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+In [1]:
+
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+
+%load_ext autoreload
+%autoreload 2
+
+
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+
+
+In [25]:
+
+
+
+
+
+
+import os
+import pathlib
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import re
+import shutil
+import pandas as pd
+import scipy.stats
+
+import exdir
+import expipe
+from distutils.dir_util import copy_tree
+import septum_mec
+import septum_mec.analysis.data_processing as dp
+import septum_mec.analysis.registration
+from septum_mec.analysis.plotting import despine, plot_bootstrap_timeseries, violinplot, savefig
+from phase_precession import cl_corr
+from spike_statistics.core import permutation_resampling_test
+import matplotlib.mlab as mlab
+import scipy.signal as ss
+from scipy.interpolate import interp1d
+from skimage import measure
+from tqdm.notebook import tqdm_notebook as tqdm
+tqdm.pandas()
+import scipy.signal as ss
+
+
+from tqdm.notebook import tqdm_notebook as tqdm
+tqdm.pandas()
+
+import pycwt
+
+
+
+
+
+In [34]:
+
+
+
+
+
+
+plt.rcParams['figure.dpi'] = 150
+figsize_violin = (1.7, 3)
+figsize_speed = (4, 3)
+plt.rc('axes', titlesize=10)
+
+
+
+
+
+In [26]:
+
+
+
+
+
+
+project_path = dp.project_path()
+project = expipe.get_project(project_path)
+actions = project.actions
+
+output_path = pathlib.Path("output") / "lfp_speed"
+(output_path / "statistics").mkdir(exist_ok=True, parents=True)
+(output_path / "figures").mkdir(exist_ok=True, parents=True)
+
+
+
+
+
+In [6]:
+
+
+
+
+
+
+data_action = actions['lfp_speed']
+output = exdir.File(
+ data_action.data_path('results'),
+ plugins = [exdir.plugins.git_lfs, exdir.plugins.quantities])
+
+ignore = ['wavelet_power', 'wavelet_freqs', 'signal']
+results = []
+for group in output.values():
+ d = group.attrs.to_dict()
+ d.update({k: np.array(v.data) for k, v in group.items() if k not in ignore})
+ results.append(d)
+results = pd.DataFrame(results)
+
+
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+
+
+In [7]:
+
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+results.head()
+
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+Out[7]:
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+In [8]:
+
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+
+
+identify_neurons = actions['identify-neurons']
+sessions = pd.read_csv(identify_neurons.data_path('sessions'))
+
+
+
+
+
+In [9]:
+
+
+
+
+
+
+results = results.merge(sessions, on='action')
+
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+In [10]:
+
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+results.head()
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+Out[10]:
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+In [11]:
+
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+results = results.query('stim_location!="mecl" and stim_location!="mecr"')
+
+
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+
+
+In [13]:
+
+
+
+
+
+
+def action_group(row):
+ a = int(row.channel_group in [0,1,2,3])
+ return f'{row.action}-{a}'
+results['action_side_a'] = results.apply(action_group, axis=1)
+
+
+
+
+
+In [14]:
+
+
+
+
+
+
+lfp_results_hemisphere = results.sort_values(
+ by=['action_side_a', 'channel_group'], ascending=[True, False]
+).drop_duplicates(subset='action_side_a', keep='first')
+lfp_results_hemisphere.loc[:,['action_side_a','channel_group', 'min_speed']].head()
+
+
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+Out[14]:
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+In [35]:
+
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+
+
+colors = ['#1b9e77','#d95f02','#7570b3','#e7298a']
+labels = ['Baseline I', '11 Hz', 'Baseline II', '30 Hz']
+# Hz11 means that the baseline session was indeed before an 11 Hz session
+queries = ['baseline and i and Hz11', 'frequency==11', 'baseline and ii and Hz30', 'frequency==30']
+
+
+
+
+
+In [16]:
+
+
+
+
+
+
+def make_entity_date_side(row):
+ s = row.action_side_a.split('-')
+ del s[2]
+ return '-'.join(s)
+
+
+
+
+
+In [17]:
+
+
+
+
+
+
+lfp_results_hemisphere['entity_date_side'] = lfp_results_hemisphere.apply(make_entity_date_side, axis=1)
+
+
+
+
+
+In [18]:
+
+
+
+
+
+
+from functools import reduce
+
+
+
+
+
+In [19]:
+
+
+
+
+
+
+keys = [
+ 'freq_score',
+ 'power_score',
+ 'speed_bins',
+ 'mean_freq',
+ 'mean_power'
+]
+
+results = {}
+for key in keys:
+ results[key] = list()
+ for query, label in zip(queries, labels):
+ values = lfp_results_hemisphere.query(query).loc[:,['entity_date_side', key]]
+ results[key].append(values.rename({key: label}, axis=1))
+
+for key, val in results.items():
+ df = reduce(lambda left,right: pd.merge(left, right, on='entity_date_side', how='outer'), val)
+ results[key] = df.drop('entity_date_side', axis=1)
+
+
+
+
+
+Scores¶
+
+
+
+
+
+In [91]:
+
+
+
+
+
+
+vss = [
+ ['Baseline I', '11 Hz'],
+ ['Baseline I', 'Baseline II'],
+ ['Baseline II', '30 Hz'],
+ ['11 Hz', '30 Hz'],
+]
+
+
+
+
+
+In [92]:
+
+
+
+
+
+
+ylabel = {
+ 'freq_score': 'Frequency score',
+ 'power_score': 'Power score'
+}
+
+
+
+
+
+In [93]:
+
+
+
+
+
+
+for stuff in ['freq_score', 'power_score']:
+ for vs in vss:
+ base, stim = results[stuff][vs].dropna().values.T
+ plt.figure(figsize=figsize_violin)
+ plt.ylabel(ylabel[stuff])
+ violinplot(base, stim, colors=[colors[labels.index(l)] for l in vs], xticks=vs)
+ plt.ylim(-0.35, 0.5)
+ plt.yticks([-0.25, 0, 0.25, 0.5])
+ despine()
+ fig_path = output_path / "figures" / f"{stuff}_{' '.join(vs)}".replace(' ', '_')
+ savefig(fig_path)
+
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+In [94]:
+
+
+
+
+
+
+def plot_speed(results, stuff, colors, labels, filename=None, show_raw=False, ylim=None):
+ base, stim = results[stuff][labels].dropna().values.T
+ base_bins, stim_bins = results['speed_bins'][labels].dropna().values.T
+
+ base = np.array([s for s in base])
+ stim = np.array([s for s in stim])
+
+ if show_raw:
+ fig, axs = plt.subplots(1, 2, sharey=True, figsize=figsize_speed)
+
+ for b, h in zip(base_bins, base):
+ axs[1].plot(b, h)
+ axs[1].set_xlim(0.1,1)
+ axs[1].set_title(labels[0])
+
+ for b, h in zip(stim_bins, stim):
+ axs[0].plot(b, h)
+ axs[0].set_xlim(0.1,1)
+ axs[0].set_title(labels[1])
+
+ fig, ax = plt.subplots(1, 1, figsize=figsize_speed)
+ plot_bootstrap_timeseries(base_bins[0], base.T, ax=ax, label=labels[0], color=colors[0])
+ plot_bootstrap_timeseries(stim_bins[0], stim.T, ax=ax, label=labels[1], color=colors[1])
+
+ plt.xlim(0, 0.9)
+ plt.gca().spines['top'].set_visible(False)
+ plt.gca().spines['right'].set_visible(False)
+ plt.legend(frameon=False)
+ if ylim is not None:
+ plt.ylim(ylim)
+ despine()
+ if filename is not None:
+ savefig(output_path / "figures" / f"{filename}")
+
+
+
+
+
+In [75]:
+
+
+
+
+
+
+plot_speed(results, 'mean_freq',
+ colors[:2], labels[:2], filename='lfp_speed_freq_11', ylim=(7.3, 8.3))
+
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+
+In [77]:
+
+
+
+
+
+
+plot_speed(results, 'mean_freq',
+ colors[2:], labels[2:], filename='lfp_speed_freq_30', ylim=(7.3, 8.3))
+
+
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+
+
+In [78]:
+
+
+
+
+
+
+plot_speed(
+ results, 'mean_freq',
+ colors=[colors[0], colors[2]], labels=[labels[0], labels[2]], filename='lfp_speed_freq_baseline', ylim=(7.3, 8.3))
+
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+
+In [95]:
+
+
+
+
+
+
+plot_speed(
+ results, 'mean_freq',
+ colors=[colors[1], colors[3]], labels=[labels[1], labels[3]], filename='lfp_speed_freq_stim', ylim=(7.3, 8.3))
+
+
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+
+
+
+
+
+Power Familiar¶
+
+
+
+
+
+In [79]:
+
+
+
+
+
+
+plot_speed(
+ results, 'mean_power',
+ colors[:2], labels[:2], filename='lfp_speed_power_11', ylim=(5, 35))
+
+
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+
+
+
+In [80]:
+
+
+
+
+
+
+plot_speed(
+ results, 'mean_power',
+ colors[2:], labels[2:], filename='lfp_speed_power_30', ylim=(5, 35))
+
+
+
+
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+
+
+
+
+
+
+
+In [81]:
+
+
+
+
+
+
+plot_speed(
+ results, 'mean_power',
+ colors=[colors[0], colors[2]], labels=[labels[0], labels[2]], filename='lfp_speed_power_baseline', ylim=(5, 35))
+
+
+
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+
+
+
+
+In [96]:
+
+
+
+
+
+
+plot_speed(
+ results, 'mean_power',
+ colors=[colors[1], colors[3]], labels=[labels[1], labels[3]], filename='lfp_speed_power_stim', ylim=(5, 35))
+
+
+
+
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+
+
+
+
+
+Table¶
+
+
+
+
+
+In [82]:
+
+
+
+
+
+
+from septum_mec.analysis.statistics import make_statistics_table
+
+
+
+
+
+In [102]:
+
+
+
+
+
+
+stat, _ = make_statistics_table(
+ {k:v for k, v in results.items() if k in ['power_score', 'freq_score']},
+ labels, lmm_test=False, wilcoxon_test=True, use_weighted_stats=False, normality_test=True)
+stat
+
+
+
+
+
+
+
+
+
+
+
+Out[102]:
+
+
+
+
+
+
+
+
+
+
+In [103]:
+
+
+
+
+
+
+stat.to_latex(output_path / "statistics" / f"statistics.tex")
+stat.to_csv(output_path / "statistics" / f"statistics.csv")
+
+
+
+
+
+In [87]:
+
+
+
+
+
+
+for key, result in results.items():
+ result.to_latex(output_path / "statistics" / f"values_{key}.tex")
+ result.to_csv(output_path / "statistics" / f"values_{key}.csv")
+
+
+
+
+
+Register in expipe¶
+
+
+
+
+
+In [104]:
+
+
+
+
+
+
+action = project.actions["lfp_speed"]
+
+
+
+
+
+In [105]:
+
+
+
+
+
+
+outdata = {
+ "figures": "figures",
+ "statistics": "statistics"
+}
+
+for key, value in outdata.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 [ ]:
+
+
+
+
+
+
+septum_mec.analysis.registration.store_notebook(action, "20_lfp_speed.ipynb")
+
+
+
+
+
+In [ ]:
+
+
+
+
+
+
+
+
+
+
+
+
+In [ ]:
+
+
+
+
+
+
+
+