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pytel2.py
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import numpy as np
import pandas as pd
# Initializing the arrays required to store the data.
attention_values = np.array([])
meditation_values = np.array([])
delta_values = np.array([])
theta_values = np.array([])
lowAlpha_values = np.array([])
highAlpha_values = np.array([])
lowBeta_values = np.array([])
highBeta_values = np.array([])
lowGamma_values = np.array([])
highGamma_values = np.array([])
blinkStrength_values = np.array([])
time_array = np.array([])
import sys
import json
import time
from telnetlib import Telnet
tn=Telnet('localhost',13854);
start=time.clock();
i=0;
# app registration step (in this instance unnecessary)
#tn.write('{"appName": "Example", "appKey": "9f54141b4b4c567c558d3a76cb8d715cbde03096"}');
tn.write('{"enableRawOutput": true, "format": "Json"}');
blink_or_not = raw_input('Non-zero blink(1) or zero blink(0): ')
outfile="null";
if len(sys.argv)>1:
outfile=sys.argv[len(sys.argv)-1];
outfptr=open(outfile,'w');
eSenseDict={'attention':0, 'meditation':0};
waveDict={'lowGamma':0, 'highGamma':0, 'highAlpha':0, 'delta':0, 'highBeta':0, 'lowAlpha':0, 'lowBeta':0, 'theta':0};
signalLevel=0;
time_list = []
while time.clock() - start < 100:
blinkStrength=0;
line=tn.read_until('\r');
if len(line) > 20:
timediff=time.clock()-start;
dict=json.loads(str(line));
if "poorSignalLevel" in dict:
signalLevel=dict['poorSignalLevel'];
if "blinkStrength" in dict:
blinkStrength=dict['blinkStrength'];
if "eegPower" in dict:
waveDict=dict['eegPower'];
eSenseDict=dict['eSense'];
outputstr=str(timediff)+ ", "+ str(signalLevel)+", "+str(blinkStrength)+", " + str(eSenseDict['attention']) + ", " + str(eSenseDict['meditation']) + ", "+str(waveDict['lowGamma'])+", " + str(waveDict['highGamma'])+", "+ str(waveDict['highAlpha'])+", "+str(waveDict['delta'])+", "+ str(waveDict['highBeta'])+", "+str(waveDict['lowAlpha'])+", "+str(waveDict['lowBeta'])+ ", "+str(waveDict['theta']);
if blinkStrength==0 and eSenseDict['attention'] ==0 and eSenseDict['meditation'] == 0 and waveDict['lowGamma'] == 0 and waveDict['highGamma']==0 and waveDict['highAlpha']==0 and waveDict['lowAlpha']==0 and waveDict['lowBeta']==0 and waveDict['highBeta']==0 and waveDict['delta']==0 and waveDict['theta']==0:
continue
time_array = np.append(time_array, [timediff]);
blinkStrength_values = np.append(blinkStrength_values, [blinkStrength]);
lowGamma_values = np.append(lowGamma_values, [waveDict['lowGamma']]);
highGamma_values = np.append(highGamma_values, [waveDict['highGamma']]);
highAlpha_values = np.append(highAlpha_values, [waveDict['highAlpha']]);
delta_values = np.append(delta_values, [waveDict['delta']]);
lowBeta_values = np.append(lowBeta_values, [waveDict['lowBeta']]);
highBeta_values = np.append(highBeta_values, [waveDict['highBeta']]);
theta_values = np.append(theta_values, [waveDict['theta']]);
lowAlpha_values = np.append(lowAlpha_values, [waveDict['lowAlpha']]);
attention_values = np.append(attention_values, [eSenseDict['attention']]);
meditation_values = np.append(meditation_values, [eSenseDict['meditation']]);
print outputstr;
if blink_or_not:
if blinkStrength:
time_list.append(timediff)
#print time_list
else:
if blinkStrength == 0:
time_list.append(timediff)
if outfile!="null":
outfptr.write(outputstr+"\n");
person_name = raw_input('Enter the name of the person: ')
blink_label = raw_input('Enter left or right eye blink(1 for left, 2 for right): ')
#time_starting = raw_input('When does TGC start: ')
lefty_righty = raw_input('Is the person left-handed or right-handed: ')
time_blinking = input("Enter the instances of time to be stored(list format): ")
print time_blinking
# Data Recorded for a single person
data_row = pd.DataFrame({'Name': person_name, 'attention': [attention_values], 'meditation': [meditation_values], 'delta': [delta_values], 'theta': [theta_values], 'lowAlpha': [lowAlpha_values], 'highAlpha': [highAlpha_values], 'lowBeta': [lowBeta_values], 'highBeta': [highBeta_values],
'lowGamma':[lowGamma_values] , 'highGamma': [highGamma_values], 'blinkStrength': [blinkStrength_values], 'time': [time_array], 'LOR': blink_label})
'''
fd = open('humara_data_eeg.csv','a')
fd.write(str(blink_label)+','+str(person_name)+','+str([attention_values])+','+str([blinkStrength_values])+','+str([delta_values])+','+
str([highAlpha_values])+','+str([highBeta_values])+','+str([highGamma_values])+','+str([lowAlpha_values])+','+str([lowBeta_values])+','+str([lowGamma_values])+','+
str([meditation_values])+','+str([theta_values])+','+str([time_array])+','+'\n')
fd.close()
'''
dataset_pre = pd.read_csv('humara_data_eeg_pre.csv')
min_time_list = []
for time_blinking_ in time_blinking:
min_time = time_list[0]
min_diff = abs(min_time - time_blinking_)
for t in time_list:
if min_diff > abs(t - time_blinking_):
min_time = t
min_diff = abs(t - time_blinking_)
min_time_list.append(min_time)
print min_time
index = 0
for index in range(0,len(time_array)):
if time_array[index] == min_time:
break
if index == 0 or index == len(time_array) - 1:
continue
#To append....................................
dataset_pre = dataset_pre.append(pd.Series([blink_label, [attention_values[index-1:index+2]], [blinkStrength_values[index-1:index+2]], [delta_values[index-1:index+2]]
, [highAlpha_values[index-1:index+2]], [highBeta_values[index-1:index+2]], [highGamma_values[index-1:index+2]], [lowAlpha_values[index-1:index+2]], [lowBeta_values[index-1:index+2]], [lowGamma_values[index-1:index+2]], [meditation_values[index-1:index+2]],
[theta_values[index-1:index+2]], lefty_righty], index=['LOR', 'attention', 'blinkStrength', 'delta', 'highAlpha', 'highBeta', 'highGamma', 'lowAlpha', 'lowBeta', 'lowGamma', 'meditation', 'theta', 'LTYRTY']), ignore_index = True)
#............................................
dataset_pre.to_csv('humara_data_eeg_pre.csv')
# Reading the data stored till now
dataset = pd.read_csv('humara_data_eeg.csv')
from numpy import nan as Nan
dataset = dataset.append(pd.Series([blink_label, person_name, [attention_values], [blinkStrength_values], [delta_values]
, [highAlpha_values], [highBeta_values], [highGamma_values], [lowAlpha_values], [lowBeta_values], [lowGamma_values], [meditation_values],
[theta_values], lefty_righty], index=['LOR', 'Name', 'attention', 'blinkStrength', 'delta', 'highAlpha', 'highBeta', 'highGamma', 'lowAlpha', 'lowBeta', 'lowGamma', 'meditation', 'theta', 'LTYRTY']), ignore_index = True)
#Appending and storing the data in the same csv
#dataset.append(data_row)
dataset.to_csv('humara_data_eeg.csv')
tn.close();
#outfptr.close();