https://github.com/ClockConnectome/clock-connectome
Tip revision: 0c820068458bd9249785c704716acfd1a9301f1c authored by gabrielle9 on 01 August 2022, 23:54:06 UTC
Update README.md
Update README.md
Tip revision: 0c82006
BeyondClock.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "612cf948-f2db-4be3-a88e-4c55716728ac",
"metadata": {},
"source": [
"To determine whether there are neurons that have not been identified as clock neurons in the hemibrain dataset but that are nonetheless central players in the clock network, we focused on the strongest shared targets of the M and E cells."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "bb9885e0",
"metadata": {},
"outputs": [],
"source": [
"from neuprint import Client\n",
"c = Client('neuprint.janelia.org', dataset='hemibrain:v1.2.1', token='')\n",
"# insert personal token above. see https://connectome-neuprint.github.io/neuprint-python/docs/quickstart.html#client-and-authorization-token for instructions\n",
"c.fetch_version()"
]
},
{
"cell_type": "markdown",
"id": "2076cd15-e8e2-48ec-ab25-3d21a8ae871a",
"metadata": {},
"source": [
"# Strongest shared M cell targets"
]
},
{
"cell_type": "markdown",
"id": "11cb9227-6b02-4fed-9872-41f3ac404153",
"metadata": {},
"source": [
"Strong connections made by all of the M cells were retrieved. \n",
"\n",
"There were 6 neurons that received strong synaptic connections from all 4 M cells."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b653dd7a",
"metadata": {},
"outputs": [],
"source": [
"import neuron_criteria\n",
"\n",
"clock_df = neuron_criteria.getClock(l_lnv = True)\n",
"bodyIds_by_type = neuron_criteria.bodyIds_by_type(clock_df)\n",
"MIds = bodyIds_by_type['s-LNv']"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c7d2b6b0-6446-4de7-9e03-7f2252147568",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\dbing\\Documents\\Research\\Code\\clock-connectome\\connection_utils.py:208: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
" conns_df = conns_df.groupby(['bodyId_post', 'instance_post'], as_index=False)['weight', 'shared'].sum()\n"
]
},
{
"data": {
"text/plain": [
"3 355453590\n",
"9 5813047586\n",
"1 325122525\n",
"2 325455002\n",
"0 294783216\n",
"5 540998882\n",
"Name: bodyId_post, dtype: int64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from connection_utils import strong_shared_connections\n",
"\n",
"sLNv_strong_shared_targs = strong_shared_connections(MIds, 'out', 4)\n",
"\n",
"candidate_IDs = sLNv_strong_shared_targs['bodyId_post']\n",
"candidate_IDs"
]
},
{
"cell_type": "markdown",
"id": "eeefa43a",
"metadata": {},
"source": [
"Retrieve connectivity for clock and candidates"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8a232c03",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from connection_utils import clock_neuron_connections, get_input_output_conns\n",
"import pandas as pd\n",
"\n",
"clock_targets = clock_neuron_connections(clock_df, 'out', min_weight=3)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "2cd3f3dc",
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>bodyId_pre</th>\n",
" <th>bodyId_post</th>\n",
" <th>weight</th>\n",
" <th>instance_pre</th>\n",
" <th>instance_post</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>264083994</td>\n",
" <td>299272434</td>\n",
" <td>4</td>\n",
" <td>DN1a1</td>\n",
" <td>SLP443</td>\n",
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" <th>1</th>\n",
" <td>264083994</td>\n",
" <td>325814461</td>\n",
" <td>10</td>\n",
" <td>DN1a1</td>\n",
" <td>LHPV4c3</td>\n",
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" <td>4</td>\n",
" <td>DN1a1</td>\n",
" <td>SLP457</td>\n",
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" <th>3</th>\n",
" <td>264083994</td>\n",
" <td>356472645</td>\n",
" <td>3</td>\n",
" <td>DN1a1</td>\n",
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" <td>DN1a1</td>\n",
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" <tr>\n",
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" <td>SLP316_R</td>\n",
" <td>SLP387_R</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>294783216</td>\n",
" <td>387166379</td>\n",
" <td>6</td>\n",
" <td>SLP403_R</td>\n",
" <td>DN1pA_R</td>\n",
" </tr>\n",
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"<p>2648 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" bodyId_pre bodyId_post weight instance_pre instance_post\n",
"0 264083994 299272434 4 DN1a1 SLP443\n",
"1 264083994 325814461 10 DN1a1 LHPV4c3\n",
"2 264083994 326119769 4 DN1a1 SLP457\n",
"3 264083994 356472645 3 DN1a1 SLP364\n",
"4 264083994 356503889 7 DN1a1 LHPV6f4_b\n",
".. ... ... ... ... ...\n",
"215 325122525 5813046962 3 SLP316_R SLP296_R\n",
"216 325122525 5813057148 3 SLP316_R SLP387_R\n",
"113 294783216 387166379 6 SLP403_R DN1pA_R\n",
"191 294783216 324846570 3 SLP403_R DN1pA_R\n",
"192 294783216 325529237 3 SLP403_R DN1pA_R\n",
"\n",
"[2648 rows x 5 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"candidate_targets = get_input_output_conns(candidate_IDs, 'out', min_strength = 3)\n",
"conn_df = pd.concat([clock_targets, candidate_targets])\n",
"conn_df"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "3450d7b0",
"metadata": {},
"outputs": [],
"source": [
"#export table\n",
"conn_df.to_csv('clock_slnv_cand_targets.csv')"
]
},
{
"cell_type": "markdown",
"id": "22f44fa3-8ff6-4b68-ba04-6ef09713a8a8",
"metadata": {},
"source": [
"To determine whether these 6 neurons had similar patterns of connectivity to any of the other identified clock neurons, jaccard indices were computed between the 6 candidates and the identified clock neurons."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "cb56957d-7f8d-4d83-99c3-5e7e4eda18e0",
"metadata": {},
"outputs": [
{
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CRcsWU1rerAILlvxAy7UqNjC3X7s1K0pXcux2B3DO4CP4Q589aN5krco/jm06/pZP53zN4uVLVnvtuerQog2zF/3cBDRr0Vw6tGgdrVu70rqFc2nXvGVeG95CeZ2S/DKzevlKs9VxW84pwAvuPg+YY2YHAJjZNkAPoFP8fHdgXXd/6hd+3tHAsIzndwGLgBKi3svrAH8vW2lmnczsA6JG60VEvasriBuN/wS8kEWersB3wEVm9o6ZTTaznarb0MzOMLNvy77+cdsdWfz4an9QhacZ7zGVtsvcpuJGBXFchpAjhAygHDX/iPwLIQNU/YhXQxBbRQ6AFStWcOkFf2WrbbdmwA4D6q/ALISQAQjmmAoiRwgZIIwcIWQA5aj5R+RfCBkgjBwhZADlqPlH5F8IGSCMHCFkAOWo+UfkXwgZIIwcIWSAqg0BNV5HsGq3adWmNY+NfIo77r+b62+7iY8/+IgnH3l8dZS6SpVzODXkyNghmfujVevWPPL8k9x+311c+7cb+eTDj3jq0Seq+xGrTeXGx+qu10DFrJU3ady4McMevp9nXx5Jxw07MvKZ5+q7zF9UpepqjvMG1oCN23Xi8ffHcN344SxY+gMHbblzle222XBz3pj28Wqpsz5kHmfZ7r+8CuR1SiSf6rWB2cwuALoDF8aL9gH+ZGbvAScC/wR+MrN1iYadPukXfl4/YG3gpYzFQ4hee9sDHYh6Rf+1bKW7T42HyG4PrAHsX+lnNgYeB15x9+eziNWYqKfzp+7eF/gL8JiZta28obvf5O7rl32dcPKJWfz4ioqK2jF3TgkrVqwo+5nRHWPt21fcrn1RhSFYimcXl9+RVdS+iNmzfr67Zvbs2bRt15YGeRzmI4QcIWQA5SikHCFkAGhXVMTckrmVcpRUuCu0ulqLi4srbLNixQouOf9iWrduzSlnnpaX2suEkAHCOaZCyBFCBggjRwgZQDkKKUcIGSCMHCFkAOUopBwhZIAwcoSQAZSjkHKEkAHCyBFCBoC27doxr2QuKyvkmEu7onYVtmvXvoji2T/XOqd4Du2KohxNmjShZauWAKy9ztrsvtcefPzBR3lKEGnbrh1zK+WYO6eEdu0r5SiqLke0TeUcu+65B5/kMUe7onbVXtNpV801nYoZiqtsA1FD82577sEro19ZvYVXsmDJD6y7VgsaZLRKtlyrBQuWLqq03SK+nDedhcsWA/DujE/p2LJDhW26tl6fJg0b89mcb1Z/4bUw+4d5rLf2z80pHdZuw+wfvovWLZrHeuv8vG69ddpSsnhBjTc+rA6hvE6J5Fu9Hd1mdhZRY+7u7r4EwN0/cvfd3b23ux8BrAd8CmxG1Dj8lplNBQ4ELjOzyyr92GOA4fGQ1mWOB56Nh+ZeDjwMDK5cj7svBh4DDsuosTHRXM6zgVOzjDaNaJjth+Of+yHwDfDbLL8/Jy1btaLHxj0Y89IYACaMHU/7Dh3osF7FN41BOw5m4vhJzP9uPu7Oc08/y067DAFgm+235bNPP2PaN1MBeObJpxmyS9W7mlanEHKEkEE5CitHCBmiHC3pvlEPXh0d5Zg4bgLt12tfJccOgwcxecLE8hzPP/0cO+0SDQBR1uu3xdprc/aF5+Z9OJAQMkBIx1T6c4SQIZQcIWRQjsLKEUKGUHKEkEE5CitHCBlCyRFCBuUorBwhZAglRwgZohwt6dajO6++HM2pOmn8RNp3aE/7SjkGDN6B1ydOLs/xwjPPM3jnHYFo7uCyBqzly5czecIkum3UPYEc3crnhp08fiJFHdpXGPY3yjGQ1ydNZsH8KMeLz45k0JDqc/xzwiS69shfjpatWtJto+68+nLUIFzTNZ2BOw5i8oSfj6mRzzzPjjtH13TmFM9h6dJoHuPS0lImvDaOrt265i0DwOLlS/j2+zn03SBqZui5Xg/mL1nE/CUVG5jfn/k5HVt2YM1GTQDYpKgzsxZVHNJ82w03581pH+e1UTYXr015ix27b0WrpusAcNCWQxjz+b8AeP2bD9msfVc6tVoPgEN67lK+Ll9CeZ2S/NIczGD1MfyAmZ1B1JA7xN0XZCxv7/HcyWb2Z+A4YCuv9EvN7AHgHXfPHOq6OTAL6OPuX2Ys/xtQNsQ1wB1AqbufZGZdgenu/pOZNQFGAF+6+4XxfM2PE/V4/lPlGjJ+vgMt4gbqsmWvALe4+0tmtiHwDrCF/8JczPMWz6/VP+60qdO46tIrWbRwIU2bNeOiyy6mS9cuXH35UPrvMKB8CNaRzzzPiOEP4e707tuHs88/h0aNo3kCJk+czB23/p2VK1fStVtXLrrsrzRr3qw25dRaCDlCyKAchZWjkDKsrMMcINOnTuPqy69i4cJFNGvWlAsuuYjOXbtw7ZVX029Af/rHOV54diSPPDiC0lKn91a9OfO8s2nUqBGvjB7DlX+9nK7du5W/jW625Racce6Zta4pzRka1uFuxkI6puoihBwhZAglRwgZlKOwcoSQIZQcIWRQjsLKEUKGUHKEkEE5CitHCBlCyVFIGZb9tLzWOWZMm861V1zNooWLaNasGef+9Xw6denMDVddx/YD+rH9wH4AjHruBR4b8SheWkrPPr057dwzaNSoEZPHT+KBe+6jQYMGrFy5kl59enHcKSfSpEmTnGsprcM1/RnTpnP9ldewaOEimjZryjkXRzluHHod2w3ox/YDohwvPf8ijz/0CKXu9OzTi1PPiXNMmMSD99xHgwYNWblyJT379OLYk0+oVY7GDRvWKsP0adO55rKh5cfU+ZdcSOeunbnuymvoN7A//Qb2B+DF50byyIOP4KWl9OrbmzPOO4tGjRrxxuv/5u7b7wSgtNTpsVEPTjr9ZNZZd52ca7l63AO1ygDQrnkrDuuzO82arMWyn5Yz4t1RFP/wHYf22o2PZ/+XT4r/C8BWG/yWIT22odRL+X7pDzz+/hi+j3s0r9GoCVfsdiLXjruf75YsrHUtE796r1bfd/5ORzG4W19aN1uX75f+wJLly9hr2GlcssuxTPjqXSZ+9S4A+2++I0dtvTcNzHhr+n+46rVhrCiN+hXu0LUPpw/8Pxo2aMh/503notH/4MflS3OuZezxtZyylMJ6nWrTvFW6Wx1/JcZNeate7ujYscfWqd3fdW5gNrP1gRnA18AP8eL/ufs2ZnYJUcOzAZ8BJ7n7jGp+xgNUbWA+Bjjc3XeotG0ronmVNyUaKvtT4Dh3nx9/z+nASqARMA44292XmdlhRA3OH/Hz9Aavu/tJ8c8dCfQGfkPUsP2luw+K13UB7gNaxz/7Mnd/9pf+bWrbwCwikg91aWCW+lWXBmYRERERERERyU5dGpgLSV0amAtJbRuYC0ldGpgLSW0bmAtJXRqYC4kamNNh/Jf108A8uPuvuIFZaqYGZhEpZGpgLhxqYBYRERERERFZ/dTAXFjUwFw41MBcONTAnA4Tvny7Xl6IB3XfKrX7W1e0RUREREREREREREREREQkK42SLkBEREREREREREREREREJBVS2++4/qiBWUREREREREREREREREQkC6YWZjUwi4iIiIiIiIiIiIiIiIhkQw3MmoNZRERERERERERERERERESypB7MIiIiIiIiIiIiIiIiIiLZUAdmNTCLiIiIiIiIiIiIiIiIiGRDQ2RriGwREREREREREREREREREcmSejCLiIiIiIiIiIiIiIiIiGTB1IFZPZhFRERERERERERERERERLJh9fRfVr/LrLuZ/cvMppjZW2a2aTXb7Ghmb5rZp2b2iZldZbZ6m8HVwCwiIiIiIiIiIiIiIiIikhWrp6+s3AXc7e49gOuAYdVsswA41N03BfoCOwCH1iJY1tTALCIiIiIiIiIiIiIiIiJSQMysHdAbGBEvehrobGadMrdz9/fd/ev48TLgA6DL6qxNDcwiIiIiIiIiIiIiIiIiIlkwq68vO8PMvs34OqPSr9oAmOXuKwDc3YHpQMeaa7P2wIHAS6srP0Cj1fnDRURERERERERERERERERCke38yb/E3W8Cbvqlzar8+hqY2drAC8B17v5eHctbJfVgFhEREREREREREREREREpLDOA9c2sEYCZGVGv5umVNzSzFsDLwMi44Xq1UgOziIiIiIiIiIiIiIiIiEgW6muI7F/i7iXA+8Af4kUHAFPdfWrFeqw5UePyGHe/ol7D1kANzCIiIiIiIiIiIiIiIiIiWbF6+srKccBxZjYFOA84BsDM7jWzveNtTgW2BvYzsw/irwvrGHKVNAeziIiIiIiIiIiIiIiIiEiBcfcvgO2qWf6njMdXAVflsy41MIuIiIiIiIiIiIiIiIiIZMGy730cLDUwi4iIiIiIiIiIiIiIiIhkIZv5k0OnBmYRERERERERERERERERkSyoBzM0SLoAERERERERERERERERERFJB/VgFhERERERERERERERERHJhjowq4FZRERERERERERERERERCQbGiIbzN2TriFY8xbP1z+uSCV6yZH6VuqlSZdQZw0sjBkrQtgXEMb+WL5iedIl1IulAeQI5c+NddZqnnQJ9aI0gA8iofz9tmjZj0mXUGdNG6+RdAn1YnnpiqRLqBcNrWHSJdRZ44bpzwCwsjT9nwmdMF5r1wrkdUpERCQbO915YtIl1IsPz3oslEsJQXtn+n/q5QNj346/Te3+Vg9mEREREREREREREREREZEsWGqbheuPGphFRERERERERERERERERLKgIbLVwCwiIiIiIiIiIiIiIiIikiU1MKd/kkEREREREREREREREREREckL9WAWEREREREREREREREREcmC5mBWA7OIiIiIiIiIiIiIiIiISFY0B7OGyBYRERERERERERERERERkSypB7OIiIiIiIiIiIiIiIiISBY0RLYamEVEREREREREREREREREsqQWZg2RLSIiIiIiIiIiIiIiIiIiWVEPZhERERERERERERERERGRLJh6MKuBWUREREREREREREREREQkG5qDWQ3MIiIiIiIiIiIiIiIiIiJZUQ9mzcEsIiIiIiIiIiIiIiIiIiJZUg9mEREREREREREREREREZFsqAOzGphFRERERERERERERERERLKhIbI1RLaIiIiIiIiIiIiIiIiIiGRJPZhFRERERERERERERERERLKgHsxqYBYRERERERERERERERERyY7al9XALCIiIiIiIiIiIiIiIiKSDfVg1hzMIiIiIiIiIiIiIiIiIiKSJfVgFhERERERERERERERERHJgqkDsxqYRURERERERERERERERESyoSGyNUS2iIiIiIiIiIiIiIiIiIhkSQ3MIiIiIiIiIiIiIiIiIiKSFQ2RXYBmTJ/BlZdczsLvF9K8RXMuvPRiOnfpXGW7F54byYgHHqK01Om7dV/OPO8sGjWKdunrk/7J32+5jZUrV9KtR3cuuuximjZtqhy/wgyh5bjq0sv5/vuFtGjenAtqyPHicyMZMTzK0Wfrvpx5bkaOyf/k9rIc3btzYQLHVNozhJJjxvQZDL30ShYuXEjz5s254JIL6VRdhudf4OHhI/DSUvps1ZfTzz2TRo0a8dV/v+Lm627k+/kLaNioEZttvhmnnn06TZo0yVuGshx12RdLlizhonMu4IvPPgdg1NiX81p/mRD2Ryj74tvp33LNFUNZ9P1CmrVozrkXn0+nzp2qbPfSyFE8+uDDlLrTu29vTjv7dBo2+vmjpbtz1sln8NWXX/HcmJF5TAAzZ3zLTVddz6L4fe/0C86mY+cNq2w35sXRPDXiMUpLnS379OKkM0+hYaOGADz9yJOMHf0KDRo2oEmTJhx/+l/osclGieQoe/8+YxU5noxz9Kwmx2ujX6FhwwY0btKEExLIEcLnkOg16ory8/v8Sy+q4fx+gYeHP0RpaSl9tu7LGZXO7ymffRFtN3Z03mrPFMJr7cwZM7l56PXlr1Gnn39WtefFKy+O5smHH8dLnS379OTEMzLPiycY+/KreKnzm47rc9r5Z9G8RfO8ZYAwXmshep264YprWbRwIc2aN+fMi85hw2pyvPzCSzzx0GN4aSk9+/bmL2edSsNGDXnv7Xe59+93lW/3/YLvadmqJbc/cFeVn7G6fDvjW66//Ory8+Lsi8+rNsPokaN47KFHcHd69e3NKWedRsNGjSiePZsjDjqswrl0ydDLWW/93+QtA8C302dw9WVDWfj99zRv0YLz/np+tef3qOdf5JEHH8ZLS+m9VR9OO+cMGjVqxOyZs7jk/ItZubKU0tJSOm7YkbMuOIcWa7fIY4ZvufaKoeXvF+es4rx4LOO8ODU+L4pnzeaPBx1W4fX50quT2Bffct0VV2fkWMUx9eAjlMbH1Klnn1ae4/BKOS5JIEco799pzxBKjhAyKEdh5QghQyg5Qshw7o5HsEPXvvxmnbYc8MBZ/Hfet9Vut99mgzlqm71pYA14c9onDH1tGCu9FICBXXpzxg6H0bBBQ6bMncZFo+9g6U//y1sGyT/TJMz104PZzKaa2edm9kH8dUil9UeYmZvZnhnLmprZo2b2XzObYmb7Z6wrMrNnzOyj+OeelrGumZndb2Yfm9kXZnaNxXvSIteb2X/i7x1vZt0yvvcsM/skrvENM9sqY90f4u/5wMzeN7Pd4+VrmtlzcY0fmNnLZtapPv7danLdVdey93778tizT3DY4X/g6suHVtlm1sxZ3POPe/jHsLt44vkn+e6773jx+RcAWLJkCVdfMZRrbryWJ55/itZtWjN82AOrs+RqhZAjhAwQTo7rh8Y5nnmC/zv8D1xzRQ057ryHO+69i8efe5L58yrmuOaKoVx947U8/lyU48H7HlCGWgghxw1XX8fe++3NI08/xv8dfhjXXnl1tRmG3XkPt9/zDx599gm+++47Ro18EYAmTZpw+tlnMOKpR7nv4QdYvHgxjz/8aF4zQN33RaNGjfi/ww/jljv+lu/SKwhhf4SyL2669gb23GcvHnzyYX7/h0O54aprq2wze9Zs7r97GLfe9XdGPPUI87+bz0svvFRhm2effIb2Hdrnq+wK/n79rey29x7c89gDHPB/B3PrNTdW2aZ41mxG3PMA191xC/c+PpwF8+fzyotRo9/XX37FC08/x413/Y2/P3AXex6wD/+46bZ8x+C2OMe9jz3Agf93MLfUkOOhex7g+jtuYVicY0xGjpFPP8dNcY69DtiHOxLIEcLnkBuGXste++3Do888zqGHH8a1NZzf9955D7ffeyePPfck8+fNZ9Tz0WtUdH7/gZvvuDWvdVcWwmvt7Tfcwm577cHdj97PAYcexK3X3lRlm+JZsxlx73Cuu/1m7nnsARbMX8Aro6Lz4v2332XcmNe44c5b+ceIe+nSvSsP3XN/XjNAGK+1AH+79mZ23+d3DHv8QQ467BBuHnpDlW2KZ83mwXse4MY7b+G+Jx9i/vz5vPxilKP3Vn24Y/jd5V/denRjx112ymuGW6+9kT323ZMHnhjBwX/4PTcOva7KNrNnzeaBe+7jlrtuY/iTDzP/u/mMztgXzZs3564Hh5V/5bshEODGq29gz/32YsTTj/L7Px7KdVdWc0zNnMV9d93LbXffzsPPPBYdUyNHAdC6bRtuu/sOhj18P/c/Opw27dryYJ5fa2++9gZ+F58Xh6zivHjg7mHcctffeaia86J58+bc/dCw8q8k9sUt197I7/bZk+FPjuCQP/yeG66q4Zi6OzqmHnzqYRZUd0w9NKz8K4kcIbx/h5ABwsgRQgZQjkLKEUIGCCNHCBlenfImRz56CTMXzq1xm9+s05YT+x/EkY9eyp73nkqbZuuw3+aDAVir8RpcuuuxnPb8jew17DTm/fg9f952v3yVLwmxevovzepziOwD3b1n/PV42UIzWx84Dnij0vZnAf9z927ArsAdZtYyXncT8LG7bwH0BY7OaAy+IP7/FsBmQC/gwHjZ3sBAoGf8vWOBoXEdWwInA9u6e0/g78Dt8bpWwB3ArvG6k4HhGbXeDWwUr3sxfr5aLJg/nymff8Gue+wKwKCdBjN71ixmz5pdYbvxY8exw+CBtGrdCjNj3wP247UxrwLwxuv/ZuNNNim/Q3b/gw4oX5cvIeQIIUOIOXbZfdU5Jowdx8CacvwrztEpyrFfQsdUmjOEkmPB/AV8+fkUdo4z7LDjIGbPnF0lw8Rx4xkwaIfyDPscsC9jx7wGwAYdN6Br9+gepoYNG7Lxppswa+asvGWIctR9XzRp0oS+W29F8xb5651SWQj7I6h98cWX7LzbzgAMHLwDs2cVU1xlX0yg/w4DynPstf8+jHtlbPn6b6d/y/hXx3HoHw/La/0A3y9YwFdTvmTHXYYA0G/QAIpnFzNndnGF7V6fMJntBvajZauWmBl77LsnE18bX75+5YoVLFu2DIAfFy+mdds2+QtB9TnmVJPjnznmaJPnHCF8DokyTKl0fld9jZowdnyF83ufA/atdH73Tf78TvlrbXRe/JfBcQNkTedF5fN79332ZOJrEwD45r9f89stNivvEbH19tswbsxY8imE11qA7+cv4L9TvmSnXaMc/QcPZM7sYoor7Y/J4yex/cB+tGwV5fjdvnsx4dXxVX7ed3Pn8eG7H7BT/O+SD9G+mMKQOMOAwTtQPGs2xbMr7ovJ4ybSb+CA8gx77rc341/N73GzKgvmL2DKF1PYebddgPj8ruZ1auK4CQwY9PPr1N7778PYV6Lzu0mTJqyx5hoArFy5kqVLlmAN8jerWnXnRXE158WkcRPoV+m8GP9KYe2LL7+YwpDdKh1TVXJMpN8OA2gZ59hz/70rnN9JC+f9O90ZQskRQgblKKwcIWQIJUcIGQDe+/ZzShbPX+U2Q3psw7gv32b+koUAPPnha+y2ST8A+nfuyX/mfM3U+dHfRo9/8Aq7bdxv9RYtUgDy8dfC3cDpQOXxAA4hbuB192+AScA+8botgVHxusXAROCPGetGe+Qn4JWMdQBrAGvGvZrXBjLHM2gMNIsfr5uxrgFgQPPK69x9mbu/5O4er3sD6JJ1+hzNmVNCm7ZtyoeHMDOK2hcxp7jiH+lziufQvkOH8ucd1uvAnOI5Get+vou9Q4cOzC2ZS2lp6eoqu4oQcoSQAQLPUVRDjvY/52i/XgfmzPk5R1FmjvUK4JhKWQYII0fJnDm0rpShXfui8mO+THXHfuVtAJYuXcqo519g+wH9V2/hldTHvigEIeyPUPbF3JIS2rRpXT78arQv2jFnTkmF7UqK51DUvqj8efsO7SmJc5SWlnLj1ddz6tmn0Sgejjaf5s6ZS6s2rcuHwjUz2hW1Y27lDHNKaJeRoah9e0ribbp078q+hxzIMQf9kcP3O5TnHn+G40//S/5CUH2OtkXtymv8ebuqOeZm5NjvkAM5+qA/8sf9DuXZBHKE8DmkZE5Jldeoms7vovY/11lo53cIr7XzSubSqnXV86Ly+T13zlzaFmWeF0Xl23TbuAfvv/MeC+YvwN0ZP2YsS5cs4YdFi/KWI4TXWoC5JXNpXc3r1Nziqq9TmTmKOhRV2WcAr45+hb7bbc26rVpWWbe6zC0poXWbNhX3RVERJcWV3zPmUNSh8r74eZslPy7hpKOP44Qj/sxDw4azcuXK/AQor6+ENm1bV3mtLanm/K5yTGVs89NPP3HMYUexzy57MvPbmRxxzJF5qR/K9kXV86Ly+17l86Io47wAWPLjj5x41LEcd/ifeHDYA3nfF9UeU+2LfjFHdcfUiUcdx/GHJ3NMhfD+HUIGCCNHCBlAOQopRwgZIIwcIWTIVocWbZi9aF7581mL5tKhReto3dqV1i2cS7vmLVPfO1VWzax+vtKsPhuYH46Hrb7XzNoCmNkJwH/c/c1qtu8ITMt4PjVeBvA28H9m1sDM2hH1cO6Use5gM2tiZi2A/TLWvQCMB4qB2cBOwF8B3P1Dop7R35jZt0SN3ifH6+YBxwPvmdk04D7gyBpynhL/ntWn0lFV3rRdZbvMbSpuVBAHZgg5QsgA4ebIYrOCyxFCBggiR5VfX8OJsaoMACtWrODSC/7KVttuzYAdBtRfgdmqh31RCILYH4HsiyonZ001ZmyXmeOJhx9ji15b0K1H99VRXVYq/xFX07+z1ZChpHgOb77+b+59fDgPPvso+x6yPzdcVnUo4dWtrjnmFM/hjdf/zbDHh/PQs4+y3yH7c30COUL4HGJVzu9f3hc1B01OCK+1lfdFVhky9tcWvbZkv0MO5LJzLuKs40+lZevoolDmvMZ5EcBrLZDD+W2/uM2ro15m1z13r6fCspf1+U31+6JV69Y88vyT3H7fXVz7txv55MOPeOrRJ1ZPsatQu/eMiusaN27MsIfv59mXR9Jxw46MfOa5+i5zlbI/v6t/rW3VpjWPjXyKO+6/m+tvu4mPP/iIJx95vJqfsHpVOaZyPL9btWnNoyOf5I777+K6227k4w8+4qlH8n9MhfD+HUQGCCNHCBlAOWr+EfkXQgYII0cIGbKU+Tkx289eEjKrp6/0qq8G5oHuviXQG/gOGG5mnYE/Ezfw1iDzrMv8lzyTqPfxe8CDwDjgp3jdtcAM4C1gJPCvjHW9gY2B3wDrEQ2R/XcAM9uQaAjtru6+PnAz8HC8bm3gRKCvu28IHAM8ZWYVrjKY2QVAd+DC6sKY2Rlm9m3Z1z9uu2MV0atXFN+Bv2LFCiB6YSqZU7E3BER34WcOsVQ8u7j87tei9kXMnvXzXUKzZ8+mbbu2NMjj8FYh5AghA/w6c8zOGNJuzuxiiop+zlGcmWNW4R5ThZoBwsjRrqiIuSVzK2Wo2LumrM7MY7+4uLjCNitWrOCS8y+mdevWnHLmaXmpvUJ99bAvCkEI+yOUfdG2XTvmlcxlZYUccykqaldhu3btiyoMSTuneA7t4hwfffARY0a9zKH7HsIpx53M4h9+4NB9D+GHRT/kJ0NRW+bNncvKFSvLM8wtmUvbyhmK2lXIUDJnDu3ibSaPm8SGnTvRqk3U8LTzHrvyyYcf57X3UHU55pXMLa/x5+2q5ijL+s9xk+iUcI4QPoe0qzZDSfUZZlfKUEDndwivtW3aZXd+ty1qW6FXZklxSYVt9th3T26593ZuvOtvbNZzc9q0a1M+ZHY+hPBaG+Voy7ySeZVep0po2/4XXqeK51TZZx+//xH/W/Y/+mzTd/UXnllbu3bMrbQvopEhKr9nFFUY+jvaF9E2TZo0oWXc63rtddZm1z334JMPPspTgrL62lV7frer5vyumKO4yjYQNTTvtucevDL6ldVbeIaazovK73vtqmT4+byovC9232sPPs7zvqjxmMrq/K7+mNotgRwhvH+HkAHCyBFCBlCOQsoRQgYII0cIGbI1+4d5rLd22/LnHdZuw+wfvovWLZrHeuv8vG69ddpSsnhBjTcuioSiXs5Sd58e//8n4BZgALAdUSPvZ2Y2FdgWGGZmf46/bTo/9zwG2DBehrvPd/ej4/mcd4vXfxqvW+bup8frBgPzy9YR9Toe7+7fu3sp0TzKg+N1BwGfuHvZK9n9wEAzawjsAix09y/i3/EC0BLYoKw4MzsL2B/Y3d2X1PDvcJO7r1/2dcLJJ2b3D5ihZatW9Ni4B2NeGgNEc7m179CBDut1qLDdoB0HM3H8JOZ/Nx9357mnn2WneI6+bbbfls8+/Yxp30wF4Jknn2bILvmbxyqUHCFkCC1H94168MroVefYYcfBTKqUY0icY9vt4hxToxzPPvk0O+X5mEp7hlBytGzVku4b9eDVOMPEcRNov177qhkGD2LyhInlGZ5/+jl2iud9LOu91WLttTn7wnOr9rjIg/rYF4UghP0R0r7o1qM7r74czXc0afxE2ndoT/tKOQYO3oF/TpxcnuOFZ55n8M47AjD0xmt47PknefS5x/nbXbfRvEULHn3ucVqsnZ+5Z9dt2ZKu3bsxLp5T8vUJkylqX1RhWH6A7XcYwL8nvV4+TO5Lz73IwCGDAGj/m/Z8+vEnLF2yFIA3X3+DDTbsSMOG+RuGtroc7arJ0e8Xcvwn4RwhfA7J9vwetOOgCud39BpVWOd32l9r123Zki7du5XPt1rT+d1vUMXzYvTzLzJwp0Hl6+fPiy4ELVu2jIeHDeeAQw/OWwYI47UWYN1WLenaoxtj4zny/jl+EkUd2lcY4hCg/6AB/GvS6yyYH+UY9dwLDBoyuMI2Y0aNZsgeu+b19QnK9kW38nn+Jo+fGGeouC8GDB7I65Mml2d48dmRDBoS7YsF8xeUX1Rdvnw5/5wwia557lneslVLum3UnVdfjhqEazq/B+44iMkTfn6dGvnM8+y4c3R+zymew9Kl0ftFaWkpE14bR9duXfObIYvzYsDgHXi9hvOi8r6YPGES3TZKYF/06MZrL1c6pqrkGMjrEyezIM7x4jMja8zxz0RyhPH+nfYMoeQIIYNyFFaOEDKEkiOEDNl6bcpb7Nh9K1o1XQeAg7YcwpjP/wXA6998yGbtu9Kp1XoAHNJzl/J1Ei71Xwara9d9M2sGNHb37+PnZwD7uvvASttNAG5w9xfj55cCndz9yLi38xvAJu4+38xaA4vc/Scz6w28BPRy99lxb+MV7r4k/r4JwD7u/kH8u3cF9oy/9zxggLv/zsz2By4Ftnf3xWb2e+Bid/9txu/Ywt1LzGw74EWgg7svj3/uYcAQd1+Q7b/NvMXza/WPO23qNK669EoWLVxI02bNuOiyi+nStQtXXz6U/jsMKB+GbuQzzzNi+EO4O7379uHs88+hUeOo0/XkiZO549a/s3LlSrp268pFl/2VZs2brerX1rsQcoSQodBy1OUlZ/rUaVx12ZUsXLiQZs2aceGlUY5rrhhK/4ED6F+W49nneXj4Q5S606dvH846/5zyuUD+OXEyd/wtytGlW1cuujS/+yOEDIWWo9RrNyfL9KnTuPryq1i4cBHNmjXlgksuonPXLlx75dX0G9C/PMMLz47kkQdHUFrq9N6qN2eedzaNGjXildFjuPKvl9O1e7fyDwObbbkFZ5x7Zs61NLDa3+9VH/vi6MOO4Lt537FgwQJat2lN7z59uPiKS3Kupbb7oixH2vdHIe2L5SuW1yoDwPRp07nuiqtZtHARTZs149y/nk/nLp254arr2G5AP/oN7AfAi8+9wGMjHsVLS+nVpzennXtGeY4yxbNmc/xRx/HcmJG1qmVpLXN8O30GN191fZyhKWdceA4bdunErdfcyDb9t2Pb/tsD8PLIl3jq4ccpLS1lyz49OemsU2nUqBHuzvC77uPfk16ncePGrNV0LY4//S907dEt51rq8sfCt9NncFNGjjPjHLdccyPbVsrx5MOP46WlbNGnJ3/JyPFApRwn1DLHOms1r3WOQvocUlrLDyLTp05j6GVXxq9Rzbjw0ug16porrqb/wP4Vzu9Hho+gNM5w1vlnZ5zfR/LdvO/4Pj6/e/XpXavzuy5/vxXSa+2iZT/WKsO302dw89Ab+CE+L06/8Gw27NyJv11zE9v0345t+m8HROfF0488EZ3fvXty4lmnlO+Lk444Fi91flrxEzvuMoTfH3lYrRrLmzZeo1YZoLBea5eXrqh1jhnTZnDjldfyw6Iox5kXnUunLp24+eob2Lb/9mw3IHqdGv38KJ4Y8RjuzpZ9enLy2aeV51jy4xIO2+dg7hh+Nx1+s16ta2lotWucnjFtOtdfeU35a+05F59Ppy6duXFotC+2HxDti5eef5HHH3qEUnd69unFqedE+2LyhEk8eM99NGjQkJUrV9KzTy+OPfkEmjRpknMtjevQwD592nSuuWxo+Wvt+ZdcSOeunbnuymvoN7A//QZG86W/+NxIHnnwkeiY6tubM847i0aNGvHG6//m7tvvBKC01OmxUQ9OOv1k1ll3nZxrWVnLORNnTJvOtfF50Sw+LzrF58X2A/qxfXxejMo4L3pmnBeTx0/igXvuo0GDBqxcuZJefXpx3Ckn1mpf1KXH0Yxp07nuimviHE05J85xY3x+/5zjRR4f8QilpU6vPr04NSPH8ErH1HGn1O6YWqsOr1OF9P79a84QSo4QMihHYeUIIUMoOQopw0535t7hD+D8nY5icLe+tG62Lt8v/YEly5ex17DTuGSXY5nw1btM/OpdAPbffEeO2npvGpjx1vT/cNVrw1hRGo3ms0PXPpw+8P9o2KAh/503nYtG/4Mfly+tVT0fnvVY2tsdfxW+njejXrqod2mzQWr3d300MHcBngYaEl1D+xo41d2nVtpuAhUbmJsRzXXcBygFLnD3p+J1uwO3EQ19/QNwlrtPitf1BJ4AVsRfl7r7M/G6NYiGxB4ALCeah/k4d59q0RWDoURzNv8v/rknu/v78feeChwX/86fgHPdfayZrU80JPfX8fcA/M/dt/mlf5vaNjCLhEzTUUh9q0ujZqGoSwNzIQlhX0AY+6MuDcyFpLYNzIUktX8lVFKXBuZCUtsG5kJS17/fCkVtG5gLSV0amAtJXRqYC0ltG5gLSV0amAtJbRuYC0koQ1rWpYFZREQkbWrbwFxo1MCcDmpgrocGZqmZGphFqtJLjtS3EBo1Q2jQhDD2BYSxP9TAXDhS+1dCJWpgLhyh/P2mBubCoQbmwqEG5sKhBmYREZH0UQOz5NM3876tlw+Mndusn9r93eiXNxERERERERERERERERERkWB6FNSBGphFRERERERERERERERERLJgamEm/WNAioiIiIiIiIiIiIiIiIhIXqgHs4iIiIiIiIiIiIiIiIhIFtR/WQ3MIiIiIiIiIiIiIiIiIiJZMVMTs4bIFhERERERERERERERERGRrKgHs4iIiIiIiIiIiIiIiIhIVtSDWQ3MIiIiIiIiIiIiIiIiIiJZ0AjZamAWEREREREREREREREREcmKqQez5mAWEREREREREREREREREZHsqAeziIiIiIiIiIiIiIiIiEgW1H9ZDcwiIiIiIiIiIiIiIiIiItnRJMwaIltERERERERERERERERERLKjHswiIiIiIiIiIiIiIiIiIlkwDZKtBmYRERERERERERERERERkWxohGwNkS0iIiIiIiIiIiIiIiIiIllSD2YRERERERERERERERERkSxoiGw1MIuIiIiIiIiIiIiIiIiIZEfty2pgFhERERERERERERERERHJhnowaw5mERERERERERERERERERHJknowi4iIiIiIiIiIiIiIiIhkQf2X1YNZRERERERERERERERERCQ7ZvXzldWvsu5m9i8zm2Jmb5nZpjVsd4yZfWlmX5nZ3Wa2WjsZq4FZRERERERERERERERERKTw3AXc7e49gOuAYZU3MLPOwBVAf6Ab0B44ZnUWpQZmEREREREREREREREREZEsWD3994u/x6wd0BsYES96GuhsZp0qbXog8Ky7z3F3B+4EDq2/xFWpgVlEREREREREREREREREJAt5HCF7A2CWu68AiBuPpwMdK23XEZiW8XxqNdvUq9U6/vavXZvmrVb7PN9mdoa737S6f8/qFEIGUI5CEkIGCCNHCBlAOQpJCBkgjBwhZADlKCQhZIAwcuQjQ9HabVbnjwfC2BcQRo4QMkAYOULIAMpRSELIAGHkCCEDKEchCSEDhJEjhAyw+nN8eNZjq+tHVxDK/pC6qa/2PzM7AzgjY9FN1RxfXvnbavhxnsU29caixm5JKzP71t3XT7qOugghAyhHIQkhA4SRI4QMoByFJIQMEEaOEDKAchSSEDJAGDlCyADKUUhCyABh5AghAyhHIQkhA4SRI4QMoByFJIQMEEaOEDKAcojURjxE9pdAa3dfYWYGzAa2dfepGdudDXRy95Pi53sA57j7oNVVm4bIFhEREREREREREREREREpIO5eArwP/CFedAAwNbNxOfY0sJ+ZFcWN0McDq7VbvxqYRUREREREREREREREREQKz3HAcWY2BTgPOAbAzO41s70B3P1r4BLgdeAroAQYtjqL0hzM6RfCWP8hZADlKCQhZIAwcoSQAZSjkISQAcLIEUIGUI5CEkIGCCNHCBlAOQpJCBkgjBwhZADlKCQhZIAwcoSQAZSjkISQAcLIEUIGUA6RWnH3L4Dtqln+p0rP7wHuyVddmoNZRERERERERERERERERESyoiGyRUREREREREREREREREQkK2pgFhERERERERERERERERGRrKiBWUREREREREREREREREREsqIGZhERkUrM7C9m1jLpOkRERERERERERERECo0amEWknJn9NukaRApEX+BLM3vCzHYzM0u6IBEREREREREpTGbW0cz6x18dk66nrsxsUzM73Mx6Jl2LhMHMWpvZQDMrSroWEakfamAOgJmNS7qGbJnZumZ2l5m9YmZ/qbTu6aTqylX8ofHpuPGpvZndbmaLzGyymW2YdH3ZMLOmlb+AF81srfixyK+Wux8JdALGABcA083sqiRrEhERERERKVRmtmXSNeTCzJqY2XrVLE/Njfdmtk7ZyFtm1tLM9jOzjZKuq67M7JKka8iFmW1sZv8C3gRuBG4C3jSzf5nZJslWlz0zG1fW8GdmBwOvAL8DnjWzPyVaXA7M7KCMx23MbJSZLTSzCWlp+I+vO48xsylmdoOZrZmx7t9J1pYLM3sw45jaEfgMuB74yMz2TrS4LIWyL0RWFzUwp0R1jYEZjYLdk64vB3cCC4G7gP3jRtqG8bouyZWVszuBScDHRB+4ZhHth6eBmxOsKxeLgR/i/5d9bQj8GC9PLTMbnXQNdWVmW5vZ6WY2KOlacmFm25jZ2vHjNc3smviPlJvNrEXS9eXC3Re7+zDgIGAUcF7CJdUbMzs+6RqyZWbbmdn/mVn7SsuPSKqm2jCzPc1sj/hxfzO71cyOSbqu+pKmC4ohXEyEMC8opu1i4i8xs+FJ15AtM2tsZueZ2d1mtmeldbclVVd9SdtNYmb2ezO7oHKPITM7P6GSchIfT38xsxPNrJGZHWxmz5vZFWbWJOn66sLMPk66hlyZ2RqVnh8Yfw45MqGS6pXO74LyQtIFZMvMBgPFwKdm9q6ZdctY/VBCZeUkbgCcDnwdN6hNBI4FJpjZ/okWl4P4vaLCF/CXjMdp8ABwo7t3cPdt3H1rd+9A1NCcms+DQFt3nxM/Pg3Yzt0PAXoDpyRWVe4yX0+HEl273QgYCdyaSEW5u5Oo3kOBtsDYjGtqa9b4XYWnV8YxdQkwxN23AbYGLk2sqtyEsi9EVgtz96RrkCyYWSngQOYwrWXP3d0bVvuNBcbMPnD3nvHjBsA/gPWA/YG33L1XguVlrSyHmRkw293bZ6z70N0L/kK7md0HlAKnu/sP8bJv3L1zspXlxsyeqGbx7sBoAHc/OL8V1Y6ZjXX3neLHBxDdqPASsCPRHyp3JVlftszsU6Cnuy83s1uBdYHHgN2ANu5+WJL1ZcvMGgF7A0cRffB9EnjA3d9JtLB6YmbT3b3g79y1aKSLU4HPgW2B49z9mXjde+7eO8n6smVmVwC7AE2AsUTH1ChgT+AVd78iwfLqRYqOqcFEN4M1AL4CDnH3/8br0nRMHQzcQ/Q+fixwMTAT6AmcVHaeFLIaLhheRnThAXe/I78V1b+0nBcAZnYn0Xv228CfgJfd/fR4XWrOjZqkbF9cCewAfAAcCFzj7rfG61KxL8zsH0A7YC2im4vXAB4n+puv2N1PTbC8rJnZW9Us3gL4CMDdt85vRbWTedyY2QnA8cCjRJ9DxqT9c4jO7/xaRYOfAZe7e+t81lNbZvYG0Weoj4GjiT5L7enun5jZ+2m4NmVm7xH1Ll0beAfY3t0/NrOuwKMpeo1aQXTt47uMxQcCTxFd7zw6kcJyYGZfuHu1N3qual2hMbMvgE3dfaWZ/dvdt8tY97G7b55geVnLPIfN7EOgt7uvLHuekmu2Fd4TzOwCYF9gZ2B8Gt4vAMxsirv3iB+/7e5bZaz7yN23SK667ISyL0RWl0ZJFyBZmw1s6e7zKq8wsxkJ1FNb5XdPu3spcJyZ/Q14jujCe1o4RJ90q7mLPRV3bbj70XEPlbFmdom7jyYltVcykKihZlL83IDB8bI0aZXx+DRgF3f/3KKhZMYQ9fpPg1J3Xx4/HgD0jc/10fEH+7SYCbxPdCfyge7+v2TLyV0NN19AdI60qmFdofkz0MfdF5nZpsBzZtbU3UdQ8YarQrcv0AtoStRTYgN3/87M7gBeB1JxYfcXLig2y2ctdXA1MIifLya+ZmZ7uvsnpOuYOg/YmBouKAIF38AM/I2qFxPXBLYiRZ9HzKykplVEDbZpsR3RDWIeNzY/ZmZ3uvvxpOTcqKExEKL62+Wzljram+jz0/K4Z+ZIM2vm7kNJyb4A+rv75hYNIVgCtHf3JWb2LPBuwrXlojnR+/RDRP/2RvQae3aSRdVC5nFzJLCHu880s9uBf5GCzyE6vwvKrcDDVP9enaZrOmu4+0fx42FmNpVoyrB9SM/nEHP32cBsM5vp7h8DuPtXZtY44dpyMQS4huiG7rKbiQe5+1HJlpWTeWb2R+Dh+PpHWaeaP1Lxs26he5ToM+C5wNNmdiHR+b478E2ileVmDYuGJjeia1QrM9al5fyuMHWhuw81s+VEN6ynaXTAMWZ2C9HUc6+Z2WHAI0SdUNJyboSyL0RWCzUwp8e/iO6Wrm6+5ffyXEtdTDOz7d39X2UL3P2UuJF5twTrytX/4j8Ef3T3ncsWWjRU5cpVfF9BcfcXLZov4u9mdgjpfE3YHLgd6AOcF1+8usTd0zQMEVT8kNvM3T8HcPc58QgGabHEzHq4+xSiD4vNgUUWDYeYpj9y+7p7mm7eqc7viG5WWF5pedlNGGlg7r4IwN0/tWjOnlctmlohLX8YAvzk7iuIzoUv3f07AHf/wcxS855BGBcUQ7iYCGFcUAzhYiJEr6k7EfXSrLz89fyXU2uNPR7ayt1/NLP9gCfM7J6E68pFD6Kh65ZUWm5EvWfTokHZzXruXmxmQ4hu1EvTe99PAO6+zMy+cvcl8fPlcU+1tOgFXAWcARwf74+l7j4x4bpylXncNHT3mVD+OSQt+0Pnd+H4DLja3b+ovCLOkxZrmNkaZTcSu/tYi6bgGUl6PtdmjmR4e6V1qbm24+4T4mPnpvi61Emk53wocwRRp4BbzWwWUf3rE920fmSCdeXE3S81s1OJhltvS3QunEPU8Jymz+hNiTqdGICZre/u35rZOkQjQKXBZ2a2m7u/XLbA3W+Irw/ekGBduToTuJaoE8d3wLlEHTnGEt3wnQah7AuR1SI1Hzh+7dz9oFWs2yeftdTRUVTTABs3Mj+WQD21tSOwtJrljYl63KVG3NBxqEVz9vyYdD25cve5wMHxXXATzewc0vfHCEDnuMepAb8xszXdfVm8Li1/4ELUo2OMmT0EfEjUQ34UUYPm/YlWlgUzG5jxuMpw8e4+qfKyAvYB8L5XM6y3RUM2p8EKM2vn7iUA8R+FOwGvARskW1pOMi/+lM9/bWZGus7vEC4ohnAxEQK4oBjIxUSIemO2zrhxoZyZFSdQT23NNbPN4t78uPsKi4Zif5zoJtc0eB9YmHkja5m4l0FaLDazTu4+FSAexWNXohFt0jJXvJlZw7jH0L4ZCxuRktcogPi94iwzGwC8EPf4TaON4h7ABnQ3sxZx47KRnhtAdX4Xjpup+TPTefkspI6eJRrVZkzZAnefGPdCvTeponI00szWdvdF7n5b2UIz25hoKpjUcPfFwLFmtjvR33pNf+FbCopHU+7sZGZt+fnv1Bnx9apU8WjY/lstmmO2sbvPT7qmXLl7pxpW/QQckMdS6uL31S1095vMLDU3VsU3VZ0e94bvSvS5Y1rZTfcpEcS+EFldNAdzylg0pPFLZUOupJVyFI4QMpQxs/WAu4HtPCVzP5WJGzgyveDu8+NMJ7n7hUnUVRtxzScAmxJ/eAQecfd/J1pYFszs7WoWO9Fc8R08JfPdA5hZX6I54mdWs66Lu3+dQFk5iRs3ZlQ+dsysA3CZux+bTGW5MbN9gVfd/cdKy3sAB7n7VYkUliMzOwp4p6y3bKV1h7n7wwmUlROLhqOc5O5jKi0fCNzr8fxQhc6iORyvK+vhn7F8Y6L5HPdNpLBaii8mXk00jG77pOvJhZk1IxqlIE0NHFWY2WbAsvgCaebyhkRzlT+STGXZM7MNgUXuvqCadU3Sso8smit+vrt/WGl5c+CUeCjdgmZm2wIfZNwsWba8CzDI3e9LprLaM7OmwPVEw38X/NyNmcxsh0qL3nX3xWbWHjjA3Qu+4fxXcH43A05Nw/ktsrrFvUw3c/c0jQQDlI9quCFRQ+ZXld8H0yB+PfpffLPhusCWwJR49CTJs/izeIf46WyvONy3JMCi4e83AGbGI9WJ/KqpgTllzGwC0J1oiMr73f2zZCuqHeUoHJUy3Ofx0MwiAmbWCrgI+APwN3e/MuGSRESClOaLiVLY4gtze7n7c0nXUhch5AghAyiHCICZPQXcB7yc5pvVQ8gRQgZIdw4zWx+4k2iuYoimTlkT+Dtwobv/lFRtuTCzw4mG+p5HNOz3g8AsoAtwgrs/mWB5WYsbxq8FOgMj3f3vGeuedveC78VsZkVEI0bsCywCGhBNQfcccGYIDf5mNs7dd0y6jl9iZrsBw4EZREPePw20JBoZ5iB3n5BYcSIFQA3MKRTf+X1E/DWbqFEwTfOjAcpRSNKcwcyuW9V6dz8nX7XUhXIUFjNbEzidaA7jR4Er3X1eokXlKIR9EUIGCCpHx1Wtd/fp+aqltkLIAGHkCCFDJjNrA1xC1MtjzbLl7r51YkXVQig5AMxsI6K53Y4g6mHQJ+GSaiWEHCFkAOVIkpmtRXRRdwHwBHAdsCvwBVHP3yoj9hSiUHIAmNmRRFOgdQMeAh5I483qIeQIIQOkO4eZvUpU84tEr63NgHuIGjm/d/fTkqsue2b2EbAXsA4wCRji7u+YWTfgKXfvmWR92bJoCsbpwJtE0/AsAA5295Vm9r6790q0wCzEx9TLwF3xEPJlo9kcD+zu7jslWV+24hFgavKFuxf81Gdm9i5wClGj8r3An9z9RTPbDrg1jX8nidQnNTCnmJmtAdwCHJumYVsrU47CkcYMZnZJ/LAbMBB4Jn6+H/BKiobQVY4CEA918yfgYmAicLG7f5NsVbWT9n0BYWSAoHLMJRoy3jIWO7AG0DwN7xshZIAwcoSQIZOZjQReB44BzgSOA95394sTLSxHac8RX8Q6mKj+rsBaREMa/yfRwnIUQo4QMkCFHH8i6sGlHAkwsxFEDR7NgFJgKlEPoh2Bjd19r+Sqy14oOTKZWVeiBrU/ArPcvV/CJdVKCDlCyADpzGFmH7n7FhnP33D3beORIj5L0TQ877l77/jxVM+YyzgtDbMAZvZBWWN4fI3nH0TTnu0PvJWGHGb2ubtvnOu6QmNmpVT/N58Bnoa/+TKPfTP7xt07V7dO5NeqUdIFSO7MrDfRXa8HA+8AhyRaUC0pR+FIcwZ3vwzAzEYDvd39u/j5FUR3kKaCchSMT4gaN84H3gPWMrNNy1a6+6dJFZarAPZFEBkgqBxtM5+bWWOi+dbPB1IxXFoIGSCMHCFkqKSju+9t0XzkL5jZGGB00kXVQmpzmNndwAHAZKK5cl8CvkxLA1qZEHKEkAGq5LgO5UhSb3ffNB5lqBjYJR5Cd7SZfZJwbbkIJUemacCHQB9gm4RrqYsQcoSQAdKZo9TM2rj7vHiEwFKAuMdsKobHjpWa2W+Jemo2M7Nt3f0NM+sBFHxDYIY1yh7Er7HHmdnfiIaXbpJUUTlaamYD3H1y5kIzGwikaW7v2cCW1Y0IaGYzEqinNhqYWTtgbaC1mXVz9/+aWVvSczyJrDZqYE6ZeLiSxkRj//d291kJl1QrylE4QsgQ26Cs0QbA3eeb2YZJFlRLypGspkR3U15B9XdZdkmiqDpK677IFEIGCCcHZnYo0XnyCbCju3+WcEk5CyEDhJEjhAzA8vj//zOzVsD3wPrJlVNrac5xKPAu0dyBL7u7m1kah+sKIUcIGUA5Csn/ANx9Wdx7KHN+1uU1fE8hCiUHZtaLaKj1g4mOr/uIRudJlRByhJABUp/jRuBDM3ufqGH8BAAza080VHNaXEg0kpsTdTy5wsw6EH0WPC7JwnI0zcy2d/d/lS1w91PiRubdEqwrF8cDI8xsGdFNF040p/QawB+SLCxH/wK2AMZVs+69PNdSW9cDX8aPjwceMLPvgd7A1UkVJVIo1MCcPie4++tJF1EPlKNwhJAB4DMzuxcYFj8/CkjFfD2VKEeCMoeACkgq90UlIWSAAHKY2S7ANcCPwBFpfP8IIQOEkSOEDBm+iBtkRwBvAAuB95MtqVbSnKMD8Hvgr8DdZvYg0U2UaRNCjhAygHIUkjXMbBOimz8zH0PGfPEpEEoOgMeA+4FeKb5JHcLIEUIGSHEOd3/IzN4CNiOaT/2reHkxsHuixeXA3ccAbcqem9lEoCcww91LkqqrFo4CVlZeGDcyP5ZAPTlz9zeB7mbWF+gYL54OvOuenvlO3f2gVazbJ5+11Ja7jzCzUUADd//OzF4AdiGaUi8tfyeJrDaagzklzKzjqta7eyruiFOOwhFChkxmtjbRRZPBRH+kjwWucPdFiRaWI+UoXGY23d1Xed4UohD2RQgZIP05zOwVonk0LyYaXqwCd1+S75pyFUIGCCNHCBlqYmb9iIYWHO3uVS5upUWac8TDOx5N1MPjK2CEu9+RbFW5CyFHCBlAOZJmZlOJem9Vx909FaMMhZJDRETk18zMLE0N/SKrkxqYU8LM5lL9cK1rAM3dPRVzYShH4Qghg0g+mdkMd98g6TpEkmJmmcM4Zn6ANKKLogX/vhFCBggjRwgZpPCZWSNgX+Bod98j4XJqLYQcIWQA5RAxs7+uYrW7+xV5K6YOQsgRQgYII4eZdXb3b+LHBpwJDCAaBeYqd0/FPMxm1q9sRCEza0o0NHB/ohynufv3CZaXtRD2R/zvfxHRNBfrxYtnEvX0H+rui5OqLRdm1pjo378LMNLdX8xYd5u7n5xYcVmKe5HfDswATgYeITqePgP+4O4fJlieSOLUwJxS8Qv0CcD5wCh3/1PCJdWKchSOtGYws8NXtd7dH8xXLXWhHIUvbT2YQ9gXIWSAcHKISG7MbJy775h0HXWVxhxm9huiCz8O/NPdZyZcUq2EkCOEDKAchaRShslpG0q3TFpzmNn1Naw6ANgwLTeIhZAjhAwQRg4ze8/de8ePLyY6t+8D9gdmu/upSdaXrUo5bgDaAXcQzcfcyt2PSLK+bIWwP8zsSaCY6N9/ary4M9F12/Xc/YCESsuJmd0JrAu8DfwJeNndT4/Xle+nQmZm/yJqYF4XOA24hWg4/72BE919YFK1iRQCNTCnkJkdClwBfAKc7+6fJVxSrShH4UhzhvhDV2UO9CUlf4yAchSK+C7RmnyRph7Mad8XEEYGCCcHgJn9DtgIeMfdJyVdT22EkAHCyBFIhiDeN0LJAWBmvwduA/4ZL+oH/MXdn0iuqtyFkCOEDKAchSSEDBBODgAzGwJcAywHznH3f/7CtxSkEHKEkAHSmcPM3nf3XvHjd4Ah7v69ma1B9Dl382QrzE6lHO8D27v7UjNrAHyY0hyp3B9mNsXde+S6rtCY2YdAT3d3M2tG1AN7prsfn7mfClml46lCx5O0ZBBZnRolXYBkz8x2IfqQ9SNwRNmwJWmjHIUjhAzuflDmczPbCLiaaIjNVNxdCcpRQBZT/dDxRs3zpRWkAPZFEBkgnBxmdgVwGNHdx2ea2WXufnfCZeUkhAwQRo4QMsRCed8IJQfApcDWGUMjdgJeBtLWcHMp6c9xKenPAMpRSC4l/RkggBxm1hO4DlgfuNDdn022otoJIUcIGSD1OTI/K630eChpd/+fma1IpqRaMTNbi+jz30/uvhTA3UtTliOE/bHSzLq7+5eZC82sB7AyoZpqo7HHvRvd/Ucz2w94wszuSbiuXDSKz4sWQGsza+fuJXGD+ZoJ1yaSODUwp4SZvQJ0BS4GnouXlfc0cPclyVSWG+UoHCFkyGRm7YHLgT2J/ij5vbsvT7aq3ClHsty9QdI11Le07otMIWSAIHIcAPRy94Vmtj7wNJC2BsEQMkAYOULIADAb2NLd51VeYWYzEqintkLJATCvrNEGwN2nmlmVXCkQQo4QMoByFJIQMkCKc8SN4VcRDTl7OXCfu5cmWlQthJAjhAwQTI7NzayEqGG2hZm1cfd5Fs13n6Zr71sQ3XRogJvZ+u7+bdy4lqZrJSHsj7OByWb2NjCNqNG8M9EoaKmY0jA218w2c/dPANx9hZkdDDxOdLylwXCi+ZYbAX8FnjSzj4nmJ38mycJECoGGyE4JM8v8cJW50wzwtAyvqRyFI4QMAGbWgmje6D8RXZi+zt0XJVtV7pSjsJhZk7KGPzPrAmwKjHb31NwpGsK+CCEDBJWjwhxJlZ+nQQgZIIwcIWSA8iHw/+Hu46pZ97y775NAWTkLJQeAmV1C1LPjXqLPtUcD/yOawy41N1GGkCOEDKAchSSEDJDuHGa2DJgJ/AOoUqe735H3omohhBwhZIAwcpjZhpUWzXb35WbWFuifst7YVZjZusDG7v5G0rVko5r9Mcvdf0rb/oh7yO4OlA3JPJ1oDuPFyVWVGzPbDFjm7v+ttLwhcIi7P5JMZbkxsy0A3P0jM+sIHAR8nZZjSWR1UgOziKSamc0FFhENNzur8np3fynvRdWCchSW+C7RHYEmwEfAVOBjdz8+ybpyEcK+CCEDBJXjK+DkjEV/A04pe5KGHCFkgDByhJBBClOlmygrS9NNlKnPEUIGUI5CEkIGSHcOM3uAmqdOcHc/Oo/l1FoIOULIAOHkEBER+TVSA3MAzOx1d++XdB11pRyFI00ZzGwCNf8x8ht375HHcmpNOQqLmb3v7r3M7HCiO3UvMLOP3D0tQ/gEsS9CyABB5Ri/itWpyBFCBggjRwgZRERE5Gdmto27v5l0HXUVQo4QMkB6cphZY+A4oJRoxKr9gcOIbla/wlMyLZKZvQOMAEZ4NdOmpImZ/R7oArzk7h9kLD/f3a9OrLAsmdka7v6/jOcHEg0j/767P5BYYTkK4dyIRzUcCnxLNET27cAg4BPgJHefnlx1IslTA3MAzGyGu2+QdB11pRyFI4QMoByFJk05zOxTd9/UzO4DHnH318zsA3fvmXRt9SFN+6ImIWQA5SgkIWSAMHKkKUPcUL6qXjc75bOe2golh4iIJMfMprt7x1/esrCFkCOEDJCeHGb2D6AdsBawEFiDaI7Z/YFidz81wfKyZmYzgfeIRnN7iWgY/1c8ZY0HZnYlsAPwAXAgcI273xqvS8W0PJl1mtkJwPHAo8CewBh3vyLJ+rIVwrlhZmOBccDaROfGOKJ5mfcGdnD33RMsTyRxaZnYXlYtVW/0q6AchSOEDKAchSZNOcaZ2adE75PHmVlLYEXCNdWnNO2LmoSQAZSjkISQAcLIkaYMN1SzbD2ieddb5LmWugglR7XMbJy775h0HXUVQo4QMoByFJIQMkAwOSzpAupJCDlCyADpydHf3Tc3szWBEqC9uy8xs2eBdxOuLRcl7r6Xma0HHE40hc1a8TDm97n71CSLy8HeQN94HuyrgJFm1szdh5KeYyqzziOBPdx9ppndDvwLSEUDM2GcG23c/SozM2Cmu58bL/807ikv8qumBmYREZGqTga2BL5295/MrCHw54RrEhGRAuPuo8oem1lz4DyiYeDuAa5Nqq5chZDDzJquYnX3vBVSRyHkCCEDKEchCSEDhJNjFdJ0g9iqhJAjhAyQnhw/Abj7MjP7yt2XxM+Xm1mablR3AHefBVwDXGNmA4GjgA+BdRKsLRcNyoZedvdiMxsCjI6v66TlmMqss6G7zwRw9x9SdkyFcG40NLMGQFNgbTNrEe+HRkQ9skV+1dTAnBJmNpfq3wQNWDe/1dSechSOEDIAmNmmq1idmtc45Sgs8RBQH2Q8nwekag6iEPZFCBlAOQpJCBkgjBwhZCgTzy12EnAu8AKwZXxhLlUCyLGY6LNtZo+PsudpuZgIYeQIIQMoRyEJIQMEkMPM9ljF6jXzVkgdhZAjhAwQTA4zs4buvhLYN2NhI9L1ubZK7153nwRMMrOTE6inthabWaeyHtfuvsjMdgXGAL9NtLLsbWRmbxHtk+4ZjZoGNE64tlyEcG68BLxO1Jh8N/BYPL3QTsDkJAsTKQRpOZEF+lazrCWwEliU51rqQjkKRwgZAEatYt2yvFVRd8oh9S2EfRFCBlCOQhJCBggjRwgZMLP/Ixqi7kNgkLt/kXBJtRJIjtlEjeJVbggzsxkJ1FNbIeQIIQMoRyEJIQOEkePsVaz7T96qqLsQcoSQAcLIcQJRo99Kd5+WsbwjcHMyJdXKWTWtcPfF+Sykjs6nUm9rd19sZrsABT/nb6zyjRdlNyEVAf/Icy11kfpzw93PMbO946cvAH2AI4BXgNsTK0ykQFjUSUvSwsxeBn5PNBfoJ/HiB939r8lVlTvlKBwhZBARERFJgpmVAlOJ5hCr8oeVux+c75pqI4QcZvYk8A93H1fNuufdfZ8EyspZCDlCyADKUUhCyADh5BARERERATUwp46Zve/uvczsYKAf0Z1+77j7FgmXlhPlKBwhZBARERFJgpkdET/M/KOqfHhBdx+e34pqJ5QcIiKyev3CPNKUza9Z6ELIEUIGCCdHJjPbmuj62vvuPiHhcurEzD52982TriNXZrYd0BkY5+7FGcuPSOPn2rQeU2bWCjgH+Nbd/25mlwCDiDo4Xezu3ydYXtbM7ADgj0AnonmlPwVucPePk6xLpBBoiOz0KZtnYSDwsrsvj3scpI1yFI4QMojUKzNbFzgO6ErGe6W7H51UTSIiUnjcfbiZbUV0g17ZvNKfADe5+1vJVZabUHKIiMhq90vzSDdMoqhaCCFHCBkggBxmNtbdd4ofH0A09O9LwAlmdqO735VogVmK5/ytrHvZcnffOs8l1YqZ/YVoKOzPgVvN7Dh3fyZefSpQ8A3MoRxTwH1ACdDRzHYDFgLXA3sCdwGHJFhbVsxsKNCbaEjs1kTzMS8AXjCzU939+STrE0maGpjT55N4SOONgXN+6U6/AqYchSOEDCL17SlgLvBvonnJRUREqoh7R7wE3AE8QnQxdCvgZTPb3d3fTLK+bIWQw8zGU83w3jEvu0hX6ELIEUIGUI5CEkIGCCOHuzdIuob6EEKOEDJAMDlaZTw+DdjF3T83syJgDFFDWho0J2o8e4jos6ABj7LqebIL0Z+BPu6+yMw2BZ4zs6buPoKKNzIUslCOqS7uvq+ZNQJmAx3cfYWZjQY+Sri2bO0HbObuK83sHuA5d9/JzJ4AngXUwCy/ampgTp8jgd2AD919iZn9Bjgv2ZJq5UiUo1AcSfoziNS3Du4+JOkiRESk4J0DHOHuIzOWPWtmbwLnA/smUlXuQshxQzXL1iOqv0Wea6mLEHKEkAGUo5CEkAHCySEiFWXeONLM3T8HcPc5KRshsBdwFXAGcLy7F5vZUnefmHBduTJ3XwTg7p+a2Y7Aq2bWkJpv8ik0oRxTZYyoHcoguqPKzNJyc8lP7l7W8WQZceO/u38TN5yL/KppDmYREZFKzGwk8Ed3X5h0LSIiUrjM7At336iGdVPcvUe+a6qNUHKUMbPmRDdMHgfcA1ybxvf0EHKEkAGUo5CEkAHCySEiYGYLgFeJGs8GAhu6+7J43SfuvlmS9eXKzAYANwG3A3919y4Jl5QTM3sP2M3dSzKWrQe8Bmzg7gV/Q08ox5SZ3Q80A5oCPwClRL1+dwXWdfeDEiwvK2Z2H1GD/2jgYKL5pM8ws7WAD2r6G0rk10J3WYiIiMTM7Lr44Q/AO/GwPcvK1rv7OYkUJiIihWrpKtYtyVsVdRdEDjNrDJwEnAu8AGzp7rOSrSp3IeQIIQMoRyEJIQOEk0NEKjgt4/GLRI1py+JGzdQNn+vuk81sENFcuT8kW02tXAN0JZr7FwB3n2VmOwGXJVZVbk7LeJzmY+p4ohupIBrWe0i87GuiEZTS4C/ABcARwNvAtfHyRkQNziK/aurBLCIiEjOzS1a13t3T8seIiIjkgZl9ChxA9fO5PeXum+a5pFoJIYeZ/R9wBfAhcL67f5FwSbUSQo4QMoByFJIQMkA4OUREREREQA3MIiIiVZjZFu7+0S8tExGRXzczm0rNc7l5WoYUDCFHPB/dVOBdqsni7qnoYRBCjhAygHIUkhAyQDg5ROSXmdk97v7npOvIlZntDBwKdIwXTQcec/dXkquq/pjZnu7+YtJ11EYajykz+wvwuLvPTbqWujCzPsAfgU7AT8CnwB3uPifJukQKgYbIFhERqeoBoHcWy0RE5FfM3TslXUN9CCTHUfH/MxttquuRXehCyBFCBlCOQhJCBggnh4hkyJhqK9OB8Ty6qZlqy8wuB3YH7gOeJHp92hC4yswGuPvFSdZXT+4gGnK6oIVyTBENs36tmY0B7gVGe8p6O5rZqcDRwERgE2As0A5438wOcvfXk6xPJGnqwSwiIhIzszZEHxSfouJQoesAD7j7RknVJiIiIqtmZlsBZwNlQ3p/Atzk7m8lV1XuQsgRQgZQjkISQgYIJ4eI/MzMfgSeBb7MWHwacAukZ6otM/sS+K27L6+0fA3gP+7eLZnKclND4yxE13eOdfd18llPbQR0TL0P7EY0f/FRQAtgOHCfu3+VZG3ZiqcS2srdfzSzdkTXBvcws95EvZi3TbhEkUSpgVlERCQW35l4GrAeMCtj1ULgNncflkRdIiIismpmth3wElHPlLeJLiJuBRwP7O7ubyZYXtZCyBFCBlCOQhJCBggnh4hUZGYbAXcCLwA3u7ub2Tfu3jnh0nJiZv8FNnP3ZZWWrwV84u5dk6ksN2a2DLgOWFnN6tPdfd38VpS7gI6p99y9d8bzfkS9gQ8E3nP3wYkVlyUz+9Ddt4wfG1HdveLn/3H33yZaoEjC1MAsIiJSiZld7O5XJF2HiIiIZMfMngXud/eRlZbvAxzl7vsmUliOQsgRQgZQjkISQgYIJ4eIVGVmDYDzgF2Ibhp5yd27JFtVbszsIuAQounBphEN59+JqPfpE+5+ZWLF5cDM3gaOdvePq1k3w903SKCsnAVyTL1f1hhbaXlz4GB3vy+BsnJiZk8DHwGjgP8D1nH3Y8ysEfCZu3dPtECRhKmBWURERERERFLNzL6oaSoLM5vi7j3yXVNthJAjhAygHIUkhAwQTg4RqZmZbQHcDXRx93ZJ15MrMxsIHAx0jBdNB55094nJVZUbM9sDmOLu/61m3Q5pygLpPqbM7FZ3PzXpOuoiHhb7JmBLotFHznT3BWbWCtja3V9OtECRhDVKugAREZFCYWYHufuT8eM2RHPD9AfeBw539+lJ1iciIiI1WrqKdUvyVkXdhZAjhAygHIUkhAwQTg4RqYG7f2Rm/YEOSddSG+4+CZiUdB114e4vrWJdqhqXId3HVNoblwHcvQT4QzXL5wNqXJZfPTUwi4iI/Ox84Mn48VDgY+AYomFwbgX2S6guERERWbUmZrYJ0ZymVdblu5g6CCFHCBlAOQpJCBkgnBwismpNgDZm9r27/5B0Mbkws80Ad/f/mFl3YE/gI3cfm3BptWZmTYGNgP+mbX9kSO0xVZmZXeLulyVdRy7MbB1gHyr27B/p7t8nVpRIgdAQ2SIiIrHM+WHM7EOgt7uvLHvu7lsmWqCIiIhUy8ymEs0VWB1Py5x1IeQIIQMoRyEJIQOEk0NEKjKza9393PjxlsBoYBHQCjgw7hVc8MzsZOAsoCFwPdHcy28COwE3uvtdCZaXtRD2RwgZAMzsxGoWXwZcAuDud+S3otyZ2X7AHUQ9+6cS3SS2ITAQONHdn02uOpHkqYFZREQkZmafAgcQfWB8uKyxOV73gbv3TKo2ERERERERESksZvaeu/eOH48E7nP358xsW6KG2X7JVpid+Cb7fkBz4BtgI3efbmZtgVcyr48UshD2RwgZAMxsBfAS8F3G4gOBp4hurDo6kcJyYGafA7u5+9RKyzsDo91940QKEykQGiJbRETkZ02BUcTD1pnZ+u7+bTwcTmmilYmIiIiIiIhIIevo7s8BuPsb8fDMaVHq7ouBxWb2lbtPB3D3uWaW1h5qad4fZdKcYQhwDfCAuz8DYGaD3P2oZMvKScPKjcsA7v6NmTVMoB6RgqIGZhERkZi7d6ph1U9EPZtFRERERERERMq0jYcCNqLev5kaJFBPbWU2ll1SaV2a5okPYX+EkAF3n2BmQ4CbzOwQ4CRqniqiUL1tZvcBtwPTiOrvRJTl3QTrEikIamAWERH5Be6+hGiIKBERERERERGRMq8BW8WPJ5tZB3efbWa/AUoSrCtXd5pZC3f/wd2fLltoZhsDExOsK1ch7I8QMgAQ94o/1sx2J8qVph7YAMcQzU0+HOhI1MA8nWiY7+sTrEukIGgOZhEREREREREREREREVkt4unnNnP315OuRUTqR2qGVBARERERERERERERKWRm1jLpGuqTmW2ZdA31IYT9krYMZraGme1rZqcBfyBdw61Xy8yOSboGkUKhBmYRERERERERERERkRyZ2akZjzub2X+AWWb2jZltnmBp9emFpAvIlpltaWZfmNlSM3vazNpkrB6bWGE5CCEDgJkNBr4ELgeuAfYFbjezt+Lhvgueme1R+Qu4MuOxyK+ahsgWEREREREREREREcmRmb3n7r3jx48C/3T3283sAOB4d9852QqzY2Yn1rQKuNzdW+ezntoyswlEc+O+AZwGHAgMcfeZZva+u/dKsLyshJABonMDONTdvzCzbYjOh6PM7M/A79x932Qr/GVmVgr8G1iesXhbon3j7r5jIoWJFIhGSRcgIiIiIiIiIiIiIpJClvF4U3c/FMDdnzazixOqqTZuBR4GquuNlqZhjdd291Hx44vN7AtgnJkNofpshSiEDAAN3f0LAHd/08xujx/fY2ZnJVta1o4CjgXOcfe3AczsG3cfnGxZIoVBDcwiIiIiIiIiIiIiIrlb28x2J5qKsvK1dqtm+0L1GXB1WYNgprhhMy2amlkDdy8FcPcRZvYT0dDSayRbWtZCyADwg5kNdPdJZrYfUJJ0Qbly9+Fm9hpwt5l9DFxCuhr5RVYrNTCLiIiIiIiIiIiIiORuBnBO/LjEzH4TD2XcjorD6ha6m6m5p/J5+Sykjl4H9gBeLFvg7o+bmQMjEqsqNyFkADgdeMbMWgHFwD4AZlZE1Fs+Fdx9JvA7Mzse+BewZsIliRQMzcEsIiIiIiIiIiIiIlIPzKwlsAhYw92XJF3Pr52ZtXT3BUnXURdpzmBmrd39u6TrqA9m1gnY1t0fS7oWkULQIOkCRERERERERERERETSxsy2MLN3zexNM9vEzEYBM4FvgK4Jl5cTM9s47nmNmXU3syPMrE/SdeUiY3+8lbk/zGy6mW2edH3ZCCEDgJl1NLOXgX+b2Q1mtmbGun8nWFrWzOygjMetgduJhsueYGYdk6tMpDCogVlEREREREREREREJHe3AZcDdwAvA4+5e1PgVODGJAvLhZmdDUwE3jGzw4DXgN8Bz5rZXxItLjdl++N20rs/QsgAcCfwAnAo0BYYa2Yt4nVpGWb6/IzHVwMfAz2AkcCtiVQkUkA0RLaIiIiIiIiIiIiISI7M7H137xU/nu7uHTPWfeDuPRMrLgdm9h+gP9Ac+BzYzN2/MbM2wAR33yzRArMUwv4IIQOAmb3n7r0znl8A7AvsDIzPXFeoKu2LD4He7r6y7Lm7b5logSIJa5R0ASIiIiIiIiIiIiIiKWQZj8evYl2h+188x+8CM5vn7t8AuPs8M/sp4dpyEcL+CCEDQNPMJ+4+1MyWA2OBFtV/S8FZw8w2Ifp3Ly1rXI6p56b86mmIbBERERERERERERGR3M0xs7UB3P2IsoVm1gFYllhVuVtmZr8zsz8AbmYHAJjZQGDlqr+1oISwP0LIAPCZme2WucDdbwAeIT3zkzcFRsVf65rZ+gBmtg5QmmRhIoVAQ2SLiIiIiIiIiIiIiNSTeK7Zddz926RryYaZ9QXuJuqVeTRwNrAfsBQ42N3HJVhenaVtf1QnbRnMbA0Ad/9fNet+4+4z819V/TCzpkBRWU9/kV8rNTCLiIiIiIiIiIiIiEg5M2sNLHB39dQUEZEqNES2iIiIiIiIiIiIiMivlJkdlPG4jZmNAr4GxppZx+QqExGRQqUGZhERERERERERERGRX6/zMx4PBT4GNgJeAG5NpCIRESloGiJbRERERERERERERORXyszed/de8eMPgd7uvrLsubtvmWiBIiJScBolXYCIiIiIiIiIiIiIiCRmDTPbBDCgtKxxOaYeaiIiUoUamEVEREREREREREREfr2aAqOIGpgxs/Xd/VszWwcoTbQyEREpSBoiW0REREREREREREREKjCzpkCRu3+TdC0iIlJY1MAsIiIiIiIiIiIiIiIiIiJZaZB0ASIiIiIiIiIiIiIiIiIikg5qYBYRERERERERERERERERkayogVlERERERERERERERERERLKiBmYREREREREREREREREREcmKGphFRERERERERERERERERCQramAWEREREREREREREREREZGs/D+T6F49EUmVogAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 2720x480 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from visualization_utils import jaccard_vis\n",
"\n",
"candidate_jaccard_out, fig = jaccard_vis(conn_df, clock_df, clock_df['bodyId'], 'out', other_body_ids = candidate_IDs)"
]
},
{
"cell_type": "markdown",
"id": "20606c32-f74b-4c2d-bb71-981cd2b77cb5",
"metadata": {},
"source": [
"To compare the output partners among the candidates only, we compute the jaccard index and plot."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d5f156eb-1fd8-452b-a2c3-4db611bc0eed",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 480x480 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from visualization_utils import jaccard_simple\n",
"candidate_jac_out, fig = jaccard_simple(candidate_IDs, candidate_IDs, 'out', diag_mask=True)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "512f83a0-55ab-43b1-91fa-936017d55be3",
"metadata": {},
"outputs": [],
"source": [
"# save figure\n",
"fig.savefig('jaccard_M_target_outputs.svg')"
]
},
{
"cell_type": "markdown",
"id": "38f7bee7-8704-4fa1-a661-1ac3a73ca3ee",
"metadata": {},
"source": [
"The strong shared targets of M that return to the clock (with medium or strong connections) are found below. "
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "bf1730fd-e33b-4b66-8673-5f02aff66101",
"metadata": {},
"outputs": [],
"source": [
"# get connections from strong shared targets of M to clock neurons\n",
"all_candidate_IDs = sLNv_strong_shared_targs['bodyId_post']\n",
"all_candidate_targets = get_input_output_conns(all_candidate_IDs, 'out', min_strength = 3)\n",
"all_candidate_targets = all_candidate_targets[all_candidate_targets.bodyId_post.isin(clock_df['bodyId'])]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "41c83e69-bbe3-4d0d-acd2-93e006e35cfb",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-11-819992679692>:6: FutureWarning: The default value of regex will change from True to False in a future version.\n",
" all_candidate_targets['instance_pre'] = all_candidate_targets['instance_pre'].str.replace(r'\\([^()]*\\)', '')\n"
]
}
],
"source": [
"import re\n",
"\n",
"# merge clock labels onto all_candidate_targets\n",
"all_candidate_targets = all_candidate_targets.merge(clock_df[['bodyId','labels']], left_on='bodyId_post', right_on='bodyId')\n",
"#remove parenthetical addendums to target instance names\n",
"all_candidate_targets['instance_pre'] = all_candidate_targets['instance_pre'].str.replace(r'\\([^()]*\\)', '')\n",
"# remove _L and _R from candidate target instance names\n",
"all_candidate_targets['instance_pre'] = [re.sub(r'_(L|R)','',str(x)) for x in all_candidate_targets['instance_pre']]\n",
"# drop extra bodyId column\n",
"all_candidate_targets = all_candidate_targets.drop(columns='bodyId')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "63714385-bc47-48da-bdce-82810efa5bf7",
"metadata": {
"scrolled": true
},
"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>bodyId_pre</th>\n",
" <th>instance_pre</th>\n",
" <th>bodyId_post</th>\n",
" <th>instance_post</th>\n",
" <th>weight</th>\n",
" <th>labels</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5813047586</td>\n",
" <td>SLP316</td>\n",
" <td>325529237</td>\n",
" <td>DN1pA_R</td>\n",
" <td>20</td>\n",
" <td>DN1pA3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>5813047586</td>\n",
" <td>SLP316</td>\n",
" <td>5813071319</td>\n",
" <td>DN1pB_R</td>\n",
" <td>4</td>\n",
" <td>DN1pB2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>325122525</td>\n",
" <td>SLP316</td>\n",
" <td>387944118</td>\n",
" <td>DN1pA_R</td>\n",
" <td>10</td>\n",
" <td>DN1pA4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>355453590</td>\n",
" <td>SLP316</td>\n",
" <td>387944118</td>\n",
" <td>DN1pA_R</td>\n",
" <td>15</td>\n",
" <td>DN1pA4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>5813047586</td>\n",
" <td>SLP316</td>\n",
" <td>387944118</td>\n",
" <td>DN1pA_R</td>\n",
" <td>4</td>\n",
" <td>DN1pA4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>325122525</td>\n",
" <td>SLP316</td>\n",
" <td>324846570</td>\n",
" <td>DN1pA_R</td>\n",
" <td>32</td>\n",
" <td>DN1pA2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>355453590</td>\n",
" <td>SLP316</td>\n",
" <td>324846570</td>\n",
" <td>DN1pA_R</td>\n",
" <td>15</td>\n",
" <td>DN1pA2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>325122525</td>\n",
" <td>SLP316</td>\n",
" <td>387166379</td>\n",
" <td>DN1pA_R</td>\n",
" <td>20</td>\n",
" <td>DN1pA5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>5813047586</td>\n",
" <td>SLP316</td>\n",
" <td>324846570</td>\n",
" <td>DN1pA_R</td>\n",
" <td>5</td>\n",
" <td>DN1pA2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5813047586</td>\n",
" <td>SLP316</td>\n",
" <td>5813010153</td>\n",
" <td>DN1pA_R</td>\n",
" <td>11</td>\n",
" <td>DN1pA1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>355453590</td>\n",
" <td>SLP316</td>\n",
" <td>325529237</td>\n",
" <td>DN1pA_R</td>\n",
" <td>26</td>\n",
" <td>DN1pA3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>325122525</td>\n",
" <td>SLP316</td>\n",
" <td>325529237</td>\n",
" <td>DN1pA_R</td>\n",
" <td>21</td>\n",
" <td>DN1pA3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>355453590</td>\n",
" <td>SLP316</td>\n",
" <td>387166379</td>\n",
" <td>DN1pA_R</td>\n",
" <td>13</td>\n",
" <td>DN1pA5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>355453590</td>\n",
" <td>SLP316</td>\n",
" <td>5813010153</td>\n",
" <td>DN1pA_R</td>\n",
" <td>7</td>\n",
" <td>DN1pA1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>355453590</td>\n",
" <td>SLP316</td>\n",
" <td>2007068523</td>\n",
" <td>s-LNv</td>\n",
" <td>3</td>\n",
" <td>sLNv3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>325122525</td>\n",
" <td>SLP316</td>\n",
" <td>5813010153</td>\n",
" <td>DN1pA_R</td>\n",
" <td>19</td>\n",
" <td>DN1pA1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>5813047586</td>\n",
" <td>SLP316</td>\n",
" <td>387166379</td>\n",
" <td>DN1pA_R</td>\n",
" <td>10</td>\n",
" <td>DN1pA5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>294783216</td>\n",
" <td>SLP403</td>\n",
" <td>324846570</td>\n",
" <td>DN1pA_R</td>\n",
" <td>3</td>\n",
" <td>DN1pA2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>294783216</td>\n",
" <td>SLP403</td>\n",
" <td>325529237</td>\n",
" <td>DN1pA_R</td>\n",
" <td>3</td>\n",
" <td>DN1pA3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>294783216</td>\n",
" <td>SLP403</td>\n",
" <td>387166379</td>\n",
" <td>DN1pA_R</td>\n",
" <td>6</td>\n",
" <td>DN1pA5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>325455002</td>\n",
" <td>SLP403</td>\n",
" <td>324846570</td>\n",
" <td>DN1pA_R</td>\n",
" <td>4</td>\n",
" <td>DN1pA2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>325455002</td>\n",
" <td>SLP403</td>\n",
" <td>387166379</td>\n",
" <td>DN1pA_R</td>\n",
" <td>5</td>\n",
" <td>DN1pA5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>540998882</td>\n",
" <td>SMP232</td>\n",
" <td>264083994</td>\n",
" <td>DN1a_R</td>\n",
" <td>3</td>\n",
" <td>DN1a1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>540998882</td>\n",
" <td>SMP232</td>\n",
" <td>450034902</td>\n",
" <td>LPN_R</td>\n",
" <td>19</td>\n",
" <td>LPN3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>540998882</td>\n",
" <td>SMP232</td>\n",
" <td>356818551</td>\n",
" <td>LPN_R</td>\n",
" <td>15</td>\n",
" <td>LPN1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>540998882</td>\n",
" <td>SMP232</td>\n",
" <td>480029788</td>\n",
" <td>LPN_R</td>\n",
" <td>12</td>\n",
" <td>LPN2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bodyId_pre instance_pre bodyId_post instance_post weight labels\n",
"0 5813047586 SLP316 325529237 DN1pA_R 20 DN1pA3\n",
"20 5813047586 SLP316 5813071319 DN1pB_R 4 DN1pB2\n",
"19 325122525 SLP316 387944118 DN1pA_R 10 DN1pA4\n",
"18 355453590 SLP316 387944118 DN1pA_R 15 DN1pA4\n",
"17 5813047586 SLP316 387944118 DN1pA_R 4 DN1pA4\n",
"15 325122525 SLP316 324846570 DN1pA_R 32 DN1pA2\n",
"13 355453590 SLP316 324846570 DN1pA_R 15 DN1pA2\n",
"10 325122525 SLP316 387166379 DN1pA_R 20 DN1pA5\n",
"12 5813047586 SLP316 324846570 DN1pA_R 5 DN1pA2\n",
"4 5813047586 SLP316 5813010153 DN1pA_R 11 DN1pA1\n",
"1 355453590 SLP316 325529237 DN1pA_R 26 DN1pA3\n",
"2 325122525 SLP316 325529237 DN1pA_R 21 DN1pA3\n",
"8 355453590 SLP316 387166379 DN1pA_R 13 DN1pA5\n",
"5 355453590 SLP316 5813010153 DN1pA_R 7 DN1pA1\n",
"25 355453590 SLP316 2007068523 s-LNv 3 sLNv3\n",
"6 325122525 SLP316 5813010153 DN1pA_R 19 DN1pA1\n",
"7 5813047586 SLP316 387166379 DN1pA_R 10 DN1pA5\n",
"16 294783216 SLP403 324846570 DN1pA_R 3 DN1pA2\n",
"3 294783216 SLP403 325529237 DN1pA_R 3 DN1pA3\n",
"11 294783216 SLP403 387166379 DN1pA_R 6 DN1pA5\n",
"14 325455002 SLP403 324846570 DN1pA_R 4 DN1pA2\n",
"9 325455002 SLP403 387166379 DN1pA_R 5 DN1pA5\n",
"24 540998882 SMP232 264083994 DN1a_R 3 DN1a1\n",
"21 540998882 SMP232 450034902 LPN_R 19 LPN3\n",
"22 540998882 SMP232 356818551 LPN_R 15 LPN1\n",
"23 540998882 SMP232 480029788 LPN_R 12 LPN2"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# just to see the cell types of the candidates\n",
"all_candidate_targets.sort_values(by='instance_pre')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "b37c7c2a",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\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>bodyId_pre</th>\n",
" <th>instance_pre</th>\n",
" <th>bodyId_post</th>\n",
" <th>instance_post</th>\n",
" <th>weight</th>\n",
" <th>labels</th>\n",
" <th>candidate_label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>325122525</td>\n",
" <td>SLP316</td>\n",
" <td>325529237</td>\n",
" <td>DN1pA_R</td>\n",
" <td>21</td>\n",
" <td>DN1pA3</td>\n",
" <td>SLP316 (325122525)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>325122525</td>\n",
" <td>SLP316</td>\n",
" <td>387944118</td>\n",
" <td>DN1pA_R</td>\n",
" <td>10</td>\n",
" <td>DN1pA4</td>\n",
" <td>SLP316 (325122525)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>325122525</td>\n",
" <td>SLP316</td>\n",
" <td>5813010153</td>\n",
" <td>DN1pA_R</td>\n",
" <td>19</td>\n",
" <td>DN1pA1</td>\n",
" <td>SLP316 (325122525)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>325122525</td>\n",
" <td>SLP316</td>\n",
" <td>387166379</td>\n",
" <td>DN1pA_R</td>\n",
" <td>20</td>\n",
" <td>DN1pA5</td>\n",
" <td>SLP316 (325122525)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>325122525</td>\n",
" <td>SLP316</td>\n",
" <td>324846570</td>\n",
" <td>DN1pA_R</td>\n",
" <td>32</td>\n",
" <td>DN1pA2</td>\n",
" <td>SLP316 (325122525)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>355453590</td>\n",
" <td>SLP316</td>\n",
" <td>2007068523</td>\n",
" <td>s-LNv</td>\n",
" <td>3</td>\n",
" <td>sLNv3</td>\n",
" <td>SLP316 (355453590)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>355453590</td>\n",
" <td>SLP316</td>\n",
" <td>325529237</td>\n",
" <td>DN1pA_R</td>\n",
" <td>26</td>\n",
" <td>DN1pA3</td>\n",
" <td>SLP316 (355453590)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>355453590</td>\n",
" <td>SLP316</td>\n",
" <td>5813010153</td>\n",
" <td>DN1pA_R</td>\n",
" <td>7</td>\n",
" <td>DN1pA1</td>\n",
" <td>SLP316 (355453590)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>355453590</td>\n",
" <td>SLP316</td>\n",
" <td>387944118</td>\n",
" <td>DN1pA_R</td>\n",
" <td>15</td>\n",
" <td>DN1pA4</td>\n",
" <td>SLP316 (355453590)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>355453590</td>\n",
" <td>SLP316</td>\n",
" <td>387166379</td>\n",
" <td>DN1pA_R</td>\n",
" <td>13</td>\n",
" <td>DN1pA5</td>\n",
" <td>SLP316 (355453590)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>355453590</td>\n",
" <td>SLP316</td>\n",
" <td>324846570</td>\n",
" <td>DN1pA_R</td>\n",
" <td>15</td>\n",
" <td>DN1pA2</td>\n",
" <td>SLP316 (355453590)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>5813047586</td>\n",
" <td>SLP316</td>\n",
" <td>5813071319</td>\n",
" <td>DN1pB_R</td>\n",
" <td>4</td>\n",
" <td>DN1pB2</td>\n",
" <td>SLP316 (5813047586)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>5813047586</td>\n",
" <td>SLP316</td>\n",
" <td>387944118</td>\n",
" <td>DN1pA_R</td>\n",
" <td>4</td>\n",
" <td>DN1pA4</td>\n",
" <td>SLP316 (5813047586)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5813047586</td>\n",
" <td>SLP316</td>\n",
" <td>325529237</td>\n",
" <td>DN1pA_R</td>\n",
" <td>20</td>\n",
" <td>DN1pA3</td>\n",
" <td>SLP316 (5813047586)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>5813047586</td>\n",
" <td>SLP316</td>\n",
" <td>387166379</td>\n",
" <td>DN1pA_R</td>\n",
" <td>10</td>\n",
" <td>DN1pA5</td>\n",
" <td>SLP316 (5813047586)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5813047586</td>\n",
" <td>SLP316</td>\n",
" <td>5813010153</td>\n",
" <td>DN1pA_R</td>\n",
" <td>11</td>\n",
" <td>DN1pA1</td>\n",
" <td>SLP316 (5813047586)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>5813047586</td>\n",
" <td>SLP316</td>\n",
" <td>324846570</td>\n",
" <td>DN1pA_R</td>\n",
" <td>5</td>\n",
" <td>DN1pA2</td>\n",
" <td>SLP316 (5813047586)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>294783216</td>\n",
" <td>SLP403</td>\n",
" <td>387166379</td>\n",
" <td>DN1pA_R</td>\n",
" <td>6</td>\n",
" <td>DN1pA5</td>\n",
" <td>SLP403 (294783216)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>294783216</td>\n",
" <td>SLP403</td>\n",
" <td>324846570</td>\n",
" <td>DN1pA_R</td>\n",
" <td>3</td>\n",
" <td>DN1pA2</td>\n",
" <td>SLP403 (294783216)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>294783216</td>\n",
" <td>SLP403</td>\n",
" <td>325529237</td>\n",
" <td>DN1pA_R</td>\n",
" <td>3</td>\n",
" <td>DN1pA3</td>\n",
" <td>SLP403 (294783216)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>325455002</td>\n",
" <td>SLP403</td>\n",
" <td>324846570</td>\n",
" <td>DN1pA_R</td>\n",
" <td>4</td>\n",
" <td>DN1pA2</td>\n",
" <td>SLP403 (325455002)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>325455002</td>\n",
" <td>SLP403</td>\n",
" <td>387166379</td>\n",
" <td>DN1pA_R</td>\n",
" <td>5</td>\n",
" <td>DN1pA5</td>\n",
" <td>SLP403 (325455002)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>540998882</td>\n",
" <td>SMP232</td>\n",
" <td>450034902</td>\n",
" <td>LPN_R</td>\n",
" <td>19</td>\n",
" <td>LPN3</td>\n",
" <td>SMP232 (540998882)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>540998882</td>\n",
" <td>SMP232</td>\n",
" <td>356818551</td>\n",
" <td>LPN_R</td>\n",
" <td>15</td>\n",
" <td>LPN1</td>\n",
" <td>SMP232 (540998882)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>540998882</td>\n",
" <td>SMP232</td>\n",
" <td>480029788</td>\n",
" <td>LPN_R</td>\n",
" <td>12</td>\n",
" <td>LPN2</td>\n",
" <td>SMP232 (540998882)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>540998882</td>\n",
" <td>SMP232</td>\n",
" <td>264083994</td>\n",
" <td>DN1a_R</td>\n",
" <td>3</td>\n",
" <td>DN1a1</td>\n",
" <td>SMP232 (540998882)</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bodyId_pre instance_pre bodyId_post instance_post weight labels \\\n",
"2 325122525 SLP316 325529237 DN1pA_R 21 DN1pA3 \n",
"19 325122525 SLP316 387944118 DN1pA_R 10 DN1pA4 \n",
"6 325122525 SLP316 5813010153 DN1pA_R 19 DN1pA1 \n",
"10 325122525 SLP316 387166379 DN1pA_R 20 DN1pA5 \n",
"15 325122525 SLP316 324846570 DN1pA_R 32 DN1pA2 \n",
"25 355453590 SLP316 2007068523 s-LNv 3 sLNv3 \n",
"1 355453590 SLP316 325529237 DN1pA_R 26 DN1pA3 \n",
"5 355453590 SLP316 5813010153 DN1pA_R 7 DN1pA1 \n",
"18 355453590 SLP316 387944118 DN1pA_R 15 DN1pA4 \n",
"8 355453590 SLP316 387166379 DN1pA_R 13 DN1pA5 \n",
"13 355453590 SLP316 324846570 DN1pA_R 15 DN1pA2 \n",
"20 5813047586 SLP316 5813071319 DN1pB_R 4 DN1pB2 \n",
"17 5813047586 SLP316 387944118 DN1pA_R 4 DN1pA4 \n",
"0 5813047586 SLP316 325529237 DN1pA_R 20 DN1pA3 \n",
"7 5813047586 SLP316 387166379 DN1pA_R 10 DN1pA5 \n",
"4 5813047586 SLP316 5813010153 DN1pA_R 11 DN1pA1 \n",
"12 5813047586 SLP316 324846570 DN1pA_R 5 DN1pA2 \n",
"11 294783216 SLP403 387166379 DN1pA_R 6 DN1pA5 \n",
"16 294783216 SLP403 324846570 DN1pA_R 3 DN1pA2 \n",
"3 294783216 SLP403 325529237 DN1pA_R 3 DN1pA3 \n",
"14 325455002 SLP403 324846570 DN1pA_R 4 DN1pA2 \n",
"9 325455002 SLP403 387166379 DN1pA_R 5 DN1pA5 \n",
"21 540998882 SMP232 450034902 LPN_R 19 LPN3 \n",
"22 540998882 SMP232 356818551 LPN_R 15 LPN1 \n",
"23 540998882 SMP232 480029788 LPN_R 12 LPN2 \n",
"24 540998882 SMP232 264083994 DN1a_R 3 DN1a1 \n",
"\n",
" candidate_label \n",
"2 SLP316 (325122525) \n",
"19 SLP316 (325122525) \n",
"6 SLP316 (325122525) \n",
"10 SLP316 (325122525) \n",
"15 SLP316 (325122525) \n",
"25 SLP316 (355453590) \n",
"1 SLP316 (355453590) \n",
"5 SLP316 (355453590) \n",
"18 SLP316 (355453590) \n",
"8 SLP316 (355453590) \n",
"13 SLP316 (355453590) \n",
"20 SLP316 (5813047586) \n",
"17 SLP316 (5813047586) \n",
"0 SLP316 (5813047586) \n",
"7 SLP316 (5813047586) \n",
"4 SLP316 (5813047586) \n",
"12 SLP316 (5813047586) \n",
"11 SLP403 (294783216) \n",
"16 SLP403 (294783216) \n",
"3 SLP403 (294783216) \n",
"14 SLP403 (325455002) \n",
"9 SLP403 (325455002) \n",
"21 SMP232 (540998882) \n",
"22 SMP232 (540998882) \n",
"23 SMP232 (540998882) \n",
"24 SMP232 (540998882) "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# recreate labels for candidates to include type and bodyId\n",
"all_candidate_targets['candidate_label'] = all_candidate_targets['instance_pre'] + ' (' + all_candidate_targets['bodyId_pre'].map(str) + ')'\n",
"all_candidate_targets.sort_values(by='candidate_label')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "5206e888-2673-42d1-92d8-acafd52bb762",
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <th>DN1pA2</th>\n",
" <th>DN1pA3</th>\n",
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" <th>SLP316 (325122525)</th>\n",
" <td>NaN</td>\n",
" <td>19.0</td>\n",
" <td>32.0</td>\n",
" <td>21.0</td>\n",
" <td>10.0</td>\n",
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" <th>SLP316 (355453590)</th>\n",
" <td>NaN</td>\n",
" <td>7.0</td>\n",
" <td>15.0</td>\n",
" <td>26.0</td>\n",
" <td>15.0</td>\n",
" <td>13.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SLP316 (5813047586)</th>\n",
" <td>NaN</td>\n",
" <td>11.0</td>\n",
" <td>5.0</td>\n",
" <td>20.0</td>\n",
" <td>4.0</td>\n",
" <td>10.0</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SLP403 (294783216)</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>6.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SLP403 (325455002)</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP232 (540998882)</th>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>15.0</td>\n",
" <td>12.0</td>\n",
" <td>19.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"labels DN1a1 DN1pA1 DN1pA2 DN1pA3 DN1pA4 DN1pA5 DN1pB2 \\\n",
"candidate_label \n",
"SLP316 (325122525) NaN 19.0 32.0 21.0 10.0 20.0 NaN \n",
"SLP316 (355453590) NaN 7.0 15.0 26.0 15.0 13.0 NaN \n",
"SLP316 (5813047586) NaN 11.0 5.0 20.0 4.0 10.0 4.0 \n",
"SLP403 (294783216) NaN NaN 3.0 3.0 NaN 6.0 NaN \n",
"SLP403 (325455002) NaN NaN 4.0 NaN NaN 5.0 NaN \n",
"SMP232 (540998882) 3.0 NaN NaN NaN NaN NaN NaN \n",
"\n",
"labels LPN1 LPN2 LPN3 sLNv3 \n",
"candidate_label \n",
"SLP316 (325122525) NaN NaN NaN NaN \n",
"SLP316 (355453590) NaN NaN NaN 3.0 \n",
"SLP316 (5813047586) NaN NaN NaN NaN \n",
"SLP403 (294783216) NaN NaN NaN NaN \n",
"SLP403 (325455002) NaN NaN NaN NaN \n",
"SMP232 (540998882) 15.0 12.0 19.0 NaN "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# generate pivot table for strong shared targets back to clock\n",
"all_candidate_targets = all_candidate_targets.pivot(index='candidate_label', columns='labels', values='weight')\n",
"all_candidate_targets"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "b393855e-409a-48f3-badf-670659ca8b46",
"metadata": {},
"outputs": [],
"source": [
"# export table\n",
"all_candidate_targets.to_csv('M_targets_to_clock.csv')"
]
},
{
"cell_type": "markdown",
"id": "c5e3ea27-af07-443b-94ca-287b2f8a62fc",
"metadata": {
"tags": []
},
"source": [
"# Strongest shared E cells targets"
]
},
{
"cell_type": "markdown",
"id": "39c60318",
"metadata": {},
"source": [
"For each of the subgroups of evening cells, the strongest targets shared by the entire group were extracted."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "f2dfe952",
"metadata": {},
"outputs": [],
"source": [
"E1Ids = clock_df[clock_df['subphase']=='E1']['bodyId']"
]
},
{
"cell_type": "markdown",
"id": "e240bfc2",
"metadata": {},
"source": [
"The top 10 targets were then explored to get a lay of their similarity to the existing clock network even though there were more than 10 strong shared targets in some cases (for example, for E1)."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "16c11f0b-8c40-4991-8e92-f476ba4bd2f0",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\dbing\\Documents\\Research\\Code\\clock-connectome\\connection_utils.py:208: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
" conns_df = conns_df.groupby(['bodyId_post', 'instance_post'], as_index=False)['weight', 'shared'].sum()\n"
]
},
{
"data": {
"text/plain": [
"5 329732855\n",
"42 670431030\n",
"43 670772147\n",
"50 702152113\n",
"10 390331583\n",
"77 5813040712\n",
"40 668384542\n",
"81 5813111989\n",
"56 731140809\n",
"82 5901196628\n",
"Name: bodyId_post, dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Evening1_targs = strong_shared_connections(E1Ids, 'out', 2)\n",
"\n",
"candidate_IDs = Evening1_targs['bodyId_post'][0:10]\n",
"candidate_IDs"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "0317a57d",
"metadata": {},
"outputs": [],
"source": [
"candidate_targets = get_input_output_conns(candidate_IDs, 'out', min_strength = 3)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "ea12002a",
"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>bodyId_pre</th>\n",
" <th>bodyId_post</th>\n",
" <th>weight</th>\n",
" <th>instance_pre</th>\n",
" <th>instance_post</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>264083994</td>\n",
" <td>299272434</td>\n",
" <td>4</td>\n",
" <td>DN1a1</td>\n",
" <td>SLP443</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>264083994</td>\n",
" <td>325814461</td>\n",
" <td>10</td>\n",
" <td>DN1a1</td>\n",
" <td>LHPV4c3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>264083994</td>\n",
" <td>326119769</td>\n",
" <td>4</td>\n",
" <td>DN1a1</td>\n",
" <td>SLP457</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>264083994</td>\n",
" <td>356472645</td>\n",
" <td>3</td>\n",
" <td>DN1a1</td>\n",
" <td>SLP364</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>264083994</td>\n",
" <td>356503889</td>\n",
" <td>7</td>\n",
" <td>DN1a1</td>\n",
" <td>LHPV6f4_b</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>833</th>\n",
" <td>329732855</td>\n",
" <td>1036637638</td>\n",
" <td>3</td>\n",
" <td>SMP368(PDM08)_L</td>\n",
" <td>ExR3(ring)_R</td>\n",
" </tr>\n",
" <tr>\n",
" <th>834</th>\n",
" <td>329732855</td>\n",
" <td>5813002813</td>\n",
" <td>3</td>\n",
" <td>SMP368(PDM08)_L</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>835</th>\n",
" <td>329732855</td>\n",
" <td>5813020676</td>\n",
" <td>3</td>\n",
" <td>SMP368(PDM08)_L</td>\n",
" <td>SMP182_R</td>\n",
" </tr>\n",
" <tr>\n",
" <th>836</th>\n",
" <td>329732855</td>\n",
" <td>5813020817</td>\n",
" <td>3</td>\n",
" <td>SMP368(PDM08)_L</td>\n",
" <td>SMP190_R</td>\n",
" </tr>\n",
" <tr>\n",
" <th>837</th>\n",
" <td>329732855</td>\n",
" <td>5813061249</td>\n",
" <td>3</td>\n",
" <td>SMP368(PDM08)_L</td>\n",
" <td>None</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3428 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" bodyId_pre bodyId_post weight instance_pre instance_post\n",
"0 264083994 299272434 4 DN1a1 SLP443\n",
"1 264083994 325814461 10 DN1a1 LHPV4c3\n",
"2 264083994 326119769 4 DN1a1 SLP457\n",
"3 264083994 356472645 3 DN1a1 SLP364\n",
"4 264083994 356503889 7 DN1a1 LHPV6f4_b\n",
".. ... ... ... ... ...\n",
"833 329732855 1036637638 3 SMP368(PDM08)_L ExR3(ring)_R\n",
"834 329732855 5813002813 3 SMP368(PDM08)_L None\n",
"835 329732855 5813020676 3 SMP368(PDM08)_L SMP182_R\n",
"836 329732855 5813020817 3 SMP368(PDM08)_L SMP190_R\n",
"837 329732855 5813061249 3 SMP368(PDM08)_L None\n",
"\n",
"[3428 rows x 5 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"conn_df = pd.concat([clock_targets, candidate_targets])\n",
"conn_df"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "c4c33c25",
"metadata": {},
"outputs": [],
"source": [
"conn_df.to_csv('clock_e1_cand_targets.csv')"
]
},
{
"cell_type": "markdown",
"id": "98aa92ab-ac96-43bb-9edf-b89578a8b228",
"metadata": {},
"source": [
"To determine whether these 10 neurons had similar patterns of connectivity to any of the other identified clock neurons, jaccard indices were computed between the top 10 strongest shared candidates and the identified clock neurons."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "66e328da",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 3040x800 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"candidate_jaccard_out = jaccard_vis(conn_df, clock_df, clock_df['bodyId'], 'out', other_body_ids = candidate_IDs)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "fcd2e03b-e89f-4fdf-930d-cbf31219dd0c",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 800x800 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from visualization_utils import jaccard_simple\n",
"candidate_jac_out, fig = jaccard_simple(candidate_IDs, candidate_IDs, 'out', diag_mask=True)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "20f7e482-74c9-414a-9be9-a7525a279456",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 800x800 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# clustering by hand\n",
"candidate_IDs = candidate_IDs.reset_index(drop=True)\n",
"clustered_Ids = candidate_IDs[[0,4,5,3,2,6,1,7,8,9]]\n",
"\n",
"from visualization_utils import jaccard_simple\n",
"candidate_jac_out, fig = jaccard_simple(clustered_Ids, clustered_Ids, 'out', diag_mask=True)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "aa42b9cd-9acd-4aa8-a52d-3c1e8e4ad1dd",
"metadata": {},
"outputs": [],
"source": [
"# save figure\n",
"fig.savefig('jaccard_E1_target_outputs.svg')"
]
},
{
"cell_type": "markdown",
"id": "d99e1754-8042-4d14-a07c-bf39ab9de9d0",
"metadata": {},
"source": [
"The strong shared targets of E1 that return to the clock (with medium or strong connections) are found below. "
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "ad3a096a-8a03-4b13-add5-a03725494a66",
"metadata": {},
"outputs": [],
"source": [
"# get connections from strong shared targets of E1 to clock neurons\n",
"all_candidate_IDs = Evening1_targs['bodyId_post']\n",
"all_candidate_targets = get_input_output_conns(all_candidate_IDs, 'out', min_strength = 3)\n",
"all_candidate_targets = all_candidate_targets[all_candidate_targets.bodyId_post.isin(clock_df['bodyId'])]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "5cff43c3-c3ef-4a7f-84e4-68ca9bac43d8",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-26-c14192aa3d57>:4: FutureWarning: The default value of regex will change from True to False in a future version.\n",
" all_candidate_targets['instance_pre'] = all_candidate_targets['instance_pre'].str.replace(r'\\([^()]*\\)', '')\n"
]
}
],
"source": [
"# merge clock labels onto all_candidate_targets\n",
"all_candidate_targets = all_candidate_targets.merge(clock_df[['bodyId','labels']], left_on='bodyId_post', right_on='bodyId')\n",
"#remove parenthetical addendums to target instance names\n",
"all_candidate_targets['instance_pre'] = all_candidate_targets['instance_pre'].str.replace(r'\\([^()]*\\)', '')\n",
"# remove _L and _R from candidate target instance names\n",
"all_candidate_targets['instance_pre'] = [re.sub(r'_(L|R)','',str(x)) for x in all_candidate_targets['instance_pre']]\n",
"# drop extra bodyId column\n",
"all_candidate_targets = all_candidate_targets.drop(columns='bodyId')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "180e9852-cb82-4f57-8fb6-37b83aef6676",
"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>bodyId_pre</th>\n",
" <th>instance_pre</th>\n",
" <th>bodyId_post</th>\n",
" <th>instance_post</th>\n",
" <th>weight</th>\n",
" <th>labels</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>702152113</td>\n",
" <td>AVLP075</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>10</td>\n",
" <td>LNd5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>674108632</td>\n",
" <td>AVLP075</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>12</td>\n",
" <td>LNd4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>674108632</td>\n",
" <td>AVLP075</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>15</td>\n",
" <td>LNd5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>702152113</td>\n",
" <td>AVLP075</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>11</td>\n",
" <td>LNd4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>1072063538</td>\n",
" <td>DNp27</td>\n",
" <td>511051477</td>\n",
" <td>5th s-LNv</td>\n",
" <td>3</td>\n",
" <td>5th sLNv</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>1072063538</td>\n",
" <td>DNp27</td>\n",
" <td>5813069648</td>\n",
" <td>LNd_R</td>\n",
" <td>5</td>\n",
" <td>LNd6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>388881226</td>\n",
" <td>LHPV6m1</td>\n",
" <td>5813022274</td>\n",
" <td>DN1a_R</td>\n",
" <td>10</td>\n",
" <td>DN1a2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>388881226</td>\n",
" <td>LHPV6m1</td>\n",
" <td>264083994</td>\n",
" <td>DN1a_R</td>\n",
" <td>19</td>\n",
" <td>DN1a1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5813111989</td>\n",
" <td>None</td>\n",
" <td>546977514</td>\n",
" <td>LPN(PDL18)_L</td>\n",
" <td>3</td>\n",
" <td>LPN4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5813026589</td>\n",
" <td>SMP082</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>23</td>\n",
" <td>LNd5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>298254517</td>\n",
" <td>SMP082</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>13</td>\n",
" <td>LNd5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>298254517</td>\n",
" <td>SMP082</td>\n",
" <td>480029788</td>\n",
" <td>LPN_R</td>\n",
" <td>10</td>\n",
" <td>LPN2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>5813026589</td>\n",
" <td>SMP082</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>23</td>\n",
" <td>LNd4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>5813026589</td>\n",
" <td>SMP082</td>\n",
" <td>480029788</td>\n",
" <td>LPN_R</td>\n",
" <td>12</td>\n",
" <td>LPN2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>298254517</td>\n",
" <td>SMP082</td>\n",
" <td>450034902</td>\n",
" <td>LPN_R</td>\n",
" <td>10</td>\n",
" <td>LPN3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>5813026589</td>\n",
" <td>SMP082</td>\n",
" <td>450034902</td>\n",
" <td>LPN_R</td>\n",
" <td>14</td>\n",
" <td>LPN3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>298254517</td>\n",
" <td>SMP082</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>15</td>\n",
" <td>LNd4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>390331583</td>\n",
" <td>SMP368</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>7</td>\n",
" <td>LNd5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>329732855</td>\n",
" <td>SMP368</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>5</td>\n",
" <td>LNd5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>329732855</td>\n",
" <td>SMP368</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>5</td>\n",
" <td>LNd4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>390331583</td>\n",
" <td>SMP368</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>7</td>\n",
" <td>LNd4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5813040712</td>\n",
" <td>SMP512</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>23</td>\n",
" <td>LNd5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>358639742</td>\n",
" <td>SMP512</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>22</td>\n",
" <td>LNd4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>699687305</td>\n",
" <td>SMP512</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>16</td>\n",
" <td>LNd4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>699687305</td>\n",
" <td>SMP512</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>19</td>\n",
" <td>LNd5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>5813040712</td>\n",
" <td>SMP512</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>22</td>\n",
" <td>LNd4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>358639742</td>\n",
" <td>SMP512</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>18</td>\n",
" <td>LNd5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>330415332</td>\n",
" <td>SMP513</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>21</td>\n",
" <td>LNd4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>481070027</td>\n",
" <td>SMP513</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>12</td>\n",
" <td>LNd4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>481070027</td>\n",
" <td>SMP513</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>24</td>\n",
" <td>LNd5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>330415332</td>\n",
" <td>SMP513</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>18</td>\n",
" <td>LNd5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>574511458</td>\n",
" <td>SMP514</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>19</td>\n",
" <td>LNd4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>574511458</td>\n",
" <td>SMP514</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>24</td>\n",
" <td>LNd5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>453130054</td>\n",
" <td>SMP516</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>19</td>\n",
" <td>LNd5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>453130054</td>\n",
" <td>SMP516</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>18</td>\n",
" <td>LNd4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bodyId_pre instance_pre bodyId_post instance_post weight labels\n",
"3 702152113 AVLP075 5813021192 LNd_R 10 LNd5\n",
"18 674108632 AVLP075 5813056917 LNd_R 12 LNd4\n",
"5 674108632 AVLP075 5813021192 LNd_R 15 LNd5\n",
"16 702152113 AVLP075 5813056917 LNd_R 11 LNd4\n",
"32 1072063538 DNp27 511051477 5th s-LNv 3 5th sLNv\n",
"31 1072063538 DNp27 5813069648 LNd_R 5 LNd6\n",
"34 388881226 LHPV6m1 5813022274 DN1a_R 10 DN1a2\n",
"33 388881226 LHPV6m1 264083994 DN1a_R 19 DN1a1\n",
"0 5813111989 None 546977514 LPN(PDL18)_L 3 LPN4\n",
"2 5813026589 SMP082 5813021192 LNd_R 23 LNd5\n",
"13 298254517 SMP082 5813021192 LNd_R 13 LNd5\n",
"30 298254517 SMP082 480029788 LPN_R 10 LPN2\n",
"15 5813026589 SMP082 5813056917 LNd_R 23 LNd4\n",
"29 5813026589 SMP082 480029788 LPN_R 12 LPN2\n",
"28 298254517 SMP082 450034902 LPN_R 10 LPN3\n",
"27 5813026589 SMP082 450034902 LPN_R 14 LPN3\n",
"26 298254517 SMP082 5813056917 LNd_R 15 LNd4\n",
"9 390331583 SMP368 5813021192 LNd_R 7 LNd5\n",
"12 329732855 SMP368 5813021192 LNd_R 5 LNd5\n",
"25 329732855 SMP368 5813056917 LNd_R 5 LNd4\n",
"22 390331583 SMP368 5813056917 LNd_R 7 LNd4\n",
"1 5813040712 SMP512 5813021192 LNd_R 23 LNd5\n",
"23 358639742 SMP512 5813056917 LNd_R 22 LNd4\n",
"17 699687305 SMP512 5813056917 LNd_R 16 LNd4\n",
"4 699687305 SMP512 5813021192 LNd_R 19 LNd5\n",
"14 5813040712 SMP512 5813056917 LNd_R 22 LNd4\n",
"10 358639742 SMP512 5813021192 LNd_R 18 LNd5\n",
"24 330415332 SMP513 5813056917 LNd_R 21 LNd4\n",
"20 481070027 SMP513 5813056917 LNd_R 12 LNd4\n",
"7 481070027 SMP513 5813021192 LNd_R 24 LNd5\n",
"11 330415332 SMP513 5813021192 LNd_R 18 LNd5\n",
"19 574511458 SMP514 5813056917 LNd_R 19 LNd4\n",
"6 574511458 SMP514 5813021192 LNd_R 24 LNd5\n",
"8 453130054 SMP516 5813021192 LNd_R 19 LNd5\n",
"21 453130054 SMP516 5813056917 LNd_R 18 LNd4"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# just to see the cell types of the candidates\n",
"all_candidate_targets.sort_values(by='instance_pre')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "9dcf2e5d",
"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>bodyId_pre</th>\n",
" <th>instance_pre</th>\n",
" <th>bodyId_post</th>\n",
" <th>instance_post</th>\n",
" <th>weight</th>\n",
" <th>labels</th>\n",
" <th>candidate_label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>674108632</td>\n",
" <td>AVLP075</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>12</td>\n",
" <td>LNd4</td>\n",
" <td>AVLP075 (674108632)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>674108632</td>\n",
" <td>AVLP075</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>15</td>\n",
" <td>LNd5</td>\n",
" <td>AVLP075 (674108632)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>702152113</td>\n",
" <td>AVLP075</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>10</td>\n",
" <td>LNd5</td>\n",
" <td>AVLP075 (702152113)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>702152113</td>\n",
" <td>AVLP075</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>11</td>\n",
" <td>LNd4</td>\n",
" <td>AVLP075 (702152113)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>1072063538</td>\n",
" <td>DNp27</td>\n",
" <td>511051477</td>\n",
" <td>5th s-LNv</td>\n",
" <td>3</td>\n",
" <td>5th sLNv</td>\n",
" <td>DNp27 (1072063538)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>1072063538</td>\n",
" <td>DNp27</td>\n",
" <td>5813069648</td>\n",
" <td>LNd_R</td>\n",
" <td>5</td>\n",
" <td>LNd6</td>\n",
" <td>DNp27 (1072063538)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>388881226</td>\n",
" <td>LHPV6m1</td>\n",
" <td>5813022274</td>\n",
" <td>DN1a_R</td>\n",
" <td>10</td>\n",
" <td>DN1a2</td>\n",
" <td>LHPV6m1 (388881226)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>388881226</td>\n",
" <td>LHPV6m1</td>\n",
" <td>264083994</td>\n",
" <td>DN1a_R</td>\n",
" <td>19</td>\n",
" <td>DN1a1</td>\n",
" <td>LHPV6m1 (388881226)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5813111989</td>\n",
" <td>None</td>\n",
" <td>546977514</td>\n",
" <td>LPN(PDL18)_L</td>\n",
" <td>3</td>\n",
" <td>LPN4</td>\n",
" <td>None (5813111989)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>298254517</td>\n",
" <td>SMP082</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>13</td>\n",
" <td>LNd5</td>\n",
" <td>SMP082 (298254517)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>298254517</td>\n",
" <td>SMP082</td>\n",
" <td>480029788</td>\n",
" <td>LPN_R</td>\n",
" <td>10</td>\n",
" <td>LPN2</td>\n",
" <td>SMP082 (298254517)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>298254517</td>\n",
" <td>SMP082</td>\n",
" <td>450034902</td>\n",
" <td>LPN_R</td>\n",
" <td>10</td>\n",
" <td>LPN3</td>\n",
" <td>SMP082 (298254517)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>298254517</td>\n",
" <td>SMP082</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>15</td>\n",
" <td>LNd4</td>\n",
" <td>SMP082 (298254517)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>5813026589</td>\n",
" <td>SMP082</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>23</td>\n",
" <td>LNd4</td>\n",
" <td>SMP082 (5813026589)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5813026589</td>\n",
" <td>SMP082</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>23</td>\n",
" <td>LNd5</td>\n",
" <td>SMP082 (5813026589)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>5813026589</td>\n",
" <td>SMP082</td>\n",
" <td>480029788</td>\n",
" <td>LPN_R</td>\n",
" <td>12</td>\n",
" <td>LPN2</td>\n",
" <td>SMP082 (5813026589)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>5813026589</td>\n",
" <td>SMP082</td>\n",
" <td>450034902</td>\n",
" <td>LPN_R</td>\n",
" <td>14</td>\n",
" <td>LPN3</td>\n",
" <td>SMP082 (5813026589)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>329732855</td>\n",
" <td>SMP368</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>5</td>\n",
" <td>LNd5</td>\n",
" <td>SMP368 (329732855)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>329732855</td>\n",
" <td>SMP368</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>5</td>\n",
" <td>LNd4</td>\n",
" <td>SMP368 (329732855)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>390331583</td>\n",
" <td>SMP368</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>7</td>\n",
" <td>LNd5</td>\n",
" <td>SMP368 (390331583)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>390331583</td>\n",
" <td>SMP368</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>7</td>\n",
" <td>LNd4</td>\n",
" <td>SMP368 (390331583)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>358639742</td>\n",
" <td>SMP512</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>18</td>\n",
" <td>LNd5</td>\n",
" <td>SMP512 (358639742)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>358639742</td>\n",
" <td>SMP512</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>22</td>\n",
" <td>LNd4</td>\n",
" <td>SMP512 (358639742)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5813040712</td>\n",
" <td>SMP512</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>23</td>\n",
" <td>LNd5</td>\n",
" <td>SMP512 (5813040712)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>5813040712</td>\n",
" <td>SMP512</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>22</td>\n",
" <td>LNd4</td>\n",
" <td>SMP512 (5813040712)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>699687305</td>\n",
" <td>SMP512</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>16</td>\n",
" <td>LNd4</td>\n",
" <td>SMP512 (699687305)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>699687305</td>\n",
" <td>SMP512</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>19</td>\n",
" <td>LNd5</td>\n",
" <td>SMP512 (699687305)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>330415332</td>\n",
" <td>SMP513</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>18</td>\n",
" <td>LNd5</td>\n",
" <td>SMP513 (330415332)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>330415332</td>\n",
" <td>SMP513</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>21</td>\n",
" <td>LNd4</td>\n",
" <td>SMP513 (330415332)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>481070027</td>\n",
" <td>SMP513</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>12</td>\n",
" <td>LNd4</td>\n",
" <td>SMP513 (481070027)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>481070027</td>\n",
" <td>SMP513</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>24</td>\n",
" <td>LNd5</td>\n",
" <td>SMP513 (481070027)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>574511458</td>\n",
" <td>SMP514</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>19</td>\n",
" <td>LNd4</td>\n",
" <td>SMP514 (574511458)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>574511458</td>\n",
" <td>SMP514</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>24</td>\n",
" <td>LNd5</td>\n",
" <td>SMP514 (574511458)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>453130054</td>\n",
" <td>SMP516</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>19</td>\n",
" <td>LNd5</td>\n",
" <td>SMP516 (453130054)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>453130054</td>\n",
" <td>SMP516</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>18</td>\n",
" <td>LNd4</td>\n",
" <td>SMP516 (453130054)</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bodyId_pre instance_pre bodyId_post instance_post weight labels \\\n",
"18 674108632 AVLP075 5813056917 LNd_R 12 LNd4 \n",
"5 674108632 AVLP075 5813021192 LNd_R 15 LNd5 \n",
"3 702152113 AVLP075 5813021192 LNd_R 10 LNd5 \n",
"16 702152113 AVLP075 5813056917 LNd_R 11 LNd4 \n",
"32 1072063538 DNp27 511051477 5th s-LNv 3 5th sLNv \n",
"31 1072063538 DNp27 5813069648 LNd_R 5 LNd6 \n",
"34 388881226 LHPV6m1 5813022274 DN1a_R 10 DN1a2 \n",
"33 388881226 LHPV6m1 264083994 DN1a_R 19 DN1a1 \n",
"0 5813111989 None 546977514 LPN(PDL18)_L 3 LPN4 \n",
"13 298254517 SMP082 5813021192 LNd_R 13 LNd5 \n",
"30 298254517 SMP082 480029788 LPN_R 10 LPN2 \n",
"28 298254517 SMP082 450034902 LPN_R 10 LPN3 \n",
"26 298254517 SMP082 5813056917 LNd_R 15 LNd4 \n",
"15 5813026589 SMP082 5813056917 LNd_R 23 LNd4 \n",
"2 5813026589 SMP082 5813021192 LNd_R 23 LNd5 \n",
"29 5813026589 SMP082 480029788 LPN_R 12 LPN2 \n",
"27 5813026589 SMP082 450034902 LPN_R 14 LPN3 \n",
"12 329732855 SMP368 5813021192 LNd_R 5 LNd5 \n",
"25 329732855 SMP368 5813056917 LNd_R 5 LNd4 \n",
"9 390331583 SMP368 5813021192 LNd_R 7 LNd5 \n",
"22 390331583 SMP368 5813056917 LNd_R 7 LNd4 \n",
"10 358639742 SMP512 5813021192 LNd_R 18 LNd5 \n",
"23 358639742 SMP512 5813056917 LNd_R 22 LNd4 \n",
"1 5813040712 SMP512 5813021192 LNd_R 23 LNd5 \n",
"14 5813040712 SMP512 5813056917 LNd_R 22 LNd4 \n",
"17 699687305 SMP512 5813056917 LNd_R 16 LNd4 \n",
"4 699687305 SMP512 5813021192 LNd_R 19 LNd5 \n",
"11 330415332 SMP513 5813021192 LNd_R 18 LNd5 \n",
"24 330415332 SMP513 5813056917 LNd_R 21 LNd4 \n",
"20 481070027 SMP513 5813056917 LNd_R 12 LNd4 \n",
"7 481070027 SMP513 5813021192 LNd_R 24 LNd5 \n",
"19 574511458 SMP514 5813056917 LNd_R 19 LNd4 \n",
"6 574511458 SMP514 5813021192 LNd_R 24 LNd5 \n",
"8 453130054 SMP516 5813021192 LNd_R 19 LNd5 \n",
"21 453130054 SMP516 5813056917 LNd_R 18 LNd4 \n",
"\n",
" candidate_label \n",
"18 AVLP075 (674108632) \n",
"5 AVLP075 (674108632) \n",
"3 AVLP075 (702152113) \n",
"16 AVLP075 (702152113) \n",
"32 DNp27 (1072063538) \n",
"31 DNp27 (1072063538) \n",
"34 LHPV6m1 (388881226) \n",
"33 LHPV6m1 (388881226) \n",
"0 None (5813111989) \n",
"13 SMP082 (298254517) \n",
"30 SMP082 (298254517) \n",
"28 SMP082 (298254517) \n",
"26 SMP082 (298254517) \n",
"15 SMP082 (5813026589) \n",
"2 SMP082 (5813026589) \n",
"29 SMP082 (5813026589) \n",
"27 SMP082 (5813026589) \n",
"12 SMP368 (329732855) \n",
"25 SMP368 (329732855) \n",
"9 SMP368 (390331583) \n",
"22 SMP368 (390331583) \n",
"10 SMP512 (358639742) \n",
"23 SMP512 (358639742) \n",
"1 SMP512 (5813040712) \n",
"14 SMP512 (5813040712) \n",
"17 SMP512 (699687305) \n",
"4 SMP512 (699687305) \n",
"11 SMP513 (330415332) \n",
"24 SMP513 (330415332) \n",
"20 SMP513 (481070027) \n",
"7 SMP513 (481070027) \n",
"19 SMP514 (574511458) \n",
"6 SMP514 (574511458) \n",
"8 SMP516 (453130054) \n",
"21 SMP516 (453130054) "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# recreate labels for candidates to include type and bodyId\n",
"all_candidate_targets['candidate_label'] = all_candidate_targets['instance_pre'] + ' (' + all_candidate_targets['bodyId_pre'].map(str) + ')'\n",
"all_candidate_targets.sort_values(by='candidate_label')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "38c8a613-9c79-4700-a535-588dc217e3c4",
"metadata": {},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th>labels</th>\n",
" <th>5th sLNv</th>\n",
" <th>DN1a1</th>\n",
" <th>DN1a2</th>\n",
" <th>LNd4</th>\n",
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" <th>AVLP075 (674108632)</th>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>DNp27 (1072063538)</th>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>LHPV6m1 (388881226)</th>\n",
" <td>NaN</td>\n",
" <td>19.0</td>\n",
" <td>10.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>None (5813111989)</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP082 (298254517)</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>15.0</td>\n",
" <td>13.0</td>\n",
" <td>NaN</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>SMP082 (5813026589)</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>23.0</td>\n",
" <td>23.0</td>\n",
" <td>NaN</td>\n",
" <td>12.0</td>\n",
" <td>14.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP368 (329732855)</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP368 (390331583)</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7.0</td>\n",
" <td>7.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP512 (358639742)</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>22.0</td>\n",
" <td>18.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>SMP512 (5813040712)</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>22.0</td>\n",
" <td>23.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP512 (699687305)</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>16.0</td>\n",
" <td>19.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>SMP513 (330415332)</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>21.0</td>\n",
" <td>18.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <th>SMP516 (453130054)</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>18.0</td>\n",
" <td>19.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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],
"text/plain": [
"labels 5th sLNv DN1a1 DN1a2 LNd4 LNd5 LNd6 LPN2 LPN3 \\\n",
"candidate_label \n",
"AVLP075 (674108632) NaN NaN NaN 12.0 15.0 NaN NaN NaN \n",
"AVLP075 (702152113) NaN NaN NaN 11.0 10.0 NaN NaN NaN \n",
"DNp27 (1072063538) 3.0 NaN NaN NaN NaN 5.0 NaN NaN \n",
"LHPV6m1 (388881226) NaN 19.0 10.0 NaN NaN NaN NaN NaN \n",
"None (5813111989) NaN NaN NaN NaN NaN NaN NaN NaN \n",
"SMP082 (298254517) NaN NaN NaN 15.0 13.0 NaN 10.0 10.0 \n",
"SMP082 (5813026589) NaN NaN NaN 23.0 23.0 NaN 12.0 14.0 \n",
"SMP368 (329732855) NaN NaN NaN 5.0 5.0 NaN NaN NaN \n",
"SMP368 (390331583) NaN NaN NaN 7.0 7.0 NaN NaN NaN \n",
"SMP512 (358639742) NaN NaN NaN 22.0 18.0 NaN NaN NaN \n",
"SMP512 (5813040712) NaN NaN NaN 22.0 23.0 NaN NaN NaN \n",
"SMP512 (699687305) NaN NaN NaN 16.0 19.0 NaN NaN NaN \n",
"SMP513 (330415332) NaN NaN NaN 21.0 18.0 NaN NaN NaN \n",
"SMP513 (481070027) NaN NaN NaN 12.0 24.0 NaN NaN NaN \n",
"SMP514 (574511458) NaN NaN NaN 19.0 24.0 NaN NaN NaN \n",
"SMP516 (453130054) NaN NaN NaN 18.0 19.0 NaN NaN NaN \n",
"\n",
"labels LPN4 \n",
"candidate_label \n",
"AVLP075 (674108632) NaN \n",
"AVLP075 (702152113) NaN \n",
"DNp27 (1072063538) NaN \n",
"LHPV6m1 (388881226) NaN \n",
"None (5813111989) 3.0 \n",
"SMP082 (298254517) NaN \n",
"SMP082 (5813026589) NaN \n",
"SMP368 (329732855) NaN \n",
"SMP368 (390331583) NaN \n",
"SMP512 (358639742) NaN \n",
"SMP512 (5813040712) NaN \n",
"SMP512 (699687305) NaN \n",
"SMP513 (330415332) NaN \n",
"SMP513 (481070027) NaN \n",
"SMP514 (574511458) NaN \n",
"SMP516 (453130054) NaN "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# generate pivot table for strong shared targets back to clock\n",
"all_candidate_targets = all_candidate_targets.pivot(index='candidate_label', columns='labels', values='weight')\n",
"all_candidate_targets"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "951734cd-7dc1-4b90-bd3f-7c88a9883e9c",
"metadata": {},
"outputs": [],
"source": [
"# export table\n",
"all_candidate_targets.to_csv('E1_targets_to_clock.csv')"
]
},
{
"cell_type": "markdown",
"id": "842ac31c-b16c-487f-ab52-86d1d22af26e",
"metadata": {},
"source": [
"E2 also had more than 10 strong shared targets, but here we focused on the connectivity patterns of the top 10."
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "75f937ae",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\dbing\\Documents\\Research\\Code\\clock-connectome\\connection_utils.py:208: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
" conns_df = conns_df.groupby(['bodyId_post', 'instance_post'], as_index=False)['weight', 'shared'].sum()\n"
]
},
{
"data": {
"text/plain": [
"15 390331583\n",
"59 5813021666\n",
"5 329732855\n",
"2 327588446\n",
"23 421068062\n",
"6 356140100\n",
"18 417143726\n",
"27 451148838\n",
"64 5813057153\n",
"63 5813056917\n",
"Name: bodyId_post, dtype: int64"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"E2Ids = clock_df[clock_df['subphase']=='E2']['bodyId']\n",
"Evening2_targs = strong_shared_connections(E2Ids, 'out', 2)\n",
"\n",
"candidate_IDs = Evening2_targs['bodyId_post'][0:10]\n",
"candidate_IDs"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "36c67003",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\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>bodyId_pre</th>\n",
" <th>bodyId_post</th>\n",
" <th>weight</th>\n",
" <th>instance_pre</th>\n",
" <th>instance_post</th>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>264083994</td>\n",
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" <td>4</td>\n",
" <td>DN1a1</td>\n",
" <td>SLP443</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>264083994</td>\n",
" <td>325814461</td>\n",
" <td>10</td>\n",
" <td>DN1a1</td>\n",
" <td>LHPV4c3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>264083994</td>\n",
" <td>326119769</td>\n",
" <td>4</td>\n",
" <td>DN1a1</td>\n",
" <td>SLP457</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>264083994</td>\n",
" <td>356472645</td>\n",
" <td>3</td>\n",
" <td>DN1a1</td>\n",
" <td>SLP364</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>264083994</td>\n",
" <td>356503889</td>\n",
" <td>7</td>\n",
" <td>DN1a1</td>\n",
" <td>LHPV6f4_b</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>924</th>\n",
" <td>327588446</td>\n",
" <td>951446091</td>\n",
" <td>3</td>\n",
" <td>SMP162_R</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>925</th>\n",
" <td>327588446</td>\n",
" <td>954161608</td>\n",
" <td>3</td>\n",
" <td>SMP162_R</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>926</th>\n",
" <td>327588446</td>\n",
" <td>1018273031</td>\n",
" <td>3</td>\n",
" <td>SMP162_R</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>927</th>\n",
" <td>327588446</td>\n",
" <td>5813060768</td>\n",
" <td>3</td>\n",
" <td>SMP162_R</td>\n",
" <td>SMP600(SCB077)_R</td>\n",
" </tr>\n",
" <tr>\n",
" <th>928</th>\n",
" <td>327588446</td>\n",
" <td>5813077945</td>\n",
" <td>3</td>\n",
" <td>SMP162_R</td>\n",
" <td>SMP543(PDM27)_L</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3564 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" bodyId_pre bodyId_post weight instance_pre instance_post\n",
"0 264083994 299272434 4 DN1a1 SLP443\n",
"1 264083994 325814461 10 DN1a1 LHPV4c3\n",
"2 264083994 326119769 4 DN1a1 SLP457\n",
"3 264083994 356472645 3 DN1a1 SLP364\n",
"4 264083994 356503889 7 DN1a1 LHPV6f4_b\n",
".. ... ... ... ... ...\n",
"924 327588446 951446091 3 SMP162_R None\n",
"925 327588446 954161608 3 SMP162_R None\n",
"926 327588446 1018273031 3 SMP162_R None\n",
"927 327588446 5813060768 3 SMP162_R SMP600(SCB077)_R\n",
"928 327588446 5813077945 3 SMP162_R SMP543(PDM27)_L\n",
"\n",
"[3564 rows x 5 columns]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"candidate_targets = get_input_output_conns(candidate_IDs, 'out', min_strength = 3)\n",
"conn_df = pd.concat([clock_targets, candidate_targets])\n",
"conn_df"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "614e1c4e-15b7-4d0c-abf8-5defa12af596",
"metadata": {},
"outputs": [],
"source": [
"# export table\n",
"conn_df.to_csv('clock_e2_cand_targets.csv')"
]
},
{
"cell_type": "markdown",
"id": "40a52c1e-5959-449a-8fc4-1a74e23f1771",
"metadata": {},
"source": [
"To determine whether these 10 neurons had similar patterns of connectivity to any of the other identified clock neurons, jaccard indices were computed between the top 10 strongest shared candidates and the identified clock neurons."
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "d179ba7c-63e6-4179-bbae-48cca968bbca",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 3040x800 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"candidate_jaccard_out = jaccard_vis(conn_df, clock_df, clock_df['bodyId'], 'out', other_body_ids = candidate_IDs)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "1a3a87a6-05f2-414e-aa32-f7a417e392a4",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 800x800 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from visualization_utils import jaccard_simple\n",
"candidate_jac_out, fig = jaccard_simple(candidate_IDs, candidate_IDs, 'out', diag_mask=False)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "4a6e96cd-011c-4a72-b05a-eda7664afdb0",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 800x800 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# clustering by hand\n",
"candidate_IDs = candidate_IDs.reset_index(drop=True)\n",
"clustered_Ids = candidate_IDs[[0,2,9,1,8,5,7,4,3,6]]\n",
"\n",
"from visualization_utils import jaccard_simple\n",
"candidate_jac_out, fig = jaccard_simple(clustered_Ids, clustered_Ids, 'out', diag_mask=True)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "5537ba09-7c1f-4916-868b-8f7b00c76de3",
"metadata": {},
"outputs": [],
"source": [
"# save figure\n",
"fig.savefig('jaccard_E2_target_outputs.svg')"
]
},
{
"cell_type": "markdown",
"id": "89b18668-0746-4ae4-9968-f61464f24f87",
"metadata": {},
"source": [
"The strong shared targets of E2 that return to the clock (with medium or strong connections) are found below. "
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "75b55837-91f4-4cfd-b71b-47c133d056ba",
"metadata": {},
"outputs": [],
"source": [
"# get connections from strong shared targets of E2 to clock neurons\n",
"all_candidate_IDs = Evening2_targs['bodyId_post']\n",
"all_candidate_targets = get_input_output_conns(all_candidate_IDs, 'out', min_strength = 3)\n",
"all_candidate_targets = all_candidate_targets[all_candidate_targets.bodyId_post.isin(clock_df['bodyId'])]"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "799e99bb-7dd7-40d3-9625-22c6bcc35b41",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-39-819992679692>:6: FutureWarning: The default value of regex will change from True to False in a future version.\n",
" all_candidate_targets['instance_pre'] = all_candidate_targets['instance_pre'].str.replace(r'\\([^()]*\\)', '')\n"
]
}
],
"source": [
"import re\n",
"\n",
"# merge clock labels onto all_candidate_targets\n",
"all_candidate_targets = all_candidate_targets.merge(clock_df[['bodyId','labels']], left_on='bodyId_post', right_on='bodyId')\n",
"#remove parenthetical addendums to target instance names\n",
"all_candidate_targets['instance_pre'] = all_candidate_targets['instance_pre'].str.replace(r'\\([^()]*\\)', '')\n",
"# remove _L and _R from candidate target instance names\n",
"all_candidate_targets['instance_pre'] = [re.sub(r'_(L|R)','',str(x)) for x in all_candidate_targets['instance_pre']]\n",
"# drop extra bodyId column\n",
"all_candidate_targets = all_candidate_targets.drop(columns='bodyId')"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "57e9bda7-b92d-441e-8cdb-cfdb8690be5a",
"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>bodyId_pre</th>\n",
" <th>instance_pre</th>\n",
" <th>bodyId_post</th>\n",
" <th>instance_post</th>\n",
" <th>weight</th>\n",
" <th>labels</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>325529237</td>\n",
" <td>DN1pA</td>\n",
" <td>5813069648</td>\n",
" <td>LNd_R</td>\n",
" <td>30</td>\n",
" <td>LNd6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>387166379</td>\n",
" <td>DN1pA</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>5</td>\n",
" <td>LNd5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>325529237</td>\n",
" <td>DN1pA</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>5</td>\n",
" <td>LNd4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>387166379</td>\n",
" <td>DN1pA</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>8</td>\n",
" <td>LNd4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>325529237</td>\n",
" <td>DN1pA</td>\n",
" <td>511051477</td>\n",
" <td>5th s-LNv</td>\n",
" <td>33</td>\n",
" <td>5th sLNv</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>387166379</td>\n",
" <td>DN1pA</td>\n",
" <td>511051477</td>\n",
" <td>5th s-LNv</td>\n",
" <td>25</td>\n",
" <td>5th sLNv</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>387166379</td>\n",
" <td>DN1pA</td>\n",
" <td>5813069648</td>\n",
" <td>LNd_R</td>\n",
" <td>30</td>\n",
" <td>LNd6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>325529237</td>\n",
" <td>DN1pA</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>8</td>\n",
" <td>LNd5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>5813021192</td>\n",
" <td>LNd</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>5</td>\n",
" <td>LNd4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>420027427</td>\n",
" <td>SMP219</td>\n",
" <td>5813064789</td>\n",
" <td>LNd_R</td>\n",
" <td>3</td>\n",
" <td>LNd3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>357949102</td>\n",
" <td>SMP222</td>\n",
" <td>356818551</td>\n",
" <td>LPN_R</td>\n",
" <td>3</td>\n",
" <td>LPN1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>357949102</td>\n",
" <td>SMP222</td>\n",
" <td>480029788</td>\n",
" <td>LPN_R</td>\n",
" <td>5</td>\n",
" <td>LPN2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>417143726</td>\n",
" <td>SMP223</td>\n",
" <td>450034902</td>\n",
" <td>LPN_R</td>\n",
" <td>20</td>\n",
" <td>LPN3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>540325242</td>\n",
" <td>SMP223</td>\n",
" <td>480029788</td>\n",
" <td>LPN_R</td>\n",
" <td>3</td>\n",
" <td>LPN2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>417143726</td>\n",
" <td>SMP223</td>\n",
" <td>356818551</td>\n",
" <td>LPN_R</td>\n",
" <td>14</td>\n",
" <td>LPN1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>449335798</td>\n",
" <td>SMP223</td>\n",
" <td>387166379</td>\n",
" <td>DN1pA_R</td>\n",
" <td>5</td>\n",
" <td>DN1pA5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>540325242</td>\n",
" <td>SMP223</td>\n",
" <td>5813071319</td>\n",
" <td>DN1pB_R</td>\n",
" <td>3</td>\n",
" <td>DN1pB2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>540325242</td>\n",
" <td>SMP223</td>\n",
" <td>386834269</td>\n",
" <td>DN1pB_R</td>\n",
" <td>5</td>\n",
" <td>DN1pB1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>449335798</td>\n",
" <td>SMP223</td>\n",
" <td>480029788</td>\n",
" <td>LPN_R</td>\n",
" <td>7</td>\n",
" <td>LPN2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>417143726</td>\n",
" <td>SMP223</td>\n",
" <td>480029788</td>\n",
" <td>LPN_R</td>\n",
" <td>18</td>\n",
" <td>LPN2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5813087531</td>\n",
" <td>SMP223</td>\n",
" <td>450034902</td>\n",
" <td>LPN_R</td>\n",
" <td>14</td>\n",
" <td>LPN3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>449335798</td>\n",
" <td>SMP223</td>\n",
" <td>450034902</td>\n",
" <td>LPN_R</td>\n",
" <td>3</td>\n",
" <td>LPN3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>540325242</td>\n",
" <td>SMP223</td>\n",
" <td>450034902</td>\n",
" <td>LPN_R</td>\n",
" <td>3</td>\n",
" <td>LPN3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5813087531</td>\n",
" <td>SMP223</td>\n",
" <td>480029788</td>\n",
" <td>LPN_R</td>\n",
" <td>16</td>\n",
" <td>LPN2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>5813087531</td>\n",
" <td>SMP223</td>\n",
" <td>5813010153</td>\n",
" <td>DN1pA_R</td>\n",
" <td>4</td>\n",
" <td>DN1pA1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>449335798</td>\n",
" <td>SMP223</td>\n",
" <td>5813010153</td>\n",
" <td>DN1pA_R</td>\n",
" <td>4</td>\n",
" <td>DN1pA1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>329732855</td>\n",
" <td>SMP368</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>5</td>\n",
" <td>LNd5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>390331583</td>\n",
" <td>SMP368</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>7</td>\n",
" <td>LNd5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>329732855</td>\n",
" <td>SMP368</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>5</td>\n",
" <td>LNd4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>390331583</td>\n",
" <td>SMP368</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>7</td>\n",
" <td>LNd4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>5813022753</td>\n",
" <td>aMe</td>\n",
" <td>5813069648</td>\n",
" <td>LNd_R</td>\n",
" <td>8</td>\n",
" <td>LNd6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>5813040744</td>\n",
" <td>aMe</td>\n",
" <td>1884625521</td>\n",
" <td>l-LNv</td>\n",
" <td>29</td>\n",
" <td>lLNv1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>5813022753</td>\n",
" <td>aMe</td>\n",
" <td>1884625521</td>\n",
" <td>l-LNv</td>\n",
" <td>23</td>\n",
" <td>lLNv1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>5813040744</td>\n",
" <td>aMe</td>\n",
" <td>5813026773</td>\n",
" <td>l-LNv</td>\n",
" <td>21</td>\n",
" <td>lLNv4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>5813022753</td>\n",
" <td>aMe</td>\n",
" <td>5813026773</td>\n",
" <td>l-LNv</td>\n",
" <td>16</td>\n",
" <td>lLNv4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>5813040744</td>\n",
" <td>aMe</td>\n",
" <td>5813001741</td>\n",
" <td>l-LNv</td>\n",
" <td>20</td>\n",
" <td>lLNv3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>5813022753</td>\n",
" <td>aMe</td>\n",
" <td>5813001741</td>\n",
" <td>l-LNv</td>\n",
" <td>25</td>\n",
" <td>lLNv3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>5813040744</td>\n",
" <td>aMe</td>\n",
" <td>2065745704</td>\n",
" <td>l-LNv</td>\n",
" <td>16</td>\n",
" <td>lLNv2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>5813022753</td>\n",
" <td>aMe</td>\n",
" <td>2065745704</td>\n",
" <td>l-LNv</td>\n",
" <td>16</td>\n",
" <td>lLNv2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>5813040744</td>\n",
" <td>aMe</td>\n",
" <td>1664980698</td>\n",
" <td>s-LNv</td>\n",
" <td>12</td>\n",
" <td>sLNv2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>5813022753</td>\n",
" <td>aMe</td>\n",
" <td>1664980698</td>\n",
" <td>s-LNv</td>\n",
" <td>26</td>\n",
" <td>sLNv2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>5813022753</td>\n",
" <td>aMe</td>\n",
" <td>511051477</td>\n",
" <td>5th s-LNv</td>\n",
" <td>6</td>\n",
" <td>5th sLNv</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>5813040744</td>\n",
" <td>aMe</td>\n",
" <td>511051477</td>\n",
" <td>5th s-LNv</td>\n",
" <td>3</td>\n",
" <td>5th sLNv</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>5813022753</td>\n",
" <td>aMe</td>\n",
" <td>2007068523</td>\n",
" <td>s-LNv</td>\n",
" <td>23</td>\n",
" <td>sLNv3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>5813040744</td>\n",
" <td>aMe</td>\n",
" <td>2007068523</td>\n",
" <td>s-LNv</td>\n",
" <td>8</td>\n",
" <td>sLNv3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>5813040744</td>\n",
" <td>aMe</td>\n",
" <td>5813069648</td>\n",
" <td>LNd_R</td>\n",
" <td>11</td>\n",
" <td>LNd6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>5813040744</td>\n",
" <td>aMe</td>\n",
" <td>2068801704</td>\n",
" <td>s-LNv</td>\n",
" <td>9</td>\n",
" <td>sLNv1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>5813022753</td>\n",
" <td>aMe</td>\n",
" <td>1975347348</td>\n",
" <td>s-LNv</td>\n",
" <td>22</td>\n",
" <td>sLNv4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>5813040744</td>\n",
" <td>aMe</td>\n",
" <td>1975347348</td>\n",
" <td>s-LNv</td>\n",
" <td>9</td>\n",
" <td>sLNv4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>5813022753</td>\n",
" <td>aMe</td>\n",
" <td>2068801704</td>\n",
" <td>s-LNv</td>\n",
" <td>18</td>\n",
" <td>sLNv1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>5813021666</td>\n",
" <td>aMe22</td>\n",
" <td>511051477</td>\n",
" <td>5th s-LNv</td>\n",
" <td>71</td>\n",
" <td>5th sLNv</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>5813021666</td>\n",
" <td>aMe22</td>\n",
" <td>5813069648</td>\n",
" <td>LNd_R</td>\n",
" <td>81</td>\n",
" <td>LNd6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bodyId_pre instance_pre bodyId_post instance_post weight labels\n",
"25 325529237 DN1pA 5813069648 LNd_R 30 LNd6\n",
"49 387166379 DN1pA 5813021192 LNd_R 5 LNd5\n",
"41 325529237 DN1pA 5813056917 LNd_R 5 LNd4\n",
"39 387166379 DN1pA 5813056917 LNd_R 8 LNd4\n",
"36 325529237 DN1pA 511051477 5th s-LNv 33 5th sLNv\n",
"35 387166379 DN1pA 511051477 5th s-LNv 25 5th sLNv\n",
"24 387166379 DN1pA 5813069648 LNd_R 30 LNd6\n",
"51 325529237 DN1pA 5813021192 LNd_R 8 LNd5\n",
"37 5813021192 LNd 5813056917 LNd_R 5 LNd4\n",
"45 420027427 SMP219 5813064789 LNd_R 3 LNd3\n",
"47 357949102 SMP222 356818551 LPN_R 3 LPN1\n",
"4 357949102 SMP222 480029788 LPN_R 5 LPN2\n",
"8 417143726 SMP223 450034902 LPN_R 20 LPN3\n",
"1 540325242 SMP223 480029788 LPN_R 3 LPN2\n",
"46 417143726 SMP223 356818551 LPN_R 14 LPN1\n",
"44 449335798 SMP223 387166379 DN1pA_R 5 DN1pA5\n",
"43 540325242 SMP223 5813071319 DN1pB_R 3 DN1pB2\n",
"42 540325242 SMP223 386834269 DN1pB_R 5 DN1pB1\n",
"2 449335798 SMP223 480029788 LPN_R 7 LPN2\n",
"3 417143726 SMP223 480029788 LPN_R 18 LPN2\n",
"5 5813087531 SMP223 450034902 LPN_R 14 LPN3\n",
"7 449335798 SMP223 450034902 LPN_R 3 LPN3\n",
"6 540325242 SMP223 450034902 LPN_R 3 LPN3\n",
"0 5813087531 SMP223 480029788 LPN_R 16 LPN2\n",
"9 5813087531 SMP223 5813010153 DN1pA_R 4 DN1pA1\n",
"10 449335798 SMP223 5813010153 DN1pA_R 4 DN1pA1\n",
"50 329732855 SMP368 5813021192 LNd_R 5 LNd5\n",
"48 390331583 SMP368 5813021192 LNd_R 7 LNd5\n",
"40 329732855 SMP368 5813056917 LNd_R 5 LNd4\n",
"38 390331583 SMP368 5813056917 LNd_R 7 LNd4\n",
"22 5813022753 aMe 5813069648 LNd_R 8 LNd6\n",
"11 5813040744 aMe 1884625521 l-LNv 29 lLNv1\n",
"12 5813022753 aMe 1884625521 l-LNv 23 lLNv1\n",
"13 5813040744 aMe 5813026773 l-LNv 21 lLNv4\n",
"14 5813022753 aMe 5813026773 l-LNv 16 lLNv4\n",
"15 5813040744 aMe 5813001741 l-LNv 20 lLNv3\n",
"16 5813022753 aMe 5813001741 l-LNv 25 lLNv3\n",
"17 5813040744 aMe 2065745704 l-LNv 16 lLNv2\n",
"18 5813022753 aMe 2065745704 l-LNv 16 lLNv2\n",
"19 5813040744 aMe 1664980698 s-LNv 12 sLNv2\n",
"20 5813022753 aMe 1664980698 s-LNv 26 sLNv2\n",
"33 5813022753 aMe 511051477 5th s-LNv 6 5th sLNv\n",
"32 5813040744 aMe 511051477 5th s-LNv 3 5th sLNv\n",
"31 5813022753 aMe 2007068523 s-LNv 23 sLNv3\n",
"30 5813040744 aMe 2007068523 s-LNv 8 sLNv3\n",
"21 5813040744 aMe 5813069648 LNd_R 11 LNd6\n",
"28 5813040744 aMe 2068801704 s-LNv 9 sLNv1\n",
"27 5813022753 aMe 1975347348 s-LNv 22 sLNv4\n",
"26 5813040744 aMe 1975347348 s-LNv 9 sLNv4\n",
"29 5813022753 aMe 2068801704 s-LNv 18 sLNv1\n",
"34 5813021666 aMe22 511051477 5th s-LNv 71 5th sLNv\n",
"23 5813021666 aMe22 5813069648 LNd_R 81 LNd6"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# just to see the cell types of the candidates\n",
"all_candidate_targets.sort_values(by='instance_pre')"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "35661f02",
"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>bodyId_pre</th>\n",
" <th>instance_pre</th>\n",
" <th>bodyId_post</th>\n",
" <th>instance_post</th>\n",
" <th>weight</th>\n",
" <th>labels</th>\n",
" <th>candidate_label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>325529237</td>\n",
" <td>DN1pA</td>\n",
" <td>5813069648</td>\n",
" <td>LNd_R</td>\n",
" <td>30</td>\n",
" <td>LNd6</td>\n",
" <td>DN1pA (325529237)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>325529237</td>\n",
" <td>DN1pA</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>5</td>\n",
" <td>LNd4</td>\n",
" <td>DN1pA (325529237)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>325529237</td>\n",
" <td>DN1pA</td>\n",
" <td>511051477</td>\n",
" <td>5th s-LNv</td>\n",
" <td>33</td>\n",
" <td>5th sLNv</td>\n",
" <td>DN1pA (325529237)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>325529237</td>\n",
" <td>DN1pA</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>8</td>\n",
" <td>LNd5</td>\n",
" <td>DN1pA (325529237)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>387166379</td>\n",
" <td>DN1pA</td>\n",
" <td>5813069648</td>\n",
" <td>LNd_R</td>\n",
" <td>30</td>\n",
" <td>LNd6</td>\n",
" <td>DN1pA (387166379)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>387166379</td>\n",
" <td>DN1pA</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>5</td>\n",
" <td>LNd5</td>\n",
" <td>DN1pA (387166379)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>387166379</td>\n",
" <td>DN1pA</td>\n",
" <td>511051477</td>\n",
" <td>5th s-LNv</td>\n",
" <td>25</td>\n",
" <td>5th sLNv</td>\n",
" <td>DN1pA (387166379)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>387166379</td>\n",
" <td>DN1pA</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>8</td>\n",
" <td>LNd4</td>\n",
" <td>DN1pA (387166379)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>5813021192</td>\n",
" <td>LNd</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>5</td>\n",
" <td>LNd4</td>\n",
" <td>LNd (5813021192)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>420027427</td>\n",
" <td>SMP219</td>\n",
" <td>5813064789</td>\n",
" <td>LNd_R</td>\n",
" <td>3</td>\n",
" <td>LNd3</td>\n",
" <td>SMP219 (420027427)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>357949102</td>\n",
" <td>SMP222</td>\n",
" <td>480029788</td>\n",
" <td>LPN_R</td>\n",
" <td>5</td>\n",
" <td>LPN2</td>\n",
" <td>SMP222 (357949102)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>357949102</td>\n",
" <td>SMP222</td>\n",
" <td>356818551</td>\n",
" <td>LPN_R</td>\n",
" <td>3</td>\n",
" <td>LPN1</td>\n",
" <td>SMP222 (357949102)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>417143726</td>\n",
" <td>SMP223</td>\n",
" <td>356818551</td>\n",
" <td>LPN_R</td>\n",
" <td>14</td>\n",
" <td>LPN1</td>\n",
" <td>SMP223 (417143726)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>417143726</td>\n",
" <td>SMP223</td>\n",
" <td>480029788</td>\n",
" <td>LPN_R</td>\n",
" <td>18</td>\n",
" <td>LPN2</td>\n",
" <td>SMP223 (417143726)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>417143726</td>\n",
" <td>SMP223</td>\n",
" <td>450034902</td>\n",
" <td>LPN_R</td>\n",
" <td>20</td>\n",
" <td>LPN3</td>\n",
" <td>SMP223 (417143726)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>449335798</td>\n",
" <td>SMP223</td>\n",
" <td>450034902</td>\n",
" <td>LPN_R</td>\n",
" <td>3</td>\n",
" <td>LPN3</td>\n",
" <td>SMP223 (449335798)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>449335798</td>\n",
" <td>SMP223</td>\n",
" <td>480029788</td>\n",
" <td>LPN_R</td>\n",
" <td>7</td>\n",
" <td>LPN2</td>\n",
" <td>SMP223 (449335798)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>449335798</td>\n",
" <td>SMP223</td>\n",
" <td>5813010153</td>\n",
" <td>DN1pA_R</td>\n",
" <td>4</td>\n",
" <td>DN1pA1</td>\n",
" <td>SMP223 (449335798)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>449335798</td>\n",
" <td>SMP223</td>\n",
" <td>387166379</td>\n",
" <td>DN1pA_R</td>\n",
" <td>5</td>\n",
" <td>DN1pA5</td>\n",
" <td>SMP223 (449335798)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>540325242</td>\n",
" <td>SMP223</td>\n",
" <td>5813071319</td>\n",
" <td>DN1pB_R</td>\n",
" <td>3</td>\n",
" <td>DN1pB2</td>\n",
" <td>SMP223 (540325242)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>540325242</td>\n",
" <td>SMP223</td>\n",
" <td>386834269</td>\n",
" <td>DN1pB_R</td>\n",
" <td>5</td>\n",
" <td>DN1pB1</td>\n",
" <td>SMP223 (540325242)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>540325242</td>\n",
" <td>SMP223</td>\n",
" <td>480029788</td>\n",
" <td>LPN_R</td>\n",
" <td>3</td>\n",
" <td>LPN2</td>\n",
" <td>SMP223 (540325242)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>540325242</td>\n",
" <td>SMP223</td>\n",
" <td>450034902</td>\n",
" <td>LPN_R</td>\n",
" <td>3</td>\n",
" <td>LPN3</td>\n",
" <td>SMP223 (540325242)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5813087531</td>\n",
" <td>SMP223</td>\n",
" <td>480029788</td>\n",
" <td>LPN_R</td>\n",
" <td>16</td>\n",
" <td>LPN2</td>\n",
" <td>SMP223 (5813087531)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5813087531</td>\n",
" <td>SMP223</td>\n",
" <td>450034902</td>\n",
" <td>LPN_R</td>\n",
" <td>14</td>\n",
" <td>LPN3</td>\n",
" <td>SMP223 (5813087531)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>5813087531</td>\n",
" <td>SMP223</td>\n",
" <td>5813010153</td>\n",
" <td>DN1pA_R</td>\n",
" <td>4</td>\n",
" <td>DN1pA1</td>\n",
" <td>SMP223 (5813087531)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>329732855</td>\n",
" <td>SMP368</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>5</td>\n",
" <td>LNd5</td>\n",
" <td>SMP368 (329732855)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>329732855</td>\n",
" <td>SMP368</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>5</td>\n",
" <td>LNd4</td>\n",
" <td>SMP368 (329732855)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>390331583</td>\n",
" <td>SMP368</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>7</td>\n",
" <td>LNd5</td>\n",
" <td>SMP368 (390331583)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>390331583</td>\n",
" <td>SMP368</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>7</td>\n",
" <td>LNd4</td>\n",
" <td>SMP368 (390331583)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>5813022753</td>\n",
" <td>aMe</td>\n",
" <td>5813069648</td>\n",
" <td>LNd_R</td>\n",
" <td>8</td>\n",
" <td>LNd6</td>\n",
" <td>aMe (5813022753)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>5813022753</td>\n",
" <td>aMe</td>\n",
" <td>1884625521</td>\n",
" <td>l-LNv</td>\n",
" <td>23</td>\n",
" <td>lLNv1</td>\n",
" <td>aMe (5813022753)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>5813022753</td>\n",
" <td>aMe</td>\n",
" <td>5813026773</td>\n",
" <td>l-LNv</td>\n",
" <td>16</td>\n",
" <td>lLNv4</td>\n",
" <td>aMe (5813022753)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>5813022753</td>\n",
" <td>aMe</td>\n",
" <td>5813001741</td>\n",
" <td>l-LNv</td>\n",
" <td>25</td>\n",
" <td>lLNv3</td>\n",
" <td>aMe (5813022753)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>5813022753</td>\n",
" <td>aMe</td>\n",
" <td>1664980698</td>\n",
" <td>s-LNv</td>\n",
" <td>26</td>\n",
" <td>sLNv2</td>\n",
" <td>aMe (5813022753)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>5813022753</td>\n",
" <td>aMe</td>\n",
" <td>2065745704</td>\n",
" <td>l-LNv</td>\n",
" <td>16</td>\n",
" <td>lLNv2</td>\n",
" <td>aMe (5813022753)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>5813022753</td>\n",
" <td>aMe</td>\n",
" <td>511051477</td>\n",
" <td>5th s-LNv</td>\n",
" <td>6</td>\n",
" <td>5th sLNv</td>\n",
" <td>aMe (5813022753)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>5813022753</td>\n",
" <td>aMe</td>\n",
" <td>2007068523</td>\n",
" <td>s-LNv</td>\n",
" <td>23</td>\n",
" <td>sLNv3</td>\n",
" <td>aMe (5813022753)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>5813022753</td>\n",
" <td>aMe</td>\n",
" <td>2068801704</td>\n",
" <td>s-LNv</td>\n",
" <td>18</td>\n",
" <td>sLNv1</td>\n",
" <td>aMe (5813022753)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>5813022753</td>\n",
" <td>aMe</td>\n",
" <td>1975347348</td>\n",
" <td>s-LNv</td>\n",
" <td>22</td>\n",
" <td>sLNv4</td>\n",
" <td>aMe (5813022753)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>5813040744</td>\n",
" <td>aMe</td>\n",
" <td>1664980698</td>\n",
" <td>s-LNv</td>\n",
" <td>12</td>\n",
" <td>sLNv2</td>\n",
" <td>aMe (5813040744)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>5813040744</td>\n",
" <td>aMe</td>\n",
" <td>2065745704</td>\n",
" <td>l-LNv</td>\n",
" <td>16</td>\n",
" <td>lLNv2</td>\n",
" <td>aMe (5813040744)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>5813040744</td>\n",
" <td>aMe</td>\n",
" <td>511051477</td>\n",
" <td>5th s-LNv</td>\n",
" <td>3</td>\n",
" <td>5th sLNv</td>\n",
" <td>aMe (5813040744)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>5813040744</td>\n",
" <td>aMe</td>\n",
" <td>5813001741</td>\n",
" <td>l-LNv</td>\n",
" <td>20</td>\n",
" <td>lLNv3</td>\n",
" <td>aMe (5813040744)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>5813040744</td>\n",
" <td>aMe</td>\n",
" <td>2007068523</td>\n",
" <td>s-LNv</td>\n",
" <td>8</td>\n",
" <td>sLNv3</td>\n",
" <td>aMe (5813040744)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>5813040744</td>\n",
" <td>aMe</td>\n",
" <td>5813026773</td>\n",
" <td>l-LNv</td>\n",
" <td>21</td>\n",
" <td>lLNv4</td>\n",
" <td>aMe (5813040744)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>5813040744</td>\n",
" <td>aMe</td>\n",
" <td>1884625521</td>\n",
" <td>l-LNv</td>\n",
" <td>29</td>\n",
" <td>lLNv1</td>\n",
" <td>aMe (5813040744)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>5813040744</td>\n",
" <td>aMe</td>\n",
" <td>2068801704</td>\n",
" <td>s-LNv</td>\n",
" <td>9</td>\n",
" <td>sLNv1</td>\n",
" <td>aMe (5813040744)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>5813040744</td>\n",
" <td>aMe</td>\n",
" <td>5813069648</td>\n",
" <td>LNd_R</td>\n",
" <td>11</td>\n",
" <td>LNd6</td>\n",
" <td>aMe (5813040744)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>5813040744</td>\n",
" <td>aMe</td>\n",
" <td>1975347348</td>\n",
" <td>s-LNv</td>\n",
" <td>9</td>\n",
" <td>sLNv4</td>\n",
" <td>aMe (5813040744)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>5813021666</td>\n",
" <td>aMe22</td>\n",
" <td>5813069648</td>\n",
" <td>LNd_R</td>\n",
" <td>81</td>\n",
" <td>LNd6</td>\n",
" <td>aMe22 (5813021666)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>5813021666</td>\n",
" <td>aMe22</td>\n",
" <td>511051477</td>\n",
" <td>5th s-LNv</td>\n",
" <td>71</td>\n",
" <td>5th sLNv</td>\n",
" <td>aMe22 (5813021666)</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bodyId_pre instance_pre bodyId_post instance_post weight labels \\\n",
"25 325529237 DN1pA 5813069648 LNd_R 30 LNd6 \n",
"41 325529237 DN1pA 5813056917 LNd_R 5 LNd4 \n",
"36 325529237 DN1pA 511051477 5th s-LNv 33 5th sLNv \n",
"51 325529237 DN1pA 5813021192 LNd_R 8 LNd5 \n",
"24 387166379 DN1pA 5813069648 LNd_R 30 LNd6 \n",
"49 387166379 DN1pA 5813021192 LNd_R 5 LNd5 \n",
"35 387166379 DN1pA 511051477 5th s-LNv 25 5th sLNv \n",
"39 387166379 DN1pA 5813056917 LNd_R 8 LNd4 \n",
"37 5813021192 LNd 5813056917 LNd_R 5 LNd4 \n",
"45 420027427 SMP219 5813064789 LNd_R 3 LNd3 \n",
"4 357949102 SMP222 480029788 LPN_R 5 LPN2 \n",
"47 357949102 SMP222 356818551 LPN_R 3 LPN1 \n",
"46 417143726 SMP223 356818551 LPN_R 14 LPN1 \n",
"3 417143726 SMP223 480029788 LPN_R 18 LPN2 \n",
"8 417143726 SMP223 450034902 LPN_R 20 LPN3 \n",
"7 449335798 SMP223 450034902 LPN_R 3 LPN3 \n",
"2 449335798 SMP223 480029788 LPN_R 7 LPN2 \n",
"10 449335798 SMP223 5813010153 DN1pA_R 4 DN1pA1 \n",
"44 449335798 SMP223 387166379 DN1pA_R 5 DN1pA5 \n",
"43 540325242 SMP223 5813071319 DN1pB_R 3 DN1pB2 \n",
"42 540325242 SMP223 386834269 DN1pB_R 5 DN1pB1 \n",
"1 540325242 SMP223 480029788 LPN_R 3 LPN2 \n",
"6 540325242 SMP223 450034902 LPN_R 3 LPN3 \n",
"0 5813087531 SMP223 480029788 LPN_R 16 LPN2 \n",
"5 5813087531 SMP223 450034902 LPN_R 14 LPN3 \n",
"9 5813087531 SMP223 5813010153 DN1pA_R 4 DN1pA1 \n",
"50 329732855 SMP368 5813021192 LNd_R 5 LNd5 \n",
"40 329732855 SMP368 5813056917 LNd_R 5 LNd4 \n",
"48 390331583 SMP368 5813021192 LNd_R 7 LNd5 \n",
"38 390331583 SMP368 5813056917 LNd_R 7 LNd4 \n",
"22 5813022753 aMe 5813069648 LNd_R 8 LNd6 \n",
"12 5813022753 aMe 1884625521 l-LNv 23 lLNv1 \n",
"14 5813022753 aMe 5813026773 l-LNv 16 lLNv4 \n",
"16 5813022753 aMe 5813001741 l-LNv 25 lLNv3 \n",
"20 5813022753 aMe 1664980698 s-LNv 26 sLNv2 \n",
"18 5813022753 aMe 2065745704 l-LNv 16 lLNv2 \n",
"33 5813022753 aMe 511051477 5th s-LNv 6 5th sLNv \n",
"31 5813022753 aMe 2007068523 s-LNv 23 sLNv3 \n",
"29 5813022753 aMe 2068801704 s-LNv 18 sLNv1 \n",
"27 5813022753 aMe 1975347348 s-LNv 22 sLNv4 \n",
"19 5813040744 aMe 1664980698 s-LNv 12 sLNv2 \n",
"17 5813040744 aMe 2065745704 l-LNv 16 lLNv2 \n",
"32 5813040744 aMe 511051477 5th s-LNv 3 5th sLNv \n",
"15 5813040744 aMe 5813001741 l-LNv 20 lLNv3 \n",
"30 5813040744 aMe 2007068523 s-LNv 8 sLNv3 \n",
"13 5813040744 aMe 5813026773 l-LNv 21 lLNv4 \n",
"11 5813040744 aMe 1884625521 l-LNv 29 lLNv1 \n",
"28 5813040744 aMe 2068801704 s-LNv 9 sLNv1 \n",
"21 5813040744 aMe 5813069648 LNd_R 11 LNd6 \n",
"26 5813040744 aMe 1975347348 s-LNv 9 sLNv4 \n",
"23 5813021666 aMe22 5813069648 LNd_R 81 LNd6 \n",
"34 5813021666 aMe22 511051477 5th s-LNv 71 5th sLNv \n",
"\n",
" candidate_label \n",
"25 DN1pA (325529237) \n",
"41 DN1pA (325529237) \n",
"36 DN1pA (325529237) \n",
"51 DN1pA (325529237) \n",
"24 DN1pA (387166379) \n",
"49 DN1pA (387166379) \n",
"35 DN1pA (387166379) \n",
"39 DN1pA (387166379) \n",
"37 LNd (5813021192) \n",
"45 SMP219 (420027427) \n",
"4 SMP222 (357949102) \n",
"47 SMP222 (357949102) \n",
"46 SMP223 (417143726) \n",
"3 SMP223 (417143726) \n",
"8 SMP223 (417143726) \n",
"7 SMP223 (449335798) \n",
"2 SMP223 (449335798) \n",
"10 SMP223 (449335798) \n",
"44 SMP223 (449335798) \n",
"43 SMP223 (540325242) \n",
"42 SMP223 (540325242) \n",
"1 SMP223 (540325242) \n",
"6 SMP223 (540325242) \n",
"0 SMP223 (5813087531) \n",
"5 SMP223 (5813087531) \n",
"9 SMP223 (5813087531) \n",
"50 SMP368 (329732855) \n",
"40 SMP368 (329732855) \n",
"48 SMP368 (390331583) \n",
"38 SMP368 (390331583) \n",
"22 aMe (5813022753) \n",
"12 aMe (5813022753) \n",
"14 aMe (5813022753) \n",
"16 aMe (5813022753) \n",
"20 aMe (5813022753) \n",
"18 aMe (5813022753) \n",
"33 aMe (5813022753) \n",
"31 aMe (5813022753) \n",
"29 aMe (5813022753) \n",
"27 aMe (5813022753) \n",
"19 aMe (5813040744) \n",
"17 aMe (5813040744) \n",
"32 aMe (5813040744) \n",
"15 aMe (5813040744) \n",
"30 aMe (5813040744) \n",
"13 aMe (5813040744) \n",
"11 aMe (5813040744) \n",
"28 aMe (5813040744) \n",
"21 aMe (5813040744) \n",
"26 aMe (5813040744) \n",
"23 aMe22 (5813021666) \n",
"34 aMe22 (5813021666) "
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# recreate labels for candidates to include type and bodyId\n",
"all_candidate_targets['candidate_label'] = all_candidate_targets['instance_pre'] + ' (' + all_candidate_targets['bodyId_pre'].map(str) + ')'\n",
"all_candidate_targets.sort_values(by='candidate_label')"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "ca4e512a-4b97-4387-97fd-6d7703270e84",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" vertical-align: middle;\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>labels</th>\n",
" <th>5th sLNv</th>\n",
" <th>DN1pA1</th>\n",
" <th>DN1pA5</th>\n",
" <th>DN1pB1</th>\n",
" <th>DN1pB2</th>\n",
" <th>LNd3</th>\n",
" <th>LNd4</th>\n",
" <th>LNd5</th>\n",
" <th>LNd6</th>\n",
" <th>LPN1</th>\n",
" <th>LPN2</th>\n",
" <th>LPN3</th>\n",
" <th>lLNv1</th>\n",
" <th>lLNv2</th>\n",
" <th>lLNv3</th>\n",
" <th>lLNv4</th>\n",
" <th>sLNv1</th>\n",
" <th>sLNv2</th>\n",
" <th>sLNv3</th>\n",
" <th>sLNv4</th>\n",
" </tr>\n",
" <tr>\n",
" <th>candidate_label</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>DN1pA (325529237)</th>\n",
" <td>33.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>5.0</td>\n",
" <td>8.0</td>\n",
" <td>30.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DN1pA (387166379)</th>\n",
" <td>25.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" <td>30.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LNd (5813021192)</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP219 (420027427)</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP222 (357949102)</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP223 (417143726)</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>14.0</td>\n",
" <td>18.0</td>\n",
" <td>20.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP223 (449335798)</th>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP223 (540325242)</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP223 (5813087531)</th>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>16.0</td>\n",
" <td>14.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP368 (329732855)</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP368 (390331583)</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7.0</td>\n",
" <td>7.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aMe (5813022753)</th>\n",
" <td>6.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>23.0</td>\n",
" <td>16.0</td>\n",
" <td>25.0</td>\n",
" <td>16.0</td>\n",
" <td>18.0</td>\n",
" <td>26.0</td>\n",
" <td>23.0</td>\n",
" <td>22.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aMe (5813040744)</th>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>11.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>29.0</td>\n",
" <td>16.0</td>\n",
" <td>20.0</td>\n",
" <td>21.0</td>\n",
" <td>9.0</td>\n",
" <td>12.0</td>\n",
" <td>8.0</td>\n",
" <td>9.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aMe22 (5813021666)</th>\n",
" <td>71.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>81.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"labels 5th sLNv DN1pA1 DN1pA5 DN1pB1 DN1pB2 LNd3 LNd4 \\\n",
"candidate_label \n",
"DN1pA (325529237) 33.0 NaN NaN NaN NaN NaN 5.0 \n",
"DN1pA (387166379) 25.0 NaN NaN NaN NaN NaN 8.0 \n",
"LNd (5813021192) NaN NaN NaN NaN NaN NaN 5.0 \n",
"SMP219 (420027427) NaN NaN NaN NaN NaN 3.0 NaN \n",
"SMP222 (357949102) NaN NaN NaN NaN NaN NaN NaN \n",
"SMP223 (417143726) NaN NaN NaN NaN NaN NaN NaN \n",
"SMP223 (449335798) NaN 4.0 5.0 NaN NaN NaN NaN \n",
"SMP223 (540325242) NaN NaN NaN 5.0 3.0 NaN NaN \n",
"SMP223 (5813087531) NaN 4.0 NaN NaN NaN NaN NaN \n",
"SMP368 (329732855) NaN NaN NaN NaN NaN NaN 5.0 \n",
"SMP368 (390331583) NaN NaN NaN NaN NaN NaN 7.0 \n",
"aMe (5813022753) 6.0 NaN NaN NaN NaN NaN NaN \n",
"aMe (5813040744) 3.0 NaN NaN NaN NaN NaN NaN \n",
"aMe22 (5813021666) 71.0 NaN NaN NaN NaN NaN NaN \n",
"\n",
"labels LNd5 LNd6 LPN1 LPN2 LPN3 lLNv1 lLNv2 lLNv3 lLNv4 \\\n",
"candidate_label \n",
"DN1pA (325529237) 8.0 30.0 NaN NaN NaN NaN NaN NaN NaN \n",
"DN1pA (387166379) 5.0 30.0 NaN NaN NaN NaN NaN NaN NaN \n",
"LNd (5813021192) NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"SMP219 (420027427) NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"SMP222 (357949102) NaN NaN 3.0 5.0 NaN NaN NaN NaN NaN \n",
"SMP223 (417143726) NaN NaN 14.0 18.0 20.0 NaN NaN NaN NaN \n",
"SMP223 (449335798) NaN NaN NaN 7.0 3.0 NaN NaN NaN NaN \n",
"SMP223 (540325242) NaN NaN NaN 3.0 3.0 NaN NaN NaN NaN \n",
"SMP223 (5813087531) NaN NaN NaN 16.0 14.0 NaN NaN NaN NaN \n",
"SMP368 (329732855) 5.0 NaN NaN NaN NaN NaN NaN NaN NaN \n",
"SMP368 (390331583) 7.0 NaN NaN NaN NaN NaN NaN NaN NaN \n",
"aMe (5813022753) NaN 8.0 NaN NaN NaN 23.0 16.0 25.0 16.0 \n",
"aMe (5813040744) NaN 11.0 NaN NaN NaN 29.0 16.0 20.0 21.0 \n",
"aMe22 (5813021666) NaN 81.0 NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
"labels sLNv1 sLNv2 sLNv3 sLNv4 \n",
"candidate_label \n",
"DN1pA (325529237) NaN NaN NaN NaN \n",
"DN1pA (387166379) NaN NaN NaN NaN \n",
"LNd (5813021192) NaN NaN NaN NaN \n",
"SMP219 (420027427) NaN NaN NaN NaN \n",
"SMP222 (357949102) NaN NaN NaN NaN \n",
"SMP223 (417143726) NaN NaN NaN NaN \n",
"SMP223 (449335798) NaN NaN NaN NaN \n",
"SMP223 (540325242) NaN NaN NaN NaN \n",
"SMP223 (5813087531) NaN NaN NaN NaN \n",
"SMP368 (329732855) NaN NaN NaN NaN \n",
"SMP368 (390331583) NaN NaN NaN NaN \n",
"aMe (5813022753) 18.0 26.0 23.0 22.0 \n",
"aMe (5813040744) 9.0 12.0 8.0 9.0 \n",
"aMe22 (5813021666) NaN NaN NaN NaN "
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# generate pivot table for strong shared targets back to clock\n",
"all_candidate_targets = all_candidate_targets.pivot(index='candidate_label', columns='labels', values='weight')\n",
"all_candidate_targets"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "c5a5de00-9f79-420e-9aab-3120d790b6c3",
"metadata": {},
"outputs": [],
"source": [
"# export table\n",
"all_candidate_targets.to_csv('E2_targets_to_clock.csv')"
]
},
{
"cell_type": "markdown",
"id": "fa6d5121",
"metadata": {},
"source": [
"However, E3 looks a little different. There are only three neurons that are shared by all three of the E3s! However, knowing that the LNds as a larger group are fairly un-uniform anyway, we wanted to know more about shared targets even if they weren't shared by all of E3, so sharing from any 2 was considered sufficient."
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "a9bd9139",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\dbing\\Documents\\Research\\Code\\clock-connectome\\connection_utils.py:208: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
" conns_df = conns_df.groupby(['bodyId_post', 'instance_post'], as_index=False)['weight', 'shared'].sum()\n"
]
},
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" <td>327933679</td>\n",
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" <td>35</td>\n",
" <td>3</td>\n",
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"text/plain": [
" bodyId_post instance_post weight shared\n",
"5 297243542 SMP335_R 75 3\n",
"30 360254108 SMP334_R 42 3\n",
"24 327933679 SMP486_c_R 35 3"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"E3Ids = clock_df[clock_df['subphase']=='E3']['bodyId']\n",
"Evening3_targs = strong_shared_connections(E3Ids, 'out', 3)\n",
"Evening3_targs"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "fdb3ecad",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\dbing\\Documents\\Research\\Code\\clock-connectome\\connection_utils.py:208: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
" conns_df = conns_df.groupby(['bodyId_post', 'instance_post'], as_index=False)['weight', 'shared'].sum()\n"
]
},
{
"data": {
"text/plain": [
"5 297243542\n",
"42 5813039910\n",
"20 327587644\n",
"30 360254108\n",
"24 327933679\n",
"14 298279629\n",
"25 327937494\n",
"44 5813092319\n",
"13 298266950\n",
"26 328273806\n",
"Name: bodyId_post, dtype: int64"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"E3Ids = clock_df[clock_df['subphase']=='E3']['bodyId']\n",
"Evening3_targs = strong_shared_connections(E3Ids, 'out', 2)\n",
"Evening3_targs\n",
"\n",
"candidate_IDs = Evening3_targs['bodyId_post'][0:10]\n",
"candidate_IDs"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "83a54626",
"metadata": {},
"outputs": [
{
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" <td>4</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>264083994</td>\n",
" <td>356503889</td>\n",
" <td>7</td>\n",
" <td>DN1a1</td>\n",
" <td>LHPV6f4_b</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>557</th>\n",
" <td>297243542</td>\n",
" <td>571709547</td>\n",
" <td>3</td>\n",
" <td>SMP335_R</td>\n",
" <td>SMP233_R</td>\n",
" </tr>\n",
" <tr>\n",
" <th>558</th>\n",
" <td>297243542</td>\n",
" <td>5813056917</td>\n",
" <td>3</td>\n",
" <td>SMP335_R</td>\n",
" <td>LNd_R</td>\n",
" </tr>\n",
" <tr>\n",
" <th>559</th>\n",
" <td>297243542</td>\n",
" <td>5813064828</td>\n",
" <td>3</td>\n",
" <td>SMP335_R</td>\n",
" <td>SMP349_R</td>\n",
" </tr>\n",
" <tr>\n",
" <th>560</th>\n",
" <td>297243542</td>\n",
" <td>5813067452</td>\n",
" <td>3</td>\n",
" <td>SMP335_R</td>\n",
" <td>SMP509_R</td>\n",
" </tr>\n",
" <tr>\n",
" <th>561</th>\n",
" <td>297243542</td>\n",
" <td>5813087531</td>\n",
" <td>3</td>\n",
" <td>SMP335_R</td>\n",
" <td>SMP223_R</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3142 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" bodyId_pre bodyId_post weight instance_pre instance_post\n",
"0 264083994 299272434 4 DN1a1 SLP443\n",
"1 264083994 325814461 10 DN1a1 LHPV4c3\n",
"2 264083994 326119769 4 DN1a1 SLP457\n",
"3 264083994 356472645 3 DN1a1 SLP364\n",
"4 264083994 356503889 7 DN1a1 LHPV6f4_b\n",
".. ... ... ... ... ...\n",
"557 297243542 571709547 3 SMP335_R SMP233_R\n",
"558 297243542 5813056917 3 SMP335_R LNd_R\n",
"559 297243542 5813064828 3 SMP335_R SMP349_R\n",
"560 297243542 5813067452 3 SMP335_R SMP509_R\n",
"561 297243542 5813087531 3 SMP335_R SMP223_R\n",
"\n",
"[3142 rows x 5 columns]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"candidate_targets = get_input_output_conns(candidate_IDs, 'out', min_strength = 3)\n",
"conn_df = pd.concat([clock_targets, candidate_targets])\n",
"conn_df"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "d6a7d426-6d47-4850-b19f-c74efc161fd0",
"metadata": {},
"outputs": [],
"source": [
"# export table\n",
"conn_df.to_csv('clock_e3_cand_targets.csv')"
]
},
{
"cell_type": "markdown",
"id": "e90269c6-4e1c-411f-8606-2800a0ebc4c5",
"metadata": {},
"source": [
"To determine whether these 10 neurons had similar patterns of connectivity to any of the other identified clock neurons, jaccard indices were computed between the top 10 strongest shared candidates and the identified clock neurons."
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "c90d8d64-2ac8-41d4-b4a1-b87ceead2e54",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 3040x800 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"candidate_jaccard_out = jaccard_vis(conn_df, clock_df, clock_df['bodyId'], 'out', other_body_ids = candidate_IDs)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "af90968f-e19a-4635-8710-27db36e26b10",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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uF/jjyMzIZO9e/++UPp+P1ctX0rxFiWsqKzmOOFqd1Iqlb3r9sWIVySkpJfujaxfWrDoQx/w58+jWozsA55x/Ll/+70s2/bARgLmz59C9R+jmPFaHGKB6HN/HxtWneasWrPDmH7+zai1JKclFpnMAtO/cgX+tXc+OXH9fvDF/EZ27dylSZ+kbS+h+yYWFp8tDqTr0BUC9+vVo3KIJ61esBeDDde/RICmhxC8YB5OYksSGL77kZ++z6pP3/k3DRsdRowr65UhYFf2LdFYRp6bNrB/+gWN359yOgOXJzrkM7+e/4r9A66yC+ahm1hT4D9DQObcrYL2TgSXAbc65EoPVgHo3AZc65/7gPW8ObHbO7TOzWsA0/NnfB80sEdjhlR3llX3pnHu4lO06oE5BFris+A7l9DF/CurNHdjtZrq0aEt8zLHs3LubPb/+TM/UvgzucSurv/uINd99BMAVp3bl5rN7UcOMDzZ/wfDlqez3+X8T7dS8DXd3vIaoGlF8u30zg5a8wE+/7j3Yy5Zqzo0lr4Yvr62bt/DU8DHsyttF7Zja9HtwAI2bNeGZUU9yTvvzOLf9eQC8tfBNXp8+C5/P57+VTP87C+cY3n7DrTifY//+fXS5sDtX33RtUKdG6h4VE1QMmzduYsSjw8nL20VMTG0eHDyIps2bMWrYSNp3aE/7Tv7fiRbOW8irU6fh8zlan9Wa/vcPKIzhz9fdTM72HHbu2EF8g3jObNOahx4tsdsdUsEt2YKKY9NmRj0ygl15edSOifHffqx5Ux4bNop2HdvTrmN7ABbPX8irU1/F+Xyc2bY1/bxbRb23/l0mjH0RAJ/P0erEVtxx9z+pd2y9w25LzajgT2Nu2riJ4UOGFcYx6JGHaNa8GSMfHUH7Th3oUNAfcxcwbcorOOdo3bYNAwbeS3RN/+uuW7OOcc88T35+Ps1bNGfQIw8TExvc/hHpMezcuzvoOMLp+I6qEdyAZcumLTwxbDS7d+2idkwM9wy6jybNmvDUyDGc2/58zutwPgBLFrzBrGkzcc5/u6t/DuhbGMOen/Zw7WV/ZNyUCaQc1zCodgDk+4LPIoZTX+TuCX7ebfqWbUwYM5Yfd+3mmNrHcOuAf3J8kxOY9OQLtD6vLa3PO4t9v+7jnpvuYP++/ez5aQ91j61Lu26duOqWa3HOMeulV/lo/ftE16rJMcccw/V3/JkmLQ7/spezG59aZSO88565qUrmrb171+SIHtUe8YDWzI4HtgDfAwWfjr84584xs8H4B4IGfAnc4V3kVbDuUKCRl70N3ObbQFtgU8Di+4oPbksZ0N4C3A3k47/H7kpggHPuZzO7Ani0WFn/ggvRim23cEB7sPgO9d4EO6ANJ0cyoA0nwQ5ow8mRDGjDyZEMaKViHcmANpwEO6ANJ0cyoA0nRzKgDSca0EaeI/5mcc5tpYxbmDnnHgEeOci6D5WxvFznz5xzk4HJAc9TKePiMufcXGBuObdrAT+XGZ+IiIhIhdKIIyihvZGkiIiIiEgF07k/ERERkTBRHS7QqgrK0IqIiIhIRFOGVkRERCRMKD8bHGVoRURERCSiaUArIiIiIhFNUw5EREREwkQwf9BClKEVERERkQinDK2IiIhI2FCGNhjK0IqIiIhIRFOGVkRERCRMaAptcJShFREREZGIpgGtiIiIiEQ0TTkQERERCROmi8KCogytiIiIiEQ0ZWhFREREwoQuCguOMrQiIiIiEtGUoRUREREJG0rRBkMZWhERERGJaBrQioiIiEhE05QDERERkTCh23YFRxlaEREREYloytCKiIiIhAndtis4ytCKiIiISERThlZEREQkTGgObXA0oK1Ec24cXdVNOGJ9ptxX1U2oEG/fNraqm3DEfPm+qm5CxYiq6gZIgY+3fl3VTagQHZudWdVNOGK7fvmpqptQIeodHVvVTZDfKE05EBEREZGIpgytiIiISLjQjIOgKEMrIiIiIhFNGVoRERGRMKGLwoKjDK2IiIiIRDQNaEVEREQkomnKgYiIiEiY0ISD4ChDKyIiIiIRTRlaERERkXBhytEGQxlaEREREYloytCKiIiIhAndtis4ytCKiIiISETTgFZEREREIpqmHIiIiIiECV0TFhxlaEVEREQkoilDKyIiIhImdFFYcJShFREREZGIpgGtiIiIiEQ0DWhFREREJKJpQCsiIiIiEU0XhYmIiIiECdN9u4KiDK2IiIiIRDRlaEVERETChG7bFRxlaEVEREQkoilDKyIiIhImNIU2OBrQhqFtW7bx5PDH2bUzj9g6sdz9QH8aNW1cot7SxUt4fdpr+HyO09ucwR333ElUdBQAc16dxYolb+NzjuNPOJ6+D/Qntk5syGK4r+uNdGreluPqJdBncn++3b611Hq9T+nCzef0oobV4P1N/2XE8lTynQ+Ajs1a06/TtUTViGJD9iYGLRnH3n2/hCwGgC2btzBiyFB27syjTmwsA4cMommzpiXqLZ6/iOlTXsHn89Hm7Lb0u68/0dHR7Nmzh0H3PsCGL7/211uxJKTtL7B181ZGDR3Brp15xNSJ5b6HBtKkaZMS9d5c+AYzpk7H5xyt27am74C7iYo+8DHhnKP/P/vx3TffMX/pwhBG4Ldl8xaGDX6UPO/YeHDIQ6X2x6L5C5k2+RV8Pkfbs9tyz/3+/gBYv/Ydnn/6OfLz82nRqiWDHnmI2rVrK4bDlJOexZxx09iz+0eOjjmGK/5+HYnHpxSps3nDDyxKfQ2A/Px8Gp/YnP+7qQ/RNWuyIyuHp/s+SuIJB9a5+u5biEtOCFkMWzdvYcQjw8nbuZPYOnUY+PADNCmlL95YsJjpU6fhfD5an9WGu++9h+joaNK3pfHwwEH48n3k+3w0atyIAQ/cS526dUMWA0Dalm08NeJxdu3cRUydGPoOLPmdkZmewdMjnuD7b76l4fHH8dSk54uUf7D+PV4aNxFffj5NWjTj7gcGcEztY0IZBmlbtvH0iDHsyttFTGwMdz1wD42alIzjmZFP8P0339Hw+ON4cuJzRco//Nf7vDRuIvn5+TRt3oy+D/QPeRxSNco95cDMNprZV2b2qfe4ylve1szeNbNPzOxLM7s3YJ0/m9l/zGy/mf2j2Pbu8Mo+9f6/s1j5IDP7znsMDVh+XkAbvjCz8WZ2VED5ADP7r5n9z8zmmdmxAWXXm9lnXvkKM2sUUPasF6Mzs1OKtSXRzN4ys2+8dduX930LxvOPP81FvS5h4syX6XPNlTwz6skSdTLS0pk2cQqPjXuKSa9NZkfuDpYt9g+WPvnwI1a8tZwx45/hxWmTaNayOVMnvFyZTS7h7Q3vc9OMwWzLyy6zznH1Eri9/ZXcNGMIl066iwYx9eh9ahcAjql5FEMuvJW+C56gZ2pftv+0k7+e2ztUzS80ZsRoeva+jBlzX+PqG65l9NARJeqkbUtj0osTGTvpRWbOn03u9lzeWLAYgOjoaK654TqeGvdMqJtexJOjx3DpZT2ZOns6f7ruasYMH12iTnpaOi9PSOWZ8c8z7fVXyc3J5c1FbxapM2/2XJJTkkPV7BIeGz6aXr0vZ+a8WVx7w3WMfLT0/pj4wkReSB3PrAWzycnJYfGCRQDs2bOHkUNHMOqJ0cxa8DrxDeKZkjpZMQRhwaSZtO12Pn2ffpj2Pbszf/yrJeokNz6Ovw0fwB2j7+cfjw3kp127+XD5+sLyo2OO4Y7R9xc+QjmYBRgz8nF69u7F9Dkzufr6axg9bFSJOunb0kgdP5HnJ4zj1bmv+Y+Lhf7jOz6hAc9PeIHU6ZOZPGMqCYkJTEmdEtIYAMaOeYYLe17C+Bkv0efqP/Ls6KdK1KkdU5vr/noj/R++v0TZ3j17eW70Uzw4YjATZk4mLj6OWVNnhKLpRYwd8ywX9rqYF19N5YprruS5UuOI4bq/3Mg9D99Xomzvnr08O/opHhw+mAkzXqZ+fByzXgl9HFI1DncO7R+cc2d4j9e8ZROBkc65M4F2QH8z+51X9hHwR6DkJx1Mc86d6pw7I2C90wDMrCNwNXAa8DvgYjO70FvvM+Asb71TgQTgNm+9C4AbgPOcc78DPgWGe2UnAaOBHs65U4CpwAsB7XkdaA9sKqWto4D3nHMtgZuB6WZWKdntnTt28N2Gb+naoxsA7Tp3ICM9g8z0jCL11q9ex3kd21E/rj5mxiWXX8qa5asB+P6b7/n9aacUZmzOOv8cVi5dURnNLdPHW78i68fcg9bp3uocVn7zIbl78gCY/dlyLjq5HQDtm57BF5nfszE3DYDXPl3GRSe1q9xGF7MjN5cNX22gx8X+Xa9zty6kp6WTnpZepN7qFavo2KUjcfFxmBmX9bmc5UvfBqBWrVq0PbstsXXqhLTtgXbk7uCbr7/hgosuAKBjl06kp2WQUSyONStX075Th8I4el5xGSuXHdhvtm7eyqq3V3L19deGtP0F/P3xNRdeEtgfaSX6Y9WKlXQK6I/L+/Qu7I/31r/LSSefTGMvO33FlX0KyxRD+f2Yt5v0H7ZyeoezAPj9OWewIyuHHVk5RerVOqpW4Vmj/P357P91X9jcksh/XGzggot6ANCpa2cySju+V66mQ+eA4/uKy1mxbDngP76POtqfT8nPz2fvnr3UqBHa+Hbu2Ml3G76li/edcX7n9mSW8p1Rp25dfn/aKRx9zNEltvHR+x/S4sRWnNDYn+O55PKerF2xutLbHmjnjp18/823dL7Ai6NTezLTM0uJow6/O+0Ujj669DhantiS4xufAMAlvS9lXYjjqBhWRY/IVlEXhR3r/R8D/ArkAjjnPnPOfQn4iq/gnMsLeFob//QH5z2/CpjsnPvJOfcL8BL+AS7OuT3OuX1evVrAMQHbPx1Y55zb7T1fDFzv/XwK8KlzLjOg7GIzi/e2u9Y5V/p5cf+gfKxX70MgE//gt8JlZ2YT1yC+8EvAzEhMSiQ7M6tIvazMbBKTkwqfJyUnkeXVaXlSKz7998fsyN2Bc45Vy1awd88edu/aVRlNDlpKnQak79pe+DxtVzYpdeL9ZXWLleVlkxhbP6RXf2ZlZhGf0KDwNK+ZkZSURGZG0Q/YzIxMkpIPZC2TG6aQmZlJuMjOyqJBg/jCqQNmRmJyIpnF96mMTJIC9qnklGSyvDh8Ph9PjHycuwb0JdrbN0MtMzOLBsX7I7n0/khOOXAaO6VhCpkZmQFlB/oqJSWF7KxsfL4SH1GVojrEAJCXs4M69esRFXXgc6peg/rk5ewoUXdHVg5j7xvFqL/ez1HHHE3b7gd+Mf1lz8+8+MDjjLt/NKvmLAlpDFmZmSWO78TkJLIyih67JY7vlOTCvgDYt28ft1x7E716/B/btm7lxltuDk0Anu1Z2cTFF/3OSEhKIDuz7LNjxWVnZpGQnFj4PDEliZzs7SHtj1LjSDzcOLJJKPa9mJOdE9I4pOoc7oB2ujc9YJKZFZwbuhkYamabgQ3AQOdcRtmbOMDM/mBmX+DPij7unPuPV9SIopnSjd6ygvWamNmnwHZgFzDBK/o3cIGZJZk/DXAdUMfM4vBna9uYWQuv7g34fyUpOTm1aBvjgRrOucCjqkh7KlrxQZtzrvR6Vnqd01qfTu8//YEhAwZxz213ERfvHyQGzoUMF44D7S5v3KFUPJsU2N4y64VBu0sonhUrq40B9QLf/1nTZ3LamafRolXLymhd+RXvj7Le6jKOjVI2EXrVIQYomdApI476ifHcMfp+7h0/gv379/O/Dz4DoE79uvQfN5S/jRjATYP+waavvmP94pWV2+ZiSoRQ5mdt6ccFQM2aNUmdPpn5by3ihMaNWDB3fgW3shzKeXgfdBPhsFMVj6Osnar8m4hIys8G53AGtB2dc6cDrYEcoGCi0ABggHOuEfB7YLiZnVieDTrnXnfO/R44Ebih2HqBe7IVW2+jN+UgGTgKuMJbvhp4AngDeBcoOHe0zzn3LfB34BUz+wCoA+QB+zi04kdVqX1vZv3MbGvB46UXJpVj00UlJCWwPTub/P35/hd2juysbBKSEovUS0xKIDP9QJYgKzOLxIA6l1x+Kc+kjuXJCc9yyumn0iCxQcgvGjmU9N3baVj3wJy5lLoNSN/tP2WZvms7DesdKGtYL4GsH3cE9QEXrILM+P79+wF/X2RlZhXJ1oA/C5CRfuA0ZUZ6BklJSYSLhMREtmdlk18kjmySiu9TyUlFTu9lZmSS6MXx+aefs/SNt7j68qu487Z/8uPu3Vx9+VXs3rWbUEkqtT8yS++PtGL94WVtkpKTSE87EGN6ejoJiQnUqBGaOxhWhxgA6sXXZ1fOTvLzD3xO5eXsoF58/TLXOeroozj1/DZ8/s6/AYiuWZPYev6pOLVjY2jd+Vw2ffVd5Tfek5iURHZWdpG+yM7MKnLmC0oe35nFzmQUqFmzJhdfegnLliyt3IYX0yAxgZzs7UW+M7ZnZZOQVP75yAlJiWQFfp+k+7PXodynSo9j+2HGkVAkw56ZkUl8QnxI45CqU+5eds5t9v7fBzwNdDCzBkBv59wsr+x74H3g/MNphHNuo7fepd6izUCTgCqNvWXF1/sRmAlcG7DsRedcW+fcucBaYGvBFATn3Fzn3HnOubPxZ3WPBg76CeqcywEIyEgfrD1POueOL3j8+e9/OWjcpTm2fn2at2xROHdx/ep1JCUnkVTsQpzzO3Xg3bXrC6cVvDl/MR27dy4sz93uHxj+/PPPTEudQp9r/njYbalsyzd8QNeWZxFXux4AV57enaVf/QuA9T98xinJzWkS1xCAq87oUVgWKvXj4mh5YqvCL6jVK1aRnJJCSsOiV3J37tqZtavWkpuTi3OOBXPm061H95C29WDqx9WnRauWvP2Wf57l2lVrSE5JJrlYHB27dOKdNesK41g0dwFdLugKwIgnRjFzwWxmzH+NZ8c/R2ydOsyY/xp16oZubnD9uDhandSKpW8eqj+6sCagP+bPmVfYH+ecfy5f/u9LNv2wEYC5s+fQvccFiuEwxdarQ0qT4/ls3YcAfPH+pxybEE/9xPgi9XIzDvxyXpCdTWrkP6Z/zNt9oGzfPv73wWekNDk+ZDHUj6tPyxNb8vZbywD/HPLkhskl+qJT106sWx1wfM+dT9cL/H2RmZHB3r17Af+0nFXLV9G8RfOQxQBwbP1jadayBau874x/rX6n1O+Mg2l9Tlu++eprtmzyf629OX8RHbp1qpT2lsUfR3NWv+3FseYdEoOKYwNbN20B4M15i+nQtXNlNLdSmVmVPCKdlee0rpnFADWdczu95/2Ay4EuQDb+Qe0ab4D7CXCFN9e0YP3JwL+dc88HLDvZm19bMFj8F3C7c+5tM+sMPA+cA+wH1gODnHNvmVlzYLNzbp+Z1QKmAd845x70tpXinEs3s9rAfGCRc+65YmVR+OflbnfO3VMs1o3Apc65/xZr/0bn3BAzOwuYAzRzzu0/2Pv2bfamoNKJWzdv4anh/luX1I6pTb8HB9C4WROeGfUk57Q/j3PbnwfAWwvf5PXps/D5fP7bdvW/s3A+2O033IrzOfbv30eXC7tz9U3XBrXD9plS8krS8hjY7Wa6tGhLfMyx7Ny7mz2//kzP1L4M7nErq7/7iDXffQTAFad25eaze1HDjA82f8Hw5ans9/m/5Do1b8PdHa8hqkYU327fzKAlL/DTr3uDas/bt40Nar3NGzcx4pFh5OXtIiYmhgeHDKJp82aMGjqS9h3b075TBwAWzlvAq1Omebe7akP/gQMK++LP195EzvYcdu7YQXyDeM5s05qHhg4+7Lbs21+ekwllxLFpM48NHentUzHc9/BAmjZrypjhj3Feh3a06+if17h4/iJmTpuB8/k4s01r+t7XrzCOAhlp6fzt5tuCvm3XUTVrBR3Hpo2bGD5kGLvy8qgdE8OgRx6iWfNmjHx0BO07daBDQX/MXcC0Ka/gvP4YMPBeomv641i3Zh3jnnme/Px8mrdozqBHHiYmNiboNkVyDCu/+XfQcWSnZTLvhWns2f0TR9U+miv+fj1JJ6Qwf/yrnNjmVE5ueyofrXyXf725Cqth+Hw+mv2+FRdeezk1a9Xkiw8+ZeWsN4uUXXTd5UTXrHnYbenY7MygYti8aTMjHxnOrrw8YmJiGDj4QZo2b8Zjw0bRrmN72nX0XyqxaP5C/+3sfD5at21DP+8Wau+tf5fxY18EwPl8tDyxFf+4+07qHVvvsNuy65efgooB/N8ZT494gt3ed0bfB/vTuGkTnh31FOe0P5dz2p/Hvl9/5a9/upl9v+5jz08/Ua/+sXTp0Y0b//ZnAN5/511efmESvvx8Gjdryt0P9qd2zOHvU0cyVWzr5i08M/IJduft9sfxwD00atqE50Y/xdntDsRx69V/LhJH5x5dufG2A3FMfjGV/Px8mjRrSt8H7gkqjhOTmlbZCO/SSXdVyby1xX95JqJHteUd0DbDP4iLwn+6/XvgLufcRjPrjv/uAdFATWC8c+4Zb73r8N8hoD7+i8V+Ano65z4xs3FAJ/yn/M1bb1zAaz4M3OQ9nemce8BbfgtwN5DvveZK/FMefvbK/4M/81wLeAUY6rwgzewt/HNfawFveuv94pWNBS7DP41hO/Cjc66FV5bkbaupF8ftzrk1h3rfgh3QhpNgB7ThJtgBbTg5kgFtODmSAa1UrCMZ0IaTYAe04eRIBrThJByufagIGtBGnnJdJeRNJSj1E8M5txxoU0bZNPwZ1NLKbj/Eaz4KPFrK8lQg9SDrnXqQsosOUnYHcEcZZZlAj4O1V0RERORIhfJuPtWJZkqLiIiISEQLv/s4iYiIiPxWKUEbFGVoRURERCSiaUArIiIiIhFNUw5EREREwoQuCguOMrQiIiIiEtGUoRUREREJE8rPBkcZWhERERGJaMrQioiIiISJYP5MvShDKyIiIiIRTgNaEREREYlomnIgIiIiEjY05SAYytCKiIiISERThlZEREQkTOiasOAoQysiIiIiEU0ZWhEREZEwoT99GxxlaEVEREQkomlAKyIiIiIRTVMORERERMKEJhwERxlaEREREYloytCKiIiIhAvdtysoytCKiIiISLmYWUsz+5eZbTCzD8zsd6XUMTN73My+MLPPzWyVmbWozHZpQCsiIiISJqyKHodhPDDBOdcKeAxILaVOL6AjcIZz7jRgBTDi8F7m8GhAKyIiIiKHZGaJQGtgmrdoDtDUzJqUUv0o4GgzM6AusLUy26Y5tJWo7lExVd2EI/b2bWOrugkV4oLxd1R1E47Y0r8+V9VNkGqmS4u2Vd2ECrHft7+qm3DE6h9Tt6qbUCE0/bPaOwFIc87tB3DOOTPbDDQCNgbUWwR0BjKA3cA2oFNlNkwZWhEREZEwYWZV9ehnZlsDHv3KaKIr3uRS6rQGTgKOAxrin3LwfMW9SyUpQysiIiLyG+ecexJ48hDVtgDHm1m0c26/N53gBGBzsXo3AaucczsBzGwK8GbFtrgoZWhFREREwoRV0b/ycM5lAZ8A13mL+gAbnXMbi1X9HuhmZjW95z2B/x75u1M2ZWhFREREpLxuAyab2QPALuBGADObBCx0zi0ExgInA/8xs1+BdG+9SqMBrYiIiIiUi3Pua+C8Upb/JeDnX4C/hrJdGtCKiIiIhAvdKSIomkMrIiIiIhFNGVoRERGRMFHeC7SkKGVoRURERCSiKUMrIiIiEiaUnw2OMrQiIiIiEtE0oBURERGRiKYpByIiIiLhwjTpIBjK0IqIiIhIRFOGVkRERCRM6LZdwVGGVkREREQimjK0IiIiImFCU2iDowytiIiIiEQ0DWhFREREJKJpyoGIiIhImNBFYcFRhlZEREREIpoGtCIiIiIS0TSgFREREZGIpjm0IiIiImHCdN+uoChDKyIiIiIRTRnaMLRl8xZGDBnGzrw86sTGMnDwgzRt1rREvcULFjF9yjR8Ph9tzmpLv/vuITo6mj179jDovgfZ8OXX/nrL3wx1CEBBHEPZudOLY8ig0uOYv4jpU17xx3F2W/rd1/9AHPc+cCCOFUtCHQL3db2RTs3bcly9BPpM7s+327eWWq/3KV24+Zxe1LAavL/pv4xYnkq+8wHQsVlr+nW6lqgaUWzI3sSgJePYu++XUIbB1s1bGPHIcPJ27iS2Th0GPvwATUrpizcWLGb61Gk4n4/WZ7Xh7nv9+1T6tjQeHjgIX76PfJ+PRo0bMeCBe6lTt25I49iyeQvDBj9K3s48YuvE8uCQh0rdpxbNX8i0ya/g8znant2We+7371MA69e+w/NPP0d+fj4tWrVk0CMPUbt2bcUQRBzDhzxaeHw/UEYci+cvZNoUfxxtzm7LPfcFxLHuHcYWxNGyJQ+GOI6tm7cw8pERhcfF/Q8PLPO4eHXq9MLjou+9/QqPi8EDHyI/34fPOy76P3AvderWCVkMUD36oiCO6nBsSNWokAytmS0zs8/N7FMzW2dmZ3jLXzKzr73lawuWe2Wve8sLHj4z6+WVDTGzrICy6QHrHW1mk83sP2b2XzNbaGYNvLIbim1zu5nNDVi3kZkt8tr0lZn9s5RYXjIzZ2axpZQN9spOqYj3rSxjRj5Gz969mDFnJlffcC2jh40sUSdtWxqTXpzI2IkvMHPeLHJzcnhj4WIAoqOjueb6a3lq7NOV2cxDGjNiND17X8aMua/54xg6okSdwjgmvcjM+bPJ3Z7LGwsC4rjhOp4a90yom17o7Q3vc9OMwWzLyy6zznH1Eri9/ZXcNGMIl066iwYx9eh9ahcAjql5FEMuvJW+C56gZ2pftv+0k7+e2ztUzS80ZuTj9Ozdi+lzZnL19dcwetioEnXSt6WROn4iz08Yx6tzXyM3J5c3vX0qPqEBz094gdTpk5k8YyoJiQlMSZ0S6jB4bPhoevW+nJnzZnHtDdcx8tHS96mJL0zkhdTxzFowm5ycHBYvWATAnj17GDl0BKOeGM2sBa8T3yCeKamTFUMQHh/hxTF3FtfccB2jyji+J744kXGTxvPa/Nnkbi8ax6ihIxj5xGhem++PY+pLk0MawxMjx3Bp755MmzODP11/NY8NG12iTvq2NF4aP4nnJoxl+tyZ3nHxBuA/Lp6bMI7U6S/z8owpNEhMYKr6ImjV5dg4UlZF/yJdRU05+KNz7jTn3BnAE8BL3vL5wO+95Y8BswpWcM79wTl3hlf2FyAXWBqwzakF5c65awOW3wbEAqc5504BMoF7vW0GrnMGkA5MBzD/pJR53nZPBE4GZgcGYWY9AVdagGbWGjgX2HwY78th25G7gw1fbaDHxRcC0LlrZ9K3pZOell6k3uqVq+jYuRNx8XGYGZf1uZzlS5cDUKtWLdqe3ZbYOqHNEgTakZtbNI5uXUhPKyWOFavo2KVjsTjeBsIjjo+3fkXWj7kHrdO91Tms/OZDcvfkATD7s+VcdHI7ANo3PYMvMr9nY24aAK99uoyLTmpXuY0uZkfuDr75egMXXNQDgE5dO5NRWl+sXE2HzgF9ccXlrFh2YJ866uijAMjPz2fvnr3UqBHaD0D/PvU1F14SuE+llYhj1YqVdArYpy7v07twn3pv/bucdPLJNG7aBIArruxTWKYYDj+Oosd3yThWr1hZ5PguEse/vDia+OPoHfK+2MGGYsdFaZ9Ra4odF72uuOwgx8UerEZoZ/JVh74IjCPSjw2pOhVy5DnndgY8rQf4vOULnXP7veXvAY3NrLTX/DMwzTlX3vOwtYGaZhaNf3Bb4jywmZ0NJAELvUXdgL3Oudle25xzLiOgfjwwGOhXyraOAsYCt1PGgLeiZGVmEp/QoPD0iZmRlJxEZkZmkXqZGZkkpSQXPk9OSSlRpyplZWaVjCMpicyMjCL1MjMySUoOiKNhCpmZ4RNHeaTUaUD6ru2Fz9N2ZZNSJ95fVrdYWV42ibH1Q/rbcGn7VGJyElml7VOBfZGSXGSf2rdvH7dcexO9evwf27Zu5cZbbg5NAAXty8yiQanHRsl9KjklpfB5SsMDx4a/7ECMKSkpZGdl4/P5QhBB9YgByoijjOM7OflAHIHHd/HPsJSGoY0jKzOLBgnxJfqi9OMiqfB5ckpykTr+4+JmLutxKdu2buPGW24KSfsL21cN+gKqz7FREcyq5hHpKuxXSTObamZbgGHAjaVUuQt40znnK7be0cDVQGqx+leb2WdmttLMugQsHw/sArLwZ2frAc+X8nq3AK845/Z5z38HZJvZTDP7xMzmmVmzgPpjgSHOubxStvUo/gH3D6WUVbji+5VzpY+hi+yAZdSpSsWv1HRl/C5QpF4YxlEegbEVH6yW1X+hVP59ysqsU7NmTVKnT2b+W4s4oXEjFsydX8GtLIfi+1RZb22RXapopSr/4K4OMUDJOMpRLdziKO+xWvS4KFrmPy5eZt5bC2nUuBELw+G4KEe1cOsLoPocG1IlKmxA65y7wTl3AjAIeDywzMyuA/6If7pAcX2Ab5xz/wlY9iLQxDl3OvAQ8JqZNfbKuuM/XpOBFGAn8HCx16sNXEXRQXJNb92hzrkzgSXATK/+lcCvzrnFxRtnZucBZwHjDvEWYGb9zGxrwePF51841ColJCYlkZ2Vzf79/sS2c46szKwiGQKApOQkMtIO/OaakZFRok5VSkxKJDszq5Q4kovUS0pOIiP9wCmljPQMkpLCJ47ySN+9nYZ1Ewqfp9RtQPruHH/Zru00rHegrGG9BLJ+3FHm4L4ylLZPZWdmkVjaPhXQF8UzUwVq1qzJxZdewrIlS0uUVaakUvepzNL3qbRi+5QXR1JyEukBx016ejoJiQnUCNFp4uoQAxxeHOmB+1TA8V38Myw9LbRxJCYllvpZW/pxcaCdmRkZJeqA/7i46NJLWLZkWeU2vJjq0BdQfY6NimFV9IhsFd7LzrkpQBfvFD5mdhX+U/kXOOeySlnlFoplZ51zGQWZVefceuAToK1X/DdgnnPuZ+fcr/jnyAZmcAH+AHzpnPtfwLJNwCfOuS+859OANmYW5a3f1cw2mtlGr/wLMzsV6AScBPzglR0PLDWzi0uJ/Unn3PEFj7/94+8HeadKVz+uPi1PbFU4WFi9cjXJDZNJaZhSpF7nLp1Zu3oNuTm5OOdYMGc+3Xp0O+zXqyz14+KKxrFiFckpKSXj6NqZtavWFouje1U0OWjLN3xA15ZnEVe7HgBXnt6dpV/9C4D1P3zGKcnNaRLXEICrzuhRWBYq/n2qJW+/5f+iXVPGPtWpayfWrQ7oi7nz6XqBvy8yMzLYu3cvAD6fj1XLV9G8RfMQxxFHq5NasfTNQ+1TXVgTsE/NnzOvcJ865/xz+fJ/X7Lph40AzJ09h+49LlAMQcRRnuO7U9cuRY7v+XPm0d2L49zzvDg2+uOYN3sO3ULaF/VpUY7jomPXzkWOi4VzF9D1Av9nbWZGZpHjYvXylVVyXER6XxTEUR2ODak6dqSnQ82sLhDrnEvznvcGngNOAK4EhgPdnXObSlm3KfAfoKFzblfA8uOdc1u9n1sC7wAdnHMbzOxZIAb/hWTgz5z6nHN3BKy/Gv8UgUkBy2KAz4GOzrltZnYFMNjLAhdvlwPqOOd+LKVsI3Cpc+6/h3pvsnZtD+rN3bxxEyMeHU5e3i5iYmrz4OBBNG3ejFHDRtK+Q3vad+oAwMJ5C3l16jR8Pkfrs1rT//4BhfOP/nzdzeRsz2Hnjh3EN4jnzDateejRhw/2sqU7gvM3mzduYsQjw7w4YnhwiBfH0JG07xgYxwJenTINn3O0btuG/gMD4rj2ppJxDB182G25YPwdh65UioHdbqZLi7bExxzLzr272fPrz/RM7cvgHrey+ruPWPPdRwBccWpXbj67FzXM+GDzFwxfnsp+Xz4AnZq34e6O1xBVI4pvt29m0JIX+OnXvYfdlqV/fS6oGAA2b9rMyEeGsysvj5iYGP+t4Jo347Fho2jXsT3tOrYH/LfDmTF1Oj6fj9Zt29DPux3Oe+vfZfzYFwFwPh8tT2zFP+6+k3rH1jvstkRHRQUdx6aNmxg+ZBi78vKoHRPDoEceolnzZox8dATtO3WgQ8E+NXcB06a8gvP2qQED7yW6pn+fWrdmHeOeeZ78/Hyat2jOoEceJiY2Jug2RXIMR/Lxv3njJoY/Mow8b596cIg/jlFDR9C+Y4cix/f0Ka/gc442bdvQf+C9hcf3O2vWMe5ZfxzNWjRn0JDg4tjv23/oSqXFsGkzox4ZUdgX/uOiaYnjYvH8hbw69VWcz8eZbVsXOS4meMeFz+dodWIr7rj7n8EdFzWCv4tmOPXFkZzyD6djo0FsXJWlLG949eEqmac29ZpHIzpNWxED2hOAOcAx+C8Gywb6O+c+NbN9QAaQE7BKN+dcjrfuUKCRc+7GYtucArQB9gP5wEjn3OteWRwwAf+cWAf8D7jNOZfrlTcHPsU/SN5dbLsXAqPx59Z3ArcHZGwD61XpgDasVJMJScEOaMPJkQxow8mRDGilYoXB9O4KEeyANpwcyYA2nFSTr4wqHdDeWEUD2ikRPqA94iPIObcFOLuMspqHWPehMpaXdlFZQVku/ikFZZV/B5R6nyfn3FKK3hqsrG2U2anOuSaHWl9EREREQqd6/EooIiIiUg0Uv0OQlE+kXfonIiIiIlKEBrQiIiIiEtE05UBEREQkTGjCQXCUoRURERGRiKYMrYiIiEi40EVhQVGGVkREREQimjK0IiIiImHCNIs2KMrQioiIiEhE04BWRERERCKaphyIiIiIhAlNOAiOMrQiIiIiEtGUoRUREREJE6bbdgVFGVoRERERiWga0IqIiIhIRNOAVkREREQimga0IiIiIhLRdFGYiIiISJjQRWHBUYZWRERERCKaMrQiIiIiYcL0pxWCogytiIiIiEQ0ZWhFREREwoTys8FRhlZEREREIpoGtCIiIiIS0TTlQERERCRc6LZdQdGAthLlO19VN+GI+fIjPwaApX99rqqbcMQunPjPqm5ChXj7trFV3YQKUaMafOn8sv/Xqm5ChXC4qm7CEas29x6N/K6QCKUBrYiIiEiYqCa/2oSc5tCKiIiISETTgFZEREREIpqmHIiIiIiEiWoznzrElKEVERERkYimDK2IiIhImDBdFhYUZWhFREREJKJpQCsiIiIiEU0DWhERERGJaBrQioiIiEhE00VhIiIiImFCt+0KjjK0IiIiIhLRlKEVERERCRPKzwZHGVoRERERiWjK0IqIiIiECc2hDY4ytCIiIiIS0TSgFREREZGIpgGtiIiIiEQ0DWhFREREJKLpojARERGRMKGLwoKjDK2IiIiIRDRlaEVERETChOlPKwRFGVoRERERiWga0IqIiIhIRNOUgzC0dfMWRj4ygrydO4mtU4f7Hx5Ik2ZNS9R7Y8FiXp06Hefz0fqsNvS9tx/R0dGkb0tj8MCHyM/34fP5aNS4Ef0fuJc6deuEOI6tjBo6gl0784ipE8t9Dw2kSdMmJeq9ufANZkydjs85WrdtTd8BdxMVfWDXdM7R/5/9+O6b75i/dGEII/D3xYhHhhf2xcCHHyizL6ZPnVbYF3ffe09hXzw8cBC+fB/5Xl8MeOBe6tStG7IY7ut6I52at+W4egn0mdyfb7dvLbVe71O6cPM5vahhNXh/038ZsTyVfOcDoGOz1vTrdC1RNaLYkL2JQUvGsXffLyGLocCWzVsYMWQoO3fmUSc2loFDBtG0lP5YPH8R06e8gs/no83Zbel3X3+io6PZs2cPg+59gA1ffu2vt2JJqENgy+YtDBv8KHk784itE8uDQx4qNYZF8xcybfIr+HyOtme35Z77/TEArF/7Ds8//Rz5+fm0aNWSQY88RO3atUMax9bNWxk9dERhHPce5PieGXB83+Ud3xlp6Vx/5bVFYh8y8lEaHn9cSGN4bOjIgBjup3EpMSxZ+AYzp76KzznObNuauwb0LYzhhmIxDA5xDOB9Tg0ZTl7eTmJj6zBw8EE+p6YEfE7d5/+c+u7b73j6sSfZmbuDqOhofn/q77lrwN3UqlVLcUjEqJAMrZktM7PPzexTM1tnZmd4y83MhpjZBjP7r5mtDlintpnNMLNvvfIrAsru9OoXbPOqgLKbzGynt/xTM1tVSnuONrP/mdm/A5bFmtlSM9tuZttLWeccb3sbzGyFmaUElF1oZh+Z2Sdeu26siPetLE+MHMOlvXsybc4M/nT91Tw2bHSJOunb0nhp/CSemzCW6XNnkpuTy5sL3wAgPqEBz00YR+r0l3l5xhQaJCYwNXVyZTa5VE+OHsOll/Vk6uzp/Om6qxkzvJQ40tJ5eUIqz4x/nmmvv+qPY9GbRerMmz2X5JTkUDW7iDEjH6dn715MnzOTq6+/htHDRpWok74tjdTxE3l+wjhenfua1xeLAX9fPD/hBVKnT2byjKkkJCYwJXVKSGN4e8P73DRjMNvyssusc1y9BG5vfyU3zRjCpZPuokFMPXqf2gWAY2oexZALb6XvgifomdqX7T/t5K/n9g5V84sYM2I0PXtfxoy5r3H1DdcyeuiIEnXStqUx6cWJjJ30IjPnzyZ3ey5vLPD3R3R0NNfccB1PjXsm1E0v9Njw0fTqfTkz583i2huuY+Sjpccw8YWJvJA6nlkLZpOTk8PiBYsA2LNnDyOHjmDUE6OZteB14hvEM6UKju+nRo/h/7zj+6qDHN+TJ6Ty9PjneaWU4zs2NpYJr6QWPkI9EHx69BP832WXMmX2NK667k+MGf5YiTr+GF7i6fHPMfX16ezIyWVJsRjGv5Ja+Ah1DFDsc+qGg3xOvTiR5yeO49V5RT+njqpVi74D7uaV118ldfrL/PTjT7w2fWaow6g2cUjVqKgpB390zp3mnDsDeAJ4yVt+J3AqcIpz7hTg6oB1+gO/OOdaABcC48ysvlf2BdDOOXca0BN43swaB6y73Dl3hvfoUkp7hgPvFlu2D3gM6F68svnvkTEd6OucawUsAZ4MKHsVuNk5dyZwKTDezCol3bkjdwcbvt7ABRf1AKBT186kp6WTnpZepN6alavp0LkjcfFxmBm9rriMFcuWA1CrVi2OOvooAPLz89m7Zw9WI7SzS3bk7uCbr7/hgosuAKBjl06kp2WQUUoc7Tt1KIyj5xWXsXLZisLyrZu3surtlVx9/bUhbT8UxFC0LzJK6YvVxfrisisuP0hf7KVGjdBO+P9461dk/Zh70DrdW53Dym8+JHdPHgCzP1vORSe3A6B90zP4IvN7NuamAfDap8u46KR2ldvoUuzIzWXDVxvocfGFAHTu1qXUY2P1ilV07BLQH30uZ/nStwF/f7Q9uy2xdUJ7tqKAP4avufCSwBjSSsSwasVKOgXEcHmf3oUxvLf+XU46+eTCTOIVV/YpLAtdHCWP74xSju+1K1fTrtjxvSrg+K5KBcd3dy+GDl06kZGWXkoMa2jXqQP1vRguvaJXkc+oqrYjdwfffLWBCy4O+JzaVo7PqT6Xs2Kp/3Pq+EYn0LxlCwCioqI46Xcnkb4tTXFUETOrkkekq5BRjnNuZ8DTeoDP+3kAcJ9z7levXuCeeRUw1lv+A7AWuMx7vsI5l+f9vAXIBE4oT1vMrAPQEnilWBt/cc6tAHaWslpb/IPr1d7z8cDlZlYzoM6x3v91gRygUs63ZmVm0SAhvvDUopmRlJxEVkZmkXqZGZkkJScVPk9OSS5SZ9++fdxy7c1c1uNStm3dxo233FQZzS1TdlYWDRrEF04dMDMSkxPJzMwqUi+rtDgy/XH4fD6eGPk4dw3oS3R0VOgaX9C2zEziExoU6YvEMvviQAY5OSWZzBJ9cRO9evwf27Zu5cZbbg5NAIchpU4D0ncdOHGRtiublDrx/rK6xcryskmMrR/yK3GzMrNK9EdSUhKZGRlF6pXoj4YpZGYW7bOqkpmZRYPiMSSXHkNySuFJIlIaphTuU/6yA/GlpKSQnZWNz+cjVLKzsogv5fjOOsTxnRRwfAPs+eknbr/5Vm674S9MTZ1Mfn5+aAKgIIYGxWJIOmQM/s+oA3X2/LSH22++jb/d8FdeSZ0S0hjgMD+nUsr+nCqwd+9e3liwmPM7hPaX1uoSh1SdCkvbmdlUM9sCDANuNLO6QALQ28ze8x5XBazSCNgU8Hyjt6z4drsD9YGPAhZ38qYHrDezPwTUjQGeBv5+mM0v0hbn3G5gN5DinHPAH4G5ZrYJeAe4sWCQXhmKDxT8TSilXsBvVMWr1KxZk9TpLzPvrYU0atyIhXPnV3QzD634b3xlxEGROA7UmTV9JqedeRotWrWsjNaVS/EhW/n6omgdf19MZv5bizihcSMWVEVflIPjQLvLuw+GWvEsQmCby6wXJm0vVDyGsppXJISilcIhmVIio1OOYyOwTlyDeGYufJ1xL0/g8eee5D+ffs7sV1+rjKaWqcT+dJifUXEN4pmxcDbjXh7PY889wX8+/ZzXX51VKW09mIr4nALYv38/jzwwmLbnnk37Th0qsonlUl3iOFJWRY9IV2EDWufcDc65E4BBwONATaAWcIxz7lz8g8InzeyUwNUCfi7xfprZqcDLwFXOub3e4sVAY296w1+Ap8zsXK/scWCsc25bMCEUf3mvDdHAQOAy51xjoBswxcziSmlvPzPbWvAY//yLh92IxKREsrOy2b9/v79RzpGVmUViQIYAICk5iYz0A1mdzIyMEnXAP5i66NJLWLZk2WG35UgkJCayPSub/CJxZJOUlFikXmJyEplF4sgkMckfx+effs7SN97i6suv4s7b/smPu3dz9eVXsXvX7pDEkJiUVKIvssvsiwMnH4pnzwvUrFmTiy+9hGVLllZuw4OQvns7DesmFD5PqduA9N05/rJd22lY70BZw3oJZP24o8zBZGVJTEokOzOrxLERmI2Fkv2RkZ5BUlLJ/qgKSaXGkFl6DGnFYvD2qaTkJNLTDhwz6enpJCQmUCOE04rKOr4TSzm+M8o4vmvVqkX9OP8ss7r16nJxz0v4z6efhygCfwzZxWLIzswqNYaSn1H+OsVjuCjEMcBhfk6llf05tX//fgYPfIi4+HjuvOeu0DQ+QHWJQ6pOhX8COuemAAXzWn8EpnnLNwPr8Z/eB9gMNAlYtbG3DAAz+x3+weufnXPvBGx/u3Nuj/fzl8CbQME5hfbAw2a2EZgJnGpmX5Sj2UXa4s2PrQOkA2cADZ1z673X/BBIA04vJfYnnXPHFzxu+8ffyvHSRdWPq0+LE1vy9lv+AeialatJbphMSsOUIvU6du3MutVryc3JxTnHwrkL6HpBN8B/gO/d6x//+3w+Vi9fSfMWzQ+7LUeiflx9WrRqydtv+ef2rV21huSUZJKLx9GlE++sWVcYx6K5C+hyQVcARjwxipkLZjNj/ms8O/45YuvUYcb810J2t4b6cfVpWY6+6NS1U5G+WDB3Pl0v8E/VzszIKNIXq5avCnlflMfyDR/QteVZxNWuB8CVp3dn6Vf/AmD9D59xSnJzmsQ1BOCqM3oUloVS/bg4Wp7YqvAXgtUrVpGcklKiPzp37czaVQH9MWc+3XqUmDpfJerHxdHqpFYsffNQMXRhTUAM8+fMK4zhnPPP5cv/fcmmHzYCMHf2HLr3uCDEcZTv+O7QpRPryzi+d+TuKBy8/Prrr6xbvZYWJ4bubIw/hhYs92JYt2oNSaXG0JH1a9axw4th8dyFZcbwTohjKIij5YkteXvJIT6nuhT7nJozn67ePlWQ0axbty4DHry3SuZTVpc4pOrYkZ5K9KYWxDrn0rznvYHn8M95HQ986pwruODrE+AK59zHZjYEaOKcu8nMmgLvASc753LN7GT8F2bd5pxbWuz1jivIwJpZEv4pALc551YWq9cZGOOca1tseRPg3865BgHLagDfALc451abWX+grXPuT95rfAOc5Zz72sxaAB8Apx4qE5yelxXUm7t502ZGPTKCXXl51I6JYeDgB2navCmPDRtFu47tadexPQCL5y/k1amv4nw+zmzbmn7ebX3eW/8uE8b6s8M+n6PVia244+5/Uu/YeofdliOZl7d502YeGzqSXXm7qB0Tw30PD6Rps6aMGf4Y53VoR7uO7bw4FjFz2gx/HG1a0/e+foXzqApkpKXzt5tvC/q2XVE1gpuDu3nTZkY+MpxdeXnEFPZFsxJ9sWj+Qv+tx3w+WrdtU6Qvxnt94Xw+Wp7Yin/cfWdQfXHhxH8GFcPAbjfTpUVb4mOOZefe3ez59Wd6pvZlcI9bWf3dR6z5zj+b54pTu3Lz2b2oYcYHm79g+PJU9vv88wE7NW/D3R2vIapGFN9u38ygJS/w0697D/ayZXr7trFBrQeweeMmRjwyjLy8XcTExPDgkEE0bd6MUUNH0r5j+8LTiwvnLeDVKdO8W0W1of/AAYX71J+vvYmc7Tns3LGD+AbxnNmmNQ8NHXzYbakR5Jflpo2bGD5kWOHxPeiRh2jWvBkjHx1B+04d6FAQw9wFTJvyCs6LYcDAe4mu6Y9h3Zp1jHvmefLz82neojmDHnmYmNiYw27Lz/uCnzm1ZdNmRnvHd4x3fDfxju/zO7TjfO/4fiPg+D4j4Phet2otkye+RI0aNcjPz+fMNmdy2523B3WLpWDPFmzZtJnHho7yYqjNvV4MT3ifUQdiWMxr017F53Oc2eZM7gqIYcrEl6hRI4r8/HzOaHMmt93596BiqBkV/F00N2/czMhHy/ic6tCedp28z6l5AZ9TZx34nHp7yTKGPfwozVs2L5xudMrpp3L3ffcE3aZIjyO5bkKVjYb7LXiiSuZJPXnZPRH9G0BFDGhPAOYAx+C/GCwb6O+c+9TMGuCfMlBwI7nnnHPjvfVi8N8NoY233gPOude9srfxZ3ID59je55xbamYj8F88tg9/hvlF59y4UtrVmWIDWjP7GEgBEvFnX1c55673ys4DXvTi2AZcFzBwvhp4wGunASOcc4e8F0iwA9pwEsoLTSpTsAPacBLsgDbcHMmANpwEO6ANJ0cyoA0noZ7+UhmOZEArFU8D2shzxEeQdxeCs8so247/tlullf2E/04HpZWVef7MOfcA/sHlodq1mgPTGwqWtT5I/XcpZRqBVzYDmHGo1xQRERE5MhE9rqwy+tO3IiIiIhLRNKAVERERkYimSTsiIiIiYUITDoKjDK2IiIiIRDRlaEVERETChO6fGxxlaEVEREQkoilDKyIiIhImlJ8NjjK0IiIiIhLRNKAVERERkYimKQciIiIi4UIXhQVFGVoRERERiWjK0IqIiIiECdNlYUFRhlZEREREIpoytCIiIiJhQvnZ4ChDKyIiIiIRTQNaEREREYlomnIgIiIiEiZMt+0KijK0IiIiIhLRNKAVERERkYimAa2IiIiIRDTNoRUREREJE5pDGxxlaEVEREQkomlAKyIiIiLlYmYtzexfZrbBzD4ws9+VUe9UM1ttZl+a2ddmdkVltktTDkRERETCRARMOBgPTHDOTTazPwCpwHmBFcysNjAfuNE5946ZRQP1K7NRytCKiIiIyCGZWSLQGpjmLZoDNDWzJsWqXgO865x7B8A5t985l12ZbVOGthLVjKoGb29UVTdACrx929iqbkKFuGD8HVXdhAqx4m/jqroJR+zomrWqugkVIt/nq+omHLGoGsoviZ+Fd472BCDNObcfwDnnzGwz0AjYGFDvd8DPZrYYOB74HLinMge1OoJEREREfuPMrJ+ZbQ149Cujqiu+ail1agIXArcBZwJbgErNylSDFKKIiIhINVFFt+1yzj0JPHmIaluA480s2jm33/z3GDsB2Fys3iZglXNuG4CZTQferOg2B1KGVkREREQOyTmXBXwCXOct6gNsdM5tLFZ1FnCWmdX1nl8EfFaZbVOGVkRERETK6zZgspk9AOwCbgQws0nAQufcQufcZjMbCbxrZvuBbcCtldkoDWhFREREwkRYXxIGOOe+pthturzlfyn2fCowNVTt0pQDEREREYloytCKiIiIhAmroovCIp0ytCIiIiIS0TSgFREREZGIpikHIiIiImFCEw6CowytiIiIiEQ0ZWhFREREwoZytMFQhlZEREREIpoytCIiIiJhQrftCo4ytCIiIiIS0TSgFREREZGIpikHIiIiImFCEw6CowytiIiIiEQ0ZWhFREREwoQuCguOMrQiIiIiEtE0oBURERGRiKYBrYiIiIhENA1oRURERCSi6aIwERERkTBhunFXUJShFREREZEqY2YDij2vaWZjD2cbytCGoS2btzBs8KPk7cwjtk4sDw55iKbNmpaot2j+QqZNfgWfz9H27Lbcc39/oqP9Xbp+7Ts8//Rz5Ofn06JVSwY98hC1a9dWHL/BGAriGDFkKDt35lEnNpaBQwaVGsfi+YuYPuUVfD4fbc5uS7/7/HHs2bOHQfc+wIYvv/bXW7EkpO0HuK/rjXRq3pbj6iXQZ3J/vt2+tdR6vU/pws3n9KKG1eD9Tf9lxPJU8p0PgI7NWtOv07VE1YhiQ/YmBi0Zx959v4QyjGq1T0V6HP7jYhh5eXnExsbywOAHaVLacbFgEdOnTMP5fLQ5qy1333cP0dHRfPftdzz12BPszN1BVHQ0p5x6CncNuJtatWqFLIaCOCK9L6pTHEfqN3rbrg5m1gm4HogDZgPvHc4GKiRDa2bLzOxzM/vUzNaZ2Rne8pfM7Gtv+dqC5V7Z697ygofPzHp5ZUlmNtfb5ldm1jdgvd4Br/WFmQ03r/fN7LyA7X1hZuPN7KiAdRuZ2SKvTV+Z2T+95b8r1paNZpYbsN5FZvZv73XfM7PTK+J9K8tjw0fTq/flzJw3i2tvuI6Rj44oUSdtWxoTX5jIC6njmbVgNjk5OSxesAiAPXv2MHLoCEY9MZpZC14nvkE8U1InV2aTS1Ud4qgOMQCMGTGanr0vY8bc17j6hmsZPbT0OCa9OJGxk15k5vzZ5G7P5Y0FiwGIjo7mmhuu46lxz4S66YXe3vA+N80YzLa87DLrHFcvgdvbX8lNM4Zw6aS7aBBTj96ndgHgmJpHMeTCW+m74Al6pvZl+087+eu5vUPV/ELVZZ+qDnGMGfkYvXr34tU5M7nmhmsZPWxkqTGkvjiRsRNfYMa8WeTk5PDGQv9xUatWLe4e0I9pr8/gpemT+fHHH3lt+oyQxgDVoy+g+sQhh8851wtYDXwCrAJGOeduP5xtVNSUgz86505zzp0BPAG85C2fD/zeW/4YMKtgBefcH5xzZ3hlfwFygaVe8ZPAf5xzpwFtgT+b2Vle2XKgYL0zgQuAnl7ZZ8BZXtmpQAJwG4A36J0HTHXOnQicjP83AJxz/ytoi7fuYmC6t159YBpwvdee+wrKKsOO3Fw2fPU1F15yIQCdu3UhPS2N9LT0IvVWrVhJpy4diYuPw8y4vE9vli99G4D31r/LSSefTOOmTQC44so+hWWhUh3iqA4xHIhjAz0uDowjvUQcq1esomNAHJf1ubywrbVq1aLt2W2JrVMnpG0P9PHWr8j6Mfegdbq3OoeV33xI7p48AGZ/tpyLTm4HQPumZ/BF5vdszE0D4LVPl3HRSe0qt9HFVK99KrLj2JG7g2++2sAF3nHRqWtn0reVPC7WrFxFh86dihwXK5YuB+CERifQvGULAKKiojjpdyeTti0tZDH444j8vqhOcUhwzKwm0BjYCTjgsE9zVMiA1jm3M+BpPcDnLV/onNvvLX8PaGxmpb3mn4FpzrmCc3+nA2942/gRWIM/DY1zbrdz3vlDOBo4KuD19jjn9nlltYBjCsqAbsBe51zBINY55zKKN8TL6F4DpHqLmgNZzrkvvfXWeHG0PtT7EozMzCwaJDQoPH1iZiQlJ5GZUbSpmRmZJKekFD5PaZhCZkZmQFnygbKUFLKzsvH5fIRKdYijOsQAkJWZRXzxOJJKjyMp+UBbkxumkJmZGbJ2VoSUOg1I37W98HnarmxS6sT7y+oWK8vLJjG2fkgvwKgu+1R1iCMrM7PEcZGYnFTYvgKltbN4HYC9e/fyxoJFnN+hfeU2vJjq0BdQfeKQoP0L/zTYs4H2wN/NLPXgqxRVYReFmdlUM9sCDANuLKXKXcCbAYPRgvWOBq7mwAAS4EPgGjOrYWaJwIVAk4B1zjezz4EsYAXe4Ncra2JmnwLbgV3ABK/od0C2mc00s0/MbJ6ZNSulnVcAPzjnPvWefwMkmNm53vZ7A7GB7alwxebPOFdWvcA6RSuFxRSc6hBHdYiBknOyHKUHUqRemcGGt8DYig9Wi/dNlagm+1R1iKPEy5cRhB0kBoD9+/cz5IGHOevcs+nQqUPFNbC8qkFfANUnDgnGU865vzvnfnXObQE64j9zX24VNqB1zt3gnDsBGAQ8HlhmZtcBf8Q7/V9MH+Ab59x/ApbdA9QFPgamAiuBgswrzrl/eaf/TwDOAjoElG30pg0k48/eXuEV1QS6A0Odc2cCS4CZpbTnzwQMrp1zeV4bR5nZR0Bn4H+B7QmIs5+ZbS14vPDcuFI2f3BJSYlkZ2axf//+gtcnK7No5gwgKTmJjIBTMRnpGSQlJxWWpacd+K02PT2dhMQEatQI3U0tqkMc1SEGgMRS48gqPY70YnEkJYWsnRUhffd2GtZNKHyeUrcB6btz/GW7ttOw3oGyhvUSyPpxR5mD+8pQXfap6hBHYlIS2VnZpRwXRff54u3MyMgoUmf//v0MHvgQ8fHx3HlP35C0vUj7qkFfQPWJoyKYWZU8qpJz7lUzO8PMrvEW1cE//bTcKryXnXNTgC5mFg9gZlcBg4ELnHNZpaxyC0Wzszjncp1zf/bmtF7kLf5fKa+VjT87e2UpZT/iH7Be6y3aBHzinPvCez4NaGNmUQXrmFlj4Hzg1WLbWuuc6+ycawPcCzQEvizlNZ90zh1f8Pj7Pw9rPjMA9ePiaHVSK5a+6Z9OvHrFKpJTUkhpmFKkXueuXVizai25Obk455g/Zx7denQH4Jzzz+XL/33Jph82AjB39hy697jgsNtyJKpDHNUhhoI4Wp7YimVLDhVHZ9YGxLFgzvzCOCLF8g0f0LXlWcTVrgfAlad3Z+lX/wJg/Q+fcUpyc5rENQTgqjN6FJaFSnXapyI9jvpx9Wl5Yive9o6LNStXk9wwuUQMnbp0Zt3qNcWOi27Agcxsnbp1GfDgfVUyKKgOfVGd4pDgmNnfgCnAUG9RPId5vZId6Sk4M6sLxDrn0rznvYHn8GdPrwSGA92dc5tKWbcp8B+goXNuV8DyeGCXc26fN1f1TeBM51y6mZ2IP6PrM7M6+DOtU5xzE82sObDZW68W/kHrN865B80sBvgc6Oic22ZmVwCDnXOnB7zuEKCFc+66Yu1Mcc6lez8PA052zvU51Huz/cfcoN7cTRs3MXzIMHbl5VE7JoZBjzxEs+bNGPnoCNp36lB4Smvh3AVMm/IKzjlat23DgIH3El3TP/9o3Zp1jHvmefLz82neojmDHnmYmNiYYJoTtOoQRzjF4DuCY3Xzxk2MeGQYeXm7iImJ4cEhg2javBmjho6kfcf2tC+IY94CXp0yDZ8XR/+BAwrntP352pvI2Z7Dzh07iG8Qz5ltWvPQ0MGH3ZYLxt8RVAwDu91MlxZtiY85lp17d7Pn15/pmdqXwT1uZfV3H7Hmu48AuOLUrtx8di9qmPHB5i8YvjyV/b58ADo1b8PdHa8hqkYU327fzKAlL/DTr3uDas+Kvx3+GRgIr33qSIRTHPlBzpHcvHETIx8d7h0XtXlgsP+4GD1sJO06HDguFs1byKtTp+HzOVqf1Zp77vcfF8uWLGXYw4/SvGWLwrPgp5x+Gv3uu+ew2xJ1BFnEcOqLIxFOcTSIjauylOXwtydVydyoBy/4S5XFbGaf4E8o/ss7i46Z/dc5d0q5t1EBA9oTgDkcuAArG+jvnPvUzPYBGUBOwCrdnHM53rpDgUbOuRuLbfNi/IPifcBub3trvbJB+C/a2gdEAa8DjzjnnJndAtwN5OOfXLwSGOCc+9lb90JgNP4ZODuB2wsytt5dEH4AbnbOrSrWnkn4JylHA+8C/yx2IVypgh3QipTmSAa04STYAW24CXZAKxUv2AFtODmSAa1UPA1oQ8vM3nfOnWNmnwQMaD/1ppCWyxH/YQVv8u7ZZZTVPMS6D5WxfAnQooyyYfgvPCutLJVi0xeKlS/lwK3Bipc5yrjQyzn3l7K2KSIiIiJHJNvMWuG/ZRdmdj2w5XA2oL8UJiIiIhImqvoCrSrSF//1Syea2UZgDwf+xkC5aEArIiIiIlXGOfetd3vUE/FPC/3aOZd/ONvQgFZEREQkbPx2MrRm9rsyik40M5xzJe5wVRYNaEVERESkKryBf96sAY3w/0Es8P/V2U1A0/JuSANaERERkTDx28nPgnOuKYCZPQesdc7N9p7/AWh7ONvSfUJEREREpCqdVTCYBXDOvY7/L7OWmwa0IiIiIlKVaptZh4InZtYeqH04G9CUAxEREZEw8Ru9bdcdwAwz+8l7fgxw9eFsQANaEREREakyzrl1ZtaMA7ft+so59+vhbEMDWhEREZEw8ZvMz/rtB3Lwj02Tvdt2bS7vyhrQioiIiEiVMbObgGeBfYDPW+yAxPJuQwNaERERkXDx25xD+xBwtnPuq2A3oLsciIiIiEhVyj6SwSxoQCsiIiIiVWuumf3DzOLMrHbB43A2oCkHIiIiImHiNznhAEZ5/z/LgT+F64Co8m5AA1oRERERqTLOuSOeMaABrYiIiEiYsN9qjvYIaUArIiIiIiFnZiucc93MLBv/FIPCIsA553TbLhEREZFI8xv707fXef+3PdINaUArIiIiIiHnnEv3/t90pNvSbbtEREREJKJpQCsiIiIiEU0DWhERERGJaJpDKyIiIhImfksXhZnZ7Qcrd86NK++2NKAViRA1qsmH3Iq/lfvzKax1e/Ggn8MRobr0RVQNnWwUiVBnef83ADoBK7zn3YC3AQ1oRURERCJN9UhdlI9z7mYAM5sPnO6c+8F73gR47HC2pV9rRURERKQqNSkYzAI45zYCrQ5nAxrQioiIiEhV2m5mD5lZivcYBGw/nA1oyoGIiIhImLDf1KSDQjcAzwL/xf8ncFd6y8pNA1oRERERqRJmFgXc5Zz7w5FsRwNaERERkXBRTe5oU17OuXwzO/tIt6M5tCIiIiJSlRaZ2X1mlmhmtQseh7MBZWhFREREpCqN8f4fiX8OrXn/R5V3AxrQioiIiISJ39aEAz/n3BHPGNCAVkRERESqnJlFA7UKnjvn9pR3XQ1oRURERMKE/cYuCgPwLgpLBU6maJJaUw5EREREJCI8C/wFeBHoCNwJ7D2cDeguByIiIiJhwqroUcVqOufeB6Kdc7udc8OBXoezAQ1oRURERKQq7ff+zzGzM8ysAdD4cDagKQciIiIiUpVmmlk8MAJYi398+vDhbEADWhEREZGwEQYTAELMOfeU9+Myb2B7tHNu9+FsQ1MORERERKTKmNmtZhYH4JzbB9Qys78ezjY0oBUREREJE2ZWJY8qdrtzLrfgiXMuB7jjcDagAa2IiIiIVKXSRtSHNUbVgFZEREQkTPxGb9uVbmZ9Cp54P2cczgZ0UZiIiIiIVKW7gflmNtp7/itw2eFsQANaEREREakyzrkvzex3wIneoq+dc/mHsw0NaEVERETCRBhcoBVyZtYTWOec+5/3vL6ZtXPOLS7vNjSHVkRERESq0lDn3M6A5zuBoYezAQ1oRURERCRsOOcchzlG1ZSDMLRl8xaGDX6UvJ15xNaJ5cEhD9G0WdMS9RbNX8i0ya/g8znant2We+7vT3S0v0vXr32H559+jvz8fFq0asmgRx6idu3aiuM3GIPiCK847ut6I52at+W4egn0mdyfb7dvLbVe71O6cPM5vahhNXh/038ZsTyVfOcDoGOz1vTrdC1RNaLYkL2JQUvGsXffLyGLAapHX1SHGBRH+MUhQdllZuc4594HMLNzgdD+pTAzO9rM5pvZBjP71MzeMrMmXtlZZrbezD73yroGrNfczFZ4y78ysyfMrIZXdqeZ/TdgvasC1rvfW1bw2GVmT3plNcxsjLfuV2aWama1AtZtZGaLzOxrr/yf3vKGZrbUW/65mc0q+IsVXvlFZvZvr+w9Mzv9SN+3g3ls+Gh69b6cmfNmce0N1zHy0REl6qRtS2PiCxN5IXU8sxbMJicnh8ULFgGwZ88eRg4dwagnRjNrwevEN4hnSurkymxyqapDHNUhBlAc4RTH2xve56YZg9mWl11mnePqJXB7+yu5acYQLp10Fw1i6tH71C4AHFPzKIZceCt9FzxBz9S+bP9pJ389t3eoml+oOvRFdYgBFEe4xXGkrIr+VbH78N/lYLmZrQDmAP0OZwMVNeVgAnCic+4MYDEwwfyzmucBg5xzpwF/AqaY2THeOmOABd46ZwA9gIu8si+Adt56PYHnzawxgHNulHPuDG+9s/Hf2mG6t94twGlAa+Bkb9ldAAHtmeqcO9Ern+3Vycc/f+NE7zU3AaO89eoD04DrvbL7Al6vwu3IzWXDV19z4SUXAtC5WxfS09JIT0svUm/VipV06tKRuPg4zIzL+/Rm+dK3AXhv/bucdPLJNG7aBIArruxTWBYq1SGO6hCD4gi/OD7e+hVZP+YetE73Vuew8psPyd2TB8Dsz5Zz0cntAGjf9Ay+yPyejblpALz26TIuOqld5Ta6mOrQF9UhBsURfnFIcJxz7wK/A54EngB+75z74HC2ccQDWufcz865N735DgDvAc2AeCDOObfKq/cV/km+FwesXs/7/xigJpDu1V3hnMvzft4CZAInlPLylwNbnXMfec9PB5Y753712vMmcL1X1g3Y65yb7W3XOecyvJ8znXPvBGz3fS8GgOZAlnPuS6/uGqCxmbUu51t0WDIzs2iQ0KDw9ImZkZScRGZG0fsLZ2ZkkpySUvg8pWEKmRmZAWXJB8pSUsjOysbn81VGk0tVHeKoDjGA4gi3OMojpU4D0ndtL3yetiublDrx/rK6xcryskmMrR/SDEt16IvqEAMojnCLQ4LnnNsBLANWA7+a2WHNFamMi8LuBBY557YDmQV/+cHMzgFaAU28en2BK80sDUjDnzn9pPjGzKw7UB/4qHgZ/oxsasDzD4HLzKyON9XgTwGv9zsg28xmmtknZjbPzJpRjJlF4f/7wYu8Rd8ACd58DsysNxAbsN2KV+yWHYW/KpSoF1inaKWwuOtHdYijOsQAiqPsTYQtx4F2Fx+sFo+pSlSHvqgOMYDiKHsTEcmsah5VG7OdbWb/AX7GP3e24FFuFTqgNbMHgJbAg96iy4C/mNnHwO3AO8A+r+w24BXnXEOgMXBN4Bxbb3unAi8DVznn9hYrOwFoT9HT/1OBpcBaYCX+qQsFr1cT6I5/asGZwBJgZrFtGjAOfyb5OQAvU9wHGGVmHwGdgf8FbDdw/X5mtrXg8cJz4w72dpUqKSmR7Mws9u/fj/f6ZGVmkpScXLRechIZAadiMtIzSEpOKixLTzvwW216ejoJiQnUqBG6m1pUhziqQwygOMItjvJI372dhnUTCp+n1G1A+u4cf9mu7TSsd6CsYb0Esn7cUWQAXNmqQ19UhxhAcYRbHBK0Z4G/AP8BjgUeBgYczgYqrJfNrD9wBXCxc24PgHPuc+fcxc651s65G4GG+AeD4M/kTvHqZeEfYHYK2N7v8M/H/XOx6QAFbgYWOucKJ6N50wgedc6d6ZxrD3wV8HqbgE+cc194z6cBbbyMbIFn8U9tuMo55wvY7lrnXGfnXBvgXi+OL4s3yDn3pHPu+ILH3/95+6HfuGLqx8XR6qRWLH1zKQCrV6wiOSWFlIYpRep17tqFNavWkpuTi3OO+XPm0a1HdwDOOf9cvvzfl2z6YSMAc2fPoXuPCw67LUeiOsRRHWJQHOEXR3ks3/ABXVueRVxt/6ysK0/vztKv/gXA+h8+45Tk5jSJawjAVWf0KCwLlerQF9UhBsURfnFUDKuiR5Wq6d3hINo5t9s5NxzodTgbsIo4dWVm/YBrge7eHIiC5ckF81TN7K/4s7JnOeecmX0OPOGcm2JmMfizqqOcc7PN7GT8A9zbnHNLS3k9A74DbnXOLQ9YfjRwtHNup5k1AJYDDznnFnmv8TnQ0Tm3zcyuAAY750731n0Wf3b5cufcL8VeL8U5l+79PAw42TnX51Dvy/Yfc4N6czdt3MTwIcPYlZdH7ZgYBj3yEM2aN2PkoyNo36kDHTp1AGDh3AVMm/IKzjlat23DgIH3El3TP/9o3Zp1jHvmefLz82neojmDHnmYmNiYYJoTtOoQR3WIQXFUThzdXjz8X1gBBna7mS4t2hIfcyw79+5mz68/0zO1L4N73Mrq7z5izXf+2VVXnNqVm8/uRQ0zPtj8BcOXp7Lf5/9LkJ2at+HujtcQVSOKb7dvZtCSF/jp170He9lSrfjb4Z9FKhBOffFbjkFxVE4cDWLjqmyE98L62VUyp+jv7a6sspjN7APn3Nlmthr/lNStwIfOuZL3bStrG0c6oDWz44EtwPccmO/wi3PuHDMbjH+ga/gzmnd4F3lhZmcCzwN18E8HmA884A123wba4s+qFrivYHBrZt2ASUCzgIvRMLMkYA3+uxZEAU87514MKL8QGO21Zydwu3PuCzNrh386xFdAwWD2B+dcb2+9SfinN0QD7wL/dEX/okWpgh3Qikj4C3ZAG06OZEArUp1pQBtaZnY3/mmjbYDX8Y+3HnbOjSnvNo74Dys457ZSRq7aOfcI8EgZZZ8Apd5rxjl30HMEzrkVQIlRu3MuEzjpIOstxT/Htvjy9Rwk3+6c+8vB2iMiIiJSEayqr9AKIW96KfjHZkn4M7MFY8OfzOyo4mfNy6K/FCYiIiIiVeGNUpYVZKhrArFmdo9z7qVDbUgDWhEREZEw8dvJz8Kh5siaWQr+u1YdckCre1mIiIiISNjxLsifUJ66ytCKiIiIhIlQ/tW/SOCce6o89ZShFREREZGIpgGtiIiIiEQ0TTkQERERCReacRAUZWhFREREJKIpQysiIiISJnRRWHCUoRURERGRiKYMrYiIiEiY+C396duKpAytiIiIiEQ0DWhFREREJKJpyoGIiIhImNCEg+AoQysiIiIiEU0ZWhEREZFwoYvCgqIMrYiIiIhENGVoRURERMKE8rPBUYZWRERERCKaBrQiIiIiEtE05UBEREQkTJgmHQRFGVoRERERKRcza2lm/zKzDWb2gZn97iB1jzaz/5nZvyu7XRrQioiIiIQJM6uSx2EYD0xwzrUCHgNSD1J3OPDuEbwd5aYBrYiIiIgckpklAq2Bad6iOUBTM2tSSt0OQEvglVC0TQNaERERESmPE4A059x+AOecAzYDjQIrmVkM8DTw91A1TBeFVaKde3dXdROO2Mdbv67qJlSILi3aVnUTjtgv+3+t6iZUiKNr1qrqJlSIFX8bV9VNOGLdXry9qptQIZbd+nxVN+GIbcvLquomVIi6R8dUdRMqRIPYuKpuQsiZWT+gX8CiJ51zT5ZS1RVftZQ6jwNjnXPbzKxlRbXxYDSgFREREfmN8wavpQ1gA20BjjezaOfcfvNPvj0Bf5Y2UHvgEjN7GDgaqG9mXzjnfl/hDfdoyoGIiIhImAjni8Kcc1nAJ8B13qI+wEbn3MZi9U5zzjVxzjUB/gT8pzIHs6ABrYiIiIiU323AbWa2AbgfuAXAzCaZWa+qapSmHIiIiIiEiXD/swrOua+B80pZ/pcy6q8GKv1CFmVoRURERCSiaUArIiIiIhFNUw5EREREwoSF/aSD8KQMrYiIiIhENGVoRURERMKFErRBUYZWRERERCKaMrQiIiIiYUJzaIOjDK2IiIiIRDQNaEVEREQkomnKgYiIiEiYMNOUg2AoQysiIiIiEU0ZWhEREZEwofxscJShFREREZGIpgytiIiISNhQjjYYytCKiIiISETTgFZEREREIpqmHIiIiIiECd21KzjK0IqIiIhIRFOGVkRERCRMmC4KC4oytCIiIiIS0ZShFREREQkT+tO3wdGANgxt27KNJ4c/zq6decTWieXuB/rTqGnjEvWWLl7C69New+dznN7mDO64506ioqMAmPPqLFYseRufcxx/wvH0faA/sXViQxpHTnoWc8ZNY8/uHzk65hiu+Pt1JB6fUqTO5g0/sCj1NQDy8/NpfGJz/u+mPkTXrMmOrBye7vsoiSccWOfqu28hLjkhZDFs2byF4UMeZefOPOrExvLAkIdo2qxpiXqL5y9k2pRX8Pkcbc5uyz339Sc62n94rV/3DmOffo78/HxatGzJg488RO3atUMWA8DWzVsZPXQEed4+de9DA2nStEmJem8ufIOZU6fjc47WbVtz14C7iYqOJiMtneuvvLZI7ENGPkrD448LYRT+/hg2+NHCOB4soz8WzV/ItMn+/mh7dlvuuT+gP9a+w/MF/dGqJYNC3B/VIYb7ut5Ip+ZtOa5eAn0m9+fb7VtLrdf7lC7cfE4valgN3t/0X0YsTyXf+QDo2Kw1/TpdS1SNKDZkb2LQknHs3fdLyGIAf1+MGDKMvLw8YmNjeWDwgzQp7fhesIjpU6bhfD7anNWWu++7h+joaL779jueeuwJdubuICo6mlNOPYW7BtxNrVq1QhpH+tY0XnjsWXbn7SImNoa/3Xsnxzc+oUid7IwsXnjsWTZ++wPJx6cwYtyYIuXbM7N5+bkJpG9Nw8y4oNfFXNT7/0IZRrX57pOqUSFTDsxsmZl9bmafmtk6MzvDW/6SmX3tLV9bsNwre91bXvDwmVkvryzJzOZ62/zKzPoGrNc74LW+MLPh5v06Y2Y3FNvmdjObW6ytZmYrzGx7seXXm9lnZvZfr7xRKXEONjNnZqdUxPtWlucff5qLel3CxJkv0+eaK3lm1JMl6mSkpTNt4hQeG/cUk16bzI7cHSxbvASATz78iBVvLWfM+Gd4cdokmrVsztQJL1dmk0u1YNJM2nY7n75PP0z7nt2ZP/7VEnWSGx/H34YP4I7R9/OPxwby067dfLh8fWH50THHcMfo+wsfoRzMAjw+YjS9el/OzLmzuOaG6xg1dESJOmnb0pj44kTGTRrPa/Nnk7s9h8ULFgGwZ88eRg0dwcgnRvPa/NeJbxDP1JcmhzQGgKdGj+H/LuvJ1NnTueq6qxkzfHSJOulp6UyekMrT45/nlddfJTcnlzcXvVlYHhsby4RXUgsfoR7MAjw23OuPebO49obrGPloGf3xwkReSB3PrAWzyckp2h8jh45g1BOjmbXA3x9TUicrhsP09ob3uWnGYLblZZdZ57h6Cdze/kpumjGESyfdRYOYevQ+tQsAx9Q8iiEX3krfBU/QM7Uv23/ayV/P7R2q5hcaM/IxevXuxatzZnLNDdcyetjIEnXStqWR+uJExk58gRnzZpGTk8MbCxcDUKtWLe4e0I9pr8/gpemT+fHHH3lt+oxQh8Gkp1+g2//14Kkp4+h5VW8mjHm+RJ1jah/DH2++hn88cHeJMuccTw4ZRYcLOvPk5LGMeek5zu10fiiaXkR1+e6TqlFRc2j/6Jw7zTl3BvAE8JK3fD7we2/5Y8CsghWcc39wzp3hlf0FyAWWesVPAv9xzp0GtAX+bGZneWXLgYL1zgQuAHp625xasE2vPB2YXqyt/wA2Bi4ws5OA0UAP59wpwFTghWJ1WgPnApsP4305bDt37OC7Dd/StUc3ANp17kBGegaZ6RlF6q1fvY7zOrajflx9zIxLLr+UNctXA/D9N9/z+9NOKczYnHX+OaxcuqIym13Cj3m7Sf9hK6d38Hfb7885gx1ZOezIyilSr9ZRtQp/s87fn8/+X/eFzemWHbm5bPjqa3pcfCEAnbt1IT0tjfS09CL1Vq9YSccuHYmLj8PMuLxPb5YvfRuA9/71LiedfDKNmzQBoPeVfQrLQhfHDr75+hsuuOgCADp26URGWgYZxeJYu3I17Tp1KIyj5xWXsWpZaPebgynojwsvOXh/rFqxkk5l9cd6rz+87PQVIe6P6hADwMdbvyLrx9yD1une6hxWfvMhuXvyAJj92XIuOrkdAO2bnsEXmd+zMTcNgNc+XcZFJ7Wr3EYXsyN3B998tYELvOO7U9fOpG9LL9EXa1auokPnToV9cVmfy1mxdDkAJzQ6geYtWwAQFRXFSb87mbRtaSGNI2/HTjZ+8z3tu3cC4OwO55GVkUV2RlaRerF163DSqb/j6KOPLrGN/378ObVq1eLcTv4+MDOOjatf+Y0PUF2++6TqVMiA1jm3M+BpPcDnLV/onNvvLX8PaGxmpb3mn4FpzrmC802nA2942/gRWANc7z3f7Zx3zgqOBo4qeL1AZnY2kAQsDFjWEvgTMKpY9VOAT51zmd7zxcDFZhbvrXcUMBa4HXBlvhEVIDszm7gG8YWDPDMjMSmR7MyiH05ZmdkkJicVPk9KTiLLq9PypFZ8+u+P2ZG7A+ccq5atYO+ePezetasym15EXs4O6tSvR1TUgTjqNahPXs6OEnV3ZOUw9r5RjPrr/Rx1zNG07X7gi+2XPT/z4gOPM+7+0ayaswSfr0RXV5rMzCwaJDQoPM1rZiQlJZGZUfQDNjMjk+TkA9MikhumkJmZWViWlJJcWJbSMIXsrOyQxpGdlUV8g3iiAuJITE4s3F8KZGVkkhS4T6Ukk5WZWfh8z08/cfvNt3LbDX9haupk8vPzQxOAp9T+SC6jP1IO9EdKwxQyMzIDygL6IyW0/VEdYiivlDoNSN914ERY2q5sUurE+8vqFivLyyYxtn5Ir+7OyswkvlhfJCYnFb7PBUp7v4vXAdi7dy9vLFjE+R3aV27Di8nJzqF+fFyRz9oGiQ3YnlV29ry4bZu3UOfYejw77Anuv60fTwweRWZaxqFXrEDV5btPqk6FzaE1s6lAF+/pRaVUuQt4M2AwWrDe0cDVQMeAxR8C15jZv4EGwIXAVwHrnA+8CLQCxuENfou5BXjFObfPW6cGMBG4A9hXrO6nQBsza+Gc+xa4Af8fU24M5ACP4h9w/xCK7GHxD3XnSh9DBzYlsM5prU+n95/+wJABg4iKiuL8Tv4P2IIBTcgUf6vK+FWgfmI8d4y+n19+/oXXn5/C/z74jNPOb0Od+nXpP24osfXqsOfHn5j1zMusX7ySDr26V3rTCxXr77J+mymrL0rZRJUosd+WuU9ZqXXiGsQzc+Hr1I+rz668XQwdNITZr77Gn66/pjKaW7bi/VFmhwTWCbP+qA4xlJMLOGLK+7kWSiXexsP8rC2wf/9+hjzwMGedezYdOnWouAaWV3n3qTLs35/Pfz/+jEefG80JTRqxYvEynhv+BMPGPl6BjTy0avPdd4R0267gVNhtu5xzNzjnTgAGAUWOAjO7DvgjcFspq/YBvnHO/Sdg2T1AXeBj/Kf/VxIwCHXO/cubjnACcBZQ5BPEzGoDVwGpAYv7A2udc5+W0vZvgb8Dr5jZB0AdIA/YZ2bnea8x7hBvAWbWz8y2FjxeemHSoVYpISEpge3Z2eTvzy9oG9lZ2SQkJRapl5iUQGb6gSxBVmYWiQF1Lrn8Up5JHcuTE57llNNPpUFig5BeNFIvvj67cnYWZvGcc+Tl7KBefNmnsY46+ihOPb8Nn7/zbwCia9Yktl4dAGrHxtC687ls+uq7ym+8J8nLDuzf7z/J4JwjKzOTpOTkovWSk0hPP3CaMjM9g6SkpMKyjIBMR3paOgmJCdSoEbo75iUkJrI9K5v8InFkF9lfABKTk8gIOL2XmZFJohdHrVq1qO+dgqxbry4X97yE/3z6eYgi8Duc/gicTpGRnlGYeU5KTiI9sD/SQ9sf1SGG8krfvZ2GdQ/MeU+p24D03f4pR+m7ttOw3oGyhvUSyPpxR5EBcGVLTEoiOyu7WF9kFTlLASXf74yMjCJ19u/fz+CBDxEfH8+d9/QNSdsDxSfEk5udU+SzNid7Ow0Sy3+9QUJSAk1aNOOEJv5LR9p378T333yPL4RnYarLd59UnQr/BHTOTQG6BJyuvwoYDFzgnMsqZZVbKDrwxDmX65z7szcXtiDb+79SXisbf3b2ymJFfwC+dM4FrtMRuMnMNgLvAPXNbKOZ1fe2Ndc5d55z7mxgAv7pDN8BnYCTgB+8dY8HlprZxaW050nn3PEFjz///S9lvU1lOrZ+fZq3bMFKb+7i+tXrSEpOKnLaGuD8Th14d+36wlMrb85fTMfunQvLc7f7vzh+/vlnpqVOoc81fzzsthyJ2Hp1SGlyPJ+t+xCAL97/lGMT4qmfGF+kXm7GgQ+w/fv3878PPiOpUUPAPw+3sGzfPv73wWekNDk+ZDHUj4uj5YmtWLbEP7V79YpVJKekkNKw6J0aOnXtwtpVa8nNycU5x/w58+jew59FPve8c/nyf1+yaeNGAObNnkO3HheELAZ/HPVp0aolb7/ln2e5dtUaklOSSS4WR4cunVi/Zl1hHIvmLqDLBV0B/3zDgi/+X3/9lXWr19LixJYhjiOOVie1YumbB++Pzl27sKZYf3Tz+uOc873++GEjAHNnz6F7CPujOsRQXss3fEDXlmcRV7seAFee3p2lX/0LgPU/fMYpyc1pEuc/1q86o0dhWajUj6tPyxNb8bZ3fK9ZuZrkhsklj+8unVm3ek1hXyyYM59u3jzPgsxsnbp1GfDgfVUy/79e/WNp0qIp7yxfA8AH694lISmRhOTEQ6x5wOlntSZ3e07h98ZnH37MCU0aUcObxhAK1eW7ryKYVc0j0tmRnvYxs7pArHMuzXveG3gOf/b0SmA40N05t6mUdZsC/wEaOud2BSyPB3Y55/Z5F2O9CZzpnEs3sxPxZ3R9ZlYHWAJMcc5NDFh/Nf4pAqWmSM2sCfBv51yDgGUp3vaj8F/Utt05d08p624ELnXO/fdQ78232ZuCenO3bt7CU8PHsCtvF7VjatPvwQE0btaEZ0Y9yTntz+Pc9ucB8NbCN3l9+ix8Pp//1iX97yycD3b7DbfifI79+/fR5cLuXH3TtUF92H689etgQgAgOy2TeS9MY8/unziq9tFc8ffrSTohhfnjX+XENqdycttT+Wjlu/zrzVVYDcPn89Hs96248NrLqVmrJl988CkrZ71ZpOyi6y4numbNw25LlxZtg4ph88ZNDH/Ef1ufmJgYHhzyEM2aN2PU0BG079iB9t7pxYXzFjB9yiv4nKNN2zb0H3hvYV+8s2Yd4559nvz8fJq1aM6gIQ8TExtz2G35Zf+vQcUAsGXTZkYPHcmuvF3ExMRw38MDadKsKWOGP8b5Hdpxfkf/vOU35i9i5rQZOJ+PM9q0pu99/YiOjmbdqrVMnvgSNWrUID8/nzPbnMltd94e1O2Jjq4Z/C2NNm3cxPAhw9iVl0ftmBgGPeLvj5GPjqB9pw6Fp3sXzl3AtCmv4Jyjdds2DBh4L9E1/f2xbs06xj3j74/mLZoz6JHg+qM6xNDtxduDimFgt5vp0qIt8THHsnPvbvb8+jM9U/syuMetrP7uI9Z89xEAV5zalZvP7kUNMz7Y/AXDl6ey3+f/JbVT8zbc3fEaompE8e32zQxa8gI//bo3qPYsu7XkVf3lsXnjJkY+Opy8vF3ExNTmgcGDaNq8GaOHjaRdh/aFx/eieQt5deo0fD5H67Nac8/9A4iOjmbZkqUMe/hRmrdsUXiS+JTTT6PffSW+Og5pW15p+Z7ySduyjRcfe5bdu3ZzTExt/n7vnZzQpBETnhhL6/POou35Z7Pv1330veHv7Nu3jz0/7aHesfVo370TV//legA++/ATZkyainNQO7Y2f77ztsKM7eGoe3Twx1I4ffe1SGhcZUO82Z8uq5L5OFee0SOih7UVMaA9AZgDHIP/4qxsoL9z7lMz2wdk4J+HWqCbcy7HW3co0Mg5d2OxbV6Mf1C8D9jtbW+tVzYIuMYriwJeBx5xXiBm1hz/nNiGzrndZbS5CSUHtG8BjYBa+AfQAwIuUgtcdyOVPKANJ0cyoA0nwQ5ow8mRDGjDyZEMaKViBTugDTfBDmjDyZEMaMPJkQxow4kGtJHniGdKO+e2AGeXUXbQVJpz7qEyli8BWpRRNgwYdpBtfod/DuzBXncj/ovNApeVdiFbaes2KU89ERERkcMX0ePKKhNeVxGIiIiIiBymyLqXhYiIiEg1Fi5/XCjSKEMrIiIiIhFNA1oRERERiWiaciAiIiISJjThIDjK0IqIiIhIRFOGVkRERCRMmHK0QVGGVkREREQimjK0IiIiIuFCCdqgKEMrIiIiIhFNA1oRERERiWiaciAiIiISJnRRWHCUoRURERGRiKYMrYiIiEiYMCVog6IMrYiIiIhENGVoRURERMKE5tAGRxlaEREREYloGtCKiIiISETTlAMRERGRsKEpB8FQhlZEREREIpoytCIiIiJhQrftCo4ytCIiIiIS0ZShFREREQkTum1XcJShFREREZGIpgxtJYqqEVXVTThiHZudWdVNqBD7ffuruglHzOGqugkVIt/nq+omVIioGpGfD1h26/NV3YQK0WPCP6q6CUfszb88U9VNqBDKLkpV0YBWREREJEzoorDgRH6KQURERER+05ShFREREQkbStEGQxlaEREREYloGtCKiIiISETTlAMRERGRMGG6KiwoytCKiIiISERThlZEREQkTCg/GxxlaEVEREQkoilDKyIiIhIm9NfWgqMMrYiIiIhENA1oRURERCSiacqBiIiISLjQjIOgKEMrIiIiIhFNGVoRERGRMKGLwoKjDK2IiIiIRDRlaEVERETChP70bXCUoRURERGRiKYBrYiIiIhENE05EBEREQkTmnAQHGVoRURERCSiKUMrIiIiEjaUow2GMrQiIiIiEtGUoRUREREJE7prV3CUoRURERGRiKYMbRjatmUrY4aOZldeHjGxsdwz6F4aN21Sot5bi95k1iszcT4fZ7RtzT/630VUdBQff/gRk54fX1hv546d1I+rz9jJ40tsozJt3byFEY8MJ2/nTmLr1GHgww/QpFnTEvXeWLCY6VOn4Xz/3959x0dRdQ0c/x0CqIQW0hHpYBcEFKV3GyroY/fB+qiv+igIWFEUpQrYUZoCgiCKdJDexN7Qxwai1FRIKAIqJOf9YybJZpNoEsjuznq+fPbD7szd3XNyZ2bv3rlzN5tm5zSnz4N9KV++PMk7k3jikQFkZ2WTlZ1N7Tq16f/og1SpWjWgOQx9akhuDg8/8UiRObw1ZVpuDr0ffCA3h4GPPE5WVjbZbg79Hn2QKlWrBCwHJ48djHh6KHv37KVylco8+PjDhW5Ti+ctZMaUt8hW5ewWzbi/f28iypcnJSmZXlfdQD2f3AcOHUTNWicGMAvYvm07Q558hr1791K5cmUeHfhYofWxYO58pk12tqnm57Sgz0PONrX55808N2IUezIyiShfnjPOPIP7+/ehYsWKAc3hmYGDcuvisScfz/d3zTF/zjymTnqT7Gylxbkt6PtwP8qXdw7Z69d+wMvPv0RWVhYNGzdiwFOPU6lSpYDlkJOH1+vioU430b5BC06sFsuVk/rx864dhZbreUZHbml5GeWkHJ9s/R9Dlk8kS7MBaFe/GQ+0v4GIchFsTN/KgMVjOHT4j4DlAM5xatigIezbs5fIKpV56PFHqVu/boFyi+YtYPrkaWSr0qxFc3o/2IeI8nnNAFWl37192LxpM3OWzg9gBo6cPHKOtw89XsTx1s1DNZuzWzSnz4MPEFG+PMlJSQx8+HGys/OOt30fCfzx1gRHsXtoRWSLiPwoIl+7t2vc5S1E5CMR+UpEfhCRB32ec6uIfCsiR0TkXr/Xu8dd97X7/31+6weIyGb39rTP8nIiMlJE/ufGM1FEKrrrzhSRte7yb0VknIgc566LFJFPRGSDe3tfROr6vO6Lbo4qImf4xXKOiKwXkW/ceDsV9+9WGi8Of46LLr+EiW9P4aobruG5ISMLlElJSmbK+EmMeu15Xn/nTTIyMnh/wSIAmp3TnDGTx+XeGjZuSKduncsy5EKNHPosl/a8jGmzZnDdv69n+DPDCpRJ3pnExLHjeXncGN56720ydmewaN4CAKJjY3h53KtMnDaJSdOnEBsXy+SJkwOaw6ihI+ne81KmzprOtf++jhHPDC80h9fHTuClca8w7b0Zbg4Lc3N4adwYJk57gzemTyYmLpYpEycFNAeA54eP4pLLuzP5nalcc+O1jBw8okCZ5KRkJo17nefHvsSUd6eRuTuDxfMX5a6vXLkyY9+cmHsLdGMWYOTQEVzW8zLemjWD63vdwPBnhhYok7QziYmvjeeV8a8yffZMdu/ezUJ3m6pYsSJ9+j/A1Hen8/q0Sfz222+8PW16QHMYMXg4l/XswYzZM7mh140MHTSk0BzGvzqeVyeOZebcd9i9ezcL5joNjIMHDzL06SEMGzWcmXPfJTommslB2KbCoS6WbfyEm6cPZOfe9CLLnFgtlrvbXMXN05+k+4T7iYmsRs8zOwJwQoXjePKCO+g9dxSXTuzNrgN7+M95PQMVfq7Rw0bSvcdlTHn3La698XpGDi7kOJWUxBtjJ/LCuFeYOms6GRm7c49TOWa/8x4JiQmBCrsAJ49LefPd6Vx743U8W2QeE3hx3CtMnTWDzIwMFuYcb2Oc4+2EqW/w+luTiYmNZcrrkwKcxdGTIP3zupIOOfiXqjZ1b2+7y8YDQ1X1bKA10E9ETnPXfQFcDbxVyGtNVdUzVbWpz/POAhCRdsB1wFnAacBFInKB+7zb3OXNgFPdZfe7//8O3KuqpwBNgWpAX3fdIaCLqjZR1SbA+8Bon3jeBdoAW32DFOc36GYDA1T1LOBaYLKInPC3f61S2JORyc8bN9H5gq4AtOnYjtTkFFKSU/KVW7dqLa3atSaqRg1EhEt6XMrqZasKvN7u9F1s+OJrOl/YtSzCLVJmRiabftpI1wu7AdC+UwdSkpJJTkrOV271ytW07dCOGtFOHpdf0YMVS5cDzgfecccfB0BWVhaHDh6iXLnA7XSZGZls9MshuZAc1vjlcNkVl/9FDgeRcoEd6ZNTF13cbaBtx/akJCWT4pfH2pVraN2+LVFuHt2vuIyVS1cENNa/kpmRyaYfN9L1IudQ0L5TB5J3FlYfq2jboX3eNnVlD1YscerjpNon0aBRQwAiIiI45bRTSdqZFMAcMtj4409ccLGTQ4fOHUlOSiqQw6oVK2nfMW+b6nFlT5YvWQbAx+s/4pRTT83tYb/iqitz1wUuD+/XBcCXO34k7beMvyzTpXFLVm76jIyDewF4Z8NyLjy1NQBt6jXlu9Rf2JLhxP3210u58JTWZRu0H2f/3kRXd/9u16k9yYXs32tWrKFN+7a5dXFpz8tZuSxv/96xbTurlq3gul43BDT+HP7H23bu8bZgHqtp076dXx5FHG8PHaSc2MjKf4pjVdPV3f8jgT+BDABV3aCqPwDZ/k9Q1b0+DyvhDH9Q9/E1wCRVPaCqfwCv4zRwAZoAy1X1T1VVYBHwb/c1N6nqN+79LOAzoL77OFtV90NuI7Wqb1yqulZVCzvfFA3UUNVVbrkfgT3ARcX6y5RQelo60THRRJSPwI2V2Pg40lPS8pdLTSM+IT73cXxiPOmp+csALFu8lBbnn0v1GlFlEW6R0lJTiY6NyT1FKiLEJcSTlpKar1xqSirxCXk9AgmJCaT6lDl8+DC33XAzl3W7hJ07dnDTbbcEJgEgLTWNmNjofDnEF5lDXl0kJCbkK+PkcAuXd+vOzh07uem2mwMSf470tDSiY2JyTy3m1oXf9pJWWB4+ZQ4eOMjdt9zJXb3+w5sTJ5OVlRWYBHLiK2KbSi2kPnx7mRITEwuUATh06BAL586nVds2ZRu4b2ypacT45RCfEE9qSv4vrE4OibmPE2vm5VBYfulp6WRnFzjMlplwqIviSqwSQ/K+XbmPk/alk1gl2llX1W/d3nTiKkcFtLcrPTWNmJhov/07jtTU/H/ntNRU4hN9j7WJucep7OxsRg19lvv798mt00BLKySP+IT4IvL46+Pt7TfeQo8LnONtrwAfb48FEQnKzetK2qCd5p7KnyAise6yW4CnRWQbsBF4RFVTin6JPCLyLxH5DqdX9FlV/dZdVZv8PaVb3GXgNFIvF5Eq7lCDa4G6hbx2JHA7MN9v+XIgBafn+D7/5/lT1V1Aqohc6T6/JdC4sPc8Zvw2LNW/L1dUmWUL3+eC7mXS9v5b/ruHFhGk5Msjf5kKFSowcdok5rw/n5Pq1Gbue3OOcZR/zf+DqXg55F/n5PAGs9+fR+06tZkX4ByAAgerovKgiLqoERPN9HnvMOaNsYx4aRTffv0N7741s0xi/SsFDrlF1odvkYJljhw5wpOPPsE5551L2/Ztj12AxVHs/du3TP5CofDZExZ1UUxKXtzFPSYElP8GUWRIPvu3T6GZ02ZwVtMmNGzc6NjHVhLFPE5Jvjzyq1ChAhOmvsF7i+dxUu3azJs95xgHaUJVSRq07dxT9c2A3UDOYMb+QH9VrQ2cDgwWkZOL84Kq+q6qng6cDPTye57vduq7lU8BlgBrgZXAd8Bh39cVkQrA28BSVZ3r955dgER3/YDixAlcDtwuIl8CdwMf+L+n+74PiMiOnNuEMeOK+fJ5YuNi2ZW2i6wjWTnxsistjdiEuPzl4uNI9RmGkJaSSmx8/jLffvUNf/z+B81btihxHEcrLj6e9LR0jhw5Ajh5pKemEefTAwgQnxBPSnLeKSX/3s4cFSpU4KLuF7N08ZKyDdxHXHxcgRzSiswhry5SU1IKlAEnhwu7X8zSxUvLNnA/sXFOHln+deG3vcQlxOfbplJTUnPLVKxYkSi3l79qtapceOnFfPv1NwHKwI2vkG0qze9MBTj1kZyUl0dKSkq+MkeOHGHgI48THR3NfX17ByT23Nji40hPTfPLIf9ZCnC3KZ9TrSnJeTn455ecnExsXCzlAjiUJRzqoriS9++iZtXY3MeJVWNI3r/bWbdvFzWr5a2rWS2WtN8y8zUWy1psfBy7/PbvtNQ04uPz10VcfDypvsfa5Lzj1DdfbWDJwsVc1+Nq7rvjXn7bv5/relzN/n37A5ZHXAnyyHe8TS76eHtR94tZFuDjrQmeYh8BVXWb+/9h4HmgrYjEAD1Vdaa77hfgE6BVSYJQ1S3u87q7i7aRvwe0jrsMdQxS1bNVtQ3wI/B9TkG3MTsTSCZvbK3/+2XjjP39dzHj+0ZVL1LVZqp6E1DT9z19yo1W1Vo5t9vvvqM4L59P9RpRNGjckBXumLgPVq0lPjGhwED9Nh3a8uHa9WRmZKCqLJwznw5dOuYrs2ThYrpcfAEREREljuNoRdWIotHJjVj2vnMwWbNyNQk1E0ismZivXPtO7Vm3ei0Zu5085r43h05duwBOw/DQoUOAc0ps1fJVNGjYIKA5NCxGDu06dciXw7z35tKpa2c3h9R8OaxevjKgOeTm0bghy993tql1q9Y425RfHm07tmP9mnVkunkseG8eHbs61z9mZmTmNl7+/PNPPli9loYnB7Y3x9mmGrPM/VJT5DbVsQPrVq/J26ZmzaGze1FkTm9glapV6f/YQwE/zRZVowaNT2nMkkVODqtXrCIhMbFADh06dWTNqrxtas6s2XTu5uwXLVudxw/f/8DWX7cA8N47s+jSLbBj5MOhLopr+cZP6dToHGpUqgbAVU26sOTHDwFY/+sGzkhoQN0aNQG4pmm33HWBkneccvbvtSvXkFDI/t2uU3s+WLMuty7mz55LR/c4NWT0cGbMe5fpc2by4riXqVylCtPnzAzo7AD+x9u1K1cXkUcHPlizNl8eRR1vV61YSf0AH29N8EhxTpe4p+8rqOoe9/EDQA+gI5CO06hd4zZwvwKuUNXPfJ4/CfhcVV/2WXaqO74Wd/jCh8DdqrpMRDoALwMtgSPAepyLst4XkeOB41V1j/t+y4HHVXW+iJTH6XndA9yuPsmJSDxwWFUz3Me9gatUNd8IfhHZAnRX1f/5LEvIGUYhIv8B7gTO0b/54/26e0epvqZv37qdUc8MZ/++fVSKjKTvgIeoW78uzw0dyXltWnF+W+f7wuK5C5k5dQaqSpPmTflv/965458OHjjIDZdfzZjJ40g8sWZpwgDghPLHlfq527ZuY+hTg53pxyIjeWTgY9RrUJ8Rzwyjdbs2tG7njJebP2ce06dMIzs7m2YtmvOAOz3Rx+s/YuwrrwGg2dk0Orkx9/a5j2rVq5U4ltL2mGzbuo1hTw1h3969VMrNoV6BHBbMmcdbU95Cs7M5u0WzfDmMc3PIzlYan9yYe/r8t1Q5HMku/ZjV7Vu3MeLpYezbu4/IyEo86E4/NmrwCM5v25pW7ZzdYOGcBbw99S2ys5Wzm5/N/Q8504+tW7WWyeNfp1y5CLKysmja/GzuvO//SjXFUsWICqXOY9uWrQwdNJi9bh6PDhxAvQb1Gf7MUFq3bUMb95T1/NnzeGvKVLKzlWbnNKPvw/0pX748Sxcv4ZknBtGgUcPc0z5nNDmLBx7qW/SbFiGilD2iW7dsZfCTz+RuUwOeepz6DeozdNAQ2rRvm3vafd57c5k6+U3UnWKp/yMPUr6Cs3+vW7OOMS+8TFZWFg0aNmDAU08QWTmyxLFkHcW421Cqi27j7v37QoV4pPMtdGzYgujI6uw5tJ+Df/7OpRN7M7DbHaze/AVrNn8BwBVnduKWcy+jnAifbvuOwcsn5u6P7Rs0p0+764koF8HPu7YxYPGrHPjzUIljWXT7C6XKAZzj1IhBQ3O3qYcGPkq9+vUYOXg457dt7XOcms+MN/OOU73dKdR8pSQlc9fNd5R62q6jGT+8bes2hg/KO94+PPAx6tWvx7ODh9Gqbf7j7XSfPPo8lHe8HT8m73jbKOd4W63kx9ua1eOC9g1rxcZPgjKOpXPjlqH5rbKYitugrQ/MAiJwTv//AtyvqltEpAswHOeirgrAWFV9wX3ejcAwIArnYrEDwKWq+pWIjAHa45y6F/d5Y3ze8wngZvfhDFV91F0eD6wBstx4nlfV19x1NwBTgW/IG7KwXlXvEZHmOL2y5d332wz0UdVf3ee+gjO0IAHYBfymqg3ddQOBG9zn/QDco6rb/+7vVtoGbSg5mgZtKAnkKcCycjQN2lByNA3aUFLaBm0oOZoGbSgpbYM2lBxNgzaUhMP0TxDcBu3KjZ8G5QOrU+NzPV15xWrQmtKxBm3osAZt6LAGbeiwBm3osAZtaLEGrffYL4UZY4wxxoSIEB1OHvK838VgjDHGGGP+0axBa4wxxhhjPM2GHBhjjDHGhIhwGYccaNZDa4wxxhhjPM16aI0xxhhjQoV10JaK9dAaY4wxxhhPsx5aY4wxxpgQYWNoS8d6aI0xxhhjjKdZg9YYY4wxxniaDTkwxhhjjAkRNuSgdKyH1hhjjDHGeJr10BpjjDHGhArroC0V66E1xhhjjDGeZj20xhhjjDEhwsbQlo710BpjjDHGGE+zBq0xxhhjjPE0G3JgjDHGGBMixEYclIr10BpjjDHGGE+zHlpjjDHGmBBhF4WVjvXQGmOMMcYYT7MGrTHGGGNMyJAg3YoZnUgjEflQRDaKyKciclohZTqJyCci8r2I/E9EBouU7ehga9AaY4wxxpjiGguMU9XGwAhgYiFlMoHrVPU0oAXQHriuLIOyBq0xxhhjjPlbIhIHNAOmuotmAfVEpK5vOVX9SlV/ce//DnwN1C/L2OyiMGOMMcaYEBHi03adBCSp6hEAVVUR2QbUBrYU9gQRSQD+BVxcloFZg7YMZWVnBTuEo7bvjwPBDuGYiDqharBDOGplPPwoYCLK2YmhULFzb1qwQzgmFt3+QrBDOGoXT7g/2CEcE3NuHhnsEEwpicgDwAM+i0ar6uhCiqr/U//iNasC84ERqvrl0UdZNGvQGmOMMcaEiGBN2+U2XgtrwPraDtQSkfKqesS90OskYJt/QRGpArwPzCuiYXxMWVeJMcYYY4z5W6qaBnwF3OguuhLYoqpbfMuJSGWcxuwSVX06ELFZg9YYY4wxxhTXncCdIrIReBi4DUBEJojIZW6Z+4FzgZ4i8rV7e6wsg7IhB8YYY4wxISLUL5dQ1Z+A8wtZfrvP/cHA4EDGZT20xhhjjDHG06yH1hhjjDEmZIR4F22Ish5aY4wxxhjjadZDa4wxxhgTIoI1bZfXWQ+tMcYYY4zxNGvQGmOMMcYYT7MhB8YYY4wxISLUp+0KVdZDa4wxxhhjPM16aI0xxhhjQoRdFFY61kNrjDHGGGM8zXpojTHGGGNChXXQlor10BpjjDHGGE+zBq0xxhhjjPE0G3JgjDHGGBMi7KKw0rEeWmOMMcYY42nWQ2uMMcYYEyLshxVKx3pojTHGGGOMp1kPrTHGGGNMiLAxtKVjPbTGGGOMMcbTrEFrjDHGGGM8zYYchKCd23cyevCz7Nuzl8pVKtPn0X7UrlenQLklCxbz7tS3yc5WmjRvyj197yOifAQAs96ayYrFy8hWpdZJtej9aD8qV6kc0DyStu/kuSHPsm/PPiKrRNL7kYJ5pCan8PyQUfyy6Wdq1jqR5ya8nG/9p+s/5vUx48nOyqJuw/r0ebQ/J1Q6IWA5bN+2ncFPDmLPnr1UqVyZR598nHr16xUot2DOPKZOfpPsbKX5uS3o+1A/ypd3dq/16z7gledfIisri4aNGvHYU49TqVKlgOUAsGPbdoY8OZi9e/dQuXIVHhn4KHULyWPh3AVMmzwVzc6m2TnN6fNQX8qXL8/mnzfz/IjR7MnIJKJ8eU4/83Tu79+HihUrBjSP7du288zAQex1943HiqiP+XPmMXWSUx8tzm1B34d96mPtB7ycUx+NGzEgwPURDjkAJO9I4tURL7J/7z4iK0dy14P3UavOSfnKpKek8eqIF9ny868k1EpkyJiR+dbvSk3njZfGkbwjCRGh62UXcWHPSwKWw45t2xk2aAj79uwlskplHnr8UerWr1ug3KJ5C5g+eRrZqjRr0ZzeD/Yhonzex6eq0u/ePmzetJk5S+cHLH6AhzrdRPsGLTixWixXTurHz7t2FFqu5xkduaXlZZSTcnyy9X8MWT6RLM0GoF39ZjzQ/gYiykWwMX0rAxaP4dDhPwKZBgA7t+9g5DPD2bd3L5GVK9P3sQepU69ugXLvz1/EzKkz0OxsmrZoxr197yeifARffvYFE14em1tuT+YeoqKjeOWNsQVeI7TZkIPSOOoeWhE5XkTmiMhGEflaRN4XkbruunNEZL2IfOOu6+TzvAYissJd/qOIjBKRcu66+0Tkfz7Pu8bvPduLyGci8p373PN91p0pIqtF5AcR+UlErvBZ19993e9FZLaIVPdZpz7v97WItPVZ10hEPnRz/FRETjvav9tfefnZ57nwsosZP+MNrrz+Kl4YNrpAmZSkZKaOn8yIMc8x4e1JZGZksnTBYgC++uwLVry/nJFjX+C1qROo36gBU8a9UZYhF+qVkS9wwaUXM3b661x53dW8OPy5AmUqRVbixv/cRL8nHi6w7tDBQ7w0/DkeGzKQcTMmUSO6BjOnTA9E6LmeHTKcy3r2YMZ7M7m+140Me3pIgTJJO5MY/9p4xkwYy9tz3iFj124WzHU+1A4ePMiwp4cwdNRw3p7zLtEx0Ux5fVJAcwAYOfRZLu15GdNmzeC6Xtcz/JlhBcok70xi4mvjeXn8GN6a/TYZuzNYNG8BAMdVrEjv/n148923mDjtDQ78doC3p80IdBqMGOzWx+yZ3NDrRoYOKqI+Xh3PqxPHMnPuO+zenb8+hj49hGGjhjNzrlMfkydOshxKYcLzr9L5km48N3kMl17Tk3EjXy5Q5oRKJ3D1Lddz76N9CqxTVUY/OYy2XTswetIrjHz9Jc5r3yoQoecaPWwk3XtcxpR33+LaG69n5ODhBcokJyXxxtiJvDDuFabOmk5Gxm4WzVuYr8zsd94jITEhUGHns2zjJ9w8fSA796YXWebEarHc3eYqbp7+JN0n3E9MZDV6ntkRgBMqHMeTF9xB77mjuHRib3Yd2MN/zusZqPDzeXHEc1x02SVMnDGFq264hueGjixQJiUpmSnjJzHq1ed5feabZOzO4P0FiwBodk5zxkwel3treHJDOnXrHOg0TJAcqyEH44CTVbUpsAAYJyICzAYGqOpZwLXAZBHJ6V4bCcx1n9MU6AZc6K77DmjtPu9S4GURqQMgIjWByUAvVT3dfe4P7rpKwBz3PU8FTgfWueu6Ar2A81X1NOBrYLBfHq1Utal7W+ezfCwwTlUbAyOAiaX+S/2NPZmZbN74c+5O2LpDW1KSU0hNTslXbv3qdZzfrjVRNaIQES7u0Z01y1cD8MumXzj9rDNye2zOadWSlUtWlFXIReSxh80bf6ajm0erDm1ILSSPKlWrcvpZZ3D8CccXeI0vPvmMhic35qQ6tQG4uMelrF2xusxjz5GZkcHGH3+i20UXANChc0eSk5JITkrOV271ipW069iOGtE1EBF6XNmT5UuWAfDxhx9xyqmnUqduXQB6XnVl7rrA5ZHJph830vWibgC079SBlJ3JBfNYuZq2HfLyuPzKHqxYshyAWrVPokGjhgBERERwymmnkLwzKcB5OPVxwcV/XR+rVqykfVH1sd6tD7fX54oA10c45ACwN3MPWzb9Qpsu7QE4t+35pKWkkZ6Slq9c5apVOOXM0zj++IL79/++/IaKFStyXvvWAIgI1WtElX3wrsyMTDb9tImuF3YFoF2n9iQnJZPiVxdrVqyhTfu2uXVxac/LWbks73i6Y9t2Vi1bwXW9bghY7L6+3PEjab9l/GWZLo1bsnLTZ2Qc3AvAOxuWc+Gpzt+9Tb2mfJf6C1synP357a+XcuEprcs26ELsyczk542b6HyBUx9tOrQjNTmFFL/PjHWr1tKqfWuiajj1cUmPS1m9fFWB19udvosNX3xNZ7d+vUQkODevO+oGrar+rqqLVFXdRR8D9YFooIaqrnLL/QjsAS7yeXo19/8TgApAslt2harude9vB1KBnHNZdwNTVfUHn/ff4667HvhIVT9w1x1R1ZyvrU2Adaq63328APj33+UnInFAM2Cqu2gWUC+nF/pYS09Np0ZMdO7QAREhLj6O9NT8HxRpqenEJcTnPo5PiCfNLdPolMZ8/fmXZGZkoqqsWrqCQwcPsn/fvrIIuVC70tKpEZ0/j9j4WNJTi+5F8JeemkZsQlzu47jEeHan7yI7O/uYx1uY1NQ0YmJjck/zigjx8fGkpuQ/wKampJKQkJj7OKFmIqmpqbnr4n16bhJrJpKelh6wHADSUlOJ9ssjLiGetJTUfOX8Y01ITCDVrwzAoUOHWDh3Aa3aBvZDr9D6SCiiPhLz6iOxZmJuHs46n/pIDGx9hEMOALvTdxMVXYOIiLz9OyYuhl1pxd+/d27bTpXq1XjxmVE8fOcDjBo4jNSklL9/4jGSnppGTEx07tABZ7+Iy913c6Sl+u8Xibn7TnZ2NqOGPsv9/fvk1mkoSqwSQ/K+XbmPk/alk1gl2llX1W/d3nTiKkcF/Er79NR0omP8PzMKfvalp6YR7/vZlxhfoAzAssVLaXHeuVSPCtyXJBNcZXFR2H3AfFXdBaSKyJUAItISaAzUdcv1Bq4SkSQgCZiiql/5v5iIdAGigC/cRacBJ4jIcndowEtuz2zOut9FZIG7boqIxLrrPge6iki823t8I1BFRGr4vN1qEdkgIqNFJNJddhKQpKpHANyG+zag9lH8jf6S/4Ek77uCXzkpvMxZzZrQ89p/8WT/AfS9835qRDsHrohAH3D9jodFpPHXLxHsr41+719UCkXVRSEvERT+IRS9Tclfljly5AhPPTqQFuedS5v2bQusL3P+9VFkhfiWCbH6CIccoPh5FOHIkSz+9+UGet54FcPGjqbpOc14afCoYxhgMfj/IYvMwWe/8Ck0c9oMzmrahIaNGx372I4x37iL+xkTeMXdpuRvyyxb9D4XdL+o8JUhToL0z+uOaYNWRB4FGgGPuYsuB24XkS9xelY/AA676+4E3lTVmkAd4HrfMbbu650JvAFco6qH3MUVgA7AVUALnF7eJ33WXeC+9tnAduAVAFVdDYwCFgIf4fYG+8RTR1VbAK2AWOBZn1D8d5lCa15EHhCRHTm311+dUFixvxQbH8uu9HSyjmThxk16Wjqx8XH5ysXFx5KanNeTkJaaRpxPmYt7dOeFia8wetyLnNHkTGLiYgJ60UhMXCy703fly2NXWjqx8bF/88w8sfFxpPnmmOz0NJYrF5jJOeLd3oEjR44ATg5pqanEJ+QfKxefEE9yct5pytTkFOLj43PXpfj0OiUnJRMbFxuwHADi4uNJT0vPl0d6alq+Hv68WH3ySEnN1xNy5MgRBj7yODWio7mv7/2BCd43vhLUh28eKckpuXnEJ8ST7FsfyYGtj3DIASA6NpqM9N1kZeXt37vTdxETV5L9O5a6DetzUl2nb6BNl/b8sukXst3XLGux8XHsSksnK19dpOXuuzni4uNJ9du/c/adb77awJKFi7mux9Xcd8e9/LZ/P9f1uJr9+/YTSpL376Jm1by6SawaQ/L+3c66fbuoWS1vXc1qsaT9lpmvARwIzmef/2dGWoHPvtj4uHxnNNJSUguU+fbrb/jj9z9o3rJF2QduQsYxOwKKSD/gCuAiVT0IoKrfqOpFqtpMVW8CagLfu0+5D2csLKqaBiwG2vu83mk4wwJuzRlC4NoKLFTVTLfXdAZwrs+6Vaq60+1JneazDlV9TVVbqOp5wFpgR84QBFXd5v5/ABgD5HQ/bQdqiUh5Ny7B6bXd5v83UNXRqlor53br/91e0j8j1aOiaNCoISuXOmO01q9eR3xCfL5TXgCt2rflo7Xrc4cVLJqzgHZdOuSuz9jlHKx+//13pk6czJXXX13iWI5G9ajq1G/UkFVuHh+u/qDQPP5Ks5Yt2PTjT2zf6vypF82ZT9vO7f/mWcdOVI0aNDq5MUsXLwFg9YpVJCQmklgzMV+59p06snbVWjJ2Z6CqzJk1my7dugBw3vnn8cP3P7B1yxYAZr8zi87dAjumK6pGFI1ObsSyxUsBWLNyNQk1Ewrm0bE961bn5TF31hw6uXnk9MxWrVqV/o89GJSe86gaNWh8SmOWLPrr+ujQqSNr/Oqjs5tHy1Zuffy6BYD33plFlwDWRzjkAFAtqjp1G9bjg+VrAPh03UfExsflGyL0d5qc04yMXbtzj1UbPvuSk+rWppw7jKGsRdWIouHJjVj2vjP+eO3KNSQkJpDgVxftOrXngzXrcuti/uy5dOzqXBswZPRwZsx7l+lzZvLiuJepXKUK0+fMpErVKgHJobiWb/yUTo3OoUYlZ5TfVU26sOTHDwFY/+sGzkhoQN0aNQG4pmm33HWBVD0qigaNG7LCHQ/+weq1xCckFLjYrk2Htny4Zj2ZGU59LJwznw6dO+Yrs2TBYrpcfEHukBjzzyDH4lSDiDwA3AB0UdVMn+UJqpri3v8PTs/pOaqqIvINMEpVJ7un99cCw1T1HRE5FaeBe6eqLvF7r1bAcPe9/hCRF3FGAtwvIrWBJUBLVd3nxtVeVS93n5uoqsk+F4/NV9WXRCQK+ENVD7ozLYzGGf/by33eamCSqk4SkX8B/dxG8V/6OX1rqf64O7Zt57nBI9m3dx+VIivxwGP9qVO/Li8MG03LNudzXhtnUof35y3i3Wkzyc7Odqbt6ndf7jiuu3vdgWYrR44cpuMFXbju5htK1QjJPortY8e27Tw/ZBT73Tx6P9aPOvXq8uKw52jZ5jxatjmfw3/+yX+uvYXDfx7m4IEDVIuqTsdunbnprlsB+OSDj3jj1QlkZ2VRp349+jzWj0qRkX/zzgVFnVC1VDls27KVwU89w969e4mMjOSxJx+nfoP6DHt6CG3atc097T5v9lymTX6TbFWat2hOv0cezK2LD9asY8yLL5OVlUX9hg0Y8OQTRFYueQ5ZWvqeq21btjF00GBnOpzISB4Z+Bj1GtRnxDPDaN22Da3btwFg/ux5TJ8yjWx32q4H3Kmili1eyjNPDKJBowa5p6bOaHImfR7qW+JYypcr/YfM1i1bGfzkM+zbu5dKkZEMeMqpj6GDhtCmfVva5tTHe3OZOvlN1J1mqf8jD1K+glMf69asY8wLTn00aNiAAU+Vrj7CIYdtmaUfs5q0fSevjXiR/fv2c0JkJf7vwfs4qW5txo16hWbnn0OLVudy+M/D9O71fxw+fJiDBw5SrXo12nRpz3W3O5cvbPjsK6ZPmIIqVKpciVvvuzO3x7Yk4t3xoCW1bes2RgwamlsXDw18lHr16zFy8HDOb9ua1u2c/WLBnPnMePMtNDubs1s0o7c7nZ2vlKRk7rr5jlJP23XxhNKd9Xik8y10bNiC6Mjq7Dm0n4N//s6lE3szsNsdrN78BWs2O6P1rjizE7ecexnlRPh023cMXj6RI9nOMaV9g+b0aXc9EeUi+HnXNgYsfpUDfx76q7ct0pybC85MUFzbt25n1ODh7N+3j0qVIuk74CHq1q/Lc0NHcl6bVpzf1pkFY/G8hc60Xao0adaU//bvnVsfBw8c5IbLr2bM5HEknliz1LHUi6kVtHPwG3b+GJQxIE1OPMXT4w6OukErIrVwejF/AXLOs/yhqi1FZCBOQ1dwZiK4x73ICxE5G3gZqIIzVGAO8Kjb2F2GM5xgq89bPZTTuBWRB4FbgCPA/4C7ci4iE5FewEPuup3AHaq6w133LU6vdEXgTeBp9/3Ox5nJQHHm5v0SuF9VM9znnQxMwrnQbR9wk6p+93d/m9I2aEPJ0TRoQ0lpG7Sh5GgatKHkaBq05tg6mgZtKCltgzaUlLZBG2qOpkEbSqxB6z1HfZWQ21gs9I+gqk8BTxWx7iug0MukVfUvz5+p6gic6bMKWzcFmFLEujOLWP4RcNZfvN9PwPlFrTfGGGOMOTY83a4MGvvpW2OMMcYY42nWoDXGGGOMMZ4WujNBG2OMMcb8w4TDnLDBYD20xhhjjDHG06yH1hhjjDEmRITELwF6kPXQGmOMMcYYT7MeWmOMMcaYEGFjaEvHemiNMcYYY4ynWYPWGGOMMcZ4mg05MMYYY4wJFTbioFSsh9YYY4wxxnia9dAaY4wxxoQIuyisdKyH1hhjjDHGeJr10BpjjDHGhAjroS0d66E1xhhjjDGeZg1aY4wxxhjjaTbkwBhjjDEmVNiIg1KxHlpjjDHGGONp1kNrjDHGGBMi7KKw0rEeWmOMMcYY42nWQ2uMMcYYEyLEOmhLxXpojTHGGGOMp1mD1hhjjDHGeJoNOTDGGGOMCRF2UVjpWA+tMcYYY4zxNOuhLUMZB/cFO4SjVu34ysEO4ZgIi0H2GuwATLipenxksEM4JsKhR2vOzSODHcIx0WNSv2CHcExs6Dcj2CGYErIeWmOMMcYY42nWQ2uMMcYYEyIkLE4pBp710BpjjDHGGE+zBq0xxhhjjPE0G3JgjDHGGBMiwuEix2CwHlpjjDHGGONp1kNrjDHGGBMi7Jqw0rEeWmOMMcYY42nWoDXGGGOMMZ5mQw6MMcYYY0KGjTkoDeuhNcYYY4wxnmY9tMYYY4wxIcL6Z0vHemiNMcYYY4ynWQ+tMcYYY0yIEJu3q1Ssh9YYY4wxxniaNWiNMcYYY4yn2ZADY4wxxpgQIXZZWKlYD60xxhhjjPE066E1xhhjjAkV1kFbKtZDa4wxxhhjPM16aI0xxhhjQoSNoS0d66E1xhhjjDGeZg1aY4wxxhjjaTbkwBhjjDEmRNiAg9KxHlpjjDHGGONp1kMbglJ2JjP22Zf4be9+KlWO5I5+93BinZPylUlPSWPcyJfZ+vMWEk5MYNArI/KtX/jOXNYtXU1ERAQVKlag1z23Uf/khoFMg6TtO3l+yEj27d1HZOVI7n+0L7Xr1slXJjU5hReGjuKXTZupWetERo9/Kd/6zz78hNfHjCcrK4t6DerT+9F+nFDphIDlsH3bdp4ZOIi9e/ZSuUplHnvycerVr1eg3Pw585g66U2ys5UW57ag78P9KF/e2b3Wr/2Al59/iaysLBo2bsSApx6nUqVKAcsBYMe27Qx5cjB79+6hcuUqPDLwUeoWksfCuQuYNnkqmp1Ns3Oa0+ehvpQvX57NP2/m+RGj2ZORSUT58px+5unc378PFStWDGge4VAf4ZADwM7tOxk9+Fn2uXn0ebQftevVKVBuyYLFvDv1bbKzlSbNm3JP3/uIKB8BwKy3ZrJi8TKyVal1Ui16P9qPylUqByyHHdu2M2zQEPbu2UPlKlV46PFHCt8v5i1g+uRpqGZzdovm9HnwASLKlyc5KYmBDz9OdnY22dnZ1K5Tm76PPEiVqlUClgPAzu07GPnMcPbt3Utk5cr0fexB6tSrW6Dc+/MXMXPqDDQ7m6YtmnFv3/uJKB/Bl599wYSXx+aW25O5h6joKF55Y2yB1ygrD3W6ifYNWnBitViunNSPn3ftKLRczzM6ckvLyygn5fhk6/8YsnwiWZoNQLv6zXig/Q1ElItgY/pWBiwew6HDfwQsh2NFxPpoS+OY9NCKyFIR+UZEvhaRdSLS1F3+uoj85C5fm7PcXddARFa4634UkVEiUs5dd5+I/M/nNa/xed7D7rKc2z4RGe2uO99n+XciMlZEjvN5bj/3db8WkY9F5ByfdS3d5RvduBJ91h0nIi+LyCb3dacei79bUV5/fiwdL+7Ks2+8xCVXXc6E0a8WKHNCpRP4183Xcfcj9xdYt3XzFpbNXcyTLw5h8Gsj6XrZRUx+eUJZhlyoV0a+yAWXXcRrb03kiuuv4qXhzxUoUykykhtvv4m+TzxUYN2hg4d4cfhzPDZ4IOOmv0FUdA1mvjk9EKHnGjF4OJf17MGM2TO5odeNDB00pECZpJ1JjH91PK9OHMvMue+we/duFsydD8DBgwcZ+vQQho0azsy57xIdE83kiZMCmgPAyKHPcmnPy5g2awbX9bqe4c8MK1AmeWcSE18bz8vjx/DW7LfJ2J3BonkLADiuYkV69+/Dm+++xcRpb3DgtwO8PW1GoNMIi/oIhxwAXn72eS687GLGz3iDK6+/iheGjS5QJiUpmanjJzNizHNMeHsSmRmZLF2wGICvPvuCFe8vZ+TYF3ht6gTqN2rAlHFvBDSH0cNG0r3Hpbz57nSuvfE6nh08vECZ5KQk3hg7gRfHvcLUWTPIzMhg4byFAETHxPDSuDFMmPoGr781mZjYWKa8PimgOQC8OOI5LrrsEibOmMJVN1zDc0NHFiiTkpTMlPGTGPXq87w+800ydmfw/oJFADQ7pzljJo/LvTU8uSGdunUOaA7LNn7CzdMHsnNvepFlTqwWy91truLm6U/SfcL9xERWo+eZHQE4ocJxPHnBHfSeO4pLJ/Zm14E9/Oe8noEK34SAYzXk4GpVPUtVmwKjgNfd5XOA093lI4CZPs8ZCcx11zUFugEXuuu+A1qr6lnApcDLIlIHQFWHqWpT93nnAn8C09znbQDOcdedCcQCdwKISBPgv8B57vqXgVfcdeK+Rm9VbQwsBnyPzsOAbKCxqp4O9C/VX6kY9mbuZevPv9C6czsAzml7HukpaaSnpOUrV7lqFU4+41SOO/64wl6GrCNZ/PG788304IED1IipUVYhF2pP5h5+2fQzHbo6B8VW7duQmpxKanJKvnJVqlbhtLPO4Pjjjy/wGl988hmNTm5ELbd3+uKe3Vm3YnWZx54jMyODjT/+xAUXXwBAh84dSU5KIjkpOV+5VStW0r5jO2pE10BE6HFlT5YvWQbAx+s/4pRTT83tLbniqitz1wUuj0w2/biRrhd1A6B9pw6k7EwukMfqlatp2yEvj8uv7MGKJcsBqFX7JBo0cnr4IyIiOOW0U0jemRTgPLxfH+GQA8CezEw2b/w5t9HTukNbUpJTCuzf61ev4/x2rYmqEYWIcHGP7qxZvhqAXzb9wulnnZHbs3xOq5asXLIiYDlkZmSy8aeNdL3Q2S/adepAclIyKX51sWbFatq0z6uLS3tezsplzn5RsWLF3GNwVlYWhw4dpJwEdiTfnsxMft64ic4XdAWgTYd2pCankOJXF+tWraVV+9ZE1XDyuKTHpaxevqrA6+1O38WGL76m84VdAxJ/ji93/Ejabxl/WaZL45as3PQZGQf3AvDOhuVceGprANrUa8p3qb+wJcM5Lr399VIuPKV12QZdZiRIN287Jnuequ7xeVgNp/GHqs5T1SPu8o+BOjm9sD5lAU4AKgDJ7vNWqOpe9/52IBXIf87d0QPYoapfuGUPquphd11F93WzfcpXACLd+9WBnHMaLYA/VHW1+3gs0ENEKohIJHAL8Kiqqvs++Y94x1BG+i6qR9cgIsI5JSciRMfFsDttV7Ffo06Dulx4ZXce6HU3911/B++/t4B/33NbWYVcqF1p6dSIjs49tSgixMbFkp5a9Ldvf+mp6cQmxOc+jk+IZ3f6brKzs//iWcdOamoaMbExuad5RYT4hHhSU/J/UKSmpJKQmNuhT2LNRFJTUn3WJeStS0wkPS09YDkApKWmEu2XR1xCPGlujDlSU1KJ94k1ITEhNw9fhw4dYuHcBbRqG9gPi3Coj3DIAZx9s0ZM/v07Lj6O9NT8X7zTUtOJ89uH09wyjU5pzNeff0lmRiaqyqqlKzh08CD79+0LSA5pqWnExEQT4V8Xqal+5VKJT8zLISExId++c/jwYW6/8RZ6XNCdnTt20uu2mwMSf4701HSi/eoitpC6SE9NI963LhLjC5QBWLZ4KS3OO5fqUVFlG3gpJFaJIXlf3mdh0r50EqtEO+uq+q3bm05c5Sib0/Uf5Jh9lRSRKSKyHXgGuKmQIvcDi1Q156jbG7hKRJKAJGCKqn5VyOt2AaKALwp5zduAiX7l64rI18AuYB8wDkBVN+D0uv4qIjuAPjg9tgC1ga05r6Gq+4H9QCLQANgNDBCRz90hFWV6LsZ/+Izbji62XanpfPXR54ya9AovvjWOC6/ozqvDXjiGERaTfx6ULI9CXiLw/CqjyKoQ3zL5C4XCcCj/EIrapnzHbhVW5siRIzz16EBanHcubdq3PZYhFk841Ec45EDByd+L3qYKL3NWsyb0vPZfPNl/AH3vvJ8a0U7DJKeBGRAF6qKIHHxy9S9RoUIFJkx9g/cWz+Ok2rWZN3vOMQ6yOIq5TfnmUUSZZYve54LuFx2bsMqA7+dIcbdB889wzBq0qtpLVU8CBgDP+q4TkRuBq3FP/7vuBN5U1ZpAHeB6Eenk97wzgTeAa1T1kN+6k4A25A03yIljizukIAE4DrjCLV8HuAxooKq1gOf8nuu/J+TsKRWA+sD3qtoCuBeYISKx/n8DEXlARHbk3N6aMMW/yN+qERtDRnoGWVlZOfmQkb6b6LiYYr/GJ2s/pFbd2lSPdr5ht+3WkZ++/YFs9zUDISYult3pu8g6kpfHrrRdxMYX+LMVKTY+Nl9PSGpKKtGx0ZQrF5hTevFuL8eRI85JBlV1emsSEvKXS4jPd5oyJTkltyckPiGe5KS83rfk5GRi42IDlgNAXHw86Wnp+fJIT03L13OWE6tvHqkpqfl6dI4cOcLARx6nRnQ09/UtOHa7rIVDfYRDDuDsm7vS0/Pt3+lp6cTGx+UrFxcfS2py3j6clppGnE+Zi3t054WJrzB63Iuc0eRMYuJiAnZxW1x8HLvS0snKVxdpxMfH+5WLz3f6PjU5pcC+A07D9qLuF7Ns8dKyDdyPUxf+x9q0AnURGx+X70xAWkpqgTLffv0Nf/z+B81btij7wEshef8ualbN+wxJrBpD8v7dzrp9u6hZLW9dzWqxpP2WWaqOlGATCc7N6475EVBVJwMdRSQawL2gayDQVVV9z2/cB0x2n5OGM261fc5KETkNWADcqqofFPJWtwDzVLXQQTeq+hswA7jBXXQV8D+f4QJvAO1EJALYBtT1ee8qQBWcIRBbcYYtTHNfdwPwK3B6Ie85WlVr5dyuv71XoX+jv1Itqhp1GtZl/Yq1AHy27mNi4mOJTYj7m2fmiUuMZ+N3P/D7Iec7wFcff07N2idSzh3GEAjVo6pTv1EDVi9zxsR9uOYD4hLi853S/jvNWrZg048b2bF1OwCLZi+gbacOZRFuoaJq1KDxKY1ZsmgJAKtXrCIhMZHEmon5ynXo1JE1q9aSsTsDVWXOrNl07tYFgJatzuOH739g669bAHjvnVl06RbYsWlRNaJodHKj3A/aNStXk1AzoUAe7Tu2Z93qvDzmzppDJzePnJ7ZqlWr0v+xB4NyFW441Ec45ABQPSqKBo0asnKps3+vX72O+EL271bt2/LR2vW5wwoWzVlAuy4dctdn7HIaI7///jtTJ07myuuvDlgOUTWiaHhyI5a97+wXa1euJiExgQS/umjXqQMfrMmri/mz59LJvTYgNSWVQ+5xNjs7m1UrVlK/YYOA5QBuXTRuyAp3HPUHq9cSn5CQb1gKQJsObflwzXoyM5w8Fs6ZT4fOHfOVWbJgMV0uviB3yFuoWb7xUzo1OocalZzRilc16cKSHz8EYP2vGzgjoQF1a9QE4Jqm3XLXmX8GOdouehGpClRW1ST3cU/gJZwxr1cBg4EuqrrV73nfAKNUdbI7TnUtMExV3xGRU3EauHeq6pJC3lOAzcAdqrrcZ3kDYJuqHhaRisBUYJOqPiYiVwBPAq1U9TcRuRZ4XFVPd8f1bgJuU9XVItIPaKGq17qvuxR4XlUXuT29nwNn/d1Y2k+3fluqP27y9p2MG/kKv+3bzwmVTuCO/v+lVt2TmDD6VZqd34Jm55/D4T8P0/fmezhy+AgHDxykavWqtO7cnmtuuwFVZebrb/HF+k8oX7ECJ5xwAv++51bqNqxf4liqHV/6KXR2bNvOC0NHsX/vfipFVqL3o32pXa8uLw1/jnNbn0fLNudz+M8/ueO6Wzn852EOHjhAtajqdOjWiZvuvBWATz74iEmvTSQrK4u69evR+9G+VIqM/Jt3Lig6strfFyrE1i1bGfzkM+zbu5dKkZEMeOpx6jeoz9BBQ2jTvi1t3dPu896by9TJb6KqNGvRnP6PPEj5Cs6p03Vr1jHmhZfJysqiQcMGDHjqCSIrlzyHI9ml72HftmUbQwcNdqb1iYzkkYGPUa9BfUY8M4zWbdvQun0bAObPnsf0KdPIdqftesCdKmrZ4qU888QgGjRqkHua74wmZ9Lnob4ljqV8udJ/WIZSfYRDDnsO7S91Hju2bee5wc60fJUiK/HAY/2pU78uLwwbTcs253Nem/MBeH/eIt6dNpPs7Gxn2q5+9+WOIb671x1otnLkyGE6XtCF626+oVRflipVKN1Uftu2bmP4oCG5dfHwwMeoV78ezw4eRqu2bWjdztkvFsyZx/Q330Kzszm7RTP6POTsFx+v/4jxY14DIDtbaXRyY+7p81+qVSv58eaPI3+WKgeA7Vu3M2rwcPbv20elSpH0HfAQdevX5bmhIzmvTSvOb9sKgMXzFjrTdqnSpFlT/tu/d25dHDxwkBsuv5oxk8eReGLNUsfSY1K/Uj3vkc630LFhC6Ijq7Pn0H4O/vk7l07szcBud7B68xes2eyMOrzizE7ccu5llBPh023fMXj5xNxjY/sGzenT7noiykXw865tDFj8Kgf+PPRXb1ukDf1mBK3PckdmSlC6lWtFJXi6n/ZYNGhPAmaRdwFWOtBPVb8WkcNACs4Y1BydVXW3iJyNM9NAFZzT+nNwL7wSkWU4F2r5NoIfymncumNYJwD11ScBEbkNZ2xsFs4cuyuB/qr6u9sIHgL0BP7AGSP735xxuyJyPvCam8dO4EZV3emuq48zc0O0+9pPqersv/vblLZBG0qOpkEbSkrboA0lR9OgDSVH06A1x9bRNGhDSWkbtKHkaBq0oaS0DdpQYw1a7znqBq0pmjVoQ4c1aEOHNWhDhzVoQ4c1aEOLNWi9x34pzBhjjDEmRHi6VRlEgZ0B2hhjjDHGmGPMemiNMcYYY0JFOMyhFQTWQ2uMMcYYYzzNemiNMcYYY0KE/Vxv6VgPrTHGGGOM8TRr0BpjjDHGGE+zIQfGGGOMMSHCrgkrHeuhNcYYY4wxnmY9tMYYY4wxIcIuCisd66E1xhhjjDGeZj20xhhjjDGhwjpoS8V6aI0xxhhjjKdZg9YYY4wxxniaDTkwxhhjjAkRdlFY6VgPrTHGGGOM8TTroTXGGGOMCRHWP1s61kNrjDHGGGM8zXpojTHGGGNChf32balYD60xxhhjjPE0a9AaY4wxxhhPswatMcYYY0yIkCD9K3Z8Io1E5EMR2Sgin4rIaUWUu01ENonIZhEZJyJlOszVGrTGGGOMMaa4xgLjVLUxMAKY6F9AROoBTwNtgIZAAnBbWQZlDVpjjDHGmBAhEpxb8WKTOKAZMNVdNAuoJyJ1/Yr+C5itqqmqqsBrwHXH5A9UBGvQGmOMMcaY4jgJSFLVIwBuY3UbUNuvXG1gq8/jLYWUOaZs2q4ydG6dM8t87g0ReUBVR5f1+5SlcMgBLI9QEg45QNnnEVO5Rlm9dC6ri9BS1nls6DejrF46V7jURVFiKtcIyrxdIvIA8IDPotFF/J3V/6lFvKQWo8wxI07j2niViOxQ1VrBjuNohEMOYHmEknDIAcIjj3DIASyPUBIOOXiVO+RgExCtqkdERIBk4DxV3eJTrj9QV1XvcR9fDDyoqh3KKjYbcmCMMcYYY/6WqqYBXwE3uouuBLb4NmZds4CeIhLvNnrvAsq0+94atMYYY4wxprjuBO4UkY3Aw7izF4jIBBG5DEBVfwEGAuuBzUAahcyGcCzZGFrvC4dxROGQA1geoSQccoDwyCMccgDLI5SEQw6epao/AecXsvx2v8fjgfGBisvG0BpjjDHGGE+zIQfGGGOMMcbTrEFrjDHGGGM8zRq0xhhjjDHG06xBa4wxxhhjPM1mOTABJyLlgDbk/QzeNuADVc0OXlRHT0QuUNUlwY6jJEQkEvjDnSC7OtAE2KiqycGNrOREpClQFzgM/OBOG+M54ZJHDi/uFwAichxwEXl18b2qrgpqUKUkIrXxOd6q6rZgxnM0RCQaOB34SVVTgx2PCR02y4GHuAel8UA9YB4wQFV/d9d9pKoFptEINSLSGpgGpOD8zrMAdYB44EZV/SCI4RWbiJxWyOIlQDec/er7AIdUYiLSCxgL7AJuAqYASUB94P9U9Z0ghldsInIWzjZVB4gEvgdOBFYAt6nqviCGV2zhkEc47BcAItIRmAzsARoD63Dq4jegp6ruDF50xScipwCv43xmbMM53p4E/IqzTf0QxPCKRUSmAP1VNVVEOuFMzv8rzheN/6jqvGDGZ0KHNWg9REQWAQuBj4H7gIbAhaq6X0S+UtWzgxpgMYjIN8Ctqvq53/JzgNdV9czgRFYyIpKN0yD3VQvYAaiq1g98VCXj1sWlQDVgLdBFVT8XkYbAu6raNJjxFZeIfAg8rKprRaQn0A54CHgCOElVbwpqgMUUDnmEw34BICJfAtep6k8i0hK4S1VvEZH/AJeoao/gRlg8IvIx8KyqzvJb/i+cnyE9NziRFZ+IfJvzuSAia4D/quo3IlIHmK2qzYIboQkVNobWWxJU9RVV/cL9cFsIrBCRaoBXvpkc79+YBVDVz4DjghBPaT0F/AB0UNV6qloP2OHe98SHNnBEVbeq6jfAnpx6UdWf8c72BFBJVdcCqOpsoI2q/qmqAyhk8u8QFg55hMN+ARDhTh6Pqn4CnOneHw+cGszASijKvzELoKrv4nyR9QLfz4VK7vEKVd2KDZs0PqxB6y2VfB+o6hBgJs4pySpBiajkNovIE+44KMAZEyUiA3FOI3mCqj4FPAZMF5G7chYHMaTSyBaR00WkDRApIucBiEhjICK4oZXIYTdmRORcnNPCObKCE1KpeD6PMNkvAPaLSDsAt7c8LcjxlNYuEfm3e90C4FzDICI3AbuDGFdJLBGR50WkErBcRG4Qx0V4JwcTAPbtxlt+EJELVfX9nAWqOtI9zTcyiHGVRC9gGLBFRIS8D7t3gH8HLapSUNWvRKQDMEhEVgAVgxtRiT0GrMGpg2uAp0UkEecU8Z3BDKyEHgfWi0gqEAtcCSAiCYAnxmS7wiKPMNgvAPoA74lIFJAKXA4gIvE445y94iaccfIviEgSzr5eC/gKuDmIcZVEX2A4sBOnAfsQMAmnI+fW4IVlQo2NofUQ96pbVPWPQtad6JULFXKISA0AVc0IdixHy+3dbK+qw4MdS2mJSATQFNiuqp7qkXJnaGgAbPLCxVNFCZc8cnh9vxCRaFX1fC+giMTiXAwGzv6dHsx4SsPtoW0AVAC2hkO9mGPLGrQeJyKnq+p3wY7DeLcu3IZsovswWVU9cXrbhC63Z7MOznRXm3NmYzGmtOw4Zf6OjaH1EBGp5H8DFojICe59TxORjcGOobjCoS5EJF5E3gL2A58DX+KMHXzLHXrgCSKyS0ReEBFPzJBRFBGpLiJjRWSpiNzrt67AhT2hSERqicgCnKngvsCZ7ipDREaISIXgRndseOw41drnfiUReVlENojIJPdsQMgLl+OUKXvWoPWW33B26t98bnWAA+7ykCcipxV1AyoHO74S8HxdAFNxGh1xqpqgqnE48wF/6a7ziv04x7LVIvKZiNwlIlWDHVQpvAbsxRnzeIWIzHJ7pcCZG9gL3sC5UDUW6AeMxpkDNQ54NohxlUgYHade8rk/CKiKMz4+E3ghKBGVXGHHqTi8d5wyZcyGHHiIiLwOZAN9VHW/u+xXd2ocT3AvYNuCM8G3vxNV1RMXkIRJXfyoqqeUdF2oEZEvVbWZiFTEuZDqFpxprmYDE3Kmwgp1IvJ1zty/7lXprwI1gSuAT70yz7SqnuXz+GNVPc9tmP+gqo2DGF6xhdFxKnd+chH5Cmilqofc7WuDF+b9DpfjlCl71kPrIap6KzAHZ+7Zi3IWBy+iUtmKM79mPf8bztXEnhAmdXFIRNr6LxRnuiLPjXl052ydrqrdcH4a8xecq6G9Ine+TVXNVtU7caaym4N3ZgrIFpEYABGpj/OlD3e84+FgBlZCYXGcAsRnGNRhVT0EzvYFHAluaMUWVscpU3Zs2i6PUdUFIvIR8LKIXIP36nAezunTpELWzQ1wLEclDOriLmCqiPyO8wGuOKeHjwNuDGZgJVSgF02d36p/0r15xVYRaaWqH+YsUNX7RORF4MIgxlUSo4ANbm9gc+D/IHfqsW3BDKyEwuU4dRbOcCgBVERqqeoOETkB73RohctxypQxG3LgYSJyFc4v8twT7Fj+6bxcFyLSAqjtPtwGfKEeOjCIyKnqgd+k/zvuBS5ZhU2Z5t/QDWUicjJwBvC1qm4OdjymIPeCsFNU9eNgx1JcXj9OmbJnDVoTFOE4rY+IiB1gQ0O41EU45CEi1VR1b7Dj+KdzG7GHVfVAsGM5WuGwX5hjzyunHAwgIvV87pcTkX4iMldEnvTKlDjhMq2PiNwjInHu/Xoi8gnwp4h8JSJe+q33QonI5GDHUFzhUhfhkkchvgp2ACUlInXcWSZmikiCiLwiIvtEZJ2I1Al2fMUlIlXFmaprL86vbO0Tka0icnewYysuEWkhIp+IyLsikigiq3B+JvpbEWkS7PhM6LAGrbf4zkX5GNANmA6chnd++jYspvUB7vE5NTwSmIAzJc7TwJigRXXsdAx2ACUQLnXh+TxEJM3/BtT2ue8VrwJrgW+BpThjaRvhHIOfC2JcJfUGkIazP7+I87OxVwOXi8hjwQysBF50b6tw6uRdnP1iMPmnJTP/cDbkwEP8pmD5HOiiqnvE+Unczz0yBUu4TOuTO11MzrRRPuty6ymU/UUDQ4DqquqJHvNwqAsIjzxEZAXO7BLDcK6iF5yzMG0AVHVr8KIrPnGnUBMRwflVqgSfdRtU1RM9gyLyP1U9w+fxh6rayp314CtVPTmI4RWL3+feNlWtXdg6Y6yH1lt8v31kqeoeAFX9A+9MwRIu0/psFJEr3Ps/iUhOQ6RmEGMqKQG6AOcUcvNSb1o41AWEQR6q2hnYgDNdWlVV3YIzbnOrVxqzLgVwx2l+W9g6j1C38YqIxAIVAFT1IPBnMAMrgfLiTD0WB0T7DMuJBI4PbmgmlHhtmqF/ujPdXjUBqohIjKruEpHyeKcuw2Van3uA2SLSG2c88CduTicCXhmf9gUQrarf+K8QkZQgxFNa4VAXECZ5qOrLIrIEGCcia/Bmx8kfIhKpqgdUtWvOQnEuZs0KYlwlNQX4SETWAV1xjr85x1uvNMwnAz/gfMY9AbwjIt/i9Pq/F8zATGixIQceUsjFCMmq+qf7zbuNqs4ORlwl5fY8nU4YTOsjIp1xxjBXwJkjcbHb+xHy3B6Ow6rqlZ6av+TluvAVRnkIzjj5dqp6abDjKQm3V/OQ/5X0bu9gLVX9MjiRlZyIdAGa4AxLWxPseEpDRM4CUNVvRKQ2cBXwi1c+80xgWIPWBI2E0TQyxhhjjAkeL54K+scSkQoicq+I3C0i5UXkanGm7XpanN+xD3k+08jswaPTyAC4U8j0cC9mCxvuqTxPCYf9AsCNvZ+IfC0ie0QkXUTWiMjFwY6tuMJlv3C3qYdFZJyIdPdb55kr6926iHbvx4jI2yLyi4jM9srY7L/Yvwd5af82Zc96aD1ERF7Fmd7qBGAvzk//vQ1cAaSo6v1BDK9YRGQWzkUjC4B/AztxroIeBKxV1cFBDK/YRCQdSMapjzeBiar6Y3CjKhkR+bSQxWcB3wCo6rmBjah0wmG/ABCR8Tjj4xcB1wL/A34CHgbGqOr4IIZXLOGwXwCIyGtAdeAz4HbgfVXt467LNwNFKBORH4DTVTVbRCYBO4BpwEVAJ1Xt/lfPDwXhsn+bsmcNWg8RkW9V9UwROR7nKvQEVT3ofkv9wiPTdnl+GhnImy5GRM4FbgWuAb4HJgJve2EYhYh8D6zHaXiIe5uO05jCK+PtwmG/AKc+VPU0935FYLmqthNnVpC1OetCWTjsF+BMzQU0VVV1x5rPAHaq6l3ioami/LapfHF7JY9w2b9N2bMhB95yGMD9mdjNOReKuBf1eGXarnCYRgbypvX5VFXvAmoCr+H0OicFM7ASOBunx+MB4CdVXY1zIcwarzRmXeGwXwBk+ZyqrwhUBlDVXXjnyvpw2C8AKuRcEOY2wnsCcW4vupekisj57v0d7uwGiEgV3GOvB4TL/m3KmFemejIOEZEId87WHj4LvTRtVzhMIwNOb2YuVT2E09P5pjjz64Y8d/7ifiLSFpgvIq8EO6ZSCof9AmAJsFBElgOX405JJCLV8E4ent8vXOkicoaq/g9AVY+IyNU4p7rP+uunhpT7cKaC+xBIwZkKbhXOXNNDgxpZ8YXL/m3KmA058BAROQ9nqqvf/ZbXBzqo6uvBiaxkRKQrzoeCl6eR+beqvhnsOI4Vt9f8WZzp3zzxK0g5wmi/EOA2nCmWPlPVKe7y44BqmvezuCErXPYLETkD+F1Vf/ZbHgFco6pvBSeykhORE4DryT8V3ExV3R7UwIopXPZvU/asQWuMMcYYYzzNxtB6nIgsDnYMJRFGUyzVEJFhInKv+3igiKwSkZfEmV/Xc0TkXBHpIyIdgh1LSYlId3GntxKRNiLygojcFuy4SkJERopI02DHcbTCpC7Ki0hf8fAUagAi0lJEqrr3j3ePWStF5Dl3HG3IC5f9wpQ966H1EBGZWcjii4DFAKp6dWAjKrlwmYJFRObgXHFbGaiKk8s0oDvOz8leE7zoikdEVqhqZ/f+lcBzOFNGdQJGqerYYMZXXCLyNNAN50KqFcC5wEKculiqqk8HMbxiE5G9wB84UytNBKap6p6gBlVCYVQXnp9CDXJnMmnq/qLkCzhTkc0ALgRiVPWGYMZXHOGwX5jAsAath4hICs6Hw9qcRTjjHvsBqOrkIIVWbOEyBYuIfKOqZ7kXJiQDie6FIwJ844U8fKftcS/S+4+q/igi8cASVW0a1ACLSZwfgzgbqIRz4ctJqrrb7YFar6qeuIhHRL7CaQD2xJnyqjUwD5igqquCGVtxhVFdeH4KNcg/TaKIfAm0UNVs9/EGL4yXD4f9wgSGDTnwljOBKkBz4B1VnQT8pqqTvdCYdYXbFCyCc6WtALhT/Xhlv/L9Nhup7gT4qpoKZAcnpFI5rKpHVHUfsElVdwOo6n68M90VOJvPYVWdqaoX4lzE8yMwQUQ2Bzm24gqXugiHKdQADopIY/f+btw83Ea6V6btCof9wgSATXnhIaqaDlwtIjcAa0TkQbw11RWEzxQsX7hDQCoB7wOTRGQ2cAHORPJeUM/NQYATReR4nyuJPTOeGfD9mdW7cu64veVeysN/yqvtwNPA0yLSKTghlVi41EU4TKEG0B9YIiJv4vxC4woRWQh0BN4IamTFFw77hQkAL+2YxqWq08SZS3AczvhNL/k/nJ6BLFXd6rO8Ns4YTq+4C7jTvT8WZ07dO4FfgAeDFVQJ9fa5vwCncf67OL/xPjcoEZXOQBGJVNUDqvqRz/JGgGemV8IZ21goVV0ZyECOQrjURX/yplAbnzOFGvA70D5oUZWQqq4RkVbA3UA9nOFR0cDDfvUTysJhvzABYGNojTHGGGOMp1kPrceIyEnASTgXUP3hs7yrqi4LXmTFJ84PK1yH0ysLsA2YoapLgxfV0XHrpQXwrf9k7KEsHLYnABFpjvPzqnVxxml/j3M1emow4yqpcKiPMKqLsDhOhck25fkcTNnzysUrBhDnpxe/wDnF/bPk/UY3wPDgRFUyIjIIGAJ8hvOzt6Pd+4PdKX88wR2TlnO/A/A5zpCDdSLSM0hhlUg4bE8AInI/8DrO8exUIB1narivRKR1MGMriXCojzCqi3A5ToXDNuX5HEyAqKrdPHIDvgRqufe74PQYdHIffxXs+IqZwyagYiHLjwN+DnZ8JcjjK5/7y4FW7v1GOD9bGvQYi5GD57cnN9bvcWZpAKfxtMi93wz4ONjx/ZPqI4zqIlyOU+GwTXk+B7sF5mY9tN5STlV3AKjqcuASYKJ7aswrg6GFws8MlMPvatYQ5/v3jlbVDwFUdRPeGcoTDtsTOFNFHXDvpwOJAKr6Jc40d14RDvURLnURLsepcNimwiEHEwBe+eA1jggRqaLOnI6o6rcicgnOjy14ZbaDScBnIjIJ2IpzQKoL3IR3ppEBZ5qrETgfbjE+U5FB/qmLQlk4bE/gnIYciBP39Tg9OjlTwXlpqqhwqI9wqYtJhMdxKhy2qXDIwQSA9dB6yzicC49yqer3wMU4Y4xCnqo+A9yDM4VML5wPiHrAf911XjEGOAD8BkzAmQoHETkR90PcAzy/Pbn+D2eoxxtANaCvu7wq8N9gBVUK4VAfYVEXYXScCodtKhxyMAFg03YZY4wxxhhPsx5aDxGR7iLilZ8rLJKInCEip7v3G4lIHxHpHOy4SiKM6qKaiPQSkQHurZeIVA92XCURLnUBICIXikgT934HERkoIlcGO67iCrO68PxxCry/TUF45GDKnvXQeoiIZOH8HvdUYKKqfhfkkEpMRP4L9MMZZ/oszqm8T4DOwChVHRvE8IotTOqiJ87QiTU44wQFqAO0A+5W1dlBDK/YwqEuAETkWZyfTi4PTAFuABYDnYD3VXVAEMMrljCqi3A5ToXDNuX5HExgWIPWQ0TkK+B24FacCb83AhOB6ar6WzBjKy4R2QC0BioDvwInq+o2EYkFlqrq2UENsJjCpC5+BC5U1S1+y+sBi1X1lKAEVkLhUBcAIvI90BSIBHYAdVR1l4hEAp+q6unBjK84wqguwuU4FQ7blOdzMIFhQw68RVX1C1W9B2c6nJeAa4EkEXk9uKEVW7aq/qaqKcBmVd0GoKrpeGsKlnCoiwj/xiyAqv6Kd2ZqgPCoC4A/VPVPVc0E9qjqLgB3GqzDwQ2t2MKlLsLlOBUO21Q45GACwBq0HqWqf6jqNFXtDDTB+ebqBb4NpYF+67w0rU8uD9fFZyLyuog0F5EYEYl277+OR68e9nBdAOwWkXtF5DFgl4j0deulF85sGp7i8boIl+NUOGxT4ZCDCQBr0HrL5sIWquqvqvpEoIMppddEpAqAqs7KWSgip+CM5fSKcKiL23BOp04GfnHvT8IZT3tr8MIqsXCoC4A7cMYFNgMuxZkK7lfgAZwppLwgXOoiXI5T4bBN+edQA+/lYALAxtAaY4wxxhhPs18K8xgROQ64COdXaw4D36vqqqAGdYyISBNV3RDsOI6WiES54708RUROw5nA/BtV/TrI4RwTXquLQvbv71R1dTBjOla8VhdF8dpxyrYp809hQw48REQ6ApuAQcAwoAfwioh86v5CldfND3YAxSUiTUTkJxE5JCKzRCTGZ/WKoAVWAiKyUkTi3ftXA0txfid9tojcHtTgSiAc6gKK3L/HeGn/Dpe6+BteOk7ZNmX+MayH1ltGAV1V9ScRaQncpapdReQ/wCs4B6uQJiJ3F7UKZ1oWr3gBZwzXx0BvYJ2IdFHVnTi5eEGsqqa693sD56vqdhGJwhknOCFokZVMONQFhMH+TZjURRgdp2ybMv8Y1qD1lghV/QlAVT8RkVfc++NFpF9wQyu2F4BpFD71jZeuHq6qqgvd+4+LyE/AShHpgnem9akoIhGqmoUznn47gKpmioiXPijCoS4gPPbvcKmLcDlO2TZl/jGsQest+0WknaqudX/lKS3YAZXCD8DQnIOsL/cA5RWVRKScqmYDqOpUETmMcwrsuOCGVmzTgRki8hAwy50WZxrOeLtfgxpZyYRDXUB47N/hUhfhcpyybcr8Y1iD1lv6AO+JSA0gBbgcwB0HOS2YgZXAcxTdw/FwIAM5SuuBi4EFOQtU9W0RUZyf/Qx5qvqkiNyPM7wgFqdeHsRp6N4SzNhKyPN14QqH/Ttc6iJcjlO2TZl/DJu2y4NEJFpVdwc7DpOfl6+4defcrIDzS0+ezMGXx+sirPZvL9dFuLBtyvwT2CwHHiIitUXkfeAjERkpIsf7rPsoiKGViIicIiJx7v1GInKTiDQPdlwlISJnicgX7tXCp4rIQmCniGwTkTODHV9xuFcPfyEinwC1gDeBHW4OZwU5vGILh7qA8Ni/w6UuIGyOU7ZNmX8Ma9B6y2s4U8Zch3OKeIXbswZwfJHPCiEi0h/nFPfnInIDsJy8qaLuDWpwJfMSzlQ4rwDvAzNUtRJwP86VxV7wIk4OY8jLIRInh5HBDKyEwqEuIAz2b8KkLsLoOGXblPnHsCEHHiIiX6pqM5/Hj+JMu9IVWOW7LlSJyHdAG6Ay8CNwhqr+Ks7cgqtV9YygBlhMIvKVqp7t3t+mqrV91n2tqk2DFlwxhUMOEFZ5hMP+HS51ES7HKdumzD+GXRTmLZV8H6jqEBH5E+dqzyqFPyXk/OGOfcoUkV2q+iuAqu5yr1z1Ct9prfx/qc0rU16FQw4QPnmEw/4dLnURLscp26bMP4YNOfCWH0TkQt8FqjoSeAtoEJyQSux3EblERG4EVESuBBCRdkBWcEMrkVQRqQqgqjflLBSRROD3oEVVMuGQA4RPHuGwf4dLXYTLccq2KfOPYUMOPESc3+RGVf8oZN2J7i+nhDQRaQGMw5kQ+1agP9ATOARcraorgxjeUXPHp1VT1R3BjqW0wiEH8F4e4bB/F8WDdREWxynbpsw/iTVoTdCJSDSQmTNxtjHGhBo7ThkT2mzIgQkoEbnK536MOwXLLzhX39Yu+pnGGBMYdpwyxnusQWsC7RGf+0OAb4GTcaaWeSEoERljTH52nDLGY2zIgQkovylYNgDNVDUr57GqNglqgMaYfzw7ThnjPTZtlwm040TkVJzpVrJzPiRc9u3KGBMK7DhljMdYg9YEWiVgIe78gSJSS1V3iEg1wC62MMaEAjtOGeMxNuTAhAQRqQTE50xgbowxocaOU8aELmvQGmOMMcYYT7NZDowxxhhjjKdZg9YYY4wxxniaNWiNMcYYY4ynWYPWGGOMMcZ4mjVojTHGGGOMp1mD1hhjjDHGeNr/A7vls24R60dFAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 800x800 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from visualization_utils import jaccard_simple\n",
"candidate_jac_out, fig = jaccard_simple(candidate_IDs, candidate_IDs, 'out', diag_mask=False)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "13a57b10-e23e-44ec-8b16-93934f6e8a09",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 800x800 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# clustering by hand\n",
"candidate_IDs = candidate_IDs.reset_index(drop=True)\n",
"#clustered_Ids = candidate_IDs[[0,9,1,6,8,7,3,2,4,5]]\n",
"clustered_Ids = candidate_IDs[[6,1,8,9,7,2,5,0,3,4]]\n",
"\n",
"from visualization_utils import jaccard_simple\n",
"candidate_jac_out, fig = jaccard_simple(clustered_Ids, clustered_Ids, 'out', diag_mask=True)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "2691f9d3-d0f9-448c-9179-bf232db7dcdd",
"metadata": {},
"outputs": [],
"source": [
"# save figure\n",
"fig.savefig('jaccard_E3_target_outputs.svg')"
]
},
{
"cell_type": "markdown",
"id": "100501b8-b59e-4ef7-ad30-eec9121d4f6a",
"metadata": {},
"source": [
"The strong shared targets of E3 that return to the clock (with medium or strong connections) are found below. "
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "0c48def2-b5f0-445d-a901-c5f5832d50cc",
"metadata": {},
"outputs": [],
"source": [
"# get connections from strong shared targets of E3 to clock neurons\n",
"all_candidate_IDs = Evening3_targs['bodyId_post']\n",
"all_candidate_targets = get_input_output_conns(all_candidate_IDs, 'out', min_strength = 3)\n",
"all_candidate_targets = all_candidate_targets[all_candidate_targets.bodyId_post.isin(clock_df['bodyId'])]"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "68fdbc9c-d9d5-4428-9b06-1881dcb1c9d5",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-53-c14192aa3d57>:4: FutureWarning: The default value of regex will change from True to False in a future version.\n",
" all_candidate_targets['instance_pre'] = all_candidate_targets['instance_pre'].str.replace(r'\\([^()]*\\)', '')\n"
]
}
],
"source": [
"# merge clock labels onto all_candidate_targets\n",
"all_candidate_targets = all_candidate_targets.merge(clock_df[['bodyId','labels']], left_on='bodyId_post', right_on='bodyId')\n",
"#remove parenthetical addendums to target instance names\n",
"all_candidate_targets['instance_pre'] = all_candidate_targets['instance_pre'].str.replace(r'\\([^()]*\\)', '')\n",
"# remove _L and _R from candidate target instance names\n",
"all_candidate_targets['instance_pre'] = [re.sub(r'_(L|R)','',str(x)) for x in all_candidate_targets['instance_pre']]\n",
"# drop extra bodyId column\n",
"all_candidate_targets = all_candidate_targets.drop(columns='bodyId')"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "13de0c5e-ba15-4a1d-964c-988e26bdf243",
"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>bodyId_pre</th>\n",
" <th>instance_pre</th>\n",
" <th>bodyId_post</th>\n",
" <th>instance_post</th>\n",
" <th>weight</th>\n",
" <th>labels</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>297230760</td>\n",
" <td>5-HTPMPD01</td>\n",
" <td>296544364</td>\n",
" <td>LNd_R</td>\n",
" <td>3</td>\n",
" <td>LNd1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>328273806</td>\n",
" <td>SMP226</td>\n",
" <td>5813064789</td>\n",
" <td>LNd_R</td>\n",
" <td>31</td>\n",
" <td>LNd3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>328273806</td>\n",
" <td>SMP226</td>\n",
" <td>448260940</td>\n",
" <td>LNd_R</td>\n",
" <td>17</td>\n",
" <td>LNd2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>328273806</td>\n",
" <td>SMP226</td>\n",
" <td>296544364</td>\n",
" <td>LNd_R</td>\n",
" <td>7</td>\n",
" <td>LNd1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>297243542</td>\n",
" <td>SMP335</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>3</td>\n",
" <td>LNd4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>297243542</td>\n",
" <td>SMP335</td>\n",
" <td>5813064789</td>\n",
" <td>LNd_R</td>\n",
" <td>28</td>\n",
" <td>LNd3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>297243542</td>\n",
" <td>SMP335</td>\n",
" <td>448260940</td>\n",
" <td>LNd_R</td>\n",
" <td>17</td>\n",
" <td>LNd2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>297243542</td>\n",
" <td>SMP335</td>\n",
" <td>296544364</td>\n",
" <td>LNd_R</td>\n",
" <td>53</td>\n",
" <td>LNd1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5813092319</td>\n",
" <td>SMP346</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>5</td>\n",
" <td>LNd4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5813092319</td>\n",
" <td>SMP346</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>4</td>\n",
" <td>LNd5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5813092319</td>\n",
" <td>SMP346</td>\n",
" <td>450034902</td>\n",
" <td>LPN_R</td>\n",
" <td>3</td>\n",
" <td>LPN3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5813092319</td>\n",
" <td>SMP346</td>\n",
" <td>480029788</td>\n",
" <td>LPN_R</td>\n",
" <td>3</td>\n",
" <td>LPN2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>267214250</td>\n",
" <td>pC1b</td>\n",
" <td>5813064789</td>\n",
" <td>LNd_R</td>\n",
" <td>11</td>\n",
" <td>LNd3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>267214250</td>\n",
" <td>pC1b</td>\n",
" <td>448260940</td>\n",
" <td>LNd_R</td>\n",
" <td>5</td>\n",
" <td>LNd2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bodyId_pre instance_pre bodyId_post instance_post weight labels\n",
"13 297230760 5-HTPMPD01 296544364 LNd_R 3 LNd1\n",
"5 328273806 SMP226 5813064789 LNd_R 31 LNd3\n",
"8 328273806 SMP226 448260940 LNd_R 17 LNd2\n",
"11 328273806 SMP226 296544364 LNd_R 7 LNd1\n",
"1 297243542 SMP335 5813056917 LNd_R 3 LNd4\n",
"6 297243542 SMP335 5813064789 LNd_R 28 LNd3\n",
"9 297243542 SMP335 448260940 LNd_R 17 LNd2\n",
"12 297243542 SMP335 296544364 LNd_R 53 LNd1\n",
"0 5813092319 SMP346 5813056917 LNd_R 5 LNd4\n",
"2 5813092319 SMP346 5813021192 LNd_R 4 LNd5\n",
"3 5813092319 SMP346 450034902 LPN_R 3 LPN3\n",
"4 5813092319 SMP346 480029788 LPN_R 3 LPN2\n",
"7 267214250 pC1b 5813064789 LNd_R 11 LNd3\n",
"10 267214250 pC1b 448260940 LNd_R 5 LNd2"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# just to see the cell types of the candidates\n",
"all_candidate_targets.sort_values(by='instance_pre')"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "0edd0563",
"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>bodyId_pre</th>\n",
" <th>instance_pre</th>\n",
" <th>bodyId_post</th>\n",
" <th>instance_post</th>\n",
" <th>weight</th>\n",
" <th>labels</th>\n",
" <th>candidate_label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>297230760</td>\n",
" <td>5-HTPMPD01</td>\n",
" <td>296544364</td>\n",
" <td>LNd_R</td>\n",
" <td>3</td>\n",
" <td>LNd1</td>\n",
" <td>5-HTPMPD01 (297230760)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>328273806</td>\n",
" <td>SMP226</td>\n",
" <td>5813064789</td>\n",
" <td>LNd_R</td>\n",
" <td>31</td>\n",
" <td>LNd3</td>\n",
" <td>SMP226 (328273806)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>328273806</td>\n",
" <td>SMP226</td>\n",
" <td>448260940</td>\n",
" <td>LNd_R</td>\n",
" <td>17</td>\n",
" <td>LNd2</td>\n",
" <td>SMP226 (328273806)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>328273806</td>\n",
" <td>SMP226</td>\n",
" <td>296544364</td>\n",
" <td>LNd_R</td>\n",
" <td>7</td>\n",
" <td>LNd1</td>\n",
" <td>SMP226 (328273806)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>297243542</td>\n",
" <td>SMP335</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>3</td>\n",
" <td>LNd4</td>\n",
" <td>SMP335 (297243542)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>297243542</td>\n",
" <td>SMP335</td>\n",
" <td>5813064789</td>\n",
" <td>LNd_R</td>\n",
" <td>28</td>\n",
" <td>LNd3</td>\n",
" <td>SMP335 (297243542)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>297243542</td>\n",
" <td>SMP335</td>\n",
" <td>448260940</td>\n",
" <td>LNd_R</td>\n",
" <td>17</td>\n",
" <td>LNd2</td>\n",
" <td>SMP335 (297243542)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>297243542</td>\n",
" <td>SMP335</td>\n",
" <td>296544364</td>\n",
" <td>LNd_R</td>\n",
" <td>53</td>\n",
" <td>LNd1</td>\n",
" <td>SMP335 (297243542)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5813092319</td>\n",
" <td>SMP346</td>\n",
" <td>5813056917</td>\n",
" <td>LNd_R</td>\n",
" <td>5</td>\n",
" <td>LNd4</td>\n",
" <td>SMP346 (5813092319)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5813092319</td>\n",
" <td>SMP346</td>\n",
" <td>5813021192</td>\n",
" <td>LNd_R</td>\n",
" <td>4</td>\n",
" <td>LNd5</td>\n",
" <td>SMP346 (5813092319)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5813092319</td>\n",
" <td>SMP346</td>\n",
" <td>450034902</td>\n",
" <td>LPN_R</td>\n",
" <td>3</td>\n",
" <td>LPN3</td>\n",
" <td>SMP346 (5813092319)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5813092319</td>\n",
" <td>SMP346</td>\n",
" <td>480029788</td>\n",
" <td>LPN_R</td>\n",
" <td>3</td>\n",
" <td>LPN2</td>\n",
" <td>SMP346 (5813092319)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>267214250</td>\n",
" <td>pC1b</td>\n",
" <td>5813064789</td>\n",
" <td>LNd_R</td>\n",
" <td>11</td>\n",
" <td>LNd3</td>\n",
" <td>pC1b (267214250)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>267214250</td>\n",
" <td>pC1b</td>\n",
" <td>448260940</td>\n",
" <td>LNd_R</td>\n",
" <td>5</td>\n",
" <td>LNd2</td>\n",
" <td>pC1b (267214250)</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bodyId_pre instance_pre bodyId_post instance_post weight labels \\\n",
"13 297230760 5-HTPMPD01 296544364 LNd_R 3 LNd1 \n",
"5 328273806 SMP226 5813064789 LNd_R 31 LNd3 \n",
"8 328273806 SMP226 448260940 LNd_R 17 LNd2 \n",
"11 328273806 SMP226 296544364 LNd_R 7 LNd1 \n",
"1 297243542 SMP335 5813056917 LNd_R 3 LNd4 \n",
"6 297243542 SMP335 5813064789 LNd_R 28 LNd3 \n",
"9 297243542 SMP335 448260940 LNd_R 17 LNd2 \n",
"12 297243542 SMP335 296544364 LNd_R 53 LNd1 \n",
"0 5813092319 SMP346 5813056917 LNd_R 5 LNd4 \n",
"2 5813092319 SMP346 5813021192 LNd_R 4 LNd5 \n",
"3 5813092319 SMP346 450034902 LPN_R 3 LPN3 \n",
"4 5813092319 SMP346 480029788 LPN_R 3 LPN2 \n",
"7 267214250 pC1b 5813064789 LNd_R 11 LNd3 \n",
"10 267214250 pC1b 448260940 LNd_R 5 LNd2 \n",
"\n",
" candidate_label \n",
"13 5-HTPMPD01 (297230760) \n",
"5 SMP226 (328273806) \n",
"8 SMP226 (328273806) \n",
"11 SMP226 (328273806) \n",
"1 SMP335 (297243542) \n",
"6 SMP335 (297243542) \n",
"9 SMP335 (297243542) \n",
"12 SMP335 (297243542) \n",
"0 SMP346 (5813092319) \n",
"2 SMP346 (5813092319) \n",
"3 SMP346 (5813092319) \n",
"4 SMP346 (5813092319) \n",
"7 pC1b (267214250) \n",
"10 pC1b (267214250) "
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# recreate labels for candidates to include type and bodyId\n",
"all_candidate_targets['candidate_label'] = all_candidate_targets['instance_pre'] + ' (' + all_candidate_targets['bodyId_pre'].map(str) + ')'\n",
"all_candidate_targets.sort_values(by='candidate_label')"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "cc3a9ee8-be9b-4916-9d5c-9c7aebb3dbff",
"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>labels</th>\n",
" <th>LNd1</th>\n",
" <th>LNd2</th>\n",
" <th>LNd3</th>\n",
" <th>LNd4</th>\n",
" <th>LNd5</th>\n",
" <th>LPN2</th>\n",
" <th>LPN3</th>\n",
" </tr>\n",
" <tr>\n",
" <th>candidate_label</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5-HTPMPD01 (297230760)</th>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP226 (328273806)</th>\n",
" <td>7.0</td>\n",
" <td>17.0</td>\n",
" <td>31.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP335 (297243542)</th>\n",
" <td>53.0</td>\n",
" <td>17.0</td>\n",
" <td>28.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP346 (5813092319)</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pC1b (267214250)</th>\n",
" <td>NaN</td>\n",
" <td>5.0</td>\n",
" <td>11.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"labels LNd1 LNd2 LNd3 LNd4 LNd5 LPN2 LPN3\n",
"candidate_label \n",
"5-HTPMPD01 (297230760) 3.0 NaN NaN NaN NaN NaN NaN\n",
"SMP226 (328273806) 7.0 17.0 31.0 NaN NaN NaN NaN\n",
"SMP335 (297243542) 53.0 17.0 28.0 3.0 NaN NaN NaN\n",
"SMP346 (5813092319) NaN NaN NaN 5.0 4.0 3.0 3.0\n",
"pC1b (267214250) NaN 5.0 11.0 NaN NaN NaN NaN"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# generate pivot table for strong shared targets back to clock\n",
"all_candidate_targets = all_candidate_targets.pivot(index='candidate_label', columns='labels', values='weight')\n",
"all_candidate_targets"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "212de316-0ab8-47c0-8160-37c20a099acf",
"metadata": {},
"outputs": [],
"source": [
"# export table\n",
"all_candidate_targets.to_csv('E3_targets_to_clock.csv')"
]
},
{
"cell_type": "markdown",
"id": "abe4565d",
"metadata": {},
"source": [
"# Regional Breakdown"
]
},
{
"cell_type": "markdown",
"id": "7222e3b8",
"metadata": {},
"source": [
"We're interested in exploring what regions of the brain are innervated by the various clock groups. Where the main body of the cell being innervated came from did not matter so much as the region each individual synapse is in. First, the morning cells' had the following regional output breakdown:"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "16919368",
"metadata": {},
"outputs": [],
"source": [
"from neuprint import fetch_adjacencies\n",
"\n",
"neurons_df, conns_df = fetch_adjacencies(MIds, None, min_total_weight = 3)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "ff72708d",
"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>labels</th>\n",
" <th>sLNv1</th>\n",
" <th>sLNv2</th>\n",
" <th>sLNv3</th>\n",
" <th>sLNv4</th>\n",
" </tr>\n",
" <tr>\n",
" <th>roi</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AME(R)</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NotPrimary</th>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PLP(R)</th>\n",
" <td>22</td>\n",
" <td>21</td>\n",
" <td>23</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PVLP(R)</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SLP(R)</th>\n",
" <td>253</td>\n",
" <td>322</td>\n",
" <td>356</td>\n",
" <td>259</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP(R)</th>\n",
" <td>71</td>\n",
" <td>59</td>\n",
" <td>36</td>\n",
" <td>51</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"labels sLNv1 sLNv2 sLNv3 sLNv4\n",
"roi \n",
"AME(R) 0 1 0 0\n",
"NotPrimary 7 5 8 12\n",
"PLP(R) 22 21 23 17\n",
"PVLP(R) 0 1 0 0\n",
"SLP(R) 253 322 356 259\n",
"SMP(R) 71 59 36 51"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"conns_df = conns_df.merge(clock_df[['bodyId', 'labels']], left_on = 'bodyId_pre', right_on = 'bodyId')\n",
"output_by_region = pd.pivot_table(conns_df, values = 'weight', index = 'roi', columns = 'labels', fill_value = 0, aggfunc = 'sum')\n",
"output_by_region"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "aa7532af",
"metadata": {},
"outputs": [],
"source": [
"# export table\n",
"output_by_region.to_csv('morning_mediumstrong_output.csv')"
]
},
{
"cell_type": "markdown",
"id": "95e3e43a",
"metadata": {},
"source": [
"And had inputs from the following regions"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "f71b53a2",
"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",
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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>labels</th>\n",
" <th>sLNv1</th>\n",
" <th>sLNv2</th>\n",
" <th>sLNv3</th>\n",
" <th>sLNv4</th>\n",
" </tr>\n",
" <tr>\n",
" <th>roi</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AME(R)</th>\n",
" <td>38</td>\n",
" <td>45</td>\n",
" <td>46</td>\n",
" <td>37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NotPrimary</th>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PLP(R)</th>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PVLP(R)</th>\n",
" <td>13</td>\n",
" <td>19</td>\n",
" <td>15</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SLP(R)</th>\n",
" <td>16</td>\n",
" <td>9</td>\n",
" <td>21</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP(R)</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"labels sLNv1 sLNv2 sLNv3 sLNv4\n",
"roi \n",
"AME(R) 38 45 46 37\n",
"NotPrimary 3 2 6 1\n",
"PLP(R) 3 7 7 3\n",
"PVLP(R) 13 19 15 19\n",
"SLP(R) 16 9 21 15\n",
"SMP(R) 1 3 5 1"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"neurons_df, conns_df = fetch_adjacencies(None, MIds, min_total_weight = 3)\n",
"conns_df = conns_df.merge(clock_df[['bodyId', 'labels']], left_on = 'bodyId_post', right_on = 'bodyId')\n",
"input_by_region = pd.pivot_table(conns_df, values = 'weight', index = 'roi', columns = 'labels', fill_value = 0, aggfunc = 'sum')\n",
"input_by_region"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "00357796",
"metadata": {},
"outputs": [],
"source": [
"# export table\n",
"input_by_region.to_csv('morning_mediumstrong_input.csv')"
]
},
{
"cell_type": "markdown",
"id": "704ffb9c",
"metadata": {},
"source": [
"Doing the same for the outputs and inputs of evening cells yields the following:"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "9e8795c6-d5e4-48a1-b629-60bbe3b527d6",
"metadata": {},
"outputs": [],
"source": [
"EIds = clock_df[clock_df['phase']=='evening']['bodyId']"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "7b10af9c",
"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>labels</th>\n",
" <th>5th sLNv</th>\n",
" <th>LNd1</th>\n",
" <th>LNd2</th>\n",
" <th>LNd3</th>\n",
" <th>LNd4</th>\n",
" <th>LNd5</th>\n",
" <th>LNd6</th>\n",
" </tr>\n",
" <tr>\n",
" <th>roi</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AME(R)</th>\n",
" <td>125</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ICL(R)</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LO(R)</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NotPrimary</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PLP(R)</th>\n",
" <td>197</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PVLP(R)</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SCL(R)</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SIP(R)</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SLP(R)</th>\n",
" <td>245</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>25</td>\n",
" <td>28</td>\n",
" <td>115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP(L)</th>\n",
" <td>203</td>\n",
" <td>116</td>\n",
" <td>127</td>\n",
" <td>0</td>\n",
" <td>1246</td>\n",
" <td>945</td>\n",
" <td>459</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP(R)</th>\n",
" <td>803</td>\n",
" <td>598</td>\n",
" <td>496</td>\n",
" <td>779</td>\n",
" <td>622</td>\n",
" <td>970</td>\n",
" <td>736</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"labels 5th sLNv LNd1 LNd2 LNd3 LNd4 LNd5 LNd6\n",
"roi \n",
"AME(R) 125 0 0 0 0 0 59\n",
"ICL(R) 0 0 0 0 0 0 1\n",
"LO(R) 0 0 0 0 0 0 7\n",
"NotPrimary 3 1 0 2 0 0 7\n",
"PLP(R) 197 0 0 0 1 1 201\n",
"PVLP(R) 5 0 0 0 0 0 1\n",
"SCL(R) 1 0 0 0 0 0 0\n",
"SIP(R) 0 1 0 0 0 0 0\n",
"SLP(R) 245 2 5 3 25 28 115\n",
"SMP(L) 203 116 127 0 1246 945 459\n",
"SMP(R) 803 598 496 779 622 970 736"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"neurons_df, conns_df = fetch_adjacencies(EIds, None, min_total_weight = 3)\n",
"conns_df = conns_df.merge(clock_df[['bodyId', 'labels']], left_on = 'bodyId_pre', right_on = 'bodyId')\n",
"output_by_region = pd.pivot_table(conns_df, values = 'weight', index = 'roi', columns = 'labels', fill_value = 0, aggfunc = 'sum')\n",
"output_by_region"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "f55f376d",
"metadata": {},
"outputs": [],
"source": [
"# export table\n",
"output_by_region.to_csv('evening_mediumstrong_output.csv')"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "a01b9957",
"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>labels</th>\n",
" <th>5th sLNv</th>\n",
" <th>LNd1</th>\n",
" <th>LNd2</th>\n",
" <th>LNd3</th>\n",
" <th>LNd4</th>\n",
" <th>LNd5</th>\n",
" <th>LNd6</th>\n",
" </tr>\n",
" <tr>\n",
" <th>roi</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AME(R)</th>\n",
" <td>456</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ICL(R)</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LO(R)</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ME(R)</th>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NotPrimary</th>\n",
" <td>27</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PLP(R)</th>\n",
" <td>460</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>123</td>\n",
" <td>82</td>\n",
" <td>719</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PVLP(R)</th>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SCL(R)</th>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SIP(R)</th>\n",
" <td>0</td>\n",
" <td>30</td>\n",
" <td>25</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SLP(R)</th>\n",
" <td>28</td>\n",
" <td>57</td>\n",
" <td>180</td>\n",
" <td>153</td>\n",
" <td>211</td>\n",
" <td>223</td>\n",
" <td>170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP(L)</th>\n",
" <td>50</td>\n",
" <td>17</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" <td>256</td>\n",
" <td>223</td>\n",
" <td>106</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMP(R)</th>\n",
" <td>195</td>\n",
" <td>317</td>\n",
" <td>183</td>\n",
" <td>306</td>\n",
" <td>361</td>\n",
" <td>355</td>\n",
" <td>128</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"labels 5th sLNv LNd1 LNd2 LNd3 LNd4 LNd5 LNd6\n",
"roi \n",
"AME(R) 456 0 0 0 0 0 273\n",
"ICL(R) 0 0 0 0 0 0 1\n",
"LO(R) 0 0 0 0 0 0 19\n",
"ME(R) 2 0 0 0 0 0 2\n",
"NotPrimary 27 1 2 0 0 2 21\n",
"PLP(R) 460 0 0 0 123 82 719\n",
"PVLP(R) 6 0 0 0 0 0 5\n",
"SCL(R) 4 0 0 0 11 11 1\n",
"SIP(R) 0 30 25 4 0 0 0\n",
"SLP(R) 28 57 180 153 211 223 170\n",
"SMP(L) 50 17 40 0 256 223 106\n",
"SMP(R) 195 317 183 306 361 355 128"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"neurons_df, conns_df = fetch_adjacencies(None, EIds, min_total_weight = 3)\n",
"conns_df = conns_df.merge(clock_df[['bodyId', 'labels']], left_on = 'bodyId_post', right_on = 'bodyId')\n",
"input_by_region = pd.pivot_table(conns_df, values = 'weight', index = 'roi', columns = 'labels', fill_value = 0, aggfunc = 'sum')\n",
"input_by_region"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "9944f2e6",
"metadata": {},
"outputs": [],
"source": [
"# export table\n",
"input_by_region.to_csv('evening_mediumstrong_input.csv')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
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