In [1]:
%load_ext autoreload
%autoreload 2
In [2]:
import os
import expipe
import pathlib
import numpy as np
import spatial_maps.stats as stats
import septum_mec.analysis.data_processing as dp
import head_direction.head as head
import spatial_maps as sp
import pnnmec.registration
import speed_cells.speed as spd
import re
import joblib
from distutils.dir_util import copy_tree
import multiprocessing
import shutil
import psutil
import pandas as pd
import matplotlib.pyplot as plt
import pnnmec
import scipy.ndimage.measurements
import quantities as pq
import exdir
from tqdm import tqdm_notebook as tqdm
09:19:10 [I] klustakwik KlustaKwik2 version 0.2.6
In [3]:
%matplotlib inline
In [4]:
max_speed = 1 # m/s only used for speed score
min_speed = 0.02 # m/s only used for speed score
position_sampling_rate = 100 # for interpolation
position_low_pass_frequency = 6 # for low pass filtering of position

box_size = [1.0, 1.0]
bin_size=0.02
smoothing = 0.05
In [6]:
project_path = dp.project_path()

project = expipe.get_project(project_path)
actions = project.actions
In [ ]:
 
In [6]:
identify_neurons = actions['identify-neurons']
sessions = pd.read_csv(identify_neurons.data_path('sessions'))
# units = pd.read_csv(identify_neurons.data_path('units'))
In [45]:
data_loader = dp.Data()
In [47]:
units = []
for action in sessions.action.values:
    for ch in range(8):
        for unit_name in data_loader.unit_names(action, ch):
            units.append({'unit_name': unit_name, 'action': action, 'channel_group': ch})
In [49]:
units = pd.DataFrame(units)
In [50]:
session_units = pd.merge(sessions, units, on='action')
In [51]:
first_row = session_units.iloc[0]
In [3]:
output = pathlib.Path('output/shuffling')
output.mkdir(parents=True, exist_ok=True)
output_exdir = exdir.File(output / "shuffling.exdir")
output_units = output_exdir.require_group("units")
In [53]:
from elephant.spike_train_surrogates import dither_spike_train

def process(row):
    memory_in_gb = psutil.virtual_memory().available / 1024 / 1024 / 1024
    if memory_in_gb < 2:
        print("Running out of memory! Restart your kernel.")
        return
    
    action_id = row['action']
    channel_id = int(row['channel_group'])
    unit_id = int(row['unit_name'])
    
    action = actions[action_id]
    
    cell_name = "{}_{}_{}".format(action_id, channel_id, unit_id)
    
    if cell_name in output_units:
        print("Skipping existing", cell_name)
        return
    
    print("Processing", cell_name)
    
    output_group = output_units.require_group(cell_name)
    
    data_path = pathlib.Path(project_path) / "actions" / action_id / "data" / "main.exdir"

    # common values for all units == faster calculations
    x, y, t, speed = dp.load_tracking(
        data_path, position_sampling_rate, position_low_pass_frequency, box_size)
    ang, ang_t = dp.load_head_direction(
        data_path, position_sampling_rate, position_low_pass_frequency, box_size)
    sptr = dp.load_spike_train(data_path, channel_id, unit_id, t[-1])

    box_size_, bin_size_ = sp.maps._adjust_bin_size(box_size=box_size, bin_size=bin_size)
    xbins, ybins = sp.maps._make_bins(box_size_, bin_size_)
    occupancy_map = sp.maps._occupancy_map(x, y, t, xbins, ybins)
    
    smooth_occupancy_map = sp.maps.smooth_map(occupancy_map, bin_size=bin_size_, smoothing=smoothing)
    
    def calculate(spike_times):
        # common
        spike_map = sp.maps._spike_map(x, y, t, spike_times, xbins, ybins)
        smooth_spike_map = sp.maps.smooth_map(spike_map, bin_size=bin_size_, smoothing=smoothing)
        rate_map = smooth_spike_map / smooth_occupancy_map
        
        # gridness
        gridness = sp.gridness(rate_map)
        
        # border score
        fields = sp.separate_fields_by_laplace(rate_map)
        border_score = sp.border_score(rate_map, fields)
        
        # spatial information
        px = stats.prob_dist(x, y, xbins)
        information_rate = sp.information_rate(rate_map, px)
        
        # speed
        speed_score = spd.speed_correlation(
            speed, t, spike_times, min_speed=min_speed, max_speed=max_speed)
        
        # head direction
        ang_bin, ang_rate = head.head_direction_rate(spike_times, ang, ang_t)
        
        head_mean_ang, head_mean_vec_len = head.head_direction_score(ang_bin, ang_rate)
        
        statistics = {
            "gridness": gridness,
            "border_score": border_score,
            "information_rate": np.asscalar(information_rate),
            "speed_score": speed_score,
            "head_mean_ang": head_mean_ang,
            "head_mean_vec_len": head_mean_vec_len
        }
        
        return rate_map, statistics
    
    # Calculate for cell first
    rate_map, statistics = calculate(sptr)
    
    # Copy attrs from row
    attributes = row.to_dict()
    attributes.update(statistics)
    
    for key, value in attributes.items():
        if isinstance(value, (np.generic, np.ndarray)):
            attributes[key] = np.asscalar(value)
    
    output_group.attrs = attributes
    output_group['rate_map'] = rate_map
    
    # Calculate shuffled
    sample_count = 1000
    spike_trains = dither_spike_train(sptr, shift=30*pq.s, n=sample_count, edges=True)
   
    shuffling_data = []
    
    for i, spike_times in enumerate(spike_trains):
        rate_map, statistics = calculate(spike_times)
        shuffling_data.append(statistics)
        
    shuffling_data = pd.DataFrame(shuffling_data)
        
    quantiles = shuffling_data.quantile(0.95, axis=0)
    
    # TODO make it easier to create raw data in Exdir
    raw_path = output_group.root_directory / output_group.relative_path / "results"
    raw_path.mkdir(exist_ok=True)
    
    shuffling_data.to_csv(raw_path / "shuffling_data.csv", index=False)
    quantiles.to_csv(raw_path / "quantiles.csv")
    
    output_group['shuffled_rate_map_example'] = rate_map
In [54]:
# process(first_row)

Shuffle a random sample

In [55]:
# args = []
# for index, row in session_units.sample(100, random_state=1).iterrows():
#     args.append(row)

# with multiprocessing.Pool(processes=4) as pool:
#     result = pool.map(process, args)

Shuffle all

In [56]:
args = []
for index, row in session_units.iterrows():
    args.append(row)

with multiprocessing.Pool(processes=4) as pool:
    result = pool.map(process, args)
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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.
  warnings.warn("Instantaneous firing rate approximation contains "
/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
  'a.item() instead', DeprecationWarning, stacklevel=1)
/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.
  warnings.warn("Instantaneous firing rate approximation contains "
/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
  'a.item() instead', DeprecationWarning, stacklevel=1)
/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.
  warnings.warn("Instantaneous firing rate approximation contains "
/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
  'a.item() instead', DeprecationWarning, stacklevel=1)
/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.
  warnings.warn("Instantaneous firing rate approximation contains "
/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
  'a.item() instead', DeprecationWarning, stacklevel=1)
/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.
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/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.
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/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in log2
  return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *
/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2
  return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *
/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in multiply
  return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *
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  return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *
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  return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *
/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2
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/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in multiply
  return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *
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Gather all results and statistics

In [57]:
cell_statistics_path = output / "statistics"
cell_statistics_path.mkdir(exist_ok=True)
In [58]:
all_statistics = []
for cell_id, cell in output_units.items():
    all_statistics.append(cell.attrs.to_dict())
In [59]:
df_statistics = pd.DataFrame(all_statistics)
df_statistics.head()
Out[59]:
action action_id baseline border_score channel_group control entity frequency gridness head_mean_ang ... information_rate max_depth_delta max_dissimilarity session speed_score stim_location stimulated tag unit_id unit_name
0 1833-010719-1 NaN True 0.180203 0.0 NaN 1833 NaN 0.018309 6.262674 ... 1.636751 NaN NaN 1 0.195105 NaN False baseline i NaN 127.0
1 1833-010719-1 NaN True 0.282701 0.0 NaN 1833 NaN 0.229324 5.993547 ... 0.439478 NaN NaN 1 0.173469 NaN False baseline i NaN 161.0
2 1833-010719-1 NaN True 0.212317 0.0 NaN 1833 NaN 0.458029 3.510934 ... 0.407217 NaN NaN 1 0.007090 NaN False baseline i NaN 191.0
3 1833-010719-1 NaN True 0.216728 0.0 NaN 1833 NaN 0.500521 2.186690 ... 1.351648 NaN NaN 1 0.034591 NaN False baseline i NaN 223.0
4 1833-010719-1 NaN True 0.237254 0.0 NaN 1833 NaN -0.035198 0.149628 ... 0.350674 NaN NaN 1 0.166268 NaN False baseline i NaN 225.0

5 rows × 23 columns

In [60]:
df_statistics.to_csv(cell_statistics_path / "cell_statistics.csv", index=False)
In [61]:
quantiles_95 = []
quantiles_99 = []

for cell_id, cell in output_units.items():
    results = cell['results']
    shuffling_data_path = results.root_directory / results.relative_path / "shuffling_data.csv"
    shuffling_data = pd.read_csv(shuffling_data_path)
    quantile_95 = shuffling_data.quantile(0.95, axis=0)
    quantile_99 = shuffling_data.quantile(0.99, axis=0)
    
    def add_attrs(quantile):
        quantile['action'] = cell.attrs['action']
        quantile['channel_group'] = cell.attrs['channel_group']
        quantile['unit_name'] = cell.attrs['unit_name']
    
    add_attrs(quantile_95)
    add_attrs(quantile_99)
        
    quantiles_95.append(quantile_95)
    quantiles_99.append(quantile_99)
In [62]:
pd_quantiles_95 = pd.DataFrame(quantiles_95)
pd_quantiles_99 = pd.DataFrame(quantiles_99)
In [63]:
pd_quantiles_95.head()
Out[63]:
border_score gridness head_mean_ang head_mean_vec_len information_rate speed_score action channel_group unit_name
0.95 0.348023 0.275109 3.012689 0.086792 0.707197 0.149071 1833-010719-1 0.0 127.0
0.95 0.362380 0.166475 3.133138 0.037271 0.482486 0.132212 1833-010719-1 0.0 161.0
0.95 0.367498 0.266865 5.586395 0.182843 0.271188 0.062821 1833-010719-1 0.0 191.0
0.95 0.331942 0.312155 5.955767 0.090786 0.354018 0.052009 1833-010719-1 0.0 223.0
0.95 0.325842 0.180495 5.262721 0.103584 0.210427 0.094041 1833-010719-1 0.0 225.0
In [64]:
pd_quantiles_99.head()
Out[64]:
border_score gridness head_mean_ang head_mean_vec_len information_rate speed_score action channel_group unit_name
0.99 0.380905 0.973869 6.203531 0.098137 1.258079 0.189375 1833-010719-1 0.0 127.0
0.99 0.390729 0.451171 5.920679 0.041201 0.501343 0.203524 1833-010719-1 0.0 161.0
0.99 0.408019 0.495850 6.046085 0.204525 0.384131 0.074477 1833-010719-1 0.0 191.0
0.99 0.362179 0.540011 6.199585 0.109680 0.714685 0.069614 1833-010719-1 0.0 223.0
0.99 0.360166 0.532935 6.191290 0.118948 0.342249 0.159087 1833-010719-1 0.0 225.0
In [65]:
pd_quantiles_95.to_csv(cell_statistics_path / "cell_quantiles_95.csv", index=False)
pd_quantiles_99.to_csv(cell_statistics_path / "cell_quantiles_99.csv", index=False)

Quick verification of results

In [66]:
from scipy.interpolate import interp1d
def summarize(row, value):
    action_id = row['action']
    channel_id = int(row['channel_group'])
    unit_id = int(row['unit_name'])
    
    cell_name = "{}_{}_{}".format(action_id, channel_id, unit_id)
    cell_group = output_exdir["units"][cell_name]
    results_group = cell_group["results"]
    
    # TODO simplify this in Exdir
    shuffling_path = results_group.root_directory / results_group.relative_path / "shuffling_data.csv"
    shuffling_data = pd.read_csv(shuffling_path)
    quantiles = shuffling_data.quantile(0.95, axis=0)
    
    action = actions[action_id]
    data_path = pathlib.Path(project_path) / "actions" / action_id / "data" / "main.exdir"
#     unit_path = dp.unit_path(channel_id, unit_id)
    
#     x1, y1, t1, x2, y2, t2 = dp.load_leds(data_path)
#     x, y, t, speed = dp.load_tracking(
#         data_path, sampling_rate=position_sampling_rate, 
#         low_pass_frequency=position_low_pass_frequency)
#     spike_times = dp.load_spike_train(data_path, unit_path, t)
    
    # common values for all units == faster calculations
    x, y, t, speed = dp.load_tracking(
        data_path, position_sampling_rate, position_low_pass_frequency, box_size)
    spike_times = dp.load_spike_train(data_path, channel_id, unit_id, t[-1])

    
    title = "{}\n{}: {:.2f}\n(threshold: {:.2f})".format(cell_name, value, row[value], quantiles.T[value])
    
    if value not in ["head_mean_vec_len", 'speed_score']:
        spatial_map = sp.SpatialMap(x, y, t, spike_times, box_size=1.0, bin_size=0.02)
        rate_map = spatial_map.rate_map(0.03)
        plt.imshow(rate_map)
        
#         plt.plot(x, y, alpha=.5, color='grey')
#         plt.xticks([])
#         plt.yticks([])
#         sx = interp1d(t, x)(spike_times)
#         sy = interp1d(t, y)(spike_times)
#         plt.scatter(sx, sy, color='r', s=1)
#         plt.xlim(0,1)
#         plt.ylim(0,1)
#         plt.gca().set_aspect(1)
    elif value == "head_mean_vec_len":
        ang, ang_t = dp.load_head_direction(
            data_path, position_sampling_rate, position_low_pass_frequency, box_size)
        ang_bin, ang_rate = head.head_direction_rate(spike_times, ang, ang_t)
        head_mean_ang, head_mean_vec_len = head.head_direction_score(ang_bin, ang_rate)
        plt.plot(ang_bin, ang_rate)
        title = title + '\n'
    else:
        binsize = 0.02
        speed_score, inst_speed, rate, times = spd.speed_correlation(
            speed, t, spike_times, return_data=True)
        speed_bins = np.arange(min_speed, max_speed + binsize, binsize)
        ia = np.digitize(inst_speed, bins=speed_bins, right=True)
        mean_rate = np.zeros_like(speed_bins)
        for i in range(len(speed_bins)):
            mean_rate[i] = np.mean(rate[ia==i])
        
        plt.plot(speed_bins, mean_rate)
        aspect = (max_speed - min_speed) / (np.nanmax(mean_rate) - np.nanmin(mean_rate))
        plt.gca().set_aspect(aspect)
        
    plt.title(title)

def top(value):
    projection = 'polar' if value == 'head_mean_vec_len' else None
    plt.figure(figsize=(14,26))
    top = df_statistics.sort_values(by=value, ascending=False).head(30)
    counter = 1
    for index, row in top.iterrows():
        plt.subplot(6, 5, counter, projection=projection)
        summarize(row, value)
        counter += 1
#     plt.tight_layout()

Top gridness

In [67]:
top("gridness")

Top border score

In [68]:
top("border_score")

Top spatial information

In [69]:
top("information_rate")

Top head direction

In [70]:
top("head_mean_vec_len")

Top speed score

In [71]:
top("speed_score")
/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.
  warnings.warn("Instantaneous firing rate approximation contains "
/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.
  out=out, **kwargs)
/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)

Store results in Expipe action

In [7]:
shuffling_action = project.require_action("shuffling")
In [8]:
shuffling_action.data["shuffling"] = "shuffling.exdir"
In [9]:
copy_tree(output, str(shuffling_action.data_path()))
Out[9]:
['/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-3_6_168/attributes.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-3_6_168/shuffled_rate_map_example/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-3_6_168/shuffled_rate_map_example/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-3_6_168/results/quantiles.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-3_6_168/results/shuffling_data.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-3_6_168/rate_map/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-3_6_168/rate_map/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-3_6_168/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-4_1_84/attributes.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-4_1_84/shuffled_rate_map_example/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-4_1_84/shuffled_rate_map_example/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-4_1_84/results/quantiles.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-4_1_84/results/shuffling_data.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-4_1_84/rate_map/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-4_1_84/rate_map/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-4_1_84/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-1_7_152/attributes.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-1_7_152/shuffled_rate_map_example/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-1_7_152/shuffled_rate_map_example/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-1_7_152/results/quantiles.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-1_7_152/results/shuffling_data.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-1_7_152/rate_map/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-1_7_152/rate_map/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-120619-1_7_152/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-200619-2_6_112/attributes.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-200619-2_6_112/shuffled_rate_map_example/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-200619-2_6_112/shuffled_rate_map_example/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-200619-2_6_112/results/quantiles.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-200619-2_6_112/results/shuffling_data.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-200619-2_6_112/rate_map/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-200619-2_6_112/rate_map/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-200619-2_6_112/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-3_4_72/attributes.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-3_4_72/shuffled_rate_map_example/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-3_4_72/shuffled_rate_map_example/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-3_4_72/results/quantiles.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-3_4_72/results/shuffling_data.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-3_4_72/rate_map/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-3_4_72/rate_map/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-3_4_72/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-020719-2_1_152/attributes.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-020719-2_1_152/shuffled_rate_map_example/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-020719-2_1_152/shuffled_rate_map_example/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-020719-2_1_152/results/quantiles.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-020719-2_1_152/results/shuffling_data.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-020719-2_1_152/rate_map/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-020719-2_1_152/rate_map/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-020719-2_1_152/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-3_7_26/attributes.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-3_7_26/shuffled_rate_map_example/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-3_7_26/shuffled_rate_map_example/exdir.yaml',
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 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-220319-2_3_57/rate_map/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-220319-2_3_57/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-1_0_120/attributes.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-1_0_120/shuffled_rate_map_example/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-1_0_120/shuffled_rate_map_example/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-1_0_120/results/quantiles.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-1_0_120/results/shuffling_data.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-1_0_120/rate_map/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-1_0_120/rate_map/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-260619-1_0_120/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-2_6_45/attributes.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-2_6_45/shuffled_rate_map_example/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-2_6_45/shuffled_rate_map_example/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-2_6_45/results/quantiles.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-2_6_45/results/shuffling_data.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-2_6_45/rate_map/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-2_6_45/rate_map/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1834-220319-2_6_45/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-060319-2_5_118/attributes.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-060319-2_5_118/shuffled_rate_map_example/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-060319-2_5_118/shuffled_rate_map_example/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-060319-2_5_118/results/quantiles.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-060319-2_5_118/results/shuffling_data.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-060319-2_5_118/rate_map/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-060319-2_5_118/rate_map/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1849-060319-2_5_118/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-010719-2_7_211/attributes.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-010719-2_7_211/shuffled_rate_map_example/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-010719-2_7_211/shuffled_rate_map_example/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-010719-2_7_211/results/quantiles.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-010719-2_7_211/results/shuffling_data.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-010719-2_7_211/rate_map/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-010719-2_7_211/rate_map/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1833-010719-2_7_211/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-2_4_119/attributes.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-2_4_119/shuffled_rate_map_example/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-2_4_119/shuffled_rate_map_example/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-2_4_119/results/quantiles.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-2_4_119/results/shuffling_data.csv',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-2_4_119/rate_map/data.npy',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-2_4_119/rate_map/exdir.yaml',
 '/media/storage/expipe/septum-mec/actions/shuffling/data/shuffling.exdir/units/1839-290519-2_4_119/exdir.yaml',
 ...]
In [10]:
from septum_mec.analysis.registration import store_notebook
store_notebook(
    shuffling_action, "10-shuffling.ipynb")

delete nonexisting neurons

In [ ]:
# for u in (output / 'shuffling.exdir' / 'units').iterdir():
#     if not u.is_dir():
#         continue
#     action_id, channel_group, unit_name = u.stem.split('_')
#     real_path = pathlib.Path(
#         f'/media/storage/expipe/septum-mec/actions/{action_id}/data/main.exdir/processing/electrophysiology/channel_group_{channel_group}/UnitTimes/{unit_name}'
#     )
#     print(u.stem, real_path.exists())
#     if not real_path.exists():
#         shutil.rmtree(u)
In [ ]:
 
In [ ]:
 
In [ ]: