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User Calibration Initial Push #2

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155 changes: 155 additions & 0 deletions Training/DataCreateUpdated.py
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import numpy as np
import matplotlib.pyplot as plt
import os
from scipy.fft import fft
from string import digits
from datetime import datetime, timedelta
import requests

from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder

# define example
labels = {'NONE' : 0, 'L_EYE' : 1, 'R_EYE' : 2, 'JAW_CLENCH' : 3, 'BROW_UP' : 4, 'BROW_DOWN': 5}
labelInts = np.array([0, 1, 2, 3, 4, 5])
# integer encode
label_encoder = LabelEncoder()
integer_encoded = label_encoder.fit_transform(labelInts)
# binary encode
onehot_encoder = OneHotEncoder(sparse=False)
integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
onehot_encoded = onehot_encoder.fit_transform(integer_encoded)

def getOneHot(label):
return onehot_encoded[labels[label]]

def getOneHotLabels():
output = {}
for label in labels:
output[label] = getOneHot(label)
return output

def getTitle(recordingFile):
return recordingFile.split("-")[-1].split(".")[0].translate({ord(k): None for k in digits})

# pass none if dont want granularity
# pass none to dataLimit if want all the data
def getData(path, granularity, channels, dataLimit):
dataRaw = []
dataStartLine = 6
count = 0
with open(path, 'r') as data_file:
for line in data_file:
if count >= dataStartLine:
dataRaw.append(line.strip().split(','))
else:
count += 1
dataRaw = np.char.strip(np.array(dataRaw))

dataChannels = dataRaw[:, 1:5]
timeChannels = dataRaw[:, 15]

if granularity is None:
granularity = 1
# the current channel of data
if dataLimit is None:
dataLimit = len(dataChannels)

channelData = dataChannels[:,channels][:dataLimit:granularity].transpose()
y_channels = channelData.astype(float)
inds = np.arange(channelData.shape[1])
t = np.array([datetime.strptime(time[11:],'%H:%M:%S.%f') for time in timeChannels])
return y_channels,inds,t

def getLabel(path):
dataRaw = []
first = True
basetime = None
with open(path, 'r') as label_file:
for line in label_file:
if(first):
dt_obj = datetime.strptime(line[11:].strip(),'%H:%M:%S.%f')
basetime = dt_obj
# dataRaw.append(dt_obj)
first = False
else:
dr = np.char.strip(np.array(line[1:-1].split(", ")))
# print(dr)
for i in range(len(dr)):
if dr[i] == '1':
dataRaw.append(basetime + timedelta(seconds=i))
# print(dataRaw)
return dataRaw


def groupbyInterval(data, labels, interval, actionType):
#data tuple (x,y,z). labels: datetimes. interval(ms): int
y_channels,inds,t = data
interval_ms = timedelta(milliseconds=interval)

split_inds = []
cutoff_times = [t[0]+interval_ms]
for ind in range(t.shape[0]):
time = t[ind]
if time >= cutoff_times[-1]:
split_inds.append(ind)
cutoff_times.append(cutoff_times[-1] + interval_ms)

ind_groups = np.split(inds, split_inds)
y_channels_groups = np.split(y_channels, split_inds, axis=1)
t_groups = np.split(t, split_inds)

#find min group size
min_group_size = ind_groups[0].shape[0]
for i in range(len(split_inds)-1):
if ind_groups[i].shape[0] < min_group_size:
min_group_size = ind_groups[i].shape[0]

#rectangularize jagged arrays
for i in range(len(split_inds)):
ind_groups[i] = ind_groups[i][:min_group_size]
y_channels_groups[i] = y_channels_groups[i][:,:min_group_size]
t_groups[i] = t_groups[i][:min_group_size]

#drop short last group
ind_groups = np.array(ind_groups[:-1])
y_channels_groups = np.array(y_channels_groups[:-1]).transpose((1,0,2))
t_groups = np.array(t_groups[:-1])

#assign labels to groups
NO_ACTION = 0
ACTION = 1

if actionType:
NO_ACTION = getOneHot("NONE")
ACTION = getOneHot(actionType)

l_groups = np.array([NO_ACTION] * ind_groups.shape[0])

lnum=0
for ind in range(len(cutoff_times)):
if lnum==len(labels):
break

cutoff_time = cutoff_times[ind]
if labels[lnum] < cutoff_time:
l_groups[ind] = ACTION
lnum+=1

return y_channels_groups, ind_groups, t_groups, l_groups


#THIS IS THE MAIN METHOD FOR INTERACTION
# Inputs:
# datafile, labelfile, interval, channels requested
def getObservations(dataPath, labelPath, interval, channels, actionType):
data = getData(dataPath, None, channels, None)
action_times = getLabel(labelPath)
observations = groupbyInterval(data, action_times, interval, actionType)

return observations





200 changes: 200 additions & 0 deletions calibration.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#Import libraries and function loading data\n",
"\n",
"from Training import DataCreateUpdated as dc\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import sklearn\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.model_selection import train_test_split\n",
"import random\n",
"\n",
"# rec_paths = [\"OpenBCI-RAW-2021-12-02_19-19-53.txt\", \n",
"# \"OpenBCI-RAW-2021-12-02_19-31-12.txt\", \n",
"# \"Recordings/Spring_2022/OpenBCISession_2022-02-16_Evan_JawClench_2/OpenBCI-RAW-2022-02-16_19-30-28.txt\",\n",
"# \"Recordings/Spring_2022/OpenBCISession_2022-02-16_Evan_LeftBlink_1/OpenBCI-RAW-2022-02-16_19-33-20.txt\",\n",
"# \"Recordings/Spring_2022/OpenBCISession_2022-02-16_Evan_LeftBlink_2/OpenBCI-RAW-2022-02-16_19-39-23.txt\",\n",
"# \"Recordings/Spring_2022/OpenBCISession_2022-02-16_Evan_RightBlink_1/OpenBCI-RAW-2022-02-16_19-42-03.txt\"]\n",
"# label_paths = [\"JawClench_labels_Ansh_12-02-21-1918.txt\", \n",
"# \"JawClench_labels_Ansh_12-02-21-1930.txt\", \n",
"# \"Recordings/Spring_2022/Evan_JawClench_1.txt\",\n",
"# \"Recordings/Spring_2022/Evan_LeftBlink_1.txt\",\n",
"# \"Recordings/Spring_2022/Evan_LeftBlink_2.txt\",\n",
"# \"Recordings/Spring_2022/Evan_RightBlink_1.txt\"]\n",
"# label_types = [\"JAW_CLENCH\", \n",
"# \"JAW_CLENCH\", \n",
"# \"JAW_CLENCH\",\n",
"# \"L_EYE\",\n",
"# \"L_EYE\",\n",
"# \"R_EYE\"]\n",
"\n",
"\n",
"rec_paths = [\"Recordings/Spring_2022/OpenBCISession_2022-03-23_Sam_BrowLower_2/OpenBCI-RAW-2022-03-23_20-43-17.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-23_Sam_BrowLower_1/OpenBCI-RAW-2022-03-23_20-41-22.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-23_Sam_BrowRaise_1/OpenBCI-RAW-2022-03-23_20-37-06.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-23_Sam_BrowRaise_2/OpenBCI-RAW-2022-03-23_20-39-04.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-23_Sam_JawClench_1/OpenBCI-RAW-2022-03-23_20-33-09.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-23_Sam_JawClench_2/OpenBCI-RAW-2022-03-23_20-34-58.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-23_Sam_LeftEye_1/OpenBCI-RAW-2022-03-23_20-21-37.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-23_Sam_LeftEye_2/OpenBCI-RAW-2022-03-23_20-25-30.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-23_Sam_RightEye_1/OpenBCI-RAW-2022-03-23_20-27-22.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-23_Sam_RightEye_2/OpenBCI-RAW-2022-03-23_20-29-16.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-26_Sam_BrowLower_1/OpenBCI-RAW-2022-03-26_18-33-15.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-26_Sam_BrowLower_2/OpenBCI-RAW-2022-03-26_18-35-00.txt\",\n",
" # \"Recordings/Spring_2022/OpenBCISession_2022-03-26_Sam_BrowRaise_1/OpenBCI-RAW-2022-03-26_18-38-07.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-26_Sam_BrowRaise_2/OpenBCI-RAW-2022-03-26_18-39-58.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-26_Sam_JawClench_1/OpenBCI-RAW-2022-03-26_18-21-33.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-26_Sam_JawClench_2/OpenBCI-RAW-2022-03-26_18-23-24.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-26_Sam_LeftBlink_1/OpenBCI-RAW-2022-03-26_18-25-33.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-26_Sam_LeftBlink_2/OpenBCI-RAW-2022-03-26_18-30-33.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-26_Sam_RightBlink_1/OpenBCI-RAW-2022-03-26_18-04-32.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-26_Sam_RightBlink_2/OpenBCI-RAW-2022-03-26_18-07-12.txt\"]\n",
"\n",
"label_paths = [\"Recordings/Spring_2022/OpenBCISession_2022-03-23_Sam_BrowLower_2/labels.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-23_Sam_BrowLower_1/labels.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-23_Sam_BrowRaise_1/labels.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-23_Sam_BrowRaise_2/labels.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-23_Sam_JawClench_1/labels.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-23_Sam_JawClench_2/labels.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-23_Sam_LeftEye_1/labels.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-23_Sam_LeftEye_2/labels.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-23_Sam_RightEye_1/labels.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-23_Sam_RightEye_2/labels.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-26_Sam_BrowLower_1/labels.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-26_Sam_BrowLower_2/labels.txt\",\n",
" # \"Recordings/Spring_2022/OpenBCISession_2022-03-26_Sam_BrowRaise_1/labels.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-26_Sam_BrowRaise_2/labels.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-26_Sam_JawClench_1/labels.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-26_Sam_JawClench_2/labels.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-26_Sam_LeftBlink_1/labels.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-26_Sam_LeftBlink_2/labels.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-26_Sam_RightBlink_1/labels.txt\",\n",
" \"Recordings/Spring_2022/OpenBCISession_2022-03-26_Sam_RightBlink_2/labels.txt\"]\n",
"\n",
"label_types = [\"BROW_DOWN\", \"BROW_DOWN\", \"BROW_UP\", \"BROW_UP\", \"JAW_CLENCH\", \"JAW_CLENCH\", \"L_EYE\", \"L_EYE\", \"R_EYE\", \"R_EYE\", \"BROW_DOWN\", \"BROW_DOWN\", \"BROW_UP\", \"JAW_CLENCH\", \"JAW_CLENCH\", \"L_EYE\", \"L_EYE\", \"R_EYE\", \"R_EYE\"]\n",
"\n",
"#Chooses which input data to use\n",
"inputsToUse = np.arange(len(rec_paths))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"\n",
"def getMeanAbsDeviation(dchannel):\n",
" return np.transpose(np.mean(np.abs(dchannel - np.mean(dchannel, axis=2, keepdims=True)), axis=2))\n",
"\n",
"def getMeanSquaredDeviation(dchannel):\n",
" return np.transpose(np.mean(np.square(dchannel - np.mean(dchannel, axis=2, keepdims=True)), axis=2))\n",
"\n",
"def getMean(dchannel):\n",
" return np.transpose(np.mean(dchannel, axis=2))\n",
"\n",
"def getPercentile(dchannel, percent):\n",
" return np.transpose(np.percentile(dchannel, percent, axis=2))\n",
"\n",
"def getPercentile10(dchannel, percent=10):\n",
" return np.transpose(np.percentile(dchannel, percent, axis=2))\n",
"\n",
"def getPercentile90(dchannel, percent=90):\n",
" return np.transpose(np.percentile(dchannel, percent, axis=2))\n",
"\n",
"def getPercentile15(dchannel, percent=15):\n",
" return np.transpose(np.percentile(dchannel, percent, axis=2))\n",
"\n",
"def getPercentile85(dchannel, percent=85):\n",
" return np.transpose(np.percentile(dchannel, percent, axis=2))\n",
"\n",
"def getPercentile5(dchannel, percent=5):\n",
" return np.transpose(np.percentile(dchannel, percent, axis=2))\n",
"\n",
"def getPercentile95(dchannel, percent=95):\n",
" return np.transpose(np.percentile(dchannel, percent, axis=2))\n",
"\n",
"\n",
"def getSpread(dchannel):\n",
" return np.transpose(np.max(dchannel, axis=2) - np.min(dchannel, axis=2))\n",
"\n",
"def getSpreadPercentile(dchannel, low=5, high=95):\n",
" return getPercentile(dchannel, high) - getPercentile(dchannel, low)\n",
"\n",
"def getPeakCount(dchannel, w=3):\n",
" ret = np.zeros((dchannel.shape[1], dchannel.shape[0]))\n",
" for ch in range(dchannel.shape[0]):\n",
" for sample in range(dchannel.shape[1]):\n",
" count = 0\n",
" for tind in range(w, dchannel.shape[2]-w):\n",
" isPeak = True\n",
" for x in range(1, w+1):\n",
" isPeak &= (dchannel[ch, sample, tind] > dchannel[ch, sample, tind-x] and dchannel[ch, sample, tind] > dchannel[ch, sample, tind+x])\n",
" if(isPeak):\n",
" count+=1\n",
" ret[sample, ch] = count\n",
" return ret\n",
"\n",
"# Loads in X and Y\n",
"functsList = [getMean, getMeanSquaredDeviation, getMeanAbsDeviation, getSpreadPercentile, getPeakCount, getPercentile10, getPercentile90]\n",
"\n",
"for featureFunc in functsList:\n",
" y = None\n",
" fX = None\n",
" for i in inputsToUse:\n",
" obs = dc.getObservations(rec_paths[i], label_paths[i], 1000, [0,1,2,3], label_types[i])\n",
" y_channels_groups = obs"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"#Calibration Testing for Individual EEG Channels\n",
"import numpy as np\n",
"\n",
"def Calibrate(data):\n",
" # Find the mean of the data\n",
" mean = getMean(data)\n",
" # Find the standard deviation of the data\n",
" std_abs = getMeanAbsDeviation(data)\n",
" std_squared = getMeanSquaredDeviation(data)\n",
" # Find the minimum and maximum values of the data\n",
" min = np.min(data)\n",
" max = np.max(data)\n",
" # Find the range of the data\n",
" range = max - min\n",
" # Find the offset\n",
" offset = mean - min\n",
" # Return the calibration values\n",
" return offset, mean, std_abs, std_squared"
]
}
],
"metadata": {
"interpreter": {
"hash": "b484e7466d1310c7063c3e2acaced4c395fa7098aef2c94d8ed134d16efa77f6"
},
"kernelspec": {
"display_name": "Python 3.7.11 ('neuro')",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.7.11"
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"orig_nbformat": 4
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"nbformat": 4,
"nbformat_minor": 2
}