+
+
+
+
+
+
+
+
+
+
+In [1]:
+
+
+
+
+
+
+%load_ext autoreload
+%autoreload 2
+
+
+
+
+
+In [91]:
+
+
+
+
+
+
+import os
+import expipe
+import pathlib
+import numpy as np
+import spatial_maps.stats as stats
+import septum_mec
+import septum_mec.analysis.data_processing as dp
+import septum_mec.analysis.registration
+import head_direction.head as head
+import spatial_maps as sp
+import speed_cells.speed as spd
+import re
+import joblib
+import multiprocessing
+import shutil
+import psutil
+import pandas as pd
+import matplotlib.pyplot as plt
+from distutils.dir_util import copy_tree
+from neo import SpikeTrain
+import scipy
+
+from tqdm import tqdm_notebook as tqdm
+from tqdm._tqdm_notebook import tqdm_notebook
+tqdm_notebook.pandas()
+
+from spike_statistics.core import permutation_resampling
+
+from spikewaveform.core import calculate_waveform_features_from_template, cluster_waveform_features
+
+from septum_mec.analysis.plotting import violinplot
+
+
+
+
+
+In [40]:
+
+
+
+
+
+
+%matplotlib inline
+color_control = '#4393c3'
+color_stimulated = '#d6604d'
+
+color_bs = '#5aae61'
+color_ns = '#9970ab'
+
+figsize_violin = (1.7, 3)
+figsize_gen = (4, 3)
+
+output_path = pathlib.Path("output") / "waveform-analysis"
+(output_path / "statistics").mkdir(exist_ok=True, parents=True)
+(output_path / "figures").mkdir(exist_ok=True, parents=True)
+output_path.mkdir(exist_ok=True)
+
+
+
+
+
+In [4]:
+
+
+
+
+
+
+data_loader = dp.Data()
+actions = data_loader.actions
+project = data_loader.project
+
+
+
+
+
+In [5]:
+
+
+
+
+
+
+identify_neurons = actions['identify-neurons']
+sessions = pd.read_csv(identify_neurons.data_path('sessions'))
+units = pd.read_csv(identify_neurons.data_path('units'))
+session_units = pd.merge(sessions, units, on='action')
+#########################3
+# session_units = session_units.drop_duplicates('unit_id')
+#################################
+print('N cells:',session_units.shape[0])
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [6]:
+
+
+
+
+
+
+session_units.head()
+
+
+
+
+
+
+
+
+
+
+
+Out[6]:
+
+
+
+
+
+
+
+
+
+
+In [7]:
+
+
+
+
+
+
+session_units.groupby('action').count().unit_name.hist()
+
+
+
+
+
+
+
+
+
+
+
+Out[7]:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Process all data¶
+
+
+
+
+
+In [8]:
+
+
+
+
+
+
+def features(row):
+ action_id = row['action']
+ channel_id = row['channel_group']
+ unit = row['unit_name']
+ template = data_loader.template(action_id, channel_id, unit)
+ spike_times = data_loader.spike_train(action_id, channel_id, unit)
+ half_widths, peak_to_troughs = calculate_waveform_features_from_template(
+ template.data, template.sampling_rate)
+ peak_amps = template.data.min(axis=1)
+ half_widths = half_widths * 1000 # to ms
+ peak_to_troughs = peak_to_troughs * 1000 # to ms
+ idxs = np.argsort(peak_amps)
+ peak_to_trough = np.nan
+ for p2t in peak_to_troughs[idxs]:
+ if np.isfinite(p2t) and p2t > .1:
+ peak_to_trough = p2t
+ break
+ half_width = np.nan
+ for hw in half_widths[idxs]:
+ if np.isfinite(hw):
+ half_width = hw
+ break
+
+ return pd.Series({
+ 'half_width': half_width,
+ 'peak_to_trough': peak_to_trough,
+ 'average_firing_rate': float(len(spike_times) / spike_times.t_stop),
+ 'template': template.data[idxs[0]]
+ })
+
+
+
+
+
+In [9]:
+
+
+
+
+
+
+results = session_units.merge(
+ session_units.progress_apply(features, axis=1),
+ left_index=True, right_index=True)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [10]:
+
+
+
+
+
+
+df = results.loc[:, ['half_width', 'peak_to_trough']].dropna()
+
+idxs_df = cluster_waveform_features(df.half_width, df.peak_to_trough)
+
+results.loc[df.index, 'bs'] = idxs_df
+
+
+
+
+
+In [86]:
+
+
+
+
+
+
+plt.figure(figsize=figsize_gen)
+size = 5
+mew = .5
+marker_bs = '.'
+marker_ns = '+'
+
+plt.scatter(
+ results.query('bs==0')['half_width'],
+ results.query('bs==0')['peak_to_trough'],
+ c=color_ns, s=size, marker=marker_ns, linewidth=mew, label='NS')
+
+plt.scatter(
+ results.query('bs==1')['half_width'],
+ results.query('bs==1')['peak_to_trough'],
+ c=color_bs, s=size, marker=marker_bs, linewidth=mew, label='BS')
+
+plt.xlabel('half width')
+plt.ylabel('peak to through')
+
+plt.legend(bbox_to_anchor=(0,1.02,1,0.2), loc="lower left",
+ mode="expand", borderaxespad=0, ncol=2)
+
+plt.savefig(output_path / "figures" / "clusters.svg", bbox_inches="tight")
+plt.savefig(output_path / "figures" / "clusters.png", dpi=600, bbox_inches="tight")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [85]:
+
+
+
+
+
+
+plt.figure(figsize=figsize_gen)
+
+size = 5
+mew = .5
+marker_bs = '.'
+marker_ns = '+'
+
+plt.scatter(
+ results.query('bs==0')['half_width'],
+ results.query('bs==0')['peak_to_trough'],
+ c=color_ns, s=size, marker=marker_ns, linewidth=mew, label='NS')
+
+plt.scatter(
+ results.query('bs==1')['half_width'],
+ results.query('bs==1')['peak_to_trough'],
+ c=color_bs, s=size, marker=marker_bs, linewidth=mew, label='BS')
+
+plt.scatter(
+ results.query('bs==1 and average_firing_rate > 10')['half_width'],
+ results.query('bs==1 and average_firing_rate > 10')['peak_to_trough'],
+ c='red', s=size, marker=marker_bs, linewidth=mew, label='BS rate > 10 Hz')
+
+plt.xlabel('half width')
+plt.ylabel('peak to through')
+
+plt.legend(bbox_to_anchor=(0,1.02,1,0.2), loc="lower left",
+ mode="expand", borderaxespad=0, ncol=2)
+
+plt.savefig(output_path / "figures" / "clusters_and_rate.svg", bbox_inches="tight")
+plt.savefig(output_path / "figures" / "clusters_and_rate.png", dpi=600, bbox_inches="tight")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [31]:
+
+
+
+
+
+
+stim = results.query('stimulated').loc[:, ['half_width', 'peak_to_trough']].dropna()
+
+idxs_stim = cluster_waveform_features(stim.half_width, stim.peak_to_trough)
+
+results.loc[stim.index, 'bs_stim'] = idxs_stim
+
+
+
+
+
+In [32]:
+
+
+
+
+
+
+control = results.query('not stimulated').loc[:, ['half_width', 'peak_to_trough']].dropna()
+
+idxs_control = cluster_waveform_features(control.half_width, control.peak_to_trough)
+
+results.loc[control.index, 'bs_ctrl'] = idxs_control
+
+
+
+
+
+In [87]:
+
+
+
+
+
+
+plt.figure(figsize=figsize_gen)
+size = 5
+mew = .5
+marker_bs = '.'
+marker_ns = '+'
+plt.scatter(
+ results.query('bs_stim==0')['half_width'],
+ results.query('bs_stim==0')['peak_to_trough'],
+ c=color_stimulated, s=size, marker=marker_ns,
+ linewidth=mew, label='Stimulated NS', alpha=.5)
+
+plt.scatter(
+ results.query('bs_stim==1')['half_width'],
+ results.query('bs_stim==1')['peak_to_trough'],
+ c=color_stimulated, s=size, marker=marker_bs,
+ linewidth=mew, label='Stimulated BS', alpha=.5)
+
+
+plt.scatter(
+ results.query('bs_ctrl==0')['half_width'],
+ results.query('bs_ctrl==0')['peak_to_trough'],
+ c=color_control, s=size, marker=marker_ns,
+ linewidth=mew, label='Control NS', alpha=.5)
+
+plt.scatter(
+ results.query('bs_ctrl==1')['half_width'],
+ results.query('bs_ctrl==1')['peak_to_trough'],
+ c=color_control, s=size, marker=marker_bs,
+ linewidth=mew, label='Control BS', alpha=.5)
+
+plt.xlabel('half width')
+plt.ylabel('peak to through')
+plt.legend(bbox_to_anchor=(0,1.02,1,0.2), loc="lower left",
+ mode="expand", borderaxespad=0, ncol=2)
+
+plt.savefig(output_path / "figures" / "compare-clusters.svg", bbox_inches="tight")
+plt.savefig(output_path / "figures" / "compare-clusters.png", dpi=600, bbox_inches="tight")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [47]:
+
+
+
+
+
+
+results.average_firing_rate = results.apply(lambda x: float(x.average_firing_rate), axis=1)
+
+
+
+
+
+In [64]:
+
+
+
+
+
+
+results.frequency = results.apply(
+ lambda x:
+ float(x.frequency.replace('Hz', '')) if isinstance(x.frequency, str)
+ else float(x.frequency), axis=1)
+
+
+
+
+
+In [74]:
+
+
+
+
+
+
+bins=100
+density=True
+cumulative=True
+histtype='step'
+lw = 2
+
+plt.figure(figsize=figsize_gen)
+plt.title('Narrow spiking')
+_, bins, _ = plt.hist(
+ results.query('bs_ctrl==0')['average_firing_rate'],
+ bins=bins, density=density, cumulative=cumulative,
+ histtype=histtype, color=color_control, lw=lw, label='Control');
+
+_, bins, _ = plt.hist(
+ results.query('bs_stim==0')['average_firing_rate'],
+ bins=bins, density=density, cumulative=cumulative,
+ histtype=histtype, color=color_stimulated, lw=lw, label='Stimulated');
+
+plt.xlim(-.5, 56)
+# plt.legend(bbox_to_anchor=(0,1.02,1,0.2), loc="lower left",
+# mode="expand", borderaxespad=0, ncol=2)
+
+plt.savefig(output_path / "figures" / "cumulative_ns.svg", bbox_inches="tight")
+plt.savefig(output_path / "figures" / "cumulative_ns.png", dpi=600, bbox_inches="tight")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [75]:
+
+
+
+
+
+
+bins=100
+density=True
+cumulative=True
+histtype='step'
+lw = 2
+
+plt.figure(figsize=figsize_gen)
+plt.title('Narrow spiking')
+_, bins, _ = plt.hist(
+ results.query('bs_ctrl==0')['average_firing_rate'],
+ bins=bins, density=density, cumulative=cumulative,
+ histtype=histtype, color=color_control, lw=lw, label='Control');
+
+_, bins, _ = plt.hist(
+ results.query('bs_stim==0 and frequency==30')['average_firing_rate'],
+ bins=bins, density=density, cumulative=cumulative,
+ histtype=histtype, color=color_stimulated, lw=lw, label='Stimulated');
+
+plt.xlim(-.5, 56)
+# plt.legend(bbox_to_anchor=(0,1.02,1,0.2), loc="lower left",
+# mode="expand", borderaxespad=0, ncol=2)
+
+plt.savefig(output_path / "figures" / "cumulative_ns_30.svg", bbox_inches="tight")
+plt.savefig(output_path / "figures" / "cumulative_ns_30.png", dpi=600, bbox_inches="tight")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [76]:
+
+
+
+
+
+
+bins=100
+density=True
+cumulative=True
+histtype='step'
+lw = 2
+
+plt.figure(figsize=figsize_gen)
+plt.title('Narrow spiking')
+_, bins, _ = plt.hist(
+ results.query('bs_ctrl==0')['average_firing_rate'],
+ bins=bins, density=density, cumulative=cumulative,
+ histtype=histtype, color=color_control, lw=lw, label='Control');
+
+_, bins, _ = plt.hist(
+ results.query('bs_stim==0 and frequency==11')['average_firing_rate'],
+ bins=bins, density=density, cumulative=cumulative,
+ histtype=histtype, color=color_stimulated, lw=lw, label='Stimulated');
+
+plt.xlim(-.5, 56)
+# plt.legend(bbox_to_anchor=(0,1.02,1,0.2), loc="lower left",
+# mode="expand", borderaxespad=0, ncol=2)
+
+plt.savefig(output_path / "figures" / "cumulative_ns_11.svg", bbox_inches="tight")
+plt.savefig(output_path / "figures" / "cumulative_ns_11.png", dpi=600, bbox_inches="tight")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [78]:
+
+
+
+
+
+
+bins = 100
+
+plt.figure(figsize=figsize_gen)
+plt.title('Broad spiking')
+_, bins, _ = plt.hist(
+ results.query('bs_ctrl==1')['average_firing_rate'],
+ bins=bins, density=density, cumulative=cumulative,
+ histtype=histtype, color=color_control, lw=lw);
+
+_, bins, _ = plt.hist(
+ results.query('bs_stim==1')['average_firing_rate'],
+ bins=bins, density=density, cumulative=cumulative,
+ histtype=histtype, color=color_stimulated, lw=lw);
+
+plt.xlim(-.5, 44)
+
+plt.savefig(output_path / "figures" / "cumulative_bs.svg", bbox_inches="tight")
+plt.savefig(output_path / "figures" / "cumulative_bs.png", dpi=600, bbox_inches="tight")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [80]:
+
+
+
+
+
+
+bins = 100
+
+plt.figure(figsize=figsize_gen)
+plt.title('Broad spiking')
+_, bins, _ = plt.hist(
+ results.query('bs_ctrl==1')['average_firing_rate'],
+ bins=bins, density=density, cumulative=cumulative,
+ histtype=histtype, color=color_control, lw=lw);
+
+_, bins, _ = plt.hist(
+ results.query('bs_stim==1 and frequency==11')['average_firing_rate'],
+ bins=bins, density=density, cumulative=cumulative,
+ histtype=histtype, color=color_stimulated, lw=lw);
+
+plt.xlim(-.5, 44)
+
+plt.savefig(output_path / "figures" / "cumulative_bs_11.svg", bbox_inches="tight")
+plt.savefig(output_path / "figures" / "cumulative_bs_11.png", dpi=600, bbox_inches="tight")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [79]:
+
+
+
+
+
+
+bins = 100
+
+plt.figure(figsize=figsize_gen)
+plt.title('Broad spiking')
+_, bins, _ = plt.hist(
+ results.query('bs_ctrl==1')['average_firing_rate'],
+ bins=bins, density=density, cumulative=cumulative,
+ histtype=histtype, color=color_control, lw=lw);
+
+_, bins, _ = plt.hist(
+ results.query('bs_stim==1 and frequency==30')['average_firing_rate'],
+ bins=bins, density=density, cumulative=cumulative,
+ histtype=histtype, color=color_stimulated, lw=lw);
+
+plt.xlim(-.5, 44)
+
+plt.savefig(output_path / "figures" / "cumulative_bs_30.svg", bbox_inches="tight")
+plt.savefig(output_path / "figures" / "cumulative_bs_30.png", dpi=600, bbox_inches="tight")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [73]:
+
+
+
+
+
+
+plt.figure(figsize=figsize_violin)
+# test = 'permutation_resampling'
+test = 'mann_whitney'
+
+plt.title('Broad spiking')
+violinplot(
+ results.query('bs_ctrl==1')['average_firing_rate'].to_numpy(),
+ results.query('bs_stim==1')['average_firing_rate'].to_numpy(),
+ test=test)
+
+plt.savefig(output_path / "figures" / "rates_bs.svg", bbox_inches="tight")
+plt.savefig(output_path / "figures" / "rates_bs.png", dpi=600, bbox_inches="tight")
+
+plt.figure(figsize=figsize_violin)
+plt.title('Narrow spiking')
+violinplot(
+ results.query('bs_ctrl==0')['average_firing_rate'].to_numpy(),
+ results.query('bs_stim==0')['average_firing_rate'].to_numpy(),
+ test=test)
+
+plt.savefig(output_path / "figures" / "rates_ns.svg", bbox_inches="tight")
+plt.savefig(output_path / "figures" / "rates_ns.png", dpi=600, bbox_inches="tight")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [81]:
+
+
+
+
+
+
+plt.figure(figsize=figsize_violin)
+# test = 'permutation_resampling'
+test = 'mann_whitney'
+
+plt.title('Broad spiking')
+violinplot(
+ results.query('bs_ctrl==1')['average_firing_rate'].to_numpy(),
+ results.query('bs_stim==1 and frequency==11')['average_firing_rate'].to_numpy(),
+ test=test)
+
+plt.savefig(output_path / "figures" / "rates_bs_11.svg", bbox_inches="tight")
+plt.savefig(output_path / "figures" / "rates_bs_11.png", dpi=600, bbox_inches="tight")
+
+plt.figure(figsize=figsize_violin)
+plt.title('Narrow spiking')
+violinplot(
+ results.query('bs_ctrl==0')['average_firing_rate'].to_numpy(),
+ results.query('bs_stim==0 and frequency==11')['average_firing_rate'].to_numpy(),
+ test=test)
+
+plt.savefig(output_path / "figures" / "rates_ns_11.svg", bbox_inches="tight")
+plt.savefig(output_path / "figures" / "rates_ns_11.png", dpi=600, bbox_inches="tight")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [82]:
+
+
+
+
+
+
+plt.figure(figsize=figsize_violin)
+# test = 'permutation_resampling'
+test = 'mann_whitney'
+
+plt.title('Broad spiking')
+violinplot(
+ results.query('bs_ctrl==1')['average_firing_rate'].to_numpy(),
+ results.query('bs_stim==1 and frequency==30')['average_firing_rate'].to_numpy(),
+ test=test)
+
+plt.savefig(output_path / "figures" / "rates_bs.svg", bbox_inches="tight")
+plt.savefig(output_path / "figures" / "rates_bs.png", dpi=600, bbox_inches="tight")
+
+plt.figure(figsize=figsize_violin)
+plt.title('Narrow spiking')
+violinplot(
+ results.query('bs_ctrl==0')['average_firing_rate'].to_numpy(),
+ results.query('bs_stim==0 and frequency==30')['average_firing_rate'].to_numpy(),
+ test=test)
+
+plt.savefig(output_path / "figures" / "rates_ns_30.svg", bbox_inches="tight")
+plt.savefig(output_path / "figures" / "rates_ns_30.png", dpi=600, bbox_inches="tight")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [101]:
+
+
+
+
+
+
+columns = [
+ 'average_firing_rate',
+ 'half_width',
+ 'peak_to_trough'
+]
+
+
+def summarize(data):
+ return "{:.2f} ± {:.2f} ({})".format(data.mean(), data.sem(), sum(~np.isnan(data)))
+
+
+bs = pd.DataFrame()
+
+bs['Control'] = results.query('bs_ctrl==1')[columns].agg(summarize)
+bs['Stimulated'] = results.query('bs_stim==1')[columns].agg(summarize)
+
+ns = pd.DataFrame()
+
+ns['Control'] = results.query('bs_ctrl==0')[columns].agg(summarize)
+ns['Stimulated'] = results.query('bs_stim==0')[columns].agg(summarize)
+
+
+def MWU(column, df, cluster, extra):
+ '''
+ Mann Whitney U
+ '''
+ Uvalue, pvalue = scipy.stats.mannwhitneyu(
+ df.query('bs_ctrl=={} {}'.format(cluster, extra))[column].dropna(),
+ df.query('bs_stim=={} {}'.format(cluster, extra))[column].dropna(),
+ alternative='two-sided')
+
+ return "{:.2f}, {:.3f}".format(Uvalue, pvalue)
+
+
+def PRS(column, df, cluster, extra):
+ '''
+ Permutation ReSampling
+ '''
+ pvalue, observed_diff, diffs = permutation_resampling(
+ df.query('bs_ctrl=={} {}'.format(cluster, extra))[column].dropna(),
+ df.query('bs_stim=={} {}'.format(cluster, extra))[column].dropna())
+
+ return "{:.2f}, {:.3f}".format(observed_diff, pvalue)
+
+
+bs['MWU'] = list(map(lambda x: MWU(x, results, 1, ''), columns))
+bs['PRS'] = list(map(lambda x: PRS(x, results, 1, ''), columns))
+
+ns['MWU'] = list(map(lambda x: MWU(x, results, 0, ''), columns))
+ns['PRS'] = list(map(lambda x: PRS(x, results, 0, ''), columns))
+
+bs.to_latex(output_path / "statistics" / "broad_spiking.tex")
+bs.to_csv(output_path / "statistics" / "broad_spiking.csv")
+
+ns.to_latex(output_path / "statistics" / "narrow_spiking.tex")
+ns.to_csv(output_path / "statistics" / "narrow_spiking.csv")
+
+
+
+
+
+In [99]:
+
+
+
+
+
+
+columns = [
+ 'average_firing_rate',
+ 'half_width',
+ 'peak_to_trough'
+]
+
+
+def summarize(data):
+ return "{:.2f} ± {:.2f} ({})".format(data.mean(), data.sem(), sum(~np.isnan(data)))
+
+
+bs = pd.DataFrame()
+
+bs['Control'] = results.query('bs_ctrl==1')[columns].agg(summarize)
+bs['Stimulated'] = results.query('bs_stim==1 and frequency==11')[columns].agg(summarize)
+
+ns = pd.DataFrame()
+
+ns['Control'] = results.query('bs_ctrl==0')[columns].agg(summarize)
+ns['Stimulated'] = results.query('bs_stim==0 and frequency==11')[columns].agg(summarize)
+
+
+def MWU(column, df, cluster, extra):
+ '''
+ Mann Whitney U
+ '''
+ Uvalue, pvalue = scipy.stats.mannwhitneyu(
+ df.query('bs_ctrl=={} {}'.format(cluster, extra))[column].dropna(),
+ df.query('bs_stim=={} {} and frequency==11'.format(cluster, extra))[column].dropna(),
+ alternative='two-sided')
+
+ return "{:.2f}, {:.3f}".format(Uvalue, pvalue)
+
+
+def PRS(column, df, cluster, extra):
+ '''
+ Permutation ReSampling
+ '''
+ pvalue, observed_diff, diffs = permutation_resampling(
+ df.query('bs_ctrl=={} {}'.format(cluster, extra))[column].dropna(),
+ df.query('bs_stim=={} {} and frequency==11'.format(cluster, extra))[column].dropna())
+
+ return "{:.2f}, {:.3f}".format(observed_diff, pvalue)
+
+
+bs['MWU'] = list(map(lambda x: MWU(x, results, 1, ''), columns))
+bs['PRS'] = list(map(lambda x: PRS(x, results, 1, ''), columns))
+
+ns['MWU'] = list(map(lambda x: MWU(x, results, 0, ''), columns))
+ns['PRS'] = list(map(lambda x: PRS(x, results, 0, ''), columns))
+
+bs.to_latex(output_path / "statistics" / "broad_spiking_11.tex")
+bs.to_csv(output_path / "statistics" / "broad_spiking_11.csv")
+
+ns.to_latex(output_path / "statistics" / "narrow_spiking_11.tex")
+ns.to_csv(output_path / "statistics" / "narrow_spiking_11.csv")
+
+
+
+
+
+In [100]:
+
+
+
+
+
+
+columns = [
+ 'average_firing_rate',
+ 'half_width',
+ 'peak_to_trough'
+]
+
+
+def summarize(data):
+ return "{:.2f} ± {:.2f} ({})".format(data.mean(), data.sem(), sum(~np.isnan(data)))
+
+
+bs = pd.DataFrame()
+
+bs['Control'] = results.query('bs_ctrl==1')[columns].agg(summarize)
+bs['Stimulated'] = results.query('bs_stim==1 and frequency==30')[columns].agg(summarize)
+
+ns = pd.DataFrame()
+
+ns['Control'] = results.query('bs_ctrl==0')[columns].agg(summarize)
+ns['Stimulated'] = results.query('bs_stim==0 and frequency==30')[columns].agg(summarize)
+
+
+def MWU(column, df, cluster, extra):
+ '''
+ Mann Whitney U
+ '''
+ Uvalue, pvalue = scipy.stats.mannwhitneyu(
+ df.query('bs_ctrl=={} {}'.format(cluster, extra))[column].dropna(),
+ df.query('bs_stim=={} {} and frequency==30'.format(cluster, extra))[column].dropna(),
+ alternative='two-sided')
+
+ return "{:.2f}, {:.3f}".format(Uvalue, pvalue)
+
+
+def PRS(column, df, cluster, extra):
+ '''
+ Permutation ReSampling
+ '''
+ pvalue, observed_diff, diffs = permutation_resampling(
+ df.query('bs_ctrl=={} {}'.format(cluster, extra))[column].dropna(),
+ df.query('bs_stim=={} {} and frequency==30'.format(cluster, extra))[column].dropna())
+
+ return "{:.2f}, {:.3f}".format(observed_diff, pvalue)
+
+
+bs['MWU'] = list(map(lambda x: MWU(x, results, 1, ''), columns))
+bs['PRS'] = list(map(lambda x: PRS(x, results, 1, ''), columns))
+
+ns['MWU'] = list(map(lambda x: MWU(x, results, 0, ''), columns))
+ns['PRS'] = list(map(lambda x: PRS(x, results, 0, ''), columns))
+
+bs.to_latex(output_path / "statistics" / "broad_spiking_30.tex")
+bs.to_csv(output_path / "statistics" / "broad_spiking_30.csv")
+
+ns.to_latex(output_path / "statistics" / "narrow_spiking_30.tex")
+ns.to_csv(output_path / "statistics" / "narrow_spiking_30.csv")
+
+
+
+
+
+In [97]:
+
+
+
+
+
+
+bs
+
+
+
+
+
+
+
+
+
+
+
+Out[97]:
+
+
+
+
+
+
+
+
+
+
+In [98]:
+
+
+
+
+
+
+ns
+
+
+
+
+
+
+
+
+
+
+
+Out[98]:
+
+
+
+
+
+
+
+
+
+
+example waveforms¶
+
+
+
+
+
+In [102]:
+
+
+
+
+
+
+def normalize(a):
+ t = a - a.min()
+ return t / t.max()
+
+
+
+
+
+In [ ]:
+
+
+
+
+
+
+'half_width','peak_to_trough'
+
+
+
+
+
+In [142]:
+
+
+
+
+
+
+plt.figure(figsize=figsize_gen)
+
+
+lw = 3
+
+row = results.query('bs==1').sort_values('half_width', ascending=False).iloc[50]
+template = data_loader.template(
+ row.action, row.channel_group, row.unit_name)
+
+mean_wf = template.data
+peak_wf = mean_wf[np.argmin(mean_wf.min(1))]
+plt.plot(normalize(peak_wf.T), color=color_bs, lw=lw)
+
+
+row = results.query('bs==0').sort_values('half_width').iloc[10]
+template = data_loader.template(
+ row.action, row.channel_group, row.unit_name)
+
+mean_wf = template.data
+peak_wf = mean_wf[np.argmin(mean_wf.min(1))]
+plt.plot(normalize(peak_wf.T), color=color_ns, lw=lw)
+
+# plt.savefig(output_path / "figures" / "example_waveforms.svg", bbox_inches="tight")
+# plt.savefig(output_path / "figures" / "example_waveforms.png", dpi=600, bbox_inches="tight")
+
+
+
+
+
+
+
+
+
+
+
+Out[142]:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Store results in Expipe action¶
+
+
+
+
+
+In [105]:
+
+
+
+
+
+
+action = project.require_action("waveform-analysis")
+
+
+
+
+
+In [106]:
+
+
+
+
+
+
+stuff = {
+ "figures": "figures",
+ "statistics": "statistics"
+}
+
+for key, value in stuff.items():
+ action.data[key] = value
+ data_path = action.data_path(key)
+ data_path.parent.mkdir(exist_ok=True, parents=True)
+ source = output_path / value
+ if source.is_file():
+ shutil.copy(source, data_path)
+ else:
+ copy_tree(str(source), str(data_path))
+
+
+
+
+
+In [108]:
+
+
+
+
+
+
+septum_mec.analysis.registration.store_notebook(action, "10_waveform_analysis.ipynb")
+