{
"cells": [
{
"cell_type": "code",
"execution_count": 145,
"metadata": {},
"outputs": [],
"source": [
"import h5py\n",
"import pandas as pd\n",
"from raster_maker import SonataWriter\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import scipy.signal as ss\n",
"import scipy"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('Connections.csv')"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {},
"outputs": [],
"source": [
"p_delta = 0.1 # change p_delta of the nodes to 100% modulation\n",
"num_cells = int(p_delta * df[(df['Source Population']=='exc_stim')&(df.Name.str.contains('dend'))]['Node ID'].nunique())\n",
"cells_to_change = np.random.choice(df[(df['Source Population']=='exc_stim')&\n",
" (df.Name.str.contains('dend'))]['Node ID'].unique(),\n",
" num_cells,\n",
" replace=False)"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {},
"outputs": [],
"source": [
"depth_of_mod = 1\n",
"freq = 64\n",
"phase= 0#2*np.pi/3\n",
"tsim = 400 # seconds\n",
"t = np.arange(0,tsim,.001)\n",
"\n",
"mod_trace = depth_of_mod*(np.sin((2 * np.pi * freq * t ) - phase) + 1) + (1-depth_of_mod)\n",
"\n",
"#numbPoints = scipy.stats.poisson(rate_temp/1000).rvs()#Poisson number of points\n",
"#simSpks=np.where(numbPoints>0)[0]"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {},
"outputs": [],
"source": [
"f = h5py.File('exc_stim_spikes.h5','r')"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {},
"outputs": [],
"source": [
"mask = np.isin(f['spikes']['exc_stim']['node_ids'][:], cells_to_change)\n",
"anti_mask = ~mask"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {},
"outputs": [],
"source": [
"old_timestamps = f['spikes']['exc_stim']['timestamps'][anti_mask]\n",
"old_nodeids = f['spikes']['exc_stim']['node_ids'][anti_mask]"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {},
"outputs": [],
"source": [
"fr_df = pd.DataFrame(np.concatenate((f['spikes']['exc_stim']['timestamps'][mask].reshape(-1,1),\n",
" f['spikes']['exc_stim']['node_ids'][mask].reshape(-1,1)),axis=1),\n",
" columns = ['timestamps','node_ids'])\n",
"fr_df = (fr_df.groupby('node_ids')['timestamps'].count()/tsim).reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {},
"outputs": [
{
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