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@ikuokuo
Last active Jun 16, 2020
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How to use: numpy, matplotlib, pandas, scipy
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# pylint: disable=missing-docstring
import platform
import sys
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
if platform.system() == 'Darwin':
matplotlib.use('TkAgg')
def _interp(xvalues, yvalues, kind='cubic', step=0.01):
from scipy import interpolate
# https://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html
xnew = np.arange(xvalues[0], xvalues[-1], step)
func = interpolate.interp1d(xvalues, yvalues, kind=kind)
return xnew, func(xnew)
def _plot(x, y, fig_name='fig.png', show_secs=0, interp_kind=None,
interp_step=0.01, ax_kwargs=None, plot_kwargs=None, grid=True,
legend_kwargs=None, legend_noline=True):
fig, ax = plt.subplots()
if interp_kind:
x, y = _interp(x, y, kind=interp_kind, step=interp_step)
if plot_kwargs:
ax.plot(x, y, **plot_kwargs)
else:
ax.plot(x, y)
if ax_kwargs:
ax.set(**ax_kwargs)
if grid:
ax.grid()
if legend_kwargs:
leg = ax.legend(**legend_kwargs)
else:
leg = ax.legend()
if legend_noline:
leg.get_frame().set_linewidth(0.0)
if fig_name:
fig.savefig(fig_name)
print('save figure to {}'.format(fig_name))
if show_secs > 0:
plt.show(block=False)
plt.pause(show_secs)
plt.close()
elif show_secs < 0:
plt.show()
def _load(path):
print("Load: {}".format(path))
# id, (data, timestamp)
dtype = {'names': ('data', 'timestamp'), 'formats': ('i4', 'f8')}
return np.loadtxt(path, dtype=dtype, delimiter=",", skiprows=1,
usecols=(1, 2))
def _main():
if len(sys.argv) < 2:
sys.exit('python data_interp.py *.txt')
datas = _load(sys.argv[1])
if datas.size <= 0:
sys.exit('Datas not found: {}'.format(sys.argv[1]))
# https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes
ax_kwargs= {
'xlabel': 'timestamp (s)',
'ylabel': 'data',
# 'ylim': (0, 20),
}
# https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib-axes-axes-plot
plot_kwargs = {
'label': 'data',
'color': '#ed0dd9',
'linestyle': '-',
'solid_joinstyle': 'round',
}
# https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend
legend_kwargs = {
'bbox_to_anchor': (1, 1.1),
'edgecolor': None,
}
_plot(datas['timestamp'], datas['data'], fig_name=None, show_secs=-1,
interp_kind='cubic', interp_step=0.01,
ax_kwargs=ax_kwargs, plot_kwargs=plot_kwargs, grid=True,
legend_kwargs=legend_kwargs, legend_noline=True)
if __name__ == "__main__":
_main()
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# pylint: disable=missing-docstring
import sys
import matplotlib.pyplot as plt
import numpy as np
def _plot(paths, nonzero):
fig, ax = plt.subplots()
for i, p in enumerate(paths):
print("Load: {}".format(p))
# id, (data), timestamp
datas = np.loadtxt(p, dtype=np.int32, delimiter=",", skiprows=1, usecols=(1))
# nonzero
if nonzero:
datas_nonzero = datas[datas.nonzero()]
print(" size_nonzero: {}, size_zero: {}".format(
datas_nonzero.size, datas.size - datas_nonzero.size))
datas = datas_nonzero
else:
print(" size: {}".format(datas.size))
# average
data_avg = np.mean(datas)
# data_avg = np.average(datas)
# standard deviation
# data_std = np.std(datas)
# sample standard deviation
data_std = np.std(datas, ddof=1)
print(" avg: {:.2f}, std: {:.2f}, sum: {}".format(
data_avg, data_std, np.sum(datas)))
ax.plot(range(len(datas)), datas, label=str(i))
ax.legend()
plt.show()
def _parse_args():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
"--nonzero",
action="store_true",
help="show datas without zero value: %(default)s")
args, unknown = parser.parse_known_args()
print("Args")
print(" nonzero: {}".format(args.nonzero))
return args, unknown
def _main():
args, paths = _parse_args()
if not paths:
sys.exit("python data_plot.py *.txt ...")
_plot(paths, nonzero=args.nonzero)
if __name__ == "__main__":
_main()
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# pylint: disable=missing-docstring
import sys
import matplotlib.pyplot as plt
import numpy as np
def _plot(path, xlabel_per_n=None):
print("Load: {}".format(path))
stamps = np.loadtxt(path, dtype=np.float64, delimiter=",", skiprows=1, usecols=(2))
if stamps.size <= 0:
return
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))
fig.subplots_adjust(left=0.1, right=0.9, bottom=0.15, top=0.9, wspace=0.2)
# timestamp diff
stamps_diff = np.diff(stamps)
ax1.plot(range(len(stamps_diff)), stamps_diff, label="timestamp diff (s)")
ax1.set_xlabel("avg: {:.3f} s".format(np.mean(stamps_diff)), labelpad=15)
ax1.legend()
# count per second
stamps_int = np.array(stamps, dtype='int')
stamps_int = stamps_int - stamps_int[0]
import pandas as pd
stamps_s = pd.Series(data=stamps_int)
stamps_s = stamps_s.value_counts(sort=False)
# print(stamps_s)
ax2 = plt.subplot(1, 2, 2)
stamps_s.plot.bar(label="count per second", rot=0)
ax2.set_xlabel("avg: {:.3f}".format(np.mean(stamps_s.values)), labelpad=15)
if xlabel_per_n:
for n, label in enumerate(ax2.xaxis.get_ticklabels()):
if n % xlabel_per_n != 0:
label.set_visible(False)
ax2.legend()
# show
plt.show()
def _main():
if len(sys.argv) < 2:
sys.exit("python stamp_diff.py *.txt")
_plot(sys.argv[1])
if __name__ == "__main__":
_main()
@ikuokuo

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@ikuokuo ikuokuo commented Jun 16, 2020

  1. python data_plot.py data0.txt

data_plot

  1. python data_plot.py data*.txt

data_plot1

  1. python data_interp.py data0.txt

data_interp

  1. python stamp_diff.py data0.txt

stamp_diff

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@ikuokuo ikuokuo commented Jun 16, 2020

  1. data0.txt
id, data, timestamp
0, 55, 1592207702.688805
1, 41, 1592207702.783134
2, 57, 1592207702.883619
3, 59, 1592207702.980597
4, 58, 1592207703.08313
5, 41, 1592207703.183011
6, 52, 1592207703.281802
7, 46, 1592207703.387034
8, 41, 1592207703.482777
9, 72, 1592207703.582849
11, 46, 1592207703.783618
12, 59, 1592207703.883357
13, 56, 1592207703.983407
14, 57, 1592207704.082893
15, 64, 1592207704.183343
17, 45, 1592207704.381702
18, 45, 1592207704.4813
19, 52, 1592207704.582907
20, 42, 1592207704.68303
21, 55, 1592207704.78293
  1. data1.txt
id, data, timestamp
0, 49, 1592207702.688805
1, 60, 1592207702.783134
2, 45, 1592207702.883619
3, 51, 1592207702.980597
4, 49, 1592207703.08313
5, 58, 1592207703.183011
6, 49, 1592207703.281802
7, 54, 1592207703.387034
8, 59, 1592207703.482777
9, 43, 1592207703.582849
11, 46, 1592207703.783618
12, 43, 1592207703.883357
13, 69, 1592207703.983407
14, 54, 1592207704.082893
15, 58, 1592207704.183343
17, 58, 1592207704.381702
18, 49, 1592207704.4813
19, 59, 1592207704.582907
20, 55, 1592207704.68303
21, 59, 1592207704.78293
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