From a139b3a8d4d0002b1fc7ceb4028fe6a83f5b4a2f Mon Sep 17 00:00:00 2001 From: Marshall Perrin Date: Fri, 16 Jan 2026 09:34:44 -0500 Subject: [PATCH 1/2] add draft tutorial notebooks for MIRI reductions --- docs/source/tutorials/index.rst | 27 + ...utorial_1_Data_Reductions_for_BREADS.ipynb | 7622 +++++++++++++++++ ...orial_2_MRS_Forward_Modeling_and_SNR.ipynb | 1064 +++ .../MIRI_Tutorial_3_MRS_Cube-ish_build.ipynb | 1667 ++++ 4 files changed, 10380 insertions(+) create mode 100644 docs/source/tutorials/index.rst create mode 100644 docs/source/tutorials/jwst/MIRI_Tutorial_1_Data_Reductions_for_BREADS.ipynb create mode 100644 docs/source/tutorials/jwst/MIRI_Tutorial_2_MRS_Forward_Modeling_and_SNR.ipynb create mode 100644 docs/source/tutorials/jwst/MIRI_Tutorial_3_MRS_Cube-ish_build.ipynb diff --git a/docs/source/tutorials/index.rst b/docs/source/tutorials/index.rst new file mode 100644 index 0000000..4fb609b --- /dev/null +++ b/docs/source/tutorials/index.rst @@ -0,0 +1,27 @@ + +BREADS tutorials +================ + + + +General +------- + - Using atmosphere models via species. `Tutorial notebook here `_ . + + +.. toctree:: + :maxdepth: 1 + :caption: JWST + + jwst/NIRSpec_Tutorial_1_Placeholder.ipynb + jwst/MIRI_Tutorial_1_Data_Reductions_for_BREADS.ipynb + jwst/MIRI_Tutorial_2_MRS_Forward_Modeling_and_SNR.ipynb + jwst/MIRI_Tutorial_3_MRS_Cube-ish_build.ipynbi + + +.. toctree:: + :maxdepth: 1 + :caption: KPIC + + kpic/KPIC_Tutorial_1_Placeholder.ipynb + diff --git a/docs/source/tutorials/jwst/MIRI_Tutorial_1_Data_Reductions_for_BREADS.ipynb b/docs/source/tutorials/jwst/MIRI_Tutorial_1_Data_Reductions_for_BREADS.ipynb new file mode 100644 index 0000000..e80413f --- /dev/null +++ b/docs/source/tutorials/jwst/MIRI_Tutorial_1_Data_Reductions_for_BREADS.ipynb @@ -0,0 +1,7622 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "39d359e7-5cd3-4427-8115-7a84c1004f3b", + "metadata": {}, + "source": [ + "# BREADS MIRI Tutorial 1: MIRI Data Reductions for BREADS IFU high contrast\n", + "\n", + "\n", + "This notebook demonstrates how to reduce MIRI MRS data, starting from uncal files, to produce the custom/optimized cal files that will be used for forward modeling. \n", + "\n", + "
\n", + "This is the first in a series of notebooks demonstrating data reduction for MIRI using BREADS. \n", + "
  1. Tutorial 1 (This notebook): Pipeline data reductions to get ready for forward modeling
    2. \n", + "
  2. Tutorial 2: Forward modeling and measuring SNR of a companion
    3. \n", + "
  3. Tutorial 3: Generating data-cube-like representations of the forward modeled data.
    4. \n", + "
\n", + "\n", + "\n", + "## Introduction\n", + "\n", + "This makes use of the JWST pipeline (invoked via utility functions in BREADS) and custom code within BREADS, particularly for fitting and handling the fringe patterns in the MRS.\n", + "\n", + "To forward model JWST/MIRI data with breads we want to: \n", + " 1. Reduce the raw `uncal` FITS files to calibrated `cal` FITS files, using the JWST pipeline with a handful of small customizations.\n", + " 2. A larger customization is some code for dealing with the fringing within MRS. Breads has some functions to estimate and calibrate the fringing in science data. To do this it needs a calibration dataset observed similarly to the science data. If a PSF calibrator has been observed, it will work well for this.\n", + "\n", + "\n", + "### Getting an Example dataset for this tutorial: GQ Lup B\n", + "We will demonstrate this with a reduction of the data on GQ Lup B from JWST program 1640 observation 9; we'll use observation 10 from that program as the fringe calibrator. \n", + "\n", + "You can download these data using the [jwst_mast_query](https://github.com/spacetelescope/jwst_mast_query) package, using this command line: \n", + "\n", + "```\n", + "jwst_download.py -i miri --propID 1294 --obsnums 3 4 -f uncal --sca mirifushort -d 2023-01-10 2023-01-12 --skip_propID2outsubdir\n", + "```\n", + "\n", + "This tutorial assumes you have the resulting files (24 of them: 2 targets × 3 MRS sub-bands × 4 dithers; total of 9 GB) and they have been placed in a subdirectory `data` under this current working directory. For instance you can do: \n", + "\n", + "```\n", + "mkdir data\n", + "cd data\n", + "jwst_download.py -i miri --propID 1294 --obsnums 3 4 -f uncal --sca mirifushort -d 2023-01-10 2023-01-12 --skip_propID2outsubdir\n", + "\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "baf02e4e-679b-4162-91f2-5ffabb26b06c", + "metadata": {}, + "source": [ + "## Set Paths and Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e4b64573-4ee2-4311-ad8d-b29b0bcc8e49", + "metadata": {}, + "outputs": [], + "source": [ + "# Root directory for where the data files are, including the uncal input files\n", + "# and the output files will be in subdirectories created under here. \n", + "data_dir = \"./data\" \n", + "\n", + "# Which target is which? \n", + "# These must match the target names as defined in APT and written in FITS header keywords for TARGNAME\n", + "science_target_name = \"* bet Pic\"\n", + "flat_reference_target_name = \"* N Car\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "73e408b7-45f9-46ba-815f-4bc95606cff6", + "metadata": {}, + "outputs": [], + "source": [ + "# The need for this will eventually be removed\n", + "import os\n", + "\n", + "# We need to tell BREADS where to store reduced fringe flat files. This can be shared among multiple programs.\n", + "# In this case let's just use a subdirectory here, which suffices for this tutorial.\n", + "\n", + "os.environ['FLAT_PATH'] = './miri_fringe_flats'\n", + "os.makedirs(os.environ['FLAT_PATH'], exist_ok=True)\n" + ] + }, + { + "cell_type": "markdown", + "id": "2f8b25fb-a6ac-47a4-aab6-d4aae31ef41c", + "metadata": {}, + "source": [ + "## Which MRS bands to reduce? \n", + "\n", + "You can select just a subset of the data to reduce, to get shorter runtimes. If particular spectral ranges are of interest just select those bands. \n", + "\n", + "The syntax here is like `'12A'` for bands 1A and 2A (observed simultaneously on the short wave detector), `'12B'`, `'34A'`, etc. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2d01ad0c-c1dc-4bb7-8b6b-cb3dd7107257", + "metadata": {}, + "outputs": [], + "source": [ + "list_bands = ['12A']" + ] + }, + { + "cell_type": "markdown", + "id": "7fc824ee-40f3-4aff-a2cf-21b6de5f6bef", + "metadata": {}, + "source": [ + "## Stage 1 reductions" + ] + }, + { + "cell_type": "markdown", + "id": "0c43ae6a-cbb8-48cc-9035-335ac41b1873", + "metadata": {}, + "source": [ + "### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "295837f7-7362-47bc-975e-e13e82320ae0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n" + ] + } + ], + "source": [ + "import os, glob\n", + "from breads.jwst_tools.reduction_utils import run_stage1_miri, run_stage2_miri, run_stage3_miri, flat_fringing_stage1\n", + "from breads.jwst_tools.flat_miri_utils import run_miri_flat_running_mean" + ] + }, + { + "cell_type": "markdown", + "id": "6ba5c6a1-9be9-42f1-a863-fdf296c395c4", + "metadata": {}, + "source": [ + "### Run the detector1 pipeline\n", + "\n", + "This runs the jwst pipeline's `calwebb_detector1` pipeline, with some small tweaks. \n", + "- Inputs: finds `uncal.fits` files in the path given by `uncaldir`\n", + "- Outputs: writes `rate.fit`s file in new subdirectories organized by `[targetname]/[MRS band]`\n", + "\n", + "This will take a while to run (many minutes), but only needs to be run once. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4b6f11f5-c05f-4ba9-a397-22cc54db8ce6", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Searching in ./data for files matching jw*_uncal.fits\n", + "\tFound 8 input files to process\n", + "\tjw01294003001_03102_00001_mirifushort_uncal.fits\n", + "\tjw01294003001_03102_00002_mirifushort_uncal.fits\n", + "\tjw01294003001_03102_00003_mirifushort_uncal.fits\n", + "\tjw01294003001_03102_00004_mirifushort_uncal.fits\n", + "\tjw01294004001_03102_00001_mirifushort_uncal.fits\n", + "\tjw01294004001_03102_00002_mirifushort_uncal.fits\n", + "\tjw01294004001_03102_00003_mirifushort_uncal.fits\n", + "\tjw01294004001_03102_00004_mirifushort_uncal.fits\n", + "DEBUG target_names ['* bet Pic', '* N Car']\n", + "48657.247317708\n", + "DEBUG target_name * bet Pic\n", + "Processing file 1 of 4.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-12 13:17:26,610 - CRDS - INFO - Calibration SW Found: jwst 1.20.2 (/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/jwst-1.20.2.dist-info)\n", + "2025-11-12 13:17:27,576 - stpipe.step - INFO - PARS-EMICORRSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-emicorrstep_0003.asdf\n", + "2025-11-12 13:17:27,589 - stpipe.step - INFO - PARS-DARKCURRENTSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-darkcurrentstep_0001.asdf\n", + "2025-11-12 13:17:27,597 - stpipe.step - INFO - PARS-JUMPSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-jumpstep_0007.asdf\n", + "2025-11-12 13:17:27,605 - stpipe.pipeline - INFO - PARS-DETECTOR1PIPELINE parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-detector1pipeline_0010.asdf\n", + "2025-11-12 13:17:27,616 - stpipe.step - INFO - Detector1Pipeline instance created.\n", + "2025-11-12 13:17:27,617 - stpipe.step - INFO - GroupScaleStep instance created.\n", + "2025-11-12 13:17:27,617 - stpipe.step - INFO - DQInitStep instance created.\n", + "2025-11-12 13:17:27,618 - stpipe.step - INFO - EmiCorrStep instance created.\n", + "2025-11-12 13:17:27,619 - stpipe.step - INFO - SaturationStep instance created.\n", + "2025-11-12 13:17:27,619 - stpipe.step - INFO - IPCStep instance created.\n", + "2025-11-12 13:17:27,620 - stpipe.step - INFO - SuperBiasStep instance created.\n", + "2025-11-12 13:17:27,621 - stpipe.step - INFO - RefPixStep instance created.\n", + "2025-11-12 13:17:27,621 - stpipe.step - INFO - RscdStep instance created.\n", + "2025-11-12 13:17:27,622 - stpipe.step - INFO - FirstFrameStep instance created.\n", + "2025-11-12 13:17:27,622 - stpipe.step - INFO - LastFrameStep instance created.\n", + "2025-11-12 13:17:27,623 - stpipe.step - INFO - LinearityStep instance created.\n", + "2025-11-12 13:17:27,624 - stpipe.step - INFO - DarkCurrentStep instance created.\n", + "2025-11-12 13:17:27,624 - stpipe.step - INFO - ResetStep instance created.\n", + "2025-11-12 13:17:27,625 - stpipe.step - INFO - PersistenceStep instance created.\n", + "2025-11-12 13:17:27,625 - stpipe.step - INFO - ChargeMigrationStep instance created.\n", + "2025-11-12 13:17:27,626 - stpipe.step - INFO - JumpStep instance created.\n", + "2025-11-12 13:17:27,627 - stpipe.step - INFO - CleanFlickerNoiseStep instance created.\n", + "2025-11-12 13:17:27,628 - stpipe.step - INFO - RampFitStep instance created.\n", + "2025-11-12 13:17:27,628 - stpipe.step - INFO - GainScaleStep instance created.\n", + "2025-11-12 13:17:27,691 - stpipe.step - INFO - Step Detector1Pipeline running with args ('./data/jw01294003001_03102_00001_mirifushort_uncal.fits',).\n", + "2025-11-12 13:17:27,702 - stpipe.step - INFO - Step Detector1Pipeline parameters are:\n", + " pre_hooks: []\n", + " post_hooks: []\n", + " output_file: None\n", + " output_dir: ./data/* bet Pic/12A/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: False\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: True\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: 1.0\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: 100\n", + " four_group_rejection_threshold: 5.0\n", + " maximum_cores: all\n", + " flag_4_neighbors: True\n", + " max_jump_to_flag_neighbors: 1000.0\n", + " min_jump_to_flag_neighbors: 10.0\n", + " after_jump_flag_dn1: 0.0\n", + " after_jump_flag_time1: 0.0\n", + " after_jump_flag_dn2: 0.0\n", + " after_jump_flag_time2: 0.0\n", + " expand_large_events: False\n", + " min_sat_area: 1.0\n", + " min_jump_area: 5.0\n", + " expand_factor: 2.0\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: True\n", + " max_shower_amplitude: 4.0\n", + " extend_snr_threshold: 1.2\n", + " extend_min_area: 90\n", + " extend_inner_radius: 1.0\n", + " extend_outer_radius: 2.6\n", + " extend_ellipse_expand_ratio: 1.1\n", + " time_masked_after_shower: 15.0\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: all\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", + "2025-11-12 13:17:27,725 - stpipe.pipeline - INFO - Prefetching reference files for dataset: 'jw01294003001_03102_00001_mirifushort_uncal.fits' reftypes = ['dark', 'emicorr', 'gain', 'linearity', 'mask', 'readnoise', 'reset', 'rscd', 'saturation', 'superbias']\n", + "2025-11-12 13:17:27,729 - stpipe.pipeline - INFO - Prefetch for DARK reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_dark_0112.fits'.\n", + "2025-11-12 13:17:27,730 - stpipe.pipeline - INFO - Prefetch for EMICORR reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_emicorr_0003.asdf'.\n", + "2025-11-12 13:17:27,730 - stpipe.pipeline - INFO - Prefetch for GAIN reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits'.\n", + "2025-11-12 13:17:27,730 - stpipe.pipeline - INFO - Prefetch for LINEARITY reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_linearity_0030.fits'.\n", + "2025-11-12 13:17:27,731 - stpipe.pipeline - INFO - Prefetch for MASK reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_mask_0046.fits'.\n", + "2025-11-12 13:17:27,731 - stpipe.pipeline - INFO - Prefetch for READNOISE reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits'.\n", + "2025-11-12 13:17:27,732 - stpipe.pipeline - INFO - Prefetch for RESET reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_reset_0078.fits'.\n", + "2025-11-12 13:17:27,732 - stpipe.pipeline - INFO - Prefetch for RSCD reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_rscd_0018.fits'.\n", + "2025-11-12 13:17:27,733 - stpipe.pipeline - INFO - Prefetch for SATURATION reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_saturation_0033.fits'.\n", + "2025-11-12 13:17:27,733 - stpipe.pipeline - INFO - Prefetch for SUPERBIAS reference file is 'N/A'.\n", + "2025-11-12 13:17:27,733 - jwst.pipeline.calwebb_detector1 - INFO - Starting calwebb_detector1 ...\n", + "2025-11-12 13:17:27,956 - stpipe.step - INFO - Step group_scale running with args (,).\n", + "2025-11-12 13:17:28,011 - jwst.group_scale.group_scale_step - INFO - NFRAMES and FRMDIVSR are equal; correction not needed\n", + "2025-11-12 13:17:28,011 - jwst.group_scale.group_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:17:28,012 - stpipe.step - INFO - Step group_scale done\n", + "2025-11-12 13:17:28,081 - stpipe.step - INFO - Step dq_init running with args (,).\n", + "2025-11-12 13:17:28,088 - jwst.dq_init.dq_init_step - INFO - Using MASK reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_mask_0046.fits\n", + "2025-11-12 13:17:28,153 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:28,218 - CRDS - INFO - Calibration SW Found: jwst 1.20.2 (/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/jwst-1.20.2.dist-info)\n", + "2025-11-12 13:17:28,269 - stpipe.step - INFO - Step dq_init done\n", + "2025-11-12 13:17:28,334 - stpipe.step - INFO - Step emicorr running with args (,).\n", + "2025-11-12 13:17:28,377 - jwst.emicorr.emicorr_step - INFO - Using CRDS reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_emicorr_0003.asdf\n", + "2025-11-12 13:17:28,391 - jwst.emicorr.emicorr - INFO - Using reference file to get subarray case.\n", + "2025-11-12 13:17:28,392 - jwst.emicorr.emicorr - INFO - With configuration: Subarray=FULL, Read_pattern=FASTR1, Detector=MIRIFUSHORT\n", + "2025-11-12 13:17:28,392 - jwst.emicorr.emicorr - INFO - Will correct data for the following 1 frequencies: \n", + "2025-11-12 13:17:28,393 - jwst.emicorr.emicorr - INFO - ['Hz10']\n", + "2025-11-12 13:17:28,393 - jwst.emicorr.emicorr - INFO - Running EMI fit with algorithm = 'joint'.\n", + "2025-11-12 13:17:33,001 - stpipe.step - INFO - Step emicorr done\n", + "2025-11-12 13:17:33,066 - stpipe.step - INFO - Step saturation running with args (,).\n", + "2025-11-12 13:17:33,111 - jwst.saturation.saturation_step - INFO - Using SATURATION reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_saturation_0033.fits\n", + "2025-11-12 13:17:33,111 - jwst.saturation.saturation_step - INFO - Using SUPERBIAS reference file N/A\n", + "2025-11-12 13:17:33,134 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:33,134 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:33,148 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:33,152 - jwst.saturation.saturation - INFO - Using read_pattern with nframes 1\n", + "2025-11-12 13:17:33,366 - stcal.saturation.saturation - INFO - Detected 99 saturated pixels\n", + "2025-11-12 13:17:33,379 - stcal.saturation.saturation - INFO - Detected 3 A/D floor pixels\n", + "2025-11-12 13:17:33,383 - stpipe.step - INFO - Step saturation done\n", + "2025-11-12 13:17:33,447 - stpipe.step - INFO - Step ipc running with args (,).\n", + "2025-11-12 13:17:33,448 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:17:33,506 - stpipe.step - INFO - Step firstframe running with args (,).\n", + "2025-11-12 13:17:33,561 - jwst.firstframe.firstframe_sub - INFO - Number of usable bright pixels with first group not set to DO_NOT_USE: 0\n", + "2025-11-12 13:17:33,563 - stpipe.step - INFO - Step firstframe done\n", + "2025-11-12 13:17:33,627 - stpipe.step - INFO - Step lastframe running with args (,).\n", + "2025-11-12 13:17:33,670 - stpipe.step - INFO - Step lastframe done\n", + "2025-11-12 13:17:33,736 - stpipe.step - INFO - Step reset running with args (,).\n", + "2025-11-12 13:17:33,781 - jwst.reset.reset_step - INFO - Using RESET reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_reset_0078.fits\n", + "2025-11-12 13:17:33,834 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:33,834 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:33,850 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:33,887 - stpipe.step - INFO - Step reset done\n", + "2025-11-12 13:17:33,952 - stpipe.step - INFO - Step linearity running with args (,).\n", + "2025-11-12 13:17:33,994 - jwst.linearity.linearity_step - INFO - Using Linearity reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_linearity_0030.fits\n", + "2025-11-12 13:17:34,018 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:34,019 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:34,029 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:34,233 - stpipe.step - INFO - Step linearity done\n", + "2025-11-12 13:17:34,299 - stpipe.step - INFO - Step rscd running with args (,).\n", + "2025-11-12 13:17:34,343 - jwst.rscd.rscd_step - INFO - Using RSCD reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_rscd_0018.fits\n", + "2025-11-12 13:17:34,363 - jwst.rscd.rscd_sub - INFO - Number of groups to skip for integrations 2 and higher: 2\n", + "2025-11-12 13:17:34,363 - jwst.rscd.rscd_sub - WARNING - Too few groups to apply RSCD correction\n", + "2025-11-12 13:17:34,364 - jwst.rscd.rscd_sub - WARNING - RSCD step will be skipped\n", + "2025-11-12 13:17:34,365 - stpipe.step - INFO - Step rscd done\n", + "2025-11-12 13:17:34,430 - stpipe.step - INFO - Step dark_current running with args (,).\n", + "2025-11-12 13:17:34,438 - jwst.dark_current.dark_current_step - INFO - Using DARK reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_dark_0112.fits\n", + "2025-11-12 13:17:35,026 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:35,041 - jwst.dark_current.dark_current_step - INFO - Using Poisson noise from average dark current 1.0 e-/sec\n", + "2025-11-12 13:17:35,042 - stcal.dark_current.dark_sub - INFO - Science data nints=14, ngroups=5, nframes=1, groupgap=0\n", + "2025-11-12 13:17:35,042 - stcal.dark_current.dark_sub - INFO - Dark data nints=2, ngroups=360, nframes=1, groupgap=0\n", + "2025-11-12 13:17:35,365 - stpipe.step - INFO - Step dark_current done\n", + "2025-11-12 13:17:35,431 - stpipe.step - INFO - Step refpix running with args (,).\n", + "2025-11-12 13:17:35,432 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:17:35,497 - stpipe.step - INFO - Step charge_migration running with args (,).\n", + "2025-11-12 13:17:35,498 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:17:35,569 - stpipe.step - INFO - Step jump running with args (,).\n", + "2025-11-12 13:17:35,609 - jwst.jump.jump_step - INFO - CR rejection threshold = 4 sigma\n", + "2025-11-12 13:17:35,609 - jwst.jump.jump_step - INFO - Maximum cores to use = all\n", + "2025-11-12 13:17:35,615 - jwst.jump.jump_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:17:35,617 - jwst.jump.jump_step - INFO - Using READNOISE reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits\n", + "2025-11-12 13:17:35,674 - stcal.jump.jump - INFO - Executing two-point difference method\n", + "2025-11-12 13:17:35,674 - stcal.jump.jump - INFO - Creating 12 processes for jump detection \n", + "2025-11-12 13:17:39,134 - stcal.jump.jump - INFO - Flagging Showers\n", + "2025-11-12 13:17:43,687 - stcal.jump.jump - INFO - Total showers= 24\n", + "2025-11-12 13:17:43,687 - stcal.jump.jump - INFO - Total elapsed time = 8.01293 sec\n", + "2025-11-12 13:17:43,698 - jwst.jump.jump_step - INFO - The execution time in seconds: 8.123754\n", + "2025-11-12 13:17:43,701 - stpipe.step - INFO - Step jump done\n", + "2025-11-12 13:17:43,776 - stpipe.step - INFO - Step clean_flicker_noise running with args (,).\n", + "2025-11-12 13:17:43,777 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:17:43,841 - stpipe.step - INFO - Step ramp_fit running with args (,).\n", + "2025-11-12 13:17:43,912 - jwst.ramp_fitting.ramp_fit_step - INFO - Using READNOISE reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits\n", + "2025-11-12 13:17:43,913 - jwst.ramp_fitting.ramp_fit_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:17:43,931 - jwst.ramp_fitting.ramp_fit_step - INFO - Using algorithm = OLS_C\n", + "2025-11-12 13:17:43,932 - jwst.ramp_fitting.ramp_fit_step - INFO - Using weighting = optimal\n", + "2025-11-12 13:17:44,105 - stcal.ramp_fitting.ols_fit - INFO - Number of multiprocessing slices: 12\n", + "2025-11-12 13:17:44,144 - stcal.ramp_fitting.ols_fit - INFO - Number of leading groups that are flagged as DO_NOT_USE: 1\n", + "2025-11-12 13:17:44,146 - stcal.ramp_fitting.ols_fit - INFO - MIRI dataset has all pixels in the final group flagged as DO_NOT_USE.\n", + "2025-11-12 13:17:44,149 - stcal.ramp_fitting.ols_fit - INFO - Number of processors used for multiprocessing: 12\n", + "2025-11-12 13:17:46,200 - stpipe.step - INFO - Step ramp_fit done\n", + "2025-11-12 13:17:46,278 - stpipe.step - INFO - Step gain_scale running with args (,).\n", + "2025-11-12 13:17:46,297 - jwst.gain_scale.gain_scale_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:17:46,308 - jwst.gain_scale.gain_scale_step - INFO - GAINFACT not found in gain reference file\n", + "2025-11-12 13:17:46,308 - jwst.gain_scale.gain_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:17:46,309 - stpipe.step - INFO - Step gain_scale done\n", + "2025-11-12 13:17:46,377 - stpipe.step - INFO - Step gain_scale running with args (,).\n", + "2025-11-12 13:17:46,417 - jwst.gain_scale.gain_scale_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:17:46,427 - jwst.gain_scale.gain_scale_step - INFO - GAINFACT not found in gain reference file\n", + "2025-11-12 13:17:46,428 - jwst.gain_scale.gain_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:17:46,429 - stpipe.step - INFO - Step gain_scale done\n", + "2025-11-12 13:17:46,581 - stpipe.step - INFO - Saved model in ./data/* bet Pic/12A/stage1/jw01294003001_03102_00001_mirifushort_rateints.fits\n", + "2025-11-12 13:17:46,581 - jwst.pipeline.calwebb_detector1 - INFO - ... ending calwebb_detector1\n", + "2025-11-12 13:17:46,582 - jwst.stpipe.core - INFO - Results used CRDS context: jwst_1464.pmap\n", + "2025-11-12 13:17:46,630 - stpipe.step - INFO - Saved model in ./data/* bet Pic/12A/stage1/jw01294003001_03102_00001_mirifushort_rate.fits\n", + "2025-11-12 13:17:46,631 - stpipe.step - INFO - Step Detector1Pipeline done\n", + "2025-11-12 13:17:46,631 - jwst.stpipe.core - INFO - Results used jwst version: 1.20.2\n", + "/Users/mperrin/Dropbox (Personal)/Documents/software/git/breads/breads/jwst_tools/reduction_utils.py:1390: ResourceWarning: unclosed file <_io.BufferedReader name='./data/jw01294003001_03102_00001_mirifushort_uncal.fits'>\n", + " hdu_uncal = fits.open(file)\n", + "2025-11-12 13:17:46,668 - stpipe.step - INFO - PARS-EMICORRSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-emicorrstep_0003.asdf\n", + "2025-11-12 13:17:46,681 - stpipe.step - INFO - PARS-DARKCURRENTSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-darkcurrentstep_0001.asdf\n", + "2025-11-12 13:17:46,689 - stpipe.step - INFO - PARS-JUMPSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-jumpstep_0007.asdf\n", + "2025-11-12 13:17:46,698 - stpipe.pipeline - INFO - PARS-DETECTOR1PIPELINE parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-detector1pipeline_0010.asdf\n", + "2025-11-12 13:17:46,710 - stpipe.step - INFO - Detector1Pipeline instance created.\n", + "2025-11-12 13:17:46,711 - stpipe.step - INFO - GroupScaleStep instance created.\n", + "2025-11-12 13:17:46,711 - stpipe.step - INFO - DQInitStep instance created.\n", + "2025-11-12 13:17:46,712 - stpipe.step - INFO - EmiCorrStep instance created.\n", + "2025-11-12 13:17:46,712 - stpipe.step - INFO - SaturationStep instance created.\n", + "2025-11-12 13:17:46,713 - stpipe.step - INFO - IPCStep instance created.\n", + "2025-11-12 13:17:46,713 - stpipe.step - INFO - SuperBiasStep instance created.\n", + "2025-11-12 13:17:46,714 - stpipe.step - INFO - RefPixStep instance created.\n", + "2025-11-12 13:17:46,715 - stpipe.step - INFO - RscdStep instance created.\n", + "2025-11-12 13:17:46,716 - stpipe.step - INFO - FirstFrameStep instance created.\n", + "2025-11-12 13:17:46,716 - stpipe.step - INFO - LastFrameStep instance created.\n", + "2025-11-12 13:17:46,716 - stpipe.step - INFO - LinearityStep instance created.\n", + "2025-11-12 13:17:46,717 - stpipe.step - INFO - DarkCurrentStep instance created.\n", + "2025-11-12 13:17:46,717 - stpipe.step - INFO - ResetStep instance created.\n", + "2025-11-12 13:17:46,718 - stpipe.step - INFO - PersistenceStep instance created.\n", + "2025-11-12 13:17:46,718 - stpipe.step - INFO - ChargeMigrationStep instance created.\n", + "2025-11-12 13:17:46,719 - stpipe.step - INFO - JumpStep instance created.\n", + "2025-11-12 13:17:46,720 - stpipe.step - INFO - CleanFlickerNoiseStep instance created.\n", + "2025-11-12 13:17:46,721 - stpipe.step - INFO - RampFitStep instance created.\n", + "2025-11-12 13:17:46,721 - stpipe.step - INFO - GainScaleStep instance created.\n", + "2025-11-12 13:17:46,810 - stpipe.step - INFO - Step Detector1Pipeline running with args ('./data/jw01294003001_03102_00002_mirifushort_uncal.fits',).\n", + "2025-11-12 13:17:46,824 - stpipe.step - INFO - Step Detector1Pipeline parameters are:\n", + " pre_hooks: []\n", + " post_hooks: []\n", + " output_file: None\n", + " output_dir: ./data/* bet Pic/12A/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: False\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: True\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: 1.0\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: 100\n", + " four_group_rejection_threshold: 5.0\n", + " maximum_cores: all\n", + " flag_4_neighbors: True\n", + " max_jump_to_flag_neighbors: 1000.0\n", + " min_jump_to_flag_neighbors: 10.0\n", + " after_jump_flag_dn1: 0.0\n", + " after_jump_flag_time1: 0.0\n", + " after_jump_flag_dn2: 0.0\n", + " after_jump_flag_time2: 0.0\n", + " expand_large_events: False\n", + " min_sat_area: 1.0\n", + " min_jump_area: 5.0\n", + " expand_factor: 2.0\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: True\n", + " max_shower_amplitude: 4.0\n", + " extend_snr_threshold: 1.2\n", + " extend_min_area: 90\n", + " extend_inner_radius: 1.0\n", + " extend_outer_radius: 2.6\n", + " extend_ellipse_expand_ratio: 1.1\n", + " time_masked_after_shower: 15.0\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: all\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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing file 2 of 4.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-12 13:17:46,849 - stpipe.pipeline - INFO - Prefetching reference files for dataset: 'jw01294003001_03102_00002_mirifushort_uncal.fits' reftypes = ['dark', 'emicorr', 'gain', 'linearity', 'mask', 'readnoise', 'reset', 'rscd', 'saturation', 'superbias']\n", + "2025-11-12 13:17:46,851 - stpipe.pipeline - INFO - Prefetch for DARK reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_dark_0112.fits'.\n", + "2025-11-12 13:17:46,852 - stpipe.pipeline - INFO - Prefetch for EMICORR reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_emicorr_0003.asdf'.\n", + "2025-11-12 13:17:46,852 - stpipe.pipeline - INFO - Prefetch for GAIN reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits'.\n", + "2025-11-12 13:17:46,853 - stpipe.pipeline - INFO - Prefetch for LINEARITY reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_linearity_0030.fits'.\n", + "2025-11-12 13:17:46,853 - stpipe.pipeline - INFO - Prefetch for MASK reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_mask_0046.fits'.\n", + "2025-11-12 13:17:46,853 - stpipe.pipeline - INFO - Prefetch for READNOISE reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits'.\n", + "2025-11-12 13:17:46,854 - stpipe.pipeline - INFO - Prefetch for RESET reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_reset_0078.fits'.\n", + "2025-11-12 13:17:46,854 - stpipe.pipeline - INFO - Prefetch for RSCD reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_rscd_0018.fits'.\n", + "2025-11-12 13:17:46,855 - stpipe.pipeline - INFO - Prefetch for SATURATION reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_saturation_0033.fits'.\n", + "2025-11-12 13:17:46,855 - stpipe.pipeline - INFO - Prefetch for SUPERBIAS reference file is 'N/A'.\n", + "2025-11-12 13:17:46,855 - jwst.pipeline.calwebb_detector1 - INFO - Starting calwebb_detector1 ...\n", + "2025-11-12 13:17:47,104 - stpipe.step - INFO - Step group_scale running with args (,).\n", + "2025-11-12 13:17:47,165 - jwst.group_scale.group_scale_step - INFO - NFRAMES and FRMDIVSR are equal; correction not needed\n", + "2025-11-12 13:17:47,165 - jwst.group_scale.group_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:17:47,167 - stpipe.step - INFO - Step group_scale done\n", + "2025-11-12 13:17:47,236 - stpipe.step - INFO - Step dq_init running with args (,).\n", + "2025-11-12 13:17:47,242 - jwst.dq_init.dq_init_step - INFO - Using MASK reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_mask_0046.fits\n", + "2025-11-12 13:17:47,296 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:47,385 - stpipe.step - INFO - Step dq_init done\n", + "2025-11-12 13:17:47,454 - stpipe.step - INFO - Step emicorr running with args (,).\n", + "2025-11-12 13:17:47,492 - jwst.emicorr.emicorr_step - INFO - Using CRDS reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_emicorr_0003.asdf\n", + "2025-11-12 13:17:47,506 - jwst.emicorr.emicorr - INFO - Using reference file to get subarray case.\n", + "2025-11-12 13:17:47,506 - jwst.emicorr.emicorr - INFO - With configuration: Subarray=FULL, Read_pattern=FASTR1, Detector=MIRIFUSHORT\n", + "2025-11-12 13:17:47,507 - jwst.emicorr.emicorr - INFO - Will correct data for the following 1 frequencies: \n", + "2025-11-12 13:17:47,507 - jwst.emicorr.emicorr - INFO - ['Hz10']\n", + "2025-11-12 13:17:47,508 - jwst.emicorr.emicorr - INFO - Running EMI fit with algorithm = 'joint'.\n", + "2025-11-12 13:17:52,180 - stpipe.step - INFO - Step emicorr done\n", + "2025-11-12 13:17:52,249 - stpipe.step - INFO - Step saturation running with args (,).\n", + "2025-11-12 13:17:52,289 - jwst.saturation.saturation_step - INFO - Using SATURATION reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_saturation_0033.fits\n", + "2025-11-12 13:17:52,290 - jwst.saturation.saturation_step - INFO - Using SUPERBIAS reference file N/A\n", + "2025-11-12 13:17:52,309 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:52,309 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:52,319 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:52,322 - jwst.saturation.saturation - INFO - Using read_pattern with nframes 1\n", + "2025-11-12 13:17:52,546 - stcal.saturation.saturation - INFO - Detected 102 saturated pixels\n", + "2025-11-12 13:17:52,557 - stcal.saturation.saturation - INFO - Detected 11 A/D floor pixels\n", + "2025-11-12 13:17:52,561 - stpipe.step - INFO - Step saturation done\n", + "2025-11-12 13:17:52,629 - stpipe.step - INFO - Step ipc running with args (,).\n", + "2025-11-12 13:17:52,629 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:17:52,687 - stpipe.step - INFO - Step firstframe running with args (,).\n", + "2025-11-12 13:17:52,742 - jwst.firstframe.firstframe_sub - INFO - Number of usable bright pixels with first group not set to DO_NOT_USE: 0\n", + "2025-11-12 13:17:52,744 - stpipe.step - INFO - Step firstframe done\n", + "2025-11-12 13:17:52,814 - stpipe.step - INFO - Step lastframe running with args (,).\n", + "2025-11-12 13:17:52,856 - stpipe.step - INFO - Step lastframe done\n", + "2025-11-12 13:17:52,920 - stpipe.step - INFO - Step reset running with args (,).\n", + "2025-11-12 13:17:52,959 - jwst.reset.reset_step - INFO - Using RESET reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_reset_0078.fits\n", + "2025-11-12 13:17:52,999 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:52,999 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:53,015 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:53,055 - stpipe.step - INFO - Step reset done\n", + "2025-11-12 13:17:53,124 - stpipe.step - INFO - Step linearity running with args (,).\n", + "2025-11-12 13:17:53,166 - jwst.linearity.linearity_step - INFO - Using Linearity reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_linearity_0030.fits\n", + "2025-11-12 13:17:53,187 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:53,188 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:53,197 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:53,403 - stpipe.step - INFO - Step linearity done\n", + "2025-11-12 13:17:53,471 - stpipe.step - INFO - Step rscd running with args (,).\n", + "2025-11-12 13:17:53,512 - jwst.rscd.rscd_step - INFO - Using RSCD reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_rscd_0018.fits\n", + "2025-11-12 13:17:53,531 - jwst.rscd.rscd_sub - INFO - Number of groups to skip for integrations 2 and higher: 2\n", + "2025-11-12 13:17:53,532 - jwst.rscd.rscd_sub - WARNING - Too few groups to apply RSCD correction\n", + "2025-11-12 13:17:53,532 - jwst.rscd.rscd_sub - WARNING - RSCD step will be skipped\n", + "2025-11-12 13:17:53,533 - stpipe.step - INFO - Step rscd done\n", + "2025-11-12 13:17:53,602 - stpipe.step - INFO - Step dark_current running with args (,).\n", + "2025-11-12 13:17:53,607 - jwst.dark_current.dark_current_step - INFO - Using DARK reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_dark_0112.fits\n", + "2025-11-12 13:17:54,081 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:17:54,097 - jwst.dark_current.dark_current_step - INFO - Using Poisson noise from average dark current 1.0 e-/sec\n", + "2025-11-12 13:17:54,098 - stcal.dark_current.dark_sub - INFO - Science data nints=14, ngroups=5, nframes=1, groupgap=0\n", + "2025-11-12 13:17:54,098 - stcal.dark_current.dark_sub - INFO - Dark data nints=2, ngroups=360, nframes=1, groupgap=0\n", + "2025-11-12 13:17:54,570 - stpipe.step - INFO - Step dark_current done\n", + "2025-11-12 13:17:54,644 - stpipe.step - INFO - Step refpix running with args (,).\n", + "2025-11-12 13:17:54,645 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:17:54,709 - stpipe.step - INFO - Step charge_migration running with args (,).\n", + "2025-11-12 13:17:54,710 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:17:54,767 - stpipe.step - INFO - Step jump running with args (,).\n", + "2025-11-12 13:17:54,808 - jwst.jump.jump_step - INFO - CR rejection threshold = 4 sigma\n", + "2025-11-12 13:17:54,809 - jwst.jump.jump_step - INFO - Maximum cores to use = all\n", + "2025-11-12 13:17:54,811 - jwst.jump.jump_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:17:54,812 - jwst.jump.jump_step - INFO - Using READNOISE reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits\n", + "2025-11-12 13:17:54,863 - stcal.jump.jump - INFO - Executing two-point difference method\n", + "2025-11-12 13:17:54,864 - stcal.jump.jump - INFO - Creating 12 processes for jump detection \n", + "2025-11-12 13:17:58,470 - stcal.jump.jump - INFO - Flagging Showers\n", + "2025-11-12 13:18:03,218 - stcal.jump.jump - INFO - Total showers= 18\n", + "2025-11-12 13:18:03,218 - stcal.jump.jump - INFO - Total elapsed time = 8.35493 sec\n", + "2025-11-12 13:18:03,231 - jwst.jump.jump_step - INFO - The execution time in seconds: 8.458758\n", + "2025-11-12 13:18:03,235 - stpipe.step - INFO - Step jump done\n", + "2025-11-12 13:18:03,315 - stpipe.step - INFO - Step clean_flicker_noise running with args (,).\n", + "2025-11-12 13:18:03,315 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:18:03,376 - stpipe.step - INFO - Step ramp_fit running with args (,).\n", + "2025-11-12 13:18:03,446 - jwst.ramp_fitting.ramp_fit_step - INFO - Using READNOISE reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits\n", + "2025-11-12 13:18:03,446 - jwst.ramp_fitting.ramp_fit_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:18:03,464 - jwst.ramp_fitting.ramp_fit_step - INFO - Using algorithm = OLS_C\n", + "2025-11-12 13:18:03,464 - jwst.ramp_fitting.ramp_fit_step - INFO - Using weighting = optimal\n", + "2025-11-12 13:18:03,667 - stcal.ramp_fitting.ols_fit - INFO - Number of multiprocessing slices: 12\n", + "2025-11-12 13:18:03,711 - stcal.ramp_fitting.ols_fit - INFO - Number of leading groups that are flagged as DO_NOT_USE: 1\n", + "2025-11-12 13:18:03,713 - stcal.ramp_fitting.ols_fit - INFO - MIRI dataset has all pixels in the final group flagged as DO_NOT_USE.\n", + "2025-11-12 13:18:03,715 - stcal.ramp_fitting.ols_fit - INFO - Number of processors used for multiprocessing: 12\n", + "2025-11-12 13:18:05,851 - stpipe.step - INFO - Step ramp_fit done\n", + "2025-11-12 13:18:05,930 - stpipe.step - INFO - Step gain_scale running with args (,).\n", + "2025-11-12 13:18:05,944 - jwst.gain_scale.gain_scale_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:18:05,954 - jwst.gain_scale.gain_scale_step - INFO - GAINFACT not found in gain reference file\n", + "2025-11-12 13:18:05,955 - jwst.gain_scale.gain_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:18:05,956 - stpipe.step - INFO - Step gain_scale done\n", + "2025-11-12 13:18:06,023 - stpipe.step - INFO - Step gain_scale running with args (,).\n", + "2025-11-12 13:18:06,061 - jwst.gain_scale.gain_scale_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:18:06,070 - jwst.gain_scale.gain_scale_step - INFO - GAINFACT not found in gain reference file\n", + "2025-11-12 13:18:06,070 - jwst.gain_scale.gain_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:18:06,071 - stpipe.step - INFO - Step gain_scale done\n", + "2025-11-12 13:18:06,209 - stpipe.step - INFO - Saved model in ./data/* bet Pic/12A/stage1/jw01294003001_03102_00002_mirifushort_rateints.fits\n", + "2025-11-12 13:18:06,209 - jwst.pipeline.calwebb_detector1 - INFO - ... ending calwebb_detector1\n", + "2025-11-12 13:18:06,210 - jwst.stpipe.core - INFO - Results used CRDS context: jwst_1464.pmap\n", + "2025-11-12 13:18:06,267 - stpipe.step - INFO - Saved model in ./data/* bet Pic/12A/stage1/jw01294003001_03102_00002_mirifushort_rate.fits\n", + "2025-11-12 13:18:06,267 - stpipe.step - INFO - Step Detector1Pipeline done\n", + "2025-11-12 13:18:06,268 - jwst.stpipe.core - INFO - Results used jwst version: 1.20.2\n", + "/Users/mperrin/Dropbox (Personal)/Documents/software/git/breads/breads/jwst_tools/reduction_utils.py:1390: ResourceWarning: unclosed file <_io.BufferedReader name='./data/jw01294003001_03102_00002_mirifushort_uncal.fits'>\n", + " hdu_uncal = fits.open(file)\n", + "2025-11-12 13:18:06,301 - stpipe.step - INFO - PARS-EMICORRSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-emicorrstep_0003.asdf\n", + "2025-11-12 13:18:06,313 - stpipe.step - INFO - PARS-DARKCURRENTSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-darkcurrentstep_0001.asdf\n", + "2025-11-12 13:18:06,320 - stpipe.step - INFO - PARS-JUMPSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-jumpstep_0007.asdf\n", + "2025-11-12 13:18:06,328 - stpipe.pipeline - INFO - PARS-DETECTOR1PIPELINE parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-detector1pipeline_0010.asdf\n", + "2025-11-12 13:18:06,340 - stpipe.step - INFO - Detector1Pipeline instance created.\n", + "2025-11-12 13:18:06,340 - stpipe.step - INFO - GroupScaleStep instance created.\n", + "2025-11-12 13:18:06,341 - stpipe.step - INFO - DQInitStep instance created.\n", + "2025-11-12 13:18:06,341 - stpipe.step - INFO - EmiCorrStep instance created.\n", + "2025-11-12 13:18:06,342 - stpipe.step - INFO - SaturationStep instance created.\n", + "2025-11-12 13:18:06,342 - stpipe.step - INFO - IPCStep instance created.\n", + "2025-11-12 13:18:06,343 - stpipe.step - INFO - SuperBiasStep instance created.\n", + "2025-11-12 13:18:06,343 - stpipe.step - INFO - RefPixStep instance created.\n", + "2025-11-12 13:18:06,344 - stpipe.step - INFO - RscdStep instance created.\n", + "2025-11-12 13:18:06,344 - stpipe.step - INFO - FirstFrameStep instance created.\n", + "2025-11-12 13:18:06,344 - stpipe.step - INFO - LastFrameStep instance created.\n", + "2025-11-12 13:18:06,345 - stpipe.step - INFO - LinearityStep instance created.\n", + "2025-11-12 13:18:06,345 - stpipe.step - INFO - DarkCurrentStep instance created.\n", + "2025-11-12 13:18:06,346 - stpipe.step - INFO - ResetStep instance created.\n", + "2025-11-12 13:18:06,346 - stpipe.step - INFO - PersistenceStep instance created.\n", + "2025-11-12 13:18:06,347 - stpipe.step - INFO - ChargeMigrationStep instance created.\n", + "2025-11-12 13:18:06,348 - stpipe.step - INFO - JumpStep instance created.\n", + "2025-11-12 13:18:06,349 - stpipe.step - INFO - CleanFlickerNoiseStep instance created.\n", + "2025-11-12 13:18:06,349 - stpipe.step - INFO - RampFitStep instance created.\n", + "2025-11-12 13:18:06,350 - stpipe.step - INFO - GainScaleStep instance created.\n", + "2025-11-12 13:18:06,431 - stpipe.step - INFO - Step Detector1Pipeline running with args ('./data/jw01294003001_03102_00003_mirifushort_uncal.fits',).\n", + "2025-11-12 13:18:06,442 - stpipe.step - INFO - Step Detector1Pipeline parameters are:\n", + " pre_hooks: []\n", + " post_hooks: []\n", + " output_file: None\n", + " output_dir: ./data/* bet Pic/12A/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: False\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: True\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: 1.0\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: 100\n", + " four_group_rejection_threshold: 5.0\n", + " maximum_cores: all\n", + " flag_4_neighbors: True\n", + " max_jump_to_flag_neighbors: 1000.0\n", + " min_jump_to_flag_neighbors: 10.0\n", + " after_jump_flag_dn1: 0.0\n", + " after_jump_flag_time1: 0.0\n", + " after_jump_flag_dn2: 0.0\n", + " after_jump_flag_time2: 0.0\n", + " expand_large_events: False\n", + " min_sat_area: 1.0\n", + " min_jump_area: 5.0\n", + " expand_factor: 2.0\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: True\n", + " max_shower_amplitude: 4.0\n", + " extend_snr_threshold: 1.2\n", + " extend_min_area: 90\n", + " extend_inner_radius: 1.0\n", + " extend_outer_radius: 2.6\n", + " extend_ellipse_expand_ratio: 1.1\n", + " time_masked_after_shower: 15.0\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: all\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", + "2025-11-12 13:18:06,464 - stpipe.pipeline - INFO - Prefetching reference files for dataset: 'jw01294003001_03102_00003_mirifushort_uncal.fits' reftypes = ['dark', 'emicorr', 'gain', 'linearity', 'mask', 'readnoise', 'reset', 'rscd', 'saturation', 'superbias']\n", + "2025-11-12 13:18:06,467 - stpipe.pipeline - INFO - Prefetch for DARK reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_dark_0112.fits'.\n", + "2025-11-12 13:18:06,467 - stpipe.pipeline - INFO - Prefetch for EMICORR reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_emicorr_0003.asdf'.\n", + "2025-11-12 13:18:06,467 - stpipe.pipeline - INFO - Prefetch for GAIN reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits'.\n", + "2025-11-12 13:18:06,468 - stpipe.pipeline - INFO - Prefetch for LINEARITY reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_linearity_0030.fits'.\n", + "2025-11-12 13:18:06,469 - stpipe.pipeline - INFO - Prefetch for MASK reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_mask_0046.fits'.\n", + "2025-11-12 13:18:06,469 - stpipe.pipeline - INFO - Prefetch for READNOISE reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits'.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing file 3 of 4.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-12 13:18:06,470 - stpipe.pipeline - INFO - Prefetch for RESET reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_reset_0078.fits'.\n", + "2025-11-12 13:18:06,470 - stpipe.pipeline - INFO - Prefetch for RSCD reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_rscd_0018.fits'.\n", + "2025-11-12 13:18:06,471 - stpipe.pipeline - INFO - Prefetch for SATURATION reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_saturation_0033.fits'.\n", + "2025-11-12 13:18:06,471 - stpipe.pipeline - INFO - Prefetch for SUPERBIAS reference file is 'N/A'.\n", + "2025-11-12 13:18:06,472 - jwst.pipeline.calwebb_detector1 - INFO - Starting calwebb_detector1 ...\n", + "2025-11-12 13:18:06,716 - stpipe.step - INFO - Step group_scale running with args (,).\n", + "2025-11-12 13:18:06,778 - jwst.group_scale.group_scale_step - INFO - NFRAMES and FRMDIVSR are equal; correction not needed\n", + "2025-11-12 13:18:06,779 - jwst.group_scale.group_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:18:06,780 - stpipe.step - INFO - Step group_scale done\n", + "2025-11-12 13:18:06,847 - stpipe.step - INFO - Step dq_init running with args (,).\n", + "2025-11-12 13:18:06,852 - jwst.dq_init.dq_init_step - INFO - Using MASK reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_mask_0046.fits\n", + "2025-11-12 13:18:06,904 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:06,981 - stpipe.step - INFO - Step dq_init done\n", + "2025-11-12 13:18:07,050 - stpipe.step - INFO - Step emicorr running with args (,).\n", + "2025-11-12 13:18:07,088 - jwst.emicorr.emicorr_step - INFO - Using CRDS reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_emicorr_0003.asdf\n", + "2025-11-12 13:18:07,100 - jwst.emicorr.emicorr - INFO - Using reference file to get subarray case.\n", + "2025-11-12 13:18:07,101 - jwst.emicorr.emicorr - INFO - With configuration: Subarray=FULL, Read_pattern=FASTR1, Detector=MIRIFUSHORT\n", + "2025-11-12 13:18:07,101 - jwst.emicorr.emicorr - INFO - Will correct data for the following 1 frequencies: \n", + "2025-11-12 13:18:07,102 - jwst.emicorr.emicorr - INFO - ['Hz10']\n", + "2025-11-12 13:18:07,102 - jwst.emicorr.emicorr - INFO - Running EMI fit with algorithm = 'joint'.\n", + "2025-11-12 13:18:11,830 - stpipe.step - INFO - Step emicorr done\n", + "2025-11-12 13:18:11,902 - stpipe.step - INFO - Step saturation running with args (,).\n", + "2025-11-12 13:18:11,942 - jwst.saturation.saturation_step - INFO - Using SATURATION reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_saturation_0033.fits\n", + "2025-11-12 13:18:11,943 - jwst.saturation.saturation_step - INFO - Using SUPERBIAS reference file N/A\n", + "2025-11-12 13:18:11,962 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:11,962 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:11,974 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:11,978 - jwst.saturation.saturation - INFO - Using read_pattern with nframes 1\n", + "2025-11-12 13:18:12,197 - stcal.saturation.saturation - INFO - Detected 118 saturated pixels\n", + "2025-11-12 13:18:12,208 - stcal.saturation.saturation - INFO - Detected 7 A/D floor pixels\n", + "2025-11-12 13:18:12,212 - stpipe.step - INFO - Step saturation done\n", + "2025-11-12 13:18:12,279 - stpipe.step - INFO - Step ipc running with args (,).\n", + "2025-11-12 13:18:12,280 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:18:12,336 - stpipe.step - INFO - Step firstframe running with args (,).\n", + "2025-11-12 13:18:12,392 - jwst.firstframe.firstframe_sub - INFO - Number of usable bright pixels with first group not set to DO_NOT_USE: 1\n", + "2025-11-12 13:18:12,394 - stpipe.step - INFO - Step firstframe done\n", + "2025-11-12 13:18:12,465 - stpipe.step - INFO - Step lastframe running with args (,).\n", + "2025-11-12 13:18:12,506 - stpipe.step - INFO - Step lastframe done\n", + "2025-11-12 13:18:12,580 - stpipe.step - INFO - Step reset running with args (,).\n", + "2025-11-12 13:18:12,619 - jwst.reset.reset_step - INFO - Using RESET reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_reset_0078.fits\n", + "2025-11-12 13:18:12,663 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:12,664 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:12,672 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:12,709 - stpipe.step - INFO - Step reset done\n", + "2025-11-12 13:18:12,778 - stpipe.step - INFO - Step linearity running with args (,).\n", + "2025-11-12 13:18:12,817 - jwst.linearity.linearity_step - INFO - Using Linearity reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_linearity_0030.fits\n", + "2025-11-12 13:18:12,839 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:12,839 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:12,855 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:13,065 - stpipe.step - INFO - Step linearity done\n", + "2025-11-12 13:18:13,135 - stpipe.step - INFO - Step rscd running with args (,).\n", + "2025-11-12 13:18:13,173 - jwst.rscd.rscd_step - INFO - Using RSCD reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_rscd_0018.fits\n", + "2025-11-12 13:18:13,191 - jwst.rscd.rscd_sub - INFO - Number of groups to skip for integrations 2 and higher: 2\n", + "2025-11-12 13:18:13,191 - jwst.rscd.rscd_sub - WARNING - Too few groups to apply RSCD correction\n", + "2025-11-12 13:18:13,192 - jwst.rscd.rscd_sub - WARNING - RSCD step will be skipped\n", + "2025-11-12 13:18:13,193 - stpipe.step - INFO - Step rscd done\n", + "2025-11-12 13:18:13,262 - stpipe.step - INFO - Step dark_current running with args (,).\n", + "2025-11-12 13:18:13,268 - jwst.dark_current.dark_current_step - INFO - Using DARK reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_dark_0112.fits\n", + "2025-11-12 13:18:13,715 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:13,730 - jwst.dark_current.dark_current_step - INFO - Using Poisson noise from average dark current 1.0 e-/sec\n", + "2025-11-12 13:18:13,731 - stcal.dark_current.dark_sub - INFO - Science data nints=14, ngroups=5, nframes=1, groupgap=0\n", + "2025-11-12 13:18:13,731 - stcal.dark_current.dark_sub - INFO - Dark data nints=2, ngroups=360, nframes=1, groupgap=0\n", + "2025-11-12 13:18:14,094 - stpipe.step - INFO - Step dark_current done\n", + "2025-11-12 13:18:14,162 - stpipe.step - INFO - Step refpix running with args (,).\n", + "2025-11-12 13:18:14,163 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:18:14,221 - stpipe.step - INFO - Step charge_migration running with args (,).\n", + "2025-11-12 13:18:14,222 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:18:14,280 - stpipe.step - INFO - Step jump running with args (,).\n", + "2025-11-12 13:18:14,318 - jwst.jump.jump_step - INFO - CR rejection threshold = 4 sigma\n", + "2025-11-12 13:18:14,319 - jwst.jump.jump_step - INFO - Maximum cores to use = all\n", + "2025-11-12 13:18:14,321 - jwst.jump.jump_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:18:14,322 - jwst.jump.jump_step - INFO - Using READNOISE reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits\n", + "2025-11-12 13:18:14,376 - stcal.jump.jump - INFO - Executing two-point difference method\n", + "2025-11-12 13:18:14,376 - stcal.jump.jump - INFO - Creating 12 processes for jump detection \n", + "2025-11-12 13:18:17,978 - stcal.jump.jump - INFO - Flagging Showers\n", + "2025-11-12 13:18:22,665 - stcal.jump.jump - INFO - Total showers= 18\n", + "2025-11-12 13:18:22,665 - stcal.jump.jump - INFO - Total elapsed time = 8.28896 sec\n", + "2025-11-12 13:18:22,677 - jwst.jump.jump_step - INFO - The execution time in seconds: 8.391872\n", + "2025-11-12 13:18:22,680 - stpipe.step - INFO - Step jump done\n", + "2025-11-12 13:18:22,757 - stpipe.step - INFO - Step clean_flicker_noise running with args (,).\n", + "2025-11-12 13:18:22,757 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:18:22,814 - stpipe.step - INFO - Step ramp_fit running with args (,).\n", + "2025-11-12 13:18:22,881 - jwst.ramp_fitting.ramp_fit_step - INFO - Using READNOISE reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits\n", + "2025-11-12 13:18:22,881 - jwst.ramp_fitting.ramp_fit_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:18:22,898 - jwst.ramp_fitting.ramp_fit_step - INFO - Using algorithm = OLS_C\n", + "2025-11-12 13:18:22,898 - jwst.ramp_fitting.ramp_fit_step - INFO - Using weighting = optimal\n", + "2025-11-12 13:18:23,114 - stcal.ramp_fitting.ols_fit - INFO - Number of multiprocessing slices: 12\n", + "2025-11-12 13:18:23,150 - stcal.ramp_fitting.ols_fit - INFO - Number of leading groups that are flagged as DO_NOT_USE: 1\n", + "2025-11-12 13:18:23,152 - stcal.ramp_fitting.ols_fit - INFO - MIRI dataset has all pixels in the final group flagged as DO_NOT_USE.\n", + "2025-11-12 13:18:23,154 - stcal.ramp_fitting.ols_fit - INFO - Number of processors used for multiprocessing: 12\n", + "2025-11-12 13:18:25,459 - stpipe.step - INFO - Step ramp_fit done\n", + "2025-11-12 13:18:25,538 - stpipe.step - INFO - Step gain_scale running with args (,).\n", + "2025-11-12 13:18:25,551 - jwst.gain_scale.gain_scale_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:18:25,560 - jwst.gain_scale.gain_scale_step - INFO - GAINFACT not found in gain reference file\n", + "2025-11-12 13:18:25,560 - jwst.gain_scale.gain_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:18:25,562 - stpipe.step - INFO - Step gain_scale done\n", + "2025-11-12 13:18:25,622 - stpipe.step - INFO - Step gain_scale running with args (,).\n", + "2025-11-12 13:18:25,659 - jwst.gain_scale.gain_scale_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:18:25,668 - jwst.gain_scale.gain_scale_step - INFO - GAINFACT not found in gain reference file\n", + "2025-11-12 13:18:25,668 - jwst.gain_scale.gain_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:18:25,669 - stpipe.step - INFO - Step gain_scale done\n", + "2025-11-12 13:18:25,801 - stpipe.step - INFO - Saved model in ./data/* bet Pic/12A/stage1/jw01294003001_03102_00003_mirifushort_rateints.fits\n", + "2025-11-12 13:18:25,801 - jwst.pipeline.calwebb_detector1 - INFO - ... ending calwebb_detector1\n", + "2025-11-12 13:18:25,802 - jwst.stpipe.core - INFO - Results used CRDS context: jwst_1464.pmap\n", + "2025-11-12 13:18:25,842 - stpipe.step - INFO - Saved model in ./data/* bet Pic/12A/stage1/jw01294003001_03102_00003_mirifushort_rate.fits\n", + "2025-11-12 13:18:25,842 - stpipe.step - INFO - Step Detector1Pipeline done\n", + "2025-11-12 13:18:25,843 - jwst.stpipe.core - INFO - Results used jwst version: 1.20.2\n", + "/Users/mperrin/Dropbox (Personal)/Documents/software/git/breads/breads/jwst_tools/reduction_utils.py:1390: ResourceWarning: unclosed file <_io.BufferedReader name='./data/jw01294003001_03102_00003_mirifushort_uncal.fits'>\n", + " hdu_uncal = fits.open(file)\n", + "2025-11-12 13:18:25,875 - stpipe.step - INFO - PARS-EMICORRSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-emicorrstep_0003.asdf\n", + "2025-11-12 13:18:25,885 - stpipe.step - INFO - PARS-DARKCURRENTSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-darkcurrentstep_0001.asdf\n", + "2025-11-12 13:18:25,893 - stpipe.step - INFO - PARS-JUMPSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-jumpstep_0007.asdf\n", + "2025-11-12 13:18:25,901 - stpipe.pipeline - INFO - PARS-DETECTOR1PIPELINE parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-detector1pipeline_0010.asdf\n", + "2025-11-12 13:18:25,912 - stpipe.step - INFO - Detector1Pipeline instance created.\n", + "2025-11-12 13:18:25,912 - stpipe.step - INFO - GroupScaleStep instance created.\n", + "2025-11-12 13:18:25,912 - stpipe.step - INFO - DQInitStep instance created.\n", + "2025-11-12 13:18:25,913 - stpipe.step - INFO - EmiCorrStep instance created.\n", + "2025-11-12 13:18:25,913 - stpipe.step - INFO - SaturationStep instance created.\n", + "2025-11-12 13:18:25,914 - stpipe.step - INFO - IPCStep instance created.\n", + "2025-11-12 13:18:25,914 - stpipe.step - INFO - SuperBiasStep instance created.\n", + "2025-11-12 13:18:25,915 - stpipe.step - INFO - RefPixStep instance created.\n", + "2025-11-12 13:18:25,915 - stpipe.step - INFO - RscdStep instance created.\n", + "2025-11-12 13:18:25,916 - stpipe.step - INFO - FirstFrameStep instance created.\n", + "2025-11-12 13:18:25,916 - stpipe.step - INFO - LastFrameStep instance created.\n", + "2025-11-12 13:18:25,916 - stpipe.step - INFO - LinearityStep instance created.\n", + "2025-11-12 13:18:25,917 - stpipe.step - INFO - DarkCurrentStep instance created.\n", + "2025-11-12 13:18:25,917 - stpipe.step - INFO - ResetStep instance created.\n", + "2025-11-12 13:18:25,918 - stpipe.step - INFO - PersistenceStep instance created.\n", + "2025-11-12 13:18:25,918 - stpipe.step - INFO - ChargeMigrationStep instance created.\n", + "2025-11-12 13:18:25,919 - stpipe.step - INFO - JumpStep instance created.\n", + "2025-11-12 13:18:25,920 - stpipe.step - INFO - CleanFlickerNoiseStep instance created.\n", + "2025-11-12 13:18:25,920 - stpipe.step - INFO - RampFitStep instance created.\n", + "2025-11-12 13:18:25,921 - stpipe.step - INFO - GainScaleStep instance created.\n", + "2025-11-12 13:18:25,994 - stpipe.step - INFO - Step Detector1Pipeline running with args ('./data/jw01294003001_03102_00004_mirifushort_uncal.fits',).\n", + "2025-11-12 13:18:26,006 - stpipe.step - INFO - Step Detector1Pipeline parameters are:\n", + " pre_hooks: []\n", + " post_hooks: []\n", + " output_file: None\n", + " output_dir: ./data/* bet Pic/12A/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: False\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: True\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: 1.0\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: 100\n", + " four_group_rejection_threshold: 5.0\n", + " maximum_cores: all\n", + " flag_4_neighbors: True\n", + " max_jump_to_flag_neighbors: 1000.0\n", + " min_jump_to_flag_neighbors: 10.0\n", + " after_jump_flag_dn1: 0.0\n", + " after_jump_flag_time1: 0.0\n", + " after_jump_flag_dn2: 0.0\n", + " after_jump_flag_time2: 0.0\n", + " expand_large_events: False\n", + " min_sat_area: 1.0\n", + " min_jump_area: 5.0\n", + " expand_factor: 2.0\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: True\n", + " max_shower_amplitude: 4.0\n", + " extend_snr_threshold: 1.2\n", + " extend_min_area: 90\n", + " extend_inner_radius: 1.0\n", + " extend_outer_radius: 2.6\n", + " extend_ellipse_expand_ratio: 1.1\n", + " time_masked_after_shower: 15.0\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: all\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", + "2025-11-12 13:18:26,029 - stpipe.pipeline - INFO - Prefetching reference files for dataset: 'jw01294003001_03102_00004_mirifushort_uncal.fits' reftypes = ['dark', 'emicorr', 'gain', 'linearity', 'mask', 'readnoise', 'reset', 'rscd', 'saturation', 'superbias']\n", + "2025-11-12 13:18:26,032 - stpipe.pipeline - INFO - Prefetch for DARK reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_dark_0112.fits'.\n", + "2025-11-12 13:18:26,032 - stpipe.pipeline - INFO - Prefetch for EMICORR reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_emicorr_0003.asdf'.\n", + "2025-11-12 13:18:26,032 - stpipe.pipeline - INFO - Prefetch for GAIN reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits'.\n", + "2025-11-12 13:18:26,033 - stpipe.pipeline - INFO - Prefetch for LINEARITY reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_linearity_0030.fits'.\n", + "2025-11-12 13:18:26,033 - stpipe.pipeline - INFO - Prefetch for MASK reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_mask_0046.fits'.\n", + "2025-11-12 13:18:26,034 - stpipe.pipeline - INFO - Prefetch for READNOISE reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits'.\n", + "2025-11-12 13:18:26,034 - stpipe.pipeline - INFO - Prefetch for RESET reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_reset_0078.fits'.\n", + "2025-11-12 13:18:26,035 - stpipe.pipeline - INFO - Prefetch for RSCD reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_rscd_0018.fits'.\n", + "2025-11-12 13:18:26,035 - stpipe.pipeline - INFO - Prefetch for SATURATION reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_saturation_0033.fits'.\n", + "2025-11-12 13:18:26,035 - stpipe.pipeline - INFO - Prefetch for SUPERBIAS reference file is 'N/A'.\n", + "2025-11-12 13:18:26,036 - jwst.pipeline.calwebb_detector1 - INFO - Starting calwebb_detector1 ...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing file 4 of 4.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-12 13:18:26,294 - stpipe.step - INFO - Step group_scale running with args (,).\n", + "2025-11-12 13:18:26,354 - jwst.group_scale.group_scale_step - INFO - NFRAMES and FRMDIVSR are equal; correction not needed\n", + "2025-11-12 13:18:26,355 - jwst.group_scale.group_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:18:26,356 - stpipe.step - INFO - Step group_scale done\n", + "2025-11-12 13:18:26,425 - stpipe.step - INFO - Step dq_init running with args (,).\n", + "2025-11-12 13:18:26,431 - jwst.dq_init.dq_init_step - INFO - Using MASK reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_mask_0046.fits\n", + "2025-11-12 13:18:26,482 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:26,564 - stpipe.step - INFO - Step dq_init done\n", + "2025-11-12 13:18:26,633 - stpipe.step - INFO - Step emicorr running with args (,).\n", + "2025-11-12 13:18:26,675 - jwst.emicorr.emicorr_step - INFO - Using CRDS reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_emicorr_0003.asdf\n", + "2025-11-12 13:18:26,687 - jwst.emicorr.emicorr - INFO - Using reference file to get subarray case.\n", + "2025-11-12 13:18:26,687 - jwst.emicorr.emicorr - INFO - With configuration: Subarray=FULL, Read_pattern=FASTR1, Detector=MIRIFUSHORT\n", + "2025-11-12 13:18:26,688 - jwst.emicorr.emicorr - INFO - Will correct data for the following 1 frequencies: \n", + "2025-11-12 13:18:26,688 - jwst.emicorr.emicorr - INFO - ['Hz10']\n", + "2025-11-12 13:18:26,688 - jwst.emicorr.emicorr - INFO - Running EMI fit with algorithm = 'joint'.\n", + "2025-11-12 13:18:31,427 - stpipe.step - INFO - Step emicorr done\n", + "2025-11-12 13:18:31,500 - stpipe.step - INFO - Step saturation running with args (,).\n", + "2025-11-12 13:18:31,545 - jwst.saturation.saturation_step - INFO - Using SATURATION reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_saturation_0033.fits\n", + "2025-11-12 13:18:31,546 - jwst.saturation.saturation_step - INFO - Using SUPERBIAS reference file N/A\n", + "2025-11-12 13:18:31,568 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:31,569 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:31,586 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:31,590 - jwst.saturation.saturation - INFO - Using read_pattern with nframes 1\n", + "2025-11-12 13:18:31,827 - stcal.saturation.saturation - INFO - Detected 96 saturated pixels\n", + "2025-11-12 13:18:31,838 - stcal.saturation.saturation - INFO - Detected 9 A/D floor pixels\n", + "2025-11-12 13:18:31,845 - stpipe.step - INFO - Step saturation done\n", + "2025-11-12 13:18:31,916 - stpipe.step - INFO - Step ipc running with args (,).\n", + "2025-11-12 13:18:31,917 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:18:31,977 - stpipe.step - INFO - Step firstframe running with args (,).\n", + "2025-11-12 13:18:32,036 - jwst.firstframe.firstframe_sub - INFO - Number of usable bright pixels with first group not set to DO_NOT_USE: 0\n", + "2025-11-12 13:18:32,037 - stpipe.step - INFO - Step firstframe done\n", + "2025-11-12 13:18:32,106 - stpipe.step - INFO - Step lastframe running with args (,).\n", + "2025-11-12 13:18:32,149 - stpipe.step - INFO - Step lastframe done\n", + "2025-11-12 13:18:32,218 - stpipe.step - INFO - Step reset running with args (,).\n", + "2025-11-12 13:18:32,257 - jwst.reset.reset_step - INFO - Using RESET reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_reset_0078.fits\n", + "2025-11-12 13:18:32,298 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:32,298 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:32,314 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:32,353 - stpipe.step - INFO - Step reset done\n", + "2025-11-12 13:18:32,422 - stpipe.step - INFO - Step linearity running with args (,).\n", + "2025-11-12 13:18:32,462 - jwst.linearity.linearity_step - INFO - Using Linearity reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_linearity_0030.fits\n", + "2025-11-12 13:18:32,485 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:32,486 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:32,501 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:32,723 - stpipe.step - INFO - Step linearity done\n", + "2025-11-12 13:18:32,798 - stpipe.step - INFO - Step rscd running with args (,).\n", + "2025-11-12 13:18:32,839 - jwst.rscd.rscd_step - INFO - Using RSCD reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_rscd_0018.fits\n", + "2025-11-12 13:18:32,860 - jwst.rscd.rscd_sub - INFO - Number of groups to skip for integrations 2 and higher: 2\n", + "2025-11-12 13:18:32,860 - jwst.rscd.rscd_sub - WARNING - Too few groups to apply RSCD correction\n", + "2025-11-12 13:18:32,861 - jwst.rscd.rscd_sub - WARNING - RSCD step will be skipped\n", + "2025-11-12 13:18:32,862 - stpipe.step - INFO - Step rscd done\n", + "2025-11-12 13:18:32,939 - stpipe.step - INFO - Step dark_current running with args (,).\n", + "2025-11-12 13:18:32,946 - jwst.dark_current.dark_current_step - INFO - Using DARK reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_dark_0112.fits\n", + "2025-11-12 13:18:33,396 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:33,412 - jwst.dark_current.dark_current_step - INFO - Using Poisson noise from average dark current 1.0 e-/sec\n", + "2025-11-12 13:18:33,412 - stcal.dark_current.dark_sub - INFO - Science data nints=14, ngroups=5, nframes=1, groupgap=0\n", + "2025-11-12 13:18:33,413 - stcal.dark_current.dark_sub - INFO - Dark data nints=2, ngroups=360, nframes=1, groupgap=0\n", + "2025-11-12 13:18:33,739 - stpipe.step - INFO - Step dark_current done\n", + "2025-11-12 13:18:33,806 - stpipe.step - INFO - Step refpix running with args (,).\n", + "2025-11-12 13:18:33,807 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:18:33,868 - stpipe.step - INFO - Step charge_migration running with args (,).\n", + "2025-11-12 13:18:33,869 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:18:33,927 - stpipe.step - INFO - Step jump running with args (,).\n", + "2025-11-12 13:18:33,967 - jwst.jump.jump_step - INFO - CR rejection threshold = 4 sigma\n", + "2025-11-12 13:18:33,967 - jwst.jump.jump_step - INFO - Maximum cores to use = all\n", + "2025-11-12 13:18:33,969 - jwst.jump.jump_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:18:33,971 - jwst.jump.jump_step - INFO - Using READNOISE reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits\n", + "2025-11-12 13:18:34,022 - stcal.jump.jump - INFO - Executing two-point difference method\n", + "2025-11-12 13:18:34,023 - stcal.jump.jump - INFO - Creating 12 processes for jump detection \n", + "2025-11-12 13:18:37,611 - stcal.jump.jump - INFO - Flagging Showers\n", + "2025-11-12 13:18:42,286 - stcal.jump.jump - INFO - Total showers= 12\n", + "2025-11-12 13:18:42,286 - stcal.jump.jump - INFO - Total elapsed time = 8.26356 sec\n", + "2025-11-12 13:18:42,297 - jwst.jump.jump_step - INFO - The execution time in seconds: 8.363858\n", + "2025-11-12 13:18:42,301 - stpipe.step - INFO - Step jump done\n", + "2025-11-12 13:18:42,385 - stpipe.step - INFO - Step clean_flicker_noise running with args (,).\n", + "2025-11-12 13:18:42,385 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:18:42,450 - stpipe.step - INFO - Step ramp_fit running with args (,).\n", + "2025-11-12 13:18:42,517 - jwst.ramp_fitting.ramp_fit_step - INFO - Using READNOISE reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits\n", + "2025-11-12 13:18:42,517 - jwst.ramp_fitting.ramp_fit_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:18:42,536 - jwst.ramp_fitting.ramp_fit_step - INFO - Using algorithm = OLS_C\n", + "2025-11-12 13:18:42,537 - jwst.ramp_fitting.ramp_fit_step - INFO - Using weighting = optimal\n", + "2025-11-12 13:18:42,714 - stcal.ramp_fitting.ols_fit - INFO - Number of multiprocessing slices: 12\n", + "2025-11-12 13:18:42,751 - stcal.ramp_fitting.ols_fit - INFO - Number of leading groups that are flagged as DO_NOT_USE: 1\n", + "2025-11-12 13:18:42,753 - stcal.ramp_fitting.ols_fit - INFO - MIRI dataset has all pixels in the final group flagged as DO_NOT_USE.\n", + "2025-11-12 13:18:42,755 - stcal.ramp_fitting.ols_fit - INFO - Number of processors used for multiprocessing: 12\n", + "2025-11-12 13:18:45,010 - stpipe.step - INFO - Step ramp_fit done\n", + "2025-11-12 13:18:45,109 - stpipe.step - INFO - Step gain_scale running with args (,).\n", + "2025-11-12 13:18:45,124 - jwst.gain_scale.gain_scale_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:18:45,135 - jwst.gain_scale.gain_scale_step - INFO - GAINFACT not found in gain reference file\n", + "2025-11-12 13:18:45,136 - jwst.gain_scale.gain_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:18:45,137 - stpipe.step - INFO - Step gain_scale done\n", + "2025-11-12 13:18:45,255 - stpipe.step - INFO - Step gain_scale running with args (,).\n", + "2025-11-12 13:18:45,303 - jwst.gain_scale.gain_scale_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:18:45,313 - jwst.gain_scale.gain_scale_step - INFO - GAINFACT not found in gain reference file\n", + "2025-11-12 13:18:45,314 - jwst.gain_scale.gain_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:18:45,316 - stpipe.step - INFO - Step gain_scale done\n", + "2025-11-12 13:18:45,474 - stpipe.step - INFO - Saved model in ./data/* bet Pic/12A/stage1/jw01294003001_03102_00004_mirifushort_rateints.fits\n", + "2025-11-12 13:18:45,475 - jwst.pipeline.calwebb_detector1 - INFO - ... ending calwebb_detector1\n", + "2025-11-12 13:18:45,475 - jwst.stpipe.core - INFO - Results used CRDS context: jwst_1464.pmap\n", + "2025-11-12 13:18:45,525 - stpipe.step - INFO - Saved model in ./data/* bet Pic/12A/stage1/jw01294003001_03102_00004_mirifushort_rate.fits\n", + "2025-11-12 13:18:45,526 - stpipe.step - INFO - Step Detector1Pipeline done\n", + "2025-11-12 13:18:45,526 - jwst.stpipe.core - INFO - Results used jwst version: 1.20.2\n", + "/Users/mperrin/Dropbox (Personal)/Documents/software/git/breads/breads/jwst_tools/reduction_utils.py:1390: ResourceWarning: unclosed file <_io.BufferedReader name='./data/jw01294003001_03102_00004_mirifushort_uncal.fits'>\n", + " hdu_uncal = fits.open(file)\n", + "2025-11-12 13:18:45,571 - stpipe.step - INFO - PARS-EMICORRSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-emicorrstep_0003.asdf\n", + "2025-11-12 13:18:45,584 - stpipe.step - INFO - PARS-DARKCURRENTSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-darkcurrentstep_0001.asdf\n", + "2025-11-12 13:18:45,595 - stpipe.step - INFO - PARS-JUMPSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-jumpstep_0007.asdf\n", + "2025-11-12 13:18:45,604 - stpipe.pipeline - INFO - PARS-DETECTOR1PIPELINE parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-detector1pipeline_0010.asdf\n", + "2025-11-12 13:18:45,620 - stpipe.step - INFO - Detector1Pipeline instance created.\n", + "2025-11-12 13:18:45,621 - stpipe.step - INFO - GroupScaleStep instance created.\n", + "2025-11-12 13:18:45,621 - stpipe.step - INFO - DQInitStep instance created.\n", + "2025-11-12 13:18:45,622 - stpipe.step - INFO - EmiCorrStep instance created.\n", + "2025-11-12 13:18:45,623 - stpipe.step - INFO - SaturationStep instance created.\n", + "2025-11-12 13:18:45,623 - stpipe.step - INFO - IPCStep instance created.\n", + "2025-11-12 13:18:45,624 - stpipe.step - INFO - SuperBiasStep instance created.\n", + "2025-11-12 13:18:45,625 - stpipe.step - INFO - RefPixStep instance created.\n", + "2025-11-12 13:18:45,625 - stpipe.step - INFO - RscdStep instance created.\n", + "2025-11-12 13:18:45,625 - stpipe.step - INFO - FirstFrameStep instance created.\n", + "2025-11-12 13:18:45,626 - stpipe.step - INFO - LastFrameStep instance created.\n", + "2025-11-12 13:18:45,626 - stpipe.step - INFO - LinearityStep instance created.\n", + "2025-11-12 13:18:45,627 - stpipe.step - INFO - DarkCurrentStep instance created.\n", + "2025-11-12 13:18:45,628 - stpipe.step - INFO - ResetStep instance created.\n", + "2025-11-12 13:18:45,629 - stpipe.step - INFO - PersistenceStep instance created.\n", + "2025-11-12 13:18:45,629 - stpipe.step - INFO - ChargeMigrationStep instance created.\n", + "2025-11-12 13:18:45,631 - stpipe.step - INFO - JumpStep instance created.\n", + "2025-11-12 13:18:45,631 - stpipe.step - INFO - CleanFlickerNoiseStep instance created.\n", + "2025-11-12 13:18:45,632 - stpipe.step - INFO - RampFitStep instance created.\n", + "2025-11-12 13:18:45,632 - stpipe.step - INFO - GainScaleStep instance created.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DEBUG target_name * N Car\n", + "Processing file 1 of 4.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-12 13:18:45,739 - stpipe.step - INFO - Step Detector1Pipeline running with args ('./data/jw01294004001_03102_00001_mirifushort_uncal.fits',).\n", + "2025-11-12 13:18:45,751 - stpipe.step - INFO - Step Detector1Pipeline parameters are:\n", + " pre_hooks: []\n", + " post_hooks: []\n", + " output_file: None\n", + " output_dir: ./data/* N Car/12A/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: False\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: True\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: 1.0\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: 100\n", + " four_group_rejection_threshold: 5.0\n", + " maximum_cores: all\n", + " flag_4_neighbors: True\n", + " max_jump_to_flag_neighbors: 1000.0\n", + " min_jump_to_flag_neighbors: 10.0\n", + " after_jump_flag_dn1: 0.0\n", + " after_jump_flag_time1: 0.0\n", + " after_jump_flag_dn2: 0.0\n", + " after_jump_flag_time2: 0.0\n", + " expand_large_events: False\n", + " min_sat_area: 1.0\n", + " min_jump_area: 5.0\n", + " expand_factor: 2.0\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: True\n", + " max_shower_amplitude: 4.0\n", + " extend_snr_threshold: 1.2\n", + " extend_min_area: 90\n", + " extend_inner_radius: 1.0\n", + " extend_outer_radius: 2.6\n", + " extend_ellipse_expand_ratio: 1.1\n", + " time_masked_after_shower: 15.0\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: all\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", + "2025-11-12 13:18:45,776 - stpipe.pipeline - INFO - Prefetching reference files for dataset: 'jw01294004001_03102_00001_mirifushort_uncal.fits' reftypes = ['dark', 'emicorr', 'gain', 'linearity', 'mask', 'readnoise', 'reset', 'rscd', 'saturation', 'superbias']\n", + "2025-11-12 13:18:45,779 - stpipe.pipeline - INFO - Prefetch for DARK reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_dark_0112.fits'.\n", + "2025-11-12 13:18:45,780 - stpipe.pipeline - INFO - Prefetch for EMICORR reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_emicorr_0003.asdf'.\n", + "2025-11-12 13:18:45,781 - stpipe.pipeline - INFO - Prefetch for GAIN reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits'.\n", + "2025-11-12 13:18:45,782 - stpipe.pipeline - INFO - Prefetch for LINEARITY reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_linearity_0030.fits'.\n", + "2025-11-12 13:18:45,782 - stpipe.pipeline - INFO - Prefetch for MASK reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_mask_0046.fits'.\n", + "2025-11-12 13:18:45,783 - stpipe.pipeline - INFO - Prefetch for READNOISE reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits'.\n", + "2025-11-12 13:18:45,784 - stpipe.pipeline - INFO - Prefetch for RESET reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_reset_0078.fits'.\n", + "2025-11-12 13:18:45,784 - stpipe.pipeline - INFO - Prefetch for RSCD reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_rscd_0018.fits'.\n", + "2025-11-12 13:18:45,785 - stpipe.pipeline - INFO - Prefetch for SATURATION reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_saturation_0033.fits'.\n", + "2025-11-12 13:18:45,785 - stpipe.pipeline - INFO - Prefetch for SUPERBIAS reference file is 'N/A'.\n", + "2025-11-12 13:18:45,785 - jwst.pipeline.calwebb_detector1 - INFO - Starting calwebb_detector1 ...\n", + "2025-11-12 13:18:46,185 - stpipe.step - INFO - Step group_scale running with args (,).\n", + "2025-11-12 13:18:46,295 - jwst.group_scale.group_scale_step - INFO - NFRAMES and FRMDIVSR are equal; correction not needed\n", + "2025-11-12 13:18:46,296 - jwst.group_scale.group_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:18:46,297 - stpipe.step - INFO - Step group_scale done\n", + "2025-11-12 13:18:46,392 - stpipe.step - INFO - Step dq_init running with args (,).\n", + "2025-11-12 13:18:46,399 - jwst.dq_init.dq_init_step - INFO - Using MASK reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_mask_0046.fits\n", + "2025-11-12 13:18:46,521 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:46,667 - stpipe.step - INFO - Step dq_init done\n", + "2025-11-12 13:18:46,760 - stpipe.step - INFO - Step emicorr running with args (,).\n", + "2025-11-12 13:18:46,875 - jwst.emicorr.emicorr_step - INFO - Using CRDS reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_emicorr_0003.asdf\n", + "2025-11-12 13:18:46,891 - jwst.emicorr.emicorr - INFO - Using reference file to get subarray case.\n", + "2025-11-12 13:18:46,891 - jwst.emicorr.emicorr - INFO - With configuration: Subarray=FULL, Read_pattern=FASTR1, Detector=MIRIFUSHORT\n", + "2025-11-12 13:18:46,891 - jwst.emicorr.emicorr - INFO - Will correct data for the following 1 frequencies: \n", + "2025-11-12 13:18:46,892 - jwst.emicorr.emicorr - INFO - ['Hz10']\n", + "2025-11-12 13:18:46,892 - jwst.emicorr.emicorr - INFO - Running EMI fit with algorithm = 'joint'.\n", + "2025-11-12 13:18:53,301 - stpipe.step - INFO - Step emicorr done\n", + "2025-11-12 13:18:53,384 - stpipe.step - INFO - Step saturation running with args (,).\n", + "2025-11-12 13:18:53,466 - jwst.saturation.saturation_step - INFO - Using SATURATION reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_saturation_0033.fits\n", + "2025-11-12 13:18:53,466 - jwst.saturation.saturation_step - INFO - Using SUPERBIAS reference file N/A\n", + "2025-11-12 13:18:53,485 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:53,486 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:53,495 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:53,504 - jwst.saturation.saturation - INFO - Using read_pattern with nframes 1\n", + "2025-11-12 13:18:54,008 - stcal.saturation.saturation - INFO - Detected 101 saturated pixels\n", + "2025-11-12 13:18:54,031 - stcal.saturation.saturation - INFO - Detected 25 A/D floor pixels\n", + "2025-11-12 13:18:54,037 - stpipe.step - INFO - Step saturation done\n", + "2025-11-12 13:18:54,118 - stpipe.step - INFO - Step ipc running with args (,).\n", + "2025-11-12 13:18:54,119 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:18:54,184 - stpipe.step - INFO - Step firstframe running with args (,).\n", + "2025-11-12 13:18:54,289 - jwst.firstframe.firstframe_sub - INFO - Number of usable bright pixels with first group not set to DO_NOT_USE: 0\n", + "2025-11-12 13:18:54,291 - stpipe.step - INFO - Step firstframe done\n", + "2025-11-12 13:18:54,371 - stpipe.step - INFO - Step lastframe running with args (,).\n", + "2025-11-12 13:18:54,467 - stpipe.step - INFO - Step lastframe done\n", + "2025-11-12 13:18:54,546 - stpipe.step - INFO - Step reset running with args (,).\n", + "2025-11-12 13:18:54,640 - jwst.reset.reset_step - INFO - Using RESET reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_reset_0078.fits\n", + "2025-11-12 13:18:54,682 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:54,682 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:54,692 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:54,772 - stpipe.step - INFO - Step reset done\n", + "2025-11-12 13:18:54,857 - stpipe.step - INFO - Step linearity running with args (,).\n", + "2025-11-12 13:18:54,943 - jwst.linearity.linearity_step - INFO - Using Linearity reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_linearity_0030.fits\n", + "2025-11-12 13:18:54,965 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:54,965 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:54,981 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:55,450 - stpipe.step - INFO - Step linearity done\n", + "2025-11-12 13:18:55,535 - stpipe.step - INFO - Step rscd running with args (,).\n", + "2025-11-12 13:18:55,616 - jwst.rscd.rscd_step - INFO - Using RSCD reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_rscd_0018.fits\n", + "2025-11-12 13:18:55,636 - jwst.rscd.rscd_sub - INFO - Number of groups to skip for integrations 2 and higher: 2\n", + "2025-11-12 13:18:55,636 - jwst.rscd.rscd_sub - WARNING - Too few groups to apply RSCD correction\n", + "2025-11-12 13:18:55,637 - jwst.rscd.rscd_sub - WARNING - RSCD step will be skipped\n", + "2025-11-12 13:18:55,638 - stpipe.step - INFO - Step rscd done\n", + "2025-11-12 13:18:55,720 - stpipe.step - INFO - Step dark_current running with args (,).\n", + "2025-11-12 13:18:55,728 - jwst.dark_current.dark_current_step - INFO - Using DARK reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_dark_0112.fits\n", + "2025-11-12 13:18:56,287 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:18:56,303 - jwst.dark_current.dark_current_step - INFO - Using Poisson noise from average dark current 1.0 e-/sec\n", + "2025-11-12 13:18:56,303 - stcal.dark_current.dark_sub - INFO - Science data nints=28, ngroups=5, nframes=1, groupgap=0\n", + "2025-11-12 13:18:56,304 - stcal.dark_current.dark_sub - INFO - Dark data nints=2, ngroups=360, nframes=1, groupgap=0\n", + "2025-11-12 13:18:56,958 - stpipe.step - INFO - Step dark_current done\n", + "2025-11-12 13:18:57,044 - stpipe.step - INFO - Step refpix running with args (,).\n", + "2025-11-12 13:18:57,045 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:18:57,112 - stpipe.step - INFO - Step charge_migration running with args (,).\n", + "2025-11-12 13:18:57,113 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:18:57,193 - stpipe.step - INFO - Step jump running with args (,).\n", + "2025-11-12 13:18:57,300 - jwst.jump.jump_step - INFO - CR rejection threshold = 4 sigma\n", + "2025-11-12 13:18:57,300 - jwst.jump.jump_step - INFO - Maximum cores to use = all\n", + "2025-11-12 13:18:57,302 - jwst.jump.jump_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:18:57,304 - jwst.jump.jump_step - INFO - Using READNOISE reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits\n", + "2025-11-12 13:18:57,406 - stcal.jump.jump - INFO - Executing two-point difference method\n", + "2025-11-12 13:18:57,406 - stcal.jump.jump - INFO - Creating 12 processes for jump detection \n", + "2025-11-12 13:19:03,987 - stcal.jump.jump - INFO - Flagging Showers\n", + "2025-11-12 13:19:13,848 - stcal.jump.jump - INFO - Total showers= 11\n", + "2025-11-12 13:19:13,848 - stcal.jump.jump - INFO - Total elapsed time = 16.442 sec\n", + "2025-11-12 13:19:13,867 - jwst.jump.jump_step - INFO - The execution time in seconds: 16.668711\n", + "2025-11-12 13:19:13,872 - stpipe.step - INFO - Step jump done\n", + "2025-11-12 13:19:13,970 - stpipe.step - INFO - Step clean_flicker_noise running with args (,).\n", + "2025-11-12 13:19:13,971 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:19:14,038 - stpipe.step - INFO - Step ramp_fit running with args (,).\n", + "2025-11-12 13:19:14,167 - jwst.ramp_fitting.ramp_fit_step - INFO - Using READNOISE reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits\n", + "2025-11-12 13:19:14,167 - jwst.ramp_fitting.ramp_fit_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:19:14,186 - jwst.ramp_fitting.ramp_fit_step - INFO - Using algorithm = OLS_C\n", + "2025-11-12 13:19:14,187 - jwst.ramp_fitting.ramp_fit_step - INFO - Using weighting = optimal\n", + "2025-11-12 13:19:14,563 - stcal.ramp_fitting.ols_fit - INFO - Number of multiprocessing slices: 12\n", + "2025-11-12 13:19:14,635 - stcal.ramp_fitting.ols_fit - INFO - Number of leading groups that are flagged as DO_NOT_USE: 1\n", + "2025-11-12 13:19:14,639 - stcal.ramp_fitting.ols_fit - INFO - MIRI dataset has all pixels in the final group flagged as DO_NOT_USE.\n", + "2025-11-12 13:19:14,643 - stcal.ramp_fitting.ols_fit - INFO - Number of processors used for multiprocessing: 12\n", + "2025-11-12 13:19:17,792 - stpipe.step - INFO - Step ramp_fit done\n", + "2025-11-12 13:19:17,883 - stpipe.step - INFO - Step gain_scale running with args (,).\n", + "2025-11-12 13:19:17,897 - jwst.gain_scale.gain_scale_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:19:17,907 - jwst.gain_scale.gain_scale_step - INFO - GAINFACT not found in gain reference file\n", + "2025-11-12 13:19:17,907 - jwst.gain_scale.gain_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:19:17,908 - stpipe.step - INFO - Step gain_scale done\n", + "2025-11-12 13:19:17,977 - stpipe.step - INFO - Step gain_scale running with args (,).\n", + "2025-11-12 13:19:18,066 - jwst.gain_scale.gain_scale_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:19:18,076 - jwst.gain_scale.gain_scale_step - INFO - GAINFACT not found in gain reference file\n", + "2025-11-12 13:19:18,076 - jwst.gain_scale.gain_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:19:18,077 - stpipe.step - INFO - Step gain_scale done\n", + "2025-11-12 13:19:18,340 - stpipe.step - INFO - Saved model in ./data/* N Car/12A/stage1/jw01294004001_03102_00001_mirifushort_rateints.fits\n", + "2025-11-12 13:19:18,340 - jwst.pipeline.calwebb_detector1 - INFO - ... ending calwebb_detector1\n", + "2025-11-12 13:19:18,341 - jwst.stpipe.core - INFO - Results used CRDS context: jwst_1464.pmap\n", + "2025-11-12 13:19:18,391 - stpipe.step - INFO - Saved model in ./data/* N Car/12A/stage1/jw01294004001_03102_00001_mirifushort_rate.fits\n", + "2025-11-12 13:19:18,391 - stpipe.step - INFO - Step Detector1Pipeline done\n", + "2025-11-12 13:19:18,392 - jwst.stpipe.core - INFO - Results used jwst version: 1.20.2\n", + "/Users/mperrin/Dropbox (Personal)/Documents/software/git/breads/breads/jwst_tools/reduction_utils.py:1390: ResourceWarning: unclosed file <_io.BufferedReader name='./data/jw01294004001_03102_00001_mirifushort_uncal.fits'>\n", + " hdu_uncal = fits.open(file)\n", + "2025-11-12 13:19:18,427 - stpipe.step - INFO - PARS-EMICORRSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-emicorrstep_0003.asdf\n", + "2025-11-12 13:19:18,438 - stpipe.step - INFO - PARS-DARKCURRENTSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-darkcurrentstep_0001.asdf\n", + "2025-11-12 13:19:18,445 - stpipe.step - INFO - PARS-JUMPSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-jumpstep_0007.asdf\n", + "2025-11-12 13:19:18,456 - stpipe.pipeline - INFO - PARS-DETECTOR1PIPELINE parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-detector1pipeline_0010.asdf\n", + "2025-11-12 13:19:18,467 - stpipe.step - INFO - Detector1Pipeline instance created.\n", + "2025-11-12 13:19:18,467 - stpipe.step - INFO - GroupScaleStep instance created.\n", + "2025-11-12 13:19:18,468 - stpipe.step - INFO - DQInitStep instance created.\n", + "2025-11-12 13:19:18,468 - stpipe.step - INFO - EmiCorrStep instance created.\n", + "2025-11-12 13:19:18,469 - stpipe.step - INFO - SaturationStep instance created.\n", + "2025-11-12 13:19:18,469 - stpipe.step - INFO - IPCStep instance created.\n", + "2025-11-12 13:19:18,470 - stpipe.step - INFO - SuperBiasStep instance created.\n", + "2025-11-12 13:19:18,470 - stpipe.step - INFO - RefPixStep instance created.\n", + "2025-11-12 13:19:18,471 - stpipe.step - INFO - RscdStep instance created.\n", + "2025-11-12 13:19:18,471 - stpipe.step - INFO - FirstFrameStep instance created.\n", + "2025-11-12 13:19:18,472 - stpipe.step - INFO - LastFrameStep instance created.\n", + "2025-11-12 13:19:18,472 - stpipe.step - INFO - LinearityStep instance created.\n", + "2025-11-12 13:19:18,473 - stpipe.step - INFO - DarkCurrentStep instance created.\n", + "2025-11-12 13:19:18,473 - stpipe.step - INFO - ResetStep instance created.\n", + "2025-11-12 13:19:18,474 - stpipe.step - INFO - PersistenceStep instance created.\n", + "2025-11-12 13:19:18,474 - stpipe.step - INFO - ChargeMigrationStep instance created.\n", + "2025-11-12 13:19:18,475 - stpipe.step - INFO - JumpStep instance created.\n", + "2025-11-12 13:19:18,476 - stpipe.step - INFO - CleanFlickerNoiseStep instance created.\n", + "2025-11-12 13:19:18,476 - stpipe.step - INFO - RampFitStep instance created.\n", + "2025-11-12 13:19:18,477 - stpipe.step - INFO - GainScaleStep instance created.\n", + "2025-11-12 13:19:18,568 - stpipe.step - INFO - Step Detector1Pipeline running with args ('./data/jw01294004001_03102_00002_mirifushort_uncal.fits',).\n", + "2025-11-12 13:19:18,580 - stpipe.step - INFO - Step Detector1Pipeline parameters are:\n", + " pre_hooks: []\n", + " post_hooks: []\n", + " output_file: None\n", + " output_dir: ./data/* N Car/12A/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: False\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: True\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: 1.0\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: 100\n", + " four_group_rejection_threshold: 5.0\n", + " maximum_cores: all\n", + " flag_4_neighbors: True\n", + " max_jump_to_flag_neighbors: 1000.0\n", + " min_jump_to_flag_neighbors: 10.0\n", + " after_jump_flag_dn1: 0.0\n", + " after_jump_flag_time1: 0.0\n", + " after_jump_flag_dn2: 0.0\n", + " after_jump_flag_time2: 0.0\n", + " expand_large_events: False\n", + " min_sat_area: 1.0\n", + " min_jump_area: 5.0\n", + " expand_factor: 2.0\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: True\n", + " max_shower_amplitude: 4.0\n", + " extend_snr_threshold: 1.2\n", + " extend_min_area: 90\n", + " extend_inner_radius: 1.0\n", + " extend_outer_radius: 2.6\n", + " extend_ellipse_expand_ratio: 1.1\n", + " time_masked_after_shower: 15.0\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: all\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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing file 2 of 4.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-12 13:19:18,605 - stpipe.pipeline - INFO - Prefetching reference files for dataset: 'jw01294004001_03102_00002_mirifushort_uncal.fits' reftypes = ['dark', 'emicorr', 'gain', 'linearity', 'mask', 'readnoise', 'reset', 'rscd', 'saturation', 'superbias']\n", + "2025-11-12 13:19:18,607 - stpipe.pipeline - INFO - Prefetch for DARK reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_dark_0112.fits'.\n", + "2025-11-12 13:19:18,608 - stpipe.pipeline - INFO - Prefetch for EMICORR reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_emicorr_0003.asdf'.\n", + "2025-11-12 13:19:18,609 - stpipe.pipeline - INFO - Prefetch for GAIN reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits'.\n", + "2025-11-12 13:19:18,609 - stpipe.pipeline - INFO - Prefetch for LINEARITY reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_linearity_0030.fits'.\n", + "2025-11-12 13:19:18,610 - stpipe.pipeline - INFO - Prefetch for MASK reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_mask_0046.fits'.\n", + "2025-11-12 13:19:18,610 - stpipe.pipeline - INFO - Prefetch for READNOISE reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits'.\n", + "2025-11-12 13:19:18,610 - stpipe.pipeline - INFO - Prefetch for RESET reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_reset_0078.fits'.\n", + "2025-11-12 13:19:18,611 - stpipe.pipeline - INFO - Prefetch for RSCD reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_rscd_0018.fits'.\n", + "2025-11-12 13:19:18,611 - stpipe.pipeline - INFO - Prefetch for SATURATION reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_saturation_0033.fits'.\n", + "2025-11-12 13:19:18,612 - stpipe.pipeline - INFO - Prefetch for SUPERBIAS reference file is 'N/A'.\n", + "2025-11-12 13:19:18,612 - jwst.pipeline.calwebb_detector1 - INFO - Starting calwebb_detector1 ...\n", + "2025-11-12 13:19:18,959 - stpipe.step - INFO - Step group_scale running with args (,).\n", + "2025-11-12 13:19:19,068 - jwst.group_scale.group_scale_step - INFO - NFRAMES and FRMDIVSR are equal; correction not needed\n", + "2025-11-12 13:19:19,069 - jwst.group_scale.group_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:19:19,070 - stpipe.step - INFO - Step group_scale done\n", + "2025-11-12 13:19:19,161 - stpipe.step - INFO - Step dq_init running with args (,).\n", + "2025-11-12 13:19:19,167 - jwst.dq_init.dq_init_step - INFO - Using MASK reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_mask_0046.fits\n", + "2025-11-12 13:19:19,282 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:19:19,407 - stpipe.step - INFO - Step dq_init done\n", + "2025-11-12 13:19:19,492 - stpipe.step - INFO - Step emicorr running with args (,).\n", + "2025-11-12 13:19:19,587 - jwst.emicorr.emicorr_step - INFO - Using CRDS reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_emicorr_0003.asdf\n", + "2025-11-12 13:19:19,600 - jwst.emicorr.emicorr - INFO - Using reference file to get subarray case.\n", + "2025-11-12 13:19:19,600 - jwst.emicorr.emicorr - INFO - With configuration: Subarray=FULL, Read_pattern=FASTR1, Detector=MIRIFUSHORT\n", + "2025-11-12 13:19:19,601 - jwst.emicorr.emicorr - INFO - Will correct data for the following 1 frequencies: \n", + "2025-11-12 13:19:19,601 - jwst.emicorr.emicorr - INFO - ['Hz10']\n", + "2025-11-12 13:19:19,601 - jwst.emicorr.emicorr - INFO - Running EMI fit with algorithm = 'joint'.\n", + "2025-11-12 13:19:25,616 - stpipe.step - INFO - Step emicorr done\n", + "2025-11-12 13:19:25,696 - stpipe.step - INFO - Step saturation running with args (,).\n", + "2025-11-12 13:19:25,781 - jwst.saturation.saturation_step - INFO - Using SATURATION reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_saturation_0033.fits\n", + "2025-11-12 13:19:25,782 - jwst.saturation.saturation_step - INFO - Using SUPERBIAS reference file N/A\n", + "2025-11-12 13:19:25,802 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:19:25,803 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:19:25,818 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:19:25,821 - jwst.saturation.saturation - INFO - Using read_pattern with nframes 1\n", + "2025-11-12 13:19:26,264 - stcal.saturation.saturation - INFO - Detected 96 saturated pixels\n", + "2025-11-12 13:19:26,286 - stcal.saturation.saturation - INFO - Detected 10 A/D floor pixels\n", + "2025-11-12 13:19:26,290 - stpipe.step - INFO - Step saturation done\n", + "2025-11-12 13:19:26,366 - stpipe.step - INFO - Step ipc running with args (,).\n", + "2025-11-12 13:19:26,367 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:19:26,428 - stpipe.step - INFO - Step firstframe running with args (,).\n", + "2025-11-12 13:19:26,554 - jwst.firstframe.firstframe_sub - INFO - Number of usable bright pixels with first group not set to DO_NOT_USE: 0\n", + "2025-11-12 13:19:26,555 - stpipe.step - INFO - Step firstframe done\n", + "2025-11-12 13:19:26,634 - stpipe.step - INFO - Step lastframe running with args (,).\n", + "2025-11-12 13:19:26,724 - stpipe.step - INFO - Step lastframe done\n", + "2025-11-12 13:19:26,799 - stpipe.step - INFO - Step reset running with args (,).\n", + "2025-11-12 13:19:26,888 - jwst.reset.reset_step - INFO - Using RESET reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_reset_0078.fits\n", + "2025-11-12 13:19:26,931 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:19:26,932 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:19:26,948 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:19:27,018 - stpipe.step - INFO - Step reset done\n", + "2025-11-12 13:19:27,095 - stpipe.step - INFO - Step linearity running with args (,).\n", + "2025-11-12 13:19:27,173 - jwst.linearity.linearity_step - INFO - Using Linearity reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_linearity_0030.fits\n", + "2025-11-12 13:19:27,193 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:19:27,193 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:19:27,203 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:19:27,619 - stpipe.step - INFO - Step linearity done\n", + "2025-11-12 13:19:27,697 - stpipe.step - INFO - Step rscd running with args (,).\n", + "2025-11-12 13:19:27,774 - jwst.rscd.rscd_step - INFO - Using RSCD reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_rscd_0018.fits\n", + "2025-11-12 13:19:27,792 - jwst.rscd.rscd_sub - INFO - Number of groups to skip for integrations 2 and higher: 2\n", + "2025-11-12 13:19:27,793 - jwst.rscd.rscd_sub - WARNING - Too few groups to apply RSCD correction\n", + "2025-11-12 13:19:27,793 - jwst.rscd.rscd_sub - WARNING - RSCD step will be skipped\n", + "2025-11-12 13:19:27,794 - stpipe.step - INFO - Step rscd done\n", + "2025-11-12 13:19:27,872 - stpipe.step - INFO - Step dark_current running with args (,).\n", + "2025-11-12 13:19:27,879 - jwst.dark_current.dark_current_step - INFO - Using DARK reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_dark_0112.fits\n", + "2025-11-12 13:19:28,389 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:19:28,403 - jwst.dark_current.dark_current_step - INFO - Using Poisson noise from average dark current 1.0 e-/sec\n", + "2025-11-12 13:19:28,404 - stcal.dark_current.dark_sub - INFO - Science data nints=28, ngroups=5, nframes=1, groupgap=0\n", + "2025-11-12 13:19:28,404 - stcal.dark_current.dark_sub - INFO - Dark data nints=2, ngroups=360, nframes=1, groupgap=0\n", + "2025-11-12 13:19:28,828 - stpipe.step - INFO - Step dark_current done\n", + "2025-11-12 13:19:28,904 - stpipe.step - INFO - Step refpix running with args (,).\n", + "2025-11-12 13:19:28,904 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:19:28,965 - stpipe.step - INFO - Step charge_migration running with args (,).\n", + "2025-11-12 13:19:28,965 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:19:29,027 - stpipe.step - INFO - Step jump running with args (,).\n", + "2025-11-12 13:19:29,115 - jwst.jump.jump_step - INFO - CR rejection threshold = 4 sigma\n", + "2025-11-12 13:19:29,115 - jwst.jump.jump_step - INFO - Maximum cores to use = all\n", + "2025-11-12 13:19:29,117 - jwst.jump.jump_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:19:29,119 - jwst.jump.jump_step - INFO - Using READNOISE reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits\n", + "2025-11-12 13:19:29,206 - stcal.jump.jump - INFO - Executing two-point difference method\n", + "2025-11-12 13:19:29,207 - stcal.jump.jump - INFO - Creating 12 processes for jump detection \n", + "2025-11-12 13:19:35,807 - stcal.jump.jump - INFO - Flagging Showers\n", + "2025-11-12 13:19:45,600 - stcal.jump.jump - INFO - Total showers= 37\n", + "2025-11-12 13:19:45,601 - stcal.jump.jump - INFO - Total elapsed time = 16.3935 sec\n", + "2025-11-12 13:19:45,621 - jwst.jump.jump_step - INFO - The execution time in seconds: 16.588015\n", + "2025-11-12 13:19:45,626 - stpipe.step - INFO - Step jump done\n", + "2025-11-12 13:19:45,711 - stpipe.step - INFO - Step clean_flicker_noise running with args (,).\n", + "2025-11-12 13:19:45,712 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:19:45,775 - stpipe.step - INFO - Step ramp_fit running with args (,).\n", + "2025-11-12 13:19:45,901 - jwst.ramp_fitting.ramp_fit_step - INFO - Using READNOISE reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits\n", + "2025-11-12 13:19:45,901 - jwst.ramp_fitting.ramp_fit_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:19:45,918 - jwst.ramp_fitting.ramp_fit_step - INFO - Using algorithm = OLS_C\n", + "2025-11-12 13:19:45,918 - jwst.ramp_fitting.ramp_fit_step - INFO - Using weighting = optimal\n", + "2025-11-12 13:19:46,266 - stcal.ramp_fitting.ols_fit - INFO - Number of multiprocessing slices: 12\n", + "2025-11-12 13:19:46,336 - stcal.ramp_fitting.ols_fit - INFO - Number of leading groups that are flagged as DO_NOT_USE: 1\n", + "2025-11-12 13:19:46,340 - stcal.ramp_fitting.ols_fit - INFO - MIRI dataset has all pixels in the final group flagged as DO_NOT_USE.\n", + "2025-11-12 13:19:46,344 - stcal.ramp_fitting.ols_fit - INFO - Number of processors used for multiprocessing: 12\n", + "2025-11-12 13:19:49,451 - stpipe.step - INFO - Step ramp_fit done\n", + "2025-11-12 13:19:49,541 - stpipe.step - INFO - Step gain_scale running with args (,).\n", + "2025-11-12 13:19:49,556 - jwst.gain_scale.gain_scale_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:19:49,566 - jwst.gain_scale.gain_scale_step - INFO - GAINFACT not found in gain reference file\n", + "2025-11-12 13:19:49,566 - jwst.gain_scale.gain_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:19:49,567 - stpipe.step - INFO - Step gain_scale done\n", + "2025-11-12 13:19:49,632 - stpipe.step - INFO - Step gain_scale running with args (,).\n", + "2025-11-12 13:19:49,713 - jwst.gain_scale.gain_scale_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:19:49,722 - jwst.gain_scale.gain_scale_step - INFO - GAINFACT not found in gain reference file\n", + "2025-11-12 13:19:49,722 - jwst.gain_scale.gain_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:19:49,724 - stpipe.step - INFO - Step gain_scale done\n", + "2025-11-12 13:19:49,968 - stpipe.step - INFO - Saved model in ./data/* N Car/12A/stage1/jw01294004001_03102_00002_mirifushort_rateints.fits\n", + "2025-11-12 13:19:49,968 - jwst.pipeline.calwebb_detector1 - INFO - ... ending calwebb_detector1\n", + "2025-11-12 13:19:49,969 - jwst.stpipe.core - INFO - Results used CRDS context: jwst_1464.pmap\n", + "2025-11-12 13:19:50,017 - stpipe.step - INFO - Saved model in ./data/* N Car/12A/stage1/jw01294004001_03102_00002_mirifushort_rate.fits\n", + "2025-11-12 13:19:50,018 - stpipe.step - INFO - Step Detector1Pipeline done\n", + "2025-11-12 13:19:50,018 - jwst.stpipe.core - INFO - Results used jwst version: 1.20.2\n", + "/Users/mperrin/Dropbox (Personal)/Documents/software/git/breads/breads/jwst_tools/reduction_utils.py:1390: ResourceWarning: unclosed file <_io.BufferedReader name='./data/jw01294004001_03102_00002_mirifushort_uncal.fits'>\n", + " hdu_uncal = fits.open(file)\n", + "2025-11-12 13:19:50,053 - stpipe.step - INFO - PARS-EMICORRSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-emicorrstep_0003.asdf\n", + "2025-11-12 13:19:50,063 - stpipe.step - INFO - PARS-DARKCURRENTSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-darkcurrentstep_0001.asdf\n", + "2025-11-12 13:19:50,071 - stpipe.step - INFO - PARS-JUMPSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-jumpstep_0007.asdf\n", + "2025-11-12 13:19:50,078 - stpipe.pipeline - INFO - PARS-DETECTOR1PIPELINE parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-detector1pipeline_0010.asdf\n", + "2025-11-12 13:19:50,090 - stpipe.step - INFO - Detector1Pipeline instance created.\n", + "2025-11-12 13:19:50,090 - stpipe.step - INFO - GroupScaleStep instance created.\n", + "2025-11-12 13:19:50,091 - stpipe.step - INFO - DQInitStep instance created.\n", + "2025-11-12 13:19:50,091 - stpipe.step - INFO - EmiCorrStep instance created.\n", + "2025-11-12 13:19:50,092 - stpipe.step - INFO - SaturationStep instance created.\n", + "2025-11-12 13:19:50,092 - stpipe.step - INFO - IPCStep instance created.\n", + "2025-11-12 13:19:50,093 - stpipe.step - INFO - SuperBiasStep instance created.\n", + "2025-11-12 13:19:50,093 - stpipe.step - INFO - RefPixStep instance created.\n", + "2025-11-12 13:19:50,094 - stpipe.step - INFO - RscdStep instance created.\n", + "2025-11-12 13:19:50,094 - stpipe.step - INFO - FirstFrameStep instance created.\n", + "2025-11-12 13:19:50,095 - stpipe.step - INFO - LastFrameStep instance created.\n", + "2025-11-12 13:19:50,095 - stpipe.step - INFO - LinearityStep instance created.\n", + "2025-11-12 13:19:50,096 - stpipe.step - INFO - DarkCurrentStep instance created.\n", + "2025-11-12 13:19:50,096 - stpipe.step - INFO - ResetStep instance created.\n", + "2025-11-12 13:19:50,097 - stpipe.step - INFO - PersistenceStep instance created.\n", + "2025-11-12 13:19:50,097 - stpipe.step - INFO - ChargeMigrationStep instance created.\n", + "2025-11-12 13:19:50,098 - stpipe.step - INFO - JumpStep instance created.\n", + "2025-11-12 13:19:50,098 - stpipe.step - INFO - CleanFlickerNoiseStep instance created.\n", + "2025-11-12 13:19:50,099 - stpipe.step - INFO - RampFitStep instance created.\n", + "2025-11-12 13:19:50,099 - stpipe.step - INFO - GainScaleStep instance created.\n", + "2025-11-12 13:19:50,182 - stpipe.step - INFO - Step Detector1Pipeline running with args ('./data/jw01294004001_03102_00003_mirifushort_uncal.fits',).\n", + "2025-11-12 13:19:50,194 - stpipe.step - INFO - Step Detector1Pipeline parameters are:\n", + " pre_hooks: []\n", + " post_hooks: []\n", + " output_file: None\n", + " output_dir: ./data/* N Car/12A/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: False\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: True\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: 1.0\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: 100\n", + " four_group_rejection_threshold: 5.0\n", + " maximum_cores: all\n", + " flag_4_neighbors: True\n", + " max_jump_to_flag_neighbors: 1000.0\n", + " min_jump_to_flag_neighbors: 10.0\n", + " after_jump_flag_dn1: 0.0\n", + " after_jump_flag_time1: 0.0\n", + " after_jump_flag_dn2: 0.0\n", + " after_jump_flag_time2: 0.0\n", + " expand_large_events: False\n", + " min_sat_area: 1.0\n", + " min_jump_area: 5.0\n", + " expand_factor: 2.0\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: True\n", + " max_shower_amplitude: 4.0\n", + " extend_snr_threshold: 1.2\n", + " extend_min_area: 90\n", + " extend_inner_radius: 1.0\n", + " extend_outer_radius: 2.6\n", + " extend_ellipse_expand_ratio: 1.1\n", + " time_masked_after_shower: 15.0\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: all\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", + "2025-11-12 13:19:50,217 - stpipe.pipeline - INFO - Prefetching reference files for dataset: 'jw01294004001_03102_00003_mirifushort_uncal.fits' reftypes = ['dark', 'emicorr', 'gain', 'linearity', 'mask', 'readnoise', 'reset', 'rscd', 'saturation', 'superbias']\n", + "2025-11-12 13:19:50,219 - stpipe.pipeline - INFO - Prefetch for DARK reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_dark_0112.fits'.\n", + "2025-11-12 13:19:50,219 - stpipe.pipeline - INFO - Prefetch for EMICORR reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_emicorr_0003.asdf'.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing file 3 of 4.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-12 13:19:50,220 - stpipe.pipeline - INFO - Prefetch for GAIN reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits'.\n", + "2025-11-12 13:19:50,220 - stpipe.pipeline - INFO - Prefetch for LINEARITY reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_linearity_0030.fits'.\n", + "2025-11-12 13:19:50,221 - stpipe.pipeline - INFO - Prefetch for MASK reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_mask_0046.fits'.\n", + "2025-11-12 13:19:50,221 - stpipe.pipeline - INFO - Prefetch for READNOISE reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits'.\n", + "2025-11-12 13:19:50,222 - stpipe.pipeline - INFO - Prefetch for RESET reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_reset_0078.fits'.\n", + "2025-11-12 13:19:50,222 - stpipe.pipeline - INFO - Prefetch for RSCD reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_rscd_0018.fits'.\n", + "2025-11-12 13:19:50,222 - stpipe.pipeline - INFO - Prefetch for SATURATION reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_saturation_0033.fits'.\n", + "2025-11-12 13:19:50,223 - stpipe.pipeline - INFO - Prefetch for SUPERBIAS reference file is 'N/A'.\n", + "2025-11-12 13:19:50,223 - jwst.pipeline.calwebb_detector1 - INFO - Starting calwebb_detector1 ...\n", + "2025-11-12 13:19:50,556 - stpipe.step - INFO - Step group_scale running with args (,).\n", + "2025-11-12 13:19:50,654 - jwst.group_scale.group_scale_step - INFO - NFRAMES and FRMDIVSR are equal; correction not needed\n", + "2025-11-12 13:19:50,655 - jwst.group_scale.group_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:19:50,656 - stpipe.step - INFO - Step group_scale done\n", + "2025-11-12 13:19:50,735 - stpipe.step - INFO - Step dq_init running with args (,).\n", + "2025-11-12 13:19:50,741 - jwst.dq_init.dq_init_step - INFO - Using MASK reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_mask_0046.fits\n", + "2025-11-12 13:19:50,851 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:19:50,981 - stpipe.step - INFO - Step dq_init done\n", + "2025-11-12 13:19:51,061 - stpipe.step - INFO - Step emicorr running with args (,).\n", + "2025-11-12 13:19:51,152 - jwst.emicorr.emicorr_step - INFO - Using CRDS reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_emicorr_0003.asdf\n", + "2025-11-12 13:19:51,165 - jwst.emicorr.emicorr - INFO - Using reference file to get subarray case.\n", + "2025-11-12 13:19:51,165 - jwst.emicorr.emicorr - INFO - With configuration: Subarray=FULL, Read_pattern=FASTR1, Detector=MIRIFUSHORT\n", + "2025-11-12 13:19:51,166 - jwst.emicorr.emicorr - INFO - Will correct data for the following 1 frequencies: \n", + "2025-11-12 13:19:51,166 - jwst.emicorr.emicorr - INFO - ['Hz10']\n", + "2025-11-12 13:19:51,166 - jwst.emicorr.emicorr - INFO - Running EMI fit with algorithm = 'joint'.\n", + "2025-11-12 13:19:57,345 - stpipe.step - INFO - Step emicorr done\n", + "2025-11-12 13:19:57,422 - stpipe.step - INFO - Step saturation running with args (,).\n", + "2025-11-12 13:19:57,500 - jwst.saturation.saturation_step - INFO - Using SATURATION reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_saturation_0033.fits\n", + "2025-11-12 13:19:57,501 - jwst.saturation.saturation_step - INFO - Using SUPERBIAS reference file N/A\n", + "2025-11-12 13:19:57,521 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:19:57,522 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:19:57,531 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:19:57,535 - jwst.saturation.saturation - INFO - Using read_pattern with nframes 1\n", + "2025-11-12 13:19:57,994 - stcal.saturation.saturation - INFO - Detected 96 saturated pixels\n", + "2025-11-12 13:19:58,016 - stcal.saturation.saturation - INFO - Detected 16 A/D floor pixels\n", + "2025-11-12 13:19:58,020 - stpipe.step - INFO - Step saturation done\n", + "2025-11-12 13:19:58,099 - stpipe.step - INFO - Step ipc running with args (,).\n", + "2025-11-12 13:19:58,100 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:19:58,163 - stpipe.step - INFO - Step firstframe running with args (,).\n", + "2025-11-12 13:19:58,284 - jwst.firstframe.firstframe_sub - INFO - Number of usable bright pixels with first group not set to DO_NOT_USE: 0\n", + "2025-11-12 13:19:58,285 - stpipe.step - INFO - Step firstframe done\n", + "2025-11-12 13:19:58,364 - stpipe.step - INFO - Step lastframe running with args (,).\n", + "2025-11-12 13:19:58,456 - stpipe.step - INFO - Step lastframe done\n", + "2025-11-12 13:19:58,535 - stpipe.step - INFO - Step reset running with args (,).\n", + "2025-11-12 13:19:58,629 - jwst.reset.reset_step - INFO - Using RESET reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_reset_0078.fits\n", + "2025-11-12 13:19:58,668 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:19:58,669 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:19:58,684 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:19:58,752 - stpipe.step - INFO - Step reset done\n", + "2025-11-12 13:19:58,830 - stpipe.step - INFO - Step linearity running with args (,).\n", + "2025-11-12 13:19:58,914 - jwst.linearity.linearity_step - INFO - Using Linearity reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_linearity_0030.fits\n", + "2025-11-12 13:19:58,934 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:19:58,935 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:19:58,950 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:19:59,345 - stpipe.step - INFO - Step linearity done\n", + "2025-11-12 13:19:59,427 - stpipe.step - INFO - Step rscd running with args (,).\n", + "2025-11-12 13:19:59,503 - jwst.rscd.rscd_step - INFO - Using RSCD reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_rscd_0018.fits\n", + "2025-11-12 13:19:59,522 - jwst.rscd.rscd_sub - INFO - Number of groups to skip for integrations 2 and higher: 2\n", + "2025-11-12 13:19:59,522 - jwst.rscd.rscd_sub - WARNING - Too few groups to apply RSCD correction\n", + "2025-11-12 13:19:59,522 - jwst.rscd.rscd_sub - WARNING - RSCD step will be skipped\n", + "2025-11-12 13:19:59,524 - stpipe.step - INFO - Step rscd done\n", + "2025-11-12 13:19:59,602 - stpipe.step - INFO - Step dark_current running with args (,).\n", + "2025-11-12 13:19:59,609 - jwst.dark_current.dark_current_step - INFO - Using DARK reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_dark_0112.fits\n", + "2025-11-12 13:20:00,110 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:20:00,125 - jwst.dark_current.dark_current_step - INFO - Using Poisson noise from average dark current 1.0 e-/sec\n", + "2025-11-12 13:20:00,125 - stcal.dark_current.dark_sub - INFO - Science data nints=28, ngroups=5, nframes=1, groupgap=0\n", + "2025-11-12 13:20:00,126 - stcal.dark_current.dark_sub - INFO - Dark data nints=2, ngroups=360, nframes=1, groupgap=0\n", + "2025-11-12 13:20:00,542 - stpipe.step - INFO - Step dark_current done\n", + "2025-11-12 13:20:00,618 - stpipe.step - INFO - Step refpix running with args (,).\n", + "2025-11-12 13:20:00,619 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:20:00,682 - stpipe.step - INFO - Step charge_migration running with args (,).\n", + "2025-11-12 13:20:00,683 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:20:00,744 - stpipe.step - INFO - Step jump running with args (,).\n", + "2025-11-12 13:20:00,831 - jwst.jump.jump_step - INFO - CR rejection threshold = 4 sigma\n", + "2025-11-12 13:20:00,831 - jwst.jump.jump_step - INFO - Maximum cores to use = all\n", + "2025-11-12 13:20:00,833 - jwst.jump.jump_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:20:00,834 - jwst.jump.jump_step - INFO - Using READNOISE reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits\n", + "2025-11-12 13:20:00,918 - stcal.jump.jump - INFO - Executing two-point difference method\n", + "2025-11-12 13:20:00,919 - stcal.jump.jump - INFO - Creating 12 processes for jump detection \n", + "2025-11-12 13:20:07,523 - stcal.jump.jump - INFO - Flagging Showers\n", + "2025-11-12 13:20:17,470 - stcal.jump.jump - INFO - Total showers= 15\n", + "2025-11-12 13:20:17,471 - stcal.jump.jump - INFO - Total elapsed time = 16.5524 sec\n", + "2025-11-12 13:20:17,492 - jwst.jump.jump_step - INFO - The execution time in seconds: 16.743326\n", + "2025-11-12 13:20:17,496 - stpipe.step - INFO - Step jump done\n", + "2025-11-12 13:20:17,585 - stpipe.step - INFO - Step clean_flicker_noise running with args (,).\n", + "2025-11-12 13:20:17,586 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:20:17,651 - stpipe.step - INFO - Step ramp_fit running with args (,).\n", + "2025-11-12 13:20:17,785 - jwst.ramp_fitting.ramp_fit_step - INFO - Using READNOISE reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits\n", + "2025-11-12 13:20:17,785 - jwst.ramp_fitting.ramp_fit_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:20:17,805 - jwst.ramp_fitting.ramp_fit_step - INFO - Using algorithm = OLS_C\n", + "2025-11-12 13:20:17,806 - jwst.ramp_fitting.ramp_fit_step - INFO - Using weighting = optimal\n", + "2025-11-12 13:20:18,176 - stcal.ramp_fitting.ols_fit - INFO - Number of multiprocessing slices: 12\n", + "2025-11-12 13:20:18,248 - stcal.ramp_fitting.ols_fit - INFO - Number of leading groups that are flagged as DO_NOT_USE: 1\n", + "2025-11-12 13:20:18,252 - stcal.ramp_fitting.ols_fit - INFO - MIRI dataset has all pixels in the final group flagged as DO_NOT_USE.\n", + "2025-11-12 13:20:18,255 - stcal.ramp_fitting.ols_fit - INFO - Number of processors used for multiprocessing: 12\n", + "2025-11-12 13:20:21,484 - stpipe.step - INFO - Step ramp_fit done\n", + "2025-11-12 13:20:21,586 - stpipe.step - INFO - Step gain_scale running with args (,).\n", + "2025-11-12 13:20:21,602 - jwst.gain_scale.gain_scale_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:20:21,612 - jwst.gain_scale.gain_scale_step - INFO - GAINFACT not found in gain reference file\n", + "2025-11-12 13:20:21,612 - jwst.gain_scale.gain_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:20:21,613 - stpipe.step - INFO - Step gain_scale done\n", + "2025-11-12 13:20:21,683 - stpipe.step - INFO - Step gain_scale running with args (,).\n", + "2025-11-12 13:20:21,760 - jwst.gain_scale.gain_scale_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:20:21,770 - jwst.gain_scale.gain_scale_step - INFO - GAINFACT not found in gain reference file\n", + "2025-11-12 13:20:21,770 - jwst.gain_scale.gain_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:20:21,772 - stpipe.step - INFO - Step gain_scale done\n", + "2025-11-12 13:20:22,024 - stpipe.step - INFO - Saved model in ./data/* N Car/12A/stage1/jw01294004001_03102_00003_mirifushort_rateints.fits\n", + "2025-11-12 13:20:22,024 - jwst.pipeline.calwebb_detector1 - INFO - ... ending calwebb_detector1\n", + "2025-11-12 13:20:22,025 - jwst.stpipe.core - INFO - Results used CRDS context: jwst_1464.pmap\n", + "2025-11-12 13:20:22,078 - stpipe.step - INFO - Saved model in ./data/* N Car/12A/stage1/jw01294004001_03102_00003_mirifushort_rate.fits\n", + "2025-11-12 13:20:22,078 - stpipe.step - INFO - Step Detector1Pipeline done\n", + "2025-11-12 13:20:22,079 - jwst.stpipe.core - INFO - Results used jwst version: 1.20.2\n", + "/Users/mperrin/Dropbox (Personal)/Documents/software/git/breads/breads/jwst_tools/reduction_utils.py:1390: ResourceWarning: unclosed file <_io.BufferedReader name='./data/jw01294004001_03102_00003_mirifushort_uncal.fits'>\n", + " hdu_uncal = fits.open(file)\n", + "2025-11-12 13:20:22,114 - stpipe.step - INFO - PARS-EMICORRSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-emicorrstep_0003.asdf\n", + "2025-11-12 13:20:22,124 - stpipe.step - INFO - PARS-DARKCURRENTSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-darkcurrentstep_0001.asdf\n", + "2025-11-12 13:20:22,134 - stpipe.step - INFO - PARS-JUMPSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-jumpstep_0007.asdf\n", + "2025-11-12 13:20:22,144 - stpipe.pipeline - INFO - PARS-DETECTOR1PIPELINE parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-detector1pipeline_0010.asdf\n", + "2025-11-12 13:20:22,156 - stpipe.step - INFO - Detector1Pipeline instance created.\n", + "2025-11-12 13:20:22,157 - stpipe.step - INFO - GroupScaleStep instance created.\n", + "2025-11-12 13:20:22,157 - stpipe.step - INFO - DQInitStep instance created.\n", + "2025-11-12 13:20:22,158 - stpipe.step - INFO - EmiCorrStep instance created.\n", + "2025-11-12 13:20:22,158 - stpipe.step - INFO - SaturationStep instance created.\n", + "2025-11-12 13:20:22,159 - stpipe.step - INFO - IPCStep instance created.\n", + "2025-11-12 13:20:22,159 - stpipe.step - INFO - SuperBiasStep instance created.\n", + "2025-11-12 13:20:22,160 - stpipe.step - INFO - RefPixStep instance created.\n", + "2025-11-12 13:20:22,160 - stpipe.step - INFO - RscdStep instance created.\n", + "2025-11-12 13:20:22,161 - stpipe.step - INFO - FirstFrameStep instance created.\n", + "2025-11-12 13:20:22,161 - stpipe.step - INFO - LastFrameStep instance created.\n", + "2025-11-12 13:20:22,161 - stpipe.step - INFO - LinearityStep instance created.\n", + "2025-11-12 13:20:22,162 - stpipe.step - INFO - DarkCurrentStep instance created.\n", + "2025-11-12 13:20:22,162 - stpipe.step - INFO - ResetStep instance created.\n", + "2025-11-12 13:20:22,163 - stpipe.step - INFO - PersistenceStep instance created.\n", + "2025-11-12 13:20:22,164 - stpipe.step - INFO - ChargeMigrationStep instance created.\n", + "2025-11-12 13:20:22,165 - stpipe.step - INFO - JumpStep instance created.\n", + "2025-11-12 13:20:22,165 - stpipe.step - INFO - CleanFlickerNoiseStep instance created.\n", + "2025-11-12 13:20:22,166 - stpipe.step - INFO - RampFitStep instance created.\n", + "2025-11-12 13:20:22,167 - stpipe.step - INFO - GainScaleStep instance created.\n", + "2025-11-12 13:20:22,265 - stpipe.step - INFO - Step Detector1Pipeline running with args ('./data/jw01294004001_03102_00004_mirifushort_uncal.fits',).\n", + "2025-11-12 13:20:22,278 - stpipe.step - INFO - Step Detector1Pipeline parameters are:\n", + " pre_hooks: []\n", + " post_hooks: []\n", + " output_file: None\n", + " output_dir: ./data/* N Car/12A/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: False\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: True\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: 1.0\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: 100\n", + " four_group_rejection_threshold: 5.0\n", + " maximum_cores: all\n", + " flag_4_neighbors: True\n", + " max_jump_to_flag_neighbors: 1000.0\n", + " min_jump_to_flag_neighbors: 10.0\n", + " after_jump_flag_dn1: 0.0\n", + " after_jump_flag_time1: 0.0\n", + " after_jump_flag_dn2: 0.0\n", + " after_jump_flag_time2: 0.0\n", + " expand_large_events: False\n", + " min_sat_area: 1.0\n", + " min_jump_area: 5.0\n", + " expand_factor: 2.0\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: True\n", + " max_shower_amplitude: 4.0\n", + " extend_snr_threshold: 1.2\n", + " extend_min_area: 90\n", + " extend_inner_radius: 1.0\n", + " extend_outer_radius: 2.6\n", + " extend_ellipse_expand_ratio: 1.1\n", + " time_masked_after_shower: 15.0\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: all\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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing file 4 of 4.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-12 13:20:22,303 - stpipe.pipeline - INFO - Prefetching reference files for dataset: 'jw01294004001_03102_00004_mirifushort_uncal.fits' reftypes = ['dark', 'emicorr', 'gain', 'linearity', 'mask', 'readnoise', 'reset', 'rscd', 'saturation', 'superbias']\n", + "2025-11-12 13:20:22,306 - stpipe.pipeline - INFO - Prefetch for DARK reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_dark_0112.fits'.\n", + "2025-11-12 13:20:22,307 - stpipe.pipeline - INFO - Prefetch for EMICORR reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_emicorr_0003.asdf'.\n", + "2025-11-12 13:20:22,307 - stpipe.pipeline - INFO - Prefetch for GAIN reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits'.\n", + "2025-11-12 13:20:22,308 - stpipe.pipeline - INFO - Prefetch for LINEARITY reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_linearity_0030.fits'.\n", + "2025-11-12 13:20:22,308 - stpipe.pipeline - INFO - Prefetch for MASK reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_mask_0046.fits'.\n", + "2025-11-12 13:20:22,309 - stpipe.pipeline - INFO - Prefetch for READNOISE reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits'.\n", + "2025-11-12 13:20:22,309 - stpipe.pipeline - INFO - Prefetch for RESET reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_reset_0078.fits'.\n", + "2025-11-12 13:20:22,310 - stpipe.pipeline - INFO - Prefetch for RSCD reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_rscd_0018.fits'.\n", + "2025-11-12 13:20:22,310 - stpipe.pipeline - INFO - Prefetch for SATURATION reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_saturation_0033.fits'.\n", + "2025-11-12 13:20:22,310 - stpipe.pipeline - INFO - Prefetch for SUPERBIAS reference file is 'N/A'.\n", + "2025-11-12 13:20:22,311 - jwst.pipeline.calwebb_detector1 - INFO - Starting calwebb_detector1 ...\n", + "2025-11-12 13:20:22,709 - stpipe.step - INFO - Step group_scale running with args (,).\n", + "2025-11-12 13:20:22,822 - jwst.group_scale.group_scale_step - INFO - NFRAMES and FRMDIVSR are equal; correction not needed\n", + "2025-11-12 13:20:22,823 - jwst.group_scale.group_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:20:22,824 - stpipe.step - INFO - Step group_scale done\n", + "2025-11-12 13:20:22,907 - stpipe.step - INFO - Step dq_init running with args (,).\n", + "2025-11-12 13:20:22,913 - jwst.dq_init.dq_init_step - INFO - Using MASK reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_mask_0046.fits\n", + "2025-11-12 13:20:23,026 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:20:23,178 - stpipe.step - INFO - Step dq_init done\n", + "2025-11-12 13:20:23,261 - stpipe.step - INFO - Step emicorr running with args (,).\n", + "2025-11-12 13:20:23,370 - jwst.emicorr.emicorr_step - INFO - Using CRDS reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_emicorr_0003.asdf\n", + "2025-11-12 13:20:23,388 - jwst.emicorr.emicorr - INFO - Using reference file to get subarray case.\n", + "2025-11-12 13:20:23,388 - jwst.emicorr.emicorr - INFO - With configuration: Subarray=FULL, Read_pattern=FASTR1, Detector=MIRIFUSHORT\n", + "2025-11-12 13:20:23,389 - jwst.emicorr.emicorr - INFO - Will correct data for the following 1 frequencies: \n", + "2025-11-12 13:20:23,389 - jwst.emicorr.emicorr - INFO - ['Hz10']\n", + "2025-11-12 13:20:23,390 - jwst.emicorr.emicorr - INFO - Running EMI fit with algorithm = 'joint'.\n", + "2025-11-12 13:20:29,589 - stpipe.step - INFO - Step emicorr done\n", + "2025-11-12 13:20:29,668 - stpipe.step - INFO - Step saturation running with args (,).\n", + "2025-11-12 13:20:29,745 - jwst.saturation.saturation_step - INFO - Using SATURATION reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_saturation_0033.fits\n", + "2025-11-12 13:20:29,745 - jwst.saturation.saturation_step - INFO - Using SUPERBIAS reference file N/A\n", + "2025-11-12 13:20:29,765 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:20:29,766 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:20:29,778 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:20:29,782 - jwst.saturation.saturation - INFO - Using read_pattern with nframes 1\n", + "2025-11-12 13:20:30,266 - stcal.saturation.saturation - INFO - Detected 97 saturated pixels\n", + "2025-11-12 13:20:30,288 - stcal.saturation.saturation - INFO - Detected 17 A/D floor pixels\n", + "2025-11-12 13:20:30,292 - stpipe.step - INFO - Step saturation done\n", + "2025-11-12 13:20:30,372 - stpipe.step - INFO - Step ipc running with args (,).\n", + "2025-11-12 13:20:30,372 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:20:30,434 - stpipe.step - INFO - Step firstframe running with args (,).\n", + "2025-11-12 13:20:30,552 - jwst.firstframe.firstframe_sub - INFO - Number of usable bright pixels with first group not set to DO_NOT_USE: 1\n", + "2025-11-12 13:20:30,554 - stpipe.step - INFO - Step firstframe done\n", + "2025-11-12 13:20:30,636 - stpipe.step - INFO - Step lastframe running with args (,).\n", + "2025-11-12 13:20:30,731 - stpipe.step - INFO - Step lastframe done\n", + "2025-11-12 13:20:30,813 - stpipe.step - INFO - Step reset running with args (,).\n", + "2025-11-12 13:20:30,904 - jwst.reset.reset_step - INFO - Using RESET reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_reset_0078.fits\n", + "2025-11-12 13:20:30,945 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:20:30,945 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:20:30,961 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:20:31,037 - stpipe.step - INFO - Step reset done\n", + "2025-11-12 13:20:31,123 - stpipe.step - INFO - Step linearity running with args (,).\n", + "2025-11-12 13:20:31,201 - jwst.linearity.linearity_step - INFO - Using Linearity reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_linearity_0030.fits\n", + "2025-11-12 13:20:31,220 - stdatamodels.dynamicdq - WARNING - Keyword RESERVED_4 does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:20:31,221 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:20:31,235 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_RESET does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:20:31,644 - stpipe.step - INFO - Step linearity done\n", + "2025-11-12 13:20:31,722 - stpipe.step - INFO - Step rscd running with args (,).\n", + "2025-11-12 13:20:31,799 - jwst.rscd.rscd_step - INFO - Using RSCD reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_rscd_0018.fits\n", + "2025-11-12 13:20:31,819 - jwst.rscd.rscd_sub - INFO - Number of groups to skip for integrations 2 and higher: 2\n", + "2025-11-12 13:20:31,820 - jwst.rscd.rscd_sub - WARNING - Too few groups to apply RSCD correction\n", + "2025-11-12 13:20:31,820 - jwst.rscd.rscd_sub - WARNING - RSCD step will be skipped\n", + "2025-11-12 13:20:31,821 - stpipe.step - INFO - Step rscd done\n", + "2025-11-12 13:20:31,905 - stpipe.step - INFO - Step dark_current running with args (,).\n", + "2025-11-12 13:20:31,911 - jwst.dark_current.dark_current_step - INFO - Using DARK reference file /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_dark_0112.fits\n", + "2025-11-12 13:20:32,407 - stdatamodels.dynamicdq - WARNING - Keyword UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:20:32,421 - jwst.dark_current.dark_current_step - INFO - Using Poisson noise from average dark current 1.0 e-/sec\n", + "2025-11-12 13:20:32,422 - stcal.dark_current.dark_sub - INFO - Science data nints=28, ngroups=5, nframes=1, groupgap=0\n", + "2025-11-12 13:20:32,422 - stcal.dark_current.dark_sub - INFO - Dark data nints=2, ngroups=360, nframes=1, groupgap=0\n", + "2025-11-12 13:20:32,917 - stpipe.step - INFO - Step dark_current done\n", + "2025-11-12 13:20:32,995 - stpipe.step - INFO - Step refpix running with args (,).\n", + "2025-11-12 13:20:32,996 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:20:33,058 - stpipe.step - INFO - Step charge_migration running with args (,).\n", + "2025-11-12 13:20:33,059 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:20:33,123 - stpipe.step - INFO - Step jump running with args (,).\n", + "2025-11-12 13:20:33,223 - jwst.jump.jump_step - INFO - CR rejection threshold = 4 sigma\n", + "2025-11-12 13:20:33,224 - jwst.jump.jump_step - INFO - Maximum cores to use = all\n", + "2025-11-12 13:20:33,225 - jwst.jump.jump_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:20:33,227 - jwst.jump.jump_step - INFO - Using READNOISE reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits\n", + "2025-11-12 13:20:33,316 - stcal.jump.jump - INFO - Executing two-point difference method\n", + "2025-11-12 13:20:33,317 - stcal.jump.jump - INFO - Creating 12 processes for jump detection \n", + "2025-11-12 13:20:39,917 - stcal.jump.jump - INFO - Flagging Showers\n", + "2025-11-12 13:20:49,989 - stcal.jump.jump - INFO - Total showers= 5\n", + "2025-11-12 13:20:49,990 - stcal.jump.jump - INFO - Total elapsed time = 16.6734 sec\n", + "2025-11-12 13:20:50,011 - jwst.jump.jump_step - INFO - The execution time in seconds: 16.883176\n", + "2025-11-12 13:20:50,017 - stpipe.step - INFO - Step jump done\n", + "2025-11-12 13:20:50,106 - stpipe.step - INFO - Step clean_flicker_noise running with args (,).\n", + "2025-11-12 13:20:50,106 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:20:50,170 - stpipe.step - INFO - Step ramp_fit running with args (,).\n", + "2025-11-12 13:20:50,299 - jwst.ramp_fitting.ramp_fit_step - INFO - Using READNOISE reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_readnoise_0087.fits\n", + "2025-11-12 13:20:50,299 - jwst.ramp_fitting.ramp_fit_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:20:50,318 - jwst.ramp_fitting.ramp_fit_step - INFO - Using algorithm = OLS_C\n", + "2025-11-12 13:20:50,318 - jwst.ramp_fitting.ramp_fit_step - INFO - Using weighting = optimal\n", + "2025-11-12 13:20:50,669 - stcal.ramp_fitting.ols_fit - INFO - Number of multiprocessing slices: 12\n", + "2025-11-12 13:20:50,741 - stcal.ramp_fitting.ols_fit - INFO - Number of leading groups that are flagged as DO_NOT_USE: 1\n", + "2025-11-12 13:20:50,745 - stcal.ramp_fitting.ols_fit - INFO - MIRI dataset has all pixels in the final group flagged as DO_NOT_USE.\n", + "2025-11-12 13:20:50,749 - stcal.ramp_fitting.ols_fit - INFO - Number of processors used for multiprocessing: 12\n", + "2025-11-12 13:20:54,120 - stpipe.step - INFO - Step ramp_fit done\n", + "2025-11-12 13:20:54,208 - stpipe.step - INFO - Step gain_scale running with args (,).\n", + "2025-11-12 13:20:54,223 - jwst.gain_scale.gain_scale_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:20:54,234 - jwst.gain_scale.gain_scale_step - INFO - GAINFACT not found in gain reference file\n", + "2025-11-12 13:20:54,234 - jwst.gain_scale.gain_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:20:54,235 - stpipe.step - INFO - Step gain_scale done\n", + "2025-11-12 13:20:54,303 - stpipe.step - INFO - Step gain_scale running with args (,).\n", + "2025-11-12 13:20:54,374 - jwst.gain_scale.gain_scale_step - INFO - Using GAIN reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_gain_0054.fits\n", + "2025-11-12 13:20:54,384 - jwst.gain_scale.gain_scale_step - INFO - GAINFACT not found in gain reference file\n", + "2025-11-12 13:20:54,384 - jwst.gain_scale.gain_scale_step - INFO - Step will be skipped\n", + "2025-11-12 13:20:54,386 - stpipe.step - INFO - Step gain_scale done\n", + "2025-11-12 13:20:54,654 - stpipe.step - INFO - Saved model in ./data/* N Car/12A/stage1/jw01294004001_03102_00004_mirifushort_rateints.fits\n", + "2025-11-12 13:20:54,654 - jwst.pipeline.calwebb_detector1 - INFO - ... ending calwebb_detector1\n", + "2025-11-12 13:20:54,655 - jwst.stpipe.core - INFO - Results used CRDS context: jwst_1464.pmap\n", + "2025-11-12 13:20:54,711 - stpipe.step - INFO - Saved model in ./data/* N Car/12A/stage1/jw01294004001_03102_00004_mirifushort_rate.fits\n", + "2025-11-12 13:20:54,712 - stpipe.step - INFO - Step Detector1Pipeline done\n", + "2025-11-12 13:20:54,712 - jwst.stpipe.core - INFO - Results used jwst version: 1.20.2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total Runtime: 208.7108 seconds\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/wj/p62fp78j0z1b4xyr2yql1prw00042w/T/ipykernel_22998/3892037653.py:1: ResourceWarning: unclosed file <_io.BufferedReader name='./data/jw01294004001_03102_00004_mirifushort_uncal.fits'>\n", + " rate_files, targnames = run_stage1_miri(data_dir,\n" + ] + } + ], + "source": [ + "rate_files, targnames = run_stage1_miri(data_dir, \n", + " overwrite=False, # If output files already exist, no need to re-compute them.\n", + " maximum_cores=\"all\", # This is passed to pipeline's jump and ramp_fit steps. \n", + " skip_dark=False, # Sometimes master background sub is preferable to master dark sub, for some datasets. \n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "57909859-d028-48c0-b0d0-98f961d19d59", + "metadata": {}, + "source": [ + "The output files are going to be in subdirectories organized by target name. For example files like this: " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "da24c74e-b456-46c6-9dd6-2badc9b34d94", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['./data/* bet Pic/12A/stage1/jw01294003001_03102_00002_mirifushort_rate.fits',\n", + " './data/* bet Pic/12A/stage1/jw01294003001_03102_00002_mirifushort_rateints.fits',\n", + " './data/* bet Pic/12A/stage1/jw01294003001_03102_00004_mirifushort_rate.fits',\n", + " './data/* bet Pic/12A/stage1/jw01294003001_03102_00003_mirifushort_rateints.fits',\n", + " './data/* bet Pic/12A/stage1/jw01294003001_03102_00003_mirifushort_rate.fits',\n", + " './data/* bet Pic/12A/stage1/jw01294003001_03102_00001_mirifushort_rate.fits',\n", + " './data/* bet Pic/12A/stage1/jw01294003001_03102_00004_mirifushort_rateints.fits',\n", + " './data/* bet Pic/12A/stage1/jw01294003001_03102_00001_mirifushort_rateints.fits']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "glob.glob(os.path.join(data_dir, science_target_name, list_bands[0], \"stage1\", \"*.fits\"))" + ] + }, + { + "cell_type": "markdown", + "id": "4ced934a-d32e-4677-8451-ec7f818f5b14", + "metadata": {}, + "source": [ + "### Handle the MIRI MRS fringing\n", + "\n", + "These are extra steps, custom to breads, for dealing with fringing.\n", + "\n", + "The function `run_miri_flat_running_mean` wil take the rate files of the reference star target and compute a running mean column-wise to estimate the continuum in between the fringes. Then the column is divided by this continuum to get only the fringes. These flats are stored in the miri_flat folder.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c1ea9476-d465-47df-a4bd-346053528879", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing flat for band 12A\n", + "[DEBUG] Writing flat image to ./miri_fringe_flats/12A\n", + "Computing running mean flat for jw01294004001_03102_00002_mirifushort_rate.fits\n", + "==> Estimated fringe flat written to ./miri_fringe_flats/12A/jw01294004001_03102_00002_mirifushort_flat.fits\n", + "Computing running mean flat for jw01294004001_03102_00004_mirifushort_rate.fits\n", + "==> Estimated fringe flat written to ./miri_fringe_flats/12A/jw01294004001_03102_00004_mirifushort_flat.fits\n", + "Computing running mean flat for jw01294004001_03102_00003_mirifushort_rate.fits\n", + "==> Estimated fringe flat written to ./miri_fringe_flats/12A/jw01294004001_03102_00003_mirifushort_flat.fits\n", + "Computing running mean flat for jw01294004001_03102_00001_mirifushort_rate.fits\n", + "==> Estimated fringe flat written to ./miri_fringe_flats/12A/jw01294004001_03102_00001_mirifushort_flat.fits\n" + ] + } + ], + "source": [ + "# Here we pass in the target name for the second target, which in this case is the reference star. \n", + "\n", + "run_miri_flat_running_mean(data_dir, \n", + " flat_reference_target_name, \n", + " output_dir=None, \n", + " list_bands=list_bands, \n", + " overwrite=True # If output files already exist, no need to re-compute them.\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4343a7c6-5d55-48c1-a207-c0fe981ffe69", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "48867.785647166\n", + "Searching in ./data/* bet Pic/12A/stage1 for files matching jw*_rate.fits\n", + "\tFound 4 input files to process\n", + "\tjw01294003001_03102_00001_mirifushort_rate.fits\n", + "\tjw01294003001_03102_00002_mirifushort_rate.fits\n", + "\tjw01294003001_03102_00003_mirifushort_rate.fits\n", + "\tjw01294003001_03102_00004_mirifushort_rate.fits\n", + "0 ./data/* bet Pic/12A/stage1/jw01294003001_03102_00001_mirifushort_rate.fits\n", + "Searching fringes flat files in: ./miri_fringe_flats\n", + "Band: SHORT\n", + "Brightest column for Channel CH2: 578\n", + "jw01294004001_03102_00001_mirifushort_flat.fits 11.83430662363583\n", + "Flat selected: jw01294004001_03102_00001_mirifushort_flat.fits\n", + "==> Plot saved to ./fig_fringes_jw01294003001_03102_00001_mirifushort_rate.png\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mperrin/Dropbox (Personal)/Documents/software/git/breads/breads/jwst_tools/reduction_utils.py:1530: ResourceWarning: unclosed file <_io.BufferedReader name='./data/* bet Pic/12A/stage1/jw01294003001_03102_00001_mirifushort_rate.fits'>\n", + " best_flat, flat_name, std_min = best_flat_selection(rate_file, flat_path_rate, channel,\n", + "/Users/mperrin/Dropbox (Personal)/Documents/software/git/breads/breads/jwst_tools/reduction_utils.py:1530: ResourceWarning: unclosed file <_io.BufferedReader name='./miri_fringe_flats/12A/jw01294004001_03102_00001_mirifushort_flat.fits'>\n", + " best_flat, flat_name, std_min = best_flat_selection(rate_file, flat_path_rate, channel,\n", + "/Users/mperrin/Dropbox (Personal)/Documents/software/git/breads/breads/jwst_tools/reduction_utils.py:1538: RuntimeWarning: invalid value encountered in divide\n", + " hdu_copy['ERR'].data /= best_flat\n", + "WARNING: VerifyWarning: Keyword name 'FLAT_STD_MIN' is greater than 8 characters or contains characters not allowed by the FITS standard; a HIERARCH card will be created. [astropy.io.fits.card]\n", + "WARNING: nan_treatment='interpolate', however, NaN values detected post convolution. A contiguous region of NaN values, larger than the kernel size, are present in the input array. Increase the kernel size to avoid this. [astropy.convolution.convolve]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==> Wrote fringe-corrected file to ./data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00001_mirifushort_rate.fits\n", + "1 ./data/* bet Pic/12A/stage1/jw01294003001_03102_00002_mirifushort_rate.fits\n", + "Searching fringes flat files in: ./miri_fringe_flats\n", + "Band: SHORT\n", + "Brightest column for Channel CH2: 826\n", + "jw01294004001_03102_00002_mirifushort_flat.fits 6.021316704790502\n", + "Flat selected: jw01294004001_03102_00002_mirifushort_flat.fits\n", + "==> Plot saved to ./fig_fringes_jw01294003001_03102_00002_mirifushort_rate.png\n", + "==> Wrote fringe-corrected file to ./data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00002_mirifushort_rate.fits\n", + "2 ./data/* bet Pic/12A/stage1/jw01294003001_03102_00003_mirifushort_rate.fits\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mperrin/Dropbox (Personal)/Documents/software/git/breads/breads/jwst_tools/reduction_utils.py:1530: ResourceWarning: unclosed file <_io.BufferedReader name='./data/* bet Pic/12A/stage1/jw01294003001_03102_00002_mirifushort_rate.fits'>\n", + " best_flat, flat_name, std_min = best_flat_selection(rate_file, flat_path_rate, channel,\n", + "/Users/mperrin/Dropbox (Personal)/Documents/software/git/breads/breads/jwst_tools/reduction_utils.py:1530: ResourceWarning: unclosed file <_io.BufferedReader name='./miri_fringe_flats/12A/jw01294004001_03102_00002_mirifushort_flat.fits'>\n", + " best_flat, flat_name, std_min = best_flat_selection(rate_file, flat_path_rate, channel,\n", + "/Users/mperrin/Dropbox (Personal)/Documents/software/git/breads/breads/jwst_tools/reduction_utils.py:1530: ResourceWarning: unclosed file <_io.BufferedReader name='./data/* bet Pic/12A/stage1/jw01294003001_03102_00003_mirifushort_rate.fits'>\n", + " best_flat, flat_name, std_min = best_flat_selection(rate_file, flat_path_rate, channel,\n", + "/Users/mperrin/Dropbox (Personal)/Documents/software/git/breads/breads/jwst_tools/reduction_utils.py:1530: ResourceWarning: unclosed file <_io.BufferedReader name='./miri_fringe_flats/12A/jw01294004001_03102_00003_mirifushort_flat.fits'>\n", + " best_flat, flat_name, std_min = best_flat_selection(rate_file, flat_path_rate, channel,\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Searching fringes flat files in: ./miri_fringe_flats\n", + "Band: SHORT\n", + "Brightest column for Channel CH2: 994\n", + "jw01294004001_03102_00003_mirifushort_flat.fits 11.753547556496155\n", + "Flat selected: jw01294004001_03102_00003_mirifushort_flat.fits\n", + "==> Plot saved to ./fig_fringes_jw01294003001_03102_00003_mirifushort_rate.png\n", + "==> Wrote fringe-corrected file to ./data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00003_mirifushort_rate.fits\n", + "3 ./data/* bet Pic/12A/stage1/jw01294003001_03102_00004_mirifushort_rate.fits\n", + "Searching fringes flat files in: ./miri_fringe_flats\n", + "Band: SHORT\n", + "Brightest column for Channel CH2: 825\n", + "jw01294004001_03102_00004_mirifushort_flat.fits 8.09204589565036\n", + "Flat selected: jw01294004001_03102_00004_mirifushort_flat.fits\n", + "==> Plot saved to ./fig_fringes_jw01294003001_03102_00004_mirifushort_rate.png\n", + "==> Wrote fringe-corrected file to ./data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00004_mirifushort_rate.fits\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mperrin/Dropbox (Personal)/Documents/software/git/breads/breads/jwst_tools/reduction_utils.py:1530: ResourceWarning: unclosed file <_io.BufferedReader name='./data/* bet Pic/12A/stage1/jw01294003001_03102_00004_mirifushort_rate.fits'>\n", + " best_flat, flat_name, std_min = best_flat_selection(rate_file, flat_path_rate, channel,\n", + "/Users/mperrin/Dropbox (Personal)/Documents/software/git/breads/breads/jwst_tools/reduction_utils.py:1530: ResourceWarning: unclosed file <_io.BufferedReader name='./miri_fringe_flats/12A/jw01294004001_03102_00004_mirifushort_flat.fits'>\n", + " best_flat, flat_name, std_min = best_flat_selection(rate_file, flat_path_rate, channel,\n" + ] + } + ], + "source": [ + "flat_fringing_stage1(data_dir, science_target_name, list_bands=list_bands, flat_path=None, flat_extended=True, overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "id": "f63efe1a-fb23-463e-8d75-442871fcd460", + "metadata": {}, + "source": [ + "The fringe flat correction step makes some plots which you can optionally look at. These give a small view of how well the fringing pattern matches between science data and calibrator, for a small spectral region. This is mostly useful if you are trying to use fringe calibrator data from a different program or another time, and want to see how well it is working. If there are multiple fringe flats available in the directory, then the software will automatically select the one that best matches." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "fccacb64-60e0-4335-a7ea-7c7ea35a7a0e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=22998) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n", + " pid, fd = os.forkpty()\n" + ] + } + ], + "source": [ + "!open fig*png" + ] + }, + { + "cell_type": "markdown", + "id": "8f3b5789-8fad-4052-8d2a-fb643e1c07ae", + "metadata": {}, + "source": [ + "## Stage 2 reductions\n", + "\n", + "This outputs `cal.fits` files for the selected MRS sub-bands, written into the output subdirectories organized by [targetname]/[MRS band]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d337efce-d547-4750-9c34-16184fb2e47b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run stage 2\n", + "48868.88941425\n", + "Processing the custom flatted rate files in ./data/* bet Pic/12A/stage1_flat for stage 2.\n", + "Searching in ./data/* bet Pic/12A/stage1_flat for files matching jw*_rate.fits\n", + "\tFound 4 input files to process\n", + "\tjw01294003001_03102_00001_mirifushort_rate.fits\n", + "\tjw01294003001_03102_00002_mirifushort_rate.fits\n", + "\tjw01294003001_03102_00003_mirifushort_rate.fits\n", + "\tjw01294003001_03102_00004_mirifushort_rate.fits\n", + "0 ./data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00001_mirifushort_rate.fits\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-12 13:20:57,732 - stpipe.step - INFO - PARS-RESAMPLESPECSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-resamplespecstep_0001.asdf\n", + "2025-11-12 13:20:57,739 - stpipe.step - INFO - PARS-EXTRACT1DSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-extract1dstep_0001.asdf\n", + "2025-11-12 13:20:57,749 - stpipe.step - INFO - PARS-RESAMPLESPECSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-resamplespecstep_0001.asdf\n", + "2025-11-12 13:20:57,757 - stpipe.step - INFO - PARS-EXTRACT1DSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-extract1dstep_0001.asdf\n", + "2025-11-12 13:20:57,764 - stpipe.pipeline - INFO - PARS-SPEC2PIPELINE parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-spec2pipeline_0005.asdf\n", + "2025-11-12 13:20:57,779 - stpipe.step - INFO - Spec2Pipeline instance created.\n", + "2025-11-12 13:20:57,780 - stpipe.step - INFO - AssignWcsStep instance created.\n", + "2025-11-12 13:20:57,781 - stpipe.step - INFO - BadpixSelfcalStep instance created.\n", + "2025-11-12 13:20:57,781 - stpipe.step - INFO - MSAFlagOpenStep instance created.\n", + "2025-11-12 13:20:57,782 - stpipe.step - INFO - NSCleanStep instance created.\n", + "2025-11-12 13:20:57,783 - stpipe.step - INFO - BackgroundStep instance created.\n", + "2025-11-12 13:20:57,783 - stpipe.step - INFO - ImprintStep instance created.\n", + "2025-11-12 13:20:57,784 - stpipe.step - INFO - Extract2dStep instance created.\n", + "2025-11-12 13:20:57,787 - stpipe.step - INFO - MasterBackgroundMosStep instance created.\n", + "2025-11-12 13:20:57,788 - stpipe.step - INFO - FlatFieldStep instance created.\n", + "2025-11-12 13:20:57,789 - stpipe.step - INFO - PathLossStep instance created.\n", + "2025-11-12 13:20:57,789 - stpipe.step - INFO - BarShadowStep instance created.\n", + "2025-11-12 13:20:57,790 - stpipe.step - INFO - PhotomStep instance created.\n", + "2025-11-12 13:20:57,790 - stpipe.step - INFO - PixelReplaceStep instance created.\n", + "2025-11-12 13:20:57,791 - stpipe.step - INFO - ResampleSpecStep instance created.\n", + "2025-11-12 13:20:57,792 - stpipe.step - INFO - Extract1dStep instance created.\n", + "2025-11-12 13:20:57,793 - stpipe.step - INFO - WavecorrStep instance created.\n", + "2025-11-12 13:20:57,793 - stpipe.step - INFO - FlatFieldStep instance created.\n", + "2025-11-12 13:20:57,794 - stpipe.step - INFO - SourceTypeStep instance created.\n", + "2025-11-12 13:20:57,794 - stpipe.step - INFO - StraylightStep instance created.\n", + "2025-11-12 13:20:57,795 - stpipe.step - INFO - FringeStep instance created.\n", + "2025-11-12 13:20:57,796 - stpipe.step - INFO - ResidualFringeStep instance created.\n", + "2025-11-12 13:20:57,796 - stpipe.step - INFO - PathLossStep instance created.\n", + "2025-11-12 13:20:57,797 - stpipe.step - INFO - BarShadowStep instance created.\n", + "2025-11-12 13:20:57,797 - stpipe.step - INFO - WfssContamStep instance created.\n", + "2025-11-12 13:20:57,798 - stpipe.step - INFO - PhotomStep instance created.\n", + "2025-11-12 13:20:57,799 - stpipe.step - INFO - PixelReplaceStep instance created.\n", + "2025-11-12 13:20:57,799 - stpipe.step - INFO - ResampleSpecStep instance created.\n", + "2025-11-12 13:20:57,800 - stpipe.step - INFO - CubeBuildStep instance created.\n", + "2025-11-12 13:20:57,801 - stpipe.step - INFO - Extract1dStep instance created.\n", + "2025-11-12 13:20:57,889 - stpipe.step - INFO - Step Spec2Pipeline running with args ('./data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00001_mirifushort_rate.fits',).\n", + "2025-11-12 13:20:57,909 - stpipe.step - INFO - Step Spec2Pipeline parameters are:\n", + " pre_hooks: []\n", + " post_hooks: []\n", + " output_file: None\n", + " output_dir: ./data/* bet Pic/12A/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: True\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: True\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: True\n", + " bkg_sigma_clip: 3.0\n", + " ifu_rfcorr: True\n", + " ifu_set_srctype: None\n", + " ifu_rscale: None\n", + " ifu_covar_scale: 1.8\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: True\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: True\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: True\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: True\n", + " bkg_sigma_clip: 3.0\n", + " ifu_rfcorr: True\n", + " ifu_set_srctype: None\n", + " ifu_rscale: None\n", + " ifu_covar_scale: 1.8\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", + "2025-11-12 13:20:57,936 - stpipe.pipeline - INFO - Prefetching reference files for dataset: 'jw01294003001_03102_00001_mirifushort_rate.fits' reftypes = ['area', 'barshadow', 'camera', 'collimator', 'disperser', 'distortion', 'filteroffset', 'fore', 'fpa', 'ifufore', 'ifupost', 'ifuslicer', 'msa', 'msaoper', 'ote', 'pathloss', 'photom', 'regions', 'specwcs', 'wavecorr', 'wavelengthrange']\n", + "2025-11-12 13:20:57,939 - stpipe.pipeline - INFO - Prefetch for AREA reference file is 'N/A'.\n", + "2025-11-12 13:20:57,939 - stpipe.pipeline - INFO - Prefetch for BARSHADOW reference file is 'N/A'.\n", + "2025-11-12 13:20:57,939 - stpipe.pipeline - INFO - Prefetch for CAMERA reference file is 'N/A'.\n", + "2025-11-12 13:20:57,940 - stpipe.pipeline - INFO - Prefetch for COLLIMATOR reference file is 'N/A'.\n", + "2025-11-12 13:20:57,940 - stpipe.pipeline - INFO - Prefetch for DISPERSER reference file is 'N/A'.\n", + "2025-11-12 13:20:57,940 - stpipe.pipeline - INFO - Prefetch for DISTORTION reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_distortion_0140.asdf'.\n", + "2025-11-12 13:20:57,941 - stpipe.pipeline - INFO - Prefetch for FILTEROFFSET reference file is 'N/A'.\n", + "2025-11-12 13:20:57,941 - stpipe.pipeline - INFO - Prefetch for FORE reference file is 'N/A'.\n", + "2025-11-12 13:20:57,941 - stpipe.pipeline - INFO - Prefetch for FPA reference file is 'N/A'.\n", + "2025-11-12 13:20:57,942 - stpipe.pipeline - INFO - Prefetch for IFUFORE reference file is 'N/A'.\n", + "2025-11-12 13:20:57,942 - stpipe.pipeline - INFO - Prefetch for IFUPOST reference file is 'N/A'.\n", + "2025-11-12 13:20:57,942 - stpipe.pipeline - INFO - Prefetch for IFUSLICER reference file is 'N/A'.\n", + "2025-11-12 13:20:57,943 - stpipe.pipeline - INFO - Prefetch for MSA reference file is 'N/A'.\n", + "2025-11-12 13:20:57,943 - stpipe.pipeline - INFO - Prefetch for MSAOPER reference file is 'N/A'.\n", + "2025-11-12 13:20:57,943 - stpipe.pipeline - INFO - Prefetch for OTE reference file is 'N/A'.\n", + "2025-11-12 13:20:57,944 - stpipe.pipeline - INFO - Prefetch for PATHLOSS reference file is 'N/A'.\n", + "2025-11-12 13:20:57,944 - stpipe.pipeline - INFO - Prefetch for PHOTOM reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_photom_0224.fits'.\n", + "2025-11-12 13:20:57,944 - stpipe.pipeline - INFO - Prefetch for REGIONS reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_regions_0142.asdf'.\n", + "2025-11-12 13:20:57,945 - stpipe.pipeline - INFO - Prefetch for SPECWCS reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_specwcs_0139.asdf'.\n", + "2025-11-12 13:20:57,945 - stpipe.pipeline - INFO - Prefetch for WAVECORR reference file is 'N/A'.\n", + "2025-11-12 13:20:57,946 - stpipe.pipeline - INFO - Prefetch for WAVELENGTHRANGE reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_wavelengthrange_0011.asdf'.\n", + "2025-11-12 13:20:57,946 - jwst.pipeline.calwebb_spec2 - INFO - Starting calwebb_spec2 ...\n", + "2025-11-12 13:20:57,947 - jwst.pipeline.calwebb_spec2 - INFO - Processing product data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00001_mirifushort\n", + "2025-11-12 13:20:57,948 - jwst.pipeline.calwebb_spec2 - INFO - Working on input ./data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00001_mirifushort_rate.fits ...\n", + "2025-11-12 13:20:58,005 - py.warnings - WARNING - /Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stdatamodels/fits_support.py:699: ValidationWarning: While validating meta.cal_step.flat_field the following error occurred:\n", + "'jw01294004001_03102_00001_mirifushort_flat.fits' is not one of ['SKIPPED', 'COMPLETE']\n", + "\n", + "Failed validating 'enum' in schema:\n", + " {'blend_table': True,\n", + " 'enum': ['SKIPPED', 'COMPLETE'],\n", + " 'fits_keyword': 'S_FLAT',\n", + " 'title': 'Flat Field Correction',\n", + " 'type': 'string'}\n", + "\n", + "On instance:\n", + " 'jw01294004001_03102_00001_mirifushort_flat.fits'\n", + " if validate.value_change(path, result, schema, context):\n", + "\n", + "2025-11-12 13:20:58,091 - stpipe.step - INFO - Step assign_wcs running with args (,).\n", + "2025-11-12 13:20:58,565 - jwst.assign_wcs.miri - INFO - Applied Barycentric velocity correction : 0.9999872600819685\n", + "2025-11-12 13:20:59,265 - jwst.assign_wcs.miri - INFO - Created a MIRI mir_mrs pipeline with references {'distortion': '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_distortion_0140.asdf', 'filteroffset': None, 'specwcs': '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_specwcs_0139.asdf', 'regions': '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_regions_0142.asdf', 'wavelengthrange': '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_wavelengthrange_0011.asdf', 'camera': None, 'collimator': None, 'disperser': None, 'fore': None, 'fpa': None, 'msa': None, 'ote': None, 'ifupost': None, 'ifufore': None, 'ifuslicer': None}\n", + "2025-11-12 13:20:59,818 - stcal.alignment.util - INFO - Update S_REGION to POLYGON ICRS 86.820456118 -51.066908719 86.823033018 -51.066908719 86.823033018 -51.065250481 86.820456118 -51.065250481\n", + "2025-11-12 13:20:59,819 - jwst.assign_wcs.assign_wcs - INFO - COMPLETED assign_wcs\n", + "2025-11-12 13:20:59,822 - stpipe.step - INFO - Step assign_wcs done\n", + "2025-11-12 13:20:59,931 - stpipe.step - INFO - Step badpix_selfcal running with args (, [], []).\n", + "2025-11-12 13:20:59,932 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:00,019 - stpipe.step - INFO - Step msa_flagging running with args (,).\n", + "2025-11-12 13:21:00,020 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:00,101 - stpipe.step - INFO - Step nsclean running with args (,).\n", + "2025-11-12 13:21:00,101 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:00,179 - stpipe.step - INFO - Step imprint_subtract running with args (, []).\n", + "2025-11-12 13:21:00,180 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:00,258 - stpipe.step - INFO - Step bkg_subtract running with args (, []).\n", + "2025-11-12 13:21:00,259 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:00,340 - stpipe.step - INFO - Step srctype running with args (,).\n", + "2025-11-12 13:21:00,673 - jwst.srctype.srctype - INFO - Input EXP_TYPE is MIR_MRS\n", + "2025-11-12 13:21:00,673 - jwst.srctype.srctype - INFO - Input SRCTYAPT = EXTENDED\n", + "2025-11-12 13:21:00,674 - jwst.srctype.srctype - INFO - Using input source type = EXTENDED\n", + "2025-11-12 13:21:00,675 - stpipe.step - INFO - Step srctype done\n", + "2025-11-12 13:21:00,775 - stpipe.step - INFO - Step straylight running with args (,).\n", + "2025-11-12 13:21:00,776 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:00,873 - stpipe.step - INFO - Step flat_field running with args (,).\n", + "2025-11-12 13:21:00,874 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:00,970 - stpipe.step - INFO - Step fringe running with args (,).\n", + "2025-11-12 13:21:00,971 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:01,069 - stpipe.step - INFO - Step pathloss running with args (,).\n", + "2025-11-12 13:21:01,069 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:01,168 - stpipe.step - INFO - Step barshadow running with args (,).\n", + "2025-11-12 13:21:01,169 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:01,266 - stpipe.step - INFO - Step photom running with args (,).\n", + "2025-11-12 13:21:01,284 - jwst.photom.photom_step - INFO - Using photom reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_photom_0224.fits\n", + "2025-11-12 13:21:01,285 - jwst.photom.photom_step - INFO - Using area reference file: N/A\n", + "2025-11-12 13:21:01,618 - jwst.photom.photom - INFO - Using instrument: MIRI\n", + "2025-11-12 13:21:01,619 - jwst.photom.photom - INFO - detector: MIRIFUSHORT\n", + "2025-11-12 13:21:01,619 - jwst.photom.photom - INFO - exp_type: MIR_MRS\n", + "2025-11-12 13:21:01,619 - jwst.photom.photom - INFO - band: SHORT\n", + "2025-11-12 13:21:01,661 - stdatamodels.dynamicdq - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:21:01,665 - jwst.photom.photom - INFO - Attempting to obtain PIXAR_SR and PIXAR_A2 values from PHOTOM reference file.\n", + "2025-11-12 13:21:01,666 - jwst.photom.photom - INFO - Values for PIXAR_SR and PIXAR_A2 obtained from PHOTOM reference file.\n", + "2025-11-12 13:21:01,673 - jwst.photom.photom - INFO - Applying MRS IFU time dependent correction.\n", + "2025-11-12 13:21:01,997 - stpipe.step - INFO - Step photom done\n", + "2025-11-12 13:21:02,122 - stpipe.step - INFO - Step residual_fringe running with args (,).\n", + "2025-11-12 13:21:02,123 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:02,577 - stpipe.step - INFO - Step pixel_replace running with args (,).\n", + "2025-11-12 13:21:02,578 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:02,691 - stpipe.step - INFO - Step cube_build running with args (,).\n", + "2025-11-12 13:21:02,691 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:03,153 - stpipe.step - INFO - Step extract_1d running with args (,).\n", + "2025-11-12 13:21:03,154 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:03,155 - jwst.pipeline.calwebb_spec2 - INFO - Finished processing product data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00001_mirifushort\n", + "2025-11-12 13:21:03,158 - jwst.pipeline.calwebb_spec2 - INFO - Ending calwebb_spec2\n", + "2025-11-12 13:21:03,159 - jwst.stpipe.core - INFO - Results used CRDS context: jwst_1464.pmap\n", + "2025-11-12 13:21:03,176 - py.warnings - WARNING - /Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/astropy/io/fits/card.py:271: VerifyWarning: Keyword name 'FLAT_STD_MIN' is greater than 8 characters or contains characters not allowed by the FITS standard; a HIERARCH card will be created.\n", + " warnings.warn(\n", + "\n", + "2025-11-12 13:21:03,447 - stpipe.step - INFO - Saved model in ./data/* bet Pic/12A/stage2/jw01294003001_03102_00001_mirifushort_cal.fits\n", + "2025-11-12 13:21:03,447 - stpipe.step - INFO - Step Spec2Pipeline done\n", + "2025-11-12 13:21:03,448 - jwst.stpipe.core - INFO - Results used jwst version: 1.20.2\n", + "2025-11-12 13:21:03,497 - stpipe.step - INFO - PARS-RESAMPLESPECSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-resamplespecstep_0001.asdf\n", + "2025-11-12 13:21:03,504 - stpipe.step - INFO - PARS-EXTRACT1DSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-extract1dstep_0001.asdf\n", + "2025-11-12 13:21:03,517 - stpipe.step - INFO - PARS-RESAMPLESPECSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-resamplespecstep_0001.asdf\n", + "2025-11-12 13:21:03,524 - stpipe.step - INFO - PARS-EXTRACT1DSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-extract1dstep_0001.asdf\n", + "2025-11-12 13:21:03,531 - stpipe.pipeline - INFO - PARS-SPEC2PIPELINE parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-spec2pipeline_0005.asdf\n", + "2025-11-12 13:21:03,547 - stpipe.step - INFO - Spec2Pipeline instance created.\n", + "2025-11-12 13:21:03,548 - stpipe.step - INFO - AssignWcsStep instance created.\n", + "2025-11-12 13:21:03,548 - stpipe.step - INFO - BadpixSelfcalStep instance created.\n", + "2025-11-12 13:21:03,549 - stpipe.step - INFO - MSAFlagOpenStep instance created.\n", + "2025-11-12 13:21:03,549 - stpipe.step - INFO - NSCleanStep instance created.\n", + "2025-11-12 13:21:03,550 - stpipe.step - INFO - BackgroundStep instance created.\n", + "2025-11-12 13:21:03,551 - stpipe.step - INFO - ImprintStep instance created.\n", + "2025-11-12 13:21:03,552 - stpipe.step - INFO - Extract2dStep instance created.\n", + "2025-11-12 13:21:03,555 - stpipe.step - INFO - MasterBackgroundMosStep instance created.\n", + "2025-11-12 13:21:03,555 - stpipe.step - INFO - FlatFieldStep instance created.\n", + "2025-11-12 13:21:03,556 - stpipe.step - INFO - PathLossStep instance created.\n", + "2025-11-12 13:21:03,556 - stpipe.step - INFO - BarShadowStep instance created.\n", + "2025-11-12 13:21:03,557 - stpipe.step - INFO - PhotomStep instance created.\n", + "2025-11-12 13:21:03,558 - stpipe.step - INFO - PixelReplaceStep instance created.\n", + "2025-11-12 13:21:03,558 - stpipe.step - INFO - ResampleSpecStep instance created.\n", + "2025-11-12 13:21:03,559 - stpipe.step - INFO - Extract1dStep instance created.\n", + "2025-11-12 13:21:03,560 - stpipe.step - INFO - WavecorrStep instance created.\n", + "2025-11-12 13:21:03,560 - stpipe.step - INFO - FlatFieldStep instance created.\n", + "2025-11-12 13:21:03,561 - stpipe.step - INFO - SourceTypeStep instance created.\n", + "2025-11-12 13:21:03,561 - stpipe.step - INFO - StraylightStep instance created.\n", + "2025-11-12 13:21:03,562 - stpipe.step - INFO - FringeStep instance created.\n", + "2025-11-12 13:21:03,563 - stpipe.step - INFO - ResidualFringeStep instance created.\n", + "2025-11-12 13:21:03,563 - stpipe.step - INFO - PathLossStep instance created.\n", + "2025-11-12 13:21:03,563 - stpipe.step - INFO - BarShadowStep instance created.\n", + "2025-11-12 13:21:03,564 - stpipe.step - INFO - WfssContamStep instance created.\n", + "2025-11-12 13:21:03,565 - stpipe.step - INFO - PhotomStep instance created.\n", + "2025-11-12 13:21:03,565 - stpipe.step - INFO - PixelReplaceStep instance created.\n", + "2025-11-12 13:21:03,566 - stpipe.step - INFO - ResampleSpecStep instance created.\n", + "2025-11-12 13:21:03,567 - stpipe.step - INFO - CubeBuildStep instance created.\n", + "2025-11-12 13:21:03,568 - stpipe.step - INFO - Extract1dStep instance created.\n", + "2025-11-12 13:21:03,770 - stpipe.step - INFO - Step Spec2Pipeline running with args ('./data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00002_mirifushort_rate.fits',).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 ./data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00002_mirifushort_rate.fits\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-12 13:21:03,789 - stpipe.step - INFO - Step Spec2Pipeline parameters are:\n", + " pre_hooks: []\n", + " post_hooks: []\n", + " output_file: None\n", + " output_dir: ./data/* bet Pic/12A/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: True\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: True\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: True\n", + " bkg_sigma_clip: 3.0\n", + " ifu_rfcorr: True\n", + " ifu_set_srctype: None\n", + " ifu_rscale: None\n", + " ifu_covar_scale: 1.8\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: True\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: True\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: True\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: True\n", + " bkg_sigma_clip: 3.0\n", + " ifu_rfcorr: True\n", + " ifu_set_srctype: None\n", + " ifu_rscale: None\n", + " ifu_covar_scale: 1.8\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", + "2025-11-12 13:21:03,818 - stpipe.pipeline - INFO - Prefetching reference files for dataset: 'jw01294003001_03102_00002_mirifushort_rate.fits' reftypes = ['area', 'barshadow', 'camera', 'collimator', 'disperser', 'distortion', 'filteroffset', 'fore', 'fpa', 'ifufore', 'ifupost', 'ifuslicer', 'msa', 'msaoper', 'ote', 'pathloss', 'photom', 'regions', 'specwcs', 'wavecorr', 'wavelengthrange']\n", + "2025-11-12 13:21:03,820 - stpipe.pipeline - INFO - Prefetch for AREA reference file is 'N/A'.\n", + "2025-11-12 13:21:03,820 - stpipe.pipeline - INFO - Prefetch for BARSHADOW reference file is 'N/A'.\n", + "2025-11-12 13:21:03,820 - stpipe.pipeline - INFO - Prefetch for CAMERA reference file is 'N/A'.\n", + "2025-11-12 13:21:03,820 - stpipe.pipeline - INFO - Prefetch for COLLIMATOR reference file is 'N/A'.\n", + "2025-11-12 13:21:03,821 - stpipe.pipeline - INFO - Prefetch for DISPERSER reference file is 'N/A'.\n", + "2025-11-12 13:21:03,821 - stpipe.pipeline - INFO - Prefetch for DISTORTION reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_distortion_0140.asdf'.\n", + "2025-11-12 13:21:03,822 - stpipe.pipeline - INFO - Prefetch for FILTEROFFSET reference file is 'N/A'.\n", + "2025-11-12 13:21:03,822 - stpipe.pipeline - INFO - Prefetch for FORE reference file is 'N/A'.\n", + "2025-11-12 13:21:03,822 - stpipe.pipeline - INFO - Prefetch for FPA reference file is 'N/A'.\n", + "2025-11-12 13:21:03,823 - stpipe.pipeline - INFO - Prefetch for IFUFORE reference file is 'N/A'.\n", + "2025-11-12 13:21:03,823 - stpipe.pipeline - INFO - Prefetch for IFUPOST reference file is 'N/A'.\n", + "2025-11-12 13:21:03,823 - stpipe.pipeline - INFO - Prefetch for IFUSLICER reference file is 'N/A'.\n", + "2025-11-12 13:21:03,823 - stpipe.pipeline - INFO - Prefetch for MSA reference file is 'N/A'.\n", + "2025-11-12 13:21:03,824 - stpipe.pipeline - INFO - Prefetch for MSAOPER reference file is 'N/A'.\n", + "2025-11-12 13:21:03,824 - stpipe.pipeline - INFO - Prefetch for OTE reference file is 'N/A'.\n", + "2025-11-12 13:21:03,824 - stpipe.pipeline - INFO - Prefetch for PATHLOSS reference file is 'N/A'.\n", + "2025-11-12 13:21:03,825 - stpipe.pipeline - INFO - Prefetch for PHOTOM reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_photom_0224.fits'.\n", + "2025-11-12 13:21:03,825 - stpipe.pipeline - INFO - Prefetch for REGIONS reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_regions_0142.asdf'.\n", + "2025-11-12 13:21:03,826 - stpipe.pipeline - INFO - Prefetch for SPECWCS reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_specwcs_0139.asdf'.\n", + "2025-11-12 13:21:03,826 - stpipe.pipeline - INFO - Prefetch for WAVECORR reference file is 'N/A'.\n", + "2025-11-12 13:21:03,826 - stpipe.pipeline - INFO - Prefetch for WAVELENGTHRANGE reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_wavelengthrange_0011.asdf'.\n", + "2025-11-12 13:21:03,827 - jwst.pipeline.calwebb_spec2 - INFO - Starting calwebb_spec2 ...\n", + "2025-11-12 13:21:03,827 - jwst.pipeline.calwebb_spec2 - INFO - Processing product data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00002_mirifushort\n", + "2025-11-12 13:21:03,827 - jwst.pipeline.calwebb_spec2 - INFO - Working on input ./data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00002_mirifushort_rate.fits ...\n", + "2025-11-12 13:21:03,880 - py.warnings - WARNING - /Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stdatamodels/fits_support.py:699: ValidationWarning: While validating meta.cal_step.flat_field the following error occurred:\n", + "'jw01294004001_03102_00002_mirifushort_flat.fits' is not one of ['SKIPPED', 'COMPLETE']\n", + "\n", + "Failed validating 'enum' in schema:\n", + " {'blend_table': True,\n", + " 'enum': ['SKIPPED', 'COMPLETE'],\n", + " 'fits_keyword': 'S_FLAT',\n", + " 'title': 'Flat Field Correction',\n", + " 'type': 'string'}\n", + "\n", + "On instance:\n", + " 'jw01294004001_03102_00002_mirifushort_flat.fits'\n", + " if validate.value_change(path, result, schema, context):\n", + "\n", + "2025-11-12 13:21:03,963 - stpipe.step - INFO - Step assign_wcs running with args (,).\n", + "2025-11-12 13:21:04,378 - jwst.assign_wcs.miri - INFO - Applied Barycentric velocity correction : 0.9999872582140571\n", + "2025-11-12 13:21:05,083 - jwst.assign_wcs.miri - INFO - Created a MIRI mir_mrs pipeline with references {'distortion': '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_distortion_0140.asdf', 'filteroffset': None, 'specwcs': '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_specwcs_0139.asdf', 'regions': '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_regions_0142.asdf', 'wavelengthrange': '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_wavelengthrange_0011.asdf', 'camera': None, 'collimator': None, 'disperser': None, 'fore': None, 'fpa': None, 'msa': None, 'ote': None, 'ifupost': None, 'ifufore': None, 'ifuslicer': None}\n", + "2025-11-12 13:21:05,630 - stcal.alignment.util - INFO - Update S_REGION to POLYGON ICRS 86.819476301 -51.066843587 86.822053206 -51.066843587 86.822053206 -51.065185345 86.819476301 -51.065185345\n", + "2025-11-12 13:21:05,631 - jwst.assign_wcs.assign_wcs - INFO - COMPLETED assign_wcs\n", + "2025-11-12 13:21:05,634 - stpipe.step - INFO - Step assign_wcs done\n", + "2025-11-12 13:21:05,757 - stpipe.step - INFO - Step badpix_selfcal running with args (, [], []).\n", + "2025-11-12 13:21:05,758 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:05,848 - stpipe.step - INFO - Step msa_flagging running with args (,).\n", + "2025-11-12 13:21:05,849 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:05,939 - stpipe.step - INFO - Step nsclean running with args (,).\n", + "2025-11-12 13:21:05,940 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:06,024 - stpipe.step - INFO - Step imprint_subtract running with args (, []).\n", + "2025-11-12 13:21:06,025 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:06,109 - stpipe.step - INFO - Step bkg_subtract running with args (, []).\n", + "2025-11-12 13:21:06,110 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:06,196 - stpipe.step - INFO - Step srctype running with args (,).\n", + "2025-11-12 13:21:06,529 - jwst.srctype.srctype - INFO - Input EXP_TYPE is MIR_MRS\n", + "2025-11-12 13:21:06,529 - jwst.srctype.srctype - INFO - Input SRCTYAPT = EXTENDED\n", + "2025-11-12 13:21:06,530 - jwst.srctype.srctype - INFO - Using input source type = EXTENDED\n", + "2025-11-12 13:21:06,531 - stpipe.step - INFO - Step srctype done\n", + "2025-11-12 13:21:06,640 - stpipe.step - INFO - Step straylight running with args (,).\n", + "2025-11-12 13:21:06,641 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:06,745 - stpipe.step - INFO - Step flat_field running with args (,).\n", + "2025-11-12 13:21:06,746 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:06,851 - stpipe.step - INFO - Step fringe running with args (,).\n", + "2025-11-12 13:21:06,852 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:06,962 - stpipe.step - INFO - Step pathloss running with args (,).\n", + "2025-11-12 13:21:06,963 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:07,068 - stpipe.step - INFO - Step barshadow running with args (,).\n", + "2025-11-12 13:21:07,069 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:07,175 - stpipe.step - INFO - Step photom running with args (,).\n", + "2025-11-12 13:21:07,183 - jwst.photom.photom_step - INFO - Using photom reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_photom_0224.fits\n", + "2025-11-12 13:21:07,183 - jwst.photom.photom_step - INFO - Using area reference file: N/A\n", + "2025-11-12 13:21:07,518 - jwst.photom.photom - INFO - Using instrument: MIRI\n", + "2025-11-12 13:21:07,518 - jwst.photom.photom - INFO - detector: MIRIFUSHORT\n", + "2025-11-12 13:21:07,519 - jwst.photom.photom - INFO - exp_type: MIR_MRS\n", + "2025-11-12 13:21:07,519 - jwst.photom.photom - INFO - band: SHORT\n", + "2025-11-12 13:21:07,552 - stdatamodels.dynamicdq - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:21:07,556 - jwst.photom.photom - INFO - Attempting to obtain PIXAR_SR and PIXAR_A2 values from PHOTOM reference file.\n", + "2025-11-12 13:21:07,556 - jwst.photom.photom - INFO - Values for PIXAR_SR and PIXAR_A2 obtained from PHOTOM reference file.\n", + "2025-11-12 13:21:07,563 - jwst.photom.photom - INFO - Applying MRS IFU time dependent correction.\n", + "2025-11-12 13:21:07,878 - stpipe.step - INFO - Step photom done\n", + "2025-11-12 13:21:08,009 - stpipe.step - INFO - Step residual_fringe running with args (,).\n", + "2025-11-12 13:21:08,010 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:08,470 - stpipe.step - INFO - Step pixel_replace running with args (,).\n", + "2025-11-12 13:21:08,470 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:08,596 - stpipe.step - INFO - Step cube_build running with args (,).\n", + "2025-11-12 13:21:08,597 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:09,065 - stpipe.step - INFO - Step extract_1d running with args (,).\n", + "2025-11-12 13:21:09,066 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:09,067 - jwst.pipeline.calwebb_spec2 - INFO - Finished processing product data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00002_mirifushort\n", + "2025-11-12 13:21:09,070 - jwst.pipeline.calwebb_spec2 - INFO - Ending calwebb_spec2\n", + "2025-11-12 13:21:09,071 - jwst.stpipe.core - INFO - Results used CRDS context: jwst_1464.pmap\n", + "2025-11-12 13:21:09,087 - py.warnings - WARNING - /Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/astropy/io/fits/card.py:271: VerifyWarning: Keyword name 'FLAT_STD_MIN' is greater than 8 characters or contains characters not allowed by the FITS standard; a HIERARCH card will be created.\n", + " warnings.warn(\n", + "\n", + "2025-11-12 13:21:09,355 - stpipe.step - INFO - Saved model in ./data/* bet Pic/12A/stage2/jw01294003001_03102_00002_mirifushort_cal.fits\n", + "2025-11-12 13:21:09,355 - stpipe.step - INFO - Step Spec2Pipeline done\n", + "2025-11-12 13:21:09,356 - jwst.stpipe.core - INFO - Results used jwst version: 1.20.2\n", + "2025-11-12 13:21:09,404 - stpipe.step - INFO - PARS-RESAMPLESPECSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-resamplespecstep_0001.asdf\n", + "2025-11-12 13:21:09,410 - stpipe.step - INFO - PARS-EXTRACT1DSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-extract1dstep_0001.asdf\n", + "2025-11-12 13:21:09,421 - stpipe.step - INFO - PARS-RESAMPLESPECSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-resamplespecstep_0001.asdf\n", + "2025-11-12 13:21:09,429 - stpipe.step - INFO - PARS-EXTRACT1DSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-extract1dstep_0001.asdf\n", + "2025-11-12 13:21:09,434 - stpipe.pipeline - INFO - PARS-SPEC2PIPELINE parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-spec2pipeline_0005.asdf\n", + "2025-11-12 13:21:09,450 - stpipe.step - INFO - Spec2Pipeline instance created.\n", + "2025-11-12 13:21:09,451 - stpipe.step - INFO - AssignWcsStep instance created.\n", + "2025-11-12 13:21:09,452 - stpipe.step - INFO - BadpixSelfcalStep instance created.\n", + "2025-11-12 13:21:09,452 - stpipe.step - INFO - MSAFlagOpenStep instance created.\n", + "2025-11-12 13:21:09,453 - stpipe.step - INFO - NSCleanStep instance created.\n", + "2025-11-12 13:21:09,454 - stpipe.step - INFO - BackgroundStep instance created.\n", + "2025-11-12 13:21:09,454 - stpipe.step - INFO - ImprintStep instance created.\n", + "2025-11-12 13:21:09,455 - stpipe.step - INFO - Extract2dStep instance created.\n", + "2025-11-12 13:21:09,458 - stpipe.step - INFO - MasterBackgroundMosStep instance created.\n", + "2025-11-12 13:21:09,458 - stpipe.step - INFO - FlatFieldStep instance created.\n", + "2025-11-12 13:21:09,459 - stpipe.step - INFO - PathLossStep instance created.\n", + "2025-11-12 13:21:09,459 - stpipe.step - INFO - BarShadowStep instance created.\n", + "2025-11-12 13:21:09,460 - stpipe.step - INFO - PhotomStep instance created.\n", + "2025-11-12 13:21:09,460 - stpipe.step - INFO - PixelReplaceStep instance created.\n", + "2025-11-12 13:21:09,461 - stpipe.step - INFO - ResampleSpecStep instance created.\n", + "2025-11-12 13:21:09,462 - stpipe.step - INFO - Extract1dStep instance created.\n", + "2025-11-12 13:21:09,463 - stpipe.step - INFO - WavecorrStep instance created.\n", + "2025-11-12 13:21:09,463 - stpipe.step - INFO - FlatFieldStep instance created.\n", + "2025-11-12 13:21:09,464 - stpipe.step - INFO - SourceTypeStep instance created.\n", + "2025-11-12 13:21:09,464 - stpipe.step - INFO - StraylightStep instance created.\n", + "2025-11-12 13:21:09,465 - stpipe.step - INFO - FringeStep instance created.\n", + "2025-11-12 13:21:09,466 - stpipe.step - INFO - ResidualFringeStep instance created.\n", + "2025-11-12 13:21:09,466 - stpipe.step - INFO - PathLossStep instance created.\n", + "2025-11-12 13:21:09,467 - stpipe.step - INFO - BarShadowStep instance created.\n", + "2025-11-12 13:21:09,467 - stpipe.step - INFO - WfssContamStep instance created.\n", + "2025-11-12 13:21:09,468 - stpipe.step - INFO - PhotomStep instance created.\n", + "2025-11-12 13:21:09,469 - stpipe.step - INFO - PixelReplaceStep instance created.\n", + "2025-11-12 13:21:09,469 - stpipe.step - INFO - ResampleSpecStep instance created.\n", + "2025-11-12 13:21:09,470 - stpipe.step - INFO - CubeBuildStep instance created.\n", + "2025-11-12 13:21:09,471 - stpipe.step - INFO - Extract1dStep instance created.\n", + "2025-11-12 13:21:09,692 - stpipe.step - INFO - Step Spec2Pipeline running with args ('./data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00003_mirifushort_rate.fits',).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 ./data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00003_mirifushort_rate.fits\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-12 13:21:09,713 - stpipe.step - INFO - Step Spec2Pipeline parameters are:\n", + " pre_hooks: []\n", + " post_hooks: []\n", + " output_file: None\n", + " output_dir: ./data/* bet Pic/12A/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: True\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: True\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: True\n", + " bkg_sigma_clip: 3.0\n", + " ifu_rfcorr: True\n", + " ifu_set_srctype: None\n", + " ifu_rscale: None\n", + " ifu_covar_scale: 1.8\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: True\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: True\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: True\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: True\n", + " bkg_sigma_clip: 3.0\n", + " ifu_rfcorr: True\n", + " ifu_set_srctype: None\n", + " ifu_rscale: None\n", + " ifu_covar_scale: 1.8\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", + "2025-11-12 13:21:09,739 - stpipe.pipeline - INFO - Prefetching reference files for dataset: 'jw01294003001_03102_00003_mirifushort_rate.fits' reftypes = ['area', 'barshadow', 'camera', 'collimator', 'disperser', 'distortion', 'filteroffset', 'fore', 'fpa', 'ifufore', 'ifupost', 'ifuslicer', 'msa', 'msaoper', 'ote', 'pathloss', 'photom', 'regions', 'specwcs', 'wavecorr', 'wavelengthrange']\n", + "2025-11-12 13:21:09,741 - stpipe.pipeline - INFO - Prefetch for AREA reference file is 'N/A'.\n", + "2025-11-12 13:21:09,741 - stpipe.pipeline - INFO - Prefetch for BARSHADOW reference file is 'N/A'.\n", + "2025-11-12 13:21:09,742 - stpipe.pipeline - INFO - Prefetch for CAMERA reference file is 'N/A'.\n", + "2025-11-12 13:21:09,742 - stpipe.pipeline - INFO - Prefetch for COLLIMATOR reference file is 'N/A'.\n", + "2025-11-12 13:21:09,742 - stpipe.pipeline - INFO - Prefetch for DISPERSER reference file is 'N/A'.\n", + "2025-11-12 13:21:09,743 - stpipe.pipeline - INFO - Prefetch for DISTORTION reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_distortion_0140.asdf'.\n", + "2025-11-12 13:21:09,743 - stpipe.pipeline - INFO - Prefetch for FILTEROFFSET reference file is 'N/A'.\n", + "2025-11-12 13:21:09,743 - stpipe.pipeline - INFO - Prefetch for FORE reference file is 'N/A'.\n", + "2025-11-12 13:21:09,744 - stpipe.pipeline - INFO - Prefetch for FPA reference file is 'N/A'.\n", + "2025-11-12 13:21:09,744 - stpipe.pipeline - INFO - Prefetch for IFUFORE reference file is 'N/A'.\n", + "2025-11-12 13:21:09,744 - stpipe.pipeline - INFO - Prefetch for IFUPOST reference file is 'N/A'.\n", + "2025-11-12 13:21:09,745 - stpipe.pipeline - INFO - Prefetch for IFUSLICER reference file is 'N/A'.\n", + "2025-11-12 13:21:09,745 - stpipe.pipeline - INFO - Prefetch for MSA reference file is 'N/A'.\n", + "2025-11-12 13:21:09,745 - stpipe.pipeline - INFO - Prefetch for MSAOPER reference file is 'N/A'.\n", + "2025-11-12 13:21:09,746 - stpipe.pipeline - INFO - Prefetch for OTE reference file is 'N/A'.\n", + "2025-11-12 13:21:09,746 - stpipe.pipeline - INFO - Prefetch for PATHLOSS reference file is 'N/A'.\n", + "2025-11-12 13:21:09,746 - stpipe.pipeline - INFO - Prefetch for PHOTOM reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_photom_0224.fits'.\n", + "2025-11-12 13:21:09,747 - stpipe.pipeline - INFO - Prefetch for REGIONS reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_regions_0142.asdf'.\n", + "2025-11-12 13:21:09,747 - stpipe.pipeline - INFO - Prefetch for SPECWCS reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_specwcs_0139.asdf'.\n", + "2025-11-12 13:21:09,748 - stpipe.pipeline - INFO - Prefetch for WAVECORR reference file is 'N/A'.\n", + "2025-11-12 13:21:09,748 - stpipe.pipeline - INFO - Prefetch for WAVELENGTHRANGE reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_wavelengthrange_0011.asdf'.\n", + "2025-11-12 13:21:09,748 - jwst.pipeline.calwebb_spec2 - INFO - Starting calwebb_spec2 ...\n", + "2025-11-12 13:21:09,749 - jwst.pipeline.calwebb_spec2 - INFO - Processing product data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00003_mirifushort\n", + "2025-11-12 13:21:09,749 - jwst.pipeline.calwebb_spec2 - INFO - Working on input ./data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00003_mirifushort_rate.fits ...\n", + "2025-11-12 13:21:09,802 - py.warnings - WARNING - /Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stdatamodels/fits_support.py:699: ValidationWarning: While validating meta.cal_step.flat_field the following error occurred:\n", + "'jw01294004001_03102_00003_mirifushort_flat.fits' is not one of ['SKIPPED', 'COMPLETE']\n", + "\n", + "Failed validating 'enum' in schema:\n", + " {'blend_table': True,\n", + " 'enum': ['SKIPPED', 'COMPLETE'],\n", + " 'fits_keyword': 'S_FLAT',\n", + " 'title': 'Flat Field Correction',\n", + " 'type': 'string'}\n", + "\n", + "On instance:\n", + " 'jw01294004001_03102_00003_mirifushort_flat.fits'\n", + " if validate.value_change(path, result, schema, context):\n", + "\n", + "2025-11-12 13:21:09,886 - stpipe.step - INFO - Step assign_wcs running with args (,).\n", + "2025-11-12 13:21:10,301 - jwst.assign_wcs.miri - INFO - Applied Barycentric velocity correction : 0.9999872569465458\n", + "2025-11-12 13:21:11,007 - jwst.assign_wcs.miri - INFO - Created a MIRI mir_mrs pipeline with references {'distortion': '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_distortion_0140.asdf', 'filteroffset': None, 'specwcs': '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_specwcs_0139.asdf', 'regions': '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_regions_0142.asdf', 'wavelengthrange': '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_wavelengthrange_0011.asdf', 'camera': None, 'collimator': None, 'disperser': None, 'fore': None, 'fpa': None, 'msa': None, 'ote': None, 'ifupost': None, 'ifufore': None, 'ifuslicer': None}\n", + "2025-11-12 13:21:11,507 - stcal.alignment.util - INFO - Update S_REGION to POLYGON ICRS 86.820286375 -51.066929108 86.822863276 -51.066929108 86.822863276 -51.065270870 86.820286375 -51.065270870\n", + "2025-11-12 13:21:11,508 - jwst.assign_wcs.assign_wcs - INFO - COMPLETED assign_wcs\n", + "2025-11-12 13:21:11,511 - stpipe.step - INFO - Step assign_wcs done\n", + "2025-11-12 13:21:11,636 - stpipe.step - INFO - Step badpix_selfcal running with args (, [], []).\n", + "2025-11-12 13:21:11,637 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:11,725 - stpipe.step - INFO - Step msa_flagging running with args (,).\n", + "2025-11-12 13:21:11,726 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:11,811 - stpipe.step - INFO - Step nsclean running with args (,).\n", + "2025-11-12 13:21:11,812 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:11,896 - stpipe.step - INFO - Step imprint_subtract running with args (, []).\n", + "2025-11-12 13:21:11,897 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:11,981 - stpipe.step - INFO - Step bkg_subtract running with args (, []).\n", + "2025-11-12 13:21:11,981 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:12,069 - stpipe.step - INFO - Step srctype running with args (,).\n", + "2025-11-12 13:21:12,407 - jwst.srctype.srctype - INFO - Input EXP_TYPE is MIR_MRS\n", + "2025-11-12 13:21:12,408 - jwst.srctype.srctype - INFO - Input SRCTYAPT = EXTENDED\n", + "2025-11-12 13:21:12,408 - jwst.srctype.srctype - INFO - Using input source type = EXTENDED\n", + "2025-11-12 13:21:12,409 - stpipe.step - INFO - Step srctype done\n", + "2025-11-12 13:21:12,520 - stpipe.step - INFO - Step straylight running with args (,).\n", + "2025-11-12 13:21:12,520 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:12,630 - stpipe.step - INFO - Step flat_field running with args (,).\n", + "2025-11-12 13:21:12,630 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:12,737 - stpipe.step - INFO - Step fringe running with args (,).\n", + "2025-11-12 13:21:12,737 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:12,845 - stpipe.step - INFO - Step pathloss running with args (,).\n", + "2025-11-12 13:21:12,845 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:12,953 - stpipe.step - INFO - Step barshadow running with args (,).\n", + "2025-11-12 13:21:12,954 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:13,061 - stpipe.step - INFO - Step photom running with args (,).\n", + "2025-11-12 13:21:13,069 - jwst.photom.photom_step - INFO - Using photom reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_photom_0224.fits\n", + "2025-11-12 13:21:13,069 - jwst.photom.photom_step - INFO - Using area reference file: N/A\n", + "2025-11-12 13:21:13,407 - jwst.photom.photom - INFO - Using instrument: MIRI\n", + "2025-11-12 13:21:13,408 - jwst.photom.photom - INFO - detector: MIRIFUSHORT\n", + "2025-11-12 13:21:13,408 - jwst.photom.photom - INFO - exp_type: MIR_MRS\n", + "2025-11-12 13:21:13,409 - jwst.photom.photom - INFO - band: SHORT\n", + "2025-11-12 13:21:13,442 - stdatamodels.dynamicdq - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:21:13,447 - jwst.photom.photom - INFO - Attempting to obtain PIXAR_SR and PIXAR_A2 values from PHOTOM reference file.\n", + "2025-11-12 13:21:13,447 - jwst.photom.photom - INFO - Values for PIXAR_SR and PIXAR_A2 obtained from PHOTOM reference file.\n", + "2025-11-12 13:21:13,454 - jwst.photom.photom - INFO - Applying MRS IFU time dependent correction.\n", + "2025-11-12 13:21:13,766 - stpipe.step - INFO - Step photom done\n", + "2025-11-12 13:21:13,906 - stpipe.step - INFO - Step residual_fringe running with args (,).\n", + "2025-11-12 13:21:13,908 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:14,376 - stpipe.step - INFO - Step pixel_replace running with args (,).\n", + "2025-11-12 13:21:14,377 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:14,505 - stpipe.step - INFO - Step cube_build running with args (,).\n", + "2025-11-12 13:21:14,506 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:14,989 - stpipe.step - INFO - Step extract_1d running with args (,).\n", + "2025-11-12 13:21:14,989 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:14,990 - jwst.pipeline.calwebb_spec2 - INFO - Finished processing product data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00003_mirifushort\n", + "2025-11-12 13:21:14,994 - jwst.pipeline.calwebb_spec2 - INFO - Ending calwebb_spec2\n", + "2025-11-12 13:21:14,994 - jwst.stpipe.core - INFO - Results used CRDS context: jwst_1464.pmap\n", + "2025-11-12 13:21:15,010 - py.warnings - WARNING - /Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/astropy/io/fits/card.py:271: VerifyWarning: Keyword name 'FLAT_STD_MIN' is greater than 8 characters or contains characters not allowed by the FITS standard; a HIERARCH card will be created.\n", + " warnings.warn(\n", + "\n", + "2025-11-12 13:21:15,284 - stpipe.step - INFO - Saved model in ./data/* bet Pic/12A/stage2/jw01294003001_03102_00003_mirifushort_cal.fits\n", + "2025-11-12 13:21:15,284 - stpipe.step - INFO - Step Spec2Pipeline done\n", + "2025-11-12 13:21:15,284 - jwst.stpipe.core - INFO - Results used jwst version: 1.20.2\n", + "2025-11-12 13:21:15,335 - stpipe.step - INFO - PARS-RESAMPLESPECSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-resamplespecstep_0001.asdf\n", + "2025-11-12 13:21:15,341 - stpipe.step - INFO - PARS-EXTRACT1DSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-extract1dstep_0001.asdf\n", + "2025-11-12 13:21:15,352 - stpipe.step - INFO - PARS-RESAMPLESPECSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-resamplespecstep_0001.asdf\n", + "2025-11-12 13:21:15,358 - stpipe.step - INFO - PARS-EXTRACT1DSTEP parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-extract1dstep_0001.asdf\n", + "2025-11-12 13:21:15,364 - stpipe.pipeline - INFO - PARS-SPEC2PIPELINE parameters found: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_pars-spec2pipeline_0005.asdf\n", + "2025-11-12 13:21:15,379 - stpipe.step - INFO - Spec2Pipeline instance created.\n", + "2025-11-12 13:21:15,380 - stpipe.step - INFO - AssignWcsStep instance created.\n", + "2025-11-12 13:21:15,381 - stpipe.step - INFO - BadpixSelfcalStep instance created.\n", + "2025-11-12 13:21:15,382 - stpipe.step - INFO - MSAFlagOpenStep instance created.\n", + "2025-11-12 13:21:15,382 - stpipe.step - INFO - NSCleanStep instance created.\n", + "2025-11-12 13:21:15,383 - stpipe.step - INFO - BackgroundStep instance created.\n", + "2025-11-12 13:21:15,384 - stpipe.step - INFO - ImprintStep instance created.\n", + "2025-11-12 13:21:15,384 - stpipe.step - INFO - Extract2dStep instance created.\n", + "2025-11-12 13:21:15,387 - stpipe.step - INFO - MasterBackgroundMosStep instance created.\n", + "2025-11-12 13:21:15,388 - stpipe.step - INFO - FlatFieldStep instance created.\n", + "2025-11-12 13:21:15,388 - stpipe.step - INFO - PathLossStep instance created.\n", + "2025-11-12 13:21:15,389 - stpipe.step - INFO - BarShadowStep instance created.\n", + "2025-11-12 13:21:15,389 - stpipe.step - INFO - PhotomStep instance created.\n", + "2025-11-12 13:21:15,390 - stpipe.step - INFO - PixelReplaceStep instance created.\n", + "2025-11-12 13:21:15,391 - stpipe.step - INFO - ResampleSpecStep instance created.\n", + "2025-11-12 13:21:15,392 - stpipe.step - INFO - Extract1dStep instance created.\n", + "2025-11-12 13:21:15,392 - stpipe.step - INFO - WavecorrStep instance created.\n", + "2025-11-12 13:21:15,393 - stpipe.step - INFO - FlatFieldStep instance created.\n", + "2025-11-12 13:21:15,393 - stpipe.step - INFO - SourceTypeStep instance created.\n", + "2025-11-12 13:21:15,394 - stpipe.step - INFO - StraylightStep instance created.\n", + "2025-11-12 13:21:15,394 - stpipe.step - INFO - FringeStep instance created.\n", + "2025-11-12 13:21:15,395 - stpipe.step - INFO - ResidualFringeStep instance created.\n", + "2025-11-12 13:21:15,396 - stpipe.step - INFO - PathLossStep instance created.\n", + "2025-11-12 13:21:15,396 - stpipe.step - INFO - BarShadowStep instance created.\n", + "2025-11-12 13:21:15,397 - stpipe.step - INFO - WfssContamStep instance created.\n", + "2025-11-12 13:21:15,397 - stpipe.step - INFO - PhotomStep instance created.\n", + "2025-11-12 13:21:15,398 - stpipe.step - INFO - PixelReplaceStep instance created.\n", + "2025-11-12 13:21:15,399 - stpipe.step - INFO - ResampleSpecStep instance created.\n", + "2025-11-12 13:21:15,400 - stpipe.step - INFO - CubeBuildStep instance created.\n", + "2025-11-12 13:21:15,401 - stpipe.step - INFO - Extract1dStep instance created.\n", + "2025-11-12 13:21:15,646 - stpipe.step - INFO - Step Spec2Pipeline running with args ('./data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00004_mirifushort_rate.fits',).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3 ./data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00004_mirifushort_rate.fits\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-12 13:21:15,667 - stpipe.step - INFO - Step Spec2Pipeline parameters are:\n", + " pre_hooks: []\n", + " post_hooks: []\n", + " output_file: None\n", + " output_dir: ./data/* bet Pic/12A/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: True\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: True\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: True\n", + " bkg_sigma_clip: 3.0\n", + " ifu_rfcorr: True\n", + " ifu_set_srctype: None\n", + " ifu_rscale: None\n", + " ifu_covar_scale: 1.8\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: True\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: True\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: True\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: True\n", + " bkg_sigma_clip: 3.0\n", + " ifu_rfcorr: True\n", + " ifu_set_srctype: None\n", + " ifu_rscale: None\n", + " ifu_covar_scale: 1.8\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", + "2025-11-12 13:21:15,695 - stpipe.pipeline - INFO - Prefetching reference files for dataset: 'jw01294003001_03102_00004_mirifushort_rate.fits' reftypes = ['area', 'barshadow', 'camera', 'collimator', 'disperser', 'distortion', 'filteroffset', 'fore', 'fpa', 'ifufore', 'ifupost', 'ifuslicer', 'msa', 'msaoper', 'ote', 'pathloss', 'photom', 'regions', 'specwcs', 'wavecorr', 'wavelengthrange']\n", + "2025-11-12 13:21:15,697 - stpipe.pipeline - INFO - Prefetch for AREA reference file is 'N/A'.\n", + "2025-11-12 13:21:15,698 - stpipe.pipeline - INFO - Prefetch for BARSHADOW reference file is 'N/A'.\n", + "2025-11-12 13:21:15,698 - stpipe.pipeline - INFO - Prefetch for CAMERA reference file is 'N/A'.\n", + "2025-11-12 13:21:15,698 - stpipe.pipeline - INFO - Prefetch for COLLIMATOR reference file is 'N/A'.\n", + "2025-11-12 13:21:15,699 - stpipe.pipeline - INFO - Prefetch for DISPERSER reference file is 'N/A'.\n", + "2025-11-12 13:21:15,699 - stpipe.pipeline - INFO - Prefetch for DISTORTION reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_distortion_0140.asdf'.\n", + "2025-11-12 13:21:15,700 - stpipe.pipeline - INFO - Prefetch for FILTEROFFSET reference file is 'N/A'.\n", + "2025-11-12 13:21:15,700 - stpipe.pipeline - INFO - Prefetch for FORE reference file is 'N/A'.\n", + "2025-11-12 13:21:15,700 - stpipe.pipeline - INFO - Prefetch for FPA reference file is 'N/A'.\n", + "2025-11-12 13:21:15,701 - stpipe.pipeline - INFO - Prefetch for IFUFORE reference file is 'N/A'.\n", + "2025-11-12 13:21:15,701 - stpipe.pipeline - INFO - Prefetch for IFUPOST reference file is 'N/A'.\n", + "2025-11-12 13:21:15,701 - stpipe.pipeline - INFO - Prefetch for IFUSLICER reference file is 'N/A'.\n", + "2025-11-12 13:21:15,701 - stpipe.pipeline - INFO - Prefetch for MSA reference file is 'N/A'.\n", + "2025-11-12 13:21:15,702 - stpipe.pipeline - INFO - Prefetch for MSAOPER reference file is 'N/A'.\n", + "2025-11-12 13:21:15,702 - stpipe.pipeline - INFO - Prefetch for OTE reference file is 'N/A'.\n", + "2025-11-12 13:21:15,702 - stpipe.pipeline - INFO - Prefetch for PATHLOSS reference file is 'N/A'.\n", + "2025-11-12 13:21:15,703 - stpipe.pipeline - INFO - Prefetch for PHOTOM reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_photom_0224.fits'.\n", + "2025-11-12 13:21:15,703 - stpipe.pipeline - INFO - Prefetch for REGIONS reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_regions_0142.asdf'.\n", + "2025-11-12 13:21:15,703 - stpipe.pipeline - INFO - Prefetch for SPECWCS reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_specwcs_0139.asdf'.\n", + "2025-11-12 13:21:15,704 - stpipe.pipeline - INFO - Prefetch for WAVECORR reference file is 'N/A'.\n", + "2025-11-12 13:21:15,704 - stpipe.pipeline - INFO - Prefetch for WAVELENGTHRANGE reference file is '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_wavelengthrange_0011.asdf'.\n", + "2025-11-12 13:21:15,705 - jwst.pipeline.calwebb_spec2 - INFO - Starting calwebb_spec2 ...\n", + "2025-11-12 13:21:15,705 - jwst.pipeline.calwebb_spec2 - INFO - Processing product data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00004_mirifushort\n", + "2025-11-12 13:21:15,706 - jwst.pipeline.calwebb_spec2 - INFO - Working on input ./data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00004_mirifushort_rate.fits ...\n", + "2025-11-12 13:21:15,760 - py.warnings - WARNING - /Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stdatamodels/fits_support.py:699: ValidationWarning: While validating meta.cal_step.flat_field the following error occurred:\n", + "'jw01294004001_03102_00004_mirifushort_flat.fits' is not one of ['SKIPPED', 'COMPLETE']\n", + "\n", + "Failed validating 'enum' in schema:\n", + " {'blend_table': True,\n", + " 'enum': ['SKIPPED', 'COMPLETE'],\n", + " 'fits_keyword': 'S_FLAT',\n", + " 'title': 'Flat Field Correction',\n", + " 'type': 'string'}\n", + "\n", + "On instance:\n", + " 'jw01294004001_03102_00004_mirifushort_flat.fits'\n", + " if validate.value_change(path, result, schema, context):\n", + "\n", + "2025-11-12 13:21:15,844 - stpipe.step - INFO - Step assign_wcs running with args (,).\n", + "2025-11-12 13:21:16,255 - jwst.assign_wcs.miri - INFO - Applied Barycentric velocity correction : 0.999987255045279\n", + "2025-11-12 13:21:16,967 - jwst.assign_wcs.miri - INFO - Created a MIRI mir_mrs pipeline with references {'distortion': '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_distortion_0140.asdf', 'filteroffset': None, 'specwcs': '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_specwcs_0139.asdf', 'regions': '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_regions_0142.asdf', 'wavelengthrange': '/Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_wavelengthrange_0011.asdf', 'camera': None, 'collimator': None, 'disperser': None, 'fore': None, 'fpa': None, 'msa': None, 'ote': None, 'ifupost': None, 'ifufore': None, 'ifuslicer': None}\n", + "2025-11-12 13:21:17,504 - stcal.alignment.util - INFO - Update S_REGION to POLYGON ICRS 86.819306821 -51.066864857 86.821883729 -51.066864857 86.821883729 -51.065206615 86.819306821 -51.065206615\n", + "2025-11-12 13:21:17,505 - jwst.assign_wcs.assign_wcs - INFO - COMPLETED assign_wcs\n", + "2025-11-12 13:21:17,508 - stpipe.step - INFO - Step assign_wcs done\n", + "2025-11-12 13:21:17,632 - stpipe.step - INFO - Step badpix_selfcal running with args (, [], []).\n", + "2025-11-12 13:21:17,633 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:17,722 - stpipe.step - INFO - Step msa_flagging running with args (,).\n", + "2025-11-12 13:21:17,723 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:17,809 - stpipe.step - INFO - Step nsclean running with args (,).\n", + "2025-11-12 13:21:17,810 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:17,894 - stpipe.step - INFO - Step imprint_subtract running with args (, []).\n", + "2025-11-12 13:21:17,894 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:17,982 - stpipe.step - INFO - Step bkg_subtract running with args (, []).\n", + "2025-11-12 13:21:17,982 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:18,070 - stpipe.step - INFO - Step srctype running with args (,).\n", + "2025-11-12 13:21:18,401 - jwst.srctype.srctype - INFO - Input EXP_TYPE is MIR_MRS\n", + "2025-11-12 13:21:18,401 - jwst.srctype.srctype - INFO - Input SRCTYAPT = EXTENDED\n", + "2025-11-12 13:21:18,401 - jwst.srctype.srctype - INFO - Using input source type = EXTENDED\n", + "2025-11-12 13:21:18,403 - stpipe.step - INFO - Step srctype done\n", + "2025-11-12 13:21:18,515 - stpipe.step - INFO - Step straylight running with args (,).\n", + "2025-11-12 13:21:18,515 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:18,629 - stpipe.step - INFO - Step flat_field running with args (,).\n", + "2025-11-12 13:21:18,630 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:18,738 - stpipe.step - INFO - Step fringe running with args (,).\n", + "2025-11-12 13:21:18,739 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:18,848 - stpipe.step - INFO - Step pathloss running with args (,).\n", + "2025-11-12 13:21:18,849 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:18,958 - stpipe.step - INFO - Step barshadow running with args (,).\n", + "2025-11-12 13:21:18,959 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:19,070 - stpipe.step - INFO - Step photom running with args (,).\n", + "2025-11-12 13:21:19,078 - jwst.photom.photom_step - INFO - Using photom reference file: /Users/mperrin/data/crds_cache/references/jwst/miri/jwst_miri_photom_0224.fits\n", + "2025-11-12 13:21:19,079 - jwst.photom.photom_step - INFO - Using area reference file: N/A\n", + "2025-11-12 13:21:19,417 - jwst.photom.photom - INFO - Using instrument: MIRI\n", + "2025-11-12 13:21:19,418 - jwst.photom.photom - INFO - detector: MIRIFUSHORT\n", + "2025-11-12 13:21:19,418 - jwst.photom.photom - INFO - exp_type: MIR_MRS\n", + "2025-11-12 13:21:19,419 - jwst.photom.photom - INFO - band: SHORT\n", + "2025-11-12 13:21:19,451 - stdatamodels.dynamicdq - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n", + "2025-11-12 13:21:19,456 - jwst.photom.photom - INFO - Attempting to obtain PIXAR_SR and PIXAR_A2 values from PHOTOM reference file.\n", + "2025-11-12 13:21:19,456 - jwst.photom.photom - INFO - Values for PIXAR_SR and PIXAR_A2 obtained from PHOTOM reference file.\n", + "2025-11-12 13:21:19,464 - jwst.photom.photom - INFO - Applying MRS IFU time dependent correction.\n", + "2025-11-12 13:21:19,806 - stpipe.step - INFO - Step photom done\n", + "2025-11-12 13:21:19,952 - stpipe.step - INFO - Step residual_fringe running with args (,).\n", + "2025-11-12 13:21:19,953 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:20,421 - stpipe.step - INFO - Step pixel_replace running with args (,).\n", + "2025-11-12 13:21:20,422 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:20,555 - stpipe.step - INFO - Step cube_build running with args (,).\n", + "2025-11-12 13:21:20,556 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:21,038 - stpipe.step - INFO - Step extract_1d running with args (,).\n", + "2025-11-12 13:21:21,039 - stpipe.step - INFO - Step skipped.\n", + "2025-11-12 13:21:21,040 - jwst.pipeline.calwebb_spec2 - INFO - Finished processing product data/* bet Pic/12A/stage1_flat/jw01294003001_03102_00004_mirifushort\n", + "2025-11-12 13:21:21,044 - jwst.pipeline.calwebb_spec2 - INFO - Ending calwebb_spec2\n", + "2025-11-12 13:21:21,045 - jwst.stpipe.core - INFO - Results used CRDS context: jwst_1464.pmap\n", + "2025-11-12 13:21:21,061 - py.warnings - WARNING - /Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/astropy/io/fits/card.py:271: VerifyWarning: Keyword name 'FLAT_STD_MIN' is greater than 8 characters or contains characters not allowed by the FITS standard; a HIERARCH card will be created.\n", + " warnings.warn(\n", + "\n", + "2025-11-12 13:21:21,330 - stpipe.step - INFO - Saved model in ./data/* bet Pic/12A/stage2/jw01294003001_03102_00004_mirifushort_cal.fits\n", + "2025-11-12 13:21:21,331 - stpipe.step - INFO - Step Spec2Pipeline done\n", + "2025-11-12 13:21:21,331 - jwst.stpipe.core - INFO - Results used jwst version: 1.20.2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Runtime so far: 23.6870 seconds\n", + "Total Runtime: 23.6870 seconds\n" + ] + } + ], + "source": [ + "## run_stage2\n", + "print(\"Run stage 2\")\n", + "cal_files = run_stage2_miri(data_dir, science_target_name, \n", + " list_bands=list_bands, \n", + " custom_flatted=True, # default is true\n", + " skip_cubes=True, # default is true\n", + " skip_fringe=True, # default is false\n", + " skip_residual_fringes=True, # default is false\n", + " skip_flatfield=True, # default is false\n", + " skip_straylight=True, # default is true\n", + " overwrite=False)\n" + ] + }, + { + "cell_type": "markdown", + "id": "0da545f2-8ff8-4a99-94ba-e39890bc77bc", + "metadata": {}, + "source": [ + "Again, the output files are going to be in subdirectories organized by target name, sub-band, and now \"stage2\". For example files like this: " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "4b2bd93e-1cc3-496b-b273-9e60c35fb54c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['./data/* bet Pic/12A/stage2/jw01294003001_03102_00001_mirifushort_cal.fits',\n", + " './data/* bet Pic/12A/stage2/jw01294003001_03102_00002_mirifushort_cal.fits',\n", + " './data/* bet Pic/12A/stage2/jw01294003001_03102_00003_mirifushort_cal.fits',\n", + " './data/* bet Pic/12A/stage2/jw01294003001_03102_00004_mirifushort_cal.fits']" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cal_files = glob.glob(os.path.join(data_dir, science_target_name, list_bands[0], \"stage2\", \"*.fits\"))\n", + "cal_files.sort()\n", + "cal_files" + ] + }, + { + "cell_type": "markdown", + "id": "55c2215b-0753-4e00-b012-ceb605e50417", + "metadata": {}, + "source": [ + "### And that's it. Done with reductions.\n", + "\n", + "This completes the reduction through stages 1 and 2 of the pipeline.\n", + "\n", + "Proceed to the MRS forward modeling notebook to invoke BREADS forward modeling and detect the companion." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a80c4efc-b6e7-40d7-9c82-4ecb0a1711dc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import breads.jwst_tools.plotting\n", + "\n", + "plt.figure(figsize=(12,8))\n", + "breads.jwst_tools.plotting.plot_2d_image(cal_files[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f24b276-c09e-45e0-ae90-86f42a1e1390", + "metadata": {}, + "outputs": [], + "source": [ + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/tutorials/jwst/MIRI_Tutorial_2_MRS_Forward_Modeling_and_SNR.ipynb b/docs/source/tutorials/jwst/MIRI_Tutorial_2_MRS_Forward_Modeling_and_SNR.ipynb new file mode 100644 index 0000000..9725324 --- /dev/null +++ b/docs/source/tutorials/jwst/MIRI_Tutorial_2_MRS_Forward_Modeling_and_SNR.ipynb @@ -0,0 +1,1064 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "190ccf4e-ca08-4a51-84ba-c67427db19e1", + "metadata": {}, + "source": [ + "# BREADS MIRI Tutorial 2: MRS High Contrast Analyses and SNR Maps using BREADS\n", + "\n", + "\n", + "
\n", + "This is the second in a series of notebooks demonstrating data reduction for MIRI using BREADS. \n", + "
  1. Tutorial 1: Pipeline data reductions to get ready for forward modeling
    2. \n", + "
  2. Tutorial 2 (This notebook): Forward modeling and measuring SNR of a companion
    3. \n", + "
  3. Tutorial 3: Generating data-cube-like representations of the forward modeled data.
    4. \n", + "
\n", + "\n", + "\n", + "
\n", + "Work in progress. This notebook needs more explanations and pedagogy. \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "e01eea63-cf05-424f-a338-9eb1f853a8b3", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4eeb20fe-766f-474c-898d-f4dc25f69fc5", + "metadata": {}, + "outputs": [], + "source": [ + "import os\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\n", + "\n", + "import numpy as np\n", + "import multiprocessing as mp\n", + "import h5py\n", + "from scipy.interpolate import RegularGridInterpolator\n", + "from scipy.optimize import lsq_linear\n", + "import glob\n", + "\n", + "from breads.instruments.jwstmiri_cal import JWSTMiri_cal\n", + "from breads.grid_search import grid_search\n", + "from breads.fm.hc_atmgrid_splinefm_jwst_miri_cal import hc_atmgrid_splinefm_jwst_miri_cal\n", + "from breads.fit import fitfm\n", + "from breads.jwst_tools.flat_miri_utils import plot_flat, plot_starsub_fit\n", + "#from breads.jwst_tools.fit_miri_psf_centroid import *\n", + "from breads.jwst_tools.reduction_utils import find_files_to_process\n", + "from copy import copy\n", + "\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f70d3876-7f0c-4976-8cb7-848e75d0912e", + "metadata": {}, + "outputs": [], + "source": [ + "# What does this do ???\n", + "os.environ['OBJC_DISABLE_INITIALIZE_FORK_SAFETY'] = 'YES'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b47621fb-b286-4c5e-866d-47b560337eaa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MKL did not import\n" + ] + } + ], + "source": [ + "try:\n", + " import mkl\n", + " mkl.set_num_threads(1)\n", + " print(\"MKL has been imported\")\n", + "except:\n", + " print(\"MKL did not import\")\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "id": "16c209ef-98c2-4bf9-8411-e39dcbb8d4b6", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "32983d3a-96d4-4003-882d-0e774cd7b135", + "metadata": {}, + "outputs": [], + "source": [ + "# Number of threads to be used for multithreading\n", + "numthreads = 20\n", + "\n", + "# Number of nodes\n", + "nodes = 40" + ] + }, + { + "cell_type": "markdown", + "id": "73e041d7-c07f-452f-84cb-3e40e6c369b2", + "metadata": {}, + "source": [ + "## Paths" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6e25a560-2e06-4f83-9aa4-74ce128990a9", + "metadata": {}, + "outputs": [], + "source": [ + "# Directories to update\n", + "# External_dir is where to look for external files like the btsettl grid\n", + "external_dir = './' \n", + "\n", + "# Where are the data files located? \n", + "data_dir = \"./data\" " + ] + }, + { + "cell_type": "markdown", + "id": "0773a7f6-3a4a-4937-abd9-30f6868217a2", + "metadata": {}, + "source": [ + "## More Parameter Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e1c02fbd-8756-4a6d-8959-2d100422f435", + "metadata": {}, + "outputs": [], + "source": [ + "# Definition of the wavelength sampling on which the detector images are interpolated (for each detector)\n", + "ra_offset = None\n", + "dec_offset = None" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e1c07bf8-1e07-479d-8848-b25d5a1200f1", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "####################\n", + "channel = '1'\n", + "channel_reduction = '1A'\n", + "channel_band = '12A'\n", + "target_name = '* bet Pic'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "63303005-00d9-44b4-9283-426f58ab8853", + "metadata": {}, + "outputs": [], + "source": [ + "#plot_flat(data_dir, target_name, band_list=['12A'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "c5042240-6af4-41f7-8e75-92fd4c426614", + "metadata": {}, + "source": [ + "## Function Definitions" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e233396a-09a1-458c-bf4e-f46294401aad", + "metadata": {}, + "outputs": [], + "source": [ + "def retrieve_data(data_dir, target_name, channel_band):\n", + " cal_dir = os.path.join(data_dir, target_name, channel_band, 'stage2')\n", + " print(f'Searching cal files in {cal_dir}')\n", + " filelist = find_files_to_process(cal_dir, filetype='cal.fits')\n", + " return filelist\n", + "\n", + "def create_fm_saving_paths(data_dir, target_name, channel_band, N_nodes):\n", + " utils_dir = os.path.join(data_dir, target_name, f'utils_fm_{N_nodes}_nodes', channel_band)\n", + " if not os.path.exists(utils_dir):\n", + " os.makedirs(utils_dir)\n", + " out_dir = os.path.join(data_dir, target_name, f'fm_outputs_{N_nodes}_nodes', channel_band)\n", + " if not os.path.exists(out_dir):\n", + " os.makedirs(out_dir)\n", + " return utils_dir, out_dir\n", + "\n", + "def create_miri_nodes_sampling(channel_band, N_nodes):\n", + " if channel_band == '1A':\n", + " x_nodes = np.linspace(4.82, 5.79, N_nodes, endpoint=True) #4.89 5.75\n", + " elif channel_band == '2A':\n", + " x_nodes = np.linspace(7.42, 8.82, N_nodes, endpoint=True) #7.49 8.78\n", + " elif channel_band == '1B':\n", + " x_nodes = np.linspace(5.61, 6.69, N_nodes, endpoint=True) #5.65 6.64\n", + " elif channel_band == '2B':\n", + " x_nodes = np.linspace(8.66, 10.14, N_nodes, endpoint=True)\n", + " elif channel_band == '1C':\n", + " x_nodes = np.linspace(6.46, 7.72, N_nodes, endpoint=True) #6.51 7.67\n", + " elif channel_band == '2C':\n", + " x_nodes = np.linspace(10.00, 11.712, N_nodes, endpoint=True)\n", + " else:\n", + " raise NotImplementedError\n", + "\n", + " return x_nodes\n", + "\n", + "def interp_model_grid(grid_dir, grid_file_name, verbose=False):\n", + " with h5py.File(os.path.join(grid_dir, grid_file_name), 'r') as hf:\n", + " \n", + " grid_specs = np.array(hf.get(\"spec\"))\n", + " grid_temps = np.array(hf.get(\"temps\"))\n", + " print(grid_temps)\n", + " grid_loggs = np.array(hf.get(\"loggs\"))\n", + " grid_wvs = np.array(hf.get(\"wvs\"))\n", + " \n", + " if verbose:\n", + " print(\"[DEBUG] wavelength bounds:\", np.nanmin(grid_wvs), np.nanmax(grid_wvs))\n", + " print(\"[DEBUG] nan in atmospheric grid?\", np.isnan(grid_specs).any())\n", + " \n", + " # add flux calibration correction because we are fitting a pure WebbPSF model later, which comes with systematics\n", + " myinterpgrid = RegularGridInterpolator((grid_temps, grid_loggs), grid_specs, method=\"linear\", bounds_error=False, fill_value=np.nan)\n", + " #dra_comp, ddec_comp = None, None\n", + " #fix_parameters = [teff, logg, vsini, rv, dra_comp, ddec_comp]\n", + " return myinterpgrid, grid_wvs" + ] + }, + { + "cell_type": "markdown", + "id": "996246e3-b229-477a-99cd-f57b05491113", + "metadata": {}, + "source": [ + "## Load Cal Files" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "151226ce-0612-4625-a1f2-0a7044e33782", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Searching cal files in ./data/* bet Pic/12A/stage2\n", + "Searching in ./data/* bet Pic/12A/stage2 for files matching jw*_cal.fits\n", + "\tFound 4 input files to process\n", + "\tjw01294003001_03102_00001_mirifushort_cal.fits\n", + "\tjw01294003001_03102_00002_mirifushort_cal.fits\n", + "\tjw01294003001_03102_00003_mirifushort_cal.fits\n", + "\tjw01294003001_03102_00004_mirifushort_cal.fits\n" + ] + } + ], + "source": [ + "filelist = retrieve_data(data_dir, target_name, channel_band)" + ] + }, + { + "cell_type": "markdown", + "id": "303c9f1e-0185-4f6f-beb4-d4f9338f9a52", + "metadata": {}, + "source": [ + "## Prepare for forward modeling: create output paths, set up node spacings" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "aaec4f76-d94b-47d7-8719-13199eed0cbc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('./data/* bet Pic/utils_fm_40_nodes/1A',\n", + " './data/* bet Pic/fm_outputs_40_nodes/1A')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "utils_dir, out_dir = create_fm_saving_paths(data_dir, target_name, channel_reduction, nodes)\n", + "utils_dir, out_dir" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "8d1b072f-f2d4-4158-9b6d-3e12d033b5f0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([4.82 , 4.84487179, 4.86974359, 4.89461538, 4.91948718,\n", + " 4.94435897, 4.96923077, 4.99410256, 5.01897436, 5.04384615,\n", + " 5.06871795, 5.09358974, 5.11846154, 5.14333333, 5.16820513,\n", + " 5.19307692, 5.21794872, 5.24282051, 5.26769231, 5.2925641 ,\n", + " 5.3174359 , 5.34230769, 5.36717949, 5.39205128, 5.41692308,\n", + " 5.44179487, 5.46666667, 5.49153846, 5.51641026, 5.54128205,\n", + " 5.56615385, 5.59102564, 5.61589744, 5.64076923, 5.66564103,\n", + " 5.69051282, 5.71538462, 5.74025641, 5.76512821, 5.79 ])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_nodes = create_miri_nodes_sampling(channel_reduction, nodes)\n", + "x_nodes" + ] + }, + { + "cell_type": "markdown", + "id": "7ebba299-81f2-42a8-b52c-820450ee282f", + "metadata": {}, + "source": [ + "## Main SNR analysis processing loop" + ] + }, + { + "cell_type": "markdown", + "id": "5529227d-5ca8-4d21-987c-08360c76249b", + "metadata": {}, + "source": [ + "### Parameters for the loop" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0b079371-e2f3-4e06-a559-0cca1bc30312", + "metadata": {}, + "outputs": [], + "source": [ + "read_grid = True # Should we read in the atmosphere model file from a precomputed model grid? \n", + "atmos_model_filename = \"BT-settl_MIR.hdf5\" # What filename should be used from that grid? \n", + "\n", + "# Parameters for model grid\n", + "model_teff = 1100 # 1700\n", + "model_logg = 4.0\n", + "model_vsini = 0\n", + "\n", + "\n", + "# Something about applying regularization. Details TBD...\n", + "regularization_step = True\n", + "regularization = True\n", + "\n", + "# Test the forward model for a fixed value of the non linear parameter.\n", + "# Make sure it does not crash and look the way you want\n", + "# THis runs the analysis for just a single position dra ddec\n", + "test = False\n", + "\n", + "\n", + "\n", + "\n", + "# Run the grid search over a parameter space to estimate planet SNR as a function of position and RV? \n", + "# This is the main SNR output analysis.\n", + "grid_search_snr=True\n", + "\n", + "# Parameters for that grid\n", + "rvs = np.array([60]) # What RV(s) to check? \n", + "ras = np.arange(-1.0, 1.0, 0.1) # Delta RA for spatial sampling\n", + "decs = np.arange(-1.0, 1.0, 0.1) # Delta Dec for spatial sampling\n" + ] + }, + { + "cell_type": "markdown", + "id": "d38297af-b182-47e0-9007-354c3f662a66", + "metadata": {}, + "source": [ + "### Main loop" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "27b609c5-e407-45ff-88c0-25479130eef6", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Running SNR analysis for ./data/* bet Pic/12A/stage2/jw01294003001_03102_00001_mirifushort_cal.fits (file 1 of 4)\n", + "Reading data from ./data/* bet Pic/12A/stage2/jw01294003001_03102_00001_mirifushort_cal.fits\n", + "Photom file selected: jwst_miri_photom_0224.fits\n", + "Photom file selected: jwst_miri_photom_0210.fits\n", + "Wavelength map loaded\n", + "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", + "Loading data for compute_med_filt_badpix cached in ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00001_mirifushort_cal_roughbadpix.fits\n", + "Loading data for compute_coordinates_arrays cached in ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00001_mirifushort_cal_relcoords.fits\n", + "Loading data for compute_quick_webbpsf_model cached in ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_mirifushort_quick_webbpsf.fits\n", + "Loading data for compute_new_coords_from_webbPSFfit cached in ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00001_mirifushort_cal_newcen_wpsf.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 ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00001_mirifushort_cal_starspec_contnorm.fits\n", + "Loading data for compute_starsubtraction cached in ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00001_mirifushort_cal_starsub.fits\n", + "[ 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800]\n", + "Grid search\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/numpy/lib/nanfunctions.py:1217: RuntimeWarning: All-NaN slice encountered\n", + " return function_base._ureduce(a, func=_nanmedian, keepdims=keepdims,\n", + "/var/folders/wj/p62fp78j0z1b4xyr2yql1prw00042w/T/ipykernel_68249/624996590.py:47: RuntimeWarning: All-NaN slice encountered\n", + " reg_mean_map[wherenan] = np.tile(np.nanmedian(spline_paras, axis=1)[:, None], (1, spline_paras.shape[1]))[\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==> ./data/* bet Pic/fm_outputs_40_nodes/1A/jw01294003001_03102_00001_mirifushort_cal_out.hdf5\n", + "\n", + "Running SNR analysis for ./data/* bet Pic/12A/stage2/jw01294003001_03102_00002_mirifushort_cal.fits (file 2 of 4)\n", + "Reading data from ./data/* bet Pic/12A/stage2/jw01294003001_03102_00002_mirifushort_cal.fits\n", + "Photom file selected: jwst_miri_photom_0224.fits\n", + "Photom file selected: jwst_miri_photom_0210.fits\n", + "Wavelength map loaded\n", + "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", + "Loading data for compute_med_filt_badpix cached in ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00002_mirifushort_cal_roughbadpix.fits\n", + "Loading data for compute_coordinates_arrays cached in ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00002_mirifushort_cal_relcoords.fits\n", + "Loading data for compute_quick_webbpsf_model cached in ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_mirifushort_quick_webbpsf.fits\n", + "Loading data for compute_new_coords_from_webbPSFfit cached in ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00002_mirifushort_cal_newcen_wpsf.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 ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00002_mirifushort_cal_starspec_contnorm.fits\n", + "Loading data for compute_starsubtraction cached in ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00002_mirifushort_cal_starsub.fits\n", + "[ 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800]\n", + "Grid search\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==> ./data/* bet Pic/fm_outputs_40_nodes/1A/jw01294003001_03102_00002_mirifushort_cal_out.hdf5\n", + "\n", + "Running SNR analysis for ./data/* bet Pic/12A/stage2/jw01294003001_03102_00003_mirifushort_cal.fits (file 3 of 4)\n", + "Reading data from ./data/* bet Pic/12A/stage2/jw01294003001_03102_00003_mirifushort_cal.fits\n", + "Photom file selected: jwst_miri_photom_0224.fits\n", + "Photom file selected: jwst_miri_photom_0210.fits\n", + "Wavelength map loaded\n", + "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", + "Loading data for compute_med_filt_badpix cached in ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00003_mirifushort_cal_roughbadpix.fits\n", + "Loading data for compute_coordinates_arrays cached in ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00003_mirifushort_cal_relcoords.fits\n", + "Loading data for compute_quick_webbpsf_model cached in ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_mirifushort_quick_webbpsf.fits\n", + "Loading data for compute_new_coords_from_webbPSFfit cached in ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00003_mirifushort_cal_newcen_wpsf.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 ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00003_mirifushort_cal_starspec_contnorm.fits\n", + "Loading data for compute_starsubtraction cached in ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00003_mirifushort_cal_starsub.fits\n", + "[ 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800]\n", + "Grid search\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==> ./data/* bet Pic/fm_outputs_40_nodes/1A/jw01294003001_03102_00003_mirifushort_cal_out.hdf5\n", + "\n", + "Running SNR analysis for ./data/* bet Pic/12A/stage2/jw01294003001_03102_00004_mirifushort_cal.fits (file 4 of 4)\n", + "Reading data from ./data/* bet Pic/12A/stage2/jw01294003001_03102_00004_mirifushort_cal.fits\n", + "Photom file selected: jwst_miri_photom_0224.fits\n", + "Photom file selected: jwst_miri_photom_0210.fits\n", + "Wavelength map loaded\n", + "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", + "Loading data for compute_med_filt_badpix cached in ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00004_mirifushort_cal_roughbadpix.fits\n", + "Loading data for compute_coordinates_arrays cached in ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00004_mirifushort_cal_relcoords.fits\n", + "Loading data for compute_quick_webbpsf_model cached in ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_mirifushort_quick_webbpsf.fits\n", + "Loading data for compute_new_coords_from_webbPSFfit cached in ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00004_mirifushort_cal_newcen_wpsf.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 ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00004_mirifushort_cal_starspec_contnorm.fits\n", + "Loading data for compute_starsubtraction cached in ./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00004_mirifushort_cal_starsub.fits\n", + "[ 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800]\n", + "Grid search\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n", + "/Users/mperrin/miniconda3/envs/stenv-py3.12-2025.07.16/lib/python3.12/site-packages/stsci/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " __import__('pkg_resources').declare_namespace(__name__)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==> ./data/* bet Pic/fm_outputs_40_nodes/1A/jw01294003001_03102_00004_mirifushort_cal_out.hdf5\n" + ] + } + ], + "source": [ + "mypool = mp.Pool(processes=numthreads)\n", + "\n", + "for i, filename in enumerate(filelist):\n", + " print(f\"\\nRunning SNR analysis for {filename} (file {i+1} of {len(filelist)})\")\n", + "\n", + " preproc_task_list = []\n", + " preproc_task_list.append([\"compute_med_filt_badpix\", {\"window_size\": 10, \"mad_threshold\": 20}, True, True])\n", + " preproc_task_list.append([\"compute_coordinates_arrays\",])\n", + " preproc_task_list.append([\"compute_quick_webbpsf_model\", {\"image_mask\": None, \"oversample\": 10}, True, True])\n", + " preproc_task_list.append([\"compute_new_coords_from_webbPSFfit\", {\"IWA\": 0.3, \"OWA\": 2, \"apply_offset\": True}, True, True])\n", + " preproc_task_list.append([\"convert_MJy_per_sr_to_MJy\"])\n", + " #preproc_task_list.append([\"apply_coords_offset\", {\"coords_offset\": centroid}])\n", + " #preproc_task_list.append([\"compute_charge_bleeding_mask\", {\"threshold2mask\": 0.15}])\n", + " preproc_task_list.append([\"compute_starspectrum_contnorm\", {\"x_nodes\": x_nodes, \"mppool\": mypool}, True, True])\n", + " preproc_task_list.append([\"compute_starsubtraction\", {\"mppool\": mypool}, True, True])\n", + " \n", + " dataobj = JWSTMiri_cal(filename, channel_reduction=channel, utils_dir=utils_dir,\n", + " save_utils=True, load_utils=True, preproc_task_list=preproc_task_list)\n", + "\n", + " tmp_badpixels = copy(dataobj.bad_pixels)\n", + " subtracted_im, star_model, spline_paras, _ = dataobj.reload_starsubtraction()\n", + " wv_sampling = np.nanmedian(dataobj.wavelengths, 1)\n", + "\n", + " if read_grid: # read and normalize BTSettl model grid used in FM\n", + " myinterpgrid, grid_wvs = interp_model_grid(external_dir, atmos_model_filename)\n", + " # #minwv, maxwv = np.min(dataobj.wavelengths), np.max(dataobj.wavelengths)\n", + " # with h5py.File(os.path.join(external_dir, atmos_model_filename), 'r') as hf:\n", + " # grid_specs = np.array(hf.get(\"spec\"))\n", + " # grid_temps = np.array(hf.get(\"temps\"))\n", + " # grid_loggs = np.array(hf.get(\"loggs\"))\n", + " # grid_wvs = np.array(hf.get(\"wvs\"))\n", + "\n", + " # # add flux calibration correction because we are fitting a pure WebbPSF model later, which comes with systematics\n", + " # # grid_specs = grid_specs / np.polyval(flux_calib_paras, grid_wvs)[None, None, :]\n", + "\n", + " # myinterpgrid = RegularGridInterpolator((grid_temps, grid_loggs), grid_specs, method=\"linear\",\n", + " # bounds_error=False, fill_value=np.nan)\n", + " # teff, logg, vsini, \n", + " rv, dra_comp, ddec_comp = None, None, None\n", + " fix_parameters = [model_teff, model_logg, model_vsini, rv, dra_comp, ddec_comp]\n", + "\n", + " if regularization_step: # Definition of the priors\n", + " subtracted_im, star_model, spline_paras, x_nodes = dataobj.reload_starsubtraction()\n", + "\n", + " wherenan = np.where(np.isnan(spline_paras))\n", + " reg_mean_map = copy(spline_paras)\n", + " reg_mean_map[wherenan] = np.tile(np.nanmedian(spline_paras, axis=1)[:, None], (1, spline_paras.shape[1]))[\n", + " wherenan]\n", + " if regularization:\n", + " reg_std_map = np.zeros(spline_paras.shape) + 300e-12 #np.nanmedian(spline_paras)\n", + " else:\n", + " reg_std_map = np.abs(spline_paras)\n", + " reg_std_map[wherenan] = \\\n", + " np.tile(np.nanmax(np.abs(spline_paras), axis=1)[:, None], (1, spline_paras.shape[1]))[wherenan]\n", + " reg_std_map = reg_std_map\n", + " reg_std_map = np.clip(reg_std_map, 1e-11, np.inf) # in MJy\n", + "\n", + " fm_paras = {\"atm_grid\": myinterpgrid, \"atm_grid_wvs\": grid_wvs, \"channel\":channel, \"star_func\": dataobj.star_func,\n", + " \"radius_as\": 0.25, \"badpixfraction\": 0.75, \"nodes\": x_nodes, \"fix_parameters\": fix_parameters,\n", + " \"regularization\": \"user\", \"reg_mean_map\": reg_mean_map,\n", + " \"reg_std_map\": reg_std_map} #0.15 for 1A\n", + " fm_func = hc_atmgrid_splinefm_jwst_miri_cal\n", + "\n", + " # /!\\ Optional but recommended\n", + " # Test the forward model for a fixed value of the non linear parameter.\n", + " # Make sure it does not crash and look the way you want\n", + " if test:\n", + " # fm_paras[\"fix_parameters\"]= [None,None,None,sc_fib]\n", + " ra_offset, dec_offset = -0.188, 0.356 #-0.253, 0.922 #0, 0.5 #1.64, 0.50 #-0.626-0.21, -0.311-0.03 #-0.188-0.21, 0.356+0.03 #1.450, 0.490 #0.3, 0.3\n", + " nonlin_paras = [0.0, ra_offset, dec_offset] # rv (km/s), dra (arcsec),ddec (arcsec),\n", + "\n", + " # d is the data vector at the specified location\n", + " # M is the linear component of the model. M is a function of the non linear parameters x,y,rv\n", + " # s is the vector of uncertainties corresponding to d\n", + " d, M, s, extra_outputs = fm_func(nonlin_paras, dataobj, return_extra_outputs=True, **fm_paras)\n", + " where_finite = extra_outputs[\"where_trace_finite\"]\n", + " w = extra_outputs[\"wvs\"]\n", + " x = extra_outputs[\"ras\"]\n", + " y = extra_outputs[\"decs\"]\n", + " rows = extra_outputs[\"rows\"]\n", + " d_reg, s_reg = extra_outputs[\"regularization\"]\n", + " reg_wvs = extra_outputs[\"regularization_wvs\"]\n", + " reg_rows = extra_outputs[\"regularization_rows\"]\n", + " unique_rows = np.unique(rows)\n", + "\n", + " shifted_w = w + (wv_sampling[-1] - wv_sampling[0]) * (rows - np.nanmin(unique_rows))\n", + " shifted_reg_wvs = reg_wvs + (wv_sampling[-1] - wv_sampling[0]) * (reg_rows - np.nanmin(unique_rows))\n", + "\n", + " validpara = np.where(np.max(np.abs(M), axis=0) != 0)\n", + " M = M[:, validpara[0]]\n", + " primary_hdu = fits.PrimaryHDU()\n", + "\n", + " ds = d\n", + " Ms = M\n", + "\n", + " paras = lsq_linear(Ms, ds).x\n", + " m = np.dot(Ms, paras)\n", + " r = d - m\n", + "\n", + " primary_hdu = fits.PrimaryHDU()\n", + "\n", + " hdu1 = fits.ImageHDU(data=ds, name='DATA')\n", + " hdu2 = fits.ImageHDU(data=Ms, name='MODEL_MATRIX')\n", + " hdu3 = fits.ImageHDU(data=r, name='RESIDUALS')\n", + " hdu4 = fits.ImageHDU(data=paras, name='LINPARAS')\n", + " hdu5 = fits.ImageHDU(data=w, name='WAVE')\n", + " hdu6 = fits.ImageHDU(data=s, name='NOISE')\n", + " hdu7 = fits.ImageHDU(data=rows, name='ROWS')\n", + " hdul = fits.HDUList([primary_hdu, hdu1, hdu2, hdu3, hdu4, hdu5, hdu6, hdu7])\n", + "\n", + " hdul.writeto(f'lsq_fit.fits', overwrite=True)\n", + "\n", + "\n", + " residuals = np.ones(np.size(s) + np.size(validpara[0])) + np.nan\n", + " residuals_H0 = np.ones(np.size(s) + np.size(validpara[0])) + np.nan\n", + " noise4residuals = np.ones(np.size(s) + np.size(validpara[0])) + np.nan\n", + " log_prob, log_prob_H0, rchi2, linparas, linparas_err = fitfm(nonlin_paras, dataobj, fm_func, fm_paras,\n", + " computeH0=True, bounds=None,\n", + " residuals=residuals, residuals_H0=residuals_H0,\n", + " noise4residuals=noise4residuals,\n", + " scale_noise=True,\n", + " marginalize_noise_scaling=False)\n", + " paras = linparas[validpara]\n", + " print(\"best fit\", linparas[0:5])\n", + " print(\"best fit err\", linparas_err[0:5])\n", + " print(\"best fit snr\", linparas[0:5] / linparas_err[0:5])\n", + " print(\"rchi2\", rchi2)\n", + "\n", + " if grid_search_snr:\n", + " print(\"Grid search\")\n", + " log_prob, log_prob_H0, rchi2, linparas, linparas_err = grid_search([rvs, ras, decs], dataobj, fm_func,\n", + " fm_paras, numthreads=numthreads,\n", + " computeH0=False)\n", + " outoftheoven_filename = os.path.join(out_dir,\n", + " os.path.basename(filename).replace(\".fits\",\n", + " \"_out.hdf5\"))\n", + " print(f\"==> {outoftheoven_filename}\")\n", + " with h5py.File(outoftheoven_filename, 'w') as hf:\n", + " hf.create_dataset(\"rvs\", data=rvs)\n", + " hf.create_dataset(\"ras\", data=ras)\n", + " hf.create_dataset(\"decs\", data=decs)\n", + " hf.create_dataset(\"log_prob\", data=log_prob)\n", + " hf.create_dataset(\"log_prob_H0\", data=log_prob_H0)\n", + " hf.create_dataset(\"rchi2\", data=rchi2)\n", + " hf.create_dataset(\"linparas\", data=linparas)\n", + " hf.create_dataset(\"linparas_err\", data=linparas_err)\n", + "\n", + "mypool.close()\n", + "mypool.join()" + ] + }, + { + "cell_type": "markdown", + "id": "9cd8d230-2580-4fc8-a7b5-585391f349b4", + "metadata": {}, + "source": [ + "The outputs are a bunch of HDF5 files: " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "f1d03401-9736-4abf-8ec1-42bafd8e19ed", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['./data/* bet Pic/fm_outputs_40_nodes/1A/jw01294003001_03102_00002_mirifushort_cal_out.hdf5',\n", + " './data/* bet Pic/fm_outputs_40_nodes/1A/jw01294003001_03102_00003_mirifushort_cal_out.hdf5',\n", + " './data/* bet Pic/fm_outputs_40_nodes/1A/jw01294003001_03102_00001_mirifushort_cal_out.hdf5',\n", + " './data/* bet Pic/fm_outputs_40_nodes/1A/jw01294003001_03102_00004_mirifushort_cal_out.hdf5']" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "glob.glob(os.path.join(data_dir, target_name, f\"fm_outputs_{nodes}_nodes\", channel_reduction, \"*.*\"))" + ] + }, + { + "cell_type": "markdown", + "id": "a919dba4-243a-4b07-b258-eed60cb6eb19", + "metadata": {}, + "source": [ + "As is standard for BREADS, the intermediate data products are all saved in a so-called \"utils\" subdirectory:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d20b614a-19ee-4d24-a3cc-a3a672deaecf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['./data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00001_mirifushort_cal_newcen_wpsf.fits',\n", + " './data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00001_mirifushort_cal_relcoords.fits',\n", + " './data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00001_mirifushort_cal_roughbadpix.fits',\n", + " './data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00001_mirifushort_cal_starspec_contnorm.fits',\n", + " './data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00001_mirifushort_cal_starsub.fits',\n", + " './data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00002_mirifushort_cal_newcen_wpsf.fits',\n", + " './data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00002_mirifushort_cal_relcoords.fits',\n", + " './data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00002_mirifushort_cal_roughbadpix.fits',\n", + " './data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00002_mirifushort_cal_starspec_contnorm.fits',\n", + " './data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00002_mirifushort_cal_starsub.fits',\n", + " './data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00003_mirifushort_cal_newcen_wpsf.fits',\n", + " './data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00003_mirifushort_cal_relcoords.fits',\n", + " './data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00003_mirifushort_cal_roughbadpix.fits',\n", + " './data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00003_mirifushort_cal_starspec_contnorm.fits',\n", + " './data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00003_mirifushort_cal_starsub.fits',\n", + " './data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00004_mirifushort_cal_newcen_wpsf.fits',\n", + " './data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00004_mirifushort_cal_relcoords.fits',\n", + " './data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00004_mirifushort_cal_roughbadpix.fits',\n", + " './data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00004_mirifushort_cal_starspec_contnorm.fits',\n", + " './data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00004_mirifushort_cal_starsub.fits',\n", + " './data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_mirifushort_quick_webbpsf.fits']" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fns = glob.glob(os.path.join(data_dir, target_name, f\"utils_fm_{nodes}_nodes\", channel_reduction, \"*.*\"))\n", + "fns.sort()\n", + "fns" + ] + }, + { + "cell_type": "markdown", + "id": "354a2d89-3005-4cfb-88e1-936ca58d5ba2", + "metadata": {}, + "source": [ + "## Plot Outputs" + ] + }, + { + "cell_type": "markdown", + "id": "806b4790-8b54-491f-acaf-4ae2447de6d1", + "metadata": {}, + "source": [ + "Alexis says: Basically we take a look at the linparas output where the first term of the vector is the scaling flux of the tested template and we make the ratio with the first term of linparas_err to get the SNR" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "4f8b0653-5a0d-4c0e-add2-78f5f276734b", + "metadata": {}, + "outputs": [], + "source": [ + "companion_offsets = [[0.2828, 0.4603], # [dRA, dDec] for each companion.\n", + " # Values taken from whereistheplanet.org for the date of observation.\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "35bfd54a-d4a5-41b4-ad94-fe676336f470", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mperrin/Dropbox (Personal)/Documents/software/git/breads/breads/jwst_tools/open_fm_outputs.py:148: RuntimeWarning: invalid value encountered in divide\n", + " snr_map = (flux_p[index, k] / flux_error[index, k]).T\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "from breads.jwst_tools.open_fm_outputs import *\n", + "import matplotlib\n", + "\n", + "\n", + "open_fm_outputs_miri(data_dir, target_name, nodes, list_bands=['1A'], companion_offsets=companion_offsets)\n", + "\n", + " #plot_combined_hf_starspectrum(\"/Users/abidot/Desktop/miri_data_4829_new/\", \"HD 218396\", 160, list_bands=['3B'])\n", + " #coor_ptheta = [[2, 2.5|]]\n", + "\n", + " #open_fm_outputs_miri(\"/Users/abidot/Desktop/miri_data_8714_gitest/\", \"* eps Ind Ab\", 85, list_bands=['2A'], coor_ptheta=coor_ptheta)\n", + " #plot_combined_hf_starspectrum(\"/Users/abidot/Desktop/miri_data_8714_gitest/\", '* eps Ind Ab', 85, list_bands=['1A'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36de7fbf-8025-4adf-9427-1cef5da1200e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/tutorials/jwst/MIRI_Tutorial_3_MRS_Cube-ish_build.ipynb b/docs/source/tutorials/jwst/MIRI_Tutorial_3_MRS_Cube-ish_build.ipynb new file mode 100644 index 0000000..cf674a4 --- /dev/null +++ b/docs/source/tutorials/jwst/MIRI_Tutorial_3_MRS_Cube-ish_build.ipynb @@ -0,0 +1,1667 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6c3e3636-3237-4e5a-8627-3e9833f91145", + "metadata": {}, + "source": [ + "# BREADS MIRI Tutorial 3: Building forward modeled data cubes (ish)\n", + "\n", + "\n", + "
\n", + "This is the third in a series of notebooks demonstrating data reduction for MIRI using BREADS. \n", + "
  1. Tutorial 1: Pipeline data reductions to get ready for forward modeling
    2. \n", + "
  2. Tutorial 2: Forward modeling and measuring SNR of a companion
    3. \n", + "
  3. Tutorial 3 (This notebook): Generating data-cube-like representations of the forward modeled data.
    4. \n", + "
\n", + "\n", + "\n", + "
\n", + "Work in progress. This notebook needs more explanations and pedagogy. \n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6e62eb80-140a-4922-aca4-4ad80974cced", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import numpy as np\n", + "from astropy.io import fits\n", + "\n", + "\n", + "# Print out what pipeline version we're using\n", + "from breads.jwst_tools.reduction_utils import find_files_to_process\n", + "from breads.jwst_tools.reduction_utils import compute_normalized_stellar_spectrum_miri\n", + "from breads.jwst_tools.reduction_utils import compute_starlight_subtraction_miri\n", + "from breads.jwst_tools.reduction_utils import get_combined_regwvs_miri\n", + "from breads.instruments.jwstmiri_cal import build_cube, build_cube_para\n", + "\n", + "import multiprocessing as mp\n", + "\n", + "os.environ[\"OMP_NUM_THREADS\"] = \"1\"\n", + "os.environ[\"OPENBLAS_NUM_THREADS\"] = \"1\"\n", + "os.environ[\"MKL_NUM_THREADS\"] = \"1\"\n", + "os.environ[\"VECLIB_MAXIMUM_THREADS\"] = \"1\"\n", + "os.environ[\"NUMEXPR_NUM_THREADS\"]= \"1\"\n", + "\n", + "\n", + "os.environ['OBJC_DISABLE_INITIALIZE_FORK_SAFETY'] = 'YES'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8bdf837c-43d9-4bc2-87f4-84aa5f56ddcc", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def create_miri_nodes_wave_sampling(channel_band, N_nodes):\n", + " \"\"\" Creates wavelength sampling and sets aperture for photometry estimation\n", + " and FOV for STPSF computation \n", + "\n", + " Using smaller FOVs and apertures here is an optimization to keep the runtime from being too slow\n", + " \"\"\"\n", + " if channel_band == '1A':\n", + " x_nodes = np.linspace(4.89, 5.75, N_nodes, endpoint=True)\n", + " wave = np.linspace(4.89, 5.75, 1050, endpoint=True)\n", + " aperture = 0.2\n", + " fov = 0.6\n", + " elif channel_band == '1B':\n", + " x_nodes = np.linspace(5.65, 6.64, N_nodes, endpoint=True)\n", + " wave = np.linspace(5.65, 6.64, 1050, endpoint=True)\n", + " aperture = 0.2\n", + " fov = 0.6\n", + " elif channel_band == '1C':\n", + " x_nodes = np.linspace(6.51, 7.67, N_nodes, endpoint=True)\n", + " wave = np.linspace(6.51, 7.67, 1050, endpoint=True)\n", + " aperture = 0.2\n", + " fov = 0.6 \n", + " elif channel_band == '2A':\n", + " x_nodes = np.linspace(7.47, 8.80, N_nodes, endpoint=True)\n", + " wave = np.linspace(7.47, 8.80, 1050, endpoint=True)\n", + " aperture = 0.2\n", + " fov = 0.8\n", + " elif channel_band == '2B':\n", + " x_nodes = np.linspace(8.66, 10.14, N_nodes, endpoint=True)\n", + " wave = np.linspace(8.66, 10.14, 1050, endpoint=True)\n", + " aperture = 0.3\n", + " fov = 0.8\n", + "\n", + " elif channel_band == '2C':\n", + " x_nodes = np.linspace(10.00, 11.712, N_nodes, endpoint=True)\n", + " wave = np.linspace(10.00, 11.712, 1050, endpoint=True)\n", + " aperture = 0.3\n", + " fov = 0.8\n", + " elif channel_band == '3A':\n", + " x_nodes = np.linspace(11.55, 13.47, N_nodes, endpoint=True)\n", + " wave = np.linspace(11.55, 13.47, 1050, endpoint=True)\n", + " aperture = 0.4\n", + " fov = 0.9\n", + " elif channel_band == '3B':\n", + " x_nodes = np.linspace(13.34, 15.56, N_nodes, endpoint=True)\n", + " wave = np.linspace(13.34, 15.56, 1050, endpoint=True)\n", + " aperture = 0.4\n", + " fov = 0.9\n", + " elif channel_band == '3C':\n", + " x_nodes = np.linspace(15.3, 18.1, N_nodes, endpoint=True)\n", + " wave = np.linspace(15.3, 18.1, 1050, endpoint=True)\n", + " aperture = 0.4\n", + " fov = 0.9\n", + " elif channel_band == '4A':\n", + " x_nodes = np.linspace(17.7, 20.94, N_nodes, endpoint=True)\n", + " wave = np.linspace(17.7, 20.94, 1050, endpoint=True)\n", + " aperture = 0.5\n", + " fov = 0.9\n", + " elif channel_band == '4B':\n", + " x_nodes = np.linspace(20.69, 24.48, N_nodes, endpoint=True)\n", + " wave = np.linspace(20.69, 24.48, 1050, endpoint=True)\n", + " aperture = 0.5\n", + " fov = 0.9\n", + " else:\n", + " raise NotImplementedError\n", + "\n", + " return x_nodes, wave, aperture, fov\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e29c5a55-752c-45a4-a62d-dca0bb98b25e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[35mBT-settl_MIR.hdf5\u001b[m\u001b[m\n", + "\u001b[35mCRDS_file\u001b[m\u001b[m\n", + "\u001b[1m\u001b[36mdata\u001b[m\u001b[m\n", + "fig_fringes_jw01294003001_03102_00001_mirifushort_rate.png\n", + "fig_fringes_jw01294003001_03102_00002_mirifushort_rate.png\n", + "fig_fringes_jw01294003001_03102_00003_mirifushort_rate.png\n", + "fig_fringes_jw01294003001_03102_00004_mirifushort_rate.png\n", + "Tutorial_MIRI-Cube ish build.ipynb\n", + "Tutorial_MIRI-MRS_Forward_Modeling_and_SNR.ipynb\n", + "Tutorial_MIRI-MRS_Reductions_for_BREADS.ipynb\n", + "Untitled.ipynb\n" + ] + } + ], + "source": [ + "!ls" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6b19d169-15fb-4b4d-b426-844c2ccd0cab", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "numthreads = 30\n", + "\n", + "# Main directory for the data and reduced products\n", + "uncaldir = \"data\"\n", + "crds_dir = \"./CRDS_file\"\n", + "os.environ['CUSTOM_CRDS_PATH'] = crds_dir\n", + "targetname = '* bet Pic'\n", + "targetname_simbad = 'Beta Pic'\n", + "channel = '1'\n", + "band = '1A'\n", + "obs_band = '12A'\n", + "\n", + "N_nodes = 40\n", + "\n", + "output_dir = os.path.join(uncaldir, targetname, \"cube_outputs\", band)\n", + "if not os.path.exists(output_dir):\n", + " os.makedirs(output_dir)\n", + "\n", + "ra_vec = np.arange(-2.5, 2.5, 0.13)\n", + "dec_vec = np.arange(-2.5, 2.5, 0.13)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "358615bc-9b9b-4666-9c71-d75ba6ebf128", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Searching cal files in data/* bet Pic/12A/stage2\n", + "Searching in data/* bet Pic/12A/stage2 for files matching jw*_*_cal.fits\n", + "\tFound 4 input files to process\n", + "\tjw01294003001_03102_00001_mirifushort_cal.fits\n", + "\tjw01294003001_03102_00002_mirifushort_cal.fits\n", + "\tjw01294003001_03102_00003_mirifushort_cal.fits\n", + "\tjw01294003001_03102_00004_mirifushort_cal.fits\n" + ] + } + ], + "source": [ + "\n", + "filename_filter = f\"*_cal.fits\"\n", + "stage2 = os.path.join(uncaldir, targetname, obs_band, \"stage2\")\n", + "print(f\"Searching cal files in {stage2}\")\n", + "utils_dir = os.path.join(uncaldir, targetname, f\"utils_fm_{N_nodes}_nodes\", band)\n", + "cleaned_cal_files = find_files_to_process(stage2, filetype=filename_filter)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "45393725-7d1d-4bb9-a9b3-6f15fa7b7b9a", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "hdulist_sc = fits.open(cleaned_cal_files[0])\n", + "wave2d = hdulist_sc[\"WAVELENGTH\"].data\n", + "\n", + "wv_nodes, wv_sampling, aperture, fov = create_miri_nodes_wave_sampling(band, N_nodes)\n", + "\n", + "splitbasename = os.path.basename(cleaned_cal_files[0]).split(\"_\")\n", + "filename_suffix = \"_webbpsf\"\n", + "cube_filename = os.path.join(output_dir, splitbasename[0]+\"_\"+splitbasename[1]+\"_\"+splitbasename[3]+\"_spectral_cube_ish.fits\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b3860a42-58a7-4a82-87e3-1ebfbfe99b55", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extract star spectrum\n", + "[4.89 4.91205128 4.93410256 4.95615385 4.97820513 5.00025641\n", + " 5.02230769 5.04435897 5.06641026 5.08846154 5.11051282 5.1325641\n", + " 5.15461538 5.17666667 5.19871795 5.22076923 5.24282051 5.26487179\n", + " 5.28692308 5.30897436 5.33102564 5.35307692 5.37512821 5.39717949\n", + " 5.41923077 5.44128205 5.46333333 5.48538462 5.5074359 5.52948718\n", + " 5.55153846 5.57358974 5.59564103 5.61769231 5.63974359 5.66179487\n", + " 5.68384615 5.70589744 5.72794872 5.75 ]\n", + "data/* bet Pic/12A/stage2/jw01294003001_03102_00001_mirifushort_cal.fits\n", + "Reading data from data/* bet Pic/12A/stage2/jw01294003001_03102_00001_mirifushort_cal.fits\n", + "Wavelength map loaded\n", + "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", + "Loading data for compute_med_filt_badpix cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00001_mirifushort_cal_roughbadpix.fits\n", + "Loading data for compute_coordinates_arrays cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00001_mirifushort_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: (0, 0)\n", + "Applying relative coordinate offset (0, 0)\n", + "Loading data for compute_starspectrum_contnorm cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00001_mirifushort_cal_starspec_contnorm.fits\n", + "Loading data for compute_starsubtraction cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00001_mirifushort_cal_starsub.fits\n", + "data/* bet Pic/12A/stage2/jw01294003001_03102_00002_mirifushort_cal.fits\n", + "Reading data from data/* bet Pic/12A/stage2/jw01294003001_03102_00002_mirifushort_cal.fits\n", + "Wavelength map loaded\n", + "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", + "Loading data for compute_med_filt_badpix cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00002_mirifushort_cal_roughbadpix.fits\n", + "Loading data for compute_coordinates_arrays cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00002_mirifushort_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: (0, 0)\n", + "Applying relative coordinate offset (0, 0)\n", + "Loading data for compute_starspectrum_contnorm cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00002_mirifushort_cal_starspec_contnorm.fits\n", + "Loading data for compute_starsubtraction cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00002_mirifushort_cal_starsub.fits\n", + "data/* bet Pic/12A/stage2/jw01294003001_03102_00003_mirifushort_cal.fits\n", + "Reading data from data/* bet Pic/12A/stage2/jw01294003001_03102_00003_mirifushort_cal.fits\n", + "Wavelength map loaded\n", + "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", + "Loading data for compute_med_filt_badpix cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00003_mirifushort_cal_roughbadpix.fits\n", + "Loading data for compute_coordinates_arrays cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00003_mirifushort_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: (0, 0)\n", + "Applying relative coordinate offset (0, 0)\n", + "Loading data for compute_starspectrum_contnorm cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00003_mirifushort_cal_starspec_contnorm.fits\n", + "Loading data for compute_starsubtraction cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00003_mirifushort_cal_starsub.fits\n", + "data/* bet Pic/12A/stage2/jw01294003001_03102_00004_mirifushort_cal.fits\n", + "Reading data from data/* bet Pic/12A/stage2/jw01294003001_03102_00004_mirifushort_cal.fits\n", + "Wavelength map loaded\n", + "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", + "Loading data for compute_med_filt_badpix cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00004_mirifushort_cal_roughbadpix.fits\n", + "Loading data for compute_coordinates_arrays cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00004_mirifushort_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: (0, 0)\n", + "Applying relative coordinate offset (0, 0)\n", + "Loading data for compute_starspectrum_contnorm cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00004_mirifushort_cal_starspec_contnorm.fits\n", + "Loading data for compute_starsubtraction cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00004_mirifushort_cal_starsub.fits\n", + "[DEBUG] combining spectrum (1191018,) 8.566664276114517\n", + "[DEBUG] combining spectrum (1191018,) 5.748682300855786\n" + ] + } + ], + "source": [ + "\n", + "mypool = mp.Pool(processes=numthreads)\n", + "\n", + "##############\n", + "## Compute normalized spectrum of the star in the FOV\n", + "print('Extract star spectrum')\n", + "print(wv_nodes)\n", + "combined_star_func = compute_normalized_stellar_spectrum_miri(cleaned_cal_files, channel, utils_dir, wave2d, target_name=targetname_simbad, fit_centroid=True,\n", + " wv_nodes=wv_nodes,\n", + " mask_charge_transfer_radius = None,\n", + " mppool=mypool,\n", + " ra_dec_point_sources=None, overwrite=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e1971d6c-112b-4bea-acbe-d7c25d2d9d82", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Subtract star spectrum\n", + "data/* bet Pic/12A/stage2/jw01294003001_03102_00001_mirifushort_cal.fits\n", + "Reading data from data/* bet Pic/12A/stage2/jw01294003001_03102_00001_mirifushort_cal.fits\n", + "Wavelength map loaded\n", + "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", + "Loading data for compute_med_filt_badpix cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00001_mirifushort_cal_roughbadpix.fits\n", + "Loading data for compute_coordinates_arrays cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00001_mirifushort_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: None\n", + "Applying relative coordinate offset [0, 0]\n", + "data/* bet Pic/12A/stage2/jw01294003001_03102_00002_mirifushort_cal.fits\n", + "Reading data from data/* bet Pic/12A/stage2/jw01294003001_03102_00002_mirifushort_cal.fits\n", + "Wavelength map loaded\n", + "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", + "Loading data for compute_med_filt_badpix cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00002_mirifushort_cal_roughbadpix.fits\n", + "Loading data for compute_coordinates_arrays cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00002_mirifushort_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: None\n", + "Applying relative coordinate offset [0, 0]\n", + "data/* bet Pic/12A/stage2/jw01294003001_03102_00003_mirifushort_cal.fits\n", + "Reading data from data/* bet Pic/12A/stage2/jw01294003001_03102_00003_mirifushort_cal.fits\n", + "Wavelength map loaded\n", + "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", + "Loading data for compute_med_filt_badpix cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00003_mirifushort_cal_roughbadpix.fits\n", + "Loading data for compute_coordinates_arrays cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00003_mirifushort_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: None\n", + "Applying relative coordinate offset [0, 0]\n", + "data/* bet Pic/12A/stage2/jw01294003001_03102_00004_mirifushort_cal.fits\n", + "Reading data from data/* bet Pic/12A/stage2/jw01294003001_03102_00004_mirifushort_cal.fits\n", + "Wavelength map loaded\n", + "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", + "Loading data for compute_med_filt_badpix cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00004_mirifushort_cal_roughbadpix.fits\n", + "Loading data for compute_coordinates_arrays cached in data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00004_mirifushort_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: None\n", + "Applying relative coordinate offset [0, 0]\n" + ] + } + ], + "source": [ + "\n", + "##############\n", + "## Fit the normalized spectrum of the star everywhere by modulating the continuum\n", + "# This does a few things:\n", + "# - subtracts the starlight everywhere as kind of fancy high-pass filter. New files saved in [utils_dir]/starsub1d.\n", + "# - Updates the bad pixel map in the original data object with a sigma clipping\n", + "print('Subtract star spectrum')\n", + "dataobj_list = compute_starlight_subtraction_miri(cleaned_cal_files, channel, utils_dir, crds_dir, wave2d, combined_star_func=combined_star_func,\n", + " coords_offset=None, wv_nodes=wv_nodes, mppool=mypool)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5e79da38-2e34-437f-af23-3c971e33f68d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starsub1d path for combined regwvs miri: data/* bet Pic/utils_fm_40_nodes/1A/starsub1d/jw01294003001_03102_00001_mirifushort_cal.fits\n", + "Reading data from data/* bet Pic/utils_fm_40_nodes/1A/starsub1d/jw01294003001_03102_00001_mirifushort_cal.fits\n", + "Wavelength map loaded\n", + "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", + "regwvs path for combined regwvs miri: data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00001_mirifushort_cal_starsub1d_regwvs.fits\n", + "[DEBUG] get combined regwvs miri checking wv_sampling (1050,)\n", + "starsub1d path for combined regwvs miri: data/* bet Pic/utils_fm_40_nodes/1A/starsub1d/jw01294003001_03102_00002_mirifushort_cal.fits\n", + "Reading data from data/* bet Pic/utils_fm_40_nodes/1A/starsub1d/jw01294003001_03102_00002_mirifushort_cal.fits\n", + "Wavelength map loaded\n", + "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", + "regwvs path for combined regwvs miri: data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00002_mirifushort_cal_starsub1d_regwvs.fits\n", + "[DEBUG] get combined regwvs miri checking wv_sampling (1050,)\n", + "starsub1d path for combined regwvs miri: data/* bet Pic/utils_fm_40_nodes/1A/starsub1d/jw01294003001_03102_00003_mirifushort_cal.fits\n", + "Reading data from data/* bet Pic/utils_fm_40_nodes/1A/starsub1d/jw01294003001_03102_00003_mirifushort_cal.fits\n", + "Wavelength map loaded\n", + "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", + "regwvs path for combined regwvs miri: data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00003_mirifushort_cal_starsub1d_regwvs.fits\n", + "[DEBUG] get combined regwvs miri checking wv_sampling (1050,)\n", + "starsub1d path for combined regwvs miri: data/* bet Pic/utils_fm_40_nodes/1A/starsub1d/jw01294003001_03102_00004_mirifushort_cal.fits\n", + "Reading data from data/* bet Pic/utils_fm_40_nodes/1A/starsub1d/jw01294003001_03102_00004_mirifushort_cal.fits\n", + "Wavelength map loaded\n", + "\tUnpacking data quality bitmasks. DQ array is of type uint32\n", + "regwvs path for combined regwvs miri: data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_00004_mirifushort_cal_starsub1d_regwvs.fits\n", + "[DEBUG] get combined regwvs miri checking wv_sampling (1050,)\n", + "DEBUG intializing combine_dataobj_list\n" + ] + } + ], + "source": [ + "\n", + "##############\n", + "## Interpolation the wavelength dimension onto a regular wavelength grid and return combined dataset object\n", + "regwvs_combdataobj = get_combined_regwvs_miri(dataobj_list, channel, wv_sampling=wv_sampling,\n", + " use_starsub1d=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ecfe8b6f-2a90-4895-903b-d20ad503574b", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Fit a model PSF (WebbPSF) to the combined point cloud of dataobj_list\n", + "# Save output as fitpsf_filename\n", + "init_centroid = (0, 0)\n", + "ann_width = None\n", + "padding = 0.0\n", + "sector_area = None\n", + "debug_init,debug_end = None, None\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2d4cfcad-d449-4feb-8604-082de24d5c1f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing PSFs. This has to iterate over many wavelengths, so is slow.\n", + "Loading telescope state as of observation date\n", + "iterating query, tdelta=3.0\n", + "\n", + "MAST OPD query around UTC: 2023-01-11T05:11:27.552\n", + " MJD: 59955.21629111111\n", + "\n", + "OPD immediately preceding the given datetime:\n", + "\tURI:\t mast:JWST/product/R2023011003-NRCA3_FP1-1.fits\n", + "\tDate (MJD):\t 59953.6985\n", + "\tDelta time:\t -1.5178 days\n", + "\n", + "OPD immediately following the given datetime:\n", + "\tURI:\t mast:JWST/product/R2023011203-NRCA3_FP1-1.fits\n", + "\tDate (MJD):\t 59955.7773\n", + "\tDelta time:\t 0.5610 days\n", + "User requested choosing OPD time closest in time to 2023-01-11T05:11:27.552, which is R2023011203-NRCA3_FP1-1.fits, delta time 0.561 days\n", + "Importing and format-converting OPD from /Users/mperrin/software/webbpsf-data/MAST_JWST_WSS_OPDs/R2023011203-NRCA3_FP1-1.fits\n", + "Backing out SI WFE and OTE field dependence at the WF sensing field point (NRCA3_FP1)\n", + "Current index of wavelength 0, Current wavelength 4.89, Total number of wavelength 1050\n", + "Current index of wavelength 1, Current wavelength 4.8908198284080076, Total number of wavelength 1050\n", + "Current index of wavelength 2, Current wavelength 4.8916396568160145, Total number of wavelength 1050\n", + "Current index of wavelength 3, Current wavelength 4.892459485224022, Total number of wavelength 1050\n", + "Current index of wavelength 4, Current wavelength 4.89327931363203, Total number of wavelength 1050\n", + "Current index of wavelength 5, Current wavelength 4.894099142040038, Total number of wavelength 1050\n", + "Current index of wavelength 6, Current wavelength 4.894918970448045, Total number of wavelength 1050\n", + "Current index of wavelength 7, Current wavelength 4.895738798856053, Total number of wavelength 1050\n", + "Current index of wavelength 8, Current wavelength 4.896558627264061, Total number of wavelength 1050\n", + "Current index of wavelength 9, Current wavelength 4.897378455672069, Total number of wavelength 1050\n", + "Current index of wavelength 10, Current wavelength 4.898198284080076, Total number of wavelength 1050\n", + "Current index of wavelength 11, Current wavelength 4.899018112488084, Total number of wavelength 1050\n", + "Current index of wavelength 12, Current wavelength 4.8998379408960915, Total number of wavelength 1050\n", + "Current index of wavelength 13, Current wavelength 4.9006577693040985, Total number of wavelength 1050\n", + "Current index of wavelength 14, Current wavelength 4.901477597712106, Total number of wavelength 1050\n", + "Current index of wavelength 15, Current wavelength 4.902297426120114, Total number of wavelength 1050\n", + "Current index of wavelength 16, Current wavelength 4.903117254528122, Total number of wavelength 1050\n", + "Current index of wavelength 17, Current wavelength 4.903937082936129, Total number of wavelength 1050\n", + "Current index of wavelength 18, Current wavelength 4.904756911344137, Total number of wavelength 1050\n", + "Current index of wavelength 19, Current wavelength 4.905576739752145, Total number of wavelength 1050\n", + "Current index of wavelength 20, Current wavelength 4.906396568160152, Total number of wavelength 1050\n", + "Current index of wavelength 21, Current wavelength 4.90721639656816, Total number of wavelength 1050\n", + "Current index of wavelength 22, Current wavelength 4.9080362249761675, Total number of wavelength 1050\n", + "Current index of wavelength 23, Current wavelength 4.908856053384175, Total number of wavelength 1050\n", + "Current index of wavelength 24, Current wavelength 4.909675881792182, Total number of wavelength 1050\n", + "Current index of wavelength 25, Current wavelength 4.91049571020019, Total number of wavelength 1050\n", + "Current index of wavelength 26, Current wavelength 4.911315538608198, Total number of wavelength 1050\n", + "Current index of wavelength 27, Current wavelength 4.912135367016206, Total number of wavelength 1050\n", + "Current index of wavelength 28, Current wavelength 4.912955195424213, Total number of wavelength 1050\n", + "Current index of wavelength 29, Current wavelength 4.913775023832221, Total number of wavelength 1050\n", + "Current index of wavelength 30, Current wavelength 4.914594852240229, Total number of wavelength 1050\n", + "Current index of wavelength 31, Current wavelength 4.915414680648236, Total number of wavelength 1050\n", + "Current index of wavelength 32, Current wavelength 4.916234509056244, Total number of wavelength 1050\n", + "Current index of wavelength 33, Current wavelength 4.9170543374642515, Total number of wavelength 1050\n", + "Current index of wavelength 34, Current wavelength 4.917874165872259, Total number of wavelength 1050\n", + "Current index of wavelength 35, Current wavelength 4.918693994280266, Total number of wavelength 1050\n", + "Current index of wavelength 36, Current wavelength 4.919513822688274, Total number of wavelength 1050\n", + "Current index of wavelength 37, Current wavelength 4.920333651096282, Total number of wavelength 1050\n", + "Current index of wavelength 38, Current wavelength 4.921153479504289, Total number of wavelength 1050\n", + "Current index of wavelength 39, Current wavelength 4.921973307912297, Total number of wavelength 1050\n", + "Current index of wavelength 40, Current wavelength 4.922793136320305, Total number of wavelength 1050\n", + "Current index of wavelength 41, Current wavelength 4.923612964728313, Total number of wavelength 1050\n", + "Current index of wavelength 42, Current wavelength 4.92443279313632, Total number of wavelength 1050\n", + "Current index of wavelength 43, Current wavelength 4.9252526215443275, Total number of wavelength 1050\n", + "Current index of wavelength 44, Current wavelength 4.926072449952335, Total number of wavelength 1050\n", + "Current index of wavelength 45, Current wavelength 4.926892278360343, Total number of wavelength 1050\n", + "Current index of wavelength 46, Current wavelength 4.92771210676835, Total number of wavelength 1050\n", + "Current index of wavelength 47, Current wavelength 4.928531935176358, Total number of wavelength 1050\n", + "Current index of wavelength 48, Current wavelength 4.929351763584366, Total number of wavelength 1050\n", + "Current index of wavelength 49, Current wavelength 4.930171591992373, Total number of wavelength 1050\n", + "Current index of wavelength 50, Current wavelength 4.930991420400381, Total number of wavelength 1050\n", + "Current index of wavelength 51, Current wavelength 4.931811248808389, Total number of wavelength 1050\n", + "Current index of wavelength 52, Current wavelength 4.932631077216397, Total number of wavelength 1050\n", + "Current index of wavelength 53, Current wavelength 4.933450905624404, Total number of wavelength 1050\n", + "Current index of wavelength 54, Current wavelength 4.9342707340324115, Total number of wavelength 1050\n", + "Current index of wavelength 55, Current wavelength 4.935090562440419, Total number of wavelength 1050\n", + "Current index of wavelength 56, Current wavelength 4.935910390848427, Total number of wavelength 1050\n", + "Current index of wavelength 57, Current wavelength 4.936730219256434, Total number of wavelength 1050\n", + "Current index of wavelength 58, Current wavelength 4.937550047664442, Total number of wavelength 1050\n", + "Current index of wavelength 59, Current wavelength 4.93836987607245, Total number of wavelength 1050\n", + "Current index of wavelength 60, Current wavelength 4.939189704480457, Total number of wavelength 1050\n", + "Current index of wavelength 61, Current wavelength 4.940009532888465, Total number of wavelength 1050\n", + "Current index of wavelength 62, Current wavelength 4.940829361296473, Total number of wavelength 1050\n", + "Current index of wavelength 63, Current wavelength 4.9416491897044805, Total number of wavelength 1050\n", + "Current index of wavelength 64, Current wavelength 4.9424690181124875, Total number of wavelength 1050\n", + "Current index of wavelength 65, Current wavelength 4.943288846520495, Total number of wavelength 1050\n", + "Current index of wavelength 66, Current wavelength 4.944108674928503, Total number of wavelength 1050\n", + "Current index of wavelength 67, Current wavelength 4.94492850333651, Total number of wavelength 1050\n", + "Current index of wavelength 68, Current wavelength 4.945748331744518, Total number of wavelength 1050\n", + "Current index of wavelength 69, Current wavelength 4.946568160152526, Total number of wavelength 1050\n", + "Current index of wavelength 70, Current wavelength 4.947387988560534, Total number of wavelength 1050\n", + "Current index of wavelength 71, Current wavelength 4.948207816968541, Total number of wavelength 1050\n", + "Current index of wavelength 72, Current wavelength 4.949027645376549, Total number of wavelength 1050\n", + "Current index of wavelength 73, Current wavelength 4.949847473784557, Total number of wavelength 1050\n", + "Current index of wavelength 74, Current wavelength 4.9506673021925645, Total number of wavelength 1050\n", + "Current index of wavelength 75, Current wavelength 4.951487130600571, Total number of wavelength 1050\n", + "Current index of wavelength 76, Current wavelength 4.952306959008579, Total number of wavelength 1050\n", + "Current index of wavelength 77, Current wavelength 4.953126787416587, Total number of wavelength 1050\n", + "Current index of wavelength 78, Current wavelength 4.953946615824594, Total number of wavelength 1050\n", + "Current index of wavelength 79, Current wavelength 4.954766444232602, Total number of wavelength 1050\n", + "Current index of wavelength 80, Current wavelength 4.95558627264061, Total number of wavelength 1050\n", + "Current index of wavelength 81, Current wavelength 4.956406101048618, Total number of wavelength 1050\n", + "Current index of wavelength 82, Current wavelength 4.957225929456625, Total number of wavelength 1050\n", + "Current index of wavelength 83, Current wavelength 4.958045757864633, Total number of wavelength 1050\n", + "Current index of wavelength 84, Current wavelength 4.9588655862726405, Total number of wavelength 1050\n", + "Current index of wavelength 85, Current wavelength 4.9596854146806475, Total number of wavelength 1050\n", + "Current index of wavelength 86, Current wavelength 4.960505243088655, Total number of wavelength 1050\n", + "Current index of wavelength 87, Current wavelength 4.961325071496663, Total number of wavelength 1050\n", + "Current index of wavelength 88, Current wavelength 4.962144899904671, Total number of wavelength 1050\n", + "Current index of wavelength 89, Current wavelength 4.962964728312678, Total number of wavelength 1050\n", + "Current index of wavelength 90, Current wavelength 4.963784556720686, Total number of wavelength 1050\n", + "Current index of wavelength 91, Current wavelength 4.964604385128694, Total number of wavelength 1050\n", + "Current index of wavelength 92, Current wavelength 4.965424213536702, Total number of wavelength 1050\n", + "Current index of wavelength 93, Current wavelength 4.966244041944709, Total number of wavelength 1050\n", + "Current index of wavelength 94, Current wavelength 4.967063870352717, Total number of wavelength 1050\n", + "Current index of wavelength 95, Current wavelength 4.967883698760724, Total number of wavelength 1050\n", + "Current index of wavelength 96, Current wavelength 4.968703527168731, Total number of wavelength 1050\n", + "Current index of wavelength 97, Current wavelength 4.969523355576739, Total number of wavelength 1050\n", + "Current index of wavelength 98, Current wavelength 4.970343183984747, Total number of wavelength 1050\n", + "Current index of wavelength 99, Current wavelength 4.971163012392755, Total number of wavelength 1050\n", + "Current index of wavelength 100, Current wavelength 4.971982840800762, Total number of wavelength 1050\n", + "Current index of wavelength 101, Current wavelength 4.97280266920877, Total number of wavelength 1050\n", + "Current index of wavelength 102, Current wavelength 4.973622497616778, Total number of wavelength 1050\n", + "Current index of wavelength 103, Current wavelength 4.974442326024786, Total number of wavelength 1050\n", + "Current index of wavelength 104, Current wavelength 4.975262154432793, Total number of wavelength 1050\n", + "Current index of wavelength 105, Current wavelength 4.9760819828408005, Total number of wavelength 1050\n", + "Current index of wavelength 106, Current wavelength 4.976901811248808, Total number of wavelength 1050\n", + "Current index of wavelength 107, Current wavelength 4.977721639656815, Total number of wavelength 1050\n", + "Current index of wavelength 108, Current wavelength 4.978541468064823, Total number of wavelength 1050\n", + "Current index of wavelength 109, Current wavelength 4.979361296472831, Total number of wavelength 1050\n", + "Current index of wavelength 110, Current wavelength 4.980181124880839, Total number of wavelength 1050\n", + "Current index of wavelength 111, Current wavelength 4.981000953288846, Total number of wavelength 1050\n", + "Current index of wavelength 112, Current wavelength 4.981820781696854, Total number of wavelength 1050\n", + "Current index of wavelength 113, Current wavelength 4.982640610104862, Total number of wavelength 1050\n", + "Current index of wavelength 114, Current wavelength 4.983460438512869, Total number of wavelength 1050\n", + "Current index of wavelength 115, Current wavelength 4.984280266920877, Total number of wavelength 1050\n", + "Current index of wavelength 116, Current wavelength 4.985100095328884, Total number of wavelength 1050\n", + "Current index of wavelength 117, Current wavelength 4.985919923736892, Total number of wavelength 1050\n", + "Current index of wavelength 118, Current wavelength 4.986739752144899, Total number of wavelength 1050\n", + "Current index of wavelength 119, Current wavelength 4.987559580552907, Total number of wavelength 1050\n", + "Current index of wavelength 120, Current wavelength 4.988379408960915, Total number of wavelength 1050\n", + "Current index of wavelength 121, Current wavelength 4.989199237368923, Total number of wavelength 1050\n", + "Current index of wavelength 122, Current wavelength 4.99001906577693, Total number of wavelength 1050\n", + "Current index of wavelength 123, Current wavelength 4.990838894184938, Total number of wavelength 1050\n", + "Current index of wavelength 124, Current wavelength 4.991658722592946, Total number of wavelength 1050\n", + "Current index of wavelength 125, Current wavelength 4.992478551000953, Total number of wavelength 1050\n", + "Current index of wavelength 126, Current wavelength 4.9932983794089605, Total number of wavelength 1050\n", + "Current index of wavelength 127, Current wavelength 4.994118207816968, Total number of wavelength 1050\n", + "Current index of wavelength 128, Current wavelength 4.994938036224976, Total number of wavelength 1050\n", + "Current index of wavelength 129, Current wavelength 4.995757864632983, Total number of wavelength 1050\n", + "Current index of wavelength 130, Current wavelength 4.996577693040991, Total number of wavelength 1050\n", + "Current index of wavelength 131, Current wavelength 4.997397521448999, Total number of wavelength 1050\n", + "Current index of wavelength 132, Current wavelength 4.998217349857006, Total number of wavelength 1050\n", + "Current index of wavelength 133, Current wavelength 4.999037178265014, Total number of wavelength 1050\n", + "Current index of wavelength 134, Current wavelength 4.999857006673022, Total number of wavelength 1050\n", + "Current index of wavelength 135, Current wavelength 5.00067683508103, Total number of wavelength 1050\n", + "Current index of wavelength 136, Current wavelength 5.0014966634890365, Total number of wavelength 1050\n", + "Current index of wavelength 137, Current wavelength 5.002316491897044, Total number of wavelength 1050\n", + "Current index of wavelength 138, Current wavelength 5.003136320305052, Total number of wavelength 1050\n", + "Current index of wavelength 139, Current wavelength 5.00395614871306, Total number of wavelength 1050\n", + "Current index of wavelength 140, Current wavelength 5.004775977121067, Total number of wavelength 1050\n", + "Current index of wavelength 141, Current wavelength 5.005595805529075, Total number of wavelength 1050\n", + "Current index of wavelength 142, Current wavelength 5.006415633937083, Total number of wavelength 1050\n", + "Current index of wavelength 143, Current wavelength 5.00723546234509, Total number of wavelength 1050\n", + "Current index of wavelength 144, Current wavelength 5.008055290753098, Total number of wavelength 1050\n", + "Current index of wavelength 145, Current wavelength 5.008875119161106, Total number of wavelength 1050\n", + "Current index of wavelength 146, Current wavelength 5.0096949475691135, Total number of wavelength 1050\n", + "Current index of wavelength 147, Current wavelength 5.0105147759771205, Total number of wavelength 1050\n", + "Current index of wavelength 148, Current wavelength 5.011334604385128, Total number of wavelength 1050\n", + "Current index of wavelength 149, Current wavelength 5.012154432793136, Total number of wavelength 1050\n", + "Current index of wavelength 150, Current wavelength 5.012974261201144, Total number of wavelength 1050\n", + "Current index of wavelength 151, Current wavelength 5.013794089609151, Total number of wavelength 1050\n", + "Current index of wavelength 152, Current wavelength 5.014613918017159, Total number of wavelength 1050\n", + "Current index of wavelength 153, Current wavelength 5.015433746425167, Total number of wavelength 1050\n", + "Current index of wavelength 154, Current wavelength 5.016253574833174, Total number of wavelength 1050\n", + "Current index of wavelength 155, Current wavelength 5.017073403241182, Total number of wavelength 1050\n", + "Current index of wavelength 156, Current wavelength 5.0178932316491895, Total number of wavelength 1050\n", + "Current index of wavelength 157, Current wavelength 5.018713060057197, Total number of wavelength 1050\n", + "Current index of wavelength 158, Current wavelength 5.019532888465204, Total number of wavelength 1050\n", + "Current index of wavelength 159, Current wavelength 5.020352716873212, Total number of wavelength 1050\n", + "Current index of wavelength 160, Current wavelength 5.02117254528122, Total number of wavelength 1050\n", + "Current index of wavelength 161, Current wavelength 5.021992373689228, Total number of wavelength 1050\n", + "Current index of wavelength 162, Current wavelength 5.022812202097235, Total number of wavelength 1050\n", + "Current index of wavelength 163, Current wavelength 5.023632030505243, Total number of wavelength 1050\n", + "Current index of wavelength 164, Current wavelength 5.024451858913251, Total number of wavelength 1050\n", + "Current index of wavelength 165, Current wavelength 5.025271687321258, Total number of wavelength 1050\n", + "Current index of wavelength 166, Current wavelength 5.026091515729266, Total number of wavelength 1050\n", + "Current index of wavelength 167, Current wavelength 5.0269113441372735, Total number of wavelength 1050\n", + "Current index of wavelength 168, Current wavelength 5.027731172545281, Total number of wavelength 1050\n", + "Current index of wavelength 169, Current wavelength 5.028551000953288, Total number of wavelength 1050\n", + "Current index of wavelength 170, Current wavelength 5.029370829361296, Total number of wavelength 1050\n", + "Current index of wavelength 171, Current wavelength 5.030190657769304, Total number of wavelength 1050\n", + "Current index of wavelength 172, Current wavelength 5.031010486177311, Total number of wavelength 1050\n", + "Current index of wavelength 173, Current wavelength 5.031830314585319, Total number of wavelength 1050\n", + "Current index of wavelength 174, Current wavelength 5.032650142993327, Total number of wavelength 1050\n", + "Current index of wavelength 175, Current wavelength 5.033469971401335, Total number of wavelength 1050\n", + "Current index of wavelength 176, Current wavelength 5.034289799809342, Total number of wavelength 1050\n", + "Current index of wavelength 177, Current wavelength 5.0351096282173495, Total number of wavelength 1050\n", + "Current index of wavelength 178, Current wavelength 5.035929456625357, Total number of wavelength 1050\n", + "Current index of wavelength 179, Current wavelength 5.036749285033364, Total number of wavelength 1050\n", + "Current index of wavelength 180, Current wavelength 5.037569113441372, Total number of wavelength 1050\n", + "Current index of wavelength 181, Current wavelength 5.03838894184938, Total number of wavelength 1050\n", + "Current index of wavelength 182, Current wavelength 5.039208770257388, Total number of wavelength 1050\n", + "Current index of wavelength 183, Current wavelength 5.040028598665395, Total number of wavelength 1050\n", + "Current index of wavelength 184, Current wavelength 5.040848427073403, Total number of wavelength 1050\n", + "Current index of wavelength 185, Current wavelength 5.041668255481411, Total number of wavelength 1050\n", + "Current index of wavelength 186, Current wavelength 5.042488083889419, Total number of wavelength 1050\n", + "Current index of wavelength 187, Current wavelength 5.043307912297426, Total number of wavelength 1050\n", + "Current index of wavelength 188, Current wavelength 5.0441277407054335, Total number of wavelength 1050\n", + "Current index of wavelength 189, Current wavelength 5.044947569113441, Total number of wavelength 1050\n", + "Current index of wavelength 190, Current wavelength 5.045767397521448, Total number of wavelength 1050\n", + "Current index of wavelength 191, Current wavelength 5.046587225929456, Total number of wavelength 1050\n", + "Current index of wavelength 192, Current wavelength 5.047407054337464, Total number of wavelength 1050\n", + "Current index of wavelength 193, Current wavelength 5.048226882745472, Total number of wavelength 1050\n", + "Current index of wavelength 194, Current wavelength 5.049046711153479, Total number of wavelength 1050\n", + "Current index of wavelength 195, Current wavelength 5.049866539561487, Total number of wavelength 1050\n", + "Current index of wavelength 196, Current wavelength 5.050686367969495, Total number of wavelength 1050\n", + "Current index of wavelength 197, Current wavelength 5.0515061963775025, Total number of wavelength 1050\n", + "Current index of wavelength 198, Current wavelength 5.0523260247855095, Total number of wavelength 1050\n", + "Current index of wavelength 199, Current wavelength 5.053145853193517, Total number of wavelength 1050\n", + "Current index of wavelength 200, Current wavelength 5.053965681601525, Total number of wavelength 1050\n", + "Current index of wavelength 201, Current wavelength 5.054785510009532, Total number of wavelength 1050\n", + "Current index of wavelength 202, Current wavelength 5.05560533841754, Total number of wavelength 1050\n", + "Current index of wavelength 203, Current wavelength 5.056425166825548, Total number of wavelength 1050\n", + "Current index of wavelength 204, Current wavelength 5.057244995233556, Total number of wavelength 1050\n", + "Current index of wavelength 205, Current wavelength 5.058064823641563, Total number of wavelength 1050\n", + "Current index of wavelength 206, Current wavelength 5.058884652049571, Total number of wavelength 1050\n", + "Current index of wavelength 207, Current wavelength 5.059704480457579, Total number of wavelength 1050\n", + "Current index of wavelength 208, Current wavelength 5.0605243088655865, Total number of wavelength 1050\n", + "Current index of wavelength 209, Current wavelength 5.0613441372735934, Total number of wavelength 1050\n", + "Current index of wavelength 210, Current wavelength 5.062163965681601, Total number of wavelength 1050\n", + "Current index of wavelength 211, Current wavelength 5.062983794089609, Total number of wavelength 1050\n", + "Current index of wavelength 212, Current wavelength 5.063803622497616, Total number of wavelength 1050\n", + "Current index of wavelength 213, Current wavelength 5.064623450905624, Total number of wavelength 1050\n", + "Current index of wavelength 214, Current wavelength 5.065443279313632, Total number of wavelength 1050\n", + "Current index of wavelength 215, Current wavelength 5.06626310772164, Total number of wavelength 1050\n", + "Current index of wavelength 216, Current wavelength 5.067082936129647, Total number of wavelength 1050\n", + "Current index of wavelength 217, Current wavelength 5.067902764537655, Total number of wavelength 1050\n", + "Current index of wavelength 218, Current wavelength 5.0687225929456625, Total number of wavelength 1050\n", + "Current index of wavelength 219, Current wavelength 5.0695424213536695, Total number of wavelength 1050\n", + "Current index of wavelength 220, Current wavelength 5.070362249761677, Total number of wavelength 1050\n", + "Current index of wavelength 221, Current wavelength 5.071182078169685, Total number of wavelength 1050\n", + "Current index of wavelength 222, Current wavelength 5.072001906577693, Total number of wavelength 1050\n", + "Current index of wavelength 223, Current wavelength 5.0728217349857, Total number of wavelength 1050\n", + "Current index of wavelength 224, Current wavelength 5.073641563393708, Total number of wavelength 1050\n", + "Current index of wavelength 225, Current wavelength 5.074461391801716, Total number of wavelength 1050\n", + "Current index of wavelength 226, Current wavelength 5.075281220209723, Total number of wavelength 1050\n", + "Current index of wavelength 227, Current wavelength 5.076101048617731, Total number of wavelength 1050\n", + "Current index of wavelength 228, Current wavelength 5.076920877025739, Total number of wavelength 1050\n", + "Current index of wavelength 229, Current wavelength 5.0777407054337464, Total number of wavelength 1050\n", + "Current index of wavelength 230, Current wavelength 5.078560533841753, Total number of wavelength 1050\n", + "Current index of wavelength 231, Current wavelength 5.079380362249761, Total number of wavelength 1050\n", + "Current index of wavelength 232, Current wavelength 5.080200190657769, Total number of wavelength 1050\n", + "Current index of wavelength 233, Current wavelength 5.081020019065777, Total number of wavelength 1050\n", + "Current index of wavelength 234, Current wavelength 5.081839847473784, Total number of wavelength 1050\n", + "Current index of wavelength 235, Current wavelength 5.082659675881792, Total number of wavelength 1050\n", + "Current index of wavelength 236, Current wavelength 5.0834795042898, Total number of wavelength 1050\n", + "Current index of wavelength 237, Current wavelength 5.084299332697807, Total number of wavelength 1050\n", + "Current index of wavelength 238, Current wavelength 5.085119161105815, Total number of wavelength 1050\n", + "Current index of wavelength 239, Current wavelength 5.0859389895138225, Total number of wavelength 1050\n", + "Current index of wavelength 240, Current wavelength 5.08675881792183, Total number of wavelength 1050\n", + "Current index of wavelength 241, Current wavelength 5.087578646329837, Total number of wavelength 1050\n", + "Current index of wavelength 242, Current wavelength 5.088398474737845, Total number of wavelength 1050\n", + "Current index of wavelength 243, Current wavelength 5.089218303145853, Total number of wavelength 1050\n", + "Current index of wavelength 244, Current wavelength 5.090038131553861, Total number of wavelength 1050\n", + "Current index of wavelength 245, Current wavelength 5.090857959961868, Total number of wavelength 1050\n", + "Current index of wavelength 246, Current wavelength 5.091677788369876, Total number of wavelength 1050\n", + "Current index of wavelength 247, Current wavelength 5.092497616777884, Total number of wavelength 1050\n", + "Current index of wavelength 248, Current wavelength 5.093317445185891, Total number of wavelength 1050\n", + "Current index of wavelength 249, Current wavelength 5.094137273593899, Total number of wavelength 1050\n", + "Current index of wavelength 250, Current wavelength 5.094957102001906, Total number of wavelength 1050\n", + "Current index of wavelength 251, Current wavelength 5.095776930409914, Total number of wavelength 1050\n", + "Current index of wavelength 252, Current wavelength 5.096596758817921, Total number of wavelength 1050\n", + "Current index of wavelength 253, Current wavelength 5.097416587225929, Total number of wavelength 1050\n", + "Current index of wavelength 254, Current wavelength 5.098236415633937, Total number of wavelength 1050\n", + "Current index of wavelength 255, Current wavelength 5.099056244041945, Total number of wavelength 1050\n", + "Current index of wavelength 256, Current wavelength 5.099876072449952, Total number of wavelength 1050\n", + "Current index of wavelength 257, Current wavelength 5.10069590085796, Total number of wavelength 1050\n", + "Current index of wavelength 258, Current wavelength 5.101515729265968, Total number of wavelength 1050\n", + "Current index of wavelength 259, Current wavelength 5.102335557673975, Total number of wavelength 1050\n", + "Current index of wavelength 260, Current wavelength 5.1031553860819825, Total number of wavelength 1050\n", + "Current index of wavelength 261, Current wavelength 5.10397521448999, Total number of wavelength 1050\n", + "Current index of wavelength 262, Current wavelength 5.104795042897998, Total number of wavelength 1050\n", + "Current index of wavelength 263, Current wavelength 5.105614871306005, Total number of wavelength 1050\n", + "Current index of wavelength 264, Current wavelength 5.106434699714013, Total number of wavelength 1050\n", + "Current index of wavelength 265, Current wavelength 5.107254528122021, Total number of wavelength 1050\n", + "Current index of wavelength 266, Current wavelength 5.108074356530028, Total number of wavelength 1050\n", + "Current index of wavelength 267, Current wavelength 5.108894184938036, Total number of wavelength 1050\n", + "Current index of wavelength 268, Current wavelength 5.109714013346044, Total number of wavelength 1050\n", + "Current index of wavelength 269, Current wavelength 5.110533841754052, Total number of wavelength 1050\n", + "Current index of wavelength 270, Current wavelength 5.1113536701620585, Total number of wavelength 1050\n", + "Current index of wavelength 271, Current wavelength 5.112173498570066, Total number of wavelength 1050\n", + "Current index of wavelength 272, Current wavelength 5.112993326978074, Total number of wavelength 1050\n", + "Current index of wavelength 273, Current wavelength 5.113813155386082, Total number of wavelength 1050\n", + "Current index of wavelength 274, Current wavelength 5.114632983794089, Total number of wavelength 1050\n", + "Current index of wavelength 275, Current wavelength 5.115452812202097, Total number of wavelength 1050\n", + "Current index of wavelength 276, Current wavelength 5.116272640610105, Total number of wavelength 1050\n", + "Current index of wavelength 277, Current wavelength 5.117092469018112, Total number of wavelength 1050\n", + "Current index of wavelength 278, Current wavelength 5.11791229742612, Total number of wavelength 1050\n", + "Current index of wavelength 279, Current wavelength 5.118732125834128, Total number of wavelength 1050\n", + "Current index of wavelength 280, Current wavelength 5.1195519542421355, Total number of wavelength 1050\n", + "Current index of wavelength 281, Current wavelength 5.1203717826501425, Total number of wavelength 1050\n", + "Current index of wavelength 282, Current wavelength 5.12119161105815, Total number of wavelength 1050\n", + "Current index of wavelength 283, Current wavelength 5.122011439466158, Total number of wavelength 1050\n", + "Current index of wavelength 284, Current wavelength 5.122831267874165, Total number of wavelength 1050\n", + "Current index of wavelength 285, Current wavelength 5.123651096282173, Total number of wavelength 1050\n", + "Current index of wavelength 286, Current wavelength 5.124470924690181, Total number of wavelength 1050\n", + "Current index of wavelength 287, Current wavelength 5.125290753098189, Total number of wavelength 1050\n", + "Current index of wavelength 288, Current wavelength 5.126110581506196, Total number of wavelength 1050\n", + "Current index of wavelength 289, Current wavelength 5.126930409914204, Total number of wavelength 1050\n", + "Current index of wavelength 290, Current wavelength 5.1277502383222116, Total number of wavelength 1050\n", + "Current index of wavelength 291, Current wavelength 5.128570066730219, Total number of wavelength 1050\n", + "Current index of wavelength 292, Current wavelength 5.129389895138226, Total number of wavelength 1050\n", + "Current index of wavelength 293, Current wavelength 5.130209723546234, Total number of wavelength 1050\n", + "Current index of wavelength 294, Current wavelength 5.131029551954242, Total number of wavelength 1050\n", + "Current index of wavelength 295, Current wavelength 5.131849380362249, Total number of wavelength 1050\n", + "Current index of wavelength 296, Current wavelength 5.132669208770257, Total number of wavelength 1050\n", + "Current index of wavelength 297, Current wavelength 5.133489037178265, Total number of wavelength 1050\n", + "Current index of wavelength 298, Current wavelength 5.134308865586273, Total number of wavelength 1050\n", + "Current index of wavelength 299, Current wavelength 5.13512869399428, Total number of wavelength 1050\n", + "Current index of wavelength 300, Current wavelength 5.135948522402288, Total number of wavelength 1050\n", + "Current index of wavelength 301, Current wavelength 5.1367683508102955, Total number of wavelength 1050\n", + "Current index of wavelength 302, Current wavelength 5.137588179218303, Total number of wavelength 1050\n", + "Current index of wavelength 303, Current wavelength 5.13840800762631, Total number of wavelength 1050\n", + "Current index of wavelength 304, Current wavelength 5.139227836034318, Total number of wavelength 1050\n", + "Current index of wavelength 305, Current wavelength 5.140047664442326, Total number of wavelength 1050\n", + "Current index of wavelength 306, Current wavelength 5.140867492850333, Total number of wavelength 1050\n", + "Current index of wavelength 307, Current wavelength 5.141687321258341, Total number of wavelength 1050\n", + "Current index of wavelength 308, Current wavelength 5.142507149666349, Total number of wavelength 1050\n", + "Current index of wavelength 309, Current wavelength 5.143326978074357, Total number of wavelength 1050\n", + "Current index of wavelength 310, Current wavelength 5.144146806482364, Total number of wavelength 1050\n", + "Current index of wavelength 311, Current wavelength 5.1449666348903715, Total number of wavelength 1050\n", + "Current index of wavelength 312, Current wavelength 5.145786463298379, Total number of wavelength 1050\n", + "Current index of wavelength 313, Current wavelength 5.146606291706386, Total number of wavelength 1050\n", + "Current index of wavelength 314, Current wavelength 5.147426120114394, Total number of wavelength 1050\n", + "Current index of wavelength 315, Current wavelength 5.148245948522402, Total number of wavelength 1050\n", + "Current index of wavelength 316, Current wavelength 5.14906577693041, Total number of wavelength 1050\n", + "Current index of wavelength 317, Current wavelength 5.149885605338417, Total number of wavelength 1050\n", + "Current index of wavelength 318, Current wavelength 5.150705433746425, Total number of wavelength 1050\n", + "Current index of wavelength 319, Current wavelength 5.151525262154433, Total number of wavelength 1050\n", + "Current index of wavelength 320, Current wavelength 5.15234509056244, Total number of wavelength 1050\n", + "Current index of wavelength 321, Current wavelength 5.153164918970448, Total number of wavelength 1050\n", + "Current index of wavelength 322, Current wavelength 5.1539847473784555, Total number of wavelength 1050\n", + "Current index of wavelength 323, Current wavelength 5.154804575786463, Total number of wavelength 1050\n", + "Current index of wavelength 324, Current wavelength 5.15562440419447, Total number of wavelength 1050\n", + "Current index of wavelength 325, Current wavelength 5.156444232602478, Total number of wavelength 1050\n", + "Current index of wavelength 326, Current wavelength 5.157264061010486, Total number of wavelength 1050\n", + "Current index of wavelength 327, Current wavelength 5.158083889418494, Total number of wavelength 1050\n", + "Current index of wavelength 328, Current wavelength 5.158903717826501, Total number of wavelength 1050\n", + "Current index of wavelength 329, Current wavelength 5.159723546234509, Total number of wavelength 1050\n", + "Current index of wavelength 330, Current wavelength 5.160543374642517, Total number of wavelength 1050\n", + "Current index of wavelength 331, Current wavelength 5.161363203050524, Total number of wavelength 1050\n", + "Current index of wavelength 332, Current wavelength 5.1621830314585315, Total number of wavelength 1050\n", + "Current index of wavelength 333, Current wavelength 5.163002859866539, Total number of wavelength 1050\n", + "Current index of wavelength 334, Current wavelength 5.163822688274547, Total number of wavelength 1050\n", + "Current index of wavelength 335, Current wavelength 5.164642516682554, Total number of wavelength 1050\n", + "Current index of wavelength 336, Current wavelength 5.165462345090562, Total number of wavelength 1050\n", + "Current index of wavelength 337, Current wavelength 5.16628217349857, Total number of wavelength 1050\n", + "Current index of wavelength 338, Current wavelength 5.167102001906578, Total number of wavelength 1050\n", + "Current index of wavelength 339, Current wavelength 5.167921830314585, Total number of wavelength 1050\n", + "Current index of wavelength 340, Current wavelength 5.168741658722593, Total number of wavelength 1050\n", + "Current index of wavelength 341, Current wavelength 5.169561487130601, Total number of wavelength 1050\n", + "Current index of wavelength 342, Current wavelength 5.170381315538608, Total number of wavelength 1050\n", + "Current index of wavelength 343, Current wavelength 5.1712011439466155, Total number of wavelength 1050\n", + "Current index of wavelength 344, Current wavelength 5.172020972354623, Total number of wavelength 1050\n", + "Current index of wavelength 345, Current wavelength 5.172840800762631, Total number of wavelength 1050\n", + "Current index of wavelength 346, Current wavelength 5.173660629170638, Total number of wavelength 1050\n", + "Current index of wavelength 347, Current wavelength 5.174480457578646, Total number of wavelength 1050\n", + "Current index of wavelength 348, Current wavelength 5.175300285986654, Total number of wavelength 1050\n", + "Current index of wavelength 349, Current wavelength 5.176120114394662, Total number of wavelength 1050\n", + "Current index of wavelength 350, Current wavelength 5.176939942802669, Total number of wavelength 1050\n", + "Current index of wavelength 351, Current wavelength 5.177759771210677, Total number of wavelength 1050\n", + "Current index of wavelength 352, Current wavelength 5.1785795996186845, Total number of wavelength 1050\n", + "Current index of wavelength 353, Current wavelength 5.1793994280266915, Total number of wavelength 1050\n", + "Current index of wavelength 354, Current wavelength 5.180219256434699, Total number of wavelength 1050\n", + "Current index of wavelength 355, Current wavelength 5.181039084842707, Total number of wavelength 1050\n", + "Current index of wavelength 356, Current wavelength 5.181858913250715, Total number of wavelength 1050\n", + "Current index of wavelength 357, Current wavelength 5.182678741658722, Total number of wavelength 1050\n", + "Current index of wavelength 358, Current wavelength 5.18349857006673, Total number of wavelength 1050\n", + "Current index of wavelength 359, Current wavelength 5.184318398474738, Total number of wavelength 1050\n", + "Current index of wavelength 360, Current wavelength 5.185138226882745, Total number of wavelength 1050\n", + "Current index of wavelength 361, Current wavelength 5.185958055290753, Total number of wavelength 1050\n", + "Current index of wavelength 362, Current wavelength 5.186777883698761, Total number of wavelength 1050\n", + "Current index of wavelength 363, Current wavelength 5.1875977121067685, Total number of wavelength 1050\n", + "Current index of wavelength 364, Current wavelength 5.188417540514775, Total number of wavelength 1050\n", + "Current index of wavelength 365, Current wavelength 5.189237368922783, Total number of wavelength 1050\n", + "Current index of wavelength 366, Current wavelength 5.190057197330791, Total number of wavelength 1050\n", + "Current index of wavelength 367, Current wavelength 5.190877025738798, Total number of wavelength 1050\n", + "Current index of wavelength 368, Current wavelength 5.191696854146806, Total number of wavelength 1050\n", + "Current index of wavelength 369, Current wavelength 5.192516682554814, Total number of wavelength 1050\n", + "Current index of wavelength 370, Current wavelength 5.193336510962822, Total number of wavelength 1050\n", + "Current index of wavelength 371, Current wavelength 5.194156339370829, Total number of wavelength 1050\n", + "Current index of wavelength 372, Current wavelength 5.194976167778837, Total number of wavelength 1050\n", + "Current index of wavelength 373, Current wavelength 5.1957959961868445, Total number of wavelength 1050\n", + "Current index of wavelength 374, Current wavelength 5.196615824594852, Total number of wavelength 1050\n", + "Current index of wavelength 375, Current wavelength 5.197435653002859, Total number of wavelength 1050\n", + "Current index of wavelength 376, Current wavelength 5.198255481410867, Total number of wavelength 1050\n", + "Current index of wavelength 377, Current wavelength 5.199075309818875, Total number of wavelength 1050\n", + "Current index of wavelength 378, Current wavelength 5.199895138226882, Total number of wavelength 1050\n", + "Current index of wavelength 379, Current wavelength 5.20071496663489, Total number of wavelength 1050\n", + "Current index of wavelength 380, Current wavelength 5.201534795042898, Total number of wavelength 1050\n", + "Current index of wavelength 381, Current wavelength 5.202354623450906, Total number of wavelength 1050\n", + "Current index of wavelength 382, Current wavelength 5.203174451858913, Total number of wavelength 1050\n", + "Current index of wavelength 383, Current wavelength 5.203994280266921, Total number of wavelength 1050\n", + "Current index of wavelength 384, Current wavelength 5.204814108674928, Total number of wavelength 1050\n", + "Current index of wavelength 385, Current wavelength 5.205633937082936, Total number of wavelength 1050\n", + "Current index of wavelength 386, Current wavelength 5.206453765490943, Total number of wavelength 1050\n", + "Current index of wavelength 387, Current wavelength 5.207273593898951, Total number of wavelength 1050\n", + "Current index of wavelength 388, Current wavelength 5.208093422306959, Total number of wavelength 1050\n", + "Current index of wavelength 389, Current wavelength 5.208913250714966, Total number of wavelength 1050\n", + "Current index of wavelength 390, Current wavelength 5.209733079122974, Total number of wavelength 1050\n", + "Current index of wavelength 391, Current wavelength 5.210552907530982, Total number of wavelength 1050\n", + "Current index of wavelength 392, Current wavelength 5.21137273593899, Total number of wavelength 1050\n", + "Current index of wavelength 393, Current wavelength 5.212192564346997, Total number of wavelength 1050\n", + "Current index of wavelength 394, Current wavelength 5.2130123927550045, Total number of wavelength 1050\n", + "Current index of wavelength 395, Current wavelength 5.213832221163012, Total number of wavelength 1050\n", + "Current index of wavelength 396, Current wavelength 5.21465204957102, Total number of wavelength 1050\n", + "Current index of wavelength 397, Current wavelength 5.215471877979027, Total number of wavelength 1050\n", + "Current index of wavelength 398, Current wavelength 5.216291706387035, Total number of wavelength 1050\n", + "Current index of wavelength 399, Current wavelength 5.217111534795043, Total number of wavelength 1050\n", + "Current index of wavelength 400, Current wavelength 5.21793136320305, Total number of wavelength 1050\n", + "Current index of wavelength 401, Current wavelength 5.218751191611058, Total number of wavelength 1050\n", + "Current index of wavelength 402, Current wavelength 5.219571020019066, Total number of wavelength 1050\n", + "Current index of wavelength 403, Current wavelength 5.220390848427074, Total number of wavelength 1050\n", + "Current index of wavelength 404, Current wavelength 5.2212106768350806, Total number of wavelength 1050\n", + "Current index of wavelength 405, Current wavelength 5.222030505243088, Total number of wavelength 1050\n", + "Current index of wavelength 406, Current wavelength 5.222850333651096, Total number of wavelength 1050\n", + "Current index of wavelength 407, Current wavelength 5.223670162059104, Total number of wavelength 1050\n", + "Current index of wavelength 408, Current wavelength 5.224489990467111, Total number of wavelength 1050\n", + "Current index of wavelength 409, Current wavelength 5.225309818875119, Total number of wavelength 1050\n", + "Current index of wavelength 410, Current wavelength 5.226129647283127, Total number of wavelength 1050\n", + "Current index of wavelength 411, Current wavelength 5.226949475691134, Total number of wavelength 1050\n", + "Current index of wavelength 412, Current wavelength 5.227769304099142, Total number of wavelength 1050\n", + "Current index of wavelength 413, Current wavelength 5.22858913250715, Total number of wavelength 1050\n", + "Current index of wavelength 414, Current wavelength 5.2294089609151575, Total number of wavelength 1050\n", + "Current index of wavelength 415, Current wavelength 5.2302287893231645, Total number of wavelength 1050\n", + "Current index of wavelength 416, Current wavelength 5.231048617731172, Total number of wavelength 1050\n", + "Current index of wavelength 417, Current wavelength 5.23186844613918, Total number of wavelength 1050\n", + "Current index of wavelength 418, Current wavelength 5.232688274547187, Total number of wavelength 1050\n", + "Current index of wavelength 419, Current wavelength 5.233508102955195, Total number of wavelength 1050\n", + "Current index of wavelength 420, Current wavelength 5.234327931363203, Total number of wavelength 1050\n", + "Current index of wavelength 421, Current wavelength 5.235147759771211, Total number of wavelength 1050\n", + "Current index of wavelength 422, Current wavelength 5.235967588179218, Total number of wavelength 1050\n", + "Current index of wavelength 423, Current wavelength 5.236787416587226, Total number of wavelength 1050\n", + "Current index of wavelength 424, Current wavelength 5.237607244995234, Total number of wavelength 1050\n", + "Current index of wavelength 425, Current wavelength 5.2384270734032405, Total number of wavelength 1050\n", + "Current index of wavelength 426, Current wavelength 5.239246901811248, Total number of wavelength 1050\n", + "Current index of wavelength 427, Current wavelength 5.240066730219256, Total number of wavelength 1050\n", + "Current index of wavelength 428, Current wavelength 5.240886558627264, Total number of wavelength 1050\n", + "Current index of wavelength 429, Current wavelength 5.241706387035271, Total number of wavelength 1050\n", + "Current index of wavelength 430, Current wavelength 5.242526215443279, Total number of wavelength 1050\n", + "Current index of wavelength 431, Current wavelength 5.243346043851287, Total number of wavelength 1050\n", + "Current index of wavelength 432, Current wavelength 5.244165872259295, Total number of wavelength 1050\n", + "Current index of wavelength 433, Current wavelength 5.244985700667302, Total number of wavelength 1050\n", + "Current index of wavelength 434, Current wavelength 5.24580552907531, Total number of wavelength 1050\n", + "Current index of wavelength 435, Current wavelength 5.2466253574833175, Total number of wavelength 1050\n", + "Current index of wavelength 436, Current wavelength 5.2474451858913245, Total number of wavelength 1050\n", + "Current index of wavelength 437, Current wavelength 5.248265014299332, Total number of wavelength 1050\n", + "Current index of wavelength 438, Current wavelength 5.24908484270734, Total number of wavelength 1050\n", + "Current index of wavelength 439, Current wavelength 5.249904671115348, Total number of wavelength 1050\n", + "Current index of wavelength 440, Current wavelength 5.250724499523355, Total number of wavelength 1050\n", + "Current index of wavelength 441, Current wavelength 5.251544327931363, Total number of wavelength 1050\n", + "Current index of wavelength 442, Current wavelength 5.252364156339371, Total number of wavelength 1050\n", + "Current index of wavelength 443, Current wavelength 5.253183984747379, Total number of wavelength 1050\n", + "Current index of wavelength 444, Current wavelength 5.254003813155386, Total number of wavelength 1050\n", + "Current index of wavelength 445, Current wavelength 5.2548236415633935, Total number of wavelength 1050\n", + "Current index of wavelength 446, Current wavelength 5.255643469971401, Total number of wavelength 1050\n", + "Current index of wavelength 447, Current wavelength 5.256463298379408, Total number of wavelength 1050\n", + "Current index of wavelength 448, Current wavelength 5.257283126787416, Total number of wavelength 1050\n", + "Current index of wavelength 449, Current wavelength 5.258102955195424, Total number of wavelength 1050\n", + "Current index of wavelength 450, Current wavelength 5.258922783603432, Total number of wavelength 1050\n", + "Current index of wavelength 451, Current wavelength 5.259742612011439, Total number of wavelength 1050\n", + "Current index of wavelength 452, Current wavelength 5.260562440419447, Total number of wavelength 1050\n", + "Current index of wavelength 453, Current wavelength 5.261382268827455, Total number of wavelength 1050\n", + "Current index of wavelength 454, Current wavelength 5.262202097235463, Total number of wavelength 1050\n", + "Current index of wavelength 455, Current wavelength 5.26302192564347, Total number of wavelength 1050\n", + "Current index of wavelength 456, Current wavelength 5.2638417540514775, Total number of wavelength 1050\n", + "Current index of wavelength 457, Current wavelength 5.264661582459485, Total number of wavelength 1050\n", + "Current index of wavelength 458, Current wavelength 5.265481410867492, Total number of wavelength 1050\n", + "Current index of wavelength 459, Current wavelength 5.2663012392755, Total number of wavelength 1050\n", + "Current index of wavelength 460, Current wavelength 5.267121067683508, Total number of wavelength 1050\n", + "Current index of wavelength 461, Current wavelength 5.267940896091516, Total number of wavelength 1050\n", + "Current index of wavelength 462, Current wavelength 5.268760724499523, Total number of wavelength 1050\n", + "Current index of wavelength 463, Current wavelength 5.269580552907531, Total number of wavelength 1050\n", + "Current index of wavelength 464, Current wavelength 5.270400381315539, Total number of wavelength 1050\n", + "Current index of wavelength 465, Current wavelength 5.271220209723546, Total number of wavelength 1050\n", + "Current index of wavelength 466, Current wavelength 5.2720400381315535, Total number of wavelength 1050\n", + "Current index of wavelength 467, Current wavelength 5.272859866539561, Total number of wavelength 1050\n", + "Current index of wavelength 468, Current wavelength 5.273679694947569, Total number of wavelength 1050\n", + "Current index of wavelength 469, Current wavelength 5.274499523355576, Total number of wavelength 1050\n", + "Current index of wavelength 470, Current wavelength 5.275319351763584, Total number of wavelength 1050\n", + "Current index of wavelength 471, Current wavelength 5.276139180171592, Total number of wavelength 1050\n", + "Current index of wavelength 472, Current wavelength 5.276959008579599, Total number of wavelength 1050\n", + "Current index of wavelength 473, Current wavelength 5.277778836987607, Total number of wavelength 1050\n", + "Current index of wavelength 474, Current wavelength 5.278598665395615, Total number of wavelength 1050\n", + "Current index of wavelength 475, Current wavelength 5.279418493803623, Total number of wavelength 1050\n", + "Current index of wavelength 476, Current wavelength 5.28023832221163, Total number of wavelength 1050\n", + "Current index of wavelength 477, Current wavelength 5.2810581506196375, Total number of wavelength 1050\n", + "Current index of wavelength 478, Current wavelength 5.281877979027645, Total number of wavelength 1050\n", + "Current index of wavelength 479, Current wavelength 5.282697807435653, Total number of wavelength 1050\n", + "Current index of wavelength 480, Current wavelength 5.28351763584366, Total number of wavelength 1050\n", + "Current index of wavelength 481, Current wavelength 5.284337464251668, Total number of wavelength 1050\n", + "Current index of wavelength 482, Current wavelength 5.285157292659676, Total number of wavelength 1050\n", + "Current index of wavelength 483, Current wavelength 5.285977121067683, Total number of wavelength 1050\n", + "Current index of wavelength 484, Current wavelength 5.286796949475691, Total number of wavelength 1050\n", + "Current index of wavelength 485, Current wavelength 5.287616777883699, Total number of wavelength 1050\n", + "Current index of wavelength 486, Current wavelength 5.2884366062917065, Total number of wavelength 1050\n", + "Current index of wavelength 487, Current wavelength 5.2892564346997135, Total number of wavelength 1050\n", + "Current index of wavelength 488, Current wavelength 5.290076263107721, Total number of wavelength 1050\n", + "Current index of wavelength 489, Current wavelength 5.290896091515729, Total number of wavelength 1050\n", + "Current index of wavelength 490, Current wavelength 5.291715919923737, Total number of wavelength 1050\n", + "Current index of wavelength 491, Current wavelength 5.292535748331744, Total number of wavelength 1050\n", + "Current index of wavelength 492, Current wavelength 5.293355576739752, Total number of wavelength 1050\n", + "Current index of wavelength 493, Current wavelength 5.29417540514776, Total number of wavelength 1050\n", + "Current index of wavelength 494, Current wavelength 5.294995233555767, Total number of wavelength 1050\n", + "Current index of wavelength 495, Current wavelength 5.295815061963775, Total number of wavelength 1050\n", + "Current index of wavelength 496, Current wavelength 5.296634890371783, Total number of wavelength 1050\n", + "Current index of wavelength 497, Current wavelength 5.2974547187797905, Total number of wavelength 1050\n", + "Current index of wavelength 498, Current wavelength 5.2982745471877974, Total number of wavelength 1050\n", + "Current index of wavelength 499, Current wavelength 5.299094375595805, Total number of wavelength 1050\n", + "Current index of wavelength 500, Current wavelength 5.299914204003813, Total number of wavelength 1050\n", + "Current index of wavelength 501, Current wavelength 5.300734032411821, Total number of wavelength 1050\n", + "Current index of wavelength 502, Current wavelength 5.301553860819828, Total number of wavelength 1050\n", + "Current index of wavelength 503, Current wavelength 5.302373689227836, Total number of wavelength 1050\n", + "Current index of wavelength 504, Current wavelength 5.303193517635844, Total number of wavelength 1050\n", + "Current index of wavelength 505, Current wavelength 5.304013346043851, Total number of wavelength 1050\n", + "Current index of wavelength 506, Current wavelength 5.304833174451859, Total number of wavelength 1050\n", + "Current index of wavelength 507, Current wavelength 5.3056530028598665, Total number of wavelength 1050\n", + "Current index of wavelength 508, Current wavelength 5.306472831267874, Total number of wavelength 1050\n", + "Current index of wavelength 509, Current wavelength 5.307292659675881, Total number of wavelength 1050\n", + "Current index of wavelength 510, Current wavelength 5.308112488083889, Total number of wavelength 1050\n", + "Current index of wavelength 511, Current wavelength 5.308932316491897, Total number of wavelength 1050\n", + "Current index of wavelength 512, Current wavelength 5.309752144899904, Total number of wavelength 1050\n", + "Current index of wavelength 513, Current wavelength 5.310571973307912, Total number of wavelength 1050\n", + "Current index of wavelength 514, Current wavelength 5.31139180171592, Total number of wavelength 1050\n", + "Current index of wavelength 515, Current wavelength 5.312211630123928, Total number of wavelength 1050\n", + "Current index of wavelength 516, Current wavelength 5.313031458531935, Total number of wavelength 1050\n", + "Current index of wavelength 517, Current wavelength 5.313851286939943, Total number of wavelength 1050\n", + "Current index of wavelength 518, Current wavelength 5.3146711153479504, Total number of wavelength 1050\n", + "Current index of wavelength 519, Current wavelength 5.315490943755957, Total number of wavelength 1050\n", + "Current index of wavelength 520, Current wavelength 5.316310772163965, Total number of wavelength 1050\n", + "Current index of wavelength 521, Current wavelength 5.317130600571973, Total number of wavelength 1050\n", + "Current index of wavelength 522, Current wavelength 5.317950428979981, Total number of wavelength 1050\n", + "Current index of wavelength 523, Current wavelength 5.318770257387988, Total number of wavelength 1050\n", + "Current index of wavelength 524, Current wavelength 5.319590085795996, Total number of wavelength 1050\n", + "Current index of wavelength 525, Current wavelength 5.320409914204004, Total number of wavelength 1050\n", + "Current index of wavelength 526, Current wavelength 5.321229742612012, Total number of wavelength 1050\n", + "Current index of wavelength 527, Current wavelength 5.322049571020019, Total number of wavelength 1050\n", + "Current index of wavelength 528, Current wavelength 5.3228693994280265, Total number of wavelength 1050\n", + "Current index of wavelength 529, Current wavelength 5.323689227836034, Total number of wavelength 1050\n", + "Current index of wavelength 530, Current wavelength 5.324509056244041, Total number of wavelength 1050\n", + "Current index of wavelength 531, Current wavelength 5.325328884652049, Total number of wavelength 1050\n", + "Current index of wavelength 532, Current wavelength 5.326148713060057, Total number of wavelength 1050\n", + "Current index of wavelength 533, Current wavelength 5.326968541468065, Total number of wavelength 1050\n", + "Current index of wavelength 534, Current wavelength 5.327788369876072, Total number of wavelength 1050\n", + "Current index of wavelength 535, Current wavelength 5.32860819828408, Total number of wavelength 1050\n", + "Current index of wavelength 536, Current wavelength 5.329428026692088, Total number of wavelength 1050\n", + "Current index of wavelength 537, Current wavelength 5.330247855100096, Total number of wavelength 1050\n", + "Current index of wavelength 538, Current wavelength 5.331067683508103, Total number of wavelength 1050\n", + "Current index of wavelength 539, Current wavelength 5.33188751191611, Total number of wavelength 1050\n", + "Current index of wavelength 540, Current wavelength 5.332707340324118, Total number of wavelength 1050\n", + "Current index of wavelength 541, Current wavelength 5.333527168732125, Total number of wavelength 1050\n", + "Current index of wavelength 542, Current wavelength 5.334346997140133, Total number of wavelength 1050\n", + "Current index of wavelength 543, Current wavelength 5.335166825548141, Total number of wavelength 1050\n", + "Current index of wavelength 544, Current wavelength 5.335986653956149, Total number of wavelength 1050\n", + "Current index of wavelength 545, Current wavelength 5.336806482364156, Total number of wavelength 1050\n", + "Current index of wavelength 546, Current wavelength 5.337626310772164, Total number of wavelength 1050\n", + "Current index of wavelength 547, Current wavelength 5.338446139180172, Total number of wavelength 1050\n", + "Current index of wavelength 548, Current wavelength 5.3392659675881795, Total number of wavelength 1050\n", + "Current index of wavelength 549, Current wavelength 5.3400857959961865, Total number of wavelength 1050\n", + "Current index of wavelength 550, Current wavelength 5.340905624404194, Total number of wavelength 1050\n", + "Current index of wavelength 551, Current wavelength 5.341725452812202, Total number of wavelength 1050\n", + "Current index of wavelength 552, Current wavelength 5.342545281220209, Total number of wavelength 1050\n", + "Current index of wavelength 553, Current wavelength 5.343365109628217, Total number of wavelength 1050\n", + "Current index of wavelength 554, Current wavelength 5.344184938036225, Total number of wavelength 1050\n", + "Current index of wavelength 555, Current wavelength 5.345004766444233, Total number of wavelength 1050\n", + "Current index of wavelength 556, Current wavelength 5.34582459485224, Total number of wavelength 1050\n", + "Current index of wavelength 557, Current wavelength 5.346644423260248, Total number of wavelength 1050\n", + "Current index of wavelength 558, Current wavelength 5.347464251668256, Total number of wavelength 1050\n", + "Current index of wavelength 559, Current wavelength 5.3482840800762625, Total number of wavelength 1050\n", + "Current index of wavelength 560, Current wavelength 5.34910390848427, Total number of wavelength 1050\n", + "Current index of wavelength 561, Current wavelength 5.349923736892278, Total number of wavelength 1050\n", + "Current index of wavelength 562, Current wavelength 5.350743565300286, Total number of wavelength 1050\n", + "Current index of wavelength 563, Current wavelength 5.351563393708293, Total number of wavelength 1050\n", + "Current index of wavelength 564, Current wavelength 5.352383222116301, Total number of wavelength 1050\n", + "Current index of wavelength 565, Current wavelength 5.353203050524309, Total number of wavelength 1050\n", + "Current index of wavelength 566, Current wavelength 5.354022878932316, Total number of wavelength 1050\n", + "Current index of wavelength 567, Current wavelength 5.354842707340324, Total number of wavelength 1050\n", + "Current index of wavelength 568, Current wavelength 5.355662535748332, Total number of wavelength 1050\n", + "Current index of wavelength 569, Current wavelength 5.3564823641563395, Total number of wavelength 1050\n", + "Current index of wavelength 570, Current wavelength 5.3573021925643465, Total number of wavelength 1050\n", + "Current index of wavelength 571, Current wavelength 5.358122020972354, Total number of wavelength 1050\n", + "Current index of wavelength 572, Current wavelength 5.358941849380362, Total number of wavelength 1050\n", + "Current index of wavelength 573, Current wavelength 5.35976167778837, Total number of wavelength 1050\n", + "Current index of wavelength 574, Current wavelength 5.360581506196377, Total number of wavelength 1050\n", + "Current index of wavelength 575, Current wavelength 5.361401334604385, Total number of wavelength 1050\n", + "Current index of wavelength 576, Current wavelength 5.362221163012393, Total number of wavelength 1050\n", + "Current index of wavelength 577, Current wavelength 5.3630409914204, Total number of wavelength 1050\n", + "Current index of wavelength 578, Current wavelength 5.363860819828408, Total number of wavelength 1050\n", + "Current index of wavelength 579, Current wavelength 5.3646806482364155, Total number of wavelength 1050\n", + "Current index of wavelength 580, Current wavelength 5.365500476644423, Total number of wavelength 1050\n", + "Current index of wavelength 581, Current wavelength 5.36632030505243, Total number of wavelength 1050\n", + "Current index of wavelength 582, Current wavelength 5.367140133460438, Total number of wavelength 1050\n", + "Current index of wavelength 583, Current wavelength 5.367959961868446, Total number of wavelength 1050\n", + "Current index of wavelength 584, Current wavelength 5.368779790276454, Total number of wavelength 1050\n", + "Current index of wavelength 585, Current wavelength 5.369599618684461, Total number of wavelength 1050\n", + "Current index of wavelength 586, Current wavelength 5.370419447092469, Total number of wavelength 1050\n", + "Current index of wavelength 587, Current wavelength 5.371239275500477, Total number of wavelength 1050\n", + "Current index of wavelength 588, Current wavelength 5.372059103908484, Total number of wavelength 1050\n", + "Current index of wavelength 589, Current wavelength 5.372878932316492, Total number of wavelength 1050\n", + "Current index of wavelength 590, Current wavelength 5.3736987607244995, Total number of wavelength 1050\n", + "Current index of wavelength 591, Current wavelength 5.374518589132507, Total number of wavelength 1050\n", + "Current index of wavelength 592, Current wavelength 5.375338417540514, Total number of wavelength 1050\n", + "Current index of wavelength 593, Current wavelength 5.376158245948522, Total number of wavelength 1050\n", + "Current index of wavelength 594, Current wavelength 5.37697807435653, Total number of wavelength 1050\n", + "Current index of wavelength 595, Current wavelength 5.377797902764538, Total number of wavelength 1050\n", + "Current index of wavelength 596, Current wavelength 5.378617731172545, Total number of wavelength 1050\n", + "Current index of wavelength 597, Current wavelength 5.379437559580553, Total number of wavelength 1050\n", + "Current index of wavelength 598, Current wavelength 5.380257387988561, Total number of wavelength 1050\n", + "Current index of wavelength 599, Current wavelength 5.381077216396568, Total number of wavelength 1050\n", + "Current index of wavelength 600, Current wavelength 5.3818970448045755, Total number of wavelength 1050\n", + "Current index of wavelength 601, Current wavelength 5.382716873212583, Total number of wavelength 1050\n", + "Current index of wavelength 602, Current wavelength 5.383536701620591, Total number of wavelength 1050\n", + "Current index of wavelength 603, Current wavelength 5.384356530028598, Total number of wavelength 1050\n", + "Current index of wavelength 604, Current wavelength 5.385176358436606, Total number of wavelength 1050\n", + "Current index of wavelength 605, Current wavelength 5.385996186844614, Total number of wavelength 1050\n", + "Current index of wavelength 606, Current wavelength 5.386816015252621, Total number of wavelength 1050\n", + "Current index of wavelength 607, Current wavelength 5.387635843660629, Total number of wavelength 1050\n", + "Current index of wavelength 608, Current wavelength 5.388455672068637, Total number of wavelength 1050\n", + "Current index of wavelength 609, Current wavelength 5.389275500476645, Total number of wavelength 1050\n", + "Current index of wavelength 610, Current wavelength 5.390095328884652, Total number of wavelength 1050\n", + "Current index of wavelength 611, Current wavelength 5.3909151572926595, Total number of wavelength 1050\n", + "Current index of wavelength 612, Current wavelength 5.391734985700667, Total number of wavelength 1050\n", + "Current index of wavelength 613, Current wavelength 5.392554814108674, Total number of wavelength 1050\n", + "Current index of wavelength 614, Current wavelength 5.393374642516682, Total number of wavelength 1050\n", + "Current index of wavelength 615, Current wavelength 5.39419447092469, Total number of wavelength 1050\n", + "Current index of wavelength 616, Current wavelength 5.395014299332698, Total number of wavelength 1050\n", + "Current index of wavelength 617, Current wavelength 5.395834127740705, Total number of wavelength 1050\n", + "Current index of wavelength 618, Current wavelength 5.396653956148713, Total number of wavelength 1050\n", + "Current index of wavelength 619, Current wavelength 5.397473784556721, Total number of wavelength 1050\n", + "Current index of wavelength 620, Current wavelength 5.3982936129647285, Total number of wavelength 1050\n", + "Current index of wavelength 621, Current wavelength 5.3991134413727355, Total number of wavelength 1050\n", + "Current index of wavelength 622, Current wavelength 5.399933269780743, Total number of wavelength 1050\n", + "Current index of wavelength 623, Current wavelength 5.400753098188751, Total number of wavelength 1050\n", + "Current index of wavelength 624, Current wavelength 5.401572926596758, Total number of wavelength 1050\n", + "Current index of wavelength 625, Current wavelength 5.402392755004766, Total number of wavelength 1050\n", + "Current index of wavelength 626, Current wavelength 5.403212583412774, Total number of wavelength 1050\n", + "Current index of wavelength 627, Current wavelength 5.404032411820782, Total number of wavelength 1050\n", + "Current index of wavelength 628, Current wavelength 5.404852240228789, Total number of wavelength 1050\n", + "Current index of wavelength 629, Current wavelength 5.405672068636797, Total number of wavelength 1050\n", + "Current index of wavelength 630, Current wavelength 5.406491897044805, Total number of wavelength 1050\n", + "Current index of wavelength 631, Current wavelength 5.4073117254528125, Total number of wavelength 1050\n", + "Current index of wavelength 632, Current wavelength 5.4081315538608195, Total number of wavelength 1050\n", + "Current index of wavelength 633, Current wavelength 5.408951382268827, Total number of wavelength 1050\n", + "Current index of wavelength 634, Current wavelength 5.409771210676835, Total number of wavelength 1050\n", + "Current index of wavelength 635, Current wavelength 5.410591039084842, Total number of wavelength 1050\n", + "Current index of wavelength 636, Current wavelength 5.41141086749285, Total number of wavelength 1050\n", + "Current index of wavelength 637, Current wavelength 5.412230695900858, Total number of wavelength 1050\n", + "Current index of wavelength 638, Current wavelength 5.413050524308866, Total number of wavelength 1050\n", + "Current index of wavelength 639, Current wavelength 5.413870352716873, Total number of wavelength 1050\n", + "Current index of wavelength 640, Current wavelength 5.414690181124881, Total number of wavelength 1050\n", + "Current index of wavelength 641, Current wavelength 5.4155100095328885, Total number of wavelength 1050\n", + "Current index of wavelength 642, Current wavelength 5.416329837940896, Total number of wavelength 1050\n", + "Current index of wavelength 643, Current wavelength 5.417149666348903, Total number of wavelength 1050\n", + "Current index of wavelength 644, Current wavelength 5.417969494756911, Total number of wavelength 1050\n", + "Current index of wavelength 645, Current wavelength 5.418789323164919, Total number of wavelength 1050\n", + "Current index of wavelength 646, Current wavelength 5.419609151572926, Total number of wavelength 1050\n", + "Current index of wavelength 647, Current wavelength 5.420428979980934, Total number of wavelength 1050\n", + "Current index of wavelength 648, Current wavelength 5.421248808388942, Total number of wavelength 1050\n", + "Current index of wavelength 649, Current wavelength 5.422068636796949, Total number of wavelength 1050\n", + "Current index of wavelength 650, Current wavelength 5.422888465204957, Total number of wavelength 1050\n", + "Current index of wavelength 651, Current wavelength 5.423708293612965, Total number of wavelength 1050\n", + "Current index of wavelength 652, Current wavelength 5.4245281220209725, Total number of wavelength 1050\n", + "Current index of wavelength 653, Current wavelength 5.42534795042898, Total number of wavelength 1050\n", + "Current index of wavelength 654, Current wavelength 5.426167778836987, Total number of wavelength 1050\n", + "Current index of wavelength 655, Current wavelength 5.426987607244995, Total number of wavelength 1050\n", + "Current index of wavelength 656, Current wavelength 5.427807435653003, Total number of wavelength 1050\n", + "Current index of wavelength 657, Current wavelength 5.42862726406101, Total number of wavelength 1050\n", + "Current index of wavelength 658, Current wavelength 5.429447092469018, Total number of wavelength 1050\n", + "Current index of wavelength 659, Current wavelength 5.430266920877026, Total number of wavelength 1050\n", + "Current index of wavelength 660, Current wavelength 5.431086749285033, Total number of wavelength 1050\n", + "Current index of wavelength 661, Current wavelength 5.431906577693041, Total number of wavelength 1050\n", + "Current index of wavelength 662, Current wavelength 5.4327264061010485, Total number of wavelength 1050\n", + "Current index of wavelength 663, Current wavelength 5.433546234509056, Total number of wavelength 1050\n", + "Current index of wavelength 664, Current wavelength 5.434366062917063, Total number of wavelength 1050\n", + "Current index of wavelength 665, Current wavelength 5.435185891325071, Total number of wavelength 1050\n", + "Current index of wavelength 666, Current wavelength 5.436005719733079, Total number of wavelength 1050\n", + "Current index of wavelength 667, Current wavelength 5.436825548141087, Total number of wavelength 1050\n", + "Current index of wavelength 668, Current wavelength 5.437645376549094, Total number of wavelength 1050\n", + "Current index of wavelength 669, Current wavelength 5.438465204957102, Total number of wavelength 1050\n", + "Current index of wavelength 670, Current wavelength 5.43928503336511, Total number of wavelength 1050\n", + "Current index of wavelength 671, Current wavelength 5.440104861773117, Total number of wavelength 1050\n", + "Current index of wavelength 672, Current wavelength 5.440924690181125, Total number of wavelength 1050\n", + "Current index of wavelength 673, Current wavelength 5.441744518589132, Total number of wavelength 1050\n", + "Current index of wavelength 674, Current wavelength 5.44256434699714, Total number of wavelength 1050\n", + "Current index of wavelength 675, Current wavelength 5.443384175405147, Total number of wavelength 1050\n", + "Current index of wavelength 676, Current wavelength 5.444204003813155, Total number of wavelength 1050\n", + "Current index of wavelength 677, Current wavelength 5.445023832221163, Total number of wavelength 1050\n", + "Current index of wavelength 678, Current wavelength 5.445843660629171, Total number of wavelength 1050\n", + "Current index of wavelength 679, Current wavelength 5.446663489037178, Total number of wavelength 1050\n", + "Current index of wavelength 680, Current wavelength 5.447483317445186, Total number of wavelength 1050\n", + "Current index of wavelength 681, Current wavelength 5.448303145853194, Total number of wavelength 1050\n", + "Current index of wavelength 682, Current wavelength 5.449122974261201, Total number of wavelength 1050\n", + "Current index of wavelength 683, Current wavelength 5.4499428026692085, Total number of wavelength 1050\n", + "Current index of wavelength 684, Current wavelength 5.450762631077216, Total number of wavelength 1050\n", + "Current index of wavelength 685, Current wavelength 5.451582459485224, Total number of wavelength 1050\n", + "Current index of wavelength 686, Current wavelength 5.452402287893231, Total number of wavelength 1050\n", + "Current index of wavelength 687, Current wavelength 5.453222116301239, Total number of wavelength 1050\n", + "Current index of wavelength 688, Current wavelength 5.454041944709247, Total number of wavelength 1050\n", + "Current index of wavelength 689, Current wavelength 5.454861773117255, Total number of wavelength 1050\n", + "Current index of wavelength 690, Current wavelength 5.455681601525262, Total number of wavelength 1050\n", + "Current index of wavelength 691, Current wavelength 5.45650142993327, Total number of wavelength 1050\n", + "Current index of wavelength 692, Current wavelength 5.457321258341278, Total number of wavelength 1050\n", + "Current index of wavelength 693, Current wavelength 5.4581410867492846, Total number of wavelength 1050\n", + "Current index of wavelength 694, Current wavelength 5.458960915157292, Total number of wavelength 1050\n", + "Current index of wavelength 695, Current wavelength 5.4597807435653, Total number of wavelength 1050\n", + "Current index of wavelength 696, Current wavelength 5.460600571973308, Total number of wavelength 1050\n", + "Current index of wavelength 697, Current wavelength 5.461420400381315, Total number of wavelength 1050\n", + "Current index of wavelength 698, Current wavelength 5.462240228789323, Total number of wavelength 1050\n", + "Current index of wavelength 699, Current wavelength 5.463060057197331, Total number of wavelength 1050\n", + "Current index of wavelength 700, Current wavelength 5.463879885605339, Total number of wavelength 1050\n", + "Current index of wavelength 701, Current wavelength 5.464699714013346, Total number of wavelength 1050\n", + "Current index of wavelength 702, Current wavelength 5.465519542421354, Total number of wavelength 1050\n", + "Current index of wavelength 703, Current wavelength 5.4663393708293615, Total number of wavelength 1050\n", + "Current index of wavelength 704, Current wavelength 5.4671591992373685, Total number of wavelength 1050\n", + "Current index of wavelength 705, Current wavelength 5.467979027645376, Total number of wavelength 1050\n", + "Current index of wavelength 706, Current wavelength 5.468798856053384, Total number of wavelength 1050\n", + "Current index of wavelength 707, Current wavelength 5.469618684461391, Total number of wavelength 1050\n", + "Current index of wavelength 708, Current wavelength 5.470438512869399, Total number of wavelength 1050\n", + "Current index of wavelength 709, Current wavelength 5.471258341277407, Total number of wavelength 1050\n", + "Current index of wavelength 710, Current wavelength 5.472078169685415, Total number of wavelength 1050\n", + "Current index of wavelength 711, Current wavelength 5.472897998093422, Total number of wavelength 1050\n", + "Current index of wavelength 712, Current wavelength 5.47371782650143, Total number of wavelength 1050\n", + "Current index of wavelength 713, Current wavelength 5.474537654909438, Total number of wavelength 1050\n", + "Current index of wavelength 714, Current wavelength 5.475357483317445, Total number of wavelength 1050\n", + "Current index of wavelength 715, Current wavelength 5.476177311725452, Total number of wavelength 1050\n", + "Current index of wavelength 716, Current wavelength 5.47699714013346, Total number of wavelength 1050\n", + "Current index of wavelength 717, Current wavelength 5.477816968541468, Total number of wavelength 1050\n", + "Current index of wavelength 718, Current wavelength 5.478636796949475, Total number of wavelength 1050\n", + "Current index of wavelength 719, Current wavelength 5.479456625357483, Total number of wavelength 1050\n", + "Current index of wavelength 720, Current wavelength 5.480276453765491, Total number of wavelength 1050\n", + "Current index of wavelength 721, Current wavelength 5.481096282173499, Total number of wavelength 1050\n", + "Current index of wavelength 722, Current wavelength 5.481916110581506, Total number of wavelength 1050\n", + "Current index of wavelength 723, Current wavelength 5.482735938989514, Total number of wavelength 1050\n", + "Current index of wavelength 724, Current wavelength 5.4835557673975215, Total number of wavelength 1050\n", + "Current index of wavelength 725, Current wavelength 5.484375595805529, Total number of wavelength 1050\n", + "Current index of wavelength 726, Current wavelength 5.485195424213536, Total number of wavelength 1050\n", + "Current index of wavelength 727, Current wavelength 5.486015252621544, Total number of wavelength 1050\n", + "Current index of wavelength 728, Current wavelength 5.486835081029552, Total number of wavelength 1050\n", + "Current index of wavelength 729, Current wavelength 5.487654909437559, Total number of wavelength 1050\n", + "Current index of wavelength 730, Current wavelength 5.488474737845567, Total number of wavelength 1050\n", + "Current index of wavelength 731, Current wavelength 5.489294566253575, Total number of wavelength 1050\n", + "Current index of wavelength 732, Current wavelength 5.490114394661583, Total number of wavelength 1050\n", + "Current index of wavelength 733, Current wavelength 5.49093422306959, Total number of wavelength 1050\n", + "Current index of wavelength 734, Current wavelength 5.4917540514775975, Total number of wavelength 1050\n", + "Current index of wavelength 735, Current wavelength 5.492573879885605, Total number of wavelength 1050\n", + "Current index of wavelength 736, Current wavelength 5.493393708293613, Total number of wavelength 1050\n", + "Current index of wavelength 737, Current wavelength 5.49421353670162, Total number of wavelength 1050\n", + "Current index of wavelength 738, Current wavelength 5.495033365109628, Total number of wavelength 1050\n", + "Current index of wavelength 739, Current wavelength 5.495853193517636, Total number of wavelength 1050\n", + "Current index of wavelength 740, Current wavelength 5.496673021925643, Total number of wavelength 1050\n", + "Current index of wavelength 741, Current wavelength 5.497492850333651, Total number of wavelength 1050\n", + "Current index of wavelength 742, Current wavelength 5.498312678741659, Total number of wavelength 1050\n", + "Current index of wavelength 743, Current wavelength 5.499132507149667, Total number of wavelength 1050\n", + "Current index of wavelength 744, Current wavelength 5.499952335557674, Total number of wavelength 1050\n", + "Current index of wavelength 745, Current wavelength 5.5007721639656815, Total number of wavelength 1050\n", + "Current index of wavelength 746, Current wavelength 5.501591992373689, Total number of wavelength 1050\n", + "Current index of wavelength 747, Current wavelength 5.502411820781697, Total number of wavelength 1050\n", + "Current index of wavelength 748, Current wavelength 5.503231649189704, Total number of wavelength 1050\n", + "Current index of wavelength 749, Current wavelength 5.504051477597712, Total number of wavelength 1050\n", + "Current index of wavelength 750, Current wavelength 5.50487130600572, Total number of wavelength 1050\n", + "Current index of wavelength 751, Current wavelength 5.505691134413727, Total number of wavelength 1050\n", + "Current index of wavelength 752, Current wavelength 5.506510962821735, Total number of wavelength 1050\n", + "Current index of wavelength 753, Current wavelength 5.507330791229743, Total number of wavelength 1050\n", + "Current index of wavelength 754, Current wavelength 5.50815061963775, Total number of wavelength 1050\n", + "Current index of wavelength 755, Current wavelength 5.5089704480457575, Total number of wavelength 1050\n", + "Current index of wavelength 756, Current wavelength 5.509790276453765, Total number of wavelength 1050\n", + "Current index of wavelength 757, Current wavelength 5.510610104861773, Total number of wavelength 1050\n", + "Current index of wavelength 758, Current wavelength 5.51142993326978, Total number of wavelength 1050\n", + "Current index of wavelength 759, Current wavelength 5.512249761677788, Total number of wavelength 1050\n", + "Current index of wavelength 760, Current wavelength 5.513069590085796, Total number of wavelength 1050\n", + "Current index of wavelength 761, Current wavelength 5.513889418493804, Total number of wavelength 1050\n", + "Current index of wavelength 762, Current wavelength 5.514709246901811, Total number of wavelength 1050\n", + "Current index of wavelength 763, Current wavelength 5.515529075309819, Total number of wavelength 1050\n", + "Current index of wavelength 764, Current wavelength 5.516348903717827, Total number of wavelength 1050\n", + "Current index of wavelength 765, Current wavelength 5.517168732125834, Total number of wavelength 1050\n", + "Current index of wavelength 766, Current wavelength 5.5179885605338415, Total number of wavelength 1050\n", + "Current index of wavelength 767, Current wavelength 5.518808388941849, Total number of wavelength 1050\n", + "Current index of wavelength 768, Current wavelength 5.519628217349857, Total number of wavelength 1050\n", + "Current index of wavelength 769, Current wavelength 5.520448045757864, Total number of wavelength 1050\n", + "Current index of wavelength 770, Current wavelength 5.521267874165872, Total number of wavelength 1050\n", + "Current index of wavelength 771, Current wavelength 5.52208770257388, Total number of wavelength 1050\n", + "Current index of wavelength 772, Current wavelength 5.522907530981888, Total number of wavelength 1050\n", + "Current index of wavelength 773, Current wavelength 5.523727359389895, Total number of wavelength 1050\n", + "Current index of wavelength 774, Current wavelength 5.524547187797903, Total number of wavelength 1050\n", + "Current index of wavelength 775, Current wavelength 5.5253670162059105, Total number of wavelength 1050\n", + "Current index of wavelength 776, Current wavelength 5.5261868446139175, Total number of wavelength 1050\n", + "Current index of wavelength 777, Current wavelength 5.527006673021925, Total number of wavelength 1050\n", + "Current index of wavelength 778, Current wavelength 5.527826501429933, Total number of wavelength 1050\n", + "Current index of wavelength 779, Current wavelength 5.528646329837941, Total number of wavelength 1050\n", + "Current index of wavelength 780, Current wavelength 5.529466158245948, Total number of wavelength 1050\n", + "Current index of wavelength 781, Current wavelength 5.530285986653956, Total number of wavelength 1050\n", + "Current index of wavelength 782, Current wavelength 5.531105815061964, Total number of wavelength 1050\n", + "Current index of wavelength 783, Current wavelength 5.531925643469972, Total number of wavelength 1050\n", + "Current index of wavelength 784, Current wavelength 5.532745471877979, Total number of wavelength 1050\n", + "Current index of wavelength 785, Current wavelength 5.533565300285987, Total number of wavelength 1050\n", + "Current index of wavelength 786, Current wavelength 5.5343851286939945, Total number of wavelength 1050\n", + "Current index of wavelength 787, Current wavelength 5.535204957102001, Total number of wavelength 1050\n", + "Current index of wavelength 788, Current wavelength 5.536024785510009, Total number of wavelength 1050\n", + "Current index of wavelength 789, Current wavelength 5.536844613918017, Total number of wavelength 1050\n", + "Current index of wavelength 790, Current wavelength 5.537664442326025, Total number of wavelength 1050\n", + "Current index of wavelength 791, Current wavelength 5.538484270734032, Total number of wavelength 1050\n", + "Current index of wavelength 792, Current wavelength 5.53930409914204, Total number of wavelength 1050\n", + "Current index of wavelength 793, Current wavelength 5.540123927550048, Total number of wavelength 1050\n", + "Current index of wavelength 794, Current wavelength 5.540943755958056, Total number of wavelength 1050\n", + "Current index of wavelength 795, Current wavelength 5.541763584366063, Total number of wavelength 1050\n", + "Current index of wavelength 796, Current wavelength 5.5425834127740705, Total number of wavelength 1050\n", + "Current index of wavelength 797, Current wavelength 5.543403241182078, Total number of wavelength 1050\n", + "Current index of wavelength 798, Current wavelength 5.544223069590085, Total number of wavelength 1050\n", + "Current index of wavelength 799, Current wavelength 5.545042897998093, Total number of wavelength 1050\n", + "Current index of wavelength 800, Current wavelength 5.545862726406101, Total number of wavelength 1050\n", + "Current index of wavelength 801, Current wavelength 5.546682554814108, Total number of wavelength 1050\n", + "Current index of wavelength 802, Current wavelength 5.547502383222116, Total number of wavelength 1050\n", + "Current index of wavelength 803, Current wavelength 5.548322211630124, Total number of wavelength 1050\n", + "Current index of wavelength 804, Current wavelength 5.549142040038132, Total number of wavelength 1050\n", + "Current index of wavelength 805, Current wavelength 5.549961868446139, Total number of wavelength 1050\n", + "Current index of wavelength 806, Current wavelength 5.550781696854147, Total number of wavelength 1050\n", + "Current index of wavelength 807, Current wavelength 5.5516015252621544, Total number of wavelength 1050\n", + "Current index of wavelength 808, Current wavelength 5.552421353670162, Total number of wavelength 1050\n", + "Current index of wavelength 809, Current wavelength 5.553241182078169, Total number of wavelength 1050\n", + "Current index of wavelength 810, Current wavelength 5.554061010486177, Total number of wavelength 1050\n", + "Current index of wavelength 811, Current wavelength 5.554880838894185, Total number of wavelength 1050\n", + "Current index of wavelength 812, Current wavelength 5.555700667302192, Total number of wavelength 1050\n", + "Current index of wavelength 813, Current wavelength 5.5565204957102, Total number of wavelength 1050\n", + "Current index of wavelength 814, Current wavelength 5.557340324118208, Total number of wavelength 1050\n", + "Current index of wavelength 815, Current wavelength 5.558160152526216, Total number of wavelength 1050\n", + "Current index of wavelength 816, Current wavelength 5.558979980934223, Total number of wavelength 1050\n", + "Current index of wavelength 817, Current wavelength 5.5597998093422305, Total number of wavelength 1050\n", + "Current index of wavelength 818, Current wavelength 5.560619637750238, Total number of wavelength 1050\n", + "Current index of wavelength 819, Current wavelength 5.561439466158246, Total number of wavelength 1050\n", + "Current index of wavelength 820, Current wavelength 5.562259294566253, Total number of wavelength 1050\n", + "Current index of wavelength 821, Current wavelength 5.563079122974261, Total number of wavelength 1050\n", + "Current index of wavelength 822, Current wavelength 5.563898951382269, Total number of wavelength 1050\n", + "Current index of wavelength 823, Current wavelength 5.564718779790276, Total number of wavelength 1050\n", + "Current index of wavelength 824, Current wavelength 5.565538608198284, Total number of wavelength 1050\n", + "Current index of wavelength 825, Current wavelength 5.566358436606292, Total number of wavelength 1050\n", + "Current index of wavelength 826, Current wavelength 5.5671782650143, Total number of wavelength 1050\n", + "Current index of wavelength 827, Current wavelength 5.567998093422307, Total number of wavelength 1050\n", + "Current index of wavelength 828, Current wavelength 5.568817921830314, Total number of wavelength 1050\n", + "Current index of wavelength 829, Current wavelength 5.569637750238322, Total number of wavelength 1050\n", + "Current index of wavelength 830, Current wavelength 5.57045757864633, Total number of wavelength 1050\n", + "Current index of wavelength 831, Current wavelength 5.571277407054337, Total number of wavelength 1050\n", + "Current index of wavelength 832, Current wavelength 5.572097235462345, Total number of wavelength 1050\n", + "Current index of wavelength 833, Current wavelength 5.572917063870353, Total number of wavelength 1050\n", + "Current index of wavelength 834, Current wavelength 5.57373689227836, Total number of wavelength 1050\n", + "Current index of wavelength 835, Current wavelength 5.574556720686368, Total number of wavelength 1050\n", + "Current index of wavelength 836, Current wavelength 5.575376549094376, Total number of wavelength 1050\n", + "Current index of wavelength 837, Current wavelength 5.5761963775023835, Total number of wavelength 1050\n", + "Current index of wavelength 838, Current wavelength 5.5770162059103905, Total number of wavelength 1050\n", + "Current index of wavelength 839, Current wavelength 5.577836034318398, Total number of wavelength 1050\n", + "Current index of wavelength 840, Current wavelength 5.578655862726406, Total number of wavelength 1050\n", + "Current index of wavelength 841, Current wavelength 5.579475691134414, Total number of wavelength 1050\n", + "Current index of wavelength 842, Current wavelength 5.580295519542421, Total number of wavelength 1050\n", + "Current index of wavelength 843, Current wavelength 5.581115347950429, Total number of wavelength 1050\n", + "Current index of wavelength 844, Current wavelength 5.581935176358437, Total number of wavelength 1050\n", + "Current index of wavelength 845, Current wavelength 5.582755004766444, Total number of wavelength 1050\n", + "Current index of wavelength 846, Current wavelength 5.583574833174452, Total number of wavelength 1050\n", + "Current index of wavelength 847, Current wavelength 5.58439466158246, Total number of wavelength 1050\n", + "Current index of wavelength 848, Current wavelength 5.5852144899904665, Total number of wavelength 1050\n", + "Current index of wavelength 849, Current wavelength 5.586034318398474, Total number of wavelength 1050\n", + "Current index of wavelength 850, Current wavelength 5.586854146806482, Total number of wavelength 1050\n", + "Current index of wavelength 851, Current wavelength 5.58767397521449, Total number of wavelength 1050\n", + "Current index of wavelength 852, Current wavelength 5.588493803622497, Total number of wavelength 1050\n", + "Current index of wavelength 853, Current wavelength 5.589313632030505, Total number of wavelength 1050\n", + "Current index of wavelength 854, Current wavelength 5.590133460438513, Total number of wavelength 1050\n", + "Current index of wavelength 855, Current wavelength 5.590953288846521, Total number of wavelength 1050\n", + "Current index of wavelength 856, Current wavelength 5.591773117254528, Total number of wavelength 1050\n", + "Current index of wavelength 857, Current wavelength 5.592592945662536, Total number of wavelength 1050\n", + "Current index of wavelength 858, Current wavelength 5.5934127740705435, Total number of wavelength 1050\n", + "Current index of wavelength 859, Current wavelength 5.5942326024785505, Total number of wavelength 1050\n", + "Current index of wavelength 860, Current wavelength 5.595052430886558, Total number of wavelength 1050\n", + "Current index of wavelength 861, Current wavelength 5.595872259294566, Total number of wavelength 1050\n", + "Current index of wavelength 862, Current wavelength 5.596692087702574, Total number of wavelength 1050\n", + "Current index of wavelength 863, Current wavelength 5.597511916110581, Total number of wavelength 1050\n", + "Current index of wavelength 864, Current wavelength 5.598331744518589, Total number of wavelength 1050\n", + "Current index of wavelength 865, Current wavelength 5.599151572926597, Total number of wavelength 1050\n", + "Current index of wavelength 866, Current wavelength 5.599971401334605, Total number of wavelength 1050\n", + "Current index of wavelength 867, Current wavelength 5.600791229742612, Total number of wavelength 1050\n", + "Current index of wavelength 868, Current wavelength 5.6016110581506195, Total number of wavelength 1050\n", + "Current index of wavelength 869, Current wavelength 5.602430886558627, Total number of wavelength 1050\n", + "Current index of wavelength 870, Current wavelength 5.603250714966634, Total number of wavelength 1050\n", + "Current index of wavelength 871, Current wavelength 5.604070543374642, Total number of wavelength 1050\n", + "Current index of wavelength 872, Current wavelength 5.60489037178265, Total number of wavelength 1050\n", + "Current index of wavelength 873, Current wavelength 5.605710200190658, Total number of wavelength 1050\n", + "Current index of wavelength 874, Current wavelength 5.606530028598665, Total number of wavelength 1050\n", + "Current index of wavelength 875, Current wavelength 5.607349857006673, Total number of wavelength 1050\n", + "Current index of wavelength 876, Current wavelength 5.608169685414681, Total number of wavelength 1050\n", + "Current index of wavelength 877, Current wavelength 5.608989513822689, Total number of wavelength 1050\n", + "Current index of wavelength 878, Current wavelength 5.609809342230696, Total number of wavelength 1050\n", + "Current index of wavelength 879, Current wavelength 5.6106291706387035, Total number of wavelength 1050\n", + "Current index of wavelength 880, Current wavelength 5.611448999046711, Total number of wavelength 1050\n", + "Current index of wavelength 881, Current wavelength 5.612268827454718, Total number of wavelength 1050\n", + "Current index of wavelength 882, Current wavelength 5.613088655862726, Total number of wavelength 1050\n", + "Current index of wavelength 883, Current wavelength 5.613908484270734, Total number of wavelength 1050\n", + "Current index of wavelength 884, Current wavelength 5.614728312678742, Total number of wavelength 1050\n", + "Current index of wavelength 885, Current wavelength 5.615548141086749, Total number of wavelength 1050\n", + "Current index of wavelength 886, Current wavelength 5.616367969494757, Total number of wavelength 1050\n", + "Current index of wavelength 887, Current wavelength 5.617187797902765, Total number of wavelength 1050\n", + "Current index of wavelength 888, Current wavelength 5.6180076263107726, Total number of wavelength 1050\n", + "Current index of wavelength 889, Current wavelength 5.6188274547187795, Total number of wavelength 1050\n", + "Current index of wavelength 890, Current wavelength 5.619647283126787, Total number of wavelength 1050\n", + "Current index of wavelength 891, Current wavelength 5.620467111534795, Total number of wavelength 1050\n", + "Current index of wavelength 892, Current wavelength 5.621286939942802, Total number of wavelength 1050\n", + "Current index of wavelength 893, Current wavelength 5.62210676835081, Total number of wavelength 1050\n", + "Current index of wavelength 894, Current wavelength 5.622926596758818, Total number of wavelength 1050\n", + "Current index of wavelength 895, Current wavelength 5.623746425166825, Total number of wavelength 1050\n", + "Current index of wavelength 896, Current wavelength 5.624566253574833, Total number of wavelength 1050\n", + "Current index of wavelength 897, Current wavelength 5.625386081982841, Total number of wavelength 1050\n", + "Current index of wavelength 898, Current wavelength 5.626205910390849, Total number of wavelength 1050\n", + "Current index of wavelength 899, Current wavelength 5.627025738798856, Total number of wavelength 1050\n", + "Current index of wavelength 900, Current wavelength 5.6278455672068635, Total number of wavelength 1050\n", + "Current index of wavelength 901, Current wavelength 5.628665395614871, Total number of wavelength 1050\n", + "Current index of wavelength 902, Current wavelength 5.629485224022879, Total number of wavelength 1050\n", + "Current index of wavelength 903, Current wavelength 5.630305052430886, Total number of wavelength 1050\n", + "Current index of wavelength 904, Current wavelength 5.631124880838894, Total number of wavelength 1050\n", + "Current index of wavelength 905, Current wavelength 5.631944709246902, Total number of wavelength 1050\n", + "Current index of wavelength 906, Current wavelength 5.632764537654909, Total number of wavelength 1050\n", + "Current index of wavelength 907, Current wavelength 5.633584366062917, Total number of wavelength 1050\n", + "Current index of wavelength 908, Current wavelength 5.634404194470925, Total number of wavelength 1050\n", + "Current index of wavelength 909, Current wavelength 5.6352240228789325, Total number of wavelength 1050\n", + "Current index of wavelength 910, Current wavelength 5.6360438512869395, Total number of wavelength 1050\n", + "Current index of wavelength 911, Current wavelength 5.636863679694947, Total number of wavelength 1050\n", + "Current index of wavelength 912, Current wavelength 5.637683508102955, Total number of wavelength 1050\n", + "Current index of wavelength 913, Current wavelength 5.638503336510963, Total number of wavelength 1050\n", + "Current index of wavelength 914, Current wavelength 5.63932316491897, Total number of wavelength 1050\n", + "Current index of wavelength 915, Current wavelength 5.640142993326978, Total number of wavelength 1050\n", + "Current index of wavelength 916, Current wavelength 5.640962821734986, Total number of wavelength 1050\n", + "Current index of wavelength 917, Current wavelength 5.641782650142993, Total number of wavelength 1050\n", + "Current index of wavelength 918, Current wavelength 5.642602478551001, Total number of wavelength 1050\n", + "Current index of wavelength 919, Current wavelength 5.643422306959009, Total number of wavelength 1050\n", + "Current index of wavelength 920, Current wavelength 5.6442421353670165, Total number of wavelength 1050\n", + "Current index of wavelength 921, Current wavelength 5.6450619637750235, Total number of wavelength 1050\n", + "Current index of wavelength 922, Current wavelength 5.645881792183031, Total number of wavelength 1050\n", + "Current index of wavelength 923, Current wavelength 5.646701620591039, Total number of wavelength 1050\n", + "Current index of wavelength 924, Current wavelength 5.647521448999047, Total number of wavelength 1050\n", + "Current index of wavelength 925, Current wavelength 5.648341277407054, Total number of wavelength 1050\n", + "Current index of wavelength 926, Current wavelength 5.649161105815062, Total number of wavelength 1050\n", + "Current index of wavelength 927, Current wavelength 5.64998093422307, Total number of wavelength 1050\n", + "Current index of wavelength 928, Current wavelength 5.650800762631077, Total number of wavelength 1050\n", + "Current index of wavelength 929, Current wavelength 5.651620591039085, Total number of wavelength 1050\n", + "Current index of wavelength 930, Current wavelength 5.6524404194470925, Total number of wavelength 1050\n", + "Current index of wavelength 931, Current wavelength 5.6532602478551, Total number of wavelength 1050\n", + "Current index of wavelength 932, Current wavelength 5.654080076263107, Total number of wavelength 1050\n", + "Current index of wavelength 933, Current wavelength 5.654899904671115, Total number of wavelength 1050\n", + "Current index of wavelength 934, Current wavelength 5.655719733079123, Total number of wavelength 1050\n", + "Current index of wavelength 935, Current wavelength 5.656539561487131, Total number of wavelength 1050\n", + "Current index of wavelength 936, Current wavelength 5.657359389895138, Total number of wavelength 1050\n", + "Current index of wavelength 937, Current wavelength 5.658179218303146, Total number of wavelength 1050\n", + "Current index of wavelength 938, Current wavelength 5.658999046711154, Total number of wavelength 1050\n", + "Current index of wavelength 939, Current wavelength 5.659818875119161, Total number of wavelength 1050\n", + "Current index of wavelength 940, Current wavelength 5.660638703527169, Total number of wavelength 1050\n", + "Current index of wavelength 941, Current wavelength 5.6614585319351765, Total number of wavelength 1050\n", + "Current index of wavelength 942, Current wavelength 5.662278360343183, Total number of wavelength 1050\n", + "Current index of wavelength 943, Current wavelength 5.663098188751191, Total number of wavelength 1050\n", + "Current index of wavelength 944, Current wavelength 5.663918017159199, Total number of wavelength 1050\n", + "Current index of wavelength 945, Current wavelength 5.664737845567207, Total number of wavelength 1050\n", + "Current index of wavelength 946, Current wavelength 5.665557673975215, Total number of wavelength 1050\n", + "Current index of wavelength 947, Current wavelength 5.666377502383222, Total number of wavelength 1050\n", + "Current index of wavelength 948, Current wavelength 5.66719733079123, Total number of wavelength 1050\n", + "Current index of wavelength 949, Current wavelength 5.668017159199238, Total number of wavelength 1050\n", + "Current index of wavelength 950, Current wavelength 5.668836987607245, Total number of wavelength 1050\n", + "Current index of wavelength 951, Current wavelength 5.6696568160152525, Total number of wavelength 1050\n", + "Current index of wavelength 952, Current wavelength 5.67047664442326, Total number of wavelength 1050\n", + "Current index of wavelength 953, Current wavelength 5.671296472831267, Total number of wavelength 1050\n", + "Current index of wavelength 954, Current wavelength 5.672116301239275, Total number of wavelength 1050\n", + "Current index of wavelength 955, Current wavelength 5.672936129647283, Total number of wavelength 1050\n", + "Current index of wavelength 956, Current wavelength 5.673755958055291, Total number of wavelength 1050\n", + "Current index of wavelength 957, Current wavelength 5.674575786463298, Total number of wavelength 1050\n", + "Current index of wavelength 958, Current wavelength 5.675395614871306, Total number of wavelength 1050\n", + "Current index of wavelength 959, Current wavelength 5.676215443279314, Total number of wavelength 1050\n", + "Current index of wavelength 960, Current wavelength 5.677035271687322, Total number of wavelength 1050\n", + "Current index of wavelength 961, Current wavelength 5.677855100095329, Total number of wavelength 1050\n", + "Current index of wavelength 962, Current wavelength 5.678674928503336, Total number of wavelength 1050\n", + "Current index of wavelength 963, Current wavelength 5.679494756911344, Total number of wavelength 1050\n", + "Current index of wavelength 964, Current wavelength 5.680314585319351, Total number of wavelength 1050\n", + "Current index of wavelength 965, Current wavelength 5.681134413727359, Total number of wavelength 1050\n", + "Current index of wavelength 966, Current wavelength 5.681954242135367, Total number of wavelength 1050\n", + "Current index of wavelength 967, Current wavelength 5.682774070543375, Total number of wavelength 1050\n", + "Current index of wavelength 968, Current wavelength 5.683593898951382, Total number of wavelength 1050\n", + "Current index of wavelength 969, Current wavelength 5.68441372735939, Total number of wavelength 1050\n", + "Current index of wavelength 970, Current wavelength 5.685233555767398, Total number of wavelength 1050\n", + "Current index of wavelength 971, Current wavelength 5.6860533841754055, Total number of wavelength 1050\n", + "Current index of wavelength 972, Current wavelength 5.6868732125834125, Total number of wavelength 1050\n", + "Current index of wavelength 973, Current wavelength 5.68769304099142, Total number of wavelength 1050\n", + "Current index of wavelength 974, Current wavelength 5.688512869399428, Total number of wavelength 1050\n", + "Current index of wavelength 975, Current wavelength 5.689332697807435, Total number of wavelength 1050\n", + "Current index of wavelength 976, Current wavelength 5.690152526215443, Total number of wavelength 1050\n", + "Current index of wavelength 977, Current wavelength 5.690972354623451, Total number of wavelength 1050\n", + "Current index of wavelength 978, Current wavelength 5.691792183031459, Total number of wavelength 1050\n", + "Current index of wavelength 979, Current wavelength 5.692612011439466, Total number of wavelength 1050\n", + "Current index of wavelength 980, Current wavelength 5.693431839847474, Total number of wavelength 1050\n", + "Current index of wavelength 981, Current wavelength 5.694251668255482, Total number of wavelength 1050\n", + "Current index of wavelength 982, Current wavelength 5.695071496663489, Total number of wavelength 1050\n", + "Current index of wavelength 983, Current wavelength 5.695891325071496, Total number of wavelength 1050\n", + "Current index of wavelength 984, Current wavelength 5.696711153479504, Total number of wavelength 1050\n", + "Current index of wavelength 985, Current wavelength 5.697530981887512, Total number of wavelength 1050\n", + "Current index of wavelength 986, Current wavelength 5.698350810295519, Total number of wavelength 1050\n", + "Current index of wavelength 987, Current wavelength 5.699170638703527, Total number of wavelength 1050\n", + "Current index of wavelength 988, Current wavelength 5.699990467111535, Total number of wavelength 1050\n", + "Current index of wavelength 989, Current wavelength 5.700810295519542, Total number of wavelength 1050\n", + "Current index of wavelength 990, Current wavelength 5.70163012392755, Total number of wavelength 1050\n", + "Current index of wavelength 991, Current wavelength 5.702449952335558, Total number of wavelength 1050\n", + "Current index of wavelength 992, Current wavelength 5.7032697807435655, Total number of wavelength 1050\n", + "Current index of wavelength 993, Current wavelength 5.704089609151573, Total number of wavelength 1050\n", + "Current index of wavelength 994, Current wavelength 5.70490943755958, Total number of wavelength 1050\n", + "Current index of wavelength 995, Current wavelength 5.705729265967588, Total number of wavelength 1050\n", + "Current index of wavelength 996, Current wavelength 5.706549094375596, Total number of wavelength 1050\n", + "Current index of wavelength 997, Current wavelength 5.707368922783603, Total number of wavelength 1050\n", + "Current index of wavelength 998, Current wavelength 5.708188751191611, Total number of wavelength 1050\n", + "Current index of wavelength 999, Current wavelength 5.709008579599619, Total number of wavelength 1050\n", + "Current index of wavelength 1000, Current wavelength 5.709828408007626, Total number of wavelength 1050\n", + "Current index of wavelength 1001, Current wavelength 5.710648236415634, Total number of wavelength 1050\n", + "Current index of wavelength 1002, Current wavelength 5.7114680648236416, Total number of wavelength 1050\n", + "Current index of wavelength 1003, Current wavelength 5.712287893231649, Total number of wavelength 1050\n", + "Current index of wavelength 1004, Current wavelength 5.713107721639656, Total number of wavelength 1050\n", + "Current index of wavelength 1005, Current wavelength 5.713927550047664, Total number of wavelength 1050\n", + "Current index of wavelength 1006, Current wavelength 5.714747378455672, Total number of wavelength 1050\n", + "Current index of wavelength 1007, Current wavelength 5.71556720686368, Total number of wavelength 1050\n", + "Current index of wavelength 1008, Current wavelength 5.716387035271687, Total number of wavelength 1050\n", + "Current index of wavelength 1009, Current wavelength 5.717206863679695, Total number of wavelength 1050\n", + "Current index of wavelength 1010, Current wavelength 5.718026692087703, Total number of wavelength 1050\n", + "Current index of wavelength 1011, Current wavelength 5.71884652049571, Total number of wavelength 1050\n", + "Current index of wavelength 1012, Current wavelength 5.719666348903718, Total number of wavelength 1050\n", + "Current index of wavelength 1013, Current wavelength 5.7204861773117255, Total number of wavelength 1050\n", + "Current index of wavelength 1014, Current wavelength 5.721306005719733, Total number of wavelength 1050\n", + "Current index of wavelength 1015, Current wavelength 5.72212583412774, Total number of wavelength 1050\n", + "Current index of wavelength 1016, Current wavelength 5.722945662535748, Total number of wavelength 1050\n", + "Current index of wavelength 1017, Current wavelength 5.723765490943756, Total number of wavelength 1050\n", + "Current index of wavelength 1018, Current wavelength 5.724585319351764, Total number of wavelength 1050\n", + "Current index of wavelength 1019, Current wavelength 5.725405147759771, Total number of wavelength 1050\n", + "Current index of wavelength 1020, Current wavelength 5.726224976167779, Total number of wavelength 1050\n", + "Current index of wavelength 1021, Current wavelength 5.727044804575787, Total number of wavelength 1050\n", + "Current index of wavelength 1022, Current wavelength 5.727864632983794, Total number of wavelength 1050\n", + "Current index of wavelength 1023, Current wavelength 5.7286844613918015, Total number of wavelength 1050\n", + "Current index of wavelength 1024, Current wavelength 5.729504289799809, Total number of wavelength 1050\n", + "Current index of wavelength 1025, Current wavelength 5.730324118207817, Total number of wavelength 1050\n", + "Current index of wavelength 1026, Current wavelength 5.731143946615824, Total number of wavelength 1050\n", + "Current index of wavelength 1027, Current wavelength 5.731963775023832, Total number of wavelength 1050\n", + "Current index of wavelength 1028, Current wavelength 5.73278360343184, Total number of wavelength 1050\n", + "Current index of wavelength 1029, Current wavelength 5.733603431839848, Total number of wavelength 1050\n", + "Current index of wavelength 1030, Current wavelength 5.734423260247855, Total number of wavelength 1050\n", + "Current index of wavelength 1031, Current wavelength 5.735243088655863, Total number of wavelength 1050\n", + "Current index of wavelength 1032, Current wavelength 5.736062917063871, Total number of wavelength 1050\n", + "Current index of wavelength 1033, Current wavelength 5.736882745471878, Total number of wavelength 1050\n", + "Current index of wavelength 1034, Current wavelength 5.7377025738798855, Total number of wavelength 1050\n", + "Current index of wavelength 1035, Current wavelength 5.738522402287893, Total number of wavelength 1050\n", + "Current index of wavelength 1036, Current wavelength 5.7393422306959, Total number of wavelength 1050\n", + "Current index of wavelength 1037, Current wavelength 5.740162059103908, Total number of wavelength 1050\n", + "Current index of wavelength 1038, Current wavelength 5.740981887511916, Total number of wavelength 1050\n", + "Current index of wavelength 1039, Current wavelength 5.741801715919924, Total number of wavelength 1050\n", + "Current index of wavelength 1040, Current wavelength 5.742621544327932, Total number of wavelength 1050\n", + "Current index of wavelength 1041, Current wavelength 5.743441372735939, Total number of wavelength 1050\n", + "Current index of wavelength 1042, Current wavelength 5.744261201143947, Total number of wavelength 1050\n", + "Current index of wavelength 1043, Current wavelength 5.7450810295519545, Total number of wavelength 1050\n", + "Current index of wavelength 1044, Current wavelength 5.7459008579599615, Total number of wavelength 1050\n", + "Current index of wavelength 1045, Current wavelength 5.746720686367969, Total number of wavelength 1050\n", + "Current index of wavelength 1046, Current wavelength 5.747540514775977, Total number of wavelength 1050\n", + "Current index of wavelength 1047, Current wavelength 5.748360343183984, Total number of wavelength 1050\n", + "Current index of wavelength 1048, Current wavelength 5.749180171591992, Total number of wavelength 1050\n", + "Current index of wavelength 1049, Current wavelength 5.75, Total number of wavelength 1050\n", + " Saved the computed PSFs to data/* bet Pic/utils_fm_40_nodes/1A/jw01294003001_03102_mirifushort_webbpsf.fits\n" + ] + } + ], + "source": [ + "\n", + "\n", + "# Load the webbPSF model (or compute if it does not yet exist)\n", + "webbpsf_reload = regwvs_combdataobj.reload_webbpsf_model()\n", + "if webbpsf_reload is None:\n", + " webbpsf_reload = regwvs_combdataobj.compute_webbpsf_model(\n", + " wv_sampling=wv_sampling,\n", + " image_mask=None,\n", + " oversample=10,\n", + " parallelize=False, fov=fov,\n", + " save_utils=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "113b1685-dd35-4c30-a89a-11a678d2f6a1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LOG] BUILD CUBE MIRI PARA\n", + "Setting parallel_flag = True\n", + "(4128, 1050) -3.6963767299336947 2.5502054718854867\n", + "Processing wavelength indices in range: 0 to 1050\n", + "prepping build_cube inputs... id: 1049 wave: 5.75starting parallel _build_cube_task...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████| 1050/1050 [00:31<00:00, 33.65it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cubing outputs... id: 271 wave: 5.1121734985700665" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cubing outputs... id: 1049 wave: 5.75saving data/* bet Pic/cube_outputs/1A/jw01294003001_03102_mirifushort_spectral_cube_ish.fits\n" + ] + } + ], + "source": [ + "\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", + "flux_cube, fluxerr_cube, ra_grid, dec_grid = build_cube_para(regwvs_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=aperture, N_pix_min=1,\n", + " debug_init=debug_init, debug_end=debug_end) # min max wavelength indices for partial extraction\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1585f964-7e68-40ee-b6c1-8c806e648226", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "05eb1395-1867-4ee7-9a57-b3756db35654", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 529523ef5509db873abf81acbd5ab62111fb9e77 Mon Sep 17 00:00:00 2001 From: Marshall Perrin Date: Fri, 16 Jan 2026 09:45:49 -0500 Subject: [PATCH 2/2] update docs tutorials page --- docs/source/tutorials.rst | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/source/tutorials.rst b/docs/source/tutorials.rst index e1431f4..625f785 100644 --- a/docs/source/tutorials.rst +++ b/docs/source/tutorials.rst @@ -17,7 +17,9 @@ General tutorials/jwst/nirspec_1_running_BREADS_pipeline_tutorial.ipynb tutorials/jwst/nirspec_2_analyzing_data_products_CCF_covariance.ipynb tutorials/jwst/nirspec_3_spectral_modelling_likelihood_joint_analysis.ipynb - + tutorials/jwst/MIRI_Tutorial_1_Data_Reductions_for_BREADS.ipynb + tutorials/jwst/MIRI_Tutorial_2_MRS_Forward_Modeling_and_SNR.ipynb + tutorials/jwst/MIRI_Tutorial_3_MRS_Cube-ish_build.ipynbi .. toctree:: @@ -26,4 +28,3 @@ General tutorials/kpic/KPIC_Tutorial_1_Placeholder.ipynb -