Is it possible to change the line color in a plot when values exceeds a certain y value?
Example:
import numpy as np import matplotlib.pyplot as plt a = np.array([1,2,17,20,16,3,5,4]) plt.plt(a)
This one gives the following:
I want to visualise the values that exceeds y=15. Something like the following figure:
Or something like this(with cycle linestyle):
:
Is it possible?
Answers:
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Method 1
Define a helper function (this a bare-bones one, more bells and whistles can be added). This code is a slight refactoring of this example from the documentation.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
from matplotlib.colors import ListedColormap, BoundaryNorm
def threshold_plot(ax, x, y, threshv, color, overcolor):
"""
Helper function to plot points above a threshold in a different color
Parameters
----------
ax : Axes
Axes to plot to
x, y : array
The x and y values
threshv : float
Plot using overcolor above this value
color : color
The color to use for the lower values
overcolor: color
The color to use for values over threshv
"""
# Create a colormap for red, green and blue and a norm to color
# f' < -0.5 red, f' > 0.5 blue, and the rest green
cmap = ListedColormap([color, overcolor])
norm = BoundaryNorm([np.min(y), threshv, np.max(y)], cmap.N)
# Create a set of line segments so that we can color them individually
# This creates the points as a N x 1 x 2 array so that we can stack points
# together easily to get the segments. The segments array for line collection
# needs to be numlines x points per line x 2 (x and y)
points = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
# Create the line collection object, setting the colormapping parameters.
# Have to set the actual values used for colormapping separately.
lc = LineCollection(segments, cmap=cmap, norm=norm)
lc.set_array(y)
ax.add_collection(lc)
ax.set_xlim(np.min(x), np.max(x))
ax.set_ylim(np.min(y)*1.1, np.max(y)*1.1)
return lc
Example of usage
fig, ax = plt.subplots() x = np.linspace(0, 3 * np.pi, 500) y = np.sin(x) lc = threshold_plot(ax, x, y, .75, 'k', 'r') ax.axhline(.75, color='k', ls='--') lc.set_linewidth(3)
and the output
If you want just the markers to change color, use the same norm and cmap and pass them to scatter as
cmap = ListedColormap([color, overcolor]) norm = BoundaryNorm([np.min(y), threshv, np.max(y)], cmap.N) sc = ax.scatter(x, y, c=c, norm=norm, cmap=cmap)
Method 2
Unfortunately, matplotlib doesn’t have an easy option to change the color of only part of a line. We will have to write the logic ourselves. The trick is to cut the line up into a collection of line segments, then assign a color to each of them, and then plot them.
from matplotlib import pyplot as plt
from matplotlib.collections import LineCollection
import numpy as np
# The x and y data to plot
y = np.array([1,2,17,20,16,3,5,4])
x = np.arange(len(y))
# Threshold above which the line should be red
threshold = 15
# Create line segments: 1--2, 2--17, 17--20, 20--16, 16--3, etc.
segments_x = np.r_[x[0], x[1:-1].repeat(2), x[-1]].reshape(-1, 2)
segments_y = np.r_[y[0], y[1:-1].repeat(2), y[-1]].reshape(-1, 2)
# Assign colors to the line segments
linecolors = ['red' if y_[0] > threshold and y_[1] > threshold else 'blue'
for y_ in segments_y]
# Stamp x,y coordinates of the segments into the proper format for the
# LineCollection
segments = [zip(x_, y_) for x_, y_ in zip(segments_x, segments_y)]
# Create figure
plt.figure()
ax = plt.axes()
# Add a collection of lines
ax.add_collection(LineCollection(segments, colors=linecolors))
# Set x and y limits... sadly this is not done automatically for line
# collections
ax.set_xlim(0, 8)
ax.set_ylim(0, 21)
Your second option is much easier. We first draw the line and then add the markers as a scatterplot on top of it:
from matplotlib import pyplot as plt import numpy as np # The x and y data to plot y = np.array([1,2,17,20,16,3,5,4]) x = np.arange(len(y)) # Threshold above which the markers should be red threshold = 15 # Create figure plt.figure() # Plot the line plt.plot(x, y, color='blue') # Add below threshold markers below_threshold = y < threshold plt.scatter(x[below_threshold], y[below_threshold], color='green') # Add above threshold markers above_threshold = np.logical_not(below_threshold) plt.scatter(x[above_threshold], y[above_threshold], color='red')
Method 3
Basically @RaJa provides the solution, but I think that you can do the same without loading an additional package (pandas), by using masked arrays in numpy:
import numpy as np import matplotlib.pyplot as plt a = np.array([1,2,17,20,16,3,5,4]) # use a masked array to suppress the values that are too low a_masked = np.ma.masked_less_equal(a, 15) # plot the full line plt.plot(a, 'k') # plot only the large values plt.plot(a_masked, 'r', linewidth=2) # add the threshold value (optional) plt.axhline(15, color='k', linestyle='--') plt.show()
Result:
Method 4
I don’t know wether there is a built-in function in matplolib. But you could convert your numpy array into a pandas series and then use the plot function in combination with boolean selection/masking.
import numpy as np import pandas as pd a = np.array([1,2,17,20,16,3,5,4]) aPandas = pd.Series(a) aPandas.plot() aPandas[aPandas > 15].plot(color = 'red')
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