{ "cells": [ { "cell_type": "code", "execution_count": 2, "id": "5f7ded64", "metadata": {}, "outputs": [], "source": [ "import sys, os\n", "sys.path.append(os.path.join(os.getcwd(), '..'))\n", "sys.path.append(os.path.join(os.getcwd(), '..', '..'))\n", "sys.path.append(os.path.join(os.getcwd(), '..', 'analysis'))" ] }, { "cell_type": "code", "execution_count": 3, "id": "c4461104", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import os\n", "import h5py\n", "import json\n", "from scipy.stats import pearsonr\n", "from scipy.interpolate import interp1d\n", "from scipy import signal\n", "from functools import reduce\n", "from imports import *\n", "from analysis.loading import load_session_data\n", "from session.sessions import selected_009266, selected_008229, selected_009265\n", "\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 4, "id": "20f34373", "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "IPython.OutputArea.prototype._should_scroll = function(lines) {\n", " return false;\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%javascript\n", "IPython.OutputArea.prototype._should_scroll = function(lines) {\n", " return false;\n", "}" ] }, { "cell_type": "code", "execution_count": 5, "id": "98c1b317", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['008229_hippoSIT_2022-05-17_21-44-43',\n", " '008229_hippoSIT_2022-05-16_20-36-44',\n", " '008229_hippoSIT_2022-05-20_15-54-39',\n", " '008229_hippoSIT_2022-05-18_14-36-18']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#selected_009266\n", "selected_008229\n", "#selected_009265" ] }, { "cell_type": "markdown", "id": "733dd36d", "metadata": {}, "source": [ "## Read MoSeq source file" ] }, { "cell_type": "code", "execution_count": 8, "id": "d6a24576", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/mnt/nevermind.data-share/ag-grothe/Andrey/analysis/MoSeq/results'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "source" ] }, { "cell_type": "code", "execution_count": 13, "id": "4ae08697", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found MoSeq file\n" ] } ], "source": [ "#source = '/home/sobolev/nevermind/Miguel/MoSeq/TrainedModels/MoSeqProject_ALLhippoSIT/2023_01_22-16_22_54'\n", "#source = '/home/sobolev/nevermind/Andrey/analysis/DLC/MoSeq/10fps'\n", "source = '/home/sobolev/nevermind/Andrey/analysis/MoSeq/results'\n", "#source = '/mnt/nevermind.data-share/ag-grothe/Andrey/analysis/MoSeq/results'\n", "session = selected_008229[0]\n", "\n", "filt = [s for s in os.listdir(source) if s.startswith(session)]\n", "if len(filt) > 0:\n", " moseq_file = os.path.join(source, filt[0])\n", " print(\"Found MoSeq file\")\n", "else:\n", " print(\"No MoSeq file for that session\")" ] }, { "cell_type": "code", "execution_count": 14, "id": "29f79622", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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syllables reindexedsyllables non-reindexedcentroid xcentroid yheadingestimated left_eye xestimated left_eye yestimated right_eye xestimated right_eye yestimated left_ear x...estimated right_hip ylatent_state 0latent_state 1latent_state 2latent_state 3latent_state 4latent_state 5latent_state 6latent_state 7latent_state 8
0282617.0446127.41311.3730624.6819140.0827609.6564133.6857627.2717...125.2797-2.86052.7593-1.5788-0.1955-0.9608-0.3716-1.1929-1.8936-0.7051
1282617.3593127.48521.7297611.6738151.4319597.2071163.9044625.5019...87.84864.6644-2.1807-5.26181.97271.41485.1785-0.1719-3.81243.9014
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5 rows × 40 columns

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" ], "text/plain": [ " syllables reindexed syllables non-reindexed centroid x centroid y \\\n", "0 2 82 617.0446 127.4131 \n", "1 2 82 617.3593 127.4852 \n", "2 2 82 615.6161 133.6536 \n", "3 2 82 616.2345 134.0113 \n", "4 2 82 616.5646 135.5285 \n", "\n", " heading estimated left_eye x estimated left_eye y estimated right_eye x \\\n", "0 1.3730 624.6819 140.0827 609.6564 \n", "1 1.7297 611.6738 151.4319 597.2071 \n", "2 2.1715 613.1118 145.8070 603.7877 \n", "3 2.0459 615.8642 148.2531 605.0786 \n", "4 1.9536 616.9005 149.8722 606.7854 \n", "\n", " estimated right_eye y estimated left_ear x ... estimated right_hip y \\\n", "0 133.6857 627.2717 ... 125.2797 \n", "1 163.9044 625.5019 ... 87.8486 \n", "2 143.7918 618.7785 ... 116.6650 \n", "3 144.8670 621.0288 ... 118.0985 \n", "4 147.3233 621.7898 ... 120.9462 \n", "\n", " latent_state 0 latent_state 1 latent_state 2 latent_state 3 \\\n", "0 -2.8605 2.7593 -1.5788 -0.1955 \n", "1 4.6644 -2.1807 -5.2618 1.9727 \n", "2 -1.6576 -0.0438 0.9293 0.3719 \n", "3 -1.5587 0.1691 0.6754 0.3677 \n", "4 -1.6000 0.2642 0.6043 0.5735 \n", "\n", " latent_state 4 latent_state 5 latent_state 6 latent_state 7 \\\n", "0 -0.9608 -0.3716 -1.1929 -1.8936 \n", "1 1.4148 5.1785 -0.1719 -3.8124 \n", "2 0.5609 -1.2030 0.2963 -0.2094 \n", "3 0.0905 -1.4436 -0.0062 -1.6705 \n", "4 0.0079 -2.0334 -0.0190 -1.6202 \n", "\n", " latent_state 8 \n", "0 -0.7051 \n", "1 3.9014 \n", "2 0.8537 \n", "3 -0.1696 \n", "4 0.2985 \n", "\n", "[5 rows x 40 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = pd.read_csv(moseq_file)\n", "ds.head()" ] }, { "cell_type": "markdown", "id": "ba4c8762", "metadata": {}, "source": [ "## Create moseq processed file in the session" ] }, { "cell_type": "code", "execution_count": 15, "id": "e1135957", "metadata": {}, "outputs": [], "source": [ "source = '/home/sobolev/nevermind/Andrey/data'\n", "#source = '/mnt/nevermind.data-share/ag-grothe/Andrey/data'\n", "\n", "animal = session.split('_')[0]\n", "sessionpath = os.path.join(source, animal, session)\n", "h5_file = os.path.join(sessionpath, session + '.h5')\n", "moseq_file = os.path.join(sessionpath, 'moseq.h5')" ] }, { "cell_type": "code", "execution_count": 16, "id": "57cd7a46", "metadata": {}, "outputs": [], "source": [ "with h5py.File(h5_file, 'r') as f:\n", " tl = np.array(f['processed']['timeline']) # time, X, Y, speed, etc.\n", " trials = np.array(f['processed']['trial_idxs'])\n", " cfg = json.loads(f['processed'].attrs['parameters'])" ] }, { "cell_type": "code", "execution_count": 17, "id": "1992e8a7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((239999, 7), (47998, 40))" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# compare lengths of timeline (~100Hz) and MoSeq detected coords / syllables\n", "ds1 = ds.copy()\n", "tl.shape, ds1.shape" ] }, { "cell_type": "code", "execution_count": 18, "id": "d7456dfe", "metadata": {}, "outputs": [], "source": [ "# select only required columns\n", "#columns_to_drop = ['Unnamed: 0', 'session_name', 'uuid', 'onset']\n", "#ds1 = ds1.drop(columns=columns_to_drop)" ] }, { "cell_type": "code", "execution_count": 19, "id": "d12f64b2", "metadata": {}, "outputs": [], "source": [ "def px_to_meters(cfg, x, y): # convert pixels to meters\n", " cfg_pos = cfg['position']\n", " pixel_size = cfg_pos['floor_r_in_meters'] / float(cfg_pos['floor_radius'])\n", " x_m = float(cfg_pos['arena_x'] - x) * pixel_size * (-1 if cfg_pos['flip_x'] else 1)\n", " y_m = float(cfg_pos['arena_y'] - y) * pixel_size * (-1 if cfg_pos['flip_y'] else 1)\n", " return x_m, y_m" ] }, { "cell_type": "code", "execution_count": 20, "id": "15583cbc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Converted 14 variables\n" ] } ], "source": [ "# convert pixels to meters in all variables\n", "variables = [x[:-2] for x in ds1.columns if x.find(' x') > 0]\n", "for variable in variables:\n", " var_x, var_y = variable + ' x', variable + ' y'\n", " converted = np.array([px_to_meters(cfg, x, y) for x, y in zip(ds[var_x], ds[var_y])])\n", " ds1[var_x] = converted[:, 0]\n", " ds1[var_y] = converted[:, 1]\n", " \n", "print(\"Converted %d variables\" % len(variables))" ] }, { "cell_type": "code", "execution_count": 21, "id": "4550d764", "metadata": {}, "outputs": [], "source": [ "# interpolate syllables assuming frames are evenly distributed\n", "t_start, t_end = tl[0][0], tl[-1][0]\n", "\n", "x_moseq = np.linspace(t_start, t_end, len(ds1)) # moseq timeline in seconds\n", "moseq_matrix = np.zeros((len(tl), len(ds1.columns))) # collect moseq data into numpy array\n", "\n", "curr_idx = 0\n", "for i, t in enumerate(tl[:, 0]):\n", " if curr_idx < len(x_moseq) - 1 and \\\n", " np.abs(t - x_moseq[curr_idx]) > np.abs(t - x_moseq[curr_idx + 1]):\n", " curr_idx += 1\n", " \n", " moseq_matrix[i] = np.array(ds1.iloc[curr_idx])" ] }, { "cell_type": "code", "execution_count": 22, "id": "a620621a", "metadata": {}, "outputs": [], "source": [ "# create a DataFrame from it\n", "moseq_df = pd.DataFrame(moseq_matrix, columns=ds1.columns)" ] }, { "cell_type": "code", "execution_count": 23, "id": "aab44ba2", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'X Position')" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# test centroid position\n", "fig, ax = plt.subplots(1, 1, figsize=(15, 3))\n", "ax.plot(tl[:, 1][:30000], label='Original')\n", "ax.plot(moseq_matrix[:, 2][:30000], label='DLC')\n", "ax.legend(loc='upper right')\n", "ax.set_xlabel('Time, samples (100 Hz)', fontsize=14)\n", "ax.set_ylabel('X Position', fontsize=14)" ] }, { "cell_type": "code", "execution_count": 24, "id": "c59cec3a", "metadata": {}, "outputs": [], "source": [ "# save moseq data to the session folder\n", "with h5py.File(moseq_file, 'w') as f:\n", " ds_h5 = f.create_dataset('moseq', data=moseq_matrix)\n", " ds_h5.attrs['headers'] = ', '.join(list(ds1.columns))" ] }, { "cell_type": "code", "execution_count": null, "id": "d9367434", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }