def main():
with open('cred.json') as json_file:
user = json.load(json_file)
cortex = Cortex(user, True)
# do_stuff(cortex)
record = Record(cortex)
record.run()
def __init__(self):
self.headers = {}
self.objects = None
self.count = 0
self.tick = time.time()
self.maxEventsInBuffer = 100
self.stream_types = {}
self.is_first_for_type = {
"met": True,
"eeg": True,
"fac": True,
"mot": True,
}
self.c = Cortex(user, debug_mode=False)
self.c.do_prepare_steps()
self.time_slice = 0
self.time_data = {
'engagement': {},
'excitement': {},
'stress': {},
'relaxation': {},
'interest': {},
'focus': {}
}
self.time_slice_averages = {}
def func(self):
url = "wss://localhost:6868"
user = {
"license": "",
"client_id": "",
"client_secret": "",
"debit": 100,
"number_row_data": 10
}
self.count = 0
self.cortex = Cortex(url, user)
self.headset_id = self.cortex.query_headset()
self.cortex.connect_headset()
self.cortex.request_access()
auth = self.cortex.authorize()
self.cortex.create_session(auth, self.headset_id)
status = 'load'
profile_name = 'skshreyas'
self.cortex.setup_profile(profile_name, status)
stream_list = ['com']
self.lis = []
self.cortex.subscribe(stream_list)
while True:
resp = self.cortex.ws.recv()
res = json.loads(resp)
mentalCommandList = res['com']
action = mentalCommandList[0]
#print(action)
self.FocusChanger(action)
def simple():
c = Cortex(input_dims, output_dims)
c.PARAMS['learning_rate'] = 1e-4
c.PARAMS['gamma'] = 0.8
c.PARAMS['messagedim'] = 5
c.PARAMS['sensory_hidden'] = [10]
c.PARAMS['motorM-net_hidden'] = [10]
c.PARAMS['Q-net_hidden'] = [10]
c.initialize()
e = Environment()
durs = list()
lall = 0
rall = 0
while (len(durs) < 200):
obs = e.observe() # model observes
actions = c.learning_step(
obs, reward, explore=(len(durs) < 100)) # model chooses action
flag, reward = e.simulate(
actions
) # environment simulates and determines reward for that action
# that reward is fed in next time to instruct the model what Q should have been
if flag != None:
l, r = flag
durs.append(l + r)
lall += l
rall += r
print(len(durs), flag)
print(lall, rall)
plt.plot(durs)
plt.show()
def start_chart(data):
# print('received message: ' + str(data), flush=sys.stdout)
chart_cortex = Cortex(user)
PROFILE_NAME = "RealBCI"
STREAM = "pow"
chart_cortex.do_prepare_steps()
chart_cortex.setup_profile(PROFILE_NAME, 'load')
chart_cortex.sub_request(stream=[STREAM], emit=emit)
def propagate(console):
'''
This method creates a cortex and adds some relay mappings
:param console: Used by the cortex to control console logging
'''
core = Cortex(debug=True, console=console)
core.add_relay(1, [2, 3, 4, 5])
core.add_relay(2, [3, 4])
core.add_relay(3, [4, 5, 13])
core.add_relay(4, [7, 8, 9])
core.add_relay(9, [10])
core.add_relay(10, [11, 12])
return core
def record():
try:
app.app_cortex = Cortex(user)
app.app_cortex.do_prepare_steps()
app.app_cortex.create_record(record_name, record_description)
response = app.response_class(response=json.dumps(
{"record_id": app.app_cortex.record_id}),
status=200,
mimetype='application/json')
return response
except Exception as e:
print(e, flush=sys.stdout)
response = app.response_class(response=json.dumps(
{"error": 'Get Error'}),
status=400,
mimetype='applica tion/json')
return response
def __init__(self):
self.c = Cortex(user, debug_mode=True)
self.c.do_prepare_steps()
from pyboy import PyBoy
import ctypes
from time import sleep
import os
from command_handler import command_handler
from cca_handler import cca_handler
from pyboy_controller import pyboy_controller
import time
# Initialize our variables
use_csv = True
# TEMP: feel free to change how I do this. I say 'if not using csv then use emotiv' here and in #EACHFRAMEPOG - Matt
# NOTE: still will save a csv file even though I am reading from a csv file
if not use_csv:
cortex = Cortex(None)
cortex.do_prepare_steps()
generator = cortex.sub_request(['eeg'])
# generator = cortex.sub_request_pow(['pow'])
next(generator).queue
data_columns = ["P7", "O1", "O2", "P8", "TIME"]
# data_columns = ["O1/theta","O1/alpha","O1/betaL","O1/betaH","O1/gamma",
# "O2/theta","O2/alpha","O2/betaL","O2/betaH","O2/gamma", "TIME"]
# CSV CONTROL
if use_csv:
recording_data = pd.read_csv('old_bad_recordings/first_target_0.csv')
record_length = len(recording_data.index)
channels = ['P7', 'O1', 'O2', 'P8'] # only data channels
row_index = 0
def test():
cortex = Cortex(None)
cortex.do_prepare_steps()
init()
screenSize = [1280, 720] # Set Window game size
playerScore = 0
aiScore = 0
# ASCII Menus
art1 = text2art("Lain BCI", font="sub-zero")
art2 = text2art("Connect your brain", font="small")
art3 = text2art("Training", font="sub-zero")
# INSERT CODES GENERATED BY EMOTIV
# First parameter is clientID
# Second parameter secretID
# Cortex Object initialization. Check Cortex libary I developed for more information
cortex = Cortex("XX", "XX", "wss://localhost:6868")
# Class used to intialiaze font objects. Font object is text that can be displayed on the game
class Font():
def __init__(self, color, name, size, posX, posY):
self.font = pyg.font.Font(name, size)
self.color = color
self.posX = posX
self.posY = posY
self.text = None
self.rect = None
def render(self, text):
self.text = self.font.render(str(text), 1, self.color)
self.rect = self.text.get_rect(centerx=self.posX, centery=self.posY)
import asyncio
from cortex import Cortex
import time
import pyautogui
import json
import keyboard
cortex = Cortex(
"C:\\Users\\Siwen Wang\\Desktop\\Cursor_Control_V1\\cortex_creds.txt")
async def set_up(cortex):
# await cortex.inspectApi()
print("** USER LOGIN **")
await cortex.get_user_login()
print("** GET CORTEX INFO **")
await cortex.get_cortex_info()
print("** HAS ACCESS RIGHT **")
await cortex.has_access_right()
print("** REQUEST ACCESS **")
await cortex.request_access()
print("** AUTHORIZE **")
await cortex.authorize()
print("** GET LICENSE INFO **")
await cortex.get_license_info()
print("** QUERY HEADSETS **")
await cortex.query_headsets()
if len(cortex.headsets) > 0:
print("** CREATE SESSION **")
await cortex.create_session(activate=True,
headset_id=cortex.headsets[0])
import key
import json
from cortex import Cortex
import matplotlib.pyplot as plt
import numpy as np
from liveplot import MyPlotClass, MyDataFetchClass, MyDataClass
import bci_workshop_tools as BCIw
from IPython.core.pylabtools import figsize
import time
print("licence :", key.licence)
print("client_id :", key.client_id)
print("client_secret :", key.client_secret)
streams = ['eeg']
cortex = Cortex(key.user, debug_mode=True)
cortex.do_prepare_steps()
sub_request_json = {
"jsonrpc": "2.0",
"method": "subscribe",
"params": {
"cortexToken": cortex.auth,
"session": cortex.session_id,
"streams": streams
},
"id": key.SUB_REQUEST_ID
}
print('subscribe request --------------------------------')
cortex.ws.send(json.dumps(sub_request_json))
def muscular_detectors(self):
status = 'unload'
profile_name = 'shreyas'
self.cortex.setup_profile(profile_name, status)
cortex = Cortex(self.url, self.user)
headset_id = cortex.query_headset()
cortex.connect_headset()
cortex.request_access()
auth = cortex.authorize()
cortex.create_session(auth, headset_id)
cortex.setup_profile(profile_name, status)
status = 'load'
profile_name = 'shreyas'
cortex.setup_profile(profile_name, status)
stream_list = ['fac']
lis = []
cortex.subscribe(stream_list)
while True:
resp = self.cortex.ws.recv()
res = json.loads(resp)
facCommandList = res['fac']
print(facCommandList)
blink = facCommandList[0]
frownNsurprise = facCommandList[1]
clenchNsmile = facCommandList[3]
if frownNsurprise == 'frown':
self.lis.append(frownNsurprise)
if len(self.lis) == 3:
del self.lis[0]
print(self.lis)
if self.lis[0] == self.lis[1]:
self.count = self.count + 1
if self.count > 6:
self.voicegen.say("I would like to go out")
self.voicegen.runAndWait()
self.count = 0
print(self.count)
else:
self.count = 0
if frownNsurprise == 'surprise':
self.lis.append(frownNsurprise)
if len(self.lis) == 3:
del self.lis[0]
print(self.lis)
if self.lis[0] == self.lis[1]:
self.count = self.count + 1
if self.count > 6:
self.voicegen.say("I am excited")
self.voicegen.runAndWait()
self.count = 0
print(self.count)
else:
self.count = 0
if clenchNsmile == 'clench':
self.lis.append(clenchNsmile)
if len(self.lis) == 3:
del self.lis[0]
print(self.lis)
if self.lis[0] == self.lis[1]:
self.count = self.count + 1
if self.count > 6:
self.voicegen.say("take me to the washroom")
self.voicegen.runAndWait()
print(self.count)
else:
self.count = 0
if clenchNsmile == 'smile':
self.lis.append(clenchNsmile)
if len(self.lis) == 3:
del self.lis[0]
print(self.lis)
if self.lis[0] == self.lis[1]:
self.count = self.count + 1
if self.count > 6:
self.voicegen.say("I am happy")
self.voicegen.runAndWait()
self.count = 0
print(self.count)
else:
self.count = 0
if blink == 'blink':
self.lis.append(clenchNsmile)
if len(self.lis) == 3:
del self.lis[0]
print(self.lis)
if self.lis[0] == self.lis[1]:
self.count = self.count + 1
if self.count > 10:
break
print(self.count)
else:
self.count = 0
self.mentalComm()
def mentalComm(self):
self.url = "wss://localhost:6868"
self.user = {
"license": "",
"client_id": "",
"client_secret": "",
"debit": 100,
"number_row_data": 10
}
self.count = 0
self.cortex = Cortex(self.url, self.user)
headset_id = self.cortex.query_headset()
self.cortex.connect_headset()
self.cortex.request_access()
auth = self.cortex.authorize()
self.cortex.create_session(auth, headset_id)
status = 'load'
profile_name = 'skshreyas'
self.cortex.setup_profile(profile_name, status)
stream_list = ['com']
self.lis = []
self.cortex.subscribe(stream_list)
while True:
resp = self.cortex.ws.recv()
res = json.loads(resp)
mentalCommandList = res['com']
action = mentalCommandList[0]
print(action)
if action == 'neutral':
self.count = 0
if action == 'right':
self.lis.append(action)
if len(self.lis) == 3:
del self.lis[0]
print(self.lis)
if self.lis[0] == self.lis[1]:
self.count = self.count + 1
if self.count > 6:
self.voicegen.say("please bring my things")
self.voicegen.runAndWait()
self.count = 0
print(self.count)
else:
self.count = 0
if action == 'lift':
self.lis.append(action)
if len(self.lis) == 3:
del self.lis[0]
print(self.lis)
if self.lis[0] == self.lis[1]:
self.count = self.count + 1
if self.count > 6:
self.voicegen.say("I would like to go out")
self.voicegen.runAndWait()
self.count = 0
print(self.count)
else:
self.count = 0
if action == 'drop':
self.lis.append(action)
if len(self.lis) == 3:
del self.lis[0]
print(self.lis)
if self.lis[0] == self.lis[1]:
self.count = self.count + 1
if self.count > 6:
self.voicegen.say("hi it is glad to meet you")
self.voicegen.runAndWait()
self.count = 0
print(self.count)
else:
self.count = 0
if action == 'left':
self.lis.append(action)
if len(self.lis) == 3:
del self.lis[0]
print(self.lis)
if self.lis[0] == self.lis[1]:
self.count = self.count + 1
if self.count > 6:
break
print(self.count)
else:
self.count = 0
self.muscular_detectors()
Alternating greedy and optimizing periods. For each period, report:
Number of bounces
Number of time steps
Number of agent movements
'''
es = [0.5] # [0.05, 0.1, 0.25]
for i in range(9, 10):
for nnet in nnettypes:
for e in es:
for learning_rate in learning_rates:
for gamma in gammas:
Environment.movement_cost = e
c = Cortex(input_dims, output_dims)
c.PARAMS['learning_rate'] = learning_rate
c.PARAMS['gamma'] = gamma
c.PARAMS['messagedim'] = 5
c.PARAMS['sensory_hidden'] = nnettypes[nnet]
c.PARAMS['motorM-net_hidden'] = nnettypes[nnet]
c.PARAMS['Q-net_hidden'] = nnettypes[nnet]
c.initialize(4)
env = Environment()
data = np.zeros(
(NUMBER, 5)
) # number left bounces, right bounces, total time, left mvmts, right mvmts
path = "res3/{}".format(i)