From 1ad2894b788d1d9e8e0b548b988b887880ee704c Mon Sep 17 00:00:00 2001
From: benj3542 <s200431@student.dtu.dk>
Date: Wed, 22 Feb 2023 15:34:59 +0100
Subject: [PATCH] added local changes for preamble and preprocessing
---
FeatureEstimation.py | 48 +-
Preamble.py | 211 ++---
Preprocessing.py | 5 +-
eeg_microstates3.py | 1849 ++++++++++++++++++++++++++++++++++++++++++
feature_select.py | 148 ++++
requirements.txt | 6 +-
6 files changed, 2144 insertions(+), 123 deletions(-)
create mode 100644 eeg_microstates3.py
create mode 100644 feature_select.py
diff --git a/FeatureEstimation.py b/FeatureEstimation.py
index 7ea34d0..612ce0d 100644
--- a/FeatureEstimation.py
+++ b/FeatureEstimation.py
@@ -33,9 +33,10 @@ The script was written in Spyder. The outline panel can be used to navigate
the different parts easier (Default shortcut: Ctrl + Shift + O)
"""
+
# Set working directory
import os
-wkdir = "/home/glia/EEG"
+wkdir = "/Users/benj3542/Desktop/Uni/Noter/Semester_6/Bachelor/resting-state-eeg-analysis/"
os.chdir(wkdir)
# Load all libraries from the Preamble
@@ -57,7 +58,7 @@ Subject_id = [0] * len(files)
for i in range(len(files)):
temp = files[i].split("\\")
temp = temp[-1].split("_")
- Subject_id[i] = int(temp[0])
+ Subject_id[i] = int(temp[0][-1]) # Subject_id[i] = int(temp[0])
n_subjects = len(Subject_id)
@@ -151,9 +152,15 @@ def power_band_estimation(n):
res = np.concatenate([abs_power_values,rel_power_values],axis=0)
return n, res
+"""
with concurrent.futures.ProcessPoolExecutor() as executor:
for n, result in executor.map(power_band_estimation, range(len(final_epochs))): # Function and arguments
power_bands[n] = result
+"""
+
+for i in range(len(power_bands)):
+ n, results = power_band_estimation(i)
+ power_bands[i] = results
# Combine all data into one dataframe
# First the columns are prepared
@@ -216,10 +223,11 @@ power_df = pd.DataFrame(data = {"Subject_ID": [ele for ele in Subject_id for i i
# Fix Freq_band categorical order
power_df["Freq_band"] = power_df["Freq_band"].astype("category").\
cat.reorder_categories(list(Freq_Bands.keys()), ordered=True)
+"""
# Fix Brain_region categorical order
power_df["Brain_region"] = power_df["Brain_region"].astype("category").\
cat.reorder_categories(Brain_region_labels, ordered=True)
-
+"""
# Save the dataframe
power_df.to_pickle(os.path.join(Feature_savepath,"Power_df.pkl"))
@@ -234,16 +242,29 @@ n_eye_status = len(eye_status)
# Subset frontal absolute power
power_df_sub1 = power_df[(power_df["Quant_status"] == "Absolute")&
(power_df["Brain_region"] == "Frontal")]
+# Subset frontal, midline absolute power
+power_df_sub2 = power_df[(power_df["Quant_status"] == "Absolute")&
+ (power_df["Brain_region"] == "Frontal")&
+ (power_df["Brain_side"] == "Mid")]
+
-# Calculate average frontal power
+# Calculate average frontal power theta
frontal_theta_mean_subject = power_df_sub1[power_df_sub1["Freq_band"] == "theta"].\
groupby(["Subject_ID","Group_status","Eye_status"]).mean().reset_index()
+
+# Calculate average frontal power beta
frontal_beta_mean_subject = power_df_sub1[power_df_sub1["Freq_band"] == "beta"].\
groupby(["Subject_ID","Group_status","Eye_status"]).mean().reset_index()
+# Calculate average frontal, midline power theta
+frontal_midline_theta_mean_subject = power_df_sub2[power_df_sub2["Freq_band"] == "theta"].\
+ groupby(["Subject_ID","Group_status","Eye_status"]).mean().reset_index()
+
+
# Convert from dB to raw power
frontal_theta_mean_subject["PSD"] = 10**(frontal_theta_mean_subject["PSD"]/10)
frontal_beta_mean_subject["PSD"] = 10**(frontal_beta_mean_subject["PSD"]/10)
+frontal_midline_theta_mean_subject["PSD"] = 10**(frontal_midline_theta_mean_subject["PSD"]/10)
# Calculate mean for each group and take ratio for whole group
# To confirm trend observed in PSD plots
@@ -266,6 +287,7 @@ frontal_theta_beta_ratio.insert(3, "Measurement", dummy_variable )
frontal_theta_beta_ratio.to_pickle(os.path.join(Feature_savepath,"fTBR_df.pkl"))
+"""
# %% Frequency bands asymmetry
# Defined as ln(right) - ln(left)
# Thus we should only work with the absolute values and undo the dB transformation
@@ -553,7 +575,7 @@ OOF_data_df.insert(4, "Brain_region", list(Brain_region)*int(PAF_data_df.shape[0
# Save the dataframes
OOF_data_df.to_pickle(os.path.join(Feature_savepath,"OOF_data_FOOOF_df.pkl"))
-
+"""
# %% Microstate analysis
# The function takes the data as a numpy array (n_t, n_ch)
# The data is already re-referenced to common average
@@ -616,11 +638,11 @@ plt.ylabel("Normalized to total")
# The lower CV the better.
# But the higher GEV the better.
# Based on the plots and the recommendation by vong Wegner & Laufs 2018
-# we used 4 microstates
+# we used 5 microstates
# In order to compare between groups, I fix the microstates by clustering on data from both groups
# Due to instability of maps when running multiple times, I increased n_maps from 4 to 6
-n_maps = 4
+n_maps = 5
mode = ["aahc", "kmeans", "kmedoids", "pca", "ica"][1]
# K-means is stochastic, thus I run it multiple times in order to find the maps with highest GEV
@@ -670,7 +692,7 @@ c_time2 = time.strftime("%a %d %b %Y %H:%M:%S", c_time2)
print("Started", c_time1, "\nFinished",c_time2)
# Save the results
-with open(Feature_savepath+"Microstate_4_maps_10x5_k_means_results.pkl", "wb") as file:
+with open(Feature_savepath+"Microstate_5_maps_10x5_k_means_results.pkl", "wb") as file:
pickle.dump(microstate_cluster_results, file)
# # Load
@@ -690,7 +712,7 @@ gev = [microstate_cluster_results[Best_EC_idx][3][0],microstate_cluster_results[
# Plot the maps
plt.style.use('default')
-labels = ["EC", "EO"]
+labels = ["EC", "EO"] #Eyes-closed, Eyes-open
for i in range(len(labels)):
fig, axarr = plt.subplots(1, n_maps, figsize=(20,5))
fig.patch.set_facecolor('white')
@@ -703,8 +725,8 @@ for i in range(len(labels)):
# Due the random initiation of K-means this have to be modified every time clusters are made!
# Assign map labels (e.g. 0, 2, 1, 3)
order = [0]*2
-order[0] = [3,0,1,2] # EC
-order[1] = [3,1,0,2] # EO
+order[0] = [3,0,1,2,4] # EC
+order[1] = [3,1,0,2,4] # EO
for i in range(len(order)):
maps[i] = maps[i][order[i],:] # re-order maps
gev[i] = gev[i][order[i]] # re-order GEV
@@ -716,13 +738,13 @@ for i in range(len(order)):
# Thus the sign of the map does not really reflect which areas are positive or negative (absolute)
# But more which areas are different during each state (relatively)
# I can therefore change the sign of the map for the visualizaiton
-sign_swap = [[1,-1,1,1],[1,1,1,-1]]
+sign_swap = [[1,-1,1,1,1],[1,1,1,-1,1]]
for i in range(len(order)):
for m in range(n_maps):
maps[i][m] *= sign_swap[i][m]
# Plot the maps and save
-save_path = "/home/glia/Analysis/Figures/Microstates/"
+save_path = "/Users/benj3542/Desktop/Uni/Noter/Semester_6/Bachelor/resting-state-eeg-analysis/Figures/Microstates"
labels = ["EC", "EO"]
for i in range(len(labels)):
fig, axarr = plt.subplots(1, n_maps, figsize=(20,5))
diff --git a/Preamble.py b/Preamble.py
index d6d17b7..e85613f 100644
--- a/Preamble.py
+++ b/Preamble.py
@@ -25,14 +25,14 @@ import sklearn # Machine learning
import nitime # Time series analysis
import nolds # DFA exponent
import statsmodels # multipletest
-import pysparcl # Sparse Kmeans
+#import pysparcl # Sparse Kmeans
import fooof # Peak Alpha Freq and 1/f exponents
import pandas as pd # Dataframes
import seaborn as sns # Plotting library
-import autoreject # Automatic EEG artifact detection
+# import autoreject # Automatic EEG artifact detection
import mlxtend # Sequential Forward Selection
-from mne.time_frequency import psd_multitaper
+from mne.time_frequency import *
from mne.preprocessing import (ICA, create_eog_epochs, create_ecg_epochs, corrmap)
from mne.stats import spatio_temporal_cluster_test, permutation_cluster_test
from mne.channels import find_ch_adjacency
@@ -41,7 +41,7 @@ from mne.connectivity import spectral_connectivity
import nitime.analysis as nta
import nitime.timeseries as nts
import nitime.utils as ntsu
-from nitime.viz import drawmatrix_channels, drawmatrix_channels_modified
+from nitime.viz import * # (drawmatrix_channels, drawmatrix_channels_modified)
from sklearn import preprocessing
from sklearn import manifold
@@ -74,7 +74,7 @@ from mpl_toolkits.mplot3d import Axes3D # registers 3D projections
# Non-library scripts
# EEG microstate package by von Wegner & Lauf, 2018
-from eeg_microstates import * # downloadeded from https://github.com/Frederic-vW/eeg_microstates
+from eeg_microstates3 import * # downloadeded from https://github.com/Frederic-vW/eeg_microstates
# minimum Redundancy Maximum Relevance script by Kiran Karra
from feature_select import * # downloaded from https://github.com/stochasticresearch/featureselect/blob/master/python/feature_select.py
@@ -88,115 +88,116 @@ plt.style.use('ggplot') # plotting style
# Modified sparcl cluster_permute
# # For eeg_microstates.py
-# def kmeans_return_all(data, n_maps, n_runs=10, maxerr=1e-6, maxiter=500):
-# """Modified K-means clustering as detailed in:
-# [1] Pascual-Marqui et al., IEEE TBME (1995) 42(7):658--665
-# [2] Murray et al., Brain Topography(2008) 20:249--264.
-# Variables named as in [1], step numbering as in Table I.
+def kmeans_return_all(data, n_maps, n_runs=10, maxerr=1e-6, maxiter=500):
+ """
+ Modified K-means clustering as detailed in:
+ [1] Pascual-Marqui et al., IEEE TBME (1995) 42(7):658--665
+ [2] Murray et al., Brain Topography(2008) 20:249--264.
+ Variables named as in [1], step numbering as in Table I.
-# Args:
-# data: numpy.array, size = number of EEG channels
-# n_maps: number of microstate maps
-# n_runs: number of K-means runs (optional)
-# maxerr: maximum error for convergence (optional)
-# maxiter: maximum number of iterations (optional)
-# doplot: plot the results, default=False (optional)
-# Returns:
-# maps: microstate maps (number of maps x number of channels)
-# L: sequence of microstate labels
-# gfp_peaks: indices of local GFP maxima
-# gev: global explained variance (0..1)
-# cv: value of the cross-validation criterion
-# """
-# n_t = data.shape[0]
-# n_ch = data.shape[1]
-# data = data - data.mean(axis=1, keepdims=True)
+ Args:
+ data: numpy.array, size = number of EEG channels
+ n_maps: number of microstate maps
+ n_runs: number of K-means runs (optional)
+ maxerr: maximum error for convergence (optional)
+ maxiter: maximum number of iterations (optional)
+ doplot: plot the results, default=False (optional)
+ Returns:
+ maps: microstate maps (number of maps x number of channels)
+ L: sequence of microstate labels
+ gfp_peaks: indices of local GFP maxima
+ gev: global explained variance (0..1)
+ cv: value of the cross-validation criterion
+ """
+ n_t = data.shape[0]
+ n_ch = data.shape[1]
+ data = data - data.mean(axis=1, keepdims=True)
-# # GFP peaks
-# gfp = np.std(data, axis=1)
-# gfp_peaks = locmax(gfp)
-# gfp_values = gfp[gfp_peaks]
-# gfp2 = np.sum(gfp_values**2) # normalizing constant in GEV
-# n_gfp = gfp_peaks.shape[0]
+ # GFP peaks
+ gfp = np.std(data, axis=1)
+ gfp_peaks = locmax(gfp)
+ gfp_values = gfp[gfp_peaks]
+ gfp2 = np.sum(gfp_values**2) # normalizing constant in GEV
+ n_gfp = gfp_peaks.shape[0]
-# # clustering of GFP peak maps only
-# V = data[gfp_peaks, :]
-# sumV2 = np.sum(V**2)
+ # clustering of GFP peak maps only
+ V = data[gfp_peaks, :]
+ sumV2 = np.sum(V**2)
-# # store results for each k-means run
-# cv_list = [] # cross-validation criterion for each k-means run
-# gev_list = [] # GEV of each map for each k-means run
-# gevT_list = [] # total GEV values for each k-means run
-# maps_list = [] # microstate maps for each k-means run
-# L_list = [] # microstate label sequence for each k-means run
-# for run in range(n_runs):
-# # initialize random cluster centroids (indices w.r.t. n_gfp)
-# rndi = np.random.permutation(n_gfp)[:n_maps]
-# maps = V[rndi, :]
-# # normalize row-wise (across EEG channels)
-# maps /= np.sqrt(np.sum(maps**2, axis=1, keepdims=True))
-# # initialize
-# n_iter = 0
-# var0 = 1.0
-# var1 = 0.0
-# # convergence criterion: variance estimate (step 6)
-# while ( (np.abs((var0-var1)/var0) > maxerr) & (n_iter < maxiter) ):
-# # (step 3) microstate sequence (= current cluster assignment)
-# C = np.dot(V, maps.T)
-# C /= (n_ch*np.outer(gfp[gfp_peaks], np.std(maps, axis=1)))
-# L = np.argmax(C**2, axis=1)
-# # (step 4)
-# for k in range(n_maps):
-# Vt = V[L==k, :]
-# # (step 4a)
-# Sk = np.dot(Vt.T, Vt)
-# # (step 4b)
-# evals, evecs = np.linalg.eig(Sk)
-# v = evecs[:, np.argmax(np.abs(evals))]
-# maps[k, :] = v/np.sqrt(np.sum(v**2))
-# # (step 5)
-# var1 = var0
-# var0 = sumV2 - np.sum(np.sum(maps[L, :]*V, axis=1)**2)
-# var0 /= (n_gfp*(n_ch-1))
-# n_iter += 1
-# if (n_iter < maxiter):
-# print("\t\tK-means run {:d}/{:d} converged after {:d} iterations.".format(run+1, n_runs, n_iter))
-# else:
-# print("\t\tK-means run {:d}/{:d} did NOT converge after {:d} iterations.".format(run+1, n_runs, maxiter))
+ # store results for each k-means run
+ cv_list = [] # cross-validation criterion for each k-means run
+ gev_list = [] # GEV of each map for each k-means run
+ gevT_list = [] # total GEV values for each k-means run
+ maps_list = [] # microstate maps for each k-means run
+ L_list = [] # microstate label sequence for each k-means run
+ for run in range(n_runs):
+ # initialize random cluster centroids (indices w.r.t. n_gfp)
+ rndi = np.random.permutation(n_gfp)[:n_maps]
+ maps = V[rndi, :]
+ # normalize row-wise (across EEG channels)
+ maps /= np.sqrt(np.sum(maps**2, axis=1, keepdims=True))
+ # initialize
+ n_iter = 0
+ var0 = 1.0
+ var1 = 0.0
+ # convergence criterion: variance estimate (step 6)
+ while ( (np.abs((var0-var1)/var0) > maxerr) & (n_iter < maxiter) ):
+ # (step 3) microstate sequence (= current cluster assignment)
+ C = np.dot(V, maps.T)
+ C /= (n_ch*np.outer(gfp[gfp_peaks], np.std(maps, axis=1)))
+ L = np.argmax(C**2, axis=1)
+ # (step 4)
+ for k in range(n_maps):
+ Vt = V[L==k, :]
+ # (step 4a)
+ Sk = np.dot(Vt.T, Vt)
+ # (step 4b)
+ evals, evecs = np.linalg.eig(Sk)
+ v = evecs[:, np.argmax(np.abs(evals))]
+ maps[k, :] = v/np.sqrt(np.sum(v**2))
+ # (step 5)
+ var1 = var0
+ var0 = sumV2 - np.sum(np.sum(maps[L, :]*V, axis=1)**2)
+ var0 /= (n_gfp*(n_ch-1))
+ n_iter += 1
+ if (n_iter < maxiter):
+ print("\t\tK-means run {:d}/{:d} converged after {:d} iterations.".format(run+1, n_runs, n_iter))
+ else:
+ print("\t\tK-means run {:d}/{:d} did NOT converge after {:d} iterations.".format(run+1, n_runs, maxiter))
-# # CROSS-VALIDATION criterion for this run (step 8)
-# C_ = np.dot(data, maps.T)
-# C_ /= (n_ch*np.outer(gfp, np.std(maps, axis=1)))
-# L_ = np.argmax(C_**2, axis=1)
-# var = np.sum(data**2) - np.sum(np.sum(maps[L_, :]*data, axis=1)**2)
-# var /= (n_t*(n_ch-1))
-# cv = var * (n_ch-1)**2/(n_ch-n_maps-1.)**2
+ # CROSS-VALIDATION criterion for this run (step 8)
+ C_ = np.dot(data, maps.T)
+ C_ /= (n_ch*np.outer(gfp, np.std(maps, axis=1)))
+ L_ = np.argmax(C_**2, axis=1)
+ var = np.sum(data**2) - np.sum(np.sum(maps[L_, :]*data, axis=1)**2)
+ var /= (n_t*(n_ch-1))
+ cv = var * (n_ch-1)**2/(n_ch-n_maps-1.)**2
-# # GEV (global explained variance) of cluster k
-# gev = np.zeros(n_maps)
-# for k in range(n_maps):
-# r = L==k
-# gev[k] = np.sum(gfp_values[r]**2 * C[r,k]**2)/gfp2
-# gev_total = np.sum(gev)
+ # GEV (global explained variance) of cluster k
+ gev = np.zeros(n_maps)
+ for k in range(n_maps):
+ r = L==k
+ gev[k] = np.sum(gfp_values[r]**2 * C[r,k]**2)/gfp2
+ gev_total = np.sum(gev)
-# # store
-# cv_list.append(cv)
-# gev_list.append(gev)
-# gevT_list.append(gev_total)
-# maps_list.append(maps)
-# L_list.append(L_)
+ # store
+ cv_list.append(cv)
+ gev_list.append(gev)
+ gevT_list.append(gev_total)
+ maps_list.append(maps)
+ L_list.append(L_)
-# # select best run
-# k_opt = np.argmin(cv_list)
-# #k_opt = np.argmax(gevT_list)
-# maps = maps_list[k_opt]
-# # ms_gfp = ms_list[k_opt] # microstate sequence at GFP peaks
-# gev = gev_list[k_opt]
-# L_ = L_list[k_opt]
-# # lowest cv criterion
-# cv_min = np.min(cv_list)
+ # select best run
+ k_opt = np.argmin(cv_list)
+ #k_opt = np.argmax(gevT_list)
+ maps = maps_list[k_opt]
+ # ms_gfp = ms_list[k_opt] # microstate sequence at GFP peaks
+ gev = gev_list[k_opt]
+ L_ = L_list[k_opt]
+ # lowest cv criterion
+ cv_min = np.min(cv_list)
-# return maps, L_, gfp_peaks, gev, cv_min
+ return maps, L_, gfp_peaks, gev, cv_min
# # For eeg_microstates.py
# def T_empirical(data, n_clusters, gap_idx = []):
diff --git a/Preprocessing.py b/Preprocessing.py
index 7e15f86..2779510 100644
--- a/Preprocessing.py
+++ b/Preprocessing.py
@@ -25,7 +25,7 @@ Link to the demonstration data: www.bci2000.org
# Set working directory
import os
-wkdir = "/home/glia/EEG"
+wkdir = "/Users/benj3542/Desktop/Uni/Noter/Semester_6/Bachelor/resting-state-eeg-analysis/"
os.chdir(wkdir)
# Load all libraries from the Preamble
@@ -300,7 +300,8 @@ for i in range(len(corrected_epochs)):
reject_log0[e] = ar0[e].get_reject_log(corrected_epochs[i][ee])
# Plot and save Autorejected epochs
fig = reject_log0[e].plot(orientation="horizontal", show=False)
- fig.savefig(os.path.join(save_path,"AR_" + str(Subject_id_concat[i]) + "_" + str(ee) + ".png"))
+ #fig.savefig(os.path.join(save_path,"AR_" + str(Subject_id_concat[i]) + "_" + str(ee) + ".png"))
+ fig.savefig(os.path.join(save_path,"AR_" + str(e) + "_" + str(ee) + ".png"))
# Close figure window
plt.close(fig)
# Save mean peak-to-peak voltage threshold used
diff --git a/eeg_microstates3.py b/eeg_microstates3.py
new file mode 100644
index 0000000..290cbc3
--- /dev/null
+++ b/eeg_microstates3.py
@@ -0,0 +1,1849 @@
+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+# Information theoretic analysis EEG microstates
+# Ref.: von Wegner et al., NeuroImage 2017, 158:99-111
+# FvW (12/2014)
+
+"""
+- .format() => f""
+- function documentation
+- ms sequence temporal smoothing
+- all clustering algorithms
+- ms re-ordering: TextWidget due to plt.ion/plt.ioff + console input() problems
+- dictionaries
+"""
+
+import argparse, os, sys, time
+import matplotlib.pyplot as plt
+from matplotlib.widgets import TextBox
+import numpy as np
+from scipy.interpolate import griddata
+from scipy.signal import butter, filtfilt, welch
+from scipy.stats import chi2, chi2_contingency
+from sklearn.decomposition import PCA, FastICA, TruncatedSVD
+from statsmodels.stats.multitest import multipletests
+# in case you have a compiled Cython implementation:
+# from mutinf.mutinf_cy import mutinf_cy_c_wrapper, mutinf_cy
+
+
+def read_xyz(filename):
+ """Read EEG electrode locations in xyz format
+
+ Args:
+ filename: full path to the '.xyz' file
+ Returns:
+ locs: n_channels x 3 (numpy.array)
+ """
+ ch_names = []
+ locs = []
+ with open(filename, 'r') as f:
+ l = f.readline() # header line
+ while l:
+ l = f.readline().strip().split("\t")
+ if (l != ['']):
+ ch_names.append(l[0])
+ locs.append([float(l[1]), float(l[2]), float(l[3])])
+ else:
+ l = None
+ return ch_names, np.array(locs)
+
+
+def read_edf(filename):
+ """Basic EDF file format reader
+
+ EDF specifications: http://www.edfplus.info/specs/edf.html
+
+ Args:
+ filename: full path to the '.edf' file
+ Returns:
+ chs: list of channel names
+ fs: sampling frequency in [Hz]
+ data: EEG data as numpy.array (samples x channels)
+ """
+
+ def readn(n):
+ """read n bytes."""
+ return np.fromfile(fp, sep='', dtype=np.int8, count=n)
+
+ def bytestr(bytes, i):
+ """convert byte array to string."""
+ return np.array([bytes[k] for k in range(i*8, (i+1)*8)]).tostring()
+
+ fp = open(filename, 'r')
+ x = np.fromfile(fp, sep='', dtype=np.uint8, count=256).tostring()
+ header = {}
+ header['version'] = x[0:8]
+ header['patientID'] = x[8:88]
+ header['recordingID'] = x[88:168]
+ header['startdate'] = x[168:176]
+ header['starttime'] = x[176:184]
+ header['length'] = int(x[184:192]) # header length (bytes)
+ header['reserved'] = x[192:236]
+ header['records'] = int(x[236:244]) # number of records
+ header['duration'] = float(x[244:252]) # duration of each record [sec]
+ header['channels'] = int(x[252:256]) # ns - number of signals
+ n_ch = header['channels'] # number of EEG channels
+ header['channelname'] = (readn(16*n_ch)).tostring()
+ header['transducer'] = (readn(80*n_ch)).tostring().split()
+ header['physdime'] = (readn(8*n_ch)).tostring().split()
+ header['physmin'] = []
+ b = readn(8*n_ch)
+ for i in range(n_ch):
+ header['physmin'].append(float(bytestr(b, i)))
+ header['physmax'] = []
+ b = readn(8*n_ch)
+ for i in range(n_ch):
+ header['physmax'].append(float(bytestr(b, i)))
+ header['digimin'] = []
+ b = readn(8*n_ch)
+ for i in range(n_ch):
+ header['digimin'].append(int(bytestr(b, i)))
+ header['digimax'] = []
+ b = readn(8*n_ch)
+ for i in range(n_ch):
+ header['digimax'].append(int(bytestr(b, i)))
+ header['prefilt'] = (readn(80*n_ch)).tostring().split()
+ header['samples_per_record'] = []
+ b = readn(8*n_ch)
+ for i in range(n_ch):
+ header['samples_per_record'].append(float(bytestr(b, i)))
+ nr = header['records']
+ n_per_rec = int(header['samples_per_record'][0])
+ n_total = int(nr*n_per_rec*n_ch)
+ fp.seek(header['length'],os.SEEK_SET) # header end = data start
+ data = np.fromfile(fp, sep='', dtype=np.int16, count=n_total) # count=-1
+ fp.close()
+
+ # re-order
+ #print("EDF reader:")
+ #print("[+] n_per_rec: {:d}".format(n_per_rec))
+ #print("[+] n_ch: {:d}".format(n_ch))
+ #print("[+] nr: {:d}".format(nr))
+ #print("[+] n_total: {:d}".format(n_total))
+ #print(data.shape)
+ data = np.reshape(data,(n_per_rec,n_ch,nr),order='F')
+ data = np.transpose(data,(0,2,1))
+ data = np.reshape(data,(n_per_rec*nr,n_ch),order='F')
+
+ # convert to physical dimensions
+ for k in range(data.shape[1]):
+ d_min = float(header['digimin'][k])
+ d_max = float(header['digimax'][k])
+ p_min = float(header['physmin'][k])
+ p_max = float(header['physmax'][k])
+ if ((d_max-d_min) > 0):
+ data[:,k] = p_min+(data[:,k]-d_min)/(d_max-d_min)*(p_max-p_min)
+
+ #print(header)
+ return header['channelname'].split(),\
+ header['samples_per_record'][0]/header['duration'],\
+ data
+
+
+def bp_filter(data, f_lo, f_hi, fs):
+ """Digital band pass filter (6-th order Butterworth)
+
+ Args:
+ data: numpy.array, time along axis 0
+ (f_lo, f_hi): frequency band to extract [Hz]
+ fs: sampling frequency [Hz]
+ Returns:
+ data_filt: band-pass filtered data, same shape as data
+ """
+ data_filt = np.zeros_like(data)
+ f_ny = fs/2. # Nyquist frequency
+ b_lo = f_lo/f_ny # normalized frequency [0..1]
+ b_hi = f_hi/f_ny # normalized frequency [0..1]
+ # band-pass filter parameters
+ p_lp = {"N":6, "Wn":b_hi, "btype":"lowpass", "analog":False, "output":"ba"}
+ p_hp = {"N":6, "Wn":b_lo, "btype":"highpass", "analog":False, "output":"ba"}
+ bp_b1, bp_a1 = butter(**p_lp)
+ bp_b2, bp_a2 = butter(**p_hp)
+ data_filt = filtfilt(bp_b1, bp_a1, data, axis=0)
+ data_filt = filtfilt(bp_b2, bp_a2, data_filt, axis=0)
+ return data_filt
+
+
+def plot_data(data, fs):
+ """Plot the data
+
+ Args:
+ data: numpy.array
+ fs: sampling frequency [Hz]
+ """
+ t = np.arange(len(data))/fs # time axis in seconds
+ fig = plt.figure(1, figsize=(20,4))
+ plt.plot(t, data, '-k', linewidth=1)
+ plt.xlabel("time [s]", fontsize=24)
+ plt.ylabel("potential [$\mu$V]", fontsize=24)
+ plt.tight_layout()
+ plt.show()
+
+
+def plot_psd(data, fs, n_seg=1024):
+ """Plot the power spectral density (Welch's method)
+
+ Args:
+ data: numpy.array
+ fs: sampling frequency [Hz]
+ n_seg: samples per segment, default=1024
+ """
+ freqs, psd = welch(data, fs, nperseg=n_seg)
+ fig = plt.figure(1, figsize=(16,8))
+ plt.semilogy(freqs, psd, 'k', linewidth=3)
+ #plt.loglog(freqs, psd, 'k', linewidth=3)
+ #plt.xlim(freqs.min(), freqs.max())
+ #plt.ylim(psd.min(), psd.max())
+ plt.title("Power spectral density (Welch's method n={:d})".format(n_seg))
+ plt.tight_layout()
+ plt.show()
+
+
+def topo(data, n_grid=64):
+ """Interpolate EEG topography onto a regularly spaced grid
+
+ Args:
+ data: numpy.array, size = number of EEG channels
+ n_grid: integer, interpolate to n_grid x n_grid array, default=64
+ Returns:
+ data_interpol: cubic interpolation of EEG topography, n_grid x n_grid
+ contains nan values
+ """
+ channels, locs = read_xyz('cap.xyz')
+ n_channels = len(channels)
+ #locs /= np.sqrt(np.sum(locs**2,axis=1))[:,np.newaxis]
+ locs /= np.linalg.norm(locs, 2, axis=1, keepdims=True)
+ c = findstr('Cz', channels)[0]
+ # print 'center electrode for interpolation: ' + channels[c]
+ #w = np.sqrt(np.sum((locs-locs[c])**2, axis=1))
+ w = np.linalg.norm(locs-locs[c], 2, axis=1)
+ #arclen = 2*np.arcsin(w/2)
+ arclen = np.arcsin(w/2.*np.sqrt(4.-w*w))
+ phi_re = locs[:,0]-locs[c][0]
+ phi_im = locs[:,1]-locs[c][1]
+ #print(type(phi_re), phi_re)
+ #print(type(phi_im), phi_im)
+ tmp = phi_re + 1j*phi_im
+ #tmp = map(complex, locs[:,0]-locs[c][0], locs[:,1]-locs[c][1])
+ #print(type(tmp), tmp)
+ phi = np.angle(tmp)
+ #phi = np.angle(map(complex, locs[:,0]-locs[c][0], locs[:,1]-locs[c][1]))
+ X = arclen*np.real(np.exp(1j*phi))
+ Y = arclen*np.imag(np.exp(1j*phi))
+ r = max([max(X),max(Y)])
+ Xi = np.linspace(-r,r,n_grid)
+ Yi = np.linspace(-r,r,n_grid)
+ data_ip = griddata((X, Y), data, (Xi[None,:], Yi[:,None]), method='cubic')
+ return data_ip
+
+
+def eeg2map(data):
+ """Interpolate and normalize EEG topography, ignoring nan values
+
+ Args:
+ data: numpy.array, size = number of EEG channels
+ n_grid: interger, interpolate to n_grid x n_grid array, default=64
+ Returns:
+ top_norm: normalized topography, n_grid x n_grid
+ """
+ n_grid = 64
+ top = topo(data, n_grid)
+ mn = np.nanmin(top)
+ mx = np.nanmax(top)
+ top_norm = (top-mn)/(mx-mn)
+ return top_norm
+
+
+def clustering(data, fs, chs, locs, mode, n_clusters, n_win=3, \
+ interpol=True, doplot=False):
+ """EEG microstate clustering algorithms.
+
+ Args:
+ data: numpy.array (n_t, n_ch)
+ fs : sampling frequency [Hz]
+ chs : list of channel name strings
+ locs: numpy.array (n_ch, 2) of electrode locations (x, y)
+ mode: clustering algorithm
+ n_clusters: number of clusters
+ n_win: smoothing window size 2*n_win+1 [t-n_win:t+n_win]
+ doplot: boolean
+ Returns:
+ maps: n_maps x n_channels NumPy array
+ L: microstate sequence (integers)
+ gfp_peaks: locations of local GFP maxima
+ gev: global explained variance
+ """
+ #print("[+] Clustering algorithm: {:s}".format(mode))
+
+ # --- number of EEG channels ---
+ n_ch = data.shape[1]
+ #print("[+] EEG channels: n_ch = {:d}".format(n_ch))
+
+ # --- normalized data ---
+ data_norm = data - data.mean(axis=1, keepdims=True)
+ data_norm /= data_norm.std(axis=1, keepdims=True)
+
+ # --- GFP peaks ---
+ gfp = np.nanstd(data, axis=1)
+ gfp2 = np.sum(gfp**2) # normalizing constant
+ gfp_peaks = locmax(gfp)
+ data_cluster = data[gfp_peaks,:]
+ #data_cluster = data_cluster[:100,:]
+ data_cluster_norm = data_cluster - data_cluster.mean(axis=1, keepdims=True)
+ data_cluster_norm /= data_cluster_norm.std(axis=1, keepdims=True)
+ print("[+] Data format for clustering [GFP peaks, channels]: {:d} x {:d}"\
+ .format(data_cluster.shape[0], data_cluster.shape[1]))
+
+ start = time.time()
+
+ if (mode == "aahc"):
+ print("\n[+] Clustering algorithm: AAHC.")
+ maps = aahc(data, n_clusters, doplot=False)
+
+ if (mode == "kmeans"):
+ print("\n[+] Clustering algorithm: mod. K-MEANS.")
+ maps = kmeans(data, n_maps=n_clusters, n_runs=5)
+
+ if (mode == "kmedoids"):
+ print("\n[+] Clustering algorithm: K-MEDOIDS.")
+ C = np.corrcoef(data_cluster_norm)
+ C = C**2 # ignore EEG polarity
+ kmed_maps = kmedoids(S=C, K=n_clusters, nruns=10, maxits=500)
+ maps = [int(data_cluster[kmed_maps[k],:]) for k in range(n_clusters)]
+ maps = np.array(maps)
+ del C, kmed_maps
+
+ if (mode == "pca"):
+ print("\n[+] Clustering algorithm: PCA.")
+ params = {
+ "n_components": n_clusters,
+ "copy": True,
+ "whiten": True,
+ "svd_solver": "auto",
+ }
+ pca = PCA(**params) # SKLEARN
+ pca.fit(data_cluster_norm)
+ maps = np.array([pca.components_[k,:] for k in range(n_clusters)])
+ '''
+ print("PCA explained variance: ", str(pca.explained_variance_ratio_))
+ print("PCA total explained variance: ", \
+ str(np.sum(pca.explained_variance_ratio_)))
+ '''
+ del pca, params
+
+ ''' SKLEARN:
+ params = {
+ "n_components": n_clusters,
+ "algorithm": "randomized",
+ "n_iter": 5,
+ "random_state": None,
+ "tol": 0.0,
+ }
+ svd = TruncatedSVD(**params)
+ svd.fit(data_cluster_norm)
+ maps = svd.components_
+ print("explained variance (%): ")
+ print(explained_variance_ratio_)
+ #print("explained variance: ")
+ print(explained_variance_)
+ del svd, params
+ '''
+
+ if (mode == "ica"):
+ print("\n[+] Clustering algorithm: Fast-ICA.")
+ #''' Fast-ICA: algorithm= parallel;deflation, fun=logcosh;exp;cube
+ params = {
+ "n_components": n_clusters,
+ "algorithm": "parallel",
+ "whiten": True,
+ "fun": "exp",
+ "max_iter": 200,
+ }
+ ica = FastICA(**params) # SKLEARN
+ S_ = ica.fit_transform(data_cluster_norm) # reconstructed signals
+ A_ = ica.mixing_ # estimated mixing matrix
+ IC_ = ica.components_
+ print("data: " + str(data_cluster_norm.shape))
+ print("ICs: " + str(IC_.shape))
+ print("mixing matrix: " + str(A_.shape))
+ maps = np.array([ica.components_[k,:] for k in range(n_clusters)])
+ del ica, params
+
+ end = time.time()
+ delta_t = end - start
+ print(f"[+] Computation time: {delta_t:.2f} sec")
+
+ # --- microstate sequence ---
+ print("\n[+] Microstate back-fitting:")
+ print("data_norm: ", data_norm.shape)
+ print("data_cluster_norm: ", data_cluster_norm.shape)
+ print("maps: ", maps.shape)
+
+ if interpol:
+ C = np.dot(data_cluster_norm, maps.T)/n_ch
+ L_gfp = np.argmax(C**2, axis=1) # microstates at GFP peak
+ del C
+ n_t = data_norm.shape[0]
+ L = np.zeros(n_t)
+ for t in range(n_t):
+ if t in gfp_peaks:
+ i = gfp_peaks.tolist().index(t)
+ L[t] = L_gfp[i]
+ else:
+ i = np.argmin(np.abs(t-gfp_peaks))
+ L[t] = L_gfp[i]
+ L = L.astype('int')
+ else:
+ C = np.dot(data_norm, maps.T)/n_ch
+ L = np.argmax(C**2, axis=1)
+ del C
+
+ # visualize microstate sequence
+ if False:
+ t_ = np.arange(n_t)
+ fig, ax = plt.subplots(2, 1, figsize=(15,4), sharex=True)
+ ax[0].plot(t_, gfp, '-k', lw=2)
+ for p in gfp_peaks:
+ ax[0].axvline(t_[p], c='k', lw=0.5, alpha=0.3)
+ ax[0].plot(t_[gfp_peaks], gfp[gfp_peaks], 'or', ms=10)
+ ax[1].plot(L)
+ plt.show()
+
+ ''' --- temporal smoothing ---
+ L2 = np.copy(L)
+ for i in range(n_win, len(L)-n_win):
+ s = np.array([np.sum(L[i-n_win:i+n_win]==j) for j in range(n_clusters)])
+ L2[i] = np.argmax(s)
+ L = L2.copy()
+ del L2
+ '''
+
+ # --- GEV ---
+ maps_norm = maps - maps.mean(axis=1, keepdims=True)
+ maps_norm /= maps_norm.std(axis=1, keepdims=True)
+
+ # --- correlation data, maps ---
+ C = np.dot(data_norm, maps_norm.T)/n_ch
+ #print("C.shape: " + str(C.shape))
+ #print("C.min: {C.min():.2f} Cmax: {C.max():.2f}")
+
+ # --- GEV_k & GEV ---
+ gev = np.zeros(n_clusters)
+ for k in range(n_clusters):
+ r = L==k
+ gev[k] = np.sum(gfp[r]**2 * C[r,k]**2)/gfp2
+ print(f"\n[+] Global explained variance GEV = {gev.sum():.3f}")
+ for k in range(n_clusters):
+ print(f"GEV_{k:d}: {gev[k]:.3f}")
+
+ if doplot:
+ #plt.ion()
+ # matplotlib's perceptually uniform sequential colormaps:
+ # magma, inferno, plasma, viridis
+ #cm = plt.cm.magma
+ cm = plt.cm.seismic
+ fig, axarr = plt.subplots(1, n_clusters, figsize=(20,5))
+ for imap in range(n_clusters):
+ axarr[imap].imshow(eeg2map(maps[imap, :]), cmap=cm, origin='lower')
+ axarr[imap].set_xticks([])
+ axarr[imap].set_xticklabels([])
+ axarr[imap].set_yticks([])
+ axarr[imap].set_yticklabels([])
+ title = f"Microstate maps ({mode.upper():s})"
+ axarr[0].set_title(title, fontsize=16, fontweight="bold")
+
+ # dummy function, callback from TextBox, does nothing
+ def f_dummy(text): pass
+
+ axbox = plt.axes([0.1, 0.05, 0.1, 0.1]) # l, b, w, h
+ text_box = TextBox(axbox, 'Ordering: ', initial="[0, 1, 2, 3]")
+ text_box.on_submit(f_dummy)
+ plt.show()
+ order_str = text_box.text
+
+ #plt.ion()
+ #plt.show()
+ #plt.pause(0.001)
+
+ #txt = input("enter something: ")
+ #print("txt: ", txt)
+ #plt.draw()
+ #plt.ioff()
+
+ # --- assign map labels manually ---
+ #order_str = raw_input("\n\t\tAssign map labels (e.g. 0, 2, 1, 3): ")
+ #order_str = input("\n\t\tAssign map labels (e.g. 0, 2, 1, 3): ")
+ #plt.ioff()
+ order_str = order_str.replace("[", "")
+ order_str = order_str.replace("]", "")
+ order_str = order_str.replace(",", "")
+ order_str = order_str.replace(" ", "")
+ if (len(order_str) != n_clusters):
+ if (len(order_str)==0):
+ print("Empty input string.")
+ else:
+ input_str = ", ".join(order_str)
+ print(f"Parsed manual input: {input_str:s}")
+ print("Number of labels does not equal number of clusters.")
+ print("Continue using the original assignment...\n")
+ else:
+ order = np.zeros(n_clusters, dtype=int)
+ for i, s in enumerate(order_str):
+ order[i] = int(s)
+ print("Re-ordered labels: {:s}".format(", ".join(order_str)))
+ # re-order return variables
+ maps = maps[order,:]
+ for i in range(len(L)):
+ L[i] = order[L[i]]
+ gev = gev[order]
+ # Figure
+ fig, axarr = plt.subplots(1, n_clusters, figsize=(20,5))
+ for imap in range(n_clusters):
+ axarr[imap].imshow(eeg2map(maps[imap,:]),cmap=cm,origin='lower')
+ axarr[imap].set_xticks([])
+ axarr[imap].set_xticklabels([])
+ axarr[imap].set_yticks([])
+ axarr[imap].set_yticklabels([])
+ title = "re-ordered microstate maps"
+ axarr[0].set_title(title, fontsize=16, fontweight="bold")
+ plt.show()
+ #plt.ioff()
+
+ return maps, L, gfp_peaks, gev
+
+
+def aahc(data, N_clusters, doplot=False):
+ """Atomize and Agglomerative Hierarchical Clustering Algorithm
+ AAHC (Murray et al., Brain Topography, 2008)
+
+ Args:
+ data: EEG data to cluster, numpy.array (n_samples, n_channels)
+ N_clusters: desired number of clusters
+ doplot: boolean, plot maps
+ Returns:
+ maps: n_maps x n_channels (numpy.array)
+ """
+
+ def extract_row(A, k):
+ v = A[k,:]
+ A_ = np.vstack((A[:k,:],A[k+1:,:]))
+ return A_, v
+
+ def extract_item(A, k):
+ a = A[k]
+ A_ = A[:k] + A[k+1:]
+ return A_, a
+
+ #print("\n\t--- AAHC ---")
+ nt, nch = data.shape
+
+ # --- get GFP peaks ---
+ gfp = data.std(axis=1)
+ gfp_peaks = locmax(gfp)
+ #gfp_peaks = gfp_peaks[:100]
+ #n_gfp = gfp_peaks.shape[0]
+ gfp2 = np.sum(gfp**2) # normalizing constant in GEV
+
+ # --- initialize clusters ---
+ maps = data[gfp_peaks,:]
+ # --- store original gfp peaks and indices ---
+ cluster_data = data[gfp_peaks,:]
+ #n_maps = n_gfp
+ n_maps = maps.shape[0]
+ print(f"[+] Initial number of clusters: {n_maps:d}\n")
+
+ # --- cluster indices w.r.t. original size, normalized GFP peak data ---
+ Ci = [[k] for k in range(n_maps)]
+
+ # --- main loop: atomize + agglomerate ---
+ while (n_maps > N_clusters):
+ blank_ = 80*" "
+ print(f"\r{blank_:s}\r\t\tAAHC > n: {n_maps:d} => {n_maps-1:d}", end="")
+ #stdout.write(s); stdout.flush()
+ #print("\n\tAAHC > n: {:d} => {:d}".format(n_maps, n_maps-1))
+
+ # --- correlations of the data sequence with each cluster ---
+ m_x, s_x = data.mean(axis=1, keepdims=True), data.std(axis=1)
+ m_y, s_y = maps.mean(axis=1, keepdims=True), maps.std(axis=1)
+ s_xy = 1.*nch*np.outer(s_x, s_y)
+ C = np.dot(data-m_x, np.transpose(maps-m_y)) / s_xy
+
+ # --- microstate sequence, ignore polarity ---
+ L = np.argmax(C**2, axis=1)
+
+ # --- GEV (global explained variance) of cluster k ---
+ gev = np.zeros(n_maps)
+ for k in range(n_maps):
+ r = L==k
+ gev[k] = np.sum(gfp[r]**2 * C[r,k]**2)/gfp2
+
+ # --- merge cluster with the minimum GEV ---
+ imin = np.argmin(gev)
+ #print("\tre-cluster: {:d}".format(imin))
+
+ # --- N => N-1 ---
+ maps, _ = extract_row(maps, imin)
+ Ci, reC = extract_item(Ci, imin)
+ re_cluster = [] # indices of updated clusters
+ #C_sgn = np.zeros(nt)
+ for k in reC: # map index to re-assign
+ c = cluster_data[k,:]
+ m_x, s_x = maps.mean(axis=1, keepdims=True), maps.std(axis=1)
+ m_y, s_y = c.mean(), c.std()
+ s_xy = 1.*nch*s_x*s_y
+ C = np.dot(maps-m_x, c-m_y)/s_xy
+ inew = np.argmax(C**2) # ignore polarity
+ #C_sgn[k] = C[inew]
+ re_cluster.append(inew)
+ Ci[inew].append(k)
+ n_maps = len(Ci)
+
+ # --- update clusters ---
+ re_cluster = list(set(re_cluster)) # unique list of updated clusters
+
+ ''' re-clustering by modified mean
+ for i in re_cluster:
+ idx = Ci[i]
+ c = np.zeros(nch) # new cluster average
+ # add to new cluster, polarity according to corr. sign
+ for k in idx:
+ if (C_sgn[k] >= 0):
+ c += cluster_data[k,:]
+ else:
+ c -= cluster_data[k,:]
+ c /= len(idx)
+ maps[i] = c
+ #maps[i] = (c-np.mean(c))/np.std(c) # normalize the new cluster
+ del C_sgn
+ '''
+
+ # re-clustering by eigenvector method
+ for i in re_cluster:
+ idx = Ci[i]
+ Vt = cluster_data[idx,:]
+ Sk = np.dot(Vt.T, Vt)
+ evals, evecs = np.linalg.eig(Sk)
+ c = evecs[:, np.argmax(np.abs(evals))]
+ c = np.real(c)
+ maps[i] = c/np.sqrt(np.sum(c**2))
+
+ print()
+ return maps
+
+
+def kmeans(data, n_maps, n_runs=10, maxerr=1e-6, maxiter=500):
+ """Modified K-means clustering as detailed in:
+ [1] Pascual-Marqui et al., IEEE TBME (1995) 42(7):658--665
+ [2] Murray et al., Brain Topography(2008) 20:249--264.
+ Variables named as in [1], step numbering as in Table I.
+
+ Args:
+ data: numpy.array, size = number of EEG channels
+ n_maps: number of microstate maps
+ n_runs: number of K-means runs (optional)
+ maxerr: maximum error for convergence (optional)
+ maxiter: maximum number of iterations (optional)
+ Returns:
+ maps: microstate maps (number of maps x number of channels)
+ L: sequence of microstate labels
+ gfp_peaks: indices of local GFP maxima
+ gev: global explained variance (0..1)
+ cv: value of the cross-validation criterion
+ """
+ n_t = data.shape[0]
+ n_ch = data.shape[1]
+ data = data - data.mean(axis=1, keepdims=True)
+
+ # GFP peaks
+ gfp = np.std(data, axis=1)
+ gfp_peaks = locmax(gfp)
+ gfp_values = gfp[gfp_peaks]
+ gfp2 = np.sum(gfp_values**2) # normalizing constant in GEV
+ n_gfp = gfp_peaks.shape[0]
+
+ # clustering of GFP peak maps only
+ V = data[gfp_peaks, :]
+ sumV2 = np.sum(V**2)
+
+ # store results for each k-means run
+ cv_list = [] # cross-validation criterion for each k-means run
+ gev_list = [] # GEV of each map for each k-means run
+ gevT_list = [] # total GEV values for each k-means run
+ maps_list = [] # microstate maps for each k-means run
+ L_list = [] # microstate label sequence for each k-means run
+ for run in range(n_runs):
+ # initialize random cluster centroids (indices w.r.t. n_gfp)
+ rndi = np.random.permutation(n_gfp)[:n_maps]
+ maps = V[rndi, :]
+ # normalize row-wise (across EEG channels)
+ maps /= np.sqrt(np.sum(maps**2, axis=1, keepdims=True))
+ # initialize
+ n_iter = 0
+ var0 = 1.0
+ var1 = 0.0
+ # convergence criterion: variance estimate (step 6)
+ while ( (np.abs((var0-var1)/var0) > maxerr) & (n_iter < maxiter) ):
+ # (step 3) microstate sequence (= current cluster assignment)
+ C = np.dot(V, maps.T)
+ C /= (n_ch*np.outer(gfp[gfp_peaks], np.std(maps, axis=1)))
+ L = np.argmax(C**2, axis=1)
+ # (step 4)
+ for k in range(n_maps):
+ Vt = V[L==k, :]
+ # (step 4a)
+ Sk = np.dot(Vt.T, Vt)
+ # (step 4b)
+ evals, evecs = np.linalg.eig(Sk)
+ v = evecs[:, np.argmax(np.abs(evals))]
+ v = v.real
+ maps[k, :] = v/np.sqrt(np.sum(v**2))
+ # (step 5)
+ var1 = var0
+ var0 = sumV2 - np.sum(np.sum(maps[L, :]*V, axis=1)**2)
+ var0 /= (n_gfp*(n_ch-1))
+ n_iter += 1
+ if (n_iter < maxiter):
+ print((f"\tK-means run {run+1:d}/{n_runs:d} converged after "
+ f"{n_iter:d} iterations."))
+ else:
+ print((f"\tK-means run {run+1:d}/{n_runs:d} did NOT converge "
+ f"after {maxiter:d} iterations."))
+
+ # CROSS-VALIDATION criterion for this run (step 8)
+ C_ = np.dot(data, maps.T)
+ C_ /= (n_ch*np.outer(gfp, np.std(maps, axis=1)))
+ L_ = np.argmax(C_**2, axis=1)
+ var = np.sum(data**2) - np.sum(np.sum(maps[L_, :]*data, axis=1)**2)
+ var /= (n_t*(n_ch-1))
+ cv = var * (n_ch-1)**2/(n_ch-n_maps-1.)**2
+
+ # GEV (global explained variance) of cluster k
+ gev = np.zeros(n_maps)
+ for k in range(n_maps):
+ r = L==k
+ gev[k] = np.sum(gfp_values[r]**2 * C[r,k]**2)/gfp2
+ gev_total = np.sum(gev)
+
+ # store
+ cv_list.append(cv)
+ gev_list.append(gev)
+ gevT_list.append(gev_total)
+ maps_list.append(maps)
+ L_list.append(L_)
+
+ # select best run
+ k_opt = np.argmin(cv_list)
+ #k_opt = np.argmax(gevT_list)
+ maps = maps_list[k_opt]
+ # ms_gfp = ms_list[k_opt] # microstate sequence at GFP peaks
+ gev = gev_list[k_opt]
+ L_ = L_list[k_opt]
+
+ return maps
+
+
+def kmedoids(S, K, nruns, maxits):
+ """Octave/Matlab: Copyright Brendan J. Frey and Delbert Dueck, Aug 2006
+ http://www.psi.toronto.edu/~frey/apm/kcc.m
+ Simplified by Kevin Murphy
+ Python 2.7.x - FvW, 02/2014
+
+ Args:
+ filename: full path to the '.xyz' file
+ Returns:
+ locs: n_channels x 3 (numpy.array)
+ """
+ n = S.shape[0]
+ dpsim = np.zeros((maxits,nruns))
+ idx = np.zeros((n,nruns))
+ for rep in xrange(nruns):
+ tmp = np.random.permutation(range(n))
+ mu = tmp[:K]
+ t = 0
+ done = (t==maxits)
+ while ( not done ):
+ t += 1
+ muold = mu
+ dpsim[t,rep] = 0
+ # Find class assignments
+ cl = np.argmax(S[:,mu], axis=1) # max pos. of each row
+ # Set assignments of exemplars to themselves
+ cl[mu] = range(K)
+ for j in xrange(K): # For each class, find new exemplar
+ I = np.where(cl==j)[0]
+ S_I_rowsum = np.sum(S[I][:,I],axis=0)
+ Scl = max(S_I_rowsum)
+ ii = np.argmax(S_I_rowsum)
+ dpsim[t,rep] = dpsim[t,rep] + Scl
+ mu[j] = I[ii]
+ if all(muold==mu) | (t==maxits):
+ done = 1
+ idx[:,rep] = mu[cl]
+ dpsim[t+1:,rep] = dpsim[t,rep]
+ return np.unique(idx)
+
+
+def p_empirical(data, n_clusters):
+ """Empirical symbol distribution
+
+ Args:
+ data: numpy.array, size = length of microstate sequence
+ n_clusters: number of microstate clusters
+ Returns:
+ p: empirical distribution
+ """
+ p = np.zeros(n_clusters)
+ n = len(data)
+ for i in range(n):
+ p[data[i]] += 1.0
+ p /= n
+ return p
+
+
+def T_empirical(data, n_clusters):
+ """Empirical transition matrix
+
+ Args:
+ data: numpy.array, size = length of microstate sequence
+ n_clusters: number of microstate clusters
+ Returns:
+ T: empirical transition matrix
+ """
+ T = np.zeros((n_clusters, n_clusters))
+ n = len(data)
+ for i in range(n-1):
+ T[data[i], data[i+1]] += 1.0
+ p_row = np.sum(T, axis=1)
+ for i in range(n_clusters):
+ if ( p_row[i] != 0.0 ):
+ for j in range(n_clusters):
+ T[i,j] /= p_row[i] # normalize row sums to 1.0
+ return T
+
+
+def p_equilibrium(T):
+ '''
+ get equilibrium distribution from transition matrix:
+ lambda = 1 - (left) eigenvector
+ '''
+ evals, evecs = np.linalg.eig(T.transpose())
+ i = np.where(np.isclose(evals, 1.0, atol=1e-6))[0][0] # locate max eigenval.
+ p_eq = np.abs(evecs[:,i]) # make eigenvec. to max. eigenval. non-negative
+ p_eq /= p_eq.sum() # normalized eigenvec. to max. eigenval.
+ return p_eq # stationary distribution
+
+
+def max_entropy(n_clusters):
+ """Maximum Shannon entropy of a sequence with n_clusters
+
+ Args:
+ n_clusters: number of microstate clusters
+ Returns:
+ h_max: maximum Shannon entropy
+ """
+ h_max = np.log(float(n_clusters))
+ return h_max
+
+
+def shuffle(data):
+ """Randomly shuffled copy of data (i.i.d. surrogate)
+
+ Args:
+ data: numpy array, 1D
+ Returns:
+ data_copy: shuffled data copy
+ """
+ data_c = data.copy()
+ np.random.shuffle(data_c)
+ return data_c
+
+
+def surrogate_mc(p, T, n_clusters, n):
+ """Surrogate Markov chain with symbol distribution p and
+ transition matrix T
+
+ Args:
+ p: empirical symbol distribution
+ T: empirical transition matrix
+ n_clusters: number of clusters/symbols
+ n: length of surrogate microstate sequence
+ Returns:
+ mc: surrogate Markov chain
+ """
+
+ # NumPy vectorized code
+ psum = np.cumsum(p)
+ Tsum = np.cumsum(T, axis=1)
+ # initial state according to p:
+ mc = [np.min(np.argwhere(np.random.rand() < psum))]
+ # next state according to T:
+ for i in range(1, n):
+ mc.append(np.min(np.argwhere(np.random.rand() < Tsum[mc[i-1]])))
+
+ ''' alternative implementation using loops
+ r = np.random.rand() # ~U[0,1], random threshold
+ s = 0.
+ y = p[s]
+ while (y < r):
+ s += 1.
+ y += p[s]
+ mc = [s] # initial state according to p
+
+ # iterate ...
+ for i in xrange(1,n):
+ r = np.random.rand() # ~U[0,1], random threshold
+ s = mc[i-1] # currrent state
+ t = 0. # trial state
+ y = T[s][t] # transition rate to trial state
+ while ( y < r ):
+ t += 1. # next trial state
+ y += T[s][t]
+ mc.append(t) # store current state
+ '''
+
+ return np.array(mc)
+
+
+def mutinf(x, ns, lmax):
+ """Time-lagged mutual information of symbolic sequence x with
+ ns different symbols, up to maximum lag lmax.
+ *** Symbols must be 0, 1, 2, ... to use as indices directly! ***
+
+ Args:
+ x: symbolic sequence, symbols = [0, 1, 2, ...]
+ ns: number of symbols
+ lmax: maximum time lag
+ Returns:
+ mi: time-lagged mutual information
+ """
+
+ n = len(x)
+ mi = np.zeros(lmax)
+ for l in range(lmax):
+ if ((l+1)%10 == 0):
+ print(f"mutual information lag: {l+1:d}\r", end="")
+ #sys.stdout.write(s)
+ #sys.stdout.flush()
+ nmax = n-l
+ h1 = H_1(x[:nmax], ns)
+ h2 = H_1(x[l:l+nmax], ns)
+ h12 = H_2(x[:nmax], x[l:l+nmax], ns)
+ mi[l] = h1 + h2 - h12
+ print("")
+ return mi
+
+
+def mutinf_i(x, ns, lmax):
+ """Time-lagged mutual information for each symbol of the symbolic
+ sequence x with ns different symbols, up to maximum lag lmax.
+ *** Symbols must be 0, 1, 2, ... to use as indices directly! ***
+
+ Args:
+ x: symbolic sequence, symbols = [0, 1, 2, ...]
+ ns: number of symbols
+ lmax: maximum time lag
+ Returns:
+ mi: time-lagged mutual informations for each symbol, ns x lmax
+ """
+
+ n = len(x)
+ mi = np.zeros((ns,lmax))
+ for l in range(lmax):
+ if (l%10 == 0):
+ print(f"\r\tmutual information lag: {l:d}\r", end="")
+ #sys.stdout.write(s)
+ #sys.stdout.flush()
+ nmax = n - l
+
+ # compute distributions
+ p1 = np.zeros(ns)
+ p2 = np.zeros(ns)
+ p12 = np.zeros((ns,ns))
+ for i in range(nmax):
+ i1 = int(x[i])
+ i2 = int(x[i+l])
+ p1[i1] += 1.0 # p( X_t = i1 )
+ p2[i2] += 1.0 # p( X_t+l = i2 )
+ p12[i1,i2] += 1.0 # p( X_t+l=i2 , X_t=i1 )
+ p1 /= nmax
+ p2 /= nmax
+
+ # normalize the transition matrix p( X_t+l=i2 | X_t=i1 )
+ rowsum = np.sum(p12, axis=1)
+ for i, j in np.ndindex(p12.shape):
+ p12[i,j] /= rowsum[i]
+
+ # compute entropies
+ H2 = np.sum(p2[p2>0] * np.log(p2[p2>0]))
+ for i in range(ns):
+ H12 = 0.0
+ for j in range(ns):
+ if ( p12[i,j] > 0.0 ):
+ H12 += ( p12[i,j] * np.log( p12[i,j] ) )
+ mi[i,l] = -H2 + p1[i] * H12
+ return mi
+
+
+def mutinf_CI(p, T, n, alpha, nrep, lmax):
+ """Return an array for the computation of confidence intervals (CI) for
+ the time-lagged mutual information of length lmax.
+ Null Hypothesis: Markov Chain of length n, equilibrium distribution p,
+ transition matrix T, using nrep repetitions."""
+
+ ns = len(p)
+ mi_array = np.zeros((nrep,lmax))
+ for r in range(nrep):
+ print(f"\nsurrogate MC # {r+1:d}/{nrep:d}")
+ x_mc = surrogate_mc(p, T, ns, n)
+ mi_mc = mutinf(x_mc, ns, lmax)
+ #mi_mc = mutinf_cy(X_mc, ns, lmax)
+ mi_array[r,:] = mi_mc
+ return mi_array
+
+
+def excess_entropy_rate(x, ns, kmax, doplot=False):
+ # y = ax+b: line fit to joint entropy for range of histories k
+ # a = entropy rate (slope)
+ # b = excess entropy (intersect.)
+ h_ = np.zeros(kmax)
+ for k in range(kmax): h_[k] = H_k(x, ns, k+1)
+ ks = np.arange(1,kmax+1)
+ a, b = np.polyfit(ks, h_, 1)
+ # --- Figure ---
+ if doplot:
+ plt.figure(figsize=(6,6))
+ plt.plot(ks, h_, '-sk')
+ plt.plot(ks, a*ks+b, '-b')
+ plt.xlabel("k")
+ plt.ylabel("$H_k$")
+ plt.title("Entropy rate")
+ plt.tight_layout()
+ plt.show()
+ return (a, b)
+
+
+def mc_entropy_rate(p, T):
+ """Markov chain entropy rate.
+ - \sum_i sum_j p_i T_ij log(T_ij)
+ """
+ h = 0.
+ for i, j in np.ndindex(T.shape):
+ if (T[i,j] > 0):
+ h -= ( p[i]*T[i,j]*np.log(T[i,j]) )
+ return h
+
+
+def aif_peak1(mi, fs, doplot=False):
+ '''compute time-lagged mut. inf. (AIF) and 1st peak.'''
+ dt = 1000./fs # sampling interval [ms]
+ mi_filt = np.convolve(mi, np.ones(3)/3., mode='same')
+ #mi_filt = np.convolve(mi, np.ones(5)/5., mode='same')
+ mx0 = 8 # 8
+ jmax = mx0 + locmax(mi_filt[mx0:])[0]
+ mx_mi = dt*jmax
+ if doplot:
+ offset = 5
+ tmax = 100
+ fig = plt.figure(1, figsize=(22,4))
+ t = dt*np.arange(tmax)
+ plt.plot(t[offset:tmax], mi[offset:tmax], '-ok', label='AIF')
+ plt.plot(t[offset:tmax], mi_filt[offset:tmax], '-b', label='smoothed AIF')
+ plt.plot(mx_mi, mi[jmax], 'or', markersize=15, label='peak-1')
+ plt.xlabel("time lag [ms]")
+ plt.ylabel("mut. inf. [bits]")
+ plt.legend(loc=0)
+ #plt.title("mutual information of map sequence")
+ #plt.title(s, fontsize=16, fontweight='bold')
+ plt.show()
+ input()
+
+ return jmax, mx_mi
+
+
+def H_1(x, ns):
+ """Shannon entropy of the symbolic sequence x with ns symbols.
+
+ Args:
+ x: symbolic sequence, symbols = [0, 1, 2, ...]
+ ns: number of symbols
+ Returns:
+ h: Shannon entropy of x
+ """
+
+ n = len(x)
+ p = np.zeros(ns) # symbol distribution
+ for t in range(n):
+ p[x[t]] += 1.0
+ p /= n
+ h = -np.sum(p[p>0]*np.log(p[p>0]))
+ return h
+
+
+def H_2(x, y, ns):
+ """Joint Shannon entropy of the symbolic sequences X, Y with ns symbols.
+
+ Args:
+ x, y: symbolic sequences, symbols = [0, 1, 2, ...]
+ ns: number of symbols
+ Returns:
+ h: Shannon entropy of x
+ """
+
+ if (len(x) != len(y)):
+ print("H_2 warning: sequences of different lengths, using the shorter...")
+ n = min([len(x), len(y)])
+ p = np.zeros((ns, ns)) # joint distribution
+ for t in range(n):
+ p[x[t],y[t]] += 1.0
+ p /= n
+ h = -np.sum(p[p>0]*np.log(p[p>0]))
+ return h
+
+
+def H_k(x, ns, k):
+ """Shannon's joint entropy from x[n+p:n-m]
+ x: symbolic time series
+ ns: number of symbols
+ k: length of k-history
+ """
+
+ N = len(x)
+ f = np.zeros(tuple(k*[ns]))
+ for t in range(N-k): f[tuple(x[t:t+k])] += 1.0
+ f /= (N-k) # normalize distribution
+ hk = -np.sum(f[f>0]*np.log(f[f>0]))
+ #m = np.sum(f>0)
+ #hk = hk + (m-1)/(2*N) # Miller-Madow bias correction
+ return hk
+
+
+def testMarkov0(x, ns, alpha, verbose=True):
+ """Test zero-order Markovianity of symbolic sequence x with ns symbols.
+ Null hypothesis: zero-order MC (iid) <=>
+ p(X[t]), p(X[t+1]) independent
+ cf. Kullback, Technometrics (1962)
+
+ Args:
+ x: symbolic sequence, symbols = [0, 1, 2, ...]
+ ns: number of symbols
+ alpha: significance level
+ Returns:
+ p: p-value of the Chi2 test for independence
+ """
+
+ if verbose:
+ print("ZERO-ORDER MARKOVIANITY:")
+ n = len(x)
+ f_ij = np.zeros((ns,ns))
+ f_i = np.zeros(ns)
+ f_j = np.zeros(ns)
+ # calculate f_ij p( x[t]=i, p( x[t+1]=j ) )
+ for t in range(n-1):
+ i = x[t]
+ j = x[t+1]
+ f_ij[i,j] += 1.0
+ f_i[i] += 1.0
+ f_j[j] += 1.0
+ T = 0.0 # statistic
+ for i, j in np.ndindex(f_ij.shape):
+ f = f_ij[i,j]*f_i[i]*f_j[j]
+ if (f > 0):
+ num_ = n*f_ij[i,j]
+ den_ = f_i[i]*f_j[j]
+ T += (f_ij[i,j] * np.log(num_/den_))
+ T *= 2.0
+ df = (ns-1.0) * (ns-1.0)
+ #p = chi2test(T, df, alpha)
+ p = chi2.sf(T, df, loc=0, scale=1)
+ if verbose:
+ print(f"p: {p:.2e} | t: {T:.3f} | df: {df:.1f}")
+ return p
+
+
+def testMarkov1(X, ns, alpha, verbose=True):
+ """Test first-order Markovianity of symbolic sequence X with ns symbols.
+ Null hypothesis:
+ first-order MC <=>
+ p(X[t+1] | X[t]) = p(X[t+1] | X[t], X[t-1])
+ cf. Kullback, Technometrics (1962), Tables 8.1, 8.2, 8.6.
+
+ Args:
+ x: symbolic sequence, symbols = [0, 1, 2, ...]
+ ns: number of symbols
+ alpha: significance level
+ Returns:
+ p: p-value of the Chi2 test for independence
+ """
+
+ if verbose:
+ print("\nFIRST-ORDER MARKOVIANITY:")
+ n = len(X)
+ f_ijk = np.zeros((ns,ns,ns))
+ f_ij = np.zeros((ns,ns))
+ f_jk = np.zeros((ns,ns))
+ f_j = np.zeros(ns)
+ for t in range(n-2):
+ i = X[t]
+ j = X[t+1]
+ k = X[t+2]
+ f_ijk[i,j,k] += 1.0
+ f_ij[i,j] += 1.0
+ f_jk[j,k] += 1.0
+ f_j[j] += 1.0
+ T = 0.0
+ for i, j, k in np.ndindex(f_ijk.shape):
+ f = f_ijk[i][j][k]*f_j[j]*f_ij[i][j]*f_jk[j][k]
+ if (f > 0):
+ num_ = f_ijk[i,j,k]*f_j[j]
+ den_ = f_ij[i,j]*f_jk[j,k]
+ T += (f_ijk[i,j,k]*np.log(num_/den_))
+ T *= 2.0
+ df = ns*(ns-1)*(ns-1)
+ #p = chi2test(T, df, alpha)
+ p = chi2.sf(T, df, loc=0, scale=1)
+ if verbose:
+ print(f"p: {p:.2e} | t: {T:.3f} | df: {df:.1f}")
+ return p
+
+
+def testMarkov2(X, ns, alpha, verbose=True):
+ """Test second-order Markovianity of symbolic sequence X with ns symbols.
+ Null hypothesis:
+ first-order MC <=>
+ p(X[t+1] | X[t], X[t-1]) = p(X[t+1] | X[t], X[t-1], X[t-2])
+ cf. Kullback, Technometrics (1962), Table 10.2.
+
+ Args:
+ x: symbolic sequence, symbols = [0, 1, 2, ...]
+ ns: number of symbols
+ alpha: significance level
+ Returns:
+ p: p-value of the Chi2 test for independence
+ """
+
+ if verbose:
+ print("\nSECOND-ORDER MARKOVIANITY:")
+ n = len(X)
+ f_ijkl = np.zeros((ns,ns,ns,ns))
+ f_ijk = np.zeros((ns,ns,ns))
+ f_jkl = np.zeros((ns,ns,ns))
+ f_jk = np.zeros((ns,ns))
+ for t in range(n-3):
+ i = X[t]
+ j = X[t+1]
+ k = X[t+2]
+ l = X[t+3]
+ f_ijkl[i,j,k,l] += 1.0
+ f_ijk[i,j,k] += 1.0
+ f_jkl[j,k,l] += 1.0
+ f_jk[j,k] += 1.0
+ T = 0.0
+ for i, j, k, l in np.ndindex(f_ijkl.shape):
+ f = f_ijkl[i,j,k,l]*f_ijk[i,j,k]*f_jkl[j,k,l]*f_jk[j,k]
+ if (f > 0):
+ num_ = f_ijkl[i,j,k,l]*f_jk[j,k]
+ den_ = f_ijk[i,j,k]*f_jkl[j,k,l]
+ T += (f_ijkl[i,j,k,l]*np.log(num_/den_))
+ T *= 2.0
+ df = ns*ns*(ns-1)*(ns-1)
+ #p = chi2test(T, df, alpha)
+ p = chi2.sf(T, df, loc=0, scale=1)
+ if verbose:
+ print(f"p: {p:.2e} | t: {T:.3f} | df: {df:.1f}")
+ return p
+
+
+def conditionalHomogeneityTest(X, ns, l, alpha, verbose=True):
+ """Test conditional homogeneity of non-overlapping blocks of
+ length l of symbolic sequence X with ns symbols
+ cf. Kullback, Technometrics (1962), Table 9.1.
+
+ Args:
+ x: symbolic sequence, symbols = [0, 1, 2, ...]
+ ns: number of symbols
+ l: split x into non-overlapping blocks of size l
+ alpha: significance level
+ Returns:
+ p: p-value of the Chi2 test for independence
+ """
+
+ if verbose:
+ print("\nCONDITIONAL HOMOGENEITY (three-way table):")
+ n = len(X)
+ r = int(np.floor(float(n)/float(l))) # number of blocks
+ nl = r*l
+ if verbose:
+ print("Split data in r = {:d} blocks of length {:d}.".format(r,l))
+ f_ijk = np.zeros((r,ns,ns))
+ f_ij = np.zeros((r,ns))
+ f_jk = np.zeros((ns,ns))
+ f_i = np.zeros(r)
+ f_j = np.zeros(ns)
+
+ # calculate f_ijk (time / block dep. transition matrix)
+ for i in range(r): # block index
+ for ii in range(l-1): # pos. inside the current block
+ j = X[i*l + ii]
+ k = X[i*l + ii + 1]
+ f_ijk[i,j,k] += 1.0
+ f_ij[i,j] += 1.0
+ f_jk[j,k] += 1.0
+ f_i[i] += 1.0
+ f_j[j] += 1.0
+
+ # conditional homogeneity (Markovianity stationarity)
+ T = 0.0
+ for i, j, k in np.ndindex(f_ijk.shape):
+ # conditional homogeneity
+ f = f_ijk[i,j,k]*f_j[j]*f_ij[i,j]*f_jk[j,k]
+ if (f > 0):
+ num_ = f_ijk[i,j,k]*f_j[j]
+ den_ = f_ij[i,j]*f_jk[j,k]
+ T += (f_ijk[i,j,k]*np.log(num_/den_))
+ T *= 2.0
+ df = (r-1)*(ns-1)*ns
+ #p = chi2test(T, df, alpha)
+ p = chi2.sf(T, df, loc=0, scale=1)
+ if verbose:
+ print(f"p: {p:.2e} | t: {T:.3f} | df: {df:.1f}")
+ return p
+
+
+def symmetryTest(X, ns, alpha, verbose=True):
+ """Test symmetry of the transition matrix of symbolic sequence X with
+ ns symbols
+ cf. Kullback, Technometrics (1962)
+
+ Args:
+ x: symbolic sequence, symbols = [0, 1, 2, ...]
+ ns: number of symbols
+ alpha: significance level
+ Returns:
+ p: p-value of the Chi2 test for independence
+ """
+
+ if verbose:
+ print("\nSYMMETRY:")
+ n = len(X)
+ f_ij = np.zeros((ns,ns))
+ for t in range(n-1):
+ i = X[t]
+ j = X[t+1]
+ f_ij[i,j] += 1.0
+ T = 0.0
+ for i, j in np.ndindex(f_ij.shape):
+ if (i != j):
+ f = f_ij[i,j]*f_ij[j,i]
+ if (f > 0):
+ num_ = 2*f_ij[i,j]
+ den_ = f_ij[i,j]+f_ij[j,i]
+ T += (f_ij[i,j]*np.log(num_/den_))
+ T *= 2.0
+ df = ns*(ns-1)/2
+ #p = chi2test(T, df, alpha)
+ p = chi2.sf(T, df, loc=0, scale=1)
+ if verbose:
+ print(f"p: {p:.2e} | t: {T:.3f} | df: {df:.1f}")
+ return p
+
+
+def geoTest(X, ns, dt, alpha, verbose=True):
+ """Test the geometric distribution of lifetime distributions.
+
+ Args:
+ X: symbolic sequence, symbols = [0, 1, 2, ...]
+ ns: number of symbols
+ dt: time step in [ms]
+ alpha: significance level
+ Returns:
+ p_values: for each symbol
+ """
+
+ if verbose:
+ print("\nGEOMETRIC DISTRIBUTION of lifetimes:\n")
+ tauDist = lifetimes(X, ns)
+ T = T_empirical(X, ns)
+ p_values = np.zeros(ns)
+ for s in range(ns): # test for each symbol
+ if verbose:
+ print(f"\nTesting the distribution of symbol # {s:d}")
+ # m = max_tau:
+ m = len(tauDist[s])
+ # observed lifetime distribution:
+ q_obs = np.zeros(m)
+ # theoretical lifetime distribution:
+ q_exp = np.zeros(m)
+ for j in range(m):
+ # observed frequency of lifetime j+1 for state s
+ q_obs[j] = tauDist[s][j]
+ # expected frequency
+ q_exp[j] = (1.0-T[s][s]) * (T[s][s]**j)
+ q_exp *= sum(q_obs)
+
+ t = 0.0 # chi2 statistic
+ for j in range(m):
+ if ((q_obs[j] > 0) & (q_exp[j] > 0)):
+ t += (q_obs[j]*np.log(q_obs[j]/q_exp[j]))
+ t *= 2.0
+ df = m-1
+ #p0 = chi2test(t, df, alpha)
+ p0 = chi2.sf(t, df, loc=0, scale=1)
+ if verbose:
+ print(f"p: {p0:.2e} | t: {t:.3f} | df: {df:.1f}")
+ p_values[s] = p0
+
+ g1, p1, dof1, expctd1 = chi2_contingency(np.vstack((q_obs,q_exp)), \
+ lambda_='log-likelihood')
+ if verbose:
+ print((f"G-test (log-likelihood) p: {p1:.2e}, g: {g1:.3f}, "
+ f"df: {dof1:.1f}"))
+
+ # Pearson's Chi2 test
+ g2, p2, dof2, expctd2 = chi2_contingency( np.vstack((q_obs,q_exp)) )
+ if verbose:
+ print((f"G-test (Pearson Chi2) p: {p2:.2e}, g: {g2:.3f}, "
+ f"df: {dof2:.1f}"))
+
+ p_ = tauDist[s]/np.sum(tauDist[s])
+ tau_mean = 0.0
+ tau_max = len(tauDist[s])*dt
+ for j in range(len(p_)): tau_mean += (p_[j] * j)
+ tau_mean *= dt
+ if verbose:
+ pass
+ #print("\t\tmean dwell time: {:.2f} [ms]".format(tau_mean))
+ if verbose:
+ pass
+ #print("\t\tmax. dwell time: {:.2f} [ms]\n\n".format(tau_max))
+ return p_values
+
+
+def lifetimes(X, ns):
+ """Compute the lifetime distributions for each symbol
+ in a symbolic sequence X with ns symbols.
+
+ Args:
+ x: symbolic sequence, symbols = [0, 1, 2, ...]
+ ns: number of symbols
+ Returns:
+ tauDist: list of lists, lifetime distributions for each symbol
+ """
+
+ n = len(X)
+ tauList = [[] for i in range(ns)] # unsorted lifetimes, [[],[],[],[]]
+ i = 0 # current index
+ s = X[i] # current symbol
+ tau = 1.0 # current lifetime
+ while (i < n-1):
+ i += 1
+ if ( X[i] == s ):
+ tau += 1.0
+ else:
+ tauList[int(s)].append(tau)
+ s = X[i]
+ tau = 1.0
+ tauList[int(s)].append(tau) # last state
+ # find max lifetime for each symbol
+ tau_max = [max(L) for L in tauList]
+ #print( "max lifetime for each symbol : " + str(tau_max) )
+ tauDist = [] # empty histograms for each symbol
+ for i in range(ns): tauDist.append( [0.]*int(tau_max[i]) )
+ # lifetime distributions
+ for s in range(ns):
+ for j in range(len(tauList[s])):
+ tau = tauList[s][j]
+ tauDist[s][int(tau)-1] += 1.0
+ return tauDist
+
+
+def mixing_time(X, ns):
+ """
+ Relaxation time, inverse of spectral gap
+ Arguments:
+ X: microstate label sequence
+ ns: number of unique labels
+ """
+ T_hat = T_empirical(X,ns) # transition matrix
+ ev = np.linalg.eigvals(T_hat)
+ #ev = np.real_if_close(ev)
+ ev = np.real(ev)
+ ev.sort() # ascending
+ ev2 = np.flipud(ev) # descending
+ #print("ordered eigenvalues: {:s}".format(str(ev2)))
+ sg = ev2[0] - ev2[1] # spectral gap
+ T_mix = 1.0/sg # mixing time
+ return T_mix
+
+
+def multiple_comparisons(p_values, method):
+ """Apply multiple comparisons correction code using the statsmodels package.
+ Input: array p-values.
+ 'b': 'Bonferroni'
+ 's': 'Sidak'
+ 'h': 'Holm'
+ 'hs': 'Holm-Sidak'
+ 'sh': 'Simes-Hochberg'
+ 'ho': 'Hommel'
+ 'fdr_bh': 'FDR Benjamini-Hochberg'
+ 'fdr_by': 'FDR Benjamini-Yekutieli'
+ 'fdr_tsbh': 'FDR 2-stage Benjamini-Hochberg'
+ 'fdr_tsbky': 'FDR 2-stage Benjamini-Krieger-Yekutieli'
+ 'fdr_gbs': 'FDR adaptive Gavrilov-Benjamini-Sarkar'
+
+ Args:
+ p_values: uncorrected p-values
+ method: string, one of the methods given above
+ Returns:
+ p_values_corrected: corrected p-values
+ """
+ reject, pvals_corr, alphacSidak, alphacBonf = multipletests(p_values, \
+ method=method)
+ return pvals_corr
+
+
+def locmax(x):
+ """Get local maxima of 1D-array
+
+ Args:
+ x: numeric sequence
+ Returns:
+ m: list, 1D-indices of local maxima
+ """
+
+ dx = np.diff(x) # discrete 1st derivative
+ zc = np.diff(np.sign(dx)) # zero-crossings of dx
+ m = 1 + np.where(zc == -2)[0] # indices of local max.
+ return m
+
+
+def findstr(s, L):
+ """Find string in list of strings, returns indices.
+
+ Args:
+ s: query string
+ L: list of strings to search
+ Returns:
+ x: list of indices where s is found in L
+ """
+
+ x = [i for i, l in enumerate(L) if (l==s)]
+ return x
+
+
+def print_matrix(T):
+ """Console-friendly output of the matrix T.
+
+ Args:
+ T: matrix to print
+ """
+
+ for i, j in np.ndindex(T.shape):
+ if (j == 0):
+ print("|{:.3f}".format(T[i,j]), end='')
+ elif (j == T.shape[1]-1):
+ print("{:.3f}|\n".format(T[i,j]), end='')
+ else:
+ #print "{:.3f}".format(T[i,j]), # Python-2
+ print("{:.3f}".format(T[i,j]), end='') # Python-3
+
+
+def main():
+ """Test module functions."""
+
+ os.system("clear")
+ print("\n\n--- EEG microstate analysis ---\n\n")
+ parser = argparse.ArgumentParser(description='EEG microstate analysis')
+ parser.add_argument('-i','--input', help='.edf file name', required=False)
+ parser.add_argument('-f','--filelist', help='.edf file list', required=False)
+ parser.add_argument('-d', '--directory', \
+ help='process all .edf files in this directory', \
+ required=False)
+ parser.add_argument('-m', '--markovsurrogates', \
+ help='number of Markov surrogates', required=False)
+ args = parser.parse_args()
+ argsdict = vars(args)
+ print("\nTutorial: press <Enter> to proceed to the next step\n")
+
+ # (0) make file list
+ if argsdict['input']:
+ filenames = [argsdict['input']]
+ elif argsdict['filelist']:
+ with open(argsdict['filelist'], 'rb') as f:
+ filenames = [l.strip() for l in f]
+ elif argsdict['directory']:
+ filenames = []
+ for f1 in os.listdir(argsdict['directory']):
+ f2 = os.path.join(argsdict['directory'], f1)
+ if os.path.isfile(f2):
+ if (f2[-4:] == ".edf"):
+ filenames.append(f2)
+ else:
+ filenames = ["test.edf"]
+
+ print("List of file names generated:")
+ for i, f in enumerate(filenames):
+ print(f"File {i:d}: {f:s}")
+ #input("\n\t\tpress ENTER to continue...")
+ # define lists that hold p-values for multiple comparisons
+ p_Markov0 = []
+ p_Markov1 = []
+ p_Markov2 = []
+ p_geo = []
+ p_stationarity = []
+ p_symmetry = []
+
+ # process files
+ for filename in filenames:
+
+ # (1) load edf file
+ os.system("clear")
+ print("\n[1] Load EEG file:")
+ chs, fs, data_raw = read_edf(filename)
+ print(f"\nFile: {filename:s}")
+ print("Sampling rate: {fs:.2f} Hz")
+ print(("Data shape: {data_raw.shape[0]:d} samples x "
+ "{data_raw.shape[1]:d} channels"))
+ print("Channels:")
+ for ch in chs:
+ print(f"\t{ch.decode():s}", end='')
+ print("")
+ #input("\n\n\n\t\tpress ENTER to continue...")
+
+ # (2) band-pass filter 1-35 Hz (6-th order Butterworth)
+ os.system("clear")
+ print("\n[2] Apply band-pass filter...")
+ data = bp_filter(data_raw, f_lo=1, f_hi=35, fs=fs) # (1, 35)
+ #data = np.copy(data_raw)
+ #input("\n\n\t\tpress ENTER to continue...")
+
+ # (3) visualize data
+ os.system("clear")
+ print("\n[3] Visualize data (PCA-1):")
+ pca = PCA(copy=True, n_components=1, whiten=False)
+ pca1 = pca.fit_transform(data)[:,0]
+ del pca
+ plot_data(pca1, fs)
+ #input("\n\n\t\tpress ENTER to continue...")
+
+ # (4) microstate clustering
+ os.system("clear")
+ print("\n[4] Clustering EEG topographies at GFP peaks:")
+ n_maps = 4
+ #maps, x, gfp_peaks, gev, cv = kmeans(data, n_maps=n_maps, n_runs=5, \
+ # doplot=True)
+ locs = []
+
+ # choose clustering method
+ mode = ["aahc", "kmeans", "kmedoids", "pca", "ica"][1]
+ n_win = 5 # smoothing window
+ maps, x, gfp_peaks, gev = clustering(data, fs, chs, locs, mode, n_maps,\
+ n_win, interpol=True, doplot=True)
+ #print("\t\tK-means clustering of: n = {:d} EEG maps".\
+ #format(len(gfp_peaks)))
+ #print("\t\tMicrostates: {:d} maps x {:d} channels".\
+ #print("maps: ", type(maps), maps.shape, maps.dtype)
+ #format(maps.shape[0],maps.shape[1]))
+ #print("\t\tGlobal explained variance GEV = {:.2f}".format(gev.sum()))
+ #input("\n\n\t\tpress ENTER to continue...")
+
+ # (5) basic microstate statistics
+ os.system("clear")
+ print("\n[5] Basic microstate statistics:")
+ p_hat = p_empirical(x, n_maps)
+ T_hat = T_empirical(x, n_maps)
+ print("\nEmpirical symbol distribution (=RTT):\n")
+ for i in range(n_maps):
+ print(f"p_{i:d} = {p_hat[i]:.3f}")
+ print("\nEmpirical transition matrix:\n")
+ print_matrix(T_hat)
+ T_mix = mixing_time(x, n_maps)
+ print(f"\nRelaxation time T = {T_mix:.3f}:\n")
+
+ #'''
+ # taus: list of lists, each list is the lifetimes histogram of
+ # a microstate class, in sample units
+ taus = lifetimes(x,n_maps)
+ #print(taus)
+ dt = 1000./fs # ms
+ fig, ax = plt.subplots(1, n_maps, figsize=(16,4))
+ for i in range(n_maps):
+ taus_i = taus[i] # lifetimes histogram for microstate class i
+ ts = dt*np.arange(len(taus_i)) # histogram x-axis in msec
+ # normalize histogram to get probability distribution p_
+ p_ = taus_i / np.sum(taus_i)
+ mu = np.sum(ts*p_) # mean lifetime
+ print(f"ms-{i:d} lifetimes: {mu:.2f}")
+ ax[i].plot(ts, taus[i], '-k', lw=2)
+ plt.show()
+ #'''
+
+ #''' PPS (peaks per second)
+ pps = len(gfp_peaks) / (len(x)/fs)
+ print("\nGFP peaks per sec.: {pps:.2f}")
+
+ # GEV (global explained variance A-D)
+ print("\nGlobal explained variance (GEV) per map:")
+ for i, g in enumerate(gev):
+ print(f"GEV(ms-{i:d}) = {gev[i]:.2f}")
+ print(f"\nTotal GEV: {gev.sum():.2f}")
+
+ # Cross-correlation between maps
+ print("\nCross-correlation between microstate maps:")
+ C = np.corrcoef(maps)
+ C_max = np.max(np.abs(C-np.array(np.diag(C)*np.eye(n_maps))))
+ print_matrix(C)
+ print(f"\nCmax: {C_max:.2f}")
+ input("\n\npress ENTER to continue...")
+
+ # (6) entropy of the microstate sequence
+ #os.system("clear")
+ print("\n[6a] Shannon entropy of the microstate sequence:")
+ h_hat = H_1(x, n_maps)
+ h_max = max_entropy(n_maps)
+ print(f"\nEmpirical entropy H = {h_hat:.2f} (max. entropy: {h_max:.2f})\n")
+
+ # (6b) entropy rate of the microstate sequence
+ #os.system("clear")
+ print("\n[6b] Entropy rate of the microstate sequence:")
+ kmax = 6
+ h_rate, _ = excess_entropy_rate(x, n_maps, kmax, doplot=True)
+ h_mc = mc_entropy_rate(p_hat, T_hat)
+ print(f"\nEmpirical entropy rate h = {h_rate:.2f}")
+ print(f"Theoretical MC entropy rate h = {h_mc:.2f}")
+ input("\n\n\t\tpress ENTER to continue...")
+
+ # (7) Markov tests
+ os.system("clear")
+ print("\n[7] Markovianity tests:\n")
+ alpha = 0.01
+ p0 = testMarkov0(x, n_maps, alpha)
+ p1 = testMarkov1(x, n_maps, alpha)
+ p2 = testMarkov2(x, n_maps, alpha)
+ p_geo_vals = geoTest(x, n_maps, 1000./fs, alpha)
+ p_Markov0.append(p0)
+ p_Markov1.append(p1)
+ p_Markov2.append(p2)
+ #p_geo.append(p_geo_vals)
+ input("\n\n\t\tpress ENTER to continue...")
+
+ # (8) stationarity test
+ os.system("clear")
+ print("\n[8] Stationarity test:")
+ try:
+ L = int(input("enter block size: "))
+ except:
+ L = 5000
+ p3 = conditionalHomogeneityTest(x, n_maps, L, alpha)
+ p_stationarity.append(p3)
+ input("\n\n\t\tpress ENTER to continue...")
+
+ # (9) symmetry test
+ os.system("clear")
+ print("\n[9] Symmetry test:")
+ p4 = symmetryTest(x, n_maps, alpha)
+ p_symmetry.append(p4)
+ input("\n\n\t\tpress ENTER to continue...")
+
+ # (10) Markov surrogate example
+ os.system("clear")
+ print("\n[10] Synthesize a surrogate Markov chain:")
+ x_mc = surrogate_mc(p_hat, T_hat, n_maps, len(x))
+ p_surr = p_empirical(x_mc, n_maps)
+ T_surr = T_empirical(x_mc, n_maps)
+ print("\nSurrogate symbol distribution:\n")
+ for i in range(n_maps):
+ print(f"p_{i:d} = {p_surr[i]:.3f}")
+ print( "\nSurrogate transition matrix:\n" )
+ print_matrix(T_surr)
+ input("\n\n\t\tpress ENTER to continue...")
+
+ # (11) Markov tests for surrogate (sanity check)
+ os.system("clear")
+ print("\n[11] Markovianity tests for surrogate MC:\n")
+ p0_ = testMarkov0(x_mc, n_maps, alpha)
+ p1_ = testMarkov1(x_mc, n_maps, alpha)
+ p2_ = testMarkov2(x_mc, n_maps, alpha)
+ p_geo_vals_ = geoTest(x_mc, n_maps, 1000./fs, alpha)
+ input("\n\n\t\tpress ENTER to continue...")
+
+ # (12) Stationarity test for surrogate MC
+ os.system("clear")
+ print("\n[12] Stationarity test for surrogate MC:")
+ try:
+ L = int(input("enter block size: "))
+ except:
+ L = 5000
+ p3_ = conditionalHomogeneityTest(x_mc, n_maps, L, alpha)
+ input("\n\n\t\tpress ENTER to continue...")
+
+ # (13) Symmetry test for surrogate (sanity check)
+ os.system("clear")
+ print("\n[13] Symmetry test for surrogate MC:")
+ p4_ = symmetryTest(x_mc, n_maps, alpha)
+ input("\n\n\t\tpress ENTER to continue...")
+
+ # (14) time-lagged mutual information (AIF) empirical vs. Markov model
+ os.system("clear")
+ print("\n[14] Mutual information of EEG microstates and surrogate MC:\n")
+ l_max = 100
+ #print(argsdict)
+ if argsdict['markovsurrogates']:
+ n_mc = int(argsdict['markovsurrogates'])
+ else:
+ n_mc = 10
+ aif = mutinf(x, n_maps, l_max)
+ #jmax, mx_mi = aif_peak1(aif, fs, doplot=True)
+ #print("\n\t\tAIF 1st peak: index {:d} = {:.2f} ms".format(jmax, mx_mi))
+
+ print("\n\nComputing n = {:d} Markov surrogates...".format(n_mc))
+ aif_array = mutinf_CI(p_hat, T_hat, len(x), alpha, n_mc, l_max)
+ pct = np.percentile(aif_array,[100.*alpha/2.,100.*(1-alpha/2.)],axis=0)
+ print("\n\n")
+ #'''
+
+ #'''
+ plt.ioff()
+ fig = plt.figure(1, figsize=(20,5))
+ t = np.arange(l_max)*1000./fs
+ plt.semilogy(t, aif, '-sk', linewidth=3, label='AIF (EEG)')
+ #plt.semilogy(t, pct[0,:], '-k', linewidth=1)
+ #plt.semilogy(t, pct[1,:], '-k', linewidth=1)
+ plt.fill_between(t, pct[0,:], pct[1,:], facecolor='gray', alpha=0.5, \
+ label='AIF (Markov)')
+ plt.xlabel("time lag [ms]", fontsize=24, fontweight="bold")
+ plt.ylabel("AIF [nats]", fontsize=24, fontweight="bold")
+ plt.legend(fontsize=22)
+ ax = plt.gca()
+ #ax.set_xticklabels(ax.get_xticks(), fontsize=24, fontweight='normal')
+ #ax.set_yticklabels(ax.get_yticks(), fontsize=24, fontweight='normal')
+ ax.tick_params(axis='both', labelsize=24)
+ plt.tight_layout()
+ plt.show()
+ #'''
+
+ if (len(filenames) > 1):
+ os.system("clear")
+ print("\n\t[15] Multiple comparisons correction")
+ mc_method = 'b'
+ if (len(p_Markov0) > 1):
+ p_Markov0_mc = multiple_comparisons(p_Markov0, mc_method)
+ print("\nZero-order Markov test: " + str(p_Markov0_mc))
+ if (len(p_Markov1) > 1):
+ p_Markov1_mc = multiple_comparisons(p_Markov1, mc_method)
+ print("\nFirst-order Markov test: " + str(p_Markov1_mc))
+ if (len(p_Markov2) > 1):
+ p_Markov2_mc = multiple_comparisons(p_Markov2, mc_method)
+ print("\nZero-order Markov test: " + str(p_Markov0_mc))
+ if (len(p_stationarity) > 1):
+ p_stationarity_mc = multiple_comparisons(p_stationarity, mc_method)
+ print("\nStationarity test: " + str(p_stationarity_mc))
+ if (len(p_symmetry) > 1):
+ p_symmetry_mc = multiple_comparisons(p_symmetry, mc_method)
+ print("\nSymmetry test: " + str(p_symmetry_mc))
+
+ print("\n\n\t\t--- Tutorial completed ---")
+
+
+if __name__ == '__main__':
+ np.set_printoptions(precision=3, suppress=True)
+ main()
diff --git a/feature_select.py b/feature_select.py
new file mode 100644
index 0000000..b05cdfa
--- /dev/null
+++ b/feature_select.py
@@ -0,0 +1,148 @@
+#!/usr/bin/env python
+
+'''
+This file contains functions that enable MRMR based Feature Selection.
+See the paper: Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance,
+and Min-Redundancy by H. Peng, F. Long, and C. Ding
+Author: Kiran Karra [Virginia Tech]
+ <kiran.karra@gmail.com, kiran.karra@vt.edu>
+Distribution Statement A: Approved for Public Release, Distribution Unlimited
+'''
+
+import os
+import shutil
+
+import json
+
+from tempfile import mkdtemp
+
+from tqdm import tqdm
+
+from scipy.stats import randint as sp_randint
+
+from sklearn.model_selection import RandomizedSearchCV
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import FeatureUnion, Pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import accuracy_score, roc_curve
+# from sklearn.externals import * #joblib
+# from sklearn.externals.joblib import Memory
+import joblib
+from joblib import Memory
+from sklearn.metrics import matthews_corrcoef
+
+import gplearn
+import gplearn.genetic
+import gplearn.fitness
+
+import numpy as np
+
+from joblib import Parallel, delayed
+from joblib import load, dump
+
+# from identity_transformer import IdentityTransformer # wasent used
+
+# import depmeas # couldnt find depmeas
+
+def generic_combined_scorer(x1,o1,ii_1,x2,o2,ii_2,y,h):
+ s1 = h(x1,y)
+ s2 = h(x2,y)
+ o1[ii_1] = s1
+ o2[ii_2] = s2
+"""
+def feature_select(X,y,num_features_to_select=None,K_MAX=1000,estimator=depmeas.mi_tau,n_jobs=-1,verbose=True):
+ '''
+ Implements the MRMR algorithm for feature-selection: http://ieeexplore.ieee.org/document/1453511/
+ Inputs:
+ X - A feature-matrix, of shape (N,D) where N is the number of samples and D is the number
+ of features
+ y - A vector of shape (N,1) which represents the output. Each index in y is assumed to
+ correspond to the row with the same index in X.
+ num_features_to_select - the number of features to select from the provided X matrix. If None
+ are provided, then all the features that are available are ranked/ordered.
+ (default: None)
+ K_MAX - the maximum number of top-scoring features to consider.
+ estimator - a function handle to an estimator of association (that theoretically should
+ follow the DPI assumptions)
+ n_jobs - the numer of processes to use with parallel processing in the background
+ verbose - if True, show progress
+ Output:
+ a vector of indices sorted in descending order, where each index represents the "importance"
+ of the feature, as computed by the MRMR algorithm.
+ '''
+ num_dim = X.shape[1]
+
+ if(num_features_to_select is not None):
+ num_selected_features = min(num_dim,num_features_to_select)
+ else:
+ num_selected_features = num_dim
+ K_MAX_internal = min(num_dim,K_MAX)
+
+ initial_scores = Parallel(n_jobs=n_jobs)(delayed(estimator)(X[:,ii],y) for ii in range(num_dim))
+ # rank the scores in descending order
+ sorted_scores_idxs = np.flipud(np.argsort(initial_scores))
+
+ # subset the data down so that joblib doesn't have to
+ # transport large matrices to its workers
+ X_subset = X[:,sorted_scores_idxs[0:K_MAX_internal]]
+ # memory map this for parallelization speed
+ tmp_folder = mkdtemp()
+ # TODO: why is X_subset crashing when we increase K_MAX_in? Investigate in detail, but
+ # for now, do not use memory mapping for X_subset for stability
+ # X_subset_fname = os.path.join(tmp_folder, 'X_subset')
+ # dump(X_subset, X_subset_fname)
+ # X_subset = load(X_subset_fname, mmap_mode='r')
+
+ selected_feature_idxs = np.zeros(num_selected_features,dtype=int)
+ remaining_candidate_idxs = list(range(1,K_MAX_internal))
+
+ # mi_matrix = np.empty((K_MAX_internal,num_selected_features-1))
+ # mi_matrix[:] = np.nan
+
+ relevance_vec_fname = os.path.join(tmp_folder, 'relevance_vec')
+ feature_redundance_vec_fname = os.path.join(tmp_folder, 'feature_redundance_vec')
+ mi_matrix_fname = os.path.join(tmp_folder, 'mi_matrix')
+ relevance_vec = np.memmap(relevance_vec_fname, dtype=float,
+ shape=(K_MAX_internal,), mode='w+')
+ feature_redundance_vec = np.memmap(feature_redundance_vec_fname, dtype=float,
+ shape=(K_MAX_internal,), mode='w+')
+ mi_matrix = np.memmap(mi_matrix_fname, dtype=float,
+ shape=(K_MAX_internal,num_selected_features-1), mode='w+')
+ mi_matrix[:] = np.nan
+
+ # TODO: investigate whether its worth it to parallelize the nested for-loop?
+ with tqdm(total=num_selected_features,desc='Selecting Features ...',disable=(not verbose)) as pbar:
+ pbar.update(1)
+ for k in range(1,num_selected_features):
+ ncand = len(remaining_candidate_idxs)
+ last_selected_feature = k-1
+
+ Parallel(n_jobs=n_jobs)(delayed(generic_combined_scorer)(y,relevance_vec,ii,
+ X_subset[:,selected_feature_idxs[last_selected_feature]],
+ feature_redundance_vec,ii,X_subset[:,ii],
+ estimator)
+ for ii in remaining_candidate_idxs)
+
+ # copy the redundance into the mi_matrix, which accumulates our redundance as we compute
+ mi_matrix[remaining_candidate_idxs,last_selected_feature] = feature_redundance_vec[remaining_candidate_idxs]
+ redundance_vec = np.nanmean(mi_matrix[remaining_candidate_idxs,:], axis=1)
+
+ tmp_idx = np.argmax(relevance_vec[remaining_candidate_idxs]-redundance_vec)
+ selected_feature_idxs[k] = remaining_candidate_idxs[tmp_idx]
+ del remaining_candidate_idxs[tmp_idx]
+
+ pbar.update(1)
+
+ # map the selected features back to the original dimensions
+ selected_feature_idxs = sorted_scores_idxs[selected_feature_idxs]
+
+ # clean up
+ try:
+ shutil.rmtree(tmp_folder)
+ except:
+ pass
+
+ return selected_feature_idxs
+ """
+
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 2bc2f51..55b5e14 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -6,12 +6,12 @@ nitime==0.8.1
nolds==0.5.2
statsmodels==0.11.1
spm1d==0.4.2
-pysparcl==1.4.1
+# pysparcl==1.4.1
fooof==1.0.0
pandas==1.0.3
seaborn==0.10.1
pingouin==0.3.11
-sklearn==0.24.2
+# sklearn==0.24.2 # installed newer version due to python 3.10 limitation
autoreject==0.2.1
mlxtend==0.17.0
-mayavi==4.7.1
\ No newline at end of file
+# mayavi==4.7.1 # installed 4.8.1
\ No newline at end of file
--
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