diff --git a/.gitignore b/.gitignore
index 2fa44b1..327bea4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -24,6 +24,7 @@ MANIFEST
 # Sphinx
 docs/api
 docs/_build
+docs/_static
 
 # Eclipse editor project files
 .project
@@ -154,6 +155,7 @@ nosetests.xml
 coverage.xml
 *.cover
 .hypothesis/
+ref_data/
 
 # Translations #
 ################
diff --git a/CHANGES.rst b/CHANGES.rst
index 75d2578..2561a1f 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -5,6 +5,13 @@ Release Notes
 Version History and Change Log
 ------------------------------
 
+Version 2.1.0
+=============
+- Updated the Flux PHOTFNU values to match Pandeia
+- Updated the background and noise estimates to use Pandeia
+- Updated webbpsf to version 1.1
+- Updated pandeia to version 2.0
+
 Version 2.0.0
 =============
 
diff --git a/docs/basic_tutorial.rst b/docs/basic_tutorial.rst
index b1b8ee4..a00eea8 100644
--- a/docs/basic_tutorial.rst
+++ b/docs/basic_tutorial.rst
@@ -55,7 +55,7 @@ population parameters.  In this example, we will create the following:
 
 #. A stellar population representing a global cluster with:
 
-   * 10000 stars
+   * 100 stars
    * An age of 7.5 billion years
    * A metallicity of -2.0
    * A Salpeter IMF with alpha = -2.35
@@ -81,12 +81,16 @@ population parameters.  In this example, we will create the following:
 
 .. code-block:: python
 
+  obs_prefix = 'notebook_example'
+  obs_ra = 150.0
+  obs_dec = -2.5
+
   from stips.scene_module import SceneModule
 
   scm = SceneModule(out_prefix=obs_prefix, ra=obs_ra, dec=obs_dec)
 
   stellar_parameters = {
-                        'n_stars': 10000,
+                        'n_stars': 100,
                         'age_low': 7.5e12,
                         'age_high': 7.5e12,
                         'z_low': -2.0,
@@ -98,12 +102,12 @@ population parameters.  In this example, we will create the following:
                         'distribution': 'invpow',
                         'radius': 100.0,
                         'radius_units': 'pc',
-                        'distance_low': 10.0,
-                        'distance_high': 10.0,
+                        'distance_low': 20.0,
+                        'distance_high': 20.0,
                         'offset_ra': 0.0,
-                        'offset_dec':0.0
-                        }
-
+                        'offset_dec': 0.0
+                       }
+   
   stellar_cat_file = scm.CreatePopulation(stellar_parameters)
   print("Stellar population saved to file {}".format(stellar_cat_file))
 
@@ -113,7 +117,7 @@ population parameters.  In this example, we will create the following:
                        'z_high': 0.2,
                        'rad_low': 0.01,
                        'rad_high': 2.0,
-                       'sb_v_low': 28.0,
+                       'sb_v_low': 30.0,
                        'sb_v_high': 25.0,
                        'distribution': 'uniform',
                        'clustered': False,
@@ -122,10 +126,9 @@ population parameters.  In this example, we will create the following:
                        'offset_ra': 0.0,
                        'offset_dec': 0.0,
                       }
-
+   
   galaxy_cat_file = scm.CreateGalaxies(galaxy_parameters)
-  print("Galaxy population saved to file{}".format(galaxy_cat_file))
-
+  print("Galaxy population saved to file {}".format(galaxy_cat_file))
 
 Creating a STIPS Observation
 ----------------------------
diff --git a/docs/conf.py b/docs/conf.py
index 437559d..ac96f5d 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -48,7 +48,7 @@ def setup(app):
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 sys.path.insert(0, os.path.abspath('../'))
-
+'''
 extensions = [
     'sphinx.ext.autodoc',
     'sphinx.ext.intersphinx',
@@ -59,6 +59,15 @@ def setup(app):
     'sphinx_automodapi.automodapi',
     'autoapi.extension',
     ]
+'''
+extensions = [
+    'sphinx.ext.intersphinx',
+    'sphinx.ext.autodoc',
+    'sphinx.ext.autosummary',
+    'sphinx.ext.mathjax',
+    'sphinx.ext.viewcode',
+    'sphinx_rtd_theme',
+]
 
 numpydoc_show_class_members = False
 
@@ -92,6 +101,7 @@ def setup(app):
 
 # The name of the Pygments (syntax highlighting) style to use.
 pygments_style = 'sphinx'
+#pygments_style = 'default'
 
 # -- Options for HTML output --------------------------------------------------
 
diff --git a/docs/installation.rst b/docs/installation.rst
index bade03a..0b0e0c0 100644
--- a/docs/installation.rst
+++ b/docs/installation.rst
@@ -9,9 +9,9 @@ in this section along with instructions.
 STIPS Requirements
 ##################
 
-  \• ``pandeia>=1.7``: Exposure time calculator.
+  \• ``pandeia>=3.0``: Exposure time calculator.
 
-  \• ``webbpsf>=1.0.0``: Nancy Grace Roman PSF calculator. STIPS also requires that ``poppy``, a
+  \• ``webbpsf==1.1.1``: Nancy Grace Roman PSF calculator. STIPS also requires that ``poppy``, a
   support package used by WebbPSF, have version ``>=1.0.3``.
 
   \• ``astropy``: STIPS uses Astropy in order to:
diff --git a/environment.yml b/environment.yml
index 3ee99ab..b7cefb1 100644
--- a/environment.yml
+++ b/environment.yml
@@ -31,20 +31,15 @@ dependencies:
   - esutil # installed from conda because you need the pre-compiled binaries.
 
   - pip:
-    #
     # Special Modules. These are temporary entries and require documentation.
-    #
-    # poppy 1.0.3 fixes a bug with the way poppy was calling factorial with non-integer
-    # values. Currently this is the only way to ensure that webbpsf picks up the correct
-    # poppy version. This peg should be removed as soon as webbpsf has updated to only
-    # use poppy versions that include this fix.
     - poppy==1.0.3
   
     # Core Modules
-    - webbpsf==1.0.0
-    - pandeia.engine==1.7
+    - webbpsf==1.1.1
+    - pandeia.engine==3.0
     - synphot==1.1.1
     - stsynphot==1.1.0
+    - soc_roman_tools
     
     # Major modules
     - astropy
diff --git a/environment_dev.yml b/environment_dev.yml
index 30cde4c..686185e 100644
--- a/environment_dev.yml
+++ b/environment_dev.yml
@@ -44,10 +44,11 @@ dependencies:
     - poppy==1.0.3
   
     # Core Modules
-    - webbpsf==1.0.0
-    - pandeia.engine==1.7
+    - webbpsf==1.1.0
+    - pandeia.engine==3.0
     - synphot==1.1.1
     - stsynphot==1.1.0
+    - soc_roman_tools
     
     # Major modules
     - astropy
diff --git "a/notebooks/STIPS Advanced I \342\200\223 Further Observations, Noise and Distortion.ipynb" "b/notebooks/STIPS Advanced I \342\200\223 Further Observations, Noise and Distortion.ipynb"
new file mode 100644
index 0000000..edd9d80
--- /dev/null
+++ "b/notebooks/STIPS Advanced I \342\200\223 Further Observations, Noise and Distortion.ipynb"	
@@ -0,0 +1,3839 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8bc35b04",
+   "metadata": {},
+   "source": [
+    "# STIPS Advanced Tutorial I – Further Observations (Noise and Distortion)\n",
+    "\n",
+    "The contents of this notebook assume both that you already have STIPS installed (see [Installing STIPS](https://stsci-stips.readthedocs.io/en/latest/installation.html) if not) and that you are comfortable with basic usage of STIPS functionalities (see [STIPS Basic Tutorial](https://stips.readthedocs.io/en/latest/basic_tutorial.html), or the Basic Tutorial notebook, if not).  Procedures in this tutorial will use scenes generated by the Basic Tutorial to explore further STIPS functionalities."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fe12fd1",
+   "metadata": {},
+   "source": [
+    "## Checking STIPS Import\n",
+    "\n",
+    "Before beginning, check again that the STIPS import is correct and configure and import and configure pyplot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "ee62ccf5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "STIPS Version 2.0.0 with Data Version 1.0.9 at /Users/halkowsi/STScI-STIPS/ref_data/stips_data.\n",
+      "\tSTIPS Grid Generated with 1.0.8\n",
+      "Pandeia Version 1.7 with Data Version 1.7 at /Users/halkowsi/STScI-STIPS/ref_data/pandeia_data-1.7_roman.\n",
+      "Webbpsf Version 1.0.0 with Data Version 1.0.0 at /Users/halkowsi/STScI-STIPS/ref_data/webbpsf-data.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "%config InlineBackend.figure_format = 'svg'\n",
+    "import matplotlib\n",
+    "from matplotlib import style\n",
+    "matplotlib.rcParams['axes.grid'] = False\n",
+    "matplotlib.rcParams['image.origin'] = 'lower'\n",
+    "import matplotlib.pyplot as plt\n",
+    "from astropy.io import fits\n",
+    "\n",
+    "import stips\n",
+    "\n",
+    "print(stips.__env__report__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4e2ca00",
+   "metadata": {},
+   "source": [
+    "## Generating a Smaller Scene\n",
+    "\n",
+    "To better see the scope of possible observations, we will generate a scene smaller in scale than in the Basic Tutorial –– we do that below, following the same procedure as before.  The specifications of our scene are as follows:\n",
+    "\n",
+    "* A stellar population representing a globular cluster with\n",
+    "  * 100 stars\n",
+    "  * An age of 7.5 billion years\n",
+    "  * A metallicity of -2.0\n",
+    "  * A Salpeter IMF with alpha=-2.35\n",
+    "  * A binary fraction of 10%\n",
+    "  * A clustered distribution (higher-mass stars closer to the population centre)\n",
+    "  * An inverse power-law distribution\n",
+    "  * A radius of 100 parsecs\n",
+    "  * A distance of 10 kpc\n",
+    "  * No offset from the centre of the scene being created\n",
+    "* A collection of background galaxies with\n",
+    "  * 2 galaxies\n",
+    "  * Redshifts between 0 and 0.2\n",
+    "  * Radii between 0.01 and 2.0 arcsec\n",
+    "  * V-band surface brightness magnitudes between 28 and 25\n",
+    "  * Uniform spatial distribution (unclustered) over 200 arcsec\n",
+    "  * No offset from the centre of the scene being created"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "567d53ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obs_prefix = 'adv_notebook'\n",
+    "obs_ra = 150.0\n",
+    "obs_dec = -2.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "86ea0a4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:02:25,484 INFO: Creating catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_stars_000.fits\n",
+      "2022-11-05 14:02:25,484 INFO: Creating age and metallicity numbers\n",
+      "2022-11-05 14:02:25,485 INFO: Created age and metallicity numbers\n",
+      "2022-11-05 14:02:25,486 INFO: Creating stars\n",
+      "2022-11-05 14:02:25,486 INFO: Age 1.35e+10\n",
+      "2022-11-05 14:02:25,487 INFO: Metallicity -2.000000\n",
+      "2022-11-05 14:02:25,488 INFO: Creating 100 stars\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Log level: INFO\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:02:25,873 INFO: Creating 100 objects, max radius 100.0, function invpow, scale 2.8\n",
+      "2022-11-05 14:02:25,956 INFO: Chunk 1: 106 stars\n",
+      "2022-11-05 14:02:25,978 INFO: Done creating catalogue\n",
+      "2022-11-05 14:02:25,979 INFO: Creating catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_gals_000.fits\n",
+      "2022-11-05 14:02:25,980 INFO: Wrote preamble\n",
+      "2022-11-05 14:02:25,980 INFO: Parameters are: {'n_gals': 2, 'z_low': 0.0, 'z_high': 0.2, 'rad_low': 0.01, 'rad_high': 2.0, 'sb_v_low': 28.0, 'sb_v_high': 25.0, 'distribution': 'uniform', 'clustered': False, 'radius': 200.0, 'radius_units': 'arcsec', 'offset_ra': 0.0, 'offset_dec': 0.0}\n",
+      "2022-11-05 14:02:25,982 INFO: Making Co-ordinates\n",
+      "2022-11-05 14:02:25,982 INFO: Creating 2 objects, max radius 200.0, function uniform, scale 2.8\n",
+      "2022-11-05 14:02:25,983 INFO: Converting Co-ordinates into RA,DEC\n",
+      "2022-11-05 14:02:26,003 INFO: Done creating catalogue\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stellar population saved to file /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_stars_000.fits\n",
+      "Galaxy population saved to file /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_gals_000.fits\n"
+     ]
+    }
+   ],
+   "source": [
+    "from stips.scene_module import SceneModule\n",
+    "\n",
+    "scm = SceneModule(out_prefix=obs_prefix, ra=obs_ra, dec=obs_dec)\n",
+    "\n",
+    "stellar_parameters = {\n",
+    "                      'n_stars': 100,\n",
+    "                      'age_low': 7.5e12, \n",
+    "                      'age_high': 7.5e12,\n",
+    "                      'z_low': -2.0, \n",
+    "                      'z_high': -2.0,\n",
+    "                      'imf': 'salpeter', \n",
+    "                      'alpha': -2.35,\n",
+    "                      'binary_fraction': 0.1,\n",
+    "                      'clustered': True,\n",
+    "                      'distribution': 'invpow',\n",
+    "                      'radius': 100.0, \n",
+    "                      'radius_units': 'pc',\n",
+    "                      'distance_low': 10.0, \n",
+    "                      'distance_high': 10.0,\n",
+    "                      'offset_ra': 0.0, \n",
+    "                      'offset_dec': 0.0\n",
+    "                     }\n",
+    "\n",
+    "stellar_cat_file = scm.CreatePopulation(stellar_parameters)\n",
+    "print(\"Stellar population saved to file {}\".format(stellar_cat_file))\n",
+    "\n",
+    "galaxy_parameters = {\n",
+    "                     'n_gals': 2,\n",
+    "                     'z_low': 0.0, \n",
+    "                     'z_high': 0.2,\n",
+    "                     'rad_low': 0.01, \n",
+    "                     'rad_high': 2.0,\n",
+    "                     'sb_v_low': 28.0, \n",
+    "                     'sb_v_high': 25.0,\n",
+    "                     'distribution': 'uniform', \n",
+    "                     'clustered': False,\n",
+    "                     'radius': 200.0, \n",
+    "                     'radius_units': 'arcsec',\n",
+    "                     'offset_ra': 0.0, \n",
+    "                     'offset_dec': 0.0\n",
+    "                    }\n",
+    "\n",
+    "galaxy_cat_file = scm.CreateGalaxies(galaxy_parameters)\n",
+    "print(\"Galaxy population saved to file {}\".format(galaxy_cat_file))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cfdec43",
+   "metadata": {},
+   "source": [
+    "## Observations\n",
+    "\n",
+    "With the scene now created, we can move forward with our observations.  For the purpose of seeing multiple observation styles, we'll run through a series of observations, with the following parameters.  This is meant to demonstrate the range of STIPS observation capabilities –– there are, of course, a great many possibilities for other observations, and this is only a small sample of options.  We will leave all other parameters unchanged beyond the specified modifications in each observation.  We will begin with the observation setup used in the Basic Tutorial, and compare other observations against that.\n",
+    "\n",
+    "### Observation I: Offset Modifications\n",
+    "\n",
+    "* Offset of 10 degrees in RA\n",
+    "* Rotation of 27 degrees\n",
+    "\n",
+    "### Observation II: Other Residuals\n",
+    "\n",
+    "* Flat residuals –– True\n",
+    "* Dark residauls –– True\n",
+    "* Cosmic residuals –– True\n",
+    "* Poisson residuals –– False\n",
+    "* Readnoise residuals –– False\n",
+    "\n",
+    "### Observation III: Distortions and Exposures\n",
+    "\n",
+    "#### III.1\n",
+    "\n",
+    "* Introduce distortion\n",
+    "\n",
+    "#### III.2\n",
+    "\n",
+    "* Increase exposure time to 5000\n",
+    "\n",
+    "#### III.3 \n",
+    "\n",
+    "* Decrease exposure time to 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5e26b4f",
+   "metadata": {},
+   "source": [
+    "## Basic Tutorial Observation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "1bd5f5be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:07:00,206 INFO: Got offsets as [{'offset_id': 1, 'offset_centre': False, 'offset_ra': 0.0, 'offset_dec': 0.0, 'offset_pa': 0.0}]\n",
+      "2022-11-05 14:07:00,258 INFO: Adding observation with filter F129 and offset (0.0,0.0,0.0)\n",
+      "2022-11-05 14:07:00,259 INFO: Added 1 observations\n",
+      "2022-11-05 14:07:00,261 INFO: WFI with 1 detectors. Central offset (0.0, 0.0, 0.0)\n",
+      "2022-11-05 14:07:00,262 INFO: Initializing Observation 0 of 1\n",
+      "2022-11-05 14:07:00,262 INFO: Observation Filter is F129\n",
+      "2022-11-05 14:07:00,263 INFO: Observation (RA,DEC) = (150.0,-2.5) with PA=0.0\n",
+      "2022-11-05 14:07:00,264 INFO: Resetting\n",
+      "2022-11-05 14:07:00,265 INFO: Returning background 0.15.\n",
+      "2022-11-05 14:07:00,265 INFO: Creating Detector SCA01 with (RA,DEC,PA) = (150.0,-2.5,0.0)\n",
+      "2022-11-05 14:07:00,266 INFO: Creating Detector SCA01 with offset (0.0,0.0)\n",
+      "2022-11-05 14:07:00,319 INFO: Creating Instrument with Configuration {'aperture': 'imaging', 'disperser': None, 'filter': 'f129', 'instrument': 'wfi', 'mode': 'imaging'}\n",
+      "2022-11-05 14:07:00,513 INFO: SCA01: (RA, DEC, PA) := (150.0, -2.5, 0.0), detected as (150.0, -2.5, 0.0)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating pandeia instrument roman.wfi.imaging\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:07:00,514 INFO: Detector SCA01 created\n",
+      "2022-11-05 14:07:00,514 INFO: Reset Instrument\n",
+      "2022-11-05 14:07:00,515 INFO: Running catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_stars_000.fits\n",
+      "2022-11-05 14:07:00,516 INFO: Adding catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_stars_000.fits\n",
+      "2022-11-05 14:07:00,533 INFO: Converting phoenix catalogue\n",
+      "2022-11-05 14:07:00,533 INFO: Preparing output table\n",
+      "2022-11-05 14:07:00,567 INFO: Converting chunk 2\n",
+      "2022-11-05 14:07:00,568 INFO: Converting Phoenix Table to Internal format\n",
+      "2022-11-05 14:07:00,569 INFO: 1 datasets\n",
+      "2022-11-05 14:07:00,958 INFO: Finished converting catalogue to internal format\n",
+      "2022-11-05 14:07:00,959 INFO: Adding catalogue to detector SCA01\n",
+      "2022-11-05 14:07:00,960 INFO: Adding catalogue adv_notebook_stars_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:07:00,997 INFO: Determining pixel co-ordinates\n",
+      "2022-11-05 14:07:00,998 INFO: Keeping 45 items\n",
+      "2022-11-05 14:07:01,000 INFO: Writing 45 stars\n",
+      "2022-11-05 14:07:01,001 INFO: Adding 45 point sources to AstroImage SCA01\n",
+      "2022-11-05 14:07:01,020 INFO: PSF File psf_WFI_2.0.0_F129_sca01.fits to be put at /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache\n",
+      "2022-11-05 14:07:01,021 INFO: PSF File is /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache/psf_WFI_2.0.0_F129_sca01.fits\n",
+      "2022-11-05 14:07:02,247 INFO: Adding point source 1 to AstroImage 2070.722560524683,2041.9150679944821\n",
+      "2022-11-05 14:07:02,260 INFO: Adding point source 2 to AstroImage 2084.2158307131685,2020.5898365456273\n",
+      "2022-11-05 14:07:02,274 INFO: Adding point source 3 to AstroImage 2020.0916326654587,2062.377725941959\n",
+      "2022-11-05 14:07:02,292 INFO: Adding point source 4 to AstroImage 2212.4513663786984,2050.6756963380167\n",
+      "2022-11-05 14:07:02,308 INFO: Adding point source 5 to AstroImage 2074.333731140191,2069.828192912249\n",
+      "2022-11-05 14:07:02,325 INFO: Adding point source 6 to AstroImage 2038.133559050457,1916.5833393972441\n",
+      "2022-11-05 14:07:02,342 INFO: Adding point source 7 to AstroImage 2343.7887260447783,1900.9585770057893\n",
+      "2022-11-05 14:07:02,359 INFO: Adding point source 8 to AstroImage 2686.907675398465,2605.1397456727286\n",
+      "2022-11-05 14:07:02,376 INFO: Adding point source 9 to AstroImage 2065.0,2064.9999999998854\n",
+      "2022-11-05 14:07:02,394 INFO: Adding point source 10 to AstroImage 3066.406035833374,2361.1200801485934\n",
+      "2022-11-05 14:07:02,412 INFO: Adding point source 11 to AstroImage 1182.5553343396969,2253.7720913427247\n",
+      "2022-11-05 14:07:02,430 INFO: Adding point source 12 to AstroImage 2193.6284242791144,2076.3731457782155\n",
+      "2022-11-05 14:07:02,448 INFO: Adding point source 13 to AstroImage 2813.8779329567824,1511.2515673506764\n",
+      "2022-11-05 14:07:02,466 INFO: Adding point source 14 to AstroImage 2182.309943805917,2015.3144162842866\n",
+      "2022-11-05 14:07:02,484 INFO: Adding point source 15 to AstroImage 3057.662806951754,2070.8940117453767\n",
+      "2022-11-05 14:07:02,501 INFO: Adding point source 16 to AstroImage 2058.065719450632,2052.1650298807162\n",
+      "2022-11-05 14:07:02,519 INFO: Adding point source 17 to AstroImage 2091.1427915903523,2087.830605821163\n",
+      "2022-11-05 14:07:02,536 INFO: Adding point source 18 to AstroImage 2935.0659858918716,2238.7989722696175\n",
+      "2022-11-05 14:07:02,554 INFO: Adding point source 19 to AstroImage 2307.363834564019,44.638053235246616\n",
+      "2022-11-05 14:07:02,570 INFO: Adding point source 20 to AstroImage 1680.7671502598998,584.177926715473\n",
+      "2022-11-05 14:07:02,588 INFO: Adding point source 21 to AstroImage 1092.7417651348703,3440.84712683443\n",
+      "2022-11-05 14:07:02,606 INFO: Adding point source 22 to AstroImage 2830.1701018015883,2110.572157738221\n",
+      "2022-11-05 14:07:02,623 INFO: Adding point source 23 to AstroImage 2633.6640993973206,3541.184421687627\n",
+      "2022-11-05 14:07:02,641 INFO: Adding point source 24 to AstroImage 1164.5832552158022,1650.5574925855592\n",
+      "2022-11-05 14:07:02,658 INFO: Adding point source 25 to AstroImage 3791.1938738559716,3441.3126869956523\n",
+      "2022-11-05 14:07:02,675 INFO: Adding point source 26 to AstroImage 4103.8638238646145,1496.5221231689397\n",
+      "2022-11-05 14:07:02,692 INFO: Adding point source 27 to AstroImage 1927.483392341295,2084.012838647698\n",
+      "2022-11-05 14:07:02,709 INFO: Adding point source 28 to AstroImage 178.8233678377253,2018.9980800272074\n",
+      "2022-11-05 14:07:02,726 INFO: Adding point source 29 to AstroImage 4086.428547161244,2087.534318716181\n",
+      "2022-11-05 14:07:02,743 INFO: Adding point source 30 to AstroImage 4013.0036569791146,3801.977402800462\n",
+      "2022-11-05 14:07:02,759 INFO: Adding point source 31 to AstroImage 2496.9760981816366,803.4482637257686\n",
+      "2022-11-05 14:07:02,775 INFO: Adding point source 32 to AstroImage 3845.1763908222347,2562.947057853079\n",
+      "2022-11-05 14:07:02,791 INFO: Adding point source 33 to AstroImage 2292.3383947583316,3791.6209688368463\n",
+      "2022-11-05 14:07:02,807 INFO: Adding point source 34 to AstroImage 788.8944598713454,2221.1635345445893\n",
+      "2022-11-05 14:07:02,822 INFO: Adding point source 35 to AstroImage 1640.67667391249,1859.632529783993\n",
+      "2022-11-05 14:07:02,837 INFO: Adding point source 36 to AstroImage 2080.756486917873,1948.955440355016\n",
+      "2022-11-05 14:07:02,852 INFO: Adding point source 37 to AstroImage 1941.1642102038797,2738.6736046548926\n",
+      "2022-11-05 14:07:02,866 INFO: Adding point source 38 to AstroImage 2034.0285436105394,2075.3305458540103\n",
+      "2022-11-05 14:07:02,880 INFO: Adding point source 39 to AstroImage 2832.565664059406,2472.6828743012607\n",
+      "2022-11-05 14:07:02,893 INFO: Adding point source 40 to AstroImage 2507.4896238795254,1761.0417715257663\n",
+      "2022-11-05 14:07:02,908 INFO: Adding point source 41 to AstroImage 3447.0034423518946,3015.875319404148\n",
+      "2022-11-05 14:07:02,923 INFO: Adding point source 42 to AstroImage 2577.160455836409,1447.2117649906638\n",
+      "2022-11-05 14:07:02,937 INFO: Adding point source 43 to AstroImage 3414.753743350597,1328.352052000483\n",
+      "2022-11-05 14:07:02,952 INFO: Adding point source 44 to AstroImage 2358.459104375479,1859.405996413239\n",
+      "2022-11-05 14:07:02,966 INFO: Adding point source 45 to AstroImage 2947.2962384749826,2913.584957297892\n",
+      "WARNING: VerifyWarning: Keyword name 'input_catalogue' is greater than 8 characters or contains characters not allowed by the FITS standard; a HIERARCH card will be created. [astropy.io.fits.card]\n",
+      "2022-11-05 14:07:03,024 INFO: Added catalogue adv_notebook_stars_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:07:03,025 INFO: Finished catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_stars_000.fits\n",
+      "2022-11-05 14:07:03,025 INFO: Running catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_gals_000.fits\n",
+      "2022-11-05 14:07:03,026 INFO: Adding catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_gals_000.fits\n",
+      "2022-11-05 14:07:03,040 INFO: Converting bc95 catalogue\n",
+      "2022-11-05 14:07:03,040 INFO: Preparing output table\n",
+      "2022-11-05 14:07:03,064 INFO: Converting chunk 2\n",
+      "2022-11-05 14:07:03,064 INFO: Converting BC95 Catalogue\n",
+      "2022-11-05 14:07:03,065 INFO: Normalization Bandpass is johnson,v (<class 'str'>)\n",
+      "2022-11-05 14:07:03,065 INFO: Normalization Bandpass is johnson,v\n",
+      "2022-11-05 14:07:03,227 INFO: Finished converting catalogue to internal format\n",
+      "2022-11-05 14:07:03,228 INFO: Adding catalogue to detector SCA01\n",
+      "2022-11-05 14:07:03,228 INFO: Adding catalogue adv_notebook_gals_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:07:03,257 INFO: Determining pixel co-ordinates\n",
+      "2022-11-05 14:07:03,258 INFO: Keeping 2 items\n",
+      "2022-11-05 14:07:03,259 INFO: Writing 2 galaxies\n",
+      "2022-11-05 14:07:03,260 INFO: Starting Sersic Profiles at Sat Nov  5 14:07:03 2022\n",
+      "2022-11-05 14:07:03,260 INFO: Index is 0\n",
+      "2022-11-05 14:07:06,491 INFO: PSF File psf_WFI_2.0.0_F129_sca01.fits to be put at /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache\n",
+      "2022-11-05 14:07:06,492 INFO: PSF File is /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache/psf_WFI_2.0.0_F129_sca01.fits\n",
+      "2022-11-05 14:07:12,895 INFO: Finished Galaxy 1 of 2\n",
+      "2022-11-05 14:07:12,896 INFO: Index is 1\n",
+      "2022-11-05 14:07:15,595 INFO: PSF File psf_WFI_2.0.0_F129_sca01.fits to be put at /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache\n",
+      "2022-11-05 14:07:15,596 INFO: PSF File is /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache/psf_WFI_2.0.0_F129_sca01.fits\n",
+      "2022-11-05 14:07:22,068 INFO: Finished Galaxy 2 of 2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:07:22,068 INFO: Finishing Sersic Profiles at Sat Nov  5 14:07:22 2022\n",
+      "2022-11-05 14:07:22,118 INFO: Added catalogue adv_notebook_gals_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:07:22,119 INFO: Finished catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_gals_000.fits\n",
+      "2022-11-05 14:07:22,121 INFO: Adding Error\n",
+      "2022-11-05 14:07:22,122 INFO: Adding residual error\n",
+      "2022-11-05 14:07:22,199 INFO: SCA01: (RA, DEC, PA) := (0.0, 0.0, 0.0), detected as (0.0, 0.0, 0.0)\n",
+      "2022-11-05 14:07:22,521 INFO: SCA01: (RA, DEC, PA) := (0.0, 0.0, 0.0), detected as (0.0, 0.0, 0.0)\n",
+      "2022-11-05 14:07:22,522 INFO: Created AstroImage SCA01 and imported data from FITS file err_flat_wfi.fits\n",
+      "2022-11-05 14:07:22,579 INFO: SCA01: (RA, DEC, PA) := (0.0, 0.0, 0.0), detected as (0.0, 0.0, 0.0)\n",
+      "2022-11-05 14:07:22,890 INFO: SCA01: (RA, DEC, PA) := (0.0, 0.0, 0.0), detected as (0.0, 0.0, 0.0)\n",
+      "2022-11-05 14:07:22,891 INFO: Created AstroImage SCA01 and imported data from FITS file err_rdrk_wfi.fits\n",
+      "2022-11-05 14:07:22,904 INFO: Adding error to detector SCA01\n",
+      "2022-11-05 14:07:22,905 INFO: Adding background\n",
+      "2022-11-05 14:07:22,905 INFO: Returning background 0.15.\n",
+      "2022-11-05 14:07:22,906 INFO: Background is 0.15 counts/s/pixel\n",
+      "2022-11-05 14:07:22,907 INFO: Returning background 0.15.\n",
+      "2022-11-05 14:07:22,908 INFO: Added background of 0.15 counts/s/pixel\n",
+      "2022-11-05 14:07:22,914 INFO: Inserting correct exposure time\n",
+      "2022-11-05 14:07:22,915 INFO: Cropping Down to base Detector Size\n",
+      "2022-11-05 14:07:22,915 INFO: Cropping convolved image down to detector size\n",
+      "2022-11-05 14:07:22,916 INFO: Taking [22:4110, 22:4110]\n",
+      "2022-11-05 14:07:22,934 INFO: SCA01: (RA, DEC, PA) := (150.0, -2.5, 0.0), detected as (150.0, -2.5, 0.0)\n",
+      "2022-11-05 14:07:22,938 INFO: Adding poisson noise\n",
+      "2022-11-05 14:07:23,505 INFO: Adding Poisson Noise with mean 0.000123310554600831 and standard deviation 0.4029473226487636\n",
+      "2022-11-05 14:07:23,514 INFO: Adding readnoise\n",
+      "2022-11-05 14:07:24,093 INFO: Adding readnoise with mean 0.0035705557093024254 and STDEV 11.997060775756836\n",
+      "2022-11-05 14:07:24,099 INFO: Adding flatfield residual\n",
+      "2022-11-05 14:07:24,178 INFO: Adding Flatfield residual with mean 1.009787678718567 and standard deviation 0.0006766521255485713\n",
+      "2022-11-05 14:07:24,179 INFO: Adding dark residual\n",
+      "2022-11-05 14:07:24,246 INFO: Adding Dark residual with mean 0.6935914158821106 and standard deviation 3.6687374114990234\n",
+      "2022-11-05 14:07:24,246 INFO: Adding cosmic ray residual\n",
+      "2022-11-05 14:07:26,163 INFO: Adding Cosmic Ray residual with mean 0.2784475088119507 and standard deviation 3.479156970977783\n",
+      "2022-11-05 14:07:26,168 INFO: Finished adding error\n",
+      "2022-11-05 14:07:26,181 INFO: Finished Adding Error\n",
+      "2022-11-05 14:07:26,182 INFO: Converting to FITS file\n",
+      "2022-11-05 14:07:26,185 INFO: Converting detector SCA01 to FITS extension\n",
+      "2022-11-05 14:07:26,186 INFO: Creating Extension HDU from AstroImage SCA01\n",
+      "2022-11-05 14:07:26,194 INFO: Created Extension HDU from AstroImage SCA01\n",
+      "2022-11-05 14:07:26,570 INFO: Created FITS file /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_1_0.fits\n"
+     ]
+    }
+   ],
+   "source": [
+    "from stips.observation_module import ObservationModule\n",
+    "\n",
+    "offset = {\n",
+    "          'offset_id': 1,\n",
+    "          'offset_centre': False,\n",
+    "          'offset_ra': 0.0,\n",
+    "          'offset_dec': 0.0,\n",
+    "          'offset_pa': 0.0\n",
+    "         }\n",
+    "\n",
+    "residuals = {\n",
+    "             'residual_flat': False,\n",
+    "             'residual_dark': False,\n",
+    "             'residual_cosmic': False,\n",
+    "             'residual_poisson': True,\n",
+    "             'residual_readnoise': True,\n",
+    "            }\n",
+    "\n",
+    "observation_parameters = {\n",
+    "                          'instrument': 'WFI',\n",
+    "                          'filters': ['F129'],\n",
+    "                          'detectors': 1,\n",
+    "                          'distortion': False,\n",
+    "                          'background': 0.15,\n",
+    "                          'observations_id': 1,\n",
+    "                          'exptime': 1000,\n",
+    "                          'offsets': [offset]\n",
+    "                         }\n",
+    "\n",
+    "obm = ObservationModule(observation_parameters, out_prefix=obs_prefix, ra=obs_ra, dec=obs_dec, residuals=residuals)\n",
+    "\n",
+    "obm.nextObservation()\n",
+    "\n",
+    "output_stellar_catalogues = obm.addCatalogue(stellar_cat_file)\n",
+    "output_galaxy_catalogues = obm.addCatalogue(galaxy_cat_file)\n",
+    "\n",
+    "psf_file = obm.addError()\n",
+    "\n",
+    "fits_file, mosaic_file, params = obm.finalize(mosaic=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "fff89c66",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      ],
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with fits.open(fits_file) as result_file:\n",
+    "    result_data = result_file[1].data\n",
+    "    \n",
+    "crop_result_data = result_data[1900:2200, 1900:2200]\n",
+    "\n",
+    "fig1 = plt.figure()\n",
+    "im = plt.matshow(crop_result_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ee2052a",
+   "metadata": {},
+   "source": [
+    "## Observation I"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "4b2c7d7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:18:56,850 INFO: Got offsets as [{'offset_id': 1, 'offset_centre': False, 'offset_ra': 0.0, 'offset_dec': 0.0, 'offset_pa': 0.0}]\n",
+      "2022-11-05 14:18:56,886 INFO: Adding observation with filter F129 and offset (0.0,0.0,0.0)\n",
+      "2022-11-05 14:18:56,886 INFO: Added 1 observations\n",
+      "2022-11-05 14:18:56,889 INFO: WFI with 1 detectors. Central offset (0.0, 0.0, 0.0)\n",
+      "2022-11-05 14:18:56,889 INFO: Initializing Observation 0 of 1\n",
+      "2022-11-05 14:18:56,890 INFO: Observation Filter is F129\n",
+      "2022-11-05 14:18:56,890 INFO: Observation (RA,DEC) = (150.0,-2.5) with PA=0.0\n",
+      "2022-11-05 14:18:56,891 INFO: Resetting\n",
+      "2022-11-05 14:18:56,892 INFO: Returning background 0.15.\n",
+      "2022-11-05 14:18:56,893 INFO: Creating Detector SCA01 with (RA,DEC,PA) = (150.0,-2.5,0.0)\n",
+      "2022-11-05 14:18:56,894 INFO: Creating Detector SCA01 with offset (0.0,0.0)\n",
+      "2022-11-05 14:18:56,951 INFO: Creating Instrument with Configuration {'aperture': 'imaging', 'disperser': None, 'filter': 'f129', 'instrument': 'wfi', 'mode': 'imaging'}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating pandeia instrument roman.wfi.imaging\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:18:57,154 INFO: SCA01: (RA, DEC, PA) := (150.0, -2.5, 0.0), detected as (150.0, -2.5, 0.0)\n",
+      "2022-11-05 14:18:57,155 INFO: Detector SCA01 created\n",
+      "2022-11-05 14:18:57,156 INFO: Reset Instrument\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from stips.observation_module import ObservationModule\n",
+    "\n",
+    "offset_1 = {\n",
+    "          'offset_id': 1,\n",
+    "          'offset_centre': False,\n",
+    "          'offset_ra': 40.0,\n",
+    "          'offset_dec': 20.0,\n",
+    "          'offset_pa': 27.0\n",
+    "         }\n",
+    "\n",
+    "\n",
+    "residuals_1 = {\n",
+    "             'residual_flat': False,\n",
+    "             'residual_dark': False,\n",
+    "             'residual_cosmic': False,\n",
+    "             'residual_poisson': True,\n",
+    "             'residual_readnoise': True\n",
+    "            }\n",
+    "\n",
+    "\n",
+    "observation_parameters_1 = {\n",
+    "                          'instrument': 'WFI',\n",
+    "                          'filters': ['F129'],\n",
+    "                          'detectors': 1,\n",
+    "                          'distortion': False,\n",
+    "                          'background': 0.15,\n",
+    "                          'observations_id': 1,\n",
+    "                          'exptime': 1000,\n",
+    "                          'offsets': [offset]\n",
+    "                         }\n",
+    "\n",
+    "obm_1 = ObservationModule(observation_parameters_1, out_prefix=obs_prefix, ra=obs_ra, dec=obs_dec, residual=residuals_1)\n",
+    "\n",
+    "obm_1.nextObservation()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "2c26a99f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:21:38,768 INFO: Running catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_stars_000.fits\n",
+      "2022-11-05 14:21:38,769 INFO: Adding catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_stars_000.fits\n",
+      "2022-11-05 14:21:38,787 INFO: Converting phoenix catalogue\n",
+      "2022-11-05 14:21:38,788 INFO: Preparing output table\n",
+      "2022-11-05 14:21:38,821 INFO: Converting chunk 2\n",
+      "2022-11-05 14:21:38,822 INFO: Converting Phoenix Table to Internal format\n",
+      "2022-11-05 14:21:38,823 INFO: 1 datasets\n",
+      "2022-11-05 14:21:39,244 INFO: Finished converting catalogue to internal format\n",
+      "2022-11-05 14:21:39,244 INFO: Adding catalogue to detector SCA01\n",
+      "2022-11-05 14:21:39,245 INFO: Adding catalogue adv_notebook_stars_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:21:39,288 INFO: Determining pixel co-ordinates\n",
+      "2022-11-05 14:21:39,289 INFO: Keeping 45 items\n",
+      "2022-11-05 14:21:39,290 INFO: Writing 45 stars\n",
+      "2022-11-05 14:21:39,291 INFO: Adding 45 point sources to AstroImage SCA01\n",
+      "2022-11-05 14:21:39,306 INFO: PSF File psf_WFI_2.0.0_F129_sca01.fits to be put at /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache\n",
+      "2022-11-05 14:21:39,307 INFO: PSF File is /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache/psf_WFI_2.0.0_F129_sca01.fits\n",
+      "2022-11-05 14:21:40,358 INFO: Adding point source 1 to AstroImage 2070.722560524683,2041.9150679944821\n",
+      "2022-11-05 14:21:40,369 INFO: Adding point source 2 to AstroImage 2084.2158307131685,2020.5898365456273\n",
+      "2022-11-05 14:21:40,382 INFO: Adding point source 3 to AstroImage 2020.0916326654587,2062.377725941959\n",
+      "2022-11-05 14:21:40,396 INFO: Adding point source 4 to AstroImage 2212.4513663786984,2050.6756963380167\n",
+      "2022-11-05 14:21:40,411 INFO: Adding point source 5 to AstroImage 2074.333731140191,2069.828192912249\n",
+      "2022-11-05 14:21:40,428 INFO: Adding point source 6 to AstroImage 2038.133559050457,1916.5833393972441\n",
+      "2022-11-05 14:21:40,446 INFO: Adding point source 7 to AstroImage 2343.7887260447783,1900.9585770057893\n",
+      "2022-11-05 14:21:40,463 INFO: Adding point source 8 to AstroImage 2686.907675398465,2605.1397456727286\n",
+      "2022-11-05 14:21:40,482 INFO: Adding point source 9 to AstroImage 2065.0,2064.9999999998854\n",
+      "2022-11-05 14:21:40,501 INFO: Adding point source 10 to AstroImage 3066.406035833374,2361.1200801485934\n",
+      "2022-11-05 14:21:40,518 INFO: Adding point source 11 to AstroImage 1182.5553343396969,2253.7720913427247\n",
+      "2022-11-05 14:21:40,535 INFO: Adding point source 12 to AstroImage 2193.6284242791144,2076.3731457782155\n",
+      "2022-11-05 14:21:40,553 INFO: Adding point source 13 to AstroImage 2813.8779329567824,1511.2515673506764\n",
+      "2022-11-05 14:21:40,571 INFO: Adding point source 14 to AstroImage 2182.309943805917,2015.3144162842866\n",
+      "2022-11-05 14:21:40,588 INFO: Adding point source 15 to AstroImage 3057.662806951754,2070.8940117453767\n",
+      "2022-11-05 14:21:40,606 INFO: Adding point source 16 to AstroImage 2058.065719450632,2052.1650298807162\n",
+      "2022-11-05 14:21:40,624 INFO: Adding point source 17 to AstroImage 2091.1427915903523,2087.830605821163\n",
+      "2022-11-05 14:21:40,643 INFO: Adding point source 18 to AstroImage 2935.0659858918716,2238.7989722696175\n",
+      "2022-11-05 14:21:40,663 INFO: Adding point source 19 to AstroImage 2307.363834564019,44.638053235246616\n",
+      "2022-11-05 14:21:40,681 INFO: Adding point source 20 to AstroImage 1680.7671502598998,584.177926715473\n",
+      "2022-11-05 14:21:40,702 INFO: Adding point source 21 to AstroImage 1092.7417651348703,3440.84712683443\n",
+      "2022-11-05 14:21:40,724 INFO: Adding point source 22 to AstroImage 2830.1701018015883,2110.572157738221\n",
+      "2022-11-05 14:21:40,743 INFO: Adding point source 23 to AstroImage 2633.6640993973206,3541.184421687627\n",
+      "2022-11-05 14:21:40,766 INFO: Adding point source 24 to AstroImage 1164.5832552158022,1650.5574925855592\n",
+      "2022-11-05 14:21:40,786 INFO: Adding point source 25 to AstroImage 3791.1938738559716,3441.3126869956523\n",
+      "2022-11-05 14:21:40,805 INFO: Adding point source 26 to AstroImage 4103.8638238646145,1496.5221231689397\n",
+      "2022-11-05 14:21:40,823 INFO: Adding point source 27 to AstroImage 1927.483392341295,2084.012838647698\n",
+      "2022-11-05 14:21:40,841 INFO: Adding point source 28 to AstroImage 178.8233678377253,2018.9980800272074\n",
+      "2022-11-05 14:21:40,859 INFO: Adding point source 29 to AstroImage 4086.428547161244,2087.534318716181\n",
+      "2022-11-05 14:21:40,878 INFO: Adding point source 30 to AstroImage 4013.0036569791146,3801.977402800462\n",
+      "2022-11-05 14:21:40,897 INFO: Adding point source 31 to AstroImage 2496.9760981816366,803.4482637257686\n",
+      "2022-11-05 14:21:40,915 INFO: Adding point source 32 to AstroImage 3845.1763908222347,2562.947057853079\n",
+      "2022-11-05 14:21:40,934 INFO: Adding point source 33 to AstroImage 2292.3383947583316,3791.6209688368463\n",
+      "2022-11-05 14:21:40,952 INFO: Adding point source 34 to AstroImage 788.8944598713454,2221.1635345445893\n",
+      "2022-11-05 14:21:40,971 INFO: Adding point source 35 to AstroImage 1640.67667391249,1859.632529783993\n",
+      "2022-11-05 14:21:40,988 INFO: Adding point source 36 to AstroImage 2080.756486917873,1948.955440355016\n",
+      "2022-11-05 14:21:41,006 INFO: Adding point source 37 to AstroImage 1941.1642102038797,2738.6736046548926\n",
+      "2022-11-05 14:21:41,025 INFO: Adding point source 38 to AstroImage 2034.0285436105394,2075.3305458540103\n",
+      "2022-11-05 14:21:41,044 INFO: Adding point source 39 to AstroImage 2832.565664059406,2472.6828743012607\n",
+      "2022-11-05 14:21:41,063 INFO: Adding point source 40 to AstroImage 2507.4896238795254,1761.0417715257663\n",
+      "2022-11-05 14:21:41,082 INFO: Adding point source 41 to AstroImage 3447.0034423518946,3015.875319404148\n",
+      "2022-11-05 14:21:41,100 INFO: Adding point source 42 to AstroImage 2577.160455836409,1447.2117649906638\n",
+      "2022-11-05 14:21:41,118 INFO: Adding point source 43 to AstroImage 3414.753743350597,1328.352052000483\n",
+      "2022-11-05 14:21:41,138 INFO: Adding point source 44 to AstroImage 2358.459104375479,1859.405996413239\n",
+      "2022-11-05 14:21:41,156 INFO: Adding point source 45 to AstroImage 2947.2962384749826,2913.584957297892\n",
+      "2022-11-05 14:21:41,278 INFO: Added catalogue adv_notebook_stars_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:21:41,279 INFO: Finished catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_stars_000.fits\n",
+      "2022-11-05 14:21:41,280 INFO: Running catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_gals_000.fits\n",
+      "2022-11-05 14:21:41,281 INFO: Adding catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_gals_000.fits\n",
+      "2022-11-05 14:21:41,301 INFO: Converting bc95 catalogue\n",
+      "2022-11-05 14:21:41,302 INFO: Preparing output table\n",
+      "2022-11-05 14:21:41,331 INFO: Converting chunk 2\n",
+      "2022-11-05 14:21:41,331 INFO: Converting BC95 Catalogue\n",
+      "2022-11-05 14:21:41,332 INFO: Normalization Bandpass is johnson,v (<class 'str'>)\n",
+      "2022-11-05 14:21:41,334 INFO: Normalization Bandpass is johnson,v\n",
+      "2022-11-05 14:21:41,505 INFO: Finished converting catalogue to internal format\n",
+      "2022-11-05 14:21:41,506 INFO: Adding catalogue to detector SCA01\n",
+      "2022-11-05 14:21:41,506 INFO: Adding catalogue adv_notebook_gals_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:21:41,559 INFO: Determining pixel co-ordinates\n",
+      "2022-11-05 14:21:41,560 INFO: Keeping 2 items\n",
+      "2022-11-05 14:21:41,561 INFO: Writing 2 galaxies\n",
+      "2022-11-05 14:21:41,562 INFO: Starting Sersic Profiles at Sat Nov  5 14:21:41 2022\n",
+      "2022-11-05 14:21:41,563 INFO: Index is 0\n",
+      "2022-11-05 14:21:45,244 INFO: PSF File psf_WFI_2.0.0_F129_sca01.fits to be put at /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache\n",
+      "2022-11-05 14:21:45,244 INFO: PSF File is /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache/psf_WFI_2.0.0_F129_sca01.fits\n",
+      "2022-11-05 14:21:51,863 INFO: Finished Galaxy 1 of 2\n",
+      "2022-11-05 14:21:51,864 INFO: Index is 1\n",
+      "2022-11-05 14:21:54,836 INFO: PSF File psf_WFI_2.0.0_F129_sca01.fits to be put at /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache\n",
+      "2022-11-05 14:21:54,837 INFO: PSF File is /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache/psf_WFI_2.0.0_F129_sca01.fits\n",
+      "2022-11-05 14:22:01,009 INFO: Finished Galaxy 2 of 2\n",
+      "2022-11-05 14:22:01,009 INFO: Finishing Sersic Profiles at Sat Nov  5 14:22:01 2022\n",
+      "2022-11-05 14:22:01,109 INFO: Added catalogue adv_notebook_gals_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:22:01,110 INFO: Finished catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_gals_000.fits\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:22:01,111 INFO: Adding Error\n",
+      "2022-11-05 14:22:01,111 INFO: Adding residual error\n",
+      "2022-11-05 14:22:01,112 INFO: Adding error to detector SCA01\n",
+      "2022-11-05 14:22:01,113 INFO: Adding background\n",
+      "2022-11-05 14:22:01,113 INFO: Returning background 0.15.\n",
+      "2022-11-05 14:22:01,114 INFO: Background is 0.15 counts/s/pixel\n",
+      "2022-11-05 14:22:01,115 INFO: Returning background 0.15.\n",
+      "2022-11-05 14:22:01,116 INFO: Added background of 0.15 counts/s/pixel\n",
+      "2022-11-05 14:22:01,122 INFO: Inserting correct exposure time\n",
+      "2022-11-05 14:22:01,123 INFO: Cropping Down to base Detector Size\n",
+      "2022-11-05 14:22:01,123 INFO: Cropping convolved image down to detector size\n",
+      "2022-11-05 14:22:01,124 INFO: Taking [22:4110, 22:4110]\n",
+      "2022-11-05 14:22:01,141 INFO: SCA01: (RA, DEC, PA) := (150.0, -2.5, 0.0), detected as (150.0, -2.5, 0.0)\n",
+      "2022-11-05 14:22:01,145 INFO: Adding poisson noise\n",
+      "2022-11-05 14:22:01,731 INFO: Adding Poisson Noise with mean 0.0001269056787226865 and standard deviation 0.4180971605674833\n",
+      "2022-11-05 14:22:01,742 INFO: Adding readnoise\n",
+      "2022-11-05 14:22:02,282 INFO: Adding readnoise with mean 0.0035705557093024254 and STDEV 11.997060775756836\n",
+      "2022-11-05 14:22:02,286 INFO: Finished adding error\n",
+      "2022-11-05 14:22:02,287 INFO: Finished Adding Error\n",
+      "2022-11-05 14:22:02,287 INFO: Converting to FITS file\n",
+      "2022-11-05 14:22:02,289 INFO: Converting detector SCA01 to FITS extension\n",
+      "2022-11-05 14:22:02,290 INFO: Creating Extension HDU from AstroImage SCA01\n",
+      "2022-11-05 14:22:02,296 INFO: Created Extension HDU from AstroImage SCA01\n",
+      "2022-11-05 14:22:02,557 INFO: Created FITS file /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_1_0.fits\n"
+     ]
+    }
+   ],
+   "source": [
+    "output_stellar_catalogues_1 = obm_1.addCatalogue(stellar_cat_file)\n",
+    "output_galaxy_catalogues_1 = obm_1.addCatalogue(galaxy_cat_file)\n",
+    "\n",
+    "psf_file_1 = obm_1.addError()\n",
+    "\n",
+    "fits_file_1, mosaic_file_1, params_1 = obm_1.finalize(mosaic=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "4261f44f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with fits.open(fits_file_1) as result_file_1:\n",
+    "    result_data_1 = result_file_1[1].data\n",
+    "\n",
+    "crop_result_data_1 = result_data_1[1900:2200, 1900:2200]\n",
+    "\n",
+    "fig_1 = plt.figure()\n",
+    "im_1 = plt.matshow(crop_result_data_1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b15b79b0",
+   "metadata": {},
+   "source": [
+    "## Observation II"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "c74650b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:22:14,352 INFO: Got offsets as [{'offset_id': 1, 'offset_centre': False, 'offset_ra': 0.0, 'offset_dec': 0.0, 'offset_pa': 0.0}]\n",
+      "2022-11-05 14:22:14,387 INFO: Adding observation with filter F129 and offset (0.0,0.0,0.0)\n",
+      "2022-11-05 14:22:14,388 INFO: Added 1 observations\n",
+      "2022-11-05 14:22:14,390 INFO: WFI with 1 detectors. Central offset (0.0, 0.0, 0.0)\n",
+      "2022-11-05 14:22:14,391 INFO: Initializing Observation 0 of 1\n",
+      "2022-11-05 14:22:14,391 INFO: Observation Filter is F129\n",
+      "2022-11-05 14:22:14,392 INFO: Observation (RA,DEC) = (150.0,-2.5) with PA=0.0\n",
+      "2022-11-05 14:22:14,392 INFO: Resetting\n",
+      "2022-11-05 14:22:14,393 INFO: Returning background 0.15.\n",
+      "2022-11-05 14:22:14,393 INFO: Creating Detector SCA01 with (RA,DEC,PA) = (150.0,-2.5,0.0)\n",
+      "2022-11-05 14:22:14,394 INFO: Creating Detector SCA01 with offset (0.0,0.0)\n",
+      "2022-11-05 14:22:14,447 INFO: Creating Instrument with Configuration {'aperture': 'imaging', 'disperser': None, 'filter': 'f129', 'instrument': 'wfi', 'mode': 'imaging'}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating pandeia instrument roman.wfi.imaging\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:22:14,658 INFO: SCA01: (RA, DEC, PA) := (150.0, -2.5, 0.0), detected as (150.0, -2.5, 0.0)\n",
+      "2022-11-05 14:22:14,659 INFO: Detector SCA01 created\n",
+      "2022-11-05 14:22:14,660 INFO: Reset Instrument\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "offset_2 = {\n",
+    "          'offset_id': 1,\n",
+    "          'offset_centre': False,\n",
+    "          'offset_ra': 0.0,\n",
+    "          'offset_dec': 0.0,\n",
+    "          'offset_pa': 0.0\n",
+    "         }\n",
+    "\n",
+    "\n",
+    "residuals_2 = {\n",
+    "             'residual_flat': True,\n",
+    "             'residual_dark': True,\n",
+    "             'residual_cosmic': True,\n",
+    "             'residual_poisson': False,\n",
+    "             'residual_readnoise': False\n",
+    "            }\n",
+    "\n",
+    "\n",
+    "observation_parameters_2 = {\n",
+    "                          'instrument': 'WFI',\n",
+    "                          'filters': ['F129'],\n",
+    "                          'detectors': 1,\n",
+    "                          'distortion': False,\n",
+    "                          'background': 0.15,\n",
+    "                          'observations_id': 1,\n",
+    "                          'exptime': 1000,\n",
+    "                          'offsets': [offset]\n",
+    "                         }\n",
+    "\n",
+    "obm_2 = ObservationModule(observation_parameters_2, out_prefix=obs_prefix, ra=obs_ra, dec=obs_dec, residual=residuals_2)\n",
+    "\n",
+    "obm_2.nextObservation()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "3b24e680",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:22:21,338 INFO: Running catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_stars_000.fits\n",
+      "2022-11-05 14:22:21,339 INFO: Adding catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_stars_000.fits\n",
+      "2022-11-05 14:22:21,355 INFO: Converting phoenix catalogue\n",
+      "2022-11-05 14:22:21,356 INFO: Preparing output table\n",
+      "2022-11-05 14:22:21,385 INFO: Converting chunk 2\n",
+      "2022-11-05 14:22:21,386 INFO: Converting Phoenix Table to Internal format\n",
+      "2022-11-05 14:22:21,386 INFO: 1 datasets\n",
+      "2022-11-05 14:22:21,848 INFO: Finished converting catalogue to internal format\n",
+      "2022-11-05 14:22:21,849 INFO: Adding catalogue to detector SCA01\n",
+      "2022-11-05 14:22:21,849 INFO: Adding catalogue adv_notebook_stars_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:22:21,910 INFO: Determining pixel co-ordinates\n",
+      "2022-11-05 14:22:21,911 INFO: Keeping 45 items\n",
+      "2022-11-05 14:22:21,912 INFO: Writing 45 stars\n",
+      "2022-11-05 14:22:21,913 INFO: Adding 45 point sources to AstroImage SCA01\n",
+      "2022-11-05 14:22:21,930 INFO: PSF File psf_WFI_2.0.0_F129_sca01.fits to be put at /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache\n",
+      "2022-11-05 14:22:21,930 INFO: PSF File is /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache/psf_WFI_2.0.0_F129_sca01.fits\n",
+      "2022-11-05 14:22:23,232 INFO: Adding point source 1 to AstroImage 2070.722560524683,2041.9150679944821\n",
+      "2022-11-05 14:22:23,246 INFO: Adding point source 2 to AstroImage 2084.2158307131685,2020.5898365456273\n",
+      "2022-11-05 14:22:23,262 INFO: Adding point source 3 to AstroImage 2020.0916326654587,2062.377725941959\n",
+      "2022-11-05 14:22:23,280 INFO: Adding point source 4 to AstroImage 2212.4513663786984,2050.6756963380167\n",
+      "2022-11-05 14:22:23,297 INFO: Adding point source 5 to AstroImage 2074.333731140191,2069.828192912249\n",
+      "2022-11-05 14:22:23,317 INFO: Adding point source 6 to AstroImage 2038.133559050457,1916.5833393972441\n",
+      "2022-11-05 14:22:23,335 INFO: Adding point source 7 to AstroImage 2343.7887260447783,1900.9585770057893\n",
+      "2022-11-05 14:22:23,351 INFO: Adding point source 8 to AstroImage 2686.907675398465,2605.1397456727286\n",
+      "2022-11-05 14:22:23,369 INFO: Adding point source 9 to AstroImage 2065.0,2064.9999999998854\n",
+      "2022-11-05 14:22:23,387 INFO: Adding point source 10 to AstroImage 3066.406035833374,2361.1200801485934\n",
+      "2022-11-05 14:22:23,404 INFO: Adding point source 11 to AstroImage 1182.5553343396969,2253.7720913427247\n",
+      "2022-11-05 14:22:23,421 INFO: Adding point source 12 to AstroImage 2193.6284242791144,2076.3731457782155\n",
+      "2022-11-05 14:22:23,439 INFO: Adding point source 13 to AstroImage 2813.8779329567824,1511.2515673506764\n",
+      "2022-11-05 14:22:23,457 INFO: Adding point source 14 to AstroImage 2182.309943805917,2015.3144162842866\n",
+      "2022-11-05 14:22:23,474 INFO: Adding point source 15 to AstroImage 3057.662806951754,2070.8940117453767\n",
+      "2022-11-05 14:22:23,492 INFO: Adding point source 16 to AstroImage 2058.065719450632,2052.1650298807162\n",
+      "2022-11-05 14:22:23,510 INFO: Adding point source 17 to AstroImage 2091.1427915903523,2087.830605821163\n",
+      "2022-11-05 14:22:23,528 INFO: Adding point source 18 to AstroImage 2935.0659858918716,2238.7989722696175\n",
+      "2022-11-05 14:22:23,545 INFO: Adding point source 19 to AstroImage 2307.363834564019,44.638053235246616\n",
+      "2022-11-05 14:22:23,563 INFO: Adding point source 20 to AstroImage 1680.7671502598998,584.177926715473\n",
+      "2022-11-05 14:22:23,580 INFO: Adding point source 21 to AstroImage 1092.7417651348703,3440.84712683443\n",
+      "2022-11-05 14:22:23,598 INFO: Adding point source 22 to AstroImage 2830.1701018015883,2110.572157738221\n",
+      "2022-11-05 14:22:23,615 INFO: Adding point source 23 to AstroImage 2633.6640993973206,3541.184421687627\n",
+      "2022-11-05 14:22:23,632 INFO: Adding point source 24 to AstroImage 1164.5832552158022,1650.5574925855592\n",
+      "2022-11-05 14:22:23,650 INFO: Adding point source 25 to AstroImage 3791.1938738559716,3441.3126869956523\n",
+      "2022-11-05 14:22:23,667 INFO: Adding point source 26 to AstroImage 4103.8638238646145,1496.5221231689397\n",
+      "2022-11-05 14:22:23,685 INFO: Adding point source 27 to AstroImage 1927.483392341295,2084.012838647698\n",
+      "2022-11-05 14:22:23,702 INFO: Adding point source 28 to AstroImage 178.8233678377253,2018.9980800272074\n",
+      "2022-11-05 14:22:23,720 INFO: Adding point source 29 to AstroImage 4086.428547161244,2087.534318716181\n",
+      "2022-11-05 14:22:23,738 INFO: Adding point source 30 to AstroImage 4013.0036569791146,3801.977402800462\n",
+      "2022-11-05 14:22:23,755 INFO: Adding point source 31 to AstroImage 2496.9760981816366,803.4482637257686\n",
+      "2022-11-05 14:22:23,773 INFO: Adding point source 32 to AstroImage 3845.1763908222347,2562.947057853079\n",
+      "2022-11-05 14:22:23,790 INFO: Adding point source 33 to AstroImage 2292.3383947583316,3791.6209688368463\n",
+      "2022-11-05 14:22:23,809 INFO: Adding point source 34 to AstroImage 788.8944598713454,2221.1635345445893\n",
+      "2022-11-05 14:22:23,826 INFO: Adding point source 35 to AstroImage 1640.67667391249,1859.632529783993\n",
+      "2022-11-05 14:22:23,844 INFO: Adding point source 36 to AstroImage 2080.756486917873,1948.955440355016\n",
+      "2022-11-05 14:22:23,861 INFO: Adding point source 37 to AstroImage 1941.1642102038797,2738.6736046548926\n",
+      "2022-11-05 14:22:23,880 INFO: Adding point source 38 to AstroImage 2034.0285436105394,2075.3305458540103\n",
+      "2022-11-05 14:22:23,897 INFO: Adding point source 39 to AstroImage 2832.565664059406,2472.6828743012607\n",
+      "2022-11-05 14:22:23,915 INFO: Adding point source 40 to AstroImage 2507.4896238795254,1761.0417715257663\n",
+      "2022-11-05 14:22:23,933 INFO: Adding point source 41 to AstroImage 3447.0034423518946,3015.875319404148\n",
+      "2022-11-05 14:22:23,951 INFO: Adding point source 42 to AstroImage 2577.160455836409,1447.2117649906638\n",
+      "2022-11-05 14:22:23,968 INFO: Adding point source 43 to AstroImage 3414.753743350597,1328.352052000483\n",
+      "2022-11-05 14:22:23,985 INFO: Adding point source 44 to AstroImage 2358.459104375479,1859.405996413239\n",
+      "2022-11-05 14:22:24,001 INFO: Adding point source 45 to AstroImage 2947.2962384749826,2913.584957297892\n",
+      "WARNING: VerifyWarning: Keyword name 'input_catalogue' is greater than 8 characters or contains characters not allowed by the FITS standard; a HIERARCH card will be created. [astropy.io.fits.card]\n",
+      "2022-11-05 14:22:24,104 INFO: Added catalogue adv_notebook_stars_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:22:24,105 INFO: Finished catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_stars_000.fits\n",
+      "2022-11-05 14:22:24,105 INFO: Running catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_gals_000.fits\n",
+      "2022-11-05 14:22:24,106 INFO: Adding catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_gals_000.fits\n",
+      "2022-11-05 14:22:24,126 INFO: Converting bc95 catalogue\n",
+      "2022-11-05 14:22:24,126 INFO: Preparing output table\n",
+      "2022-11-05 14:22:24,157 INFO: Converting chunk 2\n",
+      "2022-11-05 14:22:24,158 INFO: Converting BC95 Catalogue\n",
+      "2022-11-05 14:22:24,158 INFO: Normalization Bandpass is johnson,v (<class 'str'>)\n",
+      "2022-11-05 14:22:24,159 INFO: Normalization Bandpass is johnson,v\n",
+      "2022-11-05 14:22:24,352 INFO: Finished converting catalogue to internal format\n",
+      "2022-11-05 14:22:24,352 INFO: Adding catalogue to detector SCA01\n",
+      "2022-11-05 14:22:24,353 INFO: Adding catalogue adv_notebook_gals_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:22:24,409 INFO: Determining pixel co-ordinates\n",
+      "2022-11-05 14:22:24,410 INFO: Keeping 2 items\n",
+      "2022-11-05 14:22:24,411 INFO: Writing 2 galaxies\n",
+      "2022-11-05 14:22:24,412 INFO: Starting Sersic Profiles at Sat Nov  5 14:22:24 2022\n",
+      "2022-11-05 14:22:24,413 INFO: Index is 0\n",
+      "2022-11-05 14:22:28,148 INFO: PSF File psf_WFI_2.0.0_F129_sca01.fits to be put at /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache\n",
+      "2022-11-05 14:22:28,148 INFO: PSF File is /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache/psf_WFI_2.0.0_F129_sca01.fits\n",
+      "2022-11-05 14:22:35,037 INFO: Finished Galaxy 1 of 2\n",
+      "2022-11-05 14:22:35,038 INFO: Index is 1\n",
+      "2022-11-05 14:22:38,045 INFO: PSF File psf_WFI_2.0.0_F129_sca01.fits to be put at /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache\n",
+      "2022-11-05 14:22:38,045 INFO: PSF File is /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache/psf_WFI_2.0.0_F129_sca01.fits\n",
+      "2022-11-05 14:22:44,404 INFO: Finished Galaxy 2 of 2\n",
+      "2022-11-05 14:22:44,405 INFO: Finishing Sersic Profiles at Sat Nov  5 14:22:44 2022\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:22:44,476 INFO: Added catalogue adv_notebook_gals_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:22:44,477 INFO: Finished catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_gals_000.fits\n",
+      "2022-11-05 14:22:44,477 INFO: Adding Error\n",
+      "2022-11-05 14:22:44,478 INFO: Adding residual error\n",
+      "2022-11-05 14:22:44,538 INFO: SCA01: (RA, DEC, PA) := (0.0, 0.0, 0.0), detected as (0.0, 0.0, 0.0)\n",
+      "2022-11-05 14:22:44,871 INFO: SCA01: (RA, DEC, PA) := (0.0, 0.0, 0.0), detected as (0.0, 0.0, 0.0)\n",
+      "2022-11-05 14:22:44,872 INFO: Created AstroImage SCA01 and imported data from FITS file err_flat_wfi.fits\n",
+      "2022-11-05 14:22:44,931 INFO: SCA01: (RA, DEC, PA) := (0.0, 0.0, 0.0), detected as (0.0, 0.0, 0.0)\n",
+      "2022-11-05 14:22:45,222 INFO: SCA01: (RA, DEC, PA) := (0.0, 0.0, 0.0), detected as (0.0, 0.0, 0.0)\n",
+      "2022-11-05 14:22:45,223 INFO: Created AstroImage SCA01 and imported data from FITS file err_rdrk_wfi.fits\n",
+      "2022-11-05 14:22:45,237 INFO: Adding error to detector SCA01\n",
+      "2022-11-05 14:22:45,238 INFO: Adding background\n",
+      "2022-11-05 14:22:45,238 INFO: Returning background 0.15.\n",
+      "2022-11-05 14:22:45,239 INFO: Background is 0.15 counts/s/pixel\n",
+      "2022-11-05 14:22:45,240 INFO: Returning background 0.15.\n",
+      "2022-11-05 14:22:45,241 INFO: Added background of 0.15 counts/s/pixel\n",
+      "2022-11-05 14:22:45,248 INFO: Inserting correct exposure time\n",
+      "2022-11-05 14:22:45,248 INFO: Cropping Down to base Detector Size\n",
+      "2022-11-05 14:22:45,249 INFO: Cropping convolved image down to detector size\n",
+      "2022-11-05 14:22:45,250 INFO: Taking [22:4110, 22:4110]\n",
+      "2022-11-05 14:22:45,268 INFO: SCA01: (RA, DEC, PA) := (150.0, -2.5, 0.0), detected as (150.0, -2.5, 0.0)\n",
+      "2022-11-05 14:22:45,272 INFO: Adding flatfield residual\n",
+      "2022-11-05 14:22:45,352 INFO: Adding Flatfield residual with mean 1.009787678718567 and standard deviation 0.0006766521255485713\n",
+      "2022-11-05 14:22:45,353 INFO: Adding dark residual\n",
+      "2022-11-05 14:22:45,432 INFO: Adding Dark residual with mean 0.6935914158821106 and standard deviation 3.6687374114990234\n",
+      "2022-11-05 14:22:45,433 INFO: Adding cosmic ray residual\n",
+      "2022-11-05 14:22:47,114 INFO: Adding Cosmic Ray residual with mean 0.2784475088119507 and standard deviation 3.479156970977783\n",
+      "2022-11-05 14:22:47,118 INFO: Finished adding error\n",
+      "2022-11-05 14:22:47,126 INFO: Finished Adding Error\n",
+      "2022-11-05 14:22:47,127 INFO: Converting to FITS file\n",
+      "2022-11-05 14:22:47,128 INFO: Converting detector SCA01 to FITS extension\n",
+      "2022-11-05 14:22:47,129 INFO: Creating Extension HDU from AstroImage SCA01\n",
+      "2022-11-05 14:22:47,133 INFO: Created Extension HDU from AstroImage SCA01\n",
+      "2022-11-05 14:22:47,341 INFO: Created FITS file /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_1_0.fits\n"
+     ]
+    }
+   ],
+   "source": [
+    "output_stellar_catalogues_2 = obm_2.addCatalogue(stellar_cat_file)\n",
+    "output_galaxy_catalogues_2 = obm_2.addCatalogue(galaxy_cat_file)\n",
+    "\n",
+    "psf_file_2 = obm_2.addError()\n",
+    "\n",
+    "fits_file_2, mosaic_file_2, params_2 = obm_2.finalize(mosaic=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "827535a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with fits.open(fits_file_2) as result_file_2:\n",
+    "    result_data_2 = result_file_2[1].data\n",
+    "\n",
+    "crop_result_data_2 = result_data[1900:2200, 1900:2200]\n",
+    "\n",
+    "fig_2 = plt.figure()\n",
+    "im_2 = plt.matshow(crop_result_data_2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3467534c",
+   "metadata": {},
+   "source": [
+    "## Observation III.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "6fc129d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:24:15,406 INFO: Got offsets as [{'offset_id': 1, 'offset_centre': False, 'offset_ra': 0.0, 'offset_dec': 0.0, 'offset_pa': 0.0}]\n",
+      "2022-11-05 14:24:15,441 INFO: Adding observation with filter F129 and offset (0.0,0.0,0.0)\n",
+      "2022-11-05 14:24:15,442 INFO: Added 1 observations\n",
+      "2022-11-05 14:24:15,444 INFO: WFI with 1 detectors. Central offset (0.0, 0.0, 0.0)\n",
+      "2022-11-05 14:24:15,445 INFO: Initializing Observation 0 of 1\n",
+      "2022-11-05 14:24:15,445 INFO: Observation Filter is F129\n",
+      "2022-11-05 14:24:15,446 INFO: Observation (RA,DEC) = (150.0,-2.5) with PA=0.0\n",
+      "2022-11-05 14:24:15,447 INFO: Resetting\n",
+      "2022-11-05 14:24:15,448 INFO: Returning background 0.15.\n",
+      "2022-11-05 14:24:15,448 INFO: Creating Detector SCA01 with (RA,DEC,PA) = (150.0,-2.5,0.0)\n",
+      "2022-11-05 14:24:15,449 INFO: Creating Detector SCA01 with offset (0.0,0.0)\n",
+      "2022-11-05 14:24:15,507 INFO: Creating Instrument with Configuration {'aperture': 'imaging', 'disperser': None, 'filter': 'f129', 'instrument': 'wfi', 'mode': 'imaging'}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating pandeia instrument roman.wfi.imaging\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:24:15,711 INFO: SCA01: (RA, DEC, PA) := (150.0, -2.5, 0.0), detected as (150.0, -2.5, 0.0)\n",
+      "2022-11-05 14:24:15,712 INFO: Detector SCA01 created\n",
+      "2022-11-05 14:24:15,713 INFO: Reset Instrument\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "offset_3_1 = {\n",
+    "          'offset_id': 1,\n",
+    "          'offset_centre': False,\n",
+    "          'offset_ra': 0.0,\n",
+    "          'offset_dec': 0.0,\n",
+    "          'offset_pa': 0.0\n",
+    "         }\n",
+    "\n",
+    "\n",
+    "residuals_3_1 = {\n",
+    "             'residual_flat': False,\n",
+    "             'residual_dark': False,\n",
+    "             'residual_cosmic': False,\n",
+    "             'residual_poisson': True,\n",
+    "             'residual_readnoise': True\n",
+    "            }\n",
+    "\n",
+    "\n",
+    "observation_parameters_3_1 = {\n",
+    "                          'instrument': 'WFI',\n",
+    "                          'filters': ['F129'],\n",
+    "                          'detectors': 1,\n",
+    "                          'distortion': True,\n",
+    "                          'background': 0.15,\n",
+    "                          'observations_id': 1,\n",
+    "                          'exptime': 1000,\n",
+    "                          'offsets': [offset]\n",
+    "                         }\n",
+    "\n",
+    "obm_3_1 = ObservationModule(observation_parameters_3_1, out_prefix=obs_prefix, ra=obs_ra, dec=obs_dec, residual=residuals_3_1)\n",
+    "\n",
+    "obm_3_1.nextObservation()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "c19a2c79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:24:20,424 INFO: Running catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_stars_000.fits\n",
+      "2022-11-05 14:24:20,425 INFO: Adding catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_stars_000.fits\n",
+      "2022-11-05 14:24:20,444 INFO: Converting phoenix catalogue\n",
+      "2022-11-05 14:24:20,444 INFO: Preparing output table\n",
+      "2022-11-05 14:24:20,479 INFO: Converting chunk 2\n",
+      "2022-11-05 14:24:20,479 INFO: Converting Phoenix Table to Internal format\n",
+      "2022-11-05 14:24:20,481 INFO: 1 datasets\n",
+      "2022-11-05 14:24:20,851 INFO: Finished converting catalogue to internal format\n",
+      "2022-11-05 14:24:20,852 INFO: Adding catalogue to detector SCA01\n",
+      "2022-11-05 14:24:20,853 INFO: Adding catalogue adv_notebook_stars_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:24:20,903 INFO: Determining pixel co-ordinates\n",
+      "2022-11-05 14:24:20,904 INFO: Keeping 45 items\n",
+      "2022-11-05 14:24:20,905 INFO: Writing 45 stars\n",
+      "2022-11-05 14:24:20,905 INFO: Adding 45 point sources to AstroImage SCA01\n",
+      "2022-11-05 14:24:20,920 INFO: PSF File psf_WFI_2.0.0_F129_sca01.fits to be put at /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache\n",
+      "2022-11-05 14:24:20,921 INFO: PSF File is /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache/psf_WFI_2.0.0_F129_sca01.fits\n",
+      "2022-11-05 14:24:21,947 INFO: Adding point source 1 to AstroImage 2070.722560524683,2041.9150679944821\n",
+      "2022-11-05 14:24:21,960 INFO: Adding point source 2 to AstroImage 2084.2158307131685,2020.5898365456273\n",
+      "2022-11-05 14:24:21,974 INFO: Adding point source 3 to AstroImage 2020.0916326654587,2062.377725941959\n",
+      "2022-11-05 14:24:21,989 INFO: Adding point source 4 to AstroImage 2212.4513663786984,2050.6756963380167\n",
+      "2022-11-05 14:24:22,007 INFO: Adding point source 5 to AstroImage 2074.333731140191,2069.828192912249\n",
+      "2022-11-05 14:24:22,024 INFO: Adding point source 6 to AstroImage 2038.133559050457,1916.5833393972441\n",
+      "2022-11-05 14:24:22,042 INFO: Adding point source 7 to AstroImage 2343.7887260447783,1900.9585770057893\n",
+      "2022-11-05 14:24:22,059 INFO: Adding point source 8 to AstroImage 2686.907675398465,2605.1397456727286\n",
+      "2022-11-05 14:24:22,076 INFO: Adding point source 9 to AstroImage 2065.0,2064.9999999998854\n",
+      "2022-11-05 14:24:22,093 INFO: Adding point source 10 to AstroImage 3066.406035833374,2361.1200801485934\n",
+      "2022-11-05 14:24:22,110 INFO: Adding point source 11 to AstroImage 1182.5553343396969,2253.7720913427247\n",
+      "2022-11-05 14:24:22,127 INFO: Adding point source 12 to AstroImage 2193.6284242791144,2076.3731457782155\n",
+      "2022-11-05 14:24:22,144 INFO: Adding point source 13 to AstroImage 2813.8779329567824,1511.2515673506764\n",
+      "2022-11-05 14:24:22,161 INFO: Adding point source 14 to AstroImage 2182.309943805917,2015.3144162842866\n",
+      "2022-11-05 14:24:22,179 INFO: Adding point source 15 to AstroImage 3057.662806951754,2070.8940117453767\n",
+      "2022-11-05 14:24:22,196 INFO: Adding point source 16 to AstroImage 2058.065719450632,2052.1650298807162\n",
+      "2022-11-05 14:24:22,213 INFO: Adding point source 17 to AstroImage 2091.1427915903523,2087.830605821163\n",
+      "2022-11-05 14:24:22,232 INFO: Adding point source 18 to AstroImage 2935.0659858918716,2238.7989722696175\n",
+      "2022-11-05 14:24:22,250 INFO: Adding point source 19 to AstroImage 2307.363834564019,44.638053235246616\n",
+      "2022-11-05 14:24:22,269 INFO: Adding point source 20 to AstroImage 1680.7671502598998,584.177926715473\n",
+      "2022-11-05 14:24:22,288 INFO: Adding point source 21 to AstroImage 1092.7417651348703,3440.84712683443\n",
+      "2022-11-05 14:24:22,306 INFO: Adding point source 22 to AstroImage 2830.1701018015883,2110.572157738221\n",
+      "2022-11-05 14:24:22,324 INFO: Adding point source 23 to AstroImage 2633.6640993973206,3541.184421687627\n",
+      "2022-11-05 14:24:22,342 INFO: Adding point source 24 to AstroImage 1164.5832552158022,1650.5574925855592\n",
+      "2022-11-05 14:24:22,358 INFO: Adding point source 25 to AstroImage 3791.1938738559716,3441.3126869956523\n",
+      "2022-11-05 14:24:22,376 INFO: Adding point source 26 to AstroImage 4103.8638238646145,1496.5221231689397\n",
+      "2022-11-05 14:24:22,393 INFO: Adding point source 27 to AstroImage 1927.483392341295,2084.012838647698\n",
+      "2022-11-05 14:24:22,411 INFO: Adding point source 28 to AstroImage 178.8233678377253,2018.9980800272074\n",
+      "2022-11-05 14:24:22,429 INFO: Adding point source 29 to AstroImage 4086.428547161244,2087.534318716181\n",
+      "2022-11-05 14:24:22,446 INFO: Adding point source 30 to AstroImage 4013.0036569791146,3801.977402800462\n",
+      "2022-11-05 14:24:22,464 INFO: Adding point source 31 to AstroImage 2496.9760981816366,803.4482637257686\n",
+      "2022-11-05 14:24:22,482 INFO: Adding point source 32 to AstroImage 3845.1763908222347,2562.947057853079\n",
+      "2022-11-05 14:24:22,501 INFO: Adding point source 33 to AstroImage 2292.3383947583316,3791.6209688368463\n",
+      "2022-11-05 14:24:22,519 INFO: Adding point source 34 to AstroImage 788.8944598713454,2221.1635345445893\n",
+      "2022-11-05 14:24:22,538 INFO: Adding point source 35 to AstroImage 1640.67667391249,1859.632529783993\n",
+      "2022-11-05 14:24:22,556 INFO: Adding point source 36 to AstroImage 2080.756486917873,1948.955440355016\n",
+      "2022-11-05 14:24:22,574 INFO: Adding point source 37 to AstroImage 1941.1642102038797,2738.6736046548926\n",
+      "2022-11-05 14:24:22,591 INFO: Adding point source 38 to AstroImage 2034.0285436105394,2075.3305458540103\n",
+      "2022-11-05 14:24:22,609 INFO: Adding point source 39 to AstroImage 2832.565664059406,2472.6828743012607\n",
+      "2022-11-05 14:24:22,627 INFO: Adding point source 40 to AstroImage 2507.4896238795254,1761.0417715257663\n",
+      "2022-11-05 14:24:22,646 INFO: Adding point source 41 to AstroImage 3447.0034423518946,3015.875319404148\n",
+      "2022-11-05 14:24:22,668 INFO: Adding point source 42 to AstroImage 2577.160455836409,1447.2117649906638\n",
+      "2022-11-05 14:24:22,685 INFO: Adding point source 43 to AstroImage 3414.753743350597,1328.352052000483\n",
+      "2022-11-05 14:24:22,703 INFO: Adding point source 44 to AstroImage 2358.459104375479,1859.405996413239\n",
+      "2022-11-05 14:24:22,721 INFO: Adding point source 45 to AstroImage 2947.2962384749826,2913.584957297892\n",
+      "WARNING: VerifyWarning: Keyword name 'input_catalogue' is greater than 8 characters or contains characters not allowed by the FITS standard; a HIERARCH card will be created. [astropy.io.fits.card]\n",
+      "2022-11-05 14:24:22,833 INFO: Added catalogue adv_notebook_stars_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:24:22,834 INFO: Finished catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_stars_000.fits\n",
+      "2022-11-05 14:24:22,835 INFO: Running catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_gals_000.fits\n",
+      "2022-11-05 14:24:22,836 INFO: Adding catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_gals_000.fits\n",
+      "2022-11-05 14:24:22,850 INFO: Converting bc95 catalogue\n",
+      "2022-11-05 14:24:22,851 INFO: Preparing output table\n",
+      "2022-11-05 14:24:22,876 INFO: Converting chunk 2\n",
+      "2022-11-05 14:24:22,876 INFO: Converting BC95 Catalogue\n",
+      "2022-11-05 14:24:22,877 INFO: Normalization Bandpass is johnson,v (<class 'str'>)\n",
+      "2022-11-05 14:24:22,878 INFO: Normalization Bandpass is johnson,v\n",
+      "2022-11-05 14:24:23,039 INFO: Finished converting catalogue to internal format\n",
+      "2022-11-05 14:24:23,040 INFO: Adding catalogue to detector SCA01\n",
+      "2022-11-05 14:24:23,040 INFO: Adding catalogue adv_notebook_gals_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:24:23,084 INFO: Determining pixel co-ordinates\n",
+      "2022-11-05 14:24:23,085 INFO: Keeping 2 items\n",
+      "2022-11-05 14:24:23,087 INFO: Writing 2 galaxies\n",
+      "2022-11-05 14:24:23,088 INFO: Starting Sersic Profiles at Sat Nov  5 14:24:23 2022\n",
+      "2022-11-05 14:24:23,088 INFO: Index is 0\n",
+      "2022-11-05 14:24:26,773 INFO: PSF File psf_WFI_2.0.0_F129_sca01.fits to be put at /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache\n",
+      "2022-11-05 14:24:26,773 INFO: PSF File is /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache/psf_WFI_2.0.0_F129_sca01.fits\n",
+      "2022-11-05 14:24:33,723 INFO: Finished Galaxy 1 of 2\n",
+      "2022-11-05 14:24:33,724 INFO: Index is 1\n",
+      "2022-11-05 14:24:36,602 INFO: PSF File psf_WFI_2.0.0_F129_sca01.fits to be put at /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache\n",
+      "2022-11-05 14:24:36,603 INFO: PSF File is /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache/psf_WFI_2.0.0_F129_sca01.fits\n",
+      "2022-11-05 14:24:42,794 INFO: Finished Galaxy 2 of 2\n",
+      "2022-11-05 14:24:42,795 INFO: Finishing Sersic Profiles at Sat Nov  5 14:24:42 2022\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:24:42,892 INFO: Added catalogue adv_notebook_gals_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:24:42,892 INFO: Finished catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_gals_000.fits\n",
+      "2022-11-05 14:24:42,893 INFO: Adding Error\n",
+      "2022-11-05 14:24:42,894 INFO: Adding residual error\n",
+      "2022-11-05 14:24:42,894 INFO: Adding error to detector SCA01\n",
+      "2022-11-05 14:24:42,895 INFO: Adding background\n",
+      "2022-11-05 14:24:42,896 INFO: Returning background 0.15.\n",
+      "2022-11-05 14:24:42,897 INFO: Background is 0.15 counts/s/pixel\n",
+      "2022-11-05 14:24:42,898 INFO: Returning background 0.15.\n",
+      "2022-11-05 14:24:42,898 INFO: Added background of 0.15 counts/s/pixel\n",
+      "2022-11-05 14:24:42,906 INFO: Inserting correct exposure time\n",
+      "2022-11-05 14:24:42,907 INFO: Cropping Down to base Detector Size\n",
+      "2022-11-05 14:24:42,908 INFO: Cropping convolved image down to detector size\n",
+      "2022-11-05 14:24:42,909 INFO: Taking [22:4110, 22:4110]\n",
+      "2022-11-05 14:24:42,926 INFO: SCA01: (RA, DEC, PA) := (150.0, -2.5, 0.0), detected as (150.0, -2.5, 0.0)\n",
+      "2022-11-05 14:24:42,930 INFO: Adding poisson noise\n",
+      "2022-11-05 14:24:43,434 INFO: Adding Poisson Noise with mean 0.000123310554600831 and standard deviation 0.4029473226487636\n",
+      "2022-11-05 14:24:43,443 INFO: Adding readnoise\n",
+      "2022-11-05 14:24:43,962 INFO: Adding readnoise with mean 0.0035705557093024254 and STDEV 11.997060775756836\n",
+      "2022-11-05 14:24:43,965 INFO: Finished adding error\n",
+      "2022-11-05 14:24:43,966 INFO: Finished Adding Error\n",
+      "2022-11-05 14:24:43,967 INFO: Converting to FITS file\n",
+      "2022-11-05 14:24:43,968 INFO: Converting detector SCA01 to FITS extension\n",
+      "2022-11-05 14:24:43,969 INFO: Creating Extension HDU from AstroImage SCA01\n",
+      "2022-11-05 14:24:43,973 INFO: Created Extension HDU from AstroImage SCA01\n",
+      "2022-11-05 14:24:44,161 INFO: Created FITS file /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_1_0.fits\n"
+     ]
+    }
+   ],
+   "source": [
+    "output_stellar_catalogues_3_1 = obm_3_1.addCatalogue(stellar_cat_file)\n",
+    "output_galaxy_catalogues_3_1 = obm_3_1.addCatalogue(galaxy_cat_file)\n",
+    "\n",
+    "psf_file_3_1 = obm_3_1.addError()\n",
+    "\n",
+    "fits_file_3_1, mosaic_file_3_1, params_3_1 = obm_3_1.finalize(mosaic=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "a9d4f5af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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nBBryjBtTFr27tgYOhM0CLibeVs4hez1O80qAP3NWyUlIUAbDCL+bwBaEgLgmys0xQMYW4dbZu3Ad4fw4n00LTzdc28kNwmccHze8bT3bEXfr4W+DwPWsaFJTc2YaOHrK3s/sy+c1wpcHVh/e83McL7xUzh3cjZt6+PMj+8iKN5YbIzfcGBA/3fFg956HoFQm/qg38sDLyUnfmGfcmCEQCZXfh2HkAQRYeRvXDZ+B90TQQ4RbzBHrnJdQlAGoe9vL7C7jw3YCjNxvgCwTgKSCa3vkf3vmgj1eZQ7i2Rjfem640xVRFi6cs78YANy1hddfv9/C96MNYAEYbOzfjjbf8bsTP4dzCA9rvriYEB7XwPMb+5YiB57u+d9k5pa8h99zlOBvA8vpMcCfWmTvLG31cyHPeAtkHu545g04KxHuVlxzW5k5SU/hW5blWv66fuThIqedP96Q6oob6W7D/u544ZytqfmdZdHG7UqQPKK5ru3hz1eEbcPfK6BAnFdEN6+tjCWmMtafrzwsx4j09RnpckP65XcSCZzjATmfsRTMvIw2NnLTVuyxnOOv6Wa8sM8eH5YID2vERYNUF4jLhqh0VbD0vXaIq5kgiUQl/f5kVU14WPK5ntjr4nBmX/n1mcANIBWAAD5VyVt4teD3i5HP6nwjwHG5EsypCgHEOGrC+4El+npBtDfP2L7cr5C/SfXwwh7N9dwcqSw4f7x1GH66N8Q63LPH5P8G11FZcHR15NgubqTXyzMZR0wVTSpzxHnNSihzPHguN966i4YobD/A/Zc//U/JmnhAyqOb/W/XDlycw8iXeb+RB8kfhCzjCb9eWq+AW0vY+v0A5xzClweWT8PIh1axec6eDzaoTKs5/279otqs9oSbU1Xajeu6QXqAbCq3pB8JTYni93duXh2sKoBSFrxB80webv8HyF+ZIKmpJwbErUNsao5P3vZwMyK7yTsbIyDz7Mu8LKTFzP6sskGMtdH2RNGyDP5ys7kWwNJSS8Eooxh3PCP89Gg/111Y/mB3RPzzJ2RvJ56wIAvHDQSTfDfw4BEWiW6SbMfZmCLBkL9T+3N/bslSmXPswQ0hs7IYufk3i+mGkJtMnx379YYo6W0waJ8la2llrjJkYiOAxMDSGBlvU2Xe6CGuc1V4IPu2Q1o0GO/ncGOEvw4cScU4MbcESHOHM9KisfEPmTQln7P0rmkmwNXh8qHclpltU0+jjZr9nb/c+H5SQqxz+H/9HW695PfL/MQCkqoPURg5MnPWyhAhwqfFbLpyzy2y16Nd0W6MPNmkN8FmxX8vciKgpwvBkdVCEFNCrKg4G4s/PSJ+umOvUxUYfrrnS7rcBMBYwJ2vdtqmIsP4ZctTbLsw9Cqu51J2XbhYZhU3bivUqZis7MhfjjxBZQG6C8trlIWwJWqW121vP4e3DEvmuGqI0EWWbsk7+MuNf9d6CbQdGRpZxt5lJeXR4SyAgENYz0hsyFkikR1U28zORhU6JwKIPDe1LF6d75UIn+85pF43Ng5KZYH40xMQ+O82uxyEwJDYY/n9iYuwH4EiR/Z6NNpcmtcEIMBxC6sXbn53uvL93zrrpVMtJAYZcbAsq2R4f+R7TGkC0E6tUbO0X+VfnAi0vbzzpr903BjDyNJ4WRuwo2QFAjsVsj1RUaUMZpce2eHGzSrgmm507S8hczwnbUs6X/m9pLx1MXLNdwNHA2XO76vou/ec2+pBXfBGDmt+Tv+Xr0g/f0JcNYgrziPjop5IHdqGXVqCMCX3SSpkzyFKP9WNVk4NX7b80s6RMnW8cvYkJ6FSusLff7Zy1fiKy7mdXNkb5znwRD3zlyNP981ChpZE+1JTC3WrRfbORZZ92/HG09GC0ORS5qzntD4iBGmmZVA+K1kena/8u70nOCM3Vlw3cnJzcB9WNVHaIkesyPiJyxrD543d6ulAFBNVyQY7BJ7Cm5ktlnHLmy87cg4Ul7XMnjj/NOpWP7Dk00FukaP45RVhwX4Dew6J/e5E7u2tg//rN6TrjfPLE8GHcVOxRxKkOmWkOyVZ+Dp4DusZN892KSORHP71IMAY+7yPo6a4WRrxImUebneEv/YExCq2Ba7tEeuc4Mh2xfFI19tQO2waLvJhtFEKV5xHWM/hVpzhIkSizxUP7+z7Htm3N8TNkjdRN3Bz56S3Ic8IonzeclG3naHd6HqMP95j+LzkcxXyBdqOJeeqQfzzJ26A+QzjtuGN1A/CaBrgLy0vCEE6XT/wsxUEnBSpzv9/vxE7+fQgdDcB/P723ZDPVPD5uBtpcWm94GdfVnYYeSdloTuebaFn156wfIxWfrpri9iUPG3lReXf9jwNj2cZ0rLGhXPs4eYz5M9HIxZbMysfVm9Nd23hThcr5/DyLgBHtFvOna4czAvjRctGZN5uo7huDMFUdk54WvN2vPXkZgKsyz2RrbhZIP/llQdOVSK7CAOmHZG/nvhylzPEP32x/nN8XNlcrvh9xw1dl8hPnC+6y1TCx9UM4W7BPqMbeOPJ5rBeS07KbHcBYsLw3/3EOZVQ3dKsQvrxCW7e2JgD3sN3nC+6S8vBfk4Gh/Z5rh/hQkB2bCcUchgRmwLD3z2xhQgB/nnHW16G7E7J2ALpp3G6YfPng1Qvcxkz5fBCMUzzGauDtz18/4EdFRKG+zkR1wMPlfC0YZ9dFjJgbxF+uCdyvlrwMwj5gfzezA5yjAF+f+Ez9p4H0MCDINtfUbwSm4jLms/60x03+vkG/8sz1/KsQP4m3OLNcgKKMk+AqpfyeQwEwIoMOJwJGvYDkf62N56n9s+umclMfWDvXuQIW3Kr3e5IiuNJfl5ZwP3Tf///TNo/ufPN6vgkMxh366fyLctIPB0jbyKAvy5DY3frbEalKGXqpXTTPjLPZJA5cEFeb0h3a/7a/gQ8kshqSoZbb7St8W6O/OVoc0eF390oczgAoSmQ72SYXZeGRPlrT1rW11f2VNd+Yr/PZ0KcrXjDF9nUkzy/AU/3/KzKa7x1/HtlHOBCtBPena48nYdAQOVCpFVRQP2z0NOx6zF+2vAmKnPk+6uVeGE9h7+0vEm7wEXpHA9BmTMhz9hvHS4EfWRDpMwbTc7/+oL0eAd/bUnb+/0FbjHnBlFa1BgQ1mRruEjCdHhYAgDy5+PUq2oPV+SIFctf140GlECI4/GOKKuuFT7PnofRVVQ2g8z1cvZ9cI5rrhv5rt/Z4rh+ZBXWDwhPRK8VI3CHM6sz6fnd5UbQUEBAgllL9q+3Xg5tAUVEJQFABuYyq5PS2nq3a4c4r5G9Hqzfd9pvxsjNLQqPVHHPxGUNv78YyGTrQw7dJIRvn3LpybQOv3VcjCrjKItpfhKjnQaExDu5ajMOvlf8YNqsp1kFp02xAjTDyF5GWPlptZAPWCL+9GTjDP925O+TxerajptPavVU5FaSxOWMrIXdCcW3w8QOKUnK9vsLYsPSMXx5IDggnwUpkW1TC3ewomTIX25cgF+euOEVuHjZIdwt5FBKXKx1yec2BsQ7sjiUCIyMi3a8JxsllQW/08uOcp+q5Ijj2lN+5Ygio+s5v7yQkOAvN+MlYhQO4+7IymAghS5VOYEBVXRUBeH8eWPKk1QVwN2G5XxVspTveFDkf3tmqSp9or/2yN4EsVQWjSz0JAsXSuHSqqTm7NeNEe7Cm8aFBLc/8XC4DTzURGUx/nTPcnx/4jMUJDo7dVPP2PI7p80S/t9+Za+1bIQssCTyLYCSAkxsP4iq+7ZnudoP8PsTRxYA/0yM3BRyUOiox+2O/LVrRyma0OzQdkizwjYf6Yi3CdQT6qG/cO/ENXt71w/sbzMZY+QZ/PsJXucahjS2HeL9hhtSuG4EFIS9Mhde3I4npQu8oj8OJk3Wk3mk+w1L1zPZ8gAQHlZshE8XLtB+4CLKHPy5JfjzsLYbLJU5kpS5cb2gGqLh9Z5KoUKFiOGHO9bVSsO69TImIbKrZZTOG4c/P5Ld889/Y23vHPypZTmzapAdbrz5S/YXabMEnu7g/+VXuMPZZm5k4cyEqNvb4nQq0wJ4s4kaw906ckWftiQwHFthUPBmj3dLxMeN0bgIa5M9ocoMeI/0iSVbrCk10l7XKUNmd4bbHXnaFhlvn+NtWnSZI3BUlZyHbldAXSHWhRxoZ4Js/cAhv/wcdD3cyztv5zqfUFwB1z6WyU7mvKgrAzLCv//JhvO+HVkKP2wI6OxFDdJ2cOcrgaOmMtpe/Mef4U8tyRF1RdWGkNNdTMZySreWo4iysNLf5GhSqqfcE5EucrgYOc6qSvbRq4W0FkQ+Iehp+OkR2e9vvGlDtJtbCdr+cCZHVkAWp8qY7RL+NmB8WvG7f3ulikS5m1C2/3rBl1PkXHjO8cVEKSuFGkXEigBEanhlq4bK9QOGp6VJRrLfXjnTKXKkxYyDaO8w/MNnlrVFbnpEY6inxL7lICfW5wfE9ZzzrgNvA1KrJilRvr8aDSpuFnBtR5BIa/Q8I68QQGxK5C8nbtRPD6bs0JJC/37XinJgztEB+gH48TOBih3/PAnMgqD2A5UQwnhxAjsrTS7eUzPpAhUecVHZweBu3VQhAJNu7dqyR9cKBSAXVZr57GVvZGL39c1uyrjhQnJCns52F2P2p4oHF2mGogWdV0g5FQ6pKrigrj3LQHkv7tfvvH3mjegfexFIV1KVZIjLhmtBEej3PdFTAef8tefMLpKHq0yfNKuQhKisUqdUFjzI5Lbyl84YWwSP2CqF9Yxk8n4ghnC/MexC54UuRHKTXw+c28r8Ni5FeNBRkKDvnBOBYLQxjEH+/span/FxZUg69Y9uYjaJ6oM97g1xXqH412/A7ojw73+Cv7TwYSOarbu1yWkAfFhY/G/+xNo4LirbKO7CHiqYBo4zpbieo/h+5AYdRv5sgaPdrUd4WmP46R75y4m0Hunn/OHKB12Vk/j2873ITlizx/UcsSmJVrXc7C5EbjqF2IXsSgmO5yktp34SaZHq9+CpBIA0zU5oVq4b2QNKSZOaCv5tz5cs/8TNwkTE/nTjZvce6cfHidMpc0x/lZc7RmB/5GcKgc9cUFHtyTiyCDxcjjJIFz4kUsK4Jh2v+Lbnr0mJ499PcMs53Ms7T9zD1ehWsS742eW7AZje90CqnjF9hKyO76/Sp7J/S00FN2/Yg0n56WJEeFiTBBAi36cHRySi0cRmZbNb935gef7lnmvo1gmIxr8bD1uRGgnv90Seq32ul3cj/MeZiIB/f0V2bO2wwMOWB5K8m0nR4ZHu1hh/uCMiruOaa89buiqlgnE8MFQ1cjib0Dw2pfCA2b4Uf3uZbvxhNEAQRY7YUHKFGPk9jjek9RK4WxuX2evAFqJzcgIMKJXLHSnuTDX7AH/tjXoE5xDuF8iug5VG48PC5ofaw8WmhN+fiEKKgjw7tLagJjbNYHW1zuj860G4ioTW/euBLHu50dQ+IS1mrLuDMmoylhqZZw8iZGDjJZbsEeOaw9zU1H8YWyBGobEJObkbyAW8Drx9c56e4WHFsiXzpKAt6omQnGcYf36wSsG/kx2SvjxM7PnXEw8G2fxR+gt3bVlNrAShk3ELUjKgJs7JphmelmbzkSoZXnvYZsbLDtm5I/KshIZxGlqPTyvEpkZ4WLItUIDhieJU1xGJ9YcLDwuZZUGqkGx/ZpUkpHHXcfOgH7iJhhFR9HZJVOKpKohuKj+24mZC11Oulcv8bzE90/Gne+Dxzgb9maDO8c+foO4IsVJlz0BA5QMjS9eFSxym++OVoJrM58LdwpQS7tbzHYrSAt5xLHS8sedTytnqjzPhcE/gKnlHKZz0exQIyKFybblWY4IHwHo7xAkNzDOMjysO6JWv13BDYBipQgZRz/yX10mO8u0FxV9fOLw8CvoXOOhEyV7MdQP8804kGoVwToPdKgrfQ0itadHY8Fq9T2KZy+yoNITWdcNU8sjPTLJxbOivROUVb4Ls9Wj8P+V+cmVHllBDsFI8FRnivEaaFew7XvambghzbjZ3ufFk7SYdmG9Jahgf2Mt54WT6M3tFZZL4E0Eb/9dvE7qmKvzzBcN2hvHTBnAO/aelsIaoffNjnMAdAK6Z8e+V0zf9+GiHTSpyDuuFU4oxsBI5nJG9X5BuN/ZrHxDS8WE5DZGHYaqOrjfrTwHIId4ThLPDtUR4WPF7F7mVhn53pnpGdKA4XSa+5eMdRxQycAcwvQ8ZH/n30zTGaVkhIQTqOwX8ivcrhLs5mS7XliXotzeuDQEXk/A74cFeUvxpIDS/VFdkesVk/XxqWxuo62dIqwUPWSHhuxBFCX8x9DjVFcJmYUBPqgq4f/of/tdkCFeIwqMLRt1iqeKm3k94iP7tiDQMtFwIhG8hMiXVxqUiM7WzesvoQyTbIRq0rqe2G1R1Lgx0NQSazygt2ay4+ZraVAVx1UyWBmMkaLRoSHd7O5mGy4TFcnKHxzXJyDUH9ykjCJPKguWnWESgLHjbZEorC4hlDv/7C1HehlSwKKWgIoJxRhQyCZXK3SYlBSH3kqqAWUUUuC7gO2EhXW4EEhrps6tigvnbnuhaLQps58jNFBFp3MyJNgKU4YRICpWMUmJNc6fsVTR589qUFnrjU2lek4xx7XioXVuWZtKzmTyoH1gSNjXXQsu+Mh3PcOslwv2SYMpyjrgo2TeKSVR4WvNWkWej4yBl5sRlTbbJ50ci03cLfrdhRFzM4E9XrlMlTNTkp6ZMqIBih5HkoP6oZTR6oJT/JiAWkJHGYm6iNOrI5/1kJBA3BoxPK2S7KzfqZjXZfXwwN3NWonLO6M43Vm126ks54ndHlnHKbtdZnC6qO2FJzGdIQjNTOUu4FwsLkfTbF9WhZjdywTlnXi0qIfHX1mZFSbR2PKUncjDWS1GD11bbJ6WoqZTmxjI0NiX8qTVdntoTmHwKBAPickbYf4wEKU4Xqq61gd8sSWK+qm0CN5KX3ibdrREXtfTHBB7c+wFhWYlSnqdrqitB4jzCdonhy4Zk7oc1RyWnG2KZwb8e+GdSAqQvt7JSkMVUEZjIvu/5ea6dlTpOgAIMI2KZI9tduGA9KH6V3id7PVl140VpAUDGF+ISdr4Rwj9fDRhzN7L/bXAt4wH608gYQUjubrUgcHYbRGV/Q/7buwilOSrJnkWP+bC2MtJ9f+ehJ0p0V4moNc9oatRy9uwvRC2paeUs0t16pCv1oqmpWTXJGkMpYxnZ5GEjVhcl+cn+1xe4diQfdjGjtG5e8/vIOMndeqoqmtrKddO6PmwR1jOMn9YYvmyFUwyOm7RCESxDRx8eY7DSJDU1QQrVoKXEU2c7F6SrFom+cB2zzLxTUOSs/fcnkpEDy1alcrmj0LdWc6EPNZxbHS/GKHfnm9ygomYQHmNaL+yEM8n/QpC5Iqf5UU67Pt0M2csB48PCIOrkta8SEyY5Ef3xSjGsaNHUegJZZgtMkUI3BMR5TQsD7cnGYL1R/ts7N0iMyP/ynbf3My0fXKIC2wUSeItf3nj6XlqWW3mG4v1qQlXzixGLRSV00+bQTwyM5zfrR9T6QwELKv0pXPXHG7KXPdy3F1Yvwtq3nt+L4ZGANEow0JGS02em76Ub4XZHqu2HEeOymtQdj5vp1giJmrkVW4m4Xogi5cYqaTEzyY/OUXG/YY/3fc/FvZoTO5Dvzo3WCSBVI/v2NpXBKcHNarsEsm+7iTt8udks1nXU7MU1UWK3P9E973QBbu0kCPZkWim/E0nE2s5RTRIiYk410LiiHC97v6D45ZXPsB+AmiJjI2vrZ4hRbsCUzFaOI4XMFOXoB2R7YaZ7EFnrSCr2ZyHnFrmdoGm1oE+KzoT04d+tjYGOqkT2fmbZuFmyZ8o8Z3yb+TRDiuRNfjzh8r98l96gY/lZl8h2J5PUOGG8wzlCvnKKpboydkZYz7h4v72yfN1fJrJt10t5Q9UDvj5P/UyZw//6jFRlf+hXlemiwAvWSyOhx093LImErICuJ3hQ5PCvO8TFzH4+UkKUeWBYz5EWDdkrqxkPqVnFU7kubMOkv/uBBYzwQ1ORUYtYFRPhG5iI4Nu1yL9AwENlYMIOMXmWh7GRtFyemCpCKWwoO0LmUbxfRQEv7m3Xm6yRq6DOV1GQ51KhCDgiPZ0738yaECnB7898XwIuufMNaDuS9FMyv6F8dyXAESMP5bZn6T6MfG6Lhs/+bY/+53u6wIm4V+1Yxk8b8eOh6VO6W9vt6C/8WdnLgZeAzjbl4EqzSgQAOYrvB8SmYt85n3EPyO9J58v077OK7/1yg/un//C/JDcGMx9SVFBvnAldI2Rv1B05kSG3kDbB7nxF2q6sFIR3HAfIInBdz6b8NkympdbAdwK6CBxc5ay3BWrXE45WiicOn1/3tM/QUcR2Sfi+EEbL/0EepNIcAOYlov1oKjKWY9JHxKae5Dz9OFHoTuqTEqaeKc/439VyMImaQj63eoXSA5XyL+tVxoB0viD++TP7oRAFCm/4nFpRcHfhjxKmgiaw8W46xJL3Uyko3zFuFmJpCCrhvQyiZxXL1ANP/fDDvVHe9DDIXvZIq7mZJLmbeI3eOkP8AMDfBiv3UpEh/34QU6T4x88lVQYCeZZxszAdaSpzY6mohWNcNZRXna6Iy4aHrcrVHL1bs9ej3cyx5ogKRT713gL4qUId3k8c564nAt0PCJsFb9PVgvIlPRReD6LkF8qhgFOxzNkijZEgk+wLf20RtnNk3/cG+CUxmyJLSA19PTzKAul0NgMj13ImxbLEGUoXl6I2GAm4+APJ22lekw7V9YjbBU8jgFSnlQxGMxkMC3nW7y+TM9oHbqHaE/pvb3+ot40rCLAviJFeJ8NIHxBpwtN8Rqj4cKaJrYxBXDfQ+2UY7fR2J45X0PWiMezhPxC+WWb1BjqkjKRnvLyzjBZ3bu2b3LXlgaCqb/VmEZ7l8MMd+5rtAuPTSrib5VTa3W0mW0NhJunIJzZipah9l9gVJhmz+PfT5Ntyvlp/hfe9AGccQdBJbsnSTyqW7GVPc6OnLXugnhvJHc4cL8xlhPKXr8aldNeWB0aZIyxKsybMvr4DUbijtczpoij/BWlXTm1cNlKiUS2h/kIE4kY5vFnaefFedSlxjY7BEFPXjjYYZ+XEV0KGDkTaJge0rjV5P6nmkFw3Q3a4sFwcRmQvex4shwuJ86KsSFWJ2KiedSeMsA+jLyFk+Bvnr+46zatTUyPczellu6i5uTGwycUH92Y3BitDw8OSQ15BM2NTTYardcEbSiBZv79wM7wfCEULEVlPTX++IV2uLBVEL6ebLjalASzh5ycyYBI9LO3BhoDs9YiwYi+alN70uuOpfKEKPTysab4qnMlUlxMVSoAfqPVclnEcMKeDVjoczZIOYoPhTzeEZcVB7N994cvbLKcydBhpeaG3jhBw+z8/CLfzgvxwYy+2OyN/OSEpvF6XooTvkO2v5hJt/6TEsg3S41XCOw1s5um8RQlQKrJJr3e6Ap8erM/Tz5WKbOJWFpQvwTnOKH965MbO3DRDfOPIyK2WojDJWGVs18iPLdwwEePjdiWmS3K7iE0Ejtyg9DmtqBQ/c4SBQA+i7EXE2e0wzZjvFlMFtprD/fUrR0azyWvVDaMpZlJVigLdw3UBYV4hiL4w3C2IT9QVFSrrucyHOxMCK7tmfFgibpaCetIhQimBLkbaWR5vZuzrDxe4NwHEhEDh1IKxLKY4g7KAv/b0lTmLp5AOO40G5h3QdsaBzN7OGH4Ud+e3vZxGhOj9gVxOGz0sZoS9i2I6acIkaQIALOeUdEgZQT5eQ/i/Kll2nlu4eUPnLi3vxCkMMbHRPbYGFqXHu2kwnHmWK2K3EI+nyXY880LABdKZ9ghh2yBVVOIjJeDxXkqhaA7V4W6B7DbAn27Ivr6zKhBgxbUD4rpB9n6BSrvCln1ssbvZZo/i1pwW0n8+bLiYa5LEU/UBJeuGaXAsHp2kYbVGQlfQSEWzqcx56KRkdhF62IwPSy7aIfDZigLBXTtzBot3S/E65SIiKHbF+B9+ot39pzUQI28JKf/c+YriX79h+LxhZSQbIq6bySryOr1L6uwCRweiJnFdT/W+tiKZF7WH+NXofw8R+PIozyy3jWdEaPn74cRqPqcxtBvIiUVIVp3528CSUckW4hNDPOBM3arEMbgz3R/UA8ZGMDeOUdxRROLivA6QAxx+fGBlJROAVPEz8QDsJ3NnJzU6HYkbsgk+3/MF/PadSNJFJPaJfRI3QiQVS2d4w4js2xt/b1NjeFjIlStk4qZiufXxdP9A7k11IbcvdVvoB26kdUOpUeaBd2YnjFsyIdRuXBkscdWwD5tTfY5hgPvhk93KyDLO785XpC/3NGa6DYg1b5Y4FyRPbB8o6akotNyfWTYsGqR5TQe0SOa7zuK4qPjvcbO0cj2u58gOF/j9meJYGcL6550ZSDnRuVlvs55zYN0N5vuCzE/+lToo18Ml0jOUxHQBsaSEJqlcnKdlNhXnlZS5rR2W6ffvbBFOLZ/Vco7swhso/+WVi7UuRX9H2lV82sKrwdG1pVayFxT12iFJqel/fZ4EyfqsEz11ko5WukFQdR7Y4XHNBdtRFEt1es+eTw67+LA2cMvW4fuZPkLbBftB700Rb6OvC6V3GrugM2SrAm+9fda0WnDdVqXhInGzJCA2q8yYWD+3O12ldxdkXd4FIkddmj/iuiDhLHLiZ68H8TkMPMEetubWhSxD/Pd/4oykyOzB6XBRTY20ti7/8iLDbXIx/eEypeUAluKTfdvJ5uQDik1JaHq9gAuiMj5cWC59uqMp1O5qCy3Ohc1+vhqlzF0o7E3rpb2U9Incw2wnsphza6VX8a/fyCi59mbRjhiFYpaJbwi1gu5yo3X7/YZUopMIlZVVIYNcJ3kP4w93VNlXRC7z74eJ65nngpCKv6QcRKoqSIuZnagUMgej38WSerzwaUNZ0odhb1zPzbhKbe2RUc1C9g7tO1Lmaf+XZ/DnFv5+O1lLeMc+PyTEuyVS15nqxe/OZt7kz3QG13LNhUju5v5IXqayeu7WvAXkf2evR1Pox6agqLguuYHlu7ubqEuUgSILPM1nRgGjcr+c/INEoRC3S5az6m535k2J94P1l7HOkQ4nbnIBHtOa7BwIOGbEgEsL/PaNSK7wiZXdpdkq9JSpEJ7WU78pxHii1hLmIsZl8KApk1KSUk3Cqd9LvFIQXmZdskdyjr+m6NbuSBkJgFQVGBclyl/frWzQL0aG+WCLABlNdLNv7N0U6tZUHGr4zgh3pDCZSDJ+6DcWM7phKZJZEvXK3o9EWc+CdMoAV31F9L+pKQ4h7YrpSeI6jRj5gt8PfzBhUh8Us0PcLA1p4w08EznTaJd8XNQkkasRbuYIiKjqQjxOVQTrxGnNnWWYnInrdlPTR3W9mISw7WAoXfhyRwsQZQNdWx6OSrlSe0C14hgCpUoaubWUza5kiKZGmJeT1+tixpi51Xyy+CgyZN/3jB0738xbCCGIc9rV9H22luYzE1jr57FbRxBKRZ/hRJ93EwWCxIDpAZnqCmHbkNZXqa9Rh7ioiEDWpX1/Hd6rqsLdeDuriTES1fDwHmFZIXtjS+JiYgvyfW9CXXfrOBLREZNykqWUpRXnBeFpi+xwsdtd3c7jsuY4L0R4LQtSL0yB95Nk8UlZM6f4kQZHrSGCALj5dFzRjyh/YwQYb81OrAq5EVJTfTC9pRlP+HInoMmKOQGnm0HWSbxXWMJRtBvXYrzj/WRFJwNrd77xhUsZC08r/OSdKdH1H1pgXCcLudwzqWYMRLxK8bG5WzOrYTsXi4jZNE8TMESlKnFZ0x257WiFsaWVhT8TbMn2ZzbfxxsPt64X4CQ39TtAUMb10wZWWp+6Z6cyn8oqQXDj3ZIWDTUPLsihE+9YJo0/3MlhMJukVs7xWeaiE9zRilFDcjAGyr40jejS2uzNHS+8eWRw7/fUhpJUnVvuhfb/7szeL60XE+Ahn4ejmsreT1zI5ksT99LAmkos5QsaJuFth+yNOlF/o1doqguzJNTPARmAk9hBl7+0Xoh0qjYhumt7or//7VeZIbK68ideQjRgDsacIYLLdRDXzVTmnm8WEZCkr9esiFQXZFzFxIvCZiSrBdzb/gOTgvZ5Tnqx7PUA1cilIrPgDqNsSdOqLlhmujrnyalCWfL7Zla+uMuN0WWCfrm2R3a4UD2cE6Vzv30nNPz7G+dPIsdJ8xnS+84kTMrl4+LIJkgeoHFQlSEuZADb1ESrJKsvFcwEpNks2ffueJn4rP3A4byc0kkWLsZAy4Mu2OghFTmtHX5/JQAibJY0F5Gyo1eqG2gpEe9X9BnVduBlxwUr+kT/Rqc67W0gVL5Uqn2g+GSWuc344mpmPir57+88BHTI/U6EOTyIL00EGSFdT5JDUMGut02UZkI4P5xtJpZ/3yOtFzTxku/iTzfK0wDr+T6OCdKCDCg+o6NoLundGRdT3BqH9ZWFn+gmI6tF/Hb+/gcLvaHVvxC192cCKkc6oMVZYYcHADPjcilR36eod0rs3X3GTXIRRYr0jUrYMBPj0xXueMHw8z0BoyITz9KFgEFyiRU5K65+4IEcoxk9e1P0jgFpOefizjzwtmM18bCaIGlRzPvzjbbgh7P1dP7ckniqCJc4lLmuJ+SvJ1lK3Hhy6jBZJp/KQkcWRfZ+NhoVHrbmZIWUiFKpfGo2A17fTT0NSTRC5ownmsTnxh9vNGBSwERkVy5EZM8HQtVye6eMp6Q/3UwOpEx3ABg+rXjrzEpkrwfTNabVXBzKPOBp14+cIIPfndg/6oupJXj0lXxIRdbSDw+sQpqKHjtFzigxgHPLtuPprFECBU2k/OvBDj43kJmvGwngrCts50YTy97P8j6makItQSCzOBsp3Oi6Fu+W1iNCwjjz/+0vJAOIqDl/OSF7PRD5lZs6iao9OfGJmXF0Nfx0b2vDv+0N1Yx3S+PQptybuiTOCuNSqo6PMjqS8NXZgX9HZeivecnWJau8WWW+RsQLFoKIZnB1JRxcKuvHp5Uhx8iyySFhTkQ7fzmZRMoVZCB9XMN+fzLHOnMbL5lZ4v7pP/7fE01tskkWFKINDi2gU3s3NV3SAA1tkC9XRjkLx82U8qMIYSuGjaS2Q/rhwWwM4VgqOmWO1DLorHK6GUuPNi5KFM8nsbEgEhuF7qOnnX5e+A9yJJUgiQdnLHPrTWg5J0Ei0veYfvE2TGZC3TCx8AGDp01LJiWyKg2y/ZlUJhmz8HTMjdztjhzfhO2Sw/8xMA1XF1Q/mLW5sYwSPTP9qZ0YOKKO0OeXMg7drRcTWZDyJI2PGPl+w+OaZbMCFPK8oiTT+nNrym8zIu6m3EQkcW6TgxQQhpS6AYxEJ90YkC43xD9/sh45eR584X7JGaAijP2A4fMGxbf9ZKCr4IryQUUCFec1sw8lFMZsVFTVo6RvBYI8WSuu7SY/mFbEuOMI5Dn7whD5rNtxygyUKkARcjcGesT+cA9/ZlWWqReqKGLc/gTM6PVqf24YZb4ZKGtTQrRrhz/YrMUVTW9My7Sai1lTwRPcuSlNNCWkT3espyVRyN06+G9vggBxE8f7FZyo1XXQnurCJEnmHF1kyI7Ss+UZ/MveDIvS3Rrh8xZxPac5alOLcY7ET0Xwy4rpD7yb+sCPJsBiiUgTIVEqCKNG3bGYH0DrDbMSl2wK7RPD560N+JPQuuhMxtvMUm6L3LLstETRMUtqamS/vwkQVhiLPy4bc/bWEswiu4VYTFXAzUp8EzUr7UtBIvVDiZzXpoYJQvTXqRHuJ8ZRKkhAULBifFgKn7YwJkpcNRJxLSZOypKS0t1dOwsCpbj3jgGaK7Htk0PE9SN1c2OwLPji295EzBBVvCltqpL/fjhZ8Ki79ayqOtLudPPFZQP//Z3jjRv7Za9E/uOFwF2ecdPNapOtpVqqHak2VBDsrty4SbSx8X7DdQEg218naZZjfkjarqws1rI+lewB/ZVtjWfO3MJKTnIphcW+XZEjeeEPT9sVS8Dve7iuR/HLGzMT5KX5syy649VUwG4YEdbSzIoC2b/uqHNTNcC1JelY+r7snR/QtR2RutWcp1vXW95eaArk+5sNOVNGdA+S4mN5cGFCP1OlXqe5Gf66fqRiAYDmiiOIcuFIWzz/djSVeGpqOpwBwh0VfxPAShtV8LtuYDS1zN/ctUXYNORzSomZvZ2skiDCxxlV9naarB/UdlEs/9APRGCbysJN1fhXeyTNRTTSs5DJ6WLHfj3OZ9JXlTRE1hZhJzTDphazJ6YU+cPFoswBMH8vUI5jmz4wTtsMketqoqU5yVKXlCs3jAiLivkgRW4gE79nh/FxKeSKzMTTcI4ps+slqYciDg7rOcLDGu7X72yNAPqjSlXiREeJEBDLjKOCkFgGihIDZUHHhdONCn75DipB0xte1S/+2lpmolVTH0YtAHM1YkO3bIKbOdU0Nc2fvM6X4p8/U1DZDVICqZFNoIdkTpa9a8dJOb+am9xH02xSVfDE1Bq6FFu68xXplWAAGgZ5uN2RIkZldYQ03UQxspSa1UYhSvMZQRRhwZthUSCB2B+vQpou4c4Ub473Ai4ULJ/8+4liVs+SlPB0PpVYQSRYd3PL6NPkHDbWFYqvu8lPxTmkz/dioU+Kni7AVORk/vR8NvFuSbPf5cxMkTT0kUPuynxFbIYkvbR/PQAHGgWrh41KmbJvOzOI0nRYiBrAqy+pcEL9257ophxG7sQeVO0o44ryoCSZigAoswJ4g/acrflzSzc3uY3TrLSKhSg2qwuTEAVxaju3ojHtxC39JH6yFdxtoIBaGFD5nv13fFgjnc5mcRHvV6TvHaiqJ4NKyu777TR+GAP9VwbOR6nvm5Edc7yRrC/Vk1Yy8Z7WGpQqNRi+bEQyVYtaIqern3qctiMrRSUziHOCOmKTLBLsAmGL4CwjIndX+uR7gH6G7zx1/aWbanspQ/3xZuRmGi2xP3AhInoPzCpSxfZXS59lOSbX8f0GsSmQackmFLgoueP+7UggRxJ2zHJgViPVOfz7Ga4W9r70SQ65DJxvzBHUxKYAeDm1FdmNyzkH5LlHLDPkryfW4yFZrwrxLMkOUuI1NXWLAiED4OdqavgdDwZ4j7CqUHw7SA+d88/0ZP7EZSPztUH6zcHGGa7tkfZH+IZVAr69IP79j/Z7MDCDMbvc4OoK6FnqKrwft4uJqV98cKn2Dq6brBe4gSq4QcTIMlPUG1ONkvUdp8qLItwj3S1tNBI/3ZFKCIghMMcT/o0/z70fgB/u4a7CuY3sAz0gNg8EreI9B+5ctJzfcfM6gmbqlFfknD1vRc6WRh4kY+Ds813c4tJ8AqSE0JF9J6nAOaraXUxIAlCZu8AwMqf+IAQEGZXF9ZzGupI1SWe4BIREwopqVZ0DVgvewndzZLsr/PFMrvAYpnzE4Y8jC98PGNcz+LiskUaqAbLng/Uz7sRSwx1pVaBWhebsDHC+o6oGAJoTrzZ4Fuqome+/fUcm2ep4fWd5FvmlkBI3nyjh3fkqqFVhvQK6Xuzbo/H/kleznXwy7S1z1v9HWlO405UnT07it/v6iuz9zPJMRi4ucnaoNDt3bXnq6lhDKFj+dJ3KjMC5l+vIztG5o/JZUVLW5LvBeKuW3+5oYJSKnFxW8VF16xU5nQA1gXMRHktl4NqeN9bxQqrVWUndEieuypGyMGsENwae1uoudr6yhJV8DvXiNNsLYdCoMZSON9zhzJJdwlcBkGpWF/x53gMz9pbwzmZ5bqRXaFQvlKbmrEyoegCMSaQ58MnTpycJAV2R6yQho3E9l0BWer9QwTNY/IDrR7KEUjI6nFVdHlYmpsxz88kYQg9sXduqQGHWYG9hp7SjiGZrgSKnzjPPEH565DSgLMzYKzyspSQvWU7HCN8H+OztRKsH/UBlQcAFgpo1NeUTkgIa13NrpOEcTWYuEoopdoI8+UDSc10YMRefH2z+lH76QtJ2ZPKOGezKgFJRVDU/UsvDIDSrVFesvwXN1JLD3Xr2KmKui9cdF3dZEO0TY6DUiKnrVTh/vej8hC4Ul3Okv/uBp9+iMU/IuBYZjViia0il2ZjHJOEcxWQtMVL7hiLnKSreOUrh0s3pbh3Cpw3iw5ZuAolWB/50EzV95KEg81F/mnpEhIRsd2I10fXkWmYecd3Q++adyGxczhB+uOccdn9mboH0ewgR6S+/kromtyq8ZzV0uUm2eTSfVdeP02wtc9P4JRC8glDTNLwze95N1pZlYaAO+oE2EgIcjU9rgk77s6Hl7tbxdhXFjHmnilmTk5FJ3C6h9pd+L4ZIOor5+oLhHz5zLQ0jy8N2isOGpCrFpjRAxbVEceOCs0ilzPnD2bxvAPDWPl+B53ceDKu5oeipqXhrFjkpekuh0R0uZMKYw5NYG+icJzxtiLL1o6kRXM/UXGvYrx2ShF8oJSc8bYwZj5Skv/IWsOhPBF3SemFDa3/4AAIcb3S4En2W6u/C5y2yb288pXM5uTJvWd5E52aUpexPLIs+3RFgGkaiitLLKccwFZkBInQhy0U1QMQ03Vr+u9zyam6rLB6MYTLFleQhlSXZxhHoOZY5S1wlsGdeEOjeRL5MfnWUbmXOmEmmJrkXrxEZP2gGhKKN2cuet/a5JWNJBshuGKcYs47lrY5B0A/8bCnBf3q0G4Gl/AfjXOGr9n+643te0ubBnyivMfaJlp4ikg7bOcGZh7VUFFTua1nrYoKrK7iY4PcnZKfWbBsA8AZ/WjMSD0Sx43yG8W7OQ14kSqks4H57YT+YU64W15ztYgzAl0dk584E00q2N8GxINwa+Jo2Sx6kxxOy3YUVk/dU/a/mU/7E4Qx/IuneLedmSO2+vnETf30Vu8PInJG2s+rLk3xMx6dwv+QJeuJMjQwV2eVvO7P9S1sKFONmjris0T/MjeITFw1JtJfWZklx3dC5+GGNJMpw8+CX+Y6VGZkXWzkppaJ49i9pHZFWXMhKDI6SYjpuZsCtncxoldXRDsDDHWdGL/tJFS1/l84+w6IyJn1Uwu0YWd/LBks5gRVVgiu87883eqKupIxMCeNGJV48IbO3kzl4OVGD07s0syYfL++2cc0cV2VbErmWqtwsO6DC3eMVqjdL64WNBFKdcwwilhyupXlvmHOxZq8H9kXLuT2HJJZ6Rs2S3jDNKlGazFD+779L/8TvZpmGbT/Nd6VNSVUJ30oWuz6PgoecEt0hqvRUFVQZqE2/cyxlFYEucuZKOIewqpCdO/a8/eTe4JZiqOs9CRC1zPaMj9pNloxlYeCT6wJVIssG46axQw4hAncb+U5U9ef7m3jujNCw1zQXH1ydcbY9sOV4hfmZJdeYKHg4ky2IgrpO9E5jNKMjeA7dwz0ZDuEffpi4efKPv1GoW75KyIgXuYzmbEuf4w/XKWxjK7aCJQfpyTvEzRzjvbo2D0ZpQ8bATn+40JvlxrwA348TNUpO6DCTOZvQitycSKPqHLNvb3bDKpUoNgzU8Ocr8tcz0baqoFasF8NioTrpxlDJkfZeTkcFw4jsNsjJ6pD/8+8WLApAoHeiiMPDQk52oSkJnc8VBUvE9RzxfkVEUOhooSlNJ+hkRAOAJdN6TjBMej6Nv7YYtp75Hk7cvdS+Iy2ojA8bhrfEdWPEbHM08HyHkJlqnBWUqzWV6fj8SeLYbtQrqiO1qTuunVhDBCM9RHF2g7BR6FIuguZrZ2VrKriO/E0ArNUSqalRfDsQ0ZaMQSMw1KXZFrohmNcnhJChM0y9FcPnLeJmLjzOQOtGKSm1j9aIOMZaz+3QgXdmwwGx18DAPEOzLBEOacoy5L+/sz0Qbi9igncy5+OCd3AdXwA1azLnajuWBYOIIG+iIpbm2iwEZSblLu3UhJcSSLI/iQQnM86jS4mI0L/8hnxPl6yow2KxzM+O1Kbhx882h3Pnq7DQvfm7VN/OEsxRGBOEwEsG7A4imhTmiixqVQ/E9dxItE4CMeE950la+ul4RIMwM6ElLWYYH5dya/STmPZhY/kDScSv6kZdfDsYxUtL3/BpY6oO349wvz1PEW0xofj9nc8mJDEaEke5xWySeqmeUHtu+ccdzuKjyoM1OxA9TZWAOtIT+eMN6X0nciVh/YilI2/qZpqftoMZEWMYOSutSrYWdTW5uek8UEY5tHcXH9K3PQ+1qoT761d5rxfxiplPPkC31krVVOSTa3ZTs1pQ06ymNp1hmpWWsGxk/JQw/LA1vSJkppx931uYqLvckL+dJyBR+m07kIWZQwnSTRQ90hO3A+KnOyRxddNwIX+8waWE8OWOF1o3GpBJS4p5YyeyLty4nBlAET/fC+JXGhM9iS8JymLq3VS31xBeh3PIfn1B+HxPuwKN7HXOfGH8qUX8hx/YC3XjJDjVMkacip3EECdN8pFGe3xa8fd2TGyl/q2eyM/OAQJhp8xNUpiC8xzt1zQ8JF2uIh/pDbmKyznV4XdzGVsE+NedSWT8IPrBEOQkHqExy0YKEOuN8XFJCw4NBAkSCd1y/ufPZPFgK2MelUeV/EwuSO8nPqOuHyeARsZAqcpNjhSbyqwTPiK85qFZFghzscVfN3B1TRBMUF7V+TEIlFpIvz8bk0jZRlCurvT+/kqHA4wi+H5mP+SPV0lvCpyfasWyknmelKLZm+g2347kEseIJGlaJvhtO5E+kZzPRKwB7nCWQNSe89HjxUrF4t++i0cN5Unut2fyTkM0SxMAtNEEoCbSqSrpmTtSmxkeVmbnYdxYIaOPq9rYWOaAALC3/kiLSwlegwXdlUJbkp5r24yxLkV6zw+kZaALkSqHriedSZJtsiO9LpJ4tPBU7BiUuCYyxGyCgO5PWzbx5w7Dzw/sIVUJvpcHsJdo6Cwj8/90MQoWvEfx27vdIv5MJotrBysZMIzA6SIOzFeqEGRxZi8HUsZ+/c7PlXukz/fGPMleD1AnNwDITh3HIN4DRSGAkviDyOdJVUEu6dtRwiK9yXzGT2vkLyd+1pY3q98dZQMFI6hbyXuQDAjh36oCxZ+vwjMVC8Lc2wZVU93U1NRMXvhdnaRCpaZGXC+QvZ2ozj+eUfz6RlRWwlmy398495IhfqqJRisxPDyujbqoTBUL4tGceylFXdeLSe/S/HNSxpnsuBF2iEh2rH248QJwMoRXpwNIkKtuKHUOYD/pKMKVzR2E0ZUWJD6nqiQ3U3pcjDQuTj8+8SBqaiDPp+e8Whii74aAVDHLQwNq/S/PPNAFs4hNaa1I/nK0lsBJO+POV2E7iZpGxnXuv/zD/zWpJaEOx1NdTPOvQlJ85rWpty07oR1sHBGX9YfoZ2FVbMWmO0aKGi/9RKdKyQTArh2Aw4k38fWG8OMDGQZnGtRq4KY/XsnWEdYBXnbAZslRw8+fDJhJc00XCtBce/NQ6SdAIW6WpmIYvmzIyhAS+kcrcubNt5yPKRlZrdQBEbzWov/yNkNL0oemjHaHJnwNyawg/tAz3TojhKeqoN3d/sRSJstgoSopWQ9FjqN85uc34G7D4MunNRE/+Xx/SI9dMC3J9wFqs4/c261jRkgFzXn7n7co//bOMjb3Yg3R2TO1mIC3I1LfwzUzEqq/Uh9KWdtoROU0r82K0OwiFzVHNMNIxs+fPiH7tsPwpwcUX/c84J2D35+IpgLiJ8qFTVWNBMfKhvyDjSMgKbalCahTkdvtZKE+Ota4ULdpVpNlwTEIQN5onkHt+sPdinaGi4bso2s//UyZDOhNaiIByT/0aoHHPxCYn7Y/I4lCHfujbT5jVCjtSGQ6cd3YzZCKjLy8py3SjNnzaTGj45f0jAoS4EQSeFzR5j7lGdJ6aQv5D+EXBcEQf75ysK+lT57BbVaTjqzIJ6RLymoOkwuTqoT1jChnPxCKvlyJbClBWCRVqZTNI4CLloKGQJ6vXNQFLTfQ9SQV5BnCJ24EJkFdbFP5I2em6X3HUE+pIDSZR3PP3ZlgVlrS4MgNjECDDMb9tbMqIdzLaf/Do5CtG/h2IOqmJInjhWMlGST7jj0v8zCucLdhGjsA0/MqcuS7mzl78c8SEGEJ7o18HdcLOEGMfUvicVzTh5W9N9FBvz9bepHO2/yR7zWuG+B+g+zru1D3zlwH/UAa2KcNw3BGLu64lXy+LEP3NIc/kFrnjzf48xXdn++NQN3/dAfNjvyDv+e8wngnOIBsXjW00vI9NpTiIc8n6uRVDuUhcEQiVZhmeYwPUm0JKo34wXMmJmBWiydMldMJLc8tSNN3A0GJujJy8vjDneXQUec1yIB1z/Wp4wEPNrevR5ZqIsWAdwxrnFf0/pBG3f/b70YPcpcbfS5vvUmE3OnKIfhmwUNB2BVuVsPtjgL8yAYSdgfEHdmJtYFKpGJTIf9+kNx18Zuc0RYQw0il8tMaYVFxfpM5Ag0xkXheMrgl5TQtihtaRCgSqTeqG4iAaQ+rCcNxPkNY1sAPn4yU7YZgB5i7tOS+NhVL+TKfPFFyic5SR68NKVBaMvrd2TL63FXKZVUklIX1IP4o2kLxVFGZkY431ATLrNTV6PjW8fOdprmojUlkvALBB9QcN3sVG/yMHp9xuzC2DofV3qRQcHRCc21PqprSA6XMy75znWWXfpKvfei1Z//8QsRR/IZSVaL665sR+Mvf9wR8RhmzHBldnhzstlYSCROQWO1hGJEcqEtdzHgoyiaM6wXXVozMsN/StAsxIjt3ExIuiCmVFq3NIH2qiwm9qXk7wPupvpfrMpY5fP+h4b19yKMT75Ls9QANu1e5RVo2YllYAa97Uq1utLuPyzk3P8CTZH+yU4k3bTWNPoTtoeWdnVDbFbLf38yBWBcDmhk/qwze0Q/sffZn9jvXSf9HdkYNjKOY4oo948NWRi1kXcA7uJd3loPzWqhtpLghRoxPDGpxt47yGA02UaMiAC5QCEuIPWevNQrApQjx9WazMHXWSkUO7I5GAYP39LGZ10hjoKphRcbS+LCcWoGq5IkvpWVsahktEYQIwhyBc2J/LyXYrbP+LTblNJsrCyOdO1Ed0GRKOKoKSijlTckdOqcT7rETsrJaGCqQhFsrGkJueh2BqComez/bTBb9QFW96hhXDefAcuCkKmdl4WHPm4fIzdaV60dkpxZpf7BKy19uwOdHU0EQVKL/TqxLe/6IiZtP2VLOMa7vjZ8pNuU0r17UxEcurZEb3LWF+6f/0/8tEXIVvdystqGpaydE0UqssmBjq0wHYJr1aJC9QMR/6GtkU6khjb5wAJPqXOdz9yvR+FViCXHlS9KTUlj8jAXuCQoMwXIh0nxmiU0IEi6qJj8LRk2zPJQh/JYmrf54nezphBMbVzOWWXbz4A9yJ51TZS8HS8BxxwtSPyB9uefhNq85W9TT3HvecnO6D/DWHieerdDrVDALJ0ry728If/+ZfNq254LbndD/fI/ylzexVqyRqgzZqTPxsRkSqcpExcGFkswrE0MrmpjKAuP9HMXvO3lXfBe6aOE914MecIFOelE3o27k44WHo9xSlFXFidwe0tSfC/XR7U8kM7/ugcfJ7iKVpCzqfHR8WCI/3GyDo+ttVKEtg5IoNJnKXSULs9J8DTfxP3dHpPuN6WDVdCssK2oUVw3ctzdgtaAl/u4iWkGSMpwIp8PDiv6x85kZjMU1qYOppqO5htN4bTZtYyWRDqkDcUi0OihypGGwL+YklIPCUmHISEkBUYIrGuc6WuO5tkOocxlQij5MorHieo7xx3uktoXfnakgCIE3lgyM46wAfvsmQAt7uHDHgbUbo4leNQ9ObfUt+akqyFDxfNlhPWdfdu3NKj6J6VAqc2DPRNv8+SDK+BF+d7T+MlUFsucDS20BFwAgPG2FcDAILewG97afFpFz07MWS8GUCQJclRxReM/SvmdUgL+2cFVJlYbSqHLO18p/e+boIEb4tifC2ov5rG6eXAyItMxcziUCbM5DYKGSJWYAun5A8XXPEjoTVfoYyR9d1mbBqN6ow+f1hOaKWVRyjiXa+wF4fSf5PHO8YYRE7y83Hhqq2nCO9MMyJ5lCZ2+y8f3ujOHzGi5EFN8PUKWBez/YgQrh0DoZWwCw4J5UlbxltRcbowWBpuWcn1nSnbjGHTIZx/nTjdRGIQYk74h2B3JRVRubvYja43y1/Eon3qkYhQEkl4mPIrIFwIWuRNtrR99EYaGjyBH//Hk6LVcLc0dT9j1Eu5bqyhynVLvmjzeEuxWK78wNcO1gOQGICf55R/8U+ftcCIhzlqD+9YAkoky33ZC3J8ifP4v7tPQwSa57hqVchPbEngmHMwpJ9E2Lhur1ckp2ihXV+eGJwk5sV8jfzqTXSWZ5WjRUIRzZlwLszcafH1nWhWhhlyzxGnHhEo9S8T9FThpafNhivF9YZLI7X8XiYM5NVRbo/iyxzMNgvVrcEHBw15a8VOmT3JmzOit/5XOQUiYL9HVng2Z/OJvfSioL9s6aN6guccDEpwySo6h0Pukty1/fBTgTxf7tgy3kco704xOf2YkiVrc/wbUSiLI7GfKqeRKu13aADJZYl1b+Ff/2XW6SzA56zFhaW3qUcwRMBHhDVZKTmjlSKXW2qeO3phbJETnISS4kzj9ZTVnp/r4XeRFnrKmSaIYTaZdxs7AylV5AZxvRsM9l7Lq7dvAuBAkeuRmhWa/0bHchYjef1A9ayvDKjTZSUFFmXDdQox9/auFuPYIIVj+igXAO47YhLN5U1AsWOcW/Ga/o7PU4sd8vtwl02F1s7uT6QcIQOUx2ysoYA4Z/+Gzhiu58RfrhgVxDRdVU5gRQ7SDghr/2SJ8fScHbNPBve5ZfbSd0udFYKoqm+W60ZxhFvOnanpb13YDhSfijUQbT/cAZ68AY66AvTSVOKbFXvLao/79fiYquFnZAfpwRZl/f6XymnFUZ/oY7UaaLRwq88DY/3/Pmqosp41y4oygKO3DVQNhdbjKPm/EQEHW/6gU13pxGXRezufBvexHadmaETPnThd445yvd4IS5Mv78yJtdGC2prohayufwvSz67XQouKu4GPQDb35HNpe7tIZgqrg8ez9PyV8CeIX7pYmZfTua/EkJBq6Vv3NWIXveUwn0eAd3usC1HcYt7ezZSrCacpcWYU6DaddRQIw8A76/8sBWUUCRw8N7sjqaehpIyj+WOCPCUGrohHmvt50ErQDCi1TmQ5LMQSErm2V6iPZSswtnWNnhgnHbSPDLfLqJvefIYFbS5Ojb20TSlhIobhY0jxW0MC4qY514tQZ39JIxcEhYIQqFK9wca4lDC2KvMAzI/u0bmRhOAhmFxO3fTwyFEbqV//5O/VmeEZy5UZrjRIhZ/vPXKbxFwkS8jEuyHYnaGiQJx9ImiShVXcKZ9DNlEBD2ZsCo+r1oaEycz5BJ3kWqCzqOhcSW4kTGh2+pgnD9QGtFSRpyp+t0IF/oAu7fT8i+vsPNG5vLusuNSK4srlSVtn44LM857JbxRqpF/b8kNzItaICkxPf85cgSewxsK1QHWedwv37lzw2BKCzwBwfqJNkTfL7dVLaerkSHL4x9cwdx2xPLRu1hydqRCmpZG1vGpUR63LVFXM45ZggRSbJH8uejBM5UMpft7L+rBUbKnIXlpJoEcE3W8u7WIT5sxSOklka3sH4mzUrrC+OiRva8x/i0/oOjMTJJzCmZJ6e2Cn5/FuV6OXERAWiUl/5d4W6B/J0Bk+7ameI63C2YvR1ojRF/fpog7A8SIBQ5RyUVoepUFVRP/O3Zbje3Oxr6FauCPd4w2oJ1t44n4JL9QNwuuEBEMqVOYuPDAv0n4VHOagqLU0J82BoRwe9OUwacNvd3a8pkeo3nqimnyTM+zywzi3uERH+TYWSJI+ZBABgU+YGnqaWP5THGyINEQ0t6sXcXTSOiqk8yiRnw5mjmYpQ8iQU0RThsSVZIS45/1A4y3C2MrI88g5s37P12R4IkFW/X/OVo/SWlX7mEzAjo58hAgrYJMrSO8xnivGISlneIf/8T1+ii5s8bgyQhZQLi0VFc16MbA+ernzYID6SaDV82iPcr693gnWkGtQT2v3wjs6kuWeU8v0+buZPkpX5g/FmR8wLaHYnOSvtjbCShaWbf94jrBdOddW2vyFn1ZHnQpi97P7LeBVkGSsMJdwvEpy0fzGZhgR1qwZ3WC+kbCAowEFOAhiMdl+NMpB+FmPYqArmeGQWKiT30eImLGec+KUmKDn0g3enCiKiLxDdL4569kNj70b/FNbUNsfW0hXfs/XJBuTJFZ2fkkkqIYvZ2ImND5Fju6xvwtkP+z7+j2AkIc77ArVem/0qZIKDKcmkqjJsZb86BCKq+mOy3V+Tvgr7J5/I3AW3kVkSM7Ndu9GrRuSg5kwW5qdu5ERLUZi97PZn9gcmL5DTGMJqiPy5q5L+9GWDjdkeWjlpe1jl1iVVh0c4AWHX8wixBZq4TKArbJVF0UbLYQZES0LJc9K8HQRBPFmtnoyUNWJHDzl86ZO8X5M8HuqSrgFqqGpoiN9MBIiZLEJoeEo138798B4ocxbeDEeINIXUO/Y/bicC/mBtpHBHA053Fqv0hslsDaW49R16isHDdyF97eWdZ+oGlY4qgYSS6PAZS0ZI2uB7Sd02+kpTeB6TjCdiuZabHHDfzHhkDur97QPmruIudr4ykFj4cZflMtqGtAyFY17O5djGSnnUg4snknsChr5zykzYsTb2q9HfmC3nrp1TZw9koXHE1M1K4kwZf04LUblFnUYhMZ0p9L7FnQiMTE9+UeYIwewI8lhyr87qKIl+191BPnVTwVtLy0EgCRY50PCH+6QvgYQm5SrnjAJ9CVaO+ycjGGX1KGC/P7xj+40/IDzeM6xnKX954k51OcOsVDyxBrt3bHuGnx2lB//4d7m5r5AH1FKV0JojvZWc8zeQlcUjmXB89SOOCwmINdtXQG/WMTSVJ1vq82b9Ptx8A0gs/3dEUS3x5UpnbDeIPxAEstk6ArY+ZI0lU9+psZppAZXQJsSEuavb1TUXUvcgntldIfC+HC9HS44UeomVhFDzL08gzG7/o+1F/19RUJlgIT5OdoWcSLL0v/KmdLPBSouW4esQs5eXLlzdOZaDTb/WXV6OYjX/3CWFVA4eTDJgZmKgji1Tpou2RfX2Ffz+xZBJfRn8Sq3vn7Brnwq3ooqbhnQA0T8+fqKLI3pknASWVKxJYV6RvvZ9sE5vzl5KJZa6V6pLJUOK25qTPMRWCDs49vxcTfio7KLLf3/iCJd2XD56WEHwGFxsWx+UMbrWEb+l3Q6rcHPAAnt+54DJnFgcQuwnXEUHUWynlHrhbo3g9w4WE4vUsiHSJ+O9+InottDcjyctzdm0PfHpEOhwltUfi0pw4B1QF8meOWsJaLCKHwJ5fZ4lVadxWYwG9vvNWf37jM3jbmyfP8PM9f77IncyD9vsrW4Cfn+RAK0w47a/ih3PtjLECgM9kWQuqe5WxlbBOjlfJtaBCR1UoRLZHC+DUXEncWgMSLeUKoHfp8YL+7x6ZBSiHn387mtWiHR6OGle7pe8XpMcdLhz7iKdRqkt4sukz9lAyqE3y4opve54Egojp1W5uZmU+nUabBdxfv9IZ+v2CbHcl1SzRI8X1g/hjBpuFoScDJ3ze8mbRMI18apDDegZ3IEk6ezvR5k+/xKwSmwQRqoqLW7yjQzcCI439/mKnIaUnwW4CLXH/cKvK8JVC2W6y9RtG+POVNhxVRiXE6SKnL/sILxkaGi2WSnE6u1sSFJESLoktguoZ4RxwpUW8Gv9gS7OhKPIiN/KW8Rexpi/81KNfu0lFDjI6xscVxaYhEZwCeBuJgoHGVyy/x4cF3GppB27KPEcS0jeG+yWR7N1JkOzMQI/xaQV4kAiwKOmzGiLw+ZE/64dHvk8p5fCwBRLHV2FDBX9aSzvwdA+TaY2RlUnuSd8bAzWdMtB3QpRwysdUUoBUHFZuOkm2qkszSlaiQ7gj6MdqKrPQFTdwDKZlpf/9hXvi+WQXUWxYyie13G87DuUP52nccpniEOKGeSAaCYCUkDtlIsjLi48bmuesxHC3ZNKPP0q5VBLwQF1h3FTspxbNlMlXZcCZBkNxu5gWGCaCr6osNMvP3cgr9e5m0VO+6xGXDVGxPDcqlQO4wOczhEXFQNB+MAoc7QUoKVEXsLickV96PNNWrywwbmbID3QuU4/Gj45l2dvZPHL04WpevBsjEeHt0niV6ruSmlpMl3iA+OOVCoX3EzdNmVMuVeUTe0QNZme13ZhOnOJQFsi1nFWV9pmD7EwOMXe5sUeXnt29H4DVAh7sS/UzKqqKKMgjBFW8kEQeNgu++8WMc+CnrbQGl4kj6pyQym8sYc8tssPNsID8t/fpgJ4V8HJLEBtojGWT3QYJPhHC+a3jzXvjgef7kbdzjHDf35FtVn9gWRFFzeDaSWHiBrF79A7OOQOpFDyizE6R+BGxyEx8C4D9Y8bxRNwueLMtGqRZgezaIqU0ORzcLTk++nLHZ/Cy5/foerFmqeD3jChzQ6CMqRUBgSjoXYjwSMnEqADBl/DljnMY+W/+3JGuJTMbnZGUz1SXp4wwbvz5M1/GteUwu6UMRj1LVJakJxBt3xLCRuwP3ve8nUZxWFN7gGUjzPq5beSwqGgHriMQAY3idoHx09rMlsJ6brzHNAxWrxe/vNkJrgZQPAx6yxhIhWyGlMyQSn0/4nImBkCF0drMQ1Woe64fzRHZRgcpUXGyO9vt4r6/ITY1eyvN4ogsc9WZLNV0cVNiryHB3tPTpR3gfvkuurrc1BMuBLozrxoTgWoqUljznYbNgje4jDFM1e8cb7aCCbL+eOXc7O3I8dLuPNERxbQ3rhem6FBmDZX+dBOgk51kWCjJPMt4O3R0vkM/TODYqmF2vUrA1GiqG8RLlOWpi9FUJa4T9bo4HyTnMHyiD6mRu2NEvpdwz663flSFuf5COwwXwqQU8V6MjzsTn2ffdob4KlUv1ZV57MSmgJfAVTUpHj9vaF5dFXD/9O//56QCXE23SU0lfLV2MjAVHRycKBr+/JnI6dtkx56EqUAF+43lTzuyTAtRop/AoWzXm9c/vDOT2bCskR1apFlhQAelKJWorAszCUq5N9EttXbSO3kP99sLcygg3NJvb8DdmqJeRV0/omFy25g2bYzT3C4lC3rRkBlt/MOnjeTSTaRjqyZmBSOwZwUXi+jHxqe1afHUug7ey4w12E2GfjDGSdgu6cGifEZlYhQ5Rb2bpTA7OgPHFJDCGOBOUqnITFeDNpGSSLv85LP6gdWEzFsgqOoqNRQlSYsBcFwVmhLFr28Uu366m0gOak0iAApSwrht6G4uGkUMowWjsOTlCMq0e9rr1oUxU9y1ZbTb6WYUyI9kfgwjczCqDNl/+5WVyOmC+LTl5pH3pn6y6mSQMjqYuXZAbIgdaDTb8PMDb++LKP7/+hu8mDalRcPNvJhx/xzFKwcwYa7ZOAog5f7L3/9fUlyQHzmuZ8h30occzwQJtMy6tQyxlBwApUPZgrm0UzMOzkziRsxg5fZLqkwePrgmO+bHKwoatwsTmcbtkjQ0uTHjem7oK0+XepL0KLIlGyHWJeeKYt/nxsAknuc9T2ShTfl//Z0yk6qk09aRoZs6JqEFRyZgy8hE1mtv6JwbRqT3HfDDp6mn1JAOLVc2C9M9uu5DRpymvkaa1np5hroYw7Imw0ZFt1VpaGy4WxhiqmirMv4ZePkhxUiRz/cD0mbJ0c+5t1mherImkYPFhzU1oYuG81957h+VDrS96DA+rpDvr3QBaEeEZUUh7Pk2Lba6EL+c3hTsWgbCOYxPK+Tf9ux3HzeTmLqZNrg/t5M/T5GL2ddlYssId1hRWePrzioLD9UWgkh8aweihQLJOAOAzBFrM+alX6zmn8jaFsTUwBeppszeU8Tnak1pAmARSMd5BU97ATaa5S9vdKnK6e4bNwvhYfrJdFcWCJwj3AuITKizwA0O8mdwl5bSmJSM4ZEKKomxO7J5lmx3f7nxBAQmutuFJ5y6tDH7QdJkk6jT84z9lCCmriPYk30l7SfOZyxxvfhRLpj6pC8h/uNPMnMiM4cHkdjRy6bOduK2fG2Rf90RSVVeZkrAD5+kenBW4rX/8QvSokF4Ioskrpiw5HcCc6tPyEW8UlX+1dNzFJ7BmgqzpzybOI66CZQsUZdSJRCQoX1EJU5kZHTEOuet1A3In48c+ch8SpNc42pmlUV4WElJKQbAbzujqOmGSE2FfE+5VfZ2wriq4c+9WLeXcuCoI3iDcLcy3m0S1Dg2NTef9xj//MQDSSwRiZIm/hlpLywd6tZbjFpcN5JALBVbWVCoK2GjaiytDt+6+cKiIgNopPxMh/G6wSmfkrl4UM0mhQDWVglwGZc1N5+qRIbR+r00Ky0CnG5xA4b7OcGe//w//tfk308sSUK0haJR04xrmnawP1+5GNSfpJMhexf+YKbqD2dBHJNc5zRQSo6hlXHdkMFxPBNkEWNVf20J+hwoFKXwdE5hpwom3ZTPBu+pPgDMTlBlSK4bYLFcAB+MLmDZ1DamiAzmYB/YTaeWqBNYojO/Xgfj/kgwZLybI3+/iGeNnKjKsY2RgM/+ZrMzixxTfqtz000/qGdINfmwqvnT235yZxMhrLp9mYJduKIqcwIwZdOdLghfHmwWmqpiIh7XFAZb+aan9Ez4jULG1kgAszFR6w69Pd4PRE/njRnbeolscycxWs6zP8iDrFKoS/i2N6tHdWhXQjSz/AaMf3qCG6j8UHTTpG/rhdnfa0/mXw8G4MTlXBQY/PyaDxnuFgZ2xSX/XteTw1u8XTju0sqt7aYY8ygu4EWO/ocNiv/2O5xzdHbQw+d9D/fDJ6j21nxvNktIkyN0oIw25OlyNTQuLhvhzZGhoS7SdEOmTYW/MarJ3UjQjouSiNAYxUkqIKznHORKyeQPnDfxtCLJ2kXqtLJvO5a311YsDC7mTxKXjZVcabNE2ixZasq8xqzqs2zy4xCkMNUkTrvIUzXWdPJKTY3weTuBDyKWjJsFPUW/7025QcUDuYp4P8B1A7JDC7wfaNRTF5Qs3aYUnPyZhkju67Ogo2J5IFYMKZfXILzIlGeT1s57s3BM8xnSkRC2O18n2dWsAsR0iXKiRsYmwQ6buJpx8+mII0Qu3iLnqe5F3iS0MI24Jvvoag5faVaaSiHcL8X3ck7I/nxj7NzTPYGgbgJTUOSsquYzuzXonuAJrBgZnrzO8LQWxUArFvVyGSzn8NeB/bBwUgHY7W22Ib8/sxoSK3ltKTTOTEt5dfPOnw9Mw/20kcgC3qbF//Y3KIk8ebFpURNpEHBJixniokbxv/+G9HhHNpDEcMfNAvh3P0+Mn66flCJdD/ef/9N/TdRvibGPcixPVwSpx90w0hvmy4MhipPXxUjWi2jb4tOWbPA7IUl7Zzeo6we7imnmSn2YxpfF5ZzyGKHvWKmV+4nFrtc+gLCskP8qhrvjaPM6NUhyxwv1aLroRkm2bWoT7FKLKLfx5WbARNwsDTp2l5uVF2mztFyI1NS2CUwtoreRbCB3loCYtrPMPkWTcWtZFopIVNN97PNfW2OiGHS+ntOs6XRBeFhNIE8rSUrzWjR5BIzcKP36ij/Hvx54atcVkU3pZSGaNrvJenHEXszM+4SZiWe+b7khw4JkDBrnRhOgkkM6jaA+VkfKmgmbBW34b2wb0vlKwfGBlUVY0+DIv1O8HJuKmX9SWeitp4QG/3qglYQ4nWnfn7xjJFtNG0fNWKSzAa0UbfasVpJiyeHPV8MMLKfCnLy5qeAc8PIOt1rw9sw5yjBmj3MG5qRZKSJgmkl59+tXUQiXE+wuMx2vaTPi4eiPV5aMXc/Fs11M6nOADAFBF/OXIz/A4WyDbj54sTMcxul0FPs4FwIVBSLzd4fzZEVxZc9BRy21ChDGReZNqeD6gQjoreMt3HZmnQBg4ooKcz9u5kAkihe3K5bNOT1S3KVFXEjo5d2an0uywZWho/6oHPzTct4y0VOC6i1NAiUkcLQdbyR1cqtLmD2/LK74we48zmsqQjqaE6Uit9Ref2qt13BCUFf+bBKCgNLl0nrB7z4T+pfoEvW96W0MT5EuU5PKiVhwt+YBIrdWtr+yN0ri9ZlLjHnu4Q7CZ9UEqyqnzeQwUjX+Sm6mpQ7frZFdelMjZK/MVtcbEwA3nLB31JfWLDU2S9MdWq8oM1vkmZXi4XGNtF3JIUv01xK5pOSHc4wYeFob5qEHbXja2OgKMu4wf6PzFe63Fx4Km7lkZuZG1HD9yPcleIh3d1teqwK7mh3b4Wz5BKmpxDvTE8JdzBCftoTjP3quNFICzGvpE2tgs+TJ0/XUpwFIyznC44aw9hC4+GsOiVXnFrbCdqlowKrWcrGUvnDV0LpQLAj0ak9NjfjTk+y2NAlHpdE2RFYpYteetg2X1pQD49PaAk/Y51ElD8kCRKCJLQCoo/bHABebbwnUrKx5lAWjx/IMabM0sMINI/C2J+vndLEGX7WYacHbSwXE/toLJ7eZqgsR9Prv7xyP3C1k49M8ynUfnLyASfmvlogt/WI0OSjJSIYSonJSDCgI5icWURKbjbhsZC7G5Kf4+R7hfiGpU1LFPGyR5oIzKLtKx1d6Oyg/Ncvgf302FY2XkpnUP0lIimJvkXtRe0BGW3KbnW9UQwjDScdrbnf8sJErIKN+UgXPOrf0V+3ZWD6iLCYTMnkO1PxtJsNosdXwhytS27Ly0KhyqQD4MhwH8fS8F2rPZsHZybIx/qMbAgMqysJsCVQNr7A5Vcu5qSLcGOS2qK2k8b8+T/bpHubjqB/G+HrrObJzR6lQBCFmib7OX08ylxxI2PU0/9HNZ7C+eNKkusLw0z3/DoWx84x8Uv1vYjOvmzY76+1aWUk+ftnKDdQLn1NKjOs0CjAeqczQNAND3cTw6zc6CdQlrTHUcaws4JqZsX5UrGmL6NZT3LoSxlE/YHxcMQHq2gLt5LsTH7Y8AHX0ICp4J1aCFnrayuZSvxwx0Y3iJu73J2O96HNKTU00/KMzmlC/Ys1TPjxuuIEiOZ75V2FV1QwmMQ6v9uzaUug7jVwrjBnIkT7d8/ZSxUkllvN1buyc4fOaTJ5tI6ZZaqFB46f8+cgDsyALibkYFNJaDENBq0dzIJfN5oSor0oTiCsCsgzjWrIoFzWfp/aFJfMS4Zn3qKbDqSr43LJMFCoDPDmY1cTOfqc4VBFEN04zGY0W5onBE1BDKlJZID8Q6Mi+vrPXK3L4ndhM1BWcIKtuGJH99mrWDf5lTwGm2Kz743USRPYDfNuzNFQuqpyYTl4+ZjU5gh2DVvzbkeqBeQ13PCN/O/MUl02esgzYrKwBtyBKScA1H5I9y+g0r5GdWvhLi+EHeoIoSGEGVQre7I4EHGaFEY35JT3S3//I3L/zbfJrEcv3pEZJ0vuGRWViWfYoUyJUWsx4QDU1mTabJc12ZS7pTzcO+ruePZb0PC5SeBy2SyvDkblJTVHkRBaHkUE1kgKMkMw8mKOJxtgk5G0GwQpIuaLvaj2R140nSSFsyr2RAtSVPd4txbYvN2TXdSMPgn4gUCejp/GeapRUl0AE8rcLssOFUdr3G+Btz5u5yDH86ZGP/3A1baqi6GG7nICgIjd+rs6djeVzYNmsQ/5xRZJ/8f1o+ESai2G0rA2kxO/c9cRPck/O9P2KxPEZe0jvIsswmvbIzSeaKoVzTc7hQfrSibO7dLsRZZJBeJxXnC/NZywFiox52sLqsPCUMdBW4HqbnNT6wcAJ1bKh7awUcnI6paaSGVJGCpYNzDMbtMbNEnFDg1/MaowPC5O9+PeT/H7Khlw/GtVNdWBeekY3b+wkt9NaS4fjbWrAs8ysKTRaOzvJULwqeJicr6z5ew7Gs+9762/Hn+6FFTSVfflvb5TGaKiJfkaZdQKg1OfIUFEKS0FXgRXTgcPj2jxv4sMauN74jloKSzEKKFMWSKczy9LTleWr9OF8LgBCJMVvXovioDHmjLrXub9+hetHor6BKhJ3uRnryY2BIm1BppN3pHmJZ2j2doLfXziMF1tE44lq1TYGmjEBUFtAnQ2PX7ZcQ+ul2agUf/k+reW6EoWDtDCSgssclHZyyvPewJk0r5F+/jy1F4lBOZp+5MYIPL+JA4KzkVVsyskW//FuIhc4ioCN5K1wvBOrCEpsBgMN0PX0CpXdrSZIfn/mB5OygFc4aThu5EnoTzdkv71CydM8wQYMPz+w1HzY8jbqerMs4M8Tbt1myT4oz0iUDUESc3v2AasZ0u4gGjaemIgJsSkstgwAKU9XpuzGuyUftJTeapibaknQlRkc+sFob+r/glxElTGaxUGqyol6Vos5VZ5xZNNUgrYSWMleDsD3VwZ7iMLc9RyMx5kkCYuBa9yuzHSIKpIIeFiKD0b6i4b7JeKiFKCCtDnC/16yEkfx/GkFjR2t3HNHGcZ7j/jnzyyjVIycZTYH9c87QvK/vU/9qQpoq1LkVQHpx08WX6AWg5rNrn248SSd40gJkFLfIR1OVCNcWoSHFf1rypyzvZYUQT2g9ZBQzCLWOVzCH1oaOEdDKHHQps0IEU46lEcpwWmUy0tkZr6z6hjvfnshKro78GaUilADOt1sRvR5FEmf/PPRf1THK9nL3uIWkBIDOjWmi4uftgFICeEzpSHZuRPDow+nQyvW6Ep3Mj9JGf5quMvney7ssiB65B3DQMSQyMXIh7ReWlOvp6/GbLnLjZC2+IjobZs9H5B+/MRDQ5vxpjZ2vlrqjZvGeIuaPe4OZ4x3c2PXUE1f83bQh6aHgXh/Gn1KAaaO2fKjeJP44020g+KJk/F2jJuFkAoS3GyG7PWIKK5e7kQgwe8vSKcLvUBbSpIYkMIN7A4nsk9UIiTsIn9qOcY5ilPA8QoNPlXaVaqY7pPmNZ+v4zwrbZaMNztdiGSKho7InjNLybRoLAAmLugehjEQlJIN5G60TkwzUsMYClqYfb3FdadkMWAuMo/Sq8X7mj6cLkTzQzVU+HShO/qKior89WTsq9iUKH5/ZxWgYuLzlc9PSuZUZSwFa45NgsTfWV6i2FiqR27Yzonwn2+If3ri592uWdUsGoac1jnfT66k7YlgruBdKum0p2RypMQLzTkafukpp07E2duU4+c7qff7YQo1zDxPzvsNT5aaTAK9ojW72906lp9nafBTooN1VSLerxhvrMP7y808ZtK1pWJ+u5DeMpcE15H0Kpnf+MPVuJjwEJlUsNsGAOVHK1Fyi6OZ7wbeus4h39GKI36IO4MTbdcoQZLLhg8TE4Koz2H40yNpWBehLO0p9nWBEiNEMf3tSX5WKlVcNMb7tLEIQBheXLNJxA6TOkCeN5akVaHIucHzDNnXdxoUibpCDw7kGfzfvrO0u04nc1zWNMftxC9mPjPna7UVVPaL6u1IyqhpKyExz2Ejnp9BkOxFPZkdyVqIdY64mLE6EdsMTQRWG8fxYXLnVlSV2ezRGDDpy8Mf5rQpz1iWFxmyX55tBgnn7ObxOzHEPd7grz1tNatsAlyEwoeUJtRfK70rJwLhbsGS8n5DwKYj2p+dOqv4ANClXD1IQzLCQlhWoq+VQz1nUCdedvB9gFe2B9kpPIHjjCediUCH0W5FOAdIlkK4WxAar6tpVjKf8fRQuUqRkylR5gift7KQO+S/vCA8rbn41wsL7og/PVGX9kKFNGVCN4sRTpm3UgSZx/C4kBfH28/s0QvduIGbT+Z27nT9wy0X1nOOEIqcp//4IWm2qckpTfQdTevFRDiPCdmFrmZuLznuj1suMjkUOEclYhjnBJeSJtVKPwFTwPPl2fhlVtp354k7lbquHYwZgpGjCn8lcqf5ieybW+B+Mw3aZZCcfdshNoWY6Zb04hSnMH/tzZsl1SUVA15mVn/9RmBK0HF/aSe0rxtNgsVNQGzAt6ORClQY63enqYzNPPKXI/zb3hwD7NYDeHuJpC0u5xPmoCDdtQOaGcLnreRceNN8qlOCO57Ffe5CcfPhRAeDMmfM+oq9Gt3lREQubVP+SnBQq4u0YGyaFyKKJjqnlciporQKolgpfn2D++37h9kf11386Yk2jn53Yn7emqcSU2Xo4xjul3C/vRg3zobnN8qCfEdmitNTV/h2/tyxtBKUMszpCZq9ki6Fwwnx0x2y9zMHl5cbb437FUuS822yiSsJIMT7FcEReRmpIbqWH1u431/F0FWSW8UuI1Wl+fRb0GVd0nxIicvdFEfmumkcY5zRRki0ipB5QfQyD/92ZLKtLEKED/PFM2lUcTUjNH8gsovtisZSYveY8imnPs20EiFcHrdLQ6KzU0v4WkpW3iDD5DI+iPGUV/cxul67tp+yztXvc7OEvyop+2KzXHcVyz2VGb0fzNTJn1oq5rdrIX7zQKM1u2yW5dzs96PMPXVkkJZSSd0tjRIY75ZsT/IMabVA9kL0Ug9t7XP1H3+5mTlVXDWsymSO54805HKHM/y3N6LIa7lc8tx6yuw60MGuJcPGtaNwfOlj4w8Xix2AIKkpy8wwGANNlyxYVNhCzLZc8KA6qtsb/5tbTZdcaiqkYaD9BQAft0s4740OND7wlHe3jkz0T/f0RtxfEL7cmfmQ2x2nCDFFy94P7JMOJ54IMsLIX89MnpWgSawWCLPCkEcIEEP6EAfXTlJ9NOLLBfF+PNCpWgMw/eECbFfc3LkoLTT1aBgFyJGaXzIhwo8PfxxIn66syyN7j5R5pjdp2aZkAzFWjUtBW+9XHyhsjqwZ0X+xVyIfVgfuaTWX0ceMJldym0F4uFq+qJ+Iu7RI13YiWIfA073IRbHB29Z/f7dFipgsttmfr0I0puBaUebYcPygp78N6dU4SeLHXUGSdpJDIFWFHXxKr1ILST47SaI6Xlhl9APisqZVZC7UvX4E3naTq/qsEtaKhLzOKqKzKmMTVYE5tg9iS3Ghn033j0/QxOFxXU++pEqe9l56tGS3rxfGS3hYwzLqM1pLQOZztj4KEkH8mah2uFuY1CzNWYnFeSWChJ5o/Io5gQxIpSM8M0k8hdizmgBU28EnR8NZv6fFQvGv3+irsmrM914XIV2CC/qI1hX98YUO5E/cCADEV2OQQS9NddLj3cS5A5BdumnuVpdsmkUInHKP8EhgI67nCHMaCaWSNhb+QGpReFwb6Vrhf4BMGw6tJfQlio2hiIvV/IgGueyXfDvY4DvNa5bIwiO0f57ueSgIL9SfW6QqR1w0LDHPnfAgWfr649Xcv5Kn/lBtAd1c7PT2Rz7naysKBbHgVwPh9cJE0RZ0GSKKv77A3waEhzU9R+tqgu69MykRnEP+dQfN88Mw0mC3pUM1SfUtDyDAqhkAnAWWU9w1AKN5YYwssTV4U708VzMrw9OcFRU9egjWuf0JbjEXD1JNBI4W+JLKfDqMklhAfH2erCauH/q0c4fy+UKV/qlF8dcXI31n33YyhyytKkniX9v/RMcH30mfvaNhEv1tFxQXP214WK/EeEyc130fzNbf/fqdxlje2ZpS921/6cxhLdbSrxc0Vo4LjkDS3Rruv/zpf0rxbsotiE1BtoksfuVv6gPRB6aOYk4MXBl8OKewVlgLau5qpkdCJnb9yFvn08YEjmk+MyaBP19Nq6YlkiqX1QbRhqezkkCPlDsAbJNr6pCqrxEibS8e74w+Bykx4EkQxzBSGdGPky+LQOg63DUqUUaViDtzo5uQtRbSspbuMmTXcYZ/OyK1LcI//oj8+wFpt4dbLuVkvxGO945OdIIwa8iMf9ubVwuAKScvz8zCUYWvyXvzKrHeeCTyGJtS+kZ5pjEJxU5cz1Ta834gwT4kS8cy0kHbS85dSd/Uh409p/C0IdorfjdpVpnCI64bave0LHZOSrswtQgqZdqdhErXW/JVKjKCHmNghfbbd9rF3zpWGSIYN2mXtg+6FgWosRu4JCpuliKCL+j6SxUvgPi4mbIui5zO6PqOnbhrq0sDQJF129uYy7+fxP28MlmSD09rnrxljrCukf3bN/MAYVlCn4xxw/9tAZlHgi8p80THLjcpFdmYxjlLlbCqLE02rhp+uJFu1v7E0kHndQpLp7IAXt6nYXJd/oGzpxkN8LKgRpKAIYN6d+uoLTtSspPvrpJ13wOfHqaeqJS+UQCKuGpI0E3JpDzjemZzyvCwkkUyvSh1+eY8TByam4qjlXpS6sfljGXQrWf/+XiH7F++8mX+8GkSb1b8NdxtOCqYz4ioKlXsacsSKUZTeaRZZZvBiUGu35/h295kVv54JUgii1Mz6o0hUxUcZI8R4+NqMu/9fE8EUw+cnlxRiNDV3Tq+d9XQtTxMs5cDS06hBZpJ1xi4+ZTEfLywZdDDzdEYzIlsKC0aUaDPjfjvbj3J8Zsl5W4PW4quJUJbW4Zwv5yQcQD+cIa6W7MXpeMf5L99JFpoLqJufgDwb0eMD0t6Hs1LuFKofGMAjkLeuEr5fiQ7By/v0iuOlmHJKoaXiPunf/8/J+sPRGJijAzx6XRn+kh+vGHo/S85DfsTsFmKWZNHOl/5v8ep7OHNGYwd4A5nnrCXG4Yf75B/P0yEX4G9zVxVLBY+snOUzcAxR2aCYacGOx//kWx3AAa48MQS7uqc7mxx2dAhWl++zLk0gCY+rEUhMZO++GwSI7Oly5iIlP/6ZiJQyAI3Aaf4r+iBxPDJhRnHqu+MLRBVWFSlWSX4yw1hs+A4Q24G+9kqMlZ+a08VRCxz+F+f4RYNIXP9dbnFNT5AhaofqWJqSpuuV6QvT0T6RprsuvON88TdafINEkqWzvNcKxYZah3S1Iizgjl6DVO0XD+yPxWSvpO5oXI11aaSXi4yklIXuRsTlP3b0dznzC7zRoWFP3GmmrLMLoqwnvOdixxOqwr1G0pZRkWNxrSV096wCkDsGLNja1q/JPPFjxHw6p2qN3XKPbwl4SYKN7mIxY4tY62vJk3I/HRSlAWpYup/kURJ7Bzinz/ZS3CnKxGxIresbgACizMGq/jt3Uq1VDAH3Czs88ml26ztm4qeKIrgeSdoV2ZCUwbFBEtQMm/P05U3tng46s2lQ/gk5VB42pjuUbMmwpw3sf/1mSz77ZLsF3zonfIM2bGdZkzOIfW9iENvkqwrJHc1ARIbP91oSoJW4rE+C3rSMLsv3C/Z1+rzlFuJ+eSlCXv1FnXHC63+vtzTbgKYQK7zDRgG4G3HoB7ho8YFAY24nnMMUxU0NlIxq5TTRhWUmeb4IOhtz55P3zfyzDIb0fXMTLhfTRTEIkO2uzCoRjih6Zev04C8yOGGaElOajEYm5K3vo4JdkeWqDPybgkOSmVSZEI6l8vlgzLBtZ2tNRo9ZUTphXmk0XxR8AZ9R/5wRvZ6Yrk+n9lGNRH5hn2fXgLhYU0DrjHCx7ulBFIWRApDsJdMO7XRKGopy9hEnylQTXdrnhaf7m2Iyt5gNNZEWs3hLy0XQsEbx52v1GPJC7arX8cZucfweTPdtCEZA0eRNM2sUHobAP7c280ItRyRpD8MqFHkHAkIjzDlFOWa6LWlVYDa2UEOJISIfCfD/6aWGdOVdK/3wx//rpF+n2m94O8v6YIcH9Ykeov+TJOioLaFbztLCjIUUwbxSgR2IRkXNa6EqbFZ/MEvJtXCuo/R+lO7Sa8SFVYWnO9db1N+4d3GNj9lW+D7vvWm0AhbIqqhEVWCaudULB0jshttAf2B7BpdvCZQVRL3KDaPQoJQ1/RUV6TMAUj/+KeJUSIjKgRGyMUF55v+NpC5I3NRNDPG3ZVCsVzOSY0838wrFkGMliRjHkK8cKIYSTPhNTc1D5Yih/v2YhuOt5xYaWhilQh41bXBiTeM352M9K7+P/5Kl3Lv30/mNemlnPNXNdKZGzsi+/pKpHNWIy0ZQpi8F49E8bh0YoJ7lhNMjHIR4zSP0983ryw2Kgk65LoB4WGJ7PWEXHL1lA+q2X942dnMKns+SEYdCQBpPgPut5OBEYiwqt2dwtxxyXmZexf0KybTPpKPCXFlpiO09l8sI3Jj88CRiKw0Mx2gp0bgdKWDSR6i35/hf/nG31MVRAyFlkWAYrrtw3ZJtr5KvarCZm5ObCmgtK3DBe5vXwlKlQyEjPfMgohLIU1LH+7Ux6UfgMOZg/qy4IAdRJC95Di6YSRpXg+iE8W37toi31+tBDQn8pjYDnTDNAIQII7jDGopKWsLlgmpMegkDEgwy+EsOZHXyb9ViffLRjaMMLWGkSTuxYcwnSt1eHHNvMns/Wy/BucYCjqQAG4ourqlF7kYU62pZdRo7jy38ROBlI72mbKpU02TJy+2iWk+s2fjDxdedPIdIX4xnho6zz5EDGpMiiRUKH+4sEfRIIvDmXOPYbSB9vBl84fQC036SYuZ0XWgUiLvkf3+Rkj2ccMP6zg7y05Cc1rP+Ht/f+avSynqymLKe5fUHid+mRZuKcLauJmLnq5jM9wPcO9M2gkPq6n0E2oTvOP3kAw9d20JJgj3k5FosKrAetYPMha/P/MgmYs9YlNywwsLIv782fpOf245WxT1OQ1rW9oRnq703vQO6XIj2jkSkQ6f7zn60dCTugQe74kUhsBotj1JDplY5SMlU8MD4ObYLCdl+VoOGiFQaJVjoubvr4IPlNamQHxk3CjPXfvw8+0PKKPXeS3AzScL0d06jmQkOEUrjjQrOaMTYQC8J7tKWphU5rYG9J+4bviuVvNpFqjkizlNj7n+Kiur42o2KW88RylUsJP/67uBwNSNt21aEnTy+/MfPU/r0pTv2be3Pwzy/f5sKG983PDQNUbTCPef/8//r2QeklJCqL+kOlelMiezo+0ndFQcwgxWHwZgtbDUVC8zuLhdso+sc8LSq4aSIAUo5OSLM4IU/ig8vmsrHEH2iAqemMp8GK3sC1sGJxoiqWSA9dLUCKmidYW6i4UfH0jurUvxfqGBcNJeNdNgkpLeM3tx4BZhq8Vyp8RTcr0QSleypF1l5vvXAwGcW2+3pP5jQIZ47XyE4+3fZbEmtY+QMt11RNPGpxXyt8vEyXWO7Pwh0Fb96ztHHEIf9O1oqU4pc0AEst3JpF/IvJWWKtmaHLArAWwq/v1i3JsdLsIXlltGxzfeT344IZpbm7t2kwNfXXJTSlBQeJLNdxss7FQZPuZJ1A0Eo972tPRXl4OqhHvbczYqlothVdPkV+V1Mme0g1yV+dqKjOIlKy2KP92ENELmj5b//tJN4mtJuorrOcGoukLYNsQDisxs6bW817/H++/vFLJKDLOWS2lWAs/v3EzHmyE+AOC+vpFitagsnhfrJU+39wObXOn5dLaXvRykHMmp11tJGTsns94frhIYKUjZMCLbXZDvrhSQatN/OHPBC7vfvFEyP6WkZt6Y+nG7MPSQaGRmJAJ3vHCYfLiakkLlPmFRTXKhN3rTRPXfbGqWb8IF1MMhezkgzAuLQOazFQnUEIwRzyAVGKfzD0ZXGVXeyXtC20k8egRKdxexNxSRrOsG23wuJt5gRU6/la7nQFpAgzif8XvvT5wB/vod2fOBfY9u3PViYigpy0ac5gja0GXAfX8XUnfgrVuXFvWs/SrjDLoJ/bu2JneDkzxEKRfjw5oH37IheVzeiX89TBzdTgC1koe3+/5G7vHdAkld7/qBrgOF9Hchwp+5gVHkf/C/GX64kxl2nA7Kkr/HvbxzXe7OE6ruPRlX7weuZ22pUmKp2ZDkrowv3WTEMGTWeWnJa51XRGbjJ1F4f7rjqSq52W4IcHlmttyKnPn3E9xyjuztxBcP8C909NtIaw71/fHKMul8I4ReUuNHN2fW7/54JYooGztlmfVaH6k+vhvYu0gQpZW0cmPHRWO2eqYDO0mgowhwNbk0ezshbBYYVyI0VmuFXF7YjFkO+t0MhXXskS3Cua5o36DgjneIyznyE52voqg4zC5eDX/mM1GUO+uZjexdFkTwXqk7S59o0Bse1n90d879JNkCzLMleVpSGAglJSBHMwQINBWY36EWZ7fBgCpNHNIKIwm/U9kk7nTlzfDpbrI9FFJDurZmyw7nkH/fW+WB94NwJuUdSbirluZujIYas8enzCd8ubO1hJz+NppANfzHnyig3l/gJFjUfVBFKKPIH87ckIpMDyPGVY3i93fe+K/vpJDdr4gsZw5OBAc2tpJ3hCigjhxQdAP4MFMdRpsr2qzVsxVxIaL/ccNb8tLB/8uv8E4oWhSsjnxRR0lFrUqM95Kdfrzx1Jemefy84a2zaAg1Z37a6XlGYePlNp1+YXrAnKOIiLcdTUPo9yexR6w49L607EX6Yer1NBRRuKOpqQgVS49kpd1iLrQi3mT+eUeZ06qBSwn5UdKcxMrC7U/i0alCVDbi7kj1ssV2e4+4kSGuxzQqCVHiym6CNN4mWVQUJ61MXNuGQJFrNgVHxnXDhVMU5lTG7AgeTuPDkjdySOyxZPaWVnMCXO97ed6Zaff+4FIekjGHWErWGP70wGdQFwapx6ZiaaXvDOyv1HnAhtpvR97cZTHZ1j9spnyLGHloS7kIuZVMEyhaz7hoJt8gYQylZkqJyr6+2ywZ4IwuPG2BmJAfBNXUeSYA3HjD6BgqLRsSokU4oFVN8f1gbm/x775wJPB6sP6exlyVHb50nEsYP204+5Mkp7ick2Sh+tdlbeqgWMv4KFArGjYLlN9PxAC8B4oS3k4oFRKeSS524u2fH0RGUReIf/fZfDh8O5oqOvUDaTdywtrsTVkM2wXCei4DS7EDFzc0KIF1Mze3YX+4iFats9DPtFkibClrshmZl/gvkfIkL4Tft/1kpRFoexc/35sSOpbZlIjz7QWpJRiQipy3mjp4yT+pLuGvLWKZid1fL5IkGDnZosmUvyhsDCUV+2tPW8C6hH/ZE11URFhkP/7tSEhbbwBjDF2pOewGEwenxYxggOY7/PjEm20IQjzuDTXEwHkj5KC1sUyCiagJChH4MWlZzVMbYhylGxeJdovu2tLmX2azNlMGqOB4P/O2eViK9R/Lbdf2psb35yv/m5T+cdXQzkFNlYvcfj+e303uQ/0ijBgd7lbszaW9SXVlBllefF0/6v1iUwv5PGfEgnMcG80qbu6Xo2hPvYFRsaE3EJUP3NiQMZYysfxtIIo+0H5QmUfq9aqVm3/dwS3n8E5qcADsj2K0PIawZiqrv7FczN5O0Ax3jBww8rZpZChcG5Sv0VvqIZLtTvBve2SvRzIP1NdTPlj22yv6n7cccsrCiXdLnpQjwxKznfRs+4uxJJKMIQBYL5MWAlOLyRONeDu7ybL95OjligLpy4OAJhKJ1g8sta4dDX8rzvGy3cXMfZWUrTaAk1qcoSq6KNVLMmXOgIT4tAVedgh3cx4Q0kPF9UIOJvEinZUc7i4b8ZTJrX9S2xBl4kAzFAB74eHTxgbNuvHUxhBFTmBJ+hpafDQYv2wNVEhVwUOrYyJSeJDRS0kHtDSfEQABJo/WlIxVE+XQ9d1ot4k7EWyJ9zLQl/mi3XrAhBJ2A8LdgpvyaYv06Z4bZneRWaGo5l93CCs6dCNE4F9/5ebRsnXVIC4bDsPFncGLJM1dWqReenFxUUDmLUaPUXQElygW5gHIgJ4j3KUlBuFEyH1tzWYlzmumA3tGzvnTBfj6zM88E9aRMjFiTW6m9Q5ZJkmwhdhDCLIlhqzu+5td2akWhstItsP4sKSSWBn+sxLj5w17ooqjhSjzEP92tHjp4mXqu1gyTfO87Os7T63dcUqyFVaF2thrrZ7qCtgJ/C43lmroVHCppdj4Jwojw8MKYVlRR7aZTy7hs8nGAXmG1HaI9xsMT0t+xgW5sQiB6oLjFdm//CZeNt6Gz04Gv5oxgYcNjZu83JIh8gAMRKHDkr6X4ecnEhm2S0NxDaiRCG6lP/ndibdskdP8OBKKj2pFIULSuJyx95XeLqmIOGJK0JUhPoEuGQGNkc+/V/GtxHq9vEPVBtnbiQfJrTO7D9cO1u+RAOB5CMTEEluf78Cfy3XIW9Af6HXqz60NwBEZeZDmdBdzsxrlX9+4bssC7odPwqrKDPjL9mej1iHzpEHeOhoFC2slPqxN6Jt93/P9CwGA/WdtvXuSNN2wnZtyyJ8udAkYKNDN3o+06Y9iQjWfAY/3Vl3g+Q25GwI5ea9HxoF9f4ebyWxwDHAdLQUAsTY/X5FWDVyeIQGTB8xN7OxmTLV1bTdFmw0eXpgBaT6Dq0okD/L6hLnh309TKSdwuBdmDaoSCSCx935Dhs4YEGelyGrkz0JmkNcW4z/8wBvLOQuTcUMgRKzEaQBeSjp/uCJre+NsKo811gWFuppXmCKQexT/n79i/A8/w4WSqUrCmE95BreYA6crvCoDHlYsQQUddeKOhRgmQSfA1N0V+638+SAIcRQy+01UBB3iYrKRVIaJE+JEqvg5yePNhJAs6VRq99CP9lxjnXPjXVqSJ3oZfWyWRh5IRQ6vz+N+gzQr4IqMfWA/wnnO8CxCTZBid7xMAJsc7FHsRSzQpaBUJ2UZE309RzgqrOaMlMJb9tqsiNKaLKi4mhHseN3BfUA4w7JC9nbm4SxuCUpC0BFNXM7g8gxhNYP/2zP8ouHGeD/QtlJIKFpWU/RL011IuZqdu+lwlhEZ1SgNvHdmGmVE7DEA4GgJnx/g/um/+38kd5UTFpD5SomUeeS/vJCZrgiVpJPaRqlEhHu/sdoWA92q0cu8bDWZ3wCweOUkxrmoSlq1ibN0Op2pvI4So7w/CVAiKneZz2lOeMoySRziDcLI7dEsEMJmwTJMUpr0dLdY6qaasiMAE5gqtSqqUVOIxpyP2wUwRmTiFpZKnqqKjNksrZCch165hAX8WW6gS2t5EHG7FNI2EFYVit93LO9TYrTZlqU+E1sT3C/fmL+uimwN4zzebHCuLnT658ZVjfwoEPiH56Z2j0YoEEmTP10MYNJYcheCmR5Z+M0YJumVMkkAmzM6CVzRKiLpohTisyZiQV0MmorEjzxjfPeituyMOJ9ZTxVXM/tM1GVK+S3MG384GwXP8v+EQcN1rpzbkuSHTxuStaOoZPqB70f0sDrHJOGAIyklcaciJ4Pr/WK4hc7MXUgmddK2TAnicI5k7Licw/3yzfokf+3p0f+0lUVRc1MJKTZq6ufuCFeSoKwNuDINwuNG2DPUQsXmg+XgfIawrKeT5XA2GlX66dM0rLx1CJ+3iPOKKCBg5WpcNDy5f/nGDSz+NYCAMSLGzA4XS7o1apgeJN5RTXC+8UYayQXVCO5UFmSSqLpa+il/vJGlsppLhnhp6K5R1gSIUZsHRTx17KCE8iTyGpVyZRfOUN1AgIBZGWwJUubhv70BD3d2XqS6JAfxKnkUynSRA0afcfH9CPVpjXWBMC+MYG+6OeFp+tNFbBm5OCnavcr3ob/meD/nDecd1wkwcSrFxzN5L+oGPnelihEsGvl5PTB8WvOwFYSYMiaRqLWyET6Yb6VZCf+Xr+zXI1jG6u+RGV5aNsQyAsUElpQlmy88rBAXDcK2gSZy8aYmMKj9aHhYmdUG8wI7ICZkewJMlMtVyHZXtkPbBTT228Y0YtGheSLGfc48fFw1vDbXq6m30Bzu0w3IqKhO3hGBbEiTCuu5pSIhRu52scaDkGbjQrSAqieLyTZq/n0vSmNaNaQZ1fP+9SDOyjMhxF4J0rzzRJuiisW2YrXk39n17EvON7iXd9uwSeB8f21ZFs+kphcBL42nKH1xIWD8tGaJsj/xFM28zPhmItz100xIELbJu5NyK3Jl5zyopN+MulHejsIqEgL5cj55fQoE7s+tHEzi61nnSIUnwVtdB1RdrvmAWWaWhFrNGEtDCN/+3CHMC4yLErv/OEP7aUYlvKozRM6U2o5lnyg00ozINQD6mRzO1FiuF/I5L3bbaLKQZrf7c4ewnWP8vGEfPJ8JCZpyITdGBvnIgafc2CR9tX42NVp2QqhOnx+lZzwbkIOq5IZfzo3oHh7IiElSxqtkK3s7kYBy/aMNpd+dORZRWt0Q6LLeVFbGuo5Z9zQaZlY8lUIEqOJyxp5TiOMI0VhmJGnTWcK1PbMh8LYX1gI3h7mCqcGPmOj4S4soV736IIZPG44DpBxTUx/LJ/eEcdVsSC3mNajFXzqywo9Xlkf3q4lBoApskT+R0sOhtvkuymghrRfW5Hf//c8kC++OsrFzQ0z9kTIT936wzwbvhamSIX+/8HaTUBSlR+mGAQixp/XSosnMPjAySx4pMVMwJTPu9ZcWyTnEhzUVBTnjjrVaMGMk7SOvLQ+k4xXZ9z37zMBBOLybbBuck+8iI4IYbTzBA8ibvhEAfBew/8cS+Y1qh+QcN3LXI1YFUpUDd2v2TkfJlb91JBgD7PcWrGD8+Tq5gslho0ip68VWPvPITi05vsNo4JcSxMetgElaRZWFRGKzNNbKSQ+d1FS8SQNz71VIYJl7rZDX5ffRaj/YjUPeMJFRJwCNBbkWOXMJLy0riZx9LmVUU0API8dHm9H6Kw92f7zaWgj3S8rQWu4RTW42RwXlb4zrGbDidTl+WmO4m+H2D3cY7+YUqF44NNfZXna4iNGpQNRXESVulpP3PlimRRNzToNK1wrMH6OccpHs/QXDYBASywEVjA6juFcxF06hbfV6wVGEq8q6L3PU//YKfH1mAMfrgQJOPeVyZn1rGcQwRVKd/LmlYa4cOBAJVhJygb+2E+FYZ0RCAEiHI/9M5mgEvJoLoncxT0z++xQn5tpOQCP2jNrIpxkhdXcTBtFiZgrrtJpPm3274mae1WY5iMhNQA3g2Q7QKK7jvh2x/tces1eWdPlB7DzKAtnzTjiLApyIt6hJjm4dy81hRNRYcJUueT9RtVIyp7E4r0z1rmpwL4cMABS/M/I73C2YDzHjpgsPK9n4HYkAUrYz9JWE9/HvPsmC/iA/E+Tbn29wtwHjppkMngCbK8LT6drfyCnVEpguDWqHTwVNeFjR07apje/qFN2OaXKIW85YYV1aVox/+sQ25vnA8dN2RYOw7Zw2HXkGnx/VbLbH8e8bnH8oUZwGxNIjez8yy/rSGlthfFhyHne88YU0zLVLWYb+75+gzBpC3p2VGrHMJxi5EEfhKL1R20/e/DrklZjiuJpZMIguENqASxkp5QZipMHRy56K/C9PPMVCIA2tFiaLZmFUFBSbJjHPhCyQsZSs1CeksMTZVJe05Mi8jF4mOw1s1+La3KH4uoMqLPRZ8ObytMYYyP5Rg12Fvv/wbFKyCDQA6P/ukaMb6Y2n59YbSyeWOcYf76R0ZZSzF/Mropy01qterpj9ZY/mL0cebHlGNFTVALmH/9t3pHnNIJLjhVrFIjdqX/F1P21ArVK63sjsABA3EjVeFpQptWr220yjnVs7aTTBeWJ2bFnhZHxe/i/fqIqRsUpaNpLhTkQTQcZSIvOJjxtWTDMGBqnvJwCb77nzDXjY8t186A21HUmXqyVn+UuH8c9PBtzE1cxoZ3FRs6249nDdiGzH75GdWmRf36H8WoBgGMnwRKNTWZCK5oStMM4cqkPEsMhR/soHXLxfRYR4odfHSWzyQmDNXdHCwF9uyE/dNDc5nOlkLS/Fy1DbCSvD9GJSRqXXdzn1O86q7leE8GeFDWnDZw6J/aW1eRmtDORlrpdMn/nT02SxUVcmQuW8LZjNRvh8LwhfFE7qJHh1Pc2Qsld5uGeWtFryOdGppYbxXhbH7Jx9D/XG5ChiPTXmzgmCl/j3dv3UQ8tGdNeOi3B3xLhpULye2au3PY18UxIPGJbG/vs7fNvTBVyeR/5ytN7wY6mcioyMpGXFOdaCJG0FLvzxCtyt4doBxV++syyrudDj/YoHg1AM/XfOAN03mcPVEqZz64goi8Uj8oweqk1t4udUF0jrpeQSslTUJGQnCv9sdwKe7oAfnoCMPi/u2iI2JbL9mb2WWJikujDDJ85HBwneJJ0x3VqSOE7i3h4mxUqYM0yFG3tA/NMnuqQ1courCZaIwV2IRHTF60VTvxQ9V+YRD3dhRf36bDe42tn7VFcYn6hsX/2lx+y3C+qvtNbrf7oj5Lug5D85WjqkmSSmxojiqxBu244qaXEsw9MdB6oq2bncgLqi0h3A+In/313Ehv7LE/zpxsV1a+0WKN6vxjTxv77YjcLgSD6YNKsmG/Ux8spvan4uYVuQB1kBEiTixkCHY/Esyd5o0uqPN9ETkoQQlzMJeVxNLI9xpChzdxAtXMk+JCQRKFPWlL0eJmaOB016Mgf10HSXG+U46wWTZbcr63tTRT8YV1csE0XPmOpyMkIWL014Z3pNyNxRb2AvloJxwXczLlmq+ZYJtP42GPvHIrTWc5bwBXMQtPyCbGZ/uHJ2p2V6VdJ2siyMBRI+b4kwX1ta+V1ulhto+jvRVnoNhb3cJt2hzAiHH7bsq7+/CjDYsj/3bnLSlryMlFO5g+vNSspwv+SB4T3iz0/kMotzNpUYrOJyrVokzYoRDSNV9PsL25Mt5XH+9YA0q5hLKBrOlFNJFO6XrMR0ri2jB//9XS4GHnSIidXQf/5P/zUlGcSPjyuGRGbesh38hRHPcbsCPKaZi8DEZnIjZrg6G8IoamrV7QlMnOZE3ejzKfMnQcWI4C1Imp7PeOI1JW0SvdjfXVpr4JWFrr6QZiorXipxXvO2dG7KamsmQSTaDuHzPSFlwPov18vgXeZq4WEJ344WF81eYGa3md+dyHB/P8nNPU7zqP1pWlD9gLiYITYFit93CPdLikglbzwsKuQHiUr2nqZL3WDzVWrXKmF4UHeYco9Y5Rzci7WFu3Us7WYUovrnHZKaQ/3wwOH6ojEtoAthMipSoACwWV+Yk4bH3smh//kexTM1neOqRv52nual0oemKidamfMmiBVHOmkhVMNycpo2m4qqFLPnbMrjUHWMc+YyQHds2sW7wxnDnx6Rv1+oLxSZUP/THctksbOAh+VOxvuVEPoDq54PM1n0A8JPjyzd1a5ERMEaxAJxgkh1SQ70pTXmjR6+FsuuGsgPpTlkzSPz8F4dwMTFSYMJ43zGGlZ4gx89FvVF6Obz56up3eOKJNW0nNttqCGN8WEtYtZM5nQFF4IEqhDYyUS97BFnBbV4AP/ev36d7OtEchM3C0wqdUYz63wrfgh7TMoblJshzmdAzlsqzqWmF8WFDpFRFohLfj43BJqw1syDN8t0JffWojrQuWLNl57a1kY1bgzc7JEelYxzLu1n5Pvb5FGiM7Hck1u6UJDghuzlwM0/0CY920v8sbiCJ+94+Clf9m5NssR2ZQNg09fFSBRUSj46yNEaRHV4meQahIc14naJ8rcd8LaTCkgsQgRNVpKGfzvSPlIU8NnLni3I/mQzOdWeQphKqcio5L/2koAlVYw6pwsPNvu+h9+fKIDOM2RXkrnV3BcAim8Hs6nkRUFXhfiw5jMTlNpdO1ufaT5Dulsje95/cDej/YlKrPpPS7jzFePTiuj96Wo0TOMez0qjMsa7JUvZNROm2L9nto/cf/5P/zX5M2U4KAuERYUwL5AfO5O+aL2sPo1IaVJxK2N9f5IauhKXYPYC49OaSTOqtJ/Rxjt7P089Ue65uVYLO8VVsjI+LFD8vrPbLa7ncN/fGXqo7lwfNoIpKFS+IgrrbHfhxlZGjMZYKfVNnKQZeOltpGI3tH7u16MhlGHTcHGqE9msQHJ8Vsap7AekuiJ3cTW3xcD5WinWeDNkuyviojKbCV2Yxky53IgAFxmrEKEApoL2H5pJYC7QQr0DgOGeOkUbRLeDHVZ+f5mY+hK/loqct8nznrnpAyl1fzDG3Z95UN8t4f76O9JPXyyBSaU69FbJLBqAnq85b+zvB8p7nlbmJG0mxDJDVNW79r5R4tJQFsD3V6SfPpkDXnhY8yaKEThdKBCXQ89YV7pWnFhSynMwpwHNibx2BiClqkT3wwL9MoMPCcv/91ekpkZY1lxTGzGv7gLgQXKIvmvnjARi678sqOzQTE1zpV6QoJu/HFG836jTk3w7jaKKcqvFGU1mhkeiO+gHniz9/4GZ4Bzy398pB0lJxJ9CdG07ZM87Ajj7M/D5gbeZ4+zRTrK/vkzjDXXaWpFeprOyOK8R7oV1s5hNfM5umFQMt9bYJmr8YwtEIHKEZCWTOQM0tQk5s5cDy903Qu3564nwvnMYNzVCk+P2ZYbbl/n0sstiEtB6z9sxcyzZL7QryP7tG2833bSeZseaZQgPLuCvr4iFjAjEwt+1wtAZoxk9xXUj38XDHc4sF+WA8s+8ueKippHRsuHoQQfgMlohCpkRzg8B48OC70dU3WoH6d+OcLOZDZp1NpbGUXSIcSIYvOzh9xfJh+TN5G/83h+V9HCOgJcIjs0RwINjFwBuRfNhAJIJSSeA8WkFiHeLqlHc8WL8U2X+xKa0HtNc1YVknepKni2jC/IL90IoPdp/fGKPeO4EKxhMguTPlB+Fp60JA/yJ5BS1LIxNhfi4Ebe1Gt6FSIttVQuUkj7knNXhqaQmLUkAhb/2SMcTivfJGQuA5MXDKEyakJpUbS6nK0IA6orarc2CQ8tMmltB69z+RBXFT/eTN40ool2IGIUtAwDZ8w7Z60nQRanlc+bHuRAlens+2RQAVnbGdUNSgPjYjA/8LE6ipMN6NqXhClwe1XTYO6Yr1QVimaHbFIi5w+zrxWwOyLbpJgPfWgSrraCkAFxdTY5gmm0nchc1M/LXDq4s7dbL3k4ECvoB6ddvfPHLGrHKBZ2jzV942vJmPXJoPv78yAX+y3cyc/7yu3FWxx/uJp/X9wN7ld3BSl137SwLJGznYppcI/xwD3VyU+dwNyNrRNOtiGxvCLhJ25KEO2oCYs1fbAf2rw9rccVeUgzuPYn+ouig7YmMeq43uP2JacgawS0tx/CnB/btDRlJUZDYlDlkBx6CqaFFhIsR/nxlarFaiTig2o+43Tuah0lIkJpy0XQ448+9tiaVwzAibknYiE9baSGu4pLQ8vb+p//hf02QKx5tx92ZZUbBiQ9rUxD8wTRIFdd1yQ9TlWYHqH76cV7T7EfU8XpaISW7wk0tcCPySE+PucHlThrcsKqZo6AKdC0jlSC9bPh36edMvAGSnuLDyGF/kfH3CdkXw0g/Dy8WeNK3xqaexLdFDmQZxrs550rzSvxrRKYzBLQ/r5G8Q3EaCAjM5RQVNbQqBPRZa9aDins1cEbt1bUkjMKXdCHxxqxKDA8Nit0N47KCS5w5+fcTF4wErSqbSIEo9dlRS/i4WVjOAZNkR+MCx8cNKX23jjNZnfF9f4V/vDdgDRDNptzsNuR2DsOnNfL91QyJnORDpKpEqsT60pNdErfs4/35yjbgeYfwwz1VDkKKTkpzk7FByhh+mb+J65iYQ3kxQI7bpZk56cEcm4rjsBAMNHEhUVS84XgjnS8mIdK1yVSlM7p/9wQ4oPrnZyKZzhlqreGhqS4nIEtv1t2BYTzqHlEWEgqbkYqWytwcpQBwIYrhjVOhbZHb3Mua+4wE3rSaG4qZikxk+jU9VMQyndYDMxtIuyGyN3hcw3XBBsIM1xA2w4uMOMaA/OuOZZjkQmhvmjKqwzNxyXbC79RF7PvJFjxVGX+foLKW8d0OyPbnSQcYPvQ8GqmWk6aWcqYimeMzAMSE8r1FcRqQnegel72f4ToaNWl1AWXgq3BVgYtcTG9lfBCakr1D5lnaXGm/EdcN/OEMP9CesHg5I5Yel79f4fI//oDrnwkM2J/fHanYiBHpxCiA8cuW6TzHq9nnuX7krVOVZDSJkDqN4pz9fmBP+cMnsbT3NjNkfygyq9NV4qgzFL9yLujfT5aUxQ2U0c9VUPS0mFG76DH1r9sVHRcE3GOZ45EOJJQjJvj9ib9n5IjDvR+4bsXRL8m4JxWZkfPZfgQz6HLXjvTAxw03dojA/RZ43TNqriYi7t+OGH+8Q34ZULxdDdEe7lhhabWnmzU8bYAiR/dlyf56tWSVNTJMNa4kMtuDaghm1AkNSgaI43/4UU53pgsZepP7P4AEqc4nh+DNcpqjDeQHusNZxL1iX7Gac0gaJOPgSNMmre0t8jolMUt100ik7f//Zf1Jr23JlqWHDTNbxa6rU93rft39FRmZSRAUhIQAChCkhiAi4j8IENQXIEAiKRLqJNTQb+C/IdRWR4ASkJLJF+9FeHGLU+y6WnstM1NjzDnX8ZQDiYx48fzec/Zey2wWY3yDQmPn6NwOnkoN4TYCsIfcv+1ZxhbqhGAgie620ngo5OLWxu46AreEWMmqUwG2inPRRWIYnePNM+HAI+yutkfTiLK0mNi6JE8oVWJZXwLPbxwGHC/8LLYnDnrON5bDyxH8lZ9/HlbophXaT3doJwXiuEK3GuPwqUYzC9j/WOA29UhVYKprzlxg68G6WrB3OQkefT4mv6Vp5XblspqOjo4v8GzC/vr7eyl9e9R6t5KUq0tDWPLu0Pv7RjVvCIjOVZ0ZkPlAShS4K15EeTEAJ9oH7hmT9nJ1KVkkElw6HUgqcEMZ2WxMiZz29Ncb/ZJlwdI7RlYYztlBH5cjc9X79YH0aiEqdP/iO4S3A24fppxszidsCfQgHA+AukL165rvjOANwy/PplYCgMHf3iSazMthq2S/E8Jvr7wgVPjbrcY2GgeA8re1LTLLf35hzb/b0/F8N7P9jD81/c4DELlXMvZjHnPvlabMDWhXIwlT6fkYeSh2mmFF5UNVmsIjykmEBGimeRoUZKRsDyifOUJXsXf8sKQObznj+B1cWLcfF1xLnC4WpOJi4n/Hs3/1R/ZNGA5kuFOzBBRPnLkF6gpYMlWK/kJYr6YUZH8Qg6t8Bu54RrscIPz22svRPj2Jkj6I2GFMEUFB9cj1vkI3p22rXQ6saigPLe1ElcfwtcP2XwHnDw63iWNqcckXNBdiZhUNq6Ix8qBAeNki7K90CwSuiJToHZcjG2qEzYGL6DVz5nNdwt3kRVYGz2hA5Yx4MP3XN4r2v74STqVcVsfs9fi4ZL+bkiRYSd66OPdZqoPugrJg73Y3Q/rxCe5w4grh2pg9jtPFgpPhijkePde0MEeHqpvyeEC5m7gc4BxvKKEflL++ATmj/qe3/hktAw+MIXt4banSbAjMJ+I3FAT+f/iZN3ykrE4vOSp4OBjK8ymfG07ZHEunGG2C0z0yFszvL9JE34APDwzFFLmVmk+d+KecIAXi/ZSMl+8XFHAfGWGFlzWXo4Dk07Gncb89/+6F1R2Ru7UotnK6rPfIpz5f3R0lcVdKHndpJHnV09R5kGz5nOE+v6D6yxfjlmjUtSl0HLEQ7tYiLsdoPyzotD+woU4jgpR6zxkJ0u58RVpN4Y8XocQ54OWNA56y4OEkSpS4mjFM8uO9WbXc5UYNZPAofn2DaxPihyVZnRkITUY4tzh+GuA2L3BbVrg+DhEHBbpRQNjfEIcei/8BuP93EeNvkQ/H9QZ/bSmYFmyELoJdQ1iQ9jD+3Fiksu4dwz9+6Xd0Uq7n+cRgwWg7WXl4efDlVp9x0JU+3LHXeljxAXXOXib/umFP2vChTBMO/tK4lu+npElaDdGzIbWtXYK7sCXIB4EIS9xYmkkWw6kPElVzdK6rHlWYMiPVri0rs/FQ8PZMmTIWT12y0rpJcI0TBsyNB70XEy6dOJc+BWx74AH+8ZEIC2EmKeXO1FFlQaHC8Qyv7gSbtokqoHg9mGpcMW9xWnPPJSmlyk20yZkQlcOBfrLqt62hEJAz8vfU12XfT1j925bubudYKunt2UXEhznilNamPB4iP6wIlz0wLisPK+YQqrLl8Y4Dif1Zyk56wtxkzL5iJ1ySjs19+92SN4Tmsp+v8Ocbyr8xvyFNqCM1FYayMW/iohjUlhhb/EpGTvrzJ+OvoIuIH1csxWMUnH8UD6IMkMR9ncdDDotuHeLTAt2kRP1yRjepcF06fP5fBDz/7y44fifDowTAA6PP/ByH324YvDSGWvD7MyVgMcH99swXRG/+W2v49TysjANrxGpNtxLHSq5KAmtFIK64Qr8+SOAnF/i0d+VeiOCYHJsmjGHrHqb9d6zg5EFpcFtdA4QdD09/vHKFFKicSiMR1D/c0W+5mHKG0CWTjsE5WKqveByZT3jjjk9aCcj6yYnU0h3oaXQbuloskk+tYSEY27X7sDBxt8Z5o4vAw5JCkJqDQddG4GVj+HvX8LlIQ3FPTEbwCL6POBYpWZoOeTIJiCYPa/ZubeQYWEMyFCmv01HFvgk5LM1lXCtOYH88WwSUcSbfYeABKTPFRcFUJvJaqHTgQj+NJRD0InV5yrh9t7AJnEKE01zyLGYjmwC6LvLlGQ+IvH+/mhAOSp5PRRfJMXp2QLyfGiawvRsbdCquJtCwz3Q/l1KZ1qk8HggctoHfHO1GZVnY2Gom3k+RJjW6h6mYRz38NZIlOSqw/19e8Zf/7X+H//4//++w/jcR5w8VRv/jK7oZF/f3/49fUL4eUf7y1ouvh3J4xMg9nQjm42pGidvmIBSvhot85+wmNC7o9sDfG6CCx/te0CCHqhlNi8CDRBk72yMfxPPFJG7lb2spW2ubABcve+mLznx2CjGFT0d0p+wPVuWEE3swAp/o0vcHwVnIVJmg3GjPAHP+WnnZWPL6143Zp3BmsGxecoCCIGCsjpS4KNFi2l7oYj0N2Osa3V3QFeHXFxtoucMZeFwh/+0XW9mgrqSEbvnzp3FtY9M0E5yfvAzd4wz+b5/5sO9PMhqP0lTKYvdIyKgTNU0ac0qaHhYIv72+m5g6E/RC/i4LPakKuD1NpGk6lBuPmO+wb/pIaj1VlRkSZaQ/rFH97dmGJ/5w7WGtwfO2kNEvuZODXqcHiJP+jFzz53enC0vv+Qjtwwi3ZU1BcPDoliNUf/mC8PmN6ot//mrsSHe50ec4HjKQZNu7+HVS/B7RoA9T8dsa/tKi/LxBeN2juavRjRkFVu5vmE3P+G++/U/56xwCsuN3U77Iifw9M89vf3oAABO8xwV3nHlQEetXBDJGX6k/1TRitYPpXg4p8yYYDoh1z5lDq92RB8+FYmvdz+o+DmVBPaiUW35/JqkbILphPJTSjd5IyzCUdCR3vbGkE4F0eN0ByznXKVNqV92tRffdyqRzeVDyuxvV0ORijVNIFYFTKiKwrPvRkEJuAHk+7WWFt5axCWNePu6ffqO6puA0WtssF7NBsRCzWObkAJqLhE80w7i1cD98J0lKXNWluxmf9dkI7h/+/F/lLDSpPKwQR8Sy57ow8jEA+vlEbEuZ2c12ZLmuesZjEcgSlV2g5rqpwoXBh0rKqigUvluY5k73VGpJSdMx/PHMKeiWZuCwOVnJAlD6pLsYhmMcWWYOBwZyQoKpX/C8pi5SZGjqeM5VAb89oXucIeyuSKMSzd0Accj0ptm/kwOlKsUlUvPPVXdCGRB+fWEzXvQ3a65LySRMlDjJzjOuJhbwoWsRPlgRzU8rxNpj/P/6Bf/0v/8TLh8jxj8cEP77BRb/2MLfEsojVRipLuBvEXFYUrheMiDHJUKJ83hoMCFVB8FxChk2FDCk8dAQHJphkQeVYQRNzSSfl9vsgaLo1zYydNA89byY/j5oBhBE4rAX9Q952OUhTbvxcUnNqAie03TImcJy2puWO10jcLWC1cIMAOz3Yh9DpiJvoOenChzLtLTXG+LDQkJBa7QPI1S/bmUFwt8vzocI/+OvwId72ZF3vaFAftbuYQp/63EiTkT0SsTTv5srqbov+f+L/9m/zeF5xz3HL4zKMh+TytQGJZUTj3f2ckD6An/r+CXPSK1WZThkP5Pnkz7yWUldMfXL8qow9B3AUT17iBp5WPYZgtNxr4JvenSGuq79+cbGejZGnA4EDpyg4Zoac63JSzpy1jz6/NPHnhAmgnHXJbR3Y+z/OMDoW4v69YI0KOFvEeHXF6obzlJSC6XNHy7A5Yp8t+CNLLpE19wQV1yluKalW6GiGD0NS9qS5CBM4wFudwOEJqEbBQz/ukb7NEMcBFS7G5q7AepvZ3SLGvVfX+yFiR+WvP21JWjf7UCH9HR2DzO4lg9lWO95wL17kCwBWXSM/nDqcYcaN70/IT7O7SDJAypMwvbIm/LaGNEs1yXC85YDMzkA3Yn2Lr+TNkVYqfp9uv2Jg79XQTPuDnDDAV/EW9fb0w4EJefFtM/ey0LgS9lMy8RLFqzWhpXBt9J0RLcJwGfSO5u+q244zmqEc2svtP+2Zrl6peg9TUcIL1s+5zIYdPsT8uWC/P2jKX10n9hrqaWH/Ic//Ze5e5gRvDSs6VAQO0iuK36YKiELoZd5BUmQuV8adi0PGbuLzBImC64wO8feR5Ty5qLQskeNuQD/fu97tcrxbHkJZhfSzO+TGD898+P8lTx+7VO8LN3VkuJOF+Ln9HcT0pV+6fl0QvrzJxOTx+mAOIvxwKaE5q4YEdwUH5eUialKQ21IrxsmCEtqjwqq2XwPTKsKwEBK+jLnIRfptwdi7QZ/e0P7NAccUOyuuPwwQ2giypezTWlT4ekbVPzhjnK4LHgFzTfUG9bwf/JCoJCQ1C7Z7ckpIMtIvyP6QwNzANp7KEyu+fyMhzbIceer2bCyo45Th2BpMurXQEOJPxflXpzV7PW6JIMMVkHwvYg8b/fAw4oXQ9PZIIRum2y4EBNpa5afCqJVc6o357DqL4GyAF63cFUp8wlve2Dj2kpUnDueET89sBzNGbhcWXIXngEy4B7U//wFeLq3Z9FkmrcWHgDtLKOBBLOc7aRwKbH2Hg1E4ButOXfNjSf7+Wo/lDs3IugNLMNujMLKdZAJ49WSd/z2wBNySOlOmtQWiBLWeysv2z9+sJ2OfenvSog8rI3YRq4L0X/+cBGLTrAxdXxacOAwHrCuf1+a1BXw9MCXTzLsitcD/30B7uRCgiLFGZEXU/h//IXDBnFvYL3lYSN2oDwaMH02EBCs0zETD3TR9qJpNrTqo1vU8C3z+65/vEOqA7pJifZ+hMHXE8Kl4zi88PCbI8p/+kZ9bCW5f9cG8SPxhVlKbAipO0+GfGkXE6qRvr9n5RGV65pkfTFiKOXXF3Tfr/hdHTm0cFcZwuyOJhHMVWHL+Twgzj+XAe7rC43TsgvMdRBRR+CgAmBV4gF/6fr8iu1etL/C65kz8i79+Xv2paOqp6bdzTiVLTy6Oxm+zScIzxu2ArfWuDjKFLJU5DNdNiYdW5Kpo/5NrdaUNpcHFHLn5UyoaERgQrD/nC5zcOnPDfB0T3SLOFjSXEh4RUBhyTOi6C9lF6TqCFfRauQxlZM8IMhexV1EPlYXyPW0R+0psaqukLuuhyLptEs1c9/WwHTMU0rMr77t+GAIzDbIRK59mqNY972f//qGdL/kFC5wIJMH8vDJg8bgkdIWp2F/5H/XMYjD3VqkYQW/50vrzg0HT1fmerffrRDON4Q9M+6REvyvL3CDGmG94+/3dM+6X6eBj3eUqZWFTYnDZcBboBLnxqik8VbtQh8YPxb2RC0gZ1S/bISbSV9jNyqtmkAWrs1oAL8+oPu4hB/VZkpFG+EHpGen0YDDqkYgskLbLr5urez3u3MP2b00gJRtQYZIKAqEt6NNyTNk0qvOkREnrkrczpOhlNLESmI5Z4k8H8PlDG15woElPxKIgugSnzvVwH544LJcBNR+q6syZg4G+X2dpC6l+YRKoP3VxCHxccmhkypzllNK1q43QR0KMGvD5K+4miCVAeWXDRGMqh1V36bEL7jrDepJ1TLT7FwO1ofnMePTEKWy/LZGVrdEyvCICVhvAe9QbK/Ay4a9UCBk12+PyJ30RCIxSxJ3Zdc4wBIiJbSf7tjoT6mscSXLkO5esiKkrEBdwYn1Rks4ZreL81gd94m/TPEffuEH0UW4v/6K9LTqRcJ1YRM8DX1RsbaLyZau6X5JKPDTQoTaag/yts/z55tI0gqEUwN3unIyeqHiHYuZ7Pfu+XIoOClnSuyutz762jlSttR93tx6FUlKyM7RktImslyuNyQZluQBE2jdby8onvdwojet//lNVBWNncbhRAWPuzYcg0uIJl++2I/oFcOg2tSykGitaODZPBtTnD7sy6+8nIkCiMgFd7oQvXAQ/e256ZOR2s4sWuHLGhr2qQ4EfUl1MBHHFb9HGdK5ozBndYA1n/DnC47fZcdBkv47fnc20b7Lmah43fWVzIRIgqx0MSJ8eeWfp0MyIQV036/EHxp5kM2pSVYnjDtf+ywUCbJN06Gt31It5eutZfjPrYV7WZP4NxMnjHeIH+/FVSJoENd2wNO9BYdgPpHxPtHy8W4qUhsJHyw8zEU/qoE1uSh+S4R6sb30RsjxAO0P93CnC4rdVYC1E+5WBgXShztxwhdiB+r/3Dygm1mZJvmHD4and6sFX1LNDqhLhAvF21pnx/sZ5VXBM/1GFTRd5CK/5CmWBzVQlQQHpQz38xdJlaUcKi0nVGCIer5bjZFnYzbewXMyOxpQ9VCIH03COgGuAlxKCF83PGDEd5gmA4TnDRktEhWtSv/rd1NcP00JzX1YIk0GqF5OCOsj2o+LXtVypi8vy4MfH+bknYi8z12lRw7swdNsZAMytKww/JuAphQg+64sA7jrdZfGJp2ujXL7HOHEE5jrSkT0xEq4GCk5LAvK/KYDJhur71JWMMiZE22wv/Nvew6qNLVYD9iykDhttS/JoS+4yPB2sABTdzhTPD6sbPXlDyfx94ngXNAWYXeynr74bc1WSmjW7tYB9wu2ZsFzhXU/7z8b9XuWBfLrGuHYoJszkerypxUPpNkEzU8rcV1E+KYjRkSc/q7t4H+3UDVHcgB27H/8VZAUUnK4VjB/CjSdEQFhX4DwWuI96VrF9mw7kjgbyHqCUFrXsB9zksKkqUd+d2KU79veHkq/YVmnO0HUlRHEwtuBPdVyxj1VGy3+GAAwp+dQf4c0lXCOuxkfUgEHIXjkHz/KF8QbOHxZ8yTbHbmsFoBvHg9F2tWapjGPBmJkDuTZjHk7Ze/Zo1xpPPUnYiUA4DYvkYYFXNPh9mGK538zwubvSnz9n5do7gfcq3qPOKZWtPy6s/41LqeIy96t4a4dHSSlhGJKhqL7/MqXNLIsJ7yKv2/8uDIBe1oJUVsOUHhvhxyp0wOjb6tIPQ0q4Zt6CQ5li1F+29uN4nfnfrLedtTpDmlytt1czogPC+Pz0IIUervU+donW8mejegUUdTMGD2W7maInx5Mo+v2J3RPC5mI36yN6j4u+WdNR2bUjePKBAY6KIOU2U5eSgq5x3RCfHnjz/fnH9ia7Rvk4FHuOaxJizG6YUA3LpEGJbrFwOBXKrXzecDG2vLMMhETxNLx4YzTgbFL3EVVHUKYvjQsf3bHPvO7o4oje7JH4py3gT8StxB2omoR6RWCPDBtJ9YQYty1KW5+WNokUV0Pbn8io0Sy+HhqiTtZywSRtLmYuGyV0S9H/t54p93DzPY2fncy20yc1nwo51Pe7jsSsrivdGiXQ36Bb1t0T/y81O3ut0eKp08XVgeSQqsQ2Tweov3xAaOf9xRQ54xyfcFgk9DcAa5jyXJ9EEHC7sIyJzJZKQdGZhe/vDKrr2LISV5MEce1AXPTbAQ8Et+hmQjh29YMwOHXFxMJ6CHiPr8YlkJFGu504bpCTbhPC2Iqdyfrj/KwpkSwFO3jQH6OkqICVCVvUSGlu060x7I6COu9DXN016zqm7iY8EDW1NqyEK+jsGe3R058b8yxVzliupsh/O1rbzgX5ITX1YJztkMsvm1NtRV+eeZ/XgY6MaT/y4MSYXdB+f/9Gbhf8HM5nGV4eYY/tyh/fkUeVrjNK7QTj/V/UuP8acT1z8sbcf3zIfJogCJNR/COL0R43QHeoXtakP+p4RyN1LmrqUWS+fPtd+5yJ1Ik9nH8jFzD7ATDAMYr2h/vUbweLcoJAF0M8yGJbHJpud2RX+r9HPUXIub92x7AwL5U2kzE5jQaAPsj3KAW6tSMrMqioMH0crNFaPOHe9R/+WZ5AxgKTk/sSK7jyR0a5n/nUY1UiRRKbnh0EeWLZEdMx2SCdJFDmqqiOVZv4CKQi6OhHzLGL9Yn2mKcA8bSrzmH8S8ZzZ1DN3SY/7/X1KTGiPLrzsTw6qvMEjoJgNPZipYhNaNq8lQeSLugichKGxtyEkv48JC7vgUnuOHL2kIucwyI91OWanWvabXIcE2kCh5OVC062ELOiHdjxGGJar3jdF3KYVXOIHGKbBS0GI3KlsqCRPbFlADnnFnRXHkLto9TFMcbR//BA+MhBegFMyAwGXGwFDxQyTrNw3ZxPJU9LPvydCHm0aafYkpWO1oR4KYTQBgvCB7d/RCpFI/fqITLQBwENDOHbgTcxh5+VSP89B384YLiG3ej7u//9X+b86C0/YnZVmQp3D3OULweLUOBRlvpCRruWyi2FVKwgI0U680lfDanMCK1mOb3azvpEc6mC9Wdmxlv5e8AuPDWBWtcCaO/Fgf/sc9AyEWgnm8xgf/6hu7HR8at3S1suWx7peCBwwnxpyf2JNsD+4uZNO9dRPthzs+hCMQ0Pizgt0f2yN4TLzhkeeO+rUkhUxBQzsQRDgsUm3NPkhP+iSpg3J7DouN/+oDBS4N2VmH4847WHnHFA+BNMBrYPvW9HQyAScT84WIrGVyudJkferw+yoL7z7o0IXOa8XNWELNWPWkxsX0uv5NAMtpyDqXlwYOVkQi4rX+XbDxI2Wt731vb07N1Ctvc+r2fphqPh/2/p8JtXa0ARP8raWAypChgd/odXMsUWntxMgxJc/dNy8HjeNgvx9+T9wZ1H2unIC0xLJBTI+6btz3hy5OKSVSjGtenEa6rAjkAxTVj+pcD3G8vcOMhkZRvB3g6v7v+Wp8OkRYThnPEhOLf/2zSINV+pkltXjDNjHCykzMWxmwEdzjTHaxSN8G+5QF3UHojhc0JOJxMGG0kKd0nqXoliD1GdkfhZUf/lQSuaMlrZDWAN8GIt2teTHnL6USuixw7z0dw3sNl6aWqEm407PMf2o5VgAaYzCem7HEtxb8GE4oZ8acnQ9DpMCpsjyi2F7T3E9OL5v3BkoS6+wni9/eIqwmm/+4Z4dhg+DfusMKW7npDHmpmQxmQRiW6e2YJau5AGkg5OWAQafaOO8uLIDYGokeVrEMnTm07MBLsAUyDytwM/pevNBwvmAWYPz3xuXhd94g/DSzZHnqQ8P5key8AEgqa5PsRevX+1OtmB0WPhbyfcx7RRSvvVehBOWTHfeFiTN6nuCls/bI72gAOCSbm8IcLpXqHM5k82mKJWIKrmxbdQuYdmhURnE2IneAl/f7MVsQDxeYirg6H+vmCyS9XTH9pEBoZYH28g7KF2u+W8Fljj+VEcpFcyPCy40Tvjvz87o5SG6x3CK97NsI591lvIjTWEa3fnZBWU8qQhPvo1EmfZAomk7XsHV+Ayw3+2LCvcBKeKBDXNOXDrlRu9au5TnaSwvmIT6zL3fFi1Gjzw7Ude7OmFdVKpCnycEVOEhIzri2QJE0FLVcz2ksJ1xoUQz9aR/Or6EPpZOCaIN3NoCEy+veX3/Z9WtGHB/mz+IX7wxWpYkZFGpQkLIvD3ok3DgAntM0N7YpYxPLrTmw1A/iGe9fugeEmaToUijkPrrhi7kaeDKUfy2ZajR+WpKnVwdCOYXMw14abjM0jSpMvD9D040ceBssJFFqLqkT36U72swxbwa2VQ5ePWxbfXx5U8gB7xqjtL4JJYeqWE6NsDoFR5+OBlad428CNR+zNT+84PVLFsSct+gRfMUzrfjgtJzaIccczDd1lYRVC9XkL//VNniUP/+uL6aa7xxndG7KOSBKnriuo8LIlQxXA8DP9o+4scQoxodic4RleX/HBEuQgVQRjhPVJfFMt46WqEvnpTiRp/ADRdqSaVXRKa1Ak/XWiMChokUlKpprKIOV4ZvlzulCtL71g3uxsR2Mgo44lhjtde/9VF+H2Jw6F3vbsTY/y4Ououou28M4DDj/icmwj7yDTVazmPYy1rowJGmcMgjG/4EVinlOiRet0QfnzKyliuwOJ1RtZ+N86Q+Mj+F4JIuWWYhT9peW02Tt4SZGCd+j+9JGckVuPDHQpi4l0RNan9/ZwaK5jmo8R/vqZwmetAlJC93FJetrTok8CftsaBp/YhStz2d/2/F4mI8bSzUeUtsnfEe+ov0RVUg+sLUBZAN4hzseUp4lqBAkWcmO5g4JrVBmf6yIVTfIz56pA/I43hqIHw/5qE9McAvIHhtbobs/JBDMHTXHmAEl7OB0kmarrl290dFQFMBAua6BcTDM44sd7mSJH5A93cNcWcVyTbXq62jCv+A+/yQRadsljui6KtwsHSXdTxNWEzJ6K75n7hz//VzlNqVxQSpbq6FAE9g7CBPHXrt+n5HdiV2lEFTyKKIvktoMCfnQo4PYn4giHdAjEO1kbHK52QtEQeTaEubFZgrfwDPfLN+Bu8fseQmCzYXeRkkO+XLEu5SL0EFqRUeWugxtJ+RW8TQdRFvwZT43hBfJ4wADRxyV7h3dCW3NgVyU1n13kBDVm+OMZ3YcF1ScicWqfZijfGFcWV5PfUZ395oC0nFIx8yyRAYcGcVqj/OXVlujtckjVhwaHyHogDyrJqxD9pA6ppBzWly/L0ECp6GF9BK4NGS0CrmXYyk32iq1luqtTRpkt5kQvCx5GYkOyNYb+I9Nq3eVZn/6yRV5MWeLJkE8F1iQVTClRDIEJXYerfS86i9C8SBe5M1SXjqIpVR+rMKlUFVY6v5em5fnEUpfTbMThXydk+Pcuj5pWLv1sDcsiE29/vDIeTfa8/tJS4H682PfgIRgJE9CCU8720x2HCssZwvpohkkiDxq0TzM7DVXDmeZjYXoQdtPdSYpP4fvyaTRgDxC5MC+eJX1IMBiATEADMeJZvmTeTKU4sDPwgf2SP9/sykcIwiqR3dFRsgCm9B6qmgGdpPPMxshPK+TgGYW9JBMUjtPD8HXDv7sIuH1asiySnPI0LCVF98QeYXdkiSF9TFpOjCOTi4CwOZOb+bYFqhLVP7/SlV6X/GJq5sr53cmUNMUzl+Tl1y0QHIqdlNUp4fYwRhwVuD2MTeuYg5fdJqFOdvMKet21Hcv8zZ4rm6ZlOZkyFSXDWhwe8lLLfhPS57wvr7A9QAG/HM6N+iHJjZQFPK/5vcvCnO3AgMqgUW06Y3ehesi9bbnPFZQGqlJ6tpoRAosJPY1NxzmAXgAifdMVmkrs2vuR8VtVQJDmfNmVoqaHAN62SHcztD/c2ywiriZwxwvaH+4ocFD0SWDZ6Royk2hGqAzX6V+2Ivgv4dtoz6x/2yOs9+wvT5xp+HQ3MwtS/O7OPuTihdlxtPIQv4CY7AOufn5l8qxjRFOc1HaDRi0xuyTq+2yJtihZJvrjxXgeFGXXKF4PiKux7Xp038bGnC+fPzJViDndZ7jDmUMBYUfaNCw4Wpju57ytPNOH0mpqA4A04CQsTzjtY+qN1PGeMih94Mst+zZ1Vof10Uo/o8oNa+DLi5W//niV0XyQNcsJ3Y+PLGfkhs51CbzSiOuEHubWXNLnCfdwuLW43Y1s0BPvpyhOLXyb4DvBCh5ORq1rf7ynhUikfk7kUnlYw+0ORjE3iNKpsQsqjiv7jPKgJ0vnYU3yWSGIkunY9Js5kC0DJ+ExP36QXj/aTZsLuj+0HYhjMjqz94ZHzCv+XOXPr8w7nAx4WMiCnQm0XS+0l1LUOKXXhj7FGJEmQ1S/bmywlIalISUQI/JsQjvS257U9DlpCMWeJaW/3hBedkiLMcUU8llmsSylYSkGZ3E2gKQ5f5IJsayAANIE/PaE+GGJ+LjggLKlPcn9/b/8v2Q9Oeidi2zUpe8CALxukJ9WPBV1T6MqAZmC5vGQ4ttTgzgbsAwU8CpHxkHkTtEQ9UgJaTm1PAYlThk+zju0Hxf8UM4U4KZRieJlz9vv1xcGdGpirnPA8xvccGiZgnRzU+7lrzd+IYcT8sd7vI9XVp9XnA4s70Fvjaw7Ii1DZcLYLUYodpfenhL7gBlVq7hbx4cCYEKR2LcY5dW7E7JzNIVOlMVa946D5sYboSiMTXP9bopqSw1iOFIMkSYD9mGSVZ8GhQVkWnklqg91sXRzlrH6YmVBt8fVjC+V4iZEq0n95YmlvuZ7qLzsSsnf7ccVfCOCi6bHPjq5paGmXfEpAmAvpWJvyT2MTwsO/IY1q4mvb9arq0Eb4GoqLaf2ufFSoBNHgdGoReUi02P/ticr9cfvKFU7X+GC500X+32k8m+yd70dD7AskPC8ZQmtVLySskrGr+XegCvSTsofOUj0uxM81lsR/koJMx/D/yoQH4Gb4n5pEqQsoZN5QCpVvBPyU9vZcjbsrxzvStCmvvGaGKunMfVwfc/irvKhxcRct8UY1c+vYswkNjxsdbx+RfruAfF+aphv3FrxiYndZM/yEF3iLbhmdFX+eG/hIvrh5NWcGITDFXjdmmYxLiZIywnV+YBpANOoMvisncCiryQcuLFgyPSwoKD5fmmjeRVHdw8zERiAD4n6KTVp+GXNL3s6lt6F1Ub1xilk8R9++138dw6Uj7m9TJKlcjEep3OI8yG6pwVfvr9+4bBlKHpYKeGCLLXzuA820Z+T7nl+D/AwPWmuiRoJ547ls+L5g+91oDKRzOKw9297I4/lQSXPYI8V5AWwhTs3XO+IjUl3dGk5MdOAVk3EVzZceWlKUVXY0DDX4vX70yeqh0YDuOkYKEv59+Xlk5bMmLPgBNp1kf+e44vonzfChUnye4hzpS7s34N3nCvcS5sl5nAiKfT/OK6pLghMiFV/lxK0LEnn1hEDfm6EIXkx6Rdyphzs8zfg4yMb6vUBac50HNKznOXRQdJNXUdQr5NdjJGrxPYBT/XG+yx68v2DgZ6UMJamI+ouddzetPal6almGI4Bey+Xsv39ACe0xvkYVBYwamWuDB26pwWKLxszs7r1jv+98ZDm4LcDT9rvH22owW8Vvf7WXmoqXtSXl3RgVHgztebgWRLJ8Ke9G6P6yxf2bjHbKa+3ch7UQqbmw6UnrwkAZEiRPasOVfTnxRSpKniLygse3g4msLDBigwfclVScaNKG+d6LS5ggThpNuLzcD/nS+Mcb7wrOTPu1nG3p6bhg/I3R/y5ZWdJvoo3A7jOJ5Byj6DPGXE1pgQSgD+c7GZX4YD7/ELlj2RTZLFX+XfcIQ2ABdCLLa6tZG5cpJpxPetWFvb8Oy/9NFqCgdrHKYot5wXeQDwdMQua2sMPjMJid7qaizw7B7xtiTzPmaWmgGr89gTXRHraPj7yB9DMgpZ52hYnDQ5V3PHMRJ/JAH57YHlxN7VmX3d4yidJpu/MfYk4GUoePO1H4XVHS0zwpuOzXHlPgJJmi/tvaysHXUrSXwo+43Lt/V4zCrpNsiS707AhwFbdA+l+2Udt/9MX5MsV8AIiDjTPuqbrJ6XjwTtDslq7eJKG9Z5SvuOF/M6LLJRzRrsk5r/65c0gr0SIDHioST/mroLZuLb9Q1RwV8uXXV4+cc93DzNDr/vDGeXXLcLzlgeJ9uNVgTitWa28bfqY8LYzkbUNa1LufXKiN9W1VLxjr+lOFzT/4glJSkOAKyN3YV4Jg1uaXgeqk0tBIPqDLM9FNeXF8uS6iLA580EXb6g/SAzc4cS/a7Wgk8I55hmWhBpbtqU8Q7kQY/dN+r+y4JDuSPeFf97wABNmjfvt2QJd9VBH5GFRvhwt1MVrHHK8m9ikJ81HdsID6MNQPLg3u1vwVHgHQU0DLi7ThNg1pGz1LnO1O4TnLX/JmOBypoB2PpF4aIaydPdT+DNzEPjLpP6kdcyHzwWF0u/VFW69k7pbdmu/fOWBMeEE1CvFWlgwkOi1+PFext11//K3HcNVljwZ03yENB3wgJJ+Ls4lHaiuOBETP5yLtPjkIsDNptSmruaU44VggCIdLIUNbVyujQhvR8aHSTkXVzNz9StkOPz1M7L3KE7ska7/4pH7Q6Ff67TSstidI2wpJY75ReBg4gu1jolQIGxO5nF0gnTM2j8J2gORE9p0PwcWMxvjk/bFwY6RquV21BfS2DMAbV9VifS4RPV8RDg06L6/E4FG4s8xFJle23G4VxYmRwyvO6Px+e2Rw6Nvr6IF9n3ZrSuC/ZHPxpXEMnXwZC+Dr5IVlEvZ+l5UJQUK4gSCJ0QZQYYyqzmfk6IwwUne7inRS5moy+YGd20QV2Ox+gmb5nKFT5XwGb/tuItKmVpPaWT99iAgWcEoCIJOOSMq/SKAdWDlmD3o8ubzFquNoZgD2SC6P+qklwzCNQmf3+BEnoYiAOst/+6qBDY7uqEFNed3J8RPD+TQXBs+XMu5Tb1c2wlegM4HXYjTGNqYAj4P6eB2LTPC1R/ntyeq7mcTuvCvDV/osuCwJcoH/csX8yzmQSkqlJEhK+K0Ngc2R+itldcq8sVoyAOhlFQnWYMwmbcAnu6put8c4Y8NBn95ZsVxviA+Lsx5YuWf7HU1DSiNSBxnPBixkHryq3PEKpTTmbJArQhSEuAS1T3uRmM15Lt1l1sfVlJ49rPBw2/2/e0oa4W4HPdR34JndG0Hf2agqdvsRfDPuDUAKH57M62qu974wmkUnpIP5lQAoYvM55C4ung3FXlh12dSlDKcCQEY1CyBO+Lk04QEBIU160BRtanu2nKvvd7x4H5a2AGYf/zAz2Q66qsa71F83XLiPyBNL6/mcH//d/911kz1PKrtBI3zMQWtgqzIw6qfgklOvAY1pumAouhBwR4SIL5dY7EUDiTL/TQemidQRbU2+Ws6u3Hd8dLbSHSpquJcBeyIGiWPh8wh159Zr32px9W/qPstt9kLFbtXueT5xPrPPKjfTfduJu5WIW9fKlV9Dzcbsk8e1TQHS1kMgD+nPCg2+ZNlsD/Ro0drDT9jdRHoS6S8HhW4x/sZ+8HNyX72NCypGf32BlcE5BnTpNJyCv/1zVJZnYjj+fdcZfgRCKwtCu5HNaX2fOUDI61KqgpGgAmISw2+ajDV6K/bhymqX7d9KSpQLHTRPo/sPTWjziE+0uXhz7c+LHPAiXKcVIyh1kx3JdF1kUOsc8tU2tMJ+P6DaEapglESmSI03YEhL1REjRD2Il9TmNN6CzcYmPNChfI2K1DS3GT0u4wRjblT6SAfCEARjIDusTmkSwvyVWlHOl76OC5RtYTdidPLk3AOb9znuStTksz6EQKdAHIbKB80F5WFNeqDlFZTqc0T3FFcF86xP4kJUC5oc+tPEP1wGqoxLL4a3h6UPB3Dv23hHhb9y6Sj7+adhUi+tDQawA1qPojijs7zCWlgkCENYEoJwzVMpCw+NzxQNnvkmmN4tzvC69/ZJUHXy/AmUASQ5mOZvkbTSPoje2t/arjTfFjwAe+YSR9ed1wei4rfHS/I0xHLt8UUaTKE9xxiBCmhnXdAIRYvcMSPQc3etEvA7iiQZAcoE6gkSMsdTkIRr0xPqy8pqpLDretNciTfRQycr6Y08m971JfG/j51LeTZiGue3VUMrjIPUF7QX36Bm06Rb3STx0lNHEdLW1seVHCve+Q5ZwrxjlBd10UecDEC+xMggDFd8Fsl8PKGXFB2CPGOut0RmIx6rGQ37aWG6kLZHYBiwe+yroQPJJdMFyWY1svWIBMgNRoYZzXNx4iTGuU/fkH6cIdUeKOPe6+hjIuJgVLV1a7MEsrNpISZjgRRyH4gzhmm6VKmnGhYsRfLQgR72/CHGFUc8cZsRGPqIwOw3fPDVUyfNrObPd3cA3mwhT1iUVaZYmIvE0rDwkuuHIXbI7PbuBszx/35iqxfqLJMfv0qyEQZ499a5mRIPnqaSqyaOiS6iO6HBzlZAx0ScuC4k5iR5SSPqwkHEJuDnY69j04GK+qQVjHDagIvKg9/vjIDQXpqAMZbJfWrsP/cn2+If/zAzyaLLGpYUYUjN1paia/u1jFaTIYiqEp0f3hiKarTztxHi+fg0C1Gxt9MVSFSrFr8e7RAWUyYrlTOjZSZNyjrxyKpAQ7D9hfgfsW+bDVndMAvb/bf0T6bC/QBmp9WRPevJkbXztMxB3Lewf36TW4h8j7z8Qw3mQAPK4u+9uuD5T9k2YPnoZgGYqLSSaaw2Tmkp1X/e3nY+sSJ2oUvZejXbrICcV1iDILEsIX10ao7745nu2nSqGLDKf0RU3KPdtOE3YkcjXcDGn+kuDVNBmLTgTjlZWH/eEcXwlEQ8+IqJoeRECTnnKhsOtuVpMmQuLzvH1jWChZBPwB3bfr9m2Nak5fY4Px0J80//W4Ad1d5zCxznmxO1hny8Pzw0XIwemgwF9hpPkac1T3mXcI2gkBw3enKsufasIxL2aakaTbiBHnA8sNLEChSom8Q4O9SVzR+th3itKYuUywxTH9qge4dbNd7WzB7UbTkgt9h8U/f+JKdCKjye2L3IXFzTvLkc11arFgSTo/rkj2gGgWA4JG2OxpJ1yeRcV1IDlMZmPy/rIqg0FvL0mRoQuxid6Efc9ajNHJdsReMyRQzdoB3lD7yJhWF0alBuZYHuCFRIfzybMOlXJdw41F/uAHMaJiNxSg84rOkQanHs/lW7Z9CsCPOId8tqMiRysQdzlDKeBIWahrRYB4XEzu0kTP70EtjaxgjpC8nwoSRPsX/8kw1/O5sagK/2fc+qYbiU542vVWHA4LSdlDqDezuxMCpN4w27sczTxVhYWZRoecBgxrToDDJUnjZwb/uxNEtGeLycsSHuaHflLWZlhzkuJSgOe9cdfAUU22oa1oaZg0dxy9asxHhHEs7iVX2myOK1yPLpqbt6d86Zq/4++dBzWwAhRTHRItKQSR/Ws7MNYIvz3DOCcdTnAHCDg1ripDzqLZ9pT9fob06H0Lhr0TakNz2gPbDnGTs0ZCH1OHMae54yMW49yYIBkCBw+dvXGhXhDG7a2e0bgUb58LD/fCRD2zBUjXviI9w2wPQJd7sYvnpliOTDrrmhrA7MatBqikAjKRToQfAn0tvl8gyHzpVHNRi5bna0Ep7cH+4UCRRlmIIPhEp+enOPivlcSo0OexOvNFmQyZkjcXeJiKGvN2b4DrNRmavsqDW2dhuS3jIAUw7HTPlnbld1Lxuu1OpHP2eRAMyYbqI9MMjG8n5yBakhk9oubfKNUfn/toxViwllpY3uiTUtIucifSTmK7mhyVJUJORLDILU8T41x0HChKVFb5u7IGLD3ObmrnPr7xRhKviz1Km3lreHJLEazkEV2HVnC5kmMxEX6q/h2ekspMvO9cF4sOC/Y7ErXE4QYWGuzRwv71YjwmB1xKJKEg9if4CYOW0b7oez5CzTVzdYi4U5WB9pQkOZHTuTjLdE81sHtbUs45rk8flCW+WtJyhOFAArzdFHg1YYUjojVNTqx5CZYH8L3/k97UVyVqSyaVgA3X6TekgQ3ZcF5H+RDhu/O7OPkO3O5KtsuaOzcuB2d1PDfQMASNhteAwb1DD7Y/9rSvrIS0fs/Th3eO8l4GJ1UefDf/5hVrRDSugNB9zTXJHZY9fHyzFy2xdLX2T5c+vNFkPah5M3sPNJv2soRN6t6w0VGPLqXRpxuFifbI4NL89AC0FIYarbzvGEchAJ83H5Nyg7Sgi3VGHGN4ONtVrv1uZ8VbxEK4h/j1PGPEb1kfSr0vuD1EIUQ1MpUEXUa0vfOM7YTreWltTpLsZhyxtFLcxMfHUzXX9JFCiw6hw4Xi8fZr1JZmEfkDLWYBytrsF3PGM8suWpuH7ufSsPWZAYUzhZcs/S3gzar1KM8KX8sc7MjE/LHmiSeBlnFBrioYvmo3xB0qCLg24i5Tg1web6pIiPjIMItTzJz2rl/ASvzvR8Fyz3NKE1m4upLkBeSXhJH62wxl42yCOK7TfrwxP4aIokYa1QXEhPb2O73mAMcsiy95Nb3X/skU+cC3jrjcOMo5Ug9B+RZARpC1xhzPCP3+D//pmU/P3LYw7XYDRsJ+U6sK9LLiK2FPoEXbktGSVBIpyJzuiQ1xDBVBcTDhJB7gq6CJzLP72mcO9d+uxNKVm2P/TZ7ZDm31v3Wp44PA7Im8WrVDnaiq0nHBquSN1Rv7TEtwfzvQzynOWF1MeGmJYD687irHVop/mvFrdZs8dxe5oQZjGzncMD9G0muy9/SC20FXPmLgEnABxLC+gkh7MSR8maTFxPpQ94pRJP29HK2v4RLMvSDUX2ABoGN0d+yUwYDcYwHIUsgsCYCsEm+IdxQsmnjF3lSGL9IpE0oc+LagILLsGtTTb0eRyLFduJlLPQx31i5lZf5fNnr1t0/KEF0OnqTnEU+eGA+urlNlChYZDfJhbFWAi9rbrU6JkMnd9GsFloP52QpzUDDBdH4wC7Zq+XzF+ijzc8Y59JNY7yrWCZ6XjSCLQn8XKsaZP00JMhn9EzMiDokdFyi3vDkJha249IEn/b8czH1g5jDQnXkX2jPmO/ZhfRP4qhHdfXikS2B0Qv79nT325cjodaJ9yrQybxK1hfky1uEn56pqW5fxqanJCGwQ6xz9bBQcAGUcH/ZlqTnNfDvwzd6JZnXCn7fmCiFX/dccP7n5poSfu2phrIM2YlBTkZXSHM5esolJR93saVT2mXkfYmcGUcUWeCt413HFSU1J0ae2XKL9uJQOiY6NbFhb+4Q8K1ql5Q0xHdNxPOClz14bQI/WBSmwWhbRUhNB7d6Y7vyo5ddyfadcZVVLedsTpFXJLDfmfK+ZADZ44s8z139Ym4EURaEe6MSY5TgkZ7u4mSD89mczPH8ScGbOZXON8CDeozf+GPUu7NB6Kz7Ck7E8cDv7A/MbufmqmaPdtjeuHMVLlsf+xxOkPNLG6y43sFBFPOJFsoSpZkl8b4HXL32fLHSNWc758inu4tkxLFqBynMveUO0+0noYwjGIt/IkskUZeqTFFHEx6rGSIlJgxTGWh1/SjcYsv3XvGL6+UU21ObLq6HgA+fOVDNk5ZW7dj4+kJIyHyMuZkNG4TqE7nnOAsD4iB0fOUCa+o5sPkSoqd/KI/Fq3P8Liy7tE98+Sga26AfD7Cw/QlOGaznydOJyA1RzppydOZU8XeFt41gVy21rpoxgAmyyNh+xzojgERiWR3wrILQJLVEHDO2VIKhlNe8P10YYy/plTtPLrjrfNGwcF7tZJjpwgza8idaoks1CE2ub+liwI/8tXpNUU8X7KA+Iq+sRBjTgXV74GxshO0XUyHNJafy64O+1R9xfKz6QXymO+sOa0P1/R/eEJ2TmSA2Sw4zZ7TsA2e7o4DswDDOebQX9txyjBnnpzhN1FoFW8CTCfctCgDncZZCShhaUJ1wLF2xHXP9DTmT9wEnxZBdxmDrFypp/NVUGgr0wAFZGhvNL88Y6qj5QRVzMpy4OR1lAEhINYoAaFRAtE7hGF1u3EUJtmYq+S/HVoMOfpTMp6I5+1+E7d8dxPjeVWyc5JySt95qjm4C44mWy2gsKgzleHgZxFyLMZOb1EXZHanTJVUc7xQhkNUHzdkjlTBsT7OYr9FcXz7l2ql2ffLsMz3Rz4Y9PfgrUkeMn8hD0gw4vSD49wezG3L2dAJ2RscxWMR9Lsy2Bmwp2I3+wB76gaEFU+2SQsy+IH3pguJuTzRcAz+uJkjrhnNL2qhtR1kRPD0C/IdYAC6X38/tyHatYV/XxC3VIuJuOEudTGci7RZkRd6GjctR3KL1v+vDIMcueG/kV56f3uJAZkljVOb9m6IiOyLKiR/e2lpznXFXtYyNBHylkuuAecHC9nPO2rgmSBb2tOUQEZthDL6M5XiTJjxJnfHknjFhWHclcA9ARrDwmZbKmEmQ4w+P/8aoO0VHoM3yIWf+1QnhK62cDyL4rnvRiCq76PFrWTf9nSvT4ZwH9+MauZhuzk4CwW2lAiXjyGsn9V0bJrRN6mHJz5VEqwMUXPcujYzlFj466t9YtexAFO+ij/tu+X4ZBdZ10xgx5AultIKe3Jgzlfab6ejYG2Q/V5S+dJyvwMdaA2Jj3P7zmcY3s0ENmePHeq9vGAu3YI37ZSGVbw64PRJdLDQoQJVG3lm6zlBDSWxgOkxyW87ubCC4cUVLvIBO/YoJOMcLcRh7wHwuvepmTNhynlV8Gje5rTICs2DgPlHC/Gf9EPO0up4Q8cuAAQehd5LKrK1z4KzY0vhe/Nmu50sWlVtxxRDbI7sYTruOyPqzH3e7I8p4JeMAtnEYsfz4iLifFE4D3y5WLwXx14+PMN+emuF/I2tDz5a8fdnpg2/f5MUrYMJMz3OKwRv2dWRrdkjxa/u+OLOpaBgLgF0nRs6hONf9ObzrWaqCsPsPTiYXNC+8cPpk4Z/3WL4bcLRr9dUG1vzA9UNb9Mo+2zAaeLJEyPka9NL/mKCXh+E3sTy1t9abVF8MezTR1dy9/HX4QVO6Udp32Sw2q9Qx4NeCiKcwE5m1uD2Yk3vmhT8dlJzJw7M2OEoo7Yk+52B5R//SqJtFcjnaU5CQsuJenTh+ZQcNcOfn/B+V8/4fCfPWD7ny0B73H7tOL6SCqUJJNmCkcKqn0OJIXTVnUyp0PWmUdV2OrEv2yRP5Kr1H1YEJq1oQHB6+2UpyMB24BN/nIMxIhw5G0WP94b5DWuZta0V68n24kVv7ya85iL3orULVnEhh3LFy1vwht5j/F+hu7DwmBJnHa2nPoVXsC0Hnk+4Whbb+05eZhpMiBtuy6RRhSEayMd9leWB1JSu2sjrBmqONJijDQdo5vXvfs9Z2Axg2Ys6BpC92dK9KJGkkp8/8szT9nTmaKAI6doCqqF7LzC2wF5MrIbSAcJWVqBeE+mi0nv3ttf3g4ChmXPGdbMtk9jetRy8KSrya6JlDhR8yQF4waE1z0/Q2FgMkNCuCkS6OJG7DcxmyBvtnAzAohdzHBFz13hoSVZ8w0dC+50MeMwgjrtA6pf13xIp9SBZvWFSp6EDc/azg4g13SIH1fsl8Sxr/hCTdvK4yEwHCB+EKTK+dJPzwGLT0AXeWMtJyIcZxzb7k8lDt8HnD5yyBTONxSf1xaP4L69of1uzsrkcLGZSZqNbC3DneXA6ODcKlA1lCecUbhrx++s7dm1PlUURWu6q+5S3qMM6Ks6cJ8yHrJmljwHlRvpCsLtJGdtTrBtelzKMr3pqV3q65IrOXzbmqnXNUKOOlzgj1eEV4JsbLIoSgvXJSFtJ8JdtVT1sCy4rDYe+yaEkXJtWaKNSOT2xzPK1zNvNNnJ6f+vu1HqNjmkUKez05u7LJCfVox8/uHRVi2KkvCbfS+j04f91oqrhBItl6khVF2tazvD/HmJ3EpTDqF0IUy97Jhql4Z6Wn+8INUlVzRnEXfnzICQ6cBe8u6BKxr/y7MAnZiRl354RPvpjrfa/sIB0I8fOTHdEzyVloJ9VMNvw981TWq6GcZDutSvrZWlfitT68XY8ICoSooPZJTvzldqUYF+Z5oSwuc3SyZK05ENeQBYChW6yIyGaweUpTFqXUv4cl5vKBD5+C5WXKKwc3CIQ8C3wOXThMPAQSW7ZQes5gj7mw0U05QuDr87Ufn13r2fevoeubPJ1jiu7YRZe7aKw/3Dx/9DzncLY3ToLcCHpTRHOf/w3HM9VGB8N5MvQJEWI17tO3qvoGGe5wbueEb3w4NkTdwM822lzHTMQEbFVQgO3z7IcYXiy4ZJt3/5Bfmn72TMnWAMjiLIDSmKejmVSGzrZNkKeXHEkRG86VN1rcAgGJGEya4w3QmjU+RtADgqb259XJjYmtKQfxYrBn6plIJx9G98Gu8RF6wO+HNOhPbNslid3Zpxb//EyKnaYmY/t75suQzwlxbdYojytzUfujbSPHy58lDUveC1pdfx25Z/7P3ccuzjakJ7ljo61Bcagq1q4Lgjy/uDQZy1Iko1DbB5UBOp+DODYOA98NtXPkODQf8itx01tLITtNVRIO+T4Ogkus1bH1P2ugYeWeLplNSAv+JeN+2pzCEA8EUOAd39BLdFicHnM253A1QbKluKLxtDVar7XllFyghCVcKtdyQSaFx2ygZD1pmFIUsUfy/UOvdf/Jt/m8PrDtd/+YTq7WKKDIXO+M2xl0Dpfk9R7JHZfvoScL9H0XRaTvjvAiLobokKUOPqu4TRVJcIzxtKtWTU7SXqWEMmdaBiLMfjpf/S6t6Rnge1OTYgdT9LRe7j2EQnA8HGuylPzbYzJ3quK+4W5WUgJLY0ronukPzpYtiCOB/D3yhSsD8PoGB9Jqr+4xntHz+g/LajqXjFUBdd6+QRyXK6c3THc++q0HCRw4mDDP1iZfihlYV+sWnFzPn2uzmKN5HiNbyBFYyrfVf3/Yocn/2Jf5dgQtJ4KAORxspDxfLh2tCICvwOq2H+Q2GrxnFFlY3oH8PzVjg10XAZpgYSZIXtUpUvKhNVVdG4KExOOZDSpEZ43gnTVfbRowEP28KbrM3Ypfsjbv/JJ5TrMyfDrxv2aDlzV3q6kSOkrhHpUdOCmE2/PdnO1C6hW0dI1/Zsv0v73YqqMVnj6U7bIF/Bw3tZ/FZvF5LBqpJlgAfr2PEAeN1QLvS6EfBPMN+c1tY08N5sl+X3F46L64qG1enAWPwchvQqgrA7idYOHPLsTiyLvISBKB6xDJbF52Tn43YHYh5k3eBy7uOyxoNeHnTgz0OEvIQkliQ2a/OvOQ4ArF9UsK0/8GeklvXMm1qgsVl8iO587UvtsuDNdGsRThQAxx8eUWzPyBvumsLfvvJAkjWLa2OfuBvo50NiBl5cTaw3c100hz26yNunLBAXIwFmVUzajYlR1+K6IASZh4Q+VCxxWSrHD0sOOzT5NTEqOk3HYj0TLWNZcFe8lR5+Nekf0vnIxNTudOHuUYy+2hdp0I8T25lWH7aM15dlOjbBva43AEj09UlUNwV7Wu9IVl9NTMStulD7XYvA7+VhhXBukQZSqn68NyZr2F0Rhzxs82zMdkNudPZ7Cdc/3RkBG0IIgHMoP6/729t7FK+H/sJ5dyuneW/S9qpe8W97XqMpURok2EF3ucENB+SOrOYC8eHplSZV/xdu9/z/Be6Uay6qs3Pc98j+Kz4s2IuITSgPak5a5QGkC4CmTuyoEdRFPBvZtpd4DUgLs/JTLgW3O9rPrtCnNNV9VMXVwJVgJ/U9cgRfMIb42ogcjcJgDgTEFzef8jaS5KAkD0lSOrLaUS5U8KepQKxGpSTtBqQ/fMey64PEea/mPWNGMHxa4sWHuSA+IC911bvdAf73PjwgTYcofnsz+FTxvBP3dWVZ7+GgrnMJOFFkyJVKHVOqvCvTXGRikQrP04TTZqfEa++B/+e/Y2k4IS0NzY2O9wvbizgf8zY90zWiO2Ery7zj6qMsoJmTCMFG9nk8YLZESqQBHKiRRVX2vBWJHAubk4kRTLwxotAjDcQgHRyKLxv+d9uIbloj3k2MJh4ODf93MZ9rWV08s5cf/LqHP1wY8ql786pA+91KSnWBVOvFIXHoADgIet5ZBen+/l/9N9nJviN737vAL01/VUL0kUofC6FHl49qkXKJubahP8ydiTnQ/Q3Lw+r3kjXxCfrduUfMQ8beZzom8nyCbj6UDPXefawvgEnd6tLihrNksqviHM0N8XEpQSOijBdOSRqWCF83nLRJsGM+HBlEIjaZ8LLrZUbzEUHDVxqPkyhz7J9aaF2XG1++JvZIvkBJWZxLxPWMSauabacOCC2v4UFnt/bQ74zFmq+ofZYGzriGL1GeT/i5nM5wo2FfxipGRM3DOQuT9ETPozJU5KHL8mC7CyfG4XVvqVhqegVE8aRGWI19847f6/5i7QkSxF4UTVqnQTDaT6lz38gCKlAXwjdyNjnZ7yICLjeTLcZ7aozD647P4fkihycVXlxpCGt2UsFfWsQRHSy+YcgnwcBHDpR2R05Pz03/HV9bIw6oltlvjgbqtT4ZFOcr5j5PmRyGsmBGvH4ZXpbBaViaKNfwEL99s8Vmrov+gz9eTHEC1QleW2B/ZLDJVejXoqTJw6pnu7QdNXO6k5OdXx7WyHcL6z2LtyP84YTukYxRCq9dr4YZCtdUrv44rmxJrwk54XVnCnoDBiu30ntGUV94YqU/fORLXDG/ggMh+rnC+ihUb5nayYhfUev49kqp1oI+QB0k8bM680s4Xa0Erz5veTqr2PyFPZLX/HfBKthtEagyifMBXfjXWw8EHtVI8wluf3w09ipWc0oEpf9JlSTf6s3RdsjDUshpTT9dnQhKTxwJnFw29rLB8YBzXbS1iMGX1jvSpiOlWpDD2xbcYthVOSEjv2WEX0uuRQh8QaQMx3AgCqxB79qRKTV/KIf2acagoBGrjvCyRZpPkL1D94cn/v6LSc+zyZnVmVi0in//M/xNpt9eqjaZrKtkU9GXrmN5TqFE23++8wmwJfpf2azdasyyPud+Qj8bI3sHr0EfuWBtr3AbgMMTus4T8o8feLstZ+z7Ko61nTbzt5YPj0RO4W5B5cx0xN3JreWOSAyiThNQS3FVe88p0q3lg1oVvWlTRNvFy0FCTAiXdZK5rg8Sckb73ZK35D/+0j9E14blQhd7BOBoQPTGmsOWdD/vP6AE1vtCDFOamhLQVCKXZiNhblZcITxvkL97YDmut6wYkLU812ERioKod1HO6z95MeWIPiX4zYE34GTY8ymP7LnLf3om5awMtvz1myPcy5r57OpquDTsaQS9x+FWy2HWy1o4PK1NIvX2o8ubn5c/NbZYzsGzkhF7TlyOqa3tIvzuSKfH0x3yciYJuVfb+yq8Nk0G1hcZTKmTAdhkhLgao1sJOWBYkTwmHNo8kH2uyPkgAyXXtDT7HsX82gqBXSLwiue9xQqk5bT3JUqkmYsR+fsnHnalZiPqoEUGfKP+BmbVF21wp3Apf75ycKQXw+5IBMfXNx7MMwn/ubXA4QTvLg1x7gqN1ZSZqfBg1L6voSG3lqPsQYHwvOtlZl6SY3Jm8GRV2IeTBVFR/rY2F7GWSgBoW8m0yeiy2B/OnAh2Ed3dGGm3t0QadBH+yLLTsgHEuFpsLwjftvDzmWVUwHuEY2OeLNshigrIn6+9KPplw4FKIziJBSVeqkZxSvo6nk2rqB4zRrl1cAVlZ7bbEpCxpUjJrit/uAM+P0s/1ohPUtQb70BUSpVL0wGF1ApDurXwxysVRpUEwny8N8sLEqOgi7ej9NsVurlIBAsPNx7JtO8klUvT37RygFi5qTvUS2P08lxXCK97UsZkEa3YwFSTRQrnEO/nwPlC5U5Z2K65/bigQEEGbGg7dLMB/PEmYny2N5qYRJ0yp+MWKhsCBfUifMZ6x0P8IhjA6423JoD245IL/W9rDo4qIcTtLxQYCIaDDhWqt1SnilvLabjsItWah7ZjG7OmYF7xjnk04KBvNuaLL78LwVLim13N4f43//n/LYfdBdoHWoOvV+py0rNaVER8PFud6w58ENP9XNwNtHiolYkfku+lRt5bKaV7sO7jEsXnNfJ0hDSuqZBpO+S2Rfr00AehiNzHTpymZZ8ngCOlh7kuWS5CvJ/2GQVi63kvBMgFYbm3n+4ZdCknozmYvbMpabyfcS92PHP4k1If0aVMG2WBxGiZFUoBd/sT49Ckl0nzMV98jfeSh0nFyPpiZJneGbluxyAWtB33dhIlALmJzF0gonrlezoJqTFo1TtzcZqOoXFyemhZXLksmlNVIGyPIgLnOsodzv0u+HqjhLDwKN5O0GTj7F2fux4j+8JL21vWAE7ZRxKkKgfx78ys3jPX8UwGD6VoshM8XSzazMXcy/a8l1iDoeiW636VNB/zsNwfCbca8blRlwoCXSa5CPzZAA7LpCLKw4oTViXMiRBcf888FB6u7JRtZiJCfhQB+PYK789tL3A+XXjlHs6WaOuP195qH1zvWBYSV56NaVAV9YyLkfsXGcObJKiTpbp3/SnuWc6EzcmmbFo6xKcFsJpTpaPowmtjGkQ1DuciIK1IByOqkB+wP3AVENbMh+ALImg6nU5VdEPExznK50MPZC0CbymRuan6JqyPnNiqDEqHSxp2IpwVtzvw4SmDLGP5u8UPS7iqovta7C0Kb0pTEV2fOUhw19ZAWdAdXOp1ujgzMcjlzJN0OjbKGJH2XT+JAwQCREd7Hg1MIRTvZ1xGL0U0LC2E3oa2zD6epQTkfhiQabMEqWjJFU43FK98YP3hZOVxelqZFzBVQcrZYPvdvJyJtpaDM3c8y7T6JpzVioM+Cb9R/AhSslmBayPw21d+B+eG5eVU8BjNrRdnTEaGssjjIfW8x8YQKVx9tVZq5uUMGMo+UFYxaSByvK6TQQ4P9byYQmMAoBWbF/Kfal3lMnKzKbzy6hVa1D7N5GXgLqyT3RK6KPHVckqmhPT8SkDPhbs8p/Fl4kx3p6vdWFlxboK6zyVP9bQYW4acE7aLuzTwPz9D05p0CqijZXiuIPy3tfQRBfyRU9M05sOVxgO7nfVwMd2rvDCaFegPV5tOZhXwruYwQG9dwW0PEgzZ9D1bK4GlGsoh7BHFRNCpXoqEasAX+PuVqVXM9DkeAB6IS0HyXxsbZvFhkpCQlzdoolS+W8B/fePD3HaSvsrFuL91Juxuf7yHYkcsEmt/spda8Xjlf/iNf3fMNiDRUblWGZrp4U8UascPFC/bKqrtbMEf50NOsUe1+TWd0OrKz0L/LqXCEJhyupvxxtNglTIIvZqZ8P51R9TF6coyVgdGIu1z1xvweM+/uyqNdp4Lj3y+II0GkkfICX/YHm1vihjpSz03/Hy21Mum5YQH5GwkP7OkK0nehEaqhc9vnJCnDL8+8POWHbEeit33K1F6VXLoDuH+/j/9v2aT7lQlr2S5atOoRi7ZP6luUQcSCIG8RJlO+Rs/fLc/km35suWHKW70PB72jmlRsqS7GVEL8svpZNHQfjIoScLP1JIKAD/4X78Cf/6BY/RbB7fV7LsOGkGV7ufQII3wujdWpbn0rzdOymrK3RjkWBIO1UaqWt7xK81gLAZl10S4lzXLypgQHxYUEuSM7mmOsDn3pVjBAyLIzos9aCc7UUrvTPGiDzTQ/98kUsydRBBfePJBZzKwKIPlESB4xszl/PvgEOdYrVxv6FZjFK9Hlr0iOVPmTfdxieL1YGWuuc91tXMhazQuJghfXlmSa3swH3GnqG2H/p7Tcf98aVVUBNnNeckYSbZmUAlcmo9NqK9SMHoVKZVLsyH8Xz8j//BEzow+IylZIrINkMSUzUuEZXpcTGytpP+5BfCoAux0gRsPzQblN3t0P9xTQbRjGevOV0YbROIlNV2YE1tWZt1ixOxNKau9O7+j/dYFX6ipgpM8CtEIuo4TQbc72B4wzyYsIdd7USo4xA93XF4Oak6NLte+J5HYKMSE7vsVy6JMUK/W00jZ+hMGqCTu2ZyDZq+ZPvWP38OdG/I714yO1ubcb458WEVax3CUYIOEXIvhc0acod+dbWjgNwfmtovlyshvb1vajLSX6sT7t+DJnWdjZpRPWc6F9Umo4Y0sxTnJg0ybmWXfrwWSxJZpqcRehhRxdNFKyiQaUTorhrb/1Giz+LSwFCB3uclymiolvztxdXFpzJHhD+9iuKTfLF4ljGU04OEqOzxqZAd8BoLnYTKi8N5FeeCvnTFU1JRtFPL5lG2Oxqd531MIRGigZmy1rTH1KAnmYmiHuD/TIB2+bYGPDyxvJ0RcKGYE+rKJf1PnA4rTyEMJCq0KYuaFOaQDxHQ3Yyn5dEfHjgxr0t2MdquN7EUlnMbvLzzEB4IzGdWM0hvS7BAOMk94XPBZA2C/tD8yEzv85Tdeq0c2rqnm7iOXBT/A4A3xjo4Z37ki/s+frnbV+8OlD9fQWj4QZe+Pt94FrfiLRtzLKVmSDV9QlqUqMdOMCt5Y0tDq4lp6CzN4KmtFHO/asyEmYLPj/z4bC2Dp1nv/BCKVhqXJoPJkJCqJKUtZcYinUW1lIndjcpNIH5lHNfzxauBiBpxE+M2BQ5RjQwyguCgsQ3E+gRcvJQ3P2UoaO9SOFzb/F2Y25DHDR8LmYCud7NkCYHuwRX5aTNjPFtQkKhdIf/886FHzTqbEXgXNjk50HcnH1cQCXHg4AXl/oEZ2KSqjmmWeaj/zeIg4px+0/fNHGIBLlCpuf+QkczkW4cC7slinz+K1s6DYpoX7tubvWAS0H+ac9l6EZC45J+GZ37trWuD5jUqcbwLOlWdEXRtmLr+1CK8HvqSbPV/EW0dYr9iWdBeqIOm4nPJCCJ5hsmXR78ovbK/cf/Fv/m12iaNo5dVDdifZO9NXOtU+dr1TWOU6hQp5RVrmN3vkWwsn1OG0mCCOSpT//NKXoyJMjfczsUMV5pDQf3T1YaqcnPlzyVTJb47Iuz3wkVZ/Na8ipl74LYr0VJfsE0Vix2Wq3CpyK6VBgfBtK4bPq00Q83oD3K84ERZHRZAUo7SYUkUyHzPZqJYXTEpW7T/RRQMhhc2pzyt89/NogIrtCsuivynqEnhdw62Wv1PD5DqY08KyK8bk0+TVnH7MrkP+/rGfuL7L0FPg0XsorcrnVFmiZS9v4LafmGt/fb31kGOgz2mcj+j8OJxE2dMDnJxkQujzYSUyYCh7Pvx1XzJK2aaDH/25DdsYApmfih9ciEN+UHD8D4pNsogZEAJXFDrRlbbHfkZ57oxyLVVK2AjV7nwF2pYma5n+/063KjeoLv1zCDzENL5hNoZn+dggSlR0mg376d5kyMVncIiPy/72SLQYFV+3FBfLB4nDiVPQyYiJo3LSuVuHsBeb/mJqIuk84PWMQsbKMv0jh7I18KuGdaKu7GYDZFXw8ZHo9e/uOPyQ8XvYnRhNrZCoVkITg+9NlCLszlUJ/PLFBkO/S3MKng/9mJAi/23NkqyuyBFtiO9T1QZlST1OUcud3FB36X994Zee6XdLo9oW3pqKpClCGjpjt9F3T3bzIXjgrz/zJN4dbYnuBCaVp3Tsp6eVlfnGcd0fzWOZa8KYiMKIouIQL2LmYec2e3GXAKqcei+Qj/cswU1BI+VoeN7R7zcZmV5S8/bid3fm8QMoGHCnC/D1hQ73m0jTuoQ4lRWUEvViMgAVvMDAEvgSiw8z3VE7yrxHalr9Zs8KbDLkQE6Sb12X+gOmaW0aG5e0Til0GI7xeLS2tXw/ZqTB68HBi6gSBZbojwU+5uTzzKMB0krWJv/wh/9TNl2lchCLIGhxOZEzr1sFLWGzAx7vLCXX7Q78olUxrik2YitRPx9PbUYU+88vcAP2BrYrk9BCdJ3sdYJlMWgEdbqbUW0xGcpkNpk/K86HhOios3q7p3VHG33fTyo1jckfLiahYtBkZw9EHtEJkn54pAayLHqt4+lCvaWcknlQkx2it4AOFLRkkr4PksWuomeEYJrJ8NurDWTU6mSoQLXZyEPodflfFvzeZGf23uVgu7v13oY27tLS2RECoVFdYu+rp7faxEaD3mKkmlstxfXnAixPT/Wt6DqkD3dc8Ov0VkJ+3PHMQ0pu3zysBLhEkTx0GS4ePvabFfsmcYWEL2vED0suyeuq3yXqiywHRBYXjCIsdQdsntPZWJbrfWtjWmXZFSuvSINh7DnWakEP2fHAQkGpGFK1k0zLpfpRza1KJhE8kRTKWcnDim//RUuTINIl/mdpydx2N5VcvutN1DOBQJqXrfQ/cqNdmPij/r9cUdcYnjdEw4fAv6+ReKmhgHMCXxj/tuXLJyk9ujtL8wnidIC0GCOrqmRQ0gEw50I5jWpOJotAVYZAcJn2ezYIrK5U+En2uMQ05UmOxUxw/QPi6vfnfvFbUcvorjdTdEDE4pDsvSwrE4U12e4vph58m+SGCkFsO0fZeyWB8RamkcVmT2HweAi8bfnQSZmo+lin65ETk3UZEiI3hNxIuS4R3kg56O4nfZ8+5TApjwfMZxS0fpwOkJZTWymh7Y3JGoCa7ueccCaJo54MOA8Q5maejPh3iYTLb48IL1tWPufGEJe5KgyP4q9UmgDgimQy4mE4GUq51/QCAiFv60uDtutZR96z1L02vH28FwfHiOunYW27agbOVkYtMNOxxLDrMj+P+HwFic6Gc72UTUUO+rMJhyYtJgx8HdVoVyN4d22JgRfHeHqU3c75wpBMOcW07wjP/IXMcnO6Ij4uTYniri2HCZdGruixCbf99iiL3Bp42Yi8Sm7A0QDdjAOZ+LCwF1tPklx4Y0bCQ0qcLfCwFMX92dicSIkn6WTEh1BQEP7IYUbzB/q5rKQpOYk0JY0uZC+N6Ea5FgnrvQWiuO2BGsvNXm7CW090liwL/hy5h+3GbEGYSIk3hrz8mvCaRgPqVmUEH75uzDyMlJE+PZrHMf2LT1SfPM1pLp6LRUp6+TSRJXTb0Ri6pVZTT+U0I7PEN/20Ugce7twQPjwQb9szczrSdAiLfJtJ4msjmtwu8cFuJXr72spqpZe2Ff/hN9FecoAXnxZ2o6a7mWgqW0FCnkUqdxb9bWmCfb8/Mw13d5TVkhDjHha2J759v6C2V+lq6wMvkEjrWg4Cfzo3RoQAQJP28Z2DQ4Tz4e0A/21tCVeuFXWTwsPEdKwIx/a7JfvglOl3dOw1/aWFP15Q7K8yBa3ZzOeCTE50HfJqjvS0MhU3eRa0nPDEpDiZAKUbuvspF6llYZFX+hDT/VyRLlVQxeAGNce9osxAziiOpDEr7dgmpAD8+kAhq9Ts/thI70WOigV2anz25YY0KKh+l6mhftHFgaLc7L19ceF1L270m5x+tDy5JnJANR2LuFd0mQ8LaiwX0147qfRv56B5Ctk7S5/yh5PlJ8QPS6OeAZB0ohJpUsG1LF3jcozc3DgxExZNeNmy9JRsjvbDgrvGIhhXx79RNwsPHqrzkegplYYWTGyh2I00HfTY93fu9rA+8uFUridgAwbeCDwo85BOdSeTUH84SxaF/N4iMMdqTnEDwImtrBLcZs/U3/PNKoDucc4Qmi4CzQ3lX79aXgZypqro6Y7PrOzrkDPw9RVpXKN6OZkmFjeltHcWPvoe1JVVZSVAJeXjhg1LRqXW5SWldkkCfdL93PTSNAc09qIVO+qO+X9rWSXJuoK3NrgHBHjVuzMVCd0PD/zDLqIe11G+NN7l5434xGQ5fbzAyxLbNTdgPjG0QA5CTFZ7kyN9OC0J9Q2veyEyE0MX3g4I66Ng0/tkpfi0ILNRTlN3unCq+Q6HoLBUpMyFtxgueXtyf5gWE6o/pFTRcfbt04qayQkfJL8+iMOdKpA441Ss/bjgxDVLv1xyR5gHEtE2rClbcq7vA9uOdK/7GSV2mbmGeNv+TuxefF7L7yh075+f4QpxXg8H8C9blnmjGljMkOqCITjXRnIeZBG+moqaJTCb4e3AXk/KIXe82AuqE1lK4DobNqQ5SzNUJdqPS5vWMqVXCNBbQpqan1YCSsrQbHZmXXCAkhYT8yOq6DyNBhRRt+LBXM6sD03zCX/uv31FDp55DzEhfn9P+rkqfz49cOSvXNFWeKKPdwi/vtDxIbDovJiie5hRYhel1xUfYZqP6WCQl4gM2DE/UzEeK68VMSH89kpLWWamCDxxjmks9L/1XqaeooYK8sLGftKca+Z8eHM/yH6CfV+0/Y82nlngNEoc9ttDTx2TIY1aguL9VGRrLP3S/VLy33iV68Q1FzxB05y6PvVuxcUE4e3AL06wbsahaTv+XAL3Sfdz/jJH5hb6zUGQ6WDGueg9/fZIKdP+zFMREPkZS89yfSYl7dcX06garGkyRPnLG/KwRrG7soeSvsYfLiY7U5S7wovs1BQSmj8wbzxXJTW1Hx/sgMnB9y707QHxfob0hw9ITyveJMOaaovjxcrx4vXYVyLy+3BNQBO06xJvWhmmKTOGiEVnulnkjG41tqFDlgRXf2P/VX7tDwpkBrEiBCvfq992nBhLZoLqT+NyivLzmt+d7kRlNeXEC6qHs9vsWU1ISZunIyLx64riBYCyu5yRvr3A7y8sCffcg+r0O00kbamu2HMrpWBQkPWiL8BoADy/8Xs7XPh95Ey5H0A1i2hdFb2SppLVodF84jbh4Sta3l9fbICl5m5jpUrl0T1M+TwcG/hccBASvm4kE49Amjwe2hjVXO5ijTG70rc3i5O20sSLI0JKLX8k9o8fVIKTVQWRElSG+9OFO5ED9aS2y5Eam2WsnB4b9mFuLfi+G0/1+Ofv+SDL6DpsyOH339b9KVcE9roaljIoidGrJK6s7YD5lGsVnVSVDOfggxyEeVrBXzsra92ZCcO/i8K+MbrM7Y50h1wa/uwhwO+OvAHbjoOO0YA6wpy5OqkrhPUR4XlLB7XGo8nELjzvTPalMi5/OFEgMOFNHe9nclCeevnf/kQmpwoSxgPpySuE042Km7KgmuNMiRVdC94gs7ksekGEDhiKAP+27ZmZAwbgKLbR7Y4ciHlve17N+ktPK/a+TysO5FTk/uWZh+PXFzooZiOU//zCYc0dOaFpwls2T4boHmaIjws+Z8qWEQtVHg04zKnEZb+hztONRyY78/szp5l3SySZlKc7CtUJpSpZvgprp1uNedFcbnxHFC61msv3yplE/v6RP0qMfaBQ5fn3xsg1xHt1gaEJywJ+Qy2lxup291MEMTzqWDqXhAGFzYkDjEihcXYOYXPgbXdu3k2OBmarQQJXHLreKEKfQqP4dnkxcvBcRC8XiPcz9hGXK+J3d5RkvRubp6rgpFVG+Gk54W3dRpaUjpHFeFiaRM7G984ZtsGdr2g/3aF4OwoPZtKPksXfFe8mNuDIowERRrs1XQAAPx9JREFUG2cajNv7Eaove1t2u+OlT5TNVLWoZQUxim3mRjnVtQW+viD9q5/6nqksej2s7BH1ZuLhKZK09RZ4vDM5GETEnkY1Bci3Ft33d737G+i1mLLs1t2ru96sd87OyQRT8t11vK6qI/WAZjJOVZFCp8zRWDeaMaJwLqdTY7EWcbWSeKi9z02UYZwqtmyVtD70FPEyIAeuX4qtoDq8eBkPJ6SHhZmNFaFJzpFY2t62wP3SFEGa0cHwz4shJ/3har4+J1mWfnsSFlHTt2DjgRjVB+ZZdQdWYfF+ynCWNCeKnL0J1RROdJLucqMfb8BTOY2lEVfH761F8W0rCUADOz3C84aDgdddX46VxTurU0Ialcbkd2JeZcZcNiGuNsrxfgqUFSdUW3rN6MSWvkGabZvajtnvMWSFky54SMKSlKA69h7WPbYhJbTLIdz+iPg4t0DLPB5KnNnZSjEdWrgvr/yy3r18fndE9SJj+uCp9l9NbXhBJRB7ru5+atpA/kHgSuaP3wsSo+RAZlhRBK3szVtrin9/vSHNhugepoxnvt7ocJEDD8FzH6am2Qw67nWAJLRpeG/G4Vx4syhZtNrrlr97ETisC56HQEwCJO5sIqw0AeUM5WElA5qL/dm6b1XsflpMkeZjUyi5tjMiHHvqjPZpzp5rRpFHWk3tQnBNy9Lu2tmDrgMVjc1G4vPl9xf+bPsjuscZP6f5lBXTYiK2qAv89gAXiWmkgftm1ioefiK29h5xLlHoDQ8yd+sIMhaHiNsx8l1vaZ/H3G2lHx5lkU4zYpZm3DUCIu2isEpuVnZAT0EJaim2F5YxsgJAVbKWd+J0r0q0P97bS1O87E1udfvpji/o+2GBLIVxaym3Ws058SwCk2ADF7ZpOoZvWsMY+M8v0EzDqCPqIMiL3ZGN+OOK6vzDic16Gw1zVz4fhPURUW4IsDX9q8qvugj/tkfxbQc3Hkm0NCG+7krtoWb32RRZ6Njub79ZnFYODsXrgb+X6kCvjUxoWbLrxJL+tjEMVqzA3EHFqLjtCcXmzJ0gYAm16Lp+we4c8uEo08fh739GiTtzbUeNJDgscDeCluEcq4Yd7Tbx0wN7ulIx9VTWGMNTy3GVMbbRLF9OYr6t7z03ojS5MBVKnhFWZKk3BQ9LlL+8Uoxwvlk/n6uiF4oD8E3LBz3y8E8TsoT8/gJ8exGAlaxPZhN7gRTH4d/24kOsuRctCCyzzEcRtGuMgIq7i287Wy3pCshJFeKvIqWTHWo4NExH0tsN3tsJ70W4mquSux45RZKg0d3pgrQUloqoLtylQXg78u1XrWMX2efIiU0qdkNEnNwkKAuUn3cmR3JnGXRMR8jeW9qP9aDOcRJbB2iiT5xJPsSg4nha/HbhyysxFHuGtuTZmKoQ1U6u5mzsR2SPUP4kC1k5pf1GHj6Z9KXp2OBQFsHWdtaToCxYyojsDeryVn3o413veGg1Yi1IqmzPiDHejZTzkBaBwyGiP7QMQ1n01GUNDFXjrUR3ZwlRcZMxNFYbbWtQLZ1O5rJghPT6YEtkyhLFFzqkSdkfr+xv36VWZSnJ8mTIXeD+ZDI1dziTYlYWvdVoKn2YqFdw4Z+JX7+yzM+ZdO2R5Eycb3aLq7DCtVwVubbjIOydm0TXYcyLZLWR/vyJL2xd9alH54a7YDEJ58mQGlJRB8URtZ+2XppP+ltTIMLmZjGB/M32wu5wFiGEZ4K0OOLd//p/9X/P5dedybUU6ZCdY91+bZhlJn2CSsZQBODLM3C/gibcmp8KYKmynJnKnsyQGnFcESg0n/QPtfZ5smjnFO8dHg8w4rX5uaTp7ZYj+DbxBvEyAh/VfKhe10g/fuyFARI97CKzvt3x0peX0r/o3iZVRS/dEomZ/e5yaLjtgeNzHYjknjSG7R5YUVDgvq3ZnMvL5A7nfje4O9lk0G79W0sf44VKI9d0v5OYdfMhyi8b/g7aK4sA3G0P/GzVwyh9kY3jZ2N+ZrdIbJ8wQxWtYET0v/4GN5ua3I1KphKKwcet5e94t4QlJctna7Iu/UdnBtqTKjtVUIsAhR5kgEqp/7whuKos6NfTg6ZLMPK5DoJEfEBrlQiqj+f+4FGs/Jk5hUi5R5psjqxW3q2z9LM2spyYjtNsSCGKVA4AfifJg0y3mRlB+d57Ubd9/yKad20HX37Z8oE9nE0MDZ1MDmukT4/v/IKE2Lgu8i1/vOeUqKEZV6Gr7nhB/niHnhg16jHlu2v/A0sCEB3RXpAYIrSWUbsTzr4TDr+62HUZXLweEX55RpqMcPuB+yov8Vn50xN7P+dMWuauzHFXgnce1MDbFt1UDhHNBM+ZC1kZAFD4CxPo5rpE98MDe8+vr7YiUQWLm06kxGkAgfmi7axysIQlBfmkhDgdGILC789oP87EAEuFTlyOkctAAfyYSVUuJnGueKYozcaU/ym49tLwu9KHC0DxvOeUdct9ZpagT42iQ85wo1EvhEjZHAapLky6iIeVWbi8Ji2J7hUAb1jRryoJLE9H3MVKLqOKk1HyRnJ7ArHy3eKdED2audVphSElXi4CokSa8z8TDo9+rjHx74p9GUtL0lZi2bkC01K/t0sN0P70YBwZd224HRgNbJpv++cyWEvmj1dK9jKDbCFrOzhnFAJIAlUaiyPewiVE85lHA2CzB+4XfJNlisRT74D88Q5+d6KVaH/pTxutuYUFo/RpJa5ZQpCctF53PzrOdo5sy7pA2J575X0n4Z02dOCLq7Kq3NzYm6j/TaE4wndUexXOF8Tv74WClcxqYo780xVpOuKwQk5i+x00Om29Q/5wZxMx2/upY17MvmnA/ZWTPiwuJlSxiDQK0pdqWIz2l3FS26TZstslZ8FCSURraKe1HEg2sLh2dkvoVFFByCpgx/mCdL/8najbHS/QMBJ0lJNBVCNeALfMYRcpmL68z29CWKMTpluNOTnuItqPC5R//UrXQPC8/WfjHn+owxG9Icqitx29rpH++J3t0DTsxhwx7/pWVbtkceGkUSUrKvwuB56BoX21pkoZ/7I1A0AeEnCsVZjavlzMIkSoJRlYcgBln+puLffREyGpq4dWK8R3iim1Jvk0YgKs6xL5LJK+mn587FFwx4vtv5zuWDyBpi4KFKcsJAyDezH/vLHBg2Ll1bTI5rWxUsHtjxbo6dcHlkdFIBsliy+x8FzmOge8vPWnTlWSBqZmUUHIG8vxnfYSI5pVbfk9EoH3lWLguCKaID7Me4WGBqacLixBK5GpqaNeXj5VTcT5GPj6Kt5DAqiUIpanIysx3Y25f9yTXm06GnYXO6UVra6m127V7/CUvWKkOKlQ/O7ME/t4Bt42VurnAbPn1TWOQd2/fFIacR85thfbHy/GXsnzSV/Cn2XP6ByDRpdzk2shJcZw6wL7zIg6KlCSDU0Yue1tX6k59bZySAluzgOe+8WOhLMBHRBZjbZVaQ76+OmhV9ccruKoCL05oEumbGI0eGOfnabqOrnFXJf60vnWGvk9B080YVWgW9FNoq6H+LhA+vGjkBeoR3X7Y7/mAqfb6CIHOnUJ7+SBTGM5VRLf0PB1wxsmM5ZLVxSGYtAeQ28ukT6h4e4jz6dm7dD+DgDSbMg/T/BsaTHhclhf7I5qllSX/FCkTHRd4jI3Z+B+RRPvRRgcziGfLpadZ3ltzsF9ezN7SpoOKeCVnkNLXcZxD1F82XCVcGrs90LOPGCkDElz7hT1i9V/7H8uPBUuZkUhwNWfb/YzaUniWoaT5s2ODvnDxSRMFrYiQzLXJXg9RYVnqcojEuqaXghxvTGvfjHjwSEVjL/QK+cayVkMnvK76cimnrojy8PKeKO613XnKyuJurJhSLEhOzOpO6GLPez2dKGb5nS1796JdUqlXNShDrl6GBT94ElEE/Yiny5Mz6qoSnHnhtPN8cB4O35/sX4NIrQ26K9WKbIaMTuYE4dKGczuxWqI2tU0lGrudCX6RFwNxesBxebcM28AZk2KmVc5p+nDnTFo03jQ4xQLTkO9lzTT8LLjJLTwVh7k6YipuK87STlKxgPNXWd/cZqN5MPcU5l/bpCmA8T7uZULnIQyBNJdbux/hiw3ifArTJSdJyNCmFIS4SwFtHFFKrdaUbCYMVRyPgLUkVD3U9s0HgAPS+lZz7yFl3OTFLnTtWfG6Il0uFoJZMtU55AelwL46Sxwk2ExHEjpHk5fRjN7fn3pyyRZPaAs2EsC/MzvFhxQzcdiiBaHxLnlnynWKX8g7l3tOWkhjJixWIU6oXCL9MkOh/GArgLthQalvUi688ViJjcTAyj1wdWls5PBClNtJVhkwz/PrXfwP39Bmgx5o9V9sq22DXkysuyP7uOSN9d0ID2bFFXHxvph1whpT4Jg8vnCtKPdqefCpCSrAQlXKQsuw3Om20Zi6dKwNLlgWk3NAoYQgC4xY+J0pT9S2J7xfsZFfSe7ShWo1JXIKcd9lSIITzU88/2B7QGzcGBdJiJF1zB5PID7+3/932bNH0h3M/i3vcmuwoGItvgkEp+UxMUuo2WdYA1K7txWM3GeM/Ak3fFLdV9e4QqOyW36eLpQ5yiDD+1B0opBF2lYSqJS6g21ic2v1vnueuuJVoKB09LIbwWHXlMkHb6ska9XuLL8HcHLHU4UTwN0qqsTQ6ZX/D8kwzEYEe69IiVnmpSLAum7B4PXaupQNx8S2yGpTYaN2FIdkiYDTnH/Iwd7LjhQih/v+WAoh8cLnuN8ZZk+JBQod7JIloCX7mHGvDon8CPJVNeMPXekBDCreVomdEqSdlLNaPiOtiHaj1lopj6Y3vXa4aqA/7ZG94cnCr27xP72HQTa8ggPJ66DhGKt0sdcSpy298iva+D7J3P9a66kl0k2wIW4rkXiHVup8HXz+6ms0OZM6JGSTWBtYOh6pGaajhBnFcrP5MjoblzByGlF7gsjz1vzbuoLbyGi6g6S/abbEaHi/v7v/uuMqgTetqRQP++oRhdrf3xc8OSSXyytptbYaqni3/ZW/+u42Vz1OgzRAUAXeYLJ1a/wIRvrAiY9UyS4iaJVXQFAU3XeJ/OGb1vyXyYj+BMfwHC49j+njpz15VMJnJYk6oz2nmXNsGaM8Z1MI5sb4oclZXc63pYHM82kv8y5V7ocLzwEZCfqzteeQ6mT5XfJOwDkRp/Av23R/fBAOlldIk4H8OfWwin97mTDGIv8irkfbKk6yPNmQRF4qP3zN5II1BEu7m84B7c7GipSp39pMeEMAJB9Jnsg97JmVrwQqRVX8Z6AEMcV0YZyuJH8Vtjn5E4XY/7oZ+svRFeYuF+AS/os+a9vRlzPg5rPIWD/XX3x82zSh6qK44WUNS8qLKZyqRQx3s+R64CwOZt4Gwn8z96OPaXgHRKFX5g3U7mKL+JqzMtLh5O6YhFNbxoP+T01Dbyl367mREnIpE0HC+HrxtYB+iC6a2M1st0K8uK4/clkTXE+ZA+kNfBkaIErOrBBScGylh5pwhshj2poBHAeD2l6Fb1emtFX6I7sb/J4IEoGKY2u1OKF3YVT11HNvqcu+1QkAe2op1GRieSjFBZSGWUfB5Hm+Ysg7i8N9z3NDVhv+ZDrHi9mYhZ1qCB7tPanB0FTFD0TZ3uQvi/YhM2lBAxqhMOVy94uovi85q21PcD9+g3xbton8CTQ6fEuxDJ7Dy+yLHfl/i183cCNh5YqZVKx641ChPMF7nJjFVEEDqNkWJVU0dTcOMhYzfuhTNP1KAi5Td3pwrgB76QtiDKmJyhXJ6bIlJbl0cCCVfyOEj6j4enO70TcJQ2uPLioPOLQyV0apLrg0Oed5A/N7d3wT9w8MkBM8xHi4xxhvefLp8GxCQQAn+UFlurBHc7W32mfS/H73A7Z8HbkYbac9qFBH+bQtC6vKVDfPbAE5V0tX1xF9b/bHuiIlpji3/13ysBluY6t1c5fhD50RUfnStxa74A59XVsnHuWjDtd+0WyJO/i2yvweNeTwaQMNXH4ry9IdwszUaq42cq6cW0n3H+ccZimIyIPRdHzu1Ncx/oias4fHqBMTH++QlOSmA7U5y8o+sLrDi6+A7vK4hk5c6oYvLg25FSUIYLGkr0XP/u3LT+jf/oNeHqQsmjAB0hQjzrNS/MxUhVshaPi91SXhOcuZ4LQr9gHiiMDKQGHEyugz2+U6N36YUIc1yjWojBR1X/OfLDvFvbzKzhYLW66atGeWkXpcTogu0ckc6YzFV2mGykmf2AcmjQd9iW4MnBmY657FL9/5g7Ona427FKHijpz8vEELOd9X54ZGaCCd74DdN6rLQtAX9VVpWURAhARhexF1QxQl1w5iTdWIczvsyWV+u5NaSInp7I+83RMW8npYioNzfuz/ZosTwEY8wVnTqK6j0uSqxqWZ+kTtaYoApfjwbNZ13XBOyV9HlbApw/8v19vnH4JEY0n/Q15OmaeoY6VBZSj4uPwsjOJltvT1a1KjbA9GrcjC7NE0RyqgkdZIH96Mo6Lu7VM1NH+63DuJ6WOY3HftH2STtsZTSCNZZCVuCpIiwnlSDqaFhmZf1cJ2GBoStxG+vMnWo1EFxtnA4EeM1JaKQBhc7KHV4c+/npD++eP/F5Fla+T3DQmajJ9euC/WxT2Miv6r/y2I4jZOyOc5/GQKqBb2y+bi2ByNYoS6POLy7GILDhFL172fBGShPiIsz6PB3BVheanO8uPNGH2L996sb13PIjF9ZIGFaummvrfLKE47taaAss1RHS4MQ9CZkaQTBC+bSW6PBqy0J1IWkjjutfIysTTNYw4T5Mhp7OVVBRriToQhpGWyXjdcoJ87bie6iLyieWpkbE10sks9Adi/eJiItKqA8L6hPZp3i+BgX4XFTynn/dLxiW/7HnKeQd/apCGBU+At62ddmlOpgh2RysB84D2Er2FVLLj9kd76dHc+pu3FFLZ7mBxatpgU/w7NGOskxKAYZQ3W0G4a4u4Yk+kZlqdBPs9J3dpzikeyzRKunRg4fZHK3finahempthLfxRlCuignGnKzCf8lbsYp9hIIh5GHOyE5iUR3jdsx0Q+V7YXZBXdAVY3JrQB/KA7nBzz3tPM6rYvbL3AhIidTzNRvQ3in3IHZQkEOg8kd6KPY9AnXTt4hzyrbUdrWtjr6gpC/6MNyFMS+gKIEoheVD9jXn0cVQiT4aofqU3lVnqwlIZMUTlfVnHyPEaYXOwCWtaTFjCy8APm53lUjgZIhp/9dYy7loGNPnCCXse1qxiQuB3LmusPJ9wNRS85MnTgxkO9AOmx6URz/KoBu4XrJoelvLniVJoVCM/rTi1pbtbnOnS96AskJ5WhMfoSb+ckacifZVy8NUVnS80UrqGcctxJRIfebHK39a8uR5W/GDWO7v90k9PFhBDNsmgJ6qVBQXKg5pYfJH0aFBlmtMyledTA0Gh7ZjXHRPiUMTjza3vLUU14bpE8W9z4wK8LIDtni5+QRZyiV1zSukc3FDsPKcLy/OckR6JyYvTGn5/Qfy44nSxIkBY/2k/LjjVg+zE2ii5DCcTJSgnNOuuzxPvkKZjllxjCobNqJwZs638Tg2VQWAKkVEExPrE6OoGOHMayUTakj3LeMA/vxSbmBCt3a01hoo7XszjZwzTxxWc2tqqgjdcRTVJUlXJ5daLFoZsbd6LCtzpgvLnV4r0vYc/nMmjaaMBtvKFMXMQFwzqimP9UlD/4wH/HiE8+MOFU2+lKlwafr9VAf+8QXyYI024wkDwFMmf6UTJwdlO2wzNpwv3lkMBWF2ufEHlcPJvewouNFoOkNUNd5Oq3HJtNOyitwHKN9rzzf6zOcC9bRmbpasA2+bzIWaeQkOLz3jUYwvEza5xwWRl0G/H6WniuHwyNFMqd1NJpmClePSopQvPOzPcxjn1kLmWhF7FKiTJRT9dET8saca8tai+HniSyjLfHy7crcm0Ls+nFqGFlChAVgweuLD3O+ZMYLPvy2RZMyiaD3WF4nkvNOiWB8y1lbKTtOzyy9ZKovBta0ZnZPY0SUTH8K7vt4XU7Hc0BfvTxZwVKukL6yNf0ruFtRHsjfjgW0/uuMNrPy5M/OBOVLvEhwX8z1+s5egeZyzLd0dpMQhbUk+j7RlFOJCnY3PEoyxMwG5ENFUkAYafN3eIVDpZ+jm3PwJi4M1BUH8AW4L3EraceUgWgYqiw5nE9BN/jriYsL9XAYRzyA9c87R/+sBUr82Jh6+Ag9OY01p3uSHdzylRk581y1opfNvavtpAVoI4tL9HYs/ipEYalXTP1CUn7XJRubaDR+CI1AVvthG1ZNz+1XeMupIpntGfxKakt1F8WLA/0DDFEIQuRtqyUzGqc9To6bridJXl94XStY2GvEi1cxT52oRuBea6yUok8gDIA0ELVhKeck/DrpXH3pte1V6yLpGCdbkZPySuJsiDGt3jTBJcIYbW2lYXbjo2AzFUwzgd2VSPInbZHd4tTLuJGM3mpQDa9A5lmJZT/m5bDmiUqQLnuCsTVb8OI9Tj5tY77tAEXKy+NGY5nJmTUAutOonD5XhF+e9/5R+vwum6gv/br3DzmQyDKhQvooJSwJZArFwbBdg04Hcm2ZEARHEj8GPxOjpdv9zaHoBcUyhgSbo6kRVbW7pfAjIwCs87Svjk70+LsalaUl1y5gA55CSDQ533KKg9NQTK4WTA6HDis0BgUm1Lc43HU8M0k5Els7Hge6BE7zQZ9uoliTPQhC0+wKTKFS97XjpS+mo4j7s2QsaW/Y4i9fLxxEx4XTCKeVJjwgDYSkD/HS54q37RKS+rJoJq9K8uOY3539yQpiOKXYXfH3bcs5lqQ4FMAHurlC2fQidd3cMM4cTSWLWXBn6qK+7D3u3O1LXwH+8naSrl7gebHdIfvyN/VAXDReDnIPHZqpbhtFWMspJ1r3vE3NyQ/vCBC2nZDeFCz2OasIxV3IO79MZQY5tIX9vdT1F826L7uER4PZhMjLc6DwKla+tuU3dtegvpElqDLr04tG2yvT/ZJNOdr2bzMeuQCrDbjsO1qkD5eS39/Dsbj954bWf5CGbvkaGVig3cuZGBxtWmlrYKGw9ZxiogOiazMqEI/bTbuR7ZIQOtdDcTPiqN5GnAwz8NSxS/rfuqSX7nNGEL4S4NkuRKkBAhjh8RGmgGRVKk4ajmYfewsog5iG6UL3Zr2ZaaR+8aDqm8aTAF/YciIP3hO3sQ/F5/OaYWdQ9Ty/xT8Izu5Qi65WQ0PsyFalYbyEnNkyxHhKCd6Emz/DmNiJYfXMviHHodX1xNyCCVYQoAYjEk1MPvjsLDpOLFfXujmFZEu5SZvduHvddbXm/8csoCWMzo/9pzOqnwXnj0YaEpme5QbwgVT1sU9+MKrmnRLYakiwtfElVpxC//6zM/6y1TjSz6uuhvxHC4Il8bhPXJNI/0DHL65y8t0sMC7Ye5uFBu6H56tNshzgaWUpTLAH+64Pp3T8aDdZemf/mUBSNlu7FiZDIN5xCed8TGd5GHnShuuoeZhfjosCTVHIqorjR7DzyveYtebzQcOycBP+/QGPKZulYiq4MXmZwEue4vsvYQ9EUQ5/pkKBkT3D2mkZS3bUTYXw0kTTPCWJ61M7r7qRmsIdn0fn+21YprOuZDTIdA4dE9zbm+GhEYrZPmXJeSvSG33e4g4KqzaFmFH/sPn/6POS1ncktVVpblCZX7ihhQmZDfHgSOE2ytkMSzpXkGGiip+kcbjogdP82IBnCHE5Ra1T7NUP3TC/88yU5PMyar4nyhP6wMVEJMBZ3e0KGuAZiaHZFLTu/SnBhz3No+uEV9YYAMCPhzA9S05op6Qt3vsNc99Wk4kv0e76aEQV2uyA8r9jwK3bk0olARc/C54c+qAuh3N4E7X5G7DunTA/z+wptApHW2y5IT2UtuHTWO79wAkxpxVKFcn2VCLARxwStoSGR43XMUrxXK5YruD088eIRlY9Cl5kag1vX2u1PfwkllF6kmbsM9yv7PnRkPrSsZ/UeDTvzhgtsPS5Sfd6YyAcDKYD6F5q1DhBL2vK23lDUOOVtQiK+uh1QMET+uqHwaiKRNeTiifEmjQZ9fL9kW/r1iS9RYCMH6cW07lJfjzo15/BRrkuvAHrEs/v8SsFJdkq6ug8sywJu4WqZ1Ok4FgPg454fwtpU32veWFaFKu8MZ4XkD9+UZ6onyh56Vrwk8Kk5G8JaeGj/cGaCm2F6NvpwLz3F+Gzm5nE/7zPXR0FQwbs/lsIKktOTwF35Q/sJ+LT0s+LvpEEBYNq6N5JvKh+X3Z+khKu4Ezw2/RMEG5unIdKP+cEW8n3E8rktrGZ3rzWqODJHN6YMSPyyhORB5WAP3S0KErjeEr28s1UVtYxmK2q9pYI32VJG7q/Lbni7+mmLq8HUjzggZivzjbywnC6IB87AGRkMUv61tqe+PZ/odZT/qD0IVKwP/PR1W5cybR0QG7ijcnJT7PaoMq1zDwy8tp8j7I2+iA/875W9be/niYgIN8tSIacvXkMU4UqLXbjWXdkA9fRXifIi4klyQxZQH/OlCBEcb0T3OeeuqD/RGCaPeuu9fPmW55DFXTOgi4oxZJJoF749XDtwuHMTR8XKlmkd7vBsj2tSqxr+DQzi0ROV7eA9/upCpIeWM7rT8mUTm/OHOFBOcnF0FLuup46wrKJU5D+p3RKmzkarCqw5Y+ggnfyYbJg25I0kLjrH97ozwvO1lbTkT+3a+9Qk2l8aSlej6PtsymOwW6fFeNuzJ5MrnqmP0u3JRGTBZFrr+ZUuLkCp5nGOfsd6ZtQdFvyNSU6xSweJyarpJW2VIDJi7NPC/vvCElQAWNDfg8zPXDHcL2z3m6ajPshPBc5KSPy3GdrqGLSPWvOw246DA7ad7GwilYYn4d58IV9oebdQel2NjnyBnCrvnU64pAGHQUDGj+P94NzUXhrUm6kkUfqdrO7YzUo5ZzsN3jxbZrVpffzwjLiZIw8KwEOEo65XFlIID2UNyal7xJZeWhuxOh/C3r+al5NRRPIIPc7iUOB+4NgLRHdrzYdnyMfY0g1Z+7q63LBXP+97gK1SFsD7a4Ml1iQAw0Rdr1Hp4O5ge1SxWzvEz845rCI5IqWYHIBhCyqmy5/7CpWxIALXkMDCjwu37JfDxQZT9NYFBwwrd04J/xnRkGlBGSc8FdloQ0pOzJOXs+v3OSfigxwula1vRXXaUgsUPS+50hoI+F1mT3eKTIfeICyLr4seVPPDx9wp6cWzEuTBC9uLSWM7NI+bOAl4Vrosq7dNEbr+xMEjkgQzbIx9uUY+8t3fluoSTl90cHinDTcY8SOR2zhUleLqeINnNsdR2jre8SODapxnSpBL5W0Q7LxFHBbb/kxWaTwsSBjYnQ0R09xTbh/WRqPVvW74UkxGtWmVhGQa5KkgbHw2Y/9i08KeGATKbvUSL8++NqwnNxW3Hv28s1dWwNqIBgJ43Kuum8LrjA67T3y5yuHI4USAQSBV3sqLJg9rKOL8/c07wsIQ7XtB+XMIluaEB+L/+ZhNZQseuEt3d2ODGsIdFX266hjY0ReYrSdwlvmj+bW8iing/E69kEuIBzdcI7JlNoN92yKXnAS+6ZW/WC0DovzfzaMXV2PoVWnVY++bxkIlIoxru0qLY80PPhYf/+Zm/wPECf27RfVwyeEN9XHXFUklFycJn1MENR8MOrq6NecKMcm9fniaaYjYBUqb6vSIzBM9v/LPGRCK6yEkgb6vQT3pFu6i3YPHzs93yfncyF77m8Zm8anekY/p8RS597+JQgteZJVpYH4l4lOmt38j/XpVW4qcJAUFavmaRzfFWv9mNmAveVu5yIxc0556reb2hfOVJrJPD80OBy32B8pgQrp2R6HRnFvbM5ktKddNpdlUyj0LyEWynNeAgzXkJNXnZoPiyAVZzxNWMlLjJgL/zZicTVjI1VQiukXe6d44fltzjXm8EJ0+G3DGK8on9HA99zYl0F+mlwRWVTtXTfGwRAsX2zEz22ZC/w5I2J3I9ydzJv3yGpT3LtNJUSDIwVCK2ITxkKIiYeWkEthLu2xquibxp9WeVeQT31uLnlAzGcLqRZytmAK+9XFyMkCoOQPCygX/dILwekK8Ewbovz6YhTHVpzb0/nGxSCu+Rv3+wPR4Cr2cMWccreqH8vOZLeRIDpnOyVG8NJ5GWMy7dpUdltoSYPw8XyUunKDovpuwHMm8SlAXKb3t08+F/tK6Q7HZ5GJIy/p0DytJKHGWsWEqOTDgBwI0pHsjjIbWTcgobPatj9kUugizVPR8s30N92cBfKYOaENaaBBGP4UCSk0p0iyFDYdQcOx1Q2rU/Sza99HJfXxFON6TpGN1iiOnPN4RbRrhGtDOhlYvvMA81YiDbjaoPQ/YeWM7Qfljw4RdJmhM0SPxwx99xPqFIQne8AHuulBly4j3i3UT8mFW/ppLFP6uUDn5HGRw/t2SHYRZDcbxnxeEb1XQybxHyPyMldA8z6j/frZZyTUNBHg9t0Z8GkhfiHPAvfrRFfh6KZUp5ndrORGkpRDCPlMifPZ55IN7POOCbTXoP5KCkV7Ys2CLcWu4AnbwHh5Ol7xp5/B/+9F9m61H09hOPk03wZDejAwW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+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with fits.open(fits_file_3_1) as result_file_3_1:\n",
+    "    result_data_3_1 = result_file_3_1[1].data\n",
+    "\n",
+    "crop_result_data_3_1 = result_data_3_1 [1900:2200, 1900:2200]\n",
+    "\n",
+    "fig_3_1 = plt.figure()\n",
+    "im_3_1 = plt.matshow(crop_result_data_3_1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79face20",
+   "metadata": {},
+   "source": [
+    "## Observation III.2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "e6661fe3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:26:11,677 INFO: Got offsets as [{'offset_id': 1, 'offset_centre': False, 'offset_ra': 0.0, 'offset_dec': 0.0, 'offset_pa': 0.0}]\n",
+      "2022-11-05 14:26:11,712 INFO: Adding observation with filter F129 and offset (0.0,0.0,0.0)\n",
+      "2022-11-05 14:26:11,713 INFO: Added 1 observations\n",
+      "2022-11-05 14:26:11,715 INFO: WFI with 1 detectors. Central offset (0.0, 0.0, 0.0)\n",
+      "2022-11-05 14:26:11,716 INFO: Initializing Observation 0 of 1\n",
+      "2022-11-05 14:26:11,716 INFO: Observation Filter is F129\n",
+      "2022-11-05 14:26:11,717 INFO: Observation (RA,DEC) = (150.0,-2.5) with PA=0.0\n",
+      "2022-11-05 14:26:11,717 INFO: Resetting\n",
+      "2022-11-05 14:26:11,718 INFO: Returning background 0.15.\n",
+      "2022-11-05 14:26:11,719 INFO: Creating Detector SCA01 with (RA,DEC,PA) = (150.0,-2.5,0.0)\n",
+      "2022-11-05 14:26:11,720 INFO: Creating Detector SCA01 with offset (0.0,0.0)\n",
+      "2022-11-05 14:26:11,771 INFO: Creating Instrument with Configuration {'aperture': 'imaging', 'disperser': None, 'filter': 'f129', 'instrument': 'wfi', 'mode': 'imaging'}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating pandeia instrument roman.wfi.imaging\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:26:11,985 INFO: SCA01: (RA, DEC, PA) := (150.0, -2.5, 0.0), detected as (150.0, -2.5, 0.0)\n",
+      "2022-11-05 14:26:11,986 INFO: Detector SCA01 created\n",
+      "2022-11-05 14:26:11,987 INFO: Reset Instrument\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "offset_3_2 = {\n",
+    "          'offset_id': 1,\n",
+    "          'offset_centre': False,\n",
+    "          'offset_ra': 0.0,\n",
+    "          'offset_dec': 0.0,\n",
+    "          'offset_pa': 0.0\n",
+    "         }\n",
+    "\n",
+    "\n",
+    "residuals_3_2 = {\n",
+    "             'residual_flat': False,\n",
+    "             'residual_dark': False,\n",
+    "             'residual_cosmic': False,\n",
+    "             'residual_poisson': False,\n",
+    "             'residual_readnoise': False\n",
+    "            }\n",
+    "\n",
+    "\n",
+    "observation_parameters_3_2 = {\n",
+    "                          'instrument': 'WFI',\n",
+    "                          'filters': ['F129'],\n",
+    "                          'detectors': 1,\n",
+    "                          'distortion': False,\n",
+    "                          'background': 0.15,\n",
+    "                          'observations_id': 1,\n",
+    "                          'exptime': 15000,\n",
+    "                          'offsets': [offset]\n",
+    "                         }\n",
+    "\n",
+    "obm_3_2 = ObservationModule(observation_parameters, out_prefix=obs_prefix, ra=obs_ra, dec=obs_dec, residual=residuals)\n",
+    "\n",
+    "obm_3_2.nextObservation()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "5ca1bd96",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:26:13,127 INFO: Running catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_stars_000.fits\n",
+      "2022-11-05 14:26:13,129 INFO: Adding catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_stars_000.fits\n",
+      "2022-11-05 14:26:13,149 INFO: Converting phoenix catalogue\n",
+      "2022-11-05 14:26:13,150 INFO: Preparing output table\n",
+      "2022-11-05 14:26:13,180 INFO: Converting chunk 2\n",
+      "2022-11-05 14:26:13,181 INFO: Converting Phoenix Table to Internal format\n",
+      "2022-11-05 14:26:13,182 INFO: 1 datasets\n",
+      "2022-11-05 14:26:13,600 INFO: Finished converting catalogue to internal format\n",
+      "2022-11-05 14:26:13,601 INFO: Adding catalogue to detector SCA01\n",
+      "2022-11-05 14:26:13,602 INFO: Adding catalogue adv_notebook_stars_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:26:13,657 INFO: Determining pixel co-ordinates\n",
+      "2022-11-05 14:26:13,659 INFO: Keeping 45 items\n",
+      "2022-11-05 14:26:13,659 INFO: Writing 45 stars\n",
+      "2022-11-05 14:26:13,660 INFO: Adding 45 point sources to AstroImage SCA01\n",
+      "2022-11-05 14:26:13,675 INFO: PSF File psf_WFI_2.0.0_F129_sca01.fits to be put at /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache\n",
+      "2022-11-05 14:26:13,676 INFO: PSF File is /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache/psf_WFI_2.0.0_F129_sca01.fits\n",
+      "2022-11-05 14:26:14,771 INFO: Adding point source 1 to AstroImage 2070.722560524683,2041.9150679944821\n",
+      "2022-11-05 14:26:14,786 INFO: Adding point source 2 to AstroImage 2084.2158307131685,2020.5898365456273\n",
+      "2022-11-05 14:26:14,804 INFO: Adding point source 3 to AstroImage 2020.0916326654587,2062.377725941959\n",
+      "2022-11-05 14:26:14,825 INFO: Adding point source 4 to AstroImage 2212.4513663786984,2050.6756963380167\n",
+      "2022-11-05 14:26:14,846 INFO: Adding point source 5 to AstroImage 2074.333731140191,2069.828192912249\n",
+      "2022-11-05 14:26:14,863 INFO: Adding point source 6 to AstroImage 2038.133559050457,1916.5833393972441\n",
+      "2022-11-05 14:26:14,881 INFO: Adding point source 7 to AstroImage 2343.7887260447783,1900.9585770057893\n",
+      "2022-11-05 14:26:14,900 INFO: Adding point source 8 to AstroImage 2686.907675398465,2605.1397456727286\n",
+      "2022-11-05 14:26:14,918 INFO: Adding point source 9 to AstroImage 2065.0,2064.9999999998854\n",
+      "2022-11-05 14:26:14,935 INFO: Adding point source 10 to AstroImage 3066.406035833374,2361.1200801485934\n",
+      "2022-11-05 14:26:14,952 INFO: Adding point source 11 to AstroImage 1182.5553343396969,2253.7720913427247\n",
+      "2022-11-05 14:26:14,969 INFO: Adding point source 12 to AstroImage 2193.6284242791144,2076.3731457782155\n",
+      "2022-11-05 14:26:14,987 INFO: Adding point source 13 to AstroImage 2813.8779329567824,1511.2515673506764\n",
+      "2022-11-05 14:26:15,004 INFO: Adding point source 14 to AstroImage 2182.309943805917,2015.3144162842866\n",
+      "2022-11-05 14:26:15,022 INFO: Adding point source 15 to AstroImage 3057.662806951754,2070.8940117453767\n",
+      "2022-11-05 14:26:15,040 INFO: Adding point source 16 to AstroImage 2058.065719450632,2052.1650298807162\n",
+      "2022-11-05 14:26:15,057 INFO: Adding point source 17 to AstroImage 2091.1427915903523,2087.830605821163\n",
+      "2022-11-05 14:26:15,074 INFO: Adding point source 18 to AstroImage 2935.0659858918716,2238.7989722696175\n",
+      "2022-11-05 14:26:15,092 INFO: Adding point source 19 to AstroImage 2307.363834564019,44.638053235246616\n",
+      "2022-11-05 14:26:15,109 INFO: Adding point source 20 to AstroImage 1680.7671502598998,584.177926715473\n",
+      "2022-11-05 14:26:15,127 INFO: Adding point source 21 to AstroImage 1092.7417651348703,3440.84712683443\n",
+      "2022-11-05 14:26:15,145 INFO: Adding point source 22 to AstroImage 2830.1701018015883,2110.572157738221\n",
+      "2022-11-05 14:26:15,162 INFO: Adding point source 23 to AstroImage 2633.6640993973206,3541.184421687627\n",
+      "2022-11-05 14:26:15,180 INFO: Adding point source 24 to AstroImage 1164.5832552158022,1650.5574925855592\n",
+      "2022-11-05 14:26:15,197 INFO: Adding point source 25 to AstroImage 3791.1938738559716,3441.3126869956523\n",
+      "2022-11-05 14:26:15,215 INFO: Adding point source 26 to AstroImage 4103.8638238646145,1496.5221231689397\n",
+      "2022-11-05 14:26:15,232 INFO: Adding point source 27 to AstroImage 1927.483392341295,2084.012838647698\n",
+      "2022-11-05 14:26:15,250 INFO: Adding point source 28 to AstroImage 178.8233678377253,2018.9980800272074\n",
+      "2022-11-05 14:26:15,266 INFO: Adding point source 29 to AstroImage 4086.428547161244,2087.534318716181\n",
+      "2022-11-05 14:26:15,284 INFO: Adding point source 30 to AstroImage 4013.0036569791146,3801.977402800462\n",
+      "2022-11-05 14:26:15,301 INFO: Adding point source 31 to AstroImage 2496.9760981816366,803.4482637257686\n",
+      "2022-11-05 14:26:15,320 INFO: Adding point source 32 to AstroImage 3845.1763908222347,2562.947057853079\n",
+      "2022-11-05 14:26:15,338 INFO: Adding point source 33 to AstroImage 2292.3383947583316,3791.6209688368463\n",
+      "2022-11-05 14:26:15,355 INFO: Adding point source 34 to AstroImage 788.8944598713454,2221.1635345445893\n",
+      "2022-11-05 14:26:15,374 INFO: Adding point source 35 to AstroImage 1640.67667391249,1859.632529783993\n",
+      "2022-11-05 14:26:15,391 INFO: Adding point source 36 to AstroImage 2080.756486917873,1948.955440355016\n",
+      "2022-11-05 14:26:15,408 INFO: Adding point source 37 to AstroImage 1941.1642102038797,2738.6736046548926\n",
+      "2022-11-05 14:26:15,427 INFO: Adding point source 38 to AstroImage 2034.0285436105394,2075.3305458540103\n",
+      "2022-11-05 14:26:15,444 INFO: Adding point source 39 to AstroImage 2832.565664059406,2472.6828743012607\n",
+      "2022-11-05 14:26:15,462 INFO: Adding point source 40 to AstroImage 2507.4896238795254,1761.0417715257663\n",
+      "2022-11-05 14:26:15,479 INFO: Adding point source 41 to AstroImage 3447.0034423518946,3015.875319404148\n",
+      "2022-11-05 14:26:15,497 INFO: Adding point source 42 to AstroImage 2577.160455836409,1447.2117649906638\n",
+      "2022-11-05 14:26:15,513 INFO: Adding point source 43 to AstroImage 3414.753743350597,1328.352052000483\n",
+      "2022-11-05 14:26:15,531 INFO: Adding point source 44 to AstroImage 2358.459104375479,1859.405996413239\n",
+      "2022-11-05 14:26:15,549 INFO: Adding point source 45 to AstroImage 2947.2962384749826,2913.584957297892\n",
+      "WARNING: VerifyWarning: Keyword name 'input_catalogue' is greater than 8 characters or contains characters not allowed by the FITS standard; a HIERARCH card will be created. [astropy.io.fits.card]\n",
+      "2022-11-05 14:26:15,658 INFO: Added catalogue adv_notebook_stars_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:26:15,659 INFO: Finished catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_stars_000.fits\n",
+      "2022-11-05 14:26:15,660 INFO: Running catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_gals_000.fits\n",
+      "2022-11-05 14:26:15,661 INFO: Adding catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_gals_000.fits\n",
+      "2022-11-05 14:26:15,678 INFO: Converting bc95 catalogue\n",
+      "2022-11-05 14:26:15,678 INFO: Preparing output table\n",
+      "2022-11-05 14:26:15,709 INFO: Converting chunk 2\n",
+      "2022-11-05 14:26:15,709 INFO: Converting BC95 Catalogue\n",
+      "2022-11-05 14:26:15,710 INFO: Normalization Bandpass is johnson,v (<class 'str'>)\n",
+      "2022-11-05 14:26:15,711 INFO: Normalization Bandpass is johnson,v\n",
+      "2022-11-05 14:26:15,897 INFO: Finished converting catalogue to internal format\n",
+      "2022-11-05 14:26:15,898 INFO: Adding catalogue to detector SCA01\n",
+      "2022-11-05 14:26:15,898 INFO: Adding catalogue adv_notebook_gals_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:26:15,948 INFO: Determining pixel co-ordinates\n",
+      "2022-11-05 14:26:15,949 INFO: Keeping 2 items\n",
+      "2022-11-05 14:26:15,950 INFO: Writing 2 galaxies\n",
+      "2022-11-05 14:26:15,951 INFO: Starting Sersic Profiles at Sat Nov  5 14:26:15 2022\n",
+      "2022-11-05 14:26:15,952 INFO: Index is 0\n",
+      "2022-11-05 14:26:19,443 INFO: PSF File psf_WFI_2.0.0_F129_sca01.fits to be put at /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache\n",
+      "2022-11-05 14:26:19,444 INFO: PSF File is /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache/psf_WFI_2.0.0_F129_sca01.fits\n",
+      "2022-11-05 14:26:25,553 INFO: Finished Galaxy 1 of 2\n",
+      "2022-11-05 14:26:25,554 INFO: Index is 1\n",
+      "2022-11-05 14:26:28,229 INFO: PSF File psf_WFI_2.0.0_F129_sca01.fits to be put at /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache\n",
+      "2022-11-05 14:26:28,229 INFO: PSF File is /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache/psf_WFI_2.0.0_F129_sca01.fits\n",
+      "2022-11-05 14:26:33,966 INFO: Finished Galaxy 2 of 2\n",
+      "2022-11-05 14:26:33,966 INFO: Finishing Sersic Profiles at Sat Nov  5 14:26:33 2022\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:26:34,059 INFO: Added catalogue adv_notebook_gals_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:26:34,060 INFO: Finished catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_gals_000.fits\n",
+      "2022-11-05 14:26:34,060 INFO: Adding Error\n",
+      "2022-11-05 14:26:34,061 INFO: Adding residual error\n",
+      "2022-11-05 14:26:34,062 INFO: Adding error to detector SCA01\n",
+      "2022-11-05 14:26:34,063 INFO: Adding background\n",
+      "2022-11-05 14:26:34,064 INFO: Returning background 0.15.\n",
+      "2022-11-05 14:26:34,064 INFO: Background is 0.15 counts/s/pixel\n",
+      "2022-11-05 14:26:34,065 INFO: Returning background 0.15.\n",
+      "2022-11-05 14:26:34,066 INFO: Added background of 0.15 counts/s/pixel\n",
+      "2022-11-05 14:26:34,075 INFO: Inserting correct exposure time\n",
+      "2022-11-05 14:26:34,076 INFO: Cropping Down to base Detector Size\n",
+      "2022-11-05 14:26:34,077 INFO: Cropping convolved image down to detector size\n",
+      "2022-11-05 14:26:34,078 INFO: Taking [22:4110, 22:4110]\n",
+      "2022-11-05 14:26:34,098 INFO: SCA01: (RA, DEC, PA) := (150.0, -2.5, 0.0), detected as (150.0, -2.5, 0.0)\n",
+      "2022-11-05 14:26:34,102 INFO: Adding poisson noise\n",
+      "2022-11-05 14:26:34,609 INFO: Adding Poisson Noise with mean 0.000123310554600831 and standard deviation 0.4029473226487636\n",
+      "2022-11-05 14:26:34,618 INFO: Adding readnoise\n",
+      "2022-11-05 14:26:35,126 INFO: Adding readnoise with mean 0.0035705557093024254 and STDEV 11.997060775756836\n",
+      "2022-11-05 14:26:35,129 INFO: Finished adding error\n",
+      "2022-11-05 14:26:35,130 INFO: Finished Adding Error\n",
+      "2022-11-05 14:26:35,131 INFO: Converting to FITS file\n",
+      "2022-11-05 14:26:35,132 INFO: Converting detector SCA01 to FITS extension\n",
+      "2022-11-05 14:26:35,133 INFO: Creating Extension HDU from AstroImage SCA01\n",
+      "2022-11-05 14:26:35,137 INFO: Created Extension HDU from AstroImage SCA01\n",
+      "2022-11-05 14:26:35,335 INFO: Created FITS file /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_1_0.fits\n"
+     ]
+    }
+   ],
+   "source": [
+    "output_stellar_catalogues_3_2 = obm_3_2.addCatalogue(stellar_cat_file)\n",
+    "output_galaxy_catalogues_3_2 = obm_3_2.addCatalogue(galaxy_cat_file)\n",
+    "\n",
+    "psf_file_3_2 = obm_3_2.addError()\n",
+    "\n",
+    "fits_file_3_2, mosaic_file_3_2, params_3_2 = obm_3_2.finalize(mosaic=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "id": "c27bcc0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with fits.open(fits_file_3_2) as result_file_3_2:\n",
+    "    result_data_3_2 = result_file_3_2[1].data\n",
+    "\n",
+    "crop_result_data_3_2 = result_data_3_2[1900:2200, 1900:2200]\n",
+    "\n",
+    "fig_3_2 = plt.figure()\n",
+    "im_3_2 = plt.matshow(crop_result_data_3_2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d7d4dd3",
+   "metadata": {},
+   "source": [
+    "## Observation III.3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "91f99a48",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:27:08,686 INFO: Got offsets as [{'offset_id': 1, 'offset_centre': False, 'offset_ra': 0.0, 'offset_dec': 0.0, 'offset_pa': 0.0}]\n",
+      "2022-11-05 14:27:08,722 INFO: Adding observation with filter F129 and offset (0.0,0.0,0.0)\n",
+      "2022-11-05 14:27:08,723 INFO: Added 1 observations\n",
+      "2022-11-05 14:27:08,725 INFO: WFI with 1 detectors. Central offset (0.0, 0.0, 0.0)\n",
+      "2022-11-05 14:27:08,726 INFO: Initializing Observation 0 of 1\n",
+      "2022-11-05 14:27:08,726 INFO: Observation Filter is F129\n",
+      "2022-11-05 14:27:08,727 INFO: Observation (RA,DEC) = (150.0,-2.5) with PA=0.0\n",
+      "2022-11-05 14:27:08,728 INFO: Resetting\n",
+      "2022-11-05 14:27:08,728 INFO: Returning background 0.15.\n",
+      "2022-11-05 14:27:08,729 INFO: Creating Detector SCA01 with (RA,DEC,PA) = (150.0,-2.5,0.0)\n",
+      "2022-11-05 14:27:08,729 INFO: Creating Detector SCA01 with offset (0.0,0.0)\n",
+      "2022-11-05 14:27:08,780 INFO: Creating Instrument with Configuration {'aperture': 'imaging', 'disperser': None, 'filter': 'f129', 'instrument': 'wfi', 'mode': 'imaging'}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating pandeia instrument roman.wfi.imaging\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:27:08,993 INFO: SCA01: (RA, DEC, PA) := (150.0, -2.5, 0.0), detected as (150.0, -2.5, 0.0)\n",
+      "2022-11-05 14:27:08,994 INFO: Detector SCA01 created\n",
+      "2022-11-05 14:27:08,995 INFO: Reset Instrument\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "offset_3_3 = {\n",
+    "          'offset_id': 1,\n",
+    "          'offset_centre': False,\n",
+    "          'offset_ra': 0.0,\n",
+    "          'offset_dec': 0.0,\n",
+    "          'offset_pa': 0.0\n",
+    "         }\n",
+    "\n",
+    "\n",
+    "residuals_3_3 = {\n",
+    "             'residual_flat': False,\n",
+    "             'residual_dark': False,\n",
+    "             'residual_cosmic': False,\n",
+    "             'residual_poisson': False,\n",
+    "             'residual_readnoise': False\n",
+    "            }\n",
+    "\n",
+    "\n",
+    "observation_parameters_3_3 = {\n",
+    "                          'instrument': 'WFI',\n",
+    "                          'filters': ['F129'],\n",
+    "                          'detectors': 1,\n",
+    "                          'distortion': False,\n",
+    "                          'background': 0.15,\n",
+    "                          'observations_id': 1,\n",
+    "                          'exptime': 100,\n",
+    "                          'offsets': [offset]\n",
+    "                         }\n",
+    "\n",
+    "obm_3_3 = ObservationModule(observation_parameters, out_prefix=obs_prefix, ra=obs_ra, dec=obs_dec, residual=residuals)\n",
+    "\n",
+    "obm_3_3.nextObservation()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "id": "ced94b20",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:27:11,578 INFO: Running catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_stars_000.fits\n",
+      "2022-11-05 14:27:11,580 INFO: Adding catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_stars_000.fits\n",
+      "2022-11-05 14:27:11,598 INFO: Converting phoenix catalogue\n",
+      "2022-11-05 14:27:11,598 INFO: Preparing output table\n",
+      "2022-11-05 14:27:11,631 INFO: Converting chunk 2\n",
+      "2022-11-05 14:27:11,632 INFO: Converting Phoenix Table to Internal format\n",
+      "2022-11-05 14:27:11,633 INFO: 1 datasets\n",
+      "2022-11-05 14:27:11,991 INFO: Finished converting catalogue to internal format\n",
+      "2022-11-05 14:27:11,991 INFO: Adding catalogue to detector SCA01\n",
+      "2022-11-05 14:27:11,992 INFO: Adding catalogue adv_notebook_stars_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:27:12,044 INFO: Determining pixel co-ordinates\n",
+      "2022-11-05 14:27:12,044 INFO: Keeping 45 items\n",
+      "2022-11-05 14:27:12,045 INFO: Writing 45 stars\n",
+      "2022-11-05 14:27:12,046 INFO: Adding 45 point sources to AstroImage SCA01\n",
+      "2022-11-05 14:27:12,061 INFO: PSF File psf_WFI_2.0.0_F129_sca01.fits to be put at /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache\n",
+      "2022-11-05 14:27:12,062 INFO: PSF File is /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache/psf_WFI_2.0.0_F129_sca01.fits\n",
+      "2022-11-05 14:27:13,146 INFO: Adding point source 1 to AstroImage 2070.722560524683,2041.9150679944821\n",
+      "2022-11-05 14:27:13,159 INFO: Adding point source 2 to AstroImage 2084.2158307131685,2020.5898365456273\n",
+      "2022-11-05 14:27:13,173 INFO: Adding point source 3 to AstroImage 2020.0916326654587,2062.377725941959\n",
+      "2022-11-05 14:27:13,188 INFO: Adding point source 4 to AstroImage 2212.4513663786984,2050.6756963380167\n",
+      "2022-11-05 14:27:13,205 INFO: Adding point source 5 to AstroImage 2074.333731140191,2069.828192912249\n",
+      "2022-11-05 14:27:13,223 INFO: Adding point source 6 to AstroImage 2038.133559050457,1916.5833393972441\n",
+      "2022-11-05 14:27:13,241 INFO: Adding point source 7 to AstroImage 2343.7887260447783,1900.9585770057893\n",
+      "2022-11-05 14:27:13,257 INFO: Adding point source 8 to AstroImage 2686.907675398465,2605.1397456727286\n",
+      "2022-11-05 14:27:13,274 INFO: Adding point source 9 to AstroImage 2065.0,2064.9999999998854\n",
+      "2022-11-05 14:27:13,293 INFO: Adding point source 10 to AstroImage 3066.406035833374,2361.1200801485934\n",
+      "2022-11-05 14:27:13,311 INFO: Adding point source 11 to AstroImage 1182.5553343396969,2253.7720913427247\n",
+      "2022-11-05 14:27:13,331 INFO: Adding point source 12 to AstroImage 2193.6284242791144,2076.3731457782155\n",
+      "2022-11-05 14:27:13,350 INFO: Adding point source 13 to AstroImage 2813.8779329567824,1511.2515673506764\n",
+      "2022-11-05 14:27:13,370 INFO: Adding point source 14 to AstroImage 2182.309943805917,2015.3144162842866\n",
+      "2022-11-05 14:27:13,389 INFO: Adding point source 15 to AstroImage 3057.662806951754,2070.8940117453767\n",
+      "2022-11-05 14:27:13,407 INFO: Adding point source 16 to AstroImage 2058.065719450632,2052.1650298807162\n",
+      "2022-11-05 14:27:13,430 INFO: Adding point source 17 to AstroImage 2091.1427915903523,2087.830605821163\n",
+      "2022-11-05 14:27:13,449 INFO: Adding point source 18 to AstroImage 2935.0659858918716,2238.7989722696175\n",
+      "2022-11-05 14:27:13,470 INFO: Adding point source 19 to AstroImage 2307.363834564019,44.638053235246616\n",
+      "2022-11-05 14:27:13,490 INFO: Adding point source 20 to AstroImage 1680.7671502598998,584.177926715473\n",
+      "2022-11-05 14:27:13,510 INFO: Adding point source 21 to AstroImage 1092.7417651348703,3440.84712683443\n",
+      "2022-11-05 14:27:13,530 INFO: Adding point source 22 to AstroImage 2830.1701018015883,2110.572157738221\n",
+      "2022-11-05 14:27:13,550 INFO: Adding point source 23 to AstroImage 2633.6640993973206,3541.184421687627\n",
+      "2022-11-05 14:27:13,570 INFO: Adding point source 24 to AstroImage 1164.5832552158022,1650.5574925855592\n",
+      "2022-11-05 14:27:13,601 INFO: Adding point source 25 to AstroImage 3791.1938738559716,3441.3126869956523\n",
+      "2022-11-05 14:27:13,621 INFO: Adding point source 26 to AstroImage 4103.8638238646145,1496.5221231689397\n",
+      "2022-11-05 14:27:13,643 INFO: Adding point source 27 to AstroImage 1927.483392341295,2084.012838647698\n",
+      "2022-11-05 14:27:13,665 INFO: Adding point source 28 to AstroImage 178.8233678377253,2018.9980800272074\n",
+      "2022-11-05 14:27:13,686 INFO: Adding point source 29 to AstroImage 4086.428547161244,2087.534318716181\n",
+      "2022-11-05 14:27:13,707 INFO: Adding point source 30 to AstroImage 4013.0036569791146,3801.977402800462\n",
+      "2022-11-05 14:27:13,728 INFO: Adding point source 31 to AstroImage 2496.9760981816366,803.4482637257686\n",
+      "2022-11-05 14:27:13,749 INFO: Adding point source 32 to AstroImage 3845.1763908222347,2562.947057853079\n",
+      "2022-11-05 14:27:13,771 INFO: Adding point source 33 to AstroImage 2292.3383947583316,3791.6209688368463\n",
+      "2022-11-05 14:27:13,792 INFO: Adding point source 34 to AstroImage 788.8944598713454,2221.1635345445893\n",
+      "2022-11-05 14:27:13,813 INFO: Adding point source 35 to AstroImage 1640.67667391249,1859.632529783993\n",
+      "2022-11-05 14:27:13,836 INFO: Adding point source 36 to AstroImage 2080.756486917873,1948.955440355016\n",
+      "2022-11-05 14:27:13,857 INFO: Adding point source 37 to AstroImage 1941.1642102038797,2738.6736046548926\n",
+      "2022-11-05 14:27:13,879 INFO: Adding point source 38 to AstroImage 2034.0285436105394,2075.3305458540103\n",
+      "2022-11-05 14:27:13,901 INFO: Adding point source 39 to AstroImage 2832.565664059406,2472.6828743012607\n",
+      "2022-11-05 14:27:13,922 INFO: Adding point source 40 to AstroImage 2507.4896238795254,1761.0417715257663\n",
+      "2022-11-05 14:27:13,943 INFO: Adding point source 41 to AstroImage 3447.0034423518946,3015.875319404148\n",
+      "2022-11-05 14:27:13,963 INFO: Adding point source 42 to AstroImage 2577.160455836409,1447.2117649906638\n",
+      "2022-11-05 14:27:13,982 INFO: Adding point source 43 to AstroImage 3414.753743350597,1328.352052000483\n",
+      "2022-11-05 14:27:14,000 INFO: Adding point source 44 to AstroImage 2358.459104375479,1859.405996413239\n",
+      "2022-11-05 14:27:14,018 INFO: Adding point source 45 to AstroImage 2947.2962384749826,2913.584957297892\n",
+      "WARNING: VerifyWarning: Keyword name 'input_catalogue' is greater than 8 characters or contains characters not allowed by the FITS standard; a HIERARCH card will be created. [astropy.io.fits.card]\n",
+      "2022-11-05 14:27:14,153 INFO: Added catalogue adv_notebook_stars_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:27:14,154 INFO: Finished catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_stars_000.fits\n",
+      "2022-11-05 14:27:14,154 INFO: Running catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_gals_000.fits\n",
+      "2022-11-05 14:27:14,155 INFO: Adding catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_gals_000.fits\n",
+      "2022-11-05 14:27:14,171 INFO: Converting bc95 catalogue\n",
+      "2022-11-05 14:27:14,172 INFO: Preparing output table\n",
+      "2022-11-05 14:27:14,200 INFO: Converting chunk 2\n",
+      "2022-11-05 14:27:14,201 INFO: Converting BC95 Catalogue\n",
+      "2022-11-05 14:27:14,202 INFO: Normalization Bandpass is johnson,v (<class 'str'>)\n",
+      "2022-11-05 14:27:14,203 INFO: Normalization Bandpass is johnson,v\n",
+      "2022-11-05 14:27:14,397 INFO: Finished converting catalogue to internal format\n",
+      "2022-11-05 14:27:14,398 INFO: Adding catalogue to detector SCA01\n",
+      "2022-11-05 14:27:14,399 INFO: Adding catalogue adv_notebook_gals_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:27:14,453 INFO: Determining pixel co-ordinates\n",
+      "2022-11-05 14:27:14,453 INFO: Keeping 2 items\n",
+      "2022-11-05 14:27:14,454 INFO: Writing 2 galaxies\n",
+      "2022-11-05 14:27:14,455 INFO: Starting Sersic Profiles at Sat Nov  5 14:27:14 2022\n",
+      "2022-11-05 14:27:14,456 INFO: Index is 0\n",
+      "2022-11-05 14:27:17,780 INFO: PSF File psf_WFI_2.0.0_F129_sca01.fits to be put at /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache\n",
+      "2022-11-05 14:27:17,781 INFO: PSF File is /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache/psf_WFI_2.0.0_F129_sca01.fits\n",
+      "2022-11-05 14:27:24,055 INFO: Finished Galaxy 1 of 2\n",
+      "2022-11-05 14:27:24,055 INFO: Index is 1\n",
+      "2022-11-05 14:27:26,840 INFO: PSF File psf_WFI_2.0.0_F129_sca01.fits to be put at /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache\n",
+      "2022-11-05 14:27:26,841 INFO: PSF File is /Users/halkowsi/STScI-STIPS/ref_data/stips_data/psf_cache/psf_WFI_2.0.0_F129_sca01.fits\n",
+      "2022-11-05 14:27:32,966 INFO: Finished Galaxy 2 of 2\n",
+      "2022-11-05 14:27:32,967 INFO: Finishing Sersic Profiles at Sat Nov  5 14:27:32 2022\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-11-05 14:27:33,047 INFO: Added catalogue adv_notebook_gals_000_01_conv_F129.fits to AstroImage SCA01\n",
+      "2022-11-05 14:27:33,048 INFO: Finished catalogue /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_gals_000.fits\n",
+      "2022-11-05 14:27:33,049 INFO: Adding Error\n",
+      "2022-11-05 14:27:33,050 INFO: Adding residual error\n",
+      "2022-11-05 14:27:33,050 INFO: Adding error to detector SCA01\n",
+      "2022-11-05 14:27:33,051 INFO: Adding background\n",
+      "2022-11-05 14:27:33,051 INFO: Returning background 0.15.\n",
+      "2022-11-05 14:27:33,052 INFO: Background is 0.15 counts/s/pixel\n",
+      "2022-11-05 14:27:33,053 INFO: Returning background 0.15.\n",
+      "2022-11-05 14:27:33,054 INFO: Added background of 0.15 counts/s/pixel\n",
+      "2022-11-05 14:27:33,063 INFO: Inserting correct exposure time\n",
+      "2022-11-05 14:27:33,064 INFO: Cropping Down to base Detector Size\n",
+      "2022-11-05 14:27:33,065 INFO: Cropping convolved image down to detector size\n",
+      "2022-11-05 14:27:33,066 INFO: Taking [22:4110, 22:4110]\n",
+      "2022-11-05 14:27:33,085 INFO: SCA01: (RA, DEC, PA) := (150.0, -2.5, 0.0), detected as (150.0, -2.5, 0.0)\n",
+      "2022-11-05 14:27:33,089 INFO: Adding poisson noise\n",
+      "2022-11-05 14:27:33,668 INFO: Adding Poisson Noise with mean 0.000123310554600831 and standard deviation 0.4029473226487636\n",
+      "2022-11-05 14:27:33,678 INFO: Adding readnoise\n",
+      "2022-11-05 14:27:34,251 INFO: Adding readnoise with mean 0.0035705557093024254 and STDEV 11.997060775756836\n",
+      "2022-11-05 14:27:34,255 INFO: Finished adding error\n",
+      "2022-11-05 14:27:34,255 INFO: Finished Adding Error\n",
+      "2022-11-05 14:27:34,256 INFO: Converting to FITS file\n",
+      "2022-11-05 14:27:34,257 INFO: Converting detector SCA01 to FITS extension\n",
+      "2022-11-05 14:27:34,258 INFO: Creating Extension HDU from AstroImage SCA01\n",
+      "2022-11-05 14:27:34,263 INFO: Created Extension HDU from AstroImage SCA01\n",
+      "2022-11-05 14:27:34,460 INFO: Created FITS file /Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_1_0.fits\n"
+     ]
+    }
+   ],
+   "source": [
+    "output_stellar_catalogues_3_3 = obm_3_3.addCatalogue(stellar_cat_file)\n",
+    "output_galaxy_catalogues_3_3 = obm_3_3.addCatalogue(galaxy_cat_file)\n",
+    "\n",
+    "psf_file_3_3 = obm_3_3.addError()\n",
+    "\n",
+    "fits_file_3_3, mosaic_file_3_3, params_3_3 = obm_3_3.finalize(mosaic=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "id": "7636c7c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: '/Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_1_0.fits'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [150]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mfits\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfits_file_3_3\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m result_file_3_3:\n\u001b[1;32m      2\u001b[0m     result_data_3_3 \u001b[38;5;241m=\u001b[39m result_file_3_3[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mdata\n\u001b[1;32m      4\u001b[0m crop_result_data_3_3 \u001b[38;5;241m=\u001b[39m result_data_3_3[\u001b[38;5;241m1900\u001b[39m:\u001b[38;5;241m2200\u001b[39m, \u001b[38;5;241m1900\u001b[39m:\u001b[38;5;241m2200\u001b[39m]\n",
+      "File \u001b[0;32m~/anaconda3/envs/stips/lib/python3.10/site-packages/astropy/io/fits/hdu/hdulist.py:175\u001b[0m, in \u001b[0;36mfitsopen\u001b[0;34m(name, mode, memmap, save_backup, cache, lazy_load_hdus, ignore_missing_simple, **kwargs)\u001b[0m\n\u001b[1;32m    172\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m name:\n\u001b[1;32m    173\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEmpty filename: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 175\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mHDUList\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfromfile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemmap\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_backup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    176\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mlazy_load_hdus\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mignore_missing_simple\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/stips/lib/python3.10/site-packages/astropy/io/fits/hdu/hdulist.py:410\u001b[0m, in \u001b[0;36mHDUList.fromfile\u001b[0;34m(cls, fileobj, mode, memmap, save_backup, cache, lazy_load_hdus, ignore_missing_simple, **kwargs)\u001b[0m\n\u001b[1;32m    398\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m    399\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfromfile\u001b[39m(\u001b[38;5;28mcls\u001b[39m, fileobj, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, memmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    400\u001b[0m              save_backup\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, cache\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, lazy_load_hdus\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    401\u001b[0m              ignore_missing_simple\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    402\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    403\u001b[0m \u001b[38;5;124;03m    Creates an `HDUList` instance from a file-like object.\u001b[39;00m\n\u001b[1;32m    404\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    407\u001b[0m \u001b[38;5;124;03m    documentation for details of the parameters accepted by this method).\u001b[39;00m\n\u001b[1;32m    408\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 410\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_readfrom\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfileobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfileobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmemmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    411\u001b[0m \u001b[43m                         \u001b[49m\u001b[43msave_backup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_backup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    412\u001b[0m \u001b[43m                         \u001b[49m\u001b[43mignore_missing_simple\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_missing_simple\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    413\u001b[0m \u001b[43m                         \u001b[49m\u001b[43mlazy_load_hdus\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlazy_load_hdus\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/stips/lib/python3.10/site-packages/astropy/io/fits/hdu/hdulist.py:1060\u001b[0m, in \u001b[0;36mHDUList._readfrom\u001b[0;34m(cls, fileobj, data, mode, memmap, cache, lazy_load_hdus, ignore_missing_simple, **kwargs)\u001b[0m\n\u001b[1;32m   1057\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fileobj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1058\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fileobj, _File):\n\u001b[1;32m   1059\u001b[0m         \u001b[38;5;66;03m# instantiate a FITS file object (ffo)\u001b[39;00m\n\u001b[0;32m-> 1060\u001b[0m         fileobj \u001b[38;5;241m=\u001b[39m \u001b[43m_File\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfileobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmemmap\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1061\u001b[0m     \u001b[38;5;66;03m# The Astropy mode is determined by the _File initializer if the\u001b[39;00m\n\u001b[1;32m   1062\u001b[0m     \u001b[38;5;66;03m# supplied mode was None\u001b[39;00m\n\u001b[1;32m   1063\u001b[0m     mode \u001b[38;5;241m=\u001b[39m fileobj\u001b[38;5;241m.\u001b[39mmode\n",
+      "File \u001b[0;32m~/anaconda3/envs/stips/lib/python3.10/site-packages/astropy/io/fits/file.py:170\u001b[0m, in \u001b[0;36m_File.__init__\u001b[0;34m(self, fileobj, mode, memmap, overwrite, cache)\u001b[0m\n\u001b[1;32m    168\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_open_fileobj(fileobj, mode, overwrite)\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fileobj, (\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbytes\u001b[39m)):\n\u001b[0;32m--> 170\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_open_filename\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfileobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moverwrite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    172\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_open_filelike(fileobj, mode, overwrite)\n",
+      "File \u001b[0;32m~/anaconda3/envs/stips/lib/python3.10/site-packages/astropy/io/fits/file.py:562\u001b[0m, in \u001b[0;36m_File._open_filename\u001b[0;34m(self, filename, mode, overwrite)\u001b[0m\n\u001b[1;32m    559\u001b[0m ext \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39msplitext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m    561\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_try_read_compressed(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname, magic, mode, ext\u001b[38;5;241m=\u001b[39mext):\n\u001b[0;32m--> 562\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mIO_FITS_MODES\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    563\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose_on_error \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    565\u001b[0m \u001b[38;5;66;03m# Make certain we're back at the beginning of the file\u001b[39;00m\n\u001b[1;32m    566\u001b[0m \u001b[38;5;66;03m# BZ2File does not support seek when the file is open for writing, but\u001b[39;00m\n\u001b[1;32m    567\u001b[0m \u001b[38;5;66;03m# when opening a file for write, bz2.BZ2File always truncates anyway.\u001b[39;00m\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Users/halkowsi/STScI-STIPS/notebooks/adv_notebook_1_0.fits'"
+     ]
+    }
+   ],
+   "source": [
+    "with fits.open(fits_file_3_3) as result_file_3_3:\n",
+    "    result_data_3_3 = result_file_3_3[1].data\n",
+    "\n",
+    "crop_result_data_3_3 = result_data_3_3[1900:2200, 1900:2200]\n",
+    "\n",
+    "fig_3_3 = plt.figure()\n",
+    "im_3_3 = plt.matshow(crop_result_data_3_3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "401b8d8e",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "This concludes the first portion of the advanced tutorial.  For information on PSFs, please see STIPS Advanced II.  If you have further questions, check out the [STIPS documentation](https://stips.readthedocs.io/en/latest/), or reach out to the STIPS Helpdesk via email at help@stsci.edu with the subject line \"STIPS Question\"."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/STIPS Advanced II - PSFs.ipynb b/notebooks/STIPS Advanced II - PSFs.ipynb
new file mode 100644
index 0000000..3d88e4a
--- /dev/null
+++ b/notebooks/STIPS Advanced II - PSFs.ipynb	
@@ -0,0 +1,2808 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b6d71c53",
+   "metadata": {},
+   "source": [
+    "# STIPS Advanced II – PSFs, Adding Sources\n",
+    "\n",
+    "This portion of the Advanced tutorial concerns the generation and manipulation of PSFs using STIPS functions.  As with Advanced I, this notebook assumes both that you already have STIPS installed (see [Installing STIPS](https://stsci-stips.readthedocs.io/en/latest/installation.html) if not) and that you are comfortable with basic usage of STIPS functionalities (see [STIPS Basic Tutorial](https://stips.readthedocs.io/en/latest/basic_tutorial.html), or the Basic Tutorial notebook, if not).\n",
+    "\n",
+    "#### Note:\n",
+    "The ```makePSF``` package is still undergoing development –– pending the inclusion of logging functions and error catching, as well as some functional optimization.  This tutorial will be updated to reflect this in future releases."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bc7a0d4",
+   "metadata": {},
+   "source": [
+    "## Checking STIPS Import, Adding Modules\n",
+    "\n",
+    "Before beginning, check again that the STIPS import is correct, and import and configure a few modules we'll be needing.  Additionally, ensure that the generated files from the Basic Tutorial are visible in your \"notebooks\" folder/the directory from which you are working."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "775c25da",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "**WARNING**: LOCAL JWST PRD VERSION PRDOPSSOC-062 CANNOT BE CHECKED AGAINST ONLINE VERSION\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "STIPS Version 2.0.0 with Data Version 1.0.9 at /Users/sebastian/Documents/STIPS/stips_data.\n",
+      "\tSTIPS Grid Generated with 1.0.8\n",
+      "Pandeia Version 3.0 with Data Version 3.0 at /Users/sebastian/Documents/STIPS/pandeia_data-3.0_roman.\n",
+      "Webbpsf Version 1.1.1 with Data Version 1.1.1 at /Users/sebastian/Documents/STIPS/webbpsf-data.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "%config InlineBackend.figure_format = 'svg'\n",
+    "\n",
+    "from astropy.io import fits\n",
+    "from glob import glob\n",
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "from matplotlib import style\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.ticker as ticker\n",
+    "matplotlib.rcParams['axes.grid'] = False\n",
+    "matplotlib.rcParams['image.origin'] = 'lower'\n",
+    "import stips\n",
+    "\n",
+    "print(stips.__env__report__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2c65f50b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['notebook_example_1_0.fits']\n"
+     ]
+    }
+   ],
+   "source": [
+    "file_list = glob('*notebook_example*')\n",
+    "print(file_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e7c5f3a",
+   "metadata": {},
+   "source": [
+    "## Generating and Manipulating a PSF\n",
+    "\n",
+    "PSFs can be generated using FITS input files and the makePSF module within STIPS.  For this example, we will be using the example PSF file \"psf_WFI_2.0.0_F129_sca01.fits\", a PSF generated with Roman WFI F129.  This file is available in the \"notebooks\" directory of STIPS.  The generated PSF will be returned as an array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "07fa1b96",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[4.63340175e-09 4.92926905e-09 5.06057647e-09 ... 1.04694074e-08\n",
+      "  9.75042028e-09 0.00000000e+00]\n",
+      " [4.31357501e-09 4.60752880e-09 4.79127975e-09 ... 1.04253472e-08\n",
+      "  9.61258937e-09 0.00000000e+00]\n",
+      " [3.99596581e-09 4.25482504e-09 4.52264410e-09 ... 1.02763533e-08\n",
+      "  9.45093424e-09 0.00000000e+00]\n",
+      " ...\n",
+      " [5.29699428e-09 5.36962514e-09 5.49581773e-09 ... 6.47929589e-09\n",
+      "  5.97876614e-09 0.00000000e+00]\n",
+      " [4.70650252e-09 4.79887355e-09 4.91860814e-09 ... 6.71186040e-09\n",
+      "  6.11143555e-09 0.00000000e+00]\n",
+      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
+      "  0.00000000e+00 0.00000000e+00]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "example_file = fits.open('psf_WFI_2.0.0_F129_sca01.fits')\n",
+    "\n",
+    "test_psf = stips.utilities.makePSF.make_epsf(example_file[0].data[0])\n",
+    "\n",
+    "print(test_psf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e578f35a",
+   "metadata": {},
+   "source": [
+    "We can view a plot of our generated PSF using pyplot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ff1669b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
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+   ],
+   "source": [
+    "fig1 = plt.figure()\n",
+    "im1 = plt.matshow(test_psf)\n",
+    "plt.savefig('adv_notebook_epsf_orig.jpeg')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "152f0032",
+   "metadata": {},
+   "source": [
+    "The PSF can be manipulated in a variety of ways –– for purposes of this tutorial, we will perform a bicubic interpolation on our PSF, as well as calculate the fraction of light that should fall on a given pixel.  This will employ two of makePSFs primary functions, ```makePSF.bicubic``` and ```makePSF.real_psf```."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "153047e8",
+   "metadata": {},
+   "source": [
+    "### Bicubic Interpolation\n",
+    "\n",
+    "For our bicubic interpolation, we will use the following input parameters, selecting a reference point close to the source in the hopes of seeing more engaging results:\n",
+    "\n",
+    "* Reference x pixel (```ix```) – 85\n",
+    "* Reference y pixel (```iy```) – 85\n",
+    "* Pixel phase along the x-direction (```fx```) – 5\n",
+    "* Pixel phase along the y-direction (```fy```) – 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5420ad47",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-3.867468290857751"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ix = iy = 85\n",
+    "fx = fy = 5\n",
+    "\n",
+    "stips.utilities.makePSF.bicubic(test_psf, iy, ix, fx, fy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d152d26f",
+   "metadata": {},
+   "source": [
+    "Noted in the image below is the location at which the interpolation was performed (cropped for visibility)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "75f9153d",
+   "metadata": {},
+   "outputs": [
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+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot parameters\n",
+    "x_mid = 88\n",
+    "y_mid = 88\n",
+    "half = 30\n",
+    "\n",
+    "# create custom formatter for cutout region axes (run once)\n",
+    "ticks_x = ticker.FuncFormatter(lambda x, pos: f\"{x + x_mid - half:.0f}\")\n",
+    "ticks_y = ticker.FuncFormatter(lambda y, pos: f\"{y + y_mid - half:.0f}\")\n",
+    "\n",
+    "# to plot cutout region:\n",
+    "fig0, ax0 = plt.subplots(figsize=(4,4))\n",
+    "ax0.imshow(test_psf[x_mid - half:x_mid + half,\n",
+    "                    y_mid - half:y_mid + half])\n",
+    "\n",
+    "# replace default tick labels with cutout pixel indices\n",
+    "# (run in every cell that shows a plot of the cutout region)\n",
+    "ax0.set_xticks([x for x in ax0.get_xticks() if 0 <= x < half*2])\n",
+    "ax0.xaxis.set_major_formatter(ticks_x)\n",
+    "ax0.yaxis.set_major_formatter(ticks_y)\n",
+    "\n",
+    "#Coordinates of circle adjusted for crop.\n",
+    "circ_bicubic = plt.Circle((27, 27), 3, color='r', fill=False)\n",
+    "\n",
+    "ax0.add_patch(circ_bicubic)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ebeb4ec",
+   "metadata": {},
+   "source": [
+    "### Fractional Light\n",
+    "\n",
+    "Using ```makePSF.real_psf```, we can caculate the fraction of light from our epsf that should fall on any given pixel.  We will use the following parameters:\n",
+    "\n",
+    "* Relative location of source along x-axis (```dx```) (note that 0 the default the center of the boxsize) –– 5\n",
+    "* Relative location of the source along y-axis (```dy```) –– 5\n",
+    "* Center of the input psf model (```psf_center```) (our PSF has dimensions of 177 by 177 px) –– 88\n",
+    "* PSF boxsize (```boxsize```) –– 177\n",
+    "\n",
+    "#### Note:\n",
+    "\n",
+    "Currently, the default value of ```psf_center``` is set to 177, and default boxsize 44 –– this will be adjusted in future updates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c80b7c91",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.00044947279766548826"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dx = dy = 5\n",
+    "\n",
+    "#The code below is designed to modify the default values for the center\n",
+    "#and size of the PSF to fit the PSF being used –– this will be addressed\n",
+    "#in future releases.\n",
+    "\n",
+    "psf_middle = round((test_psf[0].shape[0]-1)/2)\n",
+    "\n",
+    "PSF_BOXSIZE = np.floor(psf_middle)/4\n",
+    "\n",
+    "stips.utilities.makePSF.real_psf(dx, dy, test_psf, boxsize=PSF_BOXSIZE, psf_center=psf_middle)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd5d0253",
+   "metadata": {},
+   "source": [
+    "Noted below is the location at which fractional light was evaluated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "52038cc3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.patches.Circle at 0x196626110>"
+      ]
+     },
+     "execution_count": 5,
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+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# to plot cutout region:\n",
+    "fig1_2, ax1_2 = plt.subplots(figsize=(4,4))\n",
+    "ax1_2.imshow(test_psf[x_mid - half:x_mid + half,\n",
+    "                    y_mid - half:y_mid + half])\n",
+    "\n",
+    "# replace default tick labels with cutout pixel indices\n",
+    "# (run in every cell that shows a plot of the cutout region)\n",
+    "ax1_2.set_xticks([x for x in ax1_2.get_xticks() if 0 <= x < half*2])\n",
+    "ax1_2.xaxis.set_major_formatter(ticks_x)\n",
+    "ax1_2.yaxis.set_major_formatter(ticks_y)\n",
+    "\n",
+    "#Coordinates of circle adjusted for crop.\n",
+    "circ_real_psf = plt.Circle((35, 35), 3, color='r', fill=False)\n",
+    "\n",
+    "ax1_2.add_patch(circ_real_psf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5e0432d",
+   "metadata": {},
+   "source": [
+    "## Adding New Sources to a Scene\n",
+    "\n",
+    "To add a new source to our generated scene from the Basic Tutorial, we will use the ```place_source``` function from ```makePSF``` and insert a source with the following characteristics:\n",
+    "\n",
+    "* Location (px)\n",
+    "    * x = 2000\n",
+    "    * y = 2000\n",
+    "* Flux = 15\n",
+    "\n",
+    "Note that, by default, the PSF upscale is 4.\n",
+    "\n",
+    "#### Note:\n",
+    "\n",
+    "Currently, the default value of ```psf_center``` is set to 177, and default boxsize 44 –– this will be adjusted in future updates."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d21a2c63",
+   "metadata": {},
+   "source": [
+    "### Original Scene\n",
+    "\n",
+    "Re-open the original scene from the saved output file –– by default, this file is \"notebook_example_1_0.fits\".  We will limit our area of view to the range in which the added source will be visible, between 1950 and 2150 pixels on each axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3088442d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.patches.Circle at 0x196bb6390>"
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+     },
+     "execution_count": 7,
+     "metadata": {},
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+       "   <g id=\"patch_6\">\n",
+       "    <path d=\"M 26.925 273.312 \n",
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+       "   </g>\n",
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+       "   </g>\n",
+       "  </g>\n",
+       " </g>\n",
+       " <defs>\n",
+       "  <clipPath id=\"p93b2d79abf\">\n",
+       "   <rect x=\"26.925\" y=\"7.2\" width=\"266.112\" height=\"266.112\"/>\n",
+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with fits.open('notebook_example_1_0.fits') as result_file:\n",
+    "    result_data = result_file[1].data\n",
+    "\n",
+    "fig2, ax2 = plt.subplots()\n",
+    "ax2.imshow(result_data[1950:2050, 1950:2050])\n",
+    "\n",
+    "circ_no_source = plt.Circle((50, 50), 3, color='r', fill=False)\n",
+    "\n",
+    "ax2.add_patch(circ_no_source)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1f3ba1f",
+   "metadata": {},
+   "source": [
+    "### Add New Source\n",
+    "\n",
+    "We now use the ```makePSF.place_source``` function to add in our test PSF as a source (noting that the ```boxsize``` and ```psf_center``` parameters must be modified from their defaults for this particular PSF, as was the case with the ```makePSF.real_psf example```)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "80546ce0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#The code below is designed to modify the default values for the center\n",
+    "#and size of the PSF to fit the PSF being used –– this will be addressed\n",
+    "#in future releases.\n",
+    "\n",
+    "psf_middle = round((test_psf[0].shape[0]-1)/2)\n",
+    "\n",
+    "PSF_BOXSIZE = np.floor(psf_middle)/4\n",
+    "\n",
+    "added_source = stips.utilities.makePSF.place_source(2000, 2000, 3000, result_data, test_psf, boxsize=PSF_BOXSIZE, psf_center=psf_middle)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2858999a",
+   "metadata": {},
+   "source": [
+    "We can now open our modified image and view our added source."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4d2f93fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.patches.Circle at 0x19cb0ea10>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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+       "   <cc:Work>\n",
+       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
+       "    <dc:date>2023-10-11T11:50:10.620046</dc:date>\n",
+       "    <dc:format>image/svg+xml</dc:format>\n",
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+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot parameters\n",
+    "x_mid2 = 2000\n",
+    "y_mid2 = 2000\n",
+    "half2 = 50\n",
+    "\n",
+    "# to plot cutout region:\n",
+    "fig2_1, ax2_1 = plt.subplots(figsize=(4,4))\n",
+    "ax2_1.imshow(result_data[x_mid2 - half2:x_mid2 + half2,\n",
+    "                    y_mid2 - half2:y_mid2 + half2])\n",
+    "\n",
+    "# replace default tick labels with cutout pixel indices\n",
+    "# (run in every cell that shows a plot of the cutout region)\n",
+    "ax2_1.set_xticks([x for x in ax2_1.get_xticks() if 0 <= x < half2*2])\n",
+    "ax2_1.xaxis.set_major_formatter(ticks_x)\n",
+    "ax2_1.yaxis.set_major_formatter(ticks_y)\n",
+    "\n",
+    "#Coordinates of circle adjusted for crop.\n",
+    "circ_source = plt.Circle((50, 50), 3, color='r', fill=False)\n",
+    "\n",
+    "ax2_1.add_patch(circ_source)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0430d27",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "This concludes the second portion of the advanced tutorial.  If you have further questions, check out the [STIPS documentation](https://stips.readthedocs.io/en/latest/), or reach out to the STIPS Helpdesk via email at help@stsci.edu with the subject line \"STIPS Question\"."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
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diff --git a/notebooks/STIPS Basic Tutorial.ipynb b/notebooks/STIPS Basic Tutorial.ipynb
new file mode 100644
index 0000000..8666d40
--- /dev/null
+++ b/notebooks/STIPS Basic Tutorial.ipynb	
@@ -0,0 +1,752 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# STIPS Basic Tutorial\n",
+    "\n",
+    "If you prefer to work within a Jupyter notebook, this notebook will follow the same processes as the Basic Tutorial to create a STIPS observation. Like the Basic Tutorial, it assumes that you already have STIPS installed; see [Installing STIPS](https://stsci-stips.readthedocs.io/en/latest/installation.html) in the STIPS documentation if not."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing STIPS and checking the STIPS environment\n",
+    "\n",
+    "In order to use STIPS, you must have several sets of data files installed ([Installing STIPS](https://stsci-stips.readthedocs.io/en/latest/installation.html) contains instructions on doing this). In order to test your STIPS installation, STIPS includes an environment report utility that shows which version of STIPS you have installed, as well as the versions of the most important support packages that STIPS uses. When you run the cell below, you should get output something like this:\n",
+    "\n",
+    "> STIPS Version x.y.z with Data Version x.y.z at /Some/Path/To/stips_data\n",
+    ">\n",
+    "> STIPS Grid Generated with x.y.z\n",
+    ">\n",
+    "> Pandeia Version a.b.c with Data Version a.b.c at /Some/Path/To/pandeia_refdata\n",
+    ">\n",
+    "> Webbpsf Version d.e.f with Data Version d.e.f at /Some/Path/To/WEBBPSF_PATH"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import stips\n",
+    "\n",
+    "print(stips.__env__report__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setting up some Basics\n",
+    "\n",
+    "STIPS allows you to set up some basic elements of your observation and pass them when creating and running observations. The section below shows one way to set these up."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obs_prefix = 'notebook_example'\n",
+    "obs_ra = 150.0\n",
+    "obs_dec = -2.5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a Scene to Observe\n",
+    "\n",
+    "STIPS contains functions to generate stellar populations as well as background galaxies. These functions are all present in the `SceneModule` class. In order to know what sort of populations to generate, the Scene Module requires input dictionaries to specify population parameters. In this example, we will create the following:\n",
+    "\n",
+    "* A stellar population representing a globular cluster with\n",
+    "  * 100 stars\n",
+    "  * An age of 7.5 billion years\n",
+    "  * A metallicity of -2.0\n",
+    "  * A Salpeter IMF with alpha=-2.35\n",
+    "  * A binary fraction of 10%\n",
+    "  * A clustered distribution (higher-mass stars closer to the population centre)\n",
+    "  * An inverse power-law distribution\n",
+    "  * A radius of 100 parsecs\n",
+    "  * A distance of 10 kpc\n",
+    "  * No offset from the centre of the scene being created\n",
+    "* A collection of background galaxies with\n",
+    "  * 10 galaxies\n",
+    "  * Redshifts between 0 and 0.2\n",
+    "  * Radii between 0.01 and 2.0 arcsec\n",
+    "  * V-band surface brightness magnitudes between 28 and 24\n",
+    "  * Uniform spatial distribution (unclustered) over 200 arcsec\n",
+    "  * No offset from the center of the scene being created\n",
+    "  \n",
+    "\n",
+    "##### Note: \n",
+    "Background galaxies are available in STIPS, but are neither supported nor tested.\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stips.scene_module import SceneModule\n",
+    "\n",
+    "scm = SceneModule(out_prefix=obs_prefix, ra=obs_ra, dec=obs_dec)\n",
+    "\n",
+    "stellar_parameters = {\n",
+    "                      'n_stars': 100,\n",
+    "                      'age_low': 7.5e12, \n",
+    "                      'age_high': 7.5e12,\n",
+    "                      'z_low': -2.0, \n",
+    "                      'z_high': -2.0,\n",
+    "                      'imf': 'salpeter', \n",
+    "                      'alpha': -2.35,\n",
+    "                      'binary_fraction': 0.1,\n",
+    "                      'clustered': True,\n",
+    "                      'distribution': 'invpow',\n",
+    "                      'radius': 100.0, \n",
+    "                      'radius_units': 'pc',\n",
+    "                      'distance_low': 10.0, \n",
+    "                      'distance_high': 10.0,\n",
+    "                      'offset_ra': 0.0, \n",
+    "                      'offset_dec': 0.0\n",
+    "                     }\n",
+    "\n",
+    "stellar_cat_file = scm.CreatePopulation(stellar_parameters)\n",
+    "print(\"Stellar population saved to file {}\".format(stellar_cat_file))\n",
+    "\n",
+    "galaxy_parameters = {\n",
+    "                     'n_gals': 10,\n",
+    "                     'z_low': 0.0, \n",
+    "                     'z_high': 0.2,\n",
+    "                     'rad_low': 0.01, \n",
+    "                     'rad_high': 2.0,\n",
+    "                     'sb_v_low': 28.0, \n",
+    "                     'sb_v_high': 24.0,\n",
+    "                     'distribution': 'uniform', \n",
+    "                     'clustered': False,\n",
+    "                     'radius': 200.0, \n",
+    "                     'radius_units': 'arcsec',\n",
+    "                     'offset_ra': 0.0, \n",
+    "                     'offset_dec': 0.0\n",
+    "                    }\n",
+    "\n",
+    "galaxy_cat_file = scm.CreateGalaxies(galaxy_parameters)\n",
+    "print(\"Galaxy population saved to file {}\".format(galaxy_cat_file))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a STIPS observation\n",
+    "\n",
+    "Once a scene has been created, it's possible to observe that scene as many times as you wish (and from as many places as you wish, although obviously any observation that doesn't include at least some of the scene will simply be an empty exposure). In this case, we create a single Roman WFI observation.\n",
+    "\n",
+    "STIPS uses a bit of specialialized terminology to describe its observations. In particular:\n",
+    "\n",
+    "* An *observation* is a set of exposures with a single instrument (e.g. Roman WFI), one or more filters (where each\n",
+    "  exposure in the observation will be repeated for every included filter), some number of the instrument's \n",
+    "  detectors (for WFI between 1 and 18), where each exposure in the observation will be repeated, with the \n",
+    "  appropriate inter-detector offset, for every included detector, a single chosen sky background value, and a  \n",
+    "  single exposure time (applied to each exposure in the observation), and one or more offsets.\n",
+    "* An *offset* is a single telescope pointing. For each offset specified in the observation, an exposure will be \n",
+    "  created for each detector and each filter at the offset. STIPS may, optionally, create one or more mosaics at \n",
+    "  each offset, with a single mosaic including all detectors with the same filter. In addition, STIPS can create a \n",
+    "  single combined mosaic for each filter in the combined Observation.\n",
+    "\n",
+    "In this case, we will create an observation with:\n",
+    "\n",
+    "* Roman WFI F129\n",
+    "* 1 detector\n",
+    "* No distortion\n",
+    "* A sky background of 0.15 counts/s/pixel\n",
+    "* The ID 1\n",
+    "* An exposure time of 1000 seconds\n",
+    "\n",
+    "We will use a single offset with:\n",
+    "\n",
+    "* An ID of 1\n",
+    "* No centering (if an offset is centred then, for a multi-detector observation, each detector is centred on the   \n",
+    "  offset co-ordinates individually rather than the instrument as a whole being centred there)\n",
+    "* No change in RA, DEC, or PA from the centre of the observation\n",
+    "\n",
+    "and the following residual settings:\n",
+    "\n",
+    "* Flatfield residual: off\n",
+    "* Dark current residual: off\n",
+    "* Cosmic ray removal residual: off\n",
+    "* Poisson noise residual: on\n",
+    "* Readnoise residual: on\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stips.observation_module import ObservationModule\n",
+    "\n",
+    "offset = {\n",
+    "          'offset_id': 1,\n",
+    "          'offset_centre': False,\n",
+    "          'offset_ra': 0.0,\n",
+    "          'offset_dec': 0.0,\n",
+    "          'offset_pa': 0.0\n",
+    "         }\n",
+    "\n",
+    "residuals = {\n",
+    "             'residual_flat': False,\n",
+    "             'residual_dark': False,\n",
+    "             'residual_cosmic': False,\n",
+    "             'residual_poisson': True,\n",
+    "             'residual_readnoise': True,\n",
+    "            }\n",
+    "\n",
+    "observation_parameters = {\n",
+    "                          'instrument': 'WFI',\n",
+    "                          'filters': ['F129'],\n",
+    "                          'detectors': 1,\n",
+    "                          'distortion': False,\n",
+    "                          'background': 0.15,\n",
+    "                          'observations_id': 1,\n",
+    "                          'exptime': 1000,\n",
+    "                          'offsets': [offset]\n",
+    "                         }\n",
+    "\n",
+    "obm = ObservationModule(observation_parameters, out_prefix=obs_prefix, ra=obs_ra, dec=obs_dec, residuals=residuals)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we call nextObservation() -– this function is used to move between different combinations of offset and filter.  It must be called once in order to initialize the observation module to the first observatiom before adding catalogues."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obm.nextObservation()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Observing the Scene\n",
+    "\n",
+    "In order to observe the scene, we must add the scene catalogues created above to it, add in error residuals, and finalize the observation. In so doing, we create output catalogues which are taken from the input catalogues, but only contain the sources visible to the detectors, and convert source brightnesses into units of counts/s for the detectors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_stellar_catalogues = obm.addCatalogue(stellar_cat_file)\n",
+    "output_galaxy_catalogues = obm.addCatalogue(galaxy_cat_file)\n",
+    "\n",
+    "print(\"Output Catalogues are {} and {}.\".format(output_stellar_catalogues, output_galaxy_catalogues))\n",
+    "\n",
+    "psf_file = obm.addError()\n",
+    "\n",
+    "print(\"PSF File is {}\".format(psf_file))\n",
+    "\n",
+    "fits_file, mosaic_file, params = obm.finalize(mosaic=False)\n",
+    "\n",
+    "print(\"Output FITS file is {}\".format(fits_file))\n",
+    "print(\"Output Mosaic File is {}\".format(mosaic_file))\n",
+    "print(\"Observation Parameters are {}\".format(params))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Show the Result\n",
+    "\n",
+    "We use pyplot to plot the resulting simulated image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "%config InlineBackend.figure_format = 'svg'\n",
+    "import matplotlib\n",
+    "from matplotlib import style\n",
+    "matplotlib.rcParams['axes.grid'] = False\n",
+    "matplotlib.rcParams['image.origin'] = 'lower'\n",
+    "import matplotlib.pyplot as plt\n",
+    "from astropy.io import fits\n",
+    "\n",
+    "with fits.open(fits_file) as result_file:\n",
+    "    result_data = result_file[1].data\n",
+    "\n",
+    "fig1 = plt.figure()\n",
+    "im = plt.matshow(result_data)\n",
+    "plt.savefig('notebook_example_basic_scene.jpeg')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As well as a detail from the detector center."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/svg+xml": [
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+       "<Figure size 288x288 with 1 Axes>"
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+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig2 = plt.figure()\n",
+    "im2 = plt.matshow(result_data[1800:2300, 1800:2300])\n",
+    "plt.savefig('notebook_example_basic_scene.jpeg')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
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+   "nbconvert_exporter": "python",
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diff --git a/notebooks/STIPS Tutorial.ipynb b/notebooks/STIPS Tutorial.ipynb
deleted file mode 100644
index 667f177..0000000
--- a/notebooks/STIPS Tutorial.ipynb	
+++ /dev/null
@@ -1,496 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# STIPS Basic Usage Example\n",
-    "\n",
-    "If you prefer to work within a Jupyter notebook, this notebook will follow the same processes as the Basic Tutorial to create a STIPS observation. Like the Basic Tutorial, it assumes that you already have STIPS installed; see [Installing STIPS](https://stsci-stips.readthedocs.io/en/latest/installation.html) in the STIPS documentation if not."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Importing STIPS and checking the STIPS environment\n",
-    "\n",
-    "In order to use STIPS, you must have several sets of data files installed ([Installing STIPS](https://stsci-stips.readthedocs.io/en/latest/installation.html) contains instructions on doing this). In order to test your STIPS installation, STIPS includes an environment report utility that shows which version of STIPS you have installed, as well as the versions of the most important support packages that STIPS uses. When you run the cell below, you should get output something like this:\n",
-    "\n",
-    "> STIPS Version x.y.z with Data Version x.y.z at /Some/Path/To/stips_data\n",
-    ">\n",
-    "> STIPS Grid Generated with x.y.z\n",
-    ">\n",
-    "> Pandeia Version a.b.c with Data Version a.b.c at /Some/Path/To/pandeia_refdata\n",
-    ">\n",
-    "> Webbpsf Version d.e.f with Data Version d.e.f at /Some/Path/To/WEBBPSF_PATH"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "STIPS Version 2.0.0 with Data Version 1.0.9 at /Users/halkowsi/STScI-STIPS/ref_data/stips_data.\n",
-      "\tSTIPS Grid Generated with 1.0.8\n",
-      "Pandeia Version 1.7 with Data Version 1.7 at /Users/halkowsi/STScI-STIPS/ref_data/pandeia_data-1.7_roman.\n",
-      "Webbpsf Version 1.0.0 with Data Version 1.0.0 at /Users/halkowsi/STScI-STIPS/ref_data/webbpsf-data.\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "import stips\n",
-    "\n",
-    "print(stips.__env__report__)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setting up some Basics\n",
-    "\n",
-    "STIPS allows you to set up some basic elements of your observation and pass them when creating and running observations. The section below shows one way to set these up."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "obs_prefix = 'notebook_example'\n",
-    "obs_ra = 150.0\n",
-    "obs_dec = -2.5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating a Scene to Observe\n",
-    "\n",
-    "STIPS contains functions to generate stellar populations as well as background galaxies. These functions are all present in the `SceneModule` class. In order to know what sort of populations to generate, the Scene Module requires input dictionaries to specify population parameters. In this example, we will create the following:\n",
-    "\n",
-    "* A stellar population representing a globular cluster with\n",
-    "  * 10,000 stars\n",
-    "  * An age of 7.5 billion years\n",
-    "  * A metallicity of -2.0\n",
-    "  * A Salpeter IMF with alpha=-2.35\n",
-    "  * A binary fraction of 10%\n",
-    "  * A clustered distribution (higher-mass stars closer to the population centre)\n",
-    "  * An inverse power-law distribution\n",
-    "  * A radius of 100 parsecs\n",
-    "  * A distance of 20 kpc\n",
-    "  * No offset from the centre of the scene being created\n",
-    "* A collection of background galaxies with\n",
-    "  * 100 galaxies\n",
-    "  * Redshifts between 0 and 0.2\n",
-    "  * Radii between 0.01 and 2.0 arcsec\n",
-    "  * V-band surface brightness magnitudes between 25 and 30\n",
-    "  * Uniform spatial distribution (unclustered) over 200 arcsec\n",
-    "  * No offset from the centre of the scene being created\n",
-    "  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-10-14 13:03:10,044 INFO: Creating catalogue /Users/york/Projects/stips/tmp/notebook_example_stars_000.fits\n",
-      "2020-10-14 13:03:10,045 INFO: Creating age and metallicity numbers\n",
-      "2020-10-14 13:03:10,045 INFO: Created age and metallicity numbers\n",
-      "2020-10-14 13:03:10,046 INFO: Creating stars\n",
-      "2020-10-14 13:03:10,046 INFO: Age 1.35e+10\n",
-      "2020-10-14 13:03:10,047 INFO: Metallicity -2.000000\n",
-      "2020-10-14 13:03:10,047 INFO: Creating 50000 stars\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Log level: INFO\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-10-14 13:03:10,419 INFO: Creating 50000 objects, max radius 100.0, function invpow, scale 2.8\n",
-      "2020-10-14 13:03:10,579 INFO: Chunk 1: 54987 stars\n",
-      "WARNING: UnitsWarning: 'degrees' did not parse as fits unit: At col 0, Unit 'degrees' not supported by the FITS standard.  If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n",
-      "WARNING: UnitsWarning: 'years' did not parse as fits unit: At col 0, Unit 'years' not supported by the FITS standard.  If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n",
-      "WARNING: UnitsWarning: 'solar masses' did not parse as fits unit: At col 0, Unit 'solar' not supported by the FITS standard.  If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n",
-      "WARNING: UnitsWarning: 'johnson,i' did not parse as fits unit: At col 0, Unit 'johnson' not supported by the FITS standard.  If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n",
-      "WARNING: VerifyWarning: Keyword name 'distribution' is greater than 8 characters or contains characters not allowed by the FITS standard; a HIERARCH card will be created. [astropy.io.fits.card]\n",
-      "WARNING: VerifyWarning: Keyword name 'clustered' is greater than 8 characters or contains characters not allowed by the FITS standard; a HIERARCH card will be created. [astropy.io.fits.card]\n",
-      "WARNING: VerifyWarning: Keyword name 'radius_units' is greater than 8 characters or contains characters not allowed by the FITS standard; a HIERARCH card will be created. [astropy.io.fits.card]\n",
-      "WARNING: VerifyWarning: Keyword name 'offset_ra' is greater than 8 characters or contains characters not allowed by the FITS standard; a HIERARCH card will be created. [astropy.io.fits.card]\n",
-      "WARNING: VerifyWarning: Keyword name 'offset_dec' is greater than 8 characters or contains characters not allowed by the FITS standard; a HIERARCH card will be created. [astropy.io.fits.card]\n",
-      "2020-10-14 13:03:10,695 INFO: Done creating catalogue\n",
-      "2020-10-14 13:03:10,696 INFO: Creating catalogue /Users/york/Projects/stips/tmp/notebook_example_gals_000.fits\n",
-      "2020-10-14 13:03:10,697 INFO: Wrote preamble\n",
-      "2020-10-14 13:03:10,697 INFO: Parameters are: {'n_gals': 1000, 'z_low': 0.0, 'z_high': 0.2, 'rad_low': 0.01, 'rad_high': 2.0, 'sb_v_low': 30.0, 'sb_v_high': 25.0, 'distribution': 'uniform', 'clustered': False, 'radius': 200.0, 'radius_units': 'arcsec', 'offset_ra': 0.0, 'offset_dec': 0.0}\n",
-      "2020-10-14 13:03:10,717 INFO: Making Co-ordinates\n",
-      "2020-10-14 13:03:10,718 INFO: Creating 1000 objects, max radius 200.0, function uniform, scale 2.8\n",
-      "2020-10-14 13:03:10,718 INFO: Converting Co-ordinates into RA,DEC\n",
-      "WARNING: UnitsWarning: 'johnson,v' did not parse as fits unit: At col 0, Unit 'johnson' not supported by the FITS standard.  If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n",
-      "2020-10-14 13:03:10,776 INFO: Done creating catalogue\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Stellar population saved to file /Users/york/Projects/stips/tmp/notebook_example_stars_000.fits\n",
-      "Galaxy population saved to file /Users/york/Projects/stips/tmp/notebook_example_gals_000.fits\n"
-     ]
-    }
-   ],
-   "source": [
-    "from stips.scene_module import SceneModule\n",
-    "\n",
-    "scm = SceneModule(out_prefix=obs_prefix, ra=obs_ra, dec=obs_dec)\n",
-    "\n",
-    "stellar_parameters = {\n",
-    "                      'n_stars': 100,\n",
-    "                      'age_low': 7.5e12, \n",
-    "                      'age_high': 7.5e12,\n",
-    "                      'z_low': -2.0, \n",
-    "                      'z_high': -2.0,\n",
-    "                      'imf': 'salpeter', \n",
-    "                      'alpha': -2.35,\n",
-    "                      'binary_fraction': 0.1,\n",
-    "                      'clustered': True,\n",
-    "                      'distribution': 'invpow',\n",
-    "                      'radius': 100.0, \n",
-    "                      'radius_units': 'pc',\n",
-    "                      'distance_low': 20.0, \n",
-    "                      'distance_high': 20.0,\n",
-    "                      'offset_ra': 0.0, \n",
-    "                      'offset_dec': 0.0\n",
-    "                     }\n",
-    "\n",
-    "stellar_cat_file = scm.CreatePopulation(stellar_parameters)\n",
-    "print(\"Stellar population saved to file {}\".format(stellar_cat_file))\n",
-    "\n",
-    "galaxy_parameters = {\n",
-    "                     'n_gals': 10,\n",
-    "                     'z_low': 0.0, \n",
-    "                     'z_high': 0.2,\n",
-    "                     'rad_low': 0.01, \n",
-    "                     'rad_high': 2.0,\n",
-    "                     'sb_v_low': 30.0, \n",
-    "                     'sb_v_high': 25.0,\n",
-    "                     'distribution': 'uniform', \n",
-    "                     'clustered': False,\n",
-    "                     'radius': 200.0, \n",
-    "                     'radius_units': 'arcsec',\n",
-    "                     'offset_ra': 0.0, \n",
-    "                     'offset_dec': 0.0\n",
-    "                    }\n",
-    "\n",
-    "galaxy_cat_file = scm.CreateGalaxies(galaxy_parameters)\n",
-    "print(\"Galaxy population saved to file {}\".format(galaxy_cat_file))\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating a STIPS observation\n",
-    "\n",
-    "Once a scene has been created, it's possible to observe that scene as many times as you wish (and from as many places as you wish, although obviously any observation that doesn't include at least some of the scene will simply be an empty exposure). In this case, we create a single Roman WFI observation.\n",
-    "\n",
-    "STIPS uses a bit of specialialized terminology to describe its observations. In particular:\n",
-    "\n",
-    "* An Observation is a set of exposures with a single instrument (e.g. Roman WFI), one or more filters (where each\n",
-    "  exposure in the observation will be repeated for every included filter), some number of the instrument's \n",
-    "  detectors (for WFI between 1 and 18), where each exposure in the observation will be repeated, with the \n",
-    "  appropriate inter-detector offset, for every included detector, a single chosen sky background value, and a  \n",
-    "  single exposure time (applied to each exposure in the observation), and one or more offsets.\n",
-    "* An Offset is a single telescope pointing. For each offset specified in the observation, an exposure will be \n",
-    "  created for each detector and each filter at the offset. STIPS may, optionally, create one or more mosaics at \n",
-    "  each offset, with a single mosaic including all detectors with the same filter. In addition, STIPS can create a \n",
-    "  single combined mosaic for each filter in the combined Observation.\n",
-    "\n",
-    "In this case, we will create an observation with:\n",
-    "\n",
-    "* Roman WFI F129\n",
-    "* 1 detector\n",
-    "* No distortion\n",
-    "* A background rate of 0.15 counts/s/pixel\n",
-    "* The ID 1\n",
-    "* An exposure time of 1000 seconds\n",
-    "\n",
-    "We will use a single offset with\n",
-    "\n",
-    "* An ID of 1\n",
-    "* No centring (if an offset is centred then, for a multi-detector observation, each detector is centred on the   \n",
-    "  offset co-ordinates individually rather than the instrument as a whole being centred there)\n",
-    "* No change in RA, DEC, or PA from the centre of the observation\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-10-14 13:03:25,686 INFO: Got offsets as [{'offset_id': 1, 'offset_centre': False, 'offset_ra': 0.0, 'offset_dec': 0.0, 'offset_pa': 0.0}]\n",
-      "2020-10-14 13:03:25,812 INFO: Adding observation with filter F129 and offset (0.0,0.0,0.0)\n",
-      "2020-10-14 13:03:25,813 INFO: Added 1 observations\n",
-      "2020-10-14 13:03:25,844 INFO: WFI with 1 detectors. Central offset (0.0, 0.0, 0.0)\n",
-      "2020-10-14 13:03:25,844 INFO: Initializing Observation 0 of 1\n",
-      "2020-10-14 13:03:25,845 INFO: Observation Filter is F129\n",
-      "2020-10-14 13:03:25,845 INFO: Observation (RA,DEC) = (150.000000,-2.500000) with PA=0.000000\n",
-      "2020-10-14 13:03:25,846 INFO: Resetting\n",
-      "2020-10-14 13:03:25,846 INFO: Creating Detector SCA01 with (RA,DEC,PA) = (150.0,-2.5,0.0)\n",
-      "2020-10-14 13:03:25,847 INFO: Creating Detector SCA01 with offset (0.0,0.0)\n",
-      "2020-10-14 13:03:25,885 INFO: Creating Instrument with Configuration {'aperture': 'any', 'disperser': None, 'filter': 'f129', 'instrument': 'wfirstimager', 'mode': 'imaging'}\n",
-      "2020-10-14 13:03:25,982 INFO: PSF File psf_WFI_1.0.8_F129_1_1_sca01.fits to be put at /Users/york/Data/Reference/stips_data-1.0.9/psf_cache\n",
-      "2020-10-14 13:03:25,983 INFO: PSF File is /Users/york/Data/Reference/stips_data-1.0.9/psf_cache/psf_WFI_1.0.8_F129_1_1_sca01.fits\n",
-      "2020-10-14 13:03:26,164 INFO: PSF choosing between 4096, 4096 and 176\n",
-      "2020-10-14 13:03:26,171 INFO: SCA01: Starting 1x1 PSF Grid creation at Wed Oct 14 13:03:26 2020\n",
-      "Pysynphot unavailable (or invalid source supplied)!   Assuming flat # of counts versus wavelength.\n",
-      "CAUTION: Just interpolating rather than integrating filter profile, over 10 steps\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Running instrument: WFI, filter: F129\n",
-      "  Running detector: SCA01\n",
-      "    Position 1/1: (2047, 2047) pixels\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-10-14 13:03:40,074 INFO: SCA01: Finished PSF Grid creation at Wed Oct 14 13:03:40 2020\n",
-      "2020-10-14 13:03:40,148 INFO: SCA01: (RA, DEC, PA) := (150.0, -2.5, 0.0), detected as (150.0, -2.5, 0.0)\n",
-      "2020-10-14 13:03:40,149 INFO: Detector SCA01 created\n",
-      "2020-10-14 13:03:40,150 INFO: Reset Instrument\n",
-      "2020-10-14 13:03:40,150 ERROR: Error Parsing Background: 0.15\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  Saving file: /Users/york/Data/Reference/stips_data-1.0.9/psf_cache/psf_WFI_1.0.8_F129_1_1_sca01.fits\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from stips.observation_module import ObservationModule\n",
-    "\n",
-    "offset = {\n",
-    "          'offset_id': 1,\n",
-    "          'offset_centre': False,\n",
-    "          'offset_ra': 0.0,\n",
-    "          'offset_dec': 0.0,\n",
-    "          'offset_pa': 0.0\n",
-    "         }\n",
-    "\n",
-    "observation_parameters = {\n",
-    "                          'instrument': 'WFI',\n",
-    "                          'filters': ['F129'],\n",
-    "                          'detectors': 1,\n",
-    "                          'distortion': False,\n",
-    "                          'background': 0.15,\n",
-    "                          'observations_id': 1,\n",
-    "                          'exptime': 1000,\n",
-    "                          'offsets': [offset]\n",
-    "                         }\n",
-    "\n",
-    "obm = ObservationModule(observation_parameters, out_prefix=obs_prefix, ra=obs_ra, dec=obs_dec)\n",
-    "\n",
-    "# nextObservation is called to move between different combinations of offset and filter.\n",
-    "# It must be called once in order to initialize the observation module to the first observation before\n",
-    "#     adding catalogues.\n",
-    "obm.nextObservation()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Observing the Scene\n",
-    "\n",
-    "In order to observe the scene, we must add the scene catalogues created above to it, add in error residuals, and finalize the observation. In so doing, we create output catalogues which are taken from the input catalogues, but only contain the sources visible to the detectors, and convert source brightnesses into units of counts/s for the detectors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-10-14 13:03:56,238 INFO: Running catalogue /Users/york/Projects/stips/tmp/notebook_example_stars_000.fits\n",
-      "2020-10-14 13:03:56,239 INFO: Adding catalogue /Users/york/Projects/stips/tmp/notebook_example_stars_000.fits\n",
-      "2020-10-14 13:03:56,375 INFO: Converting phoenix catalogue\n",
-      "2020-10-14 13:03:56,376 INFO: Preparing output table\n",
-      "2020-10-14 13:03:56,425 INFO: Converting chunk 2\n",
-      "2020-10-14 13:03:56,425 INFO: Converting Phoenix Table to Internal format\n",
-      "2020-10-14 13:03:56,427 INFO: 1 datasets\n",
-      "2020-10-14 13:03:57,202 INFO: Finished converting catalogue to internal format\n",
-      "2020-10-14 13:03:57,203 INFO: Adding catalogue to detector SCA01\n",
-      "2020-10-14 13:03:57,203 INFO: Adding catalogue notebook_example_stars_000_01_conv_F129.fits to AstroImage SCA01\n",
-      "2020-10-14 13:03:57,224 INFO: Determining pixel co-ordinates\n",
-      "2020-10-14 13:03:57,233 INFO: Keeping 37520 items\n",
-      "2020-10-14 13:03:57,239 INFO: Writing 37520 stars\n",
-      "2020-10-14 13:03:57,240 INFO: Adding 37520 point sources to AstroImage SCA01\n",
-      "WARNING: UnitsWarning: 'counts/s' did not parse as fits unit: At col 0, Unit 'counts' not supported by the FITS standard. Did you mean count? If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n",
-      "WARNING: VerifyWarning: Keyword name 'input_catalogue' is greater than 8 characters or contains characters not allowed by the FITS standard; a HIERARCH card will be created. [astropy.io.fits.card]\n",
-      "2020-10-14 13:03:57,457 INFO: Added catalogue notebook_example_stars_000_01_conv_F129.fits to AstroImage SCA01\n",
-      "2020-10-14 13:03:57,459 INFO: Finished catalogue /Users/york/Projects/stips/tmp/notebook_example_stars_000.fits\n",
-      "2020-10-14 13:03:57,459 INFO: Running catalogue /Users/york/Projects/stips/tmp/notebook_example_gals_000.fits\n",
-      "2020-10-14 13:03:57,460 INFO: Adding catalogue /Users/york/Projects/stips/tmp/notebook_example_gals_000.fits\n",
-      "2020-10-14 13:03:57,486 INFO: Converting bc95 catalogue\n",
-      "2020-10-14 13:03:57,486 INFO: Preparing output table\n",
-      "2020-10-14 13:03:57,533 INFO: Converting chunk 2\n",
-      "2020-10-14 13:03:57,534 INFO: Converting BC95 Catalogue\n",
-      "2020-10-14 13:03:57,534 INFO: Normalization Bandpass is johnson,v (<class 'str'>)\n",
-      "2020-10-14 13:03:57,535 INFO: Normalization Bandpass is johnson,v\n",
-      "2020-10-14 13:04:01,825 WARNING: Source 78 of 1000: Pysynphot Error Error using Synphot to perform bandpass normalization in SpectralElement\n",
-      "Model: Empirical1D\n",
-      "N_inputs: 1\n",
-      "N_outputs: 1\n",
-      "Parameters: \n",
-      "  points: (array([4700., 4750., 4800., 4850., 4900., 4950., 5000., 5050., 5100.,\n",
-      "       5150., 5200., 5250., 5300., 5350., 5400., 5450., 5500., 5550.,\n",
-      "       5600., 5650., 5700., 5750., 5800., 5850., 5900., 5950., 6000.,\n",
-      "       6050., 6100., 6150., 6200., 6250., 6300., 6350., 6400., 6450.,\n",
-      "       6500., 6550., 6600., 6650., 6700., 6750., 6800., 6850., 6900.,\n",
-      "       6950., 7000.], dtype=float32),)\n",
-      "  lookup_table: [0.    0.004 0.032 0.084 0.172 0.31  0.478 0.65  0.802 0.913 0.978 1.\n",
-      " 0.994 0.977 0.95  0.911 0.862 0.806 0.747 0.69  0.634 0.579 0.523 0.467\n",
-      " 0.413 0.363 0.317 0.274 0.234 0.2   0.168 0.14  0.114 0.089 0.067 0.05\n",
-      " 0.037 0.027 0.02  0.016 0.013 0.012 0.01  0.009 0.007 0.004 0.   ]\n",
-      "  method: linear\n",
-      "  fill_value: 0\n",
-      "  bounds_error: FalseSynphotError('Integrated flux is <= 0') encountered\n",
-      "2020-10-14 13:04:03,366 WARNING: Source 108 of 1000: Pysynphot Error Error using Synphot to perform bandpass normalization in SpectralElement\n",
-      "Model: Empirical1D\n",
-      "N_inputs: 1\n",
-      "N_outputs: 1\n",
-      "Parameters: \n",
-      "  points: (array([4700., 4750., 4800., 4850., 4900., 4950., 5000., 5050., 5100.,\n",
-      "       5150., 5200., 5250., 5300., 5350., 5400., 5450., 5500., 5550.,\n",
-      "       5600., 5650., 5700., 5750., 5800., 5850., 5900., 5950., 6000.,\n",
-      "       6050., 6100., 6150., 6200., 6250., 6300., 6350., 6400., 6450.,\n",
-      "       6500., 6550., 6600., 6650., 6700., 6750., 6800., 6850., 6900.,\n",
-      "       6950., 7000.], dtype=float32),)\n",
-      "  lookup_table: [0.    0.004 0.032 0.084 0.172 0.31  0.478 0.65  0.802 0.913 0.978 1.\n",
-      " 0.994 0.977 0.95  0.911 0.862 0.806 0.747 0.69  0.634 0.579 0.523 0.467\n",
-      " 0.413 0.363 0.317 0.274 0.234 0.2   0.168 0.14  0.114 0.089 0.067 0.05\n",
-      " 0.037 0.027 0.02  0.016 0.013 0.012 0.01  0.009 0.007 0.004 0.   ]\n",
-      "  method: linear\n",
-      "  fill_value: 0\n",
-      "  bounds_error: FalseSynphotError('Integrated flux is <= 0') encountered\n"
-     ]
-    }
-   ],
-   "source": [
-    "output_stellar_catalogues = obm.addCatalogue(stellar_cat_file)\n",
-    "output_galaxy_catalogues = obm.addCatalogue(galaxy_cat_file)\n",
-    "\n",
-    "print(\"Output Catalogues are {} and {}.\".format(output_stellar_catalogues, output_galaxy_catalogues))\n",
-    "\n",
-    "psf_file = obm.addError()\n",
-    "\n",
-    "print(\"PSF File is {}\".format(psf_file))\n",
-    "\n",
-    "fits_file, mosaic_file, params = obm.finalize(mosaic=False)\n",
-    "\n",
-    "print(\"Output FITS file is {}\".format(fits_file))\n",
-    "print(\"Output Mosaic File is {}\".format(mosaic_file))\n",
-    "print(\"Observation Parameters are {}\".format(params))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Show the Result\n",
-    "\n",
-    "We use pyplot to plot the resulting simulated image."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'svg'\n",
-    "import matplotlib\n",
-    "from matplotlib import style\n",
-    "matplotlib.rcParams['axes.grid'] = False\n",
-    "matplotlib.rcParams['image.origin'] = 'lower'\n",
-    "import matplotlib.pyplot as plt\n",
-    "from astropy.io import fits\n",
-    "\n",
-    "with fits.open(fits_file) as result_file:\n",
-    "    result_data = result_file[1].data\n",
-    "\n",
-    "fig1 = plt.figure()\n",
-    "im = plt.matshow(result_data, norm=matplotlib.colors.LogNorm())\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/psf_WFI_2.0.0_F129_sca01.fits b/notebooks/psf_WFI_2.0.0_F129_sca01.fits
new file mode 100644
index 0000000..fb47220
Binary files /dev/null and b/notebooks/psf_WFI_2.0.0_F129_sca01.fits differ
diff --git a/setup.cfg b/setup.cfg
index 58c0f66..7cf6743 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -23,10 +23,11 @@ install_requires =
     photutils
     synphot
     stsynphot
-    webbpsf==1.0.0
-    pandeia.engine==1.7
+    webbpsf==1.1.1
+    pandeia.engine==3.0
     montage-wrapper
     pyyaml
+    soc_roman_tools
 zip_safe = False
 use_2to3 = False
 include_package_data = True
diff --git a/stips/astro_image/astro_image.py b/stips/astro_image/astro_image.py
index 94747da..db48bd8 100755
--- a/stips/astro_image/astro_image.py
+++ b/stips/astro_image/astro_image.py
@@ -354,10 +354,10 @@ def imageHdu(self):
             hdu.header['CDELT2'] = self.scale[0]/3600.
         # Apparently astropy refuses to add the identity matrix to a header
         if ('PA1_1' not in hdu.header) and ('CD1_1' not in hdu.header):
-            hdu.header['PA1_1'] = 1.
-            hdu.header['PA1_2'] = 0.
-            hdu.header['PA2_1'] = 0.
-            hdu.header['PA2_2'] = 1.
+            hdu.header['CD1_1'] = -self.scale[0]/3600.
+            hdu.header['CD1_2'] = 0.
+            hdu.header['CD2_1'] = 0.
+            hdu.header['CD2_2'] = self.scale[0]/3600.
         for k, v in self.header.items():
             hdu.header[k] = v
         for item in self.history:
@@ -782,7 +782,8 @@ def addSersicProfile(self, posX, posY, flux, n, re, phi, axialRatio, *args, **kw
         g = coords.Grid(1., 1., self.xsize, self.ysize)
         xc, yc = ix, iy
 
-        src = source.Source(config=source_dict)
+        # Roman is the only telescope supported
+        src = source.Source('roman', config=source_dict)
         src.grid = g
         sersic = profile.SersicDistribution(src)
         img = deepcopy(sersic.prof)*flux/np.sum(sersic.prof)
@@ -967,9 +968,20 @@ def setExptime(self, exptime):
         not affect the data values, but it does affect errors residuals (e.g. dark
         current and cosmic ray count)
         """
+        factor = exptime / self.exptime
+        self.data *= factor
         self.exptime = exptime
         self.updateHeader('exptime', self.exptime)
 
+        #self.exptime = exptime
+        #self.updateHeader('exptime', self.exptime)
+
+    def setUnits(self):
+        """
+        Divide the image by the exposure time to the output is in e/s as opposed to e.
+        """
+        self.data /= self.exptime
+
     def addBackground(self, background):
         """
         Add a constant value per-pixel. Automatically scale that value based on oversampling.
diff --git a/stips/data/psf_WFI_2.0.0_F129_sca01.fits b/stips/data/psf_WFI_2.0.0_F129_sca01.fits
new file mode 100755
index 0000000..fb47220
Binary files /dev/null and b/stips/data/psf_WFI_2.0.0_F129_sca01.fits differ
diff --git a/stips/instruments/instrument.py b/stips/instruments/instrument.py
index 30efad4..3641499 100755
--- a/stips/instruments/instrument.py
+++ b/stips/instruments/instrument.py
@@ -18,13 +18,9 @@
 # Local Modules
 from ..stellar_module import StarGenerator
 from ..astro_image import AstroImage
-from ..utilities import GetStipsData
-from ..utilities import OffsetPosition
-from ..utilities import SelectParameter
-from ..utilities import StipsDataTable
+from ..utilities import GetStipsData, OffsetPosition, SelectParameter, get_pandeia_background, StipsDataTable
 from ..utilities.makePSF import PSF_GRID_SIZE
 
-
 class Instrument(object):
     """
     The Instrument class represents a virtual base class which will be implemented as a variety of
@@ -82,7 +78,8 @@ def __init__(self, **kwargs):
         self.DETECTOR_OFFSETS = self.DETECTOR_OFFSETS[:n_detectors]
         self.OFFSET_NAMES = self.OFFSET_NAMES[:n_detectors]
         if hasattr(self, "N_OFFSET"):
-            self.CENTRAL_OFFSET = self.N_OFFSET[n_detectors]
+            # Set the central offset to SCA01
+            self.CENTRAL_OFFSET = self.N_OFFSET[1]
         msg = "{} with {} detectors. Central offset {}"
         self._log('info', msg.format(self.DETECTOR, n_detectors,
                                      self.CENTRAL_OFFSET))
@@ -725,7 +722,7 @@ def stringify(num):
             note = "BC95_{}_{}_{}".format(model, stringify(age), mag)
             notes = np.append(notes, note)
         t = Table()
-        t['ra'] = Column(data=ras.data, dtype=np.float)
+        t['ra'] = Column(data=ras.data, dtype=float)
         t['dec'] = Column(data=decs.data)
         t['flux'] = Column(data=rates.data)
         t['type'] = Column(data=np.full_like(ras, 'sersic', dtype='S7'))
@@ -974,6 +971,7 @@ def addError(self, *args, **kwargs):
                 detector.introduceCosmicRayResidual(self.PIXEL_SIZE)
                 if 'cr' in snapshots or 'all' in snapshots:
                     detector.toFits(self.imgbase+"_{}_{}_snapshot_cr.fits".format(self.obs_count, detector.name))
+            detector.setUnits()
         self._log("info", "Finished adding error")
 
     def normalize(self, source_spectrum_or_wave_flux, norm_flux, bandpass):
@@ -1128,11 +1126,15 @@ def pixel_background_unit(self):
             msg = "Returning background {} for '{}'"
             self._log("info", msg.format(bkg, self.background_value))
             return bkg*u.ct/u.s
+        elif self.background_value == 'pandeia':
+            msg = "Returning background {} for 'pandeia'"
+            self.custom_background = get_pandeia_background(self.filter)
+            self._log("info", msg.format(self.custom_background))
+            return self.custom_background*u.ct/u.s
         elif self.background_value == 'custom':
             msg = "Returning background {} for 'custom'"
             self._log("info", msg.format(self.custom_background))
             return self.custom_background*u.ct/u.s
-
         msg = "Unknown Background {}. Returning 0."
         self._log("warning", msg.format(self.background_value))
         return 0.*u.ct/u.second
diff --git a/stips/instruments/roman_instrument.py b/stips/instruments/roman_instrument.py
index a1d343f..6791e40 100755
--- a/stips/instruments/roman_instrument.py
+++ b/stips/instruments/roman_instrument.py
@@ -29,6 +29,9 @@ def __init__(self, **kwargs):
                     }
 
     TELESCOPE = 'ROMAN'
+    # WARNING : The definition of the area is currently under discussion, the 
+    # value presented here is simply pi * r^2, where r = 0.6m, half the diameter
+    # of the Roman mirror. This is subject to change.
     AREA = 45238.93416
     DBNAME = "IsochroneGrid.db"
     MODE = 'imaging'
diff --git a/stips/instruments/wfi.py b/stips/instruments/wfi.py
index eae7af6..f44712e 100755
--- a/stips/instruments/wfi.py
+++ b/stips/instruments/wfi.py
@@ -1,7 +1,11 @@
 __filetype__ = "detector"
 
 # Local Modules
+from astropy import units as u
+from astropy.coordinates import SkyCoord, ICRS
 from .roman_instrument import RomanInstrument
+from soc_roman_tools.siaf import siaf
+import numpy as np
 
 
 class WFI(RomanInstrument):
@@ -20,6 +24,163 @@ def __init__(self, **kwargs):
         Initializes detectors (single detector for now).
         """
         self.classname = self.__class__.__name__
+
+        # N_DETECTORS is a set of options on how many of the instrument's detectors you want to use
+        self.N_DETECTORS = [1]
+        self.INSTRUMENT_OFFSET = (0., 0., 0.)  # Presumably there is one, but not determined
+        self.DETECTOR_SIZE = (4088, 4088)  # pixels
+        self.PIXEL_SIZE = 10.0  # um (Assume for now)
+        self.SCALE = [0.11, 0.11]  # Assume for now
+        self.FILTERS = ('F062', 'F087', 'F106', 'F129', 'F158', 'F184', 'F146')
+        self.DEFAULT_FILTER = 'F184'  # Assume for now
+        self.SCA_ROTATION = -60  # Rotation of SCA with respect to SIAF
+
+        # Get RA and DEC that will be used for detector offset calculations
+        # RA = kwargs.get('ra', 0)
+        DEC = kwargs.get('dec', 0)
+
+        # Calculate the detector offsets based on the Roman SIAF file from soc_roman_tools
+        # The telescope (tel) frame is defined such that (0, 0) is the center of the boresight
+        # path of the WFI in the observatory coordinate system.
+
+        # Get Roman SIAF
+        rsiaf = siaf.RomanSiaf()
+
+        # Make array of SCA's
+        self.SCA_NAMES = ["WFI01_FULL", "WFI02_FULL", "WFI03_FULL", "WFI04_FULL", "WFI05_FULL",
+                          "WFI06_FULL", "WFI07_FULL", "WFI08_FULL", "WFI09_FULL", "WFI10_FULL",
+                          "WFI11_FULL", "WFI12_FULL", "WFI13_FULL", "WFI14_FULL", "WFI15_FULL",
+                          "WFI16_FULL", "WFI17_FULL", "WFI18_FULL"]
+
+        # Default detector offsets in (arcseconds_ra,arcseconds_dec,degrees_angle)
+        self.DETECTOR_OFFSETS = [# SCA01           SCA02             SCA03
+                                 (0.0, 0.0, 0.0),  (0.0, 0.0, 0.0), (0.0, 0.0, 0.0),
+                                 # SCA04           SCA05             SCA06
+                                 (0.0, 0.0, 0.0),  (0.0, 0.0, 0.0), (0.0, 0.0, 0.0),
+                                 # SCA07           SCA08             SCA09
+                                 (0.0, 0.0, 0.0),  (0.0, 0.0, 0.0), (0.0, 0.0, 0.0),
+                                 # SCA10           SCA11             SCA12
+                                 (0.0, 0.0, 0.0),  (0.0, 0.0, 0.0), (0.0, 0.0, 0.0),
+                                 # SCA13           SCA14             SCA15
+                                 (0.0, 0.0, 0.0),  (0.0, 0.0, 0.0), (0.0, 0.0, 0.0),
+                                 # SCA16           SCA17             SCA18
+                                 (0.0, 0.0, 0.0),  (0.0, 0.0, 0.0), (0.0, 0.0, 0.0)]
+
+        # Reference point
+        s01 = rsiaf['WFI01_FULL'].sci_to_tel(self.DETECTOR_SIZE[0] / 2, self.DETECTOR_SIZE[1] / 2)
+        center = SkyCoord(s01[0] * u.arcsec, 
+                          s01[1] * u.arcsec).skyoffset_frame(self.SCA_ROTATION * u.deg)
+
+        for i, sca in enumerate(self.SCA_NAMES):
+            # Get V2, V3 coordinate pair in the telescope frame at the center of the SCA
+            V2, V3 = rsiaf[sca].sci_to_tel(self.DETECTOR_SIZE[0] / 2, self.DETECTOR_SIZE[1] / 2)
+            # Transform these to the center reference point
+            out = ICRS(V2 * u.arcsec, V3 * u.arcsec).transform_to(center)
+            # Get out offsets in X and Y direction of SCA array, convert to arcsec
+            X, Y = out.lon.value * 3600, out.lat.value * 3600
+            # Offset in arcsec, arcsec, and degrees angle, converting from deg to radians
+            self.DETECTOR_OFFSETS[i] = (X / np.cos(DEC / 180 * np.pi), Y, 0)
+
+        # Copy the results into these variables needed by some functions in STIPS
+        self.OFFSET_NAMES = ("SCA01", "SCA02", "SCA03", "SCA04", "SCA05", "SCA06",
+                             "SCA07", "SCA08", "SCA09", "SCA10", "SCA11", "SCA12",
+                             "SCA13", "SCA14", "SCA15", "SCA16", "SCA17", "SCA18")
+        self.N_OFFSET = {1: self.DETECTOR_OFFSETS[0], 2: self.DETECTOR_OFFSETS[1],
+                         3: self.DETECTOR_OFFSETS[2], 4: self.DETECTOR_OFFSETS[3],
+                         5: self.DETECTOR_OFFSETS[4], 6: self.DETECTOR_OFFSETS[5],
+                         7: self.DETECTOR_OFFSETS[6], 8: self.DETECTOR_OFFSETS[7],
+                         9: self.DETECTOR_OFFSETS[8], 10: self.DETECTOR_OFFSETS[9],
+                         11: self.DETECTOR_OFFSETS[10], 12: self.DETECTOR_OFFSETS[11],
+                         13: self.DETECTOR_OFFSETS[12], 14: self.DETECTOR_OFFSETS[13],
+                         15: self.DETECTOR_OFFSETS[14], 16: self.DETECTOR_OFFSETS[15],
+                         17: self.DETECTOR_OFFSETS[16], 18: self.DETECTOR_OFFSETS[17]}
+
+        # This is a set of offsets derived from "WFIRST-STSCI-TR1506A"
+        #   Further, it assumes no rotation or imperfection (see ASCII diagrams).
+        #       07             16
+        #       08 04       13 17
+        #       09    01 10    18
+        #          05       14
+        #          06 02 11 15
+        #             03 12
+        #
+        # The following positions come from the spreadsheet
+        # "GRISM off orders position_Zernikes_efficiency data_v4_20200424.xlsx" on
+        # the "WSM-GRISM" tab, with values in mm. Note that for all detectors values
+        # are taken from Field Position 1 (centre) for a wavelength of 1 micron.
+        #
+        # These are referred to as FPA positions because they are the detector
+        # positions on the focal plane array, albeit with the Y axis inverted to
+        # yield WFI-local co-ordinates rather than FPA-local co-ordinates.
+
+        self.FPA_GLOBAL_ROTATION = -120.  # degrees
+
+        # From WFIRST-STScI-TR1506A(3).pdf, 2.5mm=27.5", and 8.564mm=94.2"
+        self.DETECTOR_MM_ARCSEC = 11.
+
+        # I can't currently figure out rotation angles (or rather figure out which
+        # rotation angle to use), so I'm setting everything to just zero for now.
+        self.DETECTOR_ROTATION = (0., 0., 0., 0., 0., 0., 0., 0., 0.,
+                                  0., 0., 0., 0., 0., 0., 0., 0., 0.)
+
+        self.PSF_INSTRUMENT = "WFI"
+
+        # Reference Files
+        self.FLATFILE = 'err_flat_wfi.fits'  # Use for the moment
+        self.DARKFILE = 'err_rdrk_wfi.fits'  # IREF, IHB (use for the moment)
+
+        # Background Values
+        self.BACKGROUND = {'none': {'F062': 0., 'F087': 0., 'F106': 0.,
+                                    'F129': 0., 'F158': 0., 'F184': 0., 'F146': 0.},
+                           'avg':  {'F062': 1.401E+00, 'F087': 1.401E+00, 'F106': 1.401E+00, 'F129': 7.000E-01,
+                                    'F158': 7.521E-01, 'F184': 8.500E-01, 'F146': 7.000E-01}}
+        self.BACKGROUNDS_V = ['none', 'avg', 'med', 'max', 'min']
+        self.BACKGROUNDS = ['None', 'Average zodiacal background', 'Median zodiacal background',
+                            'Maximum zodiacal background', 'Minimum zodiacal background']
+        self.BGTEXT = {'none': 'None', 'avg': 'Average zodiacal background',
+                       'med': 'Median zodiacal background', 'max': 'Maximum zodiacal background',
+                       'min': 'Minimum zodiacal background', 'custom': 'Custom thermal background rate',
+                       'pandeia': 'Pandeia background rate'}
+        # PHOTFNU has units of Jy
+        # For now, just assuming similar PHOTFNU to WFC3IR.
+        # For now, just put them in the middle
+        self.PHOTPLAM = {'F062': 0.620, 'F087': 0.869, 'F106': 1.060, 'F129': 1.293,
+                         'F158': 1.577, 'F184': 1.842, 'F146': 1.464}
+
+        self.ZEROPOINTS_AB = {'F062': 26.73, 'F087': 26.39, 'F106': 26.41, 'F129': 26.43,
+                              'F158': 26.47, 'F184': 26.08, 'F146': 27.66}
+        self.PHOTFNU = {}
+        for i in self.ZEROPOINTS_AB:
+            self.PHOTFNU[i] = 10 ** (0.4 * (8.9 - self.ZEROPOINTS_AB[i]))
+
+        self.DITHERS = ("SUBPIXEL ONLY", "BOX-UVIS", "BLOB")  # Assume for now
+        self.DITHER_POINTS = {
+                                 "SUBPIXEL ONLY": ["0"],
+                                 "BOX-UVIS": ["4"],
+                                 "BLOB": ["1", "2"]
+                              }
+        self.DITHER_SIZE = {
+                             "SUBPIXEL ONLY": ["STABDARD"],
+                             "BOX-UVIS": ["Standard"],
+                             "BLOB": ["Standard"]
+                           }
+        self.DITHER_SUBPIXEL = {
+                                 "SUBPIXEL ONLY": ["LINE", "LINE-3PT", "BOX-MIN"],
+                                 "BOX-UVIS": ["NONE", "LINE", "LINE-3PT", "BOX-MIN"],
+                                 "BLOB": ["NONE", "LINE", "LINE-3PT", "BOX-MIN"]
+                               }
+        self.DITHER_OFFSETS = {
+                                 "BOX-UVIS": [(-11.071, -17.744), (11.947, -17.457), 
+                                                (11.071, 17.744), (-11.947, 17.457)],
+                                 "BLOB": [(-1.930, -1.729), (1.930, 1.729)],
+                                 "SUBPIXEL": {
+                                                 "NONE":     [(0.000, 0.000)],
+                                                 "BOX-MIN":  [(0.000, 0.000), (0.542, 0.182), (0.339, 0.485), (-0.203, 0.303)],
+                                                 "LINE":     [(0.000, 0.000), (0.474, 0.424)],
+                                                 "LINE-3PT": [(0.000, 0.000), (0.451, 0.403), (0.902, 0.806)]
+                                             }
+                              }
+
         # Initialize superclass
         super().__init__(**kwargs)
 
@@ -51,170 +212,3 @@ def handleDithers(cls, form):
 
     INSTRUMENT = "WFI"
     DETECTOR = "WFI"
-
-    OFFSET_NAMES = ("SCA01", "SCA02", "SCA03", "SCA04", "SCA05", "SCA06",
-                    "SCA07", "SCA08", "SCA09", "SCA10", "SCA11", "SCA12",
-                    "SCA13", "SCA14", "SCA15", "SCA16", "SCA17", "SCA18")
-    N_OFFSET = {1:      (0.0,    0.0,  0.0),  2:     (0.0,  464.31, 0.0),  3:     (0.0,   995.32, 0.0),
-                4:   (-533.06, 131.74, 0.0),  5:  (-533.06, 596.05, 0.0),  6:  (-533.06, 1127.06, 0.0),
-                7:  (-1066.12, 338.76, 0.0),  8: (-1066.12, 803.07, 0.0),  9: (-1066.12, 1334.08, 0.0),
-                10:   (533.06,   0.0,  0.0), 11:   (533.06, 464.31, 0.0), 12:   (533.06,  995.32, 0.0),
-                13:  (1066.12, 131.74, 0.0), 14:  (1066.12, 596.05, 0.0), 15:  (1066.12, 1127.06, 0.0),
-                16:  (1599.18, 338.76, 0.0), 17:  (1599.18, 803.07, 0.0), 18:  (1599.18, 1334.08, 0.0)}
-
-    # Each detector is 450.56 x 450.56", and according to the WFIRST-STSCI-TR1506A
-    # Document, the offsets are (27.5”) in the long direction and (94.2" and 27.5")
-    # in the short direction. Assuming 0 offset for SCA01. Then RA offsets are equal
-    # to 478.06", and DEC offsets are 478.06" for the lower SCAs and 544.76 for the
-    # upper ones. Assuming no rotation offset.
-
-    # Offsets are in (arcseconds_ra,arcseconds_dec,degrees_angle)
-    '''
-    DETECTOR_OFFSETS = (# SCA01                                        SCA02                                         SCA03                                         SCA04
-                        ((2*27.5+478.06)*( 0),(478.06-27.5/2)*( 0),0.),            ((2*27.5+478.06)*( 0),(478.06-27.5/2)*( 1),0.),            ((2*27.5+478.06)*( 0),(478.06-27.5/2)*( 2)+66.7,0.),       ((2*27.5+478.06)*(-1),(478.06-27.5/2)*( 0)+188.2*0.7,0.),
-                        # SCA05                                        SCA06                                         SCA07                                         SCA08
-                        ((2*27.5+478.06)*(-1),(478.06-27.5/2)*( 1)+188.2*0.7,0.),      ((2*27.5+478.06)*(-1),(478.06-27.5/2)*( 2)+66.7+188.2*0.7,0.), ((2*27.5+478.06)*(-2),(478.06-27.5/2)*( 0)+376.4*0.9,0.),      ((2*27.5+478.06)*(-2),(478.06-27.5/2)*( 1)+376.4*0.9,0.),
-                        # SCA09                                        SCA10                                         SCA11                                         SCA12
-                        ((2*27.5+478.06)*(-2),(478.06-27.5/2)*( 2)+66.7+376.4*0.9,0.), ((2*27.5+478.06)*( 1),(478.06-27.5/2)*( 0),0.),            ((2*27.5+478.06)*( 1),(478.06-27.5/2)*( 1),0.),            ((2*27.5+478.06)*( 1),(478.06-27.5/2)*( 2)+66.7,0.),
-                        # SCA13                                        SCA14                                         SCA15                                         SCA16
-                        ((2*27.5+478.06)*( 2),(478.06-27.5/2)*( 0)+188.2*0.7,0.),      ((2*27.5+478.06)*( 2),(478.06-27.5/2)*( 1)+188.2*0.7,0.),      ((2*27.5+478.06)*( 2),(478.06-27.5/2)*( 2)+66.7+188.2*0.7,0.), ((2*27.5+478.06)*( 3),(478.06-27.5/2)*( 0)+376.4*0.9,0.),
-                        # SCA17                                        SCA18
-                        ((2*27.5+478.06)*( 3),(478.06-27.5/2)*( 1)+376.4*0.9,0.),      ((2*27.5+478.06)*( 3),(478.06-27.5/2)*( 2)+66.7+376.4*0.9,0.))
-    '''
-
-    DETECTOR_OFFSETS = (  # SCA01                  SCA02                    SCA03
-                        (0.0, 0.0, 0.0),         (0.0, 464.31, 0.0),      (0.0, 995.32, 0.0),
-                        # SCA04                  SCA05                    SCA06
-                        (-533.06, 131.74, 0.0),  (-533.06, 596.05, 0.0),  (-533.06, 1127.06, 0.0),
-                        # SCA07                  SCA08                    SCA09
-                        (-1066.12, 338.76, 0.0), (-1066.12, 803.07, 0.0), (-1066.12, 1334.08, 0.0),
-                        # SCA10                  SCA11                    SCA12
-                        (533.06, 0.0, 0.0),      (533.06, 464.31, 0.0),   (533.06, 995.32, 0.0),
-                        # SCA13                  SCA14                    SCA15
-                        (1066.12, 131.74, 0.0),  (1066.12, 596.05, 0.0),  (1066.12, 1127.06, 0.0),
-                        # SCA16                  SCA17                    SCA18
-                        (1599.18, 338.76, 0.0),  (1599.18, 803.07, 0.0),  (1599.18, 1334.08, 0.0))
-
-    # This is a set of offsets derived from "WFIRST-STSCI-TR1506A"
-    #
-    # Since that document doesn't actually cover the column offsets, they are
-    #   assumed to be 188.2" e.g. between 08 and 07, and 376.4" e.g. between
-    #   09 and 08 (and the same for each other column)(see ASCII diagrams).
-    #   Further, it assumes no rotation or imperfection.
-    #       09             18
-    #
-    #       08 06       15 17
-    #       07    03 12    16
-    #          05       14
-    #          04 02 11 13
-    #             01 10
-
-    # The detector positions WRT to WFI Local FOV are as follows (with the
-    # X-axis pointing right and the Y-axis pointing up):
-    #
-    #             01 10
-    #          04 02 11 13
-    #          05       14
-    #       07    03 12    16
-    #       08 06       15 17
-    #
-    #       09             18
-    #
-    # with the large gaps (e.g. 09-08) showing the larger offset between the
-    # bottom row of detectors and the top two rows, or, in the case of the
-    # offsets between columns (e.g. the Y positions of 07 vs. 04 vs. 01) the
-    # staggered offsets of the columns.
-    #
-    # The following positions come from the spreadsheet
-    # "GRISM off orders position_Zernikes_efficiency data_v4_20200424.xlsx" on
-    # the "WSM-GRISM" tab, with values in mm. Note that for all detectors values
-    # are taken from Field Position 1 (centre) for a wavelength of 1 micron.
-    #
-    # These are referred to as FPA positions because they are the detector
-    # positions on the focal plane array, albeit with the Y axis inverted to
-    # yield WFI-local co-ordinates rather than FPA-local co-ordinates.
-
-    FPA_GLOBAL_ROTATION = -120.  # degrees
-
-    # Values in mm. From "FPA Position" X and Y columns (F/G in xlsx)
-    # Specifically, 1.55 um is supposed to be the "undistorted" wavelength, so
-    # I'm using the detector centre at 1.55um.
-    DETECTOR_FPA = (  # SCA01                SCA02               SCA03
-                      (-22.029, -11.956), (-22.181, 36.421), (-22.331, 80.749),
-                    # SCA04                SCA05               SCA06
-                      (-65.558, -20.505), (-66.055, 27.856), (-66.543, 71.928),
-                    # SCA07                SCA08               SCA09
-                      (-109.05,  -41.316), (-109.83,   7.001), (-110.97,  50.358),
-                    # SCA10                SCA11               SCA12
-                      (21.509, -11.955), (21.65,  36.42),  (21.787, 80.747),
-                    # SCA13                SCA14               SCA15
-                      (65.03,  -20.503), (65.517, 27.854), (65.993, 71.923),
-                    # SCA16                SCA17               SCA18
-                      (108.507, -41.309), (109.282,  7.002), (110.409, 50.353),
-                   )
-
-    # From WFIRST-STScI-TR1506A(3).pdf, 2.5mm=27.5", and 8.564mm=94.2"
-    DETECTOR_MM_ARCSEC = 11.
-
-    # I can't currently figure out rotation angles (or rather figure out which
-    # rotation angle to use), so I'm setting everything to just zero for now.
-    DETECTOR_ROTATION = (0., 0., 0., 0., 0., 0., 0., 0., 0.,
-                         0., 0., 0., 0., 0., 0., 0., 0., 0.)
-
-    # N_DETECTORS is a set of options on how many of the instrument's detectors you want to use
-    N_DETECTORS = [1]
-    INSTRUMENT_OFFSET = (0., 0., 0.)  # Presumably there is one, but not determined
-    DETECTOR_SIZE = (4088, 4088)  # pixels
-    PIXEL_SIZE = 10.0  # um (Assume for now)
-    SCALE = [0.11, 0.11]  # Assume for now
-    FILTERS = ('F062', 'F087', 'F106', 'F129', 'F158', 'F184', 'F146')
-    DEFAULT_FILTER = 'F184'  # Assume for now
-
-    PSF_INSTRUMENT = "WFI"
-
-    # Reference Files
-    FLATFILE = 'err_flat_wfi.fits'  # Use for the moment
-    DARKFILE = 'err_rdrk_wfi.fits'  # IREF, IHB (use for the moment)
-
-    # Background Values
-    BACKGROUND = {'none': {'F062': 0., 'F087': 0., 'F106': 0., 'F129': 0., 'F158': 0., 'F184': 0., 'F146': 0.},
-                  'avg':  {'F062': 1.401E+00, 'F087': 1.401E+00, 'F106': 1.401E+00, 'F129': 7.000E-01,
-                           'F158': 7.521E-01, 'F184': 8.500E-01, 'F146': 7.000E-01}}
-    BACKGROUNDS_V = ['none', 'avg', 'med', 'max', 'min']
-    BACKGROUNDS = ['None', 'Average zodiacal background', 'Median zodiacal background', 'Maximum zodiacal background', 'Minimum zodiacal background']
-    BGTEXT = {'none': 'None', 'avg': 'Average zodiacal background',
-              'med': 'Median zodiacal background', 'max': 'Maximum zodiacal background',
-              'min': 'Minimum zodiacal background', 'custom': 'Custom thermal background rate'}
-    # PHOTFNU has units of Jy
-    # For now, just assuming similar PHOTFNU to WFC3IR.
-    PHOTFNU = {'F062': 3.25e-08, 'F087': 8.87e-08, 'F106': 3.94e-08, 'F129': 3.51e-08, 'F158': 3.13e-08, 'F184': 1.18e-07, 'F146': 1.63e-08}
-    # PHOTPLAM has units of um
-    # For now, just put them in the middle
-    PHOTPLAM = {'F062': 0.6700, 'F087': 0.8735, 'F106': 1.0595, 'F129': 1.2925, 'F158': 1.577, 'F184': 1.5815, 'F146': 1.4635}
-    # For now, just put in HST-style dithers.
-    DITHERS = ("SUBPIXEL ONLY", "BOX-UVIS", "BLOB")  # Assume for now
-    DITHER_POINTS = {
-                        "SUBPIXEL ONLY": ["0"],
-                        "BOX-UVIS": ["4"],
-                        "BLOB": ["1", "2"]
-                     }
-    DITHER_SIZE = {
-                    "SUBPIXEL ONLY": ["STABDARD"],
-                    "BOX-UVIS": ["Standard"],
-                    "BLOB": ["Standard"]
-                  }
-    DITHER_SUBPIXEL = {
-                        "SUBPIXEL ONLY": ["LINE", "LINE-3PT", "BOX-MIN"],
-                        "BOX-UVIS": ["NONE", "LINE", "LINE-3PT", "BOX-MIN"],
-                        "BLOB": ["NONE", "LINE", "LINE-3PT", "BOX-MIN"]
-                      }
-    DITHER_OFFSETS = {
-                        "BOX-UVIS": [(-11.071, -17.744), (11.947, -17.457), (11.071, 17.744), (-11.947, 17.457)],
-                        "BLOB": [(-1.930, -1.729), (1.930, 1.729)],
-                        "SUBPIXEL": {
-                                        "NONE":     [(0.000, 0.000)],
-                                        "BOX-MIN":  [(0.000, 0.000), (0.542, 0.182), (0.339, 0.485), (-0.203, 0.303)],
-                                        "LINE":     [(0.000, 0.000), (0.474, 0.424)],
-                                        "LINE-3PT": [(0.000, 0.000), (0.451, 0.403), (0.902, 0.806)]
-                                    }
-                     }
diff --git a/stips/tests/test_roman.py b/stips/tests/test_roman.py
deleted file mode 100755
index 418a718..0000000
--- a/stips/tests/test_roman.py
+++ /dev/null
@@ -1,120 +0,0 @@
-from stips.scene_module import SceneModule
-from stips.observation_module import ObservationModule
-
-import pytest
-
-from tempfile import TemporaryDirectory
-
-
-def create_catalogues(out_path):
-    star_data = {
-                    'n_stars': 100,
-                    'age_low': 1.0e12, 'age_high': 1.0e12,
-                    'z_low': -2.0, 'z_high': -2.0,
-                    'imf': 'salpeter', 'alpha': -2.35,
-                    'binary_fraction': 0.1,
-                    'distribution': 'invpow', 'clustered': True,
-                    'radius': 100.0, 'radius_units': 'pc',
-                    'distance_low': 10.0, 'distance_high': 10.0,
-                    'offset_ra': 0.0, 'offset_dec': 0.0
-                }
-
-    galaxy_data = {
-                    'n_gals': 25,
-                    'z_low': 0.0, 'z_high': 0.2,
-                    'rad_low': 0.01, 'rad_high': 2.0,
-                    'sb_v_low': 30.0, 'sb_v_high': 25.0,
-                    'distribution': 'uniform', 'clustered': False,
-                    'radius': 200.0, 'radius_units': 'arcsec',
-                    'offset_ra': 0.0, 'offset_dec': 0.0
-                  }
-
-    scm = SceneModule(out_path=out_path)
-    stellar_cat_file = scm.CreatePopulation(star_data)
-    galaxy_cat_file = scm.CreateGalaxies(galaxy_data)
-
-    return stellar_cat_file, galaxy_cat_file
-
-
-def get_default_obs():
-    obs = {
-            'instrument': 'WFI',
-            'filters': ['F158'],
-            'detectors': 1,
-            'distortion': False,
-            'background': 'avg',
-            'observations_id': 1,
-            'exptime': 1000,
-            'offsets': [
-                        {
-                            'offset_id': 1,
-                            'offset_centre': False,
-                            'offset_ra': 0.5,
-                            'offset_dec': 0.0,
-                            'offset_pa': 27.0
-                        }
-                       ]
-          }
-    return obs
-
-
-def test_roman_observation():
-
-    dir_name = TemporaryDirectory()
-
-    stellar_cat_file, galaxy_cat_file = create_catalogues(dir_name.name)
-
-    obs = get_default_obs()
-
-    obm = ObservationModule(obs, out_path=dir_name.name, cat_path=dir_name.name)
-    obm.nextObservation()
-    obm.addCatalogue(stellar_cat_file)
-    obm.addCatalogue(galaxy_cat_file)
-    obm.addError()
-    obm.finalize(mosaic=False)
-
-
-@pytest.mark.veryslow
-def test_roman_observation_deluxe():
-
-    dir_name = TemporaryDirectory()
-
-    stellar_cat_file, galaxy_cat_file = create_catalogues(dir_name.name)
-
-    obs = get_default_obs()
-
-    obm = ObservationModule(obs, out_path=dir_name.name, cat_path=dir_name.name)
-    obm.nextObservation()
-    obm.addCatalogue(stellar_cat_file)
-    obm.addCatalogue(galaxy_cat_file)
-    obm.addError()
-    obm.finalize(mosaic=False)
-
-
-obs_data = [
-    (
-        {
-            'filters': ['F106'],
-        },
-    )
-]
-
-
-@pytest.mark.veryslow
-@pytest.mark.parametrize(("obs_changes"), obs_data)
-def test_obs_parameters(obs_changes):
-
-    dir_name = TemporaryDirectory()
-
-    stellar_cat_file, galaxy_cat_file = create_catalogues(dir_name.name)
-
-    obs = get_default_obs()
-    for key in obs_changes[0]:
-        obs[key] = obs_changes[0][key]
-
-    obm = ObservationModule(obs, out_path=dir_name.name, cat_path=dir_name.name)
-    obm.nextObservation()
-    obm.addCatalogue(stellar_cat_file)
-    obm.addCatalogue(galaxy_cat_file)
-    obm.addError()
-    obm.finalize(mosaic=False)
diff --git a/stips/utilities/__init__.py b/stips/utilities/__init__.py
index 5709258..ab27141 100755
--- a/stips/utilities/__init__.py
+++ b/stips/utilities/__init__.py
@@ -1,16 +1,17 @@
 __all__ = ['utilities']
 
 # Local Definitions
-from .utilities import StipsEnvironment
-from .utilities import SetupDataPaths
-from .utilities import DownloadReferenceData
-from .utilities import GetStipsDataDir
-from .utilities import GetStipsData
-from .utilities import SelectParameter
-from .utilities import OffsetPosition
-from .utilities import InstrumentList
-from .utilities import read_metadata
-from .utilities import read_table
-from .utilities import rind
-from .utilities import sersic_lum
 from .DataTable import StipsDataTable
+from .utilities import (StipsEnvironment,
+						SetupDataPaths,
+						DownloadReferenceData,
+						GetStipsDataDir,
+						GetStipsData,
+						SelectParameter,
+						OffsetPosition,
+						InstrumentList,
+						read_metadata,
+						read_table,
+						rind,
+						sersic_lum,
+						get_pandeia_background)
diff --git a/stips/utilities/makePSF.py b/stips/utilities/makePSF.py
index fe937a2..e0279f5 100644
--- a/stips/utilities/makePSF.py
+++ b/stips/utilities/makePSF.py
@@ -25,16 +25,16 @@
 
 # Convolution Constants
 # Inter Pixel Capacitance
-IPC = np.array([[0.21,  1.62,  0.2],
+IPC = np.array([[0.21,  1.62,  0.20],
                 [1.88, 91.59,  1.87],
                 [0.21,  1.66,  0.22]]) / 100.0
 # Convolution array for creating a 4x upscaled ePSF, resulting
 # in the summ of the inner 3 pixels and a corresponding fraction
 # of the edges and corners to create an effective 4x4 pixels.
 EPSF4 = np.array([[0.25, 0.5, 0.5, 0.5, 0.25],
-                  [0.5,  1.,  1.,  1.,  0.5],
-                  [0.5,  1.,  1.,  1.,  0.5],
-                  [0.5,  1.,  1.,  1.,  0.5],
+                  [0.50, 1.0, 1.0, 1.0, 0.50],
+                  [0.50, 1.0, 1.0, 1.0, 0.50],
+                  [0.50, 1.0, 1.0, 1.0, 0.50],
                   [0.25, 0.5, 0.5, 0.5, 0.25]])
 
 # The ePSF is *always* 4x upscaled
@@ -129,7 +129,7 @@ def bicubic(epsf, iy, ix, fx, fy):
     """
     Perform the bi-cubic interpolation of an image from a center
     pixel and a pixel phase. Functions originally obtained from
-    Andrea Bellini's Fortran code
+    Andrea Bellini's Fortran code.
 
     This function is significantly faster than the more complex
     scipy.interpolate.interp2d, which differs from this at the level
diff --git a/stips/utilities/tests/test_makePSF.py b/stips/utilities/tests/test_makePSF.py
new file mode 100755
index 0000000..26c71ae
--- /dev/null
+++ b/stips/utilities/tests/test_makePSF.py
@@ -0,0 +1,357 @@
+from astropy.io import fits
+from scipy import ndimage
+from stips.utilities import makePSF
+from stips.utilities.utilities import StipsEnvironment
+import numpy as np
+import os
+import pytest
+stips_version = StipsEnvironment.__stips__version__
+
+# Basic carryover constants from the original makePSF file
+# This will save time in later calculations
+
+IPC = np.array([[0.21, 1.62, 0.2],
+                [1.88, 91.59, 1.87],
+                [0.21, 1.66, 0.22]]) / 100.0
+
+EPSF4 = np.array([[0.25, 0.5, 0.5, 0.5, 0.25],
+                  [0.5, 1., 1., 1., 0.5],
+                  [0.5, 1., 1., 1., 0.5],
+                  [0.5, 1., 1., 1., 0.5],
+                  [0.25, 0.5, 0.5, 0.5, 0.25]])
+
+PSF_UPSCALE = 4
+
+PSF_BOXSIZE = 44
+PSF_BRIGHT_BOXSIZE = 88
+PSF_EXTRA_BRIGHT_BOXSIZE = 176
+
+PSF_GRID_SIZE = 3
+
+
+# Rounding (just a modification to stop potential future issues)
+def rind(x):
+    return np.round(x).astype(int)
+
+
+# Import the .fits PSF file, which will be the test PSF against which we can run all
+# comparative tests and select the part of the data we'll use to construct the test PSF
+file_dir = os.path.dirname(os.path.abspath(__file__))
+data_dir = os.path.join(file_dir, "..", "..", "data", "psf_WFI_2.0.0_F129_sca01.fits")
+file = fits.open(data_dir)
+
+file_in = file[0].data[0]
+
+# Generate the test PSF using the make_epsf function from makePSF
+# We can now use this basic PSF as a comparative standard against which to check the makePSF code
+
+test_psf = makePSF.make_epsf(file[0].data[0])
+
+# Set a few basic variables for use in testing the bicubic function of makePSF
+
+ix = 1
+iy = 2
+fx = 0.25
+fy = 0.75
+
+# Generate both a bicubic interpolation (to see fractional light output) from the bicubic function,
+# and a fractional light output from the real_psf function
+# These will both be used in later tests –– it's more efficent to generate them here
+
+test_phot = makePSF.bicubic(test_psf, iy, ix, fx, fy)
+
+test_rpsf_phot = makePSF.real_psf(10, 15, test_psf,
+                                  psf_center=88, boxsize=PSF_EXTRA_BRIGHT_BOXSIZE)
+
+
+def test_make_epsf_calcs():
+
+    # Running the individual steps of make_epsf, starting with setting the shape of the array.
+    # Empty the array to fill later
+    psf_test_mid = np.zeros_like(file_in)
+
+    # Assuming the PSF is square
+    size_test = file_in.shape[0]
+
+    # Convolve the input file with the EPSF4 array
+    psf_test_mid = ndimage.convolve(file_in, EPSF4, mode='constant', cval=0.0)
+
+    # Rather than indvidually checking each calculation against the value generated by make_epsf,
+    # we nstead run a check on a randomly selected set of the values generated.
+    # This way, we can test a sample set of the calculations,
+    # without overburdening the test by checking all of them.
+
+    # First, apply the correct scaling to the rows and columns
+
+    psf_test_mid[0:size_test, 0] = file_in[0:size_test, 0]*16.0
+
+    psf_test_mid[0:size_test, size_test - 2] = file_in[0:size_test, size_test - 2] * 16.
+
+    psf_test_mid[1, 3:(size_test - PSF_UPSCALE) + 1] = \
+        file_in[1, 3:(size_test - PSF_UPSCALE) + 1] * 16.0
+
+    psf_test_mid[size_test - 2, 3:(size_test - PSF_UPSCALE) + 1] = \
+        file_in[size_test - 2, 3:(size_test - PSF_UPSCALE) + 1] * 16.0
+
+    # Empty the array
+
+    psf_test_out = np.zeros_like(file_in)
+
+    # Run the final calculations for the pixels in the PSF image
+
+    x_range = range(PSF_UPSCALE, size_test-PSF_UPSCALE-1)
+    y_range = range(PSF_UPSCALE, size_test-PSF_UPSCALE-1)
+
+    # Add the IPC to the output PSF
+
+    for idx in x_range:
+        for idy in y_range:
+            pcen = psf_test_mid[idy, idx]
+
+            psf_test_out[idy-PSF_UPSCALE, idx-PSF_UPSCALE] += pcen*IPC[0, 0]
+            psf_test_out[idy-PSF_UPSCALE, idx] += pcen*IPC[0, 1]
+            psf_test_out[idy-PSF_UPSCALE, idx+PSF_UPSCALE] += pcen*IPC[0, 2]
+
+            psf_test_out[idy, idx-PSF_UPSCALE] += pcen*IPC[1, 0]
+            psf_test_out[idy, idx] += pcen*IPC[1, 1]
+            psf_test_out[idy, idx+PSF_UPSCALE] += pcen*IPC[1, 2]
+
+            psf_test_out[idy+PSF_UPSCALE, idx-PSF_UPSCALE] += pcen*IPC[2, 0]
+            psf_test_out[idy+PSF_UPSCALE, idx] += pcen*IPC[2, 1]
+            psf_test_out[idy+PSF_UPSCALE, idx+PSF_UPSCALE] += pcen*IPC[2, 2]
+
+    # Check the generated values at the selected points against the values in the test psf,
+    # generate previously via make_epsf.
+
+    np.testing.assert_allclose(test_psf[0:size_test, 0], psf_test_out[0:size_test, 0])
+
+    np.testing.assert_allclose(test_psf[0:size_test, size_test-2],
+                               psf_test_out[0:size_test, size_test-2])
+
+    np.testing.assert_allclose(test_psf[1, 3:(size_test-PSF_UPSCALE)+1],
+                               psf_test_out[1, 3:(size_test-PSF_UPSCALE)+1])
+
+    np.testing.assert_allclose(test_psf[size_test-2, 3:(size_test-PSF_UPSCALE)+1],
+                               psf_test_out[size_test-2, 3:(size_test-PSF_UPSCALE)+1])
+
+
+def test_bicubic():
+
+    # Run the calculations of the bicubic function "manually".
+
+    A1 = test_psf[2, 1]
+    B1 = (test_psf[2, 2]-test_psf[2, 0])/2
+    C1 = (test_psf[3, 1]-test_psf[1, 1])/2
+    D1 = (test_psf[2, 2]+test_psf[2, 0]-2*A1)/2
+    E1 = (test_psf[3, 2]-A1)
+    F1 = (test_psf[3, 1]+test_psf[1, 1]-2*A1)/2
+    V1 = (A1
+          + B1*(fx)
+          + C1*(fy)
+          + D1*(fx)**2
+          + E1*(fx)*(fy)
+          + F1*(fy)**2)
+
+    # Lower Right Value
+    A2 = test_psf[2, 2]
+    B2 = (test_psf[2, 3]-test_psf[2, 1])/2
+    C2 = (test_psf[3, 2]-test_psf[1, 2])/2
+    D2 = (test_psf[2, 3]+test_psf[2, 1]-2*A2)/2
+    E2 = -(test_psf[3, 1]-A2)
+    F2 = (test_psf[3, 2]+test_psf[1, 2]-2*A2)/2
+    V2 = (A2
+          + B2*(fx-1)
+          + C2*(fy)
+          + D2*(fx-1)**2
+          + E2*(fx-1)*(fy)
+          + F2*(fy)**2)
+
+    # Upper Left Value
+    A3 = test_psf[3, 1]
+    B3 = (test_psf[3, 2]-test_psf[3, 0])/2
+    C3 = (test_psf[4, 1]-test_psf[2, 1])/2
+    D3 = (test_psf[3, 2]+test_psf[3, 0]-2*A3)/2
+    E3 = -(test_psf[2, 2]-A3)
+    F3 = (test_psf[4, 1]+test_psf[2, 1]-2*A3)/2
+    V3 = (A3
+          + B3*(fx)
+          + C3*(fy-1)
+          + D3*(fx)**2
+          + E3*(fx)*(fy-1)
+          + F3*(fy-1)**2)
+
+    # Upper Right Value
+    A4 = test_psf[3, 2]
+    B4 = (test_psf[3, 3]-test_psf[3, 1])/2
+    C4 = (test_psf[4, 2]-test_psf[2, 2])/2
+    D4 = (test_psf[3, 3]+test_psf[3, 1]-2*A4)/2
+    E4 = (test_psf[2, 1]-A4)
+    F4 = (test_psf[4, 2]+test_psf[2, 2]-2*A4)/2
+    V4 = (A4
+          + B4*(fx-1)
+          + C4*(fy-1)
+          + D4*(fx-1)**2
+          + E4*(fx-1)*(fy-1)
+          + F4*(fy-1)**2)
+
+    check_phot = ((1-fx)*(1-fy)*V1
+                  + (fx)*(1-fy)*V2
+                  + (1-fx)*(fy)*V3
+                  + (fx)*(fy)*V4)
+
+    # Check this calculated value against the value generated previously by the bicubic function.
+
+    np.testing.assert_allclose(test_phot, check_phot)
+
+
+def test_real_psf():
+
+    # "Manually" run the calculations for the real_psf function, using some preset
+    # variables to check against these same variables (dx = 10 and dy = 15) were used
+    # to generate the test value from real_psf previously.
+
+    dx = 10
+    dy = 15
+
+    rx = 88 + 10*4
+    ry = 88 + 15*4
+    ix_rpsf = int(rx)
+    iy_rpsf = int(ry)
+    fx_rpsf = rx-ix_rpsf  # Pixel Phase
+    fy_rpsf = ry-iy_rpsf  # Pixel Phase
+    dd = np.sqrt(dx**2+dy**2)
+
+    check_rpsf_phot = ((1-fx_rpsf)*(1-fy_rpsf)*test_psf[iy_rpsf, ix_rpsf]
+                       + (fx_rpsf)*(1-fy_rpsf)*test_psf[iy_rpsf, ix_rpsf+1]
+                       + (1-fx_rpsf)*(fy_rpsf)*test_psf[iy_rpsf+1, ix_rpsf]
+                       + (fx_rpsf)*(fy_rpsf)*test_psf[iy_rpsf+1, ix_rpsf+1])
+
+    # Check the "manually" calculated value against the value generated by real_psf.
+
+    np.testing.assert_allclose(test_rpsf_phot, check_rpsf_phot)
+
+
+def test_place_source():
+
+    # Place a test source in a blank image, to ensure that this doesn't throw any exceptions.
+    # Just a check to make sure the function runs with no issues.
+
+    image = np.zeros(shape=[512, 512, 3], dtype=np.float64)
+
+    psf_middle = rind((test_psf[0].shape[0]-1)/2)
+
+    PSF_BOXSIZE = np.floor(psf_middle)/PSF_UPSCALE
+
+    test_placement = makePSF.place_source(250, 250, 15, image, test_psf,
+                                          boxsize=PSF_BOXSIZE, psf_center=psf_middle)
+
+
+def test_interpolate():
+
+    # This test is designed to test interpolations of input ePSFs in each quadrant
+    # to ensure the calculations for each quadrant run correctly.
+
+    # Get image size read in from test_psf
+
+    image_size = file_in.shape[0]
+
+    half_image = round(image_size/2)
+
+    # Generate a psf_array –– for simplicity, we set the array as test_psf three times
+
+    test_psf_array = [test_psf, test_psf, test_psf]
+
+    # Lower Left Quadrant
+
+    # Set location to the lower left quadrant
+
+    xpix_ll = 68
+    ypix_ll = 68
+
+    # Run interpolate_epsf using the test psf array, the set image size, and the
+    # specified coordinates for this quadrant.
+
+    interp_epsf_ll = makePSF.interpolate_epsf(xpix_ll, ypix_ll, test_psf_array, image_size)
+
+    # "Manually" run the calculations of the interpolate_epsf function.
+
+    xf_ll = ((xpix_ll + 4) / (half_image + 4))
+    yf_ll = ((ypix_ll + 4) / (half_image + 4))
+    check_interp_ll = (xf_ll * yf_ll * test_psf_array[1][1] +
+                       (1 - xf_ll) * (1 - yf_ll) * test_psf_array[0][0] +
+                       (xf_ll) * (1 - yf_ll) * test_psf_array[1][0] +
+                       (1 - xf_ll) * (yf_ll) * test_psf_array[0][1])
+
+    # Upper Left Quadrant
+
+    # Set location to upper left quadrant
+
+    xpix_ul = 68
+    ypix_ul = 108
+
+    # Run interpolate_epsf using the test psf array, the set image size, and the
+    # specified coordinates for this quadrant.
+
+    interp_epsf_ul = makePSF.interpolate_epsf(xpix_ul, ypix_ul, test_psf_array, image_size)
+
+    # "Manually" run the calculations of the interpolate_epsf function.
+
+    xf_ul = ((xpix_ul + 4) / (half_image + 4))
+    yf_ul = ((ypix_ul + 4 - (half_image + 4)) / (half_image + 4))
+    check_interp_ul = (xf_ul * yf_ul * test_psf_array[1][2] +
+                       (1 - xf_ul) * (1 - yf_ul) * test_psf_array[0][1] +
+                       (xf_ul) * (1 - yf_ul) * test_psf_array[1][1] +
+                       (1 - xf_ul) * (yf_ul) * test_psf_array[0][2])
+
+    # Lower Right Quadrant
+
+    # Set location to lower right quadrant
+
+    xpix_lr = 108
+    ypix_lr = 68
+
+    # Run interpolate_epsf using the test psf array, the set image size,
+    # and the specified coordinates for this quadrant.
+
+    interp_epsf_lr = makePSF.interpolate_epsf(xpix_lr, ypix_lr, test_psf_array, image_size)
+
+    # "Manually" run the calculations of the interpolate_epsf function.
+
+    xf_lr = ((xpix_lr + 4 - (half_image + 4)) / (half_image + 4))
+    yf_lr = ((ypix_lr + 4) / (half_image + 4))
+    check_interp_lr = (xf_lr * yf_lr * test_psf_array[2][1] +
+                       (1-xf_lr) * (1-yf_lr) * test_psf_array[1][0] +
+                       (xf_lr) * (1-yf_lr) * test_psf_array[2][0] +
+                       (1-xf_lr) * (yf_lr) * test_psf_array[1][1])
+
+    # Upper Right Quadrant
+
+    # Set location to upper right quadrant.
+
+    xpix_ur = 108
+    ypix_ur = 108
+
+    # Run interpolate_epsf using the test psf array, the set image size,
+    # and the specified coordinates for this quadrant.
+
+    interp_epsf_ur = makePSF.interpolate_epsf(xpix_ur, ypix_ur, test_psf_array, image_size)
+
+    # "Manually" run the calculations of the interpolate_epsf function.
+
+    xf_ur = ((xpix_ur + 4 - (half_image + 4)) / (half_image + 4))
+    yf_ur = ((ypix_ur + 4 - (half_image + 4)) / (half_image + 4))
+    check_interp_ur = (xf_ur * yf_ur * test_psf_array[2][2] +
+                       (1-xf_ur) * (1-yf_ur) * test_psf_array[1][1] +
+                       (xf_ur) * (1-yf_ur) * test_psf_array[2][1] +
+                       (1-xf_ur)*(yf_ur)*test_psf_array[1][2])
+
+    # Check that each interpolation in each qudrant generated by
+    # interpolate_epsf matches the check value.
+
+    np.testing.assert_allclose(interp_epsf_ll, check_interp_ll, atol=0.001)
+
+    np.testing.assert_allclose(interp_epsf_ul, check_interp_ul, atol=0.001)
+
+    np.testing.assert_allclose(interp_epsf_lr, check_interp_lr, atol=0.001)
+
+    np.testing.assert_allclose(interp_epsf_ur, check_interp_ur, atol=0.001)
diff --git a/stips/utilities/utilities.py b/stips/utilities/utilities.py
index 5e6fb99..523d785 100755
--- a/stips/utilities/utilities.py
+++ b/stips/utilities/utilities.py
@@ -29,6 +29,66 @@ def rind(x):
     """
     return np.round(x).astype(int)
 
+def get_pandeia_background(wfi_filter, webapp = False):
+    """
+    Import Pandeia functions to calculate the image background in a given
+    filter, in units of electrons per second.
+
+    Parameters
+    ----------
+    wfi_filter : str
+        Name of WFI filter
+    webapp: bool
+        Toggle strict API checking
+
+    Returns
+    -------
+    total_back: float
+        Total background in units of electrons per second
+    """
+    from pandeia.engine.calc_utils import build_default_calc
+    from pandeia.engine.etc3D import setup, Scene
+    from pandeia.engine.observation import Observation
+    from pandeia.engine.signal import DetectorSignal
+    from pandeia.engine.background import Background
+    import scipy.signal as sg
+
+    # Create default configuration file from Pandeia
+    calc_input = build_default_calc('roman','wfi','imaging')
+    calc_input['configuration']['instrument']['filter'] = wfi_filter.lower()
+
+    # Setup Observation
+    calc_config, instrument, strategy, scene_configuration, background, background_level, warnings = setup(calc_input, webapp=webapp)
+
+    # Create Scence
+    scene = Scene('roman', input=scene_configuration, webapp=webapp)
+
+    # Create Observation
+    observation = Observation(
+        scene=scene,
+        instrument=instrument,
+        strategy=strategy,
+        background=background,
+        background_level=background_level,
+        webapp=webapp
+        )
+
+    # Calculate signal from background, without IPC
+    observation.instrument.the_detector.ipc = False
+    my_detector_signal = DetectorSignal(observation, calc_config=calc_config, webapp=webapp, empty_scene=True)
+
+    # Extract background rate
+    rate_plus_bg = my_detector_signal.rate_plus_bg_list[0]
+    rate_per_pix = rate_plus_bg['fp_pix']
+
+    # Include IPC effects
+    kernel = observation.instrument.get_ipc_kernel()
+    detector = sg.fftconvolve(rate_per_pix, kernel, mode='same')
+
+    # Total background
+    total_back = np.mean(detector[2:-2,2:-2])
+
+    return total_back
 
 def remove_subfolder(tar_file, subfolder):
     """
@@ -265,7 +325,7 @@ def DownloadReferenceData():
 
     # webbpsf
     print("Checking webbpsf data")
-    webbpsf_url = "https://stsci.box.com/shared/static/34o0keicz2iujyilg4uz617va46ks6u9.gz"
+    webbpsf_url = "https://stsci.box.com/shared/static/t90gqazqs82d8nh25249oq1obbjfstq8.gz"
     webbpsf_data_path = os.environ[GetParameter("webbpsf_data_name", use_data=False)]
     webbpsf_data_file = "webbpsf_data.tar.gz"
     if not os.path.isdir(webbpsf_data_path):
@@ -278,14 +338,14 @@ def DownloadReferenceData():
 
     # pandeia
     print("Checking pandeia data")
-    pandeia_data_file = "pandeia_data-1.7.tar.gz"
-    pandeia_url = "https://stsci.box.com/shared/static/ycbm34uxhzafgb7te74vyl2emnr1mdty.gz"
+    pandeia_data_file = "pandeia_data-3.0.tar.gz"
+    pandeia_url = "https://stsci.box.com/shared/static/3n9e05mxkjzquxaq1gl6nqp6l0ksz5c2.gz"
     pandeia_data_path = os.environ[GetParameter("pandeia_data_name", use_data=False)]
     if not os.path.isdir(pandeia_data_path):
         print("Downloading pandeia data to {}".format(pandeia_data_path))
         os.makedirs(pandeia_data_path)
         get_compressed_file(pandeia_url, pandeia_data_file, pandeia_data_path,
-                            "pandeia_data-1.7_roman/")
+                            "pandeia_data-3.0_roman_rc3/")
     else:
         print("Found at {}".format(pandeia_data_path))