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%load_ext autoreload
%autoreload 2
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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
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%matplotlib inline
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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
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project_path = dp.project_path()
project = expipe.get_project(project_path)
actions = project.actions
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identify_neurons = actions['identify-neurons']
sessions = pd.read_csv(identify_neurons.data_path('sessions'))
# units = pd.read_csv(identify_neurons.data_path('units'))
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data_loader = dp.Data()
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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})
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units = pd.DataFrame(units)
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session_units = pd.merge(sessions, units, on='action')
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first_row = session_units.iloc[0]
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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")
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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
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# process(first_row)
Shuffle a random sample¶
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# 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¶
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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.
Skipping existing 1839-120619-4_6_90 Skipping existing 1839-120619-4_6_92 Skipping existing 1839-120619-4_7_108 Skipping existing 1839-120619-4_7_128 Processing 1839-120619-4_7_138
/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.
Processing 1834-150319-1_5_104
/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) *
Processing 1849-280219-2_7_117 Processing 1834-150319-1_7_114 Skipping existing 1849-280219-2_7_42 Skipping existing 1849-280219-2_7_43 Skipping existing 1849-280219-2_7_92 Skipping existing 1833-120619-3_0_153 Skipping existing 1833-120619-3_2_104 Skipping existing 1833-120619-3_4_100 Skipping existing 1833-120619-3_4_101 Skipping existing 1833-120619-3_4_147 Skipping existing 1833-120619-3_5_136 Skipping existing 1833-120619-3_6_118 Skipping existing 1833-120619-3_6_122 Skipping existing 1833-120619-3_6_141 Skipping existing 1833-120619-3_6_143 Skipping existing 1833-120619-3_6_145 Skipping existing 1833-120619-3_6_94 Skipping existing 1833-120619-3_7_120 Skipping existing 1834-010319-1_0_2 Skipping existing 1834-010319-1_0_39 Skipping existing 1834-010319-1_1_48 Skipping existing 1834-010319-1_3_52 Skipping existing 1849-060319-2_3_67 Skipping existing 1849-060319-2_3_94 Skipping existing 1849-060319-2_3_99 Skipping existing 1849-060319-2_4_122 Skipping existing 1849-060319-2_4_95 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Skipping existing 1833-290519-4_1_96 Skipping existing 1833-290519-4_3_129 Skipping existing 1833-290519-4_3_130 Skipping existing 1833-290519-4_3_92 Skipping existing 1833-290519-4_4_71 Skipping existing 1833-290519-4_5_115 Skipping existing 1833-290519-4_5_121 Skipping existing 1833-290519-4_5_98 Skipping existing 1833-290519-4_6_108 Skipping existing 1833-290519-4_6_65 Skipping existing 1833-290519-4_6_79 Skipping existing 1833-290519-4_6_90 Skipping existing 1833-290519-4_7_67 Skipping existing 1833-290519-4_7_75 Skipping existing 1833-290519-4_7_77 Skipping existing 1833-290519-4_7_94 Skipping existing 1849-150319-1_0_39 Skipping existing 1849-150319-1_0_74 Skipping existing 1849-150319-1_1_105 Skipping existing 1849-150319-1_1_121 Skipping existing 1849-150319-1_1_93 Skipping existing 1849-150319-1_2_62 Skipping existing 1849-150319-1_2_72 Skipping existing 1849-150319-1_2_91 Skipping existing 1849-150319-1_3_58 Skipping existing 1849-150319-1_3_64 Skipping existing 1849-150319-1_4_79 Skipping existing 1849-150319-1_4_85 Skipping existing 1849-150319-1_4_89 Skipping existing 1849-150319-1_5_107 Skipping existing 1849-150319-1_5_60 Skipping existing 1849-150319-1_5_80 Skipping existing 1849-150319-1_5_95 Skipping existing 1849-150319-1_7_56 Skipping existing 1839-060619-5_4_164 Skipping existing 1839-060619-5_5_138 Skipping existing 1839-060619-5_5_170 Skipping existing 1839-060619-5_6_142 Skipping existing 1839-060619-5_6_162 Skipping existing 1839-060619-5_6_166 Skipping existing 1839-060619-5_6_168 Skipping existing 1839-060619-5_7_136 Skipping existing 1839-060619-5_7_183 Skipping existing 1849-110319-1_0_52 Skipping existing 1849-110319-1_1_31 Skipping existing 1849-110319-1_1_41 Skipping existing 1849-110319-1_1_54 Skipping existing 1849-110319-1_1_59 Skipping existing 1849-110319-1_1_60 Skipping existing 1849-110319-1_1_64 Skipping existing 1849-110319-1_1_66 Skipping existing 1849-110319-1_2_46 Skipping existing 1849-110319-1_2_50 Skipping existing 1849-110319-1_2_62 Skipping existing 1849-110319-1_3_35 Skipping existing 1849-110319-1_5_16 Skipping existing 1849-110319-1_5_17 Skipping existing 1849-110319-1_5_29 Skipping existing 1833-020719-1_0_119 Skipping existing 1833-020719-1_0_121 Skipping existing 1833-020719-1_0_123 Skipping existing 1833-020719-1_0_145 Skipping existing 1833-020719-1_0_158 Skipping existing 1833-020719-1_1_115 Skipping existing 1833-020719-1_1_147 Skipping existing 1833-020719-1_1_155 Skipping existing 1833-020719-1_2_143 Skipping existing 1833-020719-1_2_149 Skipping existing 1833-020719-1_3_135 Skipping existing 1833-020719-1_3_140 Skipping existing 1833-020719-1_3_151 Skipping existing 1833-020719-1_3_153 Skipping existing 1833-020719-1_3_164 Skipping existing 1833-020719-1_5_109 Skipping existing 1833-020719-1_6_107 Skipping existing 1849-220319-4_0_100 Skipping existing 1849-220319-4_1_108 Skipping existing 1849-220319-4_1_117 Skipping existing 1849-220319-4_1_118 Skipping existing 1849-220319-4_1_90 Skipping existing 1849-220319-4_2_99 Skipping existing 1849-220319-4_3_122 Skipping existing 1849-220319-4_3_79 Skipping existing 1849-220319-4_3_87 Skipping existing 1849-220319-4_4_112 Skipping existing 1849-220319-4_5_102 Skipping existing 1849-220319-4_5_110 Skipping existing 1849-220319-4_5_84 Skipping existing 1833-290519-2_0_78 Skipping existing 1833-290519-2_0_82 Skipping existing 1833-290519-2_1_102 Skipping existing 1833-290519-2_1_104 Skipping existing 1833-290519-2_1_116 Skipping existing 1833-290519-2_1_120 Skipping existing 1833-290519-2_2_100 Skipping existing 1833-290519-2_2_112 Skipping existing 1833-290519-2_2_132 Skipping existing 1833-290519-2_3_108 Skipping existing 1833-290519-2_3_72 Skipping existing 1833-290519-2_3_74 Skipping existing 1833-290519-2_4_114 Skipping existing 1833-290519-2_4_122 Skipping existing 1833-290519-2_4_124 Skipping existing 1833-290519-2_5_80 Skipping existing 1833-290519-2_6_76 Skipping existing 1833-290519-2_7_126 Skipping existing 1833-290519-2_7_130 Skipping existing 1833-290519-2_7_97 Skipping existing 1833-290519-2_7_98 Skipping existing 1833-060619-2_0_76 Skipping existing 1833-060619-2_0_90 Skipping existing 1833-060619-2_1_64 Skipping existing 1833-060619-2_1_78 Skipping existing 1833-060619-2_2_106 Skipping existing 1833-060619-2_4_108 Skipping existing 1833-060619-2_5_68 Skipping existing 1833-060619-2_5_98 Skipping existing 1833-060619-2_6_105 Skipping existing 1833-060619-2_6_112 Skipping existing 1833-060619-2_6_116 Skipping existing 1833-060619-2_6_74 Skipping existing 1833-060619-2_6_82 Skipping existing 1833-060619-2_7_58 Skipping existing 1839-120619-2_0_76 Skipping existing 1839-120619-2_4_78 Skipping existing 1839-120619-2_5_104 Skipping existing 1839-120619-2_5_47 Skipping existing 1839-120619-2_6_106 Skipping existing 1839-120619-2_6_51 Skipping existing 1839-120619-2_6_86 Skipping existing 1839-120619-2_6_88 Skipping existing 1839-120619-2_7_71 Skipping existing 1839-120619-2_7_90 Skipping existing 1849-220319-5_0_79 Skipping existing 1849-220319-5_1_117 Skipping existing 1849-220319-5_2_104 Skipping existing 1849-220319-5_2_83 Skipping existing 1849-220319-5_3_27 Skipping existing 1849-220319-5_3_71 Skipping existing 1849-220319-5_3_98 Skipping existing 1849-220319-5_4_112 Skipping existing 1849-220319-5_5_34 Skipping existing 1849-220319-5_5_35 Skipping existing 1849-220319-5_5_38 Skipping existing 1849-220319-5_5_39 Skipping existing 1834-010319-4_0_7 Skipping existing 1834-010319-4_1_10 Skipping existing 1834-010319-4_3_21 Skipping existing 1834-010319-4_3_22 Skipping existing 1834-010319-4_4_29 Skipping existing 1834-010319-4_4_35 Skipping existing 1834-010319-4_4_53 Skipping existing 1834-010319-4_5_36 Skipping existing 1834-010319-4_5_37 Skipping existing 1834-010319-4_6_44 Skipping existing 1834-010319-4_6_46 Skipping existing 1834-010319-4_7_45 Skipping existing 1834-010319-4_7_48 Skipping existing 1834-010319-4_7_49 Skipping existing 1833-200619-1_0_147 Skipping existing 1833-200619-1_1_155 Skipping existing 1833-200619-1_1_159 Skipping existing 1833-200619-1_2_228 Skipping existing 1833-200619-1_2_239 Skipping existing 1833-200619-1_3_151 Skipping existing 1833-200619-1_3_184 Skipping existing 1833-200619-1_4_165 Skipping existing 1833-200619-1_6_163 Skipping existing 1833-200619-1_6_171 Skipping existing 1833-200619-1_6_206 Skipping existing 1833-200619-1_6_240 Skipping existing 1833-200619-1_7_143 Skipping existing 1839-120619-3_1_113 Skipping existing 1839-120619-3_2_129 Skipping existing 1839-120619-3_3_137 Skipping existing 1839-120619-3_4_115 Skipping existing 1839-120619-3_4_121 Skipping existing 1839-120619-3_5_131 Skipping existing 1839-120619-3_6_123 Skipping existing 1839-120619-3_6_125 Skipping existing 1839-120619-3_6_133 Skipping existing 1839-120619-3_6_71 Skipping existing 1839-120619-3_7_119 Skipping existing 1839-120619-3_7_127 Skipping existing 1834-110319-5_0_98 Skipping existing 1834-110319-5_2_18 Skipping existing 1834-110319-5_3_24 Skipping existing 1834-110319-5_4_76 Skipping existing 1834-110319-5_5_27 Skipping existing 1834-110319-5_6_30 Skipping existing 1834-110319-5_6_31 Skipping existing 1834-110319-5_7_78 Skipping existing 1834-110319-5_7_90 Skipping existing 1839-200619-2_0_104 Skipping existing 1839-200619-2_0_98 Skipping existing 1839-200619-2_1_7 Skipping existing 1839-200619-2_3_118 Skipping existing 1839-200619-2_4_100 Skipping existing 1839-200619-2_4_106 Skipping existing 1839-200619-2_4_108 Skipping existing 1839-200619-2_5_96 Skipping existing 1839-200619-2_6_112 Skipping existing 1839-200619-2_6_114 Skipping existing 1839-200619-2_6_48 Skipping existing 1839-200619-2_6_54 Skipping existing 1839-200619-2_6_78 Skipping existing 1839-200619-2_6_89 Skipping existing 1839-200619-2_6_92 Skipping existing 1839-200619-2_6_94 Skipping existing 1839-200619-2_7_116 Skipping existing 1839-200619-2_7_74 Skipping existing 1839-200619-2_7_90 Skipping existing 1833-260619-3_0_0 Skipping existing 1833-260619-3_0_1 Skipping existing 1833-260619-3_0_140 Skipping existing 1833-260619-3_0_141 Skipping existing 1833-260619-3_0_180 Skipping existing 1833-260619-3_0_182 Skipping existing 1833-260619-3_0_194 Skipping existing 1833-260619-3_0_196 Skipping existing 1833-260619-3_0_209 Skipping existing 1833-260619-3_1_119 Skipping existing 1833-260619-3_1_170 Skipping existing 1833-260619-3_1_207 Skipping existing 1833-260619-3_2_198 Skipping existing 1833-260619-3_3_160 Skipping existing 1833-260619-3_3_176 Skipping existing 1833-260619-3_5_109 Skipping existing 1833-260619-3_5_111 Skipping existing 1833-260619-3_5_113 Skipping existing 1833-260619-3_5_184 Skipping existing 1833-260619-3_6_142 Skipping existing 1833-260619-3_6_168 Skipping existing 1833-260619-3_6_192 Skipping existing 1833-260619-3_7_103 Skipping existing 1833-260619-3_7_105 Skipping existing 1833-200619-3_0_0 Skipping existing 1833-200619-3_0_91 Skipping existing 1833-200619-3_0_93 Skipping existing 1833-200619-3_1_89 Skipping existing 1833-200619-3_1_97 Skipping existing 1833-200619-3_2_142 Skipping existing 1833-200619-3_3_128 Skipping existing 1833-200619-3_3_82 Skipping existing 1833-200619-3_4_113 Skipping existing 1833-200619-3_5_122 Skipping existing 1833-200619-3_5_59 Skipping existing 1833-200619-3_6_120 Skipping existing 1833-200619-3_6_126 Skipping existing 1833-200619-3_6_132 Skipping existing 1833-200619-3_6_150 Skipping existing 1833-200619-3_6_75 Skipping existing 1833-200619-3_7_53 Skipping existing 1833-200619-3_7_65 Skipping existing 1849-280219-1_0_90 Skipping existing 1849-280219-1_1_102 Skipping existing 1849-280219-1_1_104 Skipping existing 1849-280219-1_1_112 Skipping existing 1849-280219-1_1_114 Skipping existing 1849-280219-1_1_116 Skipping existing 1849-280219-1_2_100 Skipping existing 1849-280219-1_2_16 Skipping existing 1849-280219-1_2_18 Skipping existing 1849-280219-1_2_84 Skipping existing 1849-280219-1_2_86 Skipping existing 1849-280219-1_3_110 Skipping existing 1849-280219-1_3_27 Skipping existing 1849-280219-1_3_28 Skipping existing 1849-280219-1_3_64 Skipping existing 1849-280219-1_4_66 Skipping existing 1849-280219-1_5_40 Skipping existing 1849-280219-1_6_106 Skipping existing 1849-280219-1_6_108 Skipping existing 1849-280219-1_6_44 Skipping existing 1849-280219-1_6_45 Skipping existing 1849-280219-1_6_96 Skipping existing 1849-280219-1_6_98 Skipping existing 1849-280219-1_7_52 Skipping existing 1849-280219-1_7_53 Skipping existing 1849-280219-1_7_62 Skipping existing 1833-010719-2_0_239 Skipping existing 1833-010719-2_0_254 Skipping existing 1833-010719-2_0_261 Skipping existing 1833-010719-2_0_265 Skipping existing 1833-010719-2_0_267 Skipping existing 1833-010719-2_0_4 Skipping existing 1833-010719-2_1_135 Skipping existing 1833-010719-2_1_18 Skipping existing 1833-010719-2_1_227 Skipping existing 1833-010719-2_1_229 Skipping existing 1833-010719-2_1_233 Skipping existing 1833-010719-2_1_269 Skipping existing 1833-010719-2_1_273 Skipping existing 1833-010719-2_2_249 Skipping existing 1833-010719-2_2_271 Skipping existing 1833-010719-2_2_29 Skipping existing 1833-010719-2_3_133 Skipping existing 1833-010719-2_3_157 Skipping existing 1833-010719-2_3_173 Skipping existing 1833-010719-2_3_174 Skipping existing 1833-010719-2_3_187 Skipping existing 1833-010719-2_3_188 Skipping existing 1833-010719-2_3_241 Skipping existing 1833-010719-2_3_243 Skipping existing 1833-010719-2_3_259 Skipping existing 1833-010719-2_4_223 Skipping existing 1833-010719-2_4_231 Skipping existing 1833-010719-2_5_221 Skipping existing 1833-010719-2_5_235 Skipping existing 1833-010719-2_6_225 Skipping existing 1833-010719-2_7_211 Skipping existing 1833-010719-2_7_213 Skipping existing 1833-010719-2_7_217 Processing 1834-150319-1_7_116
/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) *
Processing 1834-150319-1_7_117 Processing 1834-110319-6_6_114 Skipping existing 1834-150319-1_7_47 Skipping existing 1834-150319-1_7_76 Skipping existing 1839-120619-1_5_158 Skipping existing 1839-120619-1_6_143 Skipping existing 1839-120619-1_7_152 Skipping existing 1833-260619-2_0_152 Skipping existing 1833-260619-2_0_164 Skipping existing 1833-260619-2_0_174 Skipping existing 1833-260619-2_0_2 Skipping existing 1833-260619-2_1_123 Skipping existing 1833-260619-2_1_130 Skipping existing 1833-260619-2_1_166 Skipping existing 1833-260619-2_2_148 Skipping existing 1833-260619-2_2_168 Skipping existing 1833-260619-2_3_146 Skipping existing 1833-260619-2_5_115 Skipping existing 1833-260619-2_5_154 Skipping existing 1833-260619-2_6_140 Skipping existing 1833-260619-2_6_144 Skipping existing 1833-260619-2_6_156 Skipping existing 1833-260619-2_6_160 Skipping existing 1833-260619-2_6_170 Skipping existing 1849-010319-4_1_109 Skipping existing 1849-010319-4_1_87 Skipping existing 1849-010319-4_1_97 Skipping existing 1849-010319-4_1_99 Skipping existing 1849-010319-4_2_103 Skipping existing 1849-010319-4_2_61 Skipping existing 1849-010319-4_2_67 Skipping existing 1849-010319-4_3_115 Skipping existing 1849-010319-4_3_65 Skipping existing 1849-010319-4_3_73 Skipping existing 1849-010319-4_5_58 Skipping existing 1849-010319-4_6_79 Skipping existing 1849-010319-4_7_59 Skipping existing 1849-280219-3_0_153 Skipping existing 1849-280219-3_0_160 Skipping existing 1849-280219-3_1_180 Skipping existing 1849-280219-3_1_182 Skipping existing 1849-280219-3_2_141 Skipping existing 1849-280219-3_3_166 Skipping existing 1849-280219-3_3_170 Skipping existing 1849-280219-3_4_133 Skipping existing 1849-280219-3_4_151 Skipping existing 1849-280219-3_5_129 Skipping existing 1849-280219-3_5_137 Skipping existing 1849-280219-3_6_127 Skipping existing 1849-280219-3_6_131 Skipping existing 1849-280219-3_6_135 Skipping existing 1849-280219-3_6_139 Skipping existing 1849-280219-3_7_149 Skipping existing 1849-280219-3_7_158 Skipping existing 1849-280219-3_7_164 Skipping existing 1833-200619-4_0_1 Skipping existing 1833-200619-4_0_78 Skipping existing 1833-200619-4_0_90 Processing 1833-290519-3_1_111 Processing 1834-110319-6_6_116 Skipping existing 1834-220319-1_0_81 Skipping existing 1834-220319-1_0_87 Skipping existing 1834-220319-1_2_73 Skipping existing 1834-220319-1_2_79 Skipping existing 1834-220319-1_2_83 Skipping existing 1834-220319-1_3_11 Skipping existing 1834-220319-1_4_65 Skipping existing 1834-220319-1_5_77 Skipping existing 1834-220319-1_6_24 Skipping existing 1834-220319-1_6_26 Skipping existing 1834-220319-1_6_41 Skipping existing 1834-220319-1_6_69 Skipping existing 1834-220319-1_6_75 Skipping existing 1834-220319-1_7_29 Skipping existing 1834-220319-1_7_36 Skipping existing 1834-220319-1_7_37 Skipping existing 1834-220319-1_7_49 Skipping existing 1834-220319-1_7_67 Skipping existing 1849-060319-2_2_81 Processing 1833-290519-3_1_70 Processing 1833-290519-3_3_138
/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) *
Processing 1833-290519-3_3_96 Processing 1833-290519-3_4_80 Skipping existing 1833-290519-3_5_109 Processing 1833-290519-3_5_113 Processing 1833-290519-3_5_130 Processing 1833-290519-3_6_117 Processing 1833-290519-3_6_123 Processing 1833-290519-3_6_134 Processing 1833-290519-3_6_136 Processing 1833-290519-3_6_140 Processing 1833-290519-3_6_144 Processing 1833-290519-3_7_74 Processing 1833-290519-3_7_99 Skipping existing 1833-050619-3_1_125 Skipping existing 1833-050619-3_2_111 Skipping existing 1833-050619-3_2_133 Skipping existing 1833-050619-3_3_147 Skipping existing 1833-050619-3_4_123 Skipping existing 1833-050619-3_4_145 Skipping existing 1833-050619-3_5_127 Skipping existing 1833-050619-3_6_109 Skipping existing 1833-050619-3_6_117 Skipping existing 1833-050619-3_6_129 Skipping existing 1833-050619-3_6_143 Skipping existing 1833-050619-3_6_149 Skipping existing 1833-050619-3_7_105
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', 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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 [ ]: