def main():
if len(sys.argv) == 3:
database_filepath, model_filepath = sys.argv[1:]
print('Loading data...\n DATABASE: {}'.format(database_filepath))
X, Y, category_names = load_data(database_filepath)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)
print('Building model...')
model = build_model()
print('Training model...')
fs = 200. # Hz
high_fq = 50.0 # Hz
low_fq = 5.0 # Hz
low_fq_width = 1.0 # Hz
n_epochs = 3
n_points = 10000
noise_level = 0.4
low_sig = np.array([
simulate_pac(n_points=n_points, fs=fs, high_fq=high_fq, low_fq=low_fq,
low_fq_width=low_fq_width, noise_level=noise_level,
random_state=i) for i in range(n_epochs)
])
X = MultipleArray(low_sig, None)
model.fit(X_train, Y_train)
print('Evaluating model...')
evaluate_model(model, X_test, Y_test, category_names)
print('Saving model...\n MODEL: {}'.format(model_filepath))
save_model(model, model_filepath)
print('Trained model saved!')
else:
print('Please provide the filepath of the disaster messages database '\
'as the first argument and the filepath of the pickle file to '\
'save the model to as the second argument. \n\nExample: python '\
'train_classifier.py ../data/DisasterResponse.db classifier.pkl')
from pactools import Comodulogram, REFERENCES
from pactools import simulate_pac
fs = 200. # Hz
high_fq = 50.0 # Hz
low_fq = 5.0 # Hz
low_fq_width = 1.0 # Hz
n_points = 10000
noise_level = 0.4
t_plot = 2.0 # sec
signal = simulate_pac(n_points=n_points,
fs=fs,
high_fq=high_fq,
low_fq=low_fq,
low_fq_width=low_fq_width,
noise_level=noise_level,
random_state=0)
low_fq_range = np.linspace(1, 10, 50)
methods = [
'ozkurt', 'canolty', 'tort', 'penny', 'vanwijk', 'duprelatour', 'colgin',
'sigl', 'bispectrum'
]
n_lines = 3
n_columns = int(np.ceil(len(methods) / float(n_lines)))
fig, axs = plt.subplots(n_lines,
n_columns,
figsize=(4 * n_columns, 3 * n_lines))
###############################################################################
# Let's first create an artificial signal with PAC.
fs = 200. # Hz
high_fq = 50.0 # Hz
low_fq = 5.0 # Hz
low_fq_width = 1.0 # Hz
n_epochs = 3
n_points = 10000
noise_level = 0.4
low_sig = np.array([
simulate_pac(n_points=n_points, fs=fs, high_fq=high_fq, low_fq=low_fq,
low_fq_width=low_fq_width, noise_level=noise_level,
random_state=i) for i in range(n_epochs)
])
###############################################################################
# Let's define the model with a scikit-learn's pipeline.
#
# In a pipeline, the output of each step is given as input to the next one.
# Here #we start with `ExtractDriver`, which extracs the driver with a bandpass
# #filter, and removes it from #the modeled signal with a highpass filter. Then
# #we follow with `AddDriverDelay`, which adds a delay between the driver and
# the #modeled signal. Finally, we define the DAR model with `DARSklearn`.
model = Pipeline(steps=[
('driver', ExtractDriver(fs=fs, low_fq=4., max_low_fq=7.,
low_fq_width=low_fq_width, random_state=0)),
####################################################################################################
n_seconds = 20
fs = 200. # Hz
n_points = int(n_seconds * fs)
f_beta = (14, 28)
high_fq = 80.0 # Hz; carrier frequency
low_fq = 24.0 # Hz; driver frequency
low_fq_width = 2.0 # Hz
noise_level = 0.25
# Simulate beta-gamma pac
sig_pac = simulate_pac(n_points=n_points, fs=fs, high_fq=high_fq, low_fq=low_fq,
low_fq_width=low_fq_width, noise_level=noise_level)
# Simulate 10 Hz spiking which couples to about 60 Hz
spikes = sim_oscillation(n_seconds, fs, 10, cycle='gaussian', std=0.005)
noise = normalize_variance(pink_noise(n_points, slope=1.), variance=.5)
sig_spurious_pac = spikes + noise
# Simulate a sine wave that is the driver frequency
sig_low_fq = sim_oscillation(n_seconds, fs, low_fq)
# Add the sine wave to pink noise to make a control, no pac signal
sig_no_pac = sig_low_fq + noise
####################################################################################################
# Check comodulogram for PAC
# ~~~~~~~~~~~~~~~~~~~~~~~~~~
