Change color of line if data is less than zero

Question

I am trying to figure out if there is anything built into pyplot that will change the color of my line depending on whether or not the data is negative or positive. For example, if it is negative I’d like the line to be red and if it’s positive I’d like the line to be a different color, say black.

Is there anything in the library that lets me do this? One thing I’ve thought of is to split the data into two sets of positive and negative and plotting them separately but I’m wondering if there is a better way.

Asked By: MCT

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Source

Answer 1

You can conditionally plot data in your axes object, using a where like syntax (if you’re used to something like Pandas).

ax.plot(x[f(x)>=0], f(x)[f(x)>=0], 'g')
ax.plot(x[f(x)<0],  f(x)[f(x)<0],  'r')

Technically, it’s splitting and plotting your data in two sets, but it’s fairly compact and nice.

Answered By: Adam

Answer 2

I would just make two datasets and setting the right masks.
By using that approach i wont have lines between different positive
parts.

import matplotlib.pyplot as plt
import numpy as np

signal = 1.2*np.sin(np.linspace(0, 30, 2000))
pos_signal = signal.copy()
neg_signal = signal.copy()

pos_signal[pos_signal <= 0] = np.nan
neg_signal[neg_signal > 0] = np.nan

#plotting
plt.style.use('fivethirtyeight')
plt.plot(pos_signal, color='r')
plt.plot(neg_signal, color='b')
plt.savefig('pos_neg.png', dpi=200)
plt.show()

Example

Answered By: tillsten

Answer 3

If you use a scatter plot you can give each point a different color:

x = range(1)
x = range(10)
y = [i - 5 for i in x]
c = [i < 0 for i in y]
plt.scatter(x, y, c=c, s=80)

enter image description here

Answered By: Mike

Answer 4

I couldn’t find a clean solution on stackoverflow without vanishing line segments that cross the x-axis.

Following this approach to compute new x values at the crossing point and updating the point arrays

import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')


x = [2, 5]
y = [5,-3]

x1, x2 = x
y1, y2 = y
xf = x1 + -y1 * (x2 - x1)/(y2 - y1)

xn = [2, xf, 5]
yn = [5, 0, -3]

we get a crossing line segment with two parts.

plt.figure(figsize=(7,6))

plt.plot(xn, yn, 'X:')
plt.show()

Vectorizing this approach can be done by finding the crossing line segments, computing the crossing points and updating both point arrays at the appropriate indices for the x-axis (needs to be sorted).

x = np.linspace(-np.pi*2, np.pi*2, 40)
y = np.cos(x)

x1, x2, y1, y2 = np.stack([x[:-1],  x[1:], y[:-1], y[1:]])[:,np.diff(y < 0)]
xf = x1 + -y1 * (x2 - x1) / (y2 - y1)

i = np.searchsorted(x, xf)
x0 = np.insert(x, i, xf)
y0 = np.insert(y, i, 0)

Drawing the updated arrays as a line graph with masked arrays for non positive and non negative y-coordinates.

plt.figure(figsize=(7,6))
plt.plot(np.ma.masked_array(x0, mask=y0 < 0), np.ma.masked_array(y0, mask=y0 < 0), 'o:')
plt.plot(np.ma.masked_array(x0, mask=y0 > 0), np.ma.masked_array(y0, mask=y0 > 0), 'o:')
plt.show()

To draw the original data with colored lines

plt.figure(figsize=(7,6))
plt.plot(np.ma.masked_array(x0, mask=y0 < 0), np.ma.masked_array(y0, mask=y0 < 0), 'g-')
plt.plot(np.ma.masked_array(x0, mask=y0 > 0), np.ma.masked_array(y0, mask=y0 > 0), 'r-')
plt.plot(np.ma.masked_array(x, mask=y < 0), np.ma.masked_array(y, mask=y < 0), 'g.')
plt.plot(np.ma.masked_array(x, mask=y > 0), np.ma.masked_array(y, mask=y > 0), 'r.')
plt.show()

Answered By: Michael Szczesny

Change color of line if data is less than zero

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