TimeSeries
MCGpy deals with two types of time-series datasets: 1) a single-channel signal measured, and 2) multi-channel signals. This page provides simple examples showing how to plot each type by matplotlib.
Single channel data plot
>>> from mcgpy.timeseries import TimeSeries
>>> import numpy as np
>>> source = np.random.random(1024)
>>> data = TimeSeries(source, t0=0, sample_rate=1024)
>>> from matplotlib import pyplot as plt
>>> fig, ax = plt.subplots(1, figsize=(12, 4))
>>> ax.plot(data.times.value, data)
>>> ax.grid(True)
>>> plt.show()
Multi channel dataset plot
>>> from mcgpy.timeseries import TimeSeriesArray
>>> import numpy as np
>>> source = np.random.random((3,64))
>>> positions = np.random.random((3,3))
>>> directions = np.vander(np.linspace(0,0,3),3)
>>> dataset = TimeSeriesArray(source=source, positions=positions, directions=directions, t0=0, sample_rate=1024)
>>> from matplotlib import pyplot as plt
>>> fig, ax = plt.subplots(1, figsize=(12, 4))
>>> ax.plot(dataset.times.value, dataset.T)
>>> ax.grid(True)
>>> plt.show()
