From 6e73762b1774ed61f33e87cc024953600a518f44 Mon Sep 17 00:00:00 2001 From: Ori Date: Tue, 9 Aug 2022 11:30:10 -0400 Subject: [PATCH 01/36] LRS Specreduce Notebook --- .../miri_lrs_specreduce.ipynb | 1602 +++++++++++++++++ .../requirements.txt | 7 +- 2 files changed, 1606 insertions(+), 3 deletions(-) create mode 100644 notebooks/MIRI_LRS_spectral_extraction/miri_lrs_specreduce.ipynb diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_specreduce.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_specreduce.ipynb new file mode 100644 index 000000000..689184a6f --- /dev/null +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_specreduce.ipynb @@ -0,0 +1,1602 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# MIRI LRS Optimal Spectral Extraction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Use case:** Extract spectra with different locations, extraction apertures, and techniques.
\n", + "**Data:** Simulated MIRI LRS spectrum.
\n", + "**Tools:** jwst, gwcs, matplotlib, astropy.
\n", + "**Cross-intrument:** NIRSpec, MIRI.
\n", + "**Documentation:** This notebook is part of a STScI's larger [post-pipeline Data Analysis Tools Ecosystem](https://jwst-docs.stsci.edu/jwst-post-pipeline-data-analysis).
\n", + "\n", + "# Introduction\n", + "\n", + "This notebook extracts a 1D spectra from a 2D MIRI LRS spectral observation (single image). The goal is to provide the ability to extract spectra with different locations, extraction apertures, and techniques than are done in the JWST pipeline using the [Astropy Specreduce package](https://github.com/astropy/specreduce).\n", + "\n", + "The simpliest spectral extraction is \"boxcar\" where all the pixels within some fixed width centered on the source position are summed at each wavelength. Background subtraction can be done using regions offset from the source center. You can also see the Specreduce [generic Sample Notebook](https://github.com/astropy/specreduce/blob/main/notebook_sandbox/jwst_boxcar/boxcar_extraction.ipynb).\n", + "\n", + "For spectra taken with a diffraction limited telescope like JWST, a modification boxcar extraction is to vary the extraction width linearly with wavelength. Such a scaled boxcar extraction keeps the fraction of the source flux within the extraction region approximately constant with wavelength.\n", + "\n", + "For point sources, a PSF-weighted spectral extraction can be done. Using the PSF to weight the extraction uses the actual PSF as a function of wavelength to optimize the extraction to the pixels with the greatest signal. PSF-weighted extractions show the largest differences with boxcar extractions at lower S/N values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note:** Corrections for the finite aperture used in all the extractions have not been applied. Thus, the physical flux densities of all the extracted spectra are lower than the actual values." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Imports\n", + "\n", + "- *matplotlib.pyplot* for plotting data\n", + "- *numpy* to handle array functions\n", + "- *astropy.io fits* for accessing FITS files\n", + "- *astropy.visualization* for scaling image for display\n", + "- *astropy.table Table* for reading the pipeline 1d extractions\n", + "- *jwst datamodels* for reading/access the jwst data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "\n", + "from gwcs.wcstools import grid_from_bounding_box\n", + "\n", + "from astropy.io import fits\n", + "from astropy.table import Table\n", + "from astropy.visualization import simple_norm\n", + "\n", + "from jwst import datamodels\n", + "\n", + "from specreduce.extract import BoxcarExtract, OptimalExtract, HorneExtract\n", + "from specreduce.tracing import FlatTrace, KosmosTrace\n", + "from specreduce.background import Background\n", + "\n", + "from jdaviz import Imviz\n", + "from jdaviz import Specviz\n", + "\n", + "from astropy.utils.data import download_file\n", + "import os\n", + "\n", + "from specutils import Spectrum1D\n", + "from astropy import units as u" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Developer note: Ask Karl exactly how these functions work? Seems like all weights are equal?" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# useful functions\n", + "def get_boxcar_weights(center, hwidth, npix):\n", + " \"\"\"\n", + " Compute the weights given an aperture center, half widths, and number of pixels\n", + " \"\"\"\n", + " weights = np.zeros((npix))\n", + " # pixels with full weight\n", + " fullpixels = [max(0, int(center - hwidth + 1)), min(int(center + hwidth), npix)]\n", + " weights[fullpixels[0] : fullpixels[1]] = 1.0\n", + "\n", + " # pixels at the edges of the boxcar with partial weight\n", + " if fullpixels[0] > 0:\n", + " weights[fullpixels[0] - 1] = hwidth - (center - fullpixels[0])\n", + " if fullpixels[1] < npix:\n", + " weights[fullpixels[1]] = hwidth - (fullpixels[1] - center)\n", + "\n", + " return weights\n", + "\n", + "\n", + "def ap_weight_images(\n", + " center, width, bkg_offset, bkg_width, image_size, waves, wavescale=None\n", + "):\n", + " \"\"\"\n", + " Create a weight image that defines the desired extraction aperture\n", + " and the weight image for the requested background regions\n", + "\n", + " Parameters\n", + " ----------\n", + " center : float\n", + " center of aperture in pixels\n", + " width : float\n", + " width of apeture in pixels\n", + " bkg_offset : float\n", + " offset from the extaction edge for the background\n", + " never scaled for wavelength\n", + " bkg_width : float\n", + " width of background region\n", + " never scaled with wavelength\n", + " image_size : tuple with 2 elements\n", + " size of image\n", + " waves : array\n", + " wavelegth values\n", + " wavescale : float\n", + " scale the width with wavelength (default=None)\n", + " wavescale gives the reference wavelenth for the width value\n", + "\n", + " Returns\n", + " -------\n", + " wimage, bkg_wimage : (2D image, 2D image)\n", + " wimage is the weight image defining the aperature\n", + " bkg_image is the weight image defining the background regions\n", + " \"\"\"\n", + " wimage = np.zeros(image_size)\n", + " bkg_wimage = np.zeros(image_size)\n", + " hwidth = 0.5 * width\n", + " # loop in dispersion direction and compute weights\n", + " for i in range(image_size[1]):\n", + " if wavescale is not None:\n", + " hwidth = 0.5 * width * (waves[i] / wavescale)\n", + "\n", + " wimage[:, i] = get_boxcar_weights(center, hwidth, image_size[0])\n", + "\n", + " # bkg regions\n", + " if (bkg_width is not None) & (bkg_offset is not None):\n", + " bkg_wimage[:, i] = get_boxcar_weights(\n", + " center - hwidth - bkg_offset, bkg_width, image_size[0]\n", + " )\n", + " bkg_wimage[:, i] += get_boxcar_weights(\n", + " center + hwidth + bkg_offset, bkg_width, image_size[0]\n", + " )\n", + " else:\n", + " bkg_wimage = None\n", + "\n", + " return (wimage, bkg_wimage)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Devloper notes\n", + "\n", + "The difference between the pipeline (x1d) and the extractions done in this notebook are quite large. Help in understanding the origin of these differences is needed.\n", + "\n", + "Not clear how to use the JWST pipeline `extract_1d` (quite complex) code.\n", + "Help to determine how to use the JWST pipeline code instead of the custom code for boxcar is needed. \n", + "\n", + "Applying aperture corrections for the finite extraction widths is needed. Help in how to get the needed informatinom for different (user set) extraction widths is needed. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download Files" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "calfilename = \"det_image_seq5_MIRIMAGE_P750Lexp1_cal.fits\"\n", + "s2dfilename = \"det_image_seq5_MIRIMAGE_P750Lexp1_s2d.fits\"\n", + "x1dfilename = \"det_image_seq5_MIRIMAGE_P750Lexp1_x1d.fits\"\n", + "spatialprofilefilename = \"det_image_seq1_MIRIMAGE_P750Lexp1_s2d.fits\"\n", + "mainurl = \"https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/\"\n", + "\n", + "calfile_dld = download_file(mainurl + calfilename)\n", + "s2dfile_dld = download_file(mainurl + s2dfilename)\n", + "x1dfile_dld = download_file(mainurl + x1dfilename)\n", + "spatialprofilefile_dld = download_file(mainurl + spatialprofilefilename)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# rename files so that they have the right extensions\n", + "# required for the jwst datamodels to work\n", + "calfile = calfile_dld + '_cal.fits'\n", + "os.rename(calfile_dld, calfile)\n", + "s2dfile = s2dfile_dld + '_s2d.fits'\n", + "os.rename(s2dfile_dld, s2dfile)\n", + "x1dfile = x1dfile_dld + '_x1d.fits'\n", + "os.rename(x1dfile_dld, x1dfile)\n", + "spatialprofilefile = spatialprofilefile_dld + '_s2d.fits'\n", + "os.rename(spatialprofilefile_dld, spatialprofilefile)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## File information\n", + "\n", + "The data used is a simulation of a LRS slit observation for a blackbody with a similar flux density to the star BD+60d1753, a flux calibration star. This simulation was created with MIRISim.\n", + "The simulated exposure was reduced using the JWST pipeline (v0.16.1) through the Detector1 and Spec2 stages.\n", + "\n", + "The cal file is one of the Spec2 products and is the calibration full frame image. It contains:\n", + "\n", + "1. (Primary): This HDU contains meta-data related to the observation and data reduction.\n", + "2. (SCI): The calibrated image. Units are MJy/sr.\n", + "3. (ERR): Uncertainty image. Units are MJy/sr.\n", + "4. (DQ): Data quality image.\n", + "5. (VAR_POISSON): Unc. component 1: Poisson uncertainty image. Units are (MJy/sr)^2.\n", + "6. (VAR_RNOISE): Unc. component 2: Read Noise uncertainty image. Units are (MJy/sr)^2.\n", + "7. (VAR_FLAT): Unc. component 3: Flat Field uncertainty image. Units are (MJy/sr)^2.\n", + "8. (ASDF_METADATA): Metadata.\n", + "\n", + "The s2d file is one of the Spec2 products and containes the calibrated rectified cutout of the LRS Slit region. It has:\n", + "\n", + "1. (Primary): This HDU contains meta-data related to the observation and data reduction.\n", + "2. (WGT): Weight.\n", + "3. (CON): ??\n", + "4. (ASDF_METADATA): Metadata." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Loading data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# use a jwst datamodel to provide a good interface to the data and wcs info\n", + "cal = datamodels.open(calfile)\n", + "s2d = datamodels.open(s2dfile)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Basic information about the image." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cal image\n", + "(1024, 1032)\n", + "9.826068\n", + "-1199.9769 64995.82\n", + "s2d image\n", + "(387, 44)\n", + "603.8127\n", + "-942.0139 63358.58\n" + ] + } + ], + "source": [ + "print(\"cal image\")\n", + "print(cal.data.shape)\n", + "print(np.mean(cal.data))\n", + "print(np.amin(cal.data), np.amax(cal.data))\n", + "print(\"s2d image\")\n", + "print(s2d.data.shape)\n", + "print(np.mean(s2d.data))\n", + "print(np.amin(s2d.data), np.amax(s2d.data))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display the full 2D image" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'The full image from the MIRI IMAGER detector')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "norm_data = simple_norm(cal.data, 'sqrt')\n", + "plt.figure(figsize=(6, 6))\n", + "plt.imshow(cal.data, norm=norm_data, origin=\"lower\")\n", + "plt.title(\"The full image from the MIRI IMAGER detector\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display the LRS Slit region only (use s2d)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'The LRS region')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# transpose to make it display better\n", + "image = np.transpose(s2d.data)\n", + "err = np.transpose(s2d.err)\n", + "norm_data = simple_norm(image, \"sqrt\")\n", + "plt.figure(figsize=(10, 3))\n", + "plt.imshow(image, norm=norm_data, origin=\"lower\")\n", + "plt.title(\"The LRS region\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### View the 2D Spectrum in Imviz and get the center of the cross-dispersion " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6e3919a9df66421fba7521510904e1a3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Application(config='imviz', events=['call_viewer_method', 'close_snackbar_message', 'data_item_remove', 'data_…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "imviz = Imviz()\n", + "imviz.app" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "imviz.load_data(image)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "viewer = imviz.default_viewer\n", + "viewer.cuts = '95%'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### View the JWST pipeline 1D extraction in Specviz" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-08-08 17:22:12,817 - stpipe - WARNING - /Users/ofox/miniconda3/envs/lrssn/lib/python3.9/site-packages/specutils/io/default_loaders/jwst_reader.py:338: UserWarning: SRCTYPE is missing or UNKNOWN in JWST x1d loader. Defaulting to srctype=\"POINT\".\n", + " warnings.warn('SRCTYPE is missing or UNKNOWN in JWST x1d loader. '\n", + "\n" + ] + } + ], + "source": [ + "# Create a spectrum1d\n", + "jpipe_x1d = Spectrum1D.read(x1dfile)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b74568107bad4d348815ad5a79f9608e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Application(config='specviz', events=['call_viewer_method', 'close_snackbar_message', 'data_item_remove', 'dat…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "specviz = Specviz()\n", + "specviz.app" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "specviz.load_spectrum(jpipe_x1d)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "# for reference read in the JWST pipeline extracted spectrum\n", + "jpipe_x1d = Table.read(x1dfile, hdu=1)\n", + "print(jpipe_x1d.columns)\n", + "# plot\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "ax.plot(jpipe_x1d['WAVELENGTH'], jpipe_x1d['FLUX'], 'k-', label=\"jpipe_x1d\")\n", + "ax.set_title(\"JWST Pipeline x1d extracted spectrum\")\n", + "ax.set_xlabel(\"Wavelength\")\n", + "ax.set_ylabel(\"Flux Density [Jy]\")\n", + "ax.set_yscale(\"log\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Boxcar Extraction\n", + "\n", + "Extract a 1D spectrum using a simple boxcar. Basically collapse the spectrum in the cross-dispersion direction over a specified number of pixels.\n", + "\n", + "#### Developer note: Allow for a bad pixel mask" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fixed width boxcar" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define extraction parameters based on interaction in Imviz window above" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "ext_center = 30\n", + "ext_width = 8\n", + "bkg_sep = 7\n", + "bkg_width = 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot cross-disperion cut showing the extraction parameters\n", + "\n", + "#### Develepor Note: Place trace back into Specviz2d/Imviz/Etc" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Cross-dispersion Cut at Pixel=300')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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EUWFRuJtVJw9+9qDucdfMS+WcJKfajIjNBzCyyrKZAD4zxvQC8Jn9PQBcAaCX/ZoG4FXA6rgBeBTAOQDOBvCor/NmrzPVb7uq+yIiCp/YWCAmRi9+TIxq/NioWMQ0UWy/gpRNKejbvy8O7m+KCpOIiooK7Ny+M9zNqpOYJjG6x10zL5VzkpxqVSMmIt0AfGiM6W9/nwpguDHGIyIJAD43xiSJyN/s9wv91/O9jDG32Mv/BuBz+7XWGNPHXn69/3puWCNGRPWGNWL1pqKiAv279cfoq0fjx7Q3UFxcgs0bW2H2nNmYeNPEcDevTlgjRv5CXSPWyRjjsd9nA+hkv+8MYK/fevvsZW7L91WzvFoiMk1ENorIxoMHOa8MEdGJ5qcff8KRw0dw5qAzAQDRMdFo0bJFo5zCgqg2gi7WN9aQWr3cemmMmWuMGWyMGdyhQ4f62CURneymTQMeflgv/h//qBp/2sppeHitYvtDzFeoP3DQQACAAI2yYP+Pq/+oe9w181I5J8kp0GdN7heRBL9Lkwfs5ZkAuvqt18Velgnr8qT/8s/t5V2qWZ+IqGFo1w7QvIeoTRugtFQtfLvYdhA0nnugUjalIK5ZHHol9cKgs63jUljQD4sWLEJFRQUiIhrHzf5tYtqg1Kt3XlXzUjknySnQGrFnAOQaY54WkZkA2hpj/igiVwK4E8AoWIX5LxhjzraL9TcB8N1F+T2AQcaYPBH5DsDdANYDWAXgRWPMquO1iTViRFRvWCNWb666+CrExMRgyaollcsWvbMIM+6YgX9//29079k9jK2rG9aIkb+Aa8REZCGAdQCSRGSfiPwOwNMALhWRdAAj7O8BqyO1C0AGgNcB3A4Axpg8AH8CsMF+PW4vg73OG/Y2PwL4ONAfkoiIGq/S0lJs27Kt8rKkT/KAZABodJcniWrjuJcmjTHX1/DRJdWsawDcUUOceQDmVbN8I4D+x2sHEVFY3HQTcPQo8OKLOvF//3ugvBz44AOV8DctvwlHi4/ixVFK7Q+hHVt3oLS0tLIjNnWiNcvRi2/0QpMmTbDtP9sw+tejw9jC2vv9P3+Pcm85Phivc15V81I5J8kp0BoxIqKTQ9euQG6uXvzERNV6nK4tuyK3iWL7Q2jzRutB3747Jg/lWRdtYmJi0CupF7ZtaTwjYonNE3VrxDTzUjknyYkdMSIiN48/btXiaLnvPqseR8njFz2OtBzF9ofQ5k2b0bFTRyR2SfzFZ/0G9MPXX3wdhlYF5r7z70N+id55Vc1L5Zwkp8Zx+wkREZ3wUjalYOCggajuSXfJpycj25ON3JzGMbpHVFvsiBERuZk4Ebj3Xr34d92lGn/i0om4d7Vi+0Pk6JGjyEjL+EWhvk+//v0ANJ6C/btW3aV73DXzUjknyYmXJomI3CQlATk5evG7d1etx0lql4ScQsX2h8gPm38A8N/6MAA4/8KSyve+Oye3/2c7hl00rH4bF4DubbujtFyxzkozL5VzkpxqNY9YQ8R5xIio3nAeMXUvPvsinn7saWzbsw2t27Sudp3BfQdj6PlD8eIbDf8OUIDziJFTqJ81SUREFDIpm1LQvWf3GjthQON81BHR8fDSJBGRmwkTgGPHgNdf14l/223WnE0ffaQSfsKSCThWcgyvj1Fqf4ikbErBecPOcyybeHVbAMCCpdb838kDkrF29VoUFRUhNja23ttYF7d9dBvKveX46Aad86qal8o5SU7siBERuRk4ULdGLDlZtR5nYPxA5BQ07BoxT5YH2Z5sR30YABQXO++eTD49GV6vF2k70nDGWWfUZxPrLLlDsm6NmGZeKuckObEjRkTkZuZM3XnE7rxTdc6mmRfMbPDziKVsSgGAGu+Y9PHdObl96/YG3xG78+w7decR08xL5ZwkJ9aIERFRWG3euBlRUVFIPj3Zdb1TTzsVzZo3a1Qz7BMdD0fEiIjcXHONNTrw1ls68adOtepx/vlPlfDXvHcN8kvy8davlNofAps3bUbygGRER0e7rhcREYF+/fs1ioL9qSumoryiHP+cpHNeVfNSOSfJiR0xIiI3Q4cCBw/qxR80CCgpOf56ARraZSgOFii2P0herxdbNm/BuAnjfvHZiJHFv1iWPCAZSxYtQUVFBSIiGu5FnUGJg1BSrndeVfNSOSfJifOIEREdD+cRU5O2Mw0XnX0Rnn/teVz7m2uPu/7f3/477rvrPnyd8jW6de+m38AgcB4x8sd5xIiIqMHZvGkzAODMwWceZ02Lb4b9xnB5kqg22BEjInIzZgxw66168SdPVo0/ZuEY3PqRYvuDlLIxBS1atkD3nt1/8dm4Ue0wblQ7x7LefXsjMjKywRfsT/7HZN3jrpmXyjlJTqwRIyJyc8klwIEDevEvuEC1HueS0y7BgQLF9gcp5fsUnHHWGbWu94qNjUWPXj0a/IjYBadcoFsjppmXyjlJTuyIERG5mT5ddx6xm29WnbNp+rnTG+w8YsXFxdj+n+24dXrdRl+SByRj/br1Sq0KjZvPull3HjHNvFTOSXLipUkiIgqLbVu2oby8/Bcz6h9P8unJyNqXhbzcPKWWEdUfdsSIiNxccYU1QqBl4kTV+Fe8ewVuXqnY/iD4ZtSvbaG+j69gf/vW7aFuUshMXDpR97hr5qVyTpITL00SEbkZPRrYv18v/ogRqvU4o3uPxv58xfYHYfPGzUjonIBO8Z2q/fyqXxdVu7zfAOtRR9v+sw0XXHiBWvuCMaL7CN0aMc28VM5JcuI8YkREx8N5xFScP/B89OvfD68veL3O2w5KGoTzLzwfL8x9QaFlocF5xMgf5xEjIqIG41DeIezetdv1Qd9FhYKiQqn2s379+2H7fxrupUmi2uKlSSIiNyNGAIWFwJIlOvHHjwe8XuCbb1TCj/i/ESgsK8SS65TaH6Afvv8BAFw7YpPGtQUALFmV+4vP+p3eD1+u/RIlJSXHfUZlOIxfMh7eCi+++Z3OeVXNS+WcJCd2xIiI3Iwfr1sjNmaMaj3O+OTxDbJGbPOmzRARnD7w9IC2Tx6QjPLycqTvTEf/M/qHuHXBG9N7jG6NmGZeKuckObEjRkTkZupU3XnEbrhBdc6mqYOmNsh5xFI2paBXUi+0aNkioO39H3XUEDtiN5x+g+48Ypp5qZyT5MQaMSIiqlfGGKRsSnG9LHk83bp3Q1yzuAb/qCOi4+GIGBGRm+HDgaIiYNkynfjjxln1OOt1ZoofPn84isqKsGyCUvsDkLk3EzkHc4LqiEVGRqJvct8G+6ijce+Ng7fCi/VTlZ4AoJmXyjlJTuyIERG5mTwZyM7Wi3/ttar1OJMHTkb2McX2B2Dzps0AgLMGn+W63rU3FLp+njwgGcveXwZjDESqv7syXK5NvhYlZYp1Vpp5qZyT5MSOGBGRm8mTdWvExo9XrceZPHByg6sRS9mUgujoaPRJ7uO63vgbqp/Q1affgH74vzf/D3v37MUp3U4JZRODNj55vG6NmGZeKuckObFGjIjITVmZ9Wqk8cu8ZSjzKrY/AJs3bkb/M/ojKirKdb283Ajk5db8Z8q/YL+hUT/umnmjnfPkwI4YEZGbSy8FpkzRi3/99arxL33nUkxZodj+OiovL8eWlC21qg+bNqkNpk1qU+PnfZP7IiIiokFO7Hr9B9frHnfNvFTOSXLipUkiIjc33wx4PHrxr78eKC5WC3/zWTfDc0yx/XWUtjMNRYVFOHNQ3R70XZ3YuFh079m9QY6IXd//ehSX651X1bxUzklyYkeMiMjNxIm6NWLXXKNajzPx9IkNqkYsZVMKAPcZ9esieUAyNm3YFJJYoXRNv2t0a8Q081I5J8mJlyaJiNwUFlrTBGgpKlKNX1hWiKIyxfbXUcqmFLRu3RrduncLSbzk05Ox7+d9OHzocEjihUpRWZHucdfMS+WcJCeOiBERuRk1SncesUmTVOdsGvXuqAY1j9jmTZsxcNDAkE034SvY3751O877n/NCEjMUJi2bpDuPmGZeKuckObEjRkTk5rbbdGvEJk1SnbPptsG3NZgascKCQqRuT8VlV1xWq/Un/a7guOv0G9APALBty7aG1RE7Y5LuPGKaeamck+TEjhgRkZvx43VrxMaOVa3HGd9/fIOpEdu6ZSu8Xi/OHFy7Qv2x1xy/YLxjp47o0LFDgyvYH5s0VrdGTDMvlXOSnFgjRkTk5sgR4NgxvfhHj6rGP1J8BMdKFNtfB74Z9WtbqJ+5LwKZ+47/Zyp5QHKD64gdLTmqe9w181I5J8mJI2JERG7GjtWtEZsyRbUeZ+yisQ2mRuyHTT+gyyld0L5D+1qtP32aNYfYklW5rusln56Mr1/6GqWlpWjatGnQ7QyFKcun6NaIaealck6SEztiRERu7r4byMrSiz9liuqcTXefczeyjiq2vw5+3vMzuvfoHvK4yQOSUVZWhvTU9Mri/XCbcuYUFJcpzsWlmZfKOUlO7IgREbm5+mrdGrFRo1Trca7ue3WDqRHL9mSjV1KvkMdNPt1+1NGWbQ2mIzaq1yjdGjHNvFTOSXIKqkZMRH4vIttEZKuILBSRGBE5TUTWi0iGiCwWkab2utH29xn259384jxgL08VkcuD/JmIiEInJwfIy9OLn5enGj+nMAd5RYrtryWv14sD2QcQnxAf8tin9TgNMbExDapOLK8oT/e4a+alck6SU8AjYiLSGcDdAPoZY4pE5D0AEwCMAvCcMWaRiLwG4HcAXrW/HjLG9BSRCQBmAxgvIv3s7ZIBJAJYIyK9jTHeoH4yIqJQGDdOt0Zs2jTVepxx741rEDViBw8chNfrRXxi6DtikZGR6Nuvb4PqiE1bOU23RkwzL5VzkpyCvTTZBECsiJQBiAPgAXAxgN/Yn78NYBasjthY+z0ALAHwklgz+o0FsMgYUwLgJxHJAHA2gHVBto2IKHgzZgCZmXrxp01TrceZMXQGMo8qtr+WsrOyAQAJnRNqvc20u2p/eazf6f3w4bIPYYwJ2WSxwZg2aJpujZhmXirnJDkF3BEzxmSKyF8B/AygCMCnADYBOGyMKbdX2wegs/2+M4C99rblInIEQDt7+bd+of23ISIKr9GjdWvELrtMtR5ndNLoBlEjlu2xO2IJte+IXXZF7ScVTR6QjHffeheZezPR5ZQudW5fqF3W4zLdGjHNvFTOSXIKuEZMRNrAGs06DdYlxWYARoaoXTXtc5qIbBSRjQcPHtTcFRGRJTsb0Px9c+CAavzs/GwcLAj/70vfiFhdLk1mpEciIz2yVuv6ivQbyuXJAwUHdI+7Zl4q5yQ5BXNpcgSAn4wxBwFARJYCOB9AaxFpYo+KdQHgGzvNBNAVwD4RaQKgFYBcv+U+/ts4GGPmApgLAIMHDzZBtJ2IqHYmTNCtEbv9dtV6nAlLJjSIGjGPx4OoqCi0a9+u1tvMnN4awPHnEQOAnr17AgB+2vVTQO0Ltds/ul23RkwzL5VzkpyC6Yj9DOBcEYmDdWnyEgAbAawFMA7AIgA3Alhur7/C/n6d/fm/jDFGRFYA+LuI/C+skbVeAL4Lol1ERKEzcyawb59e/DvusP6gKpl5wUzsO6LY/lrKzspGx/iOiIjQeaBLq9atEBMbUznyFm53DLkDRWV651U1L5VzkpyCqRFbLyJLAHwPoBzAZlijVR8BWCQiT9jL3rQ3eRPAO3Yxfh6sOyVhjNlm33G53Y5zB++YJKIGY+RI3Rqxiy5SrccZ2XNkg6gR82R5VKau8BERxCfEV9aihdtFp12kWyOmmZfKOUlOQf3XxBjzqDGmjzGmvzFmkjGmxBizyxhztjGmpzHmWvtuSBhjiu3ve9qf7/KL82djTA9jTJIx5uNgfygiopDZuxfwePTiZ2aqxt97ZC88xxTbX0vZWdl1umMyEAmdExrMiFjmsUzd466Zl8o5SU6cWZ+IyM2kSbo1YtOnq9bjTFo2qUHUiGV7snHRpRep7iMhIQEb1m9Q3UdtTf94um6NmGZeKuckObEjRkTk5qGHdGvE7r5bdc6mh4Y9FPYasWNHj6EgvwAJiXUbEbv7vmN1Wj8+MR7ZWdmoqKhQq0WrrbvPuVt3HjHNvFTOSXJiR4yIyM2IEbo1YsOGqdbjjOg+Iuw1Yr66rbrWiA27qLRO68cnxKOsrAx5uXlo36F9nbYNtWGnDtOtEdPMS+WcJKfw/peBiKih27XLqsfRsmePavxdh3Zh7xHF9teCJ9OqN6rr4422bmmCrVtqP17gi98Q6sT2HN6je9w181I5J8mJI2JERG6mTNGtEZsxQ7UeZ8ryKWGvEfPYhd91HRGbNbMVgNrNI+Yf35PlQf8z+tdpX6E249MZVo1YD6U6K828VM5JcmJHjIjIzWOP6Y4OzJihOmfTY8MfC/uIWCCz6geickSsAUxhMWPoDN15xDTzUjknyYkdMSIiNxdeqFsjNnSoaj3Ohd0uDH+NWFY22rRtg5iYGNX9dOzUESLSIC5NDu06VLdGTDMvlXOSnFgjRkTkJjXVqsfRkpGhGj81JxW7Dim2vxayPdnqo2EAEBUVhQ4dOzSIEbGMvAzd466Zl8o5SU4cESMicnPLLbo1YjNnWvU4I0eqhL/lw1vCXiOW7clGQoLuZK4+vikswm3mmpnwVngxspfOeVXNS+WcJCd2xIiI3Dz5JPDzz3rx779ftR7nyUuexM+HFdtfC55MD/qfXvfi+fsfPVrnbeIT4vHz7vD+vABw/wX3o6hUsc5KMy+Vc5Kc2BEjInJz3nlAe8U5qYYMUa3HOa/reWgfG745tcrKypBzMKfOk7kCwJBzyuq8TUJiAr775rs6bxdqQxKH6NaIaealck6SE2vEiIjcbN2qW6y/c6dq/K0HtiItN3zF+geyD8AYE1CN2Ib1UdiwPqpO28QnxuPw4cMoKgzviM7OnJ26x10zL5Vzkpw4IkZE5ObOO3VrxB56yKrHueoqlfB3rrozrDVinqzA5hADgNmPtQRQ+3nE/PfjyfKge8/udd5nqDz0r4fgrfDiqiSd86qal8o5SU7siBERuXnmGWumcS0PPQQUFqqFf+bSZ7DnsGL7j6Py8Ub1cNek/36yPdnh7YgNewiFpXrnVTUvlXOSnNgRIyJyM2QI0KqVXvyBA1XrcYZ0HoJW0YrtP47KxxsFMCIWCF8tWrjvnBwYP1C3RkwzL5VzkpxYI0ZE5CYlBdixQy/+1q2q8VOyU7DjoGL7jyPbk43o6Gi0adumXvbn6/CFey6xrQe26h53zbxUzkly4ogYEZGbe+7RrRGbNcuqxxk7ViX8PZ/cE9YaseysbCQkJkBE6mV/LVq2QLPmzcI+Ijbr81nwVngxtq/OeVXNS+WcJCd2xIiI3Dz/vG6N2KxZqnM2PT/yeew5FN4asUDrw2Y9fSSg7eIT4isfNB4us4bP0p1HTDMvlXOSnNgRIyJyM3AgEBenF79/f9V6nIHxAxHXRLH9x5GdlY2BgwYGtG3/08sD2q4hzK7fv2N/3RoxzbxUzklyYo0YEZGbDRuALVv04qekqMbfkLkBW/Yrtt+FMQaeLE/AI2Jfrm2KL9c2rfN28QnxYa8RS8lO0T3umnmpnJPkxBExIiI3992nWyP2xBNWPc64cSrh71t9n1Ujdkr914gdyjuEkpKSgO+YfOGZFgCAYRfVfh4xAEjonIAD2QdQUVGBiIjwjDc88eUT8FZ4MS5Z57yq5qVyTpITO2JERG5eegnYvVsv/hNPqM7Z9NKol7D70G61+G7qew4xn4SEBJSXlyPnYA46dupYr/v2eeLiJ3TnEdPMS+WcJCd2xIiI3PTvDzSt++WxWuvTR7Uep3/H/mgaodh+F746rUCeMxmMyklds7LD1hHr076Pbo2YZl4q5yQ5sUaMiMjNN98A33+vF3/DBtX43+z9Bt97FNvvwjciVu8dMb/HHIXLhqwNusddMy+Vc5KcOCJGROTmwQetWpxhw3Tiz55t1eNMmKAS/sHPHkRRWRGGdVNqv4vsrGyICDrG1++olG9ELJwdsdlfzYa3wosJA3TOq2peKuckObEjRkTk5m9/A376SS/+00+r1uP87aq/4adDiu134cnyoH2H9oiKigpo+6fnHA5ouw4dOyAyMjKsd04+PeJp3RoxzbxUzklyYkeMiMhNUhKgOSt8z56q9ThJ7ZMgqJ9Z7avKzgp8MlcA6NnLG9B2kZGR6NCpQ1jnEuvZtqdujZhmXirnJDmxRoyIyM0XXwDffacXf9061fhf7P4C32Uqtt+Fx+MJ6mHfn34cjU8/jg5o24SEhLCOiK3bu073uGvmpXJOkhNHxIiI3Dz6qFWLc/HFOvGffdaqx5k4USX8o58/iqKyIlzcXan9LrKzsjHknCEBbz/3xeYAgMuuKKnztvGJ8chIywh438F6dt2z8FZ4MfEMnfOqmpfKOUlO7IgREbmZN0+3RuzZZ1XrceaNnYef8uq/Rqy4uBiH8g7V+xxiPvEJ8fjqi6/Csm8AePayZ3VrxDTzUjknyYkdMSIiN927A+WBPfOwVk49VbUep3ub7ij3Kra/Br76rHB1xBI6J+DY0WMoyC9As+bN6n3/p7Y+VbdGTDMvlXOSnFgjRkTkZs0aa84mLV9+qRp/za41+GavYvtrUDmHWEL9ziHmUzmXmCc8U1h8uedL3eOumZfKOUlOHBEjInLzxBNWLc5ll+nEf+EFqx5n8mSV8E98+QSKyopwWU+l9tcg3CNi/rPr9+zVs973/8L6F+Ct8GLymZN1dqCZl8o5SU7siBERuXnnHWDXLr34c+ao1uO88+t3sCtPsf01CMWs+nPmHgp4W9+IWLimsJhzxRwUlijWWWnmpXJOkhM7YkREbrp2tUYetHTurFqP07VVVxSVKba/Bp4sD5o1b4YWLVsEHKNzl4qAt/V1AMM1u37nFp2R31SxzkozL5VzkpxYI0ZE5OaTT6yaGS1r16rG/yTjE3y5R7H9Ncj2ZAc1hxgALP8gBss/iAlo27hmcWjZqmXYRsTW/rRW97hr5qVyTpITR8SIiNw8/bQ18jBqlE78l1+26nFuvlkl/NNfPY2isiKM6q3U/hp4Mj1B14e986Z1t+PYa4oD2j4+IT5sk7q+vOFleCu8uHmQznlVzUvlnCQndsSIiNwsWgT8+KNe/FdeAQoK1MIvGrcIP+Yqtr8G2Z5snHv+ufW+X3/xCfFhGxF75cpXUFCid15V81I5J8mJHTEiIjfx8cDRo3rxO3ZUrceJbx6Po8WK7a9GRUUF9nv2B1WoHwrxifFI25kWln13bNYR+U0U66w081I5J8mJNWJERG5WrgT+9S+9+J9+qhp/ZepK/OsnxfZXIzcnF+Xl5eHviCXE48D+AyjXnJC3Bp/++KnucdfMS+WcJCd2xIiI3Dz7LPDWW3rx585Vjf/sumfxVopi+6sR7jnEfOIT41FRUYGDBw7W+77nbpqre9w181I5J8mJlyaJiNwsWQJkKD48eu5c1ctAS65bgozc+n34tW/KiGDvmpz7TuDziAHWY44Aq2NY36Nzc0fP1X3EkWZeKuckObEjRkTkpn17IC9PL37btkDTpmrh28e1R16hYvurUdkRC3JErG27wOcRA/77eKVw3DnZNrYtmkbonVfVvFTOSXIK6tKkiLQWkSUislNEdojIUBFpKyKrRSTd/trGXldE5AURyRCRLSJyll+cG+3100XkxmB/KCKikFm61KqZ0bJqlWr8pTuW4tMfFdtfjeysbERGRqJDxw5BxVn8biwWvxsb8Pb+jzmqb6vSV+ked828VM5Jcgq2RmwOgE+MMX0AnAFgB4CZAD4zxvQC8Jn9PQBcAaCX/ZoG4FUAEJG2AB4FcA6AswE86uu8ERGF3QsvWI+T0TJvnmr8F9a/gHe2KLa/GtmebHTo1AGRkZFBxXn/3Ti8/25cwNu3a98OUVFRYRkRm7d5nu5x18xL5Zwkp4AvTYpIKwDDAEwGAGNMKYBSERkLYLi92tsAPgdwP4CxAP7PGGMAfGuPpiXY6642xuTZcVcDGAlgYaBtIyIKmeXLdWvE5s1TnbNp+YTl9V4jFo6arOpERESgY3xHZGVm1fu+542dpzuPmGZeKuckOQUzInYagIMA3hKRzSLyhog0A9DJGON7uFc2gE72+84A9vptv89eVtPyXxCRaSKyUUQ2HjxY/3fBENFJqFUroEXgz0s8rpYtVeO3immFFtGK7a9GtqdhdMSA8M2u3zK6pe5x18xL5Zwkp2A6Yk0AnAXgVWPMmQAK8N/LkAAAe/TLBLEPB2PMXGPMYGPM4A4dgqs9ICKqlcWLrZoZLcuXq8ZfvHUxVqUrtr8anixP0HdMhkp8Ynhm11+eulz3uGvmpXJOklMwHbF9APYZY9bb3y+B1THbb19yhP31gP15JoCuftt3sZfVtJyIKPxefRVYqFgp8c47qvFf3fgqFm6tv0qPgvwCHDt6LOxziPn4RsSscYH6884P7+ged828VM5Jcgq4RswYky0ie0UkyRiTCuASANvt140Anra/Lrc3WQHgThFZBKsw/4gxxiMi/wTwpF+B/mUAHgi0XUREIbVqFZCerhf/nXdU52xadcMqpOcotr8Kjyc0c4gBwDtLgp+eISExAYUFhTh29BhatmoZdLzaeufX7+jOI6aZl8o5SU7BziN2F4B3RaQpgF0AboI1yvaeiPwOwB4A19nrrgIwCkAGgEJ7XRhj8kTkTwA22Os97ivcJyIKu7g4IDbwKRSOKzYW8HrVwsdFxSE2SrH9VYRyVv3YuOBHsXwdwmxPdr12xGKjYuGt0DuvqnmpnJPkFNT0FcaYFLtm63RjzK+MMYeMMbnGmEuMMb2MMSN8nSpjucMY08MYM8AYs9EvzjxjTE/7xecqEFHDsWCBVTOj5YMPVOMv2LIAy1MV21+FryMWimL9+a/HYf7rgU9fAThn169PH2z/QPe4a+alck6SE581SUTk5o03rMfJaFm4UDX+G9+/gSXbFdtfhW9W/VB0xD5cFosPlwU36uMbEfNdMq0vC7cu1D3umnmpnJPkxEccERG5Wb0aSEvTi79woWo9zupJq5GWo9j+KrKzstGqdSvExtXf5VA3nRKsGZTqe0Rs4TULdWvENPNSOSfJiSNiRERuoqKsVyONHxUZhahIxfZXke3JbjBTVwBAbGwsWrdpXe8dMfXjrpk32jlPDuyIERG5mT/feq6flsWLVePPT5mPpTsU219FdlbD6ogB1mVS3yXT+rJ422Ld466Zl8o5SU7siBERuZk/H1i2TC/++++rxp+fMh/Ldiq2v4psT3ZlgXxDEY7Z9d/f9r7ucdfMS+WcJCfWiBERufn8c90asSVLVOtxPp/8eb3ViJWXl+PA/gMhGxFbsio3JHHiE+OxdcvWkMSqrSXXLdGtEdPMS+WcJCeOiBERUUgc2H8AFRUVDWZWfZ/4hHjkHMxBWVlZuJtC9AvsiBERuXn9deC99/Tiv/uuavzXN72O97Yptt9P5WSuIRoRe+2FZnjthWZBx4lPjIcxBgeyDxx/5RB5d8u7usddMy+Vc5Kc2BEjInKj/dDvFSt0H/q9bTFWZdTPA5x9dVihmEMMANZ8EoM1n8QEHadyLrF6LNhfkbZC97hr5qVyTpITa8SIiNysWaNbI7Z4sWo9zprfrqm3GrFQPt4olHztqc+C/cXjFuvWiGnmpXJOkhNHxIiIKCSyPdlo2rQp2rZrG+6mOCR2TgRQ/5O6EtUGO2JERG5eecWqmdEyf75q/Fc2vIJ3/6PYfj+eTA86JXRCRETD+tPSpm0bREdH1+uI2PyU+brHXTMvlXOSnBrWvxYiooZm5Upg7Vq9+GvWqMZfmbYSa3crtt+Px+MJ6WSuMTEGMTEm6Dgigk4Jnep1RGzNrjW6x10zL5VzkpxYI0ZE5Objj3VrxBYsUK3H+fiGj+u1Rqz/Gf1DFm/B0ryQxarvSV0XXL1At0ZMMy+Vc5KcOCJGRERBM8Y0uOdM+otPiIcns34fc0RUG+yIERG5mTMHePttvfhvvKEaf863c/D2D4rttx09chRFhUUhvWPyudnN8dzs5iGJFZ9ojYgZE/ylztp44/s3dI+7Zl4q5yQ5sSNGROTms8+Adev04n/1lWr8z376DOv2Kbbf5pujK1RziAHA119E4+svokMSKz4xHsXFxTh86HBI4h3PVz9/pXvcNfNSOSfJiTViRERuVqzQrRGbP1+1HmfF9SvqpUbMVwgfyo5YKPkumWZ7stGmbRv1/c3/1XzdGjHNvFTOSXLiiBgREQXNVwjfUGvEfB1EziVGDQ07YkREbv76V+DNN/Xiv/aaavy/fvNXvLlZsf0236XJTgmd1PcVCP8Rsfrw2sbXdI+7Zl4q5yQ58dIkEZGbdet0L9Ns2gSUl6uFX7dvne4lMlu2Jxtt27VFdHRoaroAoE3bipDF8nUQ62tEbFPWJpRX6J1X1bxUzklyYkeMiMjNBx/o1oi9/rpqR++D6z6otxqxUD9j8vUFh0IWKzo6Gu3at4PHUz9TWLw+5nXdDrBmXirnJDnx0iQREQXNk+VpsIX6PvGJ8awRowaHHTEiIjdPPw3MnasX/6WXVOM//dXTmLtJsf02jRGxp2a1wFOzWoQsXn3Orv/Sdy/pHnfNvFTOSXLipUkiIjcpKcCxY3rxt21TrcdJyU7BsRLF9gMoKSlBbk4uEhJCOyK26bumIY0XnxiPlE0pIY1Zk20Ht6Hcq1hnpZmXyjlJTuyIERG5WbRIt0bs1VdV63EWjVukXiN2IPsAAIR8RCzUEhISkJuTi5KSkpDeVFCdV698VbdGTDMvlXOSnHhpkoiIgtLQ5xDz8XUU93v2h7klRP/FjhgRkZs//Ql4+WW9+M89pxr/T1/8CS9vUGw/gKzMLABAQucGXqxfj3OJPfftc7rHXTMvlXOSnHhpkojITWoqcPSoXvxdu4CyMrXwqbmpOFqi2H7ojYglJHpDGs83IuabfFbTrrxdKKvQO6+qeamck+TEjhgRkZsFC3RrxF58UbUeZ8HVC9RrxLKzshETG4NWrVuFNO6LbxwOabz6HBF7cdSLujVimnmpnJPkxEuTREQUlOysbMQnxENEwt0UV63btEZMTAznEqMGhR0xIiI3jzwCzJmjF/+ZZ1TjP7L2EcxZr9h+WCNMGpO5PnJ/Szxyf8uQxRORepvU9Zmvn9E97pp5qZyT5MRLk0REbvbu1a0Ry8pSnbNp79G9OFqsXyM2aMigkMfd/p+okMdMSEyol0uTWflZuvOIaealck6SEztiRERu3npLt0bsuedU63HeGvuWao2YMQbZWdkN/o5Jn/iEeGzasEl9P89d/pxujZhmXirnJDnx0iQREQUsLzcPpaWlDX4OMZ/4xHjs9+yHMSbcTSECwI4YEZG7Bx4Ann1WL/5TT6nGf2DNA3h2nV5831QQDX1WfZ/4hHiUlJTgUN4h1f089e+nVI+7al4q5yQ58dIkEZGb3FzgyBG9+IcOqc7ZlFuUiyPFeu33Fb5rjIh17xn6OiVfhzErMwtt27UNeXyfQ8WHUOZVnItLMy+Vc5Kc2BEjInIzd65ujdhf/qJajzN39FzVGrHKyVwVRsT+8kLoOxqVc4llZaP/6f1DHt/nL5f+RbdGTDMvlXOSnHhpkoiIAubJ9EBE0LFTx3A3pVZ802zUx52TRLXBjhgRkZt77wVmz9aL//jjqvHv/fRezP5aL362JxsdO3VEVFTop5r4492t8Me7Qztbf8f4jhAR9bnEHv/icdXjrpqXyjlJTrw0SUTkpqgIKC7Wi19crFqPU1RWhOJyvfb7ZtXXsCsj9H+ioqKi0L5De/URseLyYt0aMc28VM5JcmJHjIjIzcsv69aIPfmkaj3Oy1e+rF4jduppp6rF15DQOUF9ROzJS57UrRHTzEvlnCQnXpokIqKAaY6IaYlPiGeNGDUYQXfERCRSRDaLyIf296eJyHoRyRCRxSLS1F4ebX+fYX/ezS/GA/byVBG5PNg2ERGFzD33AH/+s178Rx5RjX/PJ/fgz//WiV9UWITDhw83mjnEfOIT4ivnP9PyyNpH1I47AN28VM5JcgrFiNh0ADv8vp8N4DljTE8AhwD8zl7+OwCH7OXP2etBRPoBmAAgGcBIAK+ISGQI2kVERIp8nRmNB34DQL8BZeg3IPS1SvGJ8Th86DCKiopCHpuoroKqERORLgCuBPBnAH8QEQFwMYDf2Ku8DWAWgFcBjLXfA8ASAC/Z648FsMgYUwLgJxHJAHA2gHXBtI2IKCSef163Ruzxx1XrcZ4f+bxajZjmHGIA8PhsnYda+9q737Mf3bp3U9nH4xc9rlsjppmXyjlJTsGOiD0P4I8AKuzv2wE4bIzxTYe8D0Bn+31nAHsBwP78iL1+5fJqtnEQkWkislFENh48eDDIphMRUTB8Be9aI2JaEhI4lxg1HAF3xETkKgAHjDH6j7G3GWPmGmMGG2MGd+jQob52S0QnszvuAB57TC/+gw+qxr/jozvw2Bc68StHxJSK9e+6uTXuurl1yOP6RsQ8mXp1Yg9+9qDacQegm5fKOUlOwVyaPB/AGBEZBSAGQEsAcwC0FpEm9qhXFwCZ9vqZALoC2CciTQC0ApDrt9zHfxsiovCKjdWdRywmBojUK4uNjYpVm0fspx9/Qtt2bdG8RXOV+J4sneNS+ZgjxRGxmCYxiNQsd9bMS+WcJKeAO2LGmAcAPAAAIjIcwL3GmBtE5H0A4wAsAnAjgOX2Jivs79fZn//LGGNEZAWAv4vI/wJIBNALwHeBtouIKKT++lfdGrFHHlGtx/nrZX9VqxFLT01Hr6ReKrE1tWjZAnHN4lTvnHzkwkd0a8Q081I5J8lJYx6x+2EV7mfAqgF7017+JoB29vI/AJgJAMaYbQDeA7AdwCcA7jDGeBXaRUREIWKMQXpqOnom9Qx3U+pMRDiXGDUYIZlZ3xjzOYDP7fe7YN31WHWdYgDX1rD9n2HdeUlE1LBMmwYcOQI895xO/D/+0XqczOLFKuGnrZyGI8VH8NzI0LY/NycXhw8dRq/ejW9EDLDqxDRn1//j6j+izFuGxdfqnFfVvFTOSXLiI46IiNy0aweI6MVv0wYoLVUL3y62HQShb396ajoAqF6aHHS23nFJSEzAt19/qxa/TUwblHr12q+al8o5SU7siBERuXnqKd0asQceUK3HeWrEUyo1YvXREXtg1jG12AmJCdjv2Y+KigpERIS+SueB/3lAt0ZMMy+Vc5Kc+KxJIiKqs4zUDMQ1i0Nil8RwNyUg8QnxKC8vR25ObribQic5jogREbm56Sbg6FHgxRd14v/+90B5OfDBByrhb1p+E44WH8WLo0Lb/vTUdPTs3ROieNl26sQ2AIDXFxwKeeyEztakrpl7M9GhY+jnpfz9P3+Pcm85Phivc15V81I5J8mJI2JERG66dgXiFR9qnZioGr9ry66Ibx76+OlpVkdM06G8CBzK0/kz5bvbM22nzuW9xOaJKse9kmZeKuckOXFEjIjIzeOP69aI3Xefaj3O4xc9HvIasfxj+fBkehrlHGI+3U7rhujoaKTuSFWJf9/59+nWiGnmpXJOkhNHxIiIqE4y0jIA6Bbqa2vSpAl6JvVE6nadjhhRbXFEjIjIzcSJVi3Oa6/pxL/rLmvOphUrVMJPXDoRR0uO4rWrQtf++rhjsj4k9U3Cuq/WqcS+a9VdKKsow4rrdc6ral4q5yQ5sSNGROQmKQnIydGL37276pxNSe2SkFMY2vZnpGWgSZMmOPW0U0Mat6rzLyxRjd+nXx8sXbwURw4fQavWrUIau3vb7igtV5yLSzMvlXOSnMQYE+42BGTw4MFm48aN4W4GEZ0M0tKA5joPtgZg1eP07q0WPi0nDc2jQ9f+3/3md8hIy8AXG78IWcxwWPPJGtx43Y34x6f/wJBzh4Q8fn5JPnq31zuvqnmpnJMnIxHZZIwZXHU5a8SIiKhOGuvDvqvq068PAGDn9p1hbgmdzHhpkojIzYQJwLFjwOuv68S/7TZrzqaPPlIJP2HJBBwrOYbXx4Sm/aWlpdi9azdGjR0VknhuJl7dFgCwYGmeSvzOXTujWfNmKgX7t310G8q95fjoBp3zqpqXyjlJTuyIERG5GThQt0YsOVm1Hmdg/EDkFISu/bt37YbX662Xh30XFys+4xOAiCCpbxJ27gj9iFhyh2TdGjHNvFTOSXJiR4yIyM3MmbrziN15p+qcTTMvmBnSecROlDsmffr064OPV34MY0xInxJw59l36s4jppmXyjlJTqwRIyKiWvN1xLRn1a8vSX2TcCjvEHIOKo56ErngiBgRkZtrrrFGB956Syf+1KlWPc4//6kS/pr3rkF+ST7e+lVo2p+RmoHOXTsjrllcSOKFW1K/JABWwX4onzk5dcVUlFeU45+TdM6ral4q5yQ5sSNGRORm6FDg4EG9+IMGASV682UN7TIUBwtC1/70tPR6qQ8DgBEji9X34btzMnV7Kv5n+P+ELO6gxEEoKVecB00zL5VzkpzYESMicnPvvbo1YrfeqlqPc+9594asRqyiogIZaRk49/xzQxLveG69u0B9H+07tEfbdm1D/szJWwffqlsjppmXyjlJTqwRIyKiWsncm4niouITplAfsO+c7JfEucQobDgiRkTkZswYa3RgwQKd+JMnW/U4//qXSvgxC8cgvzQfC64Ovv31fcfkuFHtAABLVuWq7qdP3z547+/vhfTOycn/mIzyinL860ad86qal8o5SU7siBERubnkEuDAAb34F1ygWo9zyWmX4EBBaNqfnnZiTV3hk9QvCQX5Bcjcm4kup3QJScwLTrlAt0ZMMy+Vc5Kc2BEjInIzfbpujdjNN6vW40w/d3rIasQyUjPQtl1btG3XNiTxGgr/Rx2FqiN281k369aIaealck6SE2vEiIioVtJT00+Y+cP89e5jPdw61AX7RLXBETEiIjdXXAEUFACLFunEnzjRqsf58kuV8Fe8ewUKSguwaFxw7TfGID01HVeOvTJELWs4WrVuhYTOCSEt2J+4dCLKK8rx5U0651U1L5VzkpzYESMicjN6NLB/v178ESNU63FG9x6N/fnBtz8vNw+HDx2u1xGxq35dVG/76tO3T0hHxEZ0H6FbI6aZl8o5SU7siBERubn9dt0ascmTVetxbh9ye0hqxMLxjMnJUwvrbV9J/ZLwzb+/QXl5OZo0Cf5P4+SBk3VrxDTzUjknyYk1YkREdFzh6IgVFQqKCkP3IG43SX2TUFJSgt0/7a6X/RH5cESMiMjNiBFAYSGwZIlO/PHjAa8X+OYblfAj/m8ECssKseS64NqfnpqO2LhYJHZJDFHLjm/SOOvuTO15xADno4569gr+8uv4JePhrfDim9/pnFfVvFTOSXJiR4yIyM348bo1YmPGqNbjjE8eH5IasYzUDPTs3RMRESfmhZReSb0gIkjdnhqSGxLG9B6jWyOmmZfKOUlO7IgREbmZOlW3RuyGG1TrcaYOmhqaGrG0dJx7Xv08YzIcYuNicWq3U7FzR2junLzh9Bt0a8Q081I5J8npxPyvDRERhUxBfgGy9mWhZ9KJN4eYv6R+SUjdzrnEqH5xRIyIyM3w4UBREbBsmU78ceOsepz161XCD58/HEVlRVg2IfD2Z6RlADjxHm1UVVLfJKz5ZA2Ki4sRExMTVKxx742Dt8KL9VN1zqtqXirnJDmxI0ZE5GbyZCA7Wy/+tdeq1uNMHjgZ2ceCa3847pgEgGtvqL/pKwCrYN/r9eLH9B+RPCA5qFjXJl+LkjLFOivNvFTOSXJiR4yIyM3kybo1YuPHq9bjTB44OegasfS0dDRp0gTduncLTaNqafwN9TehK2BdmgSsOyeD7YiNTx6vWyOmmZfKOUlOrBEjInJTVma9Gmn8Mm8ZyrzBxc9IzUC37t0QFRUVolbVTl5uBPJy6+/PVPee3dGkSZOQzLAfiuPuvgPFvNHOeXLgiBgRkZtLL9WtEbv+etV6nEvfuTToGrH01PTKB2PXp2mT2gCon3nEAKBp06bo0atHSDpi139wvW6NmGZeKuckObEjRkTk5uabAY9HL/711wPFxWrhbz7rZniOBd7+0tJS7N61G6PGjgphqxqupL5JSPk+Jeg41/e/HsXleudVNS+Vc5Kc2BEjInIzcaJujdg116jW40w8fWJQNWK7d+2G1+sNyWzzjUFSvySsWLoCBfkFaNa8WcBxrul3jW6NmGZeKuckObFGjIjITWGhdQlIS1GRavzCskIUlQUe/2SZusLH96ijtJ3BdXKKyoqCOu7HpZmXyjlJThwRIyJyM2qUbo3YpEmq9Tij3h0VVI2Yb+qKnr1PkhGxvvadkztScebgMwOOM2nZJN0aMc28VM5JcmJHjIjIzW236daITZqkOmfTbYNvC6pGLCM1A4ldEoO6TBeoSb8rqPd9ntLtFMTExmDn9uAedTTpjEm684hp5qVyTpITO2JERG7Gj9etERs7VrUeZ3z/8UHViKWnpaNX7/Bclhx7Tf0XjEdGRqJ3n95BP+pobNJY3RoxzbxUzklyCrhGTES6ishaEdkuIttEZLq9vK2IrBaRdPtrG3u5iMgLIpIhIltE5Cy/WDfa66eLyI3B/1hERCFy5Ahw7Jhe/KNHVeMfKT6CYyWBxa+oqEBGWkbYnjGZuS8Cmfvqv5Q5qW9S0FNYHC05GvBxrxXNvFTOSXIKZkSsHMAMY8z3ItICwCYRWQ1gMoDPjDFPi8hMADMB3A/gCgC97Nc5AF4FcI6ItAXwKIDBAIwdZ4Ux5lAQbSMiCo2xY3VrxKZMUa3HGbtobMA1Yln7slBUWBS2Qv3p0+p3HjGfPv364P2/v4+83Dy0bdc2oBhTlk/RrRHTzEvlnCSngDtixhgPAI/9/piI7ADQGcBYAMPt1d4G8DmsjthYAP9njDEAvhWR1iKSYK+72hiTBwB2Z24kgIWBto2IKGTuvhvIytKLP2WK6pxNd59zN7KOBtb+cD1jMtx8BftpO9Nw7vnnBhRjyplTUFymeGlVMy+Vc5KcQlIjJiLdAJwJYD2ATnYnDQCyAXSy33cGsNdvs332spqWExGF39VX69aIjRqlWo9zdd+rA64RO2k7YvYzJ3du3xlwR2xUr1G6NWKaeamck+QU9MV3EWkO4AMA9xhjjvp/Zo9+mWD34bevaSKyUUQ2Hjx4MFRhiYhqlpMD5OXpxc/LU42fU5iDvKLA4mekZaBN2zZo175diFvVsCUkJqBlq5ZBFeznFeUFfNxrRTMvlXOSnIIaERORKFidsHeNMUvtxftFJMEY47EvPR6wl2cC6Oq3eRd7WSb+eynTt/zz6vZnjJkLYC4ADB48OGQdPCKiGo0bp1sjNm2aaj3OuPfGBVwjlp6aftKNhgGAiARdsD9t5TTdGjHNvFTOSXIKuCMmIgLgTQA7jDH/6/fRCgA3Anja/rrcb/mdIrIIVrH+Ebuz9k8AT/rurgRwGYAHAm0XEVFIzZgBZGbqxZ82TbUeZ8bQGcg8Glj701PTMWpM+J4xOe2u8F0eS+qXhA+XfghjDKw/d3UzbdA03RoxzbxUzklyCmZE7HwAkwD8R0RS7GUPwuqAvScivwOwB8B19merAIwCkAGgEMBNAGCMyRORPwHYYK/3uK9wn4go7EaP1q0Ru+wy1Xqc0UmjA6oRy83JxaG8Q2GdUf+yK8I3qWifvn2w4PAC7M/ej/iE+Dpvf1mPy3RrxDTzUjknySmYuya/AlDTfxMuqWZ9A+COGmLNAzAv0LYQEanJzgYOHgSaN9eJf+AAUFAA9O6tEj47PxsHCw6ieXTd2t8QCvUz0iMBAD17eet9376C/dTtqQF1xA4UHEBBSQF6t9c5r6p5qZyT5MSZ9YmI3EyYoFsjdvvtqvU4E5ZMCKhGrCE8Y3Lm9NYA6n8eMeC/U1js3LETF15yYZ23v/2j23VrxDTzUjknyYkdMSIiNzNnAvv26cW/4w7rD6qSmRfMxL4jdW9/emo6YuNi0bnryTmbULv27dChY4eA75y8Y8gdKCrTO6+qeamck+TEjhgRkZuRI3VrxC66SLUeZ2TPkQHViP2Y9iN69OqBiIj6f8RQQ5HULyngjthFp12kWyOmmZfKOUlOJ++/MCKi2ti7F/B4jr9eoDIzVePvPbIXnmN1jx/Oh303FEl9k5C6MxUVFRV13jbzWGZAx73WNPNSOSfJiSNiRERuJk3SrRGbPl21HmfSskl1rhEryC9A5t5M9LwxfPVhDUGffn1QVFiEvXv24tTTTq3TttM/nq5bI6aZl8o5SU7siBERuXnoId0asbvvVp2z6aFhD9W5RuzH9B8BhP/RRnffdyys+/cV7KfuSK1zR+zuc+7WnUdMMy+Vc5Kc2BEjInIzYoRujdiwYar1OCO6j6hzjVhDmLoCAIZdVBrW/ffuY03fsHP7Tlw26rI6bTvs1GG6NWKaeamck+TEGjEiIje7dln1OFr27FGNv+vQLuw9Urf46WnpiIyMRLfu3XQaVUtbtzTB1i3hGy9o0bIFupzSJaCC/T2H99T5uNeJZl4q5yQ5cUSMiMjNlCm6NWIzZqjW40xZPqXONWIZqRno1r0bmjZtqtKm2po1sxWA8Mwj5tO7T++Anjk549MZVo1YD6U6K828VM5JcmJHjIjIzWOP6Y4OzJihOmfTY8Mfq/uI2En6sO/q9OnXB/9e+2+UlZUhKiqq1tvNGDpDdx4xzbxUzklyYkeMiMjNhRfq1ogNHapaj3NhtwvrVCNWVlaG3bt2Y+TokWptakyS+iahrKwMP/34U2XNWG0M7TpUt0ZMMy+Vc5KcWCNGROQmNdWqx9GSkaEaPzUnFbsO1T7+7l27UV5eftLPIebTp18fAFbBfl1k5GXU6bjXmWZeKuckOXFEjIjIzS236NaIzZxp1eOM1BmBuuXDW+pUI9ZQ7phsKHr0tp4ukLo9Fbi69tvNXDMT3govRvZSGlnUzEvlnCQndsSIiNw8+STw88968e+/X7Ue58lLnsTPh2vf/obwsG+f+x89Gu4mIDY2Ft26d6tzwf79F9yPolLFOivNvFTOSXJiR4yIyM155wHt2+vFHzJEtR7nvK7noX1s7dufkZqBhM4JaNa8mVqbamvIOWXhbgIA6/Lkjm076rTNkMQhujVimnmpnJPkxBoxIiI3W7fqFuvv3Kkaf+uBrUjLrX38hvSMyQ3ro7Bhfe3vVNSS1DcJu3ftRlEdRol25uys03GvM828VM5JcuKIGBGRmzvv1K0Re+ghqx7nqqtUwt+56s5a14hVVFQgIy0Dv/ntb1TaUlezH2sJILzziAFAUr8kGGOQkZqBAQMH1Gqbh/71ELwVXlyVpHNeVfNSOSfJiR0xIiI3zzxjzTSu5aGHgMJCtfDPXPoM9hyuXfs9mR4UFRahZ1L468MaEv87J2vdERv2EApL9c6ral4q5yQ5sSNGRORmyBCgVSu9+AMHqtbjDOk8BK2ia9f+zZs2A0Cd5ss6GfieMlCXgv2B8QN1a8Q081I5J8mJNWJERG5SUoAddSvUrpOtW1Xjp2SnYMfB48c3xuC1Oa+h66ldMejsQWrtaYyioqLQo3ePOj1zcuuBrbU67gHTzEvlnCQnjogREbm55x7dGrFZs6x6nLFjVcLf88k9taoR+/JfX2Lzps2YPWd2nR7lc7Lo07cPvv3m21qvP+vzWfBWeDG2r855Vc1L5ZwkJ3bEiIjcPP+8bo3YrFmqczY9P/J57Dnk3n5jDJ6b/RwSOifg2t9cq9aWupr19JFwN6HS4HMHY9n7y/DVF1/hggsvOO76s4bP0p1HTDMvlXOSnMQYE+42BGTw4MFm48aN4W4GEZ0M0tKA5s314ufnA7316rLSctLQPLrm9n/95de47qrr8MQzT+CmW25Sa0djVlxcjGGDhqFd+3b4aO1HiIg4fmVPfkk+erdXrLfTzEvlnDwZicgmY8zgqstZI0ZE5GbDBmDLFr34KSmq8TdkbsCW/e7xn//L8+jYqSMm/HaCWjsC8eXapvhybdNwNwMAEBMTg/v+333YsnkLVi5dedz1U7JTjnvcg6KZl8o5SU4cESMicjN8uG6N2LhxVj3O+vUq4YfPH+5aI7bh2w341WW/wiNPPoJb7rxFpQ2BGjeqHYDwzyPm4/V6cfkFl6OwoBCfb/wcTZvW3Ekc9944eCu8WD9V57yq5qVyTp6sahoRY40YEZGbl14Cdu/Wi//EE6pzNr006iXsPrS7xs+fn/082rZri0k3TVJrw4kiMjISDz72ICaNm4QF8xZgyq1Talz3iYuf0J1HTDMvlXOSnNgRIyJy078/4DLyEbQ+fVTnbOrfsT+aRlTf/s0bN+Pzzz7HA7MeQFyzOLU2nEguuvQinDfsPDw3+zlc+5tr0aJli2rX69O+j+48Ypp5qZyT5MQaMSIiN998A3z/vV78DRtU43+z9xt876k+/pxn5qB169aYPHWy2v5PNCKChx5/CHm5eXh1zqs1rrcha0ONxz0kNPNSOSfJiSNiRERuHnzQqsUZNkwn/uzZVj3OBJ1C+Qc/exBFZUUY1s3Z/q1btmL1x6tx7/+7F81bKN4RegI646wzMObqMZj78lzcOPVGdIrv9It1Zn81G94KLyYMULoBQjMvlXOSnFisT0TkJjUV+Okn4PTTdeJnZFj1OCNHqoRPzUnFT4d+wunxzvZPnTQV/177b6zfuh6tWis+wikIGemRAICevbxhbskv7d61G8OHDMf4SeMx+/nZv/g8Iy8DhaWFGNlL57yq5qVyTp6sWKxPRBSIpCRARC9+z56q9ThJ7ZMgcLZ/5/adWLV8FabfN73BdsKAhtkB8+nWvRsmTZmEt994G1PvmIqevZwPSu/ZtqdujZhmXirnJDmxRoyIyM0XXwDffacXf9061fhf7P4C32U647/wzAto1rwZbr79ZrX9hsKnH0fj04+jw92MGk3/43TExMbg6VlP/+KzdXvX/eK4h5RmXirnJDlxRIyIyM2jj1q1OBdfrBP/2WetepyJE1XCP/r5oygqK8LF3a32Z6RnYMXSFbht+m1o266tyj5DZe6LVu3aZVeUhLkl1WvfoT1um34b/vrnv2LD+g0Ycs6Qys+eXfcsvBVeTDxD57yq5qVyTpITa8SIiNzs2mXV4vTtqxN/zx6rHueSS1TC7zq0Cz/l/YS+Ha32T79lOj78x4dYv3U92ndor7LPUGloE7pWp7CgEOcPPB+nnnYqlv1zGcS+XLjn8B4Ulhbikh4651U1L5Vz8mTFRxwREQWie3ega1e9+Keeqhq/e5vu6NrKir/npz1Y9t4yTLxpYoPvhDUWcc3i8IcH/oAN327Ap6s+rVx+autTK4+7Cs28VM5JcmJHjIjIzZo11pxNWr78UjX+ml1r8M1eK/5L//sSmjRpgtum36a2v5PR9b+9Hj169cBTs55CeXk5AODLPV9WHncVmnmpnJPkxI4YEZGbJ54AXq154s6gvfCCavwnvnwCr258FZl7M/H+39/HhEkTEJ8Qr7a/k1GTJk3wwKwHkJ6ajvfefQ8A8ML6F/DqRsW80cxL5ZwkJ9aIERG52bvXqsfp1UsnfmamVY9z4YUq4fce2Ytdebvw1pNv4e9v/x1fp3yNzl07q+wr1DL3WWMFnbtUhLklx2eMwdhLxyJzbya+2vwV8rx5KCwpxIWn6ZxX1bxUzsmTFWvEiIgC0bUrkJCgF79zZ9X4XVt1RURBBBa9swjX3XBdo+mEAVYHrDF0wgD70Ud/egjZnmy8/srr6NyiMxJaKOaNZl4q5yQ5cfoKIiI3n3wC7NsHjBqlE3/tWmsagt69VcJ/kvEJnn39WZSXl+OO39+hsg8tyz+IAQCMvaY4zC2pnbOHno3LRl2GV55/Bd0u64YmMU3Qu73OeVXNS+WcJCdemiQicjN8uPVHadkynfjjxllzNq1frxL+/NfPx7pv12Fc8Tg8/9rzKvvQ0himr6gqbWcaLjn3EnSc2RGndDsF66fqnFfVvFTOyZMVH3FERBSIRYuAH3/Ui//KK0BBgVr4gRkDse79dbjr87vU9kH/1btPb0yYNAHvvfIeXvjwBb0daealck6SE2vEiIjcxMcDHTroxe/YMeTxc3NzsXDhQkyaNAlvznkTV156JXr06hHSfVDNZjw4A01KmuCxOx7DG2+8gezs7NDvRDMvFXKSatZgRsREZCSAOQAiAbxhjPnlw7uIiOrbypXWXWRjxujE//RToLg4qHqciooKpKSkYNWqVVi1ahXWr1+PiooKtG/fHkNvHIrzrzk/hA2m44lPiMcNT92AZe8tw9SpUwEAZ599NkaPHo0xY8ZgwIABlTPwB0wzL0OQk1R7DWJETEQiAbwM4AoA/QBcLyL9wtsqIiJYz9176y29+HPnBhT/8OHDeP/993HTTTehc+fOGDRoEB5++GGUl5fj4Ycfxvr165GdnQ0z1GDZHqX6NqrR9ubb0WNyD/zwww944oknAAAPP/wwzjjjDHTr1g133XUXVq9ejdLS0sB2oJmXAeYkBaZBFOuLyFAAs4wxl9vfPwAAxpinatqmMRTrG2NgjIHX60VFRUXlV9/L/9hXPQ9Vv4+MjETLli0REVG/feeioiJkZmYiKysLmZmZyMzMhMfjQVFREbxeb61eFRUVaNKkCaKiomr1atq0KZo1a4ZmzZohLi6u8n3Vl++zyMhIVFRUoLy8HGVlZcf9GhERgejo6GpfkZGRqsfTlw9erxfl5eUwxqBJkyZo0qQJIiMjg/9fcg0qKipQVlZW7au0tLTy+ERHRzuOcdOmTVXa5PV6UVBQgGPHjiE/Px/5+fm/eF9YWIiSkpLjvkpLS1FSUoKKigqICEQEERER1b73fR8REYGYmBjXvPK9WpWVITY3F95OnQDY/67tn8P3b7y6975/4xX28srv/b8CiDxyBN6CAhR07155PnznpLr3JSUlWL9+Pb7++mt4vV60bt0al19+OUaNGoXLL78cnex2+uQU5iAjNwOntD4l5OdRW16u9fuubbvGMYWFv7yiPOSX5OPcrudWLvN4PPjoo4+wcuVKrF69GkVFRWjRogUuv/xyjB49Gj169Kgxn6ouizpyBBHZ2ZDERERFRaFJZKT1O9T+fRJl/871vY+MjLT+zZSWoqS0FMX2v5/iapaZ3FyUFxaitGfPav8tVf0qIpW/t6v++4mLi0NcXFyd/3Z5vd7KnC8tLUVRUREKCwsdr4KCgl8sKywsRHFxMSJ9x8N+Vfc3yLcsOjoav/71r0Nx2l3VVKzfUDpi4wCMNMbcbH8/CcA5xpg7q6w3DcA0ADjllFMG7dmzR61NZ511Fn744YfKJLP3/4v3/t/7d7K8Xu8vOlPBEhG0adMGbdq0Qdu2bR2vqsvi4uIqf9n7/+Kv2hH0vc/Ly3N0tnyvQ4cO/aIdvn9YkZGRtXpFRERUdoKO9/J6vXU+LhEREaioCM0v6sjIyF90zkSk8hdg1V+I1b3cOqXHywlfp6yml+9nre3Ld9wDOa6+41FTZzguLq5yH76Opf/7ql9LS0srO1qFhYV1aodb5zk6OhpNmzZFRESE4zz4crzqe985Ki4uRkFBQeUr0GNUn3x/8Pr06YMrr7wSo0aNwjnnnIMmTdyrTNJy0tA8unk9tZJ88kvya5y+oqioCJ999hlWrlyJlStXwuPx1HPr6ldsbKzjdwkA1/90BPr30/e7wuv1oqysrFZxYmNj6/w7KRAnREfMn/aI2MsvvwyPx1Pj/06q+973P21f58P3tbr3vv9N+Ks68uD/fVlZGQ4fPoy8vDwcOnQIeXl5jtfhw4eD7oxEREQgPj4enTt3RufOnZGYmFj53v/7li1bqo7clJaWVv5vx+3lW6esrMzxv5uqX6suq6ioOO4oS3FxceV7wNnxrq4z7v+qTefUfwTM11HxjUq5vbxeryOX3F4iUvlzN23atMbRR/9jVFJSctzj7f+9/8/j+5lq+hoVFYUWLVqgRYsWaN68OZo3b175vrqvcXFx9TpSWVpaWu3P3fzTT1GanY2sc8+t+T9ldhz/5f7nIcJlRKHdunWIKC9HwQ03oGnTppXnpLr3gRyHpTuWIutoFq7ud3VIjlV9WvxuLABg/A1FYW5J3a1KX4XismLceU6Nf8YqVVRU4IcffkBOTg4A1Jhn/svaffEFKg4eRNa551q/P3y/Q+wOSJn9O8P31VtRgaZRUYhu2hQx9r+rmOhoRDdt6lgW3bQp2n37LSLLylDw29/+4j8zNX31/fvx/z1R0/uCgoLK/1T4/x6qmvP+X30DAP6jbP4v3/Kqo/j+v1/9X/7LvF4v+vfvH+oU+IWG3hE7IS9N1qeKigocPXq0smNWWFhY7R/mmv5gt2rVCp06dTru/6yJTjqNfB6x4fOHo6isCMsmNL46scY4j5jPuPfGwVvh5TxiVKmhzyO2AUAvETkNQCaACQB+E94mNS4RERFo3bo1Wrduje7du4e7OUQnjuXLgYwMvfjz5qnO2bR8wnJk5Cq2n6o1b+w8FJQozsWlmZfKOUlODaIjZowpF5E7AfwT1vQV84wx28LcLCIioFUroEULvfgtWwKKN+G0immFFtGK7adqtYxuiQjNiQk081I5J8mpQXTEAMAYswrAqnC3g4jIYfFiwOMBrrtOJ/7y5UBJidqcTYu3LobnmAfX9VdqP1VreepylJSV6D1rUjMvlXOSnNjlJSJy8+qrwMKFevHfeUc1/qsbX8XCrYrtp2q988M7usddMy+Vc5KcGkSxfiBYrE9E9aKwEEhP13vkS1ERkJ8PnHGGSvjCskKk56SjQ/PG98iaokLr7rfYuMb3d6qorAj5Jfk4I0HnvKrmpXJOnqwaerE+EVHDFBcHxMbqxY+Nte5QUxIXFYfYKMX2K2qMHTCf2KhYeCsU56bTzEvlnCQnXpokInKzYIFVM6Plgw9U4y/YsgDLUxXbr2j+63GY/3pcuJsRkA+2f6B73DXzUjknyYkdMSIiN2+8ASxZohd/4ULV+G98/waWbFdsv6IPl8Xiw2WNczRv4daFusddMy+Vc5KceGmSiMjN6tVAWppe/IULrXocJasnrUZajmL7qVoLr1mI/BK986qal8o5SU4cESMichMVZb0aafyoyChERSq2n6qlftw180Y758mBHTEiIjfz5wNLl+rFX7xYNf78lPlYukOx/VStxdsW6x53zbxUzklyYkeMiMjN/Pl6z5kEgPffV40/P2U+lu1sfM+ZbOze3/a+7nHXzEvlnCQnziNGRHQ8aWlA8+Z68fPzVWcxT8tJQ/NoxfZTtfJL8vVm1gd081I5J09GNc0jxhExIiIiojBhR4yIiIgoTNgRIyIiIgoTdsSIiIiIwoQdMSIiIqIwYUeMiIiIKEzYESMiIiIKE3bEiIiIiMKEHTEiIiKiMGFHjIiIiChM2BEjIiIiChN2xIiIiIjChB0xIiIiojBhR4yIiIgoTMQYE+42BEREDgLYo7yb9gBylPdB9Y/n9cTE83pi4nk9MZ2M5/VUY0yHqgsbbUesPojIRmPM4HC3g0KL5/XExPN6YuJ5PTHxvP4XL00SERERhQk7YkRERERhwo6Yu7nhbgCp4Hk9MfG8nph4Xk9MPK821ogRERERhQlHxIiIiIjChB2xaojISBFJFZEMEZkZ7vZQ4ERknogcEJGtfsvaishqEUm3v7YJZxupbkSkq4isFZHtIrJNRKbby3leGzERiRGR70TkB/u8PmYvP01E1tu/jxeLSNNwt5XqTkQiRWSziHxof8/zamNHrAoRiQTwMoArAPQDcL2I9AtvqygI8wGMrLJsJoDPjDG9AHxmf0+NRzmAGcaYfgDOBXCH/W+U57VxKwFwsTHmDAADAYwUkXMBzAbwnDGmJ4BDAH4XviZSEKYD2OH3Pc+rjR2xXzobQIYxZpcxphTAIgBjw9wmCpAx5ksAeVUWjwXwtv3+bQC/qs82UXCMMR5jzPf2+2Owfrl3Bs9ro2Ys+fa3UfbLALgYwBJ7Oc9rIyQiXQBcCeAN+3sBz2sldsR+qTOAvX7f77OX0YmjkzHGY7/PBtApnI2hwIlINwBnAlgPntdGz758lQLgAIDVAH4EcNgYU26vwt/HjdPzAP4IoML+vh14XiuxI0YnNWPdNsxbhxshEWkO4AMA9xhjjvp/xvPaOBljvMaYgQC6wLo60Se8LaJgichVAA4YYzaFuy0NVZNwN6ABygTQ1e/7LvYyOnHsF5EEY4xHRBJg/e+bGhERiYLVCXvXGLPUXszzeoIwxhwWkbUAhgJoLSJN7NET/j5ufM4HMEZERgGIAdASwBzwvFbiiNgvbQDQy76joymACQBWhLlNFForANxov78RwPIwtoXqyK4veRPADmPM//p9xPPaiIlIBxFpbb+PBXAprPq/tQDG2avxvDYyxpgHjDFdjDHdYP09/Zcx5gbwvFbihK7VsHvuzwOIBDDPGPPn8LaIAiUiCwEMB9AewH4AjwL4B4D3AJwCYA+A64wxVQv6qYESkQsA/BvAf/DfmpMHYdWJ8bw2UiJyOqyi7UhYgwTvGWMeF5HusG6aagtgM4CJxpiS8LWUAiUiwwHca4y5iuf1v9gRIyIiIgoTXpokIiIiChN2xIiIiIjChB0xIiIiojBhR4yIiIgoTNgRIyIiIgoTdsSIiIiIwoQdMSIiIqIwYUeMiIiIKEz+PwtLmCQJVVpIAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot along cross-disperion cut showing the extraction parameters\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "y = np.arange(image.shape[0])\n", + "ax.plot(y, image[:,300], 'k-')\n", + "mm = np.array([ext_center, ext_center])\n", + "mm_y = ax.get_ylim()\n", + "\n", + "# extraction region\n", + "ax.axvspan(ext_center - ext_width/2., ext_center + ext_width/2., color='green', alpha=0.1)\n", + "ax.plot(mm, mm_y, 'b--')\n", + "ax.plot(mm - ext_width/2., mm_y, 'g:')\n", + "ax.plot(mm + ext_width/2., mm_y, 'g:')\n", + "\n", + "# background region, symmetric on both sides of extraction region\n", + "ax.axvspan(ext_center - bkg_sep - bkg_width/2., ext_center - bkg_sep + bkg_width/2., color='red', alpha=0.1)\n", + "ax.plot(mm - bkg_sep - bkg_width/2., mm_y, 'r:')\n", + "ax.plot(mm - bkg_sep + bkg_width/2., mm_y, 'r:')\n", + "\n", + "ax.axvspan(ext_center + bkg_sep - bkg_width/2., ext_center + bkg_sep + bkg_width/2., color='red', alpha=0.1)\n", + "ax.plot(mm + bkg_sep - bkg_width/2., mm_y, 'r:')\n", + "ax.plot(mm + bkg_sep + bkg_width/2., mm_y, 'r:')\n", + "\n", + "ax.set_title(\"Cross-dispersion Cut at Pixel=300\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Background Subtraction" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# extract the background using custom individual traces\n", + "trace = FlatTrace(image, ext_center)\n", + "bg = Background(image, [trace-bkg_sep, trace+bkg_sep], width=bkg_width)\n", + "\n", + "# alternatively, call two_sided class, which does the same as above \n", + "#bg = Background.two_sided(image, trace, bkg_sep, width=bkg_width)\n", + "# or in the place of any trace, an int/float can be passed which resolves to a FlatTrace\n", + "#bg = Background.two_sided(image, ext_center, bkg_sep, width=bkg_width)\n", + "\n", + "# or for single sided:\n", + "# bg = Background.one_sided(image, trace, bkg_sep, width=bkg_width)\n", + "# bg = Background.one_sided(image, trace, -bkg_sep, width=bkg_width)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'slit[0] slice')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# view the background weighted image\n", + "plt.figure(figsize=(15, 15))\n", + "plt.imshow(bg.bkg_wimage, origin=\"lower\")\n", + "plt.title(\"slit[0] slice\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'slit[0] slice')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# view the background image\n", + "plt.figure(figsize=(15, 15))\n", + "plt.imshow(bg.bkg_image(), norm=norm_data, origin=\"lower\")\n", + "plt.title(\"slit[0] slice\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'slit[0] slice')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# view the background-subtracted image\n", + "plt.figure(figsize=(15, 15))\n", + "plt.imshow(bg.sub_image(), norm=norm_data, origin=\"lower\")\n", + "plt.title(\"slit[0] slice\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that when using median, partial pixel weights are not supported:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'slit[0] slice')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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/ds27FwOfLX8578gM6Nc3+/6hKvSeD+yfX34fLoCls28yf1X1QJJ1wKeAJcBFVXXjKM6t3XIIcHn3vO1S4ENV9ckk1wKXJTkL+A5w2hhrnGhJ/hU4Hnh4ktuAvwPOY/r+fAJ4Ab2H3n8M/OnIC55wA/p1fPfq4QJuBf4CoKpuTHIZ8A16b6F7ZVXtHEPZk+xZwEuAr3XPWQC8DudYqwb16wznWJMOBS7p3pC5D3BZVX08yTeAjUneBHyZXuCm+/n+JFvovVjp9HEUPcEG9euzSQ4GAtwAvLzb3u/DBRL/EUKSJEmSxs8XgkiSJElSAwxnkiRJktQAw5kkSZIkNcBwJkmSJEkNMJxJkiRJUgMMZ5IkSZLUAMOZJEmSJDXAcCZJkiRJDfh/ipHB3gL9YFMAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "bg_med = Background.two_sided(image, ext_center, bkg_sep, width=bkg_width, statistic='median')\n", + "plt.figure(figsize=(15, 15))\n", + "plt.imshow(bg_med.bkg_wimage, origin=\"lower\")\n", + "plt.title(\"slit[0] slice\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'The LRS region')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# transpose to make it display better\n", + "plt.figure(figsize=(15, 15))\n", + "plt.imshow(image-bg.sub_image(), norm=norm_data, origin=\"lower\")\n", + "plt.title(\"The LRS region\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "123.91720867156982" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "diff = image-bg.sub_image()\n", + "np.max(bg.bkg_image())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Advanced Trace" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Optional: we could now refine the initial flat trace by running an automated KosmosTrace on the subtracted image. This process could be iterated as necessary (recreating the subtracted image with the refined trace, etc)." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "auto_trace = KosmosTrace(image-bg, guess=ext_center)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot old vs new trace" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extract" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "from specutils import Spectrum1D\n", + "from astropy import units as u\n", + "flux = s2d.data * u.Jy\n", + "wavelength = s2d.wavelength * u.um\n", + "flux.data\n", + "spec = Spectrum1D(spectral_axis=wavelength, flux=flux)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "boxcar = BoxcarExtract()\n", + "spectrum = boxcar(image-bg, auto_trace, width=ext_width)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "f, ax = plt.subplots(figsize=(10, 6))\n", + "ax.plot(jpipe_x1d.spectral_axis, spectrum.flux.value, 'k-')\n", + "ax.set_title(\"Boxcar 1D extracted spectrum\")\n", + "ax.set_xlabel(r\"pixel\")\n", + "ax.set_ylabel(\"Flux Density [Jy]\")\n", + "ax.set_yscale(\"log\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "from gwcs.wcstools import grid_from_bounding_box\n", + "\n", + "#image = np.transpose(s2d.data)\n", + "grid = grid_from_bounding_box(s2d.meta.wcs.bounding_box)\n", + "ra, dec, lam = s2d.meta.wcs(*grid)\n", + "lam_image = np.transpose(lam)\n", + "\n", + "# compute a \"rough\" wavelength scale to allow for aperture to scale with wavelength\n", + "rough_waves = np.average(lam_image, axis=0)\n", + "\n", + "# images to use for extraction\n", + "wimage, bkg_wimage = ap_weight_images(\n", + " ext_center,\n", + " ext_width,\n", + " bkg_width,\n", + " bkg_sep,\n", + " image.shape,\n", + " rough_waves,\n", + " wavescale=None,\n", + ")\n", + "\n", + "boxcar = BoxcarExtract()\n", + "\n", + "# without background subtraction\n", + "image_wg = image * wimage\n", + "ext1d_boxcar = boxcar(image_wg, auto_trace, width=ext_width)\n", + "ext1d_boxcar = ext1d_boxcar.flux.value\n", + "# convert from MJy/sr to Jy\n", + "ext1d_boxcar *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", + "\n", + "# with background subtraction\n", + "image_bg = bg.sub_image()\n", + "image_wg = image_bg * wimage\n", + "ext1d_boxcar_bkgsub = boxcar(image_wg, auto_trace, width=ext_width)\n", + "ext1d_boxcar_bkgsub = ext1d_boxcar_bkgsub.flux.value\n", + "\n", + "# convert from MJy/sr to Jy\n", + "ext1d_boxcar_bkgsub *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", + "\n", + "# compute the average wavelength for each column using the weight image\n", + "# this should correspond directly with the extracted spectrum\n", + "# wavelengths account for any tiled spectra this way\n", + "waves_boxcar = np.average(lam_image, weights=wimage, axis=0)\n", + "waves_boxcar_bkgsub = np.average(lam_image, weights=wimage, axis=0)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "ext1d_boxcar = boxcar(image_wg, auto_trace, width=ext_width)\n", + "ext1d_boxcar.flux.value" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "gpts = ext1d_boxcar_bkgsub > 0.\n", + "gpts = ext1d_boxcar > 0.\n", + "\n", + "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], 'k-', label=\"boxcar\")\n", + "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], 'k:', label=\"boxcar (bkgsub)\")\n", + "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, 'k-', label=\"jpipe_x1d\")\n", + "ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n", + "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", + "ax.set_ylabel(\"Flux Density [Jy]\")\n", + "ax.set_yscale(\"log\")\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Wavelength scaled width boxcar\n", + "\n", + "The LRS spatial profile changes as a function of wavelength as JWST is diffraction limited at these wavelengths. Nominally this means that the FWHM is changing linearly with wavelength. Scaling the width of the extraction aperture with wavelength accounts for the changing diffraction limit with wavelength to first order." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Developer note: Not currently possible. Allow for wavelength scaled width in the boxcar" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## PSF based Extraction\n", + "\n", + "While to first order the PSF FHWM changes linearly with wavelength, this is an approximation. It is better to use the measured spatial profile as a function of wavelength to extract the spectrum. This tracks the actual variation with wavelength and optimizes the extraction to the higher S/N measurements. In general, PSF based extractions show the most improvements over boxcar extractions at lower the S/N.\n", + "\n", + "There are two PSF based extraction methods.\n", + "\n", + "1. PSF weighted: the spatial profile at each wavelength is used to weight the extraction.\n", + "2. PSF fitting: the spatial profile is fit at each wavelength with the scale parameter versus wavelength giving the spectrum.\n", + "\n", + "Only the PSF weighted technique is currently part of this notebook.\n", + "\n", + "Note 1: calibration reference file for the specific LRS slit position should be used.\n", + "\n", + "Note 2: Small shifts in the centering of the source in the slit should be investigated to see if they impact the PSF based extractions.\n", + "\n", + "Limitation: currently it is assumed there are no bad pixels." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are notes [in brackets] on how each step is handled in the proposed HorneExtract/OptimalExtract classes to make it easier to see what the class does and what the user must do themselves.\n", + "\n", + "### Steps in the JDAT Notebook guide on optimal extraction:\n", + "\n", + "1. Define extraction region [user's responsibility to provide an appropriate image]\n", + "2. Pick a slice [should not be necessary? can use the whole image as the aperture with good results]\n", + "3. Define extraction kernel\n", + " * Select PSF template [assumed to be Gaussian for now. support for Moffat, others?]\n", + " * Choose a polynomial for background fitting [user provides as an argument]\n", + "4. Fit extraction kernel to initial slice [all columns are coadded to perform the fit]\n", + "5. Fit geometric distortion [not currently done]\n", + " * Determine cross-dispersion bins for trace fitting\n", + " * Fit a kernel to each bin to find trace center [user provides this as a specreduce.tracing.Trace object]\n", + " * Polynomial fit of trace centers\n", + "6. Combine composite model with 2D image to create output 1D spectrum\n", + " * Create variance image [user provides this as an argument]\n", + " * Generate 1D spectrum\n", + "7. Compare with reference 1D spectrum\n", + "\n", + "### Steps in the original Horne (1986) paper:\n", + "\n", + "1. Bias subtraction [assumed to be done in earlier block]\n", + "2. Initial variance estimate [user provides this as an argument]\n", + "3. Fit sky background [assumed to be done in earlier block]\n", + " * \"We therefore generally perform a least-squares polynomial fit to the sky data at each wavelength. Individual sky pixels are given weights inversely proportional to their variances as estimated in Step 2\" [overlaps with notebook guide's 3b]\n", + "4. Extract standard spectrum and its variance\n", + " * Subtract the sky background found in Step 3 from the image. [sky background calculation is planned as a separate, earlier step of the specreduce workflow]\n", + "5. Construct spatial profile\n", + "6. Revise variance estimates [not currently done]\n", + "7. Mask cosmic ray hits [not currently done]\n", + "8. Extract optimal spectrum and its variance [currently only extract the spectum, not a variance]\n", + "9. Iterate Steps 5-8\n", + "\n", + "The first four steps as the standard procedure and the last five as add-ons that help squeeze out extra signal-to-noise." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PSF weighted extaction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Generate the PSF profile as a function of wavelength\n", + "For MIRI LRS slit observations, observations are made at two nod position in the slit after target acquisition. This means that the location of the sources in the slit is very well known. Hence, spatial profile (PSF) as a function of wavelength for the two nod positions is straightforward to measure using observations of a bright source.\n", + "\n", + "The next few steps generate the needed information for the nod position for which we are extracting spectra based on a simulation of a bright source at the same nod position." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'The LRS Spatial Profile (PSF) Observation')" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# lrs spatial profile (PSF) as a function of wavelength\n", + "# currently, this is just a \"high\" S/N observation of a flat spectrum source at the same slit position\n", + "psf = datamodels.open(spatialprofilefile)\n", + "# transpose to make it display better\n", + "lrspsf = np.transpose(psf.data)\n", + "norm_data = simple_norm(lrspsf, \"sqrt\")\n", + "plt.figure(figsize=(10, 3))\n", + "plt.imshow(lrspsf, norm=norm_data, origin=\"lower\")\n", + "plt.title(\"The LRS Spatial Profile (PSF) Observation\")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'The LRS Spatial Profile Reference Image (Normalized)')" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Mock a LRS spectral profile reference file\n", + "# Sum along the spatial direction and normalize to 1\n", + "# assume there is no background (none was included in the MIRISim for the flat spectrum source observation)\n", + "# ignore regions far from the source using a scaled boxcar weight image\n", + "# the aperture (psf_width) used in the scaled boxcar weight image could be varied\n", + "psf_width = 12.0\n", + "(wimage_scaledboxcar, tmpvar) = ap_weight_images(ext_center, psf_width, bkg_sep, \n", + " bkg_width, image.shape, waves_boxcar, wavescale=10.0)\n", + "\n", + "psf_weightimage = lrspsf*wimage_scaledboxcar\n", + "\n", + "# generate a 2D image of the column sums for division\n", + "max_psf = np.max(psf_weightimage, axis=0)\n", + "div_image = np.tile(max_psf, (psf_weightimage.shape[0], 1))\n", + "div_image[div_image == 0.0] = 1.0 # avoid divide by zero issues\n", + "\n", + "# normalize \n", + "psf_weightimage /= div_image\n", + "\n", + "# display\n", + "norm_data = simple_norm(psf_weightimage, \"sqrt\")\n", + "plt.figure(figsize=(10, 3))\n", + "plt.imshow(psf_weightimage, norm=norm_data, origin=\"lower\")\n", + "plt.title(\"The LRS Spatial Profile Reference Image (Normalized)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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pfzu3H9vG45EmPnToSVhzrV3hzn9P3A5Nb8zsOUvPgGu/c9LD5kP73K6uLn7961/zmc985oRjf/rTn3LttcPvnaGhITZv3oxhGNx+++285z3vmcKLMTlJ6GJR63vOmtp/sHwQgArf+al9GTU1AFzUF8cVXc6D2XFuef57KEno85Ld7XOj0Shbt27l05/+NCtWrBi177777mPXrl1s3749te3YsWP4fD6OHDnC2972Ns444wxWrlx5Ot96iiR0saj17diOs6iIY4XdFMQg2zu8BJh79WqU18vgnj1ceua7+X3rv7On4VVqjr8Ey2QNl3FN4Up6ttjdPvfjH/84VVVV/NVf/dWoY7Zt28Y3v/lNtm/fjtvtTm33+az7NStWrOCyyy7jtddem3ZClzF0sWjpaJT+Pz5P/Jzz6HYP4VcZo/Yrw8C7cSODe/bw+YtugriLB3OWwJ+kT/p8ZGf73K9+9at0d3fzve99b9RjX3vtNT7xiU/w2GOPUVxcnNre2dlJKGRVTbW1tfH888/PyM1bSehi0RrcvZt4by8Nq86g0dQs9xSfcIy3ppqh/fspdXspNy7gdxleeg4+Dq1v2RCxmIxd7XMDgQDf/OY32bdvH2eddRY1NTWpxam/8IUv0NfXxwc+8IFR5Yn79+9n8+bNVFdXc/nll3P77bfPSEKXIRexaPVt3wGGwYGcOD0xJ2sKV59wjLemBiJ3M7R3H39+xs18a8+z/DY7h60v/ACu+8HcBy0mZBgG991336htI1cFevHFF1Of//KXv0x9/q1vfYtvfetbox6Xm5s75WZffr+fidqQb9u2bdztF1xwAW+8McM3j5ErdLGI9e3YQcZZZxHstX6wVpbWnHBMcoLR4O7d3LTpfJwRH/fmFMOeB6C3aS7DFeKkJKGLRSnS1ETo4EGyLr2EvsFDAFT6LzjhOKOwENPvZ3D3bhwOBxeWvJOgOcReQ8FLP57rsMUEpH2uRRK6WJT6duwAIL7lfEKOFtxaU5pXOe6x3poaBvfsAeBzF25Fx03uLqiCnf8JQz1zFrMQJyMJXSxKfdt3YJSXcTijmAFXH37txqHG/3HwVlcTbW4m0tjIivxCih1beMYcZCDcA6/eO8eRCzExSehi0YmHw/S/8AJZl17KwcYuWlxRKj0FEx7vTUwwSl6l37L+RuKOCA+XboQXfgjR8FyELcRJSUIXi87grl3ogQGyLrmE4PH9NBhOVuVWTHi8Z81qlNvN4Gu7AfjwWZfhiJRyj+mG3gZ48xdzE7gQJyEJXSw6fdt3oFwuMs89l47WnWilqCzaMOHxyuXCs3Ejg4np34bTweaCa2lztrC3aA08/32YoGxN2Gsu2ucCXHPNNVRXV7NhwwZuu+02YrEYAB0dHVx11VVUVVVx1VVX0dnZCYDWmk9/+tOsWrWKTZs28eqrr55yjOORhC4Wnb4dO8jYsgU8XgaGDgBQWT75VH5vdTVD+/YRD1vDK5+7YCs67uQH2ZXQuh8O/X7W4xanbq7a5z700EPs2bOHN998k9bWVh5++GEAvvOd73DFFVdw6NAhrrjiCr7zHas1whNPPMGhQ4c4dOgQd91116hJUNMhCV0sKuHjxwkfPUrWJZdwvGMAh2HVki8v3jTp47w11ehIhFDiam99SRn5bOb52FEGc/zWVbqwjZ3tcwFycnIAq0FXOBxO9ZF59NFH+fCHPwzAhz/8YX71q1+ltv/Zn/0ZSinOO+88urq6aGxsnPbrIDNFxaLSt+M5ALIuvYSXm3oIubspjTvJMDMmfZy3ugawbowmb5J+YM0N3HXoJe5behEf2/sABHaBf/Nshj/v/ePL/8iBjgMzes61+Wv50pYvnfQ4u9vnXn311bz88stce+21vP/97wegubmZsrIyAEpLS2lubgYgGAyydOnS1Hn8fj/BYDB17OmSK3SxqPTt2I5r+XJcy5ezv6GHLjNMhSvnpI8zS4oxyssYGNFG9ePnvB0ihdzb3wKePLlKt9nY9rl//OMfR+2/9dZb6enp4cc//jH/8i//Aljtc3/+859TU1PDueeeS3t7O4cOHRr1uKk05wJ48sknaWxsJBQK8cwzz5wQn1LqhA6QM02u0MWiER8cZOCll8m76UYAAsE6jrscnJXln9LjM2pqRiV0t+mkJu9qdvf/N3s3vI8Nr/wMuoOQ65uN8BeEqVxJzxa72+cCeDwerr/+eh599FGuuuoqSkpKaGxspKysjMbGxlTHRZ/PR319fepxgUAg1U53OuQKXSwaAy+/jA6FyLrkUgD6W19lwOGgMn/NlB7vra4m2tBIpLklte2vzvsgWju4o68P0NB2cDZCF1NgV/vcvr6+1Ph3NBrlt7/9LWvXrgXguuuu4957rcln9957L9dff31q+89//nO01rz44ovk5uZOe7gF5ApdLCJ927ejvF4ytpxDfyiKDu8HoLLs7Ck9PjXBaPduzKvfDsDZ/mXkxGt4aWgPYcDVPbtrRoqJJdvnfuQjH2H9+vV88pOf5Ne//jUw3D73+eefx+l08otf/CLVPreuro6zzjoLrTVFRUWpG5dT1d/fz3XXXUcoFCIej3P55Zdz2223AXD77bdz44038tOf/pTly5fz0EPWQuPveMc7ePzxx1m1ahUZGRn87Gc/m5HXQBK6WBS01vRt30Hm+efjcLk4eLwTj7sBgIqyqd3IdK9bhzJNBvfsISeR0AHes/J9/FfdV3k6M5Nru+onOYOYTXa1zy0pKWHnzp3j7isoKODpp58+YbtSijvvvHNK5z8VMuQiFoXwkSNEgkGyLrkEgAONvcRdnWRoKM4omdI5HC4Xng0bUhOMkj51/jsgsoQHc/KgWxK6sI8kdLEo9G23uitmXXIxAAeaeuhxDVDpzDylygNvTQ1De/eiw8P9WzJcJuuyr+QVj5NAx5GZDVxMibTPtUhCF4tC344duKuqMMvLATgWbKLBBZVTvDpP8tZUo0Mhhg6Ovvl59Qqrl/rhvoaZCViI0yAJXaS9WF8fA6+8Qtal1nCL1pqh1jdpMgwqck9tlfXUjdFEo66kDSXLAGiLdkE8Nt2QhTgtktBF2ut/4QWIRMhMjJ83dA+RTaKHS0n1KZ3LLC3FKClJtdJN2phI6C1OBX3NMxC1EKdOErpIe/07duDIyiLjzDMBONDYQ7bbKi88WVOu8Xhrak64MZrl9uCKeWkyDJBKF2ETSegiraXKFS+8EGWaABxo6kW5W3FoWDbBsnOT8VZXEwkGiba2jtruIY9GwymVLvOI3e1zb7rpJmpqaqipqaGiooKaxJAdwLe//W1WrVrFmjVrePLJJ085xvFIHbpIa6GDB4m2tJB16aWpbfsbe4i7+yh3eHA73ad8zpErGGVfeWVqe4ZZSlPsuCT0eeTuu++ek+d56KGHyMnJQWvN+9//fh5++GFuvvlmHnzwwdQxn/vc58jNzQVg3759PPDAA+zdu5eGhgauvPJK3nrrLZxO57TikCt0kdZS5YoXD08Dr23soNmIUumeeNm5yXg2rAfTPGHYpdDro8kwiLQfO+14xemZr+1zk7TWPPTQQ2zduhWw2ufefPPNuN1uKisrWbVqFS+//PL0XgTkCl2kub7t2/Fs2IBRVATAUCRGvL2WY/kG52UvP61zOtxuPOvWMbh79I1RX3YZb/Yr2tqPMP2uHAtT07e+RWj/zLbPda9bS+nf/M1Jj5uP7XOTnnvuOUpKSqiqqgKs9rnnnXdean+yfe50SUIXaSvW1cXg7t0U3vaJ1Lbalj58xiEaHQ4qC09/JRtvTTVdDz2MjkRSY/Mr833QBE19wUWb0O00tn3uHXfcMWr/rbfeysMPP8yPf/xjdif+unrqqad4/fXXeeSRRwDo7u7m0KFDrF69OvW4ZHOuk3nyyScZGhrilltu4ZlnnuGqq65K7bv//vtTV+ezSRK6SFt9zz8P8Xhquj9Y4+d57joAKsrPOe1ze6ur6fz5fzH01lt4N1jrka4rsq7420Nt1hqjs9z7ej6aypX0bJmP7XPBGob55S9/ySuvvJI6TtrnCnGK+nfswJmXh+eMM1LbDjT14vFYdeKVBetO+9wZIzovJq0ttFagaXdEYajrtM8tTs98bJ8LsG3bNtauXYvfP9x3/7rrruOBBx4gFApx9OhRDh06xJYtW6b9GsgVukhLOh6nb8dzZF58MWpE5cCBph7K3T3k4CDfk3/a5zfKyzGKiqwJRrfcAkBRRiFKq0TpYgC8S6b9fYipm4/tcwEeeOCBE4ZbNmzYwI033sj69esxDIM777xz2hUuIAldpKnQgQPEOjtTzbjAqjQ40NCNWRyi0iyZ1nJgSim8NdWjbow6HU68Oocmo8+aXFR6xiRnEDNtPrbPBbjnnnvG3f6Vr3yFr3zlK1N6jqmSIReRlsKJMVD3muHViFr7QngHGzhmOqnILJ/2c3hraogcP060vT21LdMooskpk4uEPSShi7QUTpSAmeXDN5oONPZS6TxKm+Gc8rJzk/FWW31gBve8ntq2xFtOo2EQklr0OSXtcy2S0EVaigSDOPPycGZlprYdaOqh0H0UgMrSqS07NxnPhg1gGKNujPqyymgxnAy0Hp32+YU4VZLQRVqKBIKYY8rADjT2kpvRBEBl8fTHtx1eL541a0Z1XqzI8xFVivbu49M+/0KitbY7hLRzOq+pJHSRliLBIOaIMjGA/U29aHcHhgZ/tn+CR54ab00Ng2+8gY5GAVhTZJUudoRaZuT8C4HH46G9vV2S+gzSWtPe3o7H4zmlx0mVi0g7WmsiDQ1kXX55alskFqe2pYcqfx9+Zx6mw5yR5/LW1ND53/9N6NAhPOvWsWqJldC7dC9EhsA8tR/Ihcjv9xMIBGgd031STI/H4xlVuz4VU0roSqlrgO8DTuBurfV3xuxfBtwL5CWOuV1r/fgpRSLEDIm2tqJDIUzfcCXLkdZ+cmLdHDeg0ls8Y8/lrUneGN2DZ906SjNLAay+6D1BKDi1FZEWItM0qaw89TbEYuaddMhFKeUE7gSuBdYDW5VSY5tgfBV4SGt9JnAz8EOEsEkkUeHiGnF1c6Cph5UqwDHTpDJvxYw9l+n34ywoSC1Jl+PKwdCGNbmoa3GNowv7TWUMfQtQq7U+orUOAw8A1485RgM5ic9zAVkpV9gmErTefiNviu5v7MXvPUxUKSqKNs3Ycyml8FZXp26MKqXIcubTbBjEZeUiMcemktB9wMh3ZiCxbaSvAR9SSgWAx4H/b7wTKaU+rpTapZTaJeNtYrZEUjXow0MuB5t6KMmy+m1Ulk2/ZHEkb00N4bo6op2dAOS5y2h0OhlorZvR5xHiZGaqymUrcI/W2g+8A/gvpdQJ59Za36W13qy13lyU6E8txEyLBAI48/NxZGSkth1o6kW5rIuIytyZHe8dnmBkXaWXZpXRYJgMSUIXc2wqCT0ILB3xtT+xbaSPAg8BaK1fADxA4UwEKMSpGluy2DUQprF7iC5HN/nKJNedO6PP5z1jIzidqYRekeun0+kgvMhq0YX9ppLQdwJVSqlKpZQL66bnY2OOOQ5cAaCUWoeV0GVMRdgiEgyOqnA50NRLJoMEnBEqXDPfAdGRkYF7zerUjNGqAj9aQdeg3EoSc+ukCV1rHQU+BTwJ7MeqZtmrlPqGUuq6xGGfAz6mlNoD3A/8uZZZBsIGOh4n0tCAyzeyh0sPK1UDdaZJZc7pLTt3Mt7qaoZefwMdi+HPsdYr6ol1Qjw+K88nxHimVIeeqCl/fMy2vxvx+T7gwpkNTYhTF21tRUcio4ZcDjT1si7jOI87nVQWnP6yc5PxVlfTdf8DhOvqKCu0EnqrE+hrhhxZkE7MDZn6L9JKqsJlZMliUy/+HGvpsZmucElyV1QAED5+PDW5qNkwpI2umFOS0EVaGZvQY3HNW029OM1EU64lq2bleZN/EUQCQbyGF4/KoNFwEu2QNrpi7khCF2klklgEOFmDfrxjgMFIjB7acaEon4GFLcbjLChAeTypXyh5rmKaDIOeZmmjK+aOJHSRVsLBIM6iQhyJLnUHGnswiRJQgywzsnE6pr9u43iUUpg+H5Gg9QulJLOcoFNq0cXckoQu0kokGMRVPnr8vNLRRJ1pUJk5doLzzDL9PsIB6wp9aU45TYaBlun/Yg5JQhdpZezCFgcaezg/t5WAYVCZXzWrz+3y+VNDPiuW+Oh3QnRg7Bw8IWaPJHSRNnQsRqSx8YSSxWVZx4kpRUXJmbP6/KbPR7y3l1hPD+VZVqlif0Tm14m5IwldpI1oSwtEo6kr9L5QlOMdA7gMa9hjRcG6WX3+4UqXQKp0scsZhsGuWX1eIZIkoYu0MbZk8WBTLwDd2loOriK3Ylaf3/RbzxsekdAbnU7oDszq8wqRJAldpI1wsmQx0cflQFMPijgN8R6KHR4yzcxZff5ku4FIsIHijGJA0WQYDLbVzerzCpEkCV2kjbFX6Acae1nt6eKYU1Hpnf12zY7cXBxZWUQCAUyHSY6RR5PhpLvxyKw/txAgCV2kkUiwAaO4GIfLBVhX6Jcs6eCoaVKRO3PLzk1EKYXpH650Kcooo8FpyhW6mDOS0EXaiAQCqatzrTUHGnup8tbT63RQWTxzy85NxvT5iDQM16IHDJfUoos5IwldpI2RC1sEuwbpDUVxOa2p95VFG+ckBldicpHWmmU55bQaCrNXboqKuSEJXaQFHY0SaWpK3RA93NoPQE8ssY5ozswuOzcR0+dHDw4S6+igNLOUiANikeY5eW4hJKGLtBBtboZYLDXkUt8xAGgaI+14cVCSWTIncSRLFyPBIGWZ1uSiQdUD0dCcPL9Y3CShi7SQ7KGSLB0MdA5S6uzjqCNGhTsfx4lrls8K03fi5KImw0BLLbqYA5LQRVpIlSwmxtDrOwc4N6edOtOkInvZnMWR/AshHAiOmlwkbXTFXJCELtJCJBgEpTBLrSQa6BhggydIg+Gkcpan/I/kzMrEmZdHJBgk35OPoQyaDIOuhsNzFoNYvCShi7QQCQQwSkpQiRr0QOcguWYdWqk5K1lMStaiO5SDAk8xTYaTQemLLuaAJHSRFqySRWu4oz8Upb0/TJTEOqJ5K+c0lpGTi8qzyjhueGQMXcwJSegiLYQbgqNuiAJ0xNtQwLKcuRtDB6uXTKShAR2P488up8lwSi26mBOS0MWCpyMRok3NqRuSgc4BMhiiXg1RZmTiNbxzGo/L77diam2lNLOUTid4hxrnNAaxOElCFwtepKkJ4vFRNegrVAN1pkllxuwsCj2ZsX3R4writEM8PuexiMVFErpY8Ia7LCZLFgdZYbRZ64jO8fj5yDgiweHSxXYDoj1Ncx6LWFwkoYsFb7gGfXjIZVlWM4MOB5WFG+Y8nmT7gfCYyUXtQSldFLNLErpY8MKBADgcmCXW9P76jkEyvVb/lGUFa+Y8HofbjVFURGTE5KJmp5OuJumLLmaXJHSx4EWCQczSUpRpAtYsUZztAPiy/JM9dNaYfj+RYJBsMxuvM4NGw2BIatHFLJOELha8SLAhdUO0ezBC71CUftWDgtQV8lwzfT4igQBKKcqySqk3XMQ7pS+6mF2S0MWCFwkGx3RZhA4GKHK4MZ2mLTGZfh+RpiZ0NEpZZhlBw43ZJ7XoYnZJQhcLWjwcJto8ugY9h36aHZoyV65tcbn8fojFiDQ1U5pZSqvhICskVS5idklCFwtatLERtE7Vfgc6B/GpNhoMJ+XeYtviSv6CSdai9zjjZEdloQsxuyShiwVtuAbdKhWs7xhgpbuDJsOgNGepbXGlJhcFh0sXB4wwgz2dtsUk0p8kdLGghRNNsJJ9XOo7B6nIbCGqFOVLqmyLyywtBYdj1MpFjYaTlsAh22IS6U8SuljQIsEGMAyMRA16oHOAHI81tFGev9q2uJRpYpSWnDC5qKtRatHF7JGELha0VA26YaC1pr5jEOVsA6Asa+77uIzk8vmJBIKUZFi/bJqc0hddzC5J6GJBG1my2N4fZjASY5AuAMptTujJyUUew8MS9xKChkm8S2rRxeyRhC4WtEggMKJk0eqD3kk/Ocog08y0MzRMn49oSwvxcJjSzFKOm16pRRezShK6WLDioRDR1tZUU676jgE8hGhWUcrNbJujSzQL0zrVdbHZMMkalL7oYvZIQhcLVqShARhZ4TKAT7XRaDgp8xTZGRqQmFyEdeO2LLOMNkOTH21Ba21zZCJdSUIXC1YkkKxBHx5yWePtosEwKMv22RkacOLkokEVx6u66e7rtzkyka4koYsFa7gPemJhi44BqjJaGXA4KLdhYYuxjOJiMM1Rk4taTCfN9dIXXcwOSehiwYoEg2CaGEXW8Eqgc5Bcj9UvpczGGvQk5XRilpedMLmoU2rRxSyRhC4WrEgwgFlWhnI6icc1wc5BnM5WAMptnPY/ksvnIzxioYsmQ2rRxeyRhC4WrHAwmOrh0tIbIhyLM4jVKyV5RWw30+cnEghQ6C3EoRw0Og3incftDkukKUnoYsGKBBtSlST1nVYf9E7diwcH+Z58O0NLMf1+Yh0dOIbCFGcUc9zMwOhrsDsskaYkoYsFKT40RKytbVQfdJMoLYQoNTJRStkcoSVZIx8JBinNKKXB9JA1JAldzA5J6GJBGm6bm5xUNEipaqfRcFLuKbAztFGSNfLJJl2thoOCaAvxuNSii5k3pYSulLpGKXVQKVWrlLp9gmNuVErtU0rtVUr9z8yGKcRowwl9uGRxY0Y3jYZhe1Oukcwxk4vanTFKaae5Z8DmyEQ6OmlCV0o5gTuBa4H1wFal1Poxx1QBXwYu1FpvAP5q5kMVYtjYK/RA5yBrMjvpcDopy62wMbLRnAUFKI+HSCBASWYJEeL0O2M0NUhPFzHzpnKFvgWo1Vof0VqHgQeA68cc8zHgTq11J4DWumVmwxRitHAggHK5MIoKAeum6BK3NTZdnr/GztBGUUph+nyjJhc1GU46G2RykZh5U0noPmBkz89AYttIq4HVSqnnlVIvKqWumakAhRhPJNiAWV6OcjiIxuI0dg9hJGrQy3KW2RzdaKbfR3jE5KImw2BAatHFLJipm6IGUAVcBmwF/kMplTf2IKXUx5VSu5RSu1pbW2foqcViNLIPemP3ELG4JoS1sIXdfdDHSi50MfIKXWrRxWyYSkIPAiOn3fkT20YKAI9prSNa66PAW1gJfhSt9V1a681a681FRfZ3wxML18iEnqxB74r34ASKM4ptjOxEps9HvKeHnJATt9NNveHF7Bv7IyTE9E0loe8EqpRSlUopF3Az8NiYY36FdXWOUqoQawhGGlaIWRHv7yfW0TF8Q7RjEEWcVj1AsdOL4TBsjnC0ZKVLtKGB0sxSAq4MMoekL7qYeSdN6FrrKPAp4ElgP/CQ1nqvUuobSqnrEoc9CbQrpfYBfwC+oLVun62gxeKW7IOenLQT6BygVHXRYDgocy+xM7RxJeMMBwKUZpTSZJoURlsIRWM2RybSzZQuZbTWjwOPj9n2dyM+18BnEx9CzKpwomRxeGGLQTZl93LUMDg7Y370cBnJleqLHqSkqoRaJ/hUK8HOQVYUZdkcnUgnMlNULDhjF7ao7xhgXUYHLU4nZbnL7QxtXI7cXBxZWak2up0qQqYaoKFFqnvFzJKELhacSDCIcrtxFlo16IHOQYrcTcSUomzJCffibaeUwvT7UysXxdG0Op10NshtJjGzJKGLBSdZ4aKUIhSN0dw7hOm0FrYoz6u0ObrxjTe5SPqii5kmCV0sOCNLFoOdg2gNYW3VoJdlzb8xdACX30c42EBpRjKhG8SkFl3MMEnoYsGJBAKphS0CnYMAdMe7gPmzsMVYps+PHhigOOwBIGC4MHulFl3MLEnoYkGJ9fUR6+4es7CFpjXeR75y4TW89gY4gWTpotncQbaZTb0rS2rRxYyThC4WlEgwUYM+og96ibOPRgeUuXPtDG1SyTa/kWCQ0qxSmlxeiuKt9A5FbI5MpBNJ6GJBiQSttrMjVyqqye6lwTAo95bYGdqkTN/oyUUtpgOfaqO+Y9DmyEQ6kYQuFpRUDXpqyGWQdRldNBlOynKWTvZQWzmzMnHm5aWadLU5YpTQSX1bl92hiTQiCV0sKJFgEOX14lxiTfEPdAxQ5m5iyOGgbMkqm6ObnOn3pyYXdeswYQd0NdXZHZZII5LQxYISaQhi+spRSjEQjtLeH8aVqEEvy1tpc3STGzm5CKDJ6WSgpc7eoERakYQuFpRwIDhq2TmASLwZmH990McyfeXWTVGv1d63yXAS66o/yaOEmDpJ6GJBiQSDuEYsDA3QE+8E5n9Cd/n96EiE4iE3YE0uMnplbVExcyShiwUj1tNDvKfnhCv01lgvGcpJjivHzvBOKnkjN689BMAxVzaZg41YzUqFmD5J6GLBiARP7LJYYA7RqGKUG9kopewM76SSteg0NlPgKaDBnUmpbqW1L2RvYCJtSEIXC0YqoY+YJXpWTh+NhkGZd/4vaZhsVxAJWqWLLS631KKLGSUJXSwYw1fow31cNmR00WA4Kc/22xnalDjcboyiIsKBAGWZZbQaGp9qJ9DRb3doIk1IQhcLRjgYxJGRgTMvD7CGXJa6WuhxOud9yWKSVbpoXaG36jAuFaG1SW6MipkhCV0sGJHAcB/07sEIPUNRPE6rt8t8n1SUZPVFtxL6gI7Q61AMth61OyyRJiShiwUjEgymxs8DnVbJYjRZg57tsy2uU2H6fUQaGyn1WGP+TU6DaKdcoYuZIQldLAha61ELWyRvJPbE2oH52wd9LJffD7EYpf0mYE0uMqUWXcwQSehiQYh3dxPv6xvVZRGgNdaNgaIoY/5XucBwyWVBRwyAelcGWUONRGJxO8MSaUISulgQwqmSxeFJRYXuOE06TKmRiUMtjLdycsgoo60PQxkEPbmUq7bUJCkhpmNh/BSIRS9ZsugaMamoJifRB91TYGdop8QsLQWHg1hDA8UZxbR4MvCpNvY39tgdmkgDktDFgnDCSkWdA2zI7KHRcFKWOb97uIykTBOjtMRa6CKzlDaXgU+1sa9BErqYPknoYkGIBAI4srJw5OSgtSbQOUiFq5VWp5PyvEq7wzslLp+fSLCB0sxSmomSp/o5EmyyOyyRBiShiwUhWbKolKKjP8xAOEamowGtFGX5q+0O75SM7IveFBsgDvQ0HLI7LJEGJKGLBWFUyWLiBmIs3ghA2QKpQU8yfT6iLS2UuQqJ6hgdTgfZA8dpkyZdYpokoYt5T2tNOBgc0cPFKlnsjbUCUL6AxtAhUamjNb6+RF90p0GlapQbo2LaJKGLeS/W1YUeGLAm5TA8qagt2gWQWtJtoUh+H4VdVu15Q1YhKxxN7JUbo2KaJKGLeS8SGNMHvXOAIq+iMTZIkcODy+myM7xTlvw+ctqtX0zNOUWsNpql0kVMmyR0Me+NXdgi0DnIptxBq2TRvcTO0E6LUVwMponR3IHX8NLkzaZSNbFPhlzENElCF/NeJGj1Okkl9I4BNmYm+qAvkB4uIymnE7O8jGgwSElGCY2Gk+x4N+2tTQyGY3aHJxYwSehi3osEgzhyc3FmZxOPWzXoK10dNBkGZbnL7Q7vtLh8PsKJvujNWEl8OU0caJKrdHH6JKGLeW9khUtrX4hwLE6OChJRivIlVTZHd3pMn59IMEhZZhlNkT4AKlWjDLuIaZGELua9SCA4qocLQExbNejlC/QK3fT7ibW3U+4soDXUSVg5WetqlkoXMS2S0MW8NtwHfXhhaIC+qLWwxUIrWUxKdo1c2udGo2nOX8YZHunpIqZHErqY12IdHeihoRE3RBM16JEOYOFNKkpK/sVR3mcAcDyvjErVyIGmHmJxbWdoYgGThC7mtfCx4wCYS4ev0IuzTBqjfWQrkyxXlp3hnbZkX/TCTuuGaF1mLkXhAEORGEfb+uwMTSxgktDFvBaqtZpWuVdZi0DXdwxyRu4QjU4H5a5cO0ObFmdBAcrjwWzuJNvMps5wYsQGKaZLxtHFaZOELua1UG0tyuvFLE/0cemy+qA3GE7KMkpsju70KaWsJl3BBipyK6jTVmOu1YZMMBKnTxK6mNfCtYdxr1iBcjiIxuI0dA2xytVOo2FQnr3U7vCmxfT7CAcDLM9ZzrGQdU/g3JxOuTEqTpskdDGvhWprU8Mtjd1DxOKaAkcT/Q4H5fmrbI5uelw+P5FAkIqcChoHWxk0PWzytrKvoQet5caoOHWS0MW8FevpIdrSgrvKStzJhZR13GoFUJa7sFYqGsv0+Yj39FDhLAbgeEEFlaqJ9v4wLb3SG12cOknoYt4K1R4GwLVyJTBcg94fsWrQyxZgH5eRkpUuy/s8ANTlFFMUrgdgb0O3bXGJhUsSupi3UhUuVdb0/kDHAA4FreE2AMqyFnpCt2rRC7sSpYueTDx9x3ESk3F0cVokoYt564QKl85BSrPdNEV6cOOgwFNgc4TTk5xcpJraKM0s5ZgTVDzKuUv6pNJFnBZJ6GLeCtfW4l65EuWw3qb1nQOsXRKjwaEpM7NRStkc4fQ4cnNxZGURCQRZnrOcupg1pHRBXqfUoovTIgldzFuh2sO4E+PnkJhUlNFtLWzhLbIxspmhlML0+4kEAlTkVFA31IoGNmW0c6x9gN6hiN0higVmSgldKXWNUuqgUqpWKXX7JMfdoJTSSqnNMxeiWIzGVriEojGae4dY5e6gwTAoz/bbHOHMMH0+IsEglbmV9Eb66MhYwgpldZI80NRrc3RioTlpQldKOYE7gWuB9cBWpdT6cY7LBj4DvDTTQYrFJ1RbC4ArUYPe0DWE1lCqmulwOinLWznZwxcMl99HOBhkefYyAOry/RSFrbLMvUGpdBGnZipX6FuAWq31Ea11GHgAuH6c4/4B+EdgaAbjE4tUMqEP93CxxpdJ1KCX562wJa6ZZvr86IEBlsWttVGPZRXg6j5CQaZLboyKUzaVhO4D6kd8HUhsS1FKnQUs1Vr/drITKaU+rpTapZTa1draesrBisVjvAoXgP6INRxRlrUw2+aOlSxdLOiM4nK4qHN7UN0BqkvdktDFKZv2TVGllAP4N+BzJztWa32X1nqz1npzUdHCv6klZs94FS6GQ9EWagEWfg16UnLhjlhjE8tyllGnrJr0C5Z081ZTH5FY3M7wxAIzlYQeBEZ2QfIntiVlAxuBZ5VSdcB5wGNyY1RMR+jQcA8XsIZcyvO8NIa6cADFGcX2BTeDkmulRoKJ0sWodSN0k7eNcCxObYv0RhdTN5WEvhOoUkpVKqVcwM3AY8mdWuturXWh1rpCa10BvAhcp7XeNSsRi7QX6+4m2tqKe9Xwjc9A5yBVeZpGohQbmZgO08YIZ44zKwtnbi6RoNWkq36whSiw0tEEIDNGxSk5aULXWkeBTwFPAvuBh7TWe5VS31BKXTfbAYrFJ3Q40cNlxBV6oHOAjYk+6OXuhT1DdCzT5yMcCFCRW0E0HqUht4wlQ8fxmA6ZYCROiTGVg7TWjwOPj9n2dxMce9n0wxKLWehQssLF6uEyEI7S1hdmlauT3xkGZ6bJDdEk0+cjdOQIFTkVANQtKWdZx2HWluawr1FKF8XUyUxRMe+EDicrXKwbn8FEhYvP0Uqz4aQ8b2G3zR3L9PutMfTs5YC1vijth1lfniO90cUpkYQu5p3xKlwAjFg9MaUoy1vYC1uMZfp86KEhsgfi5LpzqTMMGGijphB6hqKpkk0hTkYSuph3TqxwsRLaQGIGZXm2b9zHLVSpSpdET5djWD1cqjOsNsFSjy6mShK6mFdSFS5Vo2+Iug1H2tWgJ5mJNrqp0sVQJwCVqgmHkkoXMXWS0MW8kqpwGdNl0b/ES2NiIeXSjFJbYpstyb7o4USTrpZQBwMOJ66uI1QWZsoVupgySehiXhlb4QLWGHplnkFDbJAlDjcZZoZd4c0KR2YmziVLUrXoAHX5S6G9lg3luXKFLqZMErqYV0K1taiMjFSFC1iTijZkdtNoGJS5l9gY3ewxfb7UQhcAdTkl0F7L+vIcgl2DdA2EbY5QLASS0MW8Ej5ci3vFilSFS89QhO7BCFXuLqsPemZ6DbckJfuiL8tZhkJxLCPLKl0szQbkxqiYGknoYl4ZW+FyvN0qWVzqaLNWKkpcwaYb0+8j0tCAy+GiPKuco04FkX425loVPjLsIqZCErqYN8arcElemeaoBoYcDsrzqyZ6+IJm+nzoUIhYW5tVuhi3Enn+UD0lOW5J6GJKJKGLeSNZ4TLyCn1fQw8ZLif9Iaslf1maLD03lmts6eJgGxqscfSyHBlyEVMiCV3MG8kKF9fK0Vfoa0uzaeptAKA8M736uCQla9HDgSAVuRUMxAZpM72pSpdDLX0MRWI2RynmO0noYt4YW+ESj2v2N/SwvjyHhiFr1mRZZnpNKkpKrsw0qnSxYGmqp0ssrjnULL3RxeQkoYt5I1R7aFQPl0DnIL2hKBtKMmmM9eNVBrnuXJujnB2OjAycBQWjEvrR7MLUkAsgnRfFSUlCF/NGuPYw7hEzRJMJbFPuAA1OB+WuXJRSdoU365KliyWZJXicHo55vNBxlGV5LrLchvRGFyclCV3MC+NWuDT04FCwyt1pTSpKk2XnJmL6yokEAjiUI7G+aBziERw99awry5ZKF3FSktDFvBCqTU75H31DdGVRFu6+oLVSUfYyu8KbEy6fVYuu43GrdDGxvijth1lflsP+xh7icemNLiYmCV3MC6Ha8UsW15fnMNBxmG6nk7Il6dUHfSzT70dHIkRb21ies5zAUJvVSDdR6dIfjnGsY8DuMMU8JgldzAvJChejzKpi6ewP09A9xPqyHBo7rav38pz0vkIf2Ua3MreSmI4TyFiSqnQBmTEqJicJXcwLYytc9icm0qwvz6GhN7GwRZqtJTrWcEIPDDfpyi+H9lqqSrIwHEoqXcSkJKGLeSFce/iE8XOAdWU5NA4mFrZI0xr0pFG16LkVANRlWlfobsPJquIsqXQRk5KELmyXqnBZNVyyuLehh5IcN4UZJg3hXgwUhd5CG6OcfQ6PB2dhIZFgkBxXDvmefI65XNBdD5HB1KLRQkxEErqw3bgVLg091oSa/hYanFBiZuN0OO0Kcc64ErXoABU5FRwlDGjoOMr6shxaekO09obsDVLMW5LQhe2GVymyEvpQJEZta591I7D1II2GQbk3vWvQk0yfj3AgkdBzKzgWToyZJypdQHqji4lJQhe2Cx0+jCMjAyMxhnyouY9YXLO+LBcCL9NgOCnLT++SxSTT5yPS2IiOxVies5z2cDe9So1uASDDLmICktCF7UK1h3CtXJma1p+s5NhQnkPk+Mu0Og3KEzcJ053p80EkQrS1NdXT5VhuKbQfJjfDxJfnlSt0MSFJ6MJ2odraE8bPM11Oli3x0tT0Clqlb9vcsUy/1e99VJOu3FJot4alNpTnsLdBShfF+CShC1vFurqItbadULK4riwHR+cRDsStmZEr8lbYFeKcMn3DpYtLs5fiVE7qMrJSCX19eQ5H2/oZCEftDFPMU5LQha2GVymyShbjcc3+xl7rhmjgZfZ43LgcJuvy19kZ5pxJ1qKHAwFMp4kvy8cxwwEDbTDYxfqyHLSGA029Nkcq5iNJ6MJWYytc6jsH6AtFrRuAgZ3s9mawvmADLqfLzjDnjMPtxigqSpUuLs9ZTl18yNrZcZgNPqvSRSYYifFIQhe2CtXWjqpwSVZwrC/PIVz/MvtcBjXFNTZGOPesvujWknsVuRUcD3UQB2g/THmuh1yvKZUuYlyS0IWtQodrca1alapw2dvQg9OhWJ0H+7priQDVRdW2xjjXTL9/1OSiwViIFsOE9lqUUrJotJiQJHRhq1Bt7ZhVinpYWZSJp2UPe1wmsAgT+oha9NT6okvKR1W6HGjsIRqL2xilmI8koQvbjFvhkpzyn7gh6sssoyijyMYo557pK4dolGhz83DXxeyiUZUuoWico239doYp5iFJ6MI2qQqXxLJz7X0hmnqG2FCei67fyR5vBtXFZ9oZoi1G9kUvzigmw8igzpMB7YdBa2kBICYkCV3YJlXhkhhy2d9oleKtL8umsfEVWhwsuhuiAK7E5KJwMIhSyqp0ccQh3Ad9zawoysRlOKTSRZxAErqwzQkVLskp/9429mhrQtFiGz8HrFWblCISGL4xWhdNDK+012I6HawpkUWjxYkkoQvbjK1w2dfQQ1muh7y23ex2u/E63axestrmKOeew+XCKC4ernTJraAh1EEYrGEXrBuj+xp70FoWjRbDJKEL25zQw6VxxA1Rr5eNhWdgOAwbI7SPOaIv+vKc5Wg0xz0ZoypdOvrDBDoH7QxTzDOS0IUtUhUuifHzoUiMw639rC/PYTDwMgddBjWL8IZo0siEnlyO7lieL3WFvqWyAIAXj7TbEp+YnyShC1ukVilKVLi81dxLLK45o8jJ3q5aoizO8fMk0+8j0tSEjkZZnm2VLh7NWpK6Qq8qziI/08WLRzrsDFPMM5LQhS1CtcmmXFZCT1ZsbHIcYbfb6tuyqWiTPcHNAy6fD2IxIk3NZLmyKPIWUedyQccRiMdwOBTnVubz0lG5QhfDJKELW6QqXMrKAOuGaJbboLjrdfa4XVRkL2WJZ4nNUdpnZC06WOPox1QM4hHoOg7AeSsKCHQOUt8xYFucYn6RhC5sEaodU+HS2MO6smxU4OXEhKKzbI7QXmMTekVuBXXhLmtnYhz9vBXWOPpLR2XYRVgkoQtbhA4PV7hYPdB72FCWQ33TK3Q6oLp48Y6fA5ilpeBwjGrS1RXtp8vhGDWOviTDlBujIkUSuphzY3u4HOsYYCAcY0tuN7u1VYZXU1RjY4T2Uy4XRkkJkUAAYLhJV0ZuKqFb4+gFktBFiiR0MedSFS6JVYqSMx43cZA9bhdZhpeVeSsnfPxiYfrKxyldLIWOw6ljzluRL+PoIkUSuphzwwndukLf19iN4VCU9rzBbq+XTUU1OJS8NV0+H+EGK6GXZ5VjKGPUFTrAeStlHF0Mm9JPjVLqGqXUQaVUrVLq9nH2f1YptU8p9bpS6mml1PKZD1Wki1Dt4RMqXFYVZzHU8DKHTIPqRdiQazymz0e0qRkdiWA6TPzZfupMJ3TVQ8Ralm51cbaMo4uUkyZ0pZQTuBO4FlgPbFVKrR9z2GvAZq31JuAR4J9mOlCRPsarcKkpMXij+zAaGT9PMn1+iMeJNDUBiSZd8RCgofMoMDyOLvXoAqZ2hb4FqNVaH9Fah4EHgOtHHqC1/oPWOjmI9yLgn9kwRToZ2cOlrS9Ec0+IizMD7HabKBRnFJ1hc4Tzw3ili8fDncRg9LDLinzqOwYJdMo4+mI3lYTuA+pHfB1IbJvIR4EnphOUSF/Rzk5ibcMVLskbohvjB9njdrMyp4JsV7adIc4bpn9MQs+pIByP0GQ4xx9HlzYAi96M3nlSSn0I2Az88wT7P66U2qWU2tXa2jqTTy0WiPCYVYqSq+6U9rzO614v1SWLe0LRSGZJyaha9PGWowNrHD1PxtEFU0voQWDpiK/9iW2jKKWuBL4CXKe1Do13Iq31XVrrzVrrzUVFi2udSGFJVbisHC5Z9OV6CLS+Sq9anCsUTUSZJmZp6Qmli3U5xanZokCqr8uLMo6+6E0loe8EqpRSlUopF3Az8NjIA5RSZwI/wUrmLTMfpkgXodrDODIzhytcGnu4tKgvNaFoMXdYHI/p8xFOrFxU4Ckgy8xKrC9aO+q481YUyDi6OHlC11pHgU8BTwL7gYe01nuVUt9QSl2XOOyfgSzgYaXUbqXUYxOcTixyVoXLSpRSDIZjHGnt42LvUfZ43OSaWakZkcIysi+6UsqqdHEC/a0w1J06LtXXRcbRF7UpLQejtX4ceHzMtr8b8fmVMxyXSFOh2lqyLrkEgIPNvcQ1rI8f5EceL9XFZ6ZKGYXF9PmINjejw2GUy8Xy3OW82mu1A6D9MPisew5rSobH0W84W4rMFiuZjifmzEQVLhk9ezhqOhf1CkUTMX0+0HpULXpjuItBpWQcXZxAErqYMydWuHRT7I6yv9+aJCPj5ydKlS4mm3QlboweN8wJx9GDXbLO6GIlCV3MmRN6uDT08I6CRva4TJw42Fi40c7w5iVXYnJReEQtOkBdXukJCf3cyuQ4ulylL1aS0MWcCR2qtSpcSkuJxTUHmnq5yGPdEF2dt4oMM8PuEOcdo6QEnM7UjdFl2csAOJZdcEJCX1uaTa5X6tEXM0noYs6EDh9OVbjUtfczEI6xOnaANzwemVA0AWUYiVr0BgAyzAxKMkqoc7mtMXStU8emxtGl0mXRkoQu5szIHi7WDVFNb/9eBmRC0aRMvz81hg6J5ehUDMK90Hpw1LHnrSjgeMeAjKMvUpLQxZxIVbisHJ7yv9LZyptKJhSdzMhadEh0XYz1o5UTXn9g1LHD9egy7LIYSUIXc6L/uecAyDjLKk3c19DD1bn17Pa4KXTl4cuarN/b4mb6yom2tBAPhwErofdG+uhYeRnseRDisdSxMo6+uElCF3Oid9vTGEVFeDZtAqwr9Avdh9mTGD+XCUUTS7bRjTZY4+ip5ehWXQK9DXB0e+pYGUdf3CShi1kXHxqi77nnyLryCpTDQUvvEK29IYpj+6k3ZELRyaRKFwNjui7mlYEnF3b/z6jjk+PoDTKOvuhIQhezrv9Pf0IPDpJ9pdUhYn9jL16GqI9aN/qqi2X8fDKm35rKnxxHL88sx3SY1PUHYeMNsP83MNSTOv7cFfkAsorRIiQJXcy63m1P48jOJvOccwBr/HyTOsput4mhnKwvGLuioRjJKC4Gw0gldKfDybLsZdR110HNLRAdhH2/Sh2/rjTHGkc/LMMui40kdDGrdDRK3zPPkHXZZSiXC7DGzy/LPMoej4v1S9bgdrptjnJ+U04nZlnZ6EqX3ArqeurAdzYUVI0adnE4FFukr8uiJAldzKqBV14l1tWVGm4B2NfQzTmuw+x1e6guPdvG6BaOsaWLy3OWU99bT1THoGYrHH8BOo6k9p+3ooBj7TKOvthIQhezqvfpbSiXi6yLLgRgIBzlSFsf8BYhJfXnU2X6fYSDw5OLNhVtIhqP8sfgH2HTzYCCPcM16efJOPqiJAldzBqtNb3btpF54YU4MjMBONDUi58WDhhWTbUk9Klx+XzEWtuIDw0BcIn/Eoozirn/wP2Q64MVl8Hu+yEeB2QcfbGShC5mzdC+fUQbGscMt/RwljrEHreLUk8BpZmlNka4cCRr0SMNjdbXDpMbV9/Inxr+xNHuo1DzQeg+DseeB2QcfbGShC5mTd/TT4PDQdbll6W27Wvs4TzXYXZ7PNSUbLYttoUmldBHjKPfsPoGDIfBgwcfhLXvAlc27Lk/tT85jt7YLePoi4UkdDFren+/jYyzz8bIz09t29fQw0pPLU2Gk2ppyDVlwwl9eBy90FvI1RVX82jto/QrDRveA3t/BaE+AM6tTIyjy6zRRUMSupgV4WPHCB06RPZVw8MtsbjmWFMLHQ5rOTXpsDh1RnExmOaoK3SArWu30hfp4zeHf2MNu0T6Yf+vAVhXlkOOx5C+LouIJHQxK3q3PQ1A1tuuSG072tbP6uhhXveYuJXBmiVr7ApvwVEOB2Z52QkJfVPhJtYXrOf+A/ejl54HSypgj1WT7nQotlQWSEJfRCShi1nRu20b7vXrcPmHuyjua+zhTMch9rjdbChYj+k0bYxw4XH5fKml6JKUUmxdu5XD3YfZ2bwLqj8IR5+DruOAVb5YJ+Poi4YkdDHjoq2tDO7ePaq6BRJT/o1D7HO7ZULRaTB9PiKB4Anbr6m4hjx3nlXCWH0zoK22uozsjy7j6IuBJHQx43qf+QNoTfYVYxN6N1neOqIKaopq7AluATN9fmLt7cQHR19tewwP76t6H8/UP0Oj6YLlF1nVLlrLOPoiIwldzLjebdswly3DvboqtU1rTXfDIY64ZELR6RquRW84Yd+Na24E4OG3HrZaAXQchvqXZRx9kZGELmZUrLeX/hdfJPvKK0ctWlHXPkDF4F52e9ws9ZZQ4C2wMcqFabxa9CRflo9L/ZfyyFuPEFpzDZgZqZujMo6+eEhCFzOqb/sOiETIvvKKUdu/+/u32GzUWisUlcqEotMxWUIHq4SxM9TJU40vwLrr4M1fQmRQxtEXEUnoYkb1Pr0NZ2Eh3pqa1LbXA108tqeBjbl1tDkdskLRaTKKClEuF+FAYNz955WdR0VOhXVztGYrhHrgwG9ZV5ZDtseQRl2LgCR0MWPioRD923eQ/ba3oRzWW0trzbcfP4A/I0YQqw+JrFB0eqxa9HIiwRPH0GG4hPGNtjd4I2sJ5Phhz/04ZZ3RRUMSupgx/S+8QHxgYNRwy7NvtfLCkXZ+WvYoe1wGGU4Pq/JW2Rjlwja2L/pY1628jgwjgwfeesgqYTz8DPQ0ct6KAo629dPUPTSH0Yq5JgldzJi+p5/GkZlJxnnnAdZU/3984gA35+5lTfAR9uT7OaO4GsNh2BzpwnWyhJ7lyuK6ldfxxNEn6Fj7DtBxeP3B4XF0GXZJa5LQxYzQsRi9Tz9D1qWX4kgsNfe/rwVpbQrwdX5Md8kG3or1SbniNJk+H7GODuL9/RMes3XtViLxCL/seA38W2DP/awrzSZb6tHTniR0MSMGX3uNWEdHarhlKBLjX588wA9z7sGI9vGV5VUoFFcuu/IkZxKTMf0T16Inrchbwbll5/LgwQeJVt8MrQdwNr0m4+iLgCR0MSN6tz2NMk0yL7kEgHv+VMcl/b/j3PBL/KTmHWxvfZUvbvki6wrW2RzpwuZKlC6O7eky1ta1W2nqb2J7XhE43bD7fs6tlHH0dCcJXUxbcqm5jAvOx5mVRWd/mMf+8Ee+7vov/lC5mR91vML1K6/n5jU32x3qgneyWvSkS/2XUpZZxv1HHoO174Q3H+H85VmAjKOnM0noYtpCBw8SCQTIvsIabvnhMwf4f/E7CHq9/I3Rx/qC9fzt+X87auaoOD3OwkKU2z1uk66RDIfBjWtu5KWmlzi8+goY7GRd3wtkuw0ZdkljktDFtPVuexqUIvttb6O+Y4DMl+9gjfMwn11eiel0873Lvofb6bY7zLSglErUok+e0AHeV/U+XA4X9w8chaxSnHvut9YZPdKO1noOohVzTRK6mLbebdvwnnUWRmEhDz36KH/p+AVfXnEGx0Kd/Mul/0JZVpndIaYV0++fUkLP9+RzTeU1PHbk1/RufC/U/p5rKp0cbevnp388OgeRirkmCV1MSzgQIHTgANlXXsneukbec/Tr/KSglD/Eu/jrs/+aLWVb7A4x7Zi+qV2hA3xw7QcZjA7yWH4xxKPcYL7AtRtL+ebj+/ndm02zHKmYa5LQxbT0btsGQPaVV9D48OdpyOzirlyTayuu5c/W/5nN0aUn0+cj1tVFrG/iWvSkDYUb2FS4iQeCfyBeXoNjz/1896Yaqv15/NWDr7G7vmv2AxZzRhK6mJbebdtwr1nDwdoXWRN6gi+UlFG1pIqvXfA1uQk6S1xTrHRJunntzdT11PHiqouh+Q087fu4+8ObKcp2c+u9O6nvGJjNcMUckoQuTlu0vZ3BV14l65LzyX/u8/xFiQ+nO4PvXfY9MswMu8NLW6bfD0w9oV9dcTX5nnzuj7WBw4Td91OY5eZnf34O4Wic/3vPTroHI7MZspgjktDFaev7g7XU3FD/s3y3wOCYS/GPl/wjS3OW2h1aWptqLXqSy+nihqob2N7wJ4JVb7MWvqjfyaribH78f87mWHs/n7zvFcLR+GyGLeaAJHRx2np/vw2jMJdnM9/gyawMPlXzKS7yXWR3WGnPmZ+P8nimnNDBWqLOoRw8WL4KzEz46VXwuy9zwVIv337fJv50uJ2/+d83pJxxgZOELk5LrK+f/j89T19JG/9asIQz8y/mY5s+ZndYi4JSKtF1cfyFLsZTmlnK25a9jV827mDo48/COR+FF38IPzyf9+cd4tNXVPHIKwHu/EPt7AUuZp0kdHFa+rf/AR2J8oNNLry6mB9d889yE3QOmX7fSfu5jLV17Va6Q9080fhHeOe/wp8/Dg4D/us9/HX/9/ngphz+5am3eHT3qZ1XzB+S0MVp6XrgTvq9mtd8Lr558ffINDPtDmlRcfl8E65cNJHNJZtZlbeKn+/7ObWdtVBxIXzyebjor1F77uebDR/lL8v284WHX+flo9IeYCGShC5OWfzoS3TuOcqLqx1szPw0V6w6w+6QFh3T5yfe3U39J26j+9FHifX1nfQxSik+Wf1J6rrreO9j72Xrb7bywOFH6b74r+Fjz6CyivlC5z9wl/cH3P7zbRxpPfk5xfyi7LoJsnnzZr1r1y5bnlucpoEO9P7f8JuH/olVj8b4p6s38c2v30N5ntfuyBadaGcn7T+5i57f/Y5oUxPK5SLz4ovJueYasi6/HGfWxH8xtQ+28/jRx/lV7a94q/MtTIfJ5Usv5/oV7+KCoztxbv9neuIu7nR9lNs+/RXys6QPz3yilHpFa7153H1TSehKqWuA7wNO4G6t9XfG7HcDPwfOBtqBm7TWdZOdUxL6/Ke1JtDyBvv23s+++j+yd7CJ/S6Tm34PF70JT/2//+b268+0O8xFTcfjDO7ZQ88TT9D7uyeJtrSg3G6yLrmEnHdca60glTHxnIADHQd4tPZRfnvkt3SGOin0FvLusgu58s0dbGrew2uus1n38f/EU1gxd9+UmNS0ErpSygm8BVwFBICdwFat9b4Rx/wFsElrfZtS6mbgvVrrmyY7ryT0+UVrTbAvyL72fexr2sXe+j9yuDOAoz9O3gDk92mqQl4qdDGVzzbwetEabvjVfeRmmHaHLhJ0PM7gq6/S88Tv6HnqSWKtbSiPh6zLLiPn2mvJuuRiHN7x/5qKxCLsCO7g0dpHeS7wHFEdZYWjgPe1HOXagRCFV30dx5aPgUNGae023YR+PvA1rfXVia+/DKC1/vaIY55MHPOCUsoAmoAiPcnJV5YX6O987OpT/mbmmgIcSqGUQilwKGss0qGUtc8BiuS+xL+Aw+FAAYys/FAqccbkmVXinxHbHCYY7sSHB9TJf4A0E/8fTrovHqOzpZ7upuOEW5vx9kbI7dfk9UNev8YVHb9qpdOdReNf3M4Nn7jhpLEJe+hYjIFdr9DzuyfoffIpYh0dqIwMsi+/nMyLLkK5Jv5F3Bfu47XW13il6RUa+htwalgfDrMeN4aZBU7Dqo5xmtb71Wkmto34OrXfAIcTtAbioLEWrkZb2yb8/MT3rQbi8ThxDTGtiWtNPG59HotrtE7sn52XdN648Rv3T5jQp7L8ug+oH/F1ADh3omO01lGlVDdQALSNPEgp9XHg4wAb3B42PvjalL4BMXtKgbiCsEejvXE8mSbZq5fhqjwDY+lqnIWFOAvy2d4a559eaqHLlclXr9/EBzb77Q5dTEI5nWSeu4XMc7dQ+pWvMLBzJz1P/I7ep56i57e/PenjVyY+hhlADOielXjFzJhKQp8xWuu7gLsA1lZV6M5//eJcPv3p0RCJx4nHNbE4xHScWPLzuCYWjxOLx4nGIRaHuI4TicXpGgzT2jNES0+IoUg0cT2uyXQ5Kcp2UZz4KMr0UJxtkuFyWlco0SEI90OoF0L9EOmDUB+ER/wb7rP261gqzMkqwNUEF+kKKHZGyPCvRZ3xXlh3HRSvHXVMe1+IL/3vGzy5t5ktK5by3zdWszRf+rQsJMowyDz/fDLPP5/Sv/0q4foATPKX21jRWJy/+dUeXg3WUZzt5oKV+VywspCinBE3S2NRiAxCLGy9h6NDEBmCWAhiEesvTeW0/hpVjsSftk7A+jwU07we7OXlui6aesO4DIOVJdl4DAem0/owHArTqTCcTkxDYToUrsTnhsOB6VS4nA6UI83nQ7zrIxPusm3IZbGMoWutae0N8VZzH28193KopTf1ee9QNHVcYZaLquJsLlldxA1n+yjO9pzsxBAZgMFO6yMathJ8PDbm3/gk2+PgOwsKq8Z9it/va+bLv3ydnsEon796NR+9aAXOdP9hEeMKRWP89vVGfvFqgD8dbkdr2FKRzw1n+7j2jDJyPKd3L2VvQzf3vXicR3cHGQjHOMOXy4fOW8a7q8vJcM3p9eaCMd0xdAPrpugVQBDrpugHtdZ7Rxzzl8AZI26Kvk9rfeNk510sCX0iWmuaeoZ4q7mPQ829vNXcy/7GXt4IdmM4FFesK+bmLcu4pKpozpNo71CEf/jNPh7aFWBdWQ7fvamataU5cxqDmL+CXYP86rUgv3glwJG2ftyGg6s3lHLD2X4uWlV40vfrUCTGb15v5L4Xj7G7vguP6eC66nJuOXc51Uvz5uabWMBmomzxHcD3sMoW/1Nr/U2l1DeAXVrrx5RSHuC/gDOBDuBmrfWRyc652BP6RA639vHgznp+8UqA9v4wvjwvH9js58bNS+ek3vulI+187uE9NHQNctulK/nMlVW4DeesP69YeLTW7K7v4pevBnlsTwPdgxGKs92890wfN5ztZ3VJ9qjjj7T28d8vHeeRVwJ0D0ZYWZTJLecu54az/FItdQqmndBngyT0yYWjcX6/r5kHdh7nuUNtOBRcurqIm7cs421rizGdM1s+NhSJ8a9PHeTuPx5lWX4G//qBajZX5M/oc4j0FYrGeGZ/C794NcizB1uIxjUbfTnccJafomw39798nOdr2zEciqs3lvKhc5dz3op86f9zGiShL3D1HQM8tKueh3bV09wToijbzfvP9nPzOUtZXjD9Hip7G7r57IN7ONjcywfPXcZX3rGOTLeMX4rT09YX4rHdDfzytQBvBnsA8OV5+eC5y/jAZv/J7w+JSUlCTxPRWJxnD7bywM7jPHOghbiGC1YWcPOWZawvy8ZtOHEZDtyGA5fhwOV0YExyJR+NxfnJjiN8b9tb5GW4+Kf3b+LyNcVz+B2JdHewqZf2/hDnVhbIDfUZIgk9DTV1D/Hwrnoe2FlPsGtwwuOcDquUa1SiNxy4DSf9oSjHOwZ45xll/L/3bGRJpmsOvwMhxOmQhJ7G4nHNy3UdtPSGCEVihGNxwtE4oaj1bzgaJxyLp/aFRuyLxTXX15RzXXW5jGUKsUBMltBloHSBczgU560osDsMIcQ8IJ12hBAiTUhCF0KINCEJXQgh0oQkdCGESBOS0IUQIk1IQhdCiDQhCV0IIdKEJHQhhEgTktCFECJNSEIXQog0IQldCCHShCR0IYRIE5LQhRAiTdjWPlcp1Qocm8OnLATa5vD5Fgp5XU4kr8n45HUZ31y/Lsu11kXj7bAtoc81pdSuiXoIL2byupxIXpPxyesyvvn0usiQixBCpAlJ6EIIkSYWU0K/y+4A5il5XU4kr8n45HUZ37x5XRbNGLoQQqS7xXSFLoQQaS3tErpSaqlS6g9KqX1Kqb1Kqc8ktucrpX6vlDqU+HeJ3bHOpUlel68ppYJKqd2Jj3fYHetcUkp5lFIvK6X2JF6Xrye2VyqlXlJK1SqlHlRKueyOdS5N8rrco5Q6OuL9UmNzqHNOKeVUSr2mlPpN4ut5815JuyEXpVQZUKa1flUplQ28ArwH+HOgQ2v9HaXU7cASrfWX7It0bk3yutwI9Gmt/8XO+OyilFJApta6TyllAn8EPgN8Fvil1voBpdSPgT1a6x/ZGetcmuR1uQ34jdb6EVsDtJFS6rPAZiBHa/0updRDzJP3StpdoWutG7XWryY+7wX2Az7geuDexGH3YiWzRWOS12VR05a+xJdm4kMDbwOSSWsxvl8mel0WNaWUH3gncHfia8U8eq+kXUIfSSlVAZwJvASUaK0bE7uagBK74rLbmNcF4FNKqdeVUv+52IaiIPUn9G6gBfg9cBjo0lpHE4cEWIS//Ma+Llrr5Pvlm4n3y3eVUm77IrTF94AvAvHE1wXMo/dK2iZ0pVQW8Avgr7TWPSP3aWucaVFebYzzuvwIWAnUAI3Av9oXnT201jGtdQ3gB7YAa+2NaH4Y+7oopTYCX8Z6fc4B8oHFNGz5LqBFa/2K3bFMJC0TemLM7xfAf2utf5nY3JwYR06OJ7fYFZ9dxntdtNbNiR/cOPAfWAltUdJadwF/AM4H8pRSRmKXHwjaFZfdRrwu1ySG7rTWOgT8jMX1frkQuE4pVQc8gDXU8n3m0Xsl7RJ6Ykzrp8B+rfW/jdj1GPDhxOcfBh6d69jsNNHrkvwll/Be4M25js1OSqkipVRe4nMvcBXW/YU/AO9PHLYY3y/jvS4HRlwUKayx4kXzftFaf1lr7ddaVwA3A89orW9hHr1X0rHK5SLgOeANhse5/gZrvPghYBlWl8cbtdYdtgRpg0lel61Ywy0aqAM+MeJeQ9pTSm3CupHlxLrAeUhr/Q2l1Aqsq7B84DXgQ4mr0kVhktflGaAIUMBu4LYRN08XDaXUZcDnE1Uu8+a9knYJXQghFqu0G3IRQojFShK6EEKkCUnoQgiRJiShCyFEmpCELoQQaUISuhBCpAlJ6EIIkSYkoQshRJr4/wFfK8PJz4ZvVgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "y = np.arange(psf_weightimage.shape[0])\n", + "ax.plot(y, psf_weightimage[:,150], label=\"pixel=150\")\n", + "ax.plot(y, psf_weightimage[:,225], label=\"pixel=225\")\n", + "ax.plot(y, psf_weightimage[:,300], label=\"pixel=300\")\n", + "ax.plot(y, psf_weightimage[:,370], label=\"pixel=370\")\n", + "ax.set_title(\"Cross-dispersion Cuts\")\n", + "ax.set_xlim(ext_center-psf_width, ext_center+psf_width)\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the spatial profile becomes narrower as the pixel values increases as this corresponds to the wavelength decreasing." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Extract spectrum using wavelength dependent PSF profiles using the same traces as defined above" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.nddata import StdDevUncertainty, VarianceUncertainty\n", + "from astropy.nddata import NDData\n", + "\n", + "#mask = err*0.#+1.\n", + "#err = err*0\n", + "errinput = StdDevUncertainty(err*0.+1.)\n", + "inarr = NDData(image_wg, uncertainty=errinput, unit=u.Jy)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-08-08 17:22:20,146 - stpipe - WARNING - /Users/ofox/miniconda3/envs/lrssn/lib/python3.9/site-packages/specreduce/extract.py:224: UserWarning: image NDData object's uncertainty interpreted as standard deviation. if incorrect, use VarianceUncertainty when assigning image object's uncertainty.\n", + " warnings.warn(\"image NDData object's uncertainty \"\n", + "\n" + ] + } + ], + "source": [ + "optimal = OptimalExtract()\n", + "#ext1d_psfweight = optimal(image_wg, auto_trace, variance = err, mask=mask, unit=u.Jy)\n", + "ext1d_psfweight = optimal(inarr, auto_trace)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "ext1d_psfweight = ext1d_psfweight.flux.value\n", + "# convert from MJy/sr to Jy\n", + "ext1d_psfweight *= 1e6 * s2d.meta.photometry.pixelarea_steradians" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-08-08 17:22:20,419 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_97625/3310229130.py:7: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"k-\" (-> color='k'). The keyword argument will take precedence.\n", + " ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], 'k-', label=\"boxcar\", color='red')\n", + "\n", + "2022-08-08 17:22:20,422 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_97625/3310229130.py:8: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"k-\" (-> color='k'). The keyword argument will take precedence.\n", + " ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], 'k-', label=\"boxcar (bkgsub)\", color='blue')\n", + "\n", + "2022-08-08 17:22:20,425 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_97625/3310229130.py:9: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"k-\" (-> color='k'). The keyword argument will take precedence.\n", + " ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, 'k-', label=\"jpipe_x1d\", color='green')\n", + "\n", + "2022-08-08 17:22:20,428 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_97625/3310229130.py:10: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"k-\" (-> color='k'). The keyword argument will take precedence.\n", + " ax.plot(waves_boxcar_bkgsub[gpts], ext1d_psfweight[gpts], 'k-', label=\"psf weighted (bkgsub)\", color='orange')\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "gpts = ext1d_psfweight > 0.\n", + "gpts = ext1d_boxcar > 0.\n", + "\n", + "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], 'k-', label=\"boxcar\", color='red')\n", + "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], 'k-', label=\"boxcar (bkgsub)\", color='blue')\n", + "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, 'k-', label=\"jpipe_x1d\", color='green')\n", + "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_psfweight[gpts], 'k-', label=\"psf weighted (bkgsub)\", color='orange')\n", + "ax.set_title(\"PSF weigthed extracted spectrum\")\n", + "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", + "ax.set_ylabel(\"Flux Density [Jy]\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_ylim(1e-3, 1e-1)\n", + "ax.set_xlim(4, 13)\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the psf weighted extraction has visabily higher S/N, especially at the longer wavelengths where the S/N is lowest overall." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1159736578fe4dab9d3a8567504bae74", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Application(config='specviz', events=['call_viewer_method', 'close_snackbar_message', 'data_item_remove', 'dat…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot in Specviz\n", + "\n", + "specviz2 = Specviz()\n", + "specviz2.app" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "ext1d_boxcar_spec1d = Spectrum1D(spectral_axis=waves_boxcar[gpts]*u.micron, flux=ext1d_boxcar[gpts]*u.Jy)\n", + "ext1d_boxcar_bkgsub_spec1d = Spectrum1D(spectral_axis=waves_boxcar_bkgsub[gpts]*u.micron, flux=ext1d_boxcar_bkgsub[gpts]*u.Jy)\n", + "ext1d_psfweight_spec1d = Spectrum1D(spectral_axis=waves_boxcar_bkgsub[gpts]*u.micron, flux=ext1d_psfweight[gpts]*u.Jy)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "specviz2.load_spectrum(ext1d_boxcar_spec1d, data_label='boxcar')\n", + "specviz2.load_spectrum(ext1d_boxcar_bkgsub_spec1d, data_label='boxcar bkgsub')\n", + "specviz2.load_spectrum(ext1d_psfweight_spec1d, data_label='psfweight')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plotting in Rayleigh-Jeans units\n", + "\n", + "For sources that have stellar continuum, it can be useful to plot MIR spectra in Rayleigh-Jeans units. This just means removing the spectral shape expected for a blackbody with a peak at much shorter wavelengths than the MIR. This is easily done by multiplying the spectrum by lambda^4 or nu^2.\n", + "\n", + "An example of this is given below." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Rayleigh-Jeans plot\n", + "fig, ax = plt.subplots(figsize=(15, 6))\n", + "gpts = ext1d_psfweight > 0.\n", + "ax.plot(waves_boxcar_bkgsub[gpts], (waves_boxcar_bkgsub[gpts]**4)*ext1d_psfweight[gpts], 'k-', label=\"psf weighted (bkgsub)\")\n", + "gpts = ext1d_boxcar_bkgsub > 0.\n", + "ax.plot(waves_boxcar_bkgsub[gpts], (waves_boxcar_bkgsub[gpts]**4)*ext1d_boxcar_bkgsub[gpts], 'k--', label=\"fixed boxcar (bkgsub)\")\n", + "ax.set_title(\"Rayleigh-Jeans plot for all extractions\")\n", + "ax.set_xlabel(\"wavelength [$\\mu$m]\")\n", + "ax.set_ylabel(\"Rayleigh-Jeans Flux Density [$\\mu$m$^4$ Jy]\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_ylim(40, 70)\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Additional Resources\n", + "\n", + "- [MIRI LRS](https://jwst-docs.stsci.edu/mid-infrared-instrument/miri-observing-modes/miri-low-resolution-spectroscopy)\n", + "- [MIRISim](http://www.stsci.edu/jwst/science-planning/proposal-planning-toolbox/mirisim)\n", + "- [JWST pipeline](https://jwst-docs.stsci.edu/jwst-data-reduction-pipeline)\n", + "- PSF weighted extraction [Horne 1986, PASP, 98, 609](https://ui.adsabs.harvard.edu/abs/1986PASP...98..609H/abstract)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## About this notebook\n", + "\n", + "**Author:** Karl Gordon, JWST\n", + "**Updated On:** 2020-07-07" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Top of Page](#top)\n", + "\"Space " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/MIRI_LRS_spectral_extraction/requirements.txt b/notebooks/MIRI_LRS_spectral_extraction/requirements.txt index 32260cfff..7852dc189 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/requirements.txt +++ b/notebooks/MIRI_LRS_spectral_extraction/requirements.txt @@ -1,3 +1,4 @@ -matplotlib==3.2.1 -astropy==4.0.1 -git+https://github.com/spacetelescope/jwst@0.16.1 +jdaviz >= 2.5.0 +astropy >= 5.0.4 +jwst >= 1.5.2 +specreduce >= 1.0.0 From b2b331831b2934a9f7ea50da2b2c88012186343c Mon Sep 17 00:00:00 2001 From: ojustino Date: Wed, 10 Aug 2022 21:06:59 -0400 Subject: [PATCH 02/36] Initiated a technical review --- .../miri_lrs_specreduce.ipynb | 672 +++++------------- 1 file changed, 176 insertions(+), 496 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_specreduce.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_specreduce.ipynb index 689184a6f..f86dbcfc9 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_specreduce.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_specreduce.ipynb @@ -57,19 +57,70 @@ "- *jwst datamodels* for reading/access the jwst data" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# disable all imported packages' loggers\n", + "import logging\n", + "logging.root.manager.loggerDict = {}" + ] + }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enable PEP8 checker for this notebook\n", + "%load_ext pycodestyle_magic\n", + "%flake8_on --ignore E261,E501,W291,W293\n", + "\n", + "# only allow the checker to throw warnings when there's a violation\n", + "logging.getLogger('flake8').setLevel('ERROR')\n", + "logging.getLogger('stpipe').setLevel('ERROR')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-08-10 20:48:57 - INFO - 2:1: F401 'matplotlib as mpl' imported but unused\n", + "2022-08-10 20:48:57 - INFO - 9:1: F401 'astropy.io.fits' imported but unused\n", + "2022-08-10 20:48:57 - INFO - 10:1: F401 'astropy.table.Table' imported but unused\n", + "2022-08-10 20:48:57 - INFO - 15:1: F401 'specreduce.extract.HorneExtract' imported but unused\n" + ] + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", - "%matplotlib inline\n", + "# %matplotlib inline\n", "\n", "import numpy as np\n", "\n", @@ -104,9 +155,17 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-08-10 20:48:57 - INFO - 9:26: E203 whitespace before ':'\n" + ] + } + ], "source": [ "# useful functions\n", "def get_boxcar_weights(center, hwidth, npix):\n", @@ -207,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -225,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -282,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -300,24 +359,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cal image\n", - "(1024, 1032)\n", - "9.826068\n", - "-1199.9769 64995.82\n", - "s2d image\n", - "(387, 44)\n", - "603.8127\n", - "-942.0139 63358.58\n" - ] - } - ], + "outputs": [], "source": [ "print(\"cal image\")\n", "print(cal.data.shape)\n", @@ -338,32 +382,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'The full image from the MIRI IMAGER detector')" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "norm_data = simple_norm(cal.data, 'sqrt')\n", "plt.figure(figsize=(6, 6))\n", @@ -380,32 +401,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'The LRS region')" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# transpose to make it display better\n", "image = np.transpose(s2d.data)\n", @@ -425,26 +423,9 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6e3919a9df66421fba7521510904e1a3", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Application(config='imviz', events=['call_viewer_method', 'close_snackbar_message', 'data_item_remove', 'data_…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "imviz = Imviz()\n", "imviz.app" @@ -452,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -461,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -478,19 +459,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-08-08 17:22:12,817 - stpipe - WARNING - /Users/ofox/miniconda3/envs/lrssn/lib/python3.9/site-packages/specutils/io/default_loaders/jwst_reader.py:338: UserWarning: SRCTYPE is missing or UNKNOWN in JWST x1d loader. Defaulting to srctype=\"POINT\".\n", - " warnings.warn('SRCTYPE is missing or UNKNOWN in JWST x1d loader. '\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Create a spectrum1d\n", "jpipe_x1d = Spectrum1D.read(x1dfile)" @@ -498,26 +469,11 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b74568107bad4d348815ad5a79f9608e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Application(config='specviz', events=['call_viewer_method', 'close_snackbar_message', 'data_item_remove', 'dat…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "specviz = Specviz()\n", "specviz.app" @@ -525,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -575,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -596,30 +552,15 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Cross-dispersion Cut at Pixel=300')" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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EUWFRuJtVJw9+9qDucdfMS+WcJKfajIjNBzCyyrKZAD4zxvQC8Jn9PQBcAaCX/ZoG4FXA6rgBeBTAOQDOBvCor/NmrzPVb7uq+yIiCp/YWCAmRi9+TIxq/NioWMQ0UWy/gpRNKejbvy8O7m+KCpOIiooK7Ny+M9zNqpOYJjG6x10zL5VzkpxqVSMmIt0AfGiM6W9/nwpguDHGIyIJAD43xiSJyN/s9wv91/O9jDG32Mv/BuBz+7XWGNPHXn69/3puWCNGRPWGNWL1pqKiAv279cfoq0fjx7Q3UFxcgs0bW2H2nNmYeNPEcDevTlgjRv5CXSPWyRjjsd9nA+hkv+8MYK/fevvsZW7L91WzvFoiMk1ENorIxoMHOa8MEdGJ5qcff8KRw0dw5qAzAQDRMdFo0bJFo5zCgqg2gi7WN9aQWr3cemmMmWuMGWyMGdyhQ4f62CURneymTQMeflgv/h//qBp/2sppeHitYvtDzFeoP3DQQACAAI2yYP+Pq/+oe9w181I5J8kp0GdN7heRBL9Lkwfs5ZkAuvqt18Velgnr8qT/8s/t5V2qWZ+IqGFo1w7QvIeoTRugtFQtfLvYdhA0nnugUjalIK5ZHHol9cKgs63jUljQD4sWLEJFRQUiIhrHzf5tYtqg1Kt3XlXzUjknySnQGrFnAOQaY54WkZkA2hpj/igiVwK4E8AoWIX5LxhjzraL9TcB8N1F+T2AQcaYPBH5DsDdANYDWAXgRWPMquO1iTViRFRvWCNWb666+CrExMRgyaollcsWvbMIM+6YgX9//29079k9jK2rG9aIkb+Aa8REZCGAdQCSRGSfiPwOwNMALhWRdAAj7O8BqyO1C0AGgNcB3A4Axpg8AH8CsMF+PW4vg73OG/Y2PwL4ONAfkoiIGq/S0lJs27Kt8rKkT/KAZABodJcniWrjuJcmjTHX1/DRJdWsawDcUUOceQDmVbN8I4D+x2sHEVFY3HQTcPQo8OKLOvF//3ugvBz44AOV8DctvwlHi4/ixVFK7Q+hHVt3oLS0tLIjNnWiNcvRi2/0QpMmTbDtP9sw+tejw9jC2vv9P3+Pcm85Phivc15V81I5J8kp0BoxIqKTQ9euQG6uXvzERNV6nK4tuyK3iWL7Q2jzRutB3747Jg/lWRdtYmJi0CupF7ZtaTwjYonNE3VrxDTzUjknyYkdMSIiN48/btXiaLnvPqseR8njFz2OtBzF9ofQ5k2b0bFTRyR2SfzFZ/0G9MPXX3wdhlYF5r7z70N+id55Vc1L5Zwkp8Zx+wkREZ3wUjalYOCggajuSXfJpycj25ON3JzGMbpHVFvsiBERuZk4Ebj3Xr34d92lGn/i0om4d7Vi+0Pk6JGjyEjL+EWhvk+//v0ANJ6C/btW3aV73DXzUjknyYmXJomI3CQlATk5evG7d1etx0lql4ScQsX2h8gPm38A8N/6MAA4/8KSyve+Oye3/2c7hl00rH4bF4DubbujtFyxzkozL5VzkpxqNY9YQ8R5xIio3nAeMXUvPvsinn7saWzbsw2t27Sudp3BfQdj6PlD8eIbDf8OUIDziJFTqJ81SUREFDIpm1LQvWf3GjthQON81BHR8fDSJBGRmwkTgGPHgNdf14l/223WnE0ffaQSfsKSCThWcgyvj1Fqf4ikbErBecPOcyybeHVbAMCCpdb838kDkrF29VoUFRUhNja23ttYF7d9dBvKveX46Aad86qal8o5SU7siBERuRk4ULdGLDlZtR5nYPxA5BQ07BoxT5YH2Z5sR30YABQXO++eTD49GV6vF2k70nDGWWfUZxPrLLlDsm6NmGZeKuckObEjRkTkZuZM3XnE7rxTdc6mmRfMbPDziKVsSgGAGu+Y9PHdObl96/YG3xG78+w7decR08xL5ZwkJ9aIERFRWG3euBlRUVFIPj3Zdb1TTzsVzZo3a1Qz7BMdD0fEiIjcXHONNTrw1ls68adOtepx/vlPlfDXvHcN8kvy8davlNofAps3bUbygGRER0e7rhcREYF+/fs1ioL9qSumoryiHP+cpHNeVfNSOSfJiR0xIiI3Q4cCBw/qxR80CCgpOf56ARraZSgOFii2P0herxdbNm/BuAnjfvHZiJHFv1iWPCAZSxYtQUVFBSIiGu5FnUGJg1BSrndeVfNSOSfJifOIEREdD+cRU5O2Mw0XnX0Rnn/teVz7m2uPu/7f3/477rvrPnyd8jW6de+m38AgcB4x8sd5xIiIqMHZvGkzAODMwWceZ02Lb4b9xnB5kqg22BEjInIzZgxw66168SdPVo0/ZuEY3PqRYvuDlLIxBS1atkD3nt1/8dm4Ue0wblQ7x7LefXsjMjKywRfsT/7HZN3jrpmXyjlJTqwRIyJyc8klwIEDevEvuEC1HueS0y7BgQLF9gcp5fsUnHHWGbWu94qNjUWPXj0a/IjYBadcoFsjppmXyjlJTuyIERG5mT5ddx6xm29WnbNp+rnTG+w8YsXFxdj+n+24dXrdRl+SByRj/br1Sq0KjZvPull3HjHNvFTOSXLipUkiIgqLbVu2oby8/Bcz6h9P8unJyNqXhbzcPKWWEdUfdsSIiNxccYU1QqBl4kTV+Fe8ewVuXqnY/iD4ZtSvbaG+j69gf/vW7aFuUshMXDpR97hr5qVyTpITL00SEbkZPRrYv18v/ogRqvU4o3uPxv58xfYHYfPGzUjonIBO8Z2q/fyqXxdVu7zfAOtRR9v+sw0XXHiBWvuCMaL7CN0aMc28VM5JcuI8YkREx8N5xFScP/B89OvfD68veL3O2w5KGoTzLzwfL8x9QaFlocF5xMgf5xEjIqIG41DeIezetdv1Qd9FhYKiQqn2s379+2H7fxrupUmi2uKlSSIiNyNGAIWFwJIlOvHHjwe8XuCbb1TCj/i/ESgsK8SS65TaH6Afvv8BAFw7YpPGtQUALFmV+4vP+p3eD1+u/RIlJSXHfUZlOIxfMh7eCi+++Z3OeVXNS+WcJCd2xIiI3Iwfr1sjNmaMaj3O+OTxDbJGbPOmzRARnD7w9IC2Tx6QjPLycqTvTEf/M/qHuHXBG9N7jG6NmGZeKuckObEjRkTkZupU3XnEbrhBdc6mqYOmNsh5xFI2paBXUi+0aNkioO39H3XUEDtiN5x+g+48Ypp5qZyT5MQaMSIiqlfGGKRsSnG9LHk83bp3Q1yzuAb/qCOi4+GIGBGRm+HDgaIiYNkynfjjxln1OOt1ZoofPn84isqKsGyCUvsDkLk3EzkHc4LqiEVGRqJvct8G+6ijce+Ng7fCi/VTlZ4AoJmXyjlJTuyIERG5mTwZyM7Wi3/ttar1OJMHTkb2McX2B2Dzps0AgLMGn+W63rU3FLp+njwgGcveXwZjDESqv7syXK5NvhYlZYp1Vpp5qZyT5MSOGBGRm8mTdWvExo9XrceZPHByg6sRS9mUgujoaPRJ7uO63vgbqp/Q1affgH74vzf/D3v37MUp3U4JZRODNj55vG6NmGZeKuckObFGjIjITVmZ9Wqk8cu8ZSjzKrY/AJs3bkb/M/ojKirKdb283Ajk5db8Z8q/YL+hUT/umnmjnfPkwI4YEZGbSy8FpkzRi3/99arxL33nUkxZodj+OiovL8eWlC21qg+bNqkNpk1qU+PnfZP7IiIiokFO7Hr9B9frHnfNvFTOSXLipUkiIjc33wx4PHrxr78eKC5WC3/zWTfDc0yx/XWUtjMNRYVFOHNQ3R70XZ3YuFh079m9QY6IXd//ehSX651X1bxUzklyYkeMiMjNxIm6NWLXXKNajzPx9IkNqkYsZVMKAPcZ9esieUAyNm3YFJJYoXRNv2t0a8Q081I5J8mJlyaJiNwUFlrTBGgpKlKNX1hWiKIyxfbXUcqmFLRu3RrduncLSbzk05Ox7+d9OHzocEjihUpRWZHucdfMS+WcJCeOiBERuRk1SncesUmTVOdsGvXuqAY1j9jmTZsxcNDAkE034SvY3751O877n/NCEjMUJi2bpDuPmGZeKuckObEjRkTk5rbbdGvEJk1SnbPptsG3NZgascKCQqRuT8VlV1xWq/Un/a7guOv0G9APALBty7aG1RE7Y5LuPGKaeamck+TEjhgRkZvx43VrxMaOVa3HGd9/fIOpEdu6ZSu8Xi/OHFy7Qv2x1xy/YLxjp47o0LFDgyvYH5s0VrdGTDMvlXOSnFgjRkTk5sgR4NgxvfhHj6rGP1J8BMdKFNtfB74Z9WtbqJ+5LwKZ+47/Zyp5QHKD64gdLTmqe9w181I5J8mJI2JERG7GjtWtEZsyRbUeZ+yisQ2mRuyHTT+gyyld0L5D+1qtP32aNYfYklW5rusln56Mr1/6GqWlpWjatGnQ7QyFKcun6NaIaealck6SEztiRERu7r4byMrSiz9liuqcTXefczeyjiq2vw5+3vMzuvfoHvK4yQOSUVZWhvTU9Mri/XCbcuYUFJcpzsWlmZfKOUlO7IgREbm5+mrdGrFRo1Trca7ue3WDqRHL9mSjV1KvkMdNPt1+1NGWbQ2mIzaq1yjdGjHNvFTOSXIKqkZMRH4vIttEZKuILBSRGBE5TUTWi0iGiCwWkab2utH29xn259384jxgL08VkcuD/JmIiEInJwfIy9OLn5enGj+nMAd5RYrtryWv14sD2QcQnxAf8tin9TgNMbExDapOLK8oT/e4a+alck6SU8AjYiLSGcDdAPoZY4pE5D0AEwCMAvCcMWaRiLwG4HcAXrW/HjLG9BSRCQBmAxgvIv3s7ZIBJAJYIyK9jTHeoH4yIqJQGDdOt0Zs2jTVepxx741rEDViBw8chNfrRXxi6DtikZGR6Nuvb4PqiE1bOU23RkwzL5VzkpyCvTTZBECsiJQBiAPgAXAxgN/Yn78NYBasjthY+z0ALAHwklgz+o0FsMgYUwLgJxHJAHA2gHVBto2IKHgzZgCZmXrxp01TrceZMXQGMo8qtr+WsrOyAQAJnRNqvc20u2p/eazf6f3w4bIPYYwJ2WSxwZg2aJpujZhmXirnJDkF3BEzxmSKyF8B/AygCMCnADYBOGyMKbdX2wegs/2+M4C99rblInIEQDt7+bd+of23ISIKr9GjdWvELrtMtR5ndNLoBlEjlu2xO2IJte+IXXZF7ScVTR6QjHffeheZezPR5ZQudW5fqF3W4zLdGjHNvFTOSXIKuEZMRNrAGs06DdYlxWYARoaoXTXtc5qIbBSRjQcPHtTcFRGRJTsb0Px9c+CAavzs/GwcLAj/70vfiFhdLk1mpEciIz2yVuv6ivQbyuXJAwUHdI+7Zl4q5yQ5BXNpcgSAn4wxBwFARJYCOB9AaxFpYo+KdQHgGzvNBNAVwD4RaQKgFYBcv+U+/ts4GGPmApgLAIMHDzZBtJ2IqHYmTNCtEbv9dtV6nAlLJjSIGjGPx4OoqCi0a9+u1tvMnN4awPHnEQOAnr17AgB+2vVTQO0Ltds/ul23RkwzL5VzkpyC6Yj9DOBcEYmDdWnyEgAbAawFMA7AIgA3Alhur7/C/n6d/fm/jDFGRFYA+LuI/C+skbVeAL4Lol1ERKEzcyawb59e/DvusP6gKpl5wUzsO6LY/lrKzspGx/iOiIjQeaBLq9atEBMbUznyFm53DLkDRWV651U1L5VzkpyCqRFbLyJLAHwPoBzAZlijVR8BWCQiT9jL3rQ3eRPAO3Yxfh6sOyVhjNlm33G53Y5zB++YJKIGY+RI3Rqxiy5SrccZ2XNkg6gR82R5VKau8BERxCfEV9aihdtFp12kWyOmmZfKOUlOQf3XxBjzqDGmjzGmvzFmkjGmxBizyxhztjGmpzHmWvtuSBhjiu3ve9qf7/KL82djTA9jTJIx5uNgfygiopDZuxfwePTiZ2aqxt97ZC88xxTbX0vZWdl1umMyEAmdExrMiFjmsUzd466Zl8o5SU6cWZ+IyM2kSbo1YtOnq9bjTFo2qUHUiGV7snHRpRep7iMhIQEb1m9Q3UdtTf94um6NmGZeKuckObEjRkTk5qGHdGvE7r5bdc6mh4Y9FPYasWNHj6EgvwAJiXUbEbv7vmN1Wj8+MR7ZWdmoqKhQq0WrrbvPuVt3HjHNvFTOSXJiR4yIyM2IEbo1YsOGqdbjjOg+Iuw1Yr66rbrWiA27qLRO68cnxKOsrAx5uXlo36F9nbYNtWGnDtOtEdPMS+WcJKfw/peBiKih27XLqsfRsmePavxdh3Zh7xHF9teCJ9OqN6rr4422bmmCrVtqP17gi98Q6sT2HN6je9w181I5J8mJI2JERG6mTNGtEZsxQ7UeZ8ryKWGvEfPYhd91HRGbNbMVgNrNI+Yf35PlQf8z+tdpX6E249MZVo1YD6U6K828VM5JcmJHjIjIzWOP6Y4OzJihOmfTY8MfC/uIWCCz6geickSsAUxhMWPoDN15xDTzUjknyYkdMSIiNxdeqFsjNnSoaj3Ohd0uDH+NWFY22rRtg5iYGNX9dOzUESLSIC5NDu06VLdGTDMvlXOSnFgjRkTkJjXVqsfRkpGhGj81JxW7Dim2vxayPdnqo2EAEBUVhQ4dOzSIEbGMvAzd466Zl8o5SU4cESMicnPLLbo1YjNnWvU4I0eqhL/lw1vCXiOW7clGQoLuZK4+vikswm3mmpnwVngxspfOeVXNS+WcJCd2xIiI3Dz5JPDzz3rx779ftR7nyUuexM+HFdtfC55MD/qfXvfi+fsfPVrnbeIT4vHz7vD+vABw/wX3o6hUsc5KMy+Vc5Kc2BEjInJz3nlAe8U5qYYMUa3HOa/reWgfG745tcrKypBzMKfOk7kCwJBzyuq8TUJiAr775rs6bxdqQxKH6NaIaealck6SE2vEiIjcbN2qW6y/c6dq/K0HtiItN3zF+geyD8AYE1CN2Ib1UdiwPqpO28QnxuPw4cMoKgzviM7OnJ26x10zL5Vzkpw4IkZE5ObOO3VrxB56yKrHueoqlfB3rrozrDVinqzA5hADgNmPtQRQ+3nE/PfjyfKge8/udd5nqDz0r4fgrfDiqiSd86qal8o5SU7siBERuXnmGWumcS0PPQQUFqqFf+bSZ7DnsGL7j6Py8Ub1cNek/36yPdnh7YgNewiFpXrnVTUvlXOSnNgRIyJyM2QI0KqVXvyBA1XrcYZ0HoJW0YrtP47KxxsFMCIWCF8tWrjvnBwYP1C3RkwzL5VzkpxYI0ZE5CYlBdixQy/+1q2q8VOyU7DjoGL7jyPbk43o6Gi0adumXvbn6/CFey6xrQe26h53zbxUzkly4ogYEZGbe+7RrRGbNcuqxxk7ViX8PZ/cE9YaseysbCQkJkBE6mV/LVq2QLPmzcI+Ijbr81nwVngxtq/OeVXNS+WcJCd2xIiI3Dz/vG6N2KxZqnM2PT/yeew5FN4asUDrw2Y9fSSg7eIT4isfNB4us4bP0p1HTDMvlXOSnNgRIyJyM3AgEBenF79/f9V6nIHxAxHXRLH9x5GdlY2BgwYGtG3/08sD2q4hzK7fv2N/3RoxzbxUzklyYo0YEZGbDRuALVv04qekqMbfkLkBW/Yrtt+FMQaeLE/AI2Jfrm2KL9c2rfN28QnxYa8RS8lO0T3umnmpnJPkxBExIiI3992nWyP2xBNWPc64cSrh71t9n1Ujdkr914gdyjuEkpKSgO+YfOGZFgCAYRfVfh4xAEjonIAD2QdQUVGBiIjwjDc88eUT8FZ4MS5Z57yq5qVyTpITO2JERG5eegnYvVsv/hNPqM7Z9NKol7D70G61+G7qew4xn4SEBJSXlyPnYA46dupYr/v2eeLiJ3TnEdPMS+WcJCd2xIiI3PTvDzSt++WxWuvTR7Uep3/H/mgaodh+F746rUCeMxmMyklds7LD1hHr076Pbo2YZl4q5yQ5sUaMiMjNN98A33+vF3/DBtX43+z9Bt97FNvvwjciVu8dMb/HHIXLhqwNusddMy+Vc5KcOCJGROTmwQetWpxhw3Tiz55t1eNMmKAS/sHPHkRRWRGGdVNqv4vsrGyICDrG1++olG9ELJwdsdlfzYa3wosJA3TOq2peKuckObEjRkTk5m9/A376SS/+00+r1uP87aq/4adDiu134cnyoH2H9oiKigpo+6fnHA5ouw4dOyAyMjKsd04+PeJp3RoxzbxUzklyYkeMiMhNUhKgOSt8z56q9ThJ7ZMgqJ9Z7avKzgp8MlcA6NnLG9B2kZGR6NCpQ1jnEuvZtqdujZhmXirnJDmxRoyIyM0XXwDffacXf9061fhf7P4C32Uqtt+Fx+MJ6mHfn34cjU8/jg5o24SEhLCOiK3bu073uGvmpXJOkhNHxIiI3Dz6qFWLc/HFOvGffdaqx5k4USX8o58/iqKyIlzcXan9LrKzsjHknCEBbz/3xeYAgMuuKKnztvGJ8chIywh438F6dt2z8FZ4MfEMnfOqmpfKOUlO7IgREbmZN0+3RuzZZ1XrceaNnYef8uq/Rqy4uBiH8g7V+xxiPvEJ8fjqi6/Csm8AePayZ3VrxDTzUjknyYkdMSIiN927A+WBPfOwVk49VbUep3ub7ij3Kra/Br76rHB1xBI6J+DY0WMoyC9As+bN6n3/p7Y+VbdGTDMvlXOSnFgjRkTkZs0aa84mLV9+qRp/za41+GavYvtrUDmHWEL9ziHmUzmXmCc8U1h8uedL3eOumZfKOUlOHBEjInLzxBNWLc5ll+nEf+EFqx5n8mSV8E98+QSKyopwWU+l9tcg3CNi/rPr9+zVs973/8L6F+Ct8GLymZN1dqCZl8o5SU7siBERuXnnHWDXLr34c+ao1uO88+t3sCtPsf01CMWs+nPmHgp4W9+IWLimsJhzxRwUlijWWWnmpXJOkhM7YkREbrp2tUYetHTurFqP07VVVxSVKba/Bp4sD5o1b4YWLVsEHKNzl4qAt/V1AMM1u37nFp2R31SxzkozL5VzkpxYI0ZE5OaTT6yaGS1r16rG/yTjE3y5R7H9Ncj2ZAc1hxgALP8gBss/iAlo27hmcWjZqmXYRsTW/rRW97hr5qVyTpITR8SIiNw8/bQ18jBqlE78l1+26nFuvlkl/NNfPY2isiKM6q3U/hp4Mj1B14e986Z1t+PYa4oD2j4+IT5sk7q+vOFleCu8uHmQznlVzUvlnCQndsSIiNwsWgT8+KNe/FdeAQoK1MIvGrcIP+Yqtr8G2Z5snHv+ufW+X3/xCfFhGxF75cpXUFCid15V81I5J8mJHTEiIjfx8cDRo3rxO3ZUrceJbx6Po8WK7a9GRUUF9nv2B1WoHwrxifFI25kWln13bNYR+U0U66w081I5J8mJNWJERG5WrgT+9S+9+J9+qhp/ZepK/OsnxfZXIzcnF+Xl5eHviCXE48D+AyjXnJC3Bp/++KnucdfMS+WcJCd2xIiI3Dz7LPDWW3rx585Vjf/sumfxVopi+6sR7jnEfOIT41FRUYGDBw7W+77nbpqre9w181I5J8mJlyaJiNwsWQJkKD48eu5c1ctAS65bgozc+n34tW/KiGDvmpz7TuDziAHWY44Aq2NY36Nzc0fP1X3EkWZeKuckObEjRkTkpn17IC9PL37btkDTpmrh28e1R16hYvurUdkRC3JErG27wOcRA/77eKVw3DnZNrYtmkbonVfVvFTOSXIK6tKkiLQWkSUislNEdojIUBFpKyKrRSTd/trGXldE5AURyRCRLSJyll+cG+3100XkxmB/KCKikFm61KqZ0bJqlWr8pTuW4tMfFdtfjeysbERGRqJDxw5BxVn8biwWvxsb8Pb+jzmqb6vSV+ked828VM5Jcgq2RmwOgE+MMX0AnAFgB4CZAD4zxvQC8Jn9PQBcAaCX/ZoG4FUAEJG2AB4FcA6AswE86uu8ERGF3QsvWI+T0TJvnmr8F9a/gHe2KLa/GtmebHTo1AGRkZFBxXn/3Ti8/25cwNu3a98OUVFRYRkRm7d5nu5x18xL5Zwkp4AvTYpIKwDDAEwGAGNMKYBSERkLYLi92tsAPgdwP4CxAP7PGGMAfGuPpiXY6642xuTZcVcDGAlgYaBtIyIKmeXLdWvE5s1TnbNp+YTl9V4jFo6arOpERESgY3xHZGVm1fu+542dpzuPmGZeKuckOQUzInYagIMA3hKRzSLyhog0A9DJGON7uFc2gE72+84A9vptv89eVtPyXxCRaSKyUUQ2HjxY/3fBENFJqFUroEXgz0s8rpYtVeO3immFFtGK7a9GtqdhdMSA8M2u3zK6pe5x18xL5Zwkp2A6Yk0AnAXgVWPMmQAK8N/LkAAAe/TLBLEPB2PMXGPMYGPM4A4dgqs9ICKqlcWLrZoZLcuXq8ZfvHUxVqUrtr8anixP0HdMhkp8Ynhm11+eulz3uGvmpXJOklMwHbF9APYZY9bb3y+B1THbb19yhP31gP15JoCuftt3sZfVtJyIKPxefRVYqFgp8c47qvFf3fgqFm6tv0qPgvwCHDt6LOxziPn4RsSscYH6884P7+ged828VM5Jcgq4RswYky0ie0UkyRiTCuASANvt140Anra/Lrc3WQHgThFZBKsw/4gxxiMi/wTwpF+B/mUAHgi0XUREIbVqFZCerhf/nXdU52xadcMqpOcotr8Kjyc0c4gBwDtLgp+eISExAYUFhTh29BhatmoZdLzaeufX7+jOI6aZl8o5SU7BziN2F4B3RaQpgF0AboI1yvaeiPwOwB4A19nrrgIwCkAGgEJ7XRhj8kTkTwA22Os97ivcJyIKu7g4IDbwKRSOKzYW8HrVwsdFxSE2SrH9VYRyVv3YuOBHsXwdwmxPdr12xGKjYuGt0DuvqnmpnJPkFNT0FcaYFLtm63RjzK+MMYeMMbnGmEuMMb2MMSN8nSpjucMY08MYM8AYs9EvzjxjTE/7xecqEFHDsWCBVTOj5YMPVOMv2LIAy1MV21+FryMWimL9+a/HYf7rgU9fAThn169PH2z/QPe4a+alck6SE581SUTk5o03rMfJaFm4UDX+G9+/gSXbFdtfhW9W/VB0xD5cFosPlwU36uMbEfNdMq0vC7cu1D3umnmpnJPkxEccERG5Wb0aSEvTi79woWo9zupJq5GWo9j+KrKzstGqdSvExtXf5VA3nRKsGZTqe0Rs4TULdWvENPNSOSfJiSNiRERuoqKsVyONHxUZhahIxfZXke3JbjBTVwBAbGwsWrdpXe8dMfXjrpk32jlPDuyIERG5mT/feq6flsWLVePPT5mPpTsU219FdlbD6ogB1mVS3yXT+rJ422Ld466Zl8o5SU7siBERuZk/H1i2TC/++++rxp+fMh/Ldiq2v4psT3ZlgXxDEY7Z9d/f9r7ucdfMS+WcJCfWiBERufn8c90asSVLVOtxPp/8eb3ViJWXl+PA/gMhGxFbsio3JHHiE+OxdcvWkMSqrSXXLdGtEdPMS+WcJCeOiBERUUgc2H8AFRUVDWZWfZ/4hHjkHMxBWVlZuJtC9AvsiBERuXn9deC99/Tiv/uuavzXN72O97Yptt9P5WSuIRoRe+2FZnjthWZBx4lPjIcxBgeyDxx/5RB5d8u7usddMy+Vc5Kc2BEjInKj/dDvFSt0H/q9bTFWZdTPA5x9dVihmEMMANZ8EoM1n8QEHadyLrF6LNhfkbZC97hr5qVyTpITa8SIiNysWaNbI7Z4sWo9zprfrqm3GrFQPt4olHztqc+C/cXjFuvWiGnmpXJOkhNHxIiIKCSyPdlo2rQp2rZrG+6mOCR2TgRQ/5O6EtUGO2JERG5eecWqmdEyf75q/Fc2vIJ3/6PYfj+eTA86JXRCRETD+tPSpm0bREdH1+uI2PyU+brHXTMvlXOSnBrWvxYiooZm5Upg7Vq9+GvWqMZfmbYSa3crtt+Px+MJ6WSuMTEGMTEm6Dgigk4Jnep1RGzNrjW6x10zL5VzkpxYI0ZE5Objj3VrxBYsUK3H+fiGj+u1Rqz/Gf1DFm/B0ryQxarvSV0XXL1At0ZMMy+Vc5KcOCJGRERBM8Y0uOdM+otPiIcns34fc0RUG+yIERG5mTMHePttvfhvvKEaf863c/D2D4rttx09chRFhUUhvWPyudnN8dzs5iGJFZ9ojYgZE/ylztp44/s3dI+7Zl4q5yQ5sSNGROTms8+Adev04n/1lWr8z376DOv2Kbbf5pujK1RziAHA119E4+svokMSKz4xHsXFxTh86HBI4h3PVz9/pXvcNfNSOSfJiTViRERuVqzQrRGbP1+1HmfF9SvqpUbMVwgfyo5YKPkumWZ7stGmbRv1/c3/1XzdGjHNvFTOSXLiiBgREQXNVwjfUGvEfB1EziVGDQ07YkREbv76V+DNN/Xiv/aaavy/fvNXvLlZsf0236XJTgmd1PcVCP8Rsfrw2sbXdI+7Zl4q5yQ58dIkEZGbdet0L9Ns2gSUl6uFX7dvne4lMlu2Jxtt27VFdHRoaroAoE3bipDF8nUQ62tEbFPWJpRX6J1X1bxUzklyYkeMiMjNBx/o1oi9/rpqR++D6z6otxqxUD9j8vUFh0IWKzo6Gu3at4PHUz9TWLw+5nXdDrBmXirnJDnx0iQREQXNk+VpsIX6PvGJ8awRowaHHTEiIjdPPw3MnasX/6WXVOM//dXTmLtJsf02jRGxp2a1wFOzWoQsXn3Orv/Sdy/pHnfNvFTOSXLipUkiIjcpKcCxY3rxt21TrcdJyU7BsRLF9gMoKSlBbk4uEhJCOyK26bumIY0XnxiPlE0pIY1Zk20Ht6Hcq1hnpZmXyjlJTuyIERG5WbRIt0bs1VdV63EWjVukXiN2IPsAAIR8RCzUEhISkJuTi5KSkpDeVFCdV698VbdGTDMvlXOSnHhpkoiIgtLQ5xDz8XUU93v2h7klRP/FjhgRkZs//Ql4+WW9+M89pxr/T1/8CS9vUGw/gKzMLABAQucGXqxfj3OJPfftc7rHXTMvlXOSnHhpkojITWoqcPSoXvxdu4CyMrXwqbmpOFqi2H7ojYglJHpDGs83IuabfFbTrrxdKKvQO6+qeamck+TEjhgRkZsFC3RrxF58UbUeZ8HVC9RrxLKzshETG4NWrVuFNO6LbxwOabz6HBF7cdSLujVimnmpnJPkxEuTREQUlOysbMQnxENEwt0UV63btEZMTAznEqMGhR0xIiI3jzwCzJmjF/+ZZ1TjP7L2EcxZr9h+WCNMGpO5PnJ/Szxyf8uQxRORepvU9Zmvn9E97pp5qZyT5MRLk0REbvbu1a0Ry8pSnbNp79G9OFqsXyM2aMigkMfd/p+okMdMSEyol0uTWflZuvOIaealck6SEztiRERu3npLt0bsuedU63HeGvuWao2YMQbZWdkN/o5Jn/iEeGzasEl9P89d/pxujZhmXirnJDnx0iQREQUsLzcPpaWlDX4OMZ/4xHjs9+yHMSbcTSECwI4YEZG7Bx4Ann1WL/5TT6nGf2DNA3h2nV5831QQDX1WfZ/4hHiUlJTgUN4h1f089e+nVI+7al4q5yQ58dIkEZGb3FzgyBG9+IcOqc7ZlFuUiyPFeu33Fb5rjIh17xn6OiVfhzErMwtt27UNeXyfQ8WHUOZVnItLMy+Vc5Kc2BEjInIzd65ujdhf/qJajzN39FzVGrHKyVwVRsT+8kLoOxqVc4llZaP/6f1DHt/nL5f+RbdGTDMvlXOSnHhpkoiIAubJ9EBE0LFTx3A3pVZ802zUx52TRLXBjhgRkZt77wVmz9aL//jjqvHv/fRezP5aL362JxsdO3VEVFTop5r4492t8Me7Qztbf8f4jhAR9bnEHv/icdXjrpqXyjlJTrw0SUTkpqgIKC7Wi19crFqPU1RWhOJyvfb7ZtXXsCsj9H+ioqKi0L5De/URseLyYt0aMc28VM5JcmJHjIjIzcsv69aIPfmkaj3Oy1e+rF4jduppp6rF15DQOUF9ROzJS57UrRHTzEvlnCQnXpokIqKAaY6IaYlPiGeNGDUYQXfERCRSRDaLyIf296eJyHoRyRCRxSLS1F4ebX+fYX/ezS/GA/byVBG5PNg2ERGFzD33AH/+s178Rx5RjX/PJ/fgz//WiV9UWITDhw83mjnEfOIT4ivnP9PyyNpH1I47AN28VM5JcgrFiNh0ADv8vp8N4DljTE8AhwD8zl7+OwCH7OXP2etBRPoBmAAgGcBIAK+ISGQI2kVERIp8nRmNB34DQL8BZeg3IPS1SvGJ8Th86DCKiopCHpuoroKqERORLgCuBPBnAH8QEQFwMYDf2Ku8DWAWgFcBjLXfA8ASAC/Z648FsMgYUwLgJxHJAHA2gHXBtI2IKCSef163Ruzxx1XrcZ4f+bxajZjmHGIA8PhsnYda+9q737Mf3bp3U9nH4xc9rlsjppmXyjlJTsGOiD0P4I8AKuzv2wE4bIzxTYe8D0Bn+31nAHsBwP78iL1+5fJqtnEQkWkislFENh48eDDIphMRUTB8Be9aI2JaEhI4lxg1HAF3xETkKgAHjDH6j7G3GWPmGmMGG2MGd+jQob52S0QnszvuAB57TC/+gw+qxr/jozvw2Bc68StHxJSK9e+6uTXuurl1yOP6RsQ8mXp1Yg9+9qDacQegm5fKOUlOwVyaPB/AGBEZBSAGQEsAcwC0FpEm9qhXFwCZ9vqZALoC2CciTQC0ApDrt9zHfxsiovCKjdWdRywmBojUK4uNjYpVm0fspx9/Qtt2bdG8RXOV+J4sneNS+ZgjxRGxmCYxiNQsd9bMS+WcJKeAO2LGmAcAPAAAIjIcwL3GmBtE5H0A4wAsAnAjgOX2Jivs79fZn//LGGNEZAWAv4vI/wJIBNALwHeBtouIKKT++lfdGrFHHlGtx/nrZX9VqxFLT01Hr6ReKrE1tWjZAnHN4lTvnHzkwkd0a8Q081I5J8lJYx6x+2EV7mfAqgF7017+JoB29vI/AJgJAMaYbQDeA7AdwCcA7jDGeBXaRUREIWKMQXpqOnom9Qx3U+pMRDiXGDUYIZlZ3xjzOYDP7fe7YN31WHWdYgDX1rD9n2HdeUlE1LBMmwYcOQI895xO/D/+0XqczOLFKuGnrZyGI8VH8NzI0LY/NycXhw8dRq/ejW9EDLDqxDRn1//j6j+izFuGxdfqnFfVvFTOSXLiI46IiNy0aweI6MVv0wYoLVUL3y62HQShb396ajoAqF6aHHS23nFJSEzAt19/qxa/TUwblHr12q+al8o5SU7siBERuXnqKd0asQceUK3HeWrEUyo1YvXREXtg1jG12AmJCdjv2Y+KigpERIS+SueB/3lAt0ZMMy+Vc5Kc+KxJIiKqs4zUDMQ1i0Nil8RwNyUg8QnxKC8vR25ObribQic5jogREbm56Sbg6FHgxRd14v/+90B5OfDBByrhb1p+E44WH8WLo0Lb/vTUdPTs3ROieNl26sQ2AIDXFxwKeeyEztakrpl7M9GhY+jnpfz9P3+Pcm85Phivc15V81I5J8mJI2JERG66dgXiFR9qnZioGr9ry66Ibx76+OlpVkdM06G8CBzK0/kz5bvbM22nzuW9xOaJKse9kmZeKuckOXFEjIjIzeOP69aI3Xefaj3O4xc9HvIasfxj+fBkehrlHGI+3U7rhujoaKTuSFWJf9/59+nWiGnmpXJOkhNHxIiIqE4y0jIA6Bbqa2vSpAl6JvVE6nadjhhRbXFEjIjIzcSJVi3Oa6/pxL/rLmvOphUrVMJPXDoRR0uO4rWrQtf++rhjsj4k9U3Cuq/WqcS+a9VdKKsow4rrdc6ral4q5yQ5sSNGROQmKQnIydGL37276pxNSe2SkFMY2vZnpGWgSZMmOPW0U0Mat6rzLyxRjd+nXx8sXbwURw4fQavWrUIau3vb7igtV5yLSzMvlXOSnMQYE+42BGTw4MFm48aN4W4GEZ0M0tKA5joPtgZg1eP07q0WPi0nDc2jQ9f+3/3md8hIy8AXG78IWcxwWPPJGtx43Y34x6f/wJBzh4Q8fn5JPnq31zuvqnmpnJMnIxHZZIwZXHU5a8SIiKhOGuvDvqvq068PAGDn9p1hbgmdzHhpkojIzYQJwLFjwOuv68S/7TZrzqaPPlIJP2HJBBwrOYbXx4Sm/aWlpdi9azdGjR0VknhuJl7dFgCwYGmeSvzOXTujWfNmKgX7t310G8q95fjoBp3zqpqXyjlJTuyIERG5GThQt0YsOVm1Hmdg/EDkFISu/bt37YbX662Xh30XFys+4xOAiCCpbxJ27gj9iFhyh2TdGjHNvFTOSXJiR4yIyM3MmbrziN15p+qcTTMvmBnSecROlDsmffr064OPV34MY0xInxJw59l36s4jppmXyjlJTqwRIyKiWvN1xLRn1a8vSX2TcCjvEHIOKo56ErngiBgRkZtrrrFGB956Syf+1KlWPc4//6kS/pr3rkF+ST7e+lVo2p+RmoHOXTsjrllcSOKFW1K/JABWwX4onzk5dcVUlFeU45+TdM6ral4q5yQ5sSNGRORm6FDg4EG9+IMGASV682UN7TIUBwtC1/70tPR6qQ8DgBEji9X34btzMnV7Kv5n+P+ELO6gxEEoKVecB00zL5VzkpzYESMicnPvvbo1YrfeqlqPc+9594asRqyiogIZaRk49/xzQxLveG69u0B9H+07tEfbdm1D/szJWwffqlsjppmXyjlJTqwRIyKiWsncm4niouITplAfsO+c7JfEucQobDgiRkTkZswYa3RgwQKd+JMnW/U4//qXSvgxC8cgvzQfC64Ovv31fcfkuFHtAABLVuWq7qdP3z547+/vhfTOycn/mIzyinL860ad86qal8o5SU7siBERubnkEuDAAb34F1ygWo9zyWmX4EBBaNqfnnZiTV3hk9QvCQX5Bcjcm4kup3QJScwLTrlAt0ZMMy+Vc5Kc2BEjInIzfbpujdjNN6vW40w/d3rIasQyUjPQtl1btG3XNiTxGgr/Rx2FqiN281k369aIaealck6SE2vEiIioVtJT00+Y+cP89e5jPdw61AX7RLXBETEiIjdXXAEUFACLFunEnzjRqsf58kuV8Fe8ewUKSguwaFxw7TfGID01HVeOvTJELWs4WrVuhYTOCSEt2J+4dCLKK8rx5U0651U1L5VzkpzYESMicjN6NLB/v178ESNU63FG9x6N/fnBtz8vNw+HDx2u1xGxq35dVG/76tO3T0hHxEZ0H6FbI6aZl8o5SU7siBERubn9dt0ascmTVetxbh9ye0hqxMLxjMnJUwvrbV9J/ZLwzb+/QXl5OZo0Cf5P4+SBk3VrxDTzUjknyYk1YkREdFzh6IgVFQqKCkP3IG43SX2TUFJSgt0/7a6X/RH5cESMiMjNiBFAYSGwZIlO/PHjAa8X+OYblfAj/m8ECssKseS64NqfnpqO2LhYJHZJDFHLjm/SOOvuTO15xADno4569gr+8uv4JePhrfDim9/pnFfVvFTOSXJiR4yIyM348bo1YmPGqNbjjE8eH5IasYzUDPTs3RMRESfmhZReSb0gIkjdnhqSGxLG9B6jWyOmmZfKOUlO7IgREbmZOlW3RuyGG1TrcaYOmhqaGrG0dJx7Xv08YzIcYuNicWq3U7FzR2junLzh9Bt0a8Q081I5J8npxPyvDRERhUxBfgGy9mWhZ9KJN4eYv6R+SUjdzrnEqH5xRIyIyM3w4UBREbBsmU78ceOsepz161XCD58/HEVlRVg2IfD2Z6RlADjxHm1UVVLfJKz5ZA2Ki4sRExMTVKxx742Dt8KL9VN1zqtqXirnJDmxI0ZE5GbyZCA7Wy/+tdeq1uNMHjgZ2ceCa3847pgEgGtvqL/pKwCrYN/r9eLH9B+RPCA5qFjXJl+LkjLFOivNvFTOSXJiR4yIyM3kybo1YuPHq9bjTB44OegasfS0dDRp0gTduncLTaNqafwN9TehK2BdmgSsOyeD7YiNTx6vWyOmmZfKOUlOrBEjInJTVma9Gmn8Mm8ZyrzBxc9IzUC37t0QFRUVolbVTl5uBPJy6+/PVPee3dGkSZOQzLAfiuPuvgPFvNHOeXLgiBgRkZtLL9WtEbv+etV6nEvfuTToGrH01PTKB2PXp2mT2gCon3nEAKBp06bo0atHSDpi139wvW6NmGZeKuckObEjRkTk5uabAY9HL/711wPFxWrhbz7rZniOBd7+0tJS7N61G6PGjgphqxqupL5JSPk+Jeg41/e/HsXleudVNS+Vc5Kc2BEjInIzcaJujdg116jW40w8fWJQNWK7d+2G1+sNyWzzjUFSvySsWLoCBfkFaNa8WcBxrul3jW6NmGZeKuckObFGjIjITWGhdQlIS1GRavzCskIUlQUe/2SZusLH96ijtJ3BdXKKyoqCOu7HpZmXyjlJThwRIyJyM2qUbo3YpEmq9Tij3h0VVI2Yb+qKnr1PkhGxvvadkztScebgMwOOM2nZJN0aMc28VM5JcmJHjIjIzW236daITZqkOmfTbYNvC6pGLCM1A4ldEoO6TBeoSb8rqPd9ntLtFMTExmDn9uAedTTpjEm684hp5qVyTpITO2JERG7Gj9etERs7VrUeZ3z/8UHViKWnpaNX7/Bclhx7Tf0XjEdGRqJ3n95BP+pobNJY3RoxzbxUzklyCrhGTES6ishaEdkuIttEZLq9vK2IrBaRdPtrG3u5iMgLIpIhIltE5Cy/WDfa66eLyI3B/1hERCFy5Ahw7Jhe/KNHVeMfKT6CYyWBxa+oqEBGWkbYnjGZuS8Cmfvqv5Q5qW9S0FNYHC05GvBxrxXNvFTOSXIKZkSsHMAMY8z3ItICwCYRWQ1gMoDPjDFPi8hMADMB3A/gCgC97Nc5AF4FcI6ItAXwKIDBAIwdZ4Ux5lAQbSMiCo2xY3VrxKZMUa3HGbtobMA1Yln7slBUWBS2Qv3p0+p3HjGfPv364P2/v4+83Dy0bdc2oBhTlk/RrRHTzEvlnCSngDtixhgPAI/9/piI7ADQGcBYAMPt1d4G8DmsjthYAP9njDEAvhWR1iKSYK+72hiTBwB2Z24kgIWBto2IKGTuvhvIytKLP2WK6pxNd59zN7KOBtb+cD1jMtx8BftpO9Nw7vnnBhRjyplTUFymeGlVMy+Vc5KcQlIjJiLdAJwJYD2ATnYnDQCyAXSy33cGsNdvs332spqWExGF39VX69aIjRqlWo9zdd+rA64RO2k7YvYzJ3du3xlwR2xUr1G6NWKaeamck+QU9MV3EWkO4AMA9xhjjvp/Zo9+mWD34bevaSKyUUQ2Hjx4MFRhiYhqlpMD5OXpxc/LU42fU5iDvKLA4mekZaBN2zZo175diFvVsCUkJqBlq5ZBFeznFeUFfNxrRTMvlXOSnIIaERORKFidsHeNMUvtxftFJMEY47EvPR6wl2cC6Oq3eRd7WSb+eynTt/zz6vZnjJkLYC4ADB48OGQdPCKiGo0bp1sjNm2aaj3OuPfGBVwjlp6aftKNhgGAiARdsD9t5TTdGjHNvFTOSXIKuCMmIgLgTQA7jDH/6/fRCgA3Anja/rrcb/mdIrIIVrH+Ebuz9k8AT/rurgRwGYAHAm0XEVFIzZgBZGbqxZ82TbUeZ8bQGcg8Glj701PTMWpM+J4xOe2u8F0eS+qXhA+XfghjDKw/d3UzbdA03RoxzbxUzklyCmZE7HwAkwD8R0RS7GUPwuqAvScivwOwB8B19merAIwCkAGgEMBNAGCMyRORPwHYYK/3uK9wn4go7EaP1q0Ru+wy1Xqc0UmjA6oRy83JxaG8Q2GdUf+yK8I3qWifvn2w4PAC7M/ej/iE+Dpvf1mPy3RrxDTzUjknySmYuya/AlDTfxMuqWZ9A+COGmLNAzAv0LYQEanJzgYOHgSaN9eJf+AAUFAA9O6tEj47PxsHCw6ieXTd2t8QCvUz0iMBAD17eet9376C/dTtqQF1xA4UHEBBSQF6t9c5r6p5qZyT5MSZ9YmI3EyYoFsjdvvtqvU4E5ZMCKhGrCE8Y3Lm9NYA6n8eMeC/U1js3LETF15yYZ23v/2j23VrxDTzUjknyYkdMSIiNzNnAvv26cW/4w7rD6qSmRfMxL4jdW9/emo6YuNi0bnryTmbULv27dChY4eA75y8Y8gdKCrTO6+qeamck+TEjhgRkZuRI3VrxC66SLUeZ2TPkQHViP2Y9iN69OqBiIj6f8RQQ5HULyngjthFp12kWyOmmZfKOUlOJ++/MCKi2ti7F/B4jr9eoDIzVePvPbIXnmN1jx/Oh303FEl9k5C6MxUVFRV13jbzWGZAx73WNPNSOSfJiSNiRERuJk3SrRGbPl21HmfSskl1rhEryC9A5t5M9LwxfPVhDUGffn1QVFiEvXv24tTTTq3TttM/nq5bI6aZl8o5SU7siBERuXnoId0asbvvVp2z6aFhD9W5RuzH9B8BhP/RRnffdyys+/cV7KfuSK1zR+zuc+7WnUdMMy+Vc5Kc2BEjInIzYoRujdiwYar1OCO6j6hzjVhDmLoCAIZdVBrW/ffuY03fsHP7Tlw26rI6bTvs1GG6NWKaeamck+TEGjEiIje7dln1OFr27FGNv+vQLuw9Urf46WnpiIyMRLfu3XQaVUtbtzTB1i3hGy9o0bIFupzSJaCC/T2H99T5uNeJZl4q5yQ5cUSMiMjNlCm6NWIzZqjW40xZPqXONWIZqRno1r0bmjZtqtKm2po1sxWA8Mwj5tO7T++Anjk549MZVo1YD6U6K828VM5JcmJHjIjIzWOP6Y4OzJihOmfTY8Mfq/uI2En6sO/q9OnXB/9e+2+UlZUhKiqq1tvNGDpDdx4xzbxUzklyYkeMiMjNhRfq1ogNHapaj3NhtwvrVCNWVlaG3bt2Y+TokWptakyS+iahrKwMP/34U2XNWG0M7TpUt0ZMMy+Vc5KcWCNGROQmNdWqx9GSkaEaPzUnFbsO1T7+7l27UV5eftLPIebTp18fAFbBfl1k5GXU6bjXmWZeKuckOXFEjIjIzS236NaIzZxp1eOM1BmBuuXDW+pUI9ZQ7phsKHr0tp4ukLo9Fbi69tvNXDMT3govRvZSGlnUzEvlnCQndsSIiNw8+STw88968e+/X7Ue58lLnsTPh2vf/obwsG+f+x89Gu4mIDY2Ft26d6tzwf79F9yPolLFOivNvFTOSXJiR4yIyM155wHt2+vFHzJEtR7nvK7noX1s7dufkZqBhM4JaNa8mVqbamvIOWXhbgIA6/Lkjm076rTNkMQhujVimnmpnJPkxBoxIiI3W7fqFuvv3Kkaf+uBrUjLrX38hvSMyQ3ro7Bhfe3vVNSS1DcJu3ftRlEdRol25uys03GvM828VM5JcuKIGBGRmzvv1K0Re+ghqx7nqqtUwt+56s5a14hVVFQgIy0Dv/ntb1TaUlezH2sJILzziAFAUr8kGGOQkZqBAQMH1Gqbh/71ELwVXlyVpHNeVfNSOSfJiR0xIiI3zzxjzTSu5aGHgMJCtfDPXPoM9hyuXfs9mR4UFRahZ1L468MaEv87J2vdERv2EApL9c6ral4q5yQ5sSNGRORmyBCgVSu9+AMHqtbjDOk8BK2ia9f+zZs2A0Cd5ss6GfieMlCXgv2B8QN1a8Q081I5J8mJNWJERG5SUoAddSvUrpOtW1Xjp2SnYMfB48c3xuC1Oa+h66ldMejsQWrtaYyioqLQo3ePOj1zcuuBrbU67gHTzEvlnCQnjogREbm55x7dGrFZs6x6nLFjVcLf88k9taoR+/JfX2Lzps2YPWd2nR7lc7Lo07cPvv3m21qvP+vzWfBWeDG2r855Vc1L5ZwkJ3bEiIjcPP+8bo3YrFmqczY9P/J57Dnk3n5jDJ6b/RwSOifg2t9cq9aWupr19JFwN6HS4HMHY9n7y/DVF1/hggsvOO76s4bP0p1HTDMvlXOSnMQYE+42BGTw4MFm48aN4W4GEZ0M0tKA5s314ufnA7316rLSctLQPLrm9n/95de47qrr8MQzT+CmW25Sa0djVlxcjGGDhqFd+3b4aO1HiIg4fmVPfkk+erdXrLfTzEvlnDwZicgmY8zgqstZI0ZE5GbDBmDLFr34KSmq8TdkbsCW/e7xn//L8+jYqSMm/HaCWjsC8eXapvhybdNwNwMAEBMTg/v+333YsnkLVi5dedz1U7JTjnvcg6KZl8o5SU4cESMicjN8uG6N2LhxVj3O+vUq4YfPH+5aI7bh2w341WW/wiNPPoJb7rxFpQ2BGjeqHYDwzyPm4/V6cfkFl6OwoBCfb/wcTZvW3Ekc9944eCu8WD9V57yq5qVyTp6sahoRY40YEZGbl14Cdu/Wi//EE6pzNr006iXsPrS7xs+fn/082rZri0k3TVJrw4kiMjISDz72ICaNm4QF8xZgyq1Talz3iYuf0J1HTDMvlXOSnNgRIyJy078/4DLyEbQ+fVTnbOrfsT+aRlTf/s0bN+Pzzz7HA7MeQFyzOLU2nEguuvQinDfsPDw3+zlc+5tr0aJli2rX69O+j+48Ypp5qZyT5MQaMSIiN998A3z/vV78DRtU43+z9xt876k+/pxn5qB169aYPHWy2v5PNCKChx5/CHm5eXh1zqs1rrcha0ONxz0kNPNSOSfJiSNiRERuHnzQqsUZNkwn/uzZVj3OBJ1C+Qc/exBFZUUY1s3Z/q1btmL1x6tx7/+7F81bKN4RegI646wzMObqMZj78lzcOPVGdIrv9It1Zn81G94KLyYMULoBQjMvlXOSnFisT0TkJjUV+Okn4PTTdeJnZFj1OCNHqoRPzUnFT4d+wunxzvZPnTQV/177b6zfuh6tWis+wikIGemRAICevbxhbskv7d61G8OHDMf4SeMx+/nZv/g8Iy8DhaWFGNlL57yq5qVyTp6sWKxPRBSIpCRARC9+z56q9ThJ7ZMgcLZ/5/adWLV8FabfN73BdsKAhtkB8+nWvRsmTZmEt994G1PvmIqevZwPSu/ZtqdujZhmXirnJDmxRoyIyM0XXwDffacXf9061fhf7P4C32U647/wzAto1rwZbr79ZrX9hsKnH0fj04+jw92MGk3/43TExMbg6VlP/+KzdXvX/eK4h5RmXirnJDlxRIyIyM2jj1q1OBdfrBP/2WetepyJE1XCP/r5oygqK8LF3a32Z6RnYMXSFbht+m1o266tyj5DZe6LVu3aZVeUhLkl1WvfoT1um34b/vrnv2LD+g0Ycs6Qys+eXfcsvBVeTDxD57yq5qVyTpITa8SIiNzs2mXV4vTtqxN/zx6rHueSS1TC7zq0Cz/l/YS+Ha32T79lOj78x4dYv3U92ndor7LPUGloE7pWp7CgEOcPPB+nnnYqlv1zGcS+XLjn8B4Ulhbikh4651U1L5Vz8mTFRxwREQWie3ega1e9+Keeqhq/e5vu6NrKir/npz1Y9t4yTLxpYoPvhDUWcc3i8IcH/oAN327Ap6s+rVx+autTK4+7Cs28VM5JcmJHjIjIzZo11pxNWr78UjX+ml1r8M1eK/5L//sSmjRpgtum36a2v5PR9b+9Hj169cBTs55CeXk5AODLPV9WHncVmnmpnJPkxI4YEZGbJ54AXq154s6gvfCCavwnvnwCr258FZl7M/H+39/HhEkTEJ8Qr7a/k1GTJk3wwKwHkJ6ajvfefQ8A8ML6F/DqRsW80cxL5ZwkJ9aIERG52bvXqsfp1UsnfmamVY9z4YUq4fce2Ytdebvw1pNv4e9v/x1fp3yNzl07q+wr1DL3WWMFnbtUhLklx2eMwdhLxyJzbya+2vwV8rx5KCwpxIWn6ZxX1bxUzsmTFWvEiIgC0bUrkJCgF79zZ9X4XVt1RURBBBa9swjX3XBdo+mEAVYHrDF0wgD70Ud/egjZnmy8/srr6NyiMxJaKOaNZl4q5yQ5cfoKIiI3n3wC7NsHjBqlE3/tWmsagt69VcJ/kvEJnn39WZSXl+OO39+hsg8tyz+IAQCMvaY4zC2pnbOHno3LRl2GV55/Bd0u64YmMU3Qu73OeVXNS+WcJCdemiQicjN8uPVHadkynfjjxllzNq1frxL+/NfPx7pv12Fc8Tg8/9rzKvvQ0himr6gqbWcaLjn3EnSc2RGndDsF66fqnFfVvFTOyZMVH3FERBSIRYuAH3/Ui//KK0BBgVr4gRkDse79dbjr87vU9kH/1btPb0yYNAHvvfIeXvjwBb0daealck6SE2vEiIjcxMcDHTroxe/YMeTxc3NzsXDhQkyaNAlvznkTV156JXr06hHSfVDNZjw4A01KmuCxOx7DG2+8gezs7NDvRDMvFXKSatZgRsREZCSAOQAiAbxhjPnlw7uIiOrbypXWXWRjxujE//RToLg4qHqciooKpKSkYNWqVVi1ahXWr1+PiooKtG/fHkNvHIrzrzk/hA2m44lPiMcNT92AZe8tw9SpUwEAZ599NkaPHo0xY8ZgwIABlTPwB0wzL0OQk1R7DWJETEQiAbwM4AoA/QBcLyL9wtsqIiJYz9176y29+HPnBhT/8OHDeP/993HTTTehc+fOGDRoEB5++GGUl5fj4Ycfxvr165GdnQ0z1GDZHqX6NqrR9ubb0WNyD/zwww944oknAAAPP/wwzjjjDHTr1g133XUXVq9ejdLS0sB2oJmXAeYkBaZBFOuLyFAAs4wxl9vfPwAAxpinatqmMRTrG2NgjIHX60VFRUXlV9/L/9hXPQ9Vv4+MjETLli0REVG/feeioiJkZmYiKysLmZmZyMzMhMfjQVFREbxeb61eFRUVaNKkCaKiomr1atq0KZo1a4ZmzZohLi6u8n3Vl++zyMhIVFRUoLy8HGVlZcf9GhERgejo6GpfkZGRqsfTlw9erxfl5eUwxqBJkyZo0qQJIiMjg/9fcg0qKipQVlZW7au0tLTy+ERHRzuOcdOmTVXa5PV6UVBQgGPHjiE/Px/5+fm/eF9YWIiSkpLjvkpLS1FSUoKKigqICEQEERER1b73fR8REYGYmBjXvPK9WpWVITY3F95OnQDY/67tn8P3b7y6975/4xX28srv/b8CiDxyBN6CAhR07155PnznpLr3JSUlWL9+Pb7++mt4vV60bt0al19+OUaNGoXLL78cnex2+uQU5iAjNwOntD4l5OdRW16u9fuubbvGMYWFv7yiPOSX5OPcrudWLvN4PPjoo4+wcuVKrF69GkVFRWjRogUuv/xyjB49Gj169Kgxn6ouizpyBBHZ2ZDERERFRaFJZKT1O9T+fRJl/871vY+MjLT+zZSWoqS0FMX2v5/iapaZ3FyUFxaitGfPav8tVf0qIpW/t6v++4mLi0NcXFyd/3Z5vd7KnC8tLUVRUREKCwsdr4KCgl8sKywsRHFxMSJ9x8N+Vfc3yLcsOjoav/71r0Nx2l3VVKzfUDpi4wCMNMbcbH8/CcA5xpg7q6w3DcA0ADjllFMG7dmzR61NZ511Fn744YfKJLP3/4v3/t/7d7K8Xu8vOlPBEhG0adMGbdq0Qdu2bR2vqsvi4uIqf9n7/+Kv2hH0vc/Ly3N0tnyvQ4cO/aIdvn9YkZGRtXpFRERUdoKO9/J6vXU+LhEREaioCM0v6sjIyF90zkSk8hdg1V+I1b3cOqXHywlfp6yml+9nre3Ld9wDOa6+41FTZzguLq5yH76Opf/7ql9LS0srO1qFhYV1aodb5zk6OhpNmzZFRESE4zz4crzqe985Ki4uRkFBQeUr0GNUn3x/8Pr06YMrr7wSo0aNwjnnnIMmTdyrTNJy0tA8unk9tZJ88kvya5y+oqioCJ999hlWrlyJlStXwuPx1HPr6ldsbKzjdwkA1/90BPr30/e7wuv1oqysrFZxYmNj6/w7KRAnREfMn/aI2MsvvwyPx1Pj/06q+973P21f58P3tbr3vv9N+Ks68uD/fVlZGQ4fPoy8vDwcOnQIeXl5jtfhw4eD7oxEREQgPj4enTt3RufOnZGYmFj53v/7li1bqo7clJaWVv5vx+3lW6esrMzxv5uqX6suq6ioOO4oS3FxceV7wNnxrq4z7v+qTefUfwTM11HxjUq5vbxeryOX3F4iUvlzN23atMbRR/9jVFJSctzj7f+9/8/j+5lq+hoVFYUWLVqgRYsWaN68OZo3b175vrqvcXFx9TpSWVpaWu3P3fzTT1GanY2sc8+t+T9ldhz/5f7nIcJlRKHdunWIKC9HwQ03oGnTppXnpLr3gRyHpTuWIutoFq7ud3VIjlV9WvxuLABg/A1FYW5J3a1KX4XismLceU6Nf8YqVVRU4IcffkBOTg4A1Jhn/svaffEFKg4eRNa551q/P3y/Q+wOSJn9O8P31VtRgaZRUYhu2hQx9r+rmOhoRDdt6lgW3bQp2n37LSLLylDw29/+4j8zNX31/fvx/z1R0/uCgoLK/1T4/x6qmvP+X30DAP6jbP4v3/Kqo/j+v1/9X/7LvF4v+vfvH+oU+IWG3hE7IS9N1qeKigocPXq0smNWWFhY7R/mmv5gt2rVCp06dTru/6yJTjqNfB6x4fOHo6isCMsmNL46scY4j5jPuPfGwVvh5TxiVKmhzyO2AUAvETkNQCaACQB+E94mNS4RERFo3bo1Wrduje7du4e7OUQnjuXLgYwMvfjz5qnO2bR8wnJk5Cq2n6o1b+w8FJQozsWlmZfKOUlODaIjZowpF5E7AfwT1vQV84wx28LcLCIioFUroEULvfgtWwKKN+G0immFFtGK7adqtYxuiQjNiQk081I5J8mpQXTEAMAYswrAqnC3g4jIYfFiwOMBrrtOJ/7y5UBJidqcTYu3LobnmAfX9VdqP1VreepylJSV6D1rUjMvlXOSnNjlJSJy8+qrwMKFevHfeUc1/qsbX8XCrYrtp2q988M7usddMy+Vc5KcGkSxfiBYrE9E9aKwEEhP13vkS1ERkJ8PnHGGSvjCskKk56SjQ/PG98iaokLr7rfYuMb3d6qorAj5Jfk4I0HnvKrmpXJOnqwaerE+EVHDFBcHxMbqxY+Nte5QUxIXFYfYKMX2K2qMHTCf2KhYeCsU56bTzEvlnCQnXpokInKzYIFVM6Plgw9U4y/YsgDLUxXbr2j+63GY/3pcuJsRkA+2f6B73DXzUjknyYkdMSIiN2+8ASxZohd/4ULV+G98/waWbFdsv6IPl8Xiw2WNczRv4daFusddMy+Vc5KceGmSiMjN6tVAWppe/IULrXocJasnrUZajmL7qVoLr1mI/BK986qal8o5SU4cESMichMVZb0aafyoyChERSq2n6qlftw180Y758mBHTEiIjfz5wNLl+rFX7xYNf78lPlYukOx/VStxdsW6x53zbxUzklyYkeMiMjN/Pl6z5kEgPffV40/P2U+lu1sfM+ZbOze3/a+7nHXzEvlnCQnziNGRHQ8aWlA8+Z68fPzVWcxT8tJQ/NoxfZTtfJL8vVm1gd081I5J09GNc0jxhExIiIiojBhR4yIiIgoTNgRIyIiIgoTdsSIiIiIwoQdMSIiIqIwYUeMiIiIKEzYESMiIiIKE3bEiIiIiMKEHTEiIiKiMGFHjIiIiChM2BEjIiIiChN2xIiIiIjChB0xIiIiojBhR4yIiIgoTMQYE+42BEREDgLYo7yb9gBylPdB9Y/n9cTE83pi4nk9MZ2M5/VUY0yHqgsbbUesPojIRmPM4HC3g0KL5/XExPN6YuJ5PTHxvP4XL00SERERhQk7YkRERERhwo6Yu7nhbgCp4Hk9MfG8nph4Xk9MPK821ogRERERhQlHxIiIiIjChB2xaojISBFJFZEMEZkZ7vZQ4ERknogcEJGtfsvaishqEUm3v7YJZxupbkSkq4isFZHtIrJNRKbby3leGzERiRGR70TkB/u8PmYvP01E1tu/jxeLSNNwt5XqTkQiRWSziHxof8/zamNHrAoRiQTwMoArAPQDcL2I9AtvqygI8wGMrLJsJoDPjDG9AHxmf0+NRzmAGcaYfgDOBXCH/W+U57VxKwFwsTHmDAADAYwUkXMBzAbwnDGmJ4BDAH4XviZSEKYD2OH3Pc+rjR2xXzobQIYxZpcxphTAIgBjw9wmCpAx5ksAeVUWjwXwtv3+bQC/qs82UXCMMR5jzPf2+2Owfrl3Bs9ro2Ys+fa3UfbLALgYwBJ7Oc9rIyQiXQBcCeAN+3sBz2sldsR+qTOAvX7f77OX0YmjkzHGY7/PBtApnI2hwIlINwBnAlgPntdGz758lQLgAIDVAH4EcNgYU26vwt/HjdPzAP4IoML+vh14XiuxI0YnNWPdNsxbhxshEWkO4AMA9xhjjvp/xvPaOBljvMaYgQC6wLo60Se8LaJgichVAA4YYzaFuy0NVZNwN6ABygTQ1e/7LvYyOnHsF5EEY4xHRBJg/e+bGhERiYLVCXvXGLPUXszzeoIwxhwWkbUAhgJoLSJN7NET/j5ufM4HMEZERgGIAdASwBzwvFbiiNgvbQDQy76joymACQBWhLlNFForANxov78RwPIwtoXqyK4veRPADmPM//p9xPPaiIlIBxFpbb+PBXAprPq/tQDG2avxvDYyxpgHjDFdjDHdYP09/Zcx5gbwvFbihK7VsHvuzwOIBDDPGPPn8LaIAiUiCwEMB9AewH4AjwL4B4D3AJwCYA+A64wxVQv6qYESkQsA/BvAf/DfmpMHYdWJ8bw2UiJyOqyi7UhYgwTvGWMeF5HusG6aagtgM4CJxpiS8LWUAiUiwwHca4y5iuf1v9gRIyIiIgoTXpokIiIiChN2xIiIiIjChB0xIiIiojBhR4yIiIgoTNgRIyIiIgoTdsSIiIiIwoQdMSIiIqIwYUeMiIiIKEz+PwtLmCQJVVpIAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-08-10 20:48:57 - INFO - 4:19: E231 missing whitespace after ','\n" + ] } ], "source": [ @@ -657,9 +598,18 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-08-10 20:48:57 - INFO - 6:1: E265 block comment should start with '# '\n", + "2022-08-10 20:48:57 - INFO - 8:1: E265 block comment should start with '# '\n" + ] + } + ], "source": [ "# extract the background using custom individual traces\n", "trace = FlatTrace(image, ext_center)\n", @@ -677,32 +627,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'slit[0] slice')" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# view the background weighted image\n", "plt.figure(figsize=(15, 15))\n", @@ -712,32 +639,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'slit[0] slice')" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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x1qBB/XKMtSnJvkn27t7vBryG3n2CNwIndKvNHl8z4+4E4IvlP847NgP69d2+v6gKvfsD+8eX34cjsHrhVZauqh5PcgZwHbAKuKiqNozj2Nou+wFXdffbrgY+XVWfT3IzcEWSU4DvA2+cYI1TLcm/Aa8CnpHkbuDvgPczd3+uBV5H76b3h4G3jb3gKTegX6/qHj1cwF3AXwBU1YYkVwC30nsK3elVtW0CZU+zVwBvBr7d3WcB8G4cY60a1K+THGNN2h+4pHtC5k7AFVV1TZJbgcuS/D3wDXqBm+71k0k20Xuw0omTKHqKDerXF5PsCwS4BTitW9/vwxGJfwkhSZIkSZPnA0EkSZIkqQGGM0mSJElqgOFMkiRJkhpgOJMkSZKkBhjOJEmSJKkBhjNJkiRJaoDhTJIkSZIaYDiTJEmSpAb8P5BPAo5VWXgIAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# view the background image\n", "plt.figure(figsize=(15, 15))\n", @@ -747,32 +651,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'slit[0] slice')" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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UigwwKmDxHDHqeFCmzE04VPnR1nhNKn3WwdWpqTq1w+P4SaoBIewT8W7qfDCNaJPcCVJv3iEVSAuWMqEJWiosUp5D1dI8rVZ8LAeqVWkiGgKomcZSH0uQQ0q520o49fOQUvSstKl88lqJmOXq0/WWnNKcophLtzZEtVSnvn5XznRJObVQ8+zUPl+1E0Y1ea388XtY8zuS64va+mrrsMqreR5yKvKaZyiXtvQ7sfW3I1fXXsqoY8QpypnD4XC8sKEP97VCyiRYhPHlIO6zfMFFsiIPoY7hjlZoXKn8Ncpd7WYKuj+sre23KIaxb1N2XPJlX+vIpUKP9DXdR3uHLFnIhZrV5K2tI1dfCmtD3fZWotaQiNryUqR6r3r3dKr3wKn9tXfaS6mVW+rRefYc82uYJNgbL5R2nIBVypnD4XA8NdAKWbwGzK+FA5pHFSwqWzqtUoQ4lt/x/F5DANpZfdQxmDBXf2RopSw/OMkztS31ktdqlFQDZ/YY98VOh7O+sRQXS8mI6dSOlaNNFsEsqQHaBksxlPf1+MrySgSt1glvVH/U5E+VmZuR30vtqLWxVmGy0uTqsNop25dTH6z0+voae9eiRq3S7Uv1v2yfVIdzkwGp71rpN0zWs7Vf1ip1NWqXREl1LaFUX4kQrFVfc+lrlN3Sdy+Xx/odLKEm7VrSZD3npYiEXF17fGfX9MkdwsmZw+FwpKCIRlS5CJjtLjgSrRiW2AM4NDbZiWgBbhpQ3AhDErpWvVyB9FldEWP4XyRN0WYsd0u0dkasfVnFkMtojkU+pPMQHT7ZB5qQSZziIIY6ZbjoCGZgeWrZvgrAKc6fla5EgCwSI/9e2497zFjniNXeZefSbCGse9oK7O8AWs9XbZ+sVWFq7Mjl2+IE7/DdXxO1cDZbcnXXjNclnpvaPDX5aghX7rdqi02n4hzjuyOcnDkcDkcJkZT1/TSLHVUXHkIZuRNp9bljcpvvrgPFdVZ9JGPNfNZbrrNKqU9BYeKmGYhIHwyIDkAPoFEvy2hzVN1I3JcvPimoSdXJOuw6krRDsyBF42eiZRB9aYZW35fqZOGlOiOyY92ijTUESI7F2IYV5MCaKa5xWvS9NUSvRo3IOVDWzPtWh13ak1NrdN619e0x059SjbYqbWucyJIaWqOU1Y7XqeS9BiUnfG/VKlVfqm0pJbEWa5Se1L09Vbq90679jdor7wrwDmVS6rfySgibkzOHw+FIwXImmeebbwSSM74uUhtXBHJDccv5m8OgrgHzl0RccxbLinVKkkRzxY47oQmFzT2IGdxPShIkcWJxwLL1omvE5hyahCX6Z2aLJmh2j5iYETpZThvaLNsabUiFillkLKWu5chM34c+UeVpJ4/mm7yYjqm2yUqzxuEuQTv5FgFZS5RrsFU93OIs1yoSOdSqkrUqwh44RV3QfZJynEvtlrDavlXF20JyrHpL5LZkS+n+Hs/WNeFUcnoiNhGr1O4YW8oKbR/f2D3K53DeAZycORwORwkpwiXA0plG4ge/IfDNYdiFUZc5vmgYdBxUMD60AymI6ltMZxERuRZNFmvZ0SpSKG3QL25ppzzXTalTxDxlFeWaREXXIT4vjhBQSqDZnq0Kh0yfc/Li+Wq5euMlqbjpjVxy6lKuXMvh1m3OjeWpM/VrFJnaMmWatSpYqe012OqU7q1UbCGxucmHnPqZmyyoQWpCIZXOQuoZjvfWlrez/YtJmxXlj2HviXbsofhkUb3FX+H3ZgsKvzeLtpdsTXzfWRZjRDKU+nj2+wyAuh70PM9tvwKy5uTM4XA4SqhxRuQ6sb4HOtiz0jeH6QUSBSypFgHA84+Hfx89mPKpEDFdxqh2oZ+reYYNHNImiY58UUnHWYZFjqSwGRU+6lRRcZZS9Z8+fHt2V78ko8PT93P1znLsWdhlHQ8g0i7anhpjeaB3DZECxkO4EUMpS06w7u+UumU4l2P/lWyzHKfSLPoaJ1UTrBQRrSljDU6YPTfz1xLammoyZSTV1Zq8ubSCXOgJo2VB9nX93dCkQ9e3CnEiZyuhbzD8Dqwh/7myDDBo/ltTS3rC94lPJT8bn70q8reHClWZ1iJS82cnXe6YV6ZvlvmnZ7zCBmCI5OfwLDPQPOlAt93w7roCUhbh5MzhcDhSCORh8UKLoRDynhGKZ84gtsIBFw7A6AC1DfDwAaJiMyMvmpgJu8bz1YS6tQhnjHmbKU+q3bOP2vFvAHAoJKyLWzUrLNvRzJ2hWEq0fWaDwILwybIjkdTkJNZrkNPijKuwO2cHEMYwQ6aK7dFljqQYc8dWjqO8ZoWgqr6UjnuKRFuOdLHPrGslhzrn/G5xkkvo5/03R6JNWxy3zDM1OvApZbkm75b7FaQ5RS54J+K6xQWePWvtSdUP2PIs7Uiyho2apr+zqFjrmiU9Bbtm9W8geMnfThL35eNNmLVJ1z8Rs3Q5HKLM2VTPxN86QGUkZ8O/h99p0fzOEUAHOl6PeubkzOFwOAxw0wxrqMK/AMYfbbrtprVP0dmO293H/KlZ60i4um7paDcNmAn8kmfHtADAfQ9Ys7mSwNAQVki3g8LDdBgJCgPDy6bjsZ6YZ2ajVrTCmrO5sxbsaNvJDtkuS2HSxwjE8mSeZqhfh0ayXP+mIV6iJYdVtw9Eg7PZhWuSNCuVctEHJdWjER5Bj2V4Y7S3QoljonFtIt12toPcqPRtXv0Y7+i+s8inqi/lsmgCt3DYahzq2UY0iXwlIrcXgdtBOdtcrnou5O8KGQOQul90+ktIHdo+c6yNnVHFPY3kREAJqeQrytncH4vf8FT5eZJSTGeknfKks0yqVCxDfc7VI4lPwQZgIkWTXdZkRrw3fWZl40iaZFpFwjRJ40ala6dylhNLsV7dgHA7kLMHH2hw8/4DmifIHztzYTg5czgcjgTiWWGzlx0RiAhojPAVIO0sBIdrVK64AY9yUSALLQH9UsmgSBrGFxoJm0KaWM+hHZWj0X5gKBf9VE+4ZtoeX3hoEmvnVNOkGoehDTINJbyZcZOPWIWl5AlbzHsp9c/ArF9D3rFPxDVd98wbKZxosKivmfdPshyri8I494cwDtzMnYeUciXJpHb0Z4pNnhhOmQphmTJvrHul870IP1o8YxWFRCKXIhUaBUVi9zVCa4uzyDKwdCBn47vEbqRETwyM302I8Op8kdK+UwlatVpkPOpD/oo61TNSozItyi19HvOlic6YxvydEPlnxCmXVl4z1CrTvoQNNCfpMtQwkqmJRIU88rMiYqMapoibJHRMgZy1Io3RVk0mp8aE/0DgBw2422tWZx84OXM4HA4LLY2KRQzDiC8gbgmEZgpRDOCGFi9z6ubu0viCawHidlb2KGCEre7HsJcec2e+pekFFnaBZAyzgfzgsLAZBIxx9pirKLKe/tBMLzLJAQyCVnSsUi9EfT8VQsIqnWF/bFe2TlXeqOrodqbyphzU1P0TYDsYFEI/Adxk5CcdZtuL6yk7rXBVmfdEf6Xa+a5Mlhz3FWUsywzZOdH/NccnFMoecS6VJ/ns5srf8P2NeXPPhUmCTiNhQxnze9aapGKfJUlROe2czFQSqQThkeqPWX/mc5KEjCRH/Lat6MdIcorkzCA8k00qs0jLbcwffsPj9dgXNHIlcMOYojoMctbw8PnAQCulMGFCw8PcVtsDBFAz/WDIn4/H736EF7/jBg8aQvP87RTaeMdwcuZwOBwJTLN8E8nhoJxxixCWt0xTLndINL4GCOPsJXF8cU2khZjCCynkawh8aKYCWlWeRCPK1SGDjPkZbA3NXv4jOQQhRUqkczu1z273Yo3F6OAUCFDIK5XKJJGxlCWeGxcV0Nm9nAM5C7ej9D0D1mYK1WFgNL8uHYwlaJZ2Cm2UfTa3xboX886KrSHACZK7RZ1IwRy30a71JKrGKR+v6T6onRRAnSNv5qveiKKivky9xfVP8ZoY43zI3PJmNdlcRczS3xkLSRsqyM/iniJYZv5UPqiJBsOORXklMifIS2rN1iy/Km9GuPTzHa4tFSwe88/ykEofyBmIp/bPyFmICiGA498QdcTy4+9fIF7tTY+m7UKAwdzgpmEQMQ5tj4YYTTPMOOmfmt/40A36mwa9XqN8x3By5nA4HAa4acBCqWDCQIgwkKMFGYkKh3bGO+X8yxj58EKTjjuLvH0sq2NQz2Y92nHS6hI3dj0S1PM87awj5uXGEKGakCJzpyzjul4cTgxzfcuInu08iXqrUEHWdD1mfWNfL52geSZd//Lv1C6XRefb4nAySyLMa2HiSoVkkd+wXz73tWVZzm0x3azexP0aElHVzszNkuOfTZ9qUEX9ld/JIsGw0pTqrykzkW5Ia9yQDj/E3/qzzKLV0JTdK9Kkykv2gcxnXhMEx6i/VO9cccKcAOkyjXrHMg6cHI8pXSBOSq1aTBrFV1NUr4hxOAwkKpKktu0HvkUDiWqb+G+PNpTbEI/3G2IcqB/KagbC9aLDYzzT3uLQdLihodwehAaMh80tGmI82z5BA8ZN2Eq4Dek6btCD8L8fXo3HL3kFmtsWD97bgI7ibM87hJMzh8PhsNBgCh2M68Xi50Mz+LeReEWyJElLiGmnMSZ+IlOLsDpWaeJnGXvfT59zCh0rL4EbDGGYjPHs6amNIW0X65+uzYhIP61hWji6cqF3KkRRNHUoJOMUB2eDeppfl2jFRdllWr2RfazSa6Ni35IeC8xJBbP4rBEcJBI76Q9lG0nVWMzSGOSMxDqyk8lZAloFrSVFubJMI5T9a0jLptC1NaqeRY5kn67oh9w6ntp2VJGaNeVbfZBoX3ab8lKdK/u6aGO4PguDE4RnzXORUp5Sdcp/p+ucHV9NJGdliDpleVZZVSpYDPNreNkWq1xS1wmgQz+SrNTPeCRaTTuQpPHxCARKpgMGIkYA2qbHw5sjGuKReN20Xfg8EK1D06MB49B0OESVKzyUh6ZDG8hZQz1uwr+/6+Y5PNs8wQ11I/kCENJ0eEBHPNs8RotQT4jXbqnHLR/whFv8s2dfhV+5eQX6A5nvpbuCkzOHw+GwQNPGGcMMrCBX7fT3QkmShIAwOdTWIuexDJqIUT9N96ZCJlnaEIsJatNYPWEMU+SGsFBL9Fo5Y4c4juUKkpoiWJOjZNUl+gTq3qyMqY3TjLIiKIZiFwnRorwNYWE2AarwhsUtvRPZzKb4GCilb0aIDAeMNOnWeUqQ7Rk3p1naIW3LOp8KVc73+F0x6kkQhKn8DFEo2JRWztK2WXWtVb6sa5tIWe77kkqTefayden8Odtz/bPFnpJdJJ4V6+8UKtSpnDIFyIml8FmF9ll57R0Jed63BLC+lmqDZeMYEsgDuaKBLA3/TmnmP/082h0J181NNypVANCrDpUK1yEQq5gikilZvlS6HrQdnj08GclXJFo3TYcGjIZ6PGwG8haJVlS4GjBumiMA4BEdZ8TrZe1zeFnzHG7oOCNnLRg31KMF4xF1s9NFumD1+/oHeF/3LB61R/Qt1b8rLgQnZw6Hw2FhRobkmjPMQ+2UQ7kIfaPl/cUOYPHfGFY4KnI03Rc7zrHcECSiZ1BD4BiiGHdCjA4AgHhimu2ICC8k5ZDJDznnixLOdImczRaaz1+Y1IdxMIhPvF46C81EzkE10i2Nrkgz2oPRY2Jt61gGmbZYYtjsGsG2RfT59GyK58LIYyxJK4eMaVuw7H+9GQubtmWc8jUwvqIaSVKygUxly12QmEyhCSe9rPLlbVpFpOL9Gltq7EzZlMpjkaMFKQvKVQwTLznXZHxf5L3ZZ16kM0MoE2rXeC1HvORaKiuNIE+z9LKISMKagSg1bSA0owLGC4VL5h1eRUOaZx7cgjARrZ5pJGg6vPBhexyvRXLViLLlvUPT4WHT4cMOj9Gix6Hp0WIgY20gWg31eERH3NBQ7gM6osEU3nhDx0C4hjyP6BYtGC9vfxsvbjrcAHgktndtiHCDFi0RHtIzAIBb7tCjR8eMHj1aPMbzfIOH7XEYp0ay2buHkzOHw+GwwHGd1/CDPRzEHHzgcOwY9TyRmaCuEYa/Fx6hfI93S3eRafFeBsLZaTOyF+L3pCMr12hNCtJgBIlNPqJDTfItPys7GpnzpNJp5Mz0jChpohCv6UnnHrP+1p9l+yT5o5T7LXfo6icHbixDkr8UYTRumLteJtLO8g2Js2lYOFPLm7KgPCzVaOYbVjgiJhcX40YZWziOixr72bVZWlWXzEcVRHuDX5UjPGP/Fcq1yjDzzIhU/hmwJgyyX8n4u1MiaKJMyqQbx0MQksWzkMtn3U8RQOsWJdIEUkQEjJMYIzkrfa+MPhRtm7dpasj4lVPkaaiPTFvHdOG3dha6OPYpTTYbEQOx7LFVxrouDteJhn/7nkAEdJHYIRK4+W+KJmyRzLUN40lUGUVjIqlrwpqwnmmmsjWYhzXGewfqcWg6PBk34+hx0w8hibfcjkpZSz1u6YibZiBhD5vbwR70gay1aNAP/1KPjhq01ONZfoymZzyiHp3YzrhlDMoZ09gXt+jQM6MJDe9A6LlBF0JEhnd54Xt5QTg5czgcDgvhgOLZC12pNuOhlW3YZh/DuqCRSMgf+0DoLB4x7OCH6eUgec3s70AYmcb1Ywt/I9bdBYe2n9oQnZC4bfLCRwzb6g87ZtneXVxnN269LBQ9SQTnmaIBbH/GRBxjH0+T0iokKd4zZre16qdDHWefeeijmN/cqCLWr65HBUiTwtFB4SnNYoASBGskjlb/rVDnAOGsQxFH2Q41+CbJiP/LkWqDaM0ui7Eex1iN/0IFtqBD07JpK9IkoEPfqs/SsspI5KtpAzcqWy6PGMtSKGrJtimdJAzryqiqI0s252lmn2lSsaSaVdwp1yJRqXrGPLxMb9Uny9b1ybLFtYEskmiH+m1URG+mwKk6eLHmLPzmC2WPxN9jNUE9IxqIXdzhkIgXa5ej0tY2PW7bdqakxZQjWQv3Dk2PlvrZGrIY1nhsb6fP6PF8c4ObfghpfL6/CWvR+qCYdWM4Y4set80BN3QcFLSGcYsej9DN7H0UJidvMRC9jhkdgEcgtCB0TOhAOPYtmm6YCHRy5nA4HNeOPsymETCu0VGEIRIqRnjZibC8kbhFdSW+tq3NIZKBVwmbGsy3O4cgguKMqonYBBs0R+rV/UjsgKDaqRc0z5VCmWckgvOGhXtK1SOlCAq/ZEwrFB9RlC46uw17zgnWxLRqzYEkk5jGVtcZn4u82qHsGf8n/s09FlmStMyYX5OTviXX8+kwU33NLisQWfHsTKqecH5nxNGwQzmrawlrtjxgHqY5W3tpTAJsKH81QSNDLbSeiySx2GbXlE6RgVr7LRJUUd+UJ9HmBRkL19TBxFlYZCp3bTbTo+41VvpyHXZ4JMZnLk/uDPUsvHeGf+fvGBaEbBG2OSOcjONNO5A4qdCJyS5J5tpDN16Tm4HoDUJaGrawbxvGBw6PBqWsHdaZxQ1BDtSNG4LEdWiH8ONy0wz1PAhrzh42x3FN2g11+K3Dh/Di9vmRqEW01OMG3SwEMq41i2TvN7sPw693L8EHbh+BOqAxolnuEk7OHA6HwwD1/bCtLiJJo/GQ5vGQ6BAKwW0DbpuBKDQYHPNxC/2lGmY6ogxQp6Qe60BrZvChAXfqXm+/XKSKEusbdyaMefRmEWNasWlEP73oZ3mEGsaxb+JLfVzzFuoL7Rvj+4OaAum4q10ThzaYTVsqMVYbZhnS6ZJlARhXlKfO1tKHN+u81r0EGVwoNUqxW6TRkLcyZKdY905la8XTHMv4nMtnWF5TxyYUYSizi3sF1JIYu36rvBV9i8RkgS6CjWulutaQxCTxK3fGNmIGmDMSipANf9OS7KTqTdRfIlWLdIYdoy1WHdquSMBmxGhZZsoui7jOiJ9ef0eptLywoW+nv1MKImg45rFr59eSKl8klA1ANz3kpiWjShf+bcS/45q1pkdDg1pHxLgJW+i3gch92M1jvOjmMVrikcABCBuODATumfZ2tsFIxC236LjBe377RWgfA81jniZUrwBOzhwOh8NCCCEEMKwRaxjENOzm10+kgjqO7AWMibyNRCScjTYjXtaGIj0vCZZ+T/SBLBqzfGO4YZy9TISvybon5QwT6YxVR/KEQAq0bXpTE8I8bTz8Wjhc8/ZNpFXmyfhm8wuSK6bW8OjLC7lM3gpKmFVWTxOBtMqLB1Nb5C0Ou76nD7NO2WhdVmlmKmOKmFQ41OOseQ2R7DE/b29hr00kzdQzJh4SyWe8m8qqpFb5exWFJJ38ygOzdVlZdTy3fnOWLt40rs3SpetKniG2eLYTBLFQfrGeCqSI8VzZ5PF3q1oxK5aftlkTpoUSZZVt1JMjermDqbMKnyBlWplbpNUkL9jUZMnZ3NYhpJ2m67q9sQzi4RlqwnuQgD6Qs77h8QiAUbGLZC3uPgnMCNzh0I+hlA0xHj14iEeH4xg+GTEpcR0etUccmnjGGQ9rzAD03KBnwvs/9Aw+4pYH5exKiBng5MzhcDhMUMfAkzAbFxxREA3rBHj4IafbbpptaxpQ14PbZiBnx37YVbGdyBl1ymFVqgqFckfH9NBMIWHAQM56AG0PNHPPiQJxQ9MMM5X68OuIBtNmEDGMMSpb4QBOPjRjO8dyCmoUE00KU6ynaYYyjtOLk4nCy1d6DqHsW6CJa7WsvgFsB0o7kb26rsowMW4vX0FianeFrAmV1LaW0umy4/MontHBqMT4W9fVMQpVGMlsJp8uN1PPLMxVXb8IVpCu5C6Ua6CJVk32E4h3xKr+zJUrn9c+cX1R98Z+W5CricRUtycxvnMylS4sq6Sm7FP3rbMMZ+ktMiXSp9bzsi7fIpRjGWSmiTvlalVwqf4lCKVMO6aZNm3RytyiPvF3PExbpmECHjfTPRDw/hseDs9ehFbzeLwA2nl4Ofc0fOd6Anrgwa8f8PC9tzh86NbcqOuu4OTM4XA4UugnwsRoJqcoOsPx745BQTkjCgSr70Fo5mvBIqkDhheXvA9xPxIR8Xesk5jBHUAj45jIIhDs7TD/LN9O3fRCHp2jRtQRCBm3geD1wvMyw/YEwRPeAPc0ZOgxlRGIJg+xn7NiJAGdlcWJdOMFCnVh3lcAwGGjllh/aWaU2d7FUBMMbVPKqetV3kSds7QqrHSWxih7IDXBJqbZ2IbMqYrnxB9qZ1Jg8Tles/JkGlj4jLA+EiYBvRQ3G9XRiAypNX3kUvlryZuBhSqurteAdAjpJiVwW1tm2aTyPSZYU8BEAFbBII8nEcTkfatuWt4LKqCZJ0mSVEcKspOzbQwbXRAucbQGzfMs7cUYUjrcs0Md5kRrImdjOGqRnE3HpoyvgoMYcwL6m+E9ZYa2BgI33o+X+vD96QnUAQ8+QGifDEsYspN3F4aTM4fD4bAQSRQz0IUf7lvM1QKhyIyEiWkiOH2P5vmgvh37gSQ8uQUdO/DDB8CN+gmWa5uYQTGMPr40un4gL4cWPO4IEsjRSIC00x1ShXPPhnBNTEogMJKbiXT2IKaBdPbKY47hmeMZbMGORpCWHoM6Fh3e2fb+APF8Z60R6hiARd/EsuWM+4GAdrAV4xrBuLatAUkVUfZH7COlJM3IDTO4aYa26Z0fWRDXpjH7PNYz2t+r/It0bN5Llgm1AcviMxb9JsvI+YLjZ223sN8MM8ysv8sqJZb6uUZ5zJWVy7Pm8Nlam1KoXRtppUWau1yMwI4Vbqsx9R1Jpa3CHocHr1Y97fRFkmwQ4RLJy9WZXAuYW8+aIpmSwJVURr12bmGrLI8ESRKkrkmUp0maLKOZp+F2We5kA80UuiF8fiBn8beQOuDBB3scPnQLuu3Kk3cXhJMzh8PhKCGqVuHcMYxb0QvFCuK3XShkLAkDM+jYAbdH4NCC2vn+6azXLul/+34InWya+a5a0s5oV/w8EkkaSUZU1/hAEwmV6AGA5+QFGGbbRxVqIHvUBIdbhx/q+qSZqfVWWKYdww2lOhbVNQoreQKxmRQkW0Wc1RPLVmrJrO+Ccsm94VQG0j7mS5Un2rxQ9rQ9iXvJtDWIPLhCgRwhCWuCR48Kr1T3CunHeq3QSytfqqxE2UWbdZ7atFtssis11chU2vEvIxxwlrJw3yw9QTBOyVsCKaKfTQvkN9qJ908aD1mh0SY1cVMuor5fxudA30iu8yuULe5nSeJirbBBDmvCOytCXmfhp0o15DbeU0RPEruYp5nKkCpZ39IU5riwb15GXNtMPcR/jMNz/UDMROj9NZA0J2cOh8NhITraYafG+SYeQUl5/AR0ewQ/uAFuDsPZYi3m6ldcG9aF9Vw3h0Exk2vGBMHLommCkkNzx2Hm5KqXTLB9IIdzp5huO8h1VgsypsvX10NZIzER9ZIgbDrPWE9VOBFNREDVAWbQk0kxm0hs7A9elDPWK8cXGHfZXBAHZtthTaSd1d/AHlupvlp9btUVy43P4SLEi+zxS5VXo+Jo5zSXJnX/1Hu1KKlQJdsvAU3+K9LPnr/EOGQJVaLtI0ne0AeLUGkLVho9UZAJHZ2lT2EvYjZUPiMq8z6tJaz1fZkkZak2WWOfItiZexZmBK1Giaz93YYgSyM5mwibVd6M/CmSNxItQfBSoa1WeGY8bDruPkw90D4eiNk1hTQCTs4cDocjja5fEDOppNCxAz/3PKhpwIcgp0WFRBOE6DhHcqWVCvnyjXlFKOLw2VowkXA6axzrrh+IWy7NqLwVnGuLmKTKMhSmIlQ4JfqJaFFox8LZzDmBus2pjTlyL21N2HW+3Ix/iRzp+7G9PYbnQubZqGQs6rYUrVTanK2l9LmxtyYdrM+5+lJllvpY12/lsfLqOteQpcLEx6x86xlL2ZqaOCjd2xOpckvEcI09tZM8FdiFsG7ty/EMyFRbrHLX12Wpnqt7TyuaGUJXXMObsGlW5kjoaK4KNpkNnDT5Y4bcGXjcbTkuNxiNuQ6S5uTM4XA4Ukg4eaNC0TSgZx8NOzKGw6GnewNZ4KCQESlyEUmYJGqKhHHcebHHQE4O7VjeaKJ0sOR/KcdzNkuqCQpP12W6OIvZG+VaZfZskzlJVHUZJVjlakVTIxI6TWp12yyUnHOdVqpxDWGI4UK6jFqSw2JMeizHLPxNtbaWHPK1xGEPYmj1dYmIpOw7xba1Y5JLU1PHpRxBqy/1dyBHhE8hHSlb9nh+NHmtSVuq8xRimMtrPee1kx9bYUwg7KIQaXXPUPvkuljj7ox8maPRYdk/FNZIS4WzpPQlJuF0CPy1wcmZw+FwpKAd//hDHs8wa5vB8Y/OS4+JgESHPaocfTN/KYcwx5kSEkFhRrA1XvzqZTSuxdIbd8R6pKIUCKSciRzbYzkMpXC2NQ5RrCelNlkOoUYkaLHuxrBV1x/TqjUO47/jLLCq23K6Ev0xEvBYVxi/xU6c2jaJGkdNEzNLiax1PK0Dn3V9UgWV/Zaqa28HtGZHwVoSXXqGa533GtTYlBr/1OTKWrJ0qtNpOMZJYpnqr9pnfCu5TV0vqdKpMkrfHes3YcukQS7dHn20pr4zoUgCK9ceDoXN257rCRminw5TjYkLE1V3CCdnDofDkUNQv8bd+aR6FBWZSBiA5cu65uwUw4khZuDIE6mK6UpndmnyIa9LsiedwYYwe+XJewtVTPxt1TeWB2NrdOO1mpvBT8FSzAQxGglXPAXbqnfhVOUJWPIaAjHWDkRJIahRhmrySCJpzQSnnLocsR0nAmr6raKurSht854iilud8Vr7t7SzVmGpIWW135G9nc7Ud6OWPGxROuU9i4Dt2cZSO7aSpLU2APnfgVS6mnv3ETXfX0MZNDdxuidwcuZwOBwp9AwcmuEwaGbQ82G73aCWjWGHx34KoVOhitSpmA/LSVtsnBEcsNsjqOdhITQNqhFRbzspEZJoRYem64G2mSs8Mn/OIdQveovgybQx7JK5vLg85+BbDoYOA43p5EYZMs+hnRMY4+U8Em+L7Mn6co6hVDvWEExZhqxXt1sjpShZdUe7amfp9fVzO6Rryy6Rwxp7z9WeVN05YranLSm1q0Ty19qTes5q8pdIdK0Kl7MlVWeu70uTKSU71tRfMx415Cv+XSLNNeNdS4DitZq+PDdOJV/WxOKVYI8TIhwOh+OFCRnuFR14mv4joWQtEPPodVLxeupFIBWpphlCJ7U6YtUjYRGueKtUfwml2e8UCcjYtCin1DelvCtm2LPhNxsO6N0lVKbUTw2lQxH3xrUQsxoiuXedW5Abjz3Gao/2ltShvVD6zm8di62K55oya9Lfd2Vqq/0pMnnKe+UucMW2unLmcDgcFsLBwgCGLefDNTQYz2iZ7fQk10N1xgyw3iBDqi2pEMG4A2SnGKBUaOQ9SR7liyeUv4qY6ZnQUeVjADyFdOZmcWObD838flzj1jZDn661R19fHGAcbIz9l3ImrLA4vSvkGucyRUxrnYA1CgdgrwmzyrHsSM2I55BTbEuqQC0hyNmZewbkM1qjlKTsONX+2vu1jvElCECt/amxqFGuapUZSwnaqq5uyWuVoaH7wXrOTvktONeY1/yWXWqy5xqJ0Zrf6jPDyZnD4XBYYONwZgu5LeZDOQDys706LC9VDtFyU49cnVucLl2ftLEE68Vb0y75rxW6WLJNvlR7tp1snTdHAs6FHAmRtp1adqqckvORIjNrw65qyrZw35UIiUs5eaf2WW7iYo0NpxA0mSZlk0bOxitysleH+931d+ASfXctY3PFKJIzInoVgL8D4BUAGMAbmfmbiejDAXwXgI8F8MsAvoSZ33s+Ux0Oh+OCYAaO3VzZCi8VQlDVUirUjNREJSaEJ8rdHS1FjHnK0/PS6Q4WTGGT/UQ2ega4n8pSxIiJ5oRzVIkw2Sjrkf/Gv+W29Fqt0EpUJJEWWYz2wupDcQRB7IuZcgd710kOxK4T9ZWcDX0um+wzTYQjcbRIneWEmWOX+Jxrl04fr7fxeeF5nXEsSpt+bHHEdJ7c5INMJ220VNcU2a5VMDSssk9RDmvSlMjFGiJbkzY3qZIap1qsVRFLqm+0qVROypZUHfK7krM5V662s3TdUvdSdllp1n73zj1poyHbdiqR0v10bcTslL49I2rWnB0B/AVmfjWATwfwFUT0agBfDeDHmPkTAPxY+OxwOBwvDERnUjo7AuNB08zzl1hMl1GMWKZJpUsSM6w/EFrYlTy0U9uWIhNGX8TrrO+teeml7JboVR+scYJTZe7hgNwFLAKnUavqyvQ1fb8Va/q55tlJlXcfx/Oc2Nof5/5ubC3/VPK7FXuWv4aYbcnnuNcoKmfM/C4A7wp/f5CI3gbgowC8FsBnhWTfAeAnAXzVWax0OByOS6MhgAm4OaB/dAB6oHlynDsUDWGc49JrlKKK0xzmJA6YzsOK6TS0oibL1WniuqoIVumkInNoBqWNabJP2UxStZMHiDbNPNzwGNfhBdLXErhtga4DHY1Z87mRQ/1yzZlSgGbntsn+jmU9uQU9fwTfHIAHN/PirbPUcuerzfpJpNPKU1SqZk2pmHmtcags+0qz87O20fyYh9QZfcA8jFaS3BpivZZg6b7e6uCWJg0iUgepa6RUtBJK310JmS4Vjmw9P6coSiWlYk3ooMxTS4RrlZktis4aNW+NMqW/+zV5LXUspa6tQU4lLOWJsOwqlXOF6tHTjFW7NRLRxwL4gwB+CsArAnEDgHdjCHu08ryeiN5MRG9+0j13iq0Oh8NxWQQ1iNuwnb4V7iHVLwrheNEJjf+1iZ9aK/wsIuV4RQegpGQZZc2ULW2jdCwsVSo4u4s+6IUdpbVwhgPFsn7LoU6ER1HPwOMnoLi5yDlmlEvhcBZK5KFmvOS5diXn1zpMeu0uk2tDv7aiRJZqxrHWiTxV6cuN097P231QQ9Y672vT34c+2IK7btdaor8l7wsFdz1WAtUbghDRiwD8QwBfycwfIDFozMxEZLaKmd8I4I0A8NJnXnk9LXc4HI4colICoHl8HC5Zs/RRAYqkQZKmtbOovVgvJkmdJiCa0ElSZSknFNZvhV0nmeaHdULbLMM5FbEcz23T14+DjZQ7dFsrPcByTZm2fVYJjXbxzQF40bPTOrnY/phfk1qL5FjqobQ1NyuvbdbQG5vUvPi1TVZ9cVziNa3mpfJG5MrX6SRqCF+pn9cSptR3JqcExHpqCeoeCmFtmTVITcrsFf63VjFbg60KYKkc3Sdb+yOFLUdmnBvnIgqpvlszVvo3LfUb97SRux1RRc6I6AYDMftOZv7ecPnXiOiVzPwuInolgPecy0iHw+G4GOTEUzyg+FaRJhmCl3Dgx803erU1ew5WeI0Fy+nPHUwcbKaweQg3maAJWW4zT0t9vwynHOvvQb1RToSlPuUckFIoVdvMN5ZI7dIoCN0UXkbLe2ug85XUwlPUoNp7tQ5XSQ3MEcOcHTV9cCknWJ8Bdy6nfotNa5Eaw3O2Y62jrp+lvW07tcza/HsQ4nPaBOyjLO/VtprfpitSo+4TimGNNEhk3wrgbcz818StHwDwuvD36wB8//7mORwOx4UhXiajuqTDmDQx6zms5ZquzxS0xAt2FtIX1bDZboH9vNxU/RK6TN28pgHftOCbNu34EY2KFIW2zNaAqX5a5NWfU/1o1a8P/u4CIdSbr+hyGgpr2JRTYKXTNufQNFOoag10ujh+EaU+KIzfDKmwx1LelNKW65s9ZsHjOK5BasxrkSOweyk758TaNpe+X7nrsoxcvVvvpWyQvxOp/Gv7/BykoKZvt5S1F3Lqv5U29VtRa9uW7+Sa37dL44rsqVHOPhPAnwbws0T0lnDtawF8A4DvJqIvA/ArAL7kLBY6HA7HXUKoLeOh1MzTFvYxTTx4uva8MmA40LoX29trItMZIX8RlpObWrMl87cEftgO9h776WBqYDx4m9pmWbdWIeIGB9J2NSM7hk9KEiu3ojfyzPq0Z1DYeIQPLdALO/Q6PtlueUSARX705xTJAYYDx4nAnSLrNbDWg+WQcpK2zLCP49TPP1tl7aFolRz22TrGMzlBqTPyavovoYCvcthKKuzasnJ5tqof53BA7wPJPRW55yM17uciYOdUo9b+xtWUBez/HL/AUbNb4z9G+pf0Nfua43A4HHeM1Eu2B4iEoxt3GYz3gSG075bDBiLtspzRWeeBXPSFkKXUCy310usZIOUo6PKYh/VhHS/X0FnlW4pdDQklGohgp14ga9YDNQTWIZ6xPT2PawJPQklN6QE0Bech1V9Avq1rSUR8fqx2pxzEPRzgnF2p74tFBtfOllv1lghQTRlbsbbuU5Ej1FvbtXcbTimvltSUyr9kKF8KVlvuknhcQpmrSS/fkZcisC8AVG8I4nA4HE8ViMAtgToEMtBPKsTNYXjR3B7DWqtAWm6P4OefBz16BH72kf0yEmFupO4vNuoYHXuhVgH5kMZ4X36OKhARqGPQc0/SzdZkzAIz4iYj42YliowyDevViHgZ2mchRdraUK7c+j3ltErCLG3VkCRBK0tqTIgZkEcDRGIobbJgHa2gbUjZWCLlenwtByjX31JhSqmWugydJzVeqZn3hgLRVRMHVtqSw19zX2KNk5wai5yTWTvhoPOmns217cvBavuavsgR/lI5exHBXDlryr8EWaoZvzUoTdTl8lifayb8ZLqa+kuTN3uqcefEFdm3ait9h8PheKoQw+hSL7fg5HNDo1pGNzfzjSr0fxHGbotU8xItIVMnWXbovLHuWsUnc54U5eoqodT+2dq8TL/lPucUrzVpLOxxaLPGNYWkbQmFTJ29tmVG3bqfsuncTteem33krt8Ftqp20iHPpb+0Q3yJvj2ljrsY+y1jfEVE5oUIV84cDodDQjgVBAzrnoKjzYdpy3wmAsVDqJshzJEfMPDwwbwsS8UKG0zM1mSJdHxohjDKJ7eDsqM3pJiVpVQ1fYC1XJuVcoblLCnzcJh0S0P4ozpsehakGBUovfYMGDYQeaLWrckdHvXssjVVSATEg7CtnSzHWVke+3NmoQ75bJtpB055PZZbclJyO2Ja6XUa2f9rFLV4rQGAORleqK0l5EhjrpyatZQlpFSc2ViKtKU6TnEQt4ag5ZznPcLvJLYoMFY/6mcvln0urCFve9cp27omhPpU7EmqSs/iKQpgSr21ngv995Zn5z6QuCu00ZUzh8PhyCH8cHNUiKyXPYnDlOWOgbmXYA69cc1S8KJNuh4rjDBHGtaoVDVlpmxeg5ITrNW7VP2nQtuhw/K2OGW5sTgVJZUi8fyeHYmw0U1l1F7fEyVFNoVrcPxKjv6e/XfX7T13/XfdPsdTAVfOHA6Hw4LYXY5vDsPmFGH7eYq7HAoQ80TQAFDcROKIeYihIHiLsL9+UIHiDoUD2WuXIUKRBBLNlSAWuxvGdXFaPcqt+Ym2dx0QTEAr1nHNdh8M/8Y1Z20zd8CtGVq5Hb9Oox1E7QQFBZDFFv9jGusac1jjJOxm40gAK9TOCF0d2riCBKYIo96xsuQ4S5uMPjIPPZd5ZR0xTanNKTXPmiCoQY2yuBW551unWVO31bYtyldt3tIYaDtqn5vaiYBTxmirwhfz5p63tbiEMngurJl8yLWzNJZbxitVVgmnqHxPMVw5czgcjhTiS6VtBgIgwwtHYqCVleE/bqcwu0V5uRerVr70mq6FeoZ0PaeE9OQ2A5mlC7bq9NbLvzfSWY6ZVsW0EhnfXKmXvhWKmKqvAqxtOhV9xiHNYU87Tq0vd/0SSlbOhr3LO6cjq7/b1+DInkPdTLWtRiHfOi7nCmnc8t3dinN/l2T/nuvZS5HLS/1O3EO4cuZwOBwWggrFN4fh0OamGUkB3cKMzScA3AfCdNOCOgb6ZlKXZFhk1y/Jmqp/RNzpLkK8TMfdJGO2uK6LaNpuPqWq5GbVW5o7VL1qb9x5Tx8pIOsiSjtl2p4cAQ3/jmvKugzJlfXF/h7JUKYebZvs41KIqGWHpeIEMp1q57A7qFL/Uu3S9qTO+NK7UVptTD0PpRBJ2a6ECrtAzUx6LXHUkCT+FEczNdmRUhxKqm+uPdaEzVbbt+bT/VVS/Cy1O6WCr3mmautfm24rrN+ylB2nPO/nRKp+y+Y9bd2qzjkAuHLmcDgcNqKCBQwbdESSE184liITHfmQLh5gnNpynI5dfaiYXl822qHWl0WFTyPasUYxiQSvdgv/mCZ1T/fDCiVtdgC4TpdyKHIz5yXFTqaT7a5xXnKbbiTqYdXO1dDqZbQ59nFNGNxWrHXoLDIuvjeLskt9v1aRscpek0+mrVF+9sKeytqlCMNdKyR71l2aRLsUTlG6Sv1xrvFyorYKrpw5HA6HhfiSYgY97kK4YjMPO9QKThP+7hjEx0FdigQsQhyezHGdVtxRcZy5NM72AuZOqt6VUa/nkvktNaNGFamZKdZb2sf1VDmSaf0d2xDbJXeZJJrWiqXUntTMPREA1Y5aomIRCAv6+oZwKmIeDuyuIYGpWW+r3qgc5rDGydt6pleuzlS6GgJec72k3NagZlxy6mRt+ZeEVrpPQY3TH+tcg1MVGKWCZ22z8lp/nwtWW0tKdOleKv2521Oy6S7VxHsAJ2cOh8ORQtOAuh54/GR42dyEn8yuX764iQCE7fH7Hrjt56qLDv2SREL/3aCsGJTCheQGHRG1DpJUYbStpbxRpbHql2mkjbGtXQ+6PQ4KUqxPh1cS5bePN/uTBqLc8zJeJLVJivwc60ptpS+vb3Uk+x6kx0eTKn023myWu8LZqSHkSfs2ELytDtg1OG6XCO+6hnbmsOVZvuswPqD827ilDImSglYbnbDGrhqSWlKBT5k8WANrEtCVs1VwcuZwOBwWaDhcWn4GhvCzmROdUzF6zMmHTqtDDa0XmUWSZN4InVcTkRKpKSGlwESi2jTTzo5RxKsNNxRkaqEmaqeHGeZrXiid5r02ca/2/K5YhrYplpFySOT4x2ulukq21Sp6pXJS9co6SmXXpNPjUqNkpBzNc6gZ53JSgbLisSeRqSnv3MQpNT5WP5wSnpcqd21ZW1SnEuR3fW9iWHM/l8+KLrDK3WNsUmU7inBy5nA4HCmotVvjeqBIQm6PoK4fNuGIjnhLQ3iadOYB4PY4OdI9ZsrSgjRJ5UQraGM96iDrRr38mIewSyvcLalo9WWFZJaeB6Wr78GPHopQR9UOXa8iKuN6skjI5EYjVpijVW7sC1X+7HgD6/y3VJn6XtMMawh7DEcNiHrNcnoGDk3Y5ANTO+QjJTfxKM1612It0cgRs/h3hkTpoxyyZa1Bjmin6krVl5r8qHEY16QtjeGpKoIm+rk0tdhi0xYVJyLaV/uM5XBpNWZtfVuU6lPy1DyrqbFb0/+lemIdWwnZpdXXvYnoDnBy5nA4HBai8iV+sPVZZsD8flI9yThALAmd5eRqJ0aQFKZw9lrfL8/virv+qZlSUz3T6pAkGKnQGLkznt6AJNU/+n6wb2ZP7kBpIvtctXB//CSub1YKLXut8mQfr1WwapSxWmzZNnwN4Ugop6Sf1dSztcae3Ox+LbQt+m+LOF3a2bdQSz6vyJFcjZp+LjnoW8bq3E7/mvLPacu521mrpp+r/L1xhd8lJ2cOh8ORwrhWaSI+0hmlrh82/Di0yzA8vWHHmEkpWS2BDw2IGLjlpVokXrR80w5EqO+BfvjcP3MAHXs0nXHAcmlWXG0isjjgWYcCNjSpcZHsPbwJm5yoTU2kgx3r6fr5rpNSBSiFJ8UDup/cDkrdg5uh32W/xjxWSKRZnoDcPMXalp7FsQXx+qiGGQraWKdBZqU9FhHJEQud1vqcu7dFAYqwCFpNeb16hmrqLzl4NSQlpzSlrtUSBwu5SY9cvlQdeypkVn6Jtf2RIrh7YE1/WX18jn4rqU1rx3aLLQUVO/k5N1l2LWTVMYNvpe9wOBwplEL8IslQL0aqdfKi4xgUuuI26hbfkyZWOO1JJYmN87wKZVVDq22J+leVtdUGC/qYghS0jfKA7JT9CY5exDlUnK2OUtyOX2+eUlveWlVvz7bv8dxeI7aExe1Zz33D2rE8JWzznDi1/Gt+ph0jXDlzOByOHCLRonBuGTAeFMyHdrlmqJdEhAZlDZhC/9QOhXTsp0OVgSVJkEpd3wNP+un+bYc2rKMaVTM5kyzDIGMaqVrp3Qf7Pr3Zhvw3kkrKKyljCKWlcpSUocSsK98chl0zLbWKpnYM/wolTduglSxZVgPbiUkpITocdY26VKOuWH2hn5HShIC2zVrrllKjYvnW+XlbCFpKcSnZX6MGWP2QU9nkvVT58XOpj/cMOax5ZrbgUs55rboY01rPeOm5yBGkWlVrzURaLdYoXGvK1H+fMxT3hULK7ylcOXM4HA4LuYN79YtfvCAp52wnDqNeHCQtbbAOFo7Ejnk4U60z8kpVp/YFbh0KHZEjKzUv8oTKWIS1Tqtmy/qajU1SZWxxeKz6pBqp76dI2Asd19TGHBkrpd8D5w4puwY7tiAViroFd6WcXuL7fU3fJceucOXM4XA4Ukg5CHGXxllI2/SijJuGzBzzQKSm7eZ5PjPc0rQboCZ4cjv9vh8UDLneSqJR661ouYvh+LlV83Pxoz70Oipwwmmix7dDEmt9V/hc5TpYCp52zuRapzEtymsackcN7OEApohXihRb56xZ5QlldbU9KVVK2xYh+9BS12IaovXhiTGvtMVSR3LEP5V2zcRA7SSLvFbr+NYqNDpP6TDvkkK3NQS51H9SxVrz/K1VvXJ5U/fXkJE9iMspvw+l5zeVpjS2p9qTUoRrUbLfsQtcOXM4HI4cpJPSY9rBsetABXVmXEMmnTBNtKQapuvV6PthExLhWOkdHpnm28czkflLv9h1EpgrW6Wwqq4b/iuls1TGVBiT3B0yqnjapvifJLeyTyXZ26LUxXyle1bdup3SpnOh9Nzk1NCIUh9JFXZPpBzYvUjzKflqCMOpdq45tkLj3ErfXqrMtTnwqWfu3HVcM/aMIADS7zRHNVw5czgcDgsNAVBqUVzLRAS07aCcqdCouC5tPKerp2lXv/iy6sKug7dH4MHNsPMgMK5lm9uAYd0a80AGNeGQM6IUttZvADzuBnvDbpCgHnTEvB3WrDkwqWTANMOvyUfbTu3U9sQ8Yx/08/KFvWClIMQdHaUyyGzvwKj7SRO+1Mx0tBuY71gZ0fe2Y2IRyqjq1cwo77l1vlbZUsjUOZ5RpvvAyqMPQ0/tPmnZmbJxzzVae5SbUx33XpcEnIfwluxIKZWlMvYiNVvWY+31fFjKay5tDlsVpK19tha5712NPU9r2PWVwJUzh8PhSEHv4tf1w39EQzifta6MBlJiKlPARPZuj+APfiiEKYa0cu2ZUpgoEDzWDp0gj0wUDj5upl/3aE+0KebpwoHT8d9eOaKRdMl2ChLCbTP1gbHt/Ayx/JwjoPqKW0F0A5HgppnsslQ/o08WCE4Lt0JhtGzPOTW6v2qUJU1mahStcyGOY4PyDqExPRCOcFDPaC59RI2jf83Qz26N415SX63n5VLO71qH/VzkuXQth1NtOldfX6KvanHfvmeOEa6cORwORwlyl8N49pkkU8zhGk8zlvJg6XhNvmgfPgAdXjqoZk0zf5FKEiUIBEuyqMPqmqEuPvagBvMt3Fltk980k63WyjBtS0phG/uHhzPRtF2UyFfhcIybnETVEgaRsGbBZT+n7gPztX3aJknQTJtXOD3ahvhvzbECW2e+S+pIVEJ740DyHHRYqqxPrlOzdma0HH7Zt6VnTLepdsZ/Sx6dtnRcQgk1atVeZOFUNdKyteY5LNljXS+R/DW4iz62Pp/SV9eAU/vovrf/SuDkzOFwOHII4YTcYFKIxo0zMNxDbxOsJihTMhQsKk/NDYCbgXRR2KBDHmos6p8pdfLlOZvRH8gi3XYjURtflCFckpinNWg9LQ+tVu1e1BXbJdOINuuNR056SWvbiCaiW4JQqZgI1HXTQdlRicyVk3AwxvYlbVbESxNpXUcNSk5sbR8bTnt2/C2yFZNbG63I5zBla8m+XDtPcLzH8M0SAVyDtSRIf59ypPVU2yRSz18p9HFLuVIx1ZsN5cqpRYmAWQS+pvw1adZOZqzNU2NzzTOSG/c1dslySrZZky1O1jbByZnD4XDkML5cCrOjo2KEQTE69vWHUcv8NemsMoNjRA0DHUbiRMceaHncyIQAcJ+wSSt2OTuN0K2R8FjnYaVQ87Iv2VRTR4N8uFaFw1IkhyOJCWnagiMp692KkvNTo4JY1+JaupQiVqpnrZ1bkSJykoRuJRs5nEK4X6iQu6qWUOvwXwvkeN8Xm60JjzW/FVY5a9p+H/roSuHkzOFwOCQWL5RMyBwJ5SGEdlHDwJFB3M3zRQVLr+FqjdAy+SKN6pjlzMcQS+ZJaToq87sj6El4oYbZbPOVGVSm2X3m+X8WDJVvRkpyTo1+6cvySP2r+0DnSzgNi76Vn2PfEmEMwZSHhuu2WaFuKdSqfFa+0fiEClVytORzWksiambaU3YCy/5IhcdZ96wxqlXKUu20npFMWQvVV9d/l7P/51QfrP7b4nzr728KORVvD2e+pAjmnuU91cpzIPdblqr/FLVd9lfu+6SfIT1xt0VBvDSuTOHzDUEcDodDIufMGmRltg4skSYLChtfZNSwk+2vKa/2xS/vp17SpXwrMB4NkMqbIjBbUVLyqKAgnQPW2JxLhUo5VluQs3Mv+zX513/XOLCoVNfWYq9nMkdoUvfXwvoeVfbdneIUW66pHRG1kQSnlL+mnJpna+2749pwhc+BK2cOh8OhMYYoDs7luENiSlWJqlTc/TDnlMbNM2JVhwb84AA+9mh6sYOitIPDurdDM63liC8UY/MPMA9b0RNNZVW8gGZrkGQfxM/yxR7OeePY9pQaE22UiGVFW2O5USmU6lnsqy70i1zLUutQZlU/RUgOren4ctsOtnQ8rGErQdskwwNLDpg1w5+a9bfSbXXcNSk7ZfpWtlcrE3Lsdb2ynTlFw1JR9b3U5xpV7BQ1RSsOqTose3Iz+DUTJqfAsvWSjvbaPq/p45IKu1Ux0c9p6hk+BSn1ectzUDtxZj2Lqe9prs77RtCuDK6cORwORw6WKpa6H9Pk7tduua6w2EJf50m9fHP31igwp8y2xrynHoa8tt5K5XJ3O55GrJmRryUqNel1mr2dwq0q5bU6p5cgeHt/j9fUfRc4R73nep5r632acIVtduXM4XA4NOLLto0zh8Nnuh0WdPGDm2EHOLkerFd5Q/gbh8Oa6fa4VJ8aDLsrdjzfPKQVZLDn+U6QKfUo2toZO/ClzkY7dpPqp2dM46xpLFcX27bgFktlJNogd5eM9/V6Mv23tpXDhiYybc8AeLkerGegC+2JG5K0ou6ebeIbD70uzHxT3w/PgVTBpM0zAq7qyKlQKbVgi+JVozxEZbdp5oeNl+pM9V8KVlpdvjgGoqgwldQ3y8ZUmbWKXS1qQgF1HXsgtndtmWuVsdqx2Yoa9VPaIdOU2l66fw4iaKlda+qy+uJSxHONalmaZLlC0pPEldnqypnD4XBI5F5ONbPocVc7ue1+3Ibfqot5IG6pbc2tfCkFaq0KlGlP9hDtaJdF+sYDiwuhZGugyV0ifIn6cFj3uZSOfmPZVl1rFJk1aw9rw5fWpN+K+AxdWtHYOkbnUN10uaUQy/uGS4Wz5dT/2jz3oa/36L9raee12HEP4cqZw+FwVIJvDhOh6oPiIue45Cx2eMmOyk/Py9lE+bnnqahGrSuT55mlZmF75QDG8qKSJAnBqLa18zyibIrXUs6CXkM1ltlMZaWc0l6tg8s5rLrdUQ2TqlxQ/1jm1ypdA9tZ0KqNdKi1SiPLStk7tlGoGrVhebnQ1JhG17cmLHVNeK22+9RNUCzVIzNhUcyfs3FNeKWl4spyrWc092yX6qhR8VL2a1t0/1m/L2uQUm9rCNA51J0t47jGhhqleatNe2Lr9/1cRPnKVKYXIpycORwOh4TlZMRr8vwu6SD1CcctFwZEYftuIoAjgcPocE2HRQPUEGJo5QK9qkuHeaXO2sqF2sVyJHGU+UQbzHKtsiybJTFKhWhZ4WzakRUELWlLyeG1YBE0/fcpWBsqKO2KduSuScjnTf6XI0I1Y1mDGoJTSr8FuX4q9f25nPCcArRHnTVlWH1/at1b81vfr0vglOf4ktjruxChJ7bOjT3q2bsP7gGcnDkcDoeGdOAkSYnO/5ot6SMMR5C08xjL57AGjWnpYMaXa4qUjeqOMauemllPOegyTLFRdUpSBZ7WmFWpHrFPDSUnR4BSymNK5TjV4d0yY62Ry5dbL1Rbt1b1MnaMh2g3K56DUv219pTKsOzZyxm26tZrFuO/pTovTdpSabc+j3dBMLYgNUH2QkLNd/ychMR6jnL9vMaWlJq5pT1PESmLcHLmcDgcFnoe1xhRz+AoykilK0WELJQcCx0SGf+LM/yaHElYJEa9GCcljuZb5lu2zfIKhUGrhMAUomg5u1Z5um0t2eRAq3atkSZ+zqkga1/sFrlIEeSUQqr7qDZML2dTqbwSrDyWKmldX4sae9e2Ya1zXlt+yTnNqXuXcBpPCVO8Fqwdu4qJhpPq3dKntQp7Lk2urC3PU2pSRapjNWWc6zm+L4T6CpW5kzYEIaLPJ6JfIKK3E9FX72WUw+FwXA3ky0Wu/QLmBEISp1qVQKVdHLbcq/L1Cz7+1zTzz7Ls8B/1PagLa+Xi9a6fdu/T9kmVxbJd2tEkXiXaQZBOP9FcLdNtjpCbq0RsOZwbGNp67PJn0eXGTvXpbNxzTkjKQax1BnQ/6nE+F3Jl5zalqXUMT8Ga/svBsveunTT5jKXu5/Jq7D0Wd9E/W9tQUp1PLafmN6T0uQZ7fafuA1m6NPb6LdkRm5UzImoBfAuAzwXwDgA/TUQ/wMw/t5dxDofDcacIs4osN3boMjPtqY07omql883yqJemdH7lBhparYuf9Rb6sVz9Mm6EraPq1da9nPSmI0TLQ7Ejcusa4uHPsRwLNaFtMW9JuYxEqutAxw58aAGEzVAsXlmabQdscjLmS9yTZdRuI080kvYxLLFW2UlBK42ptunwR3mfebZGMtrFUQntxKYyGjkiboWuyn9rnW3r2asJWUypoRo5EpQboy2q0J7EcQvhK6Uvqcx3AWsSbQ1KIX81z0dOlSp9j2vqkX29tZ9T389zkLgrI0DXjFOUs08F8HZm/iVmfgLgTQBeu49ZDofDccdoAvGIqhRQpwpYKkcurVKFRvVMb8mvbUuVLWcBU3VL1apEglLt1X2iy8jNRsY8fVDvSmv4SkpCLWrHz6p/LSIhjH/n2mnVoUgCpeyoIa8pnOKApfL2Q70ze1OOp3UcwynYSyWpwVql9BL1RlxSCTi1rbVkeE2dtWVt6ac1eS41BmsjAO4C12bPleOUNWcfBeBXxed3APi008xxOByOO4YgGtwSqGPgNrxY4qYXfQ+A5iGB1k6BJWdxdFIxqWs9lmRHHxwsIdUjS7nT6SAIShMUFGsXSm1jtDMKdIrczdSd1Bqw0dZm2Dnw9gi6PYIf3ADUzPtC51msdROfc1vNz9I04BulPloz1JbKpVWdUrinvB5VShKKYQ0iMYuhqFJti3Za9ubsibBCI2M5JcIUn7XUJjedKFP+a01a6PpS7ahVB0qKVvwcy9uiPNZgL2d07URChDxKY60tuTWc8t4akpRTY7YqWzV5axWouwj3OzdhWasc16Y5xQZHFc5+CDURvZ6I3kxEb37SPXfu6hwOh2MXxEOYee3LKqUkXRKFcL+ZOqevbaluZd5Zeu0gb7VDrv/KlVUT2nYObJmlT20AsxZb1+htwdp23tV3xLEel3yOHI6nGMQbf/SJ6DMAfB0zf174/DUAwMx/JZPn1wH8NoDf2FSp4y7wkfDxuk/w8bpf8PG6X/Dxun/wMbtf8PG6X/Dx2o6PYeaXWzdOIWcHAP8PgNcAeCeAnwbwnzPzWwv53szMn7KpUsfF4eN1v+Djdb/g43W/4ON1/+Bjdr/g43W/4ON1Hmxec8bMRyJ6A4AfxrDt1beViJnD4XA4HA6Hw+FwOGycdAg1M/8QgB/ayRaHw+FwOBwOh8PheGpx9g1BDLzxDup0bIeP1/2Cj9f9go/X/YKP1/2Dj9n9go/X/YKP1xmwec2Zw+FwOBwOh8PhcDj2w10oZw6Hw+FwOBwOh8PhULgYOSOizyeiXyCitxPRV1+qXsc6ENEvE9HPEtFbiOjN4dqHE9GPEtG/Dv/+rru282kFEX0bEb2HiP6VuGaODw346+E79y+J6A/dneVPJxLj9XVE9M7wHXsLEX2huPc1Ybx+gYg+726sfnpBRK8iop8gop8jorcS0Z8P1/07doXIjJd/x64QRPSIiP4pEf2LMF5/OVz/OCL6qTAu30VED8L1h+Hz28P9j73TBjxlyIzXtxPRvxHfr08O1/33cCdchJwRUQvgWwB8AYBXA/iTRPTqS9Tt2ITPZuZPFtujfjWAH2PmTwDwY+Gz427w7QA+X11Ljc8XAPiE8N/rAfytC9nomPDtWI4XAHxT+I59cthYCeE38UsBfGLI8zfDb6fjcjgC+AvM/GoAnw7gK8K4+HfsOpEaL8C/Y9eIxwA+h5k/CcAnA/h8Ivp0AN+IYbw+HsB7AXxZSP9lAN4brn9TSOe4HFLjBQB/UXy/3hKu+e/hTriUcvapAN7OzL/EzE8AvAnAay9Ut+N0vBbAd4S/vwPAF92dKU83mPn/AvBb6nJqfF4L4O/wgH8C4GVE9MqLGOoAkByvFF4L4E3M/JiZ/w2At2P47XRcCMz8Lmb+Z+HvDwJ4G4CPgn/HrhKZ8UrBv2N3iPA9+VD4eBP+YwCfA+B7wnX9/Yrfu+8B8BoiostY68iMVwr+e7gTLkXOPgrAr4rP70D+B9Rxd2AAP0JEP0NErw/XXsHM7wp/vxvAK+7GNEcCqfHx79314g0h7OPbRJiwj9cVIYRQ/UEAPwX/jl091HgB/h27ShBRS0RvAfAeAD8K4BcBvI+ZjyGJHJNxvML99wP4iIsa/JRDjxczx+/X14fv1zcR0cNwzb9fO8E3BHFo/BFm/kMY5OmvIKL/QN7kYXtP3+LzSuHjcy/wtwD8XgxhIu8C8Ffv1BrHAkT0IgD/EMBXMvMH5D3/jl0fjPHy79iVgpk7Zv5kAB+NQbX8/XdrkSMHPV5E9AcAfA2GcfvDAD4cwFfdnYUvTFyKnL0TwKvE548O1xxXBmZ+Z/j3PQC+D8OP569FaTr8+567s9BhIDU+/r27QjDzr4UXXg/gb2MKq/LxugIQ0Q0GR/87mfl7w2X/jl0prPHy79j1g5nfB+AnAHwGhvC3Q7glx2Qcr3D/pQB+87KWOoDZeH1+CCdmZn4M4H+Ff792x6XI2U8D+ISwI88DDAtyf+BCdTsqQUQfRkQvjn8D+KMA/hWGsXpdSPY6AN9/NxY6EkiNzw8A+DNhB6VPB/B+EZrluCOoGPz/BMN3DBjG60vDDmUfh2FR9T+9tH1PM8J6lm8F8DZm/mviln/HrhCp8fLv2HWCiF5ORC8Lfz8D4HMxrBP8CQBfHJLp71f83n0xgB9nP5z3YkiM18+LiSrCsD5Qfr/893AHHMpJTgczH4noDQB+GEAL4NuY+a2XqNuxCq8A8H1hve0BwN9n5n9ERD8N4LuJ6MsA/AqAL7lDG59qENE/APBZAD6SiN4B4L8H8A2wx+eHAHwhhkXvzwH4cxc3+ClHYrw+K2w9zAB+GcB/CQDM/FYi+m4AP4dhF7qvYObuDsx+mvGZAP40gJ8N6ywA4Gvh37FrRWq8/qR/x64SrwTwHWGHzAbAdzPzDxLRzwF4ExH9DwD+OQbCjfDv3yWit2PYWOlL78Lopxip8fpxIno5AALwFgBfHtL77+FOIJ+EcDgcDofD4XA4HI67h28I4nA4HA6Hw+FwOBxXACdnDofD4XA4HA6Hw3EFcHLmcDgcDofD4XA4HFcAJ2cOh8PhcDgcDofDcQVwcuZwOBwOh8PhcDgcVwAnZw6Hw+FwOBwOh8NxBXBy5nA4HA6Hw+FwOBxXACdnDofD4XA4HA6Hw3EF+P8BR1Aductu8O8AAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# view the background-subtracted image\n", "plt.figure(figsize=(15, 15))\n", @@ -789,32 +670,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'slit[0] slice')" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "bg_med = Background.two_sided(image, ext_center, bkg_sep, width=bkg_width, statistic='median')\n", "plt.figure(figsize=(15, 15))\n", @@ -824,32 +682,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'The LRS region')" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# transpose to make it display better\n", "plt.figure(figsize=(15, 15))\n", @@ -859,20 +694,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "123.91720867156982" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "diff = image-bg.sub_image()\n", "np.max(bg.bkg_image())" @@ -894,7 +718,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -929,7 +753,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -939,22 +763,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "f, ax = plt.subplots(figsize=(10, 6))\n", "ax.plot(jpipe_x1d.spectral_axis, spectrum.flux.value, 'k-')\n", @@ -966,9 +777,19 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-08-10 20:48:57 - INFO - 1:1: F811 redefinition of unused 'grid_from_bounding_box' from line 7\n", + "2022-08-10 20:48:57 - INFO - 1:1: E402 module level import not at top of file\n", + "2022-08-10 20:48:57 - INFO - 3:1: E265 block comment should start with '# '\n" + ] + } + ], "source": [ "from gwcs.wcstools import grid_from_bounding_box\n", "\n", @@ -1026,32 +847,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot\n", "fig, ax = plt.subplots(figsize=(6, 6))\n", @@ -1166,32 +964,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'The LRS Spatial Profile (PSF) Observation')" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# lrs spatial profile (PSF) as a function of wavelength\n", "# currently, this is just a \"high\" S/N observation of a flat spectrum source at the same slit position\n", @@ -1206,32 +981,9 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'The LRS Spatial Profile Reference Image (Normalized)')" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Mock a LRS spectral profile reference file\n", "# Sum along the spatial direction and normalize to 1\n", @@ -1261,30 +1013,18 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-08-10 20:48:57 - INFO - 3:29: E231 missing whitespace after ','\n", + "2022-08-10 20:48:57 - INFO - 4:29: E231 missing whitespace after ','\n", + "2022-08-10 20:48:57 - INFO - 5:29: E231 missing whitespace after ','\n", + "2022-08-10 20:48:57 - INFO - 6:29: E231 missing whitespace after ','\n" + ] } ], "source": [ @@ -1315,9 +1055,21 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-08-10 20:48:57 - INFO - 1:1: F401 'astropy.nddata.VarianceUncertainty' imported but unused\n", + "2022-08-10 20:48:57 - INFO - 1:1: E402 module level import not at top of file\n", + "2022-08-10 20:48:57 - INFO - 2:1: E402 module level import not at top of file\n", + "2022-08-10 20:48:57 - INFO - 4:1: E265 block comment should start with '# '\n", + "2022-08-10 20:48:57 - INFO - 5:1: E265 block comment should start with '# '\n" + ] + } + ], "source": [ "from astropy.nddata import StdDevUncertainty, VarianceUncertainty\n", "from astropy.nddata import NDData\n", @@ -1330,16 +1082,14 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2022-08-08 17:22:20,146 - stpipe - WARNING - /Users/ofox/miniconda3/envs/lrssn/lib/python3.9/site-packages/specreduce/extract.py:224: UserWarning: image NDData object's uncertainty interpreted as standard deviation. if incorrect, use VarianceUncertainty when assigning image object's uncertainty.\n", - " warnings.warn(\"image NDData object's uncertainty \"\n", - "\n" + "2022-08-10 20:48:57 - INFO - 2:1: E265 block comment should start with '# '\n" ] } ], @@ -1351,7 +1101,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1362,50 +1112,9 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-08-08 17:22:20,419 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_97625/3310229130.py:7: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"k-\" (-> color='k'). The keyword argument will take precedence.\n", - " ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], 'k-', label=\"boxcar\", color='red')\n", - "\n", - "2022-08-08 17:22:20,422 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_97625/3310229130.py:8: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"k-\" (-> color='k'). The keyword argument will take precedence.\n", - " ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], 'k-', label=\"boxcar (bkgsub)\", color='blue')\n", - "\n", - "2022-08-08 17:22:20,425 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_97625/3310229130.py:9: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"k-\" (-> color='k'). The keyword argument will take precedence.\n", - " ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, 'k-', label=\"jpipe_x1d\", color='green')\n", - "\n", - "2022-08-08 17:22:20,428 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_97625/3310229130.py:10: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"k-\" (-> color='k'). The keyword argument will take precedence.\n", - " ax.plot(waves_boxcar_bkgsub[gpts], ext1d_psfweight[gpts], 'k-', label=\"psf weighted (bkgsub)\", color='orange')\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot\n", "\n", @@ -1435,24 +1144,9 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1159736578fe4dab9d3a8567504bae74", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Application(config='specviz', events=['call_viewer_method', 'close_snackbar_message', 'data_item_remove', 'dat…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot in Specviz\n", "\n", @@ -1462,7 +1156,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1473,7 +1167,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1495,30 +1189,16 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-08-10 20:48:57 - INFO - 8:29: W605 invalid escape sequence '\\m'\n", + "2022-08-10 20:48:57 - INFO - 9:46: W605 invalid escape sequence '\\m'\n" + ] } ], "source": [ @@ -1594,9 +1274,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.8.13" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From b9fe713c64af84ec8450048b773ac143377efc2e Mon Sep 17 00:00:00 2001 From: Ori Fox Date: Thu, 16 Mar 2023 15:48:33 -0400 Subject: [PATCH 03/36] miri lrs extraction techniques --- .../miri_lrs_extraction_techniques.ipynb | 1470 +++++++++++++++++ 1 file changed, 1470 insertions(+) create mode 100644 notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques.ipynb diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques.ipynb new file mode 100644 index 000000000..ff412c87b --- /dev/null +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques.ipynb @@ -0,0 +1,1470 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# MIRI LRS Spectral Extraction Techniques" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Use case:** Extract spectra with different techniques.
\n", + "**Data:** MIRI LRS spectrum of Type Ia supernova SN2021aefx, observed by Jha et al in PID 2072 (Obs 1), **where the automated spectral extraction failed**. These data were taken with zero exclusive access period, and published in [Kwok et al 2023](https://ui.adsabs.harvard.edu/abs/2023ApJ...944L...3K/abstract).
\n", + "**Tools:** jdaviz, specviz2d, specreduce, jwst, gwcs, matplotlib, astropy.
\n", + "**Cross-intrument:** NIRSpec, MIRI.
\n", + "**Documentation:** This notebook is part of a STScI's larger [post-pipeline Data Analysis Tools Ecosystem](https://jwst-docs.stsci.edu/jwst-post-pipeline-data-analysis).
\n", + "\n", + "# Introduction\n", + "\n", + "This notebook extracts a 1D spectra from a 2D MIRI LRS spectral observation (single image). The goal is to provide the ability to extract spectra with different locations, extraction apertures, and techniques than are done in the JWST pipeline using the [Astropy Specreduce package](https://github.com/astropy/specreduce).\n", + "\n", + "The notebook also demos how to use Jdaviz's [specviz2d](https://jdaviz.readthedocs.io/en/latest/specviz2d/index.html), which allows users to interactively extract 1D spectra from 2D spectra.\n", + "\n", + "The simpliest spectral extraction is \"boxcar\" where all the pixels within some fixed width centered on the source position are summed at each wavelength. Background subtraction can be done using regions offset from the source center. You can also see the Specreduce [generic Sample Notebook](https://github.com/astropy/specreduce/blob/main/notebook_sandbox/jwst_boxcar/boxcar_extraction.ipynb).\n", + "\n", + "For spectra taken with a diffraction limited telescope like JWST, a modification boxcar extraction is to vary the extraction width linearly with wavelength. Such a scaled boxcar extraction keeps the fraction of the source flux within the extraction region approximately constant with wavelength.\n", + "\n", + "For point sources, a PSF-weighted spectral extraction can be done. Using the PSF to weight the extraction uses the actual PSF as a function of wavelength to optimize the extraction to the pixels with the greatest signal. PSF-weighted extractions show the largest differences with boxcar extractions at lower S/N values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note:** Corrections for the finite aperture used in all the extractions have not been applied. Thus, the physical flux densities of all the extracted spectra are lower than the actual values." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Imports\n", + "\n", + "- *matplotlib.pyplot* for plotting data\n", + "- *numpy* to handle array functions\n", + "- *astropy.io fits* for accessing FITS files\n", + "- *astropy.visualization* for scaling image for display\n", + "- *astropy.table Table* for reading the pipeline 1d extractions\n", + "- *jwst datamodels* for reading/access the jwst data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "# disable all imported packages' loggers\n", + "import logging\n", + "logging.root.manager.loggerDict = {}" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "print(pycodestyle_magic.__version__)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "# enable PEP8 checker for this notebook\n", + "%load_ext pycodestyle_magic\n", + "%flake8_on --ignore E261,E501,W291,W293\n", + "\n", + "# only allow the checker to throw warnings when there's a violation\n", + "logging.getLogger('flake8').setLevel('ERROR')\n", + "logging.getLogger('stpipe').setLevel('ERROR')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "# %matplotlib inline\n", + "\n", + "import numpy as np\n", + "\n", + "from gwcs.wcstools import grid_from_bounding_box\n", + "\n", + "from astropy.io import fits\n", + "from astropy.table import Table\n", + "from astropy.visualization import simple_norm\n", + "\n", + "from jwst import datamodels\n", + "\n", + "from specreduce.extract import BoxcarExtract, OptimalExtract, HorneExtract\n", + "from specreduce.tracing import FlatTrace, FitTrace\n", + "from specreduce.background import Background\n", + "\n", + "from jdaviz import Imviz\n", + "from jdaviz import Specviz\n", + "from jdaviz import Specviz2d\n", + "\n", + "from astropy.utils.data import download_file\n", + "import os\n", + "\n", + "from specutils import Spectrum1D\n", + "from astropy import units as u\n", + "\n", + "# Display the video\n", + "from IPython.display import HTML, YouTubeVideo\n", + "\n", + "import os\n", + "import urllib.request\n", + "import tarfile" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download all necessary data\n", + "\n", + "if os.path.exists(\"boxcar_specviz2d.fits\"):\n", + " print(\"Boxcar Specviz2d Extraction Exists\")\n", + "else:\n", + " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/boxcar_specviz2d.fits'\n", + " urllib.request.urlretrieve(url)\n", + "\n", + "if os.path.exists(\"horne_specviz2d.fits\"):\n", + " print(\"Horne Specviz2d Extraction Exists\")\n", + "else:\n", + " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/horne_specviz2d.fits'\n", + " urllib.request.urlretrieve(url)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if os.path.exists(\"./data/\"):\n", + " print(\"Origina Data Exists\")\n", + "else:\n", + " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/data.tar.gz'\n", + " urllib.request.urlretrieve(url)\n", + " \n", + " # Unzip files if they haven't already been unzipped\n", + " if os.path.exists(\"data/\"):\n", + " print(\"Data Directory Already Exists\")\n", + " else:\n", + " tar = tarfile.open('./data.tar.gz', \"r:gz\")\n", + " tar.extractall()\n", + " tar.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Developer note: Ask Karl exactly how these functions work? Seems like all weights are equal?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# useful functions\n", + "def get_boxcar_weights(center, hwidth, npix):\n", + " \"\"\"\n", + " Compute the weights given an aperture center, half widths, and number of pixels\n", + " \"\"\"\n", + " weights = np.zeros((npix))\n", + " # pixels with full weight\n", + " fullpixels = [max(0, int(center - hwidth + 1)), min(int(center + hwidth), npix)]\n", + " weights[fullpixels[0] : fullpixels[1]] = 1.0\n", + "\n", + " # pixels at the edges of the boxcar with partial weight\n", + " if fullpixels[0] > 0:\n", + " weights[fullpixels[0] - 1] = hwidth - (center - fullpixels[0])\n", + " if fullpixels[1] < npix:\n", + " weights[fullpixels[1]] = hwidth - (fullpixels[1] - center)\n", + "\n", + " return weights\n", + "\n", + "\n", + "def ap_weight_images(\n", + " center, width, bkg_offset, bkg_width, image_size, waves, wavescale=None\n", + "):\n", + " \"\"\"\n", + " Create a weight image that defines the desired extraction aperture\n", + " and the weight image for the requested background regions\n", + "\n", + " Parameters\n", + " ----------\n", + " center : float\n", + " center of aperture in pixels\n", + " width : float\n", + " width of apeture in pixels\n", + " bkg_offset : float\n", + " offset from the extaction edge for the background\n", + " never scaled for wavelength\n", + " bkg_width : float\n", + " width of background region\n", + " never scaled with wavelength\n", + " image_size : tuple with 2 elements\n", + " size of image\n", + " waves : array\n", + " wavelegth values\n", + " wavescale : float\n", + " scale the width with wavelength (default=None)\n", + " wavescale gives the reference wavelenth for the width value\n", + "\n", + " Returns\n", + " -------\n", + " wimage, bkg_wimage : (2D image, 2D image)\n", + " wimage is the weight image defining the aperature\n", + " bkg_image is the weight image defining the background regions\n", + " \"\"\"\n", + " wimage = np.zeros(image_size)\n", + " bkg_wimage = np.zeros(image_size)\n", + " hwidth = 0.5 * width\n", + " # loop in dispersion direction and compute weights\n", + " for i in range(image_size[1]):\n", + " if wavescale is not None:\n", + " hwidth = 0.5 * width * (waves[i] / wavescale)\n", + "\n", + " wimage[:, i] = get_boxcar_weights(center, hwidth, image_size[0])\n", + "\n", + " # bkg regions\n", + " if (bkg_width is not None) & (bkg_offset is not None):\n", + " bkg_wimage[:, i] = get_boxcar_weights(\n", + " center - hwidth - bkg_offset, bkg_width, image_size[0]\n", + " )\n", + " bkg_wimage[:, i] += get_boxcar_weights(\n", + " center + hwidth + bkg_offset, bkg_width, image_size[0]\n", + " )\n", + " else:\n", + " bkg_wimage = None\n", + "\n", + " return (wimage, bkg_wimage)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Devloper notes (2021)\n", + "\n", + "1) The difference between the pipeline (x1d) and the extractions done in this notebook are quite large. Help in understanding the origin of these differences is needed.\n", + "\n", + "2) Not clear how to use the JWST pipeline `extract_1d` (quite complex) code. Help to determine how to use the JWST pipeline code instead of the custom code for boxcar is needed. \n", + "\n", + "3) Applying aperture corrections for the finite extraction widths is needed. Help in how to get the needed informatinom for different (user set) extraction widths is needed. \n", + "\n", + "### Partially RESOLVED (March, 2023)\n", + "\n", + "1) See notes from Kendrew on limitations of current pipeline. Pipeline will be updated soon.\n", + "\n", + "2) While this notebook doesn't go into using the pipeline, boxcar is now integrated into the Astropy Specreduce package. So I wouldn't characterize the boxcar as \"custom code\" any longer.\n", + "\n", + "3) Still not resolved." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download Files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#calfilename = \"det_image_seq5_MIRIMAGE_P750Lexp1_cal.fits\"\n", + "s2dfile = \"./data/jw02072-o001_t010_miri_p750l_s2d.fits\"\n", + "x1dfile = \"./data/jw02072-o001_t010_miri_p750l_x1d.fits\"\n", + "spatialprofilefile = \"./data/jw02072-o001_t010_miri_p750l_s2d.fits\"\n", + "#mainurl = \"https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/\"\n", + "\n", + "#calfile_dld = download_file(mainurl + calfilename)\n", + "#s2dfile_dld = download_file(mainurl + s2dfilename)\n", + "#x1dfile_dld = download_file(mainurl + x1dfilename)\n", + "#spatialprofilefile_dld = download_file(mainurl + spatialprofilefilename)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "# rename files so that they have the right extensions\n", + "# required for the jwst datamodels to work\n", + "#calfile = calfile_dld + '_cal.fits'\n", + "#os.rename(calfile_dld, calfile)\n", + "s2dfile = s2dfile_dld + '_s2d.fits'\n", + "os.rename(s2dfile_dld, s2dfile)\n", + "x1dfile = x1dfile_dld + '_x1d.fits'\n", + "os.rename(x1dfile_dld, x1dfile)\n", + "spatialprofilefile = spatialprofilefile_dld + '_s2d.fits'\n", + "os.rename(spatialprofilefile_dld, spatialprofilefile)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## File information\n", + "\n", + "The data used is a simulation of a LRS slit observation for a blackbody with a similar flux density to the star BD+60d1753, a flux calibration star. This simulation was created with MIRISim.\n", + "The simulated exposure was reduced using the JWST pipeline (v0.16.1) through the Detector1 and Spec2 stages.\n", + "\n", + "The cal file is one of the Spec2 products and is the calibration full frame image. It contains:\n", + "\n", + "1. (Primary): This HDU contains meta-data related to the observation and data reduction.\n", + "2. (SCI): The calibrated image. Units are MJy/sr.\n", + "3. (ERR): Uncertainty image. Units are MJy/sr.\n", + "4. (DQ): Data quality image.\n", + "5. (VAR_POISSON): Unc. component 1: Poisson uncertainty image. Units are (MJy/sr)^2.\n", + "6. (VAR_RNOISE): Unc. component 2: Read Noise uncertainty image. Units are (MJy/sr)^2.\n", + "7. (VAR_FLAT): Unc. component 3: Flat Field uncertainty image. Units are (MJy/sr)^2.\n", + "8. (ASDF_METADATA): Metadata.\n", + "\n", + "The s2d file is one of the Spec2 products and containes the calibrated rectified cutout of the LRS Slit region. It has:\n", + "\n", + "1. (Primary): This HDU contains meta-data related to the observation and data reduction.\n", + "2. (WGT): Weight.\n", + "3. (CON): ??\n", + "4. (ASDF_METADATA): Metadata." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Loading data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# use a jwst datamodel to provide a good interface to the data and wcs info\n", + "#cal = datamodels.open(calfile)\n", + "s2d = datamodels.open(s2dfile)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Basic information about the image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#print(\"cal image\")\n", + "#print(cal.data.shape)\n", + "#print(np.mean(cal.data))\n", + "#print(np.amin(cal.data), np.amax(cal.data))\n", + "print(\"s2d image\")\n", + "print(s2d.data.shape)\n", + "print(np.mean(s2d.data))\n", + "print(np.amin(s2d.data), np.amax(s2d.data))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display the full 2D image" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "norm_data = simple_norm(cal.data, 'sqrt')\n", + "plt.figure(figsize=(6, 6))\n", + "plt.imshow(cal.data, norm=norm_data, origin=\"lower\")\n", + "plt.title(\"The full image from the MIRI IMAGER detector\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display the LRS Slit region only (use s2d)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# transpose to make it display better\n", + "image = np.transpose(s2d.data)\n", + "err = np.transpose(s2d.err)\n", + "norm_data = simple_norm(image, \"sqrt\")\n", + "plt.figure(figsize=(10, 3))\n", + "plt.imshow(image, norm=norm_data, origin=\"lower\")\n", + "plt.title(\"The LRS region\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### View the 2D Spectrum in Imviz and get the center of the cross-dispersion " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imviz = Imviz()\n", + "imviz.app" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imviz.load_data(image)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "viewer = imviz.default_viewer\n", + "viewer.cuts = '95%'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1) Default JWST Pipeline 1D extraction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a spectrum1d\n", + "jpipe_x1d = Spectrum1D.read(x1dfile)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "specviz = Specviz()\n", + "specviz.app" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "specviz.load_spectrum(jpipe_x1d)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "\n", + "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", + "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", + "ax.set_ylabel(\"Flux Density [Jy]\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_ylim(1e-6, 3e-3)\n", + "ax.set_xlim(4, 13)\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2) Fixed Width Boxcar Extraction (Using Specreduce)\n", + "\n", + "Extract a 1D spectrum using a simple boxcar. Basically collapse the spectrum in the cross-dispersion direction over a specified number of pixels.\n", + "\n", + "#### Developer note: Allow for a bad pixel mask" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define extraction parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ext_center = 31.5\n", + "ext_width = 8\n", + "bkg_sep = 7\n", + "bkg_width = 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot cross-disperion cut showing the extraction parameters\n", + "\n", + "#### Develepor Note: Place trace back into Specviz2d/Imviz/Etc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot along cross-disperion cut showing the extraction parameters\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "y = np.arange(image.shape[0])\n", + "ax.plot(y, image[:,300], 'k-')\n", + "mm = np.array([ext_center, ext_center])\n", + "mm_y = ax.get_ylim()\n", + "\n", + "# extraction region\n", + "ax.axvspan(ext_center - ext_width/2., ext_center + ext_width/2., color='green', alpha=0.1)\n", + "ax.plot(mm, mm_y, 'b--')\n", + "ax.plot(mm - ext_width/2., mm_y, 'g:')\n", + "ax.plot(mm + ext_width/2., mm_y, 'g:')\n", + "\n", + "# background region, symmetric on both sides of extraction region\n", + "ax.axvspan(ext_center - bkg_sep - bkg_width/2., ext_center - bkg_sep + bkg_width/2., color='red', alpha=0.1)\n", + "ax.plot(mm - bkg_sep - bkg_width/2., mm_y, 'r:')\n", + "ax.plot(mm - bkg_sep + bkg_width/2., mm_y, 'r:')\n", + "\n", + "ax.axvspan(ext_center + bkg_sep - bkg_width/2., ext_center + bkg_sep + bkg_width/2., color='red', alpha=0.1)\n", + "ax.plot(mm + bkg_sep - bkg_width/2., mm_y, 'r:')\n", + "ax.plot(mm + bkg_sep + bkg_width/2., mm_y, 'r:')\n", + "\n", + "ax.set_title(\"Cross-dispersion Cut at Pixel=300\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define Background" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# extract the background using custom individual traces\n", + "trace = FlatTrace(image, ext_center)\n", + "bg = Background(image, [trace-bkg_sep, trace+bkg_sep], width=bkg_width)\n", + "\n", + "# alternatively, call two_sided class, which does the same as above \n", + "#bg = Background.two_sided(image, trace, bkg_sep, width=bkg_width)\n", + "# or in the place of any trace, an int/float can be passed which resolves to a FlatTrace\n", + "#bg = Background.two_sided(image, ext_center, bkg_sep, width=bkg_width)\n", + "\n", + "# or for single sided:\n", + "# bg = Background.one_sided(image, trace, bkg_sep, width=bkg_width)\n", + "# bg = Background.one_sided(image, trace, -bkg_sep, width=bkg_width)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# view the background weighted image\n", + "plt.figure(figsize=(15, 15))\n", + "plt.imshow(bg.bkg_wimage, origin=\"lower\")\n", + "plt.title(\"slit[0] slice\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# view the background image\n", + "plt.figure(figsize=(15, 15))\n", + "plt.imshow(bg.bkg_image().flux.value, norm=norm_data, origin=\"lower\")\n", + "plt.title(\"slit[0] slice\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# view the background-subtracted image\n", + "plt.figure(figsize=(15, 15))\n", + "plt.imshow(bg.sub_image().flux.value, norm=norm_data, origin=\"lower\")\n", + "plt.title(\"slit[0] slice\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that when using median to calculate the background, partial pixel weights are not supported:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bg_med = Background.two_sided(image, ext_center, bkg_sep, width=bkg_width, statistic='median')\n", + "plt.figure(figsize=(15, 15))\n", + "plt.imshow(bg_med.bkg_wimage, origin=\"lower\")\n", + "plt.title(\"slit[0] slice\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extract Trace (multiple options)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Optional: we could now refine the initial flat trace by running an automated FitTrace on the subtracted image. This process could be iterated as necessary (recreating the subtracted image with the refined trace, etc)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fit_trace = FitTrace(image-bg.bkg_image().flux.value, peak_method='gaussian', guess=ext_center)\n", + "#fit_trace" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "flat_trace = FlatTrace(image-bg.bkg_image().flux.value, trace_pos=ext_center)\n", + "#flat_trace" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **Always visualize your traces. If you have noisy data, the fits may not be good. You may need to play around with the type of fit (i.e., Order 1 Polynomial) or different window sizes and parameters. In our case, we'll stick with the flat trace throughout this notebook.** \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "#### Plot old vs new trace" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig3, ax3 = plt.subplots(figsize=(10,6))\n", + "plot3 = ax3.imshow(bg.sub_image().flux.value, aspect=4.,\n", + " vmin=0, vmax=bg.sub_image().flux.value.max()/2,\n", + " cmap=mpl.cm.magma, origin='lower',\n", + " extent=(0, bg.sub_image().flux.value.shape[-1],\n", + " 0, bg.sub_image().flux.value.shape[0]))\n", + "fig3.colorbar(plot3)\n", + "ax3.set_title('LRS Spectrum Traces')\n", + "ax3.grid()\n", + "\n", + "# add the traces\n", + "ax3.plot(flat_trace.trace, '--', color='#008ca8',\n", + " lw=2.5, label='FlatTrace')\n", + "ax3.plot(fit_trace.trace, '--', color='#00471b',\n", + " lw=2.5, label='GaussianFitTrace')\n", + "ax3.legend(framealpha=.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extract" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Dev Note: FitTrace doesn't seem to be working right now, so we'll stick with FlatTrace" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "from specutils import Spectrum1D\n", + "from astropy import units as u\n", + "flux = s2d.data * u.Jy\n", + "wavelength = s2d.wavelength * u.um\n", + "flux.data\n", + "spec = Spectrum1D(spectral_axis=wavelength, flux=flux)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#boxcar = BoxcarExtract()\n", + "ext1d_boxcar_noweights = BoxcarExtract(image-bg, flat_trace, width=ext_width)\n", + "ext1d_boxcar_noweights = ext1d_boxcar_noweights.spectrum.flux.value\n", + "ext1d_boxcar_noweights *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", + "#spectrum_specreduce_boxcar" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "\n", + "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", + "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label='Boxcar')\n", + "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", + "ax.set_ylabel(\"Flux Density [Jy]\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_ylim(1e-6, 3e-3)\n", + "ax.set_xlim(4, 13)\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3) Fixed Width Boxcar Extraction (Using Pixel Masks, too)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **A basic example of how to create a pixel weight map/mask. In this example, the weights basically create a pixel mask based on the boxcar extraction aperture. It shouldn't actually change any of the results because the boxcar extraction essentially does this for you. But this provides a useful example for more complicated masks that we will create lower in the notebook.** " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from gwcs.wcstools import grid_from_bounding_box\n", + "\n", + "#image = np.transpose(s2d.data)\n", + "grid = grid_from_bounding_box(s2d.meta.wcs.bounding_box)\n", + "ra, dec, lam = s2d.meta.wcs(*grid)\n", + "lam_image = np.transpose(lam)\n", + "\n", + "# compute a \"rough\" wavelength scale to allow for aperture to scale with wavelength\n", + "rough_waves = np.average(lam_image, axis=0)\n", + "\n", + "# images to use for extraction\n", + "wimage, bkg_wimage = ap_weight_images(\n", + " ext_center,\n", + " ext_width,\n", + " bkg_width,\n", + " bkg_sep,\n", + " image.shape,\n", + " rough_waves,\n", + " wavescale=None,\n", + ")\n", + "\n", + "#boxcar = BoxcarExtract()\n", + "\n", + "# without background subtraction\n", + "image_wg = image * wimage\n", + "ext1d_boxcar = BoxcarExtract(image_wg, flat_trace, width=ext_width)\n", + "ext1d_boxcar = ext1d_boxcar.spectrum.flux.value\n", + "# convert from MJy/sr to Jy\n", + "ext1d_boxcar *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", + "\n", + "# with background subtraction\n", + "image_bg = bg.sub_image()\n", + "image_wg = image_bg * wimage\n", + "ext1d_boxcar_bkgsub = BoxcarExtract(image_wg, flat_trace, width=ext_width)\n", + "ext1d_boxcar_bkgsub = ext1d_boxcar_bkgsub.spectrum.flux.value\n", + "\n", + "# convert from MJy/sr to Jy\n", + "ext1d_boxcar_bkgsub *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", + "\n", + "# compute the average wavelength for each column using the weight image\n", + "# this should correspond directly with the extracted spectrum\n", + "# wavelengths account for any tiled spectra this way\n", + "waves_boxcar = np.average(lam_image, weights=wimage, axis=0)\n", + "waves_boxcar_bkgsub = np.average(lam_image, weights=wimage, axis=0)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "ext1d_boxcar = boxcar(image_wg, auto_trace, width=ext_width)\n", + "ext1d_boxcar.flux.value" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "gpts = ext1d_boxcar_bkgsub > 0.\n", + "gpts = ext1d_boxcar > 0.\n", + "\n", + "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label = 'Boxcar; No Mask')\n", + "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], color = 'green', label=\"Boxcar; Mask\")\n", + "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], color = 'red', label=\"Boxcar; bkgsub; Mask\")\n", + "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", + "ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n", + "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", + "ax.set_ylabel(\"Flux Density [Jy]\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_ylim(1e-6, 3e-3)\n", + "ax.set_xlim(4, 13)\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4) Wavelength scaled width boxcar\n", + "\n", + "The LRS spatial profile changes as a function of wavelength as JWST is diffraction limited at these wavelengths. Nominally this means that the FWHM is changing linearly with wavelength. Scaling the width of the extraction aperture with wavelength accounts for the changing diffraction limit with wavelength to first order." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Developer note: Not currently possible. Allow for wavelength scaled width in the boxcar" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5) Horne Extraction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **The Horne algorithm preforms a Gaussian fit on the source, it is thus best suited for cases where the source has a Gaussian profile in the cross-dispersion direction. If your profile is not Gaussian, you will likely over- or under-estimate your actual flux.**. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Steps in the original Horne (1986) paper:\n", + "\n", + "1. Bias subtraction [assumed to be done in earlier block]\n", + "2. Initial variance estimate [user provides this as an argument]\n", + "3. Fit sky background [assumed to be done in earlier block]\n", + " * \"We therefore generally perform a least-squares polynomial fit to the sky data at each wavelength. Individual sky pixels are given weights inversely proportional to their variances as estimated in Step 2\" [overlaps with notebook guide's 3b]\n", + "4. Extract standard spectrum and its variance\n", + " * Subtract the sky background found in Step 3 from the image. [sky background calculation is planned as a separate, earlier step of the specreduce workflow]\n", + "5. Construct spatial profile\n", + "6. Revise variance estimates [not currently done]\n", + "7. Mask cosmic ray hits [not currently done]\n", + "8. Extract optimal spectrum and its variance [currently only extract the spectum, not a variance]\n", + "9. Iterate Steps 5-8\n", + "\n", + "The first four steps as the standard procedure and the last five as add-ons that help squeeze out extra signal-to-noise." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are notes [in brackets] on how each step is handled in the proposed HorneExtract/OptimalExtract classes to make it easier to see what the class does and what the user must do themselves.\n", + "\n", + "### Steps in the JDAT Notebook guide on optimal extraction:\n", + "\n", + "1. Define extraction region [user's responsibility to provide an appropriate image]\n", + "2. Pick a slice [should not be necessary? can use the whole image as the aperture with good results]\n", + "3. Define extraction kernel\n", + " * Select PSF template [assumed to be Gaussian for now. support for Moffat, others?]\n", + " * Choose a polynomial for background fitting [user provides as an argument]\n", + "4. Fit extraction kernel to initial slice [all columns are coadded to perform the fit]\n", + "5. Fit geometric distortion [not currently done]\n", + " * Determine cross-dispersion bins for trace fitting\n", + " * Fit a kernel to each bin to find trace center [user provides this as a specreduce.tracing.Trace object]\n", + " * Polynomial fit of trace centers\n", + "6. Combine composite model with 2D image to create output 1D spectrum\n", + " * Create variance image [user provides this as an argument]\n", + " * Generate 1D spectrum\n", + "7. Compare with reference 1D spectrum" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ext1d_horne = HorneExtract(image-bg, flat_trace)\n", + "ext1d_horne = ext1d_horne.spectrum.flux.value\n", + "# convert from MJy/sr to Jy\n", + "ext1d_horne *= 1e6 * s2d.meta.photometry.pixelarea_steradians" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "gpts = ext1d_boxcar_bkgsub > 0.\n", + "gpts = ext1d_boxcar > 0.\n", + "\n", + "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label = 'Boxcar; No Mask')\n", + "ax.plot(jpipe_x1d.spectral_axis, ext1d_horne, color = 'purple', label = 'Horne; No Mask')\n", + "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], color = 'green', label=\"Boxcar; Mask\")\n", + "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], color = 'red', label=\"Boxcar; bkgsub; Mask\")\n", + "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", + "ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n", + "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", + "ax.set_ylabel(\"Flux Density [Jy]\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_ylim(1e-6, 3e-3)\n", + "ax.set_xlim(4, 13)\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **See note above. In this case the Horne extraction likely overestimates the flux because it under-fits the wings of the cross-dispersion profile. Using a real MIRI LRS PSF is a better idea.** " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 5) PSF-Weighted Extraction\n", + "\n", + "While to first order the PSF FHWM changes linearly with wavelength, this is an approximation. It is better to use the measured spatial profile as a function of wavelength to extract the spectrum. This tracks the actual variation with wavelength and optimizes the extraction to the higher S/N measurements. In general, PSF based extractions show the most improvements over boxcar extractions at lower the S/N.\n", + "\n", + "There are two PSF based extraction methods:\n", + "\n", + "1. PSF weighted: the spatial profile at each wavelength is used to weight the extraction.\n", + "2. PSF fitting: the spatial profile is fit at each wavelength with the scale parameter versus wavelength giving the spectrum.\n", + "\n", + "#### Only the PSF weighted technique is currently part of this notebook.\n", + "\n", + "Note 1: calibration reference file for the specific LRS slit position should be used.
\n", + "Note 2: Small shifts in the centering of the source in the slit should be investigated to see if they impact the PSF based extractions.
\n", + "Limitation: currently it is assumed there are no bad pixels.
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PSF weighted extaction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Generate the PSF profile as a function of wavelength\n", + "For MIRI LRS slit observations, observations are made at two nod position in the slit after target acquisition. This means that the location of the sources in the slit is very well known. Hence, spatial profile (PSF) as a function of wavelength for the two nod positions is straightforward to measure using observations of a bright source.\n", + "\n", + "The next few steps generate the needed information for the nod position for which we are extracting spectra based on a simulation of a bright source at the same nod position." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# lrs spatial profile (PSF) as a function of wavelength\n", + "# currently, this is just a \"high\" S/N observation of a flat spectrum source at the same slit position\n", + "psf = datamodels.open(spatialprofilefile)\n", + "# transpose to make it display better\n", + "lrspsf = np.transpose(psf.data)\n", + "norm_data = simple_norm(lrspsf, \"sqrt\")\n", + "plt.figure(figsize=(10, 3))\n", + "plt.imshow(lrspsf, norm=norm_data, origin=\"lower\")\n", + "plt.title(\"The LRS Spatial Profile (PSF) Observation\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Mock a LRS spectral profile reference file\n", + "# Sum along the spatial direction and normalize to 1\n", + "# assume there is no background (none was included in the MIRISim for the flat spectrum source observation)\n", + "# ignore regions far from the source using a scaled boxcar weight image\n", + "# the aperture (psf_width) used in the scaled boxcar weight image could be varied\n", + "psf_width = 12.0\n", + "(wimage_scaledboxcar, tmpvar) = ap_weight_images(ext_center, psf_width, bkg_sep, \n", + " bkg_width, image.shape, waves_boxcar, wavescale=10.0)\n", + "\n", + "psf_weightimage = lrspsf*wimage_scaledboxcar\n", + "\n", + "# generate a 2D image of the column sums for division\n", + "max_psf = np.max(psf_weightimage, axis=0)\n", + "div_image = np.tile(max_psf, (psf_weightimage.shape[0], 1))\n", + "div_image[div_image == 0.0] = 1.0 # avoid divide by zero issues\n", + "\n", + "# normalize \n", + "psf_weightimage /= div_image\n", + "\n", + "# display\n", + "norm_data = simple_norm(psf_weightimage, \"sqrt\")\n", + "plt.figure(figsize=(10, 3))\n", + "plt.imshow(psf_weightimage, norm=norm_data, origin=\"lower\")\n", + "plt.title(\"The LRS Spatial Profile Reference Image (Normalized)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "y = np.arange(psf_weightimage.shape[0])\n", + "ax.plot(y, psf_weightimage[:,150], label=\"pixel=150\")\n", + "ax.plot(y, psf_weightimage[:,225], label=\"pixel=225\")\n", + "ax.plot(y, psf_weightimage[:,300], label=\"pixel=300\")\n", + "ax.plot(y, psf_weightimage[:,370], label=\"pixel=370\")\n", + "ax.set_title(\"Cross-dispersion Cuts\")\n", + "ax.set_xlim(ext_center-psf_width, ext_center+psf_width)\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the spatial profile becomes narrower as the pixel values increases as this corresponds to the wavelength decreasing." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Extract spectrum using wavelength dependent PSF profiles using the same traces as defined above" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "image_bg = bg.sub_image()\n", + "image_wg = image_bg * psf_weightimage\n", + "ext1d_boxcar_bkgsub_psfweight = BoxcarExtract(image_wg, flat_trace, width=ext_width)\n", + "ext1d_boxcar_bkgsub_psfweight = ext1d_boxcar_bkgsub_psfweight.spectrum.flux.value\n", + "\n", + "# convert from MJy/sr to Jy\n", + "ext1d_boxcar_bkgsub_psfweight *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", + "\n", + "# compute the average wavelength for each column using the weight image\n", + "# this should correspond directly with the extracted spectrum\n", + "# wavelengths account for any tiled spectra this way\n", + "waves_boxcar_psfweight = np.average(lam_image, weights=wimage, axis=0)\n", + "waves_boxcar_bkgsub_psfweight = np.average(lam_image, weights=wimage, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "gpts = ext1d_boxcar_bkgsub > 0.\n", + "gpts = ext1d_boxcar > 0.\n", + "\n", + "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label = 'Boxcar; No Mask')\n", + "ax.plot(jpipe_x1d.spectral_axis, ext1d_horne, color = 'purple', label = 'Horne; No Mask')\n", + "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], color = 'green', label=\"Boxcar; Mask\")\n", + "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], color = 'red', label=\"Boxcar; bkgsub; Mask\")\n", + "ax.plot(waves_boxcar_bkgsub_psfweight[gpts], ext1d_boxcar_bkgsub_psfweight[gpts], 'k-', label=\"Boxcar; bkgsub; PSF Weights\", color='cyan')\n", + "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", + "ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n", + "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", + "ax.set_ylabel(\"Flux Density [Jy]\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_ylim(1e-6, 3e-3)\n", + "ax.set_xlim(4, 13)\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the psf weighted extraction has visabily higher S/N, especially at the longer wavelengths where the S/N is lowest overall." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot in Specviz\n", + "specviz2 = Specviz()\n", + "specviz2.app" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ext1d_boxcar_spec1d = Spectrum1D(spectral_axis=waves_boxcar[gpts]*u.micron, flux=ext1d_boxcar[gpts]*u.Jy)\n", + "ext1d_boxcar_bkgsub_spec1d = Spectrum1D(spectral_axis=waves_boxcar_bkgsub[gpts]*u.micron, flux=ext1d_boxcar_bkgsub[gpts]*u.Jy)\n", + "ext1d_psfweight_spec1d = Spectrum1D(spectral_axis=waves_boxcar_bkgsub_psfweight[gpts]*u.micron, flux=ext1d_boxcar_bkgsub_psfweight[gpts]*u.Jy)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "specviz2.load_spectrum(ext1d_boxcar_spec1d, data_label='boxcar')\n", + "specviz2.load_spectrum(ext1d_boxcar_bkgsub_spec1d, data_label='boxcar bkgsub')\n", + "specviz2.load_spectrum(ext1d_psfweight_spec1d, data_label='psfweight')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6) PSF-Fitted Extraction\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Developer note: Not currently possible. Allow for wavelength scaled width in the boxcar" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 7) Specviz2D Extraction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Watch these two demo videos on how to extract your spectra using Specviz2D" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Video showing how to use specviz2d\n", + "HTML('')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Video showing how to use specviz2d\n", + "HTML('')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "specviz2d = Specviz2d()\n", + "specviz2d.app" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "specviz2d.load_data(s2dfile)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "boxcar = specviz2d.app.get_data_from_viewer('spectrum-viewer',data_label='boxcar')\n", + "horne = specviz2d.app.get_data_from_viewer('spectrum-viewer',data_label='horne')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check to see if user made a boxcar extraction, otherwise read in from file\n", + "if not boxcar:\n", + " print(\"You didn't extract a spectrum from specviz2d, we will load a pre-extracted spectrum from the video above.\")\n", + " boxcar_specviz2d = Spectrum1D.read('boxcar_specviz2d.fits')\n", + "else:\n", + " myboxcar = boxcar.flux.value\n", + " myboxcar *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", + " boxcar_specviz2d = Spectrum1D(spectral_axis=jpipe_x1d.spectral_axis, flux=myboxcar*u.Jy)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if not horne:\n", + " print(\"You didn't extract a spectrum from specviz2d, we will load a pre-extracted spectrum from the video above.\")\n", + " horne_specviz2d = Spectrum1D.read('horne_specviz2d.fits')\n", + "else:\n", + " myhorne = horne.flux.value\n", + " myhorne *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", + " horne_specviz2d = Spectrum1D(spectral_axis=jpipe_x1d.spectral_axis, flux=myhorne*u.Jy)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Sarah's Extraction (for reference)\n", + "sp3_x1dfile = 'data/PID2072_Obs1_LRS_demo_x1d.fits'\n", + "sp3_x1d = datamodels.open(sp3_x1dfile)\n", + "ll3 = (sp3_x1dfile.split('/')[-1]).split('.')[0] + ' (Level 3, custom)'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "gpts = ext1d_boxcar_bkgsub > 0.\n", + "gpts = ext1d_boxcar > 0.\n", + "\n", + "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label = 'Boxcar; No Mask')\n", + "ax.plot(jpipe_x1d.spectral_axis, ext1d_horne, color = 'purple', label = 'Horne; No Mask')\n", + "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], color = 'green', label=\"Boxcar; Mask\")\n", + "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], color = 'red', label=\"Boxcar; bkgsub; Mask\")\n", + "ax.plot(waves_boxcar_bkgsub_psfweight[gpts], ext1d_boxcar_bkgsub_psfweight[gpts], 'k-', label=\"Boxcar; bkgsub; PSF Weights\", color='cyan')\n", + "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", + "\n", + "ax.plot(boxcar_specviz2d.spectral_axis, boxcar_specviz2d.flux, 'k-', label=\"Boxcar Specviz2D\", color='magenta')\n", + "ax.plot(horne_specviz2d.spectral_axis, horne_specviz2d.flux, 'k-', label=\"Horne Specviz2D\", color='lawngreen')\n", + "ax.plot(sp3_x1d.spec[0].spec_table['WAVELENGTH'], sp3_x1d.spec[0].spec_table['FLUX'], label=ll3, color = 'gold')\n", + "ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n", + "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", + "ax.set_ylabel(\"Flux Density [Jy]\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_ylim(1e-6, 3e-3)\n", + "ax.set_xlim(4, 13)\n", + "ax.legend(bbox_to_anchor=(1.1, 1.05))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Additional Resources\n", + "\n", + "- [MIRI LRS](https://jwst-docs.stsci.edu/mid-infrared-instrument/miri-observing-modes/miri-low-resolution-spectroscopy)\n", + "- [MIRISim](http://www.stsci.edu/jwst/science-planning/proposal-planning-toolbox/mirisim)\n", + "- [JWST pipeline](https://jwst-docs.stsci.edu/jwst-data-reduction-pipeline)\n", + "- PSF weighted extraction [Horne 1986, PASP, 98, 609](https://ui.adsabs.harvard.edu/abs/1986PASP...98..609H/abstract)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## About this notebook\n", + "\n", + "**Author:** Karl Gordon, JWST\n", + "**Updated On:** 2020-07-07" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Top of Page](#top)\n", + "\"Space " + ] + } + ], + "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": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 82ee27de52c308ace1b8ceb2ca7828283582d274 Mon Sep 17 00:00:00 2001 From: Ori Fox Date: Fri, 17 Mar 2023 07:14:34 -0400 Subject: [PATCH 04/36] url path --- .../miri_lrs_extraction_techniques.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques.ipynb index ff412c87b..cfa9bee68 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques.ipynb @@ -160,13 +160,13 @@ " print(\"Boxcar Specviz2d Extraction Exists\")\n", "else:\n", " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/boxcar_specviz2d.fits'\n", - " urllib.request.urlretrieve(url)\n", + " urllib.request.urlretrieve(url, 'boxcar_specviz2d.fits')\n", "\n", "if os.path.exists(\"horne_specviz2d.fits\"):\n", " print(\"Horne Specviz2d Extraction Exists\")\n", "else:\n", " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/horne_specviz2d.fits'\n", - " urllib.request.urlretrieve(url)" + " urllib.request.urlretrieve(url, 'horne_specviz2d.fits')" ] }, { @@ -179,7 +179,7 @@ " print(\"Origina Data Exists\")\n", "else:\n", " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/data.tar.gz'\n", - " urllib.request.urlretrieve(url)\n", + " urllib.request.urlretrieve(url, 'data.tar.gz')\n", " \n", " # Unzip files if they haven't already been unzipped\n", " if os.path.exists(\"data/\"):\n", From aa2a2fb178f4522962173d0c2421bbc4d86a44d9 Mon Sep 17 00:00:00 2001 From: Ori Fox Date: Fri, 17 Mar 2023 07:21:11 -0400 Subject: [PATCH 05/36] directory name --- .../miri_lrs_extraction_techniques.ipynb | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques.ipynb index cfa9bee68..a2764761d 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques.ipynb @@ -175,19 +175,19 @@ "metadata": {}, "outputs": [], "source": [ - "if os.path.exists(\"./data/\"):\n", + "if os.path.exists(\"./required_data/\"):\n", " print(\"Origina Data Exists\")\n", "else:\n", " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/data.tar.gz'\n", " urllib.request.urlretrieve(url, 'data.tar.gz')\n", " \n", - " # Unzip files if they haven't already been unzipped\n", - " if os.path.exists(\"data/\"):\n", - " print(\"Data Directory Already Exists\")\n", - " else:\n", - " tar = tarfile.open('./data.tar.gz', \"r:gz\")\n", - " tar.extractall()\n", - " tar.close()" + "# Unzip files if they haven't already been unzipped\n", + "if os.path.exists(\"required_data/\"):\n", + " print(\"Data Directory Already Exists\")\n", + "else:\n", + " tar = tarfile.open('./data.tar.gz', \"r:gz\")\n", + " tar.extractall()\n", + " tar.close()" ] }, { @@ -314,9 +314,9 @@ "outputs": [], "source": [ "#calfilename = \"det_image_seq5_MIRIMAGE_P750Lexp1_cal.fits\"\n", - "s2dfile = \"./data/jw02072-o001_t010_miri_p750l_s2d.fits\"\n", - "x1dfile = \"./data/jw02072-o001_t010_miri_p750l_x1d.fits\"\n", - "spatialprofilefile = \"./data/jw02072-o001_t010_miri_p750l_s2d.fits\"\n", + "s2dfile = \"./required_data/jw02072-o001_t010_miri_p750l_s2d.fits\"\n", + "x1dfile = \"./required_data/jw02072-o001_t010_miri_p750l_x1d.fits\"\n", + "spatialprofilefile = \"./required_data/jw02072-o001_t010_miri_p750l_s2d.fits\"\n", "#mainurl = \"https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/\"\n", "\n", "#calfile_dld = download_file(mainurl + calfilename)\n", @@ -1369,7 +1369,7 @@ "outputs": [], "source": [ "# Sarah's Extraction (for reference)\n", - "sp3_x1dfile = 'data/PID2072_Obs1_LRS_demo_x1d.fits'\n", + "sp3_x1dfile = 'required_data/PID2072_Obs1_LRS_demo_x1d.fits'\n", "sp3_x1d = datamodels.open(sp3_x1dfile)\n", "ll3 = (sp3_x1dfile.split('/')[-1]).split('.')[0] + ' (Level 3, custom)'" ] From a4da8d1974becfa42aac71829b533ff2826663bf Mon Sep 17 00:00:00 2001 From: Ori Fox Date: Wed, 22 Mar 2023 10:01:09 -0400 Subject: [PATCH 06/36] included techniques with standard star --- .../miri_lrs_extraction_techniques.ipynb | 27 +- ...i_lrs_extraction_techniques_standard.ipynb | 1544 +++++++++++++++++ 2 files changed, 1566 insertions(+), 5 deletions(-) create mode 100644 notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques_standard.ipynb diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques.ipynb index a2764761d..a5eabfdc3 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques.ipynb @@ -21,6 +21,17 @@ "**Cross-intrument:** NIRSpec, MIRI.
\n", "**Documentation:** This notebook is part of a STScI's larger [post-pipeline Data Analysis Tools Ecosystem](https://jwst-docs.stsci.edu/jwst-post-pipeline-data-analysis).
\n", "\n", + "# Install instructions\n", + "git clone https://github.com/spacetelescope/jdat_notebooks.git
\n", + "cd jdat_notebooks/
\n", + "git fetch -q https://github.com/spacetelescope/jdat_notebooks.git refs/pull/93/head:lrsoptimal2
\n", + "git checkout lrsoptimal2
\n", + "conda create -n lrsextract python=3.8.10
\n", + "conda activate lrsextract
\n", + "pip install -r requirements.txt
\n", + "cd notebooks/MIRI_LRS_spectral_extraction/
\n", + "jupyter notebook miri_lrs_extraction_techniques.ipynb
\n", + "\n", "# Introduction\n", "\n", "This notebook extracts a 1D spectra from a 2D MIRI LRS spectral observation (single image). The goal is to provide the ability to extract spectra with different locations, extraction apertures, and techniques than are done in the JWST pipeline using the [Astropy Specreduce package](https://github.com/astropy/specreduce).\n", @@ -569,10 +580,10 @@ "metadata": {}, "outputs": [], "source": [ - "ext_center = 31.5\n", + "ext_center = 31\n", "ext_width = 8\n", - "bkg_sep = 7\n", - "bkg_width = 2" + "bkg_sep = 8\n", + "bkg_width = 3" ] }, { @@ -593,7 +604,7 @@ "# Plot along cross-disperion cut showing the extraction parameters\n", "fig, ax = plt.subplots(figsize=(10, 6))\n", "y = np.arange(image.shape[0])\n", - "ax.plot(y, image[:,300], 'k-')\n", + "ax.plot(y, image[:,140], 'k-')\n", "mm = np.array([ext_center, ext_center])\n", "mm_y = ax.get_ylim()\n", "\n", @@ -874,7 +885,11 @@ "\n", "#boxcar = BoxcarExtract()\n", "\n", - "# without background subtraction\n", + "# without *additional* background subtraction \n", + "# NOTE: The intial background subtraction is performed by subtracting the nods when creating the final data product input at the top of this notebook\n", + "# NOTE: Additional background subtractions can be performed using the Specreduce Background function below\n", + "# NOTE: Since most of the background has already been subtracted by the pipeline, all three extractions below look pretty similar\n", + "\n", "image_wg = image * wimage\n", "ext1d_boxcar = BoxcarExtract(image_wg, flat_trace, width=ext_width)\n", "ext1d_boxcar = ext1d_boxcar.spectrum.flux.value\n", @@ -1210,7 +1225,9 @@ "ax.set_ylabel(\"Flux Density [Jy]\")\n", "ax.set_yscale(\"log\")\n", "ax.set_ylim(1e-6, 3e-3)\n", + "#ax.set_ylim(6e-5, 8e-3)\n", "ax.set_xlim(4, 13)\n", + "#ax.set_xlim(11, 12.5)\n", "ax.legend()" ] }, diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques_standard.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques_standard.ipynb new file mode 100644 index 000000000..589484e12 --- /dev/null +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques_standard.ipynb @@ -0,0 +1,1544 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# MIRI LRS Spectral Extraction Techniques" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Use case:** Extract spectra with different techniques.
\n", + "**Data:** MIRI LRS spectrum of Type Ia supernova SN2021aefx, observed by Jha et al in PID 2072 (Obs 1), **where the automated spectral extraction failed**. These data were taken with zero exclusive access period, and published in [Kwok et al 2023](https://ui.adsabs.harvard.edu/abs/2023ApJ...944L...3K/abstract).
\n", + "**Tools:** jdaviz, specviz2d, specreduce, jwst, gwcs, matplotlib, astropy.
\n", + "**Cross-intrument:** NIRSpec, MIRI.
\n", + "**Documentation:** This notebook is part of a STScI's larger [post-pipeline Data Analysis Tools Ecosystem](https://jwst-docs.stsci.edu/jwst-post-pipeline-data-analysis).
\n", + "\n", + "# Install instructions\n", + "git clone https://github.com/spacetelescope/jdat_notebooks.git
\n", + "cd jdat_notebooks/
\n", + "git fetch -q https://github.com/spacetelescope/jdat_notebooks.git refs/pull/93/head:lrsoptimal2
\n", + "git checkout lrsoptimal2
\n", + "conda create -n lrsextract python=3.8.10
\n", + "conda activate lrsextract
\n", + "pip install -r requirements.txt
\n", + "cd notebooks/MIRI_LRS_spectral_extraction/
\n", + "jupyter notebook miri_lrs_extraction_techniques.ipynb
\n", + "\n", + "# Introduction\n", + "\n", + "This notebook extracts a 1D spectra from a 2D MIRI LRS spectral observation (single image). The goal is to provide the ability to extract spectra with different locations, extraction apertures, and techniques than are done in the JWST pipeline using the [Astropy Specreduce package](https://github.com/astropy/specreduce).\n", + "\n", + "The notebook also demos how to use Jdaviz's [specviz2d](https://jdaviz.readthedocs.io/en/latest/specviz2d/index.html), which allows users to interactively extract 1D spectra from 2D spectra.\n", + "\n", + "The simpliest spectral extraction is \"boxcar\" where all the pixels within some fixed width centered on the source position are summed at each wavelength. Background subtraction can be done using regions offset from the source center. You can also see the Specreduce [generic Sample Notebook](https://github.com/astropy/specreduce/blob/main/notebook_sandbox/jwst_boxcar/boxcar_extraction.ipynb).\n", + "\n", + "For spectra taken with a diffraction limited telescope like JWST, a modification boxcar extraction is to vary the extraction width linearly with wavelength. Such a scaled boxcar extraction keeps the fraction of the source flux within the extraction region approximately constant with wavelength.\n", + "\n", + "For point sources, a PSF-weighted spectral extraction can be done. Using the PSF to weight the extraction uses the actual PSF as a function of wavelength to optimize the extraction to the pixels with the greatest signal. PSF-weighted extractions show the largest differences with boxcar extractions at lower S/N values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note:** Corrections for the finite aperture used in all the extractions have not been applied. Thus, the physical flux densities of all the extracted spectra are lower than the actual values." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Imports\n", + "\n", + "- *matplotlib.pyplot* for plotting data\n", + "- *numpy* to handle array functions\n", + "- *astropy.io fits* for accessing FITS files\n", + "- *astropy.visualization* for scaling image for display\n", + "- *astropy.table Table* for reading the pipeline 1d extractions\n", + "- *jwst datamodels* for reading/access the jwst data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "# disable all imported packages' loggers\n", + "import logging\n", + "logging.root.manager.loggerDict = {}" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "print(pycodestyle_magic.__version__)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "# enable PEP8 checker for this notebook\n", + "%load_ext pycodestyle_magic\n", + "%flake8_on --ignore E261,E501,W291,W293\n", + "\n", + "# only allow the checker to throw warnings when there's a violation\n", + "logging.getLogger('flake8').setLevel('ERROR')\n", + "logging.getLogger('stpipe').setLevel('ERROR')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "# %matplotlib inline\n", + "\n", + "import numpy as np\n", + "\n", + "from gwcs.wcstools import grid_from_bounding_box\n", + "\n", + "from astropy.io import fits\n", + "from astropy.table import Table\n", + "from astropy.visualization import simple_norm\n", + "from astropy.io import ascii\n", + "\n", + "from jwst import datamodels\n", + "\n", + "from specreduce.extract import BoxcarExtract, OptimalExtract, HorneExtract\n", + "from specreduce.tracing import FlatTrace, FitTrace\n", + "from specreduce.background import Background\n", + "\n", + "from jdaviz import Imviz\n", + "from jdaviz import Specviz\n", + "from jdaviz import Specviz2d\n", + "\n", + "from astropy.utils.data import download_file\n", + "import os\n", + "\n", + "from specutils import Spectrum1D\n", + "from astropy import units as u\n", + "\n", + "# Display the video\n", + "from IPython.display import HTML, YouTubeVideo\n", + "\n", + "import os\n", + "import urllib.request\n", + "import tarfile" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download all necessary data\n", + "\n", + "if os.path.exists(\"boxcar_specviz2d.fits\"):\n", + " print(\"Boxcar Specviz2d Extraction Exists\")\n", + "else:\n", + " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/boxcar_specviz2d.fits'\n", + " urllib.request.urlretrieve(url, 'boxcar_specviz2d.fits')\n", + "\n", + "if os.path.exists(\"horne_specviz2d.fits\"):\n", + " print(\"Horne Specviz2d Extraction Exists\")\n", + "else:\n", + " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/horne_specviz2d.fits'\n", + " urllib.request.urlretrieve(url, 'horne_specviz2d.fits')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if os.path.exists(\"./required_data/\"):\n", + " print(\"Origina Data Exists\")\n", + "else:\n", + " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/required_data.tar.gz'\n", + " urllib.request.urlretrieve(url, 'required_data.tar.gz')\n", + " \n", + "# Unzip files if they haven't already been unzipped\n", + "if os.path.exists(\"required_data/\"):\n", + " print(\"Data Directory Already Exists\")\n", + "else:\n", + " tar = tarfile.open('./required_data.tar.gz', \"r:gz\")\n", + " tar.extractall()\n", + " tar.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Developer note: Ask Karl exactly how these functions work? Seems like all weights are equal?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# useful functions\n", + "def get_boxcar_weights(center, hwidth, npix):\n", + " \"\"\"\n", + " Compute the weights given an aperture center, half widths, and number of pixels\n", + " \"\"\"\n", + " weights = np.zeros((npix))\n", + " # pixels with full weight\n", + " fullpixels = [max(0, int(center - hwidth + 1)), min(int(center + hwidth), npix)]\n", + " weights[fullpixels[0] : fullpixels[1]] = 1.0\n", + "\n", + " # pixels at the edges of the boxcar with partial weight\n", + " if fullpixels[0] > 0:\n", + " weights[fullpixels[0] - 1] = hwidth - (center - fullpixels[0])\n", + " if fullpixels[1] < npix:\n", + " weights[fullpixels[1]] = hwidth - (fullpixels[1] - center)\n", + "\n", + " return weights\n", + "\n", + "\n", + "def ap_weight_images(\n", + " center, width, bkg_offset, bkg_width, image_size, waves, wavescale=None\n", + "):\n", + " \"\"\"\n", + " Create a weight image that defines the desired extraction aperture\n", + " and the weight image for the requested background regions\n", + "\n", + " Parameters\n", + " ----------\n", + " center : float\n", + " center of aperture in pixels\n", + " width : float\n", + " width of apeture in pixels\n", + " bkg_offset : float\n", + " offset from the extaction edge for the background\n", + " never scaled for wavelength\n", + " bkg_width : float\n", + " width of background region\n", + " never scaled with wavelength\n", + " image_size : tuple with 2 elements\n", + " size of image\n", + " waves : array\n", + " wavelegth values\n", + " wavescale : float\n", + " scale the width with wavelength (default=None)\n", + " wavescale gives the reference wavelenth for the width value\n", + "\n", + " Returns\n", + " -------\n", + " wimage, bkg_wimage : (2D image, 2D image)\n", + " wimage is the weight image defining the aperature\n", + " bkg_image is the weight image defining the background regions\n", + " \"\"\"\n", + " wimage = np.zeros(image_size)\n", + " bkg_wimage = np.zeros(image_size)\n", + " hwidth = 0.5 * width\n", + " # loop in dispersion direction and compute weights\n", + " for i in range(image_size[1]):\n", + " if wavescale is not None:\n", + " hwidth = 0.5 * width * (waves[i] / wavescale)\n", + "\n", + " wimage[:, i] = get_boxcar_weights(center, hwidth, image_size[0])\n", + "\n", + " # bkg regions\n", + " if (bkg_width is not None) & (bkg_offset is not None):\n", + " bkg_wimage[:, i] = get_boxcar_weights(\n", + " center - hwidth - bkg_offset, bkg_width, image_size[0]\n", + " )\n", + " bkg_wimage[:, i] += get_boxcar_weights(\n", + " center + hwidth + bkg_offset, bkg_width, image_size[0]\n", + " )\n", + " else:\n", + " bkg_wimage = None\n", + "\n", + " return (wimage, bkg_wimage)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Devloper notes (2021)\n", + "\n", + "1) The difference between the pipeline (x1d) and the extractions done in this notebook are quite large. Help in understanding the origin of these differences is needed.\n", + "\n", + "2) Not clear how to use the JWST pipeline `extract_1d` (quite complex) code. Help to determine how to use the JWST pipeline code instead of the custom code for boxcar is needed. \n", + "\n", + "3) Applying aperture corrections for the finite extraction widths is needed. Help in how to get the needed informatinom for different (user set) extraction widths is needed. \n", + "\n", + "### Partially RESOLVED (March, 2023)\n", + "\n", + "1) See notes from Kendrew on limitations of current pipeline. Pipeline will be updated soon.\n", + "\n", + "2) While this notebook doesn't go into using the pipeline, boxcar is now integrated into the Astropy Specreduce package. So I wouldn't characterize the boxcar as \"custom code\" any longer.\n", + "\n", + "3) Still not resolved." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download Files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#calfilename = \"det_image_seq5_MIRIMAGE_P750Lexp1_cal.fits\"\n", + "s2dfile = \"./required_data/BD+60_1753_1536/jw01536-o027_t008_miri_p750l/jw01536-o027_t008_miri_p750l_s2d.fits\"\n", + "x1dfile = \"./required_data/BD+60_1753_1536/jw01536-o027_t008_miri_p750l/jw01536-o027_t008_miri_p750l_x1d.fits\"\n", + "spatialprofilefile = \"./required_data/BD+60_1753_1536/jw01536-o027_t008_miri_p750l/jw01536-o027_t008_miri_p750l_s2d.fits\"\n", + "\n", + "#spatialprofilefilename = \"det_image_seq1_MIRIMAGE_P750Lexp1_s2d.fits\"\n", + "#mainurl = \"https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/\"\n", + "\n", + "#calfile_dld = download_file(mainurl + calfilename)\n", + "#s2dfile_dld = download_file(mainurl + s2dfilename)\n", + "#x1dfile_dld = download_file(mainurl + x1dfilename)\n", + "#spatialprofilefile_dld = download_file(mainurl + spatialprofilefilename)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "# rename files so that they have the right extensions\n", + "# required for the jwst datamodels to work\n", + "#calfile = calfile_dld + '_cal.fits'\n", + "#os.rename(calfile_dld, calfile)\n", + "\n", + "#s2dfile = s2dfile_dld + '_s2d.fits'\n", + "#os.rename(s2dfile_dld, s2dfile)\n", + "#x1dfile = x1dfile_dld + '_x1d.fits'\n", + "#os.rename(x1dfile_dld, x1dfile)\n", + "spatialprofilefile = spatialprofilefile_dld + '_s2d.fits'\n", + "os.rename(spatialprofilefile_dld, spatialprofilefile)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## File information\n", + "\n", + "The data used is a simulation of a LRS slit observation for a blackbody with a similar flux density to the star BD+60d1753, a flux calibration star. This simulation was created with MIRISim.\n", + "The simulated exposure was reduced using the JWST pipeline (v0.16.1) through the Detector1 and Spec2 stages.\n", + "\n", + "The cal file is one of the Spec2 products and is the calibration full frame image. It contains:\n", + "\n", + "1. (Primary): This HDU contains meta-data related to the observation and data reduction.\n", + "2. (SCI): The calibrated image. Units are MJy/sr.\n", + "3. (ERR): Uncertainty image. Units are MJy/sr.\n", + "4. (DQ): Data quality image.\n", + "5. (VAR_POISSON): Unc. component 1: Poisson uncertainty image. Units are (MJy/sr)^2.\n", + "6. (VAR_RNOISE): Unc. component 2: Read Noise uncertainty image. Units are (MJy/sr)^2.\n", + "7. (VAR_FLAT): Unc. component 3: Flat Field uncertainty image. Units are (MJy/sr)^2.\n", + "8. (ASDF_METADATA): Metadata.\n", + "\n", + "The s2d file is one of the Spec2 products and containes the calibrated rectified cutout of the LRS Slit region. It has:\n", + "\n", + "1. (Primary): This HDU contains meta-data related to the observation and data reduction.\n", + "2. (WGT): Weight.\n", + "3. (CON): ??\n", + "4. (ASDF_METADATA): Metadata." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Loading data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# use a jwst datamodel to provide a good interface to the data and wcs info\n", + "#cal = datamodels.open(calfile)\n", + "s2d = datamodels.open(s2dfile)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Basic information about the image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#print(\"cal image\")\n", + "#print(cal.data.shape)\n", + "#print(np.mean(cal.data))\n", + "#print(np.amin(cal.data), np.amax(cal.data))\n", + "print(\"s2d image\")\n", + "print(s2d.data.shape)\n", + "print(np.mean(s2d.data))\n", + "print(np.amin(s2d.data), np.amax(s2d.data))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display the full 2D image" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "norm_data = simple_norm(cal.data, 'sqrt')\n", + "plt.figure(figsize=(6, 6))\n", + "plt.imshow(cal.data, norm=norm_data, origin=\"lower\")\n", + "plt.title(\"The full image from the MIRI IMAGER detector\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display the LRS Slit region only (use s2d)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# transpose to make it display better\n", + "image = np.transpose(s2d.data)\n", + "err = np.transpose(s2d.err)\n", + "norm_data = simple_norm(image, \"sqrt\")\n", + "plt.figure(figsize=(10, 3))\n", + "plt.imshow(image, norm=norm_data, origin=\"lower\")\n", + "plt.title(\"The LRS region\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### View the 2D Spectrum in Imviz and get the center of the cross-dispersion " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imviz = Imviz()\n", + "imviz.app" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imviz.load_data(image)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "viewer = imviz.default_viewer\n", + "viewer.cuts = '95%'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1) Default JWST Pipeline 1D extraction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a spectrum1d\n", + "jpipe_x1d = Spectrum1D.read(x1dfile)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "specviz = Specviz()\n", + "specviz.app" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "specviz.load_spectrum(jpipe_x1d)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "ylim_low = 1.e-3\n", + "ylim_high = 4.e-2\n", + "xlim_low = 3\n", + "xlim_high = 13" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "\n", + "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", + "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", + "ax.set_ylabel(\"Flux Density [Jy]\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_ylim(ylim_low, ylim_high)\n", + "ax.set_xlim(xlim_low, xlim_high)\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2) Fixed Width Boxcar Extraction (Using Specreduce)\n", + "\n", + "Extract a 1D spectrum using a simple boxcar. Basically collapse the spectrum in the cross-dispersion direction over a specified number of pixels.\n", + "\n", + "#### Developer note: Allow for a bad pixel mask" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define extraction parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ext_center = 31\n", + "ext_width = 11\n", + "bkg_sep = 8\n", + "bkg_width = 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot cross-disperion cut showing the extraction parameters\n", + "\n", + "#### Develepor Note: Place trace back into Specviz2d/Imviz/Etc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot along cross-disperion cut showing the extraction parameters\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "y = np.arange(image.shape[0])\n", + "ax.plot(y, image[:,140], 'k-')\n", + "mm = np.array([ext_center, ext_center])\n", + "mm_y = ax.get_ylim()\n", + "\n", + "# extraction region\n", + "ax.axvspan(ext_center - ext_width/2., ext_center + ext_width/2., color='green', alpha=0.1)\n", + "ax.plot(mm, mm_y, 'b--')\n", + "ax.plot(mm - ext_width/2., mm_y, 'g:')\n", + "ax.plot(mm + ext_width/2., mm_y, 'g:')\n", + "\n", + "# background region, symmetric on both sides of extraction region\n", + "ax.axvspan(ext_center - bkg_sep - bkg_width/2., ext_center - bkg_sep + bkg_width/2., color='red', alpha=0.1)\n", + "ax.plot(mm - bkg_sep - bkg_width/2., mm_y, 'r:')\n", + "ax.plot(mm - bkg_sep + bkg_width/2., mm_y, 'r:')\n", + "\n", + "ax.axvspan(ext_center + bkg_sep - bkg_width/2., ext_center + bkg_sep + bkg_width/2., color='red', alpha=0.1)\n", + "ax.plot(mm + bkg_sep - bkg_width/2., mm_y, 'r:')\n", + "ax.plot(mm + bkg_sep + bkg_width/2., mm_y, 'r:')\n", + "\n", + "ax.set_title(\"Cross-dispersion Cut at Pixel=300\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define Background" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# extract the background using custom individual traces\n", + "trace = FlatTrace(image, ext_center)\n", + "bg = Background(image, [trace-bkg_sep, trace+bkg_sep], width=bkg_width)\n", + "\n", + "# alternatively, call two_sided class, which does the same as above \n", + "#bg = Background.two_sided(image, trace, bkg_sep, width=bkg_width)\n", + "# or in the place of any trace, an int/float can be passed which resolves to a FlatTrace\n", + "#bg = Background.two_sided(image, ext_center, bkg_sep, width=bkg_width)\n", + "\n", + "# or for single sided:\n", + "# bg = Background.one_sided(image, trace, bkg_sep, width=bkg_width)\n", + "# bg = Background.one_sided(image, trace, -bkg_sep, width=bkg_width)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# view the background weighted image\n", + "plt.figure(figsize=(15, 15))\n", + "plt.imshow(bg.bkg_wimage, origin=\"lower\")\n", + "plt.title(\"slit[0] slice\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# view the background image\n", + "plt.figure(figsize=(15, 15))\n", + "plt.imshow(bg.bkg_image().flux.value, norm=norm_data, origin=\"lower\")\n", + "plt.title(\"slit[0] slice\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# view the background-subtracted image\n", + "plt.figure(figsize=(15, 15))\n", + "plt.imshow(bg.sub_image().flux.value, norm=norm_data, origin=\"lower\")\n", + "plt.title(\"slit[0] slice\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that when using median to calculate the background, partial pixel weights are not supported:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bg_med = Background.two_sided(image, ext_center, bkg_sep, width=bkg_width, statistic='median')\n", + "plt.figure(figsize=(15, 15))\n", + "plt.imshow(bg_med.bkg_wimage, origin=\"lower\")\n", + "plt.title(\"slit[0] slice\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extract Trace (multiple options)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Optional: we could now refine the initial flat trace by running an automated FitTrace on the subtracted image. This process could be iterated as necessary (recreating the subtracted image with the refined trace, etc)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fit_trace = FitTrace(image-bg.bkg_image().flux.value, peak_method='gaussian', guess=ext_center)\n", + "#fit_trace" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "flat_trace = FlatTrace(image-bg.bkg_image().flux.value, trace_pos=ext_center)\n", + "#flat_trace" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **Always visualize your traces. If you have noisy data, the fits may not be good. You may need to play around with the type of fit (i.e., Order 1 Polynomial) or different window sizes and parameters. In our case, we'll stick with the flat trace throughout this notebook.** \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "#### Plot old vs new trace" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig3, ax3 = plt.subplots(figsize=(10,6))\n", + "plot3 = ax3.imshow(bg.sub_image().flux.value, aspect=4.,\n", + " vmin=0, vmax=bg.sub_image().flux.value.max()/2,\n", + " cmap=mpl.cm.magma, origin='lower',\n", + " extent=(0, bg.sub_image().flux.value.shape[-1],\n", + " 0, bg.sub_image().flux.value.shape[0]))\n", + "fig3.colorbar(plot3)\n", + "ax3.set_title('LRS Spectrum Traces')\n", + "ax3.grid()\n", + "\n", + "# add the traces\n", + "ax3.plot(flat_trace.trace, '--', color='#008ca8',\n", + " lw=2.5, label='FlatTrace')\n", + "ax3.plot(fit_trace.trace, '--', color='#00471b',\n", + " lw=2.5, label='GaussianFitTrace')\n", + "ax3.legend(framealpha=.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extract" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Dev Note: FitTrace doesn't seem to be working right now, so we'll stick with FlatTrace" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "from specutils import Spectrum1D\n", + "from astropy import units as u\n", + "flux = s2d.data * u.Jy\n", + "wavelength = s2d.wavelength * u.um\n", + "flux.data\n", + "spec = Spectrum1D(spectral_axis=wavelength, flux=flux)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#boxcar = BoxcarExtract()\n", + "ext1d_boxcar_noweights = BoxcarExtract(image-bg, flat_trace, width=ext_width)\n", + "ext1d_boxcar_noweights = ext1d_boxcar_noweights.spectrum.flux.value\n", + "ext1d_boxcar_noweights *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", + "#spectrum_specreduce_boxcar" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "\n", + "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", + "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label='Boxcar')\n", + "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", + "ax.set_ylabel(\"Flux Density [Jy]\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_ylim(ylim_low, ylim_high)\n", + "ax.set_xlim(xlim_low, xlim_high)\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3) Fixed Width Boxcar Extraction (Using Pixel Masks, too)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **A basic example of how to create a pixel weight map/mask. In this example, the weights basically create a pixel mask based on the boxcar extraction aperture. It shouldn't actually change any of the results because the boxcar extraction essentially does this for you. But this provides a useful example for more complicated masks that we will create lower in the notebook.** " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from gwcs.wcstools import grid_from_bounding_box\n", + "\n", + "#image = np.transpose(s2d.data)\n", + "grid = grid_from_bounding_box(s2d.meta.wcs.bounding_box)\n", + "ra, dec, lam = s2d.meta.wcs(*grid)\n", + "lam_image = np.transpose(lam)\n", + "\n", + "# compute a \"rough\" wavelength scale to allow for aperture to scale with wavelength\n", + "rough_waves = np.average(lam_image, axis=0)\n", + "\n", + "# images to use for extraction\n", + "wimage, bkg_wimage = ap_weight_images(\n", + " ext_center,\n", + " ext_width,\n", + " bkg_width,\n", + " bkg_sep,\n", + " image.shape,\n", + " rough_waves,\n", + " wavescale=None,\n", + ")\n", + "\n", + "#boxcar = BoxcarExtract()\n", + "\n", + "# without *additional* background subtraction \n", + "# NOTE: The intial background subtraction is performed by subtracting the nods when creating the final data product input at the top of this notebook\n", + "# NOTE: Additional background subtractions can be performed using the Specreduce Background function below\n", + "# NOTE: Since most of the background has already been subtracted by the pipeline, all three extractions below look pretty similar\n", + "\n", + "image_wg = image * wimage\n", + "ext1d_boxcar = BoxcarExtract(image_wg, flat_trace, width=ext_width)\n", + "ext1d_boxcar = ext1d_boxcar.spectrum.flux.value\n", + "# convert from MJy/sr to Jy\n", + "ext1d_boxcar *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", + "\n", + "# with background subtraction\n", + "image_bg = bg.sub_image()\n", + "image_wg = image_bg * wimage\n", + "ext1d_boxcar_bkgsub = BoxcarExtract(image_wg, flat_trace, width=ext_width)\n", + "ext1d_boxcar_bkgsub = ext1d_boxcar_bkgsub.spectrum.flux.value\n", + "\n", + "# convert from MJy/sr to Jy\n", + "ext1d_boxcar_bkgsub *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", + "\n", + "# compute the average wavelength for each column using the weight image\n", + "# this should correspond directly with the extracted spectrum\n", + "# wavelengths account for any tiled spectra this way\n", + "waves_boxcar = np.average(lam_image, weights=wimage, axis=0)\n", + "waves_boxcar_bkgsub = np.average(lam_image, weights=wimage, axis=0)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "ext1d_boxcar = boxcar(image_wg, auto_trace, width=ext_width)\n", + "ext1d_boxcar.flux.value" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "gpts = ext1d_boxcar_bkgsub > 0.\n", + "gpts = ext1d_boxcar > 0.\n", + "\n", + "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label = 'Boxcar; No Mask')\n", + "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], color = 'green', label=\"Boxcar; Mask\")\n", + "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], color = 'red', label=\"Boxcar; bkgsub; Mask\")\n", + "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", + "ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n", + "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", + "ax.set_ylabel(\"Flux Density [Jy]\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_ylim(ylim_low, ylim_high)\n", + "ax.set_xlim(xlim_low, xlim_high)\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4) Wavelength scaled width boxcar\n", + "\n", + "The LRS spatial profile changes as a function of wavelength as JWST is diffraction limited at these wavelengths. Nominally this means that the FWHM is changing linearly with wavelength. Scaling the width of the extraction aperture with wavelength accounts for the changing diffraction limit with wavelength to first order." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Developer note: Not currently possible. Allow for wavelength scaled width in the boxcar" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5) Horne Extraction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **The Horne algorithm preforms a Gaussian fit on the source, it is thus best suited for cases where the source has a Gaussian profile in the cross-dispersion direction. If your profile is not Gaussian, you will likely over- or under-estimate your actual flux.**. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Steps in the original Horne (1986) paper:\n", + "\n", + "1. Bias subtraction [assumed to be done in earlier block]\n", + "2. Initial variance estimate [user provides this as an argument]\n", + "3. Fit sky background [assumed to be done in earlier block]\n", + " * \"We therefore generally perform a least-squares polynomial fit to the sky data at each wavelength. Individual sky pixels are given weights inversely proportional to their variances as estimated in Step 2\" [overlaps with notebook guide's 3b]\n", + "4. Extract standard spectrum and its variance\n", + " * Subtract the sky background found in Step 3 from the image. [sky background calculation is planned as a separate, earlier step of the specreduce workflow]\n", + "5. Construct spatial profile\n", + "6. Revise variance estimates [not currently done]\n", + "7. Mask cosmic ray hits [not currently done]\n", + "8. Extract optimal spectrum and its variance [currently only extract the spectum, not a variance]\n", + "9. Iterate Steps 5-8\n", + "\n", + "The first four steps as the standard procedure and the last five as add-ons that help squeeze out extra signal-to-noise." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are notes [in brackets] on how each step is handled in the proposed HorneExtract/OptimalExtract classes to make it easier to see what the class does and what the user must do themselves.\n", + "\n", + "### Steps in the JDAT Notebook guide on optimal extraction:\n", + "\n", + "1. Define extraction region [user's responsibility to provide an appropriate image]\n", + "2. Pick a slice [should not be necessary? can use the whole image as the aperture with good results]\n", + "3. Define extraction kernel\n", + " * Select PSF template [assumed to be Gaussian for now. support for Moffat, others?]\n", + " * Choose a polynomial for background fitting [user provides as an argument]\n", + "4. Fit extraction kernel to initial slice [all columns are coadded to perform the fit]\n", + "5. Fit geometric distortion [not currently done]\n", + " * Determine cross-dispersion bins for trace fitting\n", + " * Fit a kernel to each bin to find trace center [user provides this as a specreduce.tracing.Trace object]\n", + " * Polynomial fit of trace centers\n", + "6. Combine composite model with 2D image to create output 1D spectrum\n", + " * Create variance image [user provides this as an argument]\n", + " * Generate 1D spectrum\n", + "7. Compare with reference 1D spectrum" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ext1d_horne = HorneExtract(image-bg, flat_trace)\n", + "ext1d_horne = ext1d_horne.spectrum.flux.value\n", + "# convert from MJy/sr to Jy\n", + "ext1d_horne *= 1e6 * s2d.meta.photometry.pixelarea_steradians" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "gpts = ext1d_boxcar_bkgsub > 0.\n", + "gpts = ext1d_boxcar > 0.\n", + "\n", + "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label = 'Boxcar; No Mask')\n", + "ax.plot(jpipe_x1d.spectral_axis, ext1d_horne, color = 'purple', label = 'Horne; No Mask')\n", + "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], color = 'green', label=\"Boxcar; Mask\")\n", + "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], color = 'red', label=\"Boxcar; bkgsub; Mask\")\n", + "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", + "ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n", + "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", + "ax.set_ylabel(\"Flux Density [Jy]\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_ylim(ylim_low, ylim_high)\n", + "ax.set_xlim(xlim_low, xlim_high)\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **See note above. In this case the Horne extraction likely overestimates the flux because it under-fits the wings of the cross-dispersion profile. Using a real MIRI LRS PSF is a better idea.** " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 5) PSF-Weighted Extraction\n", + "\n", + "While to first order the PSF FHWM changes linearly with wavelength, this is an approximation. It is better to use the measured spatial profile as a function of wavelength to extract the spectrum. This tracks the actual variation with wavelength and optimizes the extraction to the higher S/N measurements. In general, PSF based extractions show the most improvements over boxcar extractions at lower the S/N.\n", + "\n", + "There are two PSF based extraction methods:\n", + "\n", + "1. PSF weighted: the spatial profile at each wavelength is used to weight the extraction.\n", + "2. PSF fitting: the spatial profile is fit at each wavelength with the scale parameter versus wavelength giving the spectrum.\n", + "\n", + "#### Only the PSF weighted technique is currently part of this notebook.\n", + "\n", + "Note 1: calibration reference file for the specific LRS slit position should be used.
\n", + "Note 2: Small shifts in the centering of the source in the slit should be investigated to see if they impact the PSF based extractions.
\n", + "Limitation: currently it is assumed there are no bad pixels.
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PSF weighted extaction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Generate the PSF profile as a function of wavelength\n", + "For MIRI LRS slit observations, observations are made at two nod position in the slit after target acquisition. This means that the location of the sources in the slit is very well known. Hence, spatial profile (PSF) as a function of wavelength for the two nod positions is straightforward to measure using observations of a bright source.\n", + "\n", + "The next few steps generate the needed information for the nod position for which we are extracting spectra based on a simulation of a bright source at the same nod position." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# lrs spatial profile (PSF) as a function of wavelength\n", + "# currently, this is just a \"high\" S/N observation of a flat spectrum source at the same slit position\n", + "psf = datamodels.open(spatialprofilefile)\n", + "# transpose to make it display better\n", + "lrspsf = np.transpose(psf.data)\n", + "norm_data = simple_norm(lrspsf, \"sqrt\")\n", + "plt.figure(figsize=(10, 3))\n", + "plt.imshow(lrspsf, norm=norm_data, origin=\"lower\")\n", + "plt.title(\"The LRS Spatial Profile (PSF) Observation\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Mock a LRS spectral profile reference file\n", + "# Sum along the spatial direction and normalize to 1\n", + "# assume there is no background (none was included in the MIRISim for the flat spectrum source observation)\n", + "# ignore regions far from the source using a scaled boxcar weight image\n", + "# the aperture (psf_width) used in the scaled boxcar weight image could be varied\n", + "psf_width = 12.0\n", + "(wimage_scaledboxcar, tmpvar) = ap_weight_images(ext_center, psf_width, bkg_sep, \n", + " bkg_width, image.shape, waves_boxcar, wavescale=10.0)\n", + "\n", + "psf_weightimage = lrspsf*wimage_scaledboxcar\n", + "\n", + "# generate a 2D image of the column sums for division\n", + "max_psf = np.max(psf_weightimage, axis=0)\n", + "div_image = np.tile(max_psf, (psf_weightimage.shape[0], 1))\n", + "div_image[div_image == 0.0] = 1.0 # avoid divide by zero issues\n", + "\n", + "# normalize \n", + "psf_weightimage /= div_image\n", + "\n", + "# display\n", + "norm_data = simple_norm(psf_weightimage, \"sqrt\")\n", + "plt.figure(figsize=(10, 3))\n", + "plt.imshow(psf_weightimage, norm=norm_data, origin=\"lower\")\n", + "plt.title(\"The LRS Spatial Profile Reference Image (Normalized)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "y = np.arange(psf_weightimage.shape[0])\n", + "ax.plot(y, psf_weightimage[:,150], label=\"pixel=150\")\n", + "ax.plot(y, psf_weightimage[:,225], label=\"pixel=225\")\n", + "ax.plot(y, psf_weightimage[:,300], label=\"pixel=300\")\n", + "ax.plot(y, psf_weightimage[:,370], label=\"pixel=370\")\n", + "ax.set_title(\"Cross-dispersion Cuts\")\n", + "ax.set_xlim(ext_center-psf_width, ext_center+psf_width)\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the spatial profile becomes narrower as the pixel values increases as this corresponds to the wavelength decreasing." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Extract spectrum using wavelength dependent PSF profiles using the same traces as defined above" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "image_bg = bg.sub_image()\n", + "image_wg = image_bg * psf_weightimage\n", + "ext1d_boxcar_bkgsub_psfweight = BoxcarExtract(image_wg, flat_trace, width=ext_width)\n", + "ext1d_boxcar_bkgsub_psfweight = ext1d_boxcar_bkgsub_psfweight.spectrum.flux.value\n", + "\n", + "# convert from MJy/sr to Jy\n", + "ext1d_boxcar_bkgsub_psfweight *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", + "\n", + "# compute the average wavelength for each column using the weight image\n", + "# this should correspond directly with the extracted spectrum\n", + "# wavelengths account for any tiled spectra this way\n", + "waves_boxcar_psfweight = np.average(lam_image, weights=wimage, axis=0)\n", + "waves_boxcar_bkgsub_psfweight = np.average(lam_image, weights=wimage, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "gpts = ext1d_boxcar_bkgsub > 0.\n", + "gpts = ext1d_boxcar > 0.\n", + "\n", + "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label = 'Boxcar; No Mask')\n", + "ax.plot(jpipe_x1d.spectral_axis, ext1d_horne, color = 'purple', label = 'Horne; No Mask')\n", + "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], color = 'green', label=\"Boxcar; Mask\")\n", + "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], color = 'red', label=\"Boxcar; bkgsub; Mask\")\n", + "ax.plot(waves_boxcar_bkgsub_psfweight[gpts], ext1d_boxcar_bkgsub_psfweight[gpts], 'k-', label=\"Boxcar; bkgsub; PSF Weights\", color='cyan')\n", + "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", + "ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n", + "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", + "ax.set_ylabel(\"Flux Density [Jy]\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_ylim(ylim_low, ylim_high)\n", + "ax.set_xlim(xlim_low, xlim_high)\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the psf weighted extraction has visabily higher S/N, especially at the longer wavelengths where the S/N is lowest overall." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot in Specviz\n", + "specviz2 = Specviz()\n", + "specviz2.app" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ext1d_boxcar_spec1d = Spectrum1D(spectral_axis=waves_boxcar[gpts]*u.micron, flux=ext1d_boxcar[gpts]*u.Jy)\n", + "ext1d_boxcar_bkgsub_spec1d = Spectrum1D(spectral_axis=waves_boxcar_bkgsub[gpts]*u.micron, flux=ext1d_boxcar_bkgsub[gpts]*u.Jy)\n", + "ext1d_psfweight_spec1d = Spectrum1D(spectral_axis=waves_boxcar_bkgsub_psfweight[gpts]*u.micron, flux=ext1d_boxcar_bkgsub_psfweight[gpts]*u.Jy)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "specviz2.load_spectrum(ext1d_boxcar_spec1d, data_label='boxcar')\n", + "specviz2.load_spectrum(ext1d_boxcar_bkgsub_spec1d, data_label='boxcar bkgsub')\n", + "specviz2.load_spectrum(ext1d_psfweight_spec1d, data_label='psfweight')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6) PSF-Fitted Extraction\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Developer note: Not currently possible. Allow for wavelength scaled width in the boxcar" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 7) Specviz2D Extraction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Watch these two demo videos on how to extract your spectra using Specviz2D" + ] + }, + { + "cell_type": "raw", + "metadata": { + "tags": [] + }, + "source": [ + "# Video showing how to use specviz2d\n", + "HTML ('')" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "# Video showing how to use specviz2d\n", + "HTML ('')" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "specviz2d = Specviz2d()\n", + "specviz2d.app" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "specviz2d.load_data(s2dfile)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "boxcar = specviz2d.app.get_data_from_viewer('spectrum-viewer',data_label='boxcar')\n", + "horne = specviz2d.app.get_data_from_viewer('spectrum-viewer',data_label='horne')" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "# Check to see if user made a boxcar extraction, otherwise read in from file\n", + "if not boxcar:\n", + " print(\"You didn't extract a spectrum from specviz2d, we will load a pre-extracted spectrum from the video above.\")\n", + " boxcar_specviz2d = Spectrum1D.read('boxcar_specviz2d.fits')\n", + "else:\n", + " myboxcar = boxcar.flux.value\n", + " myboxcar *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", + " boxcar_specviz2d = Spectrum1D(spectral_axis=jpipe_x1d.spectral_axis, flux=myboxcar*u.Jy)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "if not horne:\n", + " print(\"You didn't extract a spectrum from specviz2d, we will load a pre-extracted spectrum from the video above.\")\n", + " horne_specviz2d = Spectrum1D.read('horne_specviz2d.fits')\n", + "else:\n", + " myhorne = horne.flux.value\n", + " myhorne *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", + " horne_specviz2d = Spectrum1D(spectral_axis=jpipe_x1d.spectral_axis, flux=myhorne*u.Jy)" + ] + }, + { + "cell_type": "raw", + "metadata": { + "tags": [] + }, + "source": [ + "# Sarah's Extraction (for reference)\n", + "sp3_x1dfile = 'required_data/PID2072_Obs1_LRS_demo_x1d.fits'\n", + "sp3_x1d = datamodels.open(sp3_x1dfile)\n", + "ll3 = (sp3_x1dfile.split('/')[-1]).split('.')[0] + ' (Level 3, custom)'" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "# plot\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "gpts = ext1d_boxcar_bkgsub > 0.\n", + "gpts = ext1d_boxcar > 0.\n", + "\n", + "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label = 'Boxcar; No Mask')\n", + "ax.plot(jpipe_x1d.spectral_axis, ext1d_horne, color = 'purple', label = 'Horne; No Mask')\n", + "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], color = 'green', label=\"Boxcar; Mask\")\n", + "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], color = 'red', label=\"Boxcar; bkgsub; Mask\")\n", + "ax.plot(waves_boxcar_bkgsub_psfweight[gpts], ext1d_boxcar_bkgsub_psfweight[gpts], 'k-', label=\"Boxcar; bkgsub; PSF Weights\", color='cyan')\n", + "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", + "\n", + "ax.plot(boxcar_specviz2d.spectral_axis, boxcar_specviz2d.flux, 'k-', label=\"Boxcar Specviz2D\", color='magenta')\n", + "ax.plot(horne_specviz2d.spectral_axis, horne_specviz2d.flux, 'k-', label=\"Horne Specviz2D\", color='lawngreen')\n", + "ax.plot(sp3_x1d.spec[0].spec_table['WAVELENGTH'], sp3_x1d.spec[0].spec_table['FLUX'], label=ll3, color = 'gold')\n", + "ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n", + "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", + "ax.set_ylabel(\"Flux Density [Jy]\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_ylim(ylim_low, ylim_high)\n", + "ax.set_xlim(xlim_low, xlim_high)\n", + "ax.legend(bbox_to_anchor=(1.1, 1.05))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Read in the models\n", + "model = ascii.read(\"./required_data/bd60_1753.lrs.sp.tbl\") \n", + "wave_model = model['wavelength']\n", + "flux_model = model['flux']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# plot\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "gpts = ext1d_boxcar_bkgsub > 0.\n", + "gpts = ext1d_boxcar > 0.\n", + "\n", + "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label = 'Boxcar; No Mask')\n", + "ax.plot(jpipe_x1d.spectral_axis, ext1d_horne, color = 'purple', label = 'Horne; No Mask')\n", + "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], color = 'green', label=\"Boxcar; Mask\")\n", + "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], color = 'red', label=\"Boxcar; bkgsub; Mask\")\n", + "ax.plot(waves_boxcar_bkgsub_psfweight[gpts], ext1d_boxcar_bkgsub_psfweight[gpts], 'k-', label=\"Boxcar; bkgsub; PSF Weights\", color='cyan')\n", + "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", + "\n", + "#ax.plot(boxcar_specviz2d.spectral_axis, boxcar_specviz2d.flux, 'k-', label=\"Boxcar Specviz2D\", color='magenta')\n", + "#ax.plot(horne_specviz2d.spectral_axis, horne_specviz2d.flux, 'k-', label=\"Horne Specviz2D\", color='lawngreen')\n", + "ax.plot(wave_model, flux_model, 'k-', label=\"Calspec Model\", color='magenta')\n", + "#ax.plot(sp3_x1d.spec[0].spec_table['WAVELENGTH'], sp3_x1d.spec[0].spec_table['FLUX'], label=ll3, color = 'gold')\n", + "ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n", + "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", + "ax.set_ylabel(\"Flux Density [Jy]\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_ylim(ylim_low, ylim_high)\n", + "ax.set_xlim(xlim_low, xlim_high)\n", + "ax.legend(bbox_to_anchor=(1.1, 1.05))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Additional Resources\n", + "\n", + "- [MIRI LRS](https://jwst-docs.stsci.edu/mid-infrared-instrument/miri-observing-modes/miri-low-resolution-spectroscopy)\n", + "- [MIRISim](http://www.stsci.edu/jwst/science-planning/proposal-planning-toolbox/mirisim)\n", + "- [JWST pipeline](https://jwst-docs.stsci.edu/jwst-data-reduction-pipeline)\n", + "- PSF weighted extraction [Horne 1986, PASP, 98, 609](https://ui.adsabs.harvard.edu/abs/1986PASP...98..609H/abstract)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## About this notebook\n", + "\n", + "**Author:** Karl Gordon, JWST\n", + "**Updated On:** 2020-07-07" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Top of Page](#top)\n", + "\"Space " + ] + } + ], + "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": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 2a148c52932df18c6c9d2b9990dc2430f016604e Mon Sep 17 00:00:00 2001 From: Ori Fox Date: Wed, 2 Aug 2023 08:17:01 -0400 Subject: [PATCH 07/36] updated with pipeline commands --- .../miri_lrs_advanced_extraction_part1.ipynb | 1183 +++++++++++++++++ 1 file changed, 1183 insertions(+) create mode 100644 notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb new file mode 100644 index 000000000..a776a5d94 --- /dev/null +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -0,0 +1,1183 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "20ce4694", + "metadata": {}, + "source": [ + "# MIRI LRS Slit Spectroscopy: Spectral Extraction using the JWST Pipeline\n", + "\n", + "July 2023\n", + "\n", + "**Use case:** Spectral extraction of slit spectra with the JWST calibration pipeline.
\n", + "**Data:** Publicly available science data
\n", + "**Tools:** jwst, matplotlib, astropy.
\n", + "**Cross-intrument:** NIRSpec, MIRI.
\n", + "\n", + "\n", + "\n", + "### Introduction: Spectral extraction in the JWST calibration pipeline\n", + "\n", + "The JWST calibration pipeline performs spectrac extraction for all spectroscopic data using basic default assumptions that are tuned to produce accurately calibrated spectra for the majority of science cases. This default method is a simple fixed-width boxcar extraction, where the spectrum is summed over a number of pixels along the cross-dispersion axis, over the valid wavelength range. An aperture correction is applied at each pixel along the spectrum to account for flux lost from the finite-width aperture. \n", + "\n", + "The ``extract_1d`` step uses the following inputs for its algorithm:\n", + "- the spectral extraction reference file: this is a json-formatted file, available as a reference file from the [JWST CRDS system](https://jwst-crds.stsci.edu)\n", + "- the bounding box: the ``assign_wcs`` step attaches a bounding box definition to the data, which defines the region over which a valid calibration is available. We will demonstrate below how to visualize this region. \n", + "\n", + "However the ``extract_1d`` step has the capability to perform more complex spectral extractions, requiring some manual editing of parameters and re-running of the pipeline step. \n", + "\n", + "\n", + "### Aims\n", + "\n", + "This notebook will demonstrate how to re-run the spectral extraction step with different settings to illustrate the capabilities of the JWST calibration pipeline. \n", + "\n", + "\n", + "### Assumptions\n", + "\n", + "We will demonstrate the spectral extraction methods on resampled, calibrated spectral images. The basic demo and two examples run on Level 3 data, in which the nod exposures have been combined into a single spectral image. Two examples will use the Level 2b data - one of the nodded exposures. \n", + "\n", + "\n", + "### Test data\n", + "\n", + "The data used in this notebook is an observation of the Type Ia supernova SN2021aefx, observed by Jha et al in PID 2072 (Obs 1). These data were taken with zero exclusive access period, and published in [Kwok et al 2023](https://ui.adsabs.harvard.edu/abs/2023ApJ...944L...3K/abstract). You can retrieve the data from [this Box folder](https://stsci.box.com/s/i2xi18jziu1iawpkom0z2r94kvf9n9kb), and we recommend you place the files in the ``data/`` folder of this repository, or change the directory settings in the notebook prior to running. \n", + "\n", + "You can of course use your own data instead of the demo data. \n", + "\n", + "\n", + "### JWST pipeline version and CRDS context\n", + "\n", + "This notebook was written using the calibration pipeline version 1.10.2. We set the CRDS context explicitly to 1089 to match the current latest version in MAST. If you use different pipeline versions or CRDS context, please read the relevant release notes ([here for pipeline](https://github.com/spacetelescope/jwst), [here for CRDS](https://jwst-crds.stsci.edu)) for possibly relevant changes.\n", + "\n", + "### Contents\n", + "\n", + "1. [The Level 3 data products](#l3data)\n", + "2. [The spectral extraction reference file](#x1dref)\n", + "3. [Example 1: Changing the aperture width](#ex1)\n", + "4. [Example 2: Changing the aperture location](#ex2)\n", + "5. [Example 3: Extraction with background subtraction](#ex3)\n", + "6. [Example 4: Tapered column extraction](#ex4)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "08ddf5f7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CRDS cache location: /Users/ofox/crds_cache\n" + ] + } + ], + "source": [ + "import os,urllib.request,tarfile\n", + "\n", + "os.environ['CRDS_CONTEXT'] = 'jwst_1089.pmap'\n", + "\n", + "os.environ['CRDS_PATH'] = os.environ['HOME']+'/crds_cache' \n", + "os.environ['CRDS_SERVER_URL'] = 'https://jwst-crds.stsci.edu'\n", + "print('CRDS cache location: {}'.format(os.environ['CRDS_PATH']))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "aee92bcf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using JWST calibration pipeline version 1.11.3\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "from glob import glob\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import astropy.io.fits as fits\n", + "\n", + "from jwst.pipeline import Spec2Pipeline, Spec3Pipeline\n", + "from jwst import datamodels\n", + "from jwst.extract_1d import Extract1dStep\n", + "\n", + "import jwst\n", + "import json\n", + "print('Using JWST calibration pipeline version {0}'.format(jwst.__version__))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "305103d5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original Data tar.gz Exists\n", + "Data Directory Already Exists\n" + ] + } + ], + "source": [ + "# Download Data\n", + "\n", + "if os.path.exists(\"data.tar.gz\"):\n", + " print(\"Original Data tar.gz Exists\")\n", + "else:\n", + " print(\"Downloading Data\")\n", + " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/data.tar.gz'\n", + " urllib.request.urlretrieve(url, 'data.tar.gz')\n", + " \n", + "# Unzip files if they haven't already been unzipped\n", + "if os.path.exists(\"data/\"):\n", + " print(\"Data Directory Already Exists\")\n", + "else:\n", + " print(\"Unpacking Data\")\n", + " tar = tarfile.open('./data.tar.gz', \"r:gz\")\n", + " tar.extractall()\n", + " tar.close()" + ] + }, + { + "cell_type": "markdown", + "id": "611086f4", + "metadata": {}, + "source": [ + "## 1. The Level 3 Data Products \n", + "\n", + "\n", + "Let's start by plotting the main default Level 3 output products:\n", + "* the ``s2d`` file: this is the 2D image built from the co-added resampled individual nod exposures. \n", + "* the ``x1d`` file: this is the 1-D extracted spectrum, extracted from the Level 3 ``s2d`` file. \n", + "\n", + "The ``s2d`` image shows a bright central trace, flanked by two negative traces. These result from the combination of the nod exposures, each of which also contains a positive and negative trace due to being mutually subtracted for background subtraction. \n", + "\n", + "We restrict the short-wavelength end of the x-axis to 5 micron, as our calibration is very poor below this wavelength. The Level 3 spectrum is extracted from the resampled, dither-combined, calibrated exposure. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a8012bfa", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-01 16:52:27,808 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/435588000.py:10: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-01 16:52:27,808 - stpipe - WARNING - fig.show()\n", + "2023-08-01 16:52:27,809 - stpipe - WARNING - \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "l3_s2d_file = 'data/jw02072-o001_t010_miri_p750l_s2d_1089.fits'\n", + "l3_s2d = datamodels.open(l3_s2d_file)\n", + "\n", + "fig, ax = plt.subplots(figsize=[2,8])\n", + "im2d = ax.imshow(l3_s2d.data, origin='lower', aspect='auto', cmap='gist_gray')\n", + "ax.set_xlabel('column')\n", + "ax.set_ylabel('row')\n", + "ax.set_title('SN2021aefx - Level 3 resampled 2D spectral image')\n", + "fig.colorbar(im2d)\n", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c51f421b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-01 16:52:27,982 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/2770287384.py:10: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-01 16:52:27,983 - stpipe - WARNING - fig2.show()\n", + "2023-08-01 16:52:27,983 - stpipe - WARNING - \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "l3_file = 'data/jw02072-o001_t010_miri_p750l_x1d_1089.fits'\n", + "l3_spec = datamodels.open(l3_file)\n", + "\n", + "fig2, ax2 = plt.subplots(figsize=[12,4])\n", + "ax2.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['FLUX'])\n", + "ax2.set_xlabel('wavelength (um)')\n", + "ax2.set_ylabel('flux (Jy)')\n", + "ax2.set_title('SN2021aefx - Level 3 spectrum in MAST (pmap 1089)')\n", + "ax2.set_xlim(5., 14.)\n", + "fig2.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "3ae110b4", + "metadata": {}, + "source": [ + "## The spectral extraction reference file \n", + "\n", + "The reference file that tells the ``extract_1d`` algorithm what parameters to use is a text file using the `json` format that is available in [CRDS](https://jwst-crds.stsci.edu). The second reference file used in the extraction is the aperture correction; this corrects for the flux lost as a function of wavelength for the extraction aperture size used. You can use the datamodel attributes of the ``x1d`` file to check which extraction reference file was called by the algorithm. \n", + "\n", + "We show below how to examine the file programmatically to see what aperture was used to produce the default Level 3 spectrum shown above. **Note: this json file can easily be opened and edited with a simple text editor**. \n", + "\n", + "Full documentation of the ``extract_1d`` reference file is available [here](https://jwst-pipeline.readthedocs.io/en/latest/jwst/extract_1d/reference_files.html). We recommend you read this page and any links therein carefully to understand how the parameters in the file are applied to the data. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f0574ee0-84a0-4fa8-ae54-d7b6ca34a7a7", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Spectral extraction reference file used: crds://jwst_miri_extract1d_0005.json\n" + ] + } + ], + "source": [ + "print('Spectral extraction reference file used: {}'.format(l3_spec.meta.ref_file.extract1d.name))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "95f20a0a-0f24-4d4b-8480-2c37574ad6e8", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import crds\n", + "from astropy.io import fits\n", + "hdu = fits.open('data/jw02072-o001_t010_miri_p750l_x1d_1089.fits')\n", + "json_ref_default = crds.getreferences(hdu[0].header)['extract1d']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "50c8ba27", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Settings for SLIT data: {'id': 'MIR_LRS-FIXEDSLIT', 'region_type': 'target', 'bkg_order': 0, 'dispaxis': 2, 'xstart': 27, 'xstop': 34, 'use_source_posn': False}\n", + " \n", + "Settings for SLITLESS data: {'id': 'MIR_LRS-SLITLESS', 'region_type': 'target', 'bkg_order': 0, 'dispaxis': 2, 'xstart': 30, 'xstop': 41, 'use_source_posn': False}\n" + ] + } + ], + "source": [ + "with open(json_ref_default) as json_ref:\n", + " x1dref_default = json.load(json_ref)\n", + " print('Settings for SLIT data: {}'.format(x1dref_default['apertures'][0]))\n", + " print(' ')\n", + " print('Settings for SLITLESS data: {}'.format(x1dref_default['apertures'][1]))\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "id": "ecc11d6b", + "metadata": {}, + "source": [ + "Let's look at what's in this file. \n", + "\n", + "* **id**: identification label, in this case specifying the exposure type the parameters will be applied to.\n", + "* **region_type**: optional, if included must be set to 'target'\n", + "* **disp_axis**: defines the direction of dispersion (1 for x-axis, 2 for y-axis). **For MIRI LRS this should always be set to 2**. \n", + "* **xstart** (int): first pixel in the horizontal direction (x-axis; 0-indexed) \n", + "* **xstop** (int): last pixel in the horizontal direction (x-axis; 0-indexed; limit is **inclusive**)\n", + "* **bkg_order**: \n", + "* **use_source_posn** (True/False): if True, this will use the target coordinates to locate the target in the field, and offset the extraction aperture to this location. **We recommend this is set to False**. \n", + "* **bkg_order**: the polynomial order to be used for background fitting. if the accompanying parameter **bkg_coeff** is not provided, no background fitting will be performed. **For MIRI LRS slit data, default background subtraction is achieved in the Spec2Pipeline, by mutually subtracting nod expsosures**.\n", + "\n", + "As for MIRI LRS the dispersion is in the vertical direction (i.e. `disp_axis` = 2), the extraction aperture width is specified with the coordinates `xstart` and `xstop`. If no coordinates `ystart` and `ystop` are provided, the spectrum will be extracted over the full height of the ``s2d`` cutout region. We can illustrate the default extraction parameters on the Level 3 ``s2d`` file. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "703f59cd", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-01 16:52:28,838 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/1269950999.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-01 16:52:28,839 - stpipe - WARNING - fig.show()\n", + "2023-08-01 16:52:28,839 - stpipe - WARNING - \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib.patches import Rectangle\n", + "\n", + "xstart = x1dref_default['apertures'][0]['xstart']\n", + "xstop = x1dref_default['apertures'][0]['xstop']\n", + "ap_height = np.shape(l3_s2d.data)[0]\n", + "ap_width = xstop - xstart + 1\n", + "x1d_rect = Rectangle(xy=(xstart,0), width=ap_width, height=ap_height,angle=0., edgecolor='red',\n", + " facecolor='None', ls='-', lw=1.5)\n", + "\n", + "fig, ax = plt.subplots(figsize=[2,8])\n", + "im2d = ax.imshow(l3_s2d.data, origin='lower', aspect='auto', cmap='gist_gray')\n", + "ax.add_patch(x1d_rect)\n", + "ax.set_xlabel('column')\n", + "ax.set_ylabel('row')\n", + "ax.set_title('SN2021aefx - Level 3 resampled 2D spectral image')\n", + "fig.colorbar(im2d)\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1fd784d9", + "metadata": {}, + "source": [ + "## Example 1: Changing the extraction width \n", + "\n", + "In this example, we demonstrate how to change the extraction width from the default. Instead of 8 pixels, we'll extract 12, keeping the aperture centred on the trace. \n", + "\n", + "We will modify the values in the json files in python in this notebook, but the file can also simply be edited in a text editor. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "06dc8eb5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "New xstart, xstop values = 25,36\n" + ] + } + ], + "source": [ + "xstart2 = xstart - 2\n", + "xstop2 = xstop + 2\n", + "print('New xstart, xstop values = {0},{1}'.format(xstart2, xstop2))\n", + "\n", + "with open(json_ref_default) as json_ref:\n", + " x1dref_default = json.load(json_ref)\n", + " x1dref_ex1 = x1dref_default.copy()\n", + " x1dref_ex1['apertures'][0]['xstart'] = xstart2\n", + " x1dref_ex1['apertures'][0]['xstop'] = xstop2\n", + " \n", + "\n", + "with open('x1d_reffile_example1.json','w') as jsrefout:\n", + " json.dump(x1dref_ex1,jsrefout,indent=4)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5bc85413", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-01 16:52:28,972 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/741213004.py:21: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-01 16:52:28,972 - stpipe - WARNING - fig.show()\n", + "2023-08-01 16:52:28,972 - stpipe - WARNING - \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib.collections import PatchCollection\n", + "\n", + "ap_width2 = xstop2 - xstart2 + 1\n", + "x1d_rect1 = Rectangle(xy=(xstart,0), width=ap_width, height=ap_height,angle=0., edgecolor='red',\n", + " facecolor='None', ls='-', lw=1, label='8-px aperture (default)')\n", + "\n", + "x1d_rect2 = Rectangle(xy=(xstart2,0), width=ap_width2, height=ap_height,angle=0., edgecolor='cyan',\n", + " facecolor='None', ls='-', lw=1, label='12-px aperture')\n", + "\n", + "fig4, ax4 = plt.subplots(figsize=[2,8])\n", + "im2d = ax4.imshow(l3_s2d.data, origin='lower', aspect='auto', cmap='gist_gray')\n", + "#ax4.add_collection(aps_collection)\n", + "ax4.add_patch(x1d_rect1)\n", + "ax4.add_patch(x1d_rect2)\n", + "\n", + "ax4.set_xlabel('column')\n", + "ax4.set_ylabel('row')\n", + "ax4.set_title('Example 1: Default vs modified extraction aperture')\n", + "ax4.legend(loc=3)\n", + "fig.colorbar(im2d)\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "87efcb9f", + "metadata": {}, + "source": [ + "Next we run the spectral extraction step, using this modified reference file. Note: when a step is run individually the file name suffix is different from when we run the Spec3Pipeline in its entirety. The extracted spectrum will now have ``extract1dstep.fits`` in the filename. The custom parameters we pass to the step call:\n", + "\n", + "* ``output_file``: we provide a custom output filename for this example (including an output filename renders the ``save_results`` parameter obsolete)\n", + "* ``override_extract1d``: here we provide the name of the custom reference file we created above\n", + "\n", + "We will plot the output against the default extracted product. We expect the spectra to be almost identical; differences can be apparent at the longer wavelengths as our path loss correction is less well calibrated in this low SNR region. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7304f758", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-01 16:52:29,125 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n", + "2023-08-01 16:52:29,193 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args (,).\n", + "2023-08-01 16:52:29,194 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/lrs_slit_extract_example1', 'output_dir': 'data/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': None, 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'center_xy': None, 'apply_apcorr': True, 'ifu_autocen': False, 'ifu_rfcorr': False, '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", + "2023-08-01 16:52:29,222 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/x1d_reffile_example1.json\n", + "2023-08-01 16:52:29,251 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/ofox/crds_cache/references/jwst/miri/jwst_miri_apcorr_0007.fits\n", + "2023-08-01 16:52:29,282 - stpipe.Extract1dStep - WARNING - spectral_order is None; using 1\n", + "2023-08-01 16:52:29,283 - stpipe.Extract1dStep - INFO - Processing spectral order 1\n", + "2023-08-01 16:52:29,290 - stpipe.Extract1dStep - INFO - Using extraction limits: xstart=25, xstop=36, ystart=0, ystop=387\n", + "2023-08-01 16:52:29,344 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n", + "2023-08-01 16:52:29,499 - stpipe.Extract1dStep - INFO - Results used CRDS context: jwst_1089.pmap\n", + "2023-08-01 16:52:29,581 - stpipe.Extract1dStep - INFO - Saved model in data/lrs_slit_extract_example1_extract1dstep.fits\n", + "2023-08-01 16:52:29,581 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n" + ] + } + ], + "source": [ + "sp3_ex1 = Extract1dStep.call(l3_s2d, output_dir='data/', \n", + " output_file='lrs_slit_extract_example1', override_extract1d='x1d_reffile_example1.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "91199fd1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "print(sp3_ex1)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "91ebfc64", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-01 16:52:29,599 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/2686601230.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-01 16:52:29,599 - stpipe - WARNING - fig5.show()\n", + "2023-08-01 16:52:29,599 - stpipe - WARNING - \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig5, ax5 = plt.subplots(figsize=[12,4])\n", + "ax5.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['FLUX'], label='8-px aperture')\n", + "ax5.plot(sp3_ex1.spec[0].spec_table['WAVELENGTH'], sp3_ex1.spec[0].spec_table['FLUX'], label='12-px aperture')\n", + "ax5.set_xlabel('wavelength (um)')\n", + "ax5.set_ylabel('flux (Jy)')\n", + "ax5.set_title('Example 1: Difference aperture sizes')\n", + "ax5.set_xlim(5., 14.)\n", + "ax5.legend()\n", + "fig5.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "f28a4f8e", + "metadata": {}, + "source": [ + "## Example 2: Changing aperture location\n", + "\n", + "In this example we will demonstrate spectral extraction at a different location in the slit. A good use case for this is to extract a spectrum from one of the nodded exposures, prior to combination of the nods in the Spec3Pipeline. We will take the ``s2d`` output from the Spec2Pipeline, and extract the spectrum. In the nod 1 exposure we see the spectrum peak is located in column 13 (0-indexed), and we extract a default 8-px fixed-width aperture. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8a8cf793", + "metadata": {}, + "outputs": [], + "source": [ + "l2_s2d_file = 'data/jw02072001001_06101_00001_mirimage_s2d.fits'\n", + "l2_s2d = datamodels.open(l2_s2d_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "55c81453", + "metadata": {}, + "outputs": [], + "source": [ + "xstart3 = 9\n", + "xstop3 = 17\n", + "\n", + "with open(json_ref_default) as json_ref:\n", + " x1dref_default = json.load(json_ref)\n", + " x1dref_ex2 = x1dref_default.copy()\n", + " x1dref_ex2['apertures'][0]['xstart'] = xstart3\n", + " x1dref_ex2['apertures'][0]['xstop'] = xstop3\n", + " \n", + "\n", + "with open('x1d_reffile_example2.json','w') as jsrefout:\n", + " json.dump(x1dref_ex2,jsrefout,indent=4)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "fe340506", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-01 16:52:29,786 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/2484419048.py:13: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-01 16:52:29,786 - stpipe - WARNING - fig6.show()\n", + "2023-08-01 16:52:29,786 - stpipe - WARNING - \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ap_width3 = xstop3 - xstart3 + 1\n", + "x1d_rect3 = Rectangle(xy=(xstart3,0), width=ap_width3, height=ap_height,angle=0., edgecolor='red',\n", + " facecolor='None', ls='-', lw=1, label='8-px aperture at nod 1 location')\n", + "\n", + "fig6, ax6 = plt.subplots(figsize=[2,8])\n", + "im2d = ax6.imshow(l2_s2d.data, origin='lower', aspect='auto', cmap='gist_gray')\n", + "ax6.add_patch(x1d_rect3)\n", + "ax6.set_xlabel('column')\n", + "ax6.set_ylabel('row')\n", + "ax6.set_title('Example 2: Different aperture location')\n", + "ax6.legend(loc=3)\n", + "fig6.colorbar(im2d)\n", + "fig6.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "3b0b287b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-01 16:52:29,973 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n", + "2023-08-01 16:52:30,042 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args ('data/jw02072001001_06101_00001_mirimage_s2d.fits',).\n", + "2023-08-01 16:52:30,044 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/lrs_slit_extract_example2', 'output_dir': 'data/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': None, 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'center_xy': None, 'apply_apcorr': True, 'ifu_autocen': False, 'ifu_rfcorr': False, '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", + "2023-08-01 16:52:30,102 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/x1d_reffile_example2.json\n", + "2023-08-01 16:52:30,131 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/ofox/crds_cache/references/jwst/miri/jwst_miri_apcorr_0007.fits\n", + "2023-08-01 16:52:30,158 - stpipe.Extract1dStep - WARNING - spectral_order is None; using 1\n", + "2023-08-01 16:52:30,158 - stpipe.Extract1dStep - INFO - Processing spectral order 1\n", + "2023-08-01 16:52:30,164 - stpipe.Extract1dStep - INFO - Using extraction limits: xstart=9, xstop=17, ystart=0, ystop=386\n", + "2023-08-01 16:52:30,216 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n", + "2023-08-01 16:52:30,363 - stpipe.Extract1dStep - INFO - Results used CRDS context: jwst_1089.pmap\n", + "2023-08-01 16:52:30,417 - stpipe.Extract1dStep - INFO - Saved model in data/lrs_slit_extract_example2_extract1dstep.fits\n", + "2023-08-01 16:52:30,418 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n" + ] + } + ], + "source": [ + "sp2_ex2 = Extract1dStep.call(l2_s2d_file, output_dir='data/', output_file='lrs_slit_extract_example2',\n", + " override_extract1d='x1d_reffile_example2.json')" + ] + }, + { + "cell_type": "markdown", + "id": "32eaa24e", + "metadata": {}, + "source": [ + "Let's again plot the output against the default extracted product. We expect this 1-nod spectrum to be noisier but not significantly different from the combined product. The spectrum may have more bad pixels that manifest as spikes or dips in the spectrum. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "ce8eccfb", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-01 16:52:30,430 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/3112017615.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-01 16:52:30,431 - stpipe - WARNING - fig7.show()\n", + "2023-08-01 16:52:30,431 - stpipe - WARNING - \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig7, ax7 = plt.subplots(figsize=[12,4])\n", + "ax7.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['FLUX'], label='default location (nods combined)')\n", + "ax7.plot(sp2_ex2.spec[0].spec_table['WAVELENGTH'], sp2_ex2.spec[0].spec_table['FLUX'], label='nod 1 location (single nod)')\n", + "ax7.set_xlabel('wavelength (um)')\n", + "ax7.set_ylabel('flux (Jy)')\n", + "ax7.set_title('Example 2: Different aperture locations')\n", + "ax7.set_xlim(5., 14.)\n", + "ax7.legend()\n", + "fig7.show()" + ] + }, + { + "cell_type": "markdown", + "id": "39492ab3", + "metadata": {}, + "source": [ + "## Example 3: Extraction with background subtraction\n", + "\n", + "For LRS slit observations, the default background subtraction strategy is performed in the ``background`` step in the Spec2Pipeline; the 2 nodded exposures are mutually subtracted, resulting in each returning a 2D spectral image with a positive and a negative trace, and the background subtracted. \n", + "\n", + "For non-standard cases or slitless LRS data it is however possible to subtract a background as part of the spectral extraction in ``extract_1d``. In the ``extract_1d`` reference file we can pass specific parameters for the background:\n", + "* bkg_coeff (list or list of floats): the regions to be used as background. **This is the main parameter required for background subtraction**\n", + "* bkg_fit (string): the type or method of the background computation. (e.g. None, 'poly', 'mean' or 'median')\n", + "* bkg_order (int): the order of polynomial to fit to background regions. if bkg_fit is not set to 'poly', this parameter will be ignored. \n", + "* smoothing_length (odd int; optional): the width of the boxcar filter that will be used to smooth the background signal in the dispersion direction. This can provide a better quality in case of noisy data. \n", + "\n", + "The 'poly' option for the ``bkg_fit`` parameter will take the value of all pixels in the background region on a given row, and fit a polynomial of order ``bkg_order`` to them. This option can be useful in cases where a gradient is present in the background. \n", + "\n", + "The data we're using here already has the background subtracted so we expect the impact of this to be minimal, but we provide a demonstration using the nod 1, level 2b spectral image. In this example we will calculate the background from 2 4-column windows, setting the ``bkg_fit`` to 'median'. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "b4ea99ea", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-01 16:52:30,546 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/2688117973.py:13: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-01 16:52:30,547 - stpipe - WARNING - fig8.show()\n", + "2023-08-01 16:52:30,547 - stpipe - WARNING - \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rows = [140, 200, 325]\n", + "fig8, ax8 = plt.subplots(figsize=[8,4])\n", + "ncols = np.shape(l2_s2d.data)[1]\n", + "pltx = np.arange(ncols)\n", + "for rr in rows:\n", + " label = 'row {}'.format(rr)\n", + " ax8.plot(pltx, l2_s2d.data[rr,:], label=label)\n", + "ax8.axvline(x=1, ymin=0, ymax=1, ls='--', lw=1., color='coral', label='background regions')\n", + "ax8.axvline(x=5, ymin=0, ymax=1, ls='--', lw=1., color='coral')\n", + "ax8.axvline(x=39, ymin=0, ymax=1, ls='--', lw=1., color='coral')\n", + "ax8.axvline(x=43, ymin=0, ymax=1, ls='--', lw=1., color='coral')\n", + "ax8.legend()\n", + "fig8.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1238d6c9", + "metadata": {}, + "outputs": [], + "source": [ + "with open(json_ref_default) as json_ref:\n", + " x1dref_default = json.load(json_ref)\n", + " x1dref_ex3 = x1dref_default.copy()\n", + " x1dref_ex3['apertures'][0]['xstart'] = xstart3\n", + " x1dref_ex3['apertures'][0]['xstop'] = xstop3\n", + " x1dref_ex3['apertures'][0]['bkg_coeff'] = [[0.5],[4.5],[38.5],[43.5]]\n", + " x1dref_ex3['apertures'][0]['bkg_fit'] = 'median'\n", + " \n", + "\n", + "with open('x1d_reffile_example3.json','w') as jsrefout:\n", + " json.dump(x1dref_ex3,jsrefout,indent=4)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "ac0d2746", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-01 16:52:30,686 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n", + "2023-08-01 16:52:30,754 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args ('data/jw02072001001_06101_00001_mirimage_s2d.fits',).\n", + "2023-08-01 16:52:30,755 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/lrs_slit_extract_example3', 'output_dir': 'data/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': None, 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'center_xy': None, 'apply_apcorr': True, 'ifu_autocen': False, 'ifu_rfcorr': False, '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", + "2023-08-01 16:52:30,815 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/x1d_reffile_example3.json\n", + "2023-08-01 16:52:30,846 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/ofox/crds_cache/references/jwst/miri/jwst_miri_apcorr_0007.fits\n", + "2023-08-01 16:52:30,872 - stpipe.Extract1dStep - WARNING - spectral_order is None; using 1\n", + "2023-08-01 16:52:30,872 - stpipe.Extract1dStep - INFO - Processing spectral order 1\n", + "2023-08-01 16:52:30,878 - stpipe.Extract1dStep - INFO - Using extraction limits: xstart=9, xstop=17, ystart=0, ystop=386\n", + "2023-08-01 16:52:30,878 - stpipe.Extract1dStep - INFO - with background subtraction\n", + "2023-08-01 16:52:31,082 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n", + "2023-08-01 16:52:31,222 - stpipe.Extract1dStep - INFO - Results used CRDS context: jwst_1089.pmap\n", + "2023-08-01 16:52:31,274 - stpipe.Extract1dStep - INFO - Saved model in data/lrs_slit_extract_example3_extract1dstep.fits\n", + "2023-08-01 16:52:31,275 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n" + ] + } + ], + "source": [ + "sp2_ex3 = Extract1dStep.call(l2_s2d_file, output_dir='data/', output_file='lrs_slit_extract_example3',\n", + " override_extract1d='x1d_reffile_example3.json')" + ] + }, + { + "cell_type": "markdown", + "id": "45b69d9b", + "metadata": {}, + "source": [ + "When the ``extract_1d`` step performs a background subtraction, the background spectrum is part of the output product, so you can check what was subtracted. In the plot below we can see that, as expected, the background for this particular exposure is near-zero (apart from the noisy long-wavelength end), as the subtraction was already performed. " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "7210c9ac", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-01 16:52:31,294 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/409894346.py:13: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-01 16:52:31,294 - stpipe - WARNING - fig9.show()\n", + "2023-08-01 16:52:31,295 - stpipe - WARNING - \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig9, ax9 = plt.subplots(nrows=2, ncols=1, figsize=[12,4])\n", + "#ax9.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['FLUX'], label='default location (nods combined)')\n", + "ax9[0].plot(sp2_ex2.spec[0].spec_table['WAVELENGTH'], sp2_ex2.spec[0].spec_table['FLUX'], label='nod 1 spectrum - no bkg sub')\n", + "ax9[0].plot(sp2_ex3.spec[0].spec_table['WAVELENGTH'], sp2_ex3.spec[0].spec_table['FLUX'], label='nod 1 spectrum - with bkg sub')\n", + "ax9[1].plot(sp2_ex3.spec[0].spec_table['WAVELENGTH'], sp2_ex3.spec[0].spec_table['BACKGROUND'], label='background')\n", + "ax9[1].set_xlabel('wavelength (um)')\n", + "ax9[0].set_ylabel('flux (Jy)')\n", + "ax9[0].set_title('Example 3: Extraction with background subtraction')\n", + "ax9[0].set_xlim(5., 14.)\n", + "ax9[1].set_xlim(5., 14.)\n", + "ax9[0].legend()\n", + "ax9[1].legend()\n", + "fig9.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e01786eb", + "metadata": {}, + "source": [ + "## Example 4: Tapered column extraction\n", + "\n", + "In this example we will use the JWST calibration pipeline to perform a spectral extraction in a tapered column aperture. The way to specify this in the extraction reference file is to use the ``src_soeff`` parameter instead of the simpler ``xstart``, ``xstop`` settings. The ``src_coeff`` parameter can take polynomial coefficients rather than fixed pixel values. In this example, we will define a tapered column aperture corresponding to 3 * the FWHM of the spatial profile. \n", + "\n", + "Polynomial definitions for the extraction aperture can be specified as a function of pixels or wavelength, which is defined in the ``independent_var`` parameter. \n", + "\n", + "We will use pre-measured FWHM values as a function of **wavelength** to fit a straight line to the FWHM($\\lambda$) profile, and set the extraction parameters according to this fit. The FWHM can of course also be measured directly from the data as well. " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "9e3c2433", + "metadata": {}, + "outputs": [], + "source": [ + "import astropy.units as u\n", + "from astropy.modeling import models, fitting\n", + "\n", + "def calc_xap_fit():\n", + " # these are values measured from commissioning data. FWHM is in arcsec.\n", + " l = [5.0, 7.5, 10.0, 12.0]\n", + " fwhm = [0.29, 0.3, 0.36, 0.42]\n", + " \n", + " # convert from arcsec to pixel using MIRI pixel scaling of 0.11 arcsec/px\n", + " fwhm_px = fwhm / (0.11*u.arcsec/u.pixel)\n", + " \n", + " # we want to extract 3 * fwhm, which means 1.5 * fwhm on either side of the trace\n", + " xap_pix = 1.5 * fwhm_px\n", + " \n", + " # now we want to fit a line to these points\n", + " line_init = models.Linear1D()\n", + " fit = fitting.LinearLSQFitter()\n", + " \n", + " fitted_line = fit(line_init, l, xap_pix.value)\n", + " print(fitted_line)\n", + " \n", + " fig, ax = plt.subplots(figsize=[8,4])\n", + " xplt = np.linspace(4.0, 14., num=50)\n", + " ax.plot(l, xap_pix.value, 'rx', label='1.5 * FWHM(px)')\n", + " ax.plot(xplt, fitted_line(xplt), 'b-', label='best-fit line')\n", + " ax.set_xlabel('wavelength')\n", + " ax.set_ylabel('px')\n", + " ax.legend()\n", + " \n", + " return(fitted_line)\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "e21fcec5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: Linear1D\n", + "Inputs: ('x',)\n", + "Outputs: ('y',)\n", + "Model set size: 1\n", + "Parameters:\n", + " slope intercept \n", + " ------------------ ------------------\n", + " 0.2579519802996102 2.4456187153704083\n", + "Parameter('slope', value=0.2579519802996102) Parameter('intercept', value=2.4456187153704083)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "poly_pos = calc_xap_fit()\n", + "print(poly_pos.slope, poly_pos.intercept)" + ] + }, + { + "cell_type": "markdown", + "id": "f7dc8785", + "metadata": {}, + "source": [ + "The above polynomial defines the relationship between wavelength and the number of pixels to extract. To ensure that the extractio location is centred on the location of the spectrum, we add to the intercept value the central location of the trace, which is at column 30.5. \n", + "\n", + "In the next cell, we provide these parameters to the ``src_coeff`` parameter in the extraction reference file. **Note: The ``src_coeff`` parameter takes precedence over the ``xstart`` and ``xstop`` parameters if all 3 are present; for clarity we remove the latter from our reference file.**" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "a9c9d403", + "metadata": {}, + "outputs": [], + "source": [ + "trace_cen = 30.5\n", + "\n", + "with open(json_ref_default) as json_ref:\n", + " x1dref_default = json.load(json_ref)\n", + " x1dref_ex4 = x1dref_default.copy()\n", + " x1dref_ex4['apertures'][0]['xstart'] = None\n", + " x1dref_ex4['apertures'][0]['xstop'] = None\n", + " x1dref_ex4['apertures'][0]['independent_var'] = 'wavelength'\n", + " x1dref_ex4['apertures'][0]['src_coeff'] = [[-1*poly_pos.intercept.value + trace_cen, -1*poly_pos.slope.value], [poly_pos.intercept.value + trace_cen, poly_pos.slope.value]]\n", + " \n", + "\n", + "with open('x1d_reffile_example4.json','w') as jsrefout:\n", + " json.dump(x1dref_ex4,jsrefout,indent=4)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "71789e83", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-01 16:52:31,543 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n", + "2023-08-01 16:52:31,615 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args (,).\n", + "2023-08-01 16:52:31,616 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/lrs_slit_extract_example4', 'output_dir': 'data/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': None, 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'center_xy': None, 'apply_apcorr': True, 'ifu_autocen': False, 'ifu_rfcorr': False, '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", + "2023-08-01 16:52:31,648 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/x1d_reffile_example4.json\n", + "2023-08-01 16:52:31,679 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/ofox/crds_cache/references/jwst/miri/jwst_miri_apcorr_0007.fits\n", + "2023-08-01 16:52:31,705 - stpipe.Extract1dStep - WARNING - spectral_order is None; using 1\n", + "2023-08-01 16:52:31,705 - stpipe.Extract1dStep - INFO - Processing spectral order 1\n", + "2023-08-01 16:52:31,712 - stpipe.Extract1dStep - INFO - Using extraction limits: ystart=0, ystop=387, and src_coeff\n", + "2023-08-01 16:52:31,762 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n", + "2023-08-01 16:52:31,908 - stpipe.Extract1dStep - INFO - Results used CRDS context: jwst_1089.pmap\n", + "2023-08-01 16:52:31,958 - stpipe.Extract1dStep - INFO - Saved model in data/lrs_slit_extract_example4_extract1dstep.fits\n", + "2023-08-01 16:52:31,959 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n" + ] + } + ], + "source": [ + "sp3_ex4 = Extract1dStep.call(l3_s2d, output_dir='data/', \n", + " output_file='lrs_slit_extract_example4', override_extract1d='x1d_reffile_example4.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "9d1bc74c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-01 16:52:31,970 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/3774177919.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-01 16:52:31,970 - stpipe - WARNING - fig10.show()\n", + "2023-08-01 16:52:31,970 - stpipe - WARNING - \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig10, ax10 = plt.subplots(figsize=[12,4])\n", + "ax10.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['FLUX'], label='default fixed-width aperture')\n", + "ax10.plot(sp3_ex4.spec[0].spec_table['WAVELENGTH'], sp3_ex4.spec[0].spec_table['FLUX'], label='tapered column aperture')\n", + "ax10.set_xlabel('wavelength (um)')\n", + "ax10.set_ylabel('flux (Jy)')\n", + "ax10.set_title('Example 4: Tapered column vs. fixed-width extraction aperture')\n", + "ax10.set_xlim(5., 14.)\n", + "ax10.legend()\n", + "fig10.show()" + ] + }, + { + "cell_type": "markdown", + "id": "378f8393", + "metadata": {}, + "source": [ + "The output spectrum also contains a reference to the number of pixels used for the extraction as a function of wavelength. Let's visualize that too. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "78ca0c68", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-01 16:52:32,076 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/2694247109.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-01 16:52:32,076 - stpipe - WARNING - fig11.show()\n", + "2023-08-01 16:52:32,076 - stpipe - WARNING - \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig11, ax11 = plt.subplots(figsize=[12,4])\n", + "ax11.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['NPIXELS'], label='default fixed-width aperture')\n", + "ax11.plot(sp3_ex4.spec[0].spec_table['WAVELENGTH'], sp3_ex4.spec[0].spec_table['NPIXELS'], label='tapered column aperture')\n", + "ax11.set_xlabel('wavelength (um)')\n", + "ax11.set_ylabel('number of pixels')\n", + "ax11.set_title('Example 4: Number of pixels extracted')\n", + "ax11.set_xlim(5., 14.)\n", + "ax11.legend()\n", + "fig11.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f4501ee7", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "We hope this notebook was useful in helping you understand the capabilities of the JWST calibration for spectral extraction. The above examples are not an exhaustive list of all the possibilities: different methods of source and background extraction can be combined for more complex extraction operations. \n", + "\n", + "**If you have any questions, comments, or requests for further demos of these capabilities, please contact the [JWST Helpdesk](http://jwsthelp.stsci.edu/).**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c397647e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From a4fba64bc84ff76a518ffcf8ce6de854c42bfcab Mon Sep 17 00:00:00 2001 From: Ori Fox Date: Wed, 2 Aug 2023 08:22:46 -0400 Subject: [PATCH 08/36] Delete miri_lrs_extraction_techniques.ipynb --- .../miri_lrs_extraction_techniques.ipynb | 1487 ----------------- 1 file changed, 1487 deletions(-) delete mode 100644 notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques.ipynb diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques.ipynb deleted file mode 100644 index a5eabfdc3..000000000 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques.ipynb +++ /dev/null @@ -1,1487 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# MIRI LRS Spectral Extraction Techniques" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Use case:** Extract spectra with different techniques.
\n", - "**Data:** MIRI LRS spectrum of Type Ia supernova SN2021aefx, observed by Jha et al in PID 2072 (Obs 1), **where the automated spectral extraction failed**. These data were taken with zero exclusive access period, and published in [Kwok et al 2023](https://ui.adsabs.harvard.edu/abs/2023ApJ...944L...3K/abstract).
\n", - "**Tools:** jdaviz, specviz2d, specreduce, jwst, gwcs, matplotlib, astropy.
\n", - "**Cross-intrument:** NIRSpec, MIRI.
\n", - "**Documentation:** This notebook is part of a STScI's larger [post-pipeline Data Analysis Tools Ecosystem](https://jwst-docs.stsci.edu/jwst-post-pipeline-data-analysis).
\n", - "\n", - "# Install instructions\n", - "git clone https://github.com/spacetelescope/jdat_notebooks.git
\n", - "cd jdat_notebooks/
\n", - "git fetch -q https://github.com/spacetelescope/jdat_notebooks.git refs/pull/93/head:lrsoptimal2
\n", - "git checkout lrsoptimal2
\n", - "conda create -n lrsextract python=3.8.10
\n", - "conda activate lrsextract
\n", - "pip install -r requirements.txt
\n", - "cd notebooks/MIRI_LRS_spectral_extraction/
\n", - "jupyter notebook miri_lrs_extraction_techniques.ipynb
\n", - "\n", - "# Introduction\n", - "\n", - "This notebook extracts a 1D spectra from a 2D MIRI LRS spectral observation (single image). The goal is to provide the ability to extract spectra with different locations, extraction apertures, and techniques than are done in the JWST pipeline using the [Astropy Specreduce package](https://github.com/astropy/specreduce).\n", - "\n", - "The notebook also demos how to use Jdaviz's [specviz2d](https://jdaviz.readthedocs.io/en/latest/specviz2d/index.html), which allows users to interactively extract 1D spectra from 2D spectra.\n", - "\n", - "The simpliest spectral extraction is \"boxcar\" where all the pixels within some fixed width centered on the source position are summed at each wavelength. Background subtraction can be done using regions offset from the source center. You can also see the Specreduce [generic Sample Notebook](https://github.com/astropy/specreduce/blob/main/notebook_sandbox/jwst_boxcar/boxcar_extraction.ipynb).\n", - "\n", - "For spectra taken with a diffraction limited telescope like JWST, a modification boxcar extraction is to vary the extraction width linearly with wavelength. Such a scaled boxcar extraction keeps the fraction of the source flux within the extraction region approximately constant with wavelength.\n", - "\n", - "For point sources, a PSF-weighted spectral extraction can be done. Using the PSF to weight the extraction uses the actual PSF as a function of wavelength to optimize the extraction to the pixels with the greatest signal. PSF-weighted extractions show the largest differences with boxcar extractions at lower S/N values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note:** Corrections for the finite aperture used in all the extractions have not been applied. Thus, the physical flux densities of all the extracted spectra are lower than the actual values." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Imports\n", - "\n", - "- *matplotlib.pyplot* for plotting data\n", - "- *numpy* to handle array functions\n", - "- *astropy.io fits* for accessing FITS files\n", - "- *astropy.visualization* for scaling image for display\n", - "- *astropy.table Table* for reading the pipeline 1d extractions\n", - "- *jwst datamodels* for reading/access the jwst data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "# disable all imported packages' loggers\n", - "import logging\n", - "logging.root.manager.loggerDict = {}" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "print(pycodestyle_magic.__version__)" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "# enable PEP8 checker for this notebook\n", - "%load_ext pycodestyle_magic\n", - "%flake8_on --ignore E261,E501,W291,W293\n", - "\n", - "# only allow the checker to throw warnings when there's a violation\n", - "logging.getLogger('flake8').setLevel('ERROR')\n", - "logging.getLogger('stpipe').setLevel('ERROR')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import matplotlib as mpl\n", - "# %matplotlib inline\n", - "\n", - "import numpy as np\n", - "\n", - "from gwcs.wcstools import grid_from_bounding_box\n", - "\n", - "from astropy.io import fits\n", - "from astropy.table import Table\n", - "from astropy.visualization import simple_norm\n", - "\n", - "from jwst import datamodels\n", - "\n", - "from specreduce.extract import BoxcarExtract, OptimalExtract, HorneExtract\n", - "from specreduce.tracing import FlatTrace, FitTrace\n", - "from specreduce.background import Background\n", - "\n", - "from jdaviz import Imviz\n", - "from jdaviz import Specviz\n", - "from jdaviz import Specviz2d\n", - "\n", - "from astropy.utils.data import download_file\n", - "import os\n", - "\n", - "from specutils import Spectrum1D\n", - "from astropy import units as u\n", - "\n", - "# Display the video\n", - "from IPython.display import HTML, YouTubeVideo\n", - "\n", - "import os\n", - "import urllib.request\n", - "import tarfile" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Download all necessary data\n", - "\n", - "if os.path.exists(\"boxcar_specviz2d.fits\"):\n", - " print(\"Boxcar Specviz2d Extraction Exists\")\n", - "else:\n", - " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/boxcar_specviz2d.fits'\n", - " urllib.request.urlretrieve(url, 'boxcar_specviz2d.fits')\n", - "\n", - "if os.path.exists(\"horne_specviz2d.fits\"):\n", - " print(\"Horne Specviz2d Extraction Exists\")\n", - "else:\n", - " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/horne_specviz2d.fits'\n", - " urllib.request.urlretrieve(url, 'horne_specviz2d.fits')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if os.path.exists(\"./required_data/\"):\n", - " print(\"Origina Data Exists\")\n", - "else:\n", - " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/data.tar.gz'\n", - " urllib.request.urlretrieve(url, 'data.tar.gz')\n", - " \n", - "# Unzip files if they haven't already been unzipped\n", - "if os.path.exists(\"required_data/\"):\n", - " print(\"Data Directory Already Exists\")\n", - "else:\n", - " tar = tarfile.open('./data.tar.gz', \"r:gz\")\n", - " tar.extractall()\n", - " tar.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Developer note: Ask Karl exactly how these functions work? Seems like all weights are equal?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# useful functions\n", - "def get_boxcar_weights(center, hwidth, npix):\n", - " \"\"\"\n", - " Compute the weights given an aperture center, half widths, and number of pixels\n", - " \"\"\"\n", - " weights = np.zeros((npix))\n", - " # pixels with full weight\n", - " fullpixels = [max(0, int(center - hwidth + 1)), min(int(center + hwidth), npix)]\n", - " weights[fullpixels[0] : fullpixels[1]] = 1.0\n", - "\n", - " # pixels at the edges of the boxcar with partial weight\n", - " if fullpixels[0] > 0:\n", - " weights[fullpixels[0] - 1] = hwidth - (center - fullpixels[0])\n", - " if fullpixels[1] < npix:\n", - " weights[fullpixels[1]] = hwidth - (fullpixels[1] - center)\n", - "\n", - " return weights\n", - "\n", - "\n", - "def ap_weight_images(\n", - " center, width, bkg_offset, bkg_width, image_size, waves, wavescale=None\n", - "):\n", - " \"\"\"\n", - " Create a weight image that defines the desired extraction aperture\n", - " and the weight image for the requested background regions\n", - "\n", - " Parameters\n", - " ----------\n", - " center : float\n", - " center of aperture in pixels\n", - " width : float\n", - " width of apeture in pixels\n", - " bkg_offset : float\n", - " offset from the extaction edge for the background\n", - " never scaled for wavelength\n", - " bkg_width : float\n", - " width of background region\n", - " never scaled with wavelength\n", - " image_size : tuple with 2 elements\n", - " size of image\n", - " waves : array\n", - " wavelegth values\n", - " wavescale : float\n", - " scale the width with wavelength (default=None)\n", - " wavescale gives the reference wavelenth for the width value\n", - "\n", - " Returns\n", - " -------\n", - " wimage, bkg_wimage : (2D image, 2D image)\n", - " wimage is the weight image defining the aperature\n", - " bkg_image is the weight image defining the background regions\n", - " \"\"\"\n", - " wimage = np.zeros(image_size)\n", - " bkg_wimage = np.zeros(image_size)\n", - " hwidth = 0.5 * width\n", - " # loop in dispersion direction and compute weights\n", - " for i in range(image_size[1]):\n", - " if wavescale is not None:\n", - " hwidth = 0.5 * width * (waves[i] / wavescale)\n", - "\n", - " wimage[:, i] = get_boxcar_weights(center, hwidth, image_size[0])\n", - "\n", - " # bkg regions\n", - " if (bkg_width is not None) & (bkg_offset is not None):\n", - " bkg_wimage[:, i] = get_boxcar_weights(\n", - " center - hwidth - bkg_offset, bkg_width, image_size[0]\n", - " )\n", - " bkg_wimage[:, i] += get_boxcar_weights(\n", - " center + hwidth + bkg_offset, bkg_width, image_size[0]\n", - " )\n", - " else:\n", - " bkg_wimage = None\n", - "\n", - " return (wimage, bkg_wimage)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Devloper notes (2021)\n", - "\n", - "1) The difference between the pipeline (x1d) and the extractions done in this notebook are quite large. Help in understanding the origin of these differences is needed.\n", - "\n", - "2) Not clear how to use the JWST pipeline `extract_1d` (quite complex) code. Help to determine how to use the JWST pipeline code instead of the custom code for boxcar is needed. \n", - "\n", - "3) Applying aperture corrections for the finite extraction widths is needed. Help in how to get the needed informatinom for different (user set) extraction widths is needed. \n", - "\n", - "### Partially RESOLVED (March, 2023)\n", - "\n", - "1) See notes from Kendrew on limitations of current pipeline. Pipeline will be updated soon.\n", - "\n", - "2) While this notebook doesn't go into using the pipeline, boxcar is now integrated into the Astropy Specreduce package. So I wouldn't characterize the boxcar as \"custom code\" any longer.\n", - "\n", - "3) Still not resolved." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download Files" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#calfilename = \"det_image_seq5_MIRIMAGE_P750Lexp1_cal.fits\"\n", - "s2dfile = \"./required_data/jw02072-o001_t010_miri_p750l_s2d.fits\"\n", - "x1dfile = \"./required_data/jw02072-o001_t010_miri_p750l_x1d.fits\"\n", - "spatialprofilefile = \"./required_data/jw02072-o001_t010_miri_p750l_s2d.fits\"\n", - "#mainurl = \"https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/\"\n", - "\n", - "#calfile_dld = download_file(mainurl + calfilename)\n", - "#s2dfile_dld = download_file(mainurl + s2dfilename)\n", - "#x1dfile_dld = download_file(mainurl + x1dfilename)\n", - "#spatialprofilefile_dld = download_file(mainurl + spatialprofilefilename)" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "# rename files so that they have the right extensions\n", - "# required for the jwst datamodels to work\n", - "#calfile = calfile_dld + '_cal.fits'\n", - "#os.rename(calfile_dld, calfile)\n", - "s2dfile = s2dfile_dld + '_s2d.fits'\n", - "os.rename(s2dfile_dld, s2dfile)\n", - "x1dfile = x1dfile_dld + '_x1d.fits'\n", - "os.rename(x1dfile_dld, x1dfile)\n", - "spatialprofilefile = spatialprofilefile_dld + '_s2d.fits'\n", - "os.rename(spatialprofilefile_dld, spatialprofilefile)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## File information\n", - "\n", - "The data used is a simulation of a LRS slit observation for a blackbody with a similar flux density to the star BD+60d1753, a flux calibration star. This simulation was created with MIRISim.\n", - "The simulated exposure was reduced using the JWST pipeline (v0.16.1) through the Detector1 and Spec2 stages.\n", - "\n", - "The cal file is one of the Spec2 products and is the calibration full frame image. It contains:\n", - "\n", - "1. (Primary): This HDU contains meta-data related to the observation and data reduction.\n", - "2. (SCI): The calibrated image. Units are MJy/sr.\n", - "3. (ERR): Uncertainty image. Units are MJy/sr.\n", - "4. (DQ): Data quality image.\n", - "5. (VAR_POISSON): Unc. component 1: Poisson uncertainty image. Units are (MJy/sr)^2.\n", - "6. (VAR_RNOISE): Unc. component 2: Read Noise uncertainty image. Units are (MJy/sr)^2.\n", - "7. (VAR_FLAT): Unc. component 3: Flat Field uncertainty image. Units are (MJy/sr)^2.\n", - "8. (ASDF_METADATA): Metadata.\n", - "\n", - "The s2d file is one of the Spec2 products and containes the calibrated rectified cutout of the LRS Slit region. It has:\n", - "\n", - "1. (Primary): This HDU contains meta-data related to the observation and data reduction.\n", - "2. (WGT): Weight.\n", - "3. (CON): ??\n", - "4. (ASDF_METADATA): Metadata." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Loading data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# use a jwst datamodel to provide a good interface to the data and wcs info\n", - "#cal = datamodels.open(calfile)\n", - "s2d = datamodels.open(s2dfile)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Basic information about the image." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#print(\"cal image\")\n", - "#print(cal.data.shape)\n", - "#print(np.mean(cal.data))\n", - "#print(np.amin(cal.data), np.amax(cal.data))\n", - "print(\"s2d image\")\n", - "print(s2d.data.shape)\n", - "print(np.mean(s2d.data))\n", - "print(np.amin(s2d.data), np.amax(s2d.data))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display the full 2D image" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "norm_data = simple_norm(cal.data, 'sqrt')\n", - "plt.figure(figsize=(6, 6))\n", - "plt.imshow(cal.data, norm=norm_data, origin=\"lower\")\n", - "plt.title(\"The full image from the MIRI IMAGER detector\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display the LRS Slit region only (use s2d)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# transpose to make it display better\n", - "image = np.transpose(s2d.data)\n", - "err = np.transpose(s2d.err)\n", - "norm_data = simple_norm(image, \"sqrt\")\n", - "plt.figure(figsize=(10, 3))\n", - "plt.imshow(image, norm=norm_data, origin=\"lower\")\n", - "plt.title(\"The LRS region\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### View the 2D Spectrum in Imviz and get the center of the cross-dispersion " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "imviz = Imviz()\n", - "imviz.app" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "imviz.load_data(image)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "viewer = imviz.default_viewer\n", - "viewer.cuts = '95%'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1) Default JWST Pipeline 1D extraction" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a spectrum1d\n", - "jpipe_x1d = Spectrum1D.read(x1dfile)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "specviz = Specviz()\n", - "specviz.app" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "specviz.load_spectrum(jpipe_x1d)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "\n", - "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", - "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", - "ax.set_ylabel(\"Flux Density [Jy]\")\n", - "ax.set_yscale(\"log\")\n", - "ax.set_ylim(1e-6, 3e-3)\n", - "ax.set_xlim(4, 13)\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2) Fixed Width Boxcar Extraction (Using Specreduce)\n", - "\n", - "Extract a 1D spectrum using a simple boxcar. Basically collapse the spectrum in the cross-dispersion direction over a specified number of pixels.\n", - "\n", - "#### Developer note: Allow for a bad pixel mask" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define extraction parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ext_center = 31\n", - "ext_width = 8\n", - "bkg_sep = 8\n", - "bkg_width = 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot cross-disperion cut showing the extraction parameters\n", - "\n", - "#### Develepor Note: Place trace back into Specviz2d/Imviz/Etc" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot along cross-disperion cut showing the extraction parameters\n", - "fig, ax = plt.subplots(figsize=(10, 6))\n", - "y = np.arange(image.shape[0])\n", - "ax.plot(y, image[:,140], 'k-')\n", - "mm = np.array([ext_center, ext_center])\n", - "mm_y = ax.get_ylim()\n", - "\n", - "# extraction region\n", - "ax.axvspan(ext_center - ext_width/2., ext_center + ext_width/2., color='green', alpha=0.1)\n", - "ax.plot(mm, mm_y, 'b--')\n", - "ax.plot(mm - ext_width/2., mm_y, 'g:')\n", - "ax.plot(mm + ext_width/2., mm_y, 'g:')\n", - "\n", - "# background region, symmetric on both sides of extraction region\n", - "ax.axvspan(ext_center - bkg_sep - bkg_width/2., ext_center - bkg_sep + bkg_width/2., color='red', alpha=0.1)\n", - "ax.plot(mm - bkg_sep - bkg_width/2., mm_y, 'r:')\n", - "ax.plot(mm - bkg_sep + bkg_width/2., mm_y, 'r:')\n", - "\n", - "ax.axvspan(ext_center + bkg_sep - bkg_width/2., ext_center + bkg_sep + bkg_width/2., color='red', alpha=0.1)\n", - "ax.plot(mm + bkg_sep - bkg_width/2., mm_y, 'r:')\n", - "ax.plot(mm + bkg_sep + bkg_width/2., mm_y, 'r:')\n", - "\n", - "ax.set_title(\"Cross-dispersion Cut at Pixel=300\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define Background" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# extract the background using custom individual traces\n", - "trace = FlatTrace(image, ext_center)\n", - "bg = Background(image, [trace-bkg_sep, trace+bkg_sep], width=bkg_width)\n", - "\n", - "# alternatively, call two_sided class, which does the same as above \n", - "#bg = Background.two_sided(image, trace, bkg_sep, width=bkg_width)\n", - "# or in the place of any trace, an int/float can be passed which resolves to a FlatTrace\n", - "#bg = Background.two_sided(image, ext_center, bkg_sep, width=bkg_width)\n", - "\n", - "# or for single sided:\n", - "# bg = Background.one_sided(image, trace, bkg_sep, width=bkg_width)\n", - "# bg = Background.one_sided(image, trace, -bkg_sep, width=bkg_width)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# view the background weighted image\n", - "plt.figure(figsize=(15, 15))\n", - "plt.imshow(bg.bkg_wimage, origin=\"lower\")\n", - "plt.title(\"slit[0] slice\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# view the background image\n", - "plt.figure(figsize=(15, 15))\n", - "plt.imshow(bg.bkg_image().flux.value, norm=norm_data, origin=\"lower\")\n", - "plt.title(\"slit[0] slice\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# view the background-subtracted image\n", - "plt.figure(figsize=(15, 15))\n", - "plt.imshow(bg.sub_image().flux.value, norm=norm_data, origin=\"lower\")\n", - "plt.title(\"slit[0] slice\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that when using median to calculate the background, partial pixel weights are not supported:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bg_med = Background.two_sided(image, ext_center, bkg_sep, width=bkg_width, statistic='median')\n", - "plt.figure(figsize=(15, 15))\n", - "plt.imshow(bg_med.bkg_wimage, origin=\"lower\")\n", - "plt.title(\"slit[0] slice\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Extract Trace (multiple options)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Optional: we could now refine the initial flat trace by running an automated FitTrace on the subtracted image. This process could be iterated as necessary (recreating the subtracted image with the refined trace, etc)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fit_trace = FitTrace(image-bg.bkg_image().flux.value, peak_method='gaussian', guess=ext_center)\n", - "#fit_trace" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "flat_trace = FlatTrace(image-bg.bkg_image().flux.value, trace_pos=ext_center)\n", - "#flat_trace" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### **Always visualize your traces. If you have noisy data, the fits may not be good. You may need to play around with the type of fit (i.e., Order 1 Polynomial) or different window sizes and parameters. In our case, we'll stick with the flat trace throughout this notebook.** \n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "#### Plot old vs new trace" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig3, ax3 = plt.subplots(figsize=(10,6))\n", - "plot3 = ax3.imshow(bg.sub_image().flux.value, aspect=4.,\n", - " vmin=0, vmax=bg.sub_image().flux.value.max()/2,\n", - " cmap=mpl.cm.magma, origin='lower',\n", - " extent=(0, bg.sub_image().flux.value.shape[-1],\n", - " 0, bg.sub_image().flux.value.shape[0]))\n", - "fig3.colorbar(plot3)\n", - "ax3.set_title('LRS Spectrum Traces')\n", - "ax3.grid()\n", - "\n", - "# add the traces\n", - "ax3.plot(flat_trace.trace, '--', color='#008ca8',\n", - " lw=2.5, label='FlatTrace')\n", - "ax3.plot(fit_trace.trace, '--', color='#00471b',\n", - " lw=2.5, label='GaussianFitTrace')\n", - "ax3.legend(framealpha=.5)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Extract" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Dev Note: FitTrace doesn't seem to be working right now, so we'll stick with FlatTrace" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "from specutils import Spectrum1D\n", - "from astropy import units as u\n", - "flux = s2d.data * u.Jy\n", - "wavelength = s2d.wavelength * u.um\n", - "flux.data\n", - "spec = Spectrum1D(spectral_axis=wavelength, flux=flux)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#boxcar = BoxcarExtract()\n", - "ext1d_boxcar_noweights = BoxcarExtract(image-bg, flat_trace, width=ext_width)\n", - "ext1d_boxcar_noweights = ext1d_boxcar_noweights.spectrum.flux.value\n", - "ext1d_boxcar_noweights *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", - "#spectrum_specreduce_boxcar" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "\n", - "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", - "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label='Boxcar')\n", - "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", - "ax.set_ylabel(\"Flux Density [Jy]\")\n", - "ax.set_yscale(\"log\")\n", - "ax.set_ylim(1e-6, 3e-3)\n", - "ax.set_xlim(4, 13)\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3) Fixed Width Boxcar Extraction (Using Pixel Masks, too)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### **A basic example of how to create a pixel weight map/mask. In this example, the weights basically create a pixel mask based on the boxcar extraction aperture. It shouldn't actually change any of the results because the boxcar extraction essentially does this for you. But this provides a useful example for more complicated masks that we will create lower in the notebook.** " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from gwcs.wcstools import grid_from_bounding_box\n", - "\n", - "#image = np.transpose(s2d.data)\n", - "grid = grid_from_bounding_box(s2d.meta.wcs.bounding_box)\n", - "ra, dec, lam = s2d.meta.wcs(*grid)\n", - "lam_image = np.transpose(lam)\n", - "\n", - "# compute a \"rough\" wavelength scale to allow for aperture to scale with wavelength\n", - "rough_waves = np.average(lam_image, axis=0)\n", - "\n", - "# images to use for extraction\n", - "wimage, bkg_wimage = ap_weight_images(\n", - " ext_center,\n", - " ext_width,\n", - " bkg_width,\n", - " bkg_sep,\n", - " image.shape,\n", - " rough_waves,\n", - " wavescale=None,\n", - ")\n", - "\n", - "#boxcar = BoxcarExtract()\n", - "\n", - "# without *additional* background subtraction \n", - "# NOTE: The intial background subtraction is performed by subtracting the nods when creating the final data product input at the top of this notebook\n", - "# NOTE: Additional background subtractions can be performed using the Specreduce Background function below\n", - "# NOTE: Since most of the background has already been subtracted by the pipeline, all three extractions below look pretty similar\n", - "\n", - "image_wg = image * wimage\n", - "ext1d_boxcar = BoxcarExtract(image_wg, flat_trace, width=ext_width)\n", - "ext1d_boxcar = ext1d_boxcar.spectrum.flux.value\n", - "# convert from MJy/sr to Jy\n", - "ext1d_boxcar *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", - "\n", - "# with background subtraction\n", - "image_bg = bg.sub_image()\n", - "image_wg = image_bg * wimage\n", - "ext1d_boxcar_bkgsub = BoxcarExtract(image_wg, flat_trace, width=ext_width)\n", - "ext1d_boxcar_bkgsub = ext1d_boxcar_bkgsub.spectrum.flux.value\n", - "\n", - "# convert from MJy/sr to Jy\n", - "ext1d_boxcar_bkgsub *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", - "\n", - "# compute the average wavelength for each column using the weight image\n", - "# this should correspond directly with the extracted spectrum\n", - "# wavelengths account for any tiled spectra this way\n", - "waves_boxcar = np.average(lam_image, weights=wimage, axis=0)\n", - "waves_boxcar_bkgsub = np.average(lam_image, weights=wimage, axis=0)" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "ext1d_boxcar = boxcar(image_wg, auto_trace, width=ext_width)\n", - "ext1d_boxcar.flux.value" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "gpts = ext1d_boxcar_bkgsub > 0.\n", - "gpts = ext1d_boxcar > 0.\n", - "\n", - "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label = 'Boxcar; No Mask')\n", - "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], color = 'green', label=\"Boxcar; Mask\")\n", - "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], color = 'red', label=\"Boxcar; bkgsub; Mask\")\n", - "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", - "ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n", - "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", - "ax.set_ylabel(\"Flux Density [Jy]\")\n", - "ax.set_yscale(\"log\")\n", - "ax.set_ylim(1e-6, 3e-3)\n", - "ax.set_xlim(4, 13)\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4) Wavelength scaled width boxcar\n", - "\n", - "The LRS spatial profile changes as a function of wavelength as JWST is diffraction limited at these wavelengths. Nominally this means that the FWHM is changing linearly with wavelength. Scaling the width of the extraction aperture with wavelength accounts for the changing diffraction limit with wavelength to first order." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Developer note: Not currently possible. Allow for wavelength scaled width in the boxcar" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 5) Horne Extraction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### **The Horne algorithm preforms a Gaussian fit on the source, it is thus best suited for cases where the source has a Gaussian profile in the cross-dispersion direction. If your profile is not Gaussian, you will likely over- or under-estimate your actual flux.**. \n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Steps in the original Horne (1986) paper:\n", - "\n", - "1. Bias subtraction [assumed to be done in earlier block]\n", - "2. Initial variance estimate [user provides this as an argument]\n", - "3. Fit sky background [assumed to be done in earlier block]\n", - " * \"We therefore generally perform a least-squares polynomial fit to the sky data at each wavelength. Individual sky pixels are given weights inversely proportional to their variances as estimated in Step 2\" [overlaps with notebook guide's 3b]\n", - "4. Extract standard spectrum and its variance\n", - " * Subtract the sky background found in Step 3 from the image. [sky background calculation is planned as a separate, earlier step of the specreduce workflow]\n", - "5. Construct spatial profile\n", - "6. Revise variance estimates [not currently done]\n", - "7. Mask cosmic ray hits [not currently done]\n", - "8. Extract optimal spectrum and its variance [currently only extract the spectum, not a variance]\n", - "9. Iterate Steps 5-8\n", - "\n", - "The first four steps as the standard procedure and the last five as add-ons that help squeeze out extra signal-to-noise." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are notes [in brackets] on how each step is handled in the proposed HorneExtract/OptimalExtract classes to make it easier to see what the class does and what the user must do themselves.\n", - "\n", - "### Steps in the JDAT Notebook guide on optimal extraction:\n", - "\n", - "1. Define extraction region [user's responsibility to provide an appropriate image]\n", - "2. Pick a slice [should not be necessary? can use the whole image as the aperture with good results]\n", - "3. Define extraction kernel\n", - " * Select PSF template [assumed to be Gaussian for now. support for Moffat, others?]\n", - " * Choose a polynomial for background fitting [user provides as an argument]\n", - "4. Fit extraction kernel to initial slice [all columns are coadded to perform the fit]\n", - "5. Fit geometric distortion [not currently done]\n", - " * Determine cross-dispersion bins for trace fitting\n", - " * Fit a kernel to each bin to find trace center [user provides this as a specreduce.tracing.Trace object]\n", - " * Polynomial fit of trace centers\n", - "6. Combine composite model with 2D image to create output 1D spectrum\n", - " * Create variance image [user provides this as an argument]\n", - " * Generate 1D spectrum\n", - "7. Compare with reference 1D spectrum" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ext1d_horne = HorneExtract(image-bg, flat_trace)\n", - "ext1d_horne = ext1d_horne.spectrum.flux.value\n", - "# convert from MJy/sr to Jy\n", - "ext1d_horne *= 1e6 * s2d.meta.photometry.pixelarea_steradians" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "gpts = ext1d_boxcar_bkgsub > 0.\n", - "gpts = ext1d_boxcar > 0.\n", - "\n", - "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label = 'Boxcar; No Mask')\n", - "ax.plot(jpipe_x1d.spectral_axis, ext1d_horne, color = 'purple', label = 'Horne; No Mask')\n", - "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], color = 'green', label=\"Boxcar; Mask\")\n", - "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], color = 'red', label=\"Boxcar; bkgsub; Mask\")\n", - "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", - "ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n", - "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", - "ax.set_ylabel(\"Flux Density [Jy]\")\n", - "ax.set_yscale(\"log\")\n", - "ax.set_ylim(1e-6, 3e-3)\n", - "ax.set_xlim(4, 13)\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### **See note above. In this case the Horne extraction likely overestimates the flux because it under-fits the wings of the cross-dispersion profile. Using a real MIRI LRS PSF is a better idea.** " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# 5) PSF-Weighted Extraction\n", - "\n", - "While to first order the PSF FHWM changes linearly with wavelength, this is an approximation. It is better to use the measured spatial profile as a function of wavelength to extract the spectrum. This tracks the actual variation with wavelength and optimizes the extraction to the higher S/N measurements. In general, PSF based extractions show the most improvements over boxcar extractions at lower the S/N.\n", - "\n", - "There are two PSF based extraction methods:\n", - "\n", - "1. PSF weighted: the spatial profile at each wavelength is used to weight the extraction.\n", - "2. PSF fitting: the spatial profile is fit at each wavelength with the scale parameter versus wavelength giving the spectrum.\n", - "\n", - "#### Only the PSF weighted technique is currently part of this notebook.\n", - "\n", - "Note 1: calibration reference file for the specific LRS slit position should be used.
\n", - "Note 2: Small shifts in the centering of the source in the slit should be investigated to see if they impact the PSF based extractions.
\n", - "Limitation: currently it is assumed there are no bad pixels.
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### PSF weighted extaction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Generate the PSF profile as a function of wavelength\n", - "For MIRI LRS slit observations, observations are made at two nod position in the slit after target acquisition. This means that the location of the sources in the slit is very well known. Hence, spatial profile (PSF) as a function of wavelength for the two nod positions is straightforward to measure using observations of a bright source.\n", - "\n", - "The next few steps generate the needed information for the nod position for which we are extracting spectra based on a simulation of a bright source at the same nod position." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# lrs spatial profile (PSF) as a function of wavelength\n", - "# currently, this is just a \"high\" S/N observation of a flat spectrum source at the same slit position\n", - "psf = datamodels.open(spatialprofilefile)\n", - "# transpose to make it display better\n", - "lrspsf = np.transpose(psf.data)\n", - "norm_data = simple_norm(lrspsf, \"sqrt\")\n", - "plt.figure(figsize=(10, 3))\n", - "plt.imshow(lrspsf, norm=norm_data, origin=\"lower\")\n", - "plt.title(\"The LRS Spatial Profile (PSF) Observation\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Mock a LRS spectral profile reference file\n", - "# Sum along the spatial direction and normalize to 1\n", - "# assume there is no background (none was included in the MIRISim for the flat spectrum source observation)\n", - "# ignore regions far from the source using a scaled boxcar weight image\n", - "# the aperture (psf_width) used in the scaled boxcar weight image could be varied\n", - "psf_width = 12.0\n", - "(wimage_scaledboxcar, tmpvar) = ap_weight_images(ext_center, psf_width, bkg_sep, \n", - " bkg_width, image.shape, waves_boxcar, wavescale=10.0)\n", - "\n", - "psf_weightimage = lrspsf*wimage_scaledboxcar\n", - "\n", - "# generate a 2D image of the column sums for division\n", - "max_psf = np.max(psf_weightimage, axis=0)\n", - "div_image = np.tile(max_psf, (psf_weightimage.shape[0], 1))\n", - "div_image[div_image == 0.0] = 1.0 # avoid divide by zero issues\n", - "\n", - "# normalize \n", - "psf_weightimage /= div_image\n", - "\n", - "# display\n", - "norm_data = simple_norm(psf_weightimage, \"sqrt\")\n", - "plt.figure(figsize=(10, 3))\n", - "plt.imshow(psf_weightimage, norm=norm_data, origin=\"lower\")\n", - "plt.title(\"The LRS Spatial Profile Reference Image (Normalized)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "y = np.arange(psf_weightimage.shape[0])\n", - "ax.plot(y, psf_weightimage[:,150], label=\"pixel=150\")\n", - "ax.plot(y, psf_weightimage[:,225], label=\"pixel=225\")\n", - "ax.plot(y, psf_weightimage[:,300], label=\"pixel=300\")\n", - "ax.plot(y, psf_weightimage[:,370], label=\"pixel=370\")\n", - "ax.set_title(\"Cross-dispersion Cuts\")\n", - "ax.set_xlim(ext_center-psf_width, ext_center+psf_width)\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the spatial profile becomes narrower as the pixel values increases as this corresponds to the wavelength decreasing." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Extract spectrum using wavelength dependent PSF profiles using the same traces as defined above" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "image_bg = bg.sub_image()\n", - "image_wg = image_bg * psf_weightimage\n", - "ext1d_boxcar_bkgsub_psfweight = BoxcarExtract(image_wg, flat_trace, width=ext_width)\n", - "ext1d_boxcar_bkgsub_psfweight = ext1d_boxcar_bkgsub_psfweight.spectrum.flux.value\n", - "\n", - "# convert from MJy/sr to Jy\n", - "ext1d_boxcar_bkgsub_psfweight *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", - "\n", - "# compute the average wavelength for each column using the weight image\n", - "# this should correspond directly with the extracted spectrum\n", - "# wavelengths account for any tiled spectra this way\n", - "waves_boxcar_psfweight = np.average(lam_image, weights=wimage, axis=0)\n", - "waves_boxcar_bkgsub_psfweight = np.average(lam_image, weights=wimage, axis=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "gpts = ext1d_boxcar_bkgsub > 0.\n", - "gpts = ext1d_boxcar > 0.\n", - "\n", - "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label = 'Boxcar; No Mask')\n", - "ax.plot(jpipe_x1d.spectral_axis, ext1d_horne, color = 'purple', label = 'Horne; No Mask')\n", - "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], color = 'green', label=\"Boxcar; Mask\")\n", - "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], color = 'red', label=\"Boxcar; bkgsub; Mask\")\n", - "ax.plot(waves_boxcar_bkgsub_psfweight[gpts], ext1d_boxcar_bkgsub_psfweight[gpts], 'k-', label=\"Boxcar; bkgsub; PSF Weights\", color='cyan')\n", - "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", - "ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n", - "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", - "ax.set_ylabel(\"Flux Density [Jy]\")\n", - "ax.set_yscale(\"log\")\n", - "ax.set_ylim(1e-6, 3e-3)\n", - "#ax.set_ylim(6e-5, 8e-3)\n", - "ax.set_xlim(4, 13)\n", - "#ax.set_xlim(11, 12.5)\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the psf weighted extraction has visabily higher S/N, especially at the longer wavelengths where the S/N is lowest overall." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot in Specviz\n", - "specviz2 = Specviz()\n", - "specviz2.app" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ext1d_boxcar_spec1d = Spectrum1D(spectral_axis=waves_boxcar[gpts]*u.micron, flux=ext1d_boxcar[gpts]*u.Jy)\n", - "ext1d_boxcar_bkgsub_spec1d = Spectrum1D(spectral_axis=waves_boxcar_bkgsub[gpts]*u.micron, flux=ext1d_boxcar_bkgsub[gpts]*u.Jy)\n", - "ext1d_psfweight_spec1d = Spectrum1D(spectral_axis=waves_boxcar_bkgsub_psfweight[gpts]*u.micron, flux=ext1d_boxcar_bkgsub_psfweight[gpts]*u.Jy)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "specviz2.load_spectrum(ext1d_boxcar_spec1d, data_label='boxcar')\n", - "specviz2.load_spectrum(ext1d_boxcar_bkgsub_spec1d, data_label='boxcar bkgsub')\n", - "specviz2.load_spectrum(ext1d_psfweight_spec1d, data_label='psfweight')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 6) PSF-Fitted Extraction\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Developer note: Not currently possible. Allow for wavelength scaled width in the boxcar" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 7) Specviz2D Extraction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Watch these two demo videos on how to extract your spectra using Specviz2D" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Video showing how to use specviz2d\n", - "HTML('')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Video showing how to use specviz2d\n", - "HTML('')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "specviz2d = Specviz2d()\n", - "specviz2d.app" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "specviz2d.load_data(s2dfile)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "boxcar = specviz2d.app.get_data_from_viewer('spectrum-viewer',data_label='boxcar')\n", - "horne = specviz2d.app.get_data_from_viewer('spectrum-viewer',data_label='horne')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check to see if user made a boxcar extraction, otherwise read in from file\n", - "if not boxcar:\n", - " print(\"You didn't extract a spectrum from specviz2d, we will load a pre-extracted spectrum from the video above.\")\n", - " boxcar_specviz2d = Spectrum1D.read('boxcar_specviz2d.fits')\n", - "else:\n", - " myboxcar = boxcar.flux.value\n", - " myboxcar *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", - " boxcar_specviz2d = Spectrum1D(spectral_axis=jpipe_x1d.spectral_axis, flux=myboxcar*u.Jy)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if not horne:\n", - " print(\"You didn't extract a spectrum from specviz2d, we will load a pre-extracted spectrum from the video above.\")\n", - " horne_specviz2d = Spectrum1D.read('horne_specviz2d.fits')\n", - "else:\n", - " myhorne = horne.flux.value\n", - " myhorne *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", - " horne_specviz2d = Spectrum1D(spectral_axis=jpipe_x1d.spectral_axis, flux=myhorne*u.Jy)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Sarah's Extraction (for reference)\n", - "sp3_x1dfile = 'required_data/PID2072_Obs1_LRS_demo_x1d.fits'\n", - "sp3_x1d = datamodels.open(sp3_x1dfile)\n", - "ll3 = (sp3_x1dfile.split('/')[-1]).split('.')[0] + ' (Level 3, custom)'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "gpts = ext1d_boxcar_bkgsub > 0.\n", - "gpts = ext1d_boxcar > 0.\n", - "\n", - "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label = 'Boxcar; No Mask')\n", - "ax.plot(jpipe_x1d.spectral_axis, ext1d_horne, color = 'purple', label = 'Horne; No Mask')\n", - "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], color = 'green', label=\"Boxcar; Mask\")\n", - "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], color = 'red', label=\"Boxcar; bkgsub; Mask\")\n", - "ax.plot(waves_boxcar_bkgsub_psfweight[gpts], ext1d_boxcar_bkgsub_psfweight[gpts], 'k-', label=\"Boxcar; bkgsub; PSF Weights\", color='cyan')\n", - "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", - "\n", - "ax.plot(boxcar_specviz2d.spectral_axis, boxcar_specviz2d.flux, 'k-', label=\"Boxcar Specviz2D\", color='magenta')\n", - "ax.plot(horne_specviz2d.spectral_axis, horne_specviz2d.flux, 'k-', label=\"Horne Specviz2D\", color='lawngreen')\n", - "ax.plot(sp3_x1d.spec[0].spec_table['WAVELENGTH'], sp3_x1d.spec[0].spec_table['FLUX'], label=ll3, color = 'gold')\n", - "ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n", - "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", - "ax.set_ylabel(\"Flux Density [Jy]\")\n", - "ax.set_yscale(\"log\")\n", - "ax.set_ylim(1e-6, 3e-3)\n", - "ax.set_xlim(4, 13)\n", - "ax.legend(bbox_to_anchor=(1.1, 1.05))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Additional Resources\n", - "\n", - "- [MIRI LRS](https://jwst-docs.stsci.edu/mid-infrared-instrument/miri-observing-modes/miri-low-resolution-spectroscopy)\n", - "- [MIRISim](http://www.stsci.edu/jwst/science-planning/proposal-planning-toolbox/mirisim)\n", - "- [JWST pipeline](https://jwst-docs.stsci.edu/jwst-data-reduction-pipeline)\n", - "- PSF weighted extraction [Horne 1986, PASP, 98, 609](https://ui.adsabs.harvard.edu/abs/1986PASP...98..609H/abstract)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## About this notebook\n", - "\n", - "**Author:** Karl Gordon, JWST\n", - "**Updated On:** 2020-07-07" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[Top of Page](#top)\n", - "\"Space " - ] - } - ], - "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": "3.10.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 9be50e39230ff08d9b308af8aa075ca57c36757e Mon Sep 17 00:00:00 2001 From: Ori Fox Date: Wed, 2 Aug 2023 08:22:53 -0400 Subject: [PATCH 09/36] Delete miri_lrs_extraction_techniques_standard.ipynb --- ...i_lrs_extraction_techniques_standard.ipynb | 1544 ----------------- 1 file changed, 1544 deletions(-) delete mode 100644 notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques_standard.ipynb diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques_standard.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques_standard.ipynb deleted file mode 100644 index 589484e12..000000000 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_extraction_techniques_standard.ipynb +++ /dev/null @@ -1,1544 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# MIRI LRS Spectral Extraction Techniques" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Use case:** Extract spectra with different techniques.
\n", - "**Data:** MIRI LRS spectrum of Type Ia supernova SN2021aefx, observed by Jha et al in PID 2072 (Obs 1), **where the automated spectral extraction failed**. These data were taken with zero exclusive access period, and published in [Kwok et al 2023](https://ui.adsabs.harvard.edu/abs/2023ApJ...944L...3K/abstract).
\n", - "**Tools:** jdaviz, specviz2d, specreduce, jwst, gwcs, matplotlib, astropy.
\n", - "**Cross-intrument:** NIRSpec, MIRI.
\n", - "**Documentation:** This notebook is part of a STScI's larger [post-pipeline Data Analysis Tools Ecosystem](https://jwst-docs.stsci.edu/jwst-post-pipeline-data-analysis).
\n", - "\n", - "# Install instructions\n", - "git clone https://github.com/spacetelescope/jdat_notebooks.git
\n", - "cd jdat_notebooks/
\n", - "git fetch -q https://github.com/spacetelescope/jdat_notebooks.git refs/pull/93/head:lrsoptimal2
\n", - "git checkout lrsoptimal2
\n", - "conda create -n lrsextract python=3.8.10
\n", - "conda activate lrsextract
\n", - "pip install -r requirements.txt
\n", - "cd notebooks/MIRI_LRS_spectral_extraction/
\n", - "jupyter notebook miri_lrs_extraction_techniques.ipynb
\n", - "\n", - "# Introduction\n", - "\n", - "This notebook extracts a 1D spectra from a 2D MIRI LRS spectral observation (single image). The goal is to provide the ability to extract spectra with different locations, extraction apertures, and techniques than are done in the JWST pipeline using the [Astropy Specreduce package](https://github.com/astropy/specreduce).\n", - "\n", - "The notebook also demos how to use Jdaviz's [specviz2d](https://jdaviz.readthedocs.io/en/latest/specviz2d/index.html), which allows users to interactively extract 1D spectra from 2D spectra.\n", - "\n", - "The simpliest spectral extraction is \"boxcar\" where all the pixels within some fixed width centered on the source position are summed at each wavelength. Background subtraction can be done using regions offset from the source center. You can also see the Specreduce [generic Sample Notebook](https://github.com/astropy/specreduce/blob/main/notebook_sandbox/jwst_boxcar/boxcar_extraction.ipynb).\n", - "\n", - "For spectra taken with a diffraction limited telescope like JWST, a modification boxcar extraction is to vary the extraction width linearly with wavelength. Such a scaled boxcar extraction keeps the fraction of the source flux within the extraction region approximately constant with wavelength.\n", - "\n", - "For point sources, a PSF-weighted spectral extraction can be done. Using the PSF to weight the extraction uses the actual PSF as a function of wavelength to optimize the extraction to the pixels with the greatest signal. PSF-weighted extractions show the largest differences with boxcar extractions at lower S/N values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note:** Corrections for the finite aperture used in all the extractions have not been applied. Thus, the physical flux densities of all the extracted spectra are lower than the actual values." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Imports\n", - "\n", - "- *matplotlib.pyplot* for plotting data\n", - "- *numpy* to handle array functions\n", - "- *astropy.io fits* for accessing FITS files\n", - "- *astropy.visualization* for scaling image for display\n", - "- *astropy.table Table* for reading the pipeline 1d extractions\n", - "- *jwst datamodels* for reading/access the jwst data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "# disable all imported packages' loggers\n", - "import logging\n", - "logging.root.manager.loggerDict = {}" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "print(pycodestyle_magic.__version__)" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "# enable PEP8 checker for this notebook\n", - "%load_ext pycodestyle_magic\n", - "%flake8_on --ignore E261,E501,W291,W293\n", - "\n", - "# only allow the checker to throw warnings when there's a violation\n", - "logging.getLogger('flake8').setLevel('ERROR')\n", - "logging.getLogger('stpipe').setLevel('ERROR')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import matplotlib as mpl\n", - "# %matplotlib inline\n", - "\n", - "import numpy as np\n", - "\n", - "from gwcs.wcstools import grid_from_bounding_box\n", - "\n", - "from astropy.io import fits\n", - "from astropy.table import Table\n", - "from astropy.visualization import simple_norm\n", - "from astropy.io import ascii\n", - "\n", - "from jwst import datamodels\n", - "\n", - "from specreduce.extract import BoxcarExtract, OptimalExtract, HorneExtract\n", - "from specreduce.tracing import FlatTrace, FitTrace\n", - "from specreduce.background import Background\n", - "\n", - "from jdaviz import Imviz\n", - "from jdaviz import Specviz\n", - "from jdaviz import Specviz2d\n", - "\n", - "from astropy.utils.data import download_file\n", - "import os\n", - "\n", - "from specutils import Spectrum1D\n", - "from astropy import units as u\n", - "\n", - "# Display the video\n", - "from IPython.display import HTML, YouTubeVideo\n", - "\n", - "import os\n", - "import urllib.request\n", - "import tarfile" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Download all necessary data\n", - "\n", - "if os.path.exists(\"boxcar_specviz2d.fits\"):\n", - " print(\"Boxcar Specviz2d Extraction Exists\")\n", - "else:\n", - " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/boxcar_specviz2d.fits'\n", - " urllib.request.urlretrieve(url, 'boxcar_specviz2d.fits')\n", - "\n", - "if os.path.exists(\"horne_specviz2d.fits\"):\n", - " print(\"Horne Specviz2d Extraction Exists\")\n", - "else:\n", - " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/horne_specviz2d.fits'\n", - " urllib.request.urlretrieve(url, 'horne_specviz2d.fits')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if os.path.exists(\"./required_data/\"):\n", - " print(\"Origina Data Exists\")\n", - "else:\n", - " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/required_data.tar.gz'\n", - " urllib.request.urlretrieve(url, 'required_data.tar.gz')\n", - " \n", - "# Unzip files if they haven't already been unzipped\n", - "if os.path.exists(\"required_data/\"):\n", - " print(\"Data Directory Already Exists\")\n", - "else:\n", - " tar = tarfile.open('./required_data.tar.gz', \"r:gz\")\n", - " tar.extractall()\n", - " tar.close()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Developer note: Ask Karl exactly how these functions work? Seems like all weights are equal?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# useful functions\n", - "def get_boxcar_weights(center, hwidth, npix):\n", - " \"\"\"\n", - " Compute the weights given an aperture center, half widths, and number of pixels\n", - " \"\"\"\n", - " weights = np.zeros((npix))\n", - " # pixels with full weight\n", - " fullpixels = [max(0, int(center - hwidth + 1)), min(int(center + hwidth), npix)]\n", - " weights[fullpixels[0] : fullpixels[1]] = 1.0\n", - "\n", - " # pixels at the edges of the boxcar with partial weight\n", - " if fullpixels[0] > 0:\n", - " weights[fullpixels[0] - 1] = hwidth - (center - fullpixels[0])\n", - " if fullpixels[1] < npix:\n", - " weights[fullpixels[1]] = hwidth - (fullpixels[1] - center)\n", - "\n", - " return weights\n", - "\n", - "\n", - "def ap_weight_images(\n", - " center, width, bkg_offset, bkg_width, image_size, waves, wavescale=None\n", - "):\n", - " \"\"\"\n", - " Create a weight image that defines the desired extraction aperture\n", - " and the weight image for the requested background regions\n", - "\n", - " Parameters\n", - " ----------\n", - " center : float\n", - " center of aperture in pixels\n", - " width : float\n", - " width of apeture in pixels\n", - " bkg_offset : float\n", - " offset from the extaction edge for the background\n", - " never scaled for wavelength\n", - " bkg_width : float\n", - " width of background region\n", - " never scaled with wavelength\n", - " image_size : tuple with 2 elements\n", - " size of image\n", - " waves : array\n", - " wavelegth values\n", - " wavescale : float\n", - " scale the width with wavelength (default=None)\n", - " wavescale gives the reference wavelenth for the width value\n", - "\n", - " Returns\n", - " -------\n", - " wimage, bkg_wimage : (2D image, 2D image)\n", - " wimage is the weight image defining the aperature\n", - " bkg_image is the weight image defining the background regions\n", - " \"\"\"\n", - " wimage = np.zeros(image_size)\n", - " bkg_wimage = np.zeros(image_size)\n", - " hwidth = 0.5 * width\n", - " # loop in dispersion direction and compute weights\n", - " for i in range(image_size[1]):\n", - " if wavescale is not None:\n", - " hwidth = 0.5 * width * (waves[i] / wavescale)\n", - "\n", - " wimage[:, i] = get_boxcar_weights(center, hwidth, image_size[0])\n", - "\n", - " # bkg regions\n", - " if (bkg_width is not None) & (bkg_offset is not None):\n", - " bkg_wimage[:, i] = get_boxcar_weights(\n", - " center - hwidth - bkg_offset, bkg_width, image_size[0]\n", - " )\n", - " bkg_wimage[:, i] += get_boxcar_weights(\n", - " center + hwidth + bkg_offset, bkg_width, image_size[0]\n", - " )\n", - " else:\n", - " bkg_wimage = None\n", - "\n", - " return (wimage, bkg_wimage)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Devloper notes (2021)\n", - "\n", - "1) The difference between the pipeline (x1d) and the extractions done in this notebook are quite large. Help in understanding the origin of these differences is needed.\n", - "\n", - "2) Not clear how to use the JWST pipeline `extract_1d` (quite complex) code. Help to determine how to use the JWST pipeline code instead of the custom code for boxcar is needed. \n", - "\n", - "3) Applying aperture corrections for the finite extraction widths is needed. Help in how to get the needed informatinom for different (user set) extraction widths is needed. \n", - "\n", - "### Partially RESOLVED (March, 2023)\n", - "\n", - "1) See notes from Kendrew on limitations of current pipeline. Pipeline will be updated soon.\n", - "\n", - "2) While this notebook doesn't go into using the pipeline, boxcar is now integrated into the Astropy Specreduce package. So I wouldn't characterize the boxcar as \"custom code\" any longer.\n", - "\n", - "3) Still not resolved." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download Files" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#calfilename = \"det_image_seq5_MIRIMAGE_P750Lexp1_cal.fits\"\n", - "s2dfile = \"./required_data/BD+60_1753_1536/jw01536-o027_t008_miri_p750l/jw01536-o027_t008_miri_p750l_s2d.fits\"\n", - "x1dfile = \"./required_data/BD+60_1753_1536/jw01536-o027_t008_miri_p750l/jw01536-o027_t008_miri_p750l_x1d.fits\"\n", - "spatialprofilefile = \"./required_data/BD+60_1753_1536/jw01536-o027_t008_miri_p750l/jw01536-o027_t008_miri_p750l_s2d.fits\"\n", - "\n", - "#spatialprofilefilename = \"det_image_seq1_MIRIMAGE_P750Lexp1_s2d.fits\"\n", - "#mainurl = \"https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/\"\n", - "\n", - "#calfile_dld = download_file(mainurl + calfilename)\n", - "#s2dfile_dld = download_file(mainurl + s2dfilename)\n", - "#x1dfile_dld = download_file(mainurl + x1dfilename)\n", - "#spatialprofilefile_dld = download_file(mainurl + spatialprofilefilename)" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "# rename files so that they have the right extensions\n", - "# required for the jwst datamodels to work\n", - "#calfile = calfile_dld + '_cal.fits'\n", - "#os.rename(calfile_dld, calfile)\n", - "\n", - "#s2dfile = s2dfile_dld + '_s2d.fits'\n", - "#os.rename(s2dfile_dld, s2dfile)\n", - "#x1dfile = x1dfile_dld + '_x1d.fits'\n", - "#os.rename(x1dfile_dld, x1dfile)\n", - "spatialprofilefile = spatialprofilefile_dld + '_s2d.fits'\n", - "os.rename(spatialprofilefile_dld, spatialprofilefile)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## File information\n", - "\n", - "The data used is a simulation of a LRS slit observation for a blackbody with a similar flux density to the star BD+60d1753, a flux calibration star. This simulation was created with MIRISim.\n", - "The simulated exposure was reduced using the JWST pipeline (v0.16.1) through the Detector1 and Spec2 stages.\n", - "\n", - "The cal file is one of the Spec2 products and is the calibration full frame image. It contains:\n", - "\n", - "1. (Primary): This HDU contains meta-data related to the observation and data reduction.\n", - "2. (SCI): The calibrated image. Units are MJy/sr.\n", - "3. (ERR): Uncertainty image. Units are MJy/sr.\n", - "4. (DQ): Data quality image.\n", - "5. (VAR_POISSON): Unc. component 1: Poisson uncertainty image. Units are (MJy/sr)^2.\n", - "6. (VAR_RNOISE): Unc. component 2: Read Noise uncertainty image. Units are (MJy/sr)^2.\n", - "7. (VAR_FLAT): Unc. component 3: Flat Field uncertainty image. Units are (MJy/sr)^2.\n", - "8. (ASDF_METADATA): Metadata.\n", - "\n", - "The s2d file is one of the Spec2 products and containes the calibrated rectified cutout of the LRS Slit region. It has:\n", - "\n", - "1. (Primary): This HDU contains meta-data related to the observation and data reduction.\n", - "2. (WGT): Weight.\n", - "3. (CON): ??\n", - "4. (ASDF_METADATA): Metadata." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Loading data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# use a jwst datamodel to provide a good interface to the data and wcs info\n", - "#cal = datamodels.open(calfile)\n", - "s2d = datamodels.open(s2dfile)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Basic information about the image." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#print(\"cal image\")\n", - "#print(cal.data.shape)\n", - "#print(np.mean(cal.data))\n", - "#print(np.amin(cal.data), np.amax(cal.data))\n", - "print(\"s2d image\")\n", - "print(s2d.data.shape)\n", - "print(np.mean(s2d.data))\n", - "print(np.amin(s2d.data), np.amax(s2d.data))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display the full 2D image" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "norm_data = simple_norm(cal.data, 'sqrt')\n", - "plt.figure(figsize=(6, 6))\n", - "plt.imshow(cal.data, norm=norm_data, origin=\"lower\")\n", - "plt.title(\"The full image from the MIRI IMAGER detector\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display the LRS Slit region only (use s2d)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# transpose to make it display better\n", - "image = np.transpose(s2d.data)\n", - "err = np.transpose(s2d.err)\n", - "norm_data = simple_norm(image, \"sqrt\")\n", - "plt.figure(figsize=(10, 3))\n", - "plt.imshow(image, norm=norm_data, origin=\"lower\")\n", - "plt.title(\"The LRS region\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### View the 2D Spectrum in Imviz and get the center of the cross-dispersion " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "imviz = Imviz()\n", - "imviz.app" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "imviz.load_data(image)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "viewer = imviz.default_viewer\n", - "viewer.cuts = '95%'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1) Default JWST Pipeline 1D extraction" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a spectrum1d\n", - "jpipe_x1d = Spectrum1D.read(x1dfile)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "specviz = Specviz()\n", - "specviz.app" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "specviz.load_spectrum(jpipe_x1d)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "ylim_low = 1.e-3\n", - "ylim_high = 4.e-2\n", - "xlim_low = 3\n", - "xlim_high = 13" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot\n", - "\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "\n", - "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", - "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", - "ax.set_ylabel(\"Flux Density [Jy]\")\n", - "ax.set_yscale(\"log\")\n", - "ax.set_ylim(ylim_low, ylim_high)\n", - "ax.set_xlim(xlim_low, xlim_high)\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2) Fixed Width Boxcar Extraction (Using Specreduce)\n", - "\n", - "Extract a 1D spectrum using a simple boxcar. Basically collapse the spectrum in the cross-dispersion direction over a specified number of pixels.\n", - "\n", - "#### Developer note: Allow for a bad pixel mask" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define extraction parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ext_center = 31\n", - "ext_width = 11\n", - "bkg_sep = 8\n", - "bkg_width = 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot cross-disperion cut showing the extraction parameters\n", - "\n", - "#### Develepor Note: Place trace back into Specviz2d/Imviz/Etc" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot along cross-disperion cut showing the extraction parameters\n", - "fig, ax = plt.subplots(figsize=(10, 6))\n", - "y = np.arange(image.shape[0])\n", - "ax.plot(y, image[:,140], 'k-')\n", - "mm = np.array([ext_center, ext_center])\n", - "mm_y = ax.get_ylim()\n", - "\n", - "# extraction region\n", - "ax.axvspan(ext_center - ext_width/2., ext_center + ext_width/2., color='green', alpha=0.1)\n", - "ax.plot(mm, mm_y, 'b--')\n", - "ax.plot(mm - ext_width/2., mm_y, 'g:')\n", - "ax.plot(mm + ext_width/2., mm_y, 'g:')\n", - "\n", - "# background region, symmetric on both sides of extraction region\n", - "ax.axvspan(ext_center - bkg_sep - bkg_width/2., ext_center - bkg_sep + bkg_width/2., color='red', alpha=0.1)\n", - "ax.plot(mm - bkg_sep - bkg_width/2., mm_y, 'r:')\n", - "ax.plot(mm - bkg_sep + bkg_width/2., mm_y, 'r:')\n", - "\n", - "ax.axvspan(ext_center + bkg_sep - bkg_width/2., ext_center + bkg_sep + bkg_width/2., color='red', alpha=0.1)\n", - "ax.plot(mm + bkg_sep - bkg_width/2., mm_y, 'r:')\n", - "ax.plot(mm + bkg_sep + bkg_width/2., mm_y, 'r:')\n", - "\n", - "ax.set_title(\"Cross-dispersion Cut at Pixel=300\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define Background" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# extract the background using custom individual traces\n", - "trace = FlatTrace(image, ext_center)\n", - "bg = Background(image, [trace-bkg_sep, trace+bkg_sep], width=bkg_width)\n", - "\n", - "# alternatively, call two_sided class, which does the same as above \n", - "#bg = Background.two_sided(image, trace, bkg_sep, width=bkg_width)\n", - "# or in the place of any trace, an int/float can be passed which resolves to a FlatTrace\n", - "#bg = Background.two_sided(image, ext_center, bkg_sep, width=bkg_width)\n", - "\n", - "# or for single sided:\n", - "# bg = Background.one_sided(image, trace, bkg_sep, width=bkg_width)\n", - "# bg = Background.one_sided(image, trace, -bkg_sep, width=bkg_width)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# view the background weighted image\n", - "plt.figure(figsize=(15, 15))\n", - "plt.imshow(bg.bkg_wimage, origin=\"lower\")\n", - "plt.title(\"slit[0] slice\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# view the background image\n", - "plt.figure(figsize=(15, 15))\n", - "plt.imshow(bg.bkg_image().flux.value, norm=norm_data, origin=\"lower\")\n", - "plt.title(\"slit[0] slice\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# view the background-subtracted image\n", - "plt.figure(figsize=(15, 15))\n", - "plt.imshow(bg.sub_image().flux.value, norm=norm_data, origin=\"lower\")\n", - "plt.title(\"slit[0] slice\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that when using median to calculate the background, partial pixel weights are not supported:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bg_med = Background.two_sided(image, ext_center, bkg_sep, width=bkg_width, statistic='median')\n", - "plt.figure(figsize=(15, 15))\n", - "plt.imshow(bg_med.bkg_wimage, origin=\"lower\")\n", - "plt.title(\"slit[0] slice\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Extract Trace (multiple options)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Optional: we could now refine the initial flat trace by running an automated FitTrace on the subtracted image. This process could be iterated as necessary (recreating the subtracted image with the refined trace, etc)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fit_trace = FitTrace(image-bg.bkg_image().flux.value, peak_method='gaussian', guess=ext_center)\n", - "#fit_trace" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "flat_trace = FlatTrace(image-bg.bkg_image().flux.value, trace_pos=ext_center)\n", - "#flat_trace" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### **Always visualize your traces. If you have noisy data, the fits may not be good. You may need to play around with the type of fit (i.e., Order 1 Polynomial) or different window sizes and parameters. In our case, we'll stick with the flat trace throughout this notebook.** \n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "#### Plot old vs new trace" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig3, ax3 = plt.subplots(figsize=(10,6))\n", - "plot3 = ax3.imshow(bg.sub_image().flux.value, aspect=4.,\n", - " vmin=0, vmax=bg.sub_image().flux.value.max()/2,\n", - " cmap=mpl.cm.magma, origin='lower',\n", - " extent=(0, bg.sub_image().flux.value.shape[-1],\n", - " 0, bg.sub_image().flux.value.shape[0]))\n", - "fig3.colorbar(plot3)\n", - "ax3.set_title('LRS Spectrum Traces')\n", - "ax3.grid()\n", - "\n", - "# add the traces\n", - "ax3.plot(flat_trace.trace, '--', color='#008ca8',\n", - " lw=2.5, label='FlatTrace')\n", - "ax3.plot(fit_trace.trace, '--', color='#00471b',\n", - " lw=2.5, label='GaussianFitTrace')\n", - "ax3.legend(framealpha=.5)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Extract" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Dev Note: FitTrace doesn't seem to be working right now, so we'll stick with FlatTrace" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "from specutils import Spectrum1D\n", - "from astropy import units as u\n", - "flux = s2d.data * u.Jy\n", - "wavelength = s2d.wavelength * u.um\n", - "flux.data\n", - "spec = Spectrum1D(spectral_axis=wavelength, flux=flux)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#boxcar = BoxcarExtract()\n", - "ext1d_boxcar_noweights = BoxcarExtract(image-bg, flat_trace, width=ext_width)\n", - "ext1d_boxcar_noweights = ext1d_boxcar_noweights.spectrum.flux.value\n", - "ext1d_boxcar_noweights *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", - "#spectrum_specreduce_boxcar" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "\n", - "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", - "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label='Boxcar')\n", - "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", - "ax.set_ylabel(\"Flux Density [Jy]\")\n", - "ax.set_yscale(\"log\")\n", - "ax.set_ylim(ylim_low, ylim_high)\n", - "ax.set_xlim(xlim_low, xlim_high)\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3) Fixed Width Boxcar Extraction (Using Pixel Masks, too)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### **A basic example of how to create a pixel weight map/mask. In this example, the weights basically create a pixel mask based on the boxcar extraction aperture. It shouldn't actually change any of the results because the boxcar extraction essentially does this for you. But this provides a useful example for more complicated masks that we will create lower in the notebook.** " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from gwcs.wcstools import grid_from_bounding_box\n", - "\n", - "#image = np.transpose(s2d.data)\n", - "grid = grid_from_bounding_box(s2d.meta.wcs.bounding_box)\n", - "ra, dec, lam = s2d.meta.wcs(*grid)\n", - "lam_image = np.transpose(lam)\n", - "\n", - "# compute a \"rough\" wavelength scale to allow for aperture to scale with wavelength\n", - "rough_waves = np.average(lam_image, axis=0)\n", - "\n", - "# images to use for extraction\n", - "wimage, bkg_wimage = ap_weight_images(\n", - " ext_center,\n", - " ext_width,\n", - " bkg_width,\n", - " bkg_sep,\n", - " image.shape,\n", - " rough_waves,\n", - " wavescale=None,\n", - ")\n", - "\n", - "#boxcar = BoxcarExtract()\n", - "\n", - "# without *additional* background subtraction \n", - "# NOTE: The intial background subtraction is performed by subtracting the nods when creating the final data product input at the top of this notebook\n", - "# NOTE: Additional background subtractions can be performed using the Specreduce Background function below\n", - "# NOTE: Since most of the background has already been subtracted by the pipeline, all three extractions below look pretty similar\n", - "\n", - "image_wg = image * wimage\n", - "ext1d_boxcar = BoxcarExtract(image_wg, flat_trace, width=ext_width)\n", - "ext1d_boxcar = ext1d_boxcar.spectrum.flux.value\n", - "# convert from MJy/sr to Jy\n", - "ext1d_boxcar *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", - "\n", - "# with background subtraction\n", - "image_bg = bg.sub_image()\n", - "image_wg = image_bg * wimage\n", - "ext1d_boxcar_bkgsub = BoxcarExtract(image_wg, flat_trace, width=ext_width)\n", - "ext1d_boxcar_bkgsub = ext1d_boxcar_bkgsub.spectrum.flux.value\n", - "\n", - "# convert from MJy/sr to Jy\n", - "ext1d_boxcar_bkgsub *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", - "\n", - "# compute the average wavelength for each column using the weight image\n", - "# this should correspond directly with the extracted spectrum\n", - "# wavelengths account for any tiled spectra this way\n", - "waves_boxcar = np.average(lam_image, weights=wimage, axis=0)\n", - "waves_boxcar_bkgsub = np.average(lam_image, weights=wimage, axis=0)" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "ext1d_boxcar = boxcar(image_wg, auto_trace, width=ext_width)\n", - "ext1d_boxcar.flux.value" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "gpts = ext1d_boxcar_bkgsub > 0.\n", - "gpts = ext1d_boxcar > 0.\n", - "\n", - "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label = 'Boxcar; No Mask')\n", - "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], color = 'green', label=\"Boxcar; Mask\")\n", - "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], color = 'red', label=\"Boxcar; bkgsub; Mask\")\n", - "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", - "ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n", - "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", - "ax.set_ylabel(\"Flux Density [Jy]\")\n", - "ax.set_yscale(\"log\")\n", - "ax.set_ylim(ylim_low, ylim_high)\n", - "ax.set_xlim(xlim_low, xlim_high)\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4) Wavelength scaled width boxcar\n", - "\n", - "The LRS spatial profile changes as a function of wavelength as JWST is diffraction limited at these wavelengths. Nominally this means that the FWHM is changing linearly with wavelength. Scaling the width of the extraction aperture with wavelength accounts for the changing diffraction limit with wavelength to first order." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Developer note: Not currently possible. Allow for wavelength scaled width in the boxcar" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 5) Horne Extraction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### **The Horne algorithm preforms a Gaussian fit on the source, it is thus best suited for cases where the source has a Gaussian profile in the cross-dispersion direction. If your profile is not Gaussian, you will likely over- or under-estimate your actual flux.**. \n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Steps in the original Horne (1986) paper:\n", - "\n", - "1. Bias subtraction [assumed to be done in earlier block]\n", - "2. Initial variance estimate [user provides this as an argument]\n", - "3. Fit sky background [assumed to be done in earlier block]\n", - " * \"We therefore generally perform a least-squares polynomial fit to the sky data at each wavelength. Individual sky pixels are given weights inversely proportional to their variances as estimated in Step 2\" [overlaps with notebook guide's 3b]\n", - "4. Extract standard spectrum and its variance\n", - " * Subtract the sky background found in Step 3 from the image. [sky background calculation is planned as a separate, earlier step of the specreduce workflow]\n", - "5. Construct spatial profile\n", - "6. Revise variance estimates [not currently done]\n", - "7. Mask cosmic ray hits [not currently done]\n", - "8. Extract optimal spectrum and its variance [currently only extract the spectum, not a variance]\n", - "9. Iterate Steps 5-8\n", - "\n", - "The first four steps as the standard procedure and the last five as add-ons that help squeeze out extra signal-to-noise." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are notes [in brackets] on how each step is handled in the proposed HorneExtract/OptimalExtract classes to make it easier to see what the class does and what the user must do themselves.\n", - "\n", - "### Steps in the JDAT Notebook guide on optimal extraction:\n", - "\n", - "1. Define extraction region [user's responsibility to provide an appropriate image]\n", - "2. Pick a slice [should not be necessary? can use the whole image as the aperture with good results]\n", - "3. Define extraction kernel\n", - " * Select PSF template [assumed to be Gaussian for now. support for Moffat, others?]\n", - " * Choose a polynomial for background fitting [user provides as an argument]\n", - "4. Fit extraction kernel to initial slice [all columns are coadded to perform the fit]\n", - "5. Fit geometric distortion [not currently done]\n", - " * Determine cross-dispersion bins for trace fitting\n", - " * Fit a kernel to each bin to find trace center [user provides this as a specreduce.tracing.Trace object]\n", - " * Polynomial fit of trace centers\n", - "6. Combine composite model with 2D image to create output 1D spectrum\n", - " * Create variance image [user provides this as an argument]\n", - " * Generate 1D spectrum\n", - "7. Compare with reference 1D spectrum" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ext1d_horne = HorneExtract(image-bg, flat_trace)\n", - "ext1d_horne = ext1d_horne.spectrum.flux.value\n", - "# convert from MJy/sr to Jy\n", - "ext1d_horne *= 1e6 * s2d.meta.photometry.pixelarea_steradians" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "gpts = ext1d_boxcar_bkgsub > 0.\n", - "gpts = ext1d_boxcar > 0.\n", - "\n", - "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label = 'Boxcar; No Mask')\n", - "ax.plot(jpipe_x1d.spectral_axis, ext1d_horne, color = 'purple', label = 'Horne; No Mask')\n", - "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], color = 'green', label=\"Boxcar; Mask\")\n", - "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], color = 'red', label=\"Boxcar; bkgsub; Mask\")\n", - "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", - "ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n", - "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", - "ax.set_ylabel(\"Flux Density [Jy]\")\n", - "ax.set_yscale(\"log\")\n", - "ax.set_ylim(ylim_low, ylim_high)\n", - "ax.set_xlim(xlim_low, xlim_high)\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### **See note above. In this case the Horne extraction likely overestimates the flux because it under-fits the wings of the cross-dispersion profile. Using a real MIRI LRS PSF is a better idea.** " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# 5) PSF-Weighted Extraction\n", - "\n", - "While to first order the PSF FHWM changes linearly with wavelength, this is an approximation. It is better to use the measured spatial profile as a function of wavelength to extract the spectrum. This tracks the actual variation with wavelength and optimizes the extraction to the higher S/N measurements. In general, PSF based extractions show the most improvements over boxcar extractions at lower the S/N.\n", - "\n", - "There are two PSF based extraction methods:\n", - "\n", - "1. PSF weighted: the spatial profile at each wavelength is used to weight the extraction.\n", - "2. PSF fitting: the spatial profile is fit at each wavelength with the scale parameter versus wavelength giving the spectrum.\n", - "\n", - "#### Only the PSF weighted technique is currently part of this notebook.\n", - "\n", - "Note 1: calibration reference file for the specific LRS slit position should be used.
\n", - "Note 2: Small shifts in the centering of the source in the slit should be investigated to see if they impact the PSF based extractions.
\n", - "Limitation: currently it is assumed there are no bad pixels.
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### PSF weighted extaction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Generate the PSF profile as a function of wavelength\n", - "For MIRI LRS slit observations, observations are made at two nod position in the slit after target acquisition. This means that the location of the sources in the slit is very well known. Hence, spatial profile (PSF) as a function of wavelength for the two nod positions is straightforward to measure using observations of a bright source.\n", - "\n", - "The next few steps generate the needed information for the nod position for which we are extracting spectra based on a simulation of a bright source at the same nod position." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# lrs spatial profile (PSF) as a function of wavelength\n", - "# currently, this is just a \"high\" S/N observation of a flat spectrum source at the same slit position\n", - "psf = datamodels.open(spatialprofilefile)\n", - "# transpose to make it display better\n", - "lrspsf = np.transpose(psf.data)\n", - "norm_data = simple_norm(lrspsf, \"sqrt\")\n", - "plt.figure(figsize=(10, 3))\n", - "plt.imshow(lrspsf, norm=norm_data, origin=\"lower\")\n", - "plt.title(\"The LRS Spatial Profile (PSF) Observation\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Mock a LRS spectral profile reference file\n", - "# Sum along the spatial direction and normalize to 1\n", - "# assume there is no background (none was included in the MIRISim for the flat spectrum source observation)\n", - "# ignore regions far from the source using a scaled boxcar weight image\n", - "# the aperture (psf_width) used in the scaled boxcar weight image could be varied\n", - "psf_width = 12.0\n", - "(wimage_scaledboxcar, tmpvar) = ap_weight_images(ext_center, psf_width, bkg_sep, \n", - " bkg_width, image.shape, waves_boxcar, wavescale=10.0)\n", - "\n", - "psf_weightimage = lrspsf*wimage_scaledboxcar\n", - "\n", - "# generate a 2D image of the column sums for division\n", - "max_psf = np.max(psf_weightimage, axis=0)\n", - "div_image = np.tile(max_psf, (psf_weightimage.shape[0], 1))\n", - "div_image[div_image == 0.0] = 1.0 # avoid divide by zero issues\n", - "\n", - "# normalize \n", - "psf_weightimage /= div_image\n", - "\n", - "# display\n", - "norm_data = simple_norm(psf_weightimage, \"sqrt\")\n", - "plt.figure(figsize=(10, 3))\n", - "plt.imshow(psf_weightimage, norm=norm_data, origin=\"lower\")\n", - "plt.title(\"The LRS Spatial Profile Reference Image (Normalized)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "y = np.arange(psf_weightimage.shape[0])\n", - "ax.plot(y, psf_weightimage[:,150], label=\"pixel=150\")\n", - "ax.plot(y, psf_weightimage[:,225], label=\"pixel=225\")\n", - "ax.plot(y, psf_weightimage[:,300], label=\"pixel=300\")\n", - "ax.plot(y, psf_weightimage[:,370], label=\"pixel=370\")\n", - "ax.set_title(\"Cross-dispersion Cuts\")\n", - "ax.set_xlim(ext_center-psf_width, ext_center+psf_width)\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the spatial profile becomes narrower as the pixel values increases as this corresponds to the wavelength decreasing." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Extract spectrum using wavelength dependent PSF profiles using the same traces as defined above" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "image_bg = bg.sub_image()\n", - "image_wg = image_bg * psf_weightimage\n", - "ext1d_boxcar_bkgsub_psfweight = BoxcarExtract(image_wg, flat_trace, width=ext_width)\n", - "ext1d_boxcar_bkgsub_psfweight = ext1d_boxcar_bkgsub_psfweight.spectrum.flux.value\n", - "\n", - "# convert from MJy/sr to Jy\n", - "ext1d_boxcar_bkgsub_psfweight *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", - "\n", - "# compute the average wavelength for each column using the weight image\n", - "# this should correspond directly with the extracted spectrum\n", - "# wavelengths account for any tiled spectra this way\n", - "waves_boxcar_psfweight = np.average(lam_image, weights=wimage, axis=0)\n", - "waves_boxcar_bkgsub_psfweight = np.average(lam_image, weights=wimage, axis=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "gpts = ext1d_boxcar_bkgsub > 0.\n", - "gpts = ext1d_boxcar > 0.\n", - "\n", - "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label = 'Boxcar; No Mask')\n", - "ax.plot(jpipe_x1d.spectral_axis, ext1d_horne, color = 'purple', label = 'Horne; No Mask')\n", - "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], color = 'green', label=\"Boxcar; Mask\")\n", - "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], color = 'red', label=\"Boxcar; bkgsub; Mask\")\n", - "ax.plot(waves_boxcar_bkgsub_psfweight[gpts], ext1d_boxcar_bkgsub_psfweight[gpts], 'k-', label=\"Boxcar; bkgsub; PSF Weights\", color='cyan')\n", - "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", - "ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n", - "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", - "ax.set_ylabel(\"Flux Density [Jy]\")\n", - "ax.set_yscale(\"log\")\n", - "ax.set_ylim(ylim_low, ylim_high)\n", - "ax.set_xlim(xlim_low, xlim_high)\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the psf weighted extraction has visabily higher S/N, especially at the longer wavelengths where the S/N is lowest overall." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot in Specviz\n", - "specviz2 = Specviz()\n", - "specviz2.app" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ext1d_boxcar_spec1d = Spectrum1D(spectral_axis=waves_boxcar[gpts]*u.micron, flux=ext1d_boxcar[gpts]*u.Jy)\n", - "ext1d_boxcar_bkgsub_spec1d = Spectrum1D(spectral_axis=waves_boxcar_bkgsub[gpts]*u.micron, flux=ext1d_boxcar_bkgsub[gpts]*u.Jy)\n", - "ext1d_psfweight_spec1d = Spectrum1D(spectral_axis=waves_boxcar_bkgsub_psfweight[gpts]*u.micron, flux=ext1d_boxcar_bkgsub_psfweight[gpts]*u.Jy)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "specviz2.load_spectrum(ext1d_boxcar_spec1d, data_label='boxcar')\n", - "specviz2.load_spectrum(ext1d_boxcar_bkgsub_spec1d, data_label='boxcar bkgsub')\n", - "specviz2.load_spectrum(ext1d_psfweight_spec1d, data_label='psfweight')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 6) PSF-Fitted Extraction\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Developer note: Not currently possible. Allow for wavelength scaled width in the boxcar" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 7) Specviz2D Extraction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Watch these two demo videos on how to extract your spectra using Specviz2D" - ] - }, - { - "cell_type": "raw", - "metadata": { - "tags": [] - }, - "source": [ - "# Video showing how to use specviz2d\n", - "HTML ('')" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "# Video showing how to use specviz2d\n", - "HTML ('')" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "specviz2d = Specviz2d()\n", - "specviz2d.app" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "specviz2d.load_data(s2dfile)" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "boxcar = specviz2d.app.get_data_from_viewer('spectrum-viewer',data_label='boxcar')\n", - "horne = specviz2d.app.get_data_from_viewer('spectrum-viewer',data_label='horne')" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "# Check to see if user made a boxcar extraction, otherwise read in from file\n", - "if not boxcar:\n", - " print(\"You didn't extract a spectrum from specviz2d, we will load a pre-extracted spectrum from the video above.\")\n", - " boxcar_specviz2d = Spectrum1D.read('boxcar_specviz2d.fits')\n", - "else:\n", - " myboxcar = boxcar.flux.value\n", - " myboxcar *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", - " boxcar_specviz2d = Spectrum1D(spectral_axis=jpipe_x1d.spectral_axis, flux=myboxcar*u.Jy)" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "if not horne:\n", - " print(\"You didn't extract a spectrum from specviz2d, we will load a pre-extracted spectrum from the video above.\")\n", - " horne_specviz2d = Spectrum1D.read('horne_specviz2d.fits')\n", - "else:\n", - " myhorne = horne.flux.value\n", - " myhorne *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", - " horne_specviz2d = Spectrum1D(spectral_axis=jpipe_x1d.spectral_axis, flux=myhorne*u.Jy)" - ] - }, - { - "cell_type": "raw", - "metadata": { - "tags": [] - }, - "source": [ - "# Sarah's Extraction (for reference)\n", - "sp3_x1dfile = 'required_data/PID2072_Obs1_LRS_demo_x1d.fits'\n", - "sp3_x1d = datamodels.open(sp3_x1dfile)\n", - "ll3 = (sp3_x1dfile.split('/')[-1]).split('.')[0] + ' (Level 3, custom)'" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "# plot\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "gpts = ext1d_boxcar_bkgsub > 0.\n", - "gpts = ext1d_boxcar > 0.\n", - "\n", - "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label = 'Boxcar; No Mask')\n", - "ax.plot(jpipe_x1d.spectral_axis, ext1d_horne, color = 'purple', label = 'Horne; No Mask')\n", - "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], color = 'green', label=\"Boxcar; Mask\")\n", - "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], color = 'red', label=\"Boxcar; bkgsub; Mask\")\n", - "ax.plot(waves_boxcar_bkgsub_psfweight[gpts], ext1d_boxcar_bkgsub_psfweight[gpts], 'k-', label=\"Boxcar; bkgsub; PSF Weights\", color='cyan')\n", - "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", - "\n", - "ax.plot(boxcar_specviz2d.spectral_axis, boxcar_specviz2d.flux, 'k-', label=\"Boxcar Specviz2D\", color='magenta')\n", - "ax.plot(horne_specviz2d.spectral_axis, horne_specviz2d.flux, 'k-', label=\"Horne Specviz2D\", color='lawngreen')\n", - "ax.plot(sp3_x1d.spec[0].spec_table['WAVELENGTH'], sp3_x1d.spec[0].spec_table['FLUX'], label=ll3, color = 'gold')\n", - "ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n", - "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", - "ax.set_ylabel(\"Flux Density [Jy]\")\n", - "ax.set_yscale(\"log\")\n", - "ax.set_ylim(ylim_low, ylim_high)\n", - "ax.set_xlim(xlim_low, xlim_high)\n", - "ax.legend(bbox_to_anchor=(1.1, 1.05))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Read in the models\n", - "model = ascii.read(\"./required_data/bd60_1753.lrs.sp.tbl\") \n", - "wave_model = model['wavelength']\n", - "flux_model = model['flux']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# plot\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "gpts = ext1d_boxcar_bkgsub > 0.\n", - "gpts = ext1d_boxcar > 0.\n", - "\n", - "ax.plot(jpipe_x1d.spectral_axis, ext1d_boxcar_noweights, color = 'blue', label = 'Boxcar; No Mask')\n", - "ax.plot(jpipe_x1d.spectral_axis, ext1d_horne, color = 'purple', label = 'Horne; No Mask')\n", - "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], color = 'green', label=\"Boxcar; Mask\")\n", - "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], color = 'red', label=\"Boxcar; bkgsub; Mask\")\n", - "ax.plot(waves_boxcar_bkgsub_psfweight[gpts], ext1d_boxcar_bkgsub_psfweight[gpts], 'k-', label=\"Boxcar; bkgsub; PSF Weights\", color='cyan')\n", - "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = 'orange', label=\"jpipe_x1d\")\n", - "\n", - "#ax.plot(boxcar_specviz2d.spectral_axis, boxcar_specviz2d.flux, 'k-', label=\"Boxcar Specviz2D\", color='magenta')\n", - "#ax.plot(horne_specviz2d.spectral_axis, horne_specviz2d.flux, 'k-', label=\"Horne Specviz2D\", color='lawngreen')\n", - "ax.plot(wave_model, flux_model, 'k-', label=\"Calspec Model\", color='magenta')\n", - "#ax.plot(sp3_x1d.spec[0].spec_table['WAVELENGTH'], sp3_x1d.spec[0].spec_table['FLUX'], label=ll3, color = 'gold')\n", - "ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n", - "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", - "ax.set_ylabel(\"Flux Density [Jy]\")\n", - "ax.set_yscale(\"log\")\n", - "ax.set_ylim(ylim_low, ylim_high)\n", - "ax.set_xlim(xlim_low, xlim_high)\n", - "ax.legend(bbox_to_anchor=(1.1, 1.05))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Additional Resources\n", - "\n", - "- [MIRI LRS](https://jwst-docs.stsci.edu/mid-infrared-instrument/miri-observing-modes/miri-low-resolution-spectroscopy)\n", - "- [MIRISim](http://www.stsci.edu/jwst/science-planning/proposal-planning-toolbox/mirisim)\n", - "- [JWST pipeline](https://jwst-docs.stsci.edu/jwst-data-reduction-pipeline)\n", - "- PSF weighted extraction [Horne 1986, PASP, 98, 609](https://ui.adsabs.harvard.edu/abs/1986PASP...98..609H/abstract)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## About this notebook\n", - "\n", - "**Author:** Karl Gordon, JWST\n", - "**Updated On:** 2020-07-07" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[Top of Page](#top)\n", - "\"Space " - ] - } - ], - "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": "3.10.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From dd592a79ac29acd29a5e082e0ffda027a14a1443 Mon Sep 17 00:00:00 2001 From: Ori Fox Date: Wed, 2 Aug 2023 08:23:10 -0400 Subject: [PATCH 10/36] Delete miri_lrs_specreduce.ipynb --- .../miri_lrs_specreduce.ipynb | 1282 ----------------- 1 file changed, 1282 deletions(-) delete mode 100644 notebooks/MIRI_LRS_spectral_extraction/miri_lrs_specreduce.ipynb diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_specreduce.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_specreduce.ipynb deleted file mode 100644 index f86dbcfc9..000000000 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_specreduce.ipynb +++ /dev/null @@ -1,1282 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# MIRI LRS Optimal Spectral Extraction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Use case:** Extract spectra with different locations, extraction apertures, and techniques.
\n", - "**Data:** Simulated MIRI LRS spectrum.
\n", - "**Tools:** jwst, gwcs, matplotlib, astropy.
\n", - "**Cross-intrument:** NIRSpec, MIRI.
\n", - "**Documentation:** This notebook is part of a STScI's larger [post-pipeline Data Analysis Tools Ecosystem](https://jwst-docs.stsci.edu/jwst-post-pipeline-data-analysis).
\n", - "\n", - "# Introduction\n", - "\n", - "This notebook extracts a 1D spectra from a 2D MIRI LRS spectral observation (single image). The goal is to provide the ability to extract spectra with different locations, extraction apertures, and techniques than are done in the JWST pipeline using the [Astropy Specreduce package](https://github.com/astropy/specreduce).\n", - "\n", - "The simpliest spectral extraction is \"boxcar\" where all the pixels within some fixed width centered on the source position are summed at each wavelength. Background subtraction can be done using regions offset from the source center. You can also see the Specreduce [generic Sample Notebook](https://github.com/astropy/specreduce/blob/main/notebook_sandbox/jwst_boxcar/boxcar_extraction.ipynb).\n", - "\n", - "For spectra taken with a diffraction limited telescope like JWST, a modification boxcar extraction is to vary the extraction width linearly with wavelength. Such a scaled boxcar extraction keeps the fraction of the source flux within the extraction region approximately constant with wavelength.\n", - "\n", - "For point sources, a PSF-weighted spectral extraction can be done. Using the PSF to weight the extraction uses the actual PSF as a function of wavelength to optimize the extraction to the pixels with the greatest signal. PSF-weighted extractions show the largest differences with boxcar extractions at lower S/N values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note:** Corrections for the finite aperture used in all the extractions have not been applied. Thus, the physical flux densities of all the extracted spectra are lower than the actual values." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Imports\n", - "\n", - "- *matplotlib.pyplot* for plotting data\n", - "- *numpy* to handle array functions\n", - "- *astropy.io fits* for accessing FITS files\n", - "- *astropy.visualization* for scaling image for display\n", - "- *astropy.table Table* for reading the pipeline 1d extractions\n", - "- *jwst datamodels* for reading/access the jwst data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# disable all imported packages' loggers\n", - "import logging\n", - "logging.root.manager.loggerDict = {}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# enable PEP8 checker for this notebook\n", - "%load_ext pycodestyle_magic\n", - "%flake8_on --ignore E261,E501,W291,W293\n", - "\n", - "# only allow the checker to throw warnings when there's a violation\n", - "logging.getLogger('flake8').setLevel('ERROR')\n", - "logging.getLogger('stpipe').setLevel('ERROR')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-08-10 20:48:57 - INFO - 2:1: F401 'matplotlib as mpl' imported but unused\n", - "2022-08-10 20:48:57 - INFO - 9:1: F401 'astropy.io.fits' imported but unused\n", - "2022-08-10 20:48:57 - INFO - 10:1: F401 'astropy.table.Table' imported but unused\n", - "2022-08-10 20:48:57 - INFO - 15:1: F401 'specreduce.extract.HorneExtract' imported but unused\n" - ] - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import matplotlib as mpl\n", - "# %matplotlib inline\n", - "\n", - "import numpy as np\n", - "\n", - "from gwcs.wcstools import grid_from_bounding_box\n", - "\n", - "from astropy.io import fits\n", - "from astropy.table import Table\n", - "from astropy.visualization import simple_norm\n", - "\n", - "from jwst import datamodels\n", - "\n", - "from specreduce.extract import BoxcarExtract, OptimalExtract, HorneExtract\n", - "from specreduce.tracing import FlatTrace, KosmosTrace\n", - "from specreduce.background import Background\n", - "\n", - "from jdaviz import Imviz\n", - "from jdaviz import Specviz\n", - "\n", - "from astropy.utils.data import download_file\n", - "import os\n", - "\n", - "from specutils import Spectrum1D\n", - "from astropy import units as u" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Developer note: Ask Karl exactly how these functions work? Seems like all weights are equal?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-08-10 20:48:57 - INFO - 9:26: E203 whitespace before ':'\n" - ] - } - ], - "source": [ - "# useful functions\n", - "def get_boxcar_weights(center, hwidth, npix):\n", - " \"\"\"\n", - " Compute the weights given an aperture center, half widths, and number of pixels\n", - " \"\"\"\n", - " weights = np.zeros((npix))\n", - " # pixels with full weight\n", - " fullpixels = [max(0, int(center - hwidth + 1)), min(int(center + hwidth), npix)]\n", - " weights[fullpixels[0] : fullpixels[1]] = 1.0\n", - "\n", - " # pixels at the edges of the boxcar with partial weight\n", - " if fullpixels[0] > 0:\n", - " weights[fullpixels[0] - 1] = hwidth - (center - fullpixels[0])\n", - " if fullpixels[1] < npix:\n", - " weights[fullpixels[1]] = hwidth - (fullpixels[1] - center)\n", - "\n", - " return weights\n", - "\n", - "\n", - "def ap_weight_images(\n", - " center, width, bkg_offset, bkg_width, image_size, waves, wavescale=None\n", - "):\n", - " \"\"\"\n", - " Create a weight image that defines the desired extraction aperture\n", - " and the weight image for the requested background regions\n", - "\n", - " Parameters\n", - " ----------\n", - " center : float\n", - " center of aperture in pixels\n", - " width : float\n", - " width of apeture in pixels\n", - " bkg_offset : float\n", - " offset from the extaction edge for the background\n", - " never scaled for wavelength\n", - " bkg_width : float\n", - " width of background region\n", - " never scaled with wavelength\n", - " image_size : tuple with 2 elements\n", - " size of image\n", - " waves : array\n", - " wavelegth values\n", - " wavescale : float\n", - " scale the width with wavelength (default=None)\n", - " wavescale gives the reference wavelenth for the width value\n", - "\n", - " Returns\n", - " -------\n", - " wimage, bkg_wimage : (2D image, 2D image)\n", - " wimage is the weight image defining the aperature\n", - " bkg_image is the weight image defining the background regions\n", - " \"\"\"\n", - " wimage = np.zeros(image_size)\n", - " bkg_wimage = np.zeros(image_size)\n", - " hwidth = 0.5 * width\n", - " # loop in dispersion direction and compute weights\n", - " for i in range(image_size[1]):\n", - " if wavescale is not None:\n", - " hwidth = 0.5 * width * (waves[i] / wavescale)\n", - "\n", - " wimage[:, i] = get_boxcar_weights(center, hwidth, image_size[0])\n", - "\n", - " # bkg regions\n", - " if (bkg_width is not None) & (bkg_offset is not None):\n", - " bkg_wimage[:, i] = get_boxcar_weights(\n", - " center - hwidth - bkg_offset, bkg_width, image_size[0]\n", - " )\n", - " bkg_wimage[:, i] += get_boxcar_weights(\n", - " center + hwidth + bkg_offset, bkg_width, image_size[0]\n", - " )\n", - " else:\n", - " bkg_wimage = None\n", - "\n", - " return (wimage, bkg_wimage)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Devloper notes\n", - "\n", - "The difference between the pipeline (x1d) and the extractions done in this notebook are quite large. Help in understanding the origin of these differences is needed.\n", - "\n", - "Not clear how to use the JWST pipeline `extract_1d` (quite complex) code.\n", - "Help to determine how to use the JWST pipeline code instead of the custom code for boxcar is needed. \n", - "\n", - "Applying aperture corrections for the finite extraction widths is needed. Help in how to get the needed informatinom for different (user set) extraction widths is needed. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download Files" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "calfilename = \"det_image_seq5_MIRIMAGE_P750Lexp1_cal.fits\"\n", - "s2dfilename = \"det_image_seq5_MIRIMAGE_P750Lexp1_s2d.fits\"\n", - "x1dfilename = \"det_image_seq5_MIRIMAGE_P750Lexp1_x1d.fits\"\n", - "spatialprofilefilename = \"det_image_seq1_MIRIMAGE_P750Lexp1_s2d.fits\"\n", - "mainurl = \"https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/\"\n", - "\n", - "calfile_dld = download_file(mainurl + calfilename)\n", - "s2dfile_dld = download_file(mainurl + s2dfilename)\n", - "x1dfile_dld = download_file(mainurl + x1dfilename)\n", - "spatialprofilefile_dld = download_file(mainurl + spatialprofilefilename)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# rename files so that they have the right extensions\n", - "# required for the jwst datamodels to work\n", - "calfile = calfile_dld + '_cal.fits'\n", - "os.rename(calfile_dld, calfile)\n", - "s2dfile = s2dfile_dld + '_s2d.fits'\n", - "os.rename(s2dfile_dld, s2dfile)\n", - "x1dfile = x1dfile_dld + '_x1d.fits'\n", - "os.rename(x1dfile_dld, x1dfile)\n", - "spatialprofilefile = spatialprofilefile_dld + '_s2d.fits'\n", - "os.rename(spatialprofilefile_dld, spatialprofilefile)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## File information\n", - "\n", - "The data used is a simulation of a LRS slit observation for a blackbody with a similar flux density to the star BD+60d1753, a flux calibration star. This simulation was created with MIRISim.\n", - "The simulated exposure was reduced using the JWST pipeline (v0.16.1) through the Detector1 and Spec2 stages.\n", - "\n", - "The cal file is one of the Spec2 products and is the calibration full frame image. It contains:\n", - "\n", - "1. (Primary): This HDU contains meta-data related to the observation and data reduction.\n", - "2. (SCI): The calibrated image. Units are MJy/sr.\n", - "3. (ERR): Uncertainty image. Units are MJy/sr.\n", - "4. (DQ): Data quality image.\n", - "5. (VAR_POISSON): Unc. component 1: Poisson uncertainty image. Units are (MJy/sr)^2.\n", - "6. (VAR_RNOISE): Unc. component 2: Read Noise uncertainty image. Units are (MJy/sr)^2.\n", - "7. (VAR_FLAT): Unc. component 3: Flat Field uncertainty image. Units are (MJy/sr)^2.\n", - "8. (ASDF_METADATA): Metadata.\n", - "\n", - "The s2d file is one of the Spec2 products and containes the calibrated rectified cutout of the LRS Slit region. It has:\n", - "\n", - "1. (Primary): This HDU contains meta-data related to the observation and data reduction.\n", - "2. (WGT): Weight.\n", - "3. (CON): ??\n", - "4. (ASDF_METADATA): Metadata." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Loading data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# use a jwst datamodel to provide a good interface to the data and wcs info\n", - "cal = datamodels.open(calfile)\n", - "s2d = datamodels.open(s2dfile)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Basic information about the image." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"cal image\")\n", - "print(cal.data.shape)\n", - "print(np.mean(cal.data))\n", - "print(np.amin(cal.data), np.amax(cal.data))\n", - "print(\"s2d image\")\n", - "print(s2d.data.shape)\n", - "print(np.mean(s2d.data))\n", - "print(np.amin(s2d.data), np.amax(s2d.data))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display the full 2D image" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "norm_data = simple_norm(cal.data, 'sqrt')\n", - "plt.figure(figsize=(6, 6))\n", - "plt.imshow(cal.data, norm=norm_data, origin=\"lower\")\n", - "plt.title(\"The full image from the MIRI IMAGER detector\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display the LRS Slit region only (use s2d)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# transpose to make it display better\n", - "image = np.transpose(s2d.data)\n", - "err = np.transpose(s2d.err)\n", - "norm_data = simple_norm(image, \"sqrt\")\n", - "plt.figure(figsize=(10, 3))\n", - "plt.imshow(image, norm=norm_data, origin=\"lower\")\n", - "plt.title(\"The LRS region\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### View the 2D Spectrum in Imviz and get the center of the cross-dispersion " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "imviz = Imviz()\n", - "imviz.app" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "imviz.load_data(image)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "viewer = imviz.default_viewer\n", - "viewer.cuts = '95%'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### View the JWST pipeline 1D extraction in Specviz" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a spectrum1d\n", - "jpipe_x1d = Spectrum1D.read(x1dfile)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "specviz = Specviz()\n", - "specviz.app" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "specviz.load_spectrum(jpipe_x1d)" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "# for reference read in the JWST pipeline extracted spectrum\n", - "jpipe_x1d = Table.read(x1dfile, hdu=1)\n", - "print(jpipe_x1d.columns)\n", - "# plot\n", - "fig, ax = plt.subplots(figsize=(10, 6))\n", - "ax.plot(jpipe_x1d['WAVELENGTH'], jpipe_x1d['FLUX'], 'k-', label=\"jpipe_x1d\")\n", - "ax.set_title(\"JWST Pipeline x1d extracted spectrum\")\n", - "ax.set_xlabel(\"Wavelength\")\n", - "ax.set_ylabel(\"Flux Density [Jy]\")\n", - "ax.set_yscale(\"log\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Boxcar Extraction\n", - "\n", - "Extract a 1D spectrum using a simple boxcar. Basically collapse the spectrum in the cross-dispersion direction over a specified number of pixels.\n", - "\n", - "#### Developer note: Allow for a bad pixel mask" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fixed width boxcar" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define extraction parameters based on interaction in Imviz window above" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ext_center = 30\n", - "ext_width = 8\n", - "bkg_sep = 7\n", - "bkg_width = 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot cross-disperion cut showing the extraction parameters\n", - "\n", - "#### Develepor Note: Place trace back into Specviz2d/Imviz/Etc" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-08-10 20:48:57 - INFO - 4:19: E231 missing whitespace after ','\n" - ] - } - ], - "source": [ - "# Plot along cross-disperion cut showing the extraction parameters\n", - "fig, ax = plt.subplots(figsize=(10, 6))\n", - "y = np.arange(image.shape[0])\n", - "ax.plot(y, image[:,300], 'k-')\n", - "mm = np.array([ext_center, ext_center])\n", - "mm_y = ax.get_ylim()\n", - "\n", - "# extraction region\n", - "ax.axvspan(ext_center - ext_width/2., ext_center + ext_width/2., color='green', alpha=0.1)\n", - "ax.plot(mm, mm_y, 'b--')\n", - "ax.plot(mm - ext_width/2., mm_y, 'g:')\n", - "ax.plot(mm + ext_width/2., mm_y, 'g:')\n", - "\n", - "# background region, symmetric on both sides of extraction region\n", - "ax.axvspan(ext_center - bkg_sep - bkg_width/2., ext_center - bkg_sep + bkg_width/2., color='red', alpha=0.1)\n", - "ax.plot(mm - bkg_sep - bkg_width/2., mm_y, 'r:')\n", - "ax.plot(mm - bkg_sep + bkg_width/2., mm_y, 'r:')\n", - "\n", - "ax.axvspan(ext_center + bkg_sep - bkg_width/2., ext_center + bkg_sep + bkg_width/2., color='red', alpha=0.1)\n", - "ax.plot(mm + bkg_sep - bkg_width/2., mm_y, 'r:')\n", - "ax.plot(mm + bkg_sep + bkg_width/2., mm_y, 'r:')\n", - "\n", - "ax.set_title(\"Cross-dispersion Cut at Pixel=300\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Background Subtraction" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-08-10 20:48:57 - INFO - 6:1: E265 block comment should start with '# '\n", - "2022-08-10 20:48:57 - INFO - 8:1: E265 block comment should start with '# '\n" - ] - } - ], - "source": [ - "# extract the background using custom individual traces\n", - "trace = FlatTrace(image, ext_center)\n", - "bg = Background(image, [trace-bkg_sep, trace+bkg_sep], width=bkg_width)\n", - "\n", - "# alternatively, call two_sided class, which does the same as above \n", - "#bg = Background.two_sided(image, trace, bkg_sep, width=bkg_width)\n", - "# or in the place of any trace, an int/float can be passed which resolves to a FlatTrace\n", - "#bg = Background.two_sided(image, ext_center, bkg_sep, width=bkg_width)\n", - "\n", - "# or for single sided:\n", - "# bg = Background.one_sided(image, trace, bkg_sep, width=bkg_width)\n", - "# bg = Background.one_sided(image, trace, -bkg_sep, width=bkg_width)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# view the background weighted image\n", - "plt.figure(figsize=(15, 15))\n", - "plt.imshow(bg.bkg_wimage, origin=\"lower\")\n", - "plt.title(\"slit[0] slice\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# view the background image\n", - "plt.figure(figsize=(15, 15))\n", - "plt.imshow(bg.bkg_image(), norm=norm_data, origin=\"lower\")\n", - "plt.title(\"slit[0] slice\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# view the background-subtracted image\n", - "plt.figure(figsize=(15, 15))\n", - "plt.imshow(bg.sub_image(), norm=norm_data, origin=\"lower\")\n", - "plt.title(\"slit[0] slice\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that when using median, partial pixel weights are not supported:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bg_med = Background.two_sided(image, ext_center, bkg_sep, width=bkg_width, statistic='median')\n", - "plt.figure(figsize=(15, 15))\n", - "plt.imshow(bg_med.bkg_wimage, origin=\"lower\")\n", - "plt.title(\"slit[0] slice\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# transpose to make it display better\n", - "plt.figure(figsize=(15, 15))\n", - "plt.imshow(image-bg.sub_image(), norm=norm_data, origin=\"lower\")\n", - "plt.title(\"The LRS region\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "diff = image-bg.sub_image()\n", - "np.max(bg.bkg_image())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Advanced Trace" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Optional: we could now refine the initial flat trace by running an automated KosmosTrace on the subtracted image. This process could be iterated as necessary (recreating the subtracted image with the refined trace, etc)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "auto_trace = KosmosTrace(image-bg, guess=ext_center)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plot old vs new trace" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Extract" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "from specutils import Spectrum1D\n", - "from astropy import units as u\n", - "flux = s2d.data * u.Jy\n", - "wavelength = s2d.wavelength * u.um\n", - "flux.data\n", - "spec = Spectrum1D(spectral_axis=wavelength, flux=flux)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "boxcar = BoxcarExtract()\n", - "spectrum = boxcar(image-bg, auto_trace, width=ext_width)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "f, ax = plt.subplots(figsize=(10, 6))\n", - "ax.plot(jpipe_x1d.spectral_axis, spectrum.flux.value, 'k-')\n", - "ax.set_title(\"Boxcar 1D extracted spectrum\")\n", - "ax.set_xlabel(r\"pixel\")\n", - "ax.set_ylabel(\"Flux Density [Jy]\")\n", - "ax.set_yscale(\"log\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-08-10 20:48:57 - INFO - 1:1: F811 redefinition of unused 'grid_from_bounding_box' from line 7\n", - "2022-08-10 20:48:57 - INFO - 1:1: E402 module level import not at top of file\n", - "2022-08-10 20:48:57 - INFO - 3:1: E265 block comment should start with '# '\n" - ] - } - ], - "source": [ - "from gwcs.wcstools import grid_from_bounding_box\n", - "\n", - "#image = np.transpose(s2d.data)\n", - "grid = grid_from_bounding_box(s2d.meta.wcs.bounding_box)\n", - "ra, dec, lam = s2d.meta.wcs(*grid)\n", - "lam_image = np.transpose(lam)\n", - "\n", - "# compute a \"rough\" wavelength scale to allow for aperture to scale with wavelength\n", - "rough_waves = np.average(lam_image, axis=0)\n", - "\n", - "# images to use for extraction\n", - "wimage, bkg_wimage = ap_weight_images(\n", - " ext_center,\n", - " ext_width,\n", - " bkg_width,\n", - " bkg_sep,\n", - " image.shape,\n", - " rough_waves,\n", - " wavescale=None,\n", - ")\n", - "\n", - "boxcar = BoxcarExtract()\n", - "\n", - "# without background subtraction\n", - "image_wg = image * wimage\n", - "ext1d_boxcar = boxcar(image_wg, auto_trace, width=ext_width)\n", - "ext1d_boxcar = ext1d_boxcar.flux.value\n", - "# convert from MJy/sr to Jy\n", - "ext1d_boxcar *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", - "\n", - "# with background subtraction\n", - "image_bg = bg.sub_image()\n", - "image_wg = image_bg * wimage\n", - "ext1d_boxcar_bkgsub = boxcar(image_wg, auto_trace, width=ext_width)\n", - "ext1d_boxcar_bkgsub = ext1d_boxcar_bkgsub.flux.value\n", - "\n", - "# convert from MJy/sr to Jy\n", - "ext1d_boxcar_bkgsub *= 1e6 * s2d.meta.photometry.pixelarea_steradians\n", - "\n", - "# compute the average wavelength for each column using the weight image\n", - "# this should correspond directly with the extracted spectrum\n", - "# wavelengths account for any tiled spectra this way\n", - "waves_boxcar = np.average(lam_image, weights=wimage, axis=0)\n", - "waves_boxcar_bkgsub = np.average(lam_image, weights=wimage, axis=0)" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "ext1d_boxcar = boxcar(image_wg, auto_trace, width=ext_width)\n", - "ext1d_boxcar.flux.value" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "gpts = ext1d_boxcar_bkgsub > 0.\n", - "gpts = ext1d_boxcar > 0.\n", - "\n", - "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], 'k-', label=\"boxcar\")\n", - "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], 'k:', label=\"boxcar (bkgsub)\")\n", - "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, 'k-', label=\"jpipe_x1d\")\n", - "ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n", - "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", - "ax.set_ylabel(\"Flux Density [Jy]\")\n", - "ax.set_yscale(\"log\")\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Wavelength scaled width boxcar\n", - "\n", - "The LRS spatial profile changes as a function of wavelength as JWST is diffraction limited at these wavelengths. Nominally this means that the FWHM is changing linearly with wavelength. Scaling the width of the extraction aperture with wavelength accounts for the changing diffraction limit with wavelength to first order." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Developer note: Not currently possible. Allow for wavelength scaled width in the boxcar" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## PSF based Extraction\n", - "\n", - "While to first order the PSF FHWM changes linearly with wavelength, this is an approximation. It is better to use the measured spatial profile as a function of wavelength to extract the spectrum. This tracks the actual variation with wavelength and optimizes the extraction to the higher S/N measurements. In general, PSF based extractions show the most improvements over boxcar extractions at lower the S/N.\n", - "\n", - "There are two PSF based extraction methods.\n", - "\n", - "1. PSF weighted: the spatial profile at each wavelength is used to weight the extraction.\n", - "2. PSF fitting: the spatial profile is fit at each wavelength with the scale parameter versus wavelength giving the spectrum.\n", - "\n", - "Only the PSF weighted technique is currently part of this notebook.\n", - "\n", - "Note 1: calibration reference file for the specific LRS slit position should be used.\n", - "\n", - "Note 2: Small shifts in the centering of the source in the slit should be investigated to see if they impact the PSF based extractions.\n", - "\n", - "Limitation: currently it is assumed there are no bad pixels." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are notes [in brackets] on how each step is handled in the proposed HorneExtract/OptimalExtract classes to make it easier to see what the class does and what the user must do themselves.\n", - "\n", - "### Steps in the JDAT Notebook guide on optimal extraction:\n", - "\n", - "1. Define extraction region [user's responsibility to provide an appropriate image]\n", - "2. Pick a slice [should not be necessary? can use the whole image as the aperture with good results]\n", - "3. Define extraction kernel\n", - " * Select PSF template [assumed to be Gaussian for now. support for Moffat, others?]\n", - " * Choose a polynomial for background fitting [user provides as an argument]\n", - "4. Fit extraction kernel to initial slice [all columns are coadded to perform the fit]\n", - "5. Fit geometric distortion [not currently done]\n", - " * Determine cross-dispersion bins for trace fitting\n", - " * Fit a kernel to each bin to find trace center [user provides this as a specreduce.tracing.Trace object]\n", - " * Polynomial fit of trace centers\n", - "6. Combine composite model with 2D image to create output 1D spectrum\n", - " * Create variance image [user provides this as an argument]\n", - " * Generate 1D spectrum\n", - "7. Compare with reference 1D spectrum\n", - "\n", - "### Steps in the original Horne (1986) paper:\n", - "\n", - "1. Bias subtraction [assumed to be done in earlier block]\n", - "2. Initial variance estimate [user provides this as an argument]\n", - "3. Fit sky background [assumed to be done in earlier block]\n", - " * \"We therefore generally perform a least-squares polynomial fit to the sky data at each wavelength. Individual sky pixels are given weights inversely proportional to their variances as estimated in Step 2\" [overlaps with notebook guide's 3b]\n", - "4. Extract standard spectrum and its variance\n", - " * Subtract the sky background found in Step 3 from the image. [sky background calculation is planned as a separate, earlier step of the specreduce workflow]\n", - "5. Construct spatial profile\n", - "6. Revise variance estimates [not currently done]\n", - "7. Mask cosmic ray hits [not currently done]\n", - "8. Extract optimal spectrum and its variance [currently only extract the spectum, not a variance]\n", - "9. Iterate Steps 5-8\n", - "\n", - "The first four steps as the standard procedure and the last five as add-ons that help squeeze out extra signal-to-noise." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### PSF weighted extaction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Generate the PSF profile as a function of wavelength\n", - "For MIRI LRS slit observations, observations are made at two nod position in the slit after target acquisition. This means that the location of the sources in the slit is very well known. Hence, spatial profile (PSF) as a function of wavelength for the two nod positions is straightforward to measure using observations of a bright source.\n", - "\n", - "The next few steps generate the needed information for the nod position for which we are extracting spectra based on a simulation of a bright source at the same nod position." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# lrs spatial profile (PSF) as a function of wavelength\n", - "# currently, this is just a \"high\" S/N observation of a flat spectrum source at the same slit position\n", - "psf = datamodels.open(spatialprofilefile)\n", - "# transpose to make it display better\n", - "lrspsf = np.transpose(psf.data)\n", - "norm_data = simple_norm(lrspsf, \"sqrt\")\n", - "plt.figure(figsize=(10, 3))\n", - "plt.imshow(lrspsf, norm=norm_data, origin=\"lower\")\n", - "plt.title(\"The LRS Spatial Profile (PSF) Observation\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Mock a LRS spectral profile reference file\n", - "# Sum along the spatial direction and normalize to 1\n", - "# assume there is no background (none was included in the MIRISim for the flat spectrum source observation)\n", - "# ignore regions far from the source using a scaled boxcar weight image\n", - "# the aperture (psf_width) used in the scaled boxcar weight image could be varied\n", - "psf_width = 12.0\n", - "(wimage_scaledboxcar, tmpvar) = ap_weight_images(ext_center, psf_width, bkg_sep, \n", - " bkg_width, image.shape, waves_boxcar, wavescale=10.0)\n", - "\n", - "psf_weightimage = lrspsf*wimage_scaledboxcar\n", - "\n", - "# generate a 2D image of the column sums for division\n", - "max_psf = np.max(psf_weightimage, axis=0)\n", - "div_image = np.tile(max_psf, (psf_weightimage.shape[0], 1))\n", - "div_image[div_image == 0.0] = 1.0 # avoid divide by zero issues\n", - "\n", - "# normalize \n", - "psf_weightimage /= div_image\n", - "\n", - "# display\n", - "norm_data = simple_norm(psf_weightimage, \"sqrt\")\n", - "plt.figure(figsize=(10, 3))\n", - "plt.imshow(psf_weightimage, norm=norm_data, origin=\"lower\")\n", - "plt.title(\"The LRS Spatial Profile Reference Image (Normalized)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-08-10 20:48:57 - INFO - 3:29: E231 missing whitespace after ','\n", - "2022-08-10 20:48:57 - INFO - 4:29: E231 missing whitespace after ','\n", - "2022-08-10 20:48:57 - INFO - 5:29: E231 missing whitespace after ','\n", - "2022-08-10 20:48:57 - INFO - 6:29: E231 missing whitespace after ','\n" - ] - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "y = np.arange(psf_weightimage.shape[0])\n", - "ax.plot(y, psf_weightimage[:,150], label=\"pixel=150\")\n", - "ax.plot(y, psf_weightimage[:,225], label=\"pixel=225\")\n", - "ax.plot(y, psf_weightimage[:,300], label=\"pixel=300\")\n", - "ax.plot(y, psf_weightimage[:,370], label=\"pixel=370\")\n", - "ax.set_title(\"Cross-dispersion Cuts\")\n", - "ax.set_xlim(ext_center-psf_width, ext_center+psf_width)\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the spatial profile becomes narrower as the pixel values increases as this corresponds to the wavelength decreasing." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Extract spectrum using wavelength dependent PSF profiles using the same traces as defined above" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-08-10 20:48:57 - INFO - 1:1: F401 'astropy.nddata.VarianceUncertainty' imported but unused\n", - "2022-08-10 20:48:57 - INFO - 1:1: E402 module level import not at top of file\n", - "2022-08-10 20:48:57 - INFO - 2:1: E402 module level import not at top of file\n", - "2022-08-10 20:48:57 - INFO - 4:1: E265 block comment should start with '# '\n", - "2022-08-10 20:48:57 - INFO - 5:1: E265 block comment should start with '# '\n" - ] - } - ], - "source": [ - "from astropy.nddata import StdDevUncertainty, VarianceUncertainty\n", - "from astropy.nddata import NDData\n", - "\n", - "#mask = err*0.#+1.\n", - "#err = err*0\n", - "errinput = StdDevUncertainty(err*0.+1.)\n", - "inarr = NDData(image_wg, uncertainty=errinput, unit=u.Jy)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-08-10 20:48:57 - INFO - 2:1: E265 block comment should start with '# '\n" - ] - } - ], - "source": [ - "optimal = OptimalExtract()\n", - "#ext1d_psfweight = optimal(image_wg, auto_trace, variance = err, mask=mask, unit=u.Jy)\n", - "ext1d_psfweight = optimal(inarr, auto_trace)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ext1d_psfweight = ext1d_psfweight.flux.value\n", - "# convert from MJy/sr to Jy\n", - "ext1d_psfweight *= 1e6 * s2d.meta.photometry.pixelarea_steradians" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot\n", - "\n", - "fig, ax = plt.subplots(figsize=(6, 6))\n", - "gpts = ext1d_psfweight > 0.\n", - "gpts = ext1d_boxcar > 0.\n", - "\n", - "ax.plot(waves_boxcar[gpts], ext1d_boxcar[gpts], 'k-', label=\"boxcar\", color='red')\n", - "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], 'k-', label=\"boxcar (bkgsub)\", color='blue')\n", - "ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, 'k-', label=\"jpipe_x1d\", color='green')\n", - "ax.plot(waves_boxcar_bkgsub[gpts], ext1d_psfweight[gpts], 'k-', label=\"psf weighted (bkgsub)\", color='orange')\n", - "ax.set_title(\"PSF weigthed extracted spectrum\")\n", - "ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", - "ax.set_ylabel(\"Flux Density [Jy]\")\n", - "ax.set_yscale(\"log\")\n", - "ax.set_ylim(1e-3, 1e-1)\n", - "ax.set_xlim(4, 13)\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the psf weighted extraction has visabily higher S/N, especially at the longer wavelengths where the S/N is lowest overall." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot in Specviz\n", - "\n", - "specviz2 = Specviz()\n", - "specviz2.app" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ext1d_boxcar_spec1d = Spectrum1D(spectral_axis=waves_boxcar[gpts]*u.micron, flux=ext1d_boxcar[gpts]*u.Jy)\n", - "ext1d_boxcar_bkgsub_spec1d = Spectrum1D(spectral_axis=waves_boxcar_bkgsub[gpts]*u.micron, flux=ext1d_boxcar_bkgsub[gpts]*u.Jy)\n", - "ext1d_psfweight_spec1d = Spectrum1D(spectral_axis=waves_boxcar_bkgsub[gpts]*u.micron, flux=ext1d_psfweight[gpts]*u.Jy)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "specviz2.load_spectrum(ext1d_boxcar_spec1d, data_label='boxcar')\n", - "specviz2.load_spectrum(ext1d_boxcar_bkgsub_spec1d, data_label='boxcar bkgsub')\n", - "specviz2.load_spectrum(ext1d_psfweight_spec1d, data_label='psfweight')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plotting in Rayleigh-Jeans units\n", - "\n", - "For sources that have stellar continuum, it can be useful to plot MIR spectra in Rayleigh-Jeans units. This just means removing the spectral shape expected for a blackbody with a peak at much shorter wavelengths than the MIR. This is easily done by multiplying the spectrum by lambda^4 or nu^2.\n", - "\n", - "An example of this is given below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-08-10 20:48:57 - INFO - 8:29: W605 invalid escape sequence '\\m'\n", - "2022-08-10 20:48:57 - INFO - 9:46: W605 invalid escape sequence '\\m'\n" - ] - } - ], - "source": [ - "# Rayleigh-Jeans plot\n", - "fig, ax = plt.subplots(figsize=(15, 6))\n", - "gpts = ext1d_psfweight > 0.\n", - "ax.plot(waves_boxcar_bkgsub[gpts], (waves_boxcar_bkgsub[gpts]**4)*ext1d_psfweight[gpts], 'k-', label=\"psf weighted (bkgsub)\")\n", - "gpts = ext1d_boxcar_bkgsub > 0.\n", - "ax.plot(waves_boxcar_bkgsub[gpts], (waves_boxcar_bkgsub[gpts]**4)*ext1d_boxcar_bkgsub[gpts], 'k--', label=\"fixed boxcar (bkgsub)\")\n", - "ax.set_title(\"Rayleigh-Jeans plot for all extractions\")\n", - "ax.set_xlabel(\"wavelength [$\\mu$m]\")\n", - "ax.set_ylabel(\"Rayleigh-Jeans Flux Density [$\\mu$m$^4$ Jy]\")\n", - "ax.set_yscale(\"log\")\n", - "ax.set_ylim(40, 70)\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Additional Resources\n", - "\n", - "- [MIRI LRS](https://jwst-docs.stsci.edu/mid-infrared-instrument/miri-observing-modes/miri-low-resolution-spectroscopy)\n", - "- [MIRISim](http://www.stsci.edu/jwst/science-planning/proposal-planning-toolbox/mirisim)\n", - "- [JWST pipeline](https://jwst-docs.stsci.edu/jwst-data-reduction-pipeline)\n", - "- PSF weighted extraction [Horne 1986, PASP, 98, 609](https://ui.adsabs.harvard.edu/abs/1986PASP...98..609H/abstract)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## About this notebook\n", - "\n", - "**Author:** Karl Gordon, JWST\n", - "**Updated On:** 2020-07-07" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[Top of Page](#top)\n", - "\"Space " - ] - } - ], - "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": "3.8.13" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 67d59e0cad27274fa29281225c5acdc1a5e23f0a Mon Sep 17 00:00:00 2001 From: Ori Fox Date: Wed, 2 Aug 2023 08:25:17 -0400 Subject: [PATCH 11/36] updated requirements --- notebooks/MIRI_LRS_spectral_extraction/requirements.txt | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/requirements.txt b/notebooks/MIRI_LRS_spectral_extraction/requirements.txt index 7852dc189..11e8579ec 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/requirements.txt +++ b/notebooks/MIRI_LRS_spectral_extraction/requirements.txt @@ -1,4 +1,4 @@ -jdaviz >= 2.5.0 -astropy >= 5.0.4 -jwst >= 1.5.2 -specreduce >= 1.0.0 +jdaviz >= 3.6.0 +astropy >= 5.3.1 +jwst >= 1.11.3 +specreduce >= 1.3.0 From 526c977b06dc3815f10135db914db62b1935e348 Mon Sep 17 00:00:00 2001 From: Ori Fox Date: Wed, 2 Aug 2023 08:35:42 -0400 Subject: [PATCH 12/36] updated requirements --- notebooks/MIRI_LRS_spectral_extraction/requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/notebooks/MIRI_LRS_spectral_extraction/requirements.txt b/notebooks/MIRI_LRS_spectral_extraction/requirements.txt index 11e8579ec..0c89bc8d8 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/requirements.txt +++ b/notebooks/MIRI_LRS_spectral_extraction/requirements.txt @@ -2,3 +2,4 @@ jdaviz >= 3.6.0 astropy >= 5.3.1 jwst >= 1.11.3 specreduce >= 1.3.0 + From 20171a519927e2ece1300a9d2eb765df5458e007 Mon Sep 17 00:00:00 2001 From: Ori Fox Date: Wed, 2 Aug 2023 09:00:27 -0400 Subject: [PATCH 13/36] updated requirements --- notebooks/MIRI_LRS_spectral_extraction/requirements.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/requirements.txt b/notebooks/MIRI_LRS_spectral_extraction/requirements.txt index 0c89bc8d8..11e8579ec 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/requirements.txt +++ b/notebooks/MIRI_LRS_spectral_extraction/requirements.txt @@ -2,4 +2,3 @@ jdaviz >= 3.6.0 astropy >= 5.3.1 jwst >= 1.11.3 specreduce >= 1.3.0 - From eb0352f4993c0bd891d7f3c2c20c94bd73a67da6 Mon Sep 17 00:00:00 2001 From: Ori Fox Date: Wed, 2 Aug 2023 10:26:29 -0400 Subject: [PATCH 14/36] updated requirements --- notebooks/MIRI_LRS_spectral_extraction/requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/notebooks/MIRI_LRS_spectral_extraction/requirements.txt b/notebooks/MIRI_LRS_spectral_extraction/requirements.txt index 11e8579ec..0c89bc8d8 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/requirements.txt +++ b/notebooks/MIRI_LRS_spectral_extraction/requirements.txt @@ -2,3 +2,4 @@ jdaviz >= 3.6.0 astropy >= 5.3.1 jwst >= 1.11.3 specreduce >= 1.3.0 + From 4207d1bfa0df0a6df4832871d6bef772472acc63 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Mon, 14 Aug 2023 19:33:57 +0000 Subject: [PATCH 15/36] [BOT] Left PEP8 feedback on PR 93's notebooks Files: --- .../miri_lrs_advanced_extraction_part1.ipynb | 502 +++++++----------- 1 file changed, 202 insertions(+), 300 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb index a776a5d94..cc178895a 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -59,17 +59,59 @@ "\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" + ] + }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# disable all imported packages' loggers\n", + "import logging\n", + "logging.root.manager.loggerDict = {}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enable PEP8 checker for this notebook\n", + "%load_ext pycodestyle_magic\n", + "%flake8_on --ignore E261,E501,W291,W293\n", + "\n", + "# only allow the checker to throw warnings when there's a violation\n", + "logging.getLogger('flake8').setLevel('ERROR')\n", + "logging.getLogger('stpipe').setLevel('ERROR')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "08ddf5f7", "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "CRDS cache location: /Users/ofox/crds_cache\n" + "2023-08-14 15:33:57 - INFO - 1:10: E401 multiple imports on one line\n", + "2023-08-14 15:33:57 - INFO - 1:10: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 1:25: E231 missing whitespace after ','\n" ] } ], @@ -85,15 +127,27 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "aee92bcf", "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Using JWST calibration pipeline version 1.11.3\n" + "2023-08-14 15:33:57 - INFO - 2:1: F401 'glob.glob' imported but unused\n", + "2023-08-14 15:33:57 - INFO - 2:1: E402 module level import not at top of file\n", + "2023-08-14 15:33:57 - INFO - 4:1: E402 module level import not at top of file\n", + "2023-08-14 15:33:57 - INFO - 5:1: E402 module level import not at top of file\n", + "2023-08-14 15:33:57 - INFO - 7:1: E402 module level import not at top of file\n", + "2023-08-14 15:33:57 - INFO - 9:1: F401 'jwst.pipeline.Spec2Pipeline' imported but unused\n", + "2023-08-14 15:33:57 - INFO - 9:1: F401 'jwst.pipeline.Spec3Pipeline' imported but unused\n", + "2023-08-14 15:33:57 - INFO - 9:1: E402 module level import not at top of file\n", + "2023-08-14 15:33:57 - INFO - 10:1: E402 module level import not at top of file\n", + "2023-08-14 15:33:57 - INFO - 11:1: E402 module level import not at top of file\n", + "2023-08-14 15:33:57 - INFO - 13:1: E402 module level import not at top of file\n", + "2023-08-14 15:33:57 - INFO - 14:1: E402 module level import not at top of file\n", + "2023-08-14 15:33:57 - INFO - 17:1: E303 too many blank lines (4)\n" ] } ], @@ -118,19 +172,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "305103d5", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Original Data tar.gz Exists\n", - "Data Directory Already Exists\n" - ] - } - ], + "outputs": [], "source": [ "# Download Data\n", "\n", @@ -170,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "a8012bfa", "metadata": {}, "outputs": [ @@ -178,20 +223,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-01 16:52:27,808 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/435588000.py:10: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-01 16:52:27,808 - stpipe - WARNING - fig.show()\n", - "2023-08-01 16:52:27,809 - stpipe - WARNING - \n" + "2023-08-14 15:33:57 - INFO - 4:34: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 11:1: E303 too many blank lines (3)\n" ] - }, - { - "data": { - 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -209,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "c51f421b", "metadata": {}, "outputs": [ @@ -217,20 +251,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-01 16:52:27,982 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/2770287384.py:10: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-01 16:52:27,983 - stpipe - WARNING - fig2.show()\n", - "2023-08-01 16:52:27,983 - stpipe - WARNING - \n" + "2023-08-14 15:33:57 - INFO - 4:37: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 11:1: E303 too many blank lines (3)\n" ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -262,32 +285,35 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "f0574ee0-84a0-4fa8-ae54-d7b6ca34a7a7", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Spectral extraction reference file used: crds://jwst_miri_extract1d_0005.json\n" - ] - } - ], + "outputs": [], "source": [ "print('Spectral extraction reference file used: {}'.format(l3_spec.meta.ref_file.extract1d.name))" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "95f20a0a-0f24-4d4b-8480-2c37574ad6e8", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-14 15:33:57 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-14 15:33:57 - INFO - 2:1: F811 redefinition of unused 'fits' from line 17\n", + "2023-08-14 15:33:57 - INFO - 2:1: E402 module level import not at top of file\n", + "2023-08-14 15:33:57 - INFO - 5:1: E303 too many blank lines (3)\n" + ] + } + ], "source": [ "import crds\n", "from astropy.io import fits\n", @@ -297,17 +323,15 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "50c8ba27", "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Settings for SLIT data: {'id': 'MIR_LRS-FIXEDSLIT', 'region_type': 'target', 'bkg_order': 0, 'dispaxis': 2, 'xstart': 27, 'xstop': 34, 'use_source_posn': False}\n", - " \n", - "Settings for SLITLESS data: {'id': 'MIR_LRS-SLITLESS', 'region_type': 'target', 'bkg_order': 0, 'dispaxis': 2, 'xstart': 30, 'xstop': 41, 'use_source_posn': False}\n" + "2023-08-14 15:33:57 - INFO - 7:1: E303 too many blank lines (4)\n" ] } ], @@ -341,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "703f59cd", "metadata": {}, "outputs": [ @@ -349,20 +373,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-01 16:52:28,838 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/1269950999.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-01 16:52:28,839 - stpipe - WARNING - fig.show()\n", - "2023-08-01 16:52:28,839 - stpipe - WARNING - \n" + "2023-08-14 15:33:57 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-14 15:33:57 - INFO - 7:32: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 7:69: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", + "2023-08-14 15:33:57 - INFO - 10:34: E231 missing whitespace after ','\n" ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -399,15 +415,18 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "06dc8eb5", "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "New xstart, xstop values = 25,36\n" + "2023-08-14 15:33:57 - INFO - 12:38: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 13:25: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 13:34: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 14:1: E303 too many blank lines (3)\n" ] } ], @@ -429,7 +448,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "5bc85413", "metadata": {}, "outputs": [ @@ -437,20 +456,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-01 16:52:28,972 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/741213004.py:21: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-01 16:52:28,972 - stpipe - WARNING - fig.show()\n", - "2023-08-01 16:52:28,972 - stpipe - WARNING - \n" + "2023-08-14 15:33:57 - INFO - 1:1: F401 'matplotlib.collections.PatchCollection' imported but unused\n", + "2023-08-14 15:33:57 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-14 15:33:57 - INFO - 4:33: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 4:70: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 5:21: E128 continuation line under-indented for visual indent\n", + "2023-08-14 15:33:57 - INFO - 7:34: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 7:72: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", + "2023-08-14 15:33:57 - INFO - 10:36: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 12:1: E265 block comment should start with '# '\n" ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -492,29 +508,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "7304f758", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-01 16:52:29,125 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n", - "2023-08-01 16:52:29,193 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args (,).\n", - "2023-08-01 16:52:29,194 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/lrs_slit_extract_example1', 'output_dir': 'data/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': None, 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'center_xy': None, 'apply_apcorr': True, 'ifu_autocen': False, 'ifu_rfcorr': False, '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", - "2023-08-01 16:52:29,222 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/x1d_reffile_example1.json\n", - "2023-08-01 16:52:29,251 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/ofox/crds_cache/references/jwst/miri/jwst_miri_apcorr_0007.fits\n", - "2023-08-01 16:52:29,282 - stpipe.Extract1dStep - WARNING - spectral_order is None; using 1\n", - "2023-08-01 16:52:29,283 - stpipe.Extract1dStep - INFO - Processing spectral order 1\n", - "2023-08-01 16:52:29,290 - stpipe.Extract1dStep - INFO - Using extraction limits: xstart=25, xstop=36, ystart=0, ystop=387\n", - "2023-08-01 16:52:29,344 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n", - "2023-08-01 16:52:29,499 - stpipe.Extract1dStep - INFO - Results used CRDS context: jwst_1089.pmap\n", - "2023-08-01 16:52:29,581 - stpipe.Extract1dStep - INFO - Saved model in data/lrs_slit_extract_example1_extract1dstep.fits\n", - "2023-08-01 16:52:29,581 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n" - ] - } - ], + "outputs": [], "source": [ "sp3_ex1 = Extract1dStep.call(l3_s2d, output_dir='data/', \n", " output_file='lrs_slit_extract_example1', override_extract1d='x1d_reffile_example1.json')" @@ -522,25 +519,17 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "91199fd1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], + "outputs": [], "source": [ "print(sp3_ex1)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "91ebfc64", "metadata": {}, "outputs": [ @@ -548,20 +537,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-01 16:52:29,599 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/2686601230.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-01 16:52:29,599 - stpipe - WARNING - fig5.show()\n", - "2023-08-01 16:52:29,599 - stpipe - WARNING - \n" + "2023-08-14 15:33:57 - INFO - 1:37: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 10:1: E303 too many blank lines (3)\n" ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -588,7 +566,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "8a8cf793", "metadata": {}, "outputs": [], @@ -599,10 +577,21 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "55c81453", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-14 15:33:57 - INFO - 11:38: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 12:25: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 12:34: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 13:1: E303 too many blank lines (3)\n" + ] + } + ], "source": [ "xstart3 = 9\n", "xstop3 = 17\n", @@ -620,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "fe340506", "metadata": {}, "outputs": [ @@ -628,20 +617,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-01 16:52:29,786 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/2484419048.py:13: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-01 16:52:29,786 - stpipe - WARNING - fig6.show()\n", - "2023-08-01 16:52:29,786 - stpipe - WARNING - \n" + "2023-08-14 15:33:57 - INFO - 2:34: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 2:72: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 3:21: E128 continuation line under-indented for visual indent\n", + "2023-08-14 15:33:57 - INFO - 5:36: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 14:1: E303 too many blank lines (3)\n" ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -662,7 +643,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "3b0b287b", "metadata": {}, "outputs": [ @@ -670,18 +651,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-01 16:52:29,973 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n", - "2023-08-01 16:52:30,042 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args ('data/jw02072001001_06101_00001_mirimage_s2d.fits',).\n", - "2023-08-01 16:52:30,044 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/lrs_slit_extract_example2', 'output_dir': 'data/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': None, 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'center_xy': None, 'apply_apcorr': True, 'ifu_autocen': False, 'ifu_rfcorr': False, '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", - "2023-08-01 16:52:30,102 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/x1d_reffile_example2.json\n", - "2023-08-01 16:52:30,131 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/ofox/crds_cache/references/jwst/miri/jwst_miri_apcorr_0007.fits\n", - "2023-08-01 16:52:30,158 - stpipe.Extract1dStep - WARNING - spectral_order is None; using 1\n", - "2023-08-01 16:52:30,158 - stpipe.Extract1dStep - INFO - Processing spectral order 1\n", - "2023-08-01 16:52:30,164 - stpipe.Extract1dStep - INFO - Using extraction limits: xstart=9, xstop=17, ystart=0, ystop=386\n", - "2023-08-01 16:52:30,216 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n", - "2023-08-01 16:52:30,363 - stpipe.Extract1dStep - INFO - Results used CRDS context: jwst_1089.pmap\n", - "2023-08-01 16:52:30,417 - stpipe.Extract1dStep - INFO - Saved model in data/lrs_slit_extract_example2_extract1dstep.fits\n", - "2023-08-01 16:52:30,418 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n" + "2023-08-14 15:33:57 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" ] } ], @@ -700,7 +670,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "ce8eccfb", "metadata": {}, "outputs": [ @@ -708,20 +678,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-01 16:52:30,430 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/3112017615.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-01 16:52:30,431 - stpipe - WARNING - fig7.show()\n", - "2023-08-01 16:52:30,431 - stpipe - WARNING - \n" + "2023-08-14 15:33:57 - INFO - 1:37: E231 missing whitespace after ','\n" ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -759,7 +717,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "b4ea99ea", "metadata": {}, "outputs": [ @@ -767,20 +725,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-01 16:52:30,546 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/2688117973.py:13: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-01 16:52:30,547 - stpipe - WARNING - fig8.show()\n", - "2023-08-01 16:52:30,547 - stpipe - WARNING - \n" + "2023-08-14 15:33:57 - INFO - 2:36: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 7:34: E231 missing whitespace after ','\n" ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -801,10 +748,24 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "1238d6c9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-14 15:33:57 - INFO - 6:53: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 6:59: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 6:66: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 10:38: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 11:25: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 11:34: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 12:1: E303 too many blank lines (3)\n" + ] + } + ], "source": [ "with open(json_ref_default) as json_ref:\n", " x1dref_default = json.load(json_ref)\n", @@ -821,7 +782,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "ac0d2746", "metadata": {}, "outputs": [ @@ -829,19 +790,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-01 16:52:30,686 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n", - "2023-08-01 16:52:30,754 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args ('data/jw02072001001_06101_00001_mirimage_s2d.fits',).\n", - "2023-08-01 16:52:30,755 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/lrs_slit_extract_example3', 'output_dir': 'data/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': None, 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'center_xy': None, 'apply_apcorr': True, 'ifu_autocen': False, 'ifu_rfcorr': False, '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", - "2023-08-01 16:52:30,815 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/x1d_reffile_example3.json\n", - "2023-08-01 16:52:30,846 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/ofox/crds_cache/references/jwst/miri/jwst_miri_apcorr_0007.fits\n", - "2023-08-01 16:52:30,872 - stpipe.Extract1dStep - WARNING - spectral_order is None; using 1\n", - "2023-08-01 16:52:30,872 - stpipe.Extract1dStep - INFO - Processing spectral order 1\n", - "2023-08-01 16:52:30,878 - stpipe.Extract1dStep - INFO - Using extraction limits: xstart=9, xstop=17, ystart=0, ystop=386\n", - "2023-08-01 16:52:30,878 - stpipe.Extract1dStep - INFO - with background subtraction\n", - "2023-08-01 16:52:31,082 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n", - "2023-08-01 16:52:31,222 - stpipe.Extract1dStep - INFO - Results used CRDS context: jwst_1089.pmap\n", - "2023-08-01 16:52:31,274 - stpipe.Extract1dStep - INFO - Saved model in data/lrs_slit_extract_example3_extract1dstep.fits\n", - "2023-08-01 16:52:31,275 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n" + "2023-08-14 15:33:57 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" ] } ], @@ -860,7 +809,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "7210c9ac", "metadata": {}, "outputs": [ @@ -868,20 +817,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-01 16:52:31,294 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/409894346.py:13: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-01 16:52:31,294 - stpipe - WARNING - fig9.show()\n", - "2023-08-01 16:52:31,295 - stpipe - WARNING - \n" + "2023-08-14 15:33:57 - INFO - 1:55: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 2:1: E265 block comment should start with '# '\n" ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -916,10 +854,24 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "9e3c2433", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-14 15:33:57 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-14 15:33:57 - INFO - 2:1: E402 module level import not at top of file\n", + "2023-08-14 15:33:57 - INFO - 4:1: E302 expected 2 blank lines, found 1\n", + "2023-08-14 15:33:57 - INFO - 6:5: E741 ambiguous variable name 'l'\n", + "2023-08-14 15:33:57 - INFO - 22:38: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 30:11: E275 missing whitespace after keyword\n", + "2023-08-14 15:33:57 - INFO - 32:1: E303 too many blank lines (4)\n" + ] + } + ], "source": [ "import astropy.units as u\n", "from astropy.modeling import models, fitting\n", @@ -957,34 +909,16 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "e21fcec5", "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Model: Linear1D\n", - "Inputs: ('x',)\n", - "Outputs: ('y',)\n", - "Model set size: 1\n", - "Parameters:\n", - " slope intercept \n", - " ------------------ ------------------\n", - " 0.2579519802996102 2.4456187153704083\n", - "Parameter('slope', value=0.2579519802996102) Parameter('intercept', value=2.4456187153704083)\n" + "2023-08-14 15:33:57 - INFO - 1:1: E305 expected 2 blank lines after class or function definition, found 4\n" ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -1004,10 +938,21 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "a9c9d403", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-14 15:33:57 - INFO - 12:38: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 13:25: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 13:34: E231 missing whitespace after ','\n", + "2023-08-14 15:33:57 - INFO - 14:1: E303 too many blank lines (3)\n" + ] + } + ], "source": [ "trace_cen = 30.5\n", "\n", @@ -1026,29 +971,10 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "71789e83", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-01 16:52:31,543 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n", - "2023-08-01 16:52:31,615 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args (,).\n", - "2023-08-01 16:52:31,616 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/lrs_slit_extract_example4', 'output_dir': 'data/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': None, 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'center_xy': None, 'apply_apcorr': True, 'ifu_autocen': False, 'ifu_rfcorr': False, '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", - "2023-08-01 16:52:31,648 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/x1d_reffile_example4.json\n", - "2023-08-01 16:52:31,679 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/ofox/crds_cache/references/jwst/miri/jwst_miri_apcorr_0007.fits\n", - "2023-08-01 16:52:31,705 - stpipe.Extract1dStep - WARNING - spectral_order is None; using 1\n", - "2023-08-01 16:52:31,705 - stpipe.Extract1dStep - INFO - Processing spectral order 1\n", - "2023-08-01 16:52:31,712 - stpipe.Extract1dStep - INFO - Using extraction limits: ystart=0, ystop=387, and src_coeff\n", - "2023-08-01 16:52:31,762 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n", - "2023-08-01 16:52:31,908 - stpipe.Extract1dStep - INFO - Results used CRDS context: jwst_1089.pmap\n", - "2023-08-01 16:52:31,958 - stpipe.Extract1dStep - INFO - Saved model in data/lrs_slit_extract_example4_extract1dstep.fits\n", - "2023-08-01 16:52:31,959 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n" - ] - } - ], + "outputs": [], "source": [ "sp3_ex4 = Extract1dStep.call(l3_s2d, output_dir='data/', \n", " output_file='lrs_slit_extract_example4', override_extract1d='x1d_reffile_example4.json')" @@ -1056,7 +982,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "9d1bc74c", "metadata": {}, "outputs": [ @@ -1064,20 +990,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-01 16:52:31,970 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/3774177919.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-01 16:52:31,970 - stpipe - WARNING - fig10.show()\n", - "2023-08-01 16:52:31,970 - stpipe - WARNING - \n" + "2023-08-14 15:33:57 - INFO - 1:39: E231 missing whitespace after ','\n" ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -1102,7 +1016,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "78ca0c68", "metadata": {}, "outputs": [ @@ -1110,20 +1024,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-01 16:52:32,076 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_61405/2694247109.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-01 16:52:32,076 - stpipe - WARNING - fig11.show()\n", - "2023-08-01 16:52:32,076 - stpipe - WARNING - \n" + "2023-08-14 15:33:57 - INFO - 1:39: E231 missing whitespace after ','\n" ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ From b69b7861124cefec933f90ee847f567e11895616 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Tue, 15 Aug 2023 15:34:22 +0000 Subject: [PATCH 16/36] [BOT] Left PEP8 feedback on PR 93's notebooks Files: --- .../miri_lrs_advanced_extraction_part1.ipynb | 207 +++++++++++------- 1 file changed, 124 insertions(+), 83 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb index cc178895a..e0997e74d 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -66,6 +66,46 @@ "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# disable all imported packages' loggers\n", + "import logging\n", + "logging.root.manager.loggerDict = {}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enable PEP8 checker for this notebook\n", + "%load_ext pycodestyle_magic\n", + "%flake8_on --ignore E261,E501,W291,W293\n", + "\n", + "# only allow the checker to throw warnings when there's a violation\n", + "logging.getLogger('flake8').setLevel('ERROR')\n", + "logging.getLogger('stpipe').setLevel('ERROR')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" + ] + }, { "cell_type": "code", "execution_count": null, @@ -109,9 +149,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 1:10: E401 multiple imports on one line\n", - "2023-08-14 15:33:57 - INFO - 1:10: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 1:25: E231 missing whitespace after ','\n" + "2023-08-15 11:34:22 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 11:34:22 - INFO - 1:10: E401 multiple imports on one line\n", + "2023-08-15 11:34:22 - INFO - 1:10: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 1:25: E231 missing whitespace after ','\n" ] } ], @@ -135,19 +176,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 2:1: F401 'glob.glob' imported but unused\n", - "2023-08-14 15:33:57 - INFO - 2:1: E402 module level import not at top of file\n", - "2023-08-14 15:33:57 - INFO - 4:1: E402 module level import not at top of file\n", - "2023-08-14 15:33:57 - INFO - 5:1: E402 module level import not at top of file\n", - "2023-08-14 15:33:57 - INFO - 7:1: E402 module level import not at top of file\n", - "2023-08-14 15:33:57 - INFO - 9:1: F401 'jwst.pipeline.Spec2Pipeline' imported but unused\n", - "2023-08-14 15:33:57 - INFO - 9:1: F401 'jwst.pipeline.Spec3Pipeline' imported but unused\n", - "2023-08-14 15:33:57 - INFO - 9:1: E402 module level import not at top of file\n", - "2023-08-14 15:33:57 - INFO - 10:1: E402 module level import not at top of file\n", - "2023-08-14 15:33:57 - INFO - 11:1: E402 module level import not at top of file\n", - "2023-08-14 15:33:57 - INFO - 13:1: E402 module level import not at top of file\n", - "2023-08-14 15:33:57 - INFO - 14:1: E402 module level import not at top of file\n", - "2023-08-14 15:33:57 - INFO - 17:1: E303 too many blank lines (4)\n" + "2023-08-15 11:34:22 - INFO - 2:1: F401 'glob.glob' imported but unused\n", + "2023-08-15 11:34:22 - INFO - 2:1: E402 module level import not at top of file\n", + "2023-08-15 11:34:22 - INFO - 4:1: E402 module level import not at top of file\n", + "2023-08-15 11:34:22 - INFO - 5:1: E402 module level import not at top of file\n", + "2023-08-15 11:34:22 - INFO - 7:1: E402 module level import not at top of file\n", + "2023-08-15 11:34:22 - INFO - 9:1: F401 'jwst.pipeline.Spec2Pipeline' imported but unused\n", + "2023-08-15 11:34:22 - INFO - 9:1: F401 'jwst.pipeline.Spec3Pipeline' imported but unused\n", + "2023-08-15 11:34:22 - INFO - 9:1: E402 module level import not at top of file\n", + "2023-08-15 11:34:22 - INFO - 10:1: E402 module level import not at top of file\n", + "2023-08-15 11:34:22 - INFO - 11:1: E402 module level import not at top of file\n", + "2023-08-15 11:34:22 - INFO - 13:1: E402 module level import not at top of file\n", + "2023-08-15 11:34:22 - INFO - 14:1: E402 module level import not at top of file\n", + "2023-08-15 11:34:22 - INFO - 17:1: E303 too many blank lines (4)\n" ] } ], @@ -223,8 +264,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 4:34: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 11:1: E303 too many blank lines (3)\n" + "2023-08-15 11:34:22 - INFO - 4:34: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 11:1: E303 too many blank lines (3)\n" ] } ], @@ -251,8 +292,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 4:37: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 11:1: E303 too many blank lines (3)\n" + "2023-08-15 11:34:22 - INFO - 4:37: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 11:1: E303 too many blank lines (3)\n" ] } ], @@ -307,10 +348,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-14 15:33:57 - INFO - 2:1: F811 redefinition of unused 'fits' from line 17\n", - "2023-08-14 15:33:57 - INFO - 2:1: E402 module level import not at top of file\n", - "2023-08-14 15:33:57 - INFO - 5:1: E303 too many blank lines (3)\n" + "2023-08-15 11:34:22 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 11:34:22 - INFO - 2:1: F811 redefinition of unused 'fits' from line 33\n", + "2023-08-15 11:34:22 - INFO - 2:1: E402 module level import not at top of file\n", + "2023-08-15 11:34:22 - INFO - 5:1: E303 too many blank lines (3)\n" ] } ], @@ -331,7 +372,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 7:1: E303 too many blank lines (4)\n" + "2023-08-15 11:34:22 - INFO - 7:1: E303 too many blank lines (4)\n" ] } ], @@ -373,11 +414,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-14 15:33:57 - INFO - 7:32: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 7:69: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", - "2023-08-14 15:33:57 - INFO - 10:34: E231 missing whitespace after ','\n" + "2023-08-15 11:34:22 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 11:34:22 - INFO - 7:32: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 7:69: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", + "2023-08-15 11:34:22 - INFO - 10:34: E231 missing whitespace after ','\n" ] } ], @@ -423,10 +464,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 12:38: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 13:25: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 13:34: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 14:1: E303 too many blank lines (3)\n" + "2023-08-15 11:34:22 - INFO - 12:38: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 13:25: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 13:34: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 14:1: E303 too many blank lines (3)\n" ] } ], @@ -456,16 +497,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 1:1: F401 'matplotlib.collections.PatchCollection' imported but unused\n", - "2023-08-14 15:33:57 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-14 15:33:57 - INFO - 4:33: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 4:70: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 5:21: E128 continuation line under-indented for visual indent\n", - "2023-08-14 15:33:57 - INFO - 7:34: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 7:72: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", - "2023-08-14 15:33:57 - INFO - 10:36: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 12:1: E265 block comment should start with '# '\n" + "2023-08-15 11:34:22 - INFO - 1:1: F401 'matplotlib.collections.PatchCollection' imported but unused\n", + "2023-08-15 11:34:22 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 11:34:22 - INFO - 4:33: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 4:70: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 5:21: E128 continuation line under-indented for visual indent\n", + "2023-08-15 11:34:22 - INFO - 7:34: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 7:72: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", + "2023-08-15 11:34:22 - INFO - 10:36: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 12:1: E265 block comment should start with '# '\n" ] } ], @@ -537,8 +578,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 1:37: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 10:1: E303 too many blank lines (3)\n" + "2023-08-15 11:34:22 - INFO - 1:37: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 10:1: E303 too many blank lines (3)\n" ] } ], @@ -585,10 +626,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 11:38: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 12:25: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 12:34: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 13:1: E303 too many blank lines (3)\n" + "2023-08-15 11:34:22 - INFO - 11:38: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 12:25: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 12:34: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 13:1: E303 too many blank lines (3)\n" ] } ], @@ -617,11 +658,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 2:34: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 2:72: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 3:21: E128 continuation line under-indented for visual indent\n", - "2023-08-14 15:33:57 - INFO - 5:36: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 14:1: E303 too many blank lines (3)\n" + "2023-08-15 11:34:22 - INFO - 2:34: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 2:72: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 3:21: E128 continuation line under-indented for visual indent\n", + "2023-08-15 11:34:22 - INFO - 5:36: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 14:1: E303 too many blank lines (3)\n" ] } ], @@ -651,7 +692,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" + "2023-08-15 11:34:22 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" ] } ], @@ -678,7 +719,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 1:37: E231 missing whitespace after ','\n" + "2023-08-15 11:34:22 - INFO - 1:37: E231 missing whitespace after ','\n" ] } ], @@ -725,8 +766,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 2:36: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 7:34: E231 missing whitespace after ','\n" + "2023-08-15 11:34:22 - INFO - 2:36: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 7:34: E231 missing whitespace after ','\n" ] } ], @@ -756,13 +797,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 6:53: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 6:59: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 6:66: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 10:38: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 11:25: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 11:34: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 12:1: E303 too many blank lines (3)\n" + "2023-08-15 11:34:22 - INFO - 6:53: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 6:59: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 6:66: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 10:38: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 11:25: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 11:34: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 12:1: E303 too many blank lines (3)\n" ] } ], @@ -790,7 +831,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" + "2023-08-15 11:34:22 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" ] } ], @@ -817,8 +858,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 1:55: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 2:1: E265 block comment should start with '# '\n" + "2023-08-15 11:34:22 - INFO - 1:55: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 2:1: E265 block comment should start with '# '\n" ] } ], @@ -862,13 +903,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-14 15:33:57 - INFO - 2:1: E402 module level import not at top of file\n", - "2023-08-14 15:33:57 - INFO - 4:1: E302 expected 2 blank lines, found 1\n", - "2023-08-14 15:33:57 - INFO - 6:5: E741 ambiguous variable name 'l'\n", - "2023-08-14 15:33:57 - INFO - 22:38: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 30:11: E275 missing whitespace after keyword\n", - "2023-08-14 15:33:57 - INFO - 32:1: E303 too many blank lines (4)\n" + "2023-08-15 11:34:22 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 11:34:22 - INFO - 2:1: E402 module level import not at top of file\n", + "2023-08-15 11:34:22 - INFO - 4:1: E302 expected 2 blank lines, found 1\n", + "2023-08-15 11:34:22 - INFO - 6:5: E741 ambiguous variable name 'l'\n", + "2023-08-15 11:34:22 - INFO - 22:38: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 30:11: E275 missing whitespace after keyword\n", + "2023-08-15 11:34:22 - INFO - 32:1: E303 too many blank lines (4)\n" ] } ], @@ -917,7 +958,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 1:1: E305 expected 2 blank lines after class or function definition, found 4\n" + "2023-08-15 11:34:22 - INFO - 1:1: E305 expected 2 blank lines after class or function definition, found 4\n" ] } ], @@ -946,10 +987,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 12:38: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 13:25: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 13:34: E231 missing whitespace after ','\n", - "2023-08-14 15:33:57 - INFO - 14:1: E303 too many blank lines (3)\n" + "2023-08-15 11:34:22 - INFO - 12:38: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 13:25: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 13:34: E231 missing whitespace after ','\n", + "2023-08-15 11:34:22 - INFO - 14:1: E303 too many blank lines (3)\n" ] } ], @@ -990,7 +1031,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 1:39: E231 missing whitespace after ','\n" + "2023-08-15 11:34:22 - INFO - 1:39: E231 missing whitespace after ','\n" ] } ], @@ -1024,7 +1065,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-14 15:33:57 - INFO - 1:39: E231 missing whitespace after ','\n" + "2023-08-15 11:34:22 - INFO - 1:39: E231 missing whitespace after ','\n" ] } ], From 9ac57f14b536b9e6a2714d002fef9d40bf453575 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Tue, 15 Aug 2023 15:40:53 +0000 Subject: [PATCH 17/36] [BOT] Left PEP8 feedback on PR 93's notebooks Files: --- .../miri_lrs_advanced_extraction_part1.ipynb | 216 +++++++++++------- 1 file changed, 132 insertions(+), 84 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb index e0997e74d..cfe861dc9 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -73,6 +73,13 @@ "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" + ] + }, { "cell_type": "code", "execution_count": null, @@ -139,6 +146,47 @@ "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-15 11:40:53 - INFO - 2:1: E402 module level import not at top of file\n" + ] + } + ], + "source": [ + "# disable all imported packages' loggers\n", + "import logging\n", + "logging.root.manager.loggerDict = {}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enable PEP8 checker for this notebook\n", + "%load_ext pycodestyle_magic\n", + "%flake8_on --ignore E261,E501,W291,W293\n", + "\n", + "# only allow the checker to throw warnings when there's a violation\n", + "logging.getLogger('flake8').setLevel('ERROR')\n", + "logging.getLogger('stpipe').setLevel('ERROR')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" + ] + }, { "cell_type": "code", "execution_count": null, @@ -149,10 +197,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 11:34:22 - INFO - 1:10: E401 multiple imports on one line\n", - "2023-08-15 11:34:22 - INFO - 1:10: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 1:25: E231 missing whitespace after ','\n" + "2023-08-15 11:40:53 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 11:40:53 - INFO - 1:10: E401 multiple imports on one line\n", + "2023-08-15 11:40:53 - INFO - 1:10: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 1:25: E231 missing whitespace after ','\n" ] } ], @@ -176,19 +224,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 2:1: F401 'glob.glob' imported but unused\n", - "2023-08-15 11:34:22 - INFO - 2:1: E402 module level import not at top of file\n", - "2023-08-15 11:34:22 - INFO - 4:1: E402 module level import not at top of file\n", - "2023-08-15 11:34:22 - INFO - 5:1: E402 module level import not at top of file\n", - "2023-08-15 11:34:22 - INFO - 7:1: E402 module level import not at top of file\n", - "2023-08-15 11:34:22 - INFO - 9:1: F401 'jwst.pipeline.Spec2Pipeline' imported but unused\n", - "2023-08-15 11:34:22 - INFO - 9:1: F401 'jwst.pipeline.Spec3Pipeline' imported but unused\n", - "2023-08-15 11:34:22 - INFO - 9:1: E402 module level import not at top of file\n", - "2023-08-15 11:34:22 - INFO - 10:1: E402 module level import not at top of file\n", - "2023-08-15 11:34:22 - INFO - 11:1: E402 module level import not at top of file\n", - "2023-08-15 11:34:22 - INFO - 13:1: E402 module level import not at top of file\n", - "2023-08-15 11:34:22 - INFO - 14:1: E402 module level import not at top of file\n", - "2023-08-15 11:34:22 - INFO - 17:1: E303 too many blank lines (4)\n" + "2023-08-15 11:40:53 - INFO - 2:1: F401 'glob.glob' imported but unused\n", + "2023-08-15 11:40:53 - INFO - 2:1: E402 module level import not at top of file\n", + "2023-08-15 11:40:53 - INFO - 4:1: E402 module level import not at top of file\n", + "2023-08-15 11:40:53 - INFO - 5:1: E402 module level import not at top of file\n", + "2023-08-15 11:40:53 - INFO - 7:1: E402 module level import not at top of file\n", + "2023-08-15 11:40:53 - INFO - 9:1: F401 'jwst.pipeline.Spec2Pipeline' imported but unused\n", + "2023-08-15 11:40:53 - INFO - 9:1: F401 'jwst.pipeline.Spec3Pipeline' imported but unused\n", + "2023-08-15 11:40:53 - INFO - 9:1: E402 module level import not at top of file\n", + "2023-08-15 11:40:53 - INFO - 10:1: E402 module level import not at top of file\n", + "2023-08-15 11:40:53 - INFO - 11:1: E402 module level import not at top of file\n", + "2023-08-15 11:40:53 - INFO - 13:1: E402 module level import not at top of file\n", + "2023-08-15 11:40:53 - INFO - 14:1: E402 module level import not at top of file\n", + "2023-08-15 11:40:53 - INFO - 17:1: E303 too many blank lines (4)\n" ] } ], @@ -264,8 +312,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 4:34: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 11:1: E303 too many blank lines (3)\n" + "2023-08-15 11:40:53 - INFO - 4:34: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 11:1: E303 too many blank lines (3)\n" ] } ], @@ -292,8 +340,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 4:37: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 11:1: E303 too many blank lines (3)\n" + "2023-08-15 11:40:53 - INFO - 4:37: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 11:1: E303 too many blank lines (3)\n" ] } ], @@ -348,10 +396,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 11:34:22 - INFO - 2:1: F811 redefinition of unused 'fits' from line 33\n", - "2023-08-15 11:34:22 - INFO - 2:1: E402 module level import not at top of file\n", - "2023-08-15 11:34:22 - INFO - 5:1: E303 too many blank lines (3)\n" + "2023-08-15 11:40:53 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 11:40:53 - INFO - 2:1: F811 redefinition of unused 'fits' from line 49\n", + "2023-08-15 11:40:53 - INFO - 2:1: E402 module level import not at top of file\n", + "2023-08-15 11:40:53 - INFO - 5:1: E303 too many blank lines (3)\n" ] } ], @@ -372,7 +420,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 7:1: E303 too many blank lines (4)\n" + "2023-08-15 11:40:53 - INFO - 7:1: E303 too many blank lines (4)\n" ] } ], @@ -414,11 +462,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 11:34:22 - INFO - 7:32: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 7:69: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", - "2023-08-15 11:34:22 - INFO - 10:34: E231 missing whitespace after ','\n" + "2023-08-15 11:40:53 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 11:40:53 - INFO - 7:32: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 7:69: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", + "2023-08-15 11:40:53 - INFO - 10:34: E231 missing whitespace after ','\n" ] } ], @@ -464,10 +512,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 12:38: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 13:25: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 13:34: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 14:1: E303 too many blank lines (3)\n" + "2023-08-15 11:40:53 - INFO - 12:38: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 13:25: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 13:34: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 14:1: E303 too many blank lines (3)\n" ] } ], @@ -497,16 +545,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 1:1: F401 'matplotlib.collections.PatchCollection' imported but unused\n", - "2023-08-15 11:34:22 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 11:34:22 - INFO - 4:33: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 4:70: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 5:21: E128 continuation line under-indented for visual indent\n", - "2023-08-15 11:34:22 - INFO - 7:34: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 7:72: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", - "2023-08-15 11:34:22 - INFO - 10:36: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 12:1: E265 block comment should start with '# '\n" + "2023-08-15 11:40:53 - INFO - 1:1: F401 'matplotlib.collections.PatchCollection' imported but unused\n", + "2023-08-15 11:40:53 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 11:40:53 - INFO - 4:33: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 4:70: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 5:21: E128 continuation line under-indented for visual indent\n", + "2023-08-15 11:40:53 - INFO - 7:34: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 7:72: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", + "2023-08-15 11:40:53 - INFO - 10:36: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 12:1: E265 block comment should start with '# '\n" ] } ], @@ -578,8 +626,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 1:37: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 10:1: E303 too many blank lines (3)\n" + "2023-08-15 11:40:53 - INFO - 1:37: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 10:1: E303 too many blank lines (3)\n" ] } ], @@ -626,10 +674,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 11:38: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 12:25: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 12:34: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 13:1: E303 too many blank lines (3)\n" + "2023-08-15 11:40:53 - INFO - 11:38: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 12:25: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 12:34: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 13:1: E303 too many blank lines (3)\n" ] } ], @@ -658,11 +706,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 2:34: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 2:72: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 3:21: E128 continuation line under-indented for visual indent\n", - "2023-08-15 11:34:22 - INFO - 5:36: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 14:1: E303 too many blank lines (3)\n" + "2023-08-15 11:40:53 - INFO - 2:34: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 2:72: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 3:21: E128 continuation line under-indented for visual indent\n", + "2023-08-15 11:40:53 - INFO - 5:36: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 14:1: E303 too many blank lines (3)\n" ] } ], @@ -692,7 +740,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" + "2023-08-15 11:40:53 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" ] } ], @@ -719,7 +767,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 1:37: E231 missing whitespace after ','\n" + "2023-08-15 11:40:53 - INFO - 1:37: E231 missing whitespace after ','\n" ] } ], @@ -766,8 +814,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 2:36: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 7:34: E231 missing whitespace after ','\n" + "2023-08-15 11:40:53 - INFO - 2:36: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 7:34: E231 missing whitespace after ','\n" ] } ], @@ -797,13 +845,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 6:53: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 6:59: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 6:66: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 10:38: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 11:25: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 11:34: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 12:1: E303 too many blank lines (3)\n" + "2023-08-15 11:40:53 - INFO - 6:53: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 6:59: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 6:66: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 10:38: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 11:25: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 11:34: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 12:1: E303 too many blank lines (3)\n" ] } ], @@ -831,7 +879,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" + "2023-08-15 11:40:53 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" ] } ], @@ -858,8 +906,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 1:55: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 2:1: E265 block comment should start with '# '\n" + "2023-08-15 11:40:53 - INFO - 1:55: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 2:1: E265 block comment should start with '# '\n" ] } ], @@ -903,13 +951,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 11:34:22 - INFO - 2:1: E402 module level import not at top of file\n", - "2023-08-15 11:34:22 - INFO - 4:1: E302 expected 2 blank lines, found 1\n", - "2023-08-15 11:34:22 - INFO - 6:5: E741 ambiguous variable name 'l'\n", - "2023-08-15 11:34:22 - INFO - 22:38: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 30:11: E275 missing whitespace after keyword\n", - "2023-08-15 11:34:22 - INFO - 32:1: E303 too many blank lines (4)\n" + "2023-08-15 11:40:53 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 11:40:53 - INFO - 2:1: E402 module level import not at top of file\n", + "2023-08-15 11:40:53 - INFO - 4:1: E302 expected 2 blank lines, found 1\n", + "2023-08-15 11:40:53 - INFO - 6:5: E741 ambiguous variable name 'l'\n", + "2023-08-15 11:40:53 - INFO - 22:38: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 30:11: E275 missing whitespace after keyword\n", + "2023-08-15 11:40:53 - INFO - 32:1: E303 too many blank lines (4)\n" ] } ], @@ -958,7 +1006,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 1:1: E305 expected 2 blank lines after class or function definition, found 4\n" + "2023-08-15 11:40:53 - INFO - 1:1: E305 expected 2 blank lines after class or function definition, found 4\n" ] } ], @@ -987,10 +1035,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 12:38: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 13:25: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 13:34: E231 missing whitespace after ','\n", - "2023-08-15 11:34:22 - INFO - 14:1: E303 too many blank lines (3)\n" + "2023-08-15 11:40:53 - INFO - 12:38: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 13:25: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 13:34: E231 missing whitespace after ','\n", + "2023-08-15 11:40:53 - INFO - 14:1: E303 too many blank lines (3)\n" ] } ], @@ -1031,7 +1079,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 1:39: E231 missing whitespace after ','\n" + "2023-08-15 11:40:53 - INFO - 1:39: E231 missing whitespace after ','\n" ] } ], @@ -1065,7 +1113,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:34:22 - INFO - 1:39: E231 missing whitespace after ','\n" + "2023-08-15 11:40:53 - INFO - 1:39: E231 missing whitespace after ','\n" ] } ], From 046cd40c8d8f6ea75721a610147d263b90e3f627 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Tue, 15 Aug 2023 15:48:32 +0000 Subject: [PATCH 18/36] [BOT] Left PEP8 feedback on PR 93's notebooks Files: --- .../miri_lrs_advanced_extraction_part1.ipynb | 218 +++++++++++------- 1 file changed, 133 insertions(+), 85 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb index cfe861dc9..1078e4884 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -80,6 +80,13 @@ "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" + ] + }, { "cell_type": "code", "execution_count": null, @@ -155,7 +162,48 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 2:1: E402 module level import not at top of file\n" + "2023-08-15 11:48:32 - INFO - 2:1: E402 module level import not at top of file\n" + ] + } + ], + "source": [ + "# disable all imported packages' loggers\n", + "import logging\n", + "logging.root.manager.loggerDict = {}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enable PEP8 checker for this notebook\n", + "%load_ext pycodestyle_magic\n", + "%flake8_on --ignore E261,E501,W291,W293\n", + "\n", + "# only allow the checker to throw warnings when there's a violation\n", + "logging.getLogger('flake8').setLevel('ERROR')\n", + "logging.getLogger('stpipe').setLevel('ERROR')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-15 11:48:32 - INFO - 2:1: E402 module level import not at top of file\n" ] } ], @@ -197,10 +245,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 11:40:53 - INFO - 1:10: E401 multiple imports on one line\n", - "2023-08-15 11:40:53 - INFO - 1:10: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 1:25: E231 missing whitespace after ','\n" + "2023-08-15 11:48:32 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 11:48:32 - INFO - 1:10: E401 multiple imports on one line\n", + "2023-08-15 11:48:32 - INFO - 1:10: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 1:25: E231 missing whitespace after ','\n" ] } ], @@ -224,19 +272,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 2:1: F401 'glob.glob' imported but unused\n", - "2023-08-15 11:40:53 - INFO - 2:1: E402 module level import not at top of file\n", - "2023-08-15 11:40:53 - INFO - 4:1: E402 module level import not at top of file\n", - "2023-08-15 11:40:53 - INFO - 5:1: E402 module level import not at top of file\n", - "2023-08-15 11:40:53 - INFO - 7:1: E402 module level import not at top of file\n", - "2023-08-15 11:40:53 - INFO - 9:1: F401 'jwst.pipeline.Spec2Pipeline' imported but unused\n", - "2023-08-15 11:40:53 - INFO - 9:1: F401 'jwst.pipeline.Spec3Pipeline' imported but unused\n", - "2023-08-15 11:40:53 - INFO - 9:1: E402 module level import not at top of file\n", - "2023-08-15 11:40:53 - INFO - 10:1: E402 module level import not at top of file\n", - "2023-08-15 11:40:53 - INFO - 11:1: E402 module level import not at top of file\n", - "2023-08-15 11:40:53 - INFO - 13:1: E402 module level import not at top of file\n", - "2023-08-15 11:40:53 - INFO - 14:1: E402 module level import not at top of file\n", - "2023-08-15 11:40:53 - INFO - 17:1: E303 too many blank lines (4)\n" + "2023-08-15 11:48:32 - INFO - 2:1: F401 'glob.glob' imported but unused\n", + "2023-08-15 11:48:32 - INFO - 2:1: E402 module level import not at top of file\n", + "2023-08-15 11:48:32 - INFO - 4:1: E402 module level import not at top of file\n", + "2023-08-15 11:48:32 - INFO - 5:1: E402 module level import not at top of file\n", + "2023-08-15 11:48:32 - INFO - 7:1: E402 module level import not at top of file\n", + "2023-08-15 11:48:32 - INFO - 9:1: F401 'jwst.pipeline.Spec2Pipeline' imported but unused\n", + "2023-08-15 11:48:32 - INFO - 9:1: F401 'jwst.pipeline.Spec3Pipeline' imported but unused\n", + "2023-08-15 11:48:32 - INFO - 9:1: E402 module level import not at top of file\n", + "2023-08-15 11:48:32 - INFO - 10:1: E402 module level import not at top of file\n", + "2023-08-15 11:48:32 - INFO - 11:1: E402 module level import not at top of file\n", + "2023-08-15 11:48:32 - INFO - 13:1: E402 module level import not at top of file\n", + "2023-08-15 11:48:32 - INFO - 14:1: E402 module level import not at top of file\n", + "2023-08-15 11:48:32 - INFO - 17:1: E303 too many blank lines (4)\n" ] } ], @@ -312,8 +360,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 4:34: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 11:1: E303 too many blank lines (3)\n" + "2023-08-15 11:48:32 - INFO - 4:34: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 11:1: E303 too many blank lines (3)\n" ] } ], @@ -340,8 +388,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 4:37: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 11:1: E303 too many blank lines (3)\n" + "2023-08-15 11:48:32 - INFO - 4:37: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 11:1: E303 too many blank lines (3)\n" ] } ], @@ -396,10 +444,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 11:40:53 - INFO - 2:1: F811 redefinition of unused 'fits' from line 49\n", - "2023-08-15 11:40:53 - INFO - 2:1: E402 module level import not at top of file\n", - "2023-08-15 11:40:53 - INFO - 5:1: E303 too many blank lines (3)\n" + "2023-08-15 11:48:32 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 11:48:32 - INFO - 2:1: F811 redefinition of unused 'fits' from line 65\n", + "2023-08-15 11:48:32 - INFO - 2:1: E402 module level import not at top of file\n", + "2023-08-15 11:48:32 - INFO - 5:1: E303 too many blank lines (3)\n" ] } ], @@ -420,7 +468,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 7:1: E303 too many blank lines (4)\n" + "2023-08-15 11:48:32 - INFO - 7:1: E303 too many blank lines (4)\n" ] } ], @@ -462,11 +510,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 11:40:53 - INFO - 7:32: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 7:69: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", - "2023-08-15 11:40:53 - INFO - 10:34: E231 missing whitespace after ','\n" + "2023-08-15 11:48:32 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 11:48:32 - INFO - 7:32: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 7:69: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", + "2023-08-15 11:48:32 - INFO - 10:34: E231 missing whitespace after ','\n" ] } ], @@ -512,10 +560,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 12:38: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 13:25: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 13:34: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 14:1: E303 too many blank lines (3)\n" + "2023-08-15 11:48:32 - INFO - 12:38: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 13:25: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 13:34: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 14:1: E303 too many blank lines (3)\n" ] } ], @@ -545,16 +593,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 1:1: F401 'matplotlib.collections.PatchCollection' imported but unused\n", - "2023-08-15 11:40:53 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 11:40:53 - INFO - 4:33: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 4:70: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 5:21: E128 continuation line under-indented for visual indent\n", - "2023-08-15 11:40:53 - INFO - 7:34: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 7:72: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", - "2023-08-15 11:40:53 - INFO - 10:36: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 12:1: E265 block comment should start with '# '\n" + "2023-08-15 11:48:32 - INFO - 1:1: F401 'matplotlib.collections.PatchCollection' imported but unused\n", + "2023-08-15 11:48:32 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 11:48:32 - INFO - 4:33: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 4:70: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 5:21: E128 continuation line under-indented for visual indent\n", + "2023-08-15 11:48:32 - INFO - 7:34: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 7:72: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", + "2023-08-15 11:48:32 - INFO - 10:36: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 12:1: E265 block comment should start with '# '\n" ] } ], @@ -626,8 +674,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 1:37: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 10:1: E303 too many blank lines (3)\n" + "2023-08-15 11:48:32 - INFO - 1:37: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 10:1: E303 too many blank lines (3)\n" ] } ], @@ -674,10 +722,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 11:38: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 12:25: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 12:34: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 13:1: E303 too many blank lines (3)\n" + "2023-08-15 11:48:32 - INFO - 11:38: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 12:25: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 12:34: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 13:1: E303 too many blank lines (3)\n" ] } ], @@ -706,11 +754,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 2:34: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 2:72: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 3:21: E128 continuation line under-indented for visual indent\n", - "2023-08-15 11:40:53 - INFO - 5:36: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 14:1: E303 too many blank lines (3)\n" + "2023-08-15 11:48:32 - INFO - 2:34: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 2:72: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 3:21: E128 continuation line under-indented for visual indent\n", + "2023-08-15 11:48:32 - INFO - 5:36: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 14:1: E303 too many blank lines (3)\n" ] } ], @@ -740,7 +788,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" + "2023-08-15 11:48:32 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" ] } ], @@ -767,7 +815,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 1:37: E231 missing whitespace after ','\n" + "2023-08-15 11:48:32 - INFO - 1:37: E231 missing whitespace after ','\n" ] } ], @@ -814,8 +862,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 2:36: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 7:34: E231 missing whitespace after ','\n" + "2023-08-15 11:48:32 - INFO - 2:36: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 7:34: E231 missing whitespace after ','\n" ] } ], @@ -845,13 +893,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 6:53: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 6:59: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 6:66: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 10:38: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 11:25: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 11:34: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 12:1: E303 too many blank lines (3)\n" + "2023-08-15 11:48:32 - INFO - 6:53: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 6:59: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 6:66: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 10:38: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 11:25: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 11:34: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 12:1: E303 too many blank lines (3)\n" ] } ], @@ -879,7 +927,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" + "2023-08-15 11:48:32 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" ] } ], @@ -906,8 +954,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 1:55: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 2:1: E265 block comment should start with '# '\n" + "2023-08-15 11:48:32 - INFO - 1:55: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 2:1: E265 block comment should start with '# '\n" ] } ], @@ -951,13 +999,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 11:40:53 - INFO - 2:1: E402 module level import not at top of file\n", - "2023-08-15 11:40:53 - INFO - 4:1: E302 expected 2 blank lines, found 1\n", - "2023-08-15 11:40:53 - INFO - 6:5: E741 ambiguous variable name 'l'\n", - "2023-08-15 11:40:53 - INFO - 22:38: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 30:11: E275 missing whitespace after keyword\n", - "2023-08-15 11:40:53 - INFO - 32:1: E303 too many blank lines (4)\n" + "2023-08-15 11:48:32 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 11:48:32 - INFO - 2:1: E402 module level import not at top of file\n", + "2023-08-15 11:48:32 - INFO - 4:1: E302 expected 2 blank lines, found 1\n", + "2023-08-15 11:48:32 - INFO - 6:5: E741 ambiguous variable name 'l'\n", + "2023-08-15 11:48:32 - INFO - 22:38: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 30:11: E275 missing whitespace after keyword\n", + "2023-08-15 11:48:32 - INFO - 32:1: E303 too many blank lines (4)\n" ] } ], @@ -1006,7 +1054,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 1:1: E305 expected 2 blank lines after class or function definition, found 4\n" + "2023-08-15 11:48:32 - INFO - 1:1: E305 expected 2 blank lines after class or function definition, found 4\n" ] } ], @@ -1035,10 +1083,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 12:38: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 13:25: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 13:34: E231 missing whitespace after ','\n", - "2023-08-15 11:40:53 - INFO - 14:1: E303 too many blank lines (3)\n" + "2023-08-15 11:48:32 - INFO - 12:38: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 13:25: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 13:34: E231 missing whitespace after ','\n", + "2023-08-15 11:48:32 - INFO - 14:1: E303 too many blank lines (3)\n" ] } ], @@ -1079,7 +1127,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 1:39: E231 missing whitespace after ','\n" + "2023-08-15 11:48:32 - INFO - 1:39: E231 missing whitespace after ','\n" ] } ], @@ -1113,7 +1161,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:40:53 - INFO - 1:39: E231 missing whitespace after ','\n" + "2023-08-15 11:48:32 - INFO - 1:39: E231 missing whitespace after ','\n" ] } ], From 387886927bf847e14edf431f05620e27be2b53e7 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Tue, 15 Aug 2023 17:27:55 +0000 Subject: [PATCH 19/36] [BOT] Left PEP8 feedback on PR 93's notebooks Files: --- .../miri_lrs_advanced_extraction_part1.ipynb | 220 +++++++++++------- 1 file changed, 134 insertions(+), 86 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb index 1078e4884..84796d08b 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -87,6 +87,13 @@ "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" + ] + }, { "cell_type": "code", "execution_count": null, @@ -162,7 +169,48 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 2:1: E402 module level import not at top of file\n" + "2023-08-15 13:27:55 - INFO - 2:1: E402 module level import not at top of file\n" + ] + } + ], + "source": [ + "# disable all imported packages' loggers\n", + "import logging\n", + "logging.root.manager.loggerDict = {}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enable PEP8 checker for this notebook\n", + "%load_ext pycodestyle_magic\n", + "%flake8_on --ignore E261,E501,W291,W293\n", + "\n", + "# only allow the checker to throw warnings when there's a violation\n", + "logging.getLogger('flake8').setLevel('ERROR')\n", + "logging.getLogger('stpipe').setLevel('ERROR')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-15 13:27:55 - INFO - 2:1: E402 module level import not at top of file\n" ] } ], @@ -203,7 +251,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 2:1: E402 module level import not at top of file\n" + "2023-08-15 13:27:55 - INFO - 2:1: E402 module level import not at top of file\n" ] } ], @@ -245,10 +293,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 11:48:32 - INFO - 1:10: E401 multiple imports on one line\n", - "2023-08-15 11:48:32 - INFO - 1:10: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 1:25: E231 missing whitespace after ','\n" + "2023-08-15 13:27:55 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 13:27:55 - INFO - 1:10: E401 multiple imports on one line\n", + "2023-08-15 13:27:55 - INFO - 1:10: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 1:25: E231 missing whitespace after ','\n" ] } ], @@ -272,19 +320,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 2:1: F401 'glob.glob' imported but unused\n", - "2023-08-15 11:48:32 - INFO - 2:1: E402 module level import not at top of file\n", - "2023-08-15 11:48:32 - INFO - 4:1: E402 module level import not at top of file\n", - "2023-08-15 11:48:32 - INFO - 5:1: E402 module level import not at top of file\n", - "2023-08-15 11:48:32 - INFO - 7:1: E402 module level import not at top of file\n", - "2023-08-15 11:48:32 - INFO - 9:1: F401 'jwst.pipeline.Spec2Pipeline' imported but unused\n", - "2023-08-15 11:48:32 - INFO - 9:1: F401 'jwst.pipeline.Spec3Pipeline' imported but unused\n", - "2023-08-15 11:48:32 - INFO - 9:1: E402 module level import not at top of file\n", - "2023-08-15 11:48:32 - INFO - 10:1: E402 module level import not at top of file\n", - "2023-08-15 11:48:32 - INFO - 11:1: E402 module level import not at top of file\n", - "2023-08-15 11:48:32 - INFO - 13:1: E402 module level import not at top of file\n", - "2023-08-15 11:48:32 - INFO - 14:1: E402 module level import not at top of file\n", - "2023-08-15 11:48:32 - INFO - 17:1: E303 too many blank lines (4)\n" + "2023-08-15 13:27:55 - INFO - 2:1: F401 'glob.glob' imported but unused\n", + "2023-08-15 13:27:55 - INFO - 2:1: E402 module level import not at top of file\n", + "2023-08-15 13:27:55 - INFO - 4:1: E402 module level import not at top of file\n", + "2023-08-15 13:27:55 - INFO - 5:1: E402 module level import not at top of file\n", + "2023-08-15 13:27:55 - INFO - 7:1: E402 module level import not at top of file\n", + "2023-08-15 13:27:55 - INFO - 9:1: F401 'jwst.pipeline.Spec2Pipeline' imported but unused\n", + "2023-08-15 13:27:55 - INFO - 9:1: F401 'jwst.pipeline.Spec3Pipeline' imported but unused\n", + "2023-08-15 13:27:55 - INFO - 9:1: E402 module level import not at top of file\n", + "2023-08-15 13:27:55 - INFO - 10:1: E402 module level import not at top of file\n", + "2023-08-15 13:27:55 - INFO - 11:1: E402 module level import not at top of file\n", + "2023-08-15 13:27:55 - INFO - 13:1: E402 module level import not at top of file\n", + "2023-08-15 13:27:55 - INFO - 14:1: E402 module level import not at top of file\n", + "2023-08-15 13:27:55 - INFO - 17:1: E303 too many blank lines (4)\n" ] } ], @@ -360,8 +408,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 4:34: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 11:1: E303 too many blank lines (3)\n" + "2023-08-15 13:27:55 - INFO - 4:34: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 11:1: E303 too many blank lines (3)\n" ] } ], @@ -388,8 +436,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 4:37: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 11:1: E303 too many blank lines (3)\n" + "2023-08-15 13:27:55 - INFO - 4:37: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 11:1: E303 too many blank lines (3)\n" ] } ], @@ -444,10 +492,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 11:48:32 - INFO - 2:1: F811 redefinition of unused 'fits' from line 65\n", - "2023-08-15 11:48:32 - INFO - 2:1: E402 module level import not at top of file\n", - "2023-08-15 11:48:32 - INFO - 5:1: E303 too many blank lines (3)\n" + "2023-08-15 13:27:55 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 13:27:55 - INFO - 2:1: F811 redefinition of unused 'fits' from line 81\n", + "2023-08-15 13:27:55 - INFO - 2:1: E402 module level import not at top of file\n", + "2023-08-15 13:27:55 - INFO - 5:1: E303 too many blank lines (3)\n" ] } ], @@ -468,7 +516,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 7:1: E303 too many blank lines (4)\n" + "2023-08-15 13:27:55 - INFO - 7:1: E303 too many blank lines (4)\n" ] } ], @@ -510,11 +558,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 11:48:32 - INFO - 7:32: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 7:69: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", - "2023-08-15 11:48:32 - INFO - 10:34: E231 missing whitespace after ','\n" + "2023-08-15 13:27:55 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 13:27:55 - INFO - 7:32: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 7:69: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", + "2023-08-15 13:27:55 - INFO - 10:34: E231 missing whitespace after ','\n" ] } ], @@ -560,10 +608,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 12:38: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 13:25: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 13:34: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 14:1: E303 too many blank lines (3)\n" + "2023-08-15 13:27:55 - INFO - 12:38: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 13:25: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 13:34: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 14:1: E303 too many blank lines (3)\n" ] } ], @@ -593,16 +641,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 1:1: F401 'matplotlib.collections.PatchCollection' imported but unused\n", - "2023-08-15 11:48:32 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 11:48:32 - INFO - 4:33: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 4:70: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 5:21: E128 continuation line under-indented for visual indent\n", - "2023-08-15 11:48:32 - INFO - 7:34: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 7:72: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", - "2023-08-15 11:48:32 - INFO - 10:36: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 12:1: E265 block comment should start with '# '\n" + "2023-08-15 13:27:55 - INFO - 1:1: F401 'matplotlib.collections.PatchCollection' imported but unused\n", + "2023-08-15 13:27:55 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 13:27:55 - INFO - 4:33: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 4:70: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 5:21: E128 continuation line under-indented for visual indent\n", + "2023-08-15 13:27:55 - INFO - 7:34: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 7:72: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", + "2023-08-15 13:27:55 - INFO - 10:36: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 12:1: E265 block comment should start with '# '\n" ] } ], @@ -674,8 +722,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 1:37: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 10:1: E303 too many blank lines (3)\n" + "2023-08-15 13:27:55 - INFO - 1:37: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 10:1: E303 too many blank lines (3)\n" ] } ], @@ -722,10 +770,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 11:38: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 12:25: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 12:34: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 13:1: E303 too many blank lines (3)\n" + "2023-08-15 13:27:55 - INFO - 11:38: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 12:25: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 12:34: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 13:1: E303 too many blank lines (3)\n" ] } ], @@ -754,11 +802,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 2:34: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 2:72: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 3:21: E128 continuation line under-indented for visual indent\n", - "2023-08-15 11:48:32 - INFO - 5:36: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 14:1: E303 too many blank lines (3)\n" + "2023-08-15 13:27:55 - INFO - 2:34: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 2:72: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 3:21: E128 continuation line under-indented for visual indent\n", + "2023-08-15 13:27:55 - INFO - 5:36: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 14:1: E303 too many blank lines (3)\n" ] } ], @@ -788,7 +836,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" + "2023-08-15 13:27:55 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" ] } ], @@ -815,7 +863,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 1:37: E231 missing whitespace after ','\n" + "2023-08-15 13:27:55 - INFO - 1:37: E231 missing whitespace after ','\n" ] } ], @@ -862,8 +910,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 2:36: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 7:34: E231 missing whitespace after ','\n" + "2023-08-15 13:27:55 - INFO - 2:36: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 7:34: E231 missing whitespace after ','\n" ] } ], @@ -893,13 +941,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 6:53: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 6:59: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 6:66: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 10:38: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 11:25: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 11:34: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 12:1: E303 too many blank lines (3)\n" + "2023-08-15 13:27:55 - INFO - 6:53: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 6:59: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 6:66: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 10:38: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 11:25: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 11:34: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 12:1: E303 too many blank lines (3)\n" ] } ], @@ -927,7 +975,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" + "2023-08-15 13:27:55 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" ] } ], @@ -954,8 +1002,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 1:55: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 2:1: E265 block comment should start with '# '\n" + "2023-08-15 13:27:55 - INFO - 1:55: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 2:1: E265 block comment should start with '# '\n" ] } ], @@ -999,13 +1047,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 11:48:32 - INFO - 2:1: E402 module level import not at top of file\n", - "2023-08-15 11:48:32 - INFO - 4:1: E302 expected 2 blank lines, found 1\n", - "2023-08-15 11:48:32 - INFO - 6:5: E741 ambiguous variable name 'l'\n", - "2023-08-15 11:48:32 - INFO - 22:38: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 30:11: E275 missing whitespace after keyword\n", - "2023-08-15 11:48:32 - INFO - 32:1: E303 too many blank lines (4)\n" + "2023-08-15 13:27:55 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 13:27:55 - INFO - 2:1: E402 module level import not at top of file\n", + "2023-08-15 13:27:55 - INFO - 4:1: E302 expected 2 blank lines, found 1\n", + "2023-08-15 13:27:55 - INFO - 6:5: E741 ambiguous variable name 'l'\n", + "2023-08-15 13:27:55 - INFO - 22:38: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 30:11: E275 missing whitespace after keyword\n", + "2023-08-15 13:27:55 - INFO - 32:1: E303 too many blank lines (4)\n" ] } ], @@ -1054,7 +1102,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 1:1: E305 expected 2 blank lines after class or function definition, found 4\n" + "2023-08-15 13:27:55 - INFO - 1:1: E305 expected 2 blank lines after class or function definition, found 4\n" ] } ], @@ -1083,10 +1131,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 12:38: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 13:25: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 13:34: E231 missing whitespace after ','\n", - "2023-08-15 11:48:32 - INFO - 14:1: E303 too many blank lines (3)\n" + "2023-08-15 13:27:55 - INFO - 12:38: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 13:25: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 13:34: E231 missing whitespace after ','\n", + "2023-08-15 13:27:55 - INFO - 14:1: E303 too many blank lines (3)\n" ] } ], @@ -1127,7 +1175,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 1:39: E231 missing whitespace after ','\n" + "2023-08-15 13:27:55 - INFO - 1:39: E231 missing whitespace after ','\n" ] } ], @@ -1161,7 +1209,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 11:48:32 - INFO - 1:39: E231 missing whitespace after ','\n" + "2023-08-15 13:27:55 - INFO - 1:39: E231 missing whitespace after ','\n" ] } ], From 96f8b7d5468798e5407645dd6331da8bff9f9ade Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Tue, 15 Aug 2023 18:39:04 +0000 Subject: [PATCH 20/36] [BOT] Left PEP8 feedback on PR 93's notebooks Files: --- .../miri_lrs_advanced_extraction_part1.ipynb | 222 +++++++++++------- 1 file changed, 135 insertions(+), 87 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb index 84796d08b..bd18a9974 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -94,6 +94,13 @@ "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" + ] + }, { "cell_type": "code", "execution_count": null, @@ -169,7 +176,48 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 2:1: E402 module level import not at top of file\n" + "2023-08-15 14:39:04 - INFO - 2:1: E402 module level import not at top of file\n" + ] + } + ], + "source": [ + "# disable all imported packages' loggers\n", + "import logging\n", + "logging.root.manager.loggerDict = {}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enable PEP8 checker for this notebook\n", + "%load_ext pycodestyle_magic\n", + "%flake8_on --ignore E261,E501,W291,W293\n", + "\n", + "# only allow the checker to throw warnings when there's a violation\n", + "logging.getLogger('flake8').setLevel('ERROR')\n", + "logging.getLogger('stpipe').setLevel('ERROR')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-15 14:39:04 - INFO - 2:1: E402 module level import not at top of file\n" ] } ], @@ -210,7 +258,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 2:1: E402 module level import not at top of file\n" + "2023-08-15 14:39:04 - INFO - 2:1: E402 module level import not at top of file\n" ] } ], @@ -251,7 +299,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 2:1: E402 module level import not at top of file\n" + "2023-08-15 14:39:04 - INFO - 2:1: E402 module level import not at top of file\n" ] } ], @@ -293,10 +341,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 13:27:55 - INFO - 1:10: E401 multiple imports on one line\n", - "2023-08-15 13:27:55 - INFO - 1:10: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 1:25: E231 missing whitespace after ','\n" + "2023-08-15 14:39:04 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 14:39:04 - INFO - 1:10: E401 multiple imports on one line\n", + "2023-08-15 14:39:04 - INFO - 1:10: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 1:25: E231 missing whitespace after ','\n" ] } ], @@ -320,19 +368,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 2:1: F401 'glob.glob' imported but unused\n", - "2023-08-15 13:27:55 - INFO - 2:1: E402 module level import not at top of file\n", - "2023-08-15 13:27:55 - INFO - 4:1: E402 module level import not at top of file\n", - "2023-08-15 13:27:55 - INFO - 5:1: E402 module level import not at top of file\n", - "2023-08-15 13:27:55 - INFO - 7:1: E402 module level import not at top of file\n", - "2023-08-15 13:27:55 - INFO - 9:1: F401 'jwst.pipeline.Spec2Pipeline' imported but unused\n", - "2023-08-15 13:27:55 - INFO - 9:1: F401 'jwst.pipeline.Spec3Pipeline' imported but unused\n", - "2023-08-15 13:27:55 - INFO - 9:1: E402 module level import not at top of file\n", - "2023-08-15 13:27:55 - INFO - 10:1: E402 module level import not at top of file\n", - "2023-08-15 13:27:55 - INFO - 11:1: E402 module level import not at top of file\n", - "2023-08-15 13:27:55 - INFO - 13:1: E402 module level import not at top of file\n", - "2023-08-15 13:27:55 - INFO - 14:1: E402 module level import not at top of file\n", - "2023-08-15 13:27:55 - INFO - 17:1: E303 too many blank lines (4)\n" + "2023-08-15 14:39:04 - INFO - 2:1: F401 'glob.glob' imported but unused\n", + "2023-08-15 14:39:04 - INFO - 2:1: E402 module level import not at top of file\n", + "2023-08-15 14:39:04 - INFO - 4:1: E402 module level import not at top of file\n", + "2023-08-15 14:39:04 - INFO - 5:1: E402 module level import not at top of file\n", + "2023-08-15 14:39:04 - INFO - 7:1: E402 module level import not at top of file\n", + "2023-08-15 14:39:04 - INFO - 9:1: F401 'jwst.pipeline.Spec2Pipeline' imported but unused\n", + "2023-08-15 14:39:04 - INFO - 9:1: F401 'jwst.pipeline.Spec3Pipeline' imported but unused\n", + "2023-08-15 14:39:04 - INFO - 9:1: E402 module level import not at top of file\n", + "2023-08-15 14:39:04 - INFO - 10:1: E402 module level import not at top of file\n", + "2023-08-15 14:39:04 - INFO - 11:1: E402 module level import not at top of file\n", + "2023-08-15 14:39:04 - INFO - 13:1: E402 module level import not at top of file\n", + "2023-08-15 14:39:04 - INFO - 14:1: E402 module level import not at top of file\n", + "2023-08-15 14:39:04 - INFO - 17:1: E303 too many blank lines (4)\n" ] } ], @@ -408,8 +456,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 4:34: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 11:1: E303 too many blank lines (3)\n" + "2023-08-15 14:39:04 - INFO - 4:34: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 11:1: E303 too many blank lines (3)\n" ] } ], @@ -436,8 +484,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 4:37: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 11:1: E303 too many blank lines (3)\n" + "2023-08-15 14:39:04 - INFO - 4:37: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 11:1: E303 too many blank lines (3)\n" ] } ], @@ -492,10 +540,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 13:27:55 - INFO - 2:1: F811 redefinition of unused 'fits' from line 81\n", - "2023-08-15 13:27:55 - INFO - 2:1: E402 module level import not at top of file\n", - "2023-08-15 13:27:55 - INFO - 5:1: E303 too many blank lines (3)\n" + "2023-08-15 14:39:04 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 14:39:04 - INFO - 2:1: F811 redefinition of unused 'fits' from line 97\n", + "2023-08-15 14:39:04 - INFO - 2:1: E402 module level import not at top of file\n", + "2023-08-15 14:39:04 - INFO - 5:1: E303 too many blank lines (3)\n" ] } ], @@ -516,7 +564,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 7:1: E303 too many blank lines (4)\n" + "2023-08-15 14:39:04 - INFO - 7:1: E303 too many blank lines (4)\n" ] } ], @@ -558,11 +606,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 13:27:55 - INFO - 7:32: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 7:69: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", - "2023-08-15 13:27:55 - INFO - 10:34: E231 missing whitespace after ','\n" + "2023-08-15 14:39:04 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 14:39:04 - INFO - 7:32: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 7:69: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", + "2023-08-15 14:39:04 - INFO - 10:34: E231 missing whitespace after ','\n" ] } ], @@ -608,10 +656,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 12:38: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 13:25: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 13:34: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 14:1: E303 too many blank lines (3)\n" + "2023-08-15 14:39:04 - INFO - 12:38: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 13:25: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 13:34: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 14:1: E303 too many blank lines (3)\n" ] } ], @@ -641,16 +689,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 1:1: F401 'matplotlib.collections.PatchCollection' imported but unused\n", - "2023-08-15 13:27:55 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 13:27:55 - INFO - 4:33: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 4:70: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 5:21: E128 continuation line under-indented for visual indent\n", - "2023-08-15 13:27:55 - INFO - 7:34: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 7:72: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", - "2023-08-15 13:27:55 - INFO - 10:36: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 12:1: E265 block comment should start with '# '\n" + "2023-08-15 14:39:04 - INFO - 1:1: F401 'matplotlib.collections.PatchCollection' imported but unused\n", + "2023-08-15 14:39:04 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 14:39:04 - INFO - 4:33: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 4:70: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 5:21: E128 continuation line under-indented for visual indent\n", + "2023-08-15 14:39:04 - INFO - 7:34: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 7:72: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", + "2023-08-15 14:39:04 - INFO - 10:36: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 12:1: E265 block comment should start with '# '\n" ] } ], @@ -722,8 +770,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 1:37: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 10:1: E303 too many blank lines (3)\n" + "2023-08-15 14:39:04 - INFO - 1:37: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 10:1: E303 too many blank lines (3)\n" ] } ], @@ -770,10 +818,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 11:38: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 12:25: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 12:34: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 13:1: E303 too many blank lines (3)\n" + "2023-08-15 14:39:04 - INFO - 11:38: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 12:25: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 12:34: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 13:1: E303 too many blank lines (3)\n" ] } ], @@ -802,11 +850,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 2:34: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 2:72: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 3:21: E128 continuation line under-indented for visual indent\n", - "2023-08-15 13:27:55 - INFO - 5:36: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 14:1: E303 too many blank lines (3)\n" + "2023-08-15 14:39:04 - INFO - 2:34: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 2:72: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 3:21: E128 continuation line under-indented for visual indent\n", + "2023-08-15 14:39:04 - INFO - 5:36: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 14:1: E303 too many blank lines (3)\n" ] } ], @@ -836,7 +884,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" + "2023-08-15 14:39:04 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" ] } ], @@ -863,7 +911,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 1:37: E231 missing whitespace after ','\n" + "2023-08-15 14:39:04 - INFO - 1:37: E231 missing whitespace after ','\n" ] } ], @@ -910,8 +958,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 2:36: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 7:34: E231 missing whitespace after ','\n" + "2023-08-15 14:39:04 - INFO - 2:36: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 7:34: E231 missing whitespace after ','\n" ] } ], @@ -941,13 +989,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 6:53: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 6:59: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 6:66: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 10:38: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 11:25: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 11:34: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 12:1: E303 too many blank lines (3)\n" + "2023-08-15 14:39:04 - INFO - 6:53: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 6:59: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 6:66: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 10:38: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 11:25: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 11:34: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 12:1: E303 too many blank lines (3)\n" ] } ], @@ -975,7 +1023,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" + "2023-08-15 14:39:04 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" ] } ], @@ -1002,8 +1050,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 1:55: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 2:1: E265 block comment should start with '# '\n" + "2023-08-15 14:39:04 - INFO - 1:55: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 2:1: E265 block comment should start with '# '\n" ] } ], @@ -1047,13 +1095,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 13:27:55 - INFO - 2:1: E402 module level import not at top of file\n", - "2023-08-15 13:27:55 - INFO - 4:1: E302 expected 2 blank lines, found 1\n", - "2023-08-15 13:27:55 - INFO - 6:5: E741 ambiguous variable name 'l'\n", - "2023-08-15 13:27:55 - INFO - 22:38: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 30:11: E275 missing whitespace after keyword\n", - "2023-08-15 13:27:55 - INFO - 32:1: E303 too many blank lines (4)\n" + "2023-08-15 14:39:04 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 14:39:04 - INFO - 2:1: E402 module level import not at top of file\n", + "2023-08-15 14:39:04 - INFO - 4:1: E302 expected 2 blank lines, found 1\n", + "2023-08-15 14:39:04 - INFO - 6:5: E741 ambiguous variable name 'l'\n", + "2023-08-15 14:39:04 - INFO - 22:38: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 30:11: E275 missing whitespace after keyword\n", + "2023-08-15 14:39:04 - INFO - 32:1: E303 too many blank lines (4)\n" ] } ], @@ -1102,7 +1150,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 1:1: E305 expected 2 blank lines after class or function definition, found 4\n" + "2023-08-15 14:39:04 - INFO - 1:1: E305 expected 2 blank lines after class or function definition, found 4\n" ] } ], @@ -1131,10 +1179,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 12:38: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 13:25: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 13:34: E231 missing whitespace after ','\n", - "2023-08-15 13:27:55 - INFO - 14:1: E303 too many blank lines (3)\n" + "2023-08-15 14:39:04 - INFO - 12:38: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 13:25: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 13:34: E231 missing whitespace after ','\n", + "2023-08-15 14:39:04 - INFO - 14:1: E303 too many blank lines (3)\n" ] } ], @@ -1175,7 +1223,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 1:39: E231 missing whitespace after ','\n" + "2023-08-15 14:39:04 - INFO - 1:39: E231 missing whitespace after ','\n" ] } ], @@ -1209,7 +1257,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 13:27:55 - INFO - 1:39: E231 missing whitespace after ','\n" + "2023-08-15 14:39:04 - INFO - 1:39: E231 missing whitespace after ','\n" ] } ], From 8ca78898a8636ed2029181f4743b0019c431d64a Mon Sep 17 00:00:00 2001 From: haticekaratay Date: Tue, 15 Aug 2023 15:22:34 -0400 Subject: [PATCH 21/36] Fix some of the style errors to trigger new CI --- .../miri_lrs_advanced_extraction_part1.ipynb | 674 ++---------------- 1 file changed, 74 insertions(+), 600 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb index bd18a9974..d915c3d87 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -59,301 +59,20 @@ "\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# disable all imported packages' loggers\n", - "import logging\n", - "logging.root.manager.loggerDict = {}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# enable PEP8 checker for this notebook\n", - "%load_ext pycodestyle_magic\n", - "%flake8_on --ignore E261,E501,W291,W293\n", - "\n", - "# only allow the checker to throw warnings when there's a violation\n", - "logging.getLogger('flake8').setLevel('ERROR')\n", - "logging.getLogger('stpipe').setLevel('ERROR')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# disable all imported packages' loggers\n", - "import logging\n", - "logging.root.manager.loggerDict = {}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# enable PEP8 checker for this notebook\n", - "%load_ext pycodestyle_magic\n", - "%flake8_on --ignore E261,E501,W291,W293\n", - "\n", - "# only allow the checker to throw warnings when there's a violation\n", - "logging.getLogger('flake8').setLevel('ERROR')\n", - "logging.getLogger('stpipe').setLevel('ERROR')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 2:1: E402 module level import not at top of file\n" - ] - } - ], - "source": [ - "# disable all imported packages' loggers\n", - "import logging\n", - "logging.root.manager.loggerDict = {}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# enable PEP8 checker for this notebook\n", - "%load_ext pycodestyle_magic\n", - "%flake8_on --ignore E261,E501,W291,W293\n", - "\n", - "# only allow the checker to throw warnings when there's a violation\n", - "logging.getLogger('flake8').setLevel('ERROR')\n", - "logging.getLogger('stpipe').setLevel('ERROR')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 2:1: E402 module level import not at top of file\n" - ] - } - ], - "source": [ - "# disable all imported packages' loggers\n", - "import logging\n", - "logging.root.manager.loggerDict = {}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# enable PEP8 checker for this notebook\n", - "%load_ext pycodestyle_magic\n", - "%flake8_on --ignore E261,E501,W291,W293\n", - "\n", - "# only allow the checker to throw warnings when there's a violation\n", - "logging.getLogger('flake8').setLevel('ERROR')\n", - "logging.getLogger('stpipe').setLevel('ERROR')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 2:1: E402 module level import not at top of file\n" - ] - } - ], - "source": [ - "# disable all imported packages' loggers\n", - "import logging\n", - "logging.root.manager.loggerDict = {}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# enable PEP8 checker for this notebook\n", - "%load_ext pycodestyle_magic\n", - "%flake8_on --ignore E261,E501,W291,W293\n", - "\n", - "# only allow the checker to throw warnings when there's a violation\n", - "logging.getLogger('flake8').setLevel('ERROR')\n", - "logging.getLogger('stpipe').setLevel('ERROR')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 2:1: E402 module level import not at top of file\n" - ] - } - ], - "source": [ - "# disable all imported packages' loggers\n", - "import logging\n", - "logging.root.manager.loggerDict = {}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# enable PEP8 checker for this notebook\n", - "%load_ext pycodestyle_magic\n", - "%flake8_on --ignore E261,E501,W291,W293\n", - "\n", - "# only allow the checker to throw warnings when there's a violation\n", - "logging.getLogger('flake8').setLevel('ERROR')\n", - "logging.getLogger('stpipe').setLevel('ERROR')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" - ] - }, { "cell_type": "code", "execution_count": null, "id": "08ddf5f7", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 14:39:04 - INFO - 1:10: E401 multiple imports on one line\n", - "2023-08-15 14:39:04 - INFO - 1:10: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 1:25: E231 missing whitespace after ','\n" - ] - } - ], + "outputs": [], "source": [ - "import os,urllib.request,tarfile\n", + "import os\n", + "import urllib.request\n", + "import tarfile\n", "\n", "os.environ['CRDS_CONTEXT'] = 'jwst_1089.pmap'\n", "\n", - "os.environ['CRDS_PATH'] = os.environ['HOME']+'/crds_cache' \n", + "os.environ['CRDS_PATH'] = os.environ['HOME']+'/crds_cache'\n", "os.environ['CRDS_SERVER_URL'] = 'https://jwst-crds.stsci.edu'\n", "print('CRDS cache location: {}'.format(os.environ['CRDS_PATH']))" ] @@ -363,30 +82,9 @@ "execution_count": null, "id": "aee92bcf", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 2:1: F401 'glob.glob' imported but unused\n", - "2023-08-15 14:39:04 - INFO - 2:1: E402 module level import not at top of file\n", - "2023-08-15 14:39:04 - INFO - 4:1: E402 module level import not at top of file\n", - "2023-08-15 14:39:04 - INFO - 5:1: E402 module level import not at top of file\n", - "2023-08-15 14:39:04 - INFO - 7:1: E402 module level import not at top of file\n", - "2023-08-15 14:39:04 - INFO - 9:1: F401 'jwst.pipeline.Spec2Pipeline' imported but unused\n", - "2023-08-15 14:39:04 - INFO - 9:1: F401 'jwst.pipeline.Spec3Pipeline' imported but unused\n", - "2023-08-15 14:39:04 - INFO - 9:1: E402 module level import not at top of file\n", - "2023-08-15 14:39:04 - INFO - 10:1: E402 module level import not at top of file\n", - "2023-08-15 14:39:04 - INFO - 11:1: E402 module level import not at top of file\n", - "2023-08-15 14:39:04 - INFO - 13:1: E402 module level import not at top of file\n", - "2023-08-15 14:39:04 - INFO - 14:1: E402 module level import not at top of file\n", - "2023-08-15 14:39:04 - INFO - 17:1: E303 too many blank lines (4)\n" - ] - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", - "from glob import glob\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -399,8 +97,7 @@ "\n", "import jwst\n", "import json\n", - "print('Using JWST calibration pipeline version {0}'.format(jwst.__version__))\n", - "\n" + "print('Using JWST calibration pipeline version {0}'.format(jwst.__version__))" ] }, { @@ -411,14 +108,13 @@ "outputs": [], "source": [ "# Download Data\n", - "\n", "if os.path.exists(\"data.tar.gz\"):\n", " print(\"Original Data tar.gz Exists\")\n", "else:\n", " print(\"Downloading Data\")\n", " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/data.tar.gz'\n", " urllib.request.urlretrieve(url, 'data.tar.gz')\n", - " \n", + "\n", "# Unzip files if they haven't already been unzipped\n", "if os.path.exists(\"data/\"):\n", " print(\"Data Directory Already Exists\")\n", @@ -451,27 +147,17 @@ "execution_count": null, "id": "a8012bfa", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 4:34: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 11:1: E303 too many blank lines (3)\n" - ] - } - ], + "outputs": [], "source": [ "l3_s2d_file = 'data/jw02072-o001_t010_miri_p750l_s2d_1089.fits'\n", "l3_s2d = datamodels.open(l3_s2d_file)\n", - "\n", - "fig, ax = plt.subplots(figsize=[2,8])\n", + "fig, ax = plt.subplots(figsize=[2, 8])\n", "im2d = ax.imshow(l3_s2d.data, origin='lower', aspect='auto', cmap='gist_gray')\n", "ax.set_xlabel('column')\n", "ax.set_ylabel('row')\n", "ax.set_title('SN2021aefx - Level 3 resampled 2D spectral image')\n", "fig.colorbar(im2d)\n", - "fig.show()\n" + "fig.show()" ] }, { @@ -479,27 +165,18 @@ "execution_count": null, "id": "c51f421b", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 4:37: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 11:1: E303 too many blank lines (3)\n" - ] - } - ], + "outputs": [], "source": [ "l3_file = 'data/jw02072-o001_t010_miri_p750l_x1d_1089.fits'\n", "l3_spec = datamodels.open(l3_file)\n", "\n", - "fig2, ax2 = plt.subplots(figsize=[12,4])\n", + "fig2, ax2 = plt.subplots(figsize=[12, 4])\n", "ax2.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['FLUX'])\n", "ax2.set_xlabel('wavelength (um)')\n", "ax2.set_ylabel('flux (Jy)')\n", "ax2.set_title('SN2021aefx - Level 3 spectrum in MAST (pmap 1089)')\n", "ax2.set_xlim(5., 14.)\n", - "fig2.show()\n" + "fig2.show()" ] }, { @@ -535,23 +212,12 @@ "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 14:39:04 - INFO - 2:1: F811 redefinition of unused 'fits' from line 97\n", - "2023-08-15 14:39:04 - INFO - 2:1: E402 module level import not at top of file\n", - "2023-08-15 14:39:04 - INFO - 5:1: E303 too many blank lines (3)\n" - ] - } - ], + "outputs": [], "source": [ "import crds\n", "from astropy.io import fits\n", "hdu = fits.open('data/jw02072-o001_t010_miri_p750l_x1d_1089.fits')\n", - "json_ref_default = crds.getreferences(hdu[0].header)['extract1d']\n" + "json_ref_default = crds.getreferences(hdu[0].header)['extract1d']" ] }, { @@ -559,22 +225,13 @@ "execution_count": null, "id": "50c8ba27", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 7:1: E303 too many blank lines (4)\n" - ] - } - ], + "outputs": [], "source": [ "with open(json_ref_default) as json_ref:\n", " x1dref_default = json.load(json_ref)\n", " print('Settings for SLIT data: {}'.format(x1dref_default['apertures'][0]))\n", " print(' ')\n", - " print('Settings for SLITLESS data: {}'.format(x1dref_default['apertures'][1]))\n", - " \n" + " print('Settings for SLITLESS data: {}'.format(x1dref_default['apertures'][1]))" ] }, { @@ -601,19 +258,7 @@ "execution_count": null, "id": "703f59cd", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 14:39:04 - INFO - 7:32: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 7:69: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", - "2023-08-15 14:39:04 - INFO - 10:34: E231 missing whitespace after ','\n" - ] - } - ], + "outputs": [], "source": [ "from matplotlib.patches import Rectangle\n", "\n", @@ -621,10 +266,10 @@ "xstop = x1dref_default['apertures'][0]['xstop']\n", "ap_height = np.shape(l3_s2d.data)[0]\n", "ap_width = xstop - xstart + 1\n", - "x1d_rect = Rectangle(xy=(xstart,0), width=ap_width, height=ap_height,angle=0., edgecolor='red',\n", - " facecolor='None', ls='-', lw=1.5)\n", + "x1d_rect = Rectangle(xy=(xstart, 0), width=ap_width, height=ap_height, angle=0., edgecolor='red',\n", + " facecolor='None', ls='-', lw=1.5)\n", "\n", - "fig, ax = plt.subplots(figsize=[2,8])\n", + "fig, ax = plt.subplots(figsize=[2, 8])\n", "im2d = ax.imshow(l3_s2d.data, origin='lower', aspect='auto', cmap='gist_gray')\n", "ax.add_patch(x1d_rect)\n", "ax.set_xlabel('column')\n", @@ -651,18 +296,7 @@ "execution_count": null, "id": "06dc8eb5", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 12:38: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 13:25: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 13:34: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 14:1: E303 too many blank lines (3)\n" - ] - } - ], + "outputs": [], "source": [ "xstart2 = xstart - 2\n", "xstop2 = xstop + 2\n", @@ -673,10 +307,9 @@ " x1dref_ex1 = x1dref_default.copy()\n", " x1dref_ex1['apertures'][0]['xstart'] = xstart2\n", " x1dref_ex1['apertures'][0]['xstop'] = xstop2\n", - " \n", "\n", - "with open('x1d_reffile_example1.json','w') as jsrefout:\n", - " json.dump(x1dref_ex1,jsrefout,indent=4)\n" + "with open('x1d_reffile_example1.json', 'w') as jsrefout:\n", + " json.dump(x1dref_ex1, jsrefout, indent=4)" ] }, { @@ -684,37 +317,20 @@ "execution_count": null, "id": "5bc85413", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 1:1: F401 'matplotlib.collections.PatchCollection' imported but unused\n", - "2023-08-15 14:39:04 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 14:39:04 - INFO - 4:33: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 4:70: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 5:21: E128 continuation line under-indented for visual indent\n", - "2023-08-15 14:39:04 - INFO - 7:34: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 7:72: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 8:21: E128 continuation line under-indented for visual indent\n", - "2023-08-15 14:39:04 - INFO - 10:36: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 12:1: E265 block comment should start with '# '\n" - ] - } - ], + "outputs": [], "source": [ "from matplotlib.collections import PatchCollection\n", "\n", "ap_width2 = xstop2 - xstart2 + 1\n", - "x1d_rect1 = Rectangle(xy=(xstart,0), width=ap_width, height=ap_height,angle=0., edgecolor='red',\n", - " facecolor='None', ls='-', lw=1, label='8-px aperture (default)')\n", + "x1d_rect1 = Rectangle(xy=(xstart, 0), width=ap_width, height=ap_height, angle=0., edgecolor='red',\n", + " facecolor='None', ls='-', lw=1, label='8-px aperture (default)')\n", "\n", - "x1d_rect2 = Rectangle(xy=(xstart2,0), width=ap_width2, height=ap_height,angle=0., edgecolor='cyan',\n", - " facecolor='None', ls='-', lw=1, label='12-px aperture')\n", + "x1d_rect2 = Rectangle(xy=(xstart2, 0), width=ap_width2, height=ap_height, angle=0., edgecolor='cyan',\n", + " facecolor='None', ls='-', lw=1, label='12-px aperture')\n", "\n", - "fig4, ax4 = plt.subplots(figsize=[2,8])\n", + "fig4, ax4 = plt.subplots(figsize=[2, 8])\n", "im2d = ax4.imshow(l3_s2d.data, origin='lower', aspect='auto', cmap='gist_gray')\n", - "#ax4.add_collection(aps_collection)\n", + "# ax4.add_collection(aps_collection)\n", "ax4.add_patch(x1d_rect1)\n", "ax4.add_patch(x1d_rect2)\n", "\n", @@ -765,18 +381,9 @@ "execution_count": null, "id": "91ebfc64", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 1:37: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 10:1: E303 too many blank lines (3)\n" - ] - } - ], + "outputs": [], "source": [ - "fig5, ax5 = plt.subplots(figsize=[12,4])\n", + "fig5, ax5 = plt.subplots(figsize=[12, 4])\n", "ax5.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['FLUX'], label='8-px aperture')\n", "ax5.plot(sp3_ex1.spec[0].spec_table['WAVELENGTH'], sp3_ex1.spec[0].spec_table['FLUX'], label='12-px aperture')\n", "ax5.set_xlabel('wavelength (um)')\n", @@ -784,7 +391,7 @@ "ax5.set_title('Example 1: Difference aperture sizes')\n", "ax5.set_xlim(5., 14.)\n", "ax5.legend()\n", - "fig5.show()\n" + "fig5.show()" ] }, { @@ -813,18 +420,7 @@ "execution_count": null, "id": "55c81453", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 11:38: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 12:25: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 12:34: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 13:1: E303 too many blank lines (3)\n" - ] - } - ], + "outputs": [], "source": [ "xstart3 = 9\n", "xstop3 = 17\n", @@ -834,10 +430,9 @@ " x1dref_ex2 = x1dref_default.copy()\n", " x1dref_ex2['apertures'][0]['xstart'] = xstart3\n", " x1dref_ex2['apertures'][0]['xstop'] = xstop3\n", - " \n", "\n", - "with open('x1d_reffile_example2.json','w') as jsrefout:\n", - " json.dump(x1dref_ex2,jsrefout,indent=4)\n" + "with open('x1d_reffile_example2.json', 'w') as jsrefout:\n", + " json.dump(x1dref_ex2, jsrefout, indent=4)" ] }, { @@ -845,23 +440,11 @@ "execution_count": null, "id": "fe340506", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 2:34: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 2:72: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 3:21: E128 continuation line under-indented for visual indent\n", - "2023-08-15 14:39:04 - INFO - 5:36: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 14:1: E303 too many blank lines (3)\n" - ] - } - ], + "outputs": [], "source": [ "ap_width3 = xstop3 - xstart3 + 1\n", - "x1d_rect3 = Rectangle(xy=(xstart3,0), width=ap_width3, height=ap_height,angle=0., edgecolor='red',\n", - " facecolor='None', ls='-', lw=1, label='8-px aperture at nod 1 location')\n", + "x1d_rect3 = Rectangle(xy=(xstart3, 0), width=ap_width3, height=ap_height, angle=0., edgecolor='red',\n", + " facecolor='None', ls='-', lw=1, label='8-px aperture at nod 1 location')\n", "\n", "fig6, ax6 = plt.subplots(figsize=[2,8])\n", "im2d = ax6.imshow(l2_s2d.data, origin='lower', aspect='auto', cmap='gist_gray')\n", @@ -871,7 +454,7 @@ "ax6.set_title('Example 2: Different aperture location')\n", "ax6.legend(loc=3)\n", "fig6.colorbar(im2d)\n", - "fig6.show()\n" + "fig6.show()" ] }, { @@ -879,18 +462,10 @@ "execution_count": null, "id": "3b0b287b", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" - ] - } - ], + "outputs": [], "source": [ "sp2_ex2 = Extract1dStep.call(l2_s2d_file, output_dir='data/', output_file='lrs_slit_extract_example2',\n", - " override_extract1d='x1d_reffile_example2.json')" + " override_extract1d='x1d_reffile_example2.json')" ] }, { @@ -906,17 +481,9 @@ "execution_count": null, "id": "ce8eccfb", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 1:37: E231 missing whitespace after ','\n" - ] - } - ], + "outputs": [], "source": [ - "fig7, ax7 = plt.subplots(figsize=[12,4])\n", + "fig7, ax7 = plt.subplots(figsize=[12, 4])\n", "ax7.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['FLUX'], label='default location (nods combined)')\n", "ax7.plot(sp2_ex2.spec[0].spec_table['WAVELENGTH'], sp2_ex2.spec[0].spec_table['FLUX'], label='nod 1 location (single nod)')\n", "ax7.set_xlabel('wavelength (um)')\n", @@ -953,24 +520,15 @@ "execution_count": null, "id": "b4ea99ea", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 2:36: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 7:34: E231 missing whitespace after ','\n" - ] - } - ], + "outputs": [], "source": [ "rows = [140, 200, 325]\n", - "fig8, ax8 = plt.subplots(figsize=[8,4])\n", + "fig8, ax8 = plt.subplots(figsize=[8, 4])\n", "ncols = np.shape(l2_s2d.data)[1]\n", "pltx = np.arange(ncols)\n", "for rr in rows:\n", " label = 'row {}'.format(rr)\n", - " ax8.plot(pltx, l2_s2d.data[rr,:], label=label)\n", + " ax8.plot(pltx, l2_s2d.data[rr, :], label=label)\n", "ax8.axvline(x=1, ymin=0, ymax=1, ls='--', lw=1., color='coral', label='background regions')\n", "ax8.axvline(x=5, ymin=0, ymax=1, ls='--', lw=1., color='coral')\n", "ax8.axvline(x=39, ymin=0, ymax=1, ls='--', lw=1., color='coral')\n", @@ -984,33 +542,18 @@ "execution_count": null, "id": "1238d6c9", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 6:53: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 6:59: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 6:66: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 10:38: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 11:25: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 11:34: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 12:1: E303 too many blank lines (3)\n" - ] - } - ], + "outputs": [], "source": [ "with open(json_ref_default) as json_ref:\n", " x1dref_default = json.load(json_ref)\n", " x1dref_ex3 = x1dref_default.copy()\n", " x1dref_ex3['apertures'][0]['xstart'] = xstart3\n", " x1dref_ex3['apertures'][0]['xstop'] = xstop3\n", - " x1dref_ex3['apertures'][0]['bkg_coeff'] = [[0.5],[4.5],[38.5],[43.5]]\n", + " x1dref_ex3['apertures'][0]['bkg_coeff'] = [[0.5], [4.5], [38.5], [43.5]]\n", " x1dref_ex3['apertures'][0]['bkg_fit'] = 'median'\n", - " \n", "\n", - "with open('x1d_reffile_example3.json','w') as jsrefout:\n", - " json.dump(x1dref_ex3,jsrefout,indent=4)\n" + "with open('x1d_reffile_example3.json', 'w') as jsrefout:\n", + " json.dump(x1dref_ex3, jsrefout, indent=4)" ] }, { @@ -1018,15 +561,7 @@ "execution_count": null, "id": "ac0d2746", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" - ] - } - ], + "outputs": [], "source": [ "sp2_ex3 = Extract1dStep.call(l2_s2d_file, output_dir='data/', output_file='lrs_slit_extract_example3',\n", " override_extract1d='x1d_reffile_example3.json')" @@ -1045,19 +580,10 @@ "execution_count": null, "id": "7210c9ac", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 1:55: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 2:1: E265 block comment should start with '# '\n" - ] - } - ], + "outputs": [], "source": [ - "fig9, ax9 = plt.subplots(nrows=2, ncols=1, figsize=[12,4])\n", - "#ax9.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['FLUX'], label='default location (nods combined)')\n", + "fig9, ax9 = plt.subplots(nrows=2, ncols=1, figsize=[12, 4])\n", + "# ax9.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['FLUX'], label='default location (nods combined)')\n", "ax9[0].plot(sp2_ex2.spec[0].spec_table['WAVELENGTH'], sp2_ex2.spec[0].spec_table['FLUX'], label='nod 1 spectrum - no bkg sub')\n", "ax9[0].plot(sp2_ex3.spec[0].spec_table['WAVELENGTH'], sp2_ex3.spec[0].spec_table['FLUX'], label='nod 1 spectrum - with bkg sub')\n", "ax9[1].plot(sp2_ex3.spec[0].spec_table['WAVELENGTH'], sp2_ex3.spec[0].spec_table['BACKGROUND'], label='background')\n", @@ -1090,54 +616,38 @@ "execution_count": null, "id": "9e3c2433", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 14:39:04 - INFO - 2:1: E402 module level import not at top of file\n", - "2023-08-15 14:39:04 - INFO - 4:1: E302 expected 2 blank lines, found 1\n", - "2023-08-15 14:39:04 - INFO - 6:5: E741 ambiguous variable name 'l'\n", - "2023-08-15 14:39:04 - INFO - 22:38: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 30:11: E275 missing whitespace after keyword\n", - "2023-08-15 14:39:04 - INFO - 32:1: E303 too many blank lines (4)\n" - ] - } - ], + "outputs": [], "source": [ "import astropy.units as u\n", "from astropy.modeling import models, fitting\n", "\n", + "\n", "def calc_xap_fit():\n", " # these are values measured from commissioning data. FWHM is in arcsec.\n", " l = [5.0, 7.5, 10.0, 12.0]\n", " fwhm = [0.29, 0.3, 0.36, 0.42]\n", - " \n", + "\n", " # convert from arcsec to pixel using MIRI pixel scaling of 0.11 arcsec/px\n", " fwhm_px = fwhm / (0.11*u.arcsec/u.pixel)\n", - " \n", + "\n", " # we want to extract 3 * fwhm, which means 1.5 * fwhm on either side of the trace\n", " xap_pix = 1.5 * fwhm_px\n", - " \n", + "\n", " # now we want to fit a line to these points\n", " line_init = models.Linear1D()\n", " fit = fitting.LinearLSQFitter()\n", - " \n", + "\n", " fitted_line = fit(line_init, l, xap_pix.value)\n", " print(fitted_line)\n", - " \n", - " fig, ax = plt.subplots(figsize=[8,4])\n", + "\n", + " fig, ax = plt.subplots(figsize=[8, 4])\n", " xplt = np.linspace(4.0, 14., num=50)\n", " ax.plot(l, xap_pix.value, 'rx', label='1.5 * FWHM(px)')\n", " ax.plot(xplt, fitted_line(xplt), 'b-', label='best-fit line')\n", " ax.set_xlabel('wavelength')\n", " ax.set_ylabel('px')\n", " ax.legend()\n", - " \n", - " return(fitted_line)\n", - " \n", - " " + " return(fitted_line)" ] }, { @@ -1145,15 +655,7 @@ "execution_count": null, "id": "e21fcec5", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 1:1: E305 expected 2 blank lines after class or function definition, found 4\n" - ] - } - ], + "outputs": [], "source": [ "poly_pos = calc_xap_fit()\n", "print(poly_pos.slope, poly_pos.intercept)" @@ -1174,18 +676,7 @@ "execution_count": null, "id": "a9c9d403", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 12:38: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 13:25: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 13:34: E231 missing whitespace after ','\n", - "2023-08-15 14:39:04 - INFO - 14:1: E303 too many blank lines (3)\n" - ] - } - ], + "outputs": [], "source": [ "trace_cen = 30.5\n", "\n", @@ -1196,10 +687,9 @@ " x1dref_ex4['apertures'][0]['xstop'] = None\n", " x1dref_ex4['apertures'][0]['independent_var'] = 'wavelength'\n", " x1dref_ex4['apertures'][0]['src_coeff'] = [[-1*poly_pos.intercept.value + trace_cen, -1*poly_pos.slope.value], [poly_pos.intercept.value + trace_cen, poly_pos.slope.value]]\n", - " \n", "\n", - "with open('x1d_reffile_example4.json','w') as jsrefout:\n", - " json.dump(x1dref_ex4,jsrefout,indent=4)\n" + "with open('x1d_reffile_example4.json', 'w') as jsrefout:\n", + " json.dump(x1dref_ex4, jsrefout, indent=4)" ] }, { @@ -1218,15 +708,7 @@ "execution_count": null, "id": "9d1bc74c", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 1:39: E231 missing whitespace after ','\n" - ] - } - ], + "outputs": [], "source": [ "fig10, ax10 = plt.subplots(figsize=[12,4])\n", "ax10.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['FLUX'], label='default fixed-width aperture')\n", @@ -1252,17 +734,9 @@ "execution_count": null, "id": "78ca0c68", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 14:39:04 - INFO - 1:39: E231 missing whitespace after ','\n" - ] - } - ], + "outputs": [], "source": [ - "fig11, ax11 = plt.subplots(figsize=[12,4])\n", + "fig11, ax11 = plt.subplots(figsize=[12, 4])\n", "ax11.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['NPIXELS'], label='default fixed-width aperture')\n", "ax11.plot(sp3_ex4.spec[0].spec_table['WAVELENGTH'], sp3_ex4.spec[0].spec_table['NPIXELS'], label='tapered column aperture')\n", "ax11.set_xlabel('wavelength (um)')\n", @@ -1310,7 +784,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.9.13" } }, "nbformat": 4, From b72094ebf40cfa3336d2e002024d9268baf58b2f Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Tue, 15 Aug 2023 19:23:33 +0000 Subject: [PATCH 22/36] [BOT] Left PEP8 feedback on PR 93's notebooks Files: --- .../miri_lrs_advanced_extraction_part1.ipynb | 135 ++++++++++++++++-- 1 file changed, 127 insertions(+), 8 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb index d915c3d87..80ee3dac0 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -59,6 +59,46 @@ "\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# disable all imported packages' loggers\n", + "import logging\n", + "logging.root.manager.loggerDict = {}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enable PEP8 checker for this notebook\n", + "%load_ext pycodestyle_magic\n", + "%flake8_on --ignore E261,E501,W291,W293\n", + "\n", + "# only allow the checker to throw warnings when there's a violation\n", + "logging.getLogger('flake8').setLevel('ERROR')\n", + "logging.getLogger('stpipe').setLevel('ERROR')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" + ] + }, { "cell_type": "code", "execution_count": null, @@ -82,7 +122,24 @@ "execution_count": null, "id": "aee92bcf", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-15 15:23:33 - INFO - 3:1: E402 module level import not at top of file\n", + "2023-08-15 15:23:33 - INFO - 4:1: E402 module level import not at top of file\n", + "2023-08-15 15:23:33 - INFO - 6:1: E402 module level import not at top of file\n", + "2023-08-15 15:23:33 - INFO - 8:1: F401 'jwst.pipeline.Spec2Pipeline' imported but unused\n", + "2023-08-15 15:23:33 - INFO - 8:1: F401 'jwst.pipeline.Spec3Pipeline' imported but unused\n", + "2023-08-15 15:23:33 - INFO - 8:1: E402 module level import not at top of file\n", + "2023-08-15 15:23:33 - INFO - 9:1: E402 module level import not at top of file\n", + "2023-08-15 15:23:33 - INFO - 10:1: E402 module level import not at top of file\n", + "2023-08-15 15:23:33 - INFO - 12:1: E402 module level import not at top of file\n", + "2023-08-15 15:23:33 - INFO - 13:1: E402 module level import not at top of file\n" + ] + } + ], "source": [ "%matplotlib inline\n", "\n", @@ -212,7 +269,17 @@ "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-15 15:23:33 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 15:23:33 - INFO - 2:1: F811 redefinition of unused 'fits' from line 18\n", + "2023-08-15 15:23:33 - INFO - 2:1: E402 module level import not at top of file\n" + ] + } + ], "source": [ "import crds\n", "from astropy.io import fits\n", @@ -258,7 +325,15 @@ "execution_count": null, "id": "703f59cd", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-15 15:23:33 - INFO - 1:1: E402 module level import not at top of file\n" + ] + } + ], "source": [ "from matplotlib.patches import Rectangle\n", "\n", @@ -317,7 +392,16 @@ "execution_count": null, "id": "5bc85413", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-15 15:23:33 - INFO - 1:1: F401 'matplotlib.collections.PatchCollection' imported but unused\n", + "2023-08-15 15:23:33 - INFO - 1:1: E402 module level import not at top of file\n" + ] + } + ], "source": [ "from matplotlib.collections import PatchCollection\n", "\n", @@ -440,7 +524,15 @@ "execution_count": null, "id": "fe340506", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-15 15:23:33 - INFO - 5:36: E231 missing whitespace after ','\n" + ] + } + ], "source": [ "ap_width3 = xstop3 - xstart3 + 1\n", "x1d_rect3 = Rectangle(xy=(xstart3, 0), width=ap_width3, height=ap_height, angle=0., edgecolor='red',\n", @@ -561,7 +653,15 @@ "execution_count": null, "id": "ac0d2746", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-15 15:23:33 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" + ] + } + ], "source": [ "sp2_ex3 = Extract1dStep.call(l2_s2d_file, output_dir='data/', output_file='lrs_slit_extract_example3',\n", " override_extract1d='x1d_reffile_example3.json')" @@ -616,7 +716,18 @@ "execution_count": null, "id": "9e3c2433", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-15 15:23:33 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 15:23:33 - INFO - 2:1: E402 module level import not at top of file\n", + "2023-08-15 15:23:33 - INFO - 7:5: E741 ambiguous variable name 'l'\n", + "2023-08-15 15:23:33 - INFO - 30:11: E275 missing whitespace after keyword\n" + ] + } + ], "source": [ "import astropy.units as u\n", "from astropy.modeling import models, fitting\n", @@ -708,7 +819,15 @@ "execution_count": null, "id": "9d1bc74c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-15 15:23:33 - INFO - 1:39: E231 missing whitespace after ','\n" + ] + } + ], "source": [ "fig10, ax10 = plt.subplots(figsize=[12,4])\n", "ax10.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['FLUX'], label='default fixed-width aperture')\n", From 8e730300c3a3782cb31b2c5ca20605c75820ee7b Mon Sep 17 00:00:00 2001 From: haticekaratay Date: Tue, 15 Aug 2023 15:36:05 -0400 Subject: [PATCH 23/36] Fix style errors --- .../miri_lrs_advanced_extraction_part1.ipynb | 143 ++---------------- 1 file changed, 12 insertions(+), 131 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb index 80ee3dac0..4d600343f 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -59,46 +59,6 @@ "\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# disable all imported packages' loggers\n", - "import logging\n", - "logging.root.manager.loggerDict = {}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# enable PEP8 checker for this notebook\n", - "%load_ext pycodestyle_magic\n", - "%flake8_on --ignore E261,E501,W291,W293\n", - "\n", - "# only allow the checker to throw warnings when there's a violation\n", - "logging.getLogger('flake8').setLevel('ERROR')\n", - "logging.getLogger('stpipe').setLevel('ERROR')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" - ] - }, { "cell_type": "code", "execution_count": null, @@ -122,24 +82,7 @@ "execution_count": null, "id": "aee92bcf", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 15:23:33 - INFO - 3:1: E402 module level import not at top of file\n", - "2023-08-15 15:23:33 - INFO - 4:1: E402 module level import not at top of file\n", - "2023-08-15 15:23:33 - INFO - 6:1: E402 module level import not at top of file\n", - "2023-08-15 15:23:33 - INFO - 8:1: F401 'jwst.pipeline.Spec2Pipeline' imported but unused\n", - "2023-08-15 15:23:33 - INFO - 8:1: F401 'jwst.pipeline.Spec3Pipeline' imported but unused\n", - "2023-08-15 15:23:33 - INFO - 8:1: E402 module level import not at top of file\n", - "2023-08-15 15:23:33 - INFO - 9:1: E402 module level import not at top of file\n", - "2023-08-15 15:23:33 - INFO - 10:1: E402 module level import not at top of file\n", - "2023-08-15 15:23:33 - INFO - 12:1: E402 module level import not at top of file\n", - "2023-08-15 15:23:33 - INFO - 13:1: E402 module level import not at top of file\n" - ] - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "\n", @@ -269,17 +212,7 @@ "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 15:23:33 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 15:23:33 - INFO - 2:1: F811 redefinition of unused 'fits' from line 18\n", - "2023-08-15 15:23:33 - INFO - 2:1: E402 module level import not at top of file\n" - ] - } - ], + "outputs": [], "source": [ "import crds\n", "from astropy.io import fits\n", @@ -325,15 +258,7 @@ "execution_count": null, "id": "703f59cd", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 15:23:33 - INFO - 1:1: E402 module level import not at top of file\n" - ] - } - ], + "outputs": [], "source": [ "from matplotlib.patches import Rectangle\n", "\n", @@ -392,16 +317,7 @@ "execution_count": null, "id": "5bc85413", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 15:23:33 - INFO - 1:1: F401 'matplotlib.collections.PatchCollection' imported but unused\n", - "2023-08-15 15:23:33 - INFO - 1:1: E402 module level import not at top of file\n" - ] - } - ], + "outputs": [], "source": [ "from matplotlib.collections import PatchCollection\n", "\n", @@ -524,15 +440,7 @@ "execution_count": null, "id": "fe340506", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 15:23:33 - INFO - 5:36: E231 missing whitespace after ','\n" - ] - } - ], + "outputs": [], "source": [ "ap_width3 = xstop3 - xstart3 + 1\n", "x1d_rect3 = Rectangle(xy=(xstart3, 0), width=ap_width3, height=ap_height, angle=0., edgecolor='red',\n", @@ -653,18 +561,10 @@ "execution_count": null, "id": "ac0d2746", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 15:23:33 - INFO - 2:29: E128 continuation line under-indented for visual indent\n" - ] - } - ], + "outputs": [], "source": [ "sp2_ex3 = Extract1dStep.call(l2_s2d_file, output_dir='data/', output_file='lrs_slit_extract_example3',\n", - " override_extract1d='x1d_reffile_example3.json')" + " override_extract1d='x1d_reffile_example3.json')" ] }, { @@ -716,18 +616,7 @@ "execution_count": null, "id": "9e3c2433", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 15:23:33 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 15:23:33 - INFO - 2:1: E402 module level import not at top of file\n", - "2023-08-15 15:23:33 - INFO - 7:5: E741 ambiguous variable name 'l'\n", - "2023-08-15 15:23:33 - INFO - 30:11: E275 missing whitespace after keyword\n" - ] - } - ], + "outputs": [], "source": [ "import astropy.units as u\n", "from astropy.modeling import models, fitting\n", @@ -758,7 +647,7 @@ " ax.set_xlabel('wavelength')\n", " ax.set_ylabel('px')\n", " ax.legend()\n", - " return(fitted_line)" + " return(fitted_line)\n" ] }, { @@ -819,17 +708,9 @@ "execution_count": null, "id": "9d1bc74c", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 15:23:33 - INFO - 1:39: E231 missing whitespace after ','\n" - ] - } - ], - "source": [ - "fig10, ax10 = plt.subplots(figsize=[12,4])\n", + "outputs": [], + "source": [ + "fig10, ax10 = plt.subplots(figsize=[12, 4])\n", "ax10.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['FLUX'], label='default fixed-width aperture')\n", "ax10.plot(sp3_ex4.spec[0].spec_table['WAVELENGTH'], sp3_ex4.spec[0].spec_table['FLUX'], label='tapered column aperture')\n", "ax10.set_xlabel('wavelength (um)')\n", From 211db2d448d9b56cbf55f14247959897d1767e36 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Tue, 15 Aug 2023 19:42:15 +0000 Subject: [PATCH 24/36] [BOT] Left PEP8 feedback on PR 93's notebooks Files: --- .../miri_lrs_advanced_extraction_part1.ipynb | 126 +++++++++++++++++- 1 file changed, 119 insertions(+), 7 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb index 4d600343f..3593c364b 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -59,6 +59,46 @@ "\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# disable all imported packages' loggers\n", + "import logging\n", + "logging.root.manager.loggerDict = {}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enable PEP8 checker for this notebook\n", + "%load_ext pycodestyle_magic\n", + "%flake8_on --ignore E261,E501,W291,W293\n", + "\n", + "# only allow the checker to throw warnings when there's a violation\n", + "logging.getLogger('flake8').setLevel('ERROR')\n", + "logging.getLogger('stpipe').setLevel('ERROR')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" + ] + }, { "cell_type": "code", "execution_count": null, @@ -82,7 +122,24 @@ "execution_count": null, "id": "aee92bcf", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-15 15:42:15 - INFO - 3:1: E402 module level import not at top of file\n", + "2023-08-15 15:42:15 - INFO - 4:1: E402 module level import not at top of file\n", + "2023-08-15 15:42:15 - INFO - 6:1: E402 module level import not at top of file\n", + "2023-08-15 15:42:15 - INFO - 8:1: F401 'jwst.pipeline.Spec2Pipeline' imported but unused\n", + "2023-08-15 15:42:15 - INFO - 8:1: F401 'jwst.pipeline.Spec3Pipeline' imported but unused\n", + "2023-08-15 15:42:15 - INFO - 8:1: E402 module level import not at top of file\n", + "2023-08-15 15:42:15 - INFO - 9:1: E402 module level import not at top of file\n", + "2023-08-15 15:42:15 - INFO - 10:1: E402 module level import not at top of file\n", + "2023-08-15 15:42:15 - INFO - 12:1: E402 module level import not at top of file\n", + "2023-08-15 15:42:15 - INFO - 13:1: E402 module level import not at top of file\n" + ] + } + ], "source": [ "%matplotlib inline\n", "\n", @@ -212,7 +269,17 @@ "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-15 15:42:15 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 15:42:15 - INFO - 2:1: F811 redefinition of unused 'fits' from line 18\n", + "2023-08-15 15:42:15 - INFO - 2:1: E402 module level import not at top of file\n" + ] + } + ], "source": [ "import crds\n", "from astropy.io import fits\n", @@ -258,7 +325,15 @@ "execution_count": null, "id": "703f59cd", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-15 15:42:15 - INFO - 1:1: E402 module level import not at top of file\n" + ] + } + ], "source": [ "from matplotlib.patches import Rectangle\n", "\n", @@ -317,7 +392,16 @@ "execution_count": null, "id": "5bc85413", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-15 15:42:15 - INFO - 1:1: F401 'matplotlib.collections.PatchCollection' imported but unused\n", + "2023-08-15 15:42:15 - INFO - 1:1: E402 module level import not at top of file\n" + ] + } + ], "source": [ "from matplotlib.collections import PatchCollection\n", "\n", @@ -440,7 +524,15 @@ "execution_count": null, "id": "fe340506", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-15 15:42:15 - INFO - 5:36: E231 missing whitespace after ','\n" + ] + } + ], "source": [ "ap_width3 = xstop3 - xstart3 + 1\n", "x1d_rect3 = Rectangle(xy=(xstart3, 0), width=ap_width3, height=ap_height, angle=0., edgecolor='red',\n", @@ -616,7 +708,19 @@ "execution_count": null, "id": "9e3c2433", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-15 15:42:15 - INFO - 1:1: E402 module level import not at top of file\n", + "2023-08-15 15:42:15 - INFO - 2:1: E402 module level import not at top of file\n", + "2023-08-15 15:42:15 - INFO - 7:5: E741 ambiguous variable name 'l'\n", + "2023-08-15 15:42:15 - INFO - 30:11: E275 missing whitespace after keyword\n", + "2023-08-15 15:42:15 - INFO - 31:1: E303 too many blank lines (3)\n" + ] + } + ], "source": [ "import astropy.units as u\n", "from astropy.modeling import models, fitting\n", @@ -655,7 +759,15 @@ "execution_count": null, "id": "e21fcec5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-15 15:42:15 - INFO - 1:1: E305 expected 2 blank lines after class or function definition, found 3\n" + ] + } + ], "source": [ "poly_pos = calc_xap_fit()\n", "print(poly_pos.slope, poly_pos.intercept)" From ffbf5ebe0b16ad1fa58aa9bbc84437142bbc6700 Mon Sep 17 00:00:00 2001 From: Ori Fox Date: Thu, 17 Aug 2023 16:23:24 -0400 Subject: [PATCH 25/36] pep 8 edits --- .../miri_lrs_advanced_extraction_part1.ipynb | 597 +++++++++++++----- 1 file changed, 437 insertions(+), 160 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb index 3593c364b..64519f7fe 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -59,110 +59,87 @@ "\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# disable all imported packages' loggers\n", - "import logging\n", - "logging.root.manager.loggerDict = {}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# enable PEP8 checker for this notebook\n", - "%load_ext pycodestyle_magic\n", - "%flake8_on --ignore E261,E501,W291,W293\n", - "\n", - "# only allow the checker to throw warnings when there's a violation\n", - "logging.getLogger('flake8').setLevel('ERROR')\n", - "logging.getLogger('stpipe').setLevel('ERROR')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "08ddf5f7", "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import urllib.request\n", - "import tarfile\n", - "\n", - "os.environ['CRDS_CONTEXT'] = 'jwst_1089.pmap'\n", - "\n", - "os.environ['CRDS_PATH'] = os.environ['HOME']+'/crds_cache'\n", - "os.environ['CRDS_SERVER_URL'] = 'https://jwst-crds.stsci.edu'\n", - "print('CRDS cache location: {}'.format(os.environ['CRDS_PATH']))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aee92bcf", - "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "2023-08-15 15:42:15 - INFO - 3:1: E402 module level import not at top of file\n", - "2023-08-15 15:42:15 - INFO - 4:1: E402 module level import not at top of file\n", - "2023-08-15 15:42:15 - INFO - 6:1: E402 module level import not at top of file\n", - "2023-08-15 15:42:15 - INFO - 8:1: F401 'jwst.pipeline.Spec2Pipeline' imported but unused\n", - "2023-08-15 15:42:15 - INFO - 8:1: F401 'jwst.pipeline.Spec3Pipeline' imported but unused\n", - "2023-08-15 15:42:15 - INFO - 8:1: E402 module level import not at top of file\n", - "2023-08-15 15:42:15 - INFO - 9:1: E402 module level import not at top of file\n", - "2023-08-15 15:42:15 - INFO - 10:1: E402 module level import not at top of file\n", - "2023-08-15 15:42:15 - INFO - 12:1: E402 module level import not at top of file\n", - "2023-08-15 15:42:15 - INFO - 13:1: E402 module level import not at top of file\n" + "CRDS cache location: /Users/ofox/crds_cache\n" ] } ], "source": [ "%matplotlib inline\n", "\n", + "import os\n", + "import urllib.request\n", + "import tarfile\n", + "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import astropy.io.fits as fits\n", + "import astropy.units as u\n", + "from astropy.modeling import models, fitting\n", "\n", + "import jwst\n", "from jwst.pipeline import Spec2Pipeline, Spec3Pipeline\n", "from jwst import datamodels\n", "from jwst.extract_1d import Extract1dStep\n", "\n", - "import jwst\n", + "from matplotlib.patches import Rectangle\n", + "from matplotlib.collections import PatchCollection\n", + "\n", + "\n", "import json\n", + "import crds\n", + "\n", "print('Using JWST calibration pipeline version {0}'.format(jwst.__version__))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, + "id": "aee92bcf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using JWST calibration pipeline version 1.11.3\n" + ] + } + ], + "source": [ + "# Set CRDS variables\n", + "\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('CRDS cache location: {}'.format(os.environ['CRDS_PATH']))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "id": "305103d5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original Data tar.gz Exists\n", + "Data Directory Already Exists\n" + ] + } + ], "source": [ "# Download Data\n", "if os.path.exists(\"data.tar.gz\"):\n", @@ -201,10 +178,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "a8012bfa", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-16 09:59:42,705 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/3079267470.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-16 09:59:42,706 - stpipe - WARNING - fig.show()\n", + "2023-08-16 09:59:42,706 - stpipe - WARNING - \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "l3_s2d_file = 'data/jw02072-o001_t010_miri_p750l_s2d_1089.fits'\n", "l3_s2d = datamodels.open(l3_s2d_file)\n", @@ -219,10 +216,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "c51f421b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-16 09:59:42,907 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/44548106.py:10: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-16 09:59:42,908 - stpipe - WARNING - fig2.show()\n", + "2023-08-16 09:59:42,908 - stpipe - WARNING - \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "l3_file = 'data/jw02072-o001_t010_miri_p750l_x1d_1089.fits'\n", "l3_spec = datamodels.open(l3_file)\n", @@ -252,47 +269,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "f0574ee0-84a0-4fa8-ae54-d7b6ca34a7a7", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Spectral extraction reference file used: crds://jwst_miri_extract1d_0005.json\n" + ] + } + ], "source": [ "print('Spectral extraction reference file used: {}'.format(l3_spec.meta.ref_file.extract1d.name))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "95f20a0a-0f24-4d4b-8480-2c37574ad6e8", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 15:42:15 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 15:42:15 - INFO - 2:1: F811 redefinition of unused 'fits' from line 18\n", - "2023-08-15 15:42:15 - INFO - 2:1: E402 module level import not at top of file\n" - ] - } - ], + "outputs": [], "source": [ - "import crds\n", - "from astropy.io import fits\n", "hdu = fits.open('data/jw02072-o001_t010_miri_p750l_x1d_1089.fits')\n", "json_ref_default = crds.getreferences(hdu[0].header)['extract1d']" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "50c8ba27", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Settings for SLIT data: {'id': 'MIR_LRS-FIXEDSLIT', 'region_type': 'target', 'bkg_order': 0, 'dispaxis': 2, 'xstart': 27, 'xstop': 34, 'use_source_posn': False}\n", + " \n", + "Settings for SLITLESS data: {'id': 'MIR_LRS-SLITLESS', 'region_type': 'target', 'bkg_order': 0, 'dispaxis': 2, 'xstart': 30, 'xstop': 41, 'use_source_posn': False}\n" + ] + } + ], "source": [ "with open(json_ref_default) as json_ref:\n", " x1dref_default = json.load(json_ref)\n", @@ -322,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "703f59cd", "metadata": {}, "outputs": [ @@ -330,13 +353,23 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 15:42:15 - INFO - 1:1: E402 module level import not at top of file\n" + "2023-08-16 09:59:43,639 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/752872122.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-16 09:59:43,640 - stpipe - WARNING - fig.show()\n", + "2023-08-16 09:59:43,640 - stpipe - WARNING - \n" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "from matplotlib.patches import Rectangle\n", - "\n", "xstart = x1dref_default['apertures'][0]['xstart']\n", "xstop = x1dref_default['apertures'][0]['xstop']\n", "ap_height = np.shape(l3_s2d.data)[0]\n", @@ -368,10 +401,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "06dc8eb5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "New xstart, xstop values = 25,36\n" + ] + } + ], "source": [ "xstart2 = xstart - 2\n", "xstop2 = xstop + 2\n", @@ -389,7 +430,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "5bc85413", "metadata": {}, "outputs": [ @@ -397,14 +438,23 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 15:42:15 - INFO - 1:1: F401 'matplotlib.collections.PatchCollection' imported but unused\n", - "2023-08-15 15:42:15 - INFO - 1:1: E402 module level import not at top of file\n" + "2023-08-16 09:59:43,800 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/3651783177.py:21: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-16 09:59:43,801 - stpipe - WARNING - fig.show()\n", + "2023-08-16 09:59:43,801 - stpipe - WARNING - \n" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "from matplotlib.collections import PatchCollection\n", - "\n", "ap_width2 = xstop2 - xstart2 + 1\n", "x1d_rect1 = Rectangle(xy=(xstart, 0), width=ap_width, height=ap_height, angle=0., edgecolor='red',\n", " facecolor='None', ls='-', lw=1, label='8-px aperture (default)')\n", @@ -441,10 +491,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "7304f758", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-16 09:59:43,961 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n", + "2023-08-16 09:59:44,023 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args (,).\n", + "2023-08-16 09:59:44,025 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/lrs_slit_extract_example1', 'output_dir': 'data/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': None, 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'center_xy': None, 'apply_apcorr': True, 'ifu_autocen': False, 'ifu_rfcorr': False, '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", + "2023-08-16 09:59:44,054 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/x1d_reffile_example1.json\n", + "2023-08-16 09:59:44,084 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/ofox/crds_cache/references/jwst/miri/jwst_miri_apcorr_0007.fits\n", + "2023-08-16 09:59:44,113 - stpipe.Extract1dStep - WARNING - spectral_order is None; using 1\n", + "2023-08-16 09:59:44,113 - stpipe.Extract1dStep - INFO - Processing spectral order 1\n", + "2023-08-16 09:59:44,120 - stpipe.Extract1dStep - INFO - Using extraction limits: xstart=25, xstop=36, ystart=0, ystop=387\n", + "2023-08-16 09:59:44,172 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n", + "2023-08-16 09:59:44,316 - stpipe.Extract1dStep - INFO - Results used CRDS context: jwst_1089.pmap\n", + "2023-08-16 09:59:44,413 - stpipe.Extract1dStep - INFO - Saved model in data/lrs_slit_extract_example1_extract1dstep.fits\n", + "2023-08-16 09:59:44,413 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n" + ] + } + ], "source": [ "sp3_ex1 = Extract1dStep.call(l3_s2d, output_dir='data/', \n", " output_file='lrs_slit_extract_example1', override_extract1d='x1d_reffile_example1.json')" @@ -452,20 +521,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "91199fd1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], "source": [ "print(sp3_ex1)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "91ebfc64", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-16 09:59:44,462 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/3663854483.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-16 09:59:44,462 - stpipe - WARNING - fig5.show()\n", + "2023-08-16 09:59:44,463 - stpipe - WARNING - \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig5, ax5 = plt.subplots(figsize=[12, 4])\n", "ax5.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['FLUX'], label='8-px aperture')\n", @@ -490,7 +587,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "8a8cf793", "metadata": {}, "outputs": [], @@ -501,7 +598,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "55c81453", "metadata": {}, "outputs": [], @@ -521,7 +618,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "fe340506", "metadata": {}, "outputs": [ @@ -529,7 +626,27 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-15 15:42:15 - INFO - 5:36: E231 missing whitespace after ','\n" + "2023-08-16 09:59:44,704 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/4031254078.py:13: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-16 09:59:44,704 - stpipe - WARNING - fig6.show()\n", + "2023-08-16 09:59:44,704 - stpipe - WARNING - \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "5:36: E231 missing whitespace after ','\n", + "2023-08-16 09:59:44,833 - stpipe - INFO - 5:36: E231 missing whitespace after ','\n" ] } ], @@ -538,7 +655,7 @@ "x1d_rect3 = Rectangle(xy=(xstart3, 0), width=ap_width3, height=ap_height, angle=0., edgecolor='red',\n", " facecolor='None', ls='-', lw=1, label='8-px aperture at nod 1 location')\n", "\n", - "fig6, ax6 = plt.subplots(figsize=[2,8])\n", + "fig6, ax6 = plt.subplots(figsize=[2, 8])\n", "im2d = ax6.imshow(l2_s2d.data, origin='lower', aspect='auto', cmap='gist_gray')\n", "ax6.add_patch(x1d_rect3)\n", "ax6.set_xlabel('column')\n", @@ -551,10 +668,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "3b0b287b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-16 09:59:44,907 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n", + "2023-08-16 09:59:44,970 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args ('data/jw02072001001_06101_00001_mirimage_s2d.fits',).\n", + "2023-08-16 09:59:44,971 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/lrs_slit_extract_example2', 'output_dir': 'data/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': None, 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'center_xy': None, 'apply_apcorr': True, 'ifu_autocen': False, 'ifu_rfcorr': False, '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", + "2023-08-16 09:59:45,027 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/x1d_reffile_example2.json\n", + "2023-08-16 09:59:45,056 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/ofox/crds_cache/references/jwst/miri/jwst_miri_apcorr_0007.fits\n", + "2023-08-16 09:59:45,082 - stpipe.Extract1dStep - WARNING - spectral_order is None; using 1\n", + "2023-08-16 09:59:45,083 - stpipe.Extract1dStep - INFO - Processing spectral order 1\n", + "2023-08-16 09:59:45,088 - stpipe.Extract1dStep - INFO - Using extraction limits: xstart=9, xstop=17, ystart=0, ystop=386\n", + "2023-08-16 09:59:45,142 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n", + "2023-08-16 09:59:45,293 - stpipe.Extract1dStep - INFO - Results used CRDS context: jwst_1089.pmap\n", + "2023-08-16 09:59:45,345 - stpipe.Extract1dStep - INFO - Saved model in data/lrs_slit_extract_example2_extract1dstep.fits\n", + "2023-08-16 09:59:45,346 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n" + ] + } + ], "source": [ "sp2_ex2 = Extract1dStep.call(l2_s2d_file, output_dir='data/', output_file='lrs_slit_extract_example2',\n", " override_extract1d='x1d_reffile_example2.json')" @@ -570,10 +706,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "ce8eccfb", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-16 09:59:45,376 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/259050671.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-16 09:59:45,377 - stpipe - WARNING - fig7.show()\n", + "2023-08-16 09:59:45,377 - stpipe - WARNING - \n" + ] + }, + { + "data": { + "image/png": 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G+HO14mkU1yenbvU+s7S05P333+eVV17B09OTe+65hz59+jBkyJASf24IIcTdQJILQgghqs358+cNNzbHjh274fWvv/6aYcOG0a9fPyZNmoSHhwdarZbZs2dz7ty5G8pf+2lsWY5fe5Nyu4pHJcyZM+eGNSCKFY8SuJWRI0eyYcMGvvnmm1Jv9CrL8ePHgcrdHlSn09GzZ08mT55c4uvFiZpi1386X9yXEydOJCwsrMQ6ro+3sr+3O3fu5JFHHuHee+9l4cKFeHt7Y25uztKlS1m1alW56yu+pqeffrrUBFSzZs2MnlfWqIWioqISj5fW7ytXrizxRvjaUT+m8NJLL7F06VLGjx9Phw4dcHR0RFEUnnzyyXKNCqqIsrzPxo8fz8MPP8y6devYvHkzU6dOZfbs2fzxxx+0bNmyWuIUQoiaRJILQgghqoVOp2PYsGE4ODgwfvx4Zs2axRNPPMFjjz1mKPP9999Tt25dfvzxR6NPjadPn14lMRV/cn5tWxEREQClLsgWHBwM6D/d7dGjx223PWnSJJYuXcq8efOMhmpXlZUrVwKUehN/O4KDg8nMzLztfqhbty6gH55ekb68XkkjDkrzww8/YGVlxebNm7G0tDQcX7p0aYnli5Nj14qIiMDGxsYwgsPe3p6ioqIKXdPNrsHZ2Zm0tDSjY/n5+WXeVaT4Pezh4VEp/V6nTh0Azpw5c0OS7MyZM4bXi7/fxYmu0nz//fcMHTqUuXPnGo7l5ubecM3l+T7XqVOHo0ePotPpjEYvnD592ugayis4OJhXXnmFV155hcjISFq0aMHcuXP5+uuvb6s+IYSozWTNBSGEENXio48+Yvfu3SxevJiZM2fSsWNHXnjhBaNdJoo/Lbz208E9e/YQHh5eJTFdvnyZn376yfA8IyODFStW0KJFi1KHNrdu3Zrg4GA+/PBDw1SDa127LWJp5syZw4cffsjrr79+wyr116qsrShXrVrFl19+SYcOHbj//vsrVNe1BgwYQHh4OJs3b77htbS0NAoLC296voeHB926deN///tfiddYlr4sia2t7Q03oqXRarUoimL0qf+FCxdYt25dieXDw8ON5v3HxMSwfv16HnjgAbRaLVqtlscff5wffvihxJvosl6Tra0tQInXERwcfMN6FosXLy515ML1wsLCcHBwYNasWUZTj8obY7E2bdrg4eHBokWLjKaV/Pbbb5w6dcqw44a7uzv33nsvS5YsITo62qiOa//Pa7XaG0aifPrppzdc38366HoPPfQQ8fHxhl1FAAoLC/n000+xs7Oja9euZbvYf2VnZ5Obm2t0LDg4GHt7e6M+EEKIu4mMXBBCCFFhv/32m+ETwGt17NiRunXrcurUKaZOncqwYcN4+OGHAf3WdS1atGD06NF89913APTp04cff/yRRx99lN69exMVFcWiRYto1KhRiTfyFVW/fn1GjBjBvn378PT0ZMmSJSQkJJT6qTWARqPhyy+/5MEHH6Rx48YMHz4cX19fYmNj+fPPP3FwcOCXX34p9fyffvqJyZMnExISQsOGDW/4hLNnz56GrRNvZyvK77//Hjs7O8MClps3b2bXrl00b96ctWvXlqmOspo0aRI///wzffr0YdiwYbRu3ZqsrCyOHTvG999/z4ULF3Bzc7tpHQsWLKBz5840bdqUkSNHUrduXRISEggPD+fSpUscOXKk3HG1bt2azz//nHfeeYd69erh4eFR6rST3r1789FHH9GrVy/+7//+j8TERBYsWEC9evU4evToDeWbNGlCWFiY0VaUAG+99ZahzHvvvceff/5J+/btGTlyJI0aNSIlJYWDBw+ydetWUlJSbnkNLVq0QKvV8v7775Oeno6lpSXdu3fHw8ODZ599lueff57HH3+cnj17cuTIETZv3nzLvi7m4ODA559/zuDBg2nVqhVPPvkk7u7uREdH8+uvv9KpUyc+++yzMtUF+pEn77//PsOHD6dr16489dRThq0oAwMDefnllw1l58+fT+fOnWnVqhWjRo0iKCiICxcu8Ouvv3L48GFA/3Ng5cqVODo60qhRI8LDw9m6datha9uy9NH1Ro0axf/+9z+GDRvGgQMHCAwM5Pvvv2fXrl3Mmzev3GuRREREcP/99zNgwAAaNWqEmZkZP/30EwkJCTz55JPlqksIIe4YptmkQgghxJ3gZltRAurSpUvVwsJCtW3btqqfn5/RdoGqqqqffPKJCqhr1qxRVVW/Hd2sWbPUOnXqqJaWlmrLli3VDRs23LBlYvF2cddvA1e8Rd7atWtLjHPfvn2GY8VbLm7evFlt1qyZamlpqTZo0OCGc6/fdq/YoUOH1Mcee0x1dXVVLS0t1Tp16qgDBgxQt23bdtM+K94ysrTHte3czlaUxQ8rKyvVz89P7dOnj7pkyRKjrR6LVXQrSlXVb2v42muvqfXq1VMtLCxUNzc3tWPHjuqHH36o5ufnG11Hadv2nTt3Th0yZIjq5eWlmpubq76+vmqfPn3U77//3lCmpO+hqpb8/YmPj1d79+6t2tvbq8Att6X86quv1JCQEMN7YOnSpYb+LKkPvv76a0P5li1blrgVYkJCgvriiy+q/v7+qrm5uerl5aXef//96uLFi2+I/fr3XLEvvvhCrVu3rqrVao2usaioSJ0yZYrq5uam2tjYqGFhYerZs2dL3Yry+j67tv2wsDDV0dFRtbKyUoODg9Vhw4YZbbVZ2nkl/Z9Ys2aN2rJlS9XS0lJ1cXFRBw0apF66dOmG848fP64++uijqpOTk2plZaWGhoaqU6dONbyempqqDh8+XHVzc1Pt7OzUsLAw9fTp0zdc38366PqtKFVV/z0prtfCwkJt2rSpunTpUqMyN3uvcs0WoMnJyeqLL76oNmjQQLW1tVUdHR3V9u3bq999991N+04IIe5kiqpWwupWQgghRC0TGBhIkyZN2LBhg6lDEbWEoii8+OKL5fpUXwghhLhbyJoLQgghhBBCCCGEqBBJLgghhBBCCCGEEKJCJLkghBBCCCGEEEKICpE1F4QQQgghhBBCCFEhMnJBCCGEEEIIIYQQFSLJBSGEEEIIIYQQQlSImakDEHo6nY7Lly9jb2+PoiimDkcIIYQQQgghxB1OVVWuXr2Kj48PGk3Fxh5IcqGGuHz5Mv7+/qYOQwghhBBCCCHEXSYmJgY/P78K1SHJhRrC3t4e0H9THRwcTByNEEIIIYQQQog7XUZGBv7+/ob70YqQ5EINUTwVwsHBQZILQgghhBBCCCGqTWVMzZcFHYUQQgghhBBCCFEhklwQQgghhBBCCCFEhUhyQQghhBBCCCGEEBUiay7UIqqqUlhYSFFRkalDEULcAbRaLWZmZrL9rRBCCCGEqDBJLtQS+fn5xMXFkZ2dbepQhBB3EBsbG7y9vbGwsDB1KEIIIYQQohaT5EItoNPpiIqKQqvV4uPjg4WFhXzSKISoEFVVyc/PJykpiaioKEJCQtBoZKacEEIIIYS4PZJcqAXy8/PR6XT4+/tjY2Nj6nCEEHcIa2trzM3NuXjxIvn5+VhZWZk6JCGEEEIIUUvJx1S1iHyqKISobPJzRQghhBBCVAb5q1IIIYQQQgghhBAVIskFIYQQQgghRO2TcBJy000dhRDiX5JcENWqW7dujB8/vlznrFu3jnr16qHVast97s0oisK6devKdc727dtRFIW0tLRKi+N2XLhwAUVROHz4cJW3lZ+fT7169di9e3eVtxUYGMi8efOqvJ3KVJaYb+e9VtFY8vPzCQwMZP/+/VXerhBCCFHtYg+ift6Rwh9fMHUkQoh/SXJB1HjPPfccTzzxBDExMcycObNK2qjOm/XyGjZsGP369TM65u/vT1xcHE2aNKny9hctWkRQUBAdO3as8rbuVHFxcTz44IPV2qaFhQUTJ05kypQp1dquEEIIUR0uHd2Ogkph5DbQFQH6nZC+3HmeMasOkltQZOIIhbj7SHJB1GiZmZkkJiYSFhaGj48P9vb2pg6pRtBqtXh5eWFmVrUbvqiqymeffcaIESOqtJ07nZeXF5aWltXe7qBBg/j77785ceJEtbcthBBCVKWUmFMAWKm5nDq2D1VVmbP5DO/8eooNR+PYdTbZxBEKcfeR5EItpaoq2fmF1f5QVbXMMWZlZTFkyBDs7Ozw9vZm7ty5N5TJy8tj4sSJ+Pr6YmtrS/v27dm+fTugn4JQnEzo3r07iqKwfft2rly5wlNPPYWvry82NjY0bdqUb7/91qjekoaqt2jRghkzZpQYa1BQEAAtW7ZEURS6detW5uv84YcfaNy4MZaWlgQGBt5wnXl5eUyZMgV/f38sLS2pV68eX331FQBFRUWMGDGCoKAgrK2tCQ0N5ZNPPjGcO2PGDJYvX8769etRFMXQByWNtNixYwft2rXD0tISb29vXn31VQoLCw2vd+vWjbFjxzJ58mRcXFzw8vIqtT+KHThwgHPnztG7d2/DseK2f/zxR+677z5sbGxo3rw54eHh5eqXxMREHn74YaytrQkKCuKbb74xel1VVWbMmEFAQACWlpb4+PgwduzYm8b7yy+/0LZtW6ysrHBzc+PRRx81vJaamsqQIUNwdnbGxsaGBx98kMjISMPry5Ytw8nJiQ0bNhAaGoqNjQ1PPPEE2dnZLF++nMDAQJydnRk7dixFRcafhly9epWnnnoKW1tbfH19WbBggdHr106LKGv//f3333Tp0gVra2v8/f0ZO3YsWVlZZe4/AGdnZzp16sTq1atv2m9CCCFEbWOedt7w7wO7t/L+pjMs3H7OcCwqOauk04QQVahqP/YUVSanoIhG0zZXe7sn3w7DxqJsb5tJkyaxY8cO1q9fj4eHB6+//joHDx6kRYsWhjJjxozh5MmTrF69Gh8fH3766Sd69erFsWPH6NixI2fOnCE0NJQffviBjh074uLiQlJSEq1bt2bKlCk4ODjw66+/MnjwYIKDg2nXrt1tXdfevXtp164dW7dupXHjxlhYWJTpvAMHDjBgwABmzJjBwIED2b17N6NHj8bV1ZVhw4YBMGTIEMLDw5k/fz7NmzcnKiqK5GR9Nl2n0+Hn58fatWtxdXVl9+7djBo1Cm9vbwYMGMDEiRM5deoUGRkZLF26FAAXFxcuX75sFEdsbCwPPfQQw4YNY8WKFZw+fZqRI0diZWVllEBYvnw5EyZMYM+ePYSHhzNs2DA6depEz549S7y+nTt3Ur9+/RJHjLzxxht8+OGHhISE8MYbb/DUU09x9uxZzMzMytQvw4YN4/Lly/z555+Ym5szduxYEhMTDfX/8MMPfPzxx6xevZrGjRsTHx/PkSNHSv1e/Prrrzz66KO88cYbrFixgvz8fDZu3Gh4fdiwYURGRvLzzz/j4ODAlClTeOihhzh58iTm5uYAZGdnM3/+fFavXs3Vq1d57LHHePTRR3FycmLjxo2cP3+exx9/nE6dOjFw4EBD3XPmzOH111/nrbfeYvPmzYwbN4769euX2q+36r9z587Rq1cv3nnnHZYsWUJSUhJjxoxhzJgxhvfBrfqvWLt27di5c2epcQghhBC1kVNO9H9PYg+y6GJLAEI97TmTcJXzklwQotpJckFUiczMTL766iu+/vpr7r//fkB/Y+vn52coEx0dzdKlS4mOjsbHxweAiRMnsmnTJpYuXcqsWbPw8PAAMHzSDuDr68vEiRMN9bz00kts3ryZ77777raTC+7u7gC4uroa2imLjz76iPvvv5+pU6cCUL9+fU6ePMmcOXMYNmwYERERfPfdd2zZsoUePXoAULduXcP55ubmvPXWW4bnQUFBhIeH89133zFgwADs7OywtrYmLy/vpnEtXLgQf39/PvvsMxRFoUGDBly+fJkpU6Ywbdo0NBr9IKVmzZoxffp0AEJCQvjss8/Ytm1bqTfBFy9eNHxvrjdx4kTDiIa33nqLxo0bc/bsWRo0aFCmfvntt9/Yu3cvbdu2BeCrr76iYcOGhvqjo6Px8vKiR48emJubExAQcNPv77vvvsuTTz5p1J/NmzcHMCQVdu3aZVg74ptvvsHf359169bRv39/AAoKCvj8888JDg4G4IknnmDlypUkJCRgZ2dHo0aNuO+++/jzzz+NkgudOnXi1VdfNVzrrl27+Pjjj2+aXLhZ/82ePZtBgwYZFjANCQlh/vz5dO3alc8//5zo6Ohb9l8xHx8fLl68WGocQgghRG2TkXkVT10SKPrnzTX6EQtv922MnaUZE747wvmkTBNGKMTdSZILtZS1uZaTb4eZpN2yOHfuHPn5+bRv395wzMXFhdDQUMPzY8eOUVRURP369Y3OzcvLw9XVtdS6i4qKmDVrFt999x2xsbHk5+eTl5eHjY1NOa+m4k6dOkXfvn2NjnXq1Il58+ZRVFTE4cOH0Wq1dO3atdQ6FixYwJIlS4iOjiYnJ4f8/Hyj0R1ljaNDhw4oimIUR2ZmJpcuXSIgIADQJxeu5e3tXeKn3cVycnKwsrIq8bVr6/L29gb0Q/UbNGhwy345deoUZmZmtG7d2vB6gwYNcHJyMjzv378/8+bNo27duvTq1YuHHnqIhx9+uNR1Jg4fPszIkSNLfK24vWvfj66uroSGhnLq1CnDMRsbG0NiAcDT05PAwEDs7OyMjl3fZx06dLjh+a12kLhZ/x05coSjR48aTXVQVRWdTkdUVBQRERG37L9i1tbWZGdn3zQWIYQQoja5EHGCZopKIRrM0NFQE8OKzince+oF8lJjqWthxsSkaaYOU4i7jiQXailFUco8PaGmyszMRKvVcuDAAbRa46TFtTdz15szZw6ffPIJ8+bNo2nTptja2jJ+/Hjy8/MNZTQazQ3rQxQUFFTuBZSBtbX1TV9fvXo1EydOZO7cuXTo0AF7e3vmzJnDnj17qiSe4uH/xRRFQafTlVrezc2NY8eO3bKu4qTGzeoqL39/f86cOcPWrVvZsmULo0ePZs6cOezYseOG64Bb93VZlNQ/5e2z22nr+v7LzMzkueeeK3GNiYCAACIiIsrcTkpKimFkjhBCCHEnSLygX6j4kkU9As1SMMtO5t79LwEqlkALDTTJ2kNWXh9sLWv338tC1CayoKOoEsHBwZibmxvdJKemphrdFLVs2ZKioiISExOpV6+e0eNmUwB27dpF3759efrpp2nevDl169a94WbL3d2duLg4w/OMjAyioqJKrbN4jYXrF+q7lYYNG7Jr164b4qtfvz5arZamTZui0+nYsWNHqdfSsWNHRo8eTcuWLalXrx7nzp0zKmNhYXHLuBo2bEh4eLhRQmXXrl3Y29sbTUUpr5YtW3L69OlyLeRZHM/N+qVBgwYUFhZy4MABw+tnzpwhLS3N6Bxra2sefvhh5s+fz/bt2wkPDy812dGsWTO2bdtWajyFhYVG78crV65w5swZGjVqVK5rK8k///xzw/OSpiiUVatWrTh58uQN/y/q1auHhYVFmfsP4Pjx47Rs2fK2YxFCCCFqmpx4/YLMuY5B4Nvq36MqBHeHpvqpjm00Z2RRRyGqmSQXRJWws7NjxIgRTJo0iT/++IPjx48zbNgww9x/0M9NHzRoEEOGDOHHH38kKiqKvXv3Mnv2bH799ddS6w4JCWHLli3s3r2bU6dO8dxzz5GQkGBUpnv37qxcuZKdO3dy7Ngxhg4desPoiGt5eHhgbW3Npk2bSEhIID09vUzX+corr7Bt2zZmzpxJREQEy5cv57PPPjOsCREYGMjQoUN55plnWLduHVFRUWzfvp3vvvvOcC379+9n8+bNREREMHXqVPbt22fURmBgIEePHuXMmTMkJyeXOAJj9OjRxMTE8NJLL3H69GnWr1/P9OnTmTBhglGfl9d9991HZmZmubcyvFW/hIaG0qtXL5577jn27NnDgQMHePbZZ41GHyxbtoyvvvqK48ePc/78eb7++musra2pU6dOiW1Onz6db7/9lunTp3Pq1CmOHTvG+++/D+j7uW/fvowcOZK///6bI0eO8PTTT+Pr63vD9I3bsWvXLj744AMiIiJYsGABa9euZdy4cbdd35QpU9i9ezdjxozh8OHDREZGsn79esaMGQOUrf+K7dy5kwceeOC2YxFCCCFqGrM0/Qcx5u71wP/fKY9uodB/GTR8BIDWmkhJLghRzSS5IKrMnDlz6NKlCw8//DA9evSgc+fORnPEAZYuXcqQIUN45ZVXCA0NpV+/fuzbt8+wRkBJ3nzzTVq1akVYWBjdunXDy8uLfv36GZV57bXX6Nq1K3369KF3797069fPaC799czMzJg/fz7/+9//8PHxKfMNZ6tWrfjuu+9YvXo1TZo0Ydq0abz99tuGHREAPv/8c5544glGjx5NgwYNGDlypGFLweeee47HHnuMgQMH0r59e65cucLo0aON2hg5ciShoaG0adMGd3f3G0YEgH6Ry40bN7J3716aN2/O888/z4gRI3jzzTfLdB2lcXV15dFHHy1xm8ObKUu/LF26FB8fH7p27cpjjz3GqFGjDAt4Ajg5OfHFF1/QqVMnmjVrxtatW/nll19KXY+jW7durF27lp9//pkWLVrQvXt39u7da9Re69at6dOnDx06dEBVVTZu3FjiFIvyeuWVV9i/fz8tW7bknXfe4aOPPiIs7PbXRGnWrBk7duwgIiKCLl260LJlS6ZNm2a0uOat+g8gPDyc9PR0nnjiiduORQghhKhJsvIKcc6NAcAloBG0fw4e+hCG/gxWjoZkQ6gSw6W4hJtVJYSoZIpa3vHOokpkZGTg6OhIeno6Dg4ORq/l5uYSFRVFUFBQqYvrCVFVjh49Ss+ePTl37txN18IQNc/AgQNp3rw5r7/+eqll5OeLEEKI2qKgSMfzKw8wK+oJPJU01Gf/QPFrfUO59Pca4Zgby+f+c3hhxCgTRCpE7XGz+9DykpELQoibatasGe+///5N16wQNU9+fj5Nmzbl5ZdfNnUoQgghRIWpqsobPx0j/HQ0nkoaAIpryaNSMz30CQfn5IPVFZ4QAtktQghRBtdOZxC1g4WFRYWnxQghhBA1xfcHLvHd/ks8ZrZff8DBD6ydSixrFtgBon+mTs5xVFU12qpbCFF1ZOSCEEIIIYQQosY6n5TJ9J9PACqvOv2hP9hmWKnlnep3BqCpGkliuizqKER1keSCEEIIIYQQosaa/vMJsvOLGOZ7GY/M02BmBa2fKbW8pU9jcrDCTsnlQkTJW1gLISqfTIsQQgghhBBC1Dzxx8jf+SkDLlxkgDn0KkrSH2/+JNiWvHsUABotCVZBBOaeIjXqELTrUD3xCnGXk+SCEEIIIYQQoubZ/h4WpzfwsPbf52mAooF7Rt/kJL0c5wYQdwpdwomqjFAIcQ1JLgghhBBCCCFqnoTjAHxR+BD+QQ3o1cQLPBqBe+gtT7XwbQpxP+GQHlHVUQoh/lXr1lxYsGABgYGBWFlZ0b59e/bu3XvT8mvXrqVBgwZYWVnRtGlTNm7caPS6qqpMmzYNb29vrK2t6dGjB5GRkYbXL1y4wIgRIwgKCsLa2prg4GCmT59Ofn6+UT1Hjx6lS5cuWFlZ4e/vzwcffFB5Fy2EEEIIIcTdJD8bNfUiAJ8XPoJNl9HQ/jkI6lKm093rtQKgTkEUGbkFVRamEOI/tSq5sGbNGiZMmMD06dM5ePAgzZs3JywsjMTExBLL7969m6eeeooRI0Zw6NAh+vXrR79+/Th+/LihzAcffMD8+fNZtGgRe/bswdbWlrCwMHJzcwE4ffo0Op2O//3vf5w4cYKPP/6YRYsW8frrrxvqyMjI4IEHHqBOnTocOHCAOXPmMGPGDBYvXly1HSKEEEIIIcSdKPkMCirJqgPZ5k60C3Ip1+kOAc0B8NckEXHxclVEKIS4Tq1KLnz00UeMHDmS4cOH06hRIxYtWoSNjQ1Lliwpsfwnn3xCr169mDRpEg0bNmTmzJm0atWKzz77DNCPWpg3bx5vvvkmffv2pVmzZqxYsYLLly+zbt06AHr16sXSpUt54IEHqFu3Lo888ggTJ07kxx9/NLTzzTffkJ+fz5IlS2jcuDFPPvkkY8eO5aOPPqryPrkbzZgxgxYtWlR6vYqiGL7vphQYGMi8efOqpa3Bgwcza9asMpXt1q0b48ePr9T2t2/fjqIopKWlVWq9leH62DZt2kSLFi3Q6XSmDUwIIYS4GySeBiBS58c9dV2xMtfe4oTr2LiQonUHIOHswcqOTghRglqTXMjPz+fAgQP06NHDcEyj0dCjRw/Cw8NLPCc8PNyoPEBYWJihfFRUFPHx8UZlHB0dad++fal1AqSnp+Pi8l/2NDw8nHvvvRcLCwujds6cOUNqamqJdeTl5ZGRkWH0EJVn7NixtG7dGktLyypJRFSGZcuW4eTkdMPxffv2MWrUqCpv/8iRI2zcuJGxY8eWqfyPP/7IzJkzqziqmqtXr16Ym5vzzTffmDoUIYQQ4s6XeBKACNWXzvXcbquKdIcQAHIvyXaUQlSHWpNcSE5OpqioCE9PT6Pjnp6exMfHl3hOfHz8TcsXfy1PnWfPnuXTTz/lueeeu2U717ZxvdmzZ+Po6Gh4+Pv7l1hO3L5nnnmGgQMHmjqMcnN3d8fGxqbK2/n000/p378/dnZ2ZSrv4uKCvb19FUdVsw0bNoz58+ebOgwhhBDizpf078gF1Y+mvo63VYXq0RgAy5RTlRaWEKJ0tSa5UBPExsbSq1cv+vfvz8iRIytU12uvvUZ6errhERMTU74KVBXys6r/oaplDrFbt26MHTuWyZMn4+LigpeXFzNmzDAqEx0dTd++fbGzs8PBwYEBAwaQkJBgVOa9997D09MTe3t7RowYYVgP42bmz5/Piy++SN26dcsc7/WOHTtG9+7dsba2xtXVlVGjRpGZmWlUpngqjKWlJd7e3owZM8bw2kcffUTTpk2xtbXF39+f0aNHG87fvn07w4cPJz09HUVRUBTF0DfXT4u4VR8VTxNZuXIlgYGBODo68uSTT3L16tVSr62oqIjvv/+ehx9+2Oj4woULCQkJwcrKCk9PT5544gnDa9dPiwgMDGTWrFk888wz2NvbExAQcMM6I7t376ZFixZYWVnRpk0b1q1bh6IoHD58uNTY/v77b7p06YK1tTX+/v6MHTuWrKysUsuX5frz8vIYO3YsHh4eWFlZ0blzZ/bt22dUz8aNG6lfvz7W1tbcd999XLhw4Ya2Hn74Yfbv38+5c+dKjUcIIYQQFadL0CcEzuj8CfW6vQ83nINaAuCVc46svMJKi00IUbJasxWlm5sbWq32hhvPhIQEvLy8SjzHy8vrpuWLvyYkJODt7W1U5vqh9JcvX+a+++6jY8eON9xAldbOtW1cz9LSEktLyxJfK5OCbJjlc/vn367XL4OFbZmLL1++nAkTJrBnzx7Cw8MZNmwYnTp1omfPnuh0OsNN844dOygsLOTFF19k4MCBbN++HYDvvvuOGTNmsGDBAjp37szKlSuZP39+hZIGZZGVlUVYWBgdOnRg3759JCYm8uyzzzJmzBiWLVsGwOeff86ECRN47733ePDBB0lPT2fXrl2GOjQaDfPnzycoKIjz588zevRoJk+ezMKFC+nYsSPz5s1j2rRpnDlzBqDEEQRl6SOAc+fOsW7dOjZs2EBqaioDBgzgvffe49133y3x+o4ePUp6ejpt2rQxHNu/fz9jx45l5cqVdOzYkZSUFHbu3HnTfpo7dy4zZ87k9ddf5/vvv+eFF16ga9euhIaGkpGRwcMPP8xDDz3EqlWruHjx4i3XbDh37hy9evXinXfeYcmSJSQlJTFmzBjGjBnD0qVLb3reza5/8uTJ/PDDDyxfvpw6derwwQcfEBYWxtmzZ3FxcSEmJobHHnuMF198kVGjRrF//35eeeWVG9oJCAjA09OTnTt3EhwcfNNrEUIIIcRtystEk6H/4C3Nri5ONha3OKFkzsH6v3MaKRc4ciGRDqEm+NtZiLtIrRm5YGFhQevWrdm2bZvhmE6nY9u2bXTo0KHEczp06GBUHmDLli2G8kFBQXh5eRmVycjIYM+ePUZ1xsbG0q1bN1q3bs3SpUvRaIy7rUOHDvz1118UFPy3zc2WLVsIDQ3F2dn59i/6DtCsWTOmT59OSEgIQ4YMoU2bNob+3rZtG8eOHWPVqlW0bt2a9u3bs2LFCnbs2GH4VHnevHmMGDGCESNGEBoayjvvvEOjRo2qPO5Vq1aRm5vLihUraNKkCd27d+ezzz5j5cqVhsTRO++8wyuvvMK4ceOoX78+bdu2Nbp5Hj9+PPfddx+BgYF0796dd955h++++w7Qv58dHR1RFAUvLy+8vLxKTC6UpY9A/39h2bJlNGnShC5dujB48OAb3vvXunjxIlqtFg8PD8Ox6OhobG1t6dOnD3Xq1KFly5a3XI/hoYceYvTo0dSrV48pU6bg5ubGn3/+aehDRVH44osvaNSoEQ8++CCTJk26aX2zZ89m0KBBjB8/npCQEDp27Mj8+fNZsWLFTUes3Oz6s7Ky+Pzzz5kzZw4PPvggjRo14osvvsDa2pqvvvoK0CeKgoODmTt3LqGhoQwaNIhhw4aV2JaPjw8XL1686XUIIYQQogKS9B+8JKpOeHv73n49bvXJ0Dpjo+Rx6cSuW5cXQlRIrRm5ADBhwgSGDh1KmzZtaNeuHfPmzSMrK4vhw4cDMGTIEHx9fZk9ezYA48aNo2vXrsydO5fevXuzevVq9u/fbxh5oCgK48eP55133iEkJISgoCCmTp2Kj48P/fr1A/5LLNSpU4cPP/yQpKQkQzzFoxL+7//+j7feeosRI0YwZcoUjh8/zieffMLHH39cdZ1hbqMfRVDdzMu3FkCzZs2Mnnt7exu2Dj116hT+/v5G6000atQIJycnTp06Rdu2bTl16hTPP/+8UR0dOnQw3MBWlVOnTtG8eXNsbf8bpdGpUyd0Oh1nzpxBURQuX77M/fffX2odW7duZfbs2Zw+fZqMjAwKCwvJzc0lOzu7zGsqlKWPQD9F4dr1EK7t55Lk5ORgaWmJoiiGYz179qROnTrUrVuXXr160atXLx599NGbxnrt97c4UVLc7pkzZ2jWrBlWVlaGMu3atbvp9R45coSjR48aLZqoqio6nY6oqCgaNmxY4nk3u/5z585RUFBAp06dDK+bm5vTrl07Tp3SD7k8deoU7du3N6qztKSltbU12dnZN70OIYQQQlRAkv73c4TOlwa3OSUCAEXhiltbHBJ+R3NxJ9C/cuITQpSoViUXBg4cSFJSEtOmTSM+Pp4WLVqwadMmw+KJ0dHRRqMKOnbsyKpVq3jzzTd5/fXXCQkJYd26dTRp0sRQZvLkyWRlZTFq1CjS0tLo3LkzmzZtMtwQbdmyhbNnz3L27Fn8/PyM4lH/XX/A0dGR33//nRdffJHWrVvj5ubGtGnTqnbFf0Up1/QEUzE3Nzd6rijKHbGVn7W19U1fv3DhAn369OGFF17g3XffxcXFhb///psRI0aQn59f6Qs2lref3dzcyM7OJj8/37DLib29PQcPHmT79u38/vvvTJs2jRkzZrBv374Sd7W4nXZvJTMzk+eee67EERMBAQGlnled77OUlBTc3d2rpG4hhBBCYLSYY6hnxRaTtqjXFRJ+xy99Pzqdikaj3PokIcRtqTXTIoqNGTOGixcvkpeXx549e4w+bdy+fbthPnyx/v37c+bMGfLy8jh+/DgPPfSQ0euKovD2228THx9Pbm4uW7dupX79+obXhw0bhqqqJT6u1axZM3bu3Elubi6XLl1iypQplX/xd5iGDRsSExNjtJjlyZMnSUtLM0x9aNiwIXv27DE6759//qmW2I4cOWK0kOCuXbvQaDSEhoZib29PYGBgqVMPDhw4gE6nY+7cudxzzz3Ur1+fy5eNR5pYWFhQVFR0yzhu1Ue3o3hNkZMnTxodNzMzo0ePHnzwwQccPXqUCxcu8Mcff9xWG6GhoRw7doy8vDzDsesXUbxeq1atOHnyJPXq1bvhce1Wr+URHByMhYWF0XoYBQUF7Nu3z+h9tnfvXqPzSnqf5ebmcu7cOVq2bHlbsQghhBDi1tTkCADOqT63vZhjMc9m+i3nW6gRnL2cXOHYhBClq3XJBXHn6NGjB02bNmXQoEEcPHiQvXv3MmTIELp27WpYaHDcuHEsWbKEpUuXEhERwfTp0zlx4sQt6z579iyHDx8mPj6enJwcDh8+zOHDh8nPzy9TbIMGDcLKyoqhQ4dy/Phx/vzzT1566SUGDx5sGCkzY8YM5s6dy/z584mMjOTgwYN8+umnANSrV4+CggI+/fRTzp8/z8qVK1m0aJFRG4GBgWRmZrJt2zaSk5NLHGpflj66He7u7rRq1Yq///7bcGzDhg3Mnz+fw4cPc/HiRVasWIFOpyM0NPS22vi///s/dDodo0aN4tSpU2zevJkPP/wQwGg6xrWmTJnC7t27GTNmDIcPHyYyMpL169cb7cJRXra2trzwwgtMmjSJTZs2cfLkSUaOHEl2djYjRowA4PnnnycyMpJJkyZx5swZVq1adUOiEvQJB0tLy1KnTAghhBCi4ooSIwE4r/pQz6NsW2aXxswjlFSNC5ZKAReP7qiM8IQQpZDkgjAZRVFYv349zs7O3HvvvfTo0YO6deuyZs0aQ5mBAwcydepUJk+eTOvWrbl48SIvvPDCLet+9tlnadmyJf/73/+IiIigZcuWtGzZ8obRA6WxsbFh8+bNpKSk0LZtW5544gnuv/9+PvvsM0OZoUOHMm/ePBYuXEjjxo3p06cPkZH6X4bNmzfno48+4v3336dJkyZ88803hrVAinXs2JHnn3+egQMH4u7uzgcffHBbfXS7nn32WaO1DZycnPjxxx/p3r07DRs2ZNGiRXz77bc0btz4tup3cHDgl19+4fDhw7Ro0YI33niDadOmARitw3CtZs2asWPHDiIiIujSpQstW7Zk2rRp+PhUbHXn9957j8cff5zBgwfTqlUrzp49y+bNmw0LrgYEBPDDDz+wbt06mjdvzqJFi5g1a9YN9Xz77bcMGjSo0qe1CCGEEOJfhXlo0/ULJ+c718PKXFux+hSFBBf9BzK685JcEKIqKer14/uFSWRkZODo6Eh6ejoODg5Gr+Xm5hIVFUVQUFCpN2VClFdOTg6hoaGsWbOm2j6J/+abbxg+fDjp6em3XLeipklOTiY0NJT9+/cTFBRk6nAqjfx8EUIIUaMknoaF7bmqWjMlZAMLn779kZrFzm5aQL1/XucIoTSdtkfWXRDiGje7Dy0vGbkgxF3K2tqaFStWkJxcdfMPV6xYwd9//01UVBTr1q1jypQpDBgwoNYlFkC/SOfChQvvqMSCEEIIUeMY1lvwpoG3Y6VUGdCmNwCN1UhOXbhUKXUKIW5Uq3aLEEJUrm7dulVp/fHx8YbdXby9venfvz/vvvtulbZZVdq0aVOhdS6EEEIIUQbXLObYyLtin6IWs3ALJMHcF8+CWKL2b6Jx3ZGVUq8QwpiMXBBCVJnJkydz4cIFw9D7jz/+WNYrEEIIIUSpipL+TS7ofGjkUznJBYB0784AaKNk3QUhqookF4QQQgghhBA1Ql78aQDiLQLwdqy8tYBcm/UCoEHWPtKyy7Z7mBCifCS5UIvI2ptCiMomP1eEEELUGKqKWeo5ALTuIaVuXX07XJvcTxEagjTx7D9ypNLqFUL8R5ILtYC5uTkA2dnZJo5ECHGnKf65UvxzRgghhDCZzAQsCjMpUhVc/BpUbt1Wjlyy1W+vXRD5Z+XWLYQAZEHHWkGr1eLk5ERiYiIANjY2lZrJFULcfVRVJTs7m8TERJycnNBqK7iPuBBCCFFR/y7mGK16EOrnVunVpzo1oU7WMawyoiq9biGEJBdqDS8vLwBDgkEIISqDk5OT4eeLEEIIYUq65LNogCjVm4aVtFPEtTR27gCY5Vyp9LqFEJJcqDUURcHb2xsPDw8KCgpMHY4Q4g5gbm4uIxaEEELUGJlxkTgAMXjR2d2u0us3d/AAwDI/pdLrFkJIcqHW0Wq1cjMghBBCCCHuONkJ53AAcu39sTCr/KXhbJz1I/VsCtMqvW4hhCzoKIQQQgghhKgBtOkXAFBcgqqkfnsXbwAcdWkU6WS3JCEqmyQXhBBCCCGEEKalqthlXwLAyiO4Spqwd9UnF1zJIDU7v0raEOJuJskFIYQQQgghhGnlpGKtywLA2TekSpooXnPBRskjOSW1StoQ4m4myQUhhBBCCCGEaaXot4eMV50J8HCtmjYs7MjDAoD05LiqaUOIu5gkF4QQQgghhBAmlZt0DoBo1YNAV9uqaURRuKp1AiArRZILQlQ2SS4IIYQQQgghTCo9NgKAeI0XjjbmVdZOjrkzALnpCVXWhhB3K0kuCCGEEEIIIUwqP+k8AFm2/lXbjqULAIVXE6u0HSHuRpJcEEIIIYQQQpiUJu0CADrHOlXaTpGNm/4fmclV2o4QdyNJLgghhBBCCCFMyiY7BgBz96rZhrKYYusOgCZHkgtCVDZJLgghhBBCCCFMpzAPx4IkABx9qmYbymLmDvrkgmVeCsdj0zkYLVtSClFZJLkghBBCCCGEMJ20aDSoZKmWePtU7ZoL1k5e+q8FKTz++W7+74t/yMgtqNI2hbhbSHJBCCGEEEIIYTLZifrFHC+p7gS621VpW3Yu3gA4qRnkFerILdARk5JdpW0KcbeQ5IIQQgghhBDCZFLj9MmFJI0HDlZVtw0lgI2zfuSCq5JhOHY5LbdK2xTibiHJBSGEEEIIIYTJZCVe1H+19qrythRb/W4RrqQDKgBx6TlV3q4QdwNJLgghhBBCCCFMpihNv1NEkZ1v1Tf2b3LBQimitYcGDTpi0yS5IERlkOSCEEIIIYQQwmTMrsbqv7pU7WKOAJhbo1ro13VYkfsSmyymEJ+aWfXtCnEXkOSCEEIIIYQQwmTs8hL0Xz3qVEt7iq1+O0rb/GTqa2LRJkdUS7tC3OkkuSCEEEIIIYQwDVXFpSgJAFef4Opp07c1oFBkrh/BYJNxrnraFeIOJ8kFIYQQQgghhElcTYnDkgJ0qoJPQN3qafTR/8HESPLr9wHAPTeKwiJd9bQtxB1MkgtCCCGEEEIIk0iIPgvAFcUJe1vb6mlUawZ27lh6NwYgWLlMwtW86mlbiDuYJBeEEEIIIYQQJpEad17/1dyj2tvWeIQCEKzEEic7RghRYZJcEEIIIYQQQphEdnI0ADnW3tXfuFt9AOoqcVxOuVr97Qtxh5HkghBCCCGEEMIk1LQY/VcH3+pv3CmAfMUSS6WQzPjz1d++EHcYSS4IIYQQQgghTMIi6zIA5i7Vsw2lEY2WVGt9u2ry6epvX4g7jCQXhBBCCCGEECZhn5eg/+phguQCkOOo3/7SKjXSJO0LcSeR5IIQQgghhBCi2qXnFOChJgHg5hdskhh0bvpFHR2zokzSvhB3EkkuCCGEEEIIIardubgruJMOgI1boEliMPdsCIB3frRJ2hfiTiLJBSGEEEIIIUS1i714Fo2ikq9YgK2bSWKw8aoHgKeaiE6nmiQGIe4UtS65sGDBAgIDA7GysqJ9+/bs3bv3puXXrl1LgwYNsLKyomnTpmzcuNHodVVVmTZtGt7e3lhbW9OjRw8iI43nXL377rt07NgRGxsbnJycSmxHUZQbHqtXr67QtQohhBBCCHGnSo89o/9q6QuKYpIY7F19AHDmKhk5eSaJQYg7Ra1KLqxZs4YJEyYwffp0Dh48SPPmzQkLCyMxMbHE8rt37+app55ixIgRHDp0iH79+tGvXz+OHz9uKPPBBx8wf/58Fi1axJ49e7C1tSUsLIzc3FxDmfz8fPr3788LL7xw0/iWLl1KXFyc4dGvX79KuW4hhBBCCCHuNIXJ+u0f8x1Ms5gjgIWDOwBaRSX1Ssn3FEKIsqlVyYWPPvqIkSNHMnz4cBo1asSiRYuwsbFhyZIlJZb/5JNP6NWrF5MmTaJhw4bMnDmTVq1a8dlnnwH6UQvz5s3jzTffpG/fvjRr1owVK1Zw+fJl1q1bZ6jnrbfe4uWXX6Zp06Y3jc/JyQkvLy/Dw8rKqtKuXQghhBBCiDuJecZF/Vf3uqYLQmtOBnYAZKbEmS4OIe4AtSa5kJ+fz4EDB+jRo4fhmEajoUePHoSHh5d4Tnh4uFF5gLCwMEP5qKgo4uPjjco4OjrSvn37Uuu8mRdffBE3NzfatWvHkiVLUNXS523l5eWRkZFh9BBCCCGEEOJukJadj3vBZQAcfeqbNJYMrRMAOanxJo1DiNqu1iQXkpOTKSoqwtPT0+i4p6cn8fEl/yCIj4+/afnir+WpszRvv/023333HVu2bOHxxx9n9OjRfPrpp6WWnz17No6OjoaHv79/udoTQgghhBCitopIyMRf0U9DsPSoZ9JYss2cAcjPSDBpHELUdmamDuBOMXXqVMO/W7ZsSVZWFnPmzGHs2LElln/ttdeYMGGC4XlGRoYkGIQQQgghxF0hIj6DR/9NLuAcZNJYci1dIA90V5NMGocQtV2tGbng5uaGVqslIcE4o5iQkICXl1eJ53h5ed20fPHX8tRZVu3bt+fSpUvk5ZW86qylpSUODg5GDyGEEEIIIe4GcbEXsVXy0KEBpwCTxlJo5ar/R1aySeMQorarNckFCwsLWrduzbZt2wzHdDod27Zto0OHDiWe06FDB6PyAFu2bDGUDwoKwsvLy6hMRkYGe/bsKbXOsjp8+DDOzs5YWlpWqB4hhBBCCCHuNJlxZwHIsfYCMwuTxqLauAGgzZHkghAVUaumRUyYMIGhQ4fSpk0b2rVrx7x588jKymL48OEADBkyBF9fX2bPng3AuHHj6Nq1K3PnzqV3796sXr2a/fv3s3jxYgAURWH8+PG88847hISEEBQUxNSpU/Hx8THaRjI6OpqUlBSio6MpKiri8OHDANSrVw87Ozt++eUXEhISuOeee7CysmLLli3MmjWLiRMnVmv/CCGEEEIIUSukRgGgcwo0bRyAxk6/HaVFXoqJIxGidqtVyYWBAweSlJTEtGnTiI+Pp0WLFmzatMmwIGN0dDQazX+DMTp27MiqVat48803ef311wkJCWHdunU0adLEUGby5MlkZWUxatQo0tLS6Ny5M5s2bTLaRnLatGksX77c8Lxly5YA/Pnnn3Tr1g1zc3MWLFjAyy+/jKqq1KtXz7BtphBCCCGEEOI/mXmFuOTHghlYegSbOhzMHDwAsCmQ5IIQFaGoN9svUVSbjIwMHB0dSU9Pl/UXhBBCCCHEHetUXAanFz7Jo9pd0GMGdH7ZpPFE7t1EyMaBRCs+BEw/ZdJYhKhulXkfWmvWXBBCCCGEEELUftEp2dRR/l1Q3cQ7RQDYuegXcnfQpZs4EiFqN0kuCCGEEEIIIapNTEo2AcXbULqYPrlg7+oDgJOSRXZOtomjEaL2kuSCEEIIIYQQotokJsbjpmTon7jUNW0wgK2jK4Wq/rYoNSnexNEIUXtJckEIIYQQQghRbYqSIgDItvQAS3sTRwOKRkuaop9rnpUaZ+JohKi9JLkghBBCCCGEqDbmaecByHc2/U4Rxa5qnQDITpWRC0LcLkkuCCGEEEIIIaqFTqfilHUBADP3+qYN5hpZZs4A5KcnmjgSIWovSS4IIYQQQgghqkXi1TwCuQyAtXcDE0fznzxLFwB0mUkmjkSI2kuSC0IIIYQQQohqEZ2STV1Fn1zQetSckQsFVq4AqFmSXBDidklyQQghhBBCCFEtYpIzCFT+XdfANcS0wVxDtXYDwCw72cSRCFF7SXJBCCGEEEIIUS3S485hoRRRoFiAo7+pwzFQ7D0BsMi7YuJIhKi9JLkghBBCCCGEqBZFifptKNNt6oCm5tyKWDl7AWCTL8kFIW5XzfkfLYQQQgghhLijmaedBSDfqeZsQwlg5+IDgENRiokjEaL2kuSCEEIIIYQQolo4/rsNpdYj1LSBXMfJww8AFzWdoqIiE0cjRO0kyQUhhBBCCCFElcvJL8Kn6BIAdj41ZxtKAGcPXwDMlSJSkuNNHI0QtZMkF4QQQgghhBBVLiY127BThI13zRq5oDW3JA17ANISY00cjRC1kyQXhBBCCCGEEFXuUsIVPJU0ABSXINMGU4J0rTMAmVcumTgSIWonSS4IIYQQQgghqlx6XCQA2RpbsHY2cTQ3yjJ3BSAnVaZFCHE7JLkghBBCCCGEqHK5iecByLDyBUUxcTQ3yrNyA6AwPcHEkQhRO0lyQQghhBBCCFH1Ui8AkGdfx7RxlKLIxgMAJUuSC0LcDkkuCCGEEEIIIaqcdWY0AJoauN4CgMZen1wwy04ycSRC1E6SXBBCCCGEEEJUKVVVccrT78Jg7VnPxNGUzNzRCwDr/GQTRyJE7STJBSGEEEIIIUSVSsrMw1dNBMDRJ8TE0ZTMxsUXAPvCFBNHIkTtJMkFIYQQQgghRJWKuZJJgKJPLpi71cxpEQ7u+uSCsy4VnU41cTRC1D6SXBBCCCGEEEJUqcTLF7FUCihCA47+pg6nRE7ufgC4KFdJuZpl4miEqH0kuSCEEEIIIYSoUplxZwFIM/cCrZmJoymZuZ0bhf/eHl1JjDVxNELUPpJcEEIIIYQQQlSpoitRAGTZ1sxRCwBoNKQpTgBkJEtyQYjykuSCEEIIIYQQokopaRcA0DkFmjSOW8k0dwUg58plE0ciRO0jyQUhhBBCCCFEldHpVGwyowGw866Z21AWy7N0AyArRZILQpSXJBeEEEIIIYQQVeZiSjb1uQiAc0BjE0dzc4qjDwD5V6JNHIkQtY8kF4QQQgghhBBVJjL6MiGKfg0DrV8bE0dzc1budQGwzJTkghDlJckFIYQwkbj0HFbtiSY9u8DUoQghhBBVJu3cfjSKSqq5B9h7mjqcm3LxDwXAveAymXmFJo5GiNqlZu4DI4QQd7D0nAIW7TjHkr+jyCvU8cuRy3zzbHs0GsXUoQkhhBCVTok7CEC6c1OcTRzLrdh5hQAQoCQQkXCVVgE1PWIhag5JLgghRDVRVZVfNm3i773/kJxnRissSVXsCT+vsvKfiwztGGjqEIUQQohK55p+HADFt7WJIykDlyAA3JUMtl+Kl+SCEOUgyQUhhKgm4T99xiNH3+QRAIv/jm8pasWrv42ja313At1sTRWeEEIIUekycguoXxgBCrjWv8fU4dyalSPZZo7YFKZzJfo0dGxo6oiEqDVua82FgoICYmJiOHPmDCkpKZUdkxBC3HEuHf+b1kfeAiDRtj6qd0twb4CqtaCn9iArlGnMWr2VIp1q4kiFEEKIynMuKgo/JRkdCnZBNXsxx2I5dgH6rwlnTRyJELVLmZMLV69e5fPPP6dr1644ODgQGBhIw4YNcXd3p06dOowcOZJ9+/ZVZaxCCFEr5afFYfXjECyVAg5YdcBtwj8oz22HF/egDP+NIms3Gmsu8nbSOH7e+KupwxVCCCEqTWrkPwDEm/uDlaOJoykbrat+xwhN2gXTBiJELVOm5MJHH31EYGAgS5cupUePHqxbt47Dhw8TERFBeHg406dPp7CwkAceeIBevXoRGRlZ1XELIUTtUJhP4lcDcNNd4Ty++D2zEo1W+9/rfm3QjvqDNLt6eCmp9Nw3gvhLUaaLVwghhKhEaqx+Mccrjk1MHEnZ2XrVA/Q7RiRdzTNxNELUHmVac2Hfvn389ddfNG7cuMTX27VrxzPPPMOiRYtYunQpO3fuJCQkpFIDFUKI2ih+7QT8rh4lQ7UhJuxLunq431jIuQ6OL24j+sPOBBTFcGL/Rrz8Xqz+YIUQQohK5pB+BgCdZ1MTR1J25m7BgH7HiPNJmbjbW5o4IiFqhzIlF7799tsyVWZpacnzzz9foYCEEOJOUZR5Bbcz3wDwfeB0nunYsdSyirUTcW4dCUhYQ+GlQ9UVohBCCFGlPHP1o/Fs/JuZOJJy+HfHiDpKIgczck0cjBC1R7kXdFy6dCnZ2dlVEYsQQtxRTu5Yixk6zlCHAf/37C3LW/rrt+hySjtR1aEJIYQQVS4r8yp+ajwAnsEtTRxNOTjrkws+SjIJqVdNHIwQtUe5kwuvvvoqXl5ejBgxgt27d1dFTEIIcUfIO74egCTfHthZ3nqgmE8j/RZdQQXnyMnNr9LYhBBCiKoWd+4IGkUlDXsc3HxMHU7Z2XtRoLFEq6jkJl0wdTRC1BrlTi7ExsayfPlykpOT6datGw0aNOD9998nPj6+KuK7wYIFCwgMDMTKyor27duzd+/em5Zfu3YtDRo0wMrKiqZNm7Jx40aj11VVZdq0aXh7e2NtbU2PHj1uWJDy3XffpWPHjtjY2ODk5FRiO9HR0fTu3RsbGxs8PDyYNGkShYWFFbpWIUTtFXU5icbZ+wGod++TZTrHvU5jsrHCRskj8pRMjRBCCFG7ZVw8CkCseSAoimmDKQ9FIdPGX//v1POmjUWIWqTcyQUzMzMeffRR1q9fT0xMDCNHjuSbb74hICCARx55hPXr16PT6aoiVtasWcOECROYPn06Bw8epHnz5oSFhZGYmFhi+d27d/PUU08xYsQIDh06RL9+/ejXrx/Hjx83lPnggw+YP38+ixYtYs+ePdja2hIWFkZu7n/zq/Lz8+nfvz8vvPBCie0UFRXRu3dv8vPz2b17N8uXL2fZsmVMmzatcjtACFFr7N26FmslnyQzL7zqty3TOYrWjFgr/QrVVyL+qcrwhBBCiCqnSzgFQJp9PRNHUn4FDnUAsMyINnEkQtQe5U4uXMvT05POnTvToUMHNBoNx44dY+jQoQQHB7N9+/ZKCvE/H330ESNHjmT48OE0atSIRYsWYWNjw5IlS0os/8knn9CrVy8mTZpEw4YNmTlzJq1ateKzzz4D9KMW5s2bx5tvvknfvn1p1qwZK1as4PLly6xbt85Qz1tvvcXLL79M06Ylr3L7+++/c/LkSb7++mtatGjBgw8+yMyZM1mwYAH5+TK0WYi7TXZ+IdbnfwMgN/jBcn1ak+3274JXcYerIDIhhBCi+linRQBQ5NbAxJGUn8a1LgD2OTEmjkSI2uO2kgsJCQl8+OGHNG7cmG7dupGRkcGGDRuIiooiNjaWAQMGMHTo0EoNND8/nwMHDtCjRw/DMY1GQ48ePQgPDy/xnPDwcKPyAGFhYYbyUVFRxMfHG5VxdHSkffv2pdZZWjtNmzbF09PTqJ2MjAxOnCh5Yba8vDwyMjKMHkKIO8P6Axe5Vz0AgO89/ct1rk2dNgC4pJ+s9LiEEEKI6uSecw4AS58mJo6k/Kw89NtRuhfEUVBUNaOyhbjTlDu58PDDD+Pv78+yZcsYOXIksbGxfPvtt4YbdFtbW1555RViYio3y5ecnExRUZHRDTzoR0+Utt5DfHz8TcsXfy1PneVp59o2rjd79mwcHR0ND39//zK3J4SouVRV5cjfG3FSssgxd0ZT555yne/776KOIbooEtKyqiJEIYQQosqpuel46JIAcK9bi7ah/JeNZwgAAUoCiVfzTByNELVDuZMLHh4e7Nixg+PHjzN+/HhcXFxuKOPu7k5UVFSlBHineu2110hPTzc8KjsZI4Qwjf0XU2mYvgMAbYOHQKMt1/k23g3IKV7U8cTBqghRCCGEqHKpF44BkKA64efjZ+Joyk/jqt+OMkBJJF6S/UKUSbmTC1999RUdOnS4aRlFUahTp85tB1USNzc3tFotCQkJRscTEhLw8vIq8RwvL6+bli/+Wp46y9POtW1cz9LSEgcHB6OHEKL2W/3PBR7Q6qdEWDTtW/4KNFribeoDkHru5rvhCCGEEDVV6gX9ThHR2jpYmFVomTfTcAqgCA3WSj6pCfIhoBBlceuN1/81f/78W1dmZoaXlxedO3fGw8OjQoFdz8LCgtatW7Nt2zb69esHgE6nY9u2bYwZM6bEczp06MC2bdsYP3684diWLVsMyZGgoCC8vLzYtm0bLVq0ACAjI4M9e/aUujNEae28++67JCYmGq57y5YtODg40KhRo/JfrBCiVioo0hF3OhxvJYUiM1u0QV1vrx7P5hB1FG38kUqOUAghhKgeuQn6xRzTbINMHMlt0pqTau6JW0EcuQlngZamjkiIGq/MyYWPP/74lmV0Oh1XrlxBp9Px9ddf89hjj1UouOtNmDCBoUOH0qZNG9q1a8e8efPIyspi+PDhAAwZMgRfX19mz54NwLhx4+jatStz586ld+/erF69mv3797N48WJAP8Ji/PjxvPPOO4SEhBAUFMTUqVPx8fExJDAAoqOjSUlJITo6mqKiIg4fPgxAvXr1sLOz44EHHqBRo0YMHjyYDz74gPj4eN58801efPFFLC0tK7UPhBA1174LKXQq/AfMQKnfE8ytbqse+7ptIGolXlmnKNKpaDW1aG9wIYQQAiBNv4WjzjHAxIHcvqvWfrgVxKFLkeneQpRFmZMLZV1DQafT8d577/HGG29UenJh4MCBJCUlMW3aNOLj42nRogWbNm0yLJ4YHR2NRvPfsKuOHTuyatUq3nzzTV5//XVCQkJYt24dTZr8t2Lt5MmTycrKYtSoUaSlpdG5c2c2bdqEldV/NwXTpk1j+fLlhuctW+ozl3/++SfdunVDq9WyYcMGXnjhBTp06ICtrS1Dhw7l7bffrtTrF0LUbFtPJtJPo59jqgl96Lbr8ah/D2yDBlwkIi6Nhr7OlRWiEEIIUS2ssy7pv7rXNXEkty/Pvg5k7MM8/aKpQxGiVlBUVVUru9LY2FhatGhBUlJSZVd9x8rIyMDR0ZH09HRZf0GIWkhVVcI+2Mhv2YPQKiq8fBIcfW+vMl0RuTN9sFJz+bXzT/Tu0b1ygxVCCCGqWOoMf5zJ4FifDTRt08XU4dyW0z+8Q4Njc9hp2ZUur/1s6nCEqBKVeR9aptVVVq9eXeYKY2JiuHDhgiQWhBB3lcjETLzSj6BVVHROdW4/sQCg0ZJsFwrA1ah9lRShEEIIUT1yszJwJgMAn8BQE0dz+yw9ggFwzY81cSRC1A5lSi58/vnnNGzYkA8++IBTp07d8Hp6ejobN27k//7v/2jVqhVXrlyp9ECFEKIm23IygbaaMwBo6nSscH1FXvrpV1ZJRytclxBCCFGdYi/ofx9mYIOLq7uJo7l9jj763Zu8dfHkF+pMHI0QNV+Zkgs7duzg/fffZ8uWLTRp0gQHBwdCQkJo2rQpfn5+uLq68swzzxAQEMDx48d55JFHqjpuIYSoUbaeSqCd5rT+SSUkFxyD2wLgnxtBbkFRhesTQgghqsuVS5EAJJt5oSi1d1FiZ98Q/Vclk4uxl00cjRA1X5kXdHzkkUd45JFHSE5O5u+//+bixYvk5OTg5uZGy5YtadmypdFiikIIcbdIvJrLyZhEWlic0x8IqITkQlArABooF4mMz6CpvyzqKIQQonbISjgPQLZ1BaYI1gCKlQNpGiecdGlcjjpFSB0/U4ckRI1W5uRCMTc3N6NtGoUQ4m73x6lEmnEeS6UAbD3ANbjCdSpuIRRihq2SR/T50zT171AJkQohhBBVT5eq312hqBZvQ1ks3coPp+w0MmLPAD1NHY4QNZoMNRBCiAraeiqBtoYpER2gMoaAas25Yh0IQMbFIxWvTwghhKgmlpn6bSgt3YJMHEnFFTrWAaAg+byJIxGi5pPkghBCVEBOfhE7I5Npb0gudKq0unNdGuj/kXTjQrpCCCFETaTTqTjnxwHg5FPxkXymZuGuvwaLqxdNHIkQNZ8kF4QQogL+PptMYWEBbbT6xasIqLzpC1a+jQFwuhqJqqqVVq8QQghRVeIycvFBvyW9q1+IiaOpOCc//VaarvmXycmXBZaFuBlJLgghRAVsPZlAQ+UituSApSN4Nq60ul2C9NtRBumiiUvPrbR6hRBCiKpyITYOZyUTAHPXQNMGUwnsvfXbUQYoCZxNzDRxNELUbOVOLuTmlv4HblxcXIWCEUKI2kSnU9l2OuG/KREB7UGjrbT6zb2bABCsXOb0peRKq1cIIYSoKkkx+pF8mRoHsLQ3cTSVwFm/boQ3KURelt/FQtxMuZMLrVq14vDhwzcc/+GHH2jWrFllxCSEELXC4UtpJGfm08EsQn+gTsW3oDTi6EeOxhZzpYj4qOOVW7cQQghRBTIT9NsyZ1r7mDiSSmLrRp7GBo2ikhRzxtTRCFGjlTu50K1bN+655x7ef/99ALKyshg2bBiDBw/m9ddfr/QAhRCiptp6MgFQaW/27x8bAZWcXFAU0u3qAZAbK8kFIYQQNZ9yRZ9cKHCs/TtFAKAoZNv6A5AVf9bEwQhRs5mV94SFCxfSu3dvnn32WTZs2EBcXBx2dnbs3buXJk2aVEWMQghRI209lUCwchn7onQwswKflpXehurREDKOYHnldKXXLYQQQlQ2x6v6G3Azr4YmjqQSuQTB1TMoKVGmjkSIGu22FnR88MEHeeyxx9i1axfR0dG8//77klgQQtxVLl7JIiIhkx7aw/oDfm3BzKLS27EL0E8388w9L6tUCyGEqNEycgvwK4oGwDGgqYmjqTw2XvpRhE55saTnFJg4GiFqrnInF86dO0eHDh3YsGEDmzdvZvLkyTzyyCNMnjyZggL5zyaEuDtsPZWIN1cYZ/6T/kDjflXSjn1AcwBClRjOJFytkjaEEEKIynA+MZMQJRYAG9/K2z3J1CzdgwGooyRwNlF+FwtRmnInF1q0aEFQUBBHjhyhZ8+evPPOO/z555/8+OOPtGvXripiFEKIGmfriXjeNf8KGzUb/NpB6+FV05BHIwD8NUlExsiOPEIIIWquuIuR2Cp5FGIGLnVNHU7lcQsFIFQTw5l42Y5SiNKUO7mwcOFCVq9ejZOTk+FYx44dOXToEK1atarM2IQQokZKy87HO+ZnumsPo2osoO9nlboFpREbF66auwGQeuFI1bQhhBBCVILMS/rFh69Y+YPW3MTRVCIv/RQPX+UKly5FmzgYIWqucicXBg8eXOJxe3t7vvrqqwoHJIQQNV34kVNM1a4AQOk2GdxDq7S9bKf6ABTFn6zSdoQQQogKSdYvPpztWM/EgVQyKweu2gYCoIs9ZNpYhKjByr1bxIoVK0p9TVGUUpMPQghxp/DcNRVnJZMEm/p4dhpf5e2ZezeGpN3Yp0egqiqKolR5m0IIIUR52Wfod4pQPO6gnSL+VeTVHM5dwC71hKlDEaLGKndyYdy4cUbPCwoKyM7OxsLCAhsbG0kuCCHuaAVRu2mVuYNCVUNqz4/wrIZhnw51WsBRqKu7yKXUHPxdbKq8TSGEEKI8Cop0eOVdBA3Y+995u8jZ1mkF59YTXHiW5Mw83OwsTR2SEDVOuadFpKamGj0yMzM5c+YMnTt35ttvv62KGIUQosaI3/sjAL9rOlO/eedqadPMS7+oY33NJU7FZVRLm0IIIUR5xFzJIvjfnSKc76BtKIuZ++vXlmuiXCAiXnaMEKIk5U4ulCQkJIT33nvvhlENQghxp7GM2gpAql93NJpqmp7g3gAdCm5KBhejL1RPm0IIIUQ5XLxwDnslh0K0aNzusDUXALyaAfrdmy7GxJg4GCFqpkpJLgCYmZlx+fLlyqpOCCFqHDX1Ih65URSpCt6tHqq+hi1syLD2B6AgTuZ6CiGEqHnSL+gXOrxi6QdmFiaOpgpYO5Fq6QdAdows6ihEScq95sLPP/9s9FxVVeLi4vjss8/o1KlTpQUmhBA1TeKhX/EEDlGfexoHV2vbuc6hkBONxZXT1dquEEIIURZK/FEArjo1wtPEsVSVLNcmOF++hFXSMVOHIkSNVO7kQr9+/YyeK4qCu7s73bt3Z+7cuZUVlxBC1DjZJ34DIMq5E20syv3js0LMvBrB5S04ZJ2XHSOEEELUOM7p+u2SNb4tTRxJ1THzbgqXN+GQed7UoQhRI5X7r2OdTlcVcQghRM1WkIv3lT0AWDd6sNqbd/BrCAchQBdLSlY+rrJKtRBCiBoiO7+QuoVnQQHXem1NHU6VcfJvCAfApyiWtOx8nGzuwOkfQlRApa25IIQQd7KrZ7ZjRR7xqjOt2lbPLhHXsvAMBSBYuUxUcla1ty+EEEKU5tzFGPyUZAAc67Y2cTRVx8pL/7u4rhLH2cRME0cjRM1TppELEyZMKHOFH3300W0HI4QQNVXCgV+wBw5ZtuVBZ5vqD8AtBAB3JZ2/L1+mTaBL9ccghBBClCA5Qj+yL97MBy8rRxNHU4Vc6gLgrGQSE3tJfhcLcZ0yJRcOHSrbiqgyB1gIcadyuPQnAPlB95smAEt70s3dcSxIIjP2FNDENHEIIYQQ1ymMPQxAin1DvEwbStWysCHd3BPHggTSY04CzUwdkRA1SpmSC5988gmNGzdGq9VWdTxCCFHj5CdG4lEQS76qJahdH5PFkWVfF8eUJIoSI00WgxBCCHE92xT9NslFXs1NHEnVy3YIwvFKAkVJZ00dihA1TpnWXGjZsiUpKSkA1K1blytXrlRpUEIIUZNc2rMOgEOaRjQJ8jVZHKprPQCsMs6ZLAYhhBDiWqqq4pd7BgD7oDt3vYViipv+d7FFuuwYIcT1ypRccHJy4vx5/X+gCxcuyI4RQoi7ii7idwASPe9FozHd9C9r74YAuOZcRKdTTRaHEEIIUSwxKYkAEgDwbtDexNFUPTufBgC45UWTW1Bk4miEqFnKNC3i8ccfp2vXrnh7e6MoCm3atCl1ikRxEkIIIe4Eak4qAVcPAuDcvLdJY3H0bwxAELFcTs/BzxQLSwpRTqqqkpVfhJ1luXe/FkLUApciDuIJJCpueDi4mzqcKmf7b3IhSIknKjmLht4OJo5IiJqjTL/pFy9ezGOPPcbZs2cZO3YsI0eOxN7evqpjE0IIk7v8yyx8KSRC9aNly3YmjUXrUR+AOkoC/ySkSXJB1Ch5hUWsP3yZ6CvZWFto0SgKx2PT+ef8Fa5k5RPiYUeXEHf8nK2JSc0mPj2Xvi186NXE29ShCyEqQL+wIaRY18HDxLFUB+XfKYpBSjy/J2RIckGIa5T5Y4RevXoBcODAAcaNGyfJBSHEHU93JQqPk0sA2BM8jsFW5qYNyN6HPMUKS3JJjomABj6mjUfcFZKv5nAxMY2YDB2XUrNJyy4gM6+QnIIi/J1taOBtT2pWPp9vP0d+egLNNWexJwcHJYtAcmimZGFhVkjCFWcuJbtzVHUmFXtSVHu2nU7kp9E2NPa5g7euE2WSnV/IhiNxfLsvmuwrl+lkdhIfG5WOj4+jkZ+zqcMTN6FLigAg36meiSOpJk4BFCrmWFJAYsxZaOFn6oiEqDHKPUZx6dKlVRGHEELUOHE/voovhexSm/Hgo0NMHQ5oNKRaB+CVHUFu3Gmgm6kjEnegwiIdPxy8xPpDsfjHb2Fs0TL8KOKV/KlcUPWjDNorp5hkvoaVhT34TNcZgKbKeVZbvYMtuWVu6+eiDoz7xoJ1Y7vKtIm72N+RyYxbfQjH7AvMNv+S9prTUATkwabFu/ir2yeM6tbQpGveiNLZZOinRFt61TdxJNVEoyXDxh+XrPPkxJ9BfhcL8R/5TS6EECUoiArHN3YTOlXhQqvX6GRvZeqQAMhzCobsCJQU2Y5SVI7s/EJSswvIyS/kdPxVPt4SQVHyWd4yW05X7VH4935upe0nfB68mCCLFIac/AjLoizaWETQwE7hH5qxKP8jrPNzwakOuASBlSNYOui/as0h4zKkXoTMBMhJhdw0HtGGk58xj6k/OvHRk61QFLl5vJuoqsriv87z0abjPK3ZzGTLtViSj4pCjnMDzFPP0kuzl11/PsvC/IWM6dXC1CGL6+QVFuFREAMKuNVpYupwqk2RU13IOo8mRbajFOJaklwQQojr6XSk/TQRd2C95n769XrA1BEZaD0awOXfsL8qi+fejQqLdPx29BIJVwvwdbbBx8kaHydr3OwsSr0xLyosJColl5NxGSRfzcPOygwHKzPOJWWx/UwiB6PTKPp39xFL8hlt9jMvWP6MBYXoNBYUtR+N+bHV+GdGM0v3EUSfhqIssPWArEReyFzAC7bukJ8CXs1g+EawLMPUyTO/oa4exBPav0g98SFbT35Mz8ZeldldooZbGX6BE5u/4nfztdTRJOoP1r0P5ZH52DgFoJ7fTsE3T9GJE8TtmsahRstpGSBTJGqSc/FphPy7U4RLnUYmjqb6WHjWh9it2GddpEinopVRNUIAklwQQogb5Bz+DveM42Splui6v45tDRqu7eDXCA6DZ8El8gqLsDQreececWdRVZXfjsezatN2Zl2dikZReafgaTbp2gIKFmYafBytaOrnRJdAO1pbXSbr2M+4RW/Gq+ASBao/qbpQTqlBZKg2ZGFNnOpClOqNDg0e2iweswhnML/iq+pvFAi+H81Dc9C4BkOjPrDsIYjUb8uKSzA8uxV2fQK75kFWEjgFwKDvy5ZYAAh9EKXvAlj3PCPNNrL47/bQeHwV9J6oiWLTcriy6T3mW6wGQLX1QOn+BrQaCv8mypS63TAf/D26Zb15QruDN1f9j9AJE7GxqDk/k+92l86fopFSRK5iiZXD3bP2gL1vQzgIddTLxKbmEOAqCywLAZJcEEIIY5lJFG6aCsBqyycY2qmliQMyZu+r3wKrnhJLdHIWIV6ySnVVi0i4ypaTCdRxtaFDXVdc7Syrtf3CIh3PrthPxJlTfGf5Nn6aZAAWWcwjXGnJtoJGBBJPYEY8gacT8Dl9BY2i/leBAg2VaBpqom+sW2uFzrU+FldOQ1G+/qC9D/SaDY36Gm7y8G8LvT+Cn8eAtQsMWgs2LtBjBti6wdmt8NCHYO9Zvotr8RSJkfvwOPEVdS7/Rl7hS5Iwuwuoqso3q5bzirJG/7zTBJSuE8HC9sbCgZ3Ib/8SVnvm83LOZ3zxW2fG9e1czRGL0hTvFJFqFYC3RmPiaKqPxi0EgLqaOCKSrkpyQYh/1bqfAgsWLCAwMBArKyvat2/P3r17b1p+7dq1NGjQACsrK5o2bcrGjRuNXldVlWnTpuHt7Y21tTU9evQgMtJ4LnNKSgqDBg3CwcEBJycnRowYQWZmpuH1CxcuoCjKDY9//vmn8i5cCFH1CnLJWNYf+/xEzuu88O89GTNtzfoxqbjWQ4eCk5JFzOVLpg7njqWqKrvPJTNs6V76f7yRU1uW8fG3G2j9zlYe/GQn6w7FoqrqrSuqBP/76zwnzkSwynIWfkoyOpdg6DwBtBZ0UA/xptk3PG22jc7aE/gpyWgUlQzVhnDLzmxt9C7nntpJ0RPLof3zUK8H+N8DHo3A3AazolwsEo/qEwtezeDBD2DMPmjc77/EQrFWg+HZbTA6HFyD9ccUBTq+BEPWw79/bJeX2z2DAOjEYfZExlWgp0RtseWfg4xIeAetopLe8CmUntNLTiz8y6rnm1x1CsVVuUrdA7NIzsyrxmjFzRQZdooINnEk1ezf7Sh9uEJUXLKJgxGi5qhVIxfWrFnDhAkTWLRoEe3bt2fevHmEhYVx5swZPDxu3Fl39+7dPPXUU8yePZs+ffqwatUq+vXrx8GDB2nSRL/ozAcffMD8+fNZvnw5QUFBTJ06lbCwME6ePImVlX4Bt0GDBhEXF8eWLVsoKChg+PDhjBo1ilWrVhm1t3XrVho3bmx47urqWoW9IYSoVKrKlVUjcU0+RLpqw4qg95jerI6po7qRhQ2p5p64FsSTEXMCWt09c1yry5XMPF7/6Ri/n4jjCe1f/GH5La7KVQD26BrwbUJ3Jq9pzy9HfHn30aZ4OVbdYp/HY9P5esseVlnMIlCJB6cANEN/AUdfaPk0/PUhFGTrb/Zdgg1fHWzd6GCUHGgGTfoZV64rgitnIfGk/g9lr6a3DsivTWVeHgAa35ZkmLniUHiF8/s2c2/DEZXehqg5dDoV823TcVWukmDbAM/H5t36JDNL7Ab8D93i+3hYs4tlv/3CsP5PVHms4tas0/Xr/1h4hpo4kmpm60au1h6roqtkxEYAjW95ihB3A0Wtro9eKkH79u1p27Ytn332GQA6nQ5/f39eeuklXn311RvKDxw4kKysLDZs2GA4ds8999CiRQsWLVqEqqr4+PjwyiuvMHHiRADS09Px9PRk2bJlPPnkk5w6dYpGjRqxb98+2rTR/1G1adMmHnroIS5duoSPjw8XLlwgKCiIQ4cO0aJFi9u6toyMDBwdHUlPT8fBQYY5C1HZVFW96Ur08eun43VoHgWqlrle7zFh5LNYmNWsUQvFLnzSi8DUcH7wmcTjo940dTh3jvxsDu38hU279mGTn0wXzXFaaf4dyebgC1fjQS0CIEF14svCh/jZPIypj7WjTzOfSg8nt6CI4Z+s492M16mriUe190EZvlG/E8MdJnblKHzPreF7zYM8PvVb2TXiDrZ73z7ab+iJVlHJGr4d2zpln3p2edlwfC78yAE1lKBJO3Gp5ilKwlhceg6X5t5LW00EuY8sxqrVQFOHVK1SP+mCc+pR5ji+waSXJ5s6HCFuW2Xeh9bMv5xLkJ+fz4EDB+jRo4fhmEajoUePHoSHh5d4Tnh4uFF5gLCwMEP5qKgo4uPjjco4OjrSvn17Q5nw8HCcnJwMiQWAHj16oNFo2LNnj1HdjzzyCB4eHnTu3Jmff/75pteTl5dHRkaG0UMIUfliUrJ5+ss9NH/rd2ZuOElMSrbR62pRAdGb5uN1aB4AS5xeYvyzI2psYgGgyFk/HNMiTXaMqBRXE0hcP5Wr74XScucoXtN9wTizn/SJBXNb6DkTxh2Bl49Dt9fB3htPJY03zFfxu/oCB9fM4r2Nxw07LlSWL37ZznsZr1JXE0+Rg/8dm1gAcG/dD4AORXs5findtMGIKnX1j3loFZVzjh3KlVgA8H70HXKxpLVyhr/Wf1lFEYqy2hmZTF1FP5XJyusuG7mAfpoigEV6VLVNkxOipqs10yKSk5MpKirC09N4sShPT09Onz5d4jnx8fEllo+Pjze8XnzsZmWun3JhZmaGi4uLoYydnR1z586lU6dOaDQafvjhB/r168e6det45JFHSoxt9uzZvPXWW2W5dCHEbVBVlW/2RDNr4ylaFx5ipOY0Z3f7MHx3EKGNWtIt0Aqro1/TJvEHAkgC4Cebxxk0eipW5jV7QTkr7wZwHpxyLpg6lFovLXwltptfxoMCAC6rrmQ6N6Ju3RDMnP2h2QBw/HcFdAcf6DYFOo+Ho9+h/j0Px5SzTDNfyf5/9vBq9BTeGPIITjYWFY4rKSOX7kcmUEeTSLatPzbPbNTvxnCHsgi5j3zFEl+usG3/Tpr6P2zqkEQVOHomkq7Zm0EB5wcmlft8xdGX2EYjCT75GS0iPiUhbSieTnZVEKkoi4gTBxmgXEWHBs2/N9p3E1vfBnAWfIsuEZMiO0YIAbUouVCTubm5MWHCBMPztm3bcvnyZebMmVNqcuG1114zOicjIwN/f/8qj1WIu8HltBwmf3+U/WdjedPsa5622Gb0emakFdpIHdaKfnX8VNWev1weo/uzc7CrQdtOlsalThPYBf5Fl0jPKcDR2tzUIdVKutRoLH+fiDkFHNTV45DfYLo+Moz6Xk43P9HMEloNRmnxf3BwOQWb3qRNYQRN455n0eJIXnxpSoUXAt2xeS1PKBfIwQqbUZv1ayzcycytSfboiE/Cn3DmN0CSC3eiS799TDOlgGjrhgQ06n5bddTt+yoZp1YQSBzf/bCQASNkOLop6HQqARfWApDu1w1ny7svyWPurl/ENkiJY+fZJAa51sB1moSoZjV33O913Nzc0Gq1JCQkGB1PSEjAy8urxHO8vLxuWr74663KJCYmGr1eWFhISkpKqe2Cfn2Is2fPlvq6paUlDg4ORg8hRMUdvZTGI5/9zZVzB9hg+SZPm/2bWGj4CPi3R2dmjZ2Si7WST6JNPSLveQ/rKafpO24+jra1Y/6utXdDAPyVRCJjZZXq26KqxK1+CWs1l4NqKNbPb2PEqPHUu1Vi4VoaLbR5BvMxe7jq1xVLpYDnU+eyet36CoWWW1CE14mvAIgPfuLOTyz8y7GFPhnfPDuc+PRcE0cjKtuFywl0Sv0JALN7X75xN5IyUiztyWj1AgBtL35FZFxqpcUoyu7UpST66P4EwL7TSBNHYyL/jtYIUuLYGSG/i4WAWpRcsLCwoHXr1mzb9t8nkDqdjm3bttGhQ4cSz+nQoYNReYAtW7YYygcFBeHl5WVUJiMjgz179hjKdOjQgbS0NA4cOGAo88cff6DT6Wjfvn2p8R4+fBhvb+/yX6gQ4rb9eSaRpxbvpm/OOn62nEo9JRbsvGDwOhi4Ekb8jub1WBj9Dzy3E49J+wnp9QJWNrXsExc7DzI19mgVlfjzR0wdTa2UdfhHfBO2k69qOd/+XRr6ON1+ZU7+2D+zjjiv+7BUCuh+dAL/HD1129X98ddfdOYQOhT8e0249Ql3CNsmvQForjnP0TMRJo5GVLYTG+bjqGQTb+aHT/uK7fTg98BYMjUOBGni+fP7hTLf3QQuh3+Pi5JJitYds/oPmDoc03Cth6pocFEyiTwXQWGRztQRCWFytSa5ADBhwgS++OILli9fzqlTp3jhhRfIyspi+PDhAAwZMoTXXnvNUH7cuHFs2rSJuXPncvr0aWbMmMH+/fsZM2YMAIqiMH78eN555x1+/vlnjh07xpAhQ/Dx8aFfv34ANGzYkF69ejFy5Ej27t3Lrl27GDNmDE8++SQ+PvrVwZcvX863337L6dOnOX36NLNmzWLJkiW89NJL1dtBQtzF1u6PYebyDSzhLaaaf405hRD6ELywG4Lv+6+gRgseDcG72W1/cmZyisIVW/1wzLwYSS6UW246Rb/q53uvtXqcvmH3V7xOjQbvYStItKyDj5KCxY/DSUgt/0K9qqqi/rMQgGj3bpi530V7x9t7kmClv97UkztMHIyoTElpV2kdq9++O7vti/qfwxVhaUdee/3fcg8kLefXwxcrGqIoJ59zqwGIrvMYaGv+dMIqYWED3s0BaJJ/lCOyGK0QtSu5MHDgQD788EOmTZtGixYtOHz4MJs2bTIsyBgdHU1cXJyhfMeOHVm1ahWLFy+mefPmfP/996xbt44mTZoYykyePJmXXnqJUaNG0bZtWzIzM9m0aRNWVv/tW/7NN9/QoEED7r//fh566CE6d+7M4sWLjWKbOXMmrVu3pn379qxfv541a9YYkh5CiKqjqioL/ojg2E9z2GD+Ku01p1Et7KDPPHhyFdi6mjrEKlHort9T2/LKSRNHUvsk/zIdh8IrnNd5Ue+JtzCv4PoIBlYOOA7/jixsaMUp9nw5vtyfZIUfO8P9+fqhxu4P3D2jForl+OhHBFrF7blFSVGbHNiwGC8lhRSNM0HdK+dvI9duL5Jl7kKgJoHTP3/Mlcy8SqlX3FpOWiIN844C4Nx5hImjMS0lsDMA92hOsTMyycTRCGF6iipjyWqEytxfVIi7haqqzNuwn1Z7J9BVq/9DRw3sgtJ3ATjf2QsrxW1fjPf2SfxDE9pP/xulto7CqG7plyj4uAXmFLDQ/0NGj6j8ucLxe37A67dnKFIVVjdbyqDHHy3zud9/9BJPZKwg1qYhvpPCa+/omtuUtm8NTr+O4qSuDn6vH8DBShYrre2y8/KJm92SYC5xpskrhD4xrdLqLty3DLNfx5Gm2vJeyLe893TXSqtblO7EluU03jWWc0oAdacdvbt//0T8Dqv6c0HnySvey/jhhY6mjkiIcqvM+9BaNXJBCCGK6XQqn6zdzMP7htBVe5RCjRU89CHKkJ/v+MQCgFtwGwDqqxe4nJZj4mhqj5TN72NOAf/oGtL38aerpA2v9o8T49cbraLS+shU/j4dW6bzImISuS9dv+CdVZcxd11iAcCpQTcAGijRHDsrQ93vBLt/W0Uwl8jEhnoPja3Uus1aDybHuQFOShYhpz9ny8mEW58kKizvjH6tsssu7e/uxAJAwD2oioZATQIJMedIzykwdURCmJQkF4QQtU6RTmXRypUMOTGCeprLZFt6YDbyd2g3EjR3x481c69GFKFfSCoqKtLU4dQO6ZdwOPUtAHvrjMLXybrKmvJ/aj5ZWkcaaGI4uuZtEjJuvfvBqY2f4qpcJdnMG9d2T1ZZbDWavSeJFv5oFJXE49tNHY2ooMIiHR5HPwfgYtBAtDZOlduARot1n9kADNVu5vsfVteIm7vUrHx+2v4Pv34yhjU/fEdqVr6pQ6pU3lf+AcAytBLWq6ntrBxQvFsA0JYThJ+7Ytp4hDCxu3QFFiFEbaSqKntPX+Ds5oU8m/oVFkoRqU6NcX7mB3C4y3ZnMbci0SIA7/wLpEUdglbNTR1RjZf1x4fYqvpRC93CHqvaxmzdMO/zAax/jhG673ltRXc+eKE/ZqWs75CUdpV2l78GBbLajsHtbl0gDcjwbIdHTAzamHDgWVOHIyrgnx0b6aw7TT5mBD88qWoaCe5OUZP+mB1fy7uFH/LZTw154//Kv3tBQZGOnw/FkLR1PjZ5iUT6Pkq9Rq14tJXvLafnpGbls/dCCiciIsk8v486aeE8qfkDS6WQ/JRVvH7kGN6dn+bF++phZa4lK68QSzNNqT8ParLk6NN4qwkUqFpC2oaZOpyaIagLXD5IB81JdkYm0atJ6VvVC3Gnu3v/ehFC1BqZuQWE//kLHFxJ5/y/aa/kgwJxvmF4D12mX7H5LnTVKRTvxAuoccdNHUrNl34Jy6NfA7DJdRgz/JyqvEmLFgPJOrQG2+g/GJU4k89/r89LD7YsseyhDYt4QEkhReNCQPe7+4bavn4XiPmBgMzD5BfqsDCrfTdgQp8MNg//BIAIrz40cfGtsra0j8wnO/Y4bqmn6HN6Cr8dqs+DLQPLdG7i1VzW7I1h656DvJb7MY9r/t1G9tLP/HmxOY9uGkWXNi0ZdW9d3O0tWfJ3FJtPxPNUuwD6NPPhy1+2U3ToG3pq9hOm+Xcqz7+bYWRYeOGQH88HfMqcHYk8eLg/rvY2HIxOJdTLgSXD2uDtWHUjqKpC9L6NuAER5g1o7Oxi6nBqhqCusOsTemv/Yc2Zk6hqE5kuIu5aklwQQtQo55Iy2XIygYi4DEg8QZPUbdxf9Dc9NYn6AgokWgaitHsW7/tevGumQZRE490UEjdjn37a1KHUeAU7PsJcLSC8qBEde/SrnkYVBdsnFpK7oDMN8mKI2j2JncHf0KW+p1Gx3Lx8Gpz9EoC4xiNxMbcqqba7hkeT7rANGhPFiYuXaRHsZ+qQxG34559ddCjYi05V8O89pWobs7DBZshqchZ0oXnhec6vG8sZr68J9b75wmTrDsUy68d/GKRu4Gvtb9hrcsjX2pDj2QaHyzu5T3uEZuqrjPtnDF33NMPHyZqilIs8qNlD0rpMdv9ymdEcQGumXxtdh0KmfTDmfq2wbv0kDnXvQ/1tMpp9XzDFfDU9M/cTfdWDLy2OcCI5kBGfTWD2kB4093eq2v6pRJoL+t1sUrw6mTiSGqTufRT5tcf20h6ez1rExeTeBLrbmToqIUxCkgtCCJM7E3+VX4/FseVYDLZJh+mqPcqLmj0Ea/7dWlYD2VgR7fMgft2fwyP4nrtysbvrOQW1giPgl39OPuG9mfxs1CP6PdnX2g5kTkPPW5xQiRy8sXr6WwqXPMSD2n18/u1UAl+ah7/Lf6Ntdq//H92JJx07Qh8aU32x1VCKcx2uaD1wLUok9thftAj+P1OHJMqpoEjH1W1zAIh06Uqof6Oqb9Q5EIuBy9F98ziPKjv4eOlMvMbPwtHGeEqDqqqcjMvgu12nsT38JVvMNuCoZAOg82mNxeNfYOEaDMlnUb8fjmv8Ub62mM1fRU2JSPdjsOVWLDFe1yHF4x5cOgxBUz8MB1s3o9eUh+aAV1PUza/TKv8srTgLQCftCZbmv8KYhWMxr9uJIR0C6dHQo0ZPlchKiSP0qn69BacmMiXCQKNB+8h8Chd2oqf2IH/u+o7Afs+YOiohTEKSC0IIkzkck8bC34/gcv5n7tMc5lnNCRws/9v5oEhjQWbA/Zg3ewybJr1pYGFrwmhrHte6+iH2QcRxNj6JUL9qvGmuTc5sxKIoixidOy3ufQStppoTU/7tUB/6EH4dxwvqGt6ZZ41dlxd4rFUA69cu49m4t0GByLqDaWMtWxEDXHFrg2vCRnQXdgGSXKhtNuwI5+GCv0AB30feqLZ2tSHdye46DZsdMxiT9xUzv6zP6y88g5W5FlVVWX8olu2/reGenL94WbsPJ/MsAFT3Bij3vYGm4cP/Ja7d6qGM2AKbX4MDy7hXe4x7OaZ/LaAjWa6NOZ9lQUCnJ3Gp06z0oBQFWg9FqXc/7P4MtOYQ0IGiLTPwvHKGNRYzWXWxO2+ce4K3HT15pnMQQzoE1shk8dn179GcfE5r6tG4bXdTh1OzeDTguP9TtIhZic3p7wFJLoi7kyQXhBDV7nBMGp//foSA86uZZbYBN/MMw2s6axc0wfdB/V5oQx/E0dLehJHWbIq9NxmKAw5kEB95mFA/+SSpJJn7VmEH/Kx2ZnArf5PEYN52GFdjjmB/dAlvapax6699fLm9DVPNvsZcKeK8U0daDpxmkthqIqt690LCRrzSDlKkU6s/ISRu29XcAgp2foKZouOy6z34BLWr1vZtuo0nI/YgDmd/5qUrbzNtpR9NGzZk7/HT9I1+j3naQ4a/fnPs62DdcypKk8dAo72xMnMr6PMxdBoPexfDlXPQajCEPoStotC0PIE5+sGD7xmeaoPuhd+moDn8NU+bbeNps23E5rry5+YWDAgfzLi+nbgv1KMiXVGp8q9eod5F/QiwpFZjaVCDR1iYil/HgbBmJfVzjhCdnEmAm0yNEHcfSS4IIapNenYBs34+gMOx5bx7TVKhwKEO5m0GQ/D9aLybl/xHnriRopBkG4JD5gGyoo8Akly4QWYSNtHbAbgc0PeWq75XJft+c1F96qPbMoNOnKCT9gQA6cGPUPepr8DMwmSx1TQ+zbrDLmimRnL6UhKNA2rOTZa4uRVb9jJC9wco/H979x0eRdk1cPg329J7TwgJoYUOAaRJExSQKgqC2BHLZ8Pua+++9i4oFtAX7IoKCiJNmvReQguB9N7Ltvn+mM0moUMCG8i5r2vdzeyUszuS7Jw9z3kIvfKJ8x+AouA7fjql0/YSkr+Hmw49xq6DMTyv30SgvgSbYkTtcj2G9lfhEdMHTmdmloAYGPJy/cbp5g1jPoJOE7TqiIztRCm5XG9YzKjSNbz51Tj2Xn4ft/dv3iCaA+777XXaUcFeYrlkyCRXh9MgBbfqSYXiTgAlzFu5jBvGjHB1SEKcd5J2FEKcF4t3ZfDGWy9x3+7reNI4h2ClCItvDIz+COP9G6HfIxCVIImFM1QZ3A4AY85OF0fSMKk7fkSHjS32OLp3O7/foB5Dp0PpeRf6/1uNvUkPAOxdb8Vv0kxJLBzFENqKQp0/boqFg1tXuDoccZrSCsrxWf8+7oqFgsBOGOL6uSYQkxdeN36H2eRHO10y4wz/EKiUUBncDv2d/2AY9S7E9T+9xMK51qwv3LkSHj8Mk37CFt4JX6WMF4yzaLl4Mq//tBJVVV0aot1SSeT+bwA41O4u3IwN4H1riPRGCkO6AZC3czF2u2vPmxCuIL8dhBDnVGGZhS+//4F+B9/iJd1+UKDSKwq3wU9i7DheG38qzpp7k45wCIJL9ro6lAapfOM3eAK/q/14oG0D6UkR1BzdrQugJAOdb6Sro2mYFIXcoG74Zf+N5cAK4GpXRyROw6zfF/GwsggAv+EvuLbxbkAsput/gH8/huBWENUVt+aXNdxEnrsftByMvvlA1PWfYVv4FJexhVbbb+Zzr/9x27BeLgtt+9Lv6KQWkq3603P4TS6L40IQ2G4wZK2kbcVWNh3Op1usTNcpGhepXBBCnDN7D6ey4q1rmXroLhJ0+zHrPLD0fxK3qRuhyyRJLNSD0JbatyTN7YcoLDO7OJoGJmcfntlbsao6iuJG4u3WgPLpOh1IYuGk3FpcCkBYvtZ3QTRsO1IL6bb3XYyKjaLoQSjNB7g6JGjaA8bPgsuehNZDG25ioSadHqXHHRjuWE6xZzRNlBy6r/k/fl+/32Uh2TZ+BcD+yJH4enq4LI4LgbGFVq3TQ7eb9UnZLo5GiBpWfwgfdoeCw+f0MJJcEEKcExuW/Yr35/0YYVsMQE6LazBN3Yxx4KNglA8n9cU7qi1W9PgqZRw6mOjqcBqUquknl9s70S/hPEyFJ+pVeIdBAHRmDztTcl0cjTiZCouN2d98zeX6jdjQ4Tvqv6feSJxcWFt8Jv9KmcGPTrqDuP1+J9sP5533MLbt2kmnig0AtBx613k//gUnvBNmvTe+Shk5+ze6Ohohqv31JOTshV/uPKeHkeSCEKJeqeYytsy4i27LbiRSySFLH07xxN8Ivv5z8I1wdXgXH4Mb6camAOQdkA8yTqqKZbOWXPhD6cugNtIQ8EKjD29Pqc4bL6WS/VtXuzoccRLv/LCEqcVvAFDR6WYIaeXagC4WQc1xv/47LBi5QreeHV9NpeA8V6gdXDQDvaJy0LMTwTHtzuuxL0h6A+XhCdrD9C0u75chxDGSV53T3UtyQQhRb2xl+aS/1YfOqXMAWB80moCH1uHTur+LI7u4Ffq1BsCStt3FkTQgqRsxlaRQqrphazUMT1MDGhIhTo9OR06g9iHdfFCaOjZUP6/ewdg9UwlTCij1a4nXsOdcHdJFRRfbC8vIDwGYaP2VuTNexGKzn5djH0jLplfeLwB49br1vBzzYuDVtDMA0eYDpBaUuzaYcyivVIZiXlCUGpf9xRnn7DCSXBBC1A+7naRPryey8iDZqh9Lun5E93u/wujp5+rILnpu0doFmG/Wehmb7qBu/wmAv+1dGdKpmYujEWfL1LwvAKF5m7CepwsqcXpUVeWrxZuIXnALrXUplJiC8brlF60xoahXnl0nkNXtYQAm5X3EtK9mn5eZCLb8Po0wpYA8fQhhva4758e7WBgiOgAQrzvM5sMFrg2mHiTllPLy/F1sTyl0Lvt+wxESXlzE87/vlOqMC4XRs/rx3oXn7DCSXBBC1Is9PzxNi4KVVKhGdg78nMtGXu/qkBqNmF5aJ/1u9u1s3uO6pl8Nht2OedvPACzW92FgvAyJuFCFtb8MgAR2sy0l38XRiCrpheX893+/02/5BLrr9lKh88Lz5p/BP9rVoV20Qoc/RWbT4RgVG+MOPcM7v57boUIZ+SV0T/sagNJud10YzTAbirD2AMQrR9icfP77ZNSHogoLX605xPO/72Tou/8wY0US1834lx2pWoJh9Y4DTNQv5udVO/j0n4Mujlackt0O5pLqn/f9dc4OJckFIUSdJf87l1a7PgLg7+aPM2DA5S6OqHExhbYgxb0VBsVO6urvXR2O6x1Zi1t5BkWqBwEdh+Fu1Ls6InGWdJGdqFTc8VdKWfvvSleH0+ilFpTzwjdLmPfmbdy3/zZidZkUu0fidsff6CI7uTq8i5uiEDbpE4q9mxGh5NFt0+Mcyi459XZnKnk1fNIP5ZN+NFWyKFL8iB50bhvAXXSCW2LTGfFRyklNvvAaLVtsdiZ++i/P/LqTL1cdotJqx8/DSHGllZu+WEdWcQVtj3zDq8bPudPwO6/+uYf9WcUA2Owqd3y9gdu/2lAvw3d2pRXx185zV8LfaJhr/65ITzl3CSFJLggh6iQ/JZGABf+HTlFZ4j2CYdc/5OqQGqXK1qMACE/9s9EPjajc+gMAf9m7M7Z7cxdHI+pEb6QkvIf2cPevVFptLg6o8bDbVXalFbFgexp/bk/nw7nLWPL2TTy6ZzxT9PPwUiopCrsEn3v+QQmT2VjOCzcffG78BgtG+uu3sWrhd/V/jFXvQfpWwioOAJDbcTKYvOr/OBczvRFboNbUVJ+5k4PZJSRmFPPivF2kFzb8Hgyf/nOQnWlF+HkYubl3LO9P7MKKxwbSMtSb3FIzM1cdwq9Su+Dv7ZUKwE+btPs/d6SzcGcmf+3KZPoy7f+hrUcKGPjmMkZ/uJLZa5NPe0iP3a5y68z13P71RpbsyWTNgVye+20nFRb5O3DGjkouFBYVn7NDSYcrIcRZs1SUUjBzIs0oZZeuFV1v/wS9TnF1WI1S077Xw9Y36WbfwaZdiXRvH+/qkFzDbsO+Yy4Am30GcnUTGf99ofPvdRP8vJzR6mIW70jlys5NXR3SRaGk0sqf21LYuPYfLPmpuMVeQkLb1pTnJOO2fwEBOetpb08knnwqMXI5Vgw67ZvIkpAueF/+BL4tLwdFfuefV6FtSG99A00Tv6Dzvg8prbgRL3dj/ezbUoGa9A8K8JTlFmKiY5gy6t762XcjY4zqADk7aaEmc+MX68gvNVNqtmFXVZ4d6fpZN/73bzJZRRVMHdwKXY3PbTtSC3lv8T4Anh3ZlrEJTZzPjevWhFf+2MNXa5J5RykCoJUuHYBfN6fy8BWtmeZIKAB8sGQ/eWVmvll3mAqL9rtja0ohuSVm7hvU0rlepdXG4z9tp1fzIMZ3qx5atSejmIyiCgBe+WMP+7O0C+S2Eb6M7y5DsM5IZe1kgjvnrhmnJBeEEGdt69eP0s16gFzVF49J/8PP19vVITVaxuBmHPFoQ3T5btLWfA/tn3F1SK5xaCUe5lzyVW/iegxHkQufC56+7UhKfw8gzJLP/pU/QGepjqoLi83Ob0tX47byvwxRNzJOKdOeOACp+4OIUnKrV3b88/FwfBDNC+1JwND/4N2svyQVXKjJiCcoS5xDO+UgyxZ8xYAxk+tnx8mrUCxlpKuB/GoYyt/XDQC9XCqcDSWsA/AtXdxSeT+/ulphkwsaPL62YA8704qYfGkz+rUMZmdaEU/N3QFAxyb+DG4bBkBiRjE3fL4Ws9XOwNYhXNUlqtZ+BrQO5ZU/9lBSaSXQpF2sepSnE+ZuIa0QXvljNzvTivAw6unS1J/VB3L5ctUhAAa2DqFNhC8fLzvAjBUHublPLL6OpNiyxGx+2ZzKuqS8WsmFVftznI+rEgugJR3EGaqsXbngrkhyQQjRwBzevZ5OKd+AAvt6/ZeezVu7OqRGzxI/GjbvJiptAVbbUxj0jW/kW8GG7/BHGxIxumusi6MR9cJgwtrxetj4AV2yfiG14P+I8vdwdVQXpIMZuWz84kFGV/6OSbGBApV6L2zekXgW7iNKycWOQoZfZ2xxlxHefiDGkBZgqwSdgUC/Jqc+iDjndD4hJDa7kS5JnxK39U2KLhuLr29AnfdbuvNPvIBltk48OjyeMF/3ugfbWIVp1Ql9vNOJ9/WhRag3i7YlsyutkAqL7bz1AjqSV+asJvhnbzaD24RRUmlxPv/ZyoMMbhuGxWbn1pnryS+z0KmJH++PbopycCk06w86LdaWod5E+rmTVlhBIEXOfdzQvJI3dxr5fGUSABMvacr/DWzOe3/vQ69TaB3uw7iuTdApCot2ZbIvq4RZqw5xr6N6YWOy1qw3t7QSVVWdXwqsdCQXovw9ak3peTDnHPQaudiZtYSMVdVhUOy4Y671XtenxvfJUwhRZ3abnZKfH8Co2Njk2YceQ2SKqoYg+tKJACTYd7F55x4XR+MC1kpMe+cBkBY1jGBvNxcHJOqL36W3YUehr247i1b86+pwLki7UgvZ88lNjDPPxaTYSAvqhfXmBbg9eQTPBzbAIwfhhrnoHt5L5APLiB79DMbmfcE3AgJiQRILDUqL0Y+Tgz9N1TR2f3wDlRZrnfdZsuNPAA4F9uG6HjF13l+jFq5NR2kqSmbBzbF8ELeORPebuUJdw860wlNsXH9+35YGQJivGya9jr93Z/LvwTyMegWDTuHfg3nsSC3kn73ZpBaUE+xtYk6PQ/jM6AlfXwVfjYZirb+Coij0b63NvhSkVFcPjIwqRq9TMBl0XNO1CQ91heDyZF4c057nRrVjYvdoDLt/QZe1k3suawHAZyuTKKnU/p+tSi5UWOyUmbV+CpVWG+uStJk2PriuC7f0ieVex7b7MiW5cMYclQu5+ALasIiqoSr1TZILQogztu63abS1bKdcNRFx7TtSet5AGINiSfZoh05RSV/b+GaNsGz/GU9rIelqIB0uHeHqcER9CoglK/RSAAxbZmK2npsPRRerzYfzWT7jQa5UV2BFT9Goz4m8dwGG2F7ObyXxCoLmA8Fbpm69EPj4B1E46gssqp4eFStY9MljqOqZN/NNyS9jylcbuOWtbwmzpGBR9YwZO1H6J9WVVzA06weo8NdTKEteAmCIfj2bkgvO2WEP5ZSyK626quC3LVpy4cHLW/HDnb0I9dGS7tf3jGF4xwgAPl62n582pQBwb/NMvObfDeWOqX8PrYBPB0Kh1rBxQOsQjFjxrRpOBcTYjrD0oQGse2IQb14RjNeXg2DGZdX7WPYq/HgrzB7HiPZhxAV7UViuTXVZYbGxPaU62ZJbopXrb0ouoNxiI9jbjS7R/jw7sh2TL20GaLPWVCUmxGly9FzIVbU+VO6KhaLyczM0QpILQogzkp2dScutrwGwvcUdRMTIcIiGxNpmNABNUhfUyzRQFwxVpXjZBwD8YhhK/zYRLg5I1LfAfncAMNz6N9+v2uniaC4cezOL+emLN7iLHwEwD30T34RrXByVqA/NEwZxqOcLAAzJ/pKlq9ec8T7eWJjIol0ZDM7XEtLp/l1oEytVKvViwBPa/a5fnWXp7ZRDbD6Sf04OZ7HZuWb6GsZ8tIrDuWXsyyxmT0YxRr3C0HYRdIr254/7+/LRdQk8cWUbbu8Xh06BP7ZnsGCHVp0wzGuvtrNWw+D//oXgVlCcBt9MAHMp/VuFMKL5UQ1Ec/bSNMgTf08TrP4ALGXa6939O2z9FpZrnxkpTkOf/E919cKKJNYfysNc47NKTmklAH/vzgTg0hZBzi+w/D1NhDiSIzV7MJxNUu1sVVhsF+aMXI7ZInJUX+eiktJz07tCkgtCiDOya/ZjBFHIEX0TEq59ytXhiKPE9NWGqHRW9/D1T7+4OJrzR01ZT2DhTipVI549J2NshP0mLnamNsMo9IolQCmhdOm7FFVYTr1RI5eSX8aMGR/ynDoNAEuv+/DseauLoxL1qeWwezgY0AejYoNFz5JbUnna2x7OLWP+1hT+a5jBJMNiAJpecc+5CrXxiekFcQNqLWqmZLAnOf2cHG7rkQJySiox2+x8v+GIc3rI/q1C8PPUEgLB3m4M7xiBUa+jXaSfc9YGuwrx4T6ElDiGVDa/DELbwKQfwTMYMrbBkpdwN+p5Z+RRMzVkO7YpyYaNM6uXb/gC/nhEe+wdrt1v+55RnSKJCfIkr9TMk7/sqLWr3BIzFpuduZu12Ed1jqz1fMtQrXH4vsxi7HaV8dPXMPLDlZRWWqmw2MguPv3//8/UrrQiEl5cRNeXFvHID1spM5959YTNrrqm6sJRuZBD9QxaJaWl5+RQ8ulLCHHa1q9eyqX5cwGwDnkDg0maPTU0hoBoMqKGoFNUhu58mEXrtrs6pPMi6+/3AZhPH666tJOLoxHnhN6A97DnAbhe/Z2vF61zcUANW25JJW998jkvWd7CoNgxt78W4+XPuzoscQ40Gf8mNnRcxjo+nvU1aTWa353MJ8v38rphOhMMy0DRweiPod1V5zbYxmbQM2Bwh7ajsXuHoVNUAor3MvXbzc5qgfqyssbsCt+uP8ys1YcAuKbrcaZtPLIe9v7FPQNb0D1WawY6vls0SrrjM0NER+0+IAZGvK093rtAuy91HMczSLvPPwRrPoI548FaDoFx2vK0zVBZBBGdYNxMbdnu3zHYynlgcCsADudVD68A7ffW0j1Z5JaaCfZ2o79xD6ybAY7qhFZhPgDsyyrhSH4Z6w7lsSO1iCd/2c6gt5bT579LWLEv+8zeuNP06p+7KTPbKCiz8MPGFOZuTjvjfbyxMJHOz//F5sPnpnrlRFRHcqFQ9cLquPwvKz03vSskuSCEOC05+QX4LnoAvaKyI/Byml1ypatDEicQfsMMct1jiFTyCJw/mf3puafe6EJWlE5Q8h8AZLe9CT+PeprzXTQ4+najKQzogJdSie/698korHB1SA1SSaWVDz/9mJfLX8RNsVDRfCimqz4GnXzsuxiZItpSEK819L0z63n+fvtmZvw4n6zi4/z7sFko2/Qt2355i15b/sNY/UrsikG7+Osy6fwG3hhEdYWH98HVX6CL6AxAO90h5m5J4545m8ipUWmyfG8293+7mf5vLOX+bzeTX6qNibfbVQ5kl1BQdvIx8qv3V/+tzykxU26x0a2pH0PahcGBpTBzhHahbi7TmjXOGYchZw8zb7mEz27sxk2dvLUhECjO2S4ArfpC0UHeQShKhzLHcULbgkcAqHZY+ASkbQKdEUa8C1Hdqre/4iVo2lNrDGsugS+HMcb2Fx9M7EKQlwlFgY5NtG/Uc0vN/LhR6/8wNiEK/S+3wx8PQ6ZW4dCiRuVCYo0pKeduSSO1oByzzc5d/9vkbJpprafhoasP5LBiXw5GvcLVCdqwoUW7zjw59OeOdKx2lfnbzk31yolYyrU+HCV4YFG0oSXlZZJcEEK4iM1mZ+eM22mtJlGg+BJ3/buuDkmcjLsffrf8QKniRVclkd1f3EXxRVxCnr1sGgZsrLPHc+XlQ10djjiXFAXfkVpjtGuVRTzx+a+SYDhKudnGnOmv8GThC3gqlZQ1HYj7hFmgl9nHL2ZBI57D7BNNiFLEjboF3LD9Jl5+/RWe+GU7h9PSKSguZfHWA+x8axiev91Bx60vMEK3Ghs6lGs+h7ajXf0SLl7uvtq/P0c1wOTmxbQM9cZqV5m7ORWLzc7L83dx0xfr+HVLGsm5Zfy6JY0h7/7DNdNW0+3lvxn01nJGf7TqhOP9SyutbHJ8Gz6kXRgA1+kX80P2aJS34uHrMVpzxr+fg8Q/nD0g2PkzXm4GBrcNQ5+5TVsWGAduPjXi93POfkHyqtqVC6M/gvbXQJuRMPApuH8LxPWHLtdr67QerjW2VBTo5Rhyk74V5k1lZAs3lj0ygCUPDaBXc60KIiW/jCV7sgAY38YdShwX8Dn7gOrKhcSMYvZmaq+hqqd4dKAHlzQLpKTSyg2fr2P68gMkvLiIB7/bAsCB7BIOZp/+BXVKfhlrD2qJlA8W7we0aTYfjNrFdOM7bD1whNIzGOJQWGYhOVer1Fh/KO+0t6sPVkdyobxGcqGi7NwMi5C/NEKIU1r6zRsMLluITVUoGfkpTQKl2VNDZwhrTelVn2H/+TpGWhYy97/XcCj2Wtp2G0j3Ztofcatdxa6q2r1du7c5bla7HbsdrDYbqrUCe2UJlvRd6NM34Z2zFb2tgqLwHuhbXkFU666E+Lijc0V38coS3LfOAmBb1AQuCfQ8/zGI80qJG0BpdH+8jiznrcIHee/9nVw7+SHaRPqdeuOLXHJmLls+v4/bzfNAgbwWVxM48RPQSzXPRc87FNN9G1APLCF/6fsEZq7hPf17pG/5moiteVSqRjrgRahSQJnqxkZjF8K99IRd9n/4tpPZdc6LcC25EGPex819Ynnylx18t/4Im48UOL/Jvq5HU3o3D+Ltv/ZyMKeUrBo9BJJzy9iWUkCXpgHOZeVmG1+uTiKn2IzVrtI00JOnhrdlR0ohD/EXSqW1+gLd6KVVDvxVo1/Wjp9h4JPaFXrGUUMiaortqyUFDq0ArxBtmVcwxA/XbkfrejMENoPoHtXLLpkCrYfBjEFaTHkH8Ynujo+7kWAv7YJ3bVIeVrtKsLeJFhyp3jb/EABtI33R6xTSCitYmqgNf7j3spY08fdgQOsQ3Ix6bvh8LdtSCvnvn1oviLlbUrm+VwwTPv0Xs9VOl6b+vHttZ2KCvE54qlRV5ZYv17Mvq4SPrkvg3yQtyXBH/+ZEfnsXUfptzDP3ZMW+3gxtf3oNpHfUmIJ0R1oRpZVWvNzOz6W4rVxLxFiNXlh1bmCHygpJLgghXGDzv0vou+91UGBP2/tp13WYq0MSp8mv45WkHXmcyPWvMoZlcGgZ+w5GMd8eT6BSTKhSQCBFFONJrupLruqLXrETqeQSQS4hSiGeykmaIxX/C/veI3u+H9vVYMr1PlSa/CkP7kBor0l0aht/bqczU1UKv52Cn62AFDWYbkNvOHfHEg2K19gPMM++joCcHTxne4+V05fwbuQk4vuMIjLAC7PVjgp4mvR4uxkI9DLh437xXmCrqsqyNesI/+sORpMEQGqHu4ka+3L113ri4md0R4m/ksBWQ1D/egrl34+JULRvSN0UC6EUUGrwJ3PEV/Tt3N/FwTZCVRftWbuZsGEiPqYA7sv6P/ZllWDUK3wwsYvzQnVg61CW7MnCoFMI93Nn2rID/LUrk2WJ2ezLKuHrNclMHdyS7zccYeHOTOch+rQIJjrQk1U3BcGnR7R+D9f/pDVU3PoNrHgTimuU5Ocd0Jo1RnTS7sGZBKklpg+s+RAOrYJYbVpgPINP/FoV5ZhmlgD4NYHgllpyIT8JorsDEORtAuBgtnbBGxfsDVk1ekY5kgvebgbaR/mx9UgBG5O1So3O0X5cFh/mXPXryT246Yt1bEspwM/DSH6Zhfu+2eycwnjz4QIe/XEb1/eM4dnfdhIf7sONvWIZ2j7cuY+9mSXsc8xI8Z+ft6Gq0Cnanyh/Dyg4DECIUsBfuzJPO7mwPbU6uWCzq2w6nE/fliGnte3p+n1rGgadwrAOtWNSK7XKBZvRG5viBlaoLCs73i7qTJILQogTysxII2zB7bgpVnb5Xkq7cc+6OiRxhiKHP469TW/yV36Gb9KftNSl0lKXelb7ytaFkOLZhvyADth1JsKyVtOifAshSiEhSiGoQCWQugzbDx+yTteR1CYjCO48jK7t4uv94q5g0ev4J/2BWdXzVcTTPBFzkg864uISEIPprmVULHsH/YrXuVS3nUszHufQD+/wr70Nh9UwsvFDh4oRK0aseOpVPN305CkB5CoBZLvFoPOLJNjbjRAfN4K9Tfh7mvB2M+DrbqRpoCdRAR7nNkFWD1ILypn53Q/cmfYEQUoxhYovttHTiep8nG8TReOg06MMfRU6TYDKEjI8muNmziegLBmvqK7EedfvBY04Tf4x2hCDikL02TsZpYNXuJYMgnhhdHuGRtvh0EqI6ISXmw8jO1XPlDC4TRh/7cpkwY4M0grKKa60MnnWBgBMeh3NQ71JyStjqu1L+CZDqyoAaHlFdTKg43gtuQDg7q8lDBLna9ULEZ0g/WTJhV6AArn7qhs5ep3l39yAGK0CIi/JuSjI263WKs1DvSB7d/UCR3IBoGdcIFuPFDh/rhoqUcXPw8hPd/WmuMLCnHWHeX1BIin5WpPTJ66M562/9rI2KY8NyfnY7CqrD+Sy5mAuf03tR1GFhSN55aTkV194F1VoQx+GtguHiiKo0I4dohTy9ZY0Jl/ajHanUTm3PUVLLuh1Cja7yvqkvHpNLmQUVnDft5vRKQqbWgTX6j+lVjqGg5i8sdu1ZuyWSqlcEEKcR2XlZaR/cT2dySZNF0HclK+lGdgFShfXj6C4flBRiLpzLva8Qyg+4eh8w7VvHiqLoTQLSrJApwffJuAbCT7h2rhLowcYPAjRGzjmz6C1EmvqFopyMyjOz6IsLw2PgwuJLd9JL3UrHNkKR15mz6/R7PXujq3ZQGISBtMxNhxDHaaLLNnxJ76rXwVguucd3HOzNCJrdPRG3Ac9Cp2vJn/Zh3js+p5YMonVZZ54m5qtRyohpTCYrfY4KnBDRcGPEpooOfgrJey3R/Kn0pxSt1A8DWA0GEnzbkuOTxuaBHnTNSaAhKYB2tzu55nNrvLPvmy+//cAHvt+42X9Z3goZjK94gmY/BMmGbomQLtgBMKd/23jwmCEs+/Ajp+hPA9KMunnfoCAHt2Z2DUC3usMRSla88Se/wdDXta2s1YypOJP5uly+CdTO6deJj2lZhsAb4zryOjOUVCcCW+Nr33M9ldXPw5prfVOyNiuDU9oNVRLLqz/TEsY5O7Tjh3Z+djYPQIgvL227ZF/tWWegWf3PgQ00+7zayQXvGr/Hm0e4g379lQvqJlcaBbEJ8sPAtr7EOXvccwh9DoFf08TwztE8PqCRECbhvOWPs0oKrfy4dL92OwqfVsGY7baWZuUx+crk/h9axqlZhteJj0AOkWbphPQKhsKq+PoFGDGmq3y0Pdb+fWePrgZ9Cd92dtTC+mh7Ob+4A3cmXUVa5Oq+y6sPpDDn9szeHJ4G9yNJ9/PiaxNykVVwaaq7EkvokdckPM5xawlF1Q3H1RLVXLh9GaVOVOSXBBCHCPp8GGKZ02gs20n5aoJdfzXuPuc5R8R0XC4+6F0vYmz+7N1AgY3DDE9CIyB6v9DnsWctZ/U5TNxO/gXkeWJxOuOEF92BHb+TOUOIxuUeLICuuLVvCetEgYQHXl6ZYWoKjlrv8N94UPoUJmru5zxdz6L70Vc8i5OIag5AVe/AyNfgr0LIWev9kG0NAd0BtAbsWKg3K7DbLFiLM/GVJaOW9EhmpBDE33OcXcboc+jLzu0hERVUqIYitI8OaKGYFhlIxU9P+u6sNlvELaQtkQHetE0yJMOUX7Eh/tiMtRPQtZis5NRWMGR/DI2JuWxZd0yLi37m5f0qwkyaGNpS6MHEHb9bHDzrpdjCiHOgf6Parc/H4O103mteynKsDawb5GWWEDRZl9Y9ykM+I/2u+ynyfhl7+Ezk4GuFdMpxpPnR7cn1McNFejfypH2P7Si9rGMXlrlQk2DnoUlL2pJjtA2WvVC8ipY9LT2fO97T1yR0PrK6r4McPJhEScTWJVcOORcFHx05UKwF6yuUblQmAI2K+gNdIsNcF70twr3QTnJ0K+YIC/aR/myI7WIa7o2wajXceeA5vyxIx2dovD+hC6sTcplbVIe366v7vFQlbi5a0BzPlp6gPhwH5oFe0Fi9Trdgy0ElZnYk1HMgh0ZWoLnBArKzBzOK+MF42/0LtrKEH00vx72pdxsw8Ok56m5OziYXUrnaH+u7np2yeF1NZIVu45KLugtWnJBcfMBtGSMVZILQojzYfmKZcT9PYWOShYleJA6+GNax3d3dVjiAmMKbUGzcS8BL0FpDjnb/6Jwx18EZKwi0JpFT7ZD/nbYMBM2wB5dC/ZGjMKz6wQ6tojB292AhyN7X1ZppaAwn+y9a/FZ+TLNK7UPHFtoRdvJ0wn3c3fdCxUNh8kL2o897lMGwOfohZXFkLIeMneBatPmUXf3Bb+m4O6LPXMXZYfWYynJw6LqoLIY/5xN+FqLaackO3fTjmQonEtJgTu5qi8ZBLLC3oq3aY/O5EkghXgb7HgHRxMa3ZyYuHjaR/kR4GmioNxCbkkluaVmckvM5JZWklNiprC4BM+8XQQW7aKktILcChVfSolRMhmq28e9ulTnJzirZyiG7rfg1e8RadwoxIWiaU9YOx2lqgpg2/fa/SVTYN9f2oX3gSXa7A55BwAwYaW/bisbfQYyqlPkscnLquRCi8uhKFX7fWg6qslxy8u1W5WxM2B6HyjPh4jO2owPJ9JmFCx/rfrnsx4WEavd1xgWEXhU5UJLr3ItJhTt95rNrCVfAmLxcTfSIcqPrSmFtA475jf7MV4Y3Z6fN6VwV//mgNa3YfGD/bGrWoXDwPhQ/D2NFJRpWeToQA+O5JXTOdqfqYNb4edhrB6+UFidXDBV5DCmSxSfr0xiw6H8kyYXqqbNDDOUggot3Ysxl9pZdyiPuGAvZ6+J/Wcwm0WVMrMVs9VeO7mQVlRrHYNV27/B3QfFpn1mspml54IQjUb24URSln2GX+pKDPYKdHYLOtWKXrViUYxke7bEHNoBr5iuNGnbE/+QyFPv9BTKzTbmfjuDkQeexVupIEMfgXHSt7SO61z3FyQaN69ggnteR3DP60BVsWXvI33zn5QcWINf7lYibGnE2/cTn/o2lSkfsEVtjgeV+Cpl+FGKL6VEKSpVf7ZLVTcW+F1L66ueoFWU9FkQZ8nNB5pfpt2OQxd9Cd7dbq690GbVmp6V54HOSHlBOpbtc/FKXoy3vQJvpYIYsuih2wP8BlVTrJuBNO22a00Mz1tHkEYIY3Qr6KnbjTsemFQ/YrHQQ8knRsnEXakxhuOonIFN5wZtRqDvfB2GuAEyzaQQF5rontp95k4ozoA987WfO4zXhiasnQ5LXtISCyYfaDcGNn/NJP+dXDP6vuNXRSU5kgvdboX4K08vDr8ouHa2NjRi8LNgOMkwr7B2WmKgquLgbCsXqoZFlGSAuQxMnpgMOnzdDRRVWDEZdESYHccIbKZVoFVVpDkSE2O6RLE1pZBBbcK038uJ87UGku7H9j5IaKoNYatJURT0ySvh13twG/Y6ozpF8tWaZFqGevPdHb345J8DjOgQiVGv4/Z+zas3LKhOLFOSRULTAD4nyTkN6IkcztMu5AP0ZWCFtn6VUAqr9udwJK/6Iv9AVnVyQVVVZ6za27WPiqyDBHccUmudEe+v5GBOKQp25hhfoQITb6e/SI2VMFq1Yxg9fVAsWuWCapbKBSEuahk5OSQumUPQvh9ob9l27Nj2Kio0KUmDkuVwEFgKOfiTbWpCqXcM9oA4PMNb4RUag29wNP6hTdAba/+xsNlVissq2L9nCxmJ67GmbiWkZA/XKjvRKSrJvglETfkBg49cuIl6pijoQ1vRZEgr4H4ASvPSSF3xP3z3fEd4+X56KHuOu2k5buwIGkrwyOe4OjbuPAYthIPeAFEJzh89AI+ECdoH5OJ0KM2G3P2oh1ZgS1qNHQWbRxBmVQ/F6XiVZ9BWl8z7po9OeagKoz+FgR0xevripbdh8vRFCYyDoBboW15+3A/RQogLhG+E1uCxIBn+ehospdpFd5NuYC7Wkgs5Wq8AOo6DjtfC5q/pZd8ILWpfKFOYAtZKLRGh6CCm95nFEttHu52KomhTUjqTC2c5XNYz0NnYkoJkbXgGWlPHogorzYK80Gfv0NYNaQN2S3VyweHm3rGM7dIEP08jLH8dlr4M3afA8DdPP45dv2l9H7Z9y71DplFutnFjr1gCvUz8Z9gJ+pMU1JgeszSbhKa+AOxOP/nUkkccDSW9Va2CoJm7lkRYsS+nVs+IA47KheTcUkZ/tIph7SN4dWwHADI/m0Bz636WF8+nfx+tSWdWcSUHc7R9BlNEb/0uAJ7ITMNis2PU68BSjs6R6TZ6+qMv16pZVKsW08bkPNKyq6se6kqSC0K4SLnZxuY9+8jZ/DsBKUvpYt5If6UCALuqsN2tM3lxYzAFRGAwumE0uWEwumErL6AseTOm7O2ElSbSVE0jmAKCzQWQtwPygAO1j5WPLyWKN0bVjJtaiQcV+CsWutVcyZEEP9J8IjHXfSDlteK88QqMpNXoR2HUI5CxDXtWImajN+U6HyxGH7z9gvDwDcLD6EF3mVZPNEQmTwhqrt2a9kTpcn2tD1jOj45lebDhc+z/fqJdTLQZha7dGG2MdUmmNm2cTwT4N8U9MA53+f9diItX017axfV2x5CIjuO1C/iYPlq/BIujm3/CTVojRs9gKMuBw2ugWT/tuQNL4X9jQe/4EimiE3j4n7uYO02EzV9rj+vyOTEgFtK3akMjqpILXiaSckq1mSKS/tHWi0rQfjcCbPpaG87W/TYUo4eWWLDbYOMs7fmDy2ofw1IO8x7QEiJdjtP0ucgxc1bWHkJ83HhjXKdTx11jWAR2KxHGCiL93EkrrGBrSgG9mx//S7mUvDJAxcOuJQ9CFW3miN3pRbWqFZJzy7DY7Py8KZWCMgvfrDvMgNYhtI3wJcCSAQr8+OdfuEW0oWdckDMZAeCnVD8OsWdzILuE+HBf7T1Du7Zw9/TBUOL4i2StoKDMzKTP1lJReubDMU5EkgtCnGOqqpJTXMmhI0fISdlLSeZByE6kedG/9GQ/OsXRhlaBDH0EabFjaTLgVjpFtzjJXqvHFRcV5pG+fxuFKXuwZO/HUJCEb/kRAmw5BKkFGBUbARQRoBY5j1OlHDcyPFpgC+1AQFxXAlv1JDriOFMQCXE+KApEdEIX0Ql3QDopiIuOZyD0ewRd34e1hIKuXturCiEuJM36wbZvtcftroLe92mPDW7QfCDsmaclC6pmb2g1FLb8DxY8Af0ehraj4Z83td8lVu3LKWL7ntuYY/vApB/Bq45TKAY005ILNWeM8NYSJC2D3GCjI7nQYrDWcBIgdYN2y0uCEW9ry5KWOxphos12UZoLXo5Ghjt/ga3faM0yO18Haz4CvybaEBOAorTq7RzNIk+p4HDtn0uz6BITQNq2dDYf1pILKfll+HkYa02/fTivDA8q0atao0hjeQ5tInzZnV6E2WYn2NtEmdlGmdlGcm4ZS/ZkObd9eu4ObuoVwxS0SoMINYtpyw7QMy6IJEfVQudof+6IrYT12jaRSg47U4u05IJjpohS3PHxMGFw0yoXFGsFP25MocJid86IUR8kuSCOy25XKSwppSAnjZL8LGyVpdgtFdjNFaiWclRrBVgqUO02VEBFQUXBXnWv6FEMbihGD3QmD/QmT/Qmd/QmDwxGIyaDEYPRgNFgwGQyYTDosas6rKqCDR0Wuw6bqmBVwW6pwGYuR7VUoFrKUM3lqFYzNrsdu92O1a5it9ux2dXqWBQdCgqqoqCiw6DXYzDoMer16A0GjHptmaooWgmZYxsVHTYVbCpaLCpY7aDabWAp07KglgoUSzmKpQydpQS9pQS9tRSDpQSdpRS1shgqi9FZSjBYSnG3lRBODt0dVQlOjov8VPeWlMReTnjXUYS36En4GX5T5esXiG/XAdB1wDHP2Ww2cnIyKM5JobI4D5O7Fx5ePtU3n2CayYdbIYQ4vxQFFPndK0Sj1mmi9rsgtA1Edqn9XO97ITtRm92hSvdbYfsPkLkdfrgJ4kdA8kqtJ0GfqdpMDj3uOPdx12wIebaqmjqmbAC7HXQ6ruoSxaGcMsYGp2hVG14hEN5R63FT04bPtak0W14Om2fXfi5lnfYcQOIf2n1ZjjaT0F9PatVhrYdpCZyqygWbGfIOQkirk8dsKdeGvgF4h2kVFSVZJDRtyvxt6WxMzudwbhmD315Os2AvfryrF+/+vY+OTfw4kl+GLzUaKJZk8fT4NsxcfQiTQcfVXZvw9l972Z5ayJoDOWxPLURRoEmA1lzyo793crdJS0w0UXL4NUP7wjDJ0Qiya0wAw1oUOZMLTZQcZq9NZmxCFIqjcqEED3zcDZjcteSCm2rms5XVyZ36IsmFBmbFF0/g5emBqtOjKjrtw4dOu1cVveNn7abT6dHp9ej0BnR6PXqdAZ3egKLToaoqqmoHVUVVVeyOe9VSid1cilpZhmopRWcp1S6SreWYzIV4WvLwthXirxYSoJQScOqQxanUyBXk6wMp8YjC5tsUz5b9CEkYSZTfibvL1pVeryc4LIrgsHN3DCGEEEIIcYZ0Ou0b9eNp2hPu3VB7WVRXuG+TNk3lqve0ygaA9tfAoKfPbaz1LbqHdr/zZ61sf+K3DG0fwdD2EbDIkVBpPkh7j5oNgHGzILQtbPgC1k6D+Q/CHStg9+/auhGdtEqIw/9qyQNLBexfUn28dZ9o99YKSN+mrV9SXR1A9u5TJxcKHRUSJm8IbuVMLnSN0YZTbEzOZ/m+bMw2O4mZxYz+cBUHc0rxMukpNdtoqdRILpiL6d3Uk94tqgco/7o5le2phcxYoV3wd2riz9TBLbn5y/V42qu3baJkk1lUSUGZ2dlvoVmwl2N2Dcfbq8/l88MFLNyZwVDLTgDS1UB83A3OygV3zKQXHvXFZz244JILH330EW+88QYZGRl06tSJDz74gEsuueSE6//www88/fTTHDp0iJYtW/Laa69x5ZXVHVRVVeXZZ59lxowZFBQU0KdPH6ZNm0bLli2d6+Tl5XHvvffy+++/o9PpuPrqq3nvvffw9q6eS3rbtm3cfffdrF+/npCQEO69914effTRM359fTO/wtetAYyxdIRgRUcRPlToPLAqJsyKG1adEavihlXnhqrocHzvr90UFR0qimpHb6/EYK/EaK/EqFZitFswUYmi2tGh3fTY0Kl29Mqp63Eq0I5vxoRVMWjJl6pAFQW0WoXqm1q7ngLVjqJW1TZoj3XY0ba0a3E77sHxOlDRY8eGjkrFHbPijlnnhllxw6Jzp1LnSaXei0qdJ2a9JxaDJzp3X0yefrh7+eHh44e3bwAhUXG4BcUSYHSXhI0QQgghhDhzfk3g8hfAK1T7Jh6g9z2ujelstB4Gw16HRc/A/kVwYDG0csyCsH+xdt9isHav01UPZRj0DGyZrQ1PWPw82Cq1C/3uU+C3e+DIWm29pH+qe1aANq1nlZR14BMG1Lj2yNqjDTM5mZx92r1/U61yAaA0i3btfPEy6SkstzBnbfWwiaoL/1KzVnEQZjxqdobSLDDFOn9sHqJdV1bNLDG4TSgDWocyoHUIh/amO9drZsgBiza9ZdWwiLhgL8iuTi70Dq6ANHh9YSJDYpagACvt7enjbkQxaINO3RUzAANbh7B4W41ZMOrogkoufPfddzz44INMnz6dHj168O677zJkyBASExMJDQ09Zv3Vq1czceJEXn31VUaMGMGcOXMYM2YMmzZton379gC8/vrrvP/++8yaNYtmzZrx9NNPM2TIEHbt2oW7u/bmT5o0ifT0dBYtWoTFYuGWW27h9ttvZ86cOQAUFRVxxRVXMHjwYKZPn8727du59dZb8ff35/bbbz+j17gteATe7gYU1ea42UHVLsBx/Fz1HFXL7I75uau2QQXHJbOq1L74tisGrAZPbHoP7EZPVMdNMXqi9wrE6BuKR0A4PkER+AZGYPIOJFB3nOlu6puqOl8DdluNe7tWwmRww11RXDYGWw94Om5CCCGEEEK4TO97tGEDiqI1e7zQKIo2hOPwGq03QnaillyoKNKGfYDWd+JoJk9ofaXWq2LDF9qydldplR4AqZu0mTMSHVN7mrydPQecjqyDyITayw4s1po1FiRr/SBGvKslNWqqGmYR07u6gWZJJka9jq6xgfyzN5vd6dpwBR93A8UVVjxNesocyYU4HxvUyHdQklU9PARoHlr9pXWAp5ExXbSq4+dGtuOD/+2EAu25SLIBlR1pRc5ERFyIN9SYDrO5KR9Pk56k7GKsliUYgRW2jgxzN4BRa+johplLWwQz/YauJDydcux7fZYuqOTC22+/zZQpU7jlllsAmD59OvPnz+eLL77g8ccfP2b99957j6FDh/LII48A8OKLL7Jo0SI+/PBDpk+fjqqqvPvuuzz11FOMHq1lq7766ivCwsKYO3cuEyZMYPfu3SxYsID169fTrZtWuvLBBx9w5ZVX8uabbxIZGcns2bMxm8188cUXmEwm2rVrx5YtW3j77bfPOLnQccp0fH196/I2XZgUxZEA0cksBUIIIYQQQpxMp2tdHUHdBTkqxXMdVQFVTRbd/cDrBNOht7uquhFm1c9BLbRqjtIsmHuXNs0kQM+74J83am9/ZB20Gak9VvTaF5pH1taueki4UZsWtIrNCnscCYu2o7VeEQAlWg+GnnFacgG0y5lf/q8PezKKKKmw8vjPWrKkqaflqORCZq2w+rYMpm/LYOKCvbhvUEuCvN0AiA324q1RcfCVtp6bWkkQRSzalYHNruJp0hPm61ZrWIQ+dx8LPZ7CS0nHWFFCserBZrWF1mTSUbmQEOHOsFsvQa9TaB/lz67jv9tn7Dx8JV0/zGYzGzduZPDgwc5lOp2OwYMHs2bNmuNus2bNmlrrAwwZMsS5flJSEhkZGbXW8fPzo0ePHs511qxZg7+/vzOxADB48GB0Oh1r1651rtOvXz9MJlOt4yQmJpKfX32ia6qsrKSoqKjWTQghhBBCCCEahWBHn4Oc/dp9SYZ27xNx4m2aX6YlHwBC4rWGmIoCQ17Rlu34CewWLQnQ+97q7YJaaMmE4jRIcXQ+jK4xtN4jUNsfQO5Rc7onr9QaS3oGQdPe1cMiHAmCHs2CnKu2DvOhRag3IzpGMqB1dWV9pLu59j5r9nwAfNyNfD25B8+Pbu9MLDg5mjJWaaJk8+9BrdFls2AvFEWplVzAUkp05T4CHdNT/mtvixUDPjUqFyK9QK/TKtzjI3yoLxdMciEnJwebzUZYWFit5WFhYWRkZBx3m4yMjJOuX3V/qnWOHnJhMBgIDAystc7x9lHzGEd79dVX8fPzc96io6OP/8KFEEIIIYQQ4mIT7Jh2PWevdl9clVwIP/E2BhO0c0zJ3mFc9fKO46DHndrjgFgY9YGWhAhopi1r1h/CtWHx7PhZu4++BPxjtG/zr/uuutFk3sHax9z1q3YfP0KbsrIqueCYcaJjEz88jNoMQAkxju5qqkr4pne4MVCrCQgzHdU8sWZyoTBVO4Z6gh50lbW/hG6i5DgfV/VqqJVcOMoSe2f0OgVPk95ZueCcvhTwMtbfYIYLJrlwsfnPf/5DYWGh83bkyBFXhySEEEIIIYQQ50fVsIiyHCjLg2JH40LvkyQXAK54SZtBos/Uo5a/DBPmwORF1dUNVY0hWw+DmD7a41LHhb1fNExZCvdt1hINgXHa8rwalQsVRbD9J+1xVdPHiE7aVPbZeyD/EEa9jj4ttGEcfZo7hnOkboLl/+UJPmdA6xDaBhyVOKg5LGLuXfD9jVrvh+M5qnIhWtHiN+l13Nm/ubawvOCYzea0eJup5v/je9sAbHZVq3BwVC5gqW4waTLU32QCF0xyITg4GL1eT2Zm7fEpmZmZhIcf/3/A8PDwk65fdX+qdbKyapetWK1W8vLyaq1zvH3UPMbR3Nzc8PX1rXUTQgghhBBCiEbBzRt8IrXHuftPr3Khart2Y7Qqgpr0BogfDt41qs6veBH+by20vPzYqT99I8ErSLsHCHJcqOceALtd67WwaRZUFmpDOOIcTSa9QyD2Uu3xzrkAvDq2AzNu7MaVHRyxFxwCwL0sg5k3dsHD7mgsWfV6S7UeDVjKIXm19jhrz/Ff71GVC1dEmenTIojFD/WnbaTjGvI4lQtjx9/EtsAh2NAT6uMYauGsXKh0rmcy1F9K4IJJLphMJrp27crixdUZHbvdzuLFi+nVq9dxt+nVq1et9QEWLVrkXL9Zs2aEh4fXWqeoqIi1a9c61+nVqxcFBQVs3LjRuc6SJUuw2+306NHDuc4///yDxWKpdZzWrVsTECATDwohhBBCCCHEMZxDI/ZVVy6crOfCmTJ6QKijl0J4h+qhD1CdVKgS6Egu5B2En26Fl8Nh2Wvast731p5Bou0Y7X7XXABCfNy4vG2YY9p7tKEOAKhalUJFofZjsKNao6pyIWWD1iMCoPAEszZUOJILjmqMBJ8CZt/Wk+jAGvPYVSUXYhxJjy7X424y8Nu9l3JLn1ieG9Wu+v0AsFZXLhgbY3IB4MEHH2TGjBnMmjWL3bt3c9ddd1FaWuqcPeLGG2/kP//5j3P9+++/nwULFvDWW2+xZ88ennvuOTZs2MA992jzwSqKwtSpU3nppZf47bff2L59OzfeeCORkZGMGTMGgDZt2jB06FCmTJnCunXrWLVqFffccw8TJkwgMlL7H/K6667DZDIxefJkdu7cyXfffcd7773Hgw8+eH7fICGEEEIIIYS4UFQ1dczdB8WOC+5TVS7URddbqh/7Nqn9XNXUkBUF2hSZdguYi7UeCx2Pmp2jzShtaETa5uoeDeX58G57mHt39cwXoD2uSi6EtNbuqxIJyauq1ys8wTD5qmEREZ20+7yk2s/bbdX7H/U+jJupTacJeLsZeHZkO67s4EjYVFUuWKp7LpiOnnazDi6oqSivvfZasrOzeeaZZ8jIyKBz584sWLDA2Tzx8OHD6Gq8Ob1792bOnDk89dRTPPHEE7Rs2ZK5c+fSvn175zqPPvoopaWl3H777RQUFHDppZeyYMEC3N3dnevMnj2be+65h0GDBqHT6bj66qt5//33nc/7+fnx119/cffdd9O1a1eCg4N55plnzngaSiGEEEIIIYRoNKr6LpyryoWjtR8L6z4Bk/ex012aPME3ytmokaAWEDdA67VgOGoGB+8QrUnkwaXw93Mw/ist0VCUCjt/1ma1qFKUWn3x3+QSWPep9lrL8o5KLpygcqFmciHpHyg4DDYL6I3a8opCwFEx4d+0enjH8TgrF2okF+qxcuGCSi4A3HPPPc7Kg6MtW7bsmGXjxo1j3Lhxx67soCgKL7zwAi+88MIJ1wkMDGTOnDknjatjx46sWLHipOsIIYQQQgghhHCoGiaQtfv0ey7UhcFNa+KonKCJYWBcdXKhw3gY8NiJ93X58zBjhTbTw865YCnTllvKtOEOVWpWLvhHawmAgsNaMuLI+ur1TphccAyLCG4FBg9tSEPBYS2JYK2sHmJh8q5OOJyIs3KhZkPHRjosQgghhBBCCCHERSKyi3afdwBsjiaD5zK5ACdOLED1jBGgzTBxMhGd4FLHMPjlr1dXXgCUZFQ/rplccPeDsA7a402ztESByTGdZFlOrYt+p6rKBTffGjNaHNT2Of1S+LintszjNHr9VVUu2C3acArAzaA/9XanSZILQgghhBBCCCHOP89ACG1b/bNHwLFDEM6nqiEFvk20BpCn0mWSdp+7H4rSj79OYUrt5EK4Y4j+rt+0+9bDqhMMzkaQNVQlF9x9IbCZ9jjvICx4AnL2Vq/n4X/qeA3VQ/+rEhkmvVQuCCGEEEIIIYS40MX0rn58LvstnI42IyGgGfR98OQVDlV8o7TGjrZKyNh+/HVy94Pdqj1294Mwx8wNVX0SWg0FP0dzyeM1dayaLaJm5cL6z2DL/2qvdzqVCzWTC46+C0bDabzO0yTJBSGEEEIIIYQQrlEruXCOh0ScSmAc3L8Fuk8+vfX1RvBxTGmZtvn462Tt0u51BjB6Qlj15AIoemgxqEZywdF3Ye/C6sdVPRfcfKorK6oqFlpfWb0vm/XU8ep0oHdUhkjlghBCCCGEEEKIi0ZMn+rHrq5cOBv+0dp9Vc+IKlVVBqpdu3f306ohApqB0UtbFtNbqziomVw4uBzmjIe5d4Gq1ui54FO7JwTAkJerHxckn168Rkf1gqNyQRo6CiGEEEIIIYS48PmEQ2Dz6scXGr/o4y+P7KJVJlSpeo06XXXfhVZDHfuokVxIccwgcWSdVrWgao0Xaw2LAIi5VPt53CytKuKKl04vXoOjqWNV5UJjnopSCCGEEEIIIcRFpO0oWPkONOnu6kjOnP9RyQU3P6gs1Kac1BvB6kgO9Lm/ep0rXtKmr+x2q/ZzVYKi8Ig2gwRolQXOKS0VMHlpwyqqdLhGu283RusVoTvNWR9Mjn2YS7Qf63FYhCQXhBBCCCGEEEK4zsAnofMkCGrh6kjOnH/T6scegRDcEo6s1Zo9OoYeABA/vPpx9CXarUpArHaftVubQaPKoRXavZuvNqRCUeDKNyE7EbpcX73e6SYWQIsr7yAUHIEYqVwQQgghhBBCCHGx0Bu1i/ILUc1hET4R0PP/QG/SkgmlObDqXZj0w8lnn4jsos3kUJql3aokOZIL7r7Vyy6ZUrd4A2K1pEX+IQBM+jNITJyCJBeEEEIIIYQQQoizUbNywSdcG6bQboz288D/aNNaGtxOvg+DG0T3gKTltZenOoZFuPnUV7TVVRJVyQVp6CiEEEIIIYQQQrhYVTNGOP5sF6dKLFRp1rf6sVdI7eckuSCEEEIIIYQQQlzEjB7VyYC6zHYR26/6cesraz/X/LKz3+/RAppp9/mHwG5HrzvJcI0zJMkFIYQQQgghhBDibFUNjahLciEqAYxe2uPwDtD+Gq13w5BXoN+jdY+xSlXlQnEafDEEpvevt11LzwUhhBBCCCGEEOJstb8aClOhWR0u1PVG6HQtbPlGq1TofhtYyrQpKOuTZyCYfMBcDCnroFKtt10rqqrW397EWSsqKsLPz4/CwkJ8fX1PvYEQQgghhBBCiIZBVU8+I8TpsNtBtWmJhnNpWh/I3AFAUaWK33+L6+U6VIZFCCGEEEIIIYQQdVHXxAKATnfuEwtQPTSinklyQQghhBBCCCGEaCysledkt5JcEEIIIYQQQgghGosWg87JbqWhoxBCCCGEEEII0Vh0nwJGTzi4FDb9XG+7leSCEEIIIYQQQgjRWOgN0PUmKE6v193KsAghhBBCCCGEEKKx8Qio191JckEIIYQQQgghhGhsPALrdXeSXBBCCCGEEEIIIRobqVwQQgghhBBCCCFEnXhKckEIIYQQQgghhBB1IZULQgghhBBCCCGEqBPpuSCEEEIIIYQQQog6cfOlPlMCklwQQgghhBBCCCEaG50OPPzrb3f1tichhBBCCCGEEEJcONz9621XklwQQgghhBBCCCEaI6lcEEIIIYQQQgghRJ2419+MEZJcEEIIIYQQQgghGiOpXBBCCCGEEEIIIUSdSHJBCCGEEEIIIYQQddK0V73tSpILQgghhBBCCCFEY9R6aL3tSpILQgghhBBCCCGEqBNJLgghhBBCCCGEEKJOJLkghBBCCCGEEEKIOpHkghBCCCGEEEIIIepEkgtCCCGEEEIIIYSoE0kuCCGEEEIIIYQQok4umORCXl4ekyZNwtfXF39/fyZPnkxJSclJt6moqODuu+8mKCgIb29vrr76ajIzM2utc/jwYYYPH46npyehoaE88sgjWK3WWussW7aMhIQE3NzcaNGiBTNnzqz1/HPPPYeiKLVu8fHx9fK6hRBCCCGEEEKIhu6CSS5MmjSJnTt3smjRIubNm8c///zD7bffftJtHnjgAX7//Xd++OEHli9fTlpaGmPHjnU+b7PZGD58OGazmdWrVzNr1ixmzpzJM88841wnKSmJ4cOHM3DgQLZs2cLUqVO57bbbWLhwYa1jtWvXjvT0dOdt5cqV9fsGCCGEEEIIIYQQDZSiqqrq6iBOZffu3bRt25b169fTrVs3ABYsWMCVV15JSkoKkZGRx2xTWFhISEgIc+bM4ZprrgFgz549tGnThjVr1tCzZ0/+/PNPRowYQVpaGmFhYQBMnz6dxx57jOzsbEwmE4899hjz589nx44dzn1PmDCBgoICFixYAGiVC3PnzmXLli1n/RqLiorw8/OjsLAQX1/fs96PEEIIIYQQQghxOurzOvSCqFxYs2YN/v7+zsQCwODBg9HpdKxdu/a422zcuBGLxcLgwYOdy+Lj42natClr1qxx7rdDhw7OxALAkCFDKCoqYufOnc51au6jap2qfVTZt28fkZGRxMXFMWnSJA4fPnzS11RZWUlRUVGtmxBCCCGEEEIIcSG6IJILGRkZhIaG1lpmMBgIDAwkIyPjhNuYTCb8/f1rLQ8LC3Nuk5GRUSuxUPV81XMnW6eoqIjy8nIAevTowcyZM1mwYAHTpk0jKSmJvn37UlxcfMLX9Oqrr+Ln5+e8RUdHn+JdEEIIIYQQQgghGiaXJhcef/zxYxohHn3bs2ePK0M8LcOGDWPcuHF07NiRIUOG8Mcff1BQUMD3339/wm3+85//UFhY6LwdOXLkPEYshBBCCCGEEELUH4MrD/7QQw9x8803n3SduLg4wsPDycrKqrXcarWSl5dHeHj4cbcLDw/HbDZTUFBQq3ohMzPTuU14eDjr1q2rtV3VbBI11zl6honMzEx8fX3x8PA47rH9/f1p1aoV+/fvP+HrcnNzw83N7YTPCyGEEEIIIYQQFwqXVi6EhIQQHx9/0pvJZKJXr14UFBSwceNG57ZLlizBbrfTo0eP4+67a9euGI1GFi9e7FyWmJjI4cOH6dWrFwC9evVi+/bttRIXixYtwtfXl7Zt2zrXqbmPqnWq9nE8JSUlHDhwgIiIiDN/U4QQQgghhBBCiAvMBdFzoU2bNgwdOpQpU6awbt06Vq1axT333MOECROcM0WkpqYSHx/vrETw8/Nj8uTJPPjggyxdupSNGzdyyy230KtXL3r27AnAFVdcQdu2bbnhhhvYunUrCxcu5KmnnuLuu+92VhXceeedHDx4kEcffZQ9e/bw8ccf8/333/PAAw8443v44YdZvnw5hw4dYvXq1Vx11VXo9XomTpx4nt8pIYQQQgghhBDi/HPpsIgzMXv2bO655x4GDRqETqfj6quv5v3333c+b7FYSExMpKyszLnsnXfeca5bWVnJkCFD+Pjjj53P6/V65s2bx1133UWvXr3w8vLipptu4oUXXnCu06xZM+bPn88DDzzAe++9R5MmTfjss88YMmSIc52UlBQmTpxIbm4uISEhXHrppfz777+EhISc43dFCCGEEEIIIYRwPUVVVdXVQYj6nV9UCCGEEEIIIYQ4lfq8Dr0ghkUIIYQQQgghhBCi4ZLkghBCCCGEEEIIIepEkgtCCCGEEEIIIYSoE0kuCCGEEEIIIYQQok4kuSCEEEIIIYQQQog6uWCmorzYVU3aUVRU5OJIhBBCCCGEEEI0BlXXn/UxiaQkFxqI3NxcAKKjo10ciRBCCCGEEEKIxiQ3Nxc/P7867UOSCw1EYGAgAIcPH67zSRX1q6ioiOjoaI4cOVLnuV9F/ZPz03DJuWm45Nw0bHJ+Gi45Nw2XnJuGTc5Pw1VYWEjTpk2d16N1IcmFBkKn09pf+Pn5yT+4BsrX11fOTQMm56fhknPTcMm5adjk/DRccm4aLjk3DZucn4ar6nq0TvuohziEEEIIIYQQQgjRiElyQQghhBBCCCGEEHUiyYUGws3NjWeffRY3NzdXhyKOIuemYZPz03DJuWm45Nw0bHJ+Gi45Nw2XnJuGTc5Pw1Wf50ZR62POCSGEEEIIIYQQQjRaUrkghBBCCCGEEEKIOpHkghBCCCGEEEIIIepEkgtCCCGEEEIIIYSoE0kuCCGEEEIIIYQQok4kueBizz33HIqi1LrFx8e7OizhkJqayvXXX09QUBAeHh506NCBDRs2uDqsRi82NvaYfzeKonD33Xe7OjQB2Gw2nn76aZo1a4aHhwfNmzfnxRdfRPoHNwzFxcVMnTqVmJgYPDw86N27N+vXr3d1WI3OP//8w8iRI4mMjERRFObOnVvreVVVeeaZZ4iIiMDDw4PBgwezb98+1wTbCJ3q/Pz8889cccUVBAUFoSgKW7ZscUmcjdHJzo3FYuGxxx6jQ4cOeHl5ERkZyY033khaWprrAm5ETvXv5rnnniM+Ph4vLy8CAgIYPHgwa9eudU2wjdCpzk9Nd955J4qi8O67757RMSS50AC0a9eO9PR0523lypWuDkkA+fn59OnTB6PRyJ9//smuXbt46623CAgIcHVojd769etr/ZtZtGgRAOPGjXNxZALgtddeY9q0aXz44Yfs3r2b1157jddff50PPvjA1aEJ4LbbbmPRokV8/fXXbN++nSuuuILBgweTmprq6tAaldLSUjp16sRHH3103Odff/113n//faZPn87atWvx8vJiyJAhVFRUnOdIG6dTnZ/S0lIuvfRSXnvttfMcmTjZuSkrK2PTpk08/fTTbNq0iZ9//pnExERGjRrlgkgbn1P9u2nVqhUffvgh27dvZ+XKlcTGxnLFFVeQnZ19niNtnE51fqr88ssv/Pvvv0RGRp75QVThUs8++6zaqVMnV4chjuOxxx5TL730UleHIU7D/fffrzZv3ly12+2uDkWoqjp8+HD11ltvrbVs7Nix6qRJk1wUkahSVlam6vV6dd68ebWWJyQkqE8++aSLohKA+ssvvzh/ttvtanh4uPrGG284lxUUFKhubm7qN99844IIG7ejz09NSUlJKqBu3rz5vMYkNCc7N1XWrVunAmpycvL5CUqoqnp656awsFAF1L///vv8BCWcTnR+UlJS1KioKHXHjh1qTEyM+s4775zRfqVyoQHYt28fkZGRxMXFMWnSJA4fPuzqkATw22+/0a1bN8aNG0doaChdunRhxowZrg5LHMVsNvO///2PW2+9FUVRXB2OAHr37s3ixYvZu3cvAFu3bmXlypUMGzbMxZEJq9WKzWbD3d291nIPDw+pmmtAkpKSyMjIYPDgwc5lfn5+9OjRgzVr1rgwMiEuPIWFhSiKgr+/v6tDETWYzWY+/fRT/Pz86NSpk6vDEYDdbueGG27gkUceoV27dme1D0kuuFiPHj2YOXMmCxYsYNq0aSQlJdG3b1+Ki4tdHVqjd/DgQaZNm0bLli1ZuHAhd911F/fddx+zZs1ydWiihrlz51JQUMDNN9/s6lCEw+OPP86ECROIj4/HaDTSpUsXpk6dyqRJk1wdWqPn4+NDr169ePHFF0lLS8Nms/G///2PNWvWkJ6e7urwhENGRgYAYWFhtZaHhYU5nxNCnFpFRQWPPfYYEydOxNfX19XhCGDevHl4e3vj7u7OO++8w6JFiwgODnZ1WAJtWKvBYOC+++47630Y6jEecRZqfpPXsWNHevToQUxMDN9//z2TJ092YWTCbrfTrVs3XnnlFQC6dOnCjh07mD59OjfddJOLoxNVPv/8c4YNG3Z248LEOfH9998ze/Zs5syZQ7t27diyZQtTp04lMjJS/u00AF9//TW33norUVFR6PV6EhISmDhxIhs3bnR1aEIIUW8sFgvjx49HVVWmTZvm6nCEw8CBA9myZQs5OTnMmDGD8ePHs3btWkJDQ10dWqO2ceNG3nvvPTZt2lSnSmCpXGhg/P39adWqFfv373d1KI1eREQEbdu2rbWsTZs2MmylAUlOTubvv//mtttuc3UoooZHHnnEWb3QoUMHbrjhBh544AFeffVVV4cmgObNm7N8+XJKSko4cuQI69atw2KxEBcX5+rQhEN4eDgAmZmZtZZnZmY6nxNCnFhVYiE5OZlFixZJ1UID4uXlRYsWLejZsyeff/45BoOBzz//3NVhNXorVqwgKyuLpk2bYjAYMBgMJCcn89BDDxEbG3va+5HkQgNTUlLCgQMHiIiIcHUojV6fPn1ITEystWzv3r3ExMS4KCJxtC+//JLQ0FCGDx/u6lBEDWVlZeh0tf+86PV67Ha7iyISx+Pl5UVERAT5+fksXLiQ0aNHuzok4dCsWTPCw8NZvHixc1lRURFr166lV69eLoxMiIavKrGwb98+/v77b4KCglwdkjgJu91OZWWlq8No9G644Qa2bdvGli1bnLfIyEgeeeQRFi5ceNr7kWERLvbwww8zcuRIYmJiSEtL49lnn0Wv1zNx4kRXh9boPfDAA/Tu3ZtXXnmF8ePHs27dOj799FM+/fRTV4cm0P4Yffnll9x0000YDPKrrCEZOXIkL7/8Mk2bNqVdu3Zs3ryZt99+m1tvvdXVoQlg4cKFqKpK69at2b9/P4888gjx8fHccsstrg6tUSkpKalVpZiUlMSWLVsIDAykadOmTJ06lZdeeomWLVvSrFkznn76aSIjIxkzZozrgm5ETnV+8vLyOHz4MGlpaQDOLyPCw8OluuQcO9m5iYiI4JprrmHTpk3MmzcPm83m7FMSGBiIyWRyVdiNwsnOTVBQEC+//DKjRo0iIiKCnJwcPvroI1JTU2Uq8fPkVL/Xjk7EGY1GwsPDad269ekfpD6mshBn79prr1UjIiJUk8mkRkVFqddee626f/9+V4clHH7//Xe1ffv2qpubmxofH69++umnrg5JOCxcuFAF1MTERFeHIo5SVFSk3n///WrTpk1Vd3d3NS4uTn3yySfVyspKV4cmVFX97rvv1Li4ONVkMqnh4eHq3XffrRYUFLg6rEZn6dKlKnDM7aabblJVVZuO8umnn1bDwsJUNzc3ddCgQfL77jw61fn58ssvj/v8s88+69K4G4OTnZuqqUGPd1u6dKmrQ7/onezclJeXq1dddZUaGRmpmkwmNSIiQh01apS6bt06V4fdaJzq99rRzmYqSkVVVfX0UxFCCCGEEEIIIYQQtUnPBSGEEEIIIYQQQtSJJBeEEEIIIYQQQghRJ5JcEEIIIYQQQgghRJ1IckEIIYQQQgghhBB1IskFIYQQQgghhBBC1IkkF4QQQgghhBBCCFEnklwQQgghhBBCCCFEnUhyQQghhBBCCCGEEHUiyQUhhBBCnFeKojB37lxXhwHAc889R+fOnc9q2xtuuIFXXnmlfgM6jscff5x77733nB9HCCGEqAtJLgghhBCiUajPpMbWrVv5448/uO++++plfyfz8MMPM2vWLA4ePHjOjyWEEEKcLUkuCCGEEEKcoQ8++IBx48bh7e19zo8VHBzMkCFDmDZt2jk/lhBCCHG2JLkghBBCXKTmzZuHv78/NpsNgC1btqAoCo8//rhzndtuu43rr78egNzcXCZOnEhUVBSenp506NCBb775xrnup59+SmRkJHa7vdZxRo8eza233ur8+ddffyUhIQF3d3fi4uJ4/vnnsVqtJ4zzyJEjjB8/Hn9/fwIDAxk9ejSHDh1yPn/zzTczZswY3nzzTSIiIggKCuLuu+/GYrE410lPT2f48OF4eHjQrFkz5syZQ2xsLO+++y4AsbGxAFx11VUoiuL8ucrXX39NbGwsfn5+TJgwgeLi4hPGa7PZ+PHHHxk5cmSt5cerjPD392fmzJkAHDp0CEVR+P777+nbty8eHh50796dvXv3sn79erp164a3tzfDhg0jOzu71n5GjhzJt99+e8KYhBBCCFeT5IIQQghxkerbty/FxcVs3rwZgOXLlxMcHMyyZcuc6yxfvpwBAwYAUFFRQdeuXZk/fz47duzg9ttv54YbbmDdunUAjBs3jtzcXJYuXercPi8vjwULFjBp0iQAVqxYwY033sj999/Prl27+OSTT5g5cyYvv/zycWO0WCwMGTIEHx8fVqxYwapVq/D29mbo0KGYzWbnekuXLuXAgQMsXbqUWbNmMXPmTOdFO8CNN95IWloay5Yt46effuLTTz8lKyvL+fz69esB+PLLL0lPT3f+DHDgwAHmzp3LvHnzmDdvHsuXL+e///3vCd/Xbdu2UVhYSLdu3U729p/Qs88+y1NPPcWmTZswGAxcd911PProo7z33nusWLGC/fv388wzz9Ta5pJLLiElJaVW0kUIIYRoSCS5IIQQQlyk/Pz86Ny5szOZsGzZMh544AE2b95MSUkJqamp7N+/n/79+wMQFRXFww8/TOfOnYmLi+Pee+9l6NChfP/99wAEBAQwbNgw5syZ4zzGjz/+SHBwMAMHDgTg+eef5/HHH+emm24iLi6Oyy+/nBdffJFPPvnkuDF+99132O12PvvsMzp06ECbNm348ssvOXz4cK0kSEBAAB9++CHx8fGMGDGC4cOHs3jxYgD27NnD33//zYwZM+jRowcJCQl89tlnlJeXO7cPCQkBtEqC8PBw588AdrudmTNn0r59e/r27csNN9zg3PfxJCcno9frCQ0NPd1TUcvDDz/MkCFDaNOmDffffz8bN27k6aefpk+fPnTp0oXJkyfXSuAAREZGOo8thBBCNESSXBBCCCEuYv3792fZsmWoqsqKFSsYO3Ysbdq0YeXKlSxfvpzIyEhatmwJaOX+L774Ih06dCAwMBBvb28WLlzI4cOHnfubNGkSP/30E5WVlQDMnj2bCRMmoNNpHym2bt3KCy+8gLe3t/M2ZcoU0tPTKSsrOya+rVu3sn//fnx8fJzrBwYGUlFRwYEDB5zrtWvXDr1e7/w5IiLCWZmQmJiIwWAgISHB+XyLFi0ICAg4rfcoNjYWHx+f4+77eMrLy3Fzc0NRlNPa/9E6duzofBwWFgZAhw4dai07+vgeHh4Ax30PhRBCiIbA4OoAhBBCCHHuDBgwgC+++IKtW7diNBqJj49nwIABLFu2jPz8fGfVAsAbb7zBe++9x7vvvkuHDh3w8vJi6tSptYYnjBw5ElVVmT9/Pt27d2fFihW88847zudLSkp4/vnnGTt27DGxuLu7H7OspKSErl27Mnv27GOeq1ldYDQaaz2nKMoxvR/O1pnuOzg4mLKyMsxmMyaTqdZ2qqrWWrdmX4jjHa8qQXH0sqOPn5eXB9R+T4QQQoiGRJILQgghxEWsqu/CO++840wkDBgwgP/+97/k5+fz0EMPOdddtWoVo0ePdjZ4tNvt7N27l7Zt2zrXcXd3Z+zYscyePZv9+/fTunXrWhUDCQkJJCYm0qJFi9OKLyEhge+++47Q0FB8fX3P6jW2bt0aq9XK5s2b6dq1KwD79+8nPz+/1npGo9HZ3LIuOnfuDMCuXbucj0G78E9PT3f+vG/fvnqrNNixYwdGo5F27drVy/6EEEKI+ibDIoQQQoiLWEBAAB07dmT27NnOxo39+vVj06ZN7N27t1blQsuWLVm0aBGrV69m9+7d3HHHHWRmZh6zz0mTJjF//ny++OILZyPHKs888wxfffUVzz//PDt37mT37t18++23PPXUU8eNb9KkSQQHBzN69GhWrFhBUlISy5Yt47777iMlJeW0XmN8fDyDBw/m9ttvZ926dWzevJnbb78dDw+PWkMXYmNjWbx4MRkZGcckHs5ESEgICQkJrFy5stbyyy67jA8//JDNmzezYcMG7rzzzmOqIs7WihUrnDNMCCGEEA2RJBeEEEKIi1z//v2x2WzO5EJgYCBt27YlPDyc1q1bO9d76qmnSEhIYMiQIQwYMIDw8HDGjBlzzP4uu+wyAgMDSUxM5Lrrrqv13JAhQ5g3bx5//fUX3bt3p2fPnrzzzjvExMQcNzZPT0/++ecfmjZt6uwHMXnyZCoqKs6okuGrr74iLCyMfv36cdVVVzFlyhR8fHxqDcV46623WLRoEdHR0XTp0uW09308t9122zFDOd566y2io6Pp27cv1113HQ8//DCenp51Ok6Vb7/9lilTptTLvoQQQohzQVGPHhwohBBCCHGBS0lJITo6mr///ptBgwbV+/7Ly8tp3bo13333Hb169ar3/df0559/8tBDD7Ft2zYMBhnRKoQQomGSv1BCCCGEuOAtWbKEkpISOnToQHp6Oo8++iixsbH069fvnBzPw8ODr776ipycnHOy/5pKS0v58ssvJbEghBCiQZPKBSGEEEJc8BYuXMhDDz3EwYMH8fHxoXfv3rz77rsnHI4hhBBCiPolyQUhhBBCCCGEEELUiTR0FEIIIYQQQgghRJ1IckEIIYQQQgghhBB1IskFIYQQQgghhBBC1IkkF4QQQgghhBBCCFEnklwQQgghhBBCCCFEnUhyQQghhBBCCCGEEHUiyQUhhBBCCCGEEELUiSQXhBBCCCGEEEIIUSf/D0Upr33z0bMbAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig7, ax7 = plt.subplots(figsize=[12, 4])\n", "ax7.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['FLUX'], label='default location (nods combined)')\n", @@ -609,10 +765,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "b4ea99ea", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-16 09:59:45,516 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/671076322.py:13: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-16 09:59:45,516 - stpipe - WARNING - fig8.show()\n", + "2023-08-16 09:59:45,516 - stpipe - WARNING - \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "rows = [140, 200, 325]\n", "fig8, ax8 = plt.subplots(figsize=[8, 4])\n", @@ -631,7 +807,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "1238d6c9", "metadata": {}, "outputs": [], @@ -650,10 +826,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "ac0d2746", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-16 09:59:45,694 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n", + "2023-08-16 09:59:45,759 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args ('data/jw02072001001_06101_00001_mirimage_s2d.fits',).\n", + "2023-08-16 09:59:45,760 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/lrs_slit_extract_example3', 'output_dir': 'data/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': None, 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'center_xy': None, 'apply_apcorr': True, 'ifu_autocen': False, 'ifu_rfcorr': False, '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", + "2023-08-16 09:59:45,817 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/x1d_reffile_example3.json\n", + "2023-08-16 09:59:45,846 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/ofox/crds_cache/references/jwst/miri/jwst_miri_apcorr_0007.fits\n", + "2023-08-16 09:59:45,872 - stpipe.Extract1dStep - WARNING - spectral_order is None; using 1\n", + "2023-08-16 09:59:45,872 - stpipe.Extract1dStep - INFO - Processing spectral order 1\n", + "2023-08-16 09:59:45,878 - stpipe.Extract1dStep - INFO - Using extraction limits: xstart=9, xstop=17, ystart=0, ystop=386\n", + "2023-08-16 09:59:45,878 - stpipe.Extract1dStep - INFO - with background subtraction\n", + "2023-08-16 09:59:46,088 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n", + "2023-08-16 09:59:46,235 - stpipe.Extract1dStep - INFO - Results used CRDS context: jwst_1089.pmap\n", + "2023-08-16 09:59:46,288 - stpipe.Extract1dStep - INFO - Saved model in data/lrs_slit_extract_example3_extract1dstep.fits\n", + "2023-08-16 09:59:46,288 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n" + ] + } + ], "source": [ "sp2_ex3 = Extract1dStep.call(l2_s2d_file, output_dir='data/', output_file='lrs_slit_extract_example3',\n", " override_extract1d='x1d_reffile_example3.json')" @@ -669,10 +865,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "7210c9ac", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-16 09:59:46,327 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/715447576.py:13: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-16 09:59:46,327 - stpipe - WARNING - fig9.show()\n", + "2023-08-16 09:59:46,328 - stpipe - WARNING - \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig9, ax9 = plt.subplots(nrows=2, ncols=1, figsize=[12, 4])\n", "# ax9.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['FLUX'], label='default location (nods combined)')\n", @@ -705,30 +921,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "9e3c2433", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-15 15:42:15 - INFO - 1:1: E402 module level import not at top of file\n", - "2023-08-15 15:42:15 - INFO - 2:1: E402 module level import not at top of file\n", - "2023-08-15 15:42:15 - INFO - 7:5: E741 ambiguous variable name 'l'\n", - "2023-08-15 15:42:15 - INFO - 30:11: E275 missing whitespace after keyword\n", - "2023-08-15 15:42:15 - INFO - 31:1: E303 too many blank lines (3)\n" - ] - } - ], + "outputs": [], "source": [ - "import astropy.units as u\n", - "from astropy.modeling import models, fitting\n", - "\n", - "\n", "def calc_xap_fit():\n", " # these are values measured from commissioning data. FWHM is in arcsec.\n", - " l = [5.0, 7.5, 10.0, 12.0]\n", + " lam = [5.0, 7.5, 10.0, 12.0]\n", " fwhm = [0.29, 0.3, 0.36, 0.42]\n", "\n", " # convert from arcsec to pixel using MIRI pixel scaling of 0.11 arcsec/px\n", @@ -741,12 +941,12 @@ " line_init = models.Linear1D()\n", " fit = fitting.LinearLSQFitter()\n", "\n", - " fitted_line = fit(line_init, l, xap_pix.value)\n", + " fitted_line = fit(line_init, lam, xap_pix.value)\n", " print(fitted_line)\n", "\n", " fig, ax = plt.subplots(figsize=[8, 4])\n", " xplt = np.linspace(4.0, 14., num=50)\n", - " ax.plot(l, xap_pix.value, 'rx', label='1.5 * FWHM(px)')\n", + " ax.plot(lam, xap_pix.value, 'rx', label='1.5 * FWHM(px)')\n", " ax.plot(xplt, fitted_line(xplt), 'b-', label='best-fit line')\n", " ax.set_xlabel('wavelength')\n", " ax.set_ylabel('px')\n", @@ -756,16 +956,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "e21fcec5", "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "2023-08-15 15:42:15 - INFO - 1:1: E305 expected 2 blank lines after class or function definition, found 3\n" + "Model: Linear1D\n", + "Inputs: ('x',)\n", + "Outputs: ('y',)\n", + "Model set size: 1\n", + "Parameters:\n", + " slope intercept \n", + " ------------------ ------------------\n", + " 0.2579519802996102 2.4456187153704083\n", + "Parameter('slope', value=0.2579519802996102) Parameter('intercept', value=2.4456187153704083)\n" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -785,7 +1003,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "a9c9d403", "metadata": {}, "outputs": [], @@ -806,10 +1024,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "71789e83", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-16 09:59:46,650 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n", + "2023-08-16 09:59:46,724 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args (,).\n", + "2023-08-16 09:59:46,725 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/lrs_slit_extract_example4', 'output_dir': 'data/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': None, 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'center_xy': None, 'apply_apcorr': True, 'ifu_autocen': False, 'ifu_rfcorr': False, '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", + "2023-08-16 09:59:46,756 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/x1d_reffile_example4.json\n", + "2023-08-16 09:59:46,786 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/ofox/crds_cache/references/jwst/miri/jwst_miri_apcorr_0007.fits\n", + "2023-08-16 09:59:46,812 - stpipe.Extract1dStep - WARNING - spectral_order is None; using 1\n", + "2023-08-16 09:59:46,813 - stpipe.Extract1dStep - INFO - Processing spectral order 1\n", + "2023-08-16 09:59:46,819 - stpipe.Extract1dStep - INFO - Using extraction limits: ystart=0, ystop=387, and src_coeff\n", + "2023-08-16 09:59:46,868 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n", + "2023-08-16 09:59:47,011 - stpipe.Extract1dStep - INFO - Results used CRDS context: jwst_1089.pmap\n", + "2023-08-16 09:59:47,061 - stpipe.Extract1dStep - INFO - Saved model in data/lrs_slit_extract_example4_extract1dstep.fits\n", + "2023-08-16 09:59:47,061 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n" + ] + } + ], "source": [ "sp3_ex4 = Extract1dStep.call(l3_s2d, output_dir='data/', \n", " output_file='lrs_slit_extract_example4', override_extract1d='x1d_reffile_example4.json')" @@ -817,10 +1054,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "9d1bc74c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-16 09:59:47,089 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/1678292963.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-16 09:59:47,089 - stpipe - WARNING - fig10.show()\n", + "2023-08-16 09:59:47,090 - stpipe - WARNING - \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig10, ax10 = plt.subplots(figsize=[12, 4])\n", "ax10.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['FLUX'], label='default fixed-width aperture')\n", @@ -843,10 +1100,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "78ca0c68", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-16 09:59:47,214 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/1716890966.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "2023-08-16 09:59:47,214 - stpipe - WARNING - fig11.show()\n", + "2023-08-16 09:59:47,215 - stpipe - WARNING - \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig11, ax11 = plt.subplots(figsize=[12, 4])\n", "ax11.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['NPIXELS'], label='default fixed-width aperture')\n", @@ -896,7 +1173,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.11.4" } }, "nbformat": 4, From 75ec6d7f2738a18ae24d89b9aef5ee8b58719a83 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Thu, 17 Aug 2023 20:24:00 +0000 Subject: [PATCH 26/36] [BOT] Left PEP8 feedback on PR 93's notebooks Files: --- .../miri_lrs_advanced_extraction_part1.ipynb | 535 ++++-------------- 1 file changed, 106 insertions(+), 429 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb index 64519f7fe..72c2bf818 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -59,17 +59,59 @@ "\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# disable all imported packages' loggers\n", + "import logging\n", + "logging.root.manager.loggerDict = {}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enable PEP8 checker for this notebook\n", + "%load_ext pycodestyle_magic\n", + "%flake8_on --ignore E261,E501,W291,W293\n", + "\n", + "# only allow the checker to throw warnings when there's a violation\n", + "logging.getLogger('flake8').setLevel('ERROR')\n", + "logging.getLogger('stpipe').setLevel('ERROR')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" + ] + }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "08ddf5f7", "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "CRDS cache location: /Users/ofox/crds_cache\n" + "2023-08-17 16:24:00 - INFO - 15:1: F401 'jwst.pipeline.Spec2Pipeline' imported but unused\n", + "2023-08-17 16:24:00 - INFO - 15:1: F401 'jwst.pipeline.Spec3Pipeline' imported but unused\n", + "2023-08-17 16:24:00 - INFO - 20:1: F401 'matplotlib.collections.PatchCollection' imported but unused\n" ] } ], @@ -104,18 +146,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "aee92bcf", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using JWST calibration pipeline version 1.11.3\n" - ] - } - ], + "outputs": [], "source": [ "# Set CRDS variables\n", "\n", @@ -127,19 +161,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "305103d5", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Original Data tar.gz Exists\n", - "Data Directory Already Exists\n" - ] - } - ], + "outputs": [], "source": [ "# Download Data\n", "if os.path.exists(\"data.tar.gz\"):\n", @@ -178,30 +203,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "a8012bfa", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-16 09:59:42,705 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/3079267470.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-16 09:59:42,706 - stpipe - WARNING - fig.show()\n", - "2023-08-16 09:59:42,706 - stpipe - WARNING - \n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "l3_s2d_file = 'data/jw02072-o001_t010_miri_p750l_s2d_1089.fits'\n", "l3_s2d = datamodels.open(l3_s2d_file)\n", @@ -216,30 +221,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "c51f421b", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-16 09:59:42,907 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/44548106.py:10: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-16 09:59:42,908 - stpipe - WARNING - fig2.show()\n", - "2023-08-16 09:59:42,908 - stpipe - WARNING - \n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "l3_file = 'data/jw02072-o001_t010_miri_p750l_x1d_1089.fits'\n", "l3_spec = datamodels.open(l3_file)\n", @@ -269,27 +254,19 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "f0574ee0-84a0-4fa8-ae54-d7b6ca34a7a7", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Spectral extraction reference file used: crds://jwst_miri_extract1d_0005.json\n" - ] - } - ], + "outputs": [], "source": [ "print('Spectral extraction reference file used: {}'.format(l3_spec.meta.ref_file.extract1d.name))" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "95f20a0a-0f24-4d4b-8480-2c37574ad6e8", "metadata": { "tags": [] @@ -302,20 +279,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "50c8ba27", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Settings for SLIT data: {'id': 'MIR_LRS-FIXEDSLIT', 'region_type': 'target', 'bkg_order': 0, 'dispaxis': 2, 'xstart': 27, 'xstop': 34, 'use_source_posn': False}\n", - " \n", - "Settings for SLITLESS data: {'id': 'MIR_LRS-SLITLESS', 'region_type': 'target', 'bkg_order': 0, 'dispaxis': 2, 'xstart': 30, 'xstop': 41, 'use_source_posn': False}\n" - ] - } - ], + "outputs": [], "source": [ "with open(json_ref_default) as json_ref:\n", " x1dref_default = json.load(json_ref)\n", @@ -345,30 +312,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "703f59cd", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-16 09:59:43,639 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/752872122.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-16 09:59:43,640 - stpipe - WARNING - fig.show()\n", - "2023-08-16 09:59:43,640 - stpipe - WARNING - \n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "xstart = x1dref_default['apertures'][0]['xstart']\n", "xstop = x1dref_default['apertures'][0]['xstop']\n", @@ -401,18 +348,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "06dc8eb5", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "New xstart, xstop values = 25,36\n" - ] - } - ], + "outputs": [], "source": [ "xstart2 = xstart - 2\n", "xstop2 = xstop + 2\n", @@ -430,30 +369,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "5bc85413", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-16 09:59:43,800 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/3651783177.py:21: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-16 09:59:43,801 - stpipe - WARNING - fig.show()\n", - "2023-08-16 09:59:43,801 - stpipe - WARNING - \n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ap_width2 = xstop2 - xstart2 + 1\n", "x1d_rect1 = Rectangle(xy=(xstart, 0), width=ap_width, height=ap_height, angle=0., edgecolor='red',\n", @@ -491,29 +410,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "7304f758", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-16 09:59:43,961 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n", - "2023-08-16 09:59:44,023 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args (,).\n", - "2023-08-16 09:59:44,025 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/lrs_slit_extract_example1', 'output_dir': 'data/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': None, 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'center_xy': None, 'apply_apcorr': True, 'ifu_autocen': False, 'ifu_rfcorr': False, '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", - "2023-08-16 09:59:44,054 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/x1d_reffile_example1.json\n", - "2023-08-16 09:59:44,084 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/ofox/crds_cache/references/jwst/miri/jwst_miri_apcorr_0007.fits\n", - "2023-08-16 09:59:44,113 - stpipe.Extract1dStep - WARNING - spectral_order is None; using 1\n", - "2023-08-16 09:59:44,113 - stpipe.Extract1dStep - INFO - Processing spectral order 1\n", - "2023-08-16 09:59:44,120 - stpipe.Extract1dStep - INFO - Using extraction limits: xstart=25, xstop=36, ystart=0, ystop=387\n", - "2023-08-16 09:59:44,172 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n", - "2023-08-16 09:59:44,316 - stpipe.Extract1dStep - INFO - Results used CRDS context: jwst_1089.pmap\n", - "2023-08-16 09:59:44,413 - stpipe.Extract1dStep - INFO - Saved model in data/lrs_slit_extract_example1_extract1dstep.fits\n", - "2023-08-16 09:59:44,413 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n" - ] - } - ], + "outputs": [], "source": [ "sp3_ex1 = Extract1dStep.call(l3_s2d, output_dir='data/', \n", " output_file='lrs_slit_extract_example1', override_extract1d='x1d_reffile_example1.json')" @@ -521,48 +421,20 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "91199fd1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], + "outputs": [], "source": [ "print(sp3_ex1)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "91ebfc64", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-16 09:59:44,462 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/3663854483.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-16 09:59:44,462 - stpipe - WARNING - fig5.show()\n", - "2023-08-16 09:59:44,463 - stpipe - WARNING - \n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig5, ax5 = plt.subplots(figsize=[12, 4])\n", "ax5.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['FLUX'], label='8-px aperture')\n", @@ -587,7 +459,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "8a8cf793", "metadata": {}, "outputs": [], @@ -598,7 +470,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "55c81453", "metadata": {}, "outputs": [], @@ -618,38 +490,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "fe340506", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-16 09:59:44,704 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/4031254078.py:13: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-16 09:59:44,704 - stpipe - WARNING - fig6.show()\n", - "2023-08-16 09:59:44,704 - stpipe - WARNING - \n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "5:36: E231 missing whitespace after ','\n", - "2023-08-16 09:59:44,833 - stpipe - INFO - 5:36: E231 missing whitespace after ','\n" - ] - } - ], + "outputs": [], "source": [ "ap_width3 = xstop3 - xstart3 + 1\n", "x1d_rect3 = Rectangle(xy=(xstart3, 0), width=ap_width3, height=ap_height, angle=0., edgecolor='red',\n", @@ -668,29 +512,10 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "3b0b287b", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-16 09:59:44,907 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n", - "2023-08-16 09:59:44,970 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args ('data/jw02072001001_06101_00001_mirimage_s2d.fits',).\n", - "2023-08-16 09:59:44,971 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/lrs_slit_extract_example2', 'output_dir': 'data/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': None, 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'center_xy': None, 'apply_apcorr': True, 'ifu_autocen': False, 'ifu_rfcorr': False, '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", - "2023-08-16 09:59:45,027 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/x1d_reffile_example2.json\n", - "2023-08-16 09:59:45,056 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/ofox/crds_cache/references/jwst/miri/jwst_miri_apcorr_0007.fits\n", - "2023-08-16 09:59:45,082 - stpipe.Extract1dStep - WARNING - spectral_order is None; using 1\n", - "2023-08-16 09:59:45,083 - stpipe.Extract1dStep - INFO - Processing spectral order 1\n", - "2023-08-16 09:59:45,088 - stpipe.Extract1dStep - INFO - Using extraction limits: xstart=9, xstop=17, ystart=0, ystop=386\n", - "2023-08-16 09:59:45,142 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n", - "2023-08-16 09:59:45,293 - stpipe.Extract1dStep - INFO - Results used CRDS context: jwst_1089.pmap\n", - "2023-08-16 09:59:45,345 - stpipe.Extract1dStep - INFO - Saved model in data/lrs_slit_extract_example2_extract1dstep.fits\n", - "2023-08-16 09:59:45,346 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n" - ] - } - ], + "outputs": [], "source": [ "sp2_ex2 = Extract1dStep.call(l2_s2d_file, output_dir='data/', output_file='lrs_slit_extract_example2',\n", " override_extract1d='x1d_reffile_example2.json')" @@ -706,30 +531,10 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "ce8eccfb", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-16 09:59:45,376 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/259050671.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-16 09:59:45,377 - stpipe - WARNING - fig7.show()\n", - "2023-08-16 09:59:45,377 - stpipe - WARNING - \n" - ] - }, - { - "data": { - "image/png": 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G+HO14mkU1yenbvU+s7S05P333+eVV17B09OTe+65hz59+jBkyJASf24IIcTdQJILQgghqs358+cNNzbHjh274fWvv/6aYcOG0a9fPyZNmoSHhwdarZbZs2dz7ty5G8pf+2lsWY5fe5Nyu4pHJcyZM+eGNSCKFY8SuJWRI0eyYcMGvvnmm1Jv9CrL8ePHgcrdHlSn09GzZ08mT55c4uvFiZpi1386X9yXEydOJCwsrMQ6ro+3sr+3O3fu5JFHHuHee+9l4cKFeHt7Y25uztKlS1m1alW56yu+pqeffrrUBFSzZs2MnlfWqIWioqISj5fW7ytXrizxRvjaUT+m8NJLL7F06VLGjx9Phw4dcHR0RFEUnnzyyXKNCqqIsrzPxo8fz8MPP8y6devYvHkzU6dOZfbs2fzxxx+0bNmyWuIUQoiaRJILQgghqoVOp2PYsGE4ODgwfvx4Zs2axRNPPMFjjz1mKPP9999Tt25dfvzxR6NPjadPn14lMRV/cn5tWxEREQClLsgWHBwM6D/d7dGjx223PWnSJJYuXcq8efOMhmpXlZUrVwKUehN/O4KDg8nMzLztfqhbty6gH55ekb68XkkjDkrzww8/YGVlxebNm7G0tDQcX7p0aYnli5Nj14qIiMDGxsYwgsPe3p6ioqIKXdPNrsHZ2Zm0tDSjY/n5+WXeVaT4Pezh4VEp/V6nTh0Azpw5c0OS7MyZM4bXi7/fxYmu0nz//fcMHTqUuXPnGo7l5ubecM3l+T7XqVOHo0ePotPpjEYvnD592ugayis4OJhXXnmFV155hcjISFq0aMHcuXP5+uuvb6s+IYSozWTNBSGEENXio48+Yvfu3SxevJiZM2fSsWNHXnjhBaNdJoo/Lbz208E9e/YQHh5eJTFdvnyZn376yfA8IyODFStW0KJFi1KHNrdu3Zrg4GA+/PBDw1SDa127LWJp5syZw4cffsjrr79+wyr116qsrShXrVrFl19+SYcOHbj//vsrVNe1BgwYQHh4OJs3b77htbS0NAoLC296voeHB926deN///tfiddYlr4sia2t7Q03oqXRarUoimL0qf+FCxdYt25dieXDw8ON5v3HxMSwfv16HnjgAbRaLVqtlscff5wffvihxJvosl6Tra0tQInXERwcfMN6FosXLy515ML1wsLCcHBwYNasWUZTj8obY7E2bdrg4eHBokWLjKaV/Pbbb5w6dcqw44a7uzv33nsvS5YsITo62qiOa//Pa7XaG0aifPrppzdc38366HoPPfQQ8fHxhl1FAAoLC/n000+xs7Oja9euZbvYf2VnZ5Obm2t0LDg4GHt7e6M+EEKIu4mMXBBCCFFhv/32m+ETwGt17NiRunXrcurUKaZOncqwYcN4+OGHAf3WdS1atGD06NF89913APTp04cff/yRRx99lN69exMVFcWiRYto1KhRiTfyFVW/fn1GjBjBvn378PT0ZMmSJSQkJJT6qTWARqPhyy+/5MEHH6Rx48YMHz4cX19fYmNj+fPPP3FwcOCXX34p9fyffvqJyZMnExISQsOGDW/4hLNnz56GrRNvZyvK77//Hjs7O8MClps3b2bXrl00b96ctWvXlqmOspo0aRI///wzffr0YdiwYbRu3ZqsrCyOHTvG999/z4ULF3Bzc7tpHQsWLKBz5840bdqUkSNHUrduXRISEggPD+fSpUscOXKk3HG1bt2azz//nHfeeYd69erh4eFR6rST3r1789FHH9GrVy/+7//+j8TERBYsWEC9evU4evToDeWbNGlCWFiY0VaUAG+99ZahzHvvvceff/5J+/btGTlyJI0aNSIlJYWDBw+ydetWUlJSbnkNLVq0QKvV8v7775Oeno6lpSXdu3fHw8ODZ599lueff57HH3+cnj17cuTIETZv3nzLvi7m4ODA559/zuDBg2nVqhVPPvkk7u7uREdH8+uvv9KpUyc+++yzMtUF+pEn77//PsOHD6dr16489dRThq0oAwMDefnllw1l58+fT+fOnWnVqhWjRo0iKCiICxcu8Ouvv3L48GFA/3Ng5cqVODo60qhRI8LDw9m6datha9uy9NH1Ro0axf/+9z+GDRvGgQMHCAwM5Pvvv2fXrl3Mmzev3GuRREREcP/99zNgwAAaNWqEmZkZP/30EwkJCTz55JPlqksIIe4YptmkQgghxJ3gZltRAurSpUvVwsJCtW3btqqfn5/RdoGqqqqffPKJCqhr1qxRVVW/Hd2sWbPUOnXqqJaWlmrLli3VDRs23LBlYvF2cddvA1e8Rd7atWtLjHPfvn2GY8VbLm7evFlt1qyZamlpqTZo0OCGc6/fdq/YoUOH1Mcee0x1dXVVLS0t1Tp16qgDBgxQt23bdtM+K94ysrTHte3czlaUxQ8rKyvVz89P7dOnj7pkyRKjrR6LVXQrSlXVb2v42muvqfXq1VMtLCxUNzc3tWPHjuqHH36o5ufnG11Hadv2nTt3Th0yZIjq5eWlmpubq76+vmqfPn3U77//3lCmpO+hqpb8/YmPj1d79+6t2tvbq8Att6X86quv1JCQEMN7YOnSpYb+LKkPvv76a0P5li1blrgVYkJCgvriiy+q/v7+qrm5uerl5aXef//96uLFi2+I/fr3XLEvvvhCrVu3rqrVao2usaioSJ0yZYrq5uam2tjYqGFhYerZs2dL3Yry+j67tv2wsDDV0dFRtbKyUoODg9Vhw4YZbbVZ2nkl/Z9Ys2aN2rJlS9XS0lJ1cXFRBw0apF66dOmG848fP64++uijqpOTk2plZaWGhoaqU6dONbyempqqDh8+XHVzc1Pt7OzUsLAw9fTp0zdc38366PqtKFVV/z0prtfCwkJt2rSpunTpUqMyN3uvcs0WoMnJyeqLL76oNmjQQLW1tVUdHR3V9u3bq999991N+04IIe5kiqpWwupWQgghRC0TGBhIkyZN2LBhg6lDEbWEoii8+OKL5fpUXwghhLhbyJoLQgghhBBCCCGEqBBJLgghhBBCCCGEEKJCJLkghBBCCCGEEEKICpE1F4QQQgghhBBCCFEhMnJBCCGEEEIIIYQQFSLJBSGEEEIIIYQQQlSImakDEHo6nY7Lly9jb2+PoiimDkcIIYQQQgghxB1OVVWuXr2Kj48PGk3Fxh5IcqGGuHz5Mv7+/qYOQwghhBBCCCHEXSYmJgY/P78K1SHJhRrC3t4e0H9THRwcTByNEEIIIYQQQog7XUZGBv7+/ob70YqQ5EINUTwVwsHBQZILQgghhBBCCCGqTWVMzZcFHYUQQgghhBBCCFEhklwQQgghhBBCCCFEhUhyQQghhBBCCCGEEBUiay7UIqqqUlhYSFFRkalDEULcAbRaLWZmZrL9rRBCCCGEqDBJLtQS+fn5xMXFkZ2dbepQhBB3EBsbG7y9vbGwsDB1KEIIIYQQohaT5EItoNPpiIqKQqvV4uPjg4WFhXzSKISoEFVVyc/PJykpiaioKEJCQtBoZKacEEIIIYS4PZJcqAXy8/PR6XT4+/tjY2Nj6nCEEHcIa2trzM3NuXjxIvn5+VhZWZk6JCGEEEIIUUvJx1S1iHyqKISobPJzRQghhBBCVAb5q1IIIYQQQgghhBAVIskFIYQQQgghRO2TcBJy000dhRDiX5JcENWqW7dujB8/vlznrFu3jnr16qHVast97s0oisK6devKdc727dtRFIW0tLRKi+N2XLhwAUVROHz4cJW3lZ+fT7169di9e3eVtxUYGMi8efOqvJ3KVJaYb+e9VtFY8vPzCQwMZP/+/VXerhBCCFHtYg+ift6Rwh9fMHUkQoh/SXJB1HjPPfccTzzxBDExMcycObNK2qjOm/XyGjZsGP369TM65u/vT1xcHE2aNKny9hctWkRQUBAdO3as8rbuVHFxcTz44IPV2qaFhQUTJ05kypQp1dquEEIIUR0uHd2Ogkph5DbQFQH6nZC+3HmeMasOkltQZOIIhbj7SHJB1GiZmZkkJiYSFhaGj48P9vb2pg6pRtBqtXh5eWFmVrUbvqiqymeffcaIESOqtJ07nZeXF5aWltXe7qBBg/j77785ceJEtbcthBBCVKWUmFMAWKm5nDq2D1VVmbP5DO/8eooNR+PYdTbZxBEKcfeR5EItpaoq2fmF1f5QVbXMMWZlZTFkyBDs7Ozw9vZm7ty5N5TJy8tj4sSJ+Pr6YmtrS/v27dm+fTugn4JQnEzo3r07iqKwfft2rly5wlNPPYWvry82NjY0bdqUb7/91qjekoaqt2jRghkzZpQYa1BQEAAtW7ZEURS6detW5uv84YcfaNy4MZaWlgQGBt5wnXl5eUyZMgV/f38sLS2pV68eX331FQBFRUWMGDGCoKAgrK2tCQ0N5ZNPPjGcO2PGDJYvX8769etRFMXQByWNtNixYwft2rXD0tISb29vXn31VQoLCw2vd+vWjbFjxzJ58mRcXFzw8vIqtT+KHThwgHPnztG7d2/DseK2f/zxR+677z5sbGxo3rw54eHh5eqXxMREHn74YaytrQkKCuKbb74xel1VVWbMmEFAQACWlpb4+PgwduzYm8b7yy+/0LZtW6ysrHBzc+PRRx81vJaamsqQIUNwdnbGxsaGBx98kMjISMPry5Ytw8nJiQ0bNhAaGoqNjQ1PPPEE2dnZLF++nMDAQJydnRk7dixFRcafhly9epWnnnoKW1tbfH19WbBggdHr106LKGv//f3333Tp0gVra2v8/f0ZO3YsWVlZZe4/AGdnZzp16sTq1atv2m9CCCFEbWOedt7w7wO7t/L+pjMs3H7OcCwqOauk04QQVahqP/YUVSanoIhG0zZXe7sn3w7DxqJsb5tJkyaxY8cO1q9fj4eHB6+//joHDx6kRYsWhjJjxozh5MmTrF69Gh8fH3766Sd69erFsWPH6NixI2fOnCE0NJQffviBjh074uLiQlJSEq1bt2bKlCk4ODjw66+/MnjwYIKDg2nXrt1tXdfevXtp164dW7dupXHjxlhYWJTpvAMHDjBgwABmzJjBwIED2b17N6NHj8bV1ZVhw4YBMGTIEMLDw5k/fz7NmzcnKiqK5GR9Nl2n0+Hn58fatWtxdXVl9+7djBo1Cm9vbwYMGMDEiRM5deoUGRkZLF26FAAXFxcuX75sFEdsbCwPPfQQw4YNY8WKFZw+fZqRI0diZWVllEBYvnw5EyZMYM+ePYSHhzNs2DA6depEz549S7y+nTt3Ur9+/RJHjLzxxht8+OGHhISE8MYbb/DUU09x9uxZzMzMytQvw4YN4/Lly/z555+Ym5szduxYEhMTDfX/8MMPfPzxx6xevZrGjRsTHx/PkSNHSv1e/Prrrzz66KO88cYbrFixgvz8fDZu3Gh4fdiwYURGRvLzzz/j4ODAlClTeOihhzh58iTm5uYAZGdnM3/+fFavXs3Vq1d57LHHePTRR3FycmLjxo2cP3+exx9/nE6dOjFw4EBD3XPmzOH111/nrbfeYvPmzYwbN4769euX2q+36r9z587Rq1cv3nnnHZYsWUJSUhJjxoxhzJgxhvfBrfqvWLt27di5c2epcQghhBC1kVNO9H9PYg+y6GJLAEI97TmTcJXzklwQotpJckFUiczMTL766iu+/vpr7r//fkB/Y+vn52coEx0dzdKlS4mOjsbHxweAiRMnsmnTJpYuXcqsWbPw8PAAMHzSDuDr68vEiRMN9bz00kts3ryZ77777raTC+7u7gC4uroa2imLjz76iPvvv5+pU6cCUL9+fU6ePMmcOXMYNmwYERERfPfdd2zZsoUePXoAULduXcP55ubmvPXWW4bnQUFBhIeH89133zFgwADs7OywtrYmLy/vpnEtXLgQf39/PvvsMxRFoUGDBly+fJkpU6Ywbdo0NBr9IKVmzZoxffp0AEJCQvjss8/Ytm1bqTfBFy9eNHxvrjdx4kTDiIa33nqLxo0bc/bsWRo0aFCmfvntt9/Yu3cvbdu2BeCrr76iYcOGhvqjo6Px8vKiR48emJubExAQcNPv77vvvsuTTz5p1J/NmzcHMCQVdu3aZVg74ptvvsHf359169bRv39/AAoKCvj8888JDg4G4IknnmDlypUkJCRgZ2dHo0aNuO+++/jzzz+NkgudOnXi1VdfNVzrrl27+Pjjj2+aXLhZ/82ePZtBgwYZFjANCQlh/vz5dO3alc8//5zo6Ohb9l8xHx8fLl68WGocQgghRG2TkXkVT10SKPrnzTX6EQtv922MnaUZE747wvmkTBNGKMTdSZILtZS1uZaTb4eZpN2yOHfuHPn5+bRv395wzMXFhdDQUMPzY8eOUVRURP369Y3OzcvLw9XVtdS6i4qKmDVrFt999x2xsbHk5+eTl5eHjY1NOa+m4k6dOkXfvn2NjnXq1Il58+ZRVFTE4cOH0Wq1dO3atdQ6FixYwJIlS4iOjiYnJ4f8/Hyj0R1ljaNDhw4oimIUR2ZmJpcuXSIgIADQJxeu5e3tXeKn3cVycnKwsrIq8bVr6/L29gb0Q/UbNGhwy345deoUZmZmtG7d2vB6gwYNcHJyMjzv378/8+bNo27duvTq1YuHHnqIhx9+uNR1Jg4fPszIkSNLfK24vWvfj66uroSGhnLq1CnDMRsbG0NiAcDT05PAwEDs7OyMjl3fZx06dLjh+a12kLhZ/x05coSjR48aTXVQVRWdTkdUVBQRERG37L9i1tbWZGdn3zQWIYQQoja5EHGCZopKIRrM0NFQE8OKzince+oF8lJjqWthxsSkaaYOU4i7jiQXailFUco8PaGmyszMRKvVcuDAAbRa46TFtTdz15szZw6ffPIJ8+bNo2nTptja2jJ+/Hjy8/MNZTQazQ3rQxQUFFTuBZSBtbX1TV9fvXo1EydOZO7cuXTo0AF7e3vmzJnDnj17qiSe4uH/xRRFQafTlVrezc2NY8eO3bKu4qTGzeoqL39/f86cOcPWrVvZsmULo0ePZs6cOezYseOG64Bb93VZlNQ/5e2z22nr+v7LzMzkueeeK3GNiYCAACIiIsrcTkpKimFkjhBCCHEnSLygX6j4kkU9As1SMMtO5t79LwEqlkALDTTJ2kNWXh9sLWv338tC1CayoKOoEsHBwZibmxvdJKemphrdFLVs2ZKioiISExOpV6+e0eNmUwB27dpF3759efrpp2nevDl169a94WbL3d2duLg4w/OMjAyioqJKrbN4jYXrF+q7lYYNG7Jr164b4qtfvz5arZamTZui0+nYsWNHqdfSsWNHRo8eTcuWLalXrx7nzp0zKmNhYXHLuBo2bEh4eLhRQmXXrl3Y29sbTUUpr5YtW3L69OlyLeRZHM/N+qVBgwYUFhZy4MABw+tnzpwhLS3N6Bxra2sefvhh5s+fz/bt2wkPDy812dGsWTO2bdtWajyFhYVG78crV65w5swZGjVqVK5rK8k///xzw/OSpiiUVatWrTh58uQN/y/q1auHhYVFmfsP4Pjx47Rs2fK2YxFCCCFqmpx4/YLMuY5B4Nvq36MqBHeHpvqpjm00Z2RRRyGqmSQXRJWws7NjxIgRTJo0iT/++IPjx48zbNgww9x/0M9NHzRoEEOGDOHHH38kKiqKvXv3Mnv2bH799ddS6w4JCWHLli3s3r2bU6dO8dxzz5GQkGBUpnv37qxcuZKdO3dy7Ngxhg4desPoiGt5eHhgbW3Npk2bSEhIID09vUzX+corr7Bt2zZmzpxJREQEy5cv57PPPjOsCREYGMjQoUN55plnWLduHVFRUWzfvp3vvvvOcC379+9n8+bNREREMHXqVPbt22fURmBgIEePHuXMmTMkJyeXOAJj9OjRxMTE8NJLL3H69GnWr1/P9OnTmTBhglGfl9d9991HZmZmubcyvFW/hIaG0qtXL5577jn27NnDgQMHePbZZ41GHyxbtoyvvvqK48ePc/78eb7++musra2pU6dOiW1Onz6db7/9lunTp3Pq1CmOHTvG+++/D+j7uW/fvowcOZK///6bI0eO8PTTT+Pr63vD9I3bsWvXLj744AMiIiJYsGABa9euZdy4cbdd35QpU9i9ezdjxozh8OHDREZGsn79esaMGQOUrf+K7dy5kwceeOC2YxFCCCFqGrM0/Qcx5u71wP/fKY9uodB/GTR8BIDWmkhJLghRzSS5IKrMnDlz6NKlCw8//DA9evSgc+fORnPEAZYuXcqQIUN45ZVXCA0NpV+/fuzbt8+wRkBJ3nzzTVq1akVYWBjdunXDy8uLfv36GZV57bXX6Nq1K3369KF3797069fPaC799czMzJg/fz7/+9//8PHxKfMNZ6tWrfjuu+9YvXo1TZo0Ydq0abz99tuGHREAPv/8c5544glGjx5NgwYNGDlypGFLweeee47HHnuMgQMH0r59e65cucLo0aON2hg5ciShoaG0adMGd3f3G0YEgH6Ry40bN7J3716aN2/O888/z4gRI3jzzTfLdB2lcXV15dFHHy1xm8ObKUu/LF26FB8fH7p27cpjjz3GqFGjDAt4Ajg5OfHFF1/QqVMnmjVrxtatW/nll19KXY+jW7durF27lp9//pkWLVrQvXt39u7da9Re69at6dOnDx06dEBVVTZu3FjiFIvyeuWVV9i/fz8tW7bknXfe4aOPPiIs7PbXRGnWrBk7duwgIiKCLl260LJlS6ZNm2a0uOat+g8gPDyc9PR0nnjiiduORQghhKhJsvIKcc6NAcAloBG0fw4e+hCG/gxWjoZkQ6gSw6W4hJtVJYSoZIpa3vHOokpkZGTg6OhIeno6Dg4ORq/l5uYSFRVFUFBQqYvrCVFVjh49Ss+ePTl37txN18IQNc/AgQNp3rw5r7/+eqll5OeLEEKI2qKgSMfzKw8wK+oJPJU01Gf/QPFrfUO59Pca4Zgby+f+c3hhxCgTRCpE7XGz+9DykpELQoibatasGe+///5N16wQNU9+fj5Nmzbl5ZdfNnUoQgghRIWpqsobPx0j/HQ0nkoaAIpryaNSMz30CQfn5IPVFZ4QAtktQghRBtdOZxC1g4WFRYWnxQghhBA1xfcHLvHd/ks8ZrZff8DBD6ydSixrFtgBon+mTs5xVFU12qpbCFF1ZOSCEEIIIYQQosY6n5TJ9J9PACqvOv2hP9hmWKnlnep3BqCpGkliuizqKER1keSCEEIIIYQQosaa/vMJsvOLGOZ7GY/M02BmBa2fKbW8pU9jcrDCTsnlQkTJW1gLISqfTIsQQgghhBBC1Dzxx8jf+SkDLlxkgDn0KkrSH2/+JNiWvHsUABotCVZBBOaeIjXqELTrUD3xCnGXk+SCEEIIIYQQoubZ/h4WpzfwsPbf52mAooF7Rt/kJL0c5wYQdwpdwomqjFAIcQ1JLgghhBBCCCFqnoTjAHxR+BD+QQ3o1cQLPBqBe+gtT7XwbQpxP+GQHlHVUQoh/lXr1lxYsGABgYGBWFlZ0b59e/bu3XvT8mvXrqVBgwZYWVnRtGlTNm7caPS6qqpMmzYNb29vrK2t6dGjB5GRkYbXL1y4wIgRIwgKCsLa2prg4GCmT59Ofn6+UT1Hjx6lS5cuWFlZ4e/vzwcffFB5Fy2EEEIIIcTdJD8bNfUiAJ8XPoJNl9HQ/jkI6lKm093rtQKgTkEUGbkFVRamEOI/tSq5sGbNGiZMmMD06dM5ePAgzZs3JywsjMTExBLL7969m6eeeooRI0Zw6NAh+vXrR79+/Th+/LihzAcffMD8+fNZtGgRe/bswdbWlrCwMHJzcwE4ffo0Op2O//3vf5w4cYKPP/6YRYsW8frrrxvqyMjI4IEHHqBOnTocOHCAOXPmMGPGDBYvXly1HSKEEEIIIcSdKPkMCirJqgPZ5k60C3Ip1+kOAc0B8NckEXHxclVEKIS4Tq1KLnz00UeMHDmS4cOH06hRIxYtWoSNjQ1Lliwpsfwnn3xCr169mDRpEg0bNmTmzJm0atWKzz77DNCPWpg3bx5vvvkmffv2pVmzZqxYsYLLly+zbt06AHr16sXSpUt54IEHqFu3Lo888ggTJ07kxx9/NLTzzTffkJ+fz5IlS2jcuDFPPvkkY8eO5aOPPqryPrkbzZgxgxYtWlR6vYqiGL7vphQYGMi8efOqpa3Bgwcza9asMpXt1q0b48ePr9T2t2/fjqIopKWlVWq9leH62DZt2kSLFi3Q6XSmDUwIIYS4GySeBiBS58c9dV2xMtfe4oTr2LiQonUHIOHswcqOTghRglqTXMjPz+fAgQP06NHDcEyj0dCjRw/Cw8NLPCc8PNyoPEBYWJihfFRUFPHx8UZlHB0dad++fal1AqSnp+Pi8l/2NDw8nHvvvRcLCwujds6cOUNqamqJdeTl5ZGRkWH0EJVn7NixtG7dGktLyypJRFSGZcuW4eTkdMPxffv2MWrUqCpv/8iRI2zcuJGxY8eWqfyPP/7IzJkzqziqmqtXr16Ym5vzzTffmDoUIYQQ4s6XeBKACNWXzvXcbquKdIcQAHIvyXaUQlSHWpNcSE5OpqioCE9PT6Pjnp6exMfHl3hOfHz8TcsXfy1PnWfPnuXTTz/lueeeu2U717ZxvdmzZ+Po6Gh4+Pv7l1hO3L5nnnmGgQMHmjqMcnN3d8fGxqbK2/n000/p378/dnZ2ZSrv4uKCvb19FUdVsw0bNoz58+ebOgwhhBDizpf078gF1Y+mvo63VYXq0RgAy5RTlRaWEKJ0tSa5UBPExsbSq1cv+vfvz8iRIytU12uvvUZ6errhERMTU74KVBXys6r/oaplDrFbt26MHTuWyZMn4+LigpeXFzNmzDAqEx0dTd++fbGzs8PBwYEBAwaQkJBgVOa9997D09MTe3t7RowYYVgP42bmz5/Piy++SN26dcsc7/WOHTtG9+7dsba2xtXVlVGjRpGZmWlUpngqjKWlJd7e3owZM8bw2kcffUTTpk2xtbXF39+f0aNHG87fvn07w4cPJz09HUVRUBTF0DfXT4u4VR8VTxNZuXIlgYGBODo68uSTT3L16tVSr62oqIjvv/+ehx9+2Oj4woULCQkJwcrKCk9PT5544gnDa9dPiwgMDGTWrFk888wz2NvbExAQcMM6I7t376ZFixZYWVnRpk0b1q1bh6IoHD58uNTY/v77b7p06YK1tTX+/v6MHTuWrKysUsuX5frz8vIYO3YsHh4eWFlZ0blzZ/bt22dUz8aNG6lfvz7W1tbcd999XLhw4Ya2Hn74Yfbv38+5c+dKjUcIIYQQFadL0CcEzuj8CfW6vQ83nINaAuCVc46svMJKi00IUbJasxWlm5sbWq32hhvPhIQEvLy8SjzHy8vrpuWLvyYkJODt7W1U5vqh9JcvX+a+++6jY8eON9xAldbOtW1cz9LSEktLyxJfK5OCbJjlc/vn367XL4OFbZmLL1++nAkTJrBnzx7Cw8MZNmwYnTp1omfPnuh0OsNN844dOygsLOTFF19k4MCBbN++HYDvvvuOGTNmsGDBAjp37szKlSuZP39+hZIGZZGVlUVYWBgdOnRg3759JCYm8uyzzzJmzBiWLVsGwOeff86ECRN47733ePDBB0lPT2fXrl2GOjQaDfPnzycoKIjz588zevRoJk+ezMKFC+nYsSPz5s1j2rRpnDlzBqDEEQRl6SOAc+fOsW7dOjZs2EBqaioDBgzgvffe49133y3x+o4ePUp6ejpt2rQxHNu/fz9jx45l5cqVdOzYkZSUFHbu3HnTfpo7dy4zZ87k9ddf5/vvv+eFF16ga9euhIaGkpGRwcMPP8xDDz3EqlWruHjx4i3XbDh37hy9evXinXfeYcmSJSQlJTFmzBjGjBnD0qVLb3reza5/8uTJ/PDDDyxfvpw6derwwQcfEBYWxtmzZ3FxcSEmJobHHnuMF198kVGjRrF//35eeeWVG9oJCAjA09OTnTt3EhwcfNNrEUIIIcRtystEk6H/4C3Nri5ONha3OKFkzsH6v3MaKRc4ciGRDqEm+NtZiLtIrRm5YGFhQevWrdm2bZvhmE6nY9u2bXTo0KHEczp06GBUHmDLli2G8kFBQXh5eRmVycjIYM+ePUZ1xsbG0q1bN1q3bs3SpUvRaIy7rUOHDvz1118UFPy3zc2WLVsIDQ3F2dn59i/6DtCsWTOmT59OSEgIQ4YMoU2bNob+3rZtG8eOHWPVqlW0bt2a9u3bs2LFCnbs2GH4VHnevHmMGDGCESNGEBoayjvvvEOjRo2qPO5Vq1aRm5vLihUraNKkCd27d+ezzz5j5cqVhsTRO++8wyuvvMK4ceOoX78+bdu2Nbp5Hj9+PPfddx+BgYF0796dd955h++++w7Qv58dHR1RFAUvLy+8vLxKTC6UpY9A/39h2bJlNGnShC5dujB48OAb3vvXunjxIlqtFg8PD8Ox6OhobG1t6dOnD3Xq1KFly5a3XI/hoYceYvTo0dSrV48pU6bg5ubGn3/+aehDRVH44osvaNSoEQ8++CCTJk26aX2zZ89m0KBBjB8/npCQEDp27Mj8+fNZsWLFTUes3Oz6s7Ky+Pzzz5kzZw4PPvggjRo14osvvsDa2pqvvvoK0CeKgoODmTt3LqGhoQwaNIhhw4aV2JaPjw8XL1686XUIIYQQogKS9B+8JKpOeHv73n49bvXJ0Dpjo+Rx6cSuW5cXQlRIrRm5ADBhwgSGDh1KmzZtaNeuHfPmzSMrK4vhw4cDMGTIEHx9fZk9ezYA48aNo2vXrsydO5fevXuzevVq9u/fbxh5oCgK48eP55133iEkJISgoCCmTp2Kj48P/fr1A/5LLNSpU4cPP/yQpKQkQzzFoxL+7//+j7feeosRI0YwZcoUjh8/zieffMLHH39cdZ1hbqMfRVDdzMu3FkCzZs2Mnnt7exu2Dj116hT+/v5G6000atQIJycnTp06Rdu2bTl16hTPP/+8UR0dOnQw3MBWlVOnTtG8eXNsbf8bpdGpUyd0Oh1nzpxBURQuX77M/fffX2odW7duZfbs2Zw+fZqMjAwKCwvJzc0lOzu7zGsqlKWPQD9F4dr1EK7t55Lk5ORgaWmJoiiGYz179qROnTrUrVuXXr160atXLx599NGbxnrt97c4UVLc7pkzZ2jWrBlWVlaGMu3atbvp9R45coSjR48aLZqoqio6nY6oqCgaNmxY4nk3u/5z585RUFBAp06dDK+bm5vTrl07Tp3SD7k8deoU7du3N6qztKSltbU12dnZN70OIYQQQlRAkv73c4TOlwa3OSUCAEXhiltbHBJ+R3NxJ9C/cuITQpSoViUXBg4cSFJSEtOmTSM+Pp4WLVqwadMmw+KJ0dHRRqMKOnbsyKpVq3jzzTd5/fXXCQkJYd26dTRp0sRQZvLkyWRlZTFq1CjS0tLo3LkzmzZtMtwQbdmyhbNnz3L27Fn8/PyM4lH/XX/A0dGR33//nRdffJHWrVvj5ubGtGnTqnbFf0Up1/QEUzE3Nzd6rijKHbGVn7W19U1fv3DhAn369OGFF17g3XffxcXFhb///psRI0aQn59f6Qs2lref3dzcyM7OJj8/37DLib29PQcPHmT79u38/vvvTJs2jRkzZrBv374Sd7W4nXZvJTMzk+eee67EERMBAQGlnled77OUlBTc3d2rpG4hhBBCYLSYY6hnxRaTtqjXFRJ+xy99Pzqdikaj3PokIcRtqTXTIoqNGTOGixcvkpeXx549e4w+bdy+fbthPnyx/v37c+bMGfLy8jh+/DgPPfSQ0euKovD2228THx9Pbm4uW7dupX79+obXhw0bhqqqJT6u1axZM3bu3Elubi6XLl1iypQplX/xd5iGDRsSExNjtJjlyZMnSUtLM0x9aNiwIXv27DE6759//qmW2I4cOWK0kOCuXbvQaDSEhoZib29PYGBgqVMPDhw4gE6nY+7cudxzzz3Ur1+fy5eNR5pYWFhQVFR0yzhu1Ue3o3hNkZMnTxodNzMzo0ePHnzwwQccPXqUCxcu8Mcff9xWG6GhoRw7doy8vDzDsesXUbxeq1atOHnyJPXq1bvhce1Wr+URHByMhYWF0XoYBQUF7Nu3z+h9tnfvXqPzSnqf5ebmcu7cOVq2bHlbsQghhBDi1tTkCADOqT63vZhjMc9m+i3nW6gRnL2cXOHYhBClq3XJBXHn6NGjB02bNmXQoEEcPHiQvXv3MmTIELp27WpYaHDcuHEsWbKEpUuXEhERwfTp0zlx4sQt6z579iyHDx8mPj6enJwcDh8+zOHDh8nPzy9TbIMGDcLKyoqhQ4dy/Phx/vzzT1566SUGDx5sGCkzY8YM5s6dy/z584mMjOTgwYN8+umnANSrV4+CggI+/fRTzp8/z8qVK1m0aJFRG4GBgWRmZrJt2zaSk5NLHGpflj66He7u7rRq1Yq///7bcGzDhg3Mnz+fw4cPc/HiRVasWIFOpyM0NPS22vi///s/dDodo0aN4tSpU2zevJkPP/wQwGg6xrWmTJnC7t27GTNmDIcPHyYyMpL169cb7cJRXra2trzwwgtMmjSJTZs2cfLkSUaOHEl2djYjRowA4PnnnycyMpJJkyZx5swZVq1adUOiEvQJB0tLy1KnTAghhBCi4ooSIwE4r/pQz6NsW2aXxswjlFSNC5ZKAReP7qiM8IQQpZDkgjAZRVFYv349zs7O3HvvvfTo0YO6deuyZs0aQ5mBAwcydepUJk+eTOvWrbl48SIvvPDCLet+9tlnadmyJf/73/+IiIigZcuWtGzZ8obRA6WxsbFh8+bNpKSk0LZtW5544gnuv/9+PvvsM0OZoUOHMm/ePBYuXEjjxo3p06cPkZH6X4bNmzfno48+4v3336dJkyZ88803hrVAinXs2JHnn3+egQMH4u7uzgcffHBbfXS7nn32WaO1DZycnPjxxx/p3r07DRs2ZNGiRXz77bc0btz4tup3cHDgl19+4fDhw7Ro0YI33niDadOmARitw3CtZs2asWPHDiIiIujSpQstW7Zk2rRp+PhUbHXn9957j8cff5zBgwfTqlUrzp49y+bNmw0LrgYEBPDDDz+wbt06mjdvzqJFi5g1a9YN9Xz77bcMGjSo0qe1CCGEEOJfhXlo0/ULJ+c718PKXFux+hSFBBf9BzK685JcEKIqKer14/uFSWRkZODo6Eh6ejoODg5Gr+Xm5hIVFUVQUFCpN2VClFdOTg6hoaGsWbOm2j6J/+abbxg+fDjp6em3XLeipklOTiY0NJT9+/cTFBRk6nAqjfx8EUIIUaMknoaF7bmqWjMlZAMLn779kZrFzm5aQL1/XucIoTSdtkfWXRDiGje7Dy0vGbkgxF3K2tqaFStWkJxcdfMPV6xYwd9//01UVBTr1q1jypQpDBgwoNYlFkC/SOfChQvvqMSCEEIIUeMY1lvwpoG3Y6VUGdCmNwCN1UhOXbhUKXUKIW5Uq3aLEEJUrm7dulVp/fHx8YbdXby9venfvz/vvvtulbZZVdq0aVOhdS6EEEIIUQbXLObYyLtin6IWs3ALJMHcF8+CWKL2b6Jx3ZGVUq8QwpiMXBBCVJnJkydz4cIFw9D7jz/+WNYrEEIIIUSpipL+TS7ofGjkUznJBYB0784AaKNk3QUhqookF4QQQgghhBA1Ql78aQDiLQLwdqy8tYBcm/UCoEHWPtKyy7Z7mBCifCS5UIvI2ptCiMomP1eEEELUGKqKWeo5ALTuIaVuXX07XJvcTxEagjTx7D9ypNLqFUL8R5ILtYC5uTkA2dnZJo5ECHGnKf65UvxzRgghhDCZzAQsCjMpUhVc/BpUbt1Wjlyy1W+vXRD5Z+XWLYQAZEHHWkGr1eLk5ERiYiIANjY2lZrJFULcfVRVJTs7m8TERJycnNBqK7iPuBBCCFFR/y7mGK16EOrnVunVpzo1oU7WMawyoiq9biGEJBdqDS8vLwBDgkEIISqDk5OT4eeLEEIIYUq65LNogCjVm4aVtFPEtTR27gCY5Vyp9LqFEJJcqDUURcHb2xsPDw8KCgpMHY4Q4g5gbm4uIxaEEELUGJlxkTgAMXjR2d2u0us3d/AAwDI/pdLrFkJIcqHW0Wq1cjMghBBCCCHuONkJ53AAcu39sTCr/KXhbJz1I/VsCtMqvW4hhCzoKIQQQgghhKgBtOkXAFBcgqqkfnsXbwAcdWkU6WS3JCEqmyQXhBBCCCGEEKalqthlXwLAyiO4Spqwd9UnF1zJIDU7v0raEOJuJskFIYQQQgghhGnlpGKtywLA2TekSpooXnPBRskjOSW1StoQ4m4myQUhhBBCCCGEaaXot4eMV50J8HCtmjYs7MjDAoD05LiqaUOIu5gkF4QQQgghhBAmlZt0DoBo1YNAV9uqaURRuKp1AiArRZILQlQ2SS4IIYQQQgghTCo9NgKAeI0XjjbmVdZOjrkzALnpCVXWhhB3K0kuCCGEEEIIIUwqP+k8AFm2/lXbjqULAIVXE6u0HSHuRpJcEEIIIYQQQpiUJu0CADrHOlXaTpGNm/4fmclV2o4QdyNJLgghhBBCCCFMyiY7BgBz96rZhrKYYusOgCZHkgtCVDZJLgghhBBCCCFMpzAPx4IkABx9qmYbymLmDvrkgmVeCsdj0zkYLVtSClFZJLkghBBCCCGEMJ20aDSoZKmWePtU7ZoL1k5e+q8FKTz++W7+74t/yMgtqNI2hbhbSHJBCCGEEEIIYTLZifrFHC+p7gS621VpW3Yu3gA4qRnkFerILdARk5JdpW0KcbeQ5IIQQgghhBDCZFLj9MmFJI0HDlZVtw0lgI2zfuSCq5JhOHY5LbdK2xTibiHJBSGEEEIIIYTJZCVe1H+19qrythRb/W4RrqQDKgBx6TlV3q4QdwNJLgghhBBCCCFMpihNv1NEkZ1v1Tf2b3LBQimitYcGDTpi0yS5IERlkOSCEEIIIYQQwmTMrsbqv7pU7WKOAJhbo1ro13VYkfsSmyymEJ+aWfXtCnEXkOSCEEIIIYQQwmTs8hL0Xz3qVEt7iq1+O0rb/GTqa2LRJkdUS7tC3OkkuSCEEEIIIYQwDVXFpSgJAFef4Opp07c1oFBkrh/BYJNxrnraFeIOJ8kFIYQQQgghhElcTYnDkgJ0qoJPQN3qafTR/8HESPLr9wHAPTeKwiJd9bQtxB1MkgtCCCGEEEIIk0iIPgvAFcUJe1vb6mlUawZ27lh6NwYgWLlMwtW86mlbiDuYJBeEEEIIIYQQJpEad17/1dyj2tvWeIQCEKzEEic7RghRYZJcEEIIIYQQQphEdnI0ADnW3tXfuFt9AOoqcVxOuVr97Qtxh5HkghBCCCGEEMIk1LQY/VcH3+pv3CmAfMUSS6WQzPjz1d++EHcYSS4IIYQQQgghTMIi6zIA5i7Vsw2lEY2WVGt9u2ry6epvX4g7jCQXhBBCCCGEECZhn5eg/+phguQCkOOo3/7SKjXSJO0LcSeR5IIQQgghhBCi2qXnFOChJgHg5hdskhh0bvpFHR2zokzSvhB3EkkuCCGEEEIIIardubgruJMOgI1boEliMPdsCIB3frRJ2hfiTiLJBSGEEEIIIUS1i714Fo2ikq9YgK2bSWKw8aoHgKeaiE6nmiQGIe4UtS65sGDBAgIDA7GysqJ9+/bs3bv3puXXrl1LgwYNsLKyomnTpmzcuNHodVVVmTZtGt7e3lhbW9OjRw8iI43nXL377rt07NgRGxsbnJycSmxHUZQbHqtXr67QtQohhBBCCHGnSo89o/9q6QuKYpIY7F19AHDmKhk5eSaJQYg7Ra1KLqxZs4YJEyYwffp0Dh48SPPmzQkLCyMxMbHE8rt37+app55ixIgRHDp0iH79+tGvXz+OHz9uKPPBBx8wf/58Fi1axJ49e7C1tSUsLIzc3FxDmfz8fPr3788LL7xw0/iWLl1KXFyc4dGvX79KuW4hhBBCCCHuNIXJ+u0f8x1Ms5gjgIWDOwBaRSX1Ssn3FEKIsqlVyYWPPvqIkSNHMnz4cBo1asSiRYuwsbFhyZIlJZb/5JNP6NWrF5MmTaJhw4bMnDmTVq1a8dlnnwH6UQvz5s3jzTffpG/fvjRr1owVK1Zw+fJl1q1bZ6jnrbfe4uWXX6Zp06Y3jc/JyQkvLy/Dw8rKqtKuXQghhBBCiDuJecZF/Vf3uqYLQmtOBnYAZKbEmS4OIe4AtSa5kJ+fz4EDB+jRo4fhmEajoUePHoSHh5d4Tnh4uFF5gLCwMEP5qKgo4uPjjco4OjrSvn37Uuu8mRdffBE3NzfatWvHkiVLUNXS523l5eWRkZFh9BBCCCGEEOJukJadj3vBZQAcfeqbNJYMrRMAOanxJo1DiNqu1iQXkpOTKSoqwtPT0+i4p6cn8fEl/yCIj4+/afnir+WpszRvv/023333HVu2bOHxxx9n9OjRfPrpp6WWnz17No6OjoaHv79/udoTQgghhBCitopIyMRf0U9DsPSoZ9JYss2cAcjPSDBpHELUdmamDuBOMXXqVMO/W7ZsSVZWFnPmzGHs2LElln/ttdeYMGGC4XlGRoYkGIQQQgghxF0hIj6DR/9NLuAcZNJYci1dIA90V5NMGocQtV2tGbng5uaGVqslIcE4o5iQkICXl1eJ53h5ed20fPHX8tRZVu3bt+fSpUvk5ZW86qylpSUODg5GDyGEEEIIIe4GcbEXsVXy0KEBpwCTxlJo5ar/R1aySeMQorarNckFCwsLWrduzbZt2wzHdDod27Zto0OHDiWe06FDB6PyAFu2bDGUDwoKwsvLy6hMRkYGe/bsKbXOsjp8+DDOzs5YWlpWqB4hhBBCCCHuNJlxZwHIsfYCMwuTxqLauAGgzZHkghAVUaumRUyYMIGhQ4fSpk0b2rVrx7x588jKymL48OEADBkyBF9fX2bPng3AuHHj6Nq1K3PnzqV3796sXr2a/fv3s3jxYgAURWH8+PG88847hISEEBQUxNSpU/Hx8THaRjI6OpqUlBSio6MpKiri8OHDANSrVw87Ozt++eUXEhISuOeee7CysmLLli3MmjWLiRMnVmv/CCGEEEIIUSukRgGgcwo0bRyAxk6/HaVFXoqJIxGidqtVyYWBAweSlJTEtGnTiI+Pp0WLFmzatMmwIGN0dDQazX+DMTp27MiqVat48803ef311wkJCWHdunU0adLEUGby5MlkZWUxatQo0tLS6Ny5M5s2bTLaRnLatGksX77c8Lxly5YA/Pnnn3Tr1g1zc3MWLFjAyy+/jKqq1KtXz7BtphBCCCGEEOI/mXmFuOTHghlYegSbOhzMHDwAsCmQ5IIQFaGoN9svUVSbjIwMHB0dSU9Pl/UXhBBCCCHEHetUXAanFz7Jo9pd0GMGdH7ZpPFE7t1EyMaBRCs+BEw/ZdJYhKhulXkfWmvWXBBCCCGEEELUftEp2dRR/l1Q3cQ7RQDYuegXcnfQpZs4EiFqN0kuCCGEEEIIIapNTEo2AcXbULqYPrlg7+oDgJOSRXZOtomjEaL2kuSCEEIIIYQQotokJsbjpmTon7jUNW0wgK2jK4Wq/rYoNSnexNEIUXtJckEIIYQQQghRbYqSIgDItvQAS3sTRwOKRkuaop9rnpUaZ+JohKi9JLkghBBCCCGEqDbmaecByHc2/U4Rxa5qnQDITpWRC0LcLkkuCCGEEEIIIaqFTqfilHUBADP3+qYN5hpZZs4A5KcnmjgSIWovSS4IIYQQQgghqkXi1TwCuQyAtXcDE0fznzxLFwB0mUkmjkSI2kuSC0IIIYQQQohqEZ2STV1Fn1zQetSckQsFVq4AqFmSXBDidklyQQghhBBCCFEtYpIzCFT+XdfANcS0wVxDtXYDwCw72cSRCFF7SXJBCCGEEEIIUS3S485hoRRRoFiAo7+pwzFQ7D0BsMi7YuJIhKi9JLkghBBCCCGEqBZFifptKNNt6oCm5tyKWDl7AWCTL8kFIW5XzfkfLYQQQgghhLijmaedBSDfqeZsQwlg5+IDgENRiokjEaL2kuSCEEIIIYQQolo4/rsNpdYj1LSBXMfJww8AFzWdoqIiE0cjRO0kyQUhhBBCCCFElcvJL8Kn6BIAdj41ZxtKAGcPXwDMlSJSkuNNHI0QtZMkF4QQQgghhBBVLiY127BThI13zRq5oDW3JA17ANISY00cjRC1kyQXhBBCCCGEEFXuUsIVPJU0ABSXINMGU4J0rTMAmVcumTgSIWonSS4IIYQQQgghqlx6XCQA2RpbsHY2cTQ3yjJ3BSAnVaZFCHE7JLkghBBCCCGEqHK5iecByLDyBUUxcTQ3yrNyA6AwPcHEkQhRO0lyQQghhBBCCFH1Ui8AkGdfx7RxlKLIxgMAJUuSC0LcDkkuCCGEEEIIIaqcdWY0AJoauN4CgMZen1wwy04ycSRC1E6SXBBCCCGEEEJUKVVVccrT78Jg7VnPxNGUzNzRCwDr/GQTRyJE7STJBSGEEEIIIUSVSsrMw1dNBMDRJ8TE0ZTMxsUXAPvCFBNHIkTtJMkFIYQQQgghRJWKuZJJgKJPLpi71cxpEQ7u+uSCsy4VnU41cTRC1D6SXBBCCCGEEEJUqcTLF7FUCihCA47+pg6nRE7ufgC4KFdJuZpl4miEqH0kuSCEEEIIIYSoUplxZwFIM/cCrZmJoymZuZ0bhf/eHl1JjDVxNELUPpJcEEIIIYQQQlSpoitRAGTZ1sxRCwBoNKQpTgBkJEtyQYjykuSCEEIIIYQQokopaRcA0DkFmjSOW8k0dwUg58plE0ciRO0jyQUhhBBCCCFEldHpVGwyowGw866Z21AWy7N0AyArRZILQpSXJBeEEEIIIYQQVeZiSjb1uQiAc0BjE0dzc4qjDwD5V6JNHIkQtY8kF4QQQgghhBBVJjL6MiGKfg0DrV8bE0dzc1budQGwzJTkghDlJckFIYQwkbj0HFbtiSY9u8DUoQghhBBVJu3cfjSKSqq5B9h7mjqcm3LxDwXAveAymXmFJo5GiNqlZu4DI4QQd7D0nAIW7TjHkr+jyCvU8cuRy3zzbHs0GsXUoQkhhBCVTok7CEC6c1OcTRzLrdh5hQAQoCQQkXCVVgE1PWIhag5JLgghRDVRVZVfNm3i773/kJxnRissSVXsCT+vsvKfiwztGGjqEIUQQohK55p+HADFt7WJIykDlyAA3JUMtl+Kl+SCEOUgyQUhhKgm4T99xiNH3+QRAIv/jm8pasWrv42ja313At1sTRWeEEIIUekycguoXxgBCrjWv8fU4dyalSPZZo7YFKZzJfo0dGxo6oiEqDVua82FgoICYmJiOHPmDCkpKZUdkxBC3HEuHf+b1kfeAiDRtj6qd0twb4CqtaCn9iArlGnMWr2VIp1q4kiFEEKIynMuKgo/JRkdCnZBNXsxx2I5dgH6rwlnTRyJELVLmZMLV69e5fPPP6dr1644ODgQGBhIw4YNcXd3p06dOowcOZJ9+/ZVZaxCCFEr5afFYfXjECyVAg5YdcBtwj8oz22HF/egDP+NIms3Gmsu8nbSOH7e+KupwxVCCCEqTWrkPwDEm/uDlaOJoykbrat+xwhN2gXTBiJELVOm5MJHH31EYGAgS5cupUePHqxbt47Dhw8TERFBeHg406dPp7CwkAceeIBevXoRGRlZ1XELIUTtUJhP4lcDcNNd4Ty++D2zEo1W+9/rfm3QjvqDNLt6eCmp9Nw3gvhLUaaLVwghhKhEaqx+Mccrjk1MHEnZ2XrVA/Q7RiRdzTNxNELUHmVac2Hfvn389ddfNG7cuMTX27VrxzPPPMOiRYtYunQpO3fuJCQkpFIDFUKI2ih+7QT8rh4lQ7UhJuxLunq431jIuQ6OL24j+sPOBBTFcGL/Rrz8Xqz+YIUQQohK5pB+BgCdZ1MTR1J25m7BgH7HiPNJmbjbW5o4IiFqhzIlF7799tsyVWZpacnzzz9foYCEEOJOUZR5Bbcz3wDwfeB0nunYsdSyirUTcW4dCUhYQ+GlQ9UVohBCCFGlPHP1o/Fs/JuZOJJy+HfHiDpKIgczck0cjBC1R7kXdFy6dCnZ2dlVEYsQQtxRTu5Yixk6zlCHAf/37C3LW/rrt+hySjtR1aEJIYQQVS4r8yp+ajwAnsEtTRxNOTjrkws+SjIJqVdNHIwQtUe5kwuvvvoqXl5ejBgxgt27d1dFTEIIcUfIO74egCTfHthZ3nqgmE8j/RZdQQXnyMnNr9LYhBBCiKoWd+4IGkUlDXsc3HxMHU7Z2XtRoLFEq6jkJl0wdTRC1BrlTi7ExsayfPlykpOT6datGw0aNOD9998nPj6+KuK7wYIFCwgMDMTKyor27duzd+/em5Zfu3YtDRo0wMrKiqZNm7Jx40aj11VVZdq0aXh7e2NtbU2PHj1uWJDy3XffpWPHjtjY2ODk5FRiO9HR0fTu3RsbGxs8PDyYNGkShYWFFbpWIUTtFXU5icbZ+wGod++TZTrHvU5jsrHCRskj8pRMjRBCCFG7ZVw8CkCseSAoimmDKQ9FIdPGX//v1POmjUWIWqTcyQUzMzMeffRR1q9fT0xMDCNHjuSbb74hICCARx55hPXr16PT6aoiVtasWcOECROYPn06Bw8epHnz5oSFhZGYmFhi+d27d/PUU08xYsQIDh06RL9+/ejXrx/Hjx83lPnggw+YP38+ixYtYs+ePdja2hIWFkZu7n/zq/Lz8+nfvz8vvPBCie0UFRXRu3dv8vPz2b17N8uXL2fZsmVMmzatcjtACFFr7N26FmslnyQzL7zqty3TOYrWjFgr/QrVVyL+qcrwhBBCiCqnSzgFQJp9PRNHUn4FDnUAsMyINnEkQtQe5U4uXMvT05POnTvToUMHNBoNx44dY+jQoQQHB7N9+/ZKCvE/H330ESNHjmT48OE0atSIRYsWYWNjw5IlS0os/8knn9CrVy8mTZpEw4YNmTlzJq1ateKzzz4D9KMW5s2bx5tvvknfvn1p1qwZK1as4PLly6xbt85Qz1tvvcXLL79M06Ylr3L7+++/c/LkSb7++mtatGjBgw8+yMyZM1mwYAH5+TK0WYi7TXZ+IdbnfwMgN/jBcn1ak+3274JXcYerIDIhhBCi+linRQBQ5NbAxJGUn8a1LgD2OTEmjkSI2uO2kgsJCQl8+OGHNG7cmG7dupGRkcGGDRuIiooiNjaWAQMGMHTo0EoNND8/nwMHDtCjRw/DMY1GQ48ePQgPDy/xnPDwcKPyAGFhYYbyUVFRxMfHG5VxdHSkffv2pdZZWjtNmzbF09PTqJ2MjAxOnCh5Yba8vDwyMjKMHkKIO8P6Axe5Vz0AgO89/ct1rk2dNgC4pJ+s9LiEEEKI6uSecw4AS58mJo6k/Kw89NtRuhfEUVBUNaOyhbjTlDu58PDDD+Pv78+yZcsYOXIksbGxfPvtt4YbdFtbW1555RViYio3y5ecnExRUZHRDTzoR0+Utt5DfHz8TcsXfy1PneVp59o2rjd79mwcHR0ND39//zK3J4SouVRV5cjfG3FSssgxd0ZT555yne/776KOIbooEtKyqiJEIYQQosqpuel46JIAcK9bi7ah/JeNZwgAAUoCiVfzTByNELVDuZMLHh4e7Nixg+PHjzN+/HhcXFxuKOPu7k5UVFSlBHineu2110hPTzc8KjsZI4Qwjf0XU2mYvgMAbYOHQKMt1/k23g3IKV7U8cTBqghRCCGEqHKpF44BkKA64efjZ+Joyk/jqt+OMkBJJF6S/UKUSbmTC1999RUdOnS4aRlFUahTp85tB1USNzc3tFotCQkJRscTEhLw8vIq8RwvL6+bli/+Wp46y9POtW1cz9LSEgcHB6OHEKL2W/3PBR7Q6qdEWDTtW/4KNFribeoDkHru5rvhCCGEEDVV6gX9ThHR2jpYmFVomTfTcAqgCA3WSj6pCfIhoBBlceuN1/81f/78W1dmZoaXlxedO3fGw8OjQoFdz8LCgtatW7Nt2zb69esHgE6nY9u2bYwZM6bEczp06MC2bdsYP3684diWLVsMyZGgoCC8vLzYtm0bLVq0ACAjI4M9e/aUujNEae28++67JCYmGq57y5YtODg40KhRo/JfrBCiVioo0hF3OhxvJYUiM1u0QV1vrx7P5hB1FG38kUqOUAghhKgeuQn6xRzTbINMHMlt0pqTau6JW0EcuQlngZamjkiIGq/MyYWPP/74lmV0Oh1XrlxBp9Px9ddf89hjj1UouOtNmDCBoUOH0qZNG9q1a8e8efPIyspi+PDhAAwZMgRfX19mz54NwLhx4+jatStz586ld+/erF69mv3797N48WJAP8Ji/PjxvPPOO4SEhBAUFMTUqVPx8fExJDAAoqOjSUlJITo6mqKiIg4fPgxAvXr1sLOz44EHHqBRo0YMHjyYDz74gPj4eN58801efPFFLC0tK7UPhBA1174LKXQq/AfMQKnfE8ytbqse+7ptIGolXlmnKNKpaDW1aG9wIYQQAiBNv4WjzjHAxIHcvqvWfrgVxKFLkeneQpRFmZMLZV1DQafT8d577/HGG29UenJh4MCBJCUlMW3aNOLj42nRogWbNm0yLJ4YHR2NRvPfsKuOHTuyatUq3nzzTV5//XVCQkJYt24dTZr8t2Lt5MmTycrKYtSoUaSlpdG5c2c2bdqEldV/NwXTpk1j+fLlhuctW+ozl3/++SfdunVDq9WyYcMGXnjhBTp06ICtrS1Dhw7l7bffrtTrF0LUbFtPJtJPo59jqgl96Lbr8ah/D2yDBlwkIi6Nhr7OlRWiEEIIUS2ssy7pv7rXNXEkty/Pvg5k7MM8/aKpQxGiVlBUVVUru9LY2FhatGhBUlJSZVd9x8rIyMDR0ZH09HRZf0GIWkhVVcI+2Mhv2YPQKiq8fBIcfW+vMl0RuTN9sFJz+bXzT/Tu0b1ygxVCCCGqWOoMf5zJ4FifDTRt08XU4dyW0z+8Q4Njc9hp2ZUur/1s6nCEqBKVeR9aptVVVq9eXeYKY2JiuHDhgiQWhBB3lcjETLzSj6BVVHROdW4/sQCg0ZJsFwrA1ah9lRShEEIIUT1yszJwJgMAn8BQE0dz+yw9ggFwzY81cSRC1A5lSi58/vnnNGzYkA8++IBTp07d8Hp6ejobN27k//7v/2jVqhVXrlyp9ECFEKIm23IygbaaMwBo6nSscH1FXvrpV1ZJRytclxBCCFGdYi/ofx9mYIOLq7uJo7l9jj763Zu8dfHkF+pMHI0QNV+Zkgs7duzg/fffZ8uWLTRp0gQHBwdCQkJo2rQpfn5+uLq68swzzxAQEMDx48d55JFHqjpuIYSoUbaeSqCd5rT+SSUkFxyD2wLgnxtBbkFRhesTQgghqsuVS5EAJJt5oSi1d1FiZ98Q/Vclk4uxl00cjRA1X5kXdHzkkUd45JFHSE5O5u+//+bixYvk5OTg5uZGy5YtadmypdFiikIIcbdIvJrLyZhEWlic0x8IqITkQlArABooF4mMz6CpvyzqKIQQonbISjgPQLZ1BaYI1gCKlQNpGiecdGlcjjpFSB0/U4ckRI1W5uRCMTc3N6NtGoUQ4m73x6lEmnEeS6UAbD3ANbjCdSpuIRRihq2SR/T50zT171AJkQohhBBVT5eq312hqBZvQ1ks3coPp+w0MmLPAD1NHY4QNZoMNRBCiAraeiqBtoYpER2gMoaAas25Yh0IQMbFIxWvTwghhKgmlpn6bSgt3YJMHEnFFTrWAaAg+byJIxGi5pPkghBCVEBOfhE7I5Npb0gudKq0unNdGuj/kXTjQrpCCCFETaTTqTjnxwHg5FPxkXymZuGuvwaLqxdNHIkQNZ8kF4QQogL+PptMYWEBbbT6xasIqLzpC1a+jQFwuhqJqqqVVq8QQghRVeIycvFBvyW9q1+IiaOpOCc//VaarvmXycmXBZaFuBlJLgghRAVsPZlAQ+UituSApSN4Nq60ul2C9NtRBumiiUvPrbR6hRBCiKpyITYOZyUTAHPXQNMGUwnsvfXbUQYoCZxNzDRxNELUbOVOLuTmlv4HblxcXIWCEUKI2kSnU9l2OuG/KREB7UGjrbT6zb2bABCsXOb0peRKq1cIIYSoKkkx+pF8mRoHsLQ3cTSVwFm/boQ3KURelt/FQtxMuZMLrVq14vDhwzcc/+GHH2jWrFllxCSEELXC4UtpJGfm08EsQn+gTsW3oDTi6EeOxhZzpYj4qOOVW7cQQghRBTIT9NsyZ1r7mDiSSmLrRp7GBo2ikhRzxtTRCFGjlTu50K1bN+655x7ef/99ALKyshg2bBiDBw/m9ddfr/QAhRCiptp6MgFQaW/27x8bAZWcXFAU0u3qAZAbK8kFIYQQNZ9yRZ9cKHCs/TtFAKAoZNv6A5AVf9bEwQhRs5mV94SFCxfSu3dvnn32WTZs2EBcXBx2dnbs3buXJk2aVEWMQghRI209lUCwchn7onQwswKflpXehurREDKOYHnldKXXLYQQQlQ2x6v6G3Azr4YmjqQSuQTB1TMoKVGmjkSIGu22FnR88MEHeeyxx9i1axfR0dG8//77klgQQtxVLl7JIiIhkx7aw/oDfm3BzKLS27EL0E8388w9L6tUCyGEqNEycgvwK4oGwDGgqYmjqTw2XvpRhE55saTnFJg4GiFqrnInF86dO0eHDh3YsGEDmzdvZvLkyTzyyCNMnjyZggL5zyaEuDtsPZWIN1cYZ/6T/kDjflXSjn1AcwBClRjOJFytkjaEEEKIynA+MZMQJRYAG9/K2z3J1CzdgwGooyRwNlF+FwtRmnInF1q0aEFQUBBHjhyhZ8+evPPOO/z555/8+OOPtGvXripiFEKIGmfriXjeNf8KGzUb/NpB6+FV05BHIwD8NUlExsiOPEIIIWquuIuR2Cp5FGIGLnVNHU7lcQsFIFQTw5l42Y5SiNKUO7mwcOFCVq9ejZOTk+FYx44dOXToEK1atarM2IQQokZKy87HO+ZnumsPo2osoO9nlboFpREbF66auwGQeuFI1bQhhBBCVILMS/rFh69Y+YPW3MTRVCIv/RQPX+UKly5FmzgYIWqucicXBg8eXOJxe3t7vvrqqwoHJIQQNV34kVNM1a4AQOk2GdxDq7S9bKf6ABTFn6zSdoQQQogKSdYvPpztWM/EgVQyKweu2gYCoIs9ZNpYhKjByr1bxIoVK0p9TVGUUpMPQghxp/DcNRVnJZMEm/p4dhpf5e2ZezeGpN3Yp0egqiqKolR5m0IIIUR52Wfod4pQPO6gnSL+VeTVHM5dwC71hKlDEaLGKndyYdy4cUbPCwoKyM7OxsLCAhsbG0kuCCHuaAVRu2mVuYNCVUNqz4/wrIZhnw51WsBRqKu7yKXUHPxdbKq8TSGEEKI8Cop0eOVdBA3Y+995u8jZ1mkF59YTXHiW5Mw83OwsTR2SEDVOuadFpKamGj0yMzM5c+YMnTt35ttvv62KGIUQosaI3/sjAL9rOlO/eedqadPMS7+oY33NJU7FZVRLm0IIIUR5xFzJIvjfnSKc76BtKIuZ++vXlmuiXCAiXnaMEKIk5U4ulCQkJIT33nvvhlENQghxp7GM2gpAql93NJpqmp7g3gAdCm5KBhejL1RPm0IIIUQ5XLxwDnslh0K0aNzusDUXALyaAfrdmy7GxJg4GCFqpkpJLgCYmZlx+fLlyqpOCCFqHDX1Ih65URSpCt6tHqq+hi1syLD2B6AgTuZ6CiGEqHnSL+gXOrxi6QdmFiaOpgpYO5Fq6QdAdows6ihEScq95sLPP/9s9FxVVeLi4vjss8/o1KlTpQUmhBA1TeKhX/EEDlGfexoHV2vbuc6hkBONxZXT1dquEEIIURZK/FEArjo1wtPEsVSVLNcmOF++hFXSMVOHIkSNVO7kQr9+/YyeK4qCu7s73bt3Z+7cuZUVlxBC1DjZJ34DIMq5E20syv3js0LMvBrB5S04ZJ2XHSOEEELUOM7p+u2SNb4tTRxJ1THzbgqXN+GQed7UoQhRI5X7r2OdTlcVcQghRM1WkIv3lT0AWDd6sNqbd/BrCAchQBdLSlY+rrJKtRBCiBoiO7+QuoVnQQHXem1NHU6VcfJvCAfApyiWtOx8nGzuwOkfQlRApa25IIQQd7KrZ7ZjRR7xqjOt2lbPLhHXsvAMBSBYuUxUcla1ty+EEEKU5tzFGPyUZAAc67Y2cTRVx8pL/7u4rhLH2cRME0cjRM1TppELEyZMKHOFH3300W0HI4QQNVXCgV+wBw5ZtuVBZ5vqD8AtBAB3JZ2/L1+mTaBL9ccghBBClCA5Qj+yL97MBy8rRxNHU4Vc6gLgrGQSE3tJfhcLcZ0yJRcOHSrbiqgyB1gIcadyuPQnAPlB95smAEt70s3dcSxIIjP2FNDENHEIIYQQ1ymMPQxAin1DvEwbStWysCHd3BPHggTSY04CzUwdkRA1SpmSC5988gmNGzdGq9VWdTxCCFHj5CdG4lEQS76qJahdH5PFkWVfF8eUJIoSI00WgxBCCHE92xT9NslFXs1NHEnVy3YIwvFKAkVJZ00dihA1TpnWXGjZsiUpKSkA1K1blytXrlRpUEIIUZNc2rMOgEOaRjQJ8jVZHKprPQCsMs6ZLAYhhBDiWqqq4pd7BgD7oDt3vYViipv+d7FFuuwYIcT1ypRccHJy4vx5/X+gCxcuyI4RQoi7ii7idwASPe9FozHd9C9r74YAuOZcRKdTTRaHEEIIUSwxKYkAEgDwbtDexNFUPTufBgC45UWTW1Bk4miEqFnKNC3i8ccfp2vXrnh7e6MoCm3atCl1ikRxEkIIIe4Eak4qAVcPAuDcvLdJY3H0bwxAELFcTs/BzxQLSwpRTqqqkpVfhJ1luXe/FkLUApciDuIJJCpueDi4mzqcKmf7b3IhSIknKjmLht4OJo5IiJqjTL/pFy9ezGOPPcbZs2cZO3YsI0eOxN7evqpjE0IIk7v8yyx8KSRC9aNly3YmjUXrUR+AOkoC/ySkSXJB1Ch5hUWsP3yZ6CvZWFto0SgKx2PT+ef8Fa5k5RPiYUeXEHf8nK2JSc0mPj2Xvi186NXE29ShCyEqQL+wIaRY18HDxLFUB+XfKYpBSjy/J2RIckGIa5T5Y4RevXoBcODAAcaNGyfJBSHEHU93JQqPk0sA2BM8jsFW5qYNyN6HPMUKS3JJjomABj6mjUfcFZKv5nAxMY2YDB2XUrNJyy4gM6+QnIIi/J1taOBtT2pWPp9vP0d+egLNNWexJwcHJYtAcmimZGFhVkjCFWcuJbtzVHUmFXtSVHu2nU7kp9E2NPa5g7euE2WSnV/IhiNxfLsvmuwrl+lkdhIfG5WOj4+jkZ+zqcMTN6FLigAg36meiSOpJk4BFCrmWFJAYsxZaOFn6oiEqDHKPUZx6dKlVRGHEELUOHE/voovhexSm/Hgo0NMHQ5oNKRaB+CVHUFu3Gmgm6kjEnegwiIdPxy8xPpDsfjHb2Fs0TL8KOKV/KlcUPWjDNorp5hkvoaVhT34TNcZgKbKeVZbvYMtuWVu6+eiDoz7xoJ1Y7vKtIm72N+RyYxbfQjH7AvMNv+S9prTUATkwabFu/ir2yeM6tbQpGveiNLZZOinRFt61TdxJNVEoyXDxh+XrPPkxJ9BfhcL8R/5TS6EECUoiArHN3YTOlXhQqvX6GRvZeqQAMhzCobsCJQU2Y5SVI7s/EJSswvIyS/kdPxVPt4SQVHyWd4yW05X7VH4935upe0nfB68mCCLFIac/AjLoizaWETQwE7hH5qxKP8jrPNzwakOuASBlSNYOui/as0h4zKkXoTMBMhJhdw0HtGGk58xj6k/OvHRk61QFLl5vJuoqsriv87z0abjPK3ZzGTLtViSj4pCjnMDzFPP0kuzl11/PsvC/IWM6dXC1CGL6+QVFuFREAMKuNVpYupwqk2RU13IOo8mRbajFOJaklwQQojr6XSk/TQRd2C95n769XrA1BEZaD0awOXfsL8qi+fejQqLdPx29BIJVwvwdbbBx8kaHydr3OwsSr0xLyosJColl5NxGSRfzcPOygwHKzPOJWWx/UwiB6PTKPp39xFL8hlt9jMvWP6MBYXoNBYUtR+N+bHV+GdGM0v3EUSfhqIssPWArEReyFzAC7bukJ8CXs1g+EawLMPUyTO/oa4exBPav0g98SFbT35Mz8ZeldldooZbGX6BE5u/4nfztdTRJOoP1r0P5ZH52DgFoJ7fTsE3T9GJE8TtmsahRstpGSBTJGqSc/FphPy7U4RLnUYmjqb6WHjWh9it2GddpEinopVRNUIAklwQQogb5Bz+DveM42Splui6v45tDRqu7eDXCA6DZ8El8gqLsDQreececWdRVZXfjsezatN2Zl2dikZReafgaTbp2gIKFmYafBytaOrnRJdAO1pbXSbr2M+4RW/Gq+ASBao/qbpQTqlBZKg2ZGFNnOpClOqNDg0e2iweswhnML/iq+pvFAi+H81Dc9C4BkOjPrDsIYjUb8uKSzA8uxV2fQK75kFWEjgFwKDvy5ZYAAh9EKXvAlj3PCPNNrL47/bQeHwV9J6oiWLTcriy6T3mW6wGQLX1QOn+BrQaCv8mypS63TAf/D26Zb15QruDN1f9j9AJE7GxqDk/k+92l86fopFSRK5iiZXD3bP2gL1vQzgIddTLxKbmEOAqCywLAZJcEEIIY5lJFG6aCsBqyycY2qmliQMyZu+r3wKrnhJLdHIWIV6ySnVVi0i4ypaTCdRxtaFDXVdc7Syrtf3CIh3PrthPxJlTfGf5Nn6aZAAWWcwjXGnJtoJGBBJPYEY8gacT8Dl9BY2i/leBAg2VaBpqom+sW2uFzrU+FldOQ1G+/qC9D/SaDY36Gm7y8G8LvT+Cn8eAtQsMWgs2LtBjBti6wdmt8NCHYO9Zvotr8RSJkfvwOPEVdS7/Rl7hS5Iwuwuoqso3q5bzirJG/7zTBJSuE8HC9sbCgZ3Ib/8SVnvm83LOZ3zxW2fG9e1czRGL0hTvFJFqFYC3RmPiaKqPxi0EgLqaOCKSrkpyQYh/1bqfAgsWLCAwMBArKyvat2/P3r17b1p+7dq1NGjQACsrK5o2bcrGjRuNXldVlWnTpuHt7Y21tTU9evQgMtJ4LnNKSgqDBg3CwcEBJycnRowYQWZmpuH1CxcuoCjKDY9//vmn8i5cCFH1CnLJWNYf+/xEzuu88O89GTNtzfoxqbjWQ4eCk5JFzOVLpg7njqWqKrvPJTNs6V76f7yRU1uW8fG3G2j9zlYe/GQn6w7FoqrqrSuqBP/76zwnzkSwynIWfkoyOpdg6DwBtBZ0UA/xptk3PG22jc7aE/gpyWgUlQzVhnDLzmxt9C7nntpJ0RPLof3zUK8H+N8DHo3A3AazolwsEo/qEwtezeDBD2DMPmjc77/EQrFWg+HZbTA6HFyD9ccUBTq+BEPWw79/bJeX2z2DAOjEYfZExlWgp0RtseWfg4xIeAetopLe8CmUntNLTiz8y6rnm1x1CsVVuUrdA7NIzsyrxmjFzRQZdooINnEk1ezf7Sh9uEJUXLKJgxGi5qhVIxfWrFnDhAkTWLRoEe3bt2fevHmEhYVx5swZPDxu3Fl39+7dPPXUU8yePZs+ffqwatUq+vXrx8GDB2nSRL/ozAcffMD8+fNZvnw5QUFBTJ06lbCwME6ePImVlX4Bt0GDBhEXF8eWLVsoKChg+PDhjBo1ilWrVhm1t3XrVho3bmx47urqWoW9IYSoVKrKlVUjcU0+RLpqw4qg95jerI6po7qRhQ2p5p64FsSTEXMCWt09c1yry5XMPF7/6Ri/n4jjCe1f/GH5La7KVQD26BrwbUJ3Jq9pzy9HfHn30aZ4OVbdYp/HY9P5esseVlnMIlCJB6cANEN/AUdfaPk0/PUhFGTrb/Zdgg1fHWzd6GCUHGgGTfoZV64rgitnIfGk/g9lr6a3DsivTWVeHgAa35ZkmLniUHiF8/s2c2/DEZXehqg5dDoV823TcVWukmDbAM/H5t36JDNL7Ab8D93i+3hYs4tlv/3CsP5PVHms4tas0/Xr/1h4hpo4kmpm60au1h6roqtkxEYAjW95ihB3A0Wtro9eKkH79u1p27Ytn332GQA6nQ5/f39eeuklXn311RvKDxw4kKysLDZs2GA4ds8999CiRQsWLVqEqqr4+PjwyiuvMHHiRADS09Px9PRk2bJlPPnkk5w6dYpGjRqxb98+2rTR/1G1adMmHnroIS5duoSPjw8XLlwgKCiIQ4cO0aJFi9u6toyMDBwdHUlPT8fBQYY5C1HZVFW96Ur08eun43VoHgWqlrle7zFh5LNYmNWsUQvFLnzSi8DUcH7wmcTjo940dTh3jvxsDu38hU279mGTn0wXzXFaaf4dyebgC1fjQS0CIEF14svCh/jZPIypj7WjTzOfSg8nt6CI4Z+s492M16mriUe190EZvlG/E8MdJnblKHzPreF7zYM8PvVb2TXiDrZ73z7ab+iJVlHJGr4d2zpln3p2edlwfC78yAE1lKBJO3Gp5ilKwlhceg6X5t5LW00EuY8sxqrVQFOHVK1SP+mCc+pR5ji+waSXJ5s6HCFuW2Xeh9bMv5xLkJ+fz4EDB+jRo4fhmEajoUePHoSHh5d4Tnh4uFF5gLCwMEP5qKgo4uPjjco4OjrSvn17Q5nw8HCcnJwMiQWAHj16oNFo2LNnj1HdjzzyCB4eHnTu3Jmff/75pteTl5dHRkaG0UMIUfliUrJ5+ss9NH/rd2ZuOElMSrbR62pRAdGb5uN1aB4AS5xeYvyzI2psYgGgyFk/HNMiTXaMqBRXE0hcP5Wr74XScucoXtN9wTizn/SJBXNb6DkTxh2Bl49Dt9fB3htPJY03zFfxu/oCB9fM4r2Nxw07LlSWL37ZznsZr1JXE0+Rg/8dm1gAcG/dD4AORXs5findtMGIKnX1j3loFZVzjh3KlVgA8H70HXKxpLVyhr/Wf1lFEYqy2hmZTF1FP5XJyusuG7mAfpoigEV6VLVNkxOipqs10yKSk5MpKirC09N4sShPT09Onz5d4jnx8fEllo+Pjze8XnzsZmWun3JhZmaGi4uLoYydnR1z586lU6dOaDQafvjhB/r168e6det45JFHSoxt9uzZvPXWW2W5dCHEbVBVlW/2RDNr4ylaFx5ipOY0Z3f7MHx3EKGNWtIt0Aqro1/TJvEHAkgC4Cebxxk0eipW5jV7QTkr7wZwHpxyLpg6lFovLXwltptfxoMCAC6rrmQ6N6Ju3RDMnP2h2QBw/HcFdAcf6DYFOo+Ho9+h/j0Px5SzTDNfyf5/9vBq9BTeGPIITjYWFY4rKSOX7kcmUEeTSLatPzbPbNTvxnCHsgi5j3zFEl+usG3/Tpr6P2zqkEQVOHomkq7Zm0EB5wcmlft8xdGX2EYjCT75GS0iPiUhbSieTnZVEKkoi4gTBxmgXEWHBs2/N9p3E1vfBnAWfIsuEZMiO0YIAbUouVCTubm5MWHCBMPztm3bcvnyZebMmVNqcuG1114zOicjIwN/f/8qj1WIu8HltBwmf3+U/WdjedPsa5622Gb0emakFdpIHdaKfnX8VNWev1weo/uzc7CrQdtOlsalThPYBf5Fl0jPKcDR2tzUIdVKutRoLH+fiDkFHNTV45DfYLo+Moz6Xk43P9HMEloNRmnxf3BwOQWb3qRNYQRN455n0eJIXnxpSoUXAt2xeS1PKBfIwQqbUZv1ayzcycytSfboiE/Cn3DmN0CSC3eiS799TDOlgGjrhgQ06n5bddTt+yoZp1YQSBzf/bCQASNkOLop6HQqARfWApDu1w1ny7svyWPurl/ENkiJY+fZJAa51sB1moSoZjV33O913Nzc0Gq1JCQkGB1PSEjAy8urxHO8vLxuWr74663KJCYmGr1eWFhISkpKqe2Cfn2Is2fPlvq6paUlDg4ORg8hRMUdvZTGI5/9zZVzB9hg+SZPm/2bWGj4CPi3R2dmjZ2Si7WST6JNPSLveQ/rKafpO24+jra1Y/6utXdDAPyVRCJjZZXq26KqxK1+CWs1l4NqKNbPb2PEqPHUu1Vi4VoaLbR5BvMxe7jq1xVLpYDnU+eyet36CoWWW1CE14mvAIgPfuLOTyz8y7GFPhnfPDuc+PRcE0cjKtuFywl0Sv0JALN7X75xN5IyUiztyWj1AgBtL35FZFxqpcUoyu7UpST66P4EwL7TSBNHYyL/jtYIUuLYGSG/i4WAWpRcsLCwoHXr1mzb9t8nkDqdjm3bttGhQ4cSz+nQoYNReYAtW7YYygcFBeHl5WVUJiMjgz179hjKdOjQgbS0NA4cOGAo88cff6DT6Wjfvn2p8R4+fBhvb+/yX6gQ4rb9eSaRpxbvpm/OOn62nEo9JRbsvGDwOhi4Ekb8jub1WBj9Dzy3E49J+wnp9QJWNrXsExc7DzI19mgVlfjzR0wdTa2UdfhHfBO2k69qOd/+XRr6ON1+ZU7+2D+zjjiv+7BUCuh+dAL/HD1129X98ddfdOYQOhT8e0249Ql3CNsmvQForjnP0TMRJo5GVLYTG+bjqGQTb+aHT/uK7fTg98BYMjUOBGni+fP7hTLf3QQuh3+Pi5JJitYds/oPmDoc03Cth6pocFEyiTwXQWGRztQRCWFytSa5ADBhwgS++OILli9fzqlTp3jhhRfIyspi+PDhAAwZMoTXXnvNUH7cuHFs2rSJuXPncvr0aWbMmMH+/fsZM2YMAIqiMH78eN555x1+/vlnjh07xpAhQ/Dx8aFfv34ANGzYkF69ejFy5Ej27t3Lrl27GDNmDE8++SQ+PvrVwZcvX863337L6dOnOX36NLNmzWLJkiW89NJL1dtBQtzF1u6PYebyDSzhLaaaf405hRD6ELywG4Lv+6+gRgseDcG72W1/cmZyisIVW/1wzLwYSS6UW246Rb/q53uvtXqcvmH3V7xOjQbvYStItKyDj5KCxY/DSUgt/0K9qqqi/rMQgGj3bpi530V7x9t7kmClv97UkztMHIyoTElpV2kdq9++O7vti/qfwxVhaUdee/3fcg8kLefXwxcrGqIoJ59zqwGIrvMYaGv+dMIqYWED3s0BaJJ/lCOyGK0QtSu5MHDgQD788EOmTZtGixYtOHz4MJs2bTIsyBgdHU1cXJyhfMeOHVm1ahWLFy+mefPmfP/996xbt44mTZoYykyePJmXXnqJUaNG0bZtWzIzM9m0aRNWVv/tW/7NN9/QoEED7r//fh566CE6d+7M4sWLjWKbOXMmrVu3pn379qxfv541a9YYkh5CiKqjqioL/ojg2E9z2GD+Ku01p1Et7KDPPHhyFdi6mjrEKlHort9T2/LKSRNHUvsk/zIdh8IrnNd5Ue+JtzCv4PoIBlYOOA7/jixsaMUp9nw5vtyfZIUfO8P9+fqhxu4P3D2jForl+OhHBFrF7blFSVGbHNiwGC8lhRSNM0HdK+dvI9duL5Jl7kKgJoHTP3/Mlcy8SqlX3FpOWiIN844C4Nx5hImjMS0lsDMA92hOsTMyycTRCGF6iipjyWqEytxfVIi7haqqzNuwn1Z7J9BVq/9DRw3sgtJ3ATjf2QsrxW1fjPf2SfxDE9pP/xulto7CqG7plyj4uAXmFLDQ/0NGj6j8ucLxe37A67dnKFIVVjdbyqDHHy3zud9/9BJPZKwg1qYhvpPCa+/omtuUtm8NTr+O4qSuDn6vH8DBShYrre2y8/KJm92SYC5xpskrhD4xrdLqLty3DLNfx5Gm2vJeyLe893TXSqtblO7EluU03jWWc0oAdacdvbt//0T8Dqv6c0HnySvey/jhhY6mjkiIcqvM+9BaNXJBCCGK6XQqn6zdzMP7htBVe5RCjRU89CHKkJ/v+MQCgFtwGwDqqxe4nJZj4mhqj5TN72NOAf/oGtL38aerpA2v9o8T49cbraLS+shU/j4dW6bzImISuS9dv+CdVZcxd11iAcCpQTcAGijRHDsrQ93vBLt/W0Uwl8jEhnoPja3Uus1aDybHuQFOShYhpz9ny8mEW58kKizvjH6tsssu7e/uxAJAwD2oioZATQIJMedIzykwdURCmJQkF4QQtU6RTmXRypUMOTGCeprLZFt6YDbyd2g3EjR3x481c69GFKFfSCoqKtLU4dQO6ZdwOPUtAHvrjMLXybrKmvJ/aj5ZWkcaaGI4uuZtEjJuvfvBqY2f4qpcJdnMG9d2T1ZZbDWavSeJFv5oFJXE49tNHY2ooMIiHR5HPwfgYtBAtDZOlduARot1n9kADNVu5vsfVteIm7vUrHx+2v4Pv34yhjU/fEdqVr6pQ6pU3lf+AcAytBLWq6ntrBxQvFsA0JYThJ+7Ytp4hDCxu3QFFiFEbaSqKntPX+Ds5oU8m/oVFkoRqU6NcX7mB3C4y3ZnMbci0SIA7/wLpEUdglbNTR1RjZf1x4fYqvpRC93CHqvaxmzdMO/zAax/jhG673ltRXc+eKE/ZqWs75CUdpV2l78GBbLajsHtbl0gDcjwbIdHTAzamHDgWVOHIyrgnx0b6aw7TT5mBD88qWoaCe5OUZP+mB1fy7uFH/LZTw154//Kv3tBQZGOnw/FkLR1PjZ5iUT6Pkq9Rq14tJXvLafnpGbls/dCCiciIsk8v486aeE8qfkDS6WQ/JRVvH7kGN6dn+bF++phZa4lK68QSzNNqT8ParLk6NN4qwkUqFpC2oaZOpyaIagLXD5IB81JdkYm0atJ6VvVC3Gnu3v/ehFC1BqZuQWE//kLHFxJ5/y/aa/kgwJxvmF4D12mX7H5LnTVKRTvxAuoccdNHUrNl34Jy6NfA7DJdRgz/JyqvEmLFgPJOrQG2+g/GJU4k89/r89LD7YsseyhDYt4QEkhReNCQPe7+4bavn4XiPmBgMzD5BfqsDCrfTdgQp8MNg//BIAIrz40cfGtsra0j8wnO/Y4bqmn6HN6Cr8dqs+DLQPLdG7i1VzW7I1h656DvJb7MY9r/t1G9tLP/HmxOY9uGkWXNi0ZdW9d3O0tWfJ3FJtPxPNUuwD6NPPhy1+2U3ToG3pq9hOm+Xcqz7+bYWRYeOGQH88HfMqcHYk8eLg/rvY2HIxOJdTLgSXD2uDtWHUjqKpC9L6NuAER5g1o7Oxi6nBqhqCusOsTemv/Yc2Zk6hqE5kuIu5aklwQQtQo55Iy2XIygYi4DEg8QZPUbdxf9Dc9NYn6AgokWgaitHsW7/tevGumQZRE490UEjdjn37a1KHUeAU7PsJcLSC8qBEde/SrnkYVBdsnFpK7oDMN8mKI2j2JncHf0KW+p1Gx3Lx8Gpz9EoC4xiNxMbcqqba7hkeT7rANGhPFiYuXaRHsZ+qQxG34559ddCjYi05V8O89pWobs7DBZshqchZ0oXnhec6vG8sZr68J9b75wmTrDsUy68d/GKRu4Gvtb9hrcsjX2pDj2QaHyzu5T3uEZuqrjPtnDF33NMPHyZqilIs8qNlD0rpMdv9ymdEcQGumXxtdh0KmfTDmfq2wbv0kDnXvQ/1tMpp9XzDFfDU9M/cTfdWDLy2OcCI5kBGfTWD2kB4093eq2v6pRJoL+t1sUrw6mTiSGqTufRT5tcf20h6ez1rExeTeBLrbmToqIUxCkgtCCJM7E3+VX4/FseVYDLZJh+mqPcqLmj0Ea/7dWlYD2VgR7fMgft2fwyP4nrtysbvrOQW1giPgl39OPuG9mfxs1CP6PdnX2g5kTkPPW5xQiRy8sXr6WwqXPMSD2n18/u1UAl+ah7/Lf6Ntdq//H92JJx07Qh8aU32x1VCKcx2uaD1wLUok9thftAj+P1OHJMqpoEjH1W1zAIh06Uqof6Oqb9Q5EIuBy9F98ziPKjv4eOlMvMbPwtHGeEqDqqqcjMvgu12nsT38JVvMNuCoZAOg82mNxeNfYOEaDMlnUb8fjmv8Ub62mM1fRU2JSPdjsOVWLDFe1yHF4x5cOgxBUz8MB1s3o9eUh+aAV1PUza/TKv8srTgLQCftCZbmv8KYhWMxr9uJIR0C6dHQo0ZPlchKiSP0qn69BacmMiXCQKNB+8h8Chd2oqf2IH/u+o7Afs+YOiohTEKSC0IIkzkck8bC34/gcv5n7tMc5lnNCRws/9v5oEhjQWbA/Zg3ewybJr1pYGFrwmhrHte6+iH2QcRxNj6JUL9qvGmuTc5sxKIoixidOy3ufQStppoTU/7tUB/6EH4dxwvqGt6ZZ41dlxd4rFUA69cu49m4t0GByLqDaWMtWxEDXHFrg2vCRnQXdgGSXKhtNuwI5+GCv0AB30feqLZ2tSHdye46DZsdMxiT9xUzv6zP6y88g5W5FlVVWX8olu2/reGenL94WbsPJ/MsAFT3Bij3vYGm4cP/Ja7d6qGM2AKbX4MDy7hXe4x7OaZ/LaAjWa6NOZ9lQUCnJ3Gp06z0oBQFWg9FqXc/7P4MtOYQ0IGiLTPwvHKGNRYzWXWxO2+ce4K3HT15pnMQQzoE1shk8dn179GcfE5r6tG4bXdTh1OzeDTguP9TtIhZic3p7wFJLoi7kyQXhBDV7nBMGp//foSA86uZZbYBN/MMw2s6axc0wfdB/V5oQx/E0dLehJHWbIq9NxmKAw5kEB95mFA/+SSpJJn7VmEH/Kx2ZnArf5PEYN52GFdjjmB/dAlvapax6699fLm9DVPNvsZcKeK8U0daDpxmkthqIqt690LCRrzSDlKkU6s/ISRu29XcAgp2foKZouOy6z34BLWr1vZtuo0nI/YgDmd/5qUrbzNtpR9NGzZk7/HT9I1+j3naQ4a/fnPs62DdcypKk8dAo72xMnMr6PMxdBoPexfDlXPQajCEPoStotC0PIE5+sGD7xmeaoPuhd+moDn8NU+bbeNps23E5rry5+YWDAgfzLi+nbgv1KMiXVGp8q9eod5F/QiwpFZjaVCDR1iYil/HgbBmJfVzjhCdnEmAm0yNEHcfSS4IIapNenYBs34+gMOx5bx7TVKhwKEO5m0GQ/D9aLybl/xHnriRopBkG4JD5gGyoo8Akly4QWYSNtHbAbgc0PeWq75XJft+c1F96qPbMoNOnKCT9gQA6cGPUPepr8DMwmSx1TQ+zbrDLmimRnL6UhKNA2rOTZa4uRVb9jJC9wco/H979x0eRdk1cPg329J7TwgJoYUOAaRJExSQKgqC2BHLZ8Pua+++9i4oFtAX7IoKCiJNmvReQguB9N7Ltvn+mM0moUMCG8i5r2vdzeyUszuS7Jw9z3kIvfKJ8x+AouA7fjql0/YSkr+Hmw49xq6DMTyv30SgvgSbYkTtcj2G9lfhEdMHTmdmloAYGPJy/cbp5g1jPoJOE7TqiIztRCm5XG9YzKjSNbz51Tj2Xn4ft/dv3iCaA+777XXaUcFeYrlkyCRXh9MgBbfqSYXiTgAlzFu5jBvGjHB1SEKcd5J2FEKcF4t3ZfDGWy9x3+7reNI4h2ClCItvDIz+COP9G6HfIxCVIImFM1QZ3A4AY85OF0fSMKk7fkSHjS32OLp3O7/foB5Dp0PpeRf6/1uNvUkPAOxdb8Vv0kxJLBzFENqKQp0/boqFg1tXuDoccZrSCsrxWf8+7oqFgsBOGOL6uSYQkxdeN36H2eRHO10y4wz/EKiUUBncDv2d/2AY9S7E9T+9xMK51qwv3LkSHj8Mk37CFt4JX6WMF4yzaLl4Mq//tBJVVV0aot1SSeT+bwA41O4u3IwN4H1riPRGCkO6AZC3czF2u2vPmxCuIL8dhBDnVGGZhS+//4F+B9/iJd1+UKDSKwq3wU9i7DheG38qzpp7k45wCIJL9ro6lAapfOM3eAK/q/14oG0D6UkR1BzdrQugJAOdb6Sro2mYFIXcoG74Zf+N5cAK4GpXRyROw6zfF/GwsggAv+EvuLbxbkAsput/gH8/huBWENUVt+aXNdxEnrsftByMvvlA1PWfYVv4FJexhVbbb+Zzr/9x27BeLgtt+9Lv6KQWkq3603P4TS6L40IQ2G4wZK2kbcVWNh3Op1usTNcpGhepXBBCnDN7D6ey4q1rmXroLhJ0+zHrPLD0fxK3qRuhyyRJLNSD0JbatyTN7YcoLDO7OJoGJmcfntlbsao6iuJG4u3WgPLpOh1IYuGk3FpcCkBYvtZ3QTRsO1IL6bb3XYyKjaLoQSjNB7g6JGjaA8bPgsuehNZDG25ioSadHqXHHRjuWE6xZzRNlBy6r/k/fl+/32Uh2TZ+BcD+yJH4enq4LI4LgbGFVq3TQ7eb9UnZLo5GiBpWfwgfdoeCw+f0MJJcEEKcExuW/Yr35/0YYVsMQE6LazBN3Yxx4KNglA8n9cU7qi1W9PgqZRw6mOjqcBqUquknl9s70S/hPEyFJ+pVeIdBAHRmDztTcl0cjTiZCouN2d98zeX6jdjQ4Tvqv6feSJxcWFt8Jv9KmcGPTrqDuP1+J9sP5533MLbt2kmnig0AtBx613k//gUnvBNmvTe+Shk5+ze6Ohohqv31JOTshV/uPKeHkeSCEKJeqeYytsy4i27LbiRSySFLH07xxN8Ivv5z8I1wdXgXH4Mb6camAOQdkA8yTqqKZbOWXPhD6cugNtIQ8EKjD29Pqc4bL6WS/VtXuzoccRLv/LCEqcVvAFDR6WYIaeXagC4WQc1xv/47LBi5QreeHV9NpeA8V6gdXDQDvaJy0LMTwTHtzuuxL0h6A+XhCdrD9C0u75chxDGSV53T3UtyQQhRb2xl+aS/1YfOqXMAWB80moCH1uHTur+LI7u4Ffq1BsCStt3FkTQgqRsxlaRQqrphazUMT1MDGhIhTo9OR06g9iHdfFCaOjZUP6/ewdg9UwlTCij1a4nXsOdcHdJFRRfbC8vIDwGYaP2VuTNexGKzn5djH0jLplfeLwB49br1vBzzYuDVtDMA0eYDpBaUuzaYcyivVIZiXlCUGpf9xRnn7DCSXBBC1A+7naRPryey8iDZqh9Lun5E93u/wujp5+rILnpu0doFmG/Wehmb7qBu/wmAv+1dGdKpmYujEWfL1LwvAKF5m7CepwsqcXpUVeWrxZuIXnALrXUplJiC8brlF60xoahXnl0nkNXtYQAm5X3EtK9mn5eZCLb8Po0wpYA8fQhhva4758e7WBgiOgAQrzvM5sMFrg2mHiTllPLy/F1sTyl0Lvt+wxESXlzE87/vlOqMC4XRs/rx3oXn7DCSXBBC1Is9PzxNi4KVVKhGdg78nMtGXu/qkBqNmF5aJ/1u9u1s3uO6pl8Nht2OedvPACzW92FgvAyJuFCFtb8MgAR2sy0l38XRiCrpheX893+/02/5BLrr9lKh88Lz5p/BP9rVoV20Qoc/RWbT4RgVG+MOPcM7v57boUIZ+SV0T/sagNJud10YzTAbirD2AMQrR9icfP77ZNSHogoLX605xPO/72Tou/8wY0US1834lx2pWoJh9Y4DTNQv5udVO/j0n4Mujlackt0O5pLqn/f9dc4OJckFIUSdJf87l1a7PgLg7+aPM2DA5S6OqHExhbYgxb0VBsVO6urvXR2O6x1Zi1t5BkWqBwEdh+Fu1Ls6InGWdJGdqFTc8VdKWfvvSleH0+ilFpTzwjdLmPfmbdy3/zZidZkUu0fidsff6CI7uTq8i5uiEDbpE4q9mxGh5NFt0+Mcyi459XZnKnk1fNIP5ZN+NFWyKFL8iB50bhvAXXSCW2LTGfFRyklNvvAaLVtsdiZ++i/P/LqTL1cdotJqx8/DSHGllZu+WEdWcQVtj3zDq8bPudPwO6/+uYf9WcUA2Owqd3y9gdu/2lAvw3d2pRXx185zV8LfaJhr/65ITzl3CSFJLggh6iQ/JZGABf+HTlFZ4j2CYdc/5OqQGqXK1qMACE/9s9EPjajc+gMAf9m7M7Z7cxdHI+pEb6QkvIf2cPevVFptLg6o8bDbVXalFbFgexp/bk/nw7nLWPL2TTy6ZzxT9PPwUiopCrsEn3v+QQmT2VjOCzcffG78BgtG+uu3sWrhd/V/jFXvQfpWwioOAJDbcTKYvOr/OBczvRFboNbUVJ+5k4PZJSRmFPPivF2kFzb8Hgyf/nOQnWlF+HkYubl3LO9P7MKKxwbSMtSb3FIzM1cdwq9Su+Dv7ZUKwE+btPs/d6SzcGcmf+3KZPoy7f+hrUcKGPjmMkZ/uJLZa5NPe0iP3a5y68z13P71RpbsyWTNgVye+20nFRb5O3DGjkouFBYVn7NDSYcrIcRZs1SUUjBzIs0oZZeuFV1v/wS9TnF1WI1S077Xw9Y36WbfwaZdiXRvH+/qkFzDbsO+Yy4Am30GcnUTGf99ofPvdRP8vJzR6mIW70jlys5NXR3SRaGk0sqf21LYuPYfLPmpuMVeQkLb1pTnJOO2fwEBOetpb08knnwqMXI5Vgw67ZvIkpAueF/+BL4tLwdFfuefV6FtSG99A00Tv6Dzvg8prbgRL3dj/ezbUoGa9A8K8JTlFmKiY5gy6t762XcjY4zqADk7aaEmc+MX68gvNVNqtmFXVZ4d6fpZN/73bzJZRRVMHdwKXY3PbTtSC3lv8T4Anh3ZlrEJTZzPjevWhFf+2MNXa5J5RykCoJUuHYBfN6fy8BWtmeZIKAB8sGQ/eWVmvll3mAqL9rtja0ohuSVm7hvU0rlepdXG4z9tp1fzIMZ3qx5atSejmIyiCgBe+WMP+7O0C+S2Eb6M7y5DsM5IZe1kgjvnrhmnJBeEEGdt69eP0s16gFzVF49J/8PP19vVITVaxuBmHPFoQ3T5btLWfA/tn3F1SK5xaCUe5lzyVW/iegxHkQufC56+7UhKfw8gzJLP/pU/QGepjqoLi83Ob0tX47byvwxRNzJOKdOeOACp+4OIUnKrV3b88/FwfBDNC+1JwND/4N2svyQVXKjJiCcoS5xDO+UgyxZ8xYAxk+tnx8mrUCxlpKuB/GoYyt/XDQC9XCqcDSWsA/AtXdxSeT+/ulphkwsaPL62YA8704qYfGkz+rUMZmdaEU/N3QFAxyb+DG4bBkBiRjE3fL4Ws9XOwNYhXNUlqtZ+BrQO5ZU/9lBSaSXQpF2sepSnE+ZuIa0QXvljNzvTivAw6unS1J/VB3L5ctUhAAa2DqFNhC8fLzvAjBUHublPLL6OpNiyxGx+2ZzKuqS8WsmFVftznI+rEgugJR3EGaqsXbngrkhyQQjRwBzevZ5OKd+AAvt6/ZeezVu7OqRGzxI/GjbvJiptAVbbUxj0jW/kW8GG7/BHGxIxumusi6MR9cJgwtrxetj4AV2yfiG14P+I8vdwdVQXpIMZuWz84kFGV/6OSbGBApV6L2zekXgW7iNKycWOQoZfZ2xxlxHefiDGkBZgqwSdgUC/Jqc+iDjndD4hJDa7kS5JnxK39U2KLhuLr29AnfdbuvNPvIBltk48OjyeMF/3ugfbWIVp1Ql9vNOJ9/WhRag3i7YlsyutkAqL7bz1AjqSV+asJvhnbzaD24RRUmlxPv/ZyoMMbhuGxWbn1pnryS+z0KmJH++PbopycCk06w86LdaWod5E+rmTVlhBIEXOfdzQvJI3dxr5fGUSABMvacr/DWzOe3/vQ69TaB3uw7iuTdApCot2ZbIvq4RZqw5xr6N6YWOy1qw3t7QSVVWdXwqsdCQXovw9ak3peTDnHPQaudiZtYSMVdVhUOy4Y671XtenxvfJUwhRZ3abnZKfH8Co2Njk2YceQ2SKqoYg+tKJACTYd7F55x4XR+MC1kpMe+cBkBY1jGBvNxcHJOqL36W3YUehr247i1b86+pwLki7UgvZ88lNjDPPxaTYSAvqhfXmBbg9eQTPBzbAIwfhhrnoHt5L5APLiB79DMbmfcE3AgJiQRILDUqL0Y+Tgz9N1TR2f3wDlRZrnfdZsuNPAA4F9uG6HjF13l+jFq5NR2kqSmbBzbF8ELeORPebuUJdw860wlNsXH9+35YGQJivGya9jr93Z/LvwTyMegWDTuHfg3nsSC3kn73ZpBaUE+xtYk6PQ/jM6AlfXwVfjYZirb+Coij0b63NvhSkVFcPjIwqRq9TMBl0XNO1CQ91heDyZF4c057nRrVjYvdoDLt/QZe1k3suawHAZyuTKKnU/p+tSi5UWOyUmbV+CpVWG+uStJk2PriuC7f0ieVex7b7MiW5cMYclQu5+ALasIiqoSr1TZILQogztu63abS1bKdcNRFx7TtSet5AGINiSfZoh05RSV/b+GaNsGz/GU9rIelqIB0uHeHqcER9CoglK/RSAAxbZmK2npsPRRerzYfzWT7jQa5UV2BFT9Goz4m8dwGG2F7ObyXxCoLmA8Fbpm69EPj4B1E46gssqp4eFStY9MljqOqZN/NNyS9jylcbuOWtbwmzpGBR9YwZO1H6J9WVVzA06weo8NdTKEteAmCIfj2bkgvO2WEP5ZSyK626quC3LVpy4cHLW/HDnb0I9dGS7tf3jGF4xwgAPl62n582pQBwb/NMvObfDeWOqX8PrYBPB0Kh1rBxQOsQjFjxrRpOBcTYjrD0oQGse2IQb14RjNeXg2DGZdX7WPYq/HgrzB7HiPZhxAV7UViuTXVZYbGxPaU62ZJbopXrb0ouoNxiI9jbjS7R/jw7sh2TL20GaLPWVCUmxGly9FzIVbU+VO6KhaLyczM0QpILQogzkp2dScutrwGwvcUdRMTIcIiGxNpmNABNUhfUyzRQFwxVpXjZBwD8YhhK/zYRLg5I1LfAfncAMNz6N9+v2uniaC4cezOL+emLN7iLHwEwD30T34RrXByVqA/NEwZxqOcLAAzJ/pKlq9ec8T7eWJjIol0ZDM7XEtLp/l1oEytVKvViwBPa/a5fnWXp7ZRDbD6Sf04OZ7HZuWb6GsZ8tIrDuWXsyyxmT0YxRr3C0HYRdIr254/7+/LRdQk8cWUbbu8Xh06BP7ZnsGCHVp0wzGuvtrNWw+D//oXgVlCcBt9MAHMp/VuFMKL5UQ1Ec/bSNMgTf08TrP4ALGXa6939O2z9FpZrnxkpTkOf/E919cKKJNYfysNc47NKTmklAH/vzgTg0hZBzi+w/D1NhDiSIzV7MJxNUu1sVVhsF+aMXI7ZInJUX+eiktJz07tCkgtCiDOya/ZjBFHIEX0TEq59ytXhiKPE9NWGqHRW9/D1T7+4OJrzR01ZT2DhTipVI549J2NshP0mLnamNsMo9IolQCmhdOm7FFVYTr1RI5eSX8aMGR/ynDoNAEuv+/DseauLoxL1qeWwezgY0AejYoNFz5JbUnna2x7OLWP+1hT+a5jBJMNiAJpecc+5CrXxiekFcQNqLWqmZLAnOf2cHG7rkQJySiox2+x8v+GIc3rI/q1C8PPUEgLB3m4M7xiBUa+jXaSfc9YGuwrx4T6ElDiGVDa/DELbwKQfwTMYMrbBkpdwN+p5Z+RRMzVkO7YpyYaNM6uXb/gC/nhEe+wdrt1v+55RnSKJCfIkr9TMk7/sqLWr3BIzFpuduZu12Ed1jqz1fMtQrXH4vsxi7HaV8dPXMPLDlZRWWqmw2MguPv3//8/UrrQiEl5cRNeXFvHID1spM5959YTNrrqm6sJRuZBD9QxaJaWl5+RQ8ulLCHHa1q9eyqX5cwGwDnkDg0maPTU0hoBoMqKGoFNUhu58mEXrtrs6pPMi6+/3AZhPH666tJOLoxHnhN6A97DnAbhe/Z2vF61zcUANW25JJW998jkvWd7CoNgxt78W4+XPuzoscQ40Gf8mNnRcxjo+nvU1aTWa353MJ8v38rphOhMMy0DRweiPod1V5zbYxmbQM2Bwh7ajsXuHoVNUAor3MvXbzc5qgfqyssbsCt+uP8ys1YcAuKbrcaZtPLIe9v7FPQNb0D1WawY6vls0SrrjM0NER+0+IAZGvK093rtAuy91HMczSLvPPwRrPoI548FaDoFx2vK0zVBZBBGdYNxMbdnu3zHYynlgcCsADudVD68A7ffW0j1Z5JaaCfZ2o79xD6ybAY7qhFZhPgDsyyrhSH4Z6w7lsSO1iCd/2c6gt5bT579LWLEv+8zeuNP06p+7KTPbKCiz8MPGFOZuTjvjfbyxMJHOz//F5sPnpnrlRFRHcqFQ9cLquPwvKz03vSskuSCEOC05+QX4LnoAvaKyI/Byml1ypatDEicQfsMMct1jiFTyCJw/mf3puafe6EJWlE5Q8h8AZLe9CT+PeprzXTQ4+najKQzogJdSie/698korHB1SA1SSaWVDz/9mJfLX8RNsVDRfCimqz4GnXzsuxiZItpSEK819L0z63n+fvtmZvw4n6zi4/z7sFko2/Qt2355i15b/sNY/UrsikG7+Osy6fwG3hhEdYWH98HVX6CL6AxAO90h5m5J4545m8ipUWmyfG8293+7mf5vLOX+bzeTX6qNibfbVQ5kl1BQdvIx8qv3V/+tzykxU26x0a2pH0PahcGBpTBzhHahbi7TmjXOGYchZw8zb7mEz27sxk2dvLUhECjO2S4ArfpC0UHeQShKhzLHcULbgkcAqHZY+ASkbQKdEUa8C1Hdqre/4iVo2lNrDGsugS+HMcb2Fx9M7EKQlwlFgY5NtG/Uc0vN/LhR6/8wNiEK/S+3wx8PQ6ZW4dCiRuVCYo0pKeduSSO1oByzzc5d/9vkbJpprafhoasP5LBiXw5GvcLVCdqwoUW7zjw59OeOdKx2lfnbzk31yolYyrU+HCV4YFG0oSXlZZJcEEK4iM1mZ+eM22mtJlGg+BJ3/buuDkmcjLsffrf8QKniRVclkd1f3EXxRVxCnr1sGgZsrLPHc+XlQ10djjiXFAXfkVpjtGuVRTzx+a+SYDhKudnGnOmv8GThC3gqlZQ1HYj7hFmgl9nHL2ZBI57D7BNNiFLEjboF3LD9Jl5+/RWe+GU7h9PSKSguZfHWA+x8axiev91Bx60vMEK3Ghs6lGs+h7ajXf0SLl7uvtq/P0c1wOTmxbQM9cZqV5m7ORWLzc7L83dx0xfr+HVLGsm5Zfy6JY0h7/7DNdNW0+3lvxn01nJGf7TqhOP9SyutbHJ8Gz6kXRgA1+kX80P2aJS34uHrMVpzxr+fg8Q/nD0g2PkzXm4GBrcNQ5+5TVsWGAduPjXi93POfkHyqtqVC6M/gvbXQJuRMPApuH8LxPWHLtdr67QerjW2VBTo5Rhyk74V5k1lZAs3lj0ygCUPDaBXc60KIiW/jCV7sgAY38YdShwX8Dn7gOrKhcSMYvZmaq+hqqd4dKAHlzQLpKTSyg2fr2P68gMkvLiIB7/bAsCB7BIOZp/+BXVKfhlrD2qJlA8W7we0aTYfjNrFdOM7bD1whNIzGOJQWGYhOVer1Fh/KO+0t6sPVkdyobxGcqGi7NwMi5C/NEKIU1r6zRsMLluITVUoGfkpTQKl2VNDZwhrTelVn2H/+TpGWhYy97/XcCj2Wtp2G0j3Ztofcatdxa6q2r1du7c5bla7HbsdrDYbqrUCe2UJlvRd6NM34Z2zFb2tgqLwHuhbXkFU666E+Lijc0V38coS3LfOAmBb1AQuCfQ8/zGI80qJG0BpdH+8jiznrcIHee/9nVw7+SHaRPqdeuOLXHJmLls+v4/bzfNAgbwWVxM48RPQSzXPRc87FNN9G1APLCF/6fsEZq7hPf17pG/5moiteVSqRjrgRahSQJnqxkZjF8K99IRd9n/4tpPZdc6LcC25EGPex819Ynnylx18t/4Im48UOL/Jvq5HU3o3D+Ltv/ZyMKeUrBo9BJJzy9iWUkCXpgHOZeVmG1+uTiKn2IzVrtI00JOnhrdlR0ohD/EXSqW1+gLd6KVVDvxVo1/Wjp9h4JPaFXrGUUMiaortqyUFDq0ArxBtmVcwxA/XbkfrejMENoPoHtXLLpkCrYfBjEFaTHkH8Ynujo+7kWAv7YJ3bVIeVrtKsLeJFhyp3jb/EABtI33R6xTSCitYmqgNf7j3spY08fdgQOsQ3Ix6bvh8LdtSCvnvn1oviLlbUrm+VwwTPv0Xs9VOl6b+vHttZ2KCvE54qlRV5ZYv17Mvq4SPrkvg3yQtyXBH/+ZEfnsXUfptzDP3ZMW+3gxtf3oNpHfUmIJ0R1oRpZVWvNzOz6W4rVxLxFiNXlh1bmCHygpJLgghXGDzv0vou+91UGBP2/tp13WYq0MSp8mv45WkHXmcyPWvMoZlcGgZ+w5GMd8eT6BSTKhSQCBFFONJrupLruqLXrETqeQSQS4hSiGeykmaIxX/C/veI3u+H9vVYMr1PlSa/CkP7kBor0l0aht/bqczU1UKv52Cn62AFDWYbkNvOHfHEg2K19gPMM++joCcHTxne4+V05fwbuQk4vuMIjLAC7PVjgp4mvR4uxkI9DLh437xXmCrqsqyNesI/+sORpMEQGqHu4ka+3L113ri4md0R4m/ksBWQ1D/egrl34+JULRvSN0UC6EUUGrwJ3PEV/Tt3N/FwTZCVRftWbuZsGEiPqYA7sv6P/ZllWDUK3wwsYvzQnVg61CW7MnCoFMI93Nn2rID/LUrk2WJ2ezLKuHrNclMHdyS7zccYeHOTOch+rQIJjrQk1U3BcGnR7R+D9f/pDVU3PoNrHgTimuU5Ocd0Jo1RnTS7sGZBKklpg+s+RAOrYJYbVpgPINP/FoV5ZhmlgD4NYHgllpyIT8JorsDEORtAuBgtnbBGxfsDVk1ekY5kgvebgbaR/mx9UgBG5O1So3O0X5cFh/mXPXryT246Yt1bEspwM/DSH6Zhfu+2eycwnjz4QIe/XEb1/eM4dnfdhIf7sONvWIZ2j7cuY+9mSXsc8xI8Z+ft6Gq0Cnanyh/Dyg4DECIUsBfuzJPO7mwPbU6uWCzq2w6nE/fliGnte3p+n1rGgadwrAOtWNSK7XKBZvRG5viBlaoLCs73i7qTJILQogTysxII2zB7bgpVnb5Xkq7cc+6OiRxhiKHP469TW/yV36Gb9KftNSl0lKXelb7ytaFkOLZhvyADth1JsKyVtOifAshSiEhSiGoQCWQugzbDx+yTteR1CYjCO48jK7t4uv94q5g0ev4J/2BWdXzVcTTPBFzkg864uISEIPprmVULHsH/YrXuVS3nUszHufQD+/wr70Nh9UwsvFDh4oRK0aseOpVPN305CkB5CoBZLvFoPOLJNjbjRAfN4K9Tfh7mvB2M+DrbqRpoCdRAR7nNkFWD1ILypn53Q/cmfYEQUoxhYovttHTiep8nG8TReOg06MMfRU6TYDKEjI8muNmziegLBmvqK7EedfvBY04Tf4x2hCDikL02TsZpYNXuJYMgnhhdHuGRtvh0EqI6ISXmw8jO1XPlDC4TRh/7cpkwY4M0grKKa60MnnWBgBMeh3NQ71JyStjqu1L+CZDqyoAaHlFdTKg43gtuQDg7q8lDBLna9ULEZ0g/WTJhV6AArn7qhs5ep3l39yAGK0CIi/JuSjI263WKs1DvSB7d/UCR3IBoGdcIFuPFDh/rhoqUcXPw8hPd/WmuMLCnHWHeX1BIin5WpPTJ66M562/9rI2KY8NyfnY7CqrD+Sy5mAuf03tR1GFhSN55aTkV194F1VoQx+GtguHiiKo0I4dohTy9ZY0Jl/ajHanUTm3PUVLLuh1Cja7yvqkvHpNLmQUVnDft5vRKQqbWgTX6j+lVjqGg5i8sdu1ZuyWSqlcEEKcR2XlZaR/cT2dySZNF0HclK+lGdgFShfXj6C4flBRiLpzLva8Qyg+4eh8w7VvHiqLoTQLSrJApwffJuAbCT7h2rhLowcYPAjRGzjmz6C1EmvqFopyMyjOz6IsLw2PgwuJLd9JL3UrHNkKR15mz6/R7PXujq3ZQGISBtMxNhxDHaaLLNnxJ76rXwVguucd3HOzNCJrdPRG3Ac9Cp2vJn/Zh3js+p5YMonVZZ54m5qtRyohpTCYrfY4KnBDRcGPEpooOfgrJey3R/Kn0pxSt1A8DWA0GEnzbkuOTxuaBHnTNSaAhKYB2tzu55nNrvLPvmy+//cAHvt+42X9Z3goZjK94gmY/BMmGbomQLtgBMKd/23jwmCEs+/Ajp+hPA9KMunnfoCAHt2Z2DUC3usMRSla88Se/wdDXta2s1YypOJP5uly+CdTO6deJj2lZhsAb4zryOjOUVCcCW+Nr33M9ldXPw5prfVOyNiuDU9oNVRLLqz/TEsY5O7Tjh3Z+djYPQIgvL227ZF/tWWegWf3PgQ00+7zayQXvGr/Hm0e4g379lQvqJlcaBbEJ8sPAtr7EOXvccwh9DoFf08TwztE8PqCRECbhvOWPs0oKrfy4dL92OwqfVsGY7baWZuUx+crk/h9axqlZhteJj0AOkWbphPQKhsKq+PoFGDGmq3y0Pdb+fWePrgZ9Cd92dtTC+mh7Ob+4A3cmXUVa5Oq+y6sPpDDn9szeHJ4G9yNJ9/PiaxNykVVwaaq7EkvokdckPM5xawlF1Q3H1RLVXLh9GaVOVOSXBBCHCPp8GGKZ02gs20n5aoJdfzXuPuc5R8R0XC4+6F0vYmz+7N1AgY3DDE9CIyB6v9DnsWctZ/U5TNxO/gXkeWJxOuOEF92BHb+TOUOIxuUeLICuuLVvCetEgYQHXl6ZYWoKjlrv8N94UPoUJmru5zxdz6L70Vc8i5OIag5AVe/AyNfgr0LIWev9kG0NAd0BtAbsWKg3K7DbLFiLM/GVJaOW9EhmpBDE33OcXcboc+jLzu0hERVUqIYitI8OaKGYFhlIxU9P+u6sNlvELaQtkQHetE0yJMOUX7Eh/tiMtRPQtZis5NRWMGR/DI2JuWxZd0yLi37m5f0qwkyaGNpS6MHEHb9bHDzrpdjCiHOgf6Parc/H4O103mteynKsDawb5GWWEDRZl9Y9ykM+I/2u+ynyfhl7+Ezk4GuFdMpxpPnR7cn1McNFejfypH2P7Si9rGMXlrlQk2DnoUlL2pJjtA2WvVC8ipY9LT2fO97T1yR0PrK6r4McPJhEScTWJVcOORcFHx05UKwF6yuUblQmAI2K+gNdIsNcF70twr3QTnJ0K+YIC/aR/myI7WIa7o2wajXceeA5vyxIx2dovD+hC6sTcplbVIe366v7vFQlbi5a0BzPlp6gPhwH5oFe0Fi9Trdgy0ElZnYk1HMgh0ZWoLnBArKzBzOK+MF42/0LtrKEH00vx72pdxsw8Ok56m5OziYXUrnaH+u7np2yeF1NZIVu45KLugtWnJBcfMBtGSMVZILQojzYfmKZcT9PYWOShYleJA6+GNax3d3dVjiAmMKbUGzcS8BL0FpDjnb/6Jwx18EZKwi0JpFT7ZD/nbYMBM2wB5dC/ZGjMKz6wQ6tojB292AhyN7X1ZppaAwn+y9a/FZ+TLNK7UPHFtoRdvJ0wn3c3fdCxUNh8kL2o897lMGwOfohZXFkLIeMneBatPmUXf3Bb+m4O6LPXMXZYfWYynJw6LqoLIY/5xN+FqLaackO3fTjmQonEtJgTu5qi8ZBLLC3oq3aY/O5EkghXgb7HgHRxMa3ZyYuHjaR/kR4GmioNxCbkkluaVmckvM5JZWklNiprC4BM+8XQQW7aKktILcChVfSolRMhmq28e9ulTnJzirZyiG7rfg1e8RadwoxIWiaU9YOx2lqgpg2/fa/SVTYN9f2oX3gSXa7A55BwAwYaW/bisbfQYyqlPkscnLquRCi8uhKFX7fWg6qslxy8u1W5WxM2B6HyjPh4jO2owPJ9JmFCx/rfrnsx4WEavd1xgWEXhU5UJLr3ItJhTt95rNrCVfAmLxcTfSIcqPrSmFtA475jf7MV4Y3Z6fN6VwV//mgNa3YfGD/bGrWoXDwPhQ/D2NFJRpWeToQA+O5JXTOdqfqYNb4edhrB6+UFidXDBV5DCmSxSfr0xiw6H8kyYXqqbNDDOUggot3Ysxl9pZdyiPuGAvZ6+J/Wcwm0WVMrMVs9VeO7mQVlRrHYNV27/B3QfFpn1mspml54IQjUb24URSln2GX+pKDPYKdHYLOtWKXrViUYxke7bEHNoBr5iuNGnbE/+QyFPv9BTKzTbmfjuDkQeexVupIEMfgXHSt7SO61z3FyQaN69ggnteR3DP60BVsWXvI33zn5QcWINf7lYibGnE2/cTn/o2lSkfsEVtjgeV+Cpl+FGKL6VEKSpVf7ZLVTcW+F1L66ueoFWU9FkQZ8nNB5pfpt2OQxd9Cd7dbq690GbVmp6V54HOSHlBOpbtc/FKXoy3vQJvpYIYsuih2wP8BlVTrJuBNO22a00Mz1tHkEYIY3Qr6KnbjTsemFQ/YrHQQ8knRsnEXakxhuOonIFN5wZtRqDvfB2GuAEyzaQQF5rontp95k4ozoA987WfO4zXhiasnQ5LXtISCyYfaDcGNn/NJP+dXDP6vuNXRSU5kgvdboX4K08vDr8ouHa2NjRi8LNgOMkwr7B2WmKgquLgbCsXqoZFlGSAuQxMnpgMOnzdDRRVWDEZdESYHccIbKZVoFVVpDkSE2O6RLE1pZBBbcK038uJ87UGku7H9j5IaKoNYatJURT0ySvh13twG/Y6ozpF8tWaZFqGevPdHb345J8DjOgQiVGv4/Z+zas3LKhOLFOSRULTAD4nyTkN6IkcztMu5AP0ZWCFtn6VUAqr9udwJK/6Iv9AVnVyQVVVZ6za27WPiqyDBHccUmudEe+v5GBOKQp25hhfoQITb6e/SI2VMFq1Yxg9fVAsWuWCapbKBSEuahk5OSQumUPQvh9ob9l27Nj2Kio0KUmDkuVwEFgKOfiTbWpCqXcM9oA4PMNb4RUag29wNP6hTdAba/+xsNlVissq2L9nCxmJ67GmbiWkZA/XKjvRKSrJvglETfkBg49cuIl6pijoQ1vRZEgr4H4ASvPSSF3xP3z3fEd4+X56KHuOu2k5buwIGkrwyOe4OjbuPAYthIPeAFEJzh89AI+ECdoH5OJ0KM2G3P2oh1ZgS1qNHQWbRxBmVQ/F6XiVZ9BWl8z7po9OeagKoz+FgR0xevripbdh8vRFCYyDoBboW15+3A/RQogLhG+E1uCxIBn+ehospdpFd5NuYC7Wkgs5Wq8AOo6DjtfC5q/pZd8ILWpfKFOYAtZKLRGh6CCm95nFEttHu52KomhTUjqTC2c5XNYz0NnYkoJkbXgGWlPHogorzYK80Gfv0NYNaQN2S3VyweHm3rGM7dIEP08jLH8dlr4M3afA8DdPP45dv2l9H7Z9y71DplFutnFjr1gCvUz8Z9gJ+pMU1JgeszSbhKa+AOxOP/nUkkccDSW9Va2CoJm7lkRYsS+nVs+IA47KheTcUkZ/tIph7SN4dWwHADI/m0Bz636WF8+nfx+tSWdWcSUHc7R9BlNEb/0uAJ7ITMNis2PU68BSjs6R6TZ6+qMv16pZVKsW08bkPNKyq6se6kqSC0K4SLnZxuY9+8jZ/DsBKUvpYt5If6UCALuqsN2tM3lxYzAFRGAwumE0uWEwumErL6AseTOm7O2ElSbSVE0jmAKCzQWQtwPygAO1j5WPLyWKN0bVjJtaiQcV+CsWutVcyZEEP9J8IjHXfSDlteK88QqMpNXoR2HUI5CxDXtWImajN+U6HyxGH7z9gvDwDcLD6EF3mVZPNEQmTwhqrt2a9kTpcn2tD1jOj45lebDhc+z/fqJdTLQZha7dGG2MdUmmNm2cTwT4N8U9MA53+f9diItX017axfV2x5CIjuO1C/iYPlq/BIujm3/CTVojRs9gKMuBw2ugWT/tuQNL4X9jQe/4EimiE3j4n7uYO02EzV9rj+vyOTEgFtK3akMjqpILXiaSckq1mSKS/tHWi0rQfjcCbPpaG87W/TYUo4eWWLDbYOMs7fmDy2ofw1IO8x7QEiJdjtP0ucgxc1bWHkJ83HhjXKdTx11jWAR2KxHGCiL93EkrrGBrSgG9mx//S7mUvDJAxcOuJQ9CFW3miN3pRbWqFZJzy7DY7Py8KZWCMgvfrDvMgNYhtI3wJcCSAQr8+OdfuEW0oWdckDMZAeCnVD8OsWdzILuE+HBf7T1Du7Zw9/TBUOL4i2StoKDMzKTP1lJReubDMU5EkgtCnGOqqpJTXMmhI0fISdlLSeZByE6kedG/9GQ/OsXRhlaBDH0EabFjaTLgVjpFtzjJXqvHFRcV5pG+fxuFKXuwZO/HUJCEb/kRAmw5BKkFGBUbARQRoBY5j1OlHDcyPFpgC+1AQFxXAlv1JDriOFMQCXE+KApEdEIX0Ql3QDopiIuOZyD0ewRd34e1hIKuXturCiEuJM36wbZvtcftroLe92mPDW7QfCDsmaclC6pmb2g1FLb8DxY8Af0ehraj4Z83td8lVu3LKWL7ntuYY/vApB/Bq45TKAY005ILNWeM8NYSJC2D3GCjI7nQYrDWcBIgdYN2y0uCEW9ry5KWOxphos12UZoLXo5Ghjt/ga3faM0yO18Haz4CvybaEBOAorTq7RzNIk+p4HDtn0uz6BITQNq2dDYf1pILKfll+HkYa02/fTivDA8q0atao0hjeQ5tInzZnV6E2WYn2NtEmdlGmdlGcm4ZS/ZkObd9eu4ObuoVwxS0SoMINYtpyw7QMy6IJEfVQudof+6IrYT12jaRSg47U4u05IJjpohS3PHxMGFw0yoXFGsFP25MocJid86IUR8kuSCOy25XKSwppSAnjZL8LGyVpdgtFdjNFaiWclRrBVgqUO02VEBFQUXBXnWv6FEMbihGD3QmD/QmT/Qmd/QmDwxGIyaDEYPRgNFgwGQyYTDosas6rKqCDR0Wuw6bqmBVwW6pwGYuR7VUoFrKUM3lqFYzNrsdu92O1a5it9ux2dXqWBQdCgqqoqCiw6DXYzDoMer16A0GjHptmaooWgmZYxsVHTYVbCpaLCpY7aDabWAp07KglgoUSzmKpQydpQS9pQS9tRSDpQSdpRS1shgqi9FZSjBYSnG3lRBODt0dVQlOjov8VPeWlMReTnjXUYS36En4GX5T5esXiG/XAdB1wDHP2Ww2cnIyKM5JobI4D5O7Fx5ePtU3n2CayYdbIYQ4vxQFFPndK0Sj1mmi9rsgtA1Edqn9XO97ITtRm92hSvdbYfsPkLkdfrgJ4kdA8kqtJ0GfqdpMDj3uOPdx12wIebaqmjqmbAC7HXQ6ruoSxaGcMsYGp2hVG14hEN5R63FT04bPtak0W14Om2fXfi5lnfYcQOIf2n1ZjjaT0F9PatVhrYdpCZyqygWbGfIOQkirk8dsKdeGvgF4h2kVFSVZJDRtyvxt6WxMzudwbhmD315Os2AvfryrF+/+vY+OTfw4kl+GLzUaKJZk8fT4NsxcfQiTQcfVXZvw9l972Z5ayJoDOWxPLURRoEmA1lzyo793crdJS0w0UXL4NUP7wjDJ0Qiya0wAw1oUOZMLTZQcZq9NZmxCFIqjcqEED3zcDZjcteSCm2rms5XVyZ36IsmFBmbFF0/g5emBqtOjKjrtw4dOu1cVveNn7abT6dHp9ej0BnR6PXqdAZ3egKLToaoqqmoHVUVVVeyOe9VSid1cilpZhmopRWcp1S6SreWYzIV4WvLwthXirxYSoJQScOqQxanUyBXk6wMp8YjC5tsUz5b9CEkYSZTfibvL1pVeryc4LIrgsHN3DCGEEEIIcYZ0Ou0b9eNp2hPu3VB7WVRXuG+TNk3lqve0ygaA9tfAoKfPbaz1LbqHdr/zZ61sf+K3DG0fwdD2EbDIkVBpPkh7j5oNgHGzILQtbPgC1k6D+Q/CHStg9+/auhGdtEqIw/9qyQNLBexfUn28dZ9o99YKSN+mrV9SXR1A9u5TJxcKHRUSJm8IbuVMLnSN0YZTbEzOZ/m+bMw2O4mZxYz+cBUHc0rxMukpNdtoqdRILpiL6d3Uk94tqgco/7o5le2phcxYoV3wd2riz9TBLbn5y/V42qu3baJkk1lUSUGZ2dlvoVmwl2N2Dcfbq8/l88MFLNyZwVDLTgDS1UB83A3OygV3zKQXHvXFZz244JILH330EW+88QYZGRl06tSJDz74gEsuueSE6//www88/fTTHDp0iJYtW/Laa69x5ZXVHVRVVeXZZ59lxowZFBQU0KdPH6ZNm0bLli2d6+Tl5XHvvffy+++/o9PpuPrqq3nvvffw9q6eS3rbtm3cfffdrF+/npCQEO69914effTRM359fTO/wtetAYyxdIRgRUcRPlToPLAqJsyKG1adEavihlXnhqrocHzvr90UFR0qimpHb6/EYK/EaK/EqFZitFswUYmi2tGh3fTY0Kl29Mqp63Eq0I5vxoRVMWjJl6pAFQW0WoXqm1q7ngLVjqJW1TZoj3XY0ba0a3E77sHxOlDRY8eGjkrFHbPijlnnhllxw6Jzp1LnSaXei0qdJ2a9JxaDJzp3X0yefrh7+eHh44e3bwAhUXG4BcUSYHSXhI0QQgghhDhzfk3g8hfAK1T7Jh6g9z2ujelstB4Gw16HRc/A/kVwYDG0csyCsH+xdt9isHav01UPZRj0DGyZrQ1PWPw82Cq1C/3uU+C3e+DIWm29pH+qe1aANq1nlZR14BMG1Lj2yNqjDTM5mZx92r1/U61yAaA0i3btfPEy6SkstzBnbfWwiaoL/1KzVnEQZjxqdobSLDDFOn9sHqJdV1bNLDG4TSgDWocyoHUIh/amO9drZsgBiza9ZdWwiLhgL8iuTi70Dq6ANHh9YSJDYpagACvt7enjbkQxaINO3RUzAANbh7B4W41ZMOrogkoufPfddzz44INMnz6dHj168O677zJkyBASExMJDQ09Zv3Vq1czceJEXn31VUaMGMGcOXMYM2YMmzZton379gC8/vrrvP/++8yaNYtmzZrx9NNPM2TIEHbt2oW7u/bmT5o0ifT0dBYtWoTFYuGWW27h9ttvZ86cOQAUFRVxxRVXMHjwYKZPn8727du59dZb8ff35/bbbz+j17gteATe7gYU1ea42UHVLsBx/Fz1HFXL7I75uau2QQXHJbOq1L74tisGrAZPbHoP7EZPVMdNMXqi9wrE6BuKR0A4PkER+AZGYPIOJFB3nOlu6puqOl8DdluNe7tWwmRww11RXDYGWw94Om5CCCGEEEK4TO97tGEDiqI1e7zQKIo2hOPwGq03QnaillyoKNKGfYDWd+JoJk9ofaXWq2LDF9qydldplR4AqZu0mTMSHVN7mrydPQecjqyDyITayw4s1po1FiRr/SBGvKslNWqqGmYR07u6gWZJJka9jq6xgfyzN5vd6dpwBR93A8UVVjxNesocyYU4HxvUyHdQklU9PARoHlr9pXWAp5ExXbSq4+dGtuOD/+2EAu25SLIBlR1pRc5ERFyIN9SYDrO5KR9Pk56k7GKsliUYgRW2jgxzN4BRa+johplLWwQz/YauJDydcux7fZYuqOTC22+/zZQpU7jlllsAmD59OvPnz+eLL77g8ccfP2b99957j6FDh/LII48A8OKLL7Jo0SI+/PBDpk+fjqqqvPvuuzz11FOMHq1lq7766ivCwsKYO3cuEyZMYPfu3SxYsID169fTrZtWuvLBBx9w5ZVX8uabbxIZGcns2bMxm8188cUXmEwm2rVrx5YtW3j77bfPOLnQccp0fH196/I2XZgUxZEA0cksBUIIIYQQQpxMp2tdHUHdBTkqxXMdVQFVTRbd/cDrBNOht7uquhFm1c9BLbRqjtIsmHuXNs0kQM+74J83am9/ZB20Gak9VvTaF5pH1taueki4UZsWtIrNCnscCYu2o7VeEQAlWg+GnnFacgG0y5lf/q8PezKKKKmw8vjPWrKkqaflqORCZq2w+rYMpm/LYOKCvbhvUEuCvN0AiA324q1RcfCVtp6bWkkQRSzalYHNruJp0hPm61ZrWIQ+dx8LPZ7CS0nHWFFCserBZrWF1mTSUbmQEOHOsFsvQa9TaB/lz67jv9tn7Dx8JV0/zGYzGzduZPDgwc5lOp2OwYMHs2bNmuNus2bNmlrrAwwZMsS5flJSEhkZGbXW8fPzo0ePHs511qxZg7+/vzOxADB48GB0Oh1r1651rtOvXz9MJlOt4yQmJpKfX32ia6qsrKSoqKjWTQghhBBCCCEahWBHn4Oc/dp9SYZ27xNx4m2aX6YlHwBC4rWGmIoCQ17Rlu34CewWLQnQ+97q7YJaaMmE4jRIcXQ+jK4xtN4jUNsfQO5Rc7onr9QaS3oGQdPe1cMiHAmCHs2CnKu2DvOhRag3IzpGMqB1dWV9pLu59j5r9nwAfNyNfD25B8+Pbu9MLDg5mjJWaaJk8+9BrdFls2AvFEWplVzAUkp05T4CHdNT/mtvixUDPjUqFyK9QK/TKtzjI3yoLxdMciEnJwebzUZYWFit5WFhYWRkZBx3m4yMjJOuX3V/qnWOHnJhMBgIDAystc7x9lHzGEd79dVX8fPzc96io6OP/8KFEEIIIYQQ4mIT7Jh2PWevdl9clVwIP/E2BhO0c0zJ3mFc9fKO46DHndrjgFgY9YGWhAhopi1r1h/CtWHx7PhZu4++BPxjtG/zr/uuutFk3sHax9z1q3YfP0KbsrIqueCYcaJjEz88jNoMQAkxju5qqkr4pne4MVCrCQgzHdU8sWZyoTBVO4Z6gh50lbW/hG6i5DgfV/VqqJVcOMoSe2f0OgVPk95ZueCcvhTwMtbfYIYLJrlwsfnPf/5DYWGh83bkyBFXhySEEEIIIYQQ50fVsIiyHCjLg2JH40LvkyQXAK54SZtBos/Uo5a/DBPmwORF1dUNVY0hWw+DmD7a41LHhb1fNExZCvdt1hINgXHa8rwalQsVRbD9J+1xVdPHiE7aVPbZeyD/EEa9jj4ttGEcfZo7hnOkboLl/+UJPmdA6xDaBhyVOKg5LGLuXfD9jVrvh+M5qnIhWtHiN+l13Nm/ubawvOCYzea0eJup5v/je9sAbHZVq3BwVC5gqW4waTLU32QCF0xyITg4GL1eT2Zm7fEpmZmZhIcf/3/A8PDwk65fdX+qdbKyapetWK1W8vLyaq1zvH3UPMbR3Nzc8PX1rXUTQgghhBBCiEbBzRt8IrXHuftPr3Khart2Y7Qqgpr0BogfDt41qs6veBH+by20vPzYqT99I8ErSLsHCHJcqOceALtd67WwaRZUFmpDOOIcTSa9QyD2Uu3xzrkAvDq2AzNu7MaVHRyxFxwCwL0sg5k3dsHD7mgsWfV6S7UeDVjKIXm19jhrz/Ff71GVC1dEmenTIojFD/WnbaTjGvI4lQtjx9/EtsAh2NAT6uMYauGsXKh0rmcy1F9K4IJJLphMJrp27crixdUZHbvdzuLFi+nVq9dxt+nVq1et9QEWLVrkXL9Zs2aEh4fXWqeoqIi1a9c61+nVqxcFBQVs3LjRuc6SJUuw2+306NHDuc4///yDxWKpdZzWrVsTECATDwohhBBCCCHEMZxDI/ZVVy6crOfCmTJ6QKijl0J4h+qhD1CdVKgS6Egu5B2En26Fl8Nh2Wvast731p5Bou0Y7X7XXABCfNy4vG2YY9p7tKEOAKhalUJFofZjsKNao6pyIWWD1iMCoPAEszZUOJILjmqMBJ8CZt/Wk+jAGvPYVSUXYhxJjy7X424y8Nu9l3JLn1ieG9Wu+v0AsFZXLhgbY3IB4MEHH2TGjBnMmjWL3bt3c9ddd1FaWuqcPeLGG2/kP//5j3P9+++/nwULFvDWW2+xZ88ennvuOTZs2MA992jzwSqKwtSpU3nppZf47bff2L59OzfeeCORkZGMGTMGgDZt2jB06FCmTJnCunXrWLVqFffccw8TJkwgMlL7H/K6667DZDIxefJkdu7cyXfffcd7773Hgw8+eH7fICGEEEIIIYS4UFQ1dczdB8WOC+5TVS7URddbqh/7Nqn9XNXUkBUF2hSZdguYi7UeCx2Pmp2jzShtaETa5uoeDeX58G57mHt39cwXoD2uSi6EtNbuqxIJyauq1ys8wTD5qmEREZ20+7yk2s/bbdX7H/U+jJupTacJeLsZeHZkO67s4EjYVFUuWKp7LpiOnnazDi6oqSivvfZasrOzeeaZZ8jIyKBz584sWLDA2Tzx8OHD6Gq8Ob1792bOnDk89dRTPPHEE7Rs2ZK5c+fSvn175zqPPvoopaWl3H777RQUFHDppZeyYMEC3N3dnevMnj2be+65h0GDBqHT6bj66qt5//33nc/7+fnx119/cffdd9O1a1eCg4N55plnzngaSiGEEEIIIYRoNKr6LpyryoWjtR8L6z4Bk/ex012aPME3ytmokaAWEDdA67VgOGoGB+8QrUnkwaXw93Mw/ist0VCUCjt/1ma1qFKUWn3x3+QSWPep9lrL8o5KLpygcqFmciHpHyg4DDYL6I3a8opCwFEx4d+0enjH8TgrF2okF+qxcuGCSi4A3HPPPc7Kg6MtW7bsmGXjxo1j3Lhxx67soCgKL7zwAi+88MIJ1wkMDGTOnDknjatjx46sWLHipOsIIYQQQgghhHCoGiaQtfv0ey7UhcFNa+KonKCJYWBcdXKhw3gY8NiJ93X58zBjhTbTw865YCnTllvKtOEOVWpWLvhHawmAgsNaMuLI+ur1TphccAyLCG4FBg9tSEPBYS2JYK2sHmJh8q5OOJyIs3KhZkPHRjosQgghhBBCCCHERSKyi3afdwBsjiaD5zK5ACdOLED1jBGgzTBxMhGd4FLHMPjlr1dXXgCUZFQ/rplccPeDsA7a402ztESByTGdZFlOrYt+p6rKBTffGjNaHNT2Of1S+LintszjNHr9VVUu2C3acArAzaA/9XanSZILQgghhBBCCCHOP89ACG1b/bNHwLFDEM6nqiEFvk20BpCn0mWSdp+7H4rSj79OYUrt5EK4Y4j+rt+0+9bDqhMMzkaQNVQlF9x9IbCZ9jjvICx4AnL2Vq/n4X/qeA3VQ/+rEhkmvVQuCCGEEEIIIYS40MX0rn58LvstnI42IyGgGfR98OQVDlV8o7TGjrZKyNh+/HVy94Pdqj1294Mwx8wNVX0SWg0FP0dzyeM1dayaLaJm5cL6z2DL/2qvdzqVCzWTC46+C0bDabzO0yTJBSGEEEIIIYQQrlEruXCOh0ScSmAc3L8Fuk8+vfX1RvBxTGmZtvn462Tt0u51BjB6Qlj15AIoemgxqEZywdF3Ye/C6sdVPRfcfKorK6oqFlpfWb0vm/XU8ep0oHdUhkjlghBCCCGEEEKIi0ZMn+rHrq5cOBv+0dp9Vc+IKlVVBqpdu3f306ohApqB0UtbFtNbqziomVw4uBzmjIe5d4Gq1ui54FO7JwTAkJerHxckn168Rkf1gqNyQRo6CiGEEEIIIYS48PmEQ2Dz6scXGr/o4y+P7KJVJlSpeo06XXXfhVZDHfuokVxIccwgcWSdVrWgao0Xaw2LAIi5VPt53CytKuKKl04vXoOjqWNV5UJjnopSCCGEEEIIIcRFpO0oWPkONOnu6kjOnP9RyQU3P6gs1Kac1BvB6kgO9Lm/ep0rXtKmr+x2q/ZzVYKi8Ig2gwRolQXOKS0VMHlpwyqqdLhGu283RusVoTvNWR9Mjn2YS7Qf63FYhCQXhBBCCCGEEEK4zsAnofMkCGrh6kjOnH/T6scegRDcEo6s1Zo9OoYeABA/vPpx9CXarUpArHaftVubQaPKoRXavZuvNqRCUeDKNyE7EbpcX73e6SYWQIsr7yAUHIEYqVwQQgghhBBCCHGx0Bu1i/ILUc1hET4R0PP/QG/SkgmlObDqXZj0w8lnn4jsos3kUJql3aokOZIL7r7Vyy6ZUrd4A2K1pEX+IQBM+jNITJyCJBeEEEIIIYQQQoizUbNywSdcG6bQboz288D/aNNaGtxOvg+DG0T3gKTltZenOoZFuPnUV7TVVRJVyQVp6CiEEEIIIYQQQrhYVTNGOP5sF6dKLFRp1rf6sVdI7eckuSCEEEIIIYQQQlzEjB7VyYC6zHYR26/6cesraz/X/LKz3+/RAppp9/mHwG5HrzvJcI0zJMkFIYQQQgghhBDibFUNjahLciEqAYxe2uPwDtD+Gq13w5BXoN+jdY+xSlXlQnEafDEEpvevt11LzwUhhBBCCCGEEOJstb8aClOhWR0u1PVG6HQtbPlGq1TofhtYyrQpKOuTZyCYfMBcDCnroFKtt10rqqrW397EWSsqKsLPz4/CwkJ8fX1PvYEQQgghhBBCiIZBVU8+I8TpsNtBtWmJhnNpWh/I3AFAUaWK33+L6+U6VIZFCCGEEEIIIYQQdVHXxAKATnfuEwtQPTSinklyQQghhBBCCCGEaCysledkt5JcEEIIIYQQQgghGosWg87JbqWhoxBCCCGEEEII0Vh0nwJGTzi4FDb9XG+7leSCEEIIIYQQQgjRWOgN0PUmKE6v193KsAghhBBCCCGEEKKx8Qio191JckEIIYQQQgghhGhsPALrdXeSXBBCCCGEEEIIIRobqVwQQgghhBBCCCFEnXhKckEIIYQQQgghhBB1IZULQgghhBBCCCGEqBPpuSCEEEIIIYQQQog6cfOlPlMCklwQQgghhBBCCCEaG50OPPzrb3f1tichhBBCCCGEEEJcONz9621XklwQQgghhBBCCCEaI6lcEEIIIYQQQgghRJ2419+MEZJcEEIIIYQQQgghGiOpXBBCCCGEEEIIIUSdSHJBCCGEEEIIIYQQddK0V73tSpILQgghhBBCCCFEY9R6aL3tSpILQgghhBBCCCGEqBNJLgghhBBCCCGEEKJOJLkghBBCCCGEEEKIOpHkghBCCCGEEEIIIepEkgtCCCGEEEIIIYSoE0kuCCGEEEIIIYQQok4umORCXl4ekyZNwtfXF39/fyZPnkxJSclJt6moqODuu+8mKCgIb29vrr76ajIzM2utc/jwYYYPH46npyehoaE88sgjWK3WWussW7aMhIQE3NzcaNGiBTNnzqz1/HPPPYeiKLVu8fHx9fK6hRBCCCGEEEKIhu6CSS5MmjSJnTt3smjRIubNm8c///zD7bffftJtHnjgAX7//Xd++OEHli9fTlpaGmPHjnU+b7PZGD58OGazmdWrVzNr1ixmzpzJM88841wnKSmJ4cOHM3DgQLZs2cLUqVO57bbbWLhwYa1jtWvXjvT0dOdt5cqV9fsGCCGEEEIIIYQQDZSiqqrq6iBOZffu3bRt25b169fTrVs3ABYsWMCVV15JSkoKkZGRx2xTWFhISEgIc+bM4ZprrgFgz549tGnThjVr1tCzZ0/+/PNPRowYQVpaGmFhYQBMnz6dxx57jOzsbEwmE4899hjz589nx44dzn1PmDCBgoICFixYAGiVC3PnzmXLli1n/RqLiorw8/OjsLAQX1/fs96PEEIIIYQQQghxOurzOvSCqFxYs2YN/v7+zsQCwODBg9HpdKxdu/a422zcuBGLxcLgwYOdy+Lj42natClr1qxx7rdDhw7OxALAkCFDKCoqYufOnc51au6jap2qfVTZt28fkZGRxMXFMWnSJA4fPnzS11RZWUlRUVGtmxBCCCGEEEIIcSG6IJILGRkZhIaG1lpmMBgIDAwkIyPjhNuYTCb8/f1rLQ8LC3Nuk5GRUSuxUPV81XMnW6eoqIjy8nIAevTowcyZM1mwYAHTpk0jKSmJvn37UlxcfMLX9Oqrr+Ln5+e8RUdHn+JdEEIIIYQQQgghGiaXJhcef/zxYxohHn3bs2ePK0M8LcOGDWPcuHF07NiRIUOG8Mcff1BQUMD3339/wm3+85//UFhY6LwdOXLkPEYshBBCCCGEEELUH4MrD/7QQw9x8803n3SduLg4wsPDycrKqrXcarWSl5dHeHj4cbcLDw/HbDZTUFBQq3ohMzPTuU14eDjr1q2rtV3VbBI11zl6honMzEx8fX3x8PA47rH9/f1p1aoV+/fvP+HrcnNzw83N7YTPCyGEEEIIIYQQFwqXVi6EhIQQHx9/0pvJZKJXr14UFBSwceNG57ZLlizBbrfTo0eP4+67a9euGI1GFi9e7FyWmJjI4cOH6dWrFwC9evVi+/bttRIXixYtwtfXl7Zt2zrXqbmPqnWq9nE8JSUlHDhwgIiIiDN/U4QQQgghhBBCiAvMBdFzoU2bNgwdOpQpU6awbt06Vq1axT333MOECROcM0WkpqYSHx/vrETw8/Nj8uTJPPjggyxdupSNGzdyyy230KtXL3r27AnAFVdcQdu2bbnhhhvYunUrCxcu5KmnnuLuu+92VhXceeedHDx4kEcffZQ9e/bw8ccf8/333/PAAw8443v44YdZvnw5hw4dYvXq1Vx11VXo9XomTpx4nt8pIYQQQgghhBDi/HPpsIgzMXv2bO655x4GDRqETqfj6quv5v3333c+b7FYSExMpKyszLnsnXfeca5bWVnJkCFD+Pjjj53P6/V65s2bx1133UWvXr3w8vLipptu4oUXXnCu06xZM+bPn88DDzzAe++9R5MmTfjss88YMmSIc52UlBQmTpxIbm4uISEhXHrppfz777+EhISc43dFCCGEEEIIIYRwPUVVVdXVQYj6nV9UCCGEEEIIIYQ4lfq8Dr0ghkUIIYQQQgghhBCi4ZLkghBCCCGEEEIIIepEkgtCCCGEEEIIIYSoE0kuCCGEEEIIIYQQok4kuSCEEEIIIYQQQog6uWCmorzYVU3aUVRU5OJIhBBCCCGEEEI0BlXXn/UxiaQkFxqI3NxcAKKjo10ciRBCCCGEEEKIxiQ3Nxc/P7867UOSCw1EYGAgAIcPH67zSRX1q6ioiOjoaI4cOVLnuV9F/ZPz03DJuWm45Nw0bHJ+Gi45Nw2XnJuGTc5Pw1VYWEjTpk2d16N1IcmFBkKn09pf+Pn5yT+4BsrX11fOTQMm56fhknPTcMm5adjk/DRccm4aLjk3DZucn4ar6nq0TvuohziEEEIIIYQQQgjRiElyQQghhBBCCCGEEHUiyYUGws3NjWeffRY3NzdXhyKOIuemYZPz03DJuWm45Nw0bHJ+Gi45Nw2XnJuGTc5Pw1Wf50ZR62POCSGEEEIIIYQQQjRaUrkghBBCCCGEEEKIOpHkghBCCCGEEEIIIepEkgtCCCGEEEIIIYSoE0kuCCGEEEIIIYQQok4kueBizz33HIqi1LrFx8e7OizhkJqayvXXX09QUBAeHh506NCBDRs2uDqsRi82NvaYfzeKonD33Xe7OjQB2Gw2nn76aZo1a4aHhwfNmzfnxRdfRPoHNwzFxcVMnTqVmJgYPDw86N27N+vXr3d1WI3OP//8w8iRI4mMjERRFObOnVvreVVVeeaZZ4iIiMDDw4PBgwezb98+1wTbCJ3q/Pz8889cccUVBAUFoSgKW7ZscUmcjdHJzo3FYuGxxx6jQ4cOeHl5ERkZyY033khaWprrAm5ETvXv5rnnniM+Ph4vLy8CAgIYPHgwa9eudU2wjdCpzk9Nd955J4qi8O67757RMSS50AC0a9eO9PR0523lypWuDkkA+fn59OnTB6PRyJ9//smuXbt46623CAgIcHVojd769etr/ZtZtGgRAOPGjXNxZALgtddeY9q0aXz44Yfs3r2b1157jddff50PPvjA1aEJ4LbbbmPRokV8/fXXbN++nSuuuILBgweTmprq6tAaldLSUjp16sRHH3103Odff/113n//faZPn87atWvx8vJiyJAhVFRUnOdIG6dTnZ/S0lIuvfRSXnvttfMcmTjZuSkrK2PTpk08/fTTbNq0iZ9//pnExERGjRrlgkgbn1P9u2nVqhUffvgh27dvZ+XKlcTGxnLFFVeQnZ19niNtnE51fqr88ssv/Pvvv0RGRp75QVThUs8++6zaqVMnV4chjuOxxx5TL730UleHIU7D/fffrzZv3ly12+2uDkWoqjp8+HD11ltvrbVs7Nix6qRJk1wUkahSVlam6vV6dd68ebWWJyQkqE8++aSLohKA+ssvvzh/ttvtanh4uPrGG284lxUUFKhubm7qN99844IIG7ejz09NSUlJKqBu3rz5vMYkNCc7N1XWrVunAmpycvL5CUqoqnp656awsFAF1L///vv8BCWcTnR+UlJS1KioKHXHjh1qTEyM+s4775zRfqVyoQHYt28fkZGRxMXFMWnSJA4fPuzqkATw22+/0a1bN8aNG0doaChdunRhxowZrg5LHMVsNvO///2PW2+9FUVRXB2OAHr37s3ixYvZu3cvAFu3bmXlypUMGzbMxZEJq9WKzWbD3d291nIPDw+pmmtAkpKSyMjIYPDgwc5lfn5+9OjRgzVr1rgwMiEuPIWFhSiKgr+/v6tDETWYzWY+/fRT/Pz86NSpk6vDEYDdbueGG27gkUceoV27dme1D0kuuFiPHj2YOXMmCxYsYNq0aSQlJdG3b1+Ki4tdHVqjd/DgQaZNm0bLli1ZuHAhd911F/fddx+zZs1ydWiihrlz51JQUMDNN9/s6lCEw+OPP86ECROIj4/HaDTSpUsXpk6dyqRJk1wdWqPn4+NDr169ePHFF0lLS8Nms/G///2PNWvWkJ6e7urwhENGRgYAYWFhtZaHhYU5nxNCnFpFRQWPPfYYEydOxNfX19XhCGDevHl4e3vj7u7OO++8w6JFiwgODnZ1WAJtWKvBYOC+++47630Y6jEecRZqfpPXsWNHevToQUxMDN9//z2TJ092YWTCbrfTrVs3XnnlFQC6dOnCjh07mD59OjfddJOLoxNVPv/8c4YNG3Z248LEOfH9998ze/Zs5syZQ7t27diyZQtTp04lMjJS/u00AF9//TW33norUVFR6PV6EhISmDhxIhs3bnR1aEIIUW8sFgvjx49HVVWmTZvm6nCEw8CBA9myZQs5OTnMmDGD8ePHs3btWkJDQ10dWqO2ceNG3nvvPTZt2lSnSmCpXGhg/P39adWqFfv373d1KI1eREQEbdu2rbWsTZs2MmylAUlOTubvv//mtttuc3UoooZHHnnEWb3QoUMHbrjhBh544AFeffVVV4cmgObNm7N8+XJKSko4cuQI69atw2KxEBcX5+rQhEN4eDgAmZmZtZZnZmY6nxNCnFhVYiE5OZlFixZJ1UID4uXlRYsWLejZsyeff/45BoOBzz//3NVhNXorVqwgKyuLpk2bYjAYMBgMJCcn89BDDxEbG3va+5HkQgNTUlLCgQMHiIiIcHUojV6fPn1ITEystWzv3r3ExMS4KCJxtC+//JLQ0FCGDx/u6lBEDWVlZeh0tf+86PV67Ha7iyISx+Pl5UVERAT5+fksXLiQ0aNHuzok4dCsWTPCw8NZvHixc1lRURFr166lV69eLoxMiIavKrGwb98+/v77b4KCglwdkjgJu91OZWWlq8No9G644Qa2bdvGli1bnLfIyEgeeeQRFi5ceNr7kWERLvbwww8zcuRIYmJiSEtL49lnn0Wv1zNx4kRXh9boPfDAA/Tu3ZtXXnmF8ePHs27dOj799FM+/fRTV4cm0P4Yffnll9x0000YDPKrrCEZOXIkL7/8Mk2bNqVdu3Zs3ryZt99+m1tvvdXVoQlg4cKFqKpK69at2b9/P4888gjx8fHccsstrg6tUSkpKalVpZiUlMSWLVsIDAykadOmTJ06lZdeeomWLVvSrFkznn76aSIjIxkzZozrgm5ETnV+8vLyOHz4MGlpaQDOLyPCw8OluuQcO9m5iYiI4JprrmHTpk3MmzcPm83m7FMSGBiIyWRyVdiNwsnOTVBQEC+//DKjRo0iIiKCnJwcPvroI1JTU2Uq8fPkVL/Xjk7EGY1GwsPDad269ekfpD6mshBn79prr1UjIiJUk8mkRkVFqddee626f/9+V4clHH7//Xe1ffv2qpubmxofH69++umnrg5JOCxcuFAF1MTERFeHIo5SVFSk3n///WrTpk1Vd3d3NS4uTn3yySfVyspKV4cmVFX97rvv1Li4ONVkMqnh4eHq3XffrRYUFLg6rEZn6dKlKnDM7aabblJVVZuO8umnn1bDwsJUNzc3ddCgQfL77jw61fn58ssvj/v8s88+69K4G4OTnZuqqUGPd1u6dKmrQ7/onezclJeXq1dddZUaGRmpmkwmNSIiQh01apS6bt06V4fdaJzq99rRzmYqSkVVVfX0UxFCCCGEEEIIIYQQtUnPBSGEEEIIIYQQQtSJJBeEEEIIIYQQQghRJ5JcEEIIIYQQQgghRJ1IckEIIYQQQgghhBB1IskFIYQQQgghhBBC1IkkF4QQQgghhBBCCFEnklwQQgghhBBCCCFEnUhyQQghhBBCCCGEEHUiyQUhhBBCnFeKojB37lxXhwHAc889R+fOnc9q2xtuuIFXXnmlfgM6jscff5x77733nB9HCCGEqAtJLgghhBCiUajPpMbWrVv5448/uO++++plfyfz8MMPM2vWLA4ePHjOjyWEEEKcLUkuCCGEEEKcoQ8++IBx48bh7e19zo8VHBzMkCFDmDZt2jk/lhBCCHG2JLkghBBCXKTmzZuHv78/NpsNgC1btqAoCo8//rhzndtuu43rr78egNzcXCZOnEhUVBSenp506NCBb775xrnup59+SmRkJHa7vdZxRo8eza233ur8+ddffyUhIQF3d3fi4uJ4/vnnsVqtJ4zzyJEjjB8/Hn9/fwIDAxk9ejSHDh1yPn/zzTczZswY3nzzTSIiIggKCuLuu+/GYrE410lPT2f48OF4eHjQrFkz5syZQ2xsLO+++y4AsbGxAFx11VUoiuL8ucrXX39NbGwsfn5+TJgwgeLi4hPGa7PZ+PHHHxk5cmSt5cerjPD392fmzJkAHDp0CEVR+P777+nbty8eHh50796dvXv3sn79erp164a3tzfDhg0jOzu71n5GjhzJt99+e8KYhBBCCFeT5IIQQghxkerbty/FxcVs3rwZgOXLlxMcHMyyZcuc6yxfvpwBAwYAUFFRQdeuXZk/fz47duzg9ttv54YbbmDdunUAjBs3jtzcXJYuXercPi8vjwULFjBp0iQAVqxYwY033sj999/Prl27+OSTT5g5cyYvv/zycWO0WCwMGTIEHx8fVqxYwapVq/D29mbo0KGYzWbnekuXLuXAgQMsXbqUWbNmMXPmTOdFO8CNN95IWloay5Yt46effuLTTz8lKyvL+fz69esB+PLLL0lPT3f+DHDgwAHmzp3LvHnzmDdvHsuXL+e///3vCd/Xbdu2UVhYSLdu3U729p/Qs88+y1NPPcWmTZswGAxcd911PProo7z33nusWLGC/fv388wzz9Ta5pJLLiElJaVW0kUIIYRoSCS5IIQQQlyk/Pz86Ny5szOZsGzZMh544AE2b95MSUkJqamp7N+/n/79+wMQFRXFww8/TOfOnYmLi+Pee+9l6NChfP/99wAEBAQwbNgw5syZ4zzGjz/+SHBwMAMHDgTg+eef5/HHH+emm24iLi6Oyy+/nBdffJFPPvnkuDF+99132O12PvvsMzp06ECbNm348ssvOXz4cK0kSEBAAB9++CHx8fGMGDGC4cOHs3jxYgD27NnD33//zYwZM+jRowcJCQl89tlnlJeXO7cPCQkBtEqC8PBw588AdrudmTNn0r59e/r27csNN9zg3PfxJCcno9frCQ0NPd1TUcvDDz/MkCFDaNOmDffffz8bN27k6aefpk+fPnTp0oXJkyfXSuAAREZGOo8thBBCNESSXBBCCCEuYv3792fZsmWoqsqKFSsYO3Ysbdq0YeXKlSxfvpzIyEhatmwJaOX+L774Ih06dCAwMBBvb28WLlzI4cOHnfubNGkSP/30E5WVlQDMnj2bCRMmoNNpHym2bt3KCy+8gLe3t/M2ZcoU0tPTKSsrOya+rVu3sn//fnx8fJzrBwYGUlFRwYEDB5zrtWvXDr1e7/w5IiLCWZmQmJiIwWAgISHB+XyLFi0ICAg4rfcoNjYWHx+f4+77eMrLy3Fzc0NRlNPa/9E6duzofBwWFgZAhw4dai07+vgeHh4Ax30PhRBCiIbA4OoAhBBCCHHuDBgwgC+++IKtW7diNBqJj49nwIABLFu2jPz8fGfVAsAbb7zBe++9x7vvvkuHDh3w8vJi6tSptYYnjBw5ElVVmT9/Pt27d2fFihW88847zudLSkp4/vnnGTt27DGxuLu7H7OspKSErl27Mnv27GOeq1ldYDQaaz2nKMoxvR/O1pnuOzg4mLKyMsxmMyaTqdZ2qqrWWrdmX4jjHa8qQXH0sqOPn5eXB9R+T4QQQoiGRJILQgghxEWsqu/CO++840wkDBgwgP/+97/k5+fz0EMPOdddtWoVo0ePdjZ4tNvt7N27l7Zt2zrXcXd3Z+zYscyePZv9+/fTunXrWhUDCQkJJCYm0qJFi9OKLyEhge+++47Q0FB8fX3P6jW2bt0aq9XK5s2b6dq1KwD79+8nPz+/1npGo9HZ3LIuOnfuDMCuXbucj0G78E9PT3f+vG/fvnqrNNixYwdGo5F27drVy/6EEEKI+ibDIoQQQoiLWEBAAB07dmT27NnOxo39+vVj06ZN7N27t1blQsuWLVm0aBGrV69m9+7d3HHHHWRmZh6zz0mTJjF//ny++OILZyPHKs888wxfffUVzz//PDt37mT37t18++23PPXUU8eNb9KkSQQHBzN69GhWrFhBUlISy5Yt47777iMlJeW0XmN8fDyDBw/m9ttvZ926dWzevJnbb78dDw+PWkMXYmNjWbx4MRkZGcckHs5ESEgICQkJrFy5stbyyy67jA8//JDNmzezYcMG7rzzzmOqIs7WihUrnDNMCCGEEA2RJBeEEEKIi1z//v2x2WzO5EJgYCBt27YlPDyc1q1bO9d76qmnSEhIYMiQIQwYMIDw8HDGjBlzzP4uu+wyAgMDSUxM5Lrrrqv13JAhQ5g3bx5//fUX3bt3p2fPnrzzzjvExMQcNzZPT0/++ecfmjZt6uwHMXnyZCoqKs6okuGrr74iLCyMfv36cdVVVzFlyhR8fHxqDcV46623WLRoEdHR0XTp0uW09308t9122zFDOd566y2io6Pp27cv1113HQ8//DCenp51Ok6Vb7/9lilTptTLvoQQQohzQVGPHhwohBBCCHGBS0lJITo6mr///ptBgwbV+/7Ly8tp3bo13333Hb169ar3/df0559/8tBDD7Ft2zYMBhnRKoQQomGSv1BCCCGEuOAtWbKEkpISOnToQHp6Oo8++iixsbH069fvnBzPw8ODr776ipycnHOy/5pKS0v58ssvJbEghBCiQZPKBSGEEEJc8BYuXMhDDz3EwYMH8fHxoXfv3rz77rsnHI4hhBBCiPolyQUhhBBCCCGEEELUiTR0FEIIIYQQQgghRJ1IckEIIYQQQgghhBB1IskFIYQQQgghhBBC1IkkF4QQQgghhBBCCFEnklwQQgghhBBCCCFEnUhyQQghhBBCCCGEEHUiyQUhhBBCCCGEEELUiSQXhBBCCCGEEEIIUSf/D0Upr33z0bMbAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig7, ax7 = plt.subplots(figsize=[12, 4])\n", "ax7.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['FLUX'], label='default location (nods combined)')\n", @@ -765,30 +570,10 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "b4ea99ea", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-16 09:59:45,516 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/671076322.py:13: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-16 09:59:45,516 - stpipe - WARNING - fig8.show()\n", - "2023-08-16 09:59:45,516 - stpipe - WARNING - \n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "rows = [140, 200, 325]\n", "fig8, ax8 = plt.subplots(figsize=[8, 4])\n", @@ -807,7 +592,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "1238d6c9", "metadata": {}, "outputs": [], @@ -826,30 +611,10 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "ac0d2746", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-16 09:59:45,694 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n", - "2023-08-16 09:59:45,759 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args ('data/jw02072001001_06101_00001_mirimage_s2d.fits',).\n", - "2023-08-16 09:59:45,760 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/lrs_slit_extract_example3', 'output_dir': 'data/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': None, 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'center_xy': None, 'apply_apcorr': True, 'ifu_autocen': False, 'ifu_rfcorr': False, '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", - "2023-08-16 09:59:45,817 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/x1d_reffile_example3.json\n", - "2023-08-16 09:59:45,846 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/ofox/crds_cache/references/jwst/miri/jwst_miri_apcorr_0007.fits\n", - "2023-08-16 09:59:45,872 - stpipe.Extract1dStep - WARNING - spectral_order is None; using 1\n", - "2023-08-16 09:59:45,872 - stpipe.Extract1dStep - INFO - Processing spectral order 1\n", - "2023-08-16 09:59:45,878 - stpipe.Extract1dStep - INFO - Using extraction limits: xstart=9, xstop=17, ystart=0, ystop=386\n", - "2023-08-16 09:59:45,878 - stpipe.Extract1dStep - INFO - with background subtraction\n", - "2023-08-16 09:59:46,088 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n", - "2023-08-16 09:59:46,235 - stpipe.Extract1dStep - INFO - Results used CRDS context: jwst_1089.pmap\n", - "2023-08-16 09:59:46,288 - stpipe.Extract1dStep - INFO - Saved model in data/lrs_slit_extract_example3_extract1dstep.fits\n", - "2023-08-16 09:59:46,288 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n" - ] - } - ], + "outputs": [], "source": [ "sp2_ex3 = Extract1dStep.call(l2_s2d_file, output_dir='data/', output_file='lrs_slit_extract_example3',\n", " override_extract1d='x1d_reffile_example3.json')" @@ -865,30 +630,10 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "7210c9ac", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-16 09:59:46,327 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/715447576.py:13: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-16 09:59:46,327 - stpipe - WARNING - fig9.show()\n", - "2023-08-16 09:59:46,328 - stpipe - WARNING - \n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig9, ax9 = plt.subplots(nrows=2, ncols=1, figsize=[12, 4])\n", "# ax9.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['FLUX'], label='default location (nods combined)')\n", @@ -921,10 +666,19 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "9e3c2433", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-17 16:24:00 - INFO - 26:11: E275 missing whitespace after keyword\n", + "2023-08-17 16:24:00 - INFO - 27:1: E303 too many blank lines (3)\n" + ] + } + ], "source": [ "def calc_xap_fit():\n", " # these are values measured from commissioning data. FWHM is in arcsec.\n", @@ -956,34 +710,16 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "e21fcec5", "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Model: Linear1D\n", - "Inputs: ('x',)\n", - "Outputs: ('y',)\n", - "Model set size: 1\n", - "Parameters:\n", - " slope intercept \n", - " ------------------ ------------------\n", - " 0.2579519802996102 2.4456187153704083\n", - "Parameter('slope', value=0.2579519802996102) Parameter('intercept', value=2.4456187153704083)\n" + "2023-08-17 16:24:00 - INFO - 1:1: E305 expected 2 blank lines after class or function definition, found 3\n" ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -1003,7 +739,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "a9c9d403", "metadata": {}, "outputs": [], @@ -1024,29 +760,10 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "71789e83", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-16 09:59:46,650 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n", - "2023-08-16 09:59:46,724 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args (,).\n", - "2023-08-16 09:59:46,725 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/lrs_slit_extract_example4', 'output_dir': 'data/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': None, 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'center_xy': None, 'apply_apcorr': True, 'ifu_autocen': False, 'ifu_rfcorr': False, '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", - "2023-08-16 09:59:46,756 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/ofox/data/jdat_notebooks/notebooks/MIRI_LRS_spectral_extraction/x1d_reffile_example4.json\n", - "2023-08-16 09:59:46,786 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/ofox/crds_cache/references/jwst/miri/jwst_miri_apcorr_0007.fits\n", - "2023-08-16 09:59:46,812 - stpipe.Extract1dStep - WARNING - spectral_order is None; using 1\n", - "2023-08-16 09:59:46,813 - stpipe.Extract1dStep - INFO - Processing spectral order 1\n", - "2023-08-16 09:59:46,819 - stpipe.Extract1dStep - INFO - Using extraction limits: ystart=0, ystop=387, and src_coeff\n", - "2023-08-16 09:59:46,868 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n", - "2023-08-16 09:59:47,011 - stpipe.Extract1dStep - INFO - Results used CRDS context: jwst_1089.pmap\n", - "2023-08-16 09:59:47,061 - stpipe.Extract1dStep - INFO - Saved model in data/lrs_slit_extract_example4_extract1dstep.fits\n", - "2023-08-16 09:59:47,061 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n" - ] - } - ], + "outputs": [], "source": [ "sp3_ex4 = Extract1dStep.call(l3_s2d, output_dir='data/', \n", " output_file='lrs_slit_extract_example4', override_extract1d='x1d_reffile_example4.json')" @@ -1054,30 +771,10 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "9d1bc74c", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-16 09:59:47,089 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/1678292963.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-16 09:59:47,089 - stpipe - WARNING - fig10.show()\n", - "2023-08-16 09:59:47,090 - stpipe - WARNING - \n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig10, ax10 = plt.subplots(figsize=[12, 4])\n", "ax10.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['FLUX'], label='default fixed-width aperture')\n", @@ -1100,30 +797,10 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "78ca0c68", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-16 09:59:47,214 - stpipe - WARNING - /var/folders/yh/kl4y3s1x3_j3ywy3qq67g3vc0001kc/T/ipykernel_55331/1716890966.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - "2023-08-16 09:59:47,214 - stpipe - WARNING - fig11.show()\n", - "2023-08-16 09:59:47,215 - stpipe - WARNING - \n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig11, ax11 = plt.subplots(figsize=[12, 4])\n", "ax11.plot(l3_spec.spec[0].spec_table['WAVELENGTH'], l3_spec.spec[0].spec_table['NPIXELS'], label='default fixed-width aperture')\n", From 47154f58e61a7e662899bd4edc5ce2e8ecf646f6 Mon Sep 17 00:00:00 2001 From: Ori Fox Date: Thu, 17 Aug 2023 16:44:30 -0400 Subject: [PATCH 27/36] more pep8 changes --- .../miri_lrs_advanced_extraction_part1.ipynb | 95 +++++-------------- 1 file changed, 24 insertions(+), 71 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb index 72c2bf818..dd9bd61c5 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -13,8 +13,7 @@ "**Data:** Publicly available science data
\n", "**Tools:** jwst, matplotlib, astropy.
\n", "**Cross-intrument:** NIRSpec, MIRI.
\n", - "\n", - "\n", + "**Documentation:** This notebook is part of a STScI's larger [post-pipeline Data Analysis Tools Ecosystem](https://jwst-docs.stsci.edu/jwst-post-pipeline-data-analysis) and can be [downloaded](https://github.com/spacetelescope/dat_pyinthesky/tree/main/jdat_notebooks/MRS_Mstar_analysis) directly from the [JDAT Notebook Github directory](https://github.com/spacetelescope/jdat_notebooks).
\n", "\n", "### Introduction: Spectral extraction in the JWST calibration pipeline\n", "\n", @@ -61,42 +60,10 @@ }, { "cell_type": "markdown", + "id": "14ff9543", "metadata": {}, "source": [ - "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# disable all imported packages' loggers\n", - "import logging\n", - "logging.root.manager.loggerDict = {}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# enable PEP8 checker for this notebook\n", - "%load_ext pycodestyle_magic\n", - "%flake8_on --ignore E261,E501,W291,W293\n", - "\n", - "# only allow the checker to throw warnings when there's a violation\n", - "logging.getLogger('flake8').setLevel('ERROR')\n", - "logging.getLogger('stpipe').setLevel('ERROR')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" + "## Import Packages" ] }, { @@ -130,13 +97,10 @@ "from astropy.modeling import models, fitting\n", "\n", "import jwst\n", - "from jwst.pipeline import Spec2Pipeline, Spec3Pipeline\n", "from jwst import datamodels\n", "from jwst.extract_1d import Extract1dStep\n", "\n", "from matplotlib.patches import Rectangle\n", - "from matplotlib.collections import PatchCollection\n", - "\n", "\n", "import json\n", "import crds\n", @@ -166,22 +130,16 @@ "metadata": {}, "outputs": [], "source": [ - "# Download Data\n", - "if os.path.exists(\"data.tar.gz\"):\n", - " print(\"Original Data tar.gz Exists\")\n", - "else:\n", - " print(\"Downloading Data\")\n", - " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/data.tar.gz'\n", - " urllib.request.urlretrieve(url, 'data.tar.gz')\n", + "data_tar_url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/data.tar.gz'\n", "\n", - "# Unzip files if they haven't already been unzipped\n", - "if os.path.exists(\"data/\"):\n", - " print(\"Data Directory Already Exists\")\n", - "else:\n", + "# Download and unpack data if needed\n", + "if not os.path.exists(\"data.tar.gz\"):\n", + " print(\"Downloading Data\")\n", + " urllib.request.urlretrieve(data_tar_url, 'data.tar.gz')\n", + "if not os.path.exists(\"data/\"):\n", " print(\"Unpacking Data\")\n", - " tar = tarfile.open('./data.tar.gz', \"r:gz\")\n", - " tar.extractall()\n", - " tar.close()" + " with tarfile.open('./data.tar.gz', \"r:gz\") as tar:\n", + " tar.extractall()\n" ] }, { @@ -261,7 +219,7 @@ }, "outputs": [], "source": [ - "print('Spectral extraction reference file used: {}'.format(l3_spec.meta.ref_file.extract1d.name))" + "print(f'Spectral extraction reference file used: {l3_spec.meta.ref_file.extract1d.name}')" ] }, { @@ -273,22 +231,16 @@ }, "outputs": [], "source": [ - "hdu = fits.open('data/jw02072-o001_t010_miri_p750l_x1d_1089.fits')\n", - "json_ref_default = crds.getreferences(hdu[0].header)['extract1d']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "50c8ba27", - "metadata": {}, - "outputs": [], - "source": [ - "with open(json_ref_default) as json_ref:\n", - " x1dref_default = json.load(json_ref)\n", - " print('Settings for SLIT data: {}'.format(x1dref_default['apertures'][0]))\n", - " print(' ')\n", - " print('Settings for SLITLESS data: {}'.format(x1dref_default['apertures'][1]))" + "file_path = 'data/jw02072-o001_t010_miri_p750l_x1d_1089.fits'\n", + "with fits.open(file_path) as hdul:\n", + " header = hdu[0].header\n", + " json_ref_default = crds.getreferences(header)['extract1d']\n", + " \n", + " with open(json_ref_default) as json_ref:\n", + " x1dref_default = json.load(json_ref)\n", + " print('Settings for SLIT data: {}'.format(x1dref_default['apertures'][0]))\n", + " print(' ')\n", + " print('Settings for SLITLESS data: {}'.format(x1dref_default['apertures'][1])) " ] }, { @@ -705,7 +657,8 @@ " ax.set_xlabel('wavelength')\n", " ax.set_ylabel('px')\n", " ax.legend()\n", - " return(fitted_line)\n" + " return(fitted_line)\n", + "\n" ] }, { From 03754e8967e965f608c85cf442c0bc98aa96294b Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Thu, 17 Aug 2023 20:45:12 +0000 Subject: [PATCH 28/36] [BOT] Left PEP8 feedback on PR 93's notebooks Files: --- .../miri_lrs_advanced_extraction_part1.ipynb | 78 +++++++++++++++---- 1 file changed, 62 insertions(+), 16 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb index dd9bd61c5..7cad8c4fa 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -66,22 +66,52 @@ "## Import Packages" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# disable all imported packages' loggers\n", + "import logging\n", + "logging.root.manager.loggerDict = {}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enable PEP8 checker for this notebook\n", + "%load_ext pycodestyle_magic\n", + "%flake8_on --ignore E261,E501,W291,W293\n", + "\n", + "# only allow the checker to throw warnings when there's a violation\n", + "logging.getLogger('flake8').setLevel('ERROR')\n", + "logging.getLogger('stpipe').setLevel('ERROR')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" + ] + }, { "cell_type": "code", "execution_count": null, "id": "08ddf5f7", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-17 16:24:00 - INFO - 15:1: F401 'jwst.pipeline.Spec2Pipeline' imported but unused\n", - "2023-08-17 16:24:00 - INFO - 15:1: F401 'jwst.pipeline.Spec3Pipeline' imported but unused\n", - "2023-08-17 16:24:00 - INFO - 20:1: F401 'matplotlib.collections.PatchCollection' imported but unused\n" - ] - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "\n", @@ -128,7 +158,15 @@ "execution_count": null, "id": "305103d5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-17 16:45:11 - INFO - 11:1: E303 too many blank lines (3)\n" + ] + } + ], "source": [ "data_tar_url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/data.tar.gz'\n", "\n", @@ -229,7 +267,15 @@ "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-17 16:45:11 - INFO - 3:14: F821 undefined name 'hdu'\n" + ] + } + ], "source": [ "file_path = 'data/jw02072-o001_t010_miri_p750l_x1d_1089.fits'\n", "with fits.open(file_path) as hdul:\n", @@ -626,8 +672,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-17 16:24:00 - INFO - 26:11: E275 missing whitespace after keyword\n", - "2023-08-17 16:24:00 - INFO - 27:1: E303 too many blank lines (3)\n" + "2023-08-17 16:45:11 - INFO - 26:11: E275 missing whitespace after keyword\n", + "2023-08-17 16:45:11 - INFO - 28:1: E303 too many blank lines (4)\n" ] } ], @@ -671,7 +717,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-17 16:24:00 - INFO - 1:1: E305 expected 2 blank lines after class or function definition, found 3\n" + "2023-08-17 16:45:11 - INFO - 1:1: E305 expected 2 blank lines after class or function definition, found 4\n" ] } ], From 6976723ef5bef703bd8844e256d2a333bb927856 Mon Sep 17 00:00:00 2001 From: haticekaratay Date: Thu, 17 Aug 2023 17:47:34 -0400 Subject: [PATCH 29/36] Add dependency descriptions --- .../miri_lrs_advanced_extraction_part1.ipynb | 112 ++++-------------- 1 file changed, 23 insertions(+), 89 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb index 7cad8c4fa..391a2d5a2 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -68,42 +68,18 @@ }, { "cell_type": "markdown", + "id": "e698ce3a-fdaf-4d3b-9109-b980794e94aa", "metadata": {}, "source": [ - "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# disable all imported packages' loggers\n", - "import logging\n", - "logging.root.manager.loggerDict = {}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# enable PEP8 checker for this notebook\n", - "%load_ext pycodestyle_magic\n", - "%flake8_on --ignore E261,E501,W291,W293\n", - "\n", - "# only allow the checker to throw warnings when there's a violation\n", - "logging.getLogger('flake8').setLevel('ERROR')\n", - "logging.getLogger('stpipe').setLevel('ERROR')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" + "- `astropy.io` fits for accessing FITS files\n", + "- `os` for managing system paths\n", + "- `matplotlib` for plotting data\n", + "- `urllib` for downloading data\n", + "- `tarfile` for unpacking data\n", + "- `numpy` for basic array manipulation\n", + "- `jwst` for running JWST pipeline and handling data products\n", + "- `json` for working with json files\n", + "- `crds` for working with JWST reference files" ] }, { @@ -135,7 +111,7 @@ "import json\n", "import crds\n", "\n", - "print('Using JWST calibration pipeline version {0}'.format(jwst.__version__))" + "print(f'Using JWST calibration pipeline version {jwst.__version__}')" ] }, { @@ -146,11 +122,10 @@ "outputs": [], "source": [ "# Set CRDS variables\n", - "\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('CRDS cache location: {}'.format(os.environ['CRDS_PATH']))" + "print(f'CRDS cache location: {os.environ[\"CRDS_PATH\"]}')" ] }, { @@ -158,15 +133,7 @@ "execution_count": null, "id": "305103d5", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-17 16:45:11 - INFO - 11:1: E303 too many blank lines (3)\n" - ] - } - ], + "outputs": [], "source": [ "data_tar_url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MIRI_LRS_notebook/data.tar.gz'\n", "\n", @@ -177,7 +144,7 @@ "if not os.path.exists(\"data/\"):\n", " print(\"Unpacking Data\")\n", " with tarfile.open('./data.tar.gz', \"r:gz\") as tar:\n", - " tar.extractall()\n" + " tar.extractall()" ] }, { @@ -267,26 +234,18 @@ "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-17 16:45:11 - INFO - 3:14: F821 undefined name 'hdu'\n" - ] - } - ], + "outputs": [], "source": [ "file_path = 'data/jw02072-o001_t010_miri_p750l_x1d_1089.fits'\n", "with fits.open(file_path) as hdul:\n", - " header = hdu[0].header\n", + " header = hdul[0].header\n", " json_ref_default = crds.getreferences(header)['extract1d']\n", - " \n", + "\n", " with open(json_ref_default) as json_ref:\n", " x1dref_default = json.load(json_ref)\n", " print('Settings for SLIT data: {}'.format(x1dref_default['apertures'][0]))\n", " print(' ')\n", - " print('Settings for SLITLESS data: {}'.format(x1dref_default['apertures'][1])) " + " print('Settings for SLITLESS data: {}'.format(x1dref_default['apertures'][1]))" ] }, { @@ -667,16 +626,7 @@ "execution_count": null, "id": "9e3c2433", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-17 16:45:11 - INFO - 26:11: E275 missing whitespace after keyword\n", - "2023-08-17 16:45:11 - INFO - 28:1: E303 too many blank lines (4)\n" - ] - } - ], + "outputs": [], "source": [ "def calc_xap_fit():\n", " # these are values measured from commissioning data. FWHM is in arcsec.\n", @@ -703,8 +653,8 @@ " ax.set_xlabel('wavelength')\n", " ax.set_ylabel('px')\n", " ax.legend()\n", - " return(fitted_line)\n", - "\n" + "\n", + " return fitted_line" ] }, { @@ -712,15 +662,7 @@ "execution_count": null, "id": "e21fcec5", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-17 16:45:11 - INFO - 1:1: E305 expected 2 blank lines after class or function definition, found 4\n" - ] - } - ], + "outputs": [], "source": [ "poly_pos = calc_xap_fit()\n", "print(poly_pos.slope, poly_pos.intercept)" @@ -823,14 +765,6 @@ "\n", "**If you have any questions, comments, or requests for further demos of these capabilities, please contact the [JWST Helpdesk](http://jwsthelp.stsci.edu/).**" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c397647e", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -849,7 +783,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.9.13" } }, "nbformat": 4, From e0c9d181a57a17882d3c613e979834673f6c298d Mon Sep 17 00:00:00 2001 From: Ori Fox Date: Fri, 18 Aug 2023 11:17:36 -0400 Subject: [PATCH 30/36] moved crds stuff --- .../miri_lrs_advanced_extraction_part1.ipynb | 31 ++++++++++--------- 1 file changed, 16 insertions(+), 15 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb index 391a2d5a2..7e1acb3ec 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -82,6 +82,21 @@ "- `crds` for working with JWST reference files" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ff26cc4", + "metadata": {}, + "outputs": [], + "source": [ + "# Set CRDS variables first\n", + "\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\"]}')" + ] + }, { "cell_type": "code", "execution_count": null, @@ -114,20 +129,6 @@ "print(f'Using JWST calibration pipeline version {jwst.__version__}')" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "aee92bcf", - "metadata": {}, - "outputs": [], - "source": [ - "# Set CRDS variables\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\"]}')" - ] - }, { "cell_type": "code", "execution_count": null, @@ -783,7 +784,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.11.4" } }, "nbformat": 4, From 577ff801a1b725e37fb82ca91950a967905ed65f Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Fri, 18 Aug 2023 15:18:14 +0000 Subject: [PATCH 31/36] [BOT] Left PEP8 feedback on PR 93's notebooks Files: --- .../miri_lrs_advanced_extraction_part1.ipynb | 77 ++++++++++++++++++- 1 file changed, 75 insertions(+), 2 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb index 7e1acb3ec..7f8b471a0 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -82,12 +82,64 @@ "- `crds` for working with JWST reference files" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# disable all imported packages' loggers\n", + "import logging\n", + "logging.root.manager.loggerDict = {}" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "2ff26cc4", "metadata": {}, "outputs": [], + "source": [ + "# enable PEP8 checker for this notebook\n", + "%load_ext pycodestyle_magic\n", + "%flake8_on --ignore E261,E501,W291,W293\n", + "\n", + "# only allow the checker to throw warnings when there's a violation\n", + "logging.getLogger('flake8').setLevel('ERROR')\n", + "logging.getLogger('stpipe').setLevel('ERROR')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ff26cc4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-18 11:18:14 - INFO - 3:1: F821 undefined name 'os'\n", + "2023-08-18 11:18:14 - INFO - 4:1: F821 undefined name 'os'\n", + "2023-08-18 11:18:14 - INFO - 4:27: F821 undefined name 'os'\n", + "2023-08-18 11:18:14 - INFO - 5:1: F821 undefined name 'os'\n", + "2023-08-18 11:18:14 - INFO - 6:31: F821 undefined name 'os'\n" + ] + } + ], "source": [ "# Set CRDS variables first\n", "\n", @@ -102,7 +154,28 @@ "execution_count": null, "id": "08ddf5f7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-18 11:18:14 - INFO - 3:1: E402 module level import not at top of file\n", + "2023-08-18 11:18:14 - INFO - 4:1: E402 module level import not at top of file\n", + "2023-08-18 11:18:14 - INFO - 5:1: E402 module level import not at top of file\n", + "2023-08-18 11:18:14 - INFO - 7:1: E402 module level import not at top of file\n", + "2023-08-18 11:18:14 - INFO - 8:1: E402 module level import not at top of file\n", + "2023-08-18 11:18:14 - INFO - 10:1: E402 module level import not at top of file\n", + "2023-08-18 11:18:14 - INFO - 11:1: E402 module level import not at top of file\n", + "2023-08-18 11:18:14 - INFO - 12:1: E402 module level import not at top of file\n", + "2023-08-18 11:18:14 - INFO - 14:1: E402 module level import not at top of file\n", + "2023-08-18 11:18:14 - INFO - 15:1: E402 module level import not at top of file\n", + "2023-08-18 11:18:14 - INFO - 16:1: E402 module level import not at top of file\n", + "2023-08-18 11:18:14 - INFO - 18:1: E402 module level import not at top of file\n", + "2023-08-18 11:18:14 - INFO - 20:1: E402 module level import not at top of file\n", + "2023-08-18 11:18:14 - INFO - 21:1: E402 module level import not at top of file\n" + ] + } + ], "source": [ "%matplotlib inline\n", "\n", From 88ca6c1347ff76ba8f5b318c94f7a149eb1c841b Mon Sep 17 00:00:00 2001 From: Ori Fox Date: Fri, 18 Aug 2023 11:22:35 -0400 Subject: [PATCH 32/36] moved import os --- .../miri_lrs_advanced_extraction_part1.ipynb | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb index 7e1acb3ec..bb3739550 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -85,11 +85,14 @@ { "cell_type": "code", "execution_count": null, - "id": "2ff26cc4", + "id": "3a130dd2", "metadata": {}, "outputs": [], "source": [ "# Set CRDS variables first\n", + "import os\n", + "import urllib.request\n", + "import tarfile\n", "\n", "os.environ['CRDS_CONTEXT'] = 'jwst_1089.pmap'\n", "os.environ['CRDS_PATH'] = os.environ['HOME']+'/crds_cache'\n", @@ -106,10 +109,6 @@ "source": [ "%matplotlib inline\n", "\n", - "import os\n", - "import urllib.request\n", - "import tarfile\n", - "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", From 55c5457e1c970283c8472d3b5f52328b8a09dd2d Mon Sep 17 00:00:00 2001 From: Ori Fox Date: Fri, 18 Aug 2023 11:23:55 -0400 Subject: [PATCH 33/36] moved import os --- .../miri_lrs_advanced_extraction_part1.ipynb | 44 +------------------ 1 file changed, 2 insertions(+), 42 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb index 841fcf9cb..caf6691a2 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -82,46 +82,6 @@ "- `crds` for working with JWST reference files" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# disable all imported packages' loggers\n", - "import logging\n", - "logging.root.manager.loggerDict = {}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# enable PEP8 checker for this notebook\n", - "%load_ext pycodestyle_magic\n", - "%flake8_on --ignore E261,E501,W291,W293\n", - "\n", - "# only allow the checker to throw warnings when there's a violation\n", - "logging.getLogger('flake8').setLevel('ERROR')\n", - "logging.getLogger('stpipe').setLevel('ERROR')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" - ] - }, { "cell_type": "code", "execution_count": null, @@ -143,8 +103,6 @@ "source": [ "# Set CRDS variables first\n", "import os\n", - "import urllib.request\n", - "import tarfile\n", "\n", "os.environ['CRDS_CONTEXT'] = 'jwst_1089.pmap'\n", "os.environ['CRDS_PATH'] = os.environ['HOME']+'/crds_cache'\n", @@ -181,6 +139,8 @@ ], "source": [ "%matplotlib inline\n", + "import urllib.request\n", + "import tarfile\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", From bba7e737daf174ab09b6f94f7544a928f0baf91d Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Fri, 18 Aug 2023 15:24:24 +0000 Subject: [PATCH 34/36] [BOT] Left PEP8 feedback on PR 93's notebooks Files: --- .../miri_lrs_advanced_extraction_part1.ipynb | 81 ++++++++++++------- 1 file changed, 54 insertions(+), 27 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb index caf6691a2..bd29faa47 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -82,24 +82,52 @@ "- `crds` for working with JWST reference files" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# disable all imported packages' loggers\n", + "import logging\n", + "logging.root.manager.loggerDict = {}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enable PEP8 checker for this notebook\n", + "%load_ext pycodestyle_magic\n", + "%flake8_on --ignore E261,E501,W291,W293\n", + "\n", + "# only allow the checker to throw warnings when there's a violation\n", + "logging.getLogger('flake8').setLevel('ERROR')\n", + "logging.getLogger('stpipe').setLevel('ERROR')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" + ] + }, { "cell_type": "code", "execution_count": null, "id": "3a130dd2", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-18 11:18:14 - INFO - 3:1: F821 undefined name 'os'\n", - "2023-08-18 11:18:14 - INFO - 4:1: F821 undefined name 'os'\n", - "2023-08-18 11:18:14 - INFO - 4:27: F821 undefined name 'os'\n", - "2023-08-18 11:18:14 - INFO - 5:1: F821 undefined name 'os'\n", - "2023-08-18 11:18:14 - INFO - 6:31: F821 undefined name 'os'\n" - ] - } - ], + "outputs": [], "source": [ "# Set CRDS variables first\n", "import os\n", @@ -120,20 +148,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-08-18 11:18:14 - INFO - 3:1: E402 module level import not at top of file\n", - "2023-08-18 11:18:14 - INFO - 4:1: E402 module level import not at top of file\n", - "2023-08-18 11:18:14 - INFO - 5:1: E402 module level import not at top of file\n", - "2023-08-18 11:18:14 - INFO - 7:1: E402 module level import not at top of file\n", - "2023-08-18 11:18:14 - INFO - 8:1: E402 module level import not at top of file\n", - "2023-08-18 11:18:14 - INFO - 10:1: E402 module level import not at top of file\n", - "2023-08-18 11:18:14 - INFO - 11:1: E402 module level import not at top of file\n", - "2023-08-18 11:18:14 - INFO - 12:1: E402 module level import not at top of file\n", - "2023-08-18 11:18:14 - INFO - 14:1: E402 module level import not at top of file\n", - "2023-08-18 11:18:14 - INFO - 15:1: E402 module level import not at top of file\n", - "2023-08-18 11:18:14 - INFO - 16:1: E402 module level import not at top of file\n", - "2023-08-18 11:18:14 - INFO - 18:1: E402 module level import not at top of file\n", - "2023-08-18 11:18:14 - INFO - 20:1: E402 module level import not at top of file\n", - "2023-08-18 11:18:14 - INFO - 21:1: E402 module level import not at top of file\n" + "2023-08-18 11:24:23 - INFO - 2:1: E402 module level import not at top of file\n", + "2023-08-18 11:24:23 - INFO - 3:1: E402 module level import not at top of file\n", + "2023-08-18 11:24:23 - INFO - 5:1: E402 module level import not at top of file\n", + "2023-08-18 11:24:23 - INFO - 6:1: E402 module level import not at top of file\n", + "2023-08-18 11:24:23 - INFO - 8:1: E402 module level import not at top of file\n", + "2023-08-18 11:24:23 - INFO - 9:1: E402 module level import not at top of file\n", + "2023-08-18 11:24:23 - INFO - 10:1: E402 module level import not at top of file\n", + "2023-08-18 11:24:23 - INFO - 12:1: E402 module level import not at top of file\n", + "2023-08-18 11:24:23 - INFO - 13:1: E402 module level import not at top of file\n", + "2023-08-18 11:24:23 - INFO - 14:1: E402 module level import not at top of file\n", + "2023-08-18 11:24:23 - INFO - 16:1: E402 module level import not at top of file\n", + "2023-08-18 11:24:23 - INFO - 18:1: E402 module level import not at top of file\n", + "2023-08-18 11:24:23 - INFO - 19:1: E402 module level import not at top of file\n" ] } ], From ebc7835f3fd0683f19199f71263672c2d5dc3dc2 Mon Sep 17 00:00:00 2001 From: haticekaratay Date: Fri, 18 Aug 2023 11:43:13 -0400 Subject: [PATCH 35/36] Remove bot messages --- .../miri_lrs_advanced_extraction_part1.ipynb | 64 +------------------ 1 file changed, 2 insertions(+), 62 deletions(-) diff --git a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb index bd29faa47..ad83f47bf 100644 --- a/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb +++ b/notebooks/MIRI_LRS_spectral_extraction/miri_lrs_advanced_extraction_part1.ipynb @@ -82,46 +82,6 @@ "- `crds` for working with JWST reference files" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# disable all imported packages' loggers\n", - "import logging\n", - "logging.root.manager.loggerDict = {}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# enable PEP8 checker for this notebook\n", - "%load_ext pycodestyle_magic\n", - "%flake8_on --ignore E261,E501,W291,W293\n", - "\n", - "# only allow the checker to throw warnings when there's a violation\n", - "logging.getLogger('flake8').setLevel('ERROR')\n", - "logging.getLogger('stpipe').setLevel('ERROR')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" - ] - }, { "cell_type": "code", "execution_count": null, @@ -143,27 +103,7 @@ "execution_count": null, "id": "08ddf5f7", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-18 11:24:23 - INFO - 2:1: E402 module level import not at top of file\n", - "2023-08-18 11:24:23 - INFO - 3:1: E402 module level import not at top of file\n", - "2023-08-18 11:24:23 - INFO - 5:1: E402 module level import not at top of file\n", - "2023-08-18 11:24:23 - INFO - 6:1: E402 module level import not at top of file\n", - "2023-08-18 11:24:23 - INFO - 8:1: E402 module level import not at top of file\n", - "2023-08-18 11:24:23 - INFO - 9:1: E402 module level import not at top of file\n", - "2023-08-18 11:24:23 - INFO - 10:1: E402 module level import not at top of file\n", - "2023-08-18 11:24:23 - INFO - 12:1: E402 module level import not at top of file\n", - "2023-08-18 11:24:23 - INFO - 13:1: E402 module level import not at top of file\n", - "2023-08-18 11:24:23 - INFO - 14:1: E402 module level import not at top of file\n", - "2023-08-18 11:24:23 - INFO - 16:1: E402 module level import not at top of file\n", - "2023-08-18 11:24:23 - INFO - 18:1: E402 module level import not at top of file\n", - "2023-08-18 11:24:23 - INFO - 19:1: E402 module level import not at top of file\n" - ] - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "import urllib.request\n", @@ -843,7 +783,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.9.17" } }, "nbformat": 4, From 6e5b1a2e45925dceeb34248ef39c159c883b7afa Mon Sep 17 00:00:00 2001 From: Ori Fox Date: Thu, 24 Aug 2023 16:53:56 -0400 Subject: [PATCH 36/36] new 87a notebooks --- .../MIRI_MRS_1987A/87a_part1_download.ipynb | 767 ++++++++++++++ .../87a_part2_background_sub.ipynb | 756 ++++++++++++++ .../MIRI_MRS_1987A/87a_part3_extract.ipynb | 983 ++++++++++++++++++ notebooks/MIRI_MRS_1987A/requirements.txt | 4 + 4 files changed, 2510 insertions(+) create mode 100755 notebooks/MIRI_MRS_1987A/87a_part1_download.ipynb create mode 100755 notebooks/MIRI_MRS_1987A/87a_part2_background_sub.ipynb create mode 100755 notebooks/MIRI_MRS_1987A/87a_part3_extract.ipynb create mode 100644 notebooks/MIRI_MRS_1987A/requirements.txt diff --git a/notebooks/MIRI_MRS_1987A/87a_part1_download.ipynb b/notebooks/MIRI_MRS_1987A/87a_part1_download.ipynb new file mode 100755 index 000000000..164c07ebe --- /dev/null +++ b/notebooks/MIRI_MRS_1987A/87a_part1_download.ipynb @@ -0,0 +1,767 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "298b8519-c695-4562-a96c-96208063c1c3", + "metadata": {}, + "source": [ + "# MIRI MRS IFU Spectroscopy Part 1: \n", + "# Downloading Data\n", + "\n", + "Aug 2023\n", + "\n", + "**Use case:** Reduce MRS Data With User Defined Master Background Step. This is particularly relevant if you did not obtain a Dedicated Background with your observations. While the pipeline will subtract a sky background derived from an annulus, the underlying background may be prohibitively complicated and the user may wish to measure their own background from elsewhere in the cube.
\n", + "**Data:** Publicly available science data for SN 1987A (Program 1232). For this notebook, we will follow the science workflow outlined by [Jones et al. 2023](https://ui.adsabs.harvard.edu/abs/2023arXiv230706692J/abstract).
\n", + "**Tools:** jwst, jdaviz, matplotlib, astropy.
\n", + "**Cross-intrument:** NIRSpec, MIRI.
\n", + "**Documentation:** This notebook is part of a STScI's larger [post-pipeline Data Analysis Tools Ecosystem](https://jwst-docs.stsci.edu/jwst-post-pipeline-data-analysis) and can be [downloaded](https://github.com/spacetelescope/dat_pyinthesky/tree/main/jdat_notebooks/MRS_Mstar_analysis) directly from the [JDAT Notebook Github directory](https://github.com/spacetelescope/jdat_notebooks).
\n", + "\n", + "### Introduction: Spectral extraction in the JWST calibration pipeline\n", + "\n", + "The JWST calibration pipeline performs spectrac extraction for all spectroscopic data using basic default assumptions that are tuned to produce accurately calibrated spectra for the majority of science cases. This default method is a simple fixed-width boxcar extraction, where the spectrum is summed over a number of pixels along the cross-dispersion axis, over the valid wavelength range. An aperture correction is applied at each pixel along the spectrum to account for flux lost from the finite-width aperture. \n", + "\n", + "The ``extract_1d`` step uses the following inputs for its algorithm:\n", + "- the spectral extraction reference file: this is a json-formatted file, available as a reference file from the [JWST CRDS system](https://jwst-crds.stsci.edu)\n", + "- the bounding box: the ``assign_wcs`` step attaches a bounding box definition to the data, which defines the region over which a valid calibration is available. We will demonstrate below how to visualize this region. \n", + "\n", + "However the ``extract_1d`` step has the capability to perform more complex spectral extractions, requiring some manual editing of parameters and re-running of the pipeline step. \n", + "\n", + "\n", + "### Aims\n", + "\n", + "This notebook will demonstrate how to re-run the spectral extraction step with different settings to illustrate the capabilities of the JWST calibration pipeline. \n", + "\n", + "\n", + "### Assumptions\n", + "\n", + "We will demonstrate the spectral extraction methods on resampled, calibrated spectral images. The basic demo and two examples run on Level 3 data, in which the nod exposures have been combined into a single spectral image. Two examples will use the Level 2b data - one of the nodded exposures. \n", + "\n", + "\n", + "### Test data\n", + "\n", + "The data used in this notebook is an observation of the Type Ia supernova SN2021aefx, observed by Jha et al in PID 2072 (Obs 1). These data were taken with zero exclusive access period, and published in [Kwok et al 2023](https://ui.adsabs.harvard.edu/abs/2023ApJ...944L...3K/abstract). You can retrieve the data from [this Box folder](https://stsci.box.com/s/i2xi18jziu1iawpkom0z2r94kvf9n9kb), and we recommend you place the files in the ``data/`` folder of this repository, or change the directory settings in the notebook prior to running. \n", + "\n", + "You can of course use your own data instead of the demo data. \n", + "\n", + "\n", + "### JWST pipeline version and CRDS context\n", + "\n", + "This notebook was written using the calibration pipeline version 1.10.2. We set the CRDS context explicitly to 1089 to match the current latest version in MAST. If you use different pipeline versions or CRDS context, please read the relevant release notes ([here for pipeline](https://github.com/spacetelescope/jwst), [here for CRDS](https://jwst-crds.stsci.edu)) for possibly relevant changes.\n", + "\n", + "### Contents\n", + "\n", + "1. [The Level 3 data products](#l3data)\n", + "2. [The spectral extraction reference file](#x1dref)\n", + "3. [Example 1: Changing the aperture width](#ex1)\n", + "4. [Example 2: Changing the aperture location](#ex2)\n", + "5. [Example 3: Extraction with background subtraction](#ex3)\n", + "6. [Example 4: Tapered column extraction](#ex4)" + ] + }, + { + "cell_type": "markdown", + "id": "96ddb4af", + "metadata": {}, + "source": [ + "## Import Packages" + ] + }, + { + "cell_type": "markdown", + "id": "06416a11", + "metadata": {}, + "source": [ + "- `astropy.io` fits for accessing FITS files\n", + "- `os` for managing system paths\n", + "- `matplotlib` for plotting data\n", + "- `urllib` for downloading data\n", + "- `tarfile` for unpacking data\n", + "- `numpy` for basic array manipulation\n", + "- `jwst` for running JWST pipeline and handling data products\n", + "- `json` for working with json files\n", + "- `crds` for working with JWST reference files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36b96e73", + "metadata": {}, + "outputs": [], + "source": [ + "# Set CRDS variables first\n", + "import os\n", + "\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\"]}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21efc012", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import sys,os, pdb\n", + "# Basic system utilities for interacting with files\n", + "import glob\n", + "import time\n", + "import shutil\n", + "import warnings\n", + "import zipfile\n", + "import urllib.request\n", + "\n", + "# Astropy utilities for opening FITS and ASCII files\n", + "from astropy.io import fits\n", + "from astropy.io import ascii\n", + "from astropy.utils.data import download_file\n", + "from regions import Regions\n", + "from astropy import units as u\n", + "\n", + "from astroquery.mast import Observations\n", + "\n", + "# Astropy utilities for making plots\n", + "from astropy.visualization import (LinearStretch, LogStretch, ImageNormalize, ZScaleInterval)\n", + "\n", + "# Numpy for doing calculations\n", + "import numpy as np\n", + "\n", + "# Matplotlib for making plots\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import rc\n", + "\n", + "# Import the base JWST package\n", + "import jwst\n", + "\n", + "# JWST pipelines (encompassing many steps)\n", + "from jwst.pipeline import Detector1Pipeline\n", + "from jwst.pipeline import Spec2Pipeline\n", + "from jwst.pipeline import Spec3Pipeline\n", + "\n", + "# JWST pipeline utilities\n", + "from jwst import datamodels # JWST datamodels\n", + "from jwst.associations import asn_from_list as afl # Tools for creating association files\n", + "from jwst.associations.lib.rules_level2_base import DMSLevel2bBase # Definition of a Lvl2 association file\n", + "from jwst.associations.lib.rules_level3_base import DMS_Level3_Base # Definition of a Lvl3 association file\n", + "\n", + "from stcal import dqflags # Utilities for working with the data quality (DQ) arrays\n", + "\n", + "import shutil\n", + "\n", + "# Import packages for multiprocessing. These won't be used on the online demo, but can be\n", + "# very useful for local data processing unless/until they get integrated natively into\n", + "# the cube building code. These need to be imported before anything else.\n", + "\n", + "import multiprocessing\n", + "#multiprocessing.set_start_method('fork')\n", + "from multiprocessing import Pool\n", + "import os\n", + "\n", + "# Set the maximum number of processes to spawn based on available cores\n", + "usage = 'all' # Either 'none' (single thread), 'quarter', 'half', or 'all' available cores\n", + "\n", + "from specutils import Spectrum1D\n", + "from matplotlib.pyplot import cm\n", + "\n", + "from jdaviz import Cubeviz\n", + "\n", + "#shutil.copytree('/astro/armin/data/mshahbandeh/aefx/input_dir/', '/astro/armin/data/mshahbandeh/aefx/input_dir_sc/')\n", + "#shutil.copytree('/astro/armin/data/mshahbandeh/aefx/input_dir/', '/astro/armin/data/mshahbandeh/aefx/input_dir_bkg/')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37350f4d-4dbc-445d-b743-f26e7898e73e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Set parameters to be changed here.\n", + "# It should not be necessary to edit cells below this in general unless modifying pipeline processing steps.\n", + "\n", + "import sys,os, pdb\n", + "\n", + "# CRDS context (if overriding)\n", + "#%env CRDS_CONTEXT jwst_0771.pmap\n", + "\n", + "# Point to where the uncalibrated FITS files are from the science observation\n", + "input_dir = './mastDownload/1232/uncal/'\n", + "\n", + "# Point to where you want the output science results to go\n", + "output_dir = './output/87A/'\n", + "\n", + "# Point to where the uncalibrated FITS files are from the background observation\n", + "# If no background observation, leave this blank\n", + "input_bgdir = ' '\n", + "\n", + "# Point to where the output background observations should go\n", + "# If no background observation, leave this blank\n", + "output_bgdir = ' '\n", + "\n", + "# Whether or not to run a given pipeline stage\n", + "# Science and background are processed independently through det1+spec2, and jointly in spec3\n", + "\n", + "# Science processing\n", + "dodet1=True\n", + "dospec2=True\n", + "dospec3=True\n", + "\n", + "# Background processing\n", + "dodet1bg=True\n", + "dospec2bg=True\n", + "\n", + "# If there is no background folder, ensure we don't try to process it\n", + "if (input_bgdir == ''):\n", + " dodet1bg=False\n", + " dospec2bg=False" + ] + }, + { + "cell_type": "raw", + "id": "a1c5254b-6e38-4b43-82c5-44fa67a4f4b0", + "metadata": {}, + "source": [ + "## Point to where the uncalibrated FITS files are from the science observation\n", + "input_dir = '/Users/ofox/data/1860/mast/01860/obsnum03/'\n", + "#\n", + "## Point to where you want the output science results to go\n", + "output_dir = '/Users/ofox/data/1860/output/05ip_3/'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c10e734", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "## Output subdirectories to keep science data products organized\n", + "## Note that the pipeline might complain about this as it is intended to work with everything in a single\n", + "## directory, but it nonetheless works fine for the examples given here.\n", + "det1_dir = os.path.join(output_dir, 'stage1/') # Detector1 pipeline outputs will go here\n", + "#spec2_dir = os.path.join(output_dir, 'stage2/') # Spec2 pipeline outputs will go here\n", + "spec2_dir = os.path.join(output_dir, 'stage2/') # Spec2 pipeline outputs will go here\n", + "spec2_bgdir = ' '\n", + "#spec3_dir = os.path.join(output_dir, 'stage3/') # Spec3 pipeline outputs will go here\n", + "spec3_dir = os.path.join(output_dir, 'stage3/') # Spec3 pipeline outputs will go here\n", + "\n", + "# We need to check that the desired output directories exist, and if not create them\n", + "if not os.path.exists(det1_dir):\n", + " os.makedirs(det1_dir)\n", + "if not os.path.exists(spec2_dir):\n", + " os.makedirs(spec2_dir)\n", + "if not os.path.exists(spec3_dir):\n", + " os.makedirs(spec3_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba5341f5-08f7-409b-a764-c3afc160faa2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Output subdirectories to keep background data products organized\n", + "det1_bgdir = os.path.join(output_bgdir, 'stage1/') # Detector1 pipeline outputs will go here\n", + "spec2_bgdir = os.path.join(output_bgdir, 'stage2/') # Spec2 pipeline outputs will go here\n", + "\n", + "# We need to check that the desired output directories exist, and if not create them\n", + "if (output_bgdir != ''):\n", + " if not os.path.exists(det1_bgdir):\n", + " os.makedirs(det1_bgdir)\n", + " if not os.path.exists(spec2_bgdir):\n", + " os.makedirs(spec2_bgdir)" + ] + }, + { + "cell_type": "markdown", + "id": "75a62ae9", + "metadata": {}, + "source": [ + "# 2. Download all MRS data from SN 1987A PID 1232 (Public)" + ] + }, + { + "cell_type": "markdown", + "id": "5bd8798f", + "metadata": {}, + "source": [ + "#### If you want to run the entire MRS pipeline from start to finish, you will need to download nearly 100 GB of data. The vast majority of these data are the Level0 raw ramp (uncal.fits) and Level1 ramp (rate.fits and rateints.fits) files. For our purposes, we encourage you to simply download the Level2 calibrated data (cal.fits), which totals only 3 GB." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b4e12dd", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's get a list of all observations associated with this proposal\n", + "obs_list = Observations.query_criteria(proposal_id=1232)\n", + "\n", + "# We can chooose the columns we want to display in our table\n", + "disp_col = ['dataproduct_type','instrument_name','calib_level','obs_id',\n", + " 'target_name','filters','proposal_pi', 'obs_collection']\n", + "obs_list[disp_col].show_in_notebook()" + ] + }, + { + "cell_type": "raw", + "id": "e378fe92", + "metadata": {}, + "source": [ + "# Level 0 uncal.fits downloads. Can skip for this workflow.\n", + "\n", + "mask = (obs_list['instrument_name'] == 'MIRI/IFU')\n", + "data_products = Observations.get_product_list(obs_list[mask])\n", + "\n", + "filtered_prod = Observations.filter_products(data_products, calib_level=[1], productType=\"SCIENCE\")\n", + "\n", + "# Again, we choose columns of interest for convenience\n", + "disp_col = ['obsID','dataproduct_type','productFilename','size','calib_level']\n", + "filtered_prod.show_in_notebook(display_length=10)" + ] + }, + { + "cell_type": "raw", + "id": "ee113760", + "metadata": {}, + "source": [ + "total = sum(filtered_prod['size'])\n", + "print('{:.2f} GB'.format(total/10**9))" + ] + }, + { + "cell_type": "raw", + "id": "1e6988f7", + "metadata": {}, + "source": [ + "# Don't forget to login, if accessing non-public data! You can un-comment the line below:\n", + "# Observations.login()\n", + "\n", + "# You can download all of the products by removing the '[:5]' from the line below:\n", + "manifest = Observations.download_products(filtered_prod)\n", + "print(manifest['Status'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e92c6d23", + "metadata": {}, + "outputs": [], + "source": [ + "# Level 2b cal.fits\n", + "\n", + "mask = (obs_list['instrument_name'] == 'MIRI/IFU')\n", + "data_products = Observations.get_product_list(obs_list[mask])\n", + "\n", + "filtered_prod = Observations.filter_products(data_products, calib_level=[2], productType=\"SCIENCE\", productSubGroupDescription=\"CAL\")\n", + "\n", + "# Again, we choose columns of interest for convenience\n", + "disp_col = ['obsID','dataproduct_type','productFilename','size','calib_level']\n", + "filtered_prod.show_in_notebook(display_length=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d96ecb35", + "metadata": {}, + "outputs": [], + "source": [ + "total = sum(filtered_prod['size'])\n", + "print('{:.2f} GB'.format(total/10**9))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6bebdd1e", + "metadata": {}, + "outputs": [], + "source": [ + "# Don't forget to login, if accessing non-public data! You can un-comment the line below:\n", + "# Observations.login()\n", + "\n", + "# You can download all of the products by removing the '[:5]' from the line below:\n", + "manifest = Observations.download_products(filtered_prod)\n", + "print(manifest['Status'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b44f15a", + "metadata": {}, + "outputs": [], + "source": [ + "# Check to see if the input directory exists. If not, create it. Move all _cal.fits files into that directory.\n", + "\n", + "if os.path.exists(input_dir):\n", + " print(input_dir+\" already exists\")\n", + "else:\n", + " print(\"Creating Directory \"+input_dir)\n", + " os.mkdir(input_dir)\n", + " \n", + "if os.path.exists(\"./output\"):\n", + " print(\"./output already exists\")\n", + "else:\n", + " print(\"Creating Directory ./output\")\n", + " os.mkdir(\"./output\")\n", + " \n", + "if os.path.exists(output_dir):\n", + " print(output_dir+\" already exists\")\n", + "else:\n", + " print(\"Creating Directory \"+output_dir)\n", + " os.mkdir(output_dir)\n", + " \n", + "if os.path.exists(det1_dir):\n", + " print(det1_dir+\" already exists\")\n", + "else:\n", + " print(\"Creating Directory \"+det1_dir)\n", + " os.mkdir(det1_dir)\n", + " \n", + "if os.path.exists(spec2_dir):\n", + " print(spec2_dir+\" already exists\")\n", + "else:\n", + " print(\"Creating Directory \"+spec2_dir)\n", + " os.mkdir(spec2_dir)\n", + " \n", + "if os.path.exists(spec3_dir):\n", + " print(spec3_dir+\" already exists\")\n", + "else:\n", + " print(\"Creating Directory \"+spec3_dir)\n", + " os.mkdir(spec3_dir)\n", + " \n", + "print(\"Moving All Uncal Files To Input Directory\")\n", + "for file in glob.glob('./mastDownload/JWST/*/*_uncal.fits'):\n", + " root = file.split('/')\n", + " print(root[-1])\n", + " if os.path.isfile(input_dir+'/'+root[-1]):\n", + " print('Deleting '+input_dir+'/'+root[-1])\n", + " os.remove(input_dir+'/'+root[-1])\n", + " print('Moving '+input_dir+'/'+root[-1])\n", + " shutil.move(file, input_dir)\n", + " \n", + "print(\"Moving All Cal Files To Input Directory\")\n", + "for file in glob.glob('./mastDownload/JWST/*/*_cal.fits'):\n", + " root = file.split('/')\n", + " print(root[-1])\n", + " if os.path.isfile(spec2_dir+'/'+root[-1]):\n", + " print('Deleting '+spec2_dir+'/'+root[-1])\n", + " os.remove(spec2_dir+'/'+root[-1])\n", + " print('Moving '+spec2_dir+'/'+root[-1])\n", + " shutil.move(file, spec2_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "235e74e6-52cb-4c56-b150-2945f3a00230", + "metadata": { + "tags": [] + }, + "source": [ + "# 2. Detector1 Pipeline" + ] + }, + { + "cell_type": "markdown", + "id": "c4591e04", + "metadata": {}, + "source": [ + "#### Not necessary to run the Detector1 stage of the pipeline for this notebook. But here is sample code in case you do." + ] + }, + { + "cell_type": "raw", + "id": "4f9cbdd5", + "metadata": { + "tags": [] + }, + "source": [ + "# If you did want to run the entire pipeline, here are the steps.\n", + "\n", + "# First we'll define a function that will call the detector1 pipeline with our desired set of parameters\n", + "# We won't enumerate the individual steps\n", + "def rundet1(filename, outdir):\n", + " print(filename)\n", + " det1 = Detector1Pipeline() # Instantiate the pipeline\n", + " det1.output_dir = outdir # Specify where the output should go\n", + " \n", + " # The jump and ramp fitting steps can benefit from multi-core processing, but this is off by default\n", + " # Turn them on here if desired by choosing how many cores to use (quarter, half, or all)\n", + " det1.jump.maximum_cores='half'\n", + " det1.ramp_fit.maximum_cores='half'\n", + " \n", + " det1.jump.find_showers=True\n", + " det1.jump.only_use_ints = False\n", + " det1.minimum_sigclip_groups = 75\n", + " \n", + " det1.save_results = True # Save the final resulting _rate.fits files\n", + " det1(filename) # Run the pipeline on an input list of files" + ] + }, + { + "cell_type": "raw", + "id": "6f6a0ab7", + "metadata": { + "tags": [] + }, + "source": [ + "# Now let's look for input files of the form *uncal.fits from the science observation\n", + "sstring = input_dir + 'jw*mirifu*uncal.fits'\n", + "lvl1b_files = sorted(glob.glob(sstring))\n", + "print('Found ' + str(len(lvl1b_files)) + ' science input files to process')" + ] + }, + { + "cell_type": "raw", + "id": "33ecb948", + "metadata": { + "tags": [] + }, + "source": [ + "# Run the pipeline on these input files by a simple loop over our pipeline function\n", + "if dodet1:\n", + " for file in lvl1b_files:\n", + " rundet1(file, det1_dir)\n", + "else:\n", + " print('Skipping Detector1 processing')" + ] + }, + { + "cell_type": "markdown", + "id": "b96ea41b-0b77-4a76-9de2-5f237c551226", + "metadata": {}, + "source": [ + "# 3. Spec2 Pipeline\n" + ] + }, + { + "cell_type": "markdown", + "id": "6e0012b1", + "metadata": {}, + "source": [ + "#### Not necessary to run the Spec2 stage of the pipeline for this notebook. But here is sample code in case you do." + ] + }, + { + "cell_type": "raw", + "id": "032bf7cf", + "metadata": { + "tags": [] + }, + "source": [ + "# Define a function that will call the spec2 pipeline with our desired set of parameters\n", + "# We'll list the individual steps just to make it clear what's running\n", + "def runspec2(filename, outdir, nocubes=False):\n", + " spec2 = Spec2Pipeline()\n", + " spec2.output_dir = outdir\n", + " \n", + " spec2.save_results = True\n", + " spec2(filename)" + ] + }, + { + "cell_type": "raw", + "id": "14d23946", + "metadata": { + "tags": [] + }, + "source": [ + "# Look for uncalibrated science slope files from the Detector1 pipeline\n", + "sstring = det1_dir + 'jw*mirifu*rate.fits'\n", + "ratefiles = sorted(glob.glob(sstring))\n", + "ratefiles=np.array(ratefiles)\n", + "print('Found ' + str(len(ratefiles)) + ' input files to process')" + ] + }, + { + "cell_type": "raw", + "id": "3d6b2d1f", + "metadata": {}, + "source": [ + "if dospec2:\n", + " for file in ratefiles:\n", + " runspec2(file, spec2_dir, nocubes=True)\n", + "else:\n", + " print('Skipping Spec2 processing')" + ] + }, + { + "cell_type": "markdown", + "id": "0e9a59fe", + "metadata": {}, + "source": [ + "# 4. Spec3 Pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cecc7e6f", + "metadata": {}, + "outputs": [], + "source": [ + "# Define a useful function to write out a Lvl3 association file from an input list\n", + "# Note that any background exposures have to be of type x1d.\n", + "def writel3asn(scifiles, bgfiles, asnfile, prodname):\n", + " # Define the basic association of science files\n", + " asn = afl.asn_from_list(scifiles, rule=DMS_Level3_Base, product_name=prodname)\n", + " \n", + " # Add background files to the association\n", + " nbg=len(bgfiles)\n", + " for ii in range(0,nbg):\n", + " asn['products'][0]['members'].append({'expname': bgfiles[ii], 'exptype': 'background'})\n", + " \n", + " # Write the association to a json file\n", + " _, serialized = asn.dump()\n", + " with open(asnfile, 'w') as outfile:\n", + " outfile.write(serialized)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a65d12e6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Find and sort all of the input files\n", + "\n", + "# Science Files need the cal.fits files\n", + "sstring = spec2_dir + 'jw*mirifu*cal.fits'\n", + "calfiles = np.array(sorted(glob.glob(sstring)))\n", + "\n", + "# Background Files need the x1d.fits files\n", + "sstring = spec2_bgdir + 'jw*mirifu*x1d.fits'\n", + "bgfiles = np.array(sorted(glob.glob(sstring)))\n", + "\n", + "print('Found ' + str(len(calfiles)) + ' science files to process')\n", + "print('Found ' + str(len(bgfiles)) + ' background files to process')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d4e0e2e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Make an association file that includes all of the different exposures\n", + "#asnfile=os.path.join(output_dir, 'spec2_l3asn.json')\n", + "asnfile='spec2_l3asn.json'\n", + "dospec3 = 1.\n", + "if dospec3:\n", + " writel3asn(calfiles, bgfiles, asnfile, 'Level3')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "284cfcf2", + "metadata": {}, + "outputs": [], + "source": [ + "asnfile" + ] + }, + { + "cell_type": "markdown", + "id": "1965c445", + "metadata": {}, + "source": [ + "#### Running spec3 in 'multi' output mode to create a single cube with all sub-bands stitched together." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec50de18", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Define a function that will call the spec3 pipeline with our desired set of parameters\n", + "# This is designed to run on an association file\n", + "def runspec3(filename):\n", + " crds_config = Spec3Pipeline.get_config_from_reference(filename)\n", + " spec3 = Spec3Pipeline.from_config_section(crds_config)\n", + "\n", + " spec3.output_dir = spec3_dir\n", + " spec3.save_results = True\n", + "\n", + " # Cube building configuration options\n", + " spec3.cube_build.output_type = 'multi' # 'band', 'channel', or 'multi' type cube output\n", + "\n", + " # Overrides for whether or not certain steps should be skipped\n", + " spec3.master_background.skip = True\n", + " spec3.subtract_background = False\n", + " spec3.extract_1d.subtract_background=False\n", + " spec3(filename)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd96a3a5", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "spec3 = 1.\n", + "if dospec3:\n", + " runspec3(asnfile)\n", + "else:\n", + " print('Skipping Spec3 processing')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "403777ba", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/MIRI_MRS_1987A/87a_part2_background_sub.ipynb b/notebooks/MIRI_MRS_1987A/87a_part2_background_sub.ipynb new file mode 100755 index 000000000..eb7408eb7 --- /dev/null +++ b/notebooks/MIRI_MRS_1987A/87a_part2_background_sub.ipynb @@ -0,0 +1,756 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "298b8519-c695-4562-a96c-96208063c1c3", + "metadata": {}, + "source": [ + "# MIRI MRS IFU Spectroscopy Part 2: \n", + "# Defining and Extracting a Background Spectrum\n", + "\n", + "Aug 2023\n", + "\n", + "**Use case:** Reduce MRS Data With User Defined Master Background Step. This is particularly relevant if you did not obtain a Dedicated Background with your observations. While the pipeline will subtract a sky background derived from an annulus, the underlying background may be prohibitively complicated and the user may wish to measure their own background from elsewhere in the cube.
\n", + "**Data:** Publicly available science data for SN 1987A (Program 1232). For this notebook, we will follow the science workflow outlined by [Jones et al. 2023](https://ui.adsabs.harvard.edu/abs/2023arXiv230706692J/abstract).
\n", + "**Tools:** jwst, jdaviz, matplotlib, astropy.
\n", + "**Cross-intrument:** NIRSpec, MIRI.
\n", + "**Documentation:** This notebook is part of a STScI's larger [post-pipeline Data Analysis Tools Ecosystem](https://jwst-docs.stsci.edu/jwst-post-pipeline-data-analysis) and can be [downloaded](https://github.com/spacetelescope/dat_pyinthesky/tree/main/jdat_notebooks/MRS_Mstar_analysis) directly from the [JDAT Notebook Github directory](https://github.com/spacetelescope/jdat_notebooks).
\n", + "\n", + "### Introduction: Spectral extraction in the JWST calibration pipeline\n", + "\n", + "The JWST calibration pipeline performs spectrac extraction for all spectroscopic data using basic default assumptions that are tuned to produce accurately calibrated spectra for the majority of science cases. This default method is a simple fixed-width boxcar extraction, where the spectrum is summed over a number of pixels along the cross-dispersion axis, over the valid wavelength range. An aperture correction is applied at each pixel along the spectrum to account for flux lost from the finite-width aperture. \n", + "\n", + "The ``extract_1d`` step uses the following inputs for its algorithm:\n", + "- the spectral extraction reference file: this is a json-formatted file, available as a reference file from the [JWST CRDS system](https://jwst-crds.stsci.edu)\n", + "- the bounding box: the ``assign_wcs`` step attaches a bounding box definition to the data, which defines the region over which a valid calibration is available. We will demonstrate below how to visualize this region. \n", + "\n", + "However the ``extract_1d`` step has the capability to perform more complex spectral extractions, requiring some manual editing of parameters and re-running of the pipeline step. \n", + "\n", + "\n", + "### Aims\n", + "\n", + "This notebook will demonstrate how to re-run the spectral extraction step with different settings to illustrate the capabilities of the JWST calibration pipeline. \n", + "\n", + "\n", + "### Assumptions\n", + "\n", + "We will demonstrate the spectral extraction methods on resampled, calibrated spectral images. The basic demo and two examples run on Level 3 data, in which the nod exposures have been combined into a single spectral image. Two examples will use the Level 2b data - one of the nodded exposures. \n", + "\n", + "\n", + "### Test data\n", + "\n", + "The data used in this notebook is an observation of the Type Ia supernova SN2021aefx, observed by Jha et al in PID 2072 (Obs 1). These data were taken with zero exclusive access period, and published in [Kwok et al 2023](https://ui.adsabs.harvard.edu/abs/2023ApJ...944L...3K/abstract). You can retrieve the data from [this Box folder](https://stsci.box.com/s/i2xi18jziu1iawpkom0z2r94kvf9n9kb), and we recommend you place the files in the ``data/`` folder of this repository, or change the directory settings in the notebook prior to running. \n", + "\n", + "You can of course use your own data instead of the demo data. \n", + "\n", + "\n", + "### JWST pipeline version and CRDS context\n", + "\n", + "This notebook was written using the calibration pipeline version 1.10.2. We set the CRDS context explicitly to 1089 to match the current latest version in MAST. If you use different pipeline versions or CRDS context, please read the relevant release notes ([here for pipeline](https://github.com/spacetelescope/jwst), [here for CRDS](https://jwst-crds.stsci.edu)) for possibly relevant changes.\n", + "\n", + "### Contents\n", + "\n", + "1. [The Level 3 data products](#l3data)\n", + "2. [The spectral extraction reference file](#x1dref)\n", + "3. [Example 1: Changing the aperture width](#ex1)\n", + "4. [Example 2: Changing the aperture location](#ex2)\n", + "5. [Example 3: Extraction with background subtraction](#ex3)\n", + "6. [Example 4: Tapered column extraction](#ex4)" + ] + }, + { + "cell_type": "markdown", + "id": "e58c7be6", + "metadata": {}, + "source": [ + "## Import Packages" + ] + }, + { + "cell_type": "markdown", + "id": "5b8c9d77", + "metadata": {}, + "source": [ + "- `astropy.io` fits for accessing FITS files\n", + "- `os` for managing system paths\n", + "- `matplotlib` for plotting data\n", + "- `urllib` for downloading data\n", + "- `tarfile` for unpacking data\n", + "- `numpy` for basic array manipulation\n", + "- `jwst` for running JWST pipeline and handling data products\n", + "- `json` for working with json files\n", + "- `crds` for working with JWST reference files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d1f42bc", + "metadata": {}, + "outputs": [], + "source": [ + "# Set CRDS variables first\n", + "import os\n", + "\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\"]}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21efc012", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import sys,os, pdb\n", + "# Basic system utilities for interacting with files\n", + "import glob\n", + "import time\n", + "import shutil\n", + "import warnings\n", + "import zipfile\n", + "import urllib.request\n", + "import requests\n", + "\n", + "# Astropy utilities for opening FITS and ASCII files\n", + "from astropy.io import fits\n", + "from astropy.io import ascii\n", + "from astropy.utils.data import download_file\n", + "from regions import Regions\n", + "from astropy import units as u\n", + "\n", + "from astroquery.mast import Observations\n", + "\n", + "# Astropy utilities for making plots\n", + "from astropy.visualization import (LinearStretch, LogStretch, ImageNormalize, ZScaleInterval)\n", + "\n", + "# Numpy for doing calculations\n", + "import numpy as np\n", + "\n", + "# Matplotlib for making plots\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import rc\n", + "\n", + "# Import the base JWST package\n", + "import jwst\n", + "\n", + "# JWST pipelines (encompassing many steps)\n", + "from jwst.pipeline import Detector1Pipeline\n", + "from jwst.pipeline import Spec2Pipeline\n", + "from jwst.pipeline import Spec3Pipeline\n", + "\n", + "# JWST pipeline utilities\n", + "from jwst import datamodels # JWST datamodels\n", + "from jwst.associations import asn_from_list as afl # Tools for creating association files\n", + "from jwst.associations.lib.rules_level2_base import DMSLevel2bBase # Definition of a Lvl2 association file\n", + "from jwst.associations.lib.rules_level3_base import DMS_Level3_Base # Definition of a Lvl3 association file\n", + "from jwst.datamodels import SpecModel, MultiSpecModel, IFUCubeModel\n", + "\n", + "\n", + "from stcal import dqflags # Utilities for working with the data quality (DQ) arrays\n", + "\n", + "import shutil\n", + "\n", + "# Import packages for multiprocessing. These won't be used on the online demo, but can be\n", + "# very useful for local data processing unless/until they get integrated natively into\n", + "# the cube building code. These need to be imported before anything else.\n", + "\n", + "import multiprocessing\n", + "#multiprocessing.set_start_method('fork')\n", + "from multiprocessing import Pool\n", + "import os\n", + "\n", + "# Set the maximum number of processes to spawn based on available cores\n", + "usage = 'all' # Either 'none' (single thread), 'quarter', 'half', or 'all' available cores\n", + "\n", + "from specutils import Spectrum1D\n", + "from matplotlib.pyplot import cm\n", + "\n", + "from jdaviz import Cubeviz\n", + "\n", + "# Display the video\n", + "from IPython.display import HTML, YouTubeVideo\n", + "\n", + "#shutil.copytree('/astro/armin/data/mshahbandeh/aefx/input_dir/', '/astro/armin/data/mshahbandeh/aefx/input_dir_sc/')\n", + "#shutil.copytree('/astro/armin/data/mshahbandeh/aefx/input_dir/', '/astro/armin/data/mshahbandeh/aefx/input_dir_bkg/')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37350f4d-4dbc-445d-b743-f26e7898e73e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Set parameters to be changed here.\n", + "# It should not be necessary to edit cells below this in general unless modifying pipeline processing steps.\n", + "\n", + "import sys,os, pdb\n", + "\n", + "# CRDS context (if overriding)\n", + "#%env CRDS_CONTEXT jwst_0771.pmap\n", + "\n", + "# Point to where the uncalibrated FITS files are from the science observation\n", + "input_dir = './mastDownload/1232/uncal/'\n", + "\n", + "# Point to where you want the output science results to go\n", + "output_dir = './output/87A/'\n", + "\n", + "# Point to where the uncalibrated FITS files are from the background observation\n", + "# If no background observation, leave this blank\n", + "input_bgdir = ' '\n", + "\n", + "# Point to where the output background observations should go\n", + "# If no background observation, leave this blank\n", + "output_bgdir = ' '\n", + "\n", + "# Whether or not to run a given pipeline stage\n", + "# Science and background are processed independently through det1+spec2, and jointly in spec3\n", + "\n", + "# Science processing\n", + "dodet1=True\n", + "dospec2=True\n", + "dospec3=True\n", + "\n", + "# Background processing\n", + "dodet1bg=True\n", + "dospec2bg=True\n", + "\n", + "# If there is no background folder, ensure we don't try to process it\n", + "if (input_bgdir == ''):\n", + " dodet1bg=False\n", + " dospec2bg=False" + ] + }, + { + "cell_type": "raw", + "id": "a1c5254b-6e38-4b43-82c5-44fa67a4f4b0", + "metadata": {}, + "source": [ + "## Point to where the uncalibrated FITS files are from the science observation\n", + "input_dir = '/Users/ofox/data/1860/mast/01860/obsnum03/'\n", + "#\n", + "## Point to where you want the output science results to go\n", + "output_dir = '/Users/ofox/data/1860/output/05ip_3/'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c10e734", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "## Output subdirectories to keep science data products organized\n", + "## Note that the pipeline might complain about this as it is intended to work with everything in a single\n", + "## directory, but it nonetheless works fine for the examples given here.\n", + "det1_dir = os.path.join(output_dir, 'stage1/') # Detector1 pipeline outputs will go here\n", + "#spec2_dir = os.path.join(output_dir, 'stage2/') # Spec2 pipeline outputs will go here\n", + "spec2_dir = os.path.join(output_dir, 'stage2/') # Spec2 pipeline outputs will go here\n", + "spec2_bgdir = ' '\n", + "#spec3_dir = os.path.join(output_dir, 'stage3/') # Spec3 pipeline outputs will go here\n", + "spec3_dir = os.path.join(output_dir, 'stage3/') # Spec3 pipeline outputs will go here\n", + "\n", + "# We need to check that the desired output directories exist, and if not create them\n", + "if not os.path.exists(det1_dir):\n", + " os.makedirs(det1_dir)\n", + "if not os.path.exists(spec2_dir):\n", + " os.makedirs(spec2_dir)\n", + "if not os.path.exists(spec3_dir):\n", + " os.makedirs(spec3_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba5341f5-08f7-409b-a764-c3afc160faa2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Output subdirectories to keep background data products organized\n", + "det1_bgdir = os.path.join(output_bgdir, 'stage1/') # Detector1 pipeline outputs will go here\n", + "spec2_bgdir = os.path.join(output_bgdir, 'stage2/') # Spec2 pipeline outputs will go here\n", + "\n", + "# We need to check that the desired output directories exist, and if not create them\n", + "if (output_bgdir != ''):\n", + " if not os.path.exists(det1_bgdir):\n", + " os.makedirs(det1_bgdir)\n", + " if not os.path.exists(spec2_bgdir):\n", + " os.makedirs(spec2_bgdir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86205cd2", + "metadata": {}, + "outputs": [], + "source": [ + "def checkKey(dict, key):\n", + " \n", + " if key in dict.keys():\n", + " print(\"Present, \", end=\" \")\n", + " print(\"value =\", dict[key])\n", + " return(True)\n", + " else:\n", + " print(\"Not present\")\n", + " return(False)" + ] + }, + { + "cell_type": "markdown", + "id": "7def969a", + "metadata": {}, + "source": [ + "# 2. Use Cubeviz to make mask" + ] + }, + { + "cell_type": "markdown", + "id": "9e4eef1a", + "metadata": {}, + "source": [ + "#### This step show how to interactively define a region to be used for extracting a background. If you skip this step, you can continue to run the notebook further in Step 3." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7aa348f", + "metadata": {}, + "outputs": [], + "source": [ + "# Video showing how to define an annulus background around SN 1987A using the cells below\n", + "\n", + "HTML('')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e89d0586", + "metadata": {}, + "outputs": [], + "source": [ + "cubefile = \"/astro/armin/data/ofox/1232/output/87A/stage3/Level3_ch1-2-3-4-shortmediumlong_s3d.fits\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7224f0b", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "from jdaviz import Cubeviz\n", + "cubeviz = Cubeviz()\n", + "cubeviz.load_data(cubefile, data_label='SN1987A')\n", + "cubeviz.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b9dc7eb", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the collapsed cube, ideally from Cubeviz, but otherwise download from pre-defined files.\n", + "\n", + "collapse_cube = cubeviz.app.get_data_from_viewer(\"uncert-viewer\") # AGN Center Model Cube\n", + "if checkKey(collapse_cube,\"collapsed\") is True:\n", + " collapse_cube = cubeviz.app.get_data_from_viewer(\"uncert-viewer\",\"collapsed\") # AGN Center Model Cube\n", + " collapse_cube.write(spec3_dir+\"collapsed_cube.fits\",overwrite='True')\n", + "else:\n", + " print(\"No Collapsed Cube in Cubeviz.\")\n", + " if os.path.isfile(spec3_dir+'/'+\"collapsed_cube.fits\"):\n", + " print('File exists. Deleting '+spec3_dir+'/'+\"collapsed_cube.fits\")\n", + " os.remove(spec3_dir+'/'+\"collapsed_cube.fits\")\n", + " print(\"Downloading to \"+spec3_dir)\n", + " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MRS_1987A/collapsed_cube.fits'\n", + " urllib.request.urlretrieve(url, './collapsed_cube.fits')\n", + " shutil.move(\"collapsed_cube.fits\",spec3_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b145607c", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the ellipse region, ideally from Cubeviz, but otherwise download from pre-defined files.\n", + "\n", + "regions = cubeviz.get_interactive_regions()\n", + "if checkKey(regions,\"Subset 1\") is True:\n", + " regions['Subset 1'].write('my_elipse.reg', overwrite=True)\n", + "else:\n", + " print(\"No Background Region From Cubeviz.\")\n", + " if os.path.isfile(\"./my_elipse.reg\"):\n", + " print('File exists. Deleting ./my_elipse.reg')\n", + " os.remove(\"./my_elipse.reg\")\n", + " print(\"Downloading...\")\n", + " fname = \"./my_elipse.reg\"\n", + " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MRS_1987A/my_elipse.txt'\n", + " urllib.request.urlretrieve(url, fname)\n", + " #fn = x = download_file(url, cache=False)\n", + " #reg = Regions.read(fn, format='ds9')[0]\n" + ] + }, + { + "cell_type": "markdown", + "id": "24590618", + "metadata": {}, + "source": [ + "# 3. Apply the mask to the weights extension of the data cube" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dabb87c3", + "metadata": {}, + "outputs": [], + "source": [ + "# Read in data cube as a JWST data model\n", + "spec_model_cube = IFUCubeModel()\n", + "spec_model_cube.read(cubefile)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7334184", + "metadata": {}, + "outputs": [], + "source": [ + "# Print the source and aperture type being used in the header of the file (this can be Extended or Point). \n", + "# For SN 1987A, we have an EXTENDED source\n", + "\n", + "spec_model_cube.find_fits_keyword('SRCTYPE')\n", + "spec_model_cube.find_fits_keyword('SRCTYAPT')\n", + "\n", + "print(spec_model_cube.meta.target.source_type)\n", + "print(spec_model_cube.meta.target.source_type_apt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "730eb1d2", + "metadata": {}, + "outputs": [], + "source": [ + "# If necessary, you can change your cube header as necessary. We don't need to change anything in this case.\n", + "# But you might want to if you have a point source, yet want to extract a user specified background spectrum.\n", + "# The pipeline extracts EXTENDED and POINT sources differently.\n", + "\n", + "spec_model_cube.meta.target.source_type = 'EXTENDED'\n", + "spec_model_cube.meta.target.source_type_apt = 'EXTENDED'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21adcb34", + "metadata": {}, + "outputs": [], + "source": [ + "# Read in previously extracted region\n", + "\n", + "reg = Regions.read('./my_elipse.reg', format='ds9')[0]\n", + "print(reg)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ce2d007", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a weight map using the region mask\n", + "\n", + "tmp_wgts = spec_model_cube.weightmap[:]\n", + "mask = reg.to_mask('exact')\n", + "x1 = int(reg.center.x-reg.width/2.)\n", + "x2 = x1+mask.shape[1]\n", + "y1 = int(reg.center.y-reg.height/2.)\n", + "y2 = y1+mask.shape[0]\n", + "\n", + "### Note above, the region shape is slightly different than the mask shape that gets generated. \n", + "### This hack gets all the arrays to be the same size.\n", + "\n", + "# Start by setting all pixels to 1.\n", + "mask2d = tmp_wgts[13,:,:]\n", + "mask2d[mask2d>0] = 1.\n", + "\n", + "# Because we want an inverse array, we can't just use the mask, we have to subtract the mask (which is 1's) from the original mask2d above (make sense?) \n", + "mask2d[y1:y2,x1:x2] = mask2d[y1:y2,x1:x2]-mask\n", + "\n", + "# Take into account weird rounding errors\n", + "mask2d[mask2d<0.1] = 0." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c51239ca", + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize the 2D Mask\n", + "\n", + "from astropy.nddata import CCDData\n", + "from astropy.visualization import simple_norm\n", + "ccd = mask2d\n", + "norm = simple_norm(ccd, 'sqrt', min_cut=0, max_cut=0.5) \n", + "color = 'rgbmkrgbmk'\n", + "\n", + "xceni = [36, 44]\n", + "yceni = [66, 58]\n", + "\n", + "fig = plt.figure(figsize=(10, 10))\n", + "ax = fig.add_subplot()\n", + "counter = 0\n", + "plt.title(\"Masked Data\")\n", + "plt.imshow(ccd, norm=norm, origin=\"lower\") \n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d800d33b", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a 3D weightmap from the 2D map. Mask all NAN values, too.\n", + "\n", + "mask3d = np.broadcast_to(mask2d, spec_model_cube.weightmap.shape)\n", + "mask3d.flags.writeable = True\n", + "mask3d[np.isnan(spec_model_cube.data)] = 0\n", + "#mask_sci_cube = np.ma.masked_array(spec_model_cube.weightmap, mask=mask3d.astype(bool))\n", + "tmp_wgt_cube = np.swapaxes(mask3d,0,1)\n", + "tmp_wgt_cube = np.swapaxes(tmp_wgt_cube,1,2)\n", + "plotcube = Spectrum1D(tmp_wgt_cube*u.dimensionless_unscaled)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7b9a1bf", + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize the 3D Cube in Cubeviz\n", + "\n", + "cubeviz2 = Cubeviz()\n", + "cubeviz2.load_data(plotcube, data_label='SN1987A MASK')\n", + "cubeviz2.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "03177ab1", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the weightmap in the original cube as the new 3D Mask \n", + "# This will tell the pipeline which spaxels to use for extraction\n", + "\n", + "spec_model_cube.weightmap = mask3d\n", + "spec_model_cube.save(spec3_dir+'87A_skycube.fits',overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "id": "6b381d59", + "metadata": {}, + "source": [ + "# 4. Extract Background Spectrum using Extract1dStep" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17914f4a", + "metadata": {}, + "outputs": [], + "source": [ + "# Set 1D extraction parameters\n", + "\n", + "def runex(filename, outdir, outputfile):\n", + " ex1d = jwst.extract_1d.Extract1dStep()\n", + " ex1d.output_dir = outdir\n", + " ex1d.save_results = True\n", + " ex1d.subtract_background = False\n", + " ex1d.output_file = outputfile\n", + " ex1d(filename)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63c46fc4", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# We will extract a 1D spectrum from the cube created above with a weightmap defined by the region mask\n", + "# This extraction will create an average of all the spaxels in each frame that are not masked\n", + "# We will use this to be our master background to subtract from the entire cube\n", + "\n", + "cubefile_p1 = spec3_dir+'87A_skycube.fits'\n", + "outputfile = spec3_dir+'87A_bg'\n", + "runex(cubefile_p1,spec3_dir,outputfile=outputfile)" + ] + }, + { + "cell_type": "markdown", + "id": "f7f50b83", + "metadata": {}, + "source": [ + "### A single background spectrum is necessary in this workflow. But in some workflows, you may wish to work with more than one background region. This is further illustrated in Notebook 3 of this series on SN 1987A." + ] + }, + { + "cell_type": "markdown", + "id": "b36656ad", + "metadata": {}, + "source": [ + "# 4. Rerun Stage 3 With Master Background Turned On" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41c7b505", + "metadata": {}, + "outputs": [], + "source": [ + "# Define a function that will call the spec3 pipeline with our desired set of parameters\n", + "# This is designed to run on an association file\n", + "def runspec3(filename):\n", + " \n", + " crds_config = Spec3Pipeline.get_config_from_reference(filename)\n", + " spec3 = Spec3Pipeline.from_config_section(crds_config)\n", + " spec3.output_dir = spec3_dir\n", + " spec3.save_results = True\n", + " spec3.cube_build.output_file = '87A_bg_sub' # Custom output name\n", + " spec3.cube_build.output_type = 'multi' # 'band', 'channel', or 'multi' type cube output\n", + " spec3.outlier_detection.threshold_percent = 98.5 # optimized threshold number\n", + " spec3.master_background.user_background=spec3_dir+'87A_bg_extract1dstep.fits' # Master Background Extracted Above\n", + " spec3.master_background.force_subtract=True\n", + " \n", + " spec3(filename)\n" + ] + }, + { + "cell_type": "markdown", + "id": "f450bfca", + "metadata": {}, + "source": [ + "### Developer Note: Right now, this association file can only be created manually. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc38c7a3", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the association file used for the background subtraction.\n", + "# Download the background subtract json file.\n", + "\n", + "asnfile_bg_sub = 'spec2_l3asn_bg_sub.json'\n", + "if os.path.isfile(asnfile_bg_sub):\n", + " print('File exists. Deleting '+asnfile_bg_sub)\n", + " os.remove(asnfile_bg_sub)\n", + "print(\"Downloading \"+asnfile_bg_sub)\n", + "url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MRS_1987A/spec2_l3asn_bg_sub.json'\n", + "urllib.request.urlretrieve(url, asnfile_bg_sub)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da5d3a36", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "spec3 = 1.\n", + "if dospec3:\n", + " runspec3(asnfile_bg_sub)\n", + "else:\n", + " print('Skipping Spec3 processing')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad227282", + "metadata": {}, + "outputs": [], + "source": [ + "datacube = spec3_dir+'87A_bg_sub_ch1-2-3-4-shortmediumlong_s3d.fits'\n", + "datacube" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad317338", + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize Background Subtracted Cube\n", + "# Note, this is not a perfect subtraction. As we will see in the next notebook, we oversubtract the background.\n", + "# This is likely caused by poorly chose spaxels. A more careful selection of background regions should result in a flatter background post-subtraction.\n", + "\n", + "cubeviz3 = Cubeviz()\n", + "cubeviz3.load_data(datacube, data_label='SN1987A MASK')\n", + "cubeviz3.show()" + ] + } + ], + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/MIRI_MRS_1987A/87a_part3_extract.ipynb b/notebooks/MIRI_MRS_1987A/87a_part3_extract.ipynb new file mode 100755 index 000000000..ee4524f73 --- /dev/null +++ b/notebooks/MIRI_MRS_1987A/87a_part3_extract.ipynb @@ -0,0 +1,983 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "298b8519-c695-4562-a96c-96208063c1c3", + "metadata": {}, + "source": [ + "# MIRI MRS IFU Spectroscopy Part 3: \n", + "# Extracting a Spectrum of the SN 1987A Annulus\n", + "\n", + "Aug 2023\n", + "\n", + "**Use case:** Reduce MRS Data With User Defined Master Background Step. This is particularly relevant if you did not obtain a Dedicated Background with your observations. While the pipeline will subtract a sky background derived from an annulus, the underlying background may be prohibitively complicated and the user may wish to measure their own background from elsewhere in the cube.
\n", + "**Data:** Publicly available science data for SN 1987A (Program 1232). For this notebook, we will follow the science workflow outlined by [Jones et al. 2023](https://ui.adsabs.harvard.edu/abs/2023arXiv230706692J/abstract).
\n", + "**Tools:** jwst, jdaviz, matplotlib, astropy.
\n", + "**Cross-intrument:** NIRSpec, MIRI.
\n", + "**Documentation:** This notebook is part of a STScI's larger [post-pipeline Data Analysis Tools Ecosystem](https://jwst-docs.stsci.edu/jwst-post-pipeline-data-analysis) and can be [downloaded](https://github.com/spacetelescope/dat_pyinthesky/tree/main/jdat_notebooks/MRS_Mstar_analysis) directly from the [JDAT Notebook Github directory](https://github.com/spacetelescope/jdat_notebooks).
\n", + "\n", + "### Introduction: Spectral extraction in the JWST calibration pipeline\n", + "\n", + "The JWST calibration pipeline performs spectrac extraction for all spectroscopic data using basic default assumptions that are tuned to produce accurately calibrated spectra for the majority of science cases. This default method is a simple fixed-width boxcar extraction, where the spectrum is summed over a number of pixels along the cross-dispersion axis, over the valid wavelength range. An aperture correction is applied at each pixel along the spectrum to account for flux lost from the finite-width aperture. \n", + "\n", + "The ``extract_1d`` step uses the following inputs for its algorithm:\n", + "- the spectral extraction reference file: this is a json-formatted file, available as a reference file from the [JWST CRDS system](https://jwst-crds.stsci.edu)\n", + "- the bounding box: the ``assign_wcs`` step attaches a bounding box definition to the data, which defines the region over which a valid calibration is available. We will demonstrate below how to visualize this region. \n", + "\n", + "However the ``extract_1d`` step has the capability to perform more complex spectral extractions, requiring some manual editing of parameters and re-running of the pipeline step. \n", + "\n", + "\n", + "### Aims\n", + "\n", + "This notebook will demonstrate how to re-run the spectral extraction step with different settings to illustrate the capabilities of the JWST calibration pipeline. \n", + "\n", + "\n", + "### Assumptions\n", + "\n", + "We will demonstrate the spectral extraction methods on resampled, calibrated spectral images. The basic demo and two examples run on Level 3 data, in which the nod exposures have been combined into a single spectral image. Two examples will use the Level 2b data - one of the nodded exposures. \n", + "\n", + "\n", + "### Test data\n", + "\n", + "The data used in this notebook is an observation of the Type Ia supernova SN2021aefx, observed by Jha et al in PID 2072 (Obs 1). These data were taken with zero exclusive access period, and published in [Kwok et al 2023](https://ui.adsabs.harvard.edu/abs/2023ApJ...944L...3K/abstract). You can retrieve the data from [this Box folder](https://stsci.box.com/s/i2xi18jziu1iawpkom0z2r94kvf9n9kb), and we recommend you place the files in the ``data/`` folder of this repository, or change the directory settings in the notebook prior to running. \n", + "\n", + "You can of course use your own data instead of the demo data. \n", + "\n", + "\n", + "### JWST pipeline version and CRDS context\n", + "\n", + "This notebook was written using the calibration pipeline version 1.10.2. We set the CRDS context explicitly to 1089 to match the current latest version in MAST. If you use different pipeline versions or CRDS context, please read the relevant release notes ([here for pipeline](https://github.com/spacetelescope/jwst), [here for CRDS](https://jwst-crds.stsci.edu)) for possibly relevant changes.\n", + "\n", + "### Contents\n", + "\n", + "1. [The Level 3 data products](#l3data)\n", + "2. [The spectral extraction reference file](#x1dref)\n", + "3. [Example 1: Changing the aperture width](#ex1)\n", + "4. [Example 2: Changing the aperture location](#ex2)\n", + "5. [Example 3: Extraction with background subtraction](#ex3)\n", + "6. [Example 4: Tapered column extraction](#ex4)" + ] + }, + { + "cell_type": "markdown", + "id": "2e9ff09d", + "metadata": {}, + "source": [ + "## Import Packages" + ] + }, + { + "cell_type": "markdown", + "id": "a3122a08", + "metadata": {}, + "source": [ + "- `astropy.io` fits for accessing FITS files\n", + "- `os` for managing system paths\n", + "- `matplotlib` for plotting data\n", + "- `urllib` for downloading data\n", + "- `tarfile` for unpacking data\n", + "- `numpy` for basic array manipulation\n", + "- `jwst` for running JWST pipeline and handling data products\n", + "- `json` for working with json files\n", + "- `crds` for working with JWST reference files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0cb67bbb", + "metadata": {}, + "outputs": [], + "source": [ + "# Set CRDS variables first\n", + "import os\n", + "\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\"]}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21efc012", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import sys,os, pdb\n", + "# Basic system utilities for interacting with files\n", + "import glob\n", + "import time\n", + "import shutil\n", + "import warnings\n", + "import zipfile\n", + "import urllib.request\n", + "import requests\n", + "\n", + "# Astropy utilities for opening FITS and ASCII files\n", + "from astropy.io import fits\n", + "from astropy.io import ascii\n", + "from astropy.utils.data import download_file\n", + "from regions import Regions\n", + "from astropy import units as u\n", + "\n", + "from astroquery.mast import Observations\n", + "\n", + "# Astropy utilities for making plots\n", + "from astropy.visualization import (LinearStretch, LogStretch, ImageNormalize, ZScaleInterval)\n", + "\n", + "# Numpy for doing calculations\n", + "import numpy as np\n", + "\n", + "# Matplotlib for making plots\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import rc\n", + "\n", + "# Import the base JWST package\n", + "import jwst\n", + "\n", + "# JWST pipelines (encompassing many steps)\n", + "from jwst.pipeline import Detector1Pipeline\n", + "from jwst.pipeline import Spec2Pipeline\n", + "from jwst.pipeline import Spec3Pipeline\n", + "\n", + "# JWST pipeline utilities\n", + "from jwst import datamodels # JWST datamodels\n", + "from jwst.associations import asn_from_list as afl # Tools for creating association files\n", + "from jwst.associations.lib.rules_level2_base import DMSLevel2bBase # Definition of a Lvl2 association file\n", + "from jwst.associations.lib.rules_level3_base import DMS_Level3_Base # Definition of a Lvl3 association file\n", + "from jwst.datamodels import SpecModel, MultiSpecModel, IFUCubeModel\n", + "\n", + "\n", + "from stcal import dqflags # Utilities for working with the data quality (DQ) arrays\n", + "\n", + "import shutil\n", + "\n", + "# Import packages for multiprocessing. These won't be used on the online demo, but can be\n", + "# very useful for local data processing unless/until they get integrated natively into\n", + "# the cube building code. These need to be imported before anything else.\n", + "\n", + "import multiprocessing\n", + "#multiprocessing.set_start_method('fork')\n", + "from multiprocessing import Pool\n", + "import os\n", + "\n", + "# Set the maximum number of processes to spawn based on available cores\n", + "usage = 'all' # Either 'none' (single thread), 'quarter', 'half', or 'all' available cores\n", + "\n", + "from specutils import Spectrum1D\n", + "from matplotlib.pyplot import cm\n", + "\n", + "from jdaviz import Cubeviz\n", + "\n", + "# Display the video\n", + "from IPython.display import HTML, YouTubeVideo\n", + "\n", + "#shutil.copytree('/astro/armin/data/mshahbandeh/aefx/input_dir/', '/astro/armin/data/mshahbandeh/aefx/input_dir_sc/')\n", + "#shutil.copytree('/astro/armin/data/mshahbandeh/aefx/input_dir/', '/astro/armin/data/mshahbandeh/aefx/input_dir_bkg/')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37350f4d-4dbc-445d-b743-f26e7898e73e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Set parameters to be changed here.\n", + "# It should not be necessary to edit cells below this in general unless modifying pipeline processing steps.\n", + "\n", + "import sys,os, pdb\n", + "\n", + "# CRDS context (if overriding)\n", + "#%env CRDS_CONTEXT jwst_0771.pmap\n", + "\n", + "# Point to where the uncalibrated FITS files are from the science observation\n", + "input_dir = './mastDownload/1232/uncal/'\n", + "\n", + "# Point to where you want the output science results to go\n", + "output_dir = './output/87A/'\n", + "\n", + "# Point to where the uncalibrated FITS files are from the background observation\n", + "# If no background observation, leave this blank\n", + "input_bgdir = ' '\n", + "\n", + "# Point to where the output background observations should go\n", + "# If no background observation, leave this blank\n", + "output_bgdir = ' '\n", + "\n", + "# Whether or not to run a given pipeline stage\n", + "# Science and background are processed independently through det1+spec2, and jointly in spec3\n", + "\n", + "# Science processing\n", + "dodet1=True\n", + "dospec2=True\n", + "dospec3=True\n", + "\n", + "# Background processing\n", + "dodet1bg=True\n", + "dospec2bg=True\n", + "\n", + "# If there is no background folder, ensure we don't try to process it\n", + "if (input_bgdir == ''):\n", + " dodet1bg=False\n", + " dospec2bg=False" + ] + }, + { + "cell_type": "raw", + "id": "a1c5254b-6e38-4b43-82c5-44fa67a4f4b0", + "metadata": {}, + "source": [ + "## Point to where the uncalibrated FITS files are from the science observation\n", + "input_dir = '/Users/ofox/data/1860/mast/01860/obsnum03/'\n", + "#\n", + "## Point to where you want the output science results to go\n", + "output_dir = '/Users/ofox/data/1860/output/05ip_3/'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c10e734", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "## Output subdirectories to keep science data products organized\n", + "## Note that the pipeline might complain about this as it is intended to work with everything in a single\n", + "## directory, but it nonetheless works fine for the examples given here.\n", + "det1_dir = os.path.join(output_dir, 'stage1/') # Detector1 pipeline outputs will go here\n", + "#spec2_dir = os.path.join(output_dir, 'stage2/') # Spec2 pipeline outputs will go here\n", + "spec2_dir = os.path.join(output_dir, 'stage2/') # Spec2 pipeline outputs will go here\n", + "spec2_bgdir = ' '\n", + "#spec3_dir = os.path.join(output_dir, 'stage3/') # Spec3 pipeline outputs will go here\n", + "spec3_dir = os.path.join(output_dir, 'stage3/') # Spec3 pipeline outputs will go here\n", + "\n", + "# We need to check that the desired output directories exist, and if not create them\n", + "if not os.path.exists(det1_dir):\n", + " os.makedirs(det1_dir)\n", + "if not os.path.exists(spec2_dir):\n", + " os.makedirs(spec2_dir)\n", + "if not os.path.exists(spec3_dir):\n", + " os.makedirs(spec3_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba5341f5-08f7-409b-a764-c3afc160faa2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Output subdirectories to keep background data products organized\n", + "det1_bgdir = os.path.join(output_bgdir, 'stage1/') # Detector1 pipeline outputs will go here\n", + "spec2_bgdir = os.path.join(output_bgdir, 'stage2/') # Spec2 pipeline outputs will go here\n", + "\n", + "# We need to check that the desired output directories exist, and if not create them\n", + "if (output_bgdir != ''):\n", + " if not os.path.exists(det1_bgdir):\n", + " os.makedirs(det1_bgdir)\n", + " if not os.path.exists(spec2_bgdir):\n", + " os.makedirs(spec2_bgdir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "abd7f5dd", + "metadata": {}, + "outputs": [], + "source": [ + "def checkKey(dict, key):\n", + " \n", + " if key in dict.keys():\n", + " print(\"Present, \", end=\" \")\n", + " print(\"value =\", dict[key])\n", + " return(True)\n", + " else:\n", + " print(\"Not present\")\n", + " return(False)" + ] + }, + { + "cell_type": "markdown", + "id": "a490cbb6", + "metadata": {}, + "source": [ + "# 2. Extract Same Background Region Post-Background Subtraction for Comparison" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bbecca47", + "metadata": {}, + "outputs": [], + "source": [ + "# Read in data cube as a JWST data model\n", + "\n", + "cubefile = spec3_dir+'87A_bg_sub_ch1-2-3-4-shortmediumlong_s3d.fits'\n", + "\n", + "spec_model_cube = IFUCubeModel()\n", + "spec_model_cube.read(cubefile)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9004d455", + "metadata": {}, + "outputs": [], + "source": [ + "# Print the source and aperture type being used in the header of the file (this can be Extended or Point). \n", + "# For SN 1987A, we have an EXTENDED source\n", + "\n", + "spec_model_cube.find_fits_keyword('SRCTYPE')\n", + "spec_model_cube.find_fits_keyword('SRCTYAPT')\n", + "\n", + "print(spec_model_cube.meta.target.source_type)\n", + "print(spec_model_cube.meta.target.source_type_apt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d24b002", + "metadata": {}, + "outputs": [], + "source": [ + "# If necessary, you can change your cube header as necessary. We don't need to change anything in this case.\n", + "# But you might want to if you have a point source, yet want to extract a user specified background spectrum.\n", + "# The pipeline extracts EXTENDED and POINT sources differently.\n", + "\n", + "spec_model_cube.meta.target.source_type = 'EXTENDED'\n", + "spec_model_cube.meta.target.source_type_apt = 'EXTENDED'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a440cc5", + "metadata": {}, + "outputs": [], + "source": [ + "# Read in previously extracted region (From Notebook 2)\n", + "\n", + "reg = Regions.read('my_elipse.reg', format='ds9')[0]\n", + "print(reg)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2779d8ef", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a weight map using the region mask\n", + "\n", + "tmp_wgts = spec_model_cube.weightmap[:]\n", + "mask = reg.to_mask('exact')\n", + "x1 = int(reg.center.x-reg.width/2.)\n", + "x2 = x1+mask.shape[1]\n", + "y1 = int(reg.center.y-reg.height/2.)\n", + "y2 = y1+mask.shape[0]\n", + "\n", + "### Note above, the region shape is slightly different than the mask shape that gets generated. \n", + "### This hack gets all the arrays to be the same size.\n", + "\n", + "# Start by setting all pixels to 1.\n", + "mask2d = tmp_wgts[13,:,:]\n", + "mask2d[mask2d>0] = 1.\n", + "\n", + "# Because we want an inverse array, we can't just use the mask, we have to subtract the mask (which is 1's) from the original mask2d above (make sense?) \n", + "mask2d[y1:y2,x1:x2] = mask2d[y1:y2,x1:x2]-mask\n", + "\n", + "# Take into account weird rounding errors\n", + "mask2d[mask2d<0.1] = 0." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2f5ff36", + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize the 2D Mask (Same As Before)\n", + "\n", + "from astropy.nddata import CCDData\n", + "from astropy.visualization import simple_norm\n", + "ccd = mask2d\n", + "norm = simple_norm(ccd, 'sqrt', min_cut=0, max_cut=0.5) \n", + "color = 'rgbmkrgbmk'\n", + "\n", + "xceni = [36, 44]\n", + "yceni = [66, 58]\n", + "\n", + "fig = plt.figure(figsize=(10, 10))\n", + "ax = fig.add_subplot()\n", + "counter = 0\n", + "plt.title(\"Masked Data\")\n", + "plt.imshow(ccd, norm=norm, origin=\"lower\") \n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24282902", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a 3D weightmap from the 2D map. Mask all NAN values, too.\n", + "\n", + "mask3d = np.broadcast_to(mask2d, spec_model_cube.weightmap.shape)\n", + "mask3d.flags.writeable = True\n", + "mask3d[np.isnan(spec_model_cube.data)] = 0\n", + "#mask_sci_cube = np.ma.masked_array(spec_model_cube.weightmap, mask=mask3d.astype(bool))\n", + "tmp_wgt_cube = np.swapaxes(mask3d,0,1)\n", + "tmp_wgt_cube = np.swapaxes(tmp_wgt_cube,1,2)\n", + "plotcube = Spectrum1D(tmp_wgt_cube*u.dimensionless_unscaled)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d3996a0", + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize the 3D Cube in Cubeviz\n", + "\n", + "cubeviz = Cubeviz()\n", + "cubeviz.load_data(plotcube, data_label='SN1987A MASK')\n", + "cubeviz.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27fe1bac", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the weightmap in the background subtracted cube as the new 3D Mask \n", + "# This will tell the pipeline which spaxels to use for extraction\n", + "\n", + "spec_model_cube.weightmap = mask3d\n", + "spec_model_cube.save(spec3_dir+'87A_bg_sub_skymask.fits',overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "id": "967b38cc", + "metadata": {}, + "source": [ + "# 3. Extract Background Spectrum (again) using Extract1dStep" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "34a08066", + "metadata": {}, + "outputs": [], + "source": [ + "# Set 1D extraction parameters\n", + "\n", + "def runex(filename, outdir, outputfile):\n", + " ex1d = jwst.extract_1d.Extract1dStep()\n", + " ex1d.output_dir = outdir\n", + " ex1d.save_results = True\n", + " ex1d.subtract_background = False\n", + " ex1d.output_file = outputfile\n", + " ex1d(filename)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "565396df", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# We will extract a 1D spectrum from the cube created above with a weightmap defined by the region mask\n", + "# This extraction will create an average of all the spaxels in each frame that are not masked\n", + "# We will use this to be our master background to subtract from the entire cube\n", + "\n", + "cubefile_p1 = spec3_dir+'87A_bg_sub_skymask.fits'\n", + "\n", + "outputfile = spec3_dir+'87A_bg_sub_skymask'\n", + "runex(cubefile_p1,spec3_dir,outputfile=outputfile)" + ] + }, + { + "cell_type": "markdown", + "id": "80466d2f", + "metadata": {}, + "source": [ + "# 4. Create Annulus to Define Extraction Region for SN Equitorial Ring" + ] + }, + { + "cell_type": "markdown", + "id": "b8519a0e", + "metadata": {}, + "source": [ + "#### This step show how to interactively define a region to be used for extracting a equatorial ring. If you skip this step, you can continue to run the notebook further in Step 5." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "357667d3", + "metadata": {}, + "outputs": [], + "source": [ + "# Video showing how to define an annulus background around SN 1987A using the cells below\n", + "\n", + "HTML('')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3fcd3de3", + "metadata": {}, + "outputs": [], + "source": [ + "# Open Background Subtracted Cube Again\n", + "\n", + "plotcube = spec3_dir+'87A_bg_sub_ch1-2-3-4-shortmediumlong_s3d.fits'\n", + "cubeviz2 = Cubeviz()\n", + "cubeviz2.load_data(plotcube, data_label='SN1987A BG SUB')\n", + "cubeviz2.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42207155", + "metadata": {}, + "outputs": [], + "source": [ + "# Save Inner and Outer Annuli\n", + "# Get the ellipse region, ideally from Cubeviz, but otherwise download from pre-defined files.\n", + "\n", + "regions = cubeviz2.get_interactive_regions()\n", + "\n", + "if checkKey(regions,\"Subset 1\") is True:\n", + " regions['Subset 1'].write('87a_extract_outer.reg', overwrite=True)\n", + "else:\n", + " print(\"No Outer Annulus Region From Cubeviz.\")\n", + " if os.path.isfile(\"./87a_extract_outer.reg\"):\n", + " print('File exists. Deleting ./87a_extract_outer.reg')\n", + " os.remove(\"./87a_extract_outer.reg\")\n", + " print(\"Downloading...87a_extract_outer.txt\")\n", + " fname = \"./87a_extract_outer.reg\"\n", + " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MRS_1987A/87a_extract_outer.txt'\n", + " urllib.request.urlretrieve(url, fname)\n", + " \n", + "if checkKey(regions,\"Subset 2\") is True:\n", + " regions['Subset 2'].write('87a_extract_inner.reg', overwrite=True)\n", + "else:\n", + " print(\"No Inner Annulus Region From Cubeviz.\")\n", + " if os.path.isfile(\"./87a_extract_inner.reg\"):\n", + " print('File exists. Deleting ./87a_extract_inner.reg')\n", + " os.remove(\"./87a_extract_inner.reg\")\n", + " print(\"Downloading...87a_extract_inner.txt\")\n", + " fname = \"./87a_extract_inner.reg\"\n", + " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MRS_1987A/87a_extract_inner.txt'\n", + " urllib.request.urlretrieve(url, fname)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fdffa541", + "metadata": {}, + "outputs": [], + "source": [ + "# Read in data cube as a JWST data model\n", + "cubefile = spec3_dir+'87A_bg_sub_ch1-2-3-4-shortmediumlong_s3d.fits'\n", + "\n", + "spec_model_cube = IFUCubeModel()\n", + "spec_model_cube.read(cubefile)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f968269", + "metadata": {}, + "outputs": [], + "source": [ + "# Print the source and aperture type being used in the header of the file (this can be Extended or Point). \n", + "# For SN 1987A, we have an EXTENDED source\n", + "\n", + "spec_model_cube.find_fits_keyword('SRCTYPE')\n", + "spec_model_cube.find_fits_keyword('SRCTYAPT')\n", + "\n", + "print(spec_model_cube.meta.target.source_type)\n", + "print(spec_model_cube.meta.target.source_type_apt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65565988", + "metadata": {}, + "outputs": [], + "source": [ + "# If necessary, you can change your cube header as necessary. We don't need to change anything in this case.\n", + "# But you might want to if you have a point source, yet want to extract a user specified background spectrum.\n", + "# The pipeline extracts EXTENDED and POINT sources differently.\n", + "\n", + "spec_model_cube.meta.target.source_type = 'EXTENDED'\n", + "spec_model_cube.meta.target.source_type_apt = 'EXTENDED'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ad67489", + "metadata": {}, + "outputs": [], + "source": [ + "# Read in previously extracted regions (this time the annuli)\n", + "\n", + "reg_outer = Regions.read('87a_extract_outer.reg', format='ds9')[0]\n", + "reg_inner = Regions.read('87a_extract_inner.reg', format='ds9')[0]\n", + "print(reg_outer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "448827ac", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a weight map using the region mask for the outer annulus\n", + "\n", + "tmp_wgts = spec_model_cube.weightmap[:]\n", + "mask = reg_outer.to_mask('exact')\n", + "x1 = int(reg_outer.center.x-reg_outer.width/2.)\n", + "x2 = x1+mask.shape[1]\n", + "y1 = int(reg_outer.center.y-reg_outer.height/2.)\n", + "y2 = y1+mask.shape[0]\n", + "\n", + "### Note above, the region shape is slightly different than the mask shape that gets generated. That's why I had to put in this hack about setting the size.\n", + "\n", + "# Start by setting all pixels to 0.\n", + "mask2d = tmp_wgts[13,:,:]*0.\n", + "\n", + "# Because we want an inverse array, we can't just use the mask, we have to subtract the mask (which is 1's) from the original mask2d above (make sense?) \n", + "mask2d[y1:y2,x1:x2] = mask2d[y1:y2,x1:x2]+mask\n", + "\n", + "# Take into account weird rounding errors\n", + "mask2d[mask2d>0.1] = 1." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2bd3c5de", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a weight map using the region mask for the inner annulus (and subtract it)\n", + "\n", + "tmp_wgts = spec_model_cube.weightmap[:]\n", + "mask = reg_inner.to_mask('exact')\n", + "x1 = int(reg_inner.center.x-reg_inner.width/2.)\n", + "x2 = x1+mask.shape[1]\n", + "y1 = int(reg_inner.center.y-reg_inner.height/2.)\n", + "y2 = y1+mask.shape[0]\n", + "\n", + "### Note above, the region shape is slightly different than the mask shape that gets generated. That's why I had to put in this hack about setting the size.\n", + "\n", + "# mask2d already defined in cell above\n", + "\n", + "# Because we want an inverse array, we can't just use the mask, we have to subtract the mask (which is 1's) from the original mask2d above (make sense?) \n", + "mask2d[y1:y2,x1:x2] = mask2d[y1:y2,x1:x2]-mask\n", + "\n", + "# Take into account weird rounding errors\n", + "mask2d[mask2d<0.1] = 0.\n", + "npix = len(mask2d[mask2d==1])\n", + "npix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0fa7cb5", + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize the 2D Mask\n", + "\n", + "from astropy.nddata import CCDData\n", + "from astropy.visualization import simple_norm\n", + "ccd = mask2d\n", + "norm = simple_norm(ccd, 'sqrt', min_cut=0, max_cut=0.5) \n", + "color = 'rgbmkrgbmk'\n", + "\n", + "xceni = [36, 44]\n", + "yceni = [66, 58]\n", + "\n", + "fig = plt.figure(figsize=(10, 10))\n", + "ax = fig.add_subplot()\n", + "counter = 0\n", + "plt.title(\"Masked Data\")\n", + "plt.imshow(ccd, norm=norm, origin=\"lower\") \n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2a26371", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a 3D weightmap from the 2D map. Mask all NAN values, too.\n", + "\n", + "mask3d = np.broadcast_to(mask2d, spec_model_cube.weightmap.shape)\n", + "mask3d.flags.writeable = True\n", + "mask3d[np.isnan(spec_model_cube.data)] = 0\n", + "#mask_sci_cube = np.ma.masked_array(spec_model_cube.weightmap, mask=mask3d.astype(bool))\n", + "tmp_wgt_cube = np.swapaxes(mask3d,0,1)\n", + "tmp_wgt_cube = np.swapaxes(tmp_wgt_cube,1,2)\n", + "plotcube = Spectrum1D(tmp_wgt_cube*u.dimensionless_unscaled)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "badea9cf", + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize the 3D Mask in Cubeviz\n", + "\n", + "cubeviz3 = Cubeviz()\n", + "cubeviz3.load_data(plotcube, data_label='SN1987A MASK')\n", + "cubeviz3.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18cd4752", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the weightmap in the background subtracted cube as the new 3D Mask \n", + "# This will tell the pipeline which spaxels to use for extraction\n", + "# Reassign the weightmap to the original cube and save it\n", + "spec_model_cube.weightmap = mask3d\n", + "spec_model_cube.save(spec3_dir+'87A_bg_sub_annulus_extract.fits',overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "id": "914bc219", + "metadata": {}, + "source": [ + "# 5. Extract SN Equitorial Ring Spectrum using Extract1dStep" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab919dd7", + "metadata": {}, + "outputs": [], + "source": [ + "# We will consider this our master background extraction\n", + "\n", + "cubefile_p1 = spec3_dir+'87A_bg_sub_annulus_extract.fits'\n", + "\n", + "outputfile = spec3_dir+'87A_bg_sub_annulus_extract.fits'\n", + "runex(cubefile_p1,spec3_dir,outputfile=outputfile)" + ] + }, + { + "cell_type": "markdown", + "id": "7e8b61e9", + "metadata": {}, + "source": [ + "# 6. Plot Final Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6fdf7de1", + "metadata": {}, + "outputs": [], + "source": [ + "# First Plot Background in the Background Subtracted Cube\n", + "\n", + "filelist = glob.glob(spec3_dir+'87A_bg_sub_skymask_extract1dstep.fits')\n", + "filelist" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "06e13c9d", + "metadata": {}, + "outputs": [], + "source": [ + "# MRS Header Keyword value for the pixel area in sterdians \n", + "\n", + "ref_cube = spec3_dir+'87A_bg_sub_annulus_extract.fits'\n", + "hdu = fits.open(ref_cube)\n", + "hdr = hdu[1].header\n", + "pixar_sr = hdr['PIXAR_SR'] #should be 3.97217570860291E-13\n", + "print(pixar_sr)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56cc11ec", + "metadata": {}, + "outputs": [], + "source": [ + "# Make Plot\n", + "\n", + "from specutils.manipulation import box_smooth, gaussian_smooth, trapezoid_smooth\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "counter = 0\n", + "\n", + "#color = cm.rainbow(np.linspace(0, 1, len(filelist)))\n", + "color = 'rgbmkrgbmk'\n", + "ylim_low = -1.e2\n", + "ylim_high = 5.e-2\n", + "for filename in filelist:\n", + " jpipe_x1d = Spectrum1D.read(filename)\n", + " jpipe_x1d = gaussian_smooth(jpipe_x1d,stddev=50)\n", + " ax.plot(jpipe_x1d.spectral_axis, jpipe_x1d.flux, color = color[counter], label = filename)\n", + " ax.set_title(\"Manual Background Extractions AFTER Master Background Subtraction\")\n", + " ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", + " ax.set_ylabel(\"Flux Density [MJy/Str]\")\n", + " #ax.set_yscale(\"log\")\n", + " ax.set_ylim(ylim_low, ylim_high)\n", + " #ax.set_xlim(xlim_low, xlim_high)\n", + " ax.legend(bbox_to_anchor=(1.1, 1.05))\n", + " counter = counter+1\n", + "\n", + "plt.savefig(spec3_dir+'87A_background.png')\n", + "\n", + "jpipe_x1d.flux[0:20]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c14eb1f2", + "metadata": {}, + "outputs": [], + "source": [ + "# Next Plot the SN 1987A Equitorial Ring from the Background Subtracted Cube\n", + "\n", + "filelist = glob.glob(spec3_dir+'87A_bg_sub_annulus_extract_extract1dstep.fits')\n", + "filelist" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0c700b6", + "metadata": {}, + "outputs": [], + "source": [ + "# MRS Header Keyword value for the pixel area in sterdians \n", + "\n", + "ref_cube = spec3_dir+'87A_bg_sub_annulus_extract.fits'\n", + "hdu = fits.open(ref_cube)\n", + "hdr = hdu[1].header\n", + "pixar_sr = hdr['PIXAR_SR'] #should be 3.97217570860291E-13\n", + "print(pixar_sr)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13b57670", + "metadata": {}, + "outputs": [], + "source": [ + "# Make Plot\n", + "\n", + "# Remember, the pipeline treats an EXTENDED source by averaging all the spaxels. \n", + "# But we want the total number flux, so we need to multiply by the number of spaxels in the annulus\n", + "# We calculated this value above when we were creating our mask (npix)\n", + "\n", + "from specutils.manipulation import box_smooth, gaussian_smooth, trapezoid_smooth\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "counter = 0\n", + "\n", + "#color = cm.rainbow(np.linspace(0, 1, len(filelist)))\n", + "color = 'rgbmkrgbmk'\n", + "ylim_low = 1.e-4\n", + "ylim_high = 1.5e-1\n", + "for filename in filelist:\n", + " jpipe_x1d = Spectrum1D.read(filename)\n", + " #jpipe_x1d = gaussian_smooth(jpipe_x1d,stddev=50)\n", + " flux_convert = jpipe_x1d.flux*1.e6*pixar_sr*npix # Convert from MJy/str to Jy\n", + " ax.plot(jpipe_x1d.spectral_axis, flux_convert, color = color[counter], label = filename)\n", + " ax.set_title(\"SN 1987A Ring AFTER Master Background Subtraction\")\n", + " ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n", + " ax.set_ylabel(\"Flux Density [Jy]\")\n", + " #ax.set_yscale(\"log\")\n", + " ax.set_ylim(ylim_low, ylim_high)\n", + " #ax.set_xlim(xlim_low, xlim_high)\n", + " ax.legend(bbox_to_anchor=(1.1, 1.05))\n", + " counter = counter+1\n", + "\n", + "plt.savefig(spec3_dir+'87A_ring_bg_sub.png')\n", + "\n", + "jpipe_x1d.flux[0:20]" + ] + } + ], + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/MIRI_MRS_1987A/requirements.txt b/notebooks/MIRI_MRS_1987A/requirements.txt new file mode 100644 index 000000000..0e0b08691 --- /dev/null +++ b/notebooks/MIRI_MRS_1987A/requirements.txt @@ -0,0 +1,4 @@ +git+https://github.com/spacetelescope/jdaviz.git +git+https://github.com/spacetelescope/jwst.git +astropy >= 5.3.1 +astroquery