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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 Setup](#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", + "* [8. 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", + "
\n", + "\n", + "
\n", + " \n", + "* *astropy.io* for accessing FITS files\n", + "* *astropy.visualization* for customizing plot normalization and stretching\n", + "* *astropy.wcs* to handling World Coordinate System (WCS) transformations\n", + "* *astroquery.mast* to query the Mikulski Archive for Space Telescopes (MAST) database\n", + "* *fnmatch* for pattern-based matching of file names\n", + "* *glob* to search for files in directories\n", + "* *json* to work with JSON (JavaScript Object Notation) data\n", + "* *jwst* for processing data through the JWST calibration pipeline\n", + "\n", + "
\n", + "\n", + "
\n", + " \n", + "* *matplotlib* for plotting data\n", + "* *matplotlib.gridspec* for creating flexible subplot layouts in Matplotlib\n", + "* *numpy* to handle array functions and numerical operations\n", + "* *os* to interact with the operating system (navigating directories, creating files)\n", + "* *shutil* to copy files\n", + "* *urllib.request* to interact with URLs and download data from the web\n", + "* *warnings* to handle warnings and how they are displayed or ignored\n", + "* *zipfile* to work with ZIP archives\n", + " \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4313a65b-8719-4cd2-b4fd-869e8c7413bb", + "metadata": {}, + "outputs": [], + "source": [ + "# ---------- Set CRDS environment variables ----------\n", + "import os\n", + "import jwst\n", + "os.environ['CRDS_CONTEXT'] = 'jwst_1210.pmap'\n", + "os.environ['CRDS_PATH'] = os.environ['HOME']+'/crds_cache_test'\n", + "os.environ['CRDS_SERVER_URL'] = 'https://jwst-crds.stsci.edu'\n", + "print(f'CRDS cache location: {os.environ[\"CRDS_PATH\"]}')\n", + "\n", + "print(\"JWST Calibration Pipeline Version={}\".format(jwst.__version__))\n", + "#print(\"Current Operational CRDS Context = {}\".format(crds.get_default_context()))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51faa94a-8f7e-4b00-be55-cea149513e4e", + "metadata": {}, + "outputs": [], + "source": [ + "# Import Library\n", + "# ---------- General Imports ----------\n", + "\n", + "import numpy as np\n", + "import warnings\n", + "import glob\n", + "import json\n", + "import urllib.request\n", + "import zipfile\n", + "import fnmatch\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "import matplotlib.gridspec as grd\n", + "from matplotlib import cm\n", + "from shutil import copy\n", + "\n", + "# ---------- Astropy/Astroquery Imports ----------\n", + "from astropy.io import fits\n", + "from astropy.wcs import WCS\n", + "from astropy.visualization import ImageNormalize, ManualInterval\n", + "from astropy.visualization import LogStretch, LinearStretch, AsinhStretch\n", + "from astroquery.mast import Observations\n", + "\n", + "# ---------- JWST Calibration Pipeline Imports ----------\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", + "\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", + "# %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,\n", + " 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.\n", + " 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=[],\n", + " y_scale=None, cmap='jet', vmin_vmax=[[[0, 15e1]]],\n", + " 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\n", + " 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.\n", + " 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\n", + " (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 = plt.figure(figsize=(8 * np.array(wavelength_slices).size, 18))\n", + " gs = grd.GridSpec(3, np.array(wavelength_slices).size, height_ratios=[1] * 3,\n", + " width_ratios=[1] * np.array(wavelength_slices).size,\n", + " 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", + " # SCI Extension:\n", + " # [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", + " # 3-D weight image giving the relative weights of the output spaxels.\n", + " wmap = s3d[4].data\n", + " # Axis 3 coordinate increment at reference point\n", + " cdelt3 = s3d[1].header['CDELT3']\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", + " colors = [\"darkred\", \"darkturquoise\", \"blue\"]\n", + " cmap_custom = cm.colors.LinearSegmentedColormap.from_list(\"\", colors)\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", + " # 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", + " # The slice of the cube we want to plot\n", + " nslice = int((slicewave - crval3)/cdelt3)\n", + " # Setup the subplot space\n", + " ax1 = plt.subplot(gs[0+plot_count], projection=wcs,\n", + " slices=('x', 'y', nslice))\n", + "\n", + " # Mean of the slice looking in the range (nslice-2):(nslice+2)\n", + " slice_mean = np.nanmean(cube[(nslice - 2):(nslice + 2), :, :], axis=0)\n", + " # Normalize & stretch\n", + " slice_norm = ImageNormalize(slice_mean, vmin=vmin_val,\n", + " vmax=vmax_val, stretch=AsinhStretch())\n", + " slice_image = ax1.imshow(slice_mean, norm=slice_norm,\n", + " origin='lower', aspect='auto', cmap=cmap)\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 {}\\nGrating/Filter: {}/{}\\n{} microns'.format(\n", + " s3d[0].header['DETECTOR'],\n", + " s3d[0].header['GRATING'],\n", + " s3d[0].header['FILTER'],\n", + " str(slicewave)\n", + " ), 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", + " # ---------- 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(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,\n", + " 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(\n", + " gs[int(total_num_plots)-np.array(wavelength_slices).size+plot_count],\n", + " projection=wcs,\n", + " slices=('x', 'y', nslice))\n", + "\n", + " # Mean of the wmap slice looking in the range (nslice-2):(nslice+2)\n", + " slice_mean_wmap = np.nanmean(wmap[(nslice-2):(nslice+2), :, :], axis=0)\n", + " # Normalize & stretch\n", + " slice_norm_wmap = ImageNormalize(slice_mean_wmap,\n", + " stretch=AsinhStretch())\n", + " slice_wmap = ax3.imshow(slice_mean_wmap, norm=slice_norm_wmap,\n", + " origin='lower', aspect='auto', cmap=cmap)\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:\n", + " fig.savefig(root+\".png\", dpi=24, bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "markdown", + "id": "347a9a54", + "metadata": {}, + "source": [ + "## 4. Directory Setup \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 = False\n", + "\n", + "# To run with pre-computed data, set `runflag=False` and specify the 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": "db35b0c0-8b69-44c0-aa29-5298aa8e0337", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a directory for the downloaded data from MAST.\n", + "mast_products_dir = output_dir+'mast_products/'\n", + "if not os.path.exists(mast_products_dir):\n", + " os.makedirs(mast_products_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7c91f0f-d8d4-4793-85e8-2a83088b500d", + "metadata": {}, + "outputs": [], + "source": [ + "# Pre-processed MAST products for comparison:\n", + "# RATE (stage 1) & CAL (stage 2&3) & S3D (stage 2&3) & X1D (stage2&3)\n", + "\n", + "if runflag:\n", + " # ---------- Pre-processed MAST Products ----------\n", + "\n", + " # Box link to the pre-proccessed data from MAST.\n", + " # We are downloading data from this Box link containing the products in MAST\n", + " # as they were at the time this notebook was created.\n", + " # Occasionally, products in MAST undergo re-processing.\n", + " # To ensure the notebook's reproducibility and maintain interpretability\n", + " # of comments throughout, we employ this approach.\n", + "\n", + " boxlink_mast = \"https://stsci.box.com/shared/static/0qw8yjib9860ishf28esjezsyqbpntq1.zip\"\n", + " boxfile_mast = os.path.join(mast_products_dir, '2729_mast_products.zip')\n", + " urllib.request.urlretrieve(boxlink_mast, boxfile_mast)\n", + "\n", + " # Extract the files from the zip file.\n", + " with zipfile.ZipFile(boxfile_mast, 'r') as zip_ref:\n", + " zip_ref.extractall(mast_products_dir)\n", + " print(\"MAST products extracted successfully!\")" + ] + }, + { + "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 search 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\n", + "# ASN files to manually run pipeline stage 1-3\n", + "filtered = Observations.filter_products(products,\n", + " productSubGroupDescription=[\"UNCAL\", \"ASN\"],\n", + " mrp_only=False)\n", + "\n", + "# Only download data for the G235H/F170LP configuration in this dataset.\n", + "subset_extensions = ['*02103*nrs?_uncal.fits', '*_spec3_00002_asn.json']\n", + "\n", + "# Print the filtered products\n", + "number = len(filtered)\n", + "for k in range(number):\n", + " if any(fnmatch.fnmatch(filtered['productFilename'][k], pattern)\n", + " for pattern in subset_extensions):\n", + " print(filtered['productFilename'][k])\n", + "\n", + "# Download the filtered products\n", + "# This creates a mastDownload directory,\n", + "# unless you set flat=True and set a download_dir.\n", + "for i in range(len(filtered)):\n", + " if runflag:\n", + " # Override any cached files and download the most up-to-date ones.\n", + " if any(fnmatch.fnmatch(filtered['productFilename'][i], pattern)\n", + " for pattern in subset_extensions):\n", + " Observations.download_products(filtered[i], mrp_only=False, cache=False,\n", + " flat=True, download_dir=mast_products_dir)\n", + " else:\n", + " # Find any cached files first before downloading new ones.\n", + " if any(fnmatch.fnmatch(filtered['productFilename'][i], pattern)\n", + " for pattern in subset_extensions):\n", + " Observations.download_products(filtered[i], mrp_only=False, cache=True,\n", + " flat=True, download_dir=mast_products_dir)\n", + "print(\"Raw data and ASN files from MAST downloaded successfully!\")" + ] + }, + { + "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)\n", + "# 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", + " # get the pixel data (the SCI extension of the fits file)\n", + " ratefile_sci = ratefile_open.data\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 {}\"\n", + " .format(ratefile_open.meta.calibration_software_version,\n", + " ratefile_open.meta.ref_file.crds.context_used))\n", + "\n", + " # Plot the slope image and small section of the\n", + " # countrate image & corresponding section of the DQ map.\n", + " detector = ratefile_open.meta.instrument.detector\n", + " dither_pos = ratefile_open.meta.dither.position_number\n", + " grating = ratefile_open.meta.instrument.grating\n", + " filter_ = ratefile_open.meta.instrument.filter\n", + "\n", + " title_sci = ('Countrate Image\\n'\n", + " 'Detector: {}\\n'\n", + " '8-Cycle Dither Position Index: {}\\n'\n", + " 'GRATING/FILTER: {}/{}'\n", + " .format(detector, dither_pos, grating, filter_))\n", + "\n", + " title_dq = ('Data Quality Map \\n'\n", + " 'Detector: {} \\n'\n", + " '8-Cycle Dither Position Index: {} \\n'\n", + " 'GRATING/FILTER: {}/{}'\n", + " .format(detector, dither_pos, grating, filter_))\n", + "\n", + " show_image(ratefile_sci, 0, 10, units='DN/s', zoom_in=[650, 700, 1250, 1300],\n", + " ysize=20, xsize=20, title=title_sci)\n", + "\n", + " show_image(ratefile_dq, 0, 10, units='Bit Value', zoom_in=[650, 700, 1250, 1300],\n", + " ysize=20, xsize=20, title=title_dq)" + ] + }, + { + "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", + "# 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:'\n", + " '3-D Cube Slices vs. Corresponding 3-D Weighted Map')\n", + "\n", + "# Print the version and CRDS pmap used to create these S3D files\n", + "# stage2_s3d_file_open.serach(key='context')\n", + "stage2_s3d_file_open = datamodels.open(s3d_stage2_list[0])\n", + "print(\"Products found in MAST used JWST calibration pipeline version: {} and {}\"\n", + " .format(stage2_s3d_file_open.meta.calibration_software_version,\n", + " stage2_s3d_file_open.meta.ref_file.crds.context_used))\n", + "\n", + "# Characteristics of the plot\n", + "\n", + "# Wavelength slices (microns) to take from the 3-D data cube\n", + "nrs1_wavelengths = [1.0, 2.3, 3.4]\n", + "nrs2_wavelengths = [1.4, 2.5, 4.0]\n", + "\n", + "# Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "nrs1_spaxel_locs = [[30, 29], [28, 39], [14, 25]]\n", + "nrs2_spaxel_locs = [[30, 29], [28, 39], [14, 25]]\n", + "\n", + "# Minimum & Maximum signal values for scaling each slice\n", + "nrs1_vmin_vmax = [[0, 2e2], [0, 1e2], [0, 1.2e3]]\n", + "nrs2_vmin_vmax = [[0, 2e2], [0, 1e2], [0, 1.2e3]]\n", + "\n", + "# Spaxel plot y-limits\n", + "nrs1_yscales = [[-80, 150], [-80, 150], [-80, 200]]\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,\n", + " wavelength_slices=[nrs1_wavelengths, nrs2_wavelengths],\n", + " spaxel_locs=[nrs1_spaxel_locs, nrs2_spaxel_locs],\n", + " 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\n", + "# 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:'\n", + " '3-D Cube Slices vs. Corresponding 3-D Weighted Map')\n", + "\n", + "# Print the version and CRDS pmap used to create these S3D files\n", + "# stage2_s3d_file_open.serach(key='context')\n", + "stage3_s3d_file_open = datamodels.open(s3d_stage3_list[0])\n", + "print(\"Products found in MAST used JWST calibration pipeline version: {} and {}\"\n", + " .format(stage3_s3d_file_open.meta.calibration_software_version,\n", + " stage3_s3d_file_open.meta.ref_file.crds.context_used))\n", + "\n", + "# Characteristics of the plot\n", + "\n", + "# Wavelength slices (microns) to take from the combined 3-D data cube\n", + "wavelengths = [1.4, 2.3, 4.0]\n", + "\n", + "# Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "spaxel_locs = [[30, 29], [28, 39], [14, 25]]\n", + "\n", + "# Minimum & Maximum signal values for scaling each slice\n", + "vmin_vmax = [[0, 2e2], [0, 1e2], [0, 1.2e3]]\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],\n", + " 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": "9db29643-52b9-4fe8-8bcd-2d23b9cd58f3", + "metadata": {}, + "outputs": [], + "source": [ + "x1d3_mast_list" + ] + }, + { + "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", + "# Print the version and CRDS pmap used to create these X1D files\n", + "# x1d3_mast.serach(key='context')\n", + "x1d3_mast_open = datamodels.open(x1d3_mast_list[0])\n", + "print(\"Products found in MAST used JWST calibration pipeline version: {} and {}\"\n", + " .format(x1d3_mast_open.meta.calibration_software_version,\n", + " x1d3_mast_open.meta.ref_file.crds.context_used))\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,\n", + " color=colors[i], label='Grating/Filter: {}/{}'\n", + " .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(r'$\\lambda [\\mu$m]', fontsize=15)\n", + "plt.ylabel('Surface Brightness (MJy/sr)', fontsize=15)\n", + "plt.title((\"Tarantula Nebula \\n\"\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. As of release DMS build B10.1rc1/CAL_VER 1.13.0, the pipeline has integrated an external algorithm, \"[NSClean](https://webb.nasa.gov/content/forScientists/publications.html)\", developed by Bernie Rauscher at GSFC to deal with 1/f noise. See https://github.com/spacetelescope/jdat_notebooks/tree/main/notebooks/NIRSpec_NSClean for a demonstration on how to execute NSClean using the JWST calibration pipeline on NIRSpec IFU data.\n", + "\n", + "
" + ] + }, + { + "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:\n", + "\n", + " for uncal_file in [sorted(glob.glob(mast_products_dir+'*02103*nrs?_uncal.fits'))[1]]:\n", + "\n", + " print(\"Applying Stage 1 Corrections & Calibrations to: \"\n", + " + 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)\n", + "# 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", + " # get the pixel data (the SCI extension of the fits file)\n", + " ratefile_sci = ratefile_open.data\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\n", + " # countrate image & corresponding section of the DQ map.\n", + " detector = ratefile_open.meta.instrument.detector\n", + " dither_pos = ratefile_open.meta.dither.position_number\n", + " grating = ratefile_open.meta.instrument.grating\n", + " filter_ = ratefile_open.meta.instrument.filter\n", + "\n", + " title_sci = ('Countrate Image\\n'\n", + " 'Detector: {}\\n'\n", + " '8-Cycle Dither Position Index: {}\\n'\n", + " 'GRATING/FILTER: {}/{}'\n", + " .format(detector, dither_pos, grating, filter_))\n", + "\n", + " title_dq = ('Data Quality Map \\n'\n", + " 'Detector: {} \\n'\n", + " '8-Cycle Dither Position Index: {} \\n'\n", + " 'GRATING/FILTER: {}/{}'\n", + " .format(detector, dither_pos, grating, filter_))\n", + "\n", + " show_image(ratefile_sci, 0, 10, units='DN/s',\n", + " zoom_in=[650, 700, 1250, 1300], ysize=20, xsize=20, title=title_sci)\n", + "\n", + " show_image(ratefile_dq, 0, 10, units='Bit Value', scale='linear',\n", + " zoom_in=[650, 700, 1250, 1300], ysize=20, xsize=20, title=title_dq)" + ] + }, + { + "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:\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: \"\n", + " + 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\n", + "# 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:'\n", + " '3-D Cube Slices vs. Corresponding 3-D Weighted Map')\n", + "\n", + "# Characteristics of the plot:\n", + "# Wavelength slices (microns) to take from the 3-D data cube\n", + "nrs1_wavelengths = [2.3]\n", + "nrs2_wavelengths = [2.5]\n", + "\n", + "# Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice)\n", + "nrs1_spaxel_locs = [[28, 39]]\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,\n", + " wavelength_slices=[nrs1_wavelengths, nrs2_wavelengths],\n", + " spaxel_locs=[nrs1_spaxel_locs, nrs2_spaxel_locs],\n", + " 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", + "# ASN file found in MAST\n", + "asnfile_mast = glob.glob(mast_products_dir+'*_spec3_00002_asn.json')[0]\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 on\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'}})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d45c93f9", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Stage 3 Products\n", + "# Combined Calibrated 3-D data cubes for\n", + "# 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'\n", + " '3-D Cube Slices vs. Corresponding 3-D Weighted Map')\n", + "\n", + "# Characteristics of the plot:\n", + "# Wavelength slices (microns) to take from the combined 3-D data cube.\n", + "wavelengths = [2.3]\n", + "# Spaxel locations for associated 1-D spectrum (one spaxel plotted per slice).\n", + "spaxel_locs = [[28, 39]]\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],\n", + " spaxel_locs=[spaxel_locs], 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_mast_list = sorted(glob.glob(mast_products_dir+'*nirspec*_x1d.fits'))\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],\n", + " label='Grating/Filter: {}/{}'.format(\n", + " 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(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\",\n", + " fontsize=20)\n", + "plt.ylim(-1e1, 2e2)\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "ff7a3626-71bb-45e5-bc1f-ffeccf930aa4", + "metadata": {}, + "source": [ + "## 8. 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", + "**Authors**: Kayli Glidic (kglidic@stsci.edu), Maria Pena-Guerrero (pena@stsci.edu), Leonardo Ubeda (lubeda@stsci.edu)\n", + "\n", + "**Update On**: 2024-05-10\n", + "\n", + "\n", + "---\n", + "
\n", + " \"Space\n", + "
\n", + " \n", + "[Top of Page](#top)" + ] + } + ], + "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": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/NIRSPEC/IFU/requirements.txt b/notebooks/NIRSPEC/IFU/requirements.txt new file mode 100644 index 0000000..7f5b269 --- /dev/null +++ b/notebooks/NIRSPEC/IFU/requirements.txt @@ -0,0 +1,7 @@ +asdf >= 3.1.0 +astropy >= 6.0.0 +astroquery >= 0.4.7.dev9069 +crds >= 11.17.19 +jwst >= 1.13.4 +matplotlib >= 3.8.3 +numpy >= 1.26.4 \ No newline at end of file From 2daa638ce06f049032be633a11b150a2153ad580 Mon Sep 17 00:00:00 2001 From: Kayli Date: Fri, 10 May 2024 19:54:05 -0400 Subject: [PATCH 02/10] Set runflag to True and fixed a style error --- notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb b/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb index 20bd523..956018e 100644 --- a/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb +++ b/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb @@ -103,7 +103,7 @@ "print(f'CRDS cache location: {os.environ[\"CRDS_PATH\"]}')\n", "\n", "print(\"JWST Calibration Pipeline Version={}\".format(jwst.__version__))\n", - "#print(\"Current Operational CRDS Context = {}\".format(crds.get_default_context()))\n" + "# print(\"Current Operational CRDS Context = {}\".format(crds.get_default_context()))" ] }, { @@ -496,7 +496,7 @@ "outputs": [], "source": [ "# To rerun the notebook and all the pipeline steps set runflag=True.\n", - "runflag = False\n", + "runflag = True\n", "\n", "# To run with pre-computed data, set `runflag=False` and specify the local directory.\n", "output_dir = './nirspec_ifu_02729_rerun/'\n", From eefd59a5eafe28bfa3537595370fcfa982bf7a04 Mon Sep 17 00:00:00 2001 From: Kayli Date: Fri, 10 May 2024 20:13:37 -0400 Subject: [PATCH 03/10] Removed a test cell accidently left in --- .../NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb b/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb index 956018e..c0d2ea0 100644 --- a/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb +++ b/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb @@ -854,16 +854,6 @@ "" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "9db29643-52b9-4fe8-8bcd-2d23b9cd58f3", - "metadata": {}, - "outputs": [], - "source": [ - "x1d3_mast_list" - ] - }, { "cell_type": "code", "execution_count": null, @@ -1283,8 +1273,6 @@ "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", "x1d3_rerun_list = sorted(glob.glob(output_dir_rerun+'*nirspec*_x1d.fits'))\n", "\n", "for i, x1d3_file in enumerate(x1d3_rerun_list):\n", From 0d75c8317248668c9774460d2a2b044dd50a1a1d Mon Sep 17 00:00:00 2001 From: Kayli Date: Fri, 10 May 2024 22:48:57 -0400 Subject: [PATCH 04/10] found and fixed what was causing file not found error --- notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb b/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb index c0d2ea0..a99a862 100644 --- a/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb +++ b/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb @@ -980,7 +980,7 @@ "\n", "if runflag:\n", "\n", - " for uncal_file in [sorted(glob.glob(mast_products_dir+'*02103*nrs?_uncal.fits'))[1]]:\n", + " for uncal_file in sorted(glob.glob(mast_products_dir+'*02103*nrs?_uncal.fits')):\n", "\n", " print(\"Applying Stage 1 Corrections & Calibrations to: \"\n", " + os.path.basename(uncal_file))\n", From a74de72521a061be475fda558f9e3e244301e634 Mon Sep 17 00:00:00 2001 From: gibsongreen Date: Mon, 24 Jun 2024 16:20:43 -0400 Subject: [PATCH 05/10] test updated requirements --- notebooks/NIRSPEC/IFU/requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/NIRSPEC/IFU/requirements.txt b/notebooks/NIRSPEC/IFU/requirements.txt index 7f5b269..69791fa 100644 --- a/notebooks/NIRSPEC/IFU/requirements.txt +++ b/notebooks/NIRSPEC/IFU/requirements.txt @@ -2,6 +2,6 @@ asdf >= 3.1.0 astropy >= 6.0.0 astroquery >= 0.4.7.dev9069 crds >= 11.17.19 -jwst >= 1.13.4 +jwst >= 1.14.0 matplotlib >= 3.8.3 -numpy >= 1.26.4 \ No newline at end of file +numpy >= 2.0.0 \ No newline at end of file From 0f98f19f40b4c3f1549b40a3d986816e1fe25596 Mon Sep 17 00:00:00 2001 From: gibsongreen Date: Mon, 24 Jun 2024 16:33:43 -0400 Subject: [PATCH 06/10] test if matplotlib 3.9 causes import error --- notebooks/NIRSPEC/IFU/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/NIRSPEC/IFU/requirements.txt b/notebooks/NIRSPEC/IFU/requirements.txt index 69791fa..bab106a 100644 --- a/notebooks/NIRSPEC/IFU/requirements.txt +++ b/notebooks/NIRSPEC/IFU/requirements.txt @@ -3,5 +3,5 @@ astropy >= 6.0.0 astroquery >= 0.4.7.dev9069 crds >= 11.17.19 jwst >= 1.14.0 -matplotlib >= 3.8.3 +matplotlib == 3.8.4 numpy >= 2.0.0 \ No newline at end of file From ca74e1ae27870ae0d31765265cd652a156c9714f Mon Sep 17 00:00:00 2001 From: gibsongreen Date: Mon, 24 Jun 2024 19:22:58 -0400 Subject: [PATCH 07/10] alternative calls to jwst pipeline classes --- notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb b/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb index a99a862..4a828b9 100644 --- a/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb +++ b/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb @@ -138,9 +138,7 @@ "\n", "# ---------- JWST Calibration Pipeline Imports ----------\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", "\n", "warnings.filterwarnings('ignore') # Set to 'default' to turn warnings back on\n", "\n", @@ -985,7 +983,7 @@ " print(\"Applying Stage 1 Corrections & Calibrations to: \"\n", " + os.path.basename(uncal_file))\n", "\n", - " result = Detector1Pipeline.call(uncal_file,\n", + " result = jwst.pipeline.Detector1Pipeline.call(uncal_file,\n", " save_results=True,\n", " output_dir=output_dir_rerun)" ] @@ -1209,7 +1207,7 @@ "# Rerun stage 3 with outlier detection on\n", "if runflag:\n", "\n", - " result = Spec3Pipeline.call(asnfile_rerun,\n", + " result = jwst.pipeline.Spec3Pipeline.call(asnfile_rerun,\n", " save_results=True,\n", " output_dir=output_dir_rerun,\n", " steps={\"outlier_detection\": {\"skip\": False,\n", From b0185e9c29ccd054893af178b82678c0758d77de Mon Sep 17 00:00:00 2001 From: gibsongreen Date: Tue, 25 Jun 2024 12:49:04 -0400 Subject: [PATCH 08/10] ensure numpy is not 2.0, styling fixes --- .../NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb | 16 +++++++++------- notebooks/NIRSPEC/IFU/requirements.txt | 6 +++--- 2 files changed, 12 insertions(+), 10 deletions(-) diff --git a/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb b/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb index 4a828b9..4026c4f 100644 --- a/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb +++ b/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb @@ -138,7 +138,9 @@ "\n", "# ---------- JWST Calibration Pipeline Imports ----------\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", "\n", "warnings.filterwarnings('ignore') # Set to 'default' to turn warnings back on\n", "\n", @@ -983,9 +985,9 @@ " print(\"Applying Stage 1 Corrections & Calibrations to: \"\n", " + os.path.basename(uncal_file))\n", "\n", - " result = jwst.pipeline.Detector1Pipeline.call(uncal_file,\n", - " save_results=True,\n", - " output_dir=output_dir_rerun)" + " result = jwst.Detector1Pipeline.call(uncal_file,\n", + " save_results=True,\n", + " output_dir=output_dir_rerun)" ] }, { @@ -1207,10 +1209,10 @@ "# Rerun stage 3 with outlier detection on\n", "if runflag:\n", "\n", - " result = jwst.pipeline.Spec3Pipeline.call(asnfile_rerun,\n", - " save_results=True,\n", - " output_dir=output_dir_rerun,\n", - " steps={\"outlier_detection\": {\"skip\": False,\n", + " result = jwst.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'}})" ] diff --git a/notebooks/NIRSPEC/IFU/requirements.txt b/notebooks/NIRSPEC/IFU/requirements.txt index bab106a..f1ed9d6 100644 --- a/notebooks/NIRSPEC/IFU/requirements.txt +++ b/notebooks/NIRSPEC/IFU/requirements.txt @@ -2,6 +2,6 @@ asdf >= 3.1.0 astropy >= 6.0.0 astroquery >= 0.4.7.dev9069 crds >= 11.17.19 -jwst >= 1.14.0 -matplotlib == 3.8.4 -numpy >= 2.0.0 \ No newline at end of file +jwst >= 1.13.4 +matplotlib >= 3.8.3 +numpy == 1.26.4 \ No newline at end of file From 71c4ff55d6beb347075e0e50cad16596db76734b Mon Sep 17 00:00:00 2001 From: gibsongreen Date: Tue, 25 Jun 2024 12:54:19 -0400 Subject: [PATCH 09/10] styling updates, double-check original pipeline calls with reverted numpy --- notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb b/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb index 4026c4f..d0f04eb 100644 --- a/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb +++ b/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb @@ -985,7 +985,7 @@ " print(\"Applying Stage 1 Corrections & Calibrations to: \"\n", " + os.path.basename(uncal_file))\n", "\n", - " result = jwst.Detector1Pipeline.call(uncal_file,\n", + " result = Detector1Pipeline.call(uncal_file,\n", " save_results=True,\n", " output_dir=output_dir_rerun)" ] @@ -1209,12 +1209,12 @@ "# Rerun stage 3 with outlier detection on\n", "if runflag:\n", "\n", - " result = jwst.Spec3Pipeline.call(asnfile_rerun,\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'}})" ] }, { From 7b5a4a9d0aa9a37e3348a4b00726f2c8fcc52826 Mon Sep 17 00:00:00 2001 From: gibsongreen Date: Tue, 25 Jun 2024 12:59:58 -0400 Subject: [PATCH 10/10] fix styling --- .../NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb b/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb index d0f04eb..a99a862 100644 --- a/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb +++ b/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb @@ -986,8 +986,8 @@ " + 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)" ] }, { @@ -1210,11 +1210,11 @@ "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", + " output_dir=output_dir_rerun,\n", + " steps={\"outlier_detection\": {\"skip\": False,\n", + " \"save_results\": True,\n", + " \"kernel_size\": '3 3'}})" ] }, {

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file mode 100644 index 0000000..20bd523 --- /dev/null +++ b/notebooks/NIRSPEC/IFU/ero_nirspec_ifu_02729_demo.ipynb @@ -0,0 +1,1365 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0d0d06b8", + "metadata": {}, + "source": [ + "\n", + "# NIRSpec IFU Pipeline Processing ERO 02729: Tarantula Nebula\n", + "

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