I'm trying to control the zorder of different plots across twinx axes. How can I get the blue noisy plots to appear in the background and the orange smoothed plots to appear in the foreground in this plot?
from matplotlib import pyplot as plt
import numpy as np
from scipy.signal import savgol_filter
random = np.random.RandomState(0)
x1 = np.linspace(-10,10,500)**3 + random.normal(0, 100, size=500)
x2 = np.linspace(-10,10,500)**2 + random.normal(0, 100, size=500)
fig,ax1 = plt.subplots()
ax1.plot(x1, zorder=0)
ax1.plot(savgol_filter(x1,99,2), zorder=1)
ax2 = ax1.twinx()
ax2.plot(x2, zorder=0)
ax2.plot(savgol_filter(x2,99,2), zorder=1)
plt.show()
Similar to this thread, though not ideal, this is an approach using twiny along with twinx.
# set up plots
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax3 = ax1.twiny()
ax4 = ax2.twiny()
# background
ax1.plot(x1)
ax2.plot(x2)
# smoothed
ax3.plot(savgol_filter(x1,99,2), c='orange')
ax4.plot(savgol_filter(x2,99,2), c='orange')
# turn off extra ticks and labels
ax3.tick_params(axis='x', which='both', bottom=False, top=False)
ax4.tick_params(axis='x', which='both', bottom=False, top=False)
ax3.set_xticklabels([])
ax4.set_xticklabels([])
# fix zorder
ax1.set_zorder(1)
ax2.set_zorder(2)
ax3.set_zorder(3)
ax4.set_zorder(4)
plt.show()
Output:
Related
I use rcParams in one script to enlarge font size in figure with twin axis:
import numpy as np
import matplotlib.pyplot as plt
# Create some mock data
t = np.arange(0.01, 10.0, 0.01)
data1 = np.exp(t)
data2 = np.sin(2 * np.pi * t)
fig, ax1 = plt.subplots()
plt.rcParams.update({'axes.titlesize': 'large',
'axes.labelsize':'large',
'ytick.labelsize': 'large',
'xtick.labelsize': 'large'})
color = 'tab:red'
ax1.set_xlabel('time (s)')
ax1.set_ylabel('exp', color=color)
ax1.plot(t, data1, color=color)
ax1.tick_params(axis='y', labelcolor=color)
ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis
color = 'tab:blue'
ax2.set_ylabel('sin', color=color) # we already handled the x-label with ax1
ax2.plot(t, data2, color=color)
ax2.tick_params(axis='y', labelcolor=color)
plt.show()
But, plt.rcParams just works for one axis.
In this example, that is the blue ticks labels.
I want to enlarge all fonts size in the figure.
Is there any simple method to achieve it?
Your problem is that you are updating rcParams after you created the left axes (ax1)
move the line plt.rcParams.update(...) before fig, ax1 = plt.subplots()
Here I plot a bar graph and a line graph in the same figure:
There are 2 y-axes, money and increase_rate, each on a different scale.
How can I set the ticks of the two y-axes to be at the same hight?
import numpy as np
import matplotlib.pyplot as plt
time = [2000,2001,2002,2003]
money = [1000,2000,4000,6000]
increase_rate =[2,1,6,12]
fig, ax1 = plt.subplots()
width = 0.75
ax1.set_xlabel("")
ax1.set_ylabel("")
ax1.bar(time, money ,width = width, color = "#9370DB", alpha=0.6)
ax1.tick_params(axis='y')
ax1.spines['right'].set_visible(False)
ax1.spines['left'].set_visible(False)
ax1.spines['top'].set_visible(False)
ax1.spines['bottom'].set_visible(False)
ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis
ax2.set_ylabel("")
ax2.plot(time, increase_rate, color = "#FFFF00", lw = 3)
ax2.tick_params(axis='y')
ax2.spines['right'].set_visible(False)
ax2.spines['left'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax2.grid(color='black', linestyle='dotted', linewidth=0.8, alpha = 0.5)
fig.tight_layout() # otherwise the right y-label is slightly clipped
plt.show()
Use the set_yticks to set the tick positions.
ax1.set_yticks(np.linspace(0, max(money), 5))
ax2.set_yticks(np.linspace(0, max(increase_rate), 5))
In pyplot, you can change the order of different graphs using the zorder option or by changing the order of the plot() commands. However, when you add an alternative axis via ax2 = twinx(), the new axis will always overlay the old axis (as described in the documentation).
Is it possible to change the order of the axis to move the alternative (twinned) y-axis to background?
In the example below, I would like to display the blue line on top of the histogram:
import numpy as np
import matplotlib.pyplot as plt
import random
# Data
x = np.arange(-3.0, 3.01, 0.1)
y = np.power(x,2)
y2 = 1/np.sqrt(2*np.pi) * np.exp(-y/2)
data = [random.gauss(0.0, 1.0) for i in range(1000)]
# Plot figure
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
ax2.hist(data, bins=40, normed=True, color='g',zorder=0)
ax2.plot(x, y2, color='r', linewidth=2, zorder=2)
ax1.plot(x, y, color='b', linewidth=2, zorder=5)
ax1.set_ylabel("Parabola")
ax2.set_ylabel("Normal distribution")
ax1.yaxis.label.set_color('b')
ax2.yaxis.label.set_color('r')
plt.show()
Edit: For some reason, I am unable to upload the image generated by this code. I will try again later.
You can set the zorder of an axes, ax.set_zorder(). One would then need to remove the background of that axes, such that the axes below is still visible.
ax2 = ax1.twinx()
ax1.set_zorder(10)
ax1.patch.set_visible(False)
In pyplot, you can change the order of different graphs using the zorder option or by changing the order of the plot() commands. However, when you add an alternative axis via ax2 = twinx(), the new axis will always overlay the old axis (as described in the documentation).
Is it possible to change the order of the axis to move the alternative (twinned) y-axis to background?
In the example below, I would like to display the blue line on top of the histogram:
import numpy as np
import matplotlib.pyplot as plt
import random
# Data
x = np.arange(-3.0, 3.01, 0.1)
y = np.power(x,2)
y2 = 1/np.sqrt(2*np.pi) * np.exp(-y/2)
data = [random.gauss(0.0, 1.0) for i in range(1000)]
# Plot figure
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
ax2.hist(data, bins=40, normed=True, color='g',zorder=0)
ax2.plot(x, y2, color='r', linewidth=2, zorder=2)
ax1.plot(x, y, color='b', linewidth=2, zorder=5)
ax1.set_ylabel("Parabola")
ax2.set_ylabel("Normal distribution")
ax1.yaxis.label.set_color('b')
ax2.yaxis.label.set_color('r')
plt.show()
Edit: For some reason, I am unable to upload the image generated by this code. I will try again later.
You can set the zorder of an axes, ax.set_zorder(). One would then need to remove the background of that axes, such that the axes below is still visible.
ax2 = ax1.twinx()
ax1.set_zorder(10)
ax1.patch.set_visible(False)
I am working on matplotlib and created some graphs like bar chart, bubble chart and others.
Can some one please explain with an example what is difference between line graph and sparkline graph and how to draw spark line graphs in python using matplotlib ?
for example with the following code
import matplotlib.pyplot as plt
import numpy as np
x=[1,2,3,4,5]
y=[5,7,2,6,2]
plt.plot(x, y)
plt.show()
the line graph generated is the following:
But I couldn't get what is the difference between a line chart and a spark lien chart for the same data. Please help me understand
A sparkline is the same as a line plot but without axes or coordinates. They can be used to show the "shape" of the data in a compact way.
You can cram several line plots in the same figure just by using subplots and changing properties of the resulting Axes for each subplot:
data = np.cumsum(np.random.rand(1000)-0.5)
data = data - np.mean(data)
fig = plt.figure()
ax1 = fig.add_subplot(411) # nrows, ncols, plot_number, top sparkline
ax1.plot(data, 'b-')
ax1.axhline(c='grey', alpha=0.5)
ax2 = fig.add_subplot(412, sharex=ax1)
ax2.plot(data, 'g-')
ax2.axhline(c='grey', alpha=0.5)
ax3 = fig.add_subplot(413, sharex=ax1)
ax3.plot(data, 'y-')
ax3.axhline(c='grey', alpha=0.5)
ax4 = fig.add_subplot(414, sharex=ax1) # bottom sparkline
ax4.plot(data, 'r-')
ax4.axhline(c='grey', alpha=0.5)
for axes in [ax1, ax2, ax3, ax4]: # remove all borders
plt.setp(axes.get_xticklabels(), visible=False)
plt.setp(axes.get_yticklabels(), visible=False)
plt.setp(axes.get_xticklines(), visible=False)
plt.setp(axes.get_yticklines(), visible=False)
plt.setp(axes.spines.values(), visible=False)
# bottom sparkline
plt.setp(ax4.get_xticklabels(), visible=True)
plt.setp(ax4.get_xticklines(), visible=True)
ax4.xaxis.tick_bottom() # but onlyt the lower x ticks not x ticks at the top
plt.tight_layout()
plt.show()
A sparkline graph is just a regular plot with all the axis removed. quite simple to do with matplotlib:
import matplotlib.pyplot as plt
import numpy as np
# create some random data
x = np.cumsum(np.random.rand(1000)-0.5)
# plot it
fig, ax = plt.subplots(1,1,figsize=(10,3))
plt.plot(x, color='k')
plt.plot(len(x)-1, x[-1], color='r', marker='o')
# remove all the axes
for k,v in ax.spines.items():
v.set_visible(False)
ax.set_xticks([])
ax.set_yticks([])
#show it
plt.show()