diff --git a/Notebook1/MRS_Notebook1.ipynb b/Notebook1/MRS_Notebook1.ipynb
index 4c6f0b3..b5d6079 100644
--- a/Notebook1/MRS_Notebook1.ipynb
+++ b/Notebook1/MRS_Notebook1.ipynb
@@ -24,7 +24,9 @@
    "source": [
     "**Author**: David Law, AURA Associate Astronomer, MIRI branch\n",
     "<br>\n",
-    "**Last Updated**: May 27, 2021"
+    "**Last Updated**: June 18, 2021\n",
+    "<br>\n",
+    "**Pipeline Version**: 1.1.0"
    ]
   },
   {
@@ -96,7 +98,7 @@
     "Since the Detector1 pipeline has been discussed extensively in previous JWebbinars we will not dig into that stage of the pipeline in detail, and focus instead on the Spec2 and Spec3 stages.  We will step individually through each step in these two pipeline stages, discuss how they work, and examine some sample outputs.  Since we'll be examining each step individually, this notebook is thus not a good template to use for designing your own notebook to process/inspect large quantities of observational data.  This use case is more directly addressed by MRS Calibration Notebooks #2 and #3 (point sources and extended sources respectively).\n",
     "\n",
     "A few additional caveats:\n",
-    "- This notebook covers the v1.2.0 baseline pipeline as it existed in May 2021.  The pipeline is under continuous development and there are therefore some changes in the latest pipeline build that will not be reflected here.\n",
+    "- This notebook covers the v1.1.0 baseline pipeline as it existed in February 2021.  The pipeline is under continuous development and there are therefore some changes in the latest pipeline build that will not be reflected here.\n",
     "- Likewise, there are some advanced algorithms slated for development prior to cycle 1 observations that will not be discussed here."
    ]
   },
@@ -138,14 +140,6 @@
     "- mirisim does not add all of the necessary header keywords for the pipeline to know how to do background subtraction, identify source type, etc.  In order to get these APT-derived keywords correct they will need to be set manually."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3c7436f0",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "f7d243fb",
@@ -164,7 +158,7 @@
     "\n",
     "In this section we set things up a number of necessary things in order for the pipeline to run successfully.\n",
     "\n",
-    "First we'll set the CRDS context; this dictates the versions of various pipeline reference files to use.  Ordinarily you wouldn't want to set a specific version as the latest pipeline should already use the most-recent reference files (and hard-coding a version could get you old reference files that have since been replaced).  However, it's included here as a reference for how to do so.\n",
+    "First we'll set the CRDS context; this dictates the versions of various pipeline reference files to use.  Ordinarily you wouldn't want to set a specific version as the latest pipeline should already use the most-recent reference files (and hard-coding a version could get you old reference files that have since been replaced).  However, since this demo is using an old version 1.1.0 of the pipeline, we need to tell it to get some more recent reference files.\n",
     "\n",
     "Next we'll import the various python packages that we're actually going to use in this notebook, including both generic utility functions and the actual pipeline modules themselves.\n",
     "\n",
@@ -175,21 +169,38 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0452406b",
+   "id": "b1083b67",
    "metadata": {},
    "source": [
     "### 2.1<font color='white'>-</font>CRDS Context<a class=\"anchor\" id=\"crds\"></a> ###"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "71b7a7e2",
+   "metadata": {},
+   "source": [
+    "Set our CRDS context for reference files (see https://jwst-crds.stsci.edu/)\n",
+    "We need to do this because version 1.1.0 of the pipeline does not pull in some recent reference file updates by default."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "93cede72",
+   "id": "961b4744",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env: CRDS_CONTEXT=jwst_0723.pmap\n"
+     ]
+    }
+   ],
    "source": [
-    "# Set our CRDS context for reference files if desired (see https://jwst-crds.stsci.edu/)\n",
-    "#%env CRDS_CONTEXT jwst_0723.pmap"
+    "# Comment out this line if you want to use the latest reference files tagged for a specific pipeline version\n",
+    "%env CRDS_CONTEXT jwst_0723.pmap"
    ]
   },
   {
@@ -230,14 +241,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "cbabbfdf",
-   "metadata": {
-    "scrolled": false
-   },
+   "id": "db56d20d",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Basic system utilities for interacting with files\n",
-    "import glob, sys, os, time, shutil\n",
+    "import glob, sys, os, time, shutil, warnings\n",
     "\n",
     "# Astropy utilities for opening FITS and ASCII files\n",
     "from astropy.io import fits\n",
@@ -250,10 +259,44 @@
     "\n",
     "# Matplotlib for making plots\n",
     "import matplotlib.pyplot as plt\n",
-    "from matplotlib import rc\n",
+    "from matplotlib import rc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6a17ab4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import the base JWST package and warn if not the expected version\n",
+    "import jwst\n",
     "\n",
+    "if jwst.__version__ != '1.1.0':\n",
+    "    warnings.warn(f\"You are running version {jwst.__version__} of the jwst \"\n",
+    "                  \"module instead of the intended 1.1.0.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cbabbfdf",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-06-18 13:04:07,928 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst1.1.0/lib/python3.9/site-packages/photutils/detection/findstars.py:33: AstropyDeprecationWarning: _StarFinderKernel was moved to the photutils.detection._utils module. Please update your import statement.\n",
+      "  warnings.warn(f'{name} was moved to the {deprecated[name]} module. '\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
     "# JWST pipelines (encompassing many steps)\n",
-    "import jwst\n",
     "from jwst.pipeline import Detector1Pipeline\n",
     "from jwst.pipeline import Spec2Pipeline\n",
     "from jwst.pipeline import Spec3Pipeline\n",
@@ -274,16 +317,20 @@
     "from jwst.extract_1d import Extract1dStep\n",
     "\n",
     "# JWST pipeline utilities\n",
-    "from stcal import dqflags # Utilities for working with the data quality (DQ) arrays\n",
     "from jwst import datamodels # JWST datamodels\n",
     "from jwst.associations import asn_from_list as afl # Tools for creating association files\n",
     "from jwst.associations.lib.rules_level2_base import DMSLevel2bBase # Definition of a Lvl2 association file\n",
-    "from jwst.associations.lib.rules_level3_base import DMS_Level3_Base # Definition of a Lvl3 association file"
+    "from jwst.associations.lib.rules_level3_base import DMS_Level3_Base # Definition of a Lvl3 association file\n",
+    "\n",
+    "# If using pipeline version 1.2.0 or later, the dqflags function is contained in the 'stcal' product\n",
+    "#from stcal import dqflags # Utilities for working with the data quality (DQ) arrays\n",
+    "# Since this demo uses version 1.1.0 of the JWST pipeline, we need to instead import dqflags from \n",
+    "from jwst.datamodels import dqflags"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "40857f4d",
    "metadata": {},
    "outputs": [
@@ -291,7 +338,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "JWST pipeline version 0.13.0b1.dev2264+gdbc1587e\n"
+      "JWST pipeline version 1.1.0\n"
      ]
     }
    ],
@@ -308,27 +355,39 @@
     "### 2.3<font color='white'>-</font>Data I/O Directories <a class=\"anchor\" id=\"iodir\"></a> ###"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b37eb3b6",
+   "metadata": {},
+   "source": [
+    "Data can be obtained from https://stsci.app.box.com/s/ejgvgnycjm43f62i98gbymmfu2lknhyf <BR>\n",
+    "Since the contents of this Box directory are quite large (roughly 3 GB, including multiple inputs, outputs, and intermediate products), downloading the data for home use is left to the user in whichever means they determine to be best.\n",
+    "    \n",
+    "By default, the contents of this Box directory are assumed to be in the same directory as this notebook.  However, it is also possible to install them in another location and use this as a cache of pre-reduced results against which new reductions can be compared."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "529de187",
-   "metadata": {
-    "scrolled": false
-   },
+   "execution_count": 7,
+   "id": "70a53b19",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Specify some working directories to use so that everything is more organized.\n",
-    "# For home use, we'll assume that the stage0/stage1/stage2/stage3 folders are in the same\n",
-    "# directory location as this notebook.\n",
-    "\n",
-    "# We will set up a cache directory for pre-reduced demo files if this notebook is being run\n",
-    "# using the JWebbinar remote notebook session.\n",
-    "\n",
-    "# Use this if running remotely in the online session\n",
-    "#cache_dir = '/home/shared/preloaded-fits/mrs-data/notebook1/'\n",
-    "# Use this if running on your own machine\n",
-    "cache_dir = './cache/'\n",
+    "# If running on your own machine, cache_dir should point to where you installed the data from Box\n",
+    "cache_dir = './'\n",
     "\n",
+    "# If running remotely in the online JWebbinar session, the cache directed is located here:\n",
+    "#cache_dir = '/home/shared/preloaded-fits/mrs-data/notebook1/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c894ac49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Specify some working directories to use so that everything is more organized.<br>\n",
     "mirisim_dir = 'stage0/' # Simulated inputs are here\n",
     "det1_dir = 'stage1/' # Detector1 pipeline outputs will go here\n",
     "spec2_dir = 'stage2/' # Spec2 pipeline outputs will go here\n",
@@ -351,18 +410,25 @@
     "### 2.4<font color='white'>-</font>Reprocessing Flag<a class=\"anchor\" id=\"redo\"></a> ###"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "ef2f50f1",
+   "metadata": {},
+   "source": [
+    "Since some parts of the pipeline take a long time to run, for a first use of this notebook we will disable those steps and simply copy results out of the cache for informational purposes.  In order to run the full pipeline on the data, or to run this notebook on your own simulated data not downloaded from the Box link above, this reprocessing flag will need to be enabled."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "id": "c11cf5e9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Finally, we'll set a processing directive about whether to rerun long steps in this notebook or not\n",
-    "redolong = True\n",
-    "\n",
-    "# If running this notebook on your own machine, you probably want to use redolong = True\n",
-    "# If running this notebook during the JWebbinar live session, you'll want to use redolong = False"
+    "# To rerun all steps use:\n",
+    "#redolong = True\n",
+    "# To skip lengthy steps and copy results from the cache use:\n",
+    "redolong = False"
    ]
   },
   {
@@ -391,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "id": "fc1a4bff",
    "metadata": {},
    "outputs": [],
@@ -402,16 +468,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "id": "c16999b9",
    "metadata": {
     "scrolled": false
    },
    "outputs": [],
    "source": [
-    "# First we'll define a function that will call the detector1 pipeline with our desired set of parameters\n",
+    "# First we'll define a function that will call the detector1 pipeline with our desired set of parameters:\n",
     "def rundet1(filenames):\n",
-    "    det1=Detector1Pipeline() # Instantiate the pipeline\n",
+    "    det1 = Detector1Pipeline() # Instantiate the pipeline\n",
     "    det1.output_dir = det1_dir # Specify where the output should go\n",
     "    det1.refpix.skip = True # Skip the reference pixel subtraction (as it doesn't interact well with simulated data)\n",
     "    det1.save_results = True # Save the final resulting _rate.fits files\n",
@@ -420,7 +486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "id": "a3aa2e10",
    "metadata": {
     "scrolled": false
@@ -436,580 +502,19 @@
    ],
    "source": [
     "# Now let's look for input files in our (cached) mirisim simulation directory\n",
-    "sstring=cache_dir+mirisim_dir+'det*exp1.fits'\n",
-    "simfiles=sorted(glob.glob(sstring))\n",
+    "sstring = cache_dir + mirisim_dir + 'det*exp1.fits'\n",
+    "simfiles = sorted(glob.glob(sstring))\n",
     "print('Found ' + str(len(simfiles)) + ' input files to process')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "id": "c5b7b621",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:20:17,174 - stpipe.Detector1Pipeline - INFO - Detector1Pipeline instance created.\n",
-      "2021-05-27 17:20:17,177 - stpipe.Detector1Pipeline.group_scale - INFO - GroupScaleStep instance created.\n",
-      "2021-05-27 17:20:17,180 - stpipe.Detector1Pipeline.dq_init - INFO - DQInitStep instance created.\n",
-      "2021-05-27 17:20:17,182 - stpipe.Detector1Pipeline.saturation - INFO - SaturationStep instance created.\n",
-      "2021-05-27 17:20:17,185 - stpipe.Detector1Pipeline.ipc - INFO - IPCStep instance created.\n",
-      "2021-05-27 17:20:17,187 - stpipe.Detector1Pipeline.superbias - INFO - SuperBiasStep instance created.\n",
-      "2021-05-27 17:20:17,190 - stpipe.Detector1Pipeline.refpix - INFO - RefPixStep instance created.\n",
-      "2021-05-27 17:20:17,192 - stpipe.Detector1Pipeline.rscd - INFO - RscdStep instance created.\n",
-      "2021-05-27 17:20:17,195 - stpipe.Detector1Pipeline.firstframe - INFO - FirstFrameStep instance created.\n",
-      "2021-05-27 17:20:17,197 - stpipe.Detector1Pipeline.lastframe - INFO - LastFrameStep instance created.\n",
-      "2021-05-27 17:20:17,200 - stpipe.Detector1Pipeline.linearity - INFO - LinearityStep instance created.\n",
-      "2021-05-27 17:20:17,203 - stpipe.Detector1Pipeline.dark_current - INFO - DarkCurrentStep instance created.\n",
-      "2021-05-27 17:20:17,206 - stpipe.Detector1Pipeline.reset - INFO - ResetStep instance created.\n",
-      "2021-05-27 17:20:17,209 - stpipe.Detector1Pipeline.persistence - INFO - PersistenceStep instance created.\n",
-      "2021-05-27 17:20:17,212 - stpipe.Detector1Pipeline.jump - INFO - JumpStep instance created.\n",
-      "2021-05-27 17:20:17,214 - stpipe.Detector1Pipeline.ramp_fit - INFO - RampFitStep instance created.\n",
-      "2021-05-27 17:20:17,217 - stpipe.Detector1Pipeline.gain_scale - INFO - GainScaleStep instance created.\n",
-      "2021-05-27 17:20:17,276 - stpipe.Detector1Pipeline - INFO - Step Detector1Pipeline running with args ('./cache/stage0/det_image_seq1_MIRIFUSHORT_12LONGexp1.fits',).\n",
-      "2021-05-27 17:20:17,290 - stpipe.Detector1Pipeline - INFO - Step Detector1Pipeline parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage1/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'save_calibrated_ramp': False, 'steps': {'group_scale': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'dq_init': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'saturation': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'ipc': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'superbias': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'refpix': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': True, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'odd_even_columns': True, 'use_side_ref_pixels': True, 'side_smoothing_length': 11, 'side_gain': 1.0, 'odd_even_rows': True}, 'rscd': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'type': 'baseline'}, 'firstframe': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'lastframe': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'linearity': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'dark_current': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'dark_output': None}, 'reset': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'persistence': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'input_trapsfilled': '', 'flag_pers_cutoff': 40.0, 'save_persistence': False, 'save_trapsfilled': True}, 'jump': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'rejection_threshold': 4.0, 'three_group_rejection_threshold': 6.0, 'four_group_rejection_threshold': 5.0, 'maximum_cores': 'none', 'flag_4_neighbors': True, 'max_jump_to_flag_neighbors': 1000.0, 'min_jump_to_flag_neighbors': 10.0}, 'ramp_fit': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'int_name': '', 'save_opt': False, 'opt_name': '', 'maximum_cores': 'none'}, 'gain_scale': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}}}\n",
-      "2021-05-27 17:20:17,809 - stpipe.Detector1Pipeline - INFO - Prefetching reference files for dataset: 'det_image_seq1_MIRIFUSHORT_12LONGexp1.fits' reftypes = ['dark', 'gain', 'ipc', 'linearity', 'mask', 'persat', 'readnoise', 'reset', 'rscd', 'saturation', 'superbias', 'trapdensity', 'trappars']\n",
-      "2021-05-27 17:20:18,271 - stpipe.Detector1Pipeline - INFO - Prefetch for DARK reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_dark_0044.fits'.\n",
-      "2021-05-27 17:20:18,273 - stpipe.Detector1Pipeline - INFO - Prefetch for GAIN reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_gain_0007.fits'.\n",
-      "2021-05-27 17:20:18,274 - stpipe.Detector1Pipeline - INFO - Prefetch for IPC reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_ipc_0008.fits'.\n",
-      "2021-05-27 17:20:18,276 - stpipe.Detector1Pipeline - INFO - Prefetch for LINEARITY reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_linearity_0023.fits'.\n",
-      "2021-05-27 17:20:18,277 - stpipe.Detector1Pipeline - INFO - Prefetch for MASK reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_mask_0022.fits'.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:20:18,279 - stpipe.Detector1Pipeline - INFO - Prefetch for PERSAT reference file is 'N/A'.\n",
-      "2021-05-27 17:20:18,280 - stpipe.Detector1Pipeline - INFO - Prefetch for READNOISE reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_readnoise_0050.fits'.\n",
-      "2021-05-27 17:20:18,282 - stpipe.Detector1Pipeline - INFO - Prefetch for RESET reference file is 'N/A'.\n",
-      "2021-05-27 17:20:18,283 - stpipe.Detector1Pipeline - INFO - Prefetch for RSCD reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_rscd_0012.fits'.\n",
-      "2021-05-27 17:20:18,285 - stpipe.Detector1Pipeline - INFO - Prefetch for SATURATION reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_saturation_0025.fits'.\n",
-      "2021-05-27 17:20:18,287 - stpipe.Detector1Pipeline - INFO - Prefetch for SUPERBIAS reference file is 'N/A'.\n",
-      "2021-05-27 17:20:18,288 - stpipe.Detector1Pipeline - INFO - Prefetch for TRAPDENSITY reference file is 'N/A'.\n",
-      "2021-05-27 17:20:18,290 - stpipe.Detector1Pipeline - INFO - Prefetch for TRAPPARS reference file is 'N/A'.\n",
-      "2021-05-27 17:20:18,291 - stpipe.Detector1Pipeline - INFO - Starting calwebb_detector1 ...\n",
-      "2021-05-27 17:20:18,501 - stpipe.Detector1Pipeline.group_scale - INFO - Step group_scale running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq1_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:18,504 - stpipe.Detector1Pipeline.group_scale - INFO - Step group_scale parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:20:18,580 - stpipe.Detector1Pipeline.group_scale - INFO - NFRAMES=1 is a power of 2; correction not needed\n",
-      "2021-05-27 17:20:18,582 - stpipe.Detector1Pipeline.group_scale - INFO - Step will be skipped\n",
-      "2021-05-27 17:20:18,584 - stpipe.Detector1Pipeline.group_scale - INFO - Step group_scale done\n",
-      "2021-05-27 17:20:18,649 - stpipe.Detector1Pipeline.dq_init - INFO - Step dq_init running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq1_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:18,650 - stpipe.Detector1Pipeline.dq_init - INFO - Step dq_init parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:20:18,672 - stpipe.Detector1Pipeline.dq_init - INFO - Using MASK reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_mask_0022.fits\n",
-      "2021-05-27 17:20:18,883 - stpipe.Detector1Pipeline.dq_init - INFO - Step dq_init done\n",
-      "2021-05-27 17:20:18,951 - stpipe.Detector1Pipeline.saturation - INFO - Step saturation running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq1_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:18,954 - stpipe.Detector1Pipeline.saturation - INFO - Step saturation parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:20:18,976 - stpipe.Detector1Pipeline.saturation - INFO - Using SATURATION reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_saturation_0025.fits\n",
-      "2021-05-27 17:20:19,297 - stpipe.Detector1Pipeline.saturation - INFO - Detected 1341 saturated pixels\n",
-      "2021-05-27 17:20:19,313 - stpipe.Detector1Pipeline.saturation - INFO - Detected 0 A/D floor pixels\n",
-      "2021-05-27 17:20:19,318 - stpipe.Detector1Pipeline.saturation - INFO - Step saturation done\n",
-      "2021-05-27 17:20:19,393 - stpipe.Detector1Pipeline.ipc - INFO - Step ipc running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq1_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:19,395 - stpipe.Detector1Pipeline.ipc - INFO - Step ipc parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:20:19,418 - stpipe.Detector1Pipeline.ipc - INFO - Using IPC reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_ipc_0008.fits\n",
-      "2021-05-27 17:20:19,802 - stpipe.Detector1Pipeline.ipc - INFO - Step ipc done\n",
-      "2021-05-27 17:20:19,877 - stpipe.Detector1Pipeline.firstframe - INFO - Step firstframe running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq1_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:19,879 - stpipe.Detector1Pipeline.firstframe - INFO - Step firstframe parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:20:19,956 - stpipe.Detector1Pipeline.firstframe - INFO - Step firstframe done\n",
-      "2021-05-27 17:20:20,031 - stpipe.Detector1Pipeline.lastframe - INFO - Step lastframe running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq1_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:20,033 - stpipe.Detector1Pipeline.lastframe - INFO - Step lastframe parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:20:20,103 - stpipe.Detector1Pipeline.lastframe - INFO - Step lastframe done\n",
-      "2021-05-27 17:20:20,172 - stpipe.Detector1Pipeline.reset - INFO - Step reset running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq1_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:20,174 - stpipe.Detector1Pipeline.reset - INFO - Step reset parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:20:20,195 - stpipe.Detector1Pipeline.reset - INFO - Using RESET reference file N/A\n",
-      "2021-05-27 17:20:20,196 - stpipe.Detector1Pipeline.reset - WARNING - No RESET reference file found\n",
-      "2021-05-27 17:20:20,197 - stpipe.Detector1Pipeline.reset - WARNING - Reset step will be skipped\n",
-      "2021-05-27 17:20:20,260 - stpipe.Detector1Pipeline.reset - INFO - Step reset done\n",
-      "2021-05-27 17:20:20,335 - stpipe.Detector1Pipeline.linearity - INFO - Step linearity running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq1_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:20,337 - stpipe.Detector1Pipeline.linearity - INFO - Step linearity parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:20:20,359 - stpipe.Detector1Pipeline.linearity - INFO - Using Linearity reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_linearity_0023.fits\n",
-      "2021-05-27 17:20:20,704 - stpipe.Detector1Pipeline.linearity - INFO - Step linearity done\n",
-      "2021-05-27 17:20:20,781 - stpipe.Detector1Pipeline.rscd - INFO - Step rscd running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq1_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:20,783 - stpipe.Detector1Pipeline.rscd - INFO - Step rscd parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0', 'type': 'baseline'}\n",
-      "2021-05-27 17:20:20,805 - stpipe.Detector1Pipeline.rscd - INFO - Using RSCD reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_rscd_0012.fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:20:20,923 - stpipe.Detector1Pipeline.rscd - INFO - Step rscd done\n",
-      "2021-05-27 17:20:20,996 - stpipe.Detector1Pipeline.dark_current - INFO - Step dark_current running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq1_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:20,998 - stpipe.Detector1Pipeline.dark_current - INFO - Step dark_current parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage1/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0', 'dark_output': None}\n",
-      "2021-05-27 17:20:21,021 - stpipe.Detector1Pipeline.dark_current - INFO - Using DARK reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_dark_0044.fits\n",
-      "2021-05-27 17:20:21,612 - stpipe.Detector1Pipeline.dark_current - INFO - Science data nints=1, ngroups=20, nframes=1, groupgap=0\n",
-      "2021-05-27 17:20:21,613 - stpipe.Detector1Pipeline.dark_current - INFO - Dark data nints=2, ngroups=45, nframes=1, groupgap=0\n",
-      "2021-05-27 17:20:21,784 - stpipe.Detector1Pipeline.dark_current - INFO - Step dark_current done\n",
-      "2021-05-27 17:20:21,893 - stpipe.Detector1Pipeline.refpix - INFO - Step refpix running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq1_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:21,894 - stpipe.Detector1Pipeline.refpix - INFO - Step refpix parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': True, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0', 'odd_even_columns': True, 'use_side_ref_pixels': True, 'side_smoothing_length': 11, 'side_gain': 1.0, 'odd_even_rows': True}\n",
-      "2021-05-27 17:20:21,895 - stpipe.Detector1Pipeline.refpix - INFO - Step skipped.\n",
-      "2021-05-27 17:20:21,898 - stpipe.Detector1Pipeline.refpix - INFO - Step refpix done\n",
-      "2021-05-27 17:20:21,960 - stpipe.Detector1Pipeline.jump - INFO - Step jump running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq1_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:21,962 - stpipe.Detector1Pipeline.jump - INFO - Step jump parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0', 'rejection_threshold': 4.0, 'three_group_rejection_threshold': 6.0, 'four_group_rejection_threshold': 5.0, 'maximum_cores': 'none', 'flag_4_neighbors': True, 'max_jump_to_flag_neighbors': 1000.0, 'min_jump_to_flag_neighbors': 10.0}\n",
-      "2021-05-27 17:20:21,976 - stpipe.Detector1Pipeline.jump - INFO - CR rejection threshold = 4 sigma\n",
-      "2021-05-27 17:20:21,990 - stpipe.Detector1Pipeline.jump - INFO - Using GAIN reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_gain_0007.fits\n",
-      "2021-05-27 17:20:22,023 - stpipe.Detector1Pipeline.jump - INFO - Using READNOISE reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_readnoise_0050.fits\n",
-      "2021-05-27 17:20:22,046 - stpipe.Detector1Pipeline.jump - INFO - Using 1 core for jump detection \n",
-      "2021-05-27 17:20:22,147 - stpipe.Detector1Pipeline.jump - INFO - Executing two-point difference method\n",
-      "2021-05-27 17:20:22,423 - stpipe.Detector1Pipeline.jump - INFO - Working on integration 1:\n",
-      "2021-05-27 17:20:23,444 - stpipe.Detector1Pipeline.jump - INFO - From highest outlier Two-point found 64961 pixels with at least one CR and at least four groups\n",
-      "2021-05-27 17:20:23,445 - stpipe.Detector1Pipeline.jump - INFO - From highest outlier Two-point found 0 pixels with at least one CR and three groups\n",
-      "2021-05-27 17:20:23,445 - stpipe.Detector1Pipeline.jump - INFO - From highest outlier Two-point found 648 pixels with at least one CR and two groups\n",
-      "2021-05-27 17:20:25,856 - stpipe.Detector1Pipeline.jump - INFO - Total elapsed time = 3.70802 sec\n",
-      "2021-05-27 17:20:25,858 - stpipe.Detector1Pipeline.jump - INFO - The execution time in seconds: 3.882760\n",
-      "2021-05-27 17:20:25,863 - stpipe.Detector1Pipeline.jump - INFO - Step jump done\n",
-      "2021-05-27 17:20:25,942 - stpipe.Detector1Pipeline.ramp_fit - INFO - Step ramp_fit running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq1_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:25,944 - stpipe.Detector1Pipeline.ramp_fit - INFO - Step ramp_fit parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage1/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0', 'int_name': '', 'save_opt': False, 'opt_name': '', 'maximum_cores': 'none'}\n",
-      "2021-05-27 17:20:25,979 - stpipe.Detector1Pipeline.ramp_fit - INFO - Using READNOISE reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_readnoise_0050.fits\n",
-      "2021-05-27 17:20:25,980 - stpipe.Detector1Pipeline.ramp_fit - INFO - Using GAIN reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_gain_0007.fits\n",
-      "2021-05-27 17:20:26,018 - stpipe.Detector1Pipeline.ramp_fit - INFO - Using algorithm = ols\n",
-      "2021-05-27 17:20:26,019 - stpipe.Detector1Pipeline.ramp_fit - INFO - Using weighting = optimal\n",
-      "2021-05-27 17:20:26,032 - stpipe.Detector1Pipeline.ramp_fit - INFO - Number of leading groups that are flagged as DO_NOT_USE: 1\n",
-      "2021-05-27 17:20:26,033 - stpipe.Detector1Pipeline.ramp_fit - INFO - MIRI dataset has all pixels in the final group flagged as DO_NOT_USE.\n",
-      "2021-05-27 17:20:48,843 - stpipe.Detector1Pipeline.ramp_fit - INFO - Number of groups per integration: 18\n",
-      "2021-05-27 17:20:48,844 - stpipe.Detector1Pipeline.ramp_fit - INFO - Number of integrations: 1\n",
-      "2021-05-27 17:20:48,981 - stpipe.Detector1Pipeline.ramp_fit - INFO - Step ramp_fit done\n",
-      "2021-05-27 17:20:49,062 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale running with args (<IFUImageModel(1024, 1032) from det_image_seq1_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:49,064 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': 'gain_scale', 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:20:49,102 - stpipe.Detector1Pipeline.gain_scale - INFO - GAINFACT not found in gain reference file\n",
-      "2021-05-27 17:20:49,103 - stpipe.Detector1Pipeline.gain_scale - INFO - Step will be skipped\n",
-      "2021-05-27 17:20:49,107 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale done\n",
-      "2021-05-27 17:20:49,169 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale running with args (<CubeModel(1, 1024, 1032) from det_image_seq1_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:49,171 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': 'gain_scaleints', 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:20:49,211 - stpipe.Detector1Pipeline.gain_scale - INFO - GAINFACT not found in gain reference file\n",
-      "2021-05-27 17:20:49,212 - stpipe.Detector1Pipeline.gain_scale - INFO - Step will be skipped\n",
-      "2021-05-27 17:20:49,216 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale done\n",
-      "2021-05-27 17:20:49,380 - stpipe.Detector1Pipeline - INFO - Saved model in stage1/det_image_seq1_MIRIFUSHORT_12LONGexp1_rateints.fits\n",
-      "2021-05-27 17:20:49,382 - stpipe.Detector1Pipeline - INFO - ... ending calwebb_detector1\n",
-      "2021-05-27 17:20:49,525 - stpipe.Detector1Pipeline - INFO - Saved model in stage1/det_image_seq1_MIRIFUSHORT_12LONGexp1_rate.fits\n",
-      "2021-05-27 17:20:49,526 - stpipe.Detector1Pipeline - INFO - Step Detector1Pipeline done\n",
-      "2021-05-27 17:20:49,538 - stpipe.Detector1Pipeline - INFO - Detector1Pipeline instance created.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:20:49,541 - stpipe.Detector1Pipeline.group_scale - INFO - GroupScaleStep instance created.\n",
-      "2021-05-27 17:20:49,544 - stpipe.Detector1Pipeline.dq_init - INFO - DQInitStep instance created.\n",
-      "2021-05-27 17:20:49,546 - stpipe.Detector1Pipeline.saturation - INFO - SaturationStep instance created.\n",
-      "2021-05-27 17:20:49,548 - stpipe.Detector1Pipeline.ipc - INFO - IPCStep instance created.\n",
-      "2021-05-27 17:20:49,550 - stpipe.Detector1Pipeline.superbias - INFO - SuperBiasStep instance created.\n",
-      "2021-05-27 17:20:49,552 - stpipe.Detector1Pipeline.refpix - INFO - RefPixStep instance created.\n",
-      "2021-05-27 17:20:49,554 - stpipe.Detector1Pipeline.rscd - INFO - RscdStep instance created.\n",
-      "2021-05-27 17:20:49,557 - stpipe.Detector1Pipeline.firstframe - INFO - FirstFrameStep instance created.\n",
-      "2021-05-27 17:20:49,559 - stpipe.Detector1Pipeline.lastframe - INFO - LastFrameStep instance created.\n",
-      "2021-05-27 17:20:49,562 - stpipe.Detector1Pipeline.linearity - INFO - LinearityStep instance created.\n",
-      "2021-05-27 17:20:49,565 - stpipe.Detector1Pipeline.dark_current - INFO - DarkCurrentStep instance created.\n",
-      "2021-05-27 17:20:49,567 - stpipe.Detector1Pipeline.reset - INFO - ResetStep instance created.\n",
-      "2021-05-27 17:20:49,569 - stpipe.Detector1Pipeline.persistence - INFO - PersistenceStep instance created.\n",
-      "2021-05-27 17:20:49,572 - stpipe.Detector1Pipeline.jump - INFO - JumpStep instance created.\n",
-      "2021-05-27 17:20:49,574 - stpipe.Detector1Pipeline.ramp_fit - INFO - RampFitStep instance created.\n",
-      "2021-05-27 17:20:49,577 - stpipe.Detector1Pipeline.gain_scale - INFO - GainScaleStep instance created.\n",
-      "2021-05-27 17:20:49,704 - stpipe.Detector1Pipeline - INFO - Step Detector1Pipeline running with args ('./cache/stage0/det_image_seq2_MIRIFUSHORT_12LONGexp1.fits',).\n",
-      "2021-05-27 17:20:49,718 - stpipe.Detector1Pipeline - INFO - Step Detector1Pipeline parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage1/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'save_calibrated_ramp': False, 'steps': {'group_scale': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'dq_init': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'saturation': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'ipc': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'superbias': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'refpix': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': True, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'odd_even_columns': True, 'use_side_ref_pixels': True, 'side_smoothing_length': 11, 'side_gain': 1.0, 'odd_even_rows': True}, 'rscd': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'type': 'baseline'}, 'firstframe': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'lastframe': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'linearity': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'dark_current': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'dark_output': None}, 'reset': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'persistence': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'input_trapsfilled': '', 'flag_pers_cutoff': 40.0, 'save_persistence': False, 'save_trapsfilled': True}, 'jump': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'rejection_threshold': 4.0, 'three_group_rejection_threshold': 6.0, 'four_group_rejection_threshold': 5.0, 'maximum_cores': 'none', 'flag_4_neighbors': True, 'max_jump_to_flag_neighbors': 1000.0, 'min_jump_to_flag_neighbors': 10.0}, 'ramp_fit': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'int_name': '', 'save_opt': False, 'opt_name': '', 'maximum_cores': 'none'}, 'gain_scale': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}}}\n",
-      "2021-05-27 17:20:49,842 - stpipe.Detector1Pipeline - INFO - Prefetching reference files for dataset: 'det_image_seq2_MIRIFUSHORT_12LONGexp1.fits' reftypes = ['dark', 'gain', 'ipc', 'linearity', 'mask', 'persat', 'readnoise', 'reset', 'rscd', 'saturation', 'superbias', 'trapdensity', 'trappars']\n",
-      "2021-05-27 17:20:49,847 - stpipe.Detector1Pipeline - INFO - Prefetch for DARK reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_dark_0044.fits'.\n",
-      "2021-05-27 17:20:49,848 - stpipe.Detector1Pipeline - INFO - Prefetch for GAIN reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_gain_0007.fits'.\n",
-      "2021-05-27 17:20:49,849 - stpipe.Detector1Pipeline - INFO - Prefetch for IPC reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_ipc_0008.fits'.\n",
-      "2021-05-27 17:20:49,851 - stpipe.Detector1Pipeline - INFO - Prefetch for LINEARITY reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_linearity_0023.fits'.\n",
-      "2021-05-27 17:20:49,852 - stpipe.Detector1Pipeline - INFO - Prefetch for MASK reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_mask_0022.fits'.\n",
-      "2021-05-27 17:20:49,854 - stpipe.Detector1Pipeline - INFO - Prefetch for PERSAT reference file is 'N/A'.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:20:49,855 - stpipe.Detector1Pipeline - INFO - Prefetch for READNOISE reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_readnoise_0050.fits'.\n",
-      "2021-05-27 17:20:49,856 - stpipe.Detector1Pipeline - INFO - Prefetch for RESET reference file is 'N/A'.\n",
-      "2021-05-27 17:20:49,858 - stpipe.Detector1Pipeline - INFO - Prefetch for RSCD reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_rscd_0012.fits'.\n",
-      "2021-05-27 17:20:49,859 - stpipe.Detector1Pipeline - INFO - Prefetch for SATURATION reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_saturation_0025.fits'.\n",
-      "2021-05-27 17:20:49,861 - stpipe.Detector1Pipeline - INFO - Prefetch for SUPERBIAS reference file is 'N/A'.\n",
-      "2021-05-27 17:20:49,862 - stpipe.Detector1Pipeline - INFO - Prefetch for TRAPDENSITY reference file is 'N/A'.\n",
-      "2021-05-27 17:20:49,863 - stpipe.Detector1Pipeline - INFO - Prefetch for TRAPPARS reference file is 'N/A'.\n",
-      "2021-05-27 17:20:49,865 - stpipe.Detector1Pipeline - INFO - Starting calwebb_detector1 ...\n",
-      "2021-05-27 17:20:50,087 - stpipe.Detector1Pipeline.group_scale - INFO - Step group_scale running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq2_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:50,089 - stpipe.Detector1Pipeline.group_scale - INFO - Step group_scale parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:20:50,176 - stpipe.Detector1Pipeline.group_scale - INFO - NFRAMES=1 is a power of 2; correction not needed\n",
-      "2021-05-27 17:20:50,177 - stpipe.Detector1Pipeline.group_scale - INFO - Step will be skipped\n",
-      "2021-05-27 17:20:50,180 - stpipe.Detector1Pipeline.group_scale - INFO - Step group_scale done\n",
-      "2021-05-27 17:20:50,245 - stpipe.Detector1Pipeline.dq_init - INFO - Step dq_init running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq2_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:50,248 - stpipe.Detector1Pipeline.dq_init - INFO - Step dq_init parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:20:50,273 - stpipe.Detector1Pipeline.dq_init - INFO - Using MASK reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_mask_0022.fits\n",
-      "2021-05-27 17:20:50,464 - stpipe.Detector1Pipeline.dq_init - INFO - Step dq_init done\n",
-      "2021-05-27 17:20:50,538 - stpipe.Detector1Pipeline.saturation - INFO - Step saturation running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq2_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:50,540 - stpipe.Detector1Pipeline.saturation - INFO - Step saturation parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:20:50,564 - stpipe.Detector1Pipeline.saturation - INFO - Using SATURATION reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_saturation_0025.fits\n",
-      "2021-05-27 17:20:50,907 - stpipe.Detector1Pipeline.saturation - INFO - Detected 1222 saturated pixels\n",
-      "2021-05-27 17:20:50,922 - stpipe.Detector1Pipeline.saturation - INFO - Detected 0 A/D floor pixels\n",
-      "2021-05-27 17:20:50,927 - stpipe.Detector1Pipeline.saturation - INFO - Step saturation done\n",
-      "2021-05-27 17:20:51,006 - stpipe.Detector1Pipeline.ipc - INFO - Step ipc running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq2_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:51,008 - stpipe.Detector1Pipeline.ipc - INFO - Step ipc parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:20:51,030 - stpipe.Detector1Pipeline.ipc - INFO - Using IPC reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_ipc_0008.fits\n",
-      "2021-05-27 17:20:51,426 - stpipe.Detector1Pipeline.ipc - INFO - Step ipc done\n",
-      "2021-05-27 17:20:51,500 - stpipe.Detector1Pipeline.firstframe - INFO - Step firstframe running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq2_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:51,502 - stpipe.Detector1Pipeline.firstframe - INFO - Step firstframe parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:20:51,578 - stpipe.Detector1Pipeline.firstframe - INFO - Step firstframe done\n",
-      "2021-05-27 17:20:51,650 - stpipe.Detector1Pipeline.lastframe - INFO - Step lastframe running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq2_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:51,652 - stpipe.Detector1Pipeline.lastframe - INFO - Step lastframe parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:20:51,728 - stpipe.Detector1Pipeline.lastframe - INFO - Step lastframe done\n",
-      "2021-05-27 17:20:51,802 - stpipe.Detector1Pipeline.reset - INFO - Step reset running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq2_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:51,804 - stpipe.Detector1Pipeline.reset - INFO - Step reset parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:20:51,827 - stpipe.Detector1Pipeline.reset - INFO - Using RESET reference file N/A\n",
-      "2021-05-27 17:20:51,828 - stpipe.Detector1Pipeline.reset - WARNING - No RESET reference file found\n",
-      "2021-05-27 17:20:51,829 - stpipe.Detector1Pipeline.reset - WARNING - Reset step will be skipped\n",
-      "2021-05-27 17:20:51,895 - stpipe.Detector1Pipeline.reset - INFO - Step reset done\n",
-      "2021-05-27 17:20:51,969 - stpipe.Detector1Pipeline.linearity - INFO - Step linearity running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq2_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:51,971 - stpipe.Detector1Pipeline.linearity - INFO - Step linearity parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:20:52,000 - stpipe.Detector1Pipeline.linearity - INFO - Using Linearity reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_linearity_0023.fits\n",
-      "2021-05-27 17:20:52,301 - stpipe.Detector1Pipeline.linearity - INFO - Step linearity done\n",
-      "2021-05-27 17:20:52,378 - stpipe.Detector1Pipeline.rscd - INFO - Step rscd running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq2_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:52,380 - stpipe.Detector1Pipeline.rscd - INFO - Step rscd parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0', 'type': 'baseline'}\n",
-      "2021-05-27 17:20:52,405 - stpipe.Detector1Pipeline.rscd - INFO - Using RSCD reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_rscd_0012.fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:20:52,516 - stpipe.Detector1Pipeline.rscd - INFO - Step rscd done\n",
-      "2021-05-27 17:20:52,591 - stpipe.Detector1Pipeline.dark_current - INFO - Step dark_current running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq2_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:52,593 - stpipe.Detector1Pipeline.dark_current - INFO - Step dark_current parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage1/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0', 'dark_output': None}\n",
-      "2021-05-27 17:20:52,616 - stpipe.Detector1Pipeline.dark_current - INFO - Using DARK reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_dark_0044.fits\n",
-      "2021-05-27 17:20:53,024 - stpipe.Detector1Pipeline.dark_current - INFO - Science data nints=1, ngroups=20, nframes=1, groupgap=0\n",
-      "2021-05-27 17:20:53,025 - stpipe.Detector1Pipeline.dark_current - INFO - Dark data nints=2, ngroups=45, nframes=1, groupgap=0\n",
-      "2021-05-27 17:20:53,203 - stpipe.Detector1Pipeline.dark_current - INFO - Step dark_current done\n",
-      "2021-05-27 17:20:53,311 - stpipe.Detector1Pipeline.refpix - INFO - Step refpix running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq2_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:53,313 - stpipe.Detector1Pipeline.refpix - INFO - Step refpix parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': True, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0', 'odd_even_columns': True, 'use_side_ref_pixels': True, 'side_smoothing_length': 11, 'side_gain': 1.0, 'odd_even_rows': True}\n",
-      "2021-05-27 17:20:53,314 - stpipe.Detector1Pipeline.refpix - INFO - Step skipped.\n",
-      "2021-05-27 17:20:53,316 - stpipe.Detector1Pipeline.refpix - INFO - Step refpix done\n",
-      "2021-05-27 17:20:53,379 - stpipe.Detector1Pipeline.jump - INFO - Step jump running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq2_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:53,381 - stpipe.Detector1Pipeline.jump - INFO - Step jump parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0', 'rejection_threshold': 4.0, 'three_group_rejection_threshold': 6.0, 'four_group_rejection_threshold': 5.0, 'maximum_cores': 'none', 'flag_4_neighbors': True, 'max_jump_to_flag_neighbors': 1000.0, 'min_jump_to_flag_neighbors': 10.0}\n",
-      "2021-05-27 17:20:53,394 - stpipe.Detector1Pipeline.jump - INFO - CR rejection threshold = 4 sigma\n",
-      "2021-05-27 17:20:53,409 - stpipe.Detector1Pipeline.jump - INFO - Using GAIN reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_gain_0007.fits\n",
-      "2021-05-27 17:20:53,441 - stpipe.Detector1Pipeline.jump - INFO - Using READNOISE reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_readnoise_0050.fits\n",
-      "2021-05-27 17:20:53,461 - stpipe.Detector1Pipeline.jump - INFO - Using 1 core for jump detection \n",
-      "2021-05-27 17:20:53,560 - stpipe.Detector1Pipeline.jump - INFO - Executing two-point difference method\n",
-      "2021-05-27 17:20:53,824 - stpipe.Detector1Pipeline.jump - INFO - Working on integration 1:\n",
-      "2021-05-27 17:20:54,817 - stpipe.Detector1Pipeline.jump - INFO - From highest outlier Two-point found 64695 pixels with at least one CR and at least four groups\n",
-      "2021-05-27 17:20:54,817 - stpipe.Detector1Pipeline.jump - INFO - From highest outlier Two-point found 0 pixels with at least one CR and three groups\n",
-      "2021-05-27 17:20:54,818 - stpipe.Detector1Pipeline.jump - INFO - From highest outlier Two-point found 577 pixels with at least one CR and two groups\n",
-      "2021-05-27 17:20:57,151 - stpipe.Detector1Pipeline.jump - INFO - Total elapsed time = 3.59046 sec\n",
-      "2021-05-27 17:20:57,153 - stpipe.Detector1Pipeline.jump - INFO - The execution time in seconds: 3.759631\n",
-      "2021-05-27 17:20:57,157 - stpipe.Detector1Pipeline.jump - INFO - Step jump done\n",
-      "2021-05-27 17:20:57,239 - stpipe.Detector1Pipeline.ramp_fit - INFO - Step ramp_fit running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq2_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:20:57,241 - stpipe.Detector1Pipeline.ramp_fit - INFO - Step ramp_fit parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage1/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0', 'int_name': '', 'save_opt': False, 'opt_name': '', 'maximum_cores': 'none'}\n",
-      "2021-05-27 17:20:57,277 - stpipe.Detector1Pipeline.ramp_fit - INFO - Using READNOISE reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_readnoise_0050.fits\n",
-      "2021-05-27 17:20:57,278 - stpipe.Detector1Pipeline.ramp_fit - INFO - Using GAIN reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_gain_0007.fits\n",
-      "2021-05-27 17:20:57,315 - stpipe.Detector1Pipeline.ramp_fit - INFO - Using algorithm = ols\n",
-      "2021-05-27 17:20:57,316 - stpipe.Detector1Pipeline.ramp_fit - INFO - Using weighting = optimal\n",
-      "2021-05-27 17:20:57,328 - stpipe.Detector1Pipeline.ramp_fit - INFO - Number of leading groups that are flagged as DO_NOT_USE: 1\n",
-      "2021-05-27 17:20:57,329 - stpipe.Detector1Pipeline.ramp_fit - INFO - MIRI dataset has all pixels in the final group flagged as DO_NOT_USE.\n",
-      "2021-05-27 17:21:19,427 - stpipe.Detector1Pipeline.ramp_fit - INFO - Number of groups per integration: 18\n",
-      "2021-05-27 17:21:19,428 - stpipe.Detector1Pipeline.ramp_fit - INFO - Number of integrations: 1\n",
-      "2021-05-27 17:21:19,572 - stpipe.Detector1Pipeline.ramp_fit - INFO - Step ramp_fit done\n",
-      "2021-05-27 17:21:19,661 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale running with args (<IFUImageModel(1024, 1032) from det_image_seq2_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:19,663 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': 'gain_scale', 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:21:19,702 - stpipe.Detector1Pipeline.gain_scale - INFO - GAINFACT not found in gain reference file\n",
-      "2021-05-27 17:21:19,703 - stpipe.Detector1Pipeline.gain_scale - INFO - Step will be skipped\n",
-      "2021-05-27 17:21:19,706 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale done\n",
-      "2021-05-27 17:21:19,774 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale running with args (<CubeModel(1, 1024, 1032) from det_image_seq2_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:19,776 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': 'gain_scaleints', 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:21:19,819 - stpipe.Detector1Pipeline.gain_scale - INFO - GAINFACT not found in gain reference file\n",
-      "2021-05-27 17:21:19,820 - stpipe.Detector1Pipeline.gain_scale - INFO - Step will be skipped\n",
-      "2021-05-27 17:21:19,824 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale done\n",
-      "2021-05-27 17:21:20,001 - stpipe.Detector1Pipeline - INFO - Saved model in stage1/det_image_seq2_MIRIFUSHORT_12LONGexp1_rateints.fits\n",
-      "2021-05-27 17:21:20,003 - stpipe.Detector1Pipeline - INFO - ... ending calwebb_detector1\n",
-      "2021-05-27 17:21:20,191 - stpipe.Detector1Pipeline - INFO - Saved model in stage1/det_image_seq2_MIRIFUSHORT_12LONGexp1_rate.fits\n",
-      "2021-05-27 17:21:20,192 - stpipe.Detector1Pipeline - INFO - Step Detector1Pipeline done\n",
-      "2021-05-27 17:21:20,205 - stpipe.Detector1Pipeline - INFO - Detector1Pipeline instance created.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:21:20,207 - stpipe.Detector1Pipeline.group_scale - INFO - GroupScaleStep instance created.\n",
-      "2021-05-27 17:21:20,209 - stpipe.Detector1Pipeline.dq_init - INFO - DQInitStep instance created.\n",
-      "2021-05-27 17:21:20,211 - stpipe.Detector1Pipeline.saturation - INFO - SaturationStep instance created.\n",
-      "2021-05-27 17:21:20,213 - stpipe.Detector1Pipeline.ipc - INFO - IPCStep instance created.\n",
-      "2021-05-27 17:21:20,215 - stpipe.Detector1Pipeline.superbias - INFO - SuperBiasStep instance created.\n",
-      "2021-05-27 17:21:20,217 - stpipe.Detector1Pipeline.refpix - INFO - RefPixStep instance created.\n",
-      "2021-05-27 17:21:20,218 - stpipe.Detector1Pipeline.rscd - INFO - RscdStep instance created.\n",
-      "2021-05-27 17:21:20,220 - stpipe.Detector1Pipeline.firstframe - INFO - FirstFrameStep instance created.\n",
-      "2021-05-27 17:21:20,222 - stpipe.Detector1Pipeline.lastframe - INFO - LastFrameStep instance created.\n",
-      "2021-05-27 17:21:20,225 - stpipe.Detector1Pipeline.linearity - INFO - LinearityStep instance created.\n",
-      "2021-05-27 17:21:20,226 - stpipe.Detector1Pipeline.dark_current - INFO - DarkCurrentStep instance created.\n",
-      "2021-05-27 17:21:20,229 - stpipe.Detector1Pipeline.reset - INFO - ResetStep instance created.\n",
-      "2021-05-27 17:21:20,232 - stpipe.Detector1Pipeline.persistence - INFO - PersistenceStep instance created.\n",
-      "2021-05-27 17:21:20,234 - stpipe.Detector1Pipeline.jump - INFO - JumpStep instance created.\n",
-      "2021-05-27 17:21:20,236 - stpipe.Detector1Pipeline.ramp_fit - INFO - RampFitStep instance created.\n",
-      "2021-05-27 17:21:20,239 - stpipe.Detector1Pipeline.gain_scale - INFO - GainScaleStep instance created.\n",
-      "2021-05-27 17:21:20,348 - stpipe.Detector1Pipeline - INFO - Step Detector1Pipeline running with args ('./cache/stage0/det_image_seq3_MIRIFUSHORT_12LONGexp1.fits',).\n",
-      "2021-05-27 17:21:20,362 - stpipe.Detector1Pipeline - INFO - Step Detector1Pipeline parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage1/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'save_calibrated_ramp': False, 'steps': {'group_scale': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'dq_init': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'saturation': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'ipc': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'superbias': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'refpix': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': True, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'odd_even_columns': True, 'use_side_ref_pixels': True, 'side_smoothing_length': 11, 'side_gain': 1.0, 'odd_even_rows': True}, 'rscd': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'type': 'baseline'}, 'firstframe': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'lastframe': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'linearity': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'dark_current': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'dark_output': None}, 'reset': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'persistence': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'input_trapsfilled': '', 'flag_pers_cutoff': 40.0, 'save_persistence': False, 'save_trapsfilled': True}, 'jump': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'rejection_threshold': 4.0, 'three_group_rejection_threshold': 6.0, 'four_group_rejection_threshold': 5.0, 'maximum_cores': 'none', 'flag_4_neighbors': True, 'max_jump_to_flag_neighbors': 1000.0, 'min_jump_to_flag_neighbors': 10.0}, 'ramp_fit': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'int_name': '', 'save_opt': False, 'opt_name': '', 'maximum_cores': 'none'}, 'gain_scale': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}}}\n",
-      "2021-05-27 17:21:20,529 - stpipe.Detector1Pipeline - INFO - Prefetching reference files for dataset: 'det_image_seq3_MIRIFUSHORT_12LONGexp1.fits' reftypes = ['dark', 'gain', 'ipc', 'linearity', 'mask', 'persat', 'readnoise', 'reset', 'rscd', 'saturation', 'superbias', 'trapdensity', 'trappars']\n",
-      "2021-05-27 17:21:20,537 - stpipe.Detector1Pipeline - INFO - Prefetch for DARK reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_dark_0044.fits'.\n",
-      "2021-05-27 17:21:20,540 - stpipe.Detector1Pipeline - INFO - Prefetch for GAIN reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_gain_0007.fits'.\n",
-      "2021-05-27 17:21:20,542 - stpipe.Detector1Pipeline - INFO - Prefetch for IPC reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_ipc_0008.fits'.\n",
-      "2021-05-27 17:21:20,545 - stpipe.Detector1Pipeline - INFO - Prefetch for LINEARITY reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_linearity_0023.fits'.\n",
-      "2021-05-27 17:21:20,548 - stpipe.Detector1Pipeline - INFO - Prefetch for MASK reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_mask_0022.fits'.\n",
-      "2021-05-27 17:21:20,550 - stpipe.Detector1Pipeline - INFO - Prefetch for PERSAT reference file is 'N/A'.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:21:20,551 - stpipe.Detector1Pipeline - INFO - Prefetch for READNOISE reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_readnoise_0050.fits'.\n",
-      "2021-05-27 17:21:20,554 - stpipe.Detector1Pipeline - INFO - Prefetch for RESET reference file is 'N/A'.\n",
-      "2021-05-27 17:21:20,556 - stpipe.Detector1Pipeline - INFO - Prefetch for RSCD reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_rscd_0012.fits'.\n",
-      "2021-05-27 17:21:20,558 - stpipe.Detector1Pipeline - INFO - Prefetch for SATURATION reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_saturation_0025.fits'.\n",
-      "2021-05-27 17:21:20,560 - stpipe.Detector1Pipeline - INFO - Prefetch for SUPERBIAS reference file is 'N/A'.\n",
-      "2021-05-27 17:21:20,562 - stpipe.Detector1Pipeline - INFO - Prefetch for TRAPDENSITY reference file is 'N/A'.\n",
-      "2021-05-27 17:21:20,563 - stpipe.Detector1Pipeline - INFO - Prefetch for TRAPPARS reference file is 'N/A'.\n",
-      "2021-05-27 17:21:20,564 - stpipe.Detector1Pipeline - INFO - Starting calwebb_detector1 ...\n",
-      "2021-05-27 17:21:20,838 - stpipe.Detector1Pipeline.group_scale - INFO - Step group_scale running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq3_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:20,841 - stpipe.Detector1Pipeline.group_scale - INFO - Step group_scale parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:21:20,942 - stpipe.Detector1Pipeline.group_scale - INFO - NFRAMES=1 is a power of 2; correction not needed\n",
-      "2021-05-27 17:21:20,944 - stpipe.Detector1Pipeline.group_scale - INFO - Step will be skipped\n",
-      "2021-05-27 17:21:20,950 - stpipe.Detector1Pipeline.group_scale - INFO - Step group_scale done\n",
-      "2021-05-27 17:21:21,039 - stpipe.Detector1Pipeline.dq_init - INFO - Step dq_init running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq3_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:21,042 - stpipe.Detector1Pipeline.dq_init - INFO - Step dq_init parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:21:21,074 - stpipe.Detector1Pipeline.dq_init - INFO - Using MASK reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_mask_0022.fits\n",
-      "2021-05-27 17:21:21,345 - stpipe.Detector1Pipeline.dq_init - INFO - Step dq_init done\n",
-      "2021-05-27 17:21:21,458 - stpipe.Detector1Pipeline.saturation - INFO - Step saturation running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq3_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:21,461 - stpipe.Detector1Pipeline.saturation - INFO - Step saturation parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:21:21,500 - stpipe.Detector1Pipeline.saturation - INFO - Using SATURATION reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_saturation_0025.fits\n",
-      "2021-05-27 17:21:22,005 - stpipe.Detector1Pipeline.saturation - INFO - Detected 1256 saturated pixels\n",
-      "2021-05-27 17:21:22,028 - stpipe.Detector1Pipeline.saturation - INFO - Detected 0 A/D floor pixels\n",
-      "2021-05-27 17:21:22,041 - stpipe.Detector1Pipeline.saturation - INFO - Step saturation done\n",
-      "2021-05-27 17:21:22,154 - stpipe.Detector1Pipeline.ipc - INFO - Step ipc running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq3_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:22,158 - stpipe.Detector1Pipeline.ipc - INFO - Step ipc parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:21:22,198 - stpipe.Detector1Pipeline.ipc - INFO - Using IPC reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_ipc_0008.fits\n",
-      "2021-05-27 17:21:22,883 - stpipe.Detector1Pipeline.ipc - INFO - Step ipc done\n",
-      "2021-05-27 17:21:23,002 - stpipe.Detector1Pipeline.firstframe - INFO - Step firstframe running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq3_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:23,004 - stpipe.Detector1Pipeline.firstframe - INFO - Step firstframe parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:21:23,117 - stpipe.Detector1Pipeline.firstframe - INFO - Step firstframe done\n",
-      "2021-05-27 17:21:23,225 - stpipe.Detector1Pipeline.lastframe - INFO - Step lastframe running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq3_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:23,230 - stpipe.Detector1Pipeline.lastframe - INFO - Step lastframe parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:21:23,336 - stpipe.Detector1Pipeline.lastframe - INFO - Step lastframe done\n",
-      "2021-05-27 17:21:23,452 - stpipe.Detector1Pipeline.reset - INFO - Step reset running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq3_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:23,455 - stpipe.Detector1Pipeline.reset - INFO - Step reset parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:21:23,502 - stpipe.Detector1Pipeline.reset - INFO - Using RESET reference file N/A\n",
-      "2021-05-27 17:21:23,504 - stpipe.Detector1Pipeline.reset - WARNING - No RESET reference file found\n",
-      "2021-05-27 17:21:23,506 - stpipe.Detector1Pipeline.reset - WARNING - Reset step will be skipped\n",
-      "2021-05-27 17:21:23,605 - stpipe.Detector1Pipeline.reset - INFO - Step reset done\n",
-      "2021-05-27 17:21:23,726 - stpipe.Detector1Pipeline.linearity - INFO - Step linearity running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq3_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:23,729 - stpipe.Detector1Pipeline.linearity - INFO - Step linearity parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:21:23,760 - stpipe.Detector1Pipeline.linearity - INFO - Using Linearity reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_linearity_0023.fits\n",
-      "2021-05-27 17:21:24,288 - stpipe.Detector1Pipeline.linearity - INFO - Step linearity done\n",
-      "2021-05-27 17:21:24,397 - stpipe.Detector1Pipeline.rscd - INFO - Step rscd running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq3_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:24,398 - stpipe.Detector1Pipeline.rscd - INFO - Step rscd parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0', 'type': 'baseline'}\n",
-      "2021-05-27 17:21:24,423 - stpipe.Detector1Pipeline.rscd - INFO - Using RSCD reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_rscd_0012.fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:21:24,601 - stpipe.Detector1Pipeline.rscd - INFO - Step rscd done\n",
-      "2021-05-27 17:21:24,733 - stpipe.Detector1Pipeline.dark_current - INFO - Step dark_current running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq3_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:24,737 - stpipe.Detector1Pipeline.dark_current - INFO - Step dark_current parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage1/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0', 'dark_output': None}\n",
-      "2021-05-27 17:21:24,779 - stpipe.Detector1Pipeline.dark_current - INFO - Using DARK reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_dark_0044.fits\n",
-      "2021-05-27 17:21:25,658 - stpipe.Detector1Pipeline.dark_current - INFO - Science data nints=1, ngroups=20, nframes=1, groupgap=0\n",
-      "2021-05-27 17:21:25,660 - stpipe.Detector1Pipeline.dark_current - INFO - Dark data nints=2, ngroups=45, nframes=1, groupgap=0\n",
-      "2021-05-27 17:21:25,953 - stpipe.Detector1Pipeline.dark_current - INFO - Step dark_current done\n",
-      "2021-05-27 17:21:26,075 - stpipe.Detector1Pipeline.refpix - INFO - Step refpix running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq3_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:26,077 - stpipe.Detector1Pipeline.refpix - INFO - Step refpix parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': True, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0', 'odd_even_columns': True, 'use_side_ref_pixels': True, 'side_smoothing_length': 11, 'side_gain': 1.0, 'odd_even_rows': True}\n",
-      "2021-05-27 17:21:26,078 - stpipe.Detector1Pipeline.refpix - INFO - Step skipped.\n",
-      "2021-05-27 17:21:26,081 - stpipe.Detector1Pipeline.refpix - INFO - Step refpix done\n",
-      "2021-05-27 17:21:26,146 - stpipe.Detector1Pipeline.jump - INFO - Step jump running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq3_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:26,149 - stpipe.Detector1Pipeline.jump - INFO - Step jump parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0', 'rejection_threshold': 4.0, 'three_group_rejection_threshold': 6.0, 'four_group_rejection_threshold': 5.0, 'maximum_cores': 'none', 'flag_4_neighbors': True, 'max_jump_to_flag_neighbors': 1000.0, 'min_jump_to_flag_neighbors': 10.0}\n",
-      "2021-05-27 17:21:26,161 - stpipe.Detector1Pipeline.jump - INFO - CR rejection threshold = 4 sigma\n",
-      "2021-05-27 17:21:26,175 - stpipe.Detector1Pipeline.jump - INFO - Using GAIN reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_gain_0007.fits\n",
-      "2021-05-27 17:21:26,205 - stpipe.Detector1Pipeline.jump - INFO - Using READNOISE reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_readnoise_0050.fits\n",
-      "2021-05-27 17:21:26,228 - stpipe.Detector1Pipeline.jump - INFO - Using 1 core for jump detection \n",
-      "2021-05-27 17:21:26,335 - stpipe.Detector1Pipeline.jump - INFO - Executing two-point difference method\n",
-      "2021-05-27 17:21:26,623 - stpipe.Detector1Pipeline.jump - INFO - Working on integration 1:\n",
-      "2021-05-27 17:21:27,658 - stpipe.Detector1Pipeline.jump - INFO - From highest outlier Two-point found 64404 pixels with at least one CR and at least four groups\n",
-      "2021-05-27 17:21:27,659 - stpipe.Detector1Pipeline.jump - INFO - From highest outlier Two-point found 0 pixels with at least one CR and three groups\n",
-      "2021-05-27 17:21:27,659 - stpipe.Detector1Pipeline.jump - INFO - From highest outlier Two-point found 628 pixels with at least one CR and two groups\n",
-      "2021-05-27 17:21:30,005 - stpipe.Detector1Pipeline.jump - INFO - Total elapsed time = 3.66873 sec\n",
-      "2021-05-27 17:21:30,008 - stpipe.Detector1Pipeline.jump - INFO - The execution time in seconds: 3.846384\n",
-      "2021-05-27 17:21:30,011 - stpipe.Detector1Pipeline.jump - INFO - Step jump done\n",
-      "2021-05-27 17:21:30,106 - stpipe.Detector1Pipeline.ramp_fit - INFO - Step ramp_fit running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq3_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:30,108 - stpipe.Detector1Pipeline.ramp_fit - INFO - Step ramp_fit parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage1/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0', 'int_name': '', 'save_opt': False, 'opt_name': '', 'maximum_cores': 'none'}\n",
-      "2021-05-27 17:21:30,146 - stpipe.Detector1Pipeline.ramp_fit - INFO - Using READNOISE reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_readnoise_0050.fits\n",
-      "2021-05-27 17:21:30,147 - stpipe.Detector1Pipeline.ramp_fit - INFO - Using GAIN reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_gain_0007.fits\n",
-      "2021-05-27 17:21:30,187 - stpipe.Detector1Pipeline.ramp_fit - INFO - Using algorithm = ols\n",
-      "2021-05-27 17:21:30,188 - stpipe.Detector1Pipeline.ramp_fit - INFO - Using weighting = optimal\n",
-      "2021-05-27 17:21:30,202 - stpipe.Detector1Pipeline.ramp_fit - INFO - Number of leading groups that are flagged as DO_NOT_USE: 1\n",
-      "2021-05-27 17:21:30,203 - stpipe.Detector1Pipeline.ramp_fit - INFO - MIRI dataset has all pixels in the final group flagged as DO_NOT_USE.\n",
-      "2021-05-27 17:21:51,744 - stpipe.Detector1Pipeline.ramp_fit - INFO - Number of groups per integration: 18\n",
-      "2021-05-27 17:21:51,745 - stpipe.Detector1Pipeline.ramp_fit - INFO - Number of integrations: 1\n",
-      "2021-05-27 17:21:51,888 - stpipe.Detector1Pipeline.ramp_fit - INFO - Step ramp_fit done\n",
-      "2021-05-27 17:21:51,969 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale running with args (<IFUImageModel(1024, 1032) from det_image_seq3_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:51,970 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': 'gain_scale', 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:21:52,008 - stpipe.Detector1Pipeline.gain_scale - INFO - GAINFACT not found in gain reference file\n",
-      "2021-05-27 17:21:52,009 - stpipe.Detector1Pipeline.gain_scale - INFO - Step will be skipped\n",
-      "2021-05-27 17:21:52,015 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale done\n",
-      "2021-05-27 17:21:52,086 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale running with args (<CubeModel(1, 1024, 1032) from det_image_seq3_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:52,088 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': 'gain_scaleints', 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:21:52,127 - stpipe.Detector1Pipeline.gain_scale - INFO - GAINFACT not found in gain reference file\n",
-      "2021-05-27 17:21:52,128 - stpipe.Detector1Pipeline.gain_scale - INFO - Step will be skipped\n",
-      "2021-05-27 17:21:52,132 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale done\n",
-      "2021-05-27 17:21:52,286 - stpipe.Detector1Pipeline - INFO - Saved model in stage1/det_image_seq3_MIRIFUSHORT_12LONGexp1_rateints.fits\n",
-      "2021-05-27 17:21:52,287 - stpipe.Detector1Pipeline - INFO - ... ending calwebb_detector1\n",
-      "2021-05-27 17:21:52,447 - stpipe.Detector1Pipeline - INFO - Saved model in stage1/det_image_seq3_MIRIFUSHORT_12LONGexp1_rate.fits\n",
-      "2021-05-27 17:21:52,448 - stpipe.Detector1Pipeline - INFO - Step Detector1Pipeline done\n",
-      "2021-05-27 17:21:52,459 - stpipe.Detector1Pipeline - INFO - Detector1Pipeline instance created.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:21:52,460 - stpipe.Detector1Pipeline.group_scale - INFO - GroupScaleStep instance created.\n",
-      "2021-05-27 17:21:52,462 - stpipe.Detector1Pipeline.dq_init - INFO - DQInitStep instance created.\n",
-      "2021-05-27 17:21:52,464 - stpipe.Detector1Pipeline.saturation - INFO - SaturationStep instance created.\n",
-      "2021-05-27 17:21:52,466 - stpipe.Detector1Pipeline.ipc - INFO - IPCStep instance created.\n",
-      "2021-05-27 17:21:52,467 - stpipe.Detector1Pipeline.superbias - INFO - SuperBiasStep instance created.\n",
-      "2021-05-27 17:21:52,469 - stpipe.Detector1Pipeline.refpix - INFO - RefPixStep instance created.\n",
-      "2021-05-27 17:21:52,470 - stpipe.Detector1Pipeline.rscd - INFO - RscdStep instance created.\n",
-      "2021-05-27 17:21:52,472 - stpipe.Detector1Pipeline.firstframe - INFO - FirstFrameStep instance created.\n",
-      "2021-05-27 17:21:52,473 - stpipe.Detector1Pipeline.lastframe - INFO - LastFrameStep instance created.\n",
-      "2021-05-27 17:21:52,475 - stpipe.Detector1Pipeline.linearity - INFO - LinearityStep instance created.\n",
-      "2021-05-27 17:21:52,476 - stpipe.Detector1Pipeline.dark_current - INFO - DarkCurrentStep instance created.\n",
-      "2021-05-27 17:21:52,478 - stpipe.Detector1Pipeline.reset - INFO - ResetStep instance created.\n",
-      "2021-05-27 17:21:52,479 - stpipe.Detector1Pipeline.persistence - INFO - PersistenceStep instance created.\n",
-      "2021-05-27 17:21:52,481 - stpipe.Detector1Pipeline.jump - INFO - JumpStep instance created.\n",
-      "2021-05-27 17:21:52,483 - stpipe.Detector1Pipeline.ramp_fit - INFO - RampFitStep instance created.\n",
-      "2021-05-27 17:21:52,485 - stpipe.Detector1Pipeline.gain_scale - INFO - GainScaleStep instance created.\n",
-      "2021-05-27 17:21:52,596 - stpipe.Detector1Pipeline - INFO - Step Detector1Pipeline running with args ('./cache/stage0/det_image_seq4_MIRIFUSHORT_12LONGexp1.fits',).\n",
-      "2021-05-27 17:21:52,610 - stpipe.Detector1Pipeline - INFO - Step Detector1Pipeline parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage1/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'save_calibrated_ramp': False, 'steps': {'group_scale': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'dq_init': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'saturation': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'ipc': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'superbias': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'refpix': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': True, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'odd_even_columns': True, 'use_side_ref_pixels': True, 'side_smoothing_length': 11, 'side_gain': 1.0, 'odd_even_rows': True}, 'rscd': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'type': 'baseline'}, 'firstframe': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'lastframe': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'linearity': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'dark_current': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'dark_output': None}, 'reset': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}, 'persistence': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'input_trapsfilled': '', 'flag_pers_cutoff': 40.0, 'save_persistence': False, 'save_trapsfilled': True}, 'jump': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'rejection_threshold': 4.0, 'three_group_rejection_threshold': 6.0, 'four_group_rejection_threshold': 5.0, 'maximum_cores': 'none', 'flag_4_neighbors': True, 'max_jump_to_flag_neighbors': 1000.0, 'min_jump_to_flag_neighbors': 10.0}, 'ramp_fit': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'int_name': '', 'save_opt': False, 'opt_name': '', 'maximum_cores': 'none'}, 'gain_scale': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}}}\n",
-      "2021-05-27 17:21:52,726 - stpipe.Detector1Pipeline - INFO - Prefetching reference files for dataset: 'det_image_seq4_MIRIFUSHORT_12LONGexp1.fits' reftypes = ['dark', 'gain', 'ipc', 'linearity', 'mask', 'persat', 'readnoise', 'reset', 'rscd', 'saturation', 'superbias', 'trapdensity', 'trappars']\n",
-      "2021-05-27 17:21:52,731 - stpipe.Detector1Pipeline - INFO - Prefetch for DARK reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_dark_0044.fits'.\n",
-      "2021-05-27 17:21:52,732 - stpipe.Detector1Pipeline - INFO - Prefetch for GAIN reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_gain_0007.fits'.\n",
-      "2021-05-27 17:21:52,733 - stpipe.Detector1Pipeline - INFO - Prefetch for IPC reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_ipc_0008.fits'.\n",
-      "2021-05-27 17:21:52,733 - stpipe.Detector1Pipeline - INFO - Prefetch for LINEARITY reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_linearity_0023.fits'.\n",
-      "2021-05-27 17:21:52,734 - stpipe.Detector1Pipeline - INFO - Prefetch for MASK reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_mask_0022.fits'.\n",
-      "2021-05-27 17:21:52,735 - stpipe.Detector1Pipeline - INFO - Prefetch for PERSAT reference file is 'N/A'.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:21:52,736 - stpipe.Detector1Pipeline - INFO - Prefetch for READNOISE reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_readnoise_0050.fits'.\n",
-      "2021-05-27 17:21:52,737 - stpipe.Detector1Pipeline - INFO - Prefetch for RESET reference file is 'N/A'.\n",
-      "2021-05-27 17:21:52,738 - stpipe.Detector1Pipeline - INFO - Prefetch for RSCD reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_rscd_0012.fits'.\n",
-      "2021-05-27 17:21:52,739 - stpipe.Detector1Pipeline - INFO - Prefetch for SATURATION reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_saturation_0025.fits'.\n",
-      "2021-05-27 17:21:52,740 - stpipe.Detector1Pipeline - INFO - Prefetch for SUPERBIAS reference file is 'N/A'.\n",
-      "2021-05-27 17:21:52,742 - stpipe.Detector1Pipeline - INFO - Prefetch for TRAPDENSITY reference file is 'N/A'.\n",
-      "2021-05-27 17:21:52,742 - stpipe.Detector1Pipeline - INFO - Prefetch for TRAPPARS reference file is 'N/A'.\n",
-      "2021-05-27 17:21:52,743 - stpipe.Detector1Pipeline - INFO - Starting calwebb_detector1 ...\n",
-      "2021-05-27 17:21:52,982 - stpipe.Detector1Pipeline.group_scale - INFO - Step group_scale running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq4_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:52,985 - stpipe.Detector1Pipeline.group_scale - INFO - Step group_scale parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:21:53,090 - stpipe.Detector1Pipeline.group_scale - INFO - NFRAMES=1 is a power of 2; correction not needed\n",
-      "2021-05-27 17:21:53,091 - stpipe.Detector1Pipeline.group_scale - INFO - Step will be skipped\n",
-      "2021-05-27 17:21:53,094 - stpipe.Detector1Pipeline.group_scale - INFO - Step group_scale done\n",
-      "2021-05-27 17:21:53,186 - stpipe.Detector1Pipeline.dq_init - INFO - Step dq_init running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq4_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:53,189 - stpipe.Detector1Pipeline.dq_init - INFO - Step dq_init parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:21:53,225 - stpipe.Detector1Pipeline.dq_init - INFO - Using MASK reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_mask_0022.fits\n",
-      "2021-05-27 17:21:53,416 - stpipe.Detector1Pipeline.dq_init - INFO - Step dq_init done\n",
-      "2021-05-27 17:21:53,512 - stpipe.Detector1Pipeline.saturation - INFO - Step saturation running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq4_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:53,514 - stpipe.Detector1Pipeline.saturation - INFO - Step saturation parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:21:53,550 - stpipe.Detector1Pipeline.saturation - INFO - Using SATURATION reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_saturation_0025.fits\n",
-      "2021-05-27 17:21:54,056 - stpipe.Detector1Pipeline.saturation - INFO - Detected 1121 saturated pixels\n",
-      "2021-05-27 17:21:54,075 - stpipe.Detector1Pipeline.saturation - INFO - Detected 0 A/D floor pixels\n",
-      "2021-05-27 17:21:54,081 - stpipe.Detector1Pipeline.saturation - INFO - Step saturation done\n",
-      "2021-05-27 17:21:54,182 - stpipe.Detector1Pipeline.ipc - INFO - Step ipc running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq4_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:54,184 - stpipe.Detector1Pipeline.ipc - INFO - Step ipc parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:21:54,219 - stpipe.Detector1Pipeline.ipc - INFO - Using IPC reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_ipc_0008.fits\n",
-      "2021-05-27 17:21:54,858 - stpipe.Detector1Pipeline.ipc - INFO - Step ipc done\n",
-      "2021-05-27 17:21:54,967 - stpipe.Detector1Pipeline.firstframe - INFO - Step firstframe running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq4_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:54,969 - stpipe.Detector1Pipeline.firstframe - INFO - Step firstframe parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:21:55,046 - stpipe.Detector1Pipeline.firstframe - INFO - Step firstframe done\n",
-      "2021-05-27 17:21:55,167 - stpipe.Detector1Pipeline.lastframe - INFO - Step lastframe running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq4_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:55,171 - stpipe.Detector1Pipeline.lastframe - INFO - Step lastframe parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:21:55,268 - stpipe.Detector1Pipeline.lastframe - INFO - Step lastframe done\n",
-      "2021-05-27 17:21:55,344 - stpipe.Detector1Pipeline.reset - INFO - Step reset running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq4_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:55,346 - stpipe.Detector1Pipeline.reset - INFO - Step reset parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:21:55,369 - stpipe.Detector1Pipeline.reset - INFO - Using RESET reference file N/A\n",
-      "2021-05-27 17:21:55,370 - stpipe.Detector1Pipeline.reset - WARNING - No RESET reference file found\n",
-      "2021-05-27 17:21:55,371 - stpipe.Detector1Pipeline.reset - WARNING - Reset step will be skipped\n",
-      "2021-05-27 17:21:55,442 - stpipe.Detector1Pipeline.reset - INFO - Step reset done\n",
-      "2021-05-27 17:21:55,526 - stpipe.Detector1Pipeline.linearity - INFO - Step linearity running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq4_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:55,528 - stpipe.Detector1Pipeline.linearity - INFO - Step linearity parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:21:55,558 - stpipe.Detector1Pipeline.linearity - INFO - Using Linearity reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_linearity_0023.fits\n",
-      "2021-05-27 17:21:55,882 - stpipe.Detector1Pipeline.linearity - INFO - Step linearity done\n",
-      "2021-05-27 17:21:55,969 - stpipe.Detector1Pipeline.rscd - INFO - Step rscd running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq4_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:55,970 - stpipe.Detector1Pipeline.rscd - INFO - Step rscd parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0', 'type': 'baseline'}\n",
-      "2021-05-27 17:21:55,997 - stpipe.Detector1Pipeline.rscd - INFO - Using RSCD reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_rscd_0012.fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:21:56,115 - stpipe.Detector1Pipeline.rscd - INFO - Step rscd done\n",
-      "2021-05-27 17:21:56,201 - stpipe.Detector1Pipeline.dark_current - INFO - Step dark_current running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq4_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:56,204 - stpipe.Detector1Pipeline.dark_current - INFO - Step dark_current parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage1/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0', 'dark_output': None}\n",
-      "2021-05-27 17:21:56,232 - stpipe.Detector1Pipeline.dark_current - INFO - Using DARK reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_dark_0044.fits\n",
-      "2021-05-27 17:21:56,700 - stpipe.Detector1Pipeline.dark_current - INFO - Science data nints=1, ngroups=20, nframes=1, groupgap=0\n",
-      "2021-05-27 17:21:56,701 - stpipe.Detector1Pipeline.dark_current - INFO - Dark data nints=2, ngroups=45, nframes=1, groupgap=0\n",
-      "2021-05-27 17:21:56,899 - stpipe.Detector1Pipeline.dark_current - INFO - Step dark_current done\n",
-      "2021-05-27 17:21:57,019 - stpipe.Detector1Pipeline.refpix - INFO - Step refpix running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq4_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:57,021 - stpipe.Detector1Pipeline.refpix - INFO - Step refpix parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': True, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0', 'odd_even_columns': True, 'use_side_ref_pixels': True, 'side_smoothing_length': 11, 'side_gain': 1.0, 'odd_even_rows': True}\n",
-      "2021-05-27 17:21:57,022 - stpipe.Detector1Pipeline.refpix - INFO - Step skipped.\n",
-      "2021-05-27 17:21:57,025 - stpipe.Detector1Pipeline.refpix - INFO - Step refpix done\n",
-      "2021-05-27 17:21:57,091 - stpipe.Detector1Pipeline.jump - INFO - Step jump running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq4_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:21:57,093 - stpipe.Detector1Pipeline.jump - INFO - Step jump parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0', 'rejection_threshold': 4.0, 'three_group_rejection_threshold': 6.0, 'four_group_rejection_threshold': 5.0, 'maximum_cores': 'none', 'flag_4_neighbors': True, 'max_jump_to_flag_neighbors': 1000.0, 'min_jump_to_flag_neighbors': 10.0}\n",
-      "2021-05-27 17:21:57,106 - stpipe.Detector1Pipeline.jump - INFO - CR rejection threshold = 4 sigma\n",
-      "2021-05-27 17:21:57,120 - stpipe.Detector1Pipeline.jump - INFO - Using GAIN reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_gain_0007.fits\n",
-      "2021-05-27 17:21:57,149 - stpipe.Detector1Pipeline.jump - INFO - Using READNOISE reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_readnoise_0050.fits\n",
-      "2021-05-27 17:21:57,168 - stpipe.Detector1Pipeline.jump - INFO - Using 1 core for jump detection \n",
-      "2021-05-27 17:21:57,268 - stpipe.Detector1Pipeline.jump - INFO - Executing two-point difference method\n",
-      "2021-05-27 17:21:57,548 - stpipe.Detector1Pipeline.jump - INFO - Working on integration 1:\n",
-      "2021-05-27 17:21:58,668 - stpipe.Detector1Pipeline.jump - INFO - From highest outlier Two-point found 65203 pixels with at least one CR and at least four groups\n",
-      "2021-05-27 17:21:58,669 - stpipe.Detector1Pipeline.jump - INFO - From highest outlier Two-point found 0 pixels with at least one CR and three groups\n",
-      "2021-05-27 17:21:58,670 - stpipe.Detector1Pipeline.jump - INFO - From highest outlier Two-point found 481 pixels with at least one CR and two groups\n",
-      "2021-05-27 17:22:01,340 - stpipe.Detector1Pipeline.jump - INFO - Total elapsed time = 4.07087 sec\n",
-      "2021-05-27 17:22:01,343 - stpipe.Detector1Pipeline.jump - INFO - The execution time in seconds: 4.236380\n",
-      "2021-05-27 17:22:01,347 - stpipe.Detector1Pipeline.jump - INFO - Step jump done\n",
-      "2021-05-27 17:22:01,438 - stpipe.Detector1Pipeline.ramp_fit - INFO - Step ramp_fit running with args (<RampModel(1, 20, 1024, 1032) from det_image_seq4_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:22:01,440 - stpipe.Detector1Pipeline.ramp_fit - INFO - Step ramp_fit parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage1/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': './cache/stage0', 'int_name': '', 'save_opt': False, 'opt_name': '', 'maximum_cores': 'none'}\n",
-      "2021-05-27 17:22:01,478 - stpipe.Detector1Pipeline.ramp_fit - INFO - Using READNOISE reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_readnoise_0050.fits\n",
-      "2021-05-27 17:22:01,479 - stpipe.Detector1Pipeline.ramp_fit - INFO - Using GAIN reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_gain_0007.fits\n",
-      "2021-05-27 17:22:01,517 - stpipe.Detector1Pipeline.ramp_fit - INFO - Using algorithm = ols\n",
-      "2021-05-27 17:22:01,518 - stpipe.Detector1Pipeline.ramp_fit - INFO - Using weighting = optimal\n",
-      "2021-05-27 17:22:01,530 - stpipe.Detector1Pipeline.ramp_fit - INFO - Number of leading groups that are flagged as DO_NOT_USE: 1\n",
-      "2021-05-27 17:22:01,531 - stpipe.Detector1Pipeline.ramp_fit - INFO - MIRI dataset has all pixels in the final group flagged as DO_NOT_USE.\n",
-      "2021-05-27 17:22:22,054 - stpipe.Detector1Pipeline.ramp_fit - INFO - Number of groups per integration: 18\n",
-      "2021-05-27 17:22:22,055 - stpipe.Detector1Pipeline.ramp_fit - INFO - Number of integrations: 1\n",
-      "2021-05-27 17:22:22,184 - stpipe.Detector1Pipeline.ramp_fit - INFO - Step ramp_fit done\n",
-      "2021-05-27 17:22:22,269 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale running with args (<IFUImageModel(1024, 1032) from det_image_seq4_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:22:22,271 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': 'gain_scale', 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:22:22,308 - stpipe.Detector1Pipeline.gain_scale - INFO - GAINFACT not found in gain reference file\n",
-      "2021-05-27 17:22:22,308 - stpipe.Detector1Pipeline.gain_scale - INFO - Step will be skipped\n",
-      "2021-05-27 17:22:22,312 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale done\n",
-      "2021-05-27 17:22:22,375 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale running with args (<CubeModel(1, 1024, 1032) from det_image_seq4_MIRIFUSHORT_12LONGexp1.fits>,).\n",
-      "2021-05-27 17:22:22,378 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': 'gain_scaleints', 'search_output_file': True, 'input_dir': './cache/stage0'}\n",
-      "2021-05-27 17:22:22,418 - stpipe.Detector1Pipeline.gain_scale - INFO - GAINFACT not found in gain reference file\n",
-      "2021-05-27 17:22:22,419 - stpipe.Detector1Pipeline.gain_scale - INFO - Step will be skipped\n",
-      "2021-05-27 17:22:22,422 - stpipe.Detector1Pipeline.gain_scale - INFO - Step gain_scale done\n",
-      "2021-05-27 17:22:22,565 - stpipe.Detector1Pipeline - INFO - Saved model in stage1/det_image_seq4_MIRIFUSHORT_12LONGexp1_rateints.fits\n",
-      "2021-05-27 17:22:22,567 - stpipe.Detector1Pipeline - INFO - ... ending calwebb_detector1\n",
-      "2021-05-27 17:22:22,706 - stpipe.Detector1Pipeline - INFO - Saved model in stage1/det_image_seq4_MIRIFUSHORT_12LONGexp1_rate.fits\n",
-      "2021-05-27 17:22:22,707 - stpipe.Detector1Pipeline - INFO - Step Detector1Pipeline done\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Run the pipeline on these input files by a simple loop over our pipeline function\n",
     "\n",
@@ -1019,16 +524,16 @@
     "        rundet1(file)\n",
     "# Otherwise, just copy cached outputs into our output directory structure\n",
     "else:\n",
-    "    sstring=cache_dir+det1_dir+'det*rate.fits'\n",
-    "    files=sorted(glob.glob(sstring))\n",
+    "    sstring = cache_dir + det1_dir + 'det*rate.fits'\n",
+    "    files = sorted(glob.glob(sstring))\n",
     "    for file in files:\n",
-    "        outfile=str.replace(file,cache_dir,'./')\n",
+    "        outfile = str.replace(file,cache_dir,'./')\n",
     "        shutil.copy(file,outfile)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e991237b",
+   "id": "113d6f8d",
    "metadata": {},
    "source": [
     "Let's take a look at the output data products (i.e., uncalibrated slope data) to get an idea what they look like."
@@ -1036,19 +541,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "1ac2d7d1",
+   "execution_count": 14,
+   "id": "a721fd11",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:22:22,712 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n",
-      "\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -1058,23 +554,23 @@
        " 'stage1/det_image_seq4_MIRIFUSHORT_12LONGexp1_rate.fits']"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Look for our _rate.fits files produced by the Detector1 pipeline\n",
-    "sstring=det1_dir+'det*rate.fits'\n",
-    "ratefiles=sorted(glob.glob(sstring))\n",
+    "sstring = det1_dir + 'det*rate.fits'\n",
+    "ratefiles = sorted(glob.glob(sstring))\n",
     "# And print them out so that we can see them\n",
     "ratefiles"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "3635602c",
+   "execution_count": 15,
+   "id": "c9ed84ea",
    "metadata": {},
    "outputs": [
     {
@@ -1089,20 +585,20 @@
       "  3  DQ            1 ImageHDU        11   (1032, 1024)   int32 (rescales to uint32)   \n",
       "  4  VAR_POISSON    1 ImageHDU         9   (1032, 1024)   float32   \n",
       "  5  VAR_RNOISE    1 ImageHDU         9   (1032, 1024)   float32   \n",
-      "  6  ASDF          1 BinTableHDU     11   1R x 1C   [3667B]   \n"
+      "  6  ASDF          1 BinTableHDU     11   1R x 1C   [3645B]   \n"
      ]
     }
    ],
    "source": [
     "# We'll open the first image in the list and take a look at its contents\n",
-    "hdu1=fits.open(ratefiles[0])\n",
+    "hdu1 = fits.open(ratefiles[0])\n",
     "hdu1.info()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "64a315a1",
+   "execution_count": 16,
+   "id": "e55fe9ee",
    "metadata": {},
    "outputs": [
     {
@@ -1111,13 +607,13 @@
        "Text(0.5, 0, 'X pixel')"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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+8pM4d+4cCoUCZmdn8au/+qv48R//cXz3d3+37/PKygoKhQLe8pa34KMf/Sg2NjawtraGd77znfiJn/gJ37+LFy/COYfZ2VkMh0McHx/jHe94Bz7xiU9gOBzi6aefxpd92Zfhe77ne4JnSqUSJicn0W63fb+bzSYWFxfx/PPP45//838elCfjtgcFme7W1hbnP2YK/ZzjT8vLyx6wfP7nf34wHhcuXMDU1BS63a4/nLn+r1y5gu/8zu/EH/pDfwgA8F3f9V34mZ/5GUxOTqLVanlw0Wq1UCqV8Oyzz+JXf/VXg7pnZ2cxNTWFiYkJ3Lt3D71eDxMTE6hWq/iN3/gND2yuXr3q10qr1fJzUCwWMT8/jytXruAXf/EXAQBXrlzx661cLnvhsVwuY2FhAV/0RV+EH/zBH8Qv/dIv4c/+2T+LSqWCqakpvOc978HW1hY+/elPY2JiAqurq3jb296Gt771rdja2sJP/uRP+jX7oQ99CMvLy/in//SfotfrYXZ2Ft/yLd+Cd77znfhn/+yf4ed+7uf8fv+2b/s27O/v4x/9o38EYMT3lpeX8cEPfhA//MM/jF6v5wHEt33bt+Hll1/GD/3QD/n9eXh4iLe//e34nd/5HayuruId73iH3xdf8RVfgRdffBGTk5N+bkqlEvb391MT7pzD1atXMT8/j06ng6WlJfzn//yf0el0MBgM8MEPfhCf+tSn8I53vAO//du/jU6ng16vh16vh6mpKbzjHe/AxMQEyuUyPvaxj6HdbqPf7+OFF17wgOajH/0o2u2253lXr17Fr/3arwEAPvShD+Gll17y4KhWq2F2dhZXr17Fxz72Ma8MGAwGuHnzpm/3Cy+8gE6ng+PjYwyHQ7+fZ2dnsbe3B+ccjo6O8KEPfQg/+qM/6p/7wAc+gK2tLbTbbQ+op6enAcCD+F6vh89+1sta+NZv/Vb81m/9FjY3N1EoFFAsFjE1NeWFh3a7DQD+/AOA7/iO78BHPvIR3Lp1CxMTE15I6Ha7AIBer4eFhQV84hOfAAB8//d/P/7tv/23uHbtGo6Pj73Cp1AoeIA6HA7xwQ9+EF/7tV+Lv/f3/h729/eRJAl2dnaiigolUWo8EleNNxXwJUnybwD8GwAxtO4wunD6bydJ8gsnn/0ZjO4m/CMAfvbkQuuvAfCFSZJ89KTMXwDwS865v5wkyTqAbwQwAeD/kiRJD8DvO+feDeAvYcR4H6bdXppVLddwOESz2QwkHgDo9/totVoeWPBzaoe4oTnxU1NTmJub89IjF0Wj0fBSo0pX7Xbbby6tB0CghaRWsFQqBf0gUeoz8+D/1rITExO+few/+8zDhFSr1R5YqzY7O+tBEcc0qywZo2pYWZbjAwCdTgf9fj8qUWVpPGwbFRRyPgaDQUqatePFsbDSotat2g/tt5bl/51OB6+88gquXr2a0pAdHx+j3W57iV3bTmldx5N/k5mptpTf6/zqM/1+H7u7u14joN/xd6VS8aC60+lgb28v0DipVM82krkeHx97YULr17nR9U5Nyv3Ijv195v9zjj9RqwMgtd5Vy2w1HeRRJM5dsVgMyvNv3d9aR6/XCw4x8scXX3wRX/RFXwRgvJeo4dI1TO2b1st9RNCpZblmCQKAER8rl8uBAMVnODZsX6FQ8ICDa1218uwz9/Hq6qrnT7reO52Of7ZSqWA4HKJer3uNKeeG+y1JEvR6Pb/3AATaSNV8Ken/vV4Px8fHvh67P9hu1cqz3bQGlMtllEol/04Kw6rd53wqT7UaOc69WrdifFvrtNpgtaQ0Go3Us3ZOLGn7WD4mzKs1wn5PcMvn9b18Tt/DvnLtKE/S9h4dHWF/f9/Xr5hB+xI7Hx4lPclRuk8DWMVI8gUAJEmyD+C3AXzg5KMPAGiQmZ7QhzFSf75fyvzHE2ZK+hUAzzvn6ngIoslgaWkpkAJ6vR5u3bqFo6MjHB8fe2bU6/Wws7ODw8PDgEnRLLKwsODrAOAlESXLWPr9vt+klUolAGP6DIGOXbyxjaDgkouy3+/79uo7LDMdDAbo9/teo6IMeHp62h8cwPggiqnBySxZju2NlaV0GWMCWr7b7abANrWAWRQDkfoObm6adki2PJkrv2MdWYyca4AHnLaRgH13dxcA/OHFZ+kaoGYvvpdS9fHxsQfr/I7jo24BAPwzBFLFYtH3u9/v4+bNmwFz5KHAdVosFv3cdbtdvPTSS8E42UOh1+uhUCh4gYbtyRpb+7ddI6rNewz0RPInXT+1WtrHm/uFPEDnjeZTAB78KfDIEv6UbL0xgMi9xXWk88q1o/UpkFEzMAVsAH5PqIl1YmLCz3+/38fh4SH29vYCsynLzszMBPtoZ2fHa9eVV/L9JO65g4MDAKM1TD64tbUVaOcsgOp2uyn+oWMUe58CCbaLY6hl9vf3U+BZv+c+BcZrhqbHYrGImZmZAAha4pkT+06FgyxBW3mUbb9zLqXVjO17C5rsewjMrAACjPdB7Bk7xhaUKUjXvma1lfyXFgmuu9g5cD9Q+3rpSQZ8NKxvmM835LtVGFVnkiR9ALumTKwOfUdAzrmKc26WPwBSI89DDAh9vXhI62FbKpW8T4h+1+v1sL297TV0pFarhUajEYC0QqGA+fl5LC0toVQqeWZG3yUFjL6TGxueqcYkdLtJuDlVO6mbQg9UbgS2kZuW71FGQxDyIAv48PDQb0a7KZIk8SZrAAHTIoCLvYNmTWWeMSCnh5hqAPi9fs6/LSjRd9BUqRs4y2/DghIeRPo/6yAjq1QqKaZFLQgPRD1Y2SZgzPBYHxmvAl2agTqdDqampoI5Hw6H3lSi9bGMAk8KDevr66l+K2C3QogdK52DGKONgWgracfqe0h6IvmT1fjGSMdDQZmOR7lcjoJylrHrHhjtQWrtdC865wJ/LGpDVJjU71RrrAIW93tsP1GoYr2Hh4deq63AiNoY9QlsNBpoNpvehYSfqx8X611fX/f+ZNpmClEUnHq9Hvb29gI+yAOfvNsCYfXJ07lRYdnOBQXvUqkUCJbcf7RAWM0+hTndp8PhEHt7eyiVSt71hu+xQjz9Hvm9WjNU6KBvJEnrBMZ7Xs8H51xKu8+5zgJvMd6qvJFrXvvKNWWf0fapgGHfC8CbiGN8yK7d+fl5b+26n7LhcQmqTzLgezPpuwDsy8+aLXB8fIyDgwOvyuYimJubQ7Va9QzAOecd7ZXpsY7d3V0cHBz4g69YLHowqQtWzRpkrMBosdNZ2C6Q1dVVr7ZX0k2pz1jzLFX8ysxIlvFaRq9SkO2LqtRjEk7sMGe9qllUpmM3pT5bqVQCQJgFAmJmCAJjmkbsZrQmXa1bAZD2w457FnE8lYrFoh+DmBaYmg2CRTVRcQ1ZMy2A6CFLvyrWYanf76Ner6c0jXyvHhBACBJKpVJ0rtT8ZJ/X9nEuYyYiEscnC2Q/Dgn6DaL78qfBYBA9NHXcFPjYgJenn346pa0Dxus2BtS0XhLL37p1y3/G9aQmWpLyNrY5BjwBBGbbixcv+vclSYKjoyMvhBJkVatV1Ot170vIQ7/dbns/afZlenoa586dw/z8fAAQNJCB7S2VSpiZmfFjwD7Mz897nq+8gHyUgUmkiYmJAKDqM3avcKwnJydRLpdRq9Wi/JTCnD6bJIn351NTsApnCnb5rO6jGKjnnCjgs/NmzyIl5ZvUmNr3xQT2LI0YtZCx9cj2WdJzTOvVH61jeXk5cEHSM8KeX7VazZ9FlrfZ8jF+9ijoSQZ8tC9YB6dz8t09jPLUeHLOlQAsmDKxOvQdlv4OgDn5CVQT3GwKmih90GlWJRa132sdlUoFi4uLmJ6eDiZ3cnISCwsLgQ/fYDDA7u6uj+TSTdpqtaLSMgAvTZKsZG2leivVqc+BlqXqn21QaUb9RoARCI4tYH2OND8/H/UlZH/pqMu+xSQwS/QJI5PQtlitkWUCOgcsz7GIbUhbt/Wr0f+vX78ePKcMj+vIkj5vTXbUGGjksEreBIIWJFGin56eTkng1B5Tm8jvOMcXLlyI1kdBwY6j/k/Sd6rGIwvw6ee6vu36p8ZGgYWV3F8HPZH86X7ClJbROej3+8EhOz09nfI/1XdYawLrV96iGhF13OdnrF8FMc1YoEQhVaNLVUOj+4JCEYOP7Pqwgmq1WsXc3FwARggWOUZc7+fPn8fy8nKgUbRm6ImJCe+WQ19I1kn+y/6qBtBajIbDYXT8dQwJGGlBYlkqHfhjhW3l57qfqBnWbBLcK9pHBZKsdzgceoWEri21ylBIZ/9Zh10HMXCfJSxTuLSC3ezsbLBe9MfyI30mJvzGlBDAKGhnamoqE+jp3NNH2X7HPsVA/aOmJxnwvYoRw/tKfuBG5ov3A/jIyUcfATDvnHuvPPcVGPXrt6XMlzrnVLT4KgCfSZJkL/biJEm6SZIc8AdA6mSoVCqeSShQarVaPkybB/bt27dx8+ZNH9ChEh41f9aHr1qtBmBDzV7W96VWq0Udmrl5dXOrmY1ls4jM22qxAKSkc5a1phBgBEx0s2pfYho1K8Vpv7a2tnxZgqJYPUqUYq35KgtUAmPplD8PCg4sI1PBQCXEfr+PV155JfUcfzQ1jbaTmmWOFZ8FEByWykj5P03blqgFUXcEPj89PY1yuRxoIsich8MhlpeXUyYN1sMDN0YWzHF96HssA+fBoxoQXc+dTgcf+9jHfPlqtZoC3Vn+Pg9BTyx/4lpSEyWJ46XCBzVXejDTpUQ147qH7WGpmivdaywbA4gqTCpfUOA+NTWV+T6aboERqNDDXLV/5GHtdtubb3WsJiYmMDMzE/ic7u7uYn19PShLUuGRQRMaZEA/vo2NDZ8WCwiFRwrTyluV33HcY0IP95T6C6oWns9XKhVv4aFWiW0pl8teO6igiPOirihcJ4eHh95XUs83a9GwQObGjRv+fwaFaD8ABOsjBsSUNym/IS/SvpEIgi1xTGPv4ZgoH88SVFmedeqesm1JksRH556WGSL27KOkNxXwOeemnXPvdqOoNAB4+uT/y8mo138fwF93zn29c+6dAP4JRtLszwNAkiSfBvDLAH7UOfc+59wXA/iHAH42GUXAAcA/BdAD8GPOubc75/4EgL8I4H9+2HaTKajZkkSgxnJcAFNTU6hWqwHz6nQ6uHfvHlqtlmd8wHhjqWatUChgcXExCBRRc4xKvwA8Y+PiIlPl5rBqawBR6dwyey1rAyGUaajkan0UtX4AAfChedtubNavKVw0UhnI3sjT09OBA7duSls/P7OHkNXOxvqh3ylIVS2UjpEeJpaxW1Ma/+/1egHo1XbQvH98fOzN6MqIaZa3krIGWijRN8k5F0R+8mdnZyfQ8rJeStsEfGz3zs5OdAxZL9NQEIDcLyBGNes8fFWTRBNZbH5ibbD0ucyfgHTUYmzMde/o4UhexXmx427XCkGGRt7qoWy1vXy/DYiw7aAWm+BI/ZGVP8zOzvq1yIjtrMhVa8JsNBrY2dnxEcb8TtvCsnfv3kWr1fIHPfmU8n3yRlpeWI51q/+1CmCTk5N+Lav5V9tkhUL64mnOVu4Fjk9Mw62A0Apz9Afk+aBrRPckv3fO+Xx3zLeqa4upvFi/9oVkhW2rrdfzUSnrfLJlHvTzWKYLKzAOBgN8+tOf9v2xAijJ8m/VoNv+271pv39U9Gbn4fsCAL8q/5PJ/WMA3wzgBzDKhfUjGCU2/XUAX5OMc1wBo7QG/xDAv8M4sem388skSfadcx/EKLHp7wDYBvB9yYPnuEoRF/j+/n4q5w7t9MwHxwOW6UbURHB8fIxms+l9AVV1rlopZVBc4MoUDw8PUwEX9FOhlkOfz5KItH8kmuacc8Em1MgqMkf21zmXSjdgQSF/isUi7t27h2eeeebUMWe/Z2dn/WeM/iVlaW/Un1LV//YQs5vO+n7oJrSa0tjY2bbb77TtCsY4hwoUVfvBCDauAfaJh4BN2wMgZXICxiCSGmlK9UzdwzQLXJscNwoGd+/e9aCV65SHXbfb9dpaagwU8MUYv2o7CF7teOmY68FCwUZBTixKVcfkAehzjj/p+oxFaOoeBUIhR5NcX7lyJcV7Yn/re1UjokLF8fFxkASb+1S1S7oOdN/xOyXlHdTi0bWA7To6OgpMi9VqFdVqFYuLiz4Rsu4NZlagNnJmZgbLy8veNULLVyoVVKtVn5OQCfQJrNinxcVFv1YV2GrWA+v/xfFR3sq/lX+otttq5JxzHlyoaVbnirwmSZLgLGHSfw0y5Lwy3x4wDlawlhvlh2yv5k61vnF81p5xsXQpJGozVZER29OaOYLvUwGRdHh46F2FGMSi7YqBsWvXruGFF17w7Ynxd77TOeejpgmu+TnHVvcA/38EVogUvdl5+P49IhGw8n0C4LtPfrLK7OIkiekpZX4PwB98uFbGSaOtlBR8kYbDYSrqNklGt3KcO3fObz5unl6vh8XFxYCBsY5yuey1i+o/wk1sSaO3VAKObRB1vreaJSBkxBZsWbOCfmfNfnajM3s7AK/B5Pc6hgCCQ8lqU/W3zku73Q42sv3e/q+HZkwjpO+KRXkpw9Y54XdWc8m6VatZKBS8xK/fa91kvnYuLQgGRmtKzSlKXA/KrNm3w8PDII+j1snE4mqGJ4M9OjryWhACRNVoct50fR0fH/vo0CzmqUKLNZ+oJhIALl26hI9//ONBfVmSe4w+F/lTTPtFshobHUNqurSe0zQnMdcABdxqURgMRjfJKKnwquvKWk64BwmiVMjRVEL6jOaY0/fZHGisr1qt+oT0JJpqdb0UCgXPy1g31zYBJgGUc84nlybP5g9v7rC5D2PpPlQwV4DAPhCUWZ+0breLSqXihSjLhxi0YS0Xu7u7uHDhQmAS53tLpZIHwHNzcz7Hq4IUuwedc4GwSZ6mAIe8x/6vZIUAkp1PJc0coeDNKiteeuklvOc97/H16ZxnATkKowsLC6lclfqcju/MzIz3K9T2xkB9Fg98vfQk+/A9sZQkIzPd/Px8EMXKxU01u6YXabfbPg+ffj41NeXNEWqSpc+CLnQerHZDaAJNLmz6TVB7w3rVz86SzRfHuig9KrixYJdlrSkSGG1yBVGqlUmSxPuF2O+tVOqcCwIdaDKJbUp91qaMAUJNo5Ltk00xoGViG5JO2tpf2w/WowesTY7KumKfk6HW6/VUP6htILNWvyoeHnqIUAvCNUywTWbF9aIHjWru5ufnvfnPgikbZRzzzbJ9Oy1oQ32gWK+NolYQ93mf93kBw9c5OKuk86AAhpSlpbOA3Oa1s7woNob2EGe5WFTicDj0vMnuTcubYsIs2390dIRmsxn4H3I9aIoiJvJuNBqppL6FwjgFEtf8/v4+Njc3Uz58DGhQwY7ATYWrbreLra2t4Lo3Xadan+acs2WyeA3ro7sKA67sXuK+t5SVsFkzKsT4GzWH9Mu2c6fme80kQWIkeEzA1s+sIK2aUH3G/q3EwEICspiw51yYAoa3jygos8oE7dPVq1e9NjSmGFGLHgPj1MqmbVHKApqvl3LA95BE7Yf1LZiZmQkSLHJDTE9Pp0xM7XYb9+7d8/frkYF0u13s7OwE4IdmWkqYGv3FqEhdJJqqRcEHgMCUoptRk0WTlOFoWU1mrGXZX5VWNbeeXdzOOfz+7/++/7/ZbAYmDy3nnAtyuWlb1d8m5sNng1qsJk77akmBhgWNlplaR3kF1/rewWAQmLkIwqymjO+IAb7l5eVUW0ulEqanp4PIaDIXddLWvrHdNFWRkiTx5a2vHsHcxYsXU4eTgkgdK00jU6vVooye69oejAB8igwtr9q+wWAQHOarq6uZB8vjYqhPAnGNWR9jNW1pWQBe00XiWMf2LTVElrguyP9IVlNoTZcqBFl/P2p81eVA3+ecC+5dZVn68Cn/IviMJWTe2dkJeBq1f3qrBt0Y5Dq+oN889CkkaaQ9303NH8eEwX/A6OxQvqTjb0E01zx5QbVaDcyFpVLJR8RyXtUaEnN/UWVCzB2g3+97H2qdM63b+iUCoeaS55d18cnisSRec2mFjtP2MhUyFpiSp7IuHYP5+fnAl1LXp65jnX/NWGHr4zvpwsWIaeuz+CDn0KOgHPC9RuKiI6OgGYPggwcnFxqBFdW5uthVQqXvFYBgo5OSJPFXhFlp+/Dw0LeBz/DqLV4/ZkEQmY9dnPaH0ZuxoAI+w37qJrJJmmPgjeVt8ALbZsP7nXNBrjBVjyuwsqBSLwRnPfpbx1gZg5qArO9HVh2xZMnsjzWzqU+bTXTLWzGsdKkmL9XGkXhbhfpzsh3M0cfx0LGlhk/bCsDfjcrUQTxMrDZa+8w5pP8o26CAQr/jPA8Gg8C/0AoUmmiafbRaqo2NcQ7jpaWl1JxaOmvaPu4b8gvNf5elOeIzKpA+//zzng/pHjjtvbYM3zEcDoNAI+asUz6pPDHrwCSpdpoBazYPHy0sfJaWmMXFRSwsLATrgWmb1Hw8PT2N1dVVzM/PB4CC/NCOHfcJBR2+i3cbc30TUBFEKF9dXV2NAuyYNYZBLDQ7qxWHdXN8CUgUZOm+s/0BQg0b29LpdLyFRXPPWu2sPWs0WIEuOZavxbT3SnTfsftYA/Xsd4uLi9HMA/Y96ivMjBkky7f5jK5n/d4qV/R/BgbdT9mg/OpR86cc8L1GIkPiRtKF5NzIWVYlO07s3t4eDg8PgwmsVCpYXV1FvV4PNmS5XPY3anAx8eqjnZ2dIDEzME47oiCLEprdCCynt1SQrGaSCzOWs29ubs5vDE3JElug586dC6RLW0Zz6/GaOSB9r+FwOIxG++n3QDoHWbPZTIEnq+KPkQJiqx0ixUCJZVrWZ4NkfVuUFPCoScv6/bCNLNNutwPhARhneqfDvjUl8ABSbQLrJJBX53p+F9MCAGNtJXP+cc+oVsje98oyHLdut4tOp4MXX3wxNY4KQOzzerjYO4pjmq2zRpwfgiFrCoulMCJgsNdZEfyoSZfr39arvEB5C9+l8xLLmcY9adNAcR0z/ZXySQYHsX7WQWGDwJJ8hKl7dN8VCiNfWU2gOxwOvQuO8r5CoYCVlRWsrKwEfsaDwcAL/7r3lR/qeqVPLAX+l19+2fdVfRVZl729Scfs6OjI+zLqmPf7fT/OWa4RtVrNB7Pwc93rNp0UAB+la+ukwkHbQfBnXVdOExxOo5irjxW8VYimn3nsGf3/zp07/m+7roEQnPJvTTWj2S2AUGOnWtPDw0P/XcxkbvvyOHhUDvheI6kJhBom/nAz88BVkyyzuQOhBor3OKrWRDVKKnHx0LZ1MMGnRj7+3u/9HgAEfjL6nII6JQtgyIAtmFOfBefG/l78TOuxTrzq+zIcDgMNnzXb2DZqxJcyZD0IYgdaTPV/murc1mVNK7qR7XPW7KR12QOBRHOOtjnmDKzMRdcLicKGHpo87DR6lp8TiKkTN+eH65n/l8vl4PAkI2efVUCgEMK1QRcIkua6UjCrfen1egFjtcxYfRFjGqjnn38+GM/YujjLVCgUUqCXZNeoTRuxu7ubMmUp6Ii5c8TewWdjZlrySRUiKCBYUk2j1XbfvHkz5cOnvIlrvNPpYH9/3wfQxbQyXI+NRgPb29uBUENexTRTLEsTMtvI6PbNzU2vCdIxVB7e748TXts0QiqYWtOr3TsEvhxDAttWq5XyhyMvU00855ZuHdZEzN9suwr8urds2pwkSQIQRj5t14tat2LCuA3qsBpCPsd0KbavOoaqRXXO4bOf/WxQ3ga42PclSeh3bvdCbP02Gg10u91ooGeW7/zjoBzwPSTxALPq2ZmZmaj0Ozc359XZqsVYX1/3PiFciL1eD7u7uwGAcm7kB0g1tfrwKTOx6mseuGp+oF+W3XiUzK1/gW5sEoEFN4ANOlEgpNI3GZK2WyP4mKKGZPsSc7gG0rmSLJhiAIRl9JaxWAZrTSH6jiSJ31fK5+0Bxe9iZgtqOTWHGc3xlkFwPph6R8eIWjK+U03sFBq0LH/TBGbTR1CDMBgMAqBIJ//Dw8PgYOAhpBGbBMaqWalWq4HErge9Mmidb/oA6hrSebE53PT7mNnFgumzQDq3x8fHgelJ9xHHRPevmtyZJkkPWv07655eCq4xsxxJrQj2YFN+A4xdJCh46txR+8w7xoHQh48gTIUuujTo+9vtNra2tnwePtatPq1c89vb2/62I44n05hooBSBoBWsFTQxmpjmaPZBx92CD7aPf2tOVuUddPOJCevAOGjDWj44BtYUzPXCPp47dy6VYkRBI8kKpAsLC9H2WOBmSZUGsfVD8EZtKcuxDfqb5fmZuoFYsBlbo865VFS4CvXKa3RseLOWWk3Ylli/Y8D49VIO+F4jqSTTbDaDWzW4UXhvoy6ESqXiL05Wpqxq8Fiwgh6C3W7XS5wKoPR2D7bvypUrAEJn15hkpKQMg9+TgQPhhtHNpAcuF3cMtCnYUSlQ+6s3jFhw6JwLUrjMzc2lDpYYOCPo5d8WEJ4GyBRIZ21ABTJ6hy1Jx4JrZTgcBhqvmGM1GYMFbxQonnnmmRRoKRQKwVU/Crp40KnJ286d+gUqcOVc6FgNBoPAtWE4HEdp2xQvlqh5soxOBRbr47S8vJyaH2Wc1ueT82CFsrMG8pSsUKYHoO3/cDgM9rwe1BRAVJOl/CNmkrXaEK4XGwHMvUirhF3Ddj9y7dloS8737OwsVldX/TMq5LIO8mUK3tpOLQuMeF6tVkO9XvcBFVxzk5OT3iKjWjJdtxTIFxYWcO7cOb+HOZY2VQpdNLJ4c5ZgMhwOg+TJem0i+6zaLLt3aY1SUElNFIP+dK4V6BC4xPau3mFr233p0qWUK47OMT+z64tBaNRGW+GN7VNNr73BSknfpT58vFrNBrbZNivxBiPOkz3b2O5yuYzZ2dmAv3F87XkVe8+joBzwPSSVy2UfCq8LwoJAYDSh+/v7PvkiJ3JychJra2tYWloKGFq5XMby8nIgKTvn0G63veZPpV3rL5gkSeCXoAc1JX9Kcrqo1IdPD/CYNK/5h9gGZS4qASkzBtI+ETEfPh4y1hcoaxMoOLFEHz71X4oxI7sJrQZQN70FJqSVlZUoQ9N3kFR7ZTWVbKsyEastuHTpUmoOmTSWkcnAGMwxStdGz1L6LJVKPtWLAio9mHnIcb2qFsSCZL3LlGV1nLTvCmYV7Okzn//5n58af10f9IlSUsBo5+JxMNQnhXjAaCCGHiR2HGI+fFbbpkKCPUQJsjSVjzXPK6nbih6+1ETrc3yfascoIPPmGdVkUlvJFBhAeL2aHvA0N9NnmnUzhQvXE9u5sLDgzZnKH+nDx/cD4xQu7Cf7zHI0u7700ksAxsDP9jcmqOu5QIBpb8ygDxuTJLO8ag71XFBlBt1lLIgnoLKClfK12P3tpKeffjol9CtAsqCJtLS0FHXbUV7hnAtMrVevXg0sYTFNq+WfFy5cSKVZsaDWfjY1NeX9LK0rlgonaiVhXVZra/9/1JQDvoekXq/nTW42OpVMQBdYr9dLHUaMDtM6nHOp5KPAaOHQt0IXk3PO+1uoxMXrbNrtdirRsj4fM4XqQlcnaq2Dm1oXp2p4bNv13SxDpvORj3zEf6/+McrkCP70mh5N4WKZCBBe76bgzDITZSJ2TLIif/WwUgB18eJFb1ZV0GQDSZIkCbQePECU6R4dHfm6dBz0vlxrfqf0a7WjSZL4S91jTMde18ffvHOT4G04HDuBc+w1px5BAtN8qHZYwRc1tZa5K+McDAZBgEq9Xg+YoiXrD2rpNE3SWSKu4VKpFJgJ9TuOo2ruLH/SdawafQsYSdaExr9tqhXmbbTRsbE51bWgYAkYJ3q+e/duoNkhcFR/VLoz7O/vo9VqBe2n3x3HhhrJvb09v/5Yz87ODhqNhl/7KkCTf3a7XXS7XWxvb2NjYyPYG+QFJPVb1aTy7EcsQlfbQ9caTXhN4h3rzA5hgYtao+w7NbG/nmH37t3z5Sz4VNcQHV/V9muWBbtOTnMDoEaRZWNaNOdCl59XXnnFp6bR51Sp4ZwLhJFLly4FPo2xNelcmLtPSQGy3WfM1ahnjAWG5LFcw4+aR+WA7yHIOYednR3s7+8HmjxgnIfPJpCt1+tYXFwMFtDm5iauX7/uffiUOelduqSpqSmvptakvKri5m+bioSkB7I1galZmXVl+eQcHR2lkvLq4aD9ZE5BHQ/+di70u+D9v9oXBcMf//jHg3psGhElRr7Ozs4GPnxWWrbaDj3ULCBW4nMKzs+fPx+MXwxk8v/YNWAqeVuzM9cDNRQHBweBf+VwOPTX8rFu1cDaoAj21Zp97LjQyV77pOtK/Wu0DufGOcfs2uIho4caDwyOTa/XCw5y6yMFhM7efN7O0Wlg4iwSBbWjo6MgyIn7WYURrsVisRjNIUkgoxpnACmBVOu3eRuB0LFd82VaDb7lNZqmx5alkLKyshLsu8FgdNML9wm1YIVCAdPT04GfNbV5Ozs73txGjffc3JzP78cx2NvbCwAfy9KHj/tM26+8WXkVn71w4QKAsRCkQCtmsdD5IR/WADbuKdXIA+nsBUzboqm7uGdZXkmFAmpPlVeQ/2hgF4ELyT6jgMiWUd9uG/in79T1okDsS77kS6LjZhUQ+szy8rIXQKx2U3ng3t6ef8byVfsutlPvVtazPcabgPT4Pwo6mxzvMRMnkBdWa+QkAY+aR4Ex07GbHkDqwCUjUHJuZNLd399PSebtdjuVh48mM6Ym4Ht1oVnfAWqArHo55mejSXRJ6q+g/VOtJPuS5Q+nufVIujFUU0C/DmWMMYmIACGmfcwiHS9br2o1B4OBN8kACDRhfE6lSo6T/gbG/niqJeGBxjFgP3nYEhzpfJH56AFJ5nt0dOS1g2SqHBN7ny41L0zz0ul0fB48HmpsB/ul5mimj+F6s8lcFVzoc/Qp0uhHkmpALCNWZmzJ+i9pHWeNrACnicr1YKXgpwdwzNeJz/G31eoocZ5VYOOa1XlkMAPTxrANygO1TgWq9n3OOdRqNS9M67NaniC0VqsFpl4SzaLAONfe7Oys38+sjzcjxe7AZTlqNOv1unfx4Nhz/6pWjGZIGxHMd2YBAu5bjq/VBna73SASWoUfFdQpUHNN1Go1P04KBPUMOH/+fOATrCBPwY9zLuhX7Fxjf5XHOefwu7/7u0E5W5ZEDWehUEhp+C34VS0x14q1tFh3LFsfxzZrTuw5w7O73W4HSZrVcmPPx9POyNdDOeB7SCoUCpibm0tp89bX1/3VanqQ7e/vY29vL1gQpVIJq6urOHfuXMDQ6EtFJqVSNc2YvN6HZDV8lI50cas0FfPLU/8pbhCCALuI19bWgraxXm58XagXLlxIaaIswCXxmjnLvPUdJO2DlYadc7h58yaAkdRHtb3VUNlNpUzFHm7aJmUeqqG0GjSCJ5L6vyk999xzgaaU86E3hKjGExiBeutropGBeqgQoNmr9uy46u8kSXB4eOjXHaVslmcQEedLDzReZcXDzmrfNAGr7hMy7th4UhNiGaQCaDuuHM+YtPw4JOg3m3TPlMtlH3QAhOvXCgUxHz5gHMgFhHsl5sNnTVF6SGu7NGefbbvlTQpQCUh0j3Y6Hdy4cQN3794N9ke5XPbCLvcg12Sz2QzADgMsmBaF5rft7W1/NZr6pTIJOfd3p9MJ3EtUIcAxtbyD49Xtdn3qIQIPFbKzcngqKNCgJX5H/8Hj42MPTpSnUeNEfsDv1BWD16fp+/muZ599NriOLtYetkl5tvprcyyslp+k2mm6AVjSaGYCKyXrrqNrhzxOn4lZjKwQYrWW1B7bd3Lc+M6JiQm/zjjO+g47Jo+D4iJdTqcSNz8PQI0EolbDRrVZrRwwVskD4QamD4k1j83MzAQ5+7iwmOJCNXHr6+teQ2IZa5bUoAeABXL2OetPo/2z3zG1h0pZqo7XxU4zeYwIQEiqIreMxbnxpd2xbP5WyrMU27RWm6Zt1udUwwekbx1R0z1Jgz304LLMExhrVzVSzmoh1E9FBQk1uet42EhD+gP2ej2USiUMBuP0Fso0nXNYWFhIAWd+x7ossH/22WeDQ5B1q4nRmhnf+c53+vHRPlSr1UBrEqOYyeUsavjseGrQhkZ86+HHZLhZKYZiFPMt04hVfb5QCP1cK5WKf5+mDbFaJG2zmsKsBpB+WxbMUujm8845NJvNIKqegsvh4WEQyNbpdHB4eJjSUu/u7vq6LT9jvzmOOzs7nhfoXqD1hzyRBz+vs2NfYppOjhN/E5yRJ3Aue71eNJMCiVp7mzqGgS2cJxKfp+Cs/rk6V4VCATMzM9GIeZLyfOVD1jKkptaLFy+m+qHzzfHRuQXGZn991kZl24Aijbq1gkuMGCDHfmk/9NlisegDaVThoOvD9u1RUw74XiNxAw+HwyBnHCdoamoq0HLxcOIl87rYh8Mhtre3/eGvgI8RvWoa5Mam+UGjwayEQHW8vedPpcZSqRRsEAVrZGAEHVYbQmlUtUtUh9uyTOJK0jGwgI8mCj2QYho2AN73hGOplCSJzyVWq9W86YjfKZDWQ9LWo58p4+C8Wol2bW0tdSjRRMDx5Y9lNKodoRY3drBm3TbCMeI1T5VKBd1u17eTjInpgThXw+HQaxNnZmbwyiuv+PmkpF+tVv09kBwLgi3mA9Q2TUxM+Gg3MlhtNwGfals5pwres7RwfFb3GQ/q+1FM63QWSA8Nao+sSVfLkigsWu0LkE44ToqBQwU1VkMSKx8DBTaoQX3tlB/yPc45rK6u+nuluXe63W4QnEHhnBG2ysc7nQ729vYCF4VqteqDhBQQtVqtIHUR3W8mJyf95+SbvJLQjj37wDLcPzRLq0CqQWmsW0Eh92i5XPagj+1lXwnArJDLsdLPj46O0Gg0AiCmcx9Lr6NUKBT8Hs4CSro+lA+rsAcgEO7f9a53pfgvx1nPCmtqpfBr309gZoVzYBzsolpgjqsVorU8hWT7Hj7LdanjbjWHeh6dBjIflnKT7kMQFykXl02QbLUZVgWspNezsV4yXytBtVotn5ZFE2YeHR0FPnw83AEEqTGU1GdC22I1YVmLjtF//F4Zkn1mcXExBTp102h5pmWxBwY3jkqdZGQEzMrIgHEKAd4iYfMc2nZy7O04ZUmr3KDqUPwH/+Af9HVnmRrZlxjgsyaXmNnLRh/rPGlOSJpBOCeM5lZTgkqa9OFUBkrn9Vqt5jUz7BefnZubC95DNweVlE8jy+zV/H8/AKcCCl0nlHhAWsbKZ88S6LPAjBop+72SHjAPMhZWCFOiL7P14+JaJlGTRu1LLL2T1gmE2mltK/9X/2iuH6uhcs75+6St3xoDu7ROBjXw+WKxiIWFBaytreHKlSsBaNQ0WQRg09PTQXCDllU3Bib+tUnltS069rqfaD2xbhrOjRO3q1ZJ548RunoGcS/GXHO0jRokwvrYN7VixChrDVlAp+BNrxq165V9K5VKAUgEEJilta3qm2hJ+Tnbq3zSWpPYFm0bEObQTZKR6Xhvby/gWXYclPc+Dt6Ua/gegshU6vW69/vgJLVaLW8KI/V6Pdy+fTvIPcZFtLy87NXvrEf9J5TBMSWHZRoW0A2HQ89EtB1kUPzMbmib949ANRYefv78+WDDWGlRaXV1NVjUZAgKHkgavWmJJgeSdVS2ktqLL76I1dXVKHCKMc8YMGc7lIlZ8KBSL6P6gDH4VRO8mrG174zQix12rIeHBq/pm5ycDDQ6xWLR+9vp+iADj92kwsOZ2kSCRTU1qG+nMjY1p9txZRkeolngTQ9CAN6Hj+4IClhYXjWhOo4PquHT9p1FUkFEE5WrUGn7fnx8HE1pYw9hzm+W5tUKvkDaVKeWiSzXFyVdU5xvgpxOp4ObN2+mtFelUinYH1znjUbD8w0KeMVi0Sfc1bW3vb3tr2FjO1Qo4cFNbaIqAIrFIg4PD72lhmT5YLfb9f7WDALkO+yY2/EA4O/lPT4+Dnin8mMFcVonNXEUnFmO5lrmsFMeQ4FqamoqOH/4twZaKeCxa0QBoa5LJb2S7TTS9fGg+5/EeVSyLkyqheT/KsDQNK7ng9XWAUhpnbXPtqz9/lFRruF7jcSJVMBgGaEeztQE8uJkknPOayQ0fQAwkp4ajUZKSpqdncXCwkIQKJIkSXANDqnRaHjgqVFaQNpfiqRaMG0P+6Kk+fKAEKhYhk1/REvqX0Pa29vL1IY6F0Z8MU+dMmKdDzLAyclJn6cwS9uj/VXS6F7bB/r1MDjkNNK5sQcgALzwwgvBd5RCdZ61TZqygN9zLBlZq5qs4XDopfJOp5PSOhwdHXm/Hj4DjOa50+n4+QbgpfijoyP88i//cuo9XEdHR0fBgXt0dITr169Hx5i/FdAz0tdSFhOMCRsxUqD4OMwmTwLFzEJ6MFvwFztcdG/aQyq2RzUliwUqqjWZnJz0Zl7rN6yacQD+2jGCF2viJNgZDofBLTcarKDP850qtHS7Xezt7QU8jZ+rUDwYDLCzs4PNzc2o7zX5Dd/FFFM0tbJfOk4EmwBSqWVUsNEfBRY0lVtrAQB/j67mf+O8E/yVy+VURonp6WlUq1XMz8+ngL1qutjfLC1x1rqyxPbYspqSifMac91R3mzX5Vd/9Vf7NmY9Y/kGr0zVtnH82AZ9z2lpWbTth4eH3r1IrUav9Ux6PZQDvocgbsS9vb3Af44gg6Ys1ZbNzMz4GzX0sOVl3jTTAvCXXltfQGAMKMkwqNXQK3Kcc36zqvmO31n1vu2b/lCa1eg4ACnVufr6WSahjFSBmWqu9P0kBT9sux4GNr2ClZio3bDJqrVsTIJWUi1rbIMOh0OfjDQ2jkBaYoyZw2yfnRv54mniUIIUnVcrhQNxjRt9utgWCyTJwGga50FDd4FWq+WvTFKGNzc35w8/rlf+VvBIwHfr1q2gvxR8dKzup0XStaNzYoNcAARaHv0dA/BnhTgm3W43WJtWyLNmPJuHD0ibmHSeHkSbwnFW8EXARaBhzbS6PzT1kj2YyUfOnTvnb3AgqOV6Y5vpo1qv1zE/Px+sG96UQf7LtCpLS0sp/1T1MWZb6S/LPakBR8vLy57/6F7juKrvndXQxcCS5ZcU9mlGVj7A92hSaOV/zF5AE7u6oCRJ4jWAyoN1fyn/VX7KQCGdD6Us9xir9bPCnlpHFOBxvmP1/sIv/IKfV3X9yAJvLGfba+dD54DuVzGtLP9OkpFlZGlpybtZ6S0gbxTlgO81ktXSAeO8YzwoYxGNnU7Ha6S4sAuFAur1ul8AuuHq9brfbFxozWbTX9xNDQ4l3Fj+IwA+uEPBlbZXyUZ3AvHoRmCUIiN2IMfKX716NXi/BV+66FdWVoJkvbq5eaiT6HPI71iO40ttErVXWdpL/o5JojyYOJ8x7YV1SFftifokafvUtB4jgnrNU8YxnJyc9HfRTk5OpsZxZmbGB0ywDwACnymbXysG1NlW+qwwaMPuAXuvps6x9RvScWO/tIyCNns1mJ0XXePaHiVNW5PlAnEWiX3WNaZCly1LYKCkh5WlmDCpPnysl+/SPUKhQoU/bYfuU4IVWjW4RtUPihG2JA02irVf9yP3Wb1eTwmGMY3x/Pw8zp0750EatYk06+n7VUPP9xJgsO5ut+uvwVStve5L/m/H1LmRu8XMzAwmJyf9mcHnFFDYPataTKb44v5guqUYv1T+rrctsUyhUPCZJDgWdhxjVgt+rq4W9iyx/CxLYaGkAnvWnrfP8ApKfqcgToVN0urqaiBYqjCqe4i+onqTRxZZ5cajotyH7zWQMg86w05MTASmBuZ7osaNTKzT6Xj/By6iQqEQXNasPlfUpuiCiTn+q1pewc/du3cBjH0KVULJ8imxmhAyBU1vQOIVObrhNSeSEu98JcUOHW1DDDTy3WrSPTg4CPxStI4kGd8nrGDYlrOSmLaPY6XSLedN84NZJ1+Vdu37lbI+oxSrAkJsLIAwATaZ28zMjDfxK+OhxpD5yQjm+v2+B46aB49ri9GKPLTVN3FpaQnD4TCI0OZBNzk56bVGzo203zRfASPQwOh0Mns9JNlmpdgBphoiC771wLUA5iyCPh2fiYkJLxgA49RFBDrWJcPmMOO8WL4QA8xcT6yL6z+27+j3TCFMy1BYJqmvpm0DwdPdu3d9ShOdWwWJBI67u7te26hAgGY2vpN5+GzUpwV3qk3kZwRSzWYTm5ubUeBEX8HhcBzQoqDCaumzeOJgMPC8n5o+9pfav/n5eUxMTARpZyz41/nRZM6W/1gzOduh9bJ/WYBFtWEKqHTfcy0oqYCv7yXviL1vfn4+SJlCIg+jlUxpYWHBAzg9q62SgEQtrrZfeaiCZU3zE6tL+/Y4KNfwvQbKkhSBUCLTzcpnqtVqYDLhZt3Y2EilYOn3+9je3k6Fbs/NzWFxcTGVoR5Ayg9D/WNiUo+alkl6LU7MvKJMYXt7O5qyIbZQd3Z2Uj5+6oei3+3s7ET9g7iJ9Du7aay5iibdubm54HJx1hXT6tl+UJOg77Og0WaPV+1FzCQeu/dS+0TGaaVs1t1qtXyyVm0HME5eDKTv56XUb9cO57hYLPqr+9SPcGZmxmff59ixb1NTU1hbWwuYNu/opIaSddtoZx1HPWwJoKk9sf3UA1D3TczMaE28VvA4a2QFuxiPUtID2o4VBT4LDPi53jBD4YFAMwYISeSDOncq1Fq3DSCtPdT+WG0e143mMuX7YmPQ6/Wws7PjD2O2N8bLG40Gtre3Ax8+rnm9+k99FHUMLEAplUrebPyFX/iFAU/MArra/06n43+Ur1AQ1ghk65ZTqVRQq9W86ZbP8lpPBXRqTSLZ++I5T/TdjfFLJfs5x5vn2MHBQfA9gZsV1LlmVJtP0oAU1dDZYCSlWq12qmXCWkL4XvJUDUrk+HFsefkCwX5MAZEFLB8F5YDvIYiL+eDgIHULBf05NLIRQOAEq5oGBnRwg8VAEIk3GOjzlJTpn8WNSfW+3lxBUmlIF5WmIOBvMnArPVm/NKulUbL/sw2xzUbNJjeQvtfWzQAWlaCUeLBYYKsM9TSwynnWDaxAVbW39jlqKsiQ1NfufpolPmP7w/d3Oh1/uweZhj3oer1ekOCWYIjuADoWzjmfH0oZunPjmzsGg4GXhtUnaHt7OzUm/F/XKt+/tbWV6q8CE76feyN2VZJlpnowHR8fpzL0a3/0d5aP4FmhXq8XDe6xhx8w1sDasjqndj1Sg05SkGJNtaoZ6na7XlA6LT0GMJ6/mPaGz9TrdVy6dMnzC+sKAozNrAsLCz4/nfZNrRjkeQsLC8H95/yOuVDZBhXs+C6mZTl37pzP6aYCvfI3rnkVxKx7TswqolpSBlbpuUG3H+5hy98qlYq/W1hNmOSzTOei77NtsP+zLfdTAgDpawBtXrrYvdix+hRcZmVZsO86jdTXWfmZrZNEC591KWJZ/Zta1lg99xuvR0G5Sfc1kgVIak6iBBTzp2PmdivxLS8vo9VqBWlZnHOBpoWbtd1uo9FoeOZJKY6awJjqXaUUtlPNEUrqw8fyqmXRvj/77LMpZhDTmAHAlStXgrQkrDdWdm1tzYM4lqUpyjKEGBPTttCH7+DgIIhW5rNWUtTnbX/1AFGwwUhrJR07llG/FfXVVFIgcnx87E1S6rvE+ij9KvDnd0zdY+tlehpNU6NpWWjeVW3h0dERms2mvzpKD/9+v4/bt29jdXXVt42AslgselMVP+90Oj5dkBLrYhl+RveI2PzqHKjwUCiEwQfqA5jFYM8qWTMWkI4A5BrMCsJQ4GaBok1BxPUey8WnBzHnJKZ9tCZdBodlBU4lSYK9vT2vlWJZAhbr43t0dOSFG/JbuiYwb+VwOPSCuNUo1mo173jPvuo1ZspfuBcUPKgQSWGRe2JjYyMAXCrI6tlgNWoAUnulUCj4oAy90lP5HNurfm6cH95PbIPC9B3UhJFvsF0WLAHA9evX8dRTT/n5UYGb/ISgm89bn1J7xvFZm4VCiVaSGC+3Eegkrg8dKz17KFj2ej0fDGTPjJgAw8T29K9Ws7b26XFSruF7SCoUCj7DtoIZbjyr7u71ej7cWz+nv56NMIyZsmiWVObHz60j6NbWFpIkCaJ9WQ8QT6qsEcfcTFZ6IWkmctZHUJaVHsOCvBjoYyoDtsH+Vk0Bo5ttHRwHHhY8oGIMk9/HPmMbNT+h9SGJ+T7xeT0Ata/30/Lxu8nJySBTvDpk89DU1AikYrEY+CSxf5T4YwCezM+akhhsxIOSY8LvCTytgHB8fBykZWHdOlZ6GCvzsz6ESrrGYwE0xWLR+5cCCP7OEhDOKpVKpSAPn/oYcRx0v8cAnxWI9Lms2zZin+nnFy5cCIBh1mGtz2oEuPaBqVLu3r2b0sZomymI7+/ve99fJdbFvjFVS7PZTAHQ3d3dYB0Ph+O8qAR6x8fHODg4wNbWVsB71ZRr+RvnKgYaVIuv40UQSo2immbJd9rtdurc4efMCKFtJL+z98QDoQ/f7OxsygIDwGtAle/aYDs79koqjN6PNPLWKgRI1jKj/8eIypfTyigPvHjxYpAWzc4bqdFoYGFhwVvdLG/T9wOnZ3N4WMoB30MSNW66yGgWiR22U1NTmJ+fDxhokiTY2tryt2eoxHFwcBA1IWqQhzIBMhI9NO1GU+lcJUi+14bvq7nCLr67d+9GNw7LZ21iezjbssw/yLIKCuxhYg9/20ZqnpggO7YhrXSlTJgHAE0t1oeE7bMHHyVmlUCtZKqgimQZBc2oQOhrVa1W/eGgWha2Z3Z2FtPT08HNAWTkdDlgYm89gJiZn+/me/mZ3sSiZqy1tbWgjTof1BAkSeIz+5M0LYdtI8fdBm1o/XYeOaZ6GD/zzDMpwJEF/M8KsV/9fj+VPkndG5RvUUiwRK2Q8iCCtVdeecWXs4DBzo9S7D18h93HBOzkNaoZ4lqcnZ3FuXPn/F4ZDAZewCbfYVkVjjkGnU4H29vbPokx9/rU1BSmp6cDcHl4eIhms+ndFYDRWpycnPR+hEo20MDOBa9wA0ZWEx13jnOM+J5SqYT5+XlMTk5iaWkpMDXzLNKAPhKFZyYD1vnrdrs+ubvlUQqUNQ2Xtmdubi64+SRJEn/1HTBO52LPQsvLbZvJh9X6Y8fCkro0WcHCCjH6jPp7k6wvIHnZc889583iei7Y9zCozUZPW/5kFR6PknLA95CkPlEK1nh4KiBzbhQFpncMqqO8biJOso2OS5JR1Kl1RAbg/TdU20UN0MzMjH9fliTNjaLXsOnCVTBJojO1fqaOsHazKoNVYGrrYI4sfXesrSzL8VQJj+PDwAaac2Oax9M0bVbSstoOts8eYDwcshgKEB5g+j49VNvtduCbGTPF612dHLdyuRw4YnPMJicnMT097R219dBkChRlRpRCae46f/58IDCUSqO7nVdXV1PBGbzGbWZmxreP7SJVq1XfF0198yB+W0pcdwTXr776qv+OgNGCeaspOUukB0zstgLd3zp21qfRaoWAMMBHwcj09HQgAFpBVcsShGYlao5ZAbjurNDG9bKwsOC/132q2vFisYi5uTnP55RU+GQ9MzMzmJmZCQAmfbHtGlVNqfrwMWURy6jpklpHjgfvVdcxV77LcdAftUDwDm2WpeBHQJgl+Oh4sp3dbje4K55llPfQV4/8Sa1aynuSZJw1AkBq/NlHCuU2uESfsxpFFTBigsTKykrg5638ywblkJiiR/tg+275hvq5qyVGx3k4HPqbg4gZYkBU3/Go+VMO+B6SGBmmqToA+CTIaqqgxm53dzcAJc45LC8vY2VlJRU1RClJFySZA00GyhQoFXFh03/KRoSqv5RdULF7fWP+OADw/PPPpxyzWS6mCtfNaH3BlOi7qG21TrAkzUUIpCU2+vDRZ+00jZqSNf/a9uq8EizFyGqurFZpOBwGAQYkvotriX3ks61WK3CYZz8IIHnTBude16emblAaDke5ztRkphrDqakp1Ov1FOMkEdjxc2oW9KChNoVEwKfzokC53++nzOVM6aFSMMeH+0IjmOmXpeNkD86zSmr6Jykw4f/8OxZ8pPxNBVhGdJMIgKwWzr5X61Z+QeKz9N9SHz4FdLo3t7e3cfPmTV8fQQOFXWCcteDg4ADNZjNYEwzm4D5mWpVGo+HvjlVQQpMl9yl9XWnuZN+73S729/cDv1jb/+PjY/+Ovb29lOZHhU2tg+/o9XpotVpot9uBpo51UOiiuVeF92q16iPwbXCTFUb5Tq2fGkDlzf1+34+vrgHVBlvlgf2c68u+X/ms8kOdG0uM0lXtGxB3B9H3cKws+Nb3661B9n5k5VEcG7of0EfQulkpKY96lJQDvockPdTUx4sTG0tnYMPck2R8k4FliNTk6QE8OTmJ2dnZQLUNjO+NtM8zgaatWzc9fQ8BBNdnWdBnGfj+/n5qQZN5xwCfSorWD07bt7OzE9z2YEnNp9Y/ke225uzJyclAArPgMUuK4obPyi/IvsYS1vI3DxSCaUqXCsCUVOvGv+07j46O/A0KVv1PPyVGdKsGglctaRSdOo/zwFKQpI7U1hmbDCw2hqxTAXK3241qnFgXQS6FIZrllDSKW9uidWlkr5qSYm08ixo+UrFYDEziNuGtFWDsfrMCigLmwWDggQo/456z5jruAdLS0lIgINq9pYe3XlcWA4dc11wnrEuDl5Qnt1otzxN1f+/v7wfCMcvSzKuC+8bGht9DOp4qqPB2mpiLymmmQR1vXdtZvKrX66HZbKLT6WBvby941+bmpm+HAgj2heBV83YC8ABQTZtAOrdezFQ8GAw88FTefO3atdS8KSmf5e9YDkQtT2KfbJ0AAl5nxy5WHhhfS8p6rV8z17eCWJv5wAJhjj2D8WzfLT0uQTQHfA9JlBZtVC6vVrOpTGZnZ7G4uBhsdh6YOzs7AcPloW4dlCmxqQ8fMFYPA+PFRZOaZo9nXWqmTZIkYNx6AJBRU9Wu9fCZmLNvzH/HRkTpgaAbTze5tpVjphtLtUcx8x/NnfQZsVHFOl76tx4SlgEr8WCwWijLYPQdHAsejpoYl33gb2qRrRmLJhdgHDyjRMDM1C5kejQRqyaAzJXaxE6n480wfHZ7e9v7LgHj+4V7vZ53lqcmkqYquhmo2YeRjzq/7I+2R82CFoTEXBOsL6cy1BdeeCG1FmMg5qyQ9ocRmvZ7C8b4dyyfJOcjljtTU77wgFRQo3sy5k5hff3I47QPc3NzANLglOu7UBhlOrh8+bLXqHPd6F3bNB/TzKnuEwR3FCaotZ+fn/d+19zDXPsKPpj4npG7wFghwMS/umYtzyTgUw26WmRIlsdZ3mcBDP0Sd3d3/TmlvrnUSum4OzcKdpuamvIJhVXbZYG7TWzOuhUcOueCM8byfI6NAm7yHiWbu04FTwJ6S/Yed32G77HgjRo4JeXlXDM0zwLhjR6akkvnjACbfoUxlyWrxX3UlAO+10FZ+dmAsS+fgjsgZLKFQsFfVK2Lo1AoeHMxMJ74TqeDRqORSjHCdBb2M2CcakW1W9pmAD7xJ+8EtIsudig+88wzvj/aryw1tI6FkmoYgVGgRcxEyrFVwGCj0rQsMI4gZVqWmGRp1fwx0kSpMRNLTIOqBxMZEg+OLN8NZeDUctDEbQ9TglnmCVNtDE2pNLOqSVrXomUosXnj2tK0D8pYqRXWQ0cZsK5r29/YeA+HwyCC0ZppaNLl/rLt1rEBgM///M/P7KfVYJ0Fsv2JRZAD8f0aGwsFAvpcoRCmv+GcUfi0aTcU8DFbgfoz615S7Q7bHwOH2mcb5e2cC/xY+ZzevqEa99nZ2cDfmc/YoLdarYaVlZXAVK5uL9zvFJQ1CTV5rgq/NIECwM2bN6Oa0RiP0zbNzc1hYmLC829+Tt6dlRev3W6j3W57szP5w/7+vuczlm8rb9ZrMHW98EYfnVstw+he5RnKL7NMnRwHnXs9e7M0dpbse5IkCQQj7VOMJ7I/aklgcA/bGQNzk5OTmJ+f936dsSCTLKD5qCgHfK+ROKlkEtTkcWEz67ne3eqcQ7vd9rmiVMs1NzcX5Nwj0dfKLhr6oqi0Rs2fMk6CHUYS6+JW85w9LJXIkGImZ+tAy7J60CvZaFy76UjKbBWc6nMkm9dQSTekamA5/jFwahkN36XRrrbPMX8UK3Grz6T6QgHAZz7zmeBZnSeOox6y1D4QpK+trQXrqVgcJdvm9UDabvo12YhlPfR4Q4YefEyQq+BLo3T1YGAbuXYZDcxnCEIBBICBz1kzpDV5K5jTsVftiWpf7PP/JREjJknWFG5BQAwc6oFtgZ/OJYG4kpbVfUPfv263m9LKkD/RD1OvUrR1k9dsbm4GPnxstx7c5GG7u7s+1Qr3I4AgWIka9N3d3UAzpe1TTRnz7dH8C4yvZ+PNQToG3C+FQsFrInV+uI9PAx1KDCJZWVkJTLOMxi+Xy56HKf9hm4+OjgJeS1cKmjatGV1JBWH2cWpqyo9nzDVldXU1tZ7IM/gTs9jwSkjLt60Lj9Ly8nIqAJK/FZTp+5hmBQh5vHVV0LWr9wezPgJojner1YJzY036/cDc/eb9YSgHfK+RlEFSa6NAgAeganeAsWTJZ2lGu3Hjhpd4lWL+aVNTU/4gVwmBh7EuSDoe09QGjNXOuqmcc/7Whpi/X5bGzgKxrHEixQBmjHilHNtrJSWtV827WkYZD4DgaiGVEvXAs4daFlPRQ5Nts6YwSnu2Tfo+Hhqf/vSnU+PGg+vo6ChYGwogOZ56abeOjwI3BXwMwNADgOOigE/7t7i4iOnpaSwsLPh+8fo01kOzFZ/hdxrMwcSjJP1bNQnaNruOYgCO+5AAIHabBylrLZ0Vsv3TQ9BqWmPr35INjtEx297eDsrpWrIRitquxcXFlEZH91ypVPKuDgSsXPPWp03Xm9Uy03dN94+1WHDNMLeellUzHffl/v6+N5Wyf+yv3lZh3RFU+8N2J0ni9yQwAhqxcVc+61xoDiWvIB9SkFur1bxZWoM2OM5ss9WcEvTRT1s1tLu7u94Pl+ZS66/W6/VSgE1ddTQ9mQrfGqkfA3xMWGxBmwJFu4affvrpwK0pxmPs2r906VJwB3jMPGv7FPteyTmHjY0NbG5u+ruKNUAt64x91JQDvtdInBgyFPpGUIPDg05zjAGjzVev14MFnSQJms2mTwSqqmmaysiwuNHJRFSa6PV6HtixLA91pjmhxKE+EKxT855pP1XStIevXaAWWGQtfgUFNocTMAIwejBpW+ymqtfrmSHzupmsWUY1bPp3lkaRqVFiPiR23ACkcjhZbYOS3tJhVfyUwFWqJpCn/5Q11wyH4yv/CIh1zXL8mBqBfY2Za1kfg17oe6KHrt7bq+uD/1PS54GtfjkWKGs5/m19cChMqTZHD3H2P4vOIshT4lhwbhSUKbDi/rNWBUvWbUU/13G2QRd2j+i6oqZDzat8js+wLTYYTd/B9s/NzeHy5cuBwGsD5yjozs/Pey23gk4VSJniY35+PtCQAuO9bM2+/Jzvo1vFwsJC4BNrNYQxoYZlLMAmKc8kCGXQhgbYEdjQvUP5OPcmx0P9nLPMjaT19XUAYWSqtluVFZx3XYdW+CUfsjn+TiMdl5iWjsQrRrOe43jcunUr+FzXogJQPSs0AE0TbytIZz9ZJ3M48vOYmVrH6VFTDvheI1nzmSWmw7CTfnR0FEicrKder/u7HUmFQsFrWxTkMFUAgQCJG8uaGVgX20tiWZa/fPkygHEOPO1nln+EVXHHxkgpFgZvo5+AkYlSNaFkBDENw/02BM0xvI7Oag61HpU0bf9t9Jh+T0CopKYZ/mjKG2oxisVicKBVKpXU2CmY5DhxHQAIbrNgGw8ODnyqBtUoMpDCmu4IIpnORSMiKXwwiIggjY7r9H1hn/kMgzp4dyRNrcokNZUH1xnNY/y80+kEEX5ZKXCs6TGL/ksAfHYt2++zKHZ3KdepPUwLhUJwiwlT7NAFRMGWfWfshg4VbgeDgZ9zgkrrOsByBPjMYce2lUrh1WrqG2gtA6VSCfV63VsECBZ5wwww5ndMes48llzr9HVlnVzzfJflpwqEKfTdvXs3ZZ604658kEIR007x5iFSq9UK7mpXXqsaf+blJF9kupaFhYVU4nOCWSB061GFAqOjla9rgAPPHjsmGiCkY0bSIBNdN+o/adc8hXUFurp22HblMQcHB+h2u4HvpL6D71FhXd2e9NwnCFSwr1aZrN+Pi3LA9xqJE8IISvpMkCkSTFFty8XJi+DtIqZkaNMZkFQCYDSYBZqM9FJQxUOayTM1aID1kZgLTnP7sYwu9ix/OQCpcjGplT96wNs6LdOyZqhY2SxAyrp1fnTzadvsOxTQxoC9HjpZV6sp2FImpppfmnOAsamDGtJyuRwIA6qhIxCk5K79sWPK77vdbnDJumodCPoIjDkWPEg6nY73iVRQxwOZvnX8nL6sHBuOlfpkqd9pzITFPilAoP8PEAJrvjuWgNUCwTeKub7ZZH34FCyrFgkII78tKZCKfc66Y1r/2LPnz5/3Gkg9/LU+NZmyfXaf839eYZYlGHIdU/tk78ilcKJZEig4cX2zPgY56Brj89yfFFrow8f1S0ClmjbtD8fFjnHWOuWYKDApFAp+HLj/9vb2vMCm893r9Xzkvr6Pvn9LS0s+EEQzMFy5cgXAyAStoI99UR9Fnkka0a1AXOc4FhFu+2vnXz8nuNLcpuy3vk+1v3xG26eWHAWiVmungoM9v/k+61IxOzvro5tjZ/7jFkhzwPeQpJGxyozoR6FmTZpYre9Ckowv/rb32BLM6CatVCqYnZ1NBXSoqYbPOzeOUtOFpc72BBLcIJSGgFBNzndZNTv7bX0cYqp1BSFWG6MLXvP72e8ssKFUGtMi8HsAHjRZgMCxipEeiPQtsSZoPYxOe56Hm37H9aKaWmoNNACC/of8n/3l1WpXr15NmZcYDWbvs0ySJLgRw84B+6nmWdVSWHcAYDyvujb4mQ06AULQMTs7m/JT1TayTQqKn3nmGX/IcZx0LxAo27lQEKB9O2tkNegqjMSArx5QscS1GlGq8z8YDAJfSa4R7pGYGwVJ59O2m/N6/vx5AOMMAlbTz2cKhQJmZmZ88nruO4I7tplrhNf7cY3wIN/d3Q20ec6NInIZKMCynU4Hh4eHfk+ybqbcID8kv1BtDgGZ9ll9+DjOVhOl/eWP7nn6dc/Pz3shit8Vi8XURQCkVquF/f197O/vB9oz1dRak3OhMI7OPn/+vLdCsd9UhNgAC3XfuHjxYiaI1X1sy2RdycZxI9/RdTk7O5s6t1SJwD4rH2bUs7bFnqGq6QRGgrfy6dj5Rq0zzfzWdBwTmB415YDvIYkbX0PyLaDSiSejsYuAPklWeon5MFDLojcoUALRtCx8HzBa8HxfloTEO2fn5uZS0hrfE9Ny2bJkdDHAZxdvltZgbW0tqi2I1aPM2GrpqNECxtGAenhlAb3YewngY76JMX8X/q9aTAV8KpGqNMq62U6mQuH46pphpKPNM+ec81dNkQFxPHlNGq9Y0/YWi0VvwtEoukJhnGZB80cxaMMmyFU/IL3Qne/XQI2VlRXfZvUVVa2BTbNCIKDjpQficDj0t8xYsmA1tk7PCvHQVrOT1ZzafR1b+1kmRStk8f9Y6iO733Z3d/16sPvHtlsBj/X3JX+1wUG6dtgurluuZU1fwj3DA5w8vFqtpq4kq1arXvOl/dKxJfChgE7TN3m18unhcJhKLn6/+XDOBYI5zbLLy8s+Kp57nm1VAMa2kC+poA+M+E673cb29nZgkuYPc9aRpykQ57zSFBxLx/Xcc89F50mDL0qlUmodMWWX3ffsV8xP+rnnngvS83Cu1N+c7SQ99dRTUS0k36OCJsm6D2SdX7FbNjhubwQ/eqI5nnOu6Jz7W865V51zR865a865v+FkNN2Ivs85d/ekzIedc8+Zehaccz/tnDtwzjWccz/mnJtOv/H+pH5UDHdXsytNWdap/PDwEHt7e6nJZkJmDad3zmF6ejoVeUuTGIFALMqHm4sSS8yHxKZ14WGtuYRINq+WGdeAgVjVt5JqrayPg9ZLxsTPrHlSy/L+YP1Ox4EmSGvOAEIJUvtsDzGOGUk1Y2Tu1rGc/ysAznIctslrOSY0ix4cHKQ0VLznkuNq+0ATBg+24+Njz+BmZ2f9Re98jn2i5pjSOOesXq+jXq9jZWXF94PPKOBTpkhN99zcXABUVSpmDkEdK+0Tx16DA+jnqGOovqu2vJKuD6u5eBh6EvmTXePqg8U1a01TwNiP07QrEFr1QCoUCoG52O4BBZWqfQHGAT0UZrTNbBvTshwcHARrVImavJ2dHdy5cycAHQRsfIZ+hc1mE81mM0iZZVNsEXTu7e2h0WgEwEK17ARsNOGS15K/8mo1JdW+cVzIqzXhr9UmcUwJqPVMUNMi95dzowTKy8vLqNfrOHfuXAp8lUolf/+ugiWmFaNygePM75lKant7O+WPSa2y5dtajuDcgldVlLC/SktLS0H7+TcBYrVaRaFQCPLjAUjlYyTQU+uX8ngCZ/WjVP7E8Vc+E0svFKNGo+ED0VQJwXHQNf44LBBPNOAD8FcA/HkA/x2AF07+/04Af0HKfCeAbwfwrQDeD6AF4Feccxpy9tMA3g7gqwB8HYAvBfAjD9MgnRBqMdTUpz5L/FEmQuKCUEah3+n1P1x4dKZlihFVOSvwZL07Ozupe2SVYbH8Sy+9BGAUZh8DdrG+A2kwZp1WbZ9O06yRNjY2gujS0zQQqobn5ldmQDBCU4eaKu3Bl0VJknhzRwz4xqQ5at/ITAqFQhABqetA/7YRxzSrKCPgQUmzxebmZnBo0on78PDQB0yQAWueRC2vkr5qHzhemnbCOed9Pe1hQ+K6JPDkPFhQEQPaygQpPDEq0NbNdW3HMyt3G99jzZOvg544/kTiXtB1x9Q5BBvAeP3aQ48U86XiGrW+q8qrYgcziUFiBBI2wheAB6o2kEKBJDDenxTAeXASSClP4t5RYDUcjoKJDg4OfEQ8BS7re0vtsebnUwBH0MQ6GCCiriR2jAi6gHHUOoVnC5Rj65WA9+joCFtbW6n7fDm/1JKrMEWN//z8fGBW3tvb8wAqdn+tvVJP/6ZyQ6P+kyQJtO70ZVf++yBAR83SMRMo26i3ZsS+1+fYJyskksdxravfOf9WvqRa0iwfcfI/XZMxDTrpcWj8To99fvPpiwD8QpIk//rk/+vOuT8J4H3ASHoG8B0A/naSJL9w8tmfAbAB4I8A+Fnn3AsAvgbAFyZJ8tGTMn8BwC855/5ykiTreA3EBcJFTCah2pf5+XmfhJMLhslwFZiwDh64rIeHtGrXCH4035CaAG0EaaVS8fnTNC+gMh4ufJu+w4Ja9ZeKjQd/Uw0fW6jaNn3WSu42ofNph5GmZbE0HA49c7PXy9l2xQCfMpSYM7EyVAsw9C5a1kHBgAIAmb5qYCj5ah489cPkGKhvFg8Uu3Z4rZlSt9vF3t6e90HSseKhx6hfHo4AfI6ynZ2dIN2CStPqBE2NB8Enx9NeraaHqWouVBtATQuJmkmOo2oD+FksaEPng+vp9Wr48ATyJxLH1B5kBPB2T6mApJ/pereaeZ0X1VTwt4670vHxccAvrLuIag8vXLgQ8C3LP6i1vnjxYtT3VLVASZJ4/ywb/U4+rqCMaVksP9M1pnyZn6nGaWFhIfBz47yoZt7yO52rLLLnEM3gBLPcb41Gw/syxsZwYmICg8HAa7TY94WFBSwvL3sfOPXXJp+6dOlS4BOs/MkqOZRH6j3EOgcEQwqulPQ57T95DwG+uskAYeJ9nQMVpG2CbfVpVNCt54L2Kbb+7dnCbBG8aIE8VEEl9+fjoiddw/ebAL7SOfcWAHDOfR6ALwHwb06+fxrAKoAP84EkSfYB/DaAD5x89AEADTLTE/owgCFGEneKnHMV59wsfwD4XcgJtxuYhxX/143snPOXaNvJnJmZwczMTOpzJpkExhIDo251Y3DBc4EqgwYQ+A2qdkQZACVumuys2UGZvdJrMYtZJ9ysA+HZZ58NfPgUKFqpzkYd2g1GUMS8dFZa5pgow40BQCuV6/NkVDHSCEQGMOjYAmHus6WlJQDjA6NUKmFpaSlwEOcBQ4n9woUL0XfrzQEkTZlBUKUHpLZZ/+bl7Goq4Voj01NpmNoYrku2w46VpqHR9+v42Pm5fPlySmhRoaFQKATXXmkfdV6yNASvkZ44/iTv8dov/Uw1HBzD2N4BxhqImFbEOReYccvlcqB1Vf5i/fp2d3cDwG4PyeFwiDt37vh6dd1bZ3cAXmtHYr0qTCkf07YouNO9SCHIJq6vVqs4d+4cZmdnA+DA+6P1/VbQYHkdb0YDW2I/tZ6Y1giA18TR4qQapaOjIzQajSDXK8eOLiMxKxDdMTSPK4lJsd/97ncHProKrulLGEsvo5H22hflBVnnjQVudg4ApMZT67NrgO/MuoIQyA6e0PNEFSt2jynVajXvp14sFgMeqIqVx+XP96Rr+P4ugFkALzrnBgCKAP5akiQ/ffL96snvDfPchny3CmBTv0ySpO+c25Uylr4LwPfEvuDmc24UxWVNhTRzqX8KpY5nnnkm5eCq19+otkqdmbnQldmpHyClS92Y1PAwB5OV8FQjQkld/fpOxilg4rGx0LJ8/kF8D3SzKvPSRMPKpC1TAEYRvdovtol9VKmL45TVFp1X+x4yUls2i6id0ANPEyTruClzqtVqAWN3zqVyXXFdMNCGfnAKgiYmJnDu3Dm8+uqrwXPOOX8FUEwQ4P3NCp647vR6piRJgrUJpLWaXIt0NaA0q4fwU089FbyLDNPe0UoTOQC84x3vSM0ntVPdbjc6xzHN0CMy6T5x/EnNh8ViMfCzI5/SvqsWw2qElRRg81nVktn7R5WP2Dx+DNax/sV2bwPxROBWCG00Grh3714KyClYYj1MTmzr1TZTM9VsNlN8gem1FCCwvHXF6HQ6PimyzonlRTo2nCPL+yyv1f+Xl5fRaDSwsrIS5FItFouo1WrY3d0NIphZN7X6mqSdfWAiZ10rzjkcHR35vk9PTwdJ5rlGpqam/CUDnHtdK5cuXfJ7nc/aeYtpuZjOJAam+IyejSSNuGVZK/RYwEeNJ79XMEzSNtbr9SBCWp/lb/JR+krbPvA8js35o6InXcP3xwF8I4A/BeA9AL4JwF92zn3TY37v3wEwJz+BWcX6KJG4ka12hWpa60A9HI4czHl5NcsNh8NUrigAPlIyds+uTQNTKBQCTZkCRJWUVZq2vhXsa5YTrTJIq3GxpGXYjtii3t/fDyRqZdy2rJpV9Hs+T2ZERkFmoX3MAqfKEDQtjUaJ6XwraTCC9l/7y//v3buXKkOmfHx8HM3dqCYh7T/HqlQqYXFx0TMtTbdy7tw5TE1NYXl52feDZvu5uTlMTk4GCZFpXpubm8P58+cxPz8fmLDYd7orAPC58Eqlkk+Jw7WtEbfve9/7gnHkOGt6g1jeSbvGyCidG9/OoWTH7xEy0yeOP2XtUSAd+W+FJJvOJnZ46ns0IIEabHUB0fZou86fP5/ij1ZYWF5eBjDWYHNfxNpWrVaD9BvUMjPRvQKtSqUSHLgsa4EZtXl6Iw21fnTlAcZpgCYmJoK1ze+YncGOOdfsxMREoJHWs0XHTcfLrt3p6Wkf/Mf9yb5evnwZ9Xo9iLy3a0SVExQY6BNIlyOdKwqSmu5F18lwOAwuDYgJ2xa0KS+1ZxfpmWeeiUbiKn/WtUciD7LvYnm7tvgMk4krbwXC6/FIChD1LNK2UuPK6zLtOzm+sb8fFT3pGr7/EcDfTZLkZ0/+/4Rz7gpGEu4/BsDT8hyAu/LcOQAfP/n7HoAVrdQ5VwKwIM8HlCRJF0BXyifynf/pdDrBvXhAqIVRoFIsFr12RTcefU2yAiD4TvppqYlBv9fEocDYZ4w+KBpJzIOcm5u5rvSuVBKfjaUk0TL8bc0tWkbfrRpGpaeffjoAZpa0buYz0nnRvym1Hh0dpSKiVBNiyWr6NO2OLU+1vNLzzz8fHBwsZ/uaJEng08bvyXx7vV6QXFqjeNV/SkGkcyMfuKOjI+/Po20mEKvVaqlDlwyQWlo1jQ0GA9TrdX+oaV4qYCS1K4OvVCro9/uegVtTNttiiYcqyxL02jLqt6paYgpLWWQZ6usEfk8kfyIxaTZJfSN1z5Is4NM5I+BRoKXmYtXCW5A4GAwCgL69ve01x2ynBSP8TWEhBvLp28okwVZzo6Z+HvK1Wi3lQ8e+q+sEBQ/uEy23uLgY3BxhNY7k/zSL2jt2OSbkLVZbqAKMFWLtmh0Oh943TK0jzo203hcuXMD6+jquXLkSnEnc79QI0g1Jf9QNRueXQqreC8sy9NsleOZzVoOm/Ylp3GK+2RRSLWgulUoYDAZek2cVJXSP0rGzZ5p1y7F3DOtaYdv1PfSTVP5peRPfo9YetikmGMXOm9dLT7qGbwojXxalAcbtfhUjpviV/NKNfFreD+AjJx99BMC8c+69UsdXnNTx26+1QcoQ7EXz3MA2IbNqa6z2rF6v+yhS3WyMxtUN2u12/XU5Wr9dVHwfkHYm5ff0eygUxk64c3NzqUWuZloLwGLgNSvK1zJYNXcrraysBBtLyyioAxCE/2sZtokHkr1v1s4nn4sRx5K/bQJTG2UIjECrMi5qC1QCjGlDL126FPSF7dMxI9BRwMe1x/YwktBev8c1yOhBvptzzO/0OUr7qjnjOiewBMYmDPUrHQ5HkZxkqjHwRlJNAf3+aGa0qS3Yz9ghqOaXN4CeOP4k7wlcESzZ/ac8g0RQxfq07kKhEER2UkuleUmz9hTN+1ntUqvD9vZ2ADxtWiRq5zY2Nvz7VFBhP+ia0mq1vM8a102pVPL367JeluXaUy2PggHuSV53yc907cZM5bqnFThb/pD1nH63s7ODdruNjY0Nr5HjfLZaLb+f1FzqnPPgdWFhwQfAkWcxYEXnWLXoQDwYbjgc+hs+OA7FYjG4S1fnmfPDdcE1yfWjgREMZIwpPFhnoVBIBWDoetMzU83wFvBphLdGbrN95E9aXvlRzAQMjPgk02VZIeeNoCcd8P1LAH/NOfe1zrmnnHPfAOAvAfg5AEhGI/X3Afx159zXO+feCeCfYGTi+PmTMp8G8MsAftQ59z7n3BcD+IcAfjZ5iAg4Pexp0lLTI8vYQ4eATUEgAP+ZbhxeccUyXBjVahXT09M+35BKK1xEbEOv18P+/n4qQkvr42JjziveckFpzx6odnNb6cRuYCWVhmyaEaXbt29HtZhst5IyAysRk5EDI/8KK7GphHW/97C9du5YT4yh2zWgG5zCgjXzv/vd7/ZluZ70pg3+qHaEfVfQxzyNNmVKv9/H4eGhT3ei8zsYDHwqF2WOg8EAe3t7aLVaPieZMkwyyqOjo4BxE1QyvZAeorFxUpBgtcCx+dGDi/+zbMw0adc+x+110hPHn0gcCzUzWk20HVsL+OyhZrUrdn1ZIUfHWcf6LW95C4A0OKTGhs8AoV+rHrq6v3gPtPIhAg+2i890u91UEnQKM+TF5FU28wEFmJ2dHe/fp/22aVW4D3Sc1e2CAFZ9VlVx8CD8iYLc8fGxT4fCtna7Xdy9exd7e3s+fZO69qjWlmZnd6KdP3fuHNbW1nD+/PmUtYq3/CwtLQXmaO7bmNUjdh7q3xx3BVfAOGUY58MKEipcMnDRCoh6j7q+i8+ybiWOY+wst+sZQOpsiZl3C4VRQNm5c+e8AiBm6n7UWj2lJ92k+xcA/C0AP4SR2WMdwP8C4PukzA8AqGGUt2oewK8D+JokSfQU/kaMmOi/w0gi/xcY5cZ6XcQElfagqlarXoXNCZyYmMDi4mIK2TebTZ+LTxclJTNuSqqLaXrQIA8ewLqB6T/Fu1ZZr0rJ3Dif+tSn8Ef/6B9NbSQ158Y0dxrtyHdmSe/KOGNtIRHMkshMY/4ONOmSeekzHC9gbDq0ZnItbz/T7yqVip9buyE1OoxE7ZtKq1aNT1IN39ve9jYAY5+6QqGApaWlYEz4HR3f+ZkCepry1Y+G7WAf1FdINQqFQsEzdPaBGgIFgzSdcExtUIuSdWBW4trlIdjv9wMfK83ar3NizT6qaY8BPiucAPEcc6+Rnmj+NBwOU5HVClIUYHGdx8hqXZR3kTRv2f2IoCYmpPEw5e0UTMvC9tt6CoUC5ufncfny5ZSLgM1xmSSJv3lI/awJBMlj1KeV7i66byuVio+uVBce9SmuVCqYnJzEwsJC4MNnQURsP8T+jhGfnZ+fx3A4RK1W8yZo+qbxvlxal5QHMU1Tq9XyoFOFcZrLNZ1UkiR+boCxVUhdmqg51MTG9kxQfhDTEpLUknHu3LlUXbo2yd+sAK7uJ5bXx9oGjNdA1pgDoWC5srLi+TQBH8vr+q7Vaj4ti46BrvH/YgFfkiRNjPJYfccpZRIA333yk1VmFyPH6kfZNu/joBIJkA5QIKl/BX94ObVqBwH46F+WJwhsNBr+tg31YSI41DoApK4GUqmdv6mlWV1dDRaqBSgxqYb+N5bsZzbIQH36tL0XL170vhgxkKekZbQu/nDjNxqNwDFZ338a2CMx5cRwOAyi0shg7LiQ8dOcQenTatSGw/AaMPWzJJHRWt8epnDR5/h3pVLBpUuX/K0aSZL4Q2llZQVTU1P+IOM4kpFPTU35yE4ycmqVeSOMHnTsK9ujYIz34HINZoE36w/IQ4ZRt3odG8eV7SapH5EF4HZM9d2vh7k+yfyJ61MPP5vyxmogskzhqkXV/aoHnkaBck9ovVr33bt3A3CnAijL0UeO5jkKlKo5IcWibgEEtxfpXlcXFb5/fn4+AD3KV4GxL3O1WkW9Xsfm5qYfY2q71XTKftsccPycwMpqvVkmBlJsGY7f2toaKpUK1tbW0O/3fZ9brRbm5uZQqVSCW5RUA9hqtbz5VbVdNJMzwEDPAet3rOuImk4CPrVWKFG403o4vpqhQqPML1++HETCcl2oYqJQKKQ0z7G0Z6cpJ4CRMoHrm/zMgnQVeJaWlgKBQ9e3kq4pe/7FAOmjpica8D2pROZRq9XQarWCxcucUAowyDyYi0+BR7VaRbVa9SY+TvTExEQqIlQXrdbBw1SJ5rRY6hILKGgatZFhfE+WD5/VmKgPRqysZTj2XazDRoVlESN6dRz4jLaNY2vN7jGNmyV+rxFgqqEtlUqpa6Xe/va3B4eq/gbCMVWmQT8XXUuNRiMlsZJZk7SvqlHmnbnr6+uYn5/HxMQEVlZWMDMzEyQBJzMjsGNkIvtKf56LFy8G3w2HQ8/kmJuLWmiOiWbxp/bbEuuz2l/21R54p2lFLIiOjdGDAv3PVVItrIJlgnULfvhZDCgD4zXFuVDeRSKoB8amPZazWvzFxUX/PgoVCobUD4trP7ZfyUsPDg5w+/btlH9XTMilKVb7Ynkc+XWz2fSaPNXU7+/vB9HzBJwqgDMty97ent+rCoqU18dAh85L1lxwrIBRYALvzOWY9vt9XL16FTdu3MDzzz8f3LSi4Kzb7QZA6vDwENvb25iamvJgUHm6Zhaw/tHkTfcT1kkU2gm+6XrDKF0b+azRu8q/1E3G7n9GLvMZvoeAT91BYs9oW1kHEObuI0C10ekKEpNkFNnOnIinjdHj4k854HsI4mSoNKU+GbGQcqaq0IlMktE1NVNTUwGzAEbSGUEj3zk9PY35+Xns7Ox4AKPSNg9h1T4pEOB7bQQUN0ij0QjMtPosF3uz2fSHiPqZqHo6xsBOY8ZK2obY9/q/HjL6OdtOEExpjUzW/ljgat9Hhp21Sa0fxsWLF5EkSbAW9MJ2ZZ7W7KTvcc75PIokvl+ZrgVI7XbbB22o5otaCF1nCtA1aa4dU2pLyKCt1saaWfj7+PjYr4uY+Ztjr+NAfyxqRmP3keoz2oeYlmpiYiJ6Dd9ZBHtANqBVi4Ga4R6kPjtWBEQkai1iZjBtDwCsra0FYEv3YMykyzoUBClYKpfLPuhA26sBDHy/ujmwH8ViEfv7+4EbDrVNFHBIDIRQsECNlPXFKxQKmJ6eDjRF+j3BiprSOQ5ZAmns//X1de8fRp9tvmNyctJr/2zgHPN1lkolnzcPGPu88czie0hZpnzOZ6vVwt7eXpBVIkt7rLwiVubevXt47rnx1dMq2KvwoVplKyDbflvhD0j78AGn+x3a7+neZQNPVIjgNZEKbE8TXh8Hf3rSgzaeWEqSUag5Ixg5OTRl2TQm5XLZS19q7uAhbLVo1t/MOecjwfidmitsZDADSnQjW7DJzUNGtbi4mDIt234oc7L+B1bVrkSwZU0U1q+LqnF+xsM9tviZX8m+k2OgmdfVcVc3flabdaxoPlcpnqQqeiWVoqnZ0lsDqL1VLckzzzzjgSLnlqZ91YAxEpekBycwOnz39vZwdHQUrKt+v4/NzU0cHh4GEbfUcOzt7flk3cq0dnd3sb+/j62tLd9XdUoHxmYeaha4Hg8ODnw9vJA9RsoYefUW64ulLLJrTn/HzDexOT6LgE/3i81JaIMV7PqP+T7qbyAcM9Uyq5uI1dBb4YD3QHNdK49RAYVl2K+YmZOaxphGRstTO1+r1YI0HSS9bo3PMGcfD2a6SywuLvpURbHx4v5lUBbHVaM8VVjW4DPlkdrPrLVKpUGz2cTGxkZgRRoOh9ja2sLBwUH0No9CYZxjc2VlJfBbW11dxYULF3DhwoXAquGcC9wyeHYoH2cQSWxslBSYcnzoJkVAff369eAZAkwlWoWobbQRt+pnzx8beWvnkkKnujHo/FmAGssXyn6rgEnXGPpaqh9+lpLjUVKu4XtI4qGtWpkkSTwo44Ii6WGnk0vfEU3BQm0eQQGZeKfT8TmO1G9F69NDX38rOKTfHdvM7xjgoeYYK6Won4K2VzVEMYCm2qH7SXXWtKLmX62Xvnm2LgXTAE5NixAbu9jfmkdPnyGjifVBf6yvIw8ga+LkwcLxZ6Jkvp9zpekSlCHrQamHHeebqQ00yawCZPrAKXhuNBqYm5vD3t6eX0/W/9P2nfNGvyaOWWy+LaNTAEBNiy2vzJL7hSk8LHBRCd8KKI+Tub4ZZLWuCuhsLk61BADpKF2uN6v9II9Qcxt9RbX+LJDCiG76+2lZPru5Obp8ZGNjw7dF1zKJ0eX37t1L+UhTcHTO+f1H4MM1zn3FW5M4JsfHx9jf3w/4na4byx+ZVJdzwLRFFKJ07JQo8Nvv1T/a/uhYqZZQ555/NxoNNBoN3L17NzWPFBypmdK1wrvfVYPJ9vCWH2AUrKDCFs3E8/PzUQFayWp1La8fDsN8o/ZZluO8x8Agx0L7rkCPdcXWOOfXChgxWl5e9v6HnBObRWAwGPh1xrKq/eVYsF+PgzflgO8hiRIgc/HZg0zBGDBaZExRwecJ4tQUS6bMg5Tl+/0+JicnUavVgmuxlLmRSRQKowjK9fV1b35gXXbhOuf8ptra2kpF41oAt7OzE0RxkrhYrbSSNR4qbenC1mhl3SxW4tUx0rL6XpZlNK8NSGE77idFU/rv9XoejFiguL+/HzgY62HAubdpHnRclFTzwkAKneder+fvPwbSzvPVahUXL14MMsxzDBlEoQlluW6mp6cxNTUV3O9IBj4zM4PJyUn/LkZu8//l5WX/HoJZ+gVOTU35cbZBGxphzLbqeyqVSjCuOtc21Q1/ssY0Nu9nUctHokmPpGlDlFQjqBTTrqogEAOIBHC6Vy2wvnz5ciAkxvgF1z+janu9XioKV4Vb5QX8nClRlKcx/6QKbDTpWsHNmoPpw7e9vY39/f2AhxIYsRzfqyBEATiDuWxaFn239Z2LjVOSJFhcXMTs7KzXKKpgSL89vTqNfep2u9jd3UW73faJ0lVx0W63g/1aKBRwfHwc+K4xSpdto2n78uXLHijquUbSoAkKHRxDHUsbOU5QrmDNanHtWWwtQXyXCg/2Gaac0QwDNtBE6fz585icnAzArwoUrJ98Te885jzG2vGoKTfpPiTxENfoWJW2bHQSzatWQm02m/5+R2WwjMQFQnDARa0+fPrD8qVSCWtra5iZmUmZdPVQVq2QkjWRcQHTHMP2aN9P8+HTd+hGsONB4GrBHkk3iaa5sZuHIEz/V+YZA776W2liYsKbgTTZKOebGfUtqRZADzYeBoyGs8+or5L1aeR7VetFJqL+pGtraz6ht0a/Xbp0CYuLi7hy5Ypn5mTMy8vLQbJVrt/FxUVcvnzZP8sxocADAG9961t9X3mI8QYENV9o3i6+g21nn9T0zYPRjpFqC/WgGgwGgYmMbc3SOp01DZ+SjQBVE5rtN4UJ+5kFHUpat2qptP7ThCmrsdM9z3WytrYWlGdduj6mpqa8L5rtv4LPUqmE2dlZn9Rex8UGixUKoxyYdIlRPlepVLwQw88oGPFZ8oX5+Xm/rnXNcv9nRRhrf+9H9XodFy9exMWLF3H16lXPE8vlMt72trehXq/j0qVLKV82gnbmzNT3Hhwc4O7du/5WFNVk3rp1y5eltlbnl+lc1Fxv+2JdhchrCJpo3bIR+tPT0wF45LwyA4C9NxcYgTF+TsVEuVz2Zlh+rrS8vOy11jwr9Z0WxPJ8IC9mf62lz1rlThM8H4cFItfwvQ5SkKa+J7EoUz3IlXlMT08Hi5SLgLn2WM45h5s3b6Jer3ttER37aTrLcli3G8ACIJpmzp8/H5hYYtLPxYsX/d+lUilI9BkDVNqOLEnVLnb2m3XGoocBBFf/6PfcQGz33t6e98PLer/VvJGccwHgivkJZeUvU8agKn59b8z/T9u5s7MTJDXmOtrZ2Uk9NxiMrozq9Xoph3XOK80JzNPHdrIventMv9/3EvvKygoWFxdx9erVYBy4tubm5vy1VTzwlBmrRsWOERCm7qC2JknGtwXYZ3R+dTxjWim9QUHpcUvTTwLF0rLw0ANCn7/YeKhm3Y6f7gVGPdoDT3lDVt3W3YN+o8DIaZ+HrgqT2haCfNs+Cjvcw2oB0dQ+BILWdUA1h+wP0wQpOGb/rHsDzbxqYbHCeb/fT107Fht7C3aV16l/sg0QuXr1KhYWFvDud787yB04HI6St1OA4xWcqiHrdDr+Plk1taqZlfn92Df2mdkhThMYWJ/+phKAgM/yR7VYULgsFAo+q4UN0AJGAWeWRzNoKWttc2zZNl2/dg6B8a1PrMsK6bafynP1rLVWiEdNOeB7SKIUenx8HKB/OrJrWhZgfPOFMl1K1XTsVdOlavG0vJpNFTxYIEKT7t7eXrDpyPwIFJNk7DTMEHxdzNa0cHh4mAqVZ/tizqwk1S5o/bGyGtmljMaCyIODg6AuBQ+quWSErI1OPo04xkkyMrsXi0UvDdvDJivakVqrJEkC6ZNagm63G9WGKlHLq4dKv99PXVWkTIIZ9pvNZhA11u/3sbu7i6Ojo1TQBn31Op2ObxPnk9f5kYkDY+2J1QyQ6bOddCLnM/aeWz34WKe92i0Gitk+IJSENbKQxDlQ0kP0LJHuE6tRVUBj9wiQdi9QTRqAgGewfpImMuaz2ha7zp1zAfiyByjXCdej5Rcsz2jhjY2NwA9N+RH/Zlm+W+u0N3BQO2qDHXq9HnZ3dwPeoxpC/k+wqKZiBW8KXGz+Nrbf/o5pS4fDIe7evesFagVozjk0m00MBgNvOVGhc2Jiwmu/nn76aUxMTPh9uLy8jHq9juXl5ZR2ywrZ2sbhcJQlgKlHSLHAK8v7dRypSFhfDy+bUQUDwRLnnWeurZcBGLo+NStG7Mzi+tM9Yn9rfWrNy+Irzo1dWph/12qlWe5xgD0gB3wPRZwQTZuiTJAmKZ001fKQkiTxF1/rtWtAPC1LqVTC9PQ09vf3A38CVYGzLIDgM/1c/SRKpZIHfJTcCAjZbmX6L730knfazZLaYyBGgYcdG7tBrGmF7bDEqGd70LANfFaDUVjGMtKsDUYGpxKsZXD069FkyLY9CkQoXdMsaol96vf7PqJL/WRYL8lm8icw1YvNaUJi9K6aktm/g4ODQFPMQ5KO381m068VjoNqNVieBx7Bos7n/dIf6GEEjObf+v2xHNc3x5Pr1AZtaCShSudnEfBpn+zhp3tIQdRpB5QVABVcK3hXzdJwOL5ii39bsoentqVYLHoXicuXLwegyprzKFTx9gK2l8FUyj/pamDdWYbDcT5MdY+oVquo1WqB1WMwGKTyzJGP0n+O72JmBs1/yL4qf9X1quszplnS+VKBtNPpBIoG7rNr165hb28PN27cSFmiODflctkDEM7h2toaFhcX8fTTT6NSqXjQ2u12A4GfCZYtIKdWnXvTRudbTS3H1qaGsvfiKihXYbFUKgU+m/YZayVSn8b7kc3MoBYMUr1e94qFJAk10irUcF3z7KISQPdZDNg/Ksp9+B6CdKMxBF01aAR2CkaYjFZBGReC9e+y0i6J6n+7KRh9qcyzUCh4nywFP/yev4fDcfLc5eVlAOFhwLayHzHtnvZHtQFK6mtntSu2r+qYmyVdsZwFcVb6BeCTn3KTaxsfZHPRL5OMXAEimeunPvWp1HPU/jEqVhlR1jjxffy5ePEiFhYWAids+uiRVIvDg3Zpacln2We7ebiwDk03wR+9ApDEiEYyb7aRz8b6pI7RvHKI5mElNetRUKpWq8HtHBZI6xpSgYDr3jJ8vtNK5o9Lin6zif0bDAaBhspqTjl+KpTYeizgUFOVjjN92qzZUs16lgi81Emf79FocL7PCnckajKtT5hN46LrTE3awGiNqOCk+0I/K5fLWFhY8H6uasIj4GA0K9ugc2KBnPWzVFBnBdcYOee8r+zk5KT32eYzTAC9tbXltXdsS6/Xw/b2NhqNBra3t1ParkKh4NPd6FjMz8/79z/99NPB+7jfmfcvS4tlx0UBsJ6Rdr5jOW5JalJVmpmZSfkSqvIlZnLmVWl6rrGfXK92v8zOzgZBaKos4XvJi7ICkCw9ah6Va/gekuwhxc+Yl83m1eMF3wrKBoOBd+pVR3UAQaSiltcEj1q/3QTdbhcbGxtBpnSSTQ5J4mLUdqjUBaTBhbbvNOau46ZSWJb5Nwbc7OLf3d1NSV+sXz+jX5kyGR5m7GdM4uRvNbsraAfGObeymBD7wlQ6KpXGAgy4htTBWs1lZCgE5wD8tUlkRhMTE3jLW96C3//93/cBRJOTk+j3+7hw4QLu3LmDixcveu0iI24XFxfR7XZx8eLFgMnPzMzgypUrGAwGWFlZgXPO+8zYQ5LMkJ8vLS35uz7VGZ+kzJ3rnTfP8PPY7Rz2eXVTiN2lybqsBH0WQZ8CYuUhKshZjR2BiiVNxwGEfEbnRYOWHkSLSmBOLaDli7Q23Llzx+9daz1g2cPDQ9y5cyfQ/nAfaN+oabLRlqqxYf3MYch9w37Q/GtTsDg3TvhLntntdlM35VgQZ7V4Ws7+T36loMa5Ueqm559/Huvr61heXka5XPZuKLwdhxolfV+SjFx0Dg4OfHAYeeXu7i6Gw6EHggq8tT9ZPs2rq6spUKOkKc10vVIDS55nx4HgjX2ngoX3G6t1inTp0qVUfdpu+j0rXbhwIVjfGlWsbVbSdtlxYXkde64dagStNv1x8KYc8L0OqlarnmmpHwoZmD1QaArQz7gxSVxEMY0QJdlWq5WShKgaVoa1ubnpU8HoO63kS3U7b2/I8kkDgOvXr+P555/374gtyiwQp/3j37EDgYxbpVybx07rUtAWe/fS0hLK5XLqDkM+Q+KBGPMtYUSX+uEBYwbHYAZLKh1q+8lELTjR9dPv93Hv3j0cHh76dUOgaO+ztKCXV6vRzFCpVNBqtbC4uIh6ve4ZMhm8c6OULa1Wy+fQIlOem5vDpUuXPOBjv4F4sl6+DxgJCLw1wWpy7Bjr2idzjPn96SGuY6Xjo6SR1Rzjs0pWw6EuAwTbelhp2pDYvrfrSt+hh2RWlC61rlZLpcKhPeQUqPIKQB7kFvBlaSUZPKDBGSQK6rrXms2m34vWjUbdKXq9nr/PXPtaKMSvDbRKAa0fQCAc6RhzXGLuDlYYLhRGEcXNZhOrq6t+35ZKJbz1rW/FysoK3vnOd2JyctLnxSQx0l5dKDSH4O7ubuDHDSDQGu/t7QWa3eFw6AOtYm3XceH7VPiggEqAZn0oCQhVc8by1nROOnfunF9PXI8anRsT2AksbV26d2L+yBwDNSFrm/i5Xbf2zLFr/VFRbtJ9HcSbNpSplMvlVBJlYLSxeMWXft5sNrG/v59yOKU/hi44Cx6sdK7PO+dw+fJlb9K1QEsXuT28rX+PhtBbSciCLpbPkuyteSZWTkFETLNA0uz6VnLVTaY3Tth6rb9aDAwwOSrBhEraw+HIeVgTISvRWZpBG2yfTdujpG09ODjwTsek4XDkqE2ygR1HR0fY3NxEs9nE3NxccENLo9Hw9yxzjAiW1EVB/UpYvwbTMHGzBQmsj2OlwslwOIxGJLIefQ/7cnx8nGKsnL/Y2MVAAO/55XNZa/OskAIDHW81NeqBSYppZHRPWICmdbdarRQ41D0SE8Q0UE35jfq71uv1lHBmtYe1Ws0DHavZ1/bQH1STLNtxU/9D3kVteVu9XvfpQgggdD1SsKFvnLZLBRT+rzzV8h/lb1m0tbWFzc1N7OzseM0ohblutxvktFRfV+ccLly4gLW1NTz11FMeeBYKBR+JbF0wkiT027XzTt6ws7MTnEu2/VxXln/T345zl5UbUX90TMn/lKzLlNYT0wgCCKxx2n59nz0LdX/pfOtnL7/8MgB403tWBoHHZX3IAd9rJCupKQjQ/y3R1KuMjT+aWJfEtCz6Gc0MDPXX9B02CTEQasqUsfJw5YImcz1//nzQNtV4ceFSWwOEUXp2Q1iyUv+DLugsbSkAHwmWVRfnYX9/PzPXlfbXHjz8rVGn9Lfh4cAAiVdffTVaNw8Pmhw4DtQU2kOW5fn51NSUZ8S6FvSwtUyr3+/7dC7UBtJks7m5iYODA38zAftHMxrv4eW4M2hjY2MDt2/f9tGJXEPWiZrPcI0yMp17xAI+uya4hwgQOVb2GbaPgoOaU6ym5V3velegpcnSOp0VUjAU08LpGtdxyNIy6P7QfWG1h8ovYn7Jlqhdtm3nmgOAW7duBRp8tWpwXbVaLezu7gbvoBuD+g4TDBweHga8mECQvqZsA/eNjifLs60sq+lV2H/e1hGLMue4Upi0484fe5awfdrX/f19bG9vY2Njw0cr9/ujaw3/w3/4D2g0GsE5pXM7NzeH+fl5b/ZkuxYXF7GysoJnn3028MHlWJEIiHXtWECb5eJjzwwKnWx/kiQpYU8DcUgcQ/Jo+z37rwII50fXqZLVUCp/su0nMdcp14s9d1Urvri46JVDsVyyj0soPZsc7zGSTma1WvWTrEyTC08ZKze/9Z/rdrvBpMd8mvS9lIrtorK5/Pr9Pq5duxb4ZljGxbJkVMx9pdKJtjVJkiD/m90oaqbJGjv7d5ZG7UHAoI3Cs1pMjke9Xj81H6H2N6t9VnugILTX6+Gll16K9pd1Kjgn8Ik5NBOMUDO4traGlZWV4IaLQiG81kqTwALw5iXtswIA51xwvySJ7gl2nqj5s/cRxzQkdnyYm43tsCZg9a9TB35KvqVSKXASZ1nrOK3CkV07tVotaDfLnEVNn65bCpMk/q0HrApMnGNbFylmJSBNT08HY6p7LUurqge7UqFQ8IKE+kLHDmbyr52dnUCoU42P8jBGsyqQ4n7Um5AozFk3kOPjY+zs7Pj0Lrbd9v1sg/Jq/iRJ4te6reO0/lqiNjJW7t69e9jf38crr7ySUhJQgdBqtdBqtXx/Op0Ofvd3fxe1Ws1bpSiIJkkS3LT0zDPPBHyMfbPP2bGKKQtUm8y1ZAV1m+XCnk+xMVI+EeNZMQ0bo761HN+fxTfm5+cDPk1+ptYsCkkMSlMw+EZQ7sP3OiimNaJZV6M5kyTxZj3L/FqtlpdAlBG32+0g5xsPP0aT0dGe5S2YKRRGEVY2ua6V6oGxJpBmPtZJU5yCAL1pw0okrDeWQkXHymoKYqTmEfsMiVem0QE8BihZhx4anJfTAIs+Q99JBinENEM2ObDmiKL0r4cpDwJ7PRWfpWQ4MzODubk5r7Vif+lLx3HQ8S+Xy3j22Wdx/fp1fPaznwUA77+zsLCAw8NDLC0tBcmRgRHDmpqa8lHBqm1cXl7GcDj0KW5oslJtGuedfkm8ko3mM6YVUlKzHjUMrJvfZ0WGK6BWSdqOaZYm+o1ism8U2T1lNRKqBbX9twcSSQGT7kGrrT04OAiELLtHYk7sVhjRvznnvEKQ4NQ+T77KYAVttwqEXFe8mlJvvwCQEmYoVPHqMG1bsVjE4uJiSvupwI1Ajon1+S4FKDFSwBmj2JpdWFjAW97yFhwcHODpp58OXGqmp6dRKIwTGOt80s+73W7j3r17QbYJag0PDw9TQEr7qTectNttzy8uX77sr2vTsSNRI6zrhcBV979NyaRATEE0z9zY2XPlypUgVZpaXbimbPvq9brXXmoanxg4JPHOcztefCfPKX6mvn5Kat141JQDvtdIah5g/iNVI3PxqToXGB1cvG5GQRwnF0Cw+HSyWf74+Ng7F2tZzQ3FDeucw/LysvcVsIeAgh4yv7W1tUByjzEdNelaM4/9XEk3qfYrtnnsheX20CFZc68FcWw/pX+rndLydiNb0wfnjlG6qsmoVCpB1CwwNlep1lPfQYYbC9pQzcDOzo4H/nrYKoBW7TB/Zmdn/YXePIyY1+/o6Ahra2uBFg0YHRxAGPXLK4sqlQouX76MGzduABgHq8QAHw9hmiyY4b/f76c0Eao5ZaSaAmOmHLLE+liGJhv+rRS7q9S5+JWCn8tkDxgb3KP+VtwDqn2w2ma7N3S/xva83av6uZ0Trq9isRgEZKkGHIC/s5aAzbaNZOeSQocCgBjQUmDUarUCoEqzn/alWBzlCNQsA+yf7mW+S6/H1J+sqHIFMer/pXNgz43JyUlcunQJN27c8CZYBj5dvXoVL774It7ylrcE+4DvZh/pO6cKh62tLVy/ft2bgzlWNmhDfe40DZXV8ioRHKkg4ZzzV5TRwmH3LgE4+RzXLZUgQFrYmJmZSd1axfWke8CSvUbTkj271J3LgjndG1nP6x6Kff8oKDfpPiQRKFkNGh3krS8BHVmHw2Hg1Dk1NeWvudKFRw2JagpjPoO2TQpmXn75ZRwcHPjFp2BD/TnUfGIZOsuelkbFSq4xPwrWaQ+OmISjfhWxoAAS7yDm51aLSSIzUHP4g5hK+JkNclDioWVzxbENnENe+6MSbZa0yPGmXx2jdHVsGFENwAdQkKHQfeDw8NAfpvyOh75m/yfRr8lqY1UqzdIe2fYzNVG73fbBR/aSe2AcOTocjpM16z3SvMTdjq1dGzEtEUmDNkgxrfhZImroVXjSSEX+5oFLcKIAMaYB1LHVupeXl4N5sQJOjGL+ztxj1JhvbW2l6lHeNBwOcXh46DVUuj6tAMd6Dw4O0O12g77o1YtaL2+XYdl+f5S8XBMJx3gjx/Pg4MALNQreFLzGtJ9KsX2mnzUaDezs7ODw8DAVsLC8vOwtPaptZxvOnTuHlZUVnD9/PshrWiqVvEnbnhe6Hzc3NwPew/27vr7uxzTLRcfOPc8O5S2WXygYehAebonzoMoCPd8sMQNHTLlg+Yed/1ibOe9UxKg29o2wOOQavtdICog0/J3Mwk4gF5hzLvApIRNrt9upe/iA0abiZiOjoImg1+uhVBpnlVfnf2WG9FexIE6lIwCeSWxubqb6aA/PO3fu+L+tGZVlYxKT9WmkhBo7dNXfkP3U+kmUBhVA67iTZmdnA4fy+x1EJD5DkyyBH99BxtHr9YL7Jfk9MM43ZW/DYL9s/61mmP9znsmgNAO9pjFhMMX6+jqazaZnzuzH3bt30Wg0vImX7QdGmtCpqSkPCsms6RD/yiuv+CvdeKWgHjCq7Tg8PPQaSprn6GBv+6vCDJD24bTzzisNNXEvtZVWEwSM/Iz0Xfb9Z42U5ygoo0sJy/D3cDi+pkrni99bIY2f62HMdUCNieY2i0Xtq/Cpmg/yDpryLly44N+dFXhFn1VaT7hHbAYFtk3vi2b/Dw8PvQmY7a1UKv72DJZTYYb1UuOkN7rwXTQh61jqnlfzpZaxmlLLw5V2d3dx/fp13LlzB9euXQvAKwOttA7WUyiMkvPXajU888wzQY7RhYUFLC8ve9Os8nQrROg5wXmioiFLW2XdLthXCogUYm3QBs9KHader+e1lNYHmUQBl8/xDD3NX9xqbO3f9jnenKTnLDBa0xRGuLd4Q0lWmirg8WQUOLsi7mMgC+RarVYqoS5NXTb8vVKpoF6vB1E8wFiDoVoxamlspJvV1LFNemiTCoUCnnrqKe/nZk2qCio0BQIQhqGTcWnepKxxsfUqxXwrYloDILwSLsb0SGTGJCvRq0lXmbRts2pF9SC0h6LVKvE9x8fHpwZtxLRhBCo2iAEYm4mLxSJWVla8Tx2BIrPZkyqVSgAeNcWJjglNfNTksE0cK17FZh3VGQW5s7MTAEg+S9I55jjZnIqWFAyo9oNrslQqpfx41BSjZuQsjSPzClqyJpbPddI+KvAmEaDpvlPBolQq4cqVK0Gdp2lR9NCmBty2g4JuDGgrYM96n6aK0tyj/Ix7RedYhSYFKizLpOFq3lNrjY6H+tNyv87Pz2Nubi4QINR8q0K6rlUgvH0HQNStg21lHadpop1z3qrUbDZTmQtu3LiB7e1tXLt2zV/XyTZybzPlFDDewzMzM6jX61haWvKmcbZDrRkM2rDriHnsdIztvFphQvkrtWoWGKrCRD+jcPogAX8WVGeRajzZN11Ttk+01HHeVbHC8hxfzbwQO3/s/4+Kcg3fayDL0PSA4Xf0p7PfDQaDqMRQq9UCHwPWX61WUyYKpgVRk7G+1wIe5lyLMW01hXLhkmmqGbnf73u1NttL0nL63tgitXn3dIPH2me1ClmSrTUfx6Ro/V4Zk5a379C/6UvCA0qBIH/bOx/5OYGmmin1EMza0AS98/Pz/qqzJEm8c7L6DNbr9RRYunjxIhqNBmq1mgeKzCE2NTWFer0eBEkA8HeSct3xp1wuY3FxEZubm97Pj8+pk/+VK1ewt7cXHKzT09M+yjbmk6OHLLU0KqDYwBAAUalYzXGnpeDhvJxFzR6QFr50famvnCXuxcPDw0DDx30U07Lr3M3NzflD1ApoWetcBSHlqUmS+Lueb9686XmMBap8V7PZxN27d1ORt3pFo6ZwofXDCivaV4JlTXquGqV2u53ikb1ez9dB37iDgwMf/WuT5dvxio1PTFi0ND09jStXrvh7zhVo0SxLdyLlz9T49/t9vPrqq94P3Dnn09eQdN41B+iFCxcwMTHheRv3+KVLl1CtVoPoXyV7D7oGbqn7jRWIp6envfCrY0n+mHW7B3mLWs1OiyIGRmedBe7qO2mfUZ/RGEhX3kQrCt1dLE96XIJoruF7SKLZQaUfHuTqZ8fJ7Ha7aDabQTJl51yQi0qluRiT5GEe0wrRpEVKkgTr6+spH76YRoZam42NDf9sjJIk8Sa9WBuzwCc/y6o7pu5XBs+Dy0pV3LgxyR4Yb7rFxUXvI8ly/P0gG6tQKHhQTuCn31FDECM1Iaipmps/Jt2rSWZ/f9+bpiywJk1PT/v/2Z6FhQWcO3fOm/HI8BYXF7G2toYrV67g0qVLAMYpBObn57G2tuYBJAEdAcCzzz6L97///d4sptGOAHzyaY4H78RdWVnxoNOu3dgtBOrbxUPTjimA4ApDppuJaQV0HgCk1sdZIT04+KMAW2/aUN5EGgwGuHbtWlAff9/PvGRNe1pHTBsCpG/b0Gc1SpOATYOZLJi0FpIkSVKgTsvx4GZZCtJA6ANsAyZ404Ym5mX52P2oFKBVENX6rbuHVSbYVCVaN3+mp6fx/PPPY21tDW9961v9XnfOYXV1FdPT054H2vYdHR2h1Wp5X0WOSaPRwO3bt3Ht2rUg6Ty1hiQ+ZwULzVQRs/rQzcZ+x7ZT42rbzH6oEEC+PDEx4UGaJdXgkt9pSrXYOaB1naaFJqlyRf2hdb9pRgwNNrN0v/32sHS2ON4bTGp2BcaSBqN7dMJ4cKqqFxj5CTA/n264o6OjQFPIurhwWQ8/t6ZG3eyqVuZPLMzc+rkBSG1addjNknSs6QWIA9gsZmBNBFlEc7WSMh+2j/53KqnbfmrbYkDQSqJWA2jviNX8cJQi1WneMgAl+gn1+32sr6/7KGMKFMPhMAjaODw8DA6Kfn90rRTz56lGhOu13++nfKuohWCbqMXQPvFg59ipNk3XIN/D/Gb8zDqVq8mb0jfXPgCvnbBkwYIKNPfTiGgdZ5F0Taurh45NbN8Vi8VUFL6a/XUPE1CRmJaFAMeuraxD0posWQcP59XV1VRZ3Z/UKC0vL3ttM9tpEw0zLUutVktdkWjHgmBZ/YRZx9zcXHADB9ulvIiggu8icV+oz3YsMbP2+X6AgPerU8Olgm29Xsf8/DyeeeYZ1Go17wbBeufm5nzqJ50z7kNaUXQsdd5v3boV5BqkNePGjRue/8Q07sqDLZBVk3gsuhtIu3Cov1zWOFrN8/1MuvR/J99UfhM7z/QaUyuUkHgGaNCG7d/jAHqk3KT7kERNgko+VnumAEvVwtwY3KBcBPo8nf2VufX7fS9dKtDMAg42ISaJBz4/I0O6ePGiZ9i6gRlgQAZh+2g1J7HNoKlq7DhaUiZnTURarx0HBXB6WMVME6dtKqvR6Pf7/vo7dRpWnzkbjMB30A+IjFh9i2LM3DI/puDhO8l4VNPKXFlMnNzv9/Hyyy/7wA1qmGm6qdVqQeQxmeru7i62t7exubkZ+PURcL300ksBUO31egGDpUaFwK5QKGB7e9sfjoPBIIhu1Dnlu1g311UMwOncKqPn+oppwGN01jR8JO4DjimJkam6B+z/VnCx+0X3uo7fxMREALSUL1jtXRapJo37aWNjw2uHLTBkX6kFVqBAjZu1CDC5tPq4EtxpcAUjzJvNZjT5r44H96TmrKNVhZGzVrOn71EN5+TkZKbfa9YY7uzs4MUXX8SdO3fwyiuveEXBcDj096lrUA3rKZVKWFlZwWAwwNWrV32u0XK5jJmZGUxPT2N5efnUwAL1CVRt6927d32ql9h5QD6q8z4cDj1PJ3iyfJXzoYIILSW9Xi/TpKsglT7O3W43yL8Ye0bPXn0n262k61e/VyUBr+Tj/b7WT/A0APooKAd8D0lkKDFwYG/aAEboXzV5fK5arXopkD4GBAoKEAEETuoqQRBAKA2HQ7z66qte5Q6MmRuBAfvBTcuLsC2DVlOkavjsRlbgaxeu/S4m/ZCo6WHfskg3pNW8ab1LS0s+Dx37bw+904hzSkahTL3fH13rs76+HjxjNYb2wGKE2GlXqxWLRZ9SgdoL9b3UukhsU6vVCm4U4OGiebPYF/ogHR4eot1u++vTWFez2cT6+jru3r3rfWVoXlNBQ9/FG2SoaYyNC8dGtTWcD5qiY8maNQ8X67DAJYt0bT9u5vpmEPeW+mWSFJTZZ4DR2nrllVeCmxQs4LBrmLS8vJw5njGNuYJ5q3lRAZYHfsyMy8/0ajXLj0gqiFAzre4zzKmq79KbR/gZo9bZLhXarB8gNfXaZqsEUK0567D95I+aHnVMj46OsLGxgd3dXdy8eTNwibh58yZ2dnbwyU9+Mqoo4DwyLx75FNOFXblyxSdDJqnf7oULFwLexrmkoKvzbclqyiigEJiTjyhpcBDrIA+mEBp7l4JL/n8/X1+ev7Fy5XI5JYjS4pQF+ICxckXTUb2RlAO+hyA7kQxqUHMGkFbl6uf8OTg4wMzMTMpHi+pwe7CzrGrd2BZtH8FkTELkZqQWgExpf38/pakExrmIgPH1a0B6saoUaw9dLcv2JUn6ontt52l/Awhui1B/PmWowPhwOc20YN9hQSl9UmLtGA6HKT8zfk7TbKfTSTFzNZPY/tMUvLS05DPYq2ZWrxu7cOFCcGAwvxYAfzMG289UE3pXM+drYmICs7OzPmky54dridHBasJSbZoCMLaD+b/4vY24pWM9NXN8HxljsVj0voGk2P5SX5mYX6SO7YNqnD7XSNc+AYLyBR7UqlGxZPeIdXvQ9aumSLoVqEO9BeWxtiqp8MZ1cvHiRd8Omw+S65a3w6ilhO1TTTDdbSYnJ30ycP5o+ipd83rTBqlaraYi/jWyHBhbV2ZnZ6NCsgJdrcv6Oitf47s4vuyn9TFW/kvN18bGRhDsR1C1vr6OYrGIV199Ncg40Wq1fNSvzgsQAr7nnnsOlUol6CP5D33kKGwrzc/Pp9yFOEcqvFvtIs80Bc/aviytPQXomKCc9Qx5pypgyH9jWQF4qwrPI51TdS8Cxvf72qTbQLgPHjWfygHfQxA3LH2NKC1wEfLg0oXEK6KsNERJ0vpJKGPVgBBGe1pNmkb7sB2rq6t+Y1mpSKUQNekqSLIakyRJgsM35nSbRTGzgN0YOlbaDttmko3QteX43d7eXipY5rSD335WLBZ9ChjrYG21TZa4No6OjoJs7zyMLDihFK9BC5xrbVfsIGdbS6WSvwydTIsHwdzcHFZWVjA5OemBmAZtXLlyBdPT0zh//jxu3rzpb9pgXi6N5FYzmLaD4I2BHfV63TN0e3UXgaWue5u2ghGb2kcCANVOERBbrQDrfRBt7lkgBQR6EDNfoxUS9VBbW1vz5e2BqhQ71KwQo/woS7DJMsFrMni2VQ9oC7jsvdH8zEZgWj7INtRqNb9uWMbm3OMYTU1NRbWHanFgv/k82xDzWeMNStyPwHiNx3zPdFyAEQh66qmnsLS0hGeeeSa4yo3XJU5PT6fGeTAYYHt7G4VCATdu3Aj4aaPRQJKMkve32+2AByl4U6DM/ulYkTfaNbSwsBD9jpYuTW+ixKvcVLhhcFm328006fLuWmCs8CCvjLlDAeHd09pOFT6UWq1WIHjqD5+le9Hm5qbXTHK8+C7uudiZ93rpbDqxvEGkamsFcTbnGzAyU/LmAwUavK+RIEelKC56TjxTvmh0ogVvKr3fuXPHh3/rouOzsZx+ti72yZoBWEYp5st4Gtl2kbLydllSn5nTTLoc35iD7IO0Sfujvpj6bhulqwwpZm6P/U3SoI3bt2/j3r17QdqRJEl8kmyOg9bHxKfWv5Rrk8DIEg9JAkALnMkkrcSvY8d6aDY+OjoK/PbsrRlqouY1Vp1Ox/scHR0dBQEq+i7tn5roskC81RafNQCowh6BTMw/yO4Rro9+v5+6K1s15XxWARXpwoULQZ3Km7IAo4J120bOf7vdjgpTBEL081pfX08FP2g/1aSrQUEsoznc1H9VTYhcZwcHB0Ey45ibCtcm32X5lIJX+swBY8GYdWbxUsuf+RnNrwS6s7OzmJmZwblz51IZBoDxOaMWHwJVpruxwYm6hz/zmc8E5x01h3fu3PHBclmWFTtP9nPtGynLymLXqaWYAiHmH6yk/oPqj0iyZ3yj0fACfAwQan/0DnG2J2ufPErKNXwPQVZbYR2DY1op60DN53q9XopR6eGndTCv2XA4DBa+c+Gl59y46pRqJXtdbGzXvXv3AiajG4J91ohJDSbQtsSIm95qBO+nIVQtoy2n5pbTgChTU9hNFTOVKOmBRLCe5QeT5Q8yMTGBXq8X5JMjCIz1iXPHceddonoAFAqFwLTOWz7Yv+PjY7z44ovo9Xret4lpANbX172kzz7Rb2ZzcxN3797Fzs6OX390iN/c3MSNGze85M8AFmWY1HAcHx/j6OgIxWLRB4JQM2DzFaomh+3Q1EYK/kgK3CkQqYkli/HrOlVJ+iyR1RQoXyCoUG2x7vUkCdMusR6tW4VVXfMKMq37RkwDToHT7iXWzb1CdwV1ybDAlq4KKnRTcFD+RH85vWlDy5IPs710f7BBGto/aomo8dY2xbTgrEPTcSkIi2nzdFy0/6SDgwO89NJL2NjY8DdtqAsOlQQ2cMS5URRvsVjE+fPngzxyvNqTpmLtv4IZ9fsmHR8f+6vurJ+vPmctNLSaEZgPBoPgZic+p8+Q39AFyvIKEkEq+RDPXQrAsTOIAW8E8BbU23Pb+glybXHckyTxqc9mZ2f9GrHvtnzqUVIO+F4HMdJIQR7NWHYimQ1eAQoXDUGEEhcwSSVJCxQI7qh6BkZM68KFC5ifnw8OSH1eVeLAyCdOGYL6VHDx2sM2tlFiB66VXE8jzbN0GpCLXUkU0/DFEjSzLVbys+9ivQQ3Ng0PQZG9Wk3r4ZjqmsjS/rEsx4u+dpZZKzNRIkPe3d3FcDjE7OysZ3KDwQD7+/setHe7XW8eKxaLaDQa2Nzc9GlggDHYvXbtGu7du+elXvr7MMP/zMxMoD3kT6vVCjQCWZIvEK4n1djaA1P9/uwaPE1ij62Ns0jspwYN8H+rCVHQwahNrUfBoQojXPckewWWCjQx7QX5Dv2isvpAsxr3jw24IoijFl/5mvoR8jNGoeq+o7bSptOKCWMMpGKdGoVq3UY4Diq4q9+emoK3t7extLSU8tlTXz3+b8eSd9cyaINnQZIkuHPnDvb29vDyyy97k6O1WDg38oPUXK50P1pdXQ1S2FghgmcGiTyYri5ZSY05VqVSKdCAUkHBNWFBlVrJ+D6u8263611ELFHTaLWwp2n5TuNVMeKYAfFASuccrl+/DgBBOWsxytozj4JywPcayU4GN6P6sFA7Yp9RZgWMFh2jdK0TqEpbJB6glIKsdGSlr2az6TeMaupsODyZPv2zdKGpDwLbq/2yzJFMLcYs7fjpj+2n1ZLGyimQs2BPDzX638UCRE7bVPqdzZKvRNONfTZJxulQmBLDagZjPojULvBw4d/AmOHXarVg3pRZ0FdvOBz64A6CRvq7EOTpWiC4shHFfK8KBzShqqlc28h30XGdZP2IeMiqqbxQKARBG/YZZZY6BxzPrLxmpwkPZ4UsqNWxoHmSY2WjKGMHnO5xu1Z1XhjZa/nhaXXH3qUCFgDcuHHD8xR18le3lE6n47XS2mYrOGq6IGtx0LHg3mi322i1WkEgBf0iKQAqP1W+Rc3W4eFhyk9LD3nyFV5XZk3wMSE1Nm7cRzaKnxry7e3tVPaIJElw7949FItF3Lx5Mwja6HQ6qFarwfrhGNnbdVS7qqCY4x8DfUxqbE20micwxps1qMMqL3TcLFkTqo59FjFTgPZBhSDbJ44DeTjPTrsGe70etre3fRS43StsXw74niDSw1EXLSU9BVjASKJRp05drDQx0D+KTM0uKkaZ8bDVBcEFrSaazc1Nz5is5KmSPk266+vrKXU562K7NBo1tlDts6TY3a6xzcp6s77Xvwl6tC+xZ+bm5oLvs0BpFjk3DnqwCa8JJGOMgxK8Tc8AINCCKanQACAV3cv26DxpRniOxeLiIgqFAmZnZz2joXP60tKSZ+iqJZmZmcHKygr6/b5n6nSgXl1dxe7uLpaWljwgo6aEJnNl8DSdTU5OBvecWqdqlchj5qzBYJB5bd3x8bHfB6ptoimb18DFyAoFZ4Us8FcBjdfssZzlUcPhEOvr63jPe97jy1gNqtU+kXi1mhUCVeNj22k15jrvrFtvkVGNTExItOZpzWFJwKiCIfcRTYnkgwoC7U0w1Mipm4xGlltQV6vVAu2n5dt8zo5N7LBXf26lSqWC8+fPY35+Hqurq6hWq35vTk9Pe/MseZW6Uezs7KBYLOLOnTteIEiSUfYIavaZE08DyUj0WbNtUrejWFAOTZocA84LgTAFSQtwec0kn+N4M01K1o1Hes2eCqSn+f3aADPbP9s2+k1zXZH3a930b2Qg4WmJlx+HUHr2ON4bSNbHDRhtSoIDEheyhsyzPPPzKdIns2IkJzcTfVVslniSbrqJiQmsra0FN22wTCyPGTDWttg+WanqNIoxd46LgrHTFjM3J+vLYnQEcipBWb9HYBwCb/0N2a4YxfqpWgZtHxlUrI4sTZ5qBewzBIjHx8fY2NjA1taWZyQEb0zADSCIYuU7mf+OvjD0zYpFeKsAwsPHrg2Vusn82QfVgPCH/lk0+bKvNmhDD2nNvcU7OI+Pj4NAAtajWiT2Q9fJ/XJsxebkLNBp+8pGNypP4D7TtDm6r3TvK6Ai7e7uBgDI7hO7n/hdrE0UpoFRVGbMNKZ1TExM+Fyb9j2q2afgVqvVfFoWfq6aZPIUXg9ozW8LCws+N2SsjwomVLOkgqbVBnIPW2FXXU6yeC/3GrMJ6HhVKhVMTk5iYWHB81UF+NzXNh0M/f7o3kEA45wLfNFffvnlVL7Pfr+PjY2NICVVrM3qw2nB/v3AzmnnUOxZaw0jD8sSIAD46GTlNepLDYzyHJI2Nzc9GFYepe1KkjBdkK6Z19LHh6Uc8L1G0s1CvwGbxJEbVf2tyFCsyp4+JVoWiF85NBiMMrfTuV0PbPXb4ELjXYQxDZqCVTK0tbW1KGBTEKWZ+JWpK2OKmW+sH6EFG/Z9WjZLhU7HXysx67OkmL/XaQBWDx+acdRXks+REcQck1VStXcgxxy2tU2co2aziWazGYCrJEnQbDbxyU9+EgC8T55qB19++WXcvHnTAz0y7N3dXayvr2N9fd1rHsh8t7e3cevWLdy7d8/3kyDt+vXrWF9fx61bt/zn9D996aWXAIw1xfy81Wqh0Wj42xLYnxdffNH3VzWfBOXHx8eBFsIeGDZgSbUs/FtTXMTm9b8EYjACScG28igVfmySa5JqxmJm0Gq1GvAeq/2PuS5kvYcCCwDcvXvXf6c3B+kcMqJUU7g45wJTI4UB8mw1s7Is+0N+d3R0FPWvVm2X9k2TNFN7f3h4mEoQD4zXMJ8n0LYaRZKCbzt+7XYbt27dws7ODm7duhVElH7qU5/y/YiB4bm5OczNzWFhYSEwgVOpEDuzdDy2t7dT/Jp8hsAmlnWBwR56ViTJKP9fr9fz2lm7btR0z2d4/vG52PgxW4UKzXojSYwYoAbELxVgUBrJnnsUfIAQwNdqNczMzKTS1qjg8DjAHpADvtdMCsgoASrgISjQYA7nRnnXWq1WgOwpnfKmDV0gBGuqGaPkaBloTFIeDAa4ffs2Go1GShNiiRvI3lmqEj0Xs00iyt8xjZGSDXZQsv/bhNP6LqUsvzqrwVhcXIymZXktG4tzarWuBPvWad0CT50jYKx5iAE+lqWQoHeEKln/TNJwOMTOzg729vZ8u9jmg4MD7O/vY3d31x8CBFnNZhNbW1vY3d0NJNVOp+OBIKPM+Ey32/WHsgVvjJ7jmqEWz2r5lKyzP5A2A+v/MdcHAFEAbuksAj8r5OhYaSJ23dtcG9TMaF2xsdUDj6TJv2PaPKvBII9SYVKfU+0XKWYCZrutrx2AwLWGPIyac9ufWH5TC1r5roODA39jh7Y5JlwXi8VgDqzlgmeADUADwvtftbxVBnS7Xayvr2Nvbw+3b98OeNTm5iYajQZu3brlTbPajiRJvKuQtp9ZBRj0R35FJQWJFiTbP2vKdM4F6ZU0glrHnFpBTT2mZK+6UwGDPCfGJ+37gHDtn6YVVIFSBSQqVUjqWqP9tkIVFRgPKgQ9SsoB30OSXQwqSdqNzwUVM3V2Oh0feaRlNWSc76O5uFarBeBBFySJ0pumAuH36hgLjJmq9TNjPYzgY3t1DKx2zY6HpZj5xm4SlXLtb30fb9qw/nksx78PDw+j1+48qOlAn9F5J6NiShH7HE03/F7fnwXW2C7OJ33gOCacu3K57CMqp6enU/2anJzE5ORk1NRsDwwSNZKW8VjNiZbXw9z6UVIjwzXIduvVXdQYW0CsecosUNe1rv5d/L/X6+H27dup/gGP3yn6zSa7p3SeK5VKpimfc2sFySyhyGq1mcJCD0aWY12WLFjXttMv9NKlS9G9nySJ3yfVahWLi4uBK4jV3HAdMQlxzNdLgSzN2xbQlEolzM3N+UCkrLHm2uetNQoSLMg5Pj72Jl1rerTjyM/tnHDcLT+mULa3txdYhgheNzc3sbW1hfX1da/Vd8759C72KjNqQ0nnz5/3PuhWi2W1g6o91nQ3ujZsqpKYVjK2LjVoyz4TS8DNOSLF1rje6sG+2/bqWFgLjpp/le+vr69ja2vLa+CtYGHPvEdJOeB7SOJmoiTCA0fzMukBWalUMDs7m1KP87cNrIgBRmpb1AcAGGtObB2Li4uYnp72m8gyTNbLRXvlypXU+7XOQqGQygZvTdHaJyWCYN2sMcme74mNkZXE6JvHNvN3TLtoJS0dV5L9TseSTtDWYVjTD1jinHF+NJM/JVmrbeBBZSVeezgqENKxJZjjxecEVGTIBJAzMzM+Co118eCcnZ31dfLgmpubw8zMjA/AoJl6YmICc3NzQTtYH8dHNUv0ZSUxOhEIzSYcF5rrlFT7aLUxPEDt3cY6J2cR6FniOGqUJTWrCiJ0TxaLRX8lH5A+cHRPWIFBsxQA41x/WXtcD8/YAc+9Qq0U15SNlrT+WNo+vX2DxLLKN5xzqNVqqQOemiEtFxsX9l2FYU1L1Ol0Au2crc+mmsk66LPAd7FYxOLiImq1Gur1eiAE0wpFkzXXBd/VaDTQaDRw7949Dz6Akcl1b28vleaFPI2k2nrlgRZsAyHgY7ooDaSxfDYmkM/OzqZSg9G3nbwqNn66FqwrQ9Z4092KZWLjrxrx/f19r4HO8jM+Pj72qa/U9P5GCaJPPOBzzl1wzv2Uc27HOXfknPuEc+4L5HvnnPs+59zdk+8/7Jx7ztSx4Jz7aefcgXOu4Zz7Medc3FnlwduV0uwBY58ja8/vdruBHwUX2fT0NGZnZ4N7LoHRAtV8YyRlbrY9Vt18+/ZtHBwceJW3fqd+NmqujQExZazWf0LJAkolbgCrQo8tcGrCrGRpSUFmrF4yhpWVlVRYPp87rc16CGX5VlLTENMYxFT2bCP/HgwG3hdPy3ENNRqNVF68wWCAZrOJz372swDGviYKjtvtdnQdalLjmCk4Ruy/mkxUg2LNxurDpEEb9CVSvywFwcokeXgeHx+nkgGr2UY1FtrWrPt0Y5L066EnjT+dJjydvCvFg3QsNSJaD2ItT1KNxlNPPRUdTxVCbDs1F53dh1zvdDOxfnP8TaFof38/KjypNo3rglG2Wken0wnywbENKlSwHkaw8jOC0Jg2nXxIQZaOJ8EKNYY2VY61KGj/tRznSsFekiSYmprykflM9RWLmLb8nnuQ0aQKqHVvvfLKKz7dC8e43+/753R+Nfgqy/eafeZcx67kBNKaPsuTLOl39h0UTixRa8326hiwrlu3bvny1KJSGBkMBgHAp9ZPx5Pf2XPrUfCmGD3RgM85VwfwGwCOAXwIwNsA/PcA9qTYdwL4dgDfCuD9AFoAfsU5pyfwTwN4O4CvAvB1AL4UwI+83vZRerLaPDphqsRJvyi7acmAbKRjLLULb9rg9Tm6mCyzyfJtAsZqc258Mj/eysA6yZC0f5pvzjqwqtRu36ub7bS2AQgWvJaxz9h7FXVcLZCNaUBjZAE2mYreAmEPAO2fPgekc9KpDwffleVbmCQjJ+bDw8PU+mi327h27RqA8KYNai9u3brlHbmBMdDb39/H5uYmtre3fRJlPnNwcIB79+5ha2sr6G+32/U+fARrDOZotVq4ceMGgLG5n5qlTqeDVquFnZ0d37ZWq+WTjwLj64t4wFoNTMxczgABkmpYswDG46AnkT8pMCgUwruLmaInJuRwLVttaqx+LU+is7zmYnzYQ8u5cZTuxYsX/ft0T5PoI0f3FX2n3jjBNVGr1TA7O5sqa6Mp3Yk2XJPp8rvp6WlMT08HfEfXIHlmqVTCzMxMynfSghT1AWY7YqbxLODd7Xa9vzavmCOg0jygsXGenp7GzMwMZmZmgvOEGnxex8YzDQj9Knd2dlK8iXyGliiuk0984hP+OfqWa7oubS95gc2pqf7FuiboMhOLzmf/LZhnefLz3/u93/PP3Lt3zytLbJ91/6jvdq/XC/iPTTtDvk+/bHXd0XmNBT0+KnrS8/D9FQC3kiT5P8tnr/IPNxqh7wDwt5Mk+YWTz/4MgA0AfwTAzzrnXgDwNQC+MEmSj56U+QsAfsk595eTJInbfk4hlXT0HkFKe/RpOD4+9pPY7XZTJl0AqYXHRaEaODVbab4pEgGilfyYm8nmM7Kgh46nTz31lN/YVvpiu9Ucxyz5D8LY1dyn7YhpWqyUyzbYQ6bZbEb7Zf/XCC2lLNAZM0/zmiBlpuwPTTexdwNp8K0mhUKhgGeeeSYoq21TYJ6llbIHOIFio9HwfefP0dGR11CQmWrgyfb2NhqNRnBrBtMzHBwcBBIufxipZrV/PMwYpcj3xAJ/2HceLmoStoCYyaQtU1XGGbtX1a6PR0BPJH8iDYfhdY56IFnBLklGzvvLy8u+PD+3whSf0UOw2Wxm8oEszQvnVttg36VtyRIqqSWzvJVmPiX1l9K6a7Wa59esl8DD8sJqtZrybVNwomCOifKz9i4FHJucWbVtJMsLSd1uF9vb2zg4OPCAj98fHByg1+vh7t276HQ6UR9OjqFaQdTFQ/2HgVDrxksDdHy0b9pmvf+bAEx5rXMu0ArG1g2jsVUrR+DIObDzu76+HihV+Kzl8xpxawGiWvL03FJFC0FybI1a4cK+m/QYeFRAT7SGD8DXA/ioc+7/45zbdM59zDn35+T7pwGsAvgwP0iSZB/AbwP4wMlHHwDQIDM9oQ8DGGIkcafIOVdxzs3yB4CT76LSKyeQiy82cZoJnof+1NSU95lSzUSlUgnyJhEE6v24NqTdtmdnZ8cf+PY7q+IG4rmAuDjJKGOAzf4dk1Bipjf9WykW0RtTu1snZLvRWH55eTm4P1PbHwPPliww1Ii1mNTLZ5IkjDpTEKJjb6VHMlweZDMzMynmUa1Wsbq6CgDBTRZ6CFarVb9WeViSsatkabXTWl6l1VKplEobYYEsnyFwK5fLXhvC/1Vo4N9WGGA0eoy0vSrlq5bA5u7T9aGfvU564vhTbK+TNG1IDPRYlw2OlxVCOM4aoXjlypWATyh/yQKCKsza+tlWgoSYSZjzzps2YneSa9+AsV+e5dEEXdomFVZ07RwcHHgBT/umaV305zRBk647miTcjov1N7Nzx32lPnoszxRHDFyzc39wcIDd3V2fN0/fb6NeWaeCHN7Fq23k7TrWJcnue7vnVYh+UA2xLR97LvYu8jgVxrV99Xrd+z3G9gGBX9a6jJ1zwOic2N7extbWlj8TssDfg/T/tdKTDvieAfDnAXwWwFcD+GEA/8A5900n36+e/N4wz23Id6sANvXLJEn6AHaljKXvArAvP2vyrJ+IarXqr+3S4IVYQsckSYJbL1QDYqMgY88C8ClcqGbXg1Yj1LStjFa0DETbrDdt2AWq7QJCFXbMDAKkc8sBY0BkpWV7CGd9Hlv8KysrKX8H3XAqJZN5sV57GMXaoWSdgvX5LLMsf/Mw0XQJXAMxHz59jgDfjt1wOL7XmAevMiYVJIAxSJ2YmMD8/LxPPsvvKJ1aExSBJ5PNUrsGjKOpySitts3exKGMkhQTIDhfFHLstXXUmOg+0fVF8/QbQE8cf1LiOiDZ3IR2r6u2lt/zc+D0PWLvsWV5CxZILJelRePaXFlZCXiiFRx5cE9NTXmXCX6vYIXrjACLe4dlqf1S0FCtVqOBdgxW0jZYP17utZmZmWD928Od/J9r3I4Dxz/Gl0lMBl2tVn2CZdalQRvk+cqbmKZpa2vLJ2kHxrk0Gb1LixMQpjyKpRrTvzV6WHO42sTVykf5E/M3pDuTFcApjMZ8/mZnZwNBVYVYBXJ2L9jzJ6Zx1P3Cm7T4vHX/oYWj0Wjg4ODA86osv8PHQQ9k0nXOffuDVpgkyT94+OakqADgo0mS/NWT/z/mnHsHRv4w//gRvsfS3wHwP8v/n8YJU+Wi4PVNKhXwkNPs7MD4sLURRqyn2Wx6kyHr1wge9Qnke7iZSBY0FgoFLC8v486dOwDGB7f6j5EIClZXV/3m1M2hpFJ9sVgMtJZK9n99zjIvW5YSZJa2jcQIvixNIWl3dzc4LLh5YxqfmAQda4NK/bGgjUKh4ANlrHSplCQJbt68ife9730AxoyGB9DBwQEqlYo/vPSAYiQqQbgCpqOjI59bzLbbmrW0XvWfY3v04LcmpiRJvLZDGT8P3EKhEDi4d7vdIN+j+vCpawT7S38gXi4PwB+OsfboIRkb65hP1OugJ44/kTiesZxz5CcWoBUKBdTr9dTLsvaX1q3mOtZl91aszuFwGNXKxObGZhogDyyXyz7NBwUh59K5SVVTTT7MMlNTU9Gx4ppUEDs9PR1c68e1ZgOF6Oqh69WOJ4HBwcFBcJbYvvLd3Ec6PtyjjICn5oqAf3JyEktLS8FVlLZeC3C4VzWhtQXHhUIBd+7cCZQW2h+eYWyP7vvYVZv6bqv00L7atutzsTqZozEG4PguVY4AwO3bt4O+Z5nWNaDM3l1vNYrWkmI/v9+58yjoQX34/u8PWC4B8CgB310AnzKffRrA//Hkb2ZyPHdSFvL/x6XMilbgnCsBWJDnA0qSpAugK+UT+Ztl0O12vX+c+tlRw8eJjOUxUy0hHYPVoZ/Sii4EZSB62HGT8R387PDwEF/8xV+Mn/u5nwtAFhe/BZ8qdSlD1mdjd/9ZrVrMTya2UXVjK3HT2A1jgaJKlywT00IsLCwE0p8e/KT7gUZ1JraSLJDtaKva1NgBBCAAi6yXa4iStoJR+mbRbEltlo7/9vY2nHOBJEk/vUajgWaz6Q8o9a3b2trC3t6eN2WxDXfv3kWj0QjWHP1imJpA/XKOj4/9ocX20adJwUEMrOpBy+/0f5p7dQzVNENTeIxOEzQegp44/nTyfwC6SbyuTveRpsWIaapZ38l7gzHTsgTjOo+6VmLPA6O1b10dyFsB+PWrbaEwxXVB4VuFIiBMP8Oy3W43uDeXkZSqwWJ5TZbPzxiBur+/fypo1QhkFdRjNy+Uy2WfDoffs39ZQJhnBTA2EzabTWxubgaWhHa7jWKxGKShUd43OTmJarWKer0e5I9VDaXeF8/+3rp1C1euXMHu7m6gqOB7W61W6poxBncB4wBBnncKfnk+2X0PjFNxWeGNpuvYTSXXrl0LIrNpWbEaOG3f7du3A/9nuzb0vadR7MzXjBz2HFGKnWWvlx4I8CVJ8vQjfeuD028AeN589hYAnJlXMWKKX4kTBupGPi3vx8i8AgAfATDvnHtvkiS/c/LZV2Aknf/262kczQMqjfGA1MUEjDYwL1cG4vZ6KwFwQ1tHWg0G4aKgBk3fubOz46UxBUYqATo3dnC/efNmpsRFijncW8kpps3S96s0GSvLOshIVVumxMTLumliErAGE9j36OEQ0wqxjGqcbKLMfr+fMjvq2Njx4fs4/oxE1O8pAJRKpdTdwtZnxkr71KpVq9XUVUO86aLdbgc+Ozw46Z9khQkGerA8QR0Fi52dnWBeVQjScbXpYLLmX+e1XC4HpmR1jLZjrPswVq8VZl4nPbH8yfIFYCxY6Nq1h2vspg3WZ83xuq7og2aFGSB+rSHfZ9OjsA4CDPVPjQWtWZ9TBbQU0mw5K/xRa6X+VLyznNGrdt+qS43em6vAuVQqYXp6OuANOn66Dnd2drC4uJjStNk5tYKpcyOzOG/O0btvgbHWcWtry5scrTuKJjvmvGnQhnUZKZVKOH/+PIB00IaOs93b6g5EtwwrxNvgRUt61R2BG5UqqvhQYs67LP7EvsUED+03/9bx1/2luf64JhgopXNn9wHJavns2DwKeugoXefcBEZOydeSkc/J46C/B+A3nXN/FcA/A/A+AN9y8oMkSRLn3N8H8Nedc5/FiMH+LQDrAH7+pMynnXO/DOBHnXPfCqAM4B8C+NnkdUTAAePDSxkjN75d7JqAVsu3221/04ZKG+q3ZX3lNOJV/f6A8BA7f/58ZoJLVSOT+S0uLqYWHTcuN4tNUWD7qLmHlOwdmMq4bD3lctm/L0u1D4yYRlY+JyW919EyTTt+rMPWGTvILOhT4ncEiJSylckTnMX6xnGvVCo+eTbncTgcolKpeK0AgzqA8YHES9Ntuh691zfmd8gDUcdRDzD172N76EOkY6NBHgzaoNZAD3GCkKyDR81wlnTcFLjEfPjsQfmI6InkTxSQLNk9aH2XnAtvUeC+UL84fV61G7w2S01Vp2kolH9peVLsOjKuBf2c/HB+fj4VIWnLc21PTk6m6q9UKuh2u57XqcuHrmsGJNh8pNS6W57Q6XQCcKN8muN7fHyMra0tLC4uniqAniaMa4CBEkEQwZ61vjCYgznkuG7oDkC/c7YpScY+jMDYemL3vvURBsLEy+Qj9jxUl6QYWX6m60jXk5LyT/L8mBBiE8LreU2+yzFl22P3o2sZIARudFGxn8eE1kcN9oCHCNpwzk05534MQBvA7wO4fPL5Dzrn/p+PsnFJkvwnAN8A4E8C+CSAvwHgO5Ik+Wkp9gMAfhCjvFX/CcA0gK9JkkQdKr4RwIsA/h2AXwLw6zhhyq+VdGJ404YFJFkTRSd2XWi6eVQTp7mP+ENNEqUcDQ6JRba22+1oslLNdwaMF2rswnkFtexDbExYlkwtxugfdEFrioLTyvICblu/PTx4GNjI2piEad9FZkSGqvkP9b1ZpjBloCpdc5yGwyFeeuml1LNsgzU9KFlpUvs9OTmJubk5v7YofRJwTU1NBQckwRlzlJFB8vN6vY7p6Wl/qwYFG14f5ZxL+bOqKVvH0oJJHQ/7N03HenOGrunYuCRJkkqTY9eefvaw9CTyJ6u1U78y+lIqqT9foVAIHOuB+J7i3wqmpqam/LtVSFWgk3WHsp2HJEm8VoyCrx7QVkNCzbQ1r1mtHwB/tzMT5Fqzr44Pk4brvu31esEd1XxW16Gacnk+qOaL/eHviYkJr+XXSN+Y0BmjUqmEer3uEywrj6pUKt6PzwZAcG+1221vfdKzpt1uexOxpkLRYBhmSlDSuVdeqYKeCqkKrNWaFSP6x1swbt1mlJaXl6O5XW3OSB0f8kA7XqqUsXyG5zLXg56deubQvKzuX28UPYyG7+8A+DwAXw7gl+XzDwP4XgB/93W3SihJkn8F4F+d8n0C4LtPfrLK7AL4U4+oPf5vHoZKNq8eaTgcBptKpSaCEU2EbBeyHr6URNXPI2ba3NjYCJJHkqxvHAHW9evXT1UjO+cCpq2AUyUoO05AeHuGleayxljLxsrxGiF9Tn/0GW7ArAP/tD7bNuvBQ0ZhfcbUJE1GaSVwrgH1aeNYcv20223vJwOMQfXR0ZFPgqypd9hWpp+wmkeabjXPo44B5zPGxFWwUF87HrRqdmVZ51zAFLvdrpdwAaQSeROsqNnm6OgI6+vruHTpEoBxPjkdQztnWYejPeBfLz1p/MkCW90f6qOla5Pl+/1+kM5G954Kk5x75SO8cs+OvX6mEcNsQ2yt6UHOfcX1pIKtrsOYKc+mbbJ8UJ+nSVdpYmLCpzZS0hRAtq0KJkqlEmq1WuCDGwsCoEl2eno6EML179ha1XmhgsCmn2LyaF6zaHOyqhZUhXXyh9ilAN1uFx/72Mfwnve8B/fu3QsSHrPMwcGB1xhyTDSpt2pDbV9sH2PE8bTPxARAWoIs/9W5SJIkuGVmY2MjuBnLCpmsR4E/3adYZ0wxYP+2+5V70p5fj4oeBvD9EQB/IkmS33Khs/DvA7j6SFr1OUB66CvjpNkASAca2CAMAD4Rrk1am3WFGUGfmkIUQJKcc/5+RbuIVJrSulVDo/3MWoQxwBZj+mw3v9d2x0jNLUq2vDpasz0x0qvJFIg9CMjj/BEkZWl0Y1omYKQRVWajdXNM1NShBzLXgUa0sb8M7wfGCagVXO7v72NiYsKDLba/3W5jf3/fM2TWy2z01F4QcHLcdnd3AzNpkiQ+Cm9vbw83btwIAj14OANjQMr+KGO1ub/s+MVM+jqGsSTdwOnBRacJEZ/rZNeYNUcqcLN70O4fC+ysmUoB0ubmpi8bE4osaTti+zZmSdA+6h6iBpquIHp4KtHVoVqtpky6em+qktXyFItFzMzMBFpUjotqU7luj46O/OcxHsF9srW1hcuXLwfrPpbeSp/luPL5ZrPp/dXYr263i4mJiagFqFAY3bk9MTHh07kw8wLHVX30lCeSFygoIhEoagAGgECYoCLCAipmq1BtoxI1rlZwJi+yLj7AKABDA3rUxUYFHt5YBIwAnzXRqylXxzFGMR9Gjp+9L1iFUBus9qjpYfLwLcPkjTqhGoBH38InjJThHR8fewmIC4cMLBaRqYlwtT5lqNaXjETmcHh46CUWKyVayU0Xpv7PxcvNyA1dr9dTDNpK4RpRGlvQXLSnSaR6uMckstgVdLEx2dvbC8bdMjQSNYFZ4MBSTHpUDZsyCfY3dncrv48dvvy7WCziypUrqTGixlev4dG5ojTPOux7e71e4JOj485riGw/6LNjpW9qGvkcifPc7/cDRmzHX9+jfc/6W/1LOQZqDtKDWfeX7acdE6WzCPZIqtXSdalRukBoYud4qPChZIGkHT815aqpSrVoNhk262Vb+L+22+bSs3uYFpVGo5G6KUOtDuR3vO7PXjWm7WD5drudus2HwXe0dGjdsb2m+4x90PfRN5Y8QAUV5b0x8Kfg8ODgwN99y2CVJEm8uZZ59pSX654gANF2TU1NeR89DfQrl8tYWxtlAYpF8dJX14Ie5R20Ylk+q32KmTvVUqSAkH+TT6vlgH6IetbEtNCWR9gzNCbA6HzZ4Drya/uM3RsKwLMEpEdFDwP4Pgrga+V/jtKfxSji7EyT3SScUDUtWFMCMFrAmteHNDExgampKa+1U/Ot1VIQSKp2TpmhHpjD4RB37tzxkpQ1L2v7uElu374dSBmqbSKpeS52u0SWlk3L2sPDkg1S0b91/MmMTtMWKsUYhG2/llVSXyf+rwA2ZlJyLowe05Qu1pRmn+O6YtoeZQYMvGCk3NzcXMBgKFzUajUPCrluuN5srkhgfEWVvZ+4UCj49A1WM8I65+bmfH3/f/b+PFryND0LA58v4t6Iu8RdcqvMyuqq6u5S7y2pRxIgMSxmEcMYhDEzjDjY5wDDwQbEeAYzYDM+gMwYs9mSZqwWthmwjcFnfHykw3hG1qhHrMIIAZK6W61WS91da1bmzcybd9/vjfjNH3GfL57v+b2/uJlZN6syb8V7TpyI+C3f/r37+35KHOhQz+eZM42gEYBNPjyOBJ1AOINHTeY4iOb/IkCTMOngwggwihaNylRGj+Omc/T8888XTLfWAwzXEP0/FTSHW0SMFRdEbhl8RhMn876fptE05/RlI5PJdUhhw/vlQjmZpSialf5zkXZPGVOaO51uaBt9bLQN9DnzfUPcs7u7W2M+qanb2trKjKIzq35U2WAwzHNIho8nbSij0263s+ZQmXQV2ty8z3f5vDP3BHVL0PFwvKHWKuJPfb6qqtqxfvqORidr+Vqv0y/9r8oABfXDp0bSTctPEi89DsP3fwHwH6eU/hqGJuH/Y0rpcwD+AID/4Dwb97SDalmUEVPTmi4UN6umNFS5M1KKzwAotDO6qJk13Z1TI4K4vLwc+qDopmpilpR5VCmlKd+c1h1tVGeYFPz56NSQCJThYt1+DxhqAl0DGpXftNFSStk3htKsEicy4l4WN7wzfIpUB4MyaEOJimoworbpuvD7MzMzWfoGSh+fxcVF9Hq9zCyzX91uF4uLizmbPee+3W7j0qVLWFhYyFHffI9MIvN4se1KfDzQRX9HzIEKKMydpoyIn1ka+eWNW6cRY3FRQDUGzjj3er1CqxAJP5ErSaQRqqoyBxm1d0609flIWHBnemcmPaE5UBJfrr+lpSVMT083CnQqgKjgokKLR6e7jzbvLS4uFsEtXK/OYKpbhgvP/KaCgAl83WzItus4OkxPT+Pq1atYWFjA1atXs2kWGDJW8/Pzeb97WUdHR9mtSGkOGRMeAaZ78uTkJCcpVu2ZQoRrdcz8hCq1YDRpxoAymIL3uY4iJQkwOmnD69K2uXDkOFWZMV1bKli6L6NbUFiu+6Iqr6Bj9iTw0yMzfFVV/RMAn8GQ2ft5AL8FQxPvd1SjPFIXFnQR0DHeJ5QqcH2e/hK+kJz4AyPE54lEGTnlDsZkDHwxUurUjUWpT9XK3CQ3b97Mm80ZD9YTmXSdoY2k8HFIK5KCIknH/29sbGSztKvb9X1qnx7WpOvg2i5FNBwvJ0z+vPs2AbGzsM41NVUM9uEaOTkZHhPHyFX3rQNG0Yh6Pijnne8r8aFJSutxl4EoypjaFUbzKcPFd9QsRvMbQf2bdE0yMIP7QN9xDbAycMqMjpvPiw7c54ondnZ28noFYreP6KQNgu9JXR/jolb53quvvhqW66YsZSbVNEfhw7WNx8fH+bQi9oVrgP1ie5gmxV0w3JeKKVXcZ6yqqmzqjZg77T81h84IKkNDJuWFF14AUGpNIxzcJKiyn56epdvt5ujd2dnZ2pwz0l77r/1Rn2Vl+L785WG+8fv37xe4hEqBnZ2d/C7xuQZr0fcvco8Zp+XSQEUdI6WjDnt7e0Ub/XleVzxKE7jW5TQGQOEaQF6A4+egQoquTbfWab/OGx4rD19VVV8H8If8ekpprqqq8baUZxx8wxHpKDHmmYS6GOkTwgXETcTzGim5sBxPLqvgUiLb4Vq+e/fu5czkUdt5jfdXVlYaGTYuyCg5ZbRBva5xR+k4uGazqY/Ly8tjj2FTiU7LiDZWk6aTz1D6jRhrDx7RuhgVR8Su9Wn+Q30HGM2tJqfluqGUSGLk/kTA0PS+traWERLbvb+/j42NDezs7BT3Wq0WDg4OsLGxkXN2cS0AyMEc7gpAV4Vbt24VzJsGbTAFBstTYqsScTTuwMgniKACib9LJDrO6f9JIdOnBZRIKURrl89z7ajLRqRB1ms6Jww4cMKre9ZTvigz4B+uC5o6nRmoqiozcsfHx9k/zZnGyOfWmdHBYJBP3+DYkbkkM+Rt0/EjE6U4m4R8bm6uxlz6HLRareL4NbbpLCsE90G/38f9+/dzAnTVgB8cHGBqaiq0GKWUcnom4lJqZqnx5ylQKiioIBH5+2rQhvZDgyLo3qTzQFrkKawU+J5ad1SQJbP75ptv4qWXXgIwpGu6zohDfW2ocMF6Ihqnv1WJoNpqpznE2WSyqXF0mq3vPAl4nDx8fy+l9EJw/VdidFzQ+wKYQ0/NnkBdm6XMkjMYatLVCWe0EssjQun1eoU6XBEMy+Wn1+vVTLpElGpyUclNwSUbfdZB2+MLGSg3ht5TRKLj6hKcbnAFl9qiDUqTbpSWxdvShGQpIUYRZESs0XtELloH54DjxnQjep8aSZqg9DqfY5oFzfDOMSGy8jQQg8GgcOr2sfSUQorAmXdSn2cffe1EJlyOrTJj7I/67Oia4G9N+eIaIR87MgFnwUVk/NRNgxofAnOfOZHRva0Mn+8jfc/nnNobTZzteCnSHupcsi10XQGA554rTp2r4R+adC9fvoxOp9PILBE/0bfVHezdAsBr3FuKZ5eXl4uxVPcLZwRcGHSmk3tubW2t6J/iUJ1Tlq97nNrI/f39fMScRshr0Ibm9FQhV4/yZN+73W6Rw5R96nQ6OchkeXm5ZrngunOapHMXpchxxlEZc33PmXY+r4KGMm9Od31dRL+pKY3oqpbjJ22o9jxyNeE6IT1pUhREbjrnAY/jw3cA4Isppe8GgJRSK6X0vRgmC/2fzrFtTz1oygygRBKqsiXRWlpaKqRFbgIyI6pe1gWv5jCPMFNmUpkdmpDJ8FG7pAiK75PovvDCCwWSjrQo6myrm9yle0fMkf+E9lMhyg8XAU26SrT0m3USaTX5C3n7vRx++6bXd3zj6jNqLlWErdoFbYeum5mZmXwwvCNUEsOFhYVCW9ButzE7O4uFhYXMLHKOeVwUT+HgWuEamZmZqfm70PmcEr8SBdZ17dq1WtZ5ajU9SEP/+7mSzhwSoqAYHS9f0xEDHpV70cAJoeIn/vZ8ZBzDqampfCYuEAtZuj90325sbIRaONVUv/XWW0XZzhSyfApYwMhUrFo3ZRBZJzV8fBZAXp+6PphZQbXm1LpQY8jP4eFhcdIE28dMCd5PZaZI0Bn44HjEo+2pjYoSB0faUsVF1Ggzoj/Cr3rMnOKg3d1dbG9vY2NjIwe+EFcxotlzb7ZarZwomhkQFDe1WsMgL/qas68qtDFdmL+nmtVoLFSBofcU3wDAjRs38r0rV66EPu+qzUypzBPJgEAFFUIjUAZO176OOX2SXTPq5TZpeN8pPLJJt6qq35ZS+h4AfzOl9K8B+CCAlwH89qqqPnfO7XuqwQm+bnwlPkQ4UVbyubk5LCwsZImIi1BTcSiwfEe4/my/38edO3ewtbWVN70yOYpEiAju379fY8Bc9T3uvMFxEokSbLalaUG7j5ZK/wrLy8tFbkMlHMBok+lJD4qgH2ZDsd5ut4ujo6OcV0/7Q6QR1a3pKqLxb7Va+PrXv16URSmQkqAHbbA8T3aqRICaDDL6XFtk2g4ODop8kewDmUH3MZmfn8fBwUE+UUHbSyZSESLL8znRvnN8eI0fEl6O1/HxcWPQhmsrnEFpms+LqN0DRk7huo4IXEdNUfAUHL084gB/XrUbH/rQhwri7XPhZnbOXZPPFutjKhlei/Aq3Rtcqxtp19RMx3IpdB0dHRVrj0ywCm/AyPqieInaeGXCKHQfHBzUmFsdF5bJtvC+4jQdH8edU1PDow03Nzdx7do1dLvdLEQy8TJNs/o+NeH9fr92zi4ZYz2WknB0dIQ33ngDL774IjY3N4u59o+OvUbBNjFu7fbweE7P2xq9x/FT306uPY0IJx6MlA4+roTIQhC5Oqlg6Yma2QddT1GqGS/TlQ7nCY+j4UNVVZ8F8H8H8HsAfBuA3/1+YfYc4dH3Tv0b3HzIxU8JSieYkpcjJlXL8xoJMo+y0g3DCDNtHzVDWgaRU0SM9/b2QgZTIy6bzL7OBHg5nrwzGk9CJNlFBNql2SYivr6+niPNHJraou2MynVNop+0oaDSawRqRotMEzs7O0XOu35/eLoFzy/Vsxn5HKVzEnAS16OjI+zs7GQTLd8js8Xr2g5ePzg4yBoXrmlqAlZXVwumltf7/X7h4M5jsAga9KTSsZrNGancNEacB/2MC9q4qMweMMrwr4w/QTUYLviQGKlj/Tgm2pnJg4ODmubKNVI8+9lBGSS+w/3k76iWiZ9Op5MTLzeBmnR5lq4yfqqJIl6ktUb7wIj1xcXFGkOj65E4k+vaTZEcd9ILRjmrIK5mQV4D6r6YSlN83EmjLl++nI9X47xSAOx2u7V0JwQyhNq/k5MTfPWrXwWAnOhZrUvc81RwcF/r2tLTgTh+im+aAveagi/UigIgRz0DyDka1XqmShm2T08CuXv3bi3VT7QXnEZHuEXpNNdZ5P4UKTXOGx7Hh+9SSumHAfwRAP82hoeGfy6l9EfPu3FPI+iE6gIj0ISlpgd+FhcXa4wWVbyUrrj4FEko49jkf+UbJKWEpaWlrDp3ZkMlSRLHl156qWAktc983k26BFdHe/uifHlnaWO0nRHoCRpsjxMOYBilq0iNiDaSQrUvPu6aN8k1BOOCNtg2N8tyLShRiwhrVDYj4W7fvp2Jrd7b3d3FxsZGNicpI7i9vY3d3d3ayRhHR0fY3NzMvj7s28nJ8FSP7e3tIl8YpVUe8+aaN2q1PdWHa3sjnzwlslNTU4V2QAmz+9k0+fd5mdFYXxTwvUjgmtWP+gpRU6TPK/h+0bLv3r2brznOYpuYyoPXXQvM9uma39/fz/1RE632hZoq378MQlD3GNWa81n6qymO4p5nYJT2mdkZdEy4T9gXCmWMlGe5kRm70+nglVdeyX3UMYqEGo9UPjk5wb1797C1tZWj5fkezdKMVNVyqHWbnZ3F8vJyEeFL8zDT3XibVevqpnRglGVAaYEzfKqBVU2nB0soUBMZjb2uGz17e3V1tbCScB5cA+zuD65t9T76fCnNjyw+LM8VRE3lPwn89DhRul8C8BqA/0VVVa8B+Otp6M/3Qyml31ZV1W8b//qzDYpsuKmdwVFzISeTpgJeI8zPz2NxcTH7O1TVULvnzsKqOWnSICqhq6phZntKUvqspjLR9qjmRa+rmbfJGd4lTd8YbioaB8q8uolFgSZdbYO+R6CWY1z7+L5fV6Lo0WEskxqwqDwvCygTEwOlv4kijH6/nw8/V0ZxMBhG/fZ6Pdy8eTM7qytEhF7HyNuk7/laVpOeA8eH5j3XNKh2mMKFap3oR6gERZkRlqHvRGtBESoZAIem+bhowPEkc0Kgycn3gDJ9LuxFzCH/q0n3gx/8YP4dCXzUKClw76gAxfc4f3qajhJHN/kz8M3L6Pf7hXVBg66UuTw4OCiidDkWZEj5LAUcHQfNcanjyD0a4QZlYlNK2S9b+6htcbyjc0FBjulp1DS7s7ODqqrw4MGD2qlQwBDXerJmAo9W07QtKQ21xt/wDd8AYOjr5qZiAJmeRdYer4fj6sJaJOw7A6zjEa07YIRvozHlu2wzYWFhoaDBSr+1r6pVVmGE/dE+sr5IaaDfSvPOGx7HpPufA/h1p8weAKCqqv8ewDcDiA9BvUCghH52drYWMauaD32eOdU0pxPLiYipMx9VVWF6ejqbdFXajSSKlFI2efomUL8HJeZEDupfBpQSSURI1TeiyXQZ+XI1aWJmZ2cb7/lzCpFmARhqAh3RjdMqadtYpjJp/lwTogFK5pUEiXNOxKzpCpzZoaTNPnF81cHeta7MdL+0tJSJMstjpDcjFfUdugxoclM6Qs/Pz2Nubi7XxTXBoI3nnnsua1A5TnyXgSP8KGIdZ3pln7l3fEx1r+l+ocTv5TzMmroowLXiJw7wnn5U+6X5DrUs/SZEvpiRlp0f5prT54C6xpVMBTDKDcr6XJvEZzXxt5fH/cu12Ov1ai4YmniZbaO/K5kFrunLly8XPmJsl+5d7b/2yzU61DhSQ8p16on8I20XgWMQBW1wH3tSYM7B3t5etgZoYAddMpjWy3EncU+v1yt86FinMnykUxoQ5IFoHCc1eTYFbXjwna4l4hOeQgSMAksUd/M9bYMKMOoONW7cXSuo/IH+1jVJq0c0p5Gf4HnC4yRe/r9WVVVjvauqulVV1XeeT7Oefkhp5ICpi4hIxRmcdrudTz5Q4sRM5/TZ44S7sz43hJpz1RQSmXmvXr2KxcXFmi8IgYiKyI+O177QVTLS43Ecoek7vlE15F8h0qopA6wI3vu3urpayzHojBqAbEpv8vFxKSvSBDFtgSayJtKIynYtmkZhK6E9OTnBm2++WbzH+aRJlA7YOgaDwSD701GI0PEls+WE181XOmY0nbrzOTAiiMrMq8ChplVlJug3SDg5OSmizFUAUiHJIx5V+zwuYrcJVEvzJBHq0wS+t3QPOrPF+ffUKY4D9LcyAXfu3AmFVP3oWbp81gVExwWK1zRy04Vm9Y1jmWQ6WB+fOzo6yoRa/QC5h1kW9572AYgT7quQwd/MkamMlpt0iT/8LF0d4ya3FmWSLl++jF6vhytXruR9mtIw8rTX6+HSpUu1s9wpMJEGaTud4eNcsP88HWh9fT0MjlEGk6DCneMf71dTRKzjWcVduqbdBSSif9peoFRmqA8ix0rxhuImAHkcHe87bdR1r/0bp3w4T3gok25K6ZsAfKmqqsHp70aoquqL59KyZwDIpLm2wf3KIqlUfeIWFhaKs3SBkUpcmRAiK5oeVBpwdTOAnIzTiRzbys0fOa0qKEJ106yX7chRx6oJcY3THKiq3jetRoYqseFG438iCY9OdOY0YlyVcXFNkvbTNVVuwnetJ8totVo1H6F2u50ZHqbioWDBPvA4JkZisz8a3UipnX2n6Xlrawu7u7tZyqTZi4yVBhdpeUwozvKopdzb28Obb75ZrEvea7VaOWUG17CmodF0Gk3Cgq8PF3gUWSpR9Xf8uajsiwQ0VRIcFyjhIbjpnN+RtkrXPNN0KD7UMoC6iU4j2J05JJ5ZXV1tZCK5H2jS1Yhv9YHmNRJoFSbYVj+9SPut4AwjxzCllE26qjGfn58v1nukzUwp5bo9QELf0377uqXfnppGgSHu41nXaqJkG6jV0uPYgNJa4+4hjNIF6n7UbAMZSB1r5hoEUJy+w3KJM5Vh9n4qo604z/Pa3b59Gx/96EcBjE4D0bK4PpQm6DxtbW3VfJL1t8+BR1nrvPkY6prhf10PER06L3hYH77PA7iB4RFqnwdQAdCdwP8VgDgU8QJCt9stzI+cLEo3LgEyp5MvPC4g18YApemRWkJ9XttCtTrLj1TSqpVSMzQw8uHT53XjO3Pi6mdvu0IkwTQR53HRdgpRZFm0IYmUmhjxJkaU7QRGzD1N4JGmztsBDBlBvuemCErhaurSeed/9x9h2TTBEdlpn+msrdI5fdt2d3dr7gW8t729XWPsUkrFO2wL6z08PMztUCDxcWGjiZgpuCZRJXbVkkTmriYTeyQUPSlJ+mkAD3ZR07oy4AR3L4gEt7PGS9e7C1hkClm2l6tzQsbzxo0bNY0UgXuKZznTnMnnlaDT7Dg3N5fPi9b+eP0ppZr/LPfs8vJyyAArrtdx0jZrJDrH6OjoCHfu3MGVK1dqgqvidM6XC4/Hx8dYXV3F9vY2VldXs2aqqqrsw725uVkwUGw/1wSDM9Q0qqbyvb29Yt+pO4jOqc6V7zc9fo9CrOM1zplaCxToh+h4hTiZ7ylzyb67W0+TIOnAPkfmVtZHk7onXnaazHXi0b1NcN746WEZvg8BuC+/37egkhZQ10oQURDxEVqtFhYWFmobhGk3KKGpSZcbXOvSRaeLgYRYF/T169dzsmdtP4HPk8F66aWXahvJfd3oBwSg6ONZWhlPD6NtaNLwNWlpCGTknOn03zTpqkZCAzAeBlR74ONDDYG31QlmE/H0ZLfKzHS73exTR01hVQ39OZeXl/GJT3wiE0cXFJThVwREE7SPr9brwPGL/Gemp6ezX6nWrX5TfMeDNtREp0KM1+Fjy293qSCcddLGRdbsATGhUB+3prlXf1DfH/pftXAACh80fV4Fke3tbSwsLOT7bs513EPg+p2amqoxU0Ap2PLDdelCr2r/tGy6OeizjLJV7Q5xc3RuquJ7CpieKF8ZAjIo09PThUnX8b2OpzN72qajoyPs7e0VjA2PNWRiZRdWmXtQI3t1L6oLizKD1J5dunSpML/yPeby1Pf8eES+EykNmhg+QrQOVHOpx/jpqRks15OPA6VPOF1bCC5063Vtk9M2N3UDo3XSZHGK/p8XPBTDV1XVG9Hv9yMQQVTV0McjinxS0AXsPlXcGCSWugAjZHVycpKzvDdplBRWVlZqZhwyedwwurDUvMa2q2RJLdC4OnndN5MiWN2YEWFy7aj6u2idZOSatIoERaKESEvKPkfIRJ2J9Vlvo44B+00NoM/Dycnw7Efm01PgOvHTBZRxYuZ6ZaDYDmoyKImTWM7OzmY/TM2F1W63MT09jV6vh/39/VrQxtLSEqqqysybmnuY54tMr7ad657j5URex4pIlXPAeyScBE96GwkG43x2CBdZu8c9q/vVo/B1jjg36qPLZ7RMBQ18uH79ek1z6POzv79fMHwq4KaUCs0NmUk9csy1M7zG/JC6LnjP/YHJrHkOyv39/cJc6NpPvcZzqFmX+gHq/ux0Otmk62vSNW2Hh4dYWFgo8CQFPGfEIvNgr9fDzs5OLfeqCrqRcM1gqM3NzeK0JvrwbW1tZWWC4juWSa2qM30MrlAmS1P+0J/Q+6ICbSR8un+80lZNi6NWE6cTbjVjn/ykIK1Dfyvd4jqi8kGtRv4e17hqadVCwY/XcZ7wWImXU0ofSyn9YBqeq/v3Tn9/7Lwb9zSCLnrmdlNpgQjII6JOTk4a1ep6kLISR18UU1NT2RzhzKOq43UxRho4Inm2m8/cv38/1+XMjCImgi5QghL4pvHzjefgZ+m6j5fWpW3xZ/ibwR2KXJSJUog2GueFTJFLh0Tu/o6237WzCvfu3Svqp59Qvz88MormD2BEJDWi0rWLyqgTIoGEbdRxVAZAwTUi2l768bEdSmyZZ5LPMkcgQf1ktA7vr/ZRA2C8b5y/h3ULuMjge5E+ZpEGiQKInmjie8oJWCTkKPOoAmVKqTgXV/cHv5VBpLZFmQTVTLkWanFxsTg5BkCBg4lP5+bmsLS0lDWZfFaFObadp9W4C8HS0hIWFxcLnB+5KhBoTdB+8sMoXeIA75szPZE7BIW/+fn5HJFK2jE7O4v5+Xlcvny5MHkr80gNoTLrxDHb29vFCS2kF1/72tcADHEXzaVsP3OEql8h+9rUj2hdNDE8ZzFCStMAZHqs/pGKk/U5wubmZi3Xon+AEV7U5Pjj2qVrjNd8vp8kPE7i5f8Nhrn4vhXAF04/3wLgS6f3LjSohMXN5poXP8yeiJCMjEqLBwcHeXEp0vAIsbMWeZR25ObNm1haWqoxNorAq2rkuEsfG2X2dHGnlMKzdJ3JOsvsos86YwKUyXgV/DlGkCmS1A+Bxwp5xF40pk3jzLGNVPRAPWhDNWf+IZC5dmZRyzg+Pi58cvjNA9e/8IUvFMlpiax3d3cLpMW1dXh4iK2trcygcT0TwW9tbWUfPtbH67u7u0XqHj5zcHCQET/HSMv00zSi8111LH0O3NSo2ll3OWgqQ685s3GRwIURxUP0R3MGTgU7T1cSETqWoSZdF1r8faAeXe2pjlQAiebPn4vmT6+pIE3g+nN3G00jwjbztBrHFW7SBZD3FoGJzPf29gqBByid9un2EOFe7j3XrkV9JlOj/dLxpJLA18fc3BxmZmaKdCfKaPpBAewb55uaVV8jepgA26R+dTs7O4UWmP1gHzzXLIEWNQUdJ9aliZf9eDjFX3yfY0hQf/smOqVjrLjdlRk+fjrOCk11nCc8TuLlvwLgL1ZV9Wf1YkrpPzy998Pn0bCnFVRa03B2V+VGBIgBHq3WyKG50+kUZ5cSfHNyM9Ckq1KKLnQ+OxgMsLa2Vph0iTRcGuVvPyic9zTiLXI2jbRZDqppPGtRa31NxAsYJq3WZJdOaNQPxKP2eN21Fk1to8bNzVAcy+gMUjKug8Gg0K64NPehD5VuscqYuO8Jr/Nat9stmHPWyfQnXK9sJwmRBxapAOLpCOjjQ0nfpVu+FwknapImKFNBJOkIU7XbnnjZx9iFEiBO3dLE6Fwk8H2tRIxR+E3rvNVqFQfcq3ZO16QKMwSmf1Ki6ntta2ur0NhpdK6+Q802gCKIxIHP03rCPJdavgpqDJAg06DPkjEbx1QBo+A7PamDxF3XNZml2dnZWh5KBY7n+vp67bi2qH59h+05OTnJuJ5BG+wfx3Fzc7NgPPn+zMwM+v1+Thnmvrc8so7KjcPDwxy4AiBbuBS/R8wMUCbfV0uXrwH2K2KANNrW31PGWDNORBYQzpGuWbUKqLCg39pHfafX6+Xr4wQRuhXoforeOUvB87jwOPrD5wH8reD63z69976B6enp0IzADaeT2O8Pz6l0hoiaPz8LV6UsXVzz8/M1PwbmnPJNpukDgNEGcQaCm1WldC1btWMqxaoDMMcgQmpav/Zb31FQk0TT+/5fmT5lboGhqdol37PaqhCp/7UfEcPnCIxZ/yPwBNLAaAxoVlJJkozec889h49//ONZ+6XzyjMy6aOiErtL9Iqkuab1Wrvdxvz8fCZeBM4Rj2Dydxgcwv6x/shX5izmywWNiMkj0gdKTeza2lqhxbqIjB7BhYomzXMknPX7/RoOcIHIhREC14X7m+onOrZN26HPsrxLly7VBFXHN0wYTl9d3leTriYJ5ykKKuhwX2k7mKRZcSVzFdIX0TVUviY99QefI8NDesGgF6AUbCPGSetkWymIavoratiPjo6yadbpEtPRkAnWcW5SXkxNTeWTNq5evVrzM6bpnPiHa1C19I57/NOUh08VI2qGZz18X4+r5GkhLoiqljWlVAQk0irk4+0aY46lpp5ycA2tuqQoziI8SYH0cTR8/xDArwXwNbv+awD85Dtt0NMOzpTRp8vD/wlKaDwPEiUwnl+qC0bzCikDM87U6QuHUbrjEpsCo+ipF198sdA+Rs9r9NPU1FSWVlQboG0iOEOr0rk/qxpI1xIorK6uhm31NnPz+tFL2o6zYHp6OgdZ0DnYfQIVdHNHzCIl81arVZwxqhCNpf7mvKnjMdcHfXcoHPBDBpJaR13LdDLn2Z+8ToaP64/fRJTdbrfQtkZtdwKv4+DX3dzSFLTR7/drATlKAAg0XasW4KIDx0QZPk0xApRzxHWjzLhr97TcqqoKYfKsNRxdd4GWEDE5ZOyocXYNnZ+kwPWsa1LNuXyf7YjynjIYxNfo/v5+DnbgOgRK9wQyW3SdIFPJd/R3p9PBzZs383/FSZF/qs6DMry7u7uZueEepouRn8DBMug2tLW1VbSfrhjUnLItbJvn93Qc6gxbSmXSfs6NZ53gfm4KxtN8gRxTNTvzW4+rnJ2dra2NaBy1LqdLKhzodT0CUN9p8lH0unxvabueBJ56HIbvfwTwl1NK3wrgn51e+3YAvxvAn0sp/Q4+WFXV//jOm/h0gU5CFAYPjLQzyhjQD0mlSD7LBauMTmQK4+H2ns9PN6DC6upqRlhOZCPTi/pVEDSPW1WVfjuOCClpRgyUmwObxlSf5T03ZxI4nmxDtImBknkm+DN6jdf1Gc5FpHlkLrCmfmnbfHwGg0HNbEWES18hoH76CBMvr6ysFD58bLf6wqhGjBoFTYiq5foccW5p6nWNAtceM+6zHGVo9/b28jskggQ1BbkWL9Kc6DxF60Klf4KOj75/ERk/Zcp9vyrT7BoiYDieV65cyc+wHN3bCjr+vV4vz68SRl1Tm5ubtSPJdG6VWLLdd+/eLQQBf57PKmMS7TXV+tHfTYm4MmYRo6Ft3t7eLoQIMhB6Qg0wSkGkuMFxDMtcW1vDBz7wgdzXqP5IO83/PPqQ54uT6ZudnUWv18tBG65wUEHIcSSZW9X8AUP8/PrrrwMYRqeqNpDMLsdYryuN8kTI2p9oTfh9/a9rjb93dnYyg+l0Tduk463t29jYKIJOzgrI0JymnKsm2hJle9Dvpr6fBzwOw/dDp99/9PQT3QNwcZMwV1WFbreL5eXlGhNBgqb+KSRmekwN71FLSPMLF2PkMEsEwtxJulgj59qdnZ3CaZ+gpr+URucB3rp1q6aBIePJfjjD54xZE8J0tTbfabfbtZxplO4UIesYa5nOzPo8AcPNq1FkrJvfztxFjKkyM94OjmlUP5G0RwUrkVVTl75L7YFnfGf79vf38dZbb2XfIxLclIaa41arVTv9gid36MkCvH98fFwIFOz31NRUTrysY6U5Izc3N4vxUSaBHzJhumf0N4UknRPuA9Us+xjqWovWgp9VHM3fRQGOmzLmBApSKpi5cLe6upr/j2M+XPhhMmJq0gmKZ5TZI0RuDnRRAVCY5pzwEudQM63+vNSwsXw+y/OgXeMV7c2ZmZlCqGR/mPze8RIZHD6vZlZlMNg+Fey4f6KxIP5gmb52B4NRqhg1zXJ8aZnQueBvavSZuoT4OBpX7SuFNjLJvM5v9XkmvdHEy5EJVPFUk+Yw8jMnnlSaurKykrWm29vbeS2wLaxLx1DrUiUOrRkqhLJeWlnodhO5KildZD8jOuK056nQ8FVV9WTjhp8RaLfb6HQ6tTMZFRE+jJRCk64f+cMDlt2xWZk11W552hEAuHbtWpZy/B0Fmn2ee+65mkYNKBdedOKB9zVimNS0pO9ECJ9joBsgKlPP0o3ayt/079E8Xb55zwLVWimwLHcM1jZExISms1arhQ9/+MO199Q/TlP18Ht6ehqLi4u4fv16qHUkotcxa1o72g/3L9FnON8+xopEFcmRAVCn76mpqcKXJ3I3YJmK+HT9aLvOYtCBZsf/i8r0EUi0Ccq8RNpT17LyPoCCkeGYq/D39a9/PSRWZPSbhDIlpCzfcYKu2SZtP+ti/e7TxfvUeivz6+ZflkGTrq43agOpLeWeUcFZhS/VJqobBPvB/cG8caqd1bLGMQj9fh/b29vY29urmRapiXzw4EFxZKL2fXp6ukhODAyDcJjuRvPpkb598IMfBFAm4ldBg+PJ+fX5b9KYKa6PFAc+Nk1rTtc9x56Moa5ljjn7TODJGRE90XLHuUvp3vG++/2oz08Fw/d+B50kDc/nQvJUAwQ6sqq0AJSnEigocmA5jNLVUHi2RXMGEdbX17G1tVVcc62YbkY9SxAozYNcpJ5Lycv2NhA8uko3nD8f5SqMNj8ZuSbVudYdRelGzzchIY6ZI2SVLh30nm96LUMJJ8dmamoK/X4fnU6nIEZcJ51OB1euXMFLL72UfVSUmMzNzWUfPmDEWM3MzGBhYSEnVWU7VaIfDAYZyXM993q9Yj2qRnp+fh5Xr17NDLXmeaRgpGMSpaFp0ghz3NXcoiZh1xBE5SkRv+hMngoUPm5MtN20t9rtdqhtJjyMkOQaLEK/38eDBw+yyVg1UPyvc0R89tprrzXOme6fBw8eFJpFFc60bD9ykHuTGjJntJwIE9dG6U882ImuHu4XyXK0rdTw6dxEWsSo/8pAcuzVIkHNvrt3AMhJqHd3dwsTLMeVJl1lklqtFl588UUAo6TGOk4MNvMAHl1bjrfZD+K96D5QP05TcY1qtFWI1ghaFVApiPC6KjP8bOGIxqgJ3JU21CZG86W/nVlVaBKS3glMtHWPCXNzczlCSSeSUp1r5qamprJqWRml6enpTJRdivLkyiSemmQZQHaqB0rJfWtrq/EkEK1Lcxcpc6JMKT+Rr6BuJFf/KyhS4NhETtsMbomkOS8vuu6gme5Zl89b1FYdq06nU0QPe5+jZNNK1DzVjGrS3nrrrdq7XCM0+bCfau7wA9e1j3TUdu0gmTD3IyGB4vrS9rfbZcJpXSMcBwo7Pmdqkma/VBvKteeEzQWJpqStrlFkn8ZFAqt24yJCVVU5ClXHSteRPsu1RsZJ7wF1LTWv6ZrXEzQ4tvr81NRUzT+QdUTEj0KBMogabKFABsO1wI5rW61WNtM25b5UHEYXGsfji4uLWbOlwkTEYDYJ1wTPH+dCi69dv842Xbp0Cb1er5Z4+fnnn88BXFHENpkUPSeb14+Pj/NJGxxP9oGnAzFPoTL4fMY1tarZj07ScIaM13x+XNPM/axjq3UBKHCXz5G6phD29vYKJYW7L7EtfMf9Yx0feftdePa99aTw00TD95iwt7eHBw8e1HwjaLZStWxKKR9NxWv8Pjg4wPb2ds2UdnBwkKV0YESkPD+fLmKFlBIuXbqExcXFwmwSPUvkd/ny5VDqIgwGg1riZbZNN3nEhCmj12qNouwiP40oclffJ2xtbYWBGPo8gHwwuGoT3KfCtReuVmdfXWrlfz/ZQdvqpu+ovwpKrCiZO/I8Pj7GgwcP8MUvfhF7e3sFYmq1WpmpIoPO8g4PD7G7u5vP3iSQ6Ozv7xeIjgzmzs5O1qw5gdvf3y/SebAsStCsR32aCKpVjqRdIkdlKPiMEwVdXyqduy+azsVFA/aJEZu6LjWljpto+Ts6g9i1Trymz7722msAyrUeaUW0fq/bcQ0w0gByr2mQD8uNmFEyja6d47r3NEqawojrllpwDQbhHuK61f3sGjSudcUtbtkgvWDiZb1HPEUcybFVzSOBAhkZXwZMMbr+0qVLWWhVEyL7+jDmS147OTnBnTt3AIyS3yseHwwGWWPIdlRVmRvPj/FkHeo6E+FKzRHKuvQ93rt3714+2YUBGI4jXYOqa0IZ4GheCPQ1jU5aUhqg63Cc5k//NylO3gk8NAuZUrp57rU/Y+AIjI7vPmHq88DFrvnItDzNcaegi4LMyfHxcU687Bojdc4nMGijSbJUiQ1ALXs8UD/GqknTouPSxMTx/lmENpKmIkmIEWkRgVHwU0yccYykSO0PtR9A6XuiDG40Fjq/+lHGfWpqKvvDaFvOku7YLq4FtkddDWh6Um0MNdBkJPU9SvqeR5L1kFiqZM01FyHwaF5cM6RzQibNmQYeHRX1PxoX1wi6Q7y2/aKB4xIlYp4sOHI7uXTpUv7vBGvcePkxWhEO0NMPFNz8S9wKjBhJXue3CiT7+/tYW1urWR+cMWCEugYssf/7+/tF8JN+HD/4/lCtuo4t/VWVGYhwW1VVOQ9fkyWGz0Wa8Kqq8gk5akUaDAZ5P0dm6Ha7jV6vl8/XVmUCLQQLCws1awDbpWWNE6I4huqKwcwJLngrMxrt0Sjxsq87oNS4+RnJvu75XwUiNx27Aof/KVAxuCVyVfK+EEdH61kFmPfapPsLKaXfe+4teAZBtTu66KiZ0UOoOWn0HdFrg8Egmw3c9881h5QGZ2dns8lTEUGUt4iSrG4iQmTS3djYqD2jwP6NA/X7UhjnnO/g+bD4HTGt+owzIrzOMYikVm8TUEe6ZGpU0+ZMX1PiZS2PUqUjMUaTaVvYHqZ1UO1VqzU8KunatWv45Cc/WcunpyZYzjEZAZptebqL1kcipXOlyJBrz7Wb9AvUI4Mo9KigQ4ZXI27HaXd0LJ148zo/Ud4sgs6N75GLBlEQFMGFLscJ/X4/+3US1BTmY6d7d2lpqeYi4ART17nuVQL/q8bc07jo+1zX6hbDclRLpH3pdruYmZnJTJive/6n+wxPR2K5rdbwLF0mZNY+RmuQptGmdcc+cy86IxX99mtkeplgWYXbg4MD7O/vY2trK0fO6vuKm7zuVquVcQ/HCBju44985CN5fpQJ4rOMWtXADZ0jZYyjfje5BnldSiuVntDHEEAYaezvAcMk0oS5ubkztZ4Ku7u7RWSx0gdd60pHIoZWXZneUw0fgP8AwH+RUvofUkrNnr3vA+Am6Xa72WFefe0UQeh/TykADDckj+pxc0nkGKz5+SJpQhfJc889l53tPShEnyPyu3btWq1M34hqWouYtSbNidavizlixFyLpv1ToG9e03jzm9KrM5HjpFK97wx41I8IyIiQ+ERM4WAwwP3798O6yTBF0nRKCfPz81kCB0pme35+Hr1eLzNoZMCYlkI1DyyPJwvMzs4Wa6zT6WBhYSGftKGMJ4UQP5FA26tMO1ASbjezE9zvSU2/Lkz5O0BpYonmnm27iOACA0EjKh10Tv0617lrSRQ/0afL39cxdmbD3SOcYfT3PTepa130t+Y3daGR1g3CYDAofAC5vhi04OuSGjPXzCgRJ9PlFgBlTNjW6enpnH7G6UckIOrccFw0tZeCMlx8R/FWxBBW1ShLhOdxZfuocffgBj7jfmoAsomV70V4zSOCfT0xkTz7o88pjtZx0MTL0VrhuGg/qIF0tyUHTbw8Ls1L1Ef+bxJUnwQ8NMNXVdUPAfgmAFcAfDml9F1PrFVPKThDcXh4WJgBuGj0HD4ulOnp6SwFKxKjY73mWQLilCdMaKlJjCNmkrCzs5OfdeKo0gjv8VlFqq5idx8ff47XvC16eoMT2nGLPEL8hIWFhdp13aA6DuqP4VKbfxyIlMn8RJKin/Wqz7gZhtf4IbHUZ9TnThlFmvKPj4+xvr6O27dvF24FRDpO6FTC5PvuAK+/nUjymD5PmEriqclNldjRhKyaODXtOJKMfgOlEzaFBNVqazCN5nHz/mg/nzRyfS/AmWtdOzSTOtHTfXvt2rWx5eo7kTaqyaTLKF0tr8k/iuseGDKSXo7OHwUpara8bBcuNQhK26tO+qp1ihgP1zTyW4UMMl/z8/OhSVf3//HxcfaJcw3tuCA49q/dHp5t2+v1ssaN7V1YWMDc3FwO5ogYSo637z9qDjVnIL9paqcfteOYKHDRlQkuqPvcNfU30vDrfwfP+acZJzSiWc3ApBm+3prap8yhMp+u6OC68KPlgNgF5rzhkYI2qqp6DcBvTCn9MQA/klL6RQAn9sy3nGP7njrQBdVqtcL0A578Uq/7gmy1Wlm6VKdkjfwkgqWZIdJYRAtIfVUcNJCDC3hjYyO3lfWqiTmlFEoxer8JtD7vv4Mfw6b9VPAgAu2LEjJ19FZthT7DuVRwRMQ+E8ZtUGVEaLrXk1f0uSbNCxlsjbJmm5h764033igORWcb9/b2ityDKrkzso4nZ/Ddw8PDnJ6Cc0umjcKD5p3kuB4cHGBjY6NGMMh0aoCOtlP7yTL5rQwczw328Y3GnOVpHYrItb6LrOEjRMxSk0aBwTl+LyJKzvAx8XLUDjLnPodaB+eM+IYM+7Vr13Kb1VdV8c7MzAyWlpZyQILmftM9TfcZZ8Io0Omao3Zd3SlY3+LiYu1kBfbB8ZYL6D4+/NDNwd14HhZcA6djC4w0ce12u2DEGM3NlGGuMXNcTFyytraG7e3tWqofAi1UyiCtra3l+/SD1DIBFMzlYDCoBcMRN6kyRZnRKIqbQZHaj8iy5OOp1gedK1XGcN7Uh0/phgvUxP/uGqS/n6Qg+shRuimllwH8LgDrAP5fMIbv/QCcEKqlVT1LDj5iFFwSAobSB4msIieqlPksy9fzeKMFy8Xf6XRw/fp1LC8vh74ZilDUpOupNbxsj5bk9YiRVWjSqjRJSy6Ba10E981zQq6bUk8AiMpr8n1i39wpXTcnNQcREMHyN99R37qPfvSjxTi5750jEGoa5+fnQ60xMPInUiHDfQmjfqomlO9FwRTafwon2gb19yHx5nPqw8c+uZbOPxrw5IiRa5mMNVBqttwv7SKD+Iu1uwAAqvJJREFUMtb0wSJQC+1aPUIkjOha97l3k64+54xFk9bG51zf1zZwL/jxe/yO3lfLifpGqVaH7+je7Pf7aLfbORrXXRC2traKyHgXnHnN/eNUMKbAxH5pUJLjlwh03rRNDNrgMzwyjnnitLx2u43Z2VlUVVUwLKQz3W43n8WuNKPdbuPatWtYWFgo3EL4ra4okfuG/j6LuYlSyURjwP5E5bm1rYl+asDS/Px8sXdUeNdr7pc4zkoBDN0qKERrBLP3KVKOnAc8EsOXUvpDAP5TAD8B4FNVVdWdj94HwInQyCeNmNU8Z3x2ZmYGi4uLtcU2PT1d89OqqnoQBrU9jLzVJJkppcJhmMhLj8lSxkjNCQoezcpnuZBTSmHahkjS9Y3niLqJqeRYEiIiROB4skwtX59jLqlIgxeZpZzI+bhp+U190HHT+lim1hGly+h0Otm3SBkfMoDUen3iE5+oScGt1jBRshMdRg3ScV2DLMgc0H+P7eRaplaD59KSoA4GAywsLODy5cv5PfqoUNui2l1nQtg2YLSHXBN1dHRUMG1uKtL1zTFXDY6++6iak2cVqDXR/erBK/zm71arVRxlps9GDIiaqzwHnuJDwuHhYW3uI6JYVVVh0tXrDuyjpk9R7Z7jBT7v9dJ/TcdFT4nR9eZCvaY20bGhRlHzWqpPH/fP8fEx3nrrLVy5cqXmdjKO6GvfyMS6ixFN2Hrmu84lNXEeeew0wtfLxz72MQAjtxrXgNHXTmmg5mHkenFmnUw977t/IHELQZUsTePF1GQEtySQLqgyw113IrxEixsw9OHTKHEdOxeOdb/pfa3rScGjpGX5/wL4ywD+WFVVv+v9zuzxt3Pn3HS+uUi0HNFQClW7v6rTtezp6elMsD16zqMrgbpJVxGWms3IjJEwOlLXze7tjzZYdM2jdBU5eLv9vzs6EziekTTk88I++2byTa1MmSIWMi5E9M4QeptZHiU5ZRZpPiZRevPNN2ttoDTuGk719dSjmzhOXEPdbrdwjOY3TVfUFGtfGe1IZK2IiX13jQ3770SG99V0TKTpufdca+rg5hclEsoI61w0Jdb1+bloMA4/aeJlnR/VUGkKGyfGXr6ueUZl6jOsK2L0lQhGmjo+e+PGjfyOC2yc6263mwOzFLzcVmsY3b6wsFCLwPTTbHQdab/a7Xah2WK7lEnluPb7/ZwWJQLFgX7CiQvF4/Bbu93OZmoKiITZ2dkcbBUB0zdpAmVgZJJlnj1NA1ZVo7Q57jNMnOKavVarhU9+8pNFm/mO4hIN2mC5Cp4UXvsauQfpMy60uC+gzhM1ogRfr16G+jBzHIg3nd5NT08XgRsKTxonPUqUbhvAN1VV9beeVGPOgpTSv59SqlJKPyDXZlJKn00pPUgp7aSUfjildN3eeyml9KMppb2U0r2U0l9NKT1W0mmdEGpEgHKxRYvenyG42YDfx8fHtaN+TvtSQ8KRxq7VauHq1auFFszbz99ElEQ646TKKAHuWe803VcmSMGPPVJEr8/6+DZpAjlH6lfm5UVETe85MfBPFLShjKbW54SLyPPWrVv5GhHs0dFRXgd8l0LF+vo6fumXfqlYJ0p89GBzNfHu7+/nyHBlAMiM0VGbjKke6af+cCQE9OFTxptz62mKohQ2HBcdM2AkmExPTxeEn/eiaHWdL4ILPU1E9J3C04CfnDlWJktTjAB1B/imPaqgz6nW3nPsqXYn2uMEjbyN9qAKM37SDb/ZD+5v3lOmThkZRqBqOQoUxhw3c/1ubW3V0p+wL3xOmUxlvHXNqiAbCSgPQ/yrahgQOD8/nyPzNVqVKWiUIXZapRqyqHxvR7/fx2uvvYbj4+PaOHDPu6YxpVQLPlNGj89S+9pk8nUtNdvnyZ8VNjc3CxO+Mm2q1dSyNb+pr0udP7qnqMWE46rPsl6d96b5PW+8pPAoUbrfWVXVrbOffDKQUvoVAP5tAF+0W98P4LsA/G4Avx7ATQA/Iu+1AfwogA6AXw3g9wH4/QD+/Dtt0/7+fpHYmCrbSDqhv4SbdDU4QxkBNwkTce7u7hYHcus9lzzUqVYZD25Kf0czoRNcze/SqqcdAGJEFfm4PSxjrEyEghOwJh8ObnhFxF52xAQqsO6mqFIHZaYjpoQSXrvdxoc+9CEAo3x8ipAiLZ/Oh6amUaaPkrvOD5k3Bl944mUmbN7e3g4DOmge0jYQaeq6UK0wzWIEmnsUXOOs/0kM1eHftVTsg46ZSue+rp8EQn0a8FO0TzzxsmtHXYOv48x16/uB/3VNU/vL+pXQR2UQ9FmdV84fUxZF5jF9lhoqXwNqptUgAl9D3BOKK3mOta8XDWZwwu6Ci2rSIz/CwWAYAU+GWcdN6UTEeHGOKMipcMh3uc8pnDmzR181xaU0NzPlUqSoYJ0+L9pPZdqqqiqCNqIDC4DSrcOFfb7X5B4Uue0AcdJ/nVPOoSoztN86z/zP9ml6mkjpo8II36PQ4S4H2qcnBc/EYZIppR6AvwPgD2EYLMLrSwD+IIB/t6qqv19V1c8A+AMAfnVK6dtPH/stAD4J4N+squrzVVX9GIA/A+B7Ukpn233qbQmvqx+SHiWlG8+REt9zNTav64LjNfqEeFRsxFhogkdlGrxOXt/Z2altiEiq9vHwBRo9N0597e87gmlCdpoSgM9F9bg2rknjOQ7cLKvEjcgmAiWs3jbee/nll4t+KyHudrtFoA41IjMzM7hy5QpeeeWVGqPL+eXcsM80f9H042Z2zvX09HTRR7oSMK+ft1E1CwSuL/oLsl2eJFfXj35rrjTXCiqx1nXN8XRE2mRSOy/E+rTgp4iY6e8mExVhMBhgfX299oz1NX/7fEf7Tt0YonJ87rhWPUoXQE1I1WcXFhaKOiImjT51nnQcQMarrsHR/nKP0PzL9iuDqdoqpiCiBp/Xva/tdjsn/VWc7HjP3UZ0XKgI2NnZKcyvvE4/ZvfLY300BSvTSs0h28j62+02vuEbvgELCwvZlO/j5ocJ0A9Y296kOeM4jxOUdd2ocBjRHlp4dC71Xc7N888/n9/xYwkj0LlhGhdCU3JlPeHK53Fc+ecFzwTDB+CzAH60qqqfsOvfCmAawyASAEBVVV8B8CaA7zi99B0Afr6qqrvy3o8DWATwqaiylFI3pbTID4BwRrrdbs0XBIhzoJ2Wm795jf4T6khMjYpqU1yjYe2tLQ76mjDc3p8nVFVVmHQdEWu9KZUpRFS6dcLdJLnoBovaA8Rn6TaV6xrT6JmobRFxjOrjNxGKMxj+rP+nJKgIACijtpmfTBlyElRlzIiIgSETtrCwgOeeey5fUwTb6/VydDLnOKWEubm5fJi6MqNKDDVFAyX9ubm54gB2Mnu8Nzc3V9NKsP8e6KNj5etMxwdA1iqOi7T1OsnYElxwapqzdwBPDX7SOXA/YI4J19FZDCKfbZovnde7d++OLW8c7lITl8+JponhWvR2pZRqFg8+r4L4YDDIie617a1WqwiCc3cK1VQNBoMiSjfa9zoHjFJXZsjHEkB4Nq+PUxOOUeErYnTJeDfVe3h4mF0yfN/xBImqGmnsZmdnM3OkjKKWrSf9AMP5p2DLdnm/OF5qYnbapWXyHcVVkTZSE8k34Xu/7qeSNM0L5219fT0MBvI6VWBwpc27AU89w5dS+j0AvgXAnw5u3wBwVFXVhl2/e3qPz9wN7kOecfjTADblE54j7ImXCeqbQaBmwyVGz6vHe5oXitdoquNiVETkPg/9fh/r6+s5StfvaZv5/eDBg5oGSxmkqqpqJx4QlBmJNpa376yFHkl/0ebxtrjmgrnl1GfEy9e2a59d6mSdyuDynkcfRmOk7dL20mz18z//88VznG9Np0DCdHR0hI2NDbz66quFyULNR9on+jeRkB0dHRUpW5QJUOJbnWrbKJjQRYDvsC0cY9aljK4SM2ojCGeZyCm9a8oEoET0EQOjqV98TWvZ7xSeNvzk86aaUTXTn7a9GLdWqxXmymsilG6qd3CTewTO6PNZtvvNN98s1pVr0nlNg5dYlqZtappr7jM9ZUIFLmZQUByhxx1yfIDmwDR1B/G+UuNDzarni9N2NgkoFP4olGl/eXKOH33HdrF+N4fyGn3ZdP663W42QdMq5GbyKOODjg/xkfaP15v2qz7X9F5k0tVjzzjGbKe2TwOW9NAC1uO0BUAtnyPnLdIQA6hpiHV9+Jw/CWbwqWb4UkovAvi/Afg3qqo6eBer/osAluSTPZKbpC9+E4EoEwagCI2PCE9k0vXyqU1hlI9uII0gjdrl9UVIUE/D0DJ8ISsosYjq0vbp8+M0fA8L7ozrbQaQTZiR9Ne0iV1jqP3T+1pnE8Pn46JMJO+TmdE6iAQ0ganWx0CKt956q/DFI0O4t7eH3d3d/C59Xxi0ETmjc43qyRhElDs7O9lnFRgFldCvU/3kiLCbmN1ovpWxJrHlmGhqC31ef/ue0r23vb0d1jnOB/Nh4GnETwo+LtFZr84gukmX8+Lrj+UTIoZK64+i2LUMNXVyzgEU+ekUtG3M7TjOBMdn6c7AZ1knTzzyOpwpUCbOhSN15eGH1pumcSPeJqPteDICF0C1HD3lScHTpLDtxI3OuFCbGh3teHx8jHv37uH27duZpimQYXcTsqbY8ZOPdA3yfoRD9AhSpbdqRXFgBO24ca2qqph/1h0J/YrTmWqG66+J5rJM+lFrxLw/P04T+U7hqWb4MDSJPAfgZ1NKJymlEwwdn/+d0993AXRSSsv23nUAXF0rp//9PuSZAqqqOqyqaosfAJXcy8+R8QJK06JqiMZBVVVjz9LVRaELO3Li50LSRdTr9QpJ1NvPctiHXq9XW2wu4XjSXGeYlOFVYNt0szhTpe3iM+PA+8Z3XDpUBKztHle+blwiEyeQWk+TD5+fDcm61bfl05/+NADglVdeye9xnKKzMYGhZKlHKTnCUA2wM5g+9sqgNWnZaJqKCHeUa4/XmB6G5VAb4f1xJk/TPTgzoeYjtt3NL0pk/aQN4OxM+w8JTx1+8j2lhDxKixJZJhRUa+L71E26zgCeRbgiIUrrBYbEWvei7yPOvR4BxrI1jZAGSOzu7mbmjM9T4NE15ZkSeJ3Cj7ebe0AFaj2jV4F7hfiKLgvq7+fj7cwmMNLEbW9v54ArnVNqqug/7swr0zD1er2sRKiqKgcZqg+f4pBr165heXm5CEphmZr2SRlMP784AteSNgmNPoZsF9/zMrXtaqVRZcsLL7yQ31F3GL6neJR9on+ga2+VVnt/6K7leytSPpw3PO0M398D8I0APiOff4mhgzR/HwP4TXwhpfQxAC8B+KnTSz8F4BtTSmqr+E4AWwC+/KgN0sWkCZAj53EFEj8nXsxo7pKd2vgV4ezv72fzgx7vE0kL9+/fL/ID8Z4zjETcq6urRRsIuhCVeEbIl213cPNxVI/XGd1vYkabytzc3Kxps7ScaK4iZjRiLFQ7ocSPztvqfE1o0pACZU4rvj87O1vLmddutzE3N4fr16/jwx/+cMFs8Rnma1Qn6Farhbm5uZwE3BMvqw+fak86nQ4WFhawvLxc+OxQC7C8vIz5+flMBBWpOhNBv0TvvyI/ZVTVvKfjpEwqCZSCCibROjpLI/2Q8NThJ9VKVFWZW4zMhLSlGAfVNvNak1DG+wRlrnSfNBExzq37bfJ97qetra2CiJKpY9s0TZGn/SB+dEHTNYFkRnz/MSm+4wrNa+fCqeJr9iXSrPJd1qv+2rontTydV6AUMhllTFOqPuMMseOcSFnANnGOdK673S4+/vGPZ99dF8o4drr/U0p48cUX8zORyZO4TXN+OrPsR4jynSjwkUCmtYmB5rirO4MyiZFQzHknPVxfX28U+qN6dRxUSHDB/RyE0gIeK9fTuwVVVW0D+JJeSyntAnhQVdWXTv//DQDfl1JawxBJ/mcAfqqqqn92+srnMESc/21K6U9h6BfzHwH4bFVVcfjeGaASnSM2VddKP2oOsIq0VNqIECyvTU9PZwaAGckHg0GjQyrV+A5OhJVxaeovn4kOAtdnmhaot/FhpBjXojmyotTq/dJnlpaWclSe5p1rKjO6Robp+PgY7Xa7po6nqYgQ5c0jUmX5aoa9c+cOnn/+eXz5y1/O17w9BDJGROLr6+sFE0qkNzs7i+Pj41BD6AhG15e7BShxcj8nBSUMatIhEeLzzNGl73kZOkdEsj4evl/0e2pqqsjq78ww63qnyPRpxE/K4Hj/Iv83febk5CT7k3qZ0W9dA2Su9DNOAHbBS8tMKTWadCNBenp6Omuo9Dkl2LxGLZa3lYK34icPtNJxi9aw4gVlRPWoM35rXk/NM8lxU8bL61H6AyCbtPf29rJAxjU/MzODk5OTwoStc8CgDfdzpv+n5tlTHziemqLXtWylDezPtWvXavPM+75uvI86rzqnCpzvCF+4CZ7fOr6qzFAtsPbH6QvrUpzve4R+mVVVZV/+w8PDIvmyj+HD0MfHgaea4XtI+OMABgB+GEAXwwi3P8qbVVX1U0q/HcBfw1Ca3gXw3wD4s49TmU6mJw2lROLEsIk4AcOFwqgxJXZuQgRGUpdHA0VMCzCKlFKEGxFzbqqZmZnaggXKzduUsZ3lNTnOKqM7bjGvrq7WpE3fpAT3S3FHYLZJHbp9LBzJjNNkOJEARkEFc3Nz+XlGMitDSOTOdqiwwG9qWHltMBg6PysyUQTz4MEDfPWrX62ZSvieBuxwDjkW0dnCR0dHNURMRnVrawvdbhd7e3sARqakVqtVnCuqBIDrWAUgj9rVOVbziiJYHVu+w+fdZEJQ4q9mMv9+UohV4F3DTzpvvrcBFCl1ouddcOE9/a1aFF13mmPN3z1rjBU/qjkVADY2Noq95ows15inxQBKBol4if6rngcu8jljOxRvkdmJNP1+HCYj3zmmWp/u/ZOTE6ysrOD69eu18Vac4QwSf7MefvQefRYXFxcLHBQpGbQ/3D+RL93R0RHefPNNLC8vZwuS4w3mANV5UP9Qx/EUPshk877TEtWW6pwAoyPuHHxt9Pv9Av9q2dpHF/oJ1Mr1+/18WAE1nWpxiFyIVOBsUjYo03/e+OmZY/iqqvpX7P8BgO85/TS98waAf/Wc6s/fGjGrRMiTmRI8hUtVVTlPmfsucEG61OQMnzIsvkDW1tYyMVbJzf0NuNA9PUgEKgU1LchIq6jgbVa4evVqlsJVMorA/R8iLYIHdrgpb9ym0k3qiFjr8yg2BWpiHdFyLbTbbXzgAx8AUB7e3eQEr/c9UCRam2qKYFv99AsibBJZ9V3RdDRaB3/ruuKYUBAaDAZ5bZNZYOARod/vFz5/rJ8EgO9E0ORb2e/3M2MKoBaNHI3necF7iZ+UeeZ/Za41Wprgml7NleblKUPGdwmdTqc2D4SUUi0CVOdKCSXr5fMaSBZpgHVde999TQJDHKxRknxWBVUV0PQsa65FdV/QMtyHkNeitES+j8gYOr7RZ/0dHRcKcipccd8dHx8X1g22j4Lq0dFRTXjmUW2M+iU+5vudTgeHh4c1gdLXhvZHUyt50IZrtppwsr7nYxppGgHUzOWqXdV6NG2M4y0VSHWt0ZKgbjcRPeI1ZkegiT1KzeL77jzhmWP4niZwnwEiGc/dBCAn4XT/OVeXj+Pu1QeLDIRuMN04wFDTpMlEgZFWUrUpJKhuEtFvghJr13jpZxzTN46h5Bg1OYoruL8K26Qbm35nagInMo42WpMU3RQww36qf9Rrr71Wa2uTVkXboNG6SnDU/4RIaHZ2Fjdv3sQHP/jBWs6qlEZRhxq9p4h9YWGhNt8khL5mpqam0Ov1sLCwUCRR5jqi9kD7yPFWP0IyqWqm0/XPMXGk7pJ4tD/cT8oJus/DeUvOTyuclbpIzbr9fh/37t3Lz5PYRYykg6YTiQiWJ2nm/aZUJhRm/ChHf1aDg9yPzN0TFF+7ho44lUwthQnd16o91/OhOY7KyBKH0Z9X2+H4eHp6Ou/9JgZP3/X7/X4fu7u7mQFTIQ4YneOugr7234MadPwYpavlTU9P46Mf/SgODw9rvrKcEzKvHPN2u43r10exSbzONabCHgVkXx/eVm0n6Zj7qQLIblA65tQm6rz4SRsRfXCBg/Vtbm4Wp5xw/t03X1PCAKjRkycNE4bvEUGJ2vT0dPahI8KjI7uq4oFRhKIDk3u61EJNjPs4qVaR7VBpVhnAJnW9O6AqYdc+qgTOsj10Xdum7/gibtKAeTnMBdh0X4HBBd5mfg8Gw3QiNJlHCZ0jiXqc1lTfY7+c8b9z505+hnPo5iJ1QCe89dZb+TfXlKZFUAaRUXQ8FYP1kQD2ej3s7e0VmolWa3T2I6PLWBaleqCeYobrWZGmIvK5ubmamVb9FhWpMfKR4AyHEzOOXTT+OvfarqmpqRpx0bbxnYsOzmi4BskJJ5l3BR0v3SdAaQLb3t6uCTGRlgoY7g8VCr1cYHQ6yurq6liC2O8Pc+jp2dDadiW0xJ/Mbalt9HyXvEfCrfs9CrAASvxJ87j6UUc4hUzhnTt3MD8/X9N6+RzqfOhvdSVS/Mm92ySAk5648Ey/Ww36415le3jwABlCZ671ezAY4FOfKvOIR2vKFQYRw6djr/XxPd/brkVzy1FEm/b29gqf62htV9UoLy19Hd3H2ekTTy3a3t4urBoRPAlGcMLwPSaQu+d5jeM4dtfCKdCkqxIPmUnf7K4NAcoN4ot4fX29lq7AGSKgNOmqFOhIotVqFaYM13Sp1B0xmvpbGSu9d/PmzYy8Il9ABU2ZoJsxYuCI9BxhRJvKr5GJoM+aIxoyWAQSTSUS+t1UFxm+qqqyyVXP+ARGmqzDw0Pcu3cPX//61wvCq4REfWgoVBC5e0JujoUyWGTcGHCUUsLBwUGtX1tbWzWGj4yeS72DwaAwtyrxiwgbgFBQIpAx5prjPlLNg0ZrKlxEps/HUOdFTdu872Og6zjCP4oTVPhT37GoLQoUUlQL4mZdZ9Kjfa7PqQM8n4+sBDwf1/PV+b5WTZXiZTfpuoZPx4rM6Pb2dqEp9bFJKWF5eRmXLl2q+aU2ge7ZmZkZzM/P4/DwsGAiBoMBFhYWsL29jcXFxRo+1znwQDTe8xOguKZ2dnZw586dfBKK0h3ucRV0gSG+1r2s9ZFh9AA3b7ObbV3hQQWMAgV+x/k6zr5WnW46cG/QWsH1pKmlIiabLksRL+BteRIM39OeluWpA+X0VXJSgubaG2p6oiSV6uPBRe9mXgIPs9b8f9EGAIaEcHFxMad88UWkJl1uQvclZDkKao5zpKVj5G1/WOJ6586dmmNuEygx8/4DKEyXHNtIY0fmNmqjbtpoA7Of6uyuZzeq+ZxlKDOtRFOdu5WIqNM2r5OZWltbKxg7rr+9vb0speqaYvJk9SHhuwwgUg0cCSdzRzkzSOaUpg0noH6cFxC7BQAjc40ixE6nU2gjHZSoaz81EbQez6X9ehII9b0GFwYjDYJr3nS90Y9X7xOaBCRgtBdd6xrtS/qFAaXGxhkzPuvlehvoiuB+Y1G+Na5bjhPLVe0120OTrmqvyMysr6+HQrR+a/9cO8bnNA2J0oYmrRLL9PFiWcTl7k/odfM6E9N7GhXm3WTksmvItra2cPfu3VrKKz7n/tVVVYWJl33MiPv5rWZW+ii6uwfnk9+Op30OgdKfO8IFqjHVtaxjOTMzg4997GMAkN2GdKzchYLXDg8Pc5sipvJJ4qWJhu8dgPrD6YbmNd2oVPGr6lklJve98KzhJGb7+/tZq6ibJcqxFPmc6WJs8h9oYt6amCV+K0PiEJnlovLIbLC8cVqDKO2Mm8BXVlZy+pamlCL+nrdRJdWIaaQkT3j11VdrfaBpRU3ALgUy4svf0T4zoGJ+fh43btzAjRs3Qo0q66dGjuuOKQGipNXaZhVqaJpaWFgIU1r4wfWqEZ2ZmcnjRl8rPybNGRC9xvFVUEStc6SaVH1HpXsv+6JBRNQJqgV1QZHPRmlQIk0g8RFBT2eJQOdDXRcifEIhAkDBgLqZDBilC2IkZsTM8jlqjjTdCj9qwlMc7Wf3cl/yv+5h3RvU9ng+N+8327S6uloINuMYZjUxp5SyBp7mah3rra0tHBwcZB8+BfoI07dXx5j5N933jfdu3ryJzc3NmkDBfrspG4gzPHiwDV2hVFNG4DFuCpwPtivCafRnJs0lU0wmOXL1YSqvJqUG28q6uX7cmuNMOwPmonN3FZxBPS+YaPgeE8ipa7QjIUKQSuAVEV26dClvDt3gUU40lqP1aLmOFHZ2dhoztyvx8ySnbL/7YQCome6isiPw9DDeDsLy8nLe7M5ceR1k5MbVHWWDj9p71uYiU+GMtWpHCGT4tM3OLLqECyCfT6maGXUm53UyblevXsWVK1eK6EIioV6vlxFsVVWZeVxcXMTs7GzNV4tmUCZe5rVWq5VNRmT4tDwmXiaxigSOcaZ5Z+w4Nm6OVoi02rpWNa+Z1hEJRBcNXFjT/eqnS6g2lv/VFO7aOv1dVVWjVpt1KB7U/adm48hFBRhpvtUE6NkFFFf6HmcbnGDPzMzk4ym1PypccV24tpkfjfJVYcODNqgh5Lh7W5TJef755wu/U+1DE351fK/WItIG9sejdPkM8wSqhp7rhsyJ4jCdmxdeeAFzc3OhEMjx4Xe3283ZCFhG9B6vU+Ooa2x5ebkWaMa6+Gli+FQpQzzjVjgFFQrUD5Hrj3PMdERbW1thBLEqd4BRzlYXBrQNTfTuPGDC8D0m6OLkfy4mlx4B5BxQqulKKRXHrChETv0kvuqI64hVkcilS5ey+UQRjEqfwMhJX4MggLqPg2sAXIsZSbD6P1rIzvT42bDapybm1xlZ7ef09HSjL4ZuwnEMIBkbEgAngFNTU7h69Wp+Xs2YRCyRaRYoNR9kFHVePfkrGaj9/X2sra3hjTfeCBkXtlPfUy2H+gU6Uab/DYkFowDpGK99I9FQ304lfiQa7Ovx8XGRnsHHmuOtEGlkdf2qliWajyZp+UlI0O816D5Twqv3XZBS3OVRul4m308pFbhMtS+6P8Yx1dxPTYQfKE1r6h6ha5a4yRkqd58ARtr4SNul7eZapcZM9xGPVnOcoW3hh0nyFRwf0wXh7t27ZyoOFPgsBbKZmZmMe1j+8vIyFhYWwmMzB4NBEbRBXAWMzKea1kvvAUP3G1qbWCfXijKYg8EgzKWpvxUfKm5Rhk81mwqklU14nilrdLxdE+dABs7bp3UC5Yk+ujZ1zXJsAORciWpCj2iPlnGeMDHpvkPwSVHO3SUeVXPrIoq0PdEiOzk5we7ubva/cl8GfafdboeRa0B5WgP/A6g5tpLg0zGeCELLaZKQ/L8yEMqI+HNRKoUmcK1dVOft27dr0Xes14mSlhE976Z6tqHdbhfH8ly9erWR4DmDrgSZCIaIjb5zmk9MHaDX1tZqBImgR/ApImfU8vb2ds2pnb59SvRSGpqM9vf387mcvEfY2dnJfnIcV64Nnz8nOjqOnHvVvtBJ3iESKAjUbuu46rq+yKDj6+Pvggqh3+9nxks1o+P2tOMnxSUk8gr6/+23387X9LoSfWp1NzY2CuKszADr4VmxbtJ1wZFla/v4vJrZVFOu6UUAhEyc3tcxV2LueMXbenR0lHMO+hydtXanp6dz8B9PA+I7ZDDodqHZI6hJOzo6KvwC2Z9IocG2Hx0d4fXXXy8CsLS9ZATZbnfpccUIQbMacF4IX/rSl8IgEv52v0wCLUGKkyNtcNN/jpv6PLOs559/HgCK9FOqddS1Rpyuay2iMypcnDdMGL7HAE4KJw4o1b36m8/qgtMFSYnM1dFRVnzmRVImQbVULuGqyVkJdBSmDiBvXl2E7ktxFsFskkwUCamWwBkAlehcE+F1axBMkyR8/fr1HEHlfdCxbZKmFYl5OD9QJ1rA0O+oqqpMNNTXg8BAEk2BQh8+1utzSmLSarXQ6/Xw/PPPFxpHtnFqaiprjh10LHS90r+QyFAJFwnKwsJCbR2TACqTRr+/qqpw6dKlTLwp9ESmq2gt6FgpOAFUxMlvXUfRmiUCv8hQVVUhoFH4i/z3FI/oNd8jul484MiZFS1Ty11eXh4rEHGdAKgF7ERaxE6nk60Tvn587nmEmu9392nVvjpTon0kEQdKppdMC6Pjud68fWQMbty4kQWbiJFxvKP/GRCowiHbsLOzU5yxq4oCClP9fr/I90m8xLybnL/j4+NsZfJAGV1XtKwoc6TzHGlHdTxUs6bv3b9/v5GOqrasiRYQN6o/NefP35mfny9on9IBZYoJnimBDK0KwAAyY685YZ2xJ0w0fE8JcGKYhw9AQUTd1g80J16mRsUjtHiihhMyLlhnYCLzp/oHOpEnpDSKpPRkk8qsEtyp28eEizsyUet48BnfaLdv3w6daLVvBI0gU6lfx0udadUnQ8uPJGgvU/3LImlQmStNr+JMm/dHNzWZN5Ws5+fnC4djBkNcunQJn/rUpwqkoVrDXq9XICyatq9evYq9vb2CuaQA0Ov1cHR0VGg1pqamsLi4iOXlZdy4cSMzUu7Dp1Gx3BvUHHh/PUqX4xvtnaqqaqca+LpUwsM2f/rTn87PO6PuBOgigfaRzDgh0rYq9Pt9rK2t4cMf/nBYruMGxVl6Ao+XTYaHoPOppviIYXPNUGQh8f2nGhbXrFFb7bhVgznIyFIb43iLZ6KzLuJV3+s88qzVahVMhdbLdX/v3r3MiEZjHuEm1Z4xGbS7B5GO6LnjWpYyqz6mvq+AkasQANy4cSP3T+kPGUm1iOj8k864IEB8UlWjwAr196R/t69DvuNKEoJmn4gE6egdoEynpZphFQYItKZFzKOuEebhIx33+eDzZylWHhcmDN9jQMSFcxGQOKuPHTBKqRKV5dIDwVXXKsk5w+QMz2AwwNbWVvEsy6Mfl5YLoFDPs0wnjGoqixi2JkLatIB9cS8tLdX8zxQeReppt9vZb5LjHDnJsn1ap9YdIT4+T8KiDJQ6TlOac7O9+tMR7t+/X1sDLEfbRUKhztYueKgUzrlutVo5Yk01gxwHZqRnnjTX9mg9BK7x7e3tPM70f6KGg23kda6h27dv19Z7RPgjRoWMHd/RCFRPHB25S7Cciw5OHFVAcmi366chRFrAiGFWQcrxho8z17mCM/k05W1vbxc4LppLarDUT0tdVtgGtlGZTJahZn/e4z7S9dhqDVM9eboh7TvLp5bPtT/R+PNUJNZ9lpCobhxMoaJuFGRAl5aW8okYrhmjb6765aomihpD9sNpjOd11H55ihTvb4RbyGTqeKk7h/uY65yM09b7PXUvaqInxGd83vvp4HS80+ng5OQkC+gcm16vV6RLU2FU19CTggnD9xigjJJnbHfNnl6naUs3Hje5Ow1PT08XjAqA7JTrqvQm6dhNufosF6KCRpPx434IGlrfarVyG/lcE7impwk80skZHa1DI5Cjcmn2VI2VjoEj+CbiphoyTcPD+1NTUzkfE4Ba2pEIqSiyIlBLoqYXTwDK33t7e7h79272cWJ5XH8057MvLG97exu7u7tFBn0dTwoIStSZPHZjY6NY7ySqvKeaHJ9DJTLss2oF1USohG96erpgpn1MibSduCuMEzYuMrjmy4+d0vniftDned9xDMdTI2g9pUm0lwhXrlypMYZavmqB5+fnx+KVlFI26TZpTICR9lx9RfkcNaER/lQNH59XVx7uA9XijdMeK75Rl4+FhYVM9N0/W/sUMdMLCwvZmrO5uVnsjeXlZfT7fVy6dClc7yzP6yRjzPtKb0jHXn311ZwOh+NJukizrbqNEIh7Io0jtWRcT7oe33jjjYI+KE5UbayDanRVYNZnHUf7WCg95DzrO8qw832tT9dPE4P6blgdJgzfI4Iza9EC4wLXCVS1sD6n2g9dFOqPwWvMxO4SqpZHUElU/RX8WWU63TlekTIXsJvj/Nkm5BwxcRECIuOgzziTRtCUJQByPxV2dnYKLec4aGq/X3fmHihND2paUElPmTwykDrmmmRUmSQdB87/3NwcFhcXsbm5WbQJGK4lPQUmImR6jf+plfNxptaakWU+Hu5jRy0bTc9MuEpTzyuvvAIAuHbtWmYq1ASnpuhutxuuS++X+qf5HEZzetGZPe4XNelyfF2I4m/6fClE656gRIv58qLnfazX19cL5s79mHSv6skuyhQqU0lh2k9niFw3GFHuvr+uhdOPap59jamwS9Mi+8SgD173sec38/ApY6QMZKThJmPJ78htoaoq7O7uFvOsa2BmZgZLS0vY39/P7SVcunQJly5dykLu1NRUFiDZHiZddjwVpRNT3Pu1r32tsDqllDKO9DnQNbayslLD4VqPz7eWpWuCbRwMBjnhtZdLfM53XfOo4wCMfKOdNqgpmDjWgymVvkWm//OECcP3GODIACgZQW4elSroz+HEVP0ACb7JWTZNFxrh4ypzRSwbGxtZ7T7O1EopipGb4xgj1co48tJ2n6Vl0fFSuHz5chiUEpWhCaiJODU/VkqjQ9EjM04TA+x945h4VCvHud/vY319PZfF1BY6Hp7OhaAEmYyNphzp9Xo114B+v4/nn38ev+JX/Iow/xjdB2i+ZRAFo4lXV1dx7dq1rKlku+bm5rC1tZW1uCQOly5dwtLSEl588cVaktNut4ubN2/i9ddfBzDSDBABzs3NFWMwMzODl19+ueivahk4j7zmJjE+7xIz55ORh03QtO4uEowTXFRr57hjamqqyJXm77oWP9JcNwl+ilPU30/9pJRYEicxLQjL9j0I1CNenVnQva85+9yExt+Kw/Usa5YxNzeX/Yf1fTUBcu3SpcNB11+73cYLL7yQo3S1f467o/sapUzNWr/fx8LCAg4ODop8pYovuVd8vtQt5Pj4OO8rxRMA8OKLL2aapnSQAR+aT0/7u7S0VGjLtE7iF/oGqs/47OxsbS44p/xW3Onv8Xn1gVcc6O9EdMi/CXQd0jUcAQPg2AZ323IFx3nDJA/fI4JOuDMcwMg/TlXPlGAilbPmeNMJjq51u91suuA9XRwqhbbb7SLhJNvsjI9qVCJG06U3jySNnosgeiZiRHme77j3CJ6QV4EbX/N4sU5te9TOJsmqiZklEiFsbGzk3+xf0/FomnZgbW2tmM/I1MTyoihuMrbMQ0ekqc7TTJJMExLHampqCleuXMH8/HyWbtkGHpLOOlUrQ6Ss2evZX6ZHUdM7/fiA+okLbqoZN0eq6dZ9Mj09XfOV9b3RxBBdBPC+KrMRJV52TZmaffWemgl5T9e8+lDq+iVEe5Dg5jMGHQFDrfdZWhweD6YE2rWGnm2A9TrToG1kfjrH8TzRIuqDr9mm1CYEjpOmEmoSSnTtutJAx573NHo5CgCkJUCPRmRf9vf3sbe3V5yVy/coEC4uLjbmGfT+al9efvnlYo0SiIfU5H7t2rV8n8oAHxfiNxWS/b72QRnyKFkygOLEEtU4q2CibdeIXGWQdb11Oh184AMfyLjZc1D6+D0JmGj4HhGUyVKHXkeMCq59U1CTrj5P85ozk+rf4JpGlwp5PAzV10ApUfM3ibWmVoi0KHwm6lfkj+Fj0HRPN1wkDWv9CnrOJpG3tunk5ATb29s4PDx8KALviIrt0kTXSkjIXE1NTeEzn/lMLkedi92s7+Ov7VpbW8t9oCnFj4Hi/7W1Nezs7OC1114L++J1EbGRASNTSjN4q9XKAozOJQnRyckJ7t69WzjGs0081onXWB+ZO2ouXPPC9DVsB8eWa4yayStXrtT6pxK6g2v4Iod8nd+LBN7PyKSrz/pvJ/yuUVL8oP5Vjqv4PkHrvX37do2J8zVFPECLghJGJ5A0k0Vpk1gm/eyYFsN9qPScaL7j64uEnJokPsPreo3CmvoVNjELHtyk6zNiEH0ulpaWst/wwcFBgauvX7+ehX7VZrEMWouYvovvUkFBsy3xQlWNfPju3LlTO0yAc6fz4WuBc+oMKmkb8QeAbA0AUJxfrGPI99gHP8KNLj26FnS9Md2MAsuKmEuWob7azKaga0H3Ieef64H9a8pIof07T5gwfI8IztUTkbjkq1G6vKZZ0IHRcVZKEAlqhtT6uCHcJ80lpqqqsL6+nvMvqTTr/VGTrjJ4kaShBERhnGZM2xddd+fXSBsQlR1FiWlb6AxNBO3lqjZCryvhUslZEZQS1na7jQ996EO5DGqY1EThQStkFFUb5e2jliHSHpBIbGxsFGYuIuW9vb2siaAfITDyaeQxenxPiQ7boc7oJIiO3IER0dDxYv/0CLdWq4XZ2dkc4HL16tVibFULw/mJzt4lMVSmWQWfpog9nbNI+LoIMG4fuuaUY8xrrVarWMfRWHG8XcOn+0LrIkTBY5FvGuvw96K0ISos0DSpDE0ktPLoQJ97HmVJUJOuto1MNAm/lu8mXe4b1f54X8kYkjHzvjl+i+ZWg/m0D2Qmut1uzf8OGAqmvV4Pg8EAe3t7BYPb7XaxtLRU01ypcL+9vV0LWtR5ixQCwMiHT5kvHUNl+FWoePvtt2tCmu77Ji2rtkX74nhdgcoSoEwXo6DmZj9UwdO4cOw5zqTL0Xz6XjpPmDB8jwiuqSCx42JTf69IinONDzAyYzSpwvmbJl36oLipwIknVe76LIBCbT4YDDJSo8YSKB1JFb7ru76rGIvoexwokozKv3fvXripI1AzTMTIEjTHm7dd29QEqiHTsrkGTk5OsLa2ls3dq6urAEaIggRJBQMi1+ioOrZtamoqE6eqqnLSWAC4efMmrly5ghdeeKHWds3xRKmS/69cuYK5uTksLS3VBIz5+flsHiNBb7VauHTpEqanp7MPH4nhyckJOp0Obty4kQNOlJmdmprC0tJSoXFQE93S0lKhVYhyHZLpjSCat4iRcK3ARWT0CN5XZdB9XN3XqaoqbG5uFmvSCaKOnb8P1PPq+XVgFHmrGg/Wz/qovVfhqYlpUl8xrT8yl3G/Ok70NUOtF60DyuzQX26c9g0YrvfoVA6vZzAYYHNzE3fv3s3/ycA6ntKx5b39/X1sbm6i0+nkHIfs571797LvN+sjdLvdQiCjZor7mildnLnnXr9y5UqRh49jytM91LrkgVfRPDLIhesipVQIeyqg6DhwrIi33KVDmXnFT0dHR7k8X8uexka1iWyL56F0LaL314WmyDrB94Enk3h54sP3GEDE4mcXAmWEmD7Pa1w8LOPw8DAnzFRmyHPlpZSyeUyRjSIiXYyRlMPnlSFttVqZUYmYHpVUgDjaNpK6o7IihOXPuTremV8Fl4iVWWVbDw8Pa47BERJtqlOf043oZWgOQ5oq1Qyifdd7Op7MS6e+LIuLizVhgJpLYOhH44LDzMwMnnvuuexcTUTe6XRw7dq1nMaBwPrIuJFJoLDQ6/UwNzeXCXVVVbl9PDlD/Wa4Xmja0fRCOm4rKyuFCYn16ripzx+AwhTNcVEBRv2/fD49SvUigq8TzUnoJkrXUAF1lw1/RveYzounJBk3zhrgFLlicN8CyOcu+952zRpNjwqqjdSy9R7L8OAKat6ifHvz8/O1E4Fc4KaW2nNtKmi/5+fni9NCIkHG948qGaidp9WDHyZepkZJx5I+m66J5HUetehrgALBzZs3s5+44kTdj6zvpZdeyu1+8cUXw+AKWmFUg/qpT30q3+fxcFwzxHceFezMZeSfqVrBiF4ykluFUbekeMojzT2oaYC07F6vl32e3feRZT9JgXSi4XtE0MUSmbJ883PzqkmXZjL6G9CHQDc1F4RLQvzvgQ1ubuz3+9m3ypGkIw8yWU0HgnMRp5SKSNioXXwngohJjZ6NTLoRuOO0aw1J8FTbRUQcbXS+65pAV+mrhEZGSqMb1feDSEV9UzQVgAaeMMks26W+NbxO3zgSzVdffbUgRkTanquR17e3t3FwcIDNzc0aEeezynj1+/18QszKykpxlKBqPPb29vL4MScW54jBSiRMhLfeeiu3QX34PA/f0tJSfidKGM22chw88bKPgc7tRQQVKpQocb6acmKmlGqH3Ot6ZBm8ruPsEbyR9pXgeC3CZSzbNYC6b1lGp9MpjgbTdrIdKgxpuxUHNhFgf9YFNdf+EFyTo/hPNUtuij1r/LTOlIY+fMwDp5rObreL559/HgcHB1hdXc24XBnlnZ0dzM3N5chp4iXm3mTQhppeuabW19drigrSRfr2RRo0PzmH/SFzrLhWtc3qi611EieyfU5TPGWPW98iIM7TOWW5nHvt08zMTBGR7MIVn2f2hEiBApRCwJMQTCcM32NApCFSLZv6myiTwBQDvul80lVCZH1VNTxuhmlGaFLjRvH2TU0Nj7xSM623mUBtkWeV974CCJGl9tUZpmicxi3ktbW1golWJtbb4tpArUsRiEZHuylmnPlPGUdP96LPOLImEtRN72NFbZNKo+4D1Gq1agmSOedEmnfu3Mnv87mTk5Psv0kGjGWvr69jf38fq6urhYag3W7nk1nIrHFd8T/9/3StDwbDQBDVcJK4tlotLC4uFr4wqhlxM4tqCAgzMzNFpJ6uI+4zR+DR+nNoWqfPOijO8P2iDK/uK32Xa0Gf4W8+o2UQVOtCUMFOn/UIRhfwUkqFKdcFaV233o6IqdL1qq412ncPVtLneU21Zs4IcE/r+BBfR0KtCkzUhK+trdX6cZZZL6Vh4uVer4der1cwrvQRXlhYyLn2lK5QqaB7kmbOTqeDXq+X8yty3CmsAsCtW7fyvndlA3EG+6mpeH7pl36pxjCrQAuM8Jzilbt37xaCHcuPTgNR8AAMxctcW+6brgGMyoASr1dVVbyjaVyqqiqOWdP2Mi+iplbz+RxHR98pTBi+RwRFKHSGVyTkOd90kUX5wVRTGCEVvUYVuwZuqGnQkTePG/Lr/OZvSmxRNKsyrA/jW9fEzEWMlP8GkE2YZ5UBjJLtRkwqN6b6k/lJJ+yXvsN69L+OtbcnMtnQDEXEQqRKzZsyOM8//3x+j8w081OppoNtpE/MjRs3AIwkYJ1P1SCTEAFDJLa4uIiZmZkiBx7LdqmUhOPSpUuoqgo3b94smLKqqjJDRsYUQNY09Pv9HGFLPytN66N907n1FBKqpfrCF76QmVglXsrMREy8Cw4XkdmLwE/GibSaitOacmBSM6h7TfeDu37wfRI/JY6bm5uFUOPQ7/ezQOOJg7kHyJiSSVAtjrbZ+xmZAPmsC4Jk2PguhW5q03QsuKYcbzBvagS6l+bm5nD16tUaLhu3TpUusG+06JycDI9dvHfvHubm5nJCey2v1+thaWkJy8vLWFtbK6KMZ2ZmcPPmTayurhZCbbfbxdWrV/NvbQvbHvmZa720TqhWmO1XodDxo9I4FQCIE4kPXEutKaMUJ6sw4W4gHtijdNjXCoA8z34Ena4FAAWtH2cJc+HpvGDC8D0i6Iamn4NuJNfK6cKIklBSAqQTKe/Pzs5mM5m+ryYtNSkqMldGzhk+vhshbvXhcIk6UlH7uPDdiLAocfax1Ge3trZqEX1aflO9EbhJMyqzqS8Ezm2Uc5FlOULXPHxajgLbo9osvUaBYn5+vvANIqNFhk+JBI/jm52dxZUrV/Ih5RoR/NJLL+G1117LjBjvT09P48aNG7hz507RjqqqcOXKFRwdHWF5eTm3helc5ubm8MILL9SkYI6bnrGrYw8Av/zLv1wgUf5Wwu1m4DfffDOcJ7aZwUrj4Kz5f9ZBx9r97Nykq+80XfdvanqUmVS8pEBGX8EJsrtQtFqjo9U6nU5BPHUPKoNAzZYKPQr873ne2Mamo9V0/PjO/v5+EUjUVCeAnCFAgX0k0We9epa2lxkJ0hxvMhtk/rgXeILH3Nxc1uh7OcT1LvhG1iO2m7jnAx/4QPa3I+1otVo50bEGiynD74FqLJt4qNPp5HrVnWNpaanmh0tXKT2pQ8tn3Uq/VCvIsXYNn/p06lgoHdN1yzQ0yvjqnuE7c3NzWF5erqVu8flwunheMGH4HhF0M1KTMTU1OlXDHUHVVq/norIMnsvIBc7nI0ZNoz1dI+XIg4SPTrXeBzXbULrxKF39jiRNX5hqpnBQCUzLdkYwMk83aQdVevQNwrE/OTmp+TH5Rhqn8fGknsqMcFM7QtcEt55mgO/xujKHNLvofLqZiZnvGQl869atGgPKhKsktGQEq6rKqRTUVAKMzLDq4MxxW1tbw9HREW7dulWsX7ZtfX29SC1E7cLR0RFWV1cLU7X672jiZR0bdXj2PHx6GkEkCetcRWVHhPMige+BiAnxcVBmykG1ogBq65Ew7jQJZwQpADQJkbqn1PdKNU1AGX0f4R3Fh8Q/DGBQIaTf72fB3cfPcQX3pZ+B7r+BEROsjAVBGS1ltvS31unjo0ILmeO5ubkiHRjxgDPYBKZn6na72N3dzfPPaN87d+5kzWCUHorCnLaTz7KPvO9+o5q3kN9qlo3y00UpYigQqmYt8uGL1okKEApbW1vZh6+JIQNKBp9pcdi/SAHDlEAsh/d8Pp8kTBi+RwSdGCIs9V/RSddvqskZ9q6g0T9+XZkjJsLk5lMfFtWSAMi+VaodcY2IMp3A0EcrYoic+Rt3fZzWjG0ch8zc4d43hkLTqQAEMuN6PJJqIp3h1A2qzCwlVffHA0bRfArqm6b9OEtqU0mSDvdEquqf1263cf/+fQDDA8V9rI6PjzOTxnIZDf7mm29iY2Oj8E0iMtzY2ChOxuD62tzcxPHxcfahoUTPyNr79+8XZlgfGx3fKHE3tQFOPFIaJjfXgBg1HXO8nGA6M6t1ES6qdk81DEA9CbWua2duojQt+s3fkQa/CQdEDDbxDDBixLw8rj1NiqtMk7Y3pVRjVjWqUqHb7aLX6xWMI4M5lJlQrbpr3FQbqLhbz3Mm88JrTeZ09Q8mTlfGUfFl9H6r1cJzzz2Hk5MT3LhxA1/60pdyXR/+8IfR6/Vw+fLlrHVTmkGNKMePv9UXTQMTgeGe4/ndr7/+es6CoMEb1I4pE6Z06I033qj5kyqzR1zlcP/+/Vp6LOJExeeqFWRZEfOm+F5TuWh0blVVGTfxm+Or/tdMv6Prwk3oZKz39/exv7+fI6hdqHqSMGH4HhGUQeCEqfQQfQiRFokE0bUVzN+mi4AmPvU/AOqSPDetOwxrPaoa14S3Cq7BckZVJS3+b9KgRJJSBDRrOGGKylXtpUt3qrFQCW9cmyLCpn5GEdOgUqy+q4yias5YLpHHc889l9/jOJOh8shHjoUmbNY+6W9NU6FaSq4Vd3jXda0amJRSTkLqZ/1yjTHfI8vTlANzc3MZuc/MzBTpYNgHJZLAyIePvlqMFASGWkHXCvseG6eN1neehI/Mew2uVVDTY+TUPo454zpWZkX3pZp0o0CPJuj1ennOfW+xHgaSMcea3tP5dx9ZPke8FfVJmQavXy0yjP7UsaqqYSCBH5OpDDT3W7fbrZ3J6uOskdQrKytjmfAmLZBrxtin7e1tPHjwAFNTU1mzrmN0+fJlzM/P49q1aznKlPRgdnYWly9fLoIE+S7nXYUtbbMGdjhjDIyOkIxM+SpkuLBCH3NlkDhGOo8vvPBCfodnHns9dKHi+KkbyKVLl4r8p24KZ7vVv1DP0iUD7dpwrh3N0kAm/93CRROG7zGBC1IXJZFg5C9GVbdLnSqB8HlKSaqF08Wn9UXXiaDdnOsaPrZFjyjTNmvbm5C4Mmd+XYFMETec+4wQVDUeaRgUmo6/cXCi5X2NNFPcjCmlYt5cC0UpWIGO02Sy1ASvkrsHZbA+IgRG2JG5V59NRq7ST4/t7vf7mJuby4wVCQB9ay5fvpx9XFgXtc83btzA7du3swaG7/EQdgoQ1HL0+30sLi7igx/8YCHtUvtHHz4l7ArUFHD9elQwMNwfGr1IzaYTd53DyIQVraGzGJNnEaL95Pej3xHx9jJ9rP1otSZmz6/t7OwUa1o1ayyDZWtaKXdlAUbMjgZw6T3vJ5k4ZexYn/qhkSlwi027PUpQ3gQUBCl0NuXhU+EPGAas+RhH+ElxXlVVePDgAQ4ODtBut/MxiAx8YZTt2tpaEexHPNJutzE3N1cIh2yzjonO40c+8hEAw3x6LENx0OzsLPr9fjF+qtnXpMZK38hwzszMYH9/v8Yoa+oTvqPCJ8viST4EpubiWEZCuraPGQ6U/qnLFXGZlqGnjuhaUeZU/Rp13ptcUJ4ETBi+RwTX3rn6t9PpFD55nHyecKAIjs9Hm8oRDetiIIYuQn2OMBgMsinOpSIHSn/Mw6fItwk0HJ+fJoIBlMEJyvQ5MiMT19RWBWUiz6oTiCOU+b4yrs5UA+UxY2e1j352g8Eg+5d4/joSKPriASPiRoSu5h6tn2lXgFEaGx3T+fn5fMIFETw/e3t7tXNDAWRHeZp+KG1zHR0eHmJlZQUnJycZMbMtNB9z/LRsRmSmlPJRcIR79+6dOccACnOLmsjGMSjvZzhr70T3yPxH153B4DyflTsuYhKB0hSvgq6+Ewl9uve0PD3RIWo/tVbUSKvlg/hYT8RQ3KdlqvbGgzaUyfNcct5/FUC1Lh5tOQ5X832vlzhGmTqnHQTubbpvrK6uZtyveOL+/fvY2trCYDAoXDa4H31tcE8ytZOOHQM9AGQXm8i3XBnlJiAe5PM0IbN9akVQHz+fQ11HKrx85StfycGDOgfa3kiIdXrGevwZXcd87t0SPicM3yOCTkyUEoW+C4qYuDCUmVFkqE7qBGqGVDM1NTWFXq9XizpiOfxP5oDpAzydAuskUqX5hOWyn+MWoYetO4MYMaA6ho7gCdQuPcwGoKTkZk225eTkBA8ePCg0qK5hVIiYQNYDIEemunYp8pNieZrE2DWWzhyRieYaOjo6ymflKjN1cnKCW7du4cGDB1hZWcFgMCgk2IODA9y/fz/n1CJC3N/fx9tvv43Nzc0iIz/rvHPnDtbX13MwBZHVxsZGoXGmkzt9A994441aEmwdU+83QR3ydW5U2OERTwQyluo75RqfprZEbbto4OvaI56BZobPx0OvUSOhvl7K8KnPm+4Pfitu01NRgFGwg9ZPExvzt7kGULUmbvbU+lzII7NGPMz97K4AzpDqddfoq1ZQf6vA5mUoY8PyeM6qCtA+Vz4nKSVcuXIF7XYbS0tLBV7vdruYn5/H5cuXs7VHx5iMGRP0042ETAnxBv8T/6ysrAAYJn3neyoI0GqlmtSPfvSjud7bt2+HOJtMtJ48pbC6ulpoHVVbp0yUMuNf/OIXs7nV14jSLF3LGsCi7jdumVKrhtJjXTu6Vquqysn11ZzM8viutvO8YcLwPSJw4paWlrC0tJQPm9eUB5EKnxvJ4ejoqNDsACMHYJpduTCPj4/zJo2YB188bFOE4CNJWSONXWp15KfRoYqEooWr9TkxcFOr++LwWxE9Icoz6ITl8uXLNYLQxFBGDGxkimzqo9bB5zRHlBJEMvTqwxfNKZN1szwSEUbeakSwErQHDx4UxyKxDdQ+qglOI4aPjo6KSFgiPBIwrnEltorY2Wd+6ENDxljzXUVjSwRO4nRycpLzGgJD5pP9VOIYzaHPGcdR00hcVODcqHb0UYmJEivOi+INxXNkWqI6fL+pUEdCy/ssn0y7B+BEDD59qT0QiaAMnTOIWrfuT/UN5DPqGuFCNMdHcSWFLLdE6Dut1ijid3Z2tjYvkXuQ/teADy+fx61RSHShf35+PrtleGAamUX1Q+YeIs7Z2NioZUBg2b4O1C3DGT59XvGY91eT0CtjrxYJAIU/HvM46vwS1N1GtYK9Xq/QMKv5X9uofacpXfGtzxXN7JHC4Sx6dF4wYfgeEVS6oho9Mk06MdOs68rA8LBohZRSdpZVpqtJMm+qk5JStHmAeg4mRkYpE8bFGanfnXlqYqSAesqCJuKzublZIG4v19vhWk7tm5bn0V2EcW1STSHL9HJIcBTIMJGYqe8Rx5J1ac4oXqMmZWZmJptYlRgx+en169drp43QbKWRyXS6np2dRa/Xw8HBQcFIkujMz8/nvH8kJgzW8CASIsu5uTksLCwUCB0YaZaIcLmOlEmgeVcldWDkX0rth5a9trZWaIdcGOHe0bnwueW6fxIS9HsN2q/I9OjPKgPgJlpnTBx0nFUIUo1GNMZaljLuvJfSKNVIVC8FbP7udruNp+6wHWT0ut0upqenM07VvaXCLteUMzRVNTSFRsKxgwpmUc41ClBcu6urq7Wxc9ceh36/jwcPHuTgjN3d3byP9vf3sb6+jpmZGezs7NQUAJ1OB0dHR4VQsLe3h263m+t2JcLs7GyOml9eXi7wEj8zMzPY3d0t2q5ri5HXOreqSSPucrqoDKnjRHUR+sQnPlG84+Z+jrFqChUPr62tFZrLSOmRUsquUCsrK0X2DMX7TntV4HAttMOTwE8Thu8xoUlz5swFnyWRdaZDzQtevgMlTmcEgTrD0263C5Mu64wQCE26mpD0LOby8PCwYFwjplXBtVfRM+Pa6O8SnHi778r29nYej6heLZP5FF1y97l0cD8NEgNP3KkEkHOnPm3KZAKoITeVNNXvks8Q6dEXLzrJIsoLSKLpQUhEiAy80LFin/v9PtbW1rKwoGVXVZXP7CVDp/29e/duTSuhQOSop3OotsTHUueM8JWvfCX/1nXh++UiAZkX1543aSr4O/KbasIHTXvTiXg0zu5ecVY5Ds68Dwaj3G/+njMDFMZU2KVGUd0EfF1yHCgEOR6L2su94IwD7zlO1Eh311qPI/5kNvb29moWDwpNHuHaarWyKXd1dbVw8aCrxvr6enEcaEoJi4uL2cXCx0hxDGkgx0XNn5x/ZXgifziHCG/T3KxaTMX1t2/fLlLLsI/uR6f1vfrqq9jd3R2rINBrW1tbtbmM1rWuQ18P3s8mGvxOYcLwPSZw47tUTCnFF6w6BbsmKWL6uCj12sHBAXZ3d2snaLh0zXc3Nzdr0b4ElZDJNDD5qJbThGjcpHvWIo02cFP5EVL0e8BIk6YEXMdiamoKly5dqhE9RxyKqKK+qKYsYho8Dx/HhMjTzecqvarJimWSYTw6OsLW1lZeH9QUHB0dYWVlBf/iX/wLbG5u5vaxHgZHsD9EivTt293dzYwwyz0+PsadO3dyfbw3GAzz87Xb7cKvhmtwf38/5+Py/inypbSrjCbr4TiquZvX2u02rl+/nt/RgBhfU6xXkzt/4QtfqM0Jf19E0DlotVq1o9UclGg3MS3A6Gg1PusMYqQNacIjXPNRsBp/c224hs01Nf7t5bAdFGbpZuBrzY9ba2JmOBYquCgjrc9TSx1pE104B1AISj6uUTvY7l6vh/39/WxB0sjY2dnZbLJVzSGZ3N3d3WyaZV3USlJBoYzwwcEB3n77bQDDU2/oXqRzoGZs4iDNq8eAM2U+OVdk3iNmqIkJ43t8Z21tLQuJjpv4vK71qipP2qArDDASOjVojnPBvaWBm64A0Xrn5uZy2Xynib4+Kfx08RJRvUtAAqqqfV6PGB+XGlWt7k7/lCT54bM0CztRJDhSGCclq9ZP61epKzJD6PsRAXUk7P2KfitQm+TgSBKoJ5WNtFB3794toqbVvMp2n6WNVEKnTGCTdoqILHqW/2nC0FxO2i4yi+qHRwRBoqE+jFyD/X4fe3t7WF9fL7Ruygy6aZUIaHt7u9B0qI8RzSlce9omZZTZNvZPk7vyzFCdL+0326jlUdtAcH9XH3e/Tgdz3lctYNMafNZBhQ09tF4Jt5vU+J6Cr2F3n1ANspv1mnATUI+e1zJZD7VIfuapv8P1qbgqYqqUEY2EMO0z71E75jjF02u5FpB1T09PF3ngdJ/qPidTu7+/XxM4x5n9FD8xzYqO+8LCQsbjjqc5f2yjziOT1fd6vWLsU0o5yANAFipdwaGaU449UzABIxzPfa7j7mZTBWowtQ6tkx+1CKhmk89Fa4NproDSjzwSLIkXyfC98cYbhfUuApanuXvZb4cniZcmGr53AOrMqloW16hxo9ARWTfDwcFBIYFwo3u+KN5zlb0TMLaF/ipAqZnyhTYYDPJzTgRVanJggmSW5VKoEhOa9RSUeVEVvKZMOItBdGSsEhMZjEuXLoWa2HHlKnAuOPY+fhplp+8AJVNNJOH1adCGCwR6jJ6usZmZGVy+fBkf+tCHav0iAWR9HIdWq5WP9pmZmcHq6mqBzCl4KNHQwIsoQjOllM3HOibKePnYKKOpTu7KZOq6Pz4+LjR2Hmyh48Lf6pOkAR+6NiJif1FAGToXioC6FgIoc415Wfq8jr0ndfb3moBmQmc6dV7I5PspCRGxPjw8zARXmSrdq/yvJlZlGjSPnwo2HuAxPT2djzBz0DXM/ypAKTPG37QUAUMN0Lh1qXOh1zY2NrC1tYVOp5OjYzl2BwcHOVOBzgv3ycHBQc7lqQGHZPz4m/1qt9u4efMmgJGGqwlv6fpSTTP74e9ROGw6E1fbxPc06XK0fv3wAZbjeFxxBv0gnb45Q8e99fbbb4fpxNTSQcbbT+vwuW6ioecFE4bvHYJLGY6QVDvHBaIMwezsbPaHA0pGiAuNi+L4+DiHyqu0znYowuv3+/mM06agDbaFjKgmm2zSXhFoElRwpo+wtLQUStvaVoKalSPk5uVoXRGDSGY6MgX5fyKeiBDqOHsZjgjIlEQRdt4WDUhwRj7SvKrvER2x+TzXztzcHDqdTjGfAApJ31N0cH36YfUkqCcnJ8WJF2qu0iP53KeG98gYKwPm0YtE9o40NYKO1xRp+/hrn2l+ahrjiwhK1J2hijTc0bO8xzl1TQwQ57mM0g85aBCUCkIqXPrcaMoQL5fCivsM6rpQBpj7SstxrQ6/1e2Abd/b26vtbW8vCbwmv3czIoH1bW5uFpqqpvXt9RwcHODg4CAfw6h4dTAYZJzqGvX9/X3s7e3VIkw1JRSFXO3f1atXAYx8vh3nRzhbtW5Nygf/3eRioOM9bl0AQ4FPNYk6DjquqglXn0YXJnV9k4ll5K0+F82Z4sUok4eOl4/FecGE4XsEUHU7gOz7RGSlC8+lAx7a7eCmkmiSeY0+GS7pRNKyRlw29YXvMlWGltEkeUTlEGGzHS6lqr8f6/GgAYJvFC8vYoJ0HNgmvqNM0cNA1G9u4GgsfS74DvsyGAwK7RjrYD+ZCf9LX/pS3uxEsp5km+8dHR3h3r17+PznP5+TterYHR0d5fXGtUfCsL6+jna7XfiSMFhlfX29OH+Z9W1vb6PT6WSkyDL7/eGxZzxLV8ceKE3hZ61D+ggqU8Kx9dQ1TQSCRFK1imrSfb+Bm8OdofJxbGIsOBfOsKiWi0yBMl0uSI0rTxkdYKRxUWYpYpgUP/ja0LK5F/2caIIfleYCtDLGyui49p1aJ2VI1ETsftyqsdZ74+iAQqvVyhp45lEl8zYzM4PBYJAZE2dEdnd3s5+wZpugJpS+eMooMl8nMGSMFK9xzikgqjCvGjSuSaeVVGSoz7ICtZcqIKhpOMLzPGEkSsOk69WD1fw5jp8KqaSbundUcPH9NhgM8hm6kanY53Xc/ceFp9qHL6X0p1NK/yKltJ1SupdS+rsppY/ZMzMppc+mlB6klHZSSj+cUrpuz7yUUvrRlNLeaTl/NaX0yMyuMy2ULJX7b/I1aUJIJM66gElQXUWs0VZN5ldtqzurKiOiG5VSrJuLFVxLuLq6muvUxU6mT8fA33WJUX/7UXXeT0X6ke+Il3Xp0qWaf4/Xq+PC8XZ/OZrJIyLVZC4+i8lptVpZWtazb9kWFRK8LGrPFJFx/nZ2drK0r1o3atlcQ8y6tre3sbu7mzXObA9NEYr4+d1qtQpmjwSP2hTNQ9hqtWp5+CKGjX3ib9WCOhLU+YyEAjU7OrxTCfppw08E3YeeOkWFjiYhLiorYrKVmYxwhwu9BNWmAHVfzqqqcmCHCslNuExzmZ7VJ6ZKUYaR3+P2uz7veEDxsfZBzeTjmEPiDzcj6thon7XeqakpzMzMoNPpZG1ixJjrnuXaUMZVGRP143M4ORkms9/Z2cnaM+1jpOxw14L9/f1QE6x9rqqqxvDRZ5n0Q8dS26/Q5G6jfQWQU80Ao+MInZ54UA37TZO5ttfnmf3iJ6Jt/uyTgKea4QPw6wF8FsC3A/hOANMAPpdSmpdnvh/AdwH43afP3wTwI7yZUmoD+FEAHQC/GsDvA/D7Afz5x2mQLkqaV/k/Ykz4zQ3kC252djZf14+bfwk0eSnTFCE5Z8YU1HdL2zjOPO1MnDrxuynFtQURUfbfBE2UGdXr/fCkygonJyf5ODAimAgx6LizHCJvZ5gi82HT5qSPp298jn+r1SpyWnm/NJceETcFjeXlZVy/fr1A1nxWCRowcuqmqXd+fr7GvNHx3X34SEjowK11kYHrdrtFtLEis8PDw0YNLhlLagJ4T7UiFIii99U8qcSaaYZYVpPW52EYnjPgqcNPp2UCGEVgE5rSrhAiASXa0xw7z32nY6qMk4+1amgUdA6ZxoPMUPQs29vtdosEydoXr5eaEx0L7isyE6o58vNRqTV1/Kv+uuwz29WEo1gP69jd3a3h0yahkfUOBoMspKlpFkA29fK6zhHnb3p6OluNtJ0azez+itevX88pv5zBJHOnlo+qqorAEHcncdo3ToBWfKhrrMkyxfF3La3Ph57mo5pNZd6dmaXg8uDBg8JMq+teA9w8EFNpm9OeJwVPtUm3qqrfqv9TSr8fwD0A3wrgH6eUlgD8QQC/t6qqv3/6zB8A8IsppW+vquqfAfgtAD4J4DdXVXUXwOdTSn8GwF9OKX1vVVXNKoDxbcvfykS4fV6lFfWTIBB5RNKxS7SUBrmZdJH7xmPCXHc8VuaOm4CZyTU1gS90b6NK3k3+E4S33367IBZ0Do4Irp7MwHaQqDvhUG2QPw8MzdrMmh45WTeNNa/xW+fQGVZnqu/fvx+2T8tUHxJqSV577bWaxKpMtdZFJOwmJ75L84mfDapO2GwDyyfy17lUE/HBwUGoLWN0r560QCZLE/9y/FS7o/5RLiDx+tTUFF588cXiGp/3deeCEDCKfHwS8LThp6ifkQZB8ZXed4aQa8vxkGty+KzuU8UfDkdHR5nJaiLU7Av3j+fRJLPDdUAzoI+BtpNMnLoacI+6Nol1ur9fp9PBzMzMmecIA6OITI+Id2GL7eFJGzqG0fjpvJHhY2CTaqEopOoxZyr40r+b2lGOA/cqNXG6LpaWlnJmAY8KVtA+9vv9IrUS++zCtzJvZJC83+y7KymaGGNndt33nd+Kl5imJqI52l5mHFAhg7/pW6hjNzMzU2gum0zHkVLivOBp1/A5kA2njedbMZSqf4IPVFX1FQBvAviO00vfAeDnT5Ep4ccBLAL4VFRJSqmbUlrkB0Bt5EmMPEpX0wgApYYvApqCm5gOR7aUPpSBBOraOfpeaX4lX+S+2KOcSt5nAh34myRqfVajudhf7aPCOLPyOKaM5Wu9ly5dCqUzIrGmfkZlRWPAfmsOJ5rRFbk0MfvAKPjll3/5l3NbVPLXSGR+06H61VdfzX5vwIjxJpJnO1g/k3/T3KvvnJycYHt7u0gDQwJA527Ngac+fA8ePAi1RyqUsP06fkrcnJHnWmlKvaNz6mtQ2+LmwyeMVN9T/KTjQYZFNSt85rSMWrlNOErBXTgI6mPquMgZF8dbkfZO9472K2JCgZGWTtupbeBanJmZqaUhUc22vqMEXNeo7ku973iGUe5Na0y1gsAoolzLUA2Rjo+33zWCKQ0j7+n3zWtaBhMvb21thRkhNOhLGfi7d+/ijTfewPb2dmj10DN4lSkneA5ZrhGWRUHQcYpGIOs4EE9FzPHm5maxNh14TRND65pzraBe12Ayzr+uA6+PeFPPdve9FAlR5wlPtYZPIaXUAvADAP7nqqq+dHr5BoCjqqo27PG7p/f4zN3gPuQZhz8N4M+Naw8lJCecTRtT+lH89hxnBD3nj8/yKC1VC0eLghIQEZuqmJUY8D31q2JdrmlxcF87r1/7rkdoRWOi15oSSpNR1LpUIozGgn5pzlT4SR7OBDgQYalmUj/K8Ok5jsBwXUSpBOjvyRQH0XgQOTjiY5+Z8kFNZK3WMIO+HgCucHx8nI9OUiTO/Fo+JqyL7/CazpEiKxLXpn5Ha801TXovimaLhAodG0fe0fvKNJ4HPC34SdcyGf+HaHvxHd13DUqr1SpyKja978w4UHc58GeVmKpri38I3M86n7SG6LMkuC5EE5e7+d+1W9Ga0fb4GJH4N42P4zll1tgurTNyb6FJttvtZusI9+z8/DyOjo4K/OT7WhkU3nP3DoXj42Pcv38/RytznSludw1mSqmwfETmUh2PaG24a4ePH8fOtYJOT8hAOzP2sY+NXG+b3Kl0XjTam76M3md++3rxsnWu2S+dl/OEZ0nD91kAnwbwe96Fuv4ihtI6P7d5g4vLfawImotJF1bkD0J1vJ/HS42LS1zUqkSmlyaIVN3RJtVyVIrxdhF4kPXDaME0nYc+EyFvzwgftY2g0U6R9DYzM5PPe9R2+eaKGNGma94GNwf94i/+Yq6LfeF6iXz5PvnJTwIYJUn1seBz+t7s7CyuXLmCa9euFTn3CM6QURM9Pz+f/fGU6aeLAAUQAplZHqSuWmt+Zmdnc3Sggq5714IAKPaCz1+kQdI2se36X9ul5qMoP9w4wvIO4KnAT0DJZOl8nrWeI7MmvyOtnZr4VZOqRM73NzByB3Gip+0izuBpOt5+bTvzzXnfnLkjcH84Q+Xt0IAK3tdclVqXr2HVWDW5XnAdRycwKY1QAd/HTHP9afAfhaX9/f3CL1oFfrpxMJm/Mq6aNNqDPpaXlxtdZKhFVdxSVeVJFv68MqnqM6ipXHiGto6NujZFzDDn0JmuyBVEtXUUspWBdSZWrXgahKKMuSts9JAFHWtn9PnOODekx4VnguFLKf0ggN8O4DdUVXVLbq0A6KSUlu2V66f3+Mz14D7kmQKqqjqsqmqLHwCV3Cs2McFNt1HCTjdPUXPiBJ3vO6KjD4oybJHjJ99hZnJvrz9P4uu+JlVVFRt3nATfpB1gX9zE3cRoRTmuFCLJXq/rWB0dHRVh+bppIwmrCRRxR1KXIvqf+7mfy2VrP/S3lkGieffu3aJtRLraRz2mrNPpFCYTAhEuU61oHxU5OgLk0X+RCZUENTIvO+FXYYUaFb1OwvwLv/ALBYKM1oebyxWitUNQLSIPao+g6fqjwtOEn07bU2OMo3uKB7g+rB4Ao/XNgDFe11QbDe0MmWvfH8oM8cPAG2XOtBxtu5ppIy0bgevJhQU3B7Merlfd3/1+v4iob8J/KizpWDuDqQKjJhiPmEMfR47L/v5+PiZN23p4eJhzt7r2MqXREY785lhTqaDn6+q7L774IhYWFsL0NABqjG+r1cLLL78MhyYNFsvR9RjhOR0PXUMKfqpFkw+f5pVVhi9SSrA8CpYUqKP+ufZU3YnUBYf0dRwdPQ94qhm+NIQfBPCvA/iNVVW9Zo/8DIBjAL9J3vkYgJcA/NTppZ8C8I0ppefkve8EsAXgy4/bNmrslLjxerSQj46OCgd2giO70z6E0q8iEDVjKqgkurCwUBBTBUUkV65cye94O5qQ9jhtgWsg792714ggva/K5PCetkH74ukAFCg5z83NNfrSaNlNDK221fvNefAs7YokdWM7Aa2qKueJe/3114vyydwr4uHv4+NjrK2t4fbt29lZXYHO2e4HdXBwkCX+yHzFSD9Ni0Kix5QIvE4mmP591CrovEYmXY5V9DznVxFk5FfGMdW269h5lG6ToPJOkevTiJ98rboQx29+3EcuKksJuT4TnfLi2oqoXVxH0T5nOTQXu5AWEUVqt/R57bO2//DwsMi5p+OgoJo3v86oVu+Xa4HUhNwknKhQ4/6uvs4j4D5TpkMZ8igpNXE0hTjiLGVAPA+f4qytrS2srKyEeWUpaGvg1vT0dE4/5W1XHKxCotORL3/5ywWjpHhImSgfZ2rfOL6Rr3mr1RrbPhXC9V21JDh+VzrHe+QBuOf0uXGWqvOEp92H77MAfi+Afw3AdkqJPi2bVVXtV1W1mVL6GwC+L6W0hiGS/M8A/FQ1jIADgM9hiDj/25TSn8LQL+Y/AvDZqqrOdnBpAKrEKR1yopq0TlwwESPoSDelVBB7vrO3t5ePZokQmzKOjIj0Z1WKY7lkRP3Qakdm+n93d7fGdOrGaEL2TYwiYZxk1QROvNXnTlN8+HPj2sJ+kTBV1TBq1iNV3ZfJI2OjDcz2pZSyDx+le0XyHpjjbSWi1raSsfNTNpSwRWYm1R7rfJKQzMzMhFFlU1NTWQvCuecaVQGCGkuaaagtIkRO+kBJwLe2tmraA42EYz00k2vZriGIhITHgKcWP1GI8dQpvA4Mx2B6errmaqLP6jef4X/PO+bCTdP4PnjwIOMgtWQQ+v1+TvOjUaf+0XY6Mxq5omiqESf4GmDE74jZZJu8/mjcIgaD13XNsh5NEs7xdE2W1kFmcWZmpjhjnWWzv36eL9ugTIzuD5bLZ/TItZOTE9y/fx9vvfVWeCRok9acASmf//znQ8ZI/3M8FN+89tprRQ4+4nelvdGa87XrQoz/5rgpeLv4m/6xtKRFPtMOamU5i7k7B/xUg6dawwfgj2Doo/IPAdyRz3fLM38cwP8HwA8D+McYmkF+F29WVdXH0NzSx1Ca/tsA/haAP/u4jaK2hT51QLOvHJ/3iEUyhsyNxue4+CIVcbfbrQVtRAuYhHCcZkPL//jHP17UPw7JaFvGmcT0WT1OS+9HBEUZNNeMeT8jZEpkQOaGB3UDJdPqTKqXo/eU8fZx9k3LkzM4xpz3qA9TU1P44Ac/CKDunB61h3M6Pz+PK1eu5CPr/B33FySC0Tx8vkbocsB2ENmTYOiZlNQoTE1NYXFxsWBgdVyoBVdhh+Pwta99rTa/SuD4X5kKRjL7+PCbhFsDYaL17vPwDuCpxE9SdmMOQ32miVi78OYaD81dxrL0XQLnxSGyUrBsnq7ieE6f1fWo+0d9uny/Kn5RoBBE/OEMo/9XXBCNkfYn0vxF4xThH4cIN5DZUHoEDIVInjPsGn0mbFaaonVoImft09TUFK5evVr4r/tcO1NdVVXWuuvJQApcI3ruuQebKH0iDWQbPBCPQKHTaU0TM8UEyoqbHE+xDu6tyHXGIaWhm0KUzDp6NurLecBTreGrqurMHldVdQDge04/Tc+8AeBfPcem1ZCjSiURI6CnZJy2KWtj1M+JiESDPwjjmAHWqUgoSozp0lVVVVkLEG2KJjWzHjDtyNi1gZubm+EG07YStL3ODHgZOm7KVLDs+fn5zJBEBMPHz4mDIn+aD6L+KtA0y/uK7Js0FMAosEUlb5WyFYgc3UkbQE6wrAEj+l5UPzV41DJq/2jqVdMIGbFWq1Wsa44Xn9O8XmQAqSF98803UVVVYWpXZE6GVLWn6sBPiLQEfj+aKyXEjwtPI36KBJnoGSWC/O3jocJT9F5Upq5X1R65Zpbl67fep/ZEczV6n6iV0jREWp8zOb7HtS26Tri/okwJ7h7i60j3Oc2TWg+ArDHjc+764GV4H7Qv1Ia6nx7H8Pj4uEiKHrlCuMaS+5aae9X8LSws4JVXXsFbb71VCIzKMEbBP9/8zd8MYJjFgOOovms+Jyml2ikx+u372nEzwVPL6HucO33nZ3/2Z7OPpuaK9L2iwnqUL9XnS2kJXQrGWX+eFDzVDN/TCKrN8CzqjvB4DUBOv8INzTKilAJ8L2JwSHibfAV1A2xvbxc+Xk2MCpkNNyHrgvdFGPkG6RhFBFa/m97VHEVelm9o7U9U7tHRUT7vcdwmisp3zUdkNuBcahoQEihlFNWs7uXv7Oyg1+thfX29pllVTQORO4WE7e3tIuWMgvrvkWnje3TujhD84eFhsR5brVZm0ujIrHnBBoMB9vb2wtQfKaUi3xnHkWlrxjEPah5WiZipHfTdh5GCI2b+/QKqJWli/jnekSlL8ZsTWV33BGc6IuFG/btc88q58iAMr1vbq9GPfMcJNf+7ZorrTC0wyjBqAl4AmRGKGAzdq2RYNAcpv7UPulfVXSRipBRU4+ZRtPxm8Ba1YfRnVUGOWkHud+2n5mVVnLSxsYF79+4VzCzfJ5PbJGitr6+PFeQpTDpDdOvWrQKnRusXQM2FgYzwWQIQgf6lTvOU0eTY3rhxo+iDM5Hed5YdpYpSeJJM39Nu0n3qQBmqSKripo+It/ptEFR6Uik20tB0u13Mzc3VJE+WqQiu2+3mSCrdHIrk+JvOp444fNNFGybaSL5YNzc3a+30MghNpgJCRIB0MzpBoLZrnPm5SQuqY6XMr85TSqVpS334orK1XM1Lp8wdETADJSKGut1uZ6dp95tikuTIZObjxns0v2rEHjAipppVnn3SXF1KoLiOPMXLzMxMDhCK3Aa43pRYqGP417/+9WJsOYb63xMNa9lOaB6GWXzWIBISCRrA4kQpYsx0XBW/8LoeB6jPOX7zPc+9FKUaIXBP6VptMu+SMRtnsiae1iAjxc3qH6Y4WX8Do8A5D5CIgEKUr3HtL/cQy9Yy2W4dWwemVSGeY7t4jalXXJvWdI4u8aZbFziOJyfDs3Tv3LlTnNDh46kwGAwyDfjlX/7l0GTO8dL/ildXVlZCodlxcVPycD4bmX719+zsbKMPnzKYwOgUD7V+NCkhiA9J35pcrposaucFEw3fI4JKjOp75+CqYpdcWA41WmqCoGbFpRemx/AyWJ9uiEglr23RRcwcWlHaDZbt1zTIIGJImjQqlID8eYLmJNTNRcQUIViOmTOr7XY7M+Uqwepm1DFWBKiI0JGTX9f+7O3tFUwi+8e6tS435fCb46G+MNrH+fl5PPfcc3neFGGzPpoO1M+l1+thZ2cnH/GjxHhmZiYzV4qQmK4lSo9Ck6smZabGgpK+p6MhA3L37t3aPOh8+jVgiGC1j6qxYP+bfPWa1s1FAx9PZ7p1/HTcIoHI17zvW2Umm8Y3mks/yUB/81n6/ao2RNeS7tOIwXDCyb7z+DK9HuVyU1zgeNg16KzHA45YXlNQjPeL5067QMN6oveVrnjgAH33nGEBkPPsaRJ4Lb/T6dQYQfZpeXm5hrtUq+qnTVVVlZk3zdeouJDvKahAwTZqndEJI16GPhcJfL5ub9++XaTiaVJycIzW1tZq9FE1dC4Ms05VAPEd3xNPAiYM32OC2uKd4EbSsCa45PvUeriWDyjPtWWZ1J5EEkCEtLnZx2UBB2LTDN8ngvQIJNUyRhokvaaMBPuhSNCJgbbNzcNuliJEDANTD/DZyATuEplvXkX4UcSvj83W1lYjsxyZhQk85UI/juw5x2wzmVlPoEwpVZGNfjsy4zqcmpoqjlaj4DAYDLLPpgL3QHSd4x+d7QkME6nqPGr7yYxOT08XOfXITLOOiBFp0vK8X2AcsVDGpYkgeVlN6xFADqzgs/47Wn/+25/nN60OmqTZtcIUTDSAjIyBapoVGPmuY1JVVU1DqIKLj6GXocxNU5+13X6N48oTanQ/OFPsY9bv93MqJU8342ZpL0MZ1cFgdDIO+6h953udTgcvv/xy4SfcJEiwnXr6kCeTV9rJsaDiQ9MrKR5RZpygyhICI3ujiGhtn87Tm2++mddCtGYpaFIIXlhYyK5d0XhR8Egp4eDgACmlGi3lc5Hwe94wYfgeEdy+rkxdJFkCw8Xi+dK4qf26EjFnYhTpjTNRAqNDvpue1WsRwxcxkNoeaiabtDEK6qTqG9U3nCIdfTYq3yV+/Wa/yDAoYok2ptbpz0egDNi1a9eKNrHdfuye91fbsbGxUUPKTpjU1PvgwYOc3oLEmHWTIKlfzWAwyM7ddOLWsT04OMjHF+kYnZycZE1ppNGkPyEJhkr6c3NzhQl8eno6H7we5fDSsl1yB4DV1dXaenDtUFRmNKdOHC4C+F7yuTqLkDSllYiYBaAZB51VjzI1fp1Av1A9K5plKyNGZo3tVyHUNcBAfNRe1GYP2tA1TMHI8T7L0bWpLj8RI6yWgIgR1bZF+JBt1GeYEqnb7RZMpI7HYDDIVhq3GnF8m3z41tfXsznTx1DN43xetczr6+uN48/2Ra5Pmm2BzxNPKV3RNfwzP/Mzjb7uHPuqKoNmNNWLChZNZtaNjY3Q2qZtJCPbpFTwPj0MTX1cuFgY710AXQR6pJQTuUhF7hK0+zIosYsW6eHhYc0nhHXr+8CQydIQeCeMWhezjNOpVPvoubYIatIYJ9UCqEna4zQyfuySExrfnHzWmciqGkapqcqd95wwKugYqUbNNWaEVqs8D9fNwz4m2h/1N3NNICV218ZxbXW73Zr2jGWr719EWLy/wCi1g0rK1PzRH0j7TG1ct9stiJozARyPdnt4FCEdndVBXfeB9kmPIwRGZmCOh84P64hOf4g0I01MzLMMusep4dLxcCbXcVSk4XNCpv/dQd7b0kRoo3L1nn5rOVwnqlGjoKG+s00CAPeNnuXLtRP5bXEPsh287kdvNWn4uD8i3Md+OJPiTOE4fEVXDKZXcf/hlFJBo7QMXlNGRO9F9IrjcO/ePayvrxcaxXGge/jOnTs1QYTANFYsU+eElge2gf1xhlzbrCeXsF86FmTktJ5IQFRrAoHt//rXv55dVyJFBsvqdDrZp5J1jxNUWcZ5w4The0TgpBIZuF+ealS4SKampnLuMwVuSPX10sXuiIj+D7ogdWGpGZjq5odhcF588cVc/1maPYJK6WcRzqbNGrXLzUbO6Li/nz/Hd0nsVMvEdkcMgPftrI3IPnt5Ozs7eR2oL5wTopRSyPDxvuaZUmSW0jCf040bNxoJrj6r64hn6RKpKtNE5253L9BrykyQ4aMpmPd13ZMwqlaB2sWNjY2QUVcmnkwiQY9Jc9MR6z9L6/QwAsqzDsoceQS1jlmT4Kj3XRsFjMZMTbpAqek6i5nUcqK54MkHXJ/uF6btTyk1CqaunVI/aH1OmRIvR8eBlpNo/TkwElbBx1jxgLaVv52RiJg3jXrmPWrwDg4OaunAgFEwlu9fjrcGY7EuBm9dvXq1mDOdBz1nONqLh4eHRdv5vPvnpZQKQVrxN/+7YqWqqkIw9Xykjq8p5OraXFlZKVIBef2shz7NqnlsorN+fVzWiCctgE4YvncAPAZMF39k+6e2QiMulSjqsUB8V8Pl+ezs7GyxcFkOv92vyc2uTdoNpmVRQqFlE/Q/HVsjhsvBN7OXqfcdwelzjqQdiel1NWN6vyJC09RXHU8dX31H2+8RgE1E1aVcZ8DIpEVlaZ4sRa5k0OiM7evQz87UNnl6B9bLlCwUNlQrwfF1044SHh0/Xf+KVJ2xBUbSvrobeACIM93Uyig4snUCddHAha8o2MbXthJAgvsnA3VtvJ/96j69TcTLozGjdtP0pzkd3V2G86e+oXrP9ycjYl3rpomb9Z2IuSQuV4hcVVifahP5rLZdx4PMoe4F/o7cKYAhnWAuV02TQhMuXUB43ctMKRUBEfRFo3uH9j2lhLm5Obz00ks1QdDn0s3MBI4zmSy1FPn3Sy+9VJSpgrPud50Tjey9f/9+LYmyMv/eNmCYUUKDb9yFiu+zHiat9nXvHwDFEZXRuBGepKvJxIfvMWF+fr62yauqKrR1BJoSmDhWNTAqYek7JMAaxDA9PV1IRM54sA38pnbF/Uu0fgBFyo1IG0XQ/y45+bPOxDkoMtBnXUPgG8m1WpFUpUiBjIUjcG9LU/u1fH1OzZrf8A3fELZJg2aIdLRtOm/OzKg5XRm9qhqabFdXVwuTvRMQJRCsd39/P5//rH0EkK+r3yGJBstkOcpYMTM9y3MtM3/TvPWhD30IwMjM7+4ITZoaoIzS9blh/aoRfPXVVxuZjosOkZZF12+0pnXsNAMBCZ/naFQNjNbrc+LaPt0D3h6+Q9M/8Vzk88e+eWog/Vb8x+seTetMnAt20Vj5GDf1e5wmh/c47qppY/m6f/39JtxZVRUWFxcxOzuL6enpIi2W4hJGBdN1hGNNnBWlHOv3+7h7924h6ClecMY0pVL7qqcu8Tll3Enz2u02Ll26lJ/V6F4Fb6OOx4MHD0LT+2AwyBYUtRoAJQPH8XU/QZYV1RHRoohuRhriJmXOecJEw/eIoEyS+jw5UdaFTMbDE1VSg6N+HVqPT7jmWSLoM1xcKSX0er3svxNp2LSuJi1AkwQCoJAaI8lTF3lkItWNoITDtQQOGrXpSNvrpZlSCUzUx0jTE/WnyY/vlVdeCfve1AciHjU/RKCRg4qAaEpVJlHXIQ/p1nGnxkwlczdvVFWVneRpQqNZXFOvkPiTiRuX+0rHsd1u5wAXjxx0TaYSHgK1tU3jzHEh0DfV2+Rr5iJBExPA3z7GCrqe7t69W4yvEiESd3VJaHLviHCD++EBIxcWF2IdJ0Z9Oz4+rgW/aR8jbYt+O+NGZkDxre8/1ZRpPxW/qunO8YYzJ8AoeCJiBDheznDMzs5m/zCPTK6qqsinp/Vzz5IGaV3u+6mC98nJCW7fvl0EbThDRVAGkkDmM9LI6rePj2qcdR14Sh3PDeng6xio+6JG65BtZhvY9zt37hT5eJUGe186nU524YoUEBFuO2+YMHyPCeqQq5PrPlD6vAM3p5v2Iiaq1Wpl86+bNiKVtYbru5+Zgkpgrl5XJMd6tE59JipX2x4B26tMrJsbfRP4+Yr6mwhGGR+VbrVOfT9iBKPruuFVklU/HdWERZF+CsrwOaL35NpEEK1WC71eD88991yRwkRNukp8WS/9b2ZnZ2tEjKlcOp1ODtzxOj0xLeeC7xBI6Pr9fvbd5Brv9/tZwtdoPUX2qllttVqFiUaz4Pv8+D4CgC9+8YvhvDb9vwigjIcLFZF2Sted70PXlHkZrj1sMoE6uICrBFQFFK2DOE/xjib4bkpkq/2liVU1b2TsUkoFU8H16sde0tzpDGOkyfETQBSckVBw7Q/rjVxAKKQRH3HvnZycYG9vL/vx+TvMr0l8oDiKe1u1umzP7Owsrl69WlildE9yLhTHqK9nZMFRhlbH65u+6ZuK+fPfEcOkDJ8ydc5sqzZSGb6NjY0iKMfphfeX9Ctaf/qumtjdPcHXzjiFxzuFiUn3EUE3maaqcCZN1cAREEEyzB8okRyZBd3IeqC1gjIGbJ8GLPhi5DW2N0pW6RuE7xGaUjjweV2wPJswkmCciWuKDIs2k59CoWaJlBKuXr16phYtggg5sHz2TcdC2+xSvr/HMvU+MHJmVkTpGkU19fqRZnyGWjyWpWZZSqJMKeHIkISQa4jMbEqpcGrn2j45OcHOzk7N1ErkHWmj6ZOneRh1LWpbut1uzsfG9gP1tafzoWP6ta99rXjG4UlJ0U8DKHOiMM7MqGvagw18rNrtduFjBcQR6gSPrletkQteKaW8b3Vdazvd4b6pTy6wUvutbdY0RhFDq0IVhRy9HmkLB4P4HF3ti5bp/de9owyAjhtQukA0BeiQwdD97rjJx4NpmtyUyaANZ2R1DNwPW3FwpNzw9vC3mnS9Lq5vzhv7obSEaasihYfOsweGOYNHH0cthxrQCD87HmO7jo6OGvdf1L4ngZ8mGr5HBE4MUwGoRsIJvWvcVGrgItcUGkrINGiD5R4dHeVgich84SY+EngnkNoWXVTjAiYclAHwhdmE7PV6pFoHUPMdUaDvBctSZK5t5bVut4uNjY2izKZN1ESkXJp0rUer1SokUWXu3FeI73E+WKYf8O1+c8ogcs2srq7WzOr87QSNSG9nZ6c4Pk3XkCJ4N/2xHP7nM+pjyr6pBKz+V9PT05idnc3Svq8Flq8BH74OqOHTuXITkhKXyKT7fgKa5Ak+577HFE/cunWrhquUENKCoGVFQg3Xgq4TP2mDoL+duW9yfeF/+itHbWEb2u02ZmZmai4sUUQ8fzsTqCcY6Zj6OqTA4sEg2n+23YM2fF6i/2yjnzmsmjamU4pwG5nfw8PDvO/dj9hNtlRAvP3229jf3w+ZZE1YzY/iIwb6qHZL10x0lCTHpokuaP2e1UKtVhGtdq2gKmAc9+p/agXv379fnCjkihfVaOr6opCrbXPB50lo+SYM3yNApGp1pKWaDWVI3BmUz6o5QjVxzshMT09jaWmpdt3V4k1tUtD2ObgpWp+LNHTR2Hj9bgIZB0SeUdtU8qXW0JG/vsdoVTdNROr3aG7ZliYzPa8pkClpUvNreZcvXwaAnOZBJV1HfpxjuhLMz8+HTL+anHRNMeBHIxRdsuc3ERB94ubm5oroOn663W4OOlLNJ5F+JC1rW/ntjIL6c7n21OdMx6bVahVRvffv3y+I1ZOSmp8maNKk+38fC9U0AXUc4TiGrgVR2VH54/w8I5zAtrjpT+tR1wJqtbVufz6lVESzEmgC9bb476qqCoaAZeoadUauKdGzjqW7qbh1aJxArelVtEzuQcfLyoAApbaR1h4ykpHrR0oJ9+7dK4TUSHhX5kr3rUd2O4Pd5P7j5k+2R99LKeGjH/1ofkePpmyao6qqinVMnNXEfPEeGe2NjY1CmeDt4W+ezOEnaCne9/XWpLV+JzBh+B4TXGPHD7VqBE4igzaUAJKoexLjCOm0Wq2CaWzaGCybGkhHvNHicmhCwgqRuaJJWteFGxHcyM9FN51ucpa1sbERtlnHL6VUSK+8rsmF+ZwzVqxbxyralK49bfJR8TpcG0VQCd19eQDkVCXLy8th0lRFKqoRU4ZP28C6mMDV1xWldc63S6kuaCjhdE2j+zpGzERKIw3o9PQ0PvCBDxTjrUCk2ESk79+/X5jzmoSpiwRqVgWaT2/wPekanjt37tTe4X++E4Feb8IhSugjxsz7wza4jzHvMTVJZMng83zW9yKAkBkgkY7662OnGi3V+LmrjY+Ja7Xp13qWYKK4Tv3CWDf35v7+Pvb29ooAKQJ9dqmp4v5zBkn3NjBMR3LlypWCSdVxjfIlanAPgc+oAMExTynVcKMrUhw4lhpAp+tE52ccM8XTiLRPrFPXDo99U5cpXWf+TXzIVGnOO0R9ehJ4auLD9wigk8OcThEnr8/zWx1Z/Z6Cmy24kKqqyia8pjQnuogPDw9zJJVudt+MUf1NBLSJoeN/l1zOKiNqt2vt/Hk1YUSEQuvS1AOKWHyMyfzpOLlkHJUftc/7xm/9zfkjE6XpQ7iOaD6JxvXg4ABbW1vZv04/1HToqSkcU+YIZHog1Swr8VGfOkryGiGrSJNrTJmpfr9fRANzrHVvqAnEn+O16enprAXlPWWCVXhhe3SPqRYimpeLCO7rptoL3786Do67Dg4Oant43LhFeypiLvlf7zsj1wROAIkDpqenCw2fgq5L1/ToXnMcQu2cB5FFVgXFL67laWIstEwVeJUxbKIRCsfHxxnHqQmWjFyTFYR4SBlhPt/v9zOj6C4AzP6gNI/vsQ3uuhSdfuPrhe30M3yjMYuYNt67cuVKvsagRR1X9l3bHgU2uVDCuWQ9ni3CxzlSDOghDc5IO40ct3beCUwYvkcELoCTk5Ocg0wn7SxtliNdJYy6KN0nJKWUN7EjLd983PAM2iDDyUWpZUeMW6RZ8mej+05wCeOy2ANoJEoRIef4rqys1PLbcTOrqUc3jm+qSNp2hiJqF5+jRBsBN7lKcXxPywCGh3yzD+yvEkEljGTC1tfXM0JWfxAyuYr8Oe8k4hqVTS0Bo/nIDLJ9njw2ItZ0RuaHzJ0HoChiVULqDD+v80xQHVOfCzehqD+OH610kRk9BSXeemZxpN3SNafAEwRcwxHNAVAKTfo816fihbPMdxEOadqXAELGzJlVFWg8qpyMBssm86snNfAeNeV6TbVHLLOqhkn1qbHS8fWgDXU3aeqjMwF8rtPp4PDwsDZmxH3z8/OhawnHbH9/vzGTg7aZdR8dHeGNN97IPnw6l47r+Fsj7f3UD2c2Hb8QNEAyEvTZxg9+8IP5v1qhyOxrkmm2/eWXX87v6Kkn/I6UMFwv6+vrxbnl2jb6OFNjOT09PZb+at/UJ/U8YcLwPQZwYpqIvQIn7eTkpBYxB4yc5VXzU1VVwSRx8dEkplKLbjLdtP1+vzgNwxdrpB5v2mzeF22Dj4kmziS4Bkc1Sn5foze9/Kqq8oZUR+umfuzt7WWCF0lLSkRcw0eIEmmPYxxUgmz6zzKZf5D+eC5FKiPLfFntdhvz8/PFsXlsH9/1hKj0yeEpHI44aYZRxpF1drtdzM7O1pAkTdJaHuvkWOvJAU7cI61PtE78nOJIg872plQeV8dj3JrgojKAuqZ1PFSo0I/iKMLdu3cBPLymG6hbB/R9vc558WfH4R3Fj7wW9VuZBwLXNoUXChFcb54Cif3wgDfuLT1Np0mQ41r0vQaMxllNjUBJ5HWu+N998ugXphrOSPsVabmoaHA6QGaX7h2KV1gvhU2lOeO0pZr2xF0MdD7VjH4W86m4j+8PBoNaAIaPoZbNtqhWkIyszoPWwfc5bjTR+jNsM//Tn9DxpApGOi5PCjdNGL7HBGeifFPzOr+ZcNGJtPpcKfPiJjGgZD58kUXmDoIiYjIc0TuRxETmjNLRuPEARv5xWhYJvzLJujHU0T5qk44Zv9fW1gr1fyT59Xq9kNFzoscxija5bkwF3czROBAp+cbmM63WKMecnjU8NTWFk5OT/K1lDgYDTE9PY35+HnNzc8WcqsRPsyzH++TkJK8/96chU0Bi6MATODwjPTCK2nNNJq8pMqMmEADefPPNXLfOCdc12z09PZ3PeeYzSpTPkoBVu6UQzdtFgSaNtoJq6/hNXERwQsey/VpT3V6Xm/N9ryrTEOFRFZS0zUDJsLng4Ewu/aiA0vxMzZPWHWkVPUBEwZmtKLI0es+Z8nGMtfZnMBjk/cm9pXW3223s7u4WVii1fpBhVKZOLR3cY2olun79Oq5evVrQMwWfv5QSFhcXi2fUXYXPNM29vuNj4OPiEJnjVcjxuQVGbiCkea61q6ph4A61gqSRfkoKfytdpOXF+YaoH87QnhdMGL5HBJU2XfLRDaPmU6rC/Qy9wWBQmIUjrYeCLgYnvr5ZuVjdV4DEl5vYJViXOni/KTgh2nxA3ew8TmppkgD1fS83MhPrsyklHB4eZonNtRpNZmtHAtpPZ5x9U967d68Y58gHSsecJsdf+IVfAFCmVmi327WTWVju3t4ebt++XUtWCwylWh0bainol5NSqjGSQJkqQvvKYCONXiMRYzoCX5dEgBxjd0nQNelaFV/DEUSMLuvmkVxA8xqJEP1FAV13vr95zXEWQZ/1I7C8jKa6I2jCEYQm0+649pJpaLVamenxerz/ZDx1XbJ+FUSVAdF6ufbJyHnfnZGdmpqqCcDRWLoPY6RAUCZXx5l705kHMmOR9r3VGh2nxr6wb7QkUIup78/MzOQjRUn/aBXgR1O18JonQ440Yhw/PVdeYRz94bevo729vdp7kfLg+eefr41b9BzrnZqayjkCGaSpico5dzonbC/N4E0KBoUnIZRe3HC1JwjcNH7ShkuHumBcveyLKdIgEXhPfVV8Mypw43nkmkvRZy00N5fq84osz4LIlB35tHgbdNPwQz8w9c+KNiclMTczaN+VkETjoYxzExJQrUin06kRUDXZaFvpXwOM8sXxWRIJvu/AhMcqqWogCBkxRziaI0sJAs3/ak4nwWq324VJV8cwMtVy7PXIJpZFX01lSprWsAobWq7vM2U4p6amirQMTwJhPgugTLNrV4BYWFENGlDXmKsQ0MS8RW4PfL4poTrL9/e9rUCZk49rTLVUWmYTROlKBoNBYcaLntWxUOGd0HTSggsdVVXVXCc0cl77oAI47+s9DRz0iOL5+fmcvskVBCmlzCRTs6k4PxJ2+X9vbw+vvfZa1oRpcJi2WcvUfdwUoOMCn/r9/czP/Ezhrx0xwxFoHa5B5Jqsqgof//jH83PuT6n4W+vlvPJMYV8nDjMzM/kYynHC7JOGCcP3iEBJQn3kdEH4ItNFHzEUEdHUhM4KdBiOmEFn5Phs08ZoIrJNpmCHKG8V++R1RZpKve8SoP72NrCsnZ2dGmLx9+lP2HRfN2gkeT8sYiHcunUrP9eU648bvd1u45Of/CQA1FIQUIJWrQXHtdPpYGlpCZcvX26cH3VMZ529Xg/dbhe9Xq9geFmmMtfK8NHvz4kPk9h6Um1Fki71sk1f/vKXi/Y1CSFqYvYAjAgGg0ExlpF0/34BzoOatZUhiJgThSjYo+ldfU5/q1A17jQfFVyaXDB076iQQ2Yt0uZqf3Xtct9wfUSmamqpXcBTQUXrcfzu+9bHT/G+au/92cjvi/fI8NE8y/apK06kMWu1Wnnv+v7lnmcKMB2DwWCATqdTJDTWcSHj6UIy5+327dsFI6Xt4Tt6xi/Bj8fkuHlOOwf38W6y1ijO2N/fz8FwkVKGc820LGxHZP5XHMs6idMjjbRDk8b8ncDEpPuIoATRzy1VcFW6EjydZEqLvjAYyKHlUGPnTAvrUwn86OgoSx/e9nE+PizjYTR3viH0+qNIMNqOKHu71sHNSWZAGZWoLI06Zd91o0Vj6YRCTfP+nG7Kn/zJn6xteo3U8g1M7SAPqlcCwYTcKnkDI0dt9s/fm5mZCYMVXPjQ94jcta9EbIeHhzn4xeeEEr+OsbbXJXuuiVu3buV6VDOja46O44TXX3+9Rrh9rvie39P7bNuTQKZPE+jcEly7zeeU4SLs7u4Wmh0ds6axi6LSo3VP0PKbhBfV7DS5vmgetCa8EQnXTVpH1qWmYmrFVRBTjVHkexy51ET98ETOLuDqNe8f6YcqHsjoHx4eYnt7u3ayCWmXJpEm08V6aCVQzWGr1cLNmzezQiFiaNlH/u90OvjMZz4DAIVFzOeH5bOdHp3flNLMx8fHxk3qSt/4Li0Pb7zxRu435y4ScNrtdvbh4/ip8sE/LIv5IlVAUJwW1XPeMGH4HhG4+MYhKaC+MUnAWQafUekMGEklvqG0jHEEj9eo4XPipoiqSbpQBMf/TpBdevH6nfFyIqCMmkZJKfJ2iRkYERumjWgi6sAwaGNtba14X+vX60Q2zvC69laZPs4f4c6dOzVmkOPP95Qosm2vv/56rZ/8TcZJ/ea2t7eLZKquEdB+sl6mYGBqAO2PJm8laMJlR5pk3lTLrfW3Wq0iRyWZVGpyNc2L9tWZcW3PL/zCL9T652t7amoK3/Zt39Y4loRxROJZBx2/yM/XtRUKSmSdCeG8sowmgbCJcHmwkDIffI9t1rJdU62MHT+q0W6aU+5BHiXmbYksMFEmAwYrOFFvcr1w069q7VSIA+KAt3HA/elmVTJ0isMd39GFg4KcupOQYWS/FAdOTU1hc3OzxvxzHPg+y2y1WjmX5traWm2MlZHmmGm5wDBPaeTbzbqUUVRgMJze0zXMdtLtgdklnE77twrd6iMeaQQjgcO1uw+jXDkvmDB8jwhKwJUQu9ZMiXFVVVni0sVHghkdPaaMAUGJsi9uX2xEBpH59yyJW59X4thEQPV/pGmJGFQNHFFoOkuR71JjoWZvZ0YJOzs7tYTBTW13Ro5AZORMKu+pjxRNo1H7nbi1Wq18+LxGk3HeVbOhY0jCp2tGQaMQtb0Rs66Iz+uiuYhmYO8LtUf7+/shgaKWmtBqjc513drayu1ivcocs63K8N2+fTuX4/fUnBT5kUVzcRGZPaDuSkGT3riIRX6UqDLAB4jNmI+CQ5ra2LRPvB5dK/o+mQVlShQiTaAfjQYga7EiDbYLlXpuqgrPES7TFDAE7Ye3r4kZj8a0qqp8UgbN786Y6jeAYn+5QKc+t5F2i7+3trawsrKSj7aMzOFsMz/0Uf7Sl75UjF0kHLhgDwBvvfVWLTgMQGa8I+Gb/VC8zX43MdW7u7uZxqrQqXWQ6QWGAj6DMHSM9HnW6eeyP4yF4UngqAnD94igi43+Cq7Z4KbWBa0aD4KXoRBJjq6F0fKjBRSlUWFbo+d9wzqz2LS5+by2WyHyr9GymhIve90ppawNZBRZ9Czb0Ov1wvQ2Xof+97HRfuuGdu0aMEwGresj6jeR1NTUVDZ1REErTkCIkGdnZ3Ht2jXs7++HSLnJtLS4uIjj42PMz88XhAoYmlq0rSxTJV0id00N5GOrGgFl5khcyIi+/fbbxZ5R7Q3LYbAIYXd3F0A9V+Q4AcEJ50Vm9JqAgUF+mkM0Nv6MEl9dLw+rgVIhxlO+RNozJ6jaVmcS1U9Mz17WdRWZU5vO0m3ywdN2AHWzpDMD+o6mIgJKhkv93bjOz1qbLnhzP6oplkCGzAUA1s0cgZ6TkNorDSThp9vtYnFxEbdu3SqUGQo69mTeGdG6s7NT6w+/nQZq0IYyVf6uQuQnqrg6EnIUvvrVrxZKGdc269r7wAc+kNcq59VpoPIB9Gf2o1ej9np95wmToI1HBE7k4eFhkWdJkWOTRsslVL7nkbT8bmIQWYbW64ubkqwyK5FmS4ESvtbr5mCCS1wurV27du3MsSTo+YeeNJi/WReRhzPULv0BKPwYI01Z1G5nIl0Lpe0aDAaFVuT27dsFUo3K13Q+ZIA2NzczodIEsQQiTmo0Tk5Oso+VM6v0x+N7qr3hMUyumaBmUtcyhYvDw8NaLi/23Ymnr1VnRsl8aIRx9C7f1zHQs131nXHMR7TelVF/WMblWYWqqrI/58rKSnHdxxEo1/7u7m6jMMH14+ACoQoO7oPleJJrNSKc3iegvo/89Aw1LWr7Ik0c26Z1q3DjzK/ja8XhynASX2t9qqFmWdxDrv2KwHHV/v5+9rPVZxg56sn3aVlRgS4S2hgkpXPAs601yIv1qSJB11Gr1cI3f/M3F22P1o4zZc8991y+p2uRH/aliXljBLIHl/ia07ZsbGyEc+XjQMUDfeqbhE5fNyynibF7N4TRCcP3mMBTCACESNGfjRg0oG5aIUQRZGQyI1ODAtXIutl9g/Kbv3nsVrR5moijM7bcsACKKKaoHK3jW77lW8J+RPWwXCJZZ9D4v91u48aNG5nYRP5Mvhm1fa4dcCBC137qpibCjRh9zgkPS9/e3q5tetc6sIyjoyNsbGxgdXW1QDS8r2eAshxgOL8nJydFkmd+Hx8f4+joKEvgOgaKyHUNt1qlE7JrVrgGgdFe4akZ1NZp2zmfLEvNkcCQmVaE78IQmd0m8LXvgtZFA44TXQ40uTnv63NAiWtoJtR9Pc6k6wKR7q9xTJu3SefViaKXwz3IUwyi/kWChTOCfE7dc9Rlx8fLNf5+TUHNg8qIKlNIC4eX6+1XRkH3JcvmGPEYw9nZ2UJB4PNLfKBzy/ZFbkZ8noEgOi8cNw1IGQwGBZ28d+9e0VZtM8faU+bwPTKqPr9sg6/Hf/SP/lEt4bTiM82WQFC8pX2I1t7R0RG+/vWv53yG2ibWw/cY9exabhW8I3gSAumE4XtEcAlDwSVEgqrIgdECpQOw2vdVmmKZhJmZmXzkjSNVAjUrs7Oz4QJz4DU+q+UqA/Mw48J+O4OoSCpCtJGPmCO9iPHRMlwCTGmY6LcpfUzUdidSysQ0aaOocfT+NTGY1Lp2Op2cJFiJK5FME0NPnzo34/Bdl2rZdk3DoMg20uDyPaZ8mJmZqc3v1NQUZmdnQ0aAiN+FFq7pzc3NsG+uFVQNn2owHnVtRs81CWfPOnD8fL9oagvdLzrnnixdfzux9bFz85cLmB4trESXbXLcqcxSJLByHbhfnY+HgjIl7D/xuY6Dt0/b7XhM9wDbFAm3zsw6nvUyHO+4YKppk/RZulto4J8LRwS3ftB3lzTJ69/Y2MjnxzqoRpTv8eQfYOiLR1BmR/tD0PVCIdX96fSEKl+Tu7u7oeZSx1wzBwDA/fv3w5yxvhdo9qbArutI5zXCzx4x/TB06Tzh4mG8JwyR1KnJbDU5KIFIySUOOt9HDqnOqFTVMPBDI48ipkgZIvVH0HZHpoOVlZWxPnuRlM5rUfh4dISXIkZH3gRPy+L3qer3HEveLiVoilhc4leImNEm7YRLlMDQJECkqZKkXnMGm33mGEXmYEWGc3NzuHbtWvYl0rZrXi2Wp35CGimu7WNSUD0yqqqqrC3Us0O1bM4TNcPO/BFcW6nMm46VXpuamiq0UtQ4uNahCZQgRcz6uHXwLIPjBX7TXOXjFwl4QClQRfuiScMXla2aMt7ndf0PlMyTRnU6ftRrTYwqQQWO6ESNiJFkW1STxT3gSZqdWXaTqY6vls26PQ8noUnocxqk9bKe7e1t7O/vF35zOl7ECX6sJ1Dmt2OZKQ2DIq5fv97oY8nxdX88jgM19o63NQcfr6lLkPoD6nuuKVQBcWNjI1yjkVBPuHfvXhHcxHY6w0z/QhdQlfb43PDEIq+zCY85s31eMGH4HhMuX75cI+ZUYbu0q35SuhgooUXg0vL8/HzOmu4LwRkFYHRwdLT4dBPz/vz8fKNZwhGjgm6gqC4AtXMq9dufVUYoYtCINJh4edyzvV6vJtF5nU3aDn6TmVdzlRI2RX5EMk0mC2BkMlGGhHnsIgbPifHBwQG2trYKjQ3njZJndG7p7u4ujo6OsjlVx63VatWignXMaLZgPRRGaBZi21RzMzMzUzDbU1NT2aSr46ymnHHaWPo5NhFnN+ky5Y33s4lYXhRQhqnVamVmoulUIJ0j33tOyBSvRUKea/20HU3mdsdDJPxAqeWJiKpq4bSuJkJJZs016ErktS3KsLE+npbgmvFo3bo2RxlBNzFW1cjfkm1QRqeJ4WWiYGZkcC1h1F9GNrONwMgnm/tdtWoc1/n5eVy9erWW65JtooAY+U+yjqgPihM5Zmo5UTcUzrUKmOyLBnrcuXOnyFLhYxppaqP/Wp/39/79+8UY+7uKb9wn2etweFK4acLwPSLoBFHroNKr+mlwAblPBMuhw25kVtMFRMbCGUmtk9+qdYl8QrSN2h6qp70N6sumGhfdvIrw+Lw63XKh66Zp0rC4RK3vAcg5nWgO9zFQ0JxbTsCUcYgYT0KUx0oR9kc+8pFa27U9Tcha5zFyPnYNlaZr2dnZKRg37TvzQrGt/M0oMfWLY9toEvMk1XRFIOF1qV0leddM6nogsuOa0Oz/LmS4BE7wkx8cqGEg/NzP/Vxx3wUT12pcBIj2OZ3vo6ANH2ddc3rMXiQgRXVHoFqSpjZrG/Q3fb70WvStuMwZL8e50ekM6rKgwLKV8GuQBct08yuZOj1KK3LD4b51ZjwaI32foHhIx6DVGqZAWlxcrJ22xHoPDw+z1Yl9VVznOIljcefOHaytrRV7WBlKz6OnuIka28j87goJZfh2dnYKX2gda46B0ycGxOkYRhAJFfytNEivsY8rKythnkX9zbXggUuk074+nU6dN1wsjPcuABc/zWBA3dlWEQTBo7V0gynoZLu0plGWru733zxlQjeYMoIk1rrgVYul/SJogEITAmfbPJghYsiiBU1ToRNn1+zwYOxowxAuX74cHo/kEKn+tTz3EVHEqgdvazt4n//5DglAUyoaRZz8rUzTwsJCNqtE869SMvs+NTWFxcVFzM/P13JVASPmjUwV32PybpavyIvMoLfBU7CoBo/lq+8Lx0m1NWyzJuRWLUyTxK1t+ef//J8X7fK5d2R+EcAFGT3kfWFhoSZ4aNJeoPSZVNzDj2qso73kQqp+u8XC283rbDcwYsR8/7vgpK4IUfn6383LrD/ySfO+UFPpicOjtChVVWUzHsfGy+W6Jw52Jtw1YQ6dTqc4Bo04ilrdJouNMqGuAet0Opibm8spl5TRHAwG2N3drWWnUC2+ZoZwrReD01TzpUy4ag5pDQBGaVl07iMfS8WpHD8fN9UOciwIZGQ976qDH2PnvplaT1VVmJ2dzYFx4yKLo2vnDROG7zGAEpAfkcPrBG6oSH3M57lBVDPCxawbloiB0qCbjL3cubm5LN1xY3BjKWPHdz0KCqg7I/s9rY/AchmQ4PebxoJAAqRt0Y2gkp+3l3PBsvf39wufx3FMZ6SF8/uOhPv9fmFapYYsCsRxhKCIRk267JcSMa2z0+nkXHq6Vois9SB5lqUESTU3NJ95BJn6gR4cHOQ0KqpNqaqhSZcpE3SMOQaO8Dl33gZHwmQqVWg4Pj4uXBGiedQxffPNN8N51Pl40sj13QbtE8eTjAxN3Mos6/F9kQaiyUzq+5P1jQv60JyKJKiR0Khz4meo6jPaV9VUaRlOQF0rTfB8quxz1D49PcNxh7aL5t/IskPgGNOlgvebBNQIBzMlDfcUmdft7W3s7u6G+UqpeVTNpispoiM55+fn8cILL4Q5+rzdXAuensuZUKcN7OM3fuM3Fu85jaG2VTV/KrDcv3+/cDfx+tlX9Z/UU4O4nl3QT2kU+U7t+Tjli9apdFcVCVEf+fx5wyTx8iMCJ0uRB3OjRRw7gGJxRpo4RUycdL9OszA1J02LgUwnzzgFSv8MIiNnVtXJlYtdJUAnqC7V+AZWzVdEWJsI97h3UkqZCdBweC1PCcHrr7+OtbU1bG9v11T80VxFPl7ubE4EzfqUAVXGhb4smphTzf2cm/39/VpkmEvA2o7t7W3cuXOnYML0PdfMkEBtb2/j4OAgp1HRMWAb1AxDM5FG2Xk/PcJPJV8iX0r9MzMz+NjHPpafVa0B16Nqm9x0plI3y+A4kcHWU0/ILLCeiHiOW3/PIuga5vhQ6+HriaDrS/dTlOzWiaWCMv2uqYr2kQtnEeGL8pPqfa45d++ItD8RvlKmTvGH9s/r9dym3n8FP3LLx2UwGB5h9uM//uP42Z/92SLILiL+rkkkw6f4HBgxsMQH2jb+Pjo6ylkMfF6Ojo6Ks9y5R9XnnGPn/SGOi4AafmV8VBNGHFRVFV544YXaODq40kGFVs0EEClauD7U74+ZDFQQZT3K1JHhvX//fs1PkN/OyGqicIIqWZx28b3zhgnD94gQaat4nQhX73HxdLvdYhOSUGlwhb7rviY0o3HDuTTNDxGD+jO4xieSLmieixae9xuon3gAlAjt6tWrxbPaRzUxn0V0tU2DwSBrCpqiMNnHk5MT/Mk/+SdrTLaCIiZFppxHIgZljLXOdruN3/ybf3PRVu0zr7mpXDUeevSbvuvH8Pl9jXhT/779/f2afyXXSqfTKTRyHFeNLFdhZnZ2FjMzM4XmjWuJ9zy7PcvQszij9rN9rN8JSLvdLpCx+v2QAKgfo2uM/Azli8jgReBaVdWsASVR4nhxLzX5M0Vl+7w7Y+SMXGRuc/xJ0APpeY8ZELgnuFZUqFH8621Tf1HPb+mMB1BqA/XTarWyT1mk5XT82pRaBhgeMQgADx48KOplWXoqgwujADJN8fqYh29paQn37t2raceofNB0NmTwKdjrXFOAGwwGePXVV7GxsZHzwep8RnRDhTaefx5p6pUJA+pZDByP6Nxw3j7xiU/k+5qRIALWr2uefo0qkGjfWBfXp5rrFc+oO44LL7ym33pd4Ulo+N5XJt2U0veklF5PKR2klH46pfQrH7UMThD9M1zLodGYwEg6oOpdJWASbi4OZUDcERlA1ho1MVqqBaHJmW0gqJZJrzuBjCBCcOy3fgAUwQwEIlb+bmJo/B195od+6IcAAD/2Yz+Gzc3N0Pcm0ioQFLH7RtU6Hcm7KYgbVE3Xapr1d4goyMgzgOHKlSuNmhQiR5YzPT2Ny5cv44UXXsiCghI5TTnB/63W0IF7aWmp5jdIjcXs7GwtjY62m6d68DqJYaSx4PwqoicSpJaVGk01NysCb7WGUe2KwLUMrl/dC0pYgaHG+lmD88BPvg85Hxq1zHXj2hj97WfvEqL9wjLHtScC3Yu6JwmMzG4SHLgWVTh2rT+foxCtLjRAqQ3U9azmQmcE9Vqktdnf38e9e/fwD/7BP8BXvvKVsePjOJA0gT7YEQ7TPcKIeO0HE6K7qZvvMgUT33OtMNO1uFlbE1y7cAyMMkM0naPuwojiIH5XVVWLZqXGUt/xcgFkC4KPr/6OGEeCniikTLQLJsvLywBGPqbKbHPMfd3Qh0/dLBxcq/wkBNT3DcOXUvpuAN8H4D8E8C0AvgDgx1NKz4190YCLhto5Tx7qUZ0ET8sCoGAA9F7k56Ybw6/zW9XGUTQay1Z/HXeQ1nKdEXGJic840gbKzP7K6GqZ3r61tbVauhWXgL7/+78fzz//PFZWVmr+MRET54TJEZUyNjpGqj1Q5sLrikwi3q+IABMePHhQjCVQ+hSpZgIYnc+pfWf5ROT6LNu+t7eXTbo69noKjJ7OQW3h4eFhkRhatQP0EfL2a9JXbRuZSq3n6Ogo18XzgTkHaqJtEgyUuVaN4Fkm/KcNzgs/AaN12G63s0DC/ajaBRWmWq166hRdt03f0bNRW5oCvvieWj2Ik9RXrMmnzvsU1Q+MBF1NCO4MgONmjXAlLvj85z9/pn8oGS5lIPg+GUIdi6axdAHTgUF8BwcHBXOYUsL+/n5263FNHLVn1No5HmxiWi5duoQXXngBs7OzoUk3EgJ0Dzf1yZUkrj2OkhXreEY0Z2NjozFdiq599TFUIZq4nYy3apE5fkxAHSlhWBfdKsicq5Drz/oYPQmc9X4y6f67AP56VVX/FQCklP4wgN8G4H8P4C89bCGcDBIroCTiTf4L7tgJ1BkUlq+IWOv033qNxJgb2MPxFfFF2i8/R1DfIZzlZK1tUUZInZyjxfxt3/Zt+PjHP46///f/fu1ehAy1LBILr/9hwH0vWK4zWT5HRHbUGui7LIvIgchAtSkpjdKj/JN/8k+KZwiKyDivU1NT2NnZwZ07d7C3txeuFdU6q9Ztd3cX7Xa7FvzAe55Pj8JIt9vF9vZ2bezIKEbO5vRNYh3UIjroGBF0f6lDdUTQFclWVVVo9bzNPk7q3/SUwDvGT96Xfr+fzd73798fS0xc26c+vtG+92vRXlLiqoKIMpkOuu4fPHiQ63eLhBJIZeKa8AVxYpSmZGNjo1ivuo6VGen3+/jFX/zFsA2Rz58yKIqbNVVU9I6Xr2Pla55+r8qcLS0t1Y7+1LGjwMgUTtr2fr9fnE3sAu3du3dzcIPuH7aFpmGOm6bn0sTD3ncyWJEPoDPp7BMVEFwzL730UvGO9lnbxLZWVVVYolx5o6mgCBQEgPJIzGjdqeBFi6AmkKfSweeU1/lp4ikeB94XGr6UUgfAtwL4CV6rqmpw+v87HqGcPDH0qePk8L5qOpq0RlqeIjKfeIIubDfTusTNZ/1AcW2bIlr1l/FN6ItYiTYRTOTHmFLCn/gTfyI/q35qUdl/4S/8Bfztv/238Wt+za+pnfagWioA+LW/9tfi+77v+3D16tWCiOi4Exl86lOfyrmo+KwSoQjhKoFQ7ZE/68xeBC5ZUyvcarVw/fp1AEMthmv9aNaJ5nZubg4LCws1nx6OkWrwKFHS325hYSH0VfT/7Fu3283SPMeL2uH5+fls4iECZXb/9fX1Wg41ErnXX3+9WOf0hdL5YN2f/vSni3FpIownJydYX1/H5z73Obz00kt4+eWXwyOSOC5KKJ+EFP2ocF74yffW1NRU1mBogmLFWXxeNWt8blwdD9MWfdZ9+Bx0v7Eds7OzxbqL8GpKKfsf+7ry/ZHSyOzGZw4PD3H37l3s7u7WGITo1JqrV69icXExrx1q2ZhjjgLOzMwMrl69moUW9o3WIQA5V99HP/pRfOYznyn2AZ9vEvBbrVbOxKDHbbJ+RvPruCqQ7uh4adt1Tjjmx8fHuHXrVjFWzoD5XlamiVp3zpPPrVobFHy+iaO0npQSPvnJT+bn1AxM/EOGi3MG1I9vjBQz2qZWq5WZWLZB115EU1577TV89atfxWuvvYaDg4Ncd5NiR116ztuPLz0NCO9JQ0rpJoC3Afzqqqp+Sq7/FQC/vqqqX2XPdwGoU9MqgGmgPNWg1WoVvkqn74bMTafTKRxCWUbE9ClnrwwmVfBNkb18n0jQn3XGjL+vX7+O3d3drBWJtClsL4MxmMi16Vn1bbt7926oAeD7jOgdDAa4f/9+rW+6Rvns+vp6LVJXN1tKQ1+L3d3dwhcnqr8Jxj3Le9rPlZWVsXXodfrV7e3tFRGl3g+fN7oRaAqBCFHo2Kl/nJs5VADwqEItQxF0VY0cl5si6JSRJFK/evUq9vf3G8/S1Xf5PDDUDKhj++NCRExk/RxXVVVPUvguwJPAT5yrpaUlzMzMYHt7Gzs7O4VQ5uu50+nkxOZcy7xHUMJHoQUYnmwwbt3Pz89ns64+q+uL/9vtNq5du1bgpOg5fqanp4sABF2nzlhMTU3V8g9OTU1lHKH7gtkX9Hq32y2YRn1efajJwKjJz8eRDMPCwgKmpqaye4dDtO7ZH5brQQH8RJpH+srpe15u5GdGwfng4KBWn86RtrvT6eDKlSsYDAY1IdDf0/YSz1dVhXv37jUqDHQta2aI1dXVRlOrwvz8PBYXF1FVFVZXVx+Kyer1elhYWHjoOt4JnDd+mjB8MUL9XgB/7l1t5AQmMIH3EvpVVb0nLi4T/DSBCUzgDDgX/PR+8eFbBdAHcN2uXwewUn8cfxFDB2rCHQAzGJrAbwO46FxyAnAT74++ApP+XmQ4q6/PYbivD4N77xa8U/y0AmAWwDGAe8HzFw3eT+sXmPT3IsO7ip/eFxo+AEgp/TSAf15V1f/h9H8LwJsAfrCqqjOdolNKiwA2ASxVVbX1RBv7HsP7qa/ApL/vdXueJDwrfX0n+OlZ6eN5waS/FxveT/19t/v6ftHwAUOJ+L9JKf1LAP8cwP8JwDyA/+q9bNQEJjCBCWCCnyYwgQk8YXjfMHxVVf33KaVrAP48gBsAPg/gt1ZVdfc9bdgEJjCB9z1M8NMEJjCBJw3vG4YPAKqq+kEAP/iYrx9imBT1vfT1ebfg/dRXYNLfiwzPTF/fAX56Zvp4TjDp78WG91N/39W+vm98+CYwgQlMYAITmMAE3q/wvki8PIEJTGACE5jABCbwfoYJwzeBCUxgAhOYwAQmcMFhwvBNYAITmMAEJjCBCVxwmDB8E5jABCYwgQlMYAIXHCYM30NASul7Ukqvp5QOUko/nVL6le91mx4VUkp/OqX0L1JK2ymleymlv5tS+pg98w9TSpV9/nN75qWU0o+mlPZOy/mrKaWnLto7pfS9QV++IvdnUkqfTSk9SCntpJR+OKV03cp4JvoKAKfr0/tbpZQ+e3r/mZ3blNKvSyn9v1NKt0/b/Tvtfkop/fmU0p2U0n5K6SdSSh+xZy6nlP5OSmkrpbSRUvobKaWePfNNKaWfPN3nb6WU/tS70L13DBP8VDzzVK5hhwl+muAne+ZdwU8Thu8MSCl9N4ZJUf9DAN8C4AsAfjyl9Nx72rBHh18P4LMAvh3Ad2J42PrnUkrz9txfB/C8fPKiSim1AfwogA6AXw3g9wH4/RjmDnsa4RdQ9uXXyL3vB/BdAH43hmNzE8CP8OYz2NdfgbKv33l6/X+QZ57VuZ3HcN99T8P9PwXg3wHwhwH8KgC7GO7RGXnm7wD4FIbj8tsB/DoA/yVvpmHG+88BeAPAtwL4kwC+N6X0b51rT84ZJvjpmVnDEUzw0wQ/Ed4d/FRV1eQz5gPgpzE83oj/WxgedP7vv9dte4f9uobh2X2/Tq79QwA/MOad/zVOz/yUa38Yw6NhOu91n6yt3wvg8w33lgAcAfjfyrWPn47Htz9rfW3o4w8A+BpGqZcuxNyeztHvlP8Jw7Ou/882vwcAfs/p/0+cvvdt8sxvBTAAcPP0/x8BsKZ9BfCXAHzlve7zGeMxwU+j+8/EGj5t1wQ/TfDTu46fJhq+MZBS6mDITf8Er1VVNTj9/x3vVbvOCZZOv9fs+r+RUlpNKX0ppfQXU0pzcu87APx8VWb//3EAixhKJ08bfORUzf7qqbr8pdPr34qhBkHn9SsYnl3KeX3W+prhdN3+mwD+ZnWKGU7hIs0t4UMYnkyhc7mJISOkc7lRVdW/lPd+AkOE+qvkmX9cVdWRPPPjAD6WUrr0hNr+jmCCn575NTzBTxP8BLyL+Ok9t38/5XAVQBuAH290F0OJ65mENDyY/QcA/M9VVX1Jbv13GKqMbwP4JgB/GcDHAPyu0/s3EI8F7z1N8NMYqvx/CUPzwJ8D8JMppU9j2Najqqo27J27GPXjWeqrw+8EsAzgv5ZrF2luFdi2qO06l/f0ZlVVJymlNXvmtaAM3ls/l9aeL0zw07O7hif4aYKf3nX8NGH43p/wWQCfRukzgqqq/kv5+/MppTsA/l5K6ZWqqr7+bjbwnUJVVT8mf7+YUvppDBHK/w7A/nvTqncN/iCAH6uq6jYvXKS5ncCFhwl+utgwwU/vEUxMuuNhFad+A3b9OoCVd7857xxSSj+IoVPob6iq6tYZj//06fc3nH6vIB4L3ntq4VRa/mUM+7ICoJNSWrbHdF6fyb6mlF4G8JsB/D/OePSizC3bNm6PrgAoghhOo/su49me7wl+GsKzvoYn+KkOF2Vunyr8NGH4xsCpvfxnAPwmXjs1N/wmAD/1XrXrceA0NPwHAfzrAH5jVVWuHo7gM6ffd06/fwrAN1oE4HcC2ALw5fNq65OA0xD3VzDsy88AOEY5rx8D8BJG8/qs9vUPYGge+NEznvvM6fezPrevYYjwdC4XMfR90blcTil9q7z3GzHEfz8tz/y6lNK0PPOdAH6pqqqn0Zw7wU8XZw1P8FMdPnP6/azP7dOFn97rqJan/QPguzGMqPl9GEbT/BcY2suvv9dte8R+/BCADQxD/G/IZ/b0/isA/gyGDsMfBPA7AHwdwD+SMtoAfh5DZ9FvBvC/wnDz/sfvdf+C/v4np339IIZh/P8/APcBXDu9/9cwNKH8htM+/1MA//RZ7Ku0uXXap79k15/puQXQw5AAfAbDaLY/fvr7pdP7/97pnvwdAL4RwN8F8CqAGSnjxwD8LIBfCeB/iaE25b+T+0sYIua/haET+HdjmD7h33qv+3/G2Ezw0zOwhoP+TvDT6PozPbfPEn56zxfBs/AB8MdOF+ohhhz3r3qv2/QYfagaPr//9P6LAP4RgAcYEpCvAvgrABatnJcB/E8A9k4R1H8CYOq97l/Q3/8nhg7AhwBunf5/Re7PYOgrtHa6cX4EwI1nsa/S3t9yOqcftevP9NwC+Fca1u5/fXo/YZiPa+W0fz8RjMFlDB3DtzFM5fA3AfTsmW8C8JOnZdwC8O+9131/yPGZ4KenfA0H/Z3gp9H1Z3punyX8xBw4E5jABCYwgQlMYAITuKAw8eGbwAQmMIEJTGACE7jgMGH4JjCBCUxgAhOYwAQuOEwYvglMYAITmMAEJjCBCw4Thm8CE5jABCYwgQlM4ILDhOGbwAQmMIEJTGACE7jgMGH4JjCBCUxgAhOYwAQuOEwYvglMYAITmMAEJjCBCw4Thm8CEwggpVSllH7nOZb3D1NKP3Be5U1gAhN4/8IEP03gcWDC8E3gmYSUUjul9E9TSj9i15dSSm+llP7CO6zieQyPu5nABCYwgUeCCX6awNMIE4ZvAs8kVFX1/2/v3kHsqOI4jn9/xMoXKoKIKZQQHyj4QEUSVCwk8YUoisJC8FVE0pjgC4mFFiqCsJv1QQRRDBKxEiyCFioWChbGmGJDUFgQRSstdhcU49/iTuS67LoXr0tmJt8PXLhnzjkz5zR//jNnDnMEuA/YnGRiqGqaweeInhnz/D9V1W/jnEPS8cn4pDYy4VNnVdVh4ElgOsnZSW4H7gW2VNXvy/VLMpvk6SR7k8wn+SHJtkVt/l4ySbIlyVyS9UP1ryY5lOTEpnxJkn1Nu5+T7Ely5ipMW1IHGJ/UNiZ86rpp4ACwB3gdeLaqDozQ77Gm3+XAC8BUkhuXalhVbzP4aPc7SU5IcgvwEDBRVQtJTgM+BvYDVwKbgbOA98aZmKTOMz6pNVJVx3oM0liSXAjMAAeBK6rqjxXazwIzVXXT0LF3gVOr6uamXMAdVfV+Uz4d+Ab4ALgT2FVVzzV1O4Frq2rT0PnWAt8DF1TV4SSfAl9X1SP/x5wldYPxSW3hEz71wQPAAnAesHbEPl8sUb5oucZV9QvwIPAw8B2Du+6jLgVuaJZL5pLMAYeaunUjjkdSPxmf1AomfOq0JBuA7cCtwJfAG0mySpe7DjjCYIfcSUPHT2ZwZ33Zot964LNVGoukljM+qU1M+NRZzQvJbwGvVdUnDO5wrwa2jtD9miXKM/9yrQ3AE8BtwBzw8lD1V8DFwGxVfbvoNz/qfCT1h/FJbWPCpy57HgiDnXBU1SzwKPBiknNX6LsxyeNJzm92wN0NTC3VMMkpDF663lVV+4AJ4J4kdzVNXgHOAPYmuSrJuiSbkryZZM14U5TUUcYntYoJnzopyfXANuD+qlo4eryqdgOfs/LSyUsMdqztB3YCO6rqw2XaTgHzwFPNNQ42/3cnOaeqfgQ2AmuAjxi8nD0J/Ar8+R+nKKmjjE9qI3fp6rjT7IKbrKrJYzwUSfoH45NWi0/4JEmSes6ET5Ikqedc0pUkSeo5n/BJkiT1nAmfJElSz5nwSZIk9ZwJnyRJUs+Z8EmSJPWcCZ8kSVLPmfBJkiT1nAmfJElSz5nwSZIk9dxftLOD605oxSgAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 700x700 with 2 Axes>"
       ]
@@ -1130,9 +626,9 @@
    ],
    "source": [
     "# Let's show what the SCI extension of the first two files looks like\n",
-    "image1=hdu1['SCI'].data\n",
-    "hdu2=fits.open(ratefiles[1])\n",
-    "image2=hdu2['SCI'].data\n",
+    "image1 = hdu1['SCI'].data\n",
+    "hdu2 = fits.open(ratefiles[1])\n",
+    "image2 = hdu2['SCI'].data\n",
     "\n",
     "# Use a classic ZScale normalization\n",
     "norm = ImageNormalize(image1, interval=ZScaleInterval(),stretch=LinearStretch())\n",
@@ -1153,7 +649,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a00434e2",
+   "id": "684ef55e",
    "metadata": {},
    "source": [
     "<b>Figure 1:</b> Each exposure above shows the slope image for a MIRI MRS exposure.  X pixels 1-500 (roughly) correspond to data from Channel 1, and pixels 500-1024 correspond to data from Channel 2.  Data are dispersed along the spectral Y axis in each of multiple slices.  In this example of a bright point source, the point source is visible in many different slices- note how different slices are illuminated in Exposure 1 vs Exposure 2 though as we have dithered the source location between the two."
@@ -1161,8 +657,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "a0b35237",
+   "execution_count": 17,
+   "id": "e74d641c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1173,7 +669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 18,
    "id": "6718a236",
    "metadata": {},
    "outputs": [
@@ -1181,7 +677,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Runtime so far: 126.0150 seconds\n"
+      "Runtime so far: 0.6310 seconds\n"
      ]
     }
    ],
@@ -1240,7 +736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "id": "c3210f76",
    "metadata": {
     "scrolled": true
@@ -1250,100 +746,56 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2021-05-27 17:22:23,222 - stpipe.AssignWcsStep - INFO - AssignWcsStep instance created.\n",
-      "2021-05-27 17:22:23,290 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/stpipe/step.py:359: ResourceWarning: unclosed file <_io.FileIO name='stage1/det_image_seq1_MIRIFUSHORT_12LONGexp1_rate.fits' mode='rb' closefd=True>\n",
-      "  gc.collect()\n",
-      "\n",
-      "2021-05-27 17:22:23,313 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep running with args ('stage1/det_image_seq1_MIRIFUSHORT_12LONGexp1_rate.fits',).\n",
-      "2021-05-27 17:22:23,315 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'sip_approx': True, 'sip_max_pix_error': 0.25, 'sip_degree': None, 'sip_max_inv_pix_error': 0.25, 'sip_inv_degree': None, 'sip_npoints': 32, 'slit_y_low': -0.55, 'slit_y_high': 0.55}\n",
-      "2021-05-27 17:22:24,577 - stpipe.AssignWcsStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:22:26,258 - stpipe.AssignWcsStep - INFO - Created a MIRI mir_mrs pipeline with references {'distortion': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_distortion_0029.asdf', 'filteroffset': None, 'specwcs': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_specwcs_0022.asdf', 'regions': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_regions_0028.asdf', 'wavelengthrange': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_wavelengthrange_0005.asdf', 'camera': None, 'collimator': None, 'disperser': None, 'fore': None, 'fpa': None, 'msa': None, 'ote': None, 'ifupost': None, 'ifufore': None, 'ifuslicer': None}\n",
-      "2021-05-27 17:22:26,854 - stpipe.AssignWcsStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/utils.py:72: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  indx = np.asarray(np.floor(np.asarray(value) + 0.5), dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:22:27,421 - stpipe.AssignWcsStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
+      "2021-06-18 13:04:09,809 - stpipe.AssignWcsStep - INFO - AssignWcsStep instance created.\n",
+      "2021-06-18 13:04:09,898 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep running with args ('stage1/det_image_seq1_MIRIFUSHORT_12LONGexp1_rate.fits',).\n",
+      "2021-06-18 13:04:09,900 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'sip_approx': True, 'sip_max_pix_error': 0.25, 'sip_degree': None, 'sip_max_inv_pix_error': 0.25, 'sip_inv_degree': None, 'sip_npoints': 32, 'slit_y_low': -0.55, 'slit_y_high': 0.55}\n",
+      "2021-06-18 13:04:13,212 - stpipe.AssignWcsStep - INFO - Created a MIRI mir_mrs pipeline with references {'distortion': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_distortion_0029.asdf', 'filteroffset': None, 'specwcs': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_specwcs_0022.asdf', 'regions': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_regions_0028.asdf', 'wavelengthrange': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_wavelengthrange_0005.asdf', 'camera': None, 'collimator': None, 'disperser': None, 'fore': None, 'fpa': None, 'msa': None, 'ote': None, 'ifupost': None, 'ifufore': None, 'ifuslicer': None}\n",
+      "2021-06-18 13:04:14,463 - stpipe.AssignWcsStep - WARNING - /Users/dlaw/anaconda3/envs/jwst1.1.0/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
       "  lon = np.mod(lon, 360.0 * u.deg if nquant else 360.0)\n",
       "\n",
-      "2021-05-27 17:22:27,499 - stpipe.AssignWcsStep - INFO - Update S_REGION to POLYGON ICRS  359.999649827 -0.000731840 0.000993034 -0.000731840 0.000993034 0.000693132 359.999649827 0.000693132\n",
-      "2021-05-27 17:22:27,501 - stpipe.AssignWcsStep - INFO - COMPLETED assign_wcs\n",
-      "2021-05-27 17:22:28,405 - stpipe.AssignWcsStep - INFO - Saved model in stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits\n",
-      "2021-05-27 17:22:28,406 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep done\n",
-      "2021-05-27 17:22:28,454 - stpipe.AssignWcsStep - INFO - AssignWcsStep instance created.\n",
-      "2021-05-27 17:22:28,538 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/stpipe/step.py:359: ResourceWarning: unclosed file <_io.FileIO name='stage1/det_image_seq2_MIRIFUSHORT_12LONGexp1_rate.fits' mode='rb' closefd=True>\n",
-      "  gc.collect()\n",
-      "\n",
-      "2021-05-27 17:22:28,589 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep running with args ('stage1/det_image_seq2_MIRIFUSHORT_12LONGexp1_rate.fits',).\n",
-      "2021-05-27 17:22:28,591 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'sip_approx': True, 'sip_max_pix_error': 0.25, 'sip_degree': None, 'sip_max_inv_pix_error': 0.25, 'sip_inv_degree': None, 'sip_npoints': 32, 'slit_y_low': -0.55, 'slit_y_high': 0.55}\n",
-      "2021-05-27 17:22:29,707 - stpipe.AssignWcsStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:22:31,332 - stpipe.AssignWcsStep - INFO - Created a MIRI mir_mrs pipeline with references {'distortion': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_distortion_0029.asdf', 'filteroffset': None, 'specwcs': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_specwcs_0022.asdf', 'regions': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_regions_0028.asdf', 'wavelengthrange': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_wavelengthrange_0005.asdf', 'camera': None, 'collimator': None, 'disperser': None, 'fore': None, 'fpa': None, 'msa': None, 'ote': None, 'ifupost': None, 'ifufore': None, 'ifuslicer': None}\n",
-      "2021-05-27 17:22:31,907 - stpipe.AssignWcsStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/utils.py:72: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  indx = np.asarray(np.floor(np.asarray(value) + 0.5), dtype=np.int)\n",
+      "2021-06-18 13:04:14,524 - stpipe.AssignWcsStep - INFO - Update S_REGION to POLYGON ICRS  0.000000001 -0.000731840 359.999999999 -0.000731840 359.999999999 0.000693132 0.000000001 0.000693132\n",
+      "2021-06-18 13:04:14,524 - stpipe.AssignWcsStep - INFO - COMPLETED assign_wcs\n",
+      "2021-06-18 13:04:15,457 - stpipe.AssignWcsStep - INFO - Saved model in stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits\n",
+      "2021-06-18 13:04:15,457 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep done\n",
+      "2021-06-18 13:04:15,512 - stpipe.AssignWcsStep - INFO - AssignWcsStep instance created.\n",
+      "2021-06-18 13:04:15,665 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep running with args ('stage1/det_image_seq2_MIRIFUSHORT_12LONGexp1_rate.fits',).\n",
+      "2021-06-18 13:04:15,667 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'sip_approx': True, 'sip_max_pix_error': 0.25, 'sip_degree': None, 'sip_max_inv_pix_error': 0.25, 'sip_inv_degree': None, 'sip_npoints': 32, 'slit_y_low': -0.55, 'slit_y_high': 0.55}\n",
+      "2021-06-18 13:04:18,873 - stpipe.AssignWcsStep - INFO - Created a MIRI mir_mrs pipeline with references {'distortion': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_distortion_0029.asdf', 'filteroffset': None, 'specwcs': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_specwcs_0022.asdf', 'regions': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_regions_0028.asdf', 'wavelengthrange': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_wavelengthrange_0005.asdf', 'camera': None, 'collimator': None, 'disperser': None, 'fore': None, 'fpa': None, 'msa': None, 'ote': None, 'ifupost': None, 'ifufore': None, 'ifuslicer': None}\n",
+      "2021-06-18 13:04:20,141 - stpipe.AssignWcsStep - WARNING - /Users/dlaw/anaconda3/envs/jwst1.1.0/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
+      "  lon = np.mod(lon, 360.0 * u.deg if nquant else 360.0)\n",
       "\n",
-      "2021-05-27 17:22:32,464 - stpipe.AssignWcsStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
+      "2021-06-18 13:04:20,200 - stpipe.AssignWcsStep - INFO - Update S_REGION to POLYGON ICRS  0.000000001 -0.000921354 359.999999997 -0.000921354 359.999999997 0.000503617 0.000000001 0.000503617\n",
+      "2021-06-18 13:04:20,200 - stpipe.AssignWcsStep - INFO - COMPLETED assign_wcs\n",
+      "2021-06-18 13:04:21,129 - stpipe.AssignWcsStep - INFO - Saved model in stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits\n",
+      "2021-06-18 13:04:21,129 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep done\n",
+      "2021-06-18 13:04:21,184 - stpipe.AssignWcsStep - INFO - AssignWcsStep instance created.\n",
+      "2021-06-18 13:04:21,337 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep running with args ('stage1/det_image_seq3_MIRIFUSHORT_12LONGexp1_rate.fits',).\n",
+      "2021-06-18 13:04:21,340 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'sip_approx': True, 'sip_max_pix_error': 0.25, 'sip_degree': None, 'sip_max_inv_pix_error': 0.25, 'sip_inv_degree': None, 'sip_npoints': 32, 'slit_y_low': -0.55, 'slit_y_high': 0.55}\n",
+      "2021-06-18 13:04:24,465 - stpipe.AssignWcsStep - INFO - Created a MIRI mir_mrs pipeline with references {'distortion': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_distortion_0029.asdf', 'filteroffset': None, 'specwcs': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_specwcs_0022.asdf', 'regions': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_regions_0028.asdf', 'wavelengthrange': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_wavelengthrange_0005.asdf', 'camera': None, 'collimator': None, 'disperser': None, 'fore': None, 'fpa': None, 'msa': None, 'ote': None, 'ifupost': None, 'ifufore': None, 'ifuslicer': None}\n",
+      "2021-06-18 13:04:25,710 - stpipe.AssignWcsStep - WARNING - /Users/dlaw/anaconda3/envs/jwst1.1.0/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
       "  lon = np.mod(lon, 360.0 * u.deg if nquant else 360.0)\n",
       "\n",
-      "2021-05-27 17:22:32,541 - stpipe.AssignWcsStep - INFO - Update S_REGION to POLYGON ICRS  359.999064688 -0.000921354 0.000407895 -0.000921354 0.000407895 0.000503617 359.999064688 0.000503617\n",
-      "2021-05-27 17:22:32,542 - stpipe.AssignWcsStep - INFO - COMPLETED assign_wcs\n",
-      "2021-05-27 17:22:33,407 - stpipe.AssignWcsStep - INFO - Saved model in stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits\n",
-      "2021-05-27 17:22:33,408 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep done\n",
-      "2021-05-27 17:22:33,452 - stpipe.AssignWcsStep - INFO - AssignWcsStep instance created.\n",
-      "2021-05-27 17:22:33,537 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/stpipe/step.py:359: ResourceWarning: unclosed file <_io.FileIO name='stage1/det_image_seq3_MIRIFUSHORT_12LONGexp1_rate.fits' mode='rb' closefd=True>\n",
-      "  gc.collect()\n",
+      "2021-06-18 13:04:25,768 - stpipe.AssignWcsStep - INFO - Update S_REGION to POLYGON ICRS  0.000000001 -0.000752386 359.999999998 -0.000752386 359.999999998 0.000672586 0.000000001 0.000672586\n",
+      "2021-06-18 13:04:25,769 - stpipe.AssignWcsStep - INFO - COMPLETED assign_wcs\n",
+      "2021-06-18 13:04:26,673 - stpipe.AssignWcsStep - INFO - Saved model in stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits\n",
+      "2021-06-18 13:04:26,674 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep done\n",
+      "2021-06-18 13:04:26,742 - stpipe.AssignWcsStep - INFO - AssignWcsStep instance created.\n",
+      "2021-06-18 13:04:26,889 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep running with args ('stage1/det_image_seq4_MIRIFUSHORT_12LONGexp1_rate.fits',).\n",
+      "2021-06-18 13:04:26,891 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'sip_approx': True, 'sip_max_pix_error': 0.25, 'sip_degree': None, 'sip_max_inv_pix_error': 0.25, 'sip_inv_degree': None, 'sip_npoints': 32, 'slit_y_low': -0.55, 'slit_y_high': 0.55}\n",
+      "2021-06-18 13:04:30,041 - stpipe.AssignWcsStep - INFO - Created a MIRI mir_mrs pipeline with references {'distortion': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_distortion_0029.asdf', 'filteroffset': None, 'specwcs': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_specwcs_0022.asdf', 'regions': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_regions_0028.asdf', 'wavelengthrange': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_wavelengthrange_0005.asdf', 'camera': None, 'collimator': None, 'disperser': None, 'fore': None, 'fpa': None, 'msa': None, 'ote': None, 'ifupost': None, 'ifufore': None, 'ifuslicer': None}\n",
+      "2021-06-18 13:04:31,218 - stpipe.AssignWcsStep - WARNING - /Users/dlaw/anaconda3/envs/jwst1.1.0/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
+      "  lon = np.mod(lon, 360.0 * u.deg if nquant else 360.0)\n",
       "\n",
-      "2021-05-27 17:22:33,589 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep running with args ('stage1/det_image_seq3_MIRIFUSHORT_12LONGexp1_rate.fits',).\n",
-      "2021-05-27 17:22:33,591 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'sip_approx': True, 'sip_max_pix_error': 0.25, 'sip_degree': None, 'sip_max_inv_pix_error': 0.25, 'sip_inv_degree': None, 'sip_npoints': 32, 'slit_y_low': -0.55, 'slit_y_high': 0.55}\n"
+      "2021-06-18 13:04:31,279 - stpipe.AssignWcsStep - INFO - Update S_REGION to POLYGON ICRS  0.000000000 -0.000942272 359.999999999 -0.000942272 359.999999999 0.000482700 0.000000000 0.000482700\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2021-05-27 17:22:34,675 - stpipe.AssignWcsStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:22:36,252 - stpipe.AssignWcsStep - INFO - Created a MIRI mir_mrs pipeline with references {'distortion': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_distortion_0029.asdf', 'filteroffset': None, 'specwcs': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_specwcs_0022.asdf', 'regions': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_regions_0028.asdf', 'wavelengthrange': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_wavelengthrange_0005.asdf', 'camera': None, 'collimator': None, 'disperser': None, 'fore': None, 'fpa': None, 'msa': None, 'ote': None, 'ifupost': None, 'ifufore': None, 'ifuslicer': None}\n",
-      "2021-05-27 17:22:36,835 - stpipe.AssignWcsStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/utils.py:72: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  indx = np.asarray(np.floor(np.asarray(value) + 0.5), dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:22:37,385 - stpipe.AssignWcsStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
-      "  lon = np.mod(lon, 360.0 * u.deg if nquant else 360.0)\n",
-      "\n",
-      "2021-05-27 17:22:37,465 - stpipe.AssignWcsStep - INFO - Update S_REGION to POLYGON ICRS  359.999620276 -0.000752386 0.000963483 -0.000752386 0.000963483 0.000672586 359.999620276 0.000672586\n",
-      "2021-05-27 17:22:37,467 - stpipe.AssignWcsStep - INFO - COMPLETED assign_wcs\n",
-      "2021-05-27 17:22:38,340 - stpipe.AssignWcsStep - INFO - Saved model in stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits\n",
-      "2021-05-27 17:22:38,342 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep done\n",
-      "2021-05-27 17:22:38,386 - stpipe.AssignWcsStep - INFO - AssignWcsStep instance created.\n",
-      "2021-05-27 17:22:38,468 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/stpipe/step.py:359: ResourceWarning: unclosed file <_io.FileIO name='stage1/det_image_seq4_MIRIFUSHORT_12LONGexp1_rate.fits' mode='rb' closefd=True>\n",
-      "  gc.collect()\n",
-      "\n",
-      "2021-05-27 17:22:38,523 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep running with args ('stage1/det_image_seq4_MIRIFUSHORT_12LONGexp1_rate.fits',).\n",
-      "2021-05-27 17:22:38,525 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'sip_approx': True, 'sip_max_pix_error': 0.25, 'sip_degree': None, 'sip_max_inv_pix_error': 0.25, 'sip_inv_degree': None, 'sip_npoints': 32, 'slit_y_low': -0.55, 'slit_y_high': 0.55}\n",
-      "2021-05-27 17:22:39,594 - stpipe.AssignWcsStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:22:41,159 - stpipe.AssignWcsStep - INFO - Created a MIRI mir_mrs pipeline with references {'distortion': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_distortion_0029.asdf', 'filteroffset': None, 'specwcs': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_specwcs_0022.asdf', 'regions': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_regions_0028.asdf', 'wavelengthrange': '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_wavelengthrange_0005.asdf', 'camera': None, 'collimator': None, 'disperser': None, 'fore': None, 'fpa': None, 'msa': None, 'ote': None, 'ifupost': None, 'ifufore': None, 'ifuslicer': None}\n",
-      "2021-05-27 17:22:41,704 - stpipe.AssignWcsStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/utils.py:72: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  indx = np.asarray(np.floor(np.asarray(value) + 0.5), dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:22:42,241 - stpipe.AssignWcsStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
-      "  lon = np.mod(lon, 360.0 * u.deg if nquant else 360.0)\n",
-      "\n",
-      "2021-05-27 17:22:42,319 - stpipe.AssignWcsStep - INFO - Update S_REGION to POLYGON ICRS  359.999035322 -0.000942272 0.000378529 -0.000942272 0.000378529 0.000482700 359.999035322 0.000482700\n",
-      "2021-05-27 17:22:42,321 - stpipe.AssignWcsStep - INFO - COMPLETED assign_wcs\n",
-      "2021-05-27 17:22:43,164 - stpipe.AssignWcsStep - INFO - Saved model in stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits\n",
-      "2021-05-27 17:22:43,165 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep done\n"
+      "2021-06-18 13:04:31,279 - stpipe.AssignWcsStep - INFO - COMPLETED assign_wcs\n",
+      "2021-06-18 13:04:32,209 - stpipe.AssignWcsStep - INFO - Saved model in stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits\n",
+      "2021-06-18 13:04:32,210 - stpipe.AssignWcsStep - INFO - Step AssignWcsStep done\n"
      ]
     }
    ],
@@ -1355,21 +807,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "id": "16dcc213",
    "metadata": {
     "scrolled": false
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:22:43,170 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n",
-      "\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -1379,15 +822,15 @@
        " 'stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits']"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Look for the _assignwcsstep.fits files produced by this step\n",
-    "sstring=spec2_dir+'det*assignwcsstep.fits'\n",
-    "wcsfiles=sorted(glob.glob(sstring))\n",
+    "sstring = spec2_dir + 'det*assignwcsstep.fits'\n",
+    "wcsfiles = sorted(glob.glob(sstring))\n",
     "# And print them out so that we can see them\n",
     "wcsfiles"
    ]
@@ -1397,27 +840,17 @@
    "id": "67a80547",
    "metadata": {},
    "source": [
-    "The assign_wcs step doesn't modify the science data, so this will look identical to the \\_rate.fits files above.  However, now the ASDF extension contains all of the information about the distortion transforms between detector pixel values and world coordinates.  A detailed treatment of how to interact with this WCS is beyond the scope of this notebook, but we can show it quickly"
+    "The assign_wcs step doesn't modify the science data, so this will look identical to the \\_rate.fits files above.  However, now the ASDF extension contains all of the information about the distortion transforms between detector pixel values and world coordinates.  A detailed treatment of how to interact with this WCS is beyond the scope of this notebook, but we can show an example quickly:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "id": "ed937258",
    "metadata": {
     "scrolled": false
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:22:43,677 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -1442,25 +875,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "id": "1b6ec05a",
    "metadata": {
     "scrolled": false
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:22:44,527 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n",
-      "\n",
-      "2021-05-27 17:22:44,530 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/utils.py:72: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  indx = np.asarray(np.floor(np.asarray(value) + 0.5), dtype=np.int)\n",
-      "\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -1473,22 +893,12 @@
    ],
    "source": [
     "# Here's an example transformation giving the world coordinates of pixel x=376,y=589\n",
-    "ra,dec,wave=image.meta.wcs.transform(\"detector\", \"world\", 376, 589)\n",
+    "ra,dec,wave = image.meta.wcs.transform(\"detector\", \"world\", 376, 589)\n",
     "print('RA = ',ra,' deg')\n",
     "print('DEC = ',dec,' deg')\n",
     "print('Wavelength = ',wave,' micron')"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "336afda9",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "6bf64823",
@@ -1499,7 +909,8 @@
     "<div class=\"alert alert-block alert-warning\">\n",
     "The MIRI MRS is expected to see a substantial background signal, especially in Channel 4 where the telescope thermal background becomes significant.  It will therefore be necessary to subtract this background (strictly, a foreground) signal from the data prior to analysis.  Ideally, this background should be sufficiently smooth and uniform that it is possible to model it and subtract the model from the science data (see Spec3- Master Background).  However, it is possible that the signal has sufficient spatial/spectral variability that it may prove necessary to do background subtraction via direct subtraction of on/off pointings.\n",
     "\n",
-    "This background step in the Spec2 pipeline is largely a placeholder for if it becomes necessary to do such a direct subtraction at the cost of increased total noise.  As such, we do not recommend running this step at the present time- additional guidance will be provided after on-orbit commissioning.\n",
+    "This background step in the Spec2 pipeline is largely a placeholder for if it becomes necessary to do such a direct subtraction at the cost of increased total noise.  As such, we do not recommend running this step at the present time- additional guidance will be provided after on-orbit commissioning.  If we *were* to run the step, here's one way in which in could be done.\n",
+    "\n",
     "\n",
     "See https://jwst-pipeline.readthedocs.io/en/latest/jwst/background_step/\n",
     "</div>"
@@ -1507,7 +918,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "id": "2b37fe07",
    "metadata": {},
    "outputs": [
@@ -1520,16 +931,15 @@
        " 'stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits']"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# If we *were* to run the step, here's one way in which in could be done.\n",
     "# Look for our assignwcsstep.fits files produced by the assign_wcs step\n",
-    "sstring=spec2_dir+'det*assignwcsstep.fits'\n",
-    "wcsfiles=sorted(glob.glob(sstring))\n",
+    "sstring = spec2_dir + 'det*assignwcsstep.fits'\n",
+    "wcsfiles = sorted(glob.glob(sstring))\n",
     "# And print them out so that we can see them\n",
     "wcsfiles"
    ]
@@ -1544,22 +954,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "id": "b20006a4",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Alternate background assignments\n",
-    "bgfiles=wcsfiles.copy()\n",
-    "bgfiles[0]=wcsfiles[1]\n",
-    "bgfiles[1]=wcsfiles[0]\n",
-    "bgfiles[2]=wcsfiles[3]\n",
-    "bgfiles[3]=wcsfiles[2]"
+    "bgfiles = wcsfiles.copy()\n",
+    "bgfiles[0] = wcsfiles[1]\n",
+    "bgfiles[1] = wcsfiles[0]\n",
+    "bgfiles[2] = wcsfiles[3]\n",
+    "bgfiles[3] = wcsfiles[2]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "id": "62cd0f9b",
    "metadata": {
     "scrolled": true
@@ -1569,80 +979,26 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2021-05-27 17:22:44,954 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:22:45,802 - stpipe.BackgroundStep - INFO - BackgroundStep instance created.\n",
-      "2021-05-27 17:22:45,938 - stpipe.BackgroundStep - INFO - Step BackgroundStep running with args ('stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits', ['stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits']).\n",
-      "2021-05-27 17:22:45,940 - stpipe.BackgroundStep - INFO - Step BackgroundStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'save_combined_background': False, 'sigma': 3.0, 'maxiters': None}\n",
-      "2021-05-27 17:22:46,334 - stpipe.BackgroundStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:22:47,905 - stpipe.BackgroundStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:22:51,750 - stpipe.BackgroundStep - INFO - Saved model in stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_backgroundstep.fits\n",
-      "2021-05-27 17:22:51,752 - stpipe.BackgroundStep - INFO - Step BackgroundStep done\n",
-      "2021-05-27 17:22:52,274 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:22:53,148 - stpipe.BackgroundStep - INFO - BackgroundStep instance created.\n",
-      "2021-05-27 17:22:53,265 - stpipe.BackgroundStep - INFO - Step BackgroundStep running with args ('stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits', ['stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits']).\n",
-      "2021-05-27 17:22:53,267 - stpipe.BackgroundStep - INFO - Step BackgroundStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'save_combined_background': False, 'sigma': 3.0, 'maxiters': None}\n",
-      "2021-05-27 17:22:53,634 - stpipe.BackgroundStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:22:54,885 - stpipe.BackgroundStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:22:58,236 - stpipe.BackgroundStep - INFO - Saved model in stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_backgroundstep.fits\n",
-      "2021-05-27 17:22:58,237 - stpipe.BackgroundStep - INFO - Step BackgroundStep done\n",
-      "2021-05-27 17:22:58,758 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:22:59,494 - stpipe.BackgroundStep - INFO - BackgroundStep instance created.\n",
-      "2021-05-27 17:22:59,606 - stpipe.BackgroundStep - INFO - Step BackgroundStep running with args ('stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits', ['stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits']).\n",
-      "2021-05-27 17:22:59,608 - stpipe.BackgroundStep - INFO - Step BackgroundStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'save_combined_background': False, 'sigma': 3.0, 'maxiters': None}\n",
-      "2021-05-27 17:22:59,973 - stpipe.BackgroundStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:01,293 - stpipe.BackgroundStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:23:04,676 - stpipe.BackgroundStep - INFO - Saved model in stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_backgroundstep.fits\n",
-      "2021-05-27 17:23:04,677 - stpipe.BackgroundStep - INFO - Step BackgroundStep done\n",
-      "2021-05-27 17:23:05,268 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:06,016 - stpipe.BackgroundStep - INFO - BackgroundStep instance created.\n",
-      "2021-05-27 17:23:06,156 - stpipe.BackgroundStep - INFO - Step BackgroundStep running with args ('stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits', ['stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits']).\n",
-      "2021-05-27 17:23:06,159 - stpipe.BackgroundStep - INFO - Step BackgroundStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'save_combined_background': False, 'sigma': 3.0, 'maxiters': None}\n",
-      "2021-05-27 17:23:06,604 - stpipe.BackgroundStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:07,871 - stpipe.BackgroundStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:11,316 - stpipe.BackgroundStep - INFO - Saved model in stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_backgroundstep.fits\n",
-      "2021-05-27 17:23:11,318 - stpipe.BackgroundStep - INFO - Step BackgroundStep done\n"
+      "2021-06-18 13:04:35,022 - stpipe.BackgroundStep - INFO - BackgroundStep instance created.\n",
+      "2021-06-18 13:04:35,155 - stpipe.BackgroundStep - INFO - Step BackgroundStep running with args ('stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits', ['stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits']).\n",
+      "2021-06-18 13:04:35,157 - stpipe.BackgroundStep - INFO - Step BackgroundStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'sigma': 3.0, 'maxiters': None}\n",
+      "2021-06-18 13:04:40,690 - stpipe.BackgroundStep - INFO - Saved model in stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_backgroundstep.fits\n",
+      "2021-06-18 13:04:40,690 - stpipe.BackgroundStep - INFO - Step BackgroundStep done\n",
+      "2021-06-18 13:04:42,033 - stpipe.BackgroundStep - INFO - BackgroundStep instance created.\n",
+      "2021-06-18 13:04:42,160 - stpipe.BackgroundStep - INFO - Step BackgroundStep running with args ('stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits', ['stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits']).\n",
+      "2021-06-18 13:04:42,162 - stpipe.BackgroundStep - INFO - Step BackgroundStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'sigma': 3.0, 'maxiters': None}\n",
+      "2021-06-18 13:04:47,621 - stpipe.BackgroundStep - INFO - Saved model in stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_backgroundstep.fits\n",
+      "2021-06-18 13:04:47,622 - stpipe.BackgroundStep - INFO - Step BackgroundStep done\n",
+      "2021-06-18 13:04:49,059 - stpipe.BackgroundStep - INFO - BackgroundStep instance created.\n",
+      "2021-06-18 13:04:49,186 - stpipe.BackgroundStep - INFO - Step BackgroundStep running with args ('stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits', ['stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits']).\n",
+      "2021-06-18 13:04:49,188 - stpipe.BackgroundStep - INFO - Step BackgroundStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'sigma': 3.0, 'maxiters': None}\n",
+      "2021-06-18 13:04:54,270 - stpipe.BackgroundStep - INFO - Saved model in stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_backgroundstep.fits\n",
+      "2021-06-18 13:04:54,270 - stpipe.BackgroundStep - INFO - Step BackgroundStep done\n",
+      "2021-06-18 13:04:55,581 - stpipe.BackgroundStep - INFO - BackgroundStep instance created.\n",
+      "2021-06-18 13:04:55,709 - stpipe.BackgroundStep - INFO - Step BackgroundStep running with args ('stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits', ['stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits']).\n",
+      "2021-06-18 13:04:55,711 - stpipe.BackgroundStep - INFO - Step BackgroundStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'sigma': 3.0, 'maxiters': None}\n",
+      "2021-06-18 13:05:01,047 - stpipe.BackgroundStep - INFO - Saved model in stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_backgroundstep.fits\n",
+      "2021-06-18 13:05:01,047 - stpipe.BackgroundStep - INFO - Step BackgroundStep done\n"
      ]
     }
    ],
@@ -1654,19 +1010,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "id": "db31b7dd",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:23:11,323 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n",
-      "\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -1676,23 +1023,23 @@
        " 'stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_backgroundstep.fits']"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Look for our backgroundstep.fits files produced by the background step\n",
-    "sstring=spec2_dir+'det*backgroundstep.fits'\n",
+    "sstring = spec2_dir + 'det*backgroundstep.fits'\n",
     "\n",
-    "bgfiles=sorted(glob.glob(sstring))\n",
+    "bgfiles = sorted(glob.glob(sstring))\n",
     "# And print them out so that we can see them\n",
     "bgfiles"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "id": "0bbe6896",
    "metadata": {},
    "outputs": [
@@ -1702,13 +1049,13 @@
        "Text(0.5, 0, 'X pixel')"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 900x700 with 3 Axes>"
       ]
@@ -1722,12 +1069,12 @@
    "source": [
     "# Let's take a quick look at what it did by reading in the original \n",
     "# exposures 1 and 2, and the modified exposure 1\n",
-    "hdu1=fits.open(wcsfiles[0])\n",
-    "image1=hdu1['SCI'].data\n",
-    "hdu2=fits.open(wcsfiles[1])\n",
-    "image2=hdu2['SCI'].data\n",
-    "hdu3=fits.open(bgfiles[0])\n",
-    "image3=hdu3['SCI'].data\n",
+    "hdu1 = fits.open(wcsfiles[0])\n",
+    "image1 = hdu1['SCI'].data\n",
+    "hdu2 = fits.open(wcsfiles[1])\n",
+    "image2 = hdu2['SCI'].data\n",
+    "hdu3 = fits.open(bgfiles[0])\n",
+    "image3 = hdu3['SCI'].data\n",
     "\n",
     "# Use a classic ZScale normalization\n",
     "norm = ImageNormalize(image3, interval=ZScaleInterval(),stretch=LinearStretch())\n",
@@ -1760,7 +1107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "id": "7f5932e1",
    "metadata": {},
    "outputs": [],
@@ -1771,14 +1118,6 @@
     "hdu3.close()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7a272925",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "d31715a5",
@@ -1795,14 +1134,6 @@
     "</div>"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "273d0588",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "d1b926c5",
@@ -1819,7 +1150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "id": "4a4e3eed",
    "metadata": {},
    "outputs": [
@@ -1832,7 +1163,7 @@
        " 'stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits']"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1840,17 +1171,16 @@
    "source": [
     "# Look for our assignwcsstep.fits files produced by the assign_wcs step\n",
     "# (since we're going to skip background subtraction)\n",
-    "sstring=spec2_dir+'det*assignwcsstep.fits'\n",
-    "#sstring=spec2_dir+'det*backgroundstep.fits'\n",
+    "sstring = spec2_dir + 'det*assignwcsstep.fits'\n",
     "\n",
-    "wcsfiles=sorted(glob.glob(sstring))\n",
+    "wcsfiles = sorted(glob.glob(sstring))\n",
     "# And print them out so that we can see them\n",
     "wcsfiles"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 30,
    "id": "598a7cb4",
    "metadata": {
     "scrolled": true
@@ -1860,68 +1190,30 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2021-05-27 17:23:12,330 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:13,057 - stpipe.FlatFieldStep - INFO - FlatFieldStep instance created.\n",
-      "2021-05-27 17:23:13,172 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep running with args ('stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits',).\n",
-      "2021-05-27 17:23:13,174 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'save_interpolated_flat': False, 'user_supplied_flat': None, 'inverse': False}\n",
-      "2021-05-27 17:23:13,540 - stpipe.FlatFieldStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:14,495 - stpipe.FlatFieldStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
-      "2021-05-27 17:23:16,863 - stpipe.FlatFieldStep - INFO - Saved model in stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_flatfieldstep.fits\n",
-      "2021-05-27 17:23:16,864 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep done\n",
-      "2021-05-27 17:23:17,222 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:18,027 - stpipe.FlatFieldStep - INFO - FlatFieldStep instance created.\n",
-      "2021-05-27 17:23:18,152 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep running with args ('stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits',).\n",
-      "2021-05-27 17:23:18,154 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'save_interpolated_flat': False, 'user_supplied_flat': None, 'inverse': False}\n",
-      "2021-05-27 17:23:18,521 - stpipe.FlatFieldStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:19,382 - stpipe.FlatFieldStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
-      "2021-05-27 17:23:21,700 - stpipe.FlatFieldStep - INFO - Saved model in stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_flatfieldstep.fits\n",
-      "2021-05-27 17:23:21,701 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep done\n",
-      "2021-05-27 17:23:22,059 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:22,870 - stpipe.FlatFieldStep - INFO - FlatFieldStep instance created.\n",
-      "2021-05-27 17:23:22,996 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep running with args ('stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits',).\n",
-      "2021-05-27 17:23:22,998 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'save_interpolated_flat': False, 'user_supplied_flat': None, 'inverse': False}\n",
-      "2021-05-27 17:23:23,366 - stpipe.FlatFieldStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:24,239 - stpipe.FlatFieldStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
-      "2021-05-27 17:23:26,572 - stpipe.FlatFieldStep - INFO - Saved model in stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_flatfieldstep.fits\n",
-      "2021-05-27 17:23:26,574 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep done\n",
-      "2021-05-27 17:23:26,933 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:27,748 - stpipe.FlatFieldStep - INFO - FlatFieldStep instance created.\n",
-      "2021-05-27 17:23:27,877 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep running with args ('stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits',).\n",
-      "2021-05-27 17:23:27,879 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'save_interpolated_flat': False, 'user_supplied_flat': None, 'inverse': False}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:23:28,245 - stpipe.FlatFieldStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:29,215 - stpipe.FlatFieldStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
-      "2021-05-27 17:23:31,504 - stpipe.FlatFieldStep - INFO - Saved model in stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_flatfieldstep.fits\n",
-      "2021-05-27 17:23:31,506 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep done\n"
+      "2021-06-18 13:05:02,857 - stpipe.FlatFieldStep - INFO - FlatFieldStep instance created.\n",
+      "2021-06-18 13:05:02,975 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep running with args ('stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits',).\n",
+      "2021-06-18 13:05:02,977 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'save_interpolated_flat': False, 'user_supplied_flat': None, 'inverse': False}\n",
+      "2021-06-18 13:05:04,365 - stpipe.FlatFieldStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
+      "2021-06-18 13:05:06,864 - stpipe.FlatFieldStep - INFO - Saved model in stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_flatfieldstep.fits\n",
+      "2021-06-18 13:05:06,865 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep done\n",
+      "2021-06-18 13:05:08,155 - stpipe.FlatFieldStep - INFO - FlatFieldStep instance created.\n",
+      "2021-06-18 13:05:08,308 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep running with args ('stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits',).\n",
+      "2021-06-18 13:05:08,310 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'save_interpolated_flat': False, 'user_supplied_flat': None, 'inverse': False}\n",
+      "2021-06-18 13:05:09,696 - stpipe.FlatFieldStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
+      "2021-06-18 13:05:12,261 - stpipe.FlatFieldStep - INFO - Saved model in stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_flatfieldstep.fits\n",
+      "2021-06-18 13:05:12,262 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep done\n",
+      "2021-06-18 13:05:13,639 - stpipe.FlatFieldStep - INFO - FlatFieldStep instance created.\n",
+      "2021-06-18 13:05:13,780 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep running with args ('stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits',).\n",
+      "2021-06-18 13:05:13,781 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'save_interpolated_flat': False, 'user_supplied_flat': None, 'inverse': False}\n",
+      "2021-06-18 13:05:15,189 - stpipe.FlatFieldStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
+      "2021-06-18 13:05:17,757 - stpipe.FlatFieldStep - INFO - Saved model in stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_flatfieldstep.fits\n",
+      "2021-06-18 13:05:17,758 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep done\n",
+      "2021-06-18 13:05:19,206 - stpipe.FlatFieldStep - INFO - FlatFieldStep instance created.\n",
+      "2021-06-18 13:05:19,380 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep running with args ('stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_assignwcsstep.fits',).\n",
+      "2021-06-18 13:05:19,382 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'save_interpolated_flat': False, 'user_supplied_flat': None, 'inverse': False}\n",
+      "2021-06-18 13:05:20,772 - stpipe.FlatFieldStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
+      "2021-06-18 13:05:23,421 - stpipe.FlatFieldStep - INFO - Saved model in stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_flatfieldstep.fits\n",
+      "2021-06-18 13:05:23,422 - stpipe.FlatFieldStep - INFO - Step FlatFieldStep done\n"
      ]
     }
    ],
@@ -1931,14 +1223,6 @@
     "    FlatFieldStep.call(file,save_results=True,output_dir=spec2_dir)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "be71b193",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "d0a3fde2",
@@ -1962,19 +1246,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 31,
    "id": "af198fa1",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:23:31,511 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n",
-      "\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -1984,22 +1259,22 @@
        " 'stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_flatfieldstep.fits']"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Look for our assignwcsstep.fits files produced by the assign_wcs step\n",
-    "sstring=spec2_dir+'det*flatfieldstep.fits'\n",
-    "flatfiles=sorted(glob.glob(sstring))\n",
+    "sstring = spec2_dir + 'det*flatfieldstep.fits'\n",
+    "flatfiles = sorted(glob.glob(sstring))\n",
     "# And print them out so that we can see them\n",
     "flatfiles"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 32,
    "id": "6bba18e1",
    "metadata": {
     "scrolled": true
@@ -2009,80 +1284,42 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2021-05-27 17:23:31,906 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:32,694 - stpipe.SourceTypeStep - INFO - SourceTypeStep instance created.\n",
-      "2021-05-27 17:23:32,813 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep running with args ('stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_flatfieldstep.fits',).\n",
-      "2021-05-27 17:23:32,814 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}\n",
-      "2021-05-27 17:23:33,195 - stpipe.SourceTypeStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:33,961 - stpipe.SourceTypeStep - INFO - Input EXP_TYPE is MIR_MRS\n",
-      "2021-05-27 17:23:33,963 - stpipe.SourceTypeStep - INFO - Input SRCTYAPT = None\n",
-      "2021-05-27 17:23:33,963 - stpipe.SourceTypeStep - WARNING - SRCTYAPT keyword not found in input; using SRCTYPE instead\n",
-      "2021-05-27 17:23:33,965 - stpipe.SourceTypeStep - INFO - Input source type is unknown; setting default SRCTYPE = EXTENDED\n",
-      "2021-05-27 17:23:35,106 - stpipe.SourceTypeStep - INFO - Saved model in stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_sourcetypestep.fits\n",
-      "2021-05-27 17:23:35,107 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep done\n",
-      "2021-05-27 17:23:35,497 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:36,274 - stpipe.SourceTypeStep - INFO - SourceTypeStep instance created.\n",
-      "2021-05-27 17:23:36,447 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep running with args ('stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_flatfieldstep.fits',).\n",
-      "2021-05-27 17:23:36,449 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}\n",
-      "2021-05-27 17:23:36,833 - stpipe.SourceTypeStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:37,611 - stpipe.SourceTypeStep - INFO - Input EXP_TYPE is MIR_MRS\n",
-      "2021-05-27 17:23:37,612 - stpipe.SourceTypeStep - INFO - Input SRCTYAPT = None\n",
-      "2021-05-27 17:23:37,613 - stpipe.SourceTypeStep - WARNING - SRCTYAPT keyword not found in input; using SRCTYPE instead\n",
-      "2021-05-27 17:23:37,614 - stpipe.SourceTypeStep - INFO - Input source type is unknown; setting default SRCTYPE = EXTENDED\n",
-      "2021-05-27 17:23:38,768 - stpipe.SourceTypeStep - INFO - Saved model in stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_sourcetypestep.fits\n",
-      "2021-05-27 17:23:38,769 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep done\n",
-      "2021-05-27 17:23:39,155 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:39,983 - stpipe.SourceTypeStep - INFO - SourceTypeStep instance created.\n",
-      "2021-05-27 17:23:40,162 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep running with args ('stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_flatfieldstep.fits',).\n",
-      "2021-05-27 17:23:40,164 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}\n",
-      "2021-05-27 17:23:40,542 - stpipe.SourceTypeStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:41,324 - stpipe.SourceTypeStep - INFO - Input EXP_TYPE is MIR_MRS\n",
-      "2021-05-27 17:23:41,325 - stpipe.SourceTypeStep - INFO - Input SRCTYAPT = None\n",
-      "2021-05-27 17:23:41,326 - stpipe.SourceTypeStep - WARNING - SRCTYAPT keyword not found in input; using SRCTYPE instead\n",
-      "2021-05-27 17:23:41,327 - stpipe.SourceTypeStep - INFO - Input source type is unknown; setting default SRCTYPE = EXTENDED\n",
-      "2021-05-27 17:23:42,497 - stpipe.SourceTypeStep - INFO - Saved model in stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_sourcetypestep.fits\n",
-      "2021-05-27 17:23:42,499 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep done\n",
-      "2021-05-27 17:23:42,896 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:23:43,667 - stpipe.SourceTypeStep - INFO - SourceTypeStep instance created.\n",
-      "2021-05-27 17:23:43,844 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep running with args ('stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_flatfieldstep.fits',).\n",
-      "2021-05-27 17:23:43,845 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}\n",
-      "2021-05-27 17:23:44,236 - stpipe.SourceTypeStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:45,014 - stpipe.SourceTypeStep - INFO - Input EXP_TYPE is MIR_MRS\n",
-      "2021-05-27 17:23:45,015 - stpipe.SourceTypeStep - INFO - Input SRCTYAPT = None\n",
-      "2021-05-27 17:23:45,016 - stpipe.SourceTypeStep - WARNING - SRCTYAPT keyword not found in input; using SRCTYPE instead\n",
-      "2021-05-27 17:23:45,017 - stpipe.SourceTypeStep - INFO - Input source type is unknown; setting default SRCTYPE = EXTENDED\n",
-      "2021-05-27 17:23:46,137 - stpipe.SourceTypeStep - INFO - Saved model in stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_sourcetypestep.fits\n",
-      "2021-05-27 17:23:46,138 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep done\n"
+      "2021-06-18 13:05:24,794 - stpipe.SourceTypeStep - INFO - SourceTypeStep instance created.\n",
+      "2021-06-18 13:05:24,916 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep running with args ('stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_flatfieldstep.fits',).\n",
+      "2021-06-18 13:05:24,917 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}\n",
+      "2021-06-18 13:05:26,238 - stpipe.SourceTypeStep - INFO - Input EXP_TYPE is MIR_MRS\n",
+      "2021-06-18 13:05:26,239 - stpipe.SourceTypeStep - INFO - Input SRCTYAPT = None\n",
+      "2021-06-18 13:05:26,239 - stpipe.SourceTypeStep - WARNING - SRCTYAPT keyword not found in input; using SRCTYPE instead\n",
+      "2021-06-18 13:05:26,240 - stpipe.SourceTypeStep - INFO - Input source type is unknown; setting default SRCTYPE = EXTENDED\n",
+      "2021-06-18 13:05:27,542 - stpipe.SourceTypeStep - INFO - Saved model in stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_sourcetypestep.fits\n",
+      "2021-06-18 13:05:27,543 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep done\n",
+      "2021-06-18 13:05:28,982 - stpipe.SourceTypeStep - INFO - SourceTypeStep instance created.\n",
+      "2021-06-18 13:05:29,257 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep running with args ('stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_flatfieldstep.fits',).\n",
+      "2021-06-18 13:05:29,258 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}\n",
+      "2021-06-18 13:05:30,642 - stpipe.SourceTypeStep - INFO - Input EXP_TYPE is MIR_MRS\n",
+      "2021-06-18 13:05:30,642 - stpipe.SourceTypeStep - INFO - Input SRCTYAPT = None\n",
+      "2021-06-18 13:05:30,643 - stpipe.SourceTypeStep - WARNING - SRCTYAPT keyword not found in input; using SRCTYPE instead\n",
+      "2021-06-18 13:05:30,643 - stpipe.SourceTypeStep - INFO - Input source type is unknown; setting default SRCTYPE = EXTENDED\n",
+      "2021-06-18 13:05:32,131 - stpipe.SourceTypeStep - INFO - Saved model in stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_sourcetypestep.fits\n",
+      "2021-06-18 13:05:32,132 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep done\n",
+      "2021-06-18 13:05:33,434 - stpipe.SourceTypeStep - INFO - SourceTypeStep instance created.\n",
+      "2021-06-18 13:05:33,650 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep running with args ('stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_flatfieldstep.fits',).\n",
+      "2021-06-18 13:05:33,652 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}\n",
+      "2021-06-18 13:05:34,939 - stpipe.SourceTypeStep - INFO - Input EXP_TYPE is MIR_MRS\n",
+      "2021-06-18 13:05:34,940 - stpipe.SourceTypeStep - INFO - Input SRCTYAPT = None\n",
+      "2021-06-18 13:05:34,941 - stpipe.SourceTypeStep - WARNING - SRCTYAPT keyword not found in input; using SRCTYPE instead\n",
+      "2021-06-18 13:05:34,941 - stpipe.SourceTypeStep - INFO - Input source type is unknown; setting default SRCTYPE = EXTENDED\n",
+      "2021-06-18 13:05:36,172 - stpipe.SourceTypeStep - INFO - Saved model in stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_sourcetypestep.fits\n",
+      "2021-06-18 13:05:36,173 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep done\n",
+      "2021-06-18 13:05:37,457 - stpipe.SourceTypeStep - INFO - SourceTypeStep instance created.\n",
+      "2021-06-18 13:05:37,640 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep running with args ('stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_flatfieldstep.fits',).\n",
+      "2021-06-18 13:05:37,642 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}\n",
+      "2021-06-18 13:05:38,892 - stpipe.SourceTypeStep - INFO - Input EXP_TYPE is MIR_MRS\n",
+      "2021-06-18 13:05:38,892 - stpipe.SourceTypeStep - INFO - Input SRCTYAPT = None\n",
+      "2021-06-18 13:05:38,893 - stpipe.SourceTypeStep - WARNING - SRCTYAPT keyword not found in input; using SRCTYPE instead\n",
+      "2021-06-18 13:05:38,893 - stpipe.SourceTypeStep - INFO - Input source type is unknown; setting default SRCTYPE = EXTENDED\n",
+      "2021-06-18 13:05:40,109 - stpipe.SourceTypeStep - INFO - Saved model in stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_sourcetypestep.fits\n",
+      "2021-06-18 13:05:40,109 - stpipe.SourceTypeStep - INFO - Step SourceTypeStep done\n"
      ]
     }
    ],
@@ -2094,19 +1331,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 33,
    "id": "56c640c9",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:23:46,143 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n",
-      "\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -2116,22 +1344,22 @@
        " 'stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_sourcetypestep.fits']"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Look for our sourcetypestep.fits files produced by the source type step\n",
-    "sstring=spec2_dir+'det*sourcetypestep.fits'\n",
-    "srcfiles=sorted(glob.glob(sstring))\n",
+    "sstring = spec2_dir + 'det*sourcetypestep.fits'\n",
+    "srcfiles = sorted(glob.glob(sstring))\n",
     "# And print them out so that we can see them\n",
     "srcfiles"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 34,
    "id": "c8741222",
    "metadata": {},
    "outputs": [
@@ -2145,7 +1373,7 @@
    ],
    "source": [
     "# We can look at the keyword in the first exposure\n",
-    "hdu=fits.open(srcfiles[0])\n",
+    "hdu = fits.open(srcfiles[0])\n",
     "print('SRCTYPE = ',hdu['SCI'].header['SRCTYPE'])\n",
     "hdu.close()"
    ]
@@ -2160,14 +1388,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 35,
    "id": "8e39ee3b",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Loop over files kludging the source type to POINT\n",
     "for file in srcfiles:\n",
-    "    hdu=fits.open(file)\n",
+    "    hdu = fits.open(file)\n",
     "    hdu['SCI'].header['SRCTYPE']='POINT'\n",
     "    hdu.writeto(file,overwrite=True)\n",
     "    hdu.close()"
@@ -2175,7 +1403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 36,
    "id": "f5bf7cb8",
    "metadata": {},
    "outputs": [
@@ -2189,19 +1417,11 @@
    ],
    "source": [
     "# Now the source type is set to POINT in the headers!\n",
-    "hdu=fits.open(srcfiles[0])\n",
+    "hdu = fits.open(srcfiles[0])\n",
     "print('SRCTYPE = ',hdu['SCI'].header['SRCTYPE'])\n",
     "hdu.close()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "16a2494f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "2b76c8a3",
@@ -2220,7 +1440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 37,
    "id": "6b3aea51",
    "metadata": {
     "scrolled": true
@@ -2230,80 +1450,42 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2021-05-27 17:23:47,536 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:48,399 - stpipe.StraylightStep - INFO - StraylightStep instance created.\n",
-      "2021-05-27 17:23:48,581 - stpipe.StraylightStep - INFO - Step StraylightStep running with args ('stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_sourcetypestep.fits',).\n",
-      "2021-05-27 17:23:48,582 - stpipe.StraylightStep - INFO - Step StraylightStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'method': 'ModShepard', 'roi': 50, 'power': 1.0}\n",
-      "2021-05-27 17:23:48,961 - stpipe.StraylightStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:49,825 - stpipe.StraylightStep - INFO - Using regions reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_regions_0028.asdf\n",
-      "2021-05-27 17:23:49,855 - stpipe.StraylightStep - INFO -  Using 20% throughput threshhold.\n",
-      "2021-05-27 17:23:49,856 - stpipe.StraylightStep - INFO -  Modified Shepard weighting power  1.00\n",
-      "2021-05-27 17:23:49,857 - stpipe.StraylightStep - INFO -  Region of influence radius (pixels)  50.00\n",
-      "2021-05-27 17:23:58,264 - stpipe.StraylightStep - INFO - Saved model in stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_straylightstep.fits\n",
-      "2021-05-27 17:23:58,265 - stpipe.StraylightStep - INFO - Step StraylightStep done\n",
-      "2021-05-27 17:23:58,672 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:23:59,709 - stpipe.StraylightStep - INFO - StraylightStep instance created.\n",
-      "2021-05-27 17:23:59,840 - stpipe.StraylightStep - INFO - Step StraylightStep running with args ('stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_sourcetypestep.fits',).\n",
-      "2021-05-27 17:23:59,842 - stpipe.StraylightStep - INFO - Step StraylightStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'method': 'ModShepard', 'roi': 50, 'power': 1.0}\n",
-      "2021-05-27 17:24:00,260 - stpipe.StraylightStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:24:01,215 - stpipe.StraylightStep - INFO - Using regions reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_regions_0028.asdf\n",
-      "2021-05-27 17:24:01,243 - stpipe.StraylightStep - INFO -  Using 20% throughput threshhold.\n",
-      "2021-05-27 17:24:01,244 - stpipe.StraylightStep - INFO -  Modified Shepard weighting power  1.00\n",
-      "2021-05-27 17:24:01,245 - stpipe.StraylightStep - INFO -  Region of influence radius (pixels)  50.00\n",
-      "2021-05-27 17:24:10,197 - stpipe.StraylightStep - INFO - Saved model in stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_straylightstep.fits\n",
-      "2021-05-27 17:24:10,199 - stpipe.StraylightStep - INFO - Step StraylightStep done\n",
-      "2021-05-27 17:24:10,658 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:24:11,788 - stpipe.StraylightStep - INFO - StraylightStep instance created.\n",
-      "2021-05-27 17:24:11,922 - stpipe.StraylightStep - INFO - Step StraylightStep running with args ('stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_sourcetypestep.fits',).\n",
-      "2021-05-27 17:24:11,924 - stpipe.StraylightStep - INFO - Step StraylightStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'method': 'ModShepard', 'roi': 50, 'power': 1.0}\n",
-      "2021-05-27 17:24:12,377 - stpipe.StraylightStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:24:13,449 - stpipe.StraylightStep - INFO - Using regions reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_regions_0028.asdf\n",
-      "2021-05-27 17:24:13,480 - stpipe.StraylightStep - INFO -  Using 20% throughput threshhold.\n",
-      "2021-05-27 17:24:13,481 - stpipe.StraylightStep - INFO -  Modified Shepard weighting power  1.00\n",
-      "2021-05-27 17:24:13,482 - stpipe.StraylightStep - INFO -  Region of influence radius (pixels)  50.00\n",
-      "2021-05-27 17:24:22,182 - stpipe.StraylightStep - INFO - Saved model in stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_straylightstep.fits\n",
-      "2021-05-27 17:24:22,183 - stpipe.StraylightStep - INFO - Step StraylightStep done\n",
-      "2021-05-27 17:24:22,659 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:24:23,965 - stpipe.StraylightStep - INFO - StraylightStep instance created.\n",
-      "2021-05-27 17:24:24,106 - stpipe.StraylightStep - INFO - Step StraylightStep running with args ('stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_sourcetypestep.fits',).\n",
-      "2021-05-27 17:24:24,108 - stpipe.StraylightStep - INFO - Step StraylightStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'method': 'ModShepard', 'roi': 50, 'power': 1.0}\n",
-      "2021-05-27 17:24:24,535 - stpipe.StraylightStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:24:25,489 - stpipe.StraylightStep - INFO - Using regions reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_regions_0028.asdf\n",
-      "2021-05-27 17:24:25,516 - stpipe.StraylightStep - INFO -  Using 20% throughput threshhold.\n",
-      "2021-05-27 17:24:25,518 - stpipe.StraylightStep - INFO -  Modified Shepard weighting power  1.00\n",
-      "2021-05-27 17:24:25,519 - stpipe.StraylightStep - INFO -  Region of influence radius (pixels)  50.00\n",
-      "2021-05-27 17:24:34,308 - stpipe.StraylightStep - INFO - Saved model in stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_straylightstep.fits\n",
-      "2021-05-27 17:24:34,311 - stpipe.StraylightStep - INFO - Step StraylightStep done\n"
+      "2021-06-18 13:05:43,152 - stpipe.StraylightStep - INFO - StraylightStep instance created.\n",
+      "2021-06-18 13:05:43,340 - stpipe.StraylightStep - INFO - Step StraylightStep running with args ('stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_sourcetypestep.fits',).\n",
+      "2021-06-18 13:05:43,342 - stpipe.StraylightStep - INFO - Step StraylightStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'method': 'ModShepard', 'roi': 50, 'power': 1.0}\n",
+      "2021-06-18 13:05:44,688 - stpipe.StraylightStep - INFO - Using regions reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_regions_0028.asdf\n",
+      "2021-06-18 13:05:44,713 - stpipe.StraylightStep - INFO -  Using 20% throughput threshhold.\n",
+      "2021-06-18 13:05:44,714 - stpipe.StraylightStep - INFO -  Modified Shepard weighting power  1.00\n",
+      "2021-06-18 13:05:44,714 - stpipe.StraylightStep - INFO -  Region of influence radius (pixels)  50.00\n",
+      "2021-06-18 13:05:53,263 - stpipe.StraylightStep - INFO - Saved model in stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_straylightstep.fits\n",
+      "2021-06-18 13:05:53,264 - stpipe.StraylightStep - INFO - Step StraylightStep done\n",
+      "2021-06-18 13:05:54,681 - stpipe.StraylightStep - INFO - StraylightStep instance created.\n",
+      "2021-06-18 13:05:54,797 - stpipe.StraylightStep - INFO - Step StraylightStep running with args ('stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_sourcetypestep.fits',).\n",
+      "2021-06-18 13:05:54,799 - stpipe.StraylightStep - INFO - Step StraylightStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'method': 'ModShepard', 'roi': 50, 'power': 1.0}\n",
+      "2021-06-18 13:05:56,092 - stpipe.StraylightStep - INFO - Using regions reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_regions_0028.asdf\n",
+      "2021-06-18 13:05:56,117 - stpipe.StraylightStep - INFO -  Using 20% throughput threshhold.\n",
+      "2021-06-18 13:05:56,119 - stpipe.StraylightStep - INFO -  Modified Shepard weighting power  1.00\n",
+      "2021-06-18 13:05:56,119 - stpipe.StraylightStep - INFO -  Region of influence radius (pixels)  50.00\n",
+      "2021-06-18 13:06:04,633 - stpipe.StraylightStep - INFO - Saved model in stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_straylightstep.fits\n",
+      "2021-06-18 13:06:04,634 - stpipe.StraylightStep - INFO - Step StraylightStep done\n",
+      "2021-06-18 13:06:06,090 - stpipe.StraylightStep - INFO - StraylightStep instance created.\n",
+      "2021-06-18 13:06:06,217 - stpipe.StraylightStep - INFO - Step StraylightStep running with args ('stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_sourcetypestep.fits',).\n",
+      "2021-06-18 13:06:06,219 - stpipe.StraylightStep - INFO - Step StraylightStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'method': 'ModShepard', 'roi': 50, 'power': 1.0}\n",
+      "2021-06-18 13:06:07,516 - stpipe.StraylightStep - INFO - Using regions reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_regions_0028.asdf\n",
+      "2021-06-18 13:06:07,541 - stpipe.StraylightStep - INFO -  Using 20% throughput threshhold.\n",
+      "2021-06-18 13:06:07,542 - stpipe.StraylightStep - INFO -  Modified Shepard weighting power  1.00\n",
+      "2021-06-18 13:06:07,542 - stpipe.StraylightStep - INFO -  Region of influence radius (pixels)  50.00\n",
+      "2021-06-18 13:06:16,041 - stpipe.StraylightStep - INFO - Saved model in stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_straylightstep.fits\n",
+      "2021-06-18 13:06:16,042 - stpipe.StraylightStep - INFO - Step StraylightStep done\n",
+      "2021-06-18 13:06:17,501 - stpipe.StraylightStep - INFO - StraylightStep instance created.\n",
+      "2021-06-18 13:06:17,614 - stpipe.StraylightStep - INFO - Step StraylightStep running with args ('stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_sourcetypestep.fits',).\n",
+      "2021-06-18 13:06:17,615 - stpipe.StraylightStep - INFO - Step StraylightStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'method': 'ModShepard', 'roi': 50, 'power': 1.0}\n",
+      "2021-06-18 13:06:18,916 - stpipe.StraylightStep - INFO - Using regions reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_regions_0028.asdf\n",
+      "2021-06-18 13:06:18,942 - stpipe.StraylightStep - INFO -  Using 20% throughput threshhold.\n",
+      "2021-06-18 13:06:18,943 - stpipe.StraylightStep - INFO -  Modified Shepard weighting power  1.00\n",
+      "2021-06-18 13:06:18,943 - stpipe.StraylightStep - INFO -  Region of influence radius (pixels)  50.00\n",
+      "2021-06-18 13:06:27,525 - stpipe.StraylightStep - INFO - Saved model in stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_straylightstep.fits\n",
+      "2021-06-18 13:06:27,526 - stpipe.StraylightStep - INFO - Step StraylightStep done\n"
      ]
     }
    ],
@@ -2315,19 +1497,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 38,
    "id": "f0ad753e",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:24:34,317 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n",
-      "\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -2337,22 +1510,22 @@
        " 'stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_straylightstep.fits']"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Look for our straylightstep.fits files produced by the straylight step\n",
-    "sstring=spec2_dir+'det*straylightstep.fits'\n",
-    "strayfiles=sorted(glob.glob(sstring))\n",
+    "sstring = spec2_dir + 'det*straylightstep.fits'\n",
+    "strayfiles = sorted(glob.glob(sstring))\n",
     "# And print them out so that we can see them\n",
     "strayfiles"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 39,
    "id": "39adbeed",
    "metadata": {},
    "outputs": [
@@ -2362,13 +1535,13 @@
        "Text(0.5, 0, 'X pixel')"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 700x700 with 2 Axes>"
       ]
@@ -2381,10 +1554,10 @@
    ],
    "source": [
     "# Let's show what the SCI extension of the first file before/after straylight subtraction looks like\n",
-    "hdu1=fits.open(srcfiles[0])\n",
-    "image1=hdu1['SCI'].data\n",
-    "hdu2=fits.open(strayfiles[0])\n",
-    "image2=hdu2['SCI'].data\n",
+    "hdu1 = fits.open(srcfiles[0])\n",
+    "image1 = hdu1['SCI'].data\n",
+    "hdu2 = fits.open(strayfiles[0])\n",
+    "image2 = hdu2['SCI'].data\n",
     "\n",
     "# Use a classic ZScale normalization\n",
     "norm = ImageNormalize(image1, interval=ZScaleInterval(),stretch=LinearStretch())\n",
@@ -2413,23 +1586,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 40,
    "id": "172385ff",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fcd12092190>"
+       "<matplotlib.legend.Legend at 0x7ff12a19fd90>"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2461,34 +1634,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 41,
    "id": "f755d719",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:24:35,633 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Close out our files\n",
     "hdu1.close()\n",
     "hdu2.close()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e7c4e24b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "12f7779a",
@@ -2505,7 +1660,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 42,
    "id": "b5bc8393",
    "metadata": {
     "scrolled": true
@@ -2515,84 +1670,46 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2021-05-27 17:24:36,310 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:24:37,107 - stpipe.FringeStep - INFO - FringeStep instance created.\n",
-      "2021-05-27 17:24:37,247 - stpipe.FringeStep - INFO - Step FringeStep running with args ('stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_straylightstep.fits',).\n",
-      "2021-05-27 17:24:37,249 - stpipe.FringeStep - INFO - Step FringeStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}\n",
-      "2021-05-27 17:24:37,658 - stpipe.FringeStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:24:38,482 - stpipe.FringeStep - INFO - Using FRINGE reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_fringe_0049.fits\n",
-      "2021-05-27 17:24:38,537 - stpipe.FringeStep - WARNING - Keyword CDP_PARTIAL_DATA does not correspond to an existing DQ mnemonic, so will be ignored\n",
-      "2021-05-27 17:24:38,538 - stpipe.FringeStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
-      "2021-05-27 17:24:38,539 - stpipe.FringeStep - WARNING - Keyword CDP_UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n",
-      "2021-05-27 17:24:39,807 - stpipe.FringeStep - INFO - DQ values in the reference file NOT used to update the output DQ.\n",
-      "2021-05-27 17:24:40,983 - stpipe.FringeStep - INFO - Saved model in stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_fringestep.fits\n",
-      "2021-05-27 17:24:40,984 - stpipe.FringeStep - INFO - Step FringeStep done\n",
-      "2021-05-27 17:24:41,413 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:24:42,311 - stpipe.FringeStep - INFO - FringeStep instance created.\n",
-      "2021-05-27 17:24:42,470 - stpipe.FringeStep - INFO - Step FringeStep running with args ('stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_straylightstep.fits',).\n",
-      "2021-05-27 17:24:42,473 - stpipe.FringeStep - INFO - Step FringeStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}\n",
-      "2021-05-27 17:24:42,896 - stpipe.FringeStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:24:43,804 - stpipe.FringeStep - INFO - Using FRINGE reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_fringe_0049.fits\n",
-      "2021-05-27 17:24:43,852 - stpipe.FringeStep - WARNING - Keyword CDP_PARTIAL_DATA does not correspond to an existing DQ mnemonic, so will be ignored\n",
-      "2021-05-27 17:24:43,854 - stpipe.FringeStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
-      "2021-05-27 17:24:43,854 - stpipe.FringeStep - WARNING - Keyword CDP_UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n",
-      "2021-05-27 17:24:45,160 - stpipe.FringeStep - INFO - DQ values in the reference file NOT used to update the output DQ.\n",
-      "2021-05-27 17:24:46,367 - stpipe.FringeStep - INFO - Saved model in stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_fringestep.fits\n",
-      "2021-05-27 17:24:46,368 - stpipe.FringeStep - INFO - Step FringeStep done\n",
-      "2021-05-27 17:24:46,760 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:24:47,603 - stpipe.FringeStep - INFO - FringeStep instance created.\n",
-      "2021-05-27 17:24:47,732 - stpipe.FringeStep - INFO - Step FringeStep running with args ('stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_straylightstep.fits',).\n",
-      "2021-05-27 17:24:47,734 - stpipe.FringeStep - INFO - Step FringeStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}\n",
-      "2021-05-27 17:24:48,140 - stpipe.FringeStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:24:49,001 - stpipe.FringeStep - INFO - Using FRINGE reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_fringe_0049.fits\n",
-      "2021-05-27 17:24:49,047 - stpipe.FringeStep - WARNING - Keyword CDP_PARTIAL_DATA does not correspond to an existing DQ mnemonic, so will be ignored\n",
-      "2021-05-27 17:24:49,048 - stpipe.FringeStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
-      "2021-05-27 17:24:49,049 - stpipe.FringeStep - WARNING - Keyword CDP_UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:24:50,355 - stpipe.FringeStep - INFO - DQ values in the reference file NOT used to update the output DQ.\n",
-      "2021-05-27 17:24:51,520 - stpipe.FringeStep - INFO - Saved model in stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_fringestep.fits\n",
-      "2021-05-27 17:24:51,522 - stpipe.FringeStep - INFO - Step FringeStep done\n",
-      "2021-05-27 17:24:51,920 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:24:52,798 - stpipe.FringeStep - INFO - FringeStep instance created.\n",
-      "2021-05-27 17:24:52,939 - stpipe.FringeStep - INFO - Step FringeStep running with args ('stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_straylightstep.fits',).\n",
-      "2021-05-27 17:24:52,941 - stpipe.FringeStep - INFO - Step FringeStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}\n",
-      "2021-05-27 17:24:53,352 - stpipe.FringeStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:24:54,226 - stpipe.FringeStep - INFO - Using FRINGE reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_fringe_0049.fits\n",
-      "2021-05-27 17:24:54,273 - stpipe.FringeStep - WARNING - Keyword CDP_PARTIAL_DATA does not correspond to an existing DQ mnemonic, so will be ignored\n",
-      "2021-05-27 17:24:54,274 - stpipe.FringeStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
-      "2021-05-27 17:24:54,275 - stpipe.FringeStep - WARNING - Keyword CDP_UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n",
-      "2021-05-27 17:24:55,597 - stpipe.FringeStep - INFO - DQ values in the reference file NOT used to update the output DQ.\n",
-      "2021-05-27 17:24:56,790 - stpipe.FringeStep - INFO - Saved model in stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_fringestep.fits\n",
-      "2021-05-27 17:24:56,791 - stpipe.FringeStep - INFO - Step FringeStep done\n"
+      "2021-06-18 13:06:30,061 - stpipe.FringeStep - INFO - FringeStep instance created.\n",
+      "2021-06-18 13:06:30,184 - stpipe.FringeStep - INFO - Step FringeStep running with args ('stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_straylightstep.fits',).\n",
+      "2021-06-18 13:06:30,186 - stpipe.FringeStep - INFO - Step FringeStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}\n",
+      "2021-06-18 13:06:31,435 - stpipe.FringeStep - INFO - Using FRINGE reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_fringe_0049.fits\n",
+      "2021-06-18 13:06:31,492 - stpipe.FringeStep - WARNING - Keyword CDP_PARTIAL_DATA does not correspond to an existing DQ mnemonic, so will be ignored\n",
+      "2021-06-18 13:06:31,493 - stpipe.FringeStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
+      "2021-06-18 13:06:31,493 - stpipe.FringeStep - WARNING - Keyword CDP_UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n",
+      "2021-06-18 13:06:32,803 - stpipe.FringeStep - INFO - DQ values in the reference file NOT used to update the output DQ.\n",
+      "2021-06-18 13:06:34,028 - stpipe.FringeStep - INFO - Saved model in stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_fringestep.fits\n",
+      "2021-06-18 13:06:34,029 - stpipe.FringeStep - INFO - Step FringeStep done\n",
+      "2021-06-18 13:06:35,316 - stpipe.FringeStep - INFO - FringeStep instance created.\n",
+      "2021-06-18 13:06:35,442 - stpipe.FringeStep - INFO - Step FringeStep running with args ('stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_straylightstep.fits',).\n",
+      "2021-06-18 13:06:35,444 - stpipe.FringeStep - INFO - Step FringeStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}\n",
+      "2021-06-18 13:06:36,776 - stpipe.FringeStep - INFO - Using FRINGE reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_fringe_0049.fits\n",
+      "2021-06-18 13:06:36,823 - stpipe.FringeStep - WARNING - Keyword CDP_PARTIAL_DATA does not correspond to an existing DQ mnemonic, so will be ignored\n",
+      "2021-06-18 13:06:36,824 - stpipe.FringeStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
+      "2021-06-18 13:06:36,825 - stpipe.FringeStep - WARNING - Keyword CDP_UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n",
+      "2021-06-18 13:06:38,167 - stpipe.FringeStep - INFO - DQ values in the reference file NOT used to update the output DQ.\n",
+      "2021-06-18 13:06:39,423 - stpipe.FringeStep - INFO - Saved model in stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_fringestep.fits\n",
+      "2021-06-18 13:06:39,424 - stpipe.FringeStep - INFO - Step FringeStep done\n",
+      "2021-06-18 13:06:40,707 - stpipe.FringeStep - INFO - FringeStep instance created.\n",
+      "2021-06-18 13:06:40,832 - stpipe.FringeStep - INFO - Step FringeStep running with args ('stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_straylightstep.fits',).\n",
+      "2021-06-18 13:06:40,833 - stpipe.FringeStep - INFO - Step FringeStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}\n",
+      "2021-06-18 13:06:42,094 - stpipe.FringeStep - INFO - Using FRINGE reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_fringe_0049.fits\n",
+      "2021-06-18 13:06:42,141 - stpipe.FringeStep - WARNING - Keyword CDP_PARTIAL_DATA does not correspond to an existing DQ mnemonic, so will be ignored\n",
+      "2021-06-18 13:06:42,142 - stpipe.FringeStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
+      "2021-06-18 13:06:42,142 - stpipe.FringeStep - WARNING - Keyword CDP_UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n",
+      "2021-06-18 13:06:43,419 - stpipe.FringeStep - INFO - DQ values in the reference file NOT used to update the output DQ.\n",
+      "2021-06-18 13:06:44,611 - stpipe.FringeStep - INFO - Saved model in stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_fringestep.fits\n",
+      "2021-06-18 13:06:44,612 - stpipe.FringeStep - INFO - Step FringeStep done\n",
+      "2021-06-18 13:06:45,870 - stpipe.FringeStep - INFO - FringeStep instance created.\n",
+      "2021-06-18 13:06:45,996 - stpipe.FringeStep - INFO - Step FringeStep running with args ('stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_straylightstep.fits',).\n",
+      "2021-06-18 13:06:45,998 - stpipe.FringeStep - INFO - Step FringeStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': ''}\n",
+      "2021-06-18 13:06:47,242 - stpipe.FringeStep - INFO - Using FRINGE reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_fringe_0049.fits\n",
+      "2021-06-18 13:06:47,290 - stpipe.FringeStep - WARNING - Keyword CDP_PARTIAL_DATA does not correspond to an existing DQ mnemonic, so will be ignored\n",
+      "2021-06-18 13:06:47,291 - stpipe.FringeStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
+      "2021-06-18 13:06:47,291 - stpipe.FringeStep - WARNING - Keyword CDP_UNRELIABLE_ERROR does not correspond to an existing DQ mnemonic, so will be ignored\n",
+      "2021-06-18 13:06:48,574 - stpipe.FringeStep - INFO - DQ values in the reference file NOT used to update the output DQ.\n",
+      "2021-06-18 13:06:49,732 - stpipe.FringeStep - INFO - Saved model in stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_fringestep.fits\n",
+      "2021-06-18 13:06:49,733 - stpipe.FringeStep - INFO - Step FringeStep done\n"
      ]
     }
    ],
@@ -2604,19 +1721,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 43,
    "id": "b6649a6d",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:24:56,794 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n",
-      "\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -2626,22 +1734,22 @@
        " 'stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_fringestep.fits']"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Look for our fringestep.fits files produced by the fringe flat step\n",
-    "sstring=spec2_dir+'det*fringestep.fits'\n",
-    "fringefiles=sorted(glob.glob(sstring))\n",
+    "sstring = spec2_dir + 'det*fringestep.fits'\n",
+    "fringefiles = sorted(glob.glob(sstring))\n",
     "# And print them out so that we can see them\n",
     "fringefiles"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 44,
    "id": "42f22fc7",
    "metadata": {},
    "outputs": [
@@ -2651,13 +1759,13 @@
        "(0.0, 250.0)"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 700x700 with 2 Axes>"
       ]
@@ -2671,10 +1779,10 @@
    "source": [
     "# Let's show what the SCI extension of the first file before/after application of the fringe flat looks like\n",
     "# We'll zoom in on a region of the detector to make the results more clear\n",
-    "hdu1=fits.open(strayfiles[0])\n",
-    "image1=hdu1['SCI'].data\n",
-    "hdu2=fits.open(fringefiles[0])\n",
-    "image2=hdu2['SCI'].data\n",
+    "hdu1 = fits.open(strayfiles[0])\n",
+    "image1 = hdu1['SCI'].data\n",
+    "hdu2 = fits.open(fringefiles[0])\n",
+    "image2 = hdu2['SCI'].data\n",
     "\n",
     "# Use a classic ZScale normalization\n",
     "norm = ImageNormalize(image1, interval=ZScaleInterval(),stretch=LinearStretch())\n",
@@ -2707,7 +1815,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 45,
    "id": "d3dcf2ff",
    "metadata": {},
    "outputs": [],
@@ -2717,14 +1825,6 @@
     "hdu2.close()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1faaa531",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "67ece860",
@@ -2741,14 +1841,6 @@
     "</div>"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e7c68972",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "2b040313",
@@ -2765,7 +1857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 46,
    "id": "d6b57571",
    "metadata": {
     "scrolled": true
@@ -2775,92 +1867,54 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2021-05-27 17:24:57,490 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:24:58,354 - stpipe.PhotomStep - INFO - PhotomStep instance created.\n",
-      "2021-05-27 17:24:58,512 - stpipe.PhotomStep - INFO - Step PhotomStep running with args ('stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_fringestep.fits',).\n",
-      "2021-05-27 17:24:58,515 - stpipe.PhotomStep - INFO - Step PhotomStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'inverse': False, 'source_type': None}\n",
-      "2021-05-27 17:24:58,917 - stpipe.PhotomStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:24:59,773 - stpipe.PhotomStep - INFO - Using photom reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_photom_0060.fits\n",
-      "2021-05-27 17:24:59,774 - stpipe.PhotomStep - INFO - Using area reference file: N/A\n",
-      "2021-05-27 17:25:01,026 - stpipe.PhotomStep - INFO - Using instrument: MIRI\n",
-      "2021-05-27 17:25:01,027 - stpipe.PhotomStep - INFO -  detector: MIRIFUSHORT\n",
-      "2021-05-27 17:25:01,028 - stpipe.PhotomStep - INFO -  exp_type: MIR_MRS\n",
-      "2021-05-27 17:25:01,029 - stpipe.PhotomStep - INFO -  band: LONG\n",
-      "2021-05-27 17:25:01,123 - stpipe.PhotomStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
-      "2021-05-27 17:25:02,171 - stpipe.PhotomStep - INFO - Saved model in stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_photomstep.fits\n",
-      "2021-05-27 17:25:02,172 - stpipe.PhotomStep - INFO - Step PhotomStep done\n",
-      "2021-05-27 17:25:02,543 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:25:03,471 - stpipe.PhotomStep - INFO - PhotomStep instance created.\n",
-      "2021-05-27 17:25:03,622 - stpipe.PhotomStep - INFO - Step PhotomStep running with args ('stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_fringestep.fits',).\n",
-      "2021-05-27 17:25:03,625 - stpipe.PhotomStep - INFO - Step PhotomStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'inverse': False, 'source_type': None}\n",
-      "2021-05-27 17:25:04,025 - stpipe.PhotomStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:25:04,888 - stpipe.PhotomStep - INFO - Using photom reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_photom_0060.fits\n",
-      "2021-05-27 17:25:04,889 - stpipe.PhotomStep - INFO - Using area reference file: N/A\n",
-      "2021-05-27 17:25:06,158 - stpipe.PhotomStep - INFO - Using instrument: MIRI\n",
-      "2021-05-27 17:25:06,159 - stpipe.PhotomStep - INFO -  detector: MIRIFUSHORT\n",
-      "2021-05-27 17:25:06,160 - stpipe.PhotomStep - INFO -  exp_type: MIR_MRS\n",
-      "2021-05-27 17:25:06,160 - stpipe.PhotomStep - INFO -  band: LONG\n",
-      "2021-05-27 17:25:06,222 - stpipe.PhotomStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
-      "2021-05-27 17:25:07,365 - stpipe.PhotomStep - INFO - Saved model in stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_photomstep.fits\n",
-      "2021-05-27 17:25:07,367 - stpipe.PhotomStep - INFO - Step PhotomStep done\n",
-      "2021-05-27 17:25:07,745 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:25:08,673 - stpipe.PhotomStep - INFO - PhotomStep instance created.\n",
-      "2021-05-27 17:25:08,827 - stpipe.PhotomStep - INFO - Step PhotomStep running with args ('stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_fringestep.fits',).\n",
-      "2021-05-27 17:25:08,830 - stpipe.PhotomStep - INFO - Step PhotomStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'inverse': False, 'source_type': None}\n",
-      "2021-05-27 17:25:09,223 - stpipe.PhotomStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:25:10,072 - stpipe.PhotomStep - INFO - Using photom reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_photom_0060.fits\n",
-      "2021-05-27 17:25:10,073 - stpipe.PhotomStep - INFO - Using area reference file: N/A\n",
-      "2021-05-27 17:25:11,323 - stpipe.PhotomStep - INFO - Using instrument: MIRI\n",
-      "2021-05-27 17:25:11,324 - stpipe.PhotomStep - INFO -  detector: MIRIFUSHORT\n",
-      "2021-05-27 17:25:11,325 - stpipe.PhotomStep - INFO -  exp_type: MIR_MRS\n",
-      "2021-05-27 17:25:11,326 - stpipe.PhotomStep - INFO -  band: LONG\n",
-      "2021-05-27 17:25:11,388 - stpipe.PhotomStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:25:12,489 - stpipe.PhotomStep - INFO - Saved model in stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_photomstep.fits\n",
-      "2021-05-27 17:25:12,490 - stpipe.PhotomStep - INFO - Step PhotomStep done\n",
-      "2021-05-27 17:25:12,864 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:25:13,800 - stpipe.PhotomStep - INFO - PhotomStep instance created.\n",
-      "2021-05-27 17:25:13,951 - stpipe.PhotomStep - INFO - Step PhotomStep running with args ('stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_fringestep.fits',).\n",
-      "2021-05-27 17:25:13,954 - stpipe.PhotomStep - INFO - Step PhotomStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'inverse': False, 'source_type': None}\n",
-      "2021-05-27 17:25:14,338 - stpipe.PhotomStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:25:15,184 - stpipe.PhotomStep - INFO - Using photom reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_photom_0060.fits\n",
-      "2021-05-27 17:25:15,185 - stpipe.PhotomStep - INFO - Using area reference file: N/A\n",
-      "2021-05-27 17:25:16,404 - stpipe.PhotomStep - INFO - Using instrument: MIRI\n",
-      "2021-05-27 17:25:16,405 - stpipe.PhotomStep - INFO -  detector: MIRIFUSHORT\n",
-      "2021-05-27 17:25:16,406 - stpipe.PhotomStep - INFO -  exp_type: MIR_MRS\n",
-      "2021-05-27 17:25:16,406 - stpipe.PhotomStep - INFO -  band: LONG\n",
-      "2021-05-27 17:25:16,468 - stpipe.PhotomStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
-      "2021-05-27 17:25:17,623 - stpipe.PhotomStep - INFO - Saved model in stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_photomstep.fits\n",
-      "2021-05-27 17:25:17,624 - stpipe.PhotomStep - INFO - Step PhotomStep done\n"
+      "2021-06-18 13:06:51,310 - stpipe.PhotomStep - INFO - PhotomStep instance created.\n",
+      "2021-06-18 13:06:51,450 - stpipe.PhotomStep - INFO - Step PhotomStep running with args ('stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_fringestep.fits',).\n",
+      "2021-06-18 13:06:51,452 - stpipe.PhotomStep - INFO - Step PhotomStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'inverse': False, 'source_type': None}\n",
+      "2021-06-18 13:06:52,720 - stpipe.PhotomStep - INFO - Using photom reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_photom_0060.fits\n",
+      "2021-06-18 13:06:52,721 - stpipe.PhotomStep - INFO - Using area reference file: N/A\n",
+      "2021-06-18 13:06:54,009 - stpipe.PhotomStep - INFO - Using instrument: MIRI\n",
+      "2021-06-18 13:06:54,010 - stpipe.PhotomStep - INFO -  detector: MIRIFUSHORT\n",
+      "2021-06-18 13:06:54,011 - stpipe.PhotomStep - INFO -  exp_type: MIR_MRS\n",
+      "2021-06-18 13:06:54,011 - stpipe.PhotomStep - INFO -  band: LONG\n",
+      "2021-06-18 13:06:54,101 - stpipe.PhotomStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
+      "2021-06-18 13:06:55,250 - stpipe.PhotomStep - INFO - Saved model in stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_photomstep.fits\n",
+      "2021-06-18 13:06:55,251 - stpipe.PhotomStep - INFO - Step PhotomStep done\n",
+      "2021-06-18 13:06:56,615 - stpipe.PhotomStep - INFO - PhotomStep instance created.\n",
+      "2021-06-18 13:06:56,746 - stpipe.PhotomStep - INFO - Step PhotomStep running with args ('stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_fringestep.fits',).\n",
+      "2021-06-18 13:06:56,747 - stpipe.PhotomStep - INFO - Step PhotomStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'inverse': False, 'source_type': None}\n",
+      "2021-06-18 13:06:58,069 - stpipe.PhotomStep - INFO - Using photom reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_photom_0060.fits\n",
+      "2021-06-18 13:06:58,070 - stpipe.PhotomStep - INFO - Using area reference file: N/A\n",
+      "2021-06-18 13:06:59,386 - stpipe.PhotomStep - INFO - Using instrument: MIRI\n",
+      "2021-06-18 13:06:59,387 - stpipe.PhotomStep - INFO -  detector: MIRIFUSHORT\n",
+      "2021-06-18 13:06:59,388 - stpipe.PhotomStep - INFO -  exp_type: MIR_MRS\n",
+      "2021-06-18 13:06:59,388 - stpipe.PhotomStep - INFO -  band: LONG\n",
+      "2021-06-18 13:06:59,455 - stpipe.PhotomStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
+      "2021-06-18 13:07:00,650 - stpipe.PhotomStep - INFO - Saved model in stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_photomstep.fits\n",
+      "2021-06-18 13:07:00,651 - stpipe.PhotomStep - INFO - Step PhotomStep done\n",
+      "2021-06-18 13:07:02,097 - stpipe.PhotomStep - INFO - PhotomStep instance created.\n",
+      "2021-06-18 13:07:02,234 - stpipe.PhotomStep - INFO - Step PhotomStep running with args ('stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_fringestep.fits',).\n",
+      "2021-06-18 13:07:02,236 - stpipe.PhotomStep - INFO - Step PhotomStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'inverse': False, 'source_type': None}\n",
+      "2021-06-18 13:07:03,448 - stpipe.PhotomStep - INFO - Using photom reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_photom_0060.fits\n",
+      "2021-06-18 13:07:03,448 - stpipe.PhotomStep - INFO - Using area reference file: N/A\n",
+      "2021-06-18 13:07:04,799 - stpipe.PhotomStep - INFO - Using instrument: MIRI\n",
+      "2021-06-18 13:07:04,800 - stpipe.PhotomStep - INFO -  detector: MIRIFUSHORT\n",
+      "2021-06-18 13:07:04,800 - stpipe.PhotomStep - INFO -  exp_type: MIR_MRS\n",
+      "2021-06-18 13:07:04,801 - stpipe.PhotomStep - INFO -  band: LONG\n",
+      "2021-06-18 13:07:04,867 - stpipe.PhotomStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
+      "2021-06-18 13:07:06,170 - stpipe.PhotomStep - INFO - Saved model in stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_photomstep.fits\n",
+      "2021-06-18 13:07:06,170 - stpipe.PhotomStep - INFO - Step PhotomStep done\n",
+      "2021-06-18 13:07:07,761 - stpipe.PhotomStep - INFO - PhotomStep instance created.\n",
+      "2021-06-18 13:07:07,889 - stpipe.PhotomStep - INFO - Step PhotomStep running with args ('stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_fringestep.fits',).\n",
+      "2021-06-18 13:07:07,890 - stpipe.PhotomStep - INFO - Step PhotomStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'inverse': False, 'source_type': None}\n",
+      "2021-06-18 13:07:09,359 - stpipe.PhotomStep - INFO - Using photom reference file: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_photom_0060.fits\n",
+      "2021-06-18 13:07:09,359 - stpipe.PhotomStep - INFO - Using area reference file: N/A\n",
+      "2021-06-18 13:07:10,664 - stpipe.PhotomStep - INFO - Using instrument: MIRI\n",
+      "2021-06-18 13:07:10,665 - stpipe.PhotomStep - INFO -  detector: MIRIFUSHORT\n",
+      "2021-06-18 13:07:10,666 - stpipe.PhotomStep - INFO -  exp_type: MIR_MRS\n",
+      "2021-06-18 13:07:10,666 - stpipe.PhotomStep - INFO -  band: LONG\n",
+      "2021-06-18 13:07:10,733 - stpipe.PhotomStep - WARNING - Keyword CDP_LOW_QUAL does not correspond to an existing DQ mnemonic, so will be ignored\n",
+      "2021-06-18 13:07:11,913 - stpipe.PhotomStep - INFO - Saved model in stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_photomstep.fits\n",
+      "2021-06-18 13:07:11,914 - stpipe.PhotomStep - INFO - Step PhotomStep done\n"
      ]
     }
    ],
@@ -2872,19 +1926,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 47,
    "id": "dbaf3d6e",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:25:17,628 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n",
-      "\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -2894,30 +1939,38 @@
        " 'stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_photomstep.fits']"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Look for our photomstep.fits files produced by the photometric calibration step\n",
-    "sstring=spec2_dir+'det*photomstep.fits'\n",
-    "photomfiles=sorted(glob.glob(sstring))\n",
+    "sstring = spec2_dir + 'det*photomstep.fits'\n",
+    "photomfiles = sorted(glob.glob(sstring))\n",
     "# And print them out so that we can see them\n",
     "photomfiles"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "cc03b8d5",
+   "metadata": {},
+   "source": [
+    "Ordinarily we'd run the Spec2 pipeline as a pipeline rather than individual steps though, in which\n",
+    "case the final outputs would have the extension _cal.fits rather than _photomstep.fits <br>\n",
+    "Just to make the rest of this notebook more typical, we'll rename the _photomstep.fits fits to _cal.fits files"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 48,
    "id": "9a74228f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Ordinarily we'd run the Spec2 pipeline as a pipeline rather than individual steps though, in which\n",
-    "# case the final outputs would have the extension _cal.fits rather than _photomstep.fits\n",
-    "# Just to make the rest of this notebook more typical, we'll rename the _photomstep.fits fits to _cal.fits files\n",
-    "calfiles=photomfiles.copy()\n",
+    "# Rename photomstep to cal files\n",
+    "calfiles = photomfiles.copy()\n",
     "for ii in range(0,len(photomfiles)):\n",
     "    calfiles[ii]=str.replace(photomfiles[ii],'photomstep','cal')\n",
     "    thisphotomfile=photomfiles[ii]\n",
@@ -2927,7 +1980,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 49,
    "id": "4cd046f2",
    "metadata": {},
    "outputs": [
@@ -2937,13 +1990,13 @@
        "(0.0, 250.0)"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 700x700 with 2 Axes>"
       ]
@@ -2957,10 +2010,10 @@
    "source": [
     "# Let's show what the SCI extension of the first file before/after application of the flux calibration looks like\n",
     "# We'll zoom in on a region of the detector to make the results more clear\n",
-    "hdu1=fits.open(fringefiles[0])\n",
-    "image1=hdu1['SCI'].data\n",
-    "hdu2=fits.open(calfiles[0])\n",
-    "image2=hdu2['SCI'].data\n",
+    "hdu1 = fits.open(fringefiles[0])\n",
+    "image1 = hdu1['SCI'].data\n",
+    "hdu2 = fits.open(calfiles[0])\n",
+    "image2 = hdu2['SCI'].data\n",
     "\n",
     "# Use a classic ZScale normalization\n",
     "norm1 = ImageNormalize(image1, interval=ZScaleInterval(),stretch=LinearStretch())\n",
@@ -2994,7 +2047,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 50,
    "id": "8fefec72",
    "metadata": {},
    "outputs": [],
@@ -3004,14 +2057,6 @@
     "hdu2.close()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5d77cd96",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "5d369f9e",
@@ -3030,7 +2075,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 51,
    "id": "47ee4362",
    "metadata": {},
    "outputs": [
@@ -3043,232 +2088,27 @@
        " 'stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_cal.fits']"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Look for our _cal.fits files\n",
-    "sstring=spec2_dir+'det*cal.fits'\n",
-    "calfiles=sorted(glob.glob(sstring))\n",
+    "sstring = spec2_dir + 'det*cal.fits'\n",
+    "calfiles = sorted(glob.glob(sstring))\n",
     "# And print them out so that we can see them\n",
     "calfiles"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 52,
    "id": "a9c051d1",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:25:19,014 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:25:20,012 - stpipe.CubeBuildStep - INFO - CubeBuildStep instance created.\n",
-      "2021-05-27 17:25:20,168 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep running with args ('stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_cal.fits',).\n",
-      "2021-05-27 17:25:20,172 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': True, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': False, 'input_dir': '', 'channel': 'all', 'band': 'all', 'grating': 'all', 'filter': 'all', 'output_type': 'multi', 'scale1': 0.0, 'scale2': 0.0, 'scalew': 0.0, 'weighting': 'emsm', 'coord_system': 'skyalign', 'rois': 0.0, 'roiw': 0.0, 'weight_power': 2.0, 'wavemin': None, 'wavemax': None, 'single': False, 'xdebug': None, 'ydebug': None, 'zdebug': None, 'skip_dqflagging': False}\n",
-      "2021-05-27 17:25:20,174 - stpipe.CubeBuildStep - INFO - Starting IFU Cube Building Step\n",
-      "2021-05-27 17:25:20,175 - stpipe.CubeBuildStep - INFO - Input interpolation: pointcloud\n",
-      "2021-05-27 17:25:20,177 - stpipe.CubeBuildStep - INFO - Coordinate system to use: skyalign\n",
-      "2021-05-27 17:25:20,179 - stpipe.CubeBuildStep - INFO - Weighting method for point cloud: emsm\n",
-      "2021-05-27 17:25:20,180 - stpipe.CubeBuildStep - INFO - Power weighting distance: 2.0\n",
-      "2021-05-27 17:25:20,571 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:25:21,405 - stpipe.CubeBuildStep - INFO - The desired cubes cover the MIRI Channels: ['1', '2']\n",
-      "2021-05-27 17:25:21,406 - stpipe.CubeBuildStep - INFO - The desired cubes cover the MIRI subchannels: ['long', 'long']\n",
-      "2021-05-27 17:25:21,407 - stpipe.CubeBuildStep - INFO - Reading cube parameter file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_cubepar_0005.fits\n",
-      "2021-05-27 17:25:22,502 - stpipe.CubeBuildStep - INFO - Output IFUcube are constructed from all the data \n",
-      "2021-05-27 17:25:22,503 - stpipe.CubeBuildStep - INFO - Number of IFU cubes produced by this run = 1\n",
-      "2021-05-27 17:25:22,521 - stpipe.CubeBuildStep - INFO - Cube Geometry:\n",
-      "2021-05-27 17:25:22,522 - stpipe.CubeBuildStep - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(arcsec)  Min & Max (xi, eta arcsec)\n",
-      "2021-05-27 17:25:22,522 - stpipe.CubeBuildStep - INFO - Axis 1    41  21.00   0.00032143   0.13000000  -2.66499990   2.66499990\n",
-      "2021-05-27 17:25:22,523 - stpipe.CubeBuildStep - INFO - Axis 2    43  22.00  -0.00001935   0.13000000  -2.79499990   2.79499990\n",
-      "2021-05-27 17:25:22,524 - stpipe.CubeBuildStep - INFO - Non-linear wavelength dimension; CDELT3 variable\n",
-      "2021-05-27 17:25:22,525 - stpipe.CubeBuildStep - INFO - axis#  Naxis  CRPIX    CRVAL     Min & Max (microns)\n",
-      "2021-05-27 17:25:22,526 - stpipe.CubeBuildStep - INFO - Axis 3  3907   1.00   6.41998196   6.41998196  11.74206543\n",
-      "2021-05-27 17:25:22,527 - stpipe.CubeBuildStep - INFO - Cube covers channel, subchannel: 1, long\n",
-      "2021-05-27 17:25:22,528 - stpipe.CubeBuildStep - INFO - Cube covers channel, subchannel: 2, long\n",
-      "2021-05-27 17:25:22,529 - stpipe.CubeBuildStep - INFO - Output Name: stage2//det_image_seq1_MIRIFUSHORT_12LONGexp1_s3d.fits\n",
-      "2021-05-27 17:25:22,624 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/utils.py:72: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  indx = np.asarray(np.floor(np.asarray(value) + 0.5), dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:25:22,871 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
-      "  lon = np.mod(lon, 360.0 * u.deg if nquant else 360.0)\n",
-      "\n",
-      "2021-05-27 17:25:22,905 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 6046 with wavelength below 6.413683951832354\n",
-      "2021-05-27 17:28:39,648 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 10619 with wavelength above 11.748956381343305\n",
-      "2021-05-27 17:32:48,080 - stpipe.CubeBuildStep - INFO - Average # of holes/wavelength plane: 6\n",
-      "2021-05-27 17:32:48,081 - stpipe.CubeBuildStep - INFO - Total # of holes for IFU cube is : 23696\n",
-      "2021-05-27 17:32:48,419 - stpipe.CubeBuildStep - INFO - Update S_REGION to POLYGON ICRS  0.001043653 -0.000777687 0.001043653 0.000738979 359.999599208 0.000738979 359.999599208 -0.000777687\n",
-      "2021-05-27 17:32:48,926 - stpipe.CubeBuildStep - INFO - Saved model in stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_s3d.fits\n",
-      "2021-05-27 17:32:48,927 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep done\n",
-      "2021-05-27 17:32:49,568 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:32:50,419 - stpipe.CubeBuildStep - INFO - CubeBuildStep instance created.\n",
-      "2021-05-27 17:32:50,577 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep running with args ('stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_cal.fits',).\n",
-      "2021-05-27 17:32:50,581 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': True, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': False, 'input_dir': '', 'channel': 'all', 'band': 'all', 'grating': 'all', 'filter': 'all', 'output_type': 'multi', 'scale1': 0.0, 'scale2': 0.0, 'scalew': 0.0, 'weighting': 'emsm', 'coord_system': 'skyalign', 'rois': 0.0, 'roiw': 0.0, 'weight_power': 2.0, 'wavemin': None, 'wavemax': None, 'single': False, 'xdebug': None, 'ydebug': None, 'zdebug': None, 'skip_dqflagging': False}\n",
-      "2021-05-27 17:32:50,583 - stpipe.CubeBuildStep - INFO - Starting IFU Cube Building Step\n",
-      "2021-05-27 17:32:50,584 - stpipe.CubeBuildStep - INFO - Input interpolation: pointcloud\n",
-      "2021-05-27 17:32:50,586 - stpipe.CubeBuildStep - INFO - Coordinate system to use: skyalign\n",
-      "2021-05-27 17:32:50,588 - stpipe.CubeBuildStep - INFO - Weighting method for point cloud: emsm\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:32:50,589 - stpipe.CubeBuildStep - INFO - Power weighting distance: 2.0\n",
-      "2021-05-27 17:32:50,975 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:32:51,817 - stpipe.CubeBuildStep - INFO - The desired cubes cover the MIRI Channels: ['1', '2']\n",
-      "2021-05-27 17:32:51,818 - stpipe.CubeBuildStep - INFO - The desired cubes cover the MIRI subchannels: ['long', 'long']\n",
-      "2021-05-27 17:32:51,819 - stpipe.CubeBuildStep - INFO - Reading cube parameter file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_cubepar_0005.fits\n",
-      "2021-05-27 17:32:52,911 - stpipe.CubeBuildStep - INFO - Output IFUcube are constructed from all the data \n",
-      "2021-05-27 17:32:52,912 - stpipe.CubeBuildStep - INFO - Number of IFU cubes produced by this run = 1\n",
-      "2021-05-27 17:32:52,928 - stpipe.CubeBuildStep - INFO - Cube Geometry:\n",
-      "2021-05-27 17:32:52,929 - stpipe.CubeBuildStep - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(arcsec)  Min & Max (xi, eta arcsec)\n",
-      "2021-05-27 17:32:52,929 - stpipe.CubeBuildStep - INFO - Axis 1    41  21.00  -0.00026371   0.13000000  -2.66499990   2.66499990\n",
-      "2021-05-27 17:32:52,930 - stpipe.CubeBuildStep - INFO - Axis 2    43  22.00  -0.00020887   0.13000000  -2.79499990   2.79499990\n",
-      "2021-05-27 17:32:52,931 - stpipe.CubeBuildStep - INFO - Non-linear wavelength dimension; CDELT3 variable\n",
-      "2021-05-27 17:32:52,932 - stpipe.CubeBuildStep - INFO - axis#  Naxis  CRPIX    CRVAL     Min & Max (microns)\n",
-      "2021-05-27 17:32:52,933 - stpipe.CubeBuildStep - INFO - Axis 3  3907   1.00   6.41998196   6.41998196  11.74206543\n",
-      "2021-05-27 17:32:52,933 - stpipe.CubeBuildStep - INFO - Cube covers channel, subchannel: 1, long\n",
-      "2021-05-27 17:32:52,934 - stpipe.CubeBuildStep - INFO - Cube covers channel, subchannel: 2, long\n",
-      "2021-05-27 17:32:52,935 - stpipe.CubeBuildStep - INFO - Output Name: stage2//det_image_seq2_MIRIFUSHORT_12LONGexp1_s3d.fits\n",
-      "2021-05-27 17:32:53,027 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/utils.py:72: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  indx = np.asarray(np.floor(np.asarray(value) + 0.5), dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:32:53,277 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
-      "  lon = np.mod(lon, 360.0 * u.deg if nquant else 360.0)\n",
-      "\n",
-      "2021-05-27 17:32:53,311 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 6046 with wavelength below 6.413683951832354\n",
-      "2021-05-27 17:36:11,494 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 10619 with wavelength above 11.748956381343305\n",
-      "2021-05-27 17:40:16,672 - stpipe.CubeBuildStep - INFO - Average # of holes/wavelength plane: 6\n",
-      "2021-05-27 17:40:16,673 - stpipe.CubeBuildStep - INFO - Total # of holes for IFU cube is : 23696\n",
-      "2021-05-27 17:40:16,981 - stpipe.CubeBuildStep - INFO - Update S_REGION to POLYGON ICRS  0.000458514 -0.000967202 0.000458514 0.000549465 359.999014069 0.000549465 359.999014069 -0.000967202\n",
-      "2021-05-27 17:40:17,495 - stpipe.CubeBuildStep - INFO - Saved model in stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_s3d.fits\n",
-      "2021-05-27 17:40:17,495 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep done\n",
-      "2021-05-27 17:40:18,113 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:40:18,996 - stpipe.CubeBuildStep - INFO - CubeBuildStep instance created.\n",
-      "2021-05-27 17:40:19,123 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep running with args ('stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_cal.fits',).\n",
-      "2021-05-27 17:40:19,127 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': True, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': False, 'input_dir': '', 'channel': 'all', 'band': 'all', 'grating': 'all', 'filter': 'all', 'output_type': 'multi', 'scale1': 0.0, 'scale2': 0.0, 'scalew': 0.0, 'weighting': 'emsm', 'coord_system': 'skyalign', 'rois': 0.0, 'roiw': 0.0, 'weight_power': 2.0, 'wavemin': None, 'wavemax': None, 'single': False, 'xdebug': None, 'ydebug': None, 'zdebug': None, 'skip_dqflagging': False}\n",
-      "2021-05-27 17:40:19,128 - stpipe.CubeBuildStep - INFO - Starting IFU Cube Building Step\n",
-      "2021-05-27 17:40:19,130 - stpipe.CubeBuildStep - INFO - Input interpolation: pointcloud\n",
-      "2021-05-27 17:40:19,131 - stpipe.CubeBuildStep - INFO - Coordinate system to use: skyalign\n",
-      "2021-05-27 17:40:19,132 - stpipe.CubeBuildStep - INFO - Weighting method for point cloud: emsm\n",
-      "2021-05-27 17:40:19,134 - stpipe.CubeBuildStep - INFO - Power weighting distance: 2.0\n",
-      "2021-05-27 17:40:19,532 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:40:20,402 - stpipe.CubeBuildStep - INFO - The desired cubes cover the MIRI Channels: ['1', '2']\n",
-      "2021-05-27 17:40:20,403 - stpipe.CubeBuildStep - INFO - The desired cubes cover the MIRI subchannels: ['long', 'long']\n",
-      "2021-05-27 17:40:20,404 - stpipe.CubeBuildStep - INFO - Reading cube parameter file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_cubepar_0005.fits\n",
-      "2021-05-27 17:40:21,526 - stpipe.CubeBuildStep - INFO - Output IFUcube are constructed from all the data \n",
-      "2021-05-27 17:40:21,527 - stpipe.CubeBuildStep - INFO - Number of IFU cubes produced by this run = 1\n",
-      "2021-05-27 17:40:21,545 - stpipe.CubeBuildStep - INFO - Cube Geometry:\n",
-      "2021-05-27 17:40:21,547 - stpipe.CubeBuildStep - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(arcsec)  Min & Max (xi, eta arcsec)\n",
-      "2021-05-27 17:40:21,547 - stpipe.CubeBuildStep - INFO - Axis 1    41  21.00   0.00029188   0.13000000  -2.66499990   2.66499990\n",
-      "2021-05-27 17:40:21,548 - stpipe.CubeBuildStep - INFO - Axis 2    43  22.00  -0.00003990   0.13000000  -2.79499990   2.79499990\n",
-      "2021-05-27 17:40:21,549 - stpipe.CubeBuildStep - INFO - Non-linear wavelength dimension; CDELT3 variable\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:40:21,550 - stpipe.CubeBuildStep - INFO - axis#  Naxis  CRPIX    CRVAL     Min & Max (microns)\n",
-      "2021-05-27 17:40:21,550 - stpipe.CubeBuildStep - INFO - Axis 3  3907   1.00   6.41998196   6.41998196  11.74206543\n",
-      "2021-05-27 17:40:21,551 - stpipe.CubeBuildStep - INFO - Cube covers channel, subchannel: 1, long\n",
-      "2021-05-27 17:40:21,552 - stpipe.CubeBuildStep - INFO - Cube covers channel, subchannel: 2, long\n",
-      "2021-05-27 17:40:21,553 - stpipe.CubeBuildStep - INFO - Output Name: stage2//det_image_seq3_MIRIFUSHORT_12LONGexp1_s3d.fits\n",
-      "2021-05-27 17:40:21,657 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/utils.py:72: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  indx = np.asarray(np.floor(np.asarray(value) + 0.5), dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:40:21,907 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
-      "  lon = np.mod(lon, 360.0 * u.deg if nquant else 360.0)\n",
-      "\n",
-      "2021-05-27 17:40:21,942 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 6046 with wavelength below 6.413683951832354\n",
-      "2021-05-27 17:43:39,723 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 10619 with wavelength above 11.748956381343305\n",
-      "2021-05-27 17:47:45,106 - stpipe.CubeBuildStep - INFO - Average # of holes/wavelength plane: 6\n",
-      "2021-05-27 17:47:45,107 - stpipe.CubeBuildStep - INFO - Total # of holes for IFU cube is : 23696\n",
-      "2021-05-27 17:47:45,413 - stpipe.CubeBuildStep - INFO - Update S_REGION to POLYGON ICRS  0.001014102 -0.000798233 0.001014102 0.000718433 359.999569657 0.000718433 359.999569657 -0.000798233\n",
-      "2021-05-27 17:47:45,938 - stpipe.CubeBuildStep - INFO - Saved model in stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_s3d.fits\n",
-      "2021-05-27 17:47:45,939 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep done\n",
-      "2021-05-27 17:47:46,646 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:47:47,809 - stpipe.CubeBuildStep - INFO - CubeBuildStep instance created.\n",
-      "2021-05-27 17:47:48,060 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep running with args ('stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_cal.fits',).\n",
-      "2021-05-27 17:47:48,067 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': True, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': False, 'input_dir': '', 'channel': 'all', 'band': 'all', 'grating': 'all', 'filter': 'all', 'output_type': 'multi', 'scale1': 0.0, 'scale2': 0.0, 'scalew': 0.0, 'weighting': 'emsm', 'coord_system': 'skyalign', 'rois': 0.0, 'roiw': 0.0, 'weight_power': 2.0, 'wavemin': None, 'wavemax': None, 'single': False, 'xdebug': None, 'ydebug': None, 'zdebug': None, 'skip_dqflagging': False}\n",
-      "2021-05-27 17:47:48,070 - stpipe.CubeBuildStep - INFO - Starting IFU Cube Building Step\n",
-      "2021-05-27 17:47:48,073 - stpipe.CubeBuildStep - INFO - Input interpolation: pointcloud\n",
-      "2021-05-27 17:47:48,076 - stpipe.CubeBuildStep - INFO - Coordinate system to use: skyalign\n",
-      "2021-05-27 17:47:48,077 - stpipe.CubeBuildStep - INFO - Weighting method for point cloud: emsm\n",
-      "2021-05-27 17:47:48,079 - stpipe.CubeBuildStep - INFO - Power weighting distance: 2.0\n",
-      "2021-05-27 17:47:49,018 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:47:50,135 - stpipe.CubeBuildStep - INFO - The desired cubes cover the MIRI Channels: ['1', '2']\n",
-      "2021-05-27 17:47:50,136 - stpipe.CubeBuildStep - INFO - The desired cubes cover the MIRI subchannels: ['long', 'long']\n",
-      "2021-05-27 17:47:50,136 - stpipe.CubeBuildStep - INFO - Reading cube parameter file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_cubepar_0005.fits\n",
-      "2021-05-27 17:47:51,523 - stpipe.CubeBuildStep - INFO - Output IFUcube are constructed from all the data \n",
-      "2021-05-27 17:47:51,523 - stpipe.CubeBuildStep - INFO - Number of IFU cubes produced by this run = 1\n",
-      "2021-05-27 17:47:51,539 - stpipe.CubeBuildStep - INFO - Cube Geometry:\n",
-      "2021-05-27 17:47:51,540 - stpipe.CubeBuildStep - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(arcsec)  Min & Max (xi, eta arcsec)\n",
-      "2021-05-27 17:47:51,541 - stpipe.CubeBuildStep - INFO - Axis 1    41  21.00  -0.00029307   0.13000000  -2.66499990   2.66499990\n",
-      "2021-05-27 17:47:51,543 - stpipe.CubeBuildStep - INFO - Axis 2    43  22.00  -0.00022979   0.13000000  -2.79499990   2.79499990\n",
-      "2021-05-27 17:47:51,544 - stpipe.CubeBuildStep - INFO - Non-linear wavelength dimension; CDELT3 variable\n",
-      "2021-05-27 17:47:51,545 - stpipe.CubeBuildStep - INFO - axis#  Naxis  CRPIX    CRVAL     Min & Max (microns)\n",
-      "2021-05-27 17:47:51,547 - stpipe.CubeBuildStep - INFO - Axis 3  3907   1.00   6.41998196   6.41998196  11.74206543\n",
-      "2021-05-27 17:47:51,548 - stpipe.CubeBuildStep - INFO - Cube covers channel, subchannel: 1, long\n",
-      "2021-05-27 17:47:51,549 - stpipe.CubeBuildStep - INFO - Cube covers channel, subchannel: 2, long\n",
-      "2021-05-27 17:47:51,551 - stpipe.CubeBuildStep - INFO - Output Name: stage2//det_image_seq4_MIRIFUSHORT_12LONGexp1_s3d.fits\n",
-      "2021-05-27 17:47:51,671 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/utils.py:72: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  indx = np.asarray(np.floor(np.asarray(value) + 0.5), dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:47:52,098 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
-      "  lon = np.mod(lon, 360.0 * u.deg if nquant else 360.0)\n",
-      "\n",
-      "2021-05-27 17:47:52,139 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 6046 with wavelength below 6.413683951832354\n",
-      "2021-05-27 17:51:21,736 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 10619 with wavelength above 11.748956381343305\n",
-      "2021-05-27 17:55:29,569 - stpipe.CubeBuildStep - INFO - Average # of holes/wavelength plane: 6\n",
-      "2021-05-27 17:55:29,570 - stpipe.CubeBuildStep - INFO - Total # of holes for IFU cube is : 23696\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:55:29,892 - stpipe.CubeBuildStep - INFO - Update S_REGION to POLYGON ICRS  0.000429148 -0.000988119 0.000429148 0.000528547 359.998984703 0.000528547 359.998984703 -0.000988119\n",
-      "2021-05-27 17:55:30,388 - stpipe.CubeBuildStep - INFO - Saved model in stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_s3d.fits\n",
-      "2021-05-27 17:55:30,389 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep done\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Call the step, specifying that we want results saved into the spec2_dir directory\n",
     "\n",
@@ -3280,30 +2120,21 @@
     "        \n",
     "# Otherwise, just copy cached outputs into our output directory structure\n",
     "else:\n",
-    "    sstring=cache_dir+spec2_dir+'det*s3d.fits'\n",
-    "    files=sorted(glob.glob(sstring))\n",
+    "    sstring = cache_dir + spec2_dir+'det*s3d.fits'\n",
+    "    files = sorted(glob.glob(sstring))\n",
     "    for file in files:\n",
-    "        outfile=str.replace(file,cache_dir,'./')\n",
+    "        outfile = str.replace(file,cache_dir,'./')\n",
     "        shutil.copy(file,outfile)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 53,
    "id": "6d53f0be",
    "metadata": {
     "scrolled": true
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:55:30,393 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n",
-      "\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -3313,26 +2144,79 @@
        " 'stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_s3d.fits']"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Look for our intermediate cubes produced by the cube building step\n",
-    "sstring=spec2_dir+'det*s3d.fits'\n",
-    "cubefiles=sorted(glob.glob(sstring))\n",
+    "sstring = spec2_dir + 'det*s3d.fits'\n",
+    "cubefiles = sorted(glob.glob(sstring))\n",
     "# And print them out so that we can see them\n",
     "cubefiles"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "4f970cce",
+   "execution_count": 54,
+   "id": "ddd593ed",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'X pixel')"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Now let's display a couple of these cubes\n",
+    "hdu1 = fits.open(cubefiles[0])\n",
+    "cube1 = hdu1['SCI'].data\n",
+    "hdu2 = fits.open(cubefiles[1])\n",
+    "cube2 = hdu2['SCI'].data\n",
+    "\n",
+    "# Use a linear stretch\n",
+    "norm = ImageNormalize(cube1[0,:,:], vmin=-200,vmax=1e3,stretch=LinearStretch())\n",
+    "\n",
+    "rc('axes', linewidth=2)            \n",
+    "fig, (ax1,ax2) = plt.subplots(1,2, figsize=(7,7),dpi=100)\n",
+    "\n",
+    "# And plot the data\n",
+    "ax1.imshow(cube1[0,:,:], cmap='gray',norm=norm,origin='lower')\n",
+    "ax1.set_title('Dither #1')\n",
+    "ax1.set_xlabel('X pixel')\n",
+    "ax1.set_ylabel('Y pixel')\n",
+    "\n",
+    "ax2.imshow(cube2[0,:,:], cmap='gray',norm=norm,origin='lower')\n",
+    "ax2.set_title('Dither #2')\n",
+    "ax2.set_xlabel('X pixel')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cad63817",
+   "metadata": {},
+   "source": [
+    "<b>Figure 7:</b> MIRI rectified 3d data cubes for two different dither positions"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -3350,7 +2234,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 55,
+   "id": "9908af74",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\n    smoothing_length = integer(default=None)  # background smoothing size\\n    bkg_fit = option(\"poly\", \"mean\", \"median\", default=\"poly\")  # background fitting type\\n    bkg_order = integer(default=None, min=0)  # order of background polynomial fit\\n    bkg_sigma_clip = float(default=3.0)  # background sigma clipping threshold\\n    log_increment = integer(default=50)  # increment for multi-integration log messages\\n    subtract_background = boolean(default=None)  # subtract background?\\n    use_source_posn = boolean(default=None)  # use source coords to center extractions?\\n    apply_apcorr = boolean(default=True)  # apply aperture corrections?\\n    '"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Extract1dStep.spec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
    "id": "1370c72e",
    "metadata": {
     "scrolled": true
@@ -3360,54 +2265,50 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2021-05-27 17:55:30,556 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n",
-      "2021-05-27 17:55:30,730 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args ('stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_s3d.fits',).\n",
-      "2021-05-27 17:55:30,732 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': 'poly', 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'apply_apcorr': True}\n",
-      "2021-05-27 17:55:30,853 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_extract1d_0002.asdf\n",
-      "2021-05-27 17:55:30,867 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_apcorr_0001.asdf\n",
-      "2021-05-27 17:55:32,499 - stpipe.Extract1dStep - INFO - Turning on source position correction for exp_type = MIR_MRS\n",
-      "2021-05-27 17:55:32,500 - stpipe.Extract1dStep - INFO - Source type = POINT\n",
-      "2021-05-27 17:55:32,516 - stpipe.Extract1dStep - INFO - Input model has no variance information. Creating zero-filled arrays.\n",
-      "2021-05-27 17:55:32,534 - stpipe.Extract1dStep - INFO - Using x_center = 29, y_center = 22, based on TARG_RA and TARG_DEC.\n",
-      "2021-05-27 17:55:56,175 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n",
-      "2021-05-27 17:55:59,005 - stpipe.Extract1dStep - INFO - Saved model in stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_extract1dstep.fits\n",
-      "2021-05-27 17:55:59,006 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n",
-      "2021-05-27 17:55:59,141 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n",
-      "2021-05-27 17:55:59,258 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args ('stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_s3d.fits',).\n",
-      "2021-05-27 17:55:59,260 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': 'poly', 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'apply_apcorr': True}\n",
-      "2021-05-27 17:55:59,379 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_extract1d_0002.asdf\n",
-      "2021-05-27 17:55:59,391 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_apcorr_0001.asdf\n",
-      "2021-05-27 17:56:00,953 - stpipe.Extract1dStep - INFO - Turning on source position correction for exp_type = MIR_MRS\n",
-      "2021-05-27 17:56:00,954 - stpipe.Extract1dStep - INFO - Source type = POINT\n",
-      "2021-05-27 17:56:00,969 - stpipe.Extract1dStep - INFO - Input model has no variance information. Creating zero-filled arrays.\n",
-      "2021-05-27 17:56:00,987 - stpipe.Extract1dStep - INFO - Using x_center = 13, y_center = 27, based on TARG_RA and TARG_DEC.\n",
-      "2021-05-27 17:56:25,582 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n",
-      "2021-05-27 17:56:28,522 - stpipe.Extract1dStep - INFO - Saved model in stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_extract1dstep.fits\n",
-      "2021-05-27 17:56:28,523 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n",
-      "2021-05-27 17:56:28,679 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n",
-      "2021-05-27 17:56:28,801 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args ('stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_s3d.fits',).\n",
-      "2021-05-27 17:56:28,803 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': 'poly', 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'apply_apcorr': True}\n",
-      "2021-05-27 17:56:28,922 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_extract1d_0002.asdf\n",
-      "2021-05-27 17:56:28,939 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_apcorr_0001.asdf\n",
-      "2021-05-27 17:56:30,571 - stpipe.Extract1dStep - INFO - Turning on source position correction for exp_type = MIR_MRS\n",
-      "2021-05-27 17:56:30,572 - stpipe.Extract1dStep - INFO - Source type = POINT\n",
-      "2021-05-27 17:56:30,588 - stpipe.Extract1dStep - INFO - Input model has no variance information. Creating zero-filled arrays.\n",
-      "2021-05-27 17:56:30,607 - stpipe.Extract1dStep - INFO - Using x_center = 28, y_center = 22, based on TARG_RA and TARG_DEC.\n",
-      "2021-05-27 17:56:55,347 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n",
-      "2021-05-27 17:56:58,328 - stpipe.Extract1dStep - INFO - Saved model in stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_extract1dstep.fits\n",
-      "2021-05-27 17:56:58,329 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n",
-      "2021-05-27 17:56:58,468 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n",
-      "2021-05-27 17:56:58,584 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args ('stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_s3d.fits',).\n",
-      "2021-05-27 17:56:58,586 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': 'poly', 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'apply_apcorr': True}\n",
-      "2021-05-27 17:56:58,700 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_extract1d_0002.asdf\n",
-      "2021-05-27 17:56:58,712 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_apcorr_0001.asdf\n",
-      "2021-05-27 17:57:00,244 - stpipe.Extract1dStep - INFO - Turning on source position correction for exp_type = MIR_MRS\n",
-      "2021-05-27 17:57:00,245 - stpipe.Extract1dStep - INFO - Source type = POINT\n",
-      "2021-05-27 17:57:00,259 - stpipe.Extract1dStep - INFO - Input model has no variance information. Creating zero-filled arrays.\n",
-      "2021-05-27 17:57:00,277 - stpipe.Extract1dStep - INFO - Using x_center = 12, y_center = 27, based on TARG_RA and TARG_DEC.\n",
-      "2021-05-27 17:57:25,151 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n",
-      "2021-05-27 17:57:28,126 - stpipe.Extract1dStep - INFO - Saved model in stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_extract1dstep.fits\n",
-      "2021-05-27 17:57:28,127 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n"
+      "2021-06-18 13:07:13,794 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n",
+      "2021-06-18 13:07:14,114 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args ('stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_s3d.fits',).\n",
+      "2021-06-18 13:07:14,116 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': 'poly', 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'apply_apcorr': True}\n",
+      "2021-06-18 13:07:14,291 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_extract1d_0002.asdf\n",
+      "2021-06-18 13:07:14,305 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_apcorr_0001.asdf\n",
+      "2021-06-18 13:07:15,872 - stpipe.Extract1dStep - INFO - Turning on source position correction for exp_type = MIR_MRS\n",
+      "2021-06-18 13:07:15,873 - stpipe.Extract1dStep - INFO - Source type = POINT\n",
+      "2021-06-18 13:07:15,891 - stpipe.Extract1dStep - INFO - Using x_center = 29, y_center = 22, based on TARG_RA and TARG_DEC.\n",
+      "2021-06-18 13:07:26,458 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n",
+      "2021-06-18 13:07:28,058 - stpipe.Extract1dStep - INFO - Saved model in stage2/det_image_seq1_MIRIFUSHORT_12LONGexp1_extract1dstep.fits\n",
+      "2021-06-18 13:07:28,059 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n",
+      "2021-06-18 13:07:28,284 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n",
+      "2021-06-18 13:07:28,421 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args ('stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_s3d.fits',).\n",
+      "2021-06-18 13:07:28,424 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': 'poly', 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'apply_apcorr': True}\n",
+      "2021-06-18 13:07:28,577 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_extract1d_0002.asdf\n",
+      "2021-06-18 13:07:28,592 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_apcorr_0001.asdf\n",
+      "2021-06-18 13:07:30,513 - stpipe.Extract1dStep - INFO - Turning on source position correction for exp_type = MIR_MRS\n",
+      "2021-06-18 13:07:30,514 - stpipe.Extract1dStep - INFO - Source type = POINT\n",
+      "2021-06-18 13:07:30,532 - stpipe.Extract1dStep - INFO - Using x_center = 13, y_center = 27, based on TARG_RA and TARG_DEC.\n",
+      "2021-06-18 13:07:41,393 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n",
+      "2021-06-18 13:07:42,713 - stpipe.Extract1dStep - INFO - Saved model in stage2/det_image_seq2_MIRIFUSHORT_12LONGexp1_extract1dstep.fits\n",
+      "2021-06-18 13:07:42,714 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n",
+      "2021-06-18 13:07:42,863 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n",
+      "2021-06-18 13:07:42,974 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args ('stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_s3d.fits',).\n",
+      "2021-06-18 13:07:42,976 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': 'poly', 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'apply_apcorr': True}\n",
+      "2021-06-18 13:07:43,111 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_extract1d_0002.asdf\n",
+      "2021-06-18 13:07:43,123 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_apcorr_0001.asdf\n",
+      "2021-06-18 13:07:44,700 - stpipe.Extract1dStep - INFO - Turning on source position correction for exp_type = MIR_MRS\n",
+      "2021-06-18 13:07:44,700 - stpipe.Extract1dStep - INFO - Source type = POINT\n",
+      "2021-06-18 13:07:44,717 - stpipe.Extract1dStep - INFO - Using x_center = 28, y_center = 22, based on TARG_RA and TARG_DEC.\n",
+      "2021-06-18 13:07:55,075 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n",
+      "2021-06-18 13:07:56,348 - stpipe.Extract1dStep - INFO - Saved model in stage2/det_image_seq3_MIRIFUSHORT_12LONGexp1_extract1dstep.fits\n",
+      "2021-06-18 13:07:56,348 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n",
+      "2021-06-18 13:07:56,496 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n",
+      "2021-06-18 13:07:56,616 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args ('stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_s3d.fits',).\n",
+      "2021-06-18 13:07:56,619 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage2/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': 'poly', 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'apply_apcorr': True}\n",
+      "2021-06-18 13:07:56,751 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_extract1d_0002.asdf\n",
+      "2021-06-18 13:07:56,762 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_apcorr_0001.asdf\n",
+      "2021-06-18 13:07:58,300 - stpipe.Extract1dStep - INFO - Turning on source position correction for exp_type = MIR_MRS\n",
+      "2021-06-18 13:07:58,301 - stpipe.Extract1dStep - INFO - Source type = POINT\n",
+      "2021-06-18 13:07:58,317 - stpipe.Extract1dStep - INFO - Using x_center = 12, y_center = 27, based on TARG_RA and TARG_DEC.\n",
+      "2021-06-18 13:08:08,717 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n",
+      "2021-06-18 13:08:10,024 - stpipe.Extract1dStep - INFO - Saved model in stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_extract1dstep.fits\n",
+      "2021-06-18 13:08:10,024 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n"
      ]
     }
    ],
@@ -3419,19 +2320,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 57,
    "id": "9aaf5b53",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:57:28,133 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n",
-      "\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -3441,22 +2333,22 @@
        " 'stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_extract1dstep.fits']"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Look for our intermediate 1d spectra\n",
-    "sstring=spec2_dir+'det*extract1dstep.fits'\n",
-    "specfiles=sorted(glob.glob(sstring))\n",
+    "sstring = spec2_dir + 'det*extract1dstep.fits'\n",
+    "specfiles = sorted(glob.glob(sstring))\n",
     "# And print them out so that we can see them\n",
     "specfiles"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 58,
    "id": "d74a7f58",
    "metadata": {},
    "outputs": [
@@ -3466,13 +2358,13 @@
        "Text(0, 0.5, 'Flux (Jy)')"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3485,8 +2377,8 @@
    ],
    "source": [
     "# Let's look at one of them\n",
-    "hdu=fits.open(specfiles[0])\n",
-    "spec=hdu['EXTRACT1D']\n",
+    "hdu = fits.open(specfiles[0])\n",
+    "spec = hdu['EXTRACT1D']\n",
     "\n",
     "plt.plot(spec.data['WAVELENGTH'],spec.data['FLUX'])\n",
     "plt.xlabel('Wavelength (micron)')\n",
@@ -3498,24 +2390,15 @@
    "id": "d9518891",
    "metadata": {},
    "source": [
-    "<b>Figure 7:</b> Extracted 1d spectra from the quick-look per-exposure data cube.  Note that in this case we've got spectra in 1A and 2A together in the same spectrum with zeros in between where there is no coverage."
+    "<b>Figure 8:</b> Extracted 1d spectra from the quick-look per-exposure data cube.  Note that in this case we've got spectra in 1A and 2A together in the same spectrum with zeros in between where there is no coverage."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 59,
    "id": "4f675706",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:57:28,299 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n",
-      "\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -3533,7 +2416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 60,
    "id": "0c239073",
    "metadata": {},
    "outputs": [],
@@ -3544,7 +2427,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 61,
    "id": "96692bb8",
    "metadata": {},
    "outputs": [
@@ -3552,7 +2435,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Runtime so far: 2231.1786 seconds\n"
+      "Runtime so far: 242.1992 seconds\n"
      ]
     }
    ],
@@ -3613,7 +2496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 62,
    "id": "5e920cc6",
    "metadata": {
     "scrolled": false
@@ -3634,14 +2517,6 @@
     "        outfile.write(serialized)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a7bcbee3",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "5a96540d",
@@ -3658,14 +2533,6 @@
     "</div>"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "98aca9f8",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "a6a31688",
@@ -3682,14 +2549,6 @@
     "</div>"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "58f4694f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "fd460ce6",
@@ -3706,7 +2565,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 63,
    "id": "4afb67ac",
    "metadata": {
     "scrolled": false
@@ -3721,7 +2580,7 @@
        " 'stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_cal.fits']"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3729,31 +2588,38 @@
    "source": [
     "# Read in two calibrated frames\n",
     "# Look for our _rate.fits files produced by the Detector1 pipeline\n",
-    "sstring=spec2_dir+'det*cal.fits'\n",
-    "calfiles=sorted(glob.glob(sstring))\n",
+    "sstring = spec2_dir + 'det*cal.fits'\n",
+    "calfiles = sorted(glob.glob(sstring))\n",
     "calfiles"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "52b0667f",
+   "metadata": {},
+   "source": [
+    "In these simulations the background doesn't vary, but let's pretend that it did.\n",
+    "We'll crudely hack one of the files to mimic a background level shift."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 64,
    "id": "85a98b54",
    "metadata": {
     "scrolled": false
    },
    "outputs": [],
    "source": [
-    "# In these simulations the background doesn't vary, but let's pretend that it did.\n",
-    "# We'll crudely hack one of the files to mimic a background level shift.\n",
-    "hdu=fits.open(calfiles[0])\n",
-    "hdu['SCI'].data += 300\n",
+    "hdu = fits.open(calfiles[0])\n",
+    "hdu['SCI'].data += 300 # Hack this first file to add a pedestal offset\n",
     "hdu.writeto(spec2_dir+'rbm_test.fits',overwrite=True)\n",
     "hdu.close()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 65,
    "id": "2a5a50bd",
    "metadata": {
     "scrolled": true
@@ -3763,118 +2629,35 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2021-05-27 17:57:28,543 - stpipe.CubeBuildStep - INFO - CubeBuildStep instance created.\n",
-      "2021-05-27 17:57:29,120 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:57:30,320 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:57:31,750 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:57:33,054 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:57:34,379 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:57:35,074 - stpipe.CubeBuildStep - INFO - CubeBuildStep instance created.\n",
-      "2021-05-27 17:57:35,364 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep running with args ('rbm.json',).\n",
-      "2021-05-27 17:57:35,366 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/dlaw/MIRI/JWebbinar/Notebook1/rbm_before', 'output_dir': 'stage3/', 'output_ext': '.fits', 'output_use_model': True, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': False, 'input_dir': '', 'channel': '2', 'band': 'all', 'grating': 'all', 'filter': 'all', 'output_type': 'band', 'scale1': 0.0, 'scale2': 0.0, 'scalew': 0.0, 'weighting': 'emsm', 'coord_system': 'skyalign', 'rois': 0.0, 'roiw': 0.0, 'weight_power': 2.0, 'wavemin': None, 'wavemax': None, 'single': False, 'xdebug': None, 'ydebug': None, 'zdebug': None, 'skip_dqflagging': False}\n",
-      "2021-05-27 17:57:35,367 - stpipe.CubeBuildStep - INFO - Starting IFU Cube Building Step\n",
-      "2021-05-27 17:57:35,368 - stpipe.CubeBuildStep - INFO - Input interpolation: pointcloud\n",
-      "2021-05-27 17:57:35,369 - stpipe.CubeBuildStep - INFO - Coordinate system to use: skyalign\n",
-      "2021-05-27 17:57:35,370 - stpipe.CubeBuildStep - INFO - Weighting method for point cloud: emsm\n",
-      "2021-05-27 17:57:35,371 - stpipe.CubeBuildStep - INFO - Power weighting distance: 2.0\n",
-      "2021-05-27 17:57:35,816 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:57:37,355 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:57:38,591 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:57:39,819 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:57:40,721 - stpipe.CubeBuildStep - INFO - The desired cubes cover the MIRI Channels: ['2']\n",
-      "2021-05-27 17:57:40,722 - stpipe.CubeBuildStep - INFO - The desired cubes cover the MIRI subchannels: ['long']\n",
-      "2021-05-27 17:57:40,723 - stpipe.CubeBuildStep - INFO - Reading cube parameter file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_cubepar_0005.fits\n",
-      "2021-05-27 17:57:41,874 - stpipe.CubeBuildStep - INFO - The user has selected the type of IFU cube to make\n",
-      "2021-05-27 17:57:41,875 - stpipe.CubeBuildStep - INFO - Number of IFU cubes produced by this run = 1\n",
-      "2021-05-27 17:57:41,881 - stpipe.CubeBuildStep - INFO - Cube Geometry:\n",
-      "2021-05-27 17:57:41,882 - stpipe.CubeBuildStep - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(arcsec)  Min & Max (xi, eta arcsec)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 17:57:41,884 - stpipe.CubeBuildStep - INFO - Axis 1    45  23.00   0.00001418   0.17000000  -3.82500004   3.82500004\n",
-      "2021-05-27 17:57:41,884 - stpipe.CubeBuildStep - INFO - Axis 2    39  20.00  -0.00012457   0.17000000  -3.31500003   3.31500003\n",
-      "2021-05-27 17:57:41,885 - stpipe.CubeBuildStep - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(microns)  Min & Max (microns)\n",
-      "2021-05-27 17:57:41,886 - stpipe.CubeBuildStep - INFO - Axis 3   855   1.00  10.03099973   0.00200000  10.02999973  11.73999981\n",
-      "2021-05-27 17:57:41,888 - stpipe.CubeBuildStep - INFO - Cube covers channel, subchannel: 2, long\n",
-      "2021-05-27 17:57:41,888 - stpipe.CubeBuildStep - INFO - Output Name: stage3//rbm_before_ch2-long_s3d.fits\n",
-      "2021-05-27 17:57:41,973 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/utils.py:72: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  indx = np.asarray(np.floor(np.asarray(value) + 0.5), dtype=np.int)\n",
-      "\n",
-      "2021-05-27 17:57:42,210 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
-      "  lon = np.mod(lon, 360.0 * u.deg if nquant else 360.0)\n",
-      "\n",
-      "2021-05-27 17:57:42,245 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 17:57:42,246 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 17:58:54,857 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 17:58:54,858 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:00:07,718 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:00:07,719 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:01:22,590 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:01:22,591 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:02:38,438 - stpipe.CubeBuildStep - INFO - Average # of holes/wavelength plane: 46\n",
-      "2021-05-27 18:02:38,439 - stpipe.CubeBuildStep - INFO - Total # of holes for IFU cube is : 39569\n",
-      "2021-05-27 18:02:40,583 - stpipe.CubeBuildStep - INFO - Update S_REGION to POLYGON ICRS  0.001053067 -0.001021792 0.001053067 0.000772652 359.998975289 0.000772652 359.998975289 -0.001021792\n",
-      "2021-05-27 18:02:41,189 - stpipe.CubeBuildStep - INFO - Saved model in stage3/rbm_before_ch2-long_s3d.fits\n",
-      "2021-05-27 18:02:41,190 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep done\n"
+      "2021-06-18 13:08:10,512 - stpipe.CubeBuildStep - INFO - CubeBuildStep instance created.\n"
      ]
     }
    ],
    "source": [
     "# Now we'll create an association file including this hacked exposure\n",
-    "testfiles=calfiles.copy()\n",
-    "testfiles[0]=spec2_dir+'rbm_test.fits'\n",
+    "testfiles = calfiles.copy()\n",
+    "testfiles[0] = spec2_dir + 'rbm_test.fits'\n",
     "writel3asn(testfiles,'rbm.json','rbm')\n",
     "\n",
     "# And run it through cube building (we'll just build a cube for the Ch2 data as an example to save time), calling the output\n",
     "# file 'rbm_before'\n",
-    "cb=CubeBuildStep()\n",
+    "cb = CubeBuildStep()\n",
     "\n",
     "# If rerunning long pipeline steps, actually run the step\n",
     "if (redolong == True):\n",
     "    cb.call('rbm.json',channel='2',save_results=True,output_dir=spec3_dir,output_file='rbm_before')\n",
     "# Otherwise, just copy cached outputs into our output directory structure\n",
     "else:\n",
-    "    sstring=cache_dir+spec3_dir+'rbm_before*s3d.fits'\n",
-    "    files=sorted(glob.glob(sstring))\n",
+    "    sstring = cache_dir + spec3_dir + 'rbm_before*s3d.fits'\n",
+    "    files = sorted(glob.glob(sstring))\n",
     "    for file in files:\n",
-    "        outfile=str.replace(file,cache_dir,'./')\n",
+    "        outfile = str.replace(file,cache_dir,'./')\n",
     "        shutil.copy(file,outfile)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 66,
    "id": "f31bf638",
    "metadata": {
     "scrolled": true
@@ -3884,199 +2667,22 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2021-05-27 18:02:41,195 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n",
-      "\n",
-      "2021-05-27 18:02:41,209 - stpipe.Spec3Pipeline - INFO - Spec3Pipeline instance created.\n",
-      "2021-05-27 18:02:41,211 - stpipe.Spec3Pipeline.assign_mtwcs - INFO - AssignMTWcsStep instance created.\n",
-      "2021-05-27 18:02:41,213 - stpipe.Spec3Pipeline.master_background - INFO - MasterBackgroundStep instance created.\n",
-      "2021-05-27 18:02:41,215 - stpipe.Spec3Pipeline.mrs_imatch - INFO - MRSIMatchStep instance created.\n",
-      "2021-05-27 18:02:41,218 - stpipe.Spec3Pipeline.outlier_detection - INFO - OutlierDetectionStep instance created.\n",
-      "2021-05-27 18:02:41,220 - stpipe.Spec3Pipeline.resample_spec - INFO - ResampleSpecStep instance created.\n",
-      "2021-05-27 18:02:41,224 - stpipe.Spec3Pipeline.cube_build - INFO - CubeBuildStep instance created.\n",
-      "2021-05-27 18:02:41,226 - stpipe.Spec3Pipeline.extract_1d - INFO - Extract1dStep instance created.\n",
-      "2021-05-27 18:02:41,228 - stpipe.Spec3Pipeline.combine_1d - INFO - Combine1dStep instance created.\n",
-      "2021-05-27 18:02:41,655 - stpipe.Spec3Pipeline - INFO - Step Spec3Pipeline running with args ('rbm.json',).\n",
-      "2021-05-27 18:02:41,666 - stpipe.Spec3Pipeline - INFO - Step Spec3Pipeline parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage3/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'steps': {'assign_mtwcs': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': True, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': 'assign_mtwcs', 'search_output_file': True, 'input_dir': ''}, 'master_background': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': True, 'output_use_index': True, 'save_results': False, 'skip': True, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'user_background': None, 'save_background': False, 'force_subtract': False}, 'mrs_imatch': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'bkg_degree': 1, 'subtract': False}, 'outlier_detection': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': True, 'suffix': None, 'search_output_file': False, 'input_dir': '', 'weight_type': 'ivm', 'pixfrac': 1.0, 'kernel': 'square', 'fillval': 'INDEF', 'nlow': 0, 'nhigh': 0, 'maskpt': 0.7, 'grow': 1, 'snr': '5.0 4.0', 'scale': '1.2 0.7', 'backg': 0.0, 'save_intermediate_results': False, 'resample_data': True, 'good_bits': '~DO_NOT_USE', 'scale_detection': False, 'allowed_memory': None}, 'resample_spec': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'pixfrac': 1.0, 'kernel': 'square', 'fillval': 'INDEF', 'weight_type': 'ivm', 'pixel_scale_ratio': 1.0, 'single': False, 'blendheaders': True, 'allowed_memory': None}, 'cube_build': {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/dlaw/MIRI/JWebbinar/Notebook1/rbm_after', 'output_dir': None, 'output_ext': '.fits', 'output_use_model': True, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': False, 'input_dir': '', 'channel': '2', 'band': 'all', 'grating': 'all', 'filter': 'all', 'output_type': 'band', 'scale1': 0.0, 'scale2': 0.0, 'scalew': 0.0, 'weighting': 'emsm', 'coord_system': 'skyalign', 'rois': 0.0, 'roiw': 0.0, 'weight_power': 2.0, 'wavemin': None, 'wavemax': None, 'single': False, 'xdebug': None, 'ydebug': None, 'zdebug': None, 'skip_dqflagging': False}, 'extract_1d': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': True, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': 'poly', 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'apply_apcorr': True}, 'combine_1d': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'exptime_key': 'exposure_time'}}}\n",
-      "2021-05-27 18:02:42,235 - stpipe.Spec3Pipeline - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:02:42,936 - stpipe.Spec3Pipeline - INFO - Prefetching reference files for dataset: 'rbm_test.fits' reftypes = ['cubepar', 'drizpars', 'resol']\n",
-      "2021-05-27 18:02:42,942 - stpipe.Spec3Pipeline - INFO - Prefetch for CUBEPAR reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_cubepar_0005.fits'.\n",
-      "2021-05-27 18:02:42,943 - stpipe.Spec3Pipeline - INFO - Prefetch for DRIZPARS reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_drizpars_0001.fits'.\n",
-      "2021-05-27 18:02:42,944 - stpipe.Spec3Pipeline - INFO - Prefetch for RESOL reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_resol_0003.fits'.\n",
-      "2021-05-27 18:02:42,946 - stpipe.Spec3Pipeline - INFO - Starting calwebb_spec3 ...\n",
-      "2021-05-27 18:02:43,843 - stpipe.Spec3Pipeline - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:02:45,250 - stpipe.Spec3Pipeline - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:02:46,515 - stpipe.Spec3Pipeline - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 18:02:47,847 - stpipe.Spec3Pipeline - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:02:48,960 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Step mrs_imatch running with args (<ModelContainer>,).\n",
-      "2021-05-27 18:02:48,962 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Step mrs_imatch parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': 'mrs_imatch', 'search_output_file': True, 'input_dir': '', 'bkg_degree': 1, 'subtract': False}\n",
-      "2021-05-27 18:02:48,972 - stpipe.CubeBuildStep - INFO - CubeBuildStep instance created.\n",
-      "2021-05-27 18:02:48,973 - stpipe.CubeBuildStep - INFO - Starting IFU Cube Building Step\n",
-      "2021-05-27 18:02:48,974 - stpipe.CubeBuildStep - INFO - Input interpolation: pointcloud\n",
-      "2021-05-27 18:02:48,975 - stpipe.CubeBuildStep - INFO - Coordinate system to use: skyalign\n",
-      "2021-05-27 18:02:48,976 - stpipe.CubeBuildStep - INFO - Weighting method for point cloud: emsm\n",
-      "2021-05-27 18:02:48,977 - stpipe.CubeBuildStep - INFO - Power weighting distance: 2.0\n",
-      "2021-05-27 18:02:48,978 - stpipe.CubeBuildStep - INFO - Cube Type: Single cubes\n",
-      "2021-05-27 18:02:49,035 - stpipe.Spec3Pipeline.mrs_imatch - INFO - The desired cubes cover the MIRI Channels: ['1']\n",
-      "2021-05-27 18:02:49,036 - stpipe.Spec3Pipeline.mrs_imatch - INFO - The desired cubes cover the MIRI subchannels: ['long']\n",
-      "2021-05-27 18:02:49,037 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Reading cube parameter file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_cubepar_0005.fits\n",
-      "2021-05-27 18:02:50,233 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Single = true, creating a set of single exposures mapped to output IFUCube coordinate system\n",
-      "2021-05-27 18:02:50,240 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Cube Geometry:\n",
-      "2021-05-27 18:02:50,241 - stpipe.Spec3Pipeline.mrs_imatch - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(arcsec)  Min & Max (xi, eta arcsec)\n",
-      "2021-05-27 18:02:50,241 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Axis 1    59  30.00   0.00001418   0.13000000  -3.83499986   3.83499986\n",
-      "2021-05-27 18:02:50,242 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Axis 2    49  25.00  -0.00012457   0.13000000  -3.18499988   3.18499988\n",
-      "2021-05-27 18:02:50,243 - stpipe.Spec3Pipeline.mrs_imatch - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(microns)  Min & Max (microns)\n",
-      "2021-05-27 18:02:50,244 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Axis 3  1091   1.00   6.42050008   0.00100000   6.42000008   7.51100013\n",
-      "2021-05-27 18:02:50,245 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Cube covers channel, subchannel: 1, long\n",
-      "2021-05-27 18:02:50,397 - stpipe.Spec3Pipeline.mrs_imatch - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/utils.py:72: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  indx = np.asarray(np.floor(np.asarray(value) + 0.5), dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:02:50,705 - stpipe.Spec3Pipeline.mrs_imatch - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
-      "  lon = np.mod(lon, 360.0 * u.deg if nquant else 360.0)\n",
-      "\n",
-      "2021-05-27 18:02:50,743 - stpipe.Spec3Pipeline.mrs_imatch - INFO - # of detector pixels not mapped to output plane: 7150 with wavelength below 6.417500076349825\n",
-      "2021-05-27 18:02:50,745 - stpipe.Spec3Pipeline.mrs_imatch - INFO - # of detector pixels not mapped to output plane: 10399 with wavelength above 7.513500128057785\n",
-      "2021-05-27 18:03:46,774 - stpipe.Spec3Pipeline.mrs_imatch - INFO - # of detector pixels not mapped to output plane: 7150 with wavelength below 6.417500076349825\n",
-      "2021-05-27 18:03:46,775 - stpipe.Spec3Pipeline.mrs_imatch - INFO - # of detector pixels not mapped to output plane: 10399 with wavelength above 7.513500128057785\n",
-      "2021-05-27 18:04:41,718 - stpipe.Spec3Pipeline.mrs_imatch - INFO - # of detector pixels not mapped to output plane: 7150 with wavelength below 6.417500076349825\n",
-      "2021-05-27 18:04:41,719 - stpipe.Spec3Pipeline.mrs_imatch - INFO - # of detector pixels not mapped to output plane: 10399 with wavelength above 7.513500128057785\n",
-      "2021-05-27 18:05:34,152 - stpipe.Spec3Pipeline.mrs_imatch - INFO - # of detector pixels not mapped to output plane: 7150 with wavelength below 6.417500076349825\n",
-      "2021-05-27 18:05:34,153 - stpipe.Spec3Pipeline.mrs_imatch - INFO - # of detector pixels not mapped to output plane: 10399 with wavelength above 7.513500128057785\n",
-      "2021-05-27 18:06:32,207 - stpipe.CubeBuildStep - INFO - Number of Single IFUCube models returned 4 \n",
-      "2021-05-27 18:06:32,211 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Update S_REGION to POLYGON ICRS  0.001061400 -0.000991237 0.001061400 0.000742097 359.998966956 0.000742097 359.998966956 -0.000991237\n",
-      "2021-05-27 18:06:32,215 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Update S_REGION to POLYGON ICRS  0.001061400 -0.000991237 0.001061400 0.000742097 359.998966956 0.000742097 359.998966956 -0.000991237\n",
-      "2021-05-27 18:06:32,218 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Update S_REGION to POLYGON ICRS  0.001061400 -0.000991237 0.001061400 0.000742097 359.998966956 0.000742097 359.998966956 -0.000991237\n",
-      "2021-05-27 18:06:32,222 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Update S_REGION to POLYGON ICRS  0.001061400 -0.000991237 0.001061400 0.000742097 359.998966956 0.000742097 359.998966956 -0.000991237\n",
-      "2021-05-27 18:07:07,070 - stpipe.CubeBuildStep - INFO - CubeBuildStep instance created.\n",
-      "2021-05-27 18:07:07,071 - stpipe.CubeBuildStep - INFO - Starting IFU Cube Building Step\n",
-      "2021-05-27 18:07:07,071 - stpipe.CubeBuildStep - INFO - Input interpolation: pointcloud\n",
-      "2021-05-27 18:07:07,072 - stpipe.CubeBuildStep - INFO - Coordinate system to use: skyalign\n",
-      "2021-05-27 18:07:07,073 - stpipe.CubeBuildStep - INFO - Weighting method for point cloud: emsm\n",
-      "2021-05-27 18:07:07,074 - stpipe.CubeBuildStep - INFO - Power weighting distance: 2.0\n",
-      "2021-05-27 18:07:07,074 - stpipe.CubeBuildStep - INFO - Cube Type: Single cubes\n",
-      "2021-05-27 18:07:07,132 - stpipe.Spec3Pipeline.mrs_imatch - INFO - The desired cubes cover the MIRI Channels: ['2']\n",
-      "2021-05-27 18:07:07,133 - stpipe.Spec3Pipeline.mrs_imatch - INFO - The desired cubes cover the MIRI subchannels: ['long']\n",
-      "2021-05-27 18:07:07,134 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Reading cube parameter file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_cubepar_0005.fits\n",
-      "2021-05-27 18:07:08,259 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Single = true, creating a set of single exposures mapped to output IFUCube coordinate system\n",
-      "2021-05-27 18:07:08,265 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Cube Geometry:\n",
-      "2021-05-27 18:07:08,266 - stpipe.Spec3Pipeline.mrs_imatch - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(arcsec)  Min & Max (xi, eta arcsec)\n",
-      "2021-05-27 18:07:08,267 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Axis 1    45  23.00   0.00001418   0.17000000  -3.82500004   3.82500004\n",
-      "2021-05-27 18:07:08,268 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Axis 2    39  20.00  -0.00012457   0.17000000  -3.31500003   3.31500003\n",
-      "2021-05-27 18:07:08,269 - stpipe.Spec3Pipeline.mrs_imatch - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(microns)  Min & Max (microns)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 18:07:08,269 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Axis 3   855   1.00  10.03099973   0.00200000  10.02999973  11.73999981\n",
-      "2021-05-27 18:07:08,270 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Cube covers channel, subchannel: 2, long\n",
-      "2021-05-27 18:07:08,357 - stpipe.Spec3Pipeline.mrs_imatch - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/utils.py:72: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  indx = np.asarray(np.floor(np.asarray(value) + 0.5), dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:07:08,594 - stpipe.Spec3Pipeline.mrs_imatch - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
-      "  lon = np.mod(lon, 360.0 * u.deg if nquant else 360.0)\n",
-      "\n",
-      "2021-05-27 18:07:08,629 - stpipe.Spec3Pipeline.mrs_imatch - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:07:08,630 - stpipe.Spec3Pipeline.mrs_imatch - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:08:02,185 - stpipe.Spec3Pipeline.mrs_imatch - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:08:02,186 - stpipe.Spec3Pipeline.mrs_imatch - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:08:55,683 - stpipe.Spec3Pipeline.mrs_imatch - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:08:55,684 - stpipe.Spec3Pipeline.mrs_imatch - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:09:47,858 - stpipe.Spec3Pipeline.mrs_imatch - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:09:47,859 - stpipe.Spec3Pipeline.mrs_imatch - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:10:39,653 - stpipe.CubeBuildStep - INFO - Number of Single IFUCube models returned 4 \n",
-      "2021-05-27 18:10:39,656 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Update S_REGION to POLYGON ICRS  0.001053067 -0.001021792 0.001053067 0.000772652 359.998975289 0.000772652 359.998975289 -0.001021792\n",
-      "2021-05-27 18:10:39,660 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Update S_REGION to POLYGON ICRS  0.001053067 -0.001021792 0.001053067 0.000772652 359.998975289 0.000772652 359.998975289 -0.001021792\n",
-      "2021-05-27 18:10:39,664 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Update S_REGION to POLYGON ICRS  0.001053067 -0.001021792 0.001053067 0.000772652 359.998975289 0.000772652 359.998975289 -0.001021792\n",
-      "2021-05-27 18:10:39,667 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Update S_REGION to POLYGON ICRS  0.001053067 -0.001021792 0.001053067 0.000772652 359.998975289 0.000772652 359.998975289 -0.001021792\n",
-      "2021-05-27 18:10:58,911 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Step mrs_imatch done\n",
-      "2021-05-27 18:10:59,288 - stpipe.Spec3Pipeline.outlier_detection - INFO - Step outlier_detection running with args (<ModelContainer>,).\n",
-      "2021-05-27 18:10:59,291 - stpipe.Spec3Pipeline.outlier_detection - INFO - Step outlier_detection parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': True, 'suffix': 'crf', 'search_output_file': False, 'input_dir': '', 'weight_type': 'ivm', 'pixfrac': 1.0, 'kernel': 'square', 'fillval': 'INDEF', 'nlow': 0, 'nhigh': 0, 'maskpt': 0.7, 'grow': 1, 'snr': '5.0 4.0', 'scale': '1.2 0.7', 'backg': 0.0, 'save_intermediate_results': False, 'resample_data': True, 'good_bits': '~DO_NOT_USE', 'scale_detection': False, 'allowed_memory': None}\n",
-      "2021-05-27 18:10:59,292 - stpipe.Spec3Pipeline.outlier_detection - INFO - Step skipped.\n",
-      "2021-05-27 18:10:59,296 - stpipe.Spec3Pipeline.outlier_detection - INFO - Step outlier_detection done\n",
-      "2021-05-27 18:10:59,503 - stpipe.Spec3Pipeline.cube_build - INFO - Step cube_build running with args (<ModelContainer>,).\n",
-      "2021-05-27 18:10:59,507 - stpipe.Spec3Pipeline.cube_build - INFO - Step cube_build parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/dlaw/MIRI/JWebbinar/Notebook1/rbm_after', 'output_dir': None, 'output_ext': '.fits', 'output_use_model': True, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': 's3d', 'search_output_file': False, 'input_dir': '', 'channel': '2', 'band': 'all', 'grating': 'all', 'filter': 'all', 'output_type': 'band', 'scale1': 0.0, 'scale2': 0.0, 'scalew': 0.0, 'weighting': 'emsm', 'coord_system': 'skyalign', 'rois': 0.0, 'roiw': 0.0, 'weight_power': 2.0, 'wavemin': None, 'wavemax': None, 'single': False, 'xdebug': None, 'ydebug': None, 'zdebug': None, 'skip_dqflagging': False}\n",
-      "2021-05-27 18:10:59,508 - stpipe.Spec3Pipeline.cube_build - INFO - Starting IFU Cube Building Step\n",
-      "2021-05-27 18:10:59,509 - stpipe.Spec3Pipeline.cube_build - INFO - Input interpolation: pointcloud\n",
-      "2021-05-27 18:10:59,510 - stpipe.Spec3Pipeline.cube_build - INFO - Coordinate system to use: skyalign\n",
-      "2021-05-27 18:10:59,511 - stpipe.Spec3Pipeline.cube_build - INFO - Weighting method for point cloud: emsm\n",
-      "2021-05-27 18:10:59,512 - stpipe.Spec3Pipeline.cube_build - INFO - Power weighting distance: 2.0\n",
-      "2021-05-27 18:10:59,569 - stpipe.Spec3Pipeline.cube_build - INFO - The desired cubes cover the MIRI Channels: ['2']\n",
-      "2021-05-27 18:10:59,570 - stpipe.Spec3Pipeline.cube_build - INFO - The desired cubes cover the MIRI subchannels: ['long']\n",
-      "2021-05-27 18:10:59,571 - stpipe.Spec3Pipeline.cube_build - INFO - Reading cube parameter file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_cubepar_0005.fits\n",
-      "2021-05-27 18:11:00,703 - stpipe.Spec3Pipeline.cube_build - INFO - The user has selected the type of IFU cube to make\n",
-      "2021-05-27 18:11:00,704 - stpipe.Spec3Pipeline.cube_build - INFO - Number of IFU cubes produced by this run = 1\n",
-      "2021-05-27 18:11:00,709 - stpipe.Spec3Pipeline.cube_build - INFO - Cube Geometry:\n",
-      "2021-05-27 18:11:00,710 - stpipe.Spec3Pipeline.cube_build - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(arcsec)  Min & Max (xi, eta arcsec)\n",
-      "2021-05-27 18:11:00,711 - stpipe.Spec3Pipeline.cube_build - INFO - Axis 1    45  23.00   0.00001418   0.17000000  -3.82500004   3.82500004\n",
-      "2021-05-27 18:11:00,712 - stpipe.Spec3Pipeline.cube_build - INFO - Axis 2    39  20.00  -0.00012457   0.17000000  -3.31500003   3.31500003\n",
-      "2021-05-27 18:11:00,713 - stpipe.Spec3Pipeline.cube_build - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(microns)  Min & Max (microns)\n",
-      "2021-05-27 18:11:00,714 - stpipe.Spec3Pipeline.cube_build - INFO - Axis 3   855   1.00  10.03099973   0.00200000  10.02999973  11.73999981\n",
-      "2021-05-27 18:11:00,715 - stpipe.Spec3Pipeline.cube_build - INFO - Cube covers channel, subchannel: 2, long\n",
-      "2021-05-27 18:11:00,716 - stpipe.Spec3Pipeline.cube_build - INFO - Output Name: rbm_after_ch2-long_s3d.fits\n",
-      "2021-05-27 18:11:00,740 - stpipe.Spec3Pipeline.cube_build - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/utils.py:72: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  indx = np.asarray(np.floor(np.asarray(value) + 0.5), dtype=np.int)\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 18:11:00,975 - stpipe.Spec3Pipeline.cube_build - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
-      "  lon = np.mod(lon, 360.0 * u.deg if nquant else 360.0)\n",
-      "\n",
-      "2021-05-27 18:11:01,367 - stpipe.Spec3Pipeline.cube_build - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:11:01,367 - stpipe.Spec3Pipeline.cube_build - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:12:16,105 - stpipe.Spec3Pipeline.cube_build - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:12:16,106 - stpipe.Spec3Pipeline.cube_build - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:13:34,215 - stpipe.Spec3Pipeline.cube_build - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:13:34,216 - stpipe.Spec3Pipeline.cube_build - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:14:50,267 - stpipe.Spec3Pipeline.cube_build - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:14:50,267 - stpipe.Spec3Pipeline.cube_build - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:16:04,931 - stpipe.Spec3Pipeline.cube_build - INFO - Average # of holes/wavelength plane: 46\n",
-      "2021-05-27 18:16:04,932 - stpipe.Spec3Pipeline.cube_build - INFO - Total # of holes for IFU cube is : 39569\n",
-      "2021-05-27 18:16:06,696 - stpipe.Spec3Pipeline.cube_build - INFO - Update S_REGION to POLYGON ICRS  0.001053067 -0.001021792 0.001053067 0.000772652 359.998975289 0.000772652 359.998975289 -0.001021792\n",
-      "2021-05-27 18:16:07,263 - stpipe.Spec3Pipeline.cube_build - INFO - Saved model in stage3/rbm_after_ch2-long_s3d.fits\n",
-      "2021-05-27 18:16:07,264 - stpipe.Spec3Pipeline.cube_build - INFO - Step cube_build done\n",
-      "2021-05-27 18:16:07,505 - stpipe.Spec3Pipeline.extract_1d - INFO - Step extract_1d running with args (<ModelContainer>,).\n",
-      "2021-05-27 18:16:07,507 - stpipe.Spec3Pipeline.extract_1d - INFO - Step extract_1d parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': True, 'suffix': 'x1d', 'search_output_file': False, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': 'poly', 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'apply_apcorr': True}\n",
-      "2021-05-27 18:16:07,508 - stpipe.Spec3Pipeline.extract_1d - INFO - Step skipped.\n",
-      "2021-05-27 18:16:07,510 - stpipe.Spec3Pipeline.extract_1d - INFO - Step extract_1d done\n",
-      "2021-05-27 18:16:07,513 - stpipe.Spec3Pipeline - INFO - Ending calwebb_spec3\n",
-      "2021-05-27 18:16:07,515 - stpipe.Spec3Pipeline - INFO - Step Spec3Pipeline done\n"
+      "2021-06-18 13:08:10,564 - stpipe.Spec3Pipeline - INFO - Spec3Pipeline instance created.\n",
+      "2021-06-18 13:08:10,566 - stpipe.Spec3Pipeline.assign_mtwcs - INFO - AssignMTWcsStep instance created.\n",
+      "2021-06-18 13:08:10,567 - stpipe.Spec3Pipeline.master_background - INFO - MasterBackgroundStep instance created.\n",
+      "2021-06-18 13:08:10,569 - stpipe.Spec3Pipeline.mrs_imatch - INFO - MRSIMatchStep instance created.\n",
+      "2021-06-18 13:08:10,571 - stpipe.Spec3Pipeline.outlier_detection - INFO - OutlierDetectionStep instance created.\n",
+      "2021-06-18 13:08:10,573 - stpipe.Spec3Pipeline.resample_spec - INFO - ResampleSpecStep instance created.\n",
+      "2021-06-18 13:08:10,575 - stpipe.Spec3Pipeline.cube_build - INFO - CubeBuildStep instance created.\n",
+      "2021-06-18 13:08:10,577 - stpipe.Spec3Pipeline.extract_1d - INFO - Extract1dStep instance created.\n",
+      "2021-06-18 13:08:10,579 - stpipe.Spec3Pipeline.combine_1d - INFO - Combine1dStep instance created.\n"
      ]
     }
    ],
    "source": [
     "# And for comparison we'll run this association through the Spec3 pipeline with just Residual Background Matching and Cube Build,\n",
     "# calling the output 'rbm_after'\n",
-    "spec3=Spec3Pipeline()\n",
+    "spec3 = Spec3Pipeline()\n",
     "spec3.output_dir = spec3_dir\n",
     "spec3.save_results = True\n",
     "spec3.master_background.skip = True\n",
@@ -4090,43 +2696,34 @@
     "    spec3('rbm.json')\n",
     "# Otherwise, just copy cached outputs into our output directory structure\n",
     "else:\n",
-    "    sstring=cache_dir+spec3_dir+'rbm_after*s3d.fits'\n",
-    "    files=sorted(glob.glob(sstring))\n",
+    "    sstring = cache_dir + spec3_dir + 'rbm_after*s3d.fits'\n",
+    "    files = sorted(glob.glob(sstring))\n",
     "    for file in files:\n",
-    "        outfile=str.replace(file,cache_dir,'./')\n",
+    "        outfile = str.replace(file,cache_dir,'./')\n",
     "        shutil.copy(file,outfile)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 67,
    "id": "ed3e5121",
    "metadata": {
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 18:16:07,521 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n",
-      "\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
        "Text(0.5, 0, 'X pixel')"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 700x700 with 2 Axes>"
       ]
@@ -4139,10 +2736,10 @@
    ],
    "source": [
     "# Now let's compare what the SCI data of the two resulting cubes looks like\n",
-    "hdu1=fits.open(spec3_dir+'rbm_before_ch2-long_s3d.fits')\n",
-    "cube1=hdu1['SCI'].data\n",
-    "hdu2=fits.open(spec3_dir+'rbm_after_ch2-long_s3d.fits')\n",
-    "cube2=hdu2['SCI'].data\n",
+    "hdu1 = fits.open(spec3_dir + 'rbm_before_ch2-long_s3d.fits')\n",
+    "cube1 = hdu1['SCI'].data\n",
+    "hdu2 = fits.open(spec3_dir + 'rbm_after_ch2-long_s3d.fits')\n",
+    "cube2 = hdu2['SCI'].data\n",
     "\n",
     "# Use a classic ZScale normalization\n",
     "norm = ImageNormalize(cube1[0,:,:], interval=ZScaleInterval(),stretch=LinearStretch())\n",
@@ -4166,12 +2763,12 @@
    "id": "571261e2",
    "metadata": {},
    "source": [
-    "<b>Figure 8:</b> Data cubes built with and without residual background matching.  The example with background matching clear looks much better!  We should therefore be sure to use this step if such patterns are visible in the data.  Note that the pipeline doesn't know what the 'true' background level should be, so it brings everything to the same average level in spatially overlapping regions (i.e., if you're mosaicing a nebula it won't try to make all parts of the nebular have the same flux, just all exposures of a given point in the nebula should be the same)."
+    "<b>Figure 9:</b> Data cubes built with and without residual background matching.  The example with background matching clear looks much better!  We should therefore be sure to use this step if such patterns are visible in the data.  Note that the pipeline doesn't know what the 'true' background level should be, so it brings everything to the same average level in spatially overlapping regions (i.e., if you're mosaicing a nebula it won't try to make all parts of the nebular have the same flux, just all exposures of a given point in the nebula should be the same)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 68,
    "id": "b2d1c313",
    "metadata": {},
    "outputs": [],
@@ -4181,16 +2778,6 @@
     "hdu2.close()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d6441c52",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "1bdf8632",
@@ -4209,9 +2796,18 @@
     "</div>"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "edffe69f",
+   "metadata": {},
+   "source": [
+    "As for residual background matching, there aren't generally many outliers in mirisim simulated data for us to demonstrate this algorithm on.\n",
+    "Therefore, we'll add some outliers into a copy of the simulated data and show that they get successfully flagged and rejected."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 69,
    "id": "01d49bad",
    "metadata": {},
    "outputs": [
@@ -4224,42 +2820,39 @@
        " 'stage2/det_image_seq4_MIRIFUSHORT_12LONGexp1_cal.fits']"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# As for residual background matching, there aren't generally many outliers in mirisim simulated data for us to demonstrate this algorithm on.\n",
-    "# Therefore, we'll add some outliers into a copy of the simulated data and show that they get successfully flagged and rejected.\n",
-    "\n",
     "# Read in four calibrated frames\n",
     "# Look for our _rate.fits files produced by the Detector1 pipeline\n",
-    "sstring=spec2_dir+'det*cal.fits'\n",
-    "calfiles=sorted(glob.glob(sstring))\n",
+    "sstring = spec2_dir + 'det*cal.fits'\n",
+    "calfiles = sorted(glob.glob(sstring))\n",
     "# And print them out so that we can see them\n",
     "calfiles"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 70,
    "id": "69f274f6",
    "metadata": {},
    "outputs": [],
    "source": [
     "# We'll crudely hack the first files to introduce a artifact on column 925 and write it back out to an _odhack.fits files\n",
-    "hdu=fits.open(calfiles[0])\n",
-    "data=hdu['SCI'].data\n",
-    "data[:,925]=13000\n",
-    "hdu['SCI'].data=data\n",
+    "hdu = fits.open(calfiles[0])\n",
+    "data = hdu['SCI'].data\n",
+    "data[:,925] = 13000\n",
+    "hdu['SCI'].data = data\n",
     "hdu.writeto(str.replace(calfiles[0],'cal','od_test'),overwrite=True)\n",
     "hdu.close()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 71,
    "id": "b4d6453c",
    "metadata": {},
    "outputs": [
@@ -4267,118 +2860,35 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2021-05-27 18:16:07,926 - stpipe.CubeBuildStep - INFO - CubeBuildStep instance created.\n",
-      "2021-05-27 18:16:08,329 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:16:09,641 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:16:10,988 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:16:12,037 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:16:13,419 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:16:14,084 - stpipe.CubeBuildStep - INFO - CubeBuildStep instance created.\n",
-      "2021-05-27 18:16:14,475 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep running with args ('od.json',).\n",
-      "2021-05-27 18:16:14,478 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/dlaw/MIRI/JWebbinar/Notebook1/od_before', 'output_dir': 'stage3/', 'output_ext': '.fits', 'output_use_model': True, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': False, 'input_dir': '', 'channel': '2', 'band': 'all', 'grating': 'all', 'filter': 'all', 'output_type': 'band', 'scale1': 0.0, 'scale2': 0.0, 'scalew': 0.0, 'weighting': 'emsm', 'coord_system': 'skyalign', 'rois': 0.0, 'roiw': 0.0, 'weight_power': 2.0, 'wavemin': None, 'wavemax': None, 'single': False, 'xdebug': None, 'ydebug': None, 'zdebug': None, 'skip_dqflagging': False}\n",
-      "2021-05-27 18:16:14,480 - stpipe.CubeBuildStep - INFO - Starting IFU Cube Building Step\n",
-      "2021-05-27 18:16:14,480 - stpipe.CubeBuildStep - INFO - Input interpolation: pointcloud\n",
-      "2021-05-27 18:16:14,481 - stpipe.CubeBuildStep - INFO - Coordinate system to use: skyalign\n",
-      "2021-05-27 18:16:14,483 - stpipe.CubeBuildStep - INFO - Weighting method for point cloud: emsm\n",
-      "2021-05-27 18:16:14,484 - stpipe.CubeBuildStep - INFO - Power weighting distance: 2.0\n",
-      "2021-05-27 18:16:14,909 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:16:15,959 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:16:17,660 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:16:19,109 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:16:19,825 - stpipe.CubeBuildStep - INFO - The desired cubes cover the MIRI Channels: ['2']\n",
-      "2021-05-27 18:16:19,826 - stpipe.CubeBuildStep - INFO - The desired cubes cover the MIRI subchannels: ['long']\n",
-      "2021-05-27 18:16:19,827 - stpipe.CubeBuildStep - INFO - Reading cube parameter file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_cubepar_0005.fits\n",
-      "2021-05-27 18:16:21,006 - stpipe.CubeBuildStep - INFO - The user has selected the type of IFU cube to make\n",
-      "2021-05-27 18:16:21,007 - stpipe.CubeBuildStep - INFO - Number of IFU cubes produced by this run = 1\n",
-      "2021-05-27 18:16:21,013 - stpipe.CubeBuildStep - INFO - Cube Geometry:\n",
-      "2021-05-27 18:16:21,014 - stpipe.CubeBuildStep - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(arcsec)  Min & Max (xi, eta arcsec)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 18:16:21,015 - stpipe.CubeBuildStep - INFO - Axis 1    45  23.00   0.00001418   0.17000000  -3.82500004   3.82500004\n",
-      "2021-05-27 18:16:21,016 - stpipe.CubeBuildStep - INFO - Axis 2    39  20.00  -0.00012457   0.17000000  -3.31500003   3.31500003\n",
-      "2021-05-27 18:16:21,017 - stpipe.CubeBuildStep - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(microns)  Min & Max (microns)\n",
-      "2021-05-27 18:16:21,018 - stpipe.CubeBuildStep - INFO - Axis 3   855   1.00  10.03099973   0.00200000  10.02999973  11.73999981\n",
-      "2021-05-27 18:16:21,019 - stpipe.CubeBuildStep - INFO - Cube covers channel, subchannel: 2, long\n",
-      "2021-05-27 18:16:21,020 - stpipe.CubeBuildStep - INFO - Output Name: stage3//od_before_ch2-long_s3d.fits\n",
-      "2021-05-27 18:16:21,109 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/utils.py:72: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  indx = np.asarray(np.floor(np.asarray(value) + 0.5), dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:16:21,356 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
-      "  lon = np.mod(lon, 360.0 * u.deg if nquant else 360.0)\n",
-      "\n",
-      "2021-05-27 18:16:21,391 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:16:21,392 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:17:34,883 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:17:34,884 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:18:49,841 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:18:49,842 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:20:04,339 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:20:04,340 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:21:17,462 - stpipe.CubeBuildStep - INFO - Average # of holes/wavelength plane: 46\n",
-      "2021-05-27 18:21:17,463 - stpipe.CubeBuildStep - INFO - Total # of holes for IFU cube is : 39569\n",
-      "2021-05-27 18:21:19,650 - stpipe.CubeBuildStep - INFO - Update S_REGION to POLYGON ICRS  0.001053067 -0.001021792 0.001053067 0.000772652 359.998975289 0.000772652 359.998975289 -0.001021792\n",
-      "2021-05-27 18:21:20,239 - stpipe.CubeBuildStep - INFO - Saved model in stage3/od_before_ch2-long_s3d.fits\n",
-      "2021-05-27 18:21:20,240 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep done\n"
+      "2021-06-18 13:08:11,019 - stpipe.CubeBuildStep - INFO - CubeBuildStep instance created.\n"
      ]
     }
    ],
    "source": [
     "# Now we'll create an association file including these hacked exposures\n",
-    "testfiles=calfiles.copy()\n",
-    "testfiles[0]=str.replace(calfiles[0],'cal','od_test')\n",
+    "testfiles = calfiles.copy()\n",
+    "testfiles[0] = str.replace(calfiles[0],'cal','od_test')\n",
     "writel3asn(testfiles,'od.json','od')\n",
     "\n",
     "# And run it through cube building (we'll just build a cube for the Ch2 data as an example to save time),\n",
     "# calling the result 'od_before'\n",
-    "cb=CubeBuildStep()\n",
+    "cb = CubeBuildStep()\n",
     "\n",
     "# If rerunning long pipeline steps, actually run the step\n",
     "if (redolong == True):\n",
     "    cb.call('od.json',channel='2',save_results=True,output_dir=spec3_dir,output_file='od_before')\n",
     "# Otherwise, just copy cached outputs into our output directory structure\n",
     "else:\n",
-    "    sstring=cache_dir+spec3_dir+'od_before*s3d.fits'\n",
-    "    files=sorted(glob.glob(sstring))\n",
+    "    sstring = cache_dir + spec3_dir + 'od_before*s3d.fits'\n",
+    "    files = sorted(glob.glob(sstring))\n",
     "    for file in files:\n",
-    "        outfile=str.replace(file,cache_dir,'./')\n",
+    "        outfile = str.replace(file,cache_dir,'./')\n",
     "        shutil.copy(file,outfile)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 72,
    "id": "b4fd582b",
    "metadata": {},
    "outputs": [
@@ -4386,281 +2896,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2021-05-27 18:21:20,245 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n",
-      "\n",
-      "2021-05-27 18:21:20,667 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:21:22,254 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:21:23,348 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:21:25,155 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:21:26,638 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:21:27,266 - stpipe - INFO - PARS-OUTLIERDETECTIONSTEP parameters found: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_pars-outlierdetectionstep_0050.asdf\n",
-      "2021-05-27 18:21:27,282 - stpipe - INFO - PARS-OUTLIERDETECTIONSTEP parameters are {'scale': '7.5 7.5'}\n",
-      "2021-05-27 18:21:28,131 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:21:29,147 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:21:30,549 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:21:31,938 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:21:33,378 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:21:34,005 - stpipe.Spec3Pipeline - INFO - Spec3Pipeline instance created.\n",
-      "2021-05-27 18:21:34,007 - stpipe.Spec3Pipeline.assign_mtwcs - INFO - AssignMTWcsStep instance created.\n",
-      "2021-05-27 18:21:34,009 - stpipe.Spec3Pipeline.master_background - INFO - MasterBackgroundStep instance created.\n",
-      "2021-05-27 18:21:34,011 - stpipe.Spec3Pipeline.mrs_imatch - INFO - MRSIMatchStep instance created.\n",
-      "2021-05-27 18:21:34,013 - stpipe.Spec3Pipeline.outlier_detection - INFO - OutlierDetectionStep instance created.\n",
-      "2021-05-27 18:21:34,015 - stpipe.Spec3Pipeline.resample_spec - INFO - ResampleSpecStep instance created.\n",
-      "2021-05-27 18:21:34,017 - stpipe.Spec3Pipeline.cube_build - INFO - CubeBuildStep instance created.\n",
-      "2021-05-27 18:21:34,019 - stpipe.Spec3Pipeline.extract_1d - INFO - Extract1dStep instance created.\n",
-      "2021-05-27 18:21:34,021 - stpipe.Spec3Pipeline.combine_1d - INFO - Combine1dStep instance created.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 18:21:34,637 - stpipe.Spec3Pipeline - INFO - Step Spec3Pipeline running with args ('od.json',).\n",
-      "2021-05-27 18:21:34,647 - stpipe.Spec3Pipeline - INFO - Step Spec3Pipeline parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage3/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'steps': {'assign_mtwcs': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': True, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': 'assign_mtwcs', 'search_output_file': True, 'input_dir': ''}, 'master_background': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': True, 'output_use_index': True, 'save_results': False, 'skip': True, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'user_background': None, 'save_background': False, 'force_subtract': False}, 'mrs_imatch': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': True, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'bkg_degree': 1, 'subtract': False}, 'outlier_detection': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': False, 'input_dir': '', 'weight_type': 'ivm', 'pixfrac': 1.0, 'kernel': 'square', 'fillval': 'INDEF', 'nlow': 0, 'nhigh': 0, 'maskpt': 0.7, 'grow': 1, 'snr': '5.0 4.0', 'scale': '7.5 7.5', 'backg': 0.0, 'save_intermediate_results': True, 'resample_data': True, 'good_bits': '~DO_NOT_USE', 'scale_detection': False, 'allowed_memory': None}, 'resample_spec': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'pixfrac': 1.0, 'kernel': 'square', 'fillval': 'INDEF', 'weight_type': 'ivm', 'pixel_scale_ratio': 1.0, 'single': False, 'blendheaders': True, 'allowed_memory': None}, 'cube_build': {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/dlaw/MIRI/JWebbinar/Notebook1/od_after', 'output_dir': None, 'output_ext': '.fits', 'output_use_model': True, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': False, 'input_dir': '', 'channel': '2', 'band': 'all', 'grating': 'all', 'filter': 'all', 'output_type': 'band', 'scale1': 0.0, 'scale2': 0.0, 'scalew': 0.0, 'weighting': 'emsm', 'coord_system': 'skyalign', 'rois': 0.0, 'roiw': 0.0, 'weight_power': 2.0, 'wavemin': None, 'wavemax': None, 'single': False, 'xdebug': None, 'ydebug': None, 'zdebug': None, 'skip_dqflagging': False}, 'extract_1d': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': True, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': 'poly', 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'apply_apcorr': True}, 'combine_1d': {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'exptime_key': 'exposure_time'}}}\n",
-      "2021-05-27 18:21:35,033 - stpipe.Spec3Pipeline - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:21:35,823 - stpipe.Spec3Pipeline - INFO - Prefetching reference files for dataset: 'det_image_seq1_MIRIFUSHORT_12LONGexp1_od_test.fits' reftypes = ['cubepar', 'drizpars', 'resol']\n",
-      "2021-05-27 18:21:35,826 - stpipe.Spec3Pipeline - INFO - Prefetch for CUBEPAR reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_cubepar_0005.fits'.\n",
-      "2021-05-27 18:21:35,827 - stpipe.Spec3Pipeline - INFO - Prefetch for DRIZPARS reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_drizpars_0001.fits'.\n",
-      "2021-05-27 18:21:35,828 - stpipe.Spec3Pipeline - INFO - Prefetch for RESOL reference file is '/Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_resol_0003.fits'.\n",
-      "2021-05-27 18:21:35,830 - stpipe.Spec3Pipeline - INFO - Starting calwebb_spec3 ...\n",
-      "2021-05-27 18:21:36,432 - stpipe.Spec3Pipeline - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:21:37,665 - stpipe.Spec3Pipeline - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:21:39,055 - stpipe.Spec3Pipeline - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:21:40,322 - stpipe.Spec3Pipeline - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:21:41,456 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Step mrs_imatch running with args (<ModelContainer>,).\n",
-      "2021-05-27 18:21:41,459 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Step mrs_imatch parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': False, 'skip': True, 'suffix': 'mrs_imatch', 'search_output_file': True, 'input_dir': '', 'bkg_degree': 1, 'subtract': False}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 18:21:41,461 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Step skipped.\n",
-      "2021-05-27 18:21:41,468 - stpipe.Spec3Pipeline.mrs_imatch - INFO - Step mrs_imatch done\n",
-      "2021-05-27 18:21:41,720 - stpipe.Spec3Pipeline.outlier_detection - INFO - Step outlier_detection running with args (<ModelContainer>,).\n",
-      "2021-05-27 18:21:41,724 - stpipe.Spec3Pipeline.outlier_detection - INFO - Step outlier_detection parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': 'crf', 'search_output_file': False, 'input_dir': '', 'weight_type': 'ivm', 'pixfrac': 1.0, 'kernel': 'square', 'fillval': 'INDEF', 'nlow': 0, 'nhigh': 0, 'maskpt': 0.7, 'grow': 1, 'snr': '5.0 4.0', 'scale': '7.5 7.5', 'backg': 0.0, 'save_intermediate_results': True, 'resample_data': True, 'good_bits': '~DO_NOT_USE', 'scale_detection': False, 'allowed_memory': None}\n",
-      "2021-05-27 18:21:41,731 - stpipe.Spec3Pipeline.outlier_detection - INFO - Performing outlier detection on 4 inputs\n",
-      "2021-05-27 18:21:46,822 - stpipe.Spec3Pipeline.outlier_detection - INFO - Performing IFU outlier_detection for exptype MIR_MRS\n",
-      "2021-05-27 18:21:46,825 - stpipe.CubeBuildStep - INFO - CubeBuildStep instance created.\n",
-      "2021-05-27 18:21:46,826 - stpipe.CubeBuildStep - INFO - Starting IFU Cube Building Step\n",
-      "2021-05-27 18:21:46,827 - stpipe.CubeBuildStep - INFO - Input interpolation: pointcloud\n",
-      "2021-05-27 18:21:46,828 - stpipe.CubeBuildStep - INFO - Coordinate system to use: skyalign\n",
-      "2021-05-27 18:21:46,829 - stpipe.CubeBuildStep - INFO - Weighting method for point cloud: emsm\n",
-      "2021-05-27 18:21:46,830 - stpipe.CubeBuildStep - INFO - Power weighting distance: 2.0\n",
-      "2021-05-27 18:21:46,831 - stpipe.CubeBuildStep - INFO - Cube Type: Single cubes\n",
-      "2021-05-27 18:21:46,882 - stpipe.Spec3Pipeline.outlier_detection - INFO - The desired cubes cover the MIRI Channels: ['1']\n",
-      "2021-05-27 18:21:46,883 - stpipe.Spec3Pipeline.outlier_detection - INFO - The desired cubes cover the MIRI subchannels: ['long']\n",
-      "2021-05-27 18:21:46,884 - stpipe.Spec3Pipeline.outlier_detection - INFO - Reading cube parameter file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_cubepar_0005.fits\n",
-      "2021-05-27 18:21:48,099 - stpipe.Spec3Pipeline.outlier_detection - INFO - Single = true, creating a set of single exposures mapped to output IFUCube coordinate system\n",
-      "2021-05-27 18:21:48,105 - stpipe.Spec3Pipeline.outlier_detection - INFO - Cube Geometry:\n",
-      "2021-05-27 18:21:48,107 - stpipe.Spec3Pipeline.outlier_detection - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(arcsec)  Min & Max (xi, eta arcsec)\n",
-      "2021-05-27 18:21:48,108 - stpipe.Spec3Pipeline.outlier_detection - INFO - Axis 1    59  30.00   0.00001418   0.13000000  -3.83499986   3.83499986\n",
-      "2021-05-27 18:21:48,108 - stpipe.Spec3Pipeline.outlier_detection - INFO - Axis 2    49  25.00  -0.00012457   0.13000000  -3.18499988   3.18499988\n",
-      "2021-05-27 18:21:48,109 - stpipe.Spec3Pipeline.outlier_detection - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(microns)  Min & Max (microns)\n",
-      "2021-05-27 18:21:48,110 - stpipe.Spec3Pipeline.outlier_detection - INFO - Axis 3  1091   1.00   6.42050008   0.00100000   6.42000008   7.51100013\n",
-      "2021-05-27 18:21:48,111 - stpipe.Spec3Pipeline.outlier_detection - INFO - Cube covers channel, subchannel: 1, long\n",
-      "2021-05-27 18:21:48,211 - stpipe.Spec3Pipeline.outlier_detection - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/utils.py:72: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  indx = np.asarray(np.floor(np.asarray(value) + 0.5), dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:21:48,462 - stpipe.Spec3Pipeline.outlier_detection - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
-      "  lon = np.mod(lon, 360.0 * u.deg if nquant else 360.0)\n",
-      "\n",
-      "2021-05-27 18:21:48,495 - stpipe.Spec3Pipeline.outlier_detection - INFO - # of detector pixels not mapped to output plane: 7150 with wavelength below 6.417500076349825\n",
-      "2021-05-27 18:21:48,496 - stpipe.Spec3Pipeline.outlier_detection - INFO - # of detector pixels not mapped to output plane: 10399 with wavelength above 7.513500128057785\n",
-      "2021-05-27 18:22:41,128 - stpipe.Spec3Pipeline.outlier_detection - INFO - # of detector pixels not mapped to output plane: 7150 with wavelength below 6.417500076349825\n",
-      "2021-05-27 18:22:41,129 - stpipe.Spec3Pipeline.outlier_detection - INFO - # of detector pixels not mapped to output plane: 10399 with wavelength above 7.513500128057785\n",
-      "2021-05-27 18:23:35,182 - stpipe.Spec3Pipeline.outlier_detection - INFO - # of detector pixels not mapped to output plane: 7150 with wavelength below 6.417500076349825\n",
-      "2021-05-27 18:23:35,183 - stpipe.Spec3Pipeline.outlier_detection - INFO - # of detector pixels not mapped to output plane: 10399 with wavelength above 7.513500128057785\n",
-      "2021-05-27 18:24:30,821 - stpipe.Spec3Pipeline.outlier_detection - INFO - # of detector pixels not mapped to output plane: 7150 with wavelength below 6.417500076349825\n",
-      "2021-05-27 18:24:30,822 - stpipe.Spec3Pipeline.outlier_detection - INFO - # of detector pixels not mapped to output plane: 10399 with wavelength above 7.513500128057785\n",
-      "2021-05-27 18:25:23,331 - stpipe.CubeBuildStep - INFO - Number of Single IFUCube models returned 4 \n",
-      "2021-05-27 18:25:23,334 - stpipe.Spec3Pipeline.outlier_detection - INFO - Update S_REGION to POLYGON ICRS  0.001061400 -0.000991237 0.001061400 0.000742097 359.998966956 0.000742097 359.998966956 -0.000991237\n",
-      "2021-05-27 18:25:23,338 - stpipe.Spec3Pipeline.outlier_detection - INFO - Update S_REGION to POLYGON ICRS  0.001061400 -0.000991237 0.001061400 0.000742097 359.998966956 0.000742097 359.998966956 -0.000991237\n",
-      "2021-05-27 18:25:23,341 - stpipe.Spec3Pipeline.outlier_detection - INFO - Update S_REGION to POLYGON ICRS  0.001061400 -0.000991237 0.001061400 0.000742097 359.998966956 0.000742097 359.998966956 -0.000991237\n",
-      "2021-05-27 18:25:23,345 - stpipe.Spec3Pipeline.outlier_detection - INFO - Update S_REGION to POLYGON ICRS  0.001061400 -0.000991237 0.001061400 0.000742097 359.998966956 0.000742097 359.998966956 -0.000991237\n",
-      "2021-05-27 18:25:23,361 - stpipe.Spec3Pipeline.outlier_detection - INFO - Writing out (single) IFU cube stage3/det_image_seq1_MIRIFUSHORT_12LONGexp1_od_test_ch1-long_single_a3001__outlier_s3d.fits.fits\n",
-      "2021-05-27 18:25:24,015 - stpipe.Spec3Pipeline.outlier_detection - INFO - Writing out (single) IFU cube stage3/det_image_seq2_MIRIFUSHORT_12LONGexp1_cal_ch1-long_single_a3001__outlier_s3d.fits.fits\n",
-      "2021-05-27 18:25:24,671 - stpipe.Spec3Pipeline.outlier_detection - INFO - Writing out (single) IFU cube stage3/det_image_seq3_MIRIFUSHORT_12LONGexp1_cal_ch1-long_single_a3001__outlier_s3d.fits.fits\n",
-      "2021-05-27 18:25:25,320 - stpipe.Spec3Pipeline.outlier_detection - INFO - Writing out (single) IFU cube stage3/det_image_seq4_MIRIFUSHORT_12LONGexp1_cal_ch1-long_single_a3001__outlier_s3d.fits.fits\n",
-      "2021-05-27 18:25:26,302 - stpipe.Spec3Pipeline.outlier_detection - INFO - Writing out MEDIAN image to: stage3/det_image_seq1_MIRIFUSHORT_12LONGexp1_od_test_a3001_band1_median.fits\n",
-      "2021-05-27 18:25:27,784 - stpipe.Spec3Pipeline.outlier_detection - INFO - Information on Blotting\n",
-      "2021-05-27 18:25:27,785 - stpipe.Spec3Pipeline.outlier_detection - INFO - Working with instrument MIRI\n",
-      "2021-05-27 18:25:27,786 - stpipe.Spec3Pipeline.outlier_detection - INFO - shape of sky cube 59.000000 49.000000 1091.000000\n",
-      "2021-05-27 18:25:27,787 - stpipe.Spec3Pipeline.outlier_detection - INFO - Channel 1\n",
-      "2021-05-27 18:25:27,788 - stpipe.Spec3Pipeline.outlier_detection - INFO - Sub-channel long\n",
-      "2021-05-27 18:25:27,789 - stpipe.Spec3Pipeline.outlier_detection - INFO - Number of input models 4 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 18:25:31,219 - stpipe.Spec3Pipeline.outlier_detection - INFO - Blotting back to det_image_seq1_MIRIFUSHORT_12LONGexp1_od_test.fits\n",
-      "2021-05-27 18:26:22,282 - stpipe.Spec3Pipeline.outlier_detection - INFO - Blotting back to det_image_seq2_MIRIFUSHORT_12LONGexp1_cal.fits\n",
-      "2021-05-27 18:27:12,425 - stpipe.Spec3Pipeline.outlier_detection - INFO - Blotting back to det_image_seq3_MIRIFUSHORT_12LONGexp1_cal.fits\n",
-      "2021-05-27 18:28:03,569 - stpipe.Spec3Pipeline.outlier_detection - INFO - Blotting back to det_image_seq4_MIRIFUSHORT_12LONGexp1_cal.fits\n",
-      "2021-05-27 18:28:53,019 - stpipe.CubeBuildStep - INFO - CubeBuildStep instance created.\n",
-      "2021-05-27 18:28:53,020 - stpipe.CubeBuildStep - INFO - Starting IFU Cube Building Step\n",
-      "2021-05-27 18:28:53,021 - stpipe.CubeBuildStep - INFO - Input interpolation: pointcloud\n",
-      "2021-05-27 18:28:53,022 - stpipe.CubeBuildStep - INFO - Coordinate system to use: skyalign\n",
-      "2021-05-27 18:28:53,022 - stpipe.CubeBuildStep - INFO - Weighting method for point cloud: emsm\n",
-      "2021-05-27 18:28:53,023 - stpipe.CubeBuildStep - INFO - Power weighting distance: 2.0\n",
-      "2021-05-27 18:28:53,024 - stpipe.CubeBuildStep - INFO - Cube Type: Single cubes\n",
-      "2021-05-27 18:28:53,076 - stpipe.Spec3Pipeline.outlier_detection - INFO - The desired cubes cover the MIRI Channels: ['2']\n",
-      "2021-05-27 18:28:53,078 - stpipe.Spec3Pipeline.outlier_detection - INFO - The desired cubes cover the MIRI subchannels: ['long']\n",
-      "2021-05-27 18:28:53,079 - stpipe.Spec3Pipeline.outlier_detection - INFO - Reading cube parameter file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_cubepar_0005.fits\n",
-      "2021-05-27 18:28:54,218 - stpipe.Spec3Pipeline.outlier_detection - INFO - Single = true, creating a set of single exposures mapped to output IFUCube coordinate system\n",
-      "2021-05-27 18:28:54,223 - stpipe.Spec3Pipeline.outlier_detection - INFO - Cube Geometry:\n",
-      "2021-05-27 18:28:54,224 - stpipe.Spec3Pipeline.outlier_detection - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(arcsec)  Min & Max (xi, eta arcsec)\n",
-      "2021-05-27 18:28:54,225 - stpipe.Spec3Pipeline.outlier_detection - INFO - Axis 1    45  23.00   0.00001418   0.17000000  -3.82500004   3.82500004\n",
-      "2021-05-27 18:28:54,226 - stpipe.Spec3Pipeline.outlier_detection - INFO - Axis 2    39  20.00  -0.00012457   0.17000000  -3.31500003   3.31500003\n",
-      "2021-05-27 18:28:54,227 - stpipe.Spec3Pipeline.outlier_detection - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(microns)  Min & Max (microns)\n",
-      "2021-05-27 18:28:54,228 - stpipe.Spec3Pipeline.outlier_detection - INFO - Axis 3   855   1.00  10.03099973   0.00200000  10.02999973  11.73999981\n",
-      "2021-05-27 18:28:54,228 - stpipe.Spec3Pipeline.outlier_detection - INFO - Cube covers channel, subchannel: 2, long\n",
-      "2021-05-27 18:28:54,324 - stpipe.Spec3Pipeline.outlier_detection - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/utils.py:72: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  indx = np.asarray(np.floor(np.asarray(value) + 0.5), dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:28:54,567 - stpipe.Spec3Pipeline.outlier_detection - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
-      "  lon = np.mod(lon, 360.0 * u.deg if nquant else 360.0)\n",
-      "\n",
-      "2021-05-27 18:28:54,602 - stpipe.Spec3Pipeline.outlier_detection - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:28:54,603 - stpipe.Spec3Pipeline.outlier_detection - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:29:49,238 - stpipe.Spec3Pipeline.outlier_detection - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:29:49,239 - stpipe.Spec3Pipeline.outlier_detection - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:30:43,180 - stpipe.Spec3Pipeline.outlier_detection - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:30:43,181 - stpipe.Spec3Pipeline.outlier_detection - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:31:38,442 - stpipe.Spec3Pipeline.outlier_detection - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:31:38,443 - stpipe.Spec3Pipeline.outlier_detection - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:32:29,111 - stpipe.CubeBuildStep - INFO - Number of Single IFUCube models returned 4 \n",
-      "2021-05-27 18:32:29,114 - stpipe.Spec3Pipeline.outlier_detection - INFO - Update S_REGION to POLYGON ICRS  0.001053067 -0.001021792 0.001053067 0.000772652 359.998975289 0.000772652 359.998975289 -0.001021792\n",
-      "2021-05-27 18:32:29,118 - stpipe.Spec3Pipeline.outlier_detection - INFO - Update S_REGION to POLYGON ICRS  0.001053067 -0.001021792 0.001053067 0.000772652 359.998975289 0.000772652 359.998975289 -0.001021792\n",
-      "2021-05-27 18:32:29,121 - stpipe.Spec3Pipeline.outlier_detection - INFO - Update S_REGION to POLYGON ICRS  0.001053067 -0.001021792 0.001053067 0.000772652 359.998975289 0.000772652 359.998975289 -0.001021792\n",
-      "2021-05-27 18:32:29,125 - stpipe.Spec3Pipeline.outlier_detection - INFO - Update S_REGION to POLYGON ICRS  0.001053067 -0.001021792 0.001053067 0.000772652 359.998975289 0.000772652 359.998975289 -0.001021792\n",
-      "2021-05-27 18:32:29,132 - stpipe.Spec3Pipeline.outlier_detection - INFO - Writing out (single) IFU cube stage3/det_image_seq1_MIRIFUSHORT_12LONGexp1_od_test_ch2-long_single_a3001__outlier_s3d.fits.fits\n",
-      "2021-05-27 18:32:29,690 - stpipe.Spec3Pipeline.outlier_detection - INFO - Writing out (single) IFU cube stage3/det_image_seq2_MIRIFUSHORT_12LONGexp1_cal_ch2-long_single_a3001__outlier_s3d.fits.fits\n",
-      "2021-05-27 18:32:30,251 - stpipe.Spec3Pipeline.outlier_detection - INFO - Writing out (single) IFU cube stage3/det_image_seq3_MIRIFUSHORT_12LONGexp1_cal_ch2-long_single_a3001__outlier_s3d.fits.fits\n",
-      "2021-05-27 18:32:30,813 - stpipe.Spec3Pipeline.outlier_detection - INFO - Writing out (single) IFU cube stage3/det_image_seq4_MIRIFUSHORT_12LONGexp1_cal_ch2-long_single_a3001__outlier_s3d.fits.fits\n",
-      "2021-05-27 18:32:31,580 - stpipe.Spec3Pipeline.outlier_detection - INFO - Writing out MEDIAN image to: stage3/det_image_seq1_MIRIFUSHORT_12LONGexp1_od_test_a3001_band2_median.fits\n",
-      "2021-05-27 18:32:32,284 - stpipe.Spec3Pipeline.outlier_detection - INFO - Information on Blotting\n",
-      "2021-05-27 18:32:32,285 - stpipe.Spec3Pipeline.outlier_detection - INFO - Working with instrument MIRI\n",
-      "2021-05-27 18:32:32,285 - stpipe.Spec3Pipeline.outlier_detection - INFO - shape of sky cube 45.000000 39.000000 855.000000\n",
-      "2021-05-27 18:32:32,286 - stpipe.Spec3Pipeline.outlier_detection - INFO - Channel 2\n",
-      "2021-05-27 18:32:32,287 - stpipe.Spec3Pipeline.outlier_detection - INFO - Sub-channel long\n",
-      "2021-05-27 18:32:32,288 - stpipe.Spec3Pipeline.outlier_detection - INFO - Number of input models 4 \n",
-      "2021-05-27 18:32:34,168 - stpipe.Spec3Pipeline.outlier_detection - INFO - Blotting back to det_image_seq1_MIRIFUSHORT_12LONGexp1_od_test.fits\n",
-      "2021-05-27 18:33:08,899 - stpipe.Spec3Pipeline.outlier_detection - INFO - Blotting back to det_image_seq2_MIRIFUSHORT_12LONGexp1_cal.fits\n",
-      "2021-05-27 18:33:43,292 - stpipe.Spec3Pipeline.outlier_detection - INFO - Blotting back to det_image_seq3_MIRIFUSHORT_12LONGexp1_cal.fits\n",
-      "2021-05-27 18:34:18,172 - stpipe.Spec3Pipeline.outlier_detection - INFO - Blotting back to det_image_seq4_MIRIFUSHORT_12LONGexp1_cal.fits\n",
-      "2021-05-27 18:34:50,442 - stpipe.Spec3Pipeline.outlier_detection - INFO - Writing out BLOT images...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 18:34:55,073 - stpipe.Spec3Pipeline.outlier_detection - INFO - Blotted files det_image_seq1_MIRIFUSHORT_12LONGexp1_od_test_a3001_0_blot.fits\n",
-      "2021-05-27 18:34:55,074 - stpipe.Spec3Pipeline.outlier_detection - INFO - Blotted files det_image_seq2_MIRIFUSHORT_12LONGexp1_a3001_1_blot.fits\n",
-      "2021-05-27 18:34:55,075 - stpipe.Spec3Pipeline.outlier_detection - INFO - Blotted files det_image_seq3_MIRIFUSHORT_12LONGexp1_a3001_2_blot.fits\n",
-      "2021-05-27 18:34:55,076 - stpipe.Spec3Pipeline.outlier_detection - INFO - Blotted files det_image_seq4_MIRIFUSHORT_12LONGexp1_a3001_3_blot.fits\n",
-      "2021-05-27 18:35:01,496 - stpipe.Spec3Pipeline.outlier_detection - INFO - Saved model in stage3/det_image_seq1_MIRIFUSHORT_12LONGexp1_od_test_a3001_crf.fits\n",
-      "2021-05-27 18:35:02,528 - stpipe.Spec3Pipeline.outlier_detection - INFO - Saved model in stage3/det_image_seq2_MIRIFUSHORT_12LONGexp1_a3001_crf.fits\n",
-      "2021-05-27 18:35:04,702 - stpipe.Spec3Pipeline.outlier_detection - INFO - Saved model in stage3/det_image_seq3_MIRIFUSHORT_12LONGexp1_a3001_crf.fits\n",
-      "2021-05-27 18:35:05,757 - stpipe.Spec3Pipeline.outlier_detection - INFO - Saved model in stage3/det_image_seq4_MIRIFUSHORT_12LONGexp1_a3001_crf.fits\n",
-      "2021-05-27 18:35:05,758 - stpipe.Spec3Pipeline.outlier_detection - INFO - Step outlier_detection done\n",
-      "2021-05-27 18:35:06,017 - stpipe.Spec3Pipeline.cube_build - INFO - Step cube_build running with args (<ModelContainer>,).\n",
-      "2021-05-27 18:35:06,020 - stpipe.Spec3Pipeline.cube_build - INFO - Step cube_build parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/dlaw/MIRI/JWebbinar/Notebook1/od_after', 'output_dir': None, 'output_ext': '.fits', 'output_use_model': True, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': 's3d', 'search_output_file': False, 'input_dir': '', 'channel': '2', 'band': 'all', 'grating': 'all', 'filter': 'all', 'output_type': 'band', 'scale1': 0.0, 'scale2': 0.0, 'scalew': 0.0, 'weighting': 'emsm', 'coord_system': 'skyalign', 'rois': 0.0, 'roiw': 0.0, 'weight_power': 2.0, 'wavemin': None, 'wavemax': None, 'single': False, 'xdebug': None, 'ydebug': None, 'zdebug': None, 'skip_dqflagging': False}\n",
-      "2021-05-27 18:35:06,021 - stpipe.Spec3Pipeline.cube_build - INFO - Starting IFU Cube Building Step\n",
-      "2021-05-27 18:35:06,022 - stpipe.Spec3Pipeline.cube_build - INFO - Input interpolation: pointcloud\n",
-      "2021-05-27 18:35:06,023 - stpipe.Spec3Pipeline.cube_build - INFO - Coordinate system to use: skyalign\n",
-      "2021-05-27 18:35:06,024 - stpipe.Spec3Pipeline.cube_build - INFO - Weighting method for point cloud: emsm\n",
-      "2021-05-27 18:35:06,024 - stpipe.Spec3Pipeline.cube_build - INFO - Power weighting distance: 2.0\n",
-      "2021-05-27 18:35:06,080 - stpipe.Spec3Pipeline.cube_build - INFO - The desired cubes cover the MIRI Channels: ['2']\n",
-      "2021-05-27 18:35:06,081 - stpipe.Spec3Pipeline.cube_build - INFO - The desired cubes cover the MIRI subchannels: ['long']\n",
-      "2021-05-27 18:35:06,081 - stpipe.Spec3Pipeline.cube_build - INFO - Reading cube parameter file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_cubepar_0005.fits\n",
-      "2021-05-27 18:35:07,221 - stpipe.Spec3Pipeline.cube_build - INFO - The user has selected the type of IFU cube to make\n",
-      "2021-05-27 18:35:07,222 - stpipe.Spec3Pipeline.cube_build - INFO - Number of IFU cubes produced by this run = 1\n",
-      "2021-05-27 18:35:07,230 - stpipe.Spec3Pipeline.cube_build - INFO - Cube Geometry:\n",
-      "2021-05-27 18:35:07,231 - stpipe.Spec3Pipeline.cube_build - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(arcsec)  Min & Max (xi, eta arcsec)\n",
-      "2021-05-27 18:35:07,231 - stpipe.Spec3Pipeline.cube_build - INFO - Axis 1    45  23.00   0.00001418   0.17000000  -3.82500004   3.82500004\n",
-      "2021-05-27 18:35:07,232 - stpipe.Spec3Pipeline.cube_build - INFO - Axis 2    39  20.00  -0.00012457   0.17000000  -3.31500003   3.31500003\n",
-      "2021-05-27 18:35:07,233 - stpipe.Spec3Pipeline.cube_build - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(microns)  Min & Max (microns)\n",
-      "2021-05-27 18:35:07,234 - stpipe.Spec3Pipeline.cube_build - INFO - Axis 3   855   1.00  10.03099973   0.00200000  10.02999973  11.73999981\n",
-      "2021-05-27 18:35:07,235 - stpipe.Spec3Pipeline.cube_build - INFO - Cube covers channel, subchannel: 2, long\n",
-      "2021-05-27 18:35:07,236 - stpipe.Spec3Pipeline.cube_build - INFO - Output Name: od_after_ch2-long_s3d.fits\n",
-      "2021-05-27 18:35:07,322 - stpipe.Spec3Pipeline.cube_build - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/utils.py:72: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  indx = np.asarray(np.floor(np.asarray(value) + 0.5), dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:35:07,561 - stpipe.Spec3Pipeline.cube_build - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
-      "  lon = np.mod(lon, 360.0 * u.deg if nquant else 360.0)\n",
-      "\n",
-      "2021-05-27 18:35:07,597 - stpipe.Spec3Pipeline.cube_build - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:35:07,598 - stpipe.Spec3Pipeline.cube_build - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:36:17,740 - stpipe.Spec3Pipeline.cube_build - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:36:17,741 - stpipe.Spec3Pipeline.cube_build - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:37:24,942 - stpipe.Spec3Pipeline.cube_build - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:37:24,943 - stpipe.Spec3Pipeline.cube_build - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:38:33,709 - stpipe.Spec3Pipeline.cube_build - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:38:33,710 - stpipe.Spec3Pipeline.cube_build - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:39:41,285 - stpipe.Spec3Pipeline.cube_build - INFO - Average # of holes/wavelength plane: 46\n",
-      "2021-05-27 18:39:41,286 - stpipe.Spec3Pipeline.cube_build - INFO - Total # of holes for IFU cube is : 39506\n",
-      "2021-05-27 18:39:42,947 - stpipe.Spec3Pipeline.cube_build - INFO - Update S_REGION to POLYGON ICRS  0.001053067 -0.001021792 0.001053067 0.000772652 359.998975289 0.000772652 359.998975289 -0.001021792\n",
-      "2021-05-27 18:39:43,474 - stpipe.Spec3Pipeline.cube_build - INFO - Saved model in stage3/od_after_ch2-long_s3d.fits\n",
-      "2021-05-27 18:39:43,475 - stpipe.Spec3Pipeline.cube_build - INFO - Step cube_build done\n",
-      "2021-05-27 18:39:43,797 - stpipe.Spec3Pipeline.extract_1d - INFO - Step extract_1d running with args (<ModelContainer>,).\n",
-      "2021-05-27 18:39:43,800 - stpipe.Spec3Pipeline.extract_1d - INFO - Step extract_1d parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': True, 'suffix': 'x1d', 'search_output_file': False, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': 'poly', 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'apply_apcorr': True}\n",
-      "2021-05-27 18:39:43,801 - stpipe.Spec3Pipeline.extract_1d - INFO - Step skipped.\n",
-      "2021-05-27 18:39:43,804 - stpipe.Spec3Pipeline.extract_1d - INFO - Step extract_1d done\n",
-      "2021-05-27 18:39:43,807 - stpipe.Spec3Pipeline - INFO - Ending calwebb_spec3\n",
-      "2021-05-27 18:39:43,808 - stpipe.Spec3Pipeline - INFO - Step Spec3Pipeline done\n"
+      "2021-06-18 13:08:17,828 - stpipe - INFO - PARS-OUTLIERDETECTIONSTEP parameters found: /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_pars-outlierdetectionstep_0050.asdf\n",
+      "2021-06-18 13:08:17,838 - stpipe - INFO - PARS-OUTLIERDETECTIONSTEP parameters are {'scale': '7.5 7.5'}\n",
+      "2021-06-18 13:08:24,667 - stpipe.Spec3Pipeline - INFO - Spec3Pipeline instance created.\n",
+      "2021-06-18 13:08:24,668 - stpipe.Spec3Pipeline.assign_mtwcs - INFO - AssignMTWcsStep instance created.\n",
+      "2021-06-18 13:08:24,670 - stpipe.Spec3Pipeline.master_background - INFO - MasterBackgroundStep instance created.\n",
+      "2021-06-18 13:08:24,672 - stpipe.Spec3Pipeline.mrs_imatch - INFO - MRSIMatchStep instance created.\n",
+      "2021-06-18 13:08:24,674 - stpipe.Spec3Pipeline.outlier_detection - INFO - OutlierDetectionStep instance created.\n",
+      "2021-06-18 13:08:24,676 - stpipe.Spec3Pipeline.resample_spec - INFO - ResampleSpecStep instance created.\n",
+      "2021-06-18 13:08:24,678 - stpipe.Spec3Pipeline.cube_build - INFO - CubeBuildStep instance created.\n",
+      "2021-06-18 13:08:24,680 - stpipe.Spec3Pipeline.extract_1d - INFO - Extract1dStep instance created.\n",
+      "2021-06-18 13:08:24,682 - stpipe.Spec3Pipeline.combine_1d - INFO - Combine1dStep instance created.\n"
      ]
     }
    ],
@@ -4689,46 +2935,37 @@
     "    spec3('od.json')\n",
     "# Otherwise, just copy cached outputs into our output directory structure\n",
     "else:\n",
-    "    sstring=cache_dir+spec3_dir+'od_after*s3d.fits'\n",
-    "    files=sorted(glob.glob(sstring))\n",
+    "    sstring = cache_dir + spec3_dir + 'od_after*s3d.fits'\n",
+    "    files = sorted(glob.glob(sstring))\n",
     "    for file in files:\n",
-    "        outfile=str.replace(file,cache_dir,'./')\n",
+    "        outfile = str.replace(file,cache_dir,'./')\n",
     "        shutil.copy(file,outfile)\n",
-    "    sstring=cache_dir+spec3_dir+'*crf.fits'\n",
-    "    files=sorted(glob.glob(sstring))\n",
+    "    sstring = cache_dir + spec3_dir + '*crf.fits'\n",
+    "    files = sorted(glob.glob(sstring))\n",
     "    for file in files:\n",
-    "        outfile=str.replace(file,cache_dir,'./')\n",
+    "        outfile = str.replace(file,cache_dir,'./')\n",
     "        shutil.copy(file,outfile)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 73,
    "id": "31e36b94",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 18:39:43,813 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n",
-      "\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fcd1cdced30>]"
+       "[<matplotlib.lines.Line2D at 0x7ff11249fb50>]"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 700x700 with 2 Axes>"
       ]
@@ -4741,9 +2978,9 @@
    ],
    "source": [
     "# Show the image and mask\n",
-    "hdu=fits.open(spec3_dir+'det_image_seq1_MIRIFUSHORT_12LONGexp1_od_test_a3001_crf.fits')\n",
-    "flux=hdu['SCI'].data\n",
-    "dq=hdu['DQ'].data\n",
+    "hdu = fits.open(spec3_dir + 'det_image_seq1_MIRIFUSHORT_12LONGexp1_od_test_a3001_crf.fits')\n",
+    "flux = hdu['SCI'].data\n",
+    "dq = hdu['DQ'].data\n",
     "\n",
     "# Use a classic ZScale normalization\n",
     "norm = ImageNormalize(flux, interval=ZScaleInterval(),stretch=LinearStretch())\n",
@@ -4773,12 +3010,21 @@
    "id": "1be5bbf2",
    "metadata": {},
    "source": [
-    "<b>Figure 9:</b> SCI and ERR extensions for a MIRI 2d calibrated detector image in which one column has been deliberately set to an erroneous value.  Note that the DQ array identifies this erroneous column after the outlier detection step has been run."
+    "<b>Figure 10:</b> SCI and ERR extensions for a MIRI 2d calibrated detector image in which one column has been deliberately set to an erroneous value.  Note that the DQ array identifies this erroneous column after the outlier detection step has been run."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0df9f20",
+   "metadata": {},
+   "source": [
+    "That DQ array is pretty messy!  What is it telling us?\n",
+    "We can see that the column is flagged in our DQ array; let's examine what the DQ flag value is"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 74,
    "id": "140630f7",
    "metadata": {},
    "outputs": [
@@ -4791,14 +3037,20 @@
     }
    ],
    "source": [
-    "# That DQ array is pretty messy!  What is it telling us?\n",
-    "# We can see that the column is flagged in our DQ array; let's examine what the DQ flag value is\n",
     "print(dq[60,925])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "a16977d9",
+   "metadata": {},
+   "source": [
+    "That's not very useful on its own, but we can ask the pipeline to tell us what this DQ flag value actually means:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 75,
    "id": "39af431d",
    "metadata": {},
    "outputs": [
@@ -4808,13 +3060,12 @@
        "{'DO_NOT_USE', 'OUTLIER'}"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# That's not very useful on its own, but we can ask the pipeline to tell us what this DQ flag value actually means:\n",
     "dqflags.dqflags_to_mnemonics(dq[60,925],mnemonic_map=datamodels.dqflags.pixel)"
    ]
   },
@@ -4828,7 +3079,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 76,
    "id": "7371ba15",
    "metadata": {},
    "outputs": [],
@@ -4839,23 +3090,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 77,
    "id": "2af46a2b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7fcd29649fd0>"
+       "<matplotlib.collections.PathCollection at 0x7ff113e471c0>"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 700x700 with 2 Axes>"
       ]
@@ -4868,12 +3119,12 @@
    ],
    "source": [
     "# Cube without outlier rejection\n",
-    "hdu1=fits.open(spec3_dir+'od_before_ch2-long_s3d.fits')\n",
-    "flux1=hdu1['SCI'].data\n",
+    "hdu1 = fits.open(spec3_dir + 'od_before_ch2-long_s3d.fits')\n",
+    "flux1 = hdu1['SCI'].data\n",
     "\n",
     "# Cube with outlier rejection\n",
-    "hdu2=fits.open(spec3_dir+'od_after_ch2-long_s3d.fits')\n",
-    "flux2=hdu2['SCI'].data\n",
+    "hdu2 = fits.open(spec3_dir + 'od_after_ch2-long_s3d.fits')\n",
+    "flux2 = hdu2['SCI'].data\n",
     "\n",
     "# Use a classic ZScale normalization\n",
     "norm = ImageNormalize(flux1, interval=ZScaleInterval(),stretch=LinearStretch())\n",
@@ -4896,12 +3147,12 @@
    "id": "c8bc37de",
    "metadata": {},
    "source": [
-    "<b>Figure 10:</b> 3d data cubes constructed with and without the outlier detection step.  The red circle denotes the location of the outlier that we injected into the data."
+    "<b>Figure 11:</b> 3d data cubes constructed with and without the outlier detection step.  The red circle denotes the location of the outlier that we injected into the data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 78,
    "id": "d342b7d3",
    "metadata": {},
    "outputs": [],
@@ -4911,14 +3162,6 @@
     "hdu2.close()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2f93c7a9",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "47a7cb05",
@@ -4939,7 +3182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 79,
    "id": "99456897",
    "metadata": {
     "scrolled": true
@@ -4949,90 +3192,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2021-05-27 18:39:44,344 - stpipe.CubeBuildStep - INFO - CubeBuildStep instance created.\n",
-      "2021-05-27 18:39:44,720 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:39:46,041 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:39:47,422 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:39:48,402 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:39:49,812 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:39:50,412 - stpipe.CubeBuildStep - INFO - CubeBuildStep instance created.\n",
-      "2021-05-27 18:39:50,900 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep running with args ('l3.json',).\n",
-      "2021-05-27 18:39:50,903 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage3/', 'output_ext': '.fits', 'output_use_model': True, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': False, 'input_dir': '', 'channel': '2', 'band': 'all', 'grating': 'all', 'filter': 'all', 'output_type': 'band', 'scale1': 0.0, 'scale2': 0.0, 'scalew': 0.0, 'weighting': 'emsm', 'coord_system': 'skyalign', 'rois': 0.0, 'roiw': 0.0, 'weight_power': 2.0, 'wavemin': None, 'wavemax': None, 'single': False, 'xdebug': None, 'ydebug': None, 'zdebug': None, 'skip_dqflagging': False}\n",
-      "2021-05-27 18:39:50,904 - stpipe.CubeBuildStep - INFO - Starting IFU Cube Building Step\n",
-      "2021-05-27 18:39:50,905 - stpipe.CubeBuildStep - INFO - Input interpolation: pointcloud\n",
-      "2021-05-27 18:39:50,906 - stpipe.CubeBuildStep - INFO - Coordinate system to use: skyalign\n",
-      "2021-05-27 18:39:50,907 - stpipe.CubeBuildStep - INFO - Weighting method for point cloud: emsm\n",
-      "2021-05-27 18:39:50,908 - stpipe.CubeBuildStep - INFO - Power weighting distance: 2.0\n",
-      "2021-05-27 18:39:51,300 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:39:52,261 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:39:53,824 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:39:55,181 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:39:55,841 - stpipe.CubeBuildStep - INFO - The desired cubes cover the MIRI Channels: ['2']\n",
-      "2021-05-27 18:39:55,842 - stpipe.CubeBuildStep - INFO - The desired cubes cover the MIRI subchannels: ['long']\n",
-      "2021-05-27 18:39:55,843 - stpipe.CubeBuildStep - INFO - Reading cube parameter file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_cubepar_0005.fits\n",
-      "2021-05-27 18:39:56,895 - stpipe.CubeBuildStep - INFO - The user has selected the type of IFU cube to make\n",
-      "2021-05-27 18:39:56,897 - stpipe.CubeBuildStep - INFO - Number of IFU cubes produced by this run = 1\n",
-      "2021-05-27 18:39:56,903 - stpipe.CubeBuildStep - INFO - Cube Geometry:\n",
-      "2021-05-27 18:39:56,904 - stpipe.CubeBuildStep - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(arcsec)  Min & Max (xi, eta arcsec)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 18:39:56,905 - stpipe.CubeBuildStep - INFO - Axis 1    45  23.00   0.00001418   0.17000000  -3.82500004   3.82500004\n",
-      "2021-05-27 18:39:56,906 - stpipe.CubeBuildStep - INFO - Axis 2    39  20.00  -0.00012457   0.17000000  -3.31500003   3.31500003\n",
-      "2021-05-27 18:39:56,907 - stpipe.CubeBuildStep - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(microns)  Min & Max (microns)\n",
-      "2021-05-27 18:39:56,908 - stpipe.CubeBuildStep - INFO - Axis 3   855   1.00  10.03099973   0.00200000  10.02999973  11.73999981\n",
-      "2021-05-27 18:39:56,909 - stpipe.CubeBuildStep - INFO - Cube covers channel, subchannel: 2, long\n",
-      "2021-05-27 18:39:56,910 - stpipe.CubeBuildStep - INFO - Output Name: stage3//l3_ch2-long_s3d.fits\n",
-      "2021-05-27 18:39:56,990 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/utils.py:72: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  indx = np.asarray(np.floor(np.asarray(value) + 0.5), dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:39:57,219 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
-      "  lon = np.mod(lon, 360.0 * u.deg if nquant else 360.0)\n",
-      "\n",
-      "2021-05-27 18:39:57,253 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:39:57,254 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:41:06,196 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:41:06,197 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:42:14,618 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:42:14,619 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:43:22,728 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:43:22,729 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:44:31,262 - stpipe.CubeBuildStep - INFO - Average # of holes/wavelength plane: 46\n",
-      "2021-05-27 18:44:31,263 - stpipe.CubeBuildStep - INFO - Total # of holes for IFU cube is : 39569\n",
-      "2021-05-27 18:44:33,394 - stpipe.CubeBuildStep - INFO - Update S_REGION to POLYGON ICRS  0.001053067 -0.001021792 0.001053067 0.000772652 359.998975289 0.000772652 359.998975289 -0.001021792\n",
-      "2021-05-27 18:44:33,937 - stpipe.CubeBuildStep - INFO - Saved model in stage3/l3_ch2-long_s3d.fits\n",
-      "2021-05-27 18:44:33,939 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep done\n"
+      "2021-06-18 13:08:25,491 - stpipe.CubeBuildStep - INFO - CubeBuildStep instance created.\n"
      ]
     }
    ],
@@ -5041,17 +3201,17 @@
     "writel3asn(calfiles,'l3.json','l3')\n",
     "\n",
     "# And run it through cube building (we'll just build a cube for the Ch2 data as an example to save time)\n",
-    "cb=CubeBuildStep()\n",
+    "cb = CubeBuildStep()\n",
     "\n",
     "# If rerunning long pipeline steps, actually run the step\n",
     "if (redolong == True):\n",
     "    cb.call('l3.json',channel='2',save_results=True,output_dir=spec3_dir)\n",
     "# Otherwise, just copy cached outputs into our output directory structure\n",
     "else:\n",
-    "    sstring=cache_dir+spec3_dir+'l3*s3d.fits'\n",
-    "    files=sorted(glob.glob(sstring))\n",
+    "    sstring = cache_dir + spec3_dir + 'l3*s3d.fits'\n",
+    "    files = sorted(glob.glob(sstring))\n",
     "    for file in files:\n",
-    "        outfile=str.replace(file,cache_dir,'./')\n",
+    "        outfile = str.replace(file,cache_dir,'./')\n",
     "        shutil.copy(file,outfile)"
    ]
   },
@@ -5065,7 +3225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 80,
    "id": "788c63af",
    "metadata": {
     "scrolled": true
@@ -5075,144 +3235,46 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2021-05-27 18:44:33,944 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n",
-      "\n",
-      "2021-05-27 18:44:33,949 - stpipe.CubeBuildStep - INFO - CubeBuildStep instance created.\n",
-      "2021-05-27 18:44:34,332 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:44:35,877 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:44:36,857 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:44:38,545 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:44:40,035 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:44:40,657 - stpipe.CubeBuildStep - INFO - CubeBuildStep instance created.\n",
-      "2021-05-27 18:44:41,185 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep running with args ('l3.json',).\n",
-      "2021-05-27 18:44:41,189 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': '/Users/dlaw/MIRI/JWebbinar/Notebook1/rotated', 'output_dir': 'stage3/', 'output_ext': '.fits', 'output_use_model': True, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': False, 'input_dir': '', 'channel': '2', 'band': 'all', 'grating': 'all', 'filter': 'all', 'output_type': 'band', 'scale1': 0.0, 'scale2': 0.0, 'scalew': 0.0, 'weighting': 'emsm', 'coord_system': 'ifualign', 'rois': 0.0, 'roiw': 0.0, 'weight_power': 2.0, 'wavemin': None, 'wavemax': None, 'single': False, 'xdebug': None, 'ydebug': None, 'zdebug': None, 'skip_dqflagging': False}\n",
-      "2021-05-27 18:44:41,190 - stpipe.CubeBuildStep - INFO - Starting IFU Cube Building Step\n",
-      "2021-05-27 18:44:41,191 - stpipe.CubeBuildStep - INFO - Input interpolation: pointcloud\n",
-      "2021-05-27 18:44:41,192 - stpipe.CubeBuildStep - INFO - Coordinate system to use: ifualign\n",
-      "2021-05-27 18:44:41,193 - stpipe.CubeBuildStep - INFO - Weighting method for point cloud: emsm\n",
-      "2021-05-27 18:44:41,194 - stpipe.CubeBuildStep - INFO - Power weighting distance: 2.0\n",
-      "2021-05-27 18:44:41,578 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:44:42,550 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:44:44,233 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:44:45,636 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/selector.py:185: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  mapper = np.asanyarray(mapper, dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:44:46,290 - stpipe.CubeBuildStep - INFO - The desired cubes cover the MIRI Channels: ['2']\n",
-      "2021-05-27 18:44:46,291 - stpipe.CubeBuildStep - INFO - The desired cubes cover the MIRI subchannels: ['long']\n",
-      "2021-05-27 18:44:46,292 - stpipe.CubeBuildStep - INFO - Reading cube parameter file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_cubepar_0005.fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 18:44:47,374 - stpipe.CubeBuildStep - INFO - The user has selected the type of IFU cube to make\n",
-      "2021-05-27 18:44:47,375 - stpipe.CubeBuildStep - INFO - Number of IFU cubes produced by this run = 1\n",
-      "2021-05-27 18:44:47,379 - stpipe.CubeBuildStep - INFO - Defining rotation between ra-dec and IFU plane using 2, long\n",
-      "2021-05-27 18:44:47,393 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/utils.py:72: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  indx = np.asarray(np.floor(np.asarray(value) + 0.5), dtype=np.int)\n",
-      "\n",
-      "2021-05-27 18:44:47,510 - stpipe.CubeBuildStep - INFO - Rotation angle between ifu and sky: -170.79528872335018\n",
-      "2021-05-27 18:44:47,514 - stpipe.CubeBuildStep - INFO - Cube Geometry:\n",
-      "2021-05-27 18:44:47,515 - stpipe.CubeBuildStep - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(arcsec)  Min & Max (xi, eta arcsec)\n",
-      "2021-05-27 18:44:47,515 - stpipe.CubeBuildStep - INFO - Axis 1    49  25.00   0.00001418   0.17000000  -4.16500004   4.16500004\n",
-      "2021-05-27 18:44:47,516 - stpipe.CubeBuildStep - INFO - Axis 2    45  23.00  -0.00012457   0.17000000  -3.82500004   3.82500004\n",
-      "2021-05-27 18:44:47,517 - stpipe.CubeBuildStep - INFO - axis#  Naxis  CRPIX    CRVAL      CDELT(microns)  Min & Max (microns)\n",
-      "2021-05-27 18:44:47,518 - stpipe.CubeBuildStep - INFO - Axis 3   855   1.00  10.03099973   0.00200000  10.02999973  11.73999981\n",
-      "2021-05-27 18:44:47,519 - stpipe.CubeBuildStep - INFO - Rotation angle between Ra-Dec and Slicer-Plane -170.79528872\n",
-      "2021-05-27 18:44:47,519 - stpipe.CubeBuildStep - INFO - Cube covers channel, subchannel: 2, long\n",
-      "2021-05-27 18:44:47,522 - stpipe.CubeBuildStep - INFO - Output Name: stage3//rotated_ch2-long_s3d.fits\n",
-      "2021-05-27 18:44:47,829 - stpipe.CubeBuildStep - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/gwcs/geometry.py:203: RuntimeWarning: invalid value encountered in remainder\n",
-      "  lon = np.mod(lon, 360.0 * u.deg if nquant else 360.0)\n",
-      "\n",
-      "2021-05-27 18:44:47,862 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:44:47,863 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:45:50,300 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:45:50,301 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:46:53,196 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:46:53,197 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:47:55,638 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 7678 with wavelength below 10.02499973308295\n",
-      "2021-05-27 18:47:55,639 - stpipe.CubeBuildStep - INFO - # of detector pixels not mapped to output plane: 11501 with wavelength above 11.744999814080074\n",
-      "2021-05-27 18:49:00,069 - stpipe.CubeBuildStep - INFO - Average # of holes/wavelength plane: 32\n",
-      "2021-05-27 18:49:00,070 - stpipe.CubeBuildStep - INFO - Total # of holes for IFU cube is : 27819\n",
-      "2021-05-27 18:49:02,194 - stpipe.CubeBuildStep - INFO - Update S_REGION to POLYGON ICRS  0.000966734 -0.001331372 0.000966734 0.001082232 359.999061622 0.001082232 359.999061622 -0.001331372\n",
-      "2021-05-27 18:49:02,751 - stpipe.CubeBuildStep - INFO - Saved model in stage3/rotated_ch2-long_s3d.fits\n",
-      "2021-05-27 18:49:02,752 - stpipe.CubeBuildStep - INFO - Step CubeBuildStep done\n"
+      "2021-06-18 13:08:25,536 - stpipe.CubeBuildStep - INFO - CubeBuildStep instance created.\n"
      ]
     }
    ],
    "source": [
     "# Build a rotated-frame cube\n",
     "\n",
-    "cb=CubeBuildStep()\n",
+    "cb = CubeBuildStep()\n",
     "\n",
     "# If rerunning long pipeline steps, actually run the step\n",
     "if (redolong == True):\n",
     "    cb.call('l3.json',channel='2',save_results=True,output_dir=spec3_dir,coord_system='ifualign',output_file='rotated')\n",
     "# Otherwise, just copy cached outputs into our output directory structure\n",
     "else:\n",
-    "    sstring=cache_dir+spec3_dir+'rotated*s3d.fits'\n",
-    "    files=sorted(glob.glob(sstring))\n",
+    "    sstring = cache_dir + spec3_dir + 'rotated*s3d.fits'\n",
+    "    files = sorted(glob.glob(sstring))\n",
     "    for file in files:\n",
-    "        outfile=str.replace(file,cache_dir,'./')\n",
+    "        outfile = str.replace(file,cache_dir,'./')\n",
     "        shutil.copy(file,outfile)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 81,
    "id": "1052a894",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 18:49:02,757 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n",
-      "\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
        "Text(0.5, 1.0, 'IFUALIGN')"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 700x700 with 2 Axes>"
       ]
@@ -5226,12 +3288,12 @@
    "source": [
     "# Let's take a quick look at these two cubes\n",
     "# Cube without outlier rejection\n",
-    "hdu1=fits.open(spec3_dir+'l3_ch2-long_s3d.fits')\n",
-    "flux1=hdu1['SCI'].data\n",
+    "hdu1 = fits.open(spec3_dir + 'l3_ch2-long_s3d.fits')\n",
+    "flux1 = hdu1['SCI'].data\n",
     "\n",
     "# Cube with outlier rejection\n",
-    "hdu2=fits.open(spec3_dir+'rotated_ch2-long_s3d.fits')\n",
-    "flux2=hdu2['SCI'].data\n",
+    "hdu2 = fits.open(spec3_dir + 'rotated_ch2-long_s3d.fits')\n",
+    "flux2 = hdu2['SCI'].data\n",
     "\n",
     "# Use a classic ZScale normalization with a logarithmic stretch to make sure that\n",
     "# we can see the actual cube footprint well\n",
@@ -5253,12 +3315,12 @@
    "id": "c6a9b2ec",
    "metadata": {},
    "source": [
-    "<b>Figure 11:</b> MIRI data cubes constructed using the 'skyalign' and 'ifualign' coordinate reference frames.  Both have full WCS information embedded in the data so that they can be (e.g.) displayed similarly using tools such as ds9."
+    "<b>Figure 12:</b> MIRI data cubes constructed using the 'skyalign' and 'ifualign' coordinate reference frames.  Both have full WCS information embedded in the data so that they can be (e.g.) displayed similarly using tools such as ds9."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 82,
    "id": "295feb99",
    "metadata": {},
    "outputs": [],
@@ -5268,14 +3330,6 @@
     "hdu2.close()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "02826c9d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "69421c74",
@@ -5294,7 +3348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 83,
    "id": "971d1cfd",
    "metadata": {},
    "outputs": [
@@ -5302,18 +3356,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2021-05-27 18:49:03,150 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n",
-      "2021-05-27 18:49:03,726 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args ('stage3/l3_ch2-long_s3d.fits',).\n",
-      "2021-05-27 18:49:03,728 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage3/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': 'poly', 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'apply_apcorr': True}\n",
-      "2021-05-27 18:49:03,934 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_extract1d_0002.asdf\n",
-      "2021-05-27 18:49:03,949 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_apcorr_0001.asdf\n",
-      "2021-05-27 18:49:05,331 - stpipe.Extract1dStep - INFO - Turning on source position correction for exp_type = MIR_MRS\n",
-      "2021-05-27 18:49:05,332 - stpipe.Extract1dStep - INFO - Source type = POINT\n",
-      "2021-05-27 18:49:05,347 - stpipe.Extract1dStep - INFO - Input model has no variance information. Creating zero-filled arrays.\n",
-      "2021-05-27 18:49:05,355 - stpipe.Extract1dStep - INFO - Using x_center = 22, y_center = 22, based on TARG_RA and TARG_DEC.\n",
-      "2021-05-27 18:49:10,216 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n",
-      "2021-05-27 18:49:10,878 - stpipe.Extract1dStep - INFO - Saved model in stage3/l3_ch2-long_extract1dstep.fits\n",
-      "2021-05-27 18:49:10,879 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n"
+      "2021-06-18 13:08:26,035 - stpipe.Extract1dStep - INFO - Extract1dStep instance created.\n",
+      "2021-06-18 13:08:26,326 - stpipe.Extract1dStep - INFO - Step Extract1dStep running with args ('stage3/l3_ch2-long_s3d.fits',).\n",
+      "2021-06-18 13:08:26,329 - stpipe.Extract1dStep - INFO - Step Extract1dStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': 'stage3/', 'output_ext': '.fits', 'output_use_model': False, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'smoothing_length': None, 'bkg_fit': 'poly', 'bkg_order': None, 'bkg_sigma_clip': 3.0, 'log_increment': 50, 'subtract_background': None, 'use_source_posn': None, 'apply_apcorr': True}\n",
+      "2021-06-18 13:08:26,548 - stpipe.Extract1dStep - INFO - Using EXTRACT1D reference file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_extract1d_0002.asdf\n",
+      "2021-06-18 13:08:26,561 - stpipe.Extract1dStep - INFO - Using APCORR file /Users/dlaw/crds_cache/references/jwst/miri/jwst_miri_apcorr_0001.asdf\n",
+      "2021-06-18 13:08:28,169 - stpipe.Extract1dStep - INFO - Turning on source position correction for exp_type = MIR_MRS\n",
+      "2021-06-18 13:08:28,170 - stpipe.Extract1dStep - INFO - Source type = POINT\n",
+      "2021-06-18 13:08:28,188 - stpipe.Extract1dStep - INFO - Using x_center = 22, y_center = 21, based on TARG_RA and TARG_DEC.\n",
+      "2021-06-18 13:08:30,428 - stpipe.Extract1dStep - INFO - Applying Aperture correction.\n",
+      "2021-06-18 13:08:30,781 - stpipe.Extract1dStep - INFO - Saved model in stage3/l3_ch2-long_extract1dstep.fits\n",
+      "2021-06-18 13:08:30,781 - stpipe.Extract1dStep - INFO - Step Extract1dStep done\n"
      ]
     },
     {
@@ -5322,45 +3375,36 @@
        "<MultiSpecModel from l3_ch2-long_extract1dstep.fits>"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Run the Extract1D step on the final 3d cube\n",
-    "cubefile=spec3_dir+'l3_ch2-long_s3d.fits'\n",
+    "cubefile = spec3_dir + 'l3_ch2-long_s3d.fits'\n",
     "Extract1dStep.call(cubefile,save_results=True,output_dir=spec3_dir)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 84,
    "id": "24ed3779",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 18:49:10,885 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n",
-      "\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
        "Text(0, 0.5, 'Flux (Jy)')"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5373,11 +3417,11 @@
    ],
    "source": [
     "# Let's look at the result\n",
-    "specfile=spec3_dir+'l3_ch2-long_extract1dstep.fits'\n",
+    "specfile = spec3_dir + 'l3_ch2-long_extract1dstep.fits'\n",
     "\n",
     "# Let's look at one of them\n",
-    "hdu=fits.open(specfile)\n",
-    "spec=hdu['EXTRACT1D']\n",
+    "hdu = fits.open(specfile)\n",
+    "spec = hdu['EXTRACT1D']\n",
     "\n",
     "plt.plot(spec.data['WAVELENGTH'],spec.data['FLUX'])\n",
     "plt.xlabel('Wavelength (micron)')\n",
@@ -5389,25 +3433,15 @@
    "id": "edae593b",
    "metadata": {},
    "source": [
-    "<b>Figure 12:</b> Extracted 1d spectrum for band 2C"
+    "<b>Figure 13:</b> Extracted 1d spectrum for band 2C"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 85,
    "id": "d320dee1",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-05-27 18:49:11,015 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
-      "  and should_run_async(code)\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Close our files behind us\n",
     "hdu.close()"
@@ -5415,7 +3449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 86,
    "id": "942a9026",
    "metadata": {},
    "outputs": [
@@ -5423,7 +3457,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Runtime so far: 5333.8839 seconds\n"
+      "Runtime so far: 262.9359 seconds\n"
      ]
     }
    ],
@@ -5456,14 +3490,6 @@
    "source": [
     "<img style=\"float: center;\" src=\"https://github.com/STScI-MIRI/MRS-ExampleNB/raw/main/assets/stsci_logo.png\" alt=\"stsci_logo\" width=\"200px\"/> "
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "959453b2",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/Notebook2/MRS_Notebook2.ipynb b/Notebook2/MRS_Notebook2.ipynb
index 1ca9a68..65d13e9 100644
--- a/Notebook2/MRS_Notebook2.ipynb
+++ b/Notebook2/MRS_Notebook2.ipynb
@@ -24,7 +24,9 @@
    "source": [
     "**Author**: David Law, AURA Associate Astronomer, MIRI branch\n",
     "<br>\n",
-    "**Last Updated**: June 01, 2021"
+    "**Last Updated**: June 18, 2021\n",
+    "<br>\n",
+    "**Pipeline Version**: 1.1.0"
    ]
   },
   {
@@ -82,7 +84,7 @@
     "We will start with a simple simulated MRS observation (created using mirisim: https://wiki.miricle.org/Public/MIRISim_Public) with a dedicated background, process the data through the Detector1 pipeline (which turns raw detector counts into uncalibrated rate images), the Spec2 pipeline (which turns uncalibrated rate images into calibrated rate images), and the Spec3 pipeline (which turns calibrated rate images into composite data cubes and extracted 1d spectra).\n",
     "\n",
     "A few additional caveats:\n",
-    "- This notebook covers the v1.2.0 baseline pipeline as it existed in May 2021.  The pipeline is under continuous development and there are therefore some changes in the latest pipeline build that will not be reflected here.\n",
+    "- This notebook covers the v1.1.0 baseline pipeline as it existed in February 2021. The pipeline is under continuous development and there are therefore some changes in the latest pipeline build that will not be reflected here.\n",
     "- Likewise, there are some advanced algorithms slated for development prior to cycle 1 observations that will not be discussed here."
    ]
   },
@@ -142,7 +144,7 @@
     "\n",
     "In this section we set things up a number of necessary things in order for the pipeline to run successfully.\n",
     "\n",
-    "First we'll set the CRDS context; this dictates the versions of various pipeline reference files to use.  Ordinarily you wouldn't want to set a specific version as the latest pipeline should already use the most-recent reference files (and hard-coding a version could get you old reference files that have since been replaced).  However, it's included here as a reference for how to do so.\n",
+    "First we'll set the CRDS context; this dictates the versions of various pipeline reference files to use.  Ordinarily you wouldn't want to set a specific version as the latest pipeline should already use the most-recent reference files (and hard-coding a version could get you old reference files that have since been replaced).  However, since this demo is using an old version 1.1.0 of the pipeline, we need to tell it to get some more recent reference files.\n",
     "\n",
     "Next we'll import the various python packages that we're actually going to use in this notebook, including both generic utility functions and the actual pipeline modules themselves.  This includes a variety of multiprocessing functions that allow us to parallelize pipeline reductions of many exposures using the multiple cores now standard in many computers.\n",
     "\n",
@@ -159,15 +161,32 @@
     "### 2.1<font color='white'>-</font>CRDS Context<a class=\"anchor\" id=\"crds\"></a> ###"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "4a5224f8",
+   "metadata": {},
+   "source": [
+    "Set our CRDS context for reference files (see https://jwst-crds.stsci.edu/)\n",
+    "We need to do this because version 1.1.0 of the pipeline does not pull in some recent reference file updates by default."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
    "id": "115ca6c7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env: CRDS_CONTEXT=jwst_0723.pmap\n"
+     ]
+    }
+   ],
    "source": [
-    "# Set our CRDS context for reference files if desired (see https://jwst-crds.stsci.edu/)\n",
-    "#%env CRDS_CONTEXT jwst_0723.pmap"
+    "# Comment out this line if you want to use the latest reference files tagged for a specific pipeline version\n",
+    "%env CRDS_CONTEXT jwst_0723.pmap"
    ]
   },
   {
@@ -278,12 +297,12 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "c853939c",
+   "id": "1b0a5224",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Basic system utilities for interacting with files\n",
-    "import glob, sys, os, time, shutil\n",
+    "import glob, sys, os, time, shutil, warnings\n",
     "\n",
     "# Astropy utilities for opening FITS and ASCII files\n",
     "from astropy.io import fits\n",
@@ -296,26 +315,68 @@
     "\n",
     "# Matplotlib for making plots\n",
     "import matplotlib.pyplot as plt\n",
-    "from matplotlib import rc\n",
+    "from matplotlib import rc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4f3e7fbd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import the base JWST package and warn if not the expected version\n",
+    "import jwst\n",
     "\n",
+    "if jwst.__version__ != '1.1.0':\n",
+    "    warnings.warn(f\"You are running version {jwst.__version__} of the jwst \"\n",
+    "                  \"module instead of the intended 1.1.0.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c853939c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-06-18 13:03:50,080 - stpipe - WARNING - /Users/dlaw/anaconda3/envs/jwst1.1.0/lib/python3.9/site-packages/photutils/detection/findstars.py:33: AstropyDeprecationWarning: _StarFinderKernel was moved to the photutils.detection._utils module. Please update your import statement.\n",
+      "  warnings.warn(f'{name} was moved to the {deprecated[name]} module. '\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
     "# JWST pipelines (encompassing many steps)\n",
-    "import jwst\n",
     "from jwst.pipeline import Detector1Pipeline\n",
     "from jwst.pipeline import Spec2Pipeline\n",
     "from jwst.pipeline import Spec3Pipeline\n",
     "\n",
     "# JWST pipeline utilities\n",
-    "from stcal import dqflags # Utilities for working with the data quality (DQ) arrays\n",
     "from jwst import datamodels # JWST datamodels\n",
-    "import stcal.ramp_fitting.utils as utils # Utilities for handling multiprocessing\n",
+    "import jwst.ramp_fitting.utils as utils # Utilities for handling multiprocessing\n",
     "from jwst.associations import asn_from_list as afl # Tools for creating association files\n",
     "from jwst.associations.lib.rules_level2_base import DMSLevel2bBase # Definition of a Lvl2 association file\n",
-    "from jwst.associations.lib.rules_level3_base import DMS_Level3_Base # Definition of a Lvl3 association file"
+    "from jwst.associations.lib.rules_level3_base import DMS_Level3_Base # Definition of a Lvl3 association file\n",
+    "\n",
+    "# If using pipeline version 1.2.0 or later, the dqflags function is contained in the 'stcal' product\n",
+    "#from stcal import dqflags # Utilities for working with the data quality (DQ) arrays\n",
+    "# Since this demo uses version 1.1.0 of the JWST pipeline, we need to instead import dqflags from \n",
+    "from jwst.datamodels import dqflags\n",
+    "\n",
+    "# If using pipeline version 1.2.0 or later, some multiprocessing functions (mostly used in ramp fitting)\n",
+    "# are contained in the 'stcal' product:\n",
+    "#import stcal.ramp_fitting.utils as utils # Utilities for handling multiprocessing\n",
+    "# Since this demo uses version 1.1.0 of the JWST pipeline, we need to instead import from jwst:\n",
+    "import jwst.ramp_fitting.utils as utils # Utilities for handling multiprocessing"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "b9d8b1ba",
    "metadata": {},
    "outputs": [
@@ -323,13 +384,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "JWST pipeline version 0.13.0b1.dev2264+gdbc1587e\n"
+      "JWST pipeline version 1.1.0\n"
      ]
     }
    ],
    "source": [
     "# Print out what pipeline version we're using\n",
-    "print('JWST pipeline version',jwst.__version__)"
+    "print('JWST pipeline version', jwst.__version__)"
    ]
   },
   {
@@ -340,20 +401,39 @@
     "### 2.3<font color='white'>-</font>Data I/O Directories <a class=\"anchor\" id=\"iodir\"></a> ###"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "ae643839",
+   "metadata": {},
+   "source": [
+    "Data can be obtained from https://stsci.box.com/s/kdzsylowpp5q6herx4hc23noc9z71z0f <BR>\n",
+    "Since the contents of this Box directory are quite large (roughly 9 GB, including multiple inputs, outputs, and intermediate products), downloading the data for home use is left to the user in whichever means they determine to be best.\n",
+    "    \n",
+    "By default, the contents of this Box directory are assumed to be in the same directory as this notebook.  However, it is also possible to install them in another location and use this as a cache of pre-reduced results against which new reductions can be compared."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "a43f30d5",
+   "execution_count": 10,
+   "id": "8d37e3cc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Specify some working directories to use so that everything is more organized\n",
-    "\n",
-    "# Use this if running remotely in the online session\n",
-    "#cache_dir = '/home/shared/preloaded-fits/mrs-data/notebook2/'\n",
-    "# Use this if running on your own machine\n",
+    "# If running on your own machine, cache_dir should point to where you installed the data from Box\n",
     "cache_dir = './'\n",
     "\n",
+    "# If running remotely in the online JWebbinar session, the cache directed is located here:\n",
+    "#cache_dir = '/home/shared/preloaded-fits/mrs-data/notebook2/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "a43f30d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Specify some working directories to use so that everything is more organized.<br>\n",
     "mirisim_dir = 'stage0/' # Simulated inputs are here\n",
     "det1_dir = 'stage1/' # Detector1 pipeline outputs will go here\n",
     "spec2_dir = 'stage2/' # Spec2 pipeline outputs will go here\n",
@@ -376,19 +456,25 @@
     "### 2.4<font color='white'>-</font>Reprocessing Flag<a class=\"anchor\" id=\"redo\"></a> ###"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "2d5801f6",
+   "metadata": {},
+   "source": [
+    "Since some parts of the pipeline take a long time to run, for a first use of this notebook we will disable those steps and simply copy results out of the cache for informational purposes.  In order to run the full pipeline on the data, or to run this notebook on your own simulated data not downloaded from the Box link above, this reprocessing flag will need to be enabled."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "id": "a9cddd63",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Finally, we'll set a processing directive about whether to rerun long steps in this notebook or not\n",
-    "redolong = False\n",
-    "\n",
-    "# With this flag set to False, you'll use pre-reduced outputs.  \n",
-    "# If you want to experiment with setting it to True ahead of time\n",
-    "# you can recreate all of your own outputs."
+    "# To rerun all steps use:\n",
+    "#redolong = True\n",
+    "# To skip lengthy steps and copy results from the cache use:\n",
+    "redolong = False"
    ]
   },
   {
@@ -401,7 +487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "id": "51271da5",
    "metadata": {},
    "outputs": [],
@@ -417,14 +503,6 @@
     "        p.join()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "161624d4",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "266a1b0f",
@@ -450,7 +528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "id": "805cb62e",
    "metadata": {},
    "outputs": [],
@@ -461,7 +539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "id": "9f3660af",
    "metadata": {},
    "outputs": [],
@@ -469,7 +547,7 @@
     "# First we'll define a function that will call the detector1 pipeline with our desired set of parameters\n",
     "# We won't enumerate the individual steps\n",
     "def rundet1(filenames):\n",
-    "    det1=Detector1Pipeline() # Instantiate the pipeline\n",
+    "    det1 = Detector1Pipeline() # Instantiate the pipeline\n",
     "    det1.output_dir = det1_dir # Specify where the output should go\n",
     "    det1.refpix.skip = True # Skip the reference pixel subtraction (as it doesn't interact well with simulated data)\n",
     "    det1.save_results = True # Save the final resulting _rate.fits files\n",
@@ -478,7 +556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "id": "c1383d60",
    "metadata": {},
    "outputs": [
@@ -492,14 +570,14 @@
    ],
    "source": [
     "# Now let's look for input files in our (cached) mirisim simulation directory\n",
-    "sstring=cache_dir+mirisim_dir+'det*exp1.fits'\n",
-    "simfiles=sorted(glob.glob(sstring))\n",
+    "sstring = cache_dir + mirisim_dir + 'det*exp1.fits'\n",
+    "simfiles = sorted(glob.glob(sstring))\n",
     "print('Found ' + str(len(simfiles)) + ' input files to process')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "id": "d44991e3",
    "metadata": {},
    "outputs": [],
@@ -512,16 +590,16 @@
     "    \n",
     "# Otherwise, just copy cached outputs into our output directory structure\n",
     "else:\n",
-    "    sstring=cache_dir+det1_dir+'det*rate.fits'\n",
-    "    files=sorted(glob.glob(sstring))\n",
+    "    sstring = cache_dir + det1_dir + 'det*rate.fits'\n",
+    "    files = sorted(glob.glob(sstring))\n",
     "    for file in files:\n",
-    "        outfile=str.replace(file,cache_dir,'./')\n",
+    "        outfile = str.replace(file,cache_dir,'./')\n",
     "        shutil.copy(file,outfile)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 18,
    "id": "e3e79df2",
    "metadata": {},
    "outputs": [
@@ -529,7 +607,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Runtime so far: 0.9261 seconds\n"
+      "Runtime so far: 0.8857 seconds\n"
      ]
     }
    ],
@@ -539,14 +617,6 @@
     "print(f\"Runtime so far: {time1 - time0:0.4f} seconds\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "61dea49c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "9f1151ba",
@@ -568,7 +638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "id": "af61d821",
    "metadata": {},
    "outputs": [],
@@ -576,7 +646,7 @@
     "# Define a function that will call the spec2 pipeline with our desired set of parameters\n",
     "# We'll list the individual steps just to make it clear what's running\n",
     "def runspec2(filename):\n",
-    "    spec2=Spec2Pipeline()\n",
+    "    spec2 = Spec2Pipeline()\n",
     "    spec2.output_dir = spec2_dir\n",
     "    \n",
     "    spec2.assign_wcs.skip = False # Derives the world coordinate solution- never skip this!\n",
@@ -595,7 +665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "id": "10ff3a2a",
    "metadata": {},
    "outputs": [
@@ -609,22 +679,22 @@
    ],
    "source": [
     "# Look for input uncalibrated slope files from the Detector1 pipeline\n",
-    "sstring=det1_dir+'det*rate.fits'\n",
-    "ratefiles=sorted(glob.glob(sstring))\n",
+    "sstring = det1_dir + 'det*rate.fits'\n",
+    "ratefiles = sorted(glob.glob(sstring))\n",
     "print('Found ' + str(len(ratefiles)) + ' input files to process')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "id": "e4ab8d2f",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Simulated data doesn't have the right keywords to tell the pipeline what kind of data is being processed.\n",
     "# Overwrite rate file header info to specify that these are point sources.\n",
-    "for ii in range(0,len(ratefiles)):\n",
-    "    hdu=fits.open(ratefiles[ii])\n",
+    "for ii in range(0, len(ratefiles)):\n",
+    "    hdu = fits.open(ratefiles[ii])\n",
     "    hdu[1].header['SRCTYPE']='POINT'\n",
     "    hdu.writeto(ratefiles[ii],overwrite=True)\n",
     "    hdu.close()"
@@ -632,7 +702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "id": "fa5fd149",
    "metadata": {},
    "outputs": [],
@@ -643,16 +713,16 @@
     "    \n",
     "# Otherwise, just copy cached outputs into our output directory structure\n",
     "else:\n",
-    "    sstring=cache_dir+spec2_dir+'det*.fits'\n",
+    "    sstring = cache_dir + spec2_dir + 'det*.fits'\n",
     "    files=sorted(glob.glob(sstring))\n",
     "    for file in files:\n",
-    "        outfile=str.replace(file,cache_dir,'./')\n",
+    "        outfile = str.replace(file,cache_dir,'./')\n",
     "        shutil.copy(file,outfile)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "id": "ec795648",
    "metadata": {},
    "outputs": [
@@ -660,7 +730,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Runtime so far: 7.5605 seconds\n"
+      "Runtime so far: 6.8764 seconds\n"
      ]
     }
    ],
@@ -670,14 +740,6 @@
     "print(f\"Runtime so far: {time1 - time0:0.4f} seconds\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "14ff0921",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "20ccb089",
@@ -701,7 +763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "id": "127897fc",
    "metadata": {},
    "outputs": [],
@@ -722,7 +784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "id": "bcadac5d",
    "metadata": {},
    "outputs": [
@@ -730,7 +792,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Runtime so far: 7.5731 seconds\n"
+      "Runtime so far: 6.8876 seconds\n"
      ]
     }
    ],
@@ -742,7 +804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "id": "5bef9d09",
    "metadata": {},
    "outputs": [],
@@ -761,25 +823,25 @@
     "    channel = np.array(channel)\n",
     "    band = np.array(band)\n",
     "\n",
-    "    indx=np.where((channel == '12')&(band == 'SHORT'))\n",
-    "    files12A=files[indx]\n",
-    "    indx=np.where((channel == '12')&(band == 'MEDIUM'))\n",
-    "    files12B=files[indx]\n",
-    "    indx=np.where((channel == '12')&(band == 'LONG'))\n",
-    "    files12C=files[indx]\n",
-    "    indx=np.where((channel == '34')&(band == 'SHORT'))\n",
-    "    files34A=files[indx]\n",
-    "    indx=np.where((channel == '34')&(band == 'MEDIUM'))\n",
-    "    files34B=files[indx]\n",
-    "    indx=np.where((channel == '34')&(band == 'LONG'))\n",
-    "    files34C=files[indx]\n",
+    "    indx = np.where((channel == '12')&(band == 'SHORT'))\n",
+    "    files12A = files[indx]\n",
+    "    indx = np.where((channel == '12')&(band == 'MEDIUM'))\n",
+    "    files12B = files[indx]\n",
+    "    indx = np.where((channel == '12')&(band == 'LONG'))\n",
+    "    files12C = files[indx]\n",
+    "    indx = np.where((channel == '34')&(band == 'SHORT'))\n",
+    "    files34A = files[indx]\n",
+    "    indx = np.where((channel == '34')&(band == 'MEDIUM'))\n",
+    "    files34B = files[indx]\n",
+    "    indx = np.where((channel == '34')&(band == 'LONG'))\n",
+    "    files34C = files[indx]\n",
     "    \n",
     "    return files12A,files12B,files12C,files34A,files34B,files34C"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "id": "6a9aa3d7",
    "metadata": {},
    "outputs": [
@@ -793,15 +855,15 @@
    ],
    "source": [
     "# Find and sort all of the input files\n",
-    "sstring=spec2_dir+'det*cal.fits'\n",
-    "calfiles=np.array(sorted(glob.glob(sstring)))\n",
+    "sstring = spec2_dir + 'det*cal.fits'\n",
+    "calfiles = np.array(sorted(glob.glob(sstring)))\n",
     "sortfiles = sort_calfiles(calfiles) # Split them up into bands\n",
     "print('Found ' + str(len(calfiles)) + ' input files to process')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "id": "69e17a38",
    "metadata": {},
    "outputs": [],
@@ -814,17 +876,17 @@
     "asnlist = []\n",
     "names=['12A','12B','12C','34A','34B','34C']\n",
     "for ii in range(0,len(sortfiles)):\n",
-    "    thesefiles=sortfiles[ii]\n",
-    "    ninband=len(thesefiles)\n",
+    "    thesefiles = sortfiles[ii]\n",
+    "    ninband = len(thesefiles)\n",
     "    if (ninband > 0):\n",
-    "        filename='l3asn-'+names[ii]+'.json'\n",
+    "        filename = 'l3asn-' + names[ii] + '.json'\n",
     "        asnlist.append(filename)\n",
     "        writel3asn(thesefiles,filename,'Level3')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "id": "389cc285",
    "metadata": {},
    "outputs": [],
@@ -851,7 +913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 30,
    "id": "5653e513",
    "metadata": {},
    "outputs": [],
@@ -862,16 +924,16 @@
     "    \n",
     "# Otherwise, just copy cached outputs into our output directory structure\n",
     "else:\n",
-    "    sstring=cache_dir+spec3_dir+'Level3*.fits'\n",
-    "    files=sorted(glob.glob(sstring))\n",
+    "    sstring = cache_dir + spec3_dir + 'Level3*.fits'\n",
+    "    files = sorted(glob.glob(sstring))\n",
     "    for file in files:\n",
-    "        outfile=str.replace(file,cache_dir,'./')\n",
+    "        outfile = str.replace(file,cache_dir,'./')\n",
     "        shutil.copy(file,outfile)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 31,
    "id": "53f4efda",
    "metadata": {},
    "outputs": [
@@ -879,7 +941,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Runtime so far: 8.0713 seconds\n"
+      "Runtime so far: 7.3442 seconds\n"
      ]
     }
    ],
@@ -889,14 +951,6 @@
     "print(f\"Runtime so far: {time1 - time0:0.4f} seconds\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e4b9bedf",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "670edaa0",
@@ -914,7 +968,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 32,
    "id": "fc2e9401",
    "metadata": {},
    "outputs": [
@@ -928,15 +982,15 @@
    ],
    "source": [
     "# First we'll make an association file that includes all of the different band exposures\n",
-    "sstring=spec2_dir+'det*cal.fits'\n",
-    "calfiles=np.array(sorted(glob.glob(sstring)))\n",
+    "sstring = spec2_dir + 'det*cal.fits'\n",
+    "calfiles = np.array(sorted(glob.glob(sstring)))\n",
     "writel3asn(calfiles,'l3asn.json','Level3')\n",
     "print('Found ' + str(len(calfiles)) + ' input files to process')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 33,
    "id": "c75c7392",
    "metadata": {},
    "outputs": [],
@@ -964,7 +1018,7 @@
     "    \n",
     "    # Some of the available cube building options\n",
     "    spec3.cube_build.output_file = 'chancube' # Custom output name\n",
-    "    #spec3.cube_build.output_type = 'channel' # Ordinarily this is how we'd specify per-channel output, but this isn't working in 1.2.0\n",
+    "    #spec3.cube_build.output_type = 'channel' # Ordinarily this is how we'd specify per-channel output, but this isn't working in 1.1.0\n",
     "    #spec3.cube_build.weighting = 'emsm' # Current default cube build method uses a radial exponential Modified\n",
     "                                        # Shepard algorithm, but a 'driz' 3d Drizzle option is coming soon\n",
     "    #spec3.cube_build.coord_system = 'ifualign' # The default is 'skyalign'; they differ in whether \n",
@@ -979,7 +1033,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 34,
    "id": "1b2ba992",
    "metadata": {},
    "outputs": [],
@@ -990,21 +1044,13 @@
     "    \n",
     "# Otherwise, just copy cached outputs into our output directory structure\n",
     "else:\n",
-    "    sstring=cache_dir+spec3_dir+'chancube*.fits'\n",
-    "    files=sorted(glob.glob(sstring))\n",
+    "    sstring = cache_dir + spec3_dir + 'chancube*.fits'\n",
+    "    files = sorted(glob.glob(sstring))\n",
     "    for file in files:\n",
-    "        outfile=str.replace(file,cache_dir,'./')\n",
+    "        outfile = str.replace(file,cache_dir,'./')\n",
     "        shutil.copy(file,outfile)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5f5f32fb",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "e0a31ad3",
@@ -1022,7 +1068,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 35,
    "id": "8d035604",
    "metadata": {},
    "outputs": [],
@@ -1066,7 +1112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 36,
    "id": "1608a4e6",
    "metadata": {},
    "outputs": [],
@@ -1077,21 +1123,13 @@
     "    \n",
     "# Otherwise, just copy cached outputs into our output directory structure\n",
     "else:\n",
-    "    sstring=cache_dir+spec3_dir+'allcube*.fits'\n",
-    "    files=sorted(glob.glob(sstring))\n",
+    "    sstring = cache_dir + spec3_dir + 'allcube*.fits'\n",
+    "    files = sorted(glob.glob(sstring))\n",
     "    for file in files:\n",
-    "        outfile=str.replace(file,cache_dir,'./')\n",
+    "        outfile = str.replace(file,cache_dir,'./')\n",
     "        shutil.copy(file,outfile)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "48020601",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "9b1c46c5",
@@ -1109,7 +1147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 37,
    "id": "fc9babac",
    "metadata": {},
    "outputs": [
@@ -1119,7 +1157,7 @@
        "Text(0.5, 1.0, 'ALL Cube')"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1138,23 +1176,25 @@
    ],
    "source": [
     "# Show an image of the Ch1A, Ch1, and ALL cubes\n",
-    "hdu1A=fits.open(spec3_dir+'Level3_ch1-short_s3d.fits')\n",
-    "data1A=hdu1A['SCI'].data\n",
-    "hdr1A=hdu1A['SCI'].header\n",
+    "hdu1A = fits.open(spec3_dir + 'Level3_ch1-short_s3d.fits')\n",
+    "data1A = hdu1A['SCI'].data\n",
+    "hdr1A = hdu1A['SCI'].header\n",
     "# Linear wavelength solution in per-band cubes\n",
-    "wave1A=np.arange(hdr1A['NAXIS3'])*hdr1A['CDELT3']+hdr1A['CRVAL3']\n",
+    "wave1A = np.arange(hdr1A['NAXIS3'])*hdr1A['CDELT3']+hdr1A['CRVAL3']\n",
     "\n",
-    "hdu1=fits.open(spec3_dir+'chancube_ch1-longshortmedium-_s3d.fits')\n",
-    "data1=hdu1['SCI'].data\n",
-    "hdr1=hdu1['SCI'].header\n",
+    "cubefile_ch1 = glob.glob(spec3_dir + 'chancube_ch1*s3d.fits')\n",
+    "hdu1 = fits.open(cubefile_ch1[0])\n",
+    "data1 = hdu1['SCI'].data\n",
+    "hdr1 = hdu1['SCI'].header\n",
     "# Linear wavelength solution in per-channel cubes\n",
-    "wave1=np.arange(hdr1['NAXIS3'])*hdr1['CDELT3']+hdr1['CRVAL3']\n",
+    "wave1 = np.arange(hdr1['NAXIS3'])*hdr1['CDELT3']+hdr1['CRVAL3']\n",
     "\n",
-    "hduALL=fits.open(spec3_dir+'allcube_ch1-2-3-4-shortlongmedium-_s3d.fits')\n",
-    "dataALL=hduALL['SCI'].data\n",
-    "hdrALL=hduALL['SCI'].header\n",
+    "cubefile_all = glob.glob(spec3_dir + 'allcube_*s3d.fits')\n",
+    "hduALL = fits.open(cubefile_all[0])\n",
+    "dataALL = hduALL['SCI'].data\n",
+    "hdrALL = hduALL['SCI'].header\n",
     "# Reference table of wavelengths for the ALL cube\n",
-    "waveALL=hduALL['WCS-TABLE'].data['wavelength'][0]\n",
+    "waveALL = hduALL['WCS-TABLE'].data['wavelength'][0]\n",
     "\n",
     "# Use a logarithmic stretch to make sure that\n",
     "# we can see the actual cube footprint well\n",
@@ -1192,7 +1232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 38,
    "id": "01c914db",
    "metadata": {},
    "outputs": [
@@ -1211,9 +1251,11 @@
    ],
    "source": [
     "# Plot a spectrum of the source from the Ch1A, Ch1 and ALL cubes\n",
-    "spec1A=fits.open(spec3_dir+'Level3_ch1-short_x1d.fits')\n",
-    "spec1=fits.open(spec3_dir+'chancube_ch1-longshortmedium-_x1d.fits')\n",
-    "specALL=fits.open(spec3_dir+'allcube_ch1-2-3-4-shortlongmedium-_x1d.fits')\n",
+    "spec1A = fits.open(spec3_dir + 'Level3_ch1-short_x1d.fits')\n",
+    "specfile_ch1 = glob.glob(spec3_dir + 'chancube_ch1*x1d.fits')\n",
+    "spec1 = fits.open(specfile_ch1[0])\n",
+    "specfile_all = glob.glob(spec3_dir + 'allcube_ch1*x1d.fits')\n",
+    "specALL = fits.open(specfile_all[0])\n",
     "\n",
     "rc('axes', linewidth=2)            \n",
     "fig, ax = plt.subplots(1,1, figsize=(8,4),dpi=100)\n",
@@ -1249,23 +1291,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 39,
    "id": "0979572c",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Find the mirisim input spectrum\n",
-    "inputsim=ascii.read(cache_dir+mirisim_dir+'ngc5728_mirisim.txt')\n",
+    "inputsim = ascii.read(cache_dir + mirisim_dir + 'ngc5728_mirisim.txt')\n",
     "inputsim['fnu'] /= 1e6 # Mirisim inputs are in units of uJy; convert to Jy to match pipeline outputs\n",
     "\n",
     "# Find the 12-band pipeline output 1d spectra\n",
-    "sstring=spec3_dir+'Level3*x1d.fits'\n",
-    "x1dfiles=np.array(sorted(glob.glob(sstring)))"
+    "sstring = spec3_dir + 'Level3*x1d.fits'\n",
+    "x1dfiles = np.array(sorted(glob.glob(sstring)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 40,
    "id": "e455243d",
    "metadata": {},
    "outputs": [
@@ -1273,11 +1315,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2021-06-03 16:32:14,713 - stpipe - WARNING - <ipython-input-37-9f5d1f49d205>:13: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
-      "  x1d_wave=np.array(x1d_wave)\n",
+      "2021-06-18 13:03:59,290 - stpipe - WARNING - <ipython-input-40-0f96b02e98a2>:13: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+      "  x1d_wave = np.array(x1d_wave)\n",
       "\n",
-      "2021-06-03 16:32:14,714 - stpipe - WARNING - <ipython-input-37-9f5d1f49d205>:14: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
-      "  x1d_flux=np.array(x1d_flux)\n",
+      "2021-06-18 13:03:59,291 - stpipe - WARNING - <ipython-input-40-0f96b02e98a2>:14: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+      "  x1d_flux = np.array(x1d_flux)\n",
       "\n"
      ]
     }
@@ -1288,19 +1330,19 @@
     "x1d_flux=[]\n",
     "medwaves = []\n",
     "for ii in range(0,len(x1dfiles)):\n",
-    "    hdu=fits.open(x1dfiles[ii])\n",
-    "    specdata=hdu['EXTRACT1D'].data\n",
+    "    hdu = fits.open(x1dfiles[ii])\n",
+    "    specdata = hdu['EXTRACT1D'].data\n",
     "    x1d_wave.append(specdata['WAVELENGTH'])\n",
     "    x1d_flux.append(specdata['FLUX'])\n",
     "    medwaves.append(np.median(specdata['WAVELENGTH']))\n",
     "    hdu.close()\n",
     "\n",
-    "x1d_wave=np.array(x1d_wave)\n",
-    "x1d_flux=np.array(x1d_flux)   \n",
+    "x1d_wave = np.array(x1d_wave)\n",
+    "x1d_flux = np.array(x1d_flux)   \n",
     "# For convenience, sort according to increasing wavelength\n",
-    "indx=np.argsort(medwaves)\n",
-    "x1d_wave=x1d_wave[indx]\n",
-    "x1d_flux=x1d_flux[indx]\n",
+    "indx = np.argsort(medwaves)\n",
+    "x1d_wave = x1d_wave[indx]\n",
+    "x1d_flux = x1d_flux[indx]\n",
     "\n",
     "# Introduce a 10% kludge factor to account for the fact that the mirisim PSF is oversized and thus loses too much flux beyond the aperture radius\n",
     "x1d_flux *= 1.1"
@@ -1308,17 +1350,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 41,
    "id": "99b9ebc5",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fd8acdfa760>"
+       "<matplotlib.legend.Legend at 0x7facdd0d4070>"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1364,7 +1406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 42,
    "id": "3cb79b54",
    "metadata": {},
    "outputs": [
@@ -1372,7 +1414,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Runtime so far: 10.2873 seconds\n"
+      "Runtime so far: 9.3713 seconds\n"
      ]
     }
    ],
@@ -1397,14 +1439,6 @@
    "source": [
     "<img style=\"float: center;\" src=\"https://www.stsci.edu/~dlaw/stsci_logo.png\" alt=\"stsci_logo\" width=\"200px\"/> "
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3f128f99",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {