In [1]:
%load_ext autoreload
%autoreload 2
In [2]:
import os
import pathlib
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
import seaborn as sns
import re
import shutil
import pandas as pd
import scipy.stats

import exdir
import expipe
from distutils.dir_util import copy_tree
import septum_mec
import septum_mec.analysis.data_processing as dp
import septum_mec.analysis.registration
from septum_mec.analysis.plotting import violinplot, despine, plot_bootstrap_timeseries
from phase_precession import cl_corr
from spike_statistics.core import permutation_resampling
import matplotlib.mlab as mlab
import scipy.signal as ss
from scipy.interpolate import interp1d
from septum_mec.analysis.plotting import regplot
from skimage import measure
from tqdm.notebook import tqdm_notebook as tqdm
tqdm.pandas()
import scipy.signal as ss


from tqdm.notebook import tqdm_notebook as tqdm
tqdm.pandas()

import pycwt

12:54:11 [I] klustakwik KlustaKwik2 version 0.2.6
/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject
  return f(*args, **kwds)
/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject
  return f(*args, **kwds)
/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject
  return f(*args, **kwds)
In [3]:
colors = ['#1b9e77','#d95f02','#7570b3','#e7298a', '#4393c3', '#d6604d']
labels = ['Baseline I', '11 Hz', 'Baseline II', '30 Hz', 'Baseline', 'Stimulated']
plt.rcParams['figure.dpi'] = 150
figsize_violin = (1.7, 3)
figsize_speed = (4, 3)
plt.rc('axes', titlesize=10)
In [4]:
project_path = dp.project_path()
project = expipe.get_project(project_path)
actions = project.actions

output_path = pathlib.Path("output") / "lfp_speed"
(output_path / "statistics").mkdir(exist_ok=True, parents=True)
(output_path / "figures").mkdir(exist_ok=True, parents=True)
In [5]:
data_action = actions['lfp_speed']
output = exdir.File(
    data_action.data_path('results'),
    plugins = [exdir.plugins.git_lfs, exdir.plugins.quantities])

ignore = ['wavelet_power', 'wavelet_freqs', 'signal']
results = []
for group in output.values():
    d = group.attrs.to_dict()
    d.update({k: np.array(v.data) for k, v in group.items() if k not in ignore})
    results.append(d)
results = pd.DataFrame(results)
In [6]:
results.head()
Out[6]:
freq_score sample_rate power_score action channel_group max_speed min_speed position_low_pass_frequency mean_freq mean_power speed speed_bins theta_freq theta_power
0 0.191729 1000.0 0.432532 1833-010719-1 0 1 0.02 6 [7.154332133229601, 7.106500202042717, 7.13862... [18.005621200653046, 18.66435212100411, 20.504... [0.02795137493203615, 0.0283076211590443, 0.02... [0.02, 0.04, 0.06, 0.08, 0.1, 0.12000000000000... [6.799999999999997, 6.799999999999997, 6.79999... [3.990633076071412, 3.992883430179942, 3.99513...
1 0.255882 1000.0 0.434938 1833-010719-1 1 1 0.02 6 [7.035831237674811, 7.05973079549096, 7.120455... [16.966011451769536, 17.60417640800431, 19.452... [0.02795137493203615, 0.0283076211590443, 0.02... [0.02, 0.04, 0.06, 0.08, 0.1, 0.12000000000000... [6.799999999999997, 6.799999999999997, 6.79999... [3.649171825378523, 3.6511305369806806, 3.6530...
2 0.169116 1000.0 0.338942 1833-010719-1 2 1 0.02 6 [7.156957284750235, 7.121730043055997, 7.17760... [14.747162413722597, 15.548073560884317, 16.81... [0.02795137493203615, 0.0283076211590443, 0.02... [0.02, 0.04, 0.06, 0.08, 0.1, 0.12000000000000... [6.799999999999997, 6.799999999999997, 6.79999... [3.069575227276876, 3.0713927350182493, 3.0732...
3 0.071480 1000.0 0.141405 1833-010719-1 3 1 0.02 6 [7.256682286107137, 7.237350035531646, 7.27254... [13.017027147293039, 12.651121743582284, 13.91... [0.02795137493203615, 0.0283076211590443, 0.02... [0.02, 0.04, 0.06, 0.08, 0.1, 0.12000000000000... [6.399999999999999, 6.399999999999999, 6.39999... [1.9508693636836856, 1.9523977795413874, 1.953...
4 0.216792 1000.0 -0.012191 1833-010719-1 4 1 0.02 6 [7.095817125902336, 7.050223640391819, 7.12869... [32.456068185302364, 23.01562486642484, 21.395... [0.02795137493203615, 0.0283076211590443, 0.02... [0.02, 0.04, 0.06, 0.08, 0.1, 0.12000000000000... [6.399999999999999, 6.399999999999999, 6.39999... [1.2545438245339104, 1.2553897239251606, 1.256...
In [7]:
identify_neurons = actions['identify-neurons']
sessions = pd.read_csv(identify_neurons.data_path('sessions'))
In [8]:
results = results.merge(sessions, on='action')

Frequency score

In [9]:
query_base_i = 'baseline and Hz11'
query_stim_11 = 'stimulated and Hz11'

query_base_ii = 'baseline and Hz30'
query_stim_30 = 'stimulated and Hz30'

query_stim_combined = 'stimulated and Hz11 or Hz30'
In [10]:
stuff = 'freq_score'
base = results.query(query_base_i)[stuff].to_numpy()
stim = results.query(query_stim_11)[stuff].to_numpy()
plt.figure(figsize=figsize_violin)
violinplot(base, stim, colors=colors)
# plt.ylim(-0.02, 0.5)
plt.savefig(output_path / "figures" / "frequency_score_11.svg", bbox_inches="tight", transparent=True)
plt.savefig(output_path / "figures" / "frequency_score_11.png", bbox_inches="tight", transparent=True)

base = results.query(query_base_ii)[stuff].to_numpy()
stim = results.query(query_stim_30)[stuff].to_numpy()
plt.figure(figsize=figsize_violin)
violinplot(base, stim, colors=colors[2:])
# plt.ylim(-0.02, 0.5)
plt.savefig(output_path / "figures" / "frequency_score_30.svg", bbox_inches="tight", transparent=True)
plt.savefig(output_path / "figures" / "frequency_score_30.png", bbox_inches="tight", transparent=True)

base = results.query(query_base_i)[stuff].to_numpy()
stim = results.query(query_stim_combined)[stuff].to_numpy()
plt.figure(figsize=figsize_violin)
violinplot(base, stim, colors=colors[4:])
# plt.ylim(-0.02, 0.5)
plt.savefig(output_path / "figures" / "frequency_score_combined.svg", bbox_inches="tight", transparent=True)
plt.savefig(output_path / "figures" / "frequency_score_combined.png", bbox_inches="tight", transparent=True)

U-test: U value 37221.0 p value 1.9679601390031718e-50
U-test: U value 16950.0 p value 1.642880876541107e-32
U-test: U value 72140.0 p value 2.0364061218728567e-30
In [11]:
def plot_speed(query_base, query_stim, stuff, colors, labels, filename=None, show_raw=False):
    base = np.array([s for s in results.query(query_base)[stuff]])
    base_bins = results.query(query_base)['speed_bins'].to_numpy()

    stim = np.array([s for s in results.query(query_stim)[stuff]])
    stim_bins = results.query(query_stim)['speed_bins'].to_numpy()
    if show_raw:
        fig, axs = plt.subplots(1, 2, sharey=True, figsize=figsize_speed)

        for b, h in zip(base_bins, base):
            axs[1].plot(b, h)
            axs[1].set_xlim(0.1,1)
            axs[1].set_title(labels[0])

        for b, h in zip(stim_bins, stim):
            axs[0].plot(b, h)
            axs[0].set_xlim(0.1,1)
            axs[0].set_title(labels[1])  

    fig, ax = plt.subplots(1, 1, figsize=figsize_speed)
    plot_bootstrap_timeseries(base_bins[0], base.T, ax=ax, label=labels[0], color=colors[0])
    plot_bootstrap_timeseries(stim_bins[0], stim.T, ax=ax, label=labels[1], color=colors[1])

    plt.xlim(0, 0.9)
    plt.gca().spines['top'].set_visible(False)
    plt.gca().spines['right'].set_visible(False)
    plt.legend(frameon=False)
    
    if filename is not None:
        plt.savefig(output_path / "figures" / f"{filename}.svg", bbox_inches="tight", transparent=True)
        plt.savefig(output_path / "figures" / f"{filename}.png", bbox_inches="tight", transparent=True)
In [12]:
plot_speed(query_base_i, query_stim_11, 'mean_freq', 
           colors, labels, filename='lfp_speed_freq_11')
In [13]:
plot_speed(query_base_ii, query_stim_30, 'mean_freq', 
           colors[2:], labels[2:], filename='lfp_speed_freq_30')
In [14]:
plot_speed(
    query_base_i, query_stim_combined, 'mean_freq', 
    colors[4:], labels[4:], filename='lfp_speed_freq_combined')

Power Familiar

In [15]:
plot_speed(
    query_base_i, query_stim_11, 'mean_power', 
    colors, labels, filename='lfp_speed_power_11')
In [16]:
plot_speed(
    query_base_ii, query_stim_30, 'mean_power', 
    colors[2:], labels[2:], filename='lfp_speed_power_30')
In [17]:
plot_speed(
    query_base_i, query_stim_combined, 'mean_power', 
    colors[4:], labels[4:], filename='lfp_speed_power_combined')

Speed

In [18]:
stuff = 'speed'
min_speed = results['min_speed'].iloc[0]
max_speed = results['max_speed'].iloc[0]

f = np.median
base = np.array([f(a) for a in results.query(query_base_i)[stuff]])
stim = np.array([f(a).mean() for a in results.query(query_stim_combined)[stuff]])
plt.figure(figsize=figsize_violin)
violinplot(stim, base)

plt.savefig(output_path / "figures" / "speed.svg", bbox_inches="tight")
plt.savefig(output_path / "figures" / "speed.png", bbox_inches="tight", transparent=True)

plt.figure(figsize=figsize_speed)
binsize = 0.02
bins = np.arange(min_speed, max_speed + binsize, binsize)
base = np.array([np.histogram(a, bins)[0] for a in results.query(query_base_i)[stuff]])
stim = np.array([np.histogram(a, bins)[0]  for a in results.query(query_stim_combined)[stuff]])

plt.bar(bins[1:], stim.mean(axis=0), width=np.diff(bins)[0], label='Stimulated', alpha=.5, color=colors[5]);
plt.bar(bins[1:], base.mean(axis=0), width=np.diff(bins)[0], label='Baseline', alpha=.5, color=colors[6]);

plt.legend(frameon=False)

plt.savefig(output_path / "figures" / "speed_histogram.svg", bbox_inches="tight")
plt.savefig(output_path / "figures" / "speed_histogram.png", bbox_inches="tight", transparent=True)

U-test: U value 31872.0 p value 4.112172727188835e-10

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-18-ea7e4968e5a4> in <module>
     19 
     20 plt.bar(bins[1:], stim.mean(axis=0), width=np.diff(bins)[0], label='Stimulated', alpha=.5, color=colors[5]);
---> 21 plt.bar(bins[1:], base.mean(axis=0), width=np.diff(bins)[0], label='Baseline', alpha=.5, color=colors[6]);
     22 
     23 plt.legend(frameon=False)

IndexError: list index out of range

Table

In [19]:
columns = [
    'power_score',
    'freq_score'
]


def summarize(data):
    return "{:.2f} ± {:.2f} ({})".format(data.mean(), data.sem(), sum(~np.isnan(data)))


def MWU(column, query1, query2):
    '''
    Mann Whitney U
    '''
    Uvalue, pvalue = scipy.stats.mannwhitneyu(
        results.query(query1)[column].dropna(), 
        results.query(query2)[column].dropna(),
        alternative='two-sided')
    
    return "{:.2f}, {:.3f}".format(Uvalue, pvalue)


def PRS(column, query1, query2):
    '''
    Permutation ReSampling
    '''
    pvalue, observed_diff, diffs = permutation_resampling(
        results.query(query1)[column].dropna(), 
        results.query(query2)[column].dropna(), statistic=np.median)
    
    return "{:.2f}, {:.3f}".format(observed_diff, pvalue)

summary_i = pd.DataFrame()

summary_i['Baseline I'] = results.query(query_base_i)[columns].agg(summarize)
summary_i['11 Hz'] = results.query(query_stim_11)[columns].agg(summarize)

summary_i['MWU'] = list(map(lambda x: MWU(x, query_base_i, query_stim_11), columns))
summary_i['PRS'] = list(map(lambda x: PRS(x, query_base_i, query_stim_11), columns))

summary_ii = pd.DataFrame()

summary_ii['Baseline II'] = results.query(query_base_ii)[columns].agg(summarize)
summary_ii['30 Hz'] = results.query(query_stim_30)[columns].agg(summarize)

summary_ii['MWU'] = list(map(lambda x: MWU(x, query_base_ii, query_stim_30), columns))
summary_ii['PRS'] = list(map(lambda x: PRS(x, query_base_ii, query_stim_30), columns))

summary_combined = pd.DataFrame()

summary_combined['Baseline I'] = results.query('not {}'.format(query_base_i))[columns].agg(summarize)
summary_combined['Combined'] = results.query('{}'.format(query_stim_combined))[columns].agg(summarize)

summary_combined['MWU'] = list(map(lambda x: MWU(x, query_base_i, query_stim_combined), columns))
summary_combined['PRS'] = list(map(lambda x: PRS(x, query_base_i, query_stim_combined), columns))

summary_i.to_latex(output_path / "statistics" / "power_freq_score_summary_i.tex")
summary_i.to_csv(output_path / "statistics" / "power_freq_score_summary_i.csv")

summary_ii.to_latex(output_path / "statistics" / "power_freq_score_summary_ii.tex")
summary_ii.to_csv(output_path / "statistics" / "power_freq_score_summary_ii.csv")

summary_combined.to_latex(output_path / "statistics" / "power_freq_score_summary_combined.tex")
summary_combined.to_csv(output_path / "statistics" / "power_freq_score_summary_combined.csv")
In [20]:
summary_i
Out[20]:
Baseline I 11 Hz MWU PRS
power_score -0.03 ± 0.01 (208) -0.03 ± 0.01 (208) 32624.00, 0.000 0.18, 0.000
freq_score -0.01 ± 0.01 (208) -0.01 ± 0.01 (208) 37221.00, 0.000 0.21, 0.000
In [21]:
summary_ii
Out[21]:
Baseline II 30 Hz MWU PRS
power_score 0.04 ± 0.01 (136) 0.04 ± 0.01 (136) 12586.00, 0.000 0.08, 0.000
freq_score 0.01 ± 0.01 (136) 0.01 ± 0.01 (136) 16950.00, 0.000 0.22, 0.000
In [22]:
summary_combined
Out[22]:
Baseline I Combined MWU PRS
power_score -0.03 ± 0.01 (208) 0.03 ± 0.01 (480) 67804.00, 0.000 0.13, 0.000
freq_score -0.01 ± 0.01 (208) 0.06 ± 0.01 (480) 72140.00, 0.000 0.17, 0.000

Register in expipe

In [23]:
action = project.actions["lfp_speed"]
In [24]:
outdata = {
    "figures": "figures",
    "statistics": "statistics"
}

for key, value in outdata.items():
    action.data[key] = value
    data_path = action.data_path(key)
    data_path.parent.mkdir(exist_ok=True, parents=True)
    source = output_path / value
    if source.is_file():
        shutil.copy(source, data_path)
    else:
        copy_tree(str(source), str(data_path))
In [25]:
septum_mec.analysis.registration.store_notebook(action, "20_lfp_speed.ipynb")
In [ ]: