From a101841cbe524d80f8fcf5621ba6e85876650313 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mikkel=20Elle=20Lepper=C3=B8d?= Date: Wed, 10 Mar 2021 13:32:52 +0100 Subject: [PATCH] actions/stimulus-lfp-response-mec-no-zscore/ --- .../attributes.yaml | 4 + .../data/20_stimulus-lfp-response-mec.html | 14624 +++++++++++++++ .../data/20_stimulus-lfp-response-mec.ipynb | 1415 ++ .../data/20_stimulus-lfp-response.html | 14652 ++++++++++++++++ .../data/20_stimulus-lfp-response.ipynb | 1610 ++ .../lfp-psd-histogram-stim_bandpower.png | Bin 0 -> 17319 bytes .../lfp-psd-histogram-stim_bandpower.svg | 3 + .../figures/lfp-psd-histogram-stim_energy.png | Bin 0 -> 16143 bytes .../figures/lfp-psd-histogram-stim_energy.svg | 3 + .../lfp-psd-histogram-stim_half_width.png | Bin 0 -> 14326 bytes .../lfp-psd-histogram-stim_half_width.svg | 3 + .../figures/lfp-psd-histogram-stim_p_max.png | Bin 0 -> 17030 bytes .../figures/lfp-psd-histogram-stim_p_max.svg | 3 + .../lfp-psd-histogram-stim_relpeak.png | Bin 0 -> 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.../statistics/values_theta_bandpower.tex | 56 + .../data/statistics/values_theta_energy.csv | 51 + .../data/statistics/values_theta_energy.tex | 56 + .../data/statistics/values_theta_freq.csv | 51 + .../data/statistics/values_theta_freq.tex | 56 + .../statistics/values_theta_half_width.csv | 51 + .../statistics/values_theta_half_width.tex | 56 + .../data/statistics/values_theta_peak.csv | 51 + .../data/statistics/values_theta_peak.tex | 56 + .../data/statistics/values_theta_relpeak.csv | 51 + .../data/statistics/values_theta_relpeak.tex | 56 + .../data/statistics/values_theta_relpower.csv | 51 + .../data/statistics/values_theta_relpower.tex | 56 + 65 files changed, 33895 insertions(+) create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/attributes.yaml create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/20_stimulus-lfp-response-mec.html create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/20_stimulus-lfp-response-mec.ipynb create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/20_stimulus-lfp-response.html create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/20_stimulus-lfp-response.ipynb create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_bandpower.png create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_bandpower.svg create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_energy.png create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_energy.svg create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_half_width.png create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_half_width.svg create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_p_max.png create mode 100644 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actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_energy.csv create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_energy.tex create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_half_width.csv create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_half_width.tex create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_p_max.csv create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_p_max.tex create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_relpeak.csv create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_relpeak.tex create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_relpower.csv create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_relpower.tex create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_strength.csv create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_stim_strength.tex create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_bandpower.csv create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_bandpower.tex create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_energy.csv create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_energy.tex create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_freq.csv create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_freq.tex create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_half_width.csv create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_half_width.tex create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_peak.csv create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_peak.tex create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_relpeak.csv create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_relpeak.tex create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_relpower.csv create mode 100644 actions/stimulus-lfp-response-mec-no-zscore/data/statistics/values_theta_relpower.tex diff --git a/actions/stimulus-lfp-response-mec-no-zscore/attributes.yaml b/actions/stimulus-lfp-response-mec-no-zscore/attributes.yaml new file mode 100644 index 000000000..ae0308d0b --- /dev/null +++ b/actions/stimulus-lfp-response-mec-no-zscore/attributes.yaml @@ -0,0 +1,4 @@ +registered: '2020-11-28T15:05:27' +data: + notebook: 20_stimulus-lfp-response.ipynb + html: 20_stimulus-lfp-response.html diff --git a/actions/stimulus-lfp-response-mec-no-zscore/data/20_stimulus-lfp-response-mec.html b/actions/stimulus-lfp-response-mec-no-zscore/data/20_stimulus-lfp-response-mec.html new file mode 100644 index 000000000..4262e4830 --- /dev/null +++ b/actions/stimulus-lfp-response-mec-no-zscore/data/20_stimulus-lfp-response-mec.html @@ -0,0 +1,14624 @@ + + + + +20_stimulus-lfp-response-mec + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+
+
In [1]:
+
+
+
%load_ext autoreload
+%autoreload 2
+
+ +
+
+
+ +
+
+
+
In [7]:
+
+
+
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
+import matplotlib
+from distutils.dir_util import copy_tree
+from neo import SpikeTrain
+import scipy
+import seaborn as sns
+from tqdm.notebook import tqdm_notebook as tqdm
+tqdm.pandas()
+
+from spike_statistics.core import permutation_resampling_test
+
+from spikewaveform.core import calculate_waveform_features_from_template, cluster_waveform_features
+
+from septum_mec.analysis.plotting import violinplot, despine
+
+ +
+
+
+ +
+
+
+
In [8]:
+
+
+
#############################
+
+perform_zscore = False
+
+if not perform_zscore:
+    zscore_str = "-no-zscore"
+else:
+    zscore_str = ""
+
+#################################
+
+ +
+
+
+ +
+
+
+
In [9]:
+
+
+
%matplotlib inline
+plt.rc('axes', titlesize=12)
+plt.rcParams.update({
+    'font.size': 12, 
+    'figure.figsize': (6, 4), 
+    'figure.dpi': 150
+})
+
+output_path = pathlib.Path("output") / ("stimulus-lfp-response-mec" + zscore_str)
+(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 [10]:
+
+
+
data_loader = dp.Data()
+actions = data_loader.actions
+project = data_loader.project
+
+ +
+
+
+ +
+
+
+
In [11]:
+
+
+
identify_neurons = actions['identify-neurons']
+sessions = pd.read_csv(identify_neurons.data_path('sessions'))
+
+ +
+
+
+ +
+
+
+
In [12]:
+
+
+
lfp_action = actions['stimulus-lfp-response' + zscore_str]
+lfp_results = pd.read_csv(lfp_action.data_path('results'))
+
+ +
+
+
+ +
+
+
+
In [13]:
+
+
+
lfp_results = pd.merge(sessions, lfp_results, how='left')
+
+ +
+
+
+ +
+
+
+
In [14]:
+
+
+
lfp_results = lfp_results.query('stim_location!="ms"')
+
+ +
+
+
+ +
+
+
+
In [15]:
+
+
+
def action_group(row):
+    a = int(row.channel_group in [0,1,2,3])
+    return f'{row.action}-{a}'
+lfp_results['action_side_a'] = lfp_results.apply(action_group, axis=1)
+
+ +
+
+
+ +
+
+
+
In [16]:
+
+
+
lfp_results['stim_strength'] = lfp_results['stim_p_max'] / lfp_results['theta_energy']
+
+ +
+
+
+ +
+
+
+
In [17]:
+
+
+
# lfp_results_hemisphere = lfp_results.sort_values(
+#     by=['action_side_a', 'stim_strength', 'signal_to_noise'], ascending=[True, False, False]
+lfp_results_hemisphere = lfp_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', 'signal_to_noise', 'stim_strength']].head()
+
+ +
+
+
+ +
+
+ + +
+ +
Out[17]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
action_side_achannel_groupsignal_to_noisestim_strength
711833-010719-1-070.001902NaN
671833-010719-1-130.003522NaN
5831833-020719-1-07-0.002942NaN
5791833-020719-1-130.012323NaN
3751833-020719-3-07-0.002042NaN
+
+
+ +
+ +
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+ +
+
+
+
In [18]:
+
+
+
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 [19]:
+
+
+
# prepare pairwise comparison: same animal same side same date different sessions
+
+ +
+
+
+ +
+
+
+
In [20]:
+
+
+
def make_entity_date_side(row):
+    s = row.action_side_a.split('-')
+    del s[2]
+    return '-'.join(s)
+
+ +
+
+
+ +
+
+
+
In [21]:
+
+
+
lfp_results_hemisphere['entity_date_side'] = lfp_results_hemisphere.apply(make_entity_date_side, axis=1)
+
+ +
+
+
+ +
+
+
+
In [22]:
+
+
+
density = True
+cumulative = True
+histtype = 'step'
+lw = 2
+if perform_zscore:
+    bins = {
+        'theta_energy': np.arange(0, .7, .03),
+        'theta_peak': np.arange(0, .7, .03),
+        'theta_freq': np.arange(4, 10, .5),
+        'theta_half_width': np.arange(0, 15, .5)
+    }
+else:
+    bins = {
+        'theta_energy': np.arange(0, .008, .0003),
+        'theta_peak': np.arange(0, .007, .0003),
+        'theta_freq': np.arange(4, 12, .5),
+        'theta_half_width': np.arange(0, 15, .5)
+    }
+xlabel = {
+    'theta_energy': 'Theta energy (dB)',
+    'theta_peak': 'Peak PSD (dB/Hz)',
+    'theta_freq': '(Hz)',
+    'theta_half_width': '(Hz)',
+}
+# key = 'theta_energy'
+# key = 'theta_peak'
+results = {}
+for key in bins:
+    results[key] = list()
+    fig = plt.figure(figsize=(3.5,2))
+    plt.suptitle(key)
+    legend_lines = []
+    for color, query, label in zip(colors, queries, labels):
+        values = lfp_results_hemisphere.query(query).loc[:,['entity_date_side', key]]
+        results[key].append(values.rename({key: label}, axis=1))
+        values[key].hist(
+            bins=bins[key], density=density, cumulative=cumulative, lw=lw, 
+            histtype=histtype, color=color)
+        legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, lw=lw, label=label))
+        
+    plt.legend(
+        handles=legend_lines,
+        bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)
+    plt.tight_layout()
+    plt.grid(False)
+    plt.xlim(right=bins[key].max() - bins[key].max()*0.025)
+    despine()
+    plt.xlabel(xlabel[key])
+    figname = f'lfp-psd-histogram-{key}'
+    fig.savefig(
+        output_path / 'figures' / f'{figname}.png', 
+        bbox_inches='tight', transparent=True)
+    fig.savefig(
+        output_path / 'figures' / f'{figname}.svg', 
+        bbox_inches='tight', transparent=True)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/lib/histograms.py:898: RuntimeWarning: invalid value encountered in true_divide
+  return n/db/n.sum(), bin_edges
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
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+ +
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+
In [23]:
+
+
+
density = True
+cumulative = True
+histtype = 'step'
+lw = 2
+if perform_zscore:
+    bins = {
+        'stim_energy': np.arange(0, .7, .01),
+        'stim_half_width': np.arange(0, 10, .5),
+        'stim_p_max': np.arange(0, 4, .01),
+        'stim_strength': np.arange(0, 160, 1)
+    }
+else:
+    bins = {
+        'stim_energy': np.arange(0, .008, .0001),
+        'stim_half_width': np.arange(0, 0.5, .001),
+        'stim_p_max': np.arange(0, .06, .0001),
+        'stim_strength': np.arange(0, 160, 1)
+    }
+xlabel = {
+    'stim_energy': 'Energy (dB)',
+    'stim_half_width': '(Hz)',
+    'stim_p_max': 'Peak PSD (dB/Hz)',
+    'stim_strength': 'Ratio',
+}
+# key = 'theta_energy'
+# key = 'theta_peak'
+for key in bins:
+    results[key] = list()
+    fig = plt.figure(figsize=(3.2,2))
+    plt.suptitle(key)
+    legend_lines = []
+    for color, query, label in zip(colors[1::2], queries[1::2], labels[1::2]):
+        values = lfp_results_hemisphere.query(query).loc[:,['entity_date_side', key]]
+        results[key].append(values.rename({key: label}, axis=1))
+        values[key].hist(
+            bins=bins[key], density=density, cumulative=cumulative, lw=lw, 
+            histtype=histtype, color=color)
+        legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, lw=lw, label=label))
+        
+    plt.legend(
+        handles=legend_lines,
+        bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)
+    plt.tight_layout()
+    plt.grid(False)
+    plt.xlim(right=bins[key].max() - bins[key].max()*0.025)
+    despine()
+    plt.xlabel(xlabel[key])
+    figname = f'lfp-psd-histogram-{key}'
+    fig.savefig(
+        output_path / 'figures' / f'{figname}.png', 
+        bbox_inches='tight', transparent=True)
+    fig.savefig(
+        output_path / 'figures' / f'{figname}.svg', 
+        bbox_inches='tight', transparent=True)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [24]:
+
+
+
from functools import reduce
+
+ +
+
+
+ +
+
+
+
In [25]:
+
+
+
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)
+
+ +
+
+
+ +
+
+
+
In [26]:
+
+
+
def summarize(data):
+    return "{:.1e} ± {:.1e} ({})".format(data.mean(), data.sem(), sum(~np.isnan(data)))
+
+
+def MWU(df, keys):
+    '''
+    Mann Whitney U
+    '''
+    Uvalue, pvalue = scipy.stats.mannwhitneyu(
+        df[keys[0]].dropna(), 
+        df[keys[1]].dropna(),
+        alternative='two-sided')
+
+    return "{:.2f}, {:.3f}".format(Uvalue, pvalue)
+
+
+def PRS(df, keys):
+    '''
+    Permutation ReSampling
+    '''
+    pvalue, observed_diff, diffs = permutation_resampling(
+        df[keys[0]].dropna(), 
+        df[keys[1]].dropna(), statistic=np.median)
+
+    return "{:.2f}, {:.3f}".format(observed_diff, pvalue)
+
+
+def wilcoxon(df, keys):
+    dff = df.loc[:,keys].dropna()
+    if len(dff[keys].dropna()) == 0:
+        statistic, pvalue = np.nan, np.nan
+    else:
+        statistic, pvalue = scipy.stats.wilcoxon(
+            dff[keys[0]], 
+            dff[keys[1]],
+            alternative='two-sided')
+
+    return "{:.2f}, {:.3f}, ({})".format(statistic, pvalue, len(dff))
+
+
+def paired_t(df, keys):
+    dff = df.loc[:,[keys[0], keys[1]]].dropna()
+    statistic, pvalue = scipy.stats.ttest_rel(
+        dff[keys[0]], 
+        dff[keys[1]])
+
+    return "{:.2f}, {:.3f}".format(statistic, pvalue)
+
+    
+def normality(df, key):
+    if len(df[key].dropna()) < 8:
+        statistic, pvalue = np.nan, np.nan
+    else:
+        statistic, pvalue = scipy.stats.normaltest(
+            df[key].dropna())
+
+    return "{:.2f}, {:.3f}".format(statistic, pvalue)
+
+
+def shapiro(df, key):
+    if len(df[key].dropna()) < 8:
+        statistic, pvalue = np.nan, np.nan
+    else:
+        statistic, pvalue = scipy.stats.shapiro(
+            df[key].dropna())
+
+    return "{:.2f}, {:.3f}".format(statistic, pvalue)
+
+def rename(name):
+    return name.replace("_field", "-field").replace("_", " ").capitalize()
+
+ +
+
+
+ +
+
+
+
In [27]:
+
+
+
results
+
+ +
+
+
+ +
+
+ + +
+ +
Out[27]:
+ + + + +
+
{'theta_energy':     Baseline I     11 Hz  Baseline II  30 Hz
+ 0     0.003500       NaN          NaN    NaN
+ 1     0.001237       NaN          NaN    NaN
+ 2     0.003269       NaN     0.004628    NaN
+ 3     0.001465       NaN     0.002155    NaN
+ 4     0.002685       NaN          NaN    NaN
+ 5     0.000772       NaN          NaN    NaN
+ 6     0.002735       NaN     0.002988    NaN
+ 7     0.000872       NaN     0.001177    NaN
+ 8     0.004156       NaN     0.003132    NaN
+ 9     0.001317       NaN     0.001268    NaN
+ 10    0.000423       NaN     0.003200    NaN
+ 11    0.000957       NaN     0.001153    NaN
+ 12    0.002135       NaN     0.001651    NaN
+ 13    0.001649       NaN     0.000871    NaN
+ 14    0.009256       NaN     0.006838    NaN
+ 15    0.003803       NaN     0.003271    NaN
+ 16    0.004999       NaN     0.003724    NaN
+ 17    0.001193       NaN     0.002239    NaN
+ 18    0.004548  0.001780          NaN    NaN
+ 19    0.004548  0.001093          NaN    NaN
+ 20    0.002307  0.001479          NaN    NaN
+ 21    0.002307  0.001122          NaN    NaN
+ 22    0.002577       NaN     0.002665    NaN
+ 23    0.001591       NaN     0.001562    NaN
+ 24    0.001850       NaN     0.002546    NaN
+ 25    0.001388       NaN     0.001375    NaN
+ 26    0.002535       NaN     0.003099    NaN
+ 27    0.001129       NaN     0.001638    NaN
+ 28    0.001581       NaN     0.001302    NaN
+ 29    0.001346       NaN     0.000808    NaN
+ 30    0.002590       NaN     0.001583    NaN
+ 31    0.001029       NaN     0.000921    NaN
+ 32    0.001280       NaN          NaN    NaN
+ 33    0.000644       NaN          NaN    NaN
+ 34    0.002180       NaN          NaN    NaN
+ 35    0.002834       NaN          NaN    NaN
+ 36    0.001891  0.001814          NaN    NaN
+ 37    0.001891  0.001557          NaN    NaN
+ 38    0.002443  0.003549          NaN    NaN
+ 39    0.002443  0.002262          NaN    NaN
+ 40    0.002762       NaN     0.001113    NaN
+ 41    0.002649       NaN     0.003236    NaN
+ 42    0.000662       NaN          NaN    NaN
+ 43    0.003067       NaN          NaN    NaN
+ 44    0.001097       NaN     0.000649    NaN
+ 45    0.002022       NaN     0.001838    NaN
+ 46    0.001199       NaN     0.001475    NaN
+ 47    0.004233       NaN     0.001936    NaN
+ 48    0.002117       NaN     0.004077    NaN
+ 49    0.002906       NaN     0.003994    NaN,
+ 'theta_peak':     Baseline I     11 Hz  Baseline II  30 Hz
+ 0     0.003070       NaN          NaN    NaN
+ 1     0.001036       NaN          NaN    NaN
+ 2     0.003435       NaN     0.003384    NaN
+ 3     0.001416       NaN     0.001389    NaN
+ 4     0.002043       NaN          NaN    NaN
+ 5     0.000472       NaN          NaN    NaN
+ 6     0.002429       NaN     0.003210    NaN
+ 7     0.000625       NaN     0.000961    NaN
+ 8     0.002038       NaN     0.002704    NaN
+ 9     0.000872       NaN     0.001010    NaN
+ 10    0.000342       NaN     0.002434    NaN
+ 11    0.000640       NaN     0.000825    NaN
+ 12    0.001660       NaN     0.001235    NaN
+ 13    0.001190       NaN     0.000620    NaN
+ 14    0.007286       NaN     0.006490    NaN
+ 15    0.002671       NaN     0.002261    NaN
+ 16    0.004189       NaN     0.003590    NaN
+ 17    0.000846       NaN     0.001383    NaN
+ 18    0.003873  0.001374          NaN    NaN
+ 19    0.003873  0.000699          NaN    NaN
+ 20    0.001684  0.001046          NaN    NaN
+ 21    0.001684  0.000645          NaN    NaN
+ 22    0.002021       NaN     0.001967    NaN
+ 23    0.001159       NaN     0.001171    NaN
+ 24    0.001540       NaN     0.002067    NaN
+ 25    0.001135       NaN     0.001147    NaN
+ 26    0.002134       NaN     0.002335    NaN
+ 27    0.000959       NaN     0.001139    NaN
+ 28    0.001189       NaN     0.000792    NaN
+ 29    0.000657       NaN     0.000317    NaN
+ 30    0.001134       NaN     0.001158    NaN
+ 31    0.000427       NaN     0.000362    NaN
+ 32    0.000670       NaN          NaN    NaN
+ 33    0.000301       NaN          NaN    NaN
+ 34    0.001377       NaN          NaN    NaN
+ 35    0.001044       NaN          NaN    NaN
+ 36    0.001698  0.001544          NaN    NaN
+ 37    0.001698  0.000898          NaN    NaN
+ 38    0.002605  0.002939          NaN    NaN
+ 39    0.002605  0.001677          NaN    NaN
+ 40    0.002632       NaN     0.000722    NaN
+ 41    0.002425       NaN     0.003022    NaN
+ 42    0.000412       NaN          NaN    NaN
+ 43    0.003115       NaN          NaN    NaN
+ 44    0.000959       NaN     0.000390    NaN
+ 45    0.002131       NaN     0.001653    NaN
+ 46    0.000515       NaN     0.000891    NaN
+ 47    0.002570       NaN     0.001760    NaN
+ 48    0.001693       NaN     0.002711    NaN
+ 49    0.003029       NaN     0.002933    NaN,
+ 'theta_freq':     Baseline I  11 Hz  Baseline II  30 Hz
+ 0          7.4    NaN          NaN    NaN
+ 1          7.4    NaN          NaN    NaN
+ 2          7.8    NaN          7.8    NaN
+ 3          7.8    NaN          7.8    NaN
+ 4          7.4    NaN          NaN    NaN
+ 5          7.4    NaN          NaN    NaN
+ 6          7.5    NaN          7.8    NaN
+ 7          7.5    NaN          7.8    NaN
+ 8          7.6    NaN          7.9    NaN
+ 9          7.6    NaN          7.9    NaN
+ 10         7.4    NaN          7.7    NaN
+ 11         7.4    NaN          7.7    NaN
+ 12         7.6    NaN          7.6    NaN
+ 13         7.5    NaN          7.6    NaN
+ 14         8.3    NaN          8.5    NaN
+ 15         8.2    NaN          8.5    NaN
+ 16         7.9    NaN          8.1    NaN
+ 17         7.7    NaN          8.2    NaN
+ 18         8.0    8.2          NaN    NaN
+ 19         8.0    8.2          NaN    NaN
+ 20         8.0    8.2          NaN    NaN
+ 21         8.0    8.2          NaN    NaN
+ 22         8.1    NaN          8.2    NaN
+ 23         8.1    NaN          8.2    NaN
+ 24         8.0    NaN          8.2    NaN
+ 25         8.1    NaN          8.2    NaN
+ 26         8.1    NaN          8.2    NaN
+ 27         8.1    NaN          8.2    NaN
+ 28         8.0    NaN          8.2    NaN
+ 29         6.3    NaN          8.3    NaN
+ 30         8.2    NaN          8.6    NaN
+ 31         6.2    NaN          8.3    NaN
+ 32         8.3    NaN          NaN    NaN
+ 33         8.4    NaN          NaN    NaN
+ 34         8.2    NaN          NaN    NaN
+ 35         7.6    NaN          NaN    NaN
+ 36         7.9    7.9          NaN    NaN
+ 37         7.9    7.9          NaN    NaN
+ 38         7.9    8.0          NaN    NaN
+ 39         7.9    7.9          NaN    NaN
+ 40         7.6    NaN          8.1    NaN
+ 41         7.6    NaN          8.1    NaN
+ 42         7.4    NaN          NaN    NaN
+ 43         7.6    NaN          NaN    NaN
+ 44         7.7    NaN          7.8    NaN
+ 45         7.7    NaN          7.8    NaN
+ 46         7.8    NaN          8.0    NaN
+ 47         8.0    NaN          8.0    NaN
+ 48         7.7    NaN          8.3    NaN
+ 49         7.7    NaN          8.0    NaN,
+ 'theta_half_width':     Baseline I     11 Hz  Baseline II  30 Hz
+ 0     0.823669       NaN          NaN    NaN
+ 1     0.845791       NaN          NaN    NaN
+ 2     0.577806       NaN     0.959215    NaN
+ 3     0.653362       NaN     1.056302    NaN
+ 4     1.059779       NaN          NaN    NaN
+ 5     1.147158       NaN          NaN    NaN
+ 6     0.787100       NaN     0.637116    NaN
+ 7     0.964045       NaN     0.633677    NaN
+ 8     1.285535       NaN     0.760866    NaN
+ 9     0.913884       NaN     0.766207    NaN
+ 10    0.776057       NaN     0.996439    NaN
+ 11    0.928426       NaN     0.941342    NaN
+ 12    0.966327       NaN     1.072370    NaN
+ 13    0.984655       NaN     1.106900    NaN
+ 14    0.960731       NaN     0.738833    NaN
+ 15    1.044175       NaN     0.879523    NaN
+ 16    0.911633       NaN     0.641326    NaN
+ 17    0.953731       NaN     0.744352    NaN
+ 18    0.749468  0.930533          NaN    NaN
+ 19    0.749468  0.935671          NaN    NaN
+ 20    0.930580  0.969152          NaN    NaN
+ 21    0.930580  1.049631          NaN    NaN
+ 22    1.009373       NaN     1.085564    NaN
+ 23    1.065057       NaN     0.944394    NaN
+ 24    0.831508       NaN     0.910906    NaN
+ 25    0.825350       NaN     0.831260    NaN
+ 26    0.844569       NaN     0.885471    NaN
+ 27    0.720270       NaN     0.915350    NaN
+ 28    0.873920       NaN     1.122807    NaN
+ 29         NaN       NaN     1.657428    NaN
+ 30    1.224911       NaN     1.120201    NaN
+ 31    6.316287       NaN          NaN    NaN
+ 32    1.015663       NaN          NaN    NaN
+ 33    1.318961       NaN          NaN    NaN
+ 34    1.124338       NaN          NaN    NaN
+ 35    8.080786       NaN          NaN    NaN
+ 36    0.755843  0.620998          NaN    NaN
+ 37    0.755843  1.215511          NaN    NaN
+ 38    0.650319  0.620433          NaN    NaN
+ 39    0.650319  0.745154          NaN    NaN
+ 40    0.677682       NaN     1.000266    NaN
+ 41    0.679729       NaN     0.541226    NaN
+ 42    0.945508       NaN          NaN    NaN
+ 43    0.604237       NaN          NaN    NaN
+ 44    0.583246       NaN     0.870935    NaN
+ 45    0.560840       NaN     0.754164    NaN
+ 46    0.843262       NaN     1.160272    NaN
+ 47    0.969953       NaN     0.680846    NaN
+ 48    0.823915       NaN     0.979878    NaN
+ 49    0.688965       NaN     0.850279    NaN,
+ 'stim_energy':       11 Hz  30 Hz
+ 0  0.002671    NaN
+ 1  0.000532    NaN
+ 2  0.000168    NaN
+ 3  0.000033    NaN
+ 4  0.000513    NaN
+ 5  0.002245    NaN
+ 6  0.000340    NaN
+ 7  0.000240    NaN,
+ 'stim_half_width':        11 Hz  30 Hz
+ 0  10.318750    NaN
+ 1   0.149163    NaN
+ 2   0.156917    NaN
+ 3   0.271082    NaN
+ 4   0.150665    NaN
+ 5   6.113351    NaN
+ 6   0.655030    NaN
+ 7   0.156293    NaN,
+ 'stim_p_max':       11 Hz  30 Hz
+ 0  0.000105    NaN
+ 1  0.004951    NaN
+ 2  0.001566    NaN
+ 3  0.000206    NaN
+ 4  0.004785    NaN
+ 5  0.000374    NaN
+ 6  0.000814    NaN
+ 7  0.002239    NaN,
+ 'stim_strength':       11 Hz  30 Hz
+ 0  0.059252    NaN
+ 1  4.530157    NaN
+ 2  1.059344    NaN
+ 3  0.183414    NaN
+ 4  2.637518    NaN
+ 5  0.239875    NaN
+ 6  0.229359    NaN
+ 7  0.989594    NaN}
+
+ +
+ +
+
+ +
+
+
+
In [28]:
+
+
+
stat = pd.DataFrame()
+
+for key, df in results.items():
+    Key = rename(key)
+    stat[Key] = df.agg(summarize)
+    stat[Key] = df.agg(summarize)
+
+    for i, c1 in enumerate(df.columns):
+        stat.loc[f'Normality {c1}', Key] = normality(df, c1)
+#         stat.loc[f'Shapiro {c1}', Key] = shapiro(df, c1)
+        for c2 in df.columns[i+1:]:
+#             stat.loc[f'MWU {c1} {c2}', Key] = MWU(df, [c1, c2])
+#             stat.loc[f'PRS {c1} {c2}', Key] = PRS(df, [c1, c2])
+            stat.loc[f'Wilcoxon {c1} {c2}', Key] = wilcoxon(df, [c1, c2])
+#             stat.loc[f'Paired T {c1} {c2}', Key] = paired_t(df, [c1, c2])
+
+stat.sort_index()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/stats/morestats.py:2863: UserWarning: Sample size too small for normal approximation.
+  warnings.warn("Sample size too small for normal approximation.")
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/stats/stats.py:1450: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=8
+  "anyway, n=%i" % int(n))
+
+
+
+ +
+ +
Out[28]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Theta energyTheta peakTheta freqTheta half widthStim energyStim half widthStim p maxStim strength
11 Hz1.8e-03 ± 2.8e-04 (8)1.4e-03 ± 2.6e-04 (8)8.1e+00 ± 5.3e-02 (8)8.9e-01 ± 7.4e-02 (8)8.4e-04 ± 3.6e-04 (8)2.2e+00 ± 1.4e+00 (8)1.9e-03 ± 7.0e-04 (8)1.2e+00 ± 5.6e-01 (8)
30 Hznan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)
Baseline I2.3e-03 ± 2.1e-04 (50)1.8e-03 ± 1.8e-04 (50)7.8e+00 ± 5.9e-02 (50)1.1e+00 ± 1.8e-01 (49)NaNNaNNaNNaN
Baseline II2.3e-03 ± 2.4e-04 (32)1.8e-03 ± 2.3e-04 (32)8.1e+00 ± 4.7e-02 (32)9.1e-01 ± 3.9e-02 (31)NaNNaNNaNNaN
Normality 11 Hz8.13, 0.0176.61, 0.0376.30, 0.0430.23, 0.8903.45, 0.1787.42, 0.0241.81, 0.4056.43, 0.040
Normality 30 Hznan, nannan, nannan, nannan, nannan, nannan, nannan, nannan, nan
Normality Baseline I41.72, 0.00031.09, 0.00029.47, 0.00081.74, 0.000NaNNaNNaNNaN
Normality Baseline II13.17, 0.00120.78, 0.0000.96, 0.61813.33, 0.001NaNNaNNaNNaN
Wilcoxon 11 Hz 30 Hznan, nan, (0)nan, nan, (0)nan, nan, (0)nan, nan, (0)nan, nan, (0)nan, nan, (0)nan, nan, (0)nan, nan, (0)
Wilcoxon 11 Hz Baseline IInan, nan, (0)nan, nan, (0)nan, nan, (0)nan, nan, (0)NaNNaNNaNNaN
Wilcoxon Baseline I 11 Hz5.00, 0.069, (8)2.00, 0.025, (8)0.00, 0.034, (8)6.00, 0.093, (8)NaNNaNNaNNaN
Wilcoxon Baseline I 30 Hznan, nan, (0)nan, nan, (0)nan, nan, (0)nan, nan, (0)NaNNaNNaNNaN
Wilcoxon Baseline I Baseline II264.00, 1.000, (32)256.00, 0.881, (32)0.00, 0.000, (32)203.00, 0.544, (30)NaNNaNNaNNaN
Wilcoxon Baseline II 30 Hznan, nan, (0)nan, nan, (0)nan, nan, (0)nan, nan, (0)NaNNaNNaNNaN
+
+
+ +
+ +
+
+ +
+
+
+
In [29]:
+
+
+
stat.to_latex(output_path / "statistics" / "statistics.tex")
+stat.to_csv(output_path / "statistics" / "statistics.csv")
+
+ +
+
+
+ +
+
+
+
+

Plot PSD

+
+
+
+
+
+
In [30]:
+
+
+
psd = pd.read_feather(pathlib.Path("output") / ("stimulus-lfp-response" + zscore_str) / 'data' / 'psd.feather')
+freqs = pd.read_feather(pathlib.Path("output") / ("stimulus-lfp-response" + zscore_str) / 'data' / 'freqs.feather')
+
+ +
+
+
+ +
+
+
+
In [31]:
+
+
+
from septum_mec.analysis.plotting import plot_bootstrap_timeseries
+
+ +
+
+
+ +
+
+
+
In [32]:
+
+
+
freq = freqs.T.iloc[0].values
+
+mask = (freq < 49)
+
+ +
+
+
+ +
+
+
+
In [33]:
+
+
+
fig, axs = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(5,2))
+axs = axs.repeat(2)
+for i, (ax, query) in enumerate(zip(axs.ravel(), queries)):
+    selection = [
+        f'{r.action}_{r.channel_group}' 
+        for i, r in lfp_results_hemisphere.query(query).iterrows()]
+    values = psd.loc[mask, selection].to_numpy()
+    values = 10 * np.log10(values)
+    plot_bootstrap_timeseries(freq[mask], values, ax=ax, lw=1, label=labels[i], color=colors[i])
+#     ax.set_title(titles[i])
+    ax.set_xlabel('Frequency Hz')
+    ax.legend(frameon=False)
+axs[0].set_ylabel('PSD (dB/Hz)')
+# axs[0].set_ylim(-31, 1)
+despine()
+
+figname = 'lfp-psd'
+fig.savefig(
+    output_path / 'figures' / f'{figname}.png', 
+    bbox_inches='tight', transparent=True)
+fig.savefig(
+    output_path / 'figures' / f'{figname}.svg', 
+    bbox_inches='tight', transparent=True)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.
+  out=out, **kwargs)
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars
+  ret = ret.dtype.type(ret / rcount)
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:154: RuntimeWarning: invalid value encountered in true_divide
+  ret, rcount, out=ret, casting='unsafe', subok=False)
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+

Store results in Expipe action

+
+
+
+
+
+
In [34]:
+
+
+
action = project.require_action("stimulus-lfp-response-mec" + zscore_str)
+
+ +
+
+
+ +
+
+
+
In [35]:
+
+
+
copy_tree(output_path, str(action.data_path()))
+
+ +
+
+
+ +
+
+ + +
+ +
Out[35]:
+ + + + +
+
['/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/statistics.tex',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/statistics.csv',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_energy.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_strength.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_peak.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_p_max.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_freq.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_energy.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_freq.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_half_width.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_half_width.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_half_width.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_energy.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_peak.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_p_max.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_half_width.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/.ipynb_checkpoints/lfp-psd-histogram-theta_energy-checkpoint.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_energy.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_strength.svg']
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septum_mec.analysis.registration.store_notebook(action, "20_stimulus-lfp-response-mec.ipynb")
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In [ ]:
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+ + + + + + diff --git a/actions/stimulus-lfp-response-mec-no-zscore/data/20_stimulus-lfp-response-mec.ipynb b/actions/stimulus-lfp-response-mec-no-zscore/data/20_stimulus-lfp-response-mec.ipynb new file mode 100644 index 000000000..83a5f6a9b --- /dev/null +++ b/actions/stimulus-lfp-response-mec-no-zscore/data/20_stimulus-lfp-response-mec.ipynb @@ -0,0 +1,1415 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import expipe\n", + "import pathlib\n", + "import numpy as np\n", + "import spatial_maps.stats as stats\n", + "import septum_mec\n", + "import septum_mec.analysis.data_processing as dp\n", + "import septum_mec.analysis.registration\n", + "import head_direction.head as head\n", + "import spatial_maps as sp\n", + "import speed_cells.speed as spd\n", + "import re\n", + "import joblib\n", + "import multiprocessing\n", + "import shutil\n", + "import psutil\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "from distutils.dir_util import copy_tree\n", + "from neo import SpikeTrain\n", + "import scipy\n", + "import seaborn as sns\n", + "from tqdm.notebook import tqdm_notebook as tqdm\n", + "tqdm.pandas()\n", + "\n", + "from spike_statistics.core import permutation_resampling_test\n", + "\n", + "from spikewaveform.core import calculate_waveform_features_from_template, cluster_waveform_features\n", + "\n", + "from septum_mec.analysis.plotting import violinplot, despine" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#############################\n", + "\n", + "perform_zscore = False\n", + "\n", + "if not perform_zscore:\n", + " zscore_str = \"-no-zscore\"\n", + "else:\n", + " zscore_str = \"\"\n", + "\n", + "#################################" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "plt.rc('axes', titlesize=12)\n", + "plt.rcParams.update({\n", + " 'font.size': 12, \n", + " 'figure.figsize': (6, 4), \n", + " 'figure.dpi': 150\n", + "})\n", + "\n", + "output_path = pathlib.Path(\"output\") / (\"stimulus-lfp-response-mec\" + zscore_str)\n", + "(output_path / \"statistics\").mkdir(exist_ok=True, parents=True)\n", + "(output_path / \"figures\").mkdir(exist_ok=True, parents=True)\n", + "output_path.mkdir(exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "data_loader = dp.Data()\n", + "actions = data_loader.actions\n", + "project = data_loader.project" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "identify_neurons = actions['identify-neurons']\n", + "sessions = pd.read_csv(identify_neurons.data_path('sessions'))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "lfp_action = actions['stimulus-lfp-response' + zscore_str]\n", + "lfp_results = pd.read_csv(lfp_action.data_path('results'))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "lfp_results = pd.merge(sessions, lfp_results, how='left')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "lfp_results = lfp_results.query('stim_location!=\"ms\"')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def action_group(row):\n", + " a = int(row.channel_group in [0,1,2,3])\n", + " return f'{row.action}-{a}'\n", + "lfp_results['action_side_a'] = lfp_results.apply(action_group, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "lfp_results['stim_strength'] = lfp_results['stim_p_max'] / lfp_results['theta_energy']" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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['#1b9e77','#d95f02','#7570b3','#e7298a']\n", + "labels = ['Baseline I', '11 Hz', 'Baseline II', '30 Hz']\n", + "# Hz11 means that the baseline session was indeed before an 11 Hz session\n", + "queries = ['baseline and i and Hz11', 'frequency==11', 'baseline and ii and Hz30', 'frequency==30']" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# prepare pairwise comparison: same animal same side same date different sessions" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def make_entity_date_side(row):\n", + " s = row.action_side_a.split('-')\n", + " del s[2]\n", + " return '-'.join(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "lfp_results_hemisphere['entity_date_side'] = lfp_results_hemisphere.apply(make_entity_date_side, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/lib/histograms.py:898: RuntimeWarning: invalid value encountered in true_divide\n", + " return n/db/n.sum(), bin_edges\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "density = True\n", + "cumulative = True\n", + "histtype = 'step'\n", + "lw = 2\n", + "if perform_zscore:\n", + " bins = {\n", + " 'theta_energy': np.arange(0, .7, .03),\n", + " 'theta_peak': np.arange(0, .7, .03),\n", + " 'theta_freq': np.arange(4, 10, .5),\n", + " 'theta_half_width': np.arange(0, 15, .5)\n", + " }\n", + "else:\n", + " bins = {\n", + " 'theta_energy': np.arange(0, .008, .0003),\n", + " 'theta_peak': np.arange(0, .007, .0003),\n", + " 'theta_freq': np.arange(4, 12, .5),\n", + " 'theta_half_width': np.arange(0, 15, .5)\n", + " }\n", + "xlabel = {\n", + " 'theta_energy': 'Theta energy (dB)',\n", + " 'theta_peak': 'Peak PSD (dB/Hz)',\n", + " 'theta_freq': '(Hz)',\n", + " 'theta_half_width': '(Hz)',\n", + "}\n", + "# key = 'theta_energy'\n", + "# key = 'theta_peak'\n", + "results = {}\n", + "for key in bins:\n", + " results[key] = list()\n", + " fig = plt.figure(figsize=(3.5,2))\n", + " plt.suptitle(key)\n", + " legend_lines = []\n", + " for color, query, label in zip(colors, queries, labels):\n", + " values = lfp_results_hemisphere.query(query).loc[:,['entity_date_side', key]]\n", + " results[key].append(values.rename({key: label}, axis=1))\n", + " values[key].hist(\n", + " bins=bins[key], density=density, cumulative=cumulative, lw=lw, \n", + " histtype=histtype, color=color)\n", + " legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, lw=lw, label=label))\n", + " \n", + " plt.legend(\n", + " handles=legend_lines,\n", + " bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n", + " plt.tight_layout()\n", + " plt.grid(False)\n", + " plt.xlim(right=bins[key].max() - bins[key].max()*0.025)\n", + " despine()\n", + " plt.xlabel(xlabel[key])\n", + " figname = f'lfp-psd-histogram-{key}'\n", + " fig.savefig(\n", + " output_path / 'figures' / f'{figname}.png', \n", + " bbox_inches='tight', transparent=True)\n", + " fig.savefig(\n", + " output_path / 'figures' / f'{figname}.svg', \n", + " bbox_inches='tight', transparent=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "density = True\n", + "cumulative = True\n", + "histtype = 'step'\n", + "lw = 2\n", + "if perform_zscore:\n", + " bins = {\n", + " 'stim_energy': np.arange(0, .7, .01),\n", + " 'stim_half_width': np.arange(0, 10, .5),\n", + " 'stim_p_max': np.arange(0, 4, .01),\n", + " 'stim_strength': np.arange(0, 160, 1)\n", + " }\n", + "else:\n", + " bins = {\n", + " 'stim_energy': np.arange(0, .008, .0001),\n", + " 'stim_half_width': np.arange(0, 0.5, .001),\n", + " 'stim_p_max': np.arange(0, .06, .0001),\n", + " 'stim_strength': np.arange(0, 160, 1)\n", + " }\n", + "xlabel = {\n", + " 'stim_energy': 'Energy (dB)',\n", + " 'stim_half_width': '(Hz)',\n", + " 'stim_p_max': 'Peak PSD (dB/Hz)',\n", + " 'stim_strength': 'Ratio',\n", + "}\n", + "# key = 'theta_energy'\n", + "# key = 'theta_peak'\n", + "for key in bins:\n", + " results[key] = list()\n", + " fig = plt.figure(figsize=(3.2,2))\n", + " plt.suptitle(key)\n", + " legend_lines = []\n", + " for color, query, label in zip(colors[1::2], queries[1::2], labels[1::2]):\n", + " values = lfp_results_hemisphere.query(query).loc[:,['entity_date_side', key]]\n", + " results[key].append(values.rename({key: label}, axis=1))\n", + " values[key].hist(\n", + " bins=bins[key], density=density, cumulative=cumulative, lw=lw, \n", + " histtype=histtype, color=color)\n", + " legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, lw=lw, label=label))\n", + " \n", + " plt.legend(\n", + " handles=legend_lines,\n", + " bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n", + " plt.tight_layout()\n", + " plt.grid(False)\n", + " plt.xlim(right=bins[key].max() - bins[key].max()*0.025)\n", + " despine()\n", + " plt.xlabel(xlabel[key])\n", + " figname = f'lfp-psd-histogram-{key}'\n", + " fig.savefig(\n", + " output_path / 'figures' / f'{figname}.png', \n", + " bbox_inches='tight', transparent=True)\n", + " fig.savefig(\n", + " output_path / 'figures' / f'{figname}.svg', \n", + " bbox_inches='tight', transparent=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "from functools import reduce" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "for key, val in results.items():\n", + " df = reduce(lambda left,right: pd.merge(left, right, on='entity_date_side', how='outer'), val)\n", + " results[key] = df.drop('entity_date_side',axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "def summarize(data):\n", + " return \"{:.1e} ± {:.1e} ({})\".format(data.mean(), data.sem(), sum(~np.isnan(data)))\n", + "\n", + "\n", + "def MWU(df, keys):\n", + " '''\n", + " Mann Whitney U\n", + " '''\n", + " Uvalue, pvalue = scipy.stats.mannwhitneyu(\n", + " df[keys[0]].dropna(), \n", + " df[keys[1]].dropna(),\n", + " alternative='two-sided')\n", + "\n", + " return \"{:.2f}, {:.3f}\".format(Uvalue, pvalue)\n", + "\n", + "\n", + "def PRS(df, keys):\n", + " '''\n", + " Permutation ReSampling\n", + " '''\n", + " pvalue, observed_diff, diffs = permutation_resampling(\n", + " df[keys[0]].dropna(), \n", + " df[keys[1]].dropna(), statistic=np.median)\n", + "\n", + " return \"{:.2f}, {:.3f}\".format(observed_diff, pvalue)\n", + "\n", + "\n", + "def wilcoxon(df, keys):\n", + " dff = df.loc[:,keys].dropna()\n", + " if len(dff[keys].dropna()) == 0:\n", + " statistic, pvalue = np.nan, np.nan\n", + " else:\n", + " statistic, pvalue = scipy.stats.wilcoxon(\n", + " dff[keys[0]], \n", + " dff[keys[1]],\n", + " alternative='two-sided')\n", + "\n", + " return \"{:.2f}, {:.3f}, ({})\".format(statistic, pvalue, len(dff))\n", + "\n", + "\n", + "def paired_t(df, keys):\n", + " dff = df.loc[:,[keys[0], keys[1]]].dropna()\n", + " statistic, pvalue = scipy.stats.ttest_rel(\n", + " dff[keys[0]], \n", + " dff[keys[1]])\n", + "\n", + " return \"{:.2f}, {:.3f}\".format(statistic, pvalue)\n", + "\n", + " \n", + "def normality(df, key):\n", + " if len(df[key].dropna()) < 8:\n", + " statistic, pvalue = np.nan, np.nan\n", + " else:\n", + " statistic, pvalue = scipy.stats.normaltest(\n", + " df[key].dropna())\n", + "\n", + " return \"{:.2f}, {:.3f}\".format(statistic, pvalue)\n", + "\n", + "\n", + "def shapiro(df, key):\n", + " if len(df[key].dropna()) < 8:\n", + " statistic, pvalue = np.nan, np.nan\n", + " else:\n", + " statistic, pvalue = scipy.stats.shapiro(\n", + " df[key].dropna())\n", + "\n", + " return \"{:.2f}, {:.3f}\".format(statistic, pvalue)\n", + "\n", + "def rename(name):\n", + " return name.replace(\"_field\", \"-field\").replace(\"_\", \" \").capitalize()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'theta_energy': Baseline I 11 Hz Baseline II 30 Hz\n", + " 0 0.003500 NaN NaN 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Theta energyTheta peakTheta freqTheta half widthStim energyStim half widthStim p maxStim strength
11 Hz1.8e-03 ± 2.8e-04 (8)1.4e-03 ± 2.6e-04 (8)8.1e+00 ± 5.3e-02 (8)8.9e-01 ± 7.4e-02 (8)8.4e-04 ± 3.6e-04 (8)2.2e+00 ± 1.4e+00 (8)1.9e-03 ± 7.0e-04 (8)1.2e+00 ± 5.6e-01 (8)
30 Hznan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)
Baseline I2.3e-03 ± 2.1e-04 (50)1.8e-03 ± 1.8e-04 (50)7.8e+00 ± 5.9e-02 (50)1.1e+00 ± 1.8e-01 (49)NaNNaNNaNNaN
Baseline II2.3e-03 ± 2.4e-04 (32)1.8e-03 ± 2.3e-04 (32)8.1e+00 ± 4.7e-02 (32)9.1e-01 ± 3.9e-02 (31)NaNNaNNaNNaN
Normality 11 Hz8.13, 0.0176.61, 0.0376.30, 0.0430.23, 0.8903.45, 0.1787.42, 0.0241.81, 0.4056.43, 0.040
Normality 30 Hznan, nannan, nannan, nannan, nannan, nannan, nannan, nannan, nan
Normality Baseline I41.72, 0.00031.09, 0.00029.47, 0.00081.74, 0.000NaNNaNNaNNaN
Normality Baseline II13.17, 0.00120.78, 0.0000.96, 0.61813.33, 0.001NaNNaNNaNNaN
Wilcoxon 11 Hz 30 Hznan, nan, (0)nan, nan, (0)nan, nan, (0)nan, nan, (0)nan, nan, (0)nan, nan, (0)nan, nan, (0)nan, nan, (0)
Wilcoxon 11 Hz Baseline IInan, nan, (0)nan, nan, (0)nan, nan, (0)nan, nan, (0)NaNNaNNaNNaN
Wilcoxon Baseline I 11 Hz5.00, 0.069, (8)2.00, 0.025, (8)0.00, 0.034, (8)6.00, 0.093, (8)NaNNaNNaNNaN
Wilcoxon Baseline I 30 Hznan, nan, (0)nan, nan, (0)nan, nan, (0)nan, nan, (0)NaNNaNNaNNaN
Wilcoxon Baseline I Baseline II264.00, 1.000, (32)256.00, 0.881, (32)0.00, 0.000, (32)203.00, 0.544, (30)NaNNaNNaNNaN
Wilcoxon Baseline II 30 Hznan, nan, (0)nan, nan, (0)nan, nan, (0)nan, nan, (0)NaNNaNNaNNaN
\n", + "
" + ], + "text/plain": [ + " Theta energy \\\n", + "11 Hz 1.8e-03 ± 2.8e-04 (8) \n", + "30 Hz nan ± nan (0) \n", + "Baseline I 2.3e-03 ± 2.1e-04 (50) \n", + "Baseline II 2.3e-03 ± 2.4e-04 (32) \n", + "Normality 11 Hz 8.13, 0.017 \n", + "Normality 30 Hz nan, nan \n", + "Normality Baseline I 41.72, 0.000 \n", + "Normality Baseline II 13.17, 0.001 \n", + "Wilcoxon 11 Hz 30 Hz nan, nan, (0) \n", + "Wilcoxon 11 Hz Baseline II nan, nan, (0) \n", + "Wilcoxon Baseline I 11 Hz 5.00, 0.069, (8) \n", + "Wilcoxon Baseline I 30 Hz nan, nan, (0) \n", + "Wilcoxon Baseline I Baseline II 264.00, 1.000, (32) \n", + "Wilcoxon Baseline II 30 Hz nan, nan, (0) \n", + "\n", + " Theta peak \\\n", + "11 Hz 1.4e-03 ± 2.6e-04 (8) \n", + "30 Hz nan ± nan (0) \n", + "Baseline I 1.8e-03 ± 1.8e-04 (50) \n", + "Baseline II 1.8e-03 ± 2.3e-04 (32) \n", + "Normality 11 Hz 6.61, 0.037 \n", + "Normality 30 Hz nan, nan \n", + "Normality Baseline I 31.09, 0.000 \n", + "Normality Baseline II 20.78, 0.000 \n", + "Wilcoxon 11 Hz 30 Hz nan, nan, (0) \n", + "Wilcoxon 11 Hz Baseline II nan, nan, (0) \n", + "Wilcoxon Baseline I 11 Hz 2.00, 0.025, (8) \n", + "Wilcoxon Baseline I 30 Hz nan, nan, (0) \n", + "Wilcoxon Baseline I Baseline II 256.00, 0.881, (32) \n", + "Wilcoxon Baseline II 30 Hz nan, nan, (0) \n", + "\n", + " Theta freq \\\n", + "11 Hz 8.1e+00 ± 5.3e-02 (8) \n", + "30 Hz nan ± nan (0) \n", + "Baseline I 7.8e+00 ± 5.9e-02 (50) \n", + "Baseline II 8.1e+00 ± 4.7e-02 (32) \n", + "Normality 11 Hz 6.30, 0.043 \n", + "Normality 30 Hz nan, nan \n", + "Normality Baseline I 29.47, 0.000 \n", + "Normality Baseline II 0.96, 0.618 \n", + "Wilcoxon 11 Hz 30 Hz nan, nan, (0) \n", + "Wilcoxon 11 Hz Baseline II nan, nan, (0) \n", + "Wilcoxon Baseline I 11 Hz 0.00, 0.034, (8) \n", + "Wilcoxon Baseline I 30 Hz nan, nan, (0) \n", + "Wilcoxon Baseline I Baseline II 0.00, 0.000, (32) \n", + "Wilcoxon Baseline II 30 Hz nan, nan, (0) \n", + "\n", + " Theta half width \\\n", + "11 Hz 8.9e-01 ± 7.4e-02 (8) \n", + "30 Hz nan ± nan (0) \n", + "Baseline I 1.1e+00 ± 1.8e-01 (49) \n", + "Baseline II 9.1e-01 ± 3.9e-02 (31) \n", + "Normality 11 Hz 0.23, 0.890 \n", + "Normality 30 Hz nan, nan \n", + "Normality Baseline I 81.74, 0.000 \n", + "Normality Baseline II 13.33, 0.001 \n", + "Wilcoxon 11 Hz 30 Hz nan, nan, (0) \n", + "Wilcoxon 11 Hz Baseline II nan, nan, (0) \n", + "Wilcoxon Baseline I 11 Hz 6.00, 0.093, (8) \n", + "Wilcoxon Baseline I 30 Hz nan, nan, (0) \n", + "Wilcoxon Baseline I Baseline II 203.00, 0.544, (30) \n", + "Wilcoxon Baseline II 30 Hz nan, nan, (0) \n", + "\n", + " Stim energy Stim half width \\\n", + "11 Hz 8.4e-04 ± 3.6e-04 (8) 2.2e+00 ± 1.4e+00 (8) \n", + "30 Hz nan ± nan (0) nan ± nan (0) \n", + "Baseline I NaN NaN \n", + "Baseline II NaN NaN \n", + "Normality 11 Hz 3.45, 0.178 7.42, 0.024 \n", + "Normality 30 Hz nan, nan nan, nan \n", + "Normality Baseline I NaN NaN \n", + "Normality Baseline II NaN NaN \n", + "Wilcoxon 11 Hz 30 Hz nan, nan, (0) nan, nan, (0) \n", + "Wilcoxon 11 Hz Baseline II NaN NaN \n", + "Wilcoxon Baseline I 11 Hz NaN NaN \n", + "Wilcoxon Baseline I 30 Hz NaN NaN \n", + "Wilcoxon Baseline I Baseline II NaN NaN \n", + "Wilcoxon Baseline II 30 Hz NaN NaN \n", + "\n", + " Stim p max Stim strength \n", + "11 Hz 1.9e-03 ± 7.0e-04 (8) 1.2e+00 ± 5.6e-01 (8) \n", + "30 Hz nan ± nan (0) nan ± nan (0) \n", + "Baseline I NaN NaN \n", + "Baseline II NaN NaN \n", + "Normality 11 Hz 1.81, 0.405 6.43, 0.040 \n", + "Normality 30 Hz nan, nan nan, nan \n", + "Normality Baseline I NaN NaN \n", + "Normality Baseline II NaN NaN \n", + "Wilcoxon 11 Hz 30 Hz nan, nan, (0) nan, nan, (0) \n", + "Wilcoxon 11 Hz Baseline II NaN NaN \n", + "Wilcoxon Baseline I 11 Hz NaN NaN \n", + "Wilcoxon Baseline I 30 Hz NaN NaN \n", + "Wilcoxon Baseline I Baseline II NaN NaN \n", + "Wilcoxon Baseline II 30 Hz NaN NaN " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "stat = pd.DataFrame()\n", + "\n", + "for key, df in results.items():\n", + " Key = rename(key)\n", + " stat[Key] = df.agg(summarize)\n", + " stat[Key] = df.agg(summarize)\n", + "\n", + " for i, c1 in enumerate(df.columns):\n", + " stat.loc[f'Normality {c1}', Key] = normality(df, c1)\n", + "# stat.loc[f'Shapiro {c1}', Key] = shapiro(df, c1)\n", + " for c2 in df.columns[i+1:]:\n", + "# stat.loc[f'MWU {c1} {c2}', Key] = MWU(df, [c1, c2])\n", + "# stat.loc[f'PRS {c1} {c2}', Key] = PRS(df, [c1, c2])\n", + " stat.loc[f'Wilcoxon {c1} {c2}', Key] = wilcoxon(df, [c1, c2])\n", + "# stat.loc[f'Paired T {c1} {c2}', Key] = paired_t(df, [c1, c2])\n", + "\n", + "stat.sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "stat.to_latex(output_path / \"statistics\" / \"statistics.tex\")\n", + "stat.to_csv(output_path / \"statistics\" / \"statistics.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot PSD" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "psd = pd.read_feather(pathlib.Path(\"output\") / (\"stimulus-lfp-response\" + zscore_str) / 'data' / 'psd.feather')\n", + "freqs = pd.read_feather(pathlib.Path(\"output\") / (\"stimulus-lfp-response\" + zscore_str) / 'data' / 'freqs.feather')" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "from septum_mec.analysis.plotting import plot_bootstrap_timeseries" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "freq = freqs.T.iloc[0].values\n", + "\n", + "mask = (freq < 49)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.\n", + " out=out, **kwargs)\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:154: RuntimeWarning: invalid value encountered in true_divide\n", + " ret, rcount, out=ret, casting='unsafe', subok=False)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fig, axs = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(5,2))\n", + "axs = axs.repeat(2)\n", + "for i, (ax, query) in enumerate(zip(axs.ravel(), queries)):\n", + " selection = [\n", + " f'{r.action}_{r.channel_group}' \n", + " for i, r in lfp_results_hemisphere.query(query).iterrows()]\n", + " values = psd.loc[mask, selection].to_numpy()\n", + " values = 10 * np.log10(values)\n", + " plot_bootstrap_timeseries(freq[mask], values, ax=ax, lw=1, label=labels[i], color=colors[i])\n", + "# ax.set_title(titles[i])\n", + " ax.set_xlabel('Frequency Hz')\n", + " ax.legend(frameon=False)\n", + "axs[0].set_ylabel('PSD (dB/Hz)')\n", + "# axs[0].set_ylim(-31, 1)\n", + "despine()\n", + "\n", + "figname = 'lfp-psd'\n", + "fig.savefig(\n", + " output_path / 'figures' / f'{figname}.png', \n", + " bbox_inches='tight', transparent=True)\n", + "fig.savefig(\n", + " output_path / 'figures' / f'{figname}.svg', \n", + " bbox_inches='tight', transparent=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Store results in Expipe action" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "action = project.require_action(\"stimulus-lfp-response-mec\" + zscore_str)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/statistics.tex',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/statistics/statistics.csv',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_energy.png',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_strength.png',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd.png',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_peak.svg',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_p_max.png',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_freq.png',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_energy.svg',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_freq.svg',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_half_width.png',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_half_width.svg',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_half_width.svg',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_energy.png',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_peak.png',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_p_max.svg',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-theta_half_width.png',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd.svg',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/.ipynb_checkpoints/lfp-psd-histogram-theta_energy-checkpoint.png',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_energy.svg',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec-no-zscore/data/figures/lfp-psd-histogram-stim_strength.svg']" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "copy_tree(output_path, str(action.data_path()))" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "septum_mec.analysis.registration.store_notebook(action, \"20_stimulus-lfp-response-mec.ipynb\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/actions/stimulus-lfp-response-mec-no-zscore/data/20_stimulus-lfp-response.html b/actions/stimulus-lfp-response-mec-no-zscore/data/20_stimulus-lfp-response.html new file mode 100644 index 000000000..31009d801 --- /dev/null +++ b/actions/stimulus-lfp-response-mec-no-zscore/data/20_stimulus-lfp-response.html @@ -0,0 +1,14652 @@ + + + + +20_stimulus-lfp-response + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+
+
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
+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
+import matplotlib
+from distutils.dir_util import copy_tree
+from neo import SpikeTrain
+import scipy
+import statsmodels
+import seaborn as sns
+from tqdm.notebook import tqdm_notebook as tqdm
+tqdm.pandas()
+
+from spike_statistics.core import permutation_resampling_test
+
+from spikewaveform.core import calculate_waveform_features_from_template, cluster_waveform_features
+
+from septum_mec.analysis.plotting import violinplot, despine
+
+ +
+
+
+ +
+
+
+
In [3]:
+
+
+
#############################
+
+zscore_str = "-no-zscore"
+# zscore_str = ""
+
+stim_loc = 'mec'
+# stim_loc = 'ms'
+
+#################################
+
+ +
+
+
+ +
+
+
+
In [4]:
+
+
+
%matplotlib inline
+plt.rc('axes', titlesize=12)
+plt.rcParams.update({
+    'font.size': 12, 
+    'figure.figsize': (6, 4), 
+    'figure.dpi': 150
+})
+
+output_path = pathlib.Path("output") / ("stimulus-lfp-response" + '-' + stim_loc + zscore_str)
+(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 [5]:
+
+
+
data_loader = dp.Data()
+actions = data_loader.actions
+project = data_loader.project
+
+ +
+
+
+ +
+
+
+
In [6]:
+
+
+
identify_neurons = actions['identify-neurons']
+sessions = pd.read_csv(identify_neurons.data_path('sessions'))
+
+ +
+
+
+ +
+
+
+
In [7]:
+
+
+
lfp_action = actions['stimulus-lfp-response' + zscore_str]
+lfp_results = pd.read_csv(lfp_action.data_path('results'))
+
+ +
+
+
+ +
+
+
+
In [8]:
+
+
+
lfp_results = pd.merge(sessions, lfp_results, how='left')
+
+ +
+
+
+ +
+
+
+
In [9]:
+
+
+
if stim_loc == 'ms':
+    lfp_results = lfp_results.query('stim_location!="mecl" and stim_location!="mecr"')
+elif stim_loc == 'mec':
+    lfp_results = lfp_results.query('stim_location!="ms"')
+else:
+    raise AssertionError('')
+
+ +
+
+
+ +
+
+
+
In [10]:
+
+
+
def action_group(row):
+    a = int(row.channel_group in [0,1,2,3])
+    return f'{row.action}-{a}'
+lfp_results['action_side_a'] = lfp_results.apply(action_group, axis=1)
+
+ +
+
+
+ +
+
+
+
In [11]:
+
+
+
lfp_results['stim_strength'] = lfp_results['stim_p_max'] / lfp_results['theta_bandpower']
+
+ +
+
+
+ +
+
+
+
In [12]:
+
+
+
# lfp_results_hemisphere = lfp_results.sort_values(
+#     by=['action_side_a', 'stim_strength', 'signal_to_noise'], ascending=[True, False, False]
+lfp_results_hemisphere = lfp_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', 'signal_to_noise', 'stim_strength']].head()
+
+ +
+
+
+ +
+
+ + +
+ +
Out[12]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
action_side_achannel_groupsignal_to_noisestim_strength
711833-010719-1-070.001902NaN
671833-010719-1-130.003522NaN
6951833-010719-2-070.0042801.401239
6911833-010719-2-130.0039743.920680
5831833-020719-1-07-0.002942NaN
+
+
+ +
+ +
+
+ +
+
+
+
In [13]:
+
+
+
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 [14]:
+
+
+
# prepare pairwise comparison: same animal same side same date different sessions
+
+ +
+
+
+ +
+
+
+
In [15]:
+
+
+
def make_entity_date_side(row):
+    s = row.action_side_a.split('-')
+    del s[2]
+    return '-'.join(s)
+
+ +
+
+
+ +
+
+
+
In [16]:
+
+
+
lfp_results_hemisphere['entity_date_side'] = lfp_results_hemisphere.apply(make_entity_date_side, axis=1)
+
+ +
+
+
+ +
+
+
+
In [17]:
+
+
+
from functools import reduce
+
+ +
+
+
+ +
+
+
+
In [18]:
+
+
+
keys = [
+    'theta_bandpower',
+    'theta_relpower',
+    'theta_relpeak',
+    'theta_peak',
+    'theta_freq',
+    'theta_half_width',
+    'stim_bandpower',
+    'stim_relpower',
+    'stim_relpeak',
+    'stim_half_width',
+    'stim_p_max',
+    'stim_strength',
+]
+
+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)
+
+ +
+
+
+ +
+
+
+
In [19]:
+
+
+
xlabel = {
+    'theta_bandpower': 'Theta bandpower (V$^2$/Hz)',
+    'theta_relpower': 'Theta relative power',
+    'theta_relpeak': 'Theta relative power',
+    'theta_peak': 'Peak PSD (V$^2$/Hz)',
+    'theta_freq': '(Hz)',
+    'theta_half_width': '(Hz)',
+}
+
+for key in xlabel:
+    fig = plt.figure(figsize=(3.5,2))
+    plt.suptitle(key)
+    legend_lines = []
+    for color, label in zip(colors, labels):
+        legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, label=label))
+    sns.kdeplot(data=results[key].loc[:,labels], cumulative=True, legend=False, palette=colors, common_norm=False)
+    plt.legend(
+        handles=legend_lines,
+        bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)
+    plt.tight_layout()
+    plt.grid(False)
+    despine()
+    plt.xlabel(xlabel[key])
+    figname = f'lfp-psd-histogram-{key}'
+    fig.savefig(
+        output_path / 'figures' / f'{figname}.png', 
+        bbox_inches='tight', transparent=True)
+    fig.savefig(
+        output_path / 'figures' / f'{figname}.svg', 
+        bbox_inches='tight', transparent=True)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  ndim = x[:, None].ndim
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  x = x[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  y = y[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  ndim = x[:, None].ndim
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  x = x[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  y = y[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  ndim = x[:, None].ndim
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  x = x[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  y = y[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  ndim = x[:, None].ndim
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  x = x[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  y = y[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  ndim = x[:, None].ndim
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  x = x[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  y = y[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  ndim = x[:, None].ndim
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  x = x[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  y = y[:, np.newaxis]
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [20]:
+
+
+
xlabel = {
+    'stim_bandpower': 'Energy (V$^2$/Hz)',
+    'stim_relpower': 'Relative power',
+    'stim_relpeak': 'Relative power',
+    'stim_half_width': '(Hz)',
+    'stim_p_max': 'Peak PSD (V$^2$/Hz)',
+    'stim_strength': 'Ratio',
+}
+for key in xlabel:
+    fig = plt.figure(figsize=(3.2,2))
+    plt.suptitle(key)
+    legend_lines = []
+    for color, label in zip(colors[1::2], labels[1::2]):
+        legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, label=label))
+    sns.kdeplot(data=results[key].loc[:, labels[1::2]], cumulative=True, legend=False, palette=colors[1::2], common_norm=False)
+    plt.legend(
+        handles=legend_lines,
+        bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)
+    plt.tight_layout()
+    plt.grid(False)
+    despine()
+    plt.xlabel(xlabel[key])
+    figname = f'lfp-psd-histogram-{key}'
+    fig.savefig(
+        output_path / 'figures' / f'{figname}.png', 
+        bbox_inches='tight', transparent=True)
+    fig.savefig(
+        output_path / 'figures' / f'{figname}.svg', 
+        bbox_inches='tight', transparent=True)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  ndim = x[:, None].ndim
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  x = x[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  y = y[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  ndim = x[:, None].ndim
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  x = x[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  y = y[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  ndim = x[:, None].ndim
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  x = x[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  y = y[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  ndim = x[:, None].ndim
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  x = x[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  y = y[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  ndim = x[:, None].ndim
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  x = x[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  y = y[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  ndim = x[:, None].ndim
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  x = x[:, np.newaxis]
+/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
+  y = y[:, np.newaxis]
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [21]:
+
+
+
def summarize(data):
+    return "{:.1e} ± {:.1e} ({})".format(data.mean(), data.sem(), sum(~np.isnan(data)))
+
+
+def MWU(df, keys):
+    '''
+    Mann Whitney U
+    '''
+    Uvalue, pvalue = scipy.stats.mannwhitneyu(
+        df[keys[0]].dropna(), 
+        df[keys[1]].dropna(),
+        alternative='two-sided')
+
+    return "{:.2f}, {:.3f}".format(Uvalue, pvalue)
+
+
+def PRS(df, keys):
+    '''
+    Permutation ReSampling
+    '''
+    pvalue, observed_diff, diffs = permutation_resampling(
+        df[keys[0]].dropna(), 
+        df[keys[1]].dropna(), statistic=np.median)
+
+    return "{:.2f}, {:.3f}".format(observed_diff, pvalue)
+
+
+def wilcoxon(df, keys):
+    dff = df.loc[:,[keys[0], keys[1]]].dropna()
+    statistic, pvalue = scipy.stats.wilcoxon(
+        dff[keys[0]], 
+        dff[keys[1]],
+        alternative='two-sided')
+
+    return "{:.1e}, {:.1e}, ({})".format(statistic, pvalue, len(dff))
+
+
+def summarize_wilcoxon(df, keys):
+    dff = df.loc[:,[keys[0], keys[1]]].dropna()
+
+
+    return"{:.1e} ± {:.1e}, {:.1e} ± {:.1e} ({})".format(dff[keys[0]].mean(), dff[keys[0]].sem(), dff[keys[1]].mean(), dff[keys[1]].sem(), len(dff))
+
+
+def paired_t(df, keys):
+    dff = df.loc[:,[keys[0], keys[1]]].dropna()
+    statistic, pvalue = scipy.stats.ttest_rel(
+        dff[keys[0]], 
+        dff[keys[1]])
+
+    return "{:.2f}, {:.3f}".format(statistic, pvalue)
+
+    
+def normality(df, key):
+    statistic, pvalue = scipy.stats.normaltest(
+        df[key].dropna())
+
+    return "{:.1e}, {:.1e}".format(statistic, pvalue)
+
+
+def shapiro(df, key):
+    statistic, pvalue = scipy.stats.shapiro(
+        df[key].dropna())
+
+    return "{:.2f}, {:.3f}".format(statistic, pvalue)
+
+def rename(name):
+    return name.replace("_field", "-field").replace("_", " ").capitalize()
+
+ +
+
+
+ +
+
+
+
In [22]:
+
+
+
stat = pd.DataFrame()
+
+for key, df in results.items():
+    Key = rename(key)
+    stat[Key] = df.agg(summarize)
+    stat[Key] = df.agg(summarize)
+
+    for i, c1 in enumerate(df.columns):
+        try:
+            stat.loc[f'Normality {c1}', Key] = normality(df, c1)
+        except:
+            stat.loc[f'Normality {c1}', Key] = np.nan
+#         stat.loc[f'Shapiro {c1}', Key] = shapiro(df, c1)
+        for c2 in df.columns[i+1:]:
+#             stat.loc[f'MWU {c1} {c2}', Key] = MWU(df, [c1, c2])
+#             stat.loc[f'PRS {c1} {c2}', Key] = PRS(df, [c1, c2])
+            try:
+                stat.loc[f'Wilcoxon {c1} {c2}', Key] = wilcoxon(df, [c1, c2])
+            except:
+                stat.loc[f'Wilcoxon {c1} {c2}', Key] = np.nan
+#             stat.loc[f'Paired T {c1} {c2}', Key] = paired_t(df, [c1, c2])
+            stat.loc[f'Paired summary {c1} {c2}', Key] = summarize_wilcoxon(df, [c1, c2])
+
+stat.sort_index()
+
+ +
+
+
+ +
+
+ + +
+ +
Out[22]:
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Theta bandpowerTheta relpowerTheta relpeakTheta peakTheta freqTheta half widthStim bandpowerStim relpowerStim relpeakStim half widthStim p maxStim strength
11 Hz8.5e-04 ± 8.0e-05 (44)8.6e-02 ± 4.6e-03 (44)2.3e-01 ± 5.2e-02 (44)3.2e-04 ± 3.1e-05 (44)7.7e+00 ± 1.3e-01 (44)1.7e+00 ± 3.0e-01 (43)9.6e-04 ± 8.0e-05 (44)1.1e-01 ± 7.4e-03 (44)1.6e+01 ± 1.9e+00 (44)3.4e-01 ± 2.5e-03 (44)2.1e-03 ± 2.3e-04 (44)3.3e+00 ± 3.8e-01 (44)
30 Hz5.3e-04 ± 6.3e-05 (34)5.3e-02 ± 4.2e-03 (34)3.0e-02 ± 5.3e-02 (34)1.9e-04 ± 2.7e-05 (34)7.4e+00 ± 1.4e-01 (34)1.2e+01 ± 1.9e+00 (34)1.8e-03 ± 3.3e-04 (34)1.6e-01 ± 2.3e-02 (34)8.6e+01 ± 1.4e+01 (34)3.0e-01 ± 2.3e-03 (23)5.3e-03 ± 1.1e-03 (34)1.3e+01 ± 2.8e+00 (34)
Baseline I2.2e-03 ± 2.2e-04 (46)2.6e-01 ± 1.6e-02 (46)6.5e+00 ± 6.7e-01 (46)1.7e-03 ± 1.8e-04 (46)7.8e+00 ± 4.0e-02 (46)7.7e-01 ± 1.8e-02 (46)nan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)
Baseline II2.2e-03 ± 2.3e-04 (32)2.7e-01 ± 1.7e-02 (32)6.3e+00 ± 7.4e-01 (32)1.6e-03 ± 2.1e-04 (32)8.1e+00 ± 4.7e-02 (32)8.4e-01 ± 3.2e-02 (32)nan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)
Normality 11 Hz2.1e+01, 2.6e-055.6e+00, 6.2e-021.2e+01, 2.3e-032.4e+01, 5.3e-061.9e+00, 3.9e-011.0e+01, 5.5e-031.7e+01, 1.8e-043.9e+00, 1.4e-015.8e+00, 5.6e-021.6e+01, 4.2e-041.5e+01, 5.8e-041.2e+01, 2.3e-03
Normality 30 Hz3.1e+01, 1.9e-077.9e+00, 2.0e-021.2e+01, 2.3e-033.8e+01, 5.3e-097.2e+00, 2.8e-021.9e+02, 2.2e-411.7e+01, 1.7e-041.5e+01, 4.8e-046.2e+00, 4.5e-026.1e-01, 7.4e-011.9e+01, 6.3e-054.3e+01, 5.1e-10
Normality Baseline I4.3e+01, 5.8e-101.6e+00, 4.6e-012.1e+00, 3.4e-013.2e+01, 1.3e-075.9e+00, 5.3e-021.9e+00, 3.8e-01NaNNaNNaNNaNNaNNaN
Normality Baseline II1.4e+01, 1.1e-033.6e-01, 8.3e-014.9e+00, 8.8e-022.5e+01, 3.4e-064.7e+00, 9.7e-021.6e+01, 2.8e-04NaNNaNNaNNaNNaNNaN
Paired summary 11 Hz 30 Hz8.2e-04 ± 8.8e-05, 5.4e-04 ± 6.6e-05 (32)8.4e-02 ± 4.5e-03, 5.5e-02 ± 4.2e-03 (32)2.0e-01 ± 5.0e-02, 4.9e-02 ± 5.4e-02 (32)3.0e-04 ± 3.1e-05, 1.9e-04 ± 2.8e-05 (32)7.6e+00 ± 1.5e-01, 7.3e+00 ± 1.4e-01 (32)1.5e+00 ± 3.5e-01, 1.1e+01 ± 2.0e+00 (31)9.7e-04 ± 9.3e-05, 1.8e-03 ± 3.4e-04 (32)1.1e-01 ± 8.6e-03, 1.6e-01 ± 2.4e-02 (32)1.8e+01 ± 2.4e+00, 8.5e+01 ± 1.4e+01 (32)3.4e-01 ± 3.6e-03, 3.0e-01 ± 2.3e-03 (21)2.1e-03 ± 2.7e-04, 5.4e-03 ± 1.1e-03 (32)3.5e+00 ± 4.9e-01, 1.3e+01 ± 3.0e+00 (32)
Paired summary 11 Hz Baseline II8.2e-04 ± 8.8e-05, 2.2e-03 ± 2.3e-04 (32)8.4e-02 ± 4.5e-03, 2.7e-01 ± 1.7e-02 (32)2.0e-01 ± 5.0e-02, 6.3e+00 ± 7.4e-01 (32)3.0e-04 ± 3.1e-05, 1.6e-03 ± 2.1e-04 (32)7.6e+00 ± 1.5e-01, 8.1e+00 ± 4.7e-02 (32)1.5e+00 ± 3.5e-01, 8.6e-01 ± 2.5e-02 (31)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)
Paired summary Baseline I 11 Hz2.2e-03 ± 2.3e-04, 8.5e-04 ± 8.0e-05 (44)2.6e-01 ± 1.6e-02, 8.6e-02 ± 4.6e-03 (44)6.3e+00 ± 6.7e-01, 2.3e-01 ± 5.2e-02 (44)1.6e-03 ± 1.9e-04, 3.2e-04 ± 3.1e-05 (44)7.8e+00 ± 4.2e-02, 7.7e+00 ± 1.3e-01 (44)7.8e-01 ± 1.8e-02, 1.7e+00 ± 3.0e-01 (43)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)
Paired summary Baseline I 30 Hz2.3e-03 ± 2.8e-04, 5.4e-04 ± 6.6e-05 (32)2.7e-01 ± 1.8e-02, 5.5e-02 ± 4.2e-03 (32)6.6e+00 ± 8.1e-01, 4.9e-02 ± 5.4e-02 (32)1.7e-03 ± 2.3e-04, 1.9e-04 ± 2.8e-05 (32)7.8e+00 ± 4.4e-02, 7.3e+00 ± 1.4e-01 (32)7.6e-01 ± 2.0e-02, 1.2e+01 ± 2.0e+00 (32)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)
Paired summary Baseline I Baseline II2.3e-03 ± 2.8e-04, 2.2e-03 ± 2.3e-04 (32)2.7e-01 ± 1.8e-02, 2.7e-01 ± 1.7e-02 (32)6.6e+00 ± 8.1e-01, 6.3e+00 ± 7.4e-01 (32)1.7e-03 ± 2.3e-04, 1.6e-03 ± 2.1e-04 (32)7.8e+00 ± 4.4e-02, 8.1e+00 ± 4.7e-02 (32)7.6e-01 ± 2.0e-02, 8.4e-01 ± 3.2e-02 (32)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)
Paired summary Baseline II 30 Hz2.2e-03 ± 2.3e-04, 5.4e-04 ± 6.6e-05 (32)2.7e-01 ± 1.7e-02, 5.5e-02 ± 4.2e-03 (32)6.3e+00 ± 7.4e-01, 4.9e-02 ± 5.4e-02 (32)1.6e-03 ± 2.1e-04, 1.9e-04 ± 2.8e-05 (32)8.1e+00 ± 4.7e-02, 7.3e+00 ± 1.4e-01 (32)8.4e-01 ± 3.2e-02, 1.2e+01 ± 2.0e+00 (32)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)
Wilcoxon 11 Hz 30 Hz1.1e+02, 3.3e-03, (32)4.1e+01, 3.0e-05, (32)1.3e+02, 1.4e-02, (32)1.2e+02, 5.6e-03, (32)1.2e+02, 4.5e-02, (32)6.7e+01, 3.9e-04, (31)2.1e+02, 2.9e-01, (32)2.0e+02, 2.2e-01, (32)3.0e+01, 1.2e-05, (32)0.0e+00, 9.5e-07, (21)1.6e+02, 5.0e-02, (32)8.8e+01, 1.0e-03, (32)
Wilcoxon 11 Hz Baseline II1.2e+01, 2.5e-06, (32)2.0e+00, 9.6e-07, (32)0.0e+00, 8.0e-07, (32)3.0e+00, 1.1e-06, (32)1.2e+02, 9.3e-03, (32)2.3e+02, 6.7e-01, (31)NaNNaNNaNNaNNaNNaN
Wilcoxon Baseline I 11 Hz3.5e+01, 7.9e-08, (44)1.0e+00, 8.2e-09, (44)2.0e+00, 8.7e-09, (44)3.0e+00, 9.4e-09, (44)3.6e+02, 4.7e-01, (44)4.3e+02, 6.2e-01, (43)NaNNaNNaNNaNNaNNaN
Wilcoxon Baseline I 30 Hz6.0e+00, 1.4e-06, (32)0.0e+00, 8.0e-07, (32)0.0e+00, 8.0e-07, (32)0.0e+00, 8.0e-07, (32)9.5e+01, 1.6e-03, (32)7.1e+01, 3.1e-04, (32)NaNNaNNaNNaNNaNNaN
Wilcoxon Baseline I Baseline II2.4e+02, 7.1e-01, (32)2.4e+02, 7.1e-01, (32)2.4e+02, 5.9e-01, (32)2.3e+02, 5.5e-01, (32)6.0e+00, 9.0e-06, (32)1.4e+02, 2.3e-02, (32)NaNNaNNaNNaNNaNNaN
Wilcoxon Baseline II 30 Hz1.6e+01, 3.5e-06, (32)0.0e+00, 8.0e-07, (32)0.0e+00, 8.0e-07, (32)3.0e+00, 1.1e-06, (32)5.0e+01, 9.9e-05, (32)7.5e+01, 4.1e-04, (32)NaNNaNNaNNaNNaNNaN
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In [23]:
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stat.to_latex(output_path / "statistics" / f"statistics.tex")
+stat.to_csv(output_path / "statistics" / f"statistics.csv")
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In [24]:
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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")
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+

Plot PSD

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In [25]:
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psd = pd.read_feather(pathlib.Path("output") / ("stimulus-lfp-response" + zscore_str) / 'data' / 'psd.feather')
+freqs = pd.read_feather(pathlib.Path("output") / ("stimulus-lfp-response" + zscore_str) / 'data' / 'freqs.feather')
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In [26]:
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from septum_mec.analysis.plotting import plot_bootstrap_timeseries
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In [27]:
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freq = freqs.T.iloc[0].values
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+mask = (freq < 49)
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In [28]:
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fig, axs = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(5,2))
+axs = axs.repeat(2)
+for i, (ax, query) in enumerate(zip(axs.ravel(), queries)):
+    selection = [
+        f'{r.action}_{r.channel_group}' 
+        for i, r in lfp_results_hemisphere.query(query).iterrows()]
+    values = psd.loc[mask, selection].to_numpy()
+    values = 10 * np.log10(values)
+    plot_bootstrap_timeseries(freq[mask], values, ax=ax, lw=1, label=labels[i], color=colors[i])
+#     ax.set_title(titles[i])
+    ax.set_xlabel('Frequency Hz')
+    ax.legend(frameon=False)
+axs[0].set_ylabel('PSD (dB/Hz)')
+# axs[0].set_ylim(-31, 1)
+despine()
+
+figname = 'lfp-psd'
+fig.savefig(
+    output_path / 'figures' / f'{figname}.png', 
+    bbox_inches='tight', transparent=True)
+fig.savefig(
+    output_path / 'figures' / f'{figname}.svg', 
+    bbox_inches='tight', transparent=True)
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Store results in Expipe action

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In [29]:
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action = project.require_action("stimulus-lfp-response" + '-' + stim_loc + zscore_str)
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In [30]:
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copy_tree(output_path, str(action.data_path()))
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Out[30]:
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+
['/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_stim_bandpower.tex',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_stim_relpeak.tex',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_theta_half_width.csv',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_theta_bandpower.csv',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_stim_relpower.csv',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_stim_half_width.tex',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_theta_energy.csv',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_theta_freq.csv',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_stim_p_max.tex',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_theta_energy.tex',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_stim_strength.csv',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_stim_energy.csv',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_theta_peak.tex',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/statistics.tex',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_stim_relpower.tex',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_theta_relpeak.tex',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_stim_strength.tex',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_stim_bandpower.csv',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_theta_freq.tex',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_stim_relpeak.csv',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/statistics.csv',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_stim_half_width.csv',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_theta_relpower.tex',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_theta_bandpower.tex',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_stim_p_max.csv',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/.ipynb_checkpoints/values_theta_bandpower-checkpoint.csv',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/.ipynb_checkpoints/statistics-checkpoint.csv',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_theta_relpeak.csv',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_theta_peak.csv',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_theta_relpower.csv',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_stim_energy.tex',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_theta_half_width.tex',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-theta_relpower.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-stim_energy.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-stim_strength.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-theta_peak.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-stim_bandpower.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-stim_relpeak.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-stim_p_max.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-theta_freq.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-stim_relpeak.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-theta_energy.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-theta_bandpower.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-theta_freq.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-stim_relpower.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-theta_relpeak.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-stim_half_width.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-stim_relpower.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-stim_half_width.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-stim_bandpower.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-theta_bandpower.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-theta_half_width.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-theta_energy.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-theta_peak.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-stim_p_max.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-theta_half_width.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-theta_relpower.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/.ipynb_checkpoints/lfp-psd-histogram-stim_bandpower-checkpoint.png',
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+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-stim_energy.svg',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-theta_relpeak.png',
+ '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/figures/lfp-psd-histogram-stim_strength.svg']
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septum_mec.analysis.registration.store_notebook(action, "20_stimulus-lfp-response.ipynb")
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+ + + + + + diff --git a/actions/stimulus-lfp-response-mec-no-zscore/data/20_stimulus-lfp-response.ipynb b/actions/stimulus-lfp-response-mec-no-zscore/data/20_stimulus-lfp-response.ipynb new file mode 100644 index 000000000..81419e0d5 --- /dev/null +++ b/actions/stimulus-lfp-response-mec-no-zscore/data/20_stimulus-lfp-response.ipynb @@ -0,0 +1,1610 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import expipe\n", + "import pathlib\n", + "import numpy as np\n", + "import spatial_maps.stats as stats\n", + "import septum_mec\n", + "import septum_mec.analysis.data_processing as dp\n", + "import septum_mec.analysis.registration\n", + "import head_direction.head as head\n", + "import spatial_maps as sp\n", + "import speed_cells.speed as spd\n", + "import re\n", + "import joblib\n", + "import multiprocessing\n", + "import shutil\n", + "import psutil\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "from distutils.dir_util import copy_tree\n", + "from neo import SpikeTrain\n", + "import scipy\n", + "import statsmodels\n", + "import seaborn as sns\n", + "from tqdm.notebook import tqdm_notebook as tqdm\n", + "tqdm.pandas()\n", + "\n", + "from spike_statistics.core import permutation_resampling_test\n", + "\n", + "from spikewaveform.core import calculate_waveform_features_from_template, cluster_waveform_features\n", + "\n", + "from septum_mec.analysis.plotting import violinplot, despine" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#############################\n", + "\n", + "zscore_str = \"-no-zscore\"\n", + "# zscore_str = \"\"\n", + "\n", + "stim_loc = 'mec'\n", + "# stim_loc = 'ms'\n", + "\n", + "#################################" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "plt.rc('axes', titlesize=12)\n", + "plt.rcParams.update({\n", + " 'font.size': 12, \n", + " 'figure.figsize': (6, 4), \n", + " 'figure.dpi': 150\n", + "})\n", + "\n", + "output_path = pathlib.Path(\"output\") / (\"stimulus-lfp-response\" + '-' + stim_loc + zscore_str)\n", + "(output_path / \"statistics\").mkdir(exist_ok=True, parents=True)\n", + "(output_path / \"figures\").mkdir(exist_ok=True, parents=True)\n", + "output_path.mkdir(exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data_loader = dp.Data()\n", + "actions = data_loader.actions\n", + "project = data_loader.project" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "identify_neurons = actions['identify-neurons']\n", + "sessions = pd.read_csv(identify_neurons.data_path('sessions'))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "lfp_action = actions['stimulus-lfp-response' + zscore_str]\n", + "lfp_results = pd.read_csv(lfp_action.data_path('results'))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "lfp_results = pd.merge(sessions, lfp_results, how='left')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "if stim_loc == 'ms':\n", + " lfp_results = lfp_results.query('stim_location!=\"mecl\" and stim_location!=\"mecr\"')\n", + "elif stim_loc == 'mec':\n", + " lfp_results = lfp_results.query('stim_location!=\"ms\"')\n", + "else:\n", + " raise AssertionError('')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def action_group(row):\n", + " a = int(row.channel_group in [0,1,2,3])\n", + " return f'{row.action}-{a}'\n", + "lfp_results['action_side_a'] = lfp_results.apply(action_group, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "lfp_results['stim_strength'] = lfp_results['stim_p_max'] / lfp_results['theta_bandpower']" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " action_side_a channel_group signal_to_noise stim_strength\n", + "71 1833-010719-1-0 7 0.001902 NaN\n", + "67 1833-010719-1-1 3 0.003522 NaN\n", + "695 1833-010719-2-0 7 0.004280 1.401239\n", + "691 1833-010719-2-1 3 0.003974 3.920680\n", + "583 1833-020719-1-0 7 -0.002942 NaN" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# lfp_results_hemisphere = lfp_results.sort_values(\n", + "# by=['action_side_a', 'stim_strength', 'signal_to_noise'], ascending=[True, False, False]\n", + "lfp_results_hemisphere = lfp_results.sort_values(\n", + " by=['action_side_a', 'channel_group'], ascending=[True, False]\n", + ").drop_duplicates(subset='action_side_a', keep='first')\n", + "lfp_results_hemisphere.loc[:,['action_side_a','channel_group', 'signal_to_noise', 'stim_strength']].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "colors = ['#1b9e77','#d95f02','#7570b3','#e7298a']\n", + "labels = ['Baseline I', '11 Hz', 'Baseline II', '30 Hz']\n", + "# Hz11 means that the baseline session was indeed before an 11 Hz session\n", + "queries = ['baseline and i and Hz11', 'frequency==11', 'baseline and ii and Hz30', 'frequency==30']" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# prepare pairwise comparison: same animal same side same date different sessions" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def make_entity_date_side(row):\n", + " s = row.action_side_a.split('-')\n", + " del s[2]\n", + " return '-'.join(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "lfp_results_hemisphere['entity_date_side'] = lfp_results_hemisphere.apply(make_entity_date_side, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "from functools import reduce" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "keys = [\n", + " 'theta_bandpower',\n", + " 'theta_relpower',\n", + " 'theta_relpeak',\n", + " 'theta_peak',\n", + " 'theta_freq',\n", + " 'theta_half_width',\n", + " 'stim_bandpower',\n", + " 'stim_relpower',\n", + " 'stim_relpeak',\n", + " 'stim_half_width',\n", + " 'stim_p_max',\n", + " 'stim_strength',\n", + "]\n", + "\n", + "results = {}\n", + "for key in keys:\n", + " results[key] = list()\n", + " for query, label in zip(queries, labels):\n", + " values = lfp_results_hemisphere.query(query).loc[:,['entity_date_side', key]]\n", + " results[key].append(values.rename({key: label}, axis=1))\n", + " \n", + "for key, val in results.items():\n", + " df = reduce(lambda left,right: pd.merge(left, right, on='entity_date_side', how='outer'), val)\n", + " results[key] = df.drop('entity_date_side', axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " ndim = x[:, None].ndim\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " x = x[:, np.newaxis]\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " y = y[:, np.newaxis]\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. 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Convert to a numpy array before indexing instead.\n", + " ndim = x[:, None].ndim\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " x = x[:, np.newaxis]\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " y = y[:, np.newaxis]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n"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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xlabel = {\n", + " 'theta_bandpower': 'Theta bandpower (V$^2$/Hz)',\n", + " 'theta_relpower': 'Theta relative power',\n", + " 'theta_relpeak': 'Theta relative power',\n", + " 'theta_peak': 'Peak PSD (V$^2$/Hz)',\n", + " 'theta_freq': '(Hz)',\n", + " 'theta_half_width': '(Hz)',\n", + "}\n", + "\n", + "for key in xlabel:\n", + " fig = plt.figure(figsize=(3.5,2))\n", + " plt.suptitle(key)\n", + " legend_lines = []\n", + " for color, label in zip(colors, labels):\n", + " legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, label=label))\n", + " sns.kdeplot(data=results[key].loc[:,labels], cumulative=True, legend=False, palette=colors, common_norm=False)\n", + " plt.legend(\n", + " handles=legend_lines,\n", + " bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n", + " plt.tight_layout()\n", + " plt.grid(False)\n", + " despine()\n", + " plt.xlabel(xlabel[key])\n", + " figname = f'lfp-psd-histogram-{key}'\n", + " fig.savefig(\n", + " output_path / 'figures' / f'{figname}.png', \n", + " bbox_inches='tight', transparent=True)\n", + " fig.savefig(\n", + " output_path / 'figures' / f'{figname}.svg', \n", + " bbox_inches='tight', transparent=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " ndim = x[:, None].ndim\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " x = x[:, np.newaxis]\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " y = y[:, np.newaxis]\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " ndim = x[:, None].ndim\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " x = x[:, np.newaxis]\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " y = y[:, np.newaxis]\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " ndim = x[:, None].ndim\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " x = x[:, np.newaxis]\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " y = y[:, np.newaxis]\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " ndim = x[:, None].ndim\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " x = x[:, np.newaxis]\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " y = y[:, np.newaxis]\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " ndim = x[:, None].ndim\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " x = x[:, np.newaxis]\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " y = y[:, np.newaxis]\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " ndim = x[:, None].ndim\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " x = x[:, np.newaxis]\n", + "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + " y = y[:, np.newaxis]\n" + ] + }, + { + "data": { + "image/png": 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w42Dn4poHksHvGeCYNiY1MGXCAmAPcNxxxzFr1izOPPNMdtttN3r16kVlZSX9+/dn33335bzzzmP27Nkceuihre6bNGkSf/rTn9h3332pq6ujpqaGyspKtm0r5iiJ7mGnnXbivvvu47TTTmPkyJFEIhEGDx7MtGnTuP322/nZz35GKLR9T/8pU6Zw7733cuKJJzJq1CgikQiRSIQhQ4Zw8MEHc/nll3P//ffz9a9/ndraWgAefvjhQF7DtGnTeOSRRzj99NOZMGECNTU1DBgwgCOOOILbb7+d448/vqB09t9/fx544AFOOOEEhgwZQnV1NWPHjuWkk05i1qxZfPWrX815rzeN24G4dsQHcSXkZtxMOHNxU4yJqs7OmYjjD4Dvqmq2Xqn+ALhQVRfnS1BVr8KtdPE7XKedjbiAuBr3g+UkYGq2pZhMeQolEuU5pEVEvo1b1eCbqnpjB+4fiFsPbQauGmY9boaM36hqUaZCEpG3JkyYsNusWXmXVzMmcPfccw8//akbIve73/2O6dOnt3EHEOzMPMYErixLgF7bQ9ZG+ALvH4qrtjkPN17pdVxV0ReBuSJyZjHyaYwxpuuUXQAUkam4bs/5xkO15S7cfI+PAqNU9QBc1++f4DoQXJ9tUmljjDGlo2x6gYpIDS5AXUxhvdxypTMV15C/BTgp2UvNm/D3ShHZA7eQ7EXe1nSiDRs2sHJlrnmzCzd+/HiqqqravtAYU7bKIgB6A1gfB3bCdfO+GLfK8+h89+Uw09ver6prspy/Hhf4jheR2iLPm2na8Pjjj6faqnbEnDlzGDVqVNsXGmPKVrlUgY7CBb/5uFXFf7kDaU3xtnNznH8e13OsHjhgB57HGGNMFyqLEiBuza7pqvrQjiTiTd80zjt8N9s13kS8H+JKlxNJL5RqApaIx5kx7XCOPeRhEi2NVPYdRrhXW3NYm6REPAHNURKxBMTiEE9APEEiFmfGwUcy44nPkYi7x2MfbqRiRJ+sQz+MKRdlEQBVdQm5ly5pj/6k35NP8ly3FhcAM2f4yEpE3spxanzhWeuZohtWsfHpm9j62iyalr1CIupbH7cizPCzb6f35BO7LoNdKL5hG9HlG4it2ED84y3E1zUQX9tAfF0DsXUNJBqaSTS0kGiMkmhsgcb2TW0Z2XMYA279OqHqsviaMGY79pfdWp1vP9/K2sl2v7o815gdkEgk2DjnT3zyz5+SaMyxhms8xtbXZpV9AEw0R4kuWUvL26tpeXs10XdWE31/PYlN+f5Ed1zLG6uILt9AZJeCfucZU3IsALYWa/uSVgqaRUBVsy6145UMd2vnc5a9RDzGx7eew8Yn/y/7BaEQoUgNkUFj6HdkofM7l45EU5SW11fS/Pxyml9YQfOrH0Fze/80d0AIqAxTM20ileMHtnm56VwdneRDRL6HW+niDFW9uZ3PWUl6tY9TVfVvbVx/FvBn3NzB3TbOdNuMdRF/UaMm51VuLkFwEyibIkokEqy++Ttsejo91WRF/QD6H/V96vc8msjQCVTU9iVUUS79t5zYyk00PrGEpjlLaH7pg4IDXqguQnh0f8LD+lAxqI6KAb5/vaqhtpKK2gjURAjVRQhVhQmFK6AiBN42FA5BRQWEQ+7Y2v26rY5O8iEiB+EmAzc+FgBb24JbAqgaN2t8Lsk6oczZ8M0O2vDota2CX82EKYz4/j1U9hmS567Sk0gkiC5ZS9OcxTQ+voTom6vy3xAJUymDiUwaQmS3oVTuMojw6P5UDKizgNVDeGOU76Gdk3yIyBG4CfDrA8hWSbMA6KOqcRFRYC/SS+C0IiIR0guCLuqkrPUIW9/8D5/c+V+p45oJUxj1o9lUVJfH/9tELE7Lax/ROGcJTXMWE1u+Iee1odoIkX1HUHXgzlRN3onI7sMIVXV4fgdTwjo6yYeI1AI/8/6VV5VJkVgA3N4CXACcglsXLtNk3PvWSHrZF7ODmlctYuUfvw4Jtwh95YCdGHHuv0o++CWaojTPX07jnMU0PbGE+NrctebhnfpR/bldqDl8PJG9RljAMx2e5ENEBLfc0yjcuOWLgHNIL3xtsACYzT9wf2BfFpELs6ww/R1ve5fNAlMcsa0b+PDqGcQbXIkoVFXHiB/cS2XfoV2cs46Jb2qk6en3aJqzhKZn3iPRkHtpucrdh1JzxC5UH7ELlRMGWnWmyeSf5OMcVX1JRL5ZwH07efc+6933ioicE2A+25TRkaYtf1HVs4LMD/TgACgiO+OGMTR4a5glzcEte3QwcJ+InKiqq71B8hcAJ+M+RGtQLoJELMrKP32dllXp2uRh3/wrNaP37cJctV9s9WaanlhC42OLaX5+BUTj2S8Mh6g6cCeqj9iFms9OIDy8T+dm1JSajk7ysZy2Fx3ubAncd2sug3DrNQIsCz47PTgAArfiJr1+CpiafFBVEyJymvf4ocAyEXkTV3UwDPchnqGq73R6jsvQmn9dTMOb/0kdD5jxP/Q+8MtdmKPCRZevp3H2IpoeW0zLG7kn6A7VRqg6dCw1n51A9WfGUdGvNue1Pc2imeE6oBxmJW+eeHOs6L3COzrJh6ouopv1UVDVGHBItnMi0pv09JP/AX7VGXnqyQEwJ1V9T0T2xdWbfwHYEzfk4RHcgrhPdGX+ysWWVx9k/UPpHt29DvgSA2f8dxfmqG3RDzbQOFtpfESJvrU653Wh/rXUHD6B6iMmUD1lNKGaSCfmsjQsmhm+GjiX8uigEV80M3ztxJtj53V1RjrBbSJyW7ES86pG78b1vXgH+IoXLANXtgFQVce0cX5qG+fXAOd7/0yRRTesYtWfz0gdV43YjWHfvLlbju9LbGth2yPKtn++RssrH+W8LrxTX6o/uws1R+xCZN8RbrydyeccyiP4gXsd5+AW0S53i8g/VSS4hcQnFJjedcA03BSTx6rqxh3IW7uUbQA03dsnf/8v4ltd/6JQVR3Dv3dXt+vxGftwI1tvfpFtD7xFYnNT1msqJwyiZtpEqo+cSOUug6wTS/v8gfIpAcZwr6cnuLwdM8HkJSI/Br4FNANfUtX3ipPFwlgANJ2u4e3H2Tz/76njwV/9DdUju8+McNEVG9jyp2dpfPCdrJ1ZwuMGUHP0rtRME5sncwdMvDl23qKZ4Z9hbYA9koicCFzhHX5bVZ/u7DxYADSdKpFIsOafF6WOq8ceSN/Dv9WFOUqLb21m6w3z2XrLi9DSugkiVF9FzfRJ1J24F5W7DbWSXpF4QcMCRw8jIlNwHRFDwJXtnZu0WCwAmk7V8NajNL73fOp4yMlXE6ro+gHfTc8uZeNFDxNf3XrlifDIvtSfeSA1X9idivpyKKgY07VEZDzwAG6+5fuAn3ZVXiwAmk617oF07+a6PY6idsKnujA3kGhsYfNvn6LhjtaT+lQM7UWv7x9K7bGTCEW6PkAbUw5EZADwEG7M36vAKapa0Ko6QbAAaDpN0/LX2LbomdTxwBkXd2FuIPbxFjacey8tb/gmoq6soP6bB1H/jclU1FmJz5hiEZFqXIlvIrAUOEZVt3ZlniwAmk6z4cl0p7CacZOp3eXgLstLy5urWH/OvcQ/Tld5Vk4cRN8rpxOR8lp5wphu4ge4yUUA1gB/FpF6INsg2WhbQ9WKwQKg6RTxpq1sfu721HHfwwKf5i+n5hdWsP47/2o1R2ft1/ahz08OJ1Rl/yWMCYh/3r8D2ri2UwbChxKJLqt+7fFE5K0JEybsNmvWrK7OSuA2zbuNVX+eCUBFbR/GXf1Bl4z7a5r3PuvPvQ8ao+6Bygr6/Oyz1H2ttOYe7SasK6wpafZz13SKzc//I7Xfe/JXuib4zV/G+u/emx7iUF1J/2uPp/qQsZ2eF2NM17MAaAIX27qerW8+mjrufdBXOj0PLW+vZsO596WCX6g2Qr8/fYnqyTt3el6MMd2DBUATuC0v3wcx194W7jOEWjmsU58/unw967/9TxJbm90DNZX0//OXqdpvVKfmwxjTvZTDHHymm9vy4r2p/V4HnEAo3Hm/u+Jbmlj/3XvSK7GHQ/T//QwLfsYYC4AmWPHmRhreeTx13Gv/4zvtuRPxBBt//BCx99alHut7+dFUHzau0/JgjOm+LACaQG1b9DSJ5m0AhKrrqZ14aBt3FM+WPz5L0xPptUTrz55C7fF7dNrzG2O6NwuAJlBb35id2q+bdDgVkepOed7GOYvZ+sdnU8fVh42j1zldN/DeGNP9WAA0gWp4/ZHUfv2eR3fKc0Y/2MDGnz6UOg6P6U/fK6cTqrBha8aYNAuAJjAt6z+keeXC1HHdntMCf85ES4yNFzxIYovr8Rmqr6L/tV+kok9N4M9tjCktFgBNYBoXzU3tVw4cTdWQ4DufbLl2Hi2vr0wd97nkKCrHDwz8eY0xpccCoAlMgy8A1k48JPDna3p2KVv/siD9nF/cg9rpkwJ/XmNMabIAaALTuGhear92YrAdUOLrG9j4k4fAm9o2PHYAvS86ItDnNMaUNguAJhCxho00ffB66jjI4Q+JRIJNlz1GfI23tFgkTL/fHmvr+Rlj8rIAaALRtPRF8FYaqajvT9XwXQN7rsaHF9I4W1PHvX/4GSKThgb2fMaY8mAB0ASicdmrqf2aMfsRqgjmTy328RY2XfZY6jhy4E7Unbp/IM9ljCkvFgBNIJqWvZzarx69XyDPkUgk2PQ/s0lsagQgVBeh7y+PtvF+xpiCWAA0gWhc+kpqv2Z0MIvNbvvXGzQ9/V7quPePD6dyVL9AnssYU35sOSRTdPHGLbSsXpQ6rg4gAEY/3MjmX6cn2a46dCy1X96r6M9jTFcTkV2AnwBHAsOAdcB84DpVfTTPfQOBi4EZwChgPTAP+I2qzm9nHqYCT3iHY1V1aRvX3wycDjylqlPb81ydyUqApuiaVryW6gATqulFZMiEoqafiCfY+LOHSTS4NQZDfWroe/nRhEJW9WnKi4hMA14DzgQGAm8DMVxQ+4+IXJXjvqHAAuA8YCjwOm6Q0BeBuSJyZvC57/4sAJqia/rwrdR+9ai9it4BpuH2l2l5YUXquM/FRxAe0quoz2FMVxORQcCdQC3wd2CEqu6jqiOBk3GB8AIROSHL7XcB44FHgVGqegAwAleSDAPXi0iPnyXCAqApuuaV6SEJVSOKO/whumIDm3//dOq4+qiJ1NhsL6Y8nQX0B5YCM1V1Y/KEqt4B/Nk7PNt/k1ddeRiwBThJVdd798RV9Urgb0AEuCjg/Hd7FgBN0fknwC7m+L9kr08aowBUDKij7/8caVWfply9jysB/lFVm7KcT840MTrj8Zne9n5VXZPlvuu97fEiUrvDuSxh1gnGFF2LvwQ4XIqW7rZ/vk7zguWp494XHUHFgLqipW9Md6Kqd+GqMnM5wNsuznh8iredS3bPA1Gg3kvjmY7msT0yOtK05QxVvTm43DgWAE1RxZu30bJmaeq4WCXA2KrNbL7qydRx9WcnUHN08YKr6RqrdruqDiiHOeuah739o4bOeCIR6Qf8ADgDF8iu9J2rAJLLrryb7X5VbRGRD3Elx4l0UgAENuJ6oeYyDhju7S/Pc13RWAA0RdWyalG6B2hlFZHBY3c4TTfX56PpNf56V9PHqj5L3qrdrroaOJfyaIqJr9rtqmuHvf2j84J6Aq+zy6XABKAaWAF8R1Wf9l3Wn/T3+id5kluLC4CDAshqVqr6CpB1WRgR2Q141ju8TFUfz3ZdsVkANEXlb/+LDJtIqCK8w2k2znqHpifTP2Z7XzjVen2Wh3Moj+AH7nWcgxt2EJTJwO6+4/7AsSLytKpu9h7ztwk05klrW5brC/W+SPFqX7whGw8BfYF/AJcULfE2lMsfn+kmmlenA1XV0F12OL3Y2q1s+pVvwPuU0dR+ac8dTtd0C38A4l2diSKJ4V5PkK4FeuGGM8zEBbGzgcdFJFmYibUzzUQH8vEirioz37+PC0lIROqAf+NKo88Dp6tqR/LUIVYCNEXlb/+LDN7xFeA3/+ZJEhvcj9VQbYQ+lx5lVZ9lYtjbPzpv1W5X/QxrAyyIqn7g7W4FbhGR+cCruI4spwA344Y+JNXkSS7Z+7MjeT6xHTPB5LumArgDOBBXnTtDVfOVWovOAqApqqg/AA4as0NpNS1YTsH8lqcAACAASURBVOO/304d9zrvUJvrs8x4QaNTOo+UG1VVEbkHOAmYSjoANuHaCAfmuT3Z9ldQSS0gv8PNaLMV+IKqrursDFgVqCkqfwmwclDm8KTCJZpjbLo8Pc1h5e5DqTspmEm1jemORGSAiOzvzQiTyzJvOwzcYHcgOQ5pTI50I7hqVIBF2a4Jmoici+vJGgdOVtVX27glEBYATdEk4jFa1qZ7L+9ID9Ctf32B2Hvr3EEI+v78KEJh+3M1PcoLuPa2fPN2Jn9lfuh7bIG3nUJ2k3G1f43AKzmuCYyIHAdc7R3+RFXv7+w8JNk3iima6PqPINaSOo50sAQY/WADW65/LnVc97V9iOwxbIfzZ0yJ+Y+3PcsrtbUiImNwk1uD60iS9A9v+2URGZAl3e9427tUdVuW84ERkf1xs9tUAH9V1ayTeXcWC4CmaPzVn+Heg6moru9QOpt//QQ0edOdDayj1w8OLUb2jCk1V+F6eu4C3OGvChWRfYHZuM4sTwP+UtQcXE/MvsB93jADRKRCRC7ETaTdgm8AfWcQkZ1xgboel/dvd+bzZ2OdYEzRRNe8n9rvaPVn04LlND2+JHXc+0dTqeiTrzObMeVJVd8Tka/gpkP7MnCciCiud+dE77L5wAn+oQOqmhCR04CngEOBZSLyJjAS11aYwE019k7nvRrADRNJzvQSAh7whkFkGyz8iqqeG3SGLACaomlZsyy135EOMIlYnM1XpqcKjOwzgprjditK3owpRar6oIjsDfwIOAqYhOs1Oxe3qsNNqtqS5b73vFLiRcAXgD29+x7BLYhb6JycxdTHt39UG9dGg8xIkgVAUzQta9Nr9EUGtj8AbnvgbaIL072ye//4cBvzZ3o8VV1CB6oLvZUgzvf+7WgensSV2gq9fibpVSmSj03d0XwUWyBtgCJiPRZ6oOiGdEe0yv4j23VvYlsLW65OT2lYM30SVXuPyHOHMcbsmKBKgMtFZDbwV+Df2YropvxE13+U2q/sNzzPldtruPMV4p9sdQdVYXqfZx1fjDHBCqoXaBiYDtwNfCQiV4vIPgE9l+kmYht8AbAdJcD41ma23vRC6rjupH0Jj+xb1LwZY0ymoALgaOBi3EKNA4HvAy+JyMsicq6I5Juix5SgeEsTsc3pxacr+xVefdlw5yvE17nZsEK1Eeq/Mbno+TPGmEyBBEBV/UBVf6Wqu+JmI7gB2ADsg5sB4EMRuVtEpnsTopoSF9uwstVxZf/CAmDW0t/Ajo0fNMaY9gg8+KjqAlX9Dm78x1eAB3Hzv50APAB8ICK/FpHiLB1uuoS/A0y492BClYVN8N9w5yvp1R7qItSfeWAg+TPGmEydVvpS1WZV/aeqzsDNRP5D3CzgQ3FjXN4SkadF5PjOypMpnuh6Xw/QAqs/E80xGv72cuq47uT9qOjfkfU5jTGm/Tp1HKA3d91JwJeAfUmPK3kNN0PBIcDBXg/SE1V1a2fmz3RcdH26CrTQ6s/GRxYS/9hbviwSpu7U/YPImjHGZBV4ABSRPsBXgVOBg72HQ8A64HbcTAaviUgYtzbUdcA04H+Bs4LOnykOfwkwXEAJMJFIsPWWF1PHtcdOIjzI2v6MMZ0nkADoBbNjcEHvWNzijCFc299jwE3AfaranLxHVWPAPSKyBTddzxexAFgyou0cAtHy4gdE30nP+lJ3+gGB5MsYY3IJqgS4Ejf8IVnF+R5uteKbVfWDNu5d6G1tmrYSEt24OrVf2Xdom9dvvTld+qv69GgiEwcHki9jjMklqCAzCLeMx79wVZxPtuPeGuD/gJcCyJcJSGxzujQXbiMAxlZuounJ9IoP9adbz09jTOcLKgCeDdypqpvbe6OqLvLuNyUktikdACv75A+A2+5/yy3IAoR36kfVwWMCzJkxxmQX1DCIYbhxfm0SkYtE5LaA8mE6QSIeazULTLjPkDzXJth2zxup49ov7UmowlZ8MMZ0vqAC4CXAmQVeewKuw4spUbEtayERTx3nC4DNL6wg9sFGd1ARovb43YPOnjHGZLXDVaAiMho4IsupoSKSLwiGcHOG7gFs2dF8mK7jr/4MRWqoqOmd89pt/3o9tV99yFjCQ3Nfa4wxQSpGG+DHwKWAf/BXApgA/LmA+0O4oRGmRMU2pXuAhvsMybmIbXxTI42PLk4d135pj8DzZowxuexwAFTVbSLyY+CXvodHA03Aqjy3xnElv1eAC3Y0H6brRH0lwHzVn42zFZqiAIT611I9dULgeTPGmFyK0gtUVe8A7kgei0gceEFVP1OM9E33Ftv0SWq/sq0A6Kk9ZhKhqnCg+TLGmHyCGgZxKbA8oLRNNxMroAQYX99A84L0n0TN0RJ4vowxJp9AAqCqXhpEuqZ7ivrbAHtnD4CNc5ZAzA3+qxjSi8i+ha8Yb4wxQShGL9DPervPqmpjxmMFU9XHdzQvpmu0GgSfYxYYf/VnzZETbeyfMabLFaME+BiuQ8tuwCLfY4l2pJEoUl5MF/C3AYZ7bz+nZ3x9A83zl6WOa6ZZ9acxpusVK+hkG1Dfnp/4VhwoYa2qQLOUAFtVfw6uJ7KfVX8aY7peMYZBbBf8sj1mylfreUC3bwNs/M+i1L5VfxpjugsLVGaHxJsaSDQ3pI4zq0DjDc00P5/u/Vl91MROy5sxxuTT6e1uIlILfA4IA0+r6rrOzoMpntiWta2Ow70GtjpuXrAcmmMAhHpXU2W9P40x3URgJUARGSkif/RmiUk+NglYDNyHWytwqYh8Nag8mODFt6Z/v4RqehGqrGp1vunp91L71Z8eQyhig9+NMd1DIAFQRAYD84FvA5/2nbqB9Jyhm4FewG1eYDQlKOYLgOH61qW/RCJB09Pvp46rPjOu0/JljDFtCaoE+ANgJLAEt7o7IjIBOASIAQeraj/g17hq2PMDyocJmL8KNNxrQKtz0SVriK/clDquPmRMZ2XLGGPaFFQAPAaIAtNUdZb32HRvO09V53v7Pwc2AO0eOG+6h9YlwNYB0F/9Wbn7UMKDe3Vavowxpi1BBcBxwCJVXep77EjcgPdHkw+oagvwPq2XUjIlJL4lHQAremUGwHT1Z7VVfxpjupmgAmAt0Jg8EJFKILkyxJMZ19bRvlljTDcS25K9BBjf3ETLyx+kji0AGmO6m6AC4EfAWBGJeMefwXV42YzrHAO4nqK40qKtHFGiclWBNj+/PDX7S6hfLZE9hnV63owxJp+gAuATQD/gShHZC7dYbgKYpaoxABEZAtyG6wQzJ6B8mIC17gST7gXqX/qo+lM7EwrbnAvGmO4lqIHwVwIn4nqD/gA312eL9zgicigu6IWBjcBvd+TJRKQOuBD4GjAWV9J8CbhaVR/uQHpjcG2T+bymqvu0N+1yE9+6PrVfUd8/te8PgFUH7dypeTLGmEIE8rNcVRU4CngBaALeAL6gqq97l3yEC75v4oZELO3oc4lIPfA4rkfpOOAtYKv3/A+JyM87kOze3nYdMC/Hv1c6mudy0qoK1OsEE1uzlejiNanHqw4a3en5MsaYtgQ2FZqqPgd8Ksfp94F9fAFxR1wHHAS8iguyKwBE5FTgJuASEZmnqo+1I81kAPyHqn6nCHksW9mqQP1zf1YM6014dL9Oz5cxxrSlSxpmVDVejOAnIuOBU3DrEZ6cDH7ec9yGG2gPcEk7k04GwDd2NI/lLJFItJoKrcLrBJNZ/RkK2eoPxpjuJ/DJsL32ub7ec+X8JlTVjvQEPRXXjjhPVd/Ocv564GLgYBHZuR3PkQyAb3YgTz1GormBRLQ5dZzsBdo839cBxtr/jDHdVGABUEQ+j+v9uXdb19LxFeGneNu52U6q6ocisgwYDRyG63Wal4j0wrUlgpUA89puJYj6AcQ+3EhsxYbUY1WTLQAaY7qnQAKgiBwCPICrYi2k/qujdWQTvO27ea5ZiguAhS5Et5eXn4+AwSJyAbAv7r1aBNypqvM6lNsy4x8EX1HTm1BlhKbnU7XQhHfuR3hEn67ImjHGtCmoEuCFuKrJ14FLgXeAbQE8T3L58U/yXJMspgwqMM1kibU/8DbudSQdCXxPRG4Czvamcuux4lvTJcBU+98LNvzBGFMaggqAn8ZNhTZNVVcH9BzgplED37RrWSQDb12ea/ySAbAGt3zTtbhVLYbh2hx/DpyJG97x3UISFJG3cpwaX2CeuqVW06B5PUBbXvow9VjVgTt1ep6MMaZQQQXAeuCtgIMfuKWVCu3JWuh8o894ab6iqn/yPb4c+KWILAX+BpwtItepaq7gVvZajwHsT+yTLa3b//Yb1RXZMsaYggQVAJcBQwNK228LrqqyJs81td62oZAEVfV24PZ8573B9bsAM3AD79tKc/dsj3slw90KyVd31KoNsH4ALS+nS38Vw3pb+58xplsLahzg3cAIETkioPSTktONDMxzTbLt7+MiPm9yFpixRUyz5MS3tq4CbX4pvfpD1f5W+jPGdG9BBcArcCWjW0VkhohUB/Q873jbMXmuSZ5bVGiiIhIRkXCeS5LvW4/uBJO5EkSzrwRYtf/IrsiSMcYULKgq0BuBD4A9gHuAmIisA5pzXJ9Q1Y5MGLkA+ALp8YCtiMgoINkV8dm2EhOR/rghFf2BE3B5z2Zfb5tt8H2P4R8HWFHVl+aF6UJ2xNr/jDHdXFAlwK8BR3v7IVygHQKMyvOvI+72tlNFRLKcP9vbPlXIhNuquh5Y5R3OzHaNiHwZ13uzmdwBskfwrwSRWBuCuLf+X+9qKicUOurEGGO6RlAlwDMCSrcVVV0sIncAJwH3iMgMVV0CICKnAD/2Lv1F5r3ePKIRYKOqrvSdugK4FThORK4ALlHVJu+eLwN/9a77jap+FMTrKhX+TjDxFVGSv6eq9h1JqMLm/zTGdG+BBEBVvSWIdHP4PrCn92+hiLyBq8JMVqlelGMliDneNbfgK+2p6m3eIr4XAD/BDXxfjOvVmmzYuhE3HrBH81eBxt9tJtkZN2IdYIwxJaDkl+lW1bW4NsBLcR1dJuF6hT4FnKCqv+pAmj/CzfpyP24g/V640uKDwLGq+k1VjRfnFZSmRCLRqhNMbFF6lEnVftYBxhjT/QW6GoTX+3MmcBywK9BXVQeLyCDgKuCqHKs4tIuqbsUteXRJO+4Z08b5x4D2rCHYoyQat0DM1wl2S5XbVlYQ2b0zhoAaY8yOCawEKCITgdeAPwLH4FZYGOCdHg2cDrwoIjOCyoMJjr/0BxCKuvkGIpOGEKqJdEWWjDGmXQIJgCLSF5iNW4FhOfD/aL1iw0ZgIa7R6B8iskcQ+TDB8QfAUKiOkDdneGTP4V2VJWOMaZegSoDn40p5DwKTvDa15PACvJ6aewL34drWfhhQPkxA/B1gkqU/gMjeI7oiO8YY025BBcAv4WZJOUtVs67UoKox4Fu4VRUODygfJiD+adBoTE/FGtnbSoDGmNIQVAAch1sNIu/8m6q6BlDAvjVLjH8MYCjqVpoK9a8lvFO/rsqSMca0S1ABME56FYZC8tAUUD5MQFoHQPdRV+01nFDIBsAbY0pDUAFwCTBeRIblu0hEdsItB7QkoHyYgPirQJMlQGv/M8aUkqAC4L24MYbXiUjWIoE3RvAvuLlCHwgoHyYgsWwBcC+ryTbGlI6gBsJfDZwJHA8sEJG7cJNhIyKfx60ScRZuUdmVwDUB5cMEZLteoCEbAmGMKS1BzQW62Qt0/wYOAPb3nX7Q24Zwwe8L3ioMpoTEfCtBhKJ1VI4bSEXvoJZ9NMaY4gtsJhhVXQjsjRsT+AywDogBm4AXgP8GdlfVl4LKgwlO3F8CjNVZ+58xpuQEOheoqjYA/+v9M2Uksw3Qxv8ZY0pN0QOg17nlUGAqbjX2gUACWA+8A8wFnlHVRLGf23SORCKx3ThA6wBjjCk1RQuAIhIBfoBbhHaA71QIFwD9PvIWm72+py8rVIrijZshHk0dV1T2sRXgjTElpyhtgCLSBzf59ZW4El8I2Ay8AcwDXgQWA1Hv3EjgWuBhEakvRh5M54lvab0SRNVu4wiFS35pSWNMD1OsEuA/cFWeMeAG4AZVfSPzIhGpAiYD3wROBj4H3IabO9SUiFZLIUVrqNrPVoA3xpSeHQ6AInIscBSud+dxqvpMrmtVtRnXBjhXRG7CDZOYISKHq+oTO5oX0zlajwG0DjDGmNJUjHqrU3BtfOfnC36ZVPUp4Ke4KtGTi5AP00miH36U2rcOMMaYUlWMALgfbjLrv3Xg3ptx1aaTi5AP00malyxN7VdU9iY8uFfXZcYYYzqoGAFwGPCeqra090ZV3Qq8hxsuYUpEy7IVqf1wH+v9aYwpTcUIgLXAxh24fz1gPUFLSHTVytR+5eChXZgTY4zpuGIEwDCuGrOjWoqUD9MJEi0xYhs+SR1XjhrZhbkxxpiOs8Bj2iW6eA2J0NbUcWRnGwJhjClNFgBNu7S89hGJym2p48p+g7swN8YY03HFGgjfV0Q+09F7i5QH0wmaX/uIRGVD6riifkCeq40xpvsqVgDcA7CB7D1Ay2srSQxIB8BwLwuAxpjSVKwq0NAO/jMlIL6+geiyda2qQMO9BnZhjowxpuOKUQIcW4Q0TAlofm0lhJsglF7AI2xVoMaYErXDAVBVlxUjI6b7cx1g0j1ACYWoqOvXdRkyxpgdYL1ATcFaXs3oAFPXj1C46GsqG2NMp7AAaAqSiMVpeWNlqwBo7X/GmFJmAdAUJLpkDYmGltYBsN4CoDGmdFkANAVpec3N/xmvtCEQxpjyYAHQFKTlVbcGYKs2QKsCNcaUMAuApiDNr20fAK0N0BhTyiwAmjbFN2wj9v46gFbDICwAGmNKmQVA06aW19Pr/1HbmNq1AGiMKWUWAE2bktWfANS3pHYtABpjSpkFQNOmFl8ATESsDdAYUx4sAJq8EtF4qgcoQDy+ObVvAdAYU8osAJq8ogs/JtHgqj0TkTiJqA2DMMaUBwuAJq/mlz5I7VfuXt/qnJUAjTGlzAKgyav5xRWp/bAvAIaqaqmoqu2KLBljTFFYADQ5JRIJml/+MHVcMa46tR/uNagrsmSMMUVjAdDkFHtvHYn13urvIagYkf5zsepPY0ypswBocmrV/rfLIOLxjaljC4DGmFJnAdDk5A+AVQfsRGzLutRxha0EYYwpcRYATU7+ABjZfxTxLWtTx1YCNMaUOguAJqvYR5uIf7QpdVy1/yhi/gBoi+EaY0qcBUCTVdP8Zan98E79CA/pRWzzJ+nHelsvUGNMabMAaLJqfi4dAKs+PRqAqD8A9hnS6XkyxphisgBotpOIJ2j2lQCrp7gAGNu4OvVYZd+hnZ4vY4wpJguAZjvRxZ8QX+vN+RmCqoNGk0gkiG1KB0ArARpjSp0FQLMdf/VnZI9hVPStId6wkUS0OfV4ZR8rARpjSpsFQLOdJn/736e86s/NH6cvqAhTUW/jAI0xpc0CoGkl0Ryl5UXfAPhPjwEg6mv/C/ceTKjC/nSMMaXNvsVMKy2vrSSxza3/R00lVfuMAGjV/mfVn8aYcmAB0LTSNG9par9q/1GEqisBiG3yDYHoax1gjDGlzwKgaaXpqXdT+9Ve9SdA1N8DtLcFQGNM6bMAaFJiH24kqumSXvXh49PnbAygMabMWAA0KY1Ppkt/4TH9qRyT7unp7wUatjZAY0wZsABoUpp8AbD68AmtzkU3+gPg4E7LkzHGBMUCoAEgvqWJ5udXpI5rfNWfANH1H6b2K/uN6LR8GWNMUCwAGgCanngXWmIAhPrWENlnZOpcIh4nusEXAAeM6vT8GWNMsVkANAA0PrIwtV/zuV0IVab/NGKbP4FYNHUc6W8B0BhT+iwAGuKbGml65v3Ucc3nd211Pro+PTNMRW0fKmp7d1rejDEmKBYADY1zlkA0DkDFgDqqJu/c6nx0XToAVlrpzxhTJiwAGhpnvZ3arz6ydfUnQHSdr/2vv3WAMcaUBwuAPVzsw42tlj+qnT5pu2tafFWg1gHGGFMuLAD2cA33vgkJtx/euR+R/bcPcFYFaowpRxYAe7BELM62e95IHdd+eS9CodB217WsWZratxKgMaZcWADswZrmvk981WZ3EA5RO2P3rNe1fJyeIaZq6ISs1xhjTKmxANiDNfz1hdR+9eETCA/utd018cYtxDauSh1HhlgANMaUBwuAPVTLm6taTX1Wf/oB2a/zlf5CldVU9h+Z9TpjjCk1FgB7qK03PZ/aj+w1nMh+2QNbsy8ARoaMI1RhfzLGmPJg32Y9UMvCj2mcranj+jMPzNr5BVqXACNDxme9xhhjSpEFwB5o8++eTg19qBw/kOojdsl5bfOqRal9C4DGmHJiAbCHaXpuGc1z0/N+9vrhZwiFc/8ZNK14PbVfPTJ7L1FjjClFFgB7kMS2FjZd8p/UcWT/UVRPzV2qS8SiNH/wZuq4eue9A82fMcZ0JguAPcjma54htmKDO6gI0efHh+ds+wNoXr2YREujOwhVUGUlQGNMGans6gwUg4jUARcCXwPGApuBl4CrVfXhDqa5M/A/wNHAEOATYA5whaq+U4x8d6bGOYtpuOWl1HH9mQcS2WNY/nvenZ/arxq+KxVVtYHlzxhjOlvJlwBFpB54HPg5MA54C9gKHAU8JCI/70CaArwMfAPoBbwG1ACnAi+LyLTi5L5ztCxew8Yfz0odh8cNoNf3Dm7zvm2L5qb2aye2fb0xxpSSkg+AwHXAQcCrwHhV3U9VRwOnAVHgEhH5XKGJiUgl8CAwELgNGK6qBwLDgT/gAuHfRWRgcV9GMFoWr2H9mXeRaGgBIFRfRf+rZxCqzl/4TyQSbFv4VOq4duKhgebTGGM6W0kHQBEZD5wCxIGTVTU1tYmq3gb82ju8pB3JngJMAJYDZ6nqNi+9ZuD7wDNAP+D8Hc1/0JqeXcq60/9OfG2DeyAEfX8zncoJg9q8t3nF67R8ku4tWrvrYUFl0xhjukRJB0BclWQYeE5V385y/npve7DXpleImd72Ni/opahqArjBO/x6O/PaaeJbmth01ZOs/+bdJDZscw9WhOh7xTHUHF7YXJ6b5v89tV8z7iAitgqEMabMlHoAnOJt52Y7qaofAsnVXtsswohIBTA5X5rAPG87TkR2KjCfnSK2ajNb/vgsaz5/o5vo2hvsTlWYvlcdS+0XCuvFGdu6no1P3JA67n3QVwLIrTHGdK1S7wWaLM68m+eapcBoYGIB6Y0Ekl0dc6W5AojhSp4TveNOlUgkSGxuIrZ6M9FFa4gu/JimeUuJLvx4u2vDO/ej3++/QGTS0ILSjjdtZdWNZxDfthGAito+9Dn0jKLm3xhjuoNSD4BDvO0nea5Z623bbvhKp5czTVWNichGYECBaXZIy8KP2XrLi8TXNpBobIGmKInGKImGFmJrtkJTNH8C1ZXUf2Myvb4xmVBtJO+lm+bfyeb5fye26ROaPniDRHND6tyAY39KuK5vMV6SMcZ0K6UeAOu8bWOea7ZlXFtIesVMExF5K8epXZcvX8706dO3OxFdsaHtIJdNJExF72pCfWsIPfwYtDEKMhFtpnn1EtL1pQO9f1BR05vIdU/CH5/KcbfpyZYsWfKAqn6hq/NhTEeVegCMUXg7ZqLtS4i18/kLSTOfeHNz89YlS5YUrxq1BVjn/StYVbYHx7OtGda/m6962ZSu5Bx49vmaHqvUA+AWoD9ubF4uyTa9hjzX+NNLqiF3KbA9aaKqJTeHWLLUWop5N22zz9eY0u8Fusbb5huUnmyn276HSO70cqbpDZRPNooVkqYxxphuqNQDYHJOzjF5rkmeW5TnGgBU9SNgYxtp7oTrAVpQmsYYY7qnUg+AC7ztlGwnRWQUkBwA/2yBaT6fL03g0952mRcwjTHGlKBSD4B3e9up3gTWmc72tk+p6tIC0/yHtz1DRLL1DkmmeXOB6RljjOmGSjoAqupi4A5cleQ9IpKa50tETgF+7B3+IvNeERkvIruKyPCMU3/D9YwbB9whIr2966tE5BrgEFw16bXFfj3GGGM6TyiR2NGe/F3LW5XhCWBP3DCGN3A9Q0d7l1ykqr/Kct9S75pbVHVmxrkDgUdxnV22AAtxAXEA0AwcrapPFP/VGGOM6SwlXQIEUNW1uPa6S3GdUibhenA+BZyQLfgVkOYLwN7AX4AN3n4c+BdwkAU/Y4wpfSVfAjTGGGM6ouRLgMYYY0xHWAA0xhjTI1kANMYY0yNZADTGGNMjWQA0xhjTI5X6ahDGIyJ1wIXA14CxwGbgJeBqVW1jVcCcae4M/A9wNG6x4E+AOcAVqvpOnvv2AC4GDgf6ASuBh4BfquqHHclLT9adPtuMNCqAp4GDgYiqdmABS2O6jg2DKAMiUo/78joItyLgm7ixkMl5UC9R1UvbmaYA87x0NgKLSU8G0Agcr6qzs9x3KPAf3HJSa4BlgAC9gPXAZ1X11Xa+xB6rO322WdL5NenZliwAmpJjVaDl4TrcF+SrwHhV3U9VRwOnAVHgEhH5XKGJeUs+PYj7grwNGK6qBwLDgT/ggtvfvVl4/PcNAO73zl/p3XcAMAI3iUB/4F855lg12XWLzzYjjbCIXEU6+BlTkiwAljgRGQ+cgpup5mRVTa0ur6q3Ab/2Di9pR7KnABOA5cBZqrrNS68Z+D7wDK5q8/yM+76PC3LzVfUnyRKBqm4GTgLew5U0TmtHXnqsbvbZJvO0C65EekF7Xosx3ZEFwNJ3Km4y8OdU9e0s56/3tgd77T6FmOltb/O+GFNUNQHc4B1+Pcd9f8lM0Evnphz3mey602eLiHwPeAs4DFiBa5c0pmRZACx9yXUL52Y76XU6WeYdHtZWYl7Hhsn50sS1HwGME5GdvPuGk56AvK37DhaRSFt5Md3js/U50NteC+wBvNDWcxrTnVkALH3JJaDezXPNUm87sYD0RgK1baS5Arfyhj/NZD4SwPtt5KOadCcOk1t3+WyT/gWIqn5frVoCWAAADeRJREFUVTcV8HzGdGsWAEvfEG/7SZ5r1nrbQe1IL2eaqhrD9R70p5m8b5OqNrWRj0Lz0tN1l882ee7fqprrx40xJccCYOmr87aNea7ZlnFtIem1N8325KPQvPR03eWzNaYsWQAsfbG2L0kpZNBne9Lzp9nR+0xu3eWzNaYsWQAsfVu8bU2ea5LtPg3tSK+9abYnH4XmpafrLp+tMWXJAmDpW+Ntcw5cJt2W83E70suZpjeYum9Gmsn7eufp4elvUyokLz1dd/lsjSlLFgBLX3LexjF5rkmeW9RWYqr6EelOELnS3Ak3Ps2fZjIfFeTu4ZlMrxHX29Dk110+W2PKkgXA0rfA207JdlJERpEOSM8WmObz+dIEPu1tl3lfqqjqetyckoXct8DrbWjy6xafrTHlygJg6bvb2071JjnOdLa3fUpVlxaY5j+87Rk55u1Mpnlzjvu+lXmDl86ZOe4z2XWnz9aYsmMBsMSp6mLgDly11T0ikhw8jYicQnrC4l9k3isi40VkV28WF7+/4QZKjwPuEJHe3vVVInINcAiuKu3ajPuuATYAh4rINckvWO/+27303vP2TRu62WdrTNmx5ZDKgDdz/xPAnriu7m/gJqVOTk12kar+Kst9S71rblHVmRnnDgQexXWI2AIsJL1kTjNwtKo+kSXNY3EzhlQB63ABT4DeuOB4iKq+tSOvtyfpTp9tlueY6uUNbDkkU4KsBFgGVHUtrk3nUlzHhUm4Xn5PASdk+4IsIM0XgL1xE1tv8PbjuOB2UK4vSFV9EDgAuAu3ft0+uC/ZW4D9Lfi1T3f6bI0pN1YCNMYY0yNZCdAYY0yPZAHQGGNMj2QB0BhjTI9kAdAYY0yPZAHQGGNMj2QB0BhjTI9kAdAYY0yPZAHQGGNMj2QB0BhjTI9kAdAYY0yPZAHQGGNMj2QB0BhjTI9kAdAYY0yPVNnVGTCmXIjIfsApwBHAWKAJeAf4nare15V5M8Zsz0qAxhTPhcDpwPPAj4BfAtXAvSJyWVdmzBizPVsP0JgiEZGDgZdUtdH3WBiYi1skeKiqruuq/BljWrMSoDFFoqrz/MHPeywG3INrbpjYJRkzxmRlAdCY4I3wtp90aS6MMa1YFWg3JCLt/VA2qmq/QDJTpkTkCuB8YA/g98CxwCJVlQLvHwO8B4SA6ar6UI7rRgJvAQtV9VN50qsE1gDvq+q+3mPJv4MzVPXmNvIzE/grgKqGCnkNOdL5DPAU8G1V/b+OpmNMKbASYPe2GJhXwL/nuiqDpUhEDsF1WLlGVZcAf/FOTRSRAwpM5jRc8PsAeCTH89QB9+I6wnyrjfQOBvoCWQNpZ1HVp4F/AL8XkQldmRdjgmbDILq3X7X1y9+0j1fSuh7YAPzKe/hBYDUwFDgZeLGApE7ztn9V1XiW56nCtf3tB3xFVV9vI71jvG2XBkDPT4AvAtcB07o4L8YExkqApqc5C9gduFpVNwCoahS4zTv/Na/nZk5eb8/xQAK4Kcv5CK4UdRTwDVW9p4B8HQOsA+YX+DoCo6rv496Po0Tk812dH2OCYgHQ9BheYLoYiJGu9kxKHg8DPttGUqd728dUdWnGc4SBO4AZwHdV9ZYC8jUK1xb5H6/XaHdwvbe9pCszYUyQrAq0zPg6Q9wFfANXnfUVYDSwFXgW+I2qPpPj/qHAf+E6hYzBBYuFwN+B6zK7+Wc837W4arNJwFrgKlX9vXfdAbh2t08Bg4FluFLGbwH18jdWVZeKyLdxX8BrgeGq2pIlnzsBS3GlsJ1V9aMC3p4TgJG4QNPqelVdKCLPAp/GVYM+muP9qQFO9A7/knGuArgF+DJwvqpeT2GS1Z8PF3h9wdrRoeoWVZ2ZPFDVF0RkITBZRA5S1QXFzpsxXc0CYPnqh6tO2wP4CHgb2A0X2D4vIjNUdZb/Bq9q735gINACLMJ19NgfN5D7VBE5WlVXZXm+XYHZQBTX63GSt00GyRuBMLAeeBM3VdgvcF/+NRlp3YnrmTnQO39/luc7FVeD8XCBwQ/gq952Vo7zN+EC4JdE5Duqui3LNTNw7+1aXAcXv6twwfM5YI2InJJx/llVfS9LmsfgAnnWzjQ7aF6ecwNxnxu4HySZHvHOfw2wAGjKjlWBlq9pwCBgmqqOVNX9cEHndVwg+qX/Yq+7fjL4/RkYoqp7qOruwC64L8C9cW1b2ewNvAGM9p5rFPCoiOzmpRcGroD/397Zh9xZl3H8s01Wy9kcyhYVbFrxlebUIVmyGe6PZFvNYlqxJOgVy6zshQKlhkovVEpIGYOY1la4cCgmmEvToAJTc6arLl3QK7Eg07UCX1r9cf1un7Oz+z7nPnvO45499/cDh/vcv/s6v/t3znN4rnO9/nhZRLyOdDVeAZxNJp88T0TsA3aU03c33K9yQ94w5HOo3t8cYHU5/VmD2HZgP3AccH6DTJX8sjUinum7dmY5nk1at/2PN9asay7ZO/SBiPj78HcyGhGxqu4BrAEqBX83cHXNy6vP6U3jXpcx0wFbgNObGyS1+Qe/OiLurRn/SETsrE4i4m+SriSVy+mS5kfE/nL506Tyuy0iDkrZj4jfS3oraRGeI2ltRNS5666IiKfKa/4BIGkT+T27OSIu75nzOeCLJf714Zq5tpCNpd8i6fgqYaXM+Qayq8oTwG3NH8tBrCDLDA6Q1vAhRMR+ST8A3kdactt7rxf3cJUV+e2a15/bci29nAPMZ3D2Z9vvQSvKj4GbyM8kgLeXv0c/j5TjMkmLI2LvuNZgzHTACnB68zjQxip4qmbsv9THlH7b83wBafFApr0DbKu7QUTslfRjMo62vmbuA/TVIxbrpopvfath7V+nXgHeSxaan0y6Ljf3XKusv+9HxNMN8/ZzUjn+tT+O2ccWUgGukXRCpcgLF5GW7H0RsbvlfYfRJv7X5nuwiLTU23Ad8GbyB8T6iPhng9we8u86m/z8rADNjMIKcHozmTrAJxpiWL1jxwBImk8moQB8XtLHG+ZcWo6n1Fx7suZ+S0nrBuDhugkj4jFJ/yLdjr3j/5N0I3AV6QbdXNb6IiZieaNYRYuqdQ4SioifSwpAZLJLbyJLpXgPsf4mwTqyRdr9A2SGfg96O8EMkfsUcAkZ470gIh5vko2IA5L2kTHPRU1yxhytOAY4c+mPT9VRtcxa0DN2KtmVpO7xiiJT13atTtme2PN8f831in0N4zeSFshKSSeXsfXAQuDXEfGrAXP2s7Ac/9NCtsruvKgakHQ6cBr5Pm4a4b6NSDqJkjxUV0w/biRtAL5STi9pcJv38+9yXDhQypijEFuABib+yQEsj4hHp2Del9LcDPq4usGI+LOku8iC8neRWaNVUsyoMbHK7dmmZ+p3yS4xKyUtiYg/MmH9be+Jm06WF6z7i6SzSPf2bOCaiGhrxVaKr+4HjjFHNbYADSXBpIrvLGuSk7Rc0hmS2loDwYQlelrDnEtJ5dhE1Wllg6RjSWX4LPC9lmuoqEo3ThwoRcY7yVKJWcA7Jc1iwu06TvfnWtLCvXOMcx5CsTR/CMwj2759puXr5gEvKaeO/5kZhxWgqbi9HC8tBd0HIWkBcA/wEHBZmwlLskmV3PH+BrGLh0xzK5mssYJsKP1i4PaIGHVroSjHhaVJ9TAqN+gG0v37cmB3RIylVVkpqF9NJtRM2Sa5ko4nLcxFZAnMxhHcra/sef67ca/NmCONFaCp+DIZ31oFbJP0vKUkaQlpEZ1AZpx+c4R5ryYzUjdK+lxpR4akWZI+RJZfNFKyPCtr76pyPJySgF2kS3Y22Y1mGHeQVuNZQJUU1N8+bTKcS1pXU+b+LFm4t5Bxxj8B60Z0364sxz0ugTAzEccApzeXS/pAS9kvNNTmtSIi9kh6B1n7thG4UNJuYC5Zc3cMqUDWjVKwHREPSvoY8A1SgV0maQ+ZdbqYLLB/fRGvq0WDdIN+lMwo3cthtAyLiGcl3U0WuK8CfjJE/jlJ3wE+S7Y2e4aMDY6LFyL+dympaCHjr5uL9Tu3TrgUyPdSnU+HHSqMGTtWgNOb19C+tmvxcJHBRMQdkpaRG8WuIUsB5pA9N3cCXys7BYw67/WSHmGiF+gZZI3ftaTC/UMRrc3QjIhdkh4mu81sbSjabsNWUgGuZcKaHMQWUgEC3NpXEzhZ1pIW5kNjnLOf3tjqmY1SNRQ3+HnldOsgWWOOVrwjvDmiFIX7KPA0MC8iDvlClj38/kIq+WURUdvJpcW95pCxrFcDp46xmH3GIWkd6fa+JyKG7Y5hzFGJY4BmSpH0I0kPSFrTIFK5AnfVKb/C+aTy+8XhKj+AstVQ1QP1g4c7T0eo2uFdeURXYcwUYgVopprfkO63ayS9qhosSTBvAzaVoYMSa5QskXQecH0ZvnYM69lGWoHv7U30MRNIOoXcNeSuiPjpkV6PMVOFFaCZar5E9pR8LfCYpJB0Pxn/ugU4FrguIvrjTJ8kY4N3ktbfzojYwSQp8cP3lPtuGizdWb5KxmNtJZsZjRWgmVJKvd4KspTgl2TbteVkzG8HuV1TXe/RB8myjCfJlmgXjnFN95FlHxdLaptk1AkkrSatv0/073ZvzEzDSTDGGGM6iS1AY4wxncQK0BhjTCexAjTGGNNJrACNMcZ0EitAY4wxncQK0BhjTCexAjTGGNNJrACNMcZ0EitAY4wxncQK0BhjTCexAjTGGNNJrACNMcZ0EitAY4wxncQK0BhjTCexAjTGGNNJrACNMcZ0EitAY4wxneT/jp+bLGLxl74AAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xlabel = {\n", + " 'stim_bandpower': 'Energy (V$^2$/Hz)',\n", + " 'stim_relpower': 'Relative power',\n", + " 'stim_relpeak': 'Relative power',\n", + " 'stim_half_width': '(Hz)',\n", + " 'stim_p_max': 'Peak PSD (V$^2$/Hz)',\n", + " 'stim_strength': 'Ratio',\n", + "}\n", + "for key in xlabel:\n", + " fig = plt.figure(figsize=(3.2,2))\n", + " plt.suptitle(key)\n", + " legend_lines = []\n", + " for color, label in zip(colors[1::2], labels[1::2]):\n", + " legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, label=label))\n", + " sns.kdeplot(data=results[key].loc[:, labels[1::2]], cumulative=True, legend=False, palette=colors[1::2], common_norm=False)\n", + " plt.legend(\n", + " handles=legend_lines,\n", + " bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n", + " plt.tight_layout()\n", + " plt.grid(False)\n", + " despine()\n", + " plt.xlabel(xlabel[key])\n", + " figname = f'lfp-psd-histogram-{key}'\n", + " fig.savefig(\n", + " output_path / 'figures' / f'{figname}.png', \n", + " bbox_inches='tight', transparent=True)\n", + " fig.savefig(\n", + " output_path / 'figures' / f'{figname}.svg', \n", + " bbox_inches='tight', transparent=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def summarize(data):\n", + " return \"{:.1e} ± {:.1e} ({})\".format(data.mean(), data.sem(), sum(~np.isnan(data)))\n", + "\n", + "\n", + "def MWU(df, keys):\n", + " '''\n", + " Mann Whitney U\n", + " '''\n", + " Uvalue, pvalue = scipy.stats.mannwhitneyu(\n", + " df[keys[0]].dropna(), \n", + " df[keys[1]].dropna(),\n", + " alternative='two-sided')\n", + "\n", + " return \"{:.2f}, {:.3f}\".format(Uvalue, pvalue)\n", + "\n", + "\n", + "def PRS(df, keys):\n", + " '''\n", + " Permutation ReSampling\n", + " '''\n", + " pvalue, observed_diff, diffs = permutation_resampling(\n", + " df[keys[0]].dropna(), \n", + " df[keys[1]].dropna(), statistic=np.median)\n", + "\n", + " return \"{:.2f}, {:.3f}\".format(observed_diff, pvalue)\n", + "\n", + "\n", + "def wilcoxon(df, keys):\n", + " dff = df.loc[:,[keys[0], keys[1]]].dropna()\n", + " statistic, pvalue = scipy.stats.wilcoxon(\n", + " dff[keys[0]], \n", + " dff[keys[1]],\n", + " alternative='two-sided')\n", + "\n", + " return \"{:.1e}, {:.1e}, ({})\".format(statistic, pvalue, len(dff))\n", + "\n", + "\n", + "def summarize_wilcoxon(df, keys):\n", + " dff = df.loc[:,[keys[0], keys[1]]].dropna()\n", + "\n", + "\n", + " return\"{:.1e} ± {:.1e}, {:.1e} ± {:.1e} ({})\".format(dff[keys[0]].mean(), dff[keys[0]].sem(), dff[keys[1]].mean(), dff[keys[1]].sem(), len(dff))\n", + "\n", + "\n", + "def paired_t(df, keys):\n", + " dff = df.loc[:,[keys[0], keys[1]]].dropna()\n", + " statistic, pvalue = scipy.stats.ttest_rel(\n", + " dff[keys[0]], \n", + " dff[keys[1]])\n", + "\n", + " return \"{:.2f}, {:.3f}\".format(statistic, pvalue)\n", + "\n", + " \n", + "def normality(df, key):\n", + " statistic, pvalue = scipy.stats.normaltest(\n", + " df[key].dropna())\n", + "\n", + " return \"{:.1e}, {:.1e}\".format(statistic, pvalue)\n", + "\n", + "\n", + "def shapiro(df, key):\n", + " statistic, pvalue = scipy.stats.shapiro(\n", + " df[key].dropna())\n", + "\n", + " return \"{:.2f}, {:.3f}\".format(statistic, pvalue)\n", + "\n", + "def rename(name):\n", + " return name.replace(\"_field\", \"-field\").replace(\"_\", \" \").capitalize()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Theta bandpowerTheta relpowerTheta relpeakTheta peakTheta freqTheta half widthStim bandpowerStim relpowerStim relpeakStim half widthStim p maxStim strength
11 Hz8.5e-04 ± 8.0e-05 (44)8.6e-02 ± 4.6e-03 (44)2.3e-01 ± 5.2e-02 (44)3.2e-04 ± 3.1e-05 (44)7.7e+00 ± 1.3e-01 (44)1.7e+00 ± 3.0e-01 (43)9.6e-04 ± 8.0e-05 (44)1.1e-01 ± 7.4e-03 (44)1.6e+01 ± 1.9e+00 (44)3.4e-01 ± 2.5e-03 (44)2.1e-03 ± 2.3e-04 (44)3.3e+00 ± 3.8e-01 (44)
30 Hz5.3e-04 ± 6.3e-05 (34)5.3e-02 ± 4.2e-03 (34)3.0e-02 ± 5.3e-02 (34)1.9e-04 ± 2.7e-05 (34)7.4e+00 ± 1.4e-01 (34)1.2e+01 ± 1.9e+00 (34)1.8e-03 ± 3.3e-04 (34)1.6e-01 ± 2.3e-02 (34)8.6e+01 ± 1.4e+01 (34)3.0e-01 ± 2.3e-03 (23)5.3e-03 ± 1.1e-03 (34)1.3e+01 ± 2.8e+00 (34)
Baseline I2.2e-03 ± 2.2e-04 (46)2.6e-01 ± 1.6e-02 (46)6.5e+00 ± 6.7e-01 (46)1.7e-03 ± 1.8e-04 (46)7.8e+00 ± 4.0e-02 (46)7.7e-01 ± 1.8e-02 (46)nan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)
Baseline II2.2e-03 ± 2.3e-04 (32)2.7e-01 ± 1.7e-02 (32)6.3e+00 ± 7.4e-01 (32)1.6e-03 ± 2.1e-04 (32)8.1e+00 ± 4.7e-02 (32)8.4e-01 ± 3.2e-02 (32)nan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)nan ± nan (0)
Normality 11 Hz2.1e+01, 2.6e-055.6e+00, 6.2e-021.2e+01, 2.3e-032.4e+01, 5.3e-061.9e+00, 3.9e-011.0e+01, 5.5e-031.7e+01, 1.8e-043.9e+00, 1.4e-015.8e+00, 5.6e-021.6e+01, 4.2e-041.5e+01, 5.8e-041.2e+01, 2.3e-03
Normality 30 Hz3.1e+01, 1.9e-077.9e+00, 2.0e-021.2e+01, 2.3e-033.8e+01, 5.3e-097.2e+00, 2.8e-021.9e+02, 2.2e-411.7e+01, 1.7e-041.5e+01, 4.8e-046.2e+00, 4.5e-026.1e-01, 7.4e-011.9e+01, 6.3e-054.3e+01, 5.1e-10
Normality Baseline I4.3e+01, 5.8e-101.6e+00, 4.6e-012.1e+00, 3.4e-013.2e+01, 1.3e-075.9e+00, 5.3e-021.9e+00, 3.8e-01NaNNaNNaNNaNNaNNaN
Normality Baseline II1.4e+01, 1.1e-033.6e-01, 8.3e-014.9e+00, 8.8e-022.5e+01, 3.4e-064.7e+00, 9.7e-021.6e+01, 2.8e-04NaNNaNNaNNaNNaNNaN
Paired summary 11 Hz 30 Hz8.2e-04 ± 8.8e-05, 5.4e-04 ± 6.6e-05 (32)8.4e-02 ± 4.5e-03, 5.5e-02 ± 4.2e-03 (32)2.0e-01 ± 5.0e-02, 4.9e-02 ± 5.4e-02 (32)3.0e-04 ± 3.1e-05, 1.9e-04 ± 2.8e-05 (32)7.6e+00 ± 1.5e-01, 7.3e+00 ± 1.4e-01 (32)1.5e+00 ± 3.5e-01, 1.1e+01 ± 2.0e+00 (31)9.7e-04 ± 9.3e-05, 1.8e-03 ± 3.4e-04 (32)1.1e-01 ± 8.6e-03, 1.6e-01 ± 2.4e-02 (32)1.8e+01 ± 2.4e+00, 8.5e+01 ± 1.4e+01 (32)3.4e-01 ± 3.6e-03, 3.0e-01 ± 2.3e-03 (21)2.1e-03 ± 2.7e-04, 5.4e-03 ± 1.1e-03 (32)3.5e+00 ± 4.9e-01, 1.3e+01 ± 3.0e+00 (32)
Paired summary 11 Hz Baseline II8.2e-04 ± 8.8e-05, 2.2e-03 ± 2.3e-04 (32)8.4e-02 ± 4.5e-03, 2.7e-01 ± 1.7e-02 (32)2.0e-01 ± 5.0e-02, 6.3e+00 ± 7.4e-01 (32)3.0e-04 ± 3.1e-05, 1.6e-03 ± 2.1e-04 (32)7.6e+00 ± 1.5e-01, 8.1e+00 ± 4.7e-02 (32)1.5e+00 ± 3.5e-01, 8.6e-01 ± 2.5e-02 (31)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)
Paired summary Baseline I 11 Hz2.2e-03 ± 2.3e-04, 8.5e-04 ± 8.0e-05 (44)2.6e-01 ± 1.6e-02, 8.6e-02 ± 4.6e-03 (44)6.3e+00 ± 6.7e-01, 2.3e-01 ± 5.2e-02 (44)1.6e-03 ± 1.9e-04, 3.2e-04 ± 3.1e-05 (44)7.8e+00 ± 4.2e-02, 7.7e+00 ± 1.3e-01 (44)7.8e-01 ± 1.8e-02, 1.7e+00 ± 3.0e-01 (43)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)
Paired summary Baseline I 30 Hz2.3e-03 ± 2.8e-04, 5.4e-04 ± 6.6e-05 (32)2.7e-01 ± 1.8e-02, 5.5e-02 ± 4.2e-03 (32)6.6e+00 ± 8.1e-01, 4.9e-02 ± 5.4e-02 (32)1.7e-03 ± 2.3e-04, 1.9e-04 ± 2.8e-05 (32)7.8e+00 ± 4.4e-02, 7.3e+00 ± 1.4e-01 (32)7.6e-01 ± 2.0e-02, 1.2e+01 ± 2.0e+00 (32)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)
Paired summary Baseline I Baseline II2.3e-03 ± 2.8e-04, 2.2e-03 ± 2.3e-04 (32)2.7e-01 ± 1.8e-02, 2.7e-01 ± 1.7e-02 (32)6.6e+00 ± 8.1e-01, 6.3e+00 ± 7.4e-01 (32)1.7e-03 ± 2.3e-04, 1.6e-03 ± 2.1e-04 (32)7.8e+00 ± 4.4e-02, 8.1e+00 ± 4.7e-02 (32)7.6e-01 ± 2.0e-02, 8.4e-01 ± 3.2e-02 (32)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)
Paired summary Baseline II 30 Hz2.2e-03 ± 2.3e-04, 5.4e-04 ± 6.6e-05 (32)2.7e-01 ± 1.7e-02, 5.5e-02 ± 4.2e-03 (32)6.3e+00 ± 7.4e-01, 4.9e-02 ± 5.4e-02 (32)1.6e-03 ± 2.1e-04, 1.9e-04 ± 2.8e-05 (32)8.1e+00 ± 4.7e-02, 7.3e+00 ± 1.4e-01 (32)8.4e-01 ± 3.2e-02, 1.2e+01 ± 2.0e+00 (32)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)nan ± nan, nan ± nan (0)
Wilcoxon 11 Hz 30 Hz1.1e+02, 3.3e-03, (32)4.1e+01, 3.0e-05, (32)1.3e+02, 1.4e-02, (32)1.2e+02, 5.6e-03, (32)1.2e+02, 4.5e-02, (32)6.7e+01, 3.9e-04, (31)2.1e+02, 2.9e-01, (32)2.0e+02, 2.2e-01, (32)3.0e+01, 1.2e-05, (32)0.0e+00, 9.5e-07, (21)1.6e+02, 5.0e-02, (32)8.8e+01, 1.0e-03, (32)
Wilcoxon 11 Hz Baseline II1.2e+01, 2.5e-06, (32)2.0e+00, 9.6e-07, (32)0.0e+00, 8.0e-07, (32)3.0e+00, 1.1e-06, (32)1.2e+02, 9.3e-03, (32)2.3e+02, 6.7e-01, (31)NaNNaNNaNNaNNaNNaN
Wilcoxon Baseline I 11 Hz3.5e+01, 7.9e-08, (44)1.0e+00, 8.2e-09, (44)2.0e+00, 8.7e-09, (44)3.0e+00, 9.4e-09, (44)3.6e+02, 4.7e-01, (44)4.3e+02, 6.2e-01, (43)NaNNaNNaNNaNNaNNaN
Wilcoxon Baseline I 30 Hz6.0e+00, 1.4e-06, (32)0.0e+00, 8.0e-07, (32)0.0e+00, 8.0e-07, (32)0.0e+00, 8.0e-07, (32)9.5e+01, 1.6e-03, (32)7.1e+01, 3.1e-04, (32)NaNNaNNaNNaNNaNNaN
Wilcoxon Baseline I Baseline II2.4e+02, 7.1e-01, (32)2.4e+02, 7.1e-01, (32)2.4e+02, 5.9e-01, (32)2.3e+02, 5.5e-01, (32)6.0e+00, 9.0e-06, (32)1.4e+02, 2.3e-02, (32)NaNNaNNaNNaNNaNNaN
Wilcoxon Baseline II 30 Hz1.6e+01, 3.5e-06, (32)0.0e+00, 8.0e-07, (32)0.0e+00, 8.0e-07, (32)3.0e+00, 1.1e-06, (32)5.0e+01, 9.9e-05, (32)7.5e+01, 4.1e-04, (32)NaNNaNNaNNaNNaNNaN
\n", + "
" + ], + "text/plain": [ + " Theta bandpower \\\n", + "11 Hz 8.5e-04 ± 8.0e-05 (44) \n", + "30 Hz 5.3e-04 ± 6.3e-05 (34) \n", + "Baseline I 2.2e-03 ± 2.2e-04 (46) \n", + "Baseline II 2.2e-03 ± 2.3e-04 (32) \n", + "Normality 11 Hz 2.1e+01, 2.6e-05 \n", + "Normality 30 Hz 3.1e+01, 1.9e-07 \n", + "Normality Baseline I 4.3e+01, 5.8e-10 \n", + "Normality Baseline II 1.4e+01, 1.1e-03 \n", + "Paired summary 11 Hz 30 Hz 8.2e-04 ± 8.8e-05, 5.4e-04 ± 6.6e-05 (32) \n", + "Paired summary 11 Hz Baseline II 8.2e-04 ± 8.8e-05, 2.2e-03 ± 2.3e-04 (32) \n", + "Paired summary Baseline I 11 Hz 2.2e-03 ± 2.3e-04, 8.5e-04 ± 8.0e-05 (44) \n", + "Paired summary Baseline I 30 Hz 2.3e-03 ± 2.8e-04, 5.4e-04 ± 6.6e-05 (32) \n", + "Paired summary Baseline I Baseline II 2.3e-03 ± 2.8e-04, 2.2e-03 ± 2.3e-04 (32) \n", + "Paired summary Baseline II 30 Hz 2.2e-03 ± 2.3e-04, 5.4e-04 ± 6.6e-05 (32) \n", + "Wilcoxon 11 Hz 30 Hz 1.1e+02, 3.3e-03, (32) \n", + "Wilcoxon 11 Hz Baseline II 1.2e+01, 2.5e-06, (32) \n", + "Wilcoxon Baseline I 11 Hz 3.5e+01, 7.9e-08, (44) \n", + "Wilcoxon Baseline I 30 Hz 6.0e+00, 1.4e-06, (32) \n", + "Wilcoxon Baseline I Baseline II 2.4e+02, 7.1e-01, (32) \n", + "Wilcoxon Baseline II 30 Hz 1.6e+01, 3.5e-06, (32) \n", + "\n", + " Theta relpower \\\n", + "11 Hz 8.6e-02 ± 4.6e-03 (44) \n", + "30 Hz 5.3e-02 ± 4.2e-03 (34) \n", + "Baseline I 2.6e-01 ± 1.6e-02 (46) \n", + "Baseline II 2.7e-01 ± 1.7e-02 (32) \n", + "Normality 11 Hz 5.6e+00, 6.2e-02 \n", + "Normality 30 Hz 7.9e+00, 2.0e-02 \n", + "Normality Baseline I 1.6e+00, 4.6e-01 \n", + "Normality Baseline II 3.6e-01, 8.3e-01 \n", + "Paired summary 11 Hz 30 Hz 8.4e-02 ± 4.5e-03, 5.5e-02 ± 4.2e-03 (32) \n", + "Paired summary 11 Hz Baseline II 8.4e-02 ± 4.5e-03, 2.7e-01 ± 1.7e-02 (32) \n", + "Paired summary Baseline I 11 Hz 2.6e-01 ± 1.6e-02, 8.6e-02 ± 4.6e-03 (44) \n", + "Paired summary Baseline I 30 Hz 2.7e-01 ± 1.8e-02, 5.5e-02 ± 4.2e-03 (32) \n", + "Paired summary Baseline I Baseline II 2.7e-01 ± 1.8e-02, 2.7e-01 ± 1.7e-02 (32) \n", + "Paired summary Baseline II 30 Hz 2.7e-01 ± 1.7e-02, 5.5e-02 ± 4.2e-03 (32) \n", + "Wilcoxon 11 Hz 30 Hz 4.1e+01, 3.0e-05, (32) \n", + "Wilcoxon 11 Hz Baseline II 2.0e+00, 9.6e-07, (32) \n", + "Wilcoxon Baseline I 11 Hz 1.0e+00, 8.2e-09, (44) \n", + "Wilcoxon Baseline I 30 Hz 0.0e+00, 8.0e-07, (32) \n", + "Wilcoxon Baseline I Baseline II 2.4e+02, 7.1e-01, (32) \n", + "Wilcoxon Baseline II 30 Hz 0.0e+00, 8.0e-07, (32) \n", + "\n", + " Theta relpeak \\\n", + "11 Hz 2.3e-01 ± 5.2e-02 (44) \n", + "30 Hz 3.0e-02 ± 5.3e-02 (34) \n", + "Baseline I 6.5e+00 ± 6.7e-01 (46) \n", + "Baseline II 6.3e+00 ± 7.4e-01 (32) \n", + "Normality 11 Hz 1.2e+01, 2.3e-03 \n", + "Normality 30 Hz 1.2e+01, 2.3e-03 \n", + "Normality Baseline I 2.1e+00, 3.4e-01 \n", + "Normality Baseline II 4.9e+00, 8.8e-02 \n", + "Paired summary 11 Hz 30 Hz 2.0e-01 ± 5.0e-02, 4.9e-02 ± 5.4e-02 (32) \n", + "Paired summary 11 Hz Baseline II 2.0e-01 ± 5.0e-02, 6.3e+00 ± 7.4e-01 (32) \n", + "Paired summary Baseline I 11 Hz 6.3e+00 ± 6.7e-01, 2.3e-01 ± 5.2e-02 (44) \n", + "Paired summary Baseline I 30 Hz 6.6e+00 ± 8.1e-01, 4.9e-02 ± 5.4e-02 (32) \n", + "Paired summary Baseline I Baseline II 6.6e+00 ± 8.1e-01, 6.3e+00 ± 7.4e-01 (32) \n", + "Paired summary Baseline II 30 Hz 6.3e+00 ± 7.4e-01, 4.9e-02 ± 5.4e-02 (32) \n", + "Wilcoxon 11 Hz 30 Hz 1.3e+02, 1.4e-02, (32) \n", + "Wilcoxon 11 Hz Baseline II 0.0e+00, 8.0e-07, (32) \n", + "Wilcoxon Baseline I 11 Hz 2.0e+00, 8.7e-09, (44) \n", + "Wilcoxon Baseline I 30 Hz 0.0e+00, 8.0e-07, (32) \n", + "Wilcoxon Baseline I Baseline II 2.4e+02, 5.9e-01, (32) \n", + "Wilcoxon Baseline II 30 Hz 0.0e+00, 8.0e-07, (32) \n", + "\n", + " Theta peak \\\n", + "11 Hz 3.2e-04 ± 3.1e-05 (44) \n", + "30 Hz 1.9e-04 ± 2.7e-05 (34) \n", + "Baseline I 1.7e-03 ± 1.8e-04 (46) \n", + "Baseline II 1.6e-03 ± 2.1e-04 (32) \n", + "Normality 11 Hz 2.4e+01, 5.3e-06 \n", + "Normality 30 Hz 3.8e+01, 5.3e-09 \n", + "Normality Baseline I 3.2e+01, 1.3e-07 \n", + "Normality Baseline II 2.5e+01, 3.4e-06 \n", + "Paired summary 11 Hz 30 Hz 3.0e-04 ± 3.1e-05, 1.9e-04 ± 2.8e-05 (32) \n", + "Paired summary 11 Hz Baseline II 3.0e-04 ± 3.1e-05, 1.6e-03 ± 2.1e-04 (32) \n", + "Paired summary Baseline I 11 Hz 1.6e-03 ± 1.9e-04, 3.2e-04 ± 3.1e-05 (44) \n", + "Paired summary Baseline I 30 Hz 1.7e-03 ± 2.3e-04, 1.9e-04 ± 2.8e-05 (32) \n", + "Paired summary Baseline I Baseline II 1.7e-03 ± 2.3e-04, 1.6e-03 ± 2.1e-04 (32) \n", + "Paired summary Baseline II 30 Hz 1.6e-03 ± 2.1e-04, 1.9e-04 ± 2.8e-05 (32) \n", + "Wilcoxon 11 Hz 30 Hz 1.2e+02, 5.6e-03, (32) \n", + "Wilcoxon 11 Hz Baseline II 3.0e+00, 1.1e-06, (32) \n", + "Wilcoxon Baseline I 11 Hz 3.0e+00, 9.4e-09, (44) \n", + "Wilcoxon Baseline I 30 Hz 0.0e+00, 8.0e-07, (32) \n", + "Wilcoxon Baseline I Baseline II 2.3e+02, 5.5e-01, (32) \n", + "Wilcoxon Baseline II 30 Hz 3.0e+00, 1.1e-06, (32) \n", + "\n", + " Theta freq \\\n", + "11 Hz 7.7e+00 ± 1.3e-01 (44) \n", + "30 Hz 7.4e+00 ± 1.4e-01 (34) \n", + "Baseline I 7.8e+00 ± 4.0e-02 (46) \n", + "Baseline II 8.1e+00 ± 4.7e-02 (32) \n", + "Normality 11 Hz 1.9e+00, 3.9e-01 \n", + "Normality 30 Hz 7.2e+00, 2.8e-02 \n", + "Normality Baseline I 5.9e+00, 5.3e-02 \n", + "Normality Baseline II 4.7e+00, 9.7e-02 \n", + "Paired summary 11 Hz 30 Hz 7.6e+00 ± 1.5e-01, 7.3e+00 ± 1.4e-01 (32) \n", + "Paired summary 11 Hz Baseline II 7.6e+00 ± 1.5e-01, 8.1e+00 ± 4.7e-02 (32) \n", + "Paired summary Baseline I 11 Hz 7.8e+00 ± 4.2e-02, 7.7e+00 ± 1.3e-01 (44) \n", + "Paired summary Baseline I 30 Hz 7.8e+00 ± 4.4e-02, 7.3e+00 ± 1.4e-01 (32) \n", + "Paired summary Baseline I Baseline II 7.8e+00 ± 4.4e-02, 8.1e+00 ± 4.7e-02 (32) \n", + "Paired summary Baseline II 30 Hz 8.1e+00 ± 4.7e-02, 7.3e+00 ± 1.4e-01 (32) \n", + "Wilcoxon 11 Hz 30 Hz 1.2e+02, 4.5e-02, (32) \n", + "Wilcoxon 11 Hz Baseline II 1.2e+02, 9.3e-03, (32) \n", + "Wilcoxon Baseline I 11 Hz 3.6e+02, 4.7e-01, (44) \n", + "Wilcoxon Baseline I 30 Hz 9.5e+01, 1.6e-03, (32) \n", + "Wilcoxon Baseline I Baseline II 6.0e+00, 9.0e-06, (32) \n", + "Wilcoxon Baseline II 30 Hz 5.0e+01, 9.9e-05, (32) \n", + "\n", + " Theta half width \\\n", + "11 Hz 1.7e+00 ± 3.0e-01 (43) \n", + "30 Hz 1.2e+01 ± 1.9e+00 (34) \n", + "Baseline I 7.7e-01 ± 1.8e-02 (46) \n", + "Baseline II 8.4e-01 ± 3.2e-02 (32) \n", + "Normality 11 Hz 1.0e+01, 5.5e-03 \n", + "Normality 30 Hz 1.9e+02, 2.2e-41 \n", + "Normality Baseline I 1.9e+00, 3.8e-01 \n", + "Normality Baseline II 1.6e+01, 2.8e-04 \n", + "Paired summary 11 Hz 30 Hz 1.5e+00 ± 3.5e-01, 1.1e+01 ± 2.0e+00 (31) \n", + "Paired summary 11 Hz Baseline II 1.5e+00 ± 3.5e-01, 8.6e-01 ± 2.5e-02 (31) \n", + "Paired summary Baseline I 11 Hz 7.8e-01 ± 1.8e-02, 1.7e+00 ± 3.0e-01 (43) \n", + "Paired summary Baseline I 30 Hz 7.6e-01 ± 2.0e-02, 1.2e+01 ± 2.0e+00 (32) \n", + "Paired summary Baseline I Baseline II 7.6e-01 ± 2.0e-02, 8.4e-01 ± 3.2e-02 (32) \n", + "Paired summary Baseline II 30 Hz 8.4e-01 ± 3.2e-02, 1.2e+01 ± 2.0e+00 (32) \n", + "Wilcoxon 11 Hz 30 Hz 6.7e+01, 3.9e-04, (31) \n", + "Wilcoxon 11 Hz Baseline II 2.3e+02, 6.7e-01, (31) \n", + "Wilcoxon Baseline I 11 Hz 4.3e+02, 6.2e-01, (43) \n", + "Wilcoxon Baseline I 30 Hz 7.1e+01, 3.1e-04, (32) \n", + "Wilcoxon Baseline I Baseline II 1.4e+02, 2.3e-02, (32) \n", + "Wilcoxon Baseline II 30 Hz 7.5e+01, 4.1e-04, (32) \n", + "\n", + " Stim bandpower \\\n", + "11 Hz 9.6e-04 ± 8.0e-05 (44) \n", + "30 Hz 1.8e-03 ± 3.3e-04 (34) \n", + "Baseline I nan ± nan (0) \n", + "Baseline II nan ± nan (0) \n", + "Normality 11 Hz 1.7e+01, 1.8e-04 \n", + "Normality 30 Hz 1.7e+01, 1.7e-04 \n", + "Normality Baseline I NaN \n", + "Normality Baseline II NaN \n", + "Paired summary 11 Hz 30 Hz 9.7e-04 ± 9.3e-05, 1.8e-03 ± 3.4e-04 (32) \n", + "Paired summary 11 Hz Baseline II nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline I 11 Hz nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline I 30 Hz nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline I Baseline II nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline II 30 Hz nan ± nan, nan ± nan (0) \n", + "Wilcoxon 11 Hz 30 Hz 2.1e+02, 2.9e-01, (32) \n", + "Wilcoxon 11 Hz Baseline II NaN \n", + "Wilcoxon Baseline I 11 Hz NaN \n", + "Wilcoxon Baseline I 30 Hz NaN \n", + "Wilcoxon Baseline I Baseline II NaN \n", + "Wilcoxon Baseline II 30 Hz NaN \n", + "\n", + " Stim relpower \\\n", + "11 Hz 1.1e-01 ± 7.4e-03 (44) \n", + "30 Hz 1.6e-01 ± 2.3e-02 (34) \n", + "Baseline I nan ± nan (0) \n", + "Baseline II nan ± nan (0) \n", + "Normality 11 Hz 3.9e+00, 1.4e-01 \n", + "Normality 30 Hz 1.5e+01, 4.8e-04 \n", + "Normality Baseline I NaN \n", + "Normality Baseline II NaN \n", + "Paired summary 11 Hz 30 Hz 1.1e-01 ± 8.6e-03, 1.6e-01 ± 2.4e-02 (32) \n", + "Paired summary 11 Hz Baseline II nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline I 11 Hz nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline I 30 Hz nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline I Baseline II nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline II 30 Hz nan ± nan, nan ± nan (0) \n", + "Wilcoxon 11 Hz 30 Hz 2.0e+02, 2.2e-01, (32) \n", + "Wilcoxon 11 Hz Baseline II NaN \n", + "Wilcoxon Baseline I 11 Hz NaN \n", + "Wilcoxon Baseline I 30 Hz NaN \n", + "Wilcoxon Baseline I Baseline II NaN \n", + "Wilcoxon Baseline II 30 Hz NaN \n", + "\n", + " Stim relpeak \\\n", + "11 Hz 1.6e+01 ± 1.9e+00 (44) \n", + "30 Hz 8.6e+01 ± 1.4e+01 (34) \n", + "Baseline I nan ± nan (0) \n", + "Baseline II nan ± nan (0) \n", + "Normality 11 Hz 5.8e+00, 5.6e-02 \n", + "Normality 30 Hz 6.2e+00, 4.5e-02 \n", + "Normality Baseline I NaN \n", + "Normality Baseline II NaN \n", + "Paired summary 11 Hz 30 Hz 1.8e+01 ± 2.4e+00, 8.5e+01 ± 1.4e+01 (32) \n", + "Paired summary 11 Hz Baseline II nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline I 11 Hz nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline I 30 Hz nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline I Baseline II nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline II 30 Hz nan ± nan, nan ± nan (0) \n", + "Wilcoxon 11 Hz 30 Hz 3.0e+01, 1.2e-05, (32) \n", + "Wilcoxon 11 Hz Baseline II NaN \n", + "Wilcoxon Baseline I 11 Hz NaN \n", + "Wilcoxon Baseline I 30 Hz NaN \n", + "Wilcoxon Baseline I Baseline II NaN \n", + "Wilcoxon Baseline II 30 Hz NaN \n", + "\n", + " Stim half width \\\n", + "11 Hz 3.4e-01 ± 2.5e-03 (44) \n", + "30 Hz 3.0e-01 ± 2.3e-03 (23) \n", + "Baseline I nan ± nan (0) \n", + "Baseline II nan ± nan (0) \n", + "Normality 11 Hz 1.6e+01, 4.2e-04 \n", + "Normality 30 Hz 6.1e-01, 7.4e-01 \n", + "Normality Baseline I NaN \n", + "Normality Baseline II NaN \n", + "Paired summary 11 Hz 30 Hz 3.4e-01 ± 3.6e-03, 3.0e-01 ± 2.3e-03 (21) \n", + "Paired summary 11 Hz Baseline II nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline I 11 Hz nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline I 30 Hz nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline I Baseline II nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline II 30 Hz nan ± nan, nan ± nan (0) \n", + "Wilcoxon 11 Hz 30 Hz 0.0e+00, 9.5e-07, (21) \n", + "Wilcoxon 11 Hz Baseline II NaN \n", + "Wilcoxon Baseline I 11 Hz NaN \n", + "Wilcoxon Baseline I 30 Hz NaN \n", + "Wilcoxon Baseline I Baseline II NaN \n", + "Wilcoxon Baseline II 30 Hz NaN \n", + "\n", + " Stim p max \\\n", + "11 Hz 2.1e-03 ± 2.3e-04 (44) \n", + "30 Hz 5.3e-03 ± 1.1e-03 (34) \n", + "Baseline I nan ± nan (0) \n", + "Baseline II nan ± nan (0) \n", + "Normality 11 Hz 1.5e+01, 5.8e-04 \n", + "Normality 30 Hz 1.9e+01, 6.3e-05 \n", + "Normality Baseline I NaN \n", + "Normality Baseline II NaN \n", + "Paired summary 11 Hz 30 Hz 2.1e-03 ± 2.7e-04, 5.4e-03 ± 1.1e-03 (32) \n", + "Paired summary 11 Hz Baseline II nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline I 11 Hz nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline I 30 Hz nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline I Baseline II nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline II 30 Hz nan ± nan, nan ± nan (0) \n", + "Wilcoxon 11 Hz 30 Hz 1.6e+02, 5.0e-02, (32) \n", + "Wilcoxon 11 Hz Baseline II NaN \n", + "Wilcoxon Baseline I 11 Hz NaN \n", + "Wilcoxon Baseline I 30 Hz NaN \n", + "Wilcoxon Baseline I Baseline II NaN \n", + "Wilcoxon Baseline II 30 Hz NaN \n", + "\n", + " Stim strength \n", + "11 Hz 3.3e+00 ± 3.8e-01 (44) \n", + "30 Hz 1.3e+01 ± 2.8e+00 (34) \n", + "Baseline I nan ± nan (0) \n", + "Baseline II nan ± nan (0) \n", + "Normality 11 Hz 1.2e+01, 2.3e-03 \n", + "Normality 30 Hz 4.3e+01, 5.1e-10 \n", + "Normality Baseline I NaN \n", + "Normality Baseline II NaN \n", + "Paired summary 11 Hz 30 Hz 3.5e+00 ± 4.9e-01, 1.3e+01 ± 3.0e+00 (32) \n", + "Paired summary 11 Hz Baseline II nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline I 11 Hz nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline I 30 Hz nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline I Baseline II nan ± nan, nan ± nan (0) \n", + "Paired summary Baseline II 30 Hz nan ± nan, nan ± nan (0) \n", + "Wilcoxon 11 Hz 30 Hz 8.8e+01, 1.0e-03, (32) \n", + "Wilcoxon 11 Hz Baseline II NaN \n", + "Wilcoxon Baseline I 11 Hz NaN \n", + "Wilcoxon Baseline I 30 Hz NaN \n", + "Wilcoxon Baseline I Baseline II NaN \n", + "Wilcoxon Baseline II 30 Hz NaN " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "stat = pd.DataFrame()\n", + "\n", + "for key, df in results.items():\n", + " Key = rename(key)\n", + " stat[Key] = df.agg(summarize)\n", + " stat[Key] = df.agg(summarize)\n", + "\n", + " for i, c1 in enumerate(df.columns):\n", + " try:\n", + " stat.loc[f'Normality {c1}', Key] = normality(df, c1)\n", + " except:\n", + " stat.loc[f'Normality {c1}', Key] = np.nan\n", + "# stat.loc[f'Shapiro {c1}', Key] = shapiro(df, c1)\n", + " for c2 in df.columns[i+1:]:\n", + "# stat.loc[f'MWU {c1} {c2}', Key] = MWU(df, [c1, c2])\n", + "# stat.loc[f'PRS {c1} {c2}', Key] = PRS(df, [c1, c2])\n", + " try:\n", + " stat.loc[f'Wilcoxon {c1} {c2}', Key] = wilcoxon(df, [c1, c2])\n", + " except:\n", + " stat.loc[f'Wilcoxon {c1} {c2}', Key] = np.nan\n", + "# stat.loc[f'Paired T {c1} {c2}', Key] = paired_t(df, [c1, c2])\n", + " stat.loc[f'Paired summary {c1} {c2}', Key] = summarize_wilcoxon(df, [c1, c2])\n", + "\n", + "stat.sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "stat.to_latex(output_path / \"statistics\" / f\"statistics.tex\")\n", + "stat.to_csv(output_path / \"statistics\" / f\"statistics.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "for key, result in results.items():\n", + " result.to_latex(output_path / \"statistics\" / f\"values_{key}.tex\")\n", + " result.to_csv(output_path / \"statistics\" / f\"values_{key}.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot PSD" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "psd = pd.read_feather(pathlib.Path(\"output\") / (\"stimulus-lfp-response\" + zscore_str) / 'data' / 'psd.feather')\n", + "freqs = pd.read_feather(pathlib.Path(\"output\") / (\"stimulus-lfp-response\" + zscore_str) / 'data' / 'freqs.feather')" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "from septum_mec.analysis.plotting import plot_bootstrap_timeseries" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "freq = freqs.T.iloc[0].values\n", + "\n", + "mask = (freq < 49)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fig, axs = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(5,2))\n", + "axs = axs.repeat(2)\n", + "for i, (ax, query) in enumerate(zip(axs.ravel(), queries)):\n", + " selection = [\n", + " f'{r.action}_{r.channel_group}' \n", + " for i, r in lfp_results_hemisphere.query(query).iterrows()]\n", + " values = psd.loc[mask, selection].to_numpy()\n", + " values = 10 * np.log10(values)\n", + " plot_bootstrap_timeseries(freq[mask], values, ax=ax, lw=1, label=labels[i], color=colors[i])\n", + "# ax.set_title(titles[i])\n", + " ax.set_xlabel('Frequency Hz')\n", + " ax.legend(frameon=False)\n", + "axs[0].set_ylabel('PSD (dB/Hz)')\n", + "# axs[0].set_ylim(-31, 1)\n", + "despine()\n", + "\n", + "figname = 'lfp-psd'\n", + "fig.savefig(\n", + " output_path / 'figures' / f'{figname}.png', \n", + " bbox_inches='tight', transparent=True)\n", + "fig.savefig(\n", + " output_path / 'figures' / f'{figname}.svg', \n", + " bbox_inches='tight', transparent=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Store results in Expipe action" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "action = project.require_action(\"stimulus-lfp-response\" + '-' + stim_loc + zscore_str)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_stim_bandpower.tex',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_stim_relpeak.tex',\n", + " '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-ms-no-zscore/data/statistics/values_theta_half_width.csv',\n", + " 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