diff --git a/Lightcurve/jwst_light_curve.ipynb b/Lightcurve/jwst_light_curve.ipynb new file mode 100644 index 0000000..217e788 --- /dev/null +++ b/Lightcurve/jwst_light_curve.ipynb @@ -0,0 +1,420 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# JWST light curve from rateint.fits file\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dataset Description\n", + "### The following datset are from jwst NIRSEPC BOTS data " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Dataset available at https://mast.stsci.edu/portal/Mashup/Clients/Mast/Portal.html" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.io import fits\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Countrate products are produced by applying the ramp_fitting step to the integrations within an exposure, in order to compute count rates from the original accumulating signal ramps. For exposures that contain multiple integrations (NINTS > 1) this is done in two ways, which results in two separate products. First, countrates are computed for each integration within the exposure, the results of which are stored in a rateints product. These products contain 3-D data arrays, where each plane of the data cube contains the countrate image for a given integration\n", + "\n", + "##### for more information visit the site https://jwst-pipeline.readthedocs.io/en/stable/jwst/data_products/science_products.html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Rateint int comes from stage 1 processing of uncall data. These data come from single exposures and are usually contained within a single FITS file. However, when the raw data volume for an individual exposure is large enough, like for time-series observations, the uncalibrated data can be broken into multiple segments less than 2GB each, so as to keep total file sizes to a reasonable level. Such broken-up exposures usually include \"segNNN\" in the file names, where NNN is 1-indexed and always includes any leading zeros https://jwst-docs.stsci.edu/accessing-jwst-data/jwst-science-data-overview" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": {}, + "outputs": [], + "source": [ + "#since jwst ligth curve are segmeneted\n", + "Required_files = [\n", + " \"jw01366003001_04101_00001-seg001_nrs2_rateints.fits\",\n", + " \"jw01366003001_04101_00001-seg002_nrs2_rateints.fits\",\n", + " \"jw01366003001_04101_00001-seg003_nrs2_rateints.fits\"\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Data Description\n", + "##### SCI: 3-D data array containing the pixel values, in units of DN/s. The first two dimensions are equal to the size of the detector readout, with the data from multiple integrations stored along the 3rd axis.\n", + "\n", + "##### ERR: 3-D data array containing uncertainty estimates on a per-integration basis. These values are based on the combined VAR_POISSON and VAR_RNOISE data (see below), given as standard deviation.\n", + "\n", + "##### DQ: 3-D data array containing DQ flags. Each plane of the cube corresponds to a given integration.\n", + "\n", + "##### INT_TIMES: A table of beginning, middle, and end time stamps for each integration in the exposure.\n", + "\n", + "##### VAR_POISSON: 3-D data array containing the per-integration variance estimates for each pixel, based on Poisson noise only.\n", + "\n", + "##### VAR_RNOISE: 3-D data array containing the per-integration variance estimates for each pixel, based on read noise only.\n", + "\n", + "##### ADSF: The data model meta data." + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filename: jw01366003001_04101_00001-seg001_nrs2_rateints.fits\n", + "No. Name Ver Type Cards Dimensions Format\n", + " 0 PRIMARY 1 PrimaryHDU 249 () \n", + " 1 SCI 1 ImageHDU 63 (2048, 32, 155) float32 \n", + " 2 ERR 1 ImageHDU 11 (2048, 32, 155) float32 \n", + " 3 DQ 1 ImageHDU 12 (2048, 32, 155) int32 (rescales to uint32) \n", + " 4 INT_TIMES 1 BinTableHDU 24 155R x 7C [J, D, D, D, D, D, D] \n", + " 5 VAR_POISSON 1 ImageHDU 10 (2048, 32, 155) float32 \n", + " 6 VAR_RNOISE 1 ImageHDU 10 (2048, 32, 155) float32 \n", + " 7 ASDF 1 BinTableHDU 11 1R x 1C [8277B] \n" + ] + } + ], + "source": [ + "#check data \n", + "file_to_check = Required_files[0]\n", + "hdul = fits.open(file_to_check)\n", + "hdul.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'XTENSION': 'IMAGE',\n", + " 'BITPIX': -32,\n", + " 'NAXIS': 3,\n", + " 'NAXIS1': 2048,\n", + " 'NAXIS2': 32,\n", + " 'NAXIS3': 155,\n", + " 'PCOUNT': 0,\n", + " 'GCOUNT': 1,\n", + " 'EXTNAME': 'SCI',\n", + " 'MJD-BEG': 59790.91951388889,\n", + " 'MJD-AVG': 59791.09184913195,\n", + " 'MJD-END': 59791.264184375,\n", + " 'TDB-BEG': 59790.92023663242,\n", + " 'TDB-MID': 59791.09255526889,\n", + " 'TDB-END': 59791.26487390614,\n", + " 'XPOSURE': 29360.1,\n", + " 'TELAPSE': 29789.053,\n", + " '': \n", + " JWST ephemeris information\n", + " \n", + " \n", + " Information about the coordinates in the file\n", + " \n", + " \n", + " Spacecraft pointing information\n", + " \n", + " \n", + " WCS parameters,\n", + " 'REFFRAME': 'EME2000',\n", + " 'EPH_TYPE': 'Definitive',\n", + " 'EPH_TIME': 59791.09184913195,\n", + " 'JWST_X': 91815476.84790646,\n", + " 'JWST_Y': -111522899.1536672,\n", + " 'JWST_Z': -48731897.45675641,\n", + " 'OBSGEO-X': 545345099.5021366,\n", + " 'OBSGEO-Y': -1291929567.847113,\n", + " 'OBSGEO-Z': -978961274.49491,\n", + " 'JWST_DX': 23.21515156724432,\n", + " 'JWST_DY': 16.56018448588144,\n", + " 'JWST_DZ': 7.282985514766873,\n", + " 'OBSGEODX': 89.20175080884282,\n", + " 'OBSGEODY': -0.811047580746933,\n", + " 'OBSGEODZ': 103.5262274780199,\n", + " 'PA_APER': 247.52683001915398,\n", + " 'VA_SCALE': 0.9999036448783407,\n", + " 'BUNIT': 'DN/s',\n", + " 'RADESYS': 'ICRS',\n", + " 'RA_V1': 217.48013207246336,\n", + " 'DEC_V1': -3.402094291064577,\n", + " 'PA_V3': 108.66460826121968,\n", + " 'S_REGION': 'POLYGON ICRS 217.326472754 -3.444794415 217.326885954 -3.444623768 217.326712385 -3.444214679 217.326299175 -3.444385300',\n", + " 'V2_REF': 321.26297,\n", + " 'V3_REF': -473.851288,\n", + " 'VPARITY': -1,\n", + " 'V3I_YANG': 138.85295105,\n", + " 'RA_REF': 217.32659258256018,\n", + " 'DEC_REF': -3.444504541495905,\n", + " 'ROLL_REF': 108.67387896915398,\n", + " 'VELOSYS': 28887.89,\n", + " 'XREF_SCI': 0,\n", + " 'YREF_SCI': 0,\n", + " 'EXTVER': 1}" + ] + }, + "execution_count": 217, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "file_to_check = Required_files[0]\n", + "hdul = fits.open(file_to_check)\n", + "hdul_dict = dict(hdul[\"SCI\"].header)\n", + "hdul_dict" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y dim = 2048\n", + "x dim = 32\n", + "num_integration = 155\n" + ] + }, + { + "data": { + "text/plain": [ + "array([nan, nan, nan, ..., nan, nan, nan], shape=(2048,), dtype='>f4')" + ] + }, + "execution_count": 218, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_integration , x_dim , y_dim = hdul[\"SCI\"].data.shape\n", + "print(f\"y dim = {y_dim}\")\n", + "print(f\"x dim = {x_dim}\")\n", + "print(f\"num_integration = {num_integration}\")\n", + "\n", + "hdul[\"SCI\"].data[0][0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Set up matplotlib\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "sl_fig, axs = plt.subplots(ncols=1, nrows=4, figsize=[8, 8])\n", + "\n", + "for i, ax in enumerate(axs.flat):\n", + " im = ax.imshow(hdul[\"SCI\"].data[i, :, :], \n", + " origin='lower', \n", + " aspect='auto', \n", + " interpolation='none',\n", + " cmap='grey')\n", + " ax.axis(\"off\") " + ] + }, + { + "cell_type": "code", + "execution_count": 210, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([785604.62805836, 791037.98383455, 788787.22850215, 787035.34815956,\n", + " 792356.27529687, 790661.87443803, 787510.5756797 , 792994.97710949,\n", + " 793491.19394794, 789897.06129328])" + ] + }, + "execution_count": 210, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#concatenating all the data into a singal array\n", + "all_flux = []\n", + "\n", + "\n", + "# Loop through all segments\n", + "for file_name in Required_files:\n", + " with fits.open(file_name) as hdul:\n", + " ini_array = hdul[\"SCI\"].data \n", + " data = np.where(np.isnan(ini_array), np.ma.array(ini_array, \n", + " mask=np.isnan(ini_array)).mean(axis=0), ini_array)\n", + " flux = np.nansum(data, axis=(1, 2)) \n", + "\n", + " all_flux.extend(flux)\n", + "\n", + "all_flux = np.array(all_flux)\n", + "all_flux[0:10] " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(np.float64(59790.9201561958), np.float64(59790.920521705826), np.float64(59790.92088721585), np.float64(59790.92087887377), np.float64(59790.92124434857), np.float64(59790.92160982337))\n", + "(155,)\n" + ] + } + ], + "source": [ + "#Time\n", + "time_check = fits.open(file_to_check)\n", + "time_arr = time_check[\"INT_TIMES\"].data\n", + "print(time_arr[1][1:])\n", + "print(time_arr.shape) \n", + "\n", + "# Its is an 1d array with [num int,times (as listed below)]\n", + "# MJD-BEG\n", + "# MJD-AVG\n", + "# MJD-END\n", + "# TDB-BEG\n", + "# TDB-MID\n", + "# TDB-END" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "metadata": {}, + "outputs": [], + "source": [ + "all_time = []\n", + "for file_name in Required_files:\n", + " with fits.open(file_name) as hdul_time:\n", + " time_lc = hdul_time[\"INT_TIMES\"].data\n", + " for i in range(time_lc.shape[0]):\n", + " all_time.append(time_lc[i][1:][0])\n", + "\n", + "all_time = np.array(all_time)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(465,)\n", + "(465,)\n" + ] + } + ], + "source": [ + "#verification since shape for time and flux should be same \n", + "print(all_time.shape)\n", + "print(all_flux.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [], + "source": [ + "from stingray import Lightcurve\n", + "\n", + "lc = Lightcurve(all_time,all_flux)" + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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