{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "55633802", "metadata": {}, "outputs": [], "source": [ "import h5py\n", "import pandas as pd\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Rectangle\n", "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n", "\n", "import vbn_utils\n", "import decoding_utils as du\n", "from analysis_utils import exponential_convolve\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "id": "7360610b", "metadata": {}, "source": [ "## Data loading" ] }, { "cell_type": "code", "execution_count": null, "id": "1af58383", "metadata": {}, "outputs": [], "source": [ "#Paths to all of the useful supplemental tables and tensors\n", "active_tensor_file = \"/Volumes/programs/mindscope/workgroups/np-behavior/vbn_data_release/supplemental_tables/vbnAllUnitSpikeTensor.hdf5\"\n", "\n", "stim_table_file = \"/Volumes/programs/mindscope/workgroups/np-behavior/vbn_data_release/supplemental_tables/master_stim_table_no_filter.csv\"\n", "unit_table_file = \"/Volumes/programs/mindscope/workgroups/np-behavior/vbn_data_release/supplemental_tables/master_units_with_responsiveness.csv\"" ] }, { "cell_type": "code", "execution_count": null, "id": "f82e1604", "metadata": {}, "outputs": [], "source": [ "units = pd.read_csv(unit_table_file)\n", "units['cortical_layer'] = units['cortical_layer'].replace('3-Feb','2/3') # 2/3 sometimes gets incorrectly reformatted as a date\n", "\n", "stim_table = pd.read_csv(stim_table_file)\n", "stim_table = stim_table.drop(columns='Unnamed: 0')\n", "\n", "active_tensor = h5py.File(active_tensor_file)" ] }, { "cell_type": "code", "execution_count": null, "id": "3a4d1adb", "metadata": {}, "outputs": [], "source": [ "structure_tree = pd.read_csv(\"/Volumes/programs/mindscope/workgroups/np-behavior/vbn_data_release/supplemental_tables/ccf_structure_tree_2017.csv\")" ] }, { "cell_type": "markdown", "id": "da17ee74", "metadata": {}, "source": [ "## Omission responses by cluster, cell type, and layer" ] }, { "cell_type": "markdown", "id": "bc2d3edc", "metadata": {}, "source": [ "### By cluster" ] }, { "cell_type": "code", "execution_count": null, "id": "1bacec57", "metadata": {}, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "def plot_omission_psth_by_group(values, filter_kwargs_fn, title_fn,\n", " inset_loc_fn=None, ylim_fn=None):\n", " fig, axes = plt.subplots(len(values), 1)\n", " fig.set_size_inches(7, 2.5 * len(values))\n", " session_list = list(active_tensor.keys())\n", " time = np.arange(-750, 750, 1)\n", "\n", " for ax, value in zip(np.atleast_1d(axes), values):\n", " unit_ids = vbn_utils.get_unit_ids(\n", " units, 'VISall', clustering='new', **filter_kwargs_fn(value)\n", " )\n", "\n", " omission_responses, _ = vbn_utils.unit_averaged_psth(\n", " active_tensor_file, stim_table, session_list, unit_ids,\n", " 'omitted', baseline_length=750, resp_window_length=750,\n", " )\n", "\n", " all_responses = np.array([\n", " exponential_convolve(ors, tau=3, symmetrical=True)\n", " for oresps in omission_responses for ors in oresps\n", " ])\n", "\n", " ax.set_title(title_fn(value), fontsize=16, pad=12, loc='right')\n", " vbn_utils.mean_sem_plot(all_responses * 1000, ax, time, color='k')\n", "\n", " if ylim_fn is not None and ylim_fn(value) is not None:\n", " ax.set_ylim(*ylim_fn(value))\n", "\n", " rect_h = (ax.get_ylim()[1] - ax.get_ylim()[0]) * 0.05\n", " ax.add_patch(Rectangle((-750, ax.get_ylim()[1]), 250, rect_h,\n", " color='gray', alpha=1, clip_on=False))\n", " ax.add_patch(Rectangle((0, ax.get_ylim()[1]), 250, rect_h,\n", " edgecolor='gray', facecolor='none',\n", " linewidth=2, clip_on=False))\n", " baseline = all_responses.mean(axis=0)[0] * 1000\n", " ax.axhline(baseline, color='k', linestyle='--', linewidth=1)\n", " ax.set_xlim(-750, 750)\n", " vbn_utils.formatFigure(fig, ax)\n", "\n", " loc, borderpad = inset_loc_fn(value) if inset_loc_fn else ('upper right', 0.5)\n", " inset_ax = inset_axes(ax, width=\"20%\", height=\"40%\", loc=loc, borderpad=borderpad)\n", " vbn_utils.mean_sem_plot(all_responses[:, 600:1100] * 1000, inset_ax,\n", " time[600:1100], color='k')\n", " inset_ax.axhline(baseline, color='k', linestyle='--', linewidth=1)\n", " vbn_utils.formatFigure(fig, inset_ax)\n", "\n", " plt.tight_layout()\n", " return fig" ] }, { "cell_type": "code", "execution_count": null, "id": "33493fee", "metadata": {}, "outputs": [], "source": [ "plot_omission_psth_by_group(\n", " values=np.arange(1, 9),\n", " filter_kwargs_fn=lambda c: dict(cell_types='all', clusters=c),\n", " title_fn=lambda c: f'Cluster {c}',\n", " inset_loc_fn=lambda c: ('lower right', 1.5) if c == 5 else ('upper right', 0.5),\n", ")" ] }, { "cell_type": "markdown", "id": "d4985683", "metadata": {}, "source": [ "### By cell type" ] }, { "cell_type": "code", "execution_count": null, "id": "c3504ebe", "metadata": {}, "outputs": [], "source": [ "plot_omission_psth_by_group(\n", " values=['RS', 'FS', 'SST', 'VIP'],\n", " filter_kwargs_fn=lambda ct: dict(cell_types=ct, clusters='all', experience='all'),\n", " title_fn=lambda ct: f'Cell Type {ct}',\n", " inset_loc_fn=lambda ct: ('lower right', 1.5) if ct == 'VIP' else ('upper right', 0.5),\n", " ylim_fn=lambda ct: (-3, 15) if ct == 'VIP' else None,\n", ")" ] }, { "cell_type": "markdown", "id": "b7085931", "metadata": {}, "source": [ "### By cortical layer" ] }, { "cell_type": "code", "execution_count": null, "id": "eab304fd", "metadata": {}, "outputs": [], "source": [ "plot_omission_psth_by_group(\n", " values=['2/3', '4', '5', '6'],\n", " filter_kwargs_fn=lambda l: dict(cell_types='all', clusters='all', layers=l),\n", " title_fn=lambda l: f'Layer {l} RS',\n", ")" ] } ], "metadata": { "kernelspec": { "display_name": "vbn_manuscript", "language": "python", "name": "python3" }, "language_info": { "name": "python", "version": "3.8.20" } }, "nbformat": 4, "nbformat_minor": 5 }