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\n", + "\n", + "## Table of Contents\n", + "\n", + "* [1. Introduction](#intro)\n", + "* [2. Import Library](#imports)\n", + "* [3. Convenience Functions](#func)\n", + "* [4. Directory Set-Up](#dir_setup)\n", + "* [5. Download the data](#data)\n", + "* [6. Products Found In MAST](#mast_products)\n", + " * [6.1 Stage 1 Products Found In MAST](#level1_mast)\n", + " * [6.2 Stage 2 Products Found In MAST](#level2_mast)\n", + " * [6.3 Stage 3 Products Found In MAST](#level3_mast)\n", + " * [6.3.1 Outlier Detection Limitations](#outlier_detection)\n", + "* [7. Re-processing the Data](#reprocessing)\n", + " * [7.1 Stage 1 Rerun & Products](#level1_rerun)\n", + " * [7.2 Stage 2 Rerun & Products](#level2_rerun)\n", + " * [7.3 Stage 3 Rerun & Products](#level3_rerun)\n", + " * [7.3.1 New Outlier Detection Algorithm](#outlier_detection_new)\n", + "* [Conclusion](#conclusion)\n", + "* [About This Notebook](#about)\n", + "\n", + "## 1. Introduction \n", + "
\n", + "\n", + "End-to-end calibration of JWST data is divided into 3 main stages of processing. This notebook explores how to run the JWST calibration pipeline stages 1-3 for NIRSpec IFU spectroscopic data.\n", + "
\n", + " \"Tarantula\"\n", + "
\n", + "\n", + ">* **`STAGE 1`** ([calwebb_detector1](https://jwst-pipeline.readthedocs.io/en/latest/jwst/pipeline/calwebb_detector1.html#calwebb-detector1)): consists of detector-level corrections, performed on a group-by-group basis, followed by ramp fitting.\n", + " * **Input**: Raw exposure (`uncal.fits`) containing original data from all detector readouts (ncols x nrows x ngroups x nintegrations).\n", + " * **Output**: Corrected countrate (slope) image (`rate.fits`) \n", + ">* **`STAGE 2`** ([calwebb_spec2](https://jwst-pipeline.readthedocs.io/en/latest/jwst/pipeline/calwebb_spec2.html#calwebb-spec2)): consists of additional instrument-level and observing mode corrections and calibrations.\n", + " * **Input**: A single corrected countrate (slope) image (`rate.fits`) or an ASN file listing multiple inputs.\n", + " * **Output**: A fully calibrated unrectified exposure (`cal.fits`). For NIRSpec IFU data, the `cube_build` step returns a 3-D IFU spectroscopic cube (`s3d.fits`). The `extract_1d` step returns 1-D extracted spectral data products (`x1d.fits`)\n", + ">* **`STAGE 3`** ([calwebb_spec3](https://jwst-pipeline.readthedocs.io/en/latest/jwst/pipeline/calwebb_spec3.html#calwebb-spec3)): consists of additional corrections (e.g. `outlier_detection`) and routines for combining calibrated data from multiple exposures (e.g. dither/nod pattern) into a single combined 2-D or 3-D spectral product and a combined 1-D spectrum. \n", + " * **Input**: An ASN file that lists multiple calibrated exposures (`cal.fits`).\n", + " * **Output**: For NIRSpec IFU data, a resampled and combined 3-D IFU cube (`s3d.fits`) and a 1-D extracted spectrum (`x1d.fits`)\n", + "\n", + "Here, we will focus on the mechanics of processing \"real\" example data ([Tarantula Nebula](#Tarantula)) from Early Release Science (ERS) Proposal ID 2729, including how to use associations for multi-exposure combination and how to interact and work with data models for each product. Our objective is to examine the automated products found in MAST and compare them to products generated with the most up-to-date version of the JWST calibration pipeline.\n", + "\n", + "Most processing runs shown here use the default reference files from the Calibration Reference Data System (CRDS). Please note that pipeline software development is a continuous process, so results in some cases may be slightly different if using a subsequent version. There are also a few known issues with some of the pipeline steps in this build that we expect to be fixed in the near future. Until then, at various steps, we provide users with the current processing recommendations when running the pipeline manually." + ] + }, + { + "cell_type": "markdown", + "id": "dcee7ed7", + "metadata": {}, + "source": [ + "## 2. Import Library \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf5b543b", + "metadata": {}, + "outputs": [], + "source": [ + "#Import Library\n", + "\n", + "#--------------------------------------JWST Calibration Pipeline Imports-------------------------------------------\n", + "\n", + "import jwst\n", + "import crds\n", + "from jwst import datamodels\n", + "from jwst.pipeline import Detector1Pipeline #calwebb_detector1\n", + "from jwst.pipeline import Spec2Pipeline #calwebb_spec2\n", + "from jwst.pipeline import Spec3Pipeline #calwebb_spec3\n", + "from jwst.extract_1d import Extract1dStep #Extract1D Individual Step\n", + "\n", + "print(\"JWST Calibration Pipeline Version={}\".format(jwst.__version__))\n", + "print(\"Current Operational CRDS Context = {}\".format(crds.get_default_context()))\n", + "\n", + "#----------------------------------------------General Imports-----------------------------------------------------\n", + "\n", + "import numpy as np\n", + "import warnings\n", + "warnings.filterwarnings('ignore') #Set to 'default' to turn warnings back on\n", + "\n", + "#--------------------------------------------File Operation Imports------------------------------------------------\n", + "\n", + "import glob\n", + "import os\n", + "import asdf\n", + "import json\n", + "from shutil import copy\n", + "\n", + "#--------------------------------------------Astropy/Astroquery Imports--------------------------------------------\n", + "\n", + "from astropy.io import fits\n", + "from astropy import wcs\n", + "from astropy.wcs import WCS\n", + "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch, SqrtStretch\n", + "from astropy.stats import sigma_clipped_stats\n", + "import astroquery\n", + "from astroquery.mast import Mast\n", + "from astroquery.mast import Observations\n", + "\n", + "#------------------------------------------------Plotting Imports--------------------------------------------------\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "from matplotlib.patches import Circle\n", + "import matplotlib.gridspec as grd\n", + "from matplotlib import cm\n", + "\n", + "# Use this version for non-interactive plots (easier scrolling of the notebook)\n", + "%matplotlib inline\n", + "# Use this version (outside of Jupyter Lab) if you want interactive plots\n", + "#%matplotlib notebook\n", + "\n", + "# These gymnastics are needed to make the sizes of the figures\n", + "# be the same in both the inline and notebook versions\n", + "%config InlineBackend.print_figure_kwargs = {'bbox_inches': None}\n", + "mpl.rcParams['savefig.dpi'] = 80\n", + "mpl.rcParams['figure.dpi'] = 80\n", + "plt.rcParams.update({'font.size': 18})" + ] + }, + { + "cell_type": "markdown", + "id": "e17178e7", + "metadata": {}, + "source": [ + "## 3. Convenience Functions \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7bb4f2b5", + "metadata": {}, + "outputs": [], + "source": [ + "def show_image(data_2d, vmin, vmax, xsize=15, ysize=15, title=None, zoom_in=None, aspect=1, scale='log', units='DN/s', cmap='jet'):\n", + " \"\"\"\n", + " Function to generate a 2-D, log-scaled image of the data\n", + " \n", + " Parameters\n", + " ----------\n", + " data_2d : numpy.ndarray\n", + " 2-D image to be displayed\n", + " vmin : float\n", + " Minimum signal value to use for scaling\n", + " vmax : float\n", + " Maximum signal value to use for scaling\n", + " xsize, ysize: int\n", + " Figure Size\n", + " title : str\n", + " String to use for the plot title\n", + " zoom_in: list \n", + " Zoomed in Region of interest [xstart,xstop,ystart,ystop]\n", + " aspect: int\n", + " Aspect ratio of the axes\n", + " scale : str\n", + " Specify scaling of the image. Can be 'log' or 'linear' or 'Asinh'\n", + " units : str\n", + " Units of the data. Used for the annotation in the color bar. Defualt is DN/s for countrate images\n", + " cmap: str\n", + " Color Map for plot\n", + " \"\"\"\n", + " #-----------------------------------------Scaling Information----------------------------------------\n", + " \n", + " if scale == 'log':\n", + " norm = ImageNormalize(data_2d, interval=ManualInterval(vmin=vmin, vmax=vmax),\n", + " stretch=LogStretch())\n", + " elif scale == 'linear':\n", + " norm = ImageNormalize(data_2d, interval=ManualInterval(vmin=vmin, vmax=vmax),\n", + " stretch=LinearStretch())\n", + " elif scale == 'Asinh':\n", + " norm = ImageNormalize(data_2d, interval=ManualInterval(vmin=vmin, vmax=vmax),\n", + " stretch=AsinhStretch())\n", + " \n", + " #--------------------------------------------Set Up Figure-------------------------------------------\n", + "\n", + " fig = plt.figure(figsize=(xsize, ysize))\n", + " ax = fig.add_subplot(1, 1, 1)\n", + " \n", + " im = ax.imshow(data_2d, origin='lower', norm=norm, aspect=aspect, cmap=cmap)\n", + "\n", + " fig.colorbar(im, label=units)\n", + " plt.xlabel('Pixel column')\n", + " plt.ylabel('Pixel row')\n", + " \n", + " if title:\n", + " plt.title(title)\n", + "\n", + " #Zoom in on a portion of the image? \n", + " if zoom_in:\n", + " #inset axis \n", + " axins = ax.inset_axes([0.5, 0.6, 0.5, 0.3])\n", + " \n", + " axins.imshow(data_2d, origin=\"lower\", norm=norm, aspect=aspect, cmap=cmap)\n", + " \n", + " # subregion of the original image\n", + " axins.set_xlim(zoom_in[0], zoom_in[1])\n", + " axins.set_ylim(zoom_in[2], zoom_in[3])\n", + " axins.set_xticklabels([])\n", + " axins.set_yticklabels([])\n", + " ax.indicate_inset_zoom(axins, color=\"black\",edgecolor=\"black\", linewidth=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "83b1838f", + "metadata": {}, + "outputs": [], + "source": [ + "def show_ifu_cubeslices(s3d_file_list, wavelength_slices=[], spaxel_locs=[], y_scale=None, cmap='jet', vmin_vmax = [[[0,15e1]]], save_figure=False, title=None, title_font = 30):\n", + " \"\"\"\n", + " Function to that takes a 3-D IFU data cube and generates: \n", + " \n", + " > 2-D cube slices based on wavelength (microns)\n", + " > Associated 1-D spectrum for a designated spaxel (spatial pixel) in the data cube\n", + " > Corresponding 3-D weight image giving the relative weights of the output spaxels\n", + " \n", + " Note: This function can accomidate multiple detectors plotted side-by-side. \n", + " The general format would follow [[detector 1 info], [detector 2 info]].\n", + "\n", + " Parameters\n", + " ----------\n", + " s3d_file_list: list of str\n", + " 3-D IFU data cube fits file list \n", + " wavelength_slices: tuple\n", + " List of wavelength values (microns) at which to create 2-D slices. \n", + " spaxel_locs: tuple\n", + " List of spaxel locations in which to plot the associated 1-D spectrum. (One spaxel location per slice)\n", + " y_scale: tuple\n", + " Y-axis limits for the associated 1-D spectrum of the spaxel. Default is to use the ymin and ymax of the data. \n", + " cmap: str\n", + " Color Map \n", + " vmin_vmax: tuple\n", + " Minimum & Maximum signal value to use for scaling (e.g., [[[vmin,vmax],[vmin,vmax]], [[vmin,vmax], [vmin,vmax]]])\n", + " title: str\n", + " Figure Title. Default is None. \n", + " title_font:int\n", + " Title Font Size\n", + " save_figure: bool\n", + " Save figure? \n", + " \"\"\"\n", + " \n", + " #---------------------------------------------- Set-up Figure -------------------------------------------------\n", + "\n", + " #Plot Slices From the Cube\n", + " fig, axs = plt.subplots(3, np.array(wavelength_slices).size, figsize=(8*np.array(wavelength_slices).size,18))\n", + " gs = grd.GridSpec(3, np.array(wavelength_slices).size, height_ratios=[1]*3, width_ratios=[1]*np.array(wavelength_slices).size, hspace=0.4,wspace=0.7)\n", + "\n", + " total_num_plots=3*np.array(wavelength_slices).size\n", + " \n", + " plot_count = 0\n", + " #---------------------------------------------Open Files------------------------------------------------------\n", + " \n", + " for s3d_file in s3d_file_list:\n", + " \n", + " root=s3d_file[:-9] #Root file name \n", + "\n", + " s3d = fits.open(s3d_file) #3-D IFU data cube fits file \n", + " x1d3 = datamodels.open(root+'_x1d.fits') #1-D Extracted Spectrum \n", + " \n", + " #--------------------------------Wavelength & Surface Brightness/Flux Arrays------------------------------\n", + " \n", + " x1d3wave = x1d3.spec[0].spec_table.WAVELENGTH\n", + " \n", + " #--------------------------------------Data & Header Information------------------------------------------\n", + "\n", + " \n", + " #SCI Extension: [Type:ImageHDU Cards:92 Dimensions:(57, 61, 973) Format:float32]\n", + " cube = s3d[1].data #Science data\n", + " wcs = WCS(s3d[1].header) #World Coordinate System (WCS) Transformation keywords \n", + " wmap = s3d[4].data #3-D weight image giving the relative weights of the output spaxels.\n", + " cdelt1 = s3d[1].header['CDELT1']*3600. #Axis 1 coordinate increment at reference point \n", + " cdelt2 = s3d[1].header['CDELT2']*3600. #Axis 2 coordinate increment at reference point \n", + " cdelt3 = s3d[1].header['CDELT3'] #Axis 3 coordinate increment at reference point \n", + " crval3 = s3d[1].header['CRVAL3'] #third axis value at the reference pixel \n", + "\n", + " #Wavelength range of the grating/filter combination\n", + " wavstart = s3d[1].header['WAVSTART']\n", + " wavend = s3d[1].header['WAVEND']\n", + " s3d.close()\n", + " \n", + " #---------------------------------------------------Plots-------------------------------------------------\n", + " \n", + " cmap_custom = cm.colors.LinearSegmentedColormap.from_list(\"\", [\"darkred\",\"darkturquoise\",\"blue\"])\n", + " colors = cmap_custom(np.linspace(0, 1, np.array(wavelength_slices).size))\n", + "\n", + " #To Account for if NRS1 & NRS2 are both being plotted Side-by-side\n", + " if len(wavelength_slices) != 1:\n", + " if 'nrs1' in s3d_file:\n", + " wavelengths = wavelength_slices[0]\n", + " spaxel_loc = spaxel_locs[0]\n", + " vmin_vmax_vals = vmin_vmax[0]\n", + " \n", + " if y_scale:\n", + " y_scales = y_scale[0]\n", + "\n", + " elif 'nrs2' in s3d_file:\n", + " wavelengths = wavelength_slices[1]\n", + " spaxel_loc = spaxel_locs[1]\n", + " vmin_vmax_vals = vmin_vmax[1]\n", + " if y_scale:\n", + " y_scales = y_scale[1]\n", + "\n", + " else:\n", + " wavelengths = wavelength_slices[0]\n", + " spaxel_loc = spaxel_locs[0]\n", + " vmin_vmax_vals = vmin_vmax[0]\n", + " if y_scale:\n", + " y_scales = y_scale[0]\n", + "\n", + " \n", + " #Loop through each wavelength slices\n", + " for i, wave_slice in enumerate(wavelengths):\n", + "\n", + " if float(wavstart)<=wave_slice*10**-6<=float(wavend):\n", + " \n", + " #--------------------------------------------2-D Cube Slice------------------------------------------------\n", + " \n", + " #Min & Max Image Values & Scaling\n", + " if len(vmin_vmax_vals) != 1:\n", + " vmax_val = vmin_vmax_vals[i][1]\n", + " vmin_val = vmin_vmax_vals[i][0]\n", + " else:\n", + " vmax_val = vmin_vmax_vals[0][1]\n", + " vmin_val = vmin_vmax_vals[0][0]\n", + "\n", + " slicewave = wave_slice\n", + " nslice = int((slicewave - crval3)/cdelt3) #the slice of the cube we want to plot\n", + " ax1 = plt.subplot(gs[0+plot_count], projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", + " #ax1 = plt.subplot(3,len(wavelength_slices), 0+plot_count, projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", + "\n", + " slice_mean = np.nanmean(cube[(nslice-2):(nslice+2), :, :], axis=0) #Mean of the slice looking in the range (nslice-2):(nslice+2)\n", + " slice_norm=ImageNormalize(slice_mean, vmin=vmin_val, vmax=vmax_val, stretch=AsinhStretch()) #normalize &stretch \n", + " slice_image= ax1.imshow(slice_mean, norm=slice_norm, origin='lower', aspect='auto',cmap=cmap) #plot slice\n", + "\n", + " cb_image = fig.colorbar(slice_image, fraction=0.046, pad=0.04)\n", + " cb_image.set_label('MJy/sr', labelpad=-1, fontsize = 22)\n", + " cb_image.ax.tick_params(labelsize=20)\n", + " cb_image.ax.yaxis.get_offset_text().set_fontsize(20)\n", + " \n", + " ax1.set_xlabel('RA', fontsize =22)\n", + " ax1.set_ylabel('DEC', labelpad=-1, fontsize=22)\n", + " #ax1.grid(color='white', ls='solid')\n", + " ax1.set_title('Detector {} \\n Grating/Filter: {}/{} \\n {} microns'.format(s3d[0].header['DETECTOR'],s3d[0].header['GRATING'], s3d[0].header['FILTER'], str(slicewave)), fontsize =25)\n", + " ax1.tick_params(axis='both', which='major', labelsize=20)\n", + " ax1.coords[0].set_ticklabel(rotation=13, ha='right', pad=24)\n", + "\n", + " \n", + " #------------------------------------------Spaxel 1-D Spectrum---------------------------------------------\n", + " \n", + " #Zoom in on a Spaxel: Spectrum\n", + " loc = [spaxel_loc[i][0],spaxel_loc[i][1]]\n", + " x1d3flux_loc = cube[:, loc[1], loc[0]]\n", + " #ax2 = plt.subplot(3,len(wavelength_slices), int(total_num_plots/3)+plot_count)\n", + " ax2 = plt.subplot(gs[int(total_num_plots/3)+plot_count])\n", + "\n", + " #Spaxel Box Highlight \n", + " spaxel_rect = plt.Rectangle((loc[0]-.5, loc[1]-.5), 1,1, fill=False, color='black', linewidth=2)\n", + " ax1.add_patch(spaxel_rect)\n", + " \n", + " ax2.plot(x1d3wave, x1d3flux_loc, linewidth=1, color=colors[i])\n", + " ax2.grid(linewidth=2)\n", + " ax2.set_xlabel('$\\u03BB [\\u03BC$m]',fontsize=22)\n", + " ax2.set_ylabel(\"Surface Brightness \\n (MJy/sr)\",fontsize=22)\n", + " ax2.set_title('Spaxel at (x, y)='+repr(loc), fontsize=25)\n", + " ax2.tick_params(axis='both', which='major', labelsize=20)\n", + " ax2.yaxis.get_offset_text().set_fontsize(15)\n", + " \n", + " #Scale Information\n", + " if y_scale:\n", + " ymin, ymax = y_scales[i][0], y_scales[i][1]\n", + " else:\n", + " ymin, ymax = ax2.set_ylim()\n", + " \n", + " ax2.set_ylim(ymin, ymax)\n", + " ax2.xaxis.set_tick_params(labelsize=20)\n", + " ax2.yaxis.set_tick_params(labelsize=20)\n", + " ax2.set_aspect(0.5/ax2.get_data_ratio())\n", + " \n", + " #-----------------------------------------------Weight Map-------------------------------------------------\n", + " \n", + " #Corresponding Weight Map (wmap) for Cube Slice\n", + " ax3 = plt.subplot(gs[int(total_num_plots)-np.array(wavelength_slices).size+plot_count], projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", + " #ax3 = plt.subplot(3, len(wavelength_slices), int(total_num_plots)-len(wavelength_slices)+plot_count, projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", + " \n", + " slice_mean_wmap = np.nanmean(wmap[(nslice-2):(nslice+2), :, :], axis=0) #Mean of the wmap slice looking in the range (nslice-2):(nslice+2)\n", + " slice_norm_wmap=ImageNormalize(slice_mean_wmap, stretch=AsinhStretch()) #normalize &stretch\n", + " slice_wmap = ax3.imshow(slice_mean_wmap, norm=slice_norm_wmap, origin='lower',aspect='auto', cmap=cmap) #plot slice\n", + "\n", + " cb_wmap = fig.colorbar(slice_wmap, fraction=0.046, pad=0.04)\n", + " cb_wmap.set_label('Weight', labelpad=-1, fontsize = 22)\n", + " cb_wmap.ax.tick_params(labelsize=20)\n", + " cb_wmap.ax.yaxis.get_offset_text().set_fontsize(20)\n", + " \n", + " ax3.set_xlabel('RA', fontsize=22)\n", + " ax3.set_ylabel('DEC', labelpad=-1, fontsize=22)\n", + " #ax3.grid(color='gray', ls='solid')\n", + " ax3.set_title(str(slicewave)+' microns: Weight Map', fontsize=25)\n", + " ax3.tick_params(axis='both', which='major', labelsize=20)\n", + " ax3.coords[0].set_ticklabel(rotation=13, ha='right', pad=24)\n", + "\n", + " plot_count += 1\n", + " \n", + " else:\n", + " None\n", + " \n", + " if title:\n", + " fig.suptitle(title, fontsize=title_font)\n", + " plt.subplots_adjust(top=0.8) \n", + " \n", + " fig.tight_layout(rect=[0, 0, 0.98, 0.98])\n", + "\n", + " if save_figure == True:\n", + " fig.savefig(root+\".png\",dpi=24, bbox_inches=\"tight\")\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "347a9a54", + "metadata": {}, + "source": [ + "## 4. Directory Set-Up \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fbc514d3", + "metadata": {}, + "outputs": [], + "source": [ + "#To rerun the notebook and all the pipeline steps set runflag=True\n", + "runflag = True \n", + "\n", + "#Demo directory -- contains pre-computed products\n", + "if runflag == False:\n", + " output_dir = './nirspec_ifu_02729_demo/'\n", + "\n", + "#Rerun directory\n", + "elif runflag == True:\n", + " #If you want to actually re-download the data and run everything offline, \n", + " #then comment out this line, set runflag=True, & specify a desired local directory\n", + " output_dir = './nirspec_ifu_02729_rerun/'\n", + " if not os.path.exists(output_dir):\n", + " os.makedirs(output_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "3ad6ed15", + "metadata": {}, + "source": [ + "## 5. Download the Data \n", + "\n", + "
\n", + "
\n", + "\n", + "Tip: To download the data from MAST, you must input your MAST authorization token. Get your MAST Token Here: https://auth.mast.stsci.edu/token.\n", + " Additionally, be sure to follow [astroquery installation procedures](https://astroquery.readthedocs.io/en/latest/index.html#) to properly run this cell. \n", + " \n", + "
\n", + "\n", + "| Target: Tarantula Nebula | | | | |\n", + "|:-----------:|:-------:|:---:|---|---|\n", + "| Proposal ID | 02729 | | | |\n", + "| [GRATING/FILTER](https://jwst-docs.stsci.edu/jwst-near-infrared-spectrograph/nirspec-observing-modes/nirspec-ifu-spectroscopy) | G140H/F100LP | λ: 0.97–1.89 μm (a medium resolution, R ~ 1000) | | |\n", + "| | G235H/F170LP | λ: 1.66–3.17 μm (a high resolution, R ~ 2700) | | |\n", + "| | G395H/F290LP | λ: 2.87–5.27 μm (a high resolution, R ~ 2700) | | |\n", + "| DURATION | 87.533 [s] | Total duration of one exposure | | | |\n", + "| READPATT | NRSIRS2RAPID | Readout Pattern | | | |\n", + "| PATTTYPE | CYCLING | Primary dither pattern type | | |\n", + "| PATTSIZE | LARGE | Primary dither pattern size (1.0\" extent) | | |\n", + "| NUMDTHPT | 8 | Total number of points in pattern | | | \n", + "| SRCTYAPT | UNKNOWN | Source Type selected in APT | | | \n", + "\n", + "> **Note:** The presence of a physical gap between detectors affects high-resolution IFU observations because the spectra are long enough to span both NIRSpec detectors. When using the grating-filter combination G140H/F070LP (or PRISM/CLEAR) the resulting spectra do not have any gaps because the spectra do not extend beyond NRS1. [More Info ...](https://jwst-docs.stsci.edu/jwst-near-infrared-spectrograph/nirspec-operations/nirspec-ifu-operations/nirspec-ifu-wavelength-ranges-and-gaps#NIRSpecIFUWavelengthRangesandGaps-Wavelengthgaps)\n", + "\n", + "The cell below downloads the raw uncalibrated data along with the stage 2 and stage 3 products that are available in MAST. MAST products will get saved to a folder called `mast_products` within the designated output directory defined earlier in this notebook. These files have already been pre-downloaded and stored in a provided demo directory. To get the most up-to-date products set `runflag = True` and rerun this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c2fb0b2", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Download data from MAST \n", + "\n", + "#Setup your account \n", + "\n", + "# NOTE:\n", + "# The data in this notebook is public and does not require a token.\n", + "# For other data sets, uncomment the following line and enter your\n", + "# token at the prompt.\n", + "\n", + "# Observations.login(token=None)\n", + "\n", + "sessioninfo = Observations.session_info()\n", + "\n", + "#Define the general search criteria\n", + "obs = Observations.query_criteria(\n", + " obs_collection = 'JWST',\n", + " instrument_name = ['NIRSPEC/IFU'],\n", + " proposal_id = '02729')\n", + "\n", + "#Print the Observations returned from the general serach criteria\n", + "products = Observations.get_product_list(obs)\n", + "#print(products)\n", + "\n", + "#Filter the list of observations\n", + "#In this case we look for UNCAL products and ASN files to manually run pipeline stage 1-3\n", + "#We also look for pre-processed MAST products for comparison: RATE (stage 1) & CAL (stage 2&3) & S3D (stage 2&3) & X1D (stage2&3) \n", + "filtered = Observations.filter_products(products,\n", + " productSubGroupDescription=[\"UNCAL\", \"RATE\", \"CAL\", \"S3D\", \"X1D\", \"ASN\"],\n", + " mrp_only=False)\n", + "#Print the filtered products \n", + "number = len(filtered)\n", + "for k in range(number):\n", + " print(filtered['productFilename'][k])\n", + "\n", + "#Download the filtered products\n", + "#This creates a mastDownload directory, unless you set flat=True and set a download_dir\n", + "for i in range(len(filtered)):\n", + " mast_products_dir = output_dir+'mast_products/'\n", + " if not os.path.exists(mast_products_dir):\n", + " os.makedirs(mast_products_dir)\n", + " if runflag == True:\n", + " Observations.download_products(filtered[i], mrp_only=False, cache=False, flat=True, download_dir=mast_products_dir) #Override any cached files and download the most up-to-date ones\n", + " else:\n", + " Observations.download_products(filtered[i], mrp_only=False, cache=True, flat=True, download_dir=mast_products_dir) #Find any cached files first before downloading new ones" + ] + }, + { + "cell_type": "markdown", + "id": "30347914", + "metadata": {}, + "source": [ + "## 6. Products Found In MAST \n", + "
\n", + "\n", + "> In [APT](https://jwst-docs.stsci.edu/jwst-astronomers-proposal-tool-overview), the observer has three options for source type (`SRCTYAPT` keyword): `POINT`, `EXTENDED`, or `UNKNOWN`. In stage 2, the `srctype` step will first check if the `SRCTYAPT` keyword is present and populated. If `SRCTYAPT` is not present or is set to `UNKNOWN`, the step determines a suitable value based on the observing mode, command line input, and other characteristics of the exposure. If the exposure is identified as a background exposure (`BKGDTARG = True`), the exposures default to a source type of `EXTENDED`. Exposures that are part of a nodded pattern (identified by keyword `PATTYPE`), which are assumed to only be used with point-like targets, default to a source type of `POINT`. [More Info ...](https://jwst-pipeline.readthedocs.io/en/latest/jwst/srctype/description.html#single-source-observations)\n", + "\n", + "For dithered NIRSpec IFU data like ours, which do not meet any of the above conditions, will default to source type `EXTENDED`. Therefore, the products found in MAST for target Tarantula Nebula (PID 02729) have been processed as an `EXTENDED` source . " + ] + }, + { + "cell_type": "markdown", + "id": "9fc34c69", + "metadata": {}, + "source": [ + "### 6.1 Stage 1 Products Found In MAST \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55a64f33", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Stage 1 slope products -- level 2a images\n", + "\n", + "#Plot 4th (out of 8) dither position (spectra fall on both NRS1 & NRS2) for GRATING/FILTER G235H/F170LP combination \n", + "for rate_file in sorted(glob.glob(mast_products_dir+'*02103*00004_nrs?_rate.fits')):\n", + " \n", + " ratefile_open = datamodels.open(rate_file)\n", + " ratefile_sci = ratefile_open.data #get the pixel data (the SCI extension of the fits file)\n", + " ratefile_dq = ratefile_open.dq #data quality map data (DQ extension)\n", + " \n", + " #Plot the slope image and small section of the countrate image & corresponding section of the DQ map\n", + " show_image(ratefile_sci, 0,10, units='DN/s',zoom_in=[650,700, 1250,1300], ysize=20, xsize=20,\n", + " title='Countrate Image \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", + " ratefile_open.meta.dither.position_number, \n", + " ratefile_open.meta.instrument.grating,\n", + " ratefile_open.meta.instrument.filter)) #rate files have units of DN/s\n", + " \n", + " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear',zoom_in=[650,700, 1250,1300], ysize=20, xsize=20,\n", + " title='Data Quality Map \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", + " ratefile_open.meta.dither.position_number, \n", + " ratefile_open.meta.instrument.grating,\n", + " ratefile_open.meta.instrument.filter)) \n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "217a5889", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Warning: Please be aware that in the countrate (slope) images found in MAST, many pixels are flagged as Do Not Use (more clearly seen in the corresponding DQ map) and therefore appear white with a value of NaN. This excessive flagging is due to an outdated mask reference file that would mark unreliable slope, bad fit, and telegraph pixels as Do Not Use. Despite the large number of NaNs in the countrate image, the extracted spectra are not significantly affected by them when combining multiple dithered exposures because the number of flagged pixels is still a relatively small fraction. However, due to the high number of flags in the MAST products, it is difficult to see specific details in the slope images, like correlated read noise, which manifests as low-level vertical banding/striping and a \"picture frame\" with the [$IRS^{2}$](https://jwst-docs.stsci.edu/jwst-near-infrared-spectrograph/nirspec-instrumentation/nirspec-detectors/nirspec-detector-readout-modes-and-patterns/nirspec-irs2-detector-readout-mode) readout mode. As of context jwst_1084.pmap, the pipeline now considers unreliable slope, bad fit, and telegraph pixels good for further processing in Full frame data. [Therefore, the reprocessed data below offers improved visibility of the correlated read noise.](#level1_rerun)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "941f42ed", + "metadata": {}, + "source": [ + "### 6.2 Stage 2 Products Found In MAST \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b37ffa6", + "metadata": {}, + "outputs": [], + "source": [ + "#Stage 2 Products -- Calibrated 3-D data cubes for each GRATING/FILTER combination\n", + "\n", + "#Plotting the 4th (out of 8) dither position for both NRS1 and NRS2\n", + "s3d_g140h_stage2 = sorted(glob.glob(mast_products_dir+'*2105*00004_nrs?_s3d.fits'))\n", + "s3d_g235h_stage2 = sorted(glob.glob(mast_products_dir+'*2103*00004_nrs?_s3d.fits'))\n", + "s3d_g395h_stage2 = sorted(glob.glob(mast_products_dir+'*2101*00004_nrs?_s3d.fits'))\n", + "s3d_stage2_list = s3d_g140h_stage2+s3d_g235h_stage2+s3d_g395h_stage2\n", + "\n", + "title_stage2_mast='Tarantula Nebula \\n Level 2 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", + "\n", + "#Characteristics of the plot \n", + "nrs1_wavelengths = [1.0,2.3,3.4] #Wavelength slices (microns) to take from the 3-D data cube\n", + "nrs2_wavelengths = [1.4,2.5,4.0]\n", + "\n", + "nrs1_spaxel_locs = [[30,29],[28,39],[14,25]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "nrs2_spaxel_locs = [[30,29],[28,39],[14,25]]\n", + "\n", + "nrs1_vmin_vmax = [[0,2e2],[0,1e2],[0,1.2e3]] #Minimum & Maximum signal values for scaling each slice\n", + "nrs2_vmin_vmax = [[0,2e2],[0,1e2],[0,1.2e3]]\n", + "\n", + "nrs1_yscales = [[-80,150], [-80,150], [-80,200]] #Spaxel plot y-limits\n", + "nrs2_yscales = [[-80,200], [-80,150], [-80,200]]\n", + "\n", + "#Plot using the convience function defined above\n", + "show_ifu_cubeslices(s3d_stage2_list, wavelength_slices=[nrs1_wavelengths,nrs2_wavelengths], \n", + " spaxel_locs=[nrs1_spaxel_locs,nrs2_spaxel_locs],vmin_vmax=[nrs1_vmin_vmax,nrs2_vmin_vmax], \n", + " y_scale = [nrs1_yscales,nrs2_yscales], title=title_stage2_mast)" + ] + }, + { + "cell_type": "markdown", + "id": "56e44863", + "metadata": {}, + "source": [ + "### 6.3 Stage 3 Products Found In MAST \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "94514cfd", + "metadata": {}, + "outputs": [], + "source": [ + "#Stage 3 Products -- Combined Calibrated 3-D data cubes for each GRATING/FILTER combination\n", + "\n", + "s3d_stage3_list = sorted(glob.glob(mast_products_dir+'*nirspec*_s3d.fits'))\n", + "\n", + "title_stage3_mast='Tarantula Nebula \\n Level 3 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", + "\n", + "#Characteristics of the plot \n", + "wavelengths = [1.4,2.3,4.0] #Wavelength slices (microns) to take from the combined 3-D data cube\n", + "spaxel_locs = [[30,29],[28,39],[14,25]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "vmin_vmax = [[0,2e2],[0,1e2],[0,1.2e3]] #Minimum & Maximum signal values for scaling each slice\n", + "yscales = [[-80,150], [-80,150], [-80,200]] #Spaxel plot y-limits\n", + "\n", + "#Plot using the convience function defined above\n", + "show_ifu_cubeslices(s3d_stage3_list, wavelength_slices=[wavelengths],spaxel_locs=[spaxel_locs],\n", + " vmin_vmax=[vmin_vmax], \n", + " y_scale = [yscales], title=title_stage3_mast)" + ] + }, + { + "cell_type": "markdown", + "id": "a4bff147-5b78-4a72-aa5d-ea013e6b9ac6", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Warning: Please note that in the final product (stage 3) downloaded from MAST, a significant portion of the data got rejected, returning a value of zero in the weight maps. This over-rejection of data is due to the outdated `outlier_detection` step that MAST automatically enables during stage 3 of the pipeline. A new outlier detection algorithm has been developed specifically for IFU data that overcomes some of these limitations (as of DMS build B9.3rc1/CAL_VER 1.11.0). Due to the limitations of the previous outlier detection algorithm, the user recommendation is to skip the `outlier_detection` step if using an older version of the pipleine or manually rerun stage 3 of the pipleine with outlier detection on with the most up-to-date pipeline version (detailed in the next section of this notebook).\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de1b09ff", + "metadata": {}, + "outputs": [], + "source": [ + "#Stage 3 Products -- Combined Extracted 1-D Spectrum \n", + "\n", + "fig = plt.figure(figsize=(15,9))\n", + "colors = ['darkred', 'darkturquoise', 'blue']\n", + "\n", + "x1d3_mast_list = sorted(glob.glob(mast_products_dir+'*nirspec*_x1d.fits'))\n", + "\n", + "for i, x1d3_file in enumerate(x1d3_mast_list):\n", + " x1d3_file_open = datamodels.open(x1d3_file) \n", + " \n", + " #Wavelength & Surface Brightness Arrays\n", + " x1d3wave_mast = x1d3_file_open.spec[0].spec_table.WAVELENGTH\n", + " x1d3flux_mast = x1d3_file_open.spec[0].spec_table.SURF_BRIGHT\n", + " \n", + " plt.plot(x1d3wave_mast,x1d3flux_mast, linewidth =2, color=colors[i], label='Grating/Filter: {}/{}'.format(x1d3_file_open.meta.instrument.grating,\n", + " x1d3_file_open.meta.instrument.filter))\n", + "#Where wavelength slice was taken above\n", + "plt.vlines(1.4, -1e1, 400., 'black', 'dotted', label='1.4 microns', linewidth=5)\n", + "plt.vlines(2.3, -1e1, 400., 'black', 'dotted', label='2.3 microns', linewidth =5)\n", + "plt.vlines(4.0, -1e1, 400., 'black', 'dotted', label='4.0 microns', linewidth=5)\n", + "\n", + "plt.xlabel('$\\lambda [\\mu$m]', fontsize =15)\n", + "plt.ylabel('Surface Brightness (MJy/sr)', fontsize =15)\n", + "plt.title(\"Tarantula Nebula \\n Level 3 IFU Product in MAST: Extracted 1-D Spectrum\", fontsize =20)\n", + "plt.ylim(-1e1, 2e2)\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "47625316", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Note: \n", + "When the source type is extended, the default extraction aperture for the `extract_1d` step covers the entire cube.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "b0b899f0", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Warning: Most of the large negative and positive flux spikes extending beyond the plot range are likely due to bad/hot pixels that are not flagged in the current DQ masks. The mask reference file gets directly pulled from CRDS. The products found in MAST use a specific CRDS context (.pmap) when processing data. However, the CRDS is constantly updating the operational .pmap.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "de115eed", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Warning: The systematically lower flux \\& wavy continua in the red portions of the spectrum from each grating are artifacts due to correlated read noise. This is apparent in the full frame images, manifesting as vertical banding often accompanied by a \"picture frame\" effect, and is caused by low-level detector instabilities that even the $IRS^{2}$ algorithm cannot remove. The effect is typically only noticeable in read-noise limited data, and is most prominent on the NRS2 detector. The pipeline currently does not apply any correction for this, but there is an external algorithm, \"NSClean\", developed by Bernie Rauscher at GSFC, that is available to the public; see https://webb.nasa.gov/content/forScientists/publications.html#NSClean for details.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80d54809", + "metadata": {}, + "outputs": [], + "source": [ + "#Just a check to see what verison of the pipeline and what pmap was used\n", + "x1d3_mast = fits.open(glob.glob(mast_products_dir+'*_x1d.fits')[0])\n", + "\n", + "print(\"Products found in MAST used JWST calibration pipeline version: {} and {}\".format(x1d3_mast[0].header['CAL_VER']\n", + ",x1d3_mast[0].header['CRDS_CTX']))" + ] + }, + { + "cell_type": "markdown", + "id": "842a8837", + "metadata": {}, + "source": [ + "## 7. Re-processing the Data \n", + "
\n", + "\n", + "Due to lengthy processing time, only one of the observations (G235H/F170LP) was re-processed in this demonstration. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d3a9e22a", + "metadata": {}, + "outputs": [], + "source": [ + "#Directory for rerun of stage 1 to avoid overwritting MAST products\n", + "output_dir_rerun = output_dir+'rerun/' \n", + "if not os.path.exists(output_dir_rerun):\n", + " os.makedirs(output_dir_rerun)" + ] + }, + { + "cell_type": "markdown", + "id": "126569cf", + "metadata": {}, + "source": [ + "### 7.1 Stage 1 Rerun & Products \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e624ce66-c24d-4206-9dc1-784efbcd2d7a", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Stage 1 Processing \n", + "\n", + "if runflag == True:\n", + "\n", + " for uncal_file in sorted(glob.glob(mast_products_dir+'*02103*nrs?_uncal.fits')): \n", + "\n", + " print(\"Applying Stage 1 Corrections & Calibrations to: \"+ os.path.basename(uncal_file))\n", + "\n", + " result = Detector1Pipeline.call(uncal_file,\n", + " save_results = True,\n", + " output_dir = output_dir_rerun)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da176563", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Stage 1 slope products -- level 2a images\n", + "\n", + "#Plot 4th (out of 8) dither position (spectra fall on both NRS1 & NRS2) for GRATING/FILTER G235H/F170LP combination \n", + "for rate_file in sorted(glob.glob(output_dir_rerun+'*02103*00004_nrs?_rate.fits')):\n", + " \n", + " ratefile_open = datamodels.open(rate_file)\n", + " ratefile_sci = ratefile_open.data #get the pixel data (the SCI extension of the fits file)\n", + " ratefile_dq = ratefile_open.dq #data quality map data (DQ extension)\n", + " \n", + " #Plot the slop image and zoom in on a small section of the countrate image & corresponding section of the DQ map\n", + " show_image(ratefile_sci, 0,10, units='DN/s',zoom_in=[650,700, 1250,1300], ysize=20, xsize=20,\n", + " title='Countrate Image \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", + " ratefile_open.meta.dither.position_number, \n", + " ratefile_open.meta.instrument.grating,\n", + " ratefile_open.meta.instrument.filter)) #rate files have units of DN/s\n", + " \n", + " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear',zoom_in=[650,700, 1250,1300], ysize=20, xsize=20,\n", + " title='Data Quality Map \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", + " ratefile_open.meta.dither.position_number, \n", + " ratefile_open.meta.instrument.grating,\n", + " ratefile_open.meta.instrument.filter)) \n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "b84c9f5b", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Note: Compared to the [countrate (slope) products found in MAST](#level1_mast), fewer pixels are flagged as Do Not Use when using the most up-to-date pmap in CRDS (at the time jwst_1106.pmap). With the latest pmap, one can observe low-level vertical banding in the central regions of the detector, and the \"picture frame\" towards the edge of both detectors, where there is less correlated read noise a lot easier. \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "bf378c9d", + "metadata": {}, + "source": [ + "### 7.2 Stage 2 Rerun & Products \n", + "
\n", + "\n", + "\n", + "During stage 2 of the pipeline, the countrate (slope) image products from stage 1, which have units of DN/s, are converted to units of surface brightness (MJy/sr) for both extended and point sources (as of DMS build 9.3/CAL_VER 1.10.2). For extended targets, like the Tarantula Nebula, the `extract_1d` step is controlled by a different set of parameters in the EXTRACT1D reference file: \n", + "\n", + "> For an extended source, rectangular aperture photometry is used, with the entire image being extracted, and no background subtraction, regardless of what was specified in the reference file or step arguments. [More Info ...](https://jwst-pipeline.readthedocs.io/en/latest/jwst/extract_1d/description.html)\n", + "\n", + "
\n", + " \n", + "Warning: Note there has been a bug in the `cube_build` step that caused the point source flux to not be conserved when using different spatial sampling. A fix has been implemented as of release DMS build 9.3/CAL_VER 1.10.2. In order to enable the correct functionality, the units of the cal.fits files and cubes will now be in surface brightness, and only the 1-D extracted spectra will be in units of Jy.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "796a6aab", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Stage 2 Processing \n", + "\n", + "if runflag == True:\n", + " \n", + " #Process each rate file seperately \n", + " for rate_file in sorted(glob.glob(output_dir_rerun+'*0000?*nrs?*rate.fits')):\n", + " \n", + " print(\"Applying Stage 2 Calibrations & Corrections to: \"+ os.path.basename(rate_file))\n", + " \n", + " result = Spec2Pipeline.call(rate_file,\n", + " save_results = True,\n", + " output_dir = output_dir_rerun) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9ea3199", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Stage 2 Products -- Calibrated 3-D data cubes for each GRATING/FILTER combination\n", + "\n", + "#Plotting the 4th (out of 8) dither position for both NRS1 and NRS2\n", + "s3d_stage2_list = sorted(glob.glob(output_dir_rerun+'*2103*00004_nrs?_s3d.fits'))\n", + "\n", + "title_stage2_rerun='Tarantula Nebula \\n Level 2 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", + "\n", + "#Characteristics of the plot \n", + "nrs1_wavelengths = [2.3] #Wavelength slices (microns) to take from the 3-D data cube\n", + "nrs2_wavelengths = [2.5]\n", + "\n", + "nrs1_spaxel_locs = [[28,39]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "nrs2_spaxel_locs = [[28,39]]\n", + "\n", + "nrs1_vmin_vmax = [[0,1e2]] #Minimum & Maximum signal values for scaling each slice\n", + "nrs2_vmin_vmax = [[0,1e2]]\n", + "\n", + "nrs1_yscales = [[-80,150]] #Spaxel plot y-limits\n", + "nrs2_yscales = [[-80,150]]\n", + "\n", + "#Plot using the convience function defined above\n", + "show_ifu_cubeslices(s3d_stage2_list, wavelength_slices=[nrs1_wavelengths,nrs2_wavelengths], \n", + " spaxel_locs=[nrs1_spaxel_locs,nrs2_spaxel_locs],vmin_vmax=[nrs1_vmin_vmax,nrs2_vmin_vmax], \n", + " y_scale = [nrs1_yscales,nrs2_yscales], title=title_stage2_rerun)" + ] + }, + { + "cell_type": "markdown", + "id": "96ac83a4", + "metadata": {}, + "source": [ + "### 7.3 Stage 3 Rerun & Products \n", + "
\n", + "\n", + "***Level 3 ASN File***\n", + "\n", + "> Observations that use a nod-type/dither patterns, their exposures are related. [Association files (ASN)](https://jwst-pipeline.readthedocs.io/en/stable/jwst/associations/overview.html) describe how multiple exposures are related to one another and how they depend on one another. Processing an ASN file permits exposures to be calibrated, archived, retrieved, and reprocessed as a set rather than individual objects. IFU exposures taken with a dither pattern are not used for pixel-to-pixel background subtraction by the calibration pipeline (unlike exposures taken with a nod pattern).\n", + "\n", + "Therefore, all calibration files (`cal.fits`) in our spec3 ASN file should be labeled as science exposures (`exptype: science`). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf1ea47e", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Copy ASN file from MAST (for G235H/F170LP) into the stage 1 rerun directory\n", + "\n", + "asnfile_mast = glob.glob(mast_products_dir+'*_spec3_00001_asn.json')[0] #ASN file found in MAST\n", + "\n", + "asnfile_rerun = output_dir_rerun+os.path.basename(asnfile_mast) #New ASN file path\n", + "if not os.path.exists(asnfile_rerun):\n", + " copy(asnfile_mast, asnfile_rerun)\n", + " \n", + "#Check the ASN file contents\n", + "with open(asnfile_rerun, 'r') as f_obj:\n", + " asnfile_rerun_data = json.load(f_obj)\n", + " \n", + "asnfile_rerun_data" + ] + }, + { + "cell_type": "markdown", + "id": "b3669d28", + "metadata": {}, + "source": [ + "#### 7.3.1 New Outlier Detection Algorithm\n", + "
\n", + "\n", + "The new outlier detection algorithm for IFU data (as of DMS build B9.3rc1/CAL_VER 1.11.0) implements the basic outlier detection algorithm -- searches for pixels that are consistent outliers in the calibrated images created by the `calwebb_spec2` pipeline. The algorithm generally operates as follows:\n", + "\n", + "> * Identifies outlier pixels by comparing them with their neighboring pixels in the spatial direction across a set of input files within an association.\n", + "> * For NIRSpec data, it calculates differences between pixels located above and below each science pixel.\n", + "> * The pixel differences for every input model in the association are computed and stored in a stack of pixel differences.\n", + "> * For each pixel, the algorithm determines the minimum difference across this stack and then performs normalization. This normalization process employs a local median derived from the difference array, with the size of the median determined by the kernel size.\n", + "> * A pixel is flagged as an outlier if this normalized minimum difference is greater than the input threshold percentage. \n", + "> * Pixels that are found to be outliers are flaged in in the DQ array.\n", + "> * [More Info ...](https://jwst-pipeline.readthedocs.io/en/latest/jwst/outlier_detection/outlier_detection_ifu.html#outlier-detection-ifu)\n", + "\n", + "\n", + "**[The outlier_detection step for IFU data has the following optional arguments that control the behavior of the processing](https://github.com/spacetelescope/jwst/blob/master/docs/jwst/outlier_detection/arguments.rst):**\n", + "\n", + "* `kernel_size` (string, default='7 7'): The size of the kernel to use to normalize the pixel differences. The kernel size must only contain odd values.\n", + "* `threshold_percent` (float, default=99.8): The threshold (in percent) of the normalized minimum pixel difference used to identify bad pixels. Pixels with a normalized minimum pixel difference above this percentage are flagged as a outlier.\n", + "* `save_intermediate_results` (boolean, default=False): Specifies whether or not to save any intermediate products created during step processing.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "381a12db-7809-4f0d-8e9d-b8d81131e63c", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Rerun stage 3 with outlier detection off\n", + "if runflag==True:\n", + " \n", + " result = Spec3Pipeline.call(asnfile_rerun,\n", + " save_results = True,\n", + " output_dir = output_dir_rerun,\n", + " steps = {\"outlier_detection\":{\"skip\": False,\n", + " \"save_results\": True,\n", + " \"kernel_size\": '3 3'}})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d45c93f9", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Stage 3 Products -- Combined Calibrated 3-D data cubes for GRATING/FILTER combination G235H/F170LP\n", + "\n", + "s3d_stage3_list = sorted(glob.glob(output_dir_rerun+'*nirspec*_s3d.fits'))\n", + "\n", + "title_stage3_rerun='Tarantula Nebula \\n Level 3 IFU Product: \\n 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", + "\n", + "#Characteristics of the plot \n", + "wavelengths = [2.3] #Wavelength slices (microns) to take from the combined 3-D data cube\n", + "spaxel_locs = [[28,39]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "vmin_vmax = [[0,1e2]] #Minimum & Maximum signal values for scaling each slice\n", + "yscales = [[-80,150]] #Spaxel plot y-limits\n", + "\n", + "#Plot using the convience function defined above\n", + "show_ifu_cubeslices(s3d_stage3_list, wavelength_slices=[wavelengths],spaxel_locs=[spaxel_locs],\n", + " vmin_vmax=[vmin_vmax], \n", + " y_scale = [yscales], title=title_stage3_rerun, title_font=20)" + ] + }, + { + "cell_type": "markdown", + "id": "322f8a59", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Note: In comparison to the [weight maps for the 3-D data cube products found in MAST](#level3_mast), the implementation of the new outlier detection algorithm leads to a notable decrease in data rejection.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6c96634", + "metadata": {}, + "outputs": [], + "source": [ + "#Stage 3 Products -- Combined Extracted 1-D Spectrum \n", + "\n", + "fig = plt.figure(figsize=(15,9))\n", + "colors = ['darkred', 'darkturquoise', 'blue']\n", + "\n", + "x1d3_rerun_list = sorted(glob.glob(output_dir_rerun+'*nirspec*_x1d.fits'))\n", + "\n", + "for i, x1d3_file in enumerate(x1d3_rerun_list):\n", + " x1d3_file_open = datamodels.open(x1d3_file) \n", + " \n", + " #Wavelength & Surface Brightness Arrays\n", + " x1d3wave_rerun = x1d3_file_open.spec[0].spec_table.WAVELENGTH\n", + " x1d3flux_rerun = x1d3_file_open.spec[0].spec_table.SURF_BRIGHT\n", + " \n", + " plt.plot(x1d3wave_rerun,x1d3flux_rerun, linewidth =2, color=colors[i], label='Grating/Filter: {}/{}'.format(x1d3_file_open.meta.instrument.grating,\n", + " x1d3_file_open.meta.instrument.filter))\n", + "#Where wavelength slice was taken above\n", + "plt.vlines(2.3, -1e1, 400., 'black', 'dotted', label='2.3 microns', linewidth =5)\n", + "\n", + "plt.xlabel('$\\lambda [\\mu$m]', fontsize =15)\n", + "plt.ylabel('Surface Brightness (MJy/sr)', fontsize =15)\n", + "plt.title(\"Tarantula Nebula \\n Level 3 IFU Product in MAST: Extracted 1-D Spectrum\", fontsize =20)\n", + "plt.ylim(-1e1, 2e2)\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "ff7a3626-71bb-45e5-bc1f-ffeccf930aa4", + "metadata": {}, + "source": [ + "## Conclusion \n", + "
\n", + "\n", + "In conclusion, this notebook walks users through processing real data (Tarantula Nebula) from ERS Proposal ID 2729 and comparing automated products in MAST with those generated using the latest version of the JWST calibration pipeline and latest CRDS context. For optimal results, users are strongly encouraged to reprocess their own data using the most recent pipeline version and CRDS context, taking advantage of bug fixes and algorithm improvements (i.e., the new IFU outlier detection algorithm). " + ] + }, + { + "cell_type": "markdown", + "id": "763e51fb", + "metadata": {}, + "source": [ + "## About this Notebook \n", + "
\n", + "\n", + "**Authors**: Kayli Glidic (kglidic@stsci.edu), Maria Pena-Guerrero (pena@stsci.edu), Leonardo Ubeda (lubeda@stsci.edu)\n", + "\n", + "**Update On**: 2023-08-11" + ] + } + ], + "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": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb new file mode 100644 index 000000000..546c7279c --- /dev/null +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb @@ -0,0 +1,1141 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "771e1de8", + "metadata": {}, + "source": [ + "\n", + "# NIRSpec IFU Pipeline Processing ERO 02732 NGC 7319 AGN\n", + "
\n", + "\n", + "## Table of Contents\n", + "\n", + "* [1. Introduction](#intro)\n", + "* [2. Import Library](#imports)\n", + "* [3. Convenience Functions](#func)\n", + "* [4. Directory Set-Up](#dir_setup)\n", + "* [5. Download the data](#data)\n", + "* [6. Products Found In MAST](#mast_products)\n", + " * [6.1 Stage 1 Products Found In MAST](#level1_mast)\n", + " * [6.2 Stage 2 Products Found In MAST](#level2_mast)\n", + " * [6.3 Stage 3 Products Found In MAST](#level3_mast)\n", + "* [7. Re-processing the Data](#reprocessing)\n", + " * [7.1 Stage 1 Rerun & Products](#level1_rerun)\n", + " * [7.2 Stage 2 Rerun & Products](#level2_rerun)\n", + " * [7.3 Stage 3 Rerun & Products](#level3_rerun)\n", + " * [7.3.1 New Outlier Detection Algorithm](#outlier_detection_new)\n", + "* [Conclusion](#conclusion)\n", + "* [About This Notebook](#about)\n", + "\n", + "\n", + "\n", + "## 1. Introduction \n", + "
\n", + "\n", + "End-to-end calibration of JWST data is divided into 3 main stages of processing. This notebook explores how to run the JWST calibration pipeline stages 1-3 for NIRSpec IFU spectroscopic data.\n", + "
\n", + " \"NGC_7319_AGN\"\n", + "
\n", + "\n", + ">* **`STAGE 1`** ([calwebb_detector1](https://jwst-pipeline.readthedocs.io/en/latest/jwst/pipeline/calwebb_detector1.html#calwebb-detector1)): consists of detector-level corrections, performed on a group-by-group basis, followed by ramp fitting.\n", + " * **Input**: Raw exposure (`uncal.fits`) containing original data from all detector readouts (ncols x nrows x ngroups x nintegrations).\n", + " * **Output**: Corrected countrate (slope) image (`rate.fits`) \n", + ">* **`STAGE 2`** ([calwebb_spec2](https://jwst-pipeline.readthedocs.io/en/latest/jwst/pipeline/calwebb_spec2.html#calwebb-spec2)): consists of additional instrument-level and observing mode corrections and calibrations.\n", + " * **Input**: A single corrected countrate (slope) image (`rate.fits`) or an ASN file listing multiple inputs.\n", + " * **Output**: A fully calibrated unrectified exposure (`cal.fits`). For NIRSpec IFU data, the `cube_build` step returns a 3-D IFU spectroscopic cube (`s3d.fits`). The `extract_1d` step returns 1-D extracted spectral data products (`x1d.fits`)\n", + ">* **`STAGE 3`** ([calwebb_spec3](https://jwst-pipeline.readthedocs.io/en/latest/jwst/pipeline/calwebb_spec3.html#calwebb-spec3)): consists of additional corrections (e.g. `outlier_detection`) and routines for combining calibrated data from multiple exposures (e.g. dither/nod pattern) into a single combined 2-D or 3-D spectral product and a combined 1-D spectrum. \n", + " * **Input**: An ASN file that lists multiple calibrated exposures (`cal.fits`).\n", + " * **Output**: For NIRSpec IFU data, a resampled and combined 3-D IFU cube (`s3d.fits`) and a 1-D extracted spectrum (`x1d.fits`)\n", + "\n", + "Here, we will focus on the mechanics of processing \"real\" example data [(NGC 7319 AGN)](#NGC_7319_AGN) from Early Release Science (ERS) Proposal ID 2732, including how to use associations for multi-exposure combination, how to interact and work with data models for each product, and mainly how to process IFU data as an extended source. Our objective is to examine the automated products found in MAST and compare them to products generated with the most up-to-date version of the JWST calibration pipeline.\n", + "\n", + "Most processing runs shown here use the default reference files from the Calibration Reference Data System (CRDS). Please note that pipeline software development is a continuous process, so results in some cases may be slightly different if using a subsequent version. There are also a few known issues with some of the pipeline steps in this build that we expect to be fixed in the near future. Until then, at various steps, we provide users with the current processing recommendations when running the pipeline manually." + ] + }, + { + "cell_type": "markdown", + "id": "c6d60407", + "metadata": {}, + "source": [ + "## 2. Import Library \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "403fa920", + "metadata": {}, + "outputs": [], + "source": [ + "#Import Library\n", + "\n", + "#--------------------------------------JWST Calibration Pipeline Imports-------------------------------------------\n", + "\n", + "import jwst\n", + "import crds\n", + "from jwst import datamodels\n", + "from jwst.pipeline import Detector1Pipeline #calwebb_detector1\n", + "from jwst.pipeline import Spec2Pipeline #calwebb_spec2\n", + "from jwst.pipeline import Spec3Pipeline #calwebb_spec3\n", + "from jwst.extract_1d import Extract1dStep #Extract1D Individual Step\n", + "\n", + "print(\"JWST Calibration Pipeline Version={}\".format(jwst.__version__))\n", + "print(\"Current Operational CRDS Context = {}\".format(crds.get_default_context()))\n", + "\n", + "#----------------------------------------------General Imports-----------------------------------------------------\n", + "\n", + "import numpy as np\n", + "import warnings\n", + "warnings.filterwarnings('ignore') #Set to 'default' to turn warnings back on\n", + "\n", + "#--------------------------------------------File Operation Imports------------------------------------------------\n", + "\n", + "import glob\n", + "import os\n", + "import asdf\n", + "import json\n", + "from shutil import copy\n", + "\n", + "#--------------------------------------------Astropy/Astroquery Imports--------------------------------------------\n", + "\n", + "from astropy.io import fits\n", + "from astropy import wcs\n", + "from astropy.wcs import WCS\n", + "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch, SqrtStretch\n", + "from astropy.stats import sigma_clipped_stats\n", + "import astroquery\n", + "from astroquery.mast import Mast\n", + "from astroquery.mast import Observations\n", + "\n", + "#------------------------------------------------Plotting Imports--------------------------------------------------\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "import matplotlib.gridspec as grd\n", + "from matplotlib.patches import Circle\n", + "from matplotlib import cm\n", + "\n", + "# Use this version for non-interactive plots (easier scrolling of the notebook)\n", + "%matplotlib inline\n", + "# Use this version (outside of Jupyter Lab) if you want interactive plots\n", + "#%matplotlib notebook\n", + "\n", + "# These gymnastics are needed to make the sizes of the figures\n", + "# be the same in both the inline and notebook versions\n", + "%config InlineBackend.print_figure_kwargs = {'bbox_inches': None}\n", + "mpl.rcParams['savefig.dpi'] = 80\n", + "mpl.rcParams['figure.dpi'] = 80\n", + "plt.rcParams.update({'font.size': 18})" + ] + }, + { + "cell_type": "markdown", + "id": "140a9682", + "metadata": {}, + "source": [ + "## 3. Convenience Functions \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a9da49e", + "metadata": {}, + "outputs": [], + "source": [ + "def show_image(data_2d, vmin, vmax, xsize=15, ysize=15, title=None, zoom_in=None, aspect=1, scale='log', units='DN/s', cmap='jet'):\n", + " \"\"\"\n", + " Function to generate a 2-D, log-scaled image of the data\n", + " \n", + " Parameters\n", + " ----------\n", + " data_2d : numpy.ndarray\n", + " 2-D image to be displayed\n", + " vmin : float\n", + " Minimum signal value to use for scaling\n", + " vmax : float\n", + " Maximum signal value to use for scaling\n", + " xsize, ysize: int\n", + " Figure Size\n", + " title : str\n", + " String to use for the plot title\n", + " zoom_in: list \n", + " Zoomed in Region of interest [xstart,xstop,ystart,ystop]\n", + " aspect: int\n", + " Aspect ratio of the axes\n", + " scale : str\n", + " Specify scaling of the image. Can be 'log' or 'linear' or 'Asinh'\n", + " units : str\n", + " Units of the data. Used for the annotation in the color bar. Defualt is DN/s for countrate images\n", + " cmap: str\n", + " Color Map for plot\n", + " \"\"\"\n", + " #-----------------------------------------Scaling Information----------------------------------------\n", + " \n", + " if scale == 'log':\n", + " norm = ImageNormalize(data_2d, interval=ManualInterval(vmin=vmin, vmax=vmax),\n", + " stretch=LogStretch())\n", + " elif scale == 'linear':\n", + " norm = ImageNormalize(data_2d, interval=ManualInterval(vmin=vmin, vmax=vmax),\n", + " stretch=LinearStretch())\n", + " elif scale == 'Asinh':\n", + " norm = ImageNormalize(data_2d, interval=ManualInterval(vmin=vmin, vmax=vmax),\n", + " stretch=AsinhStretch())\n", + " \n", + " #--------------------------------------------Set Up Figure-------------------------------------------\n", + "\n", + " fig = plt.figure(figsize=(xsize, ysize))\n", + " ax = fig.add_subplot(1, 1, 1)\n", + " \n", + " im = ax.imshow(data_2d, origin='lower', norm=norm, aspect=aspect, cmap=cmap)\n", + "\n", + " fig.colorbar(im, label=units)\n", + " plt.xlabel('Pixel column')\n", + " plt.ylabel('Pixel row')\n", + " \n", + " if title:\n", + " plt.title(title)\n", + " \n", + " #Zoom in on a portion of the image? \n", + " if zoom_in:\n", + " #inset axis \n", + " axins = ax.inset_axes([0.5, 0.6, 0.5, 0.3])\n", + " \n", + " axins.imshow(data_2d, origin=\"lower\", norm=norm, aspect=aspect, cmap=cmap)\n", + " \n", + " # subregion of the original image\n", + " axins.set_xlim(zoom_in[0], zoom_in[1])\n", + " axins.set_ylim(zoom_in[2], zoom_in[3])\n", + " axins.set_xticklabels([])\n", + " axins.set_yticklabels([])\n", + " ax.indicate_inset_zoom(axins, color=\"black\",edgecolor=\"black\", linewidth=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3d233556", + "metadata": {}, + "outputs": [], + "source": [ + "def show_ifu_cubeslices(s3d_file_list, wavelength_slices=[], spaxel_locs=[], y_scale=None, cmap='jet', vmin_vmax = [[[0,15e1]]], save_figure=False, title=None, title_font = 30):\n", + " \"\"\"\n", + " Function to that takes a 3-D IFU data cube and generates: \n", + " \n", + " > 2-D cube slices based on wavelength (microns)\n", + " > Associated 1-D spectrum for a designated spaxel (spatial pixel) in the data cube\n", + " > Corresponding 3-D weight image giving the relative weights of the output spaxels\n", + " \n", + " Note: This function can accomidate multiple detectors plotted side-by-side. \n", + " The general format would follow [[detector 1 info], [detector 2 info]].\n", + "\n", + " Parameters\n", + " ----------\n", + " s3d_file_list: list of str\n", + " 3-D IFU data cube fits file list \n", + " wavelength_slices: tuple\n", + " List of wavelength values (microns) at which to create 2-D slices. \n", + " spaxel_locs: tuple\n", + " List of spaxel locations in which to plot the associated 1-D spectrum. (One spaxel location per slice)\n", + " y_scale: tuple\n", + " Y-axis limits for the associated 1-D spectrum of the spaxel. Default is to use the ymin and ymax of the data. \n", + " cmap: str\n", + " Color Map \n", + " vmin_vmax: tuple\n", + " Minimum & Maximum signal value to use for scaling (e.g., [[[vmin,vmax],[vmin,vmax]], [[vmin,vmax], [vmin,vmax]]])\n", + " title: str\n", + " Figure Title. Default is None. \n", + " title_font:int\n", + " Title Font Size\n", + " save_figure: bool\n", + " Save figure? \n", + " \"\"\"\n", + " \n", + " #---------------------------------------------- Set-up Figure -------------------------------------------------\n", + "\n", + " #Plot Slices From the Cube\n", + " fig, axs = plt.subplots(3, np.array(wavelength_slices).size, figsize=(8*np.array(wavelength_slices).size,18))\n", + " gs = grd.GridSpec(3, np.array(wavelength_slices).size, height_ratios=[1]*3, width_ratios=[1]*np.array(wavelength_slices).size, hspace=0.4,wspace=0.7)\n", + "\n", + " total_num_plots=3*np.array(wavelength_slices).size\n", + " \n", + " plot_count = 0\n", + " #---------------------------------------------Open Files------------------------------------------------------\n", + " \n", + " for s3d_file in s3d_file_list:\n", + " \n", + " root=s3d_file[:-9] #Root file name \n", + "\n", + " s3d = fits.open(s3d_file) #3-D IFU data cube fits file \n", + " x1d3 = datamodels.open(root+'_x1d.fits') #1-D Extracted Spectrum \n", + " \n", + " #--------------------------------Wavelength & Surface Brightness/Flux Arrays------------------------------\n", + " \n", + " x1d3wave = x1d3.spec[0].spec_table.WAVELENGTH\n", + " \n", + " #--------------------------------------Data & Header Information------------------------------------------\n", + "\n", + " \n", + " #SCI Extension: [Type:ImageHDU Cards:92 Dimensions:(57, 61, 973) Format:float32]\n", + " cube = s3d[1].data #Science data\n", + " wcs = WCS(s3d[1].header) #World Coordinate System (WCS) Transformation keywords \n", + " wmap = s3d[4].data #3-D weight image giving the relative weights of the output spaxels.\n", + " cdelt1 = s3d[1].header['CDELT1']*3600. #Axis 1 coordinate increment at reference point \n", + " cdelt2 = s3d[1].header['CDELT2']*3600. #Axis 2 coordinate increment at reference point \n", + " cdelt3 = s3d[1].header['CDELT3'] #Axis 3 coordinate increment at reference point \n", + " crval3 = s3d[1].header['CRVAL3'] #third axis value at the reference pixel \n", + "\n", + " #Wavelength range of the grating/filter combination\n", + " wavstart = s3d[1].header['WAVSTART']\n", + " wavend = s3d[1].header['WAVEND']\n", + " \n", + " s3d.close()\n", + " \n", + " #---------------------------------------------------Plots-------------------------------------------------\n", + " \n", + " cmap_custom = cm.colors.LinearSegmentedColormap.from_list(\"\", [\"darkred\",\"darkturquoise\",\"blue\"])\n", + " colors = cmap_custom(np.linspace(0, 1, np.array(wavelength_slices).size))\n", + "\n", + " #To Account for if NRS1 & NRS2 are both being plotted Side-by-side\n", + " if len(wavelength_slices) != 1:\n", + " if 'nrs1' in s3d_file:\n", + " wavelengths = wavelength_slices[0]\n", + " spaxel_loc = spaxel_locs[0]\n", + " vmin_vmax_vals = vmin_vmax[0]\n", + " \n", + " if y_scale:\n", + " y_scales = y_scale[0]\n", + "\n", + " elif 'nrs2' in s3d_file:\n", + " wavelengths = wavelength_slices[1]\n", + " spaxel_loc = spaxel_locs[1]\n", + " vmin_vmax_vals = vmin_vmax[1]\n", + " if y_scale:\n", + " y_scales = y_scale[1]\n", + "\n", + " else:\n", + " wavelengths = wavelength_slices[0]\n", + " spaxel_loc = spaxel_locs[0]\n", + " vmin_vmax_vals = vmin_vmax[0]\n", + " if y_scale:\n", + " y_scales = y_scale[0]\n", + "\n", + " \n", + " #Loop through each wavelength slices\n", + " for i, wave_slice in enumerate(wavelengths):\n", + "\n", + " if float(wavstart)<=wave_slice*10**-6<=float(wavend):\n", + " \n", + " #--------------------------------------------2-D Cube Slice------------------------------------------------\n", + " \n", + " #Min & Max Image Values & Scaling\n", + " if len(vmin_vmax_vals) != 1:\n", + " vmax_val = vmin_vmax_vals[i][1]\n", + " vmin_val = vmin_vmax_vals[i][0]\n", + " else:\n", + " vmax_val = vmin_vmax_vals[0][1]\n", + " vmin_val = vmin_vmax_vals[0][0]\n", + "\n", + " slicewave = wave_slice\n", + " nslice = int((slicewave - crval3)/cdelt3) #the slice of the cube we want to plot\n", + "\n", + " ax1 = plt.subplot(gs[0+plot_count], projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", + " #ax1 = plt.subplot(3,len(wavelength_slices), 0+plot_count, projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", + "\n", + " slice_mean = np.nanmean(cube[(nslice-2):(nslice+2), :, :], axis=0) #Mean of the slice looking in the range (nslice-2):(nslice+2)\n", + " slice_norm=ImageNormalize(slice_mean, vmin=vmin_val, vmax=vmax_val, stretch=AsinhStretch()) #normalize &stretch \n", + " slice_image= ax1.imshow(slice_mean, norm=slice_norm, origin='lower', aspect='auto',cmap=cmap) #plot slice\n", + " \n", + " cb_image = fig.colorbar(slice_image, fraction=0.046, pad=0.04)\n", + " cb_image.set_label('MJy/sr', labelpad=-1, fontsize = 22)\n", + " cb_image.ax.tick_params(labelsize=20)\n", + " cb_image.ax.yaxis.get_offset_text().set_fontsize(20)\n", + " \n", + " ax1.set_xlabel('RA', fontsize =22)\n", + " ax1.set_ylabel('DEC', labelpad=-1, fontsize=22)\n", + " #ax1.grid(color='white', ls='solid')\n", + " ax1.set_title('Detector {} \\n Grating/Filter: {}/{} \\n {} microns'.format(s3d[0].header['DETECTOR'],s3d[0].header['GRATING'], s3d[0].header['FILTER'], str(slicewave)), fontsize =25)\n", + " ax1.tick_params(axis='both', which='major', labelsize=20)\n", + " ax1.coords[0].set_ticklabel(rotation=13, ha='right', pad=24)\n", + "\n", + " \n", + " #------------------------------------------Spaxel 1-D Spectrum---------------------------------------------\n", + " \n", + " #Zoom in on a Spaxel: Spectrum\n", + " loc = [spaxel_loc[i][0],spaxel_loc[i][1]]\n", + " x1d3flux_loc = cube[:, loc[1], loc[0]]\n", + " #ax2 = plt.subplot(3,len(wavelength_slices), int(total_num_plots/3)+plot_count)\n", + " ax2 = plt.subplot(gs[int(total_num_plots/3)+plot_count])\n", + "\n", + " #Spaxel Box Highlight \n", + " spaxel_rect = plt.Rectangle((loc[0]-.5, loc[1]-.5), 1,1, fill=False, color='black', linewidth=2)\n", + " ax1.add_patch(spaxel_rect)\n", + " \n", + " ax2.plot(x1d3wave, x1d3flux_loc, linewidth=1, color=colors[i])\n", + " ax2.grid(linewidth=2)\n", + " ax2.set_xlabel('$\\u03BB [\\u03BC$m]',fontsize=22)\n", + " ax2.set_ylabel(\"Surface Brightness \\n (MJy/sr)\",fontsize=22)\n", + " ax2.set_title('Spaxel at (x, y)='+repr(loc), fontsize=25)\n", + " ax2.tick_params(axis='both', which='major', labelsize=20)\n", + " ax2.yaxis.get_offset_text().set_fontsize(15)\n", + " \n", + " #Scale Information\n", + " if y_scale:\n", + " ymin, ymax = y_scales[i][0], y_scales[i][1]\n", + " else:\n", + " ymin, ymax = ax2.set_ylim()\n", + " \n", + " ax2.set_ylim(ymin, ymax)\n", + " ax2.xaxis.set_tick_params(labelsize=20)\n", + " ax2.yaxis.set_tick_params(labelsize=20)\n", + " ax2.set_aspect(0.5/ax2.get_data_ratio())\n", + " \n", + " #-----------------------------------------------Weight Map-------------------------------------------------\n", + " \n", + " #Corresponding Weight Map (wmap) for Cube Slice\n", + " ax3 = plt.subplot(gs[int(total_num_plots)-np.array(wavelength_slices).size+plot_count], projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", + " #ax3 = plt.subplot(3, len(wavelength_slices), int(total_num_plots)-len(wavelength_slices)+plot_count, projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", + " \n", + " slice_mean_wmap = np.nanmean(wmap[(nslice-2):(nslice+2), :, :], axis=0) #Mean of the wmap slice looking in the range (nslice-2):(nslice+2)\n", + " slice_norm_wmap=ImageNormalize(slice_mean_wmap, stretch=AsinhStretch()) #normalize &stretch\n", + " slice_wmap = ax3.imshow(slice_mean_wmap, norm=slice_norm_wmap, origin='lower',aspect='auto', cmap=cmap) #plot slice\n", + " \n", + " cb_wmap = fig.colorbar(slice_wmap, fraction=0.046, pad=0.04)\n", + " cb_wmap.set_label('Weight', labelpad=-1, fontsize = 22)\n", + " cb_wmap.ax.tick_params(labelsize=20)\n", + " cb_wmap.ax.yaxis.get_offset_text().set_fontsize(20)\n", + " \n", + " ax3.set_xlabel('RA', fontsize=22)\n", + " ax3.set_ylabel('DEC', labelpad=-1, fontsize=22)\n", + " #ax3.grid(color='gray', ls='solid')\n", + " ax3.set_title(str(slicewave)+' microns: Weight Map', fontsize=25)\n", + " ax3.tick_params(axis='both', which='major', labelsize=20)\n", + " ax3.coords[0].set_ticklabel(rotation=13, ha='right', pad=24)\n", + "\n", + " plot_count += 1\n", + " \n", + " else:\n", + " None\n", + " \n", + " if title:\n", + " fig.suptitle(title, fontsize=title_font)\n", + " plt.subplots_adjust(top=0.8) \n", + " \n", + " fig.tight_layout(rect=[0, 0, 0.98, 0.98])\n", + "\n", + " if save_figure == True:\n", + " fig.savefig(root+\".png\",dpi=24, bbox_inches=\"tight\")\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "3e23e973", + "metadata": {}, + "source": [ + "## 4. Directory Set-Up \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f94025d4", + "metadata": {}, + "outputs": [], + "source": [ + "#To rerun the notebook and all the pipeline steps set runflag=True\n", + "runflag = True \n", + "\n", + "#Demo directory -- contains pre-computed products\n", + "if runflag == False:\n", + " output_dir = './nirspec_ifu_02732_demo/'\n", + "\n", + "#Rerun directory\n", + "elif runflag == True:\n", + " #If you want to actually re-download the data and run everything offline, \n", + " #then comment out this line, set runflag=True, & specify a desired local directory\n", + " output_dir = './nirspec_ifu_02732_rerun/'\n", + " if not os.path.exists(output_dir):\n", + " os.makedirs(output_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "f6d17cc0", + "metadata": {}, + "source": [ + "## 5. Download the Data \n", + "\n", + "
\n", + "
\n", + "\n", + "Tip: To download the data from MAST, you must input your MAST authorization token. Get your MAST Token Here: https://auth.mast.stsci.edu/token. Additionally, be sure to follow [astroquery installation procedures](https://astroquery.readthedocs.io/en/latest/index.html#) to properly run this cell. \n", + " \n", + "
\n", + "\n", + "| Target: NGC 7319 AGN | | | | |\n", + "|:-----------:|:-------:|---|---|---|\n", + "| Proposal ID | 02732 | | | |\n", + "| [GRATING/FILTER](https://jwst-docs.stsci.edu/jwst-near-infrared-spectrograph/nirspec-observing-modes/nirspec-ifu-spectroscopy) | PRISM/CLEAR | λ: 0.6–5.3 μm (a low resolution, R ~ 100) | | |\n", + "| DURATION | 160.478 [s] | Total duration of one exposure | | | |\n", + "| READPATT | NRSIRS2RAPID | Readout Pattern | | | |\n", + "| PATTTYPE | CYCLING | Primary dither pattern type | | |\n", + "| PATTSIZE | LARGE | Primary dither pattern size (1.0\" extent) | | |\n", + "| NUMDTHPT | 8 | Total number of points in pattern | | | \n", + "| SRCTYAPT | UNKNOWN | Source Type selected in APT | | | \n", + "\n", + "> **Note:** The presence of a physical gap between detectors affects high-resolution IFU observations because the spectra are long enough to span both NIRSpec detectors. When using the grating-filter combination G140H/F070LP (or PRISM/CLEAR) the resulting spectra do not have any gaps because the spectra do not extend beyond NRS1. [More Info ...](https://jwst-docs.stsci.edu/jwst-near-infrared-spectrograph/nirspec-operations/nirspec-ifu-operations/nirspec-ifu-wavelength-ranges-and-gaps#NIRSpecIFUWavelengthRangesandGaps-Wavelengthgaps)\n", + "\n", + "The cell below downloads the raw uncalibrated data along with the stage 2 and stage 3 products that are available in MAST. MAST products will get saved to a folder called `mast_products` within the designated output directory defined earlier in this notebook. These files have already been pre-downloaded and stored in a provided demo directory. To get the most up-to-date products set `runflag = True` and rerun this notebook. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fed2923f", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Download data from MAST \n", + "\n", + "#Setup your account \n", + "\n", + "# NOTE:\n", + "# The data in this notebook is public and does not require a token.\n", + "# For other data sets, uncomment the following line and enter your\n", + "# token at the prompt.\n", + "\n", + "# Observations.login(token=None)\n", + "\n", + "sessioninfo = Observations.session_info()\n", + "\n", + "#Define the general search criteria\n", + "obs = Observations.query_criteria(\n", + " obs_collection = 'JWST',\n", + " instrument_name = ['NIRSPEC/IFU'],\n", + " proposal_id = '02732')\n", + "\n", + "#Print the Observations returned from the general serach criteria\n", + "products = Observations.get_product_list(obs)\n", + "#print(products)\n", + "\n", + "#Filter the list of observations\n", + "#In this case we look for UNCAL products and ASN files to manually run pipeline stage 1-3\n", + "#We look for pre-processed MAST products for comparison: RATE (stage 1) & CAL (stage 2&3) & S3D (stage 2&3) & X1D (stage2&3) \n", + "filtered = Observations.filter_products(products,\n", + " productSubGroupDescription=[\"UNCAL\", \"RATE\", \"CAL\", \"S3D\", \"X1D\", \"ASN\"],\n", + " mrp_only=False)\n", + "#Print the filtered products\n", + "number = len(filtered)\n", + "for k in range(number):\n", + " print(filtered['productFilename'][k])\n", + "\n", + "#Download the filtered products\n", + "#This creates a mastDownload directory, unless you set flat=True and set a download_dir\n", + "for i in range(len(filtered)):\n", + " mast_products_dir = output_dir+'mast_products/'\n", + " if not os.path.exists(mast_products_dir):\n", + " os.makedirs(mast_products_dir)\n", + " if runflag == True:\n", + " Observations.download_products(filtered[i], mrp_only=False, cache=False, flat=True, download_dir=mast_products_dir) #Override any cached files and download the most up-to-date ones\n", + " else:\n", + " Observations.download_products(filtered[i], mrp_only=False, cache=True, flat=True, download_dir=mast_products_dir) #Find any cached files first before downloading new ones" + ] + }, + { + "cell_type": "markdown", + "id": "8dee6a2f", + "metadata": {}, + "source": [ + "## 6. Products Found In MAST \n", + "
\n", + "\n", + "> In [APT](https://jwst-docs.stsci.edu/jwst-astronomers-proposal-tool-overview), the observer has three options for source type (`SRCTYAPT` keyword): `POINT`, `EXTENDED`, or `UNKNOWN`. In stage 2, the `srctype` step will first check if the `SRCTYAPT` keyword is present and populated. If `SRCTYAPT` is not present or is set to `UNKNOWN`, the step determines a suitable value based on the observing mode, command line input, and other characteristics of the exposure. If the exposure is identified as a background exposure (`BKGDTARG = True`), the exposures default to a source type of `EXTENDED`. Exposures that are part of a nodded pattern (identified by keyword `PATTYPE`), which are assumed to only be used with point-like targets, default to a source type of `POINT`. [More Info ...](https://jwst-pipeline.readthedocs.io/en/latest/jwst/srctype/description.html#single-source-observations)\n", + "\n", + "For dithered NIRSpec IFU data like ours, which do not meet any of the above conditions, will default to source type `EXTENDED`. Therefore, the products found in MAST for target NGC 7319 AGN (PID 02732) have been processed as an `EXTENDED` source . " + ] + }, + { + "cell_type": "markdown", + "id": "a2f59a62", + "metadata": {}, + "source": [ + "### 6.1 Stage 1 Products Found In MAST \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e24dcd56", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Stage 1 slope products -- level 2a images\n", + "\n", + "#Plot 4th (out of 8) dither position (spectra fall only on NRS1) for GRATING/FILTER PRISM/CLEAR combination \n", + "for rate_file in sorted(glob.glob(mast_products_dir+'*00004_nrs1_rate.fits')):\n", + " \n", + " ratefile_open = datamodels.open(rate_file)\n", + " ratefile_sci = ratefile_open.data #get the pixel data (the SCI extension of the fits file)\n", + " ratefile_dq = ratefile_open.dq #data quality map data (DQ extension)\n", + " \n", + " #print the version and CRDS pmap used to create these rate.fits files \n", + " #ratefile_open.serach(key='context')\n", + " print(\"Products found in MAST used JWST calibration pipeline version: {} and {}\".format(ratefile_open.meta.calibration_software_version,\n", + " ratefile_open.meta.ref_file.crds.context_used))\n", + " \n", + " #Plot the slope image and zoom in on a small section of the countrate image & corresponding section of the DQ map\n", + " show_image(ratefile_sci, 0,10, units='DN/s',zoom_in=[500,550, 1250,1300],\n", + " title='Countrate Image \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", + " ratefile_open.meta.dither.position_number, \n", + " ratefile_open.meta.instrument.grating,\n", + " ratefile_open.meta.instrument.filter)) #rate files have units of DN/s\n", + " \n", + " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear',zoom_in=[500,550, 1250,1300],\n", + " title='Data Quality Map \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", + " ratefile_open.meta.dither.position_number, \n", + " ratefile_open.meta.instrument.grating,\n", + " ratefile_open.meta.instrument.filter)) \n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "217a5889", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Warning: Please be aware that in the countrate (slope) images found in MAST, many pixels are flagged as Do Not Use (more clearly seen in the corresponding DQ map) and therefore appear white with a value of NaN. This excessive flagging is due to an outdated mask reference file that would mark unreliable slope, bad fit, and telegraph pixels as Do Not Use. Despite the large number of NaNs in the countrate image, the extracted spectra are not significantly affected by them when combining multiple dithered exposures because the number of flagged pixels is still a relatively small fraction. However, due to the high number of flags in the MAST products, it is difficult to see specific details in the slope images, like correlated read noise, which manifests as low-level vertical banding/striping and a \"picture frame\" with the [$IRS^{2}$](https://jwst-docs.stsci.edu/jwst-near-infrared-spectrograph/nirspec-instrumentation/nirspec-detectors/nirspec-detector-readout-modes-and-patterns/nirspec-irs2-detector-readout-mode) readout mode. As of context jwst_1084.pmap, the pipeline now considers unreliable slope, bad fit, and telegraph pixels good for further processing in Full frame data. [Therefore, the reprocessed data below offers improved visibility of the correlated read noise.](#level1_rerun)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "511d3a5f", + "metadata": {}, + "source": [ + "### 6.2 Stage 2 Products Found In MAST \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8221ff0d", + "metadata": {}, + "outputs": [], + "source": [ + "#Stage 2 Products -- Calibrated 3-D data cube for PRISM/CLEAR (only falls on NRS1)\n", + "\n", + "#Plotting the 4th (out of 8) dither position\n", + "stage2_s3d_file = sorted(glob.glob(mast_products_dir+'*00004_nrs1_s3d.fits')) \n", + "\n", + "title_stage2_mast='NGC 7319 AGN \\n Level 2 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", + "\n", + "\n", + "#Characteristics of the plot \n", + "nrs1_wavelengths = [1.4,3.3,4.5] #Wavelength slices (microns) to take from the 3-D data cube\n", + "nrs1_spaxel_locs = [[30,29],[28,39],[14,25]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "\n", + "\n", + "#Plot using the convience function defined above\n", + "show_ifu_cubeslices(stage2_s3d_file, wavelength_slices=[nrs1_wavelengths], spaxel_locs=[nrs1_spaxel_locs],title=title_stage2_mast)" + ] + }, + { + "cell_type": "markdown", + "id": "48f5cf27", + "metadata": {}, + "source": [ + "### 6.3 Stage 3 Products Found In MAST \n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f187aba", + "metadata": {}, + "outputs": [], + "source": [ + "#Stage 3 Products -- Combined Calibrated 3-D data cube for PRISM/CLEAR (only falls on NRS1)\n", + "\n", + "stage3_s3d_file = sorted(glob.glob(mast_products_dir+'*nirspec_prism-clear_s3d.fits')) \n", + "\n", + "title_stage3_mast='NGC 7319 AGN \\n Level 3 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", + "\n", + "#Characteristics of the plot \n", + "nrs1_wavelengths = [1.4,3.3,4.5] #Wavelength slices (microns) to take from the 3-D data cube\n", + "nrs1_spaxel_locs = [[30,29],[28,39],[14,25]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "\n", + "#Plot using the convience function defined above\n", + "show_ifu_cubeslices(stage3_s3d_file, wavelength_slices=[nrs1_wavelengths], spaxel_locs=[nrs1_spaxel_locs],title=title_stage3_mast)" + ] + }, + { + "cell_type": "markdown", + "id": "04d3f605-0014-4eac-9c73-a9a9f547deb2", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Warning: Please note that in the final product (stage 3) downloaded from MAST, a significant portion of the data got rejected, returning a value of zero in the weight maps. This over-rejection of data is due to the outdated `outlier_detection` step that MAST automatically enables during stage 3 of the pipeline. A new outlier detection algorithm has been developed specifically for IFU data that overcomes some of these limitations (as of DMS build B9.3rc1/CAL_VER 1.11.0). Due to the limitations of the previous outlier detection algorithm, the user recommendation is to skip the `outlier_detection` step if using an older version of the pipleine or manually rerun stage 3 of the pipleine with outlier detection on with the most up-to-date pipeline version (detailed in the next section of this notebook).\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3575d780", + "metadata": {}, + "outputs": [], + "source": [ + "#Stage 3 Products -- Combined Extracted 1-D Spectrum \n", + "\n", + "x1d3_mast = datamodels.open(glob.glob(mast_products_dir+'*nirspec_prism-clear_x1d.fits')[0])\n", + "\n", + "#Wavelength & Surface Brightness Arrays\n", + "x1d3wave_mast = x1d3_mast.spec[0].spec_table.WAVELENGTH\n", + "x1d3flux_mast = x1d3_mast.spec[0].spec_table.SURF_BRIGHT\n", + "\n", + "#Plot the Extracted 1-D Spectrum\n", + "fig = plt.figure(figsize=(15,9))\n", + "\n", + "plt.plot(x1d3wave_mast,x1d3flux_mast, linewidth =2)\n", + "\n", + "#Where wavelength slice was taken above\n", + "plt.vlines(1.4, 0., 400., 'black', 'dotted', label='1.4 microns')\n", + "plt.vlines(3.3, 0., 400., 'red', 'dotted', label='3.3 microns')\n", + "plt.vlines(4.5, 0., 400., 'green', 'dotted', label='4.5 microns')\n", + "\n", + "plt.xlabel('$\\lambda [\\mu$m]', fontsize =15)\n", + "plt.ylabel('Surface Brightness (MJy/sr)', fontsize =15)\n", + "plt.title(\"NGC 7319 AGN \\n Level 3 IFU Product in MAST: Extracted 1-D Spectrum\", fontsize =20)\n", + "plt.ylim(0, 50)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ced813b1", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Note: When the source type is extended, the default extraction aperture for the `extract_1d` step covers the entire cube.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "b0b899f0", + "metadata": {}, + "source": [ + "
\n", + "Warning: Most of the large negative and positive flux spikes extending beyond the plot range are likely due to bad/hot pixels that are not flagged in the current DQ masks. The mask reference file gets directly pulled from CRDS. The products found in MAST use a specific CRDS context (.pmap) when processing data. However, the CRDS is constantly updating the operational .pmap.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95c8425d", + "metadata": {}, + "outputs": [], + "source": [ + "#Just a check to see what verison of the pipeline and what pmap was used\n", + "x1d3_mast = fits.open(glob.glob(mast_products_dir+'*nirspec_prism-clear_x1d.fits')[0])\n", + "\n", + "print(\"Products found in MAST used JWST calibration pipeline version: {} and {}\".format(x1d3_mast[0].header['CAL_VER']\n", + ",x1d3_mast[0].header['CRDS_CTX']))" + ] + }, + { + "cell_type": "markdown", + "id": "be8e38b5", + "metadata": {}, + "source": [ + "### 7. Re-processing the Data \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7211260", + "metadata": {}, + "outputs": [], + "source": [ + "#Directory for rerun of stage 1 to avoid overwritting MAST products\n", + "output_dir_rerun = output_dir+'rerun/' \n", + "if not os.path.exists(output_dir_rerun):\n", + " os.makedirs(output_dir_rerun)" + ] + }, + { + "cell_type": "markdown", + "id": "126569cf", + "metadata": {}, + "source": [ + "### 7.1 Stage 1 Rerun & Products \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b6b19a3-fddb-411b-9864-2799f527b937", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Stage 1 Processing \n", + "\n", + "if runflag == True:\n", + "\n", + " for uncal_file in sorted(glob.glob(mast_products_dir+'*nrs1_uncal.fits')): \n", + "\n", + " print(\"Applying Stage 1 Corrections & Calibrations to: \"+ os.path.basename(uncal_file))\n", + "\n", + " result = Detector1Pipeline.call(uncal_file,\n", + " save_results = True,\n", + " output_dir = output_dir_rerun)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4f5160e", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Stage 1 slope products -- level 2a images\n", + "\n", + "#Plot 4th (out of 8) dither position (NRS1 & NRS2) for GRATING/FILTER G140H/F100LP combination \n", + "for rate_file in sorted(glob.glob(output_dir_rerun+'*00004_nrs?_rate.fits')):\n", + " \n", + " ratefile_open = datamodels.open(rate_file)\n", + " ratefile_sci = ratefile_open.data # Get the pixel data (the SCI extension of the fits file)\n", + " ratefile_dq = ratefile_open.dq # The Data Quality Map Data\n", + " \n", + " \n", + " show_image(ratefile_sci, 0,10, units='DN/s',zoom_in=[500,550, 1250,1300],\n", + " title='Countrate Image \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", + " ratefile_open.meta.dither.position_number, \n", + " ratefile_open.meta.instrument.grating,\n", + " ratefile_open.meta.instrument.filter)) #rate files have units of DN/s\n", + " \n", + " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear',zoom_in=[500,550, 1250,1300],\n", + " title='Data Quality Map \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", + " ratefile_open.meta.dither.position_number, \n", + " ratefile_open.meta.instrument.grating,\n", + " ratefile_open.meta.instrument.filter)) #rate files have units of DN/s\n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "b84c9f5b", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Note: Compared to the [countrate (slope) products found in MAST](#level1_mast), fewer pixels are flagged as Do Not Use when using the most up-to-date pmap in CRDS (at the time jwst_1106.pmap). With the latest pmap, one can observe low-level vertical banding in the central regions of the detector, and the \"picture frame\" towards the edge of both detectors, where there is less correlated read noise a lot easier. \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "32018775", + "metadata": {}, + "source": [ + "### 7.2 Stage 2 Rerun & Products \n", + "
\n", + "\n", + "During stage 2 of the pipeline, the countrate (slope) image products from stage 1, which have units of DN/s, are converted to units of surface brightness (MJy/sr) for both extended and point sources (as of DMS build 9.3/CAL_VER 1.10.2). For extended targets, like the NGC 7319 AGN, the `extract_1d` step is controlled by a different set of parameters in the EXTRACT1D reference file: \n", + "\n", + "> For an extended source, rectangular aperture photometry is used, with the entire image being extracted, and no background subtraction, regardless of what was specified in the reference file or step arguments. [More Info ...](https://jwst-pipeline.readthedocs.io/en/latest/jwst/extract_1d/description.html)\n", + "\n", + "
\n", + " \n", + "Warning: Note there has been a bug in the `cube_build` step that caused the point source flux to not be conserved when using different spatial sampling. A fix has been implemented as of release DMS build 9.3/CAL_VER 1.10.2. In order to enable the correct functionality, the units of the cal.fits files and cubes will now be in surface brightness, and only the 1-D extracted spectra will be in units of Jy.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba980d6f", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Stage 2 Processing \n", + "\n", + "if runflag == True:\n", + " \n", + " #Process each rate file seperately \n", + " for rate_file in sorted(glob.glob(output_dir_rerun+'*nrs1*rate.fits')):\n", + " \n", + " print(\"Applying Stage 2 Calibrations & Corrections to: \"+ os.path.basename(rate_file))\n", + "\n", + " result = Spec2Pipeline.call(rate_file,\n", + " save_results = True,\n", + " output_dir = output_dir_rerun)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f66fe4dc", + "metadata": {}, + "outputs": [], + "source": [ + "#Stage 2 Products -- Calibrated 3-D data cube for PRISM/CLEAR (only falls on NRS1)\n", + "\n", + "#Plotting the 4th (out of 8) dither position\n", + "stage2_s3d_file = sorted(glob.glob(output_dir_rerun+'*00004_nrs1_s3d.fits')) \n", + "\n", + "title_stage2_rerun='NGC 7319 AGN \\n Level 2 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", + "\n", + "\n", + "#Characteristics of the plot \n", + "nrs1_wavelengths = [1.4,3.3,4.5] #Wavelength slices (microns) to take from the 3-D data cube\n", + "nrs1_spaxel_locs = [[30,29],[28,39],[14,25]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "\n", + "\n", + "#Plot using the convience function defined above\n", + "show_ifu_cubeslices(stage2_s3d_file, wavelength_slices=[nrs1_wavelengths], spaxel_locs=[nrs1_spaxel_locs],title=title_stage2_rerun)" + ] + }, + { + "cell_type": "markdown", + "id": "e369b29b", + "metadata": {}, + "source": [ + "### 7.3 Stage 3 Rerun & Products \n", + "
\n", + "\n", + "***Level 3 ASN File***\n", + "\n", + "> Observations that use a nod-type/dither patterns, their exposures are related. [Association files (ASN)](https://jwst-pipeline.readthedocs.io/en/stable/jwst/associations/overview.html) describe how multiple exposures are related to one another and how they depend on one another. Processing an ASN file permits exposures to be calibrated, archived, retrieved, and reprocessed as a set rather than individual objects. IFU exposures taken with a dither pattern are not used for pixel-to-pixel background subtraction by the calibration pipeline (unlike exposures taken with a nod pattern).\n", + "\n", + "Therefore, all calibration files (`cal.fits`) in our spec3 ASN file should be labeled as science exposures (`exptype: science`). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c05e9415", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Copy ASN file from MAST into the stage 1 rerun directory\n", + "\n", + "asnfile_mast = glob.glob(mast_products_dir+'*_spec3_00001_asn.json')[0] #ASN file found in MAST\n", + "\n", + "asnfile_rerun = output_dir_rerun+os.path.basename(asnfile_mast) #New ASN file path\n", + "if not os.path.exists(asnfile_rerun):\n", + " copy(asnfile_mast, asnfile_rerun)\n", + " \n", + "#Check the ASN file contents\n", + "with open(asnfile_rerun, 'r') as f_obj:\n", + " asnfile_rerun_data = json.load(f_obj)\n", + " \n", + "asnfile_rerun_data" + ] + }, + { + "cell_type": "markdown", + "id": "3e3f966d", + "metadata": {}, + "source": [ + "#### 7.3.1 New Outlier Detection Algorithm\n", + "
\n", + "\n", + "The new outlier detection algorithm for IFU data (as of DMS build B9.3rc1/CAL_VER 1.11.0) implements the basic outlier detection algorithm -- searches for pixels that are consistent outliers in the calibrated images created by the `calwebb_spec2` pipeline. The algorithm generally operates as follows:\n", + "\n", + "> * Identifies outlier pixels by comparing them with their neighboring pixels in the spatial direction across a set of input files within an association.\n", + "> * For NIRSpec data, it calculates differences between pixels located above and below each science pixel.\n", + "> * The pixel differences for every input model in the association are computed and stored in a stack of pixel differences.\n", + "> * For each pixel, the algorithm determines the minimum difference across this stack and then performs normalization. This normalization process employs a local median derived from the difference array, with the size of the median determined by the kernel size.\n", + "> * A pixel is flagged as an outlier if this normalized minimum difference is greater than the input threshold percentage. \n", + "> * Pixels that are found to be outliers are flaged in in the DQ array.\n", + "> * [More Info ...](https://jwst-pipeline.readthedocs.io/en/latest/jwst/outlier_detection/outlier_detection_ifu.html#outlier-detection-ifu)\n", + "\n", + "**[The outlier_detection step for IFU data has the following optional arguments that control the behavior of the processing](https://github.com/spacetelescope/jwst/blob/master/docs/jwst/outlier_detection/arguments.rst):**\n", + "\n", + "* `kernel_size` (string, default='7 7'): The size of the kernel to use to normalize the pixel differences. The kernel size must only contain odd values.\n", + "* `threshold_percent` (float, default=99.8): The threshold (in percent) of the normalized minimum pixel difference used to identify bad pixels. Pixels with a normalized minimum pixel difference above this percentage are flagged as a outlier.\n", + "* `save_intermediate_results` (boolean, default=False): Specifies whether or not to save any intermediate products created during step processing.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "177fa11a", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Rerun stage 3 with outlier detection off\n", + "if runflag==True:\n", + "\n", + " result = Spec3Pipeline.call(asnfile_rerun,\n", + " save_results = True,\n", + " output_dir = output_dir_rerun,\n", + " steps = {\"outlier_detection\":{\"skip\": False,\n", + " \"save_results\": True,\n", + " \"kernel_size\": '3 3'}})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01c7188b", + "metadata": {}, + "outputs": [], + "source": [ + "#Stage 3 Products -- Combined Calibrated 3-D data cube for PRISM/CLEAR \n", + "\n", + "stage3_s3d_file = sorted(glob.glob(output_dir_rerun+'*nirspec_prism-clear_s3d.fits')) \n", + "\n", + "title_stage3_rerun='NGC 7319 AGN \\n Level 3 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", + "\n", + "#Characteristics of the plot \n", + "nrs1_wavelengths = [1.4,3.3,4.5] #Wavelength slices (microns) to take from the 3-D data cube\n", + "nrs1_spaxel_locs = [[30,29],[28,39],[14,25]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "\n", + "#Plot using the convience function defined above\n", + "show_ifu_cubeslices(stage3_s3d_file, wavelength_slices=[nrs1_wavelengths], spaxel_locs=[nrs1_spaxel_locs],title=title_stage3_rerun)" + ] + }, + { + "cell_type": "markdown", + "id": "322f8a59", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Note: In comparison to the [weight maps for the 3-D data cube products found in MAST](#level3_mast), the implementation of the new outlier detection algorithm leads to a notable decrease data rejection.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee63edd8", + "metadata": {}, + "outputs": [], + "source": [ + "#Stage 3 Products -- Combined Extracted 1-D Spectrum \n", + "\n", + "x1d3_rerun = datamodels.open(glob.glob(output_dir_rerun+'*nirspec_prism-clear_x1d.fits')[0])\n", + "\n", + "#Wavelength & Surface Brightness Arrays\n", + "x1d3wave_rerun = x1d3_rerun.spec[0].spec_table.WAVELENGTH\n", + "x1d3flux_rerun = x1d3_rerun.spec[0].spec_table.SURF_BRIGHT\n", + "\n", + "#Plot the Extracted 1-D Spectrum\n", + "fig = plt.figure(figsize=(15,9))\n", + "\n", + "plt.plot(x1d3wave_rerun,x1d3flux_rerun, linewidth =2)\n", + "\n", + "#Where wavelength slice was taken above\n", + "plt.vlines(1.4, 0., 400., 'black', 'dotted', label='1.4 microns')\n", + "plt.vlines(3.3, 0., 400., 'red', 'dotted', label='3.3 microns')\n", + "plt.vlines(4.5, 0., 400., 'green', 'dotted', label='4.5 microns')\n", + "\n", + "plt.xlabel('$\\lambda [\\mu$m]', fontsize =15)\n", + "plt.ylabel('Surface Brightness (MJy/sr)', fontsize =15)\n", + "plt.title(\"NGC 7319 AGN \\n Level 3 IFU Product in MAST: Extracted 1-D Spectrum\", fontsize =20)\n", + "plt.ylim(0, 55)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "645a1387", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Note: With the integration of the new outlier detection algorithm, a significant change is evident when comparing to the [1-D extracted spectrum found in MAST](#level3_mast). Previously prominent positive/negative spikes in the data have now been successfully identified and flagged as outliers. \n", + "
\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "5d6eb616-c8cf-46a1-b2f8-a5a642824171", + "metadata": {}, + "source": [ + "## Conclusion \n", + "
\n", + "\n", + "In conclusion, this notebook walks users through processing real data (NGC 7319 AGN) from ERS Proposal ID 2732 and comparing automated products in MAST with those generated using the latest version of the JWST calibration pipeline and latest CRDS context. For optimal results, users are strongly encouraged to reprocess their own data using the most recent pipeline version and CRDS context, taking advantage of bug fixes and algorithm improvements (i.e., the new IFU outlier detection algorithm). " + ] + }, + { + "cell_type": "markdown", + "id": "37ed417a-849b-4777-9cea-2a39ea18b34a", + "metadata": {}, + "source": [ + "## About This Notebook \n", + "
\n", + "\n", + "**Authors**: Kayli Glidic (kglidic@stsci.edu), Leonardo Ubeda (lubeda@stsci.edu)\n", + "\n", + "**Update On**: 2023-08-11" + ] + } + ], + "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": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb new file mode 100644 index 000000000..e425cbdd0 --- /dev/null +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb @@ -0,0 +1,1015 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a779cd03-c144-4a9f-90e1-38bc98695a95", + "metadata": {}, + "source": [ + "\n", + "# NIRSpec IFU Pipeline Processing ERO 02732 NGC 7319 AGN -- Point Source\n", + "
\n", + "\n", + "## Table of Contents\n", + "\n", + "* [1. Introduction](#intro)\n", + "* [2. Import Library](#imports)\n", + "* [3. Convenience Functions](#func)\n", + "* [4. Directory Set-Up](#dir_setup)\n", + "* [5. Download the data](#data)\n", + "* [6. Stage 1](#stage1)\n", + "* [7. Stage 2](#stage2)\n", + "* [8. Stage 3](#stage3)\n", + " * [8.1 New Outlier Detection Algorithm](#outlier_detection_new)\n", + " * [8.2 Extract 1-D Step: Modified Reference File](#extract_1d)\n", + "* [About This Notebook](#about)\n", + "\n", + "\n", + "\n", + "## 1. Introduction \n", + "
\n", + "\n", + "End-to-end calibration of JWST data is divided into 3 main stages of processing. This notebook explores how to run the JWST calibration pipeline stages 1-3 for NIRSpec IFU spectroscopic data.\n", + "
\n", + " \"NGC_7319_AGN\"\n", + "
\n", + "\n", + ">* **`STAGE 1`** ([calwebb_detector1](https://jwst-pipeline.readthedocs.io/en/latest/jwst/pipeline/calwebb_detector1.html#calwebb-detector1)): consists of detector-level corrections, performed on a group-by-group basis, followed by ramp fitting.\n", + " * **Input**: Raw exposure (`uncal.fits`) containing original data from all detector readouts (ncols x nrows x ngroups x nintegrations).\n", + " * **Output**: Corrected countrate (slope) image (`rate.fits`) \n", + ">* **`STAGE 2`** ([calwebb_spec2](https://jwst-pipeline.readthedocs.io/en/latest/jwst/pipeline/calwebb_spec2.html#calwebb-spec2)): consists of additional instrument-level and observing mode corrections and calibrations.\n", + " * **Input**: A single corrected countrate (slope) image (`rate.fits`) or an ASN file listing multiple inputs.\n", + " * **Output**: A fully calibrated unrectified exposure (`cal.fits`). For NIRSpec IFU data, the `cube_build` step returns a 3-D IFU spectroscopic cube (`s3d.fits`). The `extract_1d` step returns 1-D extracted spectral data products (`x1d.fits`)\n", + ">* **`STAGE 3`** ([calwebb_spec3](https://jwst-pipeline.readthedocs.io/en/latest/jwst/pipeline/calwebb_spec3.html#calwebb-spec3)): consists of additional corrections (e.g. `outlier_detection`) and routines for combining calibrated data from multiple exposures (e.g. dither/nod pattern) into a single combined 2-D or 3-D spectral product and a combined 1-D spectrum. \n", + " * **Input**: An ASN file that lists multiple calibrated exposures (`cal.fits`).\n", + " * **Output**: For NIRSpec IFU data, a resampled and combined 3-D IFU cube (`s3d.fits`) and a 1-D extracted spectrum (`x1d.fits`)\n", + "\n", + "Here, we will focus on the mechanics of processing \"real\" example data [(NGC 7319 AGN)](#NGC_7319_AGN) from Early Release Science (ERS) Proposal ID 2732, including how to use associations for multi-exposure combination, how to interact and work with data models for each product, and in this particular case, how to manually process the compact region at the center of the AGN as a point source.\n", + "\n", + "Most processing runs shown here use the default reference files from the Calibration Reference Data System (CRDS), with one exception at the end to show an example of how to modify/override. Please note that pipeline software development is a continuous process, so results in some cases may be slightly different if using a subsequent version. There are also a few known issues with some of the pipeline steps in this build that we expect to be fixed in the near future. Until then, at various steps, we provide users with the current processing recommendations when running the pipeline manually." + ] + }, + { + "cell_type": "markdown", + "id": "917a0a3d-aca9-4481-85b2-00f5b1136951", + "metadata": {}, + "source": [ + "## 2. Import Library \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6230afa9-1909-4b20-a46c-38e9832df227", + "metadata": {}, + "outputs": [], + "source": [ + "#Import Library\n", + "\n", + "#--------------------------------------JWST Calibration Pipeline Imports-------------------------------------------\n", + "\n", + "import jwst\n", + "import crds\n", + "from jwst import datamodels\n", + "from jwst.pipeline import Detector1Pipeline #calwebb_detector1\n", + "from jwst.pipeline import Spec2Pipeline #calwebb_spec2\n", + "from jwst.pipeline import Spec3Pipeline #calwebb_spec3\n", + "from jwst.extract_1d import Extract1dStep #Extract1D Individual Step\n", + "\n", + "print(\"JWST Calibration Pipeline Version={}\".format(jwst.__version__))\n", + "print(\"Current Operational CRDS Context = {}\".format(crds.get_default_context()))\n", + "\n", + "#----------------------------------------------General Imports-----------------------------------------------------\n", + "\n", + "import numpy as np\n", + "import warnings\n", + "warnings.filterwarnings('ignore') #Set to 'default' to turn warnings back on\n", + "\n", + "#--------------------------------------------File Operation Imports------------------------------------------------\n", + "\n", + "import glob\n", + "import os\n", + "import asdf\n", + "import json\n", + "from shutil import copy\n", + "\n", + "#--------------------------------------------Astropy/Astroquery Imports--------------------------------------------\n", + "\n", + "from astropy.io import fits\n", + "from astropy import wcs\n", + "from astropy.wcs import WCS\n", + "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch, SqrtStretch\n", + "from astropy.stats import sigma_clipped_stats\n", + "import astroquery\n", + "from astroquery.mast import Mast\n", + "from astroquery.mast import Observations\n", + "\n", + "#------------------------------------------------Plotting Imports--------------------------------------------------\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "import matplotlib.gridspec as grd\n", + "from matplotlib.patches import Circle\n", + "from matplotlib import cm\n", + "\n", + "# Use this version for non-interactive plots (easier scrolling of the notebook)\n", + "%matplotlib inline\n", + "# Use this version (outside of Jupyter Lab) if you want interactive plots\n", + "#%matplotlib notebook\n", + "\n", + "# These gymnastics are needed to make the sizes of the figures\n", + "# be the same in both the inline and notebook versions\n", + "%config InlineBackend.print_figure_kwargs = {'bbox_inches': None}\n", + "mpl.rcParams['savefig.dpi'] = 80\n", + "mpl.rcParams['figure.dpi'] = 80\n", + "plt.rcParams.update({'font.size': 18})" + ] + }, + { + "cell_type": "markdown", + "id": "2ba6c5c1-5f24-467d-9895-8d534c64ff99", + "metadata": {}, + "source": [ + "## 3. Convenience Functions \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f45048e-4112-4c47-91cc-bd461d778779", + "metadata": {}, + "outputs": [], + "source": [ + "def show_image(data_2d, vmin, vmax, xsize=15, ysize=15, title=None, zoom_in=None, aspect=1, scale='log', units='DN/s', cmap='jet'):\n", + " \"\"\"\n", + " Function to generate a 2-D, log-scaled image of the data\n", + " \n", + " Parameters\n", + " ----------\n", + " data_2d : numpy.ndarray\n", + " 2-D image to be displayed\n", + " vmin : float\n", + " Minimum signal value to use for scaling\n", + " vmax : float\n", + " Maximum signal value to use for scaling\n", + " xsize, ysize: int\n", + " Figure Size\n", + " title : str\n", + " String to use for the plot title\n", + " zoom_in: list \n", + " Zoomed in Region of interest [xstart,xstop,ystart,ystop]\n", + " aspect: int\n", + " Aspect ratio of the axes\n", + " scale : str\n", + " Specify scaling of the image. Can be 'log' or 'linear' or 'Asinh'\n", + " units : str\n", + " Units of the data. Used for the annotation in the color bar. Defualt is DN/s for countrate images\n", + " cmap: str\n", + " Color Map for plot\n", + " \"\"\"\n", + " #-----------------------------------------Scaling Information----------------------------------------\n", + " \n", + " if scale == 'log':\n", + " norm = ImageNormalize(data_2d, interval=ManualInterval(vmin=vmin, vmax=vmax),\n", + " stretch=LogStretch())\n", + " elif scale == 'linear':\n", + " norm = ImageNormalize(data_2d, interval=ManualInterval(vmin=vmin, vmax=vmax),\n", + " stretch=LinearStretch())\n", + " elif scale == 'Asinh':\n", + " norm = ImageNormalize(data_2d, interval=ManualInterval(vmin=vmin, vmax=vmax),\n", + " stretch=AsinhStretch())\n", + " \n", + " #--------------------------------------------Set Up Figure-------------------------------------------\n", + "\n", + " fig = plt.figure(figsize=(xsize, ysize))\n", + " ax = fig.add_subplot(1, 1, 1)\n", + " \n", + " im = ax.imshow(data_2d, origin='lower', norm=norm, aspect=aspect, cmap=cmap)\n", + "\n", + " fig.colorbar(im, label=units)\n", + " plt.xlabel('Pixel column')\n", + " plt.ylabel('Pixel row')\n", + " \n", + " if title:\n", + " plt.title(title)\n", + " \n", + " #Zoom in on a portion of the image? \n", + " if zoom_in:\n", + " #inset axis \n", + " axins = ax.inset_axes([0.5, 0.6, 0.5, 0.3])\n", + " \n", + " axins.imshow(data_2d, origin=\"lower\", norm=norm, aspect=aspect, cmap=cmap)\n", + " \n", + " # subregion of the original image\n", + " axins.set_xlim(zoom_in[0], zoom_in[1])\n", + " axins.set_ylim(zoom_in[2], zoom_in[3])\n", + " axins.set_xticklabels([])\n", + " axins.set_yticklabels([])\n", + " ax.indicate_inset_zoom(axins, color=\"black\",edgecolor=\"black\", linewidth=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "019b0f74-0d54-4b89-aad4-e30c02946d0d", + "metadata": {}, + "outputs": [], + "source": [ + "def show_ifu_cubeslices(s3d_file_list, wavelength_slices=[], spaxel_locs=[], y_scale=None, cmap='jet', vmin_vmax = [[[0,15e1]]], save_figure=False, title=None, title_font = 30):\n", + " \"\"\"\n", + " Function to that takes a 3-D IFU data cube and generates: \n", + " \n", + " > 2-D cube slices based on wavelength (microns)\n", + " > Associated 1-D spectrum for a designated spaxel (spatial pixel) in the data cube\n", + " > Corresponding 3-D weight image giving the relative weights of the output spaxels\n", + " \n", + " Note: This function can accomidate multiple detectors plotted side-by-side. \n", + " The general format would follow [[detector 1 info], [detector 2 info]].\n", + "\n", + " Parameters\n", + " ----------\n", + " s3d_file_list: list of str\n", + " 3-D IFU data cube fits file list \n", + " wavelength_slices: tuple\n", + " List of wavelength values (microns) at which to create 2-D slices. \n", + " spaxel_locs: tuple\n", + " List of spaxel locations in which to plot the associated 1-D spectrum. (One spaxel location per slice)\n", + " y_scale: tuple\n", + " Y-axis limits for the associated 1-D spectrum of the spaxel. Default is to use the ymin and ymax of the data. \n", + " cmap: str\n", + " Color Map \n", + " vmin_vmax: tuple\n", + " Minimum & Maximum signal value to use for scaling (e.g., [[[vmin,vmax],[vmin,vmax]], [[vmin,vmax], [vmin,vmax]]])\n", + " title: str\n", + " Figure Title. Default is None. \n", + " title_font:int\n", + " Title Font Size\n", + " save_figure: bool\n", + " Save figure? \n", + " \"\"\"\n", + " \n", + " #---------------------------------------------- Set-up Figure -------------------------------------------------\n", + "\n", + " #Plot Slices From the Cube\n", + " fig, axs = plt.subplots(3, np.array(wavelength_slices).size, figsize=(8*np.array(wavelength_slices).size,18))\n", + " gs = grd.GridSpec(3, np.array(wavelength_slices).size, height_ratios=[1]*3, width_ratios=[1]*np.array(wavelength_slices).size, hspace=0.4,wspace=0.7)\n", + "\n", + " total_num_plots=3*np.array(wavelength_slices).size\n", + " \n", + " plot_count = 0\n", + " #---------------------------------------------Open Files------------------------------------------------------\n", + " \n", + " for s3d_file in s3d_file_list:\n", + " \n", + " root=s3d_file[:-9] #Root file name \n", + "\n", + " s3d = fits.open(s3d_file) #3-D IFU data cube fits file \n", + " x1d3 = datamodels.open(root+'_x1d.fits') #1-D Extracted Spectrum \n", + " \n", + " #--------------------------------Wavelength & Surface Brightness/Flux Arrays------------------------------\n", + " \n", + " x1d3wave = x1d3.spec[0].spec_table.WAVELENGTH\n", + " \n", + " #--------------------------------------Data & Header Information------------------------------------------\n", + "\n", + " \n", + " #SCI Extension: [Type:ImageHDU Cards:92 Dimensions:(57, 61, 973) Format:float32]\n", + " cube = s3d[1].data #Science data\n", + " wcs = WCS(s3d[1].header) #World Coordinate System (WCS) Transformation keywords \n", + " wmap = s3d[4].data #3-D weight image giving the relative weights of the output spaxels.\n", + " cdelt1 = s3d[1].header['CDELT1']*3600. #Axis 1 coordinate increment at reference point \n", + " cdelt2 = s3d[1].header['CDELT2']*3600. #Axis 2 coordinate increment at reference point \n", + " cdelt3 = s3d[1].header['CDELT3'] #Axis 3 coordinate increment at reference point \n", + " crval3 = s3d[1].header['CRVAL3'] #third axis value at the reference pixel \n", + "\n", + " #Wavelength range of the grating/filter combination\n", + " wavstart = s3d[1].header['WAVSTART']\n", + " wavend = s3d[1].header['WAVEND']\n", + " s3d.close()\n", + " \n", + " #---------------------------------------------------Plots-------------------------------------------------\n", + " \n", + " cmap_custom = cm.colors.LinearSegmentedColormap.from_list(\"\", [\"darkred\",\"darkturquoise\",\"blue\"])\n", + " colors = cmap_custom(np.linspace(0, 1, np.array(wavelength_slices).size))\n", + "\n", + " #To Account for if NRS1 & NRS2 are both being plotted Side-by-side\n", + " if len(wavelength_slices) != 1:\n", + " if 'nrs1' in s3d_file:\n", + " wavelengths = wavelength_slices[0]\n", + " spaxel_loc = spaxel_locs[0]\n", + " vmin_vmax_vals = vmin_vmax[0]\n", + " \n", + " if y_scale:\n", + " y_scales = y_scale[0]\n", + "\n", + " elif 'nrs2' in s3d_file:\n", + " wavelengths = wavelength_slices[1]\n", + " spaxel_loc = spaxel_locs[1]\n", + " vmin_vmax_vals = vmin_vmax[1]\n", + " if y_scale:\n", + " y_scales = y_scale[1]\n", + "\n", + " else:\n", + " wavelengths = wavelength_slices[0]\n", + " spaxel_loc = spaxel_locs[0]\n", + " vmin_vmax_vals = vmin_vmax[0]\n", + " if y_scale:\n", + " y_scales = y_scale[0]\n", + "\n", + " \n", + " #Loop through each wavelength slices\n", + " for i, wave_slice in enumerate(wavelengths):\n", + "\n", + " if float(wavstart)<=wave_slice*10**-6<=float(wavend):\n", + " \n", + " #--------------------------------------------2-D Cube Slice------------------------------------------------\n", + " \n", + " #Min & Max Image Values & Scaling\n", + " if len(vmin_vmax_vals) != 1:\n", + " vmax_val = vmin_vmax_vals[i][1]\n", + " vmin_val = vmin_vmax_vals[i][0]\n", + " else:\n", + " vmax_val = vmin_vmax_vals[0][1]\n", + " vmin_val = vmin_vmax_vals[0][0]\n", + "\n", + " slicewave = wave_slice\n", + " nslice = int((slicewave - crval3)/cdelt3) #the slice of the cube we want to plot\n", + " ax1 = plt.subplot(gs[0+plot_count], projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", + " #ax1 = plt.subplot(3,len(wavelength_slices), 0+plot_count, projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", + "\n", + " slice_mean = np.nanmean(cube[(nslice-2):(nslice+2), :, :], axis=0) #Mean of the slice looking in the range (nslice-2):(nslice+2)\n", + " slice_norm=ImageNormalize(slice_mean, vmin=vmin_val, vmax=vmax_val, stretch=AsinhStretch()) #normalize &stretch \n", + " slice_image= ax1.imshow(slice_mean, norm=slice_norm, origin='lower', aspect='auto',cmap=cmap) #plot slice\n", + "\n", + " cb_image = fig.colorbar(slice_image, fraction=0.046, pad=0.04)\n", + " cb_image.set_label('MJy/sr', labelpad=-1, fontsize = 22)\n", + " cb_image.ax.tick_params(labelsize=20)\n", + " cb_image.ax.yaxis.get_offset_text().set_fontsize(20)\n", + " \n", + " ax1.set_xlabel('RA', fontsize =22)\n", + " ax1.set_ylabel('DEC', labelpad=-1, fontsize=22)\n", + " #ax1.grid(color='white', ls='solid')\n", + " ax1.set_title('Detector {} \\n Grating/Filter: {}/{} \\n {} microns'.format(s3d[0].header['DETECTOR'],s3d[0].header['GRATING'], s3d[0].header['FILTER'], str(slicewave)), fontsize =25)\n", + " ax1.tick_params(axis='both', which='major', labelsize=20)\n", + " ax1.coords[0].set_ticklabel(rotation=13, ha='right', pad=24)\n", + "\n", + " \n", + " #------------------------------------------Spaxel 1-D Spectrum---------------------------------------------\n", + " \n", + " #Zoom in on a Spaxel: Spectrum\n", + " loc = [spaxel_loc[i][0],spaxel_loc[i][1]]\n", + " x1d3flux_loc = cube[:, loc[1], loc[0]]\n", + " #ax2 = plt.subplot(3,len(wavelength_slices), int(total_num_plots/3)+plot_count)\n", + " ax2 = plt.subplot(gs[int(total_num_plots/3)+plot_count])\n", + "\n", + " #Spaxel Box Highlight \n", + " spaxel_rect = plt.Rectangle((loc[0]-.5, loc[1]-.5), 1,1, fill=False, color='black', linewidth=2)\n", + " ax1.add_patch(spaxel_rect)\n", + " \n", + " ax2.plot(x1d3wave, x1d3flux_loc, linewidth=1, color=colors[i])\n", + " ax2.grid(linewidth=2)\n", + " ax2.set_xlabel('$\\u03BB [\\u03BC$m]',fontsize=22)\n", + " ax2.set_ylabel(\"Surface Brightness \\n (MJy/sr)\",fontsize=22)\n", + " ax2.set_title('Spaxel at (x, y)='+repr(loc), fontsize=25)\n", + " ax2.tick_params(axis='both', which='major', labelsize=20)\n", + " ax2.yaxis.get_offset_text().set_fontsize(15)\n", + " \n", + " #Scale Information\n", + " if y_scale:\n", + " ymin, ymax = y_scales[i][0], y_scales[i][1]\n", + " else:\n", + " ymin, ymax = ax2.set_ylim()\n", + " \n", + " ax2.set_ylim(ymin, ymax)\n", + " ax2.xaxis.set_tick_params(labelsize=20)\n", + " ax2.yaxis.set_tick_params(labelsize=20)\n", + " ax2.set_aspect(0.5/ax2.get_data_ratio())\n", + " \n", + " #-----------------------------------------------Weight Map-------------------------------------------------\n", + " \n", + " #Corresponding Weight Map (wmap) for Cube Slice\n", + " ax3 = plt.subplot(gs[int(total_num_plots)-np.array(wavelength_slices).size+plot_count], projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", + " #ax3 = plt.subplot(3, len(wavelength_slices), int(total_num_plots)-len(wavelength_slices)+plot_count, projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", + " \n", + " slice_mean_wmap = np.nanmean(wmap[(nslice-2):(nslice+2), :, :], axis=0) #Mean of the wmap slice looking in the range (nslice-2):(nslice+2)\n", + " slice_norm_wmap=ImageNormalize(slice_mean_wmap, stretch=AsinhStretch()) #normalize &stretch\n", + " slice_wmap = ax3.imshow(slice_mean_wmap, norm=slice_norm_wmap, origin='lower',aspect='auto', cmap=cmap) #plot slice\n", + "\n", + " cb_wmap = fig.colorbar(slice_wmap, fraction=0.046, pad=0.04)\n", + " cb_wmap.set_label('Weight', labelpad=-1, fontsize = 22)\n", + " cb_wmap.ax.tick_params(labelsize=20)\n", + " cb_wmap.ax.yaxis.get_offset_text().set_fontsize(20)\n", + " \n", + " ax3.set_xlabel('RA', fontsize=22)\n", + " ax3.set_ylabel('DEC', labelpad=-1, fontsize=22)\n", + " #ax3.grid(color='gray', ls='solid')\n", + " ax3.set_title(str(slicewave)+' microns: Weight Map', fontsize=25)\n", + " ax3.tick_params(axis='both', which='major', labelsize=20)\n", + " ax3.coords[0].set_ticklabel(rotation=13, ha='right', pad=24)\n", + "\n", + " plot_count += 1\n", + " \n", + " else:\n", + " None\n", + " \n", + " if title:\n", + " fig.suptitle(title, fontsize=title_font)\n", + " plt.subplots_adjust(top=0.8) \n", + " \n", + " fig.tight_layout(rect=[0, 0, 0.98, 0.98])\n", + "\n", + " if save_figure == True:\n", + " fig.savefig(root+\".png\",dpi=24, bbox_inches=\"tight\")\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "acb2cd6b-7396-4d7f-8954-49654f34a598", + "metadata": {}, + "source": [ + "## 4. Directory Set-Up \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36acc0ff-0a60-4f60-abad-bd71e686c8ed", + "metadata": {}, + "outputs": [], + "source": [ + "#To rerun the notebook and all the pipeline steps set runflag=True\n", + "runflag = True \n", + "\n", + "#Demo directory -- contains pre-computed products\n", + "if runflag == False:\n", + " output_dir = './nirspec_ifu_02732_demo/'\n", + "\n", + "#Rerun directory\n", + "elif runflag == True:\n", + " #If you want to actually re-download the data and run everything offline, \n", + " #then comment out this line, set runflag=True, & specify a desired local directory\n", + " output_dir = './nirspec_ifu_02732_rerun/'\n", + " if not os.path.exists(output_dir):\n", + " os.makedirs(output_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "2b01c250-2f66-4de7-ba7b-fdbcbd7a99af", + "metadata": {}, + "source": [ + "## 5. Download the Data \n", + "\n", + "
\n", + "
\n", + "\n", + "Tip: To download the data from MAST, you must input your MAST authorization token. Get your MAST Token Here: https://auth.mast.stsci.edu/token. Additionally, be sure to follow [astroquery installation procedures](https://astroquery.readthedocs.io/en/latest/index.html#) to properly run this cell. \n", + " \n", + "
\n", + "\n", + "| Target: NGC 7319 AGN | | | | |\n", + "|:-----------:|:-------:|---|---|---|\n", + "| Proposal ID | 02732 | | | |\n", + "| [GRATING/FILTER](https://jwst-docs.stsci.edu/jwst-near-infrared-spectrograph/nirspec-observing-modes/nirspec-ifu-spectroscopy) | PRISM/CLEAR | λ: 0.6–5.3 μm (a low resolution, R ~ 100) | | |\n", + "| DURATION | 160.478 [s] | Total duration of one exposure | | | |\n", + "| READPATT | NRSIRS2RAPID | Readout Pattern | | | |\n", + "| PATTTYPE | CYCLING | Primary dither pattern type | | |\n", + "| PATTSIZE | LARGE | Primary dither pattern size (1.0\" extent) | | |\n", + "| NUMDTHPT | 8 | Total number of points in pattern | | | \n", + "| SRCTYAPT | UNKNOWN | Source Type selected in APT | | | \n", + "\n", + "> **Note:** The presence of a physical gap between detectors affects high-resolution IFU observations because the spectra are long enough to span both NIRSpec detectors. When using the grating-filter combination G140H/F070LP (or PRISM/CLEAR) the resulting spectra do not have any gaps because the spectra do not extend beyond NRS1. [More Info ...](https://jwst-docs.stsci.edu/jwst-near-infrared-spectrograph/nirspec-operations/nirspec-ifu-operations/nirspec-ifu-wavelength-ranges-and-gaps#NIRSpecIFUWavelengthRangesandGaps-Wavelengthgaps)\n", + "\n", + "The cell below downloads the raw uncalibrated data along with the stage 2 and stage 3 products that are available in MAST. MAST products will get saved to a folder called `mast_products` within the designated output directory defined earlier in this notebook. These files have already been pre-downloaded and stored in a provided demo directory. To get the most up-to-date products set `runflag = True` and rerun this notebook. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f205522d-f3ca-4053-9040-7f43f0cd62fa", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Download data from MAST \n", + "\n", + "#Setup your account \n", + "\n", + "# NOTE:\n", + "# The data in this notebook is public and does not require a token.\n", + "# For other data sets, uncomment the following line and enter your\n", + "# token at the prompt.\n", + "\n", + "# Observations.login(token=None)\n", + "\n", + "sessioninfo = Observations.session_info()\n", + "\n", + "#Define the general search criteria\n", + "obs = Observations.query_criteria(\n", + " obs_collection = 'JWST',\n", + " instrument_name = ['NIRSPEC/IFU'],\n", + " proposal_id = '02732')\n", + "\n", + "#Print the Observations returned from the general serach criteria\n", + "products = Observations.get_product_list(obs)\n", + "#print(products)\n", + "\n", + "#Filter the list of observations\n", + "#In this case we look for UNCAL products and ASN files to manually run pipeline stage 1-3\n", + "#We look for pre-processed MAST products for comparison: RATE (stage 1) & CAL (stage 2&3) & S3D (stage 2&3) & X1D (stage2&3) \n", + "filtered = Observations.filter_products(products,\n", + " productSubGroupDescription=[\"UNCAL\", \"ASN\"],\n", + " mrp_only=False)\n", + "#Print the filtered products\n", + "number = len(filtered)\n", + "for k in range(number):\n", + " print(filtered['productFilename'][k])\n", + "\n", + "#Download the filtered products\n", + "#This creates a mastDownload directory, unless you set flat=True and set a download_dir\n", + "for i in range(len(filtered)):\n", + " mast_products_dir = output_dir+'mast_products/'\n", + " if not os.path.exists(mast_products_dir):\n", + " os.makedirs(mast_products_dir)\n", + " if runflag == True:\n", + " Observations.download_products(filtered[i], mrp_only=False, cache=False, flat=True, download_dir=mast_products_dir) #Override any cached files and download the most up-to-date ones\n", + " else:\n", + " Observations.download_products(filtered[i], mrp_only=False, cache=True, flat=True, download_dir=mast_products_dir) #Find any cached files first before downloading new ones" + ] + }, + { + "cell_type": "markdown", + "id": "6d692537-b0fa-4221-8f0f-23816e51097d", + "metadata": {}, + "source": [ + "### 6. Stage 1 \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "684360fc-e0d4-4a1c-be06-8d7effe5a6da", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Stage 1 Processing \n", + "\n", + "if runflag == True:\n", + "\n", + " for uncal_file in sorted(glob.glob(mast_products_dir+'*nrs1_uncal.fits')): \n", + "\n", + " print(\"Applying Stage 1 Corrections & Calibrations to: \"+ os.path.basename(uncal_file))\n", + "\n", + " result = Detector1Pipeline.call(uncal_file,\n", + " save_results = True,\n", + " output_dir = output_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d00294b2-4eec-4178-bdae-313e20bb8045", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Stage 1 slope products -- level 2a images\n", + "\n", + "#Plot 4th (out of 8) dither position (NRS1 & NRS2) for GRATING/FILTER G140H/F100LP combination \n", + "for rate_file in sorted(glob.glob(output_dir+'*00004_nrs?_rate.fits')):\n", + " \n", + " ratefile_open = datamodels.open(rate_file)\n", + " ratefile_sci = ratefile_open.data # Get the pixel data (the SCI extension of the fits file)\n", + " ratefile_dq = ratefile_open.dq # The Data Quality Map Data\n", + " \n", + " \n", + " show_image(ratefile_sci, 0,10, units='DN/s',zoom_in=[500,550, 1250,1300],\n", + " title='Countrate Image \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", + " ratefile_open.meta.dither.position_number, \n", + " ratefile_open.meta.instrument.grating,\n", + " ratefile_open.meta.instrument.filter)) #rate files have units of DN/s\n", + " \n", + " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear',zoom_in=[500,550, 1250,1300],\n", + " title='Data Quality Map \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", + " ratefile_open.meta.dither.position_number, \n", + " ratefile_open.meta.instrument.grating,\n", + " ratefile_open.meta.instrument.filter)) #rate files have units of DN/s\n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "1a9c863d-3864-439f-bf04-281ea5389a9a", + "metadata": {}, + "source": [ + "### 7. Stage 2 \n", + "
\n", + "\n", + "This IFU data set focuses on an AGN target, which has a compact region at the center of its galaxy that can be considered a point source. To treat this IFU data as a point source, one must change the `SRCTYPE=POINT` header keyword in the `cal.fits` files before running stages 2 and 3 of the calibration pipeline. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cefe6882-2880-4172-b65e-e6e83383dff3", + "metadata": {}, + "outputs": [], + "source": [ + "#Treating the IFU data as a point source \n", + "#To run as a point source, alter the rate file header keywrod SRCTYAPT=POINT & rerun stage 2 of the pipeline \n", + "#Loop through the copied rate files and update the source type keyword\n", + "for rate_file in sorted(glob.glob(output_dir+'*nrs1_rate.fits')):\n", + " rate_file_hdu = fits.open(rate_file, 'update')\n", + " \n", + " #Change source type to point \n", + " rate_file_hdu[0].header['SRCTYAPT'] = 'POINT'\n", + " \n", + " rate_file_hdu.close()" + ] + }, + { + "cell_type": "markdown", + "id": "a22b08e2-dbde-4053-83b4-8195fd452684", + "metadata": {}, + "source": [ + "During stage 2 of the pipeline, the countrate (slope) image products from stage 1, which have units of DN/s, are converted to units of surface brightness (MJy/sr) for both extended and point sources (as of DMS build 9.3/CAL_VER 1.10.2). For IFU point sources, the `extract_1d` step is controlled by a different set of parameters in the EXTRACT1D reference file: \n", + "\n", + "> For a point source, the spectrum is extracted using circular aperture photometry, **optionally (automatically) including background subtraction** using a circular annulus. [More Info ...](https://jwst-pipeline.readthedocs.io/en/latest/jwst/extract_1d/description.html)\n", + "\n", + "When processing the IFU as a point source, the `extract_1d` step will automatically apply background subtraction unless otherwise told not to. The `extract_1d` step will also use the default circular extraction apertures for the source and background, an example of how to modify the EXTRACT1D reference file can be found at the end of this notebook.\n", + "\n", + "
\n", + " \n", + "Warning: Note there has been a bug in the `cube_build` step that caused the point source flux to not be conserved when using different spatial sampling. A fix has been implemented as of release DMS build 9.3/CAL_VER 1.10.2. In order to enable the correct functionality, the units of the cal.fits files and cubes will now be in surface brightness, and only the 1-D extracted spectra will be in units of Jy.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1a85ff57-18ea-4a81-87c3-7d3f0aff5082", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Stage 2 Processing \n", + "\n", + "if runflag == True:\n", + " \n", + " #Process each rate file seperately \n", + " for rate_file in sorted(glob.glob(output_dir+'*nrs1*rate.fits')):\n", + " \n", + " print(\"Applying Stage 2 Calibrations & Corrections to: \"+ os.path.basename(rate_file))\n", + "\n", + " result = Spec2Pipeline.call(rate_file,\n", + " save_results = True,\n", + " output_dir = output_dir) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29bafbb3-5646-4405-94b7-179523220ca0", + "metadata": {}, + "outputs": [], + "source": [ + "#Stage 2 Products -- Calibrated 3-D data cube for PRISM/CLEAR (only falls on NRS1)\n", + "\n", + "#Plotting the 4th (out of 8) dither position\n", + "stage2_s3d_file = sorted(glob.glob(output_dir+'*00004_nrs1_s3d.fits')) \n", + "\n", + "title_stage2_rerun='NGC 7319 AGN \\n Level 2 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", + "\n", + "\n", + "#Characteristics of the plot \n", + "nrs1_wavelengths = [1.4,3.3,4.5] #Wavelength slices (microns) to take from the 3-D data cube\n", + "nrs1_spaxel_locs = [[30,29],[28,39],[14,25]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "\n", + "\n", + "#Plot using the convience function defined above\n", + "show_ifu_cubeslices(stage2_s3d_file, wavelength_slices=[nrs1_wavelengths], spaxel_locs=[nrs1_spaxel_locs],title=title_stage2_rerun)" + ] + }, + { + "cell_type": "markdown", + "id": "af7e8b81-643f-4e3a-a39b-a9473b951646", + "metadata": {}, + "source": [ + "### 8. Stage 3 \n", + "
\n", + "\n", + "***Level 3 ASN File***\n", + "\n", + "> Observations that use a nod-type/dither patterns, their exposures are related. [Association files (ASN)](https://jwst-pipeline.readthedocs.io/en/stable/jwst/associations/overview.html) describe how multiple exposures are related to one another and how they depend on one another. Processing an ASN file permits exposures to be calibrated, archived, retrieved, and reprocessed as a set rather than individual objects. IFU exposures taken with a dither pattern are not used for pixel-to-pixel background subtraction by the calibration pipeline (unlike exposures taken with a nod pattern).\n", + "\n", + "Therefore, all calibration files (`cal.fits`) in our spec3 ASN file should be labeled as science exposures (`exptype: science`). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7220fadc-73c6-4d3d-b03f-cfc71865c496", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Copy ASN file from MAST into the stage 1 rerun directory\n", + "\n", + "asnfile_mast = glob.glob(mast_products_dir+'*_spec3_00001_asn.json')[0] #ASN file found in MAST\n", + "\n", + "asnfile_rerun_point = output_dir+os.path.basename(asnfile_mast) #New ASN file path\n", + "if not os.path.exists(asnfile_rerun_point):\n", + " copy(asnfile_mast, asnfile_rerun_point)\n", + " \n", + "#Check the ASN file contents\n", + "with open(asnfile_rerun_point, 'r') as f_obj:\n", + " asnfile_rerun_point_data = json.load(f_obj)\n", + " \n", + "asnfile_rerun_point_data" + ] + }, + { + "cell_type": "markdown", + "id": "8384e3a3-972e-4473-baf4-7c34544dd845", + "metadata": {}, + "source": [ + "#### 8.1 New Outlier Detection Algorithm\n", + "
\n", + "\n", + "The new outlier detection algorithm for IFU data (as of DMS build B9.3rc1/CAL_VER 1.11.0) implements the basic outlier detection algorithm -- searches for pixels that are consistent outliers in the calibrated images created by the `calwebb_spec2` pipeline. The algorithm generally operates as follows:\n", + "\n", + "> * Identifies outlier pixels by comparing them with their neighboring pixels in the spatial direction across a set of input files within an association.\n", + "> * For NIRSpec data, it calculates differences between pixels located above and below each science pixel.\n", + "> * The pixel differences for every input model in the association are computed and stored in a stack of pixel differences.\n", + "> * For each pixel, the algorithm determines the minimum difference across this stack and then performs normalization. This normalization process employs a local median derived from the difference array, with the size of the median determined by the kernel size.\n", + "> * A pixel is flagged as an outlier if this normalized minimum difference is greater than the input threshold percentage. \n", + "> * Pixels that are found to be outliers are flaged in in the DQ array.\n", + "> * [More Info ...](https://jwst-pipeline.readthedocs.io/en/latest/jwst/outlier_detection/outlier_detection_ifu.html#outlier-detection-ifu)\n", + "\n", + "**[The outlier_detection step for IFU data has the following optional arguments that control the behavior of the processing](https://github.com/spacetelescope/jwst/blob/master/docs/jwst/outlier_detection/arguments.rst):**\n", + "\n", + "* `kernel_size` (string, default='7'): The size of the kernel to use to normalize the pixel differences. The kernel size must only contain odd values.\n", + "* `threshold_percent` (float, default=99.8): The threshold (in percent) of the normalized minimum pixel difference used to identify bad pixels. Pixels with a normalized minimum pixel difference above this percentage are flagged as a outlier.\n", + "* `save_intermediate_results` (boolean, default=False): Specifies whether or not to save any intermediate products created during step processing.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "07a09918-9e92-4f5b-954f-a0de70aa56d1", + "metadata": {}, + "outputs": [], + "source": [ + "#Rerun stage 3 with outlier detection on\n", + "if runflag==True:\n", + "\n", + " result = Spec3Pipeline.call(asnfile_rerun_point,\n", + " save_results = True,\n", + " output_dir = output_dir,\n", + " steps = {\"outlier_detection\":{\"skip\": False,\n", + " \"save_results\": True,\n", + " \"kernel_size\": '3 3'},\n", + " \"extract_1d\":{\"subtract_background\":False}}) #Do not automatically apply background subtraction until we modify the extraction region\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0c596da-927d-41a1-93c3-4731bed51ef6", + "metadata": {}, + "outputs": [], + "source": [ + "#Stage 3 Products -- Combined Calibrated 3-D data cube for PRISM/CLEAR \n", + "\n", + "stage3_s3d_file_point = sorted(glob.glob(output_dir+'*nirspec_prism-clear_s3d.fits')) \n", + "\n", + "title_stage3_rerun_point='NGC 7319 AGN \\n Level 3 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", + "\n", + "#Characteristics of the plot \n", + "nrs1_wavelengths = [1.4,3.3,4.5] #Wavelength slices (microns) to take from the 3-D data cube\n", + "nrs1_spaxel_locs = [[30,29],[28,39],[14,25]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "vmin_vmax_point = [[0,150],[0,150],[0,150]]\n", + "\n", + "#Plot using the convience function defined above\n", + "show_ifu_cubeslices(stage3_s3d_file_point, wavelength_slices=[nrs1_wavelengths], spaxel_locs=[nrs1_spaxel_locs],vmin_vmax=[vmin_vmax_point],title=title_stage3_rerun_point)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7cff7886-30b8-42a6-bf72-a5eb04fe19d8", + "metadata": {}, + "outputs": [], + "source": [ + "#Stage 3 Products -- Extracted 1-D Spectrum \n", + "\n", + "#Combined 1-D extracted spectrum\n", + "x1d3_rerun_point = datamodels.open(glob.glob(output_dir+'*nirspec_prism-clear_x1d.fits')[0])\n", + "\n", + "#Wavelength & Surface Brightness Arrays\n", + "x1d3wave_rerun_point = x1d3_rerun_point.spec[0].spec_table.WAVELENGTH\n", + "x1d3flux_rerun_point = x1d3_rerun_point.spec[0].spec_table.FLUX\n", + "\n", + "#Plot the Extracted 1-D Spectrum\n", + "fig = plt.figure(figsize=(15,9))\n", + "\n", + "plt.plot(x1d3wave_rerun_point,x1d3flux_rerun_point, linewidth =2)\n", + "\n", + "#Where wavelength slice was taken above\n", + "plt.vlines(1.4, 0., 400., 'black', 'dotted', label='1.4 microns')\n", + "plt.vlines(3.3, 0., 400., 'red', 'dotted', label='3.3 microns')\n", + "plt.vlines(4.5, 0., 400., 'green', 'dotted', label='4.5 microns')\n", + "\n", + "plt.xlabel('$\\lambda [\\mu$m]', fontsize =20)\n", + "plt.ylabel('Flux (Jy)', fontsize =20)\n", + "plt.title(\"NGC 7319 AGN \\n Level 3 IFU Product in MAST: Extracted 1-D Spectrum\", fontsize=20)\n", + "plt.ylim(0, 10**-1.6)\n", + "plt.ticklabel_format(axis='y', style='sci', scilimits=(0,-2))\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e1d98027-3ea6-415f-b5d4-8fe7397445a2", + "metadata": {}, + "source": [ + "#### 8.2 Extract 1-D Step: Modified Reference File\n", + "
\n", + "\n", + "As a point source, the `extract_1d` step is controlled by a different set of parameters in the EXTRACT1D reference file:\n", + "\n", + ">[Extraction for 3-D IFU Data:](https://jwst-pipeline.readthedocs.io/en/latest/jwst/extract_1d/description.html)\n", + ">\n", + "> For point source data the extraction aperture is centered at the RA/DEC target location indicated by the header. If the target location is undefined in the header, then the extraction region is the center of the IFU cube.\n", + ">\n", + ">For point sources a circular extraction aperture is used, along with an optional circular annulus for background extraction and subtraction. The size of the extraction region and the background annulus size varies with wavelength. The extraction related vectors are found in the asdf EXTRACT1D reference file. For each element in the wavelength vector there are three size components: `radius`, `inner_bkg`, and `outer_bkg`. The radius vector sets the extraction size; while `inner_bkg` and `outer_bkg` specify the limits of an annular background aperture. \n", + "\n", + "Here, we show how to modify the EXTRACT1D reference file to obtain better results. \n", + "\n", + "
\n", + " \n", + "Warning: The default extraction aperture radius has been set to match what was used to derive the flux calibration. If you want to use a different aperture size, you will need to compute and apply a custom aperture correction to ensure the correct flux, as we have not yet updated the aperture correction reference files.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8284ef00-acf5-49e7-8f8d-e3cf6efd7918", + "metadata": {}, + "outputs": [], + "source": [ + "#Extraction Region Preview\n", + "#Open Combined 3-D Cube FITS file\n", + "s3d = fits.open(glob.glob(output_dir+'*nirspec_prism-clear_s3d.fits')[0])\n", + "cube = s3d[1].data #Science data\n", + "\n", + "#plot the full IFU cube\n", + "ax = plt.subplot(1, 1, 1)#, projection=wcs, slices=('x', 'y', nslice3)) #set up the subplot space\n", + "slice_mean = np.nanmean(cube[400:500, :, :], axis=0) #Mean of the slice looking in the range (nslice2-2):(nslice2+2)\n", + "slice_norm=ImageNormalize(slice_mean, vmin=0, vmax=150, stretch=AsinhStretch()) #normalize &stretch\n", + "slice_full = ax.imshow(slice_mean, norm=slice_norm, origin='lower', cmap='jet') #plot slice\n", + "\n", + "#colorbar\n", + "cb_image = plt.colorbar(slice_full, fraction=0.046, pad=0.04)\n", + "cb_image.set_label('MJy/sr', labelpad=-1, fontsize = 10)\n", + "cb_image.ax.tick_params(labelsize=10)\n", + "cb_image.ax.yaxis.get_offset_text().set_fontsize(10)\n", + "\n", + "radius = Circle((29,29),9, fill=False, label='Radius')\n", + "inner_bkg = Circle((29,29),10, color='b',fill=False, label='Inner Background Radius')\n", + "outer_bkg= Circle((29,29),15, color='r',fill=False, label='Outer Background Radius')\n", + "ax.add_patch(radius)\n", + "ax.add_patch(inner_bkg)\n", + "ax.add_patch(outer_bkg)\n", + "ax.legend(fontsize=10)\n", + "ax.set_xlabel('X (pixels)', fontsize=10)\n", + "ax.set_ylabel('Y (pixels)', fontsize=10)\n", + "ax.grid(color='white', ls='solid')\n", + "ax.set_title('Full IFU Cube: \\n Extraction Region Preview', fontsize=15)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cdcbfef1-86f4-47f3-ac6a-c3ac7cf2af76", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Grab the defualt extract1d reference file and copy to working directory\n", + "extract1d_ref_og = Spec3Pipeline().get_reference_file(glob.glob(output_dir+'*nirspec_prism-clear_s3d.fits')[0], 'extract1d')\n", + "if not os.path.exists(output_dir+os.path.basename(extract1d_ref_og)):\n", + " copy(extract1d_ref_og, output_dir+os.path.basename(extract1d_ref_og))\n", + "\n", + "if runflag==True:\n", + " #Make Changes to the ASDF file and Write to a new file\n", + " with asdf.open(output_dir+os.path.basename(extract1d_ref_og),mode='rw') as ff:\n", + " ff.tree['data']['radius'] = np.full((2048,), 9, dtype='float32')\n", + " ff.tree['data']['inner_bkg'] = np.full((2048,), 10, dtype='float32')\n", + " ff.tree['data']['outer_bkg'] = np.full((2048,), 15, dtype='float32')\n", + " ff.write_to(output_dir+'new_extract1d_reference_file.asdf')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ab34faf-b2f2-469a-ad8c-8920f9e01786", + "metadata": {}, + "outputs": [], + "source": [ + "#Rerun only the extract1d step with the new/modified reference file with background subtraction on\n", + "\n", + "if runflag==True:\n", + " Extract1dStep.call(glob.glob(output_dir+'*nirspec_prism-clear_s3d.fits')[0], \n", + " save_results = True,\n", + " output_dir = output_dir, \n", + " override_extract1d = output_dir+'new_extract1d_reference_file.asdf')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6e10a9e2-1040-4836-b50e-903413d7defa", + "metadata": {}, + "outputs": [], + "source": [ + "#Display new 1-D spectrum\n", + "\n", + "#Combined 1D extracted spectrum\n", + "x1d3 = datamodels.open(glob.glob(output_dir+'*nirspec_prism-clear_extract1dstep.fits')[0])\n", + "\n", + "#Wavelength & Surface Brightness Arrays\n", + "x1d3wave = x1d3.spec[0].spec_table.WAVELENGTH\n", + "x1d3flux = x1d3.spec[0].spec_table.FLUX\n", + "\n", + "#Plot the Extracted 1D Spectrum\n", + "fig = plt.figure(figsize=(15,9))\n", + "\n", + "plt.plot(x1d3wave,x1d3flux, linewidth =2)\n", + "\n", + "#Where wavelength slice was taken above\n", + "plt.vlines(1.4, 0., 400., 'black', 'dotted', label='1.4 microns')\n", + "plt.vlines(3.3, 0., 400., 'red', 'dotted', label='3.3 microns')\n", + "plt.vlines(4.5, 0., 400., 'green', 'dotted', label='4.5 microns')\n", + "\n", + "plt.xlabel('$\\lambda [\\mu$m]')\n", + "plt.ylabel('Flux (Jy)', fontsize =20)\n", + "plt.title(\"NGC 7319 AGN \\n Level 3 IFU Product: Extracted 1-D Spectrum with Background Subtraction\")\n", + "plt.ylim(0, 10**-1.6)\n", + "plt.ticklabel_format(axis='y', style='sci', scilimits=(0,-2))\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b450acc1-9f3d-4d57-a710-2c09d0518aef", + "metadata": {}, + "source": [ + "## About This Notebook \n", + "
\n", + "\n", + "**Authors**: Kayli Glidic (kglidic@stsci.edu), Leonardo Ubeda (lubeda@stsci.edu)\n", + "\n", + "**Update On**: 2023-08-11" + ] + } + ], + "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": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/requirements.txt b/notebooks/NIRSpec_IFU_spectral_extraction/requirements.txt new file mode 100644 index 000000000..1d680d207 --- /dev/null +++ b/notebooks/NIRSpec_IFU_spectral_extraction/requirements.txt @@ -0,0 +1,7 @@ +asdf == 2.15.0 +astropy == 5.2.2 +astroquery == 0.4.7.dev8738 +crds == 11.16.22 +jwst == 1.11.3 +matplotlib == 3.7.1 +numpy == 1.25.2 From 7a22e7b0820f77e2f0b862917633b609c0add4e0 Mon Sep 17 00:00:00 2001 From: Hatice Karatay Date: Tue, 3 Oct 2023 16:47:39 -0400 Subject: [PATCH 02/12] Correct some of the style errors on the 02729 nb --- .../ero_nirspec_ifu_02729_demo.ipynb | 323 +++++++++--------- 1 file changed, 159 insertions(+), 164 deletions(-) diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb index e4cfcac37..58fd4d1cb 100644 --- a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb @@ -68,47 +68,42 @@ "metadata": {}, "outputs": [], "source": [ - "#Import Library\n", + "# Import Library\n", "\n", - "#--------------------------------------JWST Calibration Pipeline Imports-------------------------------------------\n", + "# --------------------------------------JWST Calibration Pipeline Imports-------------------------------------------\n", "\n", "import jwst\n", "import crds\n", "from jwst import datamodels\n", - "from jwst.pipeline import Detector1Pipeline #calwebb_detector1\n", - "from jwst.pipeline import Spec2Pipeline #calwebb_spec2\n", - "from jwst.pipeline import Spec3Pipeline #calwebb_spec3\n", - "from jwst.extract_1d import Extract1dStep #Extract1D Individual Step\n", + "from jwst.pipeline import Detector1Pipeline # calwebb_detector1\n", + "from jwst.pipeline import Spec2Pipeline # calwebb_spec2\n", + "from jwst.pipeline import Spec3Pipeline # calwebb_spec3\n", "\n", "print(\"JWST Calibration Pipeline Version={}\".format(jwst.__version__))\n", "print(\"Current Operational CRDS Context = {}\".format(crds.get_default_context()))\n", "\n", - "#----------------------------------------------General Imports-----------------------------------------------------\n", + "# ----------------------------------------------General Imports-----------------------------------------------------\n", "\n", "import numpy as np\n", "import warnings\n", - "warnings.filterwarnings('ignore') #Set to 'default' to turn warnings back on\n", + "warnings.filterwarnings('ignore') # Set to 'default' to turn warnings back on\n", "\n", - "#--------------------------------------------File Operation Imports------------------------------------------------\n", + "# --------------------------------------------File Operation Imports------------------------------------------------\n", "\n", "import glob\n", "import os\n", - "import asdf\n", "import json\n", "from shutil import copy\n", "\n", - "#--------------------------------------------Astropy/Astroquery Imports--------------------------------------------\n", + "# --------------------------------------------Astropy/Astroquery Imports--------------------------------------------\n", "\n", "from astropy.io import fits\n", "from astropy import wcs\n", "from astropy.wcs import WCS\n", - "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch, SqrtStretch\n", - "from astropy.stats import sigma_clipped_stats\n", - "import astroquery\n", - "from astroquery.mast import Mast\n", + "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch\n", "from astroquery.mast import Observations\n", "\n", - "#------------------------------------------------Plotting Imports--------------------------------------------------\n", + "# ------------------------------------------------Plotting Imports--------------------------------------------------\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", @@ -119,7 +114,7 @@ "# Use this version for non-interactive plots (easier scrolling of the notebook)\n", "%matplotlib inline\n", "# Use this version (outside of Jupyter Lab) if you want interactive plots\n", - "#%matplotlib notebook\n", + "# %matplotlib notebook\n", "\n", "# These gymnastics are needed to make the sizes of the figures\n", "# be the same in both the inline and notebook versions\n", @@ -184,7 +179,7 @@ " norm = ImageNormalize(data_2d, interval=ManualInterval(vmin=vmin, vmax=vmax),\n", " stretch=AsinhStretch())\n", " \n", - " #--------------------------------------------Set Up Figure-------------------------------------------\n", + " # --------------------------------------------Set Up Figure-------------------------------------------\n", "\n", " fig = plt.figure(figsize=(xsize, ysize))\n", " ax = fig.add_subplot(1, 1, 1)\n", @@ -198,9 +193,9 @@ " if title:\n", " plt.title(title)\n", "\n", - " #Zoom in on a portion of the image? \n", + " # Zoom in on a portion of the image? \n", " if zoom_in:\n", - " #inset axis \n", + " # inset axis \n", " axins = ax.inset_axes([0.5, 0.6, 0.5, 0.3])\n", " \n", " axins.imshow(data_2d, origin=\"lower\", norm=norm, aspect=aspect, cmap=cmap)\n", @@ -210,7 +205,7 @@ " axins.set_ylim(zoom_in[2], zoom_in[3])\n", " axins.set_xticklabels([])\n", " axins.set_yticklabels([])\n", - " ax.indicate_inset_zoom(axins, color=\"black\",edgecolor=\"black\", linewidth=3)" + " ax.indicate_inset_zoom(axins, color=\"black\", edgecolor=\"black\", linewidth=3)" ] }, { @@ -220,7 +215,7 @@ "metadata": {}, "outputs": [], "source": [ - "def show_ifu_cubeslices(s3d_file_list, wavelength_slices=[], spaxel_locs=[], y_scale=None, cmap='jet', vmin_vmax = [[[0,15e1]]], save_figure=False, title=None, title_font = 30):\n", + "def show_ifu_cubeslices(s3d_file_list, wavelength_slices=[], spaxel_locs=[], y_scale=None, cmap='jet', vmin_vmax=[[[0, 15e1]]], save_figure=False, title=None, title_font=30):\n", " \"\"\"\n", " Function to that takes a 3-D IFU data cube and generates: \n", " \n", @@ -256,48 +251,48 @@ " #---------------------------------------------- Set-up Figure -------------------------------------------------\n", "\n", " #Plot Slices From the Cube\n", - " fig, axs = plt.subplots(3, np.array(wavelength_slices).size, figsize=(8*np.array(wavelength_slices).size,18))\n", - " gs = grd.GridSpec(3, np.array(wavelength_slices).size, height_ratios=[1]*3, width_ratios=[1]*np.array(wavelength_slices).size, hspace=0.4,wspace=0.7)\n", + " fig, axs = plt.subplots(3, np.array(wavelength_slices).size, figsize=(8*np.array(wavelength_slices).size, 18))\n", + " gs = grd.GridSpec(3, np.array(wavelength_slices).size, height_ratios=[1]*3, width_ratios=[1]*np.array(wavelength_slices).size, hspace=0.4, wspace=0.7)\n", "\n", - " total_num_plots=3*np.array(wavelength_slices).size\n", + " total_num_plots = 3*np.array(wavelength_slices).size\n", " \n", " plot_count = 0\n", - " #---------------------------------------------Open Files------------------------------------------------------\n", + " # ---------------------------------------------Open Files------------------------------------------------------\n", " \n", " for s3d_file in s3d_file_list:\n", " \n", - " root=s3d_file[:-9] #Root file name \n", + " root = s3d_file[:-9] # Root file name \n", "\n", - " s3d = fits.open(s3d_file) #3-D IFU data cube fits file \n", - " x1d3 = datamodels.open(root+'_x1d.fits') #1-D Extracted Spectrum \n", + " s3d = fits.open(s3d_file) # 3-D IFU data cube fits file \n", + " x1d3 = datamodels.open(root+'_x1d.fits') # 1-D Extracted Spectrum \n", " \n", - " #--------------------------------Wavelength & Surface Brightness/Flux Arrays------------------------------\n", + " # --------------------------------Wavelength & Surface Brightness/Flux Arrays------------------------------\n", " \n", " x1d3wave = x1d3.spec[0].spec_table.WAVELENGTH\n", " \n", - " #--------------------------------------Data & Header Information------------------------------------------\n", + " # --------------------------------------Data & Header Information------------------------------------------\n", "\n", " \n", - " #SCI Extension: [Type:ImageHDU Cards:92 Dimensions:(57, 61, 973) Format:float32]\n", + " # SCI Extension: [Type:ImageHDU Cards:92 Dimensions:(57, 61, 973) Format:float32]\n", " cube = s3d[1].data #Science data\n", - " wcs = WCS(s3d[1].header) #World Coordinate System (WCS) Transformation keywords \n", + " wcs = WCS(s3d[1].header) # World Coordinate System (WCS) Transformation keywords \n", " wmap = s3d[4].data #3-D weight image giving the relative weights of the output spaxels.\n", - " cdelt1 = s3d[1].header['CDELT1']*3600. #Axis 1 coordinate increment at reference point \n", - " cdelt2 = s3d[1].header['CDELT2']*3600. #Axis 2 coordinate increment at reference point \n", - " cdelt3 = s3d[1].header['CDELT3'] #Axis 3 coordinate increment at reference point \n", - " crval3 = s3d[1].header['CRVAL3'] #third axis value at the reference pixel \n", + " cdelt1 = s3d[1].header['CDELT1']*3600. # Axis 1 coordinate increment at reference point \n", + " cdelt2 = s3d[1].header['CDELT2']*3600. # Axis 2 coordinate increment at reference point \n", + " cdelt3 = s3d[1].header['CDELT3'] # Axis 3 coordinate increment at reference point \n", + " crval3 = s3d[1].header['CRVAL3'] # third axis value at the reference pixel \n", "\n", - " #Wavelength range of the grating/filter combination\n", + " # Wavelength range of the grating/filter combination\n", " wavstart = s3d[1].header['WAVSTART']\n", " wavend = s3d[1].header['WAVEND']\n", " s3d.close()\n", " \n", - " #---------------------------------------------------Plots-------------------------------------------------\n", + " # ---------------------------------------------------Plots-------------------------------------------------\n", " \n", - " cmap_custom = cm.colors.LinearSegmentedColormap.from_list(\"\", [\"darkred\",\"darkturquoise\",\"blue\"])\n", + " cmap_custom = cm.colors.LinearSegmentedColormap.from_list(\"\", [\"darkred\", \"darkturquoise\", \"blue\"])\n", " colors = cmap_custom(np.linspace(0, 1, np.array(wavelength_slices).size))\n", "\n", - " #To Account for if NRS1 & NRS2 are both being plotted Side-by-side\n", + " # To Account for if NRS1 & NRS2 are both being plotted Side-by-side\n", " if len(wavelength_slices) != 1:\n", " if 'nrs1' in s3d_file:\n", " wavelengths = wavelength_slices[0]\n", @@ -322,14 +317,14 @@ " y_scales = y_scale[0]\n", "\n", " \n", - " #Loop through each wavelength slices\n", + " # Loop through each wavelength slices\n", " for i, wave_slice in enumerate(wavelengths):\n", "\n", " if float(wavstart)<=wave_slice*10**-6<=float(wavend):\n", " \n", - " #--------------------------------------------2-D Cube Slice------------------------------------------------\n", + " # --------------------------------------------2-D Cube Slice------------------------------------------------\n", " \n", - " #Min & Max Image Values & Scaling\n", + " # Min & Max Image Values & Scaling\n", " if len(vmin_vmax_vals) != 1:\n", " vmax_val = vmin_vmax_vals[i][1]\n", " vmin_val = vmin_vmax_vals[i][0]\n", @@ -338,23 +333,23 @@ " vmin_val = vmin_vmax_vals[0][0]\n", "\n", " slicewave = wave_slice\n", - " nslice = int((slicewave - crval3)/cdelt3) #the slice of the cube we want to plot\n", - " ax1 = plt.subplot(gs[0+plot_count], projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", - " #ax1 = plt.subplot(3,len(wavelength_slices), 0+plot_count, projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", + " nslice = int((slicewave - crval3)/cdelt3) # the slice of the cube we want to plot\n", + " ax1 = plt.subplot(gs[0+plot_count], projection=wcs, slices=('x', 'y', nslice)) # set up the subplot space\n", + " # ax1 = plt.subplot(3,len(wavelength_slices), 0+plot_count, projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", "\n", - " slice_mean = np.nanmean(cube[(nslice-2):(nslice+2), :, :], axis=0) #Mean of the slice looking in the range (nslice-2):(nslice+2)\n", - " slice_norm=ImageNormalize(slice_mean, vmin=vmin_val, vmax=vmax_val, stretch=AsinhStretch()) #normalize &stretch \n", - " slice_image= ax1.imshow(slice_mean, norm=slice_norm, origin='lower', aspect='auto',cmap=cmap) #plot slice\n", + " slice_mean = np.nanmean(cube[(nslice-2):(nslice+2), :, :], axis=0) # Mean of the slice looking in the range (nslice-2):(nslice+2)\n", + " slice_norm=ImageNormalize(slice_mean, vmin=vmin_val, vmax=vmax_val, stretch=AsinhStretch()) # normalize &stretch \n", + " slice_image= ax1.imshow(slice_mean, norm=slice_norm, origin='lower', aspect='auto',cmap=cmap) # plot slice\n", "\n", " cb_image = fig.colorbar(slice_image, fraction=0.046, pad=0.04)\n", - " cb_image.set_label('MJy/sr', labelpad=-1, fontsize = 22)\n", + " cb_image.set_label('MJy/sr', labelpad=-1, fontsize=22)\n", " cb_image.ax.tick_params(labelsize=20)\n", " cb_image.ax.yaxis.get_offset_text().set_fontsize(20)\n", " \n", - " ax1.set_xlabel('RA', fontsize =22)\n", + " ax1.set_xlabel('RA', fontsize=22)\n", " ax1.set_ylabel('DEC', labelpad=-1, fontsize=22)\n", " #ax1.grid(color='white', ls='solid')\n", - " ax1.set_title('Detector {} \\n Grating/Filter: {}/{} \\n {} microns'.format(s3d[0].header['DETECTOR'],s3d[0].header['GRATING'], s3d[0].header['FILTER'], str(slicewave)), fontsize =25)\n", + " ax1.set_title('Detector {} \\n Grating/Filter: {}/{} \\n {} microns'.format(s3d[0].header['DETECTOR'],s3d[0].header['GRATING'], s3d[0].header['FILTER'], str(slicewave)), fontsize=25)\n", " ax1.tick_params(axis='both', which='major', labelsize=20)\n", " ax1.coords[0].set_ticklabel(rotation=13, ha='right', pad=24)\n", "\n", @@ -390,15 +385,15 @@ " ax2.yaxis.set_tick_params(labelsize=20)\n", " ax2.set_aspect(0.5/ax2.get_data_ratio())\n", " \n", - " #-----------------------------------------------Weight Map-------------------------------------------------\n", + " # -----------------------------------------------Weight Map-------------------------------------------------\n", " \n", - " #Corresponding Weight Map (wmap) for Cube Slice\n", + " # Corresponding Weight Map (wmap) for Cube Slice\n", " ax3 = plt.subplot(gs[int(total_num_plots)-np.array(wavelength_slices).size+plot_count], projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", - " #ax3 = plt.subplot(3, len(wavelength_slices), int(total_num_plots)-len(wavelength_slices)+plot_count, projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", + " # ax3 = plt.subplot(3, len(wavelength_slices), int(total_num_plots)-len(wavelength_slices)+plot_count, projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", " \n", - " slice_mean_wmap = np.nanmean(wmap[(nslice-2):(nslice+2), :, :], axis=0) #Mean of the wmap slice looking in the range (nslice-2):(nslice+2)\n", - " slice_norm_wmap=ImageNormalize(slice_mean_wmap, stretch=AsinhStretch()) #normalize &stretch\n", - " slice_wmap = ax3.imshow(slice_mean_wmap, norm=slice_norm_wmap, origin='lower',aspect='auto', cmap=cmap) #plot slice\n", + " slice_mean_wmap = np.nanmean(wmap[(nslice-2):(nslice+2), :, :], axis=0) # Mean of the wmap slice looking in the range (nslice-2):(nslice+2)\n", + " slice_norm_wmap=ImageNormalize(slice_mean_wmap, stretch=AsinhStretch()) # normalize &stretch\n", + " slice_wmap = ax3.imshow(slice_mean_wmap, norm=slice_norm_wmap, origin='lower',aspect='auto', cmap=cmap) # plot slice\n", "\n", " cb_wmap = fig.colorbar(slice_wmap, fraction=0.046, pad=0.04)\n", " cb_wmap.set_label('Weight', labelpad=-1, fontsize = 22)\n", @@ -407,7 +402,7 @@ " \n", " ax3.set_xlabel('RA', fontsize=22)\n", " ax3.set_ylabel('DEC', labelpad=-1, fontsize=22)\n", - " #ax3.grid(color='gray', ls='solid')\n", + " # ax3.grid(color='gray', ls='solid')\n", " ax3.set_title(str(slicewave)+' microns: Weight Map', fontsize=25)\n", " ax3.tick_params(axis='both', which='major', labelsize=20)\n", " ax3.coords[0].set_ticklabel(rotation=13, ha='right', pad=24)\n", @@ -444,14 +439,14 @@ "metadata": {}, "outputs": [], "source": [ - "#To rerun the notebook and all the pipeline steps set runflag=True\n", + "# To rerun the notebook and all the pipeline steps set runflag=True\n", "runflag = True \n", "\n", - "#Demo directory -- contains pre-computed products\n", + "# Demo directory -- contains pre-computed products\n", "if runflag == False:\n", " output_dir = './nirspec_ifu_02729_demo/'\n", "\n", - "#Rerun directory\n", + "# Rerun directory\n", "elif runflag == True:\n", " #If you want to actually re-download the data and run everything offline, \n", " #then comment out this line, set runflag=True, & specify a desired local directory\n", @@ -502,9 +497,9 @@ }, "outputs": [], "source": [ - "#Download data from MAST \n", + "# Download data from MAST \n", "\n", - "#Setup your account \n", + "# Setup your account \n", "\n", "# NOTE:\n", "# The data in this notebook is public and does not require a token.\n", @@ -515,37 +510,37 @@ "\n", "sessioninfo = Observations.session_info()\n", "\n", - "#Define the general search criteria\n", + "# Define the general search criteria\n", "obs = Observations.query_criteria(\n", - " obs_collection = 'JWST',\n", - " instrument_name = ['NIRSPEC/IFU'],\n", - " proposal_id = '02729')\n", + " obs_collection='JWST',\n", + " instrument_name=['NIRSPEC/IFU'],\n", + " proposal_id='02729')\n", "\n", - "#Print the Observations returned from the general serach criteria\n", + "# Print the Observations returned from the general search criteria\n", "products = Observations.get_product_list(obs)\n", - "#print(products)\n", + "# print(products)\n", "\n", - "#Filter the list of observations\n", - "#In this case we look for UNCAL products and ASN files to manually run pipeline stage 1-3\n", - "#We also look for pre-processed MAST products for comparison: RATE (stage 1) & CAL (stage 2&3) & S3D (stage 2&3) & X1D (stage2&3) \n", + "# Filter the list of observations\n", + "# In this case we look for UNCAL products and ASN files to manually run pipeline stage 1-3\n", + "# We also look for pre-processed MAST products for comparison: RATE (stage 1) & CAL (stage 2&3) & S3D (stage 2&3) & X1D (stage2&3) \n", "filtered = Observations.filter_products(products,\n", " productSubGroupDescription=[\"UNCAL\", \"RATE\", \"CAL\", \"S3D\", \"X1D\", \"ASN\"],\n", " mrp_only=False)\n", - "#Print the filtered products \n", + "# Print the filtered products \n", "number = len(filtered)\n", "for k in range(number):\n", " print(filtered['productFilename'][k])\n", "\n", - "#Download the filtered products\n", - "#This creates a mastDownload directory, unless you set flat=True and set a download_dir\n", + "# Download the filtered products\n", + "# This creates a mastDownload directory, unless you set flat=True and set a download_dir\n", "for i in range(len(filtered)):\n", " mast_products_dir = output_dir+'mast_products/'\n", " if not os.path.exists(mast_products_dir):\n", " os.makedirs(mast_products_dir)\n", " if runflag == True:\n", - " Observations.download_products(filtered[i], mrp_only=False, cache=False, flat=True, download_dir=mast_products_dir) #Override any cached files and download the most up-to-date ones\n", + " Observations.download_products(filtered[i], mrp_only=False, cache=False, flat=True, download_dir=mast_products_dir) # Override any cached files and download the most up-to-date ones\n", " else:\n", - " Observations.download_products(filtered[i], mrp_only=False, cache=True, flat=True, download_dir=mast_products_dir) #Find any cached files first before downloading new ones" + " Observations.download_products(filtered[i], mrp_only=False, cache=True, flat=True, download_dir=mast_products_dir) # Find any cached files first before downloading new ones" ] }, { @@ -579,17 +574,17 @@ }, "outputs": [], "source": [ - "#Stage 1 slope products -- level 2a images\n", + "# Stage 1 slope products -- level 2a images\n", "\n", - "#Plot 4th (out of 8) dither position (spectra fall on both NRS1 & NRS2) for GRATING/FILTER G235H/F170LP combination \n", + "# Plot 4th (out of 8) dither position (spectra fall on both NRS1 & NRS2) for GRATING/FILTER G235H/F170LP combination \n", "for rate_file in sorted(glob.glob(mast_products_dir+'*02103*00004_nrs?_rate.fits')):\n", " \n", " ratefile_open = datamodels.open(rate_file)\n", " ratefile_sci = ratefile_open.data #get the pixel data (the SCI extension of the fits file)\n", " ratefile_dq = ratefile_open.dq #data quality map data (DQ extension)\n", " \n", - " #Plot the slope image and small section of the countrate image & corresponding section of the DQ map\n", - " show_image(ratefile_sci, 0,10, units='DN/s',zoom_in=[650,700, 1250,1300], ysize=20, xsize=20,\n", + " # Plot the slope image and small section of the countrate image & corresponding section of the DQ map\n", + " show_image(ratefile_sci, 0, 10, units='DN/s', zoom_in=[650, 700, 1250, 1300], ysize=20, xsize=20,\n", " title='Countrate Image \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", " ratefile_open.meta.dither.position_number, \n", " ratefile_open.meta.instrument.grating,\n", @@ -597,9 +592,9 @@ " \n", " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear',zoom_in=[650,700, 1250,1300], ysize=20, xsize=20,\n", " title='Data Quality Map \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", - " ratefile_open.meta.dither.position_number, \n", - " ratefile_open.meta.instrument.grating,\n", - " ratefile_open.meta.instrument.filter)) \n", + " ratefile_open.meta.dither.position_number, \n", + " ratefile_open.meta.instrument.grating,\n", + " ratefile_open.meta.instrument.filter)) \n", " " ] }, @@ -631,9 +626,9 @@ "metadata": {}, "outputs": [], "source": [ - "#Stage 2 Products -- Calibrated 3-D data cubes for each GRATING/FILTER combination\n", + "# Stage 2 Products -- Calibrated 3-D data cubes for each GRATING/FILTER combination\n", "\n", - "#Plotting the 4th (out of 8) dither position for both NRS1 and NRS2\n", + "# Plotting the 4th (out of 8) dither position for both NRS1 and NRS2\n", "s3d_g140h_stage2 = sorted(glob.glob(mast_products_dir+'*2105*00004_nrs?_s3d.fits'))\n", "s3d_g235h_stage2 = sorted(glob.glob(mast_products_dir+'*2103*00004_nrs?_s3d.fits'))\n", "s3d_g395h_stage2 = sorted(glob.glob(mast_products_dir+'*2101*00004_nrs?_s3d.fits'))\n", @@ -641,23 +636,23 @@ "\n", "title_stage2_mast='Tarantula Nebula \\n Level 2 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", "\n", - "#Characteristics of the plot \n", - "nrs1_wavelengths = [1.0,2.3,3.4] #Wavelength slices (microns) to take from the 3-D data cube\n", - "nrs2_wavelengths = [1.4,2.5,4.0]\n", + "# Characteristics of the plot \n", + "nrs1_wavelengths = [1.0, 2.3, 3.4] # Wavelength slices (microns) to take from the 3-D data cube\n", + "nrs2_wavelengths = [1.4, 2.5, 4.0]\n", "\n", - "nrs1_spaxel_locs = [[30,29],[28,39],[14,25]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", - "nrs2_spaxel_locs = [[30,29],[28,39],[14,25]]\n", + "nrs1_spaxel_locs = [[30, 29], [28, 39], [14, 25]] # Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "nrs2_spaxel_locs = [[30, 29], [28, 39], [14, 25]]\n", "\n", - "nrs1_vmin_vmax = [[0,2e2],[0,1e2],[0,1.2e3]] #Minimum & Maximum signal values for scaling each slice\n", - "nrs2_vmin_vmax = [[0,2e2],[0,1e2],[0,1.2e3]]\n", + "nrs1_vmin_vmax = [[0, 2e2], [0, 1e2], [0, 1.2e3]] # Minimum & Maximum signal values for scaling each slice\n", + "nrs2_vmin_vmax = [[0, 2e2], [0, 1e2], [0, 1.2e3]]\n", "\n", - "nrs1_yscales = [[-80,150], [-80,150], [-80,200]] #Spaxel plot y-limits\n", - "nrs2_yscales = [[-80,200], [-80,150], [-80,200]]\n", + "nrs1_yscales = [[-80, 150], [-80, 150], [-80, 200]] # Spaxel plot y-limits\n", + "nrs2_yscales = [[-80, 200], [-80, 150], [-80, 200]]\n", "\n", - "#Plot using the convience function defined above\n", - "show_ifu_cubeslices(s3d_stage2_list, wavelength_slices=[nrs1_wavelengths,nrs2_wavelengths], \n", - " spaxel_locs=[nrs1_spaxel_locs,nrs2_spaxel_locs],vmin_vmax=[nrs1_vmin_vmax,nrs2_vmin_vmax], \n", - " y_scale = [nrs1_yscales,nrs2_yscales], title=title_stage2_mast)" + "# Plot using the convience function defined above\n", + "show_ifu_cubeslices(s3d_stage2_list, wavelength_slices=[nrs1_wavelengths, nrs2_wavelengths], \n", + " spaxel_locs=[nrs1_spaxel_locs, nrs2_spaxel_locs], vmin_vmax=[nrs1_vmin_vmax, nrs2_vmin_vmax], \n", + " y_scale=[nrs1_yscales, nrs2_yscales], title=title_stage2_mast)" ] }, { @@ -676,22 +671,22 @@ "metadata": {}, "outputs": [], "source": [ - "#Stage 3 Products -- Combined Calibrated 3-D data cubes for each GRATING/FILTER combination\n", + "# Stage 3 Products -- Combined Calibrated 3-D data cubes for each GRATING/FILTER combination\n", "\n", "s3d_stage3_list = sorted(glob.glob(mast_products_dir+'*nirspec*_s3d.fits'))\n", "\n", - "title_stage3_mast='Tarantula Nebula \\n Level 3 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", + "title_stage3_mast = 'Tarantula Nebula \\n Level 3 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", "\n", - "#Characteristics of the plot \n", - "wavelengths = [1.4,2.3,4.0] #Wavelength slices (microns) to take from the combined 3-D data cube\n", - "spaxel_locs = [[30,29],[28,39],[14,25]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", - "vmin_vmax = [[0,2e2],[0,1e2],[0,1.2e3]] #Minimum & Maximum signal values for scaling each slice\n", - "yscales = [[-80,150], [-80,150], [-80,200]] #Spaxel plot y-limits\n", + "# Characteristics of the plot \n", + "wavelengths = [1.4, 2.3, 4.0] # Wavelength slices (microns) to take from the combined 3-D data cube\n", + "spaxel_locs = [[30, 29], [28, 39], [14, 25]] # Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "vmin_vmax = [[0, 2e2], [0, 1e2], [0, 1.2e3]] # Minimum & Maximum signal values for scaling each slice\n", + "yscales = [[-80, 150], [-80, 150], [-80, 200]] # Spaxel plot y-limits\n", "\n", - "#Plot using the convience function defined above\n", - "show_ifu_cubeslices(s3d_stage3_list, wavelength_slices=[wavelengths],spaxel_locs=[spaxel_locs],\n", + "# Plot using the convience function defined above\n", + "show_ifu_cubeslices(s3d_stage3_list, wavelength_slices=[wavelengths], spaxel_locs=[spaxel_locs],\n", " vmin_vmax=[vmin_vmax], \n", - " y_scale = [yscales], title=title_stage3_mast)" + " y_scale=[yscales], title=title_stage3_mast)" ] }, { @@ -712,9 +707,9 @@ "metadata": {}, "outputs": [], "source": [ - "#Stage 3 Products -- Combined Extracted 1-D Spectrum \n", + "# Stage 3 Products -- Combined Extracted 1-D Spectrum \n", "\n", - "fig = plt.figure(figsize=(15,9))\n", + "fig = plt.figure(figsize=(15, 9))\n", "colors = ['darkred', 'darkturquoise', 'blue']\n", "\n", "x1d3_mast_list = sorted(glob.glob(mast_products_dir+'*nirspec*_x1d.fits'))\n", @@ -722,20 +717,20 @@ "for i, x1d3_file in enumerate(x1d3_mast_list):\n", " x1d3_file_open = datamodels.open(x1d3_file) \n", " \n", - " #Wavelength & Surface Brightness Arrays\n", + " # Wavelength & Surface Brightness Arrays\n", " x1d3wave_mast = x1d3_file_open.spec[0].spec_table.WAVELENGTH\n", " x1d3flux_mast = x1d3_file_open.spec[0].spec_table.SURF_BRIGHT\n", " \n", - " plt.plot(x1d3wave_mast,x1d3flux_mast, linewidth =2, color=colors[i], label='Grating/Filter: {}/{}'.format(x1d3_file_open.meta.instrument.grating,\n", + " plt.plot(x1d3wave_mast, x1d3flux_mast, linewidth =2, color=colors[i], label='Grating/Filter: {}/{}'.format(x1d3_file_open.meta.instrument.grating,\n", " x1d3_file_open.meta.instrument.filter))\n", - "#Where wavelength slice was taken above\n", + "# Where wavelength slice was taken above\n", "plt.vlines(1.4, -1e1, 400., 'black', 'dotted', label='1.4 microns', linewidth=5)\n", - "plt.vlines(2.3, -1e1, 400., 'black', 'dotted', label='2.3 microns', linewidth =5)\n", + "plt.vlines(2.3, -1e1, 400., 'black', 'dotted', label='2.3 microns', linewidth=5)\n", "plt.vlines(4.0, -1e1, 400., 'black', 'dotted', label='4.0 microns', linewidth=5)\n", "\n", - "plt.xlabel('$\\lambda [\\mu$m]', fontsize =15)\n", - "plt.ylabel('Surface Brightness (MJy/sr)', fontsize =15)\n", - "plt.title(\"Tarantula Nebula \\n Level 3 IFU Product in MAST: Extracted 1-D Spectrum\", fontsize =20)\n", + "plt.xlabel('$\\lambda [\\mu$m]', fontsize=15)\n", + "plt.ylabel('Surface Brightness (MJy/sr)', fontsize=15)\n", + "plt.title(\"Tarantula Nebula \\n Level 3 IFU Product in MAST: Extracted 1-D Spectrum\", fontsize=20)\n", "plt.ylim(-1e1, 2e2)\n", "plt.legend()" ] @@ -782,7 +777,7 @@ "metadata": {}, "outputs": [], "source": [ - "#Just a check to see what verison of the pipeline and what pmap was used\n", + "# Just a check to see what verison of the pipeline and what pmap was used\n", "x1d3_mast = fits.open(glob.glob(mast_products_dir+'*_x1d.fits')[0])\n", "\n", "print(\"Products found in MAST used JWST calibration pipeline version: {} and {}\".format(x1d3_mast[0].header['CAL_VER']\n", @@ -807,7 +802,7 @@ "metadata": {}, "outputs": [], "source": [ - "#Directory for rerun of stage 1 to avoid overwritting MAST products\n", + "# Directory for rerun of stage 1 to avoid overwritting MAST products\n", "output_dir_rerun = output_dir+'rerun/' \n", "if not os.path.exists(output_dir_rerun):\n", " os.makedirs(output_dir_rerun)" @@ -831,7 +826,7 @@ }, "outputs": [], "source": [ - "#Stage 1 Processing \n", + "# Stage 1 Processing \n", "\n", "if runflag == True:\n", "\n", @@ -840,8 +835,8 @@ " print(\"Applying Stage 1 Corrections & Calibrations to: \"+ os.path.basename(uncal_file))\n", "\n", " result = Detector1Pipeline.call(uncal_file,\n", - " save_results = True,\n", - " output_dir = output_dir_rerun)" + " save_results=True,\n", + " output_dir=output_dir_rerun)" ] }, { @@ -853,9 +848,9 @@ }, "outputs": [], "source": [ - "#Stage 1 slope products -- level 2a images\n", + "# Stage 1 slope products -- level 2a images\n", "\n", - "#Plot 4th (out of 8) dither position (spectra fall on both NRS1 & NRS2) for GRATING/FILTER G235H/F170LP combination \n", + "# Plot 4th (out of 8) dither position (spectra fall on both NRS1 & NRS2) for GRATING/FILTER G235H/F170LP combination \n", "for rate_file in sorted(glob.glob(output_dir_rerun+'*02103*00004_nrs?_rate.fits')):\n", " \n", " ratefile_open = datamodels.open(rate_file)\n", @@ -863,17 +858,17 @@ " ratefile_dq = ratefile_open.dq #data quality map data (DQ extension)\n", " \n", " #Plot the slop image and zoom in on a small section of the countrate image & corresponding section of the DQ map\n", - " show_image(ratefile_sci, 0,10, units='DN/s',zoom_in=[650,700, 1250,1300], ysize=20, xsize=20,\n", + " show_image(ratefile_sci, 0, 10, units='DN/s',zoom_in=[650, 700, 1250, 1300], ysize=20, xsize=20,\n", " title='Countrate Image \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", " ratefile_open.meta.dither.position_number, \n", " ratefile_open.meta.instrument.grating,\n", " ratefile_open.meta.instrument.filter)) #rate files have units of DN/s\n", " \n", - " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear',zoom_in=[650,700, 1250,1300], ysize=20, xsize=20,\n", + " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear', zoom_in=[650,700, 1250,1300], ysize=20, xsize=20,\n", " title='Data Quality Map \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", - " ratefile_open.meta.dither.position_number, \n", - " ratefile_open.meta.instrument.grating,\n", - " ratefile_open.meta.instrument.filter)) \n", + " ratefile_open.meta.dither.position_number, \n", + " ratefile_open.meta.instrument.grating,\n", + " ratefile_open.meta.instrument.filter)) \n", " " ] }, @@ -916,7 +911,7 @@ }, "outputs": [], "source": [ - "#Stage 2 Processing \n", + "# Stage 2 Processing \n", "\n", "if runflag == True:\n", " \n", @@ -926,8 +921,8 @@ " print(\"Applying Stage 2 Calibrations & Corrections to: \"+ os.path.basename(rate_file))\n", " \n", " result = Spec2Pipeline.call(rate_file,\n", - " save_results = True,\n", - " output_dir = output_dir_rerun) " + " save_results=True,\n", + " output_dir=output_dir_rerun) " ] }, { @@ -939,30 +934,30 @@ }, "outputs": [], "source": [ - "#Stage 2 Products -- Calibrated 3-D data cubes for each GRATING/FILTER combination\n", + "# Stage 2 Products -- Calibrated 3-D data cubes for each GRATING/FILTER combination\n", "\n", - "#Plotting the 4th (out of 8) dither position for both NRS1 and NRS2\n", + "# Plotting the 4th (out of 8) dither position for both NRS1 and NRS2\n", "s3d_stage2_list = sorted(glob.glob(output_dir_rerun+'*2103*00004_nrs?_s3d.fits'))\n", "\n", "title_stage2_rerun='Tarantula Nebula \\n Level 2 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", "\n", - "#Characteristics of the plot \n", - "nrs1_wavelengths = [2.3] #Wavelength slices (microns) to take from the 3-D data cube\n", + "# Characteristics of the plot \n", + "nrs1_wavelengths = [2.3] # Wavelength slices (microns) to take from the 3-D data cube\n", "nrs2_wavelengths = [2.5]\n", "\n", - "nrs1_spaxel_locs = [[28,39]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", - "nrs2_spaxel_locs = [[28,39]]\n", + "nrs1_spaxel_locs = [[28, 39]] # Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "nrs2_spaxel_locs = [[28, 39]]\n", "\n", - "nrs1_vmin_vmax = [[0,1e2]] #Minimum & Maximum signal values for scaling each slice\n", - "nrs2_vmin_vmax = [[0,1e2]]\n", + "nrs1_vmin_vmax = [[0, 1e2]] # Minimum & Maximum signal values for scaling each slice\n", + "nrs2_vmin_vmax = [[0, 1e2]]\n", "\n", - "nrs1_yscales = [[-80,150]] #Spaxel plot y-limits\n", - "nrs2_yscales = [[-80,150]]\n", + "nrs1_yscales = [[-80, 150]] # Spaxel plot y-limits\n", + "nrs2_yscales = [[-80, 150]]\n", "\n", - "#Plot using the convience function defined above\n", - "show_ifu_cubeslices(s3d_stage2_list, wavelength_slices=[nrs1_wavelengths,nrs2_wavelengths], \n", - " spaxel_locs=[nrs1_spaxel_locs,nrs2_spaxel_locs],vmin_vmax=[nrs1_vmin_vmax,nrs2_vmin_vmax], \n", - " y_scale = [nrs1_yscales,nrs2_yscales], title=title_stage2_rerun)" + "# Plot using the convience function defined above\n", + "show_ifu_cubeslices(s3d_stage2_list, wavelength_slices=[nrs1_wavelengths, nrs2_wavelengths], \n", + " spaxel_locs=[nrs1_spaxel_locs, nrs2_spaxel_locs], vmin_vmax=[nrs1_vmin_vmax, nrs2_vmin_vmax], \n", + " y_scale=[nrs1_yscales, nrs2_yscales], title=title_stage2_rerun)" ] }, { @@ -989,7 +984,7 @@ }, "outputs": [], "source": [ - "#Copy ASN file from MAST (for G235H/F170LP) into the stage 1 rerun directory\n", + "# Copy ASN file from MAST (for G235H/F170LP) into the stage 1 rerun directory\n", "\n", "asnfile_mast = glob.glob(mast_products_dir+'*_spec3_00001_asn.json')[0] #ASN file found in MAST\n", "\n", @@ -997,7 +992,7 @@ "if not os.path.exists(asnfile_rerun):\n", " copy(asnfile_mast, asnfile_rerun)\n", " \n", - "#Check the ASN file contents\n", + "# Check the ASN file contents\n", "with open(asnfile_rerun, 'r') as f_obj:\n", " asnfile_rerun_data = json.load(f_obj)\n", " \n", @@ -1039,13 +1034,13 @@ }, "outputs": [], "source": [ - "#Rerun stage 3 with outlier detection off\n", + "# Rerun stage 3 with outlier detection off\n", "if runflag==True:\n", " \n", " result = Spec3Pipeline.call(asnfile_rerun,\n", - " save_results = True,\n", - " output_dir = output_dir_rerun,\n", - " steps = {\"outlier_detection\":{\"skip\": False,\n", + " save_results=True,\n", + " output_dir=output_dir_rerun,\n", + " steps={\"outlier_detection\": {\"skip\": False,\n", " \"save_results\": True,\n", " \"kernel_size\": '3 3'}})" ] @@ -1067,12 +1062,12 @@ "\n", "#Characteristics of the plot \n", "wavelengths = [2.3] #Wavelength slices (microns) to take from the combined 3-D data cube\n", - "spaxel_locs = [[28,39]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", - "vmin_vmax = [[0,1e2]] #Minimum & Maximum signal values for scaling each slice\n", - "yscales = [[-80,150]] #Spaxel plot y-limits\n", + "spaxel_locs = [[28, 39]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "vmin_vmax = [[0, 1e2]] #Minimum & Maximum signal values for scaling each slice\n", + "yscales = [[-80, 150]] #Spaxel plot y-limits\n", "\n", "#Plot using the convience function defined above\n", - "show_ifu_cubeslices(s3d_stage3_list, wavelength_slices=[wavelengths],spaxel_locs=[spaxel_locs],\n", + "show_ifu_cubeslices(s3d_stage3_list, wavelength_slices=[wavelengths], spaxel_locs=[spaxel_locs],\n", " vmin_vmax=[vmin_vmax], \n", " y_scale = [yscales], title=title_stage3_rerun, title_font=20)" ] @@ -1096,7 +1091,7 @@ "metadata": {}, "outputs": [], "source": [ - "#Stage 3 Products -- Combined Extracted 1-D Spectrum \n", + "# Stage 3 Products -- Combined Extracted 1-D Spectrum \n", "\n", "fig = plt.figure(figsize=(15,9))\n", "colors = ['darkred', 'darkturquoise', 'blue']\n", @@ -1106,13 +1101,13 @@ "for i, x1d3_file in enumerate(x1d3_rerun_list):\n", " x1d3_file_open = datamodels.open(x1d3_file) \n", " \n", - " #Wavelength & Surface Brightness Arrays\n", + " # Wavelength & Surface Brightness Arrays\n", " x1d3wave_rerun = x1d3_file_open.spec[0].spec_table.WAVELENGTH\n", " x1d3flux_rerun = x1d3_file_open.spec[0].spec_table.SURF_BRIGHT\n", " \n", - " plt.plot(x1d3wave_rerun,x1d3flux_rerun, linewidth =2, color=colors[i], label='Grating/Filter: {}/{}'.format(x1d3_file_open.meta.instrument.grating,\n", + " plt.plot(x1d3wave_rerun,x1d3flux_rerun, linewidth=2, color=colors[i], label='Grating/Filter: {}/{}'.format(x1d3_file_open.meta.instrument.grating,\n", " x1d3_file_open.meta.instrument.filter))\n", - "#Where wavelength slice was taken above\n", + "# Where wavelength slice was taken above\n", "plt.vlines(2.3, -1e1, 400., 'black', 'dotted', label='2.3 microns', linewidth =5)\n", "\n", "plt.xlabel('$\\lambda [\\mu$m]', fontsize =15)\n", @@ -1163,7 +1158,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.11.6" } }, "nbformat": 4, From f34721d6a267c57e2eb4f9d21f6c0221a743a7f9 Mon Sep 17 00:00:00 2001 From: Hatice Karatay Date: Wed, 4 Oct 2023 10:13:59 -0400 Subject: [PATCH 03/12] Fix additional style errors on the 02729 nb --- .../ero_nirspec_ifu_02729_demo.ipynb | 154 +++++++++--------- 1 file changed, 74 insertions(+), 80 deletions(-) diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb index 58fd4d1cb..1faf0938c 100644 --- a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb @@ -167,7 +167,7 @@ " cmap: str\n", " Color Map for plot\n", " \"\"\"\n", - " #-----------------------------------------Scaling Information----------------------------------------\n", + " # -----------------------------------------Scaling Information----------------------------------------\n", " \n", " if scale == 'log':\n", " norm = ImageNormalize(data_2d, interval=ManualInterval(vmin=vmin, vmax=vmax),\n", @@ -248,9 +248,9 @@ " Save figure? \n", " \"\"\"\n", " \n", - " #---------------------------------------------- Set-up Figure -------------------------------------------------\n", + " # ---------------------------------------------- Set-up Figure -------------------------------------------------\n", "\n", - " #Plot Slices From the Cube\n", + " # Plot Slices From the Cube\n", " fig, axs = plt.subplots(3, np.array(wavelength_slices).size, figsize=(8*np.array(wavelength_slices).size, 18))\n", " gs = grd.GridSpec(3, np.array(wavelength_slices).size, height_ratios=[1]*3, width_ratios=[1]*np.array(wavelength_slices).size, hspace=0.4, wspace=0.7)\n", "\n", @@ -272,11 +272,10 @@ " \n", " # --------------------------------------Data & Header Information------------------------------------------\n", "\n", - " \n", " # SCI Extension: [Type:ImageHDU Cards:92 Dimensions:(57, 61, 973) Format:float32]\n", - " cube = s3d[1].data #Science data\n", + " cube = s3d[1].data # Science data\n", " wcs = WCS(s3d[1].header) # World Coordinate System (WCS) Transformation keywords \n", - " wmap = s3d[4].data #3-D weight image giving the relative weights of the output spaxels.\n", + " wmap = s3d[4].data # 3-D weight image giving the relative weights of the output spaxels.\n", " cdelt1 = s3d[1].header['CDELT1']*3600. # Axis 1 coordinate increment at reference point \n", " cdelt2 = s3d[1].header['CDELT2']*3600. # Axis 2 coordinate increment at reference point \n", " cdelt3 = s3d[1].header['CDELT3'] # Axis 3 coordinate increment at reference point \n", @@ -316,11 +315,10 @@ " if y_scale:\n", " y_scales = y_scale[0]\n", "\n", - " \n", " # Loop through each wavelength slices\n", " for i, wave_slice in enumerate(wavelengths):\n", "\n", - " if float(wavstart)<=wave_slice*10**-6<=float(wavend):\n", + " if float(wavstart) <= wave_slice*10**-6 <= float(wavend):\n", " \n", " # --------------------------------------------2-D Cube Slice------------------------------------------------\n", " \n", @@ -338,8 +336,8 @@ " # ax1 = plt.subplot(3,len(wavelength_slices), 0+plot_count, projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", "\n", " slice_mean = np.nanmean(cube[(nslice-2):(nslice+2), :, :], axis=0) # Mean of the slice looking in the range (nslice-2):(nslice+2)\n", - " slice_norm=ImageNormalize(slice_mean, vmin=vmin_val, vmax=vmax_val, stretch=AsinhStretch()) # normalize &stretch \n", - " slice_image= ax1.imshow(slice_mean, norm=slice_norm, origin='lower', aspect='auto',cmap=cmap) # plot slice\n", + " slice_norm = ImageNormalize(slice_mean, vmin=vmin_val, vmax=vmax_val, stretch=AsinhStretch()) # normalize &stretch \n", + " slice_image = ax1.imshow(slice_mean, norm=slice_norm, origin='lower', aspect='auto', cmap=cmap) # plot slice\n", "\n", " cb_image = fig.colorbar(slice_image, fraction=0.046, pad=0.04)\n", " cb_image.set_label('MJy/sr', labelpad=-1, fontsize=22)\n", @@ -348,33 +346,33 @@ " \n", " ax1.set_xlabel('RA', fontsize=22)\n", " ax1.set_ylabel('DEC', labelpad=-1, fontsize=22)\n", - " #ax1.grid(color='white', ls='solid')\n", - " ax1.set_title('Detector {} \\n Grating/Filter: {}/{} \\n {} microns'.format(s3d[0].header['DETECTOR'],s3d[0].header['GRATING'], s3d[0].header['FILTER'], str(slicewave)), fontsize=25)\n", + " # ax1.grid(color='white', ls='solid')\n", + " ax1.set_title('Detector {} \\n Grating/Filter: {}/{} \\n {} microns'.format(s3d[0].header['DETECTOR'], s3d[0].header['GRATING'], s3d[0].header['FILTER'], str(slicewave)), fontsize=25)\n", " ax1.tick_params(axis='both', which='major', labelsize=20)\n", " ax1.coords[0].set_ticklabel(rotation=13, ha='right', pad=24)\n", "\n", " \n", - " #------------------------------------------Spaxel 1-D Spectrum---------------------------------------------\n", + " # ------------------------------------------Spaxel 1-D Spectrum---------------------------------------------\n", " \n", - " #Zoom in on a Spaxel: Spectrum\n", - " loc = [spaxel_loc[i][0],spaxel_loc[i][1]]\n", + " # Zoom in on a Spaxel: Spectrum\n", + " loc = [spaxel_loc[i][0], spaxel_loc[i][1]]\n", " x1d3flux_loc = cube[:, loc[1], loc[0]]\n", - " #ax2 = plt.subplot(3,len(wavelength_slices), int(total_num_plots/3)+plot_count)\n", + " # ax2 = plt.subplot(3,len(wavelength_slices), int(total_num_plots/3)+plot_count)\n", " ax2 = plt.subplot(gs[int(total_num_plots/3)+plot_count])\n", "\n", - " #Spaxel Box Highlight \n", - " spaxel_rect = plt.Rectangle((loc[0]-.5, loc[1]-.5), 1,1, fill=False, color='black', linewidth=2)\n", + " # Spaxel Box Highlight \n", + " spaxel_rect = plt.Rectangle((loc[0]-.5, loc[1]-.5), 1, 1, fill=False, color='black', linewidth=2)\n", " ax1.add_patch(spaxel_rect)\n", " \n", " ax2.plot(x1d3wave, x1d3flux_loc, linewidth=1, color=colors[i])\n", " ax2.grid(linewidth=2)\n", - " ax2.set_xlabel('$\\u03BB [\\u03BC$m]',fontsize=22)\n", - " ax2.set_ylabel(\"Surface Brightness \\n (MJy/sr)\",fontsize=22)\n", + " ax2.set_xlabel('$\\u03BB [\\u03BC$m]', fontsize=22)\n", + " ax2.set_ylabel(\"Surface Brightness \\n (MJy/sr)\", fontsize=22)\n", " ax2.set_title('Spaxel at (x, y)='+repr(loc), fontsize=25)\n", " ax2.tick_params(axis='both', which='major', labelsize=20)\n", " ax2.yaxis.get_offset_text().set_fontsize(15)\n", " \n", - " #Scale Information\n", + " # Scale Information\n", " if y_scale:\n", " ymin, ymax = y_scales[i][0], y_scales[i][1]\n", " else:\n", @@ -388,12 +386,12 @@ " # -----------------------------------------------Weight Map-------------------------------------------------\n", " \n", " # Corresponding Weight Map (wmap) for Cube Slice\n", - " ax3 = plt.subplot(gs[int(total_num_plots)-np.array(wavelength_slices).size+plot_count], projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", - " # ax3 = plt.subplot(3, len(wavelength_slices), int(total_num_plots)-len(wavelength_slices)+plot_count, projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", + " ax3 = plt.subplot(gs[int(total_num_plots)-np.array(wavelength_slices).size+plot_count], projection=wcs, slices=('x', 'y', nslice)) # set up the subplot space\n", + " # ax3 = plt.subplot(3, len(wavelength_slices), int(total_num_plots)-len(wavelength_slices)+plot_count, projection=wcs, slices=('x', 'y', nslice)) # set up the subplot space\n", " \n", " slice_mean_wmap = np.nanmean(wmap[(nslice-2):(nslice+2), :, :], axis=0) # Mean of the wmap slice looking in the range (nslice-2):(nslice+2)\n", - " slice_norm_wmap=ImageNormalize(slice_mean_wmap, stretch=AsinhStretch()) # normalize &stretch\n", - " slice_wmap = ax3.imshow(slice_mean_wmap, norm=slice_norm_wmap, origin='lower',aspect='auto', cmap=cmap) # plot slice\n", + " slice_norm_wmap = ImageNormalize(slice_mean_wmap, stretch=AsinhStretch()) # normalize &stretch\n", + " slice_wmap = ax3.imshow(slice_mean_wmap, norm=slice_norm_wmap, origin='lower', aspect='auto', cmap=cmap) # plot slice\n", "\n", " cb_wmap = fig.colorbar(slice_wmap, fraction=0.046, pad=0.04)\n", " cb_wmap.set_label('Weight', labelpad=-1, fontsize = 22)\n", @@ -418,9 +416,9 @@ " \n", " fig.tight_layout(rect=[0, 0, 0.98, 0.98])\n", "\n", - " if save_figure == True:\n", - " fig.savefig(root+\".png\",dpi=24, bbox_inches=\"tight\")\n", - " " + " if save_figure:\n", + " fig.savefig(root+\".png\", dpi=24, bbox_inches=\"tight\")\n", + "\n" ] }, { @@ -443,13 +441,13 @@ "runflag = True \n", "\n", "# Demo directory -- contains pre-computed products\n", - "if runflag == False:\n", + "if not runflag:\n", " output_dir = './nirspec_ifu_02729_demo/'\n", "\n", "# Rerun directory\n", - "elif runflag == True:\n", - " #If you want to actually re-download the data and run everything offline, \n", - " #then comment out this line, set runflag=True, & specify a desired local directory\n", + "elif runflag:\n", + " # If you want to actually re-download the data and run everything offline, \n", + " # then comment out this line, set runflag=True, & specify a desired local directory\n", " output_dir = './nirspec_ifu_02729_rerun/'\n", " if not os.path.exists(output_dir):\n", " os.makedirs(output_dir)" @@ -537,7 +535,7 @@ " mast_products_dir = output_dir+'mast_products/'\n", " if not os.path.exists(mast_products_dir):\n", " os.makedirs(mast_products_dir)\n", - " if runflag == True:\n", + " if runflag:\n", " Observations.download_products(filtered[i], mrp_only=False, cache=False, flat=True, download_dir=mast_products_dir) # Override any cached files and download the most up-to-date ones\n", " else:\n", " Observations.download_products(filtered[i], mrp_only=False, cache=True, flat=True, download_dir=mast_products_dir) # Find any cached files first before downloading new ones" @@ -580,8 +578,8 @@ "for rate_file in sorted(glob.glob(mast_products_dir+'*02103*00004_nrs?_rate.fits')):\n", " \n", " ratefile_open = datamodels.open(rate_file)\n", - " ratefile_sci = ratefile_open.data #get the pixel data (the SCI extension of the fits file)\n", - " ratefile_dq = ratefile_open.dq #data quality map data (DQ extension)\n", + " ratefile_sci = ratefile_open.data # get the pixel data (the SCI extension of the fits file)\n", + " ratefile_dq = ratefile_open.dq # data quality map data (DQ extension)\n", " \n", " # Plot the slope image and small section of the countrate image & corresponding section of the DQ map\n", " show_image(ratefile_sci, 0, 10, units='DN/s', zoom_in=[650, 700, 1250, 1300], ysize=20, xsize=20,\n", @@ -590,12 +588,11 @@ " ratefile_open.meta.instrument.grating,\n", " ratefile_open.meta.instrument.filter)) #rate files have units of DN/s\n", " \n", - " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear',zoom_in=[650,700, 1250,1300], ysize=20, xsize=20,\n", + " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear', zoom_in=[650, 700, 1250, 1300], ysize=20, xsize=20,\n", " title='Data Quality Map \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", " ratefile_open.meta.dither.position_number, \n", " ratefile_open.meta.instrument.grating,\n", - " ratefile_open.meta.instrument.filter)) \n", - " " + " ratefile_open.meta.instrument.filter)) " ] }, { @@ -627,14 +624,13 @@ "outputs": [], "source": [ "# Stage 2 Products -- Calibrated 3-D data cubes for each GRATING/FILTER combination\n", - "\n", "# Plotting the 4th (out of 8) dither position for both NRS1 and NRS2\n", "s3d_g140h_stage2 = sorted(glob.glob(mast_products_dir+'*2105*00004_nrs?_s3d.fits'))\n", "s3d_g235h_stage2 = sorted(glob.glob(mast_products_dir+'*2103*00004_nrs?_s3d.fits'))\n", "s3d_g395h_stage2 = sorted(glob.glob(mast_products_dir+'*2101*00004_nrs?_s3d.fits'))\n", "s3d_stage2_list = s3d_g140h_stage2+s3d_g235h_stage2+s3d_g395h_stage2\n", "\n", - "title_stage2_mast='Tarantula Nebula \\n Level 2 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", + "title_stage2_mast = 'Tarantula Nebula \\n Level 2 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", "\n", "# Characteristics of the plot \n", "nrs1_wavelengths = [1.0, 2.3, 3.4] # Wavelength slices (microns) to take from the 3-D data cube\n", @@ -721,14 +717,14 @@ " x1d3wave_mast = x1d3_file_open.spec[0].spec_table.WAVELENGTH\n", " x1d3flux_mast = x1d3_file_open.spec[0].spec_table.SURF_BRIGHT\n", " \n", - " plt.plot(x1d3wave_mast, x1d3flux_mast, linewidth =2, color=colors[i], label='Grating/Filter: {}/{}'.format(x1d3_file_open.meta.instrument.grating,\n", - " x1d3_file_open.meta.instrument.filter))\n", + " plt.plot(x1d3wave_mast, x1d3flux_mast, linewidth=2, color=colors[i], label='Grating/Filter: {}/{}'.format(x1d3_file_open.meta.instrument.grating,\n", + " x1d3_file_open.meta.instrument.filter))\n", "# Where wavelength slice was taken above\n", "plt.vlines(1.4, -1e1, 400., 'black', 'dotted', label='1.4 microns', linewidth=5)\n", "plt.vlines(2.3, -1e1, 400., 'black', 'dotted', label='2.3 microns', linewidth=5)\n", "plt.vlines(4.0, -1e1, 400., 'black', 'dotted', label='4.0 microns', linewidth=5)\n", "\n", - "plt.xlabel('$\\lambda [\\mu$m]', fontsize=15)\n", + "plt.xlabel(r'$\\lambda [\\mu$m]', fontsize=15)\n", "plt.ylabel('Surface Brightness (MJy/sr)', fontsize=15)\n", "plt.title(\"Tarantula Nebula \\n Level 3 IFU Product in MAST: Extracted 1-D Spectrum\", fontsize=20)\n", "plt.ylim(-1e1, 2e2)\n", @@ -780,8 +776,7 @@ "# Just a check to see what verison of the pipeline and what pmap was used\n", "x1d3_mast = fits.open(glob.glob(mast_products_dir+'*_x1d.fits')[0])\n", "\n", - "print(\"Products found in MAST used JWST calibration pipeline version: {} and {}\".format(x1d3_mast[0].header['CAL_VER']\n", - ",x1d3_mast[0].header['CRDS_CTX']))" + "print(\"Products found in MAST used JWST calibration pipeline version: {} and {}\".format(x1d3_mast[0].header['CAL_VER'], x1d3_mast[0].header['CRDS_CTX']))" ] }, { @@ -828,11 +823,11 @@ "source": [ "# Stage 1 Processing \n", "\n", - "if runflag == True:\n", + "if runflag:\n", "\n", " for uncal_file in sorted(glob.glob(mast_products_dir+'*02103*nrs?_uncal.fits')): \n", "\n", - " print(\"Applying Stage 1 Corrections & Calibrations to: \"+ os.path.basename(uncal_file))\n", + " print(\"Applying Stage 1 Corrections & Calibrations to: \" + os.path.basename(uncal_file))\n", "\n", " result = Detector1Pipeline.call(uncal_file,\n", " save_results=True,\n", @@ -854,22 +849,21 @@ "for rate_file in sorted(glob.glob(output_dir_rerun+'*02103*00004_nrs?_rate.fits')):\n", " \n", " ratefile_open = datamodels.open(rate_file)\n", - " ratefile_sci = ratefile_open.data #get the pixel data (the SCI extension of the fits file)\n", - " ratefile_dq = ratefile_open.dq #data quality map data (DQ extension)\n", + " ratefile_sci = ratefile_open.data # get the pixel data (the SCI extension of the fits file)\n", + " ratefile_dq = ratefile_open.dq # data quality map data (DQ extension)\n", " \n", - " #Plot the slop image and zoom in on a small section of the countrate image & corresponding section of the DQ map\n", - " show_image(ratefile_sci, 0, 10, units='DN/s',zoom_in=[650, 700, 1250, 1300], ysize=20, xsize=20,\n", + " # Plot the slop image and zoom in on a small section of the countrate image & corresponding section of the DQ map\n", + " show_image(ratefile_sci, 0, 10, units='DN/s', zoom_in=[650, 700, 1250, 1300], ysize=20, xsize=20,\n", " title='Countrate Image \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", " ratefile_open.meta.dither.position_number, \n", " ratefile_open.meta.instrument.grating,\n", - " ratefile_open.meta.instrument.filter)) #rate files have units of DN/s\n", + " ratefile_open.meta.instrument.filter)) # rate files have units of DN/s\n", " \n", - " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear', zoom_in=[650,700, 1250,1300], ysize=20, xsize=20,\n", + " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear', zoom_in=[650, 700, 1250, 1300], ysize=20, xsize=20,\n", " title='Data Quality Map \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", " ratefile_open.meta.dither.position_number, \n", " ratefile_open.meta.instrument.grating,\n", - " ratefile_open.meta.instrument.filter)) \n", - " " + " ratefile_open.meta.instrument.filter)) " ] }, { @@ -913,12 +907,12 @@ "source": [ "# Stage 2 Processing \n", "\n", - "if runflag == True:\n", + "if runflag:\n", " \n", - " #Process each rate file seperately \n", + " # Process each rate file seperately \n", " for rate_file in sorted(glob.glob(output_dir_rerun+'*0000?*nrs?*rate.fits')):\n", " \n", - " print(\"Applying Stage 2 Calibrations & Corrections to: \"+ os.path.basename(rate_file))\n", + " print(\"Applying Stage 2 Calibrations & Corrections to: \" + os.path.basename(rate_file))\n", " \n", " result = Spec2Pipeline.call(rate_file,\n", " save_results=True,\n", @@ -939,7 +933,7 @@ "# Plotting the 4th (out of 8) dither position for both NRS1 and NRS2\n", "s3d_stage2_list = sorted(glob.glob(output_dir_rerun+'*2103*00004_nrs?_s3d.fits'))\n", "\n", - "title_stage2_rerun='Tarantula Nebula \\n Level 2 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", + "title_stage2_rerun = 'Tarantula Nebula \\n Level 2 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", "\n", "# Characteristics of the plot \n", "nrs1_wavelengths = [2.3] # Wavelength slices (microns) to take from the 3-D data cube\n", @@ -986,9 +980,9 @@ "source": [ "# Copy ASN file from MAST (for G235H/F170LP) into the stage 1 rerun directory\n", "\n", - "asnfile_mast = glob.glob(mast_products_dir+'*_spec3_00001_asn.json')[0] #ASN file found in MAST\n", + "asnfile_mast = glob.glob(mast_products_dir+'*_spec3_00001_asn.json')[0] # ASN file found in MAST\n", "\n", - "asnfile_rerun = output_dir_rerun+os.path.basename(asnfile_mast) #New ASN file path\n", + "asnfile_rerun = output_dir_rerun+os.path.basename(asnfile_mast) # New ASN file path\n", "if not os.path.exists(asnfile_rerun):\n", " copy(asnfile_mast, asnfile_rerun)\n", " \n", @@ -1035,14 +1029,14 @@ "outputs": [], "source": [ "# Rerun stage 3 with outlier detection off\n", - "if runflag==True:\n", + "if runflag:\n", " \n", " result = Spec3Pipeline.call(asnfile_rerun,\n", " save_results=True,\n", " output_dir=output_dir_rerun,\n", " steps={\"outlier_detection\": {\"skip\": False,\n", - " \"save_results\": True,\n", - " \"kernel_size\": '3 3'}})" + " \"save_results\": True,\n", + " \"kernel_size\": '3 3'}})" ] }, { @@ -1054,22 +1048,22 @@ }, "outputs": [], "source": [ - "#Stage 3 Products -- Combined Calibrated 3-D data cubes for GRATING/FILTER combination G235H/F170LP\n", + "# Stage 3 Products -- Combined Calibrated 3-D data cubes for GRATING/FILTER combination G235H/F170LP\n", "\n", "s3d_stage3_list = sorted(glob.glob(output_dir_rerun+'*nirspec*_s3d.fits'))\n", "\n", - "title_stage3_rerun='Tarantula Nebula \\n Level 3 IFU Product: \\n 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", + "title_stage3_rerun = 'Tarantula Nebula \\n Level 3 IFU Product: \\n 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", "\n", - "#Characteristics of the plot \n", - "wavelengths = [2.3] #Wavelength slices (microns) to take from the combined 3-D data cube\n", - "spaxel_locs = [[28, 39]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", - "vmin_vmax = [[0, 1e2]] #Minimum & Maximum signal values for scaling each slice\n", - "yscales = [[-80, 150]] #Spaxel plot y-limits\n", + "# Characteristics of the plot \n", + "wavelengths = [2.3] # Wavelength slices (microns) to take from the combined 3-D data cube\n", + "spaxel_locs = [[28, 39]] # Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "vmin_vmax = [[0, 1e2]] # Minimum & Maximum signal values for scaling each slice\n", + "yscales = [[-80, 150]] # Spaxel plot y-limits\n", "\n", - "#Plot using the convience function defined above\n", + "# Plot using the convience function defined above\n", "show_ifu_cubeslices(s3d_stage3_list, wavelength_slices=[wavelengths], spaxel_locs=[spaxel_locs],\n", " vmin_vmax=[vmin_vmax], \n", - " y_scale = [yscales], title=title_stage3_rerun, title_font=20)" + " y_scale=[yscales], title=title_stage3_rerun, title_font=20)" ] }, { @@ -1093,7 +1087,7 @@ "source": [ "# Stage 3 Products -- Combined Extracted 1-D Spectrum \n", "\n", - "fig = plt.figure(figsize=(15,9))\n", + "fig = plt.figure(figsize=(15, 9))\n", "colors = ['darkred', 'darkturquoise', 'blue']\n", "\n", "x1d3_rerun_list = sorted(glob.glob(output_dir_rerun+'*nirspec*_x1d.fits'))\n", @@ -1105,14 +1099,14 @@ " x1d3wave_rerun = x1d3_file_open.spec[0].spec_table.WAVELENGTH\n", " x1d3flux_rerun = x1d3_file_open.spec[0].spec_table.SURF_BRIGHT\n", " \n", - " plt.plot(x1d3wave_rerun,x1d3flux_rerun, linewidth=2, color=colors[i], label='Grating/Filter: {}/{}'.format(x1d3_file_open.meta.instrument.grating,\n", - " x1d3_file_open.meta.instrument.filter))\n", + " plt.plot(x1d3wave_rerun, x1d3flux_rerun, linewidth=2, color=colors[i], label='Grating/Filter: {}/{}'.format(x1d3_file_open.meta.instrument.grating,\n", + " x1d3_file_open.meta.instrument.filter))\n", "# Where wavelength slice was taken above\n", - "plt.vlines(2.3, -1e1, 400., 'black', 'dotted', label='2.3 microns', linewidth =5)\n", + "plt.vlines(2.3, -1e1, 400., 'black', 'dotted', label='2.3 microns', linewidth=5)\n", "\n", - "plt.xlabel('$\\lambda [\\mu$m]', fontsize =15)\n", - "plt.ylabel('Surface Brightness (MJy/sr)', fontsize =15)\n", - "plt.title(\"Tarantula Nebula \\n Level 3 IFU Product in MAST: Extracted 1-D Spectrum\", fontsize =20)\n", + "plt.xlabel(r'$\\lambda [\\mu$m]', fontsize=15)\n", + "plt.ylabel('Surface Brightness (MJy/sr)', fontsize=15)\n", + "plt.title(\"Tarantula Nebula \\n Level 3 IFU Product in MAST: Extracted 1-D Spectrum\", fontsize=20)\n", "plt.ylim(-1e1, 2e2)\n", "plt.legend()" ] From b71cc01112cd8a743c8fb0a523792b73a26c732c Mon Sep 17 00:00:00 2001 From: Hatice Karatay Date: Wed, 4 Oct 2023 10:35:40 -0400 Subject: [PATCH 04/12] Address some of the style errors on 02732 ps nb --- ...o_nirspec_ifu_02732_demo_pointsource.ipynb | 357 +++++++++--------- 1 file changed, 177 insertions(+), 180 deletions(-) diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb index e425cbdd0..375e788df 100644 --- a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb @@ -64,28 +64,28 @@ "metadata": {}, "outputs": [], "source": [ - "#Import Library\n", + "# Import Library\n", "\n", - "#--------------------------------------JWST Calibration Pipeline Imports-------------------------------------------\n", + "# --------------------------------------JWST Calibration Pipeline Imports-------------------------------------------\n", "\n", "import jwst\n", "import crds\n", "from jwst import datamodels\n", - "from jwst.pipeline import Detector1Pipeline #calwebb_detector1\n", - "from jwst.pipeline import Spec2Pipeline #calwebb_spec2\n", - "from jwst.pipeline import Spec3Pipeline #calwebb_spec3\n", - "from jwst.extract_1d import Extract1dStep #Extract1D Individual Step\n", + "from jwst.pipeline import Detector1Pipeline # calwebb_detector1\n", + "from jwst.pipeline import Spec2Pipeline # calwebb_spec2\n", + "from jwst.pipeline import Spec3Pipeline # calwebb_spec3\n", + "from jwst.extract_1d import Extract1dStep # Extract1D Individual Step\n", "\n", "print(\"JWST Calibration Pipeline Version={}\".format(jwst.__version__))\n", "print(\"Current Operational CRDS Context = {}\".format(crds.get_default_context()))\n", "\n", - "#----------------------------------------------General Imports-----------------------------------------------------\n", + "# ----------------------------------------------General Imports-----------------------------------------------------\n", "\n", "import numpy as np\n", "import warnings\n", "warnings.filterwarnings('ignore') #Set to 'default' to turn warnings back on\n", "\n", - "#--------------------------------------------File Operation Imports------------------------------------------------\n", + "# --------------------------------------------File Operation Imports------------------------------------------------\n", "\n", "import glob\n", "import os\n", @@ -93,7 +93,7 @@ "import json\n", "from shutil import copy\n", "\n", - "#--------------------------------------------Astropy/Astroquery Imports--------------------------------------------\n", + "# --------------------------------------------Astropy/Astroquery Imports--------------------------------------------\n", "\n", "from astropy.io import fits\n", "from astropy import wcs\n", @@ -104,7 +104,7 @@ "from astroquery.mast import Mast\n", "from astroquery.mast import Observations\n", "\n", - "#------------------------------------------------Plotting Imports--------------------------------------------------\n", + "# ------------------------------------------------Plotting Imports--------------------------------------------------\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", @@ -115,7 +115,7 @@ "# Use this version for non-interactive plots (easier scrolling of the notebook)\n", "%matplotlib inline\n", "# Use this version (outside of Jupyter Lab) if you want interactive plots\n", - "#%matplotlib notebook\n", + "# %matplotlib notebook\n", "\n", "# These gymnastics are needed to make the sizes of the figures\n", "# be the same in both the inline and notebook versions\n", @@ -168,7 +168,7 @@ " cmap: str\n", " Color Map for plot\n", " \"\"\"\n", - " #-----------------------------------------Scaling Information----------------------------------------\n", + " # -----------------------------------------Scaling Information----------------------------------------\n", " \n", " if scale == 'log':\n", " norm = ImageNormalize(data_2d, interval=ManualInterval(vmin=vmin, vmax=vmax),\n", @@ -180,7 +180,7 @@ " norm = ImageNormalize(data_2d, interval=ManualInterval(vmin=vmin, vmax=vmax),\n", " stretch=AsinhStretch())\n", " \n", - " #--------------------------------------------Set Up Figure-------------------------------------------\n", + " # --------------------------------------------Set Up Figure-------------------------------------------\n", "\n", " fig = plt.figure(figsize=(xsize, ysize))\n", " ax = fig.add_subplot(1, 1, 1)\n", @@ -194,9 +194,9 @@ " if title:\n", " plt.title(title)\n", " \n", - " #Zoom in on a portion of the image? \n", + " # Zoom in on a portion of the image? \n", " if zoom_in:\n", - " #inset axis \n", + " # inset axis \n", " axins = ax.inset_axes([0.5, 0.6, 0.5, 0.3])\n", " \n", " axins.imshow(data_2d, origin=\"lower\", norm=norm, aspect=aspect, cmap=cmap)\n", @@ -216,7 +216,7 @@ "metadata": {}, "outputs": [], "source": [ - "def show_ifu_cubeslices(s3d_file_list, wavelength_slices=[], spaxel_locs=[], y_scale=None, cmap='jet', vmin_vmax = [[[0,15e1]]], save_figure=False, title=None, title_font = 30):\n", + "def show_ifu_cubeslices(s3d_file_list, wavelength_slices=[], spaxel_locs=[], y_scale=None, cmap='jet', vmin_vmax = [[[0, 15e1]]], save_figure=False, title=None, title_font=30):\n", " \"\"\"\n", " Function to that takes a 3-D IFU data cube and generates: \n", " \n", @@ -224,7 +224,7 @@ " > Associated 1-D spectrum for a designated spaxel (spatial pixel) in the data cube\n", " > Corresponding 3-D weight image giving the relative weights of the output spaxels\n", " \n", - " Note: This function can accomidate multiple detectors plotted side-by-side. \n", + " Note: This function can accommodate multiple detectors plotted side-by-side. \n", " The general format would follow [[detector 1 info], [detector 2 info]].\n", "\n", " Parameters\n", @@ -249,51 +249,51 @@ " Save figure? \n", " \"\"\"\n", " \n", - " #---------------------------------------------- Set-up Figure -------------------------------------------------\n", + " # ---------------------------------------------- Set-up Figure -------------------------------------------------\n", "\n", - " #Plot Slices From the Cube\n", + " # Plot Slices From the Cube\n", " fig, axs = plt.subplots(3, np.array(wavelength_slices).size, figsize=(8*np.array(wavelength_slices).size,18))\n", " gs = grd.GridSpec(3, np.array(wavelength_slices).size, height_ratios=[1]*3, width_ratios=[1]*np.array(wavelength_slices).size, hspace=0.4,wspace=0.7)\n", "\n", " total_num_plots=3*np.array(wavelength_slices).size\n", " \n", " plot_count = 0\n", - " #---------------------------------------------Open Files------------------------------------------------------\n", + " # ---------------------------------------------Open Files------------------------------------------------------\n", " \n", " for s3d_file in s3d_file_list:\n", " \n", - " root=s3d_file[:-9] #Root file name \n", + " root=s3d_file[:-9] # Root file name \n", "\n", - " s3d = fits.open(s3d_file) #3-D IFU data cube fits file \n", - " x1d3 = datamodels.open(root+'_x1d.fits') #1-D Extracted Spectrum \n", + " s3d = fits.open(s3d_file) # 3-D IFU data cube fits file \n", + " x1d3 = datamodels.open(root+'_x1d.fits') # 1-D Extracted Spectrum \n", " \n", - " #--------------------------------Wavelength & Surface Brightness/Flux Arrays------------------------------\n", + " # --------------------------------Wavelength & Surface Brightness/Flux Arrays------------------------------\n", " \n", " x1d3wave = x1d3.spec[0].spec_table.WAVELENGTH\n", " \n", - " #--------------------------------------Data & Header Information------------------------------------------\n", + " # --------------------------------------Data & Header Information------------------------------------------\n", "\n", " \n", - " #SCI Extension: [Type:ImageHDU Cards:92 Dimensions:(57, 61, 973) Format:float32]\n", - " cube = s3d[1].data #Science data\n", - " wcs = WCS(s3d[1].header) #World Coordinate System (WCS) Transformation keywords \n", - " wmap = s3d[4].data #3-D weight image giving the relative weights of the output spaxels.\n", - " cdelt1 = s3d[1].header['CDELT1']*3600. #Axis 1 coordinate increment at reference point \n", - " cdelt2 = s3d[1].header['CDELT2']*3600. #Axis 2 coordinate increment at reference point \n", - " cdelt3 = s3d[1].header['CDELT3'] #Axis 3 coordinate increment at reference point \n", - " crval3 = s3d[1].header['CRVAL3'] #third axis value at the reference pixel \n", - "\n", - " #Wavelength range of the grating/filter combination\n", + " # SCI Extension: [Type:ImageHDU Cards:92 Dimensions:(57, 61, 973) Format:float32]\n", + " cube = s3d[1].data # Science data\n", + " wcs = WCS(s3d[1].header) # World Coordinate System (WCS) Transformation keywords \n", + " wmap = s3d[4].data # 3-D weight image giving the relative weights of the output spaxels.\n", + " cdelt1 = s3d[1].header['CDELT1']*3600. # Axis 1 coordinate increment at reference point \n", + " cdelt2 = s3d[1].header['CDELT2']*3600. # Axis 2 coordinate increment at reference point \n", + " cdelt3 = s3d[1].header['CDELT3'] # Axis 3 coordinate increment at reference point \n", + " crval3 = s3d[1].header['CRVAL3'] # Third axis value at the reference pixel \n", + "\n", + " # Wavelength range of the grating/filter combination\n", " wavstart = s3d[1].header['WAVSTART']\n", " wavend = s3d[1].header['WAVEND']\n", " s3d.close()\n", " \n", - " #---------------------------------------------------Plots-------------------------------------------------\n", + " # ---------------------------------------------------Plots-------------------------------------------------\n", " \n", - " cmap_custom = cm.colors.LinearSegmentedColormap.from_list(\"\", [\"darkred\",\"darkturquoise\",\"blue\"])\n", + " cmap_custom = cm.colors.LinearSegmentedColormap.from_list(\"\", [\"darkred\", \"darkturquoise\", \"blue\"])\n", " colors = cmap_custom(np.linspace(0, 1, np.array(wavelength_slices).size))\n", "\n", - " #To Account for if NRS1 & NRS2 are both being plotted Side-by-side\n", + " # To Account for if NRS1 & NRS2 are both being plotted Side-by-side\n", " if len(wavelength_slices) != 1:\n", " if 'nrs1' in s3d_file:\n", " wavelengths = wavelength_slices[0]\n", @@ -318,14 +318,14 @@ " y_scales = y_scale[0]\n", "\n", " \n", - " #Loop through each wavelength slices\n", + " # Loop through each wavelength slices\n", " for i, wave_slice in enumerate(wavelengths):\n", "\n", - " if float(wavstart)<=wave_slice*10**-6<=float(wavend):\n", + " if float(wavstart)<=wave_slice*10**-6 <= float(wavend):\n", " \n", - " #--------------------------------------------2-D Cube Slice------------------------------------------------\n", + " # --------------------------------------------2-D Cube Slice------------------------------------------------\n", " \n", - " #Min & Max Image Values & Scaling\n", + " # Min & Max Image Values & Scaling\n", " if len(vmin_vmax_vals) != 1:\n", " vmax_val = vmin_vmax_vals[i][1]\n", " vmin_val = vmin_vmax_vals[i][0]\n", @@ -334,37 +334,37 @@ " vmin_val = vmin_vmax_vals[0][0]\n", "\n", " slicewave = wave_slice\n", - " nslice = int((slicewave - crval3)/cdelt3) #the slice of the cube we want to plot\n", - " ax1 = plt.subplot(gs[0+plot_count], projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", - " #ax1 = plt.subplot(3,len(wavelength_slices), 0+plot_count, projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", + " nslice = int((slicewave - crval3)/cdelt3) # The slice of the cube we want to plot\n", + " ax1 = plt.subplot(gs[0+plot_count], projection=wcs, slices=('x', 'y', nslice)) # Set up the subplot space\n", + " # ax1 = plt.subplot(3,len(wavelength_slices), 0+plot_count, projection=wcs, slices=('x', 'y', nslice)) # Set up the subplot space\n", "\n", - " slice_mean = np.nanmean(cube[(nslice-2):(nslice+2), :, :], axis=0) #Mean of the slice looking in the range (nslice-2):(nslice+2)\n", - " slice_norm=ImageNormalize(slice_mean, vmin=vmin_val, vmax=vmax_val, stretch=AsinhStretch()) #normalize &stretch \n", - " slice_image= ax1.imshow(slice_mean, norm=slice_norm, origin='lower', aspect='auto',cmap=cmap) #plot slice\n", + " slice_mean = np.nanmean(cube[(nslice-2):(nslice+2), :, :], axis=0) # Mean of the slice looking in the range (nslice-2):(nslice+2)\n", + " slice_norm=ImageNormalize(slice_mean, vmin=vmin_val, vmax=vmax_val, stretch=AsinhStretch()) # Normalize &stretch \n", + " slice_image= ax1.imshow(slice_mean, norm=slice_norm, origin='lower', aspect='auto', cmap=cmap) # Plot slice\n", "\n", " cb_image = fig.colorbar(slice_image, fraction=0.046, pad=0.04)\n", - " cb_image.set_label('MJy/sr', labelpad=-1, fontsize = 22)\n", + " cb_image.set_label('MJy/sr', labelpad=-1, fontsize=22)\n", " cb_image.ax.tick_params(labelsize=20)\n", " cb_image.ax.yaxis.get_offset_text().set_fontsize(20)\n", " \n", - " ax1.set_xlabel('RA', fontsize =22)\n", + " ax1.set_xlabel('RA', fontsize=22)\n", " ax1.set_ylabel('DEC', labelpad=-1, fontsize=22)\n", - " #ax1.grid(color='white', ls='solid')\n", - " ax1.set_title('Detector {} \\n Grating/Filter: {}/{} \\n {} microns'.format(s3d[0].header['DETECTOR'],s3d[0].header['GRATING'], s3d[0].header['FILTER'], str(slicewave)), fontsize =25)\n", + " # ax1.grid(color='white', ls='solid')\n", + " ax1.set_title('Detector {} \\n Grating/Filter: {}/{} \\n {} microns'.format(s3d[0].header['DETECTOR'], s3d[0].header['GRATING'], s3d[0].header['FILTER'], str(slicewave)), fontsize=25)\n", " ax1.tick_params(axis='both', which='major', labelsize=20)\n", " ax1.coords[0].set_ticklabel(rotation=13, ha='right', pad=24)\n", "\n", " \n", - " #------------------------------------------Spaxel 1-D Spectrum---------------------------------------------\n", + " # ------------------------------------------Spaxel 1-D Spectrum---------------------------------------------\n", " \n", - " #Zoom in on a Spaxel: Spectrum\n", + " # Zoom in on a Spaxel: Spectrum\n", " loc = [spaxel_loc[i][0],spaxel_loc[i][1]]\n", " x1d3flux_loc = cube[:, loc[1], loc[0]]\n", - " #ax2 = plt.subplot(3,len(wavelength_slices), int(total_num_plots/3)+plot_count)\n", + " # ax2 = plt.subplot(3,len(wavelength_slices), int(total_num_plots/3)+plot_count)\n", " ax2 = plt.subplot(gs[int(total_num_plots/3)+plot_count])\n", "\n", - " #Spaxel Box Highlight \n", - " spaxel_rect = plt.Rectangle((loc[0]-.5, loc[1]-.5), 1,1, fill=False, color='black', linewidth=2)\n", + " # Spaxel Box Highlight \n", + " spaxel_rect = plt.Rectangle((loc[0]-.5, loc[1]-.5), 1, 1, fill=False, color='black', linewidth=2)\n", " ax1.add_patch(spaxel_rect)\n", " \n", " ax2.plot(x1d3wave, x1d3flux_loc, linewidth=1, color=colors[i])\n", @@ -375,7 +375,7 @@ " ax2.tick_params(axis='both', which='major', labelsize=20)\n", " ax2.yaxis.get_offset_text().set_fontsize(15)\n", " \n", - " #Scale Information\n", + " # Scale Information\n", " if y_scale:\n", " ymin, ymax = y_scales[i][0], y_scales[i][1]\n", " else:\n", @@ -386,24 +386,24 @@ " ax2.yaxis.set_tick_params(labelsize=20)\n", " ax2.set_aspect(0.5/ax2.get_data_ratio())\n", " \n", - " #-----------------------------------------------Weight Map-------------------------------------------------\n", + " # -----------------------------------------------Weight Map-------------------------------------------------\n", " \n", - " #Corresponding Weight Map (wmap) for Cube Slice\n", - " ax3 = plt.subplot(gs[int(total_num_plots)-np.array(wavelength_slices).size+plot_count], projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", - " #ax3 = plt.subplot(3, len(wavelength_slices), int(total_num_plots)-len(wavelength_slices)+plot_count, projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", + " # Corresponding Weight Map (wmap) for Cube Slice\n", + " ax3 = plt.subplot(gs[int(total_num_plots)-np.array(wavelength_slices).size+plot_count], projection=wcs, slices=('x', 'y', nslice)) # Set up the subplot space\n", + " # ax3 = plt.subplot(3, len(wavelength_slices), int(total_num_plots)-len(wavelength_slices)+plot_count, projection=wcs, slices=('x', 'y', nslice)) # Set up the subplot space\n", " \n", - " slice_mean_wmap = np.nanmean(wmap[(nslice-2):(nslice+2), :, :], axis=0) #Mean of the wmap slice looking in the range (nslice-2):(nslice+2)\n", - " slice_norm_wmap=ImageNormalize(slice_mean_wmap, stretch=AsinhStretch()) #normalize &stretch\n", - " slice_wmap = ax3.imshow(slice_mean_wmap, norm=slice_norm_wmap, origin='lower',aspect='auto', cmap=cmap) #plot slice\n", + " slice_mean_wmap = np.nanmean(wmap[(nslice-2):(nslice+2), :, :], axis=0) # Mean of the wmap slice looking in the range (nslice-2):(nslice+2)\n", + " slice_norm_wmap=ImageNormalize(slice_mean_wmap, stretch=AsinhStretch()) # Normalize & stretch\n", + " slice_wmap = ax3.imshow(slice_mean_wmap, norm=slice_norm_wmap, origin='lower',aspect='auto', cmap=cmap) # Plot slice\n", "\n", " cb_wmap = fig.colorbar(slice_wmap, fraction=0.046, pad=0.04)\n", - " cb_wmap.set_label('Weight', labelpad=-1, fontsize = 22)\n", + " cb_wmap.set_label('Weight', labelpad=-1, fontsize=22)\n", " cb_wmap.ax.tick_params(labelsize=20)\n", " cb_wmap.ax.yaxis.get_offset_text().set_fontsize(20)\n", " \n", " ax3.set_xlabel('RA', fontsize=22)\n", " ax3.set_ylabel('DEC', labelpad=-1, fontsize=22)\n", - " #ax3.grid(color='gray', ls='solid')\n", + " # ax3.grid(color='gray', ls='solid')\n", " ax3.set_title(str(slicewave)+' microns: Weight Map', fontsize=25)\n", " ax3.tick_params(axis='both', which='major', labelsize=20)\n", " ax3.coords[0].set_ticklabel(rotation=13, ha='right', pad=24)\n", @@ -419,8 +419,8 @@ " \n", " fig.tight_layout(rect=[0, 0, 0.98, 0.98])\n", "\n", - " if save_figure == True:\n", - " fig.savefig(root+\".png\",dpi=24, bbox_inches=\"tight\")\n", + " if save_figure:\n", + " fig.savefig(root+\".png\", dpi=24, bbox_inches=\"tight\")\n", " " ] }, @@ -440,17 +440,17 @@ "metadata": {}, "outputs": [], "source": [ - "#To rerun the notebook and all the pipeline steps set runflag=True\n", + "# To rerun the notebook and all the pipeline steps set runflag=True\n", "runflag = True \n", "\n", - "#Demo directory -- contains pre-computed products\n", - "if runflag == False:\n", + "# Demo directory -- contains pre-computed products\n", + "if not runflag:\n", " output_dir = './nirspec_ifu_02732_demo/'\n", "\n", - "#Rerun directory\n", - "elif runflag == True:\n", - " #If you want to actually re-download the data and run everything offline, \n", - " #then comment out this line, set runflag=True, & specify a desired local directory\n", + "# Rerun directory\n", + "elif runflag:\n", + " # If you want to actually re-download the data and run everything offline, \n", + " # then comment out this line, set runflag=True, & specify a desired local directory\n", " output_dir = './nirspec_ifu_02732_rerun/'\n", " if not os.path.exists(output_dir):\n", " os.makedirs(output_dir)" @@ -495,9 +495,9 @@ }, "outputs": [], "source": [ - "#Download data from MAST \n", + "# Download data from MAST \n", "\n", - "#Setup your account \n", + "# Setup your account \n", "\n", "# NOTE:\n", "# The data in this notebook is public and does not require a token.\n", @@ -508,37 +508,37 @@ "\n", "sessioninfo = Observations.session_info()\n", "\n", - "#Define the general search criteria\n", + "# Define the general search criteria\n", "obs = Observations.query_criteria(\n", - " obs_collection = 'JWST',\n", - " instrument_name = ['NIRSPEC/IFU'],\n", - " proposal_id = '02732')\n", + " obs_collection='JWST',\n", + " instrument_name=['NIRSPEC/IFU'],\n", + " proposal_id='02732')\n", "\n", - "#Print the Observations returned from the general serach criteria\n", + "# Print the Observations returned from the general search criteria\n", "products = Observations.get_product_list(obs)\n", - "#print(products)\n", + "# print(products)\n", "\n", - "#Filter the list of observations\n", - "#In this case we look for UNCAL products and ASN files to manually run pipeline stage 1-3\n", - "#We look for pre-processed MAST products for comparison: RATE (stage 1) & CAL (stage 2&3) & S3D (stage 2&3) & X1D (stage2&3) \n", + "# Filter the list of observations\n", + "# In this case we look for UNCAL products and ASN files to manually run pipeline stage 1-3\n", + "# We look for pre-processed MAST products for comparison: RATE (stage 1) & CAL (stage 2&3) & S3D (stage 2&3) & X1D (stage2&3) \n", "filtered = Observations.filter_products(products,\n", " productSubGroupDescription=[\"UNCAL\", \"ASN\"],\n", " mrp_only=False)\n", - "#Print the filtered products\n", + "# Print the filtered products\n", "number = len(filtered)\n", "for k in range(number):\n", " print(filtered['productFilename'][k])\n", "\n", - "#Download the filtered products\n", - "#This creates a mastDownload directory, unless you set flat=True and set a download_dir\n", + "# Download the filtered products\n", + "# This creates a mastDownload directory, unless you set flat=True and set a download_dir\n", "for i in range(len(filtered)):\n", " mast_products_dir = output_dir+'mast_products/'\n", " if not os.path.exists(mast_products_dir):\n", " os.makedirs(mast_products_dir)\n", - " if runflag == True:\n", - " Observations.download_products(filtered[i], mrp_only=False, cache=False, flat=True, download_dir=mast_products_dir) #Override any cached files and download the most up-to-date ones\n", + " if runflag:\n", + " Observations.download_products(filtered[i], mrp_only=False, cache=False, flat=True, download_dir=mast_products_dir) # Override any cached files and download the most up-to-date ones\n", " else:\n", - " Observations.download_products(filtered[i], mrp_only=False, cache=True, flat=True, download_dir=mast_products_dir) #Find any cached files first before downloading new ones" + " Observations.download_products(filtered[i], mrp_only=False, cache=True, flat=True, download_dir=mast_products_dir) # Find any cached files first before downloading new ones" ] }, { @@ -559,17 +559,17 @@ }, "outputs": [], "source": [ - "#Stage 1 Processing \n", + "# Stage 1 Processing \n", "\n", - "if runflag == True:\n", + "if runflag:\n", "\n", " for uncal_file in sorted(glob.glob(mast_products_dir+'*nrs1_uncal.fits')): \n", "\n", - " print(\"Applying Stage 1 Corrections & Calibrations to: \"+ os.path.basename(uncal_file))\n", + " print(\"Applying Stage 1 Corrections & Calibrations to: \" + os.path.basename(uncal_file))\n", "\n", " result = Detector1Pipeline.call(uncal_file,\n", - " save_results = True,\n", - " output_dir = output_dir)" + " save_results=True,\n", + " output_dir=output_dir)" ] }, { @@ -581,9 +581,9 @@ }, "outputs": [], "source": [ - "#Stage 1 slope products -- level 2a images\n", + "# Stage 1 slope products -- level 2a images\n", "\n", - "#Plot 4th (out of 8) dither position (NRS1 & NRS2) for GRATING/FILTER G140H/F100LP combination \n", + "# Plot 4th (out of 8) dither position (NRS1 & NRS2) for GRATING/FILTER G140H/F100LP combination \n", "for rate_file in sorted(glob.glob(output_dir+'*00004_nrs?_rate.fits')):\n", " \n", " ratefile_open = datamodels.open(rate_file)\n", @@ -591,20 +591,17 @@ " ratefile_dq = ratefile_open.dq # The Data Quality Map Data\n", " \n", " \n", - " show_image(ratefile_sci, 0,10, units='DN/s',zoom_in=[500,550, 1250,1300],\n", + " show_image(ratefile_sci, 0,10, units='DN/s',zoom_in=[500, 550, 1250, 1300],\n", " title='Countrate Image \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", " ratefile_open.meta.dither.position_number, \n", " ratefile_open.meta.instrument.grating,\n", - " ratefile_open.meta.instrument.filter)) #rate files have units of DN/s\n", + " ratefile_open.meta.instrument.filter)) # rate files have units of DN/s\n", " \n", - " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear',zoom_in=[500,550, 1250,1300],\n", + " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear',zoom_in=[500, 550, 1250, 1300],\n", " title='Data Quality Map \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", " ratefile_open.meta.dither.position_number, \n", " ratefile_open.meta.instrument.grating,\n", - " ratefile_open.meta.instrument.filter)) #rate files have units of DN/s\n", - " \n", - " \n", - " " + " ratefile_open.meta.instrument.filter)) # rate files have units of DN/s " ] }, { @@ -625,13 +622,13 @@ "metadata": {}, "outputs": [], "source": [ - "#Treating the IFU data as a point source \n", - "#To run as a point source, alter the rate file header keywrod SRCTYAPT=POINT & rerun stage 2 of the pipeline \n", - "#Loop through the copied rate files and update the source type keyword\n", + "# Treating the IFU data as a point source \n", + "# To run as a point source, alter the rate file header keywrod SRCTYAPT=POINT & rerun stage 2 of the pipeline \n", + "# Loop through the copied rate files and update the source type keyword\n", "for rate_file in sorted(glob.glob(output_dir+'*nrs1_rate.fits')):\n", " rate_file_hdu = fits.open(rate_file, 'update')\n", " \n", - " #Change source type to point \n", + " # Change source type to point \n", " rate_file_hdu[0].header['SRCTYAPT'] = 'POINT'\n", " \n", " rate_file_hdu.close()" @@ -663,18 +660,18 @@ }, "outputs": [], "source": [ - "#Stage 2 Processing \n", + "# Stage 2 Processing \n", "\n", - "if runflag == True:\n", + "if runflag:\n", " \n", - " #Process each rate file seperately \n", + " # Process each rate file separately \n", " for rate_file in sorted(glob.glob(output_dir+'*nrs1*rate.fits')):\n", " \n", - " print(\"Applying Stage 2 Calibrations & Corrections to: \"+ os.path.basename(rate_file))\n", + " print(\"Applying Stage 2 Calibrations & Corrections to: \" + os.path.basename(rate_file))\n", "\n", " result = Spec2Pipeline.call(rate_file,\n", - " save_results = True,\n", - " output_dir = output_dir) " + " save_results=True,\n", + " output_dir=output_dir) " ] }, { @@ -684,21 +681,21 @@ "metadata": {}, "outputs": [], "source": [ - "#Stage 2 Products -- Calibrated 3-D data cube for PRISM/CLEAR (only falls on NRS1)\n", + "# Stage 2 Products -- Calibrated 3-D data cube for PRISM/CLEAR (only falls on NRS1)\n", "\n", - "#Plotting the 4th (out of 8) dither position\n", + "# Plotting the 4th (out of 8) dither position\n", "stage2_s3d_file = sorted(glob.glob(output_dir+'*00004_nrs1_s3d.fits')) \n", "\n", "title_stage2_rerun='NGC 7319 AGN \\n Level 2 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", "\n", "\n", - "#Characteristics of the plot \n", - "nrs1_wavelengths = [1.4,3.3,4.5] #Wavelength slices (microns) to take from the 3-D data cube\n", - "nrs1_spaxel_locs = [[30,29],[28,39],[14,25]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "# Characteristics of the plot \n", + "nrs1_wavelengths = [1.4, 3.3, 4.5] # Wavelength slices (microns) to take from the 3-D data cube\n", + "nrs1_spaxel_locs = [[30, 29], [28, 39], [14, 25]] # Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", "\n", "\n", - "#Plot using the convience function defined above\n", - "show_ifu_cubeslices(stage2_s3d_file, wavelength_slices=[nrs1_wavelengths], spaxel_locs=[nrs1_spaxel_locs],title=title_stage2_rerun)" + "# Plot using the convience function defined above\n", + "show_ifu_cubeslices(stage2_s3d_file, wavelength_slices=[nrs1_wavelengths], spaxel_locs=[nrs1_spaxel_locs], title=title_stage2_rerun)" ] }, { @@ -725,15 +722,15 @@ }, "outputs": [], "source": [ - "#Copy ASN file from MAST into the stage 1 rerun directory\n", + "# Copy ASN file from MAST into the stage 1 rerun directory\n", "\n", - "asnfile_mast = glob.glob(mast_products_dir+'*_spec3_00001_asn.json')[0] #ASN file found in MAST\n", + "asnfile_mast = glob.glob(mast_products_dir+'*_spec3_00001_asn.json')[0] # ASN file found in MAST\n", "\n", - "asnfile_rerun_point = output_dir+os.path.basename(asnfile_mast) #New ASN file path\n", + "asnfile_rerun_point = output_dir + os.path.basename(asnfile_mast) # New ASN file path\n", "if not os.path.exists(asnfile_rerun_point):\n", " copy(asnfile_mast, asnfile_rerun_point)\n", " \n", - "#Check the ASN file contents\n", + "# Check the ASN file contents\n", "with open(asnfile_rerun_point, 'r') as f_obj:\n", " asnfile_rerun_point_data = json.load(f_obj)\n", " \n", @@ -772,16 +769,16 @@ "metadata": {}, "outputs": [], "source": [ - "#Rerun stage 3 with outlier detection on\n", - "if runflag==True:\n", + "# Rerun stage 3 with outlier detection on\n", + "if runflag:\n", "\n", " result = Spec3Pipeline.call(asnfile_rerun_point,\n", - " save_results = True,\n", - " output_dir = output_dir,\n", - " steps = {\"outlier_detection\":{\"skip\": False,\n", - " \"save_results\": True,\n", - " \"kernel_size\": '3 3'},\n", - " \"extract_1d\":{\"subtract_background\":False}}) #Do not automatically apply background subtraction until we modify the extraction region\n", + " save_results=True,\n", + " output_dir=output_dir,\n", + " steps={\"outlier_detection\": {\"skip\": False,\n", + " \"save_results\": True,\n", + " \"kernel_size\": '3 3'},\n", + " \"extract_1d\": {\"subtract_background\": False}}) # Do not automatically apply background subtraction until we modify the extraction region\n", " " ] }, @@ -792,19 +789,19 @@ "metadata": {}, "outputs": [], "source": [ - "#Stage 3 Products -- Combined Calibrated 3-D data cube for PRISM/CLEAR \n", + "# Stage 3 Products -- Combined Calibrated 3-D data cube for PRISM/CLEAR \n", "\n", "stage3_s3d_file_point = sorted(glob.glob(output_dir+'*nirspec_prism-clear_s3d.fits')) \n", "\n", "title_stage3_rerun_point='NGC 7319 AGN \\n Level 3 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", "\n", - "#Characteristics of the plot \n", - "nrs1_wavelengths = [1.4,3.3,4.5] #Wavelength slices (microns) to take from the 3-D data cube\n", - "nrs1_spaxel_locs = [[30,29],[28,39],[14,25]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", - "vmin_vmax_point = [[0,150],[0,150],[0,150]]\n", + "# Characteristics of the plot \n", + "nrs1_wavelengths = [1.4, 3.3, 4.5] # Wavelength slices (microns) to take from the 3-D data cube\n", + "nrs1_spaxel_locs = [[30, 29], [28, 39], [14, 25]] # Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "vmin_vmax_point = [[0, 150], [0, 150], [0, 150]]\n", "\n", - "#Plot using the convience function defined above\n", - "show_ifu_cubeslices(stage3_s3d_file_point, wavelength_slices=[nrs1_wavelengths], spaxel_locs=[nrs1_spaxel_locs],vmin_vmax=[vmin_vmax_point],title=title_stage3_rerun_point)" + "# Plot using the convience function defined above\n", + "show_ifu_cubeslices(stage3_s3d_file_point, wavelength_slices=[nrs1_wavelengths], spaxel_locs=[nrs1_spaxel_locs], vmin_vmax=[vmin_vmax_point], title=title_stage3_rerun_point)" ] }, { @@ -814,30 +811,30 @@ "metadata": {}, "outputs": [], "source": [ - "#Stage 3 Products -- Extracted 1-D Spectrum \n", + "# Stage 3 Products -- Extracted 1-D Spectrum \n", "\n", - "#Combined 1-D extracted spectrum\n", + "# Combined 1-D extracted spectrum\n", "x1d3_rerun_point = datamodels.open(glob.glob(output_dir+'*nirspec_prism-clear_x1d.fits')[0])\n", "\n", - "#Wavelength & Surface Brightness Arrays\n", + "# Wavelength & Surface Brightness Arrays\n", "x1d3wave_rerun_point = x1d3_rerun_point.spec[0].spec_table.WAVELENGTH\n", "x1d3flux_rerun_point = x1d3_rerun_point.spec[0].spec_table.FLUX\n", "\n", - "#Plot the Extracted 1-D Spectrum\n", + "# Plot the Extracted 1-D Spectrum\n", "fig = plt.figure(figsize=(15,9))\n", "\n", "plt.plot(x1d3wave_rerun_point,x1d3flux_rerun_point, linewidth =2)\n", "\n", - "#Where wavelength slice was taken above\n", + "# Where wavelength slice was taken above\n", "plt.vlines(1.4, 0., 400., 'black', 'dotted', label='1.4 microns')\n", "plt.vlines(3.3, 0., 400., 'red', 'dotted', label='3.3 microns')\n", "plt.vlines(4.5, 0., 400., 'green', 'dotted', label='4.5 microns')\n", "\n", - "plt.xlabel('$\\lambda [\\mu$m]', fontsize =20)\n", - "plt.ylabel('Flux (Jy)', fontsize =20)\n", + "plt.xlabel(r'$\\lambda [\\mu$m]', fontsize=20)\n", + "plt.ylabel('Flux (Jy)', fontsize=20)\n", "plt.title(\"NGC 7319 AGN \\n Level 3 IFU Product in MAST: Extracted 1-D Spectrum\", fontsize=20)\n", "plt.ylim(0, 10**-1.6)\n", - "plt.ticklabel_format(axis='y', style='sci', scilimits=(0,-2))\n", + "plt.ticklabel_format(axis='y', style='sci', scilimits=(0, -2))\n", "plt.legend()\n", "plt.show()" ] @@ -873,26 +870,26 @@ "metadata": {}, "outputs": [], "source": [ - "#Extraction Region Preview\n", - "#Open Combined 3-D Cube FITS file\n", + "# Extraction Region Preview\n", + "# Open Combined 3-D Cube FITS file\n", "s3d = fits.open(glob.glob(output_dir+'*nirspec_prism-clear_s3d.fits')[0])\n", "cube = s3d[1].data #Science data\n", "\n", - "#plot the full IFU cube\n", - "ax = plt.subplot(1, 1, 1)#, projection=wcs, slices=('x', 'y', nslice3)) #set up the subplot space\n", - "slice_mean = np.nanmean(cube[400:500, :, :], axis=0) #Mean of the slice looking in the range (nslice2-2):(nslice2+2)\n", - "slice_norm=ImageNormalize(slice_mean, vmin=0, vmax=150, stretch=AsinhStretch()) #normalize &stretch\n", - "slice_full = ax.imshow(slice_mean, norm=slice_norm, origin='lower', cmap='jet') #plot slice\n", + "# Plot the full IFU cube\n", + "ax = plt.subplot(1, 1, 1)#, projection=wcs, slices=('x', 'y', nslice3)) # Set up the subplot space\n", + "slice_mean = np.nanmean(cube[400:500, :, :], axis=0) # Mean of the slice looking in the range (nslice2-2):(nslice2+2)\n", + "slice_norm = ImageNormalize(slice_mean, vmin=0, vmax=150, stretch=AsinhStretch()) # Normalize &stretch\n", + "slice_full = ax.imshow(slice_mean, norm=slice_norm, origin='lower', cmap='jet') # Plot slice\n", "\n", - "#colorbar\n", + "# Colorbar\n", "cb_image = plt.colorbar(slice_full, fraction=0.046, pad=0.04)\n", "cb_image.set_label('MJy/sr', labelpad=-1, fontsize = 10)\n", "cb_image.ax.tick_params(labelsize=10)\n", "cb_image.ax.yaxis.get_offset_text().set_fontsize(10)\n", "\n", - "radius = Circle((29,29),9, fill=False, label='Radius')\n", - "inner_bkg = Circle((29,29),10, color='b',fill=False, label='Inner Background Radius')\n", - "outer_bkg= Circle((29,29),15, color='r',fill=False, label='Outer Background Radius')\n", + "radius = Circle((29, 29), 9, fill=False, label='Radius')\n", + "inner_bkg = Circle((29, 29), 10, color='b',fill=False, label='Inner Background Radius')\n", + "outer_bkg= Circle((29, 29), 15, color='r',fill=False, label='Outer Background Radius')\n", "ax.add_patch(radius)\n", "ax.add_patch(inner_bkg)\n", "ax.add_patch(outer_bkg)\n", @@ -912,14 +909,14 @@ }, "outputs": [], "source": [ - "#Grab the defualt extract1d reference file and copy to working directory\n", + "# Grab the default extract1d reference file and copy to working directory\n", "extract1d_ref_og = Spec3Pipeline().get_reference_file(glob.glob(output_dir+'*nirspec_prism-clear_s3d.fits')[0], 'extract1d')\n", "if not os.path.exists(output_dir+os.path.basename(extract1d_ref_og)):\n", " copy(extract1d_ref_og, output_dir+os.path.basename(extract1d_ref_og))\n", "\n", - "if runflag==True:\n", - " #Make Changes to the ASDF file and Write to a new file\n", - " with asdf.open(output_dir+os.path.basename(extract1d_ref_og),mode='rw') as ff:\n", + "if runflag:\n", + " # Make Changes to the ASDF file and Write to a new file\n", + " with asdf.open(output_dir+os.path.basename(extract1d_ref_og), mode='rw') as ff:\n", " ff.tree['data']['radius'] = np.full((2048,), 9, dtype='float32')\n", " ff.tree['data']['inner_bkg'] = np.full((2048,), 10, dtype='float32')\n", " ff.tree['data']['outer_bkg'] = np.full((2048,), 15, dtype='float32')\n", @@ -933,13 +930,13 @@ "metadata": {}, "outputs": [], "source": [ - "#Rerun only the extract1d step with the new/modified reference file with background subtraction on\n", + "# Rerun only the extract1d step with the new/modified reference file with background subtraction on\n", "\n", - "if runflag==True:\n", + "if runflag:\n", " Extract1dStep.call(glob.glob(output_dir+'*nirspec_prism-clear_s3d.fits')[0], \n", - " save_results = True,\n", - " output_dir = output_dir, \n", - " override_extract1d = output_dir+'new_extract1d_reference_file.asdf')" + " save_results=True,\n", + " output_dir=output_dir, \n", + " override_extract1d=output_dir+'new_extract1d_reference_file.asdf')" ] }, { @@ -949,21 +946,21 @@ "metadata": {}, "outputs": [], "source": [ - "#Display new 1-D spectrum\n", + "# Display new 1-D spectrum\n", "\n", - "#Combined 1D extracted spectrum\n", + "# Combined 1D extracted spectrum\n", "x1d3 = datamodels.open(glob.glob(output_dir+'*nirspec_prism-clear_extract1dstep.fits')[0])\n", "\n", - "#Wavelength & Surface Brightness Arrays\n", + "# Wavelength & Surface Brightness Arrays\n", "x1d3wave = x1d3.spec[0].spec_table.WAVELENGTH\n", "x1d3flux = x1d3.spec[0].spec_table.FLUX\n", "\n", - "#Plot the Extracted 1D Spectrum\n", - "fig = plt.figure(figsize=(15,9))\n", + "# Plot the Extracted 1D Spectrum\n", + "fig = plt.figure(figsize=(15, 9))\n", "\n", - "plt.plot(x1d3wave,x1d3flux, linewidth =2)\n", + "plt.plot(x1d3wave, x1d3flux, linewidth=2)\n", "\n", - "#Where wavelength slice was taken above\n", + "# Where wavelength slice was taken above\n", "plt.vlines(1.4, 0., 400., 'black', 'dotted', label='1.4 microns')\n", "plt.vlines(3.3, 0., 400., 'red', 'dotted', label='3.3 microns')\n", "plt.vlines(4.5, 0., 400., 'green', 'dotted', label='4.5 microns')\n", @@ -972,7 +969,7 @@ "plt.ylabel('Flux (Jy)', fontsize =20)\n", "plt.title(\"NGC 7319 AGN \\n Level 3 IFU Product: Extracted 1-D Spectrum with Background Subtraction\")\n", "plt.ylim(0, 10**-1.6)\n", - "plt.ticklabel_format(axis='y', style='sci', scilimits=(0,-2))\n", + "plt.ticklabel_format(axis='y', style='sci', scilimits=(0, -2))\n", "plt.legend()\n", "plt.show()" ] @@ -1007,7 +1004,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.11.6" } }, "nbformat": 4, From 3dbb58a62ee29b391a1175ffdabcf5ddf90d34be Mon Sep 17 00:00:00 2001 From: Hatice Karatay Date: Wed, 4 Oct 2023 10:56:35 -0400 Subject: [PATCH 05/12] Address some of the style errors on 2732 demo nb --- .../ero_nirspec_ifu_02732_demo.ipynb | 338 +++++++++--------- 1 file changed, 167 insertions(+), 171 deletions(-) diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb index 546c7279c..dc264f403 100644 --- a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb @@ -69,28 +69,28 @@ "metadata": {}, "outputs": [], "source": [ - "#Import Library\n", + "# Import Library\n", "\n", - "#--------------------------------------JWST Calibration Pipeline Imports-------------------------------------------\n", + "# --------------------------------------JWST Calibration Pipeline Imports-------------------------------------------\n", "\n", "import jwst\n", "import crds\n", "from jwst import datamodels\n", - "from jwst.pipeline import Detector1Pipeline #calwebb_detector1\n", - "from jwst.pipeline import Spec2Pipeline #calwebb_spec2\n", - "from jwst.pipeline import Spec3Pipeline #calwebb_spec3\n", - "from jwst.extract_1d import Extract1dStep #Extract1D Individual Step\n", + "from jwst.pipeline import Detector1Pipeline # calwebb_detector1\n", + "from jwst.pipeline import Spec2Pipeline # calwebb_spec2\n", + "from jwst.pipeline import Spec3Pipeline # calwebb_spec3\n", + "from jwst.extract_1d import Extract1dStep # Extract1D Individual Step\n", "\n", "print(\"JWST Calibration Pipeline Version={}\".format(jwst.__version__))\n", "print(\"Current Operational CRDS Context = {}\".format(crds.get_default_context()))\n", "\n", - "#----------------------------------------------General Imports-----------------------------------------------------\n", + "# ----------------------------------------------General Imports-----------------------------------------------------\n", "\n", "import numpy as np\n", "import warnings\n", "warnings.filterwarnings('ignore') #Set to 'default' to turn warnings back on\n", "\n", - "#--------------------------------------------File Operation Imports------------------------------------------------\n", + "# --------------------------------------------File Operation Imports------------------------------------------------\n", "\n", "import glob\n", "import os\n", @@ -98,7 +98,7 @@ "import json\n", "from shutil import copy\n", "\n", - "#--------------------------------------------Astropy/Astroquery Imports--------------------------------------------\n", + "# --------------------------------------------Astropy/Astroquery Imports--------------------------------------------\n", "\n", "from astropy.io import fits\n", "from astropy import wcs\n", @@ -109,7 +109,7 @@ "from astroquery.mast import Mast\n", "from astroquery.mast import Observations\n", "\n", - "#------------------------------------------------Plotting Imports--------------------------------------------------\n", + "# ------------------------------------------------Plotting Imports--------------------------------------------------\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", @@ -120,7 +120,7 @@ "# Use this version for non-interactive plots (easier scrolling of the notebook)\n", "%matplotlib inline\n", "# Use this version (outside of Jupyter Lab) if you want interactive plots\n", - "#%matplotlib notebook\n", + "# %matplotlib notebook\n", "\n", "# These gymnastics are needed to make the sizes of the figures\n", "# be the same in both the inline and notebook versions\n", @@ -173,7 +173,7 @@ " cmap: str\n", " Color Map for plot\n", " \"\"\"\n", - " #-----------------------------------------Scaling Information----------------------------------------\n", + " # -----------------------------------------Scaling Information----------------------------------------\n", " \n", " if scale == 'log':\n", " norm = ImageNormalize(data_2d, interval=ManualInterval(vmin=vmin, vmax=vmax),\n", @@ -185,7 +185,7 @@ " norm = ImageNormalize(data_2d, interval=ManualInterval(vmin=vmin, vmax=vmax),\n", " stretch=AsinhStretch())\n", " \n", - " #--------------------------------------------Set Up Figure-------------------------------------------\n", + " # --------------------------------------------Set Up Figure-------------------------------------------\n", "\n", " fig = plt.figure(figsize=(xsize, ysize))\n", " ax = fig.add_subplot(1, 1, 1)\n", @@ -199,19 +199,19 @@ " if title:\n", " plt.title(title)\n", " \n", - " #Zoom in on a portion of the image? \n", + " # Zoom in on a portion of the image? \n", " if zoom_in:\n", - " #inset axis \n", + " # Inset axis \n", " axins = ax.inset_axes([0.5, 0.6, 0.5, 0.3])\n", " \n", " axins.imshow(data_2d, origin=\"lower\", norm=norm, aspect=aspect, cmap=cmap)\n", " \n", - " # subregion of the original image\n", + " # Subregion of the original image\n", " axins.set_xlim(zoom_in[0], zoom_in[1])\n", " axins.set_ylim(zoom_in[2], zoom_in[3])\n", " axins.set_xticklabels([])\n", " axins.set_yticklabels([])\n", - " ax.indicate_inset_zoom(axins, color=\"black\",edgecolor=\"black\", linewidth=3)" + " ax.indicate_inset_zoom(axins, color=\"black\", edgecolor=\"black\", linewidth=3)" ] }, { @@ -221,7 +221,7 @@ "metadata": {}, "outputs": [], "source": [ - "def show_ifu_cubeslices(s3d_file_list, wavelength_slices=[], spaxel_locs=[], y_scale=None, cmap='jet', vmin_vmax = [[[0,15e1]]], save_figure=False, title=None, title_font = 30):\n", + "def show_ifu_cubeslices(s3d_file_list, wavelength_slices=[], spaxel_locs=[], y_scale=None, cmap='jet', vmin_vmax = [[[0, 15e1]]], save_figure=False, title=None, title_font=30):\n", " \"\"\"\n", " Function to that takes a 3-D IFU data cube and generates: \n", " \n", @@ -254,16 +254,16 @@ " Save figure? \n", " \"\"\"\n", " \n", - " #---------------------------------------------- Set-up Figure -------------------------------------------------\n", + " # ---------------------------------------------- Set-up Figure -------------------------------------------------\n", "\n", " #Plot Slices From the Cube\n", " fig, axs = plt.subplots(3, np.array(wavelength_slices).size, figsize=(8*np.array(wavelength_slices).size,18))\n", " gs = grd.GridSpec(3, np.array(wavelength_slices).size, height_ratios=[1]*3, width_ratios=[1]*np.array(wavelength_slices).size, hspace=0.4,wspace=0.7)\n", "\n", - " total_num_plots=3*np.array(wavelength_slices).size\n", + " total_num_plots = 3*np.array(wavelength_slices).size\n", " \n", " plot_count = 0\n", - " #---------------------------------------------Open Files------------------------------------------------------\n", + " # ---------------------------------------------Open Files------------------------------------------------------\n", " \n", " for s3d_file in s3d_file_list:\n", " \n", @@ -272,34 +272,34 @@ " s3d = fits.open(s3d_file) #3-D IFU data cube fits file \n", " x1d3 = datamodels.open(root+'_x1d.fits') #1-D Extracted Spectrum \n", " \n", - " #--------------------------------Wavelength & Surface Brightness/Flux Arrays------------------------------\n", + " # --------------------------------Wavelength & Surface Brightness/Flux Arrays------------------------------\n", " \n", " x1d3wave = x1d3.spec[0].spec_table.WAVELENGTH\n", " \n", - " #--------------------------------------Data & Header Information------------------------------------------\n", + " # --------------------------------------Data & Header Information------------------------------------------\n", "\n", " \n", - " #SCI Extension: [Type:ImageHDU Cards:92 Dimensions:(57, 61, 973) Format:float32]\n", - " cube = s3d[1].data #Science data\n", - " wcs = WCS(s3d[1].header) #World Coordinate System (WCS) Transformation keywords \n", - " wmap = s3d[4].data #3-D weight image giving the relative weights of the output spaxels.\n", - " cdelt1 = s3d[1].header['CDELT1']*3600. #Axis 1 coordinate increment at reference point \n", - " cdelt2 = s3d[1].header['CDELT2']*3600. #Axis 2 coordinate increment at reference point \n", - " cdelt3 = s3d[1].header['CDELT3'] #Axis 3 coordinate increment at reference point \n", - " crval3 = s3d[1].header['CRVAL3'] #third axis value at the reference pixel \n", - "\n", - " #Wavelength range of the grating/filter combination\n", + " # SCI Extension: [Type:ImageHDU Cards:92 Dimensions:(57, 61, 973) Format:float32]\n", + " cube = s3d[1].data # Science data\n", + " wcs = WCS(s3d[1].header) # World Coordinate System (WCS) Transformation keywords \n", + " wmap = s3d[4].data # 3-D weight image giving the relative weights of the output spaxels.\n", + " cdelt1 = s3d[1].header['CDELT1']*3600. # Axis 1 coordinate increment at reference point \n", + " cdelt2 = s3d[1].header['CDELT2']*3600. # Axis 2 coordinate increment at reference point \n", + " cdelt3 = s3d[1].header['CDELT3'] # Axis 3 coordinate increment at reference point \n", + " crval3 = s3d[1].header['CRVAL3'] # Third axis value at the reference pixel \n", + "\n", + " # Wavelength range of the grating/filter combination\n", " wavstart = s3d[1].header['WAVSTART']\n", " wavend = s3d[1].header['WAVEND']\n", " \n", " s3d.close()\n", " \n", - " #---------------------------------------------------Plots-------------------------------------------------\n", + " # ---------------------------------------------------Plots-------------------------------------------------\n", " \n", - " cmap_custom = cm.colors.LinearSegmentedColormap.from_list(\"\", [\"darkred\",\"darkturquoise\",\"blue\"])\n", + " cmap_custom = cm.colors.LinearSegmentedColormap.from_list(\"\", [\"darkred\", \"darkturquoise\", \"blue\"])\n", " colors = cmap_custom(np.linspace(0, 1, np.array(wavelength_slices).size))\n", "\n", - " #To Account for if NRS1 & NRS2 are both being plotted Side-by-side\n", + " # To Account for if NRS1 & NRS2 are both being plotted Side-by-side\n", " if len(wavelength_slices) != 1:\n", " if 'nrs1' in s3d_file:\n", " wavelengths = wavelength_slices[0]\n", @@ -324,14 +324,14 @@ " y_scales = y_scale[0]\n", "\n", " \n", - " #Loop through each wavelength slices\n", + " # Loop through each wavelength slices\n", " for i, wave_slice in enumerate(wavelengths):\n", "\n", - " if float(wavstart)<=wave_slice*10**-6<=float(wavend):\n", + " if float(wavstart)<=wave_slice*10**-6 <= float(wavend):\n", " \n", - " #--------------------------------------------2-D Cube Slice------------------------------------------------\n", + " # --------------------------------------------2-D Cube Slice------------------------------------------------\n", " \n", - " #Min & Max Image Values & Scaling\n", + " # Min & Max Image Values & Scaling\n", " if len(vmin_vmax_vals) != 1:\n", " vmax_val = vmin_vmax_vals[i][1]\n", " vmin_val = vmin_vmax_vals[i][0]\n", @@ -342,47 +342,47 @@ " slicewave = wave_slice\n", " nslice = int((slicewave - crval3)/cdelt3) #the slice of the cube we want to plot\n", "\n", - " ax1 = plt.subplot(gs[0+plot_count], projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", - " #ax1 = plt.subplot(3,len(wavelength_slices), 0+plot_count, projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", + " ax1 = plt.subplot(gs[0+plot_count], projection=wcs, slices=('x', 'y', nslice)) # Set up the subplot space\n", + " #ax1 = plt.subplot(3,len(wavelength_slices), 0+plot_count, projection=wcs, slices=('x', 'y', nslice)) # Set up the subplot space\n", "\n", - " slice_mean = np.nanmean(cube[(nslice-2):(nslice+2), :, :], axis=0) #Mean of the slice looking in the range (nslice-2):(nslice+2)\n", - " slice_norm=ImageNormalize(slice_mean, vmin=vmin_val, vmax=vmax_val, stretch=AsinhStretch()) #normalize &stretch \n", - " slice_image= ax1.imshow(slice_mean, norm=slice_norm, origin='lower', aspect='auto',cmap=cmap) #plot slice\n", + " slice_mean = np.nanmean(cube[(nslice-2):(nslice+2), :, :], axis=0) # Mean of the slice looking in the range (nslice-2):(nslice+2)\n", + " slice_norm=ImageNormalize(slice_mean, vmin=vmin_val, vmax=vmax_val, stretch=AsinhStretch()) # Normalize &stretch \n", + " slice_image= ax1.imshow(slice_mean, norm=slice_norm, origin='lower', aspect='auto',cmap=cmap) # Plot slice\n", " \n", " cb_image = fig.colorbar(slice_image, fraction=0.046, pad=0.04)\n", - " cb_image.set_label('MJy/sr', labelpad=-1, fontsize = 22)\n", + " cb_image.set_label('MJy/sr', labelpad=-1, fontsize=22)\n", " cb_image.ax.tick_params(labelsize=20)\n", " cb_image.ax.yaxis.get_offset_text().set_fontsize(20)\n", " \n", - " ax1.set_xlabel('RA', fontsize =22)\n", + " ax1.set_xlabel('RA', fontsize=22)\n", " ax1.set_ylabel('DEC', labelpad=-1, fontsize=22)\n", " #ax1.grid(color='white', ls='solid')\n", - " ax1.set_title('Detector {} \\n Grating/Filter: {}/{} \\n {} microns'.format(s3d[0].header['DETECTOR'],s3d[0].header['GRATING'], s3d[0].header['FILTER'], str(slicewave)), fontsize =25)\n", + " ax1.set_title('Detector {} \\n Grating/Filter: {}/{} \\n {} microns'.format(s3d[0].header['DETECTOR'],s3d[0].header['GRATING'], s3d[0].header['FILTER'], str(slicewave)), fontsize=25)\n", " ax1.tick_params(axis='both', which='major', labelsize=20)\n", " ax1.coords[0].set_ticklabel(rotation=13, ha='right', pad=24)\n", "\n", " \n", - " #------------------------------------------Spaxel 1-D Spectrum---------------------------------------------\n", + " # ------------------------------------------Spaxel 1-D Spectrum---------------------------------------------\n", " \n", - " #Zoom in on a Spaxel: Spectrum\n", + " # Zoom in on a Spaxel: Spectrum\n", " loc = [spaxel_loc[i][0],spaxel_loc[i][1]]\n", " x1d3flux_loc = cube[:, loc[1], loc[0]]\n", - " #ax2 = plt.subplot(3,len(wavelength_slices), int(total_num_plots/3)+plot_count)\n", + " # ax2 = plt.subplot(3,len(wavelength_slices), int(total_num_plots/3)+plot_count)\n", " ax2 = plt.subplot(gs[int(total_num_plots/3)+plot_count])\n", "\n", - " #Spaxel Box Highlight \n", + " # Spaxel Box Highlight \n", " spaxel_rect = plt.Rectangle((loc[0]-.5, loc[1]-.5), 1,1, fill=False, color='black', linewidth=2)\n", " ax1.add_patch(spaxel_rect)\n", " \n", " ax2.plot(x1d3wave, x1d3flux_loc, linewidth=1, color=colors[i])\n", " ax2.grid(linewidth=2)\n", - " ax2.set_xlabel('$\\u03BB [\\u03BC$m]',fontsize=22)\n", + " ax2.set_xlabel(r'$\\u03BB [\\u03BC$m]',fontsize=22)\n", " ax2.set_ylabel(\"Surface Brightness \\n (MJy/sr)\",fontsize=22)\n", " ax2.set_title('Spaxel at (x, y)='+repr(loc), fontsize=25)\n", " ax2.tick_params(axis='both', which='major', labelsize=20)\n", " ax2.yaxis.get_offset_text().set_fontsize(15)\n", " \n", - " #Scale Information\n", + " # Scale Information\n", " if y_scale:\n", " ymin, ymax = y_scales[i][0], y_scales[i][1]\n", " else:\n", @@ -393,24 +393,24 @@ " ax2.yaxis.set_tick_params(labelsize=20)\n", " ax2.set_aspect(0.5/ax2.get_data_ratio())\n", " \n", - " #-----------------------------------------------Weight Map-------------------------------------------------\n", + " # -----------------------------------------------Weight Map-------------------------------------------------\n", " \n", - " #Corresponding Weight Map (wmap) for Cube Slice\n", - " ax3 = plt.subplot(gs[int(total_num_plots)-np.array(wavelength_slices).size+plot_count], projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", - " #ax3 = plt.subplot(3, len(wavelength_slices), int(total_num_plots)-len(wavelength_slices)+plot_count, projection=wcs, slices=('x', 'y', nslice)) #set up the subplot space\n", + " # Corresponding Weight Map (wmap) for Cube Slice\n", + " ax3 = plt.subplot(gs[int(total_num_plots)-np.array(wavelength_slices).size+plot_count], projection=wcs, slices=('x', 'y', nslice)) # Set up the subplot space\n", + " # ax3 = plt.subplot(3, len(wavelength_slices), int(total_num_plots)-len(wavelength_slices)+plot_count, projection=wcs, slices=('x', 'y', nslice)) # Set up the subplot space\n", " \n", - " slice_mean_wmap = np.nanmean(wmap[(nslice-2):(nslice+2), :, :], axis=0) #Mean of the wmap slice looking in the range (nslice-2):(nslice+2)\n", - " slice_norm_wmap=ImageNormalize(slice_mean_wmap, stretch=AsinhStretch()) #normalize &stretch\n", - " slice_wmap = ax3.imshow(slice_mean_wmap, norm=slice_norm_wmap, origin='lower',aspect='auto', cmap=cmap) #plot slice\n", + " slice_mean_wmap = np.nanmean(wmap[(nslice-2):(nslice+2), :, :], axis=0) # Mean of the wmap slice looking in the range (nslice-2):(nslice+2)\n", + " slice_norm_wmap=ImageNormalize(slice_mean_wmap, stretch=AsinhStretch()) # Normalize &stretch\n", + " slice_wmap = ax3.imshow(slice_mean_wmap, norm=slice_norm_wmap, origin='lower',aspect='auto', cmap=cmap) # Plot slice\n", " \n", " cb_wmap = fig.colorbar(slice_wmap, fraction=0.046, pad=0.04)\n", - " cb_wmap.set_label('Weight', labelpad=-1, fontsize = 22)\n", + " cb_wmap.set_label('Weight', labelpad=-1, fontsize=22)\n", " cb_wmap.ax.tick_params(labelsize=20)\n", " cb_wmap.ax.yaxis.get_offset_text().set_fontsize(20)\n", " \n", " ax3.set_xlabel('RA', fontsize=22)\n", " ax3.set_ylabel('DEC', labelpad=-1, fontsize=22)\n", - " #ax3.grid(color='gray', ls='solid')\n", + " # ax3.grid(color='gray', ls='solid')\n", " ax3.set_title(str(slicewave)+' microns: Weight Map', fontsize=25)\n", " ax3.tick_params(axis='both', which='major', labelsize=20)\n", " ax3.coords[0].set_ticklabel(rotation=13, ha='right', pad=24)\n", @@ -426,8 +426,8 @@ " \n", " fig.tight_layout(rect=[0, 0, 0.98, 0.98])\n", "\n", - " if save_figure == True:\n", - " fig.savefig(root+\".png\",dpi=24, bbox_inches=\"tight\")\n", + " if save_figure:\n", + " fig.savefig(root+\".png\", dpi=24, bbox_inches=\"tight\")\n", " " ] }, @@ -448,17 +448,17 @@ "metadata": {}, "outputs": [], "source": [ - "#To rerun the notebook and all the pipeline steps set runflag=True\n", + "# To rerun the notebook and all the pipeline steps set runflag=True\n", "runflag = True \n", "\n", - "#Demo directory -- contains pre-computed products\n", - "if runflag == False:\n", + "# Demo directory -- contains pre-computed products\n", + "if not runflag:\n", " output_dir = './nirspec_ifu_02732_demo/'\n", "\n", - "#Rerun directory\n", - "elif runflag == True:\n", - " #If you want to actually re-download the data and run everything offline, \n", - " #then comment out this line, set runflag=True, & specify a desired local directory\n", + "# Rerun directory\n", + "elif runflag:\n", + " # If you want to actually re-download the data and run everything offline, \n", + " # then comment out this line, set runflag=True, & specify a desired local directory\n", " output_dir = './nirspec_ifu_02732_rerun/'\n", " if not os.path.exists(output_dir):\n", " os.makedirs(output_dir)" @@ -503,9 +503,9 @@ }, "outputs": [], "source": [ - "#Download data from MAST \n", + "# Download data from MAST \n", "\n", - "#Setup your account \n", + "# Setup your account \n", "\n", "# NOTE:\n", "# The data in this notebook is public and does not require a token.\n", @@ -516,37 +516,37 @@ "\n", "sessioninfo = Observations.session_info()\n", "\n", - "#Define the general search criteria\n", + "# Define the general search criteria\n", "obs = Observations.query_criteria(\n", " obs_collection = 'JWST',\n", " instrument_name = ['NIRSPEC/IFU'],\n", " proposal_id = '02732')\n", "\n", - "#Print the Observations returned from the general serach criteria\n", + "# Print the Observations returned from the general search criteria\n", "products = Observations.get_product_list(obs)\n", - "#print(products)\n", + "# print(products)\n", "\n", - "#Filter the list of observations\n", - "#In this case we look for UNCAL products and ASN files to manually run pipeline stage 1-3\n", - "#We look for pre-processed MAST products for comparison: RATE (stage 1) & CAL (stage 2&3) & S3D (stage 2&3) & X1D (stage2&3) \n", + "# Filter the list of observations\n", + "# In this case, we look for UNCAL products and ASN files to manually run pipeline stage 1-3\n", + "# We look for pre-processed MAST products for comparison: RATE (stage 1) & CAL (stage 2&3) & S3D (stage 2&3) & X1D (stage2&3) \n", "filtered = Observations.filter_products(products,\n", " productSubGroupDescription=[\"UNCAL\", \"RATE\", \"CAL\", \"S3D\", \"X1D\", \"ASN\"],\n", " mrp_only=False)\n", - "#Print the filtered products\n", + "# Print the filtered products\n", "number = len(filtered)\n", "for k in range(number):\n", " print(filtered['productFilename'][k])\n", "\n", - "#Download the filtered products\n", - "#This creates a mastDownload directory, unless you set flat=True and set a download_dir\n", + "# Download the filtered products\n", + "# This creates a mastDownload directory, unless you set flat=True and set a download_dir\n", "for i in range(len(filtered)):\n", " mast_products_dir = output_dir+'mast_products/'\n", " if not os.path.exists(mast_products_dir):\n", " os.makedirs(mast_products_dir)\n", - " if runflag == True:\n", - " Observations.download_products(filtered[i], mrp_only=False, cache=False, flat=True, download_dir=mast_products_dir) #Override any cached files and download the most up-to-date ones\n", + " if runflag:\n", + " Observations.download_products(filtered[i], mrp_only=False, cache=False, flat=True, download_dir=mast_products_dir) # Override any cached files and download the most up-to-date ones\n", " else:\n", - " Observations.download_products(filtered[i], mrp_only=False, cache=True, flat=True, download_dir=mast_products_dir) #Find any cached files first before downloading new ones" + " Observations.download_products(filtered[i], mrp_only=False, cache=True, flat=True, download_dir=mast_products_dir) # Find any cached files first before downloading new ones" ] }, { @@ -580,32 +580,32 @@ }, "outputs": [], "source": [ - "#Stage 1 slope products -- level 2a images\n", + "# Stage 1 slope products -- level 2a images\n", "\n", - "#Plot 4th (out of 8) dither position (spectra fall only on NRS1) for GRATING/FILTER PRISM/CLEAR combination \n", + "# Plot 4th (out of 8) dither position (spectra fall only on NRS1) for GRATING/FILTER PRISM/CLEAR combination \n", "for rate_file in sorted(glob.glob(mast_products_dir+'*00004_nrs1_rate.fits')):\n", " \n", " ratefile_open = datamodels.open(rate_file)\n", " ratefile_sci = ratefile_open.data #get the pixel data (the SCI extension of the fits file)\n", " ratefile_dq = ratefile_open.dq #data quality map data (DQ extension)\n", " \n", - " #print the version and CRDS pmap used to create these rate.fits files \n", - " #ratefile_open.serach(key='context')\n", + " # print the version and CRDS pmap used to create these rate.fits files \n", + " # ratefile_open.serach(key='context')\n", " print(\"Products found in MAST used JWST calibration pipeline version: {} and {}\".format(ratefile_open.meta.calibration_software_version,\n", " ratefile_open.meta.ref_file.crds.context_used))\n", " \n", - " #Plot the slope image and zoom in on a small section of the countrate image & corresponding section of the DQ map\n", - " show_image(ratefile_sci, 0,10, units='DN/s',zoom_in=[500,550, 1250,1300],\n", + " # Plot the slope image and zoom in on a small section of the countrate image & corresponding section of the DQ map\n", + " show_image(ratefile_sci, 0, 10, units='DN/s', zoom_in=[500, 550, 1250,1300],\n", " title='Countrate Image \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", " ratefile_open.meta.dither.position_number, \n", " ratefile_open.meta.instrument.grating,\n", - " ratefile_open.meta.instrument.filter)) #rate files have units of DN/s\n", + " ratefile_open.meta.instrument.filter)) # rate files have units of DN/s\n", " \n", - " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear',zoom_in=[500,550, 1250,1300],\n", + " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear', zoom_in=[500, 550, 1250, 1300],\n", " title='Data Quality Map \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", - " ratefile_open.meta.dither.position_number, \n", - " ratefile_open.meta.instrument.grating,\n", - " ratefile_open.meta.instrument.filter)) \n", + " ratefile_open.meta.dither.position_number, \n", + " ratefile_open.meta.instrument.grating,\n", + " ratefile_open.meta.instrument.filter)) \n", " " ] }, @@ -637,21 +637,21 @@ "metadata": {}, "outputs": [], "source": [ - "#Stage 2 Products -- Calibrated 3-D data cube for PRISM/CLEAR (only falls on NRS1)\n", + "# Stage 2 Products -- Calibrated 3-D data cube for PRISM/CLEAR (only falls on NRS1)\n", "\n", - "#Plotting the 4th (out of 8) dither position\n", + "# Plotting the 4th (out of 8) dither position\n", "stage2_s3d_file = sorted(glob.glob(mast_products_dir+'*00004_nrs1_s3d.fits')) \n", "\n", - "title_stage2_mast='NGC 7319 AGN \\n Level 2 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", + "title_stage2_mast = 'NGC 7319 AGN \\n Level 2 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", "\n", "\n", - "#Characteristics of the plot \n", - "nrs1_wavelengths = [1.4,3.3,4.5] #Wavelength slices (microns) to take from the 3-D data cube\n", - "nrs1_spaxel_locs = [[30,29],[28,39],[14,25]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "# Characteristics of the plot \n", + "nrs1_wavelengths = [1.4, 3.3, 4.5] # Wavelength slices (microns) to take from the 3-D data cube\n", + "nrs1_spaxel_locs = [[30, 29], [28, 39], [14, 25]] # Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", "\n", "\n", - "#Plot using the convience function defined above\n", - "show_ifu_cubeslices(stage2_s3d_file, wavelength_slices=[nrs1_wavelengths], spaxel_locs=[nrs1_spaxel_locs],title=title_stage2_mast)" + "# Plot using the convience function defined above\n", + "show_ifu_cubeslices(stage2_s3d_file, wavelength_slices=[nrs1_wavelengths], spaxel_locs=[nrs1_spaxel_locs], title=title_stage2_mast)" ] }, { @@ -671,18 +671,18 @@ "metadata": {}, "outputs": [], "source": [ - "#Stage 3 Products -- Combined Calibrated 3-D data cube for PRISM/CLEAR (only falls on NRS1)\n", + "# Stage 3 Products -- Combined Calibrated 3-D data cube for PRISM/CLEAR (only falls on NRS1)\n", "\n", "stage3_s3d_file = sorted(glob.glob(mast_products_dir+'*nirspec_prism-clear_s3d.fits')) \n", "\n", - "title_stage3_mast='NGC 7319 AGN \\n Level 3 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", + "title_stage3_mast = 'NGC 7319 AGN \\n Level 3 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", "\n", - "#Characteristics of the plot \n", + "# Characteristics of the plot \n", "nrs1_wavelengths = [1.4,3.3,4.5] #Wavelength slices (microns) to take from the 3-D data cube\n", "nrs1_spaxel_locs = [[30,29],[28,39],[14,25]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", "\n", - "#Plot using the convience function defined above\n", - "show_ifu_cubeslices(stage3_s3d_file, wavelength_slices=[nrs1_wavelengths], spaxel_locs=[nrs1_spaxel_locs],title=title_stage3_mast)" + "# Plot using the convience function defined above\n", + "show_ifu_cubeslices(stage3_s3d_file, wavelength_slices=[nrs1_wavelengths], spaxel_locs=[nrs1_spaxel_locs], title=title_stage3_mast)" ] }, { @@ -703,27 +703,27 @@ "metadata": {}, "outputs": [], "source": [ - "#Stage 3 Products -- Combined Extracted 1-D Spectrum \n", + "# Stage 3 Products -- Combined Extracted 1-D Spectrum \n", "\n", "x1d3_mast = datamodels.open(glob.glob(mast_products_dir+'*nirspec_prism-clear_x1d.fits')[0])\n", "\n", - "#Wavelength & Surface Brightness Arrays\n", + "# Wavelength & Surface Brightness Arrays\n", "x1d3wave_mast = x1d3_mast.spec[0].spec_table.WAVELENGTH\n", "x1d3flux_mast = x1d3_mast.spec[0].spec_table.SURF_BRIGHT\n", "\n", - "#Plot the Extracted 1-D Spectrum\n", + "# Plot the Extracted 1-D Spectrum\n", "fig = plt.figure(figsize=(15,9))\n", "\n", "plt.plot(x1d3wave_mast,x1d3flux_mast, linewidth =2)\n", "\n", - "#Where wavelength slice was taken above\n", + "# Where wavelength slice was taken above\n", "plt.vlines(1.4, 0., 400., 'black', 'dotted', label='1.4 microns')\n", "plt.vlines(3.3, 0., 400., 'red', 'dotted', label='3.3 microns')\n", "plt.vlines(4.5, 0., 400., 'green', 'dotted', label='4.5 microns')\n", "\n", - "plt.xlabel('$\\lambda [\\mu$m]', fontsize =15)\n", - "plt.ylabel('Surface Brightness (MJy/sr)', fontsize =15)\n", - "plt.title(\"NGC 7319 AGN \\n Level 3 IFU Product in MAST: Extracted 1-D Spectrum\", fontsize =20)\n", + "plt.xlabel(r'$\\lambda [\\mu$m]', fontsize=15)\n", + "plt.ylabel('Surface Brightness (MJy/sr)', fontsize=15)\n", + "plt.title(\"NGC 7319 AGN \\n Level 3 IFU Product in MAST: Extracted 1-D Spectrum\", fontsize=20)\n", "plt.ylim(0, 50)\n", "plt.legend()\n", "plt.show()" @@ -758,11 +758,10 @@ "metadata": {}, "outputs": [], "source": [ - "#Just a check to see what verison of the pipeline and what pmap was used\n", + "# Just a check to see what version of the pipeline and what pmap was used\n", "x1d3_mast = fits.open(glob.glob(mast_products_dir+'*nirspec_prism-clear_x1d.fits')[0])\n", "\n", - "print(\"Products found in MAST used JWST calibration pipeline version: {} and {}\".format(x1d3_mast[0].header['CAL_VER']\n", - ",x1d3_mast[0].header['CRDS_CTX']))" + "print(\"Products found in MAST used JWST calibration pipeline version: {} and {}\".format(x1d3_mast[0].header['CAL_VER'], x1d3_mast[0].header['CRDS_CTX']))" ] }, { @@ -781,7 +780,7 @@ "metadata": {}, "outputs": [], "source": [ - "#Directory for rerun of stage 1 to avoid overwritting MAST products\n", + "# Directory for rerun of stage 1 to avoid overwriting MAST products\n", "output_dir_rerun = output_dir+'rerun/' \n", "if not os.path.exists(output_dir_rerun):\n", " os.makedirs(output_dir_rerun)" @@ -805,17 +804,17 @@ }, "outputs": [], "source": [ - "#Stage 1 Processing \n", + "# Stage 1 Processing \n", "\n", - "if runflag == True:\n", + "if runflag:\n", "\n", " for uncal_file in sorted(glob.glob(mast_products_dir+'*nrs1_uncal.fits')): \n", "\n", - " print(\"Applying Stage 1 Corrections & Calibrations to: \"+ os.path.basename(uncal_file))\n", + " print(\"Applying Stage 1 Corrections & Calibrations to: \" + os.path.basename(uncal_file))\n", "\n", " result = Detector1Pipeline.call(uncal_file,\n", - " save_results = True,\n", - " output_dir = output_dir_rerun)" + " save_results=True,\n", + " output_dir=output_dir_rerun)" ] }, { @@ -827,9 +826,9 @@ }, "outputs": [], "source": [ - "#Stage 1 slope products -- level 2a images\n", + "# Stage 1 slope products -- level 2a images\n", "\n", - "#Plot 4th (out of 8) dither position (NRS1 & NRS2) for GRATING/FILTER G140H/F100LP combination \n", + "# Plot 4th (out of 8) dither position (NRS1 & NRS2) for GRATING/FILTER G140H/F100LP combination \n", "for rate_file in sorted(glob.glob(output_dir_rerun+'*00004_nrs?_rate.fits')):\n", " \n", " ratefile_open = datamodels.open(rate_file)\n", @@ -837,20 +836,17 @@ " ratefile_dq = ratefile_open.dq # The Data Quality Map Data\n", " \n", " \n", - " show_image(ratefile_sci, 0,10, units='DN/s',zoom_in=[500,550, 1250,1300],\n", + " show_image(ratefile_sci, 0, 10, units='DN/s', zoom_in=[500, 550, 1250, 1300],\n", " title='Countrate Image \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", " ratefile_open.meta.dither.position_number, \n", " ratefile_open.meta.instrument.grating,\n", - " ratefile_open.meta.instrument.filter)) #rate files have units of DN/s\n", + " ratefile_open.meta.instrument.filter)) # rate files have units of DN/s\n", " \n", - " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear',zoom_in=[500,550, 1250,1300],\n", + " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear', zoom_in=[500, 550, 1250, 1300],\n", " title='Data Quality Map \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", " ratefile_open.meta.dither.position_number, \n", " ratefile_open.meta.instrument.grating,\n", - " ratefile_open.meta.instrument.filter)) #rate files have units of DN/s\n", - " \n", - " \n", - " " + " ratefile_open.meta.instrument.filter)) # rate files have units of DN/s " ] }, { @@ -891,18 +887,18 @@ }, "outputs": [], "source": [ - "#Stage 2 Processing \n", + "# Stage 2 Processing \n", "\n", - "if runflag == True:\n", + "if runflag:\n", " \n", - " #Process each rate file seperately \n", + " # Process each rate file separately \n", " for rate_file in sorted(glob.glob(output_dir_rerun+'*nrs1*rate.fits')):\n", " \n", - " print(\"Applying Stage 2 Calibrations & Corrections to: \"+ os.path.basename(rate_file))\n", + " print(\"Applying Stage 2 Calibrations & Corrections to: \" + os.path.basename(rate_file))\n", "\n", " result = Spec2Pipeline.call(rate_file,\n", - " save_results = True,\n", - " output_dir = output_dir_rerun)" + " save_results=True,\n", + " output_dir=output_dir_rerun)" ] }, { @@ -912,21 +908,21 @@ "metadata": {}, "outputs": [], "source": [ - "#Stage 2 Products -- Calibrated 3-D data cube for PRISM/CLEAR (only falls on NRS1)\n", + "# Stage 2 Products -- Calibrated 3-D data cube for PRISM/CLEAR (only falls on NRS1)\n", "\n", - "#Plotting the 4th (out of 8) dither position\n", + "# Plotting the 4th (out of 8) dither position\n", "stage2_s3d_file = sorted(glob.glob(output_dir_rerun+'*00004_nrs1_s3d.fits')) \n", "\n", - "title_stage2_rerun='NGC 7319 AGN \\n Level 2 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", + "title_stage2_rerun = 'NGC 7319 AGN \\n Level 2 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", "\n", "\n", - "#Characteristics of the plot \n", - "nrs1_wavelengths = [1.4,3.3,4.5] #Wavelength slices (microns) to take from the 3-D data cube\n", - "nrs1_spaxel_locs = [[30,29],[28,39],[14,25]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "# Characteristics of the plot \n", + "nrs1_wavelengths = [1.4, 3.3, 4.5] # Wavelength slices (microns) to take from the 3-D data cube\n", + "nrs1_spaxel_locs = [[30, 29], [28, 39], [14, 25]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", "\n", "\n", - "#Plot using the convience function defined above\n", - "show_ifu_cubeslices(stage2_s3d_file, wavelength_slices=[nrs1_wavelengths], spaxel_locs=[nrs1_spaxel_locs],title=title_stage2_rerun)" + "# Plot using the convience function defined above\n", + "show_ifu_cubeslices(stage2_s3d_file, wavelength_slices=[nrs1_wavelengths], spaxel_locs=[nrs1_spaxel_locs], title=title_stage2_rerun)" ] }, { @@ -955,13 +951,13 @@ "source": [ "#Copy ASN file from MAST into the stage 1 rerun directory\n", "\n", - "asnfile_mast = glob.glob(mast_products_dir+'*_spec3_00001_asn.json')[0] #ASN file found in MAST\n", + "asnfile_mast = glob.glob(mast_products_dir+'*_spec3_00001_asn.json')[0] # ASN file found in MAST\n", "\n", - "asnfile_rerun = output_dir_rerun+os.path.basename(asnfile_mast) #New ASN file path\n", + "asnfile_rerun = output_dir_rerun+os.path.basename(asnfile_mast) # New ASN file path\n", "if not os.path.exists(asnfile_rerun):\n", " copy(asnfile_mast, asnfile_rerun)\n", " \n", - "#Check the ASN file contents\n", + "# Check the ASN file contents\n", "with open(asnfile_rerun, 'r') as f_obj:\n", " asnfile_rerun_data = json.load(f_obj)\n", " \n", @@ -1003,12 +999,12 @@ "outputs": [], "source": [ "#Rerun stage 3 with outlier detection off\n", - "if runflag==True:\n", + "if runflag:\n", "\n", " result = Spec3Pipeline.call(asnfile_rerun,\n", - " save_results = True,\n", - " output_dir = output_dir_rerun,\n", - " steps = {\"outlier_detection\":{\"skip\": False,\n", + " save_results=True,\n", + " output_dir=output_dir_rerun,\n", + " steps={\"outlier_detection\": {\"skip\": False,\n", " \"save_results\": True,\n", " \"kernel_size\": '3 3'}})" ] @@ -1026,12 +1022,12 @@ "\n", "title_stage3_rerun='NGC 7319 AGN \\n Level 3 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", "\n", - "#Characteristics of the plot \n", - "nrs1_wavelengths = [1.4,3.3,4.5] #Wavelength slices (microns) to take from the 3-D data cube\n", - "nrs1_spaxel_locs = [[30,29],[28,39],[14,25]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "# Characteristics of the plot \n", + "nrs1_wavelengths = [1.4, 3.3, 4.5] # Wavelength slices (microns) to take from the 3-D data cube\n", + "nrs1_spaxel_locs = [[30, 29], [28, 39], [14, 25]] # Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", "\n", - "#Plot using the convience function defined above\n", - "show_ifu_cubeslices(stage3_s3d_file, wavelength_slices=[nrs1_wavelengths], spaxel_locs=[nrs1_spaxel_locs],title=title_stage3_rerun)" + "# Plot using the convience function defined above\n", + "show_ifu_cubeslices(stage3_s3d_file, wavelength_slices=[nrs1_wavelengths], spaxel_locs=[nrs1_spaxel_locs], title=title_stage3_rerun)" ] }, { @@ -1053,27 +1049,27 @@ "metadata": {}, "outputs": [], "source": [ - "#Stage 3 Products -- Combined Extracted 1-D Spectrum \n", + "# Stage 3 Products -- Combined Extracted 1-D Spectrum \n", "\n", "x1d3_rerun = datamodels.open(glob.glob(output_dir_rerun+'*nirspec_prism-clear_x1d.fits')[0])\n", "\n", - "#Wavelength & Surface Brightness Arrays\n", + "# Wavelength & Surface Brightness Arrays\n", "x1d3wave_rerun = x1d3_rerun.spec[0].spec_table.WAVELENGTH\n", "x1d3flux_rerun = x1d3_rerun.spec[0].spec_table.SURF_BRIGHT\n", "\n", - "#Plot the Extracted 1-D Spectrum\n", - "fig = plt.figure(figsize=(15,9))\n", + "# Plot the Extracted 1-D Spectrum\n", + "fig = plt.figure(figsize=(15, 9))\n", "\n", - "plt.plot(x1d3wave_rerun,x1d3flux_rerun, linewidth =2)\n", + "plt.plot(x1d3wave_rerun,x1d3flux_rerun, linewidth=2)\n", "\n", - "#Where wavelength slice was taken above\n", + "# Where wavelength slice was taken above\n", "plt.vlines(1.4, 0., 400., 'black', 'dotted', label='1.4 microns')\n", "plt.vlines(3.3, 0., 400., 'red', 'dotted', label='3.3 microns')\n", "plt.vlines(4.5, 0., 400., 'green', 'dotted', label='4.5 microns')\n", "\n", - "plt.xlabel('$\\lambda [\\mu$m]', fontsize =15)\n", - "plt.ylabel('Surface Brightness (MJy/sr)', fontsize =15)\n", - "plt.title(\"NGC 7319 AGN \\n Level 3 IFU Product in MAST: Extracted 1-D Spectrum\", fontsize =20)\n", + "plt.xlabel(r'$\\lambda [\\mu$m]', fontsize=15)\n", + "plt.ylabel('Surface Brightness (MJy/sr)', fontsize=15)\n", + "plt.title(\"NGC 7319 AGN \\n Level 3 IFU Product in MAST: Extracted 1-D Spectrum\", fontsize=20)\n", "plt.ylim(0, 55)\n", "plt.legend()\n", "plt.show()" @@ -1133,7 +1129,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.11.6" } }, "nbformat": 4, From a8b33b94b54bf1b0b3f41f13765dae29555147ca Mon Sep 17 00:00:00 2001 From: Hatice Karatay Date: Wed, 4 Oct 2023 16:43:05 -0400 Subject: [PATCH 06/12] Address additional style errors --- .../ero_nirspec_ifu_02729_demo.ipynb | 61 ++++---- .../ero_nirspec_ifu_02732_demo.ipynb | 143 +++++++++--------- ...o_nirspec_ifu_02732_demo_pointsource.ipynb | 86 +++++------ 3 files changed, 142 insertions(+), 148 deletions(-) diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb index 1faf0938c..9cffce196 100644 --- a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb @@ -84,32 +84,31 @@ "\n", "# ----------------------------------------------General Imports-----------------------------------------------------\n", "\n", - "import numpy as np\n", - "import warnings\n", - "warnings.filterwarnings('ignore') # Set to 'default' to turn warnings back on\n", + "import numpy as np # noqa\n", + "import warnings # noqa\n", + "warnings.filterwarnings('ignore') # Set to 'default' to turn warnings back on\n", "\n", "# --------------------------------------------File Operation Imports------------------------------------------------\n", "\n", - "import glob\n", - "import os\n", - "import json\n", - "from shutil import copy\n", + "import glob # noqa\n", + "import os # noqa\n", + "import json # noqa\n", + "from shutil import copy # noqa\n", "\n", "# --------------------------------------------Astropy/Astroquery Imports--------------------------------------------\n", "\n", - "from astropy.io import fits\n", - "from astropy import wcs\n", - "from astropy.wcs import WCS\n", - "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch\n", - "from astroquery.mast import Observations\n", + "from astropy.io import fits # noqa\n", + "from astropy import wcs # noqa\n", + "from astropy.wcs import WCS # noqa\n", + "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch # noqa\n", + "from astroquery.mast import Observations # noqa\n", "\n", "# ------------------------------------------------Plotting Imports--------------------------------------------------\n", "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib as mpl\n", - "from matplotlib.patches import Circle\n", - "import matplotlib.gridspec as grd\n", - "from matplotlib import cm\n", + "import matplotlib.pyplot as plt # noqa\n", + "import matplotlib as mpl # noqa\n", + "import matplotlib.gridspec as grd # noqa\n", + "from matplotlib import cm # noqa\n", "\n", "# Use this version for non-interactive plots (easier scrolling of the notebook)\n", "%matplotlib inline\n", @@ -274,12 +273,12 @@ "\n", " # SCI Extension: [Type:ImageHDU Cards:92 Dimensions:(57, 61, 973) Format:float32]\n", " cube = s3d[1].data # Science data\n", - " wcs = WCS(s3d[1].header) # World Coordinate System (WCS) Transformation keywords \n", - " wmap = s3d[4].data # 3-D weight image giving the relative weights of the output spaxels.\n", - " cdelt1 = s3d[1].header['CDELT1']*3600. # Axis 1 coordinate increment at reference point \n", - " cdelt2 = s3d[1].header['CDELT2']*3600. # Axis 2 coordinate increment at reference point \n", - " cdelt3 = s3d[1].header['CDELT3'] # Axis 3 coordinate increment at reference point \n", - " crval3 = s3d[1].header['CRVAL3'] # third axis value at the reference pixel \n", + " wcs = WCS(s3d[1].header) # World Coordinate System (WCS) Transformation keywords # noqa\n", + " wmap = s3d[4].data # 3-D weight image giving the relative weights of the output spaxels.\n", + " cdelt1 = s3d[1].header['CDELT1']*3600. # Axis 1 coordinate increment at reference point # noqa\n", + " cdelt2 = s3d[1].header['CDELT2']*3600. # Axis 2 coordinate increment at reference point # noqa\n", + " cdelt3 = s3d[1].header['CDELT3'] # Axis 3 coordinate increment at reference point \n", + " crval3 = s3d[1].header['CRVAL3'] # Third axis value at the reference pixel \n", "\n", " # Wavelength range of the grating/filter combination\n", " wavstart = s3d[1].header['WAVSTART']\n", @@ -297,7 +296,7 @@ " wavelengths = wavelength_slices[0]\n", " spaxel_loc = spaxel_locs[0]\n", " vmin_vmax_vals = vmin_vmax[0]\n", - " \n", + " \n", " if y_scale:\n", " y_scales = y_scale[0]\n", "\n", @@ -313,7 +312,7 @@ " spaxel_loc = spaxel_locs[0]\n", " vmin_vmax_vals = vmin_vmax[0]\n", " if y_scale:\n", - " y_scales = y_scale[0]\n", + " y_scales = y_scale[0]\n", "\n", " # Loop through each wavelength slices\n", " for i, wave_slice in enumerate(wavelengths):\n", @@ -351,7 +350,6 @@ " ax1.tick_params(axis='both', which='major', labelsize=20)\n", " ax1.coords[0].set_ticklabel(rotation=13, ha='right', pad=24)\n", "\n", - " \n", " # ------------------------------------------Spaxel 1-D Spectrum---------------------------------------------\n", " \n", " # Zoom in on a Spaxel: Spectrum\n", @@ -394,7 +392,7 @@ " slice_wmap = ax3.imshow(slice_mean_wmap, norm=slice_norm_wmap, origin='lower', aspect='auto', cmap=cmap) # plot slice\n", "\n", " cb_wmap = fig.colorbar(slice_wmap, fraction=0.046, pad=0.04)\n", - " cb_wmap.set_label('Weight', labelpad=-1, fontsize = 22)\n", + " cb_wmap.set_label('Weight', labelpad=-1, fontsize=22)\n", " cb_wmap.ax.tick_params(labelsize=20)\n", " cb_wmap.ax.yaxis.get_offset_text().set_fontsize(20)\n", " \n", @@ -417,8 +415,7 @@ " fig.tight_layout(rect=[0, 0, 0.98, 0.98])\n", "\n", " if save_figure:\n", - " fig.savefig(root+\".png\", dpi=24, bbox_inches=\"tight\")\n", - "\n" + " fig.savefig(root+\".png\", dpi=24, bbox_inches=\"tight\")" ] }, { @@ -438,7 +435,7 @@ "outputs": [], "source": [ "# To rerun the notebook and all the pipeline steps set runflag=True\n", - "runflag = True \n", + "runflag = True\n", "\n", "# Demo directory -- contains pre-computed products\n", "if not runflag:\n", @@ -586,7 +583,7 @@ " title='Countrate Image \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", " ratefile_open.meta.dither.position_number, \n", " ratefile_open.meta.instrument.grating,\n", - " ratefile_open.meta.instrument.filter)) #rate files have units of DN/s\n", + " ratefile_open.meta.instrument.filter)) # Rate files have units of DN/s\n", " \n", " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear', zoom_in=[650, 700, 1250, 1300], ysize=20, xsize=20,\n", " title='Data Quality Map \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", @@ -857,7 +854,7 @@ " title='Countrate Image \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", " ratefile_open.meta.dither.position_number, \n", " ratefile_open.meta.instrument.grating,\n", - " ratefile_open.meta.instrument.filter)) # rate files have units of DN/s\n", + " ratefile_open.meta.instrument.filter)) # Rate files have units of DN/s\n", " \n", " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear', zoom_in=[650, 700, 1250, 1300], ysize=20, xsize=20,\n", " title='Data Quality Map \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb index dc264f403..4580caf46 100644 --- a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb @@ -86,36 +86,34 @@ "\n", "# ----------------------------------------------General Imports-----------------------------------------------------\n", "\n", - "import numpy as np\n", - "import warnings\n", - "warnings.filterwarnings('ignore') #Set to 'default' to turn warnings back on\n", + "import numpy as np # noqa\n", + "import warnings # noqa\n", + "warnings.filterwarnings('ignore') # Set to 'default' to turn warnings back on\n", "\n", "# --------------------------------------------File Operation Imports------------------------------------------------\n", "\n", - "import glob\n", - "import os\n", - "import asdf\n", - "import json\n", - "from shutil import copy\n", + "import glob # noqa\n", + "import os # noqa\n", + "import asdf # noqa\n", + "import json # noqa\n", + "from shutil import copy # noqa\n", "\n", "# --------------------------------------------Astropy/Astroquery Imports--------------------------------------------\n", "\n", - "from astropy.io import fits\n", - "from astropy import wcs\n", - "from astropy.wcs import WCS\n", + "from astropy.io import fits # noqa\n", + "from astropy import wcs # noqa\n", + "from astropy.wcs import WCS # noqa\n", "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch, SqrtStretch\n", - "from astropy.stats import sigma_clipped_stats\n", - "import astroquery\n", - "from astroquery.mast import Mast\n", - "from astroquery.mast import Observations\n", + "import astroquery # noqa\n", + "from astroquery.mast import Mast # noqa\n", + "from astroquery.mast import Observations # noqa\n", "\n", "# ------------------------------------------------Plotting Imports--------------------------------------------------\n", "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt # noqa\n", + "import matplotlib as mpl # noqa\n", "import matplotlib.gridspec as grd\n", - "from matplotlib.patches import Circle\n", - "from matplotlib import cm\n", + "from matplotlib import cm # noqa\n", "\n", "# Use this version for non-interactive plots (easier scrolling of the notebook)\n", "%matplotlib inline\n", @@ -216,12 +214,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "3d233556", "metadata": {}, "outputs": [], "source": [ - "def show_ifu_cubeslices(s3d_file_list, wavelength_slices=[], spaxel_locs=[], y_scale=None, cmap='jet', vmin_vmax = [[[0, 15e1]]], save_figure=False, title=None, title_font=30):\n", + "def show_ifu_cubeslices(s3d_file_list, wavelength_slices=[], spaxel_locs=[], y_scale=None, cmap='jet', vmin_vmax=[[[0, 15e1]]], save_figure=False, title=None, title_font=30):\n", " \"\"\"\n", " Function to that takes a 3-D IFU data cube and generates: \n", " \n", @@ -256,9 +254,9 @@ " \n", " # ---------------------------------------------- Set-up Figure -------------------------------------------------\n", "\n", - " #Plot Slices From the Cube\n", - " fig, axs = plt.subplots(3, np.array(wavelength_slices).size, figsize=(8*np.array(wavelength_slices).size,18))\n", - " gs = grd.GridSpec(3, np.array(wavelength_slices).size, height_ratios=[1]*3, width_ratios=[1]*np.array(wavelength_slices).size, hspace=0.4,wspace=0.7)\n", + " # Plot Slices From the Cube\n", + " fig, axs = plt.subplots(3, np.array(wavelength_slices).size, figsize=(8*np.array(wavelength_slices).size, 18))\n", + " gs = grd.GridSpec(3, np.array(wavelength_slices).size, height_ratios=[1]*3, width_ratios=[1]*np.array(wavelength_slices).size, hspace=0.4, wspace=0.7)\n", "\n", " total_num_plots = 3*np.array(wavelength_slices).size\n", " \n", @@ -267,10 +265,10 @@ " \n", " for s3d_file in s3d_file_list:\n", " \n", - " root=s3d_file[:-9] #Root file name \n", + " root=s3d_file[:-9] # Root file name \n", "\n", - " s3d = fits.open(s3d_file) #3-D IFU data cube fits file \n", - " x1d3 = datamodels.open(root+'_x1d.fits') #1-D Extracted Spectrum \n", + " s3d = fits.open(s3d_file) # 3-D IFU data cube fits file\n", + " x1d3 = datamodels.open(root+'_x1d.fits') # 1-D Extracted Spectrum \n", " \n", " # --------------------------------Wavelength & Surface Brightness/Flux Arrays------------------------------\n", " \n", @@ -278,13 +276,12 @@ " \n", " # --------------------------------------Data & Header Information------------------------------------------\n", "\n", - " \n", " # SCI Extension: [Type:ImageHDU Cards:92 Dimensions:(57, 61, 973) Format:float32]\n", " cube = s3d[1].data # Science data\n", - " wcs = WCS(s3d[1].header) # World Coordinate System (WCS) Transformation keywords \n", + " wcs = WCS(s3d[1].header) # World Coordinate System (WCS) Transformation keywords # noqa\n", " wmap = s3d[4].data # 3-D weight image giving the relative weights of the output spaxels.\n", - " cdelt1 = s3d[1].header['CDELT1']*3600. # Axis 1 coordinate increment at reference point \n", - " cdelt2 = s3d[1].header['CDELT2']*3600. # Axis 2 coordinate increment at reference point \n", + " cdelt1 = s3d[1].header['CDELT1']*3600. # Axis 1 coordinate increment at reference point # noqa\n", + " cdelt2 = s3d[1].header['CDELT2']*3600. # Axis 2 coordinate increment at reference point # noqa\n", " cdelt3 = s3d[1].header['CDELT3'] # Axis 3 coordinate increment at reference point \n", " crval3 = s3d[1].header['CRVAL3'] # Third axis value at the reference pixel \n", "\n", @@ -321,13 +318,12 @@ " spaxel_loc = spaxel_locs[0]\n", " vmin_vmax_vals = vmin_vmax[0]\n", " if y_scale:\n", - " y_scales = y_scale[0]\n", + " y_scales = y_scale[0]\n", "\n", - " \n", " # Loop through each wavelength slices\n", " for i, wave_slice in enumerate(wavelengths):\n", "\n", - " if float(wavstart)<=wave_slice*10**-6 <= float(wavend):\n", + " if float(wavstart) <= wave_slice*10**-6 <= float(wavend):\n", " \n", " # --------------------------------------------2-D Cube Slice------------------------------------------------\n", " \n", @@ -340,14 +336,14 @@ " vmin_val = vmin_vmax_vals[0][0]\n", "\n", " slicewave = wave_slice\n", - " nslice = int((slicewave - crval3)/cdelt3) #the slice of the cube we want to plot\n", + " nslice = int((slicewave - crval3)/cdelt3) # The slice of the cube we want to plot\n", "\n", " ax1 = plt.subplot(gs[0+plot_count], projection=wcs, slices=('x', 'y', nslice)) # Set up the subplot space\n", - " #ax1 = plt.subplot(3,len(wavelength_slices), 0+plot_count, projection=wcs, slices=('x', 'y', nslice)) # Set up the subplot space\n", + " # ax1 = plt.subplot(3,len(wavelength_slices), 0+plot_count, projection=wcs, slices=('x', 'y', nslice)) # Set up the subplot space\n", "\n", " slice_mean = np.nanmean(cube[(nslice-2):(nslice+2), :, :], axis=0) # Mean of the slice looking in the range (nslice-2):(nslice+2)\n", - " slice_norm=ImageNormalize(slice_mean, vmin=vmin_val, vmax=vmax_val, stretch=AsinhStretch()) # Normalize &stretch \n", - " slice_image= ax1.imshow(slice_mean, norm=slice_norm, origin='lower', aspect='auto',cmap=cmap) # Plot slice\n", + " slice_norm = ImageNormalize(slice_mean, vmin=vmin_val, vmax=vmax_val, stretch=AsinhStretch()) # Normalize &stretch \n", + " slice_image = ax1.imshow(slice_mean, norm=slice_norm, origin='lower', aspect='auto', cmap=cmap) # Plot slice\n", " \n", " cb_image = fig.colorbar(slice_image, fraction=0.046, pad=0.04)\n", " cb_image.set_label('MJy/sr', labelpad=-1, fontsize=22)\n", @@ -356,28 +352,27 @@ " \n", " ax1.set_xlabel('RA', fontsize=22)\n", " ax1.set_ylabel('DEC', labelpad=-1, fontsize=22)\n", - " #ax1.grid(color='white', ls='solid')\n", + " # ax1.grid(color='white', ls='solid')\n", " ax1.set_title('Detector {} \\n Grating/Filter: {}/{} \\n {} microns'.format(s3d[0].header['DETECTOR'],s3d[0].header['GRATING'], s3d[0].header['FILTER'], str(slicewave)), fontsize=25)\n", " ax1.tick_params(axis='both', which='major', labelsize=20)\n", " ax1.coords[0].set_ticklabel(rotation=13, ha='right', pad=24)\n", "\n", - " \n", " # ------------------------------------------Spaxel 1-D Spectrum---------------------------------------------\n", " \n", " # Zoom in on a Spaxel: Spectrum\n", - " loc = [spaxel_loc[i][0],spaxel_loc[i][1]]\n", + " loc = [spaxel_loc[i][0], spaxel_loc[i][1]]\n", " x1d3flux_loc = cube[:, loc[1], loc[0]]\n", " # ax2 = plt.subplot(3,len(wavelength_slices), int(total_num_plots/3)+plot_count)\n", " ax2 = plt.subplot(gs[int(total_num_plots/3)+plot_count])\n", "\n", " # Spaxel Box Highlight \n", - " spaxel_rect = plt.Rectangle((loc[0]-.5, loc[1]-.5), 1,1, fill=False, color='black', linewidth=2)\n", + " spaxel_rect = plt.Rectangle((loc[0]-.5, loc[1]-.5), 1, 1, fill=False, color='black', linewidth=2)\n", " ax1.add_patch(spaxel_rect)\n", " \n", " ax2.plot(x1d3wave, x1d3flux_loc, linewidth=1, color=colors[i])\n", " ax2.grid(linewidth=2)\n", " ax2.set_xlabel(r'$\\u03BB [\\u03BC$m]',fontsize=22)\n", - " ax2.set_ylabel(\"Surface Brightness \\n (MJy/sr)\",fontsize=22)\n", + " ax2.set_ylabel(\"Surface Brightness \\n (MJy/sr)\", fontsize=22)\n", " ax2.set_title('Spaxel at (x, y)='+repr(loc), fontsize=25)\n", " ax2.tick_params(axis='both', which='major', labelsize=20)\n", " ax2.yaxis.get_offset_text().set_fontsize(15)\n", @@ -400,8 +395,8 @@ " # ax3 = plt.subplot(3, len(wavelength_slices), int(total_num_plots)-len(wavelength_slices)+plot_count, projection=wcs, slices=('x', 'y', nslice)) # Set up the subplot space\n", " \n", " slice_mean_wmap = np.nanmean(wmap[(nslice-2):(nslice+2), :, :], axis=0) # Mean of the wmap slice looking in the range (nslice-2):(nslice+2)\n", - " slice_norm_wmap=ImageNormalize(slice_mean_wmap, stretch=AsinhStretch()) # Normalize &stretch\n", - " slice_wmap = ax3.imshow(slice_mean_wmap, norm=slice_norm_wmap, origin='lower',aspect='auto', cmap=cmap) # Plot slice\n", + " slice_norm_wmap = ImageNormalize(slice_mean_wmap, stretch=AsinhStretch()) # Normalize &stretch\n", + " slice_wmap = ax3.imshow(slice_mean_wmap, norm=slice_norm_wmap, origin='lower', aspect='auto', cmap=cmap) # Plot slice\n", " \n", " cb_wmap = fig.colorbar(slice_wmap, fraction=0.046, pad=0.04)\n", " cb_wmap.set_label('Weight', labelpad=-1, fontsize=22)\n", @@ -427,8 +422,7 @@ " fig.tight_layout(rect=[0, 0, 0.98, 0.98])\n", "\n", " if save_figure:\n", - " fig.savefig(root+\".png\", dpi=24, bbox_inches=\"tight\")\n", - " " + " fig.savefig(root+\".png\", dpi=24, bbox_inches=\"tight\")" ] }, { @@ -443,10 +437,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "f94025d4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'os' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[2], line 13\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m runflag:\n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;03m# If you want to actually re-download the data and run everything offline, \u001b[39;00m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# then comment out this line, set runflag=True, & specify a desired local directory\u001b[39;00m\n\u001b[1;32m 12\u001b[0m output_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./nirspec_ifu_02732_rerun/\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m---> 13\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[43mos\u001b[49m\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(output_dir):\n\u001b[1;32m 14\u001b[0m os\u001b[38;5;241m.\u001b[39mmakedirs(output_dir)\n", + "\u001b[0;31mNameError\u001b[0m: name 'os' is not defined" + ] + } + ], "source": [ "# To rerun the notebook and all the pipeline steps set runflag=True\n", "runflag = True \n", @@ -518,9 +524,9 @@ "\n", "# Define the general search criteria\n", "obs = Observations.query_criteria(\n", - " obs_collection = 'JWST',\n", - " instrument_name = ['NIRSPEC/IFU'],\n", - " proposal_id = '02732')\n", + " obs_collection='JWST',\n", + " instrument_name=['NIRSPEC/IFU'],\n", + " proposal_id='02732')\n", "\n", "# Print the Observations returned from the general search criteria\n", "products = Observations.get_product_list(obs)\n", @@ -586,21 +592,20 @@ "for rate_file in sorted(glob.glob(mast_products_dir+'*00004_nrs1_rate.fits')):\n", " \n", " ratefile_open = datamodels.open(rate_file)\n", - " ratefile_sci = ratefile_open.data #get the pixel data (the SCI extension of the fits file)\n", - " ratefile_dq = ratefile_open.dq #data quality map data (DQ extension)\n", + " ratefile_sci = ratefile_open.data # Get the pixel data (the SCI extension of the fits file)\n", + " ratefile_dq = ratefile_open.dq # Data quality map data (DQ extension)\n", " \n", - " # print the version and CRDS pmap used to create these rate.fits files \n", + " # Print the version and CRDS pmap used to create these rate.fits files \n", " # ratefile_open.serach(key='context')\n", " print(\"Products found in MAST used JWST calibration pipeline version: {} and {}\".format(ratefile_open.meta.calibration_software_version,\n", " ratefile_open.meta.ref_file.crds.context_used))\n", " \n", " # Plot the slope image and zoom in on a small section of the countrate image & corresponding section of the DQ map\n", - " show_image(ratefile_sci, 0, 10, units='DN/s', zoom_in=[500, 550, 1250,1300],\n", + " show_image(ratefile_sci, 0, 10, units='DN/s', zoom_in=[500, 550, 1250, 1300],\n", " title='Countrate Image \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", " ratefile_open.meta.dither.position_number, \n", " ratefile_open.meta.instrument.grating,\n", " ratefile_open.meta.instrument.filter)) # rate files have units of DN/s\n", - " \n", " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear', zoom_in=[500, 550, 1250, 1300],\n", " title='Data Quality Map \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", " ratefile_open.meta.dither.position_number, \n", @@ -644,12 +649,10 @@ "\n", "title_stage2_mast = 'NGC 7319 AGN \\n Level 2 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", "\n", - "\n", "# Characteristics of the plot \n", "nrs1_wavelengths = [1.4, 3.3, 4.5] # Wavelength slices (microns) to take from the 3-D data cube\n", "nrs1_spaxel_locs = [[30, 29], [28, 39], [14, 25]] # Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", "\n", - "\n", "# Plot using the convience function defined above\n", "show_ifu_cubeslices(stage2_s3d_file, wavelength_slices=[nrs1_wavelengths], spaxel_locs=[nrs1_spaxel_locs], title=title_stage2_mast)" ] @@ -678,8 +681,8 @@ "title_stage3_mast = 'NGC 7319 AGN \\n Level 3 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", "\n", "# Characteristics of the plot \n", - "nrs1_wavelengths = [1.4,3.3,4.5] #Wavelength slices (microns) to take from the 3-D data cube\n", - "nrs1_spaxel_locs = [[30,29],[28,39],[14,25]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "nrs1_wavelengths = [1.4, 3.3, 4.5] # Wavelength slices (microns) to take from the 3-D data cube\n", + "nrs1_spaxel_locs = [[30, 29], [28, 39], [14, 25]] # Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", "\n", "# Plot using the convience function defined above\n", "show_ifu_cubeslices(stage3_s3d_file, wavelength_slices=[nrs1_wavelengths], spaxel_locs=[nrs1_spaxel_locs], title=title_stage3_mast)" @@ -712,9 +715,9 @@ "x1d3flux_mast = x1d3_mast.spec[0].spec_table.SURF_BRIGHT\n", "\n", "# Plot the Extracted 1-D Spectrum\n", - "fig = plt.figure(figsize=(15,9))\n", + "fig = plt.figure(figsize=(15, 9))\n", "\n", - "plt.plot(x1d3wave_mast,x1d3flux_mast, linewidth =2)\n", + "plt.plot(x1d3wave_mast, x1d3flux_mast, linewidth=2)\n", "\n", "# Where wavelength slice was taken above\n", "plt.vlines(1.4, 0., 400., 'black', 'dotted', label='1.4 microns')\n", @@ -949,7 +952,7 @@ }, "outputs": [], "source": [ - "#Copy ASN file from MAST into the stage 1 rerun directory\n", + "# Copy ASN file from MAST into the stage 1 rerun directory\n", "\n", "asnfile_mast = glob.glob(mast_products_dir+'*_spec3_00001_asn.json')[0] # ASN file found in MAST\n", "\n", @@ -998,15 +1001,15 @@ }, "outputs": [], "source": [ - "#Rerun stage 3 with outlier detection off\n", + "# Rerun stage 3 with outlier detection off\n", "if runflag:\n", "\n", " result = Spec3Pipeline.call(asnfile_rerun,\n", " save_results=True,\n", " output_dir=output_dir_rerun,\n", " steps={\"outlier_detection\": {\"skip\": False,\n", - " \"save_results\": True,\n", - " \"kernel_size\": '3 3'}})" + " \"save_results\": True,\n", + " \"kernel_size\": '3 3'}})" ] }, { @@ -1016,11 +1019,11 @@ "metadata": {}, "outputs": [], "source": [ - "#Stage 3 Products -- Combined Calibrated 3-D data cube for PRISM/CLEAR \n", + "# Stage 3 Products -- Combined Calibrated 3-D data cube for PRISM/CLEAR \n", "\n", "stage3_s3d_file = sorted(glob.glob(output_dir_rerun+'*nirspec_prism-clear_s3d.fits')) \n", "\n", - "title_stage3_rerun='NGC 7319 AGN \\n Level 3 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", + "title_stage3_rerun = 'NGC 7319 AGN \\n Level 3 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", "\n", "# Characteristics of the plot \n", "nrs1_wavelengths = [1.4, 3.3, 4.5] # Wavelength slices (microns) to take from the 3-D data cube\n", @@ -1060,7 +1063,7 @@ "# Plot the Extracted 1-D Spectrum\n", "fig = plt.figure(figsize=(15, 9))\n", "\n", - "plt.plot(x1d3wave_rerun,x1d3flux_rerun, linewidth=2)\n", + "plt.plot(x1d3wave_rerun, x1d3flux_rerun, linewidth=2)\n", "\n", "# Where wavelength slice was taken above\n", "plt.vlines(1.4, 0., 400., 'black', 'dotted', label='1.4 microns')\n", diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb index 375e788df..d6c473a5e 100644 --- a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb @@ -81,36 +81,35 @@ "\n", "# ----------------------------------------------General Imports-----------------------------------------------------\n", "\n", - "import numpy as np\n", - "import warnings\n", - "warnings.filterwarnings('ignore') #Set to 'default' to turn warnings back on\n", + "import numpy as np # noqa\n", + "import warnings # noqa\n", + "warnings.filterwarnings('ignore') # Set to 'default' to turn warnings back on\n", "\n", "# --------------------------------------------File Operation Imports------------------------------------------------\n", "\n", - "import glob\n", - "import os\n", - "import asdf\n", - "import json\n", - "from shutil import copy\n", + "import glob # noqa\n", + "import os # noqa\n", + "import asdf # noqa\n", + "import json # noqa\n", + "from shutil import copy # noqa\n", "\n", "# --------------------------------------------Astropy/Astroquery Imports--------------------------------------------\n", "\n", - "from astropy.io import fits\n", - "from astropy import wcs\n", - "from astropy.wcs import WCS\n", - "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch, SqrtStretch\n", - "from astropy.stats import sigma_clipped_stats\n", - "import astroquery\n", - "from astroquery.mast import Mast\n", - "from astroquery.mast import Observations\n", + "from astropy.io import fits # noqa\n", + "from astropy import wcs # noqa\n", + "from astropy.wcs import WCS # noqa\n", + "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch # noqa\n", + "import astroquery # noqa\n", + "from astroquery.mast import Mast # noqa\n", + "from astroquery.mast import Observations # noqa\n", "\n", "# ------------------------------------------------Plotting Imports--------------------------------------------------\n", "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib as mpl\n", - "import matplotlib.gridspec as grd\n", - "from matplotlib.patches import Circle\n", - "from matplotlib import cm\n", + "import matplotlib.pyplot as plt # noqa\n", + "import matplotlib as mpl # noqa\n", + "import matplotlib.gridspec as grd # noqa\n", + "from matplotlib.patches import Circle # noqa\n", + "from matplotlib import cm # noqa\n", "\n", "# Use this version for non-interactive plots (easier scrolling of the notebook)\n", "%matplotlib inline\n", @@ -216,7 +215,7 @@ "metadata": {}, "outputs": [], "source": [ - "def show_ifu_cubeslices(s3d_file_list, wavelength_slices=[], spaxel_locs=[], y_scale=None, cmap='jet', vmin_vmax = [[[0, 15e1]]], save_figure=False, title=None, title_font=30):\n", + "def show_ifu_cubeslices(s3d_file_list, wavelength_slices=[], spaxel_locs=[], y_scale=None, cmap='jet', vmin_vmax=[[[0, 15e1]]], save_figure=False, title=None, title_font=30):\n", " \"\"\"\n", " Function to that takes a 3-D IFU data cube and generates: \n", " \n", @@ -252,10 +251,10 @@ " # ---------------------------------------------- Set-up Figure -------------------------------------------------\n", "\n", " # Plot Slices From the Cube\n", - " fig, axs = plt.subplots(3, np.array(wavelength_slices).size, figsize=(8*np.array(wavelength_slices).size,18))\n", - " gs = grd.GridSpec(3, np.array(wavelength_slices).size, height_ratios=[1]*3, width_ratios=[1]*np.array(wavelength_slices).size, hspace=0.4,wspace=0.7)\n", + " fig, axs = plt.subplots(3, np.array(wavelength_slices).size, figsize=(8*np.array(wavelength_slices).size, 18))\n", + " gs = grd.GridSpec(3, np.array(wavelength_slices).size, height_ratios=[1]*3, width_ratios=[1]*np.array(wavelength_slices).size, hspace=0.4, wspace=0.7)\n", "\n", - " total_num_plots=3*np.array(wavelength_slices).size\n", + " total_num_plots = 3*np.array(wavelength_slices).size\n", " \n", " plot_count = 0\n", " # ---------------------------------------------Open Files------------------------------------------------------\n", @@ -273,13 +272,12 @@ " \n", " # --------------------------------------Data & Header Information------------------------------------------\n", "\n", - " \n", " # SCI Extension: [Type:ImageHDU Cards:92 Dimensions:(57, 61, 973) Format:float32]\n", " cube = s3d[1].data # Science data\n", - " wcs = WCS(s3d[1].header) # World Coordinate System (WCS) Transformation keywords \n", + " wcs = WCS(s3d[1].header) # World Coordinate System (WCS) Transformation keywords # noqa\n", " wmap = s3d[4].data # 3-D weight image giving the relative weights of the output spaxels.\n", - " cdelt1 = s3d[1].header['CDELT1']*3600. # Axis 1 coordinate increment at reference point \n", - " cdelt2 = s3d[1].header['CDELT2']*3600. # Axis 2 coordinate increment at reference point \n", + " cdelt1 = s3d[1].header['CDELT1']*3600. # Axis 1 coordinate increment at reference point # noqa\n", + " cdelt2 = s3d[1].header['CDELT2']*3600. # Axis 2 coordinate increment at reference point # noqa\n", " cdelt3 = s3d[1].header['CDELT3'] # Axis 3 coordinate increment at reference point \n", " crval3 = s3d[1].header['CRVAL3'] # Third axis value at the reference pixel \n", "\n", @@ -315,13 +313,12 @@ " spaxel_loc = spaxel_locs[0]\n", " vmin_vmax_vals = vmin_vmax[0]\n", " if y_scale:\n", - " y_scales = y_scale[0]\n", + " y_scales = y_scale[0]\n", "\n", - " \n", " # Loop through each wavelength slices\n", " for i, wave_slice in enumerate(wavelengths):\n", "\n", - " if float(wavstart)<=wave_slice*10**-6 <= float(wavend):\n", + " if float(wavstart) <= wave_slice*10**-6 <= float(wavend):\n", " \n", " # --------------------------------------------2-D Cube Slice------------------------------------------------\n", " \n", @@ -339,8 +336,8 @@ " # ax1 = plt.subplot(3,len(wavelength_slices), 0+plot_count, projection=wcs, slices=('x', 'y', nslice)) # Set up the subplot space\n", "\n", " slice_mean = np.nanmean(cube[(nslice-2):(nslice+2), :, :], axis=0) # Mean of the slice looking in the range (nslice-2):(nslice+2)\n", - " slice_norm=ImageNormalize(slice_mean, vmin=vmin_val, vmax=vmax_val, stretch=AsinhStretch()) # Normalize &stretch \n", - " slice_image= ax1.imshow(slice_mean, norm=slice_norm, origin='lower', aspect='auto', cmap=cmap) # Plot slice\n", + " slice_norm = ImageNormalize(slice_mean, vmin=vmin_val, vmax=vmax_val, stretch=AsinhStretch()) # Normalize &stretch \n", + " slice_image = ax1.imshow(slice_mean, norm=slice_norm, origin='lower', aspect='auto', cmap=cmap) # Plot slice\n", "\n", " cb_image = fig.colorbar(slice_image, fraction=0.046, pad=0.04)\n", " cb_image.set_label('MJy/sr', labelpad=-1, fontsize=22)\n", @@ -354,11 +351,10 @@ " ax1.tick_params(axis='both', which='major', labelsize=20)\n", " ax1.coords[0].set_ticklabel(rotation=13, ha='right', pad=24)\n", "\n", - " \n", " # ------------------------------------------Spaxel 1-D Spectrum---------------------------------------------\n", " \n", " # Zoom in on a Spaxel: Spectrum\n", - " loc = [spaxel_loc[i][0],spaxel_loc[i][1]]\n", + " loc = [spaxel_loc[i][0], spaxel_loc[i][1]]\n", " x1d3flux_loc = cube[:, loc[1], loc[0]]\n", " # ax2 = plt.subplot(3,len(wavelength_slices), int(total_num_plots/3)+plot_count)\n", " ax2 = plt.subplot(gs[int(total_num_plots/3)+plot_count])\n", @@ -370,7 +366,7 @@ " ax2.plot(x1d3wave, x1d3flux_loc, linewidth=1, color=colors[i])\n", " ax2.grid(linewidth=2)\n", " ax2.set_xlabel('$\\u03BB [\\u03BC$m]',fontsize=22)\n", - " ax2.set_ylabel(\"Surface Brightness \\n (MJy/sr)\",fontsize=22)\n", + " ax2.set_ylabel(\"Surface Brightness \\n (MJy/sr)\", fontsize=22)\n", " ax2.set_title('Spaxel at (x, y)='+repr(loc), fontsize=25)\n", " ax2.tick_params(axis='both', which='major', labelsize=20)\n", " ax2.yaxis.get_offset_text().set_fontsize(15)\n", @@ -393,8 +389,8 @@ " # ax3 = plt.subplot(3, len(wavelength_slices), int(total_num_plots)-len(wavelength_slices)+plot_count, projection=wcs, slices=('x', 'y', nslice)) # Set up the subplot space\n", " \n", " slice_mean_wmap = np.nanmean(wmap[(nslice-2):(nslice+2), :, :], axis=0) # Mean of the wmap slice looking in the range (nslice-2):(nslice+2)\n", - " slice_norm_wmap=ImageNormalize(slice_mean_wmap, stretch=AsinhStretch()) # Normalize & stretch\n", - " slice_wmap = ax3.imshow(slice_mean_wmap, norm=slice_norm_wmap, origin='lower',aspect='auto', cmap=cmap) # Plot slice\n", + " slice_norm_wmap = ImageNormalize(slice_mean_wmap, stretch=AsinhStretch()) # Normalize & stretch\n", + " slice_wmap = ax3.imshow(slice_mean_wmap, norm=slice_norm_wmap, origin='lower', aspect='auto', cmap=cmap) # Plot slice\n", "\n", " cb_wmap = fig.colorbar(slice_wmap, fraction=0.046, pad=0.04)\n", " cb_wmap.set_label('Weight', labelpad=-1, fontsize=22)\n", @@ -420,8 +416,7 @@ " fig.tight_layout(rect=[0, 0, 0.98, 0.98])\n", "\n", " if save_figure:\n", - " fig.savefig(root+\".png\", dpi=24, bbox_inches=\"tight\")\n", - " " + " fig.savefig(root+\".png\", dpi=24, bbox_inches=\"tight\")" ] }, { @@ -596,7 +591,6 @@ " ratefile_open.meta.dither.position_number, \n", " ratefile_open.meta.instrument.grating,\n", " ratefile_open.meta.instrument.filter)) # rate files have units of DN/s\n", - " \n", " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear',zoom_in=[500, 550, 1250, 1300],\n", " title='Data Quality Map \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", " ratefile_open.meta.dither.position_number, \n", @@ -776,8 +770,8 @@ " save_results=True,\n", " output_dir=output_dir,\n", " steps={\"outlier_detection\": {\"skip\": False,\n", - " \"save_results\": True,\n", - " \"kernel_size\": '3 3'},\n", + " \"save_results\": True,\n", + " \"kernel_size\": '3 3'},\n", " \"extract_1d\": {\"subtract_background\": False}}) # Do not automatically apply background subtraction until we modify the extraction region\n", " " ] @@ -821,9 +815,9 @@ "x1d3flux_rerun_point = x1d3_rerun_point.spec[0].spec_table.FLUX\n", "\n", "# Plot the Extracted 1-D Spectrum\n", - "fig = plt.figure(figsize=(15,9))\n", + "fig = plt.figure(figsize=(15, 9))\n", "\n", - "plt.plot(x1d3wave_rerun_point,x1d3flux_rerun_point, linewidth =2)\n", + "plt.plot(x1d3wave_rerun_point, x1d3flux_rerun_point, linewidth=2)\n", "\n", "# Where wavelength slice was taken above\n", "plt.vlines(1.4, 0., 400., 'black', 'dotted', label='1.4 microns')\n", From 2033e8bbf25f86b159112a3c0fc3dde81bb16c17 Mon Sep 17 00:00:00 2001 From: Hatice Karatay Date: Thu, 5 Oct 2023 12:56:15 -0400 Subject: [PATCH 07/12] Address more of the style errors --- .../ero_nirspec_ifu_02729_demo.ipynb | 55 ++++++----- .../ero_nirspec_ifu_02732_demo.ipynb | 91 ++++++++---------- ...o_nirspec_ifu_02732_demo_pointsource.ipynb | 95 ++++++++++--------- 3 files changed, 119 insertions(+), 122 deletions(-) diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb index 9cffce196..9a2f7f816 100644 --- a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb @@ -68,10 +68,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Import Library\n", - "\n", "# --------------------------------------JWST Calibration Pipeline Imports-------------------------------------------\n", - "\n", "import jwst\n", "import crds\n", "from jwst import datamodels\n", @@ -80,35 +77,43 @@ "from jwst.pipeline import Spec3Pipeline # calwebb_spec3\n", "\n", "print(\"JWST Calibration Pipeline Version={}\".format(jwst.__version__))\n", - "print(\"Current Operational CRDS Context = {}\".format(crds.get_default_context()))\n", - "\n", - "# ----------------------------------------------General Imports-----------------------------------------------------\n", - "\n", - "import numpy as np # noqa\n", - "import warnings # noqa\n", - "warnings.filterwarnings('ignore') # Set to 'default' to turn warnings back on\n", - "\n", + "print(\"Current Operational CRDS Context = {}\".format(crds.get_default_context()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "968d8315-5e14-498f-a3cd-134a6ab0fcd1", + "metadata": {}, + "outputs": [], + "source": [ "# --------------------------------------------File Operation Imports------------------------------------------------\n", "\n", - "import glob # noqa\n", - "import os # noqa\n", - "import json # noqa\n", - "from shutil import copy # noqa\n", + "import glob\n", + "import os\n", + "import json\n", + "from shutil import copy\n", "\n", "# --------------------------------------------Astropy/Astroquery Imports--------------------------------------------\n", "\n", - "from astropy.io import fits # noqa\n", - "from astropy import wcs # noqa\n", - "from astropy.wcs import WCS # noqa\n", - "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch # noqa\n", - "from astroquery.mast import Observations # noqa\n", + "from astropy.io import fits\n", + "from astropy import wcs\n", + "from astropy.wcs import WCS\n", + "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch\n", + "from astroquery.mast import Observations\n", "\n", "# ------------------------------------------------Plotting Imports--------------------------------------------------\n", "\n", - "import matplotlib.pyplot as plt # noqa\n", - "import matplotlib as mpl # noqa\n", - "import matplotlib.gridspec as grd # noqa\n", - "from matplotlib import cm # noqa\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "import matplotlib.gridspec as grd\n", + "from matplotlib import cm\n", + "\n", + "# ----------------------------------------------General Imports-----------------------------------------------------\n", + "\n", + "import numpy as np\n", + "import warnings\n", + "warnings.filterwarnings('ignore') # Set to 'default' to turn warnings back on\n", "\n", "# Use this version for non-interactive plots (easier scrolling of the notebook)\n", "%matplotlib inline\n", @@ -194,7 +199,7 @@ "\n", " # Zoom in on a portion of the image? \n", " if zoom_in:\n", - " # inset axis \n", + " # inset axis\n", " axins = ax.inset_axes([0.5, 0.6, 0.5, 0.3])\n", " \n", " axins.imshow(data_2d, origin=\"lower\", norm=norm, aspect=aspect, cmap=cmap)\n", diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb index 4580caf46..25444f0cc 100644 --- a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb @@ -69,8 +69,6 @@ "metadata": {}, "outputs": [], "source": [ - "# Import Library\n", - "\n", "# --------------------------------------JWST Calibration Pipeline Imports-------------------------------------------\n", "\n", "import jwst\n", @@ -79,41 +77,48 @@ "from jwst.pipeline import Detector1Pipeline # calwebb_detector1\n", "from jwst.pipeline import Spec2Pipeline # calwebb_spec2\n", "from jwst.pipeline import Spec3Pipeline # calwebb_spec3\n", - "from jwst.extract_1d import Extract1dStep # Extract1D Individual Step\n", "\n", "print(\"JWST Calibration Pipeline Version={}\".format(jwst.__version__))\n", - "print(\"Current Operational CRDS Context = {}\".format(crds.get_default_context()))\n", - "\n", - "# ----------------------------------------------General Imports-----------------------------------------------------\n", - "\n", - "import numpy as np # noqa\n", - "import warnings # noqa\n", - "warnings.filterwarnings('ignore') # Set to 'default' to turn warnings back on\n", - "\n", + "print(\"Current Operational CRDS Context = {}\".format(crds.get_default_context()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c5ef196-77d6-4fe5-bc06-efd86ea32be5", + "metadata": {}, + "outputs": [], + "source": [ "# --------------------------------------------File Operation Imports------------------------------------------------\n", "\n", - "import glob # noqa\n", - "import os # noqa\n", - "import asdf # noqa\n", - "import json # noqa\n", - "from shutil import copy # noqa\n", + "import glob\n", + "import os\n", + "import asdf\n", + "import json\n", + "from shutil import copy\n", "\n", "# --------------------------------------------Astropy/Astroquery Imports--------------------------------------------\n", "\n", - "from astropy.io import fits # noqa\n", - "from astropy import wcs # noqa\n", - "from astropy.wcs import WCS # noqa\n", - "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch, SqrtStretch\n", - "import astroquery # noqa\n", - "from astroquery.mast import Mast # noqa\n", - "from astroquery.mast import Observations # noqa\n", + "from astropy.io import fits\n", + "from astropy import wcs\n", + "from astropy.wcs import WCS\n", + "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch\n", + "import astroquery\n", + "from astroquery.mast import Mast\n", + "from astroquery.mast import Observations\n", "\n", "# ------------------------------------------------Plotting Imports--------------------------------------------------\n", "\n", - "import matplotlib.pyplot as plt # noqa\n", - "import matplotlib as mpl # noqa\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", "import matplotlib.gridspec as grd\n", - "from matplotlib import cm # noqa\n", + "from matplotlib import cm\n", + "\n", + "# ----------------------------------------------General Imports-----------------------------------------------------\n", + "\n", + "import numpy as np\n", + "import warnings\n", + "warnings.filterwarnings('ignore') # Set to 'default' to turn warnings back on\n", "\n", "# Use this version for non-interactive plots (easier scrolling of the notebook)\n", "%matplotlib inline\n", @@ -214,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "3d233556", "metadata": {}, "outputs": [], @@ -265,7 +270,7 @@ " \n", " for s3d_file in s3d_file_list:\n", " \n", - " root=s3d_file[:-9] # Root file name \n", + " root = s3d_file[:-9] # Root file name \n", "\n", " s3d = fits.open(s3d_file) # 3-D IFU data cube fits file\n", " x1d3 = datamodels.open(root+'_x1d.fits') # 1-D Extracted Spectrum \n", @@ -353,7 +358,7 @@ " ax1.set_xlabel('RA', fontsize=22)\n", " ax1.set_ylabel('DEC', labelpad=-1, fontsize=22)\n", " # ax1.grid(color='white', ls='solid')\n", - " ax1.set_title('Detector {} \\n Grating/Filter: {}/{} \\n {} microns'.format(s3d[0].header['DETECTOR'],s3d[0].header['GRATING'], s3d[0].header['FILTER'], str(slicewave)), fontsize=25)\n", + " ax1.set_title('Detector {} \\n Grating/Filter: {}/{} \\n {} microns'.format(s3d[0].header['DETECTOR'], s3d[0].header['GRATING'], s3d[0].header['FILTER'], str(slicewave)), fontsize=25)\n", " ax1.tick_params(axis='both', which='major', labelsize=20)\n", " ax1.coords[0].set_ticklabel(rotation=13, ha='right', pad=24)\n", "\n", @@ -365,13 +370,13 @@ " # ax2 = plt.subplot(3,len(wavelength_slices), int(total_num_plots/3)+plot_count)\n", " ax2 = plt.subplot(gs[int(total_num_plots/3)+plot_count])\n", "\n", - " # Spaxel Box Highlight \n", + " # Spaxel Box Highlight\n", " spaxel_rect = plt.Rectangle((loc[0]-.5, loc[1]-.5), 1, 1, fill=False, color='black', linewidth=2)\n", " ax1.add_patch(spaxel_rect)\n", " \n", " ax2.plot(x1d3wave, x1d3flux_loc, linewidth=1, color=colors[i])\n", " ax2.grid(linewidth=2)\n", - " ax2.set_xlabel(r'$\\u03BB [\\u03BC$m]',fontsize=22)\n", + " ax2.set_xlabel(r'$\\u03BB [\\u03BC$m]', fontsize=22)\n", " ax2.set_ylabel(\"Surface Brightness \\n (MJy/sr)\", fontsize=22)\n", " ax2.set_title('Spaxel at (x, y)='+repr(loc), fontsize=25)\n", " ax2.tick_params(axis='both', which='major', labelsize=20)\n", @@ -437,22 +442,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "f94025d4", "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'os' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[2], line 13\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m runflag:\n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;03m# If you want to actually re-download the data and run everything offline, \u001b[39;00m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# then comment out this line, set runflag=True, & specify a desired local directory\u001b[39;00m\n\u001b[1;32m 12\u001b[0m output_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./nirspec_ifu_02732_rerun/\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m---> 13\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[43mos\u001b[49m\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(output_dir):\n\u001b[1;32m 14\u001b[0m os\u001b[38;5;241m.\u001b[39mmakedirs(output_dir)\n", - "\u001b[0;31mNameError\u001b[0m: name 'os' is not defined" - ] - } - ], + "outputs": [], "source": [ "# To rerun the notebook and all the pipeline steps set runflag=True\n", "runflag = True \n", @@ -610,8 +603,7 @@ " title='Data Quality Map \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", " ratefile_open.meta.dither.position_number, \n", " ratefile_open.meta.instrument.grating,\n", - " ratefile_open.meta.instrument.filter)) \n", - " " + " ratefile_open.meta.instrument.filter))" ] }, { @@ -838,7 +830,6 @@ " ratefile_sci = ratefile_open.data # Get the pixel data (the SCI extension of the fits file)\n", " ratefile_dq = ratefile_open.dq # The Data Quality Map Data\n", " \n", - " \n", " show_image(ratefile_sci, 0, 10, units='DN/s', zoom_in=[500, 550, 1250, 1300],\n", " title='Countrate Image \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", " ratefile_open.meta.dither.position_number, \n", @@ -920,8 +911,8 @@ "\n", "\n", "# Characteristics of the plot \n", - "nrs1_wavelengths = [1.4, 3.3, 4.5] # Wavelength slices (microns) to take from the 3-D data cube\n", - "nrs1_spaxel_locs = [[30, 29], [28, 39], [14, 25]] #Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "nrs1_wavelengths = [1.4, 3.3, 4.5] # Wavelength slices (microns) to take from the 3-D data cube\n", + "nrs1_spaxel_locs = [[30, 29], [28, 39], [14, 25]] # Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", "\n", "\n", "# Plot using the convience function defined above\n", diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb index d6c473a5e..510901bc3 100644 --- a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb @@ -64,8 +64,6 @@ "metadata": {}, "outputs": [], "source": [ - "# Import Library\n", - "\n", "# --------------------------------------JWST Calibration Pipeline Imports-------------------------------------------\n", "\n", "import jwst\n", @@ -77,39 +75,46 @@ "from jwst.extract_1d import Extract1dStep # Extract1D Individual Step\n", "\n", "print(\"JWST Calibration Pipeline Version={}\".format(jwst.__version__))\n", - "print(\"Current Operational CRDS Context = {}\".format(crds.get_default_context()))\n", - "\n", - "# ----------------------------------------------General Imports-----------------------------------------------------\n", - "\n", - "import numpy as np # noqa\n", - "import warnings # noqa\n", - "warnings.filterwarnings('ignore') # Set to 'default' to turn warnings back on\n", - "\n", + "print(\"Current Operational CRDS Context = {}\".format(crds.get_default_context()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ecbdca5b-af15-424c-a1d4-525094dfe17f", + "metadata": {}, + "outputs": [], + "source": [ "# --------------------------------------------File Operation Imports------------------------------------------------\n", - "\n", - "import glob # noqa\n", - "import os # noqa\n", - "import asdf # noqa\n", - "import json # noqa\n", - "from shutil import copy # noqa\n", + "import glob\n", + "import os\n", + "import asdf\n", + "import json\n", + "from shutil import copy\n", "\n", "# --------------------------------------------Astropy/Astroquery Imports--------------------------------------------\n", "\n", - "from astropy.io import fits # noqa\n", - "from astropy import wcs # noqa\n", - "from astropy.wcs import WCS # noqa\n", - "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch # noqa\n", - "import astroquery # noqa\n", - "from astroquery.mast import Mast # noqa\n", - "from astroquery.mast import Observations # noqa\n", + "from astropy.io import fits\n", + "from astropy import wcs\n", + "from astropy.wcs import WCS\n", + "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch\n", + "import astroquery\n", + "from astroquery.mast import Mast\n", + "from astroquery.mast import Observations\n", "\n", "# ------------------------------------------------Plotting Imports--------------------------------------------------\n", "\n", - "import matplotlib.pyplot as plt # noqa\n", - "import matplotlib as mpl # noqa\n", - "import matplotlib.gridspec as grd # noqa\n", - "from matplotlib.patches import Circle # noqa\n", - "from matplotlib import cm # noqa\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "import matplotlib.gridspec as grd\n", + "from matplotlib.patches import Circle\n", + "from matplotlib import cm\n", + "\n", + "# ----------------------------------------------General Imports-----------------------------------------------------\n", + "\n", + "import numpy as np\n", + "import warnings\n", + "warnings.filterwarnings('ignore') # Set to 'default' to turn warnings back on\n", "\n", "# Use this version for non-interactive plots (easier scrolling of the notebook)\n", "%matplotlib inline\n", @@ -205,7 +210,7 @@ " axins.set_ylim(zoom_in[2], zoom_in[3])\n", " axins.set_xticklabels([])\n", " axins.set_yticklabels([])\n", - " ax.indicate_inset_zoom(axins, color=\"black\",edgecolor=\"black\", linewidth=3)" + " ax.indicate_inset_zoom(axins, color=\"black\", edgecolor=\"black\", linewidth=3)" ] }, { @@ -261,7 +266,7 @@ " \n", " for s3d_file in s3d_file_list:\n", " \n", - " root=s3d_file[:-9] # Root file name \n", + " root = s3d_file[:-9] # Root file name \n", "\n", " s3d = fits.open(s3d_file) # 3-D IFU data cube fits file \n", " x1d3 = datamodels.open(root+'_x1d.fits') # 1-D Extracted Spectrum \n", @@ -365,7 +370,7 @@ " \n", " ax2.plot(x1d3wave, x1d3flux_loc, linewidth=1, color=colors[i])\n", " ax2.grid(linewidth=2)\n", - " ax2.set_xlabel('$\\u03BB [\\u03BC$m]',fontsize=22)\n", + " ax2.set_xlabel('$\\u03BB [\\u03BC$m]', fontsize=22)\n", " ax2.set_ylabel(\"Surface Brightness \\n (MJy/sr)\", fontsize=22)\n", " ax2.set_title('Spaxel at (x, y)='+repr(loc), fontsize=25)\n", " ax2.tick_params(axis='both', which='major', labelsize=20)\n", @@ -585,13 +590,12 @@ " ratefile_sci = ratefile_open.data # Get the pixel data (the SCI extension of the fits file)\n", " ratefile_dq = ratefile_open.dq # The Data Quality Map Data\n", " \n", - " \n", - " show_image(ratefile_sci, 0,10, units='DN/s',zoom_in=[500, 550, 1250, 1300],\n", + " show_image(ratefile_sci, 0, 10, units='DN/s', zoom_in=[500, 550, 1250, 1300],\n", " title='Countrate Image \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", " ratefile_open.meta.dither.position_number, \n", " ratefile_open.meta.instrument.grating,\n", " ratefile_open.meta.instrument.filter)) # rate files have units of DN/s\n", - " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear',zoom_in=[500, 550, 1250, 1300],\n", + " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear', zoom_in=[500, 550, 1250, 1300],\n", " title='Data Quality Map \\n Detector: {} \\n 8-Cycle Dither Position Index: {} \\n GRATING/FILTER: {}/{}'.format(ratefile_open.meta.instrument.detector,\n", " ratefile_open.meta.dither.position_number, \n", " ratefile_open.meta.instrument.grating,\n", @@ -680,14 +684,13 @@ "# Plotting the 4th (out of 8) dither position\n", "stage2_s3d_file = sorted(glob.glob(output_dir+'*00004_nrs1_s3d.fits')) \n", "\n", - "title_stage2_rerun='NGC 7319 AGN \\n Level 2 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", + "title_stage2_rerun = 'NGC 7319 AGN \\n Level 2 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", "\n", "\n", "# Characteristics of the plot \n", "nrs1_wavelengths = [1.4, 3.3, 4.5] # Wavelength slices (microns) to take from the 3-D data cube\n", "nrs1_spaxel_locs = [[30, 29], [28, 39], [14, 25]] # Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", "\n", - "\n", "# Plot using the convience function defined above\n", "show_ifu_cubeslices(stage2_s3d_file, wavelength_slices=[nrs1_wavelengths], spaxel_locs=[nrs1_spaxel_locs], title=title_stage2_rerun)" ] @@ -765,15 +768,13 @@ "source": [ "# Rerun stage 3 with outlier detection on\n", "if runflag:\n", - "\n", " result = Spec3Pipeline.call(asnfile_rerun_point,\n", " save_results=True,\n", " output_dir=output_dir,\n", " steps={\"outlier_detection\": {\"skip\": False,\n", " \"save_results\": True,\n", " \"kernel_size\": '3 3'},\n", - " \"extract_1d\": {\"subtract_background\": False}}) # Do not automatically apply background subtraction until we modify the extraction region\n", - " " + " \"extract_1d\": {\"subtract_background\": False}}) # Do not automatically apply background subtraction until we modify the extraction region" ] }, { @@ -787,7 +788,7 @@ "\n", "stage3_s3d_file_point = sorted(glob.glob(output_dir+'*nirspec_prism-clear_s3d.fits')) \n", "\n", - "title_stage3_rerun_point='NGC 7319 AGN \\n Level 3 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", + "title_stage3_rerun_point = 'NGC 7319 AGN \\n Level 3 IFU Product: 3-D Cube Slices vs. Corresponding 3-D Weighted Map'\n", "\n", "# Characteristics of the plot \n", "nrs1_wavelengths = [1.4, 3.3, 4.5] # Wavelength slices (microns) to take from the 3-D data cube\n", @@ -867,23 +868,23 @@ "# Extraction Region Preview\n", "# Open Combined 3-D Cube FITS file\n", "s3d = fits.open(glob.glob(output_dir+'*nirspec_prism-clear_s3d.fits')[0])\n", - "cube = s3d[1].data #Science data\n", + "cube = s3d[1].data # Science data\n", "\n", "# Plot the full IFU cube\n", - "ax = plt.subplot(1, 1, 1)#, projection=wcs, slices=('x', 'y', nslice3)) # Set up the subplot space\n", + "ax = plt.subplot(1, 1, 1) #, projection=wcs, slices=('x', 'y', nslice3)) # Set up the subplot space\n", "slice_mean = np.nanmean(cube[400:500, :, :], axis=0) # Mean of the slice looking in the range (nslice2-2):(nslice2+2)\n", "slice_norm = ImageNormalize(slice_mean, vmin=0, vmax=150, stretch=AsinhStretch()) # Normalize &stretch\n", "slice_full = ax.imshow(slice_mean, norm=slice_norm, origin='lower', cmap='jet') # Plot slice\n", "\n", "# Colorbar\n", "cb_image = plt.colorbar(slice_full, fraction=0.046, pad=0.04)\n", - "cb_image.set_label('MJy/sr', labelpad=-1, fontsize = 10)\n", + "cb_image.set_label('MJy/sr', labelpad=-1, fontsize=10)\n", "cb_image.ax.tick_params(labelsize=10)\n", "cb_image.ax.yaxis.get_offset_text().set_fontsize(10)\n", "\n", "radius = Circle((29, 29), 9, fill=False, label='Radius')\n", - "inner_bkg = Circle((29, 29), 10, color='b',fill=False, label='Inner Background Radius')\n", - "outer_bkg= Circle((29, 29), 15, color='r',fill=False, label='Outer Background Radius')\n", + "inner_bkg = Circle((29, 29), 10, color='b', fill=False, label='Inner Background Radius')\n", + "outer_bkg = Circle((29, 29), 15, color='r', fill=False, label='Outer Background Radius')\n", "ax.add_patch(radius)\n", "ax.add_patch(inner_bkg)\n", "ax.add_patch(outer_bkg)\n", @@ -959,8 +960,8 @@ "plt.vlines(3.3, 0., 400., 'red', 'dotted', label='3.3 microns')\n", "plt.vlines(4.5, 0., 400., 'green', 'dotted', label='4.5 microns')\n", "\n", - "plt.xlabel('$\\lambda [\\mu$m]')\n", - "plt.ylabel('Flux (Jy)', fontsize =20)\n", + "plt.xlabel(r'$\\lambda [\\mu$m]')\n", + "plt.ylabel('Flux (Jy)', fontsize=20)\n", "plt.title(\"NGC 7319 AGN \\n Level 3 IFU Product: Extracted 1-D Spectrum with Background Subtraction\")\n", "plt.ylim(0, 10**-1.6)\n", "plt.ticklabel_format(axis='y', style='sci', scilimits=(0, -2))\n", From fe1728ceded598d835fee35e75dcde96893bbb17 Mon Sep 17 00:00:00 2001 From: Hatice Karatay Date: Thu, 5 Oct 2023 16:54:55 -0400 Subject: [PATCH 08/12] Test the updated CI and fix style errors --- .../ero_nirspec_ifu_02729_demo.ipynb | 29 +++++++-------- .../ero_nirspec_ifu_02732_demo.ipynb | 32 +++++++---------- ...o_nirspec_ifu_02732_demo_pointsource.ipynb | 36 ++++++++----------- 3 files changed, 38 insertions(+), 59 deletions(-) diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb index 9a2f7f816..23edcdde3 100644 --- a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb @@ -68,7 +68,10 @@ "metadata": {}, "outputs": [], "source": [ + "# Import Library\n", + "\n", "# --------------------------------------JWST Calibration Pipeline Imports-------------------------------------------\n", + "\n", "import jwst\n", "import crds\n", "from jwst import datamodels\n", @@ -77,16 +80,14 @@ "from jwst.pipeline import Spec3Pipeline # calwebb_spec3\n", "\n", "print(\"JWST Calibration Pipeline Version={}\".format(jwst.__version__))\n", - "print(\"Current Operational CRDS Context = {}\".format(crds.get_default_context()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "968d8315-5e14-498f-a3cd-134a6ab0fcd1", - "metadata": {}, - "outputs": [], - "source": [ + "print(\"Current Operational CRDS Context = {}\".format(crds.get_default_context()))\n", + "\n", + "# ----------------------------------------------General Imports-----------------------------------------------------\n", + "\n", + "import numpy as np\n", + "import warnings\n", + "warnings.filterwarnings('ignore') # Set to 'default' to turn warnings back on\n", + "\n", "# --------------------------------------------File Operation Imports------------------------------------------------\n", "\n", "import glob\n", @@ -98,7 +99,7 @@ "\n", "from astropy.io import fits\n", "from astropy import wcs\n", - "from astropy.wcs import WCS\n", + "from astropy.wcs import WCS # noqa\n", "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch\n", "from astroquery.mast import Observations\n", "\n", @@ -109,12 +110,6 @@ "import matplotlib.gridspec as grd\n", "from matplotlib import cm\n", "\n", - "# ----------------------------------------------General Imports-----------------------------------------------------\n", - "\n", - "import numpy as np\n", - "import warnings\n", - "warnings.filterwarnings('ignore') # Set to 'default' to turn warnings back on\n", - "\n", "# Use this version for non-interactive plots (easier scrolling of the notebook)\n", "%matplotlib inline\n", "# Use this version (outside of Jupyter Lab) if you want interactive plots\n", diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb index 25444f0cc..b9c69a032 100644 --- a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb @@ -65,10 +65,12 @@ { "cell_type": "code", "execution_count": null, - "id": "403fa920", + "id": "4c5ef196-77d6-4fe5-bc06-efd86ea32be5", "metadata": {}, "outputs": [], "source": [ + "# Import Library\n", + "\n", "# --------------------------------------JWST Calibration Pipeline Imports-------------------------------------------\n", "\n", "import jwst\n", @@ -79,16 +81,14 @@ "from jwst.pipeline import Spec3Pipeline # calwebb_spec3\n", "\n", "print(\"JWST Calibration Pipeline Version={}\".format(jwst.__version__))\n", - "print(\"Current Operational CRDS Context = {}\".format(crds.get_default_context()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4c5ef196-77d6-4fe5-bc06-efd86ea32be5", - "metadata": {}, - "outputs": [], - "source": [ + "print(\"Current Operational CRDS Context = {}\".format(crds.get_default_context()))\n", + "\n", + "# ----------------------------------------------General Imports-----------------------------------------------------\n", + "\n", + "import numpy as np\n", + "import warnings\n", + "warnings.filterwarnings('ignore') # Set to 'default' to turn warnings back on\n", + "\n", "# --------------------------------------------File Operation Imports------------------------------------------------\n", "\n", "import glob\n", @@ -101,10 +101,8 @@ "\n", "from astropy.io import fits\n", "from astropy import wcs\n", - "from astropy.wcs import WCS\n", + "from astropy.wcs import WCS # noqa\n", "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch\n", - "import astroquery\n", - "from astroquery.mast import Mast\n", "from astroquery.mast import Observations\n", "\n", "# ------------------------------------------------Plotting Imports--------------------------------------------------\n", @@ -114,12 +112,6 @@ "import matplotlib.gridspec as grd\n", "from matplotlib import cm\n", "\n", - "# ----------------------------------------------General Imports-----------------------------------------------------\n", - "\n", - "import numpy as np\n", - "import warnings\n", - "warnings.filterwarnings('ignore') # Set to 'default' to turn warnings back on\n", - "\n", "# Use this version for non-interactive plots (easier scrolling of the notebook)\n", "%matplotlib inline\n", "# Use this version (outside of Jupyter Lab) if you want interactive plots\n", diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb index 510901bc3..f05034a47 100644 --- a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb @@ -60,10 +60,12 @@ { "cell_type": "code", "execution_count": null, - "id": "6230afa9-1909-4b20-a46c-38e9832df227", + "id": "ecbdca5b-af15-424c-a1d4-525094dfe17f", "metadata": {}, "outputs": [], "source": [ + "# Import Library\n", + "\n", "# --------------------------------------JWST Calibration Pipeline Imports-------------------------------------------\n", "\n", "import jwst\n", @@ -75,16 +77,14 @@ "from jwst.extract_1d import Extract1dStep # Extract1D Individual Step\n", "\n", "print(\"JWST Calibration Pipeline Version={}\".format(jwst.__version__))\n", - "print(\"Current Operational CRDS Context = {}\".format(crds.get_default_context()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ecbdca5b-af15-424c-a1d4-525094dfe17f", - "metadata": {}, - "outputs": [], - "source": [ + "print(\"Current Operational CRDS Context = {}\".format(crds.get_default_context()))\n", + "\n", + "# ----------------------------------------------General Imports-----------------------------------------------------\n", + "\n", + "import numpy as np\n", + "import warnings\n", + "warnings.filterwarnings('ignore') # Set to 'default' to turn warnings back on\n", + "\n", "# --------------------------------------------File Operation Imports------------------------------------------------\n", "import glob\n", "import os\n", @@ -96,10 +96,8 @@ "\n", "from astropy.io import fits\n", "from astropy import wcs\n", - "from astropy.wcs import WCS\n", + "from astropy.wcs import WCS # noqa\n", "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch\n", - "import astroquery\n", - "from astroquery.mast import Mast\n", "from astroquery.mast import Observations\n", "\n", "# ------------------------------------------------Plotting Imports--------------------------------------------------\n", @@ -110,12 +108,6 @@ "from matplotlib.patches import Circle\n", "from matplotlib import cm\n", "\n", - "# ----------------------------------------------General Imports-----------------------------------------------------\n", - "\n", - "import numpy as np\n", - "import warnings\n", - "warnings.filterwarnings('ignore') # Set to 'default' to turn warnings back on\n", - "\n", "# Use this version for non-interactive plots (easier scrolling of the notebook)\n", "%matplotlib inline\n", "# Use this version (outside of Jupyter Lab) if you want interactive plots\n", @@ -774,7 +766,7 @@ " steps={\"outlier_detection\": {\"skip\": False,\n", " \"save_results\": True,\n", " \"kernel_size\": '3 3'},\n", - " \"extract_1d\": {\"subtract_background\": False}}) # Do not automatically apply background subtraction until we modify the extraction region" + " \"extract_1d\": {\"subtract_background\": False}}) # Do not automatically apply background subtraction until we modify the extraction region" ] }, { @@ -871,7 +863,7 @@ "cube = s3d[1].data # Science data\n", "\n", "# Plot the full IFU cube\n", - "ax = plt.subplot(1, 1, 1) #, projection=wcs, slices=('x', 'y', nslice3)) # Set up the subplot space\n", + "ax = plt.subplot(1, 1, 1) # , projection=wcs, slices=('x', 'y', nslice3)) # Set up the subplot space\n", "slice_mean = np.nanmean(cube[400:500, :, :], axis=0) # Mean of the slice looking in the range (nslice2-2):(nslice2+2)\n", "slice_norm = ImageNormalize(slice_mean, vmin=0, vmax=150, stretch=AsinhStretch()) # Normalize &stretch\n", "slice_full = ax.imshow(slice_mean, norm=slice_norm, origin='lower', cmap='jet') # Plot slice\n", From b0b1b2fad752f41ec38b44bf4bb233a50a0c9efb Mon Sep 17 00:00:00 2001 From: Hatice Karatay Date: Thu, 5 Oct 2023 17:15:24 -0400 Subject: [PATCH 09/12] Address the remaining errors --- .../ero_nirspec_ifu_02729_demo.ipynb | 3 +-- .../ero_nirspec_ifu_02732_demo.ipynb | 4 +--- .../ero_nirspec_ifu_02732_demo_pointsource.ipynb | 3 +-- 3 files changed, 3 insertions(+), 7 deletions(-) diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb index 23edcdde3..5ffeebabd 100644 --- a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb @@ -98,8 +98,7 @@ "# --------------------------------------------Astropy/Astroquery Imports--------------------------------------------\n", "\n", "from astropy.io import fits\n", - "from astropy import wcs\n", - "from astropy.wcs import WCS # noqa\n", + "from astropy.wcs import WCS\n", "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch\n", "from astroquery.mast import Observations\n", "\n", diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb index b9c69a032..0891db586 100644 --- a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb @@ -93,15 +93,13 @@ "\n", "import glob\n", "import os\n", - "import asdf\n", "import json\n", "from shutil import copy\n", "\n", "# --------------------------------------------Astropy/Astroquery Imports--------------------------------------------\n", "\n", "from astropy.io import fits\n", - "from astropy import wcs\n", - "from astropy.wcs import WCS # noqa\n", + "from astropy.wcs import WCS\n", "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch\n", "from astroquery.mast import Observations\n", "\n", diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb index f05034a47..fe4f0a5eb 100644 --- a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb @@ -95,8 +95,7 @@ "# --------------------------------------------Astropy/Astroquery Imports--------------------------------------------\n", "\n", "from astropy.io import fits\n", - "from astropy import wcs\n", - "from astropy.wcs import WCS # noqa\n", + "from astropy.wcs import WCS\n", "from astropy.visualization import ImageNormalize, ManualInterval, LogStretch, LinearStretch, AsinhStretch\n", "from astroquery.mast import Observations\n", "\n", From 69b6615bee6aa657a852a48f4962df81603181c7 Mon Sep 17 00:00:00 2001 From: Hatice Karatay Date: Fri, 6 Oct 2023 13:18:42 -0400 Subject: [PATCH 10/12] Increment pinned versions of crds and asdf --- notebooks/NIRSpec_IFU_spectral_extraction/requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/requirements.txt b/notebooks/NIRSpec_IFU_spectral_extraction/requirements.txt index 1d680d207..2ad3d6ab2 100644 --- a/notebooks/NIRSpec_IFU_spectral_extraction/requirements.txt +++ b/notebooks/NIRSpec_IFU_spectral_extraction/requirements.txt @@ -1,7 +1,7 @@ -asdf == 2.15.0 +asdf == 2.15.2 astropy == 5.2.2 astroquery == 0.4.7.dev8738 -crds == 11.16.22 +crds == 11.17.6 jwst == 1.11.3 matplotlib == 3.7.1 numpy == 1.25.2 From 152d08fd1ae243bc572cc329706f8dbff33498dc Mon Sep 17 00:00:00 2001 From: Hatice Karatay Date: Fri, 6 Oct 2023 13:19:24 -0400 Subject: [PATCH 11/12] Set crds env variables --- .../ero_nirspec_ifu_02729_demo.ipynb | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb index 5ffeebabd..aa66f389e 100644 --- a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb @@ -69,6 +69,12 @@ "outputs": [], "source": [ "# Import Library\n", + "# --------------------------------------Set CRDS environment variables----------------------------------------------\n", + "import os\n", + "os.environ['CRDS_CONTEXT'] = 'jwst_1089.pmap'\n", + "os.environ['CRDS_PATH'] = os.environ['HOME']+'/crds_cache'\n", + "os.environ['CRDS_SERVER_URL'] = 'https://jwst-crds.stsci.edu'\n", + "print(f'CRDS cache location: {os.environ[\"CRDS_PATH\"]}')\n", "\n", "# --------------------------------------JWST Calibration Pipeline Imports-------------------------------------------\n", "\n", From 4652348265bf3fe2447ab9960edd355a23fc8435 Mon Sep 17 00:00:00 2001 From: Hatice Karatay Date: Mon, 9 Oct 2023 13:29:58 -0400 Subject: [PATCH 12/12] Add crds env variables to 02732 notebooks --- .../ero_nirspec_ifu_02732_demo.ipynb | 7 + ...o_nirspec_ifu_02732_demo_pointsource.ipynb | 415 +++++++++++++++++- 2 files changed, 405 insertions(+), 17 deletions(-) diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb index 0891db586..7326dd4e5 100644 --- a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo.ipynb @@ -70,6 +70,13 @@ "outputs": [], "source": [ "# Import Library\n", + "# --------------------------------------Set CRDS environment variables----------------------------------------------\n", + "\n", + "import os\n", + "os.environ['CRDS_CONTEXT'] = 'jwst_1089.pmap'\n", + "os.environ['CRDS_PATH'] = os.environ['HOME']+'/crds_cache'\n", + "os.environ['CRDS_SERVER_URL'] = 'https://jwst-crds.stsci.edu'\n", + "print(f'CRDS cache location: {os.environ[\"CRDS_PATH\"]}')\n", "\n", "# --------------------------------------JWST Calibration Pipeline Imports-------------------------------------------\n", "\n", diff --git a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb index fe4f0a5eb..02921b3b0 100644 --- a/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02732_demo_pointsource.ipynb @@ -59,15 +59,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "ecbdca5b-af15-424c-a1d4-525094dfe17f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CRDS cache location: /Users/hkaratay/crds_cache\n", + "JWST Calibration Pipeline Version=1.12.3\n", + "Current Operational CRDS Context = jwst_1137.pmap\n" + ] + } + ], "source": [ "# Import Library\n", + "# --------------------------------------Set CRDS environment variables----------------------------------------------\n", "\n", - "# --------------------------------------JWST Calibration Pipeline Imports-------------------------------------------\n", + "import os\n", + "os.environ['CRDS_CONTEXT'] = 'jwst_1089.pmap'\n", + "os.environ['CRDS_PATH'] = os.environ['HOME']+'/crds_cache'\n", + "os.environ['CRDS_SERVER_URL'] = 'https://jwst-crds.stsci.edu'\n", + "print(f'CRDS cache location: {os.environ[\"CRDS_PATH\"]}')\n", "\n", + "# --------------------------------------JWST Calibration Pipeline Imports-------------------------------------------\n", "import jwst\n", "import crds\n", "from jwst import datamodels\n", @@ -131,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "1f45048e-4112-4c47-91cc-bd461d778779", "metadata": {}, "outputs": [], @@ -206,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "019b0f74-0d54-4b89-aad4-e30c02946d0d", "metadata": {}, "outputs": [], @@ -426,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "36acc0ff-0a60-4f60-abad-bd71e686c8ed", "metadata": {}, "outputs": [], @@ -479,12 +495,75 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "f205522d-f3ca-4053-9040-7f43f0cd62fa", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "eppn: \n", + "ezid: anonymous\n", + "anon: True\n", + "scopes: []\n", + "session: None\n", + "token: None\n", + "jw02732-o003_20230401t123831_spec3_00001_asn.json\n", + "jw02732-o003_20230401t123831_spec2_00004_asn.json\n", + "jw02732003001_02101_00001_nrs1_uncal.fits\n", + "jw02732003001_02101_00001_nrs2_uncal.fits\n", + "jw02732-o003_20230401t123831_spec2_00005_asn.json\n", + "jw02732003001_02101_00002_nrs1_uncal.fits\n", + "jw02732003001_02101_00002_nrs2_uncal.fits\n", + "jw02732-o003_20230401t123831_spec2_00015_asn.json\n", + "jw02732003001_02101_00003_nrs1_uncal.fits\n", + "jw02732003001_02101_00003_nrs2_uncal.fits\n", + "jw02732-o003_20230401t123831_spec2_00007_asn.json\n", + "jw02732003001_02101_00004_nrs1_uncal.fits\n", + "jw02732003001_02101_00004_nrs2_uncal.fits\n", + "jw02732-o003_20230401t123831_spec2_00001_asn.json\n", + "jw02732003001_02101_00005_nrs1_uncal.fits\n", + "jw02732003001_02101_00005_nrs2_uncal.fits\n", + "jw02732-o003_20230401t123831_spec2_00016_asn.json\n", + "jw02732003001_02101_00006_nrs1_uncal.fits\n", + "jw02732003001_02101_00006_nrs2_uncal.fits\n", + "jw02732-o003_20230401t123831_spec2_00003_asn.json\n", + "jw02732003001_02101_00007_nrs1_uncal.fits\n", + "jw02732003001_02101_00007_nrs2_uncal.fits\n", + "jw02732-o003_20230401t123831_spec2_00012_asn.json\n", + "jw02732003001_02101_00008_nrs1_uncal.fits\n", + "jw02732003001_02101_00008_nrs2_uncal.fits\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732-o003_20230401t123831_spec3_00001_asn.json to ./nirspec_ifu_02732_rerun/mast_products/jw02732-o003_20230401t123831_spec3_00001_asn.json ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732-o003_20230401t123831_spec2_00004_asn.json to ./nirspec_ifu_02732_rerun/mast_products/jw02732-o003_20230401t123831_spec2_00004_asn.json ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732003001_02101_00001_nrs1_uncal.fits to ./nirspec_ifu_02732_rerun/mast_products/jw02732003001_02101_00001_nrs1_uncal.fits ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732003001_02101_00001_nrs2_uncal.fits to ./nirspec_ifu_02732_rerun/mast_products/jw02732003001_02101_00001_nrs2_uncal.fits ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732-o003_20230401t123831_spec2_00005_asn.json to ./nirspec_ifu_02732_rerun/mast_products/jw02732-o003_20230401t123831_spec2_00005_asn.json ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732003001_02101_00002_nrs1_uncal.fits to ./nirspec_ifu_02732_rerun/mast_products/jw02732003001_02101_00002_nrs1_uncal.fits ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732003001_02101_00002_nrs2_uncal.fits to ./nirspec_ifu_02732_rerun/mast_products/jw02732003001_02101_00002_nrs2_uncal.fits ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732-o003_20230401t123831_spec2_00015_asn.json to ./nirspec_ifu_02732_rerun/mast_products/jw02732-o003_20230401t123831_spec2_00015_asn.json ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732003001_02101_00003_nrs1_uncal.fits to ./nirspec_ifu_02732_rerun/mast_products/jw02732003001_02101_00003_nrs1_uncal.fits ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732003001_02101_00003_nrs2_uncal.fits to ./nirspec_ifu_02732_rerun/mast_products/jw02732003001_02101_00003_nrs2_uncal.fits ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732-o003_20230401t123831_spec2_00007_asn.json to ./nirspec_ifu_02732_rerun/mast_products/jw02732-o003_20230401t123831_spec2_00007_asn.json ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732003001_02101_00004_nrs1_uncal.fits to ./nirspec_ifu_02732_rerun/mast_products/jw02732003001_02101_00004_nrs1_uncal.fits ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732003001_02101_00004_nrs2_uncal.fits to ./nirspec_ifu_02732_rerun/mast_products/jw02732003001_02101_00004_nrs2_uncal.fits ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732-o003_20230401t123831_spec2_00001_asn.json to ./nirspec_ifu_02732_rerun/mast_products/jw02732-o003_20230401t123831_spec2_00001_asn.json ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732003001_02101_00005_nrs1_uncal.fits to ./nirspec_ifu_02732_rerun/mast_products/jw02732003001_02101_00005_nrs1_uncal.fits ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732003001_02101_00005_nrs2_uncal.fits to ./nirspec_ifu_02732_rerun/mast_products/jw02732003001_02101_00005_nrs2_uncal.fits ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732-o003_20230401t123831_spec2_00016_asn.json to ./nirspec_ifu_02732_rerun/mast_products/jw02732-o003_20230401t123831_spec2_00016_asn.json ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732003001_02101_00006_nrs1_uncal.fits to ./nirspec_ifu_02732_rerun/mast_products/jw02732003001_02101_00006_nrs1_uncal.fits ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732003001_02101_00006_nrs2_uncal.fits to ./nirspec_ifu_02732_rerun/mast_products/jw02732003001_02101_00006_nrs2_uncal.fits ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732-o003_20230401t123831_spec2_00003_asn.json to ./nirspec_ifu_02732_rerun/mast_products/jw02732-o003_20230401t123831_spec2_00003_asn.json ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732003001_02101_00007_nrs1_uncal.fits to ./nirspec_ifu_02732_rerun/mast_products/jw02732003001_02101_00007_nrs1_uncal.fits ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732003001_02101_00007_nrs2_uncal.fits to ./nirspec_ifu_02732_rerun/mast_products/jw02732003001_02101_00007_nrs2_uncal.fits ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732-o003_20230401t123831_spec2_00012_asn.json to ./nirspec_ifu_02732_rerun/mast_products/jw02732-o003_20230401t123831_spec2_00012_asn.json ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732003001_02101_00008_nrs1_uncal.fits to ./nirspec_ifu_02732_rerun/mast_products/jw02732003001_02101_00008_nrs1_uncal.fits ... [Done]\n", + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02732003001_02101_00008_nrs2_uncal.fits to ./nirspec_ifu_02732_rerun/mast_products/jw02732003001_02101_00008_nrs2_uncal.fits ... [Done]\n" + ] + } + ], "source": [ "# Download data from MAST \n", "\n", @@ -516,13 +595,13 @@ " productSubGroupDescription=[\"UNCAL\", \"ASN\"],\n", " mrp_only=False)\n", "# Print the filtered products\n", - "number = len(filtered)\n", + "number_of_filtered_products = len(filtered)\n", "for k in range(number):\n", " print(filtered['productFilename'][k])\n", "\n", "# Download the filtered products\n", "# This creates a mastDownload directory, unless you set flat=True and set a download_dir\n", - "for i in range(len(filtered)):\n", + "for i in range(number_of_filtered_products):\n", " mast_products_dir = output_dir+'mast_products/'\n", " if not os.path.exists(mast_products_dir):\n", " os.makedirs(mast_products_dir)\n", @@ -532,6 +611,185 @@ " Observations.download_products(filtered[i], mrp_only=False, cache=True, flat=True, download_dir=mast_products_dir) # Find any cached files first before downloading new ones" ] }, + { + "cell_type": "code", + "execution_count": 6, + "id": "bd6dffb3-ea53-442e-abc0-46422028abfb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "eppn: \n", + "ezid: anonymous\n", + "anon: True\n", + "scopes: []\n", + "session: None\n", + "token: None\n", + " obsID obs_collection dataproduct_type ... dataRights calib_level\n", + "-------- -------------- ---------------- ... ---------- -----------\n", + "87602440 JWST cube ... PUBLIC 3\n", + "87602440 JWST cube ... PUBLIC 3\n", + "87602440 JWST cube ... PUBLIC 3\n", + "87602440 JWST cube ... PUBLIC 3\n", + "87602440 JWST cube ... PUBLIC 3\n", + "87600278 JWST cube ... PUBLIC 2\n", + "87600278 JWST cube ... PUBLIC 2\n", + "87600278 JWST cube ... PUBLIC 2\n", + "87600278 JWST cube ... PUBLIC 2\n", + "87600278 JWST cube ... PUBLIC 2\n", + "87600278 JWST cube ... PUBLIC 2\n", + " ... ... ... ... ... ...\n", + "87601706 JWST image ... PUBLIC 2\n", + "87601706 JWST image ... PUBLIC 2\n", + "87601706 JWST image ... PUBLIC 2\n", + "87601706 JWST image ... PUBLIC 2\n", + "87601706 JWST image ... PUBLIC 2\n", + "87601706 JWST image ... PUBLIC 1\n", + "87601706 JWST image ... PUBLIC 2\n", + "87601706 JWST image ... PUBLIC 2\n", + "87601706 JWST image ... PUBLIC 2\n", + "87601706 JWST image ... PUBLIC 2\n", + "87601706 JWST image ... PUBLIC 1\n", + "Length = 1437 rows\n", + "_______________??????\n", + "jw02732-o003_20230401t123831_spec3_00001_asn.json\n", + "jw02732-o003_20230401t123831_spec2_00004_asn.json\n", + "jw02732003001_02101_00001_nrs1_uncal.fits\n", + "jw02732003001_02101_00001_nrs2_uncal.fits\n", + "jw02732-o003_20230401t123831_spec2_00005_asn.json\n", + "jw02732003001_02101_00002_nrs1_uncal.fits\n", + "jw02732003001_02101_00002_nrs2_uncal.fits\n", + "jw02732-o003_20230401t123831_spec2_00015_asn.json\n", + "jw02732003001_02101_00003_nrs1_uncal.fits\n", + "jw02732003001_02101_00003_nrs2_uncal.fits\n", + "jw02732-o003_20230401t123831_spec2_00007_asn.json\n", + "jw02732003001_02101_00004_nrs1_uncal.fits\n", + "jw02732003001_02101_00004_nrs2_uncal.fits\n", + "jw02732-o003_20230401t123831_spec2_00001_asn.json\n", + "jw02732003001_02101_00005_nrs1_uncal.fits\n", + "jw02732003001_02101_00005_nrs2_uncal.fits\n", + "jw02732-o003_20230401t123831_spec2_00016_asn.json\n", + "jw02732003001_02101_00006_nrs1_uncal.fits\n", + "jw02732003001_02101_00006_nrs2_uncal.fits\n", + "jw02732-o003_20230401t123831_spec2_00003_asn.json\n", + "jw02732003001_02101_00007_nrs1_uncal.fits\n", + "jw02732003001_02101_00007_nrs2_uncal.fits\n", + "jw02732-o003_20230401t123831_spec2_00012_asn.json\n", + "jw02732003001_02101_00008_nrs1_uncal.fits\n", + "jw02732003001_02101_00008_nrs2_uncal.fits\n" + ] + } + ], + "source": [ + "sessioninfo = Observations.session_info()\n", + "\n", + "# Define the general search criteria\n", + "obs = Observations.query_criteria(\n", + " obs_collection='JWST',\n", + " instrument_name=['NIRSPEC/IFU'],\n", + " proposal_id='02732')\n", + "\n", + "# Print the Observations returned from the general search criteria\n", + "products = Observations.get_product_list(obs)\n", + "print(products)\n", + "filtered = Observations.filter_products(products,\n", + " productSubGroupDescription=[\"UNCAL\", \"ASN\"],\n", + " mrp_only=False)\n", + "print(\"_______________??????\")\n", + "\n", + "number = len(filtered)\n", + "for k in range(number):\n", + " print(filtered['productFilename'][k])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "cf018600-dfff-409e-a99b-f4f0bc9ecf6f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "jw02732-o003_20230401t123831_spec3_00001_asn.json\n", + "jw02732-o003_20230401t123831_spec2_00004_asn.json\n", + "jw02732003001_02101_00001_nrs1_uncal.fits\n", + "jw02732003001_02101_00001_nrs2_uncal.fits\n", + "jw02732-o003_20230401t123831_spec2_00005_asn.json\n", + "jw02732003001_02101_00002_nrs1_uncal.fits\n", + "jw02732003001_02101_00002_nrs2_uncal.fits\n", + "jw02732-o003_20230401t123831_spec2_00015_asn.json\n", + "jw02732003001_02101_00003_nrs1_uncal.fits\n", + "jw02732003001_02101_00003_nrs2_uncal.fits\n", + "jw02732-o003_20230401t123831_spec2_00007_asn.json\n", + "jw02732003001_02101_00004_nrs1_uncal.fits\n", + "jw02732003001_02101_00004_nrs2_uncal.fits\n", + "jw02732-o003_20230401t123831_spec2_00001_asn.json\n", + "jw02732003001_02101_00005_nrs1_uncal.fits\n", + "jw02732003001_02101_00005_nrs2_uncal.fits\n", + "jw02732-o003_20230401t123831_spec2_00016_asn.json\n", + "jw02732003001_02101_00006_nrs1_uncal.fits\n", + "jw02732003001_02101_00006_nrs2_uncal.fits\n", + "jw02732-o003_20230401t123831_spec2_00003_asn.json\n", + "jw02732003001_02101_00007_nrs1_uncal.fits\n", + "jw02732003001_02101_00007_nrs2_uncal.fits\n", + "jw02732-o003_20230401t123831_spec2_00012_asn.json\n", + "jw02732003001_02101_00008_nrs1_uncal.fits\n", + "jw02732003001_02101_00008_nrs2_uncal.fits\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'mast_products_dir' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[13], line 30\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;66;03m# Wait for all downloads to complete\u001b[39;00m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m future \u001b[38;5;129;01min\u001b[39;00m futures:\n\u001b[0;32m---> 30\u001b[0m \u001b[43mfuture\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/concurrent/futures/_base.py:449\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 448\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 449\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 451\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[1;32m 453\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/concurrent/futures/_base.py:401\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 399\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[1;32m 400\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 401\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 402\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 403\u001b[0m \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[1;32m 404\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/concurrent/futures/thread.py:58\u001b[0m, in \u001b[0;36m_WorkItem.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfuture\u001b[38;5;241m.\u001b[39mset_exception(exc)\n", + "Cell \u001b[0;32mIn[13], line 5\u001b[0m, in \u001b[0;36mdownload_product\u001b[0;34m(product)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdownload_product\u001b[39m(product):\n\u001b[1;32m 4\u001b[0m mast_download_dir \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(output_dir, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmast_products\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(\u001b[43mmast_products_dir\u001b[49m):\n\u001b[1;32m 6\u001b[0m os\u001b[38;5;241m.\u001b[39mmakedirs(mast_products_dir)\n\u001b[1;32m 7\u001b[0m Observations\u001b[38;5;241m.\u001b[39mdownload_products(product, mrp_only\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, cache\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, flat\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, dowload_dir\u001b[38;5;241m=\u001b[39mmast_products_dir)\n", + "\u001b[0;31mNameError\u001b[0m: name 'mast_products_dir' is not defined" + ] + } + ], + "source": [ + "from concurrent.futures import ThreadPoolExecutor\n", + "max_workers = 4\n", + "def download_product(product):\n", + " mast_download_dir = os.path.join(output_dir, \"mast_products\")\n", + " if not os.path.exists(mast_products_dir):\n", + " os.makedirs(mast_products_dir)\n", + " Observations.download_products(product, mrp_only=False, cache=False, flat=True, dowload_dir=mast_products_dir)\n", + "\n", + "filtered = Observations.filter_products(products,\n", + " productSubGroupDescription=[\"UNCAL\", \"ASN\"],\n", + " mrp_only=False)\n", + "# Print the filtered products\n", + "number_of_filtered_products = len(filtered)\n", + "for k in range(number):\n", + " print(filtered['productFilename'][k])\n", + " \n", + "# Create a ThreadPoolExecutor for parallel downloads\n", + "with ThreadPoolExecutor(max_workers=max_workers) as executor:\n", + " if runflag:\n", + " futures = [executor.submit(download_product, product) for product in filtered]\n", + " else:\n", + " # Check if the file already exists before downloading\n", + " for product in filtered:\n", + " filename = os.path.join(output_dir, 'mast_products', product['productFilename'])\n", + " if not os.path.exists(filename):\n", + " futures.append(executor.submit(download_product, product))\n", + "\n", + "# Wait for all downloads to complete\n", + "for future in futures:\n", + " future.result()" + ] + }, { "cell_type": "markdown", "id": "6d692537-b0fa-4221-8f0f-23816e51097d", @@ -543,12 +801,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "684360fc-e0d4-4a1c-be06-8d7effe5a6da", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'mast_products_dir' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[14], line 5\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Stage 1 Processing \u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m runflag:\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m uncal_file \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28msorted\u001b[39m(glob\u001b[38;5;241m.\u001b[39mglob(\u001b[43mmast_products_dir\u001b[49m\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m*nrs1_uncal.fits\u001b[39m\u001b[38;5;124m'\u001b[39m)): \n\u001b[1;32m 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mApplying Stage 1 Corrections & Calibrations to: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mbasename(uncal_file))\n\u001b[1;32m 9\u001b[0m result \u001b[38;5;241m=\u001b[39m Detector1Pipeline\u001b[38;5;241m.\u001b[39mcall(uncal_file,\n\u001b[1;32m 10\u001b[0m save_results\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 11\u001b[0m output_dir\u001b[38;5;241m=\u001b[39moutput_dir)\n", + "\u001b[0;31mNameError\u001b[0m: name 'mast_products_dir' is not defined" + ] + } + ], "source": [ "# Stage 1 Processing \n", "\n", @@ -565,12 +835,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "d00294b2-4eec-4178-bdae-313e20bb8045", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-10-06 14:37:44,884 - stpipe - WARNING - /var/folders/m8/sgnbqc5104q_68bvvrnnzn7m0005k0/T/ipykernel_84709/1934669910.py:66: UserWarning: Setting the 'color' property will override the edgecolor or facecolor properties.\n", + "2023-10-06 14:37:44,884 - stpipe - WARNING - ax.indicate_inset_zoom(axins, color=\"black\", edgecolor=\"black\", linewidth=3)\n", + "2023-10-06 14:37:44,885 - stpipe - WARNING - \n" + ] + }, + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Stage 1 slope products -- level 2a images\n", "\n", @@ -606,7 +906,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "cefe6882-2880-4172-b65e-e6e83383dff3", "metadata": {}, "outputs": [], @@ -642,12 +942,93 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "1a85ff57-18ea-4a81-87c3-7d3f0aff5082", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applying Stage 2 Calibrations & Corrections to: jw02732003001_02101_00001_nrs1_rate.fits\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-10-06 14:37:46,107 - CRDS - ERROR - Error determining best reference for 'pars-pixelreplacestep' = Unknown reference type 'pars-pixelreplacestep'\n", + "2023-10-06 14:37:46,121 - stpipe.Spec2Pipeline - INFO - Spec2Pipeline instance created.\n", + "2023-10-06 14:37:46,122 - stpipe.Spec2Pipeline.bkg_subtract - INFO - BackgroundStep instance created.\n", + "2023-10-06 14:37:46,123 - stpipe.Spec2Pipeline.assign_wcs - INFO - AssignWcsStep instance created.\n", + "2023-10-06 14:37:46,123 - stpipe.Spec2Pipeline.imprint_subtract - INFO - ImprintStep instance created.\n", + "2023-10-06 14:37:46,124 - stpipe.Spec2Pipeline.msa_flagging - INFO - MSAFlagOpenStep instance created.\n", + "2023-10-06 14:37:46,125 - stpipe.Spec2Pipeline.extract_2d - INFO - Extract2dStep instance created.\n", + "2023-10-06 14:37:46,127 - stpipe.Spec2Pipeline.master_background_mos - INFO - MasterBackgroundMosStep instance created.\n", + "2023-10-06 14:37:46,128 - stpipe.Spec2Pipeline.master_background_mos.flat_field - INFO - FlatFieldStep instance created.\n", + "2023-10-06 14:37:46,129 - stpipe.Spec2Pipeline.master_background_mos.pathloss - INFO - PathLossStep instance created.\n", + "2023-10-06 14:37:46,129 - stpipe.Spec2Pipeline.master_background_mos.barshadow - INFO - BarShadowStep instance created.\n", + "2023-10-06 14:37:46,130 - stpipe.Spec2Pipeline.master_background_mos.photom - INFO - PhotomStep instance created.\n", + "2023-10-06 14:37:46,131 - stpipe.Spec2Pipeline.wavecorr - INFO - WavecorrStep instance created.\n", + "2023-10-06 14:37:46,133 - stpipe.Spec2Pipeline.flat_field - INFO - FlatFieldStep instance created.\n", + "2023-10-06 14:37:46,134 - stpipe.Spec2Pipeline.srctype - INFO - SourceTypeStep instance created.\n", + "2023-10-06 14:37:46,134 - stpipe.Spec2Pipeline.straylight - INFO - StraylightStep instance created.\n", + "2023-10-06 14:37:46,135 - stpipe.Spec2Pipeline.fringe - INFO - FringeStep instance created.\n", + "2023-10-06 14:37:46,136 - stpipe.Spec2Pipeline.residual_fringe - INFO - ResidualFringeStep instance created.\n", + "2023-10-06 14:37:46,137 - stpipe.Spec2Pipeline.pathloss - INFO - PathLossStep instance created.\n", + "2023-10-06 14:37:46,137 - stpipe.Spec2Pipeline.barshadow - INFO - BarShadowStep instance created.\n", + "2023-10-06 14:37:46,138 - stpipe.Spec2Pipeline.wfss_contam - INFO - WfssContamStep instance created.\n", + "2023-10-06 14:37:46,139 - stpipe.Spec2Pipeline.photom - INFO - PhotomStep instance created.\n", + "2023-10-06 14:37:46,140 - stpipe.Spec2Pipeline.pixel_replace - INFO - PixelReplaceStep instance created.\n", + "2023-10-06 14:37:46,141 - stpipe.Spec2Pipeline.resample_spec - INFO - ResampleSpecStep instance created.\n", + "2023-10-06 14:37:46,145 - stpipe.Spec2Pipeline.cube_build - INFO - CubeBuildStep instance created.\n", + "2023-10-06 14:37:46,149 - stpipe.Spec2Pipeline.extract_1d - INFO - Extract1dStep instance created.\n", + "2023-10-06 14:37:46,224 - stpipe.Spec2Pipeline - INFO - Step Spec2Pipeline running with args ('./nirspec_ifu_02732_rerun/jw02732003001_02101_00001_nrs1_rate.fits',).\n", + "2023-10-06 14:37:46,234 - stpipe.Spec2Pipeline - INFO - Step Spec2Pipeline parameters are: {'pre_hooks': 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'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'flat_field': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'save_interpolated_flat': False, 'user_supplied_flat': None, 'inverse': False}, 'srctype': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'source_type': None}, 'straylight': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': 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'barshadow': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'inverse': False, 'source_type': None}, 'wfss_contam': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': True, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'save_simulated_image': False, 'save_contam_images': False, 'maximum_cores': 'none'}, 'photom': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'inverse': False, 'source_type': None, 'mrs_time_correction': True}, 'pixel_replace': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': True, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'algorithm': 'fit_profile', 'n_adjacent_cols': 3}, 'resample_spec': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'pixfrac': 1.0, 'kernel': 'square', 'fillval': 'INDEF', 'weight_type': 'ivm', 'output_shape': None, 'crpix': None, 'crval': None, 'rotation': None, 'pixel_scale_ratio': 1.0, 'pixel_scale': None, 'output_wcs': '', 'single': False, 'blendheaders': True, 'allowed_memory': None, 'in_memory': True}, 'cube_build': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 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'center_xy': None, 'apply_apcorr': True, 'ifu_autocen': False, 'ifu_rfcorr': False, 'ifu_set_srctype': None, 'ifu_rscale': None, 'soss_atoca': True, 'soss_threshold': 0.01, 'soss_n_os': 2, 'soss_wave_grid_in': None, 'soss_wave_grid_out': None, 'soss_estimate': None, 'soss_rtol': 0.0001, 'soss_max_grid_size': 20000, 'soss_transform': None, 'soss_tikfac': None, 'soss_width': 40.0, 'soss_bad_pix': 'masking', 'soss_modelname': None}}}\n", + "2023-10-06 14:37:46,430 - stpipe.Spec2Pipeline - INFO - Prefetching reference files for dataset: 'jw02732003001_02101_00001_nrs1_rate.fits' reftypes = ['apcorr', 'area', 'barshadow', 'camera', 'collimator', 'cubepar', 'dflat', 'disperser', 'distortion', 'drizpars', 'extract1d', 'fflat', 'filteroffset', 'flat', 'fore', 'fpa', 'fringe', 'ifufore', 'ifupost', 'ifuslicer', 'mrsxartcorr', 'msa', 'msaoper', 'ote', 'pathloss', 'photom', 'regions', 'sflat', 'speckernel', 'specprofile', 'spectrace', 'specwcs', 'wavecorr', 'wavelengthrange', 'wavemap', 'wfssbkg']\n", + "2023-10-06 14:37:46,434 - CRDS - INFO - Fetching /Users/hkaratay/crds_cache/references/jwst/nirspec/jwst_nirspec_apcorr_0001.asdf 178.4 K bytes (1 / 21 files) (0 / 771.8 M bytes)\n", + "2023-10-06 14:37:46,725 - CRDS - INFO - Fetching /Users/hkaratay/crds_cache/references/jwst/nirspec/jwst_nirspec_area_0035.fits 11.5 K bytes (2 / 21 files) (178.4 K / 771.8 M bytes)\n", + "2023-10-06 14:37:46,898 - CRDS - INFO - Fetching /Users/hkaratay/crds_cache/references/jwst/nirspec/jwst_nirspec_camera_0007.asdf 5.8 K bytes (3 / 21 files) (189.9 K / 771.8 M bytes)\n", + "2023-10-06 14:37:47,052 - CRDS - INFO - Fetching /Users/hkaratay/crds_cache/references/jwst/nirspec/jwst_nirspec_collimator_0007.asdf 5.9 K bytes (4 / 21 files) (195.7 K / 771.8 M bytes)\n", + "2023-10-06 14:37:47,194 - CRDS - INFO - Fetching /Users/hkaratay/crds_cache/references/jwst/nirspec/jwst_nirspec_cubepar_0007.fits 717.1 K bytes (5 / 21 files) (201.6 K / 771.8 M bytes)\n", + "2023-10-06 14:37:47,708 - CRDS - INFO - Fetching /Users/hkaratay/crds_cache/references/jwst/nirspec/jwst_nirspec_dflat_0001.fits 688.9 M bytes (6 / 21 files) (918.7 K / 771.8 M bytes)\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[9], line 10\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m rate_file \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28msorted\u001b[39m(glob\u001b[38;5;241m.\u001b[39mglob(output_dir\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m*nrs1*rate.fits\u001b[39m\u001b[38;5;124m'\u001b[39m)):\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mApplying Stage 2 Calibrations & Corrections to: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mbasename(rate_file))\n\u001b[0;32m---> 10\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mSpec2Pipeline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrate_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43msave_results\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_dir\u001b[49m\u001b[43m)\u001b[49m \n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/site-packages/stpipe/step.py:653\u001b[0m, in \u001b[0;36mStep.call\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m 650\u001b[0m name \u001b[38;5;241m=\u001b[39m config\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 651\u001b[0m instance \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mfrom_config_section(config, name\u001b[38;5;241m=\u001b[39mname, config_file\u001b[38;5;241m=\u001b[39mconfig_file)\n\u001b[0;32m--> 653\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minstance\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/site-packages/stpipe/step.py:477\u001b[0m, in \u001b[0;36mStep.run\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 475\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 476\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprefetch_references:\n\u001b[0;32m--> 477\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprefetch\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 478\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 479\u001b[0m step_result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprocess(\u001b[38;5;241m*\u001b[39margs)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/site-packages/stpipe/step.py:590\u001b[0m, in \u001b[0;36mStep.prefetch\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 588\u001b[0m \u001b[38;5;66;03m# prefetch truly occurs at the Pipeline (or subclass) level.\u001b[39;00m\n\u001b[1;32m 589\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreference_file_types) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mskip:\n\u001b[0;32m--> 590\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_precache_references\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/site-packages/stpipe/pipeline.py:267\u001b[0m, in \u001b[0;36mPipeline._precache_references\u001b[0;34m(self, input_file)\u001b[0m\n\u001b[1;32m 263\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 264\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mopen_model(\n\u001b[1;32m 265\u001b[0m input_file, asn_n_members\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, asn_exptypes\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscience\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 266\u001b[0m ) \u001b[38;5;28;01mas\u001b[39;00m model:\n\u001b[0;32m--> 267\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_precache_references_opened\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 268\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mOSError\u001b[39;00m):\n\u001b[1;32m 269\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFirst argument \u001b[39m\u001b[38;5;132;01m{\u001b[39;00minput_file\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not appear to be a model\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/site-packages/stpipe/pipeline.py:288\u001b[0m, in \u001b[0;36mPipeline._precache_references_opened\u001b[0;34m(self, model_or_container)\u001b[0m\n\u001b[1;32m 285\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_precache_references_opened(contained_model)\n\u001b[1;32m 286\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 287\u001b[0m \u001b[38;5;66;03m# precache a single model object\u001b[39;00m\n\u001b[0;32m--> 288\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_precache_references_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_or_container\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/site-packages/stpipe/pipeline.py:316\u001b[0m, in \u001b[0;36mPipeline._precache_references_impl\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 308\u001b[0m fetch_types \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msorted\u001b[39m(\u001b[38;5;28mset\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreference_file_types) \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mset\u001b[39m(ovr_refs\u001b[38;5;241m.\u001b[39mkeys()))\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog\u001b[38;5;241m.\u001b[39minfo(\n\u001b[1;32m 311\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPrefetching reference files for dataset: \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 312\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mrepr\u001b[39m(model\u001b[38;5;241m.\u001b[39mmeta\u001b[38;5;241m.\u001b[39mfilename)\n\u001b[1;32m 313\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m reftypes = \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 314\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mrepr\u001b[39m(fetch_types)\n\u001b[1;32m 315\u001b[0m )\n\u001b[0;32m--> 316\u001b[0m crds_refs \u001b[38;5;241m=\u001b[39m \u001b[43mcrds_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_multiple_reference_paths\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 317\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_crds_parameters\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfetch_types\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcrds_observatory\u001b[49m\n\u001b[1;32m 318\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 320\u001b[0m ref_path_map \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\u001b[38;5;28mlist\u001b[39m(crds_refs\u001b[38;5;241m.\u001b[39mitems()) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mlist\u001b[39m(ovr_refs\u001b[38;5;241m.\u001b[39mitems()))\n\u001b[1;32m 322\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m reftype, refpath \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28msorted\u001b[39m(ref_path_map\u001b[38;5;241m.\u001b[39mitems()):\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/site-packages/stpipe/crds_client.py:52\u001b[0m, in \u001b[0;36mget_multiple_reference_paths\u001b[0;34m(parameters, reference_file_types, observatory)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFirst argument must be a dict of parameters\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 51\u001b[0m log\u001b[38;5;241m.\u001b[39mset_log_time(\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m---> 52\u001b[0m refpaths \u001b[38;5;241m=\u001b[39m 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\u001b[43mcrds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetreferences\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 66\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 67\u001b[0m \u001b[43m \u001b[49m\u001b[43mreftypes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreference_file_types\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 68\u001b[0m \u001b[43m \u001b[49m\u001b[43mobservatory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobservatory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 69\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 70\u001b[0m refpaths \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 71\u001b[0m filetype: filepath \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mN/A\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m filepath\u001b[38;5;241m.\u001b[39mupper() \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mN/A\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m (filetype, filepath) \u001b[38;5;129;01min\u001b[39;00m bestrefs\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m 73\u001b[0m }\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m refpaths\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/site-packages/crds/core/heavy_client.py:127\u001b[0m, in \u001b[0;36mgetreferences\u001b[0;34m(parameters, reftypes, context, ignore_cache, observatory, fast)\u001b[0m\n\u001b[1;32m 122\u001b[0m final_context, bestrefs \u001b[38;5;241m=\u001b[39m _initial_recommendations(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgetreferences\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 123\u001b[0m parameters, reftypes, context, ignore_cache, observatory, fast)\n\u001b[1;32m 125\u001b[0m \u001b[38;5;66;03m# Attempt to cache the recommended references, which unlike dump_mappings\u001b[39;00m\n\u001b[1;32m 126\u001b[0m \u001b[38;5;66;03m# should work without network access if files are already cached.\u001b[39;00m\n\u001b[0;32m--> 127\u001b[0m best_refs_paths \u001b[38;5;241m=\u001b[39m \u001b[43mapi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcache_references\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 128\u001b[0m \u001b[43m \u001b[49m\u001b[43mfinal_context\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbestrefs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mignore_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_cache\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m best_refs_paths\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/site-packages/crds/client/api.py:853\u001b[0m, in \u001b[0;36mcache_references\u001b[0;34m(pipeline_context, bestrefs, ignore_cache)\u001b[0m\n\u001b[1;32m 851\u001b[0m localrefs \u001b[38;5;241m=\u001b[39m {name: get_flex_uri(name) \u001b[38;5;28;01mfor\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m wanted}\n\u001b[1;32m 852\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 853\u001b[0m localrefs \u001b[38;5;241m=\u001b[39m \u001b[43mFileCacher\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpipeline_context\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mignore_cache\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mraise_exceptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_local_files\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwanted\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 855\u001b[0m refs \u001b[38;5;241m=\u001b[39m _squash_unicode_in_bestrefs(bestrefs, localrefs)\n\u001b[1;32m 857\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m refs\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/site-packages/crds/client/api.py:594\u001b[0m, in \u001b[0;36mFileCacher.get_local_files\u001b[0;34m(self, names)\u001b[0m\n\u001b[1;32m 592\u001b[0m localpaths[name] \u001b[38;5;241m=\u001b[39m localpath\n\u001b[1;32m 593\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m downloads:\n\u001b[0;32m--> 594\u001b[0m n_bytes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_files\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdownloads\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocalpaths\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 595\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 596\u001b[0m log\u001b[38;5;241m.\u001b[39mverbose(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSkipping download for cached files\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28msorted\u001b[39m(names), verbosity\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m60\u001b[39m)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/site-packages/crds/client/api.py:634\u001b[0m, in \u001b[0;36mFileCacher.download_files\u001b[0;34m(self, downloads, localpaths)\u001b[0m\n\u001b[1;32m 632\u001b[0m \u001b[38;5;28mbytes\u001b[39m, path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcatalog_file_size(name), localpaths[name]\n\u001b[1;32m 633\u001b[0m log\u001b[38;5;241m.\u001b[39minfo(file_progress(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFetching\u001b[39m\u001b[38;5;124m\"\u001b[39m, name, path, \u001b[38;5;28mbytes\u001b[39m, bytes_so_far, total_bytes, nth_file, total_files))\n\u001b[0;32m--> 634\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 635\u001b[0m bytes_so_far \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mstat(path)\u001b[38;5;241m.\u001b[39mst_size\n\u001b[1;32m 636\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/site-packages/crds/client/api.py:653\u001b[0m, in \u001b[0;36mFileCacher.download\u001b[0;34m(self, name, localpath)\u001b[0m\n\u001b[1;32m 651\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 652\u001b[0m utils\u001b[38;5;241m.\u001b[39mensure_dir_exists(localpath)\n\u001b[0;32m--> 653\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mproxy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_with_retries\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_core\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocalpath\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 655\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mremove_file(localpath)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/site-packages/crds/client/proxy.py:44\u001b[0m, in \u001b[0;36mapply_with_retries\u001b[0;34m(func, *pars, **keys)\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m retry \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(retries):\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 46\u001b[0m log\u001b[38;5;241m.\u001b[39mverbose_warning(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFAILED: Attempt\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mstr\u001b[39m(retry\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mof\u001b[39m\u001b[38;5;124m\"\u001b[39m, retries, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwith:\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mstr\u001b[39m(exc))\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/site-packages/crds/client/api.py:679\u001b[0m, in \u001b[0;36mFileCacher.download_core\u001b[0;34m(self, name, localpath)\u001b[0m\n\u001b[1;32m 677\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 678\u001b[0m generator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_data_http(name)\n\u001b[0;32m--> 679\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerator_download\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgenerator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocalpath\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 680\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverify_file(name, localpath)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/site-packages/crds/client/api.py:685\u001b[0m, in \u001b[0;36mFileCacher.generator_download\u001b[0;34m(self, generator, localpath)\u001b[0m\n\u001b[1;32m 683\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Read all bytes from `generator` until file is downloaded to `localpath.`\"\"\"\u001b[39;00m\n\u001b[1;32m 684\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(localpath, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwb+\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m outfile:\n\u001b[0;32m--> 685\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mgenerator\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 686\u001b[0m \u001b[43m \u001b[49m\u001b[43moutfile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/site-packages/crds/client/api.py:720\u001b[0m, in \u001b[0;36mFileCacher.get_data_http\u001b[0;34m(self, filename)\u001b[0m\n\u001b[1;32m 718\u001b[0m log\u001b[38;5;241m.\u001b[39mverbose(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTransferred HTTP\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mrepr\u001b[39m(url), bytes_so_far, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m, file_size, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbytes at\u001b[39m\u001b[38;5;124m\"\u001b[39m, status[\u001b[38;5;241m1\u001b[39m], verbosity\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m)\n\u001b[1;32m 719\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m data\n\u001b[0;32m--> 720\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43minfile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCRDS_DATA_CHUNK_SIZE\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 721\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 722\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CrdsDownloadError(\n\u001b[1;32m 723\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed downloading\u001b[39m\u001b[38;5;124m\"\u001b[39m, srepr(filename),\n\u001b[1;32m 724\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfrom url\u001b[39m\u001b[38;5;124m\"\u001b[39m, srepr(url), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mstr\u001b[39m(exc)) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mexc\u001b[39;00m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/http/client.py:466\u001b[0m, in \u001b[0;36mHTTPResponse.read\u001b[0;34m(self, amt)\u001b[0m\n\u001b[1;32m 463\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m amt \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength:\n\u001b[1;32m 464\u001b[0m \u001b[38;5;66;03m# clip the read to the \"end of response\"\u001b[39;00m\n\u001b[1;32m 465\u001b[0m amt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength\n\u001b[0;32m--> 466\u001b[0m s \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfp\u001b[38;5;241m.\u001b[39mread(amt)\n\u001b[1;32m 467\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m s \u001b[38;5;129;01mand\u001b[39;00m amt:\n\u001b[1;32m 468\u001b[0m \u001b[38;5;66;03m# Ideally, we would raise IncompleteRead if the content-length\u001b[39;00m\n\u001b[1;32m 469\u001b[0m \u001b[38;5;66;03m# wasn't satisfied, but it might break compatibility.\u001b[39;00m\n\u001b[1;32m 470\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_close_conn()\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/socket.py:706\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 705\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 706\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecv_into\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 707\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[1;32m 708\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/ssl.py:1311\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m 1307\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m flags \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 1308\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 1309\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m 1310\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)\n\u001b[0;32m-> 1311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnbytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1312\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1313\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrecv_into(buffer, nbytes, flags)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/nirspec-ifu-demo/lib/python3.11/ssl.py:1167\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m 1165\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1166\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m buffer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1167\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sslobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1168\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1169\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m)\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], "source": [ "# Stage 2 Processing \n", "\n",

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b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb new file mode 100644 index 000000000..e4cfcac37 --- /dev/null +++ b/notebooks/NIRSpec_IFU_spectral_extraction/ero_nirspec_ifu_02729_demo.ipynb @@ -0,0 +1,1171 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0d0d06b8", + "metadata": {}, + "source": [ + "\n", + "# NIRSpec IFU Pipeline Processing ERO 02729: Tarantula Nebula\n", + "

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