diff --git a/tools/ALARAJOYWrapper/alarajoy_QA.py b/tools/ALARAJOYWrapper/alarajoy_QA.py
deleted file mode 100644
index 3b5c719b..00000000
--- a/tools/ALARAJOYWrapper/alarajoy_QA.py
+++ /dev/null
@@ -1,210 +0,0 @@
-import subprocess
-import pandas as pd
-from string import Template
-from pathlib import Path
-import sys
-sys.path.append('..')
-from alara_output_parser import parse_tables
-
-#--------------- Running Single Parent Element Simulation(s) -----------------
-
-INPUT = 'alara.inp'
-
-# Adapted from ALARA/examples/singleElement.ala
-alara_input = Template(
-'''
-geometry rectangular
-dimension	x
-		0.0
-        1       5.0
-end
-mat_loading
-        inner_zone1  mix1
-end
-material_lib ../../data/matlib.sample
-element_lib ../../data/nuclib.std
-data_library alaralib $datalib
-mixture mix1
-        element $element              1.0     1.00
-end
-flux flux_1 ../../examples/ref_flux_files/fluxfnsfIBfw_518MW.txt  1.0   0   default
-schedule 2_year
-	2 y  flux_1  steady_state  0 s
-end
-pulsehistory steady_state
-	1	0 s
-end
-dump_file dump_singleElement
-cooling
-	1e-5 y
-	1e-2 y
-	1 y
-	100 y
-	10000 y
-end
-output interval
-        units Bq kg
-        number_density
-        specific_activity
-	total_heat
-	dose contact $datalib ../../data/ANS6_4_3
-end
-## 
-truncation  1e-7
-'''
-)
-
-def fill_alara_template(element, datalib):
-    '''
-    Substitute in the specific single parent element and path to a
-        pre-converted ALARA binary library, such as that for either FENDL2 or
-        ALARAJOY-processed FENDL3, to a template containing a generalized
-        ALARA input file text for a simple single parent element simulation.
-    Arguments:
-        element (str): Single parent element to be irradiated.
-        datalib (str): Path to the binary library.
-    
-    Returns:
-        alara_input (str): String template with appropriate variables
-            substituted in for Template identifiers.
-    '''
-
-    return alara_input.substitute(element=element, datalib=datalib)
-
-def write_alara_input_file(template):
-    '''
-    Write out the ALARA input card from the prefilled template.
-    Arguments:
-        template (str): String template with appropriate variables substituted
-            in for Template identifiers.
-    Returns:
-        None
-    '''
-
-    with open(INPUT, 'w') as f:
-        f.write(template)
-
-def run_alara(element, libname):
-    '''
-    Invoke subprocess.run() to run ALARA for the single parent element
-        irradiation simulation. Specify destination for ALARA tree file and
-        capture stdout to an output file to be read by
-        alara_pandas_parser.parse_tables().
-    Arguments:
-        element (str): Single parent element to be irradiated.
-        libname (str): Name of the source data library (i.e. fendl2, fendl3,
-            etc.)
-    Returns:
-        output (str): Path to the ALARA redirected ALARA stdout formatted as a
-            text file.
-    '''
-
-    filename_base = f'{element}_{libname}'
-    output = f'{filename_base}.out'
-    Path(output).unlink(missing_ok=True)
-    with open(output, 'w') as outfile:
-        subprocess.run(
-            ['alara', '-t', f'{filename_base}.tree', '-v', '3', INPUT],
-            stdout=outfile,
-            stderr=subprocess.STDOUT,
-            check=True
-        )
-
-    return output
-
-#---------- Loading Results of Simulation(s) into a Data Structure -----------
-
-def make_entry(datalib, variable, unit, data):
-    '''
-    Construct a dictionary entry for a single Pandas DataFrame and its
-        associated metadata for updating into a larger data dictionary.
-    Arguments:
-        datalib (str): Data source for the DataFrame (i.e. fendl2,
-            ALARAJOY-fendl3, etc.).
-        variable (str): Dependent variable evaluated in DataFrame (i.e. Number
-            Density, Specific Activity, etc.).
-        unit (str): Associated units for the above variable (i.e. atoms/kg, 
-            Bq/kg, etc.).
-        data (pandas.core.frame.DataFrame): Pandas DataFrame described by
-            the above metadata.
-    Returns:
-        entry (dict): Single data entry for dictionary containing potentially
-            multiple dataframes and associated metadata.
-    '''
-
-    return {
-        f'{datalib} {variable}': {
-            'Data Source': datalib,
-            'Variable': variable,
-            'Unit': unit,
-            'Data': data
-        }
-    }
-
-def process_data(
-        data_source,
-        inp_datalib=None,
-        inp_variable=None,
-        inp_unit=None
-        ):
-    '''
-    Flexibly create a dictionary of subdictionaries containing Pandas
-        DataFrames with associated metadata containing ALARA output data for
-        different variables. Allows for processing of either existing
-        DataFrames, with the requirement that the user provide the relevant
-        data source, evaulated variable, and its relevant units to be packaged
-        into a data dictionary. Alternatively, this function can directly
-        read in an ALARA output file and parse all tables and their metadata
-        internally.
-    Arguments:
-        data_source (dict or pandas.core.frame.DataFrame): ALARA output data.
-            If parsing directly from ALARA output files, data_source must be
-            formatted as a dictionary of the form:
-            data_source = {
-                Data Library 1 : path/to/output/file/for/data/library1,
-                Data Library 2 : path/to/output/file/for/data/library2
-            }
-            If processing a preexisting DataFrame, then data_source is just
-            the DataFrame itself.
-        inp_datalib (str or None, optional): Data source of the selected
-            DataFrame. Required if processing a preexisting DataFrame;
-            irrelevant if parsing directly from ALARA output files.
-            (Defaults to None)
-        inp_variable (str or None, optional): Evaluated variable for the
-            selected DataFrame. Required if processing a preexisting
-            DataFrame; irrelevant if parsing directly from ALARA output files.
-            (Defaults to None)
-        inp_unit (str or None, optional): Appropriate unit for the evaluated
-            variable for the selected DataFrame. Required if processing a
-            preexisting DataFrame; irrelevant if parsing directly from ALARA
-            output files.
-            (Defaults to None)
-    Returns:
-        dfs (list of dicts): List of dictionaries containing ALARA output
-            DataFrames and their metadata, of the form:
-            df_dict = {
-                'Data Source' : (Either 'fendl2' or 'fendl3'),
-                'Variable'    : (Any ALARA output variable, dependent on ALARA
-                                 run parameters),
-                'Unit'        : (Respective unit matching the above variable),
-                'Data'        : (DataFrame containing ALARA output data for
-                                 the given data source, variable, and unit)
-            }
-    '''
-
-    dfs = {}
-
-    if type(data_source) == pd.core.frame.DataFrame:
-        dfs.update(
-            make_entry(inp_datalib, inp_variable, inp_unit, data_source)
-        )
-        return dfs
-
-    for datalib, output_path in data_source.items():
-        output_tables = parse_tables(output_path)
-        for key, data in output_tables.items():
-            variable_w_unit, _ = key.split(' - ')
-            variable, unit = variable_w_unit.split(' [')
-            dfs.update(make_entry(datalib, variable, unit.strip(']'), data))
-
-    return dfs
\ No newline at end of file
diff --git a/tools/ALARAJOYWrapper/alarajoy_QA_notebook.ipynb b/tools/ALARAJOYWrapper/alarajoy_QA_notebook.ipynb
index 428aabcc..d3c9049e 100644
--- a/tools/ALARAJOYWrapper/alarajoy_QA_notebook.ipynb
+++ b/tools/ALARAJOYWrapper/alarajoy_QA_notebook.ipynb
@@ -21,13 +21,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 78,
    "id": "57313772",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import alarajoy_QA as qa\n",
-    "import importlib"
+    "from pyalara import fill_alara_template, write_alara_input_file, run_alara\n",
+    "import alara_output_processing as aop\n",
+    "import importlib\n",
+    "\n",
+    "import sys\n",
+    "sys.path.append('../alara_output_processing')\n",
+    "\n",
+    "import alara_output_plotting as alaraplot\n"
    ]
   },
   {
@@ -40,24 +46,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 79,
    "id": "4eae0d85",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'fendl2': 'fe_fendl2.out', 'fendl3': 'fe_fendl3.out'}"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "importlib.reload(qa)\n",
-    "data_soure = {\n",
+    "data_source = {\n",
     "    'fendl2' : '/groupspace/shared/n/nukecode/ALARA/data/fendl2bin', # Example path\n",
     "    'fendl3' : '../../examples/data/fendl3' # Example path\n",
     "}\n",
     "\n",
     "element = input('Select single parent element to evaluate: ').lower()\n",
     "\n",
-    "for libname, binary in data_soure.items():\n",
-    "    alara_input = qa.fill_alara_template(element, binary)\n",
-    "    qa.write_alara_input_file(alara_input)\n",
-    "    output = qa.run_alara(element,libname)\n",
-    "    data_soure[libname] = output"
+    "for libname, binary in data_source.items():\n",
+    "    alara_input = fill_alara_template(element, binary)\n",
+    "    write_alara_input_file(alara_input)\n",
+    "    output = run_alara(element,libname)\n",
+    "    data_source[libname] = output\n",
+    "data_source"
    ]
   },
   {
@@ -65,27 +82,28 @@
    "id": "20ac9949",
    "metadata": {},
    "source": [
-    "**Convert ALARA output files to DataFrames**\n",
+    "**Convert ALARA output files to Specialized ALARA DataFrame (ALARADFrame)**\n",
     "\n",
-    "Store dataframes with each table's metadata into a nested dictionary for all tables in both simulations.\n",
-    "\n",
-    "This nested dictionary is of the form:\n",
+    "Store all tabular data from both runs' output files into a single ALARADFrame of the form:\n",
     "\n",
     "```\n",
-    "df_dict = {\n",
-    "    'Data Source' : (Either 'fendl2' or 'fendl3'),\n",
-    "    'Variable'    : (Any ALARA output variable, dependent on ALARA\n",
-    "                        run parameters),\n",
-    "    'Unit'        : (Respective unit matching the above variable),\n",
-    "    'Data'        : (DataFrame containing ALARA output data for\n",
-    "                        the given data source, variable, and unit)\n",
-    "}\n",
+    "{\n",
+    "    'time'      : Cooling time of data entry,\n",
+    "    'time_unit' : Units for cooling times,\n",
+    "    'nuclide'   : Individual nuclide,\n",
+    "    'run_lbl'   : Distinguisher between runs,\n",
+    "    'block'     : ALARADFrame block integer enumerator,\n",
+    "    'block_num' : Geometric position of block,\n",
+    "    'variable'  : ALARADFrame variable integer enumerator,\n",
+    "    'var_unit'  : Unit for corresponding variable,\n",
+    "    'value'     : Float value for the corresponding variable\n",
+    "} \n",
     "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 80,
    "id": "7bd99054",
    "metadata": {},
    "outputs": [
@@ -110,65 +128,77 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>isotope</th>\n",
-       "      <th>shutdown</th>\n",
-       "      <th>1e-05y</th>\n",
-       "      <th>0.01y</th>\n",
-       "      <th>1y</th>\n",
-       "      <th>100y</th>\n",
-       "      <th>10000y</th>\n",
+       "      <th>time</th>\n",
+       "      <th>time_unit</th>\n",
+       "      <th>nuclide</th>\n",
+       "      <th>run_lbl</th>\n",
+       "      <th>block</th>\n",
+       "      <th>block_num</th>\n",
+       "      <th>variable</th>\n",
+       "      <th>var_unit</th>\n",
+       "      <th>value</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>s</td>\n",
        "      <td>h-1</td>\n",
-       "      <td>1.170900e+22</td>\n",
-       "      <td>1.170900e+22</td>\n",
-       "      <td>1.170900e+22</td>\n",
-       "      <td>1.170900e+22</td>\n",
-       "      <td>1.170900e+22</td>\n",
-       "      <td>1.170900e+22</td>\n",
+       "      <td>fendl2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>atoms/kg</td>\n",
+       "      <td>1.176100e+22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>h-2</td>\n",
-       "      <td>2.823400e+20</td>\n",
-       "      <td>2.823400e+20</td>\n",
-       "      <td>2.823400e+20</td>\n",
-       "      <td>2.823400e+20</td>\n",
-       "      <td>2.823400e+20</td>\n",
-       "      <td>2.823400e+20</td>\n",
+       "      <td>3.153600e+02</td>\n",
+       "      <td>s</td>\n",
+       "      <td>h-1</td>\n",
+       "      <td>fendl2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>atoms/kg</td>\n",
+       "      <td>1.176100e+22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>h-3</td>\n",
-       "      <td>1.620800e+17</td>\n",
-       "      <td>1.620800e+17</td>\n",
-       "      <td>1.619800e+17</td>\n",
-       "      <td>1.532500e+17</td>\n",
-       "      <td>5.979000e+14</td>\n",
-       "      <td>0.000000e+00</td>\n",
+       "      <td>3.153600e+05</td>\n",
+       "      <td>s</td>\n",
+       "      <td>h-1</td>\n",
+       "      <td>fendl2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>atoms/kg</td>\n",
+       "      <td>1.176100e+22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>he-3</td>\n",
-       "      <td>1.086300e+16</td>\n",
-       "      <td>1.086300e+16</td>\n",
-       "      <td>1.095400e+16</td>\n",
-       "      <td>1.969300e+16</td>\n",
-       "      <td>1.723400e+17</td>\n",
-       "      <td>1.729400e+17</td>\n",
+       "      <td>3.153600e+07</td>\n",
+       "      <td>s</td>\n",
+       "      <td>h-1</td>\n",
+       "      <td>fendl2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>atoms/kg</td>\n",
+       "      <td>1.176100e+22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>he-4</td>\n",
-       "      <td>2.652700e+21</td>\n",
-       "      <td>2.652700e+21</td>\n",
-       "      <td>2.652700e+21</td>\n",
-       "      <td>2.652700e+21</td>\n",
-       "      <td>2.652700e+21</td>\n",
-       "      <td>2.652700e+21</td>\n",
+       "      <td>3.153600e+09</td>\n",
+       "      <td>s</td>\n",
+       "      <td>h-1</td>\n",
+       "      <td>fendl2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>atoms/kg</td>\n",
+       "      <td>1.176100e+22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -179,107 +209,300 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>219</th>\n",
-       "      <td>fe-58r</td>\n",
-       "      <td>1.241400e+18</td>\n",
-       "      <td>1.241400e+18</td>\n",
-       "      <td>1.241400e+18</td>\n",
-       "      <td>1.241400e+18</td>\n",
-       "      <td>1.241400e+18</td>\n",
-       "      <td>1.241400e+18</td>\n",
+       "      <th>1675</th>\n",
+       "      <td>3.153600e+02</td>\n",
+       "      <td>s</td>\n",
+       "      <td>total</td>\n",
+       "      <td>fendl3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Sv/hr</td>\n",
+       "      <td>4.619700e+04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>220</th>\n",
-       "      <td>fe-58s</td>\n",
-       "      <td>1.627600e+18</td>\n",
-       "      <td>1.627600e+18</td>\n",
-       "      <td>1.627600e+18</td>\n",
-       "      <td>1.627600e+18</td>\n",
-       "      <td>1.627600e+18</td>\n",
-       "      <td>1.627600e+18</td>\n",
+       "      <th>1676</th>\n",
+       "      <td>3.153600e+05</td>\n",
+       "      <td>s</td>\n",
+       "      <td>total</td>\n",
+       "      <td>fendl3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Sv/hr</td>\n",
+       "      <td>3.624700e+03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>221</th>\n",
-       "      <td>fe-58t</td>\n",
-       "      <td>4.993900e+17</td>\n",
-       "      <td>4.993900e+17</td>\n",
-       "      <td>4.993900e+17</td>\n",
-       "      <td>4.993900e+17</td>\n",
-       "      <td>4.993900e+17</td>\n",
-       "      <td>4.993900e+17</td>\n",
+       "      <th>1677</th>\n",
+       "      <td>3.153600e+07</td>\n",
+       "      <td>s</td>\n",
+       "      <td>total</td>\n",
+       "      <td>fendl3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Sv/hr</td>\n",
+       "      <td>1.613400e+03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>222</th>\n",
-       "      <td>fe-58u</td>\n",
-       "      <td>1.755000e+18</td>\n",
-       "      <td>1.755000e+18</td>\n",
-       "      <td>1.755000e+18</td>\n",
-       "      <td>1.755000e+18</td>\n",
-       "      <td>1.755000e+18</td>\n",
-       "      <td>1.755000e+18</td>\n",
+       "      <th>1678</th>\n",
+       "      <td>3.153600e+09</td>\n",
+       "      <td>s</td>\n",
+       "      <td>total</td>\n",
+       "      <td>fendl3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Sv/hr</td>\n",
+       "      <td>2.942800e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>223</th>\n",
+       "      <th>1679</th>\n",
+       "      <td>3.153600e+11</td>\n",
+       "      <td>s</td>\n",
        "      <td>total</td>\n",
-       "      <td>1.079400e+25</td>\n",
-       "      <td>1.079400e+25</td>\n",
-       "      <td>1.079400e+25</td>\n",
-       "      <td>1.079400e+25</td>\n",
-       "      <td>1.079400e+25</td>\n",
-       "      <td>1.079400e+25</td>\n",
+       "      <td>fendl3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Sv/hr</td>\n",
+       "      <td>2.936900e-08</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>224 rows × 7 columns</p>\n",
+       "<p>2478 rows × 9 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "    isotope      shutdown        1e-05y         0.01y            1y  \\\n",
-       "0       h-1  1.170900e+22  1.170900e+22  1.170900e+22  1.170900e+22   \n",
-       "1       h-2  2.823400e+20  2.823400e+20  2.823400e+20  2.823400e+20   \n",
-       "2       h-3  1.620800e+17  1.620800e+17  1.619800e+17  1.532500e+17   \n",
-       "3      he-3  1.086300e+16  1.086300e+16  1.095400e+16  1.969300e+16   \n",
-       "4      he-4  2.652700e+21  2.652700e+21  2.652700e+21  2.652700e+21   \n",
-       "..      ...           ...           ...           ...           ...   \n",
-       "219  fe-58r  1.241400e+18  1.241400e+18  1.241400e+18  1.241400e+18   \n",
-       "220  fe-58s  1.627600e+18  1.627600e+18  1.627600e+18  1.627600e+18   \n",
-       "221  fe-58t  4.993900e+17  4.993900e+17  4.993900e+17  4.993900e+17   \n",
-       "222  fe-58u  1.755000e+18  1.755000e+18  1.755000e+18  1.755000e+18   \n",
-       "223   total  1.079400e+25  1.079400e+25  1.079400e+25  1.079400e+25   \n",
+       "              time time_unit nuclide run_lbl  block block_num  variable  \\\n",
+       "0     0.000000e+00         s     h-1  fendl2      0         1         0   \n",
+       "1     3.153600e+02         s     h-1  fendl2      0         1         0   \n",
+       "2     3.153600e+05         s     h-1  fendl2      0         1         0   \n",
+       "3     3.153600e+07         s     h-1  fendl2      0         1         0   \n",
+       "4     3.153600e+09         s     h-1  fendl2      0         1         0   \n",
+       "...            ...       ...     ...     ...    ...       ...       ...   \n",
+       "1675  3.153600e+02         s   total  fendl3      0         1         6   \n",
+       "1676  3.153600e+05         s   total  fendl3      0         1         6   \n",
+       "1677  3.153600e+07         s   total  fendl3      0         1         6   \n",
+       "1678  3.153600e+09         s   total  fendl3      0         1         6   \n",
+       "1679  3.153600e+11         s   total  fendl3      0         1         6   \n",
        "\n",
-       "             100y        10000y  \n",
-       "0    1.170900e+22  1.170900e+22  \n",
-       "1    2.823400e+20  2.823400e+20  \n",
-       "2    5.979000e+14  0.000000e+00  \n",
-       "3    1.723400e+17  1.729400e+17  \n",
-       "4    2.652700e+21  2.652700e+21  \n",
-       "..            ...           ...  \n",
-       "219  1.241400e+18  1.241400e+18  \n",
-       "220  1.627600e+18  1.627600e+18  \n",
-       "221  4.993900e+17  4.993900e+17  \n",
-       "222  1.755000e+18  1.755000e+18  \n",
-       "223  1.079400e+25  1.079400e+25  \n",
+       "      var_unit         value  \n",
+       "0     atoms/kg  1.176100e+22  \n",
+       "1     atoms/kg  1.176100e+22  \n",
+       "2     atoms/kg  1.176100e+22  \n",
+       "3     atoms/kg  1.176100e+22  \n",
+       "4     atoms/kg  1.176100e+22  \n",
+       "...        ...           ...  \n",
+       "1675     Sv/hr  4.619700e+04  \n",
+       "1676     Sv/hr  3.624700e+03  \n",
+       "1677     Sv/hr  1.613400e+03  \n",
+       "1678     Sv/hr  2.942800e-08  \n",
+       "1679     Sv/hr  2.936900e-08  \n",
        "\n",
-       "[224 rows x 7 columns]"
+       "[2478 rows x 9 columns]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dfs = qa.process_data(data_soure)\n",
+    "lib = aop.DataLibrary()\n",
+    "adf = aop.DataLibrary.make_entries(lib, data_source)\n",
+    "adf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3c60f38",
+   "metadata": {},
+   "source": [
+    "**Plot for a single data source**\n",
+    "\n",
+    "Simple plot showing the top five contributors to number density from a single data source. The function `alaraplot.plot_single_response()` can be used either singularly or comparitavely between data libraries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "1e025489",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "importlib.reload(alaraplot)\n",
+    "alaraplot.plot_single_response(\n",
+    "    adf,\n",
+    "    run_lbls='fendl3',\n",
+    "    variable='Number Density',\n",
+    "    head=6,\n",
+    "    total=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f122193b",
+   "metadata": {},
+   "source": [
+    "**Comparitavely plot data for FENDL2 and ALARAJOY-processed FENDL3 data**\n",
+    "\n",
+    "Below are two example plots showing this comparative functionality. The first follows the above single data source number density plot, here comparing the plots for both data sets.\n",
+    "\n",
+    "The second plot focuses specifically on the number density of light gas production."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "1c36abaa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "datalibs = ['fendl2', 'fendl3']\n",
     "\n",
-    "key_list = list(dfs.keys())\n",
-    "arbitrary_df = dfs[key_list[0]]['Data'] # 0 arbitrarily chosen as key_list index\n",
-    "df_time_cols = arbitrary_df.columns[1:] # All dfs have same time cols; any index valid\n",
-    "times = list(df_time_cols)\n",
+    "alaraplot.plot_single_response(\n",
+    "    adf,\n",
+    "    run_lbls=datalibs,\n",
+    "    variable='Number Density',\n",
+    "    head=6\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "7396985f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "importlib.reload(alaraplot)\n",
+    "gases = ['h', 'he']\n",
+    "alaraplot.plot_single_response(\n",
+    "    adf,\n",
+    "    run_lbls=datalibs,\n",
+    "    variable='Number Density',\n",
+    "    nuclides=gases \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48095e5b",
+   "metadata": {},
+   "source": [
+    "**Iteratively compare total values for each variable**\n",
+    "\n",
+    "The ALARA simulations initiated by `alarajoy_QA` produce tables tracking four variables against cooling times:\n",
+    "\n",
+    "1. Number Density (atoms/kg)\n",
+    "2. Specific Activity (Bq/kg)\n",
+    "3. Total Decay Heat (W/kg)\n",
+    "4. Contact Dose (Sv/hr)\n",
+    "\n",
+    "In reality, specific activity, total decay heat, and contact dose are all functions of number density (as well as the particular radiological properties of the produced radionuclides), however, the inclusion of these other variables in certain plotting scenarios can nonetheless prove valuable in studying particular decay patterns, such as identifying discrepancies in particular isotopic contributions to long term activity.\n",
+    "\n",
+    "The following example plots show the comparison of the total values for each of these variables respectively at each cooling time across both of the data libraries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "70ef1c57",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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COGWOiCiX2rVrSzcjJSIiovKNI0RERERERGSyOEJERERlFpfBEhHRi+IIERERERERmSwmREREREREZLKYEBFRqSCTyTBjxgy9suPHj6NZs2awsrKCTCbDmTNnMGPGDMhkMuME+QLatGmDNm3aFPm8/fv3QyaT8QacL2jp0qWQyWSIjIyUyp73NXlZIiMjIZPJsHTpUmOHQkRUrjEhIjIR58+fR9++feHl5QW1Wo2KFSuiY8eOmD9/vrFDy5dGo0G/fv3w5MkTzJ07F8uXL4eXl1eJXe/999+HTCbDgAEDnruN8PBwzJgxQ+9Nd0lYtWoV5s2bV6LXKCkJCQkIDQ1F3bp1YW1tDQsLC/j7++ODDz7AvXv3jB1eictO6Av7Ks2JGhFRecMbsxKZgMOHD6Nt27aoXLkyhg4dCnd3d9y+fRtHjx7FjRs3cP36dWOHiLS0NJiZmcHMLGuvl8uXL6NWrVpYtGgR3nzzTaleZmYmMjMzoVari+3aQghUrlwZZmZmiI6ORnR0NGxsbIrczoYNG9CvXz/s27cvzxvajIwMAIC5uXmR2tTpdMjIyIC5uTnk8qzPsLp164YLFy6UeOJV3G7evIkOHTogKioK/fr1Q4sWLWBubo5z585h9erVcHR0xNWrV0vk2kuXLsXw4cMREREBb29vAM//mryIc+fO4dy5c9LjpKQkjBkzBr169ULv3r2lcjc3N3To0AHp6elQKpVQKBQvLUYiIlPDXeaITMCsWbNgZ2eH48ePw97eXu/Yw4cPjRNULrkTnOy4csebM2kqLvv378edO3fw119/ITg4GJs2bcLQoUOL9RrP+6ZbLpcXa/JnLJmZmejduzeio6Oxf/9+tGjRQu/4rFmz8NVXX73UmF5mIpStTp06qFOnjvQ4JiYGY8aMQZ06dTB48OA89cvDa09EVNpxyhyRCbhx4wZq166dJ7kAAFdXV73HMpkM48aNw8qVK1GjRg2o1WrUr18fBw4cyHPu3bt3MWLECLi5uUGlUqF27dpYvHhxnnppaWmYMWMGqlevDrVajQoVKqB37964ceOG3nWz1xANGzYMrVu3BgD069dPbwpRQWuIVqxYgUaNGsHS0hIODg5o1aoVdu3aZdDzs3LlSvj5+aFt27bo0KEDVq5cmW+9u3fvYuTIkfDw8IBKpYKPjw/GjBmDjIwMLF26FP369QMAtG3bVpr6lL32J+d6lejoaJiZmSE0NDTPNa5cuQKZTIYffvgBQN41RG3atMG2bdtw69Yt6Rre3t5ISkqClZUV3n777Txt3rlzBwqFAl988UW+/dJoNHB0dMTw4cPzHEtISIBarcbkyZOlsvnz56N27drSc92gQQOsWrUq/yf3qY0bN+Ls2bOYNm1anmQIAGxtbTFr1iy9svXr16N+/fqwsLCAs7MzBg8ejLt37+Y596+//kLLli1hZWUFe3t7vPrqq7h06dIz4wHyriHKfq7XrVuHWbNmwdPTE2q1Gu3bt893FDUsLAxVqlSBhYUFGjVqhIMHDxbruqT81hANGzYM1tbWiIqKQrdu3WBtbY2KFSsiLCwMQNbU2Hbt2sHKygpeXl75vi5xcXGYOHEiKlWqBJVKBV9fX3z11VfQ6XTFEjcRUVnDhIjIBHh5eeHkyZO4cOGCQfX//vtvTJw4EYMHD8ann36Kx48fo1OnTnrnR0dHo0mTJtizZw/GjRuH7777Dr6+vhg5cqTe+hatVotu3bohNDQU9evXx7fffou3334b8fHxBcbz1ltv4cMPPwQATJgwAcuXL8e0adMKjDc0NBRvvPEGlEolPv30U4SGhqJSpUr466+/Cu1reno6Nm7ciEGDBgEABg0ahL/++gsPHjzQq3fv3j00atQIa9aswYABA/D999/jjTfewN9//42UlBS0atUKEyZMAAB8+OGHWL58OZYvX45atWrluaabmxtat26NdevW5Tm2du1aKBQKKbnKbdq0aQgMDISzs7N0jXnz5sHa2hq9evXC2rVrodVq9c5ZvXo1hBB4/fXX821TqVSiV69e2LJlizSNLNuWLVuQnp6OgQMHAgAWLVqECRMmwM/PD/PmzUNoaCgCAwPx77//5tt2tt9//x0A8MYbbzyzXralS5eif//+UiI3atQobNq0CS1atEBcXJxUb8+ePQgODsbDhw8xY8YMTJo0CYcPH0bz5s2fe0rhl19+ic2bN2Py5MmYOnUqjh49mue5++mnnzBu3Dh4enri66+/RsuWLdGzZ0/cuXPnua5ZFFqtFp07d0alSpXw9ddfw9vbG+PGjcPSpUvRqVMnNGjQAF999RVsbGwwZMgQRERESOempKSgdevWWLFiBYYMGYLvv/8ezZs3x9SpUzFp0qQSj52IqFQSRFTu7dq1SygUCqFQKETTpk3F+++/L3bu3CkyMjLy1AUgAIgTJ05IZbdu3RJqtVr06tVLKhs5cqSoUKGCiImJ0Tt/4MCBws7OTqSkpAghhFi8eLEAIObMmZPnWjqdTu+6n3zyifR43759AoBYv3693jmffPKJyPmn69q1a0Iul4tevXoJrVZbYPsF2bBhgwAgrl27JoQQIiEhQajVajF37ly9ekOGDBFyuVwcP368wH6sX79eABD79u3LU6d169aidevW0uOff/5ZABDnz5/Xq+fn5yfatWsnPc5+HnK22bVrV+Hl5ZXnGjt37hQAxI4dO/TK69Spo3ft/GSf+8cff+iVd+nSRVSpUkV6/Oqrr4ratWs/s6381KtXT9jZ2RlUNyMjQ7i6ugp/f3+RmpoqlW/dulUAENOnT5fKAgMDhaurq3j8+LFUdvbsWSGXy8WQIUOksiVLlggAIiIiQirL/ZpkP9e1atUS6enpUvl3332n91qlp6cLJycn0bBhQ6HRaKR6S5cuFQAKfa5zevToUZ6f/WwRERECgFiyZIlUNnToUAFAfP7551JZbGyssLCwEDKZTKxZs0Yqv3z5cp62Z86cKaysrMTVq1f1rjVlyhShUChEVFSUwbETEZUXHCEiMgEdO3bEkSNH0KNHD5w9exZff/01goODUbFiRemT+5yaNm2K+vXrS48rV66MV199FTt37oRWq4UQAhs3bkT37t0hhEBMTIz0FRwcjPj4eJw6dQpA1lQpZ2dnjB8/Ps91imP77C1btkCn02H69OnSpgNFaX/lypVo0KABfH19AQA2Njbo2rWr3rQ5nU6HLVu2oHv37mjQoEGeNp6nH71794aZmRnWrl0rlV24cAHh4eHPvdNdhw4d4OHhoRf7hQsXcO7cuXzXp+TUrl07ODs768UTGxuL3bt368Vjb2+PO3fu4Pjx40WKLSEhweCNKk6cOIGHDx9i7Nixemtounbtipo1a2Lbtm0AgPv37+PMmTMYNmwYHB0dpXp16tRBx44dsX379iLFmG348OF664tatmwJIGtTiOz4Hj9+jFGjRumtZ3v99dfh4ODwXNcsqpwbjdjb26NGjRqwsrJC//79pfIaNWrA3t5eihvImobYsmVLODg46P3edujQAVqtNt+psURE5R0TIiIT0bBhQ2zatAmxsbE4duwYpk6disTERPTt2xfh4eF6datVq5bn/OrVqyMlJQWPHj3Co0ePEBcXh4ULF8LFxUXvK3sdSvamCDdu3ECNGjWKfSOEbDdu3IBcLoefn1+Rz42Li8P27dvRunVrXL9+Xfpq3rw5Tpw4Ie149ujRIyQkJMDf37/Y4nZ2dkb79u31ps2tXbsWZmZmeruNFYVcLsfrr7+OLVu2ICUlBUBWwqdWqwucgpfNzMwMffr0wW+//Yb09HQAwKZNm6DRaPQSog8++ADW1tZo1KgRqlWrhpCQEBw6dKjQ2GxtbZGYmGhQP27dugUg6w19bjVr1pSOP6terVq1EBMTg+TkZIOumVPlypX1HmcnObGxsXrXzU6is5mZmUk72JUktVoNFxcXvTI7Ozt4enrmSc7t7OykuAHg2rVr+PPPP/P83nbo0AFA6dlkhYjoZWJCRGRizM3N0bBhQ3z++ef46aefoNFosH79+iK1kb34evDgwdi9e3e+X82bNy+J8IvV+vXrkZ6ejm+//RbVqlWTvrLXUhS0uUJxGThwIK5evYozZ84AANatW4f27dvD2dn5udscMmQIkpKSsGXLFgghsGrVKnTr1g12dnYGxZOYmIgdO3ZI8dSsWRN169aV6tSqVQtXrlzBmjVr0KJFC2zcuBEtWrTAJ5988sy2a9asifj4eNy+ffu5+/ayFLTFtSgld6koKD5D4tbpdOjYsWOBv7d9+vQpkZiJiEozbrtNZMKyp3/dv39fr/zatWt56l69ehWWlpbSJ9M2NjbQarXSJ8sFqVq1Kv79919oNBoolcpiily/fZ1Oh/DwcAQGBhbp3JUrV8Lf3z/fN/M///wzVq1ahdDQULi4uMDW1rbQTSmKOnWuZ8+eeOutt6RpalevXsXUqVMLPe9Z1/H390e9evWwcuVKeHp6IioqyuCb77Zq1QoVKlTA2rVr0aJFC/z111/5bmZhZWWFAQMGYMCAAcjIyEDv3r0xa9YsTJ06tcBtort3747Vq1djxYoVhfYx+wa8V65cQbt27fSOXblyRTqes15uly9fhrOzM6ysrArveBFlX/f69eto27atVJ6ZmYnIyEi9bbVLm6pVqyIpKanQ31siIlPCESIiE7Bv3758P93OXmORe8rRkSNHpDVAAHD79m389ttvCAoKgkKhgEKhQJ8+fbBx48Z8k4RHjx5J3/fp0wcxMTHSNtI5Fccn7j179oRcLsenn36aZ9vgZ7V/+/ZtHDhwAP3790ffvn3zfA0fPhzXr1/Hv//+C7lcjp49e+KPP/7AiRMnCuxH9pvvnLugPYu9vT2Cg4Oxbt06rFmzBubm5ujZs2eh51lZWSE+Pr7A42+88QZ27dqFefPmwcnJCZ07dzYoHrlcjr59++KPP/7A8uXLkZmZmWc90+PHj/Uem5ubw8/PD0IIaDSaAtvu27cvAgICMGvWLBw5ciTP8cTERCn5atCgAVxdXbFgwQJp+h4A7NixA5cuXULXrl0BABUqVEBgYCCWLVum95xfuHABu3btQpcuXQzqd1E1aNAATk5OWLRoETIzM6XylStX6k1PK4369++PI0eOYOfOnXmOxcXF6fWHiMhUcISIyASMHz8eKSkp6NWrF2rWrImMjAwcPnwYa9euhbe3d577z/j7+yM4OBgTJkyASqXCjz/+CAB698358ssvsW/fPjRu3BijRo2Cn58fnjx5glOnTmHPnj148uQJgKwpXL/++ismTZqEY8eOoWXLlkhOTsaePXswduxYvPrqqy/UN19fX0ybNg0zZ85Ey5Yt0bt3b6hUKhw/fhweHh4F3ntn1apVEEKgR48e+R7v0qULzMzMsHLlSjRu3Biff/45du3ahdatW2P06NGoVasW7t+/j/Xr1+Off/6Bvb09AgMDoVAo8NVXXyE+Ph4qlQrt2rXLc6+nnAYMGIDBgwfjxx9/RHBwcL73isqtfv36WLt2LSZNmoSGDRvC2toa3bt3l46/9tpreP/997F582aMGTOmSCNzAwYMwPz58/HJJ58gICAgz7bhQUFBcHd3R/PmzeHm5oZLly7hhx9+QNeuXZ+5aYJSqcSmTZvQoUMHtGrVCv3790fz5s2hVCpx8eJFrFq1Cg4ODpg1axaUSiW++uorDB8+HK1bt8agQYMQHR2N7777Dt7e3njnnXekdmfPno3OnTujadOmGDlyJFJTUzF//nzY2dlJ97Uqbubm5pgxYwbGjx+Pdu3aoX///oiMjMTSpUtRtWrVYtkspKS89957+P3339GtWzcMGzYM9evXR3JyMs6fP48NGzYgMjLyhaZsEhGVSUba3Y6IXqIdO3aIESNGiJo1awpra2thbm4ufH19xfjx40V0dLReXQAiJCRErFixQlSrVk2oVCpRr169fLeSjo6OFiEhIaJSpUpCqVQKd3d30b59e7Fw4UK9eikpKWLatGnCx8dHqte3b19x48YNves+z7bb2RYvXizq1asnVCqVcHBwEK1btxa7d+8u8DkJCAgQlStXftbTJtq0aSNcXV2lrZVv3bolhgwZIlxcXIRKpRJVqlQRISEhels0L1q0SFSpUkUoFAq97bJzb/GcLSEhQVhYWAgAYsWKFXmO57ftdlJSknjttdeEvb29AJDvFtxdunQRAMThw4ef2cfcdDqdqFSpkgAgPvvsszzHf/75Z9GqVSvh5OQkVCqVqFq1qnjvvfdEfHy8Qe3HxsaK6dOni4CAAGFpaSnUarXw9/cXU6dOFffv39eru3btWuk1dXR0FK+//rq4c+dOnjb37NkjmjdvLiwsLIStra3o3r27CA8P16tTlG23c//M5bf9tRBCfP/998LLy0uoVCrRqFEjcejQIVG/fn3RqVMng54LIZ5v220rK6s8dVu3bp3vduheXl6ia9euemWJiYli6tSpwtfXV5ibmwtnZ2fRrFkz8c033+S7FT8RUXknE6KUrBIlolJBJpMhJCQk3yluVHb06tUL58+fx/Xr140disnQ6XRwcXFB7969sWjRImOHQ0REBuIaIiKicub+/fvYtm0b3njjDWOHUm6lpaXlWaP266+/4smTJ2jTpo1xgiIioufCNUREROVEREQEDh06hP/9739QKpV46623jB1SuXX06FG888476NevH5ycnHDq1Cn88ssv8Pf3L/SeT0REVLowISIiKif+/vtvDB8+HJUrV8ayZcvg7u5u7JDKLW9vb1SqVAnff/89njx5AkdHRwwZMgRffvklzM3NjR0eEREVAdcQERERERGRyeIaIiIiIiIiMllMiIiIiIiIyGQxIaIy5euvv0bNmjWh0+mMHQpRHsOGDYO3t7exw3hpDO2vt7c3unXrVvIBFTNvb28MGzbM2GGUOjKZDOPGjTNqDDNmzCi1N8AdOHAg+vfvb+wwiKgImBBRmZGQkICvvvoKH3zwAeTyvD+6cXFxUKvVkMlkuHTpUr5tDBs2DNbW1gZf09A2ZTKZ9KVSqVC9enVMnz4daWlpBbbdv39/yGQyfPDBB/ke379/f5523dzc0KZNG3z++ed49OhRnnOWLl2qd07OrylTphjc79Isu48nTpzI93ibNm3g7+//kqMqGp1Oh19//RWNGzeGo6MjbGxsUL16dQwZMgRHjx4t1muFh4djxowZiIyMLNZ2S5M5c+ZAJpNhz549BdZZtGgRZDIZfv/995cYmfEdPnwYM2bMQFxcnLFDeabPP/8cW7ZsMXYYxeKDDz7Axo0bcfbsWWOHQkQGYkJEZcbixYuRmZmJQYMG5Xt8/fr1kMlkcHd3x8qVK4vlmoa2qVKpsHz5cixfvhxz5syBt7c3Zs6ciZEjR+ZbPyEhAX/88Qe8vb2xevXqPPczyWnChAlYvnw5Fi5ciPfeew+Ojo745JNPUKtWLfz111/5nvPpp59K8WR/DRw4sGidpxIzYcIEDB06FBUqVMCMGTPw1VdfoXPnzjh69Cj+/PPPYr1WeHg4QkNDy3VCNHDgQMjlcqxatarAOqtWrYKTkxM6d+78EiMzvsOHDyM0NJQJ0UtUr149NGjQAN9++62xQyEiA3HbbSozlixZgh49ekCtVud7fMWKFejSpQu8vLywatUqfPbZZy98TUPbNDMzw+DBg6XHY8eORbNmzbB69WrMmTMHbm5uevU3btwIrVaLxYsXo127djhw4ABat26db9stW7ZE37599crOnj2LoKAg9OnTB+Hh4ahQoYLe8c6dO6NBgwYG9TEtLQ3m5ub5jrpR8YuOjsaPP/6IUaNGYeHChXrH5s2bl+/I3/PIfl1NgYeHB9q2bYtNmzbhp59+gkql0jt+9+5dHDhwAKNHj4ZSqTRSlGRK+vfvj08++QQ//vhjkWYlEJFx8B0QlQkRERE4d+4cOnTokO/xqKgoHDx4EAMHDsTAgQMRERGBw4cPv9A1X6RNmUyGFi1aQAiBmzdv5jm+cuVKdOzYEW3btkWtWrWKPKJVt25dzJs3D3Fxcfjhhx8MPi97Gt6aNWvw0UcfoWLFirC0tERCQgKePHmCyZMnIyAgANbW1rC1tUXnzp3zTPvIbmPdunUIDQ1FxYoVYWNjg759+yI+Ph7p6emYOHEiXF1dYW1tjeHDhyM9PT1PLCtWrED9+vVhYWEBR0dHDBw4ELdv3y7S81AUhlzv4MGD6NevHypXrgyVSoVKlSrhnXfeQWpqap72tmzZAn9/f6jVavj7+2Pz5s0GxREREQEhBJo3b57nmEwmg6urq17ZzZs30a9fPzg6OsLS0hJNmjTBtm3b9OoU9Lp+//330k1C27ZtK02f3L9/v3Tujh070LJlS1hZWcHGxgZdu3bFxYsXi62/Oe3atQuBgYFQq9Xw8/PDpk2b9Popk8kwd+7cPOcdPnwYMpkMq1evLrDtwYMHIz4+Ps9zAwBr1qyBTqfD66+/DgD45ptv0KxZMzg5OcHCwgL169fHhg0bCo2/oHUr2dM4c4/CGfLcPnjwAMOHD4enpydUKhUqVKiAV199tdARvXPnzmHYsGGoUqUK1Go13N3dMWLECDx+/Fgv3vfeew8A4OPjI73+z2r72rVr6NOnD9zd3aFWq+Hp6YmBAwciPj4+T93snwmVSoXatWvnGd0saI1Z7udRJpMhOTkZy5Ytk2LMuXbrn3/+QcOGDaFWq1G1alX8/PPP+caemZmJmTNnomrVqlCpVPD29saHH36o9/dn0qRJcHJy0huVHz9+PGQyGb7//nupLDo6GjKZDD/99BMA/b97s2bNgqenJ9RqNdq3b4/r16/niaVjx45ITk7G7t27842ViEoXjhBRmZCdiLzyyiv5Hl+9ejWsrKzQrVs3WFhYoGrVqli5ciWaNWv23Nd80Taz33Q4ODjold+7dw/79u3DsmXLAACDBg3C3Llz8cMPPxTpE/2+ffti5MiR2LVrF2bNmqV3LD4+HjExMXplzs7O0vczZ86Eubk5Jk+ejPT0dJibmyM8PBxbtmxBv3794OPjg+joaPz8889o3bo1wsPD4eHhodfeF198AQsLC0yZMgXXr1/H/PnzoVQqIZfLERsbixkzZuDo0aNYunQpfHx8MH36dOncWbNm4eOPP0b//v3x5ptv4tGjR5g/fz5atWqF06dPw97evtD+59dHANBoNHnKDL3e+vXrkZKSgjFjxsDJyQnHjh3D/PnzcefOHaxfv15qb9euXejTpw/8/PzwxRdf4PHjx9Kb2sJ4eXlJ1+rXrx8sLS0LrBsdHY1mzZohJSUFEyZMgJOTE5YtW4YePXpgw4YN6NWrl1793K9rUFAQJkyYgO+//x4ffvghatWqBQDSv8uXL8fQoUMRHByMr776CikpKfjpp5/QokULnD59Wnoz+yL9zXbt2jUMGDAA//d//4ehQ4diyZIl6NevH/7880907NgRVapUQfPmzbFy5Uq88847eueuXLkSNjY2ePXVVwtsv3fv3hgzZgxWrVqF3r176x1btWoVvLy8pCT0u+++Q48ePfD6668jIyMDa9asQb9+/bB161Z07drV4D49i6HPbZ8+fXDx4kWMHz8e3t7eePjwIXbv3o2oqKhnblixe/du3Lx5E8OHD4e7uzsuXryIhQsX4uLFizh69ChkMhl69+6Nq1evYvXq1Zg7d670N8DFxSXfNjMyMhAcHIz09HSMHz8e7u7uuHv3LrZu3Yq4uDjY2dlJdf/55x9s2rQJY8eOhY2NDb7//nv06dMHUVFRcHJyKvJz9eabb6JRo0YYPXo0AKBq1aoAgPPnzyMoKAguLi6YMWMGMjMz8cknn+QZdQeAN998E8uWLUPfvn3x7rvv4t9//8UXX3yBS5cuSQl8y5YtMXfuXFy8eFFaa3jw4EHI5XIcPHgQEyZMkMoAoFWrVnrX+PLLLyGXyzF58mTEx8fj66+/xuuvv45///1Xr56fnx8sLCxw6NChPL+nRFQKCaIy4KOPPhIARGJiYr7HAwICxOuvvy49/vDDD4Wzs7PQaDR69YYOHSqsrKwMumZR23z06JF49OiRuH79uvjmm2+ETCYT/v7+QqfT6dX/5ptvhIWFhUhISBBCCHH16lUBQGzevFmv3r59+wQAsX79+gJjrFu3rnBwcJAeL1myRADI9ytnm1WqVBEpKSl6baWlpQmtVqtXFhERIVQqlfj000/zxOXv7y8yMjKk8kGDBgmZTCY6d+6s10bTpk2Fl5eX9DgyMlIoFAoxa9YsvXrnz58XZmZmecpze1Yfs79q1679XNfL/ZwIIcQXX3whZDKZuHXrllQWGBgoKlSoIOLi4qSyXbt2CQB6fS3IkCFDBADh4OAgevXqJb755htx6dKlPPUmTpwoAIiDBw9KZYmJicLHx0d4e3tLr9ezXtf169cLAGLfvn165YmJicLe3l6MGjVKr/zBgwfCzs5Or/xF++vl5SUAiI0bN0pl8fHxokKFCqJevXpS2c8//ywA6D0XGRkZwtnZWQwdOrTQ6/Tr10+o1WoRHx8vlV2+fFkAEFOnTpXKcj9HGRkZwt/fX7Rr1y5P3Dmv+8knn4j8/tvM/pmMiIgQQhj+3MbGxgoAYvbs2YX2Lbf8flZXr14tAIgDBw5IZbNnz9aL7VlOnz5d6N8cIYQAIMzNzcX169elsrNnzwoAYv78+VLZ0KFD8/35yO95tLKyyvc17tmzp1Cr1Xq/f+Hh4UKhUOi1cebMGQFAvPnmm3rnT548WQAQf/31lxBCiIcPHwoA4scffxRCCBEXFyfkcrno16+fcHNzk86bMGGCcHR0lP5+Z/+O1apVS6Snp0v1vvvuOwFAnD9/Pk/s1atXz/P3kIhKJ06ZozLh8ePHMDMzy3cu9rlz53D+/Hm9zRYGDRqEmJgY7Ny587muV9Q2k5OT4eLiAhcXF/j6+mLy5Mlo3rw5fvvttzxTbFauXImuXbvCxsYGAFCtWjXUr1//uTaCsLa2RmJiYp7ysLAw7N69W+8rp6FDh8LCwkKvTKVSSeuItFotHj9+DGtra9SoUQOnTp3Kc40hQ4borcdo3LgxhBAYMWKEXr3GjRvj9u3byMzMBABs2rQJOp0O/fv3R0xMjPTl7u6OatWqYd++fQb1Pb8+7t69G3Xq1NGrV5Tr5XxOkpOTERMTg2bNmkEIgdOnTwMA7t+/jzNnzmDo0KF6n5h37NgRfn5+BsW+ZMkS/PDDD/Dx8cHmzZsxefJk1KpVC+3bt8fdu3eletu3b0ejRo3QokULqcza2hqjR49GZGQkwsPD9drN73UtyO7duxEXFyf9XGd/KRQKNG7cWHpeiqO/QNY6n5yflNva2mLIkCE4ffo0Hjx4ACBr3YVardb7Xdi5cydiYmL01ugVZPDgwUhLS9Obipe90UL2dDlA/3WOjY1FfHw8WrZsme/P+fMw9Lm1sLCAubk59u/fj9jY2CJdI2cf0tLSEBMTgyZNmgDAc/cj+/XduXMnUlJSnlm3Q4cO0igOANSpUwe2trb5ThF+XlqtFjt37kTPnj1RuXJlqbxWrVoIDg7Wq7t9+3YAWVPicnr33XcBQJpK6eLigpo1a+LAgQMAgEOHDkGhUOC9995DdHQ0rl27BiBrhKhFixZ5/n4PHz5cbyS/ZcuWAJBvvx0cHPIdxSai0odT5qjMW7FiBaysrFClShVpLrdarYa3t7eUfJR0m2q1Gn/88QcA4M6dO/j666/x8OHDPG9OL126hNOnT2PIkCF6887btGmDsLAwJCQkwNbW1uA4k5KSpMQqp0aNGj1zUwUfH588ZTqdDt999x1+/PFHREREQKvVSsfymwKT8w0K8N+bqUqVKuUp1+l0iI+Ph5OTE65duwYhBKpVq5ZvbIYuei+oj7nfhBTlelFRUZg+fTp+//33PG9Qs9dQ3Lp1CwDyba+g5DE3uVyOkJAQhISE4PHjxzh06BAWLFiAHTt2YODAgdJ0nVu3bqFx48Z5zs+e8nbr1i29Lcbze10Lkv3Gr127dvkez/45LI7+AoCvr2+eN5fVq1cHkDW91N3dHfb29ujevTtWrVqFmTNnAsj6AKFixYoFxplT586d4ejoiFWrVklrUFavXo26deuidu3aUr2tW7fis88+w5kzZ/TWlxTXfW0MfW5VKhW++uorvPvuu3Bzc0OTJk3QrVs3DBkyBO7u7s+8xpMnTxAaGoo1a9bg4cOHesfyW+9jCB8fH0yaNAlz5szBypUr0bJlS/To0QODBw/WS4aBvL//QNbvXlETu2d59OgRUlNTC/zZy06CgKyfU7lcDl9fX7162T9X2T/HQFYSk33uwYMH0aBBAzRo0ACOjo44ePAg3NzccPbsWbz22mt5rpu739lTovPrtxCi1N4riYj0MSGiMsHJyQmZmZlITEzUSwCEEFi9ejWSk5Pz/bT64cOHSEpKKtIuP8/TpkKh0NvwITg4GDVr1sRbb72ld9+TFStWAADeeeedPOskgKzd54YPH25QnBqNBlevXn2ue+7kN4rw+eef4+OPP8aIESMwc+ZMODo6Qi6XY+LEifneCFehUOTbdkHl4ukiZp1OB5lMhh07duRbt7h3ZDL0elqtFh07dsSTJ0/wwQcfoGbNmrCyssLdu3cxbNiwErsZsJOTE3r06IEePXqgTZs2+Pvvv3Hr1i1prVFRGDo6BEDqz/Lly/N9821mZpz/HoYMGYL169fj8OHDCAgIwO+//46xY8catAuiUqlE//79sWjRIkRHRyMqKgrXrl3D119/LdU5ePAgevTogVatWuHHH39EhQoVoFQqsWTJkmdu2w0UnDDl/PAAKNpzO3HiRHTv3h1btmzBzp078fHHH+OLL77AX3/9hXr16hUYS//+/XH48GG89957CAwMhLW1NXQ6HTp16vRCP6vffvsthg0bht9++w27du3ChAkT8MUXX+Do0aN6a8YK+z0HDH++ipMhCUiLFi2waNEi3Lx5EwcPHkTLli2ljXAOHjwIDw8P6HQ6afQnJ0P6nS02NrbAD2KIqHRhQkRlQs2aNQFk7dCVc0rU33//jTt37uDTTz+VPjXPFhsbi9GjR2PLli0GTbcpzjYrVKiAd955B6GhoTh69CiaNGkCIQRWrVqFtm3bYuzYsXnOmTlzJlauXGlwQrRhwwakpqbmmTryvDZs2IC2bdvil19+0SuPi4vT25DhRVWtWhVCCPj4+EgjBCXJ0OudP38eV69exbJlyzBkyBCpPPd0w+xEJXsUIKcrV668UKwNGjTA33//jfv378PLywteXl75tnn58mW9WJ6loDeI2dOdXF1dC9y9Mec1XrS/169fz/OJ+dWrVwFAb/OATp06wcXFBStXrkTjxo2RkpKCN954w+DrvP7661iwYAHWrl2LiIgIyGQyvamvGzduhFqtxs6dO/W2516yZEmhbWePBsTFxelt/JFz9AEw/LnNWf/dd9/Fu+++i2vXriEwMBDffvut9AFKbrGxsdi7dy9CQ0P1NivJ7zV6nhGKgIAABAQE4KOPPsLhw4fRvHlzLFiwoMi3MnBwcMj3/ke5n6+C4nRxcYGFhYVBP3teXl7Q6XS4du2a3t/t6OhoxMXF6f2uZCc6u3fvxvHjx6WbVrdq1Qo//fQTPDw8YGVlhfr16xvW0XxkZmbi9u3b6NGjx3O3QUQvD9cQUZnQtGlTAMCJEyf0yrOntr333nvo27ev3teoUaNQrVq1Iq/NKa42x48fD0tLS3z55ZcAsuaqR0ZGYvjw4Xna7du3LwYMGIB9+/bh3r17hbZ99uxZTJw4EQ4ODggJCSlS/wqiUCjyfMq5fv16vTUtxaF3795QKBQIDQ3Ncz0hhN62wS/zetmf/OasI4TAd999p3dOhQoVEBgYiGXLlulNTdq9e3eeNT35efDgQb71MjIysHfvXr1pP126dMGxY8dw5MgRqV5ycjIWLlwIb29vg9bwWFlZAUCeN6bBwcGwtbXF559/nu/OfNn3Q3rR/ma7d++e3lbdCQkJ+PXXXxEYGKg3imJmZoZBgwZh3bp1WLp0KQICAvKsC3uW5s2bw9vbGytWrMDatWvRunXrPCMbMplMb5QiMjLSoJuCZic62etPAEjbRedk6HObkpKCtLS0PNewsbHJd6v6nH0A8o5KzJs3L0/dgl7//CQkJEhr/bIFBARALpc/M56CVK1aFfHx8Th37pxUdv/+/Xy3bLeyssoTo0KhQHBwMLZs2YKoqCip/NKlS3nWcnbp0gVA3udgzpw5AKA3zdnHxwcVK1bE3LlzodFopN0HW7ZsiRs3bmDDhg1o0qTJC42ShoeHIy0t7YV2OiWil4cjRFQmVKlSBf7+/tizZ4+0aD89PR0bN25Ex44dC7xZa48ePfDdd9/h4cOH0v1dNBpNvp90Ojo6YuTIkc/VZn6cnJwwfPhw/Pjjj7h06RJWrlwJhUJR4JqmHj16YNq0aVizZo3ewuCDBw8iLS1N2ujg0KFD+P3332FnZ4fNmzcXutbAUN26dcOnn36K4cOHo1mzZjh//jxWrlyJKlWqFEv72apWrYrPPvsMU6dORWRkJHr27AkbGxtERERg8+bNGD16NCZPnvzSr1ezZk1UrVoVkydPxt27d2Fra4uNGzfmuzbgiy++QNeuXdGiRQuMGDECT548wfz581G7dm0kJSU9M547d+6gUaNGaNeuHdq3bw93d3c8fPgQq1evlhLd7BG5KVOmYPXq1ejcuTMmTJgAR0dHLFu2DBEREdi4caNB08gCAwOhUCjw1VdfIT4+HiqVCu3atYOrqyt++uknvPHGG3jllVcwcOBAuLi4ICoqCtu2bUPz5s2le1y9SH+zVa9eHSNHjsTx48fh5uaGxYsXIzo6Ot+RmSFDhuD777/Hvn378NVXXxnUfjaZTIbXXnsNn3/+OQDg008/1TvetWtXzJkzB506dcJrr72Ghw8fIiwsDL6+vnpv3PMTFBSEypUrY+TIkXjvvfegUCiwePFi6XnLZmtra9Bze/XqVbRv3x79+/eHn58fzMzMsHnzZkRHR2PgwIEFxmFra4tWrVrh66+/hkajQcWKFbFr1y5ERETkqZs9yjFt2jQMHDgQSqUS3bt3lxKlnP766y+MGzcO/fr1Q/Xq1ZGZmYnly5dDoVCgT58+z3xu8jNw4EB88MEH6NWrFyZMmCBtPV69evU8a8/q16+PPXv2YM6cOfDw8ICPjw8aN26M0NBQ/Pnnn2jZsiXGjh2LzMxM6Wcv5+tVt25dDB06FAsXLkRcXBxat26NY8eOYdmyZejZsyfatm2rd72WLVtizZo1CAgIkEb+XnnlFVhZWeHq1av5rh8qit27d8PS0hIdO3Z8oXaI6CV5mVvaEb2IOXPmCGtra2m72Y0bNwoA4pdffinwnP379wsA4rvvvhNCZG0DiwK2aq5atepzt1nQVt43btwQCoVCvPbaa8LJyUm0bNnymX308fGRtiHO3uY1+0upVAoXFxfRqlUrMWvWLPHw4cM852dv/3v8+PF823/WVt5paWni3XffFRUqVBAWFhaiefPm4siRI6J169aidevWhbZR0LWzt9h99OiRXvnGjRtFixYthJWVlbCyshI1a9YUISEh4sqVK898jgrrY+vWrfW23S7K9cLDw0WHDh2EtbW1cHZ2FqNGjZK2E16yZEme9mrVqiVUKpXw8/MTmzZtKnCb4ZwSEhLEd999J4KDg4Wnp6dQKpXCxsZGNG3aVCxatCjPNu03btwQffv2Ffb29kKtVotGjRqJrVu36tUpbIv2RYsWiSpVqkhbFefcgnvfvn0iODhY2NnZCbVaLapWrSqGDRsmTpw4USz9FSJr++quXbuKnTt3ijp16giVSiVq1qz5zO2da9euLeRyubhz506h7ed28eJFAUCoVCoRGxub5/gvv/wiqlWrJsWxZMmSfLeCzr3tthBCnDx5UjRu3FiYm5uLypUrizlz5uTZdjtbYc9tTEyMCAkJETVr1hRWVlbCzs5ONG7cWKxbt67QPt65c0f06tVL2NvbCzs7O9GvXz9x7949AUB88sknenVnzpwpKlasKORy+TO34L5586YYMWKEqFq1qlCr1cLR0VG0bdtW7NmzR68eABESEpLn/Pyer127dgl/f39hbm4uatSoIVasWJHvc3358mXRqlUrYWFhIQDotfP333+L+vXrC3Nzc1GlShWxYMGCfNvQaDQiNDRU+Pj4CKVSKSpVqiSmTp0q0tLS8sQaFhYmAIgxY8bolXfo0EEAEHv37tUrL+h3LCIiIt+/D40bNxaDBw/Oc10iKp1kQuSzEpCoFIqPj0eVKlXw9ddfY+TIkcYOh4hKUL169eDo6Ii9e/caOxSiIjlz5gxeeeUVnDp1CoGBgcYOh4gMwDVEVGbY2dnh/fffx+zZs0tsxy8iMr4TJ07gzJkzeptbEJUVX375Jfr27ctkiKgM4QgRERGVChcuXMDJkyfx7bffIiYmBjdv3ixwLR8REVFx4QgRERGVChs2bMDw4cOh0WiwevVqJkNERPRScISIiIiIiIhMFkeIiIiIiIjIZDEhIiIiIiIik8UbsxZAp9Ph3r17sLGxgUwmM3Y4RERERPQChBBITEyEh4eHQTe2JtPBhKgA9+7dQ6VKlYwdBhEREREVo9u3b8PT09PYYVApwoSoADY2NgCyfmlsbW2NHE3J02g02LVrF4KCgqBUKo0dzkvH/rP/ptp/U+47wP6z/6bbf1Pse0JCAipVqiS9xyPKxoSoANnT5GxtbU0mIbK0tIStra3J/GHMif1n/021/6bcd4D9Z/9Nt/+m3HcuhaDcOIEyl7CwMPj5+aFhw4bGDoWIiIiIiEoYE6JcQkJCEB4ejuPHjxs7FCIiIiIiKmFMiIiIiIiIyGQxISIiIiIiIpPFhIiIiIiIiEwWEyIiIiIiIjJZTIhy4S5zRERERESmgwlRLtxljoiIiIjIdDAhIiIiIiIik8WEiIiIiIiITBYTIiIiIiIiMllMiIiIiIiIyGQxISIiIiIiIpPFhCgXbrtNRERERGQ6mBDlwm23iYiIiIhMBxMiIiIiIiIyWUyIiIiIiIjIZJkZO4DSrsWGr3FuxGfS49F/rcCBe9cKrH95cKj0/dsH1mJnVHiBdU8NmAZLpTkAYMrhzfjt5hlYKVWwNVfDRqmGjbla+n5qg06wV1kCAM7H3MX9lPis40/r2ZirYaNUwUyueNEuExERERGZDCZEhYjPSMPKK8ekxzfjHyFJk15g/Zx1r8Y9fGbdtddOwFyR9RJcenIfiZp0JGrS8SAlIU/dKQ06/XeNq8ew4sq/+bZpaWaOv3q9A09rB+ka++9clZImu1zJVvMKVWGlVAEANEKHTJ0WSigLjJmIiIiIqDxhQlQIjU6LDw5vMrh+Uep+/O/vBtdtuu5L2KksYaeyQFJGGuzMLaATOmQKHTRaLTKFDgCQkpmBa3EPkZqpgZ25BU4+jMIfkecKbPdQ3/ekhGhb6m1MXDUDlmbmeUafbJVqfNywCzys7QEA52Lu4Grcw6wRLI5UEREREVEZxYSoEO09a8LcyuKlXEsrdEjMSEd8Riri01ORkJGK5MwMAEBSZgaSMjNwNzmu0Hbe2L1E77FCJodaYQZzhQIKmQJymQwAoBMCv146CncrW1grzHEnMxlAVlKVkpmBaOiPVE1pECx9vzXyPH48/3eBMfzZYzz8nSoCADbeOI1NN07nSrBU0ihVO8+acFRbZfVTkw6NNhM25momVURERERU4pgQFSKszSDY2toa7foanRYJGamIT0/LSpRyJEvxGamIS8/6NyEjDfHS91nlCRlpEBDQCh2SMzOQnJm3/Z8vHnzm9eWQwcJMCZVCibf2rYSTygp2KgtEpySikrUDtEIHjU6LdG0m0jI1yNBpAWQlW1qdDgq5HFdjo/H33asFXmN793FSQrTs0hF8cfJPAICFmTLvKFWjrqjp4A4gay3V8YeReddcPa1ra24BhZz7hhARERFRwZgQlXJKuQJOams4qa2LfK7u6YhTQo5EKj4jLcf3T5OnjFTEpaUgKvo+ZFbqrOQqIxUanRY6iKfJVAaepCcbfO0uf/wAALA1V0OtUKKilR3M5WZQyOVQyOSQQQYBgUyhw4noW4hJS4aduRp3k2KlNlIzNUjN1CA6NVEq+0D7X1Z34N41KXnKz5rgN9HCwxcAsDXiHH6+eFBKsGzNLfRGqtp5VJfOi09PRVxGilSXI1VERERE5RcTolzCwsIQFhYGrVZr7FBemFwmh53KAnYqC1QqpK5Go8H27dvRpUsXKJVKCCGQptXkGoFKkUalEnIkVvmNVqU8neqXkJGGBKQ989rTj/2Rb7m53AzW5ipYKcyhMjODucIMP53/Gy4WNrBVWeB+UhzqOnsiU6dFhlaLNG1WApWsSUeqVgNbc7XUVlRSLE4/ul1gDFXaD5O+/yPiHKYc2Sw9zj1S9XHDrmjk5g0ACH9yD7ujLuntCJhzpMrVwhYWZtykgoiIiKi0YkKUS0hICEJCQpCQkAA7Oztjh2M0MpkMFmbmsDAzRwWroj8PGdrMrGTo6QhUfK6pfXFPR6eyy+OlJCsFCRnpEBDI0GXiSVomnuC/kanwJ/cNur4CMgzetRj2KkvYmlvAXKFAI1dvmMnlUMjlkEMGgaypfZk6LZI06YjRZsWm0WXCwkyJ1EwNgLwjVelajXSdU49uY/bp3QXG8XPb19HVOwAAsDsqHLNO7Mh3wwobczU6e/mjhoMbACA2PQV3EmP1kislR6qIiIiIih0TIioR5gozOFtYw9ni+af65Zzap79+6r/RqfwSq0yhgxYCT9JT8CQ9xaBrHo2OAAB8sv40ZJDB1lwFJysrWCvVsFJmjVCp5FmjVIfv38TN+BjYqSyQkJ6KoEp+0OgykabNRGpmBpI06UjMSEOCJk1vlCo6NRHX4x8VGIOvvauUEP1z7zrG7F+ld1ytUErJ0bQGnRFU2Q8AcCU2GuuunciTYGV/X8nGQbqHFRERERHpY0JEpU7OqX6wKdq5QgikZmqkUSn99VOpudZP5Uis0lMQm5qMDOggIJ6utcp/qt+zbrYLZE2xszO3QGVrR8w9sxeLww/D7ukoVd+qr8BMLpd2+hMCWVun67TwsLSDEAKyp8fcLGyQoEmTRqrStBqkpWrwMDVR2rwCAK7GRT9zc4zZzftgUPWGAIBD965j/IG1T7dHf3pfKnM1rMzMEZNyDxUe3UYjjyoAskapwp/cz7HuiiNVREREVP4wIaJyRSaTwVJpDkulOTyKMNUvew1V++AgpIqs6X5xetP5Ck+sEp4mUNlT7PK7we6zbLl5Bkq5AnbmFrA1V8PD2h61nm7+YGGmhFquzNo6XS5HYkYaDt27DjuVBdQKJYbXaoo0bSaSMtKRoElDYkaaNErlkGN0KDY9BQ9TE/Ewx0YVOXWIi5YSonMxd/D6rsV56mSPVL33SpCUaEUkxCDs3H4pabJR6u/4V8XO5bmmXhIRERGVNCZERDmoFGawVlrAxaKIQ1MAtDodEjVpuRIn/e3Qc07ty73jX/YW5jFpSYhJSyry9bOn+tmZZ93A183SFtXMXbH3zmWceHgLduYWUCnMMKV+JyiejkIBWfe/StdocP7qZfg5VpDKzWRyVLNzlRKs7I0yskeqdEJIdW8nxmLNtRMFxjatQWeMCWgNADjz6Db67liYJ3myfbr7X2ev2mjrWQMAkJiRhuMPb8Hu6bHsOtyogoiIiIoLEyKiYqKQy2Gvssxar/McU/1SMjPyXReVkJGWa2OKvAlXmlajP9Wv6PkUzCHHxb9XwU5lCTtzNezMLRDo4glbcwvYmVvA2lwFldwMSrkcCrkC3jZOuJ8cD3uVBSpZO+D9V4KQkD0y9XR0Knukyt3yv9GhhIw0vel/ufnYOkkJ0Y34RxiS60bDQFbiamuuxhj/1hjt3xIAEJ2SgHln9kqJU/ZIm60q619PKwe4WhY90SUiIqLyjQkRUSkgk8lgpVTBSqmCB+yLfH66NjPPFuj/rZFKyX+06mlilahJBwBkQIf7KQm4X8SpfgBgLlfATmUhJU925hawV1nA28YRduYWeJiagNVXj8PO3AKWSnMsbT/k6VopGTQ6LZI0T5OojDQ0cvOR2pXLZAhwqvg03qxdC3VCIF2biUepSdAKnVT3QUoCll/5t8AYx9dpiw/qBwMAIhMeo/f2BVLiZKNUISnpCY4c08JebYlm7lXRqmI16bm99OR+Vt9UWSNUXEdFRERUfjAhIioHVAozuFraPNcISKZOi9iUJPy260/Ua9YYKTqNNFKVe1e//NZPaYUOGTotHqUm4VFq0Yem5DIZbJRq2KuyEqmjDyKyNtV4mlh19faXvrcxV8Ncbga5TAYZgMq2jlI7zmprTArsgIRc67qykz+XHDsexmek5ruW6sTVxwCyph9mJ0T3kuPQbWuYXr3sjTPszC3Qv1p9vOXfCgCQpEnHT+f/lqb26Y1UmVvASW0Fmxw7DxIREZHxMSEiMnFmcgXsVZZwUahRx6kilErD1+cIIZD8dKpffEZKnlEo/Y0p9G/mG5eRinRtJnRCSInW87AyM5cSKNunI1N25haobOMoJSTZ0wCPR0dKdbd0HYN0bSYSMlIRm5qMf8+eQsWqPkjKzEADVy+p/QytFhWt7JGQ8d9oWs6NM+LS/4v7UWoivjv7V4GxDq3ZBLOa9gSQtcFF3+0/54hdP4nyd/JAswpVAWRtRX8nKU66+a9CLn+u54qIiIjyYkJERM9NJpPBWqmCtVKFis8x1S8tUyMlQwnpaXlGoJ6VWCU9TU6SMzOQnJmBe8nxRb6+SmEmTZnTZaThyWMV7NWW+OvOFZx6FPU0mbLAjMbdpHpyuRyyp9ulJ2ak6e2eZ2FmjmG1mkojUwl6CWIabM0tpLpx6Sm4EhddYGxDajaREqL49FQ02/C1dMxGqXo6hS8rgepYqZY0SpWp02LppSN6a6j+25TCAtZKc8hlTKiIiIiylYuEqFevXti/fz/at2+PDRs26B1LSUlBrVq10K9fP3zzzTdGipCI8qM2U0JtpoSbpW2Rz83UaZGYZ8OJtFzT/XJP80uTkqzstUg5p85F3Ltq8PUVMrk0qmOfY4qfrbka7pa2qGHvJiVU2f/aKFWITUuGrbkF3CxssSpopN76qP+m+KWhnnMl6VpJmnRYmCmle1IlatKRqEnH3eQ4AEB1ezepbmJGGmYc21pg3K9WqYuw1oOk53DQ7sVITYzD/iPpsFdbSn2yM1fDx9YF9V0r68VhZWYu3SuLiIioPCgXCdHbb7+NESNGYNmyZXmOzZo1C02aNDFCVERUkszkCjioreCgtiryuTqhQ7ImQ0qUHqckYt/RQ/D190OSNiPPtui5E6sMnRZaoUNsegpi01NwK//bOj2TtVKllzDZmv+3jqqanQuSNOnYdOO0lGht7z4elgpzQAakazV6SZSntYPUrgDwqk9dvTVU2aNq6dpM2OUYpUrMSMPR6AgAwNkbsXlifLVKXSkhytRpUXPFJ1DI5LAxV0s7EWZP82vk5o03a7eQzv3t5llYK1V5pgJamCmZUBERUalSLhKiNm3aYP/+/XnKr127hsuXL6N79+64cOHCyw+MiEol+dM39TbmanhaO0Bjq0Gs+WV08a1f6BoqIQTStJm5Npf4b1pfYfebyr6fU5ImHUk5RnmKQqUwg700Zc4iT2IV6OL534jV03J7cwuozMygkv/3Z19tpsR3zfvh8OkTqFy9KpIyNXojVbUdPaS6iU9vPKwVOsSlpyAuPUUvJqXiv533MnVahPy9Ot/YlXIFOnvVxo9tXpPKZh3fgfde6QhzRbn4L4mIiMoYo//vc+DAAcyePRsnT57E/fv3sXnzZvTs2VOvTlhYGGbPno0HDx6gbt26mD9/Pho1alRo25MnT8bs2bNx+PDhEoqeiEyNTCaDhZkSFmZKuD/HVD+NTpsjaUorMLHKb4e/hIw0CGRN9YtOTUR0PvdxKoyZTK63/shWqUZKZgLMkuNhr7aCl63T02RKDTuVBc7F3Hk63U+NK6+HIikzXW/NV3byVNnmvx3/0rWZaOnhK01hzO5H9s2Hc44Qxaen4qcLf8PfyQOvVqlb5P4QERG9KKMnRMnJyahbty5GjBiB3r175zm+du1aTJo0CQsWLEDjxo0xb948BAcH48qVK3B1dS2w3d9++w3Vq1dH9erVmRARUamhlCvgpLaGk9q68Mq56IQOiRnpeiNRcYVsSpEzIdHotMgUOjxJT8aT9GS9tk9ee2xQDFlT4NTSCFT2KFVU4hOcf3xXmkY3qnZLvcTKVqlGptAhPiMN8hwJ0ZJLWX+fd9y6wISIiIiMwugJUefOndG5c+cCj8+ZMwejRo3C8OHDAQALFizAtm3bsHjxYkyZMqXA844ePYo1a9Zg/fr1SEpKgkajga2tLaZPn55v/fT0dKSnp0uPExKybk6p0Wig0Wiep2tlSnYfTaGv+WH/2f+c/5ZmlnIzWKptUEFdtHtOCSGQqs26x1SC5r+RpyepyThx4SzcfSojKTMj61h6jo0enj5O1WY9N9k30L2DuCLHbqFQ5rg3U9YIle7pzXX/unMFiakpUJsZvu17cShLr31JYP9Nt/+m2HdT6isVjUwIIYwdRDaZTKY3ZS4jIwOWlpbYsGGD3jS6oUOHIi4uDr/99ptUtn//fvzwww95dpkDgKVLl+LChQvP3GVuxowZCA0NzVO+atUqWFpaPn+niIjKgUyhQ4rIRKrQIkVkIkWX4/unX6lCixRdrsdP/zXEaOsaqGvuWHhFIqLnkJKSgtdeew3x8fGwtS36lGcqv4w+QvQsMTEx0Gq1cHNz0yt3c3PD5cuXpccdOnTA2bNnkZycDE9PT6xfvx5NmzYt0rWmTp2KSZMmSY8TEhJQqVIlBAUFmcQvjUajwe7du9GxY8ci3ZizvGD/2X9T7f/L6LtWp0OiJu2/NVE5thffffsS9j3d7vyhixpdmncpkRgKYsqvPcD+m3L/TbHv2bN/iHIr1QmRofbs2fPM48OGDSu0DZVKBZVKladcqVSazB8KwPT6mxv7z/6bav9Lsu9KAGqVCi6wy3MswMVTSoj23rkCIZcZZbc5U37tAfbflPtvSn03lX5S0ZXq25U7OztDoVAgOlr/bu7R0dFwd3cvkWuGhYXBz88PDRs2LJH2iYjoP4HOnqholZUoJWrScOj+DSNHREREpqZUJ0Tm5uaoX78+9u7dK5XpdDrs3bu3yFPiDBUSEoLw8HAcP368RNonIqL/yGQydPfJ2l2ump0rqtq5GDkiIiIyNUZPiJKSknDmzBmcOXMGABAREYEzZ84gKioKADBp0iQsWrQIy5Ytw6VLlzBmzBgkJydLu84VN44QERG9XN196gAA7iTHwvk5tiMnIiJ6EUZfQ3TixAm0bdtWepy9scHQoUOxdOlSDBgwAI8ePcL06dPx4MEDBAYG4s8//8yz0UJxCQkJQUhICBISEmBnl3e+OxERFa86ThXhZeOIW4lPsPfOZSlBIiIiehmMnhC1adMGhe38PW7cOIwbN+4lRURERC+TTCZDN+86CDu/H4su/oN0bSb6+r5i7LCIiMhEGH3KHBERUXefAADAqUdR+PzEDumGrURERCWNCVEuXENERPTy1Xb0gJd11k1ZH6Ym4vSjO0aOiIiITAUToly4yxwR0csnk8nQo0pd6fH2WxeMGA0REZkSJkRERFQqZE+bA4BtkecLXV9KRERUHJgQERFRqVDLoQK8bZwAAHeSYnHxyT0jR0RERKaACVEuXENERGQcMpkMr+acNhfJaXNERFTymBDlwjVERETG0837v3sQXY59YMRIiIjIVDAhIiKiUqOmgxuq2DoDALryBq1ERPQSMCEiIqJSI+duc1sjzhk5GiIiMgVMiHLhGiIiIuPq/nTa3N93ryI6OcHI0RARUXnHhCgXriEiIjKuGg5uqG7nigydFk02fIVbiY+NHRIREZVjTIiIiKjU6V4la5RIo9NiR+RFI0dDRETlGRMiIiIqdXLuNvdHJNcSERFRyWFCREREpU41e1dUtXMBAJyNuYP7yfFGjoiIiMorJkRERFQq9a4SKH3/5y1OmyMiopLBhCgX7jJHRFQ6dMtxH6LfI84aMRIiIirPmBDlwl3miIhKh6p2LvB9Om3uxMNbeJyWZOSIiIioPGJCREREpVafqq8AAHxsnaGQ8b8sIiIqfvzfhYiISq2u3gEAgFuJT6ATwsjREBFRecSEiIiISq0qds7wd/SAVuiwgxsrEBFRCWBCREREpVr25gq/hP+Dw/dvGDkaIiIqb5gQERFRqdbt6bS5q3EP8dOFA0aOhoiIyhsmRLlw220iotLF29YJ1excAQAH715DiibDyBEREVF5woQoF267TURU+vTzzdptLlPo8NfdK0aOhoiIyhMmREREVOp1z3GT1s03ThsxEiIiKm+YEBERUalXycZRmja3784VpGVqjBwRERGVF0yIiIioTBhQrT4AIEOnxT/3rxs5GiIiKi+YEBERUZnQLce0uWPRkcYLhIiIyhUmREREVCZ4WjvA39EDAFDB0s7I0RARUXnBhIiIiMqMPr71AABbI88ZORIiIiovmBAREVGZ0dUr6yatx6Jv4VbCYyNHQ0RE5QETIiIiKjM8rO1Rx6kiBAQ6/z4fWp3O2CEREVEZx4Qol7CwMPj5+aFhw4bGDoWIiPLRw6cuACBBk4aTj6KMHA0REZV1TIhyCQkJQXh4OI4fP27sUIiIKB+vVqkrfb/u2kkjRkJEROUBEyIiIipTKljZSTdp3XHrAoQQRo6IiIjKMiZERERU5gys3gAAEJ+RirMxd4wcDRERlWVMiIiIqMzpWSVQ+n7ddU6bIyKi58eEiIiIyhw3S1v42rkAALZGnOO0OSIiem5MiIiIqEx6rXrWbqCWShV0TIiIiOg5MSEiIqIyqWeVepDLZLiTFIt7yXHGDoeIiMooJkRERFQmuVraoImbDwBga+R5I0dDRERlFRMiIiIqs7r71AEALLl0BLcTnxg5GiIiKovKRULUq1cvODg4oG/fvlJZXFwcGjRogMDAQPj7+2PRokVGjJCIiEpCZy9/AMC95Dj8evmokaMhIqKyqFwkRG+//TZ+/fVXvTIbGxscOHAAZ86cwb///ovPP/8cjx8/NlKERERUEpwtrFHVNmu3uS03zxo5GiIiKovKRULUpk0b2NjY6JUpFApYWloCANLT0yGE4LasRETlUPZNWu+nxONuUpxxgyEiojLH6AnRgQMH0L17d3h4eEAmk2HLli156oSFhcHb2xtqtRqNGzfGsWPHDGo7Li4OdevWhaenJ9577z04OzsXc/RERGRs/avVl75fceVfI0ZCRERlkdETouTkZNStWxdhYWH5Hl+7di0mTZqETz75BKdOnULdunURHByMhw8fFtq2vb09zp49i4iICKxatQrR0dHFHT4RERmZk/q/aXObb542cjRERFTWGD0h6ty5Mz777DP06tUr3+Nz5szBqFGjMHz4cPj5+WHBggWwtLTE4sWLDb6Gm5sb6tati4MHDxZX2EREVIoMqJ41SnQnKQ4PUxKNHA0REZUlZsYO4FkyMjJw8uRJTJ06VSqTy+Xo0KEDjhw58sxzo6OjYWlpCRsbG8THx+PAgQMYM2ZMgfXT09ORnp4uPU5ISAAAaDQaaDSaF+xJ6ZfdR1Poa37Yf/Y/57+mpLz0va9PPXx+4k8AwPaIc3i9eiODzisv/X9e7L/p9t8U+25KfaWiKdUJUUxMDLRaLdzc3PTK3dzccPnyZelxhw4dcPbsWSQnJ8PT0xPr16+HQqHA6NGjpc0Uxo8fj4CAgAKv9cUXXyA0NDRP+a5du6TNGUzB7t27jR2CUbH/7L+pKg99r2pmgxuZiTh6/gwcrscU6dzy0P8Xwf6bbv9Nqe8pKSnGDoFKqVKdEBlqz549+ZafOXPG4DamTp2KSZMmSY8TEhJQqVIlBAUFwdbW9kVDLPU0Gg12796Njh07QqlUGjucl479Z/9Ntf/lqe/JN9zx/pHNuKbKwPwuXQw6pzz1/3mw/6bbf1Pse/bsH6LcSnVC5OzsDIVCkWczhOjoaLi7uxfrtVQqFVQqFcLCwhAWFgatVgsAUCqVJvOHAjC9/ubG/rP/ptr/8tD3Lj4BmPbv77gSF41riTHwc6xg8Lnlof8vgv033f6bUt9NpZ9UdEbfVOFZzM3NUb9+fezdu1cq0+l02Lt3L5o2bVoi1wwJCUF4eDiOHz9eIu0TEVHJsFdZIsDJAwDwf/tWGjkaIiIqK4w+QpSUlITr169LjyMiInDmzBk4OjqicuXKmDRpEoYOHYoGDRqgUaNGmDdvHpKTkzF8+HAjRk1ERKVRB89aOPXoNiISYpCkSYe1UmXskIiIqJQz+gjRiRMnUK9ePdSrVw8AMGnSJNSrVw/Tp08HAAwYMADffPMNpk+fjsDAQJw5cwZ//vlnno0WiktYWBj8/PzQsGHDEmmfiIhKzpCaTQAAAsDyy7xJKxERFc7oI0Rt2rSBEOKZdcaNG4dx48a9lHhCQkIQEhKChIQE2NnZvZRrEhFR8bBXW8LbxgmRiY+x7toJjAloZeyQiIiolDP6CBEREVFx6lM1a8bB9fiHSNGkF1KbiIhMHROiXDhljoiobBvh1xxA1rS51Ve5QQ4RET0bE6JcuMscEVHZZqeygJe1IwBg9TX+LSciomdjQkREROVOH99XAABx6amFrlMlIiLTxoSIiIjKnVG1W0ClMMODlARcin1g7HCIiKgUY0KUC9cQERGVfTbmarStWAMA8EfEOSNHQ0REpRkToly4hoiIqHzo7lMHALDi8r/QaDONHA0REZVWTIiIiKhcalOxGgAgNiMF666fNHI0RERUWjEhIiKicslOZQlPK3sAwIorx4wbDBERlVpMiIiIqNzqUaUuACD8yT1odVojR0NERKURE6JcuKkCEVH5Mda/NQBAKwQ2XD9l5GiIiKg0YkKUCzdVICIqP+zVlvCwsgMA/HrlXyNHQ0REpRETIiIiKte6eWftNnfh8T3odDojR0NERKUNEyIiIirXQgKyp83psOf2JSNHQ0REpQ0TIiIiKtecLKzR0sMXAPDvw1tGjoaIiEobJkS5cFMFIqLy540aTQAAWyPOQQhh5GiIiKg0YUKUCzdVICIqf9p61oCVmTnuJsfh+MNIY4dDRESlCBMiIiIq9yzMlKjh4A4A+PzEn0aOhoiIShMmREREZBLqOHkAAM48ug2d4G5zRESUhQkRERGZhLEBbQAAmUKH7ZEXjRsMERGVGkyIiIjIJHhY28PFwhoAsDj8kJGjISKi0oIJERERmYxOlf0AAKdjOG2OiIiyMCHKhdtuExGVX2OeTpvT6LTYGRVu3GCIiKhUYEKUC7fdJiIqvyrbOMJJbQUA+N9FTpsjIiImREREZGJ6+NQFAFyNi4ZWx2lzRESmjgkRERGZlI8bdoGtuRqx6Sk48SjK2OEQEZGRMSEiIiKTYq4wQ6fKtQEA225dMHI0RERkbEyIiIjI5HT1DgAAbL55BjohjBwNEREZExMiIiIyORWt7QEASZnpuKiJNW4wRERkVEyIiIjI5NR0cIetuRoAsC/tvpGjISIiY2JCREREJqm9Z00AwPXMRGTqtEaOhoiIjIUJERERmaQ3a7cAAGgh8Pe9a0aOhoiIjIUJUS5hYWHw8/NDw4YNjR0KERGVoDpOFWFlpgIALLl8xMjREBGRsTAhyiUkJATh4eE4fvy4sUMhIqISJJPJ0NqjGgDg2MNbnDZHRGSimBAREZHJGl6zCQBAo9Pin3s3jBwNEREZAxMiIiIyWa+4VEKA0gEAsO3WeSNHQ0RExsCEiIiITJZcJkdbdQUAwI5bF6HhtDkiIpPDhIiIiEyar5ktnNRWiEtPwcF7140dDhERvWRMiIiIyKQpZDJ4WNoBAH4JP2TkaIiI6GVjQkRERCbPxcIaAHDk/g1kaDONHA0REb1MTIiIiMjkDa7WGACQodPiAG/SSkRkUswMqeTo6FikRmUyGU6dOgUvL6/nCoqIiOhlal6hCszlCmTotFh26Sg6VKpl7JCIiOglMSghiouLw7x582BnZ1doXSEExo4dC62WO/UQEVHZYK4wQxP3Kjhw7xoO3b+ODG0mzBUG/RdJRERlnMF/7QcOHAhXV1eD6o4fP/65A3oevXr1wv79+9G+fXts2LABAHD79m288cYbePjwIczMzPDxxx+jX79+LzUuIiIqO4bUbIwD964hQ6fF33evomNlP2OHREREL4FBa4h0Op3ByRAAJCYmokqVKs8dVFG9/fbb+PXXX/XKzMzMMG/ePISHh2PXrl2YOHEikpOTX1pMRERUtrSuWANmsqz/Fn+9fNTI0RAR0ctSLjZVaNOmDWxsbPTKKlSogMDAQACAu7s7nJ2d8eTJEyNER0REZYGFmRKdvWoDAI5FRyItU2PkiIiI6GUo8gTp33//Pd9ymUwGtVoNX19f+Pj4GNzegQMHMHv2bJw8eRL379/H5s2b0bNnT706YWFhmD17Nh48eIC6deti/vz5aNSokcHXOHnyJLRaLSpVqmTwOUREZHrC2gzC8XW38CAlAQfuXUMQp80REZV7RU6IevbsCZlMBiGEXnl2mUwmQ4sWLbBlyxY4ODgU2l5ycjLq1q2LESNGoHfv3nmOr127FpMmTcKCBQvQuHFjzJs3D8HBwbhy5YpB0/iePHmCIUOGYNGiRYZ3koiITJJcJkc37wD8L/wQ/og4x4SIiMgEFDkh2r17N6ZNm4ZZs2ZJozTHjh3Dxx9/jI8++gh2dnZ46623MHnyZPzyyy+Ftte5c2d07ty5wONz5szBqFGjMHz4cADAggULsG3bNixevBhTpkx5Ztvp6eno2bMnpkyZgmbNmhVaNz09XXqckJAAANBoNNBoyv+0iew+mkJf88P+s/85/zUlptx3IP/+d6xYE/8LP4TtkRfweaMUqM2UxgqvxPH1N93+m2LfTamvVDQykXuopxD+/v5YuHBhngTj0KFDGD16NC5evIg9e/ZgxIgRiIqKKlowMpnelLmMjAxYWlpiw4YNetPohg4diri4OPz2229S2f79+/HDDz9Iu8wJIfDaa6+hRo0amDFjRqHXnjFjBkJDQ/OUr1q1CpaWlkXqBxERlV1H0x5iecoNAMBo6xqoa160e/ERUemUkpKC1157DfHx8bC1tTV2OFSKFHmE6MaNG/n+ENna2uLmzZsAgGrVqiEmJuaFg4uJiYFWq4Wbm5teuZubGy5fviw97tChA86ePYvk5GR4enpi/fr10Gq1WLt2LerUqYMtW7YAAJYvX46AgIB8rzV16lRMmjRJepyQkIBKlSohKCjIJH5pNBoNdu/ejY4dO0KpLL+fhhaE/Wf/TbX/ptx3IP/+N09PxYr1X0BA4LqdwNS2XYwcZcnh62+6/TfFvmfP/iHKrcgJUf369fHee+/h119/hYuLCwDg0aNHeP/999GwYUMAwLVr117qBgZ79uzJt1yn0xnchkqlgkqlylOuVCpN5g8FYHr9zY39Z/9Ntf+m3HdAv//OSiUCXTxx+tFtHLx/A5myrB3oyjO+/qbbf1Pqu6n0k4quyNtu//LLL4iIiICnpyd8fX3h6+sLT09PREZG4n//+x8AICkpCR999NELB+fs7AyFQoHo6Gi98ujoaLi7u79w+/kJCwuDn5+flNwREZHpGeDbAACg0Wmx784VI0dDREQlqcgjRDVq1JBudnr16lWprGPHjpDLs/Kr3NtmPy9zc3PUr18fe/fuldrU6XTYu3cvxo0bVyzXyC0kJAQhISFISEiAnZ1diVyDiIhKt2AvP0w5shkAsO76CXTx9jdyREREVFKKnBDduXMHnp6e6NSpEzp16qR37OjRo2jSpEmR2ktKSsL169elxxEREThz5gwcHR1RuXJlTJo0CUOHDkWDBg3QqFEjzJs3D8nJydKuc0RERMXNxcIG/o4euPDkHv6+exWpmRmwMDM3dlhERFQCijxlLigoCE+ePMlTfujQoTwJkiFOnDiBevXqoV69egCASZMmoV69epg+fToAYMCAAfjmm28wffp0BAYG4syZM/jzzz/zbLRQXDhljoiIAKCf7ysAAI1Oh72cNkdEVG4VOSFq0qQJgoKCkJiYKJUdOHAAXbp0wSeffFLkANq0aQMhRJ6vpUuXSnXGjRuHW7duIT09Hf/++y8aN25c5OsYKiQkBOHh4Th+/HiJXYOIiEq/XlXrSUnRHxHnjBwNERGVlCInRP/73/9QuXJldO/eHenp6di3bx+6du2KTz/9FO+8805JxEhERPTSOaqtMKJWcwDA3tuXkaxJL+QMIiIqi4qcEMnlcqxZswZKpRLt2rVDjx498MUXX+Dtt98uifheOk6ZIyKibP5OHvCycUKaVoO9ty8XfgIREZU5Bm2qcO5c3qkCM2bMwKBBgzB48GC0atVKqlOnTp3ijfAl4y5zRESUTaPTQq3I+q9y441T6FGlrpEjIiKi4mZQQhQYGAiZTAYhhFSW/fjnn3/GwoULIYSATCaDVqstsWCJiIheJnOFGTJ0Wf+v/X33GpI06bBW5r2JNxERlV0GJUQRERElHQcREVGp1LtKIL49sweZQoc9ty+hZ5VAY4dERETFyKA1RF5eXvjll18QExMDLy+vZ36VdVxDREREOXXxDpC+33zjtBEjISKikmDwpgp37txB586d4enpiTFjxmDHjh3IyMgoydiMgttuExFRTtXtXeFpZQ8ga9pcYkaacQMiIqJiZXBCtHjxYjx48ACrV6+GjY0NJk6cCGdnZ/Tp0we//vprvjdrJSIiKutkMhlefTpNLlPosPv2JeMGRERExapI227L5XK0bNkSX3/9Na5cuSLdJPXnn3+Gh4cHWrVqhW+++QZ3794tqXiJiIheuq7e/tL3v904Y7xAiIio2BX5PkQ51apVC++//z4OHTqEqKgoDB06FAcPHsTq1auLK76XjmuIiIgotwCnimjomrVO9sC9a0jgtDkionLjhRKinFxdXTFy5Ej89ttvmDx5cnE1+9JxDREREeUmk8mwqcv/oZqdKzRCh11R4cYOiYiIiolB227n1KtXL8hkMoPqbtq0qcgBERERlUYymQzdfepgzpk92Bp5Dn19XzF2SEREVAyKPEJkZ2eHvXv34sSJE1LZyZMn8ddff8HW1hZ2dnbSFxERUXnSoVJNAMD+O1cRl55i5GiIiKg4FHmEyM3NDf3798eCBQugUCgAAFqtFmPHjoWtrS1mz55d7EESERGVBuuunwSQtdvcrqhw9K/WwMgRERHRiyryCNHixYsxefJkKRkCAIVCgUmTJmHx4sXFGhwREVFp0qlyben7326eM2IkRERUXIqcEGVmZuLy5ct5yi9fvgydTlcsQRkTd5kjIqKCNHH3ga1SDQA4eO8aYjltjoiozCvylLnhw4dj5MiRuHHjBho1agQA+Pfff/Hll19i+PDhxR7gyxYSEoKQkBAkJCRwHRQREekxkyvQ2bs21l47CR0Edt66iIHV+QEaEVFZVuSE6JtvvoG7uzu+/fZb3L9/HwBQoUIFvPfee3j33XeLPUAiIqLSpItXANZey1pL9HvEOSZERERlXJETIrlcjvfffx/vv/8+EhISAAC2trbFHhgREVFp1MLDF5Zm5kjJzMA/964jNi0ZDmorY4dFRETP6YVuzGprawu1Wo2kpKTiioeIiKhUUynM0LFyLQCADgI7oi4aOSIiInoRRUqIlixZgvHjx2PlypUAgKlTp8LGxgZ2dnbo2LEjHj9+XCJBEhERlSbv1G2Pt2q3AABsjThv5GiIiOhFGJwQzZo1CyEhIbh8+TImTJiAMWPGYOnSpfj000/x5Zdf4vLly/joo49KMlYiIqJSwdfeFYNrNgEAHLp/A4/TOFOCiKisMngN0dKlS/HLL79g0KBBOHHiBBo3box169ahT58+AAB/f3/83//9X4kF+rKEhYUhLCwMWq3W2KEQEVEp5mPrjACnijj/+C523LqIwTUaGzskIiJ6DgaPEEVFRaFFi6zpAQ0aNICZmRn8/f2l43Xq1JF2nSvLQkJCEB4ejuPHjxs7FCIiKsVuJz5BulYDgNPmiIjKMoMTIo1GA5VKJT02NzeHUqmUHpuZmXFUhYiITIadyhI342MAAIfuX8ej1EQjR0RERM+jSNtuh4eH48GDBwAAIQQuX74s7TAXExNT/NERERGVUrbmarSqWA1/3bkCAWDHrYsY8nRdERERlR1FSojat28PIYT0uFu3bgAAmUwGIQRkMlnxRkdERFSKdfbyx193rgAA/og4x4SIiKgMMjghioiIKMk4iIiIypzgyn6YcngTtELgyIObeJiSCFdLG2OHRURERWBwQuTl5VWScRAREZU5jmorNHGvgkP3bwAAtt+6gGG1mho5KiIiKgqDNlU4d+4cdDqdwY1evHgRmZmZzx0UERFRWdHF678dV/+IOGfESIiI6HkYlBDVq1cPjx8/NrjRpk2bIioq6rmDIiIiKis6edVGHaeKAIB/oyPwICXByBEREVFRGDRlTgiBjz/+GJaWlgY1mpGR8UJBERERlRVulrbY3mM8Xt36I04+isL2yPMY4dfc2GEREZGBDEqIWrVqhStXrhjcaNOmTWFhYfHcQRlTWFgYwsLCeE8lIiIqku4+dXDyURS2MiEiIipTDEqI9u/fX8JhlB4hISEICQlBQkIC7OzsjB0OERGVEc0r+AIAjkVH4n5yPCpY8f8QIqKywKA1RERERPRs7x3aKH2/LfK8ESMhIqKiYEJERERUDIIr+0nfb2VCRERUZjAhIiIiKgZdvP/bfvvEw1u4lxRnvGCIiMhgTIiIiIiKQVU7F1S3d5Uec5SIiKhsKHJClJycXBJxEBERlXmdc96kNZI3aSUiKguKnBC5ublhxIgR+Oeff0oiHiIiojKra45pc6cf3cbtxCdGjIaIiAxR5IRoxYoVePLkCdq1a4fq1avjyy+/xL1790oiNiIiojKllkMFeNk4SY+3RV4wYjRERGSIIidEPXv2xJYtW3D37l383//9H1atWgUvLy9069YNmzZtQmZmZknESUREVOrJZDJ832oAptbvBIDT5oiIyoLn3lTBxcUFkyZNwrlz5zBnzhzs2bMHffv2hYeHB6ZPn46UlJTijJOIiKhMqO9aGf2r1YdcJsPZmDuI4rQ5IqJS7bkToujoaHz99dfw8/PDlClT0LdvX+zduxfffvstNm3ahJ49exZjmM/Wq1cvODg4oG/fvgaVExERlSQXCxs0da8CgLvNERGVdmZFPWHTpk1YsmQJdu7cCT8/P4wdOxaDBw+Gvb29VKdZs2aoVatWccb5TG+//TZGjBiBZcuWGVRORERUkv59EIH7yfEAgK0R5zA2oLWRIyKi4qLVaqHRaIwdBhVCqVRCoVAYVLfICdHw4cMxcOBAHDp0CA0bNsy3joeHB6ZNm1bUpp9bmzZtsH//foPLiYiISpIOAjcTYgAA5x7fRWTCY3jbOhVyFhGVdklJSbhz5w6EEMYOhQohk8ng6ekJa2vrQusWOSG6f/8+LC0tn1nHwsICn3zyiUHtHThwALNnz8bJkydx//59bN68Oc90u7CwMMyePRsPHjxA3bp1MX/+fDRq1KiooRMREb0UjVy94aS2wuO0rHv3bY08j3F12hg3KCJ6IVqtFnfu3IGlpSVcXFwgk8mMHRIVQAiBR48e4c6dO6hWrVqhI0VFTohsbGxw//59uLq66pU/fvwYrq6u0Gq1RWovOTkZdevWxYgRI9C7d+88x9euXYtJkyZhwYIFaNy4MebNm4fg4GBcuXIlTwxERESlgUIuR3Dl2lh19RiArGlzTIiIyjaNRgMhBFxcXGBhYWHscKgQLi4uiIyMhEajKf6EqKAhwvT0dJibmxe1OXTu3BmdO3cu8PicOXMwatQoDB8+HACwYMECbNu2DYsXL8aUKVOKfL2CpKenIz09XXqckJAAIOuH3xTmiWb30RT6mh/2n/3P+a8pMeW+AyXb/yDPmlJCdOHJPVx9/AA+pWzaHF9/0+2/Kfa9uPrKkaGyoSivk8EJ0ffffy81/r///U9vPp5Wq8WBAwdQs2bNIoRZuIyMDJw8eRJTp06VyuRyOTp06IAjR44U67W++OILhIaG5inftWtXoVMEy5Pdu3cbOwSjYv/Zf1Nlyn0HSqb/mUIHC5kCqSJr5sTc3RvRycKz2K9THPj6m27/TanvvCWMvqVLl2LFihXYs2dPvseTk5PRsGFDHDt2DNbW1njw4AF69+6N8+fPIzQ0FJMmTSq2WGQyGW7fvg1PT0+0adMGb775JgYPHvzMc2JjY9G8eXOcOnUKarX6ha5vcEI0d+5cAFkjRAsWLNAbejI3N4e3tzcWLFjwQsHkFhMTA61WCzc3N71yNzc3XL58WXrcoUMHnD17FsnJyfD09MT69evRtGnTAsvzM3XqVL0XNiEhAZUqVUJQUBBsbW2LtV+lkUajwe7du9GxY0colUpjh/PSsf/sv6n235T7DpR8//8+nI5NN88AAK6pMvB9ly7Ffo0XwdffdPtvin3Pnv1Tlnh7e2PFihVo0aJFoXUjIyPh6+uLzMzMYrn2ggUL8Oqrr0qDIAsXLoS3tzcOHz5cLO0bYtu2bZg1axYuXrwIKysrDBw4EF999RWUSiUcHBwQFBSERYsWYfz48S90HYMTooiICABA27ZtsWnTJjg4OLzQhYtTQZltQeX5UalUUKlUCAsLQ1hYmLQWSqlUmswfCsD0+psb+8/+m2r/TbnvQMn1/9UqgYhOTcSR+zdxOS4at5Jj4Wtf+ta/8vU33f6bUt9NpZ/F5ZdffsGaNWukx1FRUfDz83upMSQkJGDGjBlo2bIlkpKS0Lt3b8yePRsffvghAGDQoEEYPXr0CydERb4x6759+15aMuTs7AyFQoHo6Gi98ujoaLi7u5fINUNCQhAeHo7jx4+XSPtERGQ62leqibWdRqF1xeoAeJNWIjLcm2++iaioKAQFBcHa2horV64EAPz444+oUqUKXFxcMHjwYMTHZ93zLCgoCFqtFtbW1rC2tkZUVBT+/fdfNGzYELa2tvDy8sL8+fMNunZUVBTu3buHgIAAAMD//d//YdmyZZg5cyasra1x+vRppKamYty4cfDw8ICnpye+/PJL6fxhw4ZhwoQJaN++PWxsbBAUFIQnT55IxxcvXoxKlSrB3d0dCxcuLDCOQYMGISgoCBYWFnBxccEbb7yht2ymQYMGuH79Ou7fv2/4E5sPg0aIJk2ahJkzZ8LKyqrQ+YJz5sx5oYByMjc3R/369bF3715pK26dToe9e/di3LhxxXYdIiKiktTdJwD77l7BHxHnMDGwvbHDIaIXJIRAambxbkhhYabU2wjgf//7H/bs2aM3ZW737t2YOXMm9u7dCy8vLwwZMgRvv/02li5dil27dsHX1xdJSUlSGzExMfj5558RGBiIU6dOoX379mjRogXq1av3zFjOnz8PX19fKZ4FCxYgLS0Nvr6++OijjwBkDSLExsbi6tWrSEhIQMeOHeHv749u3boBANatW4ddu3ahevXq6Nq1K7777juEhobiwoULmDhxInbv3o06dergrbfeMvg5OnDgAGrXri09VigU8PX1xblz51ChQgWD28nNoITo9OnT0s4cp0+fLrDe8+y6kZSUhOvXr0uPIyIicObMGTg6OqJy5cqYNGkShg4digYNGqBRo0aYN28ekpOTpV3nilvuKXNEREQvqo6zJxQyGa7EReNqXDSq27sVfhIRlVqpmRpUXzG9WNu8OvhTWCqfvWPzmjVrMHr0aGnq2ueff47AwEAsWbIk3/qvvPKK9H2DBg3QpUsXHDp0qNCEKC4u7pk3NBVCYMmSJbh+/bo0IjVmzBhs2LBBSoj69euHOnXqAAD69OmDXbt2AQA2btyI3r17o3HjxgCA6dOnY/ny5c+MJ/u8vXv34uzZs3rlNjY20ijZ8zIoIdq3b1++3xeHEydOoG3bttLj7BGooUOHYunSpRgwYAAePXqE6dOn48GDBwgMDMSff/6ZZ6OF4hISEoKQkBAkJCTAzs6uRK5BRESmIzVTg25/hEH79LYVWyPOY1I9JkREVHT37t1Ds2bNpMdeXl5IS0vTm46W08WLFzFx4kScOXMGGRkZSEtLM2hXaDs7O72RptwePXqE1NRUvTVFOp0OzZs3lx7nfK9uaWkptXf//n1UqlRJOpbz+4Ls27cPY8aMwfbt2/PchzQxMfGF37MX+T5EK1asQO/evYttK+o2bdoUeG+jbOPGjeMUOSIiKpMszJRo5eGLXbcvAQC2Rp7DpHodjBwVEb0ICzMlrg7+tNjbzC337CsPDw9ERUVJj6OioqBWq+Ho6JhvAjNu3Di0bNkSv//+OywsLDBo0KBC33cDQEBAAG7cuFHgcWdnZ6hUKty8eROOjo6FtpdThQoVpM3aAOD27dvPrP/vv/+if//+WL9+PRo0aKB3TKvV4saNG9Jap+dV5E0V3nnnHbi6uuK1117D9u3bObWMiIioEJ29/AEAMgBX4x7iSmz0s08golJNJpPBUmlerF/5LT1xdXVFZGSk9HjAgAFYtGgRLl26hOTkZEybNg39+/eHTCaDs7MzdDod7ty5I9VPTEyEvb091Go1Dh48iG3bthnUPy8vL7i7u+P8+fw3gpHL5Rg6dCjeffddxMXFQafT4dKlSzh27Fihbffp0webNm3C8ePHkZqais8++6zAuufPn0f37t3xyy+/oE2bNnmOnzx5Ej4+PvDw8DCoXwUpckJ0//59rFmzBjKZDP3790eFChUQEhLyUvckL0lhYWHw8/NDw4YNjR0KERGVEx0r14KZTI7sz2X/iDxn1HiIqGz44IMPMGXKFNjb22PVqlUICgrC1KlT0aVLF3h5eUGpVGLevHkAACsrK0yZMgWBgYGwt7dHVFQUvvrqK4SFhcHW1hbz5s1Djx49DL72yJEjsXr16gKPz507F3Z2dggICICjoyOGDBmC2NjYQtv19/fHnDlz0KtXL3h7e+tNAcxtzpw5ePz4MV577TVprVLnzp2l46tXr8aoUaMM7lNBZMKQcbMCpKSkYPPmzVi1ahX27NkDT0/PZw6vlSXZa4ji4+NN5sas27dvR5cuXUxyn372n/031f6bct+Bl9v/13f+gr/vXQMA+Nq5YF+vSc+1GVFx4utvuv03xb6/6Hu7tLQ0REREwMfHB2q1ugQiLF2Sk5PRsGFDHDt27JkbLBhLXFwcmjdvjpMnT+b7ehTl9SryGqKcLC0tERwcjNjYWNy6dQuXLl16keaIiIjKrc5e/vj73jXIAFyPf4TLsdGo5Vgy99QjInpRVlZWCA8PN3YYBbK3t8fFixeLpa0iT5kDskaGVq5ciS5duqBixYqYN28eevXqVWxBGROnzBERUUkI9vKDDDJkrSTK2lyBiIiMr8gJ0cCBA+Hq6op33nkHVapUwf79+3H9+nXMnDnToG38SruQkBCEh4fj+PHjxg6FiIjKERcLGywPGo6vm/cCAPwRcc6g3Z6IiKhkFXnKnEKhwLp16xAcHAyFQlESMREREZVLbSpWR5ImHR8d/R03E2IQ/uQ+aju92O5IRET0Yoo8QpQ9VY7JEBERUdFZK1Vo51kDAHebIyIqDQwaIfr+++8xevRoqNVqfP/998+sO2HChGIJjIiIqDzadOM0Lj65DwD4I+I8Pngl2Oi7zRERmTKDEqK5c+fi9ddfh1qtxty5cwusJ5PJynxCFBYWhrCwMN5wloiISsSDlAREJT6BHDLcSnyMC4/vIcC5orHDIiIyWQYlRBEREfl+Xx6FhIQgJCRE2queiIioOHX2qo3PT+yAeHqb1j8izzEhIiIyoiKvIfr000+RkpKSpzw1NRWffvppsQRFRERUXvnYOqOWgzuy95fbGnGeu80R0Uu3dOlSdOjQocDjycnJ8PPzQ1JSEgDgwYMHaNasGWxsbDBnzpxijUUmk+HOnTsAgDZt2mDFihWFnhMbGws/Pz+kpaW98PWLnBCFhoZKT0xOKSkpCA0NfeGAiIiIyrsu3v4AALlMhqikJzj3+K6RIyKi0sjb2xv//POPQXUjIyNhZlbkDaQLtGDBArz66quwtrYGACxcuBDe3t5ITEzEpEmTiu06z3L06FHUqVMH9vb2cHV1xdChQ6U8xMHBAUFBQVi0aNELX6fICZEQIt/Fn2fPnoWjo+MLB0RERFTedfEKyPrm6cDQHxHcbY6ISpdffvkFgwYNkh5HRUXBz8/vpcbg6+uLHTt2IC4uDpGRkdDpdHoDMIMGDcL//ve/F76OwQmRg4MDHB0dIZPJUL16dTg6OkpfdnZ26NixI/r37//CARlbWFgY/Pz80LBhQ2OHQkRE5VR1e1dUsXWG7mlGtDWSN2klIn1vvvkmoqKiEBQUBGtra6xcuRIA8OOPP6JKlSpwcXHB4MGDER8fDwAICgqCVquFtbU1rK2tERUVhX///RcNGzaEra0tvLy8MH/+fIOuHRUVhXv37iEgIOvDm//7v//DsmXLMHPmTFhbW+P06dNITU3FuHHj4OHhAU9PT3z55ZfS+cOGDcOECRPQvn172NjYICgoCE+ePJGOL168GJUqVYK7uzsWLlxYYBzOzs6oWDFrjaUQAnK5HDdu3JCON2jQANevX8f9+/cNfFbzZ/C42rx58yCEwIgRIxAaGqq34YC5uTm8vb3RtGnTFwqmNOCmCkREVNJkMhn6+r6CC4/vYe+dy7iTFIczMXdQz6WSsUMjoiJI0WQUeEwuk0FtpjSorqXSPE/Z//73P+zZswcrVqxAixYtAAC7d+/GzJkzsXfvXnh5eWHIkCF4++23sXTpUuzatQu+vr56S1tiYmLw888/IzAwEKdOnUL79u3RokUL1KtX75n9On/+PHx9faVZYQsWLEBaWhp8fX3x0UcfAch6zxwbG4urV68iISEBHTt2hL+/P7p16wYAWLduHXbt2oXq1auja9eu+O677xAaGooLFy5g4sSJ2L17N+rUqYO33nrrmbFERUWhTp06iI+Ph5WVFX7//XfpmEKhgK+vL86dO4cKFSo8s51nMTghGjp0KADAx8cHzZs3L9Y5ikRERKZmQt12AICQ/avxW8RZbI04x4SIqIypvmJ6gcfaedbArx2HS4/rrpmJ1ExNvnXvDP8y3/Lc1qxZg9GjR0tT1z7//HMEBgZiyZIl+dZ/5ZVXpO8bNGiALl264NChQ4UmRHFxcdLaofwIIbBkyRJcv35dGpEaM2YMNmzYICVE/fr1Q506dQAAffr0wa5duwAAGzduRO/evdG4cWMAwPTp07F8+fICr1W5cmXExcXh4cOHWLRoESpXrqx33MbGRhole15FXkOUnJyMvXv35infuXMnduzY8ULBEBERmZruPllvGP7gtDkiKsS9e/f0EgIvLy+kpaXpTUfL6eLFi+jYsSNcXFxgZ2eHTZs24fHjx4Vex87OLt9N1LI9evQIqamp8PPzg729Pezt7fHhhx8iOjpaquPm5iZ9b2lpKbV3//59VKr034c/Ob9/FldXV3Tq1AmvvfaaXnliYuILz+oq8jDPlClT9OYIZhNCYMqUKejcufMLBURERGRKPK3toZKb4V5yPE49ikJ9Vy9jh0REBro6uOBbzshzbUJ2duDHRW4/90ZmHh4eiIqKkh5HRUVBrVbD0dEx3wRm3LhxaNmyJX7//XdYWFhg0KBBBn3wEhAQoLdWJzdnZ2eoVCrcvHmzyJuqVahQQe++prdv3zb43MzMTFy/fl16rNVqcePGDWmt0/Mq8gjRtWvX8t1hombNmnoBEhER0bOdeXQbnX6fDx10ALjbHFFZY6k0L/Ar5/qhwuoWxNXVFZGRkdLjAQMGYNGiRbh06RKSk5Mxbdo09O/fHzKZDM7OztDpdNL9fICs0RN7e3uo1WocPHgQ27ZtM6hfXl5ecHd3x/nz5/M9LpfLMXToULz77ruIi4uDTqfDpUuXcOzYsULb7tOnDzZt2oTjx48jNTUVn332WYF1t27diitXrkAIgXv37mHatGlo166ddPzkyZPw8fGBh4eHQf0qSJETIjs7O9y8eTNP+fXr12FlZfVCwRAREZmSOs4V4WphA40uKyHaGnkeOqEzclREVFp88MEHmDJlCuzt7bFq1SoEBQVh6tSp6NKlC7y8vKBUKjFv3jwAgJWVFaZMmYLAwEDY29sjKioKX331FcLCwmBra4t58+ahR48eBl975MiRWL16dYHH586dCzs7OwQEBMDR0RFDhgxBbGxsoe36+/tjzpw56NWrF7y9vdGsWbMC6z548ACdO3eGtbU1GjRoAC8vL/z888/S8dWrV2PUqFEG96kgMlHECctvvfUWjhw5gs2bN6Nq1aoAspKhPn36oGHDhsWyF7gxhYWFISwsDFqtFlevXkV8fDxsbW2NHVaJ02g02L59O7p06QKlUln4CeUM+8/+m2r/TbnvQOno/4dHtuDXy0ehlMmhETps7vJ/aOjm/VKuXRr6b0ym3H9T7Hv2DsLP+94uLS0NERER8PHxgVqtLoEIS5fk5GQ0bNgQx44de+YGC8YSFxeH5s2b4+TJk/m+HkV5vYo8QvT111/DysoKNWvWhI+PD3x8fFCrVi04OTlh9uzZRW2u1AkJCUF4eDiOHz9u7FCIiMgEdPHyBwDIZFn/JXPaHBGVBlZWVggPDy+VyRAA2Nvb4+LFi8WSnBZ5UwU7OzscPnwYu3fvxtmzZ2FhYYE6deqgVatWLxwMERGRqWni7gMHlSVi01MAANsiz2NG426Qy4r8mSURET2H57qZkEwmQ1BQEIKCggBk7TC3Y8cO/PLLL9iwYUOxBkhERFSemckVCKrsh7XXTkApVyA6NRHHo2+hsbuPsUMjIjIJL/TxU0REBD7++GNUrlwZvXr1QlpaWnHFRUREZDKyp82pFVmfU/4RyWlzREQvS5ETovT0dKxcuRLt2rVDjRo18Pnnn2PSpEl4+PAhtm7dWhIxEhERlWstPHyxtMNQzGnRDwCwPfICtDruNkdUGvEGymVDUV4ngxOikydPYuzYsXB3d8e8efPQs2dP3L59G3K5HMHBwSaxExsREVFJUCnM0KFSLbSvVBN25mo8TE3EsYeRxg6LiHJQKBQAgIyMDCNHQobIfp2yX7dnMXgNUePGjTF+/HgcPXoUNWr8f3v3HlZVnfaP/732gc35fFDO5gFFDDzgodI84kBi2jjjTDNlVjZPg9kMP6dHfzOPZjPlt8Yar2qXv8eZTr/JsixLMU0jHSw0PISICqkBIshJgQ0b2Gz2Xt8/gB1H2cCGtWG9X9fFBeuzFov75qQ36173iuh7dERERNQlB6UKi0Mj8dGVs0jJy8KsEXdIHRIRtVCpVHB2dkZ5eTnUajUUCg4+sVdmsxnl5eVwdnaGStVzuWN1QbRgwQL861//QllZGR566CEsXrwYgiD0K1giIiL6yfbMVKQVXwEAHMjPxnMzlkLJ/3QR2QVBEDBy5Ejk5eWhoKBA6nCoBwqFAqGhoVbVK1YXRF9++SUKCwvx9ttv48knn0R9fT1WrlwJACyMiIiIbODCrWKU1OmgUapQ0VCLk6V5uHvkaKnDIqIWDg4OGDt2LNvmhgAHBwerr+L1aux2SEgINm3ahE2bNuHIkSN4++23oVKpcP/992PFihVYsWIFpkyZ0qeg7YVWq4VWq4XJZJI6FCIikpn4sCgcLLgAjVIFg6kJ+/OyWBAR2RmFQmGTh4GS/ejzdfhFixZh165dKC4uxlNPPYWDBw8iNjbWlrFJIikpCRcvXsSpU6ekDoWIiGRmYcgEqBVK6BqbH2PxRX42msz8Ax0R0UDqd2Oyl5cXnnrqKXz//fcsIoiIiPrB3cERswPHAAAclWrcMuhxouRHiaMiIhrebHqn5lBvlyMiIpJa60NanVRqAMD+vPNShkNENOxxdA0REZEdiQuNhFJQoNJQBwA4WJANI9vmiIgGDAsiIiIiO+Lt6IKEsCisHDsVng5OqDTUIf3GVanDIiIatno1ZY6IiIgG3pvzHgQAOCj24v/P/Q7787Jwb9A4iaMiIhqeen2FKC8vD5cvX+60fvnyZeTn59siJiIiIgKwZNSdAICDBRfYNkdENEB6XRA98sgjSE9P77T+3Xff4ZFHHrFFTERERLIniiI0ChU8HJxQ3ViPb4qvSB0SEdGw1OuC6Pvvv8fdd9/daX3mzJnIzMy0RUxERESy927OSSz74k3LtLmU/CyJIyIiGp56XRAJgoCamppO69XV1TCZeDmfiIjIFuYHRwAASuua/809VHABjaYmKUMiIhqWel0QzZkzB1u3bm1X/JhMJmzduhX33HOPTYOz1vLly+Hl5YUVK1a0W09JSUFERATGjh2Lf/7zn5LERkRE1Behbt6Y5BMEESLc1BpUNzbgONvmiIhsrtdT5l588UXMmTMHERERmD17NgDg+PHj0Ol0+Prrr20eoDWefvppPProo3j33Xcta01NTUhOTsbRo0fh4eGBqVOnYvny5fDx8ZEkRiIiot5KCIvC+ZtFcHdwQo3RgJT8LCwIGS91WEREw0qvrxBFRkYiKysLv/zlL1FWVoaamho8/PDDyMnJQVRU1EDE2KO5c+fCzc2t3VpGRgYmTpyIoKAguLq6Ij4+HocPH5YkPiIior6ID5sIACit0wEAvrx2EQa2zRER2VSfHswaGBiIF154AQcOHMCePXuwadMmeHt79ymAtLQ0JCYmIjAwEIIg4LPPPut0jFarRXh4OBwdHTFjxgxkZGT0eN7i4mIEBQVZtoOCglBUVNSnGImIiKQwxtMf4zz90SSa4e7gCF1jA9KKfpA6LCKiYcWqgigrKwtms9ny9u1eekuv1yM6OhparbbL/bt370ZycjI2b96Ms2fPIjo6GosXL0ZZWVmvPxYREdFQEx/W3H3h79TcCbE//7yU4RARDTtW3UMUExODkpIS+Pv7IyYmBoIgQBTFTscJgtDrSXPx8fGIj4/vdv8rr7yCNWvWYPXq1QCAHTt24MCBA3jrrbewYcOGbt8vMDCw3RWhoqIiTJ8+vdvjDQYDDAaDZVuna25PMBqNMBqNVuczVLXmKIdcu8L8mX/b13Ii59yBoZH/ilGTMdM/HApBwMrD/8LhaxdR21AHjVLd73MPhfwHkpzzl2PucsqVekcQu6psOigoKEBoaCgEQUBBQcFtjw0LC+t7MIKAvXv3YtmyZQCAxsZGODs7Y8+ePZY1AFi1ahWqqqrw+eefW9aOHTuG119/HXv27AHQPFRhwoQJOHbsmGWoQnp6erdDFZ599lls2bKl0/quXbvg7Ozc55yIiIhswSyK+J/qs6gyN+IJ1whEO/StVZ1Irurq6vDggw+iuroa7u7uUodDdsSqK0TLly9HamoqvLy88O6772L9+vWDUiRUVFTAZDIhICCg3XpAQABycnIs2wsXLsS5c+eg1+sRHByMjz/+GLNmzcLLL7+MefPmwWw245lnnrnthLmNGzciOTnZsq3T6RASEoK4uDhZ/NAYjUYcOXIEixYtglrd/786DjXMn/nLNX855w4MvfwzTyvwr5x0lPpqkHBPQr/PN9TytzU55y/H3Fu7f4g6sqogunTpEvR6Pby8vLBlyxb813/9l11dNfnqq6+6XF+6dCmWLl1q1Tk0Gg00Gk2ndbVaLZtfFID88u2I+TN/ueYv59wB+89fbzTghdOHkFp4CQDw1fUcNAmAk8o2Mdt7/gNNzvnLKXe55Em9Z/U9RKtXr8Y999wDURSxbds2uLq6dnnspk2bbBacr68vlEolSktL262XlpZixIgRNvs4bWm1Wmi12l7fC0VERDRQnFRqfHntAkrqdPDWuOCWQY9jRbmWgQtERNR3Vk2Ze+edd+Dj44OUlBQIgoCDBw9i7969nV66GpndHw4ODpg6dSpSU1Mta2azGampqZg1a5ZNP1arpKQkXLx4EadOnRqQ8xMREfWWQlBYih9fp+Y/SKbkcdocEZEtWHWFKCIiAh9++CEAQKFQIDU1Ff7+/jYJoLa2FleuXLFs5+XlITMzE97e3ggNDUVycjJWrVqFadOmYfr06di+fTv0er1l6hwREZEcJIRH4e1L6bihrwIAHCm8hPqmRjipHKQNjIhoiLOqIGqr9XlEtnL69GnMmzfPst062GDVqlV45513sHLlSpSXl2PTpk0oKSlBTEwMDh061GnQgq2wZY6IiOzRdP9w+Di64GaDHn6OrihvqMXX13NxX/gkqUMjIhrSrCqI9u3bh/j4eKjVauzbt++2x1o7xKDV3Llzu3ymUVtr167F2rVre3XevkpKSkJSUhJ0Oh08PDwG5WMSERH1RKlQYHHoROz6IQN+zm4ob6hFSt55FkRERP1kVUG0bNkyy4NZ2z4PqKO+PJiViIiIrJMQHoVdP2Tghr4aAPDV9UuoMzbCWc22OSKivrJqqILZbLbcM2Q2m7t9GQ7FkFarRWRkJGJjY6UOhYiIqJ27RtyBSO+ReGD0ZIS4eqG+yYjU6zk9vyMREXXLqoJITjhljoiI7JWDUoXD9z+NLTMSsXRUNABgf16WxFEREQ1tvS6I1q1bh1dffbXT+uuvv44//OEPtoiJiIiIepA4qvneodTrOdAbDRJHQ0Q0dPW6IPrkk09w9913d1q/6667sGfPHpsERURERN0zmk241VCHIBdPGExN+KqQbXNERH3V64Lo5s2bXU5fc3d3R0VFhU2CkhLvISIiInv3zLef4MHD/8IIZ3cAbJsjIuqPXhdEY8aMwaFDhzqtHzx4EHfccYdNgpIS7yEiIiJ7Ny8oAgBQUqcDABwtykUt2+aIiPqk1w9mTU5Oxtq1a1FeXo758+cDAFJTU/Hyyy9j+/btto6PiIiIOpgfMh4apQpF+ioEu3jiur4KR65dwvLRMVKHRkQ05PS6IHr00UdhMBjw/PPP469//SsAIDw8HG+++SYefvhhmwdIRERE7bmqNbg3cCwOF15CkKsXruurkJKfxYKIiKgP+jR2+8knn8T169dRWloKnU6HH3/8kcUQERHRIIoPiwIAlNXXAACOXs9FTWODlCEREQ1JfSqImpqa8NVXX+HTTz+FKIoAgOLiYtTW1to0OClwqAIREQ0Fi0InQCUokKerQKirFxrNJhwuvCR1WEREQ06vC6KCggJMmjQJ999/P5KSklBeXg4AePHFF7F+/XqbBzjYOFSBiIiGAk+NM+4eORoAcIeHHwAghdPmiIh6rdcF0dNPP41p06ahsrISTk5OlvXly5cjNTXVpsERERFR9/40JQ5fLl2Hv0xLAAD8p+gHVBvqJY6KiGho6fVQhePHjyM9PR0ODg7t1sPDw1FUVGSzwIiIiOj2YvxCLG9HeAYgt6oUh69dxC/GTpUwKiKioaXXV4jMZjNMJlOn9evXr8PNzc0mQREREVHvLBk1CQCwP59tc0REvdHrgiguLq7d84YEQUBtbS02b96MhIQEW8YmCQ5VICKioSRfdxPr0nbjxI0fAQBpRZdRZaiTOCoioqGj1wXRyy+/jG+//RaRkZFoaGjAgw8+aGmXe/HFFwcixkHFoQpERDSUOCiU+PTq9zhRkocxHn5oEs348tpFqcMiIhoyen0PUXBwMM6dO4cPP/wQWVlZqK2txWOPPYbf/OY37YYsEBER0cALdPVEjG8IMisKMcrdB1eqy7E/Lwsrx06TOjQioiGh1wURAKhUKvz2t7+1dSxERETUBwnhUcisKMStlla5b4qvoLJBDy9HF4kjIyKyf316MGtubi7Wrl2LBQsWYMGCBVi7di1ycnJsHRsRERFZIT5sIgAgs/w6IjwD0CSacYhtc0REVul1QfTJJ58gKioKZ86cQXR0NKKjo3H27FlMmjQJn3zyyUDESERERLcxyt0XE7xGwCSaMZoPaSUi6pVet8w988wz2LhxI5577rl265s3b8YzzzyDn//85zYLjoiIiKyTEB6FS5Ullglz39y4ilsNenizbY6I6LZ6fYXoxo0bePjhhzut//a3v8WNGzdsEpSUOHabiIiGooSwSYjwDMCcoLGI8g6ESTTjYMEFqcMiIrJ7vS6I5s6di+PHj3da/+abbzB79mybBCUljt0mIqKhKMIrAKnL/4i1d87DklF3AgBS+JBWIqIe9bplbunSpfjv//5vnDlzBjNnzgQAnDx5Eh9//DG2bNmCffv2tTuWiIiIBlfiqEn4P2cO4dsbV3GzoRY+jq5Sh0REZLd6XRD9/ve/BwC88cYbeOONN7rcBwCCIMBkMvUzPCIiIuqNOmMjsm8WY5JPEM7fLMLB/Av47fgZUodFRGS3et0yZzabrXphMURERDT4lqS8jt8dfR8RngEAgH155ySOiIjIvvXpOURERERkn+4NGgcAqDU2AABOluahrK5GypCIiOya1QXRiRMnkJKS0m7tvffew6hRo+Dv748nnngCBoPB5gESERGR9RLCogAA6SU/4k6fYJhFEQcLsiWOiojIflldED333HO4cOGn8Z3nz5/HY489hoULF2LDhg3Yv38/tm7dOiBBEhERkXWm+ofC38kNusYGTPQZCQDYz2lzRETdsrogyszMxIIFCyzbH374IWbMmIGdO3ciOTkZr776Kj766KMBCZKIiIisoxAU+FnYRACA3tjcufFdST5K63RShkVEZLesLogqKysREBBg2f7Pf/6D+Ph4y3ZsbCwKCwttGx0RERH1Wmvb3DfFVzHZNwQiRHyRz7Y5IqKuWF0QBQQEIC8vDwDQ2NiIs2fPWp5DBAA1NTVQq9W2j3CQabVaREZGIjY2VupQiIiI+mTmiFHw1DjjlkGPKf4hAPiQViKi7lhdECUkJGDDhg04fvw4Nm7cCGdnZ8yePduyPysrC6NHjx6QIAdTUlISLl68iFOnTkkdChERUZ+oFEq8Omclvvn5n/C7iXMAABmlBbihr5Y4MiIi+2N1QfTXv/4VKpUK9957L3bu3ImdO3fCwcHBsv+tt95CXFzcgARJREREvTM/OALh7j4IdPXENP+w5rY5TpsjIupEZe2Bvr6+SEtLQ3V1NVxdXaFUKtvt//jjj+Hq6mrzAImIiKh/loRPwumyAqTkZeGxyLulDoeIyK5YXRC18vDw6HLd29u738EQERGR7XxXkof/vXAcwa5eECDgVFkBivXVCHTp+t9yIiI5srpljoiIiIaWioZafHntIo5cu4RY/1AAwBf55yWOiojIvrAgIiIiGqbmBUXAUanGtdpbmOYfDgDYn8dpc0REbbEgIiIiGqac1Q6YGzQOAGAwN0GAgDPl11BUWyVtYEREdoQFERER0TCWEN78kNa0osuYMSIcAJ9JRETUFgsiIiKiYWxhyASoFUpcri7DjIBRAID9ebyPiIioFQsiIiKiYczdwRGzA8cAAIxmExSCgMyKQhTW3JI4MiIi+zCsC6Jt27Zh4sSJiIqKwr///W+pwyEiIpLEfeGTMM0/DBO8RmJmy1WiFE6bIyIC0IfnEA0V58+fx65du3DmzBmIooh58+ZhyZIl8PT0lDo0IiKiQfXLMVOxcuw0AECtsQHpJT8iJf88Hh9/l8SRERFJb9heIbp06RJmzZoFR0dHODk5ITo6GocOHZI6LCIiokEnCILl7fiwKCgEAecqruMa2+aIiOy3IEpLS0NiYiICAwMhCAI+++yzTsdotVqEh4fD0dERM2bMQEZGhmVfVFQUjh07hqqqKlRWVuLYsWMoKioaxAyIiIjsS6WhDmfLr+GuEaMBAF9cy5Y4IiIi6dltQaTX6xEdHQ2tVtvl/t27dyM5ORmbN2/G2bNnER0djcWLF6OsrAwAEBkZiXXr1mH+/Pl44IEHMHPmTCiVysFMgYiIyG5UGeow5cPn8Wjqe5YhCwcKLkgcFRGR9Oz2HqL4+HjEx8d3u/+VV17BmjVrsHr1agDAjh07cODAAbz11lvYsGEDAOB3v/sdfve73wEAHn/8cYwdO7bb8xkMBhgMBsu2TqcDABiNRhiNxn7nY+9ac5RDrl1h/sy/7Ws5kXPugLzyd1GocadPEM6UX4PJZIJSUCD7VjHKPPxlkX9X5PT170iOucspV+odQRRFUeogeiIIAvbu3Ytly5YBABobG+Hs7Iw9e/ZY1gBg1apVqKqqwueffw4AKCsrg7+/P3Jzc/GLX/wCZ8+ehUrVdQ347LPPYsuWLZ3Wd+3aBWdnZ5vnRERENNhS64vxaX0BxqncoYSAS03VWOoUisVOQVKHRjTg6urq8OCDD6K6uhru7u5Sh0N2xG6vEN1ORUUFTCYTAgIC2q0HBAQgJyfHsn3//fejuroaLi4uePvtt7sthgBg48aNSE5OtmzrdDqEhIQgLi5OFj80RqMRR44cwaJFi6BWq6UOZ9Axf+Yv1/zlnDsgv/wn1Vbi089ewRVTDf7fKT/D384cxNnGCmxb+ogs8u9Ibl//tuSYe2v3D1FHQ7IgstaJEyesPlaj0UCj0XRaV6vVsvlFAcgv346YP/OXa/5yzh2QT/53ePljkk8Qzt8sgkqphEpQ4LqpDoX11YhwHil1eJKRy9e/K3LKXS55Uu/Z7VCF2/H19YVSqURpaWm79dLSUowYMaJf59ZqtYiMjERsbGy/zkNERGSPEsKiAADHin7A3SObp80dKOC0OSKSryFZEDk4OGDq1KlITU21rJnNZqSmpmLWrFn9OndSUhIuXryIU6dO9TdMIiIiu5MQ3lwQnSj5EYuCxwNgQURE8ma3LXO1tbW4cuWKZTsvLw+ZmZnw9vZGaGgokpOTsWrVKkybNg3Tp0/H9u3bodfrLVPniIiIqLPRHn54bc6vcE/gaAgmEZszUpBbVYrLVWUY6+kvdXhERIPObgui06dPY968eZbt1oEHq1atwjvvvIOVK1eivLwcmzZtQklJCWJiYnDo0KFOgxZ6S6vVQqvVwmQy9es8RERE9mr56BgAzTfWj1d74IKxCin5WfhjzEJpAyMikoDdFkRz585FTxPB165di7Vr19r04yYlJSEpKQk6nQ4eHh42PTcREZG9meLg01wQ5Z1nQUREsjQk7yEiIiKi/vnsx0z8+shbEEQBaoUSuVWlyK0s7fkdiYiGGRZEHXDKHBERyUH2zWKcLM3DhaZKzB45BgCQkp8lcVRERIOPBVEHnDJHRERy0DptLruxEnEhEwAAKXnne2xXJyIablgQERERyVCMbzBGOLvDADOcVQ5wUChxuboMuVVsmyMieWFBREREJEMKQYHFIZEAgLTiy5gbNA4AsD+PbXNEJC8siDrgPURERCQXPwttLoiOXM9BQtgkAEBKPtvmiEheWBB1wHuIiIhILmL9wuAqqFDdWA93jSM0ShWuVpfjUmWJ1KEREQ0aFkREREQypVQoMMXBB/cGjoWnxhnzgiIAsG2OiOSFBREREZGM/dJ5FN6Z/zCmB4QjcdSdAJoLIrbNEZFcsCDqgPcQERGRnAiCYHl7Ych4aJQq5NfcxIVbxRJGRUQ0eFgQdcB7iIiISI6Ka6tw/mYRFgSPBwDszzsvcURERIODBREREZHMnSj5EdM//j94Ou0jLAlvnTbHtjkikgcWRERERDI32TcEzioHFOmrEODsDkelGgU1t3D+ZpHUoRERDTgWRERERDLnqFJjfnDzhLmvr+diYQjb5ohIPlgQERERERLCogAAXxRk4z62zRGRjLAg6oBT5oiISI7mt0yYy9NVIMTVC04qNQprK3Gu4rrUoRERDSgWRB1wyhwREcmRq1qDewPHAmhum1sUMgEAsD+fbXNENLyxICIiIiIAQHxL29xXhZcsD2lN4UNaiWiYU0kdABEREdmHRaET8NqcX2FhyHioFEq4tEye+76iEFP8QqUOj4hoQPAKEREREQEAPDXOWD46Bm4OjnBSqbEoNBJA81UiIqLhigURERERdSmxZdrc/rzzMItmiaMhIhoYLIiIiIjIQhRFvHH+P7hv/+sY6+kPV7UGN+qqcbasUOrQiIgGBAuiDjh2m4iI5EwQBKQW5uBcxXV8fT0XcS1tc/vz2TZHRMMTC6IOOHabiIjkLj5sIgDgYEG2pW0uJZ9tc0Q0PLEgIiIionYSWsZvZ5QWYIL3SLipNSit0+F02TWJIyMisj0WRERERNROoKsnYnxDIELE0eu5WBzafMVoP6fNEdEwxIKIiIiIOkkIb75KdLDgguUhrV/kn4fJzLY5IhpeWBARERFRJ633EaXfuIpJPoFwd3BEaX0NTpXlSxsYEZGNsSAiIiKiTka5+yLWPwzxYVFoMDXhZ5a2ufMSR0ZEZFssiIiIiKhLnyb8F96c9yBC3byxpLVtroBtc0Q0vLAgIiIioi4JgmB5e3bgGHg4OKG8vhbfleZJGBURkW2xICIiIqLbulxVhqvV5Zb7ilLy2TZHRMMHC6IOtFotIiMjERsbK3UoREREknvz/H8wb+8r2J6Z2mbaXDaazCaJIyMisg0WRB0kJSXh4sWLOHXqlNShEBERSe6ukaMBAF9fz8UUv1B4aZxR0VCLkyVsmyOi4YEFEREREXXrTp8gBLt6oq6pEd/euIr4sObnE/EhrUQ0XLAgIiIiom4JgmApgr4oyEbiqEmWt9k2R0TDAQsiIiIiuq2EloLoq8JLmOoXCm+NCyoNdUi/8aPEkRER9R8LIiIiIrqtqf6h8Hdyg66xAd+V5iMhvKVtLp9tc0Q09LEgIiIiottSCAr8rGXk9uFrF5EY3tw2d7DgAoxsmyOiIU4ldQBERERk/x4ePxPzgyNwz8gxUCkU8HV0RUVDLb69cRVzg8ZJHR4RUZ/xChERERH1aLzXCCwMmQBHlRoqhfKntjlOmyOiIY4FEREREfXakpa2uUMFF9BoapI4GiKivmNBRERERFapNtRj6+lDWHHw/0Osfxj8ndxQ3ViPb25clTo0IqI+G9YF0T/+8Q9MnDgRkZGRWLduHURRlDokIiKiIctRpca7OSdwsiQPWTeLLW1zKWybI6IhbNgWROXl5Xj99ddx5swZnD9/HmfOnMHJkyelDouIiGjI0ihVWBAyHkDLQ1rD7wQAHLrGtjkiGrqGbUEEAE1NTWhoaIDRaITRaIS/v7/UIREREQ1prQ9pPZifjWn+oQhoeT7R8eIrEkdGRNQ3dlsQpaWlITExEYGBgRAEAZ999lmnY7RaLcLDw+Ho6IgZM2YgIyPDss/Pzw/r169HaGgoAgMDsXDhQowePXoQMyAiIhp+5gVFwFGpxrXaW7hUWYL7WoYr7Ms7J3FkRER9Y7cFkV6vR3R0NLRabZf7d+/ejeTkZGzevBlnz55FdHQ0Fi9ejLKyMgBAZWUlUlJSkJ+fj6KiIqSnpyMtLW0wUyAiIhp2nNUOmBfc/NyhgwUXkDiquW3u8LWLaGgyShkaEVGf2O2DWePj4xEfH9/t/ldeeQVr1qzB6tWrAQA7duzAgQMH8NZbb2HDhg346quvMGbMGHh7ewMA7rvvPpw8eRJz5szp8nwGgwEGg8GyrdPpAMDSbjfcteYoh1y7wvyZf9vXciLn3AHm39f844In4GDBBRzIP491UfdihLM7Sup0+PraJSwKmTAQoQ4IOX/95Zi7nHKl3rHbguh2GhsbcebMGWzcuNGyplAosHDhQpw4cQIAEBISgvT0dDQ0NECtVuPYsWN44oknuj3n1q1bsWXLlk7rhw8fhrOzs+2TsFNHjhyROgRJMX/mL1dyzh1g/r3N32RugqfgAP8GAfu/+AITTC4ogQ47TxyG8XzeAEU5cOT89ZdT7nV1dVKHQHZqSBZEFRUVMJlMCAgIaLceEBCAnJwcAMDMmTORkJCAyZMnQ6FQYMGCBVi6dGm359y4cSOSk5Mt2zqdDiEhIYiLi4O7u/vAJGJHjEYjjhw5gkWLFkGtVksdzqBj/sxfrvnLOXeA+fcn/wfERAiCAAAIKb+Go1/uxEWzDvPjFsFRNTQ+l3L++ssx99buH6KOhmRBZK3nn38ezz//vFXHajQaaDQaaLVaaLVamEwmAIBarZbNLwpAfvl2xPyZv1zzl3PuAPPvb/6xI0ch0MUDxfpqfFuWh5+FTbRhdANPzl9/OeUulzyp9+x2qMLt+Pr6QqlUorS0tN16aWkpRowY0a9zJyUl4eLFizh16lS/zkNERDTcmUUzzpRdQ0W9Hktaps3tz+dDWoloaBmSBZGDgwOmTp2K1NRUy5rZbEZqaipmzZolYWRERETykXTsQ9x/4A18evV7LGmZNnfk2iXUc9ocEQ0hdlsQ1dbWIjMzE5mZmQCAvLw8ZGZm4tq1awCA5ORk7Ny5E++++y4uXbqEJ598Enq93jJ1rq+0Wi0iIyMRGxvb3xSIiIiGtRkjRgEAvijIxmTfEAS7eqKuqRFHr+dKHBkRkfXstiA6ffo0Jk+ejMmTJwNoLoAmT56MTZs2AQBWrlyJbdu2YdOmTYiJiUFmZiYOHTrUadBCb7FljoiIyDqt9wqdLb+GkjodloQ3XyVKYdscEQ0hdjtUYe7cuRBF8bbHrF27FmvXrh2kiIiIiKitEc7umOYfhtNlBTjU8pDWHdlpOFJ4CfVNjXBSOUgdIhFRj+z2CpFU2DJHRERkvYSwKADNbXN3+gQh1NUb9U1GpLJtjoiGCBZEHbBljoiIyHqtbXPflebhlkGPJaNaps3lsW2OiIYGFkRERETUZ6Fu3pjkEwSzKOKrwhwkttxHlFqYA73RIHF0REQ9s9t7iIiIiGho2Dj1Z3BQKhHrHw6FICDMzQcFNTeRWpiDpXdESx0eEdFt8QpRB7yHiIiIqHfmBI3FzBF3QKlQQBAEJI7iQ1qJaOhgQdQB7yEiIiLqn9a2ua+v56KWbXNEZOdYEBEREVG/5ekq8OcTn2FD+l5Eeo/EKHdfGExN+KrwktShERHdFgsiIiIi6rc6YyPezTmJj6+cQV1TIxLDOW2OiIYGFkRERETUb5HeIxHm5g2DqQlHr+diyajmtrljRT+gprFB4uiIiLrHgqgDDlUgIiLqPUEQEN/ykNaDBRcwwWsERnv4wWBqwhG2zRGRHWNB1AGHKhAREfVNQnhzQfRV4SUYTE1Y0tI2l8K2OSKyYyyIiIiIyCZifIMxwtkd+qZGfHPjCpaOan4G0bGiH6Bj2xwR2SkWRERERGQTCkFhaZv7Ij8bEV4BGOfpj0azCYevXZQ4OiKirrEgIiIiIptJCI9CsKsnQty8AABLWp5JlMKHtBKRnWJB1AGHKhAREfXdzIBROLHiv/HHmIUAgCWjmu8j+k/RZVQZ6qQMjYioSyyIOuBQBSIior4TBAGCIFi2x3kGIMIzAEa2zRGRnWJBRERERDZnNJtwvPgyTGYzElueSbQ/77zEURERdcaCiIiIiGxKFEXM/fRl/PrLf+F0WYFl/Pbx4suoZNscEdkZFkRERERkU4IgYJp/GADgi4JsjPH0xwSvEWgSzfiy4ILE0RERtceCiIiIiGwuoWX89sGCbIii+FPbXD7b5ojIvrAgIiIiIpubEzQOzioHFOurca7iuqVt7pviK6hs0EscHRHRT1gQdcCx20RERP3npFJjfnAEAOBgwQXc4eGHid4jYRLNOHiNbXNEZD9YEHXAsdtERES20do290WHtrkUTpsjIjvCgoiIiIgGxPyQ8dAoVcjTVeCHqjIsCW8uiL69cRU3G2oljo6IqBkLIiIiIhoQrmoNXrr75/hy6TqM8/RHuLsP7vQJam6b47Q5IrITLIiIiIhowPx89GRM9AmEIAgAgCVsmyMiO8OCiIiIiAZN67S59JKrqKhn2xwRSY8FEREREQ2o06UFeDptN96+mI5QN29E+wbDLIr4oiBb6tCIiFgQERER0cDKqSzBJ1e/x56rZwEAiS3DFfbnZUkZFhERABZERERENMAWh0VCgIBzFddRVFuFJaOa2+ZOluShrK5G4uiISO5YEBEREdGA8nNyw/SAcADAwYJsBLt6YbJfCESI2H4uFV9fz0VWxXUU66vRaGqSNlgikh2V1AHYG61WC61WC5PJJHUoREREw0ZCeBS+K83DFwXZeHziPUgMvxPflxfivZyTeC/nZLtjPRwc4evkBl9HV/g6uba8doGfo1ubbVf4ObnCWeVgmWBHRNQXLIg6SEpKQlJSEnQ6HTw8PKQOh4iIaFiID52Izd/tx6nSApTV1eA3EdNxrfYW8nU3UVFfi/KGWtysr0WTaEZ1YwOqGxtwtbq8x/M6KtXwc3JtXyg5usKn7euWtz00TlAIbI4hovZYEBEREdGAC3T1RIxvCDIrCvHltQt4aPxM/G3m/e2OEUURVY31qKivRUVDbfPrlrfL62txs+V161pdUyMaTEYU1laisLayxxhUggI+ji6W4slb4wxdXRkKL3yDEa7u8HF0bSmu3ODj6AK1QjlQnw4isiMsiIiIiGhQJIRHocFkhIta0+V+QRDgpXGGl8YZY+Hf4/nqjI0ob6hBRb0eFfU1lqtMza/1LftqUdGgR5WhDk2iGaX1NSitbz/IIfX7G12e31Pj3HKVqW27nktLO1/za7+W4spZ7dD7TwgR2QUWRERERDQofjdxNn4/6V6bnc9Z7YAwtQ/C3Hx6PLbR1ISbDfp2V5lK9TqcupQF95H+uGWosxRUFQ16mEQzqgx1qDLU4XK1FbGoHODn5PrTVSbH9m18bVv5PB2ceN8TkR1hQURERESDQqmQ7v4dB6UKI108MNLlp/uDjUYjQgp0SLg7AWq12rJuFs2oMtRbWvXatvCV19fgZoO+XQtfg8mIuqZGFNTcQkHNrR5jUSuUza17bYZDtBZSra9b3/ZxdIGKrXtEA4oFEREREQ2qOmMjsm8VW0Zx2xuFoIC3owu8HV0wzjPgtseKogh9U6PlfqfyhppO7XoV9TWWAqq6sQFGswkldTqU1Ol6jEVAcxuhr1NzAeXXcn+Tn5NbSyufa7sWPieVusdzElF7LIiIiIho0BTrqzHnk20wi2ac+/X/wM3BUeqQ+kUQBLiqNXBVaxDu3nPrnqGlda9tkVTedohEmwESNxv0MIsibhn0uGXQ4weU9Xh+V7Wmc9teh8l7vk6u8FRpIIqiLT4FREMeCyIiIiIaNCOd3RHo4oEfdRVIvZ6DZXfESB3SoNIoVQh08UCgS8+P9jCZzag01LUrlprb9mo7rVU01MJgakKt0YBaowEFNTd7PL8KAp7/9CL8nDo/36ljC5+3xkXSlkeigcSCiIiIiAaNIAhICI/C61nHcLDgguwKot5QKhSWKzzwuv2xoiiixmhoc5Wp9V6nn163beGrMRrQBBE36nS4YWXrnrej809tex3a9dq18jm6wJGtezSEsCAiIiKiQZUQ1lwQfX09B/VNjXBScWR1fwmCAHcHR7g7OOIOD98ej6+pr8OegymYNCsWVU0N7dr1Oj4D6lZDHUSILVP69MitKu3x/G5qjWUseWuh1GnynqMrglw9oVHyv6MkrWH7HZibm4uVK1e22/7ggw+wbNky6YIiIiIiTPIJQrCrJ67XVrVcJYqGQmhuxxJFkSOpB4GjSg1vpQbRvsHtJux1pclsQqWhrouJe13fA2U0m1BjNKDGaECeruK25/7oZ2tw18jRtkyNqNeGbUEUERGBzMxMAEBtbS3Cw8OxaNEiaYMiIiIiCIKA+LAo7LzwDdal7ca84Ah4aZwBABvS9+KDy6egViihEhRQKRRQKZTNL4IC+5b8HgHO7gCA/80+jn15WVArFFAqFC3vo2x5HwX+OvN+jGg59suCC0grvmw5j0qhhALAj/WFyM/+Dx4cPwN+Tm4AgAs3i5F9qxhKoeWcCoXlfdQKJaJ9g+GhcQIA3GyoRWldjeU4tULZ7v1c1ZohPzZbpVDCz8nN8vm5HVEUoWtssLTtNbfo1XbbumfNOYkG2rAtiNrat28fFixYABcXF6lDISIiIgAPjpuOT658j1sGPdRtCoYm0QSzKMJgaoKhi/cT8NPVo2u1t5BZUdjtx/jztATL22fKr+HdnJNdHncg8zoWhU20/Of8SOElbPv+SLfn/fy+JzHVPwwA8OnV77El40C3x+6KewxzgsYCAN7PzcBfTn7eZeGkEpR48e7lmB3YfOzR67nYnpnaUgy2FmMKy/s8Fnk3YlvGll+8VYwPfjjdsr/5uLbvNy8oAhO8RwAAbuir8e2NK4BZxPnGm1AVXoRG7dB8fkGBUR5+loEPdcZGXNdXWorB1mKy9fwapQoOHdrdBEGAh8YJHhonjPbw6/bz0oqT7sge2G1BlJaWhr///e84c+YMbty4gb1793Zqd9Nqtfj73/+OkpISREdH47XXXsP06dM7neujjz7Cww8/PEiRExERUU/Gevoj68H/gVk0tytynp2eiGemLEaT2Qyj2YQmswlGsxkmsfm1l6Oz5diHImbi3sCxMJrNzceJZphajm8ym+Dj+NMfQmcHjoGDUvXT+cwmGJqa8GNBHgKDgy1XqAAg3N0H84Mj0NRyniaxNZbm184qjeVYjVINX0dXNIktMbQcZxLNAABVm8lsxpb9RrMJ9TB2+pw0mkyWt8vra3Cm/Fq3n7/7widZ3v6xugJvX0rv9lgfRxdLQXSpsgR/OP6xZd+//vNDu2P/OmMpVkfeBQDIunkdKw7+b7fn3TD1Z1h751wAwPmKIiz74s2WAu+nq3uthd/qyLvwWOTdAIDrtZVYl7YbL9+zAqPce77fiWig2W1BpNfrER0djUcffRQPPPBAp/27d+9GcnIyduzYgRkzZmD79u1YvHgxcnNz4e/vbzlOp9MhPT0dH3744WCGT0RERFZovXeolZuDo9XPJorwCkCE1+0fnNpqduBYy9WXVkajEV9UfIGEWQnt7qNZdkeM1dPvHh4/Ew+Pn9lpXRRFNIlmKNvcD/XLMVMRFxrZXGS1FFpNlgLOjNFthiHcPXIM/jn/oU7HmVqKsiifQMuxoz38se7Oee2PE38qKNsWHV4aZ9wbNA5NpiaUVpTD3dMTJogtMZnh3aaIVAgKeGtc0CQ2F3HNH9sMEc1XdVRtvnaNZlPzVT1TU5efp2pDveXtmkYDMkrz2xWARFKy24IoPj4e8fHx3e5/5ZVXsGbNGqxevRoAsGPHDhw4cABvvfUWNmzYYDnu888/R1xcHBwdb//L1WAwwGD46eK8Ttc8gtJoNMJo7PxXnOGmNUc55NoV5s/8276WEznnDjB/5j/w+ZsAmND8H381BPg5ON/2+NZY/DUuWBA4zqpjx7j54I93zrfq2CjPEXhn3kMwGo04cuQIFi1Y1GmoQuuxk72DcOYXGzqdy2Q2o0k0QyEIlmMj3P1wfNn/gybRZCmumsxmGMXmQmqEs0e73N6Y8yv4OTgP6veeXL/PqWeCOASaNwVBaNcy19jYCGdnZ+zZs6ddG92qVatQVVWFzz//3LKWmJiIJ554AomJibf9GM8++yy2bNnSaX3Xrl1wdr79Ly8iIiIism91dXV48MEHUV1dDXd3d6nDITtit1eIbqeiogImkwkBAe0vkwcEBCAnJ8eyXV1djYyMDHzyySc9nnPjxo1ITk62bOt0OoSEhCAuLk4WPzSWvxQt6vyXIjlg/sxfrvnLOXeA+TN/+eYvx9xbu3+IOhqSBZG1PDw8UFra88PDAECj0UCj0XRaV6vVsvlFAcgv346YP/OXa/5yzh1g/sxfvvnLKXe55Em9p+j5EPvj6+sLpVLZqdgpLS3FiBEj+nVurVaLyMhIxMbG9us8RERERERk/4ZkQeTg4ICpU6ciNTXVsmY2m5GamopZs2b169xJSUm4ePEiTp061d8wiYiIiIjIztlty1xtbS2uXLli2c7Ly0NmZia8vb0RGhqK5ORkrFq1CtOmTcP06dOxfft26PV6y9Q5IiIiIiKinthtQXT69GnMmzfPst068GDVqlV45513sHLlSpSXl2PTpk0oKSlBTEwMDh061GnQQm9ptVpotVqYOBufiIiIiGjYs9uCaO7cuehpIvjatWuxdu1am37cpKQkJCUlQafTwcPDw6bnJiIiIiIi+zIk7yEiIiIiIiKyBRZEHXDKHBERERGRfLAg6oBT5oiIiIiI5IMFERERERERyRYLIiIiIiIiki0WRB3wHiIiIiIiIvlgQdQB7yEiIiIiIpIPFkRERERERCRbLIiIiIiIiEi2WBB1wHuIiIiIiIjkQyV1APYmKSkJSUlJqK6uhqenJ3Q6ndQhDQqj0Yi6ujrodDqo1Wqpwxl0zJ/5yzV/OecOMH/mL9/85Zh76//pRFGUOBKyNyyIulFTUwMACAkJkTgSIiIiIrKVmpoaeHh4SB0G2RFBZJncJbPZjOLiYri5uUEQhD6dIzY21uppdT0d293+rtZ7Wuu4PzY2FqmpqQgJCUFhYSHc3d2tirkn9pL/7bZb39bpdDbNvze5W3P8cM7flrl3td7T9/5wyr83P/sDlfvtYu7LscP5e9+a44dz/lL/uyfX/OX8sy+KImpqahAYGAiFgneN0E94hagbCoUCwcHB/TqHUqm0+pdMT8d2t7+r9Z7WOu5vu+3u7m6zX4z2kv/ttjvus1X+vcndmuOHc/62zL2rdWu/94Ghn39vfvYHKvfbxdyXY4fz9741xw/n/O3l3z1AXvnL/WefV4aoKyyPB1BSUpLNju1uf1frPa113N+bOHvDXvK/3bY95G7N8cM5f1vm3tW6nL/3O64Nxte+t+eW8/e+NccP5/z57x5/9vuyv7/f+0RdYcscAWi+0dDDwwPV1dU2+0vRUML8mb9c85dz7gDzZ/7yzV/OuRN1xCtEBADQaDTYvHkzNBqN1KFIgvkzf7nmL+fcAebP/OWbv5xzJ+qIV4iIiIiIiEi2eIWIiIiIiIhkiwURERERERHJFgsiIiIiIiKSLRZEREREREQkWyyIqEdVVVWYNm0aYmJiEBUVhZ07d0odkiTq6uoQFhaG9evXSx3KoAoPD8edd96JmJgYzJs3T+pwBl1eXh7mzZuHyMhITJo0CXq9XuqQBk1ubi5iYmIsL05OTvjss8+kDmtQ/eMf/8DEiRMRGRmJdevWQU5ziLZt24aJEyciKioK//73v6UOZ1AsX74cXl5eWLFiRbv1lJQUREREYOzYsfjnP/8pUXQDr7v8u1snGi44ZY56ZDKZYDAY4OzsDL1ej6ioKJw+fRo+Pj5Shzao/vznP+PKlSsICQnBtm3bpA5n0ISHhyM7Oxuurq5ShyKJe++9F3/7298we/Zs3Lp1C+7u7lCpVFKHNehqa2sRHh6OgoICuLi4SB3OoCgvL8fMmTNx4cIFqNVqzJkzB9u2bcOsWbOkDm3AnT9/HqtWrUJ6ejpEUcS8efNw6NAheHp6Sh3agDp27Bhqamrw7rvvYs+ePQCApqYmREZG4ujRo/Dw8MDUqVORnp4+LP8N7Cr/260TDRe8QkQ9UiqVcHZ2BgAYDAaIoiirv5ICwOXLl5GTk4P4+HipQ6FB1Pof4dmzZwMAvL29ZVkMAcC+ffuwYMEC2RRDrZqamtDQ0ACj0Qij0Qh/f3+pQxoUly5dwqxZs+Do6AgnJydER0fj0KFDUoc14ObOnQs3N7d2axkZGZg4cSKCgoLg6uqK+Ph4HD58WKIIB1ZX+d9unWi4YEEkA2lpaUhMTERgYCAEQeiy5UWr1SI8PByOjo6YMWMGMjIy2u2vqqpCdHQ0goOD8ac//Qm+vr6DFH3/2SL/9evXY+vWrYMUse3YIndBEHDvvfciNjYW77///iBFbhv9zf/y5ctwdXVFYmIipkyZghdeeGEQo+8/W3z9W3300UdYuXLlAEdsW/3N38/PD+vXr0doaCgCAwOxcOFCjB49ehAz6Lv+5h4VFYVjx46hqqoKlZWVOHbsGIqKigYxg96z5fd7W8XFxQgKCrJsBwUF2eXnYqDyJ5IDFkQyoNfrER0dDa1W2+X+3bt3Izk5GZs3b8bZs2cRHR2NxYsXo6yszHKMp6cnzp07h7y8POzatQulpaWDFX6/9Tf/zz//HOPGjcO4ceMGM2ybsMXX/ptvvsGZM2ewb98+vPDCC8jKyhqs8Putv/k3NTXh+PHjeOONN3DixAkcOXIER44cGcwU+sUWX38A0Ol0SE9PR0JCwmCEbTP9zb+yshIpKSnIz89HUVER0tPTkZaWNpgp9Fl/c2+9Z2r+/Pl44IEHMHPmTCiVysFModds9f0+VMk9f6J+EUlWAIh79+5ttzZ9+nQxKSnJsm0ymcTAwEBx69atXZ7jySefFD/++OOBDHPA9CX/DRs2iMHBwWJYWJjo4+Mjuru7i1u2bBnMsG3CFl/79evXi2+//fYARjlw+pJ/enq6GBcXZ9n/0ksviS+99NKgxGtr/fn6v/fee+JvfvObwQhzwPQl/48++kj8/e9/b9n/0ksviS+++OKgxGtLtvjZf+yxx8SUlJSBDNOm+pPz0aNHxZ///OeW7W+//VZctmyZZfvpp58W33///YEJ3EZsmX9P60TDAa8QyVxjYyPOnDmDhQsXWtYUCgUWLlyIEydOAABKS0tRU1MDAKiurkZaWhoiIiIkidfWrMl/69atKCwsRH5+PrZt24Y1a9Zg06ZNUoVsM9bkrtfrLV/72tpafP3115g4caIk8dqaNfnHxsairKwMlZWVMJvNSEtLw4QJE6QK2aasyb/VUGyX64k1+YeEhCA9PR0NDQ0wmUw4duzYsPjdZ+3XvvXKQW5uLjIyMrB48eJBj9VWevP93tH06dORnZ2NoqIi1NbW4uDBg0Puc9Gf/InkQJ53B5NFRUUFTCYTAgIC2q0HBAQgJycHAFBQUIAnnnjCMkzhqaeewqRJk6QI1+asyX+4sib30tJSLF++HEDztME1a9YgNjZ20GMdCNbkr1Kp8MILL2DOnDkQRRFxcXFYsmSJFOHanLXf+9XV1cjIyMAnn3wy2CEOKGvynzlzJhISEjB58mQoFAosWLAAS5culSJcm7L2a3///fejuroaLi4uePvtt4f0QBFrc164cCHOnTsHvV6P4OBgfPzxx5g1axZefvllzJs3D2azGc8888yQmzDX3/y7WycaLobubzcaNNOnT0dmZqbUYdiFRx55ROoQBtUdd9yBc+fOSR2GpOLj42U9XdDDw2NI3TNoa88//zyef/55qcOQhByvHHz11Vddri9dunRYFMM96S7/7taJhgu2zMmcr68vlEplp//wlJaWYsSIERJFNXjknL+ccweYP/OXb/5yzF2OObcl9/yJesKCSOYcHBwwdepUpKamWtbMZjNSU1NlcTlczvnLOXeA+TN/+eYvx9zlmHNbcs+fqCdsmZOB2tpaXLlyxbKdl5eHzMxMeHt7IzQ0FMnJyVi1ahWmTZuG6dOnY/v27dDr9Vi9erWEUduOnPOXc+4A82f+8s1fjrnLMee25J4/Ub9IPOWOBsHRo0dFAJ1eVq1aZTnmtddeE0NDQ0UHBwdx+vTp4smTJ6UL2MbknL+ccxdF5s/85Zu/HHOXY85tyT1/ov4QRFEUB6TSIiIiIiIisnO8h4iIiIiIiGSLBREREREREckWCyIiIiIiIpItFkRERERERCRbLIiIiIiIiEi2WBAREREREZFssSAiIiIiIiLZYkFERERERESyxYKIiIiIiIhkiwURERERERHJFgsiIqIh6JFHHsGyZcsk+/gPPfQQXnjhBauO/dWvfoWXX355gCMiIiLqG0EURVHqIIiI6CeCINx2/+bNm/HHP/4RoijC09NzcIJq49y5c5g/fz4KCgrg6ura4/HZ2dmYM2cO8vLy4OHhMQgREhERWY8FERGRnSkpKbG8vXv3bmzatAm5ubmWNVdXV6sKkYHy+OOPQ6VSYceOHVa/T2xsLB555BEkJSUNYGRERES9x5Y5IiI7M2LECMuLh4cHBEFot+bq6tqpZW7u3Ll46qmn8Ic//AFeXl4ICAjAzp07odfrsXr1ari5uWHMmDE4ePBgu4+VnZ2N+Ph4uLq6IiAgAA899BAqKiq6jc1kMmHPnj1ITExst/7GG29g7NixcHR0REBAAFasWNFuf2JiIj788MP+f3KIiIhsjAUREdEw8e6778LX1xcZGRl46qmn8OSTT+IXv/gF7rrrLpw9exZxcXF46KGHUFdXBwCoqqrC/PnzMXnyZJw+fRqHDh1CaWkpfvnLX3b7MbKyslBdXY1p06ZZ1k6fPo1169bhueeeQ25uLg4dOoQ5c+a0e7/p06cjIyMDBoNhYJInIiLqIxZERETDRHR0NP7yl79g7Nix2LhxIxwdHeHr64s1a9Zg7Nix2LRpE27evImsrCwAwOuvv47JkyfjhRdewPjx4zF58mS89dZbOHr0KH744YcuP0ZBQQGUSiX8/f0ta9euXYOLiwuWLFmCsLAwTJ48GevWrWv3foGBgWhsbGzXDkhERGQPWBAREQ0Td955p+VtpVIJHx8fTJo0ybIWEBAAACgrKwPQPBzh6NGjlnuSXF1dMX78eADA1atXu/wY9fX10Gg07QY/LFq0CGFhYbjjjjvw0EMP4f3337dchWrl5OQEAJ3WiYiIpMaCiIhomFCr1e22BUFot9ZaxJjNZgBAbW0tEhMTkZmZ2e7l8uXLnVreWvn6+qKurg6NjY2WNTc3N5w9exYffPABRo4ciU2bNiE6OhpVVVWWY27dugUA8PPzs0muREREtsKCiIhIpqZMmYILFy4gPDwcY8aMaffi4uLS5fvExMQAAC5evNhuXaVSYeHChXjppZeQlZWF/Px8fP3115b92dnZCA4Ohq+v74DlQ0RE1BcsiIiIZCopKQm3bt3Cr3/9a5w6dQpXr17Fl19+idWrV8NkMnX5Pn5+fpgyZQq++eYby1pKSgpeffVVZGZmoqCgAO+99x7MZjMiIiIsxxw/fhxxcXEDnhMREVFvsSAiIpKpwMBAfPvttzCZTIiLi8OkSZPwhz/8AZ6enlAouv/n4fHHH8f7779v2fb09MSnn36K+fPnY8KECdixYwc++OADTJw4EQDQ0NCAzz77DGvWrBnwnIiIiHqLD2YlIqJeqa+vR0REBHbv3o1Zs2b1ePybb76JvXv34vDhw4MQHRERUe/wChEREfWKk5MT3nvvvds+wLUttVqN1157bYCjIiIi6hteISIiIiIiItniFSIiIiIiIpItFkRERERERCRbLIiIiIiIiEi2WBAREREREZFssSAiIiIiIiLZYkFERERERESyxYKIiIiIiIhkiwURERERERHJFgsiIiIiIiKSrf8LeGmKrMyZiBUAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "variables = [\n",
+    "    'Number Density', 'Specific Activity', 'Total Decay Heat', 'Contact Dose'\n",
+    "]\n",
     "\n",
-    "# Example DataFrame\n",
-    "dfs['fendl3 Number Density']['Data']"
+    "for variable in variables:\n",
+    "    alaraplot.plot_single_response(\n",
+    "        adf,\n",
+    "        run_lbls=datalibs,\n",
+    "        variable=variable,\n",
+    "        total=True,\n",
+    "        head=1\n",
+    "    )"
    ]
   }
  ],
diff --git a/tools/ALARAJOYWrapper/pyalara.py b/tools/ALARAJOYWrapper/pyalara.py
new file mode 100644
index 00000000..ded68a6b
--- /dev/null
+++ b/tools/ALARAJOYWrapper/pyalara.py
@@ -0,0 +1,109 @@
+import subprocess
+from string import Template
+from pathlib import Path
+
+#--------------- Running Single Parent Element Simulation(s) -----------------
+
+INPUT = 'alara.inp'
+
+# Adapted from ALARA/examples/singleElement.ala
+alara_input = Template(
+'''
+geometry rectangular
+dimension	x
+		0.0
+        1       5.0
+end
+mat_loading
+        inner_zone1  mix1
+end
+material_lib ../../data/matlib.sample
+element_lib ../../data/nuclib.std
+data_library alaralib $datalib
+mixture mix1
+        element $element              1.0     1.00
+end
+flux flux_1 ../../examples/ref_flux_files/fluxfnsfIBfw_518MW.txt  1.0   0   default
+schedule 2_year
+	2 y  flux_1  steady_state  0 s
+end
+pulsehistory steady_state
+	1	0 s
+end
+dump_file dump_singleElement
+cooling
+	1e-5 y
+	1e-2 y
+	1 y
+	100 y
+	10000 y
+end
+output interval
+        units Bq kg
+        number_density
+        specific_activity
+	total_heat
+	dose contact $datalib ../../data/ANS6_4_3
+end
+## 
+truncation  1e-7
+'''
+)
+
+def fill_alara_template(element, datalib):
+    '''
+    Substitute in the specific single parent element and path to a
+        pre-converted ALARA binary library, such as that for either FENDL2 or
+        ALARAJOY-processed FENDL3, to a template containing a generalized
+        ALARA input file text for a simple single parent element simulation.
+    Arguments:
+        element (str): Single parent element to be irradiated.
+        datalib (str): Path to the binary library.
+    
+    Returns:
+        alara_input (str): String template with appropriate variables
+            substituted in for Template identifiers.
+    '''
+
+    return alara_input.substitute(element=element, datalib=datalib)
+
+def write_alara_input_file(template):
+    '''
+    Write out the ALARA input card from the prefilled template.
+    Arguments:
+        template (str): String template with appropriate variables substituted
+            in for Template identifiers.
+    Returns:
+        None
+    '''
+
+    with open(INPUT, 'w') as f:
+        f.write(template)
+
+def run_alara(element, libname):
+    '''
+    Invoke subprocess.run() to run ALARA for the single parent element
+        irradiation simulation. Specify destination for ALARA tree file and
+        capture stdout to an output file to be read by
+        alara_pandas_parser.parse_tables().
+    Arguments:
+        element (str): Single parent element to be irradiated.
+        libname (str): Name of the source data library (i.e. fendl2, fendl3,
+            etc.)
+    Returns:
+        output (str): Path to the ALARA redirected ALARA stdout formatted as a
+            text file.
+    '''
+
+    filename_base = f'{element}_{libname}'
+    output = f'{filename_base}.out'
+    Path(output).unlink(missing_ok=True)
+    with open(output, 'w') as outfile:
+        subprocess.run(
+            ['alara', '-t', f'{filename_base}.tree', '-v', '3', INPUT],
+            stdout=outfile,
+            stderr=subprocess.STDOUT,
+            check=True
+        )
+
+    return output
\ No newline at end of file
diff --git a/tools/alara_output_processing/alara_output_plotting.py b/tools/alara_output_processing/alara_output_plotting.py
new file mode 100644
index 00000000..59073c37
--- /dev/null
+++ b/tools/alara_output_processing/alara_output_plotting.py
@@ -0,0 +1,344 @@
+import matplotlib.pyplot as plt
+from matplotlib import lines
+import alara_output_processing as aop
+from numpy import array
+
+def preprocess_data(
+    adf,
+    run_lbl,
+    variable,
+    nuclides = None,
+    time_unit='s',
+    sort_by_time='',
+    head=None
+):
+    '''
+    Prepare an ALARADFrame containing data from multiple runs and potentially
+        multiple responses for plotting by filtering rows to a specified run,
+        variable, and potentially nuclides. After filtering, a pivot table is
+        created indexed by nuclide with values for each cooling time.
+
+    Arguments:
+        adf (alara_output_processing.ALARADFrame): ALARADFrame containing 
+            response data from one or more ALARA runs.
+        run_lbl (str): Distinguisher of the specified ALARA run.
+        variable (str): Name of the response variable.
+        nuclides (str, list, or None, optional): Optional parameter for
+            nuclide selection. For a single nuclide, input should be a string
+            of the form "element-A". For multiple individual nuclides, they
+            should be input as a list. To filter all nuclides of a given
+            element, provide the chemical symbol of the desired element,
+            either as a stand-alone string or in a list with other elements or
+            nuclides. To include the "total" row produced by ALARA, write
+            "total". If no nuclides or elements provided, the filtering will
+            pass through all nuclides matching the run_lbl and variable
+            parameters.
+            (Defaults to None)
+        time_unit (str, optional): Optional paramter to set units for cooling
+            times. Accepted values: 's', 'm', 'h', 'd', 'w', 'y', 'c'.
+            (Defaults to 's')
+        sort_by_time (str, optional): Option to sort the ALARADFrame by the
+            data in a particular time column.
+            (Defaults to '')
+        head (int or None, optional): Option by which to truncate the
+            ALARADFrame to a particular number of rows.
+            (Defaults to None)
+        
+    Returns:
+        times (list of floats): Chronological list of converted cooling times.
+        filtered (alara_output_processing.ALARADFrame): Modified copy of input
+            adf containing only rows that match all conditions in run_lbl,
+            variable, and (if present) nuclides.
+        piv (pandas.DataFrame): Pivot table indexed by nuclides with values
+            for each cooling time.
+    '''
+
+    filter_dict = {
+        'run_lbl' : run_lbl, 'variable' : adf.VARIABLE_ENUM[variable]
+    }
+    if nuclides:
+        filter_dict['nuclide'] = nuclides
+    
+    filtered = adf.filter_rows(filter_dict)
+
+    preset_time_unit = filtered['time_unit'].unique()[0]
+    if time_unit != preset_time_unit:
+        filtered['time'] = filtered['time'].transform(
+            lambda v: aop.convert_times(
+                array([v]), from_unit=preset_time_unit, to_unit=time_unit
+            )[0]
+        )
+        filtered['time_unit'] = [time_unit] * len(filtered)
+        
+    times = sorted(filtered['time'].unique().tolist())
+
+    piv = filtered.pivot(
+        index='nuclide',
+        columns='time',
+        values='value'
+    )
+
+    if sort_by_time:
+        sort_by_time = aop.extract_time_vals([sort_by_time])[0]
+        piv = piv.sort_values(sort_by_time, ascending=False)
+
+    if head:
+        piv = piv.head(head)
+
+    return times, filtered, piv
+
+def build_color_map(list_of_pivs, cmap_name='Dark2'):
+    '''
+    Given a list of pivot DataFrames (one per run), build a stable color map
+        keyed by nuclide name.
+
+    Arguments:
+        list_of_pivs (list of pandas.DataFrames): List of pivot tables indexed
+            by nuclide with values for each cooling time.
+        cmap_name (str, optional): Option to set the Matplotlib Colormap for
+            the plots. Reference guide for Matplotlib Colormaps can be found
+            at matplotlib.org/stable/gallery/color/colormap_reference.html
+            (Defaults to "Dark2")
+
+    Returns:
+        color_map (dict): Preset color map for each nuclide in the pivot table
+            indices.
+    '''
+
+    all_nucs = set()
+    for piv in list_of_pivs:
+        all_nucs.update(piv.index)
+  
+    cmap = plt.cm.get_cmap(cmap_name)
+
+    return {lbl: cmap(i % cmap.N) for i, lbl in enumerate(sorted(all_nucs))}
+
+def split_label(label):
+    '''
+    Split the string of a series' label to extract the isotope being plotted
+        and (conditionally) the run label attached to it. The run parameter
+        will be present parenthetically if two runs are being plotted
+        comparitavely.
+    Arguments:
+        label (str): Series label generated from 
+            ax.get_legend_handles_labels().
+    
+    Returns:
+        isotope (str): Isotope being plotted.
+        run_lbl (str): Run label of plotted isotope. Will be empty string
+            for single run plotting.
+    '''
+
+    # Label formatted as f"{element}-{A} ({run_lbl})" for comparative plots
+    if '(' in label:
+        parts = label.split('(')
+        isotope = parts[0].strip()
+        run_lbl = f'({parts[1].strip(')')})'
+    else:
+        isotope = label.strip()
+        run_lbl = ''
+    return isotope, run_lbl
+
+def reformat_isotope(isotope):
+    '''
+    Restructure the string describing an isotope to capitalize its first
+        letter and place the atomic number in a superscript for cleaner
+        presentation in legend. Skips "isotope" entries of "total" or "Other".
+    Arguments:
+        isotope (str): Identifier of the isotope of the form element-A.
+    
+    Returns:
+        isotope (str): Reformatted identifier of the isotope of the form
+            ᴬelement.
+    '''
+
+    if isotope == 'total' or isotope == 'Other':
+        return isotope
+    else:
+        element, A = isotope.split('-')
+        element = element.capitalize()
+        return f'$^{{{A}}}${element}'
+
+def construct_legend(ax, data_comp=False):
+    '''
+    Create a custom pyplot legend that exists outside of the grid itself and
+        can group like-isotopes together from compared data sets for clarity.
+    
+    Arguments:
+        ax (matplotlib.axes._axes.Axes): Matplotlib axis object of the plot
+            being constructed.
+        data_comp (bool, optional): Boolean setting for comparison between two
+            data sets.
+            (Defaults to False)
+    
+    Returns:
+        None
+    '''
+
+    handles, labels = ax.get_legend_handles_labels()
+    labels_sorted_with_handles = sorted(
+        zip(labels, handles),
+        key=lambda x: (
+            split_label(x[0]) if data_comp else (split_label(x[0])[0], x[0])
+        )
+    )
+
+    grouped_handles = []
+    grouped_labels = []
+    prev_isotope = None
+
+    for lbl, h in labels_sorted_with_handles:
+        isotope, datalib = split_label(lbl)
+        isotope = reformat_isotope(isotope)
+
+        if prev_isotope is not None and isotope != prev_isotope:
+            grouped_handles.append(plt.Line2D([], [], linestyle=''))
+            grouped_labels.append('――――――')
+        grouped_handles.append(h)
+        grouped_labels.append(f'{isotope} {datalib}')
+        prev_isotope = isotope
+
+    ax.legend(
+        grouped_handles,
+        grouped_labels,
+        loc='center left',
+        bbox_to_anchor=(1.025, 0.5),
+        borderaxespad=0.,
+        fontsize='small',
+        handlelength=1.5,
+        handletextpad=0.5,
+    )
+
+def plot_single_response(
+    adf,
+    run_lbls,
+    variable,
+    nuclides = None,
+    time_unit='s',
+    sort_by_time='shutdown',
+    head=None,
+    total=False,
+    yscale='log',
+    relative=False
+):
+    '''
+    Create a simple x-y plot of a given variable tracked in an ALARA output
+        table (as stored in an ALARADFrame) against a log timescale. Options
+        for plotting a single run, as well as two runs against each other.
+        Plot will contain unique lines for the isotopes represented in the
+        data, with options to show only certain elements and/or only the
+        largest contributors at a given cooling time. Additionally, the
+        cumulative total values across all isotopes can be plotted separately
+        from individual isotopic data using the combination of the parameters:
+        total=True, head=1.
+
+    Arguments:
+        adf (alara_output_processing.ALARADFrame): ALARADFrame containing 
+            response data from one or more ALARA runs.
+        run_lbl (str): Distinguisher of the specified ALARA run.
+        variable (str): Name of the response variable.
+        nuclides (str, list, or None, optional): Optional parameter for
+            nuclide selection. For a single nuclide, input should be a string
+            of the form "element-A". For multiple individual nuclides, they
+            should be input as a list. To filter all nuclides of a given
+            element, provide the chemical symbol of the desired element,
+            either as a stand-alone string or in a list with other elements or
+            nuclides. To include the "total" row produced by ALARA, write
+            "total". If no nuclides or elements provided, the filtering will
+            pass through all nuclides matching the run_lbl and variable
+            parameters.
+            (Defaults to None)
+        time_unit (str, optional): Optional paramter to set units for cooling
+            times. Accepted values: 's', 'm', 'h', 'd', 'w', 'y', 'c'.
+            (Defaults to 's')
+        sort_by_time (str, optional): Option to sort the ALARADFrame by the
+            data in a particular time column.
+            (Defaults to 'shutdown')
+        head (int or None, optional): Option by which to truncate the
+            ALARADFrame to a particular number of rows.
+            (Defaults to None)           
+        total (bool, optional): Option to include the cumulative total
+            contribution from all isotopes towards the select variable in the
+            plot. If total=True, the total array will be treated equivalently
+            to any of the other isotopes and will be plotted alongside them.
+            If total=True and head=1, only the total will be plotted.
+            (Defaults to False) 
+        yscale (str, optional): Option to set the y-axis scale.
+            (Defaults to 'log')
+        relative (bool, optional): Option to plot relative values with respect
+            to totals at each cooling time.
+            (Defaults to False)
+
+    Returns:
+        None
+    '''
+
+    data_comp = False
+    _, ax = plt.subplots(figsize=(10,6))
+
+    if isinstance(run_lbls, list):
+        data_comp=True
+    else:
+        run_lbls = [run_lbls]
+
+    data_list = []
+    line_styles = list(lines.lineStyles.keys())[:len(run_lbls)]
+    for run_lbl, linestyle in zip(run_lbls, line_styles):
+        times, filtered, piv = preprocess_data(
+            adf=adf,
+            run_lbl=run_lbl,
+            variable=variable,
+            nuclides=nuclides,
+            time_unit=time_unit,
+            sort_by_time=sort_by_time,
+            head=head,
+        )
+        data_list.append((run_lbl, times, filtered, piv, linestyle))
+
+    color_map = build_color_map([data[3] for data in data_list])
+    for run_lbl, times, filtered, piv, linestyle in data_list:
+        for nuc in piv.index:
+            if nuc == 'total' and not total:
+                continue
+
+            label_suffix = f' ({run_lbl})' if data_comp else ''
+
+            ax.plot(
+                times,
+                piv.loc[nuc].tolist(),
+                label=(nuc + label_suffix),
+                color=color_map[nuc],
+                linestyle=linestyle
+            )
+
+    title_suffix = f'{variable} vs Cooling Time '
+
+    if relative:
+        title_suffix += 'Relative to Total at Each Cooling Time '
+        yscale = 'linear'
+
+    if head:
+        title_suffix += (
+            f'\n(ALARADFrame Head Sorted by Values at {sort_by_time})'
+        )
+
+    title_prefix = (
+        f'{", ".join(run_lbls)} Comparison:\n' if data_comp
+        else f'{run_lbls[0]}: '
+    )
+
+    ax.set_title(title_prefix + title_suffix)
+    ax.set_ylabel(
+        f'Proportion of Total {variable}' if relative
+        else f'{variable} [{filtered['var_unit'].unique()[0]}]'
+    )
+    ax.set_xlabel(f'Time ({time_unit})')
+    ax.set_xscale('log')
+    ax.set_yscale(yscale)
+
+    construct_legend(ax, data_comp)
+
+    ax.grid(True)
+    plt.tight_layout(rect=[0, 0, 0.85, 1])
+    plt.show()
+    
\ No newline at end of file