Source code for sensormotion.plot

Matplotlib plotting functions.

Convenience wrappers around common matplotlib pyplot plot calls. This module
simplifies the creation of filter frequency response curves and general
signal plots over time.

from __future__ import print_function, division

import matplotlib.pyplot as plt
import numpy as np
import sensormotion.signal

from scipy.signal import freqz

[docs]def plot_filter_response( frequency, sample_rate, filter_type, filter_order=2, show_grid=True, fig_size=(10, 5), ): """Plot filter frequency response. Generate a plot showing the frequency response curve of a filter with the specified parameters. Parameters ---------- frequency : int or tuple of ints The cutoff frequency for the filter. If `filter_type` is set as 'bandpass' then this needs to be a tuple of integers representing the lower and upper bound frequencies. For example, for a bandpass filter with range of 2Hz and 10Hz, you would pass in the tuple (2, 10). For filter types with a single cutoff frequency then a single integer should be used. sample_rate : float Sampling rate of the signal. filter_type : {'lowpass', 'highpass', 'bandpass', 'bandstop'} Type of filter to build. filter_order: int, optional Order of the filter. show_grid : bool, optional Toggle to show grid lines on the plot. fig_size : tuple, optional Tuple containing the width and height of the resulting figure. """ b, a = sensormotion.signal.build_filter( frequency, sample_rate, filter_type, filter_order ) # Plot the frequency response w, h = freqz(b, a, worN=8000) f, axarr = plt.subplots(figsize=fig_size) axarr.plot(0.5 * sample_rate * w / np.pi, np.abs(h), "b") axarr.set_xlim(0, 0.5 * sample_rate) axarr.set_xlabel("Frequency (Hz)") axarr.grid(show_grid) # Add lines and markers at the cutoff frequency/frequencies if filter_type == "bandpass": for i in range(len(frequency)): axarr.axvline(frequency[i], color="k") axarr.plot(frequency[i], 0.5 * np.sqrt(2), "ko") else: axarr.axvline(frequency, color="k") axarr.plot(frequency, 0.5 * np.sqrt(2), "ko") plt.suptitle("Filter Frequency Response", size=16)
[docs]def plot_signal( time, signal, title="", xlab="", ylab="", line_width=1, alpha=1, color="k", subplots=False, show_grid=True, fig_size=(10, 5), ): """Plot signals over time. Convenience wrapper around pyplot to quickly create plots of signals over time. This is useful if you want to avoid remembering the syntax for matplotlib. Parameters ---------- time : ndarray Time vector of the signal (X-axis) signal : ndarray or list of dicts If plotting a single line, you can pass in a single array. If you want to plot multiple lines (either on the same plot, or as subplots) you should pass in a list of dictionaries. Each dictionary represents a separate line and contains options for that line. The dictionary can hold the follow keys: 'data' (required), 'label', 'color', 'alpha', 'line_width'. For example, a 2 line plot can be created like this: >>> plot.plot_signal(time_array, [{'data': line1_data, 'label': 'line 1', 'color': 'b'}, {'data': line2_data, 'label': 'second line', 'line_Width': 4}]) title : str, optional Title of the plot. xlab : str, optional Label for the x axis. ylab : str, optional Label for the y axis. line_width : int or float, optional Width of the plot lines. alpha : int or float, optional Alpha of the plot lines. color : str, optional Colour of the plot lines. Receives a string following the matplotlib colors API: subplots : bool, optional If multiple lines are passed in, should they be displayed on the same plot (False)? Or should each be displayed in separate subplots (True)? show_grid : bool, optional Toggle to display grid lines on the plot. fig_size : tuple, optional Set the figure size of the resulting plot. """ if type(signal) == list: # Multiple lines to be plotted if subplots: f, axarr = plt.subplots(len(signal), 1, figsize=fig_size) else: f, axarr = plt.subplots(figsize=fig_size) for i, line in enumerate(signal): # Iterate through each plot line cur_data = line["data"] # Get options for the current line try: cur_label = line["label"] except KeyError: print("Warning: Label missing for signal") cur_label = "" try: cur_color = line["color"] except KeyError: cur_color = color try: cur_alpha = line["alpha"] except KeyError: cur_alpha = alpha try: cur_linewidth = line["line_width"] except KeyError: cur_linewidth = line_width if subplots: # Show lines in separate plots, in the same figure axarr[i].plot( time, cur_data, label=cur_label, linewidth=cur_linewidth, alpha=cur_alpha, color=cur_color, ) axarr[i].set_xlim(min(time), max(time)) axarr[i].set_xlabel(xlab) axarr[i].set_ylabel(ylab) axarr[i].grid(show_grid) axarr[i].legend() f.subplots_adjust(hspace=0.5) else: # Show all lines on the same plot axarr.plot( time, cur_data, label=cur_label, linewidth=cur_linewidth, alpha=cur_alpha, color=cur_color, ) axarr.set_xlim(min(time), max(time)) axarr.set_xlabel(xlab) axarr.set_ylabel(ylab) axarr.grid(show_grid) axarr.legend() else: # Single line plot f, axarr = plt.subplots(figsize=fig_size) axarr.plot(time, signal, linewidth=line_width, alpha=alpha, color=color) axarr.set_xlim(min(time), max(time)) axarr.set_xlabel(xlab) axarr.set_ylabel(ylab) axarr.grid(show_grid) plt.suptitle(title, size=16)