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rolling_windows.py
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rolling_windows.py
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# /usr/bin/env python
# Copyright 2013, 2014 Justis Grant Peters and Sagar Jauhari
# This file is part of BCIpy.
#
# BCIpy is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# BCIpy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BCIpy. If not, see <http://www.gnu.org/licenses/>.
import pandas as pd
import numpy as np
import pylab
import matplotlib.pyplot as plt
import pyeeg
import sys
def plot_rolling_functions(series, window_size=128):
pd.rolling_median(series,window_size).plot(label='median')
pd.rolling_mean(series,window_size).plot(label='mean')
pd.rolling_std(series,window_size).plot(label='std')
pd.rolling_skew(series,window_size).plot(label='skew')
pd.rolling_kurt(series,window_size).plot(label='kurt')
pd.rolling_min(series,window_size).plot(label='min')
pd.rolling_max(series,window_size).plot(label='max')
plt.title('Various rolling window functions, window size %s' % (window_size))
plt.legend()
plt.show()
def compare_window_sizes(series, sizeList):
plots = [pd.rolling_median(series, size).plot() for size in sizeList]
plt.title('Comparison of rolling_median() with different window sizes')
plt.legend(sizeList)
plt.show()
plots = [pd.rolling_mean(series, size).plot() for size in sizeList]
plt.title('Comparison of rolling_mean() with different window sizes')
plt.legend(sizeList)
plt.show()
def downsampled_rolling_median(series, window_size=64, original_freq=512, freq=10):
step = original_freq/freq
return pd.rolling_median(series, window_size)[::step]
def plot_downsampled_rolling_median(series, window_size=64, original_freq=512, freq=10):
median = pd.rolling_median(series, window_size)
step = original_freq/freq
downsampled = pd.rolling_median(series, window_size)[::step]
pd.Series(series).plot()
downsampled.plot()
plt.title('rolling_median, window_size=%s, downsampled to %sHz' % (window_size, freq))
annotations = [
plt.annotate(int(val), (step*index, val))
for index,val in enumerate(downsampled)
if not np.isnan(val)
]
plt.legend(('original', 'rolling_median'))
plt.show()
def as_strided(series, window_size=512, step=64):
assert len(series) >= window_size
shape = ((len(series)-window_size)/step, window_size) # to find number of rows, discard one window_size and divide by step size
strides = (series.strides[0]*step, series.strides[0]) # rearrange strides to move rows by step size and columns by indvidual value size
return pd.DataFrame(
np.lib.stride_tricks.as_strided(series, shape=shape, strides=strides),
index=np.lib.stride_tricks.as_strided(series.index, shape=(shape[0],), strides=(strides[0],))
)
def rolling_power_ratio(series, bands=[0.5,4,7,12,30], sample_rate=512, window_size=512, step=64):
strided = as_strided(series, window_size=window_size, step=step)
print type(strided)
print strided
return pd.DataFrame(
[
pyeeg.bin_power(window, bands, sample_rate)[1]
for timestamp, window in strided.iterrows()
],
index=strided.index
)
def rolling_power(series, bands=[0.5,4,7,12,30], sample_rate=512):
return [ pyeeg.bin_power(window, bands, sample_rate)[0] for window in as_strided(series) ]
def plot_power_ratio(series):
[pd.Series(freq_band).plot() for freq_band in np.transpose(rolling_power_ratio(series))]
#plt.gca().add_patch(Rectangle((1,1),1,1))
plt.show()
def print_help():
print 'Usage: %s [csvfile] [cmd] ... [cmd]' % sys.argv[0]
if __name__ == "__main__":
if len(sys.argv) < 3:
print_help()
sys.exit()
filename = sys.argv[1]
f = pd.read_csv(filename)[' Value']
#ts = pd.Series(d['Value'], index=d['Time'])
shortseries = pd.Series(f[29000:30000]) # consider adding index
mainfuncs = {
'help': lambda: print_help(),
'plotroll': lambda: plot_rolling_functions(shortseries),
'compare_window_sizes': lambda: compare_window_sizes(shortseries, (32,64,128,256,512)),
'plot_downsampled_rolling_median': lambda: plot_downsampled_rolling_median(f[0:9000], window_size=64),
'plot_power_ratio': lambda: plot_power_ratio(f)
}
cmds = sys.argv[2:]
for cmd in cmds:
print 'running command %s' % cmd
mainfuncs.get(cmd, print_help)()
sys.exit()