def setup(self):
ModuleAbstract.setup(self)
self.filename = None
if 'file' in self.module_conf['outputs']['data']:
self.save_integers = True
# get the filename to write to
self.filename = self.module_conf['outputs']['data']['file']
file_extension = os.path.splitext(self.filename)[1]
# include timestamp in the filename if requested
if self.module_settings['include_timestamp_in_filename']:
timestamp = strftime("%m-%d-%Y_%H-%M-%S", localtime())
self.filename = self.filename.replace(file_extension,"-"+timestamp+file_extension)
# touch the file if overwriting is set to true, this will also set the timestamp
overwrite_existing = self.module_conf['outputs']['data']['overwrite_existing'] if 'overwrite_existing' in self.module_conf['outputs']['data'] else False
if overwrite_existing:
with open(self.filename, 'a'):
os.utime(self.filename,None)
# if we don't want the timestamp, pop it off the beginning of the headers
if not self.module_settings['include_timestamp']:
self.headers.pop(0)
if self.debug:
print self.LOGNAME + "setup"
def setup(self):
ModuleAbstract.setup(self)
if self.debug:
print self.LOGNAME + "setup"
# sampling_frequency
self.sampling_rate = self.module_settings["sampling_rate"] if "sampling_rate" in self.module_settings and self.module_settings["sampling_rate"] is not False else 250.0
# notch
self.notch_filter = self.module_settings["notch_filter"] if "notch_filter" in self.module_settings and self.module_settings["notch_filter"] is not False else None
# bandpass
self.bandpass_filter = self.module_settings["bandpass_filter"] if "bandpass_filter" in self.module_settings and self.module_settings["bandpass_filter"] is not False else None
# create the notch filter coefficients (60 Hz)
"""
For more info, see http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.signal.butter.html#scipy.signal.butter
Or http://www.mathworks.com/help/signal/ref/butter.html?s_tid=gn_loc_drop#buct3_m
"""
if self.notch_filter:
self.notch_filter_b, self.notch_filter_a = FilterCoefficients('bandstop',self.sampling_rate,np.array(self.notch_filter))
# create the bandpass filter (7-13Hz)
if self.bandpass_filter:
self.bandpass_filter_b, self.bandpass_filter_a = FilterCoefficients('bandpass',self.sampling_rate,np.array(self.bandpass_filter))
def setup(self):
ModuleAbstract.setup(self)
self.file_to_write = None
if 'file' in self.module_conf['outputs']['data']:
# set file write mode ('a', or append, by default)
file_write_mode = self.module_conf['outputs']['data']['file_write_mode'] if 'file_write_mode' in self.module_conf['outputs']['data'] else 'a'
# get the filename to write to
filename = self.module_conf['outputs']['data']['file']
# include timestamp in the filename if requested
if self.module_settings['include_timestamp_in_filename']:
timestamp = strftime("_%H-%M-%S", localtime())
filename = filename.replace('.data',timestamp+'.data')
# open the file
self.file_to_write = open(filename,file_write_mode)
# if we don't want the timestamp, pop it off the beginning of the headers
if not self.module_settings['include_timestamp']:
self.headers.pop(0)
if self.debug:
print self.LOGNAME + "setup"
def setup(self):
ModuleAbstract.setup(self)
if self.debug:
print self.LOGNAME + "setup"
self.extractSettings(
self.module_settings, {
'bandpass_filter_range': [7.0, 13.0],
'calculation_threshold': 30,
'class_label_channel': None,
'class_labels': None,
'epoch_size': 250,
'notch_filter': None,
'num_spatial_filters': 8,
'optimization_trace': False,
'sampling_rate': 250,
'separator': '\t',
'test_file': None,
'plot': False,
'include_electrodes': None
})
# sampling_rate (Hz)
self.sampling_rate = float(self.sampling_rate)
# class labels
if self.class_labels:
self.class1, self.class2 = self.class_labels
if self.notch_filter:
# create the notch filter coefficients (60 Hz)
self.notch_filter_b, self.notch_filter_a = FilterCoefficients(
'bandstop', self.sampling_rate, np.array(self.notch_filter))
if self.bandpass_filter_range:
# create the bandpass filter (7-13Hz)
self.bandpass_filter_b, self.bandpass_filter_a = FilterCoefficients(
'bandpass', self.sampling_rate,
np.array(self.bandpass_filter_range))
# X and y for classification
self.X = np.array([])
self.y = np.array([])
# holds epochs segregated into separate bins for each class (each dict key is a class)
self.epochs_by_class = dict()
# hold covariances
self.covariances_by_class = dict()
"""
initialize a standard 3-dim array just to have a data structure that is standard with other libs
In most other libraries, CSP uses a 3 dimensional epoch input: (n_epochs, n_channels, n_times)
"""
self.epochs = np.zeros((0, self.num_channels, self.epoch_size))
self.covariances = np.zeros((0, self.num_channels, self.num_channels))
self.all_spatial_filters = None
self.spatial_filters = None
# counter for epochs
self.epochsCounter = Counter()
def setup(self):
ModuleAbstract.setup(self)
"""
device_name="muse",
mock_data_enabled=True,
device_id=MOCK_DEVICE_ID,
cloudbrain_address=RABBITMQ_ADDRESS,
buffer_size=10, step_size=10,
device_port=None,
pipe_name=None,
publisher_type="pika",
device_mac=None
"""
if 'mock_data_enabled' not in self.module_settings:
self.module_settings['mock_data_enabled'] = False
if self.debug is None:
self.debug = False
if 'device_buffer' not in self.module_settings:
self.module_settings['device_buffer'] = 10
if 'publisher_type' not in self.module_settings:
self.module_settings['publisher_type'] = "pika"
if self.debug:
print self.LOGNAME + "setup"
print self.module_settings
def setup(self):
ModuleAbstract.setup(self)
if self.debug:
print self.LOGNAME + "setup"
self.extractSettings(self.module_settings, {
'bandpass_filter_range': [7.0,13.0],
'calculation_threshold': 30,
'class_label_channel': None,
'class_labels': None,
'epoch_size': 250,
'notch_filter': None,
'num_spatial_filters': 8,
'optimization_trace': False,
'sampling_rate': 250,
'separator': '\t',
'test_file': None,
'plot': False,
'include_electrodes': None
})
# sampling_rate (Hz)
self.sampling_rate = float(self.sampling_rate)
# class labels
if self.class_labels:
self.class1, self.class2 = self.class_labels
if self.notch_filter:
# create the notch filter coefficients (60 Hz)
self.notch_filter_b, self.notch_filter_a = FilterCoefficients('bandstop',self.sampling_rate,np.array(self.notch_filter))
if self.bandpass_filter_range:
# create the bandpass filter (7-13Hz)
self.bandpass_filter_b, self.bandpass_filter_a = FilterCoefficients('bandpass',self.sampling_rate,np.array(self.bandpass_filter_range))
# X and y for classification
self.X = np.array([])
self.y = np.array([])
# holds epochs segregated into separate bins for each class (each dict key is a class)
self.epochs_by_class = dict()
# hold covariances
self.covariances_by_class = dict()
"""
initialize a standard 3-dim array just to have a data structure that is standard with other libs
In most other libraries, CSP uses a 3 dimensional epoch input: (n_epochs, n_channels, n_times)
"""
self.epochs = np.zeros((0,self.num_channels,self.epoch_size))
self.covariances = np.zeros((0,self.num_channels,self.num_channels))
self.all_spatial_filters = None
self.spatial_filters = None
# counter for epochs
self.epochsCounter = Counter()
def setup(self):
ModuleAbstract.setup(self)
if self.debug:
print self.LOGNAME + "setup"
# sampling_rate (Hz)
self.sampling_rate = float(self.module_settings["sampling_rate"]) if "sampling_rate" in self.module_settings else 100.
self.samples_per_window = self.module_settings["samples_per_window"] if "samples_per_window" in self.module_settings else 500
if len(self.inputs) and "data" in self.inputs and len(self.outputs) and "data" in self.outputs:
# check if conversion is supported
input_message_type = self.inputs["data"]["message_type"]
input_data_type = self.inputs["data"]["data_type"]
output_message_type = self.outputs["data"]["message_type"]
output_data_type = self.outputs["data"]["data_type"]
def setup(self):
ModuleAbstract.setup(self)
if self.debug:
print self.LOGNAME + "setup"
if len(self.inputs) and "data" in self.inputs and len(self.outputs) and "data" in self.outputs:
# check if conversion is supported
input_message_type = self.inputs["data"]["message_type"]
input_data_type = self.inputs["data"]["data_type"]
output_message_type = self.outputs["data"]["message_type"]
output_data_type = self.outputs["data"]["data_type"]
# check that requested conversion is supported
if input_message_type == MESSAGE_TYPE_MATRIX:
if input_data_type not in self.SUPPORTED_CONVERSIONS.keys():
raise ValueError(self.LOGNAME + "Conversion from type " + input_data_type + " is not supported.")
elif output_data_type not in self.SUPPORTED_CONVERSIONS[input_data_type]:
raise ValueError(self.LOGNAME + "Conversion from type " + input_data_type + " to " + output_data_type + " is not supported.")
else:
raise ValueError(self.LOGNAME + "Input and output 'data' must be of type " + MESSAGE_TYPE_MATRIX + ". Only matrix conversions are supported so far.")
def setup(self):
ModuleAbstract.setup(self)
self.countTotal = 0
# Deprecated (leaving this in just in case we decide someday that we need to support
# multiple EEG inputs with different numbers of channels)
## override num_channels if our input is coming from a signal generator, which could have any number of channels
##if 'num_channels' in self.module_conf['inputs']['data']:
## self.num_channels = self.module_conf['inputs']['data']['num_channels']
##self.headers = ['timestamp'] + ['channel_%s' % i for i in xrange(self.num_channels)]
self.window = np.matrix(np.zeros((self.num_channels, 0)))
self.nextWindowSegment = np.matrix(np.zeros((self.num_channels, 0)))
self.timestamps = []
self.lastClassLabel = 0
self.currentClassLabel = 0
self.currentClassLabelTimepointIndex = 0
self.classLabels = []
if self.debug:
print self.LOGNAME + "setup"
def setup(self):
ModuleAbstract.setup(self)
self.countTotal = 0
# Deprecated (leaving this in just in case we decide someday that we need to support
# multiple EEG inputs with different numbers of channels)
## override num_channels if our input is coming from a signal generator, which could have any number of channels
##if 'num_channels' in self.module_conf['inputs']['data']:
## self.num_channels = self.module_conf['inputs']['data']['num_channels']
##self.headers = ['timestamp'] + ['channel_%s' % i for i in xrange(self.num_channels)]
self.window = np.matrix(np.zeros((self.num_channels,0)))
self.nextWindowSegment = np.matrix(np.zeros((self.num_channels,0)))
self.timestamps = []
self.lastClassLabel = 0
self.currentClassLabel = 0
self.currentClassLabelTimepointIndex = 0
self.classLabels = []
if self.debug:
print self.LOGNAME + "setup"
def setup(self):
ModuleAbstract.setup(self)
if self.debug:
print self.LOGNAME + "setup"
# notch
self.notch_filter = self.module_settings["notch_filter"] if "notch_filter" in self.module_settings and self.module_settings["notch_filter"] is not False else None
# bandpass
self.bandpass_filter = self.module_settings["bandpass_filter"] if "bandpass_filter" in self.module_settings and self.module_settings["bandpass_filter"] is not False else None
# create the notch filter coefficients (60 Hz)
"""
For more info, see http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.signal.butter.html#scipy.signal.butter
Or http://www.mathworks.com/help/signal/ref/butter.html?s_tid=gn_loc_drop#buct3_m
"""
if self.notch_filter:
self.notch_filter_b, self.notch_filter_a = FilterCoefficients('bandstop',250.0,np.array(self.notch_filter))
# create the bandpass filter (7-13Hz)
if self.bandpass_filter:
self.bandpass_filter_b, self.bandpass_filter_a = FilterCoefficients('bandpass',250.0,np.array(self.bandpass_filter))
def setup(self):
ModuleAbstract.setup(self)
if self.debug:
print self.LOGNAME + "setup"
def setup(self):
ModuleAbstract.setup(self)
# time counter, counts number of ticks in current period
self.period_counter = 0;
self.lines_counter = 0;
self.total_counter = 0;
# params
# sampling_rate (Hz)
self.sampling_rate = float(self.module_settings["sampling_rate"]) if "sampling_rate" in self.module_settings else 100.
# frequency (Hz)
self.frequency = float(self.module_settings["sine_frequency"]) if "sine_frequency" in self.module_settings else 10.
# range
self.range = self.module_settings["range"] if "range" in self.module_settings else [0,1]
# separator
self.separator = self.module_settings["separator"] if "separator" in self.module_settings else '\t'
# skip_lines_prefix
self.skip_lines_prefix = self.module_settings["skip_lines_prefix"] if "skip_lines_prefix" in self.module_settings else None
# skip_lines_num
self.skip_lines = self.module_settings["skip_lines"] if "skip_lines" in self.module_settings else None
# skip_columns
self.skip_columns = self.module_settings["skip_columns"] if "skip_columns" in self.module_settings else None
# include_columns
self.include_columns = self.module_settings["include_columns"] if "include_columns" in self.module_settings else None
# data contains timestamp flag
self.data_already_contains_timestamp = self.module_settings["data_already_contains_timestamp"] if "data_already_contains_timestamp" in self.module_settings else False
# timestamp_column
self.timestamp_column = self.module_settings["timestamp_column"] if "timestamp_column" in self.module_settings else 0
# class_label_column
self.class_label_column = self.module_settings["class_label_column"] if "class_label_column" in self.module_settings else None
# flag: whether to playback at the samplig_rate. if false, generate as fast as possible
self.generate_at_sampling_rate = self.module_settings["generate_at_sampling_rate"] if "generate_at_sampling_rate" in self.module_settings else True
# pattern
self.pattern = "rand"
if self.module_settings["pattern"]:
self.pattern = self.module_settings["pattern"]
# what pattern to generate
if self.pattern == "sine":
# SINE WAVE PATTERN
# formula for sine wave is:
# sine_wave = amp.*sin(2*pi*freq.*time);
# amplitude = half the distance between the min and max
amp = abs(float(self.range[1]) - float(self.range[0])) / 2
# zero will be halfway between the min and max
offset = float(self.range[1]) - amp
# np.linspace(-np.pi, np.pi, sampling_rate) --> make a range of x values, as many as sampling rate
# this is equivalent to 2*pi*time
sine_x = np.linspace(-np.pi, np.pi, self.sampling_rate)
self.sine_waves = []
# sine wave 1
sine1 = [(amp * np.sin(t * self.sampling_rate/self.frequency)) + offset for t in sine_x]
self.sine_waves.append(np.tile(sine1,self.frequency))
# sine wave 2 (double amp, triple freq)
sine2 = [((2*amp) * np.sin(3 * t * self.sampling_rate/self.frequency)) + offset for t in sine_x]
self.sine_waves.append(np.tile(sine2,self.frequency))
# default to the first sine wave (only used if sine)
self.sine_wave_to_use = 0
self.generate_pattern_func = "generateSine"
elif self.pattern == "files":
# get file list from settings
self.file_list = self.module_settings["files"]
# force file list to be list if not already
if type(self.file_list) != list:
self.file_list = [self.file_list]
self.current_file_index = -1
self.current_file = None
self.generate_pattern_func = "generateFromFiles"
else:
# RANDOM PATTERN
self.generate_pattern_func = "generateRandom"
if self.class_label_column:
self.lastClassLabel = None
def setup(self):
ModuleAbstract.setup(self)
if self.debug:
print self.LOGNAME + "setup"
def setup(self): ModuleAbstract.setup(self) # time counter, counts number of ticks in current period self.period_counter = 0; self.lines_counter = 0; self.total_counter = 0; # params # sampling_rate (Hz) self.sampling_rate = float(self.module_settings["sampling_rate"]) if "sampling_rate" in self.module_settings else 100. # frequency (Hz) self.frequency = float(self.module_settings["sine_frequency"]) if "sine_frequency" in self.module_settings else 10. # range self.range = self.module_settings["range"] if "range" in self.module_settings else [0,1] # separator self.separator = self.module_settings["separator"] if "separator" in self.module_settings else '\t' # skip_lines_prefix self.skip_lines_prefix = self.module_settings["skip_lines_prefix"] if "skip_lines_prefix" in self.module_settings else None # skip_lines_num self.skip_lines = self.module_settings["skip_lines"] if "skip_lines" in self.module_settings else None # skip_columns self.skip_columns = self.module_settings["skip_columns"] if "skip_columns" in self.module_settings else None # include_columns self.include_columns = self.module_settings["include_columns"] if "include_columns" in self.module_settings else None # data contains timestamp flag self.data_already_contains_timestamp = self.module_settings["data_already_contains_timestamp"] if "data_already_contains_timestamp" in self.module_settings else False # timestamp_column self.timestamp_column = self.module_settings["timestamp_column"] if "timestamp_column" in self.module_settings else 0 # class_label_column self.class_label_column = self.module_settings["class_label_column"] if "class_label_column" in self.module_settings else None # flag: whether to playback at the samplig_rate. if false, generate as fast as possible self.generate_at_sampling_rate = self.module_settings["generate_at_sampling_rate"] if "generate_at_sampling_rate" in self.module_settings else True # pattern self.pattern = "rand" if self.module_settings["pattern"]: self.pattern = self.module_settings["pattern"] # what pattern to generate if self.pattern == "sine": # SINE WAVE PATTERN # formula for sine wave is: # sine_wave = amp.*sin(2*pi*freq.*time); # amplitude = half the distance between the min and max amp = abs(float(self.range[1]) - float(self.range[0])) / 2 # zero will be halfway between the min and max offset = float(self.range[1]) - amp # np.linspace(-np.pi, np.pi, sampling_rate) --> make a range of x values, as many as sampling rate # this is equivalent to 2*pi*time sine_x = np.linspace(-np.pi, np.pi, self.sampling_rate) self.sine_waves = [] # sine wave 1 sine1 = [(amp * np.sin(t * self.sampling_rate/self.frequency)) + offset for t in sine_x] self.sine_waves.append(np.tile(sine1,self.frequency)) # sine wave 2 (double amp, triple freq) sine2 = [((2*amp) * np.sin(3 * t * self.sampling_rate/self.frequency)) + offset for t in sine_x] self.sine_waves.append(np.tile(sine2,self.frequency)) # default to the first sine wave (only used if sine) self.sine_wave_to_use = 0 self.generate_pattern_func = "generateSine" elif self.pattern == "files": # get file list from settings self.file_list = self.module_settings["files"] # force file list to be list if not already if type(self.file_list) != list: self.file_list = [self.file_list] self.current_file_index = -1 self.current_file = None self.generate_pattern_func = "generateFromFiles" elif self.pattern == "class_labels": self.first_class_sent = False self.inactive_class = 0 self.class_length_sec = self.module_settings["class_sec"] if "class_sec" in self.module_settings else 3 self.inactive_length_sec = self.module_settings["between_class_sec"] if "between_class_sec" in self.module_settings else 6 self.class_samples_per_trial = self.class_length_sec * self.sampling_rate self.inactive_samples_per_trial = self.inactive_length_sec * self.sampling_rate self.current_class = self.inactive_class self.initial_countdown = self.module_settings["start_delay_sec"] if "start_delay_sec" in self.module_settings else 10 self.countdown = (self.initial_countdown+1) * self.sampling_rate self.generate_pattern_func = "generateClassLabel" else: # RANDOM PATTERN self.generate_pattern_func = "generateRandom" if self.class_label_column: self.lastClassLabel = None
