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septum-mec/actions/shuffling/data/10-shuffling.ipynb

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shuffling data
2019-10-10 07:22:11 +00:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"09:19:10 [I] klustakwik KlustaKwik2 version 0.2.6\n"
]
}
],
"source": [
"import os\n",
"import expipe\n",
"import pathlib\n",
"import numpy as np\n",
"import spatial_maps.stats as stats\n",
"import septum_mec.analysis.data_processing as dp\n",
"import head_direction.head as head\n",
"import spatial_maps as sp\n",
"import pnnmec.registration\n",
"import speed_cells.speed as spd\n",
"import re\n",
"import joblib\n",
"from distutils.dir_util import copy_tree\n",
"import multiprocessing\n",
"import shutil\n",
"import psutil\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import pnnmec\n",
"import scipy.ndimage.measurements\n",
"import quantities as pq\n",
"import exdir\n",
"from tqdm import tqdm_notebook as tqdm"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"max_speed = 1 # m/s only used for speed score\n",
"min_speed = 0.02 # m/s only used for speed score\n",
"position_sampling_rate = 100 # for interpolation\n",
"position_low_pass_frequency = 6 # for low pass filtering of position\n",
"\n",
"box_size = [1.0, 1.0]\n",
"bin_size=0.02\n",
"smoothing = 0.05"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"project_path = dp.project_path()\n",
"\n",
"project = expipe.get_project(project_path)\n",
"actions = project.actions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"identify_neurons = actions['identify-neurons']\n",
"sessions = pd.read_csv(identify_neurons.data_path('sessions'))\n",
"# units = pd.read_csv(identify_neurons.data_path('units'))\n"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"data_loader = dp.Data()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"units = []\n",
"for action in sessions.action.values:\n",
" for ch in range(8):\n",
" for unit_name in data_loader.unit_names(action, ch):\n",
" units.append({'unit_name': unit_name, 'action': action, 'channel_group': ch})"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"units = pd.DataFrame(units)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"session_units = pd.merge(sessions, units, on='action')"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"first_row = session_units.iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"output = pathlib.Path('output/shuffling')\n",
"output.mkdir(parents=True, exist_ok=True)\n",
"output_exdir = exdir.File(output / \"shuffling.exdir\")\n",
"output_units = output_exdir.require_group(\"units\")"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"from elephant.spike_train_surrogates import dither_spike_train\n",
"\n",
"def process(row):\n",
" memory_in_gb = psutil.virtual_memory().available / 1024 / 1024 / 1024\n",
" if memory_in_gb < 2:\n",
" print(\"Running out of memory! Restart your kernel.\")\n",
" return\n",
" \n",
" action_id = row['action']\n",
" channel_id = int(row['channel_group'])\n",
" unit_id = int(row['unit_name'])\n",
" \n",
" action = actions[action_id]\n",
" \n",
" cell_name = \"{}_{}_{}\".format(action_id, channel_id, unit_id)\n",
" \n",
" if cell_name in output_units:\n",
" print(\"Skipping existing\", cell_name)\n",
" return\n",
" \n",
" print(\"Processing\", cell_name)\n",
" \n",
" output_group = output_units.require_group(cell_name)\n",
" \n",
" data_path = pathlib.Path(project_path) / \"actions\" / action_id / \"data\" / \"main.exdir\"\n",
"\n",
" # common values for all units == faster calculations\n",
" x, y, t, speed = dp.load_tracking(\n",
" data_path, position_sampling_rate, position_low_pass_frequency, box_size)\n",
" ang, ang_t = dp.load_head_direction(\n",
" data_path, position_sampling_rate, position_low_pass_frequency, box_size)\n",
" sptr = dp.load_spike_train(data_path, channel_id, unit_id, t[-1])\n",
"\n",
" box_size_, bin_size_ = sp.maps._adjust_bin_size(box_size=box_size, bin_size=bin_size)\n",
" xbins, ybins = sp.maps._make_bins(box_size_, bin_size_)\n",
" occupancy_map = sp.maps._occupancy_map(x, y, t, xbins, ybins)\n",
" \n",
" smooth_occupancy_map = sp.maps.smooth_map(occupancy_map, bin_size=bin_size_, smoothing=smoothing)\n",
" \n",
" def calculate(spike_times):\n",
" # common\n",
" spike_map = sp.maps._spike_map(x, y, t, spike_times, xbins, ybins)\n",
" smooth_spike_map = sp.maps.smooth_map(spike_map, bin_size=bin_size_, smoothing=smoothing)\n",
" rate_map = smooth_spike_map / smooth_occupancy_map\n",
" \n",
" # gridness\n",
" gridness = sp.gridness(rate_map)\n",
" \n",
" # border score\n",
" fields = sp.separate_fields_by_laplace(rate_map)\n",
" border_score = sp.border_score(rate_map, fields)\n",
" \n",
" # spatial information\n",
" px = stats.prob_dist(x, y, xbins)\n",
" information_rate = sp.information_rate(rate_map, px)\n",
" \n",
" # speed\n",
" speed_score = spd.speed_correlation(\n",
" speed, t, spike_times, min_speed=min_speed, max_speed=max_speed)\n",
" \n",
" # head direction\n",
" ang_bin, ang_rate = head.head_direction_rate(spike_times, ang, ang_t)\n",
" \n",
" head_mean_ang, head_mean_vec_len = head.head_direction_score(ang_bin, ang_rate)\n",
" \n",
" statistics = {\n",
" \"gridness\": gridness,\n",
" \"border_score\": border_score,\n",
" \"information_rate\": np.asscalar(information_rate),\n",
" \"speed_score\": speed_score,\n",
" \"head_mean_ang\": head_mean_ang,\n",
" \"head_mean_vec_len\": head_mean_vec_len\n",
" }\n",
" \n",
" return rate_map, statistics\n",
" \n",
" # Calculate for cell first\n",
" rate_map, statistics = calculate(sptr)\n",
" \n",
" # Copy attrs from row\n",
" attributes = row.to_dict()\n",
" attributes.update(statistics)\n",
" \n",
" for key, value in attributes.items():\n",
" if isinstance(value, (np.generic, np.ndarray)):\n",
" attributes[key] = np.asscalar(value)\n",
" \n",
" output_group.attrs = attributes\n",
" output_group['rate_map'] = rate_map\n",
" \n",
" # Calculate shuffled\n",
" sample_count = 1000\n",
" spike_trains = dither_spike_train(sptr, shift=30*pq.s, n=sample_count, edges=True)\n",
" \n",
" shuffling_data = []\n",
" \n",
" for i, spike_times in enumerate(spike_trains):\n",
" rate_map, statistics = calculate(spike_times)\n",
" shuffling_data.append(statistics)\n",
" \n",
" shuffling_data = pd.DataFrame(shuffling_data)\n",
" \n",
" quantiles = shuffling_data.quantile(0.95, axis=0)\n",
" \n",
" # TODO make it easier to create raw data in Exdir\n",
" raw_path = output_group.root_directory / output_group.relative_path / \"results\"\n",
" raw_path.mkdir(exist_ok=True)\n",
" \n",
" shuffling_data.to_csv(raw_path / \"shuffling_data.csv\", index=False)\n",
" quantiles.to_csv(raw_path / \"quantiles.csv\")\n",
" \n",
" output_group['shuffled_rate_map_example'] = rate_map\n"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"# process(first_row)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Shuffle a random sample"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"# args = []\n",
"# for index, row in session_units.sample(100, random_state=1).iterrows():\n",
"# args.append(row)\n",
"\n",
"# with multiprocessing.Pool(processes=4) as pool:\n",
"# result = pool.map(process, args)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Shuffle all"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:110: FutureWarning: The signature of `Series.to_csv` was aligned to that of `DataFrame.to_csv`, and argument 'header' will change its default value from False to True: please pass an explicit value to suppress this warning.\n"
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"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in log2\n",
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
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]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in log2\n",
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2\n",
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in multiply\n",
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing 1833-290519-3_3_96\n",
"Processing 1833-290519-3_4_80\n",
"Skipping existing 1833-290519-3_5_109\n",
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]
}
],
"source": [
"args = []\n",
"for index, row in session_units.iterrows():\n",
" args.append(row)\n",
"\n",
"with multiprocessing.Pool(processes=4) as pool:\n",
" result = pool.map(process, args)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Gather all results and statistics"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"cell_statistics_path = output / \"statistics\"\n",
"cell_statistics_path.mkdir(exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"all_statistics = []\n",
"for cell_id, cell in output_units.items():\n",
" all_statistics.append(cell.attrs.to_dict())"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
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" <th>gridness</th>\n",
" <th>head_mean_ang</th>\n",
" <th>...</th>\n",
" <th>information_rate</th>\n",
" <th>max_depth_delta</th>\n",
" <th>max_dissimilarity</th>\n",
" <th>session</th>\n",
" <th>speed_score</th>\n",
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" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>0.500521</td>\n",
" <td>2.186690</td>\n",
" <td>...</td>\n",
" <td>1.351648</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>0.034591</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline i</td>\n",
" <td>NaN</td>\n",
" <td>223.0</td>\n",
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" <tr>\n",
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" <td>1833-010719-1</td>\n",
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" <td>1833</td>\n",
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" </tbody>\n",
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"</div>"
],
"text/plain": [
" action action_id baseline border_score channel_group control \\\n",
"0 1833-010719-1 NaN True 0.180203 0.0 NaN \n",
"1 1833-010719-1 NaN True 0.282701 0.0 NaN \n",
"2 1833-010719-1 NaN True 0.212317 0.0 NaN \n",
"3 1833-010719-1 NaN True 0.216728 0.0 NaN \n",
"4 1833-010719-1 NaN True 0.237254 0.0 NaN \n",
"\n",
" entity frequency gridness head_mean_ang ... information_rate \\\n",
"0 1833 NaN 0.018309 6.262674 ... 1.636751 \n",
"1 1833 NaN 0.229324 5.993547 ... 0.439478 \n",
"2 1833 NaN 0.458029 3.510934 ... 0.407217 \n",
"3 1833 NaN 0.500521 2.186690 ... 1.351648 \n",
"4 1833 NaN -0.035198 0.149628 ... 0.350674 \n",
"\n",
" max_depth_delta max_dissimilarity session speed_score stim_location \\\n",
"0 NaN NaN 1 0.195105 NaN \n",
"1 NaN NaN 1 0.173469 NaN \n",
"2 NaN NaN 1 0.007090 NaN \n",
"3 NaN NaN 1 0.034591 NaN \n",
"4 NaN NaN 1 0.166268 NaN \n",
"\n",
" stimulated tag unit_id unit_name \n",
"0 False baseline i NaN 127.0 \n",
"1 False baseline i NaN 161.0 \n",
"2 False baseline i NaN 191.0 \n",
"3 False baseline i NaN 223.0 \n",
"4 False baseline i NaN 225.0 \n",
"\n",
"[5 rows x 23 columns]"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_statistics = pd.DataFrame(all_statistics)\n",
"df_statistics.head()"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"df_statistics.to_csv(cell_statistics_path / \"cell_statistics.csv\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"quantiles_95 = []\n",
"quantiles_99 = []\n",
"\n",
"for cell_id, cell in output_units.items():\n",
" results = cell['results']\n",
" shuffling_data_path = results.root_directory / results.relative_path / \"shuffling_data.csv\"\n",
" shuffling_data = pd.read_csv(shuffling_data_path)\n",
" quantile_95 = shuffling_data.quantile(0.95, axis=0)\n",
" quantile_99 = shuffling_data.quantile(0.99, axis=0)\n",
" \n",
" def add_attrs(quantile):\n",
" quantile['action'] = cell.attrs['action']\n",
" quantile['channel_group'] = cell.attrs['channel_group']\n",
" quantile['unit_name'] = cell.attrs['unit_name']\n",
" \n",
" add_attrs(quantile_95)\n",
" add_attrs(quantile_99)\n",
" \n",
" quantiles_95.append(quantile_95)\n",
" quantiles_99.append(quantile_99)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"pd_quantiles_95 = pd.DataFrame(quantiles_95)\n",
"pd_quantiles_99 = pd.DataFrame(quantiles_99)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
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" <th>0.95</th>\n",
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" <td>0.180495</td>\n",
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" <td>0.103584</td>\n",
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" <td>0.094041</td>\n",
" <td>1833-010719-1</td>\n",
" <td>0.0</td>\n",
" <td>225.0</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" border_score gridness head_mean_ang head_mean_vec_len \\\n",
"0.95 0.348023 0.275109 3.012689 0.086792 \n",
"0.95 0.362380 0.166475 3.133138 0.037271 \n",
"0.95 0.367498 0.266865 5.586395 0.182843 \n",
"0.95 0.331942 0.312155 5.955767 0.090786 \n",
"0.95 0.325842 0.180495 5.262721 0.103584 \n",
"\n",
" information_rate speed_score action channel_group unit_name \n",
"0.95 0.707197 0.149071 1833-010719-1 0.0 127.0 \n",
"0.95 0.482486 0.132212 1833-010719-1 0.0 161.0 \n",
"0.95 0.271188 0.062821 1833-010719-1 0.0 191.0 \n",
"0.95 0.354018 0.052009 1833-010719-1 0.0 223.0 \n",
"0.95 0.210427 0.094041 1833-010719-1 0.0 225.0 "
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd_quantiles_95.head()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
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" <td>0.109680</td>\n",
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" <td>0.0</td>\n",
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" <tr>\n",
" <th>0.99</th>\n",
" <td>0.360166</td>\n",
" <td>0.532935</td>\n",
" <td>6.191290</td>\n",
" <td>0.118948</td>\n",
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" <td>0.0</td>\n",
" <td>225.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" border_score gridness head_mean_ang head_mean_vec_len \\\n",
"0.99 0.380905 0.973869 6.203531 0.098137 \n",
"0.99 0.390729 0.451171 5.920679 0.041201 \n",
"0.99 0.408019 0.495850 6.046085 0.204525 \n",
"0.99 0.362179 0.540011 6.199585 0.109680 \n",
"0.99 0.360166 0.532935 6.191290 0.118948 \n",
"\n",
" information_rate speed_score action channel_group unit_name \n",
"0.99 1.258079 0.189375 1833-010719-1 0.0 127.0 \n",
"0.99 0.501343 0.203524 1833-010719-1 0.0 161.0 \n",
"0.99 0.384131 0.074477 1833-010719-1 0.0 191.0 \n",
"0.99 0.714685 0.069614 1833-010719-1 0.0 223.0 \n",
"0.99 0.342249 0.159087 1833-010719-1 0.0 225.0 "
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd_quantiles_99.head()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"pd_quantiles_95.to_csv(cell_statistics_path / \"cell_quantiles_95.csv\", index=False)\n",
"pd_quantiles_99.to_csv(cell_statistics_path / \"cell_quantiles_99.csv\", index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Quick verification of results"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"from scipy.interpolate import interp1d\n",
"def summarize(row, value):\n",
" action_id = row['action']\n",
" channel_id = int(row['channel_group'])\n",
" unit_id = int(row['unit_name'])\n",
" \n",
" cell_name = \"{}_{}_{}\".format(action_id, channel_id, unit_id)\n",
" cell_group = output_exdir[\"units\"][cell_name]\n",
" results_group = cell_group[\"results\"]\n",
" \n",
" # TODO simplify this in Exdir\n",
" shuffling_path = results_group.root_directory / results_group.relative_path / \"shuffling_data.csv\"\n",
" shuffling_data = pd.read_csv(shuffling_path)\n",
" quantiles = shuffling_data.quantile(0.95, axis=0)\n",
" \n",
" action = actions[action_id]\n",
" data_path = pathlib.Path(project_path) / \"actions\" / action_id / \"data\" / \"main.exdir\"\n",
"# unit_path = dp.unit_path(channel_id, unit_id)\n",
" \n",
"# x1, y1, t1, x2, y2, t2 = dp.load_leds(data_path)\n",
"# x, y, t, speed = dp.load_tracking(\n",
"# data_path, sampling_rate=position_sampling_rate, \n",
"# low_pass_frequency=position_low_pass_frequency)\n",
"# spike_times = dp.load_spike_train(data_path, unit_path, t)\n",
" \n",
" # common values for all units == faster calculations\n",
" x, y, t, speed = dp.load_tracking(\n",
" data_path, position_sampling_rate, position_low_pass_frequency, box_size)\n",
" spike_times = dp.load_spike_train(data_path, channel_id, unit_id, t[-1])\n",
"\n",
" \n",
" title = \"{}\\n{}: {:.2f}\\n(threshold: {:.2f})\".format(cell_name, value, row[value], quantiles.T[value])\n",
" \n",
" if value not in [\"head_mean_vec_len\", 'speed_score']:\n",
" spatial_map = sp.SpatialMap(x, y, t, spike_times, box_size=1.0, bin_size=0.02)\n",
" rate_map = spatial_map.rate_map(0.03)\n",
" plt.imshow(rate_map)\n",
" \n",
"# plt.plot(x, y, alpha=.5, color='grey')\n",
"# plt.xticks([])\n",
"# plt.yticks([])\n",
"# sx = interp1d(t, x)(spike_times)\n",
"# sy = interp1d(t, y)(spike_times)\n",
"# plt.scatter(sx, sy, color='r', s=1)\n",
"# plt.xlim(0,1)\n",
"# plt.ylim(0,1)\n",
"# plt.gca().set_aspect(1)\n",
" elif value == \"head_mean_vec_len\":\n",
" ang, ang_t = dp.load_head_direction(\n",
" data_path, position_sampling_rate, position_low_pass_frequency, box_size)\n",
" ang_bin, ang_rate = head.head_direction_rate(spike_times, ang, ang_t)\n",
" head_mean_ang, head_mean_vec_len = head.head_direction_score(ang_bin, ang_rate)\n",
" plt.plot(ang_bin, ang_rate)\n",
" title = title + '\\n'\n",
" else:\n",
" binsize = 0.02\n",
" speed_score, inst_speed, rate, times = spd.speed_correlation(\n",
" speed, t, spike_times, return_data=True)\n",
" speed_bins = np.arange(min_speed, max_speed + binsize, binsize)\n",
" ia = np.digitize(inst_speed, bins=speed_bins, right=True)\n",
" mean_rate = np.zeros_like(speed_bins)\n",
" for i in range(len(speed_bins)):\n",
" mean_rate[i] = np.mean(rate[ia==i])\n",
" \n",
" plt.plot(speed_bins, mean_rate)\n",
" aspect = (max_speed - min_speed) / (np.nanmax(mean_rate) - np.nanmin(mean_rate))\n",
" plt.gca().set_aspect(aspect)\n",
" \n",
" plt.title(title)\n",
"\n",
"def top(value):\n",
" projection = 'polar' if value == 'head_mean_vec_len' else None\n",
" plt.figure(figsize=(14,26))\n",
" top = df_statistics.sort_values(by=value, ascending=False).head(30)\n",
" counter = 1\n",
" for index, row in top.iterrows():\n",
" plt.subplot(6, 5, counter, projection=projection)\n",
" summarize(row, value)\n",
" counter += 1\n",
"# plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Top gridness"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAzEAAAWFCAYAAAA5HVnZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsvXe8ZVddPvzsfc65vUwv6YSZrARCQkiQUBTwlfpThICACIiI0iJVxJ+iRgQFBRQb+FJV4KVEAoLSm0hoISEhbSWZtJnMTKbdMreett8/1lpnPfuetU65c+vk+3w+yey7zy6rfFfZ6/us55tkWQaBQCAQCAQCgUAgWC9IVzsBAoFAIBAIBAKBQNAN5CNGIBAIBAKBQCAQrCvIR4xAIBAIBAKBQCBYV5CPGIFAIBAIBAKBQLCuIB8xAoFAIBAIBAKBYF1BPmIEAoFAIBAIBALBuoJ8xAgEAoFAIBAIBIJ1heJqJ8BBKZUA+AiAG7XW71JKFQD8I4DH20v+G8CbtNaZUuqJAP4GQAnALIDXaK1/ZJ/xFwAus/f8GMArtdYzgfcNAPgggItgPuberLX+nP3tKQDeDlM+dQD/V2v9FfvbSwG8yf72dfvuilLqagAD/AoAH9Bav4be+VYAm7TWl9u/ewH8PYBfBDAF4AsArtBa1wPp3QXgfQC2AugB8CGt9bvblGkPgH8A8PP21JcA/IHWutbqPntvrj7suVEAHwJwri2zf9Vav9P+9kQA77blchTA67TW17d7z1rDKtjhVgD/AmAXTNn9F4wt1pVSuwF8GMBmGPt4sdb6VntfzA4LAP4EwDMADNr0vkFrndE7XwrgWVrrX6E8/wWA5wGYBnC1vWeuRTldCODLWuudHZbrswH8EYBeAPfYvBxtc0+0HdI1rwXwO1rr8+3fBQDvAfAUe9+7tNbv7ySNawlLbIcnVK+L7A9Xyg57APwPgCtdP9Xi2j8E8Hw6tRXAsNZ6pNV9dP/C/jvavyqlToGpvx0wfeU7tdYf6+Q9awUyJrcek+kZTwbw11rrh7cu0dZjaAf39gL4IoB/0Vpfac910s4eBOAnAJ6stb6mk3etVawxm/w9AH8M4KC9/LjW+uftb6vdL67E+PxcAH8GoApgH4BXaa3vod83wPTNL11Ou1sTnhil1HkAvgHguXT6RTCdzsMAXAhjpM+xA8enYCYuFwJ4G4B/t/c8C8CTATwcwENhOrDXRl57BYAprfV5AJ4E4J+VUqfZTuYTAH7TdkovAfAppdSwUup8AH8O4Bds2jYAeD0AaK0fo7V+uL3nTwHcBWOssM+9EsDvL0jDHwE40+bxYgA7Abwqkt6PAviUff6jAbxcKfWLkWsdLocZqM8HcAGAxyBfxkFE6gMwDWmfnTA+EsArlVKPtmX2WZjO4wIArwTwadvprhuskh3+LYCbbbk9AsCjYGwOAD4O4H1a64fAdBb/oZRKWtmhfc8TADwWps4fDdP5QSm1SSn1fpiJV0JpeAmAXwbwSGtfB2x+QmVUVEq9HsBXAQxH8rTwnktgBppnW9u5DWZC0uqeaDukax4L4M0Lbn05gN0wNv9IAK9TSv1cJ+lcK1hCO3wJTrBeF9sfYpntkPB3AB7c5hoAgNb6HdRHPwFmQvC8dve16L9b9a9/CeCHtk6eCuB9SqkdnaRzLUDG5PZjslKqXyn1NgCfRucLwsExtN1N9pofAHjcgp+i7cze1wfgYzALn+saa8km7W+PgfmYeLj9z33APBDG590wi6/Pt+X7TgBX0u9PB/AjmI/1ZcWa+IgB8GqYr+tP07kCzJdqr/2vB8Cc1roM4FSt9XX2C/VsmJV/aK0/C+Cx9pphANvcbwE8C8AH7H33wlT6c2G+2l+ltb7JXnczjEFtAfCrAP5Ta33Yrsz8C4AX8kOVUpsAvB/mS3bCnv5tAN+F8VQwLgbwSa31nH3e5wA8J5LeD8F05LDPvQOms41Ca/0eAM+zz94M05iOtbrHIlQfgGmArtPfCVMvEzCTxgmt9Tfse28FMAnTQNcTVsMOr4LpQGBXVm4EcKZS6lSYDuCT9rcv2XRchNZ2+GIAb9Naz2qt5wE8G6bjB4x9H0DzwH0xgM9prcft359F3A4fAdP5xn4P4YUwnsO77d9XAPjrNve0aodQSm0H8E8wq12MZwH4iNa6qrUegym/F2J9YUnsEEtTr4vtD5fbDqGUehGAURjvZbd4F4Av2XbVDsH+u03/WgAwautkAGa1MrqavwYhY3L7MfkptjxeGvk9hNgY2g6vAfAWAD9ccL5VOwNMH/lRAEe6SONaxVqyScB8xLxAKXWdUuorSqmH2fMPhPH5QgDXa61/BgBa6/8BcJZS6iz7+2sA/CaA/V2kY1FYEx8xWuvLtdb/vuD0RwGMAbgPpmLv0Fp/wV5fsZOYfTDuwr+mZ1WUUpcDuBemk7sq8trTAeylv/cBOE1rfURr/Sk6/1YAt2mt74rds+C5bwbw3+w+01r/udb6vQAW0rh+COB5Sqkhu3LwApiOrQla649o6+5USj0VpgF9OZI3vq+ilHoHgD0A7ofpuNvdE6oPaK0zrXVVKfUxmMn2twFomC/3IetWh1LqkTArHB25MtcKVsMOtdb/obU+CABKqYtgbOAqGFvbr/M0BmdvrezwHAAPUUp9Qyl1A4xX7Jh91/u11n8O41pn/BDAM5RSW5RSKUxHG7PDH2mtf2vB+9vhHABFpdTnlVLXwwysx1vd0KodWpf8J2A+YO5bcGsnbXRNYwnt8ITr9QT6w2W1QztheC2A3w393gpKqYcCeCbM6nxbtOi/W/Wv/xeGMnIfzKT7z7TWh7pN62pBxuSOxuTPaa1fj84WBt09sTG03X2/rrUOfaxH25lS6mUASlrrD3SavrWMtWSTSqlBALcC+Eut9UUwi8xfUkoNxe6xxyfF+AzgOgDnK6UeDgBKqV+BWcjZadPxVK3197tIw6KxJj5iIvgzAIcBbIcxgE1KqTe6H7XW92utT4VZ7f+IUuoc+u0fAWyEMcwrEUYo740Ozbrl/h7Ar8F8LXdyTx/MoPqXbXNn8E4ANwH4Pgxv8moA5VY3KKV+E8Y9/Byt9YFOXqK1/kOY8rgbZl/NCUFr/UKYhr8JwJ9qrSdhVh/+yDaCFwP4JtrkZZ1gue0QQIPz/VUAv6e1/inibbMW+c3ZYQnApQCeDuOyfhyA32v1bjswfAamzr4H0zkvZd2VAPwKDNXrIhgOcUcDa6Qd/hWA/9Fafy1wS8s2uo7RtR0uZb0uoj9cNju09KJ/h1lZn+46M+bj5x9pVf6EEOlfPw6zT+IUAA8B8Ob1RmsMQMbkJcLCMfQEHhVsZ0qpRwB4hf3vZMaq2KTWelpr/RSt9dX2WZ+G+Zh6ZOwe++9JMT5rrffAeCDfr5S6DsZbdP0Sp6sjrJmN/QFcBjOhKwMoK6X+FYbr+EEAv6i1vgoAtNbX2onzw5RS/QBSrfV12mzs+iCA1yqzyfK/6dlPh/kC3wm/KetUAD8FAKXURhijTgBcqv0GJ3cP6J599PfTAPxUa31nh3ncBODdWuvft+99HoA7LEfxg+4irfXDrUv0XTBuwl+yE92WUGbPwGGt9W125eGjMHzLRcFOtH+mtd6vtZ5SSv1/AJ5tVwemtNZPoGtvgaG8rXcsqx1qrfcrpd4A4A8B/LrW+uv2t3sB7FBKJdpv+nP21soO98PQIeYBzCulPgPDzf27WAYt3eITWuu/sn8/CsYOg+nttOAI+2HsxnmcPgLTIbdEi3b4IgCHlFLPAjAE4FSl1E+14Qu3a6PrFYuxwyNYgnpdZH+4nHb4Hhjq1ieUUgBwBoAnKaVGtNYtJ4TWi/dsmEH3hBDrX5VSW2AmJ/8PAGitb1dKfQ0m/z860feuImRMttAdbOIPITaGLuZZFrF2dhaAEQBX2zZyCoCPK6XepLX+zxN431rDqtikUupMAM/QWvN8KgFQwQNgfFZmv/MdWutL7d9FAK+D2Xe2oljLHzHXwnAEv6WUKsG45n8A80X7YaXUIa3195ShBpwL43J7IoA3KqUeow316sU
"text/plain": [
"<Figure size 1008x1872 with 30 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"top(\"gridness\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Top border score"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1008x1872 with 30 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"top(\"border_score\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Top spatial information"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1008x1872 with 30 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"top(\"information_rate\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Top head direction"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1008x1872 with 30 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"top(\"head_mean_vec_len\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Top speed score"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/elephant/statistics.py:835: UserWarning: Instantaneous firing rate approximation contains negative values, possibly caused due to machine precision errors.\n",
" warnings.warn(\"Instantaneous firing rate approximation contains \"\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
" out=out, **kwargs)\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
" ret = ret.dtype.type(ret / rcount)\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1008x1872 with 30 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"top(\"speed_score\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Store results in Expipe action"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"shuffling_action = project.require_action(\"shuffling\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"shuffling_action.data[\"shuffling\"] = \"shuffling.exdir\""
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-3_6_168/attributes.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-3_6_168/shuffled_rate_map_example/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-3_6_168/shuffled_rate_map_example/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-3_6_168/results/quantiles.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-3_6_168/results/shuffling_data.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-3_6_168/rate_map/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-3_6_168/rate_map/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-3_6_168/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-4_1_84/attributes.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-4_1_84/shuffled_rate_map_example/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-4_1_84/shuffled_rate_map_example/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-4_1_84/results/quantiles.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-4_1_84/results/shuffling_data.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-4_1_84/rate_map/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-4_1_84/rate_map/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-4_1_84/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-1_7_152/attributes.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-1_7_152/shuffled_rate_map_example/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-1_7_152/shuffled_rate_map_example/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-1_7_152/results/quantiles.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-1_7_152/results/shuffling_data.csv',\n",
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" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-280219-2_6_90/rate_map/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-280219-2_6_90/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-150319-3_1_98/attributes.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-150319-3_1_98/shuffled_rate_map_example/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-150319-3_1_98/shuffled_rate_map_example/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-150319-3_1_98/results/quantiles.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-150319-3_1_98/results/shuffling_data.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-150319-3_1_98/rate_map/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-150319-3_1_98/rate_map/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-150319-3_1_98/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-150319-2_6_28/attributes.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-150319-2_6_28/shuffled_rate_map_example/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-150319-2_6_28/shuffled_rate_map_example/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-150319-2_6_28/results/quantiles.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-150319-2_6_28/results/shuffling_data.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-150319-2_6_28/rate_map/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-150319-2_6_28/rate_map/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-150319-2_6_28/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-150319-1_2_72/attributes.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-150319-1_2_72/shuffled_rate_map_example/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-150319-1_2_72/shuffled_rate_map_example/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-150319-1_2_72/results/quantiles.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-150319-1_2_72/results/shuffling_data.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-150319-1_2_72/rate_map/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-150319-1_2_72/rate_map/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-150319-1_2_72/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-1_3_11/attributes.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-1_3_11/shuffled_rate_map_example/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-1_3_11/shuffled_rate_map_example/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-1_3_11/results/quantiles.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-1_3_11/results/shuffling_data.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-1_3_11/rate_map/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-1_3_11/rate_map/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-1_3_11/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-110319-3_0_101/attributes.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-110319-3_0_101/shuffled_rate_map_example/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-110319-3_0_101/shuffled_rate_map_example/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-110319-3_0_101/results/quantiles.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-110319-3_0_101/results/shuffling_data.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-110319-3_0_101/rate_map/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-110319-3_0_101/rate_map/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-110319-3_0_101/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-010319-4_5_36/attributes.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-010319-4_5_36/shuffled_rate_map_example/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-010319-4_5_36/shuffled_rate_map_example/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-010319-4_5_36/results/quantiles.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-010319-4_5_36/results/shuffling_data.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-010319-4_5_36/rate_map/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-010319-4_5_36/rate_map/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-010319-4_5_36/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-1_5_133/attributes.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-1_5_133/shuffled_rate_map_example/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-1_5_133/shuffled_rate_map_example/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-1_5_133/results/quantiles.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-1_5_133/results/shuffling_data.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-1_5_133/rate_map/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-1_5_133/rate_map/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-1_5_133/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-010319-3_3_76/attributes.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-010319-3_3_76/shuffled_rate_map_example/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-010319-3_3_76/shuffled_rate_map_example/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-010319-3_3_76/results/quantiles.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-010319-3_3_76/results/shuffling_data.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-010319-3_3_76/rate_map/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-010319-3_3_76/rate_map/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-010319-3_3_76/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-020719-3_3_164/attributes.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-020719-3_3_164/shuffled_rate_map_example/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-020719-3_3_164/shuffled_rate_map_example/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-020719-3_3_164/results/quantiles.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-020719-3_3_164/results/shuffling_data.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-020719-3_3_164/rate_map/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-020719-3_3_164/rate_map/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-020719-3_3_164/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-220319-2_3_57/attributes.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-220319-2_3_57/shuffled_rate_map_example/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-220319-2_3_57/shuffled_rate_map_example/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-220319-2_3_57/results/quantiles.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-220319-2_3_57/results/shuffling_data.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-220319-2_3_57/rate_map/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-220319-2_3_57/rate_map/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-220319-2_3_57/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-1_0_120/attributes.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-1_0_120/shuffled_rate_map_example/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-1_0_120/shuffled_rate_map_example/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-1_0_120/results/quantiles.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-1_0_120/results/shuffling_data.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-1_0_120/rate_map/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-1_0_120/rate_map/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-1_0_120/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-2_6_45/attributes.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-2_6_45/shuffled_rate_map_example/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-2_6_45/shuffled_rate_map_example/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-2_6_45/results/quantiles.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-2_6_45/results/shuffling_data.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-2_6_45/rate_map/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-2_6_45/rate_map/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-2_6_45/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-060319-2_5_118/attributes.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-060319-2_5_118/shuffled_rate_map_example/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-060319-2_5_118/shuffled_rate_map_example/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-060319-2_5_118/results/quantiles.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-060319-2_5_118/results/shuffling_data.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-060319-2_5_118/rate_map/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-060319-2_5_118/rate_map/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-060319-2_5_118/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-010719-2_7_211/attributes.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-010719-2_7_211/shuffled_rate_map_example/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-010719-2_7_211/shuffled_rate_map_example/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-010719-2_7_211/results/quantiles.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-010719-2_7_211/results/shuffling_data.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-010719-2_7_211/rate_map/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-010719-2_7_211/rate_map/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-010719-2_7_211/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-2_4_119/attributes.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-2_4_119/shuffled_rate_map_example/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-2_4_119/shuffled_rate_map_example/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-2_4_119/results/quantiles.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-2_4_119/results/shuffling_data.csv',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-2_4_119/rate_map/data.npy',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-2_4_119/rate_map/exdir.yaml',\n",
" '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-2_4_119/exdir.yaml',\n",
" ...]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"copy_tree(output, str(shuffling_action.data_path()))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from septum_mec.analysis.registration import store_notebook\n",
"store_notebook(\n",
" shuffling_action, \"10-shuffling.ipynb\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# delete nonexisting neurons"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# for u in (output / 'shuffling.exdir' / 'units').iterdir():\n",
"# if not u.is_dir():\n",
"# continue\n",
"# action_id, channel_group, unit_name = u.stem.split('_')\n",
"# real_path = pathlib.Path(\n",
"# f'/media/storage/expipe/septum-mec/actions/{action_id}/data/main.exdir/processing/electrophysiology/channel_group_{channel_group}/UnitTimes/{unit_name}'\n",
"# )\n",
"# print(u.stem, real_path.exists())\n",
"# if not real_path.exists():\n",
"# shutil.rmtree(u)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}