{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### analysis/analyze_sensitivity_parameter_sweep\n",
    "\n",
    "- Processes neuron firing rates for simulations from a sweep of cell type sensitivity parameters (i.e. scalings of ORN/eLN/iLN/PN columns of the hemibrain connectivity matrix)\n",
    "- Plots AL features for circuits with different sensitivity parameters, choosing best one for circuit idiosyncrasy analysis (ORNs x0.1, eLNs x0.04, iLNs x0.02, PNs x0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import argparse\n",
    "import os\n",
    "import sys\n",
    "file_path = 'C:/Users/dB/deBivort/projects/ALVariability/'\n",
    "proj_dir = os.path.join(file_path.split('ALVariability')[0], 'ALVariability')\n",
    "sys.path.append(proj_dir)\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import re\n",
    "from matplotlib.gridspec import GridSpec\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.spatial.distance import pdist\n",
    "from PyPDF2 import PdfFileWriter, PdfFileReader\n",
    "import io\n",
    "from reportlab.pdfgen import canvas\n",
    "from reportlab.lib.pagesizes import letter\n",
    "from reportlab.lib.units import inch\n",
    "import seaborn as sns\n",
    "import re\n",
    "from datetime import datetime\n",
    "\n",
    "from scipy.stats import kstest, ks_2samp\n",
    "\n",
    "from utils.data_utils import make_df_AL_activity_long, make_orn_upn_frs, make_glomerular_odor_responses\n",
    "from utils.plot_utils_EXTRA import plot_ornpn_hist, plot_fig_orn_pn_stats\n",
    "\n",
    "from utils.plot_utils import set_font_sizes\n",
    "set_font_sizes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load file storing firing rates of all simulations of the sweep, originally created by running `analysis/collect_col_sweep_frs.py`:\n",
    "\n",
    "(note: the file in the next cell can be downloaded from http://lab.debivort.org/odor-loci-of-\n",
    "individuality/OLOI_AL_model/analysis/sensitivity_sweep_20210427/all_simulations_frs.p, about 500 MB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfpick = pickle.load(open('sensitivity_sweep_20210427/all_simulations_frs.p', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "project_dir = os.path.join(file_path.split('ALVariability')[0], 'ALVariability')\n",
    "df_neur_ids = pd.read_csv(os.path.join(project_dir, 'connectomics/hemibrain_v1_2/df_neur_ids.csv'), index_col=0)\n",
    "al_block = pd.read_csv(os.path.join(project_dir, 'connectomics/hemibrain_v1_2/AL_block.csv'), index_col=0)\n",
    "al_block.columns = al_block.columns.astype(np.int64)\n",
    "al_block.index = al_block.index.astype(np.int64)\n",
    "imput_table = pd.read_csv(os.path.join(project_dir, 'odor_imputation/df_odor_door_all_odors_imput_ALS.csv'), index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count number of ORNs in the 7 glomeruli measured in Bhandawat et al, 2007:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VA2    97\n",
       "DM1    86\n",
       "DL1    84\n",
       "DM2    79\n",
       "DM3    68\n",
       "DM4    60\n",
       "VM2     1\n",
       "Name: glom, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_neur_ids_bhand_orns = df_neur_ids[(df_neur_ids.altype == 'ORN') & (df_neur_ids.glom.isin(['DL1', 'DM1', 'DM2', 'DM3', 'DM4', 'VA2', 'VM2']))]\n",
    "df_neur_ids_bhand_orns['glom'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>bodyId</th>\n",
       "      <th>type</th>\n",
       "      <th>instance</th>\n",
       "      <th>altype</th>\n",
       "      <th>glom</th>\n",
       "      <th>polarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2569</th>\n",
       "      <td>2074116622</td>\n",
       "      <td>ORN_VM2</td>\n",
       "      <td>ORN_VM2</td>\n",
       "      <td>ORN</td>\n",
       "      <td>VM2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2895</th>\n",
       "      <td>727472481</td>\n",
       "      <td>VM2_adPN</td>\n",
       "      <td>VM2_adPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>VM2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2896</th>\n",
       "      <td>697132371</td>\n",
       "      <td>VM2_adPN</td>\n",
       "      <td>VM2_adPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>VM2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          bodyId      type    instance altype glom  polarity\n",
       "2569  2074116622   ORN_VM2     ORN_VM2    ORN  VM2       1.0\n",
       "2895   727472481  VM2_adPN  VM2_adPN_R    uPN  VM2       1.0\n",
       "2896   697132371  VM2_adPN  VM2_adPN_R    uPN  VM2       1.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_neur_ids[df_neur_ids.glom == 'VM2']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VM2 has only 1 ORN, so we will exclude it when making comparisons to Bhandawat et al, 2007"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>bodyId</th>\n",
       "      <th>type</th>\n",
       "      <th>instance</th>\n",
       "      <th>altype</th>\n",
       "      <th>glom</th>\n",
       "      <th>polarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2788</th>\n",
       "      <td>1977579449</td>\n",
       "      <td>VA2_adPN</td>\n",
       "      <td>VA2_adPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>VA2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2790</th>\n",
       "      <td>542634818</td>\n",
       "      <td>DM1_lPN</td>\n",
       "      <td>DM1_lPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>DM1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2794</th>\n",
       "      <td>1700937093</td>\n",
       "      <td>DL1_adPN</td>\n",
       "      <td>DL1_adPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>DL1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2795</th>\n",
       "      <td>1700946301</td>\n",
       "      <td>DL1_adPN</td>\n",
       "      <td>DL1_adPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>DL1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2808</th>\n",
       "      <td>630714299</td>\n",
       "      <td>DM2_lPN</td>\n",
       "      <td>DM2_lPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>DM2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2809</th>\n",
       "      <td>5901222910</td>\n",
       "      <td>DM2_lPN</td>\n",
       "      <td>DM2_lPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>DM2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2813</th>\n",
       "      <td>755518957</td>\n",
       "      <td>DM3_adPN</td>\n",
       "      <td>DM3_adPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>DM3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2814</th>\n",
       "      <td>1570685283</td>\n",
       "      <td>DM3_vPN</td>\n",
       "      <td>DM3_vPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>DM3</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2819</th>\n",
       "      <td>604031203</td>\n",
       "      <td>DM4_vPN</td>\n",
       "      <td>DM4_vPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>DM4</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2820</th>\n",
       "      <td>542652506</td>\n",
       "      <td>DM4_vPN</td>\n",
       "      <td>DM4_vPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>DM4</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2821</th>\n",
       "      <td>573333835</td>\n",
       "      <td>DM4_adPN</td>\n",
       "      <td>DM4_adPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>DM4</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2895</th>\n",
       "      <td>727472481</td>\n",
       "      <td>VM2_adPN</td>\n",
       "      <td>VM2_adPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>VM2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2896</th>\n",
       "      <td>697132371</td>\n",
       "      <td>VM2_adPN</td>\n",
       "      <td>VM2_adPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>VM2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          bodyId      type    instance altype glom  polarity\n",
       "2788  1977579449  VA2_adPN  VA2_adPN_R    uPN  VA2       1.0\n",
       "2790   542634818   DM1_lPN   DM1_lPN_R    uPN  DM1       1.0\n",
       "2794  1700937093  DL1_adPN  DL1_adPN_R    uPN  DL1       1.0\n",
       "2795  1700946301  DL1_adPN  DL1_adPN_R    uPN  DL1       1.0\n",
       "2808   630714299   DM2_lPN   DM2_lPN_R    uPN  DM2       1.0\n",
       "2809  5901222910   DM2_lPN   DM2_lPN_R    uPN  DM2       1.0\n",
       "2813   755518957  DM3_adPN  DM3_adPN_R    uPN  DM3       1.0\n",
       "2814  1570685283   DM3_vPN   DM3_vPN_R    uPN  DM3      -1.0\n",
       "2819   604031203   DM4_vPN   DM4_vPN_R    uPN  DM4      -1.0\n",
       "2820   542652506   DM4_vPN   DM4_vPN_R    uPN  DM4      -1.0\n",
       "2821   573333835  DM4_adPN  DM4_adPN_R    uPN  DM4       1.0\n",
       "2895   727472481  VM2_adPN  VM2_adPN_R    uPN  VM2       1.0\n",
       "2896   697132371  VM2_adPN  VM2_adPN_R    uPN  VM2       1.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_neur_ids_bhand_upns = df_neur_ids[(df_neur_ids.altype == 'uPN') & (df_neur_ids.glom.isin(['DL1', 'DM1', 'DM2', 'DM3', 'DM4', 'VA2', 'VM2']))]\n",
    "df_neur_ids_bhand_upns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "neur_inputs = pd.Series(al_block.sum(0), name='num_synapse_inputs')\n",
    "neur_inputs.index.name = 'bodyId_post'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>bodyId</th>\n",
       "      <th>type</th>\n",
       "      <th>instance</th>\n",
       "      <th>altype</th>\n",
       "      <th>glom</th>\n",
       "      <th>polarity</th>\n",
       "      <th>num_synapse_inputs</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1977579449</td>\n",
       "      <td>VA2_adPN</td>\n",
       "      <td>VA2_adPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>VA2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>542634818</td>\n",
       "      <td>DM1_lPN</td>\n",
       "      <td>DM1_lPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>DM1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1700937093</td>\n",
       "      <td>DL1_adPN</td>\n",
       "      <td>DL1_adPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>DL1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3519.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1700946301</td>\n",
       "      <td>DL1_adPN</td>\n",
       "      <td>DL1_adPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>DL1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3675.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>630714299</td>\n",
       "      <td>DM2_lPN</td>\n",
       "      <td>DM2_lPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>DM2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2804.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5901222910</td>\n",
       "      <td>DM2_lPN</td>\n",
       "      <td>DM2_lPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>DM2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2689.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>755518957</td>\n",
       "      <td>DM3_adPN</td>\n",
       "      <td>DM3_adPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>DM3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3452.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1570685283</td>\n",
       "      <td>DM3_vPN</td>\n",
       "      <td>DM3_vPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>DM3</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>604031203</td>\n",
       "      <td>DM4_vPN</td>\n",
       "      <td>DM4_vPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>DM4</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>542652506</td>\n",
       "      <td>DM4_vPN</td>\n",
       "      <td>DM4_vPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>DM4</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>573333835</td>\n",
       "      <td>DM4_adPN</td>\n",
       "      <td>DM4_adPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>DM4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>727472481</td>\n",
       "      <td>VM2_adPN</td>\n",
       "      <td>VM2_adPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>VM2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>697132371</td>\n",
       "      <td>VM2_adPN</td>\n",
       "      <td>VM2_adPN_R</td>\n",
       "      <td>uPN</td>\n",
       "      <td>VM2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        bodyId      type    instance altype glom  polarity  num_synapse_inputs\n",
       "0   1977579449  VA2_adPN  VA2_adPN_R    uPN  VA2       1.0              4223.0\n",
       "1    542634818   DM1_lPN   DM1_lPN_R    uPN  DM1       1.0              7280.0\n",
       "2   1700937093  DL1_adPN  DL1_adPN_R    uPN  DL1       1.0              3519.0\n",
       "3   1700946301  DL1_adPN  DL1_adPN_R    uPN  DL1       1.0              3675.0\n",
       "4    630714299   DM2_lPN   DM2_lPN_R    uPN  DM2       1.0              2804.0\n",
       "5   5901222910   DM2_lPN   DM2_lPN_R    uPN  DM2       1.0              2689.0\n",
       "6    755518957  DM3_adPN  DM3_adPN_R    uPN  DM3       1.0              3452.0\n",
       "7   1570685283   DM3_vPN   DM3_vPN_R    uPN  DM3      -1.0               131.0\n",
       "8    604031203   DM4_vPN   DM4_vPN_R    uPN  DM4      -1.0               182.0\n",
       "9    542652506   DM4_vPN   DM4_vPN_R    uPN  DM4      -1.0                75.0\n",
       "10   573333835  DM4_adPN  DM4_adPN_R    uPN  DM4       1.0              5274.0\n",
       "11   727472481  VM2_adPN  VM2_adPN_R    uPN  VM2       1.0                42.0\n",
       "12   697132371  VM2_adPN  VM2_adPN_R    uPN  VM2       1.0                10.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_neur_ids_bhand_upns.merge(neur_inputs, left_on='bodyId', right_on='bodyId_post', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "thermo_hygro_glomeruli = np.array(['VP1d', 'VP1l', 'VP1m', 'VP2', 'VP3', 'VP4', 'VP5'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "hemi_gloms = df_neur_ids[df_neur_ids.altype == 'ORN'].glom.value_counts().index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "njobs = len(dfpick)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotdir = './sensitivity_sweep_plots'\n",
    "if not os.path.exists(plotdir):\n",
    "    os.makedirs(plotdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "odor_names = np.array(['benzaldehyde', \n",
    "                        'butyric acid',\n",
    "                        '2,3-butanedione',\n",
    "                        '1-butanol',\n",
    "                        'cyclohexanone',\n",
    "                        'Z3-hexenol', # originally 'cis-3-hexen-1-ol',\n",
    "                        'ethyl butyrate',\n",
    "                        'ethyl acetate',\n",
    "                        'geranyl acetate',\n",
    "                        'isopentyl acetate', # originally 'isoamyl acetate',\n",
    "                        '4-methylphenol', # originally '4-methyl phenol',\n",
    "                        'methyl salicylate',\n",
    "                        '3-methylthio-1-propanol',\n",
    "                        'octanal',\n",
    "                        '2-octanone',\n",
    "                        'pentyl acetate', \n",
    "                        'E2-hexenal', # originally 'trans-2-hexenal',\n",
    "                        'gamma-valerolactone'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_dir = os.path.join(plotdir, 'all_plots2')\n",
    "if not os.path.exists(plot_dir):\n",
    "    os.makedirs(plot_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "zscore = lambda x: ((x - np.mean(x.values)).T / np.std(x.values)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "bhand_gloms = ['DL1', 'DM1', 'DM2', 'DM3', 'DM4', 'VA2']#,, 'VM2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DL1    84\n",
       "DM1    86\n",
       "DM2    79\n",
       "DM3    68\n",
       "DM4    60\n",
       "VA2    97\n",
       "Name: glom, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_neur_ids[df_neur_ids.altype == 'ORN'].glom.value_counts().loc[bhand_gloms]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DL1    2\n",
       "DM1    1\n",
       "DM2    2\n",
       "DM3    2\n",
       "DM4    3\n",
       "VA2    1\n",
       "Name: glom, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_neur_ids[df_neur_ids.altype == 'uPN'].glom.value_counts().loc[bhand_gloms]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_subtype_means(df_AL_activity):\n",
    "    \n",
    "    df_iln_activity = df_AL_activity.loc[df_AL_activity.neur_type == 'iLN'].set_index('neur_name')\n",
    "    df_eln_activity = df_AL_activity.loc[df_AL_activity.neur_type == 'eLN'].set_index('neur_name')\n",
    "\n",
    "    df_iln_on = df_iln_activity.loc[:, df_iln_activity.columns.str.contains('dur')]\n",
    "    df_iln_off = df_iln_activity.loc[:, df_iln_activity.columns.str.contains('pre')]\n",
    "    iln_off_mean = df_iln_off.mean().mean()\n",
    "    iln_on_mean = df_iln_on.mean().mean()\n",
    "\n",
    "    df_eln_on = df_eln_activity.loc[:, df_eln_activity.columns.str.contains('dur')]\n",
    "    df_eln_off = df_eln_activity.loc[:, df_eln_activity.columns.str.contains('pre')]\n",
    "    eln_off_mean = df_eln_off.mean().mean()\n",
    "    eln_on_mean = df_eln_on.mean().mean()\n",
    "\n",
    "    return iln_off_mean, iln_on_mean, eln_off_mean, eln_on_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_neur_ids_bhand  = df_neur_ids.copy()[((df_neur_ids.altype == 'ORN') & (df_neur_ids.glom.isin(bhand_gloms))) | \n",
    "              (df_neur_ids.altype == 'LN') | \n",
    "               ((df_neur_ids.altype == 'uPN') & (df_neur_ids.glom.isin(bhand_gloms))) |\n",
    "               (df_neur_ids.altype == 'mPN')\n",
    "              ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "bhand_filepath = os.path.join(project_dir, 'datasets/Bhandawat2007/fig6_pn_orn_firing_rates/fig6_pn_orn_firing_rate_histograms.csv')\n",
    "df_bhand = pd.read_csv(bhand_filepath)\n",
    "df_bhand_pn = df_bhand[df_bhand.cell_type == 'PN']\n",
    "df_bhand_orn = df_bhand[df_bhand.cell_type == 'ORN']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "doplots = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "500\n",
      "520\n"
     ]
    }
   ],
   "source": [
    "res_d = {}\n",
    "full_d = {}\n",
    "doplots = False\n",
    "for i in range(njobs):\n",
    "\n",
    "    runvals= dfpick[i]['simvals']\n",
    "    df_AL_activity = dfpick[i]['df_AL_activity']\n",
    "\n",
    "    df_AL_activity_long = make_df_AL_activity_long(df_AL_activity)\n",
    "    # remove non olfactory glomeruli\n",
    "    df_AL_activity_long = df_AL_activity_long[~df_AL_activity_long.glom.isin(thermo_hygro_glomeruli)]\n",
    "\n",
    "\n",
    "    st = 'a{}_e{}_i{}_p{}'.format(*runvals)\n",
    "\n",
    "\n",
    "    df_orn_frs, df_upn_frs = make_orn_upn_frs(df_AL_activity, odor_names, df_neur_ids,\n",
    "                                              sub_pre=True, olf_only=True)\n",
    "\n",
    "    if doplots:\n",
    "        plot_ornpn_hist(df_AL_activity_long, df_orn_frs, df_upn_frs)#, savetag=st+'_hist_onoff', \n",
    "                    #saveplot=True, savetodir=plot_dir, showplot=0)\n",
    "\n",
    "    df_orn_glom_onoff, df_upn_glom_onoff = make_glomerular_odor_responses(df_orn_frs, df_upn_frs, df_AL_activity)\n",
    "    fig_orn_pn = plt.figure(figsize=(12, 12))\n",
    "    orn_onoff_dists, upn_onoff_dists = plot_fig_orn_pn_stats(fig_orn_pn,\n",
    "                                                             df_orn_glom_onoff, \n",
    "                                                             df_upn_glom_onoff, \n",
    "                                                             'bwr', savetag=st+'_hmap_onoff', \n",
    "                                                             saveplot=0, savetodir=plot_dir, showplot=0, \n",
    "                                                             doplot=False); plt.close()\n",
    "\n",
    "\n",
    "    '''\n",
    "    ### Z Scored\n",
    "    df_orn_glom_onoff_Z = zscore(df_orn_glom_onoff); df_upn_glom_onoff_Z = zscore(df_upn_glom_onoff)\n",
    "    fig_orn_pn_Z = plt.figure(figsize=(12, 12))\n",
    "    plot_fig_orn_pn_stats(fig_orn_pn_Z, df_orn_glom_onoff_Z, df_upn_glom_onoff_Z, 'bwr', savetag=st+'_hmap_onoff_Z', \n",
    "                    saveplot=1, savetodir=plot_dir, showplot=0); plt.close()\n",
    "    '''\n",
    "\n",
    "    ### BHANDAWAT VERSION\n",
    "    df_AL_activity_bhand  = df_AL_activity.copy()[((df_AL_activity.neur_type == 'ORN') & (df_AL_activity.glom.isin(bhand_gloms))) | \n",
    "              (df_AL_activity.neur_type.isin(['iLN', 'eLN'])) | \n",
    "               ((df_AL_activity.neur_type == 'uPN') & (df_AL_activity.glom.isin(bhand_gloms))) |\n",
    "               (df_AL_activity.neur_type == 'mPN')\n",
    "              ]\n",
    "    df_AL_activity_long_bhand = make_df_AL_activity_long(df_AL_activity_bhand)\n",
    "    df_orn_frs_bhand, df_upn_frs_bhand = make_orn_upn_frs(df_AL_activity_bhand, odor_names, df_neur_ids_bhand.reset_index(),\n",
    "                                                  sub_pre=True, olf_only=True)\n",
    "    #plot_ornpn_hist(df_AL_activity_long_bhand, \n",
    "    #                df_orn_frs_bhand, df_upn_frs_bhand, savetag=st+'_hist_onoff_bhand', \n",
    "    #                saveplot=1, savetodir=plot_dir, showplot=0); plt.close()\n",
    "\n",
    "    df_orn_glom_onoff_bhand, df_upn_glom_onoff_bhand = \\\n",
    "        make_glomerular_odor_responses(df_orn_frs_bhand, df_upn_frs_bhand, df_AL_activity_bhand)\n",
    "    fig_orn_pn_bhand = plt.figure(figsize=(12, 12))\n",
    "    orn_bhand_dists, upn_bhand_dists = plot_fig_orn_pn_stats(fig_orn_pn_bhand,\n",
    "                                                             df_orn_glom_onoff_bhand, \n",
    "                                                             df_upn_glom_onoff_bhand, \n",
    "                                                             'bwr', savetag=st+'_hmap_onoff_bhand', \n",
    "                                                             saveplot=0, savetodir=plot_dir, showplot=0,\n",
    "                                                             doplot=False); plt.close()\n",
    "    \n",
    "\n",
    "\n",
    "    df_orn_OFF_frs, df_upn_OFF_frs = make_orn_upn_frs(df_AL_activity, odor_names, df_neur_ids,\n",
    "                                              sub_pre=False, olf_only=True, use_pre=True)\n",
    "\n",
    "    upn_off_mean = df_upn_OFF_frs.mean().mean()\n",
    "\n",
    "    iln_off_mean, iln_on_mean, eln_off_mean, eln_on_mean = get_subtype_means(df_AL_activity)\n",
    "\n",
    "    full_d[st] = {'df_upn_glom_onoff': df_upn_glom_onoff,\n",
    "                  'df_upn_glom_onoff_bhand': df_upn_glom_onoff_bhand,\n",
    "                  'upn_bhand_dists': upn_bhand_dists,\n",
    "                  'upn_onoff_dists': upn_onoff_dists,\n",
    "                  'df_upn_OFF_frs': df_upn_OFF_frs\n",
    "                 }\n",
    "\n",
    "\n",
    "\n",
    "    res_d[st] = {'mean_upn_onoff_dists': np.mean(upn_onoff_dists),\n",
    "                 'std_upn_onoff_dists': np.std(upn_onoff_dists),\n",
    "                 'mean_upn_bhand_dists': np.mean(upn_bhand_dists),\n",
    "                 'std_upn_bhand_dists': np.std(upn_bhand_dists), \n",
    "\n",
    "                 'mean_upn_onoff': np.mean(df_upn_glom_onoff.values),\n",
    "                 'std_upn_onoff': np.std(df_upn_glom_onoff.values),\n",
    "                 'mean_upn_bhand': np.mean(df_upn_glom_onoff_bhand.values),\n",
    "                 'std_upn_bhand': np.std(df_upn_glom_onoff_bhand.values),\n",
    "                 'max_upn_bhand': np.max(df_upn_glom_onoff_bhand.values),\n",
    "                 \n",
    "                 'upn_off_mean': upn_off_mean,\n",
    "                 'iln_off_mean': iln_off_mean, 'iln_on_mean': iln_on_mean,\n",
    "                 'eln_off_mean': eln_off_mean, 'eln_on_mean': eln_on_mean,\n",
    "                 \n",
    "                 \n",
    "\n",
    "                 'a': runvals[0], 'e': runvals[1], 'i': runvals[2], 'p': runvals[3]\n",
    "                 #'frac_upn_dists_larger': sum(upn_onoff_dists > orn_onoff_dists) / len(upn_onoff_dists),\n",
    "                 #'frac_upn_dists_larger_bhand': sum(upn_bhand_dists > orn_bhand_dists) / len(upn_bhand_dists)\n",
    "                }\n",
    "\n",
    "    if i % 20 == 0:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "def make_PCA_projections(df_orn_glom_onoff_bhand, df_upn_glom_onoff_bhand):\n",
    "    \n",
    "    # get odor x glomerulus tables for ORNs, uPNs\n",
    "    df_sim_orn = df_orn_glom_onoff_bhand.T\n",
    "    df_sim_upn = df_upn_glom_onoff_bhand.T\n",
    "    # center them\n",
    "    df_sim_orn_c = df_sim_orn - df_sim_orn.mean(0)\n",
    "    df_sim_upn_c = df_sim_upn - df_sim_upn.mean(0)\n",
    "    # perform PCA\n",
    "    ornPCA = PCA()\n",
    "    ornPCA.fit(df_sim_orn_c)\n",
    "    orn_projections = ornPCA.fit_transform(df_sim_orn_c)[:, :2]\n",
    "    upnPCA = PCA()\n",
    "    upnPCA.fit(df_sim_upn_c)\n",
    "    upn_projections = upnPCA.fit_transform(df_sim_upn_c)[:, :2]\n",
    "    \n",
    "    dfORNpca = orn_projections[:, :2]\n",
    "    dfPNpca = upn_projections[:, :2]\n",
    "    \n",
    "    return dfORNpca, dfPNpca\n",
    "\n",
    "def plot_PC_projections(dfORNpca, dfPNpca, odor_names, savetag='',\n",
    "                    saveplot=False, savetodir='./', showplot=False):\n",
    "    \n",
    "    fig, axs = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(10,4))\n",
    "    axs[0].set_title('ORNs')\n",
    "    for i in range(len(dfORNpca)):\n",
    "        axs[0].scatter(dfORNpca[i, 1], dfORNpca[i, 0], label=odor_names[i])\n",
    "    axs[0].set_xlabel('projection onto PC 2')\n",
    "    axs[0].set_ylabel('projection onto PC 1')\n",
    "    \n",
    "    axs[1].set_title('PNs')\n",
    "    for i in range(len(dfPNpca)):\n",
    "        axs[1].scatter(dfPNpca[i, 1], dfPNpca[i, 0], label=odor_names[i])\n",
    "    \n",
    "    for ax in axs:\n",
    "        #ax.axis('equal')\n",
    "        ax.axvline(0, ls='--', color='0.5')\n",
    "        ax.axhline(0, ls='--', color='0.5')\n",
    "    plt.legend(loc='upper left', bbox_to_anchor=(1.05, 1), borderaxespad=0, ncol=2)\n",
    "    if saveplot:\n",
    "        plt.savefig(os.path.join(savetodir, f'{savetag}.png'), bbox_inches='tight')\n",
    "    if showplot:\n",
    "        plt.show()\n",
    "        \n",
    "    plt.close()\n",
    "    \n",
    "    \n",
    "def plot_PC_distances(bhand_pca_orndists, bhand_pca_pndists, savetag='',\n",
    "                    saveplot=False, savetodir='./', showplot=False):\n",
    "    \n",
    "    mindist = min(min(bhand_pca_orndists), min(bhand_pca_pndists)); maxdist = max(max(bhand_pca_orndists), max(bhand_pca_pndists))\n",
    "    b = np.linspace(mindist, maxdist, 2*12)  \n",
    "\n",
    "\n",
    "    plt.figure(figsize=(13,4))\n",
    "    ax1 = plt.subplot2grid((1, 3), (0, 0), colspan=2)\n",
    "    ax2 = plt.subplot2grid((1, 3), (0, 2))\n",
    "    \n",
    "    ax1.hist(bhand_pca_orndists, alpha=0.5, bins=b, label='ORN', color='xkcd:light blue')\n",
    "    ax1.hist(bhand_pca_pndists, alpha=0.5, bins=b, label='PN', color='xkcd:navy')\n",
    "    ax1.set_xlabel('pairwise euclidean distance between\\nPCA projected odors')\n",
    "    ax1.set_ylabel('# odor pairs')\n",
    "    ax1.legend()\n",
    "    \n",
    "    ax2.scatter(bhand_pca_orndists, bhand_pca_pndists, color='k')\n",
    "    ax2.plot([mindist, maxdist], [mindist, maxdist], color='0.5', ls='--')\n",
    "    ax2.axis('equal')\n",
    "    ax2.set_xlabel('pairwise dist between ORN PCA-projected odors')\n",
    "    ax2.set_ylabel('pairwise dist between PN PCA-projected odors')\n",
    "    \n",
    "    plt.subplots_adjust(wspace=0.4)\n",
    "    \n",
    "    if saveplot:\n",
    "        plt.savefig(os.path.join(savetodir, f'{savetag}.png'), bbox_inches='tight')\n",
    "    if showplot:\n",
    "        plt.show()\n",
    "        \n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "bhand_df = pd.read_csv('../datasets/Bhandawat2007/fig3_responses/fig3_firing_rates.csv')\n",
    "df_bhand_pn_glom_by_odor = bhand_df[bhand_df.cell_type == 'PN'].pivot('glomerulus', 'odor', 'firing_rate')\n",
    "model_arr = df_bhand_pn_glom_by_odor.values.flatten()\n",
    "dur_cols = [c for c in df_AL_activity.columns if '_dur_' in c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "20\n",
      "40\n",
      "60\n",
      "80\n",
      "100\n",
      "120\n",
      "140\n",
      "160\n",
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      "460\n",
      "480\n",
      "500\n",
      "520\n"
     ]
    }
   ],
   "source": [
    "res_d_PCA = {}\n",
    "full_d_PCA = {}\n",
    "doplots = False\n",
    "for i in range(njobs):\n",
    "\n",
    "    runvals= dfpick[i]['simvals']\n",
    "    \n",
    "    st = 'a{}_e{}_i{}_p{}'.format(*runvals)\n",
    "    \n",
    "    \n",
    "    df_AL_activity = dfpick[i]['df_AL_activity']\n",
    "\n",
    "    ### BHANDAWAT VERSION\n",
    "    df_AL_activity_bhand  = df_AL_activity.copy()[((df_AL_activity.neur_type == 'ORN') & (df_AL_activity.glom.isin(bhand_gloms))) | \n",
    "              (df_AL_activity.neur_type.isin(['iLN', 'eLN'])) | \n",
    "               ((df_AL_activity.neur_type == 'uPN') & (df_AL_activity.glom.isin(bhand_gloms))) |\n",
    "               (df_AL_activity.neur_type == 'mPN')\n",
    "              ]\n",
    "    df_orn_frs_onoff_bhand, df_upn_frs_onoff_bhand = make_orn_upn_frs(df_AL_activity_bhand, odor_names, df_neur_ids_bhand.reset_index(),\n",
    "                                                  sub_pre=True, olf_only=True)\n",
    "    \n",
    "    df_orn_glom_by_odor_onoff_bhand, df_upn_glom_by_odor_onoff_bhand = \\\n",
    "        make_glomerular_odor_responses(df_orn_frs_onoff_bhand, df_upn_frs_onoff_bhand, df_AL_activity_bhand)\n",
    "    \n",
    "    dfORNpca, dfPNpca = make_PCA_projections(df_orn_glom_by_odor_onoff_bhand, df_upn_glom_by_odor_onoff_bhand)\n",
    "\n",
    "    bhand_pca_orndists = pdist(dfORNpca, metric='euclidean')\n",
    "    bhand_pca_pndists = pdist(dfPNpca, metric='euclidean')\n",
    "    \n",
    "    dm1_PN_pre_odor = df_AL_activity_bhand[df_AL_activity_bhand.neur_name == 'uPN_DM1_0']['fr_pre_odor0'].values[0]\n",
    "    va2_PN_pre_odor = df_AL_activity_bhand[df_AL_activity_bhand.neur_name == 'uPN_VA2_0']['fr_pre_odor0'].values[0]\n",
    "    vm2_PN_dur_all_odors = np.mean(df_AL_activity.loc[df_AL_activity.neur_name == 'uPN_VM2_0', dur_cols].values)\n",
    "    \n",
    "    bhand_arr = df_upn_glom_by_odor_onoff_bhand.values.flatten()\n",
    "    kstest_stat, kstest_pval = kstest(model_arr, bhand_arr)\n",
    "    kstest_2samp_stat, _ = ks_2samp(model_arr, bhand_arr)\n",
    "\n",
    "    #plot_PC_projections(dfORNpca, dfPNpca, odor_names, \n",
    "    #                     savetag=st+'_PCA_projections', saveplot=1, savetodir=plot_dir, showplot=0)\n",
    "    #plot_PC_distances(bhand_pca_orndists, bhand_pca_pndists,\n",
    "    #                     savetag=st+'_PCA_distances', saveplot=1, savetodir=plot_dir, showplot=0)\n",
    "    res_d_PCA[st] = {'mean_pca_orn_dists': np.mean(bhand_pca_orndists), \n",
    "                     'std_pca_orn_dists': np.std(bhand_pca_orndists),\n",
    "                     'mean_pca_upn_dists': np.mean(bhand_pca_pndists),\n",
    "                     'std_pca_upn_dists': np.std(bhand_pca_pndists),\n",
    "                     'max_pca_upn_dists': np.max(bhand_pca_pndists),\n",
    "                     'DM1_PN_pre_odor_fr': dm1_PN_pre_odor,\n",
    "                     'VA2_PN_pre_odor_fr': va2_PN_pre_odor,\n",
    "                     'VM2_PN_dur_odor_fr': vm2_PN_dur_all_odors,\n",
    "                     'min_PN_dur': np.min(bhand_arr),\n",
    "                     'kstest_stat': kstest_stat, 'kstest_pval': kstest_pval,\n",
    "                     'kstest_2samp_stat': kstest_2samp_stat,\n",
    "                     'a': runvals[0], 'e': runvals[1], 'i': runvals[2], 'p': runvals[3]\n",
    "                }\n",
    "    ## near 500\n",
    "    ## if st == 'a0.1_e0.25_i0.2_p6.0':\n",
    "    ##   break\n",
    "    ## near 320\n",
    "    ##if st == 'a0.1_e0.4_i0.2_p4.0':\n",
    "    ##   break\n",
    "        \n",
    "    if i % 20 == 0:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_d = {}\n",
    "for i in range(njobs):\n",
    "\n",
    "    runvals= dfpick[i]['simvals']   \n",
    "    st = 'a{}_e{}_i{}_p{}'.format(*runvals)\n",
    "    \n",
    "    cur_d = full_d[st]\n",
    "    \n",
    "    frs_upn_glom_onoff = cur_d['df_upn_glom_onoff'].values.flatten()\n",
    "    frs_upn_glom_onoff_bhand = cur_d['df_upn_glom_onoff_bhand'].values.flatten()\n",
    "    \n",
    "    frac_less_full = sum(frs_upn_glom_onoff < 100) / len(frs_upn_glom_onoff)\n",
    "    frac_less_bhand = sum(frs_upn_glom_onoff_bhand < 100) / len(frs_upn_glom_onoff_bhand)\n",
    "    \n",
    "    # squared distance from Bhandawat hist\n",
    "    # align to bhandawat data\n",
    "    b = np.arange(-200, 500, 20)\n",
    "    cnts, left_pts = np.histogram(cur_d['df_upn_glom_onoff_bhand'].values.flatten(), bins=b)\n",
    "    sim_pn_fracs = pd.Series(cnts/sum(cnts), index=left_pts[:-1], name='sim_PN')\n",
    "    sim_pn_fracs.index = sim_pn_fracs.index.rename('fr')\n",
    "    df_compare_pns_peak = pd.DataFrame(sim_pn_fracs).merge(df_bhand_pn, left_on='fr', \n",
    "                                                       right_on='firing_rate').fillna(0)\n",
    "    pn_sq_dist_bhandgloms = np.sum((df_compare_pns_peak['fraction'] - df_compare_pns_peak['sim_PN'])**2)\n",
    "    \n",
    "    cnts, left_pts = np.histogram(cur_d['df_upn_glom_onoff'].values.flatten(), bins=b)\n",
    "    sim_pn_fracs = pd.Series(cnts/sum(cnts), index=left_pts[:-1], name='sim_PN')\n",
    "    sim_pn_fracs.index = sim_pn_fracs.index.rename('fr')\n",
    "    df_compare_pns_peak = pd.DataFrame(sim_pn_fracs).merge(df_bhand_pn, left_on='fr', \n",
    "                                                       right_on='firing_rate').fillna(0)\n",
    "    pn_sq_dist_allgloms = np.sum((df_compare_pns_peak['fraction'] - df_compare_pns_peak['sim_PN'])**2)\n",
    "    \n",
    "    \n",
    "    \n",
    "    new_d[st] = {'frac_less_full': frac_less_full,\n",
    "                 'frac_less_bhand': frac_less_bhand,\n",
    "                 'pn_sq_dist_bhandgloms': pn_sq_dist_bhandgloms,\n",
    "                 'pn_sq_dist_allgloms': pn_sq_dist_allgloms,\n",
    "                 }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_res = pd.DataFrame.from_dict(res_d).T\n",
    "df_new = pd.DataFrame.from_dict(new_d).T\n",
    "df_res = pd.concat((df_res, df_new), 1)\n",
    "\n",
    "df_res_PCA = pd.DataFrame.from_dict(res_d_PCA).T\n",
    "\n",
    "df_res = pd.concat((df_res, df_res_PCA), 1)\n",
    "df_res = df_res.loc[:,~df_res.columns.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_res_cols = df_res.columns\n",
    "df_res_cols\n",
    "df_res_cols1 = [c for c in df_res_cols if c not in ['a', 'e', 'i', 'p']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_sweep_res(x_var, y_var, c_var, x_lab='', y_lab='', c_lab='', \n",
    "                   highlight_locs=['a0.1_e0.25_i0.65_p8.0']):\n",
    "    \n",
    "    x_lab = x_var if x_lab == '' else x_lab\n",
    "    y_lab = y_var if y_lab == '' else y_lab\n",
    "    c_lab = c_var if c_lab == '' else c_lab\n",
    "    \n",
    "    plot_df = df_res\n",
    "    plt.figure(figsize=(12,7))\n",
    "    # all sims\n",
    "    sc = plt.scatter(plot_df[x_var], plot_df[y_var], c=plot_df[c_var])\n",
    "    # highlight base paramter config\n",
    "    \n",
    "    for loc in highlight_locs:\n",
    "        plot_df = df_res.loc[loc]\n",
    "        plt.scatter(plot_df[x_var], plot_df[y_var], label=loc, edgecolor='w', linewidth=2, marker='*', s=500)\n",
    "    \n",
    "    #plot_df = df_res.loc['a0.1_e0.25_i0.45_p6.0']\n",
    "    #plt.scatter(plot_df[x_var], plot_df[y_var], c='r', marker='*', s=300)\n",
    "    if len(highlight_locs) > 0:\n",
    "        plt.legend(title='highlighted runs', bbox_to_anchor=(1.15, 1), loc='upper left', borderaxespad=0)\n",
    "    plt.xlabel(x_lab); plt.ylabel(y_lab)\n",
    "    plt.colorbar(sc, label=c_lab)\n",
    "    plt.show()\n",
    "    \n",
    "    return x_var, y_var, c_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>e</th>\n",
       "      <th>i</th>\n",
       "      <th>p</th>\n",
       "      <th>success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>585 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       a    e    i     p  success\n",
       "0    0.1  0.1  0.2   2.0        1\n",
       "1    0.1  0.1  0.2   4.0        1\n",
       "2    0.1  0.1  0.2   6.0        1\n",
       "3    0.1  0.1  0.2   8.0        1\n",
       "4    0.1  0.1  0.2  10.0        1\n",
       "..   ...  ...  ...   ...      ...\n",
       "580  0.1  0.5  1.0   2.0        1\n",
       "581  0.1  0.5  1.0   4.0        1\n",
       "582  0.1  0.5  1.0   6.0        1\n",
       "583  0.1  0.5  1.0   8.0        1\n",
       "584  0.1  0.5  1.0  10.0        1\n",
       "\n",
       "[585 rows x 5 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_simvals = df_res[['a', 'e', 'i', 'p']].sort_values(['a', 'e', 'i', 'p']).reset_index(drop=True)\n",
    "df_simvals['success'] = 1\n",
    "\n",
    "a_vals = df_simvals['a'].value_counts().index.values\n",
    "e_vals = df_simvals['e'].value_counts().index.values\n",
    "i_vals = df_simvals['i'].value_counts().index.values\n",
    "p_vals = df_simvals['p'].value_counts().index.values\n",
    "\n",
    "rows_to_add = []\n",
    "for a_val in a_vals:\n",
    "    sub_1df = df_simvals[df_simvals['a'] == a_val]\n",
    "\n",
    "    for e_val in e_vals:\n",
    "        sub_2df = sub_1df[sub_1df['e'] == e_val]\n",
    "        for i_val in i_vals:\n",
    "            sub_3df = sub_2df[sub_2df['i'] == i_val]\n",
    "            for p_val in p_vals:\n",
    "                sub_4df = sub_3df[sub_3df['p'] == p_val]\n",
    "                if sub_4df.shape[0] == 0:\n",
    "                    new_row = [a_val, e_val, i_val, p_val, 0]\n",
    "                    rows_to_add.append(new_row)\n",
    "                    \n",
    "df_simvals_unsuccessful = pd.DataFrame(rows_to_add, columns=['a', 'e', 'i', 'p', 'success'])\n",
    "df_simvals = pd.concat((df_simvals, df_simvals_unsuccessful)).sort_values(['a', 'e', 'i', 'p']).reset_index(drop=True)\n",
    "df_simvals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter plots of model characteristics for different sensitivity parameter sets\n",
    "\n",
    "Each point is a model with an (ORN, eLN, iLN, PN) sensitivity parameter. Axes are different model characteristics such as uPN off-odor firing rate (should be close to 0), and a distance to the Bhandawat et al, 2007 PN firing rate histogram (which is rather flat for PNs -- more uniform than the ORN distribution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_var, y_var, c_var = plot_sweep_res(\n",
    "    x_var = 'pn_sq_dist_bhandgloms', x_lab = 'squared distance to Bhandawat ON-OFF uPN firing rate hist',\n",
    "    y_var = 'upn_off_mean', y_lab = 'uPN off-odor firing rate (Hz)',\n",
    "    c_var = 'p', c_lab='PN multiplier',\n",
    "    highlight_locs=[],#'a0.1_e0.4_i0.2_p4.0']\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pn_sq_dist_bhandgloms</th>\n",
       "      <th>upn_off_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.4_i0.2_p4.0</th>\n",
       "      <td>0.027608</td>\n",
       "      <td>6.052991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.4_i0.2_p6.0</th>\n",
       "      <td>0.025386</td>\n",
       "      <td>14.531624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     pn_sq_dist_bhandgloms  upn_off_mean\n",
       "a0.1_e0.4_i0.2_p4.0               0.027608      6.052991\n",
       "a0.1_e0.4_i0.2_p6.0               0.025386     14.531624"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df = df_res[\n",
    "       #(df_res['p'] == 4) & \n",
    "       (df_res[x_var] < 0.03) &\n",
    "       (df_res[y_var] < 15) &\n",
    "       #(df_res['upn_off_mean'] < 20) &\n",
    "       #(df_res['eln_on_mean'] < 40) &\n",
    "       #(df_res['eln_off_mean'] < 20) &\n",
    "       (1) ]\n",
    "sub_df.sort_values('upn_off_mean')[df_res_cols1].sort_values('max_pca_upn_dists')[['pn_sq_dist_bhandgloms', 'upn_off_mean']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RxJWz4ycZY4wxxhizE7MkygAg4gdJuNy/McYYY9qEECWxktedmBfoBawFwhmOxRjAkihjjDHGmHZLAafj77xoqd5AMSDA8gzHYgyQwWa7xhhjjDHGJKCwwZ/GZJzNRBljjDHGtGM7+XK+PL5/v+qp+bwsc+EYE2NJlDHGGGNMO6Xs9ElUEUBpVZj8LG/t55ZEmYyz5XzGGGOMMaa9KgK46Y25tZ8XZiwSYOHChb79999/cFZW1thddtllxHPPPZefyPN+85vf9Jo0adKgdMbyxRdfZI0ZM2b3rKyssSNGjBg2efLk7ESed//993cRkXGJvs7f/va3rj169Nij9vP169e7jz766F1yc3PH9unTZ9Tf//73rs09/89//nP3AQMGjMzOzh47ZsyY3d977720l4Kurq6W3XbbbcSVV17Zu7nrHnrooaL+/fuPzMrKGnvYYYftunr16pQnlCyJ6mBUI2jVGzhbr8TZdhMaXpDpkIwxxhjTihyVtDw6oFzAs2JTJc9PXcXKzZUQq9SXm4lgHMfh+OOP361Lly6RTz/9dN5pp5226Zxzztl1/vz5vuae98ADD3S56667mn1zn6zS0lLXpEmTBo8bN67is88+mzdhwoTyE044YfCWLVuafW+/evVqz7XXXtsvmdc6//zzN8+YMWN7Fnv66acP2rJli+eDDz6Yf/XVV6+56qqrBjSVGN1///1dbrvttt433HDD6ilTpszdf//9y0444YQhS5cu9SYTw4787ne/67VkyZJAc9dMnjw5+5e//OWgq666au1HH300v7y83H3aaaelnNhaEtWBqIbQzT9BS6+F6teh6kl00yk4lS9mOjRjjDHGtILa5XzpeHRARQBvzV5L7M919Y63tddffz1v2bJlgf/85z/Lx40bV33zzTevGzt2bMX9998ftxtyOBzmzDPP7H/55ZcP7Nu3bzCdsfz73/8u8ng8+uCDD67cc889qx966KGVeXl50UceeaRLc8+76KKL+u+yyy7VybxWbm6u9u7dOwIwZ84c/4cffljw0EMPLd97772rrrjiik0/+tGPNt13333d4z33iSeeKD777LNLzjvvvC0jR44M/uMf/1hdXFwcfv755wuTiaE5X331VdZTTz1VPGjQoGa/rnvuuaf7kUceueWyyy7bNGHChKonn3xy6aeffpo/e/bslHr8WBLVkVS/DuG5oJU1BxygGkr/hDoVmYzMGGOMMSZZuwNjm3l0B3i7Jnl6uyaZqjne3PN2b0lQ77//fs64ceOGZmVljc3Kyho7ceLEwUuXLvV+/vnnOcOGDassKipyaq/dZ599yr/++uu4M2Pbtm1zL168ODB58uR548aNK082jqlTpwb22WefIYFAYM/+/fuPvP7663s4Tuylv/rqq9xx48aVu91uAFwuF+PHjy//8ssvm1wq9/jjjxfOnz8/6ze/+c26pq6Jp+5yvk8++SSnW7du4ZEjR25PCvfff//y6dOnx33dm266afWll15a0vB4dXX1DrP6BQsW+ERk3D//+c8uPXr02CMvL2/M2Wef3T8YDG5/biQS4YILLhh4/fXXryoqKoo0N96MGTNyJ06cuH0/3W677Rbu3bt3aPLkySktL7QkqgPRqjeBqsYnxAPhaW0ejzHGGGNalyJEcaXl0c6IqnqJvRdt6sGMFVv4ZtVWAGas3Mo3K7bUPr/J59WMm9LU25YtW1wnnXTS4IMPPrh0xowZc1555ZWFK1eu9F933XW91q5d6+3Ro0eo7vU9evQIr127Nu5yvuLi4uhXX321cMKECXHevDWvvLxcjj322CHjx4+v+Prrr+fccccdKx544IEet9xyS3eA9evXe3v16lWv8XD37t3Da9asiRvLxo0b3VdddVX/f/7zn8v8fr8T75pErF271tu9e/d6r9uzZ8/w+vXr477uYYcdVrH77rtv/zt74YUX8pcvX+4/9NBDE04qb7vttt6PPfbYd08//fSSd955p/CKK67YvjTy+uuv71lUVBS55JJLNu9onJKSEm+fPn3qxd61a9fwqlWrml2O2RSrzteRSFOJsoJktWkoxhhjjGkbHXQ/046oiMwHBhErW85TXy3n9ncWEIp8/x6/MhT9/gkKP7r/c7J97u3HfB4XvzlyKGdMGFB7qExElhJbCZm0srIy1+WXX772hhtuWO9yudh9991DP/zhD7dMmTIld9CgQdU+n6/euH6/3wmHw2n/H/R///d/XQsKCiL33XffaoBRo0YF169fv/r222/vde21126oqqpy+f3+hrFoKBSKmy3//Oc/73fEEUdsPeKIIypefvnlvFTjqqysdDX8OwgEAhoOh8VxHFyuppP1WbNm+S+66KJBJ5xwwqYDDjigsskLG7jxxhtXHXXUUeUAv//971dff/31/e67777Vc+bM8d9///09vvzyy3mJjFNdXe0KBAL1Yvf5fBoMBlO6w2BJVAci2aejwY9oNBslWeDdMxMhGWOMMaYVdfIS52FgIdBTVfucMWEAo/oUctkzM1i6seltCrWJ1aDiHP5++lhG9ilAVRGR1UBSS9Ua6t+/f+TnP//5phtvvLHHzJkzsxYtWpQ1f/78rNGjR1f4/X4tLS2t9z8jGAy6AoFAyjM7TZk3b15gyZIlWdnZ2WNrj6kqoVDIVV1dLX6/36m7rK0mFsnKyoo2HOull17K//TTT/Pmzp07p6VxBQIBJxQK1Xvd6upqCQQCTnMJ1Ndffx344Q9/OGSXXXapfuKJJ5Yn85qHHHLI9lmrfffdt3Lbtm3uVatWec8777yBV1xxxdqhQ4eGmnt+LZ/P5zRcRhgKhSQ7O7vR31kiLInqQMS/D5p7IZT/C6S2qIkPKXoIEXezzzXGGGOMaafWiUgZsMuovgW+1y89gD++MpsXp69u8gknj+vLn44fQY7fAxASke+AFm8QX7p0qXfvvfceNnz48MrDDz+89OKLL9746quvFnz11Ve5ffr0Cc2dO7fe0p9169Y1Wt6WDpFIRPbee++yBx98sFHC4fV6tVevXuH169fXq3C3fv16b48ePRrF8uSTT3apWco2GsBxHAHIzs4e+9///ndR7SxPIvr06RMuKSmp97pr1671FhcXN/l38PHHH2cff/zxQ4YMGVL13nvvLcrOzk5qltDr9W6/PhKJbXsKBoMyffr03Llz52bfdtttfWqOuWbNmpUzderUnI8//nhRw3Fqll7Wi33jxo2NlkUmqt0tkDXNc+X+Eun2AZJ/E1J4D9L9U8Q7PNNhGWOMMaZVCFF1peXRzlUAc4HNOX4Pd/54DIfuHrfgG4fu3p07Thldm0BtrnleWipsPf3004U5OTnO5MmTF1933XUbjjrqqPJly5b5VVX222+/innz5mWXlpZu/8v88ssvc8ePH5/26l5Dhw6tXrp0qX/o0KHBkSNHBkeOHBn8+uuvs//85z/3dLvdTJgwoXzatGm5tYUmHMdh2rRpuRMmTGgUy7333rvqm2++mTNlypS5U6ZMmXvHHXcsB5gyZcrcAw44IKnYDzzwwPINGzZ4FyxYsH0f0WeffZY7duzYuOPMnTvXN2nSpMHDhw+v+N///reooKAg6Vm7r776anv/qy+//DKna9eukQEDBoS//fbb2V9//fXc2q9r2LBhlWeeeWbJY489tizeOGPHji3/7LPPthcBWbx4sXft2rW+Aw88MKX/f+3+J8o0Ju7uSNaxiP8gRNJaZt8YY4wx7YgCDq60PDqAKLAUCAH19kbVVed4qOb6lJZjxdO1a9fohg0bvC+99FL+3Llzfddee23Pd955pygUCskxxxxT1rt379Cpp546cOrUqYFrr7225zfffJPzy1/+sgRiy9pWrFjhqZ0taYmLLrpoUygUcp122mkDp0+fHnjppZfyr7rqqv61FejOPvvsLZWVla5zzz23/7Rp0wIXXnhhv/Lycvf555+/GWKFKVasWOEB6NOnT6Q2ERs5cmSwb9++IYCRI0cGc3Nzk5oVGj58eOiAAw4oPfPMMwd99dVXWffee2/XV155petll122AWIzRStWrPDULpv72c9+NiArK8t56KGHlm/ZssW1YsUKz4oVKzzbtm1L+Bvy6quv7vfxxx9nv/LKK3m33npr7/POO2+D3+/Xul/TyJEjg36/3+nSpUt00KBBYWj8/+OSSy4pefXVV7vceeedxVOmTMk666yzBh144IHbRo0alVL5+Q7xE2WMMcYYY3YKAcC3tTLEl99tAkAEdinOQWp2s3z53Sa2VoYAfDXXp80FF1yw+YQTTth0zjnn7LLPPvsM/+ijj/L+9Kc/rVy6dGmgurpaXn755cUbN2707r///sOfffbZrk8++eSS2j0577//fu6AAQNGL1myJKVqb3UVFRU5r7766sKVK1f699tvv+EXX3zxwB//+Mcb//a3v60G6NKli/PSSy8tmjp1as5+++03fMqUKbkvv/zyotry6//+97+7DBgwYHRL44jnmWeeWZqfnx896KCDht1+++2977777mWHH354BcCSJUt8AwYMGP3+++/nbtmyxfXJJ5/kr1271jdixIhRAwYMGF37uO6663om+nonnnji5hNPPHHwueeeu8vpp5++8dZbb12742c1/v9x+OGHV9x1113L77zzzl4HH3zw7nl5edGnn356WUp/CcTKS6b63HZj/PjxOnXq1EyHYYwxxphOQkSmqer4TMcxdI+A/vPVATu+MAGHDVrYpl/TzJkzl40ePXpjkk/rBfR+fupKrn5hFt3z/Nz14zEcMLiYTxaVcOVzMykpC3LHKXtw8rh+AGuAhN5Um45lwYIFvt13333Ut99+O7tuX6q2NHPmzOLRo0cPjHfOZqKMMcYYY9op1Z1mT1StIoC3Zq/jkKHdeevyiRwwuBiAiYO78fblEzl4aDfemr2u3vXGtDWrzmeMMcYYY9oDP5AVDEc5dPfunLXP9hm4UmAV0Ldrrj//0fP25okvlxMMR/F73Vk1z8vITEUqVq9e7Rk8ePCo5q5ZuXLlrK5du6Ztr1dTJk+enH300UcPbep8Xl5edP369bNa47WLiopGN9ej6aOPPprfGq+bLpZEGWOMMca0Y07n7RPVUBGA3+vmrH0G1PZ+WgWsrzm/COihqn3rJFgAhXWuafd69OgRmTJlytzmriksLGz1BApg7733rmouFo/H02r7fj777LN5teXW49ltt91CqjqttV6/pSyJMsYYY4xpp2LNdjvMUryWqrs0L1jT+6mywTXra3tKEZuBqn1eh0miPB4Pmdrj01BWVpZmKpbhw4cn1CS3vdppfiqNMcYYY0zzRKRYRJ4Rkc0islxEftVWr62qtf2Aans/NUygalXWnN/c4HnGtBmbiTLGGGOMabekrYtCvARkA0cAecBjIuKo6r0pjOU4jiMulyuhJWE1M08OsC2RsYn1iNosIjYpYNKupoJ5k82BLYnqgNQph/BMkFzw7oHITrNW2hhjjNmp1DbbbQsiMg44ANhdVRfUHPsNcBeQdBIlIuuqqqoKcnJyqhJ8ypZkX4PEEi5jkhYKhbwi0uT3l2XuHYxT8SS6YT9066XolnPQjYehkWWZDssYY4wxHd8uwJbaBKrGTKCXiAxMdrBIJPKnZcuW+SoqKrKaKyBgTHvjOI6sWbMmNxqNPtrUNTYT1YFoaAaU3QZUx25NAUSr0M3nQbf/YbPZxhhjTOcT1TbLP9YD+SKSp6plNcdqy+AVA8uSGWzPPfd8Z/r06b9csmTJ9araE7t5bzqOKPCp4zj/bOoCS6I6EK18ksZtEBR0a2x5n29sBqIyxhhjTGtRJJ3V+YpFZGqdzx9U1QfrfP4VsBL4p4j8HMgHbqg550vlBffcc893gHdSea4x7ZklUR2Js5nvp6DqElBbEmyMMcaYZm1U1fFNnVTVoIicBDwDbCXW5Pa3wISaj40xNSyJ6kj8h0NoGtBgf6aGwWuzUMYYY0xn5LRhdT5VnQ4MEZEexAo97EasQtmKNgvCmA7A1qZ2IJJ9Inj6A1m1R2If5/0KcRVkMDJjjDHGtIbaZrvpeOyIiBSJyCci0l1V16tqCDgemK6qNhNlTB02E9WBiASg6/No5YtQ/Q64ipCcsxDfXpkOzRhjjDEdnKpuEZFs4E4RuQHYE7gOOD2jgRnTDlkS1cGIBJCcMyHnzEyHYowxxphWpkhbVucDOA14AJgFrAIuUtVX2zIAYzoCS6KMMcYYY9qxtmq2C6Cqi4BD2+wFjemgbE+UMcYYY4wxxiTBZqKMMcYYY9opVYi2YXU+Y0xiLIkyxhhjjGm3BIc23RNljEmAJVEm7TT8LVr+IESXg3ccknsh4u6d6bCMMcYYY4xJizafHxaR3UXkfREpF5HlInJ1nXMDRORdEakQkXkicnRbx2daRqs/RDedCcF3ITIfqp5FNx6HRpZlOjRjjDGmw1Fiy/nS8TDGpE+b/kSJiBd4i1jX6zHAJcB1InKmiAjwCrAJ2At4DHhRRAa1ZYwmdaqKlv4RqCb2zz5ABLQCLbszg5EZY4wxHVdbNds1xiSurZfz9QGmAJeoahWwWETeBw4C1gJDgYmqWgbMFZHDgQuAP7RxnCYVziZwtsQ7AaEpbR6OMcYYY4wxraFNkyhVXQacClAz87QfcCCxGal9gBk1CVStT4GJbRmjaQFXTjPnCtssDGOMMaazUASnbZvtGmMSkMm53VXEkqQvgBeAXsCaBtesB/q2cVwmRSJZEDgK8Dc4kwU5P81ESMYYY0yHZ8v5jGl/MvkTNanmsSdwN5ANBBtcE6TxO3IAROQiEZkqIlNLSkpaNVCTOMm/Efz7A36Q3Nif2WchWSdnOjRjjDHGGGPSImMlzlV1KoCIZBMrIvFvoKDBZX6gsonnPwg8CDB+/HiNd41pe+LKRor+hUbXQXQdeHZBXPmZDssYY4zpkBRwrLKeMe1OmyZRItIHGKeqr9Y5PBfwESssMarBU3rWHDcdjLh7grtnpsMwxhhjOjghas12jWl32vrWxjDgvyLSvc6xcUAJsf1RY0SkbnWCA4Av2zA+Y4wxxhhjjGlWWy/nm0xs5ulREfk1sCtwK/CXmnPLa87dABxLrGLfBW0cozHGGGNMu2DL+Yxpn9q6xHlYRI4F7gO+AsqAe4C/qaqKyCTgYWAasAQ4oaYsujHGGGPMTsmW8xnT/rR5YQlVXQEc38S5xcQa7xpjjDHGGGNMu5Sx6nym89LIErTiMYgsBd94JPssxN0102EZY4wxHY6q2HI+Y9ohS6JMWmnwC3TLxUAYiEJ4Blr5FBS/hLh7Zzo8Y4wxpsOJWhJlTLtjP5UmbVQVLb0WqAaiNUdDoNvQsrszGJkxxhhjjDHpYzNRJn2czRDdEO8EBD9u83CMMcaYjk4BxwpLGNPuWBJl0kcCxP65j8OV16ahGGOMMZ2D2HI+Y9oh+6k0aSOuHPAfAvganMmC7HMyEZIxxhhjjDFpZ0mUSSspuBm8ewABkDzAB1nHIdlnZjo0Y4wxpsOJNduVtDyMMeljy/lMWokrH+n6FE54PkS+A+9YXJ5emQ7LGGOM6bCids/bmHbHkiiTVqpRtOwuqHoCNAqSi5P3G1zZJ2Y6NGOMMcYYY9LCkiiTVlp2J1Q+QazMOaCbofQG1FWIBA7NZGjGGGNMh6PYUjxj2iObHzZpoxqCyifZnkBtV42W/z0TIRljjDEdnoMrLQ9jTPrYT5RJH2cb4MQ/F13dpqEYY4wxxhjTWmw5n0kfV5dYrygNNj7nHdb28RhjjDEdnCpEbTmfMe2OzUSZtBFxQ+6vgECDMwEk79eZCMkYY4zp8KzEuTHtj81EmbRy5ZyBuovQ8vsgug48w5C8qxHvHpkOzRhjjDHGmLSwJMqknQSORgJHZzoMY4wxpsOLVeezhUPGtDeWRBljjDHGtGNRbCmeMe2NJVHGGGOMMe2Ugu1nMqYdsvlhY4wxxhhjjEmCJVHGGGOMMe1WbE9UOh4JvZpIkYg8ISKbRWS1iNwqIu5W/iKN6XBsOZ8xxhhjTDvmtO2eqPuBXsCBQDfgKWAT8Ne2DMKY9s5mokyHoxpEI8tQpzzToRhjjDGdzTHAPao6W1U/JJZEHZrhmIxpd5KaiRKRfsAAoIDYXYkVqrqmNQIzpiFVRSv+Dyr+AQhoFM36EZL/R0S8mQ7PGGOMSTtViLZtYYlNwJki8i5QCBwFvNSWARjTEewwiRKR7sClwGnALlBvTllFZD7wInCfqm5olSiNAbTq5VgCpVXfH6x6BZUsJP/3GYvLGGOMaU1t3CfqF8DjQBmxFUsfADe0ZQDGdARN/lSKiFdE/gwsB44n9gM1CRgNDAb2Bk4EXgOOBZaKyC0i4m/1qM3OqeKf9RMoAKqh8hlUwxkJyRhjjOlAikVkap3HRXGu2Q2YQWxP1DHAQOCONozRmA6huZmo6cDXwFhVnd/ENVOBV4DfichY4Mqa541Ia5TGADgbmzgRBa0EKWjTcIwxxpjWpkg6+0RtVNXxTZ0UkV2Be4CBqrqq5thPgfdE5BZVXZ+uQIzp6JpLos5U1VmJDqSqM4CfiMjolodlTBzeURD6ovFxVxFIftvHY4wxxrSBNqzONw4oq02gakwD3MT2xFsSZUyNJpfzJZpAiUieiBxY53kz0xGYMQ1J3m+ALOpvywtA3nWIWDd3Y4wxpoXWAIU1hcRqDav587sMxGNMu5XQTkURiYrIw03sdxoOfJjesIxpTLwjkK7Pgf8H4OoFvn2QogdxZR2Z6dCMMcaYVqGAo5KWRwK+BL4BHhGRPURkH+BB4HFVbWpNvTE7pURLnAtwJjBGRE5W1aWtGJMxTRLvUKTovkyHYYwxxrSZtqrOp6oREfkhsX1RHwAh4AXgt20SgDEdSDI/lWcCDjBNRI5tpXiMMcYYY0yGqOoaVf2xqharam9VvUy1UWlcY3Z6ySRRK4EDgFeBl0XkLxLbiOK0SmTGdDAa3YCGF6AaynQoxhhjOos0LeVLY4U/YwyJL+cDQFWDwLkiMp1Yz4C9gb+0RmDGxKNOOVr1XwjPAM+uSNapiLtbhmPaim69AkLTQDyAoHnX4so+KaNxGWOM6fiUNq3OZ4xJUFJJVC1V/ZuIzAKeBV5Mb0jGxKfR9eimE8EpB6oAH1rxMHR5EvEOz1xcWy6B8DdAGDQYO1j6J9TTH/HtlbG4jDHGGGNM60h0Od9koLTuAVX9CNgLWJbekIyJT8vuBGczsQQKIARagW67JnMxRVZBeBYQbnCmOpbgGWOMMS1ky/mMaX8SmolS1UOaOL6ipvxl77RGZUw8wQ+AaOPjkUWoU464cts8JJyNIN7vZ6Dqiq5t+3iMMcZ0KrUlzo0x7UuTSZSInJjEOAosb3k4xjRD/LHvtMYnavYiZYBnMGgkzgkv+Pdv83CMMcYYY0zra+6d5wtxjinE3d2ogDstEXUyjjp8u20Oq6tW0zvQiz0KR+GStun30OlknQoV/wfUnfXxgP9ARAIpD6vh2RCcDJIFgWMQd8+EnyuuHDT3Mqj4O2yvAOsByUOyz085JmOMMaaWzUQZ0/40l0QNinPtIuA4YHarRdSJlEfK+cvc29gc2kzYCeN1eSnw5vOH4b8n35uX6fA6HMn9GRqeBaEpUJuIuvsiBakViFRVtPR6qHqZWD9BD5TdjRbchivrmITHceX+FPXsglY8FFve55+I5FyMuItTissYY4yppdh+JmPaoyaTKFWttzxPRGpnmtY2PJcMEdmVWCfsA4AKYhX+rlXVahF5ELiwwVN+par3pPp6mfT0imfZEFxPRGP7eKJOlHAwzGPLHufSwb/IcHQdj4gP6fIQGl4AkXng7gfePYm1K0tB6AuofhWorj0Q+2Pb71D/gUntsZLAoUjg0NTiaAOqQbTyBah+C1z5SPaZiC03NMYYY4xJSZtuJBERH/AaMBfYD+gO/Lvm9K+B4cDVwBN1nlavKmBHMmXz1O0JVK0oUWZs/QZVTf3N/05OvEPBO7TF42j1a6CVcV7ADaFPIXBUi1+jPVANoZtOg8gSahNGDX6G5l6IK/eXmQ3OGGPMDlmfKGPan7benLM3sBtwrqrOU9XJwHXAmTXnhwFTVXVdnUecd7kdg2rcKgixZWTxKySYNuUi/hY/mjneAVW/DtGlfD/jBlAF5f9CoxszFZUxxphEqJU4N6Y9auskagFwjKqW1zmmgF9EegJdaq7pFMYWjcHV4K/YhTCqYKQVl2gHJGsSEKcghUbB13mWumn1/5qYcfNCeFrbB2SMMcYY08Gl8k4+5SkUVS1R1fdrPxcRF/BL4BNiS/kiwE0islpEZorIuam+VntwZv/TKfQV4nf5AfC7/OR78zln4E8yHJkBwLsXZJ9GLJHy1vwZQArvykzPqdbiKqbJH3UpaNNQjDHGJKe2T5TNRBnTvjTXJ+pb4idMz4pIdYNjqqqjU3j9u4CxwF7AwTXHZgJ/q/n8ARGpUNXn48R3EXARQP/+/VN46dZX6Cvg9j1uZurmaaysWkXvrN7s3WU8Ppcv06EZQESQ/GvQ7FO+L3HuPxJxd810aGkl2aeiVS9RfzmfgOSAb69MhWWMMSZBlgAZ0/40V1hiGo2TqLSs/ZFYRYV7gF8AJ6vqHBGZCzytqptrLpslIoOBnwONkihVfRB4EGD8+PHtdoOR1+Vl3+J92DfTgZi4VMMQnoOGpoDkIJ7doLMlUd7haP4NUPqnWNEMHJBCpMtDfF900xhjjDHGJKq5EufntsYL1izhe5hYMYlTVfWVmtdTYHODy+cBR7RGHMaoRtDN50H4W6AKEDT4PzTnYly5nasEvSv7RDTraAjNBFcOeEZadUhjjOkArE+UMe1Tc8v5DlTVj5MdUEQOVtWPmrnkTuAM4ERVfb3O8+4EhqrqsXWuHQvMTzaGdIg4Ed5c+zYflUwm5IQZWzSGk/ueQIHX9pB0GsH3ITKbWAIFoKBVUH4/mnUK4u6WyejSTiQL/PtkOgxjjDFJUkuijGl3mlvOd5uIbABuUdUvdzSQiBwKXAV0BSY0cc0+wBXANcDUmop8tV4FPhCRy4A3gKOAs4HDEvg60u6+xf9kzra5hDTWgPWzks/5duu33LLHn8lyZ2UiJJNmWv1+/Kp1eCH0FWQdG+ecMcYYY4zZ2TWXRO0H/Ap4S0S2AS8B04GVQCVQAPQF9gV+UPP5bcAdzYx5cs2ft9Q86vICpwN/BG4HvgNOV9VPk/h60mJ11RrmlM4hpOHtx6JEqYhW8tnGLzi8x6FtHZJpDVJArGqd0+A4saILxhhjTDtgzXaNaX+a2xOlwF0i8iBwIXAasXLkbmIFJ4TYu88pwD+AB1W1tLkXU9WriM1WNeW5mkdGLatYjgs3EK53POSEWFi2yJKoTkKyT0Grnqd+1ToAL/g7T58oY4wxHZeqVeczpj1qbiYKgJrGuHcDd4tIHtCP2KzTJmC1qla0bohtr5u/GI1T3d0jHnoFesZ5humIxLs7mn8dlN4E4qGm73NN1TorQ2+MMcYYY+LbYRJVl6qWAXNbKZZ2Y3DubhT7urKuej1RotuPu8XNwd0PzGBkJt1c2aeggaMh9DVINvjGIZLUj4UxxhjTqqywhDHtjyvTAbRHIsLvhl3NiILhuMWNR9z0DvTid7tfTZGvKNPhmTQTVy4SOATxT7AEyhhjTDsTK3GejocxJn3sHWMT8r35/HroFVRFq4g6UXK9uSmNs7xiBS+vfpWVVavok9WbSb2PY5fcQWmO1hhjjDHGGNNWLInagSx3VqyURgoWli3irwvuIuyEUZSSYAlzS+fxqyGXMTx/WHoDNcYYY0ynZMv5jGl/bDlfK3pqxTOEnFC9IhUhJ8QTy5/KYFTGgGoEjSxBo5syHYoxxphmKNhyPmPaoaRmokTkAGLNb3sBNwMjgRmqurYVYuvwVlSuiHt8ddUaHHVwieWwpu05Va9B6Y1AGDSC+iYghXchroJMh2aMMcYY0yEk9C5eRLJE5FXgY2INeC8Eims+/kZEbG1aHDnu+PuostxZlkCZjNDQDNh2Leg20EogBKEv0S2/zHRoxhhj4tFYr6h0PIwx6ZPoO/nbgAnARGLJU+2c8FnAauCW9IfW8R3d80h8rvr9hnwuH0f2+EGGIjI7O614CAg2OBqG8DdoZGUmQjLGGLMDDpKWhzEmfRJNok4Dfquqn8H3G3xUdT1wE3BAK8TW4R3V6wgO634IXvEScAXwipcDiycyqc9xmQ7N7KyiayBOI2nEC86GNg/HGGOMMaYjSnRPVA7Q1DusKiCQnnA6F5e4OK3/j5nU5zg2BTfT1d8lVu3PmEzx7wuRhUC4/nENg2doRkIyxhjTNMWq8xnTHiU6E/UlcLnU70Raezv7fGBKWqPqZLLcWfTN7mMJVCemzla08im0/O9o8Eu0nS4+l+zzQHKpf/8kC3J/jrhS64VmjDGmNVmzXWPao0Rnon4DTAbmA+8SS6B+ISK7A+OBQ1snPGPaPw1NQ7dcAOoAQZAs8O4JRQ8g4s10ePWIuxsUv4KW/wtCn4CrC5LzUyRwZKZDM8YYY4zpMBJKolR1mojsDVwHnABEgR8BnwL7q+r0VouwE1FVlpQvYUNwI/2z+9E3u0+mQzItpOqgWy+tqXRXe7ASQtPQyv8iOadmLrgmiLsnUnBDpsMwxhiToHa6uMGYnVrCfaJUdS5werxzItJbVdekLapOqDxczq3z/8qGYAkCODgMzxvGpYMvweNKql2XaU8i80Gr4pyoguoXoB0mUcYYYzoW2xNlTPuTaJ+oqIjs1cS5icDCtEbVCT289BHWVK0l6ASpdoKEnDBzy+bx2po3Mh2aabGmbhFaLzBjjDHGmM6oySkQEbkJKKj9FLhKRNbHuXQcUNEKsXUaISfEzG3fEiXa4HiYj0o+5oS+kzIUmWkxz+6xQg11l/MBkIVknZKRkIwxxnQesUa5bTMTJSLnAo80cXqAqq5ok0CM6QCaW0e2HLi25mMl1mi3YZfOKLAV+HnaI+tEohpt8lzICbVhJCbdRFxQ+A90y3mgUSAE+MC/H2SdkOnwjDHGdAJtWFnvWeDtOp+7gNeApZZAGVNfk0mUqj4EPAQgIkuBE1T1mzaKq1PJcmfRO9CblVUr6x134WJs4egMRWXSRXyjodvHUP02OJvAtzd4xyBia9iNMcZ0HKpaRaz/JwAi8kugP3B4xoIypp1KaNOGqg5qLoGS9lbHuR366S7nEXAF8NS02vK5fOR58zil30kZjsykg7hykeyTkdyLEd/Ydp1AaWQxzuaf4qwfg7PhQJyKR1B1Mh2WMcaYJsSW9LX8kQwRyQOuB/6oqlta4+sypiNLqCxcTZJ0EXAQ4Ce2R4qaP7OBsUCX1giwsxiYM4Db9vgLH26YzJrqteyWuysTi/cn25Od6dDMTkQjK9FNp9Ts4dLYn2X3oJEVSMH1mQ7PGGNMHBmqzncxsW0cD2XixY1p7xKtrX07cDkwC+hBbKq3BBgF+IAbWyW6TqbQV2hFJExGacXDoEHqVxSsgqoX0LxLEZfdCzHGmPZEkXQmUcUiMrXO5w+q6oMNL5LYcoqLgb+rajhdL25MZ5JoEvVj4DZVvUZErgHGq+pJItIb+ACw5XzGdAThb4BI4+Pig8hS8FkS1RTVIFrxCFS9BChk/QjJOR+RQKZDM8aYRG1U1fEJXLcnsCvweCvHY0yHlWgjm27AOzUfzwQmANQ02L2ZWJJljGnvPLsR98deQ+Du0+bhdBSqim4+D8rvh+hSiC6D8n+hm8+x/WTGmFanaXok4Wjgq5r3ecaYOBJNokqA/JqPFwK9RKRrzefLgb7pDswYk36S81Ni2xrr8oP/YMTdMxMhdQyhLyEyF6iuc7AaIgsg9FmmojLG7Axq+kSl45GEfYDJ6QhfRD4RkaPTMZYx7UmiSdQ7wA0iMgJYAmwALhERN3BKzefGmHZOvLsjRQ+AexDgBvyQdQJSeEemQ2vfwjNBqxsf18rYOWOM6VxGAnPSOFacf0CN6dgS3RP1e2KJ1N9V9VAR+QPwIHAdsUTsylaKz7QyVeWbrTP5cMNkQk6IfbpO4IDi/fC4Ev3WMB2N+PdBur2DOpUgPkQ6//9r1RBUv4uGZoCnP5I1CXEVJj6AuwcQACobnMgGV4/0BZpmGlkBkSXg2RXx9M90OMaYVCW5Fi8NegCb0zTWI8CfRGQrML+mF5UxHV5C755UdZ2IjAH61Hz+sIgsJrY36mtV/bD1QjSt6emVz/LRho8JOkEAllR8x2cbP+d3w67GLe4MR2dak7h2jvL66pTGyro762tKuwfQ8nuhy5OId1hig/iPBLkZVKj3bkY8EGh/q1RUg+jWyyD4OYgXNIz6JyKF9yDiy3R4xpgktXWJc1XNSuNwE4HRwFQAEalo/HJakMbXM6ZNJNon6hPgZlV9q/aYqk4mTetlTWaUBEv4YP1HhOtULw05IZZXrmDGlm8Y32VcBqMzJj20/D6IrgZCNUeqY3sMtv0GKX4toTHElQ1dnkK3Xg7RlbGD7r5I4d2IK7dV4m4JLftrLIEiWFPSHgh+ipbdjeT/NqOxGWN2Oq/XPIzpVBJdx2PrWTuheaULcImr0TKBoBNk5tZZlkSZzqH6Tb5PoOqIfIc6mxPujSXewUi3N9HoWkARd++0hplWVS8Q65FZVzVUPQuWRBnT4WjbL+dLG1X9U6ZjMKY1JJpE2XrWTijXk4PQeImAGzf53vw4zzCmI2run7nkl6yKu1fqobQBVYWm/om2f7qN6XCUtl/Ol24iUgBcChwG9AROBo4FvlHVd5p7rjHtVaLV+SYSK3c5FSgXkdIGj22tF6JpLaMKRsbd9+R2uTmw2wEZiMiYVpB1Eo3LurvAuwfi6nzL8EUEvGPjn/Ql0mPTGGPSR0QGAt8SK0JWCgwh9o/yHsDrVv7cdFSJzkTZetZOyOvycs7An/DPJQ+gddb0HdbtEHoE2m/FMWOSIbkXo6Gva0qRR2OFFiQPKbwz5TFVFcIz0Or3QAJI1nGIZ5f0Bd1Ckn89uvmMWBNlwoA3Vokx77pMh2aMSZZSU9Smw7oXWEtsFqqamvXVqnqmiHiBPwJvNf10Y9qnRKvz2XrWTijiRPjP8ifqJVAA/yv5kEN6HEyPQPcMRWZM+oj4oMtjsSQq/C24+4D/wJRLu6sqWnodVL1G7P2AG614GM37La6cM9Mae6rEOwyK30Ar/gPhueAbiWSfbQ2VjemgOvKeKOBQ4CxVLa/pL1rXA8DLbR+SMS3X+RvEmCbN2jabiBNpdDzqRPm45BNO6XdSBqIyJv1EBHxjYo+WCk+tSaBq9xdFYo+yW9HAUYi7a8tfIw3E3RvJ/12mwzDGmBDQVMn0LjSugmNMh5DonijTCZVHyhvNQgFEiVIaLs1ARMa0f1r1JnGLlYobQtb1wRjTCjRNj8x4A/iziAyuc0xFpAtwDWCFJUyHZDNRO7Hd84biqNPouN/lZ4/CURmIyJgOQLxAg6a7sROAt+3jMcZ0ctLRq/P9GvgImAMsqjn2MDAI2AxcnZmwjGkZm4naiXUPdOPgbgfid31fuczn8tE/ux97FjVR3cuYnZxk/QjwNT6hUfAf3MbRGGNM+6aqJcA4YiXOZwHvA0uAG4Axqromc9EZk7o2n4kSkV2Be4ADgArgWeBaVa0WkQHA/wH7AyuAK1XVKra0ojMHnM7wgmF8sOEjQk6IfbpMYGK3/eOWPjemM1ONQOgziJaAbwzi2S3udeIdjuZeAuX3EZt9cgEOFN6NuPLaMmRjzM6iYxeWQFWriRWReCDTsRiTLgklUSKylKZ/hB2gHFgM3KeqHzUzjg94DZgL7Ad0B/5dc+4q4BVgHrAXcDzwooiMUNWlicRpkici7Fk01maezE5NIyvQzWeCloM6gKKBHyAFt9O4mBS4ci9Gs46D4GTAD4HDOmXPKWNMO6Adr9muiPwNuENVV9R83BxV1cvbIi5j0inRmainiDVJ20Zsg+B6oBtwFLHO0y8A/YH3ReRYVX27iXH2BnYD9lbVcmCeiFwH3FUz7lBgoqqWAXNF5HDgAuAPqXxxxhiTCN36S3BKiN0TqlH9Pup9Ack5Ne5zxN0bsk9vmwCNMaZjOY7YvqcVNR83RwFLokyHk2gS1R34AvihqtbW9a2dWXoF2Kqqp4vIv4DrgKaSqAXAMTUJVC0l1rl6H2BGTQJV61NgYoIxmk5KVVlS8R2Ly5dQ5C1kbNFYfC7bwG/SQ6OrIbKUegkUAFVQ9RQ0kUQZY0yb6WDL+VR1ULyPjelMEk2iTgHOqJtAAahqqGaa9mng58DzQJPdJms2F75f+7mIuIBfAp8AvYCGmwvXA30TjNF0QhEnwr2L/s6CsoVE1cEjHjzLn+T3w35Ln6zemQ7PdAYapMkaO2rtS4wx7UHHWs5nzM4g0ep8QWLL9eIZQKzbJICbWFO1RN0FjCXWJyCbxg3XgsRmqRoRkYtEZKqITC0pKUniJU1H8sGGD5lftpCgEyKiEaqdasoj5dy36P5Mh2Y6C/dAiFsQwg+BH7Z1NMYY0+GJSJmIlCb6yHS8xqQi0ZmoZ4FbRaQCeEVVS0Ukn1jxh1uAp0UkB/gZMHVHg4mIEKvQ9wvgZFWdIyLVQMOd2X6gMt4Yqvog8CDA+PHjO9hEt0nU5JJPCDmN8/KNoY2UBEvo5u+WgahMZyLigoI70a0XxcqUEwLJBncfJOe8eteqUwrBjwEF/4HtupiEagiCH0DkO/DsCv5DEbFlsMZ0SB3vXc6ddMSojUlCoknU1UAx8BixLtNhYl0lFXgSuAqYBBwC/KC5gWqW8D1MbNnfqar6Ss2p1cDoBpf3BNYmGKPphOI1A44Rok2eMyY54p8Axe+glc+Dswbx7QuBo4ht+4xxqt6Ebb8FqflnUyNo/l9wZR+foaibptESdPOPIboFqIolha4u0OU5xN010+EZY5LVwdIRVb0h0zEY09oSSqJUNQicISI3AAcBXYntX/pMVZcAiMi7QB9VjTtzVMedwBnAiar6ep3jXwK/F5EcVa2oOXZAzXGzk9qv6768suY1whqudzzfk08Pf/cMRdW8WVu/5f31H1ARrWTvLuNjDY3dcVelmnZE3D2RvEvjntPohlgCRbD+PqnSa1H/3oi7Z9sEmSAtvRGi69m+0lorIBpEy/6CFN6V0diMMTsfESkmtnd+ItAF2AB8ADzQoKCYMR1GUs12VXUhsLCJc5t29HwR2Qe4gtgeqKkiUvedx2RgOfBoTbJ2LLGKfRckE6PpXI7s9QOmb53B6qo1BJ0gPvHhEhe/2O1iYqtC25f/rnqZt9a9s30J4orKFXxS8il/HPEHqyjYkVU3VXA0gpbeBPnXI+72kdSrKgT/x/dbVWtFoPq9TIRkjGkJBTpYn6i6RGQ0sYTJT6zS80JiVZ//BFwqIhNVdUUGQzQmJYk2280h1qvpGCCHOAUpVHWXBIY6uebPW2oedXmJLQl8GJgGLAFOUNVlicRoOiefy8d1w3/PzK2zWFi2iC6+LuzbdQK53txMh9bItvA23lz7FmH9/s1ryAmxPriBLzZ9yUHdrFp/xxUConGORyH4AVryMVpwC66sY9s6sCR1sDVBxhgAtGP/6N4LLCbWJmdj7UER6U6sR+g9wImZCc2Y1CU6E/UP4HTgdWAVjRuqJERVryK2f6opi4ktFzRmO5e4GFs0hrFFYzIdSrMWlS3BLZ56SRTEEqnpW2ZYEtWR+Q+GsntpPLsDseQqCtuuQf0HIK7CNg2tIRFB/YfGikrUi9cDgWa3rBpjTGsYT2wP/Ma6B1V1g4jcBDyRmbCMaZlEk6gTgF+r6n2tGYwxHVmuJyfucRcuCr2FbRuMSSvx7IZm/wQqnwSqmrjKDcHJkDWpLUOrRyNL0fL7ITw3Fg8eILi9sITk/aFl46uCbgXJQiSQhoiNMQnp2DNRK4GBTZwrBqxPjemQEk2iIsD81gzEmExQVeaVzWf65hkE3AH2K96X3lm9UhprSN5gsj3ZBENBtM5vPI/Lw2E9DklXyCZDXPm/QQM/QLf9EaILmrgqc+90NLwQ3XQyUF3nqBv8xyBZR7W4xLkGv0BL/wDRdbHPA0cg+Tchrva3tNaYTqcD74kCLgOeEJEQ8GxNm5wAsb3vtwBXikiX2otVdXOG4jQmKcn0iTofeL8VYzGmTakq9y95gJlbZxF0grhw8fa6dzlrwOkc3D35VaUucfHb3a/izgX3sC28DReCg3LOwLPon92vFb4C09bENxYKbkQ3n0vjGako+DO3GllL/0D9BAogCqFPoPCuFhVi0chidMvPqPc1V7+HOpuRLo+lPK4xZqfwX2JFJR4A/lXTF7R2KluA/zS43t2GsRmTskSTqKXEyo/PAqbQuAGuqurlaY3MmFY2a9u32xMoAAcHRx2eWP4047uMI9eT/B32noEe3L7HzayoXElVtIpdcgfhc/l2/ETTYYhvLJp9GlQ+DYSJ/b4XyL8JcRVlLrDwt/GPaynqbELcxSkPrRWPECuuUVcIQjPQyHLEMyDlsY0xOyYdeznfpXT0BYnGxJFoEvULYCuQBxwW57wClkSZVrGk/DueWvEMyyqWk+vJ5ZheR3FEj8NbXOL8q01fb0+g6nKLmznb5jKh694pjSsiDMjp36LYTPvmyr8GzToxVrxBfBA4BnGntgw0fZqr9xOvsmASIkvijyFeiK4CS6KMaT1Kh05BVPXRTMdgTGtItNnuoNYOxJh4VlSu5Nb5f93ed2lreCsvrPov28Lb+HG/k3fw7OZ5XV4Eqbd/qZbHlVQLtbRTVb6rWMrKypX0CPRgaN4QXNKos4DJIPEOBe/QFo2h0fVo1X9j+4y8u0N0K2gZ4p8Ivn2Su1Eg3UHXxznhRlxd4hxPID6NxHpkOZuJrbpp8LOiQfAMSWlsY0znJSJ/A+5Q1RU1HzfHVjOZDqnJd4o1m/y2qqpTd8NfU2wjoGkNr6x+lbATrncs5IR4d937HN/7WALu1CuETey2P59v+mJ7gvY9ZWT+iJTHbalgNMgdC+5meeUKFMWF0MXXld8P+w153ryMxWXSS0NT0C0XgkaBUM12o1iiolVPgW8fKPwHIgluD8j/I2y7jPozRm7I/mlKBSVUo+iWn0J4Bmi8ioRZkDUJcXdLeuzvX0Mh+h1oBDyDEbtRYEwc0hELSxxHrO/nCuB4mp9Ls9VMpkNq7nZ7CbAvsT1QG9nxZLJtBDRpt6JyZdyZIre42RjcRN/sPimPvVvurhzb6xheW/M6Ii5cCApcNviX+N3+FkTdMv9d/TJLK5YR1u+Tx/XV63lk2X+4bPAlGYvLpI+qg279VZzkpOZ7XSsh9CVUvwUJNvB1Zf0AR2+GsttASwEf5JyH5F6aWpDBDyD8TfwEytULci5Ass9KbWxqi1X8AqLrQSRWhr3gLsS/T8pjGtNpdbDlfHVXMKnqwAyGYkyraS6JOh9YUvPxeW0QizGN9M7qxYZg4xYSUY3SxdfyTfyT+hzHAcX78e222fhdfsYUjSbLndXicVvik5LP6iVQAFGifLN1JhEnkvGlhiYNot+Bljd/jVaiVa8gCSZRAK7sE9CsSaAVINmJz2LFe/nq92PJXEOSg+RdibSgH5ZqCN10FugWQGv2fFSiWy+G4ncRd4+UxzbGtC8i8gHwC1Vt1CpHRPYA/qOqY9o8MGNaqMl3Y6pat27tCGK1/ae1fkjGfO/43scxt3R+vSV3PpePA4r3I9uTnZbX6OrvmlJJ89YS1fhFAFQVp6PdjjRN8IIm8P9Skk+YRVwgeWjwI5yyuyC6AtwDkLxfI/4DEx/IlQ+4aFywQkBa2Bsq+CEQpPEeqyha9RKS+7OWjW9MZ9PB/ukXkeP5/j3mwcDxIjI8zqWHA7u2VVzGpFOiC9AvBjJYu9fsrHbN3YXLB/+SnoGeCELAFeDIHj/grAFnZDq0VjOmaDSuOD+abnHzyNJHWVO1NgNRmXQSzwBw9yG2B6qpi7KQrNSKpzhV76JbLoPI/NhsUmQeuuWXaPWHiceYdQoQrzy/C/wTU4rr+wA31ewFaygUW96XIlVFQzPR6nfQyKrU4zOmvdE0PdrOocALNQ8Fbq3zed3Hz4C/t2lkxqRJorc5vwBOEJEPVZu4TW5MKxlZMILb9vgLESeCW9wtLm3e3p3W7xTmly6gKlpVrwR7WMN8uWkK07bM4PfDfsvAHCsr3ZFJ0T/QzWeCVoOGifVhctc8XBA4HvyHpjZ4+W00brxbjZbdigQOSSw+7xA0/zoovTFWyhwFvEjR/yHSwt5n3nFNvGh2ynuiNLoJ3XIuRFcCLtAQmnUskn+zFawwpu39Brib2J2i74ATgRkNrokC21S1rI1jMyYtEk2ilgM/BU4VkcXAhgbnVVVTXyBvTAJ2lr1ARb4ibtvjL3yx8UueXfkCVc73G/sdHIJOkKdXPMs1w36TwShNS4lnF+g2GYIfg7MR9eyORJeCUwq+/RDv4JTGjVW8Wxn/ZHRFUmO5sk9BA0dBaApIFvj2SqnSX734wnPRymfBVQTORmINiwEC4NkN/PFaESYw7raravpZRb4/WPUW6hmJ5KReAMOYjFM6XHU+VQ0Re++IiAwC1qg22OxrTAeX6LvSIcDndT63OsvGtKKAO8AB3fbnseVPxD2/pPy7No7ItAYRHwQOj30MwJg0jCmoq7gmQWnAlUI5cskF7wgQX4sTKKfiaSi7hdismwP4QHLA1S9WLj3nTCSFfWDqbI0lenUTKACqoPIJsCTKdHDSRkvxJPZDfhtwNrF/lp4DrlDVxp3pmx/nyjjHmrpcVfXuJEM1JuMSbbab2PoPY0zauMWN1+WN08cKcjw5GYjIdBg5v4Cyv1LTfKpGFuT+MqlhNDQd3XY1RDcAinpHI4V3pVQ9T51SKLuZWEGJWiFQN5J7QYuq/cXKsDexZE8rUh/XmJ3PX4EfAZOIzYE9BWwC/pDkOHckca0SW/pnTIfSXLPdPYF5qlpV83GzVHV6WiMzZifnEheHdD+IDzdMblSd8OieR2YwMtPeSfaZKBEo/0dNufNcyL20plhEYjS6Dt1yfv0y5+Hp6OazoPid5PcZhabG9lY1uqFdhVa/1bIkytUTXF3BWdPghAf8h6c+rjHtRRvMRIlIIfBz4FhV/azm2A3AqcmOpaq2EdF0es3NRE0F9iHWbHcqTf8IS805a7ZrdnoRJ8Kra16vSXyCjCgYwen9T6Wbvzil8X7c92QqIhV8tWkKHpeXiBPhkG4HcURPe2NomiYiSM65aPbZNUlUTtJJj1Y+B9pweVwUoqtjs1O5lyOe/kkElU38XyMC0rIV4iICBbehWy4itscqAgTAVYDkWoNqYxJ0ALHp6/drD6jqo8CjGYrHmHatuSTqXGBuzce2nM+YBPxjyb/4duvs7c1yp2+ZwYKyBdw66i/keZN/o+hxebhwlws4rd+P2RjaRHd/d3LS1B/LdH61PaNSEl1ObO9SQxGofgOtfg8tuANX1hGJjecbDxKIs7wugGQnfaO7EfFPgOLX0IqHIPId+CYiOWcgLtvCa0yCdgWWAaeLyLVALvA88PuaQhEpEZGl7GAuTVV3SXV8YzKluSTqbmAJ8BlwDnCTqi5tk6iM6YDWVa2rl0ABKEowGuLDDZM5vs+xKY+d581LKQkzOx8NL4TIHHD3A++41FsCePeG6vepv6+qlgNUQ+lv0cBBiPh3OJyIB4oeRjefx/bkTCOQewniG59ajDXUKUejy2PLF4Mfx5YNRr5FxY3k/rRFYxvTHrRRYYk8YBBwKbH+oHnAP4m9V7yiBeO+QuMkKhfYD+gO3NyCsY3JmOaSKD+wr4jMJzYr9ZSIbGvqYlXdnObYjOlQVlatwiPuekkUxPo7LalYkqGozM5CNYRuvRSCX4C4Ym9ZXHlo4EjEOxoCRySU7NSS7OPRygdqmt82VZlYIPQN+CckNqZ3OHT/FEJfgpaDd2/E3TXhmBpSjaJlf4HK54kt4atpY1h707z876inDxI4OuXXMKZdSF+J82IRmVrn8wdV9cGajyNAPnCWqi4BEJGrgMdF5EpVdVJ5QVW9It5xid3heQZIYl2wMe1Hc0nUy8DtxEpdAryzg7FsT5TZqfUI9CBK498xHvHQN6tvBiIyOxMtfyCWQFH9/T1fpxIq/4NKNpTdAV2fR9zdExpPJAu6voiW3w+Vz1C/qt72V4UkErPYuF7wT0zqOU3R8r9B5QtNxAZQhZY/YEmUMd/bqKpNTf2uASK1CVSNBUAA6AasT2cgqqoi8gCxMuq/SufYxrSFHe2JegboCjwC/IXY8j5jTBz9s/vRP7sfyyqWE6mzId8tbg7t3n62FS6vWM6TK55hcfkSst1Z/KDH4RzX+4e4kq22ZtqXqueA6vjntBI0iJb+GSn6W8JDiqsIyb8W9e2Lbv0VjZb2STZ490g55JZQVaj8D01+zbWckjaJx5hWo7RJdT7gC8AjIqNU9duaY8OBMmJlzlvDcBLvWWpMu9LkN66qRoDXAUTkYOAR2xNlTPOuGvorHl36OFO3TMVRpV92X84bdA5d/V0yHRoA66vX85d5txF0YnfuyyLlvL72TTYGN3HBLudmNjjTMjvshRmF4Aepje0/BLJ/XDMj5YotF8SDFD2YfKnztIkk0APKBb69U34FVYXwzNjD3RP8h8QaJBvT1togiVLVRSLyCvCIiFwMZAO3Av9X854wJSIS786NC+gN/JBYLypjOpxEm+2e19qBGNMZZLmz+PluFxFxzsfBwedqX2+43lj7NmGn/v6WkBPii01fcEq/E8n35mcoMtNigUOh6hW27wuKK7V9FSISm5HK/gmEvgJXAfgPznBC4eH7DhvxuEGykNzLUxpdNYRuuRhC04ForFiFBKDL04hnYGohG5OiNiosAfAT4F7gA2J7pB4DftfCMY+Lc0yBUuAuYiudjOlwbArVmFbgcbXPH61lFctw4u3bcnlZV73ekqgOTHJ/jQY/BaeM+BX1PBA4KuXxVRWiK9HId+Dqgni3gLtHyuO1XIRmb89n/QjJ+Xlyvazq0IrHIDSN7csFNQRaiW69Ail+OaUxjWnvVLUMOL/mka4xB6VrLGPak/b5Ts8Y0yr6ZfdlZeWqRolUxAnT3d8tQ1GZdBB3Nyh+G616OVb9LvRVTZW6cKz4g6s7kv/7lMZWjaBbfgbhqbH9VfjQ8r9D0d8R/0Hp/DKS4CFWzyjezFsWroJbWjZ81fM03m+lEFmMRtcjGU0gzU6n7WaiWo2I5AOF8c6p6oq2jcaYlrMkypidyDG9jmbK5qmEnO/7JnrFy7iisRT6CjMXmEkLceUiOWdBzlmoRiD4CUSXgHtX8B+ISIpFVKtfg9DXfD/DFfv+0a1XQvcvEl7Wp+qglU9B5eOx/Uz+Q5Dcy2IJYNLCNL10cUf7wxLR1NjSzLnmqUag+vVYoosPyT4Z/D9IvZeX2Xl04CRKRMYDjwND4p0m9tVZhWfT4SSURInIU8ADqjq5leMxxrSiPlm9+c3QX/Of5U+wonIlfpePQ7ofzCl9T8p0aCbNRDwQOARoeWVIrXqF+EsEawov+PZKbJzSP0DVG9+PVfUiGvwAit9EXAXJxaTN7YlKQ7GLwHFQ8X806pHl6gmuXkkPp+rEZvPqJKMa/goCxyMFN7U8XmParwcBL3AhrVflz5g2l+hM1A+B/2vNQIwxbWNw3m7cNPIGHHUQJOW74KrKsorlBJ0gu+QOandFNEw6NfWrIvEbyBpdA1WvUX+WKAJOGVr5LJJ7UVIRiUTQJm/PpzZTBKAahuD7EJ5L3CbDgQNT+5kJfRFbDlk3GdUqqHoFzTkH8eyWasimkxNt08ISrWEocLqqvprpQIxJp0STqNeBS0TkG1Xd0poBGWPaRkv6Qq2uWsOdC+6hPFKOSwRHlfMGns2+xfukMULTXkj2KWi95Xy1/OAdndgg4TmxCneNSrFX18zOJJdEqTaXyKT2jlM1iG7+CUQW1uz9iqPqRTTvt0lXJtTgx02MqRD8HCyJMs1p9vu93ZsG9Ml0EMakW6JJVDax2agTRKQE2NDgvKpqgr9Jd25zSzawYts2hhV3Y0BhYabDMTupkBNmTdUa8jy5dPV3Teq5UY1y2/w72BbeVu/4v5c+Sr/sfvTNtt+VnY7/CMiaDFWvAw7gAXEhRf9MfJ+VuzfxZ4g8kEIFPRE3iqsmnkZnccofRrJ/jLjyEh5TK5+H8Hx22MA3ui75mF1dAB+1+8m+D9UDrsLkxjKmY7kY+K+IdCGWUDW6m6CqH7d5VMa0UKJJ1FbgyVaMo9MrDVZzzssvsnDTRtwuF+FolMMG7co9R/0QjytTzSrNzuijDR/z9IpnACGqUQblDOTSwZeQ703szea80vkEo4037kc0ykclkzlrwBlpjthkmoggBTej2efGlqW5CsF/OOLKSXwQz3BwD4rN8lC3b6cXyf5JCjG5UP8PIfgGjRMphfJ70cpHoetLiLs4sUGrX2eHCZRGaxKiJOPNmoSW/yPeGfAfnvR4ZifTsZfz7Q70BZra/GeFJUyHZM1228g1/3uXuSUbCDvf/7L/YNl3PDBtCpfsZUugTNuYX7qAJ1c8Xa8635Ly77h30d+5bnhi5a8rIhXE+43u4LAtXJquUE07JN4h4I1XYCuB54pAl3+jW38dK8GOgKsrUnB7ys1rpeAGdPMyiCyicfJTDU4ELb8PKbghwQEDO7ggAFknIK7c5GN194TCe9Ftv645ooAPKXoAcWUnPZ7ZuXTwPVF3EJuBuhlYn+FYjEmbhEuci0gWseZrBwH5xCqsfAr8R1UrWie8ziEYifD+d0vqJVAA1ZEIT86aaUmUaTNvr3u3XgIFECXK8ooVrK/eQI9A9x2OMSRvMFFtvITK7/IzptBW9ZpmRFdBeDYQAKLgbEbD0xH/vikNJ6486PoCWv0BbLuU+jNcxD4P/g+4IbHxsk9HwzNiBR8ayYLs05C8q5KOU6MbYsUqNAJdXkCcktgyPu+Y1MvOG9Nx9AQuVNUPMh2IMemU0DoyEelO7C7CvcBuxG6hDQf+DsyoOW+aEIxGm5yJr4zEqf5kTCvZEopfF8YjHkoTnEUq8hVxZM8f1KvG5xMfvQO9mNAlsVLXbSHkhPmk5FPuX/wvnl7xLOuq7QZoJqlG0M0Xgm4DKojNHIWh4kE0NCXlcUUE8Y8jVu483gVJLDn0HwGBkwA/SHbNc4ug6Bmkx1Rc+dcg4k0qPqfyZbTkMLT0VrTsr7BpEhqZjfjGWQJlEqdpemTG58DYjL26Ma0k0ZmoO4gVlxilqvNqD4rIMOAN4DbAlvw1Id/vZ1BhEYs212+P4BLh4AGDMhSV2RmNLBjJqqrVRLT+HfsoUfpl9014nFP6ncSQvMF8sP4jKqNV7NN1LyZ2m4jH1T76d1dHq7lx7l/YGNxI0Anhxs0HGz7ikl1/xpgimy3LiNDXNCqqAKDVsRLnvr1THlpchahvbwh9Rf3ZqCzIPivxcUSQgj+iOefG4nUVgX9i0olTLY1uhNLraNT8t+xe1H+QlTU3ien4Jc7vAx4SkUHA10BZwwtU9b9tHpUxLZRMn6jL6iZQAKo6T0SuA+5Oe2SdzG2HH8lZLz1POBol7DgEPB6yvV6u3n9ipkMzO5Gjev6ATzZ+SkWkYnsi5XP5OLHPJALuHe0HqW904R6MLtwjbbGtr97Ae+vfZ3XVGnbN3ZUf9DiUAm9yDVhrvbvufTZUlxDW2ExvlChRJ8qD3z3M3/e8G3cLZwAiToRPNn7GF5u+xOfycUj3g9izcGzKPbd2ClpN/NkibbqceAKc0r9C5aPEejoJscqBAdAwZB2DZLek0EkLb98H3yf+1xxCq95E8i5LfWxjOo6Xav78RRPnrbCE6ZCSuW28tZnjSayX2DmN6dmLd848l8dnfcPizZvYs1dvTh+5B0VZWZkOzexE8rx5/HnkDby59m1mbZtNgTefo3oeyejCURmNa1HZYv664E7CTgQHh4Vli/hg/YfcMOI6uge6JT3elM1fb0+g6opqlFWVqxiQMyDlWB11uG3+HSyrXL59f9nCskUcULw/Zw88M+VxOz3fXrE9QY1kIYFjUhrS2XojVD9R54gCEfAdhORfhbiTK7evqmjpn6DqRRA3sRXvXujyH8Q7NOn4VMPEr/bn1CSVxiSoY89E2ZIb0yklmkRNAS4XkbdVdXujDxHxAL8iNj1rdqBPfj6/O+DATIdhdnL53nxO6/9jTuPHmQ5lu38vfYxgnYIXEY0QjUZ5duXzXDq4qZuXTWtqVs3Bwe/ypxwnwIyt37C8ckW9Ah1BJ8jHJZ9wZM8fJFScY2ckrlw0/49QeiOxZX1ObN+RdzQEjk56PFWF6qfinwy+h7hTWCARfBeqXwKC9d606paLoduHyc80SjM3ySqfwJEsJPfipBv3mp1QB06iVHV5pmMwpjUkmkT9FvgCWCwirxArUdkDmESs6sqhyb6wiPiJFau4QlXfrzn2IHBhg0t/par3JDt+e1RSWcFzs79l8ZbN7NmzFycMG0Guz355mp1bVbSKddXrGh1XlNnb5qQ05uE9DmVF5Yp6iZkgFPuK6ZnVM+VYAWZtnU3QadwnS0SYX7bAkqhmuLJPRr17oFXPg7MNCRwO/sNSKrCgWkn8RrsQd+9VImNWPh2/Mp9uhcg88A5PbsBmC2ZUQ8Xf0er3oPgVWwpqjDEdTKJ9omaJyP7AdcAZQBGwmViJ87+o6vRkXlREAsBTwIgGp4YDVwN112d0isYzc0s2cNoLzxJ2ogSjUd5dsoj7p37FK6edRfec5HuOGNNZeMWLS1w4ccqmJ7tPq9aELnuzsGwRH5d8un3/U7YniyuGXNqiWAHyvXm4cRMlWu+4Cxe5HlvZvCPiHYJ4r03DSFnE9hvFu0Wf4vaKJpfYCWjjxHmHdth3CojOR4OfIgHbH2ua1sELSxjTKSW8J0pVvwFOaukLishwYglUvNtuw4A/qGrj29Id3G/ef5vy8Pd3R6siEcLRKH/9/BP++oPkl7IY01l4XB726jKOrzdPq1c10OfycXj3pCe5gdis0NkDz+LoXkexuGwxBb4Cds8biksS6urQrInFB/D2uneIOvWTKLe42aMg8b1ljjq8vfZd3l7/DhWRSgbmDOCM/qexa+4uLY5xZ+ByuXB8h0Ho/cYnA6ekNqhnLITj3BPUavA2vOeXgMCPoOqZHV9X9V+wJMoYYzqUJpMoETkxmYGSKE85EXiXWPfD7U16RaQn0AVYkMzrdgRlwSALN21qdDyiyvvffZeBiIxpX84Z+BO2hraxpGIJbvEQcSKMKxrLMb2PatG43fzFdPMXpynKmO6Bbvx814t5YMlD2+dBstwBfjXkcryuxEthP7PiOT4smbx9b9Xi8iXcOv+vXD/8WvomUW5+p5Z3HWz6hPolxHOQ/CtTGy+6sokTLoiuA0//JAeM0PRsWV2pzbgaY4zJnOZmol5o8HntbwGJcwwSXD+hqg/UftxgDfhwYr9xbhKRo4GNwN2q+mgi47ZnXrerqTaQ+D1W1dOYLHcWvxt2NWuq1lISLKFvVh+6+rtmOqwm7Vk0lvv2vIfvKpbiFS8DcwYkNctVGankgw0fNaogGHbCvLrmdX6x28/SHXLnVH4D9ftCAYTQstuQgluSH89pYhGE+MFZDySXRElkDooLGiz9bMSVndS4Zidky/mMaXea+60/qM7jJGLN0a4CBhK7bdYHuBjYAPwoDbEMq/lzJnA08DDwgIjEXZchIheJyFQRmVpSUpKGl289AY+XAwcMwuOq/9cdcHs4dURmS0sb0570zurF6MI92nUCVcvr8jI0bwi75A5KeplgSXAjblfjGyiKsrxiRbpC7NRUoxD8mMYJShiq30ptUP8+QJxiPxoGTwolzl1d2WECBTUJmjFNqGm2m45HWxERR0SiCT7i9T4wpt1rciaqbklKEXkVuF5V761zyVpiHag9wF+B11oYy/3A06q6uebzWSIyGPg58Hyc+B4EHgQYP358u79Hc+thR3DGf59jdVmsToajyt69+/KL8RNSHrMsGOSZ2bOYvGIZvXPzOGf0WEZ075GukI0xraTY37XRniqIVRDsk51cb6O6Qk6IKZunsqR8CT38PTig237keqxwTaIk+1y08nlQh+0zXJIF2ecjrvzkB9QgCS3n8+2d/NjEyrxbVT/TTl1J89/4RcAVQD4wvy0CMibdEi0ssRtN71VaCbR4Ab+qKrGKf3XNA45o6djtQdfsbN4+8xy+XrOaVaXbGFbcjWHdUi+FvLW6iuOefoJNVZVURyK4RHh90QL+evhR/HBI8ndMjTGJCzthPi75hM83fYXP5eXQ7gczvmhcwm9oczw57F+8L59v+rJevymvy8vxvY9NKaaycBk3zP0zZeEygk4Qn8vHy2te5Q/Dftcp91iJuFHf/hD6lPqlzj3gPzK1Md3FUPwqWn5/bJbLVYTkXAApNgMWrULxAI0bP38vC8lOvEmzqoNWPAgV/wbdhnp2Q/L+gPj3TSlG00G0+1vF9TXXmkZETibWOidAbH98Cmtvjcm8RNegzAB+VdPbaTsRyQeuBT5vaSAicqeIvN7g8Fg60R0KEWHvPn05cdiIFiVQAA9Pn0ZJZQXVkdjdUkeV6kiEaz98j3A0geUjxpiURDXKrfP/yjMrn2dx+WLmls7j/777N/9Z/sSOn1zH2QPP4ogehxOoaf7bO9CbK4dczsCcASnF9cKql9gS3LK9h1XICVEVreLB7/6d0ngdgRTcBK7iWNNeAMkBd28k/7epD+oqAM8w8AwG7zDwDEp9tse/H/EL0dZwD4bit5Pqk6Vld0D5P2O9q1CILEK3XIyGZqYWo+kYNE2PDBKR3iLyEvAssAQYo6o3qmpzdxmMabcSnYn6NfA+sEpEJhMr+tAdOITYLbaD0hDLq8AHInIZ8AZwFHA2cFgaxu503v1uMaE4yVLUURZt3sTwFiZpxpj4Zmz5hpWVq+rNIAWdIJ+UfMaRPY+gZyCxJbVucXNKv5M4ue+JODjb+1mlauqWaY16VwGsqlpFZaSSbE/nK14g7l7Q7X9Q/S4aWYp4h9Q07028SmJd6lSgm06B6GqgCnChVa+h+Tfhyp6U/ICunsRvCCzQ9V1c3uQSZnUqofJx6lcjBKhGy/+OdHko+RiNaQMi8nNiM04KXKKq/8pwSMa0WKLNdr8SkZHA5cB+wChgE7F9TPeoaosrO6jqZBE5HfgjcDvwHXC6qn7a0rE7o4JA/JK4Ecch3++Pe84Y03LfbpuzfbanLhFhQdnChJMoVeWjksm8sfYtysLl7Ja7K6f2P4X+2f1SisvTTBKWjv5Y7ZWIH7KOa26+J2Fa+QxEVwG1TXed2Mdl16NZRyKJNM+tK/gu4KVxBUEPBN8G78XJjeesp8mlgeFZyY1lOgyh4zbbFZHdgf8D9gdeAn6pqmszG5Ux6ZFMs93lxDYKpo2qSoPPnwOeS+drdFbnjxnHnA3rqYp8/8vZLcLuxcX0zS/IYGT1fbpiOf83fSrry8uYOGAgF+25F91ycjIdljEpK/Dm48ZFtMEMgwC5nsS/t19c9RLvrH9v+4zW7NI5LJq7mD+N+CO9snomHVesCfC79cqmu3Cxe95QAm7rQ5SQ6rf4PoGqQ4HwHPCNS2o4jZYSm9FqKAzlT6Du3hD4IZJgkqv4iT+zBehWnM0XIvm/RTy7JRWn6QA6WBJVU3Ts98A1xFYvnaCqr2Q2KmPSK+EkSkRGENsAeBCxaiqbgE+Bv6iq3QJrY0fuuhuzx4zj4RlT8brdOI7SNz+ff/0whSUnreTJWd9w86eTtyd6S7du4aX5c3nrjHMskTId1h4FI3llTeNipCEnzPC8YXGe0VhVtIp31r9LyGncJ+q1Na9z0a4/TTqu4/scy6LyxSytWIbi4MJNnjePC3c5P+mxdl5NVVquRMlJfrbLFadc+nbr0W2/heqPkaK/JjZeaPoOzn+MbpoGxa8j7t4Jh9ka1KmA0BQQH/j2QqS5vwvTCX1DrHXNNmIta3YVkaZuxKuq3t1WgRmTLgklUSIyDviYWE+oJ4D1QE/gBOBLEZmoqtNaLUrTiIhw1X4HcN6YPfl2w3q6ZWczvFv3dlPuNhiJcMtnH9ebKQs7DmXBIP+aNoXrDjwkg9EZk7o5pXNx4cJpMCPgFS8LyhcypnD0DscoCW7EhZuGS7McHJZUfJdSXD6Xj98M/TXvrn+P+WUL6RXoxY/6HGezUMlwKpo5mfxUgGiwptluE7NHRCD4Ghr+KeJNoKrqDhMjBa1GKx5G8q9LMtr0cSpfhdI/gNS+xXBD0b+QJGfyTI027vGUJsNr/iwktk2jOQpYEmU6nERnom4HvgSOqltFRUR+C7wF3AykVlPWtEjX7GwOHjgo02E0snjzJlxx7tuGHYdPVixr+4CMSZNNoS2NEigABLaGtiU0RhdvEVGNP+uR58kj5ITwNTuL0VhVtIo/zf4z64MbcHCYxbdMLvmYP434I90D3ZIaa6flbGj6XGQx+BKbadzOMwQkAFrZ3IuiVS8j3h1XFBTfWBQ3zTfwjUAoc4tDNLIslkBRXS/v1C0/hW6fIa7OV+CkTXSwJEpVO+9GTGNqJPpNvg9wV8MylKoaInb3wBpUmHq6ZGUTjtNMFKBHjjX/NB3X8Pzd8bsaF29RVQbnJbYXJdeby95d9sIbp4rciooV/HL6FXxc8klScf1n2ZOsDa7bnuA5OFRGK7lp7s3ML11ArBVf+1MRqaQ8XJ7pMGo0tZwP0NLkh/PtC65eNFvmHCC6MaHhYisNuuzgKjd4M7cnSqteosm/x+CHbRqLMca0pkSTqM3E9kHFk0+zv3nMzqhXXh7jevXB66r/LZbl8XDRnntlKCpjWm580Th6BLrXS4B8Lh/juuxJn6zE96GcP+hcDuw2sVEiFdQQQSfI48ufYkl54kv7pmyeEvd4aaSUuxbey/VzbqQy0tyMSNvaFNzMzfNu59IZV3D5N7/m2m+vZ0XlyswGJXlNnwvNQqPrkhwwAs4WdjiN4C5KaDSNriW2R785vliD4CRodB3OtptwNh6Ps+WXLes55ZQS9y2BOqDtJVnugDpgnygR+YWIzBWRChGZLSKXSqJVVIzpABL9Zn4b+LOI1Fu0XfP5TTXnjannvmOOZXzvPvjdbnJ9PrI8Xn6z/0QmDhiY6dC2izoOby9exOVvv8Hv//cuM9dZ5VXTPI/Lwx+GXcPxvY+lT1ZvBmYP5KwBZ3DxLskVg/C4PJw98Ex+t/vV+OJsug87Yd5f/7+Ex4to00u8gk6QVZWreHpF+yh+GtUof5l3K4vKFhHVKBGNsKpqFTfPuy2zs1Ku4qbPBV9DNx6JhqYmPl7wA+JW+2v0ugmWxY+W0Ow7YfeuSJeHkqrOp5EV6MZjoeppiMyH4Hvo5p/gVL2X8Bh1SeCQ75sf1+OAb7+UxjSxPVHpeLRZvCKXAPcRm4Z9jVhzs3uA29ouCmNaV6J7on4HfAHMFpE5xApL9ABGACuAq1onPNORFQayePLEH7OmrJRNVVUM7tKFgCe1JpitIeo4/PS1l/h6zWoqw2FcIryyYB5XTNiPC8fZbJlpmt/t5/g+x3J8n2NbPFZltBK3y91om4uibA0ntscKINudRWU0XjntmCgOX2z6kgt2OTfFSNPn222zqYhUNNpbFnWifLrxc47qdURmApPmeuxFQCPo1quh2wcJFfHRyFrQpv+fxAQQ39gE4ysk9p403rvhfFzd3kpsnDq0/N6aGaLa/xfK9t5YgcMSLr++ne8A8E2A0Fd19oJlQfZPEE9qPdBMh3QhsUJk52jNWmIRuQW4VER+p9rMXR9jOoiE/nVU1U3AWGJ9ohbWPG9BzeejVXVNq0VoOrzeefmM6t6jXSVQAP9bumR7AgXgqFIViXDXl5+xsbL9LHsyndsuuYOIOI2XP/nEl1Clv1on9JmE7GDvTVibaNSagLAT5tOSz/j7on/w2LLHU1p6F3EiPL/yRe5f9C+qncYzNCENsT7YTHGH1hY4BNhBs3JnEzgJ/soTH82vofKDdw/wJpZEiacfuLrGOwPZJycWU0OhL4hbPdApr2numxwRF1J4P1JwC/h/AIFjkaJ/4cq3e60t0vGW8w0GHtX6mzHvB7KBXdo0EmNaSTLNdiuAvwN/F5EDgemqtsDZdFxvL1m0PYGqy+Ny8cWqFRw3ZPcMRGV2NrmeXCb1OY5X17y+vfGuV7wU+Ys4qNvEhMf5QY/D2RYu5c21b8evHgg7TLKaEnLC/GXuLaytXkfQCeLCxacln3HOwLM5oFviS7TuW/xPZm+b02Qy53f5GZybuaIIkn02WvlCLFEi2MRVDjtMtGppkKZnjrIh5wIk96KEW1OICBTei27+KRAiNn3pB3cvJPcXicXUaNBC4u+ziqB4UvqOEXFD4GgkcHRqMZn6MrCfKQ2ygIbvEWvXy1t1KdMpJL3BT0TcwIfAkPSHY0zbyfX6cMV58yII2e1s1sx0bsf1/iGX7XYJexSMYpecQZzQZxJ/GnFdUj2eRIRT+p3EP/a8F1cT/7R7xJ1UXNvC2/jH4n/xs6mXsKxyOUEnllg4OIQ0zH+WP0Ew2lSyUd+66vXNJlBucVPoLWCvLpnrJSSufKT4Vcj9Obh60/hXpAu8wxF3M3un6o7n6QPE+3/oQ/J/gyvvUqTZJYRxeMdA4Di+f1ftg5zLEVdTtZ92wD2wiRMRKDkMp/R2VJvqc2VMUmq/adtHQ0tjWijhmagG7AfAdHg/HjGKF+bNoTpSfymVS4QD+g/IUFRmZzWqcCSjCke2eJxsTzbju4xj2ubpROtstHLjZq8uie/1q45W85uZv4+77K6WCxdLKr5jeP6O+yetrlqNx+UhHI0z+yseDu5+ICf0mYTXldkbGOLKR3J/geZchG79dawst7gAAVcRUnhPwmOp70Diz2iFUf/hKf0i1W1/guoX+X4JXhmUXoUTXYxkn4q4eyY3YHhuMyerofJJ1JWb+kyXSYu2KgohImcATzY4/Iqq/iiF4ZqKuuPNqxkTR6pJlP0AmA5vZPce/Ga/idz22cd43bE79C4R/n38ifg9qf5oGJN5PxlwJssrVrAtvI2wE8br8lLkK+TMAaclPMbLq19tNoGC2IxUwJXYbFkPfw9C0VDcc4d3P5TTB5yacGxtQcSDFN2LRhbHmte6e4Bv36QKLUjoSxQ/0LC4hB8JTobsU5KKSZ1yqH6BxnuYolBxP1rxf2j2uUjerxNeIog4O/iNXgUVD6M5P098TJN+bfeuazjwElA3a06gxGRcd4rI1jqf134D3SMidavmqKpOSvE1jMkYm4kyO7Vzx+zJpKHD+GLVCrK8XvbvNwCfO7klT8a0N/nePG7d48/M2vYta6vW0TurN6MKRuBKIgGYvmXGDq/J9eQyKGdgQuN18RU1uVerqz9esYTUhJwwlZEK8r35SX29TRHPbpBEyfB6ostoVHYRgGo0siTpX6QaWUbcIhCxs0AIKh8H37iaIhkJyDoOKh4Fmik6ouXEvg67ubQTGA7MUtVkm6I19DGxb8qGzdcm1/zZTFM2YzqGJv9FFJGXgWtUdV7dQhKqGhWRQYBV5DOdQlFWFscMHrrjC43pQFziYkzh6KQq/NXV3AyTRzzkenK4augVCc9OLCxbhEc8cfdEfb15Kof1OAR3knu26oo4EZ5a8Swfl3wCxMrQn97vxxzQbf+Ux2wxzxAQL2iDGTjJRrzDkx9PEkliqtDKJ2P9mhIROAEqHm7+Gnd/JKHXNq2lDXs8DQeeb+kgqnpwy0Mxpn1r7jbdUUDvmo8/BLYvelfV5aotqJVr2qXSYDV/+OA9xj5wH2MfuI8/fPAepcFUZ/GNMR3ZMb2brqz2810v4u4xd9A7q3eT1zQkIk0WlVhUvphLp/+Kb7fOTjrOWk+ueJpPSj4lrGHCGqY8Us5jy59g5tZZKY/ZYr59wd0P+L6ZsuKmyvHzv21eNoc2Jzeeu39i12lF4mOW30vTs1sAAST/D4mPZ1pHG5Q4FxEfsCtwrIgsEpElInKrJF39xJidQ3O3lhYCj4nIDGLL9xquba3L1rN2cFHH4eTnnmb5tq2Endgv1OfnzmbKmtW8dcbZuF0tXxZjjOk4JnTZi883fs6sbd/We+/lwcNTK55lY3ATR/Q8POElc442/Q5OUSqiFdy76D5uH30zXXxdkoo1GA3ySclnjZK0kBPildWvMbpwj6TGSxcRF3R5Ei37K1S/QcQJMrWyiOe27ka58wrPrHyFcwaexcRuByQ2YGRBAhcFIHBM4kEGP2r6nGcMkn814ku++bhGVsTGFi/4j0Dc6VuyaVqkWESm1vn8QVV9sObjwcTeF1YAJxNLqO4ltvTukjaN0pgOoLkk6ifAdUDtb7Mc4i/uNp3Ah8u+Y2152fYECiDsOKwtK+WjZUs5bJddMxidMaatiQi/GnI5C8oW8r/1HzJtS6zaX4QIm0KbeHH1S6ypXsv5g85JaDyPuPG5fNt7YcUT1jAfrp/MSf1OSCrW0khZbFlhnDxtY2hTUmOlm7jykIIbWem9iBvn/pmQU5voxf58bNkT7FE4igJvQQKj7WC5o2SDexCSVMGKxo2etyu4GfEmvx/MKb0VKh/l+xmuP6H5t+LK/lHSYxnS3Sdqo6qOj/syqnNEpFhVa39oZkpsve7TInK5qjbzzWLMzqe5W4g/AC5T1UNrPv+5qh7S1KMNYjWtaN7GkriNZyvDYeZu3JCBiIwxmbascjn/Wf4EX2+ZWq9cOsRmeT7b+DlbQ1sTGmto/lDcCbQmnF+WyGzL99ZXr+f1NW8ScRq/vxOEXXIGJTVea/lq05T4MYowfcs3CY0h3pE0/Wu7CMm/Aen6LCKJ9xdDmuovJYi7R+Lj1HBC06Dy39RfIuhA6W9xoluSHs/ElgKl67EjdRKoWvMAL9CtpV+HMZ1Nc7/R/gTU/e1jZc07sQEFhWR7G/dnyfZ6GVBQ2KqvHYxE+GLlCr5ctZJQ1CY7jWkPtoa2ceu8v7K6ag3axD//XpeX1VWJ1RjyubxcMOg8ZAdv5ZJpMLyk/Duum/0nPi75JG7lP5/Ly4l9f5TweLVKghv5bOPnzNw6K27ikwpt8m8xdjYxzexdkgC4BxF7v5sEaWqZnSs2ZrLK7mvihELlI8mPZ9qMiJwoIutr9kbVGgtsBVparc+YTqe55XybgT+JyFs1n58pIhObuFZV9e70hmba0pG7DubmTyZTHYkQrdm74BYhx+fjyF0Ht9rrfrJ8Gb9867XtbyFcItx/zPHs1y/BDdQttHzrVm6Y/D8+W7kCn9vNibsP53cHHBQ3oTRmZzK5ZDLRHazeiTgRiv3FCY2nqry29g0EaTKdEITxReMSjvGxZY8TdBo3s3WLmxH5wzml30n0z+6X8HiqylMrnuHDDZNxiQtB8Ll8/Hb3q+ib3SfhceoKOSFeX/Mmn5R8GvfrVlXGFo5JLL7wbJpMpHQtuvls8B8IhfcgiVY6dJpKgh1wNoK7V2LjbH9aSdPnovY+PGVtcxt7MrEJqwdF5GZie6T+CvxVtZlNjcbspJpLoq4B7gEOJfbje1kz1ypgSVQH5vd4ePHHZ/Db99/hq9UrAZjQpx+3HX5kqzWe3VhZyc/eeIWqSP03ahe+9jKfnX8hhYGsVnndWlurqzjhuScpDQZxVIk4Ds/Pnc3CTRt55uTEm5Ia0xmtrVpHuJkkyiMehuQNpkege0LjLa9cwbrq9U32ihKEQm8h+3TdO6HxIk6EFZUrmxzr10OvSGicuqZtmcHkkk9iBSpq3jJWO9Xcs+hv/HWPW5NuNquq/HX+XSytWNao6IVb3LhwceaA0yj0FSY44o6WQ1ZB6GOofgOyjk9wzKZm/xXFnXxTSN/+ULUw/jn/YcmOZmq0RYlzVd0kIkcCdwHTgW3Av4BbWv/Vjel4mnx3rKpPAE8AiIgD7KOqU9oqMNP2+uTn88SJpxCsSWpaK3mq9cai+U3cXFPeWryI00e2bkWtZ+fMpjoSqVc1LBiN8u2G9czesJ6R3ZPfD2BMJlVGKvlk42csKltM76xeHNz9ILr4ilIaa3DeYKZv/Sb+TA9u9u4ynnMG/iTh8TaFNuFq4i25Cxf7dJ3Aqf1Owe9OrJqyS1xN9p3Kcqd2A+aDDR/F/XpLw2WsqFzJgJzkZsgXli9iWcXyRjG6cDGhy16c2PdHdPMnvtVEvCNQvDTfGLcKrXwBSTSJEj9o468ZgG1Xobm/RnyJ9xqTvF+iVU8CDXtjFSOBHyQ8jskMVZ0B2D53YxKQaN3qQ4C5rRmIaT/8Hk+rJ1AA26qDhCKN73SHolG2Vbd+f6o5G9ZTHef1XSIs3pzZil7GJGtraCvXfPsHXlj1X77eMpU31r7FNbOu5bvypSmNt3/xvmS7s3HXqQjnFS8j80fwwPh/cPGuFya1f2lg9gDCTuM3/y5cnNz3RC7e9acU+hKpUFfzPHExsdsBeKX+0lufeDmse2rvAYPR+P/uCNJsVcGmfFf2HaGGjXYBB4ccd05SCRQQW6JXcDs7/tWdxLSFr5lmxKEv0c0/QYOfJT6e+MHdh/plDFzgHRkr+W5S0wZ9oowxyUnoXzRVnQwMEJHnRWSDiFSLyGoReVZEEr9FZUwdB/QfgN/TeO+Rz+3mgP4DWv31h3Xrht/dOFl0FHYpSq5PTWuKOA7/W7qEh2dM49MVy5vtt2N2Xs+tfJHScNn2N/sRjVDtBHl4aWqb+QPuAH8aeR37d9uPPE8uXXxFHNf7h/xqyGV4XcnvGSz0FcZdDufg0CeJpr11nd7/x4wsGI5XvGS5s/CIh3Fd9uT4PsemNN6Ernvjc/kaHRcRBuUMTHq8DcGm9wetrU5tf5Ar64dI15fAfxTxS55nIdknJTHijor5VKPbbkx8uOp3Ibqe+u/YnVhCFp6XRFymHkuijGl3EppuEJFxwMfABmJL/NYDPYETgC9EZKKqTmu1KE2nNLZnLw4dNIgPly6lMhK7Q53t9XLkroPbZCndqSNG8cDUKQTrvIfwiIvdi4sZ1YLX31ZdzduLF1IeDjOx/wCGdE1s4308JRUVnPLC02yqrCTsOHhdLgYUFPL0SaeS57cm8uZ7M7fNjLvfaG31OioileR4spMes8BbwAWDzoVB57Y4vgVlC3GLJ+4+q682T2FMUfL343wuH1cMuYySYAnrqzfQK9CTTaHNTNn0NYNyB9EzkNzP8cHdD+LzTV+wpmotQSeIi1hxiX5ZfXl59asc0v0guvoTbxq7Odx0Se/ScGlSsdUl3mFI0d/Q0BR0y0WgDhCMVdPz7QeB4xIfLDR9x9c4S1GNILLjtwwa+hqojH8yPBO8wxKPzRhj2rFE12zdDnwJHKX6/eJuEfkt8BZwM3Bk+sMznZmIcO9Rx/L24oW8OG8OgnDy8JEcuWvyzR1Tkefz0ze/gPmbNm6f3VGU8b37JL2BvNYnK5bxs9dfBSDiRLnzCxc/Hj6S6w86NKUxf//Be6wpLSVSE18oGmXx5k3c/vkn3HTI4SnFaDonr8sHVMQ950m0UlsrCkVDTRYpqGpiGV2iHFXKwmU8tuxxtoVLEYSoRtmzaCw/2/VCXAkuI/O5vPxh2DVM2zKdqZunMbt0DmEnwsLyRSwp/45317/HlUOuYPf8oQmN18PfdNGNZJfyxSO+vaHbh1D9JjhbwTcBvOOS/LemmbLpdai6SGhYd2/AR+M9UW5wJVaExDSgbVNYwhiTnEQXKO8D3FU3gQJQ1RCxqnz7pjsws3NwiXDM4KE8fPyJPHT8CRy12+CUE5hkvb1kEcu2ba23PC6qyn9mzmBdeVnS4wUjEX7xxmtURcJURcKEHYfqSITn587h0xXLkx4v4jhMXrZ0ewJVK+Q4vLpgftLjmc7tkG4H4ZP6S9HcuBlVMDLhYg2taUjeYCLaeOmY3+Vn7y57pTRmyAlx54J7uPbbP/Lgdw+zIVhC0AlS7VQT1jAztn7D++v/l9SYHpeHCV33pshXRFW0entRiChRgk6Ify15kESrPU/qc3yTfbFO7XdyUnE1RVxFSPaZSO4liG98Cv9+Jva1xOpLJXBd1onQaMbKBZIN/qa6pJgdsuV8xrQ7iSZRm4Gm2prnA+npRmhMG/rfd0uoDDfe6O5xufhq9aqkx/ti1cq4d2qrImFemDcn6fFUm27P6Whib2jMzuOHvY5mRMFwfC4vAZcfv8tP76xe/HTQeZkODYBsTzZnDTgDn8u3vUqf3+Vnl5xBTOiaWhL19IpnmVc6n7CG4y5lDDkh/rfho5TG/nTj53F//raGt7ExlFjhmRxPNr8acjmeOkmFCxcXDrqAHlntpPqnNN4D1oh7SEJL+QDE3Q0pehhcvYEswA+eoUiXpxCx/nupEk3PwxiTPoku53sb+LOITFfVBbUHRWQocFPNeWM6lK5Z2bhFtjcXriUIBf7Eq47VijaT2ESd5JMer9vN3n368tXqVfVmyzwuF0e0YgNk0zF5XB6uGHIpq6vWsKJyJd18Xdk1d9c2m9lNxMHdD2TX3F2YXPIxFZEK9iway55FY3GnsNzQUYdPSj6LW+K8rupIaksFg0785ylKdRLLDwdk92N4/jC+3TYbgN1ydmFw3q4pxdQqAkdD5VM0Wn4HxO6z+pGC65MaUnzjYssMoytAvIg7tcIhxhjTniU6E/U7YiV8ZovINyLyjoh8A8yuOX9VawRnTGs6deQovO7Gb958Hjf790uuHwzAvn37E442Xq7kcbk4YffhKcV4y2FHUBQIkFVTxTDb66VHTi7XHHBQSuOZzq9PVm/27TqB3fJ2a1cJVK1+2X05a8AZXLzrhezVZXxKCRTEkqgdJVAAXf2pVdr0S/wlkIIkXNo9qlFumnsL326bjdb8t7BiMTfNvTmpRCye9dXrmV+6gIpI/H1wiZLcS2L7mKS28IgXcIOrHwSOQro+i/iSnykUEcQzwBKodLHlfMa0OwnNRNV0sR4LnA9MBIqABcDDwCOqWt56IRrTOoZ0LebmQ3/AHz54H7dLcFTJ9fn596QT4yZXO+KW+LsfVJVAin23+hcUMvncC3lt4XyWbN7E/7d33+FRVekDx79nSiadAAmE3qX3XkQQECwIdrFh77rW1XVXV/2tvddVLGvvBWyACIoU6b13kA7pfdr5/XEnOElmJneGkAzk/TzPPJB777xzZu5Mct8557ynU1pDTm/brlrW8RIimpktFpHjyoko/pC0wUw/MKPC9jr2OqTGmKvQtyp7NdnO7ArDAgvdRSzIWMSwBkPDbleBu4AXN73K9oLt2Cw23F43pzcazblNxkeUNCtLHUj9AYqnop3LwNoMFX8uyhI9yzwIGYonRDQyfSWmtS4AXvHdhDghjO/QidFt2rFs/17ibXa6pzfCEuG39wt2/4nNaoVyvVEerflq/RoGR7j2VbzdzkWdu0Z0XyGizYbcjcw8OIsCdyF96vZmSNpgYiJYd0qjUaig8wZLhRpmG8qohiP45cAsPOXWURrb6EzTycqOwl24A0wZ9uBhc/7miJKoN7a+xbaCrbi1B5fH6Imbvv9nmsQ1ZkD9/mHHA1AqBuLGoeLGRXR/IYSojWT5cFGrlbjdfLthHa8tXsh/ly5izs4dEcdyh5j35AwwzE+I2ubHvVN5btOLLMpcwtrcdXz65+f8Z93jOL2VD8srz6qsdE6ufM2hJFtiJE1l1sHfAiZLU/dPM12dL9uZHXRfVoh9weS58liXu75ClcMSr5Nv93xHiack7JjiOFBVQ/mkN0uIKiVJlKi1XB4Pl3zzBY/N+Y0Fu//kl21bufmn73lm3pyI4g1o2ixgIhVvt3N2e1lgUtRu+a58vt0zGaf3rwIGTq+TfcX7WZCxIKKYV7aaSLItGTvBe7L2Fe+PKPaizCW4AywMnOvOJcNkdb5iT1HQff6vg1mFnsKgc8gOFB/gtuV3MCvCaoQiykkSJUTUkSRK1FrTtm5mY8Zhitx/XSgVuV28u2Ip+/LCXycqISaGp0aMJtZmw24xPlrxdjsnN2/BqNbVs4CwENFqU/4WbJaKI8idXidLMpdFFDPNkcqz3Z9kTKMxQY/xBEiEzAjUViOeF5vJUt2N45oE3Zcemx52m9IcadiDDH3UaEq8Tj7d9TkbcjcGPCaYAncBP+z9iWc3vsBHOz9hf/GBsNsmhBC1jcxOF7VWqHWiFu3dzbgIeo/Gtu9A9/R0Jm9YT25JMae2asPAps2iskqaENUp3hpHoFFwCkWyPSniuA6rgzMbj+b7fd8H3J9kC7bEYWAF7gJ+3v8L2SXZAfdbsZASU8dUrMGpA/h2z+QK87YUihENhofVLjCKaUxscTlvbX8Hl9cVcD6Y0+vk5wO/0CG5vamY2c5sHlr7KIXuQlzaxdqcdcw+NIe7TvobHZM7hN1GUfUUUlhCiGgkSZSotVLjE4KuE5USwTpRpZrXSeH2/gOPtnm12pSN63l98UIOFxbQq1ET7hk0hPb1U2u6WeIonJTUjjhrHMXl1l+yW+ycGkFC4S/OGkfPOt1ZnrOyzHaF4uLmF5iOU+Au4F9rHibHmVOhoEQpr/aS6cyiXkzdSuPVj6lP3Zi6ZDozy7U3lmbxTU23y1+/+n1IddTniz+/YkPexoCJVI7TfEXCb/ZMId+Vf+T5evHi9Dp5e9u7PNv9KfkCKFpIEiVE1Ak6nE8ptV0ptc3kbWt1NlqIqnBR58DrRDls1ogr6Ymj9+aSRTww82c2Z2aQVVzMrO1bOe+LT9iaaW4eiohOFmXh7x3upn5MPWItscRZ4oixxDCh+UW0Tmx11PFvbncjfer2xoIFm7JhV3YuanoBg1LNf6Hx875fyHJmBU2gALxhTC7ZmLeJPFduhe1Or4ulWZENYQRondiKv510a8ChfXZlp3tKN9OxVmavDPh8c1y5ZLmyI26jEEKc6EL1RE0h9F8KO3AZkAwcrMpGCVEd2tWvz5MjTuOBmTOwWBRaa5IdDt45+1xsFpkuaJbT4+H9Fcv4Yt0avFozvn1Hru3Vhzh7+GWrS9xuXl60oMw8NQ0U+7a/NObMKmy5qG6N4xrxbPen2F6wg0JPIW0T2xBnjauS2DGWGG5rdzP57nxyXbmkOtLCLp0+L/OPSkumazRJNnPDDzfkbcQVYE6WW7tZlb2a/vX7hdW+Ur8cmMmUPd/j9paNbVd26tiTGdHwVNOxYq2xEGAtLY3GYQm84LCofspkRUghRPUJmkRpre8Itk8p1Q94GyOBeh+4O9wHVko5gKXAHVrrX3zbWgBvAYOBXcBdWuup4cYWwqyz23fktDZtWb5vH/F2O10bpke8TlRtpLXm2u++Zcm+PRT7Ep/Xlyzkl+1b+ebCS7CGmYz+mZuDJcDL79WaFfv3VUWTRQ2zKAttElsfs/iJtkQSIyxrXuQurPQYu7KT6cyiYWyDSo/dkhd8kMa2/B3hNO2I97d/xKxDv1bYnu5IZ1Bqf0Y2HEmCLd50vN51e/Hjvop/Zhs4GoQVRxxDUllPiKgU1hWOUipeKfUiMA+IA0Zpra/SWmeGvmeFOLHAp0Bnv20Ko/crA+iLkZx9rZQ6+nEeQoQQa7MzsFnzo1pot7Zaum9vmQQKoMTjYXNmBr/t3B52vAYJCbiCrLfVLDm8AgFChKuuiXlOGm26EEahuyDovkxnJrsLd5tuG4DT4wyYQJW2bFyTs8NOfIK1IcuZhUfL+nZCCBGM6SRKKXU6sA64GXgO6Kq1nhnuAyqlOgELgDbldg0H2gPXa63Xaa2fBOYD14T7GEKI6rF0b9kEqlSx2838P3eFHS/ZEcuZ7doTayvbSR5ns3FL3wERt1MIM05LH4ktyDpMADEqhiGpg0wPQUyJSQm6r1gX88ja/zB13zTT7duUvznovv0lB9hesMN0rFJb8rcF3O7UzpCLBYvqpXTV3IQQVafSJEoplaqU+hj4EWPuUx+t9f1a6+JK7hrMycDPQPnZvgOA5Vpr/wV65gY4TggRJQ4U5Afdd6gw+LfwoTx+6ijO6dAJh9WKw2olNS6eJ0eOZmCz5pE2UwhTBqcOomNSR+zl1oGyKisxlhiGNRjKZS0uMR2vXky9kPud2sXXuyeTUWJuMEeiNfQwxcfXP8XrW97EqwP35gYSaEFhAI/2EGupmvlqogrIYrtCRJ2QJc6VUldg9Do5gLuAl7UO47dzAFrrN/3i++9qBOwtd/gBILI6sEKIY65BQvCLurS4hIhiOmw2Hjt1FA8OHUae00n9uHgZZimqhUJh9euJsiorVizc3PYmutbpHHQB3mDS4ypfUFehWJG9khENKy/zHmsNXejB6XWyInslCzMWMTDVXM+t0+sMuq/QU0CCPbJ5UVvyt7Isczk2i40B9fvTOK5RRHGEECJaBf2LoJT6GRgB5AD/xij0MD7YmhFa62+Osi3xQEm5bSUYCVyg9l0PXA/QvLl8Qy1ETejWsGHQfd3TK7+ADCXWZifWFn6FPyEiNe/wfNbnrceljUW4PdqDBw/vbP8fL/d8Pux4ver25LNdnwes0FdKKUwnZ/UcoXu2AEq8Jcw+PMd0EhWqGmGOK4+02DRTcY7E05oPdn7E3MPzcXldKBQ/7ZvGxc0vZGQYVQNFWTIUT4joE+o390jfvykYvVGhaCD4QHJzioHyy8A7gIDlkrTWk4BJAH369JFfL0LUgNziEixA+e5pBeQ7g3/DXR125WTz4oL5zN+9i9S4eG7o04+z2rWXxUNFUHMOz6MkQM+My+tie8GOsKsK1oupy4TmF/Hpri/waA/eCp8Uo/Jkr5QepuLlu4MPn/Wnq6gcdrI9/CqHm/O3MPfw/CM9XBqNV3v5bNcX9Knbm5SY8n/mhSlylSNE1AmVRFV3Vbw9QPdy29IBqWssRJTalp0Z4LLQ+Hu/OaPmFsfdk5fL2Z9+RL7TiRfNwYIC7v/lZ3ZmZ3FrP5lmKcKniCz5HtHwVLrW6cofGQuYsuf7CgvbxlvjSLCZG/pafl2oQByWGE5OHWy6fQoVtDcqkjlRizOX4PK6Kmy3KAurclYxNO3ksGMKIUQ0ClpYQmu9M5xbFbRlAdBDKeX/12SIb7sQIgrlOyteLB3Z56q5nqg3liyi0O3C63dxWOR28fqSRRTUcA+ZiF5D007GYYmpsD3GYqdlQouI4xZ4CtiQuyFgslLidbImZ62pOJUVjHBYHHRI6mB6KF9lDpUcDPs+1hDVDUPtEyFUUWU+GRIoRNWqrLDEmcAtQAtgO/Cm1vr7Y9SW2cBO4D2l1MPAWRgV+6TEuRBRqm5sLIrAI03qx9VcZa/Fe3bjDrDelM1iYWtWJt0ahj9fy+318vna1Xy5bg1aay7o1IWLOnfFbpULwxPFoPoDWJa1jDU5a3F53dgtNhQWbmt3CxYV3sLRpabs+Z7v9/54ZJ5VeW7tZn/xfrrRtdJY7hDrNikUd550Ox2SwhuymmRLJNedF3Bf0/hmpuOUGlh/ADMP/lqhYIVG0yOl/GATYZokQEJEnVCFJS4EPgOygU1AH+B0pdT9WutnqrohWmuPUmoc8A6wFNgKnKO13lHVjyWEqBoDmjXHYbVS7Cl7cRdnszG4eeTf3B+tpsl12JRZcTihy+OhYYiKgsForbnu+8ks2vMnRb51sbZkZvDz1i28P/48mWd1grAoC7e1vYWtBdtYn7uBJFsifev1DXsB21KHSg4xec93AedClbIpK03jzRWhtVuCF1qxKzsdkzuE3cZLm0/gv9smVdjet14fHNaKvXKVaZHQnHGNxzJ5z3coFEopNF5uaH2d6WGLQghxPAjVE3UXMA04X2tdqJSyAW8D9wNVkkRprVW5n7cAp1RFbCHEsde1QUNObdWGX3dsp8htfNMeZ7PRM70xg5tFnkS5vV7m7NrBwfx8ejRqTPv6qWHd/5qevfl1x7YKX952SmtAw8Twk6gl+/aweO/uIwkUQJHbzfzdu7jwq8+4pe8ATmnRUpKpE4BSiraJbWibWH49+PDNO7ygkgTKRoPYBnRMMpf8WELMy4r0vde5TifiLfEUev+q4aRQnNZwZIh7hXZW4zMYWL8/K7JXYbfY6JXSk8QIilQIg0KG4gkRjUIlUZ3wJVAAWmu3Uuo/wBVKqVZa6+3V0kIhRFR7acyZTN64ns/Xrsbj9XJux85c2KlLxGs77crJ5qKvPjeKQmhjVtPwlq15ecyZWC3mhlRtyDiEzWLBVW5I366cHLxah922xXv2UOKuOKnfqzVL9+3llp++55Iu3fjn0GFhxRUntn1FoesiDUkdxMXNLzSdABV7yq8C8pdAxRzM+GzXF2USKDCG3r225b+82OO5iJOz+o76pta+EiZVUcVFIUTVCXVFkoCxRpS/Xb5/pUapEAIAq8XCeR0788X5F/P1hZdwadfuRzVP6OafvudQYQEFLidFbjfFbje/7djGp2tWmY7xzfp1FRIoMIpLbMw4HHabUuPjcViDf+dU5Hbx0eoV/JlT/lemKE9rzeqDB5j/5y4KXZFd+B8vQhWjGNngVK5qNZE4q/m5g/me4CXOQ/V4hbIwc3HA7dmuHLKcWRHFFEKI2iBUEhVovnjpxIfIZtgKIUQIe/Ny2ZqZgbfct65Fbjcfr15pOo4tSI+V1hpbBAUCTm97UshFSQEsSjF/966QxxyPPF4v6w8fYkf20V9Qb8vKZNj77zDh68+56ccp9H3rdb5Yu7oKWhmdBqUOCFiRzqqsEfXS1I+pH3RfjAp//hIQtOAFQIEn4DKNogZIdT4hoo+5ZdKFEKIaOD2eoEPtSjyVr5FTakKXbmzKOFxmDhNAanwCbevVC7tdVoul0qGEFmWhjiM27Nilftuxnf+tWEpWcRGjW7fj8u49SXY4woqxIzuL1xcvZPn+fbRKqcvNffvTI71RxG36fecO7vr5J0rcbjxa0zy5Dm+cNY6WKXXDjuXVmiu+/Yp9+Xll0tFHZs+iY1oDujZoGHE7o1Udex2uaXUl725/HzCGySkU5zc9h8ZxjcOOVy8m+Oue6ghv3qAZdktklwhaa1bnrGFR5mLslhiGpA4Ke6Fi4Ucj1fmEiEKV/Ya8Wyl1wO/n0qube5VSh/y2a63136q2aUKI2qZFnRTqxsZRlF+25HKM1cpZ7cxXHjuvY2d+37mDX3dsw6M1dosFu8XKG2eNi2iOx7Qtmyr0jpVntSiGt4xsjfLXFi/g9cWLjhTn2JyRwVfr1/L9hMtJjDHXw7A5I4Nzv/iYIrcbr9Zsy8pkzq4d3DlgMOd17Ez9+PAqzP2Zk8NNP04pk4huycpkwtdfMPeq60zPTyu1dN8eckqKK1wLlng8fLRqBU+NHB1WvOPF4NRBdE7uzLLs5Xi0h54p3SNOeA47M4IujpvnClym/Ghob/hX7lpr/rt1EiuyV1LiLUGhmHt4HmMbncnZTc6q8jYKIURNCZVE7QL6Bdi+E2P9Jn8akCRKCHFUlFK8MOYMrpryDW6vF6fHQ7zdTpOkZK7v3dd0HKvFwqtnjGXtwQMs2ruHtPh4RrZuQ6wteInoUPbn51McoLAEgN1ipU6sg7fHnoPDFv4399nFRbyyaAFOvzLxJR4PBwry+WLtaq7u2dtUnCfnzabQ5Tpyea19cZ6a9zvPL5jHuPYdeezUUUGHOpb32dpVFdba8mpNvtPJ/D93cXKLlqbilMopLg6YwHq15nBhQVixjjcpMXU4tcGwo45jVZagw0pDDcuLVIYrk0aE15O5Pm/DkQQKjN43p9fJd3u/Z3DqIOo7wu8JFqAim/ImhDiGgv7F11q3rMZ2CCEEAH0bN2Xm5Vfz1fo17M7NZUDTZoxp0y6iBKVzg4Z0roJhYj3SGxFrs1UohOCw2rhr4CCu7tE77J6ZUiv378dhtZZJogCK3W5m7dhmOolaum9vwMtrjTFM8vtNG2iUmMQdAwaZirc3Ly9gcQ6v9nIogqSnV6PGARNRq1KMbHX05cRrA4/XgwVLwCISkQ69CyXBGv66Tkszlx1JoPxZlIXVOWsY1mBoVTSt9pHhfEJEHSkQIYSIOg0TE7ml7wCeGHEa49p3jCiBqkoDmzajU1oDYv3aEWuz0SktjWt69ok4gQKoFx+PJ8BQQYtSYS0MXDc2dJW3YrebD1YuNx2vT6PAc3aK3G7eXb6UnzZvRIdRdjnebg94IejRmpTYyOeS+cstKebj1St5at7v/Lx1c4WetONdfUf9gAvuKhQdkztGFNMa4jKgTkxy2PFirbFYAsRUWHBYIit+IWqGUuptpdRvNd0OIaKVqSsTpdSsyo7RWp969M0RQojoo5Tiw/Hn8+6KpXy1bi1aa87r2JlrevWOeD2sUl3SGpCemMTO7KwyyZTDauWK7j1Nx7mhd1/+7/dfKxTT8JfnDL7OUHnZxcVB9607fIh7Z0xjw+HD3DVwsKl4c3ftxK0DJzXvrljG6e3am25bIOsPH+Lirz7H7fVQ5HYTb7fTvE4KX5x/sel5ZdHObrFzXtPxfLX7W5xeJ2AkUDGWGM5pMi6imD1TerIke2mF7QnWBFLsKWHHG5w6kJ8PzMBZLoHVaHrU7R5RG0X1V9ZTSo0ArgFmV+8jC3H8MPv1aS7GmlH+Nw/QDegCVPwNLIQQJxCHzcZNffoz84qrmTXxGm7pNyDiOVb+lFJ8MP482tWrT5zNRmJMDAl2O48OH0n3humm41zUuSvX9OxDrM0WNLHrFka833ftCLm/yO1m0tLFZBUVmYq3KcT6XHtyc023K5g7p/1InrPkSBJZ6HKxLSuT/y5ZeNSxo8no9NO4vvU1NI9vRpItiZ4pPfh353/ROC6yKowXt7iAeGs8ir/eMzHKzrWtr8ISwXIAjeMac0nzi7ErO7GWWGItsTgsDv7W7taw1sQSfjTGYrtVcTNBKZUATALmHcunJcTxzlRPlNZ6fKDtSql44Hsgu+qaJIQQtUvjpGR+unQiWzMzyCkpoXNag7CHMCqluGugUYnvszWreG/lMtxeLx6tsSqFw2bjoVPMDxgI1aNVyuX1sObgAVNFJtrVC16RrlFSkul2BXKwIJ8dOdkVtjs9HqZsXM+9g042HWtPbi7PL5jH3F07SYmN5dpefTi/Y+eIqjoeK33r9aFvvT5VEivNkcZjXR/hp33T2Zi3kQaOBpzZ6HRaJ0ZWaRJgeINh9K3bh9W5a7ErG13rdMFhDa9cv6hRjwG/AfuAITXbFCGi11FNNNBaFyqlngHexfjQCSGEMGl7dhafrF7JntxcTm7egvEdOtGmXmS9W16teWT2LL5Yu5oYqw2vhtT4eFJi4+jaoCE39ulH67rmK6N1bdCQNQcPhDxGA1nF5nqihjRvgc1iCThP6bqeR5cQhOoxsYbRm3IgP5+zPv2QPGcJXq05VFjAw7/NZGtmJvcPOXELItSLqcdlLSZUacxEeyID6/ev0pi1WXUN51NKDQQuwBhldHf1PKoQx6eqKCyRBoQ/+1QIIWqx33Zs58xPPuD9FcuYtnUz/5nzG2d9+iG5JebnLfn7YOVyvlq3hhKPhzxnCS6vh6ziYjqkpvH0qDFhJVAAl3frgcNqrfS4A/n5puJpwBOk0MPhosJwmlZBanw87eunVhjGGGu1cUGnzqbjvL18CYUuV5k1wYrcbt5buYxsk8miEMeErqIbpCqllvjdri99CKWUA3gHuENrnVU9T0yI45epJEopdVeA2z1KqeeBl4Gfj20zhRDixOHxerlnxlSK3W7cvgv2IrebvXm5/G9FZFNM312xrMIQPKfHw9QtmygxMTSvvA6paTx0yqk4rKEHLAQqgx7It+vXBa3S/M7yJWG2rqKXxpxJvbg4Eux27BYL8TY73dLTua6X+fXFFu3ZjcvrqbDdYbWyKSPjqNsoRBQ4rLXu43eb5LfvIWCz1vrLmmqcEMcTs8P5ng2yPReYCtxZNc0RQogT35aszIBrJpV4PPy4eSN/629uLSd/eUF6sLTWFLldEZWJn9ClG2e2O4mzP/uIXTk5AY8xu+jutqzgScjhwqPrifJqTVp8Ar9feS2ztm9jb14e3Rqm07dxk7DmMrVMSWHtoYNleqIAnB4vjRIjm7dV4nbz9vIlfLluDR6v5uz2HbipT/8TpmKgOPYU1Tac7xKgkVKqtHs5BrAqpfK11ubXWxCiljBbWELWkxJCiCqSYLfj8Qa+KkqwR3Zx3a9JE2Zs21phe1p8AnUcka/DlOyI5blRp3PBV59V2NcwIYGuJhczbppcJ+i+eHtk88C01ry9bAmvLVlIgdNFUkwMPdLTcfiqHHZvmB5W8nhdr75M27K5TBJlAXqmp9OsTvD2h2rfNd99y7L9e48kze8sX8qv27fx3YTLsR3F+mKiFgmjst5RGgb4fxjvBPoAl1bHgwtxvJHf4EIIUc2aJtehTb16FebwxNlsTAxjbSh/wXpKUuMTjrqyXGZRETEB5ke5vN6g85zK69ekKcFaMbxl64ja9b8Vy3hh4XxyS0rwaC/ZJcX8tnMH07du4YFZMxj07pvkhFjvqjybxVLxtVKKmEqGNAazbP9eVhzYV6bX0enx8GduDr8ESHiFqEla651a6y2lNyALKPL9XwhRjiRRQghRA948cxxNkpJJsNtJsMfgsFo5t2NnxrXvGFG86VsDX+esP3yQQpfraJrKp2tW4fRUnCtU4vaw8sB+UzE6pTWgU1qDColUrM3GjX36hd0mrTUvLZwfcFhkqaziYu6eMdV0zLeWLalQPdCrNQv3/MmevPDXslqxfz+uAK9bgcvFsn17wo4nai+lq+YmhKg6R1XiXAghRGSaJCfz68RrWLxnN4cKC+iZ3pgmyZEXOnV6AicTGnB7PZQdpVM1sZUiYJIQ+FjFB+PP554ZU5m7axegcVhtaGDspx8xsnUbHhhyCmkJCabiubxe8pzOSo/7fecOU/EAtmRmVJgPBWC3WtmelUmTpPDOT6PExKA9dUe7NpaoZWogAdJa/6v6H1WI44f0RAkhRA2xKEX/ps0466QOR5VAAYxs3TbgHJu6sXEs3rvH9LC7QMZ16EScLXAS1iO9kek4dePieOfsc1l87Y2cVD+VYrebApeTApeT7zeu5+zPPqLIbK+ZyTkigZKiYHo1aow9wGuY73Ry7Xff8s9ZMwL2yAWT7HAQ7FUP9DhCCCGOH/JbXAghTgD3DBpCanw8ceUKKWQUFXLHtB857aP3yIxwPabx7TvSM73RkQIQdouFWJuN5087PaKqfxsyDrPh8CHc+q8UwwscKsjnu40bTMWwWa1YTcz1ig1jPtO1vfoQa7MFnLvl9Hr5dsM6Hvz1F9Pxvlm/Lui+HzdvMh1HCBnOJ0T0kSRKCCFOAGnxCcy47CoeOHkYjZOSjvxy92pNgcvF7twc/u/33yKKbbda+eCc83l5zFlc3q0Ht/QdwIzLrmJk67YRxft953Y8AXqIvMDULeaSC4tSnNexc6UV7mLC6PFpnJjEsJatUUFKYBS73Xy3cX3QcvKB2hjM4j27+cfMnyNeXFnUIhrw6qq5CSGqjCRRtZzXq/lp1hquu+8jrrjzPT74egFFxZXPMxBCRJ+EmBgu6dKNQwUFFYaRubxepplMUAKxKMWprVrzyLAR3N5/4FENP8wPMZepyG2+CMZDp5xKv8ZNcQSoHFgqIYz1mKZt3cz0rZvxhpiAYrVYyDDZo3dJ1+5B93kxeqomfP05OsLy1eEMVRRCCFG1JImq5R5/bSrPvz2T9Vv2s23XYd7/agE3/fNTXC7z4/6FENEl2KV1tFxy92vSLGi58wFNmpmOE2+389G5F/DBuPODHpNRXGQ63qSliyud86RQphfe7ZneqMLwSn8ur4ddOdn8sftP0230as2rixbQ481XafvK85z20f+Yu2un6fsHU+x2sTHjMFlF5l8vUY10Fd2EEFVGqvNFmUUrdjBlxkoKi5yMGtKR04Z2xGYL/i3r0di1J5Nf52+ixPlX5a0Sp5s9+7P5bcEmRp0cWallIUTNUUoxvGUrZm3fVmbInFUpRrZqU4Mt+8uIVq2pExtLdrk1nBxWW8jem2Cs1uDfBzrd5r8Q2pWTHXJ/nM3GXQMHm54HNmv7NopClGAHo4dwY8ZhBjVrbirmk3Nn8/HqlUfibsnM5PofJvPxORfQs1FjUzHKe3PpIl5euACLUri8Hk5r3ZanR40mNkgxEVH9ZD6TENFHeqKiyBsf/c4DT09h9oLNLF65k+ffnsnfHvkStydwfafcoqNb+2XVhj0BF+EsKnaxeOWOo4othKg5jwwbQVpCAgm+QhAJdjsNExL59ymn1nDLDA6bjc/Pu5iWKSnEWq3E2Wykxsfz7tnn0DAxMex47erVD7qvTqzDdJxQvVDNkpJ5ZtTpXNWjl+l4n69dVekxdouF1il1TcUrcDr5cNXKColZsdvNy4v+MN0ufz9s2sBLC+ZT5HZR4HLi9Hj4eetm/jXLfAENIYSojaQnKkocOJzLFz8sxek3jK64xMWmbQeYt3gLpww4qcJ9/u/HdTxzfvjf2paqn5KAxVIxibLbLDRMPbpyy0KImpOemMSvV1zDtK2b2ZKZQbt69Rndpl1ElfSOlXb16zPz8qvZnp2F0+PhpPqpIQsxhJLkcFAvNpbMcj1bACPC6H1zhug1uq3fQM5oV/H3cCg5JopG1I+P5+QWLU3FO1CQj9WiIECutzkzI6y2lXp10QKKyyWPTq+X7zZt4JFhI8KaUyaOIZn/JkTUiZ6/qLXc8jV/GkNSys1FKip2MSdAErUro5Avl+zm9lPb0axefESP2bdHS+Ji7RQVO8v8frZaLZw5omtEMYUQ0cFhszGufXQPyVVK0bpuvaOOk1tSTL4zcM/8qoP7w2kRwSaOmF1U2J/FxHXvoYICo7CEiQQyPTEpYDEJBbSvnxp2+wD+zM0JuN3j9ZJTUixJVJSQ4XxCRB8ZzhclEhNiAw6ts1oUKckVk6Spa/b5/g3nAqEsm9XCq49eTIsm9XHE2IiLtVO3TjxP3HcO6WnSEyWEOD7kOZ0Be9WBCvOuQokPkTBEUo1wfebhSo8pcrv5aPVKU/Hi7Xau7N6rQrEKh83G3/oPCrt9ELyDQwOWoOU/hBBCSE9UlOjfoyUxdivP/msCbVukHdmuFDhi/prcq7VGKcU0X/I0bc0+rh/aOuLHbda4Lh+9dBW792VR4nTTqllq0IsRIYSIRo0Sk0iKcVBcbjieVSmGNm9pOk67evVZsm9Phe02i4V68eH3+Jv9Tbpg9y4mdu9p6tjRbdry5tJFZbYl2O10SmsQZusM1hCNjDlGRY1EmKSynhBRSXqiooTdbuWFh84nPS2Z+LiYI7e42JgySY1SiuW7slixOxuA5X9ms2JXVqXxK1uHpGmjurRpkSYJlBDiuGNRisdHjCLWZjsyryrGYiXZ4eCOAeZ7aCZ270lsgHljqfHxdI4gSenbuKmp45IdsaZjXvT15xWupzOKirjph8nmG+YnVPVAm5JLhGigAKV1ldyEEFVHeqKiSLtWDfF4vOQVFJOUYPxR/WThTp6evhGn+68KfYXOv8bmaw3jX59PfMxf3xjG2Cz8fXR7LunfAoCV63bz8Is/kBDn4NYrhzGgZ6tqekZCCFE9RrRqw1cXTOCtZUvYlZPNgKbNuLJHL9LiE0zHOKPdSczdtZMpm9YDRk+W3WLl7bHnBBxuXZmTm7fgl+1bKz2urcl5YSv37wtaQXDWju1hta1U4NqvhsX79oRVmEMcQ6FOlBCiRkgSFWWsVsuRBEprzSX9W9C1SQq3f7ac7YcLgt6vNLFqlZrAKxN60qVJHbxezbufz+P9rxegNRwin38+PYWnHziX3l3NrUkSqcNZ+RzKyKN543okxJsvMSyEEJHqlNaAF0afEfH9lVI8MfI0ru3Vm4V7dlM3Lo5TW7aOuKphi5SUyh8T8/OttmQFr8CngQFvv8Gzp41hSBhDGIOX0oCT6kVWrEIIIWoDSaKiWOk3n12b1uGH24bw0JQ1fL2s4nj9Uuf3bsojZ3cmwWFDa80dj37BstV/ljmmxOlm0sdzePPJS49Jm4tLXDz64o8sWL4du92Ky+3l4rG9uW7CkIi+yRVCiOrWpl592oRYe8qsz9esrvQYDaSa7C3rnNYw5P6DhQVcMflrJl90Cd0aNjIVc2SrNswI0FuWHBNDszp1TMUQx54MxRMi+siA5+NEgsPGcxf24NQOgcfln9qhAc9e0J0Eh5EXZ+cWsWbD3oDH7tqbecza+eykGSxYvh2ny0NBoROn082nU5bw7xd+YMOWyCsJCiHE8WZPXl6lxyhg7aEDpuKZvY4OZ6Hcx0eMCngh8Oyo003HEMeYrsKbEKLKSBJ1nPGfGxVqe1JiLPYglZWaNqpb5e0Coxdq1ryNZRYMBnC5Pcyat5FbH/qcfzw1Gbcn/MHdxSUuNm47wOHM/KpqrhBCHFP9mlReWEIDBUHWuCovxmruT/a27MqLDZW66+epAafb3DH9RzzeyCfiHCos4Jv1a/lh0wbync6I4wghRLSS4XzHkexCJwu2GWPilYJW9RPYnlGA1rBgWwbZhU5S4o11TmxWC5ed25/3v/qD4pK/qi85Ymxcf8mQY9K+wiJnyC+6iktcLF65gx9nrmbcad1Nx/3i+6VM+nQuVovC5fbQs3MzHrlrLIkJMtdKCBG9JnTtxjvLl1TaAVB+3adgmibXwULlNQZS48yXY5+za2fA7YVuN93/+wq3DxjEdb36hDUc+/2Vy3hy7u9YLcZKU1rDa2eM5ZSWUtQoMtp8N6QQotpIT9RxZMa6A7i9mgZJDj68uj+z7hnGB1f3Iy3Jgdur+WV92SEhl53Tj+svOZmU5DgAmqan8MhdZ9G3e8tj0r66deKJsYdeV6S4xM33v1Q+T6DUvCVbmfTpHIpLXBQUOXG6PCxb8yePvPRjRG10e7y4XIGrW0UDl8vDrj2Z5OabXyBUCBGdWqXU5frefYmxhP5Ta3ZOVJHbZSqZuW/IUFPxKlPocfPSwvm8v3K56ftsyjjMU/PmUOLxUOhyUeByUeh2cfNP35FXUlIl7aqNlK6amxCi6kRdT5RS6hLg43Kbp2itx9dAc6LK1DX7Gd6+Ac9e2I36vl6Yk9ulMe2Ok7n7i5VMXbOf83s3O3K8UooLz+rNhWf1PrJI77Hk8Wq83sp/S3uClOgN5OPJi8r0pIExPHDJqp1k5RRQt465i4+snEKeefNn5i/ZhldrunZown03nUbzxuZKC1eHb6Yu542P56C1xu3xMrRfW/5xyxhiHfbK7yyEiEr3DR7K8Jat+fdvM9mYcbjCfgUUuswNd8sqLsZhs1HoCjz8z6oUd/YfxOltTzqaJpdR5Hbz6qIFXNmjl6njv92wDleA3/EWpZi5fRvjO3SssrYJIURNirokCugEfAvc7Let1n8tX+LycGqHBlw2oEWFffUTHLx3VT8+WrCTEpcHR4DeoOqojJeTW4THEzqJcsTYGD2ss+mYGVmBy7rbrBayc4tMJVFer+aWBz9jz/5sPL75WKvW7+bGf3zCF69fV+XDAjfvOMj3v6wiL7+EU/q34+R+bbFWMpdh7uKtvP7h7DIJ45xFW0BN55E7z6rS9gkhqle/Jk05pUWrgElUjNWKx+RQraZJyQEXwFXAqFZtePXMs7FV0usViaziItxer6nYxW433gDPRwMlnuAL+4pKyHA+IaJONA7n6wSs0lrv97tl13SjaprDbuWyAS3QQX6Raq25bECLgAlUdUlKdBDqb2xcrJ2TWjXg3DE9TMfs0605VmvFBNBiUTRNN1cgY+nqnRzKyDuSQIHx96i4xMWH3yww1XtmhtPl5rPvFnPjPz5h8rSVzJiznsdencpd//dVpcU0PvxmQYUeN6fLw5yFm8krqPXfIQhx3BvTtl3QuU/DW7U2FcNutfKvocPKxLEqRVKMg38OHR5RAnV6m3aVHtM4Kdl07NFt2hFrq9h77vF6OaVFy3CbJwA0KG/V3IQQVSdak6iNNd2IaBWsRyka1mCKsds4d0xPYh1lLxSsFsXQ/u149K6xvPp/FxNjN98BesV5A0iIc5TpyYl12LjtyuHYTSaMu/dlB0yUnC4Pn32/hHNveJN1m/eZblN5JSUuHn91Gqdd9jKvvj+bEudf38QWFbtYt2kfv/2xKWSMQxmBqw5arRZycosibpsQIjr0SG/EuR07E2ezozCGt8XabPyt/yCaJJlbbBfg/E5deGvsOQxp1pxWKXW5sHMXfrzkiojXdHr1jLF0b5gedH+szcY/wphj1b9JU8a0bUe8L5GyYDzPOwYMIj0xKaI2CiFENIqq4XxKqRigDXCWUupRjCTvS+DfWutaOSM10rlM1TEHKpAbLj0Zi0Xx1U/LcXs8JCXEcvPlQzl9eJeI4jVMTeb95yfy8eRFLFm1iwapSVw6vh+9uzY3HaN1i1QslsCvhcejOZyZz52Pfsm3k24kPi4m7DY+4ltc2B2k/HxRiYtZ8zYwckiHoDF6dGrKL/M2VEj2rBYL6WnmL7BONDX1PhbiWHh02AjGte/IT5s3YbdYGN+hIx3TAq/9F8qgZs0Z1Mz878BQlFJ8e9GlZBUVsXzfXjKKCvlw9Uq2Z2XSrE4d7h44hBGt2oQV79lRYzi3Yyembt6Ew2bj3A6d6Nwg9ELBohIynE+IqBNVSRTQDqNNBcD5GAnVS0AScIv/gUqp64HrAZo3r5o/JtEo0gvImrrwtFot3HjZUK6dMITCIieJ8Y6gCYxZafWTuOOaERHfv1uHJrRqlsqWHQcrrGFVyuvVzF6wKexk73Bm/pHFhYNRCuLjQydnV180iHlLtlJU4jqSSMU6bNx8xSnYbFY8Hi8FVfR6RjuPx8sHXy/gix+WkldQQqvmqdxx9alhJc5CRCOlFH0aN6FP4yY13ZQK6sbFcWprI1m6oHPXo4qllGJwsxYMblZxDq+IkORQQkSdqBrOp7VeC6Rqra/XWq/UWn8D3AFcp5SylTt2kta6j9a6T1paWk00V4Rgs1pIToyNigt+pRQv/vsCzh7VPWgJdpfLQ1YEw+b2H8qtdFihI8bG2aNCr4vVtFFd3n32Ckad3JH0tGS6d2zCf+4dx9iRXfnwm4WcMfFVxl3zOmdd/RqTp68Iu53HSkZWAXv2ZwedqxeJ197/jY8nLyKvwOh83r7rMH9//BvWb4l8yKUQQgghRFWKtp4otNYZ5TatB+xAGiBXUSIi8XEx3HHNqQzp24Z/PDWZouKyJYJtNgu9OjcLcu/gmjepF3TdKZvVgsWimHj+QLp1qPyb5ybpKTx4+xlltn08eVGZBZNdecW8+v5vxMXGMPqUTmG3t6oczMjjoee+Z+O2A1gtivi4GE7u15ZGDeowtH+7iEvHFxY5mTxjFU5n+SIbbv73xR88/cC5VdF8IYQ4rigZzidE1Imqniil1LlKqQO+uVGlegLZwP6aaZU4kfTu2pwu7RuXKX4R67AzoFdrOrQNPrk6mOTEWM4Z06NCMQ1HjI1bJp7CV29cz+Xn9o+orVprPvpmYYWqfcUlbt79Yn5EMauC16u57aHPWLdpLy6Xh+ISN5nZhUz5eRWTPpnLlXd/wPtf/RFR7IMZeQQoxojWsP3PiuWhhRCiVtC6am5CiCoTbT1RszGWvJiklHocY47UM8AzuirHC4laSynFMw+cy9Tf1vLjrDVYLIqxI7py2tDIe3VunTiMpul1+fS7xeTmF9OjU1NuumwoLZrWP6q2Ol0eCgoDL8J5KDNwNb/qsHrDHvYfyiVQZXivV+N0uvng64Wc3K8drZunhhW7Qf2koPPL0lOjp8CG1prFK3fy/S+riHXYufCs3rRrFX6BACGEEEIcn6IqidJaZyilRgPPA8uAHOAN4IkabZg4odhsVsaO7MbYkd2qJJ5SinPG9OCcMNa/MiPGbqV+3YSACVPLJpENl6sKW3YeqnRRZbfbw+wFm8JOopQKPn9aRcH8OjASqDse+ZKlq3cd2TZt9louGdeXmy4/pQZbJoQ4IWlA1ngSIupE1XA+AK31cq31cK11ota6idb6UemFErWRUoqbrzgFR0zFoYI3X1FzF+te77H7a37gcF7Qfdv/LD9dMnxH86skK6eQh1/4gWEXvVAmgTLiwseTF3PgUM7RNlEIIcpQaJSumpsQoupEVU+UEKKsUSd3JC7WzlufzmPfwRxaNq3HjZcOpVcNlvtu2TQVq9WCxxM8mbLZrAwbeFLYsR12a8CFkcHo3YrU/KXbePqN6RzOLCDOYWPiBQO5dHy/MksBuFwe5i/bxuHMfLq2b8xJrf9a18bt8XLjA5+w/1BuyOf92fdL+dvVp0bczqNVXOLil7kbWLNxL82b1OOM4Z1JSY6v0sdwuz1YLJaoqLwphBBC1BRJooSIckP6tmVI37Y13YwjenVpRt3kOA5nFVTYZ7MqLBYrE88fQKtm4Q3lA2MeWLAErXyPnFl/LN3K3x//9sjPRSVu3vhoDvsO5XLv9aMA2LUnk1se/IwSpxu3x4tS0L9HSx69+2xsVgvzl2wlK7sgZAIFRhJzLG3bdYj5S7cRY7dx6qD2pNZLPLIvK6eQa+/7iNzcIopKXDhibPzv8/lcO2EIA3u1ovlRDgFdu2kfz02aweYdB4mx2zhjeGdunTgMh8N+tE9LCFEZ6UUSIupIEiWECIvVauHlRy7i3se+JjOnEKUUHo+Xxg3rUCcpjvPP6BVRLxRA44YpOGJsFBaVLaihFPTt0TKimI++9FPA7VOmr+SOq07FbrfywNNTyM4tLHOdsmjFDr6bsZJzx/Rkx+6MClUSAzkjzMWa/Xm9mnVb9pGXX0yX9o1JSogts//V93/j22krcHs8WC0W3vx4DvffPJpRJ3cEYNInczicmX8k0SvxlYl/7f3fmPTJHHp2acZj946LKBndvS+LOx754sjSACVONz/OWsPBjHye+sc5ET1frTW//rGJ72asxOXycNrQTpwxvEul664JUStJEiVE1JEkSggRtuZN6vHZa9eyZechJk9bwdTf1rJzTyZaazZs3c+aTXu5deKwsOPa7VZuuuxkXvtg9pGkxWJRxMXaufL8gRG1tXTR3kA2bz9ASp149h3MqXCNUlziZsrPqzh3TE9aNKlHrMNOYXHgaolGOyE7ggWbAXbtzeSuR78iJ68Ii0Xhcnu58ZKTuXBsbwBWbdjD5OkrjyRGHo8xtPHJ16czoFcrkhJi+X3hloA9ZV6tKXG6WbZ6F5M+nsNtVw0Pu32ff78kwNpdHhav3MHeA9k0bpgSdsyn3/iZGXM2HOm927jtAL/MXc+L/74QqzWy6bq/L9zMe18t4FBGHp3aNeL6S4bQpoUsxi6EEKLqRV1hCSHE8UEpRUJcDFN/W4vT5cHr1WhtJB/fTlvB5u0HI4p7zpiePHr3WLp1aEJ6WjKnDe3Iu89cQZP0lKp9Ahhzt0qH7wXi9iUrg/u0oU5yHNZAi1j5xNhtZOcWht0GrTV3PfIl+w/lUlTsoqDQidPp5s1Pfmfl+t0AzJiznhJnxaGCVqtiwbLtABWSnPKcLg/fz1wddttWrt/NnEVb8ASYqxZjt7Fnf3ZYMQF27M7g59/Xlxn+WFziZsPWAyxYvj3seACTp6/g0Zd+ZNO2A2TlFDJvyVZufOATtu06FFE8IaJGaXW+qriZoJTqoJT6RSmVr5TaqZS6tyqfjhAnCumJEkJEbP7SbQG3u1weZi/cHPHaSYN6t2FQ7zZH07QjGtRP5GBGxTLxSinatmyAUpCcGEdxSdnKgI4YG6f5hsrZbFbefOISXnh7JrMXbg5Y/EJr6N6padjtW7d5P4eyKravxOnh8x+W0r1j8Jha/1USvsRV+XDDkkoSrbKxNY+/No2ZczfiDBK7sNgZ0XpoK9b+eSRB9VdU7GLRih0M7hPeuXe7Pbz+4ewKQy6Lil28+fHciIccChEtqquynlLKDkwFfgVuBDoAnyil9mqtP66WRghxnJCeKCFExGxWS5kKd6WURWG3Rcfclqf+cU7AnqbbrjwFi0WhlOKRu84iLtZOjG8+TlysnRZN6nHhWb2PHF8vJYH/u+dsZn56B53aNSoztyjWYee0oR1p3jj84g37DmYHXXdr6w6jF2XUyR0DzmXyejUDerY68v9QlIIeYSR5i1fu5OfZ64ImUKWPGRvBHCujeEjg9oZbQn/Jqp1c/4+PKSwKXNRjYYQ9W6XcHi/7D+VWmKcnxAmqCbAIuEVrvUVr/QPwCyCL4AlRjvRECSEiNrR/O15577cK221WC6cOal/9DQqgXauGfPrKtbzw9i9s2HaABvUSue3KYfTq2uLIMV07NOGL169l6m/rOHAol15dmjG4b1tsAebm2G1WXnn0Ir6bsYoZc9bjcNgYf1r3iJ+v1RL8u6zSMuLdOjRh3GndmTx9JW6PF6tFgVLcf/NokhNjg96/lM2qcDhiuPPaEabb9fkPSwIO4Stv47aD9O3eotLjSrndHrbuDD7ErrKFnP3NWbSZB5/9HneIqomh9gWjtSYzu5DfFm7i7U/m4nIbw1VHDunIPTeMJMZu/k+n1prfFmzih5lr8Hg8jD6lM6NO7hjwvSVEUNXUE6W13gFcBKCMb8gGAUOBW6qlAUIcRySJEkJErF5KAvffPJonXp+Oxdfd4/V6ueWKU2jWuG4Nt+4vTRul8NyD54c8pm6dBC4Z19dUPEeMjQvO7MUFZ/Y6+rY1rovdZsHlrnix37ldoyP/v+3K4Zx5ahfmLdmGI8bG8IEnkVY/6cj+Oslx5AQpbHHx2X05/8xepNZNDLg/kP0Hc00dt3t/pukkatO2A9z56FcUFAUv9nEoM/iCy+U99spPESVJofyxdBtPv/kzWdmFFWL/Mm8DWnv5521nmI735OvTmTl/I8W+yoarNuzlo28Wcu6YHpwy4KQyZeqFCEzXVHW+3UBj4Afgq5pogBDRTJIoIcRRGXVyR/p2b8HcxVvxejWD+rQO62K9tmvbIo2WzVLZuvNQmSF5jhgbF53dp8yxrZun0bp54Gpzt1xxCk+9Pr1M75FFKcaO6sqNlw0Nu13NmtRj557MSo/buOUgjK48ntvj5e7/fE1OXugKhjEmhwc6nW7yC6t2Xa5N2w7wr2e/Czp3zOl088vcDdxxzQgS4h2Vxtu68xA//76uTILsdLrZuSeTF9+Zxcvv/cr9N43m9KMojS9EmFKVUkv8fp6ktZ4U5NhxGEnUf4EXgNuPdeOEOJ5IEiWEOGopyfGcNaJrTTfjuKSU4rl/nceDz37H+i37sVot2O1W7rvpNNq1NF+Y4/RhnTl4OI8Pv1mAxWLB7fYwckgH7rja/BA+fxPO7sOCpdsq7enp2cXcPKtV63abKmzRwuSiwK4AhSmO1idTFuN0hY7r8Wqyc4tMJVELV2wP2MMIRkEQj8co3jGgVyvq1kkw3c6f56zng68WkJldQKd2jbjxsqG0bSml3E9YmqrsiTqste5T+WGgtV4CoJSKB95XSt2jtZbJgUL4SBIlhBA1rF5KAq/9ZwKHM/PJLyyhWaO6Ya+VpJTiygsGcvHZfdh7IIfUeomm5ksF071jUy44qzdf/7QMl9sT8BrOalWMPqWzqXiFxS6CF4j/i+k5UcdgdFPpWmeheL2a+LgYU/HWbtxb6TFaw1c/Lee6CUNMxfxk8iLe/mzekWRvwfLtLF+7i9cfm0D71ummYojjUNWOWg1KKdUE6K21/s5v8zogBkgGDldPS4SIfpJECSFElEitl3jUc2RiHXZaN0+tkvbccsUpnHlqF+Yv3ca8xVtYuX7PkX0Oh43/PXOF6VjdOzUpsy5UMPsO5ZiKZw+juINZRSEWU/Z3MCOPunXiKz2uIEjFwPIWLNtmKokqcbrLJFB/bfdwzb0fUbdOHA/cejoDe7U29bil9uzP5v2vF7Bq/W4aN0jh8vP607Nzs7BiiBNGR+AbpVRjrXXpYn+9gUNaa0mghPAjSZQQQoigWjatT8um9blkXF9KnC7WbNhLvboJtGoWXqLmdJobfucxWSjCYqZbi78qHJqx/5C5Yhp2k72EMTHmyvybLeKx72BOyOGGWTlF3PvYN0x64hI6ndTYVMxdezO57u8fUVTiwuvV7N6Xzcr1u7n/5tGM8q2TJmpeda0TBczG6Hl6Tyl1N9AGeBJ4rLoaIMTxQmqsCiGEMMURY6d3txZhJ1AA6zbvI9Zhr/S4eiZ6eMBYANlMj1vXDk1MxQPzCZzV5BpoZpMti8njQq3Z5e+5t2aaOg7grU/mUljsKlPUpMTp5qV3Z5l+PUQ10LpqbpU+jHYBZwFuYCHwJvAi8PKxfHpCHI8kiRJCCHHM1a0Tj9fERZzZBAXgn7edTqzDHrS3yRFj4+E7zjQdz+wcMrPJRXZusanjGjVINnVcWr3EgAtHl7drb+VVFUutXL874Dyw7NwiJtz2DrMXbDIdS5wYtNa7tNZna62TtdZNtNZP6MomCwpRC8lwPiGEEMdc55MaUS8lgX0Hc8r0epTnDaP3o33rhnz00lV89dMytu48RKMGyWRkFXDwcB79erTkkvH9qJMUZzpear1EcvIqT3y82mwSVWjquJNaNTR1XEpyvKkibWbma5Wql5JAZnbgdu49kMOjL/3E/S6PDO2rSRowsfC1EKJ6SRIlhBDimFNK8dK/L+D+pyaza09mwLk9cbF2uncyVzK9VHpaMrdOHFYlbYxzmKu6Z3Z+V35h8EWFSylg/OjupuItX/unqeMmjDVVwRqAy8/tzxOvTaO4JPBQwRKnm/9++LskUTWqxhbbFUKEIMP5hBBCVIv0BnV477mJvP/8lXTv1JQY+19D92LsVho1qMMp/dvVWPtGDOmAw8Riv8mJ5nq30tNCD9OzWBQjhnSgrcn1wDKy8is9RinYtS/LVDyAEYM7MPH8gcQ6gj/vgxl5la4XJoQQtY0kUUIIIapVs8Z1eenhC7np8qG0bFqfpukpXDK+L288fgm2MOZEVbWzR3WjdfNU4mKDF8CokxRHk/Q6puL97epTg+5r1aw+D995Fv8OY87W4D5tKz1Ga1i7aZ/pmGD0Rv3wv1uCltdPSY7DFua6ZaKKVVNhCSGEeTKcTwghRLWzWS1ccGZvLjizd0035QhHjI3/PjaB2Qs3M3vBJhYs206J043Hq1HKWJvqkbvOQpmp7gB0ateIYQNO4rdyxRm6tG/MG49fEnb74uNiGNqvLb8v2hL0GKtF0bJpvbBjxzrs3Hz5UJ5+4+cyQ/tiHTauunBg2PFEFZMESIioI0mUEEII4WOzWRkxuAMjBnegsMjJT7+uYenqXTRJT+Gc0T1okp4SVrz/3Hs2m7Yd4K1P5+H2eLniKBeyffy+8bz3xXw+mrwo4Dwmu93KhHF9I4p92tBOlDjdTPpkLjl5RSQmOLj6gkGcO6ZnxO0VQogTlToRqlb26dNHL1mypKabIYQQQlSb4hIXL7w9kxlz1uPxapo0rMM9N4yiV5fmRxVXa43T6SYmxma61+1EpJRaqrU2X6XjGKnjSNeDmlxWJbGmbX8uKp6TECcC6YkSQgghjkOxDjv/uGUM994wCqfLQ3ycueqClVFK4TCxMLKoLhpMltUXQlQfSaKEEEKI45jNZq3RghxCCFEbSRIlhBBCCBHNToCpF0KcaCSJEkIIIYSIVhrwShIlRLSRhR+EEEIIIYQQIgzSEyWEEEIIEc1kOJ8QUUeSKBGV8rML+Pbln5j/3WJSUpM5529n0u/0E2+tErfLzbxvF7F42nLqptdlzNXDadK2UU03SwghRDSRJEqIqCNJlIg6BTkF3NTr72Tuz8JZ7AJgzbwNXPqv87j4vnNquHXhKSooZsn0lXhcbnqN6kZyvaQj+5zFTu4e/jA71uyiuKAEq83Cty//yAMf38GgCBfLjAZer5dd6/dgs1tp0q5RrV5nRgghhBAnJkmiRNT54c0ZZB7IPpJAARQXlPDhI19y1g2nkZiSUIOtM2/x9BU8ev6zWCwWNBqPy8Ntr13LmKtOBWDqOzPZvGwbHpcHAI/bi8ft5MnLX+brw+9ijzG3TovWmjlfL2DKa9MozC1k6PkDGX/b6cQlxkXc9rnfLuS9Bz/j4K7DNO/UlGufuJQew7tUer9Vv6/jsQkvUphbhNZeUpvU4+rHLqFNj5bEJ8ezfsEmUhrUoWP/dhWSq42Lt/DpE9/w58a9dOjXjgn/OIemJzWO+DmsW7CJ125/l83LtpFQJ55zbj+Di+8bj91hl8ROCHEc0dITJUQUUvoE+GD26dNHL1mypKabIarIXac8xOo56ytsT6gTz0Nf3k2vkd1qoFXhKcgp4KImN1BSWFJme0xcDJNWPkuTto24tsud7Fy3u8J9lUXx/OxH6TK4g6nHeuOe9/nxzRkUFxiPFRNrJ71VA15f8hSOOEfYbZ/x4WxeumkSJYXOI9sccTH854d/hEykMvdnMbHdbUfa8dcTAqvVisfjITbBgUJRNz2Fp2c8RMMWaQAsnracR85/FmeRE63BYrXgiIvhpXn/oVXXFiHbe2DnIV697R2WTF+J1WZh2EWDOOP6Ufx95KNlX38FaIhNdJCQHE9hXhGNW6dz1WMT6H9GLw7uOsRPb8/kwI5D9BzRlWEXDSImNvDipVpr1v2xiXmTFxETa+fUS06meYcmoV/YarZ/50Heuvcjtq3aQZseLbnh2StIa5p6VDG11hzek0l8UiwJdY7+y4xDuzP4+D9f4yp2cf69Y2nVuflRxVs2czWfPv4N+3ccpMuQDlz24PlVMjxWay2Jdy2klFqqte5T0+2oY2+gB6VeUCWxpu1/PSqekxAnAumJElGnfpN6KKUon+B73B5SGtSpoVaFZ/6UJVgsFS+6PG4PMz+awxUPX0jWwZyA99VeTcbeTFOPc2h3Bt+9Ph2XX6+ds9jFwV2HmfnxXM64dkRY7dZa89Z9H5VJoABKipy8dd+HvLboqaD3nfHhbLweb4CgxvMGKM43kpqSbQd48OwnmbTyObTWvHzz22Ue0+vxUpRfzKR7P+SJaf8K+piFeUXc2v8f5Gbk4fV4cbtg1idzmTdlMc6icsmc7+1UnF9ypB1bV+7g/y58jgvvOZsvn/0et9uD2+lm7rcL+fzpybz8x+MkJMdXeI1evPFNZn0yl+LCEqxWK18+9z03PHsFZ980Omhbq9Oq39dxz/CHj3yGdm/ax+9fLuCFuY/SeaC55Ly8Ka9O5bU7/of2lVqu36Qeb616jqS6iRHFe+Pu9/j6hR+P/PzzB7/Re1Q3npz+YETxZnw4m2eveR2v23gP7t9+kDlfLeCN5c9E3KM57X+zeOWWt41ecQWnXDiIBz7+GxZLeIVtsw5kM3/KYtxuD0l1E1n00zLssXbOvG4kHfq1i6htQgghapaUOBdR55zbzyAmruxQNovVQnqrBrTqenTfVFeX4oLigAmF1+2hML8IgKR6gS8+lVLUS08x9Tjr/tiE3V7xu5DighIWTV1mvsFH7ldM7uG8gPt2rtsT8r6Hd2eWGYIZiter2bt1P39u3ENhXhGHdmcEPG7tH5tCxpn58ZwKr7XL6aYgp9D06JeSQicfP/YNxYUluJ1uwHj99m07yFfPf1/h+NVz1hsJVEHJkQTRWeTkzbvfD5oYV7eHxj1V4UsIrTUPjg2eBIcy59uFvHr7u0cSKICMPZlc2Oi6iOLt3ba/TAJVaumMVcz89Pew43ncHp69+rUjCVSpkiInT1/5WkTte+Csx3jumv/+9Z7WMPvz+dzU5+8Vjj+0O4ONi7dQmFdUYd/MT+ZwWaubefX2d3j11nd44tKXmPnxHKa9M4vbBjzAa3e8G3b7ALav2cWcbxaQdSCLRVOX88tHv3Ng56GIYgXidrkrvIdEDdK6am5CiCojPVEi6nQacBK3vnINr//tfyiLwuP20LxDEx6dct9xM6Smz5gevHH3+xW2O+IdDB7XD4DTrz6Vd//5aYVkyx5r46Q+bUw9TkqDZDQV/zBabRbSmtQPu92OeAexCQ4Kcgor7GvQLHS87sM6M/1/v1KUX2zqsaw2K4W5RTRq3RCr3Xqkt8pfnfqhezm2rthRcfggRiKK0qavGQIlvK4SF7M/n8/Ehy8qs/33L+dXGKYJRqK/eOpyTps4zNyDHiNerzfg+QPIy8yPKOZTl78ccLvb6WbelIUMHtc/rHj/ufj5oPtevPEtRkwYGla8/TsP4vUEPtnrF4ROxMu7qc/f2bJse9D921bsJC87n6SURArzivjPxS+w4tc12GNseFweJvzjHC791/mA0QP19JWvVkju/E1+eSrjbh5jurcsY18mV550e8D3PUCXIe15fvb/Rfy78qFzn+aPyYuP/NxjRBee/vmhsOIVF5Xw+u3vMvfbRWit8bg9FOUVo5Si9+juPPLt34lxmJvzWZ7X6+Xjx7/my2e+w+PyMHh8P+5860biEmIjinfckARIiKgjPVEiKo256lS+OvgOT0z7F28se4bXlzxNagRJQU1p1KohF/59PI54x5GLj9gEB4PP6U+XIcZwqrNvHk2Tdo2IifVdTCiIibNz26vXmp7L1PXkjiTXS0KVGzpoi7Fx1k2nhd1ui8XCRfcZ7fbniI9h4iMXBbmXYeDYPjTv2ISYuMDziAJp3b0FNruN0VcNr3A/R7yDC+45O+T923RvQWxCxdfK7rBjM1mYI5TyrwOALSZwYQqlFDa79agfMxqVH97p78tnfwg73rYVO4PuK84zl4T7m/XpnLDvE8irt78TMoEq9dnT3wLwzJWvsWLWalzFLgpziygpcvLZk5OZ/cV8AKa/92vIBKrUc9e+brqNl7W+JWgCBbBm7kb+e+d7puP5u3fkw2USKIAVM9dwU++KvW/BuF1uJjS9ganvzCIvM5/8rAKKfOdUa82SaSu4vPXNEbUP4Io2t/LBQ19QlFeMs9jFr5/NY3zKRFxOc73gxycN3iq6CSGqjCRRImrFxMbQacBJR1WhrSZNfPhCnv7lIc68YSSjrxrOv7++l/vev/XIBXhcYhyvLX6S656+nN6jujHq8lN4/rdHj1TvM8NisfDMzH/TvGNTHPEO4pPiSExJ4L4PbqdFx6YRtfvi+8ZzyQPnEJ8ch81upU5qMre8dDWnXDgo5P2sNivP/fYIEx+5kJZdmmF32LDaKiYVFqsFR3wMd7xxw5EKhDc+N5GTz+2P3WEnPjmOmFg74287nbGVzDEacdlQYuMdZeaf2WJsNGmXzjMzH6Jd79ZGQYkQYuMdxjy8colobLwj4OOPvGwo9gDfonu9Xvqf2Sv0g1UDi8US9Dkfi47cRq0bhn0fZa3aPz35QXrewvXTW7+YOm7O1wvJy8pn4U9LcZW4y+wrLizh82emGPHenmUq3sYl20wdt3XlDtzlHi+Qb1/+yVS88lbMWhv4cVfsMB3j4/98TX5WQchjMvdls3L2mnCaBsCSn1cEHLLo9Xh55Nznwo4nhBBHQ4bzCXEMdRpwEp0GnBR0f1xCLONvPZ3xt54e8WM0at2Qt1c/z58b91CUX0zrbkbvTqSUUlzywHlcdN94ivOLiUuKMz2R3hHn4MJ7xnHhPeMoyDEWTP796wXEJ8XRrH1jDu/JJK1ZKuNuGUOb7i2P3C/GYef+D2/nxucncujPDBq3Ta9Q0CGQ+KQ4Xln4BC/f8jbLZqzEYrUy7KJB3PziVSSmJPD6YmMO0Ipf1/DG3e+zY80uYhNi8bg8uN0eHPExXPrP8xh8Tj/uGf4w+dmFaK3xerwMOa8/Y64eXuEx2/ZsxaUPns+Hj3yJxapQFgva4+Wfn95ZJRXrqsLZN4/mu9emV9h+7h1nRRSv8+CTWDsv8LC4W16+Kux43U/pxNKfVwXcV6dBUsDtIeOd3Jlvnqs4xypcblfFIaWB9D2tO/lZBVht1gpJFEDWAWNuXGGeueTObG47/T1zSdmxkJ2RQ0r9ygv7zPlmgal4P7wxg+6nVL5sgr+Xb3k76L5F08KfA3rc0KB15T2aQojqFXVJlFLKAbwCXACUAM9rrZ+u2VYJEf2ata/aEttWq/WokoKEOglc9uAFXPag+dK8KWl1SEkLrwJjessGPP7jAyHLUPcY3oU3lj1z5Gev10thbhFxSbFYrUZv2YfbXmPFrDVk7M2i08DQPaAT7j+HEZcMYeFPy4mJtTNoXN+Iq9QdCze/eBWuEjfT3/v1yLYzrh3BdU9fFlG8x3/6F+elXYXbWTbJGHPNcBIjeI/c/+HtXNDw2oD7HvzsrrDj9R4VfNkDR7z54aUNmtXnwM7DlR5368vX4vV6iYmLqTC0zmK10GtkVwA69G3Lwh8rv7hv26uVqfa16hK63P+xFKj3NRD/BcVDadw2Pew2lBZ+qZVkKJ4QUScah/M9AwwCRgI3AP9SSl1cs00SQkS7cCa+WywWElMSjiRQYCSNvUd157SJw0wNIW3QPI2xN57G6CuHR1UCBcZzuWvSjXx98F1eW/QkXx98l7+9fn2Z5xuO+KQ4vjr4Lhf/Yzz1G9eldfcWPPnzg9z9VmRzW1LS6nDnpBsqbB//tzPoPiy83gkwekCHXRx4uOm/PjeflD301b2VHjPq8pNRSmG1Wrn15WvKJGlWu5X45Diu+PeFANz73i2mHvfed829jiOvOMXUcSkNkk0dF46ExMp7hgEmPhp67mSp0uIb4ZjwwLlB97XuUXMJphCidoqqxXaVUgnAYWCs1voX37Z/AWO01kOC3U8W2xVCiONPzuFc5k1ejNvpZsBZvWjQPC3iWFpr3nvoM7587jtcxW5SGiRz11s3MXBseOuKrl+0mUfPe5bDezNRSpFUL5HCnEIS6ybw5M8P0qZbyzLHr5m3gS+ensL+HQfpfkpnLvz7ONKa/lUEZ/aX83lswotlysP7G3vTadz+mvlS8a/f+S7fvjQ15DGvLXmSk3qZq/Dp7+PHvua9Bz+rsH38bWO45aVrTMd5894P+Oq5issDlPr7+7cw6vJhYbcP4MyES3EWVSx08vXhd033gpkVNYvt2tL0wKRxVRJrevY7UfGchDgRRFsSNQiYC8RrrYt924YB04EErXXAvnxJooQQQkQrt8vN0l9W4Xa6iU2IZebHvxPjsHPWDafRtqe5oXz+fvtiPpP+/gE5h3KJTYglP6sAr8dLapN6/OOj2+l2SueI2/rTOzN57bZ3cBa7sDlsXPfUpZx7e/hz6XIz85j2zixiE2I5qV8bfvtsHikNkjn7pjHEJ8VF3L6SohL+OfYJVv66FjQ0atOQp2c8RHrLBhHHDCZqkihrqh6YGLpSqVnTc/8XFc9JiBNBtCVR5wFvaq1T/bZ1BNYBjbXW+wLdT5IoIYQQouqEmmNYW0gSJYQIJdoKS8RjFJPwV/pzmQVblFLXA9cDNG/e/Ni3TAghhKglansCFXWi6AtvIYQh2gpLFFMuWfL7uUytWK31JK11H611n7S0yMfRCyGEEEJEM+31VslNCFF1oi2J2gPUVUr516RNx+iNyqyZJgkhhBBCCCHEX6ItiVoBODFKnJcaAiwNVlRCCCGEEOLEpY3hfFVxE0JUmaiaE6W1LlRKvQ+8rpS6EqMX6h58c5+EEEIIIWoVjSy2K0QUiqokyucu4L/ALCAXeFRr/XnNNkkIIYQQQgghDFGXRGmtC4GJvpsQQgghRO2mpSiEENEm6pIoIYQQQghh0ICW4XxCRJ1oKywhhBBCCCFqgFKqjVLqe6VUllJqt1LqOaVUbE23S4hoJD1RQgghhBDRSutqGc7nW17me2AdRpXkBsC7vt13H/MGCHGckSRKCCGEECKKVdNwvn5AW6Cf1jofWK+UehB4HkmihKhAhvMJIYQQQoiNwBm+BKqUBhw11B4hopr0RAkhhBBCRLNqGM6ntT4E/FL6s1LKAtwKzDnmDy7EcUjpE2AFa6XUIWBnTbejFkgFDtd0I0TY5Lwdf+ScHZ/kvB2fgp23FlrrtOpuTHlKqWkYbawKsUCx38+TtNaTgjzui8D1QF+t9doqenwhThgnRBIlqodSaonWuk9Nt0OER87b8UfO2fFJztvxSc5bWUopBbwI3Aycr7WeUrMtEiI6yXA+IYQQQghROoTvHeBS4CJJoIQITpIoIYQQQggB8BxwCXCu1vqHmm6MENFMkigRjoDjpkXUk/N2/JFzdnyS83Z8kvMGKKUGAHcA/wCWKKXSS/dprffXVLuEiFYyJ0oIIYQQopZTSj1L8PWg7Fprd3W2R4hoJ0mUEEIIIYQQQoRBFtutxZRSDqXUJKVUllJqv1Lq7yGOvUoptUUpVaSUmqeU6ldu/4W+/YVKqSlKqQbH/hnUTlV13pRSFt92Xe6WUi1PpBYJ55z53aeeUuqAUurKctvls1ZNquq8yWeteoX5O/LnAOdlvN9++bwJIQKSOVG12zPAIGAk0BT4UCm1S2v9mf9BSqlRwOvAlcAS4BZgqlKqpdY6TynVF3gfuAlYBrwEfACMqa4nUstUyXkDWmOsRN8SKPG7a86xfgK1kKlzVs6LQJkLNvmsVbsqOW/IZ626hXPeOgEXA7P9tmWBfN6EEKHJcL5aSimVgLG44Fit9S++bf8Cxmith5Q79jKgsdb6ad/PyRh//AdrrecrpT4ALFrry3z7mwG7gHZa6y3V9qRqgSo+b2cDL2utW1bnc6htwjlnfvc5HXgBqAf8XWv9nm+7fNaqSRWfN/msVZMwf0eW/k5spbXeESCWfN6EEEHJcL7aqzvGN6Nz/bbNBfoqpcr0UGqtP/K7EI8D7gQOAmt8hwwAfvc7/k9gJzDwmLW+9qrK89YJ2HjMWyxMnzMApVQS8AZwPeAst1s+a9WnKs+bfNaqTzjnrRNQjJEYBSKfNyFEUJJE1V6NgEytdbHftgNADJAW6A5KqdFAAfBv4A6tda5frL3lDj+AMYxCVK2qPG+dgCSl1O9KqX1KqZ+UUu2PYdtrq3DP2dPANK317wH2yWet+lTleZPPWvUJ57x1ArKBz3znZZFS6oxyseTzJoQISJKo2iuesmPz8fvZEeQ+K4FewCPAe8pYUyJUrGBxROSq8rx1BOoCDwPjML6R/VUpVacqGyzMnzOl1CnAWCDYRHj5rFWfqjxv8lmrPuH8juwIJALfYcxz+gn4XinVv5JY8nkTQkhhiVqsmIp/CEp/Lgx0B99ie/uBFUqpgcCNwIIQsQLGEUelKs/byYBVa10AoJS6BPgT4yLvg6pveq1l6pz5hly+DdymtQ5WcEA+a9WnKs+bfNaqTzi/I+8HHtNaZ/t+XqmU6g3cACwMEUs+b0II6YmqxfYAdZVSMX7b0jG+Zcv0P1ApNVAp1b3c/dcBqX6x0svtTwf2VV1zhU+VnTetdXHpRV3pz8B2oMmxaHgtZvac9QPaYlQSy1dK5QONgTeUUm/4xZLPWvWosvMmn7VqZfp3pNba45dAlVrPX+dFPm9CiKAkiaq9VmBMfh7kt20IsDTAquS3AP9XbltvjD82YPRqHKl65Ktg1Ny3XVStFVTBeVNK2ZRSe5RSF5fuUEolAu2ADVXe6tptBebO2SKM17+H3+0A8JDvBvJZq04rqILzJp+1arcCk78jlVJfKaVeL3f/nvx1XuTzJoQISobz1VJa60Kl1PvA68pYFDIduAejshRKqXQgR2tdBLwGzFFK3QpMByZizLG51Bfuv8BspdQ8jD8uLwFTtdabq/Ep1QpVdd601m6l1HTgMaXUXoxvaB/D+Ib1+2p+Wie0MM9ZmbLJSikPcFBrfdC3ST5r1aQqz5t81qpPmOftO2CSUmousBi4DCNputEXTj5vQojgtNZyq6U3jEmz7wP5GBWI7vbbp4Er/X4+B1iLMUZ8ETCoXKyJGKVf84FvgbSafn4n6q2qzhuQALyCcTFXAEwBmtf08zsRb+Gcs3L3211+n3zWjr/zJp+16D1vwK0YSXAxRiI1tFws+bzJTW5yC3iTxXaFEEIIIYQQIgwyJ0oIIYQQQgghwiBJlBBCCCGEEEKEQZIoIYQQQgghhAiDJFFCCCGEEEIIEQZJooQQQgghhBAiDJJECXEMKKVUddynKu8vxNGQ958QQojaRJIoccJQSu1QSr0aYPspSqlCpdQ8pVSib1uyUuoJpdRmpVSJUipDKfWTUmr4UbbBoZR6CRgX5v2GAF/5/XylUkorpVKr4/41zdfWe8I4/mGlVP4xbo//zamU2q6UekopFVtd7TDRzhp9fF8bInrPB4gzLMDr7lZKHVBKfa6UauN37MO+/ZcHiZWtlHq4ksd73ndcrlJq8NG0XQghRO1jq+kGCHEsKaX6Az8AK4ExWut83zfm04BGwBPAJqAucBXwi1JqnNb6hwgfshFwOzAnzPtdC7T3+/lHYCCQXU33FxW9Anzi+38s0A34D1AHuLGmGhWFIn3PB3MVsMH3fyvQFngamKWU6qi1LvQ79nml1FSt9eFwHkAp1RW4E3gRYwHVFUfbaCGEELWLJFHihKWU6oGRLK0DRmut83y7hmIkGAO01gv9jp8C/AE8hJF41Rit9SHgUE3dXwCwS2u9wO/n35RSdYF/KqVu01q7aqphJ7g1Wuslfj/PU0q5gY+As4HPfNvdQDxGInRZmI9Rz/fvJ1rrxUfRViGEELWUDOcTJySlVEfgZ2ALcJrWOtdvdwPfv2Xe/1prL/BP4H+VxL5XKbVFKVWslNqqlHpQKWVRSrUEtvsO+1Ip9ZvveLtS6hGl1Cbf0MEspdQ3Sqlmvv3vAROBzr4hSsPKD8dTSrVXSk31G340TSnVzez9fcddr5Raq5QqUkptUEpdF+I5XqmUOqyUGqOUWu17rouUUh2VUucopTYqpfKVUj8opRr43S9RKfWMb+hbke8+p5WLfZKv/fm+13F0gMdvoJT6QCmV6TvuO6VUq1DnpZpkB9qolLrI95oUK6WWKKUGldt/qVJqsTKGlRYqpeYrpYb67X9PKfWVUupvSqmdvtfuV9/72D/OPb79BUqpD4G4cvsre69NLn1f+n5Wvtd4tt82q+9+N/t+7qeMoa7ZyhjWuFEpdYNvX0sCvOcDvD6B3o8pvm1XBn6py1jm+7eF37YS4N/ApYHeQ8EoY5hfaTsXKaV+U0q19LXlb8oYFry//DkUQggh/EkSJU44SqnWwC/AfowEKqfcIb8DBcC3yphb0V8pZQPQWv+itf5viNgXAf8HPA+MBt4GHgGuA/YB5/oOfQC42ff/F4DbgCeB0zAStREY36Dji/cTsA2jh6z0gtHfZIye44uAi4FU4EellNXM/ZVSdwFvYPTMjQW+BCYppSYEe65AEvBfjCGPFwHNMIYJPuZ7fncBI32Pj1LK4ot/le+5ngvsAn4qvchVSiVjXMA2BC71xX6/XFvjgF+BIb7X7XIgHfhdGT1B1cWilLL5bnHKGBp6O/B2uV6oeIzX5N/ABUAC8E3pe0opdT7wIcZrdwbG65MCfK6UivGLMxIjGf4bRs9KO+C90p3KmDP2pG/beUAMxpA0f5W916YBA3yvMUB3jKGs/fza0t/XvqlKqeYY5yLf99zGYQx/fUMZSXyw93xVa+f7d3u57S9gvN/fUEolmIz1NnCL7/9XUbbN/wfcB/wdWBpZU4UQQtQKWmu5ye2EuAE7MJKNHYAX2A3UC3LscGAnoH23fGAKRtIV6jFew5ivofy23QOM9f2/pS/e+X77PweuLhfnJeCw38/vYQxjKv35Sl+cVCDN9/8Jfvu7AI8DdUzc3wIcBt4p14aPgdeCPM/S+1/kt+0J37ahfts+BFb4/j/Wt390uVh/AEt9/78dcAHN/Paf67vfPb6fb8AYqtXB75hkIAt4yPfzw0D+MXwv6SC3LUBDv+Me9m0fEOD5dPP9fD/wSrn45/iO6eJ3/jxAI79jbvcdU993Dg8B//XbrzDm+uX7bQv5XsPoydHACN/PdwDLMT4vg/2e03rf/0/H+ELC7hevni/GrcHe8yHeT6l+21J82670/TzM93N/jC8MbL7zPgwjcdsNJJQ//0Av3/vleb/Y2cDDIdpT+lh9yj2HV4/Ve0pucpOb3OR2Yt1kTpQ40YzDuLC8CpiO0ftyYfmDtNa/+nqshmNcKJ6KkQScrZR6Qmv9QJD48zG+uV6slPoK+EFr/WyoBmmtLwJQSjUGOgAdMXpZHCafUwbGReRbSqmRGL1O00O0sbz2GBfi35dr16Um7rvI7/8HfP/6z1fJwLgYBmOuWZ7Wenq5GJ8BLyqlkoDBwGqt9Z9++6dgJBClhgObgS2lvTlAIUbhghHAo5U12tdD519y2+v717/3XWut/R+3vJcw5uGA0evTHqO3aZ5Sqq/WOsu3z0PZ12mH798U34M86WtTCsb5b48xtwfKvgd2aq33+f282/dvAsYQ1FRgqn/jlVLfAPf6bQv5XtNa71RKbcB4v8/ESCZ+8D2/IcA8jB6sn3zHT8XokYpVSnXC6BHqG6DtVWlBgG2rgMu11gXld2itlymlXgDuVEp9qo9ujtO6o7ivEEKIWkSG84kTzQZglNb6V4yhORcopa4IdKDW2qON4Xt3a617Aq0xhi7dr/zKKZe7z8cY36p7MXqCViulVpSfu+JPKTVIKbUS2AN8A5wPFFH2Ij8obczVGonRyzAeo5T5Qd/cFzMxSifRHzTzeOXkld+gy1ZH81eXvxItf6XbknzHlKmk5ktk/Itg1MdIAFzlbmMxKsGZsbXcfR/y3fy3ba0kxm6t9RLfbb7W+n8YQynbANf4HVfsO0elyiRsSql0pdSPQCbGUMbb/Y71P3/lX1f/OKXDGMtXodvv/4PJ99pUYLjvvXMyRnI6FzhZKVUH6IcvifLNj3oRoxdwGcZ7vn6AtlelKzAStb5AD4yev+7arwhMAP/GGDr6tl/iHYlIPiNCCCFqIUmixIlmpjYq04Ex/GwZ8IpvAjwASqkvlFLflr+j1noHxhwTRdly4eWPe19r3Q9jns61GEnKN4GO9V2U/oAxdLCd1jpFaz0co0fLNK31n1rrazCG9g3G6N15iAC9bAGUzglLK9e2k6p48nwmxlyn8tL99mfwV2GP0nYo/koSwGjvSv66kPa/nWeyLWPL3W+S7+a/bazJWP5W+f5tG8Z9PsHowRkIJGqtewPvhPm4Gb5/G5TbXprQhPNem4bx/AdiDJebD8zGeF+NxEi6SsuV/xO4HiOxSdZan0TZJNAs7fvX/29OYpBj1/slryu11pUmNr7E/kaMMvT3VnK4EEIIcdQkiRInLK21G2Oifizwoa/wARiT089USnUOcLd2GD0A6wPFVEq97RvGh9b6oNb6HYwL4ua+Q8oPD+uAkSC8qLXe4othAUZR9pv8oMPKlFLdlFL7lFK9tNZerfV8jEIW7hCP628DRgJzVrnt/weEHIoYprlAUoBKaRdhzIkqxujp66KUaue3fwRlh4bNBVoBO0ovpjEm+d8Z4DkEpLVe7XchvkRrvdd389+2OoLnWDqUbUsY9xkIfKa1Xuh7TwKM8f1rtjdnE7CXv4o4lDrd7/9m32uzASdGAYXlWut8jGIrKcDdGF9EOP3avkRr/aXfULrybQ/13itVWh2zsd+2k03czzSt9c8Ywy8fwij2IYQQQhwzMidKnNC01muUUo9gVE+7H2M40jMYPRpzfEOV5mMkTkMwikS8orUuXwWs1GzgA6XU48AMjIp1N/FXT1Rpr89IpdRmjAQmD3jQN08nDqMyWHdAK6WU1lpjTIRvqpQaRdk5R2DM08j1Pe7DGAnRRF+bf/QdE/T+Wmu3r71PK6UOY8yFGYpRbe2cUK9fmH4EFgIfKaX+iTG86iqMQgGlvT4fYLzG3/uOicM4J/7V7t7F6O2YoZR6wvd8r8c4Z5H0HkWquVJqgO//CmO4538weoXeD3qvihYDVyqlVmEMizsH4z0DJi/2ffOfHsKYF3cA4713IdCHv5IYU+81rXWJrxT52cBzvvi7lVLbMZKmG8q1/X6l1K3Aaowk8iGMnqXStpd5z2utVwZ4Cr8CxcBLSqn/YCT/D2KUKa9Kd2IkeamVHSiEEEIcDemJErXBUxgXgw8rpXprrQ9jXNi/C0zAKGzwPcYF+t1ULBt9hNb6Q4wS0udgzBt5GmOO0k2+/bm+x7sc+FAb5dXPw+gh+A6jut9hjATG4msHwJsYc4d+wJjY7/+YbozS2JsxSo7/iNHrcJbWel1l9/fFeM7X7vN9x5wDXKy1nhLqhQuHb27TGIyE8jHfv82AM7TWP/qOKcYoarAeoyLdE8C/MBKl0ji5GEneBozCIFMwqsqN01r/VFXtNeE2jMqCf2D0jr2AkXAP8hsyasZVGM/3fxjDMDtiFHQowEhaTPH1el6Pce6mYAydfMxvv9n3GvxVoOJ3v22zy+0Do1T6+xhzjn4ALsF4XWaUtr38ez5I27Mxkr40X5xbfMfnm3ryJvk+23dVZUwhhBAiEGV8CS6EEEIIIYQQwgzpiRJCCCGEEEKIMEgSJYQQQgghhBBhkCRKCCGEEEIIIcIgSZQQQgghhBBChEGSKCGEEEIIIYQIgyRRQgghhBBCCBEGSaKEEEIIIYQQIgySRAkhhBBCCCFEGCSJEkIIIYQQQogw/D9Qe/Qmw88aLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_var, y_var, c_var = plot_sweep_res(\n",
    "    x_var = 'kstest_stat', x_lab = 'KS statistic model -- Bhandawat uPN fr',\n",
    "    y_var = 'upn_off_mean', y_lab = 'uPN off-odor firing rate (Hz)',\n",
    "    c_var = 'p', c_lab='PN multiplier',\n",
    "    highlight_locs=['a0.1_e0.4_i0.2_p4.0']\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pn_sq_dist_bhandgloms</th>\n",
       "      <th>upn_off_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.2_p2.0</th>\n",
       "      <td>0.060490</td>\n",
       "      <td>1.102564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.3_i0.2_p4.0</th>\n",
       "      <td>0.042108</td>\n",
       "      <td>5.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.25_i0.2_p4.0</th>\n",
       "      <td>0.041499</td>\n",
       "      <td>5.517949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.2_p4.0</th>\n",
       "      <td>0.042068</td>\n",
       "      <td>6.736752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.15_i0.2_p4.0</th>\n",
       "      <td>0.046887</td>\n",
       "      <td>5.394872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.4_i0.2_p4.0</th>\n",
       "      <td>0.027608</td>\n",
       "      <td>6.052991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.2_i0.2_p4.0</th>\n",
       "      <td>0.038086</td>\n",
       "      <td>5.365812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.35_i0.2_p4.0</th>\n",
       "      <td>0.037471</td>\n",
       "      <td>5.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.2_p4.0</th>\n",
       "      <td>0.038452</td>\n",
       "      <td>6.529915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      pn_sq_dist_bhandgloms  upn_off_mean\n",
       "a0.1_e0.5_i0.2_p2.0                0.060490      1.102564\n",
       "a0.1_e0.3_i0.2_p4.0                0.042108      5.692308\n",
       "a0.1_e0.25_i0.2_p4.0               0.041499      5.517949\n",
       "a0.1_e0.5_i0.2_p4.0                0.042068      6.736752\n",
       "a0.1_e0.15_i0.2_p4.0               0.046887      5.394872\n",
       "a0.1_e0.4_i0.2_p4.0                0.027608      6.052991\n",
       "a0.1_e0.2_i0.2_p4.0                0.038086      5.365812\n",
       "a0.1_e0.35_i0.2_p4.0               0.037471      5.866667\n",
       "a0.1_e0.45_i0.2_p4.0               0.038452      6.529915"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df = df_res[\n",
    "       #(df_res['p'] == 4) & \n",
    "       (df_res[x_var] < 0.32) &\n",
    "       (df_res[y_var] < 10) &\n",
    "       #(df_res['upn_off_mean'] < 20) &\n",
    "       #(df_res['eln_on_mean'] < 40) &\n",
    "       #(df_res['eln_off_mean'] < 20) &\n",
    "       (1) ]\n",
    "sub_df.sort_values('upn_off_mean')[df_res_cols1].sort_values('max_pca_upn_dists')[['pn_sq_dist_bhandgloms', 'upn_off_mean']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Highlight a simulation with low distance from the target flat distribution of PN firing rates, and a not-too-negative minimum PN on-off firing rate (which barely are below 0 in Bhandawat et al, 2007 Figure 6b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_var, y_var, c_var = plot_sweep_res(\n",
    "    x_var = 'pn_sq_dist_bhandgloms', x_lab = 'squared distance to Bhandawat ON-OFF uPN firing rate hist',\n",
    "    y_var = 'min_PN_dur', y_lab = 'minimum PN on-off firing rate (Hz)',\n",
    "    c_var = 'p', c_lab='PN multiplier',\n",
    "    highlight_locs=['a0.1_e0.4_i0.2_p4.0']\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--> Identifying the model with ORNs x0.1, eLNs x0.04, iLNs x0.02, PNs x0.4 as the best-performing model, with small deviation to the Bhandawat 2007 et al ON-OFF PN firing rate distribution, and not-too-negative minimum PN on-off firing rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pn_sq_dist_bhandgloms</th>\n",
       "      <th>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.4_i0.2_p4.0</th>\n",
       "      <td>0.027608</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.35_i0.2_p4.0</th>\n",
       "      <td>0.037471</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.2_i0.2_p4.0</th>\n",
       "      <td>0.038086</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.2_p4.0</th>\n",
       "      <td>0.038452</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.3_p6.0</th>\n",
       "      <td>0.038618</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      pn_sq_dist_bhandgloms    a\n",
       "a0.1_e0.4_i0.2_p4.0                0.027608  0.1\n",
       "a0.1_e0.35_i0.2_p4.0               0.037471  0.1\n",
       "a0.1_e0.2_i0.2_p4.0                0.038086  0.1\n",
       "a0.1_e0.45_i0.2_p4.0               0.038452  0.1\n",
       "a0.1_e0.45_i0.3_p6.0               0.038618  0.1"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df = df_res[\n",
    "       #(df_res['p'] == 6) & \n",
    "       (df_res[x_var] < 0.041) &\n",
    "       (df_res[y_var] > -100) &\n",
    "       #(df_res['upn_off_mean'] < 15) &\n",
    "       #(df_res['eln_on_mean'] < 40) &\n",
    "       # (df_res['eln_off_mean'] < 20) &\n",
    "       (1) ]\n",
    "sub_df.sort_values('pn_sq_dist_bhandgloms')[['pn_sq_dist_bhandgloms', 'a']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_var, y_var, c_var = plot_sweep_res(\n",
    "    x_var = 'kstest_stat', x_lab = 'KS test statistic of Bhandawat uPN firing rate distribution',\n",
    "    y_var = 'upn_off_mean', y_lab = 'mean off-odor uPN firing rate (Hz)',\n",
    "    c_var = 'p', c_lab='PN multiplier',\n",
    "    highlight_locs=['a0.1_e0.4_i0.2_p4.0'],#'a0.1_e0.25_i0.2_p6.0']\n",
    "    #highlight_locs=['a0.1_e0.25_i0.65_p8.0', \n",
    "    #                'a0.1_e0.25_i0.45_p6.0',\n",
    "    #                'a0.1_e0.3_i0.45_p4.0']\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_upn_onoff_dists</th>\n",
       "      <th>std_upn_onoff_dists</th>\n",
       "      <th>mean_upn_bhand_dists</th>\n",
       "      <th>std_upn_bhand_dists</th>\n",
       "      <th>mean_upn_onoff</th>\n",
       "      <th>std_upn_onoff</th>\n",
       "      <th>mean_upn_bhand</th>\n",
       "      <th>std_upn_bhand</th>\n",
       "      <th>max_upn_bhand</th>\n",
       "      <th>upn_off_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_pca_upn_dists</th>\n",
       "      <th>std_pca_upn_dists</th>\n",
       "      <th>max_pca_upn_dists</th>\n",
       "      <th>DM1_PN_pre_odor_fr</th>\n",
       "      <th>VA2_PN_pre_odor_fr</th>\n",
       "      <th>VM2_PN_dur_odor_fr</th>\n",
       "      <th>min_PN_dur</th>\n",
       "      <th>kstest_stat</th>\n",
       "      <th>kstest_pval</th>\n",
       "      <th>kstest_2samp_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.2_p4.0</th>\n",
       "      <td>561.069537</td>\n",
       "      <td>118.254581</td>\n",
       "      <td>280.417160</td>\n",
       "      <td>93.802481</td>\n",
       "      <td>45.591644</td>\n",
       "      <td>66.109681</td>\n",
       "      <td>63.685957</td>\n",
       "      <td>89.131529</td>\n",
       "      <td>286.50</td>\n",
       "      <td>6.529915</td>\n",
       "      <td>...</td>\n",
       "      <td>220.157914</td>\n",
       "      <td>107.310734</td>\n",
       "      <td>508.552228</td>\n",
       "      <td>68.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-92.0</td>\n",
       "      <td>0.283069</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0.283069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.4_i0.2_p4.0</th>\n",
       "      <td>566.496508</td>\n",
       "      <td>118.855839</td>\n",
       "      <td>285.256129</td>\n",
       "      <td>95.201347</td>\n",
       "      <td>45.694408</td>\n",
       "      <td>66.429010</td>\n",
       "      <td>64.111883</td>\n",
       "      <td>90.134273</td>\n",
       "      <td>293.00</td>\n",
       "      <td>6.052991</td>\n",
       "      <td>...</td>\n",
       "      <td>221.199050</td>\n",
       "      <td>110.536663</td>\n",
       "      <td>494.742969</td>\n",
       "      <td>76.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-84.0</td>\n",
       "      <td>0.287037</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.287037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.35_i0.2_p4.0</th>\n",
       "      <td>568.394040</td>\n",
       "      <td>117.936114</td>\n",
       "      <td>285.012905</td>\n",
       "      <td>97.093254</td>\n",
       "      <td>45.112219</td>\n",
       "      <td>66.444472</td>\n",
       "      <td>63.020062</td>\n",
       "      <td>90.200823</td>\n",
       "      <td>305.00</td>\n",
       "      <td>5.866667</td>\n",
       "      <td>...</td>\n",
       "      <td>220.694763</td>\n",
       "      <td>109.751562</td>\n",
       "      <td>501.564582</td>\n",
       "      <td>76.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-80.0</td>\n",
       "      <td>0.292328</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.292328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.2_p4.0</th>\n",
       "      <td>565.046197</td>\n",
       "      <td>118.863910</td>\n",
       "      <td>280.537383</td>\n",
       "      <td>93.504112</td>\n",
       "      <td>46.216880</td>\n",
       "      <td>66.479202</td>\n",
       "      <td>63.301698</td>\n",
       "      <td>88.809643</td>\n",
       "      <td>283.25</td>\n",
       "      <td>6.736752</td>\n",
       "      <td>...</td>\n",
       "      <td>220.248523</td>\n",
       "      <td>106.808077</td>\n",
       "      <td>491.910384</td>\n",
       "      <td>76.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-80.0</td>\n",
       "      <td>0.296296</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.296296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.3_i0.2_p4.0</th>\n",
       "      <td>566.225195</td>\n",
       "      <td>117.633535</td>\n",
       "      <td>286.758283</td>\n",
       "      <td>97.868763</td>\n",
       "      <td>44.782580</td>\n",
       "      <td>66.324000</td>\n",
       "      <td>63.459105</td>\n",
       "      <td>90.891225</td>\n",
       "      <td>289.00</td>\n",
       "      <td>5.692308</td>\n",
       "      <td>...</td>\n",
       "      <td>224.940710</td>\n",
       "      <td>111.359087</td>\n",
       "      <td>483.852508</td>\n",
       "      <td>80.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-76.0</td>\n",
       "      <td>0.305556</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.305556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.2_i0.2_p4.0</th>\n",
       "      <td>568.066928</td>\n",
       "      <td>119.482380</td>\n",
       "      <td>286.426757</td>\n",
       "      <td>97.480844</td>\n",
       "      <td>43.629021</td>\n",
       "      <td>66.228442</td>\n",
       "      <td>61.815586</td>\n",
       "      <td>90.639696</td>\n",
       "      <td>299.00</td>\n",
       "      <td>5.365812</td>\n",
       "      <td>...</td>\n",
       "      <td>223.546464</td>\n",
       "      <td>111.501029</td>\n",
       "      <td>495.291830</td>\n",
       "      <td>64.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-76.0</td>\n",
       "      <td>0.305556</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.305556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.25_i0.2_p4.0</th>\n",
       "      <td>564.584218</td>\n",
       "      <td>118.725497</td>\n",
       "      <td>285.200934</td>\n",
       "      <td>96.499578</td>\n",
       "      <td>44.240418</td>\n",
       "      <td>66.089001</td>\n",
       "      <td>63.312500</td>\n",
       "      <td>90.158445</td>\n",
       "      <td>293.00</td>\n",
       "      <td>5.517949</td>\n",
       "      <td>...</td>\n",
       "      <td>221.808726</td>\n",
       "      <td>111.246110</td>\n",
       "      <td>483.867013</td>\n",
       "      <td>68.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-72.0</td>\n",
       "      <td>0.306878</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.306878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.2_p2.0</th>\n",
       "      <td>393.964029</td>\n",
       "      <td>85.008461</td>\n",
       "      <td>212.950745</td>\n",
       "      <td>82.155096</td>\n",
       "      <td>26.962450</td>\n",
       "      <td>46.109054</td>\n",
       "      <td>51.702160</td>\n",
       "      <td>68.006745</td>\n",
       "      <td>266.50</td>\n",
       "      <td>1.102564</td>\n",
       "      <td>...</td>\n",
       "      <td>167.150844</td>\n",
       "      <td>93.254952</td>\n",
       "      <td>382.415942</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>0.313492</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.313492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.15_i0.2_p4.0</th>\n",
       "      <td>568.005206</td>\n",
       "      <td>118.069873</td>\n",
       "      <td>286.879907</td>\n",
       "      <td>97.889750</td>\n",
       "      <td>42.596982</td>\n",
       "      <td>65.893291</td>\n",
       "      <td>62.131173</td>\n",
       "      <td>90.703323</td>\n",
       "      <td>301.00</td>\n",
       "      <td>5.394872</td>\n",
       "      <td>...</td>\n",
       "      <td>223.979962</td>\n",
       "      <td>111.217059</td>\n",
       "      <td>494.620908</td>\n",
       "      <td>64.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-76.0</td>\n",
       "      <td>0.314815</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.314815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.3_p4.0</th>\n",
       "      <td>568.655252</td>\n",
       "      <td>118.771030</td>\n",
       "      <td>287.669949</td>\n",
       "      <td>98.321139</td>\n",
       "      <td>43.231309</td>\n",
       "      <td>66.185090</td>\n",
       "      <td>62.490741</td>\n",
       "      <td>90.913906</td>\n",
       "      <td>292.50</td>\n",
       "      <td>5.181197</td>\n",
       "      <td>...</td>\n",
       "      <td>226.227176</td>\n",
       "      <td>112.067239</td>\n",
       "      <td>479.039795</td>\n",
       "      <td>60.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-68.0</td>\n",
       "      <td>0.322751</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.322751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.4_i0.3_p4.0</th>\n",
       "      <td>567.614948</td>\n",
       "      <td>117.273271</td>\n",
       "      <td>288.884940</td>\n",
       "      <td>99.110696</td>\n",
       "      <td>42.553041</td>\n",
       "      <td>66.057046</td>\n",
       "      <td>62.401235</td>\n",
       "      <td>91.328405</td>\n",
       "      <td>300.00</td>\n",
       "      <td>4.904274</td>\n",
       "      <td>...</td>\n",
       "      <td>227.208044</td>\n",
       "      <td>113.589225</td>\n",
       "      <td>502.359615</td>\n",
       "      <td>56.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-72.0</td>\n",
       "      <td>0.324074</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.324074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean_upn_onoff_dists  std_upn_onoff_dists  \\\n",
       "a0.1_e0.45_i0.2_p4.0            561.069537           118.254581   \n",
       "a0.1_e0.4_i0.2_p4.0             566.496508           118.855839   \n",
       "a0.1_e0.35_i0.2_p4.0            568.394040           117.936114   \n",
       "a0.1_e0.5_i0.2_p4.0             565.046197           118.863910   \n",
       "a0.1_e0.3_i0.2_p4.0             566.225195           117.633535   \n",
       "a0.1_e0.2_i0.2_p4.0             568.066928           119.482380   \n",
       "a0.1_e0.25_i0.2_p4.0            564.584218           118.725497   \n",
       "a0.1_e0.5_i0.2_p2.0             393.964029            85.008461   \n",
       "a0.1_e0.15_i0.2_p4.0            568.005206           118.069873   \n",
       "a0.1_e0.5_i0.3_p4.0             568.655252           118.771030   \n",
       "a0.1_e0.4_i0.3_p4.0             567.614948           117.273271   \n",
       "\n",
       "                      mean_upn_bhand_dists  std_upn_bhand_dists  \\\n",
       "a0.1_e0.45_i0.2_p4.0            280.417160            93.802481   \n",
       "a0.1_e0.4_i0.2_p4.0             285.256129            95.201347   \n",
       "a0.1_e0.35_i0.2_p4.0            285.012905            97.093254   \n",
       "a0.1_e0.5_i0.2_p4.0             280.537383            93.504112   \n",
       "a0.1_e0.3_i0.2_p4.0             286.758283            97.868763   \n",
       "a0.1_e0.2_i0.2_p4.0             286.426757            97.480844   \n",
       "a0.1_e0.25_i0.2_p4.0            285.200934            96.499578   \n",
       "a0.1_e0.5_i0.2_p2.0             212.950745            82.155096   \n",
       "a0.1_e0.15_i0.2_p4.0            286.879907            97.889750   \n",
       "a0.1_e0.5_i0.3_p4.0             287.669949            98.321139   \n",
       "a0.1_e0.4_i0.3_p4.0             288.884940            99.110696   \n",
       "\n",
       "                      mean_upn_onoff  std_upn_onoff  mean_upn_bhand  \\\n",
       "a0.1_e0.45_i0.2_p4.0       45.591644      66.109681       63.685957   \n",
       "a0.1_e0.4_i0.2_p4.0        45.694408      66.429010       64.111883   \n",
       "a0.1_e0.35_i0.2_p4.0       45.112219      66.444472       63.020062   \n",
       "a0.1_e0.5_i0.2_p4.0        46.216880      66.479202       63.301698   \n",
       "a0.1_e0.3_i0.2_p4.0        44.782580      66.324000       63.459105   \n",
       "a0.1_e0.2_i0.2_p4.0        43.629021      66.228442       61.815586   \n",
       "a0.1_e0.25_i0.2_p4.0       44.240418      66.089001       63.312500   \n",
       "a0.1_e0.5_i0.2_p2.0        26.962450      46.109054       51.702160   \n",
       "a0.1_e0.15_i0.2_p4.0       42.596982      65.893291       62.131173   \n",
       "a0.1_e0.5_i0.3_p4.0        43.231309      66.185090       62.490741   \n",
       "a0.1_e0.4_i0.3_p4.0        42.553041      66.057046       62.401235   \n",
       "\n",
       "                      std_upn_bhand  max_upn_bhand  upn_off_mean  ...  \\\n",
       "a0.1_e0.45_i0.2_p4.0      89.131529         286.50      6.529915  ...   \n",
       "a0.1_e0.4_i0.2_p4.0       90.134273         293.00      6.052991  ...   \n",
       "a0.1_e0.35_i0.2_p4.0      90.200823         305.00      5.866667  ...   \n",
       "a0.1_e0.5_i0.2_p4.0       88.809643         283.25      6.736752  ...   \n",
       "a0.1_e0.3_i0.2_p4.0       90.891225         289.00      5.692308  ...   \n",
       "a0.1_e0.2_i0.2_p4.0       90.639696         299.00      5.365812  ...   \n",
       "a0.1_e0.25_i0.2_p4.0      90.158445         293.00      5.517949  ...   \n",
       "a0.1_e0.5_i0.2_p2.0       68.006745         266.50      1.102564  ...   \n",
       "a0.1_e0.15_i0.2_p4.0      90.703323         301.00      5.394872  ...   \n",
       "a0.1_e0.5_i0.3_p4.0       90.913906         292.50      5.181197  ...   \n",
       "a0.1_e0.4_i0.3_p4.0       91.328405         300.00      4.904274  ...   \n",
       "\n",
       "                      mean_pca_upn_dists  std_pca_upn_dists  \\\n",
       "a0.1_e0.45_i0.2_p4.0          220.157914         107.310734   \n",
       "a0.1_e0.4_i0.2_p4.0           221.199050         110.536663   \n",
       "a0.1_e0.35_i0.2_p4.0          220.694763         109.751562   \n",
       "a0.1_e0.5_i0.2_p4.0           220.248523         106.808077   \n",
       "a0.1_e0.3_i0.2_p4.0           224.940710         111.359087   \n",
       "a0.1_e0.2_i0.2_p4.0           223.546464         111.501029   \n",
       "a0.1_e0.25_i0.2_p4.0          221.808726         111.246110   \n",
       "a0.1_e0.5_i0.2_p2.0           167.150844          93.254952   \n",
       "a0.1_e0.15_i0.2_p4.0          223.979962         111.217059   \n",
       "a0.1_e0.5_i0.3_p4.0           226.227176         112.067239   \n",
       "a0.1_e0.4_i0.3_p4.0           227.208044         113.589225   \n",
       "\n",
       "                      max_pca_upn_dists  DM1_PN_pre_odor_fr  \\\n",
       "a0.1_e0.45_i0.2_p4.0         508.552228                68.0   \n",
       "a0.1_e0.4_i0.2_p4.0          494.742969                76.0   \n",
       "a0.1_e0.35_i0.2_p4.0         501.564582                76.0   \n",
       "a0.1_e0.5_i0.2_p4.0          491.910384                76.0   \n",
       "a0.1_e0.3_i0.2_p4.0          483.852508                80.0   \n",
       "a0.1_e0.2_i0.2_p4.0          495.291830                64.0   \n",
       "a0.1_e0.25_i0.2_p4.0         483.867013                68.0   \n",
       "a0.1_e0.5_i0.2_p2.0          382.415942                32.0   \n",
       "a0.1_e0.15_i0.2_p4.0         494.620908                64.0   \n",
       "a0.1_e0.5_i0.3_p4.0          479.039795                60.0   \n",
       "a0.1_e0.4_i0.3_p4.0          502.359615                56.0   \n",
       "\n",
       "                      VA2_PN_pre_odor_fr  VM2_PN_dur_odor_fr  min_PN_dur  \\\n",
       "a0.1_e0.45_i0.2_p4.0                28.0                 0.0       -92.0   \n",
       "a0.1_e0.4_i0.2_p4.0                 32.0                 0.0       -84.0   \n",
       "a0.1_e0.35_i0.2_p4.0                40.0                 0.0       -80.0   \n",
       "a0.1_e0.5_i0.2_p4.0                 28.0                 0.0       -80.0   \n",
       "a0.1_e0.3_i0.2_p4.0                 24.0                 0.0       -76.0   \n",
       "a0.1_e0.2_i0.2_p4.0                 36.0                 0.0       -76.0   \n",
       "a0.1_e0.25_i0.2_p4.0                36.0                 0.0       -72.0   \n",
       "a0.1_e0.5_i0.2_p2.0                  0.0                 0.0       -36.0   \n",
       "a0.1_e0.15_i0.2_p4.0                28.0                 0.0       -76.0   \n",
       "a0.1_e0.5_i0.3_p4.0                 28.0                 0.0       -68.0   \n",
       "a0.1_e0.4_i0.3_p4.0                 24.0                 0.0       -72.0   \n",
       "\n",
       "                      kstest_stat  kstest_pval  kstest_2samp_stat  \n",
       "a0.1_e0.45_i0.2_p4.0     0.283069     0.000133           0.283069  \n",
       "a0.1_e0.4_i0.2_p4.0      0.287037     0.000101           0.287037  \n",
       "a0.1_e0.35_i0.2_p4.0     0.292328     0.000070           0.292328  \n",
       "a0.1_e0.5_i0.2_p4.0      0.296296     0.000053           0.296296  \n",
       "a0.1_e0.3_i0.2_p4.0      0.305556     0.000027           0.305556  \n",
       "a0.1_e0.2_i0.2_p4.0      0.305556     0.000027           0.305556  \n",
       "a0.1_e0.25_i0.2_p4.0     0.306878     0.000024           0.306878  \n",
       "a0.1_e0.5_i0.2_p2.0      0.313492     0.000015           0.313492  \n",
       "a0.1_e0.15_i0.2_p4.0     0.314815     0.000013           0.314815  \n",
       "a0.1_e0.5_i0.3_p4.0      0.322751     0.000007           0.322751  \n",
       "a0.1_e0.4_i0.3_p4.0      0.324074     0.000007           0.324074  \n",
       "\n",
       "[11 rows x 34 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df = df_res[\n",
    "       #(df_res['p'] == 6) & \n",
    "       (df_res[x_var] < 0.33) &\n",
    "       (df_res[y_var] < 7) &\n",
    "       #(df_res['upn_off_mean'] < 15) &\n",
    "       #(df_res['eln_on_mean'] < 40) &\n",
    "       # (df_res['eln_off_mean'] < 20) &\n",
    "       (1) ]\n",
    "sub_df.sort_values('kstest_stat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_var, y_var, c_var = plot_sweep_res(\n",
    "    x_var = 'DM1_PN_pre_odor_fr', x_lab = 'DM1 PN off-odor firing rate (Hz)',\n",
    "    y_var = 'kstest_stat', y_lab = 'KS test statistic of Bhandawat uPN firing rate distribution',\n",
    "    c_var = 'p', c_lab='PN multiplier',\n",
    "    highlight_locs=['a0.1_e0.4_i0.2_p4.0'],#, 'a0.1_e0.5_i0.2_p2.0']\n",
    "    #highlight_locs=['a0.1_e0.25_i0.65_p8.0', \n",
    "    #                'a0.1_e0.25_i0.45_p6.0',\n",
    "    #                'a0.1_e0.3_i0.45_p4.0']\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_upn_onoff_dists</th>\n",
       "      <th>std_upn_onoff_dists</th>\n",
       "      <th>mean_upn_bhand_dists</th>\n",
       "      <th>std_upn_bhand_dists</th>\n",
       "      <th>mean_upn_onoff</th>\n",
       "      <th>std_upn_onoff</th>\n",
       "      <th>mean_upn_bhand</th>\n",
       "      <th>std_upn_bhand</th>\n",
       "      <th>max_upn_bhand</th>\n",
       "      <th>upn_off_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_pca_upn_dists</th>\n",
       "      <th>std_pca_upn_dists</th>\n",
       "      <th>max_pca_upn_dists</th>\n",
       "      <th>DM1_PN_pre_odor_fr</th>\n",
       "      <th>VA2_PN_pre_odor_fr</th>\n",
       "      <th>VM2_PN_dur_odor_fr</th>\n",
       "      <th>min_PN_dur</th>\n",
       "      <th>kstest_stat</th>\n",
       "      <th>kstest_pval</th>\n",
       "      <th>kstest_2samp_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.2_p2.0</th>\n",
       "      <td>393.964029</td>\n",
       "      <td>85.008461</td>\n",
       "      <td>212.950745</td>\n",
       "      <td>82.155096</td>\n",
       "      <td>26.96245</td>\n",
       "      <td>46.109054</td>\n",
       "      <td>51.70216</td>\n",
       "      <td>68.006745</td>\n",
       "      <td>266.5</td>\n",
       "      <td>1.102564</td>\n",
       "      <td>...</td>\n",
       "      <td>167.150844</td>\n",
       "      <td>93.254952</td>\n",
       "      <td>382.415942</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>0.313492</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.313492</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     mean_upn_onoff_dists  std_upn_onoff_dists  \\\n",
       "a0.1_e0.5_i0.2_p2.0            393.964029            85.008461   \n",
       "\n",
       "                     mean_upn_bhand_dists  std_upn_bhand_dists  \\\n",
       "a0.1_e0.5_i0.2_p2.0            212.950745            82.155096   \n",
       "\n",
       "                     mean_upn_onoff  std_upn_onoff  mean_upn_bhand  \\\n",
       "a0.1_e0.5_i0.2_p2.0        26.96245      46.109054        51.70216   \n",
       "\n",
       "                     std_upn_bhand  max_upn_bhand  upn_off_mean  ...  \\\n",
       "a0.1_e0.5_i0.2_p2.0      68.006745          266.5      1.102564  ...   \n",
       "\n",
       "                     mean_pca_upn_dists  std_pca_upn_dists  max_pca_upn_dists  \\\n",
       "a0.1_e0.5_i0.2_p2.0          167.150844          93.254952         382.415942   \n",
       "\n",
       "                     DM1_PN_pre_odor_fr  VA2_PN_pre_odor_fr  \\\n",
       "a0.1_e0.5_i0.2_p2.0                32.0                 0.0   \n",
       "\n",
       "                     VM2_PN_dur_odor_fr  min_PN_dur  kstest_stat  kstest_pval  \\\n",
       "a0.1_e0.5_i0.2_p2.0                 0.0       -36.0     0.313492     0.000015   \n",
       "\n",
       "                     kstest_2samp_stat  \n",
       "a0.1_e0.5_i0.2_p2.0           0.313492  \n",
       "\n",
       "[1 rows x 34 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df = df_res[\n",
    "       #(df_res['p'] == 6) & \n",
    "       (df_res[x_var] < 40) &\n",
    "       (df_res[y_var] < 0.33) &\n",
    "       #(df_res['upn_off_mean'] < 15) &\n",
    "       #(df_res['eln_on_mean'] < 40) &\n",
    "       # (df_res['eln_off_mean'] < 20) &\n",
    "       (1) ]\n",
    "sub_df.sort_values('kstest_stat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_upn_onoff_dists</th>\n",
       "      <th>std_upn_onoff_dists</th>\n",
       "      <th>mean_upn_bhand_dists</th>\n",
       "      <th>std_upn_bhand_dists</th>\n",
       "      <th>mean_upn_onoff</th>\n",
       "      <th>std_upn_onoff</th>\n",
       "      <th>mean_upn_bhand</th>\n",
       "      <th>std_upn_bhand</th>\n",
       "      <th>max_upn_bhand</th>\n",
       "      <th>upn_off_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_pca_upn_dists</th>\n",
       "      <th>std_pca_upn_dists</th>\n",
       "      <th>max_pca_upn_dists</th>\n",
       "      <th>DM1_PN_pre_odor_fr</th>\n",
       "      <th>VA2_PN_pre_odor_fr</th>\n",
       "      <th>VM2_PN_dur_odor_fr</th>\n",
       "      <th>min_PN_dur</th>\n",
       "      <th>kstest_stat</th>\n",
       "      <th>kstest_pval</th>\n",
       "      <th>kstest_2samp_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.2_p4.0</th>\n",
       "      <td>561.069537</td>\n",
       "      <td>118.254581</td>\n",
       "      <td>280.41716</td>\n",
       "      <td>93.802481</td>\n",
       "      <td>45.591644</td>\n",
       "      <td>66.109681</td>\n",
       "      <td>63.685957</td>\n",
       "      <td>89.131529</td>\n",
       "      <td>286.5</td>\n",
       "      <td>6.529915</td>\n",
       "      <td>...</td>\n",
       "      <td>220.157914</td>\n",
       "      <td>107.310734</td>\n",
       "      <td>508.552228</td>\n",
       "      <td>68.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-92.0</td>\n",
       "      <td>0.283069</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0.283069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean_upn_onoff_dists  std_upn_onoff_dists  \\\n",
       "a0.1_e0.45_i0.2_p4.0            561.069537           118.254581   \n",
       "\n",
       "                      mean_upn_bhand_dists  std_upn_bhand_dists  \\\n",
       "a0.1_e0.45_i0.2_p4.0             280.41716            93.802481   \n",
       "\n",
       "                      mean_upn_onoff  std_upn_onoff  mean_upn_bhand  \\\n",
       "a0.1_e0.45_i0.2_p4.0       45.591644      66.109681       63.685957   \n",
       "\n",
       "                      std_upn_bhand  max_upn_bhand  upn_off_mean  ...  \\\n",
       "a0.1_e0.45_i0.2_p4.0      89.131529          286.5      6.529915  ...   \n",
       "\n",
       "                      mean_pca_upn_dists  std_pca_upn_dists  \\\n",
       "a0.1_e0.45_i0.2_p4.0          220.157914         107.310734   \n",
       "\n",
       "                      max_pca_upn_dists  DM1_PN_pre_odor_fr  \\\n",
       "a0.1_e0.45_i0.2_p4.0         508.552228                68.0   \n",
       "\n",
       "                      VA2_PN_pre_odor_fr  VM2_PN_dur_odor_fr  min_PN_dur  \\\n",
       "a0.1_e0.45_i0.2_p4.0                28.0                 0.0       -92.0   \n",
       "\n",
       "                      kstest_stat  kstest_pval  kstest_2samp_stat  \n",
       "a0.1_e0.45_i0.2_p4.0     0.283069     0.000133           0.283069  \n",
       "\n",
       "[1 rows x 34 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df = df_res[\n",
    "       #(df_res['p'] == 6) & \n",
    "       (df_res[x_var] < 75) &\n",
    "       (df_res[y_var] < 0.3) &\n",
    "       #(df_res['upn_off_mean'] < 15) &\n",
    "       #(df_res['eln_on_mean'] < 40) &\n",
    "       # (df_res['eln_off_mean'] < 20) &\n",
    "       (1) ]\n",
    "sub_df.sort_values('kstest_stat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_upn_onoff_dists</th>\n",
       "      <th>std_upn_onoff_dists</th>\n",
       "      <th>mean_upn_bhand_dists</th>\n",
       "      <th>std_upn_bhand_dists</th>\n",
       "      <th>mean_upn_onoff</th>\n",
       "      <th>std_upn_onoff</th>\n",
       "      <th>mean_upn_bhand</th>\n",
       "      <th>std_upn_bhand</th>\n",
       "      <th>max_upn_bhand</th>\n",
       "      <th>upn_off_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_pca_upn_dists</th>\n",
       "      <th>std_pca_upn_dists</th>\n",
       "      <th>max_pca_upn_dists</th>\n",
       "      <th>DM1_PN_pre_odor_fr</th>\n",
       "      <th>VA2_PN_pre_odor_fr</th>\n",
       "      <th>VM2_PN_dur_odor_fr</th>\n",
       "      <th>min_PN_dur</th>\n",
       "      <th>kstest_stat</th>\n",
       "      <th>kstest_pval</th>\n",
       "      <th>kstest_2samp_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 34 columns</p>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [mean_upn_onoff_dists, std_upn_onoff_dists, mean_upn_bhand_dists, std_upn_bhand_dists, mean_upn_onoff, std_upn_onoff, mean_upn_bhand, std_upn_bhand, max_upn_bhand, upn_off_mean, iln_off_mean, iln_on_mean, eln_off_mean, eln_on_mean, a, e, i, p, frac_less_full, frac_less_bhand, pn_sq_dist_bhandgloms, pn_sq_dist_allgloms, mean_pca_orn_dists, std_pca_orn_dists, mean_pca_upn_dists, std_pca_upn_dists, max_pca_upn_dists, DM1_PN_pre_odor_fr, VA2_PN_pre_odor_fr, VM2_PN_dur_odor_fr, min_PN_dur, kstest_stat, kstest_pval, kstest_2samp_stat]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 34 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df = df_res[\n",
    "       #(df_res['p'] == 6) & \n",
    "       (df_res[x_var] < 15) &\n",
    "       (df_res[y_var] < 0.3) &\n",
    "       #(df_res['upn_off_mean'] < 15) &\n",
    "       #(df_res['eln_on_mean'] < 40) &\n",
    "       # (df_res['eln_off_mean'] < 20) &\n",
    "       (1) ]\n",
    "sub_df.sort_values('kstest_stat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>mean_upn_onoff_dists</th>\n",
       "      <th>std_upn_onoff_dists</th>\n",
       "      <th>mean_upn_bhand_dists</th>\n",
       "      <th>std_upn_bhand_dists</th>\n",
       "      <th>mean_upn_onoff</th>\n",
       "      <th>std_upn_onoff</th>\n",
       "      <th>mean_upn_bhand</th>\n",
       "      <th>std_upn_bhand</th>\n",
       "      <th>max_upn_bhand</th>\n",
       "      <th>upn_off_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_pca_upn_dists</th>\n",
       "      <th>std_pca_upn_dists</th>\n",
       "      <th>max_pca_upn_dists</th>\n",
       "      <th>DM1_PN_pre_odor_fr</th>\n",
       "      <th>VA2_PN_pre_odor_fr</th>\n",
       "      <th>VM2_PN_dur_odor_fr</th>\n",
       "      <th>min_PN_dur</th>\n",
       "      <th>kstest_stat</th>\n",
       "      <th>kstest_pval</th>\n",
       "      <th>kstest_2samp_stat</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.2_p8.0</th>\n",
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       "    <tr>\n",
       "      <th>a0.1_e0.4_i0.2_p8.0</th>\n",
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       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.2_p8.0</th>\n",
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       "      <td>335.199110</td>\n",
       "      <td>103.905429</td>\n",
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       "      <td>323.0</td>\n",
       "      <td>25.276923</td>\n",
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       "      <td>307.0</td>\n",
       "      <td>15.977778</td>\n",
       "      <td>...</td>\n",
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       "      <td>-112.0</td>\n",
       "      <td>0.279101</td>\n",
       "      <td>1.740242e-04</td>\n",
       "      <td>0.279101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.2_p6.0</th>\n",
       "      <td>670.826541</td>\n",
       "      <td>138.636239</td>\n",
       "      <td>319.896211</td>\n",
       "      <td>102.450207</td>\n",
       "      <td>56.875536</td>\n",
       "      <td>78.564340</td>\n",
       "      <td>69.246142</td>\n",
       "      <td>100.769529</td>\n",
       "      <td>305.5</td>\n",
       "      <td>15.116239</td>\n",
       "      <td>...</td>\n",
       "      <td>248.081857</td>\n",
       "      <td>116.998290</td>\n",
       "      <td>561.472581</td>\n",
       "      <td>96.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-111.0</td>\n",
       "      <td>0.279101</td>\n",
       "      <td>1.740242e-04</td>\n",
       "      <td>0.279101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.25_i0.9_p2.0</th>\n",
       "      <td>375.817339</td>\n",
       "      <td>80.217795</td>\n",
       "      <td>211.199818</td>\n",
       "      <td>88.736338</td>\n",
       "      <td>20.595579</td>\n",
       "      <td>42.569444</td>\n",
       "      <td>43.259259</td>\n",
       "      <td>67.505767</td>\n",
       "      <td>277.5</td>\n",
       "      <td>0.512821</td>\n",
       "      <td>...</td>\n",
       "      <td>166.395736</td>\n",
       "      <td>99.333250</td>\n",
       "      <td>385.785486</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.492063</td>\n",
       "      <td>2.595701e-13</td>\n",
       "      <td>0.492063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i1.0_p2.0</th>\n",
       "      <td>376.173764</td>\n",
       "      <td>81.390950</td>\n",
       "      <td>211.024690</td>\n",
       "      <td>90.759972</td>\n",
       "      <td>20.652732</td>\n",
       "      <td>42.724264</td>\n",
       "      <td>43.472222</td>\n",
       "      <td>67.693708</td>\n",
       "      <td>285.5</td>\n",
       "      <td>0.463248</td>\n",
       "      <td>...</td>\n",
       "      <td>167.037764</td>\n",
       "      <td>100.607618</td>\n",
       "      <td>400.023087</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-24.0</td>\n",
       "      <td>0.492063</td>\n",
       "      <td>2.595701e-13</td>\n",
       "      <td>0.492063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.15_i1.0_p2.0</th>\n",
       "      <td>373.426258</td>\n",
       "      <td>78.645838</td>\n",
       "      <td>209.662789</td>\n",
       "      <td>89.580343</td>\n",
       "      <td>20.163140</td>\n",
       "      <td>42.332461</td>\n",
       "      <td>43.496142</td>\n",
       "      <td>67.420951</td>\n",
       "      <td>284.0</td>\n",
       "      <td>0.483761</td>\n",
       "      <td>...</td>\n",
       "      <td>165.102834</td>\n",
       "      <td>99.140760</td>\n",
       "      <td>392.218200</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>0.492063</td>\n",
       "      <td>2.595701e-13</td>\n",
       "      <td>0.492063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.1_i1.0_p2.0</th>\n",
       "      <td>372.062760</td>\n",
       "      <td>79.856625</td>\n",
       "      <td>209.271586</td>\n",
       "      <td>88.737513</td>\n",
       "      <td>20.197826</td>\n",
       "      <td>42.169680</td>\n",
       "      <td>42.735340</td>\n",
       "      <td>67.041094</td>\n",
       "      <td>279.0</td>\n",
       "      <td>0.441026</td>\n",
       "      <td>...</td>\n",
       "      <td>165.583602</td>\n",
       "      <td>98.367458</td>\n",
       "      <td>395.190722</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>0.492063</td>\n",
       "      <td>2.595701e-13</td>\n",
       "      <td>0.492063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.25_i1.0_p2.0</th>\n",
       "      <td>372.896142</td>\n",
       "      <td>79.438433</td>\n",
       "      <td>207.412180</td>\n",
       "      <td>87.275645</td>\n",
       "      <td>20.355960</td>\n",
       "      <td>42.298754</td>\n",
       "      <td>43.134259</td>\n",
       "      <td>66.448444</td>\n",
       "      <td>275.0</td>\n",
       "      <td>0.480342</td>\n",
       "      <td>...</td>\n",
       "      <td>164.009067</td>\n",
       "      <td>96.254157</td>\n",
       "      <td>388.428564</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-17.5</td>\n",
       "      <td>0.492063</td>\n",
       "      <td>2.595701e-13</td>\n",
       "      <td>0.492063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>536 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean_upn_onoff_dists  std_upn_onoff_dists  \\\n",
       "a0.1_e0.45_i0.2_p8.0            739.471028           152.422404   \n",
       "a0.1_e0.4_i0.2_p8.0             742.144172           151.728246   \n",
       "a0.1_e0.5_i0.2_p8.0             736.048932           150.936073   \n",
       "a0.1_e0.5_i0.2_p6.0             669.920395           142.445563   \n",
       "a0.1_e0.45_i0.2_p6.0            670.826541           138.636239   \n",
       "...                                    ...                  ...   \n",
       "a0.1_e0.25_i0.9_p2.0            375.817339            80.217795   \n",
       "a0.1_e0.45_i1.0_p2.0            376.173764            81.390950   \n",
       "a0.1_e0.15_i1.0_p2.0            373.426258            78.645838   \n",
       "a0.1_e0.1_i1.0_p2.0             372.062760            79.856625   \n",
       "a0.1_e0.25_i1.0_p2.0            372.896142            79.438433   \n",
       "\n",
       "                      mean_upn_bhand_dists  std_upn_bhand_dists  \\\n",
       "a0.1_e0.45_i0.2_p8.0            333.964259           103.543433   \n",
       "a0.1_e0.4_i0.2_p8.0             339.228490           104.633333   \n",
       "a0.1_e0.5_i0.2_p8.0             335.199110           103.905429   \n",
       "a0.1_e0.5_i0.2_p6.0             314.859562           100.726367   \n",
       "a0.1_e0.45_i0.2_p6.0            319.896211           102.450207   \n",
       "...                                    ...                  ...   \n",
       "a0.1_e0.25_i0.9_p2.0            211.199818            88.736338   \n",
       "a0.1_e0.45_i1.0_p2.0            211.024690            90.759972   \n",
       "a0.1_e0.15_i1.0_p2.0            209.662789            89.580343   \n",
       "a0.1_e0.1_i1.0_p2.0             209.271586            88.737513   \n",
       "a0.1_e0.25_i1.0_p2.0            207.412180            87.275645   \n",
       "\n",
       "                      mean_upn_onoff  std_upn_onoff  mean_upn_bhand  \\\n",
       "a0.1_e0.45_i0.2_p8.0       64.336352      86.383494       73.449846   \n",
       "a0.1_e0.4_i0.2_p8.0        63.683987      86.184698       72.613426   \n",
       "a0.1_e0.5_i0.2_p8.0        64.597762      85.958821       73.331019   \n",
       "a0.1_e0.5_i0.2_p6.0        56.604870      78.183603       68.662809   \n",
       "a0.1_e0.45_i0.2_p6.0       56.875536      78.564340       69.246142   \n",
       "...                              ...            ...             ...   \n",
       "a0.1_e0.25_i0.9_p2.0       20.595579      42.569444       43.259259   \n",
       "a0.1_e0.45_i1.0_p2.0       20.652732      42.724264       43.472222   \n",
       "a0.1_e0.15_i1.0_p2.0       20.163140      42.332461       43.496142   \n",
       "a0.1_e0.1_i1.0_p2.0        20.197826      42.169680       42.735340   \n",
       "a0.1_e0.25_i1.0_p2.0       20.355960      42.298754       43.134259   \n",
       "\n",
       "                      std_upn_bhand  max_upn_bhand  upn_off_mean  ...  \\\n",
       "a0.1_e0.45_i0.2_p8.0     104.714037          311.0     24.331624  ...   \n",
       "a0.1_e0.4_i0.2_p8.0      105.660476          316.5     23.632479  ...   \n",
       "a0.1_e0.5_i0.2_p8.0      105.057396          323.0     25.276923  ...   \n",
       "a0.1_e0.5_i0.2_p6.0       98.686532          307.0     15.977778  ...   \n",
       "a0.1_e0.45_i0.2_p6.0     100.769529          305.5     15.116239  ...   \n",
       "...                             ...            ...           ...  ...   \n",
       "a0.1_e0.25_i0.9_p2.0      67.505767          277.5      0.512821  ...   \n",
       "a0.1_e0.45_i1.0_p2.0      67.693708          285.5      0.463248  ...   \n",
       "a0.1_e0.15_i1.0_p2.0      67.420951          284.0      0.483761  ...   \n",
       "a0.1_e0.1_i1.0_p2.0       67.041094          279.0      0.441026  ...   \n",
       "a0.1_e0.25_i1.0_p2.0      66.448444          275.0      0.480342  ...   \n",
       "\n",
       "                      mean_pca_upn_dists  std_pca_upn_dists  \\\n",
       "a0.1_e0.45_i0.2_p8.0          254.932099         121.346518   \n",
       "a0.1_e0.4_i0.2_p8.0           258.800665         121.780845   \n",
       "a0.1_e0.5_i0.2_p8.0           253.760641         121.779614   \n",
       "a0.1_e0.5_i0.2_p6.0           240.291467         116.983640   \n",
       "a0.1_e0.45_i0.2_p6.0          248.081857         116.998290   \n",
       "...                                  ...                ...   \n",
       "a0.1_e0.25_i0.9_p2.0          166.395736          99.333250   \n",
       "a0.1_e0.45_i1.0_p2.0          167.037764         100.607618   \n",
       "a0.1_e0.15_i1.0_p2.0          165.102834          99.140760   \n",
       "a0.1_e0.1_i1.0_p2.0           165.583602          98.367458   \n",
       "a0.1_e0.25_i1.0_p2.0          164.009067          96.254157   \n",
       "\n",
       "                      max_pca_upn_dists  DM1_PN_pre_odor_fr  \\\n",
       "a0.1_e0.45_i0.2_p8.0         592.751862               140.0   \n",
       "a0.1_e0.4_i0.2_p8.0          579.603074               140.0   \n",
       "a0.1_e0.5_i0.2_p8.0          598.904351               124.0   \n",
       "a0.1_e0.5_i0.2_p6.0          570.209957               108.0   \n",
       "a0.1_e0.45_i0.2_p6.0         561.472581                96.0   \n",
       "...                                 ...                 ...   \n",
       "a0.1_e0.25_i0.9_p2.0         385.785486                16.0   \n",
       "a0.1_e0.45_i1.0_p2.0         400.023087                12.0   \n",
       "a0.1_e0.15_i1.0_p2.0         392.218200                12.0   \n",
       "a0.1_e0.1_i1.0_p2.0          395.190722                12.0   \n",
       "a0.1_e0.25_i1.0_p2.0         388.428564                12.0   \n",
       "\n",
       "                      VA2_PN_pre_odor_fr  VM2_PN_dur_odor_fr  min_PN_dur  \\\n",
       "a0.1_e0.45_i0.2_p8.0                60.0                 0.0      -152.0   \n",
       "a0.1_e0.4_i0.2_p8.0                 56.0                 0.0      -129.5   \n",
       "a0.1_e0.5_i0.2_p8.0                 68.0                 0.0      -144.0   \n",
       "a0.1_e0.5_i0.2_p6.0                 56.0                 0.0      -112.0   \n",
       "a0.1_e0.45_i0.2_p6.0                52.0                 0.0      -111.0   \n",
       "...                                  ...                 ...         ...   \n",
       "a0.1_e0.25_i0.9_p2.0                 0.0                 0.0       -20.0   \n",
       "a0.1_e0.45_i1.0_p2.0                 0.0                 0.0       -24.0   \n",
       "a0.1_e0.15_i1.0_p2.0                 0.0                 0.0       -16.0   \n",
       "a0.1_e0.1_i1.0_p2.0                  0.0                 0.0       -28.0   \n",
       "a0.1_e0.25_i1.0_p2.0                 0.0                 0.0       -17.5   \n",
       "\n",
       "                      kstest_stat   kstest_pval  kstest_2samp_stat  \n",
       "a0.1_e0.45_i0.2_p8.0     0.269841  3.206488e-04           0.269841  \n",
       "a0.1_e0.4_i0.2_p8.0      0.277778  1.901510e-04           0.277778  \n",
       "a0.1_e0.5_i0.2_p8.0      0.277778  1.901510e-04           0.277778  \n",
       "a0.1_e0.5_i0.2_p6.0      0.279101  1.740242e-04           0.279101  \n",
       "a0.1_e0.45_i0.2_p6.0     0.279101  1.740242e-04           0.279101  \n",
       "...                           ...           ...                ...  \n",
       "a0.1_e0.25_i0.9_p2.0     0.492063  2.595701e-13           0.492063  \n",
       "a0.1_e0.45_i1.0_p2.0     0.492063  2.595701e-13           0.492063  \n",
       "a0.1_e0.15_i1.0_p2.0     0.492063  2.595701e-13           0.492063  \n",
       "a0.1_e0.1_i1.0_p2.0      0.492063  2.595701e-13           0.492063  \n",
       "a0.1_e0.25_i1.0_p2.0     0.492063  2.595701e-13           0.492063  \n",
       "\n",
       "[536 rows x 34 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df = df_res[\n",
    "       #(df_res['p'] == 6) & \n",
    "       (df_res[x_var] > 0.48) &\n",
    "       #(df_res[y_var] < 0.03) &\n",
    "       #(df_res['upn_off_mean'] < 15) &\n",
    "       #(df_res['eln_on_mean'] < 40) &\n",
    "       # (df_res['eln_off_mean'] < 20) &\n",
    "       (1) ]\n",
    "sub_df.sort_values('kstest_stat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_var, y_var, c_var = plot_sweep_res(\n",
    "    x_var = 'DM1_PN_pre_odor_fr',\n",
    "    y_var = 'VA2_PN_pre_odor_fr',\n",
    "    c_var = 'p', c_lab='PN multiplier',\n",
    "    #highlight_locs=['a0.1_e0.25_i0.65_p8.0', \n",
    "    #                'a0.1_e0.25_i0.45_p6.0',\n",
    "    #                'a0.1_e0.3_i0.45_p4.0']\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_var, y_var, c_var = plot_sweep_res(\n",
    "    x_var = 'DM1_PN_pre_odor_fr',\n",
    "    y_var = 'max_upn_bhand',\n",
    "    c_var = 'p', c_lab='PN multiplier',\n",
    "    #highlight_locs=['a0.1_e0.25_i0.65_p8.0', \n",
    "    #                'a0.1_e0.25_i0.45_p6.0',\n",
    "    #                'a0.1_e0.3_i0.45_p4.0']\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a0.1_e0.35_i0.2_p2.0    0.787037\n",
       "a0.1_e0.2_i0.2_p2.0     0.796296\n",
       "a0.1_e0.15_i0.2_p2.0    0.796296\n",
       "a0.1_e0.3_i0.2_p2.0     0.796296\n",
       "a0.1_e0.5_i0.3_p2.0     0.796296\n",
       "                          ...   \n",
       "a0.1_e0.45_i0.8_p2.0    0.824074\n",
       "a0.1_e0.25_i0.9_p2.0    0.824074\n",
       "a0.1_e0.15_i0.9_p2.0    0.824074\n",
       "a0.1_e0.15_i0.6_p2.0    0.824074\n",
       "a0.1_e0.4_i1.0_p2.0     0.824074\n",
       "Name: frac_less_bhand, Length: 103, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df = df_res[\n",
    "       #(df_res['p'] == 6) & \n",
    "       (df_res[x_var] < 30) &\n",
    "       #(df_res[y_var] < 0.03) &\n",
    "       #(df_res['upn_off_mean'] < 15) &\n",
    "       #(df_res['eln_on_mean'] < 40) &\n",
    "       # (df_res['eln_off_mean'] < 20) &\n",
    "       (1) ]\n",
    "sub_df.sort_values('frac_less_bhand')['frac_less_bhand']#.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_var, y_var, c_var = plot_sweep_res(\n",
    "    x_var = 'pn_sq_dist_bhandgloms',\n",
    "    y_var = 'pn_sq_dist_allgloms',\n",
    "    c_var = 'p', c_lab='PN multiplier',\n",
    "    #highlight_locs=['a0.1_e0.25_i0.65_p8.0', \n",
    "    #                'a0.1_e0.25_i0.45_p6.0',\n",
    "    #                'a0.1_e0.3_i0.45_p4.0']\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "highlight_locs=['a0.1_e0.25_i0.65_p8.0', \n",
    "                 'a0.1_e0.4_i0.75_p6.0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_var, y_var, c_var = plot_sweep_res(\n",
    "    x_var = 'frac_less_full', x_lab = 'fraction of all glom PN ON-OFF responses < 100 Hz',\n",
    "    y_var = 'upn_off_mean', y_lab = 'uPN off-odor firing rate (Hz)',\n",
    "    c_var = 'p', c_lab='PN multiplier',\n",
    "    highlight_locs=[]\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_upn_onoff_dists</th>\n",
       "      <th>std_upn_onoff_dists</th>\n",
       "      <th>mean_upn_bhand_dists</th>\n",
       "      <th>std_upn_bhand_dists</th>\n",
       "      <th>mean_upn_onoff</th>\n",
       "      <th>std_upn_onoff</th>\n",
       "      <th>mean_upn_bhand</th>\n",
       "      <th>std_upn_bhand</th>\n",
       "      <th>max_upn_bhand</th>\n",
       "      <th>upn_off_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_pca_upn_dists</th>\n",
       "      <th>std_pca_upn_dists</th>\n",
       "      <th>max_pca_upn_dists</th>\n",
       "      <th>DM1_PN_pre_odor_fr</th>\n",
       "      <th>VA2_PN_pre_odor_fr</th>\n",
       "      <th>VM2_PN_dur_odor_fr</th>\n",
       "      <th>min_PN_dur</th>\n",
       "      <th>kstest_stat</th>\n",
       "      <th>kstest_pval</th>\n",
       "      <th>kstest_2samp_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.4_i0.8_p6.0</th>\n",
       "      <td>669.663060</td>\n",
       "      <td>133.063065</td>\n",
       "      <td>336.702923</td>\n",
       "      <td>111.244240</td>\n",
       "      <td>45.693714</td>\n",
       "      <td>77.019312</td>\n",
       "      <td>64.610340</td>\n",
       "      <td>105.536488</td>\n",
       "      <td>322.0</td>\n",
       "      <td>6.100855</td>\n",
       "      <td>...</td>\n",
       "      <td>261.242810</td>\n",
       "      <td>129.986657</td>\n",
       "      <td>571.939591</td>\n",
       "      <td>60.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-76.0</td>\n",
       "      <td>0.462963</td>\n",
       "      <td>8.836154e-12</td>\n",
       "      <td>0.462963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.15_i0.7_p6.0</th>\n",
       "      <td>673.074102</td>\n",
       "      <td>134.102058</td>\n",
       "      <td>340.632894</td>\n",
       "      <td>117.780425</td>\n",
       "      <td>45.058597</td>\n",
       "      <td>77.229613</td>\n",
       "      <td>62.635802</td>\n",
       "      <td>106.949771</td>\n",
       "      <td>328.5</td>\n",
       "      <td>6.347009</td>\n",
       "      <td>...</td>\n",
       "      <td>265.695284</td>\n",
       "      <td>137.122335</td>\n",
       "      <td>577.458747</td>\n",
       "      <td>68.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-96.0</td>\n",
       "      <td>0.455026</td>\n",
       "      <td>2.213274e-11</td>\n",
       "      <td>0.455026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.25_i0.75_p6.0</th>\n",
       "      <td>673.414874</td>\n",
       "      <td>132.977848</td>\n",
       "      <td>335.040700</td>\n",
       "      <td>114.012940</td>\n",
       "      <td>44.976362</td>\n",
       "      <td>77.271654</td>\n",
       "      <td>62.732253</td>\n",
       "      <td>105.186655</td>\n",
       "      <td>326.0</td>\n",
       "      <td>6.360684</td>\n",
       "      <td>...</td>\n",
       "      <td>262.376150</td>\n",
       "      <td>131.782106</td>\n",
       "      <td>567.189313</td>\n",
       "      <td>72.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-81.5</td>\n",
       "      <td>0.455026</td>\n",
       "      <td>2.213274e-11</td>\n",
       "      <td>0.455026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.3_i0.75_p6.0</th>\n",
       "      <td>675.467163</td>\n",
       "      <td>132.874608</td>\n",
       "      <td>339.963631</td>\n",
       "      <td>113.535471</td>\n",
       "      <td>45.464343</td>\n",
       "      <td>77.339848</td>\n",
       "      <td>62.219136</td>\n",
       "      <td>105.916280</td>\n",
       "      <td>324.5</td>\n",
       "      <td>6.377778</td>\n",
       "      <td>...</td>\n",
       "      <td>262.375786</td>\n",
       "      <td>133.126644</td>\n",
       "      <td>574.462084</td>\n",
       "      <td>72.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-84.0</td>\n",
       "      <td>0.453704</td>\n",
       "      <td>2.574641e-11</td>\n",
       "      <td>0.453704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.8_p6.0</th>\n",
       "      <td>671.220781</td>\n",
       "      <td>131.961677</td>\n",
       "      <td>330.878985</td>\n",
       "      <td>110.468858</td>\n",
       "      <td>45.282457</td>\n",
       "      <td>77.040621</td>\n",
       "      <td>63.478395</td>\n",
       "      <td>103.520776</td>\n",
       "      <td>324.5</td>\n",
       "      <td>6.441026</td>\n",
       "      <td>...</td>\n",
       "      <td>253.743262</td>\n",
       "      <td>128.090018</td>\n",
       "      <td>553.764072</td>\n",
       "      <td>60.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-80.0</td>\n",
       "      <td>0.453704</td>\n",
       "      <td>2.574641e-11</td>\n",
       "      <td>0.453704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.7_p10.0</th>\n",
       "      <td>827.818984</td>\n",
       "      <td>160.465263</td>\n",
       "      <td>390.148247</td>\n",
       "      <td>121.158004</td>\n",
       "      <td>59.082131</td>\n",
       "      <td>94.685433</td>\n",
       "      <td>72.739198</td>\n",
       "      <td>120.215683</td>\n",
       "      <td>360.0</td>\n",
       "      <td>14.789744</td>\n",
       "      <td>...</td>\n",
       "      <td>299.102871</td>\n",
       "      <td>142.858804</td>\n",
       "      <td>667.148459</td>\n",
       "      <td>96.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-112.0</td>\n",
       "      <td>0.453704</td>\n",
       "      <td>2.574641e-11</td>\n",
       "      <td>0.453704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.7_p10.0</th>\n",
       "      <td>824.919928</td>\n",
       "      <td>159.561153</td>\n",
       "      <td>387.096334</td>\n",
       "      <td>117.594138</td>\n",
       "      <td>59.455043</td>\n",
       "      <td>94.361615</td>\n",
       "      <td>73.059414</td>\n",
       "      <td>119.207710</td>\n",
       "      <td>346.5</td>\n",
       "      <td>14.905983</td>\n",
       "      <td>...</td>\n",
       "      <td>295.077607</td>\n",
       "      <td>139.712378</td>\n",
       "      <td>636.488931</td>\n",
       "      <td>92.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>0.435185</td>\n",
       "      <td>2.028138e-10</td>\n",
       "      <td>0.435185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.25_i0.65_p10.0</th>\n",
       "      <td>827.530285</td>\n",
       "      <td>160.278282</td>\n",
       "      <td>391.746941</td>\n",
       "      <td>120.485818</td>\n",
       "      <td>58.056454</td>\n",
       "      <td>94.447558</td>\n",
       "      <td>70.942901</td>\n",
       "      <td>120.618099</td>\n",
       "      <td>330.5</td>\n",
       "      <td>14.950427</td>\n",
       "      <td>...</td>\n",
       "      <td>303.301632</td>\n",
       "      <td>142.802057</td>\n",
       "      <td>614.509232</td>\n",
       "      <td>96.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-108.0</td>\n",
       "      <td>0.455026</td>\n",
       "      <td>2.213274e-11</td>\n",
       "      <td>0.455026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.4_i0.4_p8.0</th>\n",
       "      <td>763.632978</td>\n",
       "      <td>151.546996</td>\n",
       "      <td>361.652596</td>\n",
       "      <td>112.120552</td>\n",
       "      <td>58.941304</td>\n",
       "      <td>87.906210</td>\n",
       "      <td>70.997685</td>\n",
       "      <td>111.578477</td>\n",
       "      <td>340.5</td>\n",
       "      <td>14.953846</td>\n",
       "      <td>...</td>\n",
       "      <td>277.251910</td>\n",
       "      <td>131.221811</td>\n",
       "      <td>617.313108</td>\n",
       "      <td>116.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-112.0</td>\n",
       "      <td>0.398148</td>\n",
       "      <td>9.395075e-09</td>\n",
       "      <td>0.398148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.3_i0.65_p10.0</th>\n",
       "      <td>826.524971</td>\n",
       "      <td>164.577272</td>\n",
       "      <td>390.505324</td>\n",
       "      <td>122.214941</td>\n",
       "      <td>58.386234</td>\n",
       "      <td>94.774284</td>\n",
       "      <td>70.756173</td>\n",
       "      <td>120.253542</td>\n",
       "      <td>330.5</td>\n",
       "      <td>14.977778</td>\n",
       "      <td>...</td>\n",
       "      <td>300.727465</td>\n",
       "      <td>146.473212</td>\n",
       "      <td>645.186913</td>\n",
       "      <td>96.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-120.0</td>\n",
       "      <td>0.455026</td>\n",
       "      <td>2.213274e-11</td>\n",
       "      <td>0.455026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>208 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        mean_upn_onoff_dists  std_upn_onoff_dists  \\\n",
       "a0.1_e0.4_i0.8_p6.0               669.663060           133.063065   \n",
       "a0.1_e0.15_i0.7_p6.0              673.074102           134.102058   \n",
       "a0.1_e0.25_i0.75_p6.0             673.414874           132.977848   \n",
       "a0.1_e0.3_i0.75_p6.0              675.467163           132.874608   \n",
       "a0.1_e0.45_i0.8_p6.0              671.220781           131.961677   \n",
       "...                                      ...                  ...   \n",
       "a0.1_e0.45_i0.7_p10.0             827.818984           160.465263   \n",
       "a0.1_e0.5_i0.7_p10.0              824.919928           159.561153   \n",
       "a0.1_e0.25_i0.65_p10.0            827.530285           160.278282   \n",
       "a0.1_e0.4_i0.4_p8.0               763.632978           151.546996   \n",
       "a0.1_e0.3_i0.65_p10.0             826.524971           164.577272   \n",
       "\n",
       "                        mean_upn_bhand_dists  std_upn_bhand_dists  \\\n",
       "a0.1_e0.4_i0.8_p6.0               336.702923           111.244240   \n",
       "a0.1_e0.15_i0.7_p6.0              340.632894           117.780425   \n",
       "a0.1_e0.25_i0.75_p6.0             335.040700           114.012940   \n",
       "a0.1_e0.3_i0.75_p6.0              339.963631           113.535471   \n",
       "a0.1_e0.45_i0.8_p6.0              330.878985           110.468858   \n",
       "...                                      ...                  ...   \n",
       "a0.1_e0.45_i0.7_p10.0             390.148247           121.158004   \n",
       "a0.1_e0.5_i0.7_p10.0              387.096334           117.594138   \n",
       "a0.1_e0.25_i0.65_p10.0            391.746941           120.485818   \n",
       "a0.1_e0.4_i0.4_p8.0               361.652596           112.120552   \n",
       "a0.1_e0.3_i0.65_p10.0             390.505324           122.214941   \n",
       "\n",
       "                        mean_upn_onoff  std_upn_onoff  mean_upn_bhand  \\\n",
       "a0.1_e0.4_i0.8_p6.0          45.693714      77.019312       64.610340   \n",
       "a0.1_e0.15_i0.7_p6.0         45.058597      77.229613       62.635802   \n",
       "a0.1_e0.25_i0.75_p6.0        44.976362      77.271654       62.732253   \n",
       "a0.1_e0.3_i0.75_p6.0         45.464343      77.339848       62.219136   \n",
       "a0.1_e0.45_i0.8_p6.0         45.282457      77.040621       63.478395   \n",
       "...                                ...            ...             ...   \n",
       "a0.1_e0.45_i0.7_p10.0        59.082131      94.685433       72.739198   \n",
       "a0.1_e0.5_i0.7_p10.0         59.455043      94.361615       73.059414   \n",
       "a0.1_e0.25_i0.65_p10.0       58.056454      94.447558       70.942901   \n",
       "a0.1_e0.4_i0.4_p8.0          58.941304      87.906210       70.997685   \n",
       "a0.1_e0.3_i0.65_p10.0        58.386234      94.774284       70.756173   \n",
       "\n",
       "                        std_upn_bhand  max_upn_bhand  upn_off_mean  ...  \\\n",
       "a0.1_e0.4_i0.8_p6.0        105.536488          322.0      6.100855  ...   \n",
       "a0.1_e0.15_i0.7_p6.0       106.949771          328.5      6.347009  ...   \n",
       "a0.1_e0.25_i0.75_p6.0      105.186655          326.0      6.360684  ...   \n",
       "a0.1_e0.3_i0.75_p6.0       105.916280          324.5      6.377778  ...   \n",
       "a0.1_e0.45_i0.8_p6.0       103.520776          324.5      6.441026  ...   \n",
       "...                               ...            ...           ...  ...   \n",
       "a0.1_e0.45_i0.7_p10.0      120.215683          360.0     14.789744  ...   \n",
       "a0.1_e0.5_i0.7_p10.0       119.207710          346.5     14.905983  ...   \n",
       "a0.1_e0.25_i0.65_p10.0     120.618099          330.5     14.950427  ...   \n",
       "a0.1_e0.4_i0.4_p8.0        111.578477          340.5     14.953846  ...   \n",
       "a0.1_e0.3_i0.65_p10.0      120.253542          330.5     14.977778  ...   \n",
       "\n",
       "                        mean_pca_upn_dists  std_pca_upn_dists  \\\n",
       "a0.1_e0.4_i0.8_p6.0             261.242810         129.986657   \n",
       "a0.1_e0.15_i0.7_p6.0            265.695284         137.122335   \n",
       "a0.1_e0.25_i0.75_p6.0           262.376150         131.782106   \n",
       "a0.1_e0.3_i0.75_p6.0            262.375786         133.126644   \n",
       "a0.1_e0.45_i0.8_p6.0            253.743262         128.090018   \n",
       "...                                    ...                ...   \n",
       "a0.1_e0.45_i0.7_p10.0           299.102871         142.858804   \n",
       "a0.1_e0.5_i0.7_p10.0            295.077607         139.712378   \n",
       "a0.1_e0.25_i0.65_p10.0          303.301632         142.802057   \n",
       "a0.1_e0.4_i0.4_p8.0             277.251910         131.221811   \n",
       "a0.1_e0.3_i0.65_p10.0           300.727465         146.473212   \n",
       "\n",
       "                        max_pca_upn_dists  DM1_PN_pre_odor_fr  \\\n",
       "a0.1_e0.4_i0.8_p6.0            571.939591                60.0   \n",
       "a0.1_e0.15_i0.7_p6.0           577.458747                68.0   \n",
       "a0.1_e0.25_i0.75_p6.0          567.189313                72.0   \n",
       "a0.1_e0.3_i0.75_p6.0           574.462084                72.0   \n",
       "a0.1_e0.45_i0.8_p6.0           553.764072                60.0   \n",
       "...                                   ...                 ...   \n",
       "a0.1_e0.45_i0.7_p10.0          667.148459                96.0   \n",
       "a0.1_e0.5_i0.7_p10.0           636.488931                92.0   \n",
       "a0.1_e0.25_i0.65_p10.0         614.509232                96.0   \n",
       "a0.1_e0.4_i0.4_p8.0            617.313108               116.0   \n",
       "a0.1_e0.3_i0.65_p10.0          645.186913                96.0   \n",
       "\n",
       "                        VA2_PN_pre_odor_fr  VM2_PN_dur_odor_fr  min_PN_dur  \\\n",
       "a0.1_e0.4_i0.8_p6.0                   28.0                 0.0       -76.0   \n",
       "a0.1_e0.15_i0.7_p6.0                  28.0                 0.0       -96.0   \n",
       "a0.1_e0.25_i0.75_p6.0                 28.0                 0.0       -81.5   \n",
       "a0.1_e0.3_i0.75_p6.0                  32.0                 0.0       -84.0   \n",
       "a0.1_e0.45_i0.8_p6.0                  24.0                 0.0       -80.0   \n",
       "...                                    ...                 ...         ...   \n",
       "a0.1_e0.45_i0.7_p10.0                 32.0                 0.0      -112.0   \n",
       "a0.1_e0.5_i0.7_p10.0                  52.0                 0.0      -100.0   \n",
       "a0.1_e0.25_i0.65_p10.0                40.0                 0.0      -108.0   \n",
       "a0.1_e0.4_i0.4_p8.0                   48.0                 0.0      -112.0   \n",
       "a0.1_e0.3_i0.65_p10.0                 52.0                 0.0      -120.0   \n",
       "\n",
       "                        kstest_stat   kstest_pval  kstest_2samp_stat  \n",
       "a0.1_e0.4_i0.8_p6.0        0.462963  8.836154e-12           0.462963  \n",
       "a0.1_e0.15_i0.7_p6.0       0.455026  2.213274e-11           0.455026  \n",
       "a0.1_e0.25_i0.75_p6.0      0.455026  2.213274e-11           0.455026  \n",
       "a0.1_e0.3_i0.75_p6.0       0.453704  2.574641e-11           0.453704  \n",
       "a0.1_e0.45_i0.8_p6.0       0.453704  2.574641e-11           0.453704  \n",
       "...                             ...           ...                ...  \n",
       "a0.1_e0.45_i0.7_p10.0      0.453704  2.574641e-11           0.453704  \n",
       "a0.1_e0.5_i0.7_p10.0       0.435185  2.028138e-10           0.435185  \n",
       "a0.1_e0.25_i0.65_p10.0     0.455026  2.213274e-11           0.455026  \n",
       "a0.1_e0.4_i0.4_p8.0        0.398148  9.395075e-09           0.398148  \n",
       "a0.1_e0.3_i0.65_p10.0      0.455026  2.213274e-11           0.455026  \n",
       "\n",
       "[208 rows x 30 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df = df_res[\n",
    "       #(df_res['p'] == 4) & \n",
    "       (df_res[x_var] < 0.8) &\n",
    "       (df_res[y_var] < 15) &\n",
    "       #(df_res['upn_off_mean'] < 20) &\n",
    "       #(df_res['eln_on_mean'] < 40) &\n",
    "       #(df_res['eln_off_mean'] < 20) &\n",
    "       (1) ]\n",
    "sub_df.sort_values('upn_off_mean')[df_res_cols1]#.sort_values('max_pca_upn_dists')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mean_upn_onoff_dists',\n",
       " 'std_upn_onoff_dists',\n",
       " 'mean_upn_bhand_dists',\n",
       " 'std_upn_bhand_dists',\n",
       " 'mean_upn_onoff',\n",
       " 'std_upn_onoff',\n",
       " 'mean_upn_bhand',\n",
       " 'std_upn_bhand',\n",
       " 'max_upn_bhand',\n",
       " 'upn_off_mean',\n",
       " 'iln_off_mean',\n",
       " 'iln_on_mean',\n",
       " 'eln_off_mean',\n",
       " 'eln_on_mean',\n",
       " 'frac_less_full',\n",
       " 'frac_less_bhand',\n",
       " 'pn_sq_dist_bhandgloms',\n",
       " 'pn_sq_dist_allgloms',\n",
       " 'mean_pca_orn_dists',\n",
       " 'std_pca_orn_dists',\n",
       " 'mean_pca_upn_dists',\n",
       " 'std_pca_upn_dists',\n",
       " 'max_pca_upn_dists',\n",
       " 'DM1_PN_pre_odor_fr',\n",
       " 'VA2_PN_pre_odor_fr',\n",
       " 'VM2_PN_dur_odor_fr',\n",
       " 'min_PN_dur',\n",
       " 'kstest_stat',\n",
       " 'kstest_pval',\n",
       " 'kstest_2samp_stat']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_res_cols1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_var, y_var, c_var = plot_sweep_res(\n",
    "    x_var = 'pn_sq_dist_bhandgloms', x_lab = 'squared distance to Bhandawat ON-OFF uPN firing rate hist',\n",
    "    y_var = 'kstest_stat', y_lab = 'uPN off-odor firing rate (Hz)',\n",
    "    c_var = 'p', c_lab='PN multiplier',\n",
    "    highlight_locs=[]\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>e</th>\n",
       "      <th>i</th>\n",
       "      <th>p</th>\n",
       "      <th>success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.75</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.75</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.75</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.75</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.80</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.80</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.80</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.80</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.80</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.90</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.90</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.90</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.90</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.20</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.20</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.20</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.20</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.30</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.30</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.45</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.45</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.45</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.45</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a     e     i     p  success\n",
       "371  0.1  0.35  0.75   4.0        1\n",
       "372  0.1  0.35  0.75   6.0        1\n",
       "373  0.1  0.35  0.75   8.0        1\n",
       "374  0.1  0.35  0.75  10.0        1\n",
       "375  0.1  0.35  0.80   2.0        1\n",
       "376  0.1  0.35  0.80   4.0        1\n",
       "377  0.1  0.35  0.80   6.0        1\n",
       "378  0.1  0.35  0.80   8.0        1\n",
       "379  0.1  0.35  0.80  10.0        1\n",
       "380  0.1  0.35  0.90   2.0        1\n",
       "381  0.1  0.35  0.90   4.0        1\n",
       "382  0.1  0.35  0.90   6.0        1\n",
       "383  0.1  0.35  0.90   8.0        1\n",
       "384  0.1  0.35  0.90  10.0        1\n",
       "385  0.1  0.35  1.00   2.0        1\n",
       "386  0.1  0.35  1.00   4.0        1\n",
       "387  0.1  0.35  1.00   6.0        1\n",
       "388  0.1  0.35  1.00   8.0        1\n",
       "389  0.1  0.35  1.00  10.0        1\n",
       "390  0.1  0.40  0.20   2.0        1\n",
       "391  0.1  0.40  0.20   4.0        1\n",
       "392  0.1  0.40  0.20   6.0        1\n",
       "393  0.1  0.40  0.20   8.0        1\n",
       "394  0.1  0.40  0.20  10.0        1\n",
       "395  0.1  0.40  0.30   2.0        1\n",
       "396  0.1  0.40  0.30   4.0        1\n",
       "397  0.1  0.40  0.30   6.0        1\n",
       "398  0.1  0.40  0.30   8.0        1\n",
       "399  0.1  0.40  0.30  10.0        1\n",
       "400  0.1  0.40  0.40   2.0        1\n",
       "401  0.1  0.40  0.40   4.0        1\n",
       "402  0.1  0.40  0.40   6.0        1\n",
       "403  0.1  0.40  0.40   8.0        1\n",
       "404  0.1  0.40  0.40  10.0        1\n",
       "405  0.1  0.40  0.45   2.0        1\n",
       "406  0.1  0.40  0.45   4.0        1\n",
       "407  0.1  0.40  0.45   6.0        1\n",
       "408  0.1  0.40  0.45   8.0        1\n",
       "409  0.1  0.40  0.45  10.0        1\n",
       "410  0.1  0.40  0.50   2.0        1"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_simvals.iloc[391-20:391+20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
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       "      <td>97.868763</td>\n",
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       "      <td>1.013115e-04</td>\n",
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       "      <th>a0.1_e0.2_i0.2_p4.0</th>\n",
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       "      <td>64.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-76.0</td>\n",
       "      <td>0.305556</td>\n",
       "      <td>2.694667e-05</td>\n",
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       "      <td>65.893291</td>\n",
       "      <td>62.131173</td>\n",
       "      <td>90.703323</td>\n",
       "      <td>301.00</td>\n",
       "      <td>5.394872</td>\n",
       "      <td>...</td>\n",
       "      <td>223.979962</td>\n",
       "      <td>111.217059</td>\n",
       "      <td>494.620908</td>\n",
       "      <td>64.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-76.0</td>\n",
       "      <td>0.314815</td>\n",
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       "      <td>107.660899</td>\n",
       "      <td>304.50</td>\n",
       "      <td>19.842735</td>\n",
       "      <td>...</td>\n",
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       "      <td>126.722087</td>\n",
       "      <td>613.357795</td>\n",
       "      <td>116.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-128.0</td>\n",
       "      <td>0.314815</td>\n",
       "      <td>1.344861e-05</td>\n",
       "      <td>0.314815</td>\n",
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       "      <td>568.394040</td>\n",
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       "      <td>285.012905</td>\n",
       "      <td>97.093254</td>\n",
       "      <td>45.112219</td>\n",
       "      <td>66.444472</td>\n",
       "      <td>63.020062</td>\n",
       "      <td>90.200823</td>\n",
       "      <td>305.00</td>\n",
       "      <td>5.866667</td>\n",
       "      <td>...</td>\n",
       "      <td>220.694763</td>\n",
       "      <td>109.751562</td>\n",
       "      <td>501.564582</td>\n",
       "      <td>76.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-80.0</td>\n",
       "      <td>0.292328</td>\n",
       "      <td>7.001446e-05</td>\n",
       "      <td>0.292328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.2_p6.0</th>\n",
       "      <td>670.826541</td>\n",
       "      <td>138.636239</td>\n",
       "      <td>319.896211</td>\n",
       "      <td>102.450207</td>\n",
       "      <td>56.875536</td>\n",
       "      <td>78.564340</td>\n",
       "      <td>69.246142</td>\n",
       "      <td>100.769529</td>\n",
       "      <td>305.50</td>\n",
       "      <td>15.116239</td>\n",
       "      <td>...</td>\n",
       "      <td>248.081857</td>\n",
       "      <td>116.998290</td>\n",
       "      <td>561.472581</td>\n",
       "      <td>96.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-111.0</td>\n",
       "      <td>0.279101</td>\n",
       "      <td>1.740242e-04</td>\n",
       "      <td>0.279101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.2_p6.0</th>\n",
       "      <td>669.920395</td>\n",
       "      <td>142.445563</td>\n",
       "      <td>314.859562</td>\n",
       "      <td>100.726367</td>\n",
       "      <td>56.604870</td>\n",
       "      <td>78.183603</td>\n",
       "      <td>68.662809</td>\n",
       "      <td>98.686532</td>\n",
       "      <td>307.00</td>\n",
       "      <td>15.977778</td>\n",
       "      <td>...</td>\n",
       "      <td>240.291467</td>\n",
       "      <td>116.983640</td>\n",
       "      <td>570.209957</td>\n",
       "      <td>108.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-112.0</td>\n",
       "      <td>0.279101</td>\n",
       "      <td>1.740242e-04</td>\n",
       "      <td>0.279101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.4_i0.2_p6.0</th>\n",
       "      <td>669.982874</td>\n",
       "      <td>140.147893</td>\n",
       "      <td>318.770274</td>\n",
       "      <td>101.372736</td>\n",
       "      <td>56.368233</td>\n",
       "      <td>78.437210</td>\n",
       "      <td>69.265432</td>\n",
       "      <td>100.485247</td>\n",
       "      <td>307.00</td>\n",
       "      <td>14.531624</td>\n",
       "      <td>...</td>\n",
       "      <td>246.929901</td>\n",
       "      <td>116.653207</td>\n",
       "      <td>552.131329</td>\n",
       "      <td>100.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-105.5</td>\n",
       "      <td>0.288360</td>\n",
       "      <td>9.243349e-05</td>\n",
       "      <td>0.288360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.25_i0.2_p8.0</th>\n",
       "      <td>750.971611</td>\n",
       "      <td>154.425481</td>\n",
       "      <td>342.861936</td>\n",
       "      <td>104.408578</td>\n",
       "      <td>62.721605</td>\n",
       "      <td>87.306187</td>\n",
       "      <td>71.379630</td>\n",
       "      <td>106.671117</td>\n",
       "      <td>308.50</td>\n",
       "      <td>20.811966</td>\n",
       "      <td>...</td>\n",
       "      <td>261.898459</td>\n",
       "      <td>123.935830</td>\n",
       "      <td>580.236065</td>\n",
       "      <td>128.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-128.0</td>\n",
       "      <td>0.296296</td>\n",
       "      <td>5.282175e-05</td>\n",
       "      <td>0.296296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.25_i0.2_p6.0</th>\n",
       "      <td>674.283240</td>\n",
       "      <td>139.512790</td>\n",
       "      <td>323.126682</td>\n",
       "      <td>103.155703</td>\n",
       "      <td>55.472431</td>\n",
       "      <td>78.685851</td>\n",
       "      <td>66.793210</td>\n",
       "      <td>101.396118</td>\n",
       "      <td>308.50</td>\n",
       "      <td>12.579487</td>\n",
       "      <td>...</td>\n",
       "      <td>248.090717</td>\n",
       "      <td>118.693960</td>\n",
       "      <td>569.001946</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-105.5</td>\n",
       "      <td>0.297619</td>\n",
       "      <td>4.804357e-05</td>\n",
       "      <td>0.297619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.3_p6.0</th>\n",
       "      <td>673.424613</td>\n",
       "      <td>138.023145</td>\n",
       "      <td>327.301980</td>\n",
       "      <td>104.199625</td>\n",
       "      <td>54.106209</td>\n",
       "      <td>78.310940</td>\n",
       "      <td>67.533951</td>\n",
       "      <td>102.366292</td>\n",
       "      <td>311.00</td>\n",
       "      <td>12.090598</td>\n",
       "      <td>...</td>\n",
       "      <td>253.971826</td>\n",
       "      <td>119.268040</td>\n",
       "      <td>557.436380</td>\n",
       "      <td>100.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-104.0</td>\n",
       "      <td>0.325397</td>\n",
       "      <td>5.915565e-06</td>\n",
       "      <td>0.325397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.35_i0.2_p8.0</th>\n",
       "      <td>746.274017</td>\n",
       "      <td>152.045593</td>\n",
       "      <td>341.328811</td>\n",
       "      <td>104.965785</td>\n",
       "      <td>63.394186</td>\n",
       "      <td>86.756920</td>\n",
       "      <td>72.543210</td>\n",
       "      <td>106.534119</td>\n",
       "      <td>311.00</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>260.483706</td>\n",
       "      <td>124.057963</td>\n",
       "      <td>594.383189</td>\n",
       "      <td>120.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-124.0</td>\n",
       "      <td>0.279101</td>\n",
       "      <td>1.740242e-04</td>\n",
       "      <td>0.279101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.2_p8.0</th>\n",
       "      <td>739.471028</td>\n",
       "      <td>152.422404</td>\n",
       "      <td>333.964259</td>\n",
       "      <td>103.543433</td>\n",
       "      <td>64.336352</td>\n",
       "      <td>86.383494</td>\n",
       "      <td>73.449846</td>\n",
       "      <td>104.714037</td>\n",
       "      <td>311.00</td>\n",
       "      <td>24.331624</td>\n",
       "      <td>...</td>\n",
       "      <td>254.932099</td>\n",
       "      <td>121.346518</td>\n",
       "      <td>592.751862</td>\n",
       "      <td>140.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-152.0</td>\n",
       "      <td>0.269841</td>\n",
       "      <td>3.206488e-04</td>\n",
       "      <td>0.269841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.15_i0.2_p6.0</th>\n",
       "      <td>681.328562</td>\n",
       "      <td>138.934695</td>\n",
       "      <td>331.543858</td>\n",
       "      <td>105.154704</td>\n",
       "      <td>53.690850</td>\n",
       "      <td>78.892291</td>\n",
       "      <td>66.731481</td>\n",
       "      <td>103.598130</td>\n",
       "      <td>311.00</td>\n",
       "      <td>11.642735</td>\n",
       "      <td>...</td>\n",
       "      <td>255.320731</td>\n",
       "      <td>121.189734</td>\n",
       "      <td>567.855518</td>\n",
       "      <td>96.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-108.0</td>\n",
       "      <td>0.343915</td>\n",
       "      <td>1.310404e-06</td>\n",
       "      <td>0.343915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.2_i0.2_p6.0</th>\n",
       "      <td>677.460347</td>\n",
       "      <td>137.122425</td>\n",
       "      <td>328.307724</td>\n",
       "      <td>105.845170</td>\n",
       "      <td>54.471641</td>\n",
       "      <td>78.593601</td>\n",
       "      <td>67.233025</td>\n",
       "      <td>102.511761</td>\n",
       "      <td>311.00</td>\n",
       "      <td>12.121368</td>\n",
       "      <td>...</td>\n",
       "      <td>253.424719</td>\n",
       "      <td>122.421412</td>\n",
       "      <td>542.572236</td>\n",
       "      <td>96.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-112.0</td>\n",
       "      <td>0.310847</td>\n",
       "      <td>1.816359e-05</td>\n",
       "      <td>0.310847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.3_i0.2_p6.0</th>\n",
       "      <td>673.282850</td>\n",
       "      <td>140.305256</td>\n",
       "      <td>323.645378</td>\n",
       "      <td>102.492385</td>\n",
       "      <td>55.341381</td>\n",
       "      <td>78.329364</td>\n",
       "      <td>67.026235</td>\n",
       "      <td>101.218774</td>\n",
       "      <td>311.00</td>\n",
       "      <td>13.439316</td>\n",
       "      <td>...</td>\n",
       "      <td>248.114993</td>\n",
       "      <td>119.288213</td>\n",
       "      <td>550.294040</td>\n",
       "      <td>104.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-107.0</td>\n",
       "      <td>0.325397</td>\n",
       "      <td>5.915565e-06</td>\n",
       "      <td>0.325397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.3_i0.2_p8.0</th>\n",
       "      <td>750.453187</td>\n",
       "      <td>151.871174</td>\n",
       "      <td>344.398310</td>\n",
       "      <td>106.251491</td>\n",
       "      <td>63.228254</td>\n",
       "      <td>87.144404</td>\n",
       "      <td>72.378086</td>\n",
       "      <td>107.289677</td>\n",
       "      <td>315.00</td>\n",
       "      <td>21.350427</td>\n",
       "      <td>...</td>\n",
       "      <td>263.107534</td>\n",
       "      <td>125.060571</td>\n",
       "      <td>590.988598</td>\n",
       "      <td>124.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-128.0</td>\n",
       "      <td>0.296296</td>\n",
       "      <td>5.282175e-05</td>\n",
       "      <td>0.296296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.35_i0.2_p6.0</th>\n",
       "      <td>671.574010</td>\n",
       "      <td>138.383621</td>\n",
       "      <td>321.354493</td>\n",
       "      <td>102.505165</td>\n",
       "      <td>55.903204</td>\n",
       "      <td>78.436950</td>\n",
       "      <td>68.060185</td>\n",
       "      <td>100.437683</td>\n",
       "      <td>315.00</td>\n",
       "      <td>14.046154</td>\n",
       "      <td>...</td>\n",
       "      <td>246.456529</td>\n",
       "      <td>119.912171</td>\n",
       "      <td>559.715194</td>\n",
       "      <td>104.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-127.0</td>\n",
       "      <td>0.306878</td>\n",
       "      <td>2.443268e-05</td>\n",
       "      <td>0.306878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.1_i0.2_p6.0</th>\n",
       "      <td>680.207713</td>\n",
       "      <td>138.070705</td>\n",
       "      <td>330.481459</td>\n",
       "      <td>106.954945</td>\n",
       "      <td>52.716807</td>\n",
       "      <td>78.786897</td>\n",
       "      <td>65.419753</td>\n",
       "      <td>103.316381</td>\n",
       "      <td>315.00</td>\n",
       "      <td>11.545299</td>\n",
       "      <td>...</td>\n",
       "      <td>254.556155</td>\n",
       "      <td>123.652883</td>\n",
       "      <td>559.451203</td>\n",
       "      <td>100.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-104.0</td>\n",
       "      <td>0.334656</td>\n",
       "      <td>2.815533e-06</td>\n",
       "      <td>0.334656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.4_i0.2_p8.0</th>\n",
       "      <td>742.144172</td>\n",
       "      <td>151.728246</td>\n",
       "      <td>339.228490</td>\n",
       "      <td>104.633333</td>\n",
       "      <td>63.683987</td>\n",
       "      <td>86.184698</td>\n",
       "      <td>72.613426</td>\n",
       "      <td>105.660476</td>\n",
       "      <td>316.50</td>\n",
       "      <td>23.632479</td>\n",
       "      <td>...</td>\n",
       "      <td>258.800665</td>\n",
       "      <td>121.780845</td>\n",
       "      <td>579.603074</td>\n",
       "      <td>140.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-129.5</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>1.901510e-04</td>\n",
       "      <td>0.277778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.1_i0.2_p8.0</th>\n",
       "      <td>758.978723</td>\n",
       "      <td>153.278725</td>\n",
       "      <td>352.721761</td>\n",
       "      <td>108.002167</td>\n",
       "      <td>60.120389</td>\n",
       "      <td>87.570245</td>\n",
       "      <td>70.161265</td>\n",
       "      <td>109.752563</td>\n",
       "      <td>316.50</td>\n",
       "      <td>18.454701</td>\n",
       "      <td>...</td>\n",
       "      <td>272.439673</td>\n",
       "      <td>128.171902</td>\n",
       "      <td>610.648587</td>\n",
       "      <td>120.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-124.0</td>\n",
       "      <td>0.342593</td>\n",
       "      <td>1.463705e-06</td>\n",
       "      <td>0.342593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.15_i0.2_p8.0</th>\n",
       "      <td>757.194138</td>\n",
       "      <td>153.743298</td>\n",
       "      <td>351.314666</td>\n",
       "      <td>110.025146</td>\n",
       "      <td>61.312727</td>\n",
       "      <td>87.721214</td>\n",
       "      <td>71.520062</td>\n",
       "      <td>109.357902</td>\n",
       "      <td>316.50</td>\n",
       "      <td>18.866667</td>\n",
       "      <td>...</td>\n",
       "      <td>270.198049</td>\n",
       "      <td>130.022930</td>\n",
       "      <td>585.751355</td>\n",
       "      <td>108.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-120.0</td>\n",
       "      <td>0.325397</td>\n",
       "      <td>5.915565e-06</td>\n",
       "      <td>0.325397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.2_p10.0</th>\n",
       "      <td>795.432297</td>\n",
       "      <td>162.720819</td>\n",
       "      <td>351.205864</td>\n",
       "      <td>109.869842</td>\n",
       "      <td>69.372608</td>\n",
       "      <td>92.360227</td>\n",
       "      <td>75.091821</td>\n",
       "      <td>109.660675</td>\n",
       "      <td>317.00</td>\n",
       "      <td>33.196581</td>\n",
       "      <td>...</td>\n",
       "      <td>267.555496</td>\n",
       "      <td>131.542898</td>\n",
       "      <td>622.661252</td>\n",
       "      <td>168.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-149.5</td>\n",
       "      <td>0.287037</td>\n",
       "      <td>1.013115e-04</td>\n",
       "      <td>0.287037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.25_i0.2_p10.0</th>\n",
       "      <td>809.192413</td>\n",
       "      <td>163.503355</td>\n",
       "      <td>364.268842</td>\n",
       "      <td>109.826867</td>\n",
       "      <td>68.194013</td>\n",
       "      <td>93.898893</td>\n",
       "      <td>74.840278</td>\n",
       "      <td>113.009246</td>\n",
       "      <td>317.00</td>\n",
       "      <td>28.203419</td>\n",
       "      <td>...</td>\n",
       "      <td>275.281820</td>\n",
       "      <td>129.324825</td>\n",
       "      <td>613.700830</td>\n",
       "      <td>148.0</td>\n",
       "      <td>64.0</td>\n",
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       "      <td>-141.5</td>\n",
       "      <td>0.305556</td>\n",
       "      <td>2.694667e-05</td>\n",
       "      <td>0.305556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.35_i0.3_p6.0</th>\n",
       "      <td>679.831814</td>\n",
       "      <td>137.049163</td>\n",
       "      <td>331.503432</td>\n",
       "      <td>107.084314</td>\n",
       "      <td>53.272222</td>\n",
       "      <td>78.742271</td>\n",
       "      <td>67.509259</td>\n",
       "      <td>103.550365</td>\n",
       "      <td>320.50</td>\n",
       "      <td>10.779487</td>\n",
       "      <td>...</td>\n",
       "      <td>257.180888</td>\n",
       "      <td>123.095623</td>\n",
       "      <td>573.287232</td>\n",
       "      <td>96.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>0.325397</td>\n",
       "      <td>5.915565e-06</td>\n",
       "      <td>0.325397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.4_p6.0</th>\n",
       "      <td>680.954308</td>\n",
       "      <td>136.606108</td>\n",
       "      <td>331.880348</td>\n",
       "      <td>106.620650</td>\n",
       "      <td>52.229847</td>\n",
       "      <td>78.930543</td>\n",
       "      <td>67.479167</td>\n",
       "      <td>104.080061</td>\n",
       "      <td>320.50</td>\n",
       "      <td>9.547009</td>\n",
       "      <td>...</td>\n",
       "      <td>256.869954</td>\n",
       "      <td>123.873970</td>\n",
       "      <td>571.614668</td>\n",
       "      <td>92.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-105.5</td>\n",
       "      <td>0.362434</td>\n",
       "      <td>2.652498e-07</td>\n",
       "      <td>0.362434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.2_p8.0</th>\n",
       "      <td>736.048932</td>\n",
       "      <td>150.936073</td>\n",
       "      <td>335.199110</td>\n",
       "      <td>103.905429</td>\n",
       "      <td>64.597762</td>\n",
       "      <td>85.958821</td>\n",
       "      <td>73.331019</td>\n",
       "      <td>105.057396</td>\n",
       "      <td>323.00</td>\n",
       "      <td>25.276923</td>\n",
       "      <td>...</td>\n",
       "      <td>253.760641</td>\n",
       "      <td>121.779614</td>\n",
       "      <td>598.904351</td>\n",
       "      <td>124.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-144.0</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>1.901510e-04</td>\n",
       "      <td>0.277778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.4_i0.3_p8.0</th>\n",
       "      <td>760.973221</td>\n",
       "      <td>154.294095</td>\n",
       "      <td>352.214214</td>\n",
       "      <td>108.574319</td>\n",
       "      <td>61.057121</td>\n",
       "      <td>88.009720</td>\n",
       "      <td>71.390432</td>\n",
       "      <td>109.542054</td>\n",
       "      <td>323.00</td>\n",
       "      <td>17.998291</td>\n",
       "      <td>...</td>\n",
       "      <td>269.830351</td>\n",
       "      <td>128.531667</td>\n",
       "      <td>605.759469</td>\n",
       "      <td>120.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-124.0</td>\n",
       "      <td>0.343915</td>\n",
       "      <td>1.310404e-06</td>\n",
       "      <td>0.343915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.4_i0.3_p6.0</th>\n",
       "      <td>677.306475</td>\n",
       "      <td>139.126072</td>\n",
       "      <td>326.499725</td>\n",
       "      <td>106.967308</td>\n",
       "      <td>53.763539</td>\n",
       "      <td>78.686035</td>\n",
       "      <td>68.459105</td>\n",
       "      <td>102.415063</td>\n",
       "      <td>323.00</td>\n",
       "      <td>11.147009</td>\n",
       "      <td>...</td>\n",
       "      <td>253.281444</td>\n",
       "      <td>122.788921</td>\n",
       "      <td>570.707930</td>\n",
       "      <td>104.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-108.0</td>\n",
       "      <td>0.334656</td>\n",
       "      <td>2.815533e-06</td>\n",
       "      <td>0.334656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.3_p8.0</th>\n",
       "      <td>763.473597</td>\n",
       "      <td>155.296673</td>\n",
       "      <td>356.621930</td>\n",
       "      <td>109.411187</td>\n",
       "      <td>61.304934</td>\n",
       "      <td>88.039383</td>\n",
       "      <td>72.420525</td>\n",
       "      <td>110.667557</td>\n",
       "      <td>324.50</td>\n",
       "      <td>18.608547</td>\n",
       "      <td>...</td>\n",
       "      <td>273.335042</td>\n",
       "      <td>129.947285</td>\n",
       "      <td>607.750535</td>\n",
       "      <td>120.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-125.5</td>\n",
       "      <td>0.314815</td>\n",
       "      <td>1.344861e-05</td>\n",
       "      <td>0.314815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.3_i0.2_p10.0</th>\n",
       "      <td>806.405555</td>\n",
       "      <td>163.724933</td>\n",
       "      <td>357.379360</td>\n",
       "      <td>109.603185</td>\n",
       "      <td>68.811824</td>\n",
       "      <td>93.499984</td>\n",
       "      <td>75.290123</td>\n",
       "      <td>110.834173</td>\n",
       "      <td>325.00</td>\n",
       "      <td>28.955556</td>\n",
       "      <td>...</td>\n",
       "      <td>269.983688</td>\n",
       "      <td>133.666221</td>\n",
       "      <td>635.397340</td>\n",
       "      <td>132.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-145.5</td>\n",
       "      <td>0.305556</td>\n",
       "      <td>2.694667e-05</td>\n",
       "      <td>0.305556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.3_p6.0</th>\n",
       "      <td>674.946833</td>\n",
       "      <td>137.882755</td>\n",
       "      <td>321.763763</td>\n",
       "      <td>104.168066</td>\n",
       "      <td>53.996360</td>\n",
       "      <td>78.661491</td>\n",
       "      <td>67.987654</td>\n",
       "      <td>101.068481</td>\n",
       "      <td>325.50</td>\n",
       "      <td>11.569231</td>\n",
       "      <td>...</td>\n",
       "      <td>249.867415</td>\n",
       "      <td>119.311865</td>\n",
       "      <td>554.234242</td>\n",
       "      <td>104.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-93.5</td>\n",
       "      <td>0.325397</td>\n",
       "      <td>5.915565e-06</td>\n",
       "      <td>0.325397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.25_i0.3_p6.0</th>\n",
       "      <td>679.028106</td>\n",
       "      <td>139.780437</td>\n",
       "      <td>329.074900</td>\n",
       "      <td>107.118539</td>\n",
       "      <td>52.044576</td>\n",
       "      <td>78.722290</td>\n",
       "      <td>66.344136</td>\n",
       "      <td>103.207834</td>\n",
       "      <td>327.00</td>\n",
       "      <td>10.198291</td>\n",
       "      <td>...</td>\n",
       "      <td>254.912177</td>\n",
       "      <td>122.177143</td>\n",
       "      <td>569.813939</td>\n",
       "      <td>96.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-96.0</td>\n",
       "      <td>0.362434</td>\n",
       "      <td>2.652498e-07</td>\n",
       "      <td>0.362434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.35_i0.2_p10.0</th>\n",
       "      <td>799.744254</td>\n",
       "      <td>164.652892</td>\n",
       "      <td>356.541811</td>\n",
       "      <td>110.138877</td>\n",
       "      <td>69.372790</td>\n",
       "      <td>92.963286</td>\n",
       "      <td>75.674383</td>\n",
       "      <td>110.749424</td>\n",
       "      <td>327.50</td>\n",
       "      <td>30.246154</td>\n",
       "      <td>...</td>\n",
       "      <td>267.727372</td>\n",
       "      <td>134.584375</td>\n",
       "      <td>618.416170</td>\n",
       "      <td>140.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-156.0</td>\n",
       "      <td>0.305556</td>\n",
       "      <td>2.694667e-05</td>\n",
       "      <td>0.305556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.2_p10.0</th>\n",
       "      <td>794.221580</td>\n",
       "      <td>159.105608</td>\n",
       "      <td>347.864749</td>\n",
       "      <td>105.996955</td>\n",
       "      <td>69.294948</td>\n",
       "      <td>91.868027</td>\n",
       "      <td>74.893519</td>\n",
       "      <td>108.076661</td>\n",
       "      <td>327.50</td>\n",
       "      <td>34.608547</td>\n",
       "      <td>...</td>\n",
       "      <td>258.606555</td>\n",
       "      <td>130.078201</td>\n",
       "      <td>610.967225</td>\n",
       "      <td>144.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-156.0</td>\n",
       "      <td>0.296296</td>\n",
       "      <td>5.282175e-05</td>\n",
       "      <td>0.296296</td>\n",
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       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.3_p8.0</th>\n",
       "      <td>749.736536</td>\n",
       "      <td>156.484567</td>\n",
       "      <td>347.502543</td>\n",
       "      <td>106.582441</td>\n",
       "      <td>61.724251</td>\n",
       "      <td>87.201124</td>\n",
       "      <td>72.574846</td>\n",
       "      <td>107.892315</td>\n",
       "      <td>328.50</td>\n",
       "      <td>19.376068</td>\n",
       "      <td>...</td>\n",
       "      <td>265.271259</td>\n",
       "      <td>125.521707</td>\n",
       "      <td>611.084370</td>\n",
       "      <td>100.0</td>\n",
       "      <td>52.0</td>\n",
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       "      <td>-121.5</td>\n",
       "      <td>0.334656</td>\n",
       "      <td>2.815533e-06</td>\n",
       "      <td>0.334656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.35_i0.3_p8.0</th>\n",
       "      <td>757.991494</td>\n",
       "      <td>151.367306</td>\n",
       "      <td>353.494545</td>\n",
       "      <td>109.426199</td>\n",
       "      <td>60.725980</td>\n",
       "      <td>87.769220</td>\n",
       "      <td>72.195988</td>\n",
       "      <td>109.835127</td>\n",
       "      <td>328.50</td>\n",
       "      <td>17.420513</td>\n",
       "      <td>...</td>\n",
       "      <td>269.383778</td>\n",
       "      <td>127.749036</td>\n",
       "      <td>606.230627</td>\n",
       "      <td>96.0</td>\n",
       "      <td>52.0</td>\n",
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       "      <td>-120.0</td>\n",
       "      <td>0.334656</td>\n",
       "      <td>2.815533e-06</td>\n",
       "      <td>0.334656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.2_i0.3_p8.0</th>\n",
       "      <td>762.233017</td>\n",
       "      <td>151.034702</td>\n",
       "      <td>363.841661</td>\n",
       "      <td>111.708227</td>\n",
       "      <td>58.821482</td>\n",
       "      <td>87.929223</td>\n",
       "      <td>70.502315</td>\n",
       "      <td>112.534590</td>\n",
       "      <td>332.50</td>\n",
       "      <td>15.965812</td>\n",
       "      <td>...</td>\n",
       "      <td>278.291833</td>\n",
       "      <td>132.232383</td>\n",
       "      <td>618.353581</td>\n",
       "      <td>100.0</td>\n",
       "      <td>48.0</td>\n",
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       "      <td>-112.0</td>\n",
       "      <td>0.353175</td>\n",
       "      <td>5.962944e-07</td>\n",
       "      <td>0.353175</td>\n",
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       "    <tr>\n",
       "      <th>a0.1_e0.4_i0.3_p10.0</th>\n",
       "      <td>815.345457</td>\n",
       "      <td>163.815912</td>\n",
       "      <td>368.508546</td>\n",
       "      <td>111.968740</td>\n",
       "      <td>66.634046</td>\n",
       "      <td>94.224071</td>\n",
       "      <td>74.848765</td>\n",
       "      <td>113.917550</td>\n",
       "      <td>333.00</td>\n",
       "      <td>24.507692</td>\n",
       "      <td>...</td>\n",
       "      <td>279.596348</td>\n",
       "      <td>133.125362</td>\n",
       "      <td>624.527680</td>\n",
       "      <td>140.0</td>\n",
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       "      <td>-144.0</td>\n",
       "      <td>0.325397</td>\n",
       "      <td>5.915565e-06</td>\n",
       "      <td>0.325397</td>\n",
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       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.3_p10.0</th>\n",
       "      <td>816.757264</td>\n",
       "      <td>164.967378</td>\n",
       "      <td>374.961061</td>\n",
       "      <td>111.815683</td>\n",
       "      <td>67.860013</td>\n",
       "      <td>94.599829</td>\n",
       "      <td>76.373457</td>\n",
       "      <td>116.192735</td>\n",
       "      <td>333.00</td>\n",
       "      <td>25.842735</td>\n",
       "      <td>...</td>\n",
       "      <td>282.493521</td>\n",
       "      <td>134.939215</td>\n",
       "      <td>630.258233</td>\n",
       "      <td>120.0</td>\n",
       "      <td>40.0</td>\n",
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       "      <td>0.333333</td>\n",
       "      <td>3.134847e-06</td>\n",
       "      <td>0.333333</td>\n",
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       "    <tr>\n",
       "      <th>a0.1_e0.2_i0.3_p10.0</th>\n",
       "      <td>825.566597</td>\n",
       "      <td>163.616604</td>\n",
       "      <td>381.258516</td>\n",
       "      <td>113.086656</td>\n",
       "      <td>64.560635</td>\n",
       "      <td>94.961142</td>\n",
       "      <td>74.147377</td>\n",
       "      <td>117.404549</td>\n",
       "      <td>333.00</td>\n",
       "      <td>21.757265</td>\n",
       "      <td>...</td>\n",
       "      <td>287.159956</td>\n",
       "      <td>137.573621</td>\n",
       "      <td>622.258046</td>\n",
       "      <td>140.0</td>\n",
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       "      <td>0.388889</td>\n",
       "      <td>2.309270e-08</td>\n",
       "      <td>0.388889</td>\n",
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       "    <tr>\n",
       "      <th>a0.1_e0.4_i0.2_p10.0</th>\n",
       "      <td>805.930027</td>\n",
       "      <td>160.634293</td>\n",
       "      <td>358.479322</td>\n",
       "      <td>109.070575</td>\n",
       "      <td>69.372835</td>\n",
       "      <td>93.272888</td>\n",
       "      <td>76.444444</td>\n",
       "      <td>111.576314</td>\n",
       "      <td>333.00</td>\n",
       "      <td>31.483761</td>\n",
       "      <td>...</td>\n",
       "      <td>270.297839</td>\n",
       "      <td>131.643931</td>\n",
       "      <td>617.894751</td>\n",
       "      <td>148.0</td>\n",
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       "      <td>0.287037</td>\n",
       "      <td>1.013115e-04</td>\n",
       "      <td>0.287037</td>\n",
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       "      <th>a0.1_e0.1_i0.2_p10.0</th>\n",
       "      <td>819.930752</td>\n",
       "      <td>163.966316</td>\n",
       "      <td>375.697795</td>\n",
       "      <td>113.763427</td>\n",
       "      <td>65.673330</td>\n",
       "      <td>94.797371</td>\n",
       "      <td>72.577160</td>\n",
       "      <td>116.150959</td>\n",
       "      <td>333.00</td>\n",
       "      <td>25.104274</td>\n",
       "      <td>...</td>\n",
       "      <td>288.347633</td>\n",
       "      <td>135.993446</td>\n",
       "      <td>638.051701</td>\n",
       "      <td>140.0</td>\n",
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       "      <td>0.351852</td>\n",
       "      <td>6.682085e-07</td>\n",
       "      <td>0.351852</td>\n",
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       "      <th>a0.1_e0.3_i0.3_p10.0</th>\n",
       "      <td>819.918774</td>\n",
       "      <td>162.946003</td>\n",
       "      <td>376.131402</td>\n",
       "      <td>112.372205</td>\n",
       "      <td>65.996301</td>\n",
       "      <td>94.555906</td>\n",
       "      <td>74.606481</td>\n",
       "      <td>116.000949</td>\n",
       "      <td>334.50</td>\n",
       "      <td>22.811966</td>\n",
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       "      <td>285.590118</td>\n",
       "      <td>134.637724</td>\n",
       "      <td>638.919522</td>\n",
       "      <td>132.0</td>\n",
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       "      <td>3.134847e-06</td>\n",
       "      <td>0.333333</td>\n",
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       "    <tr>\n",
       "      <th>a0.1_e0.25_i0.3_p10.0</th>\n",
       "      <td>822.244495</td>\n",
       "      <td>163.583145</td>\n",
       "      <td>380.181901</td>\n",
       "      <td>115.188235</td>\n",
       "      <td>65.528077</td>\n",
       "      <td>94.846072</td>\n",
       "      <td>72.420525</td>\n",
       "      <td>117.220189</td>\n",
       "      <td>334.50</td>\n",
       "      <td>22.063248</td>\n",
       "      <td>...</td>\n",
       "      <td>290.262265</td>\n",
       "      <td>137.847504</td>\n",
       "      <td>662.674674</td>\n",
       "      <td>116.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-140.0</td>\n",
       "      <td>0.353175</td>\n",
       "      <td>5.962944e-07</td>\n",
       "      <td>0.353175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.35_i0.3_p10.0</th>\n",
       "      <td>821.867592</td>\n",
       "      <td>164.431727</td>\n",
       "      <td>379.764882</td>\n",
       "      <td>111.751615</td>\n",
       "      <td>66.468818</td>\n",
       "      <td>95.047153</td>\n",
       "      <td>74.648148</td>\n",
       "      <td>116.875071</td>\n",
       "      <td>334.50</td>\n",
       "      <td>23.370940</td>\n",
       "      <td>...</td>\n",
       "      <td>286.752202</td>\n",
       "      <td>135.531998</td>\n",
       "      <td>641.337320</td>\n",
       "      <td>124.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-132.0</td>\n",
       "      <td>0.353175</td>\n",
       "      <td>5.962944e-07</td>\n",
       "      <td>0.353175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.3_i0.3_p8.0</th>\n",
       "      <td>757.082905</td>\n",
       "      <td>153.992501</td>\n",
       "      <td>355.046278</td>\n",
       "      <td>111.361674</td>\n",
       "      <td>60.266735</td>\n",
       "      <td>87.862346</td>\n",
       "      <td>71.493056</td>\n",
       "      <td>110.476590</td>\n",
       "      <td>335.00</td>\n",
       "      <td>16.929915</td>\n",
       "      <td>...</td>\n",
       "      <td>272.492070</td>\n",
       "      <td>131.656001</td>\n",
       "      <td>603.036955</td>\n",
       "      <td>108.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-124.0</td>\n",
       "      <td>0.353175</td>\n",
       "      <td>5.962944e-07</td>\n",
       "      <td>0.353175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.15_i0.2_p10.0</th>\n",
       "      <td>821.265279</td>\n",
       "      <td>166.545145</td>\n",
       "      <td>372.569505</td>\n",
       "      <td>112.674411</td>\n",
       "      <td>66.962214</td>\n",
       "      <td>94.752297</td>\n",
       "      <td>74.070216</td>\n",
       "      <td>114.978554</td>\n",
       "      <td>335.50</td>\n",
       "      <td>25.786325</td>\n",
       "      <td>...</td>\n",
       "      <td>283.368986</td>\n",
       "      <td>133.411140</td>\n",
       "      <td>652.336517</td>\n",
       "      <td>128.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-144.0</td>\n",
       "      <td>0.316138</td>\n",
       "      <td>1.215566e-05</td>\n",
       "      <td>0.316138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.3_p10.0</th>\n",
       "      <td>813.350758</td>\n",
       "      <td>164.292239</td>\n",
       "      <td>371.338815</td>\n",
       "      <td>114.059056</td>\n",
       "      <td>67.025690</td>\n",
       "      <td>94.022451</td>\n",
       "      <td>74.108025</td>\n",
       "      <td>114.749933</td>\n",
       "      <td>337.00</td>\n",
       "      <td>25.282051</td>\n",
       "      <td>...</td>\n",
       "      <td>284.275328</td>\n",
       "      <td>135.942060</td>\n",
       "      <td>651.350324</td>\n",
       "      <td>128.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-149.5</td>\n",
       "      <td>0.325397</td>\n",
       "      <td>5.915565e-06</td>\n",
       "      <td>0.325397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.2_i0.2_p10.0</th>\n",
       "      <td>818.288728</td>\n",
       "      <td>165.905301</td>\n",
       "      <td>369.822802</td>\n",
       "      <td>113.723836</td>\n",
       "      <td>67.932721</td>\n",
       "      <td>94.758961</td>\n",
       "      <td>73.019290</td>\n",
       "      <td>114.291591</td>\n",
       "      <td>337.00</td>\n",
       "      <td>26.827350</td>\n",
       "      <td>...</td>\n",
       "      <td>286.046288</td>\n",
       "      <td>134.183968</td>\n",
       "      <td>648.256824</td>\n",
       "      <td>152.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-141.5</td>\n",
       "      <td>0.316138</td>\n",
       "      <td>1.215566e-05</td>\n",
       "      <td>0.316138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       mean_upn_onoff_dists  std_upn_onoff_dists  \\\n",
       "a0.1_e0.5_i0.2_p4.0              565.046197           118.863910   \n",
       "a0.1_e0.45_i0.2_p4.0             561.069537           118.254581   \n",
       "a0.1_e0.3_i0.2_p4.0              566.225195           117.633535   \n",
       "a0.1_e0.25_i0.2_p4.0             564.584218           118.725497   \n",
       "a0.1_e0.4_i0.2_p4.0              566.496508           118.855839   \n",
       "a0.1_e0.2_i0.2_p4.0              568.066928           119.482380   \n",
       "a0.1_e0.15_i0.2_p4.0             568.005206           118.069873   \n",
       "a0.1_e0.2_i0.2_p8.0              749.200167           152.843008   \n",
       "a0.1_e0.35_i0.2_p4.0             568.394040           117.936114   \n",
       "a0.1_e0.45_i0.2_p6.0             670.826541           138.636239   \n",
       "a0.1_e0.5_i0.2_p6.0              669.920395           142.445563   \n",
       "a0.1_e0.4_i0.2_p6.0              669.982874           140.147893   \n",
       "a0.1_e0.25_i0.2_p8.0             750.971611           154.425481   \n",
       "a0.1_e0.25_i0.2_p6.0             674.283240           139.512790   \n",
       "a0.1_e0.5_i0.3_p6.0              673.424613           138.023145   \n",
       "a0.1_e0.35_i0.2_p8.0             746.274017           152.045593   \n",
       "a0.1_e0.45_i0.2_p8.0             739.471028           152.422404   \n",
       "a0.1_e0.15_i0.2_p6.0             681.328562           138.934695   \n",
       "a0.1_e0.2_i0.2_p6.0              677.460347           137.122425   \n",
       "a0.1_e0.3_i0.2_p6.0              673.282850           140.305256   \n",
       "a0.1_e0.3_i0.2_p8.0              750.453187           151.871174   \n",
       "a0.1_e0.35_i0.2_p6.0             671.574010           138.383621   \n",
       "a0.1_e0.1_i0.2_p6.0              680.207713           138.070705   \n",
       "a0.1_e0.4_i0.2_p8.0              742.144172           151.728246   \n",
       "a0.1_e0.1_i0.2_p8.0              758.978723           153.278725   \n",
       "a0.1_e0.15_i0.2_p8.0             757.194138           153.743298   \n",
       "a0.1_e0.45_i0.2_p10.0            795.432297           162.720819   \n",
       "a0.1_e0.25_i0.2_p10.0            809.192413           163.503355   \n",
       "a0.1_e0.35_i0.3_p6.0             679.831814           137.049163   \n",
       "a0.1_e0.5_i0.4_p6.0              680.954308           136.606108   \n",
       "a0.1_e0.5_i0.2_p8.0              736.048932           150.936073   \n",
       "a0.1_e0.4_i0.3_p8.0              760.973221           154.294095   \n",
       "a0.1_e0.4_i0.3_p6.0              677.306475           139.126072   \n",
       "a0.1_e0.45_i0.3_p8.0             763.473597           155.296673   \n",
       "a0.1_e0.3_i0.2_p10.0             806.405555           163.724933   \n",
       "a0.1_e0.45_i0.3_p6.0             674.946833           137.882755   \n",
       "a0.1_e0.25_i0.3_p6.0             679.028106           139.780437   \n",
       "a0.1_e0.35_i0.2_p10.0            799.744254           164.652892   \n",
       "a0.1_e0.5_i0.2_p10.0             794.221580           159.105608   \n",
       "a0.1_e0.5_i0.3_p8.0              749.736536           156.484567   \n",
       "a0.1_e0.35_i0.3_p8.0             757.991494           151.367306   \n",
       "a0.1_e0.2_i0.3_p8.0              762.233017           151.034702   \n",
       "a0.1_e0.4_i0.3_p10.0             815.345457           163.815912   \n",
       "a0.1_e0.5_i0.3_p10.0             816.757264           164.967378   \n",
       "a0.1_e0.2_i0.3_p10.0             825.566597           163.616604   \n",
       "a0.1_e0.4_i0.2_p10.0             805.930027           160.634293   \n",
       "a0.1_e0.1_i0.2_p10.0             819.930752           163.966316   \n",
       "a0.1_e0.3_i0.3_p10.0             819.918774           162.946003   \n",
       "a0.1_e0.25_i0.3_p10.0            822.244495           163.583145   \n",
       "a0.1_e0.35_i0.3_p10.0            821.867592           164.431727   \n",
       "a0.1_e0.3_i0.3_p8.0              757.082905           153.992501   \n",
       "a0.1_e0.15_i0.2_p10.0            821.265279           166.545145   \n",
       "a0.1_e0.45_i0.3_p10.0            813.350758           164.292239   \n",
       "a0.1_e0.2_i0.2_p10.0             818.288728           165.905301   \n",
       "\n",
       "                       mean_upn_bhand_dists  std_upn_bhand_dists  \\\n",
       "a0.1_e0.5_i0.2_p4.0              280.537383            93.504112   \n",
       "a0.1_e0.45_i0.2_p4.0             280.417160            93.802481   \n",
       "a0.1_e0.3_i0.2_p4.0              286.758283            97.868763   \n",
       "a0.1_e0.25_i0.2_p4.0             285.200934            96.499578   \n",
       "a0.1_e0.4_i0.2_p4.0              285.256129            95.201347   \n",
       "a0.1_e0.2_i0.2_p4.0              286.426757            97.480844   \n",
       "a0.1_e0.15_i0.2_p4.0             286.879907            97.889750   \n",
       "a0.1_e0.2_i0.2_p8.0              344.477512           108.338779   \n",
       "a0.1_e0.35_i0.2_p4.0             285.012905            97.093254   \n",
       "a0.1_e0.45_i0.2_p6.0             319.896211           102.450207   \n",
       "a0.1_e0.5_i0.2_p6.0              314.859562           100.726367   \n",
       "a0.1_e0.4_i0.2_p6.0              318.770274           101.372736   \n",
       "a0.1_e0.25_i0.2_p8.0             342.861936           104.408578   \n",
       "a0.1_e0.25_i0.2_p6.0             323.126682           103.155703   \n",
       "a0.1_e0.5_i0.3_p6.0              327.301980           104.199625   \n",
       "a0.1_e0.35_i0.2_p8.0             341.328811           104.965785   \n",
       "a0.1_e0.45_i0.2_p8.0             333.964259           103.543433   \n",
       "a0.1_e0.15_i0.2_p6.0             331.543858           105.154704   \n",
       "a0.1_e0.2_i0.2_p6.0              328.307724           105.845170   \n",
       "a0.1_e0.3_i0.2_p6.0              323.645378           102.492385   \n",
       "a0.1_e0.3_i0.2_p8.0              344.398310           106.251491   \n",
       "a0.1_e0.35_i0.2_p6.0             321.354493           102.505165   \n",
       "a0.1_e0.1_i0.2_p6.0              330.481459           106.954945   \n",
       "a0.1_e0.4_i0.2_p8.0              339.228490           104.633333   \n",
       "a0.1_e0.1_i0.2_p8.0              352.721761           108.002167   \n",
       "a0.1_e0.15_i0.2_p8.0             351.314666           110.025146   \n",
       "a0.1_e0.45_i0.2_p10.0            351.205864           109.869842   \n",
       "a0.1_e0.25_i0.2_p10.0            364.268842           109.826867   \n",
       "a0.1_e0.35_i0.3_p6.0             331.503432           107.084314   \n",
       "a0.1_e0.5_i0.4_p6.0              331.880348           106.620650   \n",
       "a0.1_e0.5_i0.2_p8.0              335.199110           103.905429   \n",
       "a0.1_e0.4_i0.3_p8.0              352.214214           108.574319   \n",
       "a0.1_e0.4_i0.3_p6.0              326.499725           106.967308   \n",
       "a0.1_e0.45_i0.3_p8.0             356.621930           109.411187   \n",
       "a0.1_e0.3_i0.2_p10.0             357.379360           109.603185   \n",
       "a0.1_e0.45_i0.3_p6.0             321.763763           104.168066   \n",
       "a0.1_e0.25_i0.3_p6.0             329.074900           107.118539   \n",
       "a0.1_e0.35_i0.2_p10.0            356.541811           110.138877   \n",
       "a0.1_e0.5_i0.2_p10.0             347.864749           105.996955   \n",
       "a0.1_e0.5_i0.3_p8.0              347.502543           106.582441   \n",
       "a0.1_e0.35_i0.3_p8.0             353.494545           109.426199   \n",
       "a0.1_e0.2_i0.3_p8.0              363.841661           111.708227   \n",
       "a0.1_e0.4_i0.3_p10.0             368.508546           111.968740   \n",
       "a0.1_e0.5_i0.3_p10.0             374.961061           111.815683   \n",
       "a0.1_e0.2_i0.3_p10.0             381.258516           113.086656   \n",
       "a0.1_e0.4_i0.2_p10.0             358.479322           109.070575   \n",
       "a0.1_e0.1_i0.2_p10.0             375.697795           113.763427   \n",
       "a0.1_e0.3_i0.3_p10.0             376.131402           112.372205   \n",
       "a0.1_e0.25_i0.3_p10.0            380.181901           115.188235   \n",
       "a0.1_e0.35_i0.3_p10.0            379.764882           111.751615   \n",
       "a0.1_e0.3_i0.3_p8.0              355.046278           111.361674   \n",
       "a0.1_e0.15_i0.2_p10.0            372.569505           112.674411   \n",
       "a0.1_e0.45_i0.3_p10.0            371.338815           114.059056   \n",
       "a0.1_e0.2_i0.2_p10.0             369.822802           113.723836   \n",
       "\n",
       "                       mean_upn_onoff  std_upn_onoff  mean_upn_bhand  \\\n",
       "a0.1_e0.5_i0.2_p4.0         46.216880      66.479202       63.301698   \n",
       "a0.1_e0.45_i0.2_p4.0        45.591644      66.109681       63.685957   \n",
       "a0.1_e0.3_i0.2_p4.0         44.782580      66.324000       63.459105   \n",
       "a0.1_e0.25_i0.2_p4.0        44.240418      66.089001       63.312500   \n",
       "a0.1_e0.4_i0.2_p4.0         45.694408      66.429010       64.111883   \n",
       "a0.1_e0.2_i0.2_p4.0         43.629021      66.228442       61.815586   \n",
       "a0.1_e0.15_i0.2_p4.0        42.596982      65.893291       62.131173   \n",
       "a0.1_e0.2_i0.2_p8.0         61.931795      87.402709       70.593364   \n",
       "a0.1_e0.35_i0.2_p4.0        45.112219      66.444472       63.020062   \n",
       "a0.1_e0.45_i0.2_p6.0        56.875536      78.564340       69.246142   \n",
       "a0.1_e0.5_i0.2_p6.0         56.604870      78.183603       68.662809   \n",
       "a0.1_e0.4_i0.2_p6.0         56.368233      78.437210       69.265432   \n",
       "a0.1_e0.25_i0.2_p8.0        62.721605      87.306187       71.379630   \n",
       "a0.1_e0.25_i0.2_p6.0        55.472431      78.685851       66.793210   \n",
       "a0.1_e0.5_i0.3_p6.0         54.106209      78.310940       67.533951   \n",
       "a0.1_e0.35_i0.2_p8.0        63.394186      86.756920       72.543210   \n",
       "a0.1_e0.45_i0.2_p8.0        64.336352      86.383494       73.449846   \n",
       "a0.1_e0.15_i0.2_p6.0        53.690850      78.892291       66.731481   \n",
       "a0.1_e0.2_i0.2_p6.0         54.471641      78.593601       67.233025   \n",
       "a0.1_e0.3_i0.2_p6.0         55.341381      78.329364       67.026235   \n",
       "a0.1_e0.3_i0.2_p8.0         63.228254      87.144404       72.378086   \n",
       "a0.1_e0.35_i0.2_p6.0        55.903204      78.436950       68.060185   \n",
       "a0.1_e0.1_i0.2_p6.0         52.716807      78.786897       65.419753   \n",
       "a0.1_e0.4_i0.2_p8.0         63.683987      86.184698       72.613426   \n",
       "a0.1_e0.1_i0.2_p8.0         60.120389      87.570245       70.161265   \n",
       "a0.1_e0.15_i0.2_p8.0        61.312727      87.721214       71.520062   \n",
       "a0.1_e0.45_i0.2_p10.0       69.372608      92.360227       75.091821   \n",
       "a0.1_e0.25_i0.2_p10.0       68.194013      93.898893       74.840278   \n",
       "a0.1_e0.35_i0.3_p6.0        53.272222      78.742271       67.509259   \n",
       "a0.1_e0.5_i0.4_p6.0         52.229847      78.930543       67.479167   \n",
       "a0.1_e0.5_i0.2_p8.0         64.597762      85.958821       73.331019   \n",
       "a0.1_e0.4_i0.3_p8.0         61.057121      88.009720       71.390432   \n",
       "a0.1_e0.4_i0.3_p6.0         53.763539      78.686035       68.459105   \n",
       "a0.1_e0.45_i0.3_p8.0        61.304934      88.039383       72.420525   \n",
       "a0.1_e0.3_i0.2_p10.0        68.811824      93.499984       75.290123   \n",
       "a0.1_e0.45_i0.3_p6.0        53.996360      78.661491       67.987654   \n",
       "a0.1_e0.25_i0.3_p6.0        52.044576      78.722290       66.344136   \n",
       "a0.1_e0.35_i0.2_p10.0       69.372790      92.963286       75.674383   \n",
       "a0.1_e0.5_i0.2_p10.0        69.294948      91.868027       74.893519   \n",
       "a0.1_e0.5_i0.3_p8.0         61.724251      87.201124       72.574846   \n",
       "a0.1_e0.35_i0.3_p8.0        60.725980      87.769220       72.195988   \n",
       "a0.1_e0.2_i0.3_p8.0         58.821482      87.929223       70.502315   \n",
       "a0.1_e0.4_i0.3_p10.0        66.634046      94.224071       74.848765   \n",
       "a0.1_e0.5_i0.3_p10.0        67.860013      94.599829       76.373457   \n",
       "a0.1_e0.2_i0.3_p10.0        64.560635      94.961142       74.147377   \n",
       "a0.1_e0.4_i0.2_p10.0        69.372835      93.272888       76.444444   \n",
       "a0.1_e0.1_i0.2_p10.0        65.673330      94.797371       72.577160   \n",
       "a0.1_e0.3_i0.3_p10.0        65.996301      94.555906       74.606481   \n",
       "a0.1_e0.25_i0.3_p10.0       65.528077      94.846072       72.420525   \n",
       "a0.1_e0.35_i0.3_p10.0       66.468818      95.047153       74.648148   \n",
       "a0.1_e0.3_i0.3_p8.0         60.266735      87.862346       71.493056   \n",
       "a0.1_e0.15_i0.2_p10.0       66.962214      94.752297       74.070216   \n",
       "a0.1_e0.45_i0.3_p10.0       67.025690      94.022451       74.108025   \n",
       "a0.1_e0.2_i0.2_p10.0        67.932721      94.758961       73.019290   \n",
       "\n",
       "                       std_upn_bhand  max_upn_bhand  upn_off_mean  ...  \\\n",
       "a0.1_e0.5_i0.2_p4.0        88.809643         283.25      6.736752  ...   \n",
       "a0.1_e0.45_i0.2_p4.0       89.131529         286.50      6.529915  ...   \n",
       "a0.1_e0.3_i0.2_p4.0        90.891225         289.00      5.692308  ...   \n",
       "a0.1_e0.25_i0.2_p4.0       90.158445         293.00      5.517949  ...   \n",
       "a0.1_e0.4_i0.2_p4.0        90.134273         293.00      6.052991  ...   \n",
       "a0.1_e0.2_i0.2_p4.0        90.639696         299.00      5.365812  ...   \n",
       "a0.1_e0.15_i0.2_p4.0       90.703323         301.00      5.394872  ...   \n",
       "a0.1_e0.2_i0.2_p8.0       107.660899         304.50     19.842735  ...   \n",
       "a0.1_e0.35_i0.2_p4.0       90.200823         305.00      5.866667  ...   \n",
       "a0.1_e0.45_i0.2_p6.0      100.769529         305.50     15.116239  ...   \n",
       "a0.1_e0.5_i0.2_p6.0        98.686532         307.00     15.977778  ...   \n",
       "a0.1_e0.4_i0.2_p6.0       100.485247         307.00     14.531624  ...   \n",
       "a0.1_e0.25_i0.2_p8.0      106.671117         308.50     20.811966  ...   \n",
       "a0.1_e0.25_i0.2_p6.0      101.396118         308.50     12.579487  ...   \n",
       "a0.1_e0.5_i0.3_p6.0       102.366292         311.00     12.090598  ...   \n",
       "a0.1_e0.35_i0.2_p8.0      106.534119         311.00     22.600000  ...   \n",
       "a0.1_e0.45_i0.2_p8.0      104.714037         311.00     24.331624  ...   \n",
       "a0.1_e0.15_i0.2_p6.0      103.598130         311.00     11.642735  ...   \n",
       "a0.1_e0.2_i0.2_p6.0       102.511761         311.00     12.121368  ...   \n",
       "a0.1_e0.3_i0.2_p6.0       101.218774         311.00     13.439316  ...   \n",
       "a0.1_e0.3_i0.2_p8.0       107.289677         315.00     21.350427  ...   \n",
       "a0.1_e0.35_i0.2_p6.0      100.437683         315.00     14.046154  ...   \n",
       "a0.1_e0.1_i0.2_p6.0       103.316381         315.00     11.545299  ...   \n",
       "a0.1_e0.4_i0.2_p8.0       105.660476         316.50     23.632479  ...   \n",
       "a0.1_e0.1_i0.2_p8.0       109.752563         316.50     18.454701  ...   \n",
       "a0.1_e0.15_i0.2_p8.0      109.357902         316.50     18.866667  ...   \n",
       "a0.1_e0.45_i0.2_p10.0     109.660675         317.00     33.196581  ...   \n",
       "a0.1_e0.25_i0.2_p10.0     113.009246         317.00     28.203419  ...   \n",
       "a0.1_e0.35_i0.3_p6.0      103.550365         320.50     10.779487  ...   \n",
       "a0.1_e0.5_i0.4_p6.0       104.080061         320.50      9.547009  ...   \n",
       "a0.1_e0.5_i0.2_p8.0       105.057396         323.00     25.276923  ...   \n",
       "a0.1_e0.4_i0.3_p8.0       109.542054         323.00     17.998291  ...   \n",
       "a0.1_e0.4_i0.3_p6.0       102.415063         323.00     11.147009  ...   \n",
       "a0.1_e0.45_i0.3_p8.0      110.667557         324.50     18.608547  ...   \n",
       "a0.1_e0.3_i0.2_p10.0      110.834173         325.00     28.955556  ...   \n",
       "a0.1_e0.45_i0.3_p6.0      101.068481         325.50     11.569231  ...   \n",
       "a0.1_e0.25_i0.3_p6.0      103.207834         327.00     10.198291  ...   \n",
       "a0.1_e0.35_i0.2_p10.0     110.749424         327.50     30.246154  ...   \n",
       "a0.1_e0.5_i0.2_p10.0      108.076661         327.50     34.608547  ...   \n",
       "a0.1_e0.5_i0.3_p8.0       107.892315         328.50     19.376068  ...   \n",
       "a0.1_e0.35_i0.3_p8.0      109.835127         328.50     17.420513  ...   \n",
       "a0.1_e0.2_i0.3_p8.0       112.534590         332.50     15.965812  ...   \n",
       "a0.1_e0.4_i0.3_p10.0      113.917550         333.00     24.507692  ...   \n",
       "a0.1_e0.5_i0.3_p10.0      116.192735         333.00     25.842735  ...   \n",
       "a0.1_e0.2_i0.3_p10.0      117.404549         333.00     21.757265  ...   \n",
       "a0.1_e0.4_i0.2_p10.0      111.576314         333.00     31.483761  ...   \n",
       "a0.1_e0.1_i0.2_p10.0      116.150959         333.00     25.104274  ...   \n",
       "a0.1_e0.3_i0.3_p10.0      116.000949         334.50     22.811966  ...   \n",
       "a0.1_e0.25_i0.3_p10.0     117.220189         334.50     22.063248  ...   \n",
       "a0.1_e0.35_i0.3_p10.0     116.875071         334.50     23.370940  ...   \n",
       "a0.1_e0.3_i0.3_p8.0       110.476590         335.00     16.929915  ...   \n",
       "a0.1_e0.15_i0.2_p10.0     114.978554         335.50     25.786325  ...   \n",
       "a0.1_e0.45_i0.3_p10.0     114.749933         337.00     25.282051  ...   \n",
       "a0.1_e0.2_i0.2_p10.0      114.291591         337.00     26.827350  ...   \n",
       "\n",
       "                       mean_pca_upn_dists  std_pca_upn_dists  \\\n",
       "a0.1_e0.5_i0.2_p4.0            220.248523         106.808077   \n",
       "a0.1_e0.45_i0.2_p4.0           220.157914         107.310734   \n",
       "a0.1_e0.3_i0.2_p4.0            224.940710         111.359087   \n",
       "a0.1_e0.25_i0.2_p4.0           221.808726         111.246110   \n",
       "a0.1_e0.4_i0.2_p4.0            221.199050         110.536663   \n",
       "a0.1_e0.2_i0.2_p4.0            223.546464         111.501029   \n",
       "a0.1_e0.15_i0.2_p4.0           223.979962         111.217059   \n",
       "a0.1_e0.2_i0.2_p8.0            264.920019         126.722087   \n",
       "a0.1_e0.35_i0.2_p4.0           220.694763         109.751562   \n",
       "a0.1_e0.45_i0.2_p6.0           248.081857         116.998290   \n",
       "a0.1_e0.5_i0.2_p6.0            240.291467         116.983640   \n",
       "a0.1_e0.4_i0.2_p6.0            246.929901         116.653207   \n",
       "a0.1_e0.25_i0.2_p8.0           261.898459         123.935830   \n",
       "a0.1_e0.25_i0.2_p6.0           248.090717         118.693960   \n",
       "a0.1_e0.5_i0.3_p6.0            253.971826         119.268040   \n",
       "a0.1_e0.35_i0.2_p8.0           260.483706         124.057963   \n",
       "a0.1_e0.45_i0.2_p8.0           254.932099         121.346518   \n",
       "a0.1_e0.15_i0.2_p6.0           255.320731         121.189734   \n",
       "a0.1_e0.2_i0.2_p6.0            253.424719         122.421412   \n",
       "a0.1_e0.3_i0.2_p6.0            248.114993         119.288213   \n",
       "a0.1_e0.3_i0.2_p8.0            263.107534         125.060571   \n",
       "a0.1_e0.35_i0.2_p6.0           246.456529         119.912171   \n",
       "a0.1_e0.1_i0.2_p6.0            254.556155         123.652883   \n",
       "a0.1_e0.4_i0.2_p8.0            258.800665         121.780845   \n",
       "a0.1_e0.1_i0.2_p8.0            272.439673         128.171902   \n",
       "a0.1_e0.15_i0.2_p8.0           270.198049         130.022930   \n",
       "a0.1_e0.45_i0.2_p10.0          267.555496         131.542898   \n",
       "a0.1_e0.25_i0.2_p10.0          275.281820         129.324825   \n",
       "a0.1_e0.35_i0.3_p6.0           257.180888         123.095623   \n",
       "a0.1_e0.5_i0.4_p6.0            256.869954         123.873970   \n",
       "a0.1_e0.5_i0.2_p8.0            253.760641         121.779614   \n",
       "a0.1_e0.4_i0.3_p8.0            269.830351         128.531667   \n",
       "a0.1_e0.4_i0.3_p6.0            253.281444         122.788921   \n",
       "a0.1_e0.45_i0.3_p8.0           273.335042         129.947285   \n",
       "a0.1_e0.3_i0.2_p10.0           269.983688         133.666221   \n",
       "a0.1_e0.45_i0.3_p6.0           249.867415         119.311865   \n",
       "a0.1_e0.25_i0.3_p6.0           254.912177         122.177143   \n",
       "a0.1_e0.35_i0.2_p10.0          267.727372         134.584375   \n",
       "a0.1_e0.5_i0.2_p10.0           258.606555         130.078201   \n",
       "a0.1_e0.5_i0.3_p8.0            265.271259         125.521707   \n",
       "a0.1_e0.35_i0.3_p8.0           269.383778         127.749036   \n",
       "a0.1_e0.2_i0.3_p8.0            278.291833         132.232383   \n",
       "a0.1_e0.4_i0.3_p10.0           279.596348         133.125362   \n",
       "a0.1_e0.5_i0.3_p10.0           282.493521         134.939215   \n",
       "a0.1_e0.2_i0.3_p10.0           287.159956         137.573621   \n",
       "a0.1_e0.4_i0.2_p10.0           270.297839         131.643931   \n",
       "a0.1_e0.1_i0.2_p10.0           288.347633         135.993446   \n",
       "a0.1_e0.3_i0.3_p10.0           285.590118         134.637724   \n",
       "a0.1_e0.25_i0.3_p10.0          290.262265         137.847504   \n",
       "a0.1_e0.35_i0.3_p10.0          286.752202         135.531998   \n",
       "a0.1_e0.3_i0.3_p8.0            272.492070         131.656001   \n",
       "a0.1_e0.15_i0.2_p10.0          283.368986         133.411140   \n",
       "a0.1_e0.45_i0.3_p10.0          284.275328         135.942060   \n",
       "a0.1_e0.2_i0.2_p10.0           286.046288         134.183968   \n",
       "\n",
       "                       max_pca_upn_dists  DM1_PN_pre_odor_fr  \\\n",
       "a0.1_e0.5_i0.2_p4.0           491.910384                76.0   \n",
       "a0.1_e0.45_i0.2_p4.0          508.552228                68.0   \n",
       "a0.1_e0.3_i0.2_p4.0           483.852508                80.0   \n",
       "a0.1_e0.25_i0.2_p4.0          483.867013                68.0   \n",
       "a0.1_e0.4_i0.2_p4.0           494.742969                76.0   \n",
       "a0.1_e0.2_i0.2_p4.0           495.291830                64.0   \n",
       "a0.1_e0.15_i0.2_p4.0          494.620908                64.0   \n",
       "a0.1_e0.2_i0.2_p8.0           613.357795               116.0   \n",
       "a0.1_e0.35_i0.2_p4.0          501.564582                76.0   \n",
       "a0.1_e0.45_i0.2_p6.0          561.472581                96.0   \n",
       "a0.1_e0.5_i0.2_p6.0           570.209957               108.0   \n",
       "a0.1_e0.4_i0.2_p6.0           552.131329               100.0   \n",
       "a0.1_e0.25_i0.2_p8.0          580.236065               128.0   \n",
       "a0.1_e0.25_i0.2_p6.0          569.001946               100.0   \n",
       "a0.1_e0.5_i0.3_p6.0           557.436380               100.0   \n",
       "a0.1_e0.35_i0.2_p8.0          594.383189               120.0   \n",
       "a0.1_e0.45_i0.2_p8.0          592.751862               140.0   \n",
       "a0.1_e0.15_i0.2_p6.0          567.855518                96.0   \n",
       "a0.1_e0.2_i0.2_p6.0           542.572236                96.0   \n",
       "a0.1_e0.3_i0.2_p6.0           550.294040               104.0   \n",
       "a0.1_e0.3_i0.2_p8.0           590.988598               124.0   \n",
       "a0.1_e0.35_i0.2_p6.0          559.715194               104.0   \n",
       "a0.1_e0.1_i0.2_p6.0           559.451203               100.0   \n",
       "a0.1_e0.4_i0.2_p8.0           579.603074               140.0   \n",
       "a0.1_e0.1_i0.2_p8.0           610.648587               120.0   \n",
       "a0.1_e0.15_i0.2_p8.0          585.751355               108.0   \n",
       "a0.1_e0.45_i0.2_p10.0         622.661252               168.0   \n",
       "a0.1_e0.25_i0.2_p10.0         613.700830               148.0   \n",
       "a0.1_e0.35_i0.3_p6.0          573.287232                96.0   \n",
       "a0.1_e0.5_i0.4_p6.0           571.614668                92.0   \n",
       "a0.1_e0.5_i0.2_p8.0           598.904351               124.0   \n",
       "a0.1_e0.4_i0.3_p8.0           605.759469               120.0   \n",
       "a0.1_e0.4_i0.3_p6.0           570.707930               104.0   \n",
       "a0.1_e0.45_i0.3_p8.0          607.750535               120.0   \n",
       "a0.1_e0.3_i0.2_p10.0          635.397340               132.0   \n",
       "a0.1_e0.45_i0.3_p6.0          554.234242               104.0   \n",
       "a0.1_e0.25_i0.3_p6.0          569.813939                96.0   \n",
       "a0.1_e0.35_i0.2_p10.0         618.416170               140.0   \n",
       "a0.1_e0.5_i0.2_p10.0          610.967225               144.0   \n",
       "a0.1_e0.5_i0.3_p8.0           611.084370               100.0   \n",
       "a0.1_e0.35_i0.3_p8.0          606.230627                96.0   \n",
       "a0.1_e0.2_i0.3_p8.0           618.353581               100.0   \n",
       "a0.1_e0.4_i0.3_p10.0          624.527680               140.0   \n",
       "a0.1_e0.5_i0.3_p10.0          630.258233               120.0   \n",
       "a0.1_e0.2_i0.3_p10.0          622.258046               140.0   \n",
       "a0.1_e0.4_i0.2_p10.0          617.894751               148.0   \n",
       "a0.1_e0.1_i0.2_p10.0          638.051701               140.0   \n",
       "a0.1_e0.3_i0.3_p10.0          638.919522               132.0   \n",
       "a0.1_e0.25_i0.3_p10.0         662.674674               116.0   \n",
       "a0.1_e0.35_i0.3_p10.0         641.337320               124.0   \n",
       "a0.1_e0.3_i0.3_p8.0           603.036955               108.0   \n",
       "a0.1_e0.15_i0.2_p10.0         652.336517               128.0   \n",
       "a0.1_e0.45_i0.3_p10.0         651.350324               128.0   \n",
       "a0.1_e0.2_i0.2_p10.0          648.256824               152.0   \n",
       "\n",
       "                       VA2_PN_pre_odor_fr  VM2_PN_dur_odor_fr  min_PN_dur  \\\n",
       "a0.1_e0.5_i0.2_p4.0                  28.0                 0.0       -80.0   \n",
       "a0.1_e0.45_i0.2_p4.0                 28.0                 0.0       -92.0   \n",
       "a0.1_e0.3_i0.2_p4.0                  24.0                 0.0       -76.0   \n",
       "a0.1_e0.25_i0.2_p4.0                 36.0                 0.0       -72.0   \n",
       "a0.1_e0.4_i0.2_p4.0                  32.0                 0.0       -84.0   \n",
       "a0.1_e0.2_i0.2_p4.0                  36.0                 0.0       -76.0   \n",
       "a0.1_e0.15_i0.2_p4.0                 28.0                 0.0       -76.0   \n",
       "a0.1_e0.2_i0.2_p8.0                  60.0                 0.0      -128.0   \n",
       "a0.1_e0.35_i0.2_p4.0                 40.0                 0.0       -80.0   \n",
       "a0.1_e0.45_i0.2_p6.0                 52.0                 0.0      -111.0   \n",
       "a0.1_e0.5_i0.2_p6.0                  56.0                 0.0      -112.0   \n",
       "a0.1_e0.4_i0.2_p6.0                  56.0                 0.0      -105.5   \n",
       "a0.1_e0.25_i0.2_p8.0                 60.0                 0.0      -128.0   \n",
       "a0.1_e0.25_i0.2_p6.0                 60.0                 0.0      -105.5   \n",
       "a0.1_e0.5_i0.3_p6.0                  44.0                 0.0      -104.0   \n",
       "a0.1_e0.35_i0.2_p8.0                 56.0                 0.0      -124.0   \n",
       "a0.1_e0.45_i0.2_p8.0                 60.0                 0.0      -152.0   \n",
       "a0.1_e0.15_i0.2_p6.0                 44.0                 0.0      -108.0   \n",
       "a0.1_e0.2_i0.2_p6.0                  44.0                 0.0      -112.0   \n",
       "a0.1_e0.3_i0.2_p6.0                  44.0                 0.0      -107.0   \n",
       "a0.1_e0.3_i0.2_p8.0                  68.0                 0.0      -128.0   \n",
       "a0.1_e0.35_i0.2_p6.0                 44.0                 0.0      -127.0   \n",
       "a0.1_e0.1_i0.2_p6.0                  48.0                 0.0      -104.0   \n",
       "a0.1_e0.4_i0.2_p8.0                  56.0                 0.0      -129.5   \n",
       "a0.1_e0.1_i0.2_p8.0                  72.0                 0.0      -124.0   \n",
       "a0.1_e0.15_i0.2_p8.0                 64.0                 0.0      -120.0   \n",
       "a0.1_e0.45_i0.2_p10.0                68.0                 0.0      -149.5   \n",
       "a0.1_e0.25_i0.2_p10.0                64.0                 0.0      -141.5   \n",
       "a0.1_e0.35_i0.3_p6.0                 48.0                 0.0      -100.0   \n",
       "a0.1_e0.5_i0.4_p6.0                  36.0                 0.0      -105.5   \n",
       "a0.1_e0.5_i0.2_p8.0                  68.0                 0.0      -144.0   \n",
       "a0.1_e0.4_i0.3_p8.0                  60.0                 0.0      -124.0   \n",
       "a0.1_e0.4_i0.3_p6.0                  36.0                 0.0      -108.0   \n",
       "a0.1_e0.45_i0.3_p8.0                 68.0                 0.0      -125.5   \n",
       "a0.1_e0.3_i0.2_p10.0                 64.0                 0.0      -145.5   \n",
       "a0.1_e0.45_i0.3_p6.0                 36.0                 0.0       -93.5   \n",
       "a0.1_e0.25_i0.3_p6.0                 40.0                 0.0       -96.0   \n",
       "a0.1_e0.35_i0.2_p10.0                64.0                 0.0      -156.0   \n",
       "a0.1_e0.5_i0.2_p10.0                 64.0                 0.0      -156.0   \n",
       "a0.1_e0.5_i0.3_p8.0                  52.0                 0.0      -121.5   \n",
       "a0.1_e0.35_i0.3_p8.0                 52.0                 0.0      -120.0   \n",
       "a0.1_e0.2_i0.3_p8.0                  48.0                 0.0      -112.0   \n",
       "a0.1_e0.4_i0.3_p10.0                 60.0                 0.0      -144.0   \n",
       "a0.1_e0.5_i0.3_p10.0                 40.0                 0.0      -160.0   \n",
       "a0.1_e0.2_i0.3_p10.0                 68.0                 0.0      -140.0   \n",
       "a0.1_e0.4_i0.2_p10.0                 72.0                 0.0      -153.5   \n",
       "a0.1_e0.1_i0.2_p10.0                 68.0                 0.0      -133.5   \n",
       "a0.1_e0.3_i0.3_p10.0                 56.0                 0.0      -144.0   \n",
       "a0.1_e0.25_i0.3_p10.0                60.0                 0.0      -140.0   \n",
       "a0.1_e0.35_i0.3_p10.0                56.0                 0.0      -132.0   \n",
       "a0.1_e0.3_i0.3_p8.0                  52.0                 0.0      -124.0   \n",
       "a0.1_e0.15_i0.2_p10.0                68.0                 0.0      -144.0   \n",
       "a0.1_e0.45_i0.3_p10.0                60.0                 0.0      -149.5   \n",
       "a0.1_e0.2_i0.2_p10.0                 68.0                 0.0      -141.5   \n",
       "\n",
       "                       kstest_stat   kstest_pval  kstest_2samp_stat  \n",
       "a0.1_e0.5_i0.2_p4.0       0.296296  5.282175e-05           0.296296  \n",
       "a0.1_e0.45_i0.2_p4.0      0.283069  1.330442e-04           0.283069  \n",
       "a0.1_e0.3_i0.2_p4.0       0.305556  2.694667e-05           0.305556  \n",
       "a0.1_e0.25_i0.2_p4.0      0.306878  2.443268e-05           0.306878  \n",
       "a0.1_e0.4_i0.2_p4.0       0.287037  1.013115e-04           0.287037  \n",
       "a0.1_e0.2_i0.2_p4.0       0.305556  2.694667e-05           0.305556  \n",
       "a0.1_e0.15_i0.2_p4.0      0.314815  1.344861e-05           0.314815  \n",
       "a0.1_e0.2_i0.2_p8.0       0.314815  1.344861e-05           0.314815  \n",
       "a0.1_e0.35_i0.2_p4.0      0.292328  7.001446e-05           0.292328  \n",
       "a0.1_e0.45_i0.2_p6.0      0.279101  1.740242e-04           0.279101  \n",
       "a0.1_e0.5_i0.2_p6.0       0.279101  1.740242e-04           0.279101  \n",
       "a0.1_e0.4_i0.2_p6.0       0.288360  9.243349e-05           0.288360  \n",
       "a0.1_e0.25_i0.2_p8.0      0.296296  5.282175e-05           0.296296  \n",
       "a0.1_e0.25_i0.2_p6.0      0.297619  4.804357e-05           0.297619  \n",
       "a0.1_e0.5_i0.3_p6.0       0.325397  5.915565e-06           0.325397  \n",
       "a0.1_e0.35_i0.2_p8.0      0.279101  1.740242e-04           0.279101  \n",
       "a0.1_e0.45_i0.2_p8.0      0.269841  3.206488e-04           0.269841  \n",
       "a0.1_e0.15_i0.2_p6.0      0.343915  1.310404e-06           0.343915  \n",
       "a0.1_e0.2_i0.2_p6.0       0.310847  1.816359e-05           0.310847  \n",
       "a0.1_e0.3_i0.2_p6.0       0.325397  5.915565e-06           0.325397  \n",
       "a0.1_e0.3_i0.2_p8.0       0.296296  5.282175e-05           0.296296  \n",
       "a0.1_e0.35_i0.2_p6.0      0.306878  2.443268e-05           0.306878  \n",
       "a0.1_e0.1_i0.2_p6.0       0.334656  2.815533e-06           0.334656  \n",
       "a0.1_e0.4_i0.2_p8.0       0.277778  1.901510e-04           0.277778  \n",
       "a0.1_e0.1_i0.2_p8.0       0.342593  1.463705e-06           0.342593  \n",
       "a0.1_e0.15_i0.2_p8.0      0.325397  5.915565e-06           0.325397  \n",
       "a0.1_e0.45_i0.2_p10.0     0.287037  1.013115e-04           0.287037  \n",
       "a0.1_e0.25_i0.2_p10.0     0.305556  2.694667e-05           0.305556  \n",
       "a0.1_e0.35_i0.3_p6.0      0.325397  5.915565e-06           0.325397  \n",
       "a0.1_e0.5_i0.4_p6.0       0.362434  2.652498e-07           0.362434  \n",
       "a0.1_e0.5_i0.2_p8.0       0.277778  1.901510e-04           0.277778  \n",
       "a0.1_e0.4_i0.3_p8.0       0.343915  1.310404e-06           0.343915  \n",
       "a0.1_e0.4_i0.3_p6.0       0.334656  2.815533e-06           0.334656  \n",
       "a0.1_e0.45_i0.3_p8.0      0.314815  1.344861e-05           0.314815  \n",
       "a0.1_e0.3_i0.2_p10.0      0.305556  2.694667e-05           0.305556  \n",
       "a0.1_e0.45_i0.3_p6.0      0.325397  5.915565e-06           0.325397  \n",
       "a0.1_e0.25_i0.3_p6.0      0.362434  2.652498e-07           0.362434  \n",
       "a0.1_e0.35_i0.2_p10.0     0.305556  2.694667e-05           0.305556  \n",
       "a0.1_e0.5_i0.2_p10.0      0.296296  5.282175e-05           0.296296  \n",
       "a0.1_e0.5_i0.3_p8.0       0.334656  2.815533e-06           0.334656  \n",
       "a0.1_e0.35_i0.3_p8.0      0.334656  2.815533e-06           0.334656  \n",
       "a0.1_e0.2_i0.3_p8.0       0.353175  5.962944e-07           0.353175  \n",
       "a0.1_e0.4_i0.3_p10.0      0.325397  5.915565e-06           0.325397  \n",
       "a0.1_e0.5_i0.3_p10.0      0.333333  3.134847e-06           0.333333  \n",
       "a0.1_e0.2_i0.3_p10.0      0.388889  2.309270e-08           0.388889  \n",
       "a0.1_e0.4_i0.2_p10.0      0.287037  1.013115e-04           0.287037  \n",
       "a0.1_e0.1_i0.2_p10.0      0.351852  6.682085e-07           0.351852  \n",
       "a0.1_e0.3_i0.3_p10.0      0.333333  3.134847e-06           0.333333  \n",
       "a0.1_e0.25_i0.3_p10.0     0.353175  5.962944e-07           0.353175  \n",
       "a0.1_e0.35_i0.3_p10.0     0.353175  5.962944e-07           0.353175  \n",
       "a0.1_e0.3_i0.3_p8.0       0.353175  5.962944e-07           0.353175  \n",
       "a0.1_e0.15_i0.2_p10.0     0.316138  1.215566e-05           0.316138  \n",
       "a0.1_e0.45_i0.3_p10.0     0.325397  5.915565e-06           0.325397  \n",
       "a0.1_e0.2_i0.2_p10.0      0.316138  1.215566e-05           0.316138  \n",
       "\n",
       "[54 rows x 30 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df = df_res[\n",
    "       #(df_res['p'] == 4) & \n",
    "       (df_res[x_var] < 0.05) &\n",
    "       (df_res[y_var] < 15) &\n",
    "       #(df_res['upn_off_mean'] < 20) &\n",
    "       #(df_res['eln_on_mean'] < 40) &\n",
    "       #(df_res['eln_off_mean'] < 20) &\n",
    "       (1) ]\n",
    "sub_df.sort_values('upn_off_mean')[df_res_cols1].sort_values('max_upn_bhand')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_var, y_var, c_var = plot_sweep_res(\n",
    "    x_var = 'mean_upn_bhand', x_lab = 'mean Bhandawat ON-OFF uPN firing rate ',\n",
    "    y_var = 'upn_off_mean', y_lab = 'uPN off-odor firing rate (Hz)',\n",
    "    c_var = 'p', c_lab='PN multiplier',\n",
    "    highlight_locs=[]\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_upn_onoff_dists</th>\n",
       "      <th>std_upn_onoff_dists</th>\n",
       "      <th>mean_upn_bhand_dists</th>\n",
       "      <th>std_upn_bhand_dists</th>\n",
       "      <th>mean_upn_onoff</th>\n",
       "      <th>std_upn_onoff</th>\n",
       "      <th>mean_upn_bhand</th>\n",
       "      <th>std_upn_bhand</th>\n",
       "      <th>max_upn_bhand</th>\n",
       "      <th>upn_off_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_pca_upn_dists</th>\n",
       "      <th>std_pca_upn_dists</th>\n",
       "      <th>max_pca_upn_dists</th>\n",
       "      <th>DM1_PN_pre_odor_fr</th>\n",
       "      <th>VA2_PN_pre_odor_fr</th>\n",
       "      <th>VM2_PN_dur_odor_fr</th>\n",
       "      <th>min_PN_dur</th>\n",
       "      <th>kstest_stat</th>\n",
       "      <th>kstest_pval</th>\n",
       "      <th>kstest_2samp_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.4_i0.45_p4.0</th>\n",
       "      <td>563.954670</td>\n",
       "      <td>116.339301</td>\n",
       "      <td>288.107362</td>\n",
       "      <td>99.946816</td>\n",
       "      <td>39.650626</td>\n",
       "      <td>65.258542</td>\n",
       "      <td>60.822531</td>\n",
       "      <td>91.261101</td>\n",
       "      <td>302.50</td>\n",
       "      <td>3.912821</td>\n",
       "      <td>...</td>\n",
       "      <td>225.344186</td>\n",
       "      <td>112.728649</td>\n",
       "      <td>476.491828</td>\n",
       "      <td>64.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-68.0</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>4.899549e-08</td>\n",
       "      <td>0.380952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.3_p4.0</th>\n",
       "      <td>566.461450</td>\n",
       "      <td>117.920324</td>\n",
       "      <td>285.383310</td>\n",
       "      <td>97.802002</td>\n",
       "      <td>43.017388</td>\n",
       "      <td>66.017957</td>\n",
       "      <td>62.333333</td>\n",
       "      <td>90.365579</td>\n",
       "      <td>288.00</td>\n",
       "      <td>5.041026</td>\n",
       "      <td>...</td>\n",
       "      <td>222.749638</td>\n",
       "      <td>113.014464</td>\n",
       "      <td>476.763644</td>\n",
       "      <td>60.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-69.5</td>\n",
       "      <td>0.334656</td>\n",
       "      <td>2.815533e-06</td>\n",
       "      <td>0.334656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.4_i0.4_p4.0</th>\n",
       "      <td>566.428712</td>\n",
       "      <td>115.459782</td>\n",
       "      <td>287.038791</td>\n",
       "      <td>98.120134</td>\n",
       "      <td>40.466467</td>\n",
       "      <td>65.538212</td>\n",
       "      <td>60.522377</td>\n",
       "      <td>90.692153</td>\n",
       "      <td>296.00</td>\n",
       "      <td>4.205128</td>\n",
       "      <td>...</td>\n",
       "      <td>222.973606</td>\n",
       "      <td>111.658861</td>\n",
       "      <td>478.560876</td>\n",
       "      <td>52.0</td>\n",
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       "      <td>-72.0</td>\n",
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       "      <td>1.153222e-07</td>\n",
       "      <td>0.371693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.3_p4.0</th>\n",
       "      <td>568.655252</td>\n",
       "      <td>118.771030</td>\n",
       "      <td>287.669949</td>\n",
       "      <td>98.321139</td>\n",
       "      <td>43.231309</td>\n",
       "      <td>66.185090</td>\n",
       "      <td>62.490741</td>\n",
       "      <td>90.913906</td>\n",
       "      <td>292.50</td>\n",
       "      <td>5.181197</td>\n",
       "      <td>...</td>\n",
       "      <td>226.227176</td>\n",
       "      <td>112.067239</td>\n",
       "      <td>479.039795</td>\n",
       "      <td>60.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-68.0</td>\n",
       "      <td>0.322751</td>\n",
       "      <td>7.283579e-06</td>\n",
       "      <td>0.322751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.45_p4.0</th>\n",
       "      <td>565.257295</td>\n",
       "      <td>115.285304</td>\n",
       "      <td>289.404535</td>\n",
       "      <td>99.016690</td>\n",
       "      <td>40.244712</td>\n",
       "      <td>65.414232</td>\n",
       "      <td>60.882716</td>\n",
       "      <td>91.205643</td>\n",
       "      <td>304.00</td>\n",
       "      <td>3.991453</td>\n",
       "      <td>...</td>\n",
       "      <td>224.168864</td>\n",
       "      <td>112.253646</td>\n",
       "      <td>483.037003</td>\n",
       "      <td>52.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-68.0</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>4.899549e-08</td>\n",
       "      <td>0.380952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.3_i0.2_p4.0</th>\n",
       "      <td>566.225195</td>\n",
       "      <td>117.633535</td>\n",
       "      <td>286.758283</td>\n",
       "      <td>97.868763</td>\n",
       "      <td>44.782580</td>\n",
       "      <td>66.324000</td>\n",
       "      <td>63.459105</td>\n",
       "      <td>90.891225</td>\n",
       "      <td>289.00</td>\n",
       "      <td>5.692308</td>\n",
       "      <td>...</td>\n",
       "      <td>224.940710</td>\n",
       "      <td>111.359087</td>\n",
       "      <td>483.852508</td>\n",
       "      <td>80.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-76.0</td>\n",
       "      <td>0.305556</td>\n",
       "      <td>2.694667e-05</td>\n",
       "      <td>0.305556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.25_i0.2_p4.0</th>\n",
       "      <td>564.584218</td>\n",
       "      <td>118.725497</td>\n",
       "      <td>285.200934</td>\n",
       "      <td>96.499578</td>\n",
       "      <td>44.240418</td>\n",
       "      <td>66.089001</td>\n",
       "      <td>63.312500</td>\n",
       "      <td>90.158445</td>\n",
       "      <td>293.00</td>\n",
       "      <td>5.517949</td>\n",
       "      <td>...</td>\n",
       "      <td>221.808726</td>\n",
       "      <td>111.246110</td>\n",
       "      <td>483.867013</td>\n",
       "      <td>68.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-72.0</td>\n",
       "      <td>0.306878</td>\n",
       "      <td>2.443268e-05</td>\n",
       "      <td>0.306878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.45_p4.0</th>\n",
       "      <td>563.445111</td>\n",
       "      <td>116.235519</td>\n",
       "      <td>288.446435</td>\n",
       "      <td>100.147018</td>\n",
       "      <td>40.140355</td>\n",
       "      <td>65.155296</td>\n",
       "      <td>60.872685</td>\n",
       "      <td>90.832950</td>\n",
       "      <td>296.00</td>\n",
       "      <td>3.935043</td>\n",
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       "      <td>224.377109</td>\n",
       "      <td>115.127215</td>\n",
       "      <td>488.152311</td>\n",
       "      <td>64.0</td>\n",
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       "      <td>0.384921</td>\n",
       "      <td>3.370900e-08</td>\n",
       "      <td>0.384921</td>\n",
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       "    <tr>\n",
       "      <th>a0.1_e0.1_i0.2_p4.0</th>\n",
       "      <td>567.928504</td>\n",
       "      <td>119.383045</td>\n",
       "      <td>287.888886</td>\n",
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       "      <td>65.871090</td>\n",
       "      <td>60.962191</td>\n",
       "      <td>90.973264</td>\n",
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       "      <td>225.983349</td>\n",
       "      <td>113.230954</td>\n",
       "      <td>488.832101</td>\n",
       "      <td>64.0</td>\n",
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       "      <td>1.634190e-06</td>\n",
       "      <td>0.341270</td>\n",
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       "    <tr>\n",
       "      <th>a0.1_e0.25_i0.3_p4.0</th>\n",
       "      <td>568.295109</td>\n",
       "      <td>117.477255</td>\n",
       "      <td>288.668828</td>\n",
       "      <td>97.561770</td>\n",
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       "      <td>65.775306</td>\n",
       "      <td>61.037809</td>\n",
       "      <td>91.054331</td>\n",
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       "      <td>227.099240</td>\n",
       "      <td>111.441361</td>\n",
       "      <td>490.571992</td>\n",
       "      <td>60.0</td>\n",
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       "      <td>-72.0</td>\n",
       "      <td>0.343915</td>\n",
       "      <td>1.310404e-06</td>\n",
       "      <td>0.343915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.2_p4.0</th>\n",
       "      <td>565.046197</td>\n",
       "      <td>118.863910</td>\n",
       "      <td>280.537383</td>\n",
       "      <td>93.504112</td>\n",
       "      <td>46.216880</td>\n",
       "      <td>66.479202</td>\n",
       "      <td>63.301698</td>\n",
       "      <td>88.809643</td>\n",
       "      <td>283.25</td>\n",
       "      <td>6.736752</td>\n",
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       "      <td>220.248523</td>\n",
       "      <td>106.808077</td>\n",
       "      <td>491.910384</td>\n",
       "      <td>76.0</td>\n",
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       "      <td>5.282175e-05</td>\n",
       "      <td>0.296296</td>\n",
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       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.4_p4.0</th>\n",
       "      <td>565.912872</td>\n",
       "      <td>117.902624</td>\n",
       "      <td>291.725317</td>\n",
       "      <td>99.875713</td>\n",
       "      <td>41.285920</td>\n",
       "      <td>65.776770</td>\n",
       "      <td>61.736883</td>\n",
       "      <td>92.111890</td>\n",
       "      <td>308.00</td>\n",
       "      <td>4.360684</td>\n",
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       "      <td>227.442365</td>\n",
       "      <td>114.463606</td>\n",
       "      <td>494.581712</td>\n",
       "      <td>68.0</td>\n",
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       "      <td>0.361111</td>\n",
       "      <td>2.982082e-07</td>\n",
       "      <td>0.361111</td>\n",
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       "    <tr>\n",
       "      <th>a0.1_e0.15_i0.2_p4.0</th>\n",
       "      <td>568.005206</td>\n",
       "      <td>118.069873</td>\n",
       "      <td>286.879907</td>\n",
       "      <td>97.889750</td>\n",
       "      <td>42.596982</td>\n",
       "      <td>65.893291</td>\n",
       "      <td>62.131173</td>\n",
       "      <td>90.703323</td>\n",
       "      <td>301.00</td>\n",
       "      <td>5.394872</td>\n",
       "      <td>...</td>\n",
       "      <td>223.979962</td>\n",
       "      <td>111.217059</td>\n",
       "      <td>494.620908</td>\n",
       "      <td>64.0</td>\n",
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       "      <td>0.314815</td>\n",
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       "    <tr>\n",
       "      <th>a0.1_e0.4_i0.2_p4.0</th>\n",
       "      <td>566.496508</td>\n",
       "      <td>118.855839</td>\n",
       "      <td>285.256129</td>\n",
       "      <td>95.201347</td>\n",
       "      <td>45.694408</td>\n",
       "      <td>66.429010</td>\n",
       "      <td>64.111883</td>\n",
       "      <td>90.134273</td>\n",
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       "      <td>494.742969</td>\n",
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       "      <td>1.013115e-04</td>\n",
       "      <td>0.287037</td>\n",
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       "    <tr>\n",
       "      <th>a0.1_e0.2_i0.2_p4.0</th>\n",
       "      <td>568.066928</td>\n",
       "      <td>119.482380</td>\n",
       "      <td>286.426757</td>\n",
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       "      <td>66.228442</td>\n",
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       "      <td>90.639696</td>\n",
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       "      <td>111.501029</td>\n",
       "      <td>495.291830</td>\n",
       "      <td>64.0</td>\n",
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       "      <td>0.305556</td>\n",
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       "    <tr>\n",
       "      <th>a0.1_e0.35_i0.3_p4.0</th>\n",
       "      <td>569.196251</td>\n",
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       "      <td>288.767376</td>\n",
       "      <td>99.008985</td>\n",
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       "      <td>65.946268</td>\n",
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       "      <td>225.108061</td>\n",
       "      <td>114.395435</td>\n",
       "      <td>495.364516</td>\n",
       "      <td>72.0</td>\n",
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       "      <td>3.134847e-06</td>\n",
       "      <td>0.333333</td>\n",
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       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.55_p4.0</th>\n",
       "      <td>563.665916</td>\n",
       "      <td>114.373896</td>\n",
       "      <td>289.888498</td>\n",
       "      <td>101.597624</td>\n",
       "      <td>38.768228</td>\n",
       "      <td>64.955376</td>\n",
       "      <td>60.166667</td>\n",
       "      <td>91.586781</td>\n",
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       "      <td>3.577778</td>\n",
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       "      <td>227.031863</td>\n",
       "      <td>116.629982</td>\n",
       "      <td>495.604629</td>\n",
       "      <td>52.0</td>\n",
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       "      <td>0.399471</td>\n",
       "      <td>8.246481e-09</td>\n",
       "      <td>0.399471</td>\n",
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       "    <tr>\n",
       "      <th>a0.1_e0.3_i0.3_p4.0</th>\n",
       "      <td>567.051449</td>\n",
       "      <td>117.271546</td>\n",
       "      <td>289.204522</td>\n",
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       "      <td>65.742520</td>\n",
       "      <td>61.752315</td>\n",
       "      <td>91.333106</td>\n",
       "      <td>298.50</td>\n",
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       "      <td>112.554665</td>\n",
       "      <td>495.855144</td>\n",
       "      <td>56.0</td>\n",
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       "      <td>-68.0</td>\n",
       "      <td>0.371693</td>\n",
       "      <td>1.153222e-07</td>\n",
       "      <td>0.371693</td>\n",
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       "    <tr>\n",
       "      <th>a0.1_e0.3_i0.45_p4.0</th>\n",
       "      <td>563.112020</td>\n",
       "      <td>114.590423</td>\n",
       "      <td>286.364879</td>\n",
       "      <td>100.857136</td>\n",
       "      <td>39.190232</td>\n",
       "      <td>64.989983</td>\n",
       "      <td>60.157407</td>\n",
       "      <td>90.786504</td>\n",
       "      <td>300.00</td>\n",
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       "      <td>225.181354</td>\n",
       "      <td>114.595512</td>\n",
       "      <td>496.131350</td>\n",
       "      <td>56.0</td>\n",
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       "      <td>2.652498e-07</td>\n",
       "      <td>0.362434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.4_p4.0</th>\n",
       "      <td>565.469114</td>\n",
       "      <td>116.698430</td>\n",
       "      <td>288.044118</td>\n",
       "      <td>99.261893</td>\n",
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       "      <td>65.555911</td>\n",
       "      <td>61.783951</td>\n",
       "      <td>91.149514</td>\n",
       "      <td>304.00</td>\n",
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       "      <td>225.124647</td>\n",
       "      <td>112.283219</td>\n",
       "      <td>496.484805</td>\n",
       "      <td>60.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-68.0</td>\n",
       "      <td>0.390212</td>\n",
       "      <td>2.033775e-08</td>\n",
       "      <td>0.390212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.3_i0.4_p4.0</th>\n",
       "      <td>565.220544</td>\n",
       "      <td>115.294435</td>\n",
       "      <td>289.708661</td>\n",
       "      <td>99.362023</td>\n",
       "      <td>39.774823</td>\n",
       "      <td>65.325641</td>\n",
       "      <td>60.476080</td>\n",
       "      <td>91.466567</td>\n",
       "      <td>300.00</td>\n",
       "      <td>4.088889</td>\n",
       "      <td>...</td>\n",
       "      <td>224.807465</td>\n",
       "      <td>115.657121</td>\n",
       "      <td>496.594846</td>\n",
       "      <td>52.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-56.0</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>2.309270e-08</td>\n",
       "      <td>0.388889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.35_i0.2_p4.0</th>\n",
       "      <td>568.394040</td>\n",
       "      <td>117.936114</td>\n",
       "      <td>285.012905</td>\n",
       "      <td>97.093254</td>\n",
       "      <td>45.112219</td>\n",
       "      <td>66.444472</td>\n",
       "      <td>63.020062</td>\n",
       "      <td>90.200823</td>\n",
       "      <td>305.00</td>\n",
       "      <td>5.866667</td>\n",
       "      <td>...</td>\n",
       "      <td>220.694763</td>\n",
       "      <td>109.751562</td>\n",
       "      <td>501.564582</td>\n",
       "      <td>76.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-80.0</td>\n",
       "      <td>0.292328</td>\n",
       "      <td>7.001446e-05</td>\n",
       "      <td>0.292328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.4_i0.3_p4.0</th>\n",
       "      <td>567.614948</td>\n",
       "      <td>117.273271</td>\n",
       "      <td>288.884940</td>\n",
       "      <td>99.110696</td>\n",
       "      <td>42.553041</td>\n",
       "      <td>66.057046</td>\n",
       "      <td>62.401235</td>\n",
       "      <td>91.328405</td>\n",
       "      <td>300.00</td>\n",
       "      <td>4.904274</td>\n",
       "      <td>...</td>\n",
       "      <td>227.208044</td>\n",
       "      <td>113.589225</td>\n",
       "      <td>502.359615</td>\n",
       "      <td>56.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-72.0</td>\n",
       "      <td>0.324074</td>\n",
       "      <td>6.565517e-06</td>\n",
       "      <td>0.324074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.35_i0.4_p4.0</th>\n",
       "      <td>565.695289</td>\n",
       "      <td>116.375342</td>\n",
       "      <td>288.812397</td>\n",
       "      <td>100.222047</td>\n",
       "      <td>40.165024</td>\n",
       "      <td>65.381119</td>\n",
       "      <td>60.594907</td>\n",
       "      <td>91.261178</td>\n",
       "      <td>292.00</td>\n",
       "      <td>4.208547</td>\n",
       "      <td>...</td>\n",
       "      <td>228.206675</td>\n",
       "      <td>114.802578</td>\n",
       "      <td>503.242655</td>\n",
       "      <td>56.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-60.0</td>\n",
       "      <td>0.390212</td>\n",
       "      <td>2.033775e-08</td>\n",
       "      <td>0.390212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.2_i0.3_p4.0</th>\n",
       "      <td>567.502450</td>\n",
       "      <td>118.289386</td>\n",
       "      <td>287.101371</td>\n",
       "      <td>99.445313</td>\n",
       "      <td>41.076457</td>\n",
       "      <td>65.607622</td>\n",
       "      <td>60.714506</td>\n",
       "      <td>90.882281</td>\n",
       "      <td>298.50</td>\n",
       "      <td>4.435897</td>\n",
       "      <td>...</td>\n",
       "      <td>225.403884</td>\n",
       "      <td>114.783322</td>\n",
       "      <td>504.483342</td>\n",
       "      <td>56.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-68.0</td>\n",
       "      <td>0.362434</td>\n",
       "      <td>2.652498e-07</td>\n",
       "      <td>0.362434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.2_p4.0</th>\n",
       "      <td>561.069537</td>\n",
       "      <td>118.254581</td>\n",
       "      <td>280.417160</td>\n",
       "      <td>93.802481</td>\n",
       "      <td>45.591644</td>\n",
       "      <td>66.109681</td>\n",
       "      <td>63.685957</td>\n",
       "      <td>89.131529</td>\n",
       "      <td>286.50</td>\n",
       "      <td>6.529915</td>\n",
       "      <td>...</td>\n",
       "      <td>220.157914</td>\n",
       "      <td>107.310734</td>\n",
       "      <td>508.552228</td>\n",
       "      <td>68.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-92.0</td>\n",
       "      <td>0.283069</td>\n",
       "      <td>1.330442e-04</td>\n",
       "      <td>0.283069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.6_p4.0</th>\n",
       "      <td>563.086908</td>\n",
       "      <td>113.742552</td>\n",
       "      <td>290.501518</td>\n",
       "      <td>102.256551</td>\n",
       "      <td>38.289801</td>\n",
       "      <td>64.813705</td>\n",
       "      <td>60.039352</td>\n",
       "      <td>91.711353</td>\n",
       "      <td>308.00</td>\n",
       "      <td>3.389744</td>\n",
       "      <td>...</td>\n",
       "      <td>227.220191</td>\n",
       "      <td>116.809950</td>\n",
       "      <td>509.692847</td>\n",
       "      <td>56.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-64.0</td>\n",
       "      <td>0.417989</td>\n",
       "      <td>1.262697e-09</td>\n",
       "      <td>0.417989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.5_p4.0</th>\n",
       "      <td>565.572187</td>\n",
       "      <td>116.479085</td>\n",
       "      <td>289.862193</td>\n",
       "      <td>101.592478</td>\n",
       "      <td>39.267661</td>\n",
       "      <td>65.317685</td>\n",
       "      <td>60.254630</td>\n",
       "      <td>91.802144</td>\n",
       "      <td>306.50</td>\n",
       "      <td>3.724786</td>\n",
       "      <td>...</td>\n",
       "      <td>226.840283</td>\n",
       "      <td>115.477488</td>\n",
       "      <td>511.773036</td>\n",
       "      <td>56.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-64.0</td>\n",
       "      <td>0.399471</td>\n",
       "      <td>8.246481e-09</td>\n",
       "      <td>0.399471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       mean_upn_onoff_dists  std_upn_onoff_dists  \\\n",
       "a0.1_e0.4_i0.45_p4.0             563.954670           116.339301   \n",
       "a0.1_e0.45_i0.3_p4.0             566.461450           117.920324   \n",
       "a0.1_e0.4_i0.4_p4.0              566.428712           115.459782   \n",
       "a0.1_e0.5_i0.3_p4.0              568.655252           118.771030   \n",
       "a0.1_e0.5_i0.45_p4.0             565.257295           115.285304   \n",
       "a0.1_e0.3_i0.2_p4.0              566.225195           117.633535   \n",
       "a0.1_e0.25_i0.2_p4.0             564.584218           118.725497   \n",
       "a0.1_e0.45_i0.45_p4.0            563.445111           116.235519   \n",
       "a0.1_e0.1_i0.2_p4.0              567.928504           119.383045   \n",
       "a0.1_e0.25_i0.3_p4.0             568.295109           117.477255   \n",
       "a0.1_e0.5_i0.2_p4.0              565.046197           118.863910   \n",
       "a0.1_e0.5_i0.4_p4.0              565.912872           117.902624   \n",
       "a0.1_e0.15_i0.2_p4.0             568.005206           118.069873   \n",
       "a0.1_e0.4_i0.2_p4.0              566.496508           118.855839   \n",
       "a0.1_e0.2_i0.2_p4.0              568.066928           119.482380   \n",
       "a0.1_e0.35_i0.3_p4.0             569.196251           117.463527   \n",
       "a0.1_e0.5_i0.55_p4.0             563.665916           114.373896   \n",
       "a0.1_e0.3_i0.3_p4.0              567.051449           117.271546   \n",
       "a0.1_e0.3_i0.45_p4.0             563.112020           114.590423   \n",
       "a0.1_e0.45_i0.4_p4.0             565.469114           116.698430   \n",
       "a0.1_e0.3_i0.4_p4.0              565.220544           115.294435   \n",
       "a0.1_e0.35_i0.2_p4.0             568.394040           117.936114   \n",
       "a0.1_e0.4_i0.3_p4.0              567.614948           117.273271   \n",
       "a0.1_e0.35_i0.4_p4.0             565.695289           116.375342   \n",
       "a0.1_e0.2_i0.3_p4.0              567.502450           118.289386   \n",
       "a0.1_e0.45_i0.2_p4.0             561.069537           118.254581   \n",
       "a0.1_e0.5_i0.6_p4.0              563.086908           113.742552   \n",
       "a0.1_e0.45_i0.5_p4.0             565.572187           116.479085   \n",
       "\n",
       "                       mean_upn_bhand_dists  std_upn_bhand_dists  \\\n",
       "a0.1_e0.4_i0.45_p4.0             288.107362            99.946816   \n",
       "a0.1_e0.45_i0.3_p4.0             285.383310            97.802002   \n",
       "a0.1_e0.4_i0.4_p4.0              287.038791            98.120134   \n",
       "a0.1_e0.5_i0.3_p4.0              287.669949            98.321139   \n",
       "a0.1_e0.5_i0.45_p4.0             289.404535            99.016690   \n",
       "a0.1_e0.3_i0.2_p4.0              286.758283            97.868763   \n",
       "a0.1_e0.25_i0.2_p4.0             285.200934            96.499578   \n",
       "a0.1_e0.45_i0.45_p4.0            288.446435           100.147018   \n",
       "a0.1_e0.1_i0.2_p4.0              287.888886            99.339469   \n",
       "a0.1_e0.25_i0.3_p4.0             288.668828            97.561770   \n",
       "a0.1_e0.5_i0.2_p4.0              280.537383            93.504112   \n",
       "a0.1_e0.5_i0.4_p4.0              291.725317            99.875713   \n",
       "a0.1_e0.15_i0.2_p4.0             286.879907            97.889750   \n",
       "a0.1_e0.4_i0.2_p4.0              285.256129            95.201347   \n",
       "a0.1_e0.2_i0.2_p4.0              286.426757            97.480844   \n",
       "a0.1_e0.35_i0.3_p4.0             288.767376            99.008985   \n",
       "a0.1_e0.5_i0.55_p4.0             289.888498           101.597624   \n",
       "a0.1_e0.3_i0.3_p4.0              289.204522            98.485523   \n",
       "a0.1_e0.3_i0.45_p4.0             286.364879           100.857136   \n",
       "a0.1_e0.45_i0.4_p4.0             288.044118            99.261893   \n",
       "a0.1_e0.3_i0.4_p4.0              289.708661            99.362023   \n",
       "a0.1_e0.35_i0.2_p4.0             285.012905            97.093254   \n",
       "a0.1_e0.4_i0.3_p4.0              288.884940            99.110696   \n",
       "a0.1_e0.35_i0.4_p4.0             288.812397           100.222047   \n",
       "a0.1_e0.2_i0.3_p4.0              287.101371            99.445313   \n",
       "a0.1_e0.45_i0.2_p4.0             280.417160            93.802481   \n",
       "a0.1_e0.5_i0.6_p4.0              290.501518           102.256551   \n",
       "a0.1_e0.45_i0.5_p4.0             289.862193           101.592478   \n",
       "\n",
       "                       mean_upn_onoff  std_upn_onoff  mean_upn_bhand  \\\n",
       "a0.1_e0.4_i0.45_p4.0        39.650626      65.258542       60.822531   \n",
       "a0.1_e0.45_i0.3_p4.0        43.017388      66.017957       62.333333   \n",
       "a0.1_e0.4_i0.4_p4.0         40.466467      65.538212       60.522377   \n",
       "a0.1_e0.5_i0.3_p4.0         43.231309      66.185090       62.490741   \n",
       "a0.1_e0.5_i0.45_p4.0        40.244712      65.414232       60.882716   \n",
       "a0.1_e0.3_i0.2_p4.0         44.782580      66.324000       63.459105   \n",
       "a0.1_e0.25_i0.2_p4.0        44.240418      66.089001       63.312500   \n",
       "a0.1_e0.45_i0.45_p4.0       40.140355      65.155296       60.872685   \n",
       "a0.1_e0.1_i0.2_p4.0         42.067633      65.871090       60.962191   \n",
       "a0.1_e0.25_i0.3_p4.0        41.457589      65.775306       61.037809   \n",
       "a0.1_e0.5_i0.2_p4.0         46.216880      66.479202       63.301698   \n",
       "a0.1_e0.5_i0.4_p4.0         41.285920      65.776770       61.736883   \n",
       "a0.1_e0.15_i0.2_p4.0        42.596982      65.893291       62.131173   \n",
       "a0.1_e0.4_i0.2_p4.0         45.694408      66.429010       64.111883   \n",
       "a0.1_e0.2_i0.2_p4.0         43.629021      66.228442       61.815586   \n",
       "a0.1_e0.35_i0.3_p4.0        42.066916      65.946268       61.238426   \n",
       "a0.1_e0.5_i0.55_p4.0        38.768228      64.955376       60.166667   \n",
       "a0.1_e0.3_i0.3_p4.0         41.719567      65.742520       61.752315   \n",
       "a0.1_e0.3_i0.45_p4.0        39.190232      64.989983       60.157407   \n",
       "a0.1_e0.45_i0.4_p4.0        40.921905      65.555911       61.783951   \n",
       "a0.1_e0.3_i0.4_p4.0         39.774823      65.325641       60.476080   \n",
       "a0.1_e0.35_i0.2_p4.0        45.112219      66.444472       63.020062   \n",
       "a0.1_e0.4_i0.3_p4.0         42.553041      66.057046       62.401235   \n",
       "a0.1_e0.35_i0.4_p4.0        40.165024      65.381119       60.594907   \n",
       "a0.1_e0.2_i0.3_p4.0         41.076457      65.607622       60.714506   \n",
       "a0.1_e0.45_i0.2_p4.0        45.591644      66.109681       63.685957   \n",
       "a0.1_e0.5_i0.6_p4.0         38.289801      64.813705       60.039352   \n",
       "a0.1_e0.45_i0.5_p4.0        39.267661      65.317685       60.254630   \n",
       "\n",
       "                       std_upn_bhand  max_upn_bhand  upn_off_mean  ...  \\\n",
       "a0.1_e0.4_i0.45_p4.0       91.261101         302.50      3.912821  ...   \n",
       "a0.1_e0.45_i0.3_p4.0       90.365579         288.00      5.041026  ...   \n",
       "a0.1_e0.4_i0.4_p4.0        90.692153         296.00      4.205128  ...   \n",
       "a0.1_e0.5_i0.3_p4.0        90.913906         292.50      5.181197  ...   \n",
       "a0.1_e0.5_i0.45_p4.0       91.205643         304.00      3.991453  ...   \n",
       "a0.1_e0.3_i0.2_p4.0        90.891225         289.00      5.692308  ...   \n",
       "a0.1_e0.25_i0.2_p4.0       90.158445         293.00      5.517949  ...   \n",
       "a0.1_e0.45_i0.45_p4.0      90.832950         296.00      3.935043  ...   \n",
       "a0.1_e0.1_i0.2_p4.0        90.973264         301.00      5.193162  ...   \n",
       "a0.1_e0.25_i0.3_p4.0       91.054331         296.00      4.591453  ...   \n",
       "a0.1_e0.5_i0.2_p4.0        88.809643         283.25      6.736752  ...   \n",
       "a0.1_e0.5_i0.4_p4.0        92.111890         308.00      4.360684  ...   \n",
       "a0.1_e0.15_i0.2_p4.0       90.703323         301.00      5.394872  ...   \n",
       "a0.1_e0.4_i0.2_p4.0        90.134273         293.00      6.052991  ...   \n",
       "a0.1_e0.2_i0.2_p4.0        90.639696         299.00      5.365812  ...   \n",
       "a0.1_e0.35_i0.3_p4.0       91.452053         298.50      4.888889  ...   \n",
       "a0.1_e0.5_i0.55_p4.0       91.586781         300.00      3.577778  ...   \n",
       "a0.1_e0.3_i0.3_p4.0        91.333106         298.50      4.683761  ...   \n",
       "a0.1_e0.3_i0.45_p4.0       90.786504         300.00      3.750427  ...   \n",
       "a0.1_e0.45_i0.4_p4.0       91.149514         304.00      4.247863  ...   \n",
       "a0.1_e0.3_i0.4_p4.0        91.466567         300.00      4.088889  ...   \n",
       "a0.1_e0.35_i0.2_p4.0       90.200823         305.00      5.866667  ...   \n",
       "a0.1_e0.4_i0.3_p4.0        91.328405         300.00      4.904274  ...   \n",
       "a0.1_e0.35_i0.4_p4.0       91.261178         292.00      4.208547  ...   \n",
       "a0.1_e0.2_i0.3_p4.0        90.882281         298.50      4.435897  ...   \n",
       "a0.1_e0.45_i0.2_p4.0       89.131529         286.50      6.529915  ...   \n",
       "a0.1_e0.5_i0.6_p4.0        91.711353         308.00      3.389744  ...   \n",
       "a0.1_e0.45_i0.5_p4.0       91.802144         306.50      3.724786  ...   \n",
       "\n",
       "                       mean_pca_upn_dists  std_pca_upn_dists  \\\n",
       "a0.1_e0.4_i0.45_p4.0           225.344186         112.728649   \n",
       "a0.1_e0.45_i0.3_p4.0           222.749638         113.014464   \n",
       "a0.1_e0.4_i0.4_p4.0            222.973606         111.658861   \n",
       "a0.1_e0.5_i0.3_p4.0            226.227176         112.067239   \n",
       "a0.1_e0.5_i0.45_p4.0           224.168864         112.253646   \n",
       "a0.1_e0.3_i0.2_p4.0            224.940710         111.359087   \n",
       "a0.1_e0.25_i0.2_p4.0           221.808726         111.246110   \n",
       "a0.1_e0.45_i0.45_p4.0          224.377109         115.127215   \n",
       "a0.1_e0.1_i0.2_p4.0            225.983349         113.230954   \n",
       "a0.1_e0.25_i0.3_p4.0           227.099240         111.441361   \n",
       "a0.1_e0.5_i0.2_p4.0            220.248523         106.808077   \n",
       "a0.1_e0.5_i0.4_p4.0            227.442365         114.463606   \n",
       "a0.1_e0.15_i0.2_p4.0           223.979962         111.217059   \n",
       "a0.1_e0.4_i0.2_p4.0            221.199050         110.536663   \n",
       "a0.1_e0.2_i0.2_p4.0            223.546464         111.501029   \n",
       "a0.1_e0.35_i0.3_p4.0           225.108061         114.395435   \n",
       "a0.1_e0.5_i0.55_p4.0           227.031863         116.629982   \n",
       "a0.1_e0.3_i0.3_p4.0            226.436499         112.554665   \n",
       "a0.1_e0.3_i0.45_p4.0           225.181354         114.595512   \n",
       "a0.1_e0.45_i0.4_p4.0           225.124647         112.283219   \n",
       "a0.1_e0.3_i0.4_p4.0            224.807465         115.657121   \n",
       "a0.1_e0.35_i0.2_p4.0           220.694763         109.751562   \n",
       "a0.1_e0.4_i0.3_p4.0            227.208044         113.589225   \n",
       "a0.1_e0.35_i0.4_p4.0           228.206675         114.802578   \n",
       "a0.1_e0.2_i0.3_p4.0            225.403884         114.783322   \n",
       "a0.1_e0.45_i0.2_p4.0           220.157914         107.310734   \n",
       "a0.1_e0.5_i0.6_p4.0            227.220191         116.809950   \n",
       "a0.1_e0.45_i0.5_p4.0           226.840283         115.477488   \n",
       "\n",
       "                       max_pca_upn_dists  DM1_PN_pre_odor_fr  \\\n",
       "a0.1_e0.4_i0.45_p4.0          476.491828                64.0   \n",
       "a0.1_e0.45_i0.3_p4.0          476.763644                60.0   \n",
       "a0.1_e0.4_i0.4_p4.0           478.560876                52.0   \n",
       "a0.1_e0.5_i0.3_p4.0           479.039795                60.0   \n",
       "a0.1_e0.5_i0.45_p4.0          483.037003                52.0   \n",
       "a0.1_e0.3_i0.2_p4.0           483.852508                80.0   \n",
       "a0.1_e0.25_i0.2_p4.0          483.867013                68.0   \n",
       "a0.1_e0.45_i0.45_p4.0         488.152311                64.0   \n",
       "a0.1_e0.1_i0.2_p4.0           488.832101                64.0   \n",
       "a0.1_e0.25_i0.3_p4.0          490.571992                60.0   \n",
       "a0.1_e0.5_i0.2_p4.0           491.910384                76.0   \n",
       "a0.1_e0.5_i0.4_p4.0           494.581712                68.0   \n",
       "a0.1_e0.15_i0.2_p4.0          494.620908                64.0   \n",
       "a0.1_e0.4_i0.2_p4.0           494.742969                76.0   \n",
       "a0.1_e0.2_i0.2_p4.0           495.291830                64.0   \n",
       "a0.1_e0.35_i0.3_p4.0          495.364516                72.0   \n",
       "a0.1_e0.5_i0.55_p4.0          495.604629                52.0   \n",
       "a0.1_e0.3_i0.3_p4.0           495.855144                56.0   \n",
       "a0.1_e0.3_i0.45_p4.0          496.131350                56.0   \n",
       "a0.1_e0.45_i0.4_p4.0          496.484805                60.0   \n",
       "a0.1_e0.3_i0.4_p4.0           496.594846                52.0   \n",
       "a0.1_e0.35_i0.2_p4.0          501.564582                76.0   \n",
       "a0.1_e0.4_i0.3_p4.0           502.359615                56.0   \n",
       "a0.1_e0.35_i0.4_p4.0          503.242655                56.0   \n",
       "a0.1_e0.2_i0.3_p4.0           504.483342                56.0   \n",
       "a0.1_e0.45_i0.2_p4.0          508.552228                68.0   \n",
       "a0.1_e0.5_i0.6_p4.0           509.692847                56.0   \n",
       "a0.1_e0.45_i0.5_p4.0          511.773036                56.0   \n",
       "\n",
       "                       VA2_PN_pre_odor_fr  VM2_PN_dur_odor_fr  min_PN_dur  \\\n",
       "a0.1_e0.4_i0.45_p4.0                 16.0                 0.0       -68.0   \n",
       "a0.1_e0.45_i0.3_p4.0                 24.0                 0.0       -69.5   \n",
       "a0.1_e0.4_i0.4_p4.0                  24.0                 0.0       -72.0   \n",
       "a0.1_e0.5_i0.3_p4.0                  28.0                 0.0       -68.0   \n",
       "a0.1_e0.5_i0.45_p4.0                 20.0                 0.0       -68.0   \n",
       "a0.1_e0.3_i0.2_p4.0                  24.0                 0.0       -76.0   \n",
       "a0.1_e0.25_i0.2_p4.0                 36.0                 0.0       -72.0   \n",
       "a0.1_e0.45_i0.45_p4.0                24.0                 0.0       -72.0   \n",
       "a0.1_e0.1_i0.2_p4.0                  32.0                 0.0       -72.0   \n",
       "a0.1_e0.25_i0.3_p4.0                 28.0                 0.0       -72.0   \n",
       "a0.1_e0.5_i0.2_p4.0                  28.0                 0.0       -80.0   \n",
       "a0.1_e0.5_i0.4_p4.0                  24.0                 0.0       -72.0   \n",
       "a0.1_e0.15_i0.2_p4.0                 28.0                 0.0       -76.0   \n",
       "a0.1_e0.4_i0.2_p4.0                  32.0                 0.0       -84.0   \n",
       "a0.1_e0.2_i0.2_p4.0                  36.0                 0.0       -76.0   \n",
       "a0.1_e0.35_i0.3_p4.0                 32.0                 0.0       -73.5   \n",
       "a0.1_e0.5_i0.55_p4.0                 28.0                 0.0       -68.0   \n",
       "a0.1_e0.3_i0.3_p4.0                  28.0                 0.0       -68.0   \n",
       "a0.1_e0.3_i0.45_p4.0                 32.0                 0.0       -64.0   \n",
       "a0.1_e0.45_i0.4_p4.0                 20.0                 0.0       -68.0   \n",
       "a0.1_e0.3_i0.4_p4.0                  32.0                 0.0       -56.0   \n",
       "a0.1_e0.35_i0.2_p4.0                 40.0                 0.0       -80.0   \n",
       "a0.1_e0.4_i0.3_p4.0                  24.0                 0.0       -72.0   \n",
       "a0.1_e0.35_i0.4_p4.0                 20.0                 0.0       -60.0   \n",
       "a0.1_e0.2_i0.3_p4.0                  24.0                 0.0       -68.0   \n",
       "a0.1_e0.45_i0.2_p4.0                 28.0                 0.0       -92.0   \n",
       "a0.1_e0.5_i0.6_p4.0                  16.0                 0.0       -64.0   \n",
       "a0.1_e0.45_i0.5_p4.0                 28.0                 0.0       -64.0   \n",
       "\n",
       "                       kstest_stat   kstest_pval  kstest_2samp_stat  \n",
       "a0.1_e0.4_i0.45_p4.0      0.380952  4.899549e-08           0.380952  \n",
       "a0.1_e0.45_i0.3_p4.0      0.334656  2.815533e-06           0.334656  \n",
       "a0.1_e0.4_i0.4_p4.0       0.371693  1.153222e-07           0.371693  \n",
       "a0.1_e0.5_i0.3_p4.0       0.322751  7.283579e-06           0.322751  \n",
       "a0.1_e0.5_i0.45_p4.0      0.380952  4.899549e-08           0.380952  \n",
       "a0.1_e0.3_i0.2_p4.0       0.305556  2.694667e-05           0.305556  \n",
       "a0.1_e0.25_i0.2_p4.0      0.306878  2.443268e-05           0.306878  \n",
       "a0.1_e0.45_i0.45_p4.0     0.384921  3.370900e-08           0.384921  \n",
       "a0.1_e0.1_i0.2_p4.0       0.341270  1.634190e-06           0.341270  \n",
       "a0.1_e0.25_i0.3_p4.0      0.343915  1.310404e-06           0.343915  \n",
       "a0.1_e0.5_i0.2_p4.0       0.296296  5.282175e-05           0.296296  \n",
       "a0.1_e0.5_i0.4_p4.0       0.361111  2.982082e-07           0.361111  \n",
       "a0.1_e0.15_i0.2_p4.0      0.314815  1.344861e-05           0.314815  \n",
       "a0.1_e0.4_i0.2_p4.0       0.287037  1.013115e-04           0.287037  \n",
       "a0.1_e0.2_i0.2_p4.0       0.305556  2.694667e-05           0.305556  \n",
       "a0.1_e0.35_i0.3_p4.0      0.333333  3.134847e-06           0.333333  \n",
       "a0.1_e0.5_i0.55_p4.0      0.399471  8.246481e-09           0.399471  \n",
       "a0.1_e0.3_i0.3_p4.0       0.371693  1.153222e-07           0.371693  \n",
       "a0.1_e0.3_i0.45_p4.0      0.362434  2.652498e-07           0.362434  \n",
       "a0.1_e0.45_i0.4_p4.0      0.390212  2.033775e-08           0.390212  \n",
       "a0.1_e0.3_i0.4_p4.0       0.388889  2.309270e-08           0.388889  \n",
       "a0.1_e0.35_i0.2_p4.0      0.292328  7.001446e-05           0.292328  \n",
       "a0.1_e0.4_i0.3_p4.0       0.324074  6.565517e-06           0.324074  \n",
       "a0.1_e0.35_i0.4_p4.0      0.390212  2.033775e-08           0.390212  \n",
       "a0.1_e0.2_i0.3_p4.0       0.362434  2.652498e-07           0.362434  \n",
       "a0.1_e0.45_i0.2_p4.0      0.283069  1.330442e-04           0.283069  \n",
       "a0.1_e0.5_i0.6_p4.0       0.417989  1.262697e-09           0.417989  \n",
       "a0.1_e0.45_i0.5_p4.0      0.399471  8.246481e-09           0.399471  \n",
       "\n",
       "[28 rows x 30 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df = df_res[\n",
    "       (df_res['p'] == 4) & \n",
    "       (df_res[x_var] > 60) &\n",
    "       (df_res[y_var] < 10) &\n",
    "       #(df_res['upn_off_mean'] < 20) &\n",
    "       #(df_res['eln_on_mean'] < 40) &\n",
    "       #(df_res['eln_off_mean'] < 20) &\n",
    "       (1) ]\n",
    "sub_df.sort_values('upn_off_mean')[df_res_cols1].sort_values('max_pca_upn_dists')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_var, y_var, c_var = plot_sweep_res(\n",
    "    x_var = 'max_pca_upn_dists', x_lab = 'max pca upn dist',\n",
    "    y_var = 'upn_off_mean', y_lab = 'uPN off-odor firing rate (Hz)',\n",
    "    c_var = 'p', c_lab='PN multiplier',\n",
    "    highlight_locs=[]\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_upn_onoff_dists</th>\n",
       "      <th>std_upn_onoff_dists</th>\n",
       "      <th>mean_upn_bhand_dists</th>\n",
       "      <th>std_upn_bhand_dists</th>\n",
       "      <th>mean_upn_onoff</th>\n",
       "      <th>std_upn_onoff</th>\n",
       "      <th>mean_upn_bhand</th>\n",
       "      <th>std_upn_bhand</th>\n",
       "      <th>max_upn_bhand</th>\n",
       "      <th>upn_off_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_pca_upn_dists</th>\n",
       "      <th>std_pca_upn_dists</th>\n",
       "      <th>max_pca_upn_dists</th>\n",
       "      <th>DM1_PN_pre_odor_fr</th>\n",
       "      <th>VA2_PN_pre_odor_fr</th>\n",
       "      <th>VM2_PN_dur_odor_fr</th>\n",
       "      <th>min_PN_dur</th>\n",
       "      <th>kstest_stat</th>\n",
       "      <th>kstest_pval</th>\n",
       "      <th>kstest_2samp_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.2_i1.0_p4.0</th>\n",
       "      <td>552.858937</td>\n",
       "      <td>108.953102</td>\n",
       "      <td>288.131537</td>\n",
       "      <td>105.039712</td>\n",
       "      <td>34.280120</td>\n",
       "      <td>63.028954</td>\n",
       "      <td>56.530864</td>\n",
       "      <td>91.064336</td>\n",
       "      <td>307.0</td>\n",
       "      <td>2.403419</td>\n",
       "      <td>...</td>\n",
       "      <td>224.981062</td>\n",
       "      <td>120.811747</td>\n",
       "      <td>505.280632</td>\n",
       "      <td>44.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-52.0</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>7.569056e-12</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.1_i1.0_p4.0</th>\n",
       "      <td>549.676123</td>\n",
       "      <td>109.416865</td>\n",
       "      <td>286.455778</td>\n",
       "      <td>107.769282</td>\n",
       "      <td>34.296977</td>\n",
       "      <td>62.765736</td>\n",
       "      <td>55.432870</td>\n",
       "      <td>90.734129</td>\n",
       "      <td>308.5</td>\n",
       "      <td>2.504274</td>\n",
       "      <td>...</td>\n",
       "      <td>226.867257</td>\n",
       "      <td>121.894743</td>\n",
       "      <td>527.210512</td>\n",
       "      <td>60.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-48.0</td>\n",
       "      <td>0.473545</td>\n",
       "      <td>2.523870e-12</td>\n",
       "      <td>0.473545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.15_i1.0_p4.0</th>\n",
       "      <td>551.731958</td>\n",
       "      <td>110.612668</td>\n",
       "      <td>290.933556</td>\n",
       "      <td>106.356586</td>\n",
       "      <td>34.259300</td>\n",
       "      <td>62.982318</td>\n",
       "      <td>57.247685</td>\n",
       "      <td>91.942012</td>\n",
       "      <td>313.5</td>\n",
       "      <td>2.519658</td>\n",
       "      <td>...</td>\n",
       "      <td>226.740417</td>\n",
       "      <td>122.718263</td>\n",
       "      <td>513.323961</td>\n",
       "      <td>48.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-52.0</td>\n",
       "      <td>0.462963</td>\n",
       "      <td>8.836154e-12</td>\n",
       "      <td>0.462963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.3_i1.0_p4.0</th>\n",
       "      <td>549.411470</td>\n",
       "      <td>110.946988</td>\n",
       "      <td>287.289997</td>\n",
       "      <td>105.014482</td>\n",
       "      <td>34.731155</td>\n",
       "      <td>62.899742</td>\n",
       "      <td>57.210648</td>\n",
       "      <td>90.762945</td>\n",
       "      <td>305.5</td>\n",
       "      <td>2.535043</td>\n",
       "      <td>...</td>\n",
       "      <td>223.634167</td>\n",
       "      <td>119.441554</td>\n",
       "      <td>505.003585</td>\n",
       "      <td>44.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-48.0</td>\n",
       "      <td>0.462963</td>\n",
       "      <td>8.836154e-12</td>\n",
       "      <td>0.462963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i1.0_p4.0</th>\n",
       "      <td>555.166969</td>\n",
       "      <td>110.801657</td>\n",
       "      <td>289.322324</td>\n",
       "      <td>105.410497</td>\n",
       "      <td>34.936610</td>\n",
       "      <td>63.339675</td>\n",
       "      <td>57.827932</td>\n",
       "      <td>91.418419</td>\n",
       "      <td>309.5</td>\n",
       "      <td>2.543590</td>\n",
       "      <td>...</td>\n",
       "      <td>225.090861</td>\n",
       "      <td>119.452677</td>\n",
       "      <td>511.507046</td>\n",
       "      <td>36.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-52.0</td>\n",
       "      <td>0.445767</td>\n",
       "      <td>6.311673e-11</td>\n",
       "      <td>0.445767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean_upn_onoff_dists  std_upn_onoff_dists  \\\n",
       "a0.1_e0.2_i1.0_p4.0             552.858937           108.953102   \n",
       "a0.1_e0.1_i1.0_p4.0             549.676123           109.416865   \n",
       "a0.1_e0.15_i1.0_p4.0            551.731958           110.612668   \n",
       "a0.1_e0.3_i1.0_p4.0             549.411470           110.946988   \n",
       "a0.1_e0.45_i1.0_p4.0            555.166969           110.801657   \n",
       "\n",
       "                      mean_upn_bhand_dists  std_upn_bhand_dists  \\\n",
       "a0.1_e0.2_i1.0_p4.0             288.131537           105.039712   \n",
       "a0.1_e0.1_i1.0_p4.0             286.455778           107.769282   \n",
       "a0.1_e0.15_i1.0_p4.0            290.933556           106.356586   \n",
       "a0.1_e0.3_i1.0_p4.0             287.289997           105.014482   \n",
       "a0.1_e0.45_i1.0_p4.0            289.322324           105.410497   \n",
       "\n",
       "                      mean_upn_onoff  std_upn_onoff  mean_upn_bhand  \\\n",
       "a0.1_e0.2_i1.0_p4.0        34.280120      63.028954       56.530864   \n",
       "a0.1_e0.1_i1.0_p4.0        34.296977      62.765736       55.432870   \n",
       "a0.1_e0.15_i1.0_p4.0       34.259300      62.982318       57.247685   \n",
       "a0.1_e0.3_i1.0_p4.0        34.731155      62.899742       57.210648   \n",
       "a0.1_e0.45_i1.0_p4.0       34.936610      63.339675       57.827932   \n",
       "\n",
       "                      std_upn_bhand  max_upn_bhand  upn_off_mean  ...  \\\n",
       "a0.1_e0.2_i1.0_p4.0       91.064336          307.0      2.403419  ...   \n",
       "a0.1_e0.1_i1.0_p4.0       90.734129          308.5      2.504274  ...   \n",
       "a0.1_e0.15_i1.0_p4.0      91.942012          313.5      2.519658  ...   \n",
       "a0.1_e0.3_i1.0_p4.0       90.762945          305.5      2.535043  ...   \n",
       "a0.1_e0.45_i1.0_p4.0      91.418419          309.5      2.543590  ...   \n",
       "\n",
       "                      mean_pca_upn_dists  std_pca_upn_dists  \\\n",
       "a0.1_e0.2_i1.0_p4.0           224.981062         120.811747   \n",
       "a0.1_e0.1_i1.0_p4.0           226.867257         121.894743   \n",
       "a0.1_e0.15_i1.0_p4.0          226.740417         122.718263   \n",
       "a0.1_e0.3_i1.0_p4.0           223.634167         119.441554   \n",
       "a0.1_e0.45_i1.0_p4.0          225.090861         119.452677   \n",
       "\n",
       "                      max_pca_upn_dists  DM1_PN_pre_odor_fr  \\\n",
       "a0.1_e0.2_i1.0_p4.0          505.280632                44.0   \n",
       "a0.1_e0.1_i1.0_p4.0          527.210512                60.0   \n",
       "a0.1_e0.15_i1.0_p4.0         513.323961                48.0   \n",
       "a0.1_e0.3_i1.0_p4.0          505.003585                44.0   \n",
       "a0.1_e0.45_i1.0_p4.0         511.507046                36.0   \n",
       "\n",
       "                      VA2_PN_pre_odor_fr  VM2_PN_dur_odor_fr  min_PN_dur  \\\n",
       "a0.1_e0.2_i1.0_p4.0                 24.0                 0.0       -52.0   \n",
       "a0.1_e0.1_i1.0_p4.0                 12.0                 0.0       -48.0   \n",
       "a0.1_e0.15_i1.0_p4.0                12.0                 0.0       -52.0   \n",
       "a0.1_e0.3_i1.0_p4.0                 20.0                 0.0       -48.0   \n",
       "a0.1_e0.45_i1.0_p4.0                16.0                 0.0       -52.0   \n",
       "\n",
       "                      kstest_stat   kstest_pval  kstest_2samp_stat  \n",
       "a0.1_e0.2_i1.0_p4.0      0.464286  7.569056e-12           0.464286  \n",
       "a0.1_e0.1_i1.0_p4.0      0.473545  2.523870e-12           0.473545  \n",
       "a0.1_e0.15_i1.0_p4.0     0.462963  8.836154e-12           0.462963  \n",
       "a0.1_e0.3_i1.0_p4.0      0.462963  8.836154e-12           0.462963  \n",
       "a0.1_e0.45_i1.0_p4.0     0.445767  6.311673e-11           0.445767  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df = df_res[\n",
    "       (df_res['p'] == 4) & \n",
    "       (df_res[x_var] > 60) &\n",
    "       (df_res[y_var] < 10) &\n",
    "       #(df_res['upn_off_mean'] < 20) &\n",
    "       #(df_res['eln_on_mean'] < 40) &\n",
    "       #(df_res['eln_off_mean'] < 20) &\n",
    "       (1) ]\n",
    "sub_df.sort_values('upn_off_mean').iloc[:5][df_res_cols1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_upn_onoff_dists</th>\n",
       "      <th>std_upn_onoff_dists</th>\n",
       "      <th>mean_upn_bhand_dists</th>\n",
       "      <th>std_upn_bhand_dists</th>\n",
       "      <th>mean_upn_onoff</th>\n",
       "      <th>std_upn_onoff</th>\n",
       "      <th>mean_upn_bhand</th>\n",
       "      <th>std_upn_bhand</th>\n",
       "      <th>max_upn_bhand</th>\n",
       "      <th>upn_off_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_pca_upn_dists</th>\n",
       "      <th>std_pca_upn_dists</th>\n",
       "      <th>max_pca_upn_dists</th>\n",
       "      <th>DM1_PN_pre_odor_fr</th>\n",
       "      <th>VA2_PN_pre_odor_fr</th>\n",
       "      <th>VM2_PN_dur_odor_fr</th>\n",
       "      <th>min_PN_dur</th>\n",
       "      <th>kstest_stat</th>\n",
       "      <th>kstest_pval</th>\n",
       "      <th>kstest_2samp_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.4_p6.0</th>\n",
       "      <td>676.514075</td>\n",
       "      <td>138.008563</td>\n",
       "      <td>326.353766</td>\n",
       "      <td>103.888703</td>\n",
       "      <td>51.604798</td>\n",
       "      <td>78.402726</td>\n",
       "      <td>67.898148</td>\n",
       "      <td>102.251237</td>\n",
       "      <td>320.5</td>\n",
       "      <td>9.509402</td>\n",
       "      <td>...</td>\n",
       "      <td>251.486339</td>\n",
       "      <td>122.233677</td>\n",
       "      <td>553.421490</td>\n",
       "      <td>84.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-84.0</td>\n",
       "      <td>0.379630</td>\n",
       "      <td>5.544730e-08</td>\n",
       "      <td>0.379630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.45_p6.0</th>\n",
       "      <td>672.243476</td>\n",
       "      <td>137.054119</td>\n",
       "      <td>326.295462</td>\n",
       "      <td>106.657731</td>\n",
       "      <td>50.500499</td>\n",
       "      <td>77.963141</td>\n",
       "      <td>66.725309</td>\n",
       "      <td>102.289820</td>\n",
       "      <td>319.0</td>\n",
       "      <td>8.748718</td>\n",
       "      <td>...</td>\n",
       "      <td>252.864775</td>\n",
       "      <td>122.506500</td>\n",
       "      <td>560.688921</td>\n",
       "      <td>96.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-92.0</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>2.309270e-08</td>\n",
       "      <td>0.388889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.45_i0.8_p6.0</th>\n",
       "      <td>671.220781</td>\n",
       "      <td>131.961677</td>\n",
       "      <td>330.878985</td>\n",
       "      <td>110.468858</td>\n",
       "      <td>45.282457</td>\n",
       "      <td>77.040621</td>\n",
       "      <td>63.478395</td>\n",
       "      <td>103.520776</td>\n",
       "      <td>324.5</td>\n",
       "      <td>6.441026</td>\n",
       "      <td>...</td>\n",
       "      <td>253.743262</td>\n",
       "      <td>128.090018</td>\n",
       "      <td>553.764072</td>\n",
       "      <td>60.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-80.0</td>\n",
       "      <td>0.453704</td>\n",
       "      <td>2.574641e-11</td>\n",
       "      <td>0.453704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.25_i0.7_p6.0</th>\n",
       "      <td>672.015957</td>\n",
       "      <td>131.928933</td>\n",
       "      <td>330.912548</td>\n",
       "      <td>110.991422</td>\n",
       "      <td>45.598425</td>\n",
       "      <td>77.139325</td>\n",
       "      <td>62.357253</td>\n",
       "      <td>103.615535</td>\n",
       "      <td>320.5</td>\n",
       "      <td>6.487179</td>\n",
       "      <td>...</td>\n",
       "      <td>254.934588</td>\n",
       "      <td>128.738157</td>\n",
       "      <td>553.083243</td>\n",
       "      <td>68.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-73.5</td>\n",
       "      <td>0.436508</td>\n",
       "      <td>1.755879e-10</td>\n",
       "      <td>0.436508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.5_i0.45_p6.0</th>\n",
       "      <td>677.427826</td>\n",
       "      <td>138.484951</td>\n",
       "      <td>329.109667</td>\n",
       "      <td>105.659870</td>\n",
       "      <td>50.844499</td>\n",
       "      <td>78.333413</td>\n",
       "      <td>66.686728</td>\n",
       "      <td>102.575736</td>\n",
       "      <td>312.5</td>\n",
       "      <td>8.970940</td>\n",
       "      <td>...</td>\n",
       "      <td>254.954235</td>\n",
       "      <td>122.904828</td>\n",
       "      <td>569.858488</td>\n",
       "      <td>76.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-93.5</td>\n",
       "      <td>0.371693</td>\n",
       "      <td>1.153222e-07</td>\n",
       "      <td>0.371693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.2_i0.8_p6.0</th>\n",
       "      <td>669.468131</td>\n",
       "      <td>131.624866</td>\n",
       "      <td>337.703995</td>\n",
       "      <td>113.112020</td>\n",
       "      <td>44.444408</td>\n",
       "      <td>76.775583</td>\n",
       "      <td>62.799383</td>\n",
       "      <td>105.592011</td>\n",
       "      <td>326.0</td>\n",
       "      <td>6.100855</td>\n",
       "      <td>...</td>\n",
       "      <td>263.324645</td>\n",
       "      <td>131.442311</td>\n",
       "      <td>579.808620</td>\n",
       "      <td>80.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-72.0</td>\n",
       "      <td>0.462963</td>\n",
       "      <td>8.836154e-12</td>\n",
       "      <td>0.462963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.4_i1.0_p6.0</th>\n",
       "      <td>668.224272</td>\n",
       "      <td>131.026949</td>\n",
       "      <td>336.176934</td>\n",
       "      <td>115.149098</td>\n",
       "      <td>43.745960</td>\n",
       "      <td>76.563100</td>\n",
       "      <td>62.477623</td>\n",
       "      <td>105.392612</td>\n",
       "      <td>326.0</td>\n",
       "      <td>5.547009</td>\n",
       "      <td>...</td>\n",
       "      <td>263.422542</td>\n",
       "      <td>132.329914</td>\n",
       "      <td>579.990662</td>\n",
       "      <td>72.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-72.0</td>\n",
       "      <td>0.472222</td>\n",
       "      <td>2.957301e-12</td>\n",
       "      <td>0.472222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.3_i1.0_p6.0</th>\n",
       "      <td>667.381337</td>\n",
       "      <td>129.490142</td>\n",
       "      <td>339.749972</td>\n",
       "      <td>116.196038</td>\n",
       "      <td>43.579212</td>\n",
       "      <td>76.328853</td>\n",
       "      <td>62.514660</td>\n",
       "      <td>105.942615</td>\n",
       "      <td>330.0</td>\n",
       "      <td>5.473504</td>\n",
       "      <td>...</td>\n",
       "      <td>264.658927</td>\n",
       "      <td>135.017359</td>\n",
       "      <td>575.798177</td>\n",
       "      <td>64.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-73.5</td>\n",
       "      <td>0.472222</td>\n",
       "      <td>2.957301e-12</td>\n",
       "      <td>0.472222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.35_i0.9_p6.0</th>\n",
       "      <td>676.377737</td>\n",
       "      <td>130.699705</td>\n",
       "      <td>338.339191</td>\n",
       "      <td>117.176255</td>\n",
       "      <td>44.370915</td>\n",
       "      <td>77.203186</td>\n",
       "      <td>63.385031</td>\n",
       "      <td>106.086123</td>\n",
       "      <td>330.0</td>\n",
       "      <td>5.852991</td>\n",
       "      <td>...</td>\n",
       "      <td>264.878862</td>\n",
       "      <td>135.076980</td>\n",
       "      <td>582.029153</td>\n",
       "      <td>68.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-84.0</td>\n",
       "      <td>0.455026</td>\n",
       "      <td>2.213274e-11</td>\n",
       "      <td>0.455026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a0.1_e0.15_i0.7_p6.0</th>\n",
       "      <td>673.074102</td>\n",
       "      <td>134.102058</td>\n",
       "      <td>340.632894</td>\n",
       "      <td>117.780425</td>\n",
       "      <td>45.058597</td>\n",
       "      <td>77.229613</td>\n",
       "      <td>62.635802</td>\n",
       "      <td>106.949771</td>\n",
       "      <td>328.5</td>\n",
       "      <td>6.347009</td>\n",
       "      <td>...</td>\n",
       "      <td>265.695284</td>\n",
       "      <td>137.122335</td>\n",
       "      <td>577.458747</td>\n",
       "      <td>68.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-96.0</td>\n",
       "      <td>0.455026</td>\n",
       "      <td>2.213274e-11</td>\n",
       "      <td>0.455026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       mean_upn_onoff_dists  std_upn_onoff_dists  \\\n",
       "a0.1_e0.45_i0.4_p6.0             676.514075           138.008563   \n",
       "a0.1_e0.45_i0.45_p6.0            672.243476           137.054119   \n",
       "a0.1_e0.45_i0.8_p6.0             671.220781           131.961677   \n",
       "a0.1_e0.25_i0.7_p6.0             672.015957           131.928933   \n",
       "a0.1_e0.5_i0.45_p6.0             677.427826           138.484951   \n",
       "...                                     ...                  ...   \n",
       "a0.1_e0.2_i0.8_p6.0              669.468131           131.624866   \n",
       "a0.1_e0.4_i1.0_p6.0              668.224272           131.026949   \n",
       "a0.1_e0.3_i1.0_p6.0              667.381337           129.490142   \n",
       "a0.1_e0.35_i0.9_p6.0             676.377737           130.699705   \n",
       "a0.1_e0.15_i0.7_p6.0             673.074102           134.102058   \n",
       "\n",
       "                       mean_upn_bhand_dists  std_upn_bhand_dists  \\\n",
       "a0.1_e0.45_i0.4_p6.0             326.353766           103.888703   \n",
       "a0.1_e0.45_i0.45_p6.0            326.295462           106.657731   \n",
       "a0.1_e0.45_i0.8_p6.0             330.878985           110.468858   \n",
       "a0.1_e0.25_i0.7_p6.0             330.912548           110.991422   \n",
       "a0.1_e0.5_i0.45_p6.0             329.109667           105.659870   \n",
       "...                                     ...                  ...   \n",
       "a0.1_e0.2_i0.8_p6.0              337.703995           113.112020   \n",
       "a0.1_e0.4_i1.0_p6.0              336.176934           115.149098   \n",
       "a0.1_e0.3_i1.0_p6.0              339.749972           116.196038   \n",
       "a0.1_e0.35_i0.9_p6.0             338.339191           117.176255   \n",
       "a0.1_e0.15_i0.7_p6.0             340.632894           117.780425   \n",
       "\n",
       "                       mean_upn_onoff  std_upn_onoff  mean_upn_bhand  \\\n",
       "a0.1_e0.45_i0.4_p6.0        51.604798      78.402726       67.898148   \n",
       "a0.1_e0.45_i0.45_p6.0       50.500499      77.963141       66.725309   \n",
       "a0.1_e0.45_i0.8_p6.0        45.282457      77.040621       63.478395   \n",
       "a0.1_e0.25_i0.7_p6.0        45.598425      77.139325       62.357253   \n",
       "a0.1_e0.5_i0.45_p6.0        50.844499      78.333413       66.686728   \n",
       "...                               ...            ...             ...   \n",
       "a0.1_e0.2_i0.8_p6.0         44.444408      76.775583       62.799383   \n",
       "a0.1_e0.4_i1.0_p6.0         43.745960      76.563100       62.477623   \n",
       "a0.1_e0.3_i1.0_p6.0         43.579212      76.328853       62.514660   \n",
       "a0.1_e0.35_i0.9_p6.0        44.370915      77.203186       63.385031   \n",
       "a0.1_e0.15_i0.7_p6.0        45.058597      77.229613       62.635802   \n",
       "\n",
       "                       std_upn_bhand  max_upn_bhand  upn_off_mean  ...  \\\n",
       "a0.1_e0.45_i0.4_p6.0      102.251237          320.5      9.509402  ...   \n",
       "a0.1_e0.45_i0.45_p6.0     102.289820          319.0      8.748718  ...   \n",
       "a0.1_e0.45_i0.8_p6.0      103.520776          324.5      6.441026  ...   \n",
       "a0.1_e0.25_i0.7_p6.0      103.615535          320.5      6.487179  ...   \n",
       "a0.1_e0.5_i0.45_p6.0      102.575736          312.5      8.970940  ...   \n",
       "...                              ...            ...           ...  ...   \n",
       "a0.1_e0.2_i0.8_p6.0       105.592011          326.0      6.100855  ...   \n",
       "a0.1_e0.4_i1.0_p6.0       105.392612          326.0      5.547009  ...   \n",
       "a0.1_e0.3_i1.0_p6.0       105.942615          330.0      5.473504  ...   \n",
       "a0.1_e0.35_i0.9_p6.0      106.086123          330.0      5.852991  ...   \n",
       "a0.1_e0.15_i0.7_p6.0      106.949771          328.5      6.347009  ...   \n",
       "\n",
       "                       mean_pca_upn_dists  std_pca_upn_dists  \\\n",
       "a0.1_e0.45_i0.4_p6.0           251.486339         122.233677   \n",
       "a0.1_e0.45_i0.45_p6.0          252.864775         122.506500   \n",
       "a0.1_e0.45_i0.8_p6.0           253.743262         128.090018   \n",
       "a0.1_e0.25_i0.7_p6.0           254.934588         128.738157   \n",
       "a0.1_e0.5_i0.45_p6.0           254.954235         122.904828   \n",
       "...                                   ...                ...   \n",
       "a0.1_e0.2_i0.8_p6.0            263.324645         131.442311   \n",
       "a0.1_e0.4_i1.0_p6.0            263.422542         132.329914   \n",
       "a0.1_e0.3_i1.0_p6.0            264.658927         135.017359   \n",
       "a0.1_e0.35_i0.9_p6.0           264.878862         135.076980   \n",
       "a0.1_e0.15_i0.7_p6.0           265.695284         137.122335   \n",
       "\n",
       "                       max_pca_upn_dists  DM1_PN_pre_odor_fr  \\\n",
       "a0.1_e0.45_i0.4_p6.0          553.421490                84.0   \n",
       "a0.1_e0.45_i0.45_p6.0         560.688921                96.0   \n",
       "a0.1_e0.45_i0.8_p6.0          553.764072                60.0   \n",
       "a0.1_e0.25_i0.7_p6.0          553.083243                68.0   \n",
       "a0.1_e0.5_i0.45_p6.0          569.858488                76.0   \n",
       "...                                  ...                 ...   \n",
       "a0.1_e0.2_i0.8_p6.0           579.808620                80.0   \n",
       "a0.1_e0.4_i1.0_p6.0           579.990662                72.0   \n",
       "a0.1_e0.3_i1.0_p6.0           575.798177                64.0   \n",
       "a0.1_e0.35_i0.9_p6.0          582.029153                68.0   \n",
       "a0.1_e0.15_i0.7_p6.0          577.458747                68.0   \n",
       "\n",
       "                       VA2_PN_pre_odor_fr  VM2_PN_dur_odor_fr  min_PN_dur  \\\n",
       "a0.1_e0.45_i0.4_p6.0                 28.0                 0.0       -84.0   \n",
       "a0.1_e0.45_i0.45_p6.0                36.0                 0.0       -92.0   \n",
       "a0.1_e0.45_i0.8_p6.0                 24.0                 0.0       -80.0   \n",
       "a0.1_e0.25_i0.7_p6.0                 36.0                 0.0       -73.5   \n",
       "a0.1_e0.5_i0.45_p6.0                 36.0                 0.0       -93.5   \n",
       "...                                   ...                 ...         ...   \n",
       "a0.1_e0.2_i0.8_p6.0                  28.0                 0.0       -72.0   \n",
       "a0.1_e0.4_i1.0_p6.0                  32.0                 0.0       -72.0   \n",
       "a0.1_e0.3_i1.0_p6.0                  28.0                 0.0       -73.5   \n",
       "a0.1_e0.35_i0.9_p6.0                 32.0                 0.0       -84.0   \n",
       "a0.1_e0.15_i0.7_p6.0                 28.0                 0.0       -96.0   \n",
       "\n",
       "                       kstest_stat   kstest_pval  kstest_2samp_stat  \n",
       "a0.1_e0.45_i0.4_p6.0      0.379630  5.544730e-08           0.379630  \n",
       "a0.1_e0.45_i0.45_p6.0     0.388889  2.309270e-08           0.388889  \n",
       "a0.1_e0.45_i0.8_p6.0      0.453704  2.574641e-11           0.453704  \n",
       "a0.1_e0.25_i0.7_p6.0      0.436508  1.755879e-10           0.436508  \n",
       "a0.1_e0.5_i0.45_p6.0      0.371693  1.153222e-07           0.371693  \n",
       "...                            ...           ...                ...  \n",
       "a0.1_e0.2_i0.8_p6.0       0.462963  8.836154e-12           0.462963  \n",
       "a0.1_e0.4_i1.0_p6.0       0.472222  2.957301e-12           0.472222  \n",
       "a0.1_e0.3_i1.0_p6.0       0.472222  2.957301e-12           0.472222  \n",
       "a0.1_e0.35_i0.9_p6.0      0.455026  2.213274e-11           0.455026  \n",
       "a0.1_e0.15_i0.7_p6.0      0.455026  2.213274e-11           0.455026  \n",
       "\n",
       "[92 rows x 34 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df = df_res[\n",
    "       (df_res['p'] == 6) & \n",
    "       #(df_res[x_var] > 60) &\n",
    "       #(df_res[y_var] < 10) &\n",
    "        (df_res['upn_off_mean'] < 10) &\n",
    "        (df_res['mean_upn_bhand'] > 60) &\n",
    "       #(df_res['eln_on_mean'] < 40) &\n",
    "       #(df_res['eln_off_mean'] < 20) &\n",
    "       (1) ]\n",
    "sub_df.sort_values('mean_pca_upn_dists')#.iloc[:5][df_res_cols1]"
   ]
  }
 ],
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