I have a subplot inside an existing axes frame, and in the subplot there are some lines or filled contourf plot. What I want to do is to plot some lines in the existing bigger frame, and these lines must be on top of the lines/contourf in the subfigure. The following is a small example. Basically I want the blue and/or green lines to be on top of the red line. It seems setting the zorder has no effect for lines belonging to different sub-axes.
import matplotlib.pyplot as plt
fig = plt.figure()
plt.plot([0,2], color='blue', zorder=300)
ax0 = gca()
ax = fig.add_axes([0.3,0.3,0.3,0.3], zorder=0, axisbg='none')
ax.plot([0,1],[1,0], linewidth=40, color='red', zorder=-100)
ax0.plot([0.55,0.55],[0,2], linewidth=20, color='green', zorder=200)
I kind of find a solution, which may not be perfect but works for my needs. The trick is to set the big axes frame to a high zorder, and set its background color to transparent, so that the small axes frame in the background can be seen, and in this way the lines in the main frame will naturally be on top of the lines in the small frame.
import matplotlib.pyplot as plt
fig = plt.figure()
plt.plot([0,2], color='blue')
ax0 = gca()
ax0.set_zorder(100)
ax0.set_axis_bgcolor('none')
ax = fig.add_axes([0.3,0.3,0.3,0.3], zorder=0, axisbg='none')
ax.plot([0,1],[1,0], linewidth=40, color='red')
ax0.plot([0.55,0.55],[0,2], linewidth=20, color='green')
Related
I'm trying to fit the sizes of 2x2 subplots so they line up correctly.
I want to create the following subplot/axes structure:
ax1 has a set aspect ratio in data coordinates (e.g. data is not scaled when aspect=1)
ax2 and ax3 have a set (box) aspect ratio in display/figure coordinates (e.g. they appear as squares when box_aspect=1)
ax1 and ax3 share the x axis limits and have the same width
ax1 and ax2 share the y axis limits and have the same height
ax4 fits in the last box so that it has the width of ax2 and the height of ax3
I need this for the following project (it's an animation):
Imagine having ax1 as the world space in which a point is moving. (the coordinates should not be warped)
The adjacent plots show the x(ax3) and y(ax2) coordinates of the point over time. (I want to be able to set the aspect ratio of these subplots)
The last axes/plot is not related to any of the coordinates but should fit in nicely.
I created a minimal example and added descriptive text to the picture:
import matplotlib.pyplot as plt
fig = plt.figure()
ax1 = plt.subplot(221, anchor='SE', aspect=1, xlim=(0,1), ylim=(0,2))
ax2 = plt.subplot(222, anchor='SW', box_aspect=1, sharey=ax1, xlim=(0,3))
ax3 = plt.subplot(223, anchor='NE', box_aspect=1, sharex=ax1, ylim=(0,4))
ax4 = plt.subplot(224, anchor='NW', xlim=(0,5), ylim=(0,6))
plt.show()
Notes:
the anchors are set so there is not so much whitespace between the subplots
the x and y limits are arbitrarily chosen and should not matter
I encountered a similar issue as I was plotting GeoJson data in matplotlib.
I solved it by creating a one subplot figure setting aspect=1
# import the required libraries
import geopandas as gpd
import matplotlib.pyplot as plt
# Define the file path
fp = r"\Your-file-full-path\file.geojson"
# Read the GeoJSON file similarly as Shapefile
mygeojson = gpd.read_file(fp)
# Create a figure with one subplot
fig = plt.figure()
# Plot the grid with column-to-plot (as you set cmap, scheme, and aspect hyper-params)
mygeojson.plot(aspect=1, column = 'geojson-column-to-plot', cmap = 'gist_rainbow', scheme = 'equalinterval', k=9, linewidth=0, legend=True);
# Add title
plt.title("Your GeoDataFrame object title");
# Remove white space around the figure
plt.tight_layout()
I have a figure with 11 scatter plots as subplots. I would like the legend (same across all 11 subplots) to replace the 12th subplot. Is there a way to put the legend there and have it be the same size as the subplots?
Matplotlib scatter plot of 11 subplots
Sort of a manual approach, but here it is:
You can "remove" an axis using ax.clear() and ax.set_axis_off(). Then you can create patches with specific colors and labels, and create a legend in the desired ax based on them.
Try this:
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import numpy as np
# Create figure with subplots
fig, axes = plt.subplots(figsize=(16, 16), ncols=4, nrows=3, sharex=True, sharey=True)
# Plot some random data
for row in axes:
for ax in row:
ax.scatter(np.random.random(5), np.random.random(5), color='green')
ax.scatter(np.random.random(2), np.random.random(2), color='red')
ax.scatter(np.random.random(3), np.random.random(3), color='orange')
ax.set_title('some title')
# Clear bottom-right ax
bottom_right_ax = axes[-1][-1]
bottom_right_ax.clear() # clears the random data I plotted previously
bottom_right_ax.set_axis_off() # removes the XY axes
# Manually create legend handles (patches)
red_patch = mpatches.Patch(color='red', label='Red data')
green_patch = mpatches.Patch(color='green', label='Green data')
orange_patch = mpatches.Patch(color='orange', label='Orange data')
# Add legend to bottom-right ax
bottom_right_ax.legend(handles=[red_patch, green_patch, orange_patch], loc='center')
# Show figure
plt.show()
Output:
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 have two different sets of data with a common index, and I want to represent the first one as a barplot and the second one as a lineplot in the same graph. My current approach is similar to the following.
ax = pt.a.plot(alpha = .75, kind = 'bar')
ax2 = ax.twinx()
ax2.plot(ax.get_xticks(), pt.b.values, alpha = .75, color = 'r')
And the result is similar to this
This image is really nice and almost right. My only problem is that ax.twinx() seems to create a new canvas on top of the previous one, and the white lines are clearly seen on top of the barplot.
Is there any way to plot this without including the white lines?
You can use twinx() method along with seaborn to create a seperate y-axis, one for the lineplot and the other for the barplot. To control the style of the plot (default style of seaborn is darkgrid), you can use set_style method and specify the preferred theme. If you set style=None it resets to white background without the gridlines. You can also try whitegrid. If you want to further customize the gridlines, you can do it on the axis level using the ax2.grid(False).
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
matplotlib.rc_file_defaults()
ax1 = sns.set_style(style=None, rc=None )
fig, ax1 = plt.subplots(figsize=(12,6))
sns.lineplot(data = df['y_var_1'], marker='o', sort = False, ax=ax1)
ax2 = ax1.twinx()
sns.barplot(data = df, x='x_var', y='y_var_2', alpha=0.5, ax=ax2)
You have to remove grid lines of the second axis. Add to the code ax2.grid(False). However y-ticks of the second axis will be not align to y-ticks of the first y-axis, like here:
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.plot(pd.Series(np.random.uniform(0,1,size=10)), color='g')
ax2 = ax1.twinx()
ax2.plot(pd.Series(np.random.uniform(0,17,size=10)), color='r')
ax2.grid(False)
plt.show()
Define data
x = np.linspace(0,2*np.pi,100)
y = 2*np.sin(x)
Plot
fig = plt.figure()
ax = plt.axes()
fig.add_subplot(ax)
ax.plot(x,y)
Add second axis
newax = plt.axes(axisbg='none')
Gives me ValueError: Unknown element o, even though it does the same thing as what I am about to describe. I can also see that this works (no error) to do the same thing:
newax = plt.axes()
fig.add_subplot(newax)
newax.set_axis_bgcolor('none')
However, it turns the background color of the original figure "gray" (or whatever the figure background is)? I don't understand, as I thought this would make newax transparent except for the axes and box around the figure. Even if I switch the order, same thing:
plt.close('all')
fig = plt.figure()
newax = plt.axes()
fig.add_subplot(newax)
newax.set_axis_bgcolor('none')
ax = plt.axes()
fig.add_subplot(ax)
ax.plot(x,y)
This is surprising because I thought the background of one would be overlaid on the other, but in either case it is the newax background that appears to be visible (or at least this is the color I see).
What is going on here?
You're not actually adding a new axes.
Matplotlib is detecting that there's already a plot in that position and returning it instead of a new axes object.
(Check it for yourself. ax and newax will be the same object.)
There's probably not a reason why you'd want to, but here's how you'd do it.
(Also, don't call newax = plt.axes() and then call fig.add_subplot(newax) You're doing the same thing twice.)
Edit: With newer (>=1.2, I think?) versions of matplotlib, you can accomplish the same thing as the example below by using the label kwarg to fig.add_subplot. E.g. newax = fig.add_subplot(111, label='some unique string')
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
# If you just call `plt.axes()` or equivalently `fig.add_subplot()` matplotlib
# will just return `ax` again. It _won't_ create a new axis unless we
# call fig.add_axes() or reset fig._seen
newax = fig.add_axes(ax.get_position(), frameon=False)
ax.plot(range(10), 'r-')
newax.plot(range(50), 'g-')
newax.axis('equal')
plt.show()
Of course, this looks awful, but it's what you're asking for...
I'm guessing from your earlier questions that you just want to add a second x-axis? If so, this is a completely different thing.
If you want the y-axes linked, then do something like this (somewhat verbose...):
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
newax = ax.twiny()
# Make some room at the bottom
fig.subplots_adjust(bottom=0.20)
# I'm guessing you want them both on the bottom...
newax.set_frame_on(True)
newax.patch.set_visible(False)
newax.xaxis.set_ticks_position('bottom')
newax.xaxis.set_label_position('bottom')
newax.spines['bottom'].set_position(('outward', 40))
ax.plot(range(10), 'r-')
newax.plot(range(21), 'g-')
ax.set_xlabel('Red Thing')
newax.set_xlabel('Green Thing')
plt.show()
If you want to have a hidden, unlinked y-axis, and an entirely new x-axis, then you'd do something like this:
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
newax = fig.add_axes(ax.get_position())
newax.patch.set_visible(False)
newax.yaxis.set_visible(False)
for spinename, spine in newax.spines.iteritems():
if spinename != 'bottom':
spine.set_visible(False)
newax.spines['bottom'].set_position(('outward', 25))
ax.plot(range(10), 'r-')
x = np.linspace(0, 6*np.pi)
newax.plot(x, 0.001 * np.cos(x), 'g-')
plt.show()
Note that the y-axis values for anything plotted on newax are never shown.
If you wanted, you could even take this one step further, and have independent x and y axes (I'm not quite sure what the point of it would be, but it looks neat...):
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2, right=0.85)
newax = fig.add_axes(ax.get_position())
newax.patch.set_visible(False)
newax.yaxis.set_label_position('right')
newax.yaxis.set_ticks_position('right')
newax.spines['bottom'].set_position(('outward', 35))
ax.plot(range(10), 'r-')
ax.set_xlabel('Red X-axis', color='red')
ax.set_ylabel('Red Y-axis', color='red')
x = np.linspace(0, 6*np.pi)
newax.plot(x, 0.001 * np.cos(x), 'g-')
newax.set_xlabel('Green X-axis', color='green')
newax.set_ylabel('Green Y-axis', color='green')
plt.show()
You can also just add an extra spine at the bottom of the plot. Sometimes this is easier, especially if you don't want ticks or numerical things along it. Not to plug one of my own answers too much, but there's an example of that here: How do I plot multiple X or Y axes in matplotlib?
As one last thing, be sure to look at the parasite axes examples if you want to have the different x and y axes linked through a specific transformation.