{ "cells": [ { "cell_type": "markdown", "id": "8456912d-5559-449e-b7f4-11e89ab35513", "metadata": {}, "source": [ "# NIRSpec Tutorial 1: Data Reductions for BREADS\n", "\n", "## Configuring the BREADS pipeline analysis" ] }, { "cell_type": "markdown", "id": "ec443daf-3f1d-446f-b452-45ee5ee4481e", "metadata": {}, "source": [ "For performance reasons, we configure these various environement variables to prevent numpy from internally parallelizing certain function calls internally which are used during parallelized steps in the analysis pipeline. This cell must be run *before* numpy is imported for them to be recognized." ] }, { "cell_type": "code", "id": "e83a363c-c5f8-44f9-ab25-19806ca2bc69", "metadata": { "ExecuteTime": { "end_time": "2026-02-20T23:57:23.550604Z", "start_time": "2026-02-20T23:57:23.548061Z" } }, "source": [ "import os\n", "\n", "os.environ[\"OMP_NUM_THREADS\"] = \"1\" # export OMP_NUM_THREADS=1\n", "os.environ[\"OPENBLAS_NUM_THREADS\"] = \"1\" # export OPENBLAS_NUM_THREADS=1 \n", "os.environ[\"MKL_NUM_THREADS\"] = \"1\" # export MKL_NUM_THREADS=1\n", "os.environ[\"VECLIB_MAXIMUM_THREADS\"] = \"1\" # export VECLIB_MAXIMUM_THREADS=1\n", "os.environ[\"NUMEXPR_NUM_THREADS\"] = \"1\" # export NUMEXPR_NUM_THREADS=1" ], "outputs": [], "execution_count": 1 }, { "cell_type": "markdown", "id": "1bb51801-4fc9-4488-8408-a6798feec8ed", "metadata": {}, "source": [ "**This cell must be modified!** Specify the directory where you previously stored the data products" ] }, { "cell_type": "code", "id": "99ee1b47-bbab-4eb5-9818-0d4c80208dff", "metadata": { "ExecuteTime": { "end_time": "2026-02-20T23:57:23.779126Z", "start_time": "2026-02-20T23:57:23.776948Z" } }, "source": "base_path = '/astro/epsig/tutorial/'", "outputs": [], "execution_count": 2 }, { "cell_type": "markdown", "id": "dace14a4-d5d8-4e7f-bd62-7e3f27f4da09", "metadata": {}, "source": [ "Creating even more subdirectories for organizing data products." ] }, { "cell_type": "code", "id": "69e4160a-74b0-406b-97de-089156568bdb", "metadata": { "ExecuteTime": { "end_time": "2026-02-20T23:57:24.536473Z", "start_time": "2026-02-20T23:57:24.533698Z" } }, "source": [ "data_path = base_path+'data/'\n", "raw_path = data_path+'raw/'\n", "output_path = base_path+'outputs/'\n", "PSF_path = base_path+'PSFs/' \n", "\n", "create_paths = [base_path,data_path,raw_path,output_path,PSF_path]\n", "for path in create_paths:\n", " if not os.path.exists(path):\n", " os.mkdir(path)" ], "outputs": [], "execution_count": 3 }, { "cell_type": "markdown", "id": "e4cf8cf5-4220-49aa-8487-c3c953b7642c", "metadata": {}, "source": [ "**This cell must be modified!** The JWST pipeline requires configuring an environment variable ```CRDS_PATH``` for storing various calibration reference data, while the BREADS pipeline requires installation of STPSF/WEBBPSF for computing synthethic point spread functions used during the modelling." ] }, { "cell_type": "code", "id": "7e9fff50-a2f9-4676-b55e-b5e90c90aa4d", "metadata": { "ExecuteTime": { "end_time": "2026-02-20T23:57:25.583853Z", "start_time": "2026-02-20T23:57:25.581686Z" } }, "source": "os.environ['STPSF_PATH'] = '/fast/jruffio/data/stpsf-data/'", "outputs": [], "execution_count": 4 }, { "cell_type": "markdown", "id": "1fb92a60-6631-4d84-a0a8-9cea75078e9e", "metadata": {}, "source": [ "Time to import a bunch of stuff." ] }, { "cell_type": "code", "id": "f202a4cc-3ddf-4cbc-8a14-fcf81bc79fd4", "metadata": { "ExecuteTime": { "end_time": "2026-02-20T23:57:29.416364Z", "start_time": "2026-02-20T23:57:26.576386Z" } }, "source": [ "import time\n", "import jwst\n", "print('JWST pipeline version',jwst.__version__) # Print out what pipeline version we're using\n", "\n", "from multiprocess import Pool\n", "import numpy as np\n", "import datetime\n", "from copy import copy\n", "from glob import glob\n", "\n", "from astropy.io import fits\n", "from astropy import constants as const\n", "from astropy.table import Table\n", "import astropy.units as u\n", "\n", "from scipy.interpolate import interp1d\n", "from scipy.interpolate import LinearNDInterpolator\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "\n", "from breads.jwst_tools.reduction_utils import find_files_to_process\n", "from breads.jwst_tools.reduction_utils import run_stage1,run_stage2\n", "from breads.jwst_tools.reduction_utils import run_coordinate_recenter\n", "from breads.jwst_tools.reduction_utils import run_noise_clean\n", "from breads.jwst_tools.reduction_utils import compute_normalized_stellar_spectrum\n", "from breads.jwst_tools.reduction_utils import compute_starlight_subtraction\n", "from breads.jwst_tools.reduction_utils import get_combined_regwvs\n", "from breads.jwst_tools.reduction_utils import get_2D_point_cloud_interpolator\n", "from breads.jwst_tools.reduction_utils import save_combined_regwvs\n", "\n", "from breads.instruments.jwst_IFUs import build_cube\n", "from breads.utils import rotate_coordinates\n", "\n", "numthreads = 4#os.cpu_count()" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "JWST pipeline version 1.20.2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jruffio/anaconda3/envs/breads_2026/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], "execution_count": 5 }, { "cell_type": "markdown", "id": "aa3690e1-30e1-4dff-ae08-fd0750c3d3ac", "metadata": {}, "source": [ "The follwing saves the pre-configured non-uniform nodes defined by Ruffio et al. 2025." ] }, { "cell_type": "code", "id": "a2d85dca-de2b-4680-b6ae-883ee19dd48c", "metadata": { "ExecuteTime": { "end_time": "2026-02-20T23:57:30.841084Z", "start_time": "2026-02-20T23:57:30.800392Z" } }, "source": [ "wv_nodes_dict = {'nrs1': np.array([2.859, 2.879, 2.899, 2.919, 2.939, 2.959, 2.979, 2.999, 3.019,\n", " 3.039, 3.059, 3.079, 3.099, 3.119, 3.139, 3.159, 3.189, 3.219,\n", " 3.249, 3.279, 3.309, 3.339, 3.369, 3.399, 3.429, 3.459, 3.499,\n", " 3.539, 3.579, 3.619, 3.659, 3.699, 3.739, 3.779, 3.839, 3.899,\n", " 3.959, 4.019, 4.079, 4.139]),\n", " 'nrs2': np.array([4.081, 4.141, 4.201, 4.261, 4.321, 4.361, 4.401, 4.441, 4.481,\n", " 4.521, 4.561, 4.601, 4.641, 4.671, 4.701, 4.731, 4.761, 4.791,\n", " 4.821, 4.851, 4.881, 4.911, 4.941, 4.971, 4.991, 5.011, 5.031,\n", " 5.051, 5.071, 5.091, 5.111, 5.131, 5.151, 5.171, 5.191, 5.211,\n", " 5.231, 5.251, 5.271, 5.291])}\n", "\n", "np.save(base_path+'nonuniform_nodes.npy',wv_nodes_dict)\n", "wv_nodes_dict" ], "outputs": [ { "data": { "text/plain": [ "{'nrs1': array([2.859, 2.879, 2.899, 2.919, 2.939, 2.959, 2.979, 2.999, 3.019,\n", " 3.039, 3.059, 3.079, 3.099, 3.119, 3.139, 3.159, 3.189, 3.219,\n", " 3.249, 3.279, 3.309, 3.339, 3.369, 3.399, 3.429, 3.459, 3.499,\n", " 3.539, 3.579, 3.619, 3.659, 3.699, 3.739, 3.779, 3.839, 3.899,\n", " 3.959, 4.019, 4.079, 4.139]),\n", " 'nrs2': array([4.081, 4.141, 4.201, 4.261, 4.321, 4.361, 4.401, 4.441, 4.481,\n", " 4.521, 4.561, 4.601, 4.641, 4.671, 4.701, 4.731, 4.761, 4.791,\n", " 4.821, 4.851, 4.881, 4.911, 4.941, 4.971, 4.991, 5.011, 5.031,\n", " 5.051, 5.071, 5.091, 5.111, 5.131, 5.151, 5.171, 5.191, 5.211,\n", " 5.231, 5.251, 5.271, 5.291])}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 6 }, { "cell_type": "markdown", "id": "c61d1f8f-2a09-4526-8d19-ef0e63d447e2", "metadata": {}, "source": [ "This cell configures various aspects in the analysis.\n", "\n", "- targetname: SIMBAD resolve-able name for the host to obtain sky coordinates\n", "- grating: 'G395H' is supported for now. Other gratings are WIP.\n", "- mask_charge_transfer_radius: either 'None' or float (in arcsec) half-width of rectangular region to mask around stellar charge bleed. 0.1 is a reasonable value\n", "- model_charge_transfer: boolean. (Experimental) subtraction of unilluminated pixels in rate files using Lorentzian-based model. " ] }, { "cell_type": "code", "id": "0a3b039d-fc97-4980-8811-17ed5aca71fa", "metadata": { "ExecuteTime": { "end_time": "2026-02-20T23:57:32.396342Z", "start_time": "2026-02-20T23:57:32.394042Z" } }, "source": [ "targetname = 'HD 19467'\n", "grating = 'G395H'\n", "mask_charge_transfer_radius = None\n", "model_charge_transfer = False" ], "outputs": [], "execution_count": 7 }, { "cell_type": "markdown", "id": "1ec17e00-3d4c-4a92-9632-bc11c1b711ca", "metadata": {}, "source": [ "### Running the pipeline" ] }, { "cell_type": "markdown", "id": "212945ec-28ff-44ea-91ed-3724a4be1c17", "metadata": {}, "source": [ "Below is a behemoth of a function which combines multiple steps in the analysis chain into a single call. The intention is to use this function to conveniently to loop over both detectors ```nrs1``` and ```nrs2``` and various sequence numbers while running the same code. It could also be used to iterate over other parameters such as ```targetname``` if you have multiple datasets on different objects. For pedagogical purposes it would be preferable to split it apart for more localized explanations, however, what follows will be (1) the broad purposes of this function (2) the long function itself and (3) calling the function with a simple loop over both detectors." ] }, { "cell_type": "markdown", "id": "08c5b95a-ae90-4624-8471-4dbf3d6b1eed", "metadata": {}, "source": [ "Step 0: Preparations\n", "- The detector argument selects the node specification from the dictionary loaded ealier\n", "- The filename_filter and detector are used to construct the glob string to find the observation files\n", "- Paths to various ```/data/``` subdirectories are created for intermediate data products, including unique idenfiers for charge transfer modelling or joint sequence modelling to avoid mixtures.\n", "\n", "Step 1: JWST pipeline\n", "- Stages 1 and 2 of the JWST pipeline are run on the observations found with the glob string.\n", "- The exact parameters passed to the pipeline are hard coded inside the ```run_stage1``` and ```run_stage2``` functions\n", "\n", "Step 2: Sky coordinate calibration\n", "- This is a two-step iterative process to correct the JWST pipeline provided coordinates.\n", "- Inside ```run_coordiante_recenter```, ```fitpsf``` is used to find the best-fit coordinates for the star as a function of wavelength by fitting a PSF model to the stage 2 calibrated data.\n", "- The first iteration is initialized at (0,0), while the second iteration is initialized at the best fit centroid of the previous step.\n", "\n", "Step 3: 1/f noise clean (and) charge transfer modelling\n", "- The coordinate corrections are applied to the post-stage 1 rate files.\n", "- A simple linear 1/f noise correction is applied to the rate data to remove striping artifacts\n", "- If ```model_charge_transfer=True```, then a more complicated lorentzian-based model of the charge bleeding profile is also subtracted from the rate files. This step is experiemental and the Lorentzian model is still in development. For the tutorial we have disabled it for this analysis since HD 19467 B is widely separated it is non-issue. But for targets very close in ( < 0.3\" separation) this could be very helpful.\n", "\n", "Step 4: JWST stage 2 pipeline again (post charge bleed model)\n", "- Stage 2 of the JWST pipeline is run to produce cal files which have 1/f noise and charge bleeding corrections applied\n", "\n", "Step 5: Computing the (high pass filtered) stellar spectrum\n", "- ```compute_normalized_stellar_spectrum``` is called on the cal files to obtain the high-frequency components of the starlight model.\n", "- The argument ```ra_dec_point_sources``` can be used to mask out the region corresponding to the planet signal or other interlopers in the FOV. This argument expects an ```(ra,dec)``` tuple in units of milliarcseconds. However, this argument is skipped here.\n", "\n", "Step 6: Subtracting starlight model (spline and high frequency components)\n", "- ```compute_starlight_subtraction``` is called on cal files using the starlight spectrum computed previously. This combines the spline continuum model with the high frequency stellar spectrum to produce continuum-subtracted detector data.\n", "\n", "Step 7: Creating the point-cloud interpolator (for unsubtracted PSF visualization)\n", "- ```get_combined_regwvs``` is used with the arugment ```use_starsub=False``` to interpolate the non-star-subtracted detector data onto a regular wavelength grid and to stack the sequence of data objects into single instance\n", "- if ```JOINT=False``` and we are only modelling a single sequence we call ```regwvs_combdataobj.set_coords2ifu()``` to transform from sky to IFU coordinates, otherwise the ```JOINT``` modelling remains in sky coordinates to stack observations at different roll angles / dither positions properly.\n", "- ```get_2D_point_cloud_interpolator``` is called to transform the stacked detector data into an interpolation function internally relying on ```matplotlib.tri.LinearTriInterpolator``` to make a linear interpolation triangular mesh of the non-uniform spaced point cloud data\n", "- ```save_combined_regwvs``` is called to save this object into the ```/PSFs/``` subfolder for later access, while the ```analysis_chain()``` function returns the ```pointcloud_interp``` object so we can directly call it later in this script.\n", "\n", "Step 8: Preparing for ```build_cube```\n", "- ```get_combined_regwvs``` is called with ```use_starsub=True```. This will do the same regular wavelength interpolation and sequence stacking from Step 7 but this time on the detector data with the starlight subtracted. This forms the primary input for the final step for building the spectral cube.\n", "- Similarly, if ```JOINT=False``` and we are only modelling a single sequence we call ```regwvs_starsub_combdataobj.set_coords2ifu()``` to transform from sky to IFU coordinates, otherwise the ```JOINT``` modelling remains in sky coordinates to stack obserations at different roll angles / dither positions properly.\n", "- The parameters for ```ra_vec``` and ```dec_vec``` which are the coordinate position inputs to ```build_cube``` as 1D vectors are extracted from the minimum/maximum coordinates of the combined data object. For the joint modelling these values are hard coded and may need to be modified for different datasets.\n", "- The WebbPSF/STPSF model is computed/reloaded to prepare for the final step.\n", "\n", "Step 9: Running ```build_cube```\n", "- ```build_cube``` in essence is performing spectral extraction on the starlight-subtracted/regular-wavelength-interpolated detector sequences\n", "- At each position in the outer product of ```ra_vec``` and ```dec_vec```, the WebbPSF model is fit to the data in a region of size ```aper_radius``` (arcsec)\n", "- The final cubes built are saved in the ```/outputs/``` subdirectory and form the primary data product which will be analyzed in subsequent notebooks\n", "- ```debug_init,debug_end = None,None``` is the setting to run the full analysis, however, specifying interger wavelength indices can be used to analyze smaller subsets of the data or for debugging purposes." ] }, { "cell_type": "code", "id": "7514beb2-c6ee-434c-873e-7595df32079c", "metadata": { "ExecuteTime": { "end_time": "2026-02-20T23:57:34.978706Z", "start_time": "2026-02-20T23:57:34.976937Z" } }, "source": [], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "df290383-7f29-4dcf-a5d1-2a44981249e7", "metadata": { "ExecuteTime": { "end_time": "2026-02-20T23:57:38.029905Z", "start_time": "2026-02-20T23:57:38.018663Z" } }, "source": [ "def analysis_chain():\n", " \n", " ###### Step 0 ######\n", " \n", " wv_nodes = wv_nodes_dict[detector]\n", "\n", " uncal_filename_filter = filename_filter+'_*_'+detector+'_uncal.fits'\n", " cal_filename_filter = filename_filter+'_*_'+detector+'_cal.fits'\n", "\n", " if model_charge_transfer:\n", " MCT_path_append = '_MCT'\n", " else:\n", " MCT_path_append = ''\n", " \n", " if JOINT:\n", " joint_path_append = '_joint'\n", " else:\n", " joint_path_append = ''\n", " \n", " uncal_files = find_files_to_process(raw_path, filetype=uncal_filename_filter)\n", " stage1_outdir = os.path.join(data_path, grating + \"_stage1\")\n", " stage2_outdir = os.path.join(data_path, grating+\"_stage2\")\n", " utils_before_cleaning_dir = os.path.join(data_path, grating+\"_utils_before_cleaning\")\n", " stage1_clean_outdir = os.path.join(data_path, grating+\"_stage1_cleaned\"+MCT_path_append)\n", " stage2_clean_outdir = os.path.join(data_path, grating+\"_stage2_cleaned\"+MCT_path_append)\n", " utils_dir = os.path.join(data_path, grating+\"_utils\"+MCT_path_append)\n", "\n", " ###### Step 1 ######\n", " \n", " rate_files = run_stage1(uncal_files, stage1_outdir, overwrite=False, maximum_cores=\"1\")\n", " cal_files = run_stage2(rate_files, stage2_outdir, skip_cubes=True, overwrite=False)\n", "\n", " ###### Step 2 ######\n", " mypool = Pool(processes=numthreads)\n", " \n", " poly_p_RA,poly_p_dec = run_coordinate_recenter(cal_files,utils_before_cleaning_dir,\n", " init_centroid = (0,0),wv_sampling=None, N_wvs_nodes=40,\n", " mask_charge_transfer_radius = mask_charge_transfer_radius,\n", " IWA=0.3,OWA=1.0,\n", " debug_init=None,debug_end=None,\n", " mppool = mypool,\n", " save_plots=True,\n", " overwrite=False,\n", " filename_suffix = \"_webbpsf_init\",\n", " targetname=targetname)\n", "\n", " poly_p_RA,poly_p_dec = run_coordinate_recenter(cal_files,utils_before_cleaning_dir,\n", " init_centroid = (poly_p_RA[-1],poly_p_dec[-1]),wv_sampling=None, N_wvs_nodes=40,\n", " mask_charge_transfer_radius = mask_charge_transfer_radius,\n", " IWA=0.3,OWA=1.0,\n", " debug_init=None,debug_end=None,\n", " mppool = mypool,\n", " save_plots=True,\n", " overwrite=False,\n", " filename_suffix = \"_webbpsf\",\n", " targetname=targetname)\n", " \n", " ###### Step 3 ######\n", " \n", " if model_charge_transfer:\n", " print('!NOISE CLEAN! starting in: {}'.format(stage1_clean_outdir))\n", " new_rate_files = run_noise_clean(rate_files, stage2_outdir, stage1_clean_outdir,\n", " N_nodes=40,\n", " model_charge_transfer=model_charge_transfer, utils_dir=utils_before_cleaning_dir,\n", " coords_offset=(poly_p_RA,poly_p_dec), overwrite=False)\n", " \n", " ###### Step 4 ######\n", " \n", " cleaned_cal_files = run_stage2(new_rate_files, stage2_clean_outdir, skip_cubes=True, overwrite=False)\n", "\n", " ###### Step 5 ######\n", "\n", " combined_star_func = compute_normalized_stellar_spectrum(cleaned_cal_files, utils_dir,\n", " coords_offset=(poly_p_RA,poly_p_dec),\n", " wv_nodes=wv_nodes,\n", " mask_charge_transfer_radius = mask_charge_transfer_radius,\n", " mppool=mypool,\n", " ra_dec_point_sources=None, overwrite=False,\n", " targetname=targetname)\n", " ###### Step 6 ######\n", " \n", " dataobj_list = compute_starlight_subtraction(cleaned_cal_files, utils_dir, combined_star_func=combined_star_func,\n", " coords_offset=(poly_p_RA,poly_p_dec), mppool=mypool, targetname=targetname, wv_nodes=wv_nodes)\n", " \n", " ###### Step 7 ######\n", " \n", " regwvs_combdataobj = get_combined_regwvs(dataobj_list,\n", " mask_charge_transfer_radius=mask_charge_transfer_radius,\n", " use_starsub=False)\n", " if JOINT:\n", " pass #remain in sky coords\n", " out_filename = os.path.join(PSF_path,targetname+\"_\"+grating+\"_\"+detector+\"_2d_point_cloud\"+joint_path_append+\".fits\")\n", " else:\n", " regwvs_combdataobj.set_coords2ifu()\n", " out_filename = os.path.join(PSF_path,targetname+\"_\"+grating+\"_\"+detector+\"_2d_point_cloud.fits\")\n", " \n", " # 2D interpolator object median wavelength\n", " wv_sampling = regwvs_combdataobj.wv_sampling\n", " wv0 = np.nanmedian(regwvs_combdataobj.wavelengths)\n", " wv0_id = np.argmin(np.abs(wv_sampling-wv0))\n", " pointcloud_interp = get_2D_point_cloud_interpolator(regwvs_combdataobj,wv0)\n", " save_combined_regwvs(regwvs_combdataobj, out_filename)\n", "\n", " ###### Step 8 ######\n", " \n", " if JOINT:\n", " dramin, dramax = -2, 2\n", " ddecmin, ddecmax = -2, 2\n", " else:\n", " dramin, dramax = np.nanmin(regwvs_combdataobj.dra_as_array),np.nanmax(regwvs_combdataobj.dra_as_array)\n", " ddecmin, ddecmax = np.nanmin(regwvs_combdataobj.ddec_as_array),np.nanmax(regwvs_combdataobj.ddec_as_array)\n", " print(dramin, dramax, ddecmin, ddecmax)\n", " ra_vec = np.linspace(dramin,dramax,60)\n", " dec_vec = np.linspace(ddecmin,ddecmax,60)\n", " print(ra_vec, dec_vec)\n", " \n", " cleaned_cal_files = find_files_to_process(stage2_clean_outdir, filetype=cal_filename_filter)\n", " \n", " splitbasename = os.path.basename(cleaned_cal_files[0]).split(\"_\")\n", " filename_suffix = \"_webbpsf\"\n", " poly2d_centroid_filename = os.path.join(utils_before_cleaning_dir, splitbasename[0] + \"_\" + splitbasename[1] + \"_\" + splitbasename[3] + \"_poly2d_centroid\" + filename_suffix + \".txt\")\n", "\n", " if JOINT:\n", " cube_filename = os.path.join(output_path,splitbasename[0]+\"_\"+splitbasename[1]+\"_\"+splitbasename[3]+\"_spectral_cube_ish\"+joint_path_append+\".fits\")\n", " else:\n", " cube_filename = os.path.join(output_path,splitbasename[0]+\"_\"+splitbasename[1]+\"_\"+splitbasename[3]+\"_spectral_cube_ish.fits\")\n", " \n", " # regwvs WITH starsub\n", " regwvs_starsub_combdataobj = get_combined_regwvs(dataobj_list,\n", " mask_charge_transfer_radius=mask_charge_transfer_radius,\n", " use_starsub=True)\n", " if JOINT:\n", " pass #remain in sky coords\n", " else:\n", " regwvs_starsub_combdataobj.set_coords2ifu()\n", " \n", " # Fit a model PSF (WebbPSF) to the combined point cloud of dataobj_list\n", " debug_init,debug_end = None,None\n", " #debug_init,debug_end = 1000,1100 # min max wavelength indices for partial extraction\n", " aper_radius = 0.15\n", " \n", " webbpsf_reload = regwvs_starsub_combdataobj.reload_webbpsf_model()\n", " if webbpsf_reload is None:\n", " webbpsf_reload = regwvs_starsub_combdataobj.compute_webbpsf_model(\n", " wv_sampling=regwvs_starsub_combdataobj.wv_sampling,\n", " image_mask=None,\n", " pixelscale=0.1, oversample=10,\n", " parallelize=True, mppool=mypool,\n", " save_utils=True)\n", " wpsfs, wpsfs_header, wepsfs, webbpsf_wvs, webbpsf_X, webbpsf_Y, wpsf_oversample, wpsf_pixelscale = webbpsf_reload\n", " webbpsf_X = np.tile(webbpsf_X[None, :, :], (wepsfs.shape[0], 1, 1))\n", " webbpsf_Y = np.tile(webbpsf_Y[None, :, :], (wepsfs.shape[0], 1, 1))\n", "\n", " ###### Step 9 ######\n", " \n", " t0 = time.time()\n", " flux_cube,fluxerr_cube,ra_grid, dec_grid = \\\n", " build_cube(regwvs_starsub_combdataobj, # combined point cloud\n", " wepsfs, webbpsf_X, webbpsf_Y, # webbPSF model for flux extraction\n", " ra_vec, dec_vec, # spatial sampling of final cube\n", " out_filename=cube_filename,linear_interp=True,mppool=mypool,aper_radius=aper_radius,\n", " debug_init=debug_init,debug_end=debug_end) \n", " t1 = time.time()\n", " print('build cube ran in {} seconds...'.format(np.round(t1-t0)))\n", "\n", " return pointcloud_interp,ra_vec,dec_vec\n" ], "outputs": [], "execution_count": 8 }, { "cell_type": "markdown", "id": "0d76c3a2-7620-4e9f-a2f8-363eddd2d27a", "metadata": {}, "source": [ "Now it is time to actually call the ```analysis_chain()``` function. There are a couple of variables which are iterated over\n", "\n", "- JOINT: boolean. If True, combine multiple sequences using sky coordinates before constructing the spectral cube. If False, requires specifying ```seq_num``` and will only analyze a single sequence using IFU coordinates instead.\n", "- seq_num: part of the filename identifier after the program id. The joint analysis uses 012, 013, and 014 and accomplishes this with the glob wildcard.\n", "\n", "Additionally, if you are adapting this script to run on different datasets than the test dataset, the filename filter will need to be modified to match.\n", "\n", "The cell first individually analyzes sequences 012, 013, 014, constructing cubes in IFU coordinates by iterating over the sequence numbers and detector strings with JOINT = False. Lastly, the cell combines all the sequences in sky coordinates using JOINT = True and modifying the filename filter to include the glob wildcard ```'01[2,3,4]'``` in place of the sequence number." ] }, { "cell_type": "code", "id": "a7c0ea6d-c3b3-47ce-b001-e956255cf302", "metadata": { "scrolled": true, "ExecuteTime": { "end_time": "2026-02-21T02:23:50.722377Z", "start_time": "2026-02-21T01:53:45.519771Z" } }, "source": [ "for seq_num,JOINT in [('013',False)]: # For a partial test run, otherwise use the line below to run the full analysis on all sequences and both detectors\n", "# for seq_num,JOINT in [('012',False),('013',False),('014',False),('01[2,3,4]',True)]:\n", " filename_filter = 'jw01414'+seq_num+'001_02101' \n", " for detector in ['nrs1','nrs2']: # or ['nrs1','nrs2'] for processing both detectors\n", " pointcloud_interp,ra_vec,dec_vec = analysis_chain()" ], "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-02-20 17:53:45,566 - stpipe.step - INFO - PARS-DARKCURRENTSTEP parameters found: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pars-darkcurrentstep_0005.asdf\n", "2026-02-20 17:53:45,595 - stpipe.step - INFO - PARS-JUMPSTEP parameters found: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pars-jumpstep_0003.asdf\n", "2026-02-20 17:53:45,605 - stpipe.pipeline - INFO - PARS-DETECTOR1PIPELINE parameters found: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pars-detector1pipeline_0004.asdf\n", "2026-02-20 17:53:45,617 - stpipe.step - INFO - Detector1Pipeline instance created.\n", "2026-02-20 17:53:45,618 - stpipe.step - INFO - GroupScaleStep instance created.\n", "2026-02-20 17:53:45,619 - stpipe.step - INFO - DQInitStep instance created.\n", "2026-02-20 17:53:45,619 - stpipe.step - INFO - EmiCorrStep instance created.\n", "2026-02-20 17:53:45,620 - stpipe.step - INFO - SaturationStep instance created.\n", "2026-02-20 17:53:45,620 - stpipe.step - INFO - IPCStep instance created.\n", "2026-02-20 17:53:45,621 - stpipe.step - INFO - SuperBiasStep instance created.\n", "2026-02-20 17:53:45,622 - stpipe.step - INFO - RefPixStep instance created.\n", "2026-02-20 17:53:45,622 - stpipe.step - INFO - RscdStep instance created.\n", "2026-02-20 17:53:45,623 - stpipe.step - INFO - FirstFrameStep instance created.\n", "2026-02-20 17:53:45,623 - stpipe.step - INFO - LastFrameStep instance created.\n", "2026-02-20 17:53:45,624 - stpipe.step - INFO - LinearityStep instance created.\n", "2026-02-20 17:53:45,624 - stpipe.step - INFO - DarkCurrentStep instance created.\n", "2026-02-20 17:53:45,625 - stpipe.step - INFO - ResetStep instance created.\n", "2026-02-20 17:53:45,626 - stpipe.step - INFO - PersistenceStep instance created.\n", "2026-02-20 17:53:45,626 - stpipe.step - INFO - ChargeMigrationStep instance created.\n", "2026-02-20 17:53:45,627 - stpipe.step - INFO - JumpStep instance created.\n", "2026-02-20 17:53:45,628 - stpipe.step - INFO - CleanFlickerNoiseStep instance created.\n", "2026-02-20 17:53:45,629 - stpipe.step - INFO - RampFitStep instance created.\n", "2026-02-20 17:53:45,629 - stpipe.step - INFO - GainScaleStep instance created.\n", "2026-02-20 17:53:45,874 - stpipe.step - INFO - Step Detector1Pipeline running with args ('/stow/jruffio/data/JWST/tutorial/data/raw/jw01414013001_02101_00001_nrs1_uncal.fits',).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Searching in /stow/jruffio/data/JWST/tutorial/data/raw/ for files matching jw01414013001_02101_*_nrs1_uncal.fits\n", "\tFound 2 input files to process\n", "\tjw01414013001_02101_00001_nrs1_uncal.fits\n", "\tjw01414013001_02101_00002_nrs1_uncal.fits\n", "Stage 1 Processing file 1 of 2.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-20 17:53:45,892 - stpipe.step - INFO - Step Detector1Pipeline parameters are:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: /stow/jruffio/data/JWST/tutorial/data/G395H_stage1\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: True\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " save_calibrated_ramp: False\n", " steps:\n", " group_scale:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " dq_init:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " emicorr:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " algorithm: joint\n", " nints_to_phase: None\n", " nbins: None\n", " scale_reference: True\n", " onthefly_corr_freq: None\n", " use_n_cycles: 3\n", " fit_ints_separately: False\n", " user_supplied_reffile: None\n", " save_intermediate_results: False\n", " saturation:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " n_pix_grow_sat: 0\n", " use_readpatt: True\n", " ipc:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " superbias:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " refpix:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " odd_even_columns: True\n", " use_side_ref_pixels: True\n", " side_smoothing_length: 11\n", " side_gain: 1.0\n", " odd_even_rows: True\n", " ovr_corr_mitigation_ftr: 3.0\n", " preserve_irs2_refpix: False\n", " irs2_mean_subtraction: False\n", " refpix_algorithm: median\n", " sigreject: 4.0\n", " gaussmooth: 1.0\n", " halfwidth: 30\n", " rscd:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " firstframe:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " bright_use_group1: True\n", " lastframe:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " linearity:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " dark_current:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " dark_output: None\n", " average_dark_current: 0.0082\n", " reset:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " persistence:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " input_trapsfilled: ''\n", " flag_pers_cutoff: 40.0\n", " save_persistence: False\n", " save_trapsfilled: True\n", " modify_input: False\n", " charge_migration:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " signal_threshold: 25000.0\n", " jump:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " rejection_threshold: 4.0\n", " three_group_rejection_threshold: 6.0\n", " four_group_rejection_threshold: 5.0\n", " maximum_cores: '1'\n", " flag_4_neighbors: True\n", " max_jump_to_flag_neighbors: 1000\n", " min_jump_to_flag_neighbors: 30\n", " after_jump_flag_dn1: 0\n", " after_jump_flag_time1: 0\n", " after_jump_flag_dn2: 0\n", " after_jump_flag_time2: 0\n", " expand_large_events: True\n", " min_sat_area: 1\n", " min_jump_area: 5\n", " expand_factor: 2\n", " use_ellipses: False\n", " sat_required_snowball: True\n", " min_sat_radius_extend: 2.5\n", " sat_expand: 2\n", " edge_size: 25\n", " mask_snowball_core_next_int: True\n", " snowball_time_masked_next_int: 4000\n", " find_showers: False\n", " max_shower_amplitude: 4.0\n", " extend_snr_threshold: 0.0\n", " extend_min_area: 0\n", " extend_inner_radius: 0\n", " extend_outer_radius: 0.0\n", " extend_ellipse_expand_ratio: 0.0\n", " time_masked_after_shower: 0\n", " min_diffs_single_pass: 10\n", " max_extended_radius: 200\n", " minimum_groups: 3\n", " minimum_sigclip_groups: 100\n", " only_use_ints: True\n", " clean_flicker_noise:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " autoparam: False\n", " fit_method: median\n", " fit_by_channel: False\n", " background_method: median\n", " background_box_size: None\n", " mask_science_regions: False\n", " apply_flat_field: False\n", " n_sigma: 2.0\n", " fit_histogram: False\n", " single_mask: True\n", " user_mask: None\n", " save_mask: False\n", " save_background: False\n", " save_noise: False\n", " ramp_fit:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " algorithm: OLS_C\n", " int_name: ''\n", " save_opt: False\n", " opt_name: ''\n", " suppress_one_group: True\n", " firstgroup: None\n", " lastgroup: None\n", " maximum_cores: '1'\n", " gain_scale:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", "2026-02-20 17:53:45,917 - stpipe.pipeline - INFO - Prefetching reference files for dataset: 'jw01414013001_02101_00001_nrs1_uncal.fits' reftypes = ['dark', 'gain', 'linearity', 'mask', 'readnoise', 'refpix', 'reset', 'rscd', 'saturation', 'sirskernel', 'superbias']\n", "2026-02-20 17:53:45,920 - stpipe.pipeline - INFO - Prefetch for DARK reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_dark_0489.fits'.\n", "2026-02-20 17:53:45,920 - stpipe.pipeline - INFO - Prefetch for GAIN reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_gain_0023.fits'.\n", "2026-02-20 17:53:45,920 - stpipe.pipeline - INFO - Prefetch for LINEARITY reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_linearity_0022.fits'.\n", "2026-02-20 17:53:45,921 - stpipe.pipeline - INFO - Prefetch for MASK reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_mask_0078.fits'.\n", "2026-02-20 17:53:45,922 - stpipe.pipeline - INFO - Prefetch for READNOISE reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_readnoise_0038.fits'.\n", "2026-02-20 17:53:45,922 - stpipe.pipeline - INFO - Prefetch for REFPIX reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_refpix_0019.fits'.\n", "2026-02-20 17:53:45,923 - stpipe.pipeline - INFO - Prefetch for RESET reference file is 'N/A'.\n", "2026-02-20 17:53:45,923 - stpipe.pipeline - INFO - Prefetch for RSCD reference file is 'N/A'.\n", "2026-02-20 17:53:45,923 - stpipe.pipeline - INFO - Prefetch for SATURATION reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_saturation_0031.fits'.\n", "2026-02-20 17:53:45,924 - stpipe.pipeline - INFO - Prefetch for SIRSKERNEL reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_sirskernel_0001.asdf'.\n", "2026-02-20 17:53:45,924 - stpipe.pipeline - INFO - Prefetch for SUPERBIAS reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_superbias_0507.fits'.\n", "2026-02-20 17:53:45,925 - jwst.pipeline.calwebb_detector1 - INFO - Starting calwebb_detector1 ...\n", "2026-02-20 17:53:46,300 - stpipe.step - INFO - Step group_scale running with args (,).\n", "2026-02-20 17:53:46,362 - jwst.group_scale.group_scale_step - INFO - NFRAMES and FRMDIVSR are equal; correction not needed\n", "2026-02-20 17:53:46,363 - jwst.group_scale.group_scale_step - INFO - Step will be skipped\n", "2026-02-20 17:53:46,365 - stpipe.step - INFO - Step group_scale done\n", "2026-02-20 17:53:46,512 - stpipe.step - INFO - Step dq_init running with args (,).\n", "2026-02-20 17:53:46,520 - jwst.dq_init.dq_init_step - INFO - Using MASK reference file /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_mask_0078.fits\n", "2026-02-20 17:53:46,893 - stpipe.step - INFO - Step dq_init done\n", "2026-02-20 17:53:47,039 - stpipe.step - INFO - Step saturation running with args (,).\n", "2026-02-20 17:53:47,118 - jwst.saturation.saturation_step - INFO - Using SATURATION reference file /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_saturation_0031.fits\n", "2026-02-20 17:53:47,119 - jwst.saturation.saturation_step - INFO - Using SUPERBIAS reference file /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_superbias_0507.fits\n", "2026-02-20 17:53:47,852 - jwst.saturation.saturation - INFO - Using read_pattern with nframes 1\n", "2026-02-20 17:53:48,248 - jwst.saturation.saturation - INFO - Detected 91633 saturated pixels\n", "2026-02-20 17:53:48,280 - jwst.saturation.saturation - INFO - Detected 1 A/D floor pixels\n", "2026-02-20 17:53:48,288 - stpipe.step - INFO - Step saturation done\n", "2026-02-20 17:53:48,434 - stpipe.step - INFO - Step ipc running with args (,).\n", "2026-02-20 17:53:48,435 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:53:48,565 - stpipe.step - INFO - Step superbias running with args (,).\n", "2026-02-20 17:53:48,640 - jwst.superbias.superbias_step - INFO - Using SUPERBIAS reference file /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_superbias_0507.fits\n", "2026-02-20 17:53:48,744 - stpipe.step - INFO - Step superbias done\n", "2026-02-20 17:53:48,891 - stpipe.step - INFO - Step refpix running with args (,).\n", "2026-02-20 17:53:48,999 - jwst.refpix.irs2_subtract_reference - INFO - Total bad reference pixels flagged: 28\n", "2026-02-20 17:53:49,045 - jwst.refpix.irs2_subtract_reference - INFO - Total bad reference pixels replaced: 28\n", "2026-02-20 17:53:49,048 - jwst.refpix.refpix_step - INFO - Using refpix reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_refpix_0019.fits\n", "2026-02-20 17:53:49,651 - jwst.refpix.irs2_subtract_reference - INFO - Working on integration 1 out of 1\n", "2026-02-20 17:54:06,102 - stpipe.step - INFO - Step refpix done\n", "2026-02-20 17:54:06,243 - stpipe.step - INFO - Step linearity running with args (,).\n", "2026-02-20 17:54:06,286 - jwst.linearity.linearity_step - INFO - Using Linearity reference file /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_linearity_0022.fits\n", "2026-02-20 17:54:06,860 - stdatamodels.dynamicdq - WARNING - Keyword BAD_LIN_CORR does not correspond to an existing DQ mnemonic, so will be ignored\n", "2026-02-20 17:54:07,219 - stpipe.step - INFO - Step linearity done\n", "2026-02-20 17:54:07,364 - stpipe.step - INFO - Step dark_current running with args (,).\n", "2026-02-20 17:54:07,380 - jwst.dark_current.dark_current_step - INFO - Using DARK reference file /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_dark_0489.fits\n", "2026-02-20 17:54:38,834 - jwst.dark_current.dark_current_step - INFO - Using Poisson noise from average dark current 0.0082 e-/sec\n", "2026-02-20 17:54:38,834 - stcal.dark_current.dark_sub - INFO - Science data nints=1, ngroups=15, nframes=1, groupgap=0\n", "2026-02-20 17:54:38,835 - stcal.dark_current.dark_sub - INFO - Dark data nints=1, ngroups=200, nframes=1, groupgap=0\n", "2026-02-20 17:54:39,319 - stpipe.step - INFO - Step dark_current done\n", "2026-02-20 17:54:39,475 - stpipe.step - INFO - Step charge_migration running with args (,).\n", "2026-02-20 17:54:39,476 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:54:39,626 - stpipe.step - INFO - Step jump running with args (,).\n", "2026-02-20 17:54:39,679 - jwst.jump.jump_step - INFO - CR rejection threshold = 4 sigma\n", "2026-02-20 17:54:39,680 - jwst.jump.jump_step - INFO - Maximum cores to use = 1\n", "2026-02-20 17:54:39,683 - jwst.jump.jump_step - INFO - Using GAIN reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_gain_0023.fits\n", "2026-02-20 17:54:39,685 - jwst.jump.jump_step - INFO - Using READNOISE reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_readnoise_0038.fits\n", "2026-02-20 17:54:40,112 - stcal.jump.jump - INFO - Executing two-point difference method\n", "2026-02-20 17:54:42,359 - stcal.jump.jump - INFO - Flagging Snowballs\n", "2026-02-20 17:54:42,728 - stcal.jump.jump - INFO - Total snowballs = 449\n", "2026-02-20 17:54:42,729 - stcal.jump.jump - INFO - Total elapsed time = 2.61534 sec\n", "2026-02-20 17:54:42,746 - jwst.jump.jump_step - INFO - The execution time in seconds: 3.106448\n", "2026-02-20 17:54:42,748 - stpipe.step - INFO - Step jump done\n", "2026-02-20 17:54:42,897 - stpipe.step - INFO - Step clean_flicker_noise running with args (,).\n", "2026-02-20 17:54:42,898 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:54:43,031 - stpipe.step - INFO - Step ramp_fit running with args (,).\n", "2026-02-20 17:54:43,095 - jwst.ramp_fitting.ramp_fit_step - INFO - Using READNOISE reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_readnoise_0038.fits\n", "2026-02-20 17:54:43,096 - jwst.ramp_fitting.ramp_fit_step - INFO - Using GAIN reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_gain_0023.fits\n", "2026-02-20 17:54:43,127 - jwst.ramp_fitting.ramp_fit_step - INFO - Using algorithm = OLS_C\n", "2026-02-20 17:54:43,127 - jwst.ramp_fitting.ramp_fit_step - INFO - Using weighting = optimal\n", "2026-02-20 17:54:43,565 - stcal.ramp_fitting.ols_fit - INFO - Number of multiprocessing slices: 1\n", "2026-02-20 17:54:46,513 - stcal.ramp_fitting.ols_fit - INFO - Ramp Fitting C Time: 2.9438271522521973\n", "2026-02-20 17:54:46,568 - stpipe.step - INFO - Step ramp_fit done\n", "2026-02-20 17:54:46,717 - stpipe.step - INFO - Step gain_scale running with args (,).\n", "2026-02-20 17:54:46,739 - jwst.gain_scale.gain_scale - INFO - Rescaling by 1.0\n", "2026-02-20 17:54:46,745 - stpipe.step - INFO - Step gain_scale done\n", "2026-02-20 17:54:46,892 - stpipe.step - INFO - Step gain_scale running with args (,).\n", "2026-02-20 17:54:46,913 - jwst.gain_scale.gain_scale - INFO - Rescaling by 1.0\n", "2026-02-20 17:54:46,919 - stpipe.step - INFO - Step gain_scale done\n", "2026-02-20 17:54:47,034 - stpipe.step - INFO - Saved model in /stow/jruffio/data/JWST/tutorial/data/G395H_stage1/jw01414013001_02101_00001_nrs1_rateints.fits\n", "2026-02-20 17:54:47,035 - jwst.pipeline.calwebb_detector1 - INFO - ... ending calwebb_detector1\n", "2026-02-20 17:54:47,036 - jwst.stpipe.core - INFO - Results used CRDS context: jwst_1464.pmap\n", "2026-02-20 17:54:47,130 - stpipe.step - INFO - Saved model in /stow/jruffio/data/JWST/tutorial/data/G395H_stage1/jw01414013001_02101_00001_nrs1_rate.fits\n", "2026-02-20 17:54:47,131 - stpipe.step - INFO - Step Detector1Pipeline done\n", "2026-02-20 17:54:47,131 - jwst.stpipe.core - INFO - Results used jwst version: 1.20.2\n", "2026-02-20 17:54:47,173 - stpipe.step - INFO - PARS-DARKCURRENTSTEP parameters found: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pars-darkcurrentstep_0005.asdf\n", "2026-02-20 17:54:47,182 - stpipe.step - INFO - PARS-JUMPSTEP parameters found: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pars-jumpstep_0003.asdf\n", "2026-02-20 17:54:47,191 - stpipe.pipeline - INFO - PARS-DETECTOR1PIPELINE parameters found: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pars-detector1pipeline_0004.asdf\n", "2026-02-20 17:54:47,204 - stpipe.step - INFO - Detector1Pipeline instance created.\n", "2026-02-20 17:54:47,204 - stpipe.step - INFO - GroupScaleStep instance created.\n", "2026-02-20 17:54:47,205 - stpipe.step - INFO - DQInitStep instance created.\n", "2026-02-20 17:54:47,206 - stpipe.step - INFO - EmiCorrStep instance created.\n", "2026-02-20 17:54:47,206 - stpipe.step - INFO - SaturationStep instance created.\n", "2026-02-20 17:54:47,207 - stpipe.step - INFO - IPCStep instance created.\n", "2026-02-20 17:54:47,208 - stpipe.step - INFO - SuperBiasStep instance created.\n", "2026-02-20 17:54:47,209 - stpipe.step - INFO - RefPixStep instance created.\n", "2026-02-20 17:54:47,209 - stpipe.step - INFO - RscdStep instance created.\n", "2026-02-20 17:54:47,210 - stpipe.step - INFO - FirstFrameStep instance created.\n", "2026-02-20 17:54:47,210 - stpipe.step - INFO - LastFrameStep instance created.\n", "2026-02-20 17:54:47,211 - stpipe.step - INFO - LinearityStep instance created.\n", "2026-02-20 17:54:47,212 - stpipe.step - INFO - DarkCurrentStep instance created.\n", "2026-02-20 17:54:47,212 - stpipe.step - INFO - ResetStep instance created.\n", "2026-02-20 17:54:47,213 - stpipe.step - INFO - PersistenceStep instance created.\n", "2026-02-20 17:54:47,213 - stpipe.step - INFO - ChargeMigrationStep instance created.\n", "2026-02-20 17:54:47,215 - stpipe.step - INFO - JumpStep instance created.\n", "2026-02-20 17:54:47,215 - stpipe.step - INFO - CleanFlickerNoiseStep instance created.\n", "2026-02-20 17:54:47,216 - stpipe.step - INFO - RampFitStep instance created.\n", "2026-02-20 17:54:47,217 - stpipe.step - INFO - GainScaleStep instance created.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tStage 1 Runtime so far: 61.6115 seconds\n", "Stage 1 Processing file 2 of 2.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-20 17:54:47,364 - stpipe.step - INFO - Step Detector1Pipeline running with args ('/stow/jruffio/data/JWST/tutorial/data/raw/jw01414013001_02101_00002_nrs1_uncal.fits',).\n", "2026-02-20 17:54:47,381 - stpipe.step - INFO - Step Detector1Pipeline parameters are:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: /stow/jruffio/data/JWST/tutorial/data/G395H_stage1\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: True\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " save_calibrated_ramp: False\n", " steps:\n", " group_scale:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " dq_init:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " emicorr:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " algorithm: joint\n", " nints_to_phase: None\n", " nbins: None\n", " scale_reference: True\n", " onthefly_corr_freq: None\n", " use_n_cycles: 3\n", " fit_ints_separately: False\n", " user_supplied_reffile: None\n", " save_intermediate_results: False\n", " saturation:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " n_pix_grow_sat: 0\n", " use_readpatt: True\n", " ipc:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " superbias:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " refpix:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " odd_even_columns: True\n", " use_side_ref_pixels: True\n", " side_smoothing_length: 11\n", " side_gain: 1.0\n", " odd_even_rows: True\n", " ovr_corr_mitigation_ftr: 3.0\n", " preserve_irs2_refpix: False\n", " irs2_mean_subtraction: False\n", " refpix_algorithm: median\n", " sigreject: 4.0\n", " gaussmooth: 1.0\n", " halfwidth: 30\n", " rscd:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " firstframe:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " bright_use_group1: True\n", " lastframe:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " linearity:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " dark_current:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " dark_output: None\n", " average_dark_current: 0.0082\n", " reset:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " persistence:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " input_trapsfilled: ''\n", " flag_pers_cutoff: 40.0\n", " save_persistence: False\n", " save_trapsfilled: True\n", " modify_input: False\n", " charge_migration:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " signal_threshold: 25000.0\n", " jump:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " rejection_threshold: 4.0\n", " three_group_rejection_threshold: 6.0\n", " four_group_rejection_threshold: 5.0\n", " maximum_cores: '1'\n", " flag_4_neighbors: True\n", " max_jump_to_flag_neighbors: 1000\n", " min_jump_to_flag_neighbors: 30\n", " after_jump_flag_dn1: 0\n", " after_jump_flag_time1: 0\n", " after_jump_flag_dn2: 0\n", " after_jump_flag_time2: 0\n", " expand_large_events: True\n", " min_sat_area: 1\n", " min_jump_area: 5\n", " expand_factor: 2\n", " use_ellipses: False\n", " sat_required_snowball: True\n", " min_sat_radius_extend: 2.5\n", " sat_expand: 2\n", " edge_size: 25\n", " mask_snowball_core_next_int: True\n", " snowball_time_masked_next_int: 4000\n", " find_showers: False\n", " max_shower_amplitude: 4.0\n", " extend_snr_threshold: 0.0\n", " extend_min_area: 0\n", " extend_inner_radius: 0\n", " extend_outer_radius: 0.0\n", " extend_ellipse_expand_ratio: 0.0\n", " time_masked_after_shower: 0\n", " min_diffs_single_pass: 10\n", " max_extended_radius: 200\n", " minimum_groups: 3\n", " minimum_sigclip_groups: 100\n", " only_use_ints: True\n", " clean_flicker_noise:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " autoparam: False\n", " fit_method: median\n", " fit_by_channel: False\n", " background_method: median\n", " background_box_size: None\n", " mask_science_regions: False\n", " apply_flat_field: False\n", " n_sigma: 2.0\n", " fit_histogram: False\n", " single_mask: True\n", " user_mask: None\n", " save_mask: False\n", " save_background: False\n", " save_noise: False\n", " ramp_fit:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " algorithm: OLS_C\n", " int_name: ''\n", " save_opt: False\n", " opt_name: ''\n", " suppress_one_group: True\n", " firstgroup: None\n", " lastgroup: None\n", " maximum_cores: '1'\n", " gain_scale:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", "2026-02-20 17:54:47,405 - stpipe.pipeline - INFO - Prefetching reference files for dataset: 'jw01414013001_02101_00002_nrs1_uncal.fits' reftypes = ['dark', 'gain', 'linearity', 'mask', 'readnoise', 'refpix', 'reset', 'rscd', 'saturation', 'sirskernel', 'superbias']\n", "2026-02-20 17:54:47,408 - stpipe.pipeline - INFO - Prefetch for DARK reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_dark_0489.fits'.\n", "2026-02-20 17:54:47,408 - stpipe.pipeline - INFO - Prefetch for GAIN reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_gain_0023.fits'.\n", "2026-02-20 17:54:47,409 - stpipe.pipeline - INFO - Prefetch for LINEARITY reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_linearity_0022.fits'.\n", "2026-02-20 17:54:47,409 - stpipe.pipeline - INFO - Prefetch for MASK reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_mask_0078.fits'.\n", "2026-02-20 17:54:47,410 - stpipe.pipeline - INFO - Prefetch for READNOISE reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_readnoise_0038.fits'.\n", "2026-02-20 17:54:47,410 - stpipe.pipeline - INFO - Prefetch for REFPIX reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_refpix_0019.fits'.\n", "2026-02-20 17:54:47,411 - stpipe.pipeline - INFO - Prefetch for RESET reference file is 'N/A'.\n", "2026-02-20 17:54:47,411 - stpipe.pipeline - INFO - Prefetch for RSCD reference file is 'N/A'.\n", "2026-02-20 17:54:47,411 - stpipe.pipeline - INFO - Prefetch for SATURATION reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_saturation_0031.fits'.\n", "2026-02-20 17:54:47,413 - stpipe.pipeline - INFO - Prefetch for SIRSKERNEL reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_sirskernel_0001.asdf'.\n", "2026-02-20 17:54:47,413 - stpipe.pipeline - INFO - Prefetch for SUPERBIAS reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_superbias_0507.fits'.\n", "2026-02-20 17:54:47,414 - jwst.pipeline.calwebb_detector1 - INFO - Starting calwebb_detector1 ...\n", "2026-02-20 17:54:47,788 - stpipe.step - INFO - Step group_scale running with args (,).\n", "2026-02-20 17:54:47,850 - jwst.group_scale.group_scale_step - INFO - NFRAMES and FRMDIVSR are equal; correction not needed\n", "2026-02-20 17:54:47,851 - jwst.group_scale.group_scale_step - INFO - Step will be skipped\n", "2026-02-20 17:54:47,852 - stpipe.step - INFO - Step group_scale done\n", "2026-02-20 17:54:47,998 - stpipe.step - INFO - Step dq_init running with args (,).\n", "2026-02-20 17:54:48,007 - jwst.dq_init.dq_init_step - INFO - Using MASK reference file /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_mask_0078.fits\n", "2026-02-20 17:54:48,234 - stpipe.step - INFO - Step dq_init done\n", "2026-02-20 17:54:48,382 - stpipe.step - INFO - Step saturation running with args (,).\n", "2026-02-20 17:54:48,460 - jwst.saturation.saturation_step - INFO - Using SATURATION reference file /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_saturation_0031.fits\n", "2026-02-20 17:54:48,461 - jwst.saturation.saturation_step - INFO - Using SUPERBIAS reference file /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_superbias_0507.fits\n", "2026-02-20 17:54:48,545 - jwst.saturation.saturation - INFO - Using read_pattern with nframes 1\n", "2026-02-20 17:54:48,943 - jwst.saturation.saturation - INFO - Detected 88604 saturated pixels\n", "2026-02-20 17:54:48,974 - jwst.saturation.saturation - INFO - Detected 0 A/D floor pixels\n", "2026-02-20 17:54:48,983 - stpipe.step - INFO - Step saturation done\n", "2026-02-20 17:54:49,128 - stpipe.step - INFO - Step ipc running with args (,).\n", "2026-02-20 17:54:49,129 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:54:49,262 - stpipe.step - INFO - Step superbias running with args (,).\n", "2026-02-20 17:54:49,337 - jwst.superbias.superbias_step - INFO - Using SUPERBIAS reference file /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_superbias_0507.fits\n", "2026-02-20 17:54:49,442 - stpipe.step - INFO - Step superbias done\n", "2026-02-20 17:54:49,589 - stpipe.step - INFO - Step refpix running with args (,).\n", "2026-02-20 17:54:49,698 - jwst.refpix.irs2_subtract_reference - INFO - Total bad reference pixels flagged: 48\n", "2026-02-20 17:54:49,744 - jwst.refpix.irs2_subtract_reference - INFO - Total bad reference pixels replaced: 48\n", "2026-02-20 17:54:49,747 - jwst.refpix.refpix_step - INFO - Using refpix reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_refpix_0019.fits\n", "2026-02-20 17:54:49,926 - jwst.refpix.irs2_subtract_reference - INFO - Working on integration 1 out of 1\n", "2026-02-20 17:55:06,385 - stpipe.step - INFO - Step refpix done\n", "2026-02-20 17:55:06,529 - stpipe.step - INFO - Step linearity running with args (,).\n", "2026-02-20 17:55:06,571 - jwst.linearity.linearity_step - INFO - Using Linearity reference file /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_linearity_0022.fits\n", "2026-02-20 17:55:06,617 - stdatamodels.dynamicdq - WARNING - Keyword BAD_LIN_CORR does not correspond to an existing DQ mnemonic, so will be ignored\n", "2026-02-20 17:55:06,974 - stpipe.step - INFO - Step linearity done\n", "2026-02-20 17:55:07,122 - stpipe.step - INFO - Step dark_current running with args (,).\n", "2026-02-20 17:55:07,138 - jwst.dark_current.dark_current_step - INFO - Using DARK reference file /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_dark_0489.fits\n", "2026-02-20 17:55:08,269 - jwst.dark_current.dark_current_step - INFO - Using Poisson noise from average dark current 0.0082 e-/sec\n", "2026-02-20 17:55:08,270 - stcal.dark_current.dark_sub - INFO - Science data nints=1, ngroups=15, nframes=1, groupgap=0\n", "2026-02-20 17:55:08,271 - stcal.dark_current.dark_sub - INFO - Dark data nints=1, ngroups=200, nframes=1, groupgap=0\n", "2026-02-20 17:55:08,754 - stpipe.step - INFO - Step dark_current done\n", "2026-02-20 17:55:08,899 - stpipe.step - INFO - Step charge_migration running with args (,).\n", "2026-02-20 17:55:08,901 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:55:09,031 - stpipe.step - INFO - Step jump running with args (,).\n", "2026-02-20 17:55:09,083 - jwst.jump.jump_step - INFO - CR rejection threshold = 4 sigma\n", "2026-02-20 17:55:09,084 - jwst.jump.jump_step - INFO - Maximum cores to use = 1\n", "2026-02-20 17:55:09,086 - jwst.jump.jump_step - INFO - Using GAIN reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_gain_0023.fits\n", "2026-02-20 17:55:09,089 - jwst.jump.jump_step - INFO - Using READNOISE reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_readnoise_0038.fits\n", "2026-02-20 17:55:09,256 - stcal.jump.jump - INFO - Executing two-point difference method\n", "2026-02-20 17:55:11,490 - stcal.jump.jump - INFO - Flagging Snowballs\n", "2026-02-20 17:55:11,813 - stcal.jump.jump - INFO - Total snowballs = 382\n", "2026-02-20 17:55:11,814 - stcal.jump.jump - INFO - Total elapsed time = 2.55744 sec\n", "2026-02-20 17:55:11,831 - jwst.jump.jump_step - INFO - The execution time in seconds: 2.787854\n", "2026-02-20 17:55:11,833 - stpipe.step - INFO - Step jump done\n", "2026-02-20 17:55:11,978 - stpipe.step - INFO - Step clean_flicker_noise running with args (,).\n", "2026-02-20 17:55:11,979 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:55:12,110 - stpipe.step - INFO - Step ramp_fit running with args (,).\n", "2026-02-20 17:55:12,175 - jwst.ramp_fitting.ramp_fit_step - INFO - Using READNOISE reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_readnoise_0038.fits\n", "2026-02-20 17:55:12,175 - jwst.ramp_fitting.ramp_fit_step - INFO - Using GAIN reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_gain_0023.fits\n", "2026-02-20 17:55:12,206 - jwst.ramp_fitting.ramp_fit_step - INFO - Using algorithm = OLS_C\n", "2026-02-20 17:55:12,206 - jwst.ramp_fitting.ramp_fit_step - INFO - Using weighting = optimal\n", "2026-02-20 17:55:12,678 - stcal.ramp_fitting.ols_fit - INFO - Number of multiprocessing slices: 1\n", "2026-02-20 17:55:15,532 - stcal.ramp_fitting.ols_fit - INFO - Ramp Fitting C Time: 2.8500783443450928\n", "2026-02-20 17:55:15,587 - stpipe.step - INFO - Step ramp_fit done\n", "2026-02-20 17:55:15,732 - stpipe.step - INFO - Step gain_scale running with args (,).\n", "2026-02-20 17:55:15,752 - jwst.gain_scale.gain_scale - INFO - Rescaling by 1.0\n", "2026-02-20 17:55:15,758 - stpipe.step - INFO - Step gain_scale done\n", "2026-02-20 17:55:15,902 - stpipe.step - INFO - Step gain_scale running with args (,).\n", "2026-02-20 17:55:15,922 - jwst.gain_scale.gain_scale - INFO - Rescaling by 1.0\n", "2026-02-20 17:55:15,928 - stpipe.step - INFO - Step gain_scale done\n", "2026-02-20 17:55:16,049 - stpipe.step - INFO - Saved model in /stow/jruffio/data/JWST/tutorial/data/G395H_stage1/jw01414013001_02101_00002_nrs1_rateints.fits\n", "2026-02-20 17:55:16,050 - jwst.pipeline.calwebb_detector1 - INFO - ... ending calwebb_detector1\n", "2026-02-20 17:55:16,051 - jwst.stpipe.core - INFO - Results used CRDS context: jwst_1464.pmap\n", "2026-02-20 17:55:16,146 - stpipe.step - INFO - Saved model in /stow/jruffio/data/JWST/tutorial/data/G395H_stage1/jw01414013001_02101_00002_nrs1_rate.fits\n", "2026-02-20 17:55:16,147 - stpipe.step - INFO - Step Detector1Pipeline done\n", "2026-02-20 17:55:16,147 - jwst.stpipe.core - INFO - Results used jwst version: 1.20.2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tStage 1 Runtime so far: 90.6279 seconds\n", "Stage 1 Total Runtime: 90.6280 seconds\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-20 17:55:17,858 - stpipe.step - INFO - PARS-RESAMPLESPECSTEP parameters found: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pars-resamplespecstep_0001.asdf\n", "2026-02-20 17:55:17,872 - stpipe.step - INFO - PARS-RESAMPLESPECSTEP parameters found: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pars-resamplespecstep_0001.asdf\n", "2026-02-20 17:55:17,891 - stpipe.step - INFO - Spec2Pipeline instance created.\n", "2026-02-20 17:55:17,892 - stpipe.step - INFO - AssignWcsStep instance created.\n", "2026-02-20 17:55:17,893 - stpipe.step - INFO - BadpixSelfcalStep instance created.\n", "2026-02-20 17:55:17,893 - stpipe.step - INFO - MSAFlagOpenStep instance created.\n", "2026-02-20 17:55:17,894 - stpipe.step - INFO - NSCleanStep instance created.\n", "2026-02-20 17:55:17,895 - stpipe.step - INFO - BackgroundStep instance created.\n", "2026-02-20 17:55:17,896 - stpipe.step - INFO - ImprintStep instance created.\n", "2026-02-20 17:55:17,896 - stpipe.step - INFO - Extract2dStep instance created.\n", "2026-02-20 17:55:17,900 - stpipe.step - INFO - MasterBackgroundMosStep instance created.\n", "2026-02-20 17:55:17,901 - stpipe.step - INFO - FlatFieldStep instance created.\n", "2026-02-20 17:55:17,902 - stpipe.step - INFO - PathLossStep instance created.\n", "2026-02-20 17:55:17,902 - stpipe.step - INFO - BarShadowStep instance created.\n", "2026-02-20 17:55:17,903 - stpipe.step - INFO - PhotomStep instance created.\n", "2026-02-20 17:55:17,903 - stpipe.step - INFO - PixelReplaceStep instance created.\n", "2026-02-20 17:55:17,905 - stpipe.step - INFO - ResampleSpecStep instance created.\n", "2026-02-20 17:55:17,906 - stpipe.step - INFO - Extract1dStep instance created.\n", "2026-02-20 17:55:17,907 - stpipe.step - INFO - WavecorrStep instance created.\n", "2026-02-20 17:55:17,908 - stpipe.step - INFO - FlatFieldStep instance created.\n", "2026-02-20 17:55:17,909 - stpipe.step - INFO - SourceTypeStep instance created.\n", "2026-02-20 17:55:17,909 - stpipe.step - INFO - StraylightStep instance created.\n", "2026-02-20 17:55:17,910 - stpipe.step - INFO - FringeStep instance created.\n", "2026-02-20 17:55:17,911 - stpipe.step - INFO - ResidualFringeStep instance created.\n", "2026-02-20 17:55:17,911 - stpipe.step - INFO - PathLossStep instance created.\n", "2026-02-20 17:55:17,912 - stpipe.step - INFO - BarShadowStep instance created.\n", "2026-02-20 17:55:17,913 - stpipe.step - INFO - WfssContamStep instance created.\n", "2026-02-20 17:55:17,913 - stpipe.step - INFO - PhotomStep instance created.\n", "2026-02-20 17:55:17,914 - stpipe.step - INFO - PixelReplaceStep instance created.\n", "2026-02-20 17:55:17,915 - stpipe.step - INFO - ResampleSpecStep instance created.\n", "2026-02-20 17:55:17,916 - stpipe.step - INFO - CubeBuildStep instance created.\n", "2026-02-20 17:55:17,917 - stpipe.step - INFO - Extract1dStep instance created.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Plots saved to /stow/jruffio/data/JWST/tutorial/data/G395H_stage1/plots_stage1_jw01414013001_nrs1.pdf\n", "Stage 2 Processing file 1 of 2.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-20 17:55:18,070 - stpipe.step - INFO - Step Spec2Pipeline running with args ('/stow/jruffio/data/JWST/tutorial/data/G395H_stage1/jw01414013001_02101_00001_nrs1_rate.fits',).\n", "2026-02-20 17:55:18,098 - stpipe.step - INFO - Step Spec2Pipeline parameters are:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: /stow/jruffio/data/JWST/tutorial/data/G395H_stage2\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: True\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " save_bsub: False\n", " fail_on_exception: True\n", " save_wfss_esec: False\n", " steps:\n", " assign_wcs:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " sip_approx: True\n", " sip_max_pix_error: 0.01\n", " sip_degree: None\n", " sip_max_inv_pix_error: 0.01\n", " sip_inv_degree: None\n", " sip_npoints: 12\n", " slit_y_low: -0.55\n", " slit_y_high: 0.55\n", " nrs_ifu_slice_wcs: False\n", " badpix_selfcal:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " flagfrac_lower: 0.001\n", " flagfrac_upper: 0.001\n", " kernel_size: 15\n", " force_single: False\n", " save_flagged_bkg: False\n", " msa_flagging:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " nsclean:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " fit_method: fft\n", " fit_by_channel: False\n", " background_method: None\n", " background_box_size: None\n", " mask_spectral_regions: True\n", " n_sigma: 5.0\n", " fit_histogram: False\n", " single_mask: False\n", " user_mask: None\n", " save_mask: False\n", " save_background: False\n", " save_noise: False\n", " bkg_subtract:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " bkg_list: None\n", " save_combined_background: False\n", " sigma: 3.0\n", " maxiters: None\n", " soss_source_percentile: 35.0\n", " soss_bkg_percentile: None\n", " wfss_mmag_extract: None\n", " wfss_maxiter: 5\n", " wfss_rms_stop: 0.0\n", " wfss_outlier_percent: 1.0\n", " imprint_subtract:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " extract_2d:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " slit_names: None\n", " source_ids: None\n", " extract_orders: None\n", " grism_objects: None\n", " tsgrism_extract_height: None\n", " wfss_extract_half_height: 5\n", " wfss_mmag_extract: None\n", " wfss_nbright: 1000\n", " master_background_mos:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: True\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " sigma_clip: 3.0\n", " median_kernel: 1\n", " force_subtract: False\n", " save_background: False\n", " user_background: None\n", " inverse: False\n", " steps:\n", " flat_field:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " save_interpolated_flat: False\n", " user_supplied_flat: None\n", " inverse: False\n", " pathloss:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " user_slit_loc: None\n", " barshadow:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " photom:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " apply_time_correction: True\n", " pixel_replace:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: True\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " algorithm: fit_profile\n", " n_adjacent_cols: 3\n", " resample_spec:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " pixfrac: 1.0\n", " kernel: square\n", " fillval: NaN\n", " weight_type: exptime\n", " output_shape: None\n", " pixel_scale_ratio: 1.0\n", " pixel_scale: None\n", " output_wcs: ''\n", " single: False\n", " blendheaders: True\n", " in_memory: True\n", " extract_1d:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " subtract_background: None\n", " apply_apcorr: True\n", " extraction_type: box\n", " use_source_posn: None\n", " position_offset: 0.0\n", " model_nod_pair: True\n", " optimize_psf_location: True\n", " smoothing_length: None\n", " bkg_fit: None\n", " bkg_order: None\n", " log_increment: 50\n", " save_profile: False\n", " save_scene_model: False\n", " save_residual_image: False\n", " center_xy: None\n", " ifu_autocen: False\n", " bkg_sigma_clip: 3.0\n", " ifu_rfcorr: True\n", " ifu_set_srctype: None\n", " ifu_rscale: None\n", " ifu_covar_scale: 1.0\n", " soss_atoca: True\n", " soss_threshold: 0.01\n", " soss_n_os: 2\n", " soss_wave_grid_in: None\n", " soss_wave_grid_out: None\n", " soss_estimate: None\n", " soss_rtol: 0.0001\n", " soss_max_grid_size: 20000\n", " soss_tikfac: None\n", " soss_width: 40.0\n", " soss_bad_pix: masking\n", " soss_modelname: None\n", " soss_order_3: True\n", " wavecorr:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " flat_field:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " save_interpolated_flat: False\n", " user_supplied_flat: None\n", " inverse: False\n", " srctype:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " source_type: None\n", " straylight:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " clean_showers: False\n", " shower_plane: 3\n", " shower_x_stddev: 18.0\n", " shower_y_stddev: 5.0\n", " shower_low_reject: 0.1\n", " shower_high_reject: 99.9\n", " save_shower_model: False\n", " fringe:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " residual_fringe:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: residual_fringe\n", " search_output_file: False\n", " input_dir: ''\n", " save_intermediate_results: False\n", " ignore_region_min: None\n", " ignore_region_max: None\n", " pathloss:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " user_slit_loc: None\n", " barshadow:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " wfss_contam:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " save_simulated_image: False\n", " save_contam_images: False\n", " maximum_cores: none\n", " orders: None\n", " magnitude_limit: None\n", " wl_oversample: 2\n", " max_pixels_per_chunk: 50000\n", " photom:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " apply_time_correction: True\n", " pixel_replace:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: True\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " algorithm: fit_profile\n", " n_adjacent_cols: 3\n", " resample_spec:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " pixfrac: 1.0\n", " kernel: square\n", " fillval: NaN\n", " weight_type: exptime\n", " output_shape: None\n", " pixel_scale_ratio: 1.0\n", " pixel_scale: None\n", " output_wcs: ''\n", " single: False\n", " blendheaders: True\n", " in_memory: True\n", " cube_build:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: True\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: s3d\n", " search_output_file: False\n", " input_dir: ''\n", " pipeline: 3\n", " channel: all\n", " band: all\n", " grating: all\n", " filter: all\n", " output_type: None\n", " scalexy: 0.0\n", " scalew: 0.0\n", " weighting: drizzle\n", " coord_system: skyalign\n", " ra_center: None\n", " dec_center: None\n", " cube_pa: None\n", " nspax_x: None\n", " nspax_y: None\n", " rois: 0.0\n", " roiw: 0.0\n", " weight_power: 2.0\n", " wavemin: None\n", " wavemax: None\n", " single: False\n", " skip_dqflagging: False\n", " offset_file: None\n", " debug_spaxel: -1 -1 -1\n", " extract_1d:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " subtract_background: None\n", " apply_apcorr: True\n", " extraction_type: box\n", " use_source_posn: None\n", " position_offset: 0.0\n", " model_nod_pair: True\n", " optimize_psf_location: True\n", " smoothing_length: None\n", " bkg_fit: None\n", " bkg_order: None\n", " log_increment: 50\n", " save_profile: False\n", " save_scene_model: False\n", " save_residual_image: False\n", " center_xy: None\n", " ifu_autocen: False\n", " bkg_sigma_clip: 3.0\n", " ifu_rfcorr: True\n", " ifu_set_srctype: None\n", " ifu_rscale: None\n", " ifu_covar_scale: 1.0\n", " soss_atoca: True\n", " soss_threshold: 0.01\n", " soss_n_os: 2\n", " soss_wave_grid_in: None\n", " soss_wave_grid_out: None\n", " soss_estimate: None\n", " soss_rtol: 0.0001\n", " soss_max_grid_size: 20000\n", " soss_tikfac: None\n", " soss_width: 40.0\n", " soss_bad_pix: masking\n", " soss_modelname: None\n", " soss_order_3: True\n", "2026-02-20 17:55:18,126 - stpipe.pipeline - INFO - Prefetching reference files for dataset: 'jw01414013001_02101_00001_nrs1_rate.fits' reftypes = ['area', 'barshadow', 'bkg', 'camera', 'collimator', 'dflat', 'disperser', 'distortion', 'fflat', 'filteroffset', 'flat', 'fore', 'fpa', 'fringe', 'ifufore', 'ifupost', 'ifuslicer', 'mrsxartcorr', 'msa', 'msaoper', 'ote', 'pathloss', 'photom', 'regions', 'sflat', 'specwcs', 'wavecorr', 'wavelengthrange']\n", "2026-02-20 17:55:18,130 - stpipe.pipeline - INFO - Prefetch for AREA reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_area_0034.fits'.\n", "2026-02-20 17:55:18,130 - stpipe.pipeline - INFO - Prefetch for BARSHADOW reference file is 'N/A'.\n", "2026-02-20 17:55:18,131 - stpipe.pipeline - INFO - Prefetch for BKG reference file is 'N/A'.\n", "2026-02-20 17:55:18,131 - stpipe.pipeline - INFO - Prefetch for CAMERA reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_camera_0010.asdf'.\n", "2026-02-20 17:55:18,132 - stpipe.pipeline - INFO - Prefetch for COLLIMATOR reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_collimator_0010.asdf'.\n", "2026-02-20 17:55:18,132 - stpipe.pipeline - INFO - Prefetch for DFLAT reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_dflat_0001.fits'.\n", "2026-02-20 17:55:18,133 - stpipe.pipeline - INFO - Prefetch for DISPERSER reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_disperser_0083.asdf'.\n", "2026-02-20 17:55:18,133 - stpipe.pipeline - INFO - Prefetch for DISTORTION reference file is 'N/A'.\n", "2026-02-20 17:55:18,134 - stpipe.pipeline - INFO - Prefetch for FFLAT reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fflat_0174.fits'.\n", "2026-02-20 17:55:18,134 - stpipe.pipeline - INFO - Prefetch for FILTEROFFSET reference file is 'N/A'.\n", "2026-02-20 17:55:18,135 - stpipe.pipeline - INFO - Prefetch for FLAT reference file is 'N/A'.\n", "2026-02-20 17:55:18,135 - stpipe.pipeline - INFO - Prefetch for FORE reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fore_0065.asdf'.\n", "2026-02-20 17:55:18,135 - stpipe.pipeline - INFO - Prefetch for FPA reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fpa_0011.asdf'.\n", "2026-02-20 17:55:18,136 - stpipe.pipeline - INFO - Prefetch for FRINGE reference file is 'N/A'.\n", "2026-02-20 17:55:18,136 - stpipe.pipeline - INFO - Prefetch for IFUFORE reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifufore_0015.asdf'.\n", "2026-02-20 17:55:18,137 - stpipe.pipeline - INFO - Prefetch for IFUPOST reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifupost_0016.asdf'.\n", "2026-02-20 17:55:18,137 - stpipe.pipeline - INFO - Prefetch for IFUSLICER reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifuslicer_0015.asdf'.\n", "2026-02-20 17:55:18,138 - stpipe.pipeline - INFO - Prefetch for MRSXARTCORR reference file is 'N/A'.\n", "2026-02-20 17:55:18,138 - stpipe.pipeline - INFO - Prefetch for MSA reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_msa_0011.asdf'.\n", "2026-02-20 17:55:18,139 - stpipe.pipeline - INFO - Prefetch for MSAOPER reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_msaoper_0011.json'.\n", "2026-02-20 17:55:18,139 - stpipe.pipeline - INFO - Prefetch for OTE reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ote_0013.asdf'.\n", "2026-02-20 17:55:18,140 - stpipe.pipeline - INFO - Prefetch for PATHLOSS reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pathloss_0003.fits'.\n", "2026-02-20 17:55:18,140 - stpipe.pipeline - INFO - Prefetch for PHOTOM reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_photom_0016.fits'.\n", "2026-02-20 17:55:18,141 - stpipe.pipeline - INFO - Prefetch for REGIONS reference file is 'N/A'.\n", "2026-02-20 17:55:18,141 - stpipe.pipeline - INFO - Prefetch for SFLAT reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_sflat_0219.fits'.\n", "2026-02-20 17:55:18,141 - stpipe.pipeline - INFO - Prefetch for SPECWCS reference file is 'N/A'.\n", "2026-02-20 17:55:18,142 - stpipe.pipeline - INFO - Prefetch for WAVECORR reference file is 'N/A'.\n", "2026-02-20 17:55:18,142 - stpipe.pipeline - INFO - Prefetch for WAVELENGTHRANGE reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_wavelengthrange_0008.asdf'.\n", "2026-02-20 17:55:18,143 - jwst.pipeline.calwebb_spec2 - INFO - Starting calwebb_spec2 ...\n", "2026-02-20 17:55:18,143 - jwst.pipeline.calwebb_spec2 - INFO - Processing product /stow/jruffio/data/JWST/tutorial/data/G395H_stage1/jw01414013001_02101_00001_nrs1\n", "2026-02-20 17:55:18,144 - jwst.pipeline.calwebb_spec2 - INFO - Working on input /stow/jruffio/data/JWST/tutorial/data/G395H_stage1/jw01414013001_02101_00001_nrs1_rate.fits ...\n", "2026-02-20 17:55:18,334 - stpipe.step - INFO - Step assign_wcs running with args (,).\n", "2026-02-20 17:55:18,384 - jwst.assign_wcs.nirspec - INFO - gwa_ytilt is 0.144672647 deg\n", "2026-02-20 17:55:18,384 - jwst.assign_wcs.nirspec - INFO - gwa_xtilt is 0.319160342 deg\n", "2026-02-20 17:55:18,385 - jwst.assign_wcs.nirspec - INFO - theta_y correction: 0.0001127116201274071 deg\n", "2026-02-20 17:55:18,386 - jwst.assign_wcs.nirspec - INFO - theta_x correction: 8.739014225467499e-05 deg\n", "2026-02-20 17:55:18,450 - jwst.assign_wcs.nirspec - INFO - Applied Barycentric velocity correction : 0.9999124176092914\n", "2026-02-20 17:55:19,801 - jwst.assign_wcs.nirspec - INFO - Created a NIRSPEC nrs_ifu pipeline with references {'distortion': None, 'filteroffset': None, 'specwcs': None, 'regions': None, 'wavelengthrange': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_wavelengthrange_0008.asdf', 'camera': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_camera_0010.asdf', 'collimator': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_collimator_0010.asdf', 'disperser': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_disperser_0083.asdf', 'fore': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fore_0065.asdf', 'fpa': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fpa_0011.asdf', 'msa': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_msa_0011.asdf', 'ote': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ote_0013.asdf', 'ifupost': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifupost_0016.asdf', 'ifufore': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifufore_0015.asdf', 'ifuslicer': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifuslicer_0015.asdf'}\n", "2026-02-20 17:55:24,398 - stcal.alignment.util - INFO - Update S_REGION to POLYGON ICRS 46.826578143 -13.764355816 46.827850044 -13.764355816 46.827850044 -13.763091425 46.826578143 -13.763091425\n", "2026-02-20 17:55:27,749 - jwst.assign_wcs.assign_wcs - INFO - COMPLETED assign_wcs\n", "2026-02-20 17:55:27,753 - stpipe.step - INFO - Step assign_wcs done\n", "2026-02-20 17:55:28,040 - stpipe.step - INFO - Step badpix_selfcal running with args (, [], []).\n", "2026-02-20 17:55:28,041 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:55:28,299 - stpipe.step - INFO - Step msa_flagging running with args (,).\n", "2026-02-20 17:55:30,074 - jwst.msaflagopen.msaflagopen_step - INFO - Using reference file /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_msaoper_0011.json\n", "2026-02-20 17:55:30,151 - jwst.msaflagopen.msaflag_open - INFO - 23 failed open shutters\n", "2026-02-20 17:55:35,464 - stpipe.step - INFO - Step msa_flagging done\n", "2026-02-20 17:55:36,151 - stpipe.step - INFO - Step nsclean running with args (,).\n", "2026-02-20 17:55:36,153 - jwst.nsclean.nsclean_step - WARNING - The 'nsclean' step is a deprecated alias to 'clean_flicker_noise' and will be removed in future builds.\n", "2026-02-20 17:55:36,160 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Input exposure type is NRS_IFU, detector=NRS1\n", "2026-02-20 17:55:38,458 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Flagging failed-open MSA shutters for scene masking\n", "2026-02-20 17:55:38,459 - stpipe.step - INFO - MSAFlagOpenStep instance created.\n", "2026-02-20 17:55:47,076 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Creating mask\n", "2026-02-20 17:55:47,077 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Finding slice pixels for an IFU image\n", "2026-02-20 17:55:48,363 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Masking the fixed slit region for IFU data.\n", "2026-02-20 17:55:48,504 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Cleaning image jw01414013001_02101_00001_nrs1_rate.fits\n", "2026-02-20 17:55:49,679 - stpipe.step - INFO - Step nsclean done\n", "2026-02-20 17:55:50,613 - stpipe.step - INFO - Step imprint_subtract running with args (, []).\n", "2026-02-20 17:55:50,615 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:55:50,984 - stpipe.step - INFO - Step bkg_subtract running with args (, []).\n", "2026-02-20 17:55:50,985 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:55:51,356 - stpipe.step - INFO - Step srctype running with args (,).\n", "2026-02-20 17:55:54,034 - jwst.srctype.srctype - INFO - Input EXP_TYPE is NRS_IFU\n", "2026-02-20 17:55:54,035 - jwst.srctype.srctype - INFO - Input SRCTYAPT = POINT\n", "2026-02-20 17:55:54,035 - jwst.srctype.srctype - INFO - Using input source type = POINT\n", "2026-02-20 17:55:54,037 - stpipe.step - INFO - Step srctype done\n", "2026-02-20 17:55:54,510 - stpipe.step - INFO - Step straylight running with args (,).\n", "2026-02-20 17:55:54,511 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:55:54,965 - stpipe.step - INFO - Step flat_field running with args (,).\n", "2026-02-20 17:55:54,978 - jwst.flatfield.flat_field_step - INFO - No reference found for type FLAT\n", "2026-02-20 17:55:54,999 - jwst.flatfield.flat_field_step - INFO - Using FFLAT reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fflat_0174.fits\n", "2026-02-20 17:55:55,390 - jwst.flatfield.flat_field_step - INFO - Using SFLAT reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_sflat_0219.fits\n", "2026-02-20 17:55:58,411 - jwst.flatfield.flat_field_step - INFO - Using DFLAT reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_dflat_0001.fits\n", "2026-02-20 17:56:04,447 - stpipe.step - INFO - Step flat_field done\n", "2026-02-20 17:56:05,051 - stpipe.step - INFO - Step fringe running with args (,).\n", "2026-02-20 17:56:05,053 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:56:05,503 - stpipe.step - INFO - Step pathloss running with args (,).\n", "2026-02-20 17:56:05,512 - jwst.pathloss.pathloss_step - INFO - Using PATHLOSS reference file /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pathloss_0003.fits\n", "2026-02-20 17:56:05,526 - jwst.pathloss.pathloss - INFO - Input exposure type is NRS_IFU\n", "2026-02-20 17:56:08,579 - stpipe.step - INFO - Step pathloss done\n", "2026-02-20 17:56:09,193 - stpipe.step - INFO - Step barshadow running with args (,).\n", "2026-02-20 17:56:09,195 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:56:09,656 - stpipe.step - INFO - Step photom running with args (,).\n", "2026-02-20 17:56:09,667 - jwst.photom.photom_step - INFO - Using photom reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_photom_0016.fits\n", "2026-02-20 17:56:09,668 - jwst.photom.photom_step - INFO - Using area reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_area_0034.fits\n", "2026-02-20 17:56:11,269 - jwst.photom.photom - INFO - Using instrument: NIRSPEC\n", "2026-02-20 17:56:11,270 - jwst.photom.photom - INFO - detector: NRS1\n", "2026-02-20 17:56:11,270 - jwst.photom.photom - INFO - exp_type: NRS_IFU\n", "2026-02-20 17:56:11,271 - jwst.photom.photom - INFO - filter: F290LP\n", "2026-02-20 17:56:11,271 - jwst.photom.photom - INFO - grating: G395H\n", "2026-02-20 17:56:11,297 - jwst.photom.photom - INFO - PHOTMJSR value: 3.83378e+12\n", "2026-02-20 17:56:12,668 - stpipe.step - INFO - Step photom done\n", "2026-02-20 17:56:13,287 - stpipe.step - INFO - Step residual_fringe running with args (,).\n", "2026-02-20 17:56:13,288 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:56:16,003 - stpipe.step - INFO - Step pixel_replace running with args (,).\n", "2026-02-20 17:56:16,004 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:56:16,475 - stpipe.step - INFO - Step cube_build running with args (,).\n", "2026-02-20 17:56:16,476 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:56:19,161 - stpipe.step - INFO - Step extract_1d running with args (,).\n", "2026-02-20 17:56:19,163 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:56:19,164 - jwst.pipeline.calwebb_spec2 - INFO - Finished processing product /stow/jruffio/data/JWST/tutorial/data/G395H_stage1/jw01414013001_02101_00001_nrs1\n", "2026-02-20 17:56:19,165 - jwst.pipeline.calwebb_spec2 - INFO - Ending calwebb_spec2\n", "2026-02-20 17:56:19,165 - jwst.stpipe.core - INFO - Results used CRDS context: jwst_1464.pmap\n", "2026-02-20 17:56:20,537 - stpipe.step - INFO - Saved model in /stow/jruffio/data/JWST/tutorial/data/G395H_stage2/jw01414013001_02101_00001_nrs1_cal.fits\n", "2026-02-20 17:56:20,538 - stpipe.step - INFO - Step Spec2Pipeline done\n", "2026-02-20 17:56:20,539 - jwst.stpipe.core - INFO - Results used jwst version: 1.20.2\n", "2026-02-20 17:56:20,591 - stpipe.step - INFO - PARS-RESAMPLESPECSTEP parameters found: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pars-resamplespecstep_0001.asdf\n", "2026-02-20 17:56:20,604 - stpipe.step - INFO - PARS-RESAMPLESPECSTEP parameters found: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pars-resamplespecstep_0001.asdf\n", "2026-02-20 17:56:20,623 - stpipe.step - INFO - Spec2Pipeline instance created.\n", "2026-02-20 17:56:20,624 - stpipe.step - INFO - AssignWcsStep instance created.\n", "2026-02-20 17:56:20,625 - stpipe.step - INFO - BadpixSelfcalStep instance created.\n", "2026-02-20 17:56:20,626 - stpipe.step - INFO - MSAFlagOpenStep instance created.\n", "2026-02-20 17:56:20,627 - stpipe.step - INFO - NSCleanStep instance created.\n", "2026-02-20 17:56:20,627 - stpipe.step - INFO - BackgroundStep instance created.\n", "2026-02-20 17:56:20,628 - stpipe.step - INFO - ImprintStep instance created.\n", "2026-02-20 17:56:20,629 - stpipe.step - INFO - Extract2dStep instance created.\n", "2026-02-20 17:56:20,632 - stpipe.step - INFO - MasterBackgroundMosStep instance created.\n", "2026-02-20 17:56:20,633 - stpipe.step - INFO - FlatFieldStep instance created.\n", "2026-02-20 17:56:20,634 - stpipe.step - INFO - PathLossStep instance created.\n", "2026-02-20 17:56:20,635 - stpipe.step - INFO - BarShadowStep instance created.\n", "2026-02-20 17:56:20,635 - stpipe.step - INFO - PhotomStep instance created.\n", "2026-02-20 17:56:20,636 - stpipe.step - INFO - PixelReplaceStep instance created.\n", "2026-02-20 17:56:20,637 - stpipe.step - INFO - ResampleSpecStep instance created.\n", "2026-02-20 17:56:20,638 - stpipe.step - INFO - Extract1dStep instance created.\n", "2026-02-20 17:56:20,639 - stpipe.step - INFO - WavecorrStep instance created.\n", "2026-02-20 17:56:20,639 - stpipe.step - INFO - FlatFieldStep instance created.\n", "2026-02-20 17:56:20,640 - stpipe.step - INFO - SourceTypeStep instance created.\n", "2026-02-20 17:56:20,641 - stpipe.step - INFO - StraylightStep instance created.\n", "2026-02-20 17:56:20,642 - stpipe.step - INFO - FringeStep instance created.\n", "2026-02-20 17:56:20,643 - stpipe.step - INFO - ResidualFringeStep instance created.\n", "2026-02-20 17:56:20,643 - stpipe.step - INFO - PathLossStep instance created.\n", "2026-02-20 17:56:20,644 - stpipe.step - INFO - BarShadowStep instance created.\n", "2026-02-20 17:56:20,645 - stpipe.step - INFO - WfssContamStep instance created.\n", "2026-02-20 17:56:20,645 - stpipe.step - INFO - PhotomStep instance created.\n", "2026-02-20 17:56:20,646 - stpipe.step - INFO - PixelReplaceStep instance created.\n", "2026-02-20 17:56:20,647 - stpipe.step - INFO - ResampleSpecStep instance created.\n", "2026-02-20 17:56:20,648 - stpipe.step - INFO - CubeBuildStep instance created.\n", "2026-02-20 17:56:20,649 - stpipe.step - INFO - Extract1dStep instance created.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tStage 2 Runtime so far: 62.7351 seconds\n", "Stage 2 Processing file 2 of 2.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-20 17:56:21,788 - stpipe.step - INFO - Step Spec2Pipeline running with args ('/stow/jruffio/data/JWST/tutorial/data/G395H_stage1/jw01414013001_02101_00002_nrs1_rate.fits',).\n", "2026-02-20 17:56:21,815 - stpipe.step - INFO - Step Spec2Pipeline parameters are:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: /stow/jruffio/data/JWST/tutorial/data/G395H_stage2\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: True\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " save_bsub: False\n", " fail_on_exception: True\n", " save_wfss_esec: False\n", " steps:\n", " assign_wcs:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " sip_approx: True\n", " sip_max_pix_error: 0.01\n", " sip_degree: None\n", " sip_max_inv_pix_error: 0.01\n", " sip_inv_degree: None\n", " sip_npoints: 12\n", " slit_y_low: -0.55\n", " slit_y_high: 0.55\n", " nrs_ifu_slice_wcs: False\n", " badpix_selfcal:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " flagfrac_lower: 0.001\n", " flagfrac_upper: 0.001\n", " kernel_size: 15\n", " force_single: False\n", " save_flagged_bkg: False\n", " msa_flagging:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " nsclean:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " fit_method: fft\n", " fit_by_channel: False\n", " background_method: None\n", " background_box_size: None\n", " mask_spectral_regions: True\n", " n_sigma: 5.0\n", " fit_histogram: False\n", " single_mask: False\n", " user_mask: None\n", " save_mask: False\n", " save_background: False\n", " save_noise: False\n", " bkg_subtract:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " bkg_list: None\n", " save_combined_background: False\n", " sigma: 3.0\n", " maxiters: None\n", " soss_source_percentile: 35.0\n", " soss_bkg_percentile: None\n", " wfss_mmag_extract: None\n", " wfss_maxiter: 5\n", " wfss_rms_stop: 0.0\n", " wfss_outlier_percent: 1.0\n", " imprint_subtract:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " extract_2d:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " slit_names: None\n", " source_ids: None\n", " extract_orders: None\n", " grism_objects: None\n", " tsgrism_extract_height: None\n", " wfss_extract_half_height: 5\n", " wfss_mmag_extract: None\n", " wfss_nbright: 1000\n", " master_background_mos:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: True\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " sigma_clip: 3.0\n", " median_kernel: 1\n", " force_subtract: False\n", " save_background: False\n", " user_background: None\n", " inverse: False\n", " steps:\n", " flat_field:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " save_interpolated_flat: False\n", " user_supplied_flat: None\n", " inverse: False\n", " pathloss:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " user_slit_loc: None\n", " barshadow:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " photom:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " apply_time_correction: True\n", " pixel_replace:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: True\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " algorithm: fit_profile\n", " n_adjacent_cols: 3\n", " resample_spec:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " pixfrac: 1.0\n", " kernel: square\n", " fillval: NaN\n", " weight_type: exptime\n", " output_shape: None\n", " pixel_scale_ratio: 1.0\n", " pixel_scale: None\n", " output_wcs: ''\n", " single: False\n", " blendheaders: True\n", " in_memory: True\n", " extract_1d:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " subtract_background: None\n", " apply_apcorr: True\n", " extraction_type: box\n", " use_source_posn: None\n", " position_offset: 0.0\n", " model_nod_pair: True\n", " optimize_psf_location: True\n", " smoothing_length: None\n", " bkg_fit: None\n", " bkg_order: None\n", " log_increment: 50\n", " save_profile: False\n", " save_scene_model: False\n", " save_residual_image: False\n", " center_xy: None\n", " ifu_autocen: False\n", " bkg_sigma_clip: 3.0\n", " ifu_rfcorr: True\n", " ifu_set_srctype: None\n", " ifu_rscale: None\n", " ifu_covar_scale: 1.0\n", " soss_atoca: True\n", " soss_threshold: 0.01\n", " soss_n_os: 2\n", " soss_wave_grid_in: None\n", " soss_wave_grid_out: None\n", " soss_estimate: None\n", " soss_rtol: 0.0001\n", " soss_max_grid_size: 20000\n", " soss_tikfac: None\n", " soss_width: 40.0\n", " soss_bad_pix: masking\n", " soss_modelname: None\n", " soss_order_3: True\n", " wavecorr:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " flat_field:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " save_interpolated_flat: False\n", " user_supplied_flat: None\n", " inverse: False\n", " srctype:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " source_type: None\n", " straylight:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " clean_showers: False\n", " shower_plane: 3\n", " shower_x_stddev: 18.0\n", " shower_y_stddev: 5.0\n", " shower_low_reject: 0.1\n", " shower_high_reject: 99.9\n", " save_shower_model: False\n", " fringe:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " residual_fringe:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: residual_fringe\n", " search_output_file: False\n", " input_dir: ''\n", " save_intermediate_results: False\n", " ignore_region_min: None\n", " ignore_region_max: None\n", " pathloss:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " user_slit_loc: None\n", " barshadow:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " wfss_contam:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " save_simulated_image: False\n", " save_contam_images: False\n", " maximum_cores: none\n", " orders: None\n", " magnitude_limit: None\n", " wl_oversample: 2\n", " max_pixels_per_chunk: 50000\n", " photom:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " apply_time_correction: True\n", " pixel_replace:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: True\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " algorithm: fit_profile\n", " n_adjacent_cols: 3\n", " resample_spec:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " pixfrac: 1.0\n", " kernel: square\n", " fillval: NaN\n", " weight_type: exptime\n", " output_shape: None\n", " pixel_scale_ratio: 1.0\n", " pixel_scale: None\n", " output_wcs: ''\n", " single: False\n", " blendheaders: True\n", " in_memory: True\n", " cube_build:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: True\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: s3d\n", " search_output_file: False\n", " input_dir: ''\n", " pipeline: 3\n", " channel: all\n", " band: all\n", " grating: all\n", " filter: all\n", " output_type: None\n", " scalexy: 0.0\n", " scalew: 0.0\n", " weighting: drizzle\n", " coord_system: skyalign\n", " ra_center: None\n", " dec_center: None\n", " cube_pa: None\n", " nspax_x: None\n", " nspax_y: None\n", " rois: 0.0\n", " roiw: 0.0\n", " weight_power: 2.0\n", " wavemin: None\n", " wavemax: None\n", " single: False\n", " skip_dqflagging: False\n", " offset_file: None\n", " debug_spaxel: -1 -1 -1\n", " extract_1d:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " subtract_background: None\n", " apply_apcorr: True\n", " extraction_type: box\n", " use_source_posn: None\n", " position_offset: 0.0\n", " model_nod_pair: True\n", " optimize_psf_location: True\n", " smoothing_length: None\n", " bkg_fit: None\n", " bkg_order: None\n", " log_increment: 50\n", " save_profile: False\n", " save_scene_model: False\n", " save_residual_image: False\n", " center_xy: None\n", " ifu_autocen: False\n", " bkg_sigma_clip: 3.0\n", " ifu_rfcorr: True\n", " ifu_set_srctype: None\n", " ifu_rscale: None\n", " ifu_covar_scale: 1.0\n", " soss_atoca: True\n", " soss_threshold: 0.01\n", " soss_n_os: 2\n", " soss_wave_grid_in: None\n", " soss_wave_grid_out: None\n", " soss_estimate: None\n", " soss_rtol: 0.0001\n", " soss_max_grid_size: 20000\n", " soss_tikfac: None\n", " soss_width: 40.0\n", " soss_bad_pix: masking\n", " soss_modelname: None\n", " soss_order_3: True\n", "2026-02-20 17:56:21,843 - stpipe.pipeline - INFO - Prefetching reference files for dataset: 'jw01414013001_02101_00002_nrs1_rate.fits' reftypes = ['area', 'barshadow', 'bkg', 'camera', 'collimator', 'dflat', 'disperser', 'distortion', 'fflat', 'filteroffset', 'flat', 'fore', 'fpa', 'fringe', 'ifufore', 'ifupost', 'ifuslicer', 'mrsxartcorr', 'msa', 'msaoper', 'ote', 'pathloss', 'photom', 'regions', 'sflat', 'specwcs', 'wavecorr', 'wavelengthrange']\n", "2026-02-20 17:56:21,846 - stpipe.pipeline - INFO - Prefetch for AREA reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_area_0034.fits'.\n", "2026-02-20 17:56:21,847 - stpipe.pipeline - INFO - Prefetch for BARSHADOW reference file is 'N/A'.\n", "2026-02-20 17:56:21,848 - stpipe.pipeline - INFO - Prefetch for BKG reference file is 'N/A'.\n", "2026-02-20 17:56:21,848 - stpipe.pipeline - INFO - Prefetch for CAMERA reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_camera_0010.asdf'.\n", "2026-02-20 17:56:21,849 - stpipe.pipeline - INFO - Prefetch for COLLIMATOR reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_collimator_0010.asdf'.\n", "2026-02-20 17:56:21,849 - stpipe.pipeline - INFO - Prefetch for DFLAT reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_dflat_0001.fits'.\n", "2026-02-20 17:56:21,850 - stpipe.pipeline - INFO - Prefetch for DISPERSER reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_disperser_0083.asdf'.\n", "2026-02-20 17:56:21,850 - stpipe.pipeline - INFO - Prefetch for DISTORTION reference file is 'N/A'.\n", "2026-02-20 17:56:21,851 - stpipe.pipeline - INFO - Prefetch for FFLAT reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fflat_0174.fits'.\n", "2026-02-20 17:56:21,851 - stpipe.pipeline - INFO - Prefetch for FILTEROFFSET reference file is 'N/A'.\n", "2026-02-20 17:56:21,852 - stpipe.pipeline - INFO - Prefetch for FLAT reference file is 'N/A'.\n", "2026-02-20 17:56:21,852 - stpipe.pipeline - INFO - Prefetch for FORE reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fore_0065.asdf'.\n", "2026-02-20 17:56:21,853 - stpipe.pipeline - INFO - Prefetch for FPA reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fpa_0011.asdf'.\n", "2026-02-20 17:56:21,853 - stpipe.pipeline - INFO - Prefetch for FRINGE reference file is 'N/A'.\n", "2026-02-20 17:56:21,854 - stpipe.pipeline - INFO - Prefetch for IFUFORE reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifufore_0015.asdf'.\n", "2026-02-20 17:56:21,854 - stpipe.pipeline - INFO - Prefetch for IFUPOST reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifupost_0016.asdf'.\n", "2026-02-20 17:56:21,855 - stpipe.pipeline - INFO - Prefetch for IFUSLICER reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifuslicer_0015.asdf'.\n", "2026-02-20 17:56:21,855 - stpipe.pipeline - INFO - Prefetch for MRSXARTCORR reference file is 'N/A'.\n", "2026-02-20 17:56:21,856 - stpipe.pipeline - INFO - Prefetch for MSA reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_msa_0011.asdf'.\n", "2026-02-20 17:56:21,856 - stpipe.pipeline - INFO - Prefetch for MSAOPER reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_msaoper_0011.json'.\n", "2026-02-20 17:56:21,857 - stpipe.pipeline - INFO - Prefetch for OTE reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ote_0013.asdf'.\n", "2026-02-20 17:56:21,857 - stpipe.pipeline - INFO - Prefetch for PATHLOSS reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pathloss_0003.fits'.\n", "2026-02-20 17:56:21,858 - stpipe.pipeline - INFO - Prefetch for PHOTOM reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_photom_0016.fits'.\n", "2026-02-20 17:56:21,858 - stpipe.pipeline - INFO - Prefetch for REGIONS reference file is 'N/A'.\n", "2026-02-20 17:56:21,859 - stpipe.pipeline - INFO - Prefetch for SFLAT reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_sflat_0219.fits'.\n", "2026-02-20 17:56:21,859 - stpipe.pipeline - INFO - Prefetch for SPECWCS reference file is 'N/A'.\n", "2026-02-20 17:56:21,860 - stpipe.pipeline - INFO - Prefetch for WAVECORR reference file is 'N/A'.\n", "2026-02-20 17:56:21,860 - stpipe.pipeline - INFO - Prefetch for WAVELENGTHRANGE reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_wavelengthrange_0008.asdf'.\n", "2026-02-20 17:56:21,861 - jwst.pipeline.calwebb_spec2 - INFO - Starting calwebb_spec2 ...\n", "2026-02-20 17:56:21,861 - jwst.pipeline.calwebb_spec2 - INFO - Processing product /stow/jruffio/data/JWST/tutorial/data/G395H_stage1/jw01414013001_02101_00002_nrs1\n", "2026-02-20 17:56:21,862 - jwst.pipeline.calwebb_spec2 - INFO - Working on input /stow/jruffio/data/JWST/tutorial/data/G395H_stage1/jw01414013001_02101_00002_nrs1_rate.fits ...\n", "2026-02-20 17:56:22,054 - stpipe.step - INFO - Step assign_wcs running with args (,).\n", "2026-02-20 17:56:22,102 - jwst.assign_wcs.nirspec - INFO - gwa_ytilt is 0.144672647 deg\n", "2026-02-20 17:56:22,103 - jwst.assign_wcs.nirspec - INFO - gwa_xtilt is 0.319160342 deg\n", "2026-02-20 17:56:22,103 - jwst.assign_wcs.nirspec - INFO - theta_y correction: 0.0001127116201274071 deg\n", "2026-02-20 17:56:22,104 - jwst.assign_wcs.nirspec - INFO - theta_x correction: 8.739014225467499e-05 deg\n", "2026-02-20 17:56:22,168 - jwst.assign_wcs.nirspec - INFO - Applied Barycentric velocity correction : 0.9999124176092914\n", "2026-02-20 17:56:23,522 - jwst.assign_wcs.nirspec - INFO - Created a NIRSPEC nrs_ifu pipeline with references {'distortion': None, 'filteroffset': None, 'specwcs': None, 'regions': None, 'wavelengthrange': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_wavelengthrange_0008.asdf', 'camera': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_camera_0010.asdf', 'collimator': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_collimator_0010.asdf', 'disperser': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_disperser_0083.asdf', 'fore': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fore_0065.asdf', 'fpa': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fpa_0011.asdf', 'msa': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_msa_0011.asdf', 'ote': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ote_0013.asdf', 'ifupost': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifupost_0016.asdf', 'ifufore': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifufore_0015.asdf', 'ifuslicer': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifuslicer_0015.asdf'}\n", "2026-02-20 17:56:28,166 - stcal.alignment.util - INFO - Update S_REGION to POLYGON ICRS 46.826532286 -13.764239869 46.827804187 -13.764239869 46.827804187 -13.762975478 46.826532286 -13.762975478\n", "2026-02-20 17:56:31,551 - jwst.assign_wcs.assign_wcs - INFO - COMPLETED assign_wcs\n", "2026-02-20 17:56:31,555 - stpipe.step - INFO - Step assign_wcs done\n", "2026-02-20 17:56:31,836 - stpipe.step - INFO - Step badpix_selfcal running with args (, [], []).\n", "2026-02-20 17:56:31,837 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:56:32,097 - stpipe.step - INFO - Step msa_flagging running with args (,).\n", "2026-02-20 17:56:33,892 - jwst.msaflagopen.msaflagopen_step - INFO - Using reference file /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_msaoper_0011.json\n", "2026-02-20 17:56:33,972 - jwst.msaflagopen.msaflag_open - INFO - 23 failed open shutters\n", "2026-02-20 17:56:39,332 - stpipe.step - INFO - Step msa_flagging done\n", "2026-02-20 17:56:40,036 - stpipe.step - INFO - Step nsclean running with args (,).\n", "2026-02-20 17:56:40,037 - jwst.nsclean.nsclean_step - WARNING - The 'nsclean' step is a deprecated alias to 'clean_flicker_noise' and will be removed in future builds.\n", "2026-02-20 17:56:40,044 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Input exposure type is NRS_IFU, detector=NRS1\n", "2026-02-20 17:56:42,361 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Flagging failed-open MSA shutters for scene masking\n", "2026-02-20 17:56:42,362 - stpipe.step - INFO - MSAFlagOpenStep instance created.\n", "2026-02-20 17:56:50,964 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Creating mask\n", "2026-02-20 17:56:50,965 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Finding slice pixels for an IFU image\n", "2026-02-20 17:56:52,233 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Masking the fixed slit region for IFU data.\n", "2026-02-20 17:56:52,396 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Cleaning image jw01414013001_02101_00002_nrs1_rate.fits\n", "2026-02-20 17:56:53,558 - stpipe.step - INFO - Step nsclean done\n", "2026-02-20 17:56:54,470 - stpipe.step - INFO - Step imprint_subtract running with args (, []).\n", "2026-02-20 17:56:54,472 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:56:54,833 - stpipe.step - INFO - Step bkg_subtract running with args (, []).\n", "2026-02-20 17:56:54,834 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:56:55,193 - stpipe.step - INFO - Step srctype running with args (,).\n", "2026-02-20 17:56:57,803 - jwst.srctype.srctype - INFO - Input EXP_TYPE is NRS_IFU\n", "2026-02-20 17:56:57,803 - jwst.srctype.srctype - INFO - Input SRCTYAPT = POINT\n", "2026-02-20 17:56:57,804 - jwst.srctype.srctype - INFO - Using input source type = POINT\n", "2026-02-20 17:56:57,806 - stpipe.step - INFO - Step srctype done\n", "2026-02-20 17:56:58,265 - stpipe.step - INFO - Step straylight running with args (,).\n", "2026-02-20 17:56:58,266 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:56:58,716 - stpipe.step - INFO - Step flat_field running with args (,).\n", "2026-02-20 17:56:58,730 - jwst.flatfield.flat_field_step - INFO - No reference found for type FLAT\n", "2026-02-20 17:56:58,752 - jwst.flatfield.flat_field_step - INFO - Using FFLAT reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fflat_0174.fits\n", "2026-02-20 17:56:58,812 - jwst.flatfield.flat_field_step - INFO - Using SFLAT reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_sflat_0219.fits\n", "2026-02-20 17:56:58,954 - jwst.flatfield.flat_field_step - INFO - Using DFLAT reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_dflat_0001.fits\n", "2026-02-20 17:57:04,990 - stpipe.step - INFO - Step flat_field done\n", "2026-02-20 17:57:05,604 - stpipe.step - INFO - Step fringe running with args (,).\n", "2026-02-20 17:57:05,606 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:57:06,058 - stpipe.step - INFO - Step pathloss running with args (,).\n", "2026-02-20 17:57:06,067 - jwst.pathloss.pathloss_step - INFO - Using PATHLOSS reference file /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pathloss_0003.fits\n", "2026-02-20 17:57:06,081 - jwst.pathloss.pathloss - INFO - Input exposure type is NRS_IFU\n", "2026-02-20 17:57:09,189 - stpipe.step - INFO - Step pathloss done\n", "2026-02-20 17:57:09,814 - stpipe.step - INFO - Step barshadow running with args (,).\n", "2026-02-20 17:57:09,815 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:57:10,281 - stpipe.step - INFO - Step photom running with args (,).\n", "2026-02-20 17:57:10,292 - jwst.photom.photom_step - INFO - Using photom reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_photom_0016.fits\n", "2026-02-20 17:57:10,293 - jwst.photom.photom_step - INFO - Using area reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_area_0034.fits\n", "2026-02-20 17:57:11,896 - jwst.photom.photom - INFO - Using instrument: NIRSPEC\n", "2026-02-20 17:57:11,896 - jwst.photom.photom - INFO - detector: NRS1\n", "2026-02-20 17:57:11,897 - jwst.photom.photom - INFO - exp_type: NRS_IFU\n", "2026-02-20 17:57:11,897 - jwst.photom.photom - INFO - filter: F290LP\n", "2026-02-20 17:57:11,898 - jwst.photom.photom - INFO - grating: G395H\n", "2026-02-20 17:57:11,923 - jwst.photom.photom - INFO - PHOTMJSR value: 3.83378e+12\n", "2026-02-20 17:57:13,315 - stpipe.step - INFO - Step photom done\n", "2026-02-20 17:57:13,961 - stpipe.step - INFO - Step residual_fringe running with args (,).\n", "2026-02-20 17:57:13,962 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:57:16,745 - stpipe.step - INFO - Step pixel_replace running with args (,).\n", "2026-02-20 17:57:16,746 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:57:17,228 - stpipe.step - INFO - Step cube_build running with args (,).\n", "2026-02-20 17:57:17,230 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:57:19,931 - stpipe.step - INFO - Step extract_1d running with args (,).\n", "2026-02-20 17:57:19,932 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 17:57:19,933 - jwst.pipeline.calwebb_spec2 - INFO - Finished processing product /stow/jruffio/data/JWST/tutorial/data/G395H_stage1/jw01414013001_02101_00002_nrs1\n", "2026-02-20 17:57:19,934 - jwst.pipeline.calwebb_spec2 - INFO - Ending calwebb_spec2\n", "2026-02-20 17:57:19,935 - jwst.stpipe.core - INFO - Results used CRDS context: jwst_1464.pmap\n", "2026-02-20 17:57:21,318 - stpipe.step - INFO - Saved model in /stow/jruffio/data/JWST/tutorial/data/G395H_stage2/jw01414013001_02101_00002_nrs1_cal.fits\n", "2026-02-20 17:57:21,318 - stpipe.step - INFO - Step Spec2Pipeline done\n", "2026-02-20 17:57:21,319 - jwst.stpipe.core - INFO - Results used jwst version: 1.20.2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tStage 2 Runtime so far: 123.5153 seconds\n", "Stage 2 Total Runtime: 123.5154 seconds\n", "Plots saved to /stow/jruffio/data/JWST/tutorial/data/G395H_stage2/plots_stage2_jw01414013001_nrs1.pdf\n", "/stow/jruffio/data/JWST/tutorial/data/G395H_stage2/jw01414013001_02101_00001_nrs1_cal.fits\n", "/stow/jruffio/data/JWST/tutorial/data/G395H_stage2/jw01414013001_02101_00002_nrs1_cal.fits\n", "N files: 2\n", "/stow/jruffio/data/JWST/tutorial/data/G395H_stage2/jw01414013001_02101_00001_nrs1_cal.fits\n", "Reading data from /stow/jruffio/data/JWST/tutorial/data/G395H_stage2/jw01414013001_02101_00001_nrs1_cal.fits\n", "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", "Running compute_med_filt_badpix with parameters:\n", "\t save_utils: True\n", "\t window_size: 50\n", "\t mad_threshold: 50\n", "Initializing row_err and bad_pixels for nirspec\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jruffio/anaconda3/envs/breads_2026/lib/python3.12/site-packages/scipy/ndimage/_filters.py:2420: RuntimeWarning: All-NaN slice encountered\n", " _nd_image.generic_filter(input, function, footprint, output, mode,\n", "/home/jruffio/code/breads/breads/instruments/jwstnirspec_cal.py:198: SmallSampleWarning: One or more sample arguments is too small; all returned values will be NaN. See documentation for sample size requirements.\n", " row_err_masking = row_err/median_abs_deviation(row_err[np.where(np.isfinite(self.bad_pixels[rowid,:]))])\n", "/home/jruffio/code/breads/breads/instruments/jwstnirspec_cal.py:198: RuntimeWarning: invalid value encountered in divide\n", " row_err_masking = row_err/median_abs_deviation(row_err[np.where(np.isfinite(self.bad_pixels[rowid,:]))])\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Saved the quick bad pixel map to /stow/jruffio/data/JWST/tutorial/data/G395H_utils_before_cleaning/jw01414013001_02101_00001_nrs1_cal_roughbadpix.fits\n", "Running compute_coordinates_arrays with parameters:\n", "\t save_utils: True\n", "\t targname: HD 19467\n", "Computing coordinates arrays.\n", "Computing coords for 30 slices...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|███████████████████████████████████████████████████████████████| 30/30 [00:01<00:00, 21.86it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Retrieving SIMBAD coordinates for HD 19467\n", "Coordinates at J2000: 03h07m18.57505144s -13d45m42.41798215s\n", "Coordinates at 2024-01-25: 03h07m18.56069141s -13d45m48.69016027s\n", " Saved the computed coordinates arrays to /stow/jruffio/data/JWST/tutorial/data/G395H_utils_before_cleaning/jw01414013001_02101_00001_nrs1_cal_relcoords.fits\n", "Running convert_MJy_per_sr_to_MJy with parameters:\n", "\t save_utils: True\n", "Running compute_starspectrum_contnorm with parameters:\n", "\t save_utils: True\n", "\t N_nodes: 40\n", "\t threshold_badpix: 100\n", "\t mppool: \n", "Computing stellar spectrum (continuum normalized)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: divide by zero encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: divide by zero encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: divide by zero encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: divide by zero encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: divide by zero encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: divide by zero encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: divide by zero encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: divide by zero encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: divide by zero encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: divide by zero encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: divide by zero encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: divide by zero encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: divide by zero encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: divide by zero encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Saved the continuum normalized star spectrum to /stow/jruffio/data/JWST/tutorial/data/G395H_utils_before_cleaning/jw01414013001_02101_00001_nrs1_cal_starspec_contnorm.fits\n", "Running compute_starsubtraction with parameters:\n", "\t save_utils: True\n", "\t starsub_dir: starsub1d_tmp\n", "\t threshold_badpix: 10\n", "\t mppool: \n", "Computing star subtraction.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:1411: RuntimeWarning: All-NaN slice encountered\n", " reg_mean_map[wherenan] = np.tile(np.nanmedian(spline_paras0, axis=1)[:, None], (1, spline_paras0.shape[1]))[wherenan]\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:1413: RuntimeWarning: All-NaN slice encountered\n", " reg_std_map[wherenan] = np.tile(np.nanmax(np.abs(spline_paras0), axis=1)[:, None], (1, spline_paras0.shape[1]))[wherenan]\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: divide by zero encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: invalid value encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: invalid value encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: invalid value encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: invalid value encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Running compute_interpdata_regwvs with parameters:\n", "\t save_utils: True\n", "\t wv_sampling: [2.85950398 2.87102127 2.88253856 2.89405584 2.90557313 2.91709042\n", " 2.9286077 2.94012499 2.95164227 2.96315956 2.97467685 2.98619413\n", " 2.99771142 3.00922871 3.02074599 3.03226328 3.04378057 3.05529785\n", " 3.06681514 3.07833242 3.08984971 3.101367 3.11288428 3.12440157\n", " 3.13591886 3.14743614 3.15895343 3.17047071 3.181988 3.19350529\n", " 3.20502257 3.21653986 3.22805715 3.23957443 3.25109172 3.262609\n", " 3.27412629 3.28564358 3.29716086 3.30867815 3.32019544 3.33171272\n", " 3.34323001 3.3547473 3.36626458 3.37778187 3.38929915 3.40081644\n", " 3.41233373 3.42385101 3.4353683 3.44688559 3.45840287 3.46992016\n", " 3.48143744 3.49295473 3.50447202 3.5159893 3.52750659 3.53902388\n", " 3.55054116 3.56205845 3.57357574 3.58509302 3.59661031 3.60812759\n", " 3.61964488 3.63116217 3.64267945 3.65419674 3.66571403 3.67723131\n", " 3.6887486 3.70026588 3.71178317 3.72330046 3.73481774 3.74633503\n", " 3.75785232 3.7693696 3.78088689 3.79240417 3.80392146 3.81543875\n", " 3.82695603 3.83847332 3.84999061 3.86150789 3.87302518 3.88454247\n", " 3.89605975 3.90757704 3.91909432 3.93061161 3.9421289 3.95364618\n", " 3.96516347 3.97668076 3.98819804 3.99971533 4.01123261 4.0227499\n", " 4.03426719 4.04578447 4.05730176 4.06881905 4.08033633 4.09185362]\n", "/stow/jruffio/data/JWST/tutorial/data/G395H_stage2/jw01414013001_02101_00002_nrs1_cal.fits\n", "Reading data from /stow/jruffio/data/JWST/tutorial/data/G395H_stage2/jw01414013001_02101_00002_nrs1_cal.fits\n", "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", "Running compute_med_filt_badpix with parameters:\n", "\t save_utils: True\n", "\t window_size: 50\n", "\t mad_threshold: 50\n", "Initializing row_err and bad_pixels for nirspec\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jruffio/anaconda3/envs/breads_2026/lib/python3.12/site-packages/scipy/ndimage/_filters.py:2420: RuntimeWarning: All-NaN slice encountered\n", " _nd_image.generic_filter(input, function, footprint, output, mode,\n", "/home/jruffio/code/breads/breads/instruments/jwstnirspec_cal.py:198: SmallSampleWarning: One or more sample arguments is too small; all returned values will be NaN. See documentation for sample size requirements.\n", " row_err_masking = row_err/median_abs_deviation(row_err[np.where(np.isfinite(self.bad_pixels[rowid,:]))])\n", "/home/jruffio/code/breads/breads/instruments/jwstnirspec_cal.py:198: RuntimeWarning: invalid value encountered in divide\n", " row_err_masking = row_err/median_abs_deviation(row_err[np.where(np.isfinite(self.bad_pixels[rowid,:]))])\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Saved the quick bad pixel map to /stow/jruffio/data/JWST/tutorial/data/G395H_utils_before_cleaning/jw01414013001_02101_00002_nrs1_cal_roughbadpix.fits\n", "Running compute_coordinates_arrays with parameters:\n", "\t save_utils: True\n", "\t targname: HD 19467\n", "Computing coordinates arrays.\n", "Computing coords for 30 slices...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|███████████████████████████████████████████████████████████████| 30/30 [00:01<00:00, 22.19it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Retrieving SIMBAD coordinates for HD 19467\n", "Coordinates at J2000: 03h07m18.57505144s -13d45m42.41798215s\n", "Coordinates at 2024-01-25: 03h07m18.56069141s -13d45m48.69016027s\n", " Saved the computed coordinates arrays to /stow/jruffio/data/JWST/tutorial/data/G395H_utils_before_cleaning/jw01414013001_02101_00002_nrs1_cal_relcoords.fits\n", "Running convert_MJy_per_sr_to_MJy with parameters:\n", "\t save_utils: True\n", "Running compute_starspectrum_contnorm with parameters:\n", "\t save_utils: True\n", "\t N_nodes: 40\n", "\t threshold_badpix: 100\n", "\t mppool: \n", "Computing stellar spectrum (continuum normalized)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: divide by zero encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Saved the continuum normalized star spectrum to /stow/jruffio/data/JWST/tutorial/data/G395H_utils_before_cleaning/jw01414013001_02101_00002_nrs1_cal_starspec_contnorm.fits\n", "Running compute_starsubtraction with parameters:\n", "\t save_utils: True\n", "\t starsub_dir: starsub1d_tmp\n", "\t threshold_badpix: 10\n", "\t mppool: \n", "Computing star subtraction.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:1411: RuntimeWarning: All-NaN slice encountered\n", " reg_mean_map[wherenan] = np.tile(np.nanmedian(spline_paras0, axis=1)[:, None], (1, spline_paras0.shape[1]))[wherenan]\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:1413: RuntimeWarning: All-NaN slice encountered\n", " reg_std_map[wherenan] = np.tile(np.nanmax(np.abs(spline_paras0), axis=1)[:, None], (1, spline_paras0.shape[1]))[wherenan]\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: invalid value encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: invalid value encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: invalid value encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: invalid value encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: invalid value encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: invalid value encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Running compute_interpdata_regwvs with parameters:\n", "\t save_utils: True\n", "\t wv_sampling: [2.85950398 2.87102127 2.88253856 2.89405584 2.90557313 2.91709042\n", " 2.9286077 2.94012499 2.95164227 2.96315956 2.97467685 2.98619413\n", " 2.99771142 3.00922871 3.02074599 3.03226328 3.04378057 3.05529785\n", " 3.06681514 3.07833242 3.08984971 3.101367 3.11288428 3.12440157\n", " 3.13591886 3.14743614 3.15895343 3.17047071 3.181988 3.19350529\n", " 3.20502257 3.21653986 3.22805715 3.23957443 3.25109172 3.262609\n", " 3.27412629 3.28564358 3.29716086 3.30867815 3.32019544 3.33171272\n", " 3.34323001 3.3547473 3.36626458 3.37778187 3.38929915 3.40081644\n", " 3.41233373 3.42385101 3.4353683 3.44688559 3.45840287 3.46992016\n", " 3.48143744 3.49295473 3.50447202 3.5159893 3.52750659 3.53902388\n", " 3.55054116 3.56205845 3.57357574 3.58509302 3.59661031 3.60812759\n", " 3.61964488 3.63116217 3.64267945 3.65419674 3.66571403 3.67723131\n", " 3.6887486 3.70026588 3.71178317 3.72330046 3.73481774 3.74633503\n", " 3.75785232 3.7693696 3.78088689 3.79240417 3.80392146 3.81543875\n", " 3.82695603 3.83847332 3.84999061 3.86150789 3.87302518 3.88454247\n", " 3.89605975 3.90757704 3.91909432 3.93061161 3.9421289 3.95364618\n", " 3.96516347 3.97668076 3.98819804 3.99971533 4.01123261 4.0227499\n", " 4.03426719 4.04578447 4.05730176 4.06881905 4.08033633 4.09185362]\n", "Computing PSFs. This has to iterate over many wavelengths, so is slow.\n", "iterating query, tdelta=3.0\n", "\n", "MAST OPD query around UTC: 2024-01-25T17:47:35.716\n", " MJD: 60334.741385601854\n", "\n", "OPD immediately preceding the given datetime:\n", "\tURI:\t mast:JWST/product/R2024012502-NRCA3_FP1-1.fits\n", "\tDate (MJD):\t 60334.2955\n", "\tDelta time:\t -0.4459 days\n", "\n", "OPD immediately following the given datetime:\n", "\tURI:\t mast:JWST/product/R2024012702-NRCA3_FP1-1.fits\n", "\tDate (MJD):\t 60336.5976\n", "\tDelta time:\t 1.8562 days\n", "User requested choosing OPD time closest in time to 2024-01-25T17:47:35.716, which is R2024012502-NRCA3_FP1-1.fits, delta time -0.446 days\n", "Importing and format-converting OPD from /fast/jruffio/data/stpsf-data/MAST_JWST_WSS_OPDs/R2024012502-NRCA3_FP1-1.fits\n", "Backing out SI WFE and OTE field dependence at the WF sensing field point (NRCA3_FP1)\n", "Sensing inst model using apername NRCA3_FP1\n", "Using sensing field point SI WFE model from file wss_target_phase_fp1.fits\n", "\tPerforming serial calculation of PSF at 108 wavelengths.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|█████████████████████████████████████████████████████████████| 108/108 [01:15<00:00, 1.43it/s]\n", "WARNING: VerifyWarning: Card is too long, comment will be truncated. [astropy.io.fits.card]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Saved the computed PSFs to /stow/jruffio/data/JWST/tutorial/data/G395H_utils_before_cleaning/jw01414013001_02101_nrs1_webbpsf.fits\n", "Make sure interpdata_regwvs was already done \n", "fitpsf wavelength indices: 0 108\n", "\tPerforming parallelized PSF fit at 108 wavelengths.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|█████████████████████████████████████████████████████████████| 108/108 [00:37<00:00, 2.87it/s]\n", "100%|██████████████████████████████████████████████████████████| 108/108 [00:00<00:00, 31113.73it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2.9827389478683473 3.968618655204773\n", "RA correction nrs1 [-0.00138935 -0.00269562 -0.05754739]\n", "Dec correction nrs1 [-0.00432524 -0.010629 -0.17953026]\n", "(1, 108, 5)\n", "Saving /stow/jruffio/data/JWST/tutorial/data/G395H_utils_before_cleaning/20260220_180128_centroid_calibration.png\n", "/stow/jruffio/data/JWST/tutorial/data/G395H_stage2/jw01414013001_02101_00001_nrs1_cal.fits\n", "/stow/jruffio/data/JWST/tutorial/data/G395H_stage2/jw01414013001_02101_00002_nrs1_cal.fits\n", "N files: 2\n", "/stow/jruffio/data/JWST/tutorial/data/G395H_stage2/jw01414013001_02101_00001_nrs1_cal.fits\n", "Reading data from /stow/jruffio/data/JWST/tutorial/data/G395H_stage2/jw01414013001_02101_00001_nrs1_cal.fits\n", "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", "Loading data for compute_med_filt_badpix cached in /stow/jruffio/data/JWST/tutorial/data/G395H_utils_before_cleaning/jw01414013001_02101_00001_nrs1_cal_roughbadpix.fits\n", "Loading data for compute_coordinates_arrays cached in /stow/jruffio/data/JWST/tutorial/data/G395H_utils_before_cleaning/jw01414013001_02101_00001_nrs1_cal_relcoords.fits\n", "Running convert_MJy_per_sr_to_MJy with parameters:\n", "\t save_utils: True\n", "Loading data for compute_starspectrum_contnorm cached in /stow/jruffio/data/JWST/tutorial/data/G395H_utils_before_cleaning/jw01414013001_02101_00001_nrs1_cal_starspec_contnorm.fits\n", "Loading data for compute_starsubtraction cached in /stow/jruffio/data/JWST/tutorial/data/G395H_utils_before_cleaning/jw01414013001_02101_00001_nrs1_cal_starsub.fits\n", "Loading data for compute_interpdata_regwvs cached in /stow/jruffio/data/JWST/tutorial/data/G395H_utils_before_cleaning/jw01414013001_02101_00001_nrs1_cal_regwvs.fits\n", "/stow/jruffio/data/JWST/tutorial/data/G395H_stage2/jw01414013001_02101_00002_nrs1_cal.fits\n", "Reading data from /stow/jruffio/data/JWST/tutorial/data/G395H_stage2/jw01414013001_02101_00002_nrs1_cal.fits\n", "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", "Loading data for compute_med_filt_badpix cached in /stow/jruffio/data/JWST/tutorial/data/G395H_utils_before_cleaning/jw01414013001_02101_00002_nrs1_cal_roughbadpix.fits\n", "Loading data for compute_coordinates_arrays cached in /stow/jruffio/data/JWST/tutorial/data/G395H_utils_before_cleaning/jw01414013001_02101_00002_nrs1_cal_relcoords.fits\n", "Running convert_MJy_per_sr_to_MJy with parameters:\n", "\t save_utils: True\n", "Loading data for compute_starspectrum_contnorm cached in /stow/jruffio/data/JWST/tutorial/data/G395H_utils_before_cleaning/jw01414013001_02101_00002_nrs1_cal_starspec_contnorm.fits\n", "Loading data for compute_starsubtraction cached in /stow/jruffio/data/JWST/tutorial/data/G395H_utils_before_cleaning/jw01414013001_02101_00002_nrs1_cal_starsub.fits\n", "Loading data for compute_interpdata_regwvs cached in /stow/jruffio/data/JWST/tutorial/data/G395H_utils_before_cleaning/jw01414013001_02101_00002_nrs1_cal_regwvs.fits\n", "Make sure interpdata_regwvs was already done \n", "fitpsf wavelength indices: 0 108\n", "\tPerforming parallelized PSF fit at 108 wavelengths.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|█████████████████████████████████████████████████████████████| 108/108 [00:37<00:00, 2.89it/s]\n", "100%|██████████████████████████████████████████████████████████| 108/108 [00:00<00:00, 35177.82it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2.9827389478683473 3.968618655204773\n", "RA correction nrs1 [-0.00058284 -0.0022742 -0.0584407 ]\n", "Dec correction nrs1 [ 0.00050445 -0.01076225 -0.18053904]\n", "(1, 108, 5)\n", "Saving /stow/jruffio/data/JWST/tutorial/data/G395H_utils_before_cleaning/20260220_180212_centroid_calibration.png\n", "Noise Clean: Processing file 1 of 2: jw01414013001_02101_00001_nrs1_rate.fits\n", "Reading data from /stow/jruffio/data/JWST/tutorial/data/G395H_stage2/jw01414013001_02101_00001_nrs1_cal.fits\n", "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", "Applying relative coordinate offset (array([-0.00058284, -0.0022742 , -0.0584407 ]), array([ 0.00050445, -0.01076225, -0.18053904]))\n", "/stow/jruffio/data/JWST/tutorial/data/G395H_stage2/jw01414013001_02101_00001_nrs1_cal.fits\n", "['/stow/jruffio/data/JWST/tutorial/data/G395H_stage2/jw01414013001_02101_00001_nrs1_cal.fits']\n", "\tUnpacking data quality bitmasks. DQ array is of type uint32\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jruffio/anaconda3/envs/breads_2026/lib/python3.12/site-packages/scipy/ndimage/_filters.py:2420: RuntimeWarning: All-NaN slice encountered\n", " _nd_image.generic_filter(input, function, footprint, output, mode,\n", "/home/jruffio/code/breads/breads/jwst_tools/reduction_utils.py:788: SmallSampleWarning: One or more sample arguments is too small; all returned values will be NaN. See documentation for sample size requirements.\n", " row_data_masking = row_data / median_abs_deviation(row_data[np.where(np.isfinite(bad_pixels[rowid, :]))])\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tNoise Clean Runtime so far: 50.7002 seconds\n", "\n", "Noise Clean: Processing file 2 of 2: jw01414013001_02101_00002_nrs1_rate.fits\n", "Reading data from /stow/jruffio/data/JWST/tutorial/data/G395H_stage2/jw01414013001_02101_00002_nrs1_cal.fits\n", "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", "Applying relative coordinate offset (array([-0.00058284, -0.0022742 , -0.0584407 ]), array([ 0.00050445, -0.01076225, -0.18053904]))\n", "/stow/jruffio/data/JWST/tutorial/data/G395H_stage2/jw01414013001_02101_00002_nrs1_cal.fits\n", "['/stow/jruffio/data/JWST/tutorial/data/G395H_stage2/jw01414013001_02101_00002_nrs1_cal.fits']\n", "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", "\tNoise Clean Runtime so far: 101.0560 seconds\n", "\n", "Noise Clean Total Runtime: 101.0561 seconds\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-20 18:03:57,070 - stpipe.step - INFO - PARS-RESAMPLESPECSTEP parameters found: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pars-resamplespecstep_0001.asdf\n", "2026-02-20 18:03:57,084 - stpipe.step - INFO - PARS-RESAMPLESPECSTEP parameters found: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pars-resamplespecstep_0001.asdf\n", "2026-02-20 18:03:57,103 - stpipe.step - INFO - Spec2Pipeline instance created.\n", "2026-02-20 18:03:57,104 - stpipe.step - INFO - AssignWcsStep instance created.\n", "2026-02-20 18:03:57,104 - stpipe.step - INFO - BadpixSelfcalStep instance created.\n", "2026-02-20 18:03:57,105 - stpipe.step - INFO - MSAFlagOpenStep instance created.\n", "2026-02-20 18:03:57,106 - stpipe.step - INFO - NSCleanStep instance created.\n", "2026-02-20 18:03:57,106 - stpipe.step - INFO - BackgroundStep instance created.\n", "2026-02-20 18:03:57,107 - stpipe.step - INFO - ImprintStep instance created.\n", "2026-02-20 18:03:57,108 - stpipe.step - INFO - Extract2dStep instance created.\n", "2026-02-20 18:03:57,113 - stpipe.step - INFO - MasterBackgroundMosStep instance created.\n", "2026-02-20 18:03:57,113 - stpipe.step - INFO - FlatFieldStep instance created.\n", "2026-02-20 18:03:57,114 - stpipe.step - INFO - PathLossStep instance created.\n", "2026-02-20 18:03:57,115 - stpipe.step - INFO - BarShadowStep instance created.\n", "2026-02-20 18:03:57,115 - stpipe.step - INFO - PhotomStep instance created.\n", "2026-02-20 18:03:57,116 - stpipe.step - INFO - PixelReplaceStep instance created.\n", "2026-02-20 18:03:57,117 - stpipe.step - INFO - ResampleSpecStep instance created.\n", "2026-02-20 18:03:57,119 - stpipe.step - INFO - Extract1dStep instance created.\n", "2026-02-20 18:03:57,120 - stpipe.step - INFO - WavecorrStep instance created.\n", "2026-02-20 18:03:57,121 - stpipe.step - INFO - FlatFieldStep instance created.\n", "2026-02-20 18:03:57,121 - stpipe.step - INFO - SourceTypeStep instance created.\n", "2026-02-20 18:03:57,122 - stpipe.step - INFO - StraylightStep instance created.\n", "2026-02-20 18:03:57,124 - stpipe.step - INFO - FringeStep instance created.\n", "2026-02-20 18:03:57,125 - stpipe.step - INFO - ResidualFringeStep instance created.\n", "2026-02-20 18:03:57,125 - stpipe.step - INFO - PathLossStep instance created.\n", "2026-02-20 18:03:57,126 - stpipe.step - INFO - BarShadowStep instance created.\n", "2026-02-20 18:03:57,127 - stpipe.step - INFO - WfssContamStep instance created.\n", "2026-02-20 18:03:57,127 - stpipe.step - INFO - PhotomStep instance created.\n", "2026-02-20 18:03:57,128 - stpipe.step - INFO - PixelReplaceStep instance created.\n", "2026-02-20 18:03:57,129 - stpipe.step - INFO - ResampleSpecStep instance created.\n", "2026-02-20 18:03:57,130 - stpipe.step - INFO - CubeBuildStep instance created.\n", "2026-02-20 18:03:57,131 - stpipe.step - INFO - Extract1dStep instance created.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Plots saved to /stow/jruffio/data/JWST/tutorial/data/G395H_stage1_cleaned/plots_noiseclean_jw01414013001_nrs1.pdf\n", "Stage 2 Processing file 1 of 2.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-20 18:03:57,339 - stpipe.step - INFO - Step Spec2Pipeline running with args ('/stow/jruffio/data/JWST/tutorial/data/G395H_stage1_cleaned/jw01414013001_02101_00001_nrs1_rate.fits',).\n", "2026-02-20 18:03:57,367 - stpipe.step - INFO - Step Spec2Pipeline parameters are:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: /stow/jruffio/data/JWST/tutorial/data/G395H_stage2_cleaned\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: True\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " save_bsub: False\n", " fail_on_exception: True\n", " save_wfss_esec: False\n", " steps:\n", " assign_wcs:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " sip_approx: True\n", " sip_max_pix_error: 0.01\n", " sip_degree: None\n", " sip_max_inv_pix_error: 0.01\n", " sip_inv_degree: None\n", " sip_npoints: 12\n", " slit_y_low: -0.55\n", " slit_y_high: 0.55\n", " nrs_ifu_slice_wcs: False\n", " badpix_selfcal:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " flagfrac_lower: 0.001\n", " flagfrac_upper: 0.001\n", " kernel_size: 15\n", " force_single: False\n", " save_flagged_bkg: False\n", " msa_flagging:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " nsclean:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " fit_method: fft\n", " fit_by_channel: False\n", " background_method: None\n", " background_box_size: None\n", " mask_spectral_regions: True\n", " n_sigma: 5.0\n", " fit_histogram: False\n", " single_mask: False\n", " user_mask: None\n", " save_mask: False\n", " save_background: False\n", " save_noise: False\n", " bkg_subtract:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " bkg_list: None\n", " save_combined_background: False\n", " sigma: 3.0\n", " maxiters: None\n", " soss_source_percentile: 35.0\n", " soss_bkg_percentile: None\n", " wfss_mmag_extract: None\n", " wfss_maxiter: 5\n", " wfss_rms_stop: 0.0\n", " wfss_outlier_percent: 1.0\n", " imprint_subtract:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " extract_2d:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " slit_names: None\n", " source_ids: None\n", " extract_orders: None\n", " grism_objects: None\n", " tsgrism_extract_height: None\n", " wfss_extract_half_height: 5\n", " wfss_mmag_extract: None\n", " wfss_nbright: 1000\n", " master_background_mos:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: True\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " sigma_clip: 3.0\n", " median_kernel: 1\n", " force_subtract: False\n", " save_background: False\n", " user_background: None\n", " inverse: False\n", " steps:\n", " flat_field:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " save_interpolated_flat: False\n", " user_supplied_flat: None\n", " inverse: False\n", " pathloss:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " user_slit_loc: None\n", " barshadow:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " photom:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " apply_time_correction: True\n", " pixel_replace:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: True\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " algorithm: fit_profile\n", " n_adjacent_cols: 3\n", " resample_spec:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " pixfrac: 1.0\n", " kernel: square\n", " fillval: NaN\n", " weight_type: exptime\n", " output_shape: None\n", " pixel_scale_ratio: 1.0\n", " pixel_scale: None\n", " output_wcs: ''\n", " single: False\n", " blendheaders: True\n", " in_memory: True\n", " extract_1d:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " subtract_background: None\n", " apply_apcorr: True\n", " extraction_type: box\n", " use_source_posn: None\n", " position_offset: 0.0\n", " model_nod_pair: True\n", " optimize_psf_location: True\n", " smoothing_length: None\n", " bkg_fit: None\n", " bkg_order: None\n", " log_increment: 50\n", " save_profile: False\n", " save_scene_model: False\n", " save_residual_image: False\n", " center_xy: None\n", " ifu_autocen: False\n", " bkg_sigma_clip: 3.0\n", " ifu_rfcorr: True\n", " ifu_set_srctype: None\n", " ifu_rscale: None\n", " ifu_covar_scale: 1.0\n", " soss_atoca: True\n", " soss_threshold: 0.01\n", " soss_n_os: 2\n", " soss_wave_grid_in: None\n", " soss_wave_grid_out: None\n", " soss_estimate: None\n", " soss_rtol: 0.0001\n", " soss_max_grid_size: 20000\n", " soss_tikfac: None\n", " soss_width: 40.0\n", " soss_bad_pix: masking\n", " soss_modelname: None\n", " soss_order_3: True\n", " wavecorr:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " flat_field:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " save_interpolated_flat: False\n", " user_supplied_flat: None\n", " inverse: False\n", " srctype:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " source_type: None\n", " straylight:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " clean_showers: False\n", " shower_plane: 3\n", " shower_x_stddev: 18.0\n", " shower_y_stddev: 5.0\n", " shower_low_reject: 0.1\n", " shower_high_reject: 99.9\n", " save_shower_model: False\n", " fringe:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " residual_fringe:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: residual_fringe\n", " search_output_file: False\n", " input_dir: ''\n", " save_intermediate_results: False\n", " ignore_region_min: None\n", " ignore_region_max: None\n", " pathloss:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " user_slit_loc: None\n", " barshadow:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " wfss_contam:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " save_simulated_image: False\n", " save_contam_images: False\n", " maximum_cores: none\n", " orders: None\n", " magnitude_limit: None\n", " wl_oversample: 2\n", " max_pixels_per_chunk: 50000\n", " photom:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " apply_time_correction: True\n", " pixel_replace:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: True\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " algorithm: fit_profile\n", " n_adjacent_cols: 3\n", " resample_spec:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " pixfrac: 1.0\n", " kernel: square\n", " fillval: NaN\n", " weight_type: exptime\n", " output_shape: None\n", " pixel_scale_ratio: 1.0\n", " pixel_scale: None\n", " output_wcs: ''\n", " single: False\n", " blendheaders: True\n", " in_memory: True\n", " cube_build:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: True\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: s3d\n", " search_output_file: False\n", " input_dir: ''\n", " pipeline: 3\n", " channel: all\n", " band: all\n", " grating: all\n", " filter: all\n", " output_type: None\n", " scalexy: 0.0\n", " scalew: 0.0\n", " weighting: drizzle\n", " coord_system: skyalign\n", " ra_center: None\n", " dec_center: None\n", " cube_pa: None\n", " nspax_x: None\n", " nspax_y: None\n", " rois: 0.0\n", " roiw: 0.0\n", " weight_power: 2.0\n", " wavemin: None\n", " wavemax: None\n", " single: False\n", " skip_dqflagging: False\n", " offset_file: None\n", " debug_spaxel: -1 -1 -1\n", " extract_1d:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " subtract_background: None\n", " apply_apcorr: True\n", " extraction_type: box\n", " use_source_posn: None\n", " position_offset: 0.0\n", " model_nod_pair: True\n", " optimize_psf_location: True\n", " smoothing_length: None\n", " bkg_fit: None\n", " bkg_order: None\n", " log_increment: 50\n", " save_profile: False\n", " save_scene_model: False\n", " save_residual_image: False\n", " center_xy: None\n", " ifu_autocen: False\n", " bkg_sigma_clip: 3.0\n", " ifu_rfcorr: True\n", " ifu_set_srctype: None\n", " ifu_rscale: None\n", " ifu_covar_scale: 1.0\n", " soss_atoca: True\n", " soss_threshold: 0.01\n", " soss_n_os: 2\n", " soss_wave_grid_in: None\n", " soss_wave_grid_out: None\n", " soss_estimate: None\n", " soss_rtol: 0.0001\n", " soss_max_grid_size: 20000\n", " soss_tikfac: None\n", " soss_width: 40.0\n", " soss_bad_pix: masking\n", " soss_modelname: None\n", " soss_order_3: True\n", "2026-02-20 18:03:57,395 - stpipe.pipeline - INFO - Prefetching reference files for dataset: 'jw01414013001_02101_00001_nrs1_rate.fits' reftypes = ['area', 'barshadow', 'bkg', 'camera', 'collimator', 'dflat', 'disperser', 'distortion', 'fflat', 'filteroffset', 'flat', 'fore', 'fpa', 'fringe', 'ifufore', 'ifupost', 'ifuslicer', 'mrsxartcorr', 'msa', 'msaoper', 'ote', 'pathloss', 'photom', 'regions', 'sflat', 'specwcs', 'wavecorr', 'wavelengthrange']\n", "2026-02-20 18:03:57,399 - stpipe.pipeline - INFO - Prefetch for AREA reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_area_0034.fits'.\n", "2026-02-20 18:03:57,399 - stpipe.pipeline - INFO - Prefetch for BARSHADOW reference file is 'N/A'.\n", "2026-02-20 18:03:57,400 - stpipe.pipeline - INFO - Prefetch for BKG reference file is 'N/A'.\n", "2026-02-20 18:03:57,400 - stpipe.pipeline - INFO - Prefetch for CAMERA reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_camera_0010.asdf'.\n", "2026-02-20 18:03:57,400 - stpipe.pipeline - INFO - Prefetch for COLLIMATOR reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_collimator_0010.asdf'.\n", "2026-02-20 18:03:57,401 - stpipe.pipeline - INFO - Prefetch for DFLAT reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_dflat_0001.fits'.\n", "2026-02-20 18:03:57,401 - stpipe.pipeline - INFO - Prefetch for DISPERSER reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_disperser_0083.asdf'.\n", "2026-02-20 18:03:57,402 - stpipe.pipeline - INFO - Prefetch for DISTORTION reference file is 'N/A'.\n", "2026-02-20 18:03:57,402 - stpipe.pipeline - INFO - Prefetch for FFLAT reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fflat_0174.fits'.\n", "2026-02-20 18:03:57,402 - stpipe.pipeline - INFO - Prefetch for FILTEROFFSET reference file is 'N/A'.\n", "2026-02-20 18:03:57,403 - stpipe.pipeline - INFO - Prefetch for FLAT reference file is 'N/A'.\n", "2026-02-20 18:03:57,403 - stpipe.pipeline - INFO - Prefetch for FORE reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fore_0065.asdf'.\n", "2026-02-20 18:03:57,404 - stpipe.pipeline - INFO - Prefetch for FPA reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fpa_0011.asdf'.\n", "2026-02-20 18:03:57,404 - stpipe.pipeline - INFO - Prefetch for FRINGE reference file is 'N/A'.\n", "2026-02-20 18:03:57,405 - stpipe.pipeline - INFO - Prefetch for IFUFORE reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifufore_0015.asdf'.\n", "2026-02-20 18:03:57,405 - stpipe.pipeline - INFO - Prefetch for IFUPOST reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifupost_0016.asdf'.\n", "2026-02-20 18:03:57,406 - stpipe.pipeline - INFO - Prefetch for IFUSLICER reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifuslicer_0015.asdf'.\n", "2026-02-20 18:03:57,406 - stpipe.pipeline - INFO - Prefetch for MRSXARTCORR reference file is 'N/A'.\n", "2026-02-20 18:03:57,407 - stpipe.pipeline - INFO - Prefetch for MSA reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_msa_0011.asdf'.\n", "2026-02-20 18:03:57,407 - stpipe.pipeline - INFO - Prefetch for MSAOPER reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_msaoper_0011.json'.\n", "2026-02-20 18:03:57,408 - stpipe.pipeline - INFO - Prefetch for OTE reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ote_0013.asdf'.\n", "2026-02-20 18:03:57,408 - stpipe.pipeline - INFO - Prefetch for PATHLOSS reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pathloss_0003.fits'.\n", "2026-02-20 18:03:57,409 - stpipe.pipeline - INFO - Prefetch for PHOTOM reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_photom_0016.fits'.\n", "2026-02-20 18:03:57,409 - stpipe.pipeline - INFO - Prefetch for REGIONS reference file is 'N/A'.\n", "2026-02-20 18:03:57,410 - stpipe.pipeline - INFO - Prefetch for SFLAT reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_sflat_0219.fits'.\n", "2026-02-20 18:03:57,410 - stpipe.pipeline - INFO - Prefetch for SPECWCS reference file is 'N/A'.\n", "2026-02-20 18:03:57,410 - stpipe.pipeline - INFO - Prefetch for WAVECORR reference file is 'N/A'.\n", "2026-02-20 18:03:57,411 - stpipe.pipeline - INFO - Prefetch for WAVELENGTHRANGE reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_wavelengthrange_0008.asdf'.\n", "2026-02-20 18:03:57,411 - jwst.pipeline.calwebb_spec2 - INFO - Starting calwebb_spec2 ...\n", "2026-02-20 18:03:57,412 - jwst.pipeline.calwebb_spec2 - INFO - Processing product /stow/jruffio/data/JWST/tutorial/data/G395H_stage1_cleaned/jw01414013001_02101_00001_nrs1\n", "2026-02-20 18:03:57,412 - jwst.pipeline.calwebb_spec2 - INFO - Working on input /stow/jruffio/data/JWST/tutorial/data/G395H_stage1_cleaned/jw01414013001_02101_00001_nrs1_rate.fits ...\n", "2026-02-20 18:03:57,690 - stpipe.step - INFO - Step assign_wcs running with args (,).\n", "2026-02-20 18:03:57,738 - jwst.assign_wcs.nirspec - INFO - gwa_ytilt is 0.144672647 deg\n", "2026-02-20 18:03:57,738 - jwst.assign_wcs.nirspec - INFO - gwa_xtilt is 0.319160342 deg\n", "2026-02-20 18:03:57,739 - jwst.assign_wcs.nirspec - INFO - theta_y correction: 0.0001127116201274071 deg\n", "2026-02-20 18:03:57,740 - jwst.assign_wcs.nirspec - INFO - theta_x correction: 8.739014225467499e-05 deg\n", "2026-02-20 18:03:57,804 - jwst.assign_wcs.nirspec - INFO - Applied Barycentric velocity correction : 0.9999124176092914\n", "2026-02-20 18:03:59,258 - jwst.assign_wcs.nirspec - INFO - Created a NIRSPEC nrs_ifu pipeline with references {'distortion': None, 'filteroffset': None, 'specwcs': None, 'regions': None, 'wavelengthrange': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_wavelengthrange_0008.asdf', 'camera': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_camera_0010.asdf', 'collimator': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_collimator_0010.asdf', 'disperser': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_disperser_0083.asdf', 'fore': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fore_0065.asdf', 'fpa': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fpa_0011.asdf', 'msa': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_msa_0011.asdf', 'ote': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ote_0013.asdf', 'ifupost': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifupost_0016.asdf', 'ifufore': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifufore_0015.asdf', 'ifuslicer': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifuslicer_0015.asdf'}\n", "2026-02-20 18:04:03,686 - stcal.alignment.util - INFO - Update S_REGION to POLYGON ICRS 46.826578143 -13.764355816 46.827850044 -13.764355816 46.827850044 -13.763091425 46.826578143 -13.763091425\n", "2026-02-20 18:04:07,082 - jwst.assign_wcs.assign_wcs - INFO - COMPLETED assign_wcs\n", "2026-02-20 18:04:07,086 - stpipe.step - INFO - Step assign_wcs done\n", "2026-02-20 18:04:07,422 - stpipe.step - INFO - Step badpix_selfcal running with args (, [], []).\n", "2026-02-20 18:04:07,423 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 18:04:07,716 - stpipe.step - INFO - Step msa_flagging running with args (,).\n", "2026-02-20 18:04:09,550 - jwst.msaflagopen.msaflagopen_step - INFO - Using reference file /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_msaoper_0011.json\n", "2026-02-20 18:04:09,627 - jwst.msaflagopen.msaflag_open - INFO - 23 failed open shutters\n", "2026-02-20 18:04:15,161 - stpipe.step - INFO - Step msa_flagging done\n", "2026-02-20 18:04:15,714 - stpipe.step - INFO - Step nsclean running with args (,).\n", "2026-02-20 18:04:15,716 - jwst.nsclean.nsclean_step - WARNING - The 'nsclean' step is a deprecated alias to 'clean_flicker_noise' and will be removed in future builds.\n", "2026-02-20 18:04:15,722 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Input exposure type is NRS_IFU, detector=NRS1\n", "2026-02-20 18:04:18,113 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Flagging failed-open MSA shutters for scene masking\n", "2026-02-20 18:04:18,114 - stpipe.step - INFO - MSAFlagOpenStep instance created.\n", "2026-02-20 18:04:27,062 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Creating mask\n", "2026-02-20 18:04:27,064 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Finding slice pixels for an IFU image\n", "2026-02-20 18:04:28,391 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Masking the fixed slit region for IFU data.\n", "2026-02-20 18:04:28,558 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Cleaning image jw01414013001_02101_00001_nrs1_rate.fits\n", "2026-02-20 18:04:29,772 - stpipe.step - INFO - Step nsclean done\n", "2026-02-20 18:04:30,716 - stpipe.step - INFO - Step imprint_subtract running with args (, []).\n", "2026-02-20 18:04:30,718 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 18:04:31,126 - stpipe.step - INFO - Step bkg_subtract running with args (, []).\n", "2026-02-20 18:04:31,127 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 18:04:31,526 - stpipe.step - INFO - Step srctype running with args (,).\n", "2026-02-20 18:04:34,194 - jwst.srctype.srctype - INFO - Input EXP_TYPE is NRS_IFU\n", "2026-02-20 18:04:34,195 - jwst.srctype.srctype - INFO - Input SRCTYAPT = POINT\n", "2026-02-20 18:04:34,196 - jwst.srctype.srctype - INFO - Using input source type = POINT\n", "2026-02-20 18:04:34,197 - stpipe.step - INFO - Step srctype done\n", "2026-02-20 18:04:34,705 - stpipe.step - INFO - Step straylight running with args (,).\n", "2026-02-20 18:04:34,706 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 18:04:35,195 - stpipe.step - INFO - Step flat_field running with args (,).\n", "2026-02-20 18:04:35,209 - jwst.flatfield.flat_field_step - INFO - No reference found for type FLAT\n", "2026-02-20 18:04:35,229 - jwst.flatfield.flat_field_step - INFO - Using FFLAT reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fflat_0174.fits\n", "2026-02-20 18:04:35,286 - jwst.flatfield.flat_field_step - INFO - Using SFLAT reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_sflat_0219.fits\n", "2026-02-20 18:04:35,430 - jwst.flatfield.flat_field_step - INFO - Using DFLAT reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_dflat_0001.fits\n", "2026-02-20 18:04:41,495 - stpipe.step - INFO - Step flat_field done\n", "2026-02-20 18:04:42,139 - stpipe.step - INFO - Step fringe running with args (,).\n", "2026-02-20 18:04:42,141 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 18:04:42,631 - stpipe.step - INFO - Step pathloss running with args (,).\n", "2026-02-20 18:04:42,640 - jwst.pathloss.pathloss_step - INFO - Using PATHLOSS reference file /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pathloss_0003.fits\n", "2026-02-20 18:04:42,655 - jwst.pathloss.pathloss - INFO - Input exposure type is NRS_IFU\n", "2026-02-20 18:04:45,704 - stpipe.step - INFO - Step pathloss done\n", "2026-02-20 18:04:46,371 - stpipe.step - INFO - Step barshadow running with args (,).\n", "2026-02-20 18:04:46,372 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 18:04:46,864 - stpipe.step - INFO - Step photom running with args (,).\n", "2026-02-20 18:04:46,877 - jwst.photom.photom_step - INFO - Using photom reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_photom_0016.fits\n", "2026-02-20 18:04:46,878 - jwst.photom.photom_step - INFO - Using area reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_area_0034.fits\n", "2026-02-20 18:04:48,519 - jwst.photom.photom - INFO - Using instrument: NIRSPEC\n", "2026-02-20 18:04:48,520 - jwst.photom.photom - INFO - detector: NRS1\n", "2026-02-20 18:04:48,520 - jwst.photom.photom - INFO - exp_type: NRS_IFU\n", "2026-02-20 18:04:48,521 - jwst.photom.photom - INFO - filter: F290LP\n", "2026-02-20 18:04:48,521 - jwst.photom.photom - INFO - grating: G395H\n", "2026-02-20 18:04:48,547 - jwst.photom.photom - INFO - PHOTMJSR value: 3.83378e+12\n", "2026-02-20 18:04:49,930 - stpipe.step - INFO - Step photom done\n", "2026-02-20 18:04:50,644 - stpipe.step - INFO - Step residual_fringe running with args (,).\n", "2026-02-20 18:04:50,646 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 18:04:53,433 - stpipe.step - INFO - Step pixel_replace running with args (,).\n", "2026-02-20 18:04:53,434 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 18:04:53,940 - stpipe.step - INFO - Step cube_build running with args (,).\n", "2026-02-20 18:04:53,941 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 18:04:56,713 - stpipe.step - INFO - Step extract_1d running with args (,).\n", "2026-02-20 18:04:56,714 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 18:04:56,715 - jwst.pipeline.calwebb_spec2 - INFO - Finished processing product /stow/jruffio/data/JWST/tutorial/data/G395H_stage1_cleaned/jw01414013001_02101_00001_nrs1\n", "2026-02-20 18:04:56,717 - jwst.pipeline.calwebb_spec2 - INFO - Ending calwebb_spec2\n", "2026-02-20 18:04:56,717 - jwst.stpipe.core - INFO - Results used CRDS context: jwst_1464.pmap\n", "2026-02-20 18:04:58,104 - stpipe.step - INFO - Saved model in /stow/jruffio/data/JWST/tutorial/data/G395H_stage2_cleaned/jw01414013001_02101_00001_nrs1_cal.fits\n", "2026-02-20 18:04:58,105 - stpipe.step - INFO - Step Spec2Pipeline done\n", "2026-02-20 18:04:58,105 - jwst.stpipe.core - INFO - Results used jwst version: 1.20.2\n", "2026-02-20 18:04:58,158 - stpipe.step - INFO - PARS-RESAMPLESPECSTEP parameters found: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pars-resamplespecstep_0001.asdf\n", "2026-02-20 18:04:58,172 - stpipe.step - INFO - PARS-RESAMPLESPECSTEP parameters found: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pars-resamplespecstep_0001.asdf\n", "2026-02-20 18:04:58,191 - stpipe.step - INFO - Spec2Pipeline instance created.\n", "2026-02-20 18:04:58,192 - stpipe.step - INFO - AssignWcsStep instance created.\n", "2026-02-20 18:04:58,193 - stpipe.step - INFO - BadpixSelfcalStep instance created.\n", "2026-02-20 18:04:58,194 - stpipe.step - INFO - MSAFlagOpenStep instance created.\n", "2026-02-20 18:04:58,194 - stpipe.step - INFO - NSCleanStep instance created.\n", "2026-02-20 18:04:58,195 - stpipe.step - INFO - BackgroundStep instance created.\n", "2026-02-20 18:04:58,196 - stpipe.step - INFO - ImprintStep instance created.\n", "2026-02-20 18:04:58,197 - stpipe.step - INFO - Extract2dStep instance created.\n", "2026-02-20 18:04:58,200 - stpipe.step - INFO - MasterBackgroundMosStep instance created.\n", "2026-02-20 18:04:58,201 - stpipe.step - INFO - FlatFieldStep instance created.\n", "2026-02-20 18:04:58,202 - stpipe.step - INFO - PathLossStep instance created.\n", "2026-02-20 18:04:58,203 - stpipe.step - INFO - BarShadowStep instance created.\n", "2026-02-20 18:04:58,203 - stpipe.step - INFO - PhotomStep instance created.\n", "2026-02-20 18:04:58,204 - stpipe.step - INFO - PixelReplaceStep instance created.\n", "2026-02-20 18:04:58,205 - stpipe.step - INFO - ResampleSpecStep instance created.\n", "2026-02-20 18:04:58,206 - stpipe.step - INFO - Extract1dStep instance created.\n", "2026-02-20 18:04:58,207 - stpipe.step - INFO - WavecorrStep instance created.\n", "2026-02-20 18:04:58,207 - stpipe.step - INFO - FlatFieldStep instance created.\n", "2026-02-20 18:04:58,208 - stpipe.step - INFO - SourceTypeStep instance created.\n", "2026-02-20 18:04:58,209 - stpipe.step - INFO - StraylightStep instance created.\n", "2026-02-20 18:04:58,210 - stpipe.step - INFO - FringeStep instance created.\n", "2026-02-20 18:04:58,210 - stpipe.step - INFO - ResidualFringeStep instance created.\n", "2026-02-20 18:04:58,211 - stpipe.step - INFO - PathLossStep instance created.\n", "2026-02-20 18:04:58,212 - stpipe.step - INFO - BarShadowStep instance created.\n", "2026-02-20 18:04:58,213 - stpipe.step - INFO - WfssContamStep instance created.\n", "2026-02-20 18:04:58,214 - stpipe.step - INFO - PhotomStep instance created.\n", "2026-02-20 18:04:58,214 - stpipe.step - INFO - PixelReplaceStep instance created.\n", "2026-02-20 18:04:58,215 - stpipe.step - INFO - ResampleSpecStep instance created.\n", "2026-02-20 18:04:58,216 - stpipe.step - INFO - CubeBuildStep instance created.\n", "2026-02-20 18:04:58,218 - stpipe.step - INFO - Extract1dStep instance created.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tStage 2 Runtime so far: 61.0905 seconds\n", "Stage 2 Processing file 2 of 2.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-02-20 18:04:59,438 - stpipe.step - INFO - Step Spec2Pipeline running with args ('/stow/jruffio/data/JWST/tutorial/data/G395H_stage1_cleaned/jw01414013001_02101_00002_nrs1_rate.fits',).\n", "2026-02-20 18:04:59,466 - stpipe.step - INFO - Step Spec2Pipeline parameters are:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: /stow/jruffio/data/JWST/tutorial/data/G395H_stage2_cleaned\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: True\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " save_bsub: False\n", " fail_on_exception: True\n", " save_wfss_esec: False\n", " steps:\n", " assign_wcs:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " sip_approx: True\n", " sip_max_pix_error: 0.01\n", " sip_degree: None\n", " sip_max_inv_pix_error: 0.01\n", " sip_inv_degree: None\n", " sip_npoints: 12\n", " slit_y_low: -0.55\n", " slit_y_high: 0.55\n", " nrs_ifu_slice_wcs: False\n", " badpix_selfcal:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " flagfrac_lower: 0.001\n", " flagfrac_upper: 0.001\n", " kernel_size: 15\n", " force_single: False\n", " save_flagged_bkg: False\n", " msa_flagging:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " nsclean:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " fit_method: fft\n", " fit_by_channel: False\n", " background_method: None\n", " background_box_size: None\n", " mask_spectral_regions: True\n", " n_sigma: 5.0\n", " fit_histogram: False\n", " single_mask: False\n", " user_mask: None\n", " save_mask: False\n", " save_background: False\n", " save_noise: False\n", " bkg_subtract:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " bkg_list: None\n", " save_combined_background: False\n", " sigma: 3.0\n", " maxiters: None\n", " soss_source_percentile: 35.0\n", " soss_bkg_percentile: None\n", " wfss_mmag_extract: None\n", " wfss_maxiter: 5\n", " wfss_rms_stop: 0.0\n", " wfss_outlier_percent: 1.0\n", " imprint_subtract:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " extract_2d:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " slit_names: None\n", " source_ids: None\n", " extract_orders: None\n", " grism_objects: None\n", " tsgrism_extract_height: None\n", " wfss_extract_half_height: 5\n", " wfss_mmag_extract: None\n", " wfss_nbright: 1000\n", " master_background_mos:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: True\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " sigma_clip: 3.0\n", " median_kernel: 1\n", " force_subtract: False\n", " save_background: False\n", " user_background: None\n", " inverse: False\n", " steps:\n", " flat_field:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " save_interpolated_flat: False\n", " user_supplied_flat: None\n", " inverse: False\n", " pathloss:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " user_slit_loc: None\n", " barshadow:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " photom:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " apply_time_correction: True\n", " pixel_replace:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: True\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " algorithm: fit_profile\n", " n_adjacent_cols: 3\n", " resample_spec:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " pixfrac: 1.0\n", " kernel: square\n", " fillval: NaN\n", " weight_type: exptime\n", " output_shape: None\n", " pixel_scale_ratio: 1.0\n", " pixel_scale: None\n", " output_wcs: ''\n", " single: False\n", " blendheaders: True\n", " in_memory: True\n", " extract_1d:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " subtract_background: None\n", " apply_apcorr: True\n", " extraction_type: box\n", " use_source_posn: None\n", " position_offset: 0.0\n", " model_nod_pair: True\n", " optimize_psf_location: True\n", " smoothing_length: None\n", " bkg_fit: None\n", " bkg_order: None\n", " log_increment: 50\n", " save_profile: False\n", " save_scene_model: False\n", " save_residual_image: False\n", " center_xy: None\n", " ifu_autocen: False\n", " bkg_sigma_clip: 3.0\n", " ifu_rfcorr: True\n", " ifu_set_srctype: None\n", " ifu_rscale: None\n", " ifu_covar_scale: 1.0\n", " soss_atoca: True\n", " soss_threshold: 0.01\n", " soss_n_os: 2\n", " soss_wave_grid_in: None\n", " soss_wave_grid_out: None\n", " soss_estimate: None\n", " soss_rtol: 0.0001\n", " soss_max_grid_size: 20000\n", " soss_tikfac: None\n", " soss_width: 40.0\n", " soss_bad_pix: masking\n", " soss_modelname: None\n", " soss_order_3: True\n", " wavecorr:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " flat_field:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " save_interpolated_flat: False\n", " user_supplied_flat: None\n", " inverse: False\n", " srctype:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " source_type: None\n", " straylight:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " clean_showers: False\n", " shower_plane: 3\n", " shower_x_stddev: 18.0\n", " shower_y_stddev: 5.0\n", " shower_low_reject: 0.1\n", " shower_high_reject: 99.9\n", " save_shower_model: False\n", " fringe:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " residual_fringe:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: residual_fringe\n", " search_output_file: False\n", " input_dir: ''\n", " save_intermediate_results: False\n", " ignore_region_min: None\n", " ignore_region_max: None\n", " pathloss:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " user_slit_loc: None\n", " barshadow:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " wfss_contam:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " save_simulated_image: False\n", " save_contam_images: False\n", " maximum_cores: none\n", " orders: None\n", " magnitude_limit: None\n", " wl_oversample: 2\n", " max_pixels_per_chunk: 50000\n", " photom:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " inverse: False\n", " source_type: None\n", " apply_time_correction: True\n", " pixel_replace:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: True\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " algorithm: fit_profile\n", " n_adjacent_cols: 3\n", " resample_spec:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: False\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " pixfrac: 1.0\n", " kernel: square\n", " fillval: NaN\n", " weight_type: exptime\n", " output_shape: None\n", " pixel_scale_ratio: 1.0\n", " pixel_scale: None\n", " output_wcs: ''\n", " single: False\n", " blendheaders: True\n", " in_memory: True\n", " cube_build:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: True\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: s3d\n", " search_output_file: False\n", " input_dir: ''\n", " pipeline: 3\n", " channel: all\n", " band: all\n", " grating: all\n", " filter: all\n", " output_type: None\n", " scalexy: 0.0\n", " scalew: 0.0\n", " weighting: drizzle\n", " coord_system: skyalign\n", " ra_center: None\n", " dec_center: None\n", " cube_pa: None\n", " nspax_x: None\n", " nspax_y: None\n", " rois: 0.0\n", " roiw: 0.0\n", " weight_power: 2.0\n", " wavemin: None\n", " wavemax: None\n", " single: False\n", " skip_dqflagging: False\n", " offset_file: None\n", " debug_spaxel: -1 -1 -1\n", " extract_1d:\n", " pre_hooks: []\n", " post_hooks: []\n", " output_file: None\n", " output_dir: None\n", " output_ext: .fits\n", " output_use_model: False\n", " output_use_index: True\n", " save_results: False\n", " skip: True\n", " suffix: None\n", " search_output_file: True\n", " input_dir: ''\n", " subtract_background: None\n", " apply_apcorr: True\n", " extraction_type: box\n", " use_source_posn: None\n", " position_offset: 0.0\n", " model_nod_pair: True\n", " optimize_psf_location: True\n", " smoothing_length: None\n", " bkg_fit: None\n", " bkg_order: None\n", " log_increment: 50\n", " save_profile: False\n", " save_scene_model: False\n", " save_residual_image: False\n", " center_xy: None\n", " ifu_autocen: False\n", " bkg_sigma_clip: 3.0\n", " ifu_rfcorr: True\n", " ifu_set_srctype: None\n", " ifu_rscale: None\n", " ifu_covar_scale: 1.0\n", " soss_atoca: True\n", " soss_threshold: 0.01\n", " soss_n_os: 2\n", " soss_wave_grid_in: None\n", " soss_wave_grid_out: None\n", " soss_estimate: None\n", " soss_rtol: 0.0001\n", " soss_max_grid_size: 20000\n", " soss_tikfac: None\n", " soss_width: 40.0\n", " soss_bad_pix: masking\n", " soss_modelname: None\n", " soss_order_3: True\n", "2026-02-20 18:04:59,494 - stpipe.pipeline - INFO - Prefetching reference files for dataset: 'jw01414013001_02101_00002_nrs1_rate.fits' reftypes = ['area', 'barshadow', 'bkg', 'camera', 'collimator', 'dflat', 'disperser', 'distortion', 'fflat', 'filteroffset', 'flat', 'fore', 'fpa', 'fringe', 'ifufore', 'ifupost', 'ifuslicer', 'mrsxartcorr', 'msa', 'msaoper', 'ote', 'pathloss', 'photom', 'regions', 'sflat', 'specwcs', 'wavecorr', 'wavelengthrange']\n", "2026-02-20 18:04:59,498 - stpipe.pipeline - INFO - Prefetch for AREA reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_area_0034.fits'.\n", "2026-02-20 18:04:59,499 - stpipe.pipeline - INFO - Prefetch for BARSHADOW reference file is 'N/A'.\n", "2026-02-20 18:04:59,499 - stpipe.pipeline - INFO - Prefetch for BKG reference file is 'N/A'.\n", "2026-02-20 18:04:59,500 - stpipe.pipeline - INFO - Prefetch for CAMERA reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_camera_0010.asdf'.\n", "2026-02-20 18:04:59,500 - stpipe.pipeline - INFO - Prefetch for COLLIMATOR reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_collimator_0010.asdf'.\n", "2026-02-20 18:04:59,501 - stpipe.pipeline - INFO - Prefetch for DFLAT reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_dflat_0001.fits'.\n", "2026-02-20 18:04:59,501 - stpipe.pipeline - INFO - Prefetch for DISPERSER reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_disperser_0083.asdf'.\n", "2026-02-20 18:04:59,502 - stpipe.pipeline - INFO - Prefetch for DISTORTION reference file is 'N/A'.\n", "2026-02-20 18:04:59,502 - stpipe.pipeline - INFO - Prefetch for FFLAT reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fflat_0174.fits'.\n", "2026-02-20 18:04:59,503 - stpipe.pipeline - INFO - Prefetch for FILTEROFFSET reference file is 'N/A'.\n", "2026-02-20 18:04:59,503 - stpipe.pipeline - INFO - Prefetch for FLAT reference file is 'N/A'.\n", "2026-02-20 18:04:59,504 - stpipe.pipeline - INFO - Prefetch for FORE reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fore_0065.asdf'.\n", "2026-02-20 18:04:59,504 - stpipe.pipeline - INFO - Prefetch for FPA reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fpa_0011.asdf'.\n", "2026-02-20 18:04:59,505 - stpipe.pipeline - INFO - Prefetch for FRINGE reference file is 'N/A'.\n", "2026-02-20 18:04:59,505 - stpipe.pipeline - INFO - Prefetch for IFUFORE reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifufore_0015.asdf'.\n", "2026-02-20 18:04:59,506 - stpipe.pipeline - INFO - Prefetch for IFUPOST reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifupost_0016.asdf'.\n", "2026-02-20 18:04:59,506 - stpipe.pipeline - INFO - Prefetch for IFUSLICER reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifuslicer_0015.asdf'.\n", "2026-02-20 18:04:59,507 - stpipe.pipeline - INFO - Prefetch for MRSXARTCORR reference file is 'N/A'.\n", "2026-02-20 18:04:59,507 - stpipe.pipeline - INFO - Prefetch for MSA reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_msa_0011.asdf'.\n", "2026-02-20 18:04:59,508 - stpipe.pipeline - INFO - Prefetch for MSAOPER reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_msaoper_0011.json'.\n", "2026-02-20 18:04:59,508 - stpipe.pipeline - INFO - Prefetch for OTE reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ote_0013.asdf'.\n", "2026-02-20 18:04:59,509 - stpipe.pipeline - INFO - Prefetch for PATHLOSS reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pathloss_0003.fits'.\n", "2026-02-20 18:04:59,509 - stpipe.pipeline - INFO - Prefetch for PHOTOM reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_photom_0016.fits'.\n", "2026-02-20 18:04:59,510 - stpipe.pipeline - INFO - Prefetch for REGIONS reference file is 'N/A'.\n", "2026-02-20 18:04:59,510 - stpipe.pipeline - INFO - Prefetch for SFLAT reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_sflat_0219.fits'.\n", "2026-02-20 18:04:59,511 - stpipe.pipeline - INFO - Prefetch for SPECWCS reference file is 'N/A'.\n", "2026-02-20 18:04:59,511 - stpipe.pipeline - INFO - Prefetch for WAVECORR reference file is 'N/A'.\n", "2026-02-20 18:04:59,511 - stpipe.pipeline - INFO - Prefetch for WAVELENGTHRANGE reference file is '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_wavelengthrange_0008.asdf'.\n", "2026-02-20 18:04:59,512 - jwst.pipeline.calwebb_spec2 - INFO - Starting calwebb_spec2 ...\n", "2026-02-20 18:04:59,513 - jwst.pipeline.calwebb_spec2 - INFO - Processing product /stow/jruffio/data/JWST/tutorial/data/G395H_stage1_cleaned/jw01414013001_02101_00002_nrs1\n", "2026-02-20 18:04:59,513 - jwst.pipeline.calwebb_spec2 - INFO - Working on input /stow/jruffio/data/JWST/tutorial/data/G395H_stage1_cleaned/jw01414013001_02101_00002_nrs1_rate.fits ...\n", "2026-02-20 18:04:59,793 - stpipe.step - INFO - Step assign_wcs running with args (,).\n", "2026-02-20 18:04:59,841 - jwst.assign_wcs.nirspec - INFO - gwa_ytilt is 0.144672647 deg\n", "2026-02-20 18:04:59,842 - jwst.assign_wcs.nirspec - INFO - gwa_xtilt is 0.319160342 deg\n", "2026-02-20 18:04:59,843 - jwst.assign_wcs.nirspec - INFO - theta_y correction: 0.0001127116201274071 deg\n", "2026-02-20 18:04:59,844 - jwst.assign_wcs.nirspec - INFO - theta_x correction: 8.739014225467499e-05 deg\n", "2026-02-20 18:04:59,908 - jwst.assign_wcs.nirspec - INFO - Applied Barycentric velocity correction : 0.9999124176092914\n", "2026-02-20 18:05:01,364 - jwst.assign_wcs.nirspec - INFO - Created a NIRSPEC nrs_ifu pipeline with references {'distortion': None, 'filteroffset': None, 'specwcs': None, 'regions': None, 'wavelengthrange': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_wavelengthrange_0008.asdf', 'camera': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_camera_0010.asdf', 'collimator': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_collimator_0010.asdf', 'disperser': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_disperser_0083.asdf', 'fore': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fore_0065.asdf', 'fpa': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fpa_0011.asdf', 'msa': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_msa_0011.asdf', 'ote': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ote_0013.asdf', 'ifupost': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifupost_0016.asdf', 'ifufore': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifufore_0015.asdf', 'ifuslicer': '/stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_ifuslicer_0015.asdf'}\n", "2026-02-20 18:05:05,728 - stcal.alignment.util - INFO - Update S_REGION to POLYGON ICRS 46.826532286 -13.764239869 46.827804187 -13.764239869 46.827804187 -13.762975478 46.826532286 -13.762975478\n", "2026-02-20 18:05:09,118 - jwst.assign_wcs.assign_wcs - INFO - COMPLETED assign_wcs\n", "2026-02-20 18:05:09,122 - stpipe.step - INFO - Step assign_wcs done\n", "2026-02-20 18:05:09,442 - stpipe.step - INFO - Step badpix_selfcal running with args (, [], []).\n", "2026-02-20 18:05:09,443 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 18:05:09,737 - stpipe.step - INFO - Step msa_flagging running with args (,).\n", "2026-02-20 18:05:11,567 - jwst.msaflagopen.msaflagopen_step - INFO - Using reference file /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_msaoper_0011.json\n", "2026-02-20 18:05:11,644 - jwst.msaflagopen.msaflag_open - INFO - 23 failed open shutters\n", "2026-02-20 18:05:17,167 - stpipe.step - INFO - Step msa_flagging done\n", "2026-02-20 18:05:17,725 - stpipe.step - INFO - Step nsclean running with args (,).\n", "2026-02-20 18:05:17,726 - jwst.nsclean.nsclean_step - WARNING - The 'nsclean' step is a deprecated alias to 'clean_flicker_noise' and will be removed in future builds.\n", "2026-02-20 18:05:17,733 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Input exposure type is NRS_IFU, detector=NRS1\n", "2026-02-20 18:05:20,126 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Flagging failed-open MSA shutters for scene masking\n", "2026-02-20 18:05:20,127 - stpipe.step - INFO - MSAFlagOpenStep instance created.\n", "2026-02-20 18:05:29,027 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Creating mask\n", "2026-02-20 18:05:29,027 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Finding slice pixels for an IFU image\n", "2026-02-20 18:05:30,307 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Masking the fixed slit region for IFU data.\n", "2026-02-20 18:05:30,475 - jwst.clean_flicker_noise.clean_flicker_noise - INFO - Cleaning image jw01414013001_02101_00002_nrs1_rate.fits\n", "2026-02-20 18:05:31,701 - stpipe.step - INFO - Step nsclean done\n", "2026-02-20 18:05:32,671 - stpipe.step - INFO - Step imprint_subtract running with args (, []).\n", "2026-02-20 18:05:32,672 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 18:05:33,076 - stpipe.step - INFO - Step bkg_subtract running with args (, []).\n", "2026-02-20 18:05:33,077 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 18:05:33,473 - stpipe.step - INFO - Step srctype running with args (,).\n", "2026-02-20 18:05:36,131 - jwst.srctype.srctype - INFO - Input EXP_TYPE is NRS_IFU\n", "2026-02-20 18:05:36,132 - jwst.srctype.srctype - INFO - Input SRCTYAPT = POINT\n", "2026-02-20 18:05:36,132 - jwst.srctype.srctype - INFO - Using input source type = POINT\n", "2026-02-20 18:05:36,134 - stpipe.step - INFO - Step srctype done\n", "2026-02-20 18:05:36,639 - stpipe.step - INFO - Step straylight running with args (,).\n", "2026-02-20 18:05:36,640 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 18:05:37,129 - stpipe.step - INFO - Step flat_field running with args (,).\n", "2026-02-20 18:05:37,142 - jwst.flatfield.flat_field_step - INFO - No reference found for type FLAT\n", "2026-02-20 18:05:37,162 - jwst.flatfield.flat_field_step - INFO - Using FFLAT reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_fflat_0174.fits\n", "2026-02-20 18:05:37,219 - jwst.flatfield.flat_field_step - INFO - Using SFLAT reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_sflat_0219.fits\n", "2026-02-20 18:05:37,359 - jwst.flatfield.flat_field_step - INFO - Using DFLAT reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_dflat_0001.fits\n", "2026-02-20 18:05:43,465 - stpipe.step - INFO - Step flat_field done\n", "2026-02-20 18:05:44,124 - stpipe.step - INFO - Step fringe running with args (,).\n", "2026-02-20 18:05:44,126 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 18:05:44,622 - stpipe.step - INFO - Step pathloss running with args (,).\n", "2026-02-20 18:05:44,630 - jwst.pathloss.pathloss_step - INFO - Using PATHLOSS reference file /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_pathloss_0003.fits\n", "2026-02-20 18:05:44,644 - jwst.pathloss.pathloss - INFO - Input exposure type is NRS_IFU\n", "2026-02-20 18:05:47,702 - stpipe.step - INFO - Step pathloss done\n", "2026-02-20 18:05:48,365 - stpipe.step - INFO - Step barshadow running with args (,).\n", "2026-02-20 18:05:48,366 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 18:05:48,855 - stpipe.step - INFO - Step photom running with args (,).\n", "2026-02-20 18:05:48,865 - jwst.photom.photom_step - INFO - Using photom reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_photom_0016.fits\n", "2026-02-20 18:05:48,866 - jwst.photom.photom_step - INFO - Using area reference file: /stow/jruffio/data/JWST/crds_cache/references/jwst/nirspec/jwst_nirspec_area_0034.fits\n", "2026-02-20 18:05:50,469 - jwst.photom.photom - INFO - Using instrument: NIRSPEC\n", "2026-02-20 18:05:50,469 - jwst.photom.photom - INFO - detector: NRS1\n", "2026-02-20 18:05:50,470 - jwst.photom.photom - INFO - exp_type: NRS_IFU\n", "2026-02-20 18:05:50,470 - jwst.photom.photom - INFO - filter: F290LP\n", "2026-02-20 18:05:50,471 - jwst.photom.photom - INFO - grating: G395H\n", "2026-02-20 18:05:50,497 - jwst.photom.photom - INFO - PHOTMJSR value: 3.83378e+12\n", "2026-02-20 18:05:51,863 - stpipe.step - INFO - Step photom done\n", "2026-02-20 18:05:52,527 - stpipe.step - INFO - Step residual_fringe running with args (,).\n", "2026-02-20 18:05:52,529 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 18:05:55,364 - stpipe.step - INFO - Step pixel_replace running with args (,).\n", "2026-02-20 18:05:55,365 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 18:05:55,875 - stpipe.step - INFO - Step cube_build running with args (,).\n", "2026-02-20 18:05:55,876 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 18:05:58,619 - stpipe.step - INFO - Step extract_1d running with args (,).\n", "2026-02-20 18:05:58,620 - stpipe.step - INFO - Step skipped.\n", "2026-02-20 18:05:58,621 - jwst.pipeline.calwebb_spec2 - INFO - Finished processing product /stow/jruffio/data/JWST/tutorial/data/G395H_stage1_cleaned/jw01414013001_02101_00002_nrs1\n", "2026-02-20 18:05:58,622 - jwst.pipeline.calwebb_spec2 - INFO - Ending calwebb_spec2\n", "2026-02-20 18:05:58,623 - jwst.stpipe.core - INFO - Results used CRDS context: jwst_1464.pmap\n", "2026-02-20 18:06:00,012 - stpipe.step - INFO - Saved model in /stow/jruffio/data/JWST/tutorial/data/G395H_stage2_cleaned/jw01414013001_02101_00002_nrs1_cal.fits\n", "2026-02-20 18:06:00,013 - stpipe.step - INFO - Step Spec2Pipeline done\n", "2026-02-20 18:06:00,014 - jwst.stpipe.core - INFO - Results used jwst version: 1.20.2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tStage 2 Runtime so far: 122.9989 seconds\n", "Stage 2 Total Runtime: 122.9989 seconds\n", "Plots saved to /stow/jruffio/data/JWST/tutorial/data/G395H_stage2_cleaned/plots_stage2_jw01414013001_nrs1.pdf\n", "/stow/jruffio/data/JWST/tutorial/data/G395H_stage2_cleaned/jw01414013001_02101_00001_nrs1_cal.fits\n", "Reading data from /stow/jruffio/data/JWST/tutorial/data/G395H_stage2_cleaned/jw01414013001_02101_00001_nrs1_cal.fits\n", "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", "Running compute_med_filt_badpix with parameters:\n", "\t save_utils: True\n", "\t window_size: 50\n", "\t mad_threshold: 50\n", "Initializing row_err and bad_pixels for nirspec\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jruffio/anaconda3/envs/breads_2026/lib/python3.12/site-packages/scipy/ndimage/_filters.py:2420: RuntimeWarning: All-NaN slice encountered\n", " _nd_image.generic_filter(input, function, footprint, output, mode,\n", "/home/jruffio/code/breads/breads/instruments/jwstnirspec_cal.py:198: SmallSampleWarning: One or more sample arguments is too small; all returned values will be NaN. See documentation for sample size requirements.\n", " row_err_masking = row_err/median_abs_deviation(row_err[np.where(np.isfinite(self.bad_pixels[rowid,:]))])\n", "/home/jruffio/code/breads/breads/instruments/jwstnirspec_cal.py:198: RuntimeWarning: invalid value encountered in divide\n", " row_err_masking = row_err/median_abs_deviation(row_err[np.where(np.isfinite(self.bad_pixels[rowid,:]))])\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Saved the quick bad pixel map to /stow/jruffio/data/JWST/tutorial/data/G395H_utils/jw01414013001_02101_00001_nrs1_cal_roughbadpix.fits\n", "Running compute_coordinates_arrays with parameters:\n", "\t save_utils: True\n", "\t targname: HD 19467\n", "Computing coordinates arrays.\n", "Computing coords for 30 slices...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|███████████████████████████████████████████████████████████████| 30/30 [00:01<00:00, 22.21it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Retrieving SIMBAD coordinates for HD 19467\n", "Coordinates at J2000: 03h07m18.57505144s -13d45m42.41798215s\n", "Coordinates at 2024-01-25: 03h07m18.56069141s -13d45m48.69016027s\n", " Saved the computed coordinates arrays to /stow/jruffio/data/JWST/tutorial/data/G395H_utils/jw01414013001_02101_00001_nrs1_cal_relcoords.fits\n", "Running convert_MJy_per_sr_to_MJy with parameters:\n", "\t save_utils: True\n", "Running apply_coords_offset with parameters:\n", "\t save_utils: True\n", "\t coords_offset: (array([-0.00058284, -0.0022742 , -0.0584407 ]), array([ 0.00050445, -0.01076225, -0.18053904]))\n", "Applying relative coordinate offset (array([-0.00058284, -0.0022742 , -0.0584407 ]), array([ 0.00050445, -0.01076225, -0.18053904]))\n", "Running compute_starspectrum_contnorm with parameters:\n", "\t save_utils: True\n", "\t x_nodes: [2.859 2.879 2.899 2.919 2.939 2.959 2.979 2.999 3.019 3.039 3.059 3.079\n", " 3.099 3.119 3.139 3.159 3.189 3.219 3.249 3.279 3.309 3.339 3.369 3.399\n", " 3.429 3.459 3.499 3.539 3.579 3.619 3.659 3.699 3.739 3.779 3.839 3.899\n", " 3.959 4.019 4.079 4.139]\n", "\t threshold_badpix: 100\n", "\t mppool: \n", "Computing stellar spectrum (continuum normalized)\n", "Saved the continuum normalized star spectrum to /stow/jruffio/data/JWST/tutorial/data/G395H_utils/jw01414013001_02101_00001_nrs1_cal_starspec_contnorm.fits\n", "Running compute_starsubtraction with parameters:\n", "\t save_utils: True\n", "\t starsub_dir: starsub1d\n", "\t threshold_badpix: 10\n", "\t mppool: \n", "Computing star subtraction.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:1411: RuntimeWarning: All-NaN slice encountered\n", " reg_mean_map[wherenan] = np.tile(np.nanmedian(spline_paras0, axis=1)[:, None], (1, spline_paras0.shape[1]))[wherenan]\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:1413: RuntimeWarning: All-NaN slice encountered\n", " reg_std_map[wherenan] = np.tile(np.nanmax(np.abs(spline_paras0), axis=1)[:, None], (1, spline_paras0.shape[1]))[wherenan]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "/stow/jruffio/data/JWST/tutorial/data/G395H_stage2_cleaned/jw01414013001_02101_00002_nrs1_cal.fits\n", "Reading data from /stow/jruffio/data/JWST/tutorial/data/G395H_stage2_cleaned/jw01414013001_02101_00002_nrs1_cal.fits\n", "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", "Running compute_med_filt_badpix with parameters:\n", "\t save_utils: True\n", "\t window_size: 50\n", "\t mad_threshold: 50\n", "Initializing row_err and bad_pixels for nirspec\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jruffio/anaconda3/envs/breads_2026/lib/python3.12/site-packages/scipy/ndimage/_filters.py:2420: RuntimeWarning: All-NaN slice encountered\n", " _nd_image.generic_filter(input, function, footprint, output, mode,\n", "/home/jruffio/code/breads/breads/instruments/jwstnirspec_cal.py:198: SmallSampleWarning: One or more sample arguments is too small; all returned values will be NaN. See documentation for sample size requirements.\n", " row_err_masking = row_err/median_abs_deviation(row_err[np.where(np.isfinite(self.bad_pixels[rowid,:]))])\n", "/home/jruffio/code/breads/breads/instruments/jwstnirspec_cal.py:198: RuntimeWarning: invalid value encountered in divide\n", " row_err_masking = row_err/median_abs_deviation(row_err[np.where(np.isfinite(self.bad_pixels[rowid,:]))])\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Saved the quick bad pixel map to /stow/jruffio/data/JWST/tutorial/data/G395H_utils/jw01414013001_02101_00002_nrs1_cal_roughbadpix.fits\n", "Running compute_coordinates_arrays with parameters:\n", "\t save_utils: True\n", "\t targname: HD 19467\n", "Computing coordinates arrays.\n", "Computing coords for 30 slices...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|███████████████████████████████████████████████████████████████| 30/30 [00:01<00:00, 22.59it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Retrieving SIMBAD coordinates for HD 19467\n", "Coordinates at J2000: 03h07m18.57505144s -13d45m42.41798215s\n", "Coordinates at 2024-01-25: 03h07m18.56069141s -13d45m48.69016027s\n", " Saved the computed coordinates arrays to /stow/jruffio/data/JWST/tutorial/data/G395H_utils/jw01414013001_02101_00002_nrs1_cal_relcoords.fits\n", "Running convert_MJy_per_sr_to_MJy with parameters:\n", "\t save_utils: True\n", "Running apply_coords_offset with parameters:\n", "\t save_utils: True\n", "\t coords_offset: (array([-0.00058284, -0.0022742 , -0.0584407 ]), array([ 0.00050445, -0.01076225, -0.18053904]))\n", "Applying relative coordinate offset (array([-0.00058284, -0.0022742 , -0.0584407 ]), array([ 0.00050445, -0.01076225, -0.18053904]))\n", "Running compute_starspectrum_contnorm with parameters:\n", "\t save_utils: True\n", "\t x_nodes: [2.859 2.879 2.899 2.919 2.939 2.959 2.979 2.999 3.019 3.039 3.059 3.079\n", " 3.099 3.119 3.139 3.159 3.189 3.219 3.249 3.279 3.309 3.339 3.369 3.399\n", " 3.429 3.459 3.499 3.539 3.579 3.619 3.659 3.699 3.739 3.779 3.839 3.899\n", " 3.959 4.019 4.079 4.139]\n", "\t threshold_badpix: 100\n", "\t mppool: \n", "Computing stellar spectrum (continuum normalized)\n", "Saved the continuum normalized star spectrum to /stow/jruffio/data/JWST/tutorial/data/G395H_utils/jw01414013001_02101_00002_nrs1_cal_starspec_contnorm.fits\n", "Running compute_starsubtraction with parameters:\n", "\t save_utils: True\n", "\t starsub_dir: starsub1d\n", "\t threshold_badpix: 10\n", "\t mppool: \n", "Computing star subtraction.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:1411: RuntimeWarning: All-NaN slice encountered\n", " reg_mean_map[wherenan] = np.tile(np.nanmedian(spline_paras0, axis=1)[:, None], (1, spline_paras0.shape[1]))[wherenan]\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:1413: RuntimeWarning: All-NaN slice encountered\n", " reg_std_map[wherenan] = np.tile(np.nanmax(np.abs(spline_paras0), axis=1)[:, None], (1, spline_paras0.shape[1]))[wherenan]\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: invalid value encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: invalid value encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n", "/home/jruffio/code/breads/breads/instruments/jwst_IFUs.py:2003: RuntimeWarning: invalid value encountered in divide\n", " where_bad = np.where((np.abs(norm_res_row) / meddev > threshold) | np.isnan(norm_res_row))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "/stow/jruffio/data/JWST/tutorial/data/G395H_stage2_cleaned/jw01414013001_02101_00001_nrs1_cal.fits\n", "Reading data from /stow/jruffio/data/JWST/tutorial/data/G395H_stage2_cleaned/jw01414013001_02101_00001_nrs1_cal.fits\n", "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", "Loading data for compute_med_filt_badpix cached in /stow/jruffio/data/JWST/tutorial/data/G395H_utils/jw01414013001_02101_00001_nrs1_cal_roughbadpix.fits\n", "Loading data for compute_coordinates_arrays cached in /stow/jruffio/data/JWST/tutorial/data/G395H_utils/jw01414013001_02101_00001_nrs1_cal_relcoords.fits\n", "Running convert_MJy_per_sr_to_MJy with parameters:\n", "\t save_utils: True\n", "Running apply_coords_offset with parameters:\n", "\t save_utils: True\n", "\t coords_offset: (array([-0.00058284, -0.0022742 , -0.0584407 ]), array([ 0.00050445, -0.01076225, -0.18053904]))\n", "Applying relative coordinate offset (array([-0.00058284, -0.0022742 , -0.0584407 ]), array([ 0.00050445, -0.01076225, -0.18053904]))\n", "/stow/jruffio/data/JWST/tutorial/data/G395H_stage2_cleaned/jw01414013001_02101_00002_nrs1_cal.fits\n", "Reading data from /stow/jruffio/data/JWST/tutorial/data/G395H_stage2_cleaned/jw01414013001_02101_00002_nrs1_cal.fits\n", "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", "Loading data for compute_med_filt_badpix cached in /stow/jruffio/data/JWST/tutorial/data/G395H_utils/jw01414013001_02101_00002_nrs1_cal_roughbadpix.fits\n", "Loading data for compute_coordinates_arrays cached in /stow/jruffio/data/JWST/tutorial/data/G395H_utils/jw01414013001_02101_00002_nrs1_cal_relcoords.fits\n", "Running convert_MJy_per_sr_to_MJy with parameters:\n", "\t save_utils: True\n", "Running apply_coords_offset with parameters:\n", "\t save_utils: True\n", "\t coords_offset: (array([-0.00058284, -0.0022742 , -0.0584407 ]), array([ 0.00050445, -0.01076225, -0.18053904]))\n", "Applying relative coordinate offset (array([-0.00058284, -0.0022742 , -0.0584407 ]), array([ 0.00050445, -0.01076225, -0.18053904]))\n", "-1.4856883494560706 1.8791713967148662 -1.3970566452280107 2.430515655328636\n", "[-1.48568835 -1.42865683 -1.37162531 -1.31459379 -1.25756226 -1.20053074\n", " -1.14349922 -1.0864677 -1.02943618 -0.97240466 -0.91537314 -0.85834162\n", " -0.8013101 -0.74427857 -0.68724705 -0.63021553 -0.57318401 -0.51615249\n", " -0.45912097 -0.40208945 -0.34505793 -0.28802641 -0.23099488 -0.17396336\n", " -0.11693184 -0.05990032 -0.0028688 0.05416272 0.11119424 0.16822576\n", " 0.22525728 0.28228881 0.33932033 0.39635185 0.45338337 0.51041489\n", " 0.56744641 0.62447793 0.68150945 0.73854097 0.7955725 0.85260402\n", " 0.90963554 0.96666706 1.02369858 1.0807301 1.13776162 1.19479314\n", " 1.25182466 1.30885619 1.36588771 1.42291923 1.47995075 1.53698227\n", " 1.59401379 1.65104531 1.70807683 1.76510835 1.82213988 1.8791714 ] [-1.39705665 -1.33218254 -1.26730843 -1.20243432 -1.13756022 -1.07268611\n", " -1.007812 -0.9429379 -0.87806379 -0.81318968 -0.74831558 -0.68344147\n", " -0.61856736 -0.55369326 -0.48881915 -0.42394504 -0.35907094 -0.29419683\n", " -0.22932272 -0.16444862 -0.09957451 -0.0347004 0.0301737 0.09504781\n", " 0.15992192 0.22479602 0.28967013 0.35454424 0.41941834 0.48429245\n", " 0.54916656 0.61404067 0.67891477 0.74378888 0.80866299 0.87353709\n", " 0.9384112 1.00328531 1.06815941 1.13303352 1.19790763 1.26278173\n", " 1.32765584 1.39252995 1.45740405 1.52227816 1.58715227 1.65202637\n", " 1.71690048 1.78177459 1.84664869 1.9115228 1.97639691 2.04127101\n", " 2.10614512 2.17101923 2.23589333 2.30076744 2.36564155 2.43051566]\n", "Searching in /stow/jruffio/data/JWST/tutorial/data/G395H_stage2_cleaned for files matching jw01414013001_02101_*_nrs1_cal.fits\n", "\tFound 2 input files to process\n", "\tjw01414013001_02101_00001_nrs1_cal.fits\n", "\tjw01414013001_02101_00002_nrs1_cal.fits\n", "Reading data from /stow/jruffio/data/JWST/tutorial/data/G395H_utils/starsub1d/jw01414013001_02101_00001_nrs1_cal.fits\n", "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", "Reading data from /stow/jruffio/data/JWST/tutorial/data/G395H_utils/starsub1d/jw01414013001_02101_00002_nrs1_cal.fits\n", "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", "Computing PSFs. This has to iterate over many wavelengths, so is slow.\n", "iterating query, tdelta=3.0\n", "\n", "MAST OPD query around UTC: 2024-01-25T17:47:35.716\n", " MJD: 60334.741385601854\n", "\n", "OPD immediately preceding the given datetime:\n", "\tURI:\t mast:JWST/product/R2024012502-NRCA3_FP1-1.fits\n", "\tDate (MJD):\t 60334.2955\n", "\tDelta time:\t -0.4459 days\n", "\n", "OPD immediately following the given datetime:\n", "\tURI:\t mast:JWST/product/R2024012702-NRCA3_FP1-1.fits\n", "\tDate (MJD):\t 60336.5976\n", "\tDelta time:\t 1.8562 days\n", "User requested choosing OPD time closest in time to 2024-01-25T17:47:35.716, which is R2024012502-NRCA3_FP1-1.fits, delta time -0.446 days\n", "Importing and format-converting OPD from /fast/jruffio/data/stpsf-data/MAST_JWST_WSS_OPDs/R2024012502-NRCA3_FP1-1.fits\n", "Backing out SI WFE and OTE field dependence at the WF sensing field point (NRCA3_FP1)\n", "Sensing inst model using apername NRCA3_FP1\n", "Using sensing field point SI WFE model from file wss_target_phase_fp1.fits\n", "\tPerforming parallelized calculation of PSF at 1837 wavelengths.\n", "Writing pupilopd tempfile : /tmp/tmpkp8akpcx\n", "preparing parameter list...\n", "1836,4.101362466812134,18377\n", "starting parallel _get_wpsf_task ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|███████████████████████████████████████████████████████████| 1837/1837 [02:09<00:00, 14.22it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "collating pool outputs...\n", "12" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1836\n", "done.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: VerifyWarning: Card is too long, comment will be truncated. [astropy.io.fits.card]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Saved the computed PSFs to /stow/jruffio/data/JWST/tutorial/data/G395H_utils/jw01414013001_02101_nrs1_webbpsf.fits\n", "Setting parallel_flag = True\n", "Processing wavelength indices in range: 0 to 1837\n", "prepping build_cube inputs... id: 1836 wave: 4.1013624668121345starting parallel _build_cube_task...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " 0%| | 0/1837 [00:00" ], "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } } ], "execution_count": 11 }, { "cell_type": "code", "execution_count": null, "id": "1f39bcc9-6251-44f6-bd2f-04396163bcea", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "df711554-023c-4b9a-a6e8-4053dbffc74f", "metadata": {}, "source": [ "### Examining the outputs" ] }, { "cell_type": "markdown", "id": "e5b2b32a-5ef6-4b5e-a2af-f8b433abc8b0", "metadata": {}, "source": [ "Below is simple plot showing the interpolation of the point cloud computed in Step 7 for nrs2 only. The interpolation is computed at the median wavelength wv0, but this can be computed at any wavelength by changing the argument to ```get_2D_point_cloud_interpolator```. The image should show the basic structure of a Webb PSF." ] }, { "cell_type": "code", "id": "c5475afb-bcb3-45f9-89d7-30d1d857ace6", "metadata": { "ExecuteTime": { "end_time": "2026-02-21T02:24:02.867271Z", "start_time": "2026-02-21T02:24:02.676373Z" } }, "source": [ "dramin, dramax, ddecmin, ddecmax = np.min(ra_vec),np.max(ra_vec),np.min(dec_vec),np.max(dec_vec) \n", "extent=[dramin, dramax, ddecmin, ddecmax]\n", "\n", "N = 240\n", "ra_vec = np.linspace(dramin,dramax,N)\n", "dec_vec = np.linspace(ddecmin,ddecmax,N)\n", "inp = np.meshgrid(ra_vec,dec_vec)\n", "out = pointcloud_interp(inp[0],inp[1])\n", "\n", "fig =plt.figure(figsize=(5,5),dpi=200)\n", "im = plt.imshow(np.log10(abs(out)),origin='lower',extent=extent,vmin=-12,vmax=-8,aspect='auto')\n", "\n", "plt.xlim([dramax-.1,dramin+.2])\n", "plt.ylim([ddecmin+.1,ddecmax-.1])\n", "cbar = fig.colorbar(im, fraction=0.05, pad=0.04)\n", "cbar.set_label(r'log$_{10} \\left(\\frac{\\mathrm{flux}}{\\mathrm{MJy}}\\right)$')\n", "plt.xlabel('IFU x (arcsec)')\n", "plt.ylabel('IFU y (arcsec)')\n", "plt.title('Interpolation of non starsub point cloud')\n", "plt.show()" ], "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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" 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