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analysis.py
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analysis.py
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import log
logger = log.get_logger(__name__)
import experiment as expmnt
import channelconverter as chconv
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import lib.detect_peaks as dp
import time
import scipy
from scipy.signal import stft, get_window
from sklearn.linear_model import Ridge
def split(data, sample_rate, ms):
"""
Splits an array, <data>, into equal sized buckets. Each bucket
should contain <ms> seconds of data.
"""
seconds = ms / 1000
bucket_size = int(seconds * sample_rate)
# reshape is way faster than array_split.
buckets = len(data) // bucket_size
leftover = data[buckets*bucket_size:]
data = data[:buckets*bucket_size]
if len(leftover) > 0:
print('WARNING: Lost leftover of size {}/{} when splitting data'
.format(len(leftover), len(data)+len(leftover)))
data = data.reshape(buckets, -1)
return data
def simple_moving_average(data, N):
"""
Convolves a SMA kernel of widow size <N> over the data stream
contained in <data>.
"""
return np.convolve(np.absolute(data), np.ones((N,))/N, mode='valid')
def spectral_analysis(data):
"""
A simple analysis for how to do spectral analysis on a stream,
<data>, in real time. Also illustrates how plots can be updated
live.
"""
sample_rate = 10000
seconds = 35
data_length = seconds * sample_rate
seg_length = 500
noverlap = 0.8
segments = data_length // (seg_length*(1-noverlap))
segments_per_second = segments // seconds
num_buckets = 50
f, t, Zxx = stft(data[:data_length], fs=10000, window='hamming',
nperseg=seg_length, noverlap=seg_length*noverlap)
f = f[:num_buckets]
Zxx = np.abs(Zxx[:num_buckets, :])
# Normal method of doing this -- looks a slight bit different with
# vmin, vmax, cmap.
# fig = plt.pcolormesh(t, f, np.abs(Zxx))
# plt.show()
# Main figure.
fig = plt.figure(1)
# Axes of imshow for raw data.
ax_raw = fig.add_subplot(211)
im_raw, = ax_raw.plot(data[:data_length], color='#EB9904')
# Axes of imshow for spectrum.
ax_spec = fig.add_subplot(212)
im_spec = ax_spec.imshow(Zxx, extent=[0, t[-1], 0, f[-1]],
aspect='auto', origin='lowest', cmap='jet')
pause = False
def on_click(event):
if event.key == 'p':
nonlocal pause
pause ^= True
ticks = 0
def update_data(n):
if pause:
return
nonlocal ticks
ticks += 1
# Update raw plot.
start = int(ticks/10 * sample_rate)
end = int((ticks+30)/10 * sample_rate)
ax_raw.clear()
ax_raw.set_ylim(-100, 100)
ax_raw.plot(data[start:end], color='#EB9904')
# Update spectrogram.
start = int(ticks * segments_per_second*0.1)
end = int((ticks+30) * segments_per_second*0.1)
im_spec.set_extent([ticks/10, (ticks+30)/10, 0, f[-1]])
im_spec.set_data(Zxx[:, start:end])
fig.canvas.mpl_connect('key_press_event', on_click)
ani = animation.FuncAnimation(fig, update_data, interval=100, blit=False, repeat=False)
plt.show()
experiment_fp = 'mea_data/1.h5'
# Only used such that you don't have to keep passing the channel and
# experiment to all these analysis functions, as they all take the
# same types of arguments.
def pass_channel(func):
"""
A simple decorator to use on the examples contained in this
file. This will automatically pass a channel and experiment to
such analysis functions.
"""
def analysis_func():
ch = chconv.MCSChannelConverter.mcsviz_to_channel[21]
experiment = expmnt.Experiment(experiment_fp)
func(ch, experiment)
return analysis_func
@pass_channel
def bucketing_example(ch, experiment):
"""
Example for bucketing data, here plotting the 13th bucket (giving
the 12th second).
"""
ch_data, unit = experiment.get_channel_data(ch)
ch_data = split(ch_data, experiment.sample_rate, 1000)
plt.plot(ch_data[12])
plt.show()
@pass_channel
def plotting_example(ch, experiment):
"""
Examples for plotting.
"""
experiment.plot_channel(ch, start=11.8, end=12.8)
# experiment.plot_channels(start=10, end=11)
@pass_channel
def peak_detection_example(ch, experiment):
"""
Peak detection example.
"""
ch_data, unit = experiment.get_channel_data(ch)
ch_data = split(ch_data, experiment.sample_rate, 1000)
ch_data = ch_data[12]
dp.detect_peaks(ch_data, show=True)
@pass_channel
def peak_detection_summary_example(ch, experiment):
"""
Peak detection example, plotting the amount of peaks of each
bucket.
"""
ch_data, unit = experiment.get_channel_data(ch)
ch_data = split(ch_data, experiment.sample_rate, 1000)
threshold = 15*1e-6
peaks = [len(dp.detect_peaks(x, mph=threshold)) for x in ch_data]
valleys = [len(dp.detect_peaks(x, valley=True, mph=-threshold)) for x in ch_data]
plt.plot(peaks)
plt.plot(valleys)
plt.show()
@pass_channel
def simple_moving_average_example(ch, experiment):
"""
SMA over a given dataset of a window size.
"""
ch_data, unit = experiment.get_channel_data(ch)
window_width = 10
sma = simple_moving_average(ch_data, window_width)
plt.plot(sma)
plt.show()
@pass_channel
def ridge_regression_example(ch, experiment):
"""
Try to do ridge regression over some training data. Does not do
anything at all for now.
"""
ch_data, unit = experiment.get_channel_data(0)
f, t, Zxx = stft(ch_data, fs=10000, window='hamming',
nperseg=500, noverlap=500*0.8)
samples = np.transpose(np.abs(Zxx))
samples_per_sec = round(len(ch_data) / samples.shape[0])
positives = samples[12*100:int(12*100+0.6*100)]
negatives = samples[:60]
print(positives, negatives)
print(np.max(positives), np.max(negatives))
# Create training data.
x_train = np.append(positives, negatives, axis=0)
y_train = [True for x in range(60)] + [False for x in range(60)]
# Fit model.
clf = Ridge(alpha=1.0)
clf.fit(x_train, y_train)
clf.predict(samples[12*100].reshape(-1, 1).T)
@pass_channel
def spectral_analysis_example(ch, experiment):
"""
Spectral analysis.
"""
ch_timestamps, ch_data = experiment.get_channel_plot_data(0)
spectral_analysis(ch_data)
def main():
spectral_analysis_example()
if __name__ == '__main__':
main()