import matplotlib.pyplot as plt from matplotlib.collections import LineCollection from matplotlib import colors as mcolors
import numpy as np
# In order to efficiently plot many lines in a single set of axes, # Matplotlib has the ability to add the lines all at once. Here is a # simple example showing how it is done.
x = np.arange(100) # Here are many sets of y to plot vs x ys = x[:50, np.newaxis] + x[np.newaxis, :]
# Mask some values to test masked array support: segs = np.ma.masked_where((segs > 50) & (segs < 60), segs)
# We need to set the plot limits. fig, ax = plt.subplots() ax.set_xlim(x.min(), x.max()) ax.set_ylim(ys.min(), ys.max())
# colors is sequence of rgba tuples # linestyle is a string or dash tuple. Legal string values are # solid|dashed|dashdot|dotted. The dash tuple is (offset, onoffseq) # where onoffseq is an even length tuple of on and off ink in points. # If linestyle is omitted, 'solid' is used # See :class:`matplotlib.collections.LineCollection` for more information colors = [mcolors.to_rgba(c) for c in plt.rcParams['axes.prop_cycle'].by_key()['color']]
N = 50 x = np.arange(N) # Here are many sets of y to plot vs x ys = [x + i for i in x]
# We need to set the plot limits, they will not autoscale fig, ax = plt.subplots() ax.set_xlim(np.min(x), np.max(x)) ax.set_ylim(np.min(ys), np.max(ys))
# colors is sequence of rgba tuples # linestyle is a string or dash tuple. Legal string values are # solid|dashed|dashdot|dotted. The dash tuple is (offset, onoffseq) # where onoffseq is an even length tuple of on and off ink in points. # If linestyle is omitted, 'solid' is used # See :class:`matplotlib.collections.LineCollection` for more information
# Make a sequence of x,y pairs line_segments = LineCollection([np.column_stack([x, y]) for y in ys], linewidths=(0.5, 1, 1.5, 2), linestyles='solid') line_segments.set_array(x) ax.add_collection(line_segments) axcb = fig.colorbar(line_segments) axcb.set_label('Line Number') ax.set_title('Line Collection with mapped colors') plt.sci(line_segments) # This allows interactive changing of the colormap. plt.show()