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+{
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+ "cells": [
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {},
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+ "source": [
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+ "## GET POWER SPECTRA"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 1,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "<module 'LFP_functions' from 'e:\\\\BT_Code\\\\LFP_functions.py'>"
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+ ]
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+ },
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+ "execution_count": 1,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "import numpy as np\n",
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+ "import matplotlib.pyplot as plt\n",
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+ "import pickle\n",
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+ "import xarray as xr\n",
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+ "import pandas as pd\n",
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+ "import sys, os\n",
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+ "\n",
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+ "import importlib\n",
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+ "import LFP_functions\n",
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+ "importlib.reload(LFP_functions)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 2,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "# Disable\n",
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+ "def blockPrint():\n",
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+ " sys.stdout = open(os.devnull, 'w')\n",
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+ "\n",
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+ "# Restore\n",
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+ "def enablePrint():\n",
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+ " sys.stdout = sys.__stdout__"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {},
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+ "source": [
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+ "We import the lfps for all sessions and save them on the same dictionaries"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 3,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Loaded data for session 1\n",
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+ "Loaded data for session 2\n",
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+ "Loaded data for session 3\n",
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+ "Loaded data for session 4\n",
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+ "Loaded data for session 5\n",
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+ "Loaded data for session 6\n",
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+ "Loaded data for session 7\n",
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+ "Loaded data for session 8\n",
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+ "Loaded data for session 9\n",
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+ "Loaded data for session 10\n",
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+ "Loaded data for session 11\n",
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+ "Loaded data for session 12\n",
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+ "Loaded data for session 13\n",
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+ "Loaded data for session 14\n",
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+ "Loaded data for session 15\n",
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+ "Loaded data for session 16\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "import pickle\n",
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+ "\n",
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+ "# Initialize the dictionaries to store the loaded data\n",
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+ "lfps_act = {}\n",
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+ "lfps_pas = {}\n",
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+ "chans = {}\n",
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+ "\n",
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+ "\n",
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+ "for i in range(1,17):\n",
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+ " # Format the file names with the current number\n",
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+ " act_file = f'aligned_LFP_area/aligned_lfps_act_en_V1_{i}.pickle'\n",
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+ " pas_file = f'aligned_LFP_area/aligned_lfps_pas_en_V1_{i}.pickle'\n",
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+ " chan_file = f'channels/chans_V1_{i}.pickle'\n",
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+ " \n",
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+ " # Open and load the data from the files\n",
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+ " with open(act_file, 'rb') as f:\n",
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+ " lfps_act[i] = pickle.load(f)\n",
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+ " with open(pas_file, 'rb') as f:\n",
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+ " lfps_pas[i] = pickle.load(f)\n",
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+ " with open(chan_file, 'rb') as f:\n",
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+ " chans[i] = pickle.load(f)\n",
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+ " print(f'Loaded data for session {i}')\n",
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+ "\n",
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+ "#open the images file\n",
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+ "with open('images.pickle', 'rb') as f:\n",
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+ " images = pickle.load(f)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 4,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "from multitaper import mtspec\n",
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+ "\n",
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+ "def get_spectra(lfps,pres):\n",
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+ " '''\n",
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+ " given a dictionary of lfps, return the power spectra in the same format,\n",
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+ " where we first have the sessions, then the images, and for each combination there's a 3d xarray\n",
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+ " containing the power spectra for each channel and presentation id\n",
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+ "\n",
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+ " INPUTS:\n",
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+ " lfps: a dictionary of lfps, where the first key is the session, the second key is the image, and the value is an xarray\n",
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+ " containing the lfp data for that session and image\n",
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+ " pres: the image onset for the novel image (1 if it is the first onset, 2 if it is the second, etc)\n",
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+ " \n",
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+ " OUTPUTS:\n",
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+ " lfps_spectra: the dictionary of lfps\n",
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+ " '''\n",
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+ " blockPrint()\n",
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+ " lfps_spectra = {}\n",
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+ " for i in lfps.keys():\n",
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+ " lfps_spectra[i] = {}\n",
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+ " for j in lfps[i].keys():\n",
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+ " # we are now inside the aligned lfp of a session i and image j\n",
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+ " # we will now create another xarray to store the spectrogram\n",
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+ " lfp = lfps[i][j]\n",
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+ " lfps_spectra[i][j] = get_powers(lfp,pres)\n",
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+ " enablePrint()\n",
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+ " return lfps_spectra\n",
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+ " \n",
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+ "\n",
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+ "def get_powers(lfp,pres):\n",
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+ " '''\n",
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+ " given a lfp for a specific image and session, return the power spectrum for each channel\n",
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+ " and presentation id in the form of an xarray\n",
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+ "\n",
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+ " INPUTS:\n",
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+ " lfp: an xarray containing the lfp data for a specific image and session\n",
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+ " pres: the image onset for the novel image (1 if it is the first onset, 2 if it is the second, etc)\n",
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+ " '''\n",
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+ "\n",
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+ " #set start and end times\n",
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+ " ti, tf = LFP_functions.pres_times(pres)\n",
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+ " lfp1 = lfp.sel(time_from_presentation_onset=slice(ti,tf))\n",
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+ " #energy normalization for each presentation\n",
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+ " for pres in lfp1.presentation_id.values:\n",
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+ " lfp1.loc[{'presentation_id':pres}] = LFP_functions.energy_normalization(lfp1.sel(presentation_id=pres))\n",
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+ " presentation_ids = lfp1.presentation_id.values.tolist()\n",
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+ " channels = lfp1.channel.values.tolist()\n",
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+ "\n",
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+ "\n",
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+ " for i,pres in enumerate(presentation_ids): #for every presentation ID\n",
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+ " for j,c in enumerate(channels): #for every channel\n",
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+ " #obtain the spectrogram for the current channel and presentation id\n",
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+ " _,sigspec,lfpspecs,_=mtspec.spectrogram(lfp_res.isel(channel=j,presentation_id=i).values, 1/500, (tf-ti)-1/500, olap=0, nw=1, kspec=2, fmin=0, fmax=100, iadapt=0)\n",
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+ " if i==0 and j==0:\n",
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+ " #create the 3d numpy array to store the spectrogram\n",
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+ " data = np.zeros((len(channels),len(presentation_ids),sigspec.shape[0]))\n",
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+ " dims = ['channel','presentation_id','frequency']\n",
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+ " data[j,i,:] = lfpspecs[:,0]\n",
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+ "\n",
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+ " #create the xarray\n",
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+ " da = xr.DataArray(data,\n",
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+ " coords=[channels,presentation_ids,sigspec[:,0]],\n",
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+ " dims=['channel','presentation_id','frequency'])\n",
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+ " return da"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {},
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+ "source": [
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+ "When specifying pres=1, we refer to the first presentation of the images (0-250ms from the onset), whilst with pres=0, we obtain the power spectra of the baseline (250-0ms before the onset)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 6,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "spectra_act_1 = get_spectra(lfps_act,1)\n",
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+ "spectra_act_0 = get_spectra(lfps_act,0)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 7,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "with open('power_spectra/spectra_act_1.pickle', 'wb') as f:\n",
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+ " pickle.dump(spectra_act_1, f)\n",
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+ "with open('power_spectra/spectra_act_0.pickle', 'wb') as f:\n",
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+ " pickle.dump(spectra_act_0, f)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 8,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "spectra_pas_1 = get_spectra(lfps_pas,1)\n",
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+ "spectra_pas_0 = get_spectra(lfps_pas,0)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 9,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "#save the power spectra\n",
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+ "with open('power_spectra/spectra_pas_1.pickle', 'wb') as f:\n",
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+ " pickle.dump(spectra_pas_1, f)\n",
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+ "with open('power_spectra/spectra_pas_0.pickle', 'wb') as f:\n",
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+ " pickle.dump(spectra_pas_0, f)\n"
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+ ]
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+ }
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+ ],
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+ "metadata": {
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+ "kernelspec": {
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+ "display_name": "allensdk",
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+ "language": "python",
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+ "name": "python3"
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+ },
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+ "language_info": {
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+ "codemirror_mode": {
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+ "name": "ipython",
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+ "version": 3
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+ },
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+ "file_extension": ".py",
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+ "mimetype": "text/x-python",
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+ "name": "python",
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+ "nbconvert_exporter": "python",
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+ "pygments_lexer": "ipython3",
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+ "version": "3.8.18"
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+ }
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+ },
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+ "nbformat": 4,
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+ "nbformat_minor": 2
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+}
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