diff --git a/.DS_Store b/.DS_Store index f732453..52d055e 100644 Binary files a/.DS_Store and b/.DS_Store differ diff --git a/__pycache__/compute_vector_feature.cpython-312.pyc b/__pycache__/compute_vector_feature.cpython-312.pyc index 8b3f624..06cc6cf 100644 Binary files a/__pycache__/compute_vector_feature.cpython-312.pyc and b/__pycache__/compute_vector_feature.cpython-312.pyc differ diff --git a/__pycache__/compute_vector_feature.cpython-313.pyc b/__pycache__/compute_vector_feature.cpython-313.pyc new file mode 100644 index 0000000..4441a7f Binary files /dev/null and b/__pycache__/compute_vector_feature.cpython-313.pyc differ diff --git a/compute_vector_feature.py b/compute_vector_feature.py index 327c9a8..6ddb91f 100644 --- a/compute_vector_feature.py +++ b/compute_vector_feature.py @@ -24,8 +24,8 @@ def compute_feature_vector(eegdata, Fs): NFFT = nextpow2(winSampleLength) Y = np.fft.fft(dataWinCenteredHam, n=NFFT, axis=0)/winSampleLength - PSD = 2*np.abs(Y[0:NFFT/2,:]) - f = Fs/2*np.linspace(0,1,NFFT/2) + PSD = 2*np.abs(Y[0:NFFT//2,:]) + f = Fs/2*np.linspace(0,1,NFFT//2) # SPECTRAL FEATURES # Average of band powers diff --git a/data.py b/data.py new file mode 100644 index 0000000..c69c086 --- /dev/null +++ b/data.py @@ -0,0 +1,23 @@ +from compute_vector_feature import * +import numpy as np +import pandas as pd + +def data(): + # sampling rate + Fs = 256 + temp_file = "data/hasini/brain_1761337390.csv" + + df = pd.read_csv(temp_file) + + df.drop(columns = 'timestamps',inplace=True) + + eegdata = df.to_numpy() + print(eegdata.shape) + + ret = compute_feature_vector(eegdata, Fs) + return ret + + + +if __name__ == '__main__': + print(data()) \ No newline at end of file diff --git a/data/.DS_Store b/data/.DS_Store index a936d39..b3712c3 100644 Binary files a/data/.DS_Store and b/data/.DS_Store differ diff --git a/main.ipynb b/main.ipynb index ea20ae2..b0dd168 100644 --- a/main.ipynb +++ b/main.ipynb @@ -2,24 +2,77 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 40, + "id": "b7a45b11", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 41, "id": "d5051d3d", "metadata": {}, "outputs": [], "source": [ "from compute_vector_feature import *\n", - "import numpy as np" + "import numpy as np\n", + "import pandas as pd" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "id": "56752746", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(75768, 5)\n", + "131072\n" + ] + }, + { + "ename": "TypeError", + "evalue": "slice indices must be integers or None or have an __index__ method", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[43]\u001b[39m\u001b[32m, line 12\u001b[39m\n\u001b[32m 9\u001b[39m eegdata = df.to_numpy()\n\u001b[32m 10\u001b[39m \u001b[38;5;28mprint\u001b[39m(eegdata.shape)\n\u001b[32m---> \u001b[39m\u001b[32m12\u001b[39m ret = \u001b[43mcompute_feature_vector\u001b[49m\u001b[43m(\u001b[49m\u001b[43meegdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mFs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 13\u001b[39m ret\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/workspaces/git.anishalle.me/musi/compute_vector_feature.py:20\u001b[39m, in \u001b[36mcompute_feature_vector\u001b[39m\u001b[34m(eegdata, Fs)\u001b[39m\n\u001b[32m 17\u001b[39m # 1. Compute the PSD\n\u001b[32m 18\u001b[39m winSampleLength, nbCh = eegdata.shape\n\u001b[32m---> \u001b[39m\u001b[32m20\u001b[39m # Apply Hamming window\n\u001b[32m 21\u001b[39m w = np.hamming(winSampleLength)\n\u001b[32m 22\u001b[39m dataWinCentered = eegdata - np.mean(eegdata, axis=0) # Remove offset\n", + "\u001b[31mTypeError\u001b[39m: slice indices must be integers or None or have an __index__ method" + ] + } + ], "source": [ "# sampling rate\n", - "Fs = 256" + "Fs = 256\n", + "temp_file = \"data/hasini/brain_1761337390.csv\"\n", + "\n", + "df = pd.read_csv(temp_file)\n", + "\n", + "df.drop(columns = 'timestamps',inplace=True)\n", + "\n", + "eegdata = df.to_numpy()\n", + "print(eegdata.shape)\n", + "\n", + "ret = compute_feature_vector(eegdata, Fs)\n", + "ret\n" ] } ], diff --git a/pyproject.toml b/pyproject.toml index 056e35e..c73c13c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,4 +8,6 @@ dependencies = [ "ipykernel>=7.0.1", "muselsl>=2.3.1", "numpy>=2.3.3", + "ray>=2.50.1", + "scikit-learn>=1.7.2", ] diff --git a/utils.py b/utils.py new file mode 100644 index 0000000..3d3031c --- /dev/null +++ b/utils.py @@ -0,0 +1,745 @@ +#Utility functions for working with 3D gaze data. +#Authors: Brendan David-John, Candace Peacock +import itertools +import numpy as np +import pandas as pd +import os +import sys +import math +import pandas as pd +import numpy as np +import json +import glob # This was missing +from sklearn.preprocessing import scale +from sklearn.utils import resample + + +subjs_skip = [] +discrete_feats = ['sac_det','fix_det'] + +#https://pubmed.ncbi.nlm.nih.gov/25713524/ & https://www.sciencedirect.com/science/article/abs/pii/0025556475900759 +max_sacc_speed = 800 + +#https://www.geeksforgeeks.org/python-make-a-list-of-intervals-with-sequential-numbers/ +def intervals_extract(iterable): + iterable = sorted(set(iterable)) + for key, group in itertools.groupby(enumerate(iterable), + lambda t: t[1] - t[0]): + group = list(group) + yield [group[0][1], group[-1][1]] + +#find consecutive chunks of true values in bools, and return start and end indicies +#input: 1d np array +#output: 2d np array with indices +def intervals_from_bool(bools): + #get python list of indicies + bool_idx = np.where(bools == True)[0].tolist() + + #pull out the intervals + intervals = intervals_extract(bool_idx) + + #convert to flat np array + intervals = [item for sublist in intervals for item in sublist] + + #unroll it so start and end points fall into 1st/2nd column + intervals = np.reshape(intervals, (-1, 2)) + + return intervals + +''' +Function to compute angular gaze velocity using the a numerically stable atan2 method +Inputs: gaze_vectors: Nx3 array with N 3D gaze vectors (x,y,z), + dt: 1D array with N elements containing timestep for each gaze sample +Output: 1D array with N elements containing angular gaze velocity in degrees +''' +def compute_gaze_velocity(gaze_vectors,dt): + #get array of i+1 vectors + gaze_vectors_1 = gaze_vectors[0:-1,:] + gaze_vectors_2 = gaze_vectors[1:,:] + + #numerically stable method: atan2(norm(u-v),norm(u+v)) assuming u and v are already normalized + gaze_dist = 2*np.arctan2(np.sqrt(np.square(gaze_vectors_1-gaze_vectors_2).sum(axis=1)),np.sqrt(np.square(gaze_vectors_1+gaze_vectors_2).sum(axis=1))) #2*np.arctan2(mag(u-v),mag(u+v)) + gaze_velocity = np.rad2deg(gaze_dist) / dt + + #make nans and infs zero + np.nan_to_num(gaze_velocity,copy=False,posinf=0.0,neginf=0.0) + + #correct outliers over 800 deg/s by interpolating across good samples + gaze_velocity_good_idx = (gaze_velocity <= max_sacc_speed).nonzero()[0] + gaze_velocity = np.interp(range(gaze_velocity.shape[0]),gaze_velocity_good_idx,gaze_velocity[gaze_velocity_good_idx]) + + #append zero for first element, so size matches input + return np.insert(gaze_velocity,0,0.0) + +''' +Function to compute angular gaze velocity using the arccos dot method +Inputs: gaze_vectors: Nx3 array with N 3D gaze vectors (x,y,z), + dt :1D array with N elements containing timestep for each gaze sample +Output: 1D array with N elements containing angular gaze velocity in degrees +''' +def compute_gaze_velocity_naive(gaze_vectors,dt): + #get array of i+1 vectors + gaze_vectors_1 = gaze_vectors[0:-1,:] + gaze_vectors_2 = gaze_vectors[1:,:] + + #arc cos of dot product method, assuming vectors are normalized + gaze_dist_naive = np.arccos(np.sum(gaze_vectors_1*gaze_vectors_2, axis=1)) + gaze_velocity_naive = np.rad2deg(gaze_dist_naive) / dt + + #make nans and infs zero + np.nan_to_num(gaze_velocity_naive,copy=False,posinf=0.0,neginf=0.0) + + #correct outliers over 800 deg/s by interpolating across good samples + gaze_velocity_naive_good_idx = (gaze_velocity_naive <= max_sacc_speed).nonzero()[0] + gaze_velocity_naive = np.interp(range(gaze_velocity_naive.shape[0]),gaze_velocity_naive_good_idx,gaze_velocity_naive[gaze_velocity_naive_good_idx]) + + #append zero for first element, so size matches input + return np.insert(gaze_velocity_naive,0,0.0) + +''' +Function to classify gaze samples as saccade events using velocity thresholding. +Inputs: gaze_velocity: 1D array with N elements representing gaze velocity in degrees, + time_stamps: 1D array with N elements representing timestamps in seconds, + ivt_threshold: scalar in degrees, saccade samples are classified above this value + min_dur: scalar in seconds, saccades shorter than this are discarded +Output: 2 element tuple. + First element: 1D boolean array with N elements where True indiciates a sample is part of saccade, and False if not + Second element: 2D array that is Mx2, where M is the number of saccade events detected and the first column is start index, second end index +''' +def saccade_classification_ivt(gaze_velocity,time_stamps,ivt_threshold=100.0,min_dur=.012,max_dur=2.0): + #find samples over threshold + sac_bool = gaze_velocity > ivt_threshold + sac_intervals = intervals_from_bool(sac_bool) + + #correct saccades shorter than minimum duration + sacc_durs = time_stamps[sac_intervals[:,1]] - time_stamps[sac_intervals[:,0]] + short_sacc_idx = np.where((sacc_dursmax_dur))[0] + + #use short_sacc_idx to remove the bad intervals, and set those bad intervals to False in sac_bool + bad_sac_intervals = sac_intervals[short_sacc_idx,:] + + sac_intervals_clean = np.delete(sac_intervals,short_sacc_idx,0) + + num_bad_intervals = len(bad_sac_intervals) + for i in range(num_bad_intervals): + curr_interval = bad_sac_intervals[i,:] + sac_bool[curr_interval[0]:curr_interval[1]+1] = False + + return (sac_bool,sac_intervals_clean) + +''' +Function to classify gaze samples as fixation events using velocity thresholding. +Inputs: gaze_velocity: 1D array with N elements representing gaze velocity in degrees, + time_stamps: 1D array with N elements representing timestamps in seconds, + ivt_threshold: scalar in degrees, fixation samples are classified below this value + min_dur: scalar in seconds, fixations shorter than this are discarded + max_dur: scalar in secodns, fixations longer than this are discarded +Output: 2 element tuple. + First element: 1D boolean array with N elements where True indiciates a sample is part of saccade, and False if not + Second element: 2D array that is Mx2, where M is the number of saccade events detected and the first column is start index, second end index +''' +def fixation_classification_ivt(gaze_velocity,time_stamps,ivt_threshold=20.0,min_dur=0.100,max_dur=2.0): + #find samples over threshold + fix_bool = gaze_velocity < ivt_threshold + + fix_intervals = intervals_from_bool(fix_bool) + + #correct fixation shorter than minimum duration + fix_durs = time_stamps[fix_intervals[:,1]] - time_stamps[fix_intervals[:,0]] + short_fix_idx = np.where((fix_dursmax_dur))[0] + #short_fix_idx = np.where(fix_durs= t0 and time_stamps[r]<= t1): + last = r + elif time_stamps[r] > t1: + break + + window_range = [current,last] + + #now check the dispersion in this window + dispersion = compute_dispersion(gaze_vectors[current:last+1,:]) + + if (dispersion <= max_disp): + #add new points + while(dispersion <= max_disp and last + 1 < len(gaze_vectors)): + last += 1 + window_range = [current,last] + #print current, last, "*" + #print "*" + dispersion = compute_dispersion(gaze_vectors[current:last+1,:]) + + #dispersion threshold is exceeded + #fixation at the centroid [current,last] + fix_bool[current:last+1] = True + + current = last + 1 #this will move the pointer to a novel window + + else: + current += 1 + last = current + + #correct with saccade bool, find points marked as both, change to False in fix_bool + conflict_idx = np.where((fix_bool == True) & (sac_bool == True))[0] + fix_bool[conflict_idx] = False + + fix_intervals = intervals_from_bool(fix_bool) + + #correct fixation shorter than minimum duration or longer than max + fix_durs = time_stamps[fix_intervals[:,1]] - time_stamps[fix_intervals[:,0]] + short_fix_idx = np.where((fix_dursmax_dur))[0] + + #use short_fix to remove the bad intervals, and set those bad intervals to False in fix_bool + bad_fix_intervals = fix_intervals[short_fix_idx,:] + fix_intervals_clean = np.delete(fix_intervals,short_fix_idx,0) + + num_bad_intervals = len(bad_fix_intervals) + for i in range(num_bad_intervals): + curr_interval = bad_fix_intervals[i,:] + fix_bool[curr_interval[0]:curr_interval[1]+1] = False + + return (fix_bool,fix_intervals_clean) + +''' +Summary: Compute a continous time series for feature values by averaging over events in the last N seconds +Inputs: features: 2D array with rows corresponding to events, and columns corresponding to feature values + fixation_times: 1D array containing the timestamps corresponding to each row of fixation_times + time_stamps: 1D array representing every sample in the time series, containing times in seconds + N: Scalar indicating the number of seconds to consider past events within for computing average feature values +Output: 2D array, with rows corresponding to each sample in time_stamps and a column for each feature signal. +''' +def compute_feature_signal_time_avg(features,fixation_times,time_stamps,N): + num_features = features.shape[1] + num_rows = len(time_stamps) + feature_signal = np.zeros((num_rows,num_features),dtype=float) + + #loop over each time stamp, grab which elements have time greater than current time -2 but less than current time, average, add to row + for i in range(num_rows): + curr_time = time_stamps[i] + + #get events N seconds prior to curr_time + curr_events = features[(fixation_times>=curr_time-N) & (fixation_times<=curr_time),:] + + curr_row = np.zeros((1,num_features),dtype=float) + if len(curr_events) >0: + curr_row = np.mean(curr_events,0) + feature_signal[i,:] = curr_row + + return feature_signal + +''' +Summary: Compute a continous time series for feature values by averaging over the past N events +Inputs: features: 2D array with rows corresponding to events, and columns corresponding to feature values + fixation_times: 1D array containing the timestamps corresponding to each row of fixation_times + time_stamps: 1D array representing every sample in the time series, containing times in seconds + N: Scalar indicating the number of past events to consider when computing average feature values +Output: 2D array, with rows corresponding to each sample in time_stamps and a column for each feature signal. +''' +def compute_feature_signal_event_avg(features,fixation_times,time_stamps,N): + num_features = features.shape[1] + num_rows = len(time_stamps) + feature_signal = np.zeros((num_rows,num_features),dtype=float) + + #loop over each time stamp, grab past N elements, average, add to row + for i in range(num_rows): + curr_time = time_stamps[i] + + #get events prior to curr_time + curr_events = features[(fixation_times<=curr_time),:] + + #grab last N of them if possible + prior_events = curr_events[-N:,:] + + curr_row = np.zeros((1,num_features),dtype=float) + if len(prior_events) >0: + curr_row = np.mean(prior_events,0) + feature_signal[i,:] = curr_row + + return feature_signal + +#wrapper function to call feature signal methods +def compute_feature_signal(method,features,event_times,time_stamps,N): + if method == 'time_avg': + return compute_feature_signal_time_avg(features,event_times,time_stamps,N) + elif method == 'event_avg': + return compute_feature_signal_event_avg(features,event_times,time_stamps,N) + elif method == 'event_discrete': + return compute_feature_signal_event_discrete(features,event_times,time_stamps,N) + else: + return None + +#returns three column np array (x,y,z) containing head corrected gaze vectors +def head_correction(df,param): + gaze_vectors = df[['combined.gazeDirection.x','combined.gazeDirection.y','combined.gazeDirection.z']].values + + num_rows = np.size(gaze_vectors,0) + transformed_gaze_vectors = np.zeros(gaze_vectors.shape) + + if param.transform_type == 'coordinate_frame': + #transform vectors + head_pos = df[['head.pos.x','head.pos.y','head.pos.z']].values + head_up = df[['head.right.x','head.right.y','head.right.z']].values + head_right = df[['head.up.x','head.up.y','head.up.z']].values + head_dir = df[['head.dir.x','head.dir.y','head.dir.z']].values + + for i in range(num_rows): + #get current gaze vector and transforms + gaze_vector = gaze_vectors[i,:] + curr_head_up = head_up[i,:] + curr_head_right = head_right[i,:] + curr_head_dir = head_dir[i,:] + + '''Transformation matrix of the form: + [ R.x, R.y, R.z, 0, + U.x, U.y, U.z, 0, + D.x, D.y, D.z, 0, + 0, 0, 0, 1 ] + ''' + #make transformation matrix + transform_mat = np.ma.row_stack((np.append(curr_head_right ,0),\ + np.append(curr_head_up ,0),\ + np.append(curr_head_dir,0),\ + np.array([0.0, 0.0, 0.0, 1.0]))) + + + #add homogenous coordinate to vector + gaze_vector = np.append(gaze_vector,1.0) + + #right-multiply row vector with transpose of matrix as defined above + transformed_gaze_vector = np.matmul(gaze_vector,transform_mat.T) + + #place transformed gaze_vector back into gaze_vectors w/o homogenous coordinate + transformed_gaze_vectors[i,:] = transformed_gaze_vector[0:3] + + #normalize all rows again to account for any rounding errors + row_norm_vals = np.sqrt(np.square(transformed_gaze_vectors).sum(axis=1)) + transformed_gaze_vectors = transformed_gaze_vectors/row_norm_vals[:,None] + + elif param.transform_type == 'quaternions': + q_x = df['RotationX'].values + q_y = df['RotationY'].values + q_z = df['RotationZ'].values + q_w = df['RotationW'].values + + for i in range(num_rows): + #get current gaze vector and transforms + gaze_vector = gaze_vectors[i,:] + + curr_q_x = q_x[i] + curr_q_y = q_y[i] + curr_q_z = q_z[i] + curr_q_w = q_w[i] + + # first column + one_one = np.square(curr_q_w) + np.square(curr_q_x) - np.square(curr_q_y) - np.square(curr_q_z) + two_one = 2 * (curr_q_w*curr_q_z + curr_q_x*curr_q_y) + three_one = 2 * (curr_q_x*curr_q_z - curr_q_w*curr_q_y) + + # second column + one_two = 2* (curr_q_x*curr_q_y - curr_q_w*curr_q_z) + two_two = np.square(curr_q_w) + np.square(curr_q_x) - np.square(curr_q_y) - np.square(curr_q_z) + three_two = 2* (curr_q_w*curr_q_x - curr_q_y*curr_q_z) + + # third column + one_three = 2* (curr_q_w*curr_q_y - curr_q_x*curr_q_z) + two_three = 2* (curr_q_y*curr_q_z - curr_q_w*curr_q_x) + three_three = np.square(curr_q_w) + np.square(curr_q_x) - np.square(curr_q_y) - np.square(curr_q_z) + + # compute rotation matrix as per Diaz et al. + rot_mat = np.array([[one_one, two_one, three_one],\ + [one_two, two_two, three_two],\ + [one_three, two_three, three_three]]) + + transformed_gaze_vectors[i,:] = np.matmul(rot_mat,gaze_vector) + + #normalize all rows again to account for any rounding errors + row_norm_vals = np.sqrt(np.square(transformed_gaze_vectors).sum(axis=1)) + transformed_gaze_vectors = transformed_gaze_vectors/row_norm_vals[:,None] + + return transformed_gaze_vectors + +def window_signal_overlap(X, y, window_size=10): + M, N = X.shape + windows = np.array([]) + lbls = [] + + FP_indxs = np.where(y==1)[0] + TP_indxs = np.where(y==-1)[0] + combined = np.hstack((FP_indxs, TP_indxs)) + + for i in combined: + window = np.array([]) + starts = range(max(0, i-window_size+1), i+1) + ends = range(i+1, min(M, i+window_size)) + + for (s, e) in zip(starts, ends): + window = X[s:e] + curr_size = window.shape[0] + window = window.reshape(-1, curr_size*N) + + # window is clipped from front or back, add zero padding + if curr_size < window_size: + padding_size = window_size*N - curr_size*N + padding = np.zeros(shape=(1,padding_size)) + if i-window_size < 0: + window = np.hstack((padding,window)) + else: + window = np.hstack((window, padding)) + + if windows.shape[0] == 0: + windows = window + else: + windows = np.vstack((windows, window)) + lbl = y[i] + lbls.append(lbl) + + + lbls = np.array(lbls, dtype=int).reshape(-1,1) + return (windows, lbls) + + +def window_signal_center(X, y, window_size=10): + M, N = X.shape + windows = np.array([]) + lbls = [] + + FP_indxs = np.where(y==1)[0] + TP_indxs = np.where(y==-1)[0] + combined = np.hstack((FP_indxs, TP_indxs)) + + for i in combined: + start = max(0, i-int(window_size/2)) + end = min(M, i+int(window_size/2)) + + window = X[start:end, :] + curr_size = window.shape[0] + window = window.reshape(-1, curr_size*N) + + # window is clipped from front or back, add zero padding + if curr_size < window_size: + padding_size = window_size*N - curr_size*N + padding = np.zeros(shape=(1,padding_size)) + if i-int(window_size/2) < 0: + window = np.hstack((padding,window)) + else: + window = np.hstack((window, padding)) + + if windows.shape[0] == 0: + windows = window + else: + windows = np.vstack((windows, window)) + lbl = y[i] + lbls.append(lbl) + + lbls = np.array(lbls, dtype=int).reshape(-1,1) + return (windows, lbls) + +def window_signal_start(X, y, window_size=10): + M, N = X.shape + windows = np.array([]) + lbls = [] + + FP_indxs = np.where(y==1)[0] + TP_indxs = np.where(y==-1)[0] + combined = np.hstack((FP_indxs, TP_indxs)) + + for i in combined: + start = i + end = min(M, i+window_size) + + window = X[start:end, :] + curr_size = window.shape[0] + window = window.reshape(-1, curr_size*N) + + # window is clipped from front or back, add zero padding + if curr_size < window_size: + padding_size = window_size*N - curr_size*N + padding = np.zeros(shape=(1,padding_size)) + window = np.hstack((window, padding)) + + if windows.shape[0] == 0: + windows = window + else: + windows = np.vstack((windows, window)) + lbl = y[i] + lbls.append(lbl) + + lbls = np.array(lbls, dtype=int).reshape(-1,1) + return (windows, lbls) + +def window_signal(X, y, mode='center', window_size=10): + if mode == 'center': + return window_signal_center(X, y, window_size=window_size) + elif mode == 'start': + return window_signal_start(X, y, window_size=window_size) + elif mode == 'overlap': + return window_signal_overlap(X, y, window_size=window_size) + +def get_header(features, window_size): + header = "" + for i in range(window_size): + h = ['%s_%d'%(f, i) for f in features] + h = ",".join(h) + if not len(header): + header = h + continue + header = '%s,%s'%(header, h) + header = '%s,%s'%(header, 'label') + return header + +def is_discrete_feat(feat): + isdisc = False + for f in discrete_feats: + if f in feat: + isdisc = True + break + return isdisc + +def read_data(path): + files = glob.glob(os.path.join(path, '*')) + y = [] + X = [] + subjs = [] + discrete_cols = [] + cols = [] + + for file in files: + subj = file.split(os.sep)[-1] + subj = subj.split('.')[0] + subj = subj.split('_')[0] + + if subj in subjs_skip: + continue + + data = pd.read_csv(file) + + if not len(discrete_cols): + cols = data.columns + discrete_cols = [] + for i in range(len(cols)): + if is_discrete_feat(cols[i]): + discrete_cols.append(cols[i]) + + + if not len(y): + y = data['label'].to_numpy().reshape(-1, 1) + else: + y = np.vstack((y, data['label'].to_numpy().reshape(-1,1))) + + curr_X = data[data.columns[data.columns != 'label']].to_numpy() + + if not len(X): + X = curr_X + else: + X = np.vstack((X, curr_X)) + + M = curr_X.shape[0] + curr_subjs = np.repeat(subj, M).reshape(-1, 1) + if not len(subjs): + subjs = curr_subjs + else: + subjs = np.vstack((subjs, curr_subjs)) + + return (X, y, subjs, discrete_cols, cols) + + +def read_feat_data(path): + ''' Reads feature data in director 'path' and stores them in PD data frame''' + files = glob.glob(os.path.join(path, '*')) + + combined = pd.DataFrame() + for file in files: + data = pd.read_csv(file, dtype={'selection_type': 'str'}, index_col=0) + + filename = file.split(os.sep)[-1] + filename = filename.split('.')[0] + subj = filename.split('_')[0] + data['sbj'] = subj + block = filename.split('_')[1] + data['block'] = block + + combined = combined.append(data) + + return combined + +def read_window_data(main_dir, MODE, WINDOW_SIZE, data_type='train'): + data_dir = os.path.join(main_dir, 'w_%s_%d'%(MODE, WINDOW_SIZE)) + data_dir = os.path.join(data_dir, data_type) + X, y, subjs, discrete_cols, cols = read_data(main_dir) + return X, y, subjs, discrete_cols, cols + + +def normalize_subj_data(X, y, subjs, exclude_idxs): + X_out, y_out = [], [] + sbjs = np.unique(subjs) + for sbj in sbjs: + subj_idxs = np.where(subjs==sbj)[0] + X_subj, y_subj = X[subj_idxs], y[subj_idxs] + + if X_subj.shape[0] < 1: + continue + + if len(exclude_idxs): + mask = ~np.isin(np.arange(X.shape[1]), exclude_idxs) + X_subj[:,mask] = scale(X_subj[:,mask]) + else: + X_Subj = scale(X_subj) + + if not len(X_out): + X_out = X_subj + y_out = y_subj + else: + X_out = np.vstack((X_out, X_subj)) + y_out = np.vstack((y_out, y_subj)) + return X_out, y_out + +def get_feat_indxs(columns, query_cols): + indxs = [] + for i in range(len(columns)): + c = columns[i] + c = c.split('_') + c.pop() + c = '_'.join(c) + if c in query_cols: + indxs.append(i) + return indxs + +def filter_features(featfilepath, X, headers, count_thresh=1): + feat_counter = pd.read_csv(featfilepath,squeeze=True) + + imp_indxs = np.where(feat_counter['num']>count_thresh)[0] + important_feats = feat_counter['feat'].values[imp_indxs] + indxs = get_feat_indxs(headers, important_feats) + X = X[:,indxs] + return X + + +def compute_feature_signal_time_avg(features,fixation_times,time_stamps,N): + num_features = features.shape[1] + num_rows = len(time_stamps) + feature_signal = np.zeros((num_rows,num_features),dtype=float) + + #loop over each time stamp, grab which elements have time greater than current time -2 but less than current time, average, add to row + for i in range(num_rows): + curr_time = time_stamps[i] + + #get events N seconds prior to curr_time + curr_events = features[(fixation_times>=curr_time-N) & (fixation_times<=curr_time),:] + + curr_row = np.zeros((1,num_features),dtype=float) + if len(curr_events) >0: + curr_row = np.mean(curr_events,0) + feature_signal[i,:] = curr_row + + return feature_signal + +''' +Summary: Compute a continous time series for feature values by averaging over the past N events +Inputs: features: 2D array with rows corresponding to events, and columns corresponding to feature values + fixation_times: 1D array containing the timestamps corresponding to each row of fixation_times + time_stamps: 1D array representing every sample in the time series, containing times in seconds + N: Scalar indicating the number of past events to consider when computing average feature values +Output: 2D array, with rows corresponding to each sample in time_stamps and a column for each feature signal. +''' +def compute_feature_signal_event_avg(features,fixation_times,time_stamps,N): + num_features = features.shape[1] + num_rows = len(time_stamps) + feature_signal = np.zeros((num_rows,num_features),dtype=float) + + #loop over each time stamp, grab past N elements, average, add to row + for i in range(num_rows): + curr_time = time_stamps[i] + + #get events prior to curr_time + curr_events = features[(fixation_times<=curr_time),:] + + #grab last N of them if possible + prior_events = curr_events[-N:,:] + + curr_row = np.zeros((1,num_features),dtype=float) + if len(prior_events) >0: + curr_row = np.mean(prior_events,0) + feature_signal[i,:] = curr_row + + return feature_signal + +#wrapper function to call feature signal methods +def compute_feature_signal(method,features,event_times,time_stamps,N): + if method == 'time_avg': + return compute_feature_signal_time_avg(features,event_times,time_stamps,N) + elif method == 'event_avg': + return compute_feature_signal_event_avg(features,event_times,time_stamps,N) + elif method == 'event_discrete': + return compute_feature_signal_event_discrete(features,event_times,time_stamps,N) + else: + return None + +def get_header(features, window_size): + header = "" + for i in range(window_size): + h = ['%s_%d'%(f, i) for f in features] + h = ",".join(h) + if not len(header): + header = h + continue + header = '%s,%s'%(header, h) + header = 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classification + y = data['lost'].to_numpy() + indxs = np.where(y==1) + mask = np.full(shape=y.shape, fill_value=-1, dtype=int) + mask[indxs] = 1 + y = mask + + + # Get the features + X = data[feat_headers].to_numpy() + + # Get the windowed data + windows, lbls = window_signal(X, y, mode='center', window_size=window_size) + + return windows, lbls + +# Multiprocessing the windowing of the entire directory for each window size +@ray.remote +def process_file(file_path, output_dir, window_size): + print(f"Processing file: {file_path}") + + out_feats = ['pwr-delta', 'pwr-theta', 'pwr-low-alpha', 'pwr-high-alpha', + 'pwr-low-beta', 'pwr-high-beta', + 'pwr-delta/beta', 'pwr-theta/beta', 'pwr-alpha/beta', 'pwr-alpha-theta'] + + """Process individual files in parallel""" + print(f"Processing file: {file_path} for window size {window_size}") + + # Thread-safe windowing operation + windows, lbls = windowing(file_path, window_size) + + # Create DataFrame with dynamic column names + feature_cols = get_header(out_feats, window_size).split(',') + windowed_df = pd.DataFrame( + np.hstack((windows, lbls)), + columns=feature_cols + ) + + # Save with window size in filename + output_path = output_dir / f"{file_path.stem}_windowed_{window_size}.csv" + windowed_df.to_csv(output_path, index=False) + + return output_path + +@ray.remote +def window_files(input_dir, output_dir, window_size): + output_dir = Path(output_dir) / f"window_size_{window_size}" + output_dir.mkdir(parents=True, exist_ok=True) + + # Collect all CSV paths + csv_paths = [] + for root, _, files in os.walk(input_dir): + for f in files: + if f.endswith('.csv'): + csv_paths.append(Path(root)/f) + + # Process files in parallel + file_tasks = [process_file.remote(path, output_dir, window_size) for path in csv_paths] + results = ray.get(file_tasks) + + print(f"Processed {len(results)} files for window size {window_size}") + return results + +def main(): + window_sizes = [50] + input_dirs = ['train/raw', 'test/raw'] + output_dirs = ['train', 'test'] + + tasks = [(input_dir, output_dir, window_size) + for window_size in window_sizes + for input_dir, output_dir in zip(input_dirs, output_dirs)] + + # Use Ray for outer parallelization + outer_tasks = [window_files.remote(input_dir, output_dir, window_size) + for input_dir, output_dir, window_size in tasks] + + # Get all results + all_results = ray.get(outer_tasks) + + # Flatten results if needed + flat_results = [item for sublist in all_results for item in sublist] + print(f"Total processed files: {len(flat_results)}") + +if __name__ == '__main__': + main()