ] }, { @@ -1485,47 +1465,47 @@ } ], "source": [ - "plt.figure(figsize=[20,10])\n", - "plt.subplot(2,3,1)\n", + "plt.figure(figsize=[20, 10])\n", + "plt.subplot(2, 3, 1)\n", "with fits.open(\"./DQICORR/o5f301010_flt.fits\") as hdu:\n", " ex1 = hdu[1].data\n", - " img = plt.imshow(ex1,origin='lower',cmap=\"plasma\",vmax=1600,vmin=1200)\n", - " plt.colorbar(img,fraction=0.046, pad=0.04)\n", + " img = plt.imshow(ex1, origin='lower', cmap=\"plasma\", vmax=1600, vmin=1200)\n", + " plt.colorbar(img, fraction=0.046, pad=0.04)\n", " plt.title(\"DQICORR\")\n", "\n", - "plt.subplot(2,3,2)\n", + "plt.subplot(2, 3, 2)\n", "with fits.open(\"./BLEVCORR/o5f301010_flt.fits\") as hdu:\n", " ex1 = hdu[1].data\n", - " img = plt.imshow(ex1,origin='lower',cmap=\"plasma\",vmax=-200,vmin=200)\n", - " plt.colorbar(img,fraction=0.046, pad=0.04)\n", + " img = plt.imshow(ex1, origin='lower', cmap=\"plasma\", vmax=-200, vmin=200)\n", + " plt.colorbar(img, fraction=0.046, pad=0.04)\n", " plt.title(\"BLEVCORR\")\n", - " \n", - "plt.subplot(2,3,3)\n", + "\n", + "plt.subplot(2, 3, 3)\n", "with fits.open(\"./BIASCORR/o5f301010_flt.fits\") as hdu:\n", " ex1 = hdu[1].data\n", - " img = plt.imshow(ex1,origin='lower',cmap=\"plasma\",vmax=-200,vmin=200)\n", - " plt.colorbar(img,fraction=0.046, pad=0.04)\n", + " img = plt.imshow(ex1, origin='lower', cmap=\"plasma\", vmax=-200, vmin=200)\n", + " plt.colorbar(img, fraction=0.046, pad=0.04)\n", " plt.title(\"BIASCORR\")\n", - " \n", - "plt.subplot(2,3,4)\n", + "\n", + "plt.subplot(2, 3, 4)\n", "with fits.open(\"./CRCORR/o5f301010_crj.fits\") as hdu:\n", " ex1 = hdu[1].data\n", - " img = plt.imshow(ex1,origin='lower',cmap=\"plasma\",vmax=-200,vmin=200)\n", - " plt.colorbar(img,fraction=0.046, pad=0.04)\n", + " img = plt.imshow(ex1, origin='lower', cmap=\"plasma\", vmax=-200, vmin=200)\n", + " plt.colorbar(img, fraction=0.046, pad=0.04)\n", " plt.title(\"CRCORR\")\n", - " \n", - "plt.subplot(2,3,5)\n", + "\n", + "plt.subplot(2, 3, 5)\n", "with fits.open(\"./DARKCORR/o5f301010_crj.fits\") as hdu:\n", " ex1 = hdu[1].data\n", - " img = plt.imshow(ex1,origin='lower',cmap=\"plasma\",vmax=-200,vmin=200)\n", - " plt.colorbar(img,fraction=0.046, pad=0.04)\n", + " img = plt.imshow(ex1, origin='lower', cmap=\"plasma\", vmax=-200, vmin=200)\n", + " plt.colorbar(img, fraction=0.046, pad=0.04)\n", " plt.title(\"DARKCORR\")\n", - " \n", - "plt.subplot(2,3,6)\n", + "\n", + "plt.subplot(2, 3, 6)\n", "with fits.open(\"./FLATCORR/o5f301010_crj.fits\") as hdu:\n", " ex1 = hdu[1].data\n", - " img = plt.imshow(ex1,origin='lower',cmap=\"plasma\",vmax=-200,vmin=200)\n", - " plt.colorbar(img,fraction=0.046, pad=0.04)\n", + " img = plt.imshow(ex1, origin='lower', cmap=\"plasma\", vmax=-200, vmin=200)\n", + " plt.colorbar(img, fraction=0.046, pad=0.04)\n", " plt.title(\"FLATCORR\")" ] }, @@ -1537,24 +1517,25 @@ "\n", "---\n", "## About this Notebook \n", - "**Author:** [Keyi Ding](kding@stsci.edu)\n", + "**Author:** Keyi Ding\n", "\n", - "**Updated On:** 2023-01-05\n", + "**Updated On:** 2023-05-18\n", "\n", "\n", "> *This tutorial was generated to be in compliance with the [STScI style guides](https://github.com/spacetelescope/style-guides) and would like to cite the [Jupyter guide](https://github.com/spacetelescope/style-guides/blob/master/templates/example_notebook.ipynb) in particular.*\n", + "\n", "## Citations \n", "\n", "If you use `astropy`, `matplotlib`, `astroquery`, or `numpy` for published research, please cite the\n", "authors. Follow these links for more information about citations:\n", "\n", - "* [Citing `astropy`/`numpy`/`matplotlib`](https://www.scipy.org/citing.html)\n", + "* [Citing `astropy`](https://www.astropy.org/acknowledging.html)/[`numpy`](https://numpy.org/citing-numpy/) /[`matplotlib`](https://matplotlib.org/stable/users/project/citing.html)\n", "* [Citing `astroquery`](https://astroquery.readthedocs.io/en/latest/)\n", "\n", "---\n", "\n", "[Top of Page](#top)\n", - "\"Space " + "\"Space \n" ] }, { @@ -1600,7 +1581,7 @@ "width": "409.6px" }, "toc_section_display": true, - "toc_window_display": true + "toc_window_display": false } }, "nbformat": 4, diff --git a/cross-correlation/cross-correlation.html b/cross-correlation/cross-correlation.html index 022efd1..0c88f31 100644 --- a/cross-correlation/cross-correlation.html +++ b/cross-correlation/cross-correlation.html @@ -13114,7 +13114,7 @@

    Introduction&#

    Import Necessary Packages

      -
    • astropy.io fits astropy.table Table for accessing FITS files
    • +
    • astropy.io fits for accessing FITS files
    • astroquery.mast Observations for finding and downloading data from the MAST archive
    • astropy.modeling fitting astropy.modeling.models Polynomial1Dfor fitting polynomials
    • scipy.signal correlate for performing cross-correlation
    • @@ -13132,24 +13132,33 @@

      Import Necessary PackagesIn [1]:

    -
    from astropy.io import fits
    +
    # Import for: Reading in fits file
    +from astropy.io import fits
    +
    +# Import for: Downloading necessary files. 
    +# (Not necessary if you choose to collect data from MAST)
     from astroquery.mast import Observations
     
    +# Imort for: Model Fitting
     from astropy.modeling import fitting
     from astropy.modeling.models import Polynomial1D
     
    +# Import for: Performing cross-correlatoin
     from scipy.signal import correlate
     from scipy.signal import correlation_lags
     
    +# Import for: Plotting
     import matplotlib.pyplot as plt
    -import matplotlib
    -import matplotlib.cm as cm
     
    +# Import for: Quick Calculation and Data Analysis
     import numpy as np
     
    -import os,shutil
    +# Import for: Managing system variables and paths
    +import os
    +import shutil
     from pathlib import Path
     
    +# Import for operations on STIS Data
     import stistools
     
    @@ -13194,7 +13203,7 @@

    Import Necessary Packages
    -

    Collect Data Set From the MAST Archive Using Astroquery

    In this notebook, we need two download and explore their correlation.

    +

    Collect Data Set From the MAST Archive Using Astroquery

    In this notebook, we need to download two datasets and explore their correlation.

    @@ -13210,11 +13219,11 @@

    Collect Data Se # Search target object by obs_id target_id = "odj101050" ref_id = "odj101060" -target = Observations.query_criteria(obs_id=[target_id,ref_id]) +target = Observations.query_criteria(obs_id=[target_id, ref_id]) # get a list of files assiciated with that target target_list = Observations.get_product_list(target) # Download only the SCIENCE fits files -Observations.download_products(target_list,productType="SCIENCE") +Observations.download_products(target_list, productType="SCIENCE")

    @@ -13250,7 +13259,7 @@

    Collect Data Se
    Table length=8 - +
    @@ -13282,7 +13291,7 @@

    _x1d Spectra of the ObservationsIn [3]:
    -
    -
    pip_x1d = os.path.join("./mastDownload/HST","{}".format(target_id),"{}_x1d.fits".format(target_id))
    +
     
    -
    ref_x1d = os.path.join("./mastDownload/HST","{}".format(ref_id),"{}_x1d.fits".format(ref_id))
    +
    ref_x1d = os.path.join("./mastDownload/HST", "{}".format(ref_id), "{}_x1d.fits".format(ref_id))
     
     with fits.open(ref_x1d) as hdu1, fits.open(shifted_x1d) as hdu2:
         wl = hdu1[1].data["WAVELENGTH"][0]
         ref_flux = hdu1[1].data["FLUX"][0]
    -    
    +
         shifted_wl = hdu2[1].data["WAVELENGTH"][0]
         shifted_flux = hdu2[1].data["FLUX"][0]
    -    
    -    shifted_flux = np.interp(wl,shifted_wl,shifted_flux)
    +
    +    shifted_flux = np.interp(wl, shifted_wl, shifted_flux)
     
     fig = plt.figure(figsize=(20, 10))
    -plt.plot(wl,ref_flux,alpha=0.5,label="Reference spectrum ({})".format(target_id))
    -plt.plot(wl,shifted_flux,alpha=0.5,label="Shifted spectrum ({})".format(ref_id))
    +plt.plot(wl, ref_flux, alpha=0.5, label="Reference spectrum ({})".format(target_id))
    +plt.plot(wl, shifted_flux, alpha=0.5, label="Shifted spectrum ({})".format(ref_id))
     plt.legend(loc="best")
     plt.xlabel("Wavelength [Å]")
     plt.ylabel("Flux [ergs/s/cm$^2$/Å]")
    @@ -13765,18 +13774,18 @@ 

    Lag and Cross-Correlation Coeffic
    def cross_correlate(shifted_flux, ref_flux):
         assert len(shifted_flux) == len(ref_flux), "Arrays must be same size"
    -    
    +
         # Normalize inputs:
         shifted_flux = shifted_flux - shifted_flux.mean()
         shifted_flux /= shifted_flux.std()
         ref_flux = ref_flux - ref_flux.mean()
         ref_flux /= ref_flux.std()
    -    
    +
         # centered at the median of len(a)
    -    lag = correlation_lags(len(shifted_flux), len(ref_flux),mode="same") 
    +    lag = correlation_lags(len(shifted_flux), len(ref_flux), mode="same")
         # find the cross-correlation coefficient
         cc = correlate(shifted_flux, ref_flux, mode="same") / float(len(ref_flux))
    -        
    + 
         return lag, cc
     
    @@ -13801,23 +13810,23 @@

    Polynimial Fitting and Zero Poi
    fig = plt.figure(figsize=(10, 6))
    -lag,cc = cross_correlate(shifted_flux1,ref_flux1)
    -plt.plot(lag,cc,".-",label="cross-correlation coefficient")
    +lag, cc = cross_correlate(shifted_flux1, ref_flux1)
    +plt.plot(lag, cc, ".-", label="cross-correlation coefficient")
     
     # fit quadratic near the peak to find the pixel shift
     fitter = fitting.LinearLSQFitter()
     # get the 5 points near the peak
     width = 5
     low, hi = np.argmax(cc) - width//2, np.argmax(cc) + width//2 + 1
    -fit = fitter(Polynomial1D(degree=2),x=lag[low:hi],y=cc[low:hi])
    -x_c = np.arange(-10,0,0.01)
    -plt.plot(x_c, fit(x_c), alpha=0.5,label="fitted quadratic curve")
    +fit = fitter(Polynomial1D(degree=2), x=lag[low:hi], y=cc[low:hi])
    +x_c = np.arange(-10, 0, 0.01)
    +plt.plot(x_c, fit(x_c), alpha=0.5, label="fitted quadratic curve")
     # finding the maxima
     shift1 = -fit.parameters[1] / (2. * fit.parameters[2])
    -plt.plot([shift1,shift1],[0,1],alpha=0.5,label="quadratic curve maxima")
    +plt.plot([shift1, shift1], [0, 1], alpha=0.5, label="quadratic curve maxima")
     
    -plt.xlim(-20,20)
    -plt.ylim(0,1)
    +plt.xlim(-20, 20)
    +plt.ylim(0, 1)
     plt.xlabel("Lag [pix]")
     plt.ylabel("Cross-correlation coeff")
     plt.title("15168-01, G140M/C1222 Observations")
    @@ -13840,7 +13849,7 @@ 

    Polynimial Fitting and Zero Poi
    -
    <matplotlib.legend.Legend at 0x7fdce279d090>
    +
    <matplotlib.legend.Legend at 0x7fd1f1075a90>

    @@ -13917,23 +13926,23 @@

    Polynimial Fitting and Zero Poi
    fig = plt.figure(figsize=(10, 6))
    -lag,cc = cross_correlate(shifted_flux2,ref_flux2)
    -plt.plot(lag,cc,".-",label="cross-correlation coefficient")
    +lag, cc = cross_correlate(shifted_flux2, ref_flux2)
    +plt.plot(lag, cc, ".-", label="cross-correlation coefficient")
     
     # fit quadratic near the peak to find the pixel shift
     fitter = fitting.LinearLSQFitter()
     # get the 5 points near the peak
     width = 5
     low, hi = np.argmax(cc) - width//2, np.argmax(cc) + width//2 + 1
    -fit = fitter(Polynomial1D(degree=2),x=lag[low:hi],y=cc[low:hi])
    -x_c = np.arange(-10,0,0.01)
    -plt.plot(x_c, fit(x_c), alpha=0.5,label="fitted quadratic curve")
    +fit = fitter(Polynomial1D(degree=2), x=lag[low:hi], y=cc[low:hi])
    +x_c = np.arange(-10, 0, 0.01)
    +plt.plot(x_c, fit(x_c), alpha=0.5, label="fitted quadratic curve")
     # finding the maxima
     shift2 = -fit.parameters[1] / (2. * fit.parameters[2])
    -plt.plot([shift2,shift2],[0,1],alpha=0.5,label="quadratic curve maxima")
    +plt.plot([shift2, shift2], [0, 1], alpha=0.5, label="quadratic curve maxima")
     
    -plt.xlim(-20,20)
    -plt.ylim(0,1)
    +plt.xlim(-20, 20)
    +plt.ylim(0, 1)
     plt.xlabel("Lag [pix]")
     plt.ylabel("Cross-correlation coeff")
     plt.title("15168-01, G140M/C1222 Observations")
    @@ -14036,14 +14045,14 @@ 

    Recalibrate Spectrum
    # get SHIFTA1, SHIFTA1 keywords from the first science extension
     shifted_flt = Path("./Shifted/{}_flt.fits".format(target_id))
     # since we have turned off WAVECOR at the beginning, SHIFTA1 should be 0
    -SHIFTA1 = fits.getval(shifted_flt,"SHIFTA1",1)
    -SHIFTA2 = fits.getval(shifted_flt,"SHIFTA2",1)
    -assert (SHIFTA1==0 and SHIFTA2 == 0)
    +SHIFTA1 = fits.getval(shifted_flt, "SHIFTA1", 1)
    +SHIFTA2 = fits.getval(shifted_flt, "SHIFTA2", 1)
    +assert (SHIFTA1 == 0 and SHIFTA2 == 0)
     # update SHIFTA1 (only in the spectral direction)
     SHIFTA1 += shift
     # update the the SHIFTA1, SHIFTA1 keywords in the _raw fits file first science extension
    -fits.setval(pip_raw,"SHIFTA1",value=SHIFTA1,ext=1)
    -fits.setval(pip_raw,"SHIFTA2",value=SHIFTA2,ext=1)
    +fits.setval(pip_raw, "SHIFTA1", value=SHIFTA1, ext=1)
    +fits.setval(pip_raw, "SHIFTA2", value=SHIFTA2, ext=1)
     

    @@ -14064,8 +14073,8 @@

    Recalibrate SpectrumIn [14]:

    -
    fits.setval(pip_raw,"WAVECORR",value="OMIT",ext=0)
    -assert fits.getval(pip_raw,keyword="WAVECORR",ext=0) == "OMIT"
    +
    fits.setval(pip_raw, "WAVECORR", value="OMIT", ext=0)
    +assert fits.getval(pip_raw, keyword="WAVECORR", ext=0) == "OMIT"
     
    @@ -14091,7 +14100,7 @@

    Recalibrate Spectrumshutil.rmtree("./Recalibration") Path("./Recalibration").mkdir(exist_ok=True) # Recalibration -res = stistools.calstis.calstis(pip_raw,verbose=False,outroot="./Recalibration/") +res = stistools.calstis.calstis(pip_raw, verbose=False, outroot="./Recalibration/") # calstis returns 0 if calibration completes; if not, raise assertion error assert res == 0, f"CalSTIS exited with an error: {res}" recal_x1d = Path("./Recalibration/{}_x1d.fits".format(target_id)) @@ -14113,13 +14122,13 @@

    Recalibrate Spectrum
     *** CALSTIS-0 -- Version 3.4.2 (19-Jan-2018) ***
    -Begin    14-Apr-2023 14:21:23 EDT
    +Begin    02-May-2023 20:59:12 EDT
     
     Input    ./mastDownload/HST/odj101050/odj101050_raw.fits
     Outroot  ./Recalibration/odj101050_raw.fits
     
     *** CALSTIS-1 -- Version 3.4.2 (19-Jan-2018) ***
    -Begin    14-Apr-2023 14:21:24 EDT
    +Begin    02-May-2023 20:59:14 EDT
     Input    ./mastDownload/HST/odj101050/odj101050_raw.fits
     Output   ./Recalibration/odj101050_flt.fits
     OBSMODE  ACCUM
    @@ -14127,7 +14136,7 @@ 

    Recalibrate SpectrumRecalibrate SpectrumRecalibrate SpectrumRecalibrate SpectrumRecalibrate SpectrumRecalibrate Spectrum
    fig = plt.figure(figsize=(20, 20))
    -plt.subplot(2,1,1)
    +plt.subplot(2, 1, 1)
     with fits.open(pip_x1d) as hdu1, fits.open(shifted_x1d) as hdu2:
         pip_wl = hdu1[1].data["WAVELENGTH"][0]
         pip_flux = hdu1[1].data["FLUX"][0]
    -    
    +
         shifted_wl = hdu2[1].data["WAVELENGTH"][0]
         shifted_flux = hdu2[1].data["FLUX"][0]
    -    
    -plt.plot(pip_wl,pip_flux,label="Pipeline Spectrum ({})".format(target_id),alpha=0.5)
    -plt.plot(shifted_wl,shifted_flux,label="Shifted spectrum ({})".format(target_id),alpha=0.5)
    +
    +plt.plot(pip_wl, pip_flux, label="Pipeline Spectrum ({})".format(target_id), alpha=0.5)
    +plt.plot(shifted_wl, shifted_flux, label="Shifted spectrum ({})".format(target_id), alpha=0.5)
     plt.legend(loc="best")
     plt.xlabel("Wavelength [Å]")
     plt.ylabel("Flux [ergs/s/cm$^2$/Å]")
     plt.title("Pipeline and Shifted _x1d Spectrum")
     
    -plt.subplot(2,1,2)
    +plt.subplot(2, 1, 2)
     with fits.open(pip_x1d) as hdu1, fits.open(recal_x1d) as hdu2:
         wl1 = hdu1[1].data["WAVELENGTH"][0][10:-10]
         wl2 = hdu2[1].data["WAVELENGTH"][0][10:-10]
    -    
    +
         flux1 = hdu1[1].data["FLUX"][0][10:-10]
         flux2 = hdu2[1].data["FLUX"][0][10:-10]
    -plt.plot(wl1,flux1,label="Pipeline Spectrum ({})".format(target_id),alpha=0.3)
    -plt.plot(wl2,flux2,label="Recalibrated Spectrum ({})".format(target_id),alpha=0.3)
    +plt.plot(wl1, flux1, label="Pipeline Spectrum ({})".format(target_id), alpha=0.3)
    +plt.plot(wl2, flux2, label="Recalibrated Spectrum ({})".format(target_id), alpha=0.3)
     plt.legend(loc="best")
     plt.xlabel("Wavelength [Å]")
     plt.ylabel("Flux [ergs/s/cm$^2$/Å]")
    @@ -14401,15 +14410,15 @@ 

    Recalibrate Spectrum

    About this Notebook

    -

    Author: Keyi Ding

    -

    Updated On: 2023-04-14

    +

    Author: Keyi Ding

    +

    Updated On: 2023-05-18

    This tutorial was generated to be in compliance with the STScI style guides and would like to cite the Jupyter guide in particular.

    Citations

    If you use astropy, matplotlib, astroquery, or numpy for published research, please cite the authors. Follow these links for more information about citations:


    diff --git a/cross-correlation/cross-correlation.ipynb b/cross-correlation/cross-correlation.ipynb index 02e5c7c..639a627 100644 --- a/cross-correlation/cross-correlation.ipynb +++ b/cross-correlation/cross-correlation.ipynb @@ -35,7 +35,7 @@ "metadata": {}, "source": [ "### Import Necessary Packages\n", - "- `astropy.io fits` `astropy.table Table` for accessing FITS files\n", + "- `astropy.io fits` for accessing FITS files\n", "- `astroquery.mast Observations` for finding and downloading data from the [MAST](https://mast.stsci.edu/portal/Mashup/Clients/Mast/Portal.html) archive\n", "- `astropy.modeling fitting` `astropy.modeling.models Polynomial1D`for fitting polynomials\n", "- `scipy.signal correlate` for performing cross-correlation\n", @@ -71,24 +71,33 @@ } ], "source": [ + "# Import for: Reading in fits file\n", "from astropy.io import fits\n", + "\n", + "# Import for: Downloading necessary files. \n", + "# (Not necessary if you choose to collect data from MAST)\n", "from astroquery.mast import Observations\n", "\n", + "# Imort for: Model Fitting\n", "from astropy.modeling import fitting\n", "from astropy.modeling.models import Polynomial1D\n", "\n", + "# Import for: Performing cross-correlatoin\n", "from scipy.signal import correlate\n", "from scipy.signal import correlation_lags\n", "\n", + "# Import for: Plotting\n", "import matplotlib.pyplot as plt\n", - "import matplotlib\n", - "import matplotlib.cm as cm\n", "\n", + "# Import for: Quick Calculation and Data Analysis\n", "import numpy as np\n", "\n", - "import os,shutil\n", + "# Import for: Managing system variables and paths\n", + "import os\n", + "import shutil\n", "from pathlib import Path\n", "\n", + "# Import for operations on STIS Data\n", "import stistools" ] }, @@ -125,7 +134,7 @@ "data": { "text/html": [ "Table length=8\n", - "

    Local PathStatusMessageURL
    str47str8objectobject
    ./mastDownload/HST/odj101050/odj101050_raw.fitsCOMPLETENoneNone
    \n", + "
    \n", "\n", "\n", "\n", @@ -165,11 +174,11 @@ "# Search target object by obs_id\n", "target_id = \"odj101050\"\n", "ref_id = \"odj101060\"\n", - "target = Observations.query_criteria(obs_id=[target_id,ref_id])\n", + "target = Observations.query_criteria(obs_id=[target_id, ref_id])\n", "# get a list of files assiciated with that target\n", "target_list = Observations.get_product_list(target)\n", "# Download only the SCIENCE fits files\n", - "Observations.download_products(target_list,productType=\"SCIENCE\")" + "Observations.download_products(target_list, productType=\"SCIENCE\")" ] }, { @@ -196,13 +205,13 @@ "text": [ "\n", "*** CALSTIS-0 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 14-Apr-2023 14:20:35 EDT\n", + "Begin 02-May-2023 20:57:06 EDT\n", "\n", "Input ./mastDownload/HST/odj101050/odj101050_raw.fits\n", "Outroot ./Shifted/odj101050_raw.fits\n", "\n", "*** CALSTIS-1 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 14-Apr-2023 14:20:38 EDT\n", + "Begin 02-May-2023 20:57:15 EDT\n", "Input ./mastDownload/HST/odj101050/odj101050_raw.fits\n", "Output ./Shifted/odj101050_flt.fits\n", "OBSMODE ACCUM\n", @@ -210,7 +219,7 @@ "OPT_ELEM G140M\n", "DETECTOR FUV-MAMA\n", "\n", - "Imset 1 Begin 14:20:39 EDT\n", + "Imset 1 Begin 20:57:15 EDT\n", "\n", "DQICORR PERFORM\n", "DQITAB oref$uce15153o_bpx.fits\n", @@ -252,27 +261,27 @@ "\n", "STATFLAG PERFORM\n", "STATFLAG COMPLETE\n", - "Imset 1 End 14:21:10 EDT\n", + "Imset 1 End 20:58:25 EDT\n", "\n", - "End 14-Apr-2023 14:21:10 EDT\n", + "End 02-May-2023 20:58:25 EDT\n", "\n", "*** CALSTIS-1 complete ***\n", "\n", "*** CALSTIS-7 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 14-Apr-2023 14:21:10 EDT\n", + "Begin 02-May-2023 20:58:25 EDT\n", "Input ./Shifted/odj101050_flt.fits\n", "Output ./Shifted/odj101050_x2d.fits\n", "OBSMODE ACCUM\n", "APERTURE 52X0.2\n", "OPT_ELEM G140M\n", "DETECTOR FUV-MAMA\n", - "Imset 1 Begin 14:21:18 EDT\n", + "Imset 1 Begin 20:58:35 EDT\n", "Warning Wavecal processing has not been performed.\n", "\n", "HELCORR PERFORM\n", "HELCORR COMPLETE\n", "\n", - "Order 1 Begin 14:21:19 EDT\n", + "Order 1 Begin 20:58:35 EDT\n", "\n", "X2DCORR PERFORM\n", "DISPCORR PERFORM\n", @@ -316,15 +325,15 @@ "FLUXCORR COMPLETE\n", "X2DCORR COMPLETE\n", "DISPCORR COMPLETE\n", - "Order 1 End 14:21:21 EDT\n", - "Imset 1 End 14:21:21 EDT\n", + "Order 1 End 20:59:08 EDT\n", + "Imset 1 End 20:59:08 EDT\n", "\n", - "End 14-Apr-2023 14:21:21 EDT\n", + "End 02-May-2023 20:59:08 EDT\n", "\n", "*** CALSTIS-7 complete ***\n", "\n", "*** CALSTIS-6 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 14-Apr-2023 14:21:21 EDT\n", + "Begin 02-May-2023 20:59:08 EDT\n", "\n", "Warning Grating-aperture throughput correction table (GACTAB) was not found,\n", " and no gac corrections will be applied\n", @@ -344,9 +353,9 @@ "SPTRCTAB PEDIGREE=INFLIGHT 27/02/1997 06/02/1999\n", "SPTRCTAB DESCRIP =New G140L traces and updated Echelle A2CENTER values\n", "\n", - "Imset 1 Begin 14:21:21 EDT\n", + "Imset 1 Begin 20:59:10 EDT\n", " Input read into memory.\n", - "Order 1 Begin 14:21:21 EDT\n", + "Order 1 Begin 20:59:11 EDT\n", "X1DCORR PERFORM\n", "BACKCORR PERFORM\n", "******** Calling Slfit ***********BACKCORR COMPLETE\n", @@ -387,23 +396,23 @@ "SGEOCORR OMIT\n", "\n", " Row 1 written to disk.\n", - "Order 1 End 14:21:22 EDT\n", + "Order 1 End 20:59:11 EDT\n", "\n", - "Imset 1 End 14:21:22 EDT\n", + "Imset 1 End 20:59:11 EDT\n", "\n", "Warning Keyword `XTRACALG' is being added to header.\n", - "End 14-Apr-2023 14:21:22 EDT\n", + "End 02-May-2023 20:59:11 EDT\n", "\n", "*** CALSTIS-6 complete ***\n", "\n", - "End 14-Apr-2023 14:21:22 EDT\n", + "End 02-May-2023 20:59:11 EDT\n", "\n", "*** CALSTIS-0 complete ***\n" ] } ], "source": [ - "pip_raw = os.path.join(\"./mastDownload/HST\",\"{}\".format(target_id),\"{}_raw.fits\".format(target_id))\n", + "pip_raw = os.path.join(\"./mastDownload/HST\", \"{}\".format(target_id), \"{}_raw.fits\".format(target_id))\n", "# Set the \"WAVECORR\" switch in the raw fits file header to \"OMIT\"\n", "fits.setval(pip_raw, \"WAVECORR\", value=\"OMIT\")\n", "\n", @@ -413,7 +422,7 @@ " shutil.rmtree(shifted_dir)\n", "Path(shifted_dir).mkdir(exist_ok=True)\n", "# Recalibration\n", - "res = stistools.calstis.calstis(pip_raw,verbose=False,outroot=\"./Shifted/\")\n", + "res = stistools.calstis.calstis(pip_raw, verbose=False, outroot=\"./Shifted/\")\n", "# calstis returns 0 if calibration completes; if not, raise assertion error\n", "assert res == 0, f\"CalSTIS exited with an error: {res}\"" ] @@ -457,19 +466,19 @@ } ], "source": [ - "pip_x1d = os.path.join(\"./mastDownload/HST\",\"{}\".format(target_id),\"{}_x1d.fits\".format(target_id))\n", + "pip_x1d = os.path.join(\"./mastDownload/HST\", \"{}\".format(target_id), \"{}_x1d.fits\".format(target_id))\n", "shifted_x1d = Path(\"./Shifted/{}_x1d.fits\".format(target_id))\n", "\n", "with fits.open(pip_x1d) as hdu1, fits.open(shifted_x1d) as hdu2:\n", " pip_wl = hdu1[1].data[\"WAVELENGTH\"][0]\n", " pip_flux = hdu1[1].data[\"FLUX\"][0]\n", - " \n", + "\n", " shifted_wl = hdu2[1].data[\"WAVELENGTH\"][0]\n", " shifted_flux = hdu2[1].data[\"FLUX\"][0]\n", "\n", "fig = plt.figure(figsize=(20, 10))\n", - "plt.plot(pip_wl,pip_flux,label=\"Pipeline Spectrum ({})\".format(target_id),alpha=0.5)\n", - "plt.plot(shifted_wl,shifted_flux,label=\"Shifted spectrum ({})\".format(target_id),alpha=0.5)\n", + "plt.plot(pip_wl, pip_flux, label=\"Pipeline Spectrum ({})\".format(target_id), alpha=0.5)\n", + "plt.plot(shifted_wl, shifted_flux, label=\"Shifted spectrum ({})\".format(target_id), alpha=0.5)\n", "plt.legend(loc=\"best\")\n", "plt.xlabel(\"Wavelength [Å]\")\n", "plt.ylabel(\"Flux [ergs/s/cm$^2$/Å]\")\n", @@ -515,20 +524,20 @@ } ], "source": [ - "ref_x1d = os.path.join(\"./mastDownload/HST\",\"{}\".format(ref_id),\"{}_x1d.fits\".format(ref_id))\n", + "ref_x1d = os.path.join(\"./mastDownload/HST\", \"{}\".format(ref_id), \"{}_x1d.fits\".format(ref_id))\n", "\n", "with fits.open(ref_x1d) as hdu1, fits.open(shifted_x1d) as hdu2:\n", " wl = hdu1[1].data[\"WAVELENGTH\"][0]\n", " ref_flux = hdu1[1].data[\"FLUX\"][0]\n", - " \n", + "\n", " shifted_wl = hdu2[1].data[\"WAVELENGTH\"][0]\n", " shifted_flux = hdu2[1].data[\"FLUX\"][0]\n", - " \n", - " shifted_flux = np.interp(wl,shifted_wl,shifted_flux)\n", + "\n", + " shifted_flux = np.interp(wl, shifted_wl, shifted_flux)\n", "\n", "fig = plt.figure(figsize=(20, 10))\n", - "plt.plot(wl,ref_flux,alpha=0.5,label=\"Reference spectrum ({})\".format(target_id))\n", - "plt.plot(wl,shifted_flux,alpha=0.5,label=\"Shifted spectrum ({})\".format(ref_id))\n", + "plt.plot(wl, ref_flux, alpha=0.5, label=\"Reference spectrum ({})\".format(target_id))\n", + "plt.plot(wl, shifted_flux, alpha=0.5, label=\"Shifted spectrum ({})\".format(ref_id))\n", "plt.legend(loc=\"best\")\n", "plt.xlabel(\"Wavelength [Å]\")\n", "plt.ylabel(\"Flux [ergs/s/cm$^2$/Å]\")\n", @@ -629,18 +638,18 @@ "source": [ "def cross_correlate(shifted_flux, ref_flux):\n", " assert len(shifted_flux) == len(ref_flux), \"Arrays must be same size\"\n", - " \n", + "\n", " # Normalize inputs:\n", " shifted_flux = shifted_flux - shifted_flux.mean()\n", " shifted_flux /= shifted_flux.std()\n", " ref_flux = ref_flux - ref_flux.mean()\n", " ref_flux /= ref_flux.std()\n", - " \n", + "\n", " # centered at the median of len(a)\n", - " lag = correlation_lags(len(shifted_flux), len(ref_flux),mode=\"same\") \n", + " lag = correlation_lags(len(shifted_flux), len(ref_flux), mode=\"same\")\n", " # find the cross-correlation coefficient\n", " cc = correlate(shifted_flux, ref_flux, mode=\"same\") / float(len(ref_flux))\n", - " \n", + " \n", " return lag, cc" ] }, @@ -666,7 +675,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -688,23 +697,23 @@ ], "source": [ "fig = plt.figure(figsize=(10, 6))\n", - "lag,cc = cross_correlate(shifted_flux1,ref_flux1)\n", - "plt.plot(lag,cc,\".-\",label=\"cross-correlation coefficient\")\n", + "lag, cc = cross_correlate(shifted_flux1, ref_flux1)\n", + "plt.plot(lag, cc, \".-\", label=\"cross-correlation coefficient\")\n", "\n", "# fit quadratic near the peak to find the pixel shift\n", "fitter = fitting.LinearLSQFitter()\n", "# get the 5 points near the peak\n", "width = 5\n", "low, hi = np.argmax(cc) - width//2, np.argmax(cc) + width//2 + 1\n", - "fit = fitter(Polynomial1D(degree=2),x=lag[low:hi],y=cc[low:hi])\n", - "x_c = np.arange(-10,0,0.01)\n", - "plt.plot(x_c, fit(x_c), alpha=0.5,label=\"fitted quadratic curve\")\n", + "fit = fitter(Polynomial1D(degree=2), x=lag[low:hi], y=cc[low:hi])\n", + "x_c = np.arange(-10, 0, 0.01)\n", + "plt.plot(x_c, fit(x_c), alpha=0.5, label=\"fitted quadratic curve\")\n", "# finding the maxima\n", "shift1 = -fit.parameters[1] / (2. * fit.parameters[2])\n", - "plt.plot([shift1,shift1],[0,1],alpha=0.5,label=\"quadratic curve maxima\")\n", + "plt.plot([shift1, shift1], [0, 1], alpha=0.5, label=\"quadratic curve maxima\")\n", "\n", - "plt.xlim(-20,20)\n", - "plt.ylim(0,1)\n", + "plt.xlim(-20, 20)\n", + "plt.ylim(0, 1)\n", "plt.xlabel(\"Lag [pix]\")\n", "plt.ylabel(\"Cross-correlation coeff\")\n", "plt.title(\"15168-01, G140M/C1222 Observations\")\n", @@ -773,23 +782,23 @@ ], "source": [ "fig = plt.figure(figsize=(10, 6))\n", - "lag,cc = cross_correlate(shifted_flux2,ref_flux2)\n", - "plt.plot(lag,cc,\".-\",label=\"cross-correlation coefficient\")\n", + "lag, cc = cross_correlate(shifted_flux2, ref_flux2)\n", + "plt.plot(lag, cc, \".-\", label=\"cross-correlation coefficient\")\n", "\n", "# fit quadratic near the peak to find the pixel shift\n", "fitter = fitting.LinearLSQFitter()\n", "# get the 5 points near the peak\n", "width = 5\n", "low, hi = np.argmax(cc) - width//2, np.argmax(cc) + width//2 + 1\n", - "fit = fitter(Polynomial1D(degree=2),x=lag[low:hi],y=cc[low:hi])\n", - "x_c = np.arange(-10,0,0.01)\n", - "plt.plot(x_c, fit(x_c), alpha=0.5,label=\"fitted quadratic curve\")\n", + "fit = fitter(Polynomial1D(degree=2), x=lag[low:hi], y=cc[low:hi])\n", + "x_c = np.arange(-10, 0, 0.01)\n", + "plt.plot(x_c, fit(x_c), alpha=0.5, label=\"fitted quadratic curve\")\n", "# finding the maxima\n", "shift2 = -fit.parameters[1] / (2. * fit.parameters[2])\n", - "plt.plot([shift2,shift2],[0,1],alpha=0.5,label=\"quadratic curve maxima\")\n", + "plt.plot([shift2, shift2], [0, 1], alpha=0.5, label=\"quadratic curve maxima\")\n", "\n", - "plt.xlim(-20,20)\n", - "plt.ylim(0,1)\n", + "plt.xlim(-20, 20)\n", + "plt.ylim(0, 1)\n", "plt.xlabel(\"Lag [pix]\")\n", "plt.ylabel(\"Cross-correlation coeff\")\n", "plt.title(\"15168-01, G140M/C1222 Observations\")\n", @@ -845,14 +854,14 @@ "# get SHIFTA1, SHIFTA1 keywords from the first science extension\n", "shifted_flt = Path(\"./Shifted/{}_flt.fits\".format(target_id))\n", "# since we have turned off WAVECOR at the beginning, SHIFTA1 should be 0\n", - "SHIFTA1 = fits.getval(shifted_flt,\"SHIFTA1\",1)\n", - "SHIFTA2 = fits.getval(shifted_flt,\"SHIFTA2\",1)\n", - "assert (SHIFTA1==0 and SHIFTA2 == 0)\n", + "SHIFTA1 = fits.getval(shifted_flt, \"SHIFTA1\", 1)\n", + "SHIFTA2 = fits.getval(shifted_flt, \"SHIFTA2\", 1)\n", + "assert (SHIFTA1 == 0 and SHIFTA2 == 0)\n", "# update SHIFTA1 (only in the spectral direction)\n", "SHIFTA1 += shift\n", "# update the the SHIFTA1, SHIFTA1 keywords in the _raw fits file first science extension\n", - "fits.setval(pip_raw,\"SHIFTA1\",value=SHIFTA1,ext=1)\n", - "fits.setval(pip_raw,\"SHIFTA2\",value=SHIFTA2,ext=1)" + "fits.setval(pip_raw, \"SHIFTA1\", value=SHIFTA1, ext=1)\n", + "fits.setval(pip_raw, \"SHIFTA2\", value=SHIFTA2, ext=1)" ] }, { @@ -870,8 +879,8 @@ "metadata": {}, "outputs": [], "source": [ - "fits.setval(pip_raw,\"WAVECORR\",value=\"OMIT\",ext=0)\n", - "assert fits.getval(pip_raw,keyword=\"WAVECORR\",ext=0) == \"OMIT\"" + "fits.setval(pip_raw, \"WAVECORR\", value=\"OMIT\", ext=0)\n", + "assert fits.getval(pip_raw, keyword=\"WAVECORR\", ext=0) == \"OMIT\"" ] }, { @@ -894,13 +903,13 @@ "text": [ "\n", "*** CALSTIS-0 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 14-Apr-2023 14:21:23 EDT\n", + "Begin 02-May-2023 20:59:12 EDT\n", "\n", "Input ./mastDownload/HST/odj101050/odj101050_raw.fits\n", "Outroot ./Recalibration/odj101050_raw.fits\n", "\n", "*** CALSTIS-1 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 14-Apr-2023 14:21:24 EDT\n", + "Begin 02-May-2023 20:59:14 EDT\n", "Input ./mastDownload/HST/odj101050/odj101050_raw.fits\n", "Output ./Recalibration/odj101050_flt.fits\n", "OBSMODE ACCUM\n", @@ -908,7 +917,7 @@ "OPT_ELEM G140M\n", "DETECTOR FUV-MAMA\n", "\n", - "Imset 1 Begin 14:21:24 EDT\n", + "Imset 1 Begin 20:59:14 EDT\n", "\n", "DQICORR PERFORM\n", "DQITAB oref$uce15153o_bpx.fits\n", @@ -950,27 +959,27 @@ "\n", "STATFLAG PERFORM\n", "STATFLAG COMPLETE\n", - "Imset 1 End 14:21:25 EDT\n", + "Imset 1 End 20:59:16 EDT\n", "\n", - "End 14-Apr-2023 14:21:25 EDT\n", + "End 02-May-2023 20:59:16 EDT\n", "\n", "*** CALSTIS-1 complete ***\n", "\n", "*** CALSTIS-7 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 14-Apr-2023 14:21:25 EDT\n", + "Begin 02-May-2023 20:59:16 EDT\n", "Input ./Recalibration/odj101050_flt.fits\n", "Output ./Recalibration/odj101050_x2d.fits\n", "OBSMODE ACCUM\n", "APERTURE 52X0.2\n", "OPT_ELEM G140M\n", "DETECTOR FUV-MAMA\n", - "Imset 1 Begin 14:21:27 EDT\n", + "Imset 1 Begin 20:59:18 EDT\n", "Warning Wavecal processing has not been performed.\n", "\n", "HELCORR PERFORM\n", "HELCORR COMPLETE\n", "\n", - "Order 1 Begin 14:21:27 EDT\n", + "Order 1 Begin 20:59:18 EDT\n", "\n", "X2DCORR PERFORM\n", "DISPCORR PERFORM\n", @@ -1014,15 +1023,15 @@ "FLUXCORR COMPLETE\n", "X2DCORR COMPLETE\n", "DISPCORR COMPLETE\n", - "Order 1 End 14:21:28 EDT\n", - "Imset 1 End 14:21:28 EDT\n", + "Order 1 End 20:59:20 EDT\n", + "Imset 1 End 20:59:20 EDT\n", "\n", - "End 14-Apr-2023 14:21:28 EDT\n", + "End 02-May-2023 20:59:20 EDT\n", "\n", "*** CALSTIS-7 complete ***\n", "\n", "*** CALSTIS-6 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 14-Apr-2023 14:21:28 EDT\n", + "Begin 02-May-2023 20:59:20 EDT\n", "\n", "Warning Grating-aperture throughput correction table (GACTAB) was not found,\n", " and no gac corrections will be applied\n", @@ -1042,9 +1051,9 @@ "SPTRCTAB PEDIGREE=INFLIGHT 27/02/1997 06/02/1999\n", "SPTRCTAB DESCRIP =New G140L traces and updated Echelle A2CENTER values\n", "\n", - "Imset 1 Begin 14:21:29 EDT\n", + "Imset 1 Begin 20:59:21 EDT\n", " Input read into memory.\n", - "Order 1 Begin 14:21:29 EDT\n", + "Order 1 Begin 20:59:22 EDT\n", "X1DCORR PERFORM\n", "BACKCORR PERFORM\n", "******** Calling Slfit ***********BACKCORR COMPLETE\n", @@ -1085,16 +1094,16 @@ "SGEOCORR OMIT\n", "\n", " Row 1 written to disk.\n", - "Order 1 End 14:21:30 EDT\n", + "Order 1 End 20:59:22 EDT\n", "\n", - "Imset 1 End 14:21:30 EDT\n", + "Imset 1 End 20:59:22 EDT\n", "\n", "Warning Keyword `XTRACALG' is being added to header.\n", - "End 14-Apr-2023 14:21:30 EDT\n", + "End 02-May-2023 20:59:22 EDT\n", "\n", "*** CALSTIS-6 complete ***\n", "\n", - "End 14-Apr-2023 14:21:30 EDT\n", + "End 02-May-2023 20:59:22 EDT\n", "\n", "*** CALSTIS-0 complete ***\n" ] @@ -1106,7 +1115,7 @@ " shutil.rmtree(\"./Recalibration\")\n", "Path(\"./Recalibration\").mkdir(exist_ok=True)\n", "# Recalibration\n", - "res = stistools.calstis.calstis(pip_raw,verbose=False,outroot=\"./Recalibration/\")\n", + "res = stistools.calstis.calstis(pip_raw, verbose=False, outroot=\"./Recalibration/\")\n", "# calstis returns 0 if calibration completes; if not, raise assertion error\n", "assert res == 0, f\"CalSTIS exited with an error: {res}\"\n", "recal_x1d = Path(\"./Recalibration/{}_x1d.fits\".format(target_id))" @@ -1141,30 +1150,30 @@ ], "source": [ "fig = plt.figure(figsize=(20, 20))\n", - "plt.subplot(2,1,1)\n", + "plt.subplot(2, 1, 1)\n", "with fits.open(pip_x1d) as hdu1, fits.open(shifted_x1d) as hdu2:\n", " pip_wl = hdu1[1].data[\"WAVELENGTH\"][0]\n", " pip_flux = hdu1[1].data[\"FLUX\"][0]\n", - " \n", + "\n", " shifted_wl = hdu2[1].data[\"WAVELENGTH\"][0]\n", " shifted_flux = hdu2[1].data[\"FLUX\"][0]\n", - " \n", - "plt.plot(pip_wl,pip_flux,label=\"Pipeline Spectrum ({})\".format(target_id),alpha=0.5)\n", - "plt.plot(shifted_wl,shifted_flux,label=\"Shifted spectrum ({})\".format(target_id),alpha=0.5)\n", + "\n", + "plt.plot(pip_wl, pip_flux, label=\"Pipeline Spectrum ({})\".format(target_id), alpha=0.5)\n", + "plt.plot(shifted_wl, shifted_flux, label=\"Shifted spectrum ({})\".format(target_id), alpha=0.5)\n", "plt.legend(loc=\"best\")\n", "plt.xlabel(\"Wavelength [Å]\")\n", "plt.ylabel(\"Flux [ergs/s/cm$^2$/Å]\")\n", "plt.title(\"Pipeline and Shifted _x1d Spectrum\")\n", "\n", - "plt.subplot(2,1,2)\n", + "plt.subplot(2, 1, 2)\n", "with fits.open(pip_x1d) as hdu1, fits.open(recal_x1d) as hdu2:\n", " wl1 = hdu1[1].data[\"WAVELENGTH\"][0][10:-10]\n", " wl2 = hdu2[1].data[\"WAVELENGTH\"][0][10:-10]\n", - " \n", + "\n", " flux1 = hdu1[1].data[\"FLUX\"][0][10:-10]\n", " flux2 = hdu2[1].data[\"FLUX\"][0][10:-10]\n", - "plt.plot(wl1,flux1,label=\"Pipeline Spectrum ({})\".format(target_id),alpha=0.3)\n", - "plt.plot(wl2,flux2,label=\"Recalibrated Spectrum ({})\".format(target_id),alpha=0.3)\n", + "plt.plot(wl1, flux1, label=\"Pipeline Spectrum ({})\".format(target_id), alpha=0.3)\n", + "plt.plot(wl2, flux2, label=\"Recalibrated Spectrum ({})\".format(target_id), alpha=0.3)\n", "plt.legend(loc=\"best\")\n", "plt.xlabel(\"Wavelength [Å]\")\n", "plt.ylabel(\"Flux [ergs/s/cm$^2$/Å]\")\n", @@ -1180,9 +1189,9 @@ "\n", "---\n", "## About this Notebook \n", - "**Author:** [Keyi Ding](kding@stsci.edu)\n", + "**Author:** Keyi Ding\n", "\n", - "**Updated On:** 2023-04-14\n", + "**Updated On:** 2023-05-18\n", "\n", "\n", "> *This tutorial was generated to be in compliance with the [STScI style guides](https://github.com/spacetelescope/style-guides) and would like to cite the [Jupyter guide](https://github.com/spacetelescope/style-guides/blob/master/templates/example_notebook.ipynb) in particular.*\n", @@ -1191,13 +1200,13 @@ "If you use `astropy`, `matplotlib`, `astroquery`, or `numpy` for published research, please cite the\n", "authors. Follow these links for more information about citations:\n", "\n", - "* [Citing `astropy`/`numpy`/`matplotlib`](https://www.scipy.org/citing.html)\n", + "* [Citing `astropy`](https://www.astropy.org/acknowledging.html)/[`numpy`](https://numpy.org/citing-numpy/)/[`matplotlib`](https://matplotlib.org/stable/users/project/citing.html)\n", "* [Citing `astroquery`](https://astroquery.readthedocs.io/en/latest/)\n", "\n", "---\n", "\n", "[Top of Page](#top)\n", - "\"Space " + "\"Space \n" ] }, { diff --git a/custom_ccd_darks/custom_ccd_darks.html b/custom_ccd_darks/custom_ccd_darks.html index a1da169..5752220 100644 --- a/custom_ccd_darks/custom_ccd_darks.html +++ b/custom_ccd_darks/custom_ccd_darks.html @@ -13142,7 +13142,8 @@

    Import Necessary Packagesfrom astroquery.mast import Observations # Import for: Managing system variables and paths -import os,shutil +import os +import shutil from pathlib import Path # Import for: Quick Calculation and Data Analysis @@ -13154,14 +13155,13 @@

    Import Necessary Packagesfrom refstis.weekdark import make_weekdark # Import for: Plotting and specifying plotting parameters +from matplotlib import pyplot as plt import matplotlib %matplotlib inline -from matplotlib import pyplot as plt -from matplotlib.ticker import FixedLocator matplotlib.rcParams['image.origin'] = 'lower' matplotlib.rcParams['image.cmap'] = 'plasma' matplotlib.rcParams['image.interpolation'] = 'none' -matplotlib.rcParams['figure.figsize'] = (20,10) +matplotlib.rcParams['figure.figsize'] = (20, 10) @@ -13221,14 +13221,14 @@

    Collect Data Se shutil.rmtree('./mastDownload') # change this field in you have a specific dataset to be explored -obs_id="oeik1s030" +obs_id = "oeik1s030" # Search target by obs_id target = Observations.query_criteria(obs_id=obs_id) # get a list of files assiciated with that target FUV_list = Observations.get_product_list(target) # Download fits files -result = Observations.download_products(FUV_list,extension='fits') -crj = os.path.join("./mastDownload/HST","{}".format(obs_id),"{}_crj.fits".format(obs_id)) +result = Observations.download_products(FUV_list, extension='fits') +crj = os.path.join("./mastDownload/HST", "{}".format(obs_id), "{}_crj.fits".format(obs_id)) @@ -13249,8 +13249,8 @@

    Default Dark FileIn [3]:
    -
    darkfile = fits.getval(crj,ext=0, keyword='DARKFILE')
    -print("The default dark file of observation {id} is: {df}".format(id=obs_id,df=darkfile))
    +
    darkfile = fits.getval(crj, ext=0, keyword='DARKFILE')
    +print("The default dark file of observation {id} is: {df}".format(id=obs_id, df=darkfile))
     
    @@ -13337,11 +13337,11 @@

    Make Basedark
    %%capture --no-display
     # copy the dark file obs_id from the STIS Annealing Periods table, and put them into a list
     rootnames = "oeen8lqwq, oeen8ms5q, oeen8nvcq, oeen8oxnq, oeen8pa3q, oeen8qckq, oeen8reyq, oeen8sguq, "\
    -"oeen8taaq, oeen8udiq, oeen8vh3q, oeen8wicq, oeen8xkaq, oeen8yn4q, oeen8zpyq, oeen90rtq, oeen91u0q, oeen92wdq, "\
    -"oeen93ysq, oeen94arq, oeen95dlq, oeen96g4q, oeen97b7q, oeen98c4q, oeen99gxq, oeen9ah3q, oeen9bm7q, oeen9cmkq, "\
    -"oeen9dryq, oeen9et6q, oeen9fxwq, oeen9gy4q, oeen9icqq, oeen9hcwq, oeen9jgwq, oeen9kh4q, oeen9lafq, oeen9majq, "\
    -"oeen9nh9q, oeen9ohiq, oeen9qovq, oeen9ppcq, oeen9rucq, oeen9supq, oeen9tydq, oeen9uytq, oeen9velq, oeen9wf3q, "\
    -"oeen9xjeq, oeen9yjmq, oeen9za2q, oeena0aaq, oeena1g5q, oeena2gcq, oeena3kjq, oeena4knq".split(', ')
    +    "oeen8taaq, oeen8udiq, oeen8vh3q, oeen8wicq, oeen8xkaq, oeen8yn4q, oeen8zpyq, oeen90rtq, oeen91u0q, oeen92wdq, "\
    +    "oeen93ysq, oeen94arq, oeen95dlq, oeen96g4q, oeen97b7q, oeen98c4q, oeen99gxq, oeen9ah3q, oeen9bm7q, oeen9cmkq, "\
    +    "oeen9dryq, oeen9et6q, oeen9fxwq, oeen9gy4q, oeen9icqq, oeen9hcwq, oeen9jgwq, oeen9kh4q, oeen9lafq, oeen9majq, "\
    +    "oeen9nh9q, oeen9ohiq, oeen9qovq, oeen9ppcq, oeen9rucq, oeen9supq, oeen9tydq, oeen9uytq, oeen9velq, oeen9wf3q, "\
    +    "oeen9xjeq, oeen9yjmq, oeen9za2q, oeena0aaq, oeena1g5q, oeena2gcq, oeena3kjq, oeena4knq".split(', ')
     # search in astroquery based on obs_id
     search = Observations.query_criteria(obs_id=rootnames)
     pl = Observations.get_product_list(search)
    @@ -13352,9 +13352,9 @@ 

    Make Basedark# store all the paths to the superdark frames into a list anneal_dark = [] for root in rootnames: - file_path = os.path.join("./mastDownload/HST","{}".format(root),"{}_flt.fits".format(root)) + file_path = os.path.join("./mastDownload/HST", "{}".format(root), "{}_flt.fits".format(root)) # check CCD amplifier - CCDAMP = fits.getval(file_path,keyword='CCDAMP',ext=0) + CCDAMP = fits.getval(file_path, keyword='CCDAMP', ext=0) assert (CCDAMP == 'D') anneal_dark.append(file_path)

    @@ -13489,7 +13489,7 @@

    Make Basedark
    with fits.open(new_basedark) as hdu:
         new_basedark_data = hdu[1].data
    -cb = plt.imshow(new_basedark_data,cmap='plasma',vmax=1)
    +cb = plt.imshow(new_basedark_data, cmap='plasma', vmax=1)
     plt.colorbar(cb)
     
    @@ -13509,7 +13509,7 @@

    Make Basedark -
    <matplotlib.colorbar.Colorbar at 0x7fce88e3e490>
    +
    <matplotlib.colorbar.Colorbar at 0x7f800953be10>

    @@ -13557,7 +13557,7 @@

    Make Weekdark @@ -13592,11 +13592,11 @@

    Make Weekdark
    ['./mastDownload/HST/oeena0aaq/oeena0aaq_flt.fits',
    - './mastDownload/HST/oeena4knq/oeena4knq_flt.fits',
    - './mastDownload/HST/oeena2gcq/oeena2gcq_flt.fits',
    - './mastDownload/HST/oeena1g5q/oeena1g5q_flt.fits',
      './mastDownload/HST/oeen9za2q/oeen9za2q_flt.fits',
    - './mastDownload/HST/oeena3kjq/oeena3kjq_flt.fits']
    + './mastDownload/HST/oeena1g5q/oeena1g5q_flt.fits', + './mastDownload/HST/oeena2gcq/oeena2gcq_flt.fits', + './mastDownload/HST/oeena3kjq/oeena3kjq_flt.fits', + './mastDownload/HST/oeena4knq/oeena4knq_flt.fits'] @@ -13622,7 +13622,7 @@

    Make Weekdark# remove the new_basefark file if it already exists if os.path.exists(new_weekdark): os.remove(new_weekdark) -make_weekdark(component_flt,new_weekdark,thebasedark = new_basedark) +make_weekdark(component_flt, new_weekdark, thebasedark=new_basedark) @@ -13646,17 +13646,17 @@

    Make WeekdarkCalibrate with New WeekdarkIn [10]: @@ -13756,7 +13756,7 @@

    Calibrate with New Weekdarkif os.path.exists('./new_dark'): shutil.rmtree('./new_dark') Path('./new_dark').mkdir(exist_ok=True) -res = stistools.calstis.calstis(raw,wavecal=wav,outroot="./new_dark/") +res = stistools.calstis.calstis(raw, wavecal=wav, outroot="./new_dark/") assert res == 0, 'CalSTIS returned an error!' @@ -13776,7 +13776,7 @@

    Calibrate with New Weekdark
     *** CALSTIS-0 -- Version 3.4.2 (19-Jan-2018) ***
    -Begin    14-Apr-2023 14:20:52 EDT
    +Begin    02-May-2023 16:09:01 EDT
     
     Input    ./mastDownload/HST/oeik1s030/oeik1s030_raw.fits
     Outroot  ./new_dark/oeik1s030_raw.fits
    @@ -13788,7 +13788,7 @@ 

    Calibrate with New WeekdarkCalibrate with New WeekdarkCalibrate with New WeekdarkCalibrate with New WeekdarkCalibrate with New WeekdarkCalibrate with New WeekdarkCalibrate with New WeekdarkCalibrate with New WeekdarkCalibrate with New WeekdarkCalibrate with New WeekdarkCalibrate with New WeekdarkCalibrate with New WeekdarkCalibrate with New WeekdarkCalibrate with New WeekdarkCalibrate with New WeekdarkCalibrate with New WeekdarkCalibrate with New WeekdarkCalibrate with New WeekdarkCalibrate with New WeekdarkCalibrate with New WeekdarkCalibrate with New WeekdarkCalibrate with New WeekdarkCalibrate with New Weekdark
    with fits.open('new_weekdark.fits') as hdu:
         new_weekdark_data = hdu[1].data
    -cb = plt.imshow(new_weekdark_data,cmap='plasma',vmax=1)
    +cb = plt.imshow(new_weekdark_data, cmap='plasma', vmax=1)
     plt.colorbar(cb)
     
    @@ -14472,7 +14472,7 @@

    Calibrate with New Weekdark -
    <matplotlib.colorbar.Colorbar at 0x7fced848f150>
    +
    <matplotlib.colorbar.Colorbar at 0x7f8029a049d0>
    @@ -14511,8 +14511,8 @@

    Calibrate with New Weekdark
    with fits.open("55a20445o_drk.fits") as hdu:
         old_weekdark_data = hdu[1].data
    -cb = plt.imshow(new_weekdark_data/old_weekdark_data,cmap='RdBu_r', vmin=0.5, vmax=1.5)
    -plt.colorbar(cb,label="new weekdark/pipeline weekdark")
    +cb = plt.imshow(new_weekdark_data/old_weekdark_data, cmap='RdBu_r', vmin=0.5, vmax=1.5)
    +plt.colorbar(cb, label="new weekdark/pipeline weekdark")
     
    @@ -14531,7 +14531,7 @@

    Calibrate with New Weekdark -
    <matplotlib.colorbar.Colorbar at 0x7fcea90bf350>
    +
    <matplotlib.colorbar.Colorbar at 0x7f800928d590>
    @@ -14571,21 +14571,21 @@

    Comparison With the Default Dark
    # Plot the calibrated _crj images
     # The left panel is the defalt _crj image from the pipline
     # the right panel is calibrated with our customized dark file
    -plt.subplot(1,2,1)
    +plt.subplot(1, 2, 1)
     with fits.open(crj) as hdu:
         ex1 = hdu[1].data
    -    cb = plt.imshow(ex1,vmin=0,vmax=100)
    -    plt.colorbar(cb,fraction=0.046, pad=0.04)
    -    plt.xlim(550,650)
    -    plt.ylim(150,250)
    +    cb = plt.imshow(ex1, vmin=0, vmax=100)
    +    plt.colorbar(cb, fraction=0.046, pad=0.04)
    +    plt.xlim(550, 650)
    +    plt.ylim(150, 250)
         plt.title("Pipeline")
    -plt.subplot(1,2,2)
    +plt.subplot(1, 2, 2)
     with fits.open("./new_dark/oeik1s030_crj.fits") as hdu:
         ex1 = hdu[1].data
    -    cb = plt.imshow(ex1,vmin=0,vmax=100)
    -    plt.colorbar(cb,fraction=0.046, pad=0.04)
    -    plt.xlim(550,650)
    -    plt.ylim(150,250)
    +    cb = plt.imshow(ex1, vmin=0, vmax=100)
    +    plt.colorbar(cb, fraction=0.046, pad=0.04)
    +    plt.xlim(550, 650)
    +    plt.ylim(150, 250)
         plt.title("Customized Dark")
     plt.tight_layout()
     
    @@ -14630,30 +14630,30 @@

    Comparison With the Default Dark
    In [16]:
    -
    plt.figure(figsize=(20,25))
    +
    plt.figure(figsize=(20, 25))
     # get the spectrum of the default pipline _sx1 data
    -pip = Table.read("./mastDownload/HST/oeik1s030/oeik1s030_sx1.fits",1)
    -wl,pip_flux = pip[0]["WAVELENGTH","FLUX"]
    +pip = Table.read("./mastDownload/HST/oeik1s030/oeik1s030_sx1.fits", 1)
    +wl, pip_flux = pip[0]["WAVELENGTH", "FLUX"]
     # get the flux of the customized new_dark _sx1 data
    -cus = Table.read("./new_dark/oeik1s030_sx1.fits",1)
    -cus_wl,cus_flux = cus[0]["WAVELENGTH","FLUX"]
    +cus = Table.read("./new_dark/oeik1s030_sx1.fits", 1)
    +cus_wl,cus_flux = cus[0]["WAVELENGTH", "FLUX"]
     # interpolant flux so that the wavelengths matches
    -interp_flux = np.interp(wl,cus_wl,cus_flux)
    +interp_flux = np.interp(wl, cus_wl, cus_flux)
     # plot the pipeline spectrum
    -plt.subplot(3,1,1)
    -plt.plot(wl,pip_flux)
    +plt.subplot(3, 1, 1)
    +plt.plot(wl, pip_flux)
     plt.xlabel("Wavelength [Å]")
     plt.ylabel("Flux [ergs/s/cm$^2$/Å]")
     plt.title("Pipeline Spectrum")
     # plot the pipeline spectrum
    -plt.subplot(3,1,2)
    -plt.plot(cus_wl,cus_flux)
    +plt.subplot(3, 1, 2)
    +plt.plot(cus_wl, cus_flux)
     plt.xlabel("Wavelength [Å]")
     plt.ylabel("Flux [ergs/s/cm$^2$/Å]")
     plt.title("Recalibrated Spectrum")
     # plot the spectra difference
    -plt.subplot(3,1,3)
    -plt.plot(wl,interp_flux-pip_flux)
    +plt.subplot(3, 1, 3)
    +plt.plot(wl, interp_flux-pip_flux)
     plt.xlabel("Wavelength [Å]")
     plt.ylabel("Flux Difference [ergs/s/cm$^2$/Å]")
     plt.title("Difference")
    @@ -14687,7 +14687,7 @@ 

    Comparison With the Default Dark
    -
    @@ -14703,15 +14703,15 @@

    Comparison With the Default Dark

    About this Notebook

    -

    Author: Keyi Ding

    -

    Updated On: 2023-04-14

    +

    Author: Keyi Ding

    +

    Updated On: 2023-05-18

    This tutorial was generated to be in compliance with the STScI style guides and would like to cite the Jupyter guide in particular.

    Citations

    If you use astropy, matplotlib, astroquery, or numpy for published research, please cite the authors. Follow these links for more information about citations:


    diff --git a/custom_ccd_darks/custom_ccd_darks.ipynb b/custom_ccd_darks/custom_ccd_darks.ipynb index 31b18e4..5d0cac9 100644 --- a/custom_ccd_darks/custom_ccd_darks.ipynb +++ b/custom_ccd_darks/custom_ccd_darks.ipynb @@ -82,7 +82,8 @@ "from astroquery.mast import Observations\n", "\n", "# Import for: Managing system variables and paths\n", - "import os,shutil\n", + "import os\n", + "import shutil\n", "from pathlib import Path\n", "\n", "# Import for: Quick Calculation and Data Analysis\n", @@ -94,14 +95,13 @@ "from refstis.weekdark import make_weekdark\n", "\n", "# Import for: Plotting and specifying plotting parameters\n", + "from matplotlib import pyplot as plt\n", "import matplotlib\n", "%matplotlib inline\n", - "from matplotlib import pyplot as plt\n", - "from matplotlib.ticker import FixedLocator\n", "matplotlib.rcParams['image.origin'] = 'lower'\n", "matplotlib.rcParams['image.cmap'] = 'plasma'\n", "matplotlib.rcParams['image.interpolation'] = 'none'\n", - "matplotlib.rcParams['figure.figsize'] = (20,10)" + "matplotlib.rcParams['figure.figsize'] = (20, 10)" ] }, { @@ -126,14 +126,14 @@ " shutil.rmtree('./mastDownload')\n", "\n", "# change this field in you have a specific dataset to be explored\n", - "obs_id=\"oeik1s030\"\n", + "obs_id = \"oeik1s030\"\n", "# Search target by obs_id\n", "target = Observations.query_criteria(obs_id=obs_id)\n", "# get a list of files assiciated with that target\n", "FUV_list = Observations.get_product_list(target)\n", "# Download fits files\n", - "result = Observations.download_products(FUV_list,extension='fits')\n", - "crj = os.path.join(\"./mastDownload/HST\",\"{}\".format(obs_id),\"{}_crj.fits\".format(obs_id))" + "result = Observations.download_products(FUV_list, extension='fits')\n", + "crj = os.path.join(\"./mastDownload/HST\", \"{}\".format(obs_id), \"{}_crj.fits\".format(obs_id))" ] }, { @@ -160,8 +160,8 @@ } ], "source": [ - "darkfile = fits.getval(crj,ext=0, keyword='DARKFILE')\n", - "print(\"The default dark file of observation {id} is: {df}\".format(id=obs_id,df=darkfile))" + "darkfile = fits.getval(crj, ext=0, keyword='DARKFILE')\n", + "print(\"The default dark file of observation {id} is: {df}\".format(id=obs_id, df=darkfile))" ] }, { @@ -217,11 +217,11 @@ "%%capture --no-display\n", "# copy the dark file obs_id from the STIS Annealing Periods table, and put them into a list\n", "rootnames = \"oeen8lqwq, oeen8ms5q, oeen8nvcq, oeen8oxnq, oeen8pa3q, oeen8qckq, oeen8reyq, oeen8sguq, \"\\\n", - "\"oeen8taaq, oeen8udiq, oeen8vh3q, oeen8wicq, oeen8xkaq, oeen8yn4q, oeen8zpyq, oeen90rtq, oeen91u0q, oeen92wdq, \"\\\n", - "\"oeen93ysq, oeen94arq, oeen95dlq, oeen96g4q, oeen97b7q, oeen98c4q, oeen99gxq, oeen9ah3q, oeen9bm7q, oeen9cmkq, \"\\\n", - "\"oeen9dryq, oeen9et6q, oeen9fxwq, oeen9gy4q, oeen9icqq, oeen9hcwq, oeen9jgwq, oeen9kh4q, oeen9lafq, oeen9majq, \"\\\n", - "\"oeen9nh9q, oeen9ohiq, oeen9qovq, oeen9ppcq, oeen9rucq, oeen9supq, oeen9tydq, oeen9uytq, oeen9velq, oeen9wf3q, \"\\\n", - "\"oeen9xjeq, oeen9yjmq, oeen9za2q, oeena0aaq, oeena1g5q, oeena2gcq, oeena3kjq, oeena4knq\".split(', ')\n", + " \"oeen8taaq, oeen8udiq, oeen8vh3q, oeen8wicq, oeen8xkaq, oeen8yn4q, oeen8zpyq, oeen90rtq, oeen91u0q, oeen92wdq, \"\\\n", + " \"oeen93ysq, oeen94arq, oeen95dlq, oeen96g4q, oeen97b7q, oeen98c4q, oeen99gxq, oeen9ah3q, oeen9bm7q, oeen9cmkq, \"\\\n", + " \"oeen9dryq, oeen9et6q, oeen9fxwq, oeen9gy4q, oeen9icqq, oeen9hcwq, oeen9jgwq, oeen9kh4q, oeen9lafq, oeen9majq, \"\\\n", + " \"oeen9nh9q, oeen9ohiq, oeen9qovq, oeen9ppcq, oeen9rucq, oeen9supq, oeen9tydq, oeen9uytq, oeen9velq, oeen9wf3q, \"\\\n", + " \"oeen9xjeq, oeen9yjmq, oeen9za2q, oeena0aaq, oeena1g5q, oeena2gcq, oeena3kjq, oeena4knq\".split(', ')\n", "# search in astroquery based on obs_id\n", "search = Observations.query_criteria(obs_id=rootnames)\n", "pl = Observations.get_product_list(search)\n", @@ -232,9 +232,9 @@ "# store all the paths to the superdark frames into a list\n", "anneal_dark = []\n", "for root in rootnames:\n", - " file_path = os.path.join(\"./mastDownload/HST\",\"{}\".format(root),\"{}_flt.fits\".format(root))\n", + " file_path = os.path.join(\"./mastDownload/HST\", \"{}\".format(root), \"{}_flt.fits\".format(root))\n", " # check CCD amplifier\n", - " CCDAMP = fits.getval(file_path,keyword='CCDAMP',ext=0)\n", + " CCDAMP = fits.getval(file_path, keyword='CCDAMP', ext=0)\n", " assert (CCDAMP == 'D')\n", " anneal_dark.append(file_path)" ] @@ -353,7 +353,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -376,7 +376,7 @@ "source": [ "with fits.open(new_basedark) as hdu:\n", " new_basedark_data = hdu[1].data\n", - "cb = plt.imshow(new_basedark_data,cmap='plasma',vmax=1)\n", + "cb = plt.imshow(new_basedark_data, cmap='plasma', vmax=1)\n", "plt.colorbar(cb)" ] }, @@ -408,11 +408,11 @@ "data": { "text/plain": [ "['./mastDownload/HST/oeena0aaq/oeena0aaq_flt.fits',\n", - " './mastDownload/HST/oeena4knq/oeena4knq_flt.fits',\n", - " './mastDownload/HST/oeena2gcq/oeena2gcq_flt.fits',\n", - " './mastDownload/HST/oeena1g5q/oeena1g5q_flt.fits',\n", " './mastDownload/HST/oeen9za2q/oeen9za2q_flt.fits',\n", - " './mastDownload/HST/oeena3kjq/oeena3kjq_flt.fits']" + " './mastDownload/HST/oeena1g5q/oeena1g5q_flt.fits',\n", + " './mastDownload/HST/oeena2gcq/oeena2gcq_flt.fits',\n", + " './mastDownload/HST/oeena3kjq/oeena3kjq_flt.fits',\n", + " './mastDownload/HST/oeena4knq/oeena4knq_flt.fits']" ] }, "execution_count": 8, @@ -423,7 +423,7 @@ "source": [ "%%capture --no-display\n", "# search for darks taken during the weekly period (observation date, observation date + 3 days)\n", - "hdr = fits.getheader(crj,0)\n", + "hdr = fits.getheader(crj, 0)\n", "component_darks = Observations.query_criteria(\n", " target_name='DARK',\n", " t_min=[hdr['TEXPSTRT']-3, hdr['TEXPSTRT']],\n", @@ -432,11 +432,11 @@ "dark_list = Observations.get_product_list(component_darks)\n", "dark_list = dark_list[dark_list['productSubGroupDescription'] == 'FLT']\n", "# Download fits files\n", - "result = Observations.download_products(dark_list,extension='fits')\n", + "result = Observations.download_products(dark_list, extension='fits')\n", "# store all the paths to the superdark frames into a list\n", "component_flt = []\n", "for root in component_darks['obs_id']:\n", - " file_path = os.path.join(\"./mastDownload/HST\",\"{}\".format(root),\"{}_flt.fits\".format(root))\n", + " file_path = os.path.join(\"./mastDownload/HST\", \"{}\".format(root), \"{}_flt.fits\".format(root))\n", " component_flt.append(file_path)\n", "component_flt" ] @@ -466,17 +466,17 @@ "With : oref$55a2044do_bia.fits\n", " : new_basedark.fits\n", "BIAS correction already done for ./mastDownload/HST/oeena0aaq/oeena0aaq_flt.fits\n", - "BIAS correction already done for ./mastDownload/HST/oeena4knq/oeena4knq_flt.fits\n", - "BIAS correction already done for ./mastDownload/HST/oeena2gcq/oeena2gcq_flt.fits\n", - "BIAS correction already done for ./mastDownload/HST/oeena1g5q/oeena1g5q_flt.fits\n", "BIAS correction already done for ./mastDownload/HST/oeen9za2q/oeen9za2q_flt.fits\n", + "BIAS correction already done for ./mastDownload/HST/oeena1g5q/oeena1g5q_flt.fits\n", + "BIAS correction already done for ./mastDownload/HST/oeena2gcq/oeena2gcq_flt.fits\n", "BIAS correction already done for ./mastDownload/HST/oeena3kjq/oeena3kjq_flt.fits\n", + "BIAS correction already done for ./mastDownload/HST/oeena4knq/oeena4knq_flt.fits\n", "TEMPCORR = COMPLETE, no temperature correction applied to ./mastDownload/HST/oeena0aaq/oeena0aaq_flt.fits\n", - "TEMPCORR = COMPLETE, no temperature correction applied to ./mastDownload/HST/oeena4knq/oeena4knq_flt.fits\n", - "TEMPCORR = COMPLETE, no temperature correction applied to ./mastDownload/HST/oeena2gcq/oeena2gcq_flt.fits\n", - "TEMPCORR = COMPLETE, no temperature correction applied to ./mastDownload/HST/oeena1g5q/oeena1g5q_flt.fits\n", "TEMPCORR = COMPLETE, no temperature correction applied to ./mastDownload/HST/oeen9za2q/oeen9za2q_flt.fits\n", + "TEMPCORR = COMPLETE, no temperature correction applied to ./mastDownload/HST/oeena1g5q/oeena1g5q_flt.fits\n", + "TEMPCORR = COMPLETE, no temperature correction applied to ./mastDownload/HST/oeena2gcq/oeena2gcq_flt.fits\n", "TEMPCORR = COMPLETE, no temperature correction applied to ./mastDownload/HST/oeena3kjq/oeena3kjq_flt.fits\n", + "TEMPCORR = COMPLETE, no temperature correction applied to ./mastDownload/HST/oeena4knq/oeena4knq_flt.fits\n", "Joining images to new_weekdark_joined.fits\n", "new_weekdark_joined.fits\n", "crcorr found = OMIT\n", @@ -501,7 +501,7 @@ "# remove the new_basefark file if it already exists\n", "if os.path.exists(new_weekdark):\n", " os.remove(new_weekdark)\n", - "make_weekdark(component_flt,new_weekdark,thebasedark = new_basedark)" + "make_weekdark(component_flt, new_weekdark, thebasedark=new_basedark)" ] }, { @@ -521,8 +521,8 @@ "metadata": {}, "outputs": [], "source": [ - "raw = os.path.join(\"./mastDownload/HST\",\"{}\".format(obs_id),\"{}_raw.fits\".format(obs_id))\n", - "wav = os.path.join(\"./mastDownload/HST\",\"{}\".format(obs_id),\"{}_wav.fits\".format(obs_id))" + "raw = os.path.join(\"./mastDownload/HST\", \"{}\".format(obs_id), \"{}_raw.fits\".format(obs_id))\n", + "wav = os.path.join(\"./mastDownload/HST\", \"{}\".format(obs_id), \"{}_wav.fits\".format(obs_id))" ] }, { @@ -546,7 +546,7 @@ "# set the value of DARKFILE to the filename of the new week dark\n", "fits.setval(raw, ext=0, keyword='DARKFILE', value=new_weekdark)\n", "# make sure that the value is set correctly\n", - "fits.getval(raw,ext=0, keyword='DARKFILE')" + "fits.getval(raw, ext=0, keyword='DARKFILE')" ] }, { @@ -569,7 +569,7 @@ "text": [ "\n", "*** CALSTIS-0 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 14-Apr-2023 14:20:52 EDT\n", + "Begin 02-May-2023 16:09:01 EDT\n", "\n", "Input ./mastDownload/HST/oeik1s030/oeik1s030_raw.fits\n", "Outroot ./new_dark/oeik1s030_raw.fits\n", @@ -581,7 +581,7 @@ "Warning The value from the science header will be used for the wavecal.\n", "\n", "*** CALSTIS-1 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 14-Apr-2023 14:20:58 EDT\n", + "Begin 02-May-2023 16:09:03 EDT\n", "Input ./mastDownload/HST/oeik1s030/oeik1s030_raw.fits\n", "Output ./new_dark/oeik1s030_blv_tmp.fits\n", "OBSMODE ACCUM\n", @@ -589,7 +589,7 @@ "OPT_ELEM G750L\n", "DETECTOR CCD\n", "\n", - "Imset 1 Begin 14:20:58 EDT\n", + "Imset 1 Begin 16:09:03 EDT\n", "Epcfile oeik1snkj_epc.fits\n", "Warning EPCTAB `oeik1snkj_epc.fits' not found.\n", "\n", @@ -625,9 +625,9 @@ "SHADCORR OMIT\n", "\n", "PHOTCORR OMIT\n", - "Imset 1 End 14:21:05 EDT\n", + "Imset 1 End 16:09:07 EDT\n", "\n", - "Imset 2 Begin 14:21:05 EDT\n", + "Imset 2 Begin 16:09:07 EDT\n", "Epcfile oeik1snlj_epc.fits\n", "Warning EPCTAB `oeik1snlj_epc.fits' not found.\n", "\n", @@ -650,9 +650,9 @@ "FLATCORR OMIT\n", "\n", "SHADCORR OMIT\n", - "Imset 2 End 14:21:06 EDT\n", + "Imset 2 End 16:09:07 EDT\n", "\n", - "Imset 3 Begin 14:21:06 EDT\n", + "Imset 3 Begin 16:09:07 EDT\n", "Epcfile oeik1snmj_epc.fits\n", "Warning EPCTAB `oeik1snmj_epc.fits' not found.\n", "\n", @@ -675,9 +675,9 @@ "FLATCORR OMIT\n", "\n", "SHADCORR OMIT\n", - "Imset 3 End 14:21:07 EDT\n", + "Imset 3 End 16:09:07 EDT\n", "\n", - "Imset 4 Begin 14:21:07 EDT\n", + "Imset 4 Begin 16:09:07 EDT\n", "Epcfile oeik1snnj_epc.fits\n", "Warning EPCTAB `oeik1snnj_epc.fits' not found.\n", "\n", @@ -700,14 +700,14 @@ "FLATCORR OMIT\n", "\n", "SHADCORR OMIT\n", - "Imset 4 End 14:21:08 EDT\n", + "Imset 4 End 16:09:07 EDT\n", "\n", - "End 14-Apr-2023 14:21:08 EDT\n", + "End 02-May-2023 16:09:07 EDT\n", "\n", "*** CALSTIS-1 complete ***\n", "\n", "*** CALSTIS-2 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 14-Apr-2023 14:21:08 EDT\n", + "Begin 02-May-2023 16:09:07 EDT\n", "Input ./new_dark/oeik1s030_blv_tmp.fits\n", "Output ./new_dark/oeik1s030_crj_tmp.fits\n", "\n", @@ -716,12 +716,12 @@ "CRREJTAB oref$j3m1403io_crr.fits\n", "CRCORR COMPLETE\n", "\n", - "End 14-Apr-2023 14:21:08 EDT\n", + "End 02-May-2023 16:09:08 EDT\n", "\n", "*** CALSTIS-2 complete ***\n", "\n", "*** CALSTIS-1 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 14-Apr-2023 14:21:08 EDT\n", + "Begin 02-May-2023 16:09:08 EDT\n", "Input ./new_dark/oeik1s030_crj_tmp.fits\n", "Output ./new_dark/oeik1s030_crj.fits\n", "OBSMODE ACCUM\n", @@ -729,7 +729,7 @@ "OPT_ELEM G750L\n", "DETECTOR CCD\n", "\n", - "Imset 1 Begin 14:21:08 EDT\n", + "Imset 1 Begin 16:09:08 EDT\n", "\n", "CCDTAB oref$16j1600do_ccd.fits\n", "CCDTAB PEDIGREE=GROUND\n", @@ -765,14 +765,14 @@ "\n", "STATFLAG PERFORM\n", "STATFLAG COMPLETE\n", - "Imset 1 End 14:21:20 EDT\n", + "Imset 1 End 16:09:17 EDT\n", "\n", - "End 14-Apr-2023 14:21:20 EDT\n", + "End 02-May-2023 16:09:17 EDT\n", "\n", "*** CALSTIS-1 complete ***\n", "\n", "*** CALSTIS-1 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 14-Apr-2023 14:21:20 EDT\n", + "Begin 02-May-2023 16:09:17 EDT\n", "Input ./new_dark/oeik1s030_blv_tmp.fits\n", "Output ./new_dark/oeik1s030_flt.fits\n", "OBSMODE ACCUM\n", @@ -780,7 +780,7 @@ "OPT_ELEM G750L\n", "DETECTOR CCD\n", "\n", - "Imset 1 Begin 14:21:20 EDT\n", + "Imset 1 Begin 16:09:17 EDT\n", "Epcfile oeik1snkj_epc.fits\n", "Warning EPCTAB `oeik1snkj_epc.fits' not found.\n", "\n", @@ -818,9 +818,9 @@ "\n", "STATFLAG PERFORM\n", "STATFLAG COMPLETE\n", - "Imset 1 End 14:21:21 EDT\n", + "Imset 1 End 16:09:18 EDT\n", "\n", - "Imset 2 Begin 14:21:21 EDT\n", + "Imset 2 Begin 16:09:18 EDT\n", "Epcfile oeik1snlj_epc.fits\n", "Warning EPCTAB `oeik1snlj_epc.fits' not found.\n", "\n", @@ -842,9 +842,9 @@ "\n", "STATFLAG PERFORM\n", "STATFLAG COMPLETE\n", - "Imset 2 End 14:21:21 EDT\n", + "Imset 2 End 16:09:18 EDT\n", "\n", - "Imset 3 Begin 14:21:21 EDT\n", + "Imset 3 Begin 16:09:18 EDT\n", "Epcfile oeik1snmj_epc.fits\n", "Warning EPCTAB `oeik1snmj_epc.fits' not found.\n", "\n", @@ -866,9 +866,9 @@ "\n", "STATFLAG PERFORM\n", "STATFLAG COMPLETE\n", - "Imset 3 End 14:21:22 EDT\n", + "Imset 3 End 16:09:19 EDT\n", "\n", - "Imset 4 Begin 14:21:22 EDT\n", + "Imset 4 Begin 16:09:19 EDT\n", "Epcfile oeik1snnj_epc.fits\n", "Warning EPCTAB `oeik1snnj_epc.fits' not found.\n", "\n", @@ -890,14 +890,14 @@ "\n", "STATFLAG PERFORM\n", "STATFLAG COMPLETE\n", - "Imset 4 End 14:21:22 EDT\n", + "Imset 4 End 16:09:19 EDT\n", "\n", - "End 14-Apr-2023 14:21:22 EDT\n", + "End 02-May-2023 16:09:19 EDT\n", "\n", "*** CALSTIS-1 complete ***\n", "\n", "*** CALSTIS-1 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 14-Apr-2023 14:21:22 EDT\n", + "Begin 02-May-2023 16:09:19 EDT\n", "Input ./mastDownload/HST/oeik1s030/oeik1s030_wav.fits\n", "Output ./new_dark/oeik1s030_fwv_tmp.fits\n", "OBSMODE ACCUM\n", @@ -905,7 +905,7 @@ "OPT_ELEM G750L\n", "DETECTOR CCD\n", "\n", - "Imset 1 Begin 14:21:23 EDT\n", + "Imset 1 Begin 16:09:19 EDT\n", "Epcfile oeik1snoj_epc.fits\n", "Warning EPCTAB `oeik1snoj_epc.fits' not found.\n", "\n", @@ -961,23 +961,23 @@ "\n", "STATFLAG PERFORM\n", "STATFLAG COMPLETE\n", - "Imset 1 End 14:21:24 EDT\n", + "Imset 1 End 16:09:20 EDT\n", "\n", - "End 14-Apr-2023 14:21:24 EDT\n", + "End 02-May-2023 16:09:20 EDT\n", "\n", "*** CALSTIS-1 complete ***\n", "\n", "*** CALSTIS-7 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 14-Apr-2023 14:21:24 EDT\n", + "Begin 02-May-2023 16:09:20 EDT\n", "Input ./new_dark/oeik1s030_fwv_tmp.fits\n", "Output ./new_dark/oeik1s030_w2d_tmp.fits\n", "OBSMODE ACCUM\n", "APERTURE 52X0.1\n", "OPT_ELEM G750L\n", "DETECTOR CCD\n", - "Imset 1 Begin 14:21:24 EDT\n", + "Imset 1 Begin 16:09:20 EDT\n", "\n", - "Order 1 Begin 14:21:24 EDT\n", + "Order 1 Begin 16:09:20 EDT\n", "\n", "X2DCORR PERFORM\n", "DISPCORR PERFORM\n", @@ -1003,22 +1003,22 @@ "SPTRCTAB DESCRIP =Lindler/Bohlin/Dressel/Holfeltz postlaunch calibration\n", "X2DCORR COMPLETE\n", "DISPCORR COMPLETE\n", - "Order 1 End 14:21:27 EDT\n", - "Imset 1 End 14:21:27 EDT\n", + "Order 1 End 16:09:23 EDT\n", + "Imset 1 End 16:09:23 EDT\n", "\n", - "End 14-Apr-2023 14:21:27 EDT\n", + "End 02-May-2023 16:09:23 EDT\n", "\n", "*** CALSTIS-7 complete ***\n", "\n", "*** CALSTIS-4 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 14-Apr-2023 14:21:27 EDT\n", + "Begin 02-May-2023 16:09:23 EDT\n", "Input ./new_dark/oeik1s030_w2d_tmp.fits\n", "OBSMODE ACCUM\n", "APERTURE 52X0.1\n", "OPT_ELEM G750L\n", "DETECTOR CCD\n", "\n", - "Imset 1 Begin 14:21:29 EDT\n", + "Imset 1 Begin 16:09:24 EDT\n", "\n", "WAVECORR PERFORM\n", "WCPTAB oref$16j1600co_wcp.fits\n", @@ -1039,72 +1039,72 @@ " Shift in dispersion direction is 2.957 pixels.\n", " Shift in spatial direction is -1.950 pixels.\n", "WAVECORR COMPLETE\n", - "Imset 1 End 14:21:29 EDT\n", + "Imset 1 End 16:09:24 EDT\n", "\n", - "End 14-Apr-2023 14:21:29 EDT\n", + "End 02-May-2023 16:09:24 EDT\n", "\n", "*** CALSTIS-4 complete ***\n", "\n", "*** CALSTIS-12 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 14-Apr-2023 14:21:29 EDT\n", + "Begin 02-May-2023 16:09:24 EDT\n", "Wavecal ./new_dark/oeik1s030_w2d_tmp.fits\n", "Science ./new_dark/oeik1s030_crj.fits\n", "OBSMODE ACCUM\n", "APERTURE 52X2\n", "OPT_ELEM G750L\n", "DETECTOR CCD\n", - "Imset 1 Begin 14:21:29 EDT\n", + "Imset 1 Begin 16:09:24 EDT\n", " SHIFTA1 set to 2.95707\n", " SHIFTA2 set to -1.94995\n", - "Imset 1 End 14:21:29 EDT\n", + "Imset 1 End 16:09:24 EDT\n", "\n", - "End 14-Apr-2023 14:21:29 EDT\n", + "End 02-May-2023 16:09:24 EDT\n", "\n", "*** CALSTIS-12 complete ***\n", "\n", "*** CALSTIS-12 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 14-Apr-2023 14:21:29 EDT\n", + "Begin 02-May-2023 16:09:24 EDT\n", "Wavecal ./new_dark/oeik1s030_w2d_tmp.fits\n", "Science ./new_dark/oeik1s030_flt.fits\n", "OBSMODE ACCUM\n", "APERTURE 52X2\n", "OPT_ELEM G750L\n", "DETECTOR CCD\n", - "Imset 1 Begin 14:21:29 EDT\n", + "Imset 1 Begin 16:09:24 EDT\n", " SHIFTA1 set to 2.95707\n", " SHIFTA2 set to -1.94995\n", - "Imset 1 End 14:21:29 EDT\n", - "Imset 2 Begin 14:21:29 EDT\n", + "Imset 1 End 16:09:24 EDT\n", + "Imset 2 Begin 16:09:24 EDT\n", " SHIFTA1 set to 2.95707\n", " SHIFTA2 set to -1.94995\n", - "Imset 2 End 14:21:29 EDT\n", - "Imset 3 Begin 14:21:29 EDT\n", + "Imset 2 End 16:09:24 EDT\n", + "Imset 3 Begin 16:09:24 EDT\n", " SHIFTA1 set to 2.95707\n", " SHIFTA2 set to -1.94995\n", - "Imset 3 End 14:21:29 EDT\n", - "Imset 4 Begin 14:21:29 EDT\n", + "Imset 3 End 16:09:24 EDT\n", + "Imset 4 Begin 16:09:24 EDT\n", " SHIFTA1 set to 2.95707\n", " SHIFTA2 set to -1.94995\n", - "Imset 4 End 14:21:29 EDT\n", + "Imset 4 End 16:09:24 EDT\n", "\n", - "End 14-Apr-2023 14:21:29 EDT\n", + "End 02-May-2023 16:09:24 EDT\n", "\n", "*** CALSTIS-12 complete ***\n", "\n", "*** CALSTIS-7 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 14-Apr-2023 14:21:29 EDT\n", + "Begin 02-May-2023 16:09:24 EDT\n", "Input ./new_dark/oeik1s030_crj.fits\n", "Output ./new_dark/oeik1s030_sx2.fits\n", "OBSMODE ACCUM\n", "APERTURE 52X2\n", "OPT_ELEM G750L\n", "DETECTOR CCD\n", - "Imset 1 Begin 14:21:30 EDT\n", + "Imset 1 Begin 16:09:28 EDT\n", "\n", "HELCORR PERFORM\n", "HELCORR COMPLETE\n", "\n", - "Order 1 Begin 14:21:30 EDT\n", + "Order 1 Begin 16:09:28 EDT\n", "\n", "X2DCORR PERFORM\n", "DISPCORR PERFORM\n", @@ -1130,10 +1130,10 @@ "SPTRCTAB DESCRIP =Lindler/Bohlin/Dressel/Holfeltz postlaunch calibration\n", "\n", "FLUXCORR PERFORM\n", - "PHOTTAB oref$64719312o_pht.fits\n", + "PHOTTAB oref$74e15479o_pht.fits\n", "PHOTTAB PEDIGREE=INFLIGHT 27/02/1997 14/04/1998\n", - "PHOTTAB DESCRIP =Updated sensitivity curves for G230LB and G430L\n", - "PHOTTAB DESCRIP =Bohlin Sensitivity of 6-Aug-2003\n", + "PHOTTAB DESCRIP =Updated sensitivity curves for G750L\n", + "PHOTTAB DESCRIP =Updated Sensitivities 2023\n", "APERTAB oref$y2r1559to_apt.fits\n", "APERTAB PEDIGREE=MODEL\n", "APERTAB DESCRIP =Added/updated values for 31X0.05NDA,31X0.05NDB,31X0.05NDC apertures\n", @@ -1148,15 +1148,15 @@ "FLUXCORR COMPLETE\n", "X2DCORR COMPLETE\n", "DISPCORR COMPLETE\n", - "Order 1 End 14:21:30 EDT\n", - "Imset 1 End 14:21:30 EDT\n", + "Order 1 End 16:09:29 EDT\n", + "Imset 1 End 16:09:29 EDT\n", "\n", - "End 14-Apr-2023 14:21:30 EDT\n", + "End 02-May-2023 16:09:29 EDT\n", "\n", "*** CALSTIS-7 complete ***\n", "\n", "*** CALSTIS-6 -- Version 3.4.2 (19-Jan-2018) ***\n", - "Begin 14-Apr-2023 14:21:30 EDT\n", + "Begin 02-May-2023 16:09:29 EDT\n", "\n", "Input ./new_dark/oeik1s030_crj.fits\n", "Output ./new_dark/oeik1s030_sx1.fits\n", @@ -1174,9 +1174,9 @@ "SPTRCTAB PEDIGREE=INFLIGHT 02/09/1997 13/02/1998\n", "SPTRCTAB DESCRIP =New traces to account for angle change with time\n", "\n", - "Imset 1 Begin 14:21:31 EDT\n", + "Imset 1 Begin 16:09:29 EDT\n", " Input read into memory.\n", - "Order 1 Begin 14:21:32 EDT\n", + "Order 1 Begin 16:09:30 EDT\n", "X1DCORR PERFORM\n", "BACKCORR PERFORM\n", "******** Calling Slfit ***********BACKCORR COMPLETE\n", @@ -1199,9 +1199,9 @@ "HELCORR PERFORM\n", "HELCORR COMPLETE\n", "\n", - "PHOTTAB oref$64719312o_pht.fits\n", + "PHOTTAB oref$74e15479o_pht.fits\n", "PHOTTAB PEDIGREE=INFLIGHT 27/02/1997 16/03/2021\n", - "PHOTTAB DESCRIP =Updated sensitivity curves for G230LB and G430L\n", + "PHOTTAB DESCRIP =Updated sensitivity curves for G750L\n", "APERTAB oref$y2r1559to_apt.fits\n", "APERTAB PEDIGREE=MODEL\n", "APERTAB DESCRIP =Added/updated values for 31X0.05NDA,31X0.05NDB,31X0.05NDC apertures\n", @@ -1217,29 +1217,23 @@ "CCDTAB oref$16j1600do_ccd.fits\n", "CCDTAB PEDIGREE=GROUND\n", "CCDTAB DESCRIP =Updated amp=D gain=4 atodgain and corresponding readnoise values---\n", - "CCDTAB DESCRIP =Oct. 1996 Air Calibration\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "CCDTAB DESCRIP =Oct. 1996 Air Calibration\n", "CTECORR COMPLETE\n", "FLUXCORR COMPLETE\n", "SGEOCORR OMIT\n", "\n", " Row 1 written to disk.\n", - "Order 1 End 14:21:32 EDT\n", + "Order 1 End 16:09:30 EDT\n", "\n", " trace was rotated by = 0.0878885 degree.\n", - "Imset 1 End 14:21:32 EDT\n", + "Imset 1 End 16:09:30 EDT\n", "\n", "Warning Keyword `XTRACALG' is being added to header.\n", - "End 14-Apr-2023 14:21:32 EDT\n", + "End 02-May-2023 16:09:30 EDT\n", "\n", "*** CALSTIS-6 complete ***\n", "\n", - "End 14-Apr-2023 14:21:32 EDT\n", + "End 02-May-2023 16:09:30 EDT\n", "\n", "*** CALSTIS-0 complete ***\n" ] @@ -1250,7 +1244,7 @@ "if os.path.exists('./new_dark'):\n", " shutil.rmtree('./new_dark')\n", "Path('./new_dark').mkdir(exist_ok=True)\n", - "res = stistools.calstis.calstis(raw,wavecal=wav,outroot=\"./new_dark/\")\n", + "res = stistools.calstis.calstis(raw, wavecal=wav, outroot=\"./new_dark/\")\n", "assert res == 0, 'CalSTIS returned an error!'" ] }, @@ -1263,7 +1257,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -1286,7 +1280,7 @@ "source": [ "with fits.open('new_weekdark.fits') as hdu:\n", " new_weekdark_data = hdu[1].data\n", - "cb = plt.imshow(new_weekdark_data,cmap='plasma',vmax=1)\n", + "cb = plt.imshow(new_weekdark_data, cmap='plasma', vmax=1)\n", "plt.colorbar(cb)" ] }, @@ -1307,7 +1301,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 14, @@ -1330,8 +1324,8 @@ "source": [ "with fits.open(\"55a20445o_drk.fits\") as hdu:\n", " old_weekdark_data = hdu[1].data\n", - "cb = plt.imshow(new_weekdark_data/old_weekdark_data,cmap='RdBu_r', vmin=0.5, vmax=1.5)\n", - "plt.colorbar(cb,label=\"new weekdark/pipeline weekdark\")" + "cb = plt.imshow(new_weekdark_data/old_weekdark_data, cmap='RdBu_r', vmin=0.5, vmax=1.5)\n", + "plt.colorbar(cb, label=\"new weekdark/pipeline weekdark\")" ] }, { @@ -1366,21 +1360,21 @@ "# Plot the calibrated _crj images\n", "# The left panel is the defalt _crj image from the pipline\n", "# the right panel is calibrated with our customized dark file\n", - "plt.subplot(1,2,1)\n", + "plt.subplot(1, 2, 1)\n", "with fits.open(crj) as hdu:\n", " ex1 = hdu[1].data\n", - " cb = plt.imshow(ex1,vmin=0,vmax=100)\n", - " plt.colorbar(cb,fraction=0.046, pad=0.04)\n", - " plt.xlim(550,650)\n", - " plt.ylim(150,250)\n", + " cb = plt.imshow(ex1, vmin=0, vmax=100)\n", + " plt.colorbar(cb, fraction=0.046, pad=0.04)\n", + " plt.xlim(550, 650)\n", + " plt.ylim(150, 250)\n", " plt.title(\"Pipeline\")\n", - "plt.subplot(1,2,2)\n", + "plt.subplot(1, 2, 2)\n", "with fits.open(\"./new_dark/oeik1s030_crj.fits\") as hdu:\n", " ex1 = hdu[1].data\n", - " cb = plt.imshow(ex1,vmin=0,vmax=100)\n", - " plt.colorbar(cb,fraction=0.046, pad=0.04)\n", - " plt.xlim(550,650)\n", - " plt.ylim(150,250)\n", + " cb = plt.imshow(ex1, vmin=0, vmax=100)\n", + " plt.colorbar(cb, fraction=0.046, pad=0.04)\n", + " plt.xlim(550, 650)\n", + " plt.ylim(150, 250)\n", " plt.title(\"Customized Dark\")\n", "plt.tight_layout()" ] @@ -1408,7 +1402,7 @@ }, { "data": { - "image/png": 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    " ] @@ -1420,30 +1414,30 @@ } ], "source": [ - "plt.figure(figsize=(20,25))\n", + "plt.figure(figsize=(20, 25))\n", "# get the spectrum of the default pipline _sx1 data\n", - "pip = Table.read(\"./mastDownload/HST/oeik1s030/oeik1s030_sx1.fits\",1)\n", - "wl,pip_flux = pip[0][\"WAVELENGTH\",\"FLUX\"]\n", + "pip = Table.read(\"./mastDownload/HST/oeik1s030/oeik1s030_sx1.fits\", 1)\n", + "wl, pip_flux = pip[0][\"WAVELENGTH\", \"FLUX\"]\n", "# get the flux of the customized new_dark _sx1 data\n", - "cus = Table.read(\"./new_dark/oeik1s030_sx1.fits\",1)\n", - "cus_wl,cus_flux = cus[0][\"WAVELENGTH\",\"FLUX\"]\n", + "cus = Table.read(\"./new_dark/oeik1s030_sx1.fits\", 1)\n", + "cus_wl,cus_flux = cus[0][\"WAVELENGTH\", \"FLUX\"]\n", "# interpolant flux so that the wavelengths matches\n", - "interp_flux = np.interp(wl,cus_wl,cus_flux)\n", + "interp_flux = np.interp(wl, cus_wl, cus_flux)\n", "# plot the pipeline spectrum\n", - "plt.subplot(3,1,1)\n", - "plt.plot(wl,pip_flux)\n", + "plt.subplot(3, 1, 1)\n", + "plt.plot(wl, pip_flux)\n", "plt.xlabel(\"Wavelength [Å]\")\n", "plt.ylabel(\"Flux [ergs/s/cm$^2$/Å]\")\n", "plt.title(\"Pipeline Spectrum\")\n", "# plot the pipeline spectrum\n", - "plt.subplot(3,1,2)\n", - "plt.plot(cus_wl,cus_flux)\n", + "plt.subplot(3, 1, 2)\n", + "plt.plot(cus_wl, cus_flux)\n", "plt.xlabel(\"Wavelength [Å]\")\n", "plt.ylabel(\"Flux [ergs/s/cm$^2$/Å]\")\n", "plt.title(\"Recalibrated Spectrum\")\n", "# plot the spectra difference\n", - "plt.subplot(3,1,3)\n", - "plt.plot(wl,interp_flux-pip_flux)\n", + "plt.subplot(3, 1, 3)\n", + "plt.plot(wl, interp_flux-pip_flux)\n", "plt.xlabel(\"Wavelength [Å]\")\n", "plt.ylabel(\"Flux Difference [ergs/s/cm$^2$/Å]\")\n", "plt.title(\"Difference\")\n", @@ -1458,9 +1452,9 @@ "\n", "---\n", "## About this Notebook \n", - "**Author:** [Keyi Ding](kding@stsci.edu)\n", + "**Author:** Keyi Ding\n", "\n", - "**Updated On:** 2023-04-14\n", + "**Updated On:** 2023-05-18\n", "\n", "\n", "> *This tutorial was generated to be in compliance with the [STScI style guides](https://github.com/spacetelescope/style-guides) and would like to cite the [Jupyter guide](https://github.com/spacetelescope/style-guides/blob/master/templates/example_notebook.ipynb) in particular.*\n", @@ -1469,13 +1463,13 @@ "If you use `astropy`, `matplotlib`, `astroquery`, or `numpy` for published research, please cite the\n", "authors. Follow these links for more information about citations:\n", "\n", - "* [Citing `astropy`/`numpy`/`matplotlib`](https://www.scipy.org/citing.html)\n", + "* [Citing `astropy`](https://www.astropy.org/acknowledging.html)/[`numpy`](https://numpy.org/citing-numpy/)/[`matplotlib`](https://matplotlib.org/stable/users/project/citing.html)\n", "* [Citing `astroquery`](https://astroquery.readthedocs.io/en/latest/)\n", "\n", "---\n", "\n", "[Top of Page](#top)\n", - "\"Space " + "\"Space \n" ] }, { @@ -1521,7 +1515,7 @@ "width": "409.6px" }, "toc_section_display": true, - "toc_window_display": true + "toc_window_display": false } }, "nbformat": 4, diff --git a/etc_resampling/Resampling.ipynb b/etc_resampling/Resampling.ipynb new file mode 100644 index 0000000..882a290 --- /dev/null +++ b/etc_resampling/Resampling.ipynb @@ -0,0 +1,922 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "777bb591", + "metadata": {}, + "source": [ + "# Resampling Echelle Spectrum for the Pandeia ETC" + ] + }, + { + "cell_type": "markdown", + "id": "c3c71bd6", + "metadata": {}, + "source": [ + "## Introduction\n", + "In May 2023, the Pandeia Exposure Time Calculator (ETC) system is released and replaces the old ETC system for the Hubble Space Telescope (HST). Pandeia is a pixel-based exposure time calculator paired with a modern graphical user interface. It's based on a Python engine that calculates three-dimensional data cubes from user-specified spatial and spectral properties of one or more sources.\n", + "\n", + "Pandeia has the functionality to conduct the ETC calculation based on user-supplied STIS spectra. While STIS CCD and FUV-MAMA spectra can be directly uploaded, the resolution of the NUV-MAMA echelle spectroscopy is too high for the ETC to parse the spectra.\n", + "\n", + "In this notebook, we demonstrate the steps to downsample NUV-MAMA echelle spectra to reduce resolution, and to reformat the spectra fits file for the ETC. (The file reformatting is applicable to CCD and FUV-MAMA as well)." + ] + }, + { + "cell_type": "markdown", + "id": "a1cc846a", + "metadata": {}, + "source": [ + "### Import Necessary Packages\n", + "- `astropy.io.fits` and `astropy.table.Table` for accessing FITS files\n", + "- `astroquery.mast.Observations` for finding and downloading data from the [MAST](https://mast.stsci.edu/portal/Mashup/Clients/Mast/Portal.html) archive\n", + "- `astropy.units` for specifying astronomical units\n", + "- `numpy` to handle array functions\n", + "- `stistools.splice` for manipulating echelle spectra\n", + "- `specutils.Spectrum1D` and `specutils.manipulation.FluxConservingResampler` for resampling spectra\n", + "- `matplotlib` for plotting data" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "a9cbaa8d", + "metadata": {}, + "outputs": [], + "source": [ + "# Import for: Reading in fits file\n", + "from astropy.io import fits\n", + "from astropy.table import Table\n", + "\n", + "# Import for: Downloading necessary files.\n", + "# (Not necessary if you choose to collect data from MAST)\n", + "from astroquery.mast import Observations\n", + "\n", + "# Import for: Specifying astronomical units\n", + "import astropy.units as u\n", + "\n", + "# Import for: Quick Calculation and Data Analysis\n", + "import numpy as np\n", + "\n", + "# Import for: Manipulating STIS Spectra\n", + "from stistools.splice import splice\n", + "\n", + "# Import for: Resampling Spectra\n", + "from specutils import Spectrum1D\n", + "from specutils.manipulation import FluxConservingResampler \n", + "\n", + "# Import for: Plotting and specifying plotting parameters\n", + "import matplotlib\n", + "%matplotlib inline\n", + "from matplotlib import pyplot as plt\n", + "matplotlib.rcParams['figure.figsize'] = [15, 5]\n", + "\n", + "import pysynphot" + ] + }, + { + "cell_type": "markdown", + "id": "1f495d78", + "metadata": {}, + "source": [ + "### Collect Data Set From the MAST Archive Using Astroquery\n", + "There are other ways to download data from MAST such as using CyberDuck. The steps of collecting data is beyond the scope of this notebook, and we are only showing how to use astroquery.\n", + "\n", + "The dataset OCTX01030 is observed in NUV-MAMA echelle mode with the E230M grating and $0.2 \\times 0.2$ aperture. The exposure time is 1872 seconds" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c8908cb8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: Found cached file ./mastDownload/HST/octx01030/octx01030_x1d.fits with expected size 7629120. [astroquery.query]\n" + ] + }, + { + "data": { + "text/plain": [ + "'./mastDownload/HST/octx01030/octx01030_x1d.fits'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Search target object by obs_id\n", + "obs = Observations.query_criteria(obs_id=['OCTX01030'])\n", + "pl = Observations.get_product_list(obs)\n", + "# Download the _x1d spectrum\n", + "pl = pl[pl['productSubGroupDescription'].filled() == 'X1D']\n", + "dl = Observations.download_products(pl)\n", + "filename = dl['Local Path'][0]\n", + "filename" + ] + }, + { + "cell_type": "markdown", + "id": "972920bf", + "metadata": {}, + "source": [ + "## Introduction to STIS Echelle Spectroscopy\n", + "In echelle spectroscopy, the spectra is dispersed into different spectral orders to have higher spectral resolution. STIS has four echelle grating modes which provide spectroscopic coverage from ~1145 Å to 3100 Å at resolving powers from R ~30,000 to R ~114,000.\n", + "\n", + "In STIS fits files, the echelle data is organized in a table format such that each spectral order (SPORDER) is stored in a single row of the table. The complete spectra can be accessed by iterating over the rows in the first extension.\n", + "\n", + "For more information, see [Echelle Spectroscopy in the Ultraviolet](https://hst-docs.stsci.edu/stisihb/chapter-4-spectroscopy/4-3-echelle-spectroscopy-in-the-ultraviolet) and [view_data](https://htmlpreview.github.io/?https://github.com/spacetelescope/STIS-Notebooks/blob/main/view_data/view_data.html)." + ] + }, + { + "cell_type": "markdown", + "id": "958149b6", + "metadata": {}, + "source": [ + "### Plotting Echelle Spectra\n", + "We first read in the echelle spectra from the fits file first extension, and plot the spectra of different SPORDER in alternative colors. As shown in the plot, adjacent spectral orders have wavelength overlaps." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "287ffe21", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-2e-13, 3e-12)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Read in data from the first extension\n", + "d = fits.getdata(filename, ext=1)\n", + "\n", + "# Iterate through the spectral orders and plot the spectrum in alternative colors\n", + "for order in d:\n", + " g = (order['DQ'] & fits.getval(filename, ext=1, keyword='SDQFLAGS')) == 0\n", + " plt.plot(order['WAVELENGTH'][g], order['FLUX'][g], alpha=0.6, linewidth=1, color=f\"C{order['SPORDER'] % 2}\")\n", + "\n", + "plt.xlabel('Wavelength (Å)')\n", + "plt.ylabel('Flux (ergs/s/cm$^{2}$/Å)')\n", + "plt.grid(True, alpha=0.2)\n", + "plt.ylim(-2e-13, 3e-12)" + ] + }, + { + "cell_type": "markdown", + "id": "65ed37f2", + "metadata": {}, + "source": [ + "### Splicing Echelle Spectra\n", + "TODO: add some explanation on splice" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f7ff9a79", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-2e-13, 3e-12)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "spliced = splice(filename)\n", + "plt.plot(spliced['WAVELENGTH'], spliced['FLUX'], linewidth=1)\n", + "\n", + "plt.xlabel('Wavelength (Å)')\n", + "plt.ylabel('Flux (ergs/s/cm$^{2}$/Å)')\n", + "plt.grid(True, alpha=0.2)\n", + "plt.ylim(-2e-13, 3e-12)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "dc59eb55", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    Table length=19355\n", + "

    Local PathStatusMessageURL
    str47str8objectobject
    ./mastDownload/HST/odj101050/odj101050_raw.fitsCOMPLETENoneNone
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    WAVELENGTHFLUXERRORDQ
    float64float64float64int64
    2274.303666679877-1.849007702319199e-154.733922220160965e-172628
    2274.341169378231-7.661628493096928e-146.486138382336452e-162628
    2274.3786718871556-7.851790777887308e-143.647482774257922e-142628
    2274.416174206652-1.4809891662093295e-141.4965484838240332e-142628
    2274.4536763367231.167646013921662e-142.9525867699126823e-142628
    2274.49117827737253.008735906359812e-144.1828787476902093e-14576
    ............
    3118.6623277392093.4798355603299358e-123.031892770698208e-132048
    3118.7123584783363.080259354873438e-122.894739569575533e-132048
    3118.76238893072472.9949254881644904e-122.8455005279121037e-132048
    3118.812419096381.7453214437926357e-122.2274707306511182e-132048
    3118.8624489753071.6564510180172576e-122.1144121609834032e-132564
    3118.91247856751121.342889449813811e-121.9490850177317914e-132564
    3118.96250787299641.4040871330195381e-121.99993830090478e-132564
    " + ], + "text/plain": [ + "\n", + " WAVELENGTH FLUX ERROR DQ \n", + " float64 float64 float64 int64\n", + "------------------ ----------------------- ---------------------- -----\n", + " 2274.303666679877 -1.849007702319199e-15 4.733922220160965e-17 2628\n", + " 2274.341169378231 -7.661628493096928e-14 6.486138382336452e-16 2628\n", + "2274.3786718871556 -7.851790777887308e-14 3.647482774257922e-14 2628\n", + " 2274.416174206652 -1.4809891662093295e-14 1.4965484838240332e-14 2628\n", + " 2274.453676336723 1.167646013921662e-14 2.9525867699126823e-14 2628\n", + "2274.4911782773725 3.008735906359812e-14 4.1828787476902093e-14 576\n", + " ... ... ... ...\n", + " 3118.662327739209 3.4798355603299358e-12 3.031892770698208e-13 2048\n", + " 3118.712358478336 3.080259354873438e-12 2.894739569575533e-13 2048\n", + "3118.7623889307247 2.9949254881644904e-12 2.8455005279121037e-13 2048\n", + " 3118.81241909638 1.7453214437926357e-12 2.2274707306511182e-13 2048\n", + " 3118.862448975307 1.6564510180172576e-12 2.1144121609834032e-13 2564\n", + "3118.9124785675112 1.342889449813811e-12 1.9490850177317914e-13 2564\n", + "3118.9625078729964 1.4040871330195381e-12 1.99993830090478e-13 2564" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spliced" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "deea24cb", + "metadata": {}, + "outputs": [], + "source": [ + "t = Table(data={'WAVELENGTH': spliced['WAVELENGTH'], 'FLUX': spliced['FLUX'],},\n", + " units={'WAVELENGTH': u.Angstrom, 'FLUX': u.erg * u.s**-1 * u.cm**-2 * u.Angstrom**-1})\n", + "\n", + "t.write('echelle_nuv_full_spec.fits', overwrite=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9788c497", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sampling: 19355 --> 4224 (21.8%)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kding/miniconda3/envs/stistools_leo/lib/python3.9/site-packages/astropy/units/quantity.py:673: RuntimeWarning: invalid value encountered in divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" + ] + } + ], + "source": [ + "fluxcon = FluxConservingResampler()\n", + "lamb = spliced['WAVELENGTH']\n", + "flux = spliced['FLUX']\n", + "\n", + "# Zero out Lyman-𝜶 air glow line:\n", + "flux[(lamb >= (1215.67 - 5)) & (lamb < 1215.67 + 5)] = 0.\n", + "\n", + "# Apply units:\n", + "lamb *= u.AA\n", + "flux *= u.Unit('erg cm-2 s-1 AA-1')\n", + "\n", + "input_spec = Spectrum1D(spectral_axis=lamb, flux=flux)\n", + "\n", + "new_disp_grid = np.arange(spliced['WAVELENGTH'][0], spliced['WAVELENGTH'][-1], 0.2) * u.AA\n", + "print (f\"Sampling: {len(lamb)} --> {len(new_disp_grid)} ({len(new_disp_grid) / len(lamb) * 100:.1f}%)\")\n", + "new_spec_fluxcon = fluxcon(input_spec, new_disp_grid)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "f3d84f70", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2232.070724620221, 3161.1954499326525)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2, 1)\n", + "fig.set_size_inches(15, 5*len(axes))\n", + "\n", + "mask = (new_spec_fluxcon.flux > 0) \n", + "axes[0].plot(spliced['WAVELENGTH'], spliced['FLUX'], linewidth=1, label='original', alpha=0.5)\n", + "axes[0].plot(new_spec_fluxcon.wavelength[mask], new_spec_fluxcon.flux[mask], label='sampled', alpha=0.5)\n", + "axes[0].set_ylim(0,0.3e-11)\n", + "axes[0].legend(loc='best')\n", + "axes[0].set_ylabel('Flux (ergs/s/cm$^{2}$/Å)')\n", + "\n", + "bp = pysynphot.ObsBandpass('stis,E230M')\n", + "axes[1].plot(bp.wave, bp.throughput)\n", + "axes[1].set_xlabel('Wavelength (Å)')\n", + "axes[1].set_ylabel('Throughput')\n", + "axes[1].set_xlim(*axes[0].get_xlim())" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "18e911c6", + "metadata": {}, + "outputs": [], + "source": [ + "def rle(inarray):\n", + " \"\"\"Run Length Encoding\n", + " Partial credit to R rle function. \n", + " Multi datatype arrays catered for including non-Numpy\n", + "\n", + " From https://stackoverflow.com/a/32681075\n", + "\n", + " RETURNS\n", + " -------\n", + " tuple (runlengths, startpositions, values)\n", + " \"\"\"\n", + " ia = np.asarray(inarray) # force numpy\n", + " n = len(ia)\n", + " if n == 0: \n", + " return (None, None, None)\n", + " else:\n", + " y = ia[1:] != ia[:-1] # pairwise unequal (string safe)\n", + " i = np.append(np.where(y), n - 1) # must include last element posi\n", + " z = np.diff(np.append(-1, i)) # run lengths\n", + " p = np.cumsum(np.append(0, z))[:-1] # positions\n", + "\n", + " return z, p, ia[i]" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "17503981", + "metadata": {}, + "outputs": [], + "source": [ + "wave = spliced['WAVELENGTH']\n", + "flux = spliced['FLUX']\n", + "runlengths, startpositions, values = rle(spliced['FLUX'])\n", + "smooth_indices = (runlengths >= 10) & (values > 0)\n", + "smooth_start = startpositions[smooth_indices]\n", + "smooth_stop = startpositions[smooth_indices] + runlengths[smooth_indices] # exclusive index on right" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "703822cc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([], dtype=int64)" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "smooth_start" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "1c23cdb7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1100.0, 1500.0)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "wave_spliced = []\n", + "flux_spliced = []\n", + "i = 0\n", + "for start, stop, steps in zip(smooth_start, smooth_stop, runlengths[smooth_indices]):\n", + " if i != 0:\n", + " # Prior segment of line:\n", + " wave_spliced += list(wave[i:start])\n", + " flux_spliced += list(flux[i:start])\n", + "\n", + " # Segment of continuum:\n", + " w = np.linspace(wave[start], wave[stop], num=steps//30, endpoint=False)\n", + " f = spl(w)\n", + " wave_spliced += list(w)\n", + " flux_spliced += list(f)\n", + "\n", + " i = stop\n", + "\n", + "wave_spliced += list(wave[i:])\n", + "flux_spliced += list(wave[i:])\n", + "\n", + "wave_spliced = np.array(wave_spliced)\n", + "flux_spliced = np.array(flux_spliced)\n", + "\n", + "plt.plot(wave, flux, alpha=0.6, label='Orig')\n", + "plt.axhline(1e-14, alpha=0.3, color='k')\n", + "plt.plot(wave_spliced, flux_spliced, alpha=0.6, label='Reduced')\n", + "plt.legend(loc='best')\n", + "\n", + "plt.grid(True, alpha=0.2)\n", + "plt.axvspan(1134, 1431, color='C1', alpha=0.2)\n", + "plt.xlim(1100, 1500)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b9005c7f", + "metadata": {}, + "outputs": [], + "source": [ + "t = Table(data={'WAVELENGTH': new_spec_fluxcon.wavelength[mask], 'FLUX': new_spec_fluxcon.flux[mask],},\n", + " units={'WAVELENGTH': u.Angstrom, 'FLUX': u.erg * u.s**-1 * u.cm**-2 * u.Angstrom**-1})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b467f704", + "metadata": {}, + "outputs": [], + "source": [ + "t.write('echelle_nuv_sampled_spec.fits', overwrite=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cbfa5bb3", + "metadata": {}, + "outputs": [], + "source": [ + "np.mean(spliced['WAVELENGTH'][1:-1]-spliced['WAVELENGTH'][0:-2])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d0f82052", + "metadata": {}, + "outputs": [], + "source": [ + "min(spliced['WAVELENGTH'])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "040414bf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SIMPLE = T / file does conform to FITS standard \n", + "BITPIX = 16 / number of bits per data pixel \n", + "NAXIS = 0 / number of data axes \n", + "EXTEND = T / FITS file may contain extensions \n", + "COMMENT FITS (Flexible Image Transport System) format is defined in 'Astronomy\n", + "COMMENT and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H \n", + "ORIGIN = 'HSTIO/CFITSIO March 2010' / FITS file originator \n", + "DATE = '2022-12-19' / date this file was written (yyyy-mm-dd) \n", + "NEXTEND = 8 / Number of extensions \n", + "FILENAME= 'octx01030_x1d.fits' / name of file \n", + "FILETYPE= 'SCI ' / type of data found in data file \n", + " \n", + "TELESCOP= 'HST' / telescope used to acquire data \n", + "INSTRUME= 'STIS ' / identifier for instrument used to acquire data \n", + "EQUINOX = 2000.0 / equinox of celestial coord. system \n", + " \n", + " / DATA DESCRIPTION KEYWORDS \n", + " \n", + "ROOTNAME= 'octx01030 ' / rootname of the observation set\n", + "PRIMESI = 'STIS ' / instrument designated as prime \n", + " \n", + " / TARGET INFORMATION \n", + " \n", + "TARGNAME= '-RHO-CNC ' / proposer's target name \n", + "RA_TARG = 1.331466900606E+02 / right ascension of the target (deg) (J2000) \n", + "DEC_TARG= 2.832976685276E+01 / declination of the target (deg) (J2000) \n", + " \n", + " / PROPOSAL INFORMATION \n", + " \n", + "PROPOSID= 14094 / PEP proposal identifier \n", + "LINENUM = '01.009 ' / proposal logsheet line number \n", + "PR_INV_L= 'Bourrier ' / last name of principal investigator\n", + "PR_INV_F= 'Vincent ' / first name of principal investigator \n", + "PR_INV_M= ' ' / middle name / initial of principal investigat\n", + " \n", + " / SUMMARY EXPOSURE INFORMATION \n", + " \n", + "TDATEOBS= '2016-04-17' / UT date of start of first exposure in file \n", + "TTIMEOBS= '18:27:57' / UT start time of first exposure in file \n", + "TEXPSTRT= 5.749576941854E+04 / start time (MJD) of 1st exposure in file \n", + "TEXPEND = 57495.79302984 / end time (MJD) of last exposure in the file \n", + "TEXPTIME= 1872. / total exposure time (seconds) \n", + "QUALCOM1= ' '\n", + "QUALCOM2= ' '\n", + "QUALCOM3= ' '\n", + "QUALITY = ' '\n", + " \n", + " / TARGET OFFSETS (POSTARGS) \n", + " \n", + "POSTARG1= 0.000000 / POSTARG in axis 1 direction \n", + "POSTARG2= 0.000000 / POSTARG in axis 2 direction \n", + " \n", + " / DIAGNOSTIC KEYWORDS \n", + " \n", + "OVERFLOW= 0 / Number of science data overflows \n", + "PROCTIME= 5.993286201389E+04 / pipeline processing time (MJD) \n", + "OPUS_VER= 'HSTDP 2022_3 ' / data processing software system versi\n", + "AWSYSVER= 'v0.4.38 ' / cloud infrastructure package version \n", + "AWSDPVER= 'v0.2.20 ' / cloud docker image version \n", + "CSYS_VER= 'caldp_20221010' / calibration software system version id \n", + "CAL_VER = '3.4.2 (19-Jan-2018)' / CALSTIS code version \n", + " \n", + " / SCIENCE INSTRUMENT CONFIGURATION \n", + " \n", + "CFSTATUS= 'SUPPORTED ' / configuration status (support., avail., eng.) \n", + "OBSTYPE = 'SPECTROSCOPIC ' / observation type - imaging or spectroscopic \n", + "OBSMODE = 'ACCUM ' / operating mode \n", + "PHOTMODE= 'STIS 0.2X0.2 E230M NUVMAMA 2707 MJD#57495.7694' / obser \n", + "SCLAMP = 'NONE ' / lamp status, NONE or name of lamp which is on \n", + "LAMPSET = '0.0 ' / spectral cal lamp current value (milliamps) \n", + "NRPTEXP = 8 / number of repeat exposures in set: default 1 \n", + "SUBARRAY= F / data from a subarray (T) or full frame (F) \n", + "DETECTOR= 'NUV-MAMA ' / detector in use: NUV-MAMA, FUV-MAMA, or CCD \n", + "OPT_ELEM= 'E230M ' / optical element in use \n", + "APERTURE= '0.2X0.2 ' / aperture name \n", + "PROPAPER= '0.2X0.2 ' / proposed aperture name \n", + "FILTER = 'Clear ' / filter in use \n", + "APER_FOV= '0.2x0.2 ' / aperture field of view \n", + "CENWAVE = 2707 / central wavelength of spectrum \n", + "REPELLER= 'OFF' / repeller voltage: applies only to fuv-mama \n", + " \n", + " / MAMA OFFSETS \n", + " \n", + "MOFFSET1= 0 / axis 1 MAMA offset (low-res pixels) \n", + "MOFFSET2= 0 / axis 2 MAMA offset (low-res pixels) \n", + " \n", + " / LOCAL RATE CHECK IMAGE \n", + " \n", + "LRC_XSTS= F / Local Rate check image exists (T/F) \n", + "LRC_FAIL= F / Local Rate Check Failed (T/F) \n", + " \n", + " / READOUT DEFINITION PARAMETERS \n", + " \n", + "CENTERA1= 513 / subarray axis1 center pt in unbinned dect. pix \n", + "CENTERA2= 513 / subarray axis2 center pt in unbinned dect. pix \n", + "SIZAXIS1= 1024 / subarray axis1 size in unbinned detector pixels\n", + "SIZAXIS2= 1024 / subarray axis2 size in unbinned detector pixels\n", + "BINAXIS1= 1 / axis1 data bin size in unbinned detector pixels\n", + "BINAXIS2= 1 / axis2 data bin size in unbinned detector pixels\n", + " \n", + " / CALIBRATION SWITCHES: PERFORM, OMIT, COMPLETE \n", + " \n", + "DQICORR = 'COMPLETE' / data quality initialization \n", + "RPTCORR = 'PERFORM ' / add individual repeat observations \n", + "DOPPCORR= 'PERFORM ' / convolve ref. files with orbital Doppler shift \n", + "LORSCORR= 'COMPLETE' / Convert MAMA data to Lo-Res before processing \n", + "GLINCORR= 'COMPLETE' / correct for global detector non-linearities \n", + "LFLGCORR= 'COMPLETE' / flag pixels for local and global nonlinearities\n", + "DARKCORR= 'COMPLETE' / Subtract dark image \n", + "FLATCORR= 'COMPLETE' / flat field data \n", + "STATFLAG= T / Calculate statistics? \n", + "WAVECORR= 'COMPLETE' / use wavecal to adjust wavelength zeropoint \n", + "X1DCORR = 'COMPLETE' / Perform 1-D spectral extraction \n", + "BACKCORR= 'COMPLETE' / subtract background (sky and interorder) \n", + "HELCORR = 'COMPLETE' / convert to heliocenttric wavelengths \n", + "DISPCORR= 'COMPLETE' / apply 2-dimensional dispersion solutions \n", + "FLUXCORR= 'COMPLETE' / convert to absolute flux units \n", + "X2DCORR = 'OMIT ' / rectify 2-D spectral image \n", + "SC2DCORR= 'COMPLETE' / 2-D scattered light correction algorithm \n", + " \n", + " / CALIBRATION REFERENCE FILES \n", + " \n", + "BPIXTAB = 'oref$v2s21472o_bpx.fits' / bad pixel table \n", + "DARKFILE= 'oref$tcl1742bo_drk.fits' / dark image file name \n", + "PFLTFILE= 'oref$mbj1658do_pfl.fits' / pixel to pixel flat field file name \n", + "LFLTFILE= 'oref$h5s1140ko_lfl.fits' / low order flat \n", + "PHOTTAB = 'oref$6cg2020mo_pht.fits' / Photometric throughput table \n", + "IMPHTTAB= 'oref$59l1632po_imp.fits' / Imaging photometric table \n", + "APERTAB = 'oref$y2r1559to_apt.fits' / relative aperture throughput table \n", + "MLINTAB = 'oref$j9r16559o_lin.fits' / MAMA linearity correction table \n", + "WAVECAL = 'octx01030_wav.fits ' / wavecal image file name \n", + "APDESTAB= 'oref$16j16005o_apd.fits' / aperture description table \n", + "SPTRCTAB= 'oref$q8l14502o_1dt.fits' / spectrum trace table \n", + "DISPTAB = 'oref$m7p16111o_dsp.fits' / dispersion coefficient table \n", + "INANGTAB= 'oref$l3m1437ro_iac.fits' / incidence angle correction table \n", + "LAMPTAB = 'oref$l421050oo_lmp.fits' / template calibration lamp spectra table \n", + "SDCTAB = 'oref$16j1600bo_sdc.fits' / 2-D spatial distortion correction table \n", + "XTRACTAB= 'oref$l3m1437to_1dx.fits' / parameters for 1-D spectral extraction tab\n", + "PCTAB = 'oref$y2r16003o_pct.fits' / Photometry correction table \n", + "MOFFTAB = 'oref$h4s1350ko_moc.fits' / MAMA offsets table \n", + "WCPTAB = 'oref$16j1600co_wcp.fits' / wavecal parameters table \n", + "CDSTAB = 'oref$k8m0958bo_cds.fits' / Cross-Disperser Scattering table \n", + "ECHSCTAB= 'oref$k8m0958co_ech.fits' / Echelle Scattering table \n", + "EXSTAB = 'oref$k8m0958do_exs.fits' / Echelle Cross-Dispersion Scattering table \n", + "RIPTAB = 'oref$6cg2020po_rip.fits' / Echelle Ripple table \n", + "HALOTAB = 'oref$k8m0958eo_hal.fits' / Detector Halo table \n", + "TELTAB = 'oref$k8m0958ho_tel.fits' / Telescope Point Spread Function table \n", + "SRWTAB = 'oref$k8m0958go_srw.fits' / Scattering Reference Wavelengths table \n", + "TDSTAB = 'oref$3ah15289o_tds.fits' / time-dependent sensitivity algorithm used \n", + "TDCTAB = 'oref$36k1559ko_tdc.fits' / Coefficient table for NUV MAMA dark scalin\n", + "GACTAB = 'N/A ' / grating-aperture correction table \n", + " \n", + " / TARGET ACQUISITION DATASET IDENTIFIERS \n", + " \n", + "ACQNAME = 'octx01U7% ' / rootname of acquisition exposure \n", + "ACQTYPE = ' ' / type of acquisition \n", + "PEAKNAM1= 'octx01V6% ' / rootname of 1st peakup exposure \n", + "PEAKNAM2= ' ' / rootname of 2nd peakup exposure \n", + " \n", + " / OTFR KEYWORDS \n", + " \n", + "T_SGSTAR= ' ' / OMS calculated guide star control \n", + " \n", + " / PATTERN KEYWORDS \n", + " \n", + "PATTERN1= 'NONE ' / primary pattern type \n", + "P1_SHAPE= ' ' / primary pattern shape \n", + "P1_PURPS= ' ' / primary pattern purpose \n", + "P1_NPTS = 0 / number of points in primary pattern \n", + "P1_PSPAC= 0.000000 / point spacing for primary pattern (arc-sec) \n", + "P1_LSPAC= 0.000000 / line spacing for primary pattern (arc-sec) \n", + "P1_ANGLE= 0.000000 / angle between sides of parallelogram patt (deg)\n", + "P1_FRAME= ' ' / coordinate frame of primary pattern \n", + "P1_ORINT= 0.000000 / orientation of pattern to coordinate frame (deg\n", + "P1_CENTR= ' ' / center pattern relative to pointing (yes/no) \n", + " \n", + " / ARCHIVE SEARCH KEYWORDS \n", + " \n", + "BANDWID = 808.0 / bandwidth of the data \n", + "SPECRES = 30000.0 / approx. resolving power at central wavelength \n", + "CENTRWV = 2707.0 / central wavelength of the data \n", + "MINWAVE = 2303.0 / minimum wavelength in spectrum \n", + "MAXWAVE = 3111.0 / maximum wavelength in spectrum \n", + "PLATESC = 2.9000016E-02 / plate scale (arcsec/pixel) \n", + " \n", + " / PAPER PRODUCT SUPPORT KEYWORDS \n", + " \n", + "PROPTTL1= 'Characterization of the extended atmosphere and the nature of the ho'\n", + "PROPTTL2= 't super-Earth 55 Cnc e and the warm Jupiter 55 Cnc b '\n", + "OBSET_ID= '01' / observation set id \n", + "TARDESCR= 'STAR;EXTRA-SOLAR PLANETARY SYSTEM;G V-IV '\n", + "MTFLAG = ' ' / moving target flag; T if it is a moving target \n", + "PARALLAX= 8.103000000000E-02 / target parallax from proposal \n", + "MU_RA = -4.858000000000E-01 / target proper motion from proposal (degrees RA)\n", + "MU_DEC = -2.340500000000E-01 / target proper motion from proposal (deg. DEC) \n", + "MU_EPOCH= 'J2000.0 ' / epoch of proper motion from proposal \n", + " \n", + " / ASSOCIATION KEYWORDS \n", + " \n", + "ASN_ID = 'OCTX01030 ' / unique identifier assigned to association \n", + "ASN_TAB = 'octx01030_asn.fits ' / name of the association table \n", + " \n", + " / POINTING INFORMATION \n", + " \n", + "PA_V3 = 287.844391 / position angle of V3-axis of HST (deg) \n", + "CRDS_CTX= 'hst_1058.pmap' \n", + "CRDS_VER= '11.16.9, b11.4.0, 64d96076d89b32a5687a6b77bb910ab93b3a99b3' \n", + "BIASFILE= 'N/A ' \n", + "CCDTAB = 'N/A ' \n", + "CRREJTAB= 'N/A ' \n", + "IDCTAB = 'N/A ' \n", + "HISTORY DQICORR complete ... \n", + "HISTORY values checked for saturation \n", + "HISTORY DQ array initialized ... \n", + "HISTORY reference table oref$v2s21472o_bpx.fits \n", + "HISTORY INFLIGHT 14/05/1997 19/07/2010 \n", + "HISTORY bpixtab updated with vignetted corners. opt_elem column added.----- \n", + "HISTORY Uncertainty array initialized. \n", + "HISTORY LORSCORR complete; image converted from high-res to low-res. \n", + "HISTORY GLINCORR complete ... \n", + "HISTORY LFLGCORR complete ... \n", + "HISTORY reference table oref$j9r16559o_lin.fits \n", + "HISTORY GROUND \n", + "HISTORY T. Danks gathered Info \n", + "HISTORY T. Danks Gathered Info \n", + "HISTORY DARKCORR complete ... \n", + "HISTORY reference image oref$tcl1742bo_drk.fits \n", + "HISTORY INFLIGHT 06/01/2009 \n", + "HISTORY Avg of 72 post-SM4 dark frames from 11395, 11402 and 11857--------- \n", + "HISTORY reference table oref$36k1559ko_tdc.fits \n", + "HISTORY INFLIGHT 01/01/2014 10/06/2019 \n", + "HISTORY Post-SM4 dark correction table with 2 additional break points. \n", + "HISTORY FLATCORR complete ... \n", + "HISTORY reference image oref$mbj1658do_pfl.fits \n", + "HISTORY INFLIGHT 17/04/1998 17/05/2002 \n", + "HISTORY On-orbit NUV P-flat created from proposals 7647,8429,8863,8923 \n", + "HISTORY reference image oref$h5s1140ko_lfl.fits \n", + "HISTORY DUMMY \n", + "HISTORY Dummy file created by R. Katsanis \n", + "HISTORY DOPPCORR applied to DQICORR, DARKCORR, FLATCORR \n", + "HISTORY WAVECORR complete ... \n", + "HISTORY wavecal = octx01030_fwv_tmp.fits \n", + "HISTORY X1DCORR performed ... \n", + "HISTORY reference table oref$q8l14502o_1dt.fits \n", + "HISTORY INFLIGHT 27/02/1997 06/02/1999 \n", + "HISTORY New G230L traces and updated Echelle A2CENTER values \n", + "HISTORY reference table oref$l3m1437to_1dx.fits \n", + "HISTORY GROUND \n", + "HISTORY Analysis from prop. 7064 and ground data \n", + "HISTORY BACKCORR performed ... \n", + "HISTORY reference table oref$l3m1437to_1dx.fits \n", + "HISTORY GROUND \n", + "HISTORY Analysis from prop. 7064 and ground data \n", + "HISTORY DISPCORR performed ... \n", + "HISTORY reference table oref$m7p16111o_dsp.fits \n", + "HISTORY INFLIGHT 11/10/1997 29/04/1999 \n", + "HISTORY Dispersion coefficients reference table \n", + "HISTORY reference table oref$l3m1437ro_iac.fits \n", + "HISTORY Prelaunch Calibration/Lindler and Models \n", + "HISTORY reference table oref$16j16005o_apd.fits \n", + "HISTORY INFLIGHT 01/03/1997 13/06/2017 \n", + "HISTORY Aligned long-slit bar positions for single-bar cases.-------------- \n", + "HISTORY HELCORR performed (no reference file needed) \n", + "HISTORY FLUXCORR performed ... \n", + "HISTORY reference table oref$6cg2020mo_pht.fits \n", + "HISTORY INFLIGHT 27/02/1997 16/03/2021 \n", + "HISTORY Updated sensitivity curves for E230M \n", + "HISTORY reference table oref$y2r16003o_pct.fits \n", + "HISTORY INFLIGHT 18/05/1997 19/12/1998 \n", + "HISTORY Added/updated values for 31X0.05NDA,31X0.05NDB,31X0.05NDC apertures \n", + "HISTORY reference table oref$y2r1559to_apt.fits \n", + "HISTORY GROUND \n", + "HISTORY Added/updated values for 31X0.05NDA,31X0.05NDB,31X0.05NDC apertures \n", + "HISTORY reference table oref$3ah15289o_tds.fits \n", + "HISTORY INFLIGHT 01/04/1997 04/07/2019 \n", + "HISTORY Updated time sensitivities for MIRNUV and L, M, and H modes. \n", + "XTRACALG= 'UNWEIGHTED' / extraction algorithm \n", + "HISTORY Heliocentric correction = 28.6738 km/s \n", + "HISTORY SC2DCORR performed ... \n", + "HISTORY reference table oref$k8m0958co_ech.fits \n", + "HISTORY INFLIGHT 01/04/1996 22/06/1999 \n", + "HISTORY Built from reference files provided by IDT \n", + "HISTORY reference table oref$k8m0958do_exs.fits \n", + "HISTORY INFLIGHT 01/04/1996 22/06/1999 \n", + "HISTORY Built from reference files provided by IDT \n", + "HISTORY reference table oref$k8m0958bo_cds.fits \n", + "HISTORY INFLIGHT 01/04/1996 11/02/1999 \n", + "HISTORY Built from reference files provided by IDT \n", + "HISTORY reference table oref$6cg2020po_rip.fits \n", + "HISTORY INFLIGHT 28/11/2009 06/01/2010 \n", + "HISTORY Updated blaze function for E230M 1978, 2707 and 2415 \n", + "HISTORY reference table oref$k8m0958go_srw.fits \n", + "HISTORY GROUND 01/10/1996 \n", + "HISTORY Built from reference files provided by IDT \n", + "HISTORY reference table oref$k8m0958eo_hal.fits \n", + "HISTORY INFLIGHT 01/04/1996 22/06/1999 \n", + "HISTORY Built from reference files provided by IDT \n", + "HISTORY reference table oref$k8m0958ho_tel.fits \n", + "HISTORY GROUND 01/01/92 \n", + "HISTORY Built from reference files provided by IDT " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fits.getheader(filename)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "73d9461f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": true, + "title_cell": "Learning Goals", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/extraction/1D_Extraction.html b/extraction/1D_Extraction.html index b125190..93a9dea 100644 --- a/extraction/1D_Extraction.html +++ b/extraction/1D_Extraction.html @@ -13120,7 +13120,6 @@

    0.1 Import Necessary PackagesMAST archive
  • os for managing system paths
  • numpy to handle array functions
  • -
  • stistools for operations on STIS Data
  • matplotlib for plotting data
  • @@ -13136,62 +13135,26 @@

    0.1 Import Necessary Packagesfrom astropy.io import fits from astropy.table import Table -# Import for: Downloading necessary files. (Not necessary if you choose to collect data from MAST) +# Import for: Downloading necessary files. +# (Not necessary if you choose to collect data from MAST) from astroquery.mast import Observations -# Import for: Managing system variables and paths -import os - # Import for: Quick Calculation and Data Analysis import numpy as np -# Import for: Operations on STIS Data -import stistools +# Import for: Managing system variables and paths +import os # Import for: Plotting and specifying plotting parameters +from matplotlib import pyplot as plt import matplotlib %matplotlib inline -from matplotlib import pyplot as plt -from matplotlib.ticker import FixedLocator -
    -
    - - -
    - -
    - - -
    -
    /Users/kding/miniconda3/envs/stis/lib/python3.7/site-packages/stsci/tools/nmpfit.py:8: UserWarning: NMPFIT is deprecated - stsci.tools v 3.5 is the last version to contain it.
    -  warnings.warn("NMPFIT is deprecated - stsci.tools v 3.5 is the last version to contain it.")
    -/Users/kding/miniconda3/envs/stis/lib/python3.7/site-packages/stsci/tools/gfit.py:18: UserWarning: GFIT is deprecated - stsci.tools v 3.4.12 is the last version to contain it.Use astropy.modeling instead.
    -  warnings.warn("GFIT is deprecated - stsci.tools v 3.4.12 is the last version to contain it."
    -
    -
    -
    - -
    - -
    - - -
    -
    The following tasks in the stistools package can be run with TEAL:
    -   basic2d      calstis     ocrreject     wavecal        x1d          x2d
    -
    -
    -
    - -
    -
    -

    +
    @@ -13424,55 +13389,55 @@

    2 Plot the Extraction Region
    def show_extraction_regions(x1d_filename, flt_filename, sci_ext=1, row=0, xrange=None, yrange=None):
    -    
    +
         fig, axes = plt.subplots(1, 2, sharey=True)
         fig.tight_layout()
         fig.subplots_adjust(wspace=0.2, hspace=0.2, top=0.88)
         fig.set_figwidth(10)
         fig.set_figheight(5)
         fig.suptitle(os.path.basename(x1d_filename))
    -    
    +
         x1d = fits.getdata(x1d_filename, ext=sci_ext)[row]
         flt = fits.getdata(flt_filename, ext=('SCI', sci_ext))
    -    
    +
         # LEFT & Right PLOTS:
         for ax in axes[0:2]:
             # Display the 2D FLT spectrum on the left:
    -        ax.imshow(flt, origin='lower', interpolation='none', aspect='auto', 
    -                   vmin=-6, vmax=15)
    +        ax.imshow(flt, origin='lower', interpolation='none', aspect='auto',
    +                  vmin=-6, vmax=15)
             ax.set_xlabel('X')
             ax.set_ylabel('Y')
     
         # Right PLOT:
         axes[1].set_title(f"A2CENTER={x1d['A2CENTER']:.2f}")
         # Extraction region in red:
    -    axes[1].plot(np.arange(1024), 
    +    axes[1].plot(np.arange(1024),
                      x1d['EXTRLOCY'] - 1, 'r:', alpha=0.6)
    -    axes[1].plot(np.arange(1024), 
    +    axes[1].plot(np.arange(1024),
                      x1d['EXTRLOCY'] - 1 - x1d['EXTRSIZE']//2,
                      color='red', alpha=0.6)
    -    axes[1].plot(np.arange(1024), 
    +    axes[1].plot(np.arange(1024),
                      x1d['EXTRLOCY'] - 1 + x1d['EXTRSIZE']//2,
                      color='red', alpha=0.6)
         # Background regions in orange:
    -    axes[1].plot(np.arange(1024), 
    +    axes[1].plot(np.arange(1024),
                      x1d['EXTRLOCY'] - 1 + x1d['BK1OFFST'] - x1d['BK1SIZE']//2,
                      color='orange', alpha=0.6)
    -    axes[1].plot(np.arange(1024), 
    +    axes[1].plot(np.arange(1024),
                      x1d['EXTRLOCY'] - 1 + x1d['BK1OFFST'] + x1d['BK1SIZE']//2,
                      color='orange', alpha=0.6)
    -    axes[1].plot(np.arange(1024), 
    -                 x1d['EXTRLOCY'] - 1 + x1d['BK2OFFST'] - x1d['BK2SIZE']//2, 
    +    axes[1].plot(np.arange(1024),
    +                 x1d['EXTRLOCY'] - 1 + x1d['BK2OFFST'] - x1d['BK2SIZE']//2,
                      color='orange', alpha=0.6)
    -    axes[1].plot(np.arange(1024), 
    -                 x1d['EXTRLOCY'] - 1 + x1d['BK2OFFST'] + x1d['BK2SIZE']//2, 
    +    axes[1].plot(np.arange(1024),
    +                 x1d['EXTRLOCY'] - 1 + x1d['BK2OFFST'] + x1d['BK2SIZE']//2,
                      color='orange', alpha=0.6)
    -    
    +
         axes[0].set_xlim(-0.5, 1023.5)
         axes[0].set_ylim(-0.5, 1023.5)
         axes[1].set_xlim(-0.5, 1023.5)
         axes[1].set_ylim(-0.5, 1023.5)
    -    
    +
         if xrange is not None:
             axes[0].set_xlim(xrange[0], xrange[1])
             axes[1].set_xlim(xrange[0], xrange[1])
    @@ -13627,7 +13592,9 @@ 

    3 Extracted Spectra of STIS Echelle # get a list of files assiciated with that target echelle_list = Observations.get_product_list(target) # Download fits files -result = Observations.download_products(echelle_list, extension=['_flt.fits', '_x1d.fits'], productType=['SCIENCE',]) +result = Observations.download_products(echelle_list, + extension=['_flt.fits', '_x1d.fits'], + productType=['SCIENCE']) echelle_flt = os.path.join(f'./mastDownload/HST/{obs_id}/{obs_id}_flt.fits') echelle_x1d = os.path.join(f'./mastDownload/HST/{obs_id}/{obs_id}_x1d.fits')

    @@ -13646,8 +13613,8 @@

    3 Extracted Spectra of STIS Echelle
    -
    Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/octx01030_x1d.fits to ./mastDownload/HST/octx01030/octx01030_x1d.fits ... [Done]
    -Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/octx01030_flt.fits to ./mastDownload/HST/octx01030/octx01030_flt.fits ... [Done]
    +
    INFO: Found cached file ./mastDownload/HST/octx01030/octx01030_x1d.fits with expected size 7629120. [astroquery.query]
    +INFO: Found cached file ./mastDownload/HST/octx01030/octx01030_flt.fits with expected size 84139200. [astroquery.query]
     

    @@ -13699,7 +13666,7 @@

    3 Extracted Spectra of STIS Echelle
    Table length=24 -

    SPORDERWAVELENGTH [1024]FLUX [1024]EXTRLOCY [1024]A2CENTEREXTRSIZEBK1SIZEBK2SIZEBK1OFFSTBK2OFFST
    Angstromserg / (Angstrom cm2 s)pixpixpixpixpixpixpix
    int16float64float32float32float32float32float32float32float32float32
    +
    @@ -13815,15 +13782,15 @@

    3 Extracted Spectra of STIS Echelle

    About this Notebook

    -

    Author: Keyi Ding

    -

    Updated On: 2023-01-05

    +

    Author: Keyi Ding

    +

    Updated On: 2023-05-18

    This tutorial was generated to be in compliance with the STScI style guides and would like to cite the Jupyter guide in particular.

    Citations

    If you use astropy, matplotlib, astroquery, or numpy for published research, please cite the authors. Follow these links for more information about citations:


    diff --git a/extraction/1D_Extraction.ipynb b/extraction/1D_Extraction.ipynb index d511ae3..4391bdf 100644 --- a/extraction/1D_Extraction.ipynb +++ b/extraction/1D_Extraction.ipynb @@ -42,7 +42,6 @@ "- `astroquery.mast.Observations` for finding and downloading data from the [MAST](https://mast.stsci.edu/portal/Mashup/Clients/Mast/Portal.html) archive\n", "- `os` for managing system paths\n", "- `numpy` to handle array functions\n", - "- `stistools` for operations on STIS Data\n", "- `matplotlib` for plotting data" ] }, @@ -51,48 +50,26 @@ "execution_count": 1, "id": "bb98f7e5", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kding/miniconda3/envs/stis/lib/python3.7/site-packages/stsci/tools/nmpfit.py:8: UserWarning: NMPFIT is deprecated - stsci.tools v 3.5 is the last version to contain it.\n", - " warnings.warn(\"NMPFIT is deprecated - stsci.tools v 3.5 is the last version to contain it.\")\n", - "/Users/kding/miniconda3/envs/stis/lib/python3.7/site-packages/stsci/tools/gfit.py:18: UserWarning: GFIT is deprecated - stsci.tools v 3.4.12 is the last version to contain it.Use astropy.modeling instead.\n", - " warnings.warn(\"GFIT is deprecated - stsci.tools v 3.4.12 is the last version to contain it.\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The following tasks in the stistools package can be run with TEAL:\n", - " basic2d calstis ocrreject wavecal x1d x2d\n" - ] - } - ], + "outputs": [], "source": [ "# Import for: Reading in fits file\n", "from astropy.io import fits\n", "from astropy.table import Table\n", "\n", - "# Import for: Downloading necessary files. (Not necessary if you choose to collect data from MAST)\n", + "# Import for: Downloading necessary files.\n", + "# (Not necessary if you choose to collect data from MAST)\n", "from astroquery.mast import Observations\n", "\n", - "# Import for: Managing system variables and paths\n", - "import os\n", - "\n", "# Import for: Quick Calculation and Data Analysis\n", "import numpy as np\n", "\n", - "# Import for: Operations on STIS Data\n", - "import stistools\n", + "# Import for: Managing system variables and paths\n", + "import os\n", "\n", "# Import for: Plotting and specifying plotting parameters\n", - "import matplotlib\n", - "%matplotlib inline\n", "from matplotlib import pyplot as plt\n", - "from matplotlib.ticker import FixedLocator" + "import matplotlib\n", + "%matplotlib inline" ] }, { @@ -128,8 +105,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/odj102010_x1d.fits to ./mastDownload/HST/odj102010/odj102010_x1d.fits ... [Done]\n", - "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/odj102010_flt.fits to ./mastDownload/HST/odj102010/odj102010_flt.fits ... [Done]\n" + "INFO: Found cached file ./mastDownload/HST/odj102010/odj102010_x1d.fits with expected size 77760. [astroquery.query]\n", + "INFO: Found cached file ./mastDownload/HST/odj102010/odj102010_flt.fits with expected size 10535040. [astroquery.query]\n" ] } ], @@ -211,7 +188,7 @@ "data": { "text/html": [ "Table length=1\n", - "

    SPORDERWAVELENGTH [1024]FLUX [1024]EXTRLOCY [1024]EXTRSIZEBK1SIZEBK2SIZEBK1OFFSTBK2OFFST
    Angstromserg / (Angstrom cm2 s)pixpixpixpixpixpix
    int16float64float32float32float32float32float32float32float32
    \n", + "
    \n", "\n", "\n", "\n", @@ -233,7 +210,9 @@ } ], "source": [ - "cols = ['SPORDER', 'WAVELENGTH', 'FLUX', 'EXTRLOCY', 'A2CENTER', 'EXTRSIZE','BK1SIZE', 'BK2SIZE', 'BK1OFFST', 'BK2OFFST']\n", + "cols = ['SPORDER', 'WAVELENGTH', 'FLUX', 'EXTRLOCY',\n", + " 'A2CENTER', 'EXTRSIZE', 'BK1SIZE', 'BK2SIZE',\n", + " 'BK1OFFST', 'BK2OFFST']\n", "Table.read(x1d_filename)[cols]" ] }, @@ -267,55 +246,55 @@ "outputs": [], "source": [ "def show_extraction_regions(x1d_filename, flt_filename, sci_ext=1, row=0, xrange=None, yrange=None):\n", - " \n", + "\n", " fig, axes = plt.subplots(1, 2, sharey=True)\n", " fig.tight_layout()\n", " fig.subplots_adjust(wspace=0.2, hspace=0.2, top=0.88)\n", " fig.set_figwidth(10)\n", " fig.set_figheight(5)\n", " fig.suptitle(os.path.basename(x1d_filename))\n", - " \n", + "\n", " x1d = fits.getdata(x1d_filename, ext=sci_ext)[row]\n", " flt = fits.getdata(flt_filename, ext=('SCI', sci_ext))\n", - " \n", + "\n", " # LEFT & Right PLOTS:\n", " for ax in axes[0:2]:\n", " # Display the 2D FLT spectrum on the left:\n", - " ax.imshow(flt, origin='lower', interpolation='none', aspect='auto', \n", - " vmin=-6, vmax=15)\n", + " ax.imshow(flt, origin='lower', interpolation='none', aspect='auto',\n", + " vmin=-6, vmax=15)\n", " ax.set_xlabel('X')\n", " ax.set_ylabel('Y')\n", "\n", " # Right PLOT:\n", " axes[1].set_title(f\"A2CENTER={x1d['A2CENTER']:.2f}\")\n", " # Extraction region in red:\n", - " axes[1].plot(np.arange(1024), \n", + " axes[1].plot(np.arange(1024),\n", " x1d['EXTRLOCY'] - 1, 'r:', alpha=0.6)\n", - " axes[1].plot(np.arange(1024), \n", + " axes[1].plot(np.arange(1024),\n", " x1d['EXTRLOCY'] - 1 - x1d['EXTRSIZE']//2,\n", " color='red', alpha=0.6)\n", - " axes[1].plot(np.arange(1024), \n", + " axes[1].plot(np.arange(1024),\n", " x1d['EXTRLOCY'] - 1 + x1d['EXTRSIZE']//2,\n", " color='red', alpha=0.6)\n", " # Background regions in orange:\n", - " axes[1].plot(np.arange(1024), \n", + " axes[1].plot(np.arange(1024),\n", " x1d['EXTRLOCY'] - 1 + x1d['BK1OFFST'] - x1d['BK1SIZE']//2,\n", " color='orange', alpha=0.6)\n", - " axes[1].plot(np.arange(1024), \n", + " axes[1].plot(np.arange(1024),\n", " x1d['EXTRLOCY'] - 1 + x1d['BK1OFFST'] + x1d['BK1SIZE']//2,\n", " color='orange', alpha=0.6)\n", - " axes[1].plot(np.arange(1024), \n", - " x1d['EXTRLOCY'] - 1 + x1d['BK2OFFST'] - x1d['BK2SIZE']//2, \n", + " axes[1].plot(np.arange(1024),\n", + " x1d['EXTRLOCY'] - 1 + x1d['BK2OFFST'] - x1d['BK2SIZE']//2,\n", " color='orange', alpha=0.6)\n", - " axes[1].plot(np.arange(1024), \n", - " x1d['EXTRLOCY'] - 1 + x1d['BK2OFFST'] + x1d['BK2SIZE']//2, \n", + " axes[1].plot(np.arange(1024),\n", + " x1d['EXTRLOCY'] - 1 + x1d['BK2OFFST'] + x1d['BK2SIZE']//2,\n", " color='orange', alpha=0.6)\n", - " \n", + "\n", " axes[0].set_xlim(-0.5, 1023.5)\n", " axes[0].set_ylim(-0.5, 1023.5)\n", " axes[1].set_xlim(-0.5, 1023.5)\n", " axes[1].set_ylim(-0.5, 1023.5)\n", - " \n", + "\n", " if xrange is not None:\n", " axes[0].set_xlim(xrange[0], xrange[1])\n", " axes[1].set_xlim(xrange[0], xrange[1])\n", @@ -428,8 +407,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/octx01030_x1d.fits to ./mastDownload/HST/octx01030/octx01030_x1d.fits ... [Done]\n", - "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/octx01030_flt.fits to ./mastDownload/HST/octx01030/octx01030_flt.fits ... [Done]\n" + "INFO: Found cached file ./mastDownload/HST/octx01030/octx01030_x1d.fits with expected size 7629120. [astroquery.query]\n", + "INFO: Found cached file ./mastDownload/HST/octx01030/octx01030_flt.fits with expected size 84139200. [astroquery.query]\n" ] } ], @@ -441,7 +420,9 @@ "# get a list of files assiciated with that target\n", "echelle_list = Observations.get_product_list(target)\n", "# Download fits files\n", - "result = Observations.download_products(echelle_list, extension=['_flt.fits', '_x1d.fits'], productType=['SCIENCE',])\n", + "result = Observations.download_products(echelle_list,\n", + " extension=['_flt.fits', '_x1d.fits'],\n", + " productType=['SCIENCE'])\n", "echelle_flt = os.path.join(f'./mastDownload/HST/{obs_id}/{obs_id}_flt.fits')\n", "echelle_x1d = os.path.join(f'./mastDownload/HST/{obs_id}/{obs_id}_x1d.fits')" ] @@ -471,7 +452,7 @@ "data": { "text/html": [ "Table length=24\n", - "
    SPORDERWAVELENGTH [1024]FLUX [1024]EXTRLOCY [1024]A2CENTEREXTRSIZEBK1SIZEBK2SIZEBK1OFFSTBK2OFFST
    Angstromserg / (Angstrom cm2 s)pixpixpixpixpixpixpix
    int16float64float32float32float32float32float32float32float32float32
    \n", + "
    \n", "\n", "\n", "\n", @@ -591,9 +572,9 @@ "\n", "---\n", "## About this Notebook \n", - "**Author:** [Keyi Ding](kding@stsci.edu)\n", + "**Author:** Keyi Ding\n", "\n", - "**Updated On:** 2023-01-05\n", + "**Updated On:** 2023-05-18\n", "\n", "\n", "> *This tutorial was generated to be in compliance with the [STScI style guides](https://github.com/spacetelescope/style-guides) and would like to cite the [Jupyter guide](https://github.com/spacetelescope/style-guides/blob/master/templates/example_notebook.ipynb) in particular.*\n", @@ -602,13 +583,13 @@ "If you use `astropy`, `matplotlib`, `astroquery`, or `numpy` for published research, please cite the\n", "authors. Follow these links for more information about citations:\n", "\n", - "* [Citing `astropy`/`numpy`/`matplotlib`](https://www.scipy.org/citing.html)\n", + "* [Citing `astropy`](https://www.astropy.org/acknowledging.html)/[`numpy`](https://numpy.org/citing-numpy/)/[`matplotlib`](https://matplotlib.org/stable/users/project/citing.html)\n", "* [Citing `astroquery`](https://astroquery.readthedocs.io/en/latest/)\n", "\n", "---\n", "\n", "[Top of Page](#top)\n", - "\"Space " + "\"Space \n" ] }, { diff --git a/syn_phot/filters.png b/syn_phot/filters.png new file mode 100644 index 0000000..417b5a5 Binary files /dev/null and b/syn_phot/filters.png differ diff --git a/syn_phot/syn_phot.ipynb b/syn_phot/syn_phot.ipynb new file mode 100644 index 0000000..e1bb638 --- /dev/null +++ b/syn_phot/syn_phot.ipynb @@ -0,0 +1,678 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4051590e", + "metadata": { + "toc": true + }, + "source": [ + "

    Table of Contents

    \n", + "
    " + ] + }, + { + "cell_type": "markdown", + "id": "64d96a15", + "metadata": {}, + "source": [ + "# Synthetic Photometry with STIS Spectra" + ] + }, + { + "cell_type": "markdown", + "id": "0226227b", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The goal of synthetic photometry is to simulate observed photometric data with spectra and the instrumental response function of the photometric filters. In the notebook, we present the process of creating synthetic photometry using STIS spectra, and the [`synphot`](https://synphot.readthedocs.io/en/latest/) and [`stsynphot`](https://stsynphot.readthedocs.io/en/latest/index.html) Python packages. We also reproduced ACS observation of star `WD1657+343` using STIS spectra, and compared the data with photometry from real ACS observations.\n", + "\n", + "[`synphot`](https://synphot.readthedocs.io/en/latest/) is a Python package for simulating photometric data. It is the successor of the [`Astrolib PySynphot`](https://pysynphot.readthedocs.io/en/latest/) Python package. [`synphot`](https://synphot.readthedocs.io/en/latest/) has the funcionalities to create spectra, bandpasses, and observations, manipulating spectra and data, and computing photometric properties.\n", + "\n", + "[`stsynphot`](https://stsynphot.readthedocs.io/en/latest/index.html) is an extension of [`synphot`](https://synphot.readthedocs.io/en/latest/) that implements synthetic photometry package specific to HST. It includes HST instrument-specific data and functionalities, and is used in complement with [`synphot`](https://synphot.readthedocs.io/en/latest/)." + ] + }, + { + "cell_type": "markdown", + "id": "f33a8fff", + "metadata": {}, + "source": [ + "### Import Necessary Packages\n", + "- `synphot` `stsynphot` for computing synthetic photometry and simulating observations\n", + "- `astropy.io fits` `astropy.table` for accessing FITS files\n", + "- `astroquery.mast Observations` for finding and downloading data from the [MAST](https://mast.stsci.edu/portal/Mashup/Clients/Mast/Portal.html) archive\n", + "- `os`,`pathlib` for managing system paths\n", + "- `numpy` to handle array functions\n", + "- `stistools` for calibrating STIS data\n", + "- `matplotlib` for plotting data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a1c89846", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The following tasks in the stistools package can be run with TEAL:\n", + " basic2d calstis ocrreject wavecal x1d x2d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kding/miniconda3/envs/stis/lib/python3.7/site-packages/stsci/tools/nmpfit.py:10: UserWarning: NMPFIT is deprecated - stsci.tools v 3.5 is the last version to contain it.\n", + " warnings.warn(\"NMPFIT is deprecated - stsci.tools v 3.5 is the last version to contain it.\")\n", + "/Users/kding/miniconda3/envs/stis/lib/python3.7/site-packages/stsci/tools/gfit.py:20: UserWarning: GFIT is deprecated - stsci.tools v 3.4.12 is the last version to contain it.Use astropy.modeling instead.\n", + " warnings.warn(\"GFIT is deprecated - stsci.tools v 3.4.12 is the last version to contain it.\"\n" + ] + } + ], + "source": [ + "# Import for: computing synthetic photometry and simulating observations\n", + "from astropy import units as u\n", + "from synphot import units, SourceSpectrum,Observation,SpectralElement\n", + "from synphot.models import Empirical1D\n", + "from synphot.specio import write_fits_spec\n", + "import stsynphot as stsyn \n", + "\n", + "# Import for: Reading in fits file\n", + "from astropy.table import Table\n", + "from astropy.io import fits\n", + "\n", + "# Import for: Downloading necessary files. (Not necessary if you choose to collect data from MAST)\n", + "from astroquery.mast import Observations\n", + "\n", + "# Import for: Managing system variables and paths\n", + "from pathlib import Path\n", + "import os\n", + "\n", + "# Import for: Plotting and specifying plotting parameters\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "import matplotlib.cm as cm\n", + "from IPython.display import display\n", + "\n", + "# Import for: Quick Calculation and Data Analysis\n", + "import numpy as np\n", + "\n", + "# Import for: operations on STIS Data\n", + "import stistools" + ] + }, + { + "cell_type": "markdown", + "id": "e8b7f09f", + "metadata": {}, + "source": [ + "### Collect Data Set From the MAST Archive Using Astroquery\n", + "In this notebook, we use the spectrum of the star WD1657+343 as an example. We first collect the dataset OEHK05020 from the MAST archive. The spectrum was observed using the G750L grating and an exposure time of 1560 seconds" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e76209b6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: Found cached file ./mastDownload/HST/oehk05020/oehk05020_sx1.fits with expected size 80640. [astroquery.query]\n" + ] + } + ], + "source": [ + "# change this field in you have a specific dataset to be explored\n", + "obs_id=\"OEHK05020\"\n", + "# Search target objscy by obs_id\n", + "target = Observations.query_criteria(obs_id=obs_id)\n", + "# get a list of files assiciated with that target\n", + "FUV_list = Observations.get_product_list(target)\n", + "# Download fits files\n", + "result = Observations.download_products(FUV_list,extension='_sx1.fits')\n", + "sx1 = os.path.join(\"./mastDownload/HST\",\"{}\".format(obs_id),\"{}_sx1.fits\".format(obs_id))" + ] + }, + { + "cell_type": "markdown", + "id": "e16dfc91", + "metadata": {}, + "source": [ + "## Spectrum Preprocessing\n", + "Since the synthetic photometry is computed based on the flux in the observed spectrum, the calculated value is sensitive to the data quality of the spectrum. We first need to preprocess the spectrum to ensure that the data quality meets our need to generate synthetic photometry.\n", + "\n", + "We first open up the _sx1 fits file and get the wavelength, flux, and data quality of the spectrum:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8e3701d2", + "metadata": {}, + "outputs": [], + "source": [ + "with fits.open(sx1) as hdu:\n", + " data = hdu[1].data\n", + "wl, flux, dq = data['WAVELENGTH'][0],data['FLUX'][0],data['DQ'][0]" + ] + }, + { + "cell_type": "markdown", + "id": "b0023354", + "metadata": {}, + "source": [ + "Plot the spectrum without any processing:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bcda7cb4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Flux (ergs/s/cm$^2$/Å)')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "matplotlib.rcParams['figure.figsize'] = [20, 7]\n", + "plt.style.use('bmh')\n", + "\n", + "plt.plot(wl, flux, \n", + " marker='.', markersize=2, markerfacecolor='w', markeredgewidth=0)\n", + "plt.xlabel('Wavelength (Å)')\n", + "plt.ylabel('Flux (ergs/s/cm$^2$/Å)')" + ] + }, + { + "cell_type": "markdown", + "id": "790384f9", + "metadata": {}, + "source": [ + "As shown in the plot above, there are three \"spikes\" in the spectrum that are likely to be caused by bad data qualities. Since the synthetic photometry is computed numerically over the spectrum, the spikes are likely to confuse the algorithm. Thus we hope to remove the data points marked with serious data quality flags (SDQFLAGS).\n", + "\n", + "The SDQFLAGS field is stored in the first extension of the fits file. It is a 16-bits binary encoded integer that markes the data quality flags considered serious. To remove the data points with serious data quality flags, we first get the value of SDQFLAGS from the header, and then create a mask using SDQFLAGS and the dq array. The mask is applied to both the wavelength and the flux arrays:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1ebb6047", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Flux (ergs/s/cm$^2$/Å)')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# get SDQFLAGS from the header of the first extension\n", + "sdqFlags = fits.getval(sx1, ext=1, keyword=\"SDQFLAGS\")\n", + "# use the bitwise and operation between SDQFLAGS and the dq array\n", + "# so that only data points without serious data quality flags are preserved\n", + "g = (dq & sdqFlags) == 0\n", + "\n", + "# apply the mask to both the wavelength and flux arrays\n", + "wl_masked = wl[g]\n", + "flux_masked = flux[g]\n", + "\n", + "plt.plot(wl_masked, flux_masked, \n", + " marker='.', markersize=2, markerfacecolor='w', markeredgewidth=0) \n", + "plt.xlabel('Wavelength (Å)')\n", + "plt.ylabel('Flux (ergs/s/cm$^2$/Å)')" + ] + }, + { + "cell_type": "markdown", + "id": "9984ee8f", + "metadata": {}, + "source": [ + "## Creating Source Spectrum\n", + "A source spectrum is used to represent astronomical sources. To generate source spectrum object in the synphot package, we can use predefined spectrum or model, or load supported spectrum from FITS file or ASCII file, or construct source spectrum from arrays as an empirical model. \n", + "\n", + "In this notebook, we will only demonstrate the procedures to create source spectrum from arrays. That is because the STIS fits files are not in a format readable by synphot, and we need additional preprocessings to prepare the spectrum. To create a source spectrum from arrays, specify the model of the spectrum to be **Empirical1D**, pass the preprocessed wavelength array into the constructor as the **points** parameter, and the flux into the constructor as the **lookup_table** parameter.\n", + "\n", + "The **points** parameter takes in an array of wavelength in Angstrom by default, and therefore we do not need to manipulate the wavelength array. However, we need to specify the unit of the flux array by multiplying the array by units.FLAM. **It is essential to confirm that the flux is passed in the correct units. Since we are using STIS spectrum, we assume that the energy is in FLAM =ergs/s/cm$^2$/Å. If spectrum from other sources is used, please enture that the unit is specified correctly.**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c1dce019", + "metadata": {}, + "outputs": [], + "source": [ + "# specify the unit of the flux array to be FLAM\n", + "flux_masked = flux_masked * units.FLAM" + ] + }, + { + "cell_type": "markdown", + "id": "242aa48e", + "metadata": {}, + "source": [ + "Now we can create an SourceSpectrum object by passing the parameters to the constructor:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "65efce86", + "metadata": {}, + "outputs": [], + "source": [ + "# sp = SourceSpectrum.from_file(\"{}_source_spectrum.fits\".format(obs_id.lower()))\n", + "sp = SourceSpectrum(Empirical1D, points=wl_masked, lookup_table=flux_masked, keep_neg=False)" + ] + }, + { + "cell_type": "markdown", + "id": "458f9730", + "metadata": {}, + "source": [ + "The SourceSpectrum object has a built-in [`plot`](https://synphot.readthedocs.io/en/latest/api/synphot.spectrum.SourceSpectrum.html#synphot.spectrum.SourceSpectrum.plot) function which wraps matplotlib functions to plot the spectrum." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c919296b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sp.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "efa95fc3", + "metadata": {}, + "source": [ + "## Creating a Bandpass\n", + "The `synphot` package has the functionality to create a bandpass with various methods including reading from file, using pre-defined filter, etc. For STScI mission, it is required to access HST filters through `stsynphot`, which contains STScI mission-specific information. In this notebook, we will use `stsynphot` to create bandpasses of the ACS filters, and apply those bandpasses to generate synthetic photometry with STIS spectrum. For more information on ACS photometry, see: [`ACS Data Analysis`](https://hst-docs.stsci.edu/acsdhb/chapter-5-acs-data-analysis).\n", + "\n", + "The bandpass object is constructed through the [`band`](https://stsynphot.readthedocs.io/en/latest/api/stsynphot.spectrum.band.html#stsynphot.spectrum.band) function. The `band` function takes in an observation mode string that specifies the instrument, detector, and the filter. For instance, to create a bandpass for HST/ACS camera using WFC1 detector and F775W filter, use:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "831d5f1d", + "metadata": {}, + "outputs": [], + "source": [ + "bp = stsyn.band('acs, wfc1, F775W')" + ] + }, + { + "cell_type": "markdown", + "id": "fafc0636", + "metadata": {}, + "source": [ + "The `band` function automatically parses the input string and created the bandpass object using the corresponding throughput files in the reference file directory.\n", + "\n", + "Similar to SourceSpectrum, the Bandpass can also be plotted using the built-in `plot` function to show the sensitivity curve as a function of wavelength:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ea6650bd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# zoom in to the 5000 - 10000 Angstrom wavelength range\n", + "bp.plot(left=5000,right=10000)" + ] + }, + { + "cell_type": "markdown", + "id": "9d1dae0c", + "metadata": {}, + "source": [ + "We also need to check of the wavelength of the bandpass overlaps with the wavelength of the source spectrum using the [`check_overlap`](https://synphot.readthedocs.io/en/latest/api/synphot.spectrum.SpectralElement.html#synphot.spectrum.SpectralElement.check_overlap) function. The function has 4 possible outputs:\n", + "\n", + " - full: Source spectrum is fully defined within bandpass waveset\n", + " - partial_most: 99% of spectrum's flux is in the overlap (not a concern)\n", + " - partial_notmost: Source spectrum needs significant extrapolation (guessing)\n", + " - none: No overlap at all" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "09638560", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'partial_most'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bp.check_overlap(sp)" + ] + }, + { + "cell_type": "markdown", + "id": "80e7f853", + "metadata": {}, + "source": [ + "For the bandpass and source spectrum we use in thie notebook, we get a 'partial_most'. This matches our expectation since the wavelength range of the bandpass is covered by the wavelength of the spectrum, and we removed some data points in the spectrum preprocessing. Therefore the overlap is not a concern, and we can force synphot to create an observation even though the source spectrum and bandpass do not fully overlap." + ] + }, + { + "cell_type": "markdown", + "id": "61541bb8", + "metadata": {}, + "source": [ + "We can also get predefined filters using the [`from_filter`](https://synphot.readthedocs.io/en/latest/api/synphot.spectrum.SpectralElement.html#synphot.spectrum.SpectralElement.from_filter) function in the SpectralElement. The predefined filters include the Johnson-Cousins UBVRI passbands, and the Bessel JHK passbands." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e601168d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# get the bandpass b,v,i in the Johnson-Cousins system\n", + "b = SpectralElement.from_filter('johnson_b')\n", + "v = SpectralElement.from_filter('johnson_v')\n", + "i = SpectralElement.from_filter('johnson_i')\n", + "\n", + "# plot the filters\n", + "plt.plot(b.waveset, b(b.waveset),label=\"Johnson B\")\n", + "plt.plot(v.waveset, v(v.waveset),label=\"Johnson V\")\n", + "plt.plot(i.waveset, i(i.waveset),label=\"Johnson I\")\n", + "plt.xlabel('Wavelength (Å)')\n", + "plt.legend(loc='best')" + ] + }, + { + "cell_type": "markdown", + "id": "c05402e1", + "metadata": {}, + "source": [ + "Some of the HST filters are designed to match the Johnson-Cousins system. The figure from [(Casagrande & VandenBerg,2014)](https://ui.adsabs.harvard.edu/abs/2014MNRAS.444..392C/abstract) shows the correspondence between the filters.\n", + "
    \n", + " \n", + "
    " + ] + }, + { + "cell_type": "markdown", + "id": "cb1553e0", + "metadata": {}, + "source": [ + "## Creating an Observation\n", + "An [`Observation`](https://synphot.readthedocs.io/en/latest/synphot/observation.html) is a special type of SourceSpectrum, where the source is convolved with a Bandpass.\n", + "\n", + "With the SourceSpectrum and Bandpass available, we can now create an observation that simulates real photometric observation of the bandpass. The Observation object can be simply constructed by passing the SourceSpectrum and Bandpass to the Observation constructor:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "39578bfe", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: Source spectrum will be extrapolated (at constant value for empirical model). [synphot.observation]\n" + ] + } + ], + "source": [ + "# create an observation using sp and bp. \n", + "# We force the creation of the observation by extrapolating the source spectrum\n", + "obs = Observation(sp, bp, force='extrap')" + ] + }, + { + "cell_type": "markdown", + "id": "0bece575", + "metadata": {}, + "source": [ + "Now we can calculate the flux of the bandpass using the [`effstim`](https://synphot.readthedocs.io/en/latest/synphot/formulae.html#synphot-formula-effstim) function. For an observation, effstim calculates the predicted effective stimulus in given flux unit.\n", + "\n", + "To calculate the flux in the unit of count/s, we also need to specify the telescope collecting area. The collecting area of HST is 45238.93416 $cm^2$:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "57af8720", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$2709.9056 \\; \\mathrm{\\frac{ct}{s}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "obs.effstim('count',area=45238.93416 * units.AREA)" + ] + }, + { + "cell_type": "markdown", + "id": "655c1686", + "metadata": {}, + "source": [ + "The apparent magnitude of the observation can also be simulated using effstim. The magnitude can be calculated in either ST mag system (in flux density per unit wavelength) or the AB mag system (in flux density per unit frequency). For more information on different photometric systems, see [`Photometry`](https://hst-docs.stsci.edu/acsdhb/chapter-5-acs-data-analysis/5-1-photometry) in the ACS Data Handbook. To specify the photometric system, pass the STmag or ABmag units in [`astropy.units`](https://docs.astropy.org/en/stable/units/index.html#module-astropy.units) to effstim:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "2807afa6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$17.808991 \\; \\mathrm{mag}$$\\mathrm{\\left( \\mathrm{ST} \\right)}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "obs.effstim(u.STmag)" + ] + }, + { + "cell_type": "markdown", + "id": "c6b11fb1", + "metadata": {}, + "source": [ + "For reference, in a real ACS observation of the star WD1657+343, the F775W band flux is 2446.5 count/s, and the magnitude is 17.921 mag in the STmag system. The simulated flux and magnitude is in general agreement with the real observation." + ] + }, + { + "cell_type": "markdown", + "id": "92b5b8fa", + "metadata": {}, + "source": [ + "\n", + "---\n", + "## About this Notebook \n", + "**Author:** [Keyi Ding](kding@stsci.edu)\n", + "\n", + "**Updated On:** 2023-03-16\n", + "\n", + "\n", + "> *This tutorial was generated to be in compliance with the [STScI style guides](https://github.com/spacetelescope/style-guides) and would like to cite the [Jupyter guide](https://github.com/spacetelescope/style-guides/blob/master/templates/example_notebook.ipynb) in particular.*\n", + "## Citations \n", + "\n", + "If you use `astropy`, `matplotlib`, `astroquery`, or `numpy` for published research, please cite the\n", + "authors. Follow these links for more information about citations:\n", + "\n", + "* [Citing `astropy`/`numpy`/`matplotlib`](https://www.scipy.org/citing.html)\n", + "* [Citing `astroquery`](https://astroquery.readthedocs.io/en/latest/)\n", + "\n", + "---\n", + "\n", + "[Top of Page](#top)\n", + "\"Space " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "651eb421", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": true, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/target_acquisition/target_acquisition.html b/target_acquisition/target_acquisition.html index 8c9fc89..9b2527f 100644 --- a/target_acquisition/target_acquisition.html +++ b/target_acquisition/target_acquisition.html @@ -3,7 +3,7 @@ -imaging_acquisition +target_acquisition
    SPORDERWAVELENGTH [1024]FLUX [1024]EXTRLOCY [1024]EXTRSIZEBK1SIZEBK2SIZEBK1OFFSTBK2OFFST
    Angstromserg / (Angstrom cm2 s)pixpixpixpixpixpix
    int16float64float32float32float32float32float32float32float32