{ "cells": [ { "cell_type": "code", "execution_count": 37, "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(), '..', '..'))" ] }, { "cell_type": "code", "execution_count": 38, "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\n", "\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 39, "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": 40, "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": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#selected_009266\n", "selected_008229" ] }, { "cell_type": "markdown", "id": "733dd36d", "metadata": {}, "source": [ "## Read MoSeq source file" ] }, { "cell_type": "code", "execution_count": 41, "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", "\n", "source = '/home/sobolev/nevermind/Andrey/analysis/MoSeq/results'\n", "#session = selected_009266[18]\n", "session = selected_008229[3]\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": 42, "id": "29f79622", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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
0082496.4256364.0172-1.0080502.2657348.2704506.5237368.8014489.8594...369.3536-2.79343.7192-3.52890.2810-1.6455-0.0245-1.5734-1.8662-1.0475
1082497.8193361.1044-1.2820506.8656342.6798519.6453335.4263496.2881...391.88303.4440-1.9281-6.53563.21551.88882.5745-0.2975-5.42463.0990
2082502.6127359.1499-1.2535507.2455339.1818515.6492343.7710499.0871...382.14500.1586-0.1186-1.75820.20750.31970.54771.16711.01971.2166
3082511.3403347.9349-1.1288516.3820327.7793526.3872332.2752508.5156...372.54400.2880-0.2780-0.65480.2365-0.03540.06750.64310.51040.8013
4082519.1863336.1914-1.0357526.0042316.9872534.7120323.6790517.6688...359.59430.0007-0.1615-0.91460.05230.0245-0.10000.34050.60650.8340
\n", "

5 rows × 40 columns

\n", "
" ], "text/plain": [ " syllables reindexed syllables non-reindexed centroid x centroid y \\\n", "0 0 82 496.4256 364.0172 \n", "1 0 82 497.8193 361.1044 \n", "2 0 82 502.6127 359.1499 \n", "3 0 82 511.3403 347.9349 \n", "4 0 82 519.1863 336.1914 \n", "\n", " heading estimated left_eye x estimated left_eye y estimated right_eye x \\\n", "0 -1.0080 502.2657 348.2704 506.5237 \n", "1 -1.2820 506.8656 342.6798 519.6453 \n", "2 -1.2535 507.2455 339.1818 515.6492 \n", "3 -1.1288 516.3820 327.7793 526.3872 \n", "4 -1.0357 526.0042 316.9872 534.7120 \n", "\n", " estimated right_eye y estimated left_ear x ... estimated right_hip y \\\n", "0 368.8014 489.8594 ... 369.3536 \n", "1 335.4263 496.2881 ... 391.8830 \n", "2 343.7710 499.0871 ... 382.1450 \n", "3 332.2752 508.5156 ... 372.5440 \n", "4 323.6790 517.6688 ... 359.5943 \n", "\n", " latent_state 0 latent_state 1 latent_state 2 latent_state 3 \\\n", "0 -2.7934 3.7192 -3.5289 0.2810 \n", "1 3.4440 -1.9281 -6.5356 3.2155 \n", "2 0.1586 -0.1186 -1.7582 0.2075 \n", "3 0.2880 -0.2780 -0.6548 0.2365 \n", "4 0.0007 -0.1615 -0.9146 0.0523 \n", "\n", " latent_state 4 latent_state 5 latent_state 6 latent_state 7 \\\n", "0 -1.6455 -0.0245 -1.5734 -1.8662 \n", "1 1.8888 2.5745 -0.2975 -5.4246 \n", "2 0.3197 0.5477 1.1671 1.0197 \n", "3 -0.0354 0.0675 0.6431 0.5104 \n", "4 0.0245 -0.1000 0.3405 0.6065 \n", "\n", " latent_state 8 \n", "0 -1.0475 \n", "1 3.0990 \n", "2 1.2166 \n", "3 0.8013 \n", "4 0.8340 \n", "\n", "[5 rows x 40 columns]" ] }, "execution_count": 42, "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": 43, "id": "e1135957", "metadata": {}, "outputs": [], "source": [ "source = '/home/sobolev/nevermind/Andrey/data'\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": 44, "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": 45, "id": "1992e8a7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((240001, 7), (48000, 40))" ] }, "execution_count": 45, "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": 46, "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": 47, "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": 48, "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": 49, "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": 50, "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": 51, "id": "aab44ba2", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'X Position')" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "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": 52, "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": "fa586643", "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 }