I am trying to use different math font sets for two axes in the same figure, with no success. I have searched this issue using google and I have read the matplotlib's official guide on how to use the math font. But I can not find ways to achieve this effect. My complete code is as follows:
import matplotlib.pyplot as plt
import matplotlib as mpl
fig, (ax1, ax2) = plt.subplots(ncols=2)
mpl.rcParams['mathtext.fontset'] = 'cm' # use font "cm" for first axes
ax1.text(0.3, 0.5, r"$xyz$", fontsize=50)
ax1.set_title('before')
ax1.axis('off')
ax1.set_aspect('equal')
mpl.rcParams['mathtext.fontset'] = 'stixsans' # use font "stixsans" for second axes
ax2.text(0.3, 0.5, r"$xyz$", fontsize=50)
ax2.set_title('after')
ax2.axis('off'）
ax2.set_aspect('equal')
plt.show()
The resulting figure shows that both the axes use the "stixsans" font, see picture here.
It seems that mpl.rcParams['mathtext.fontset'] = 'stixsans' in the later part has overruled the previous setting mpl.rcParams['mathtext.fontset'] = 'cm'. Any idea how to prevent this from happening and use "cm" and "stixsans" font for the two axes respectively?
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I am making a scatter plot in matplotlib and need to change the background of the actual plot to black. I know how to change the face color of the plot using:
fig = plt.figure()
fig.patch.set_facecolor('xkcd:mint green')
My issue is that this changes the color of the space around the plot. How to I change the actual background color of the plot?
Use the set_facecolor(color) method of the axes object, which you've created one of the following ways:
You created a figure and axis/es together
fig, ax = plt.subplots(nrows=1, ncols=1)
You created a figure, then axis/es later
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1) # nrows, ncols, index
You used the stateful API (if you're doing anything more than a few lines, and especially if you have multiple plots, the object-oriented methods above make life easier because you can refer to specific figures, plot on certain axes, and customize either)
plt.plot(...)
ax = plt.gca()
Then you can use set_facecolor:
ax.set_facecolor('xkcd:salmon')
ax.set_facecolor((1.0, 0.47, 0.42))
As a refresher for what colors can be:
matplotlib.colors
Matplotlib recognizes the following formats to specify a color:
an RGB or RGBA tuple of float values in [0, 1] (e.g., (0.1, 0.2, 0.5) or (0.1, 0.2, 0.5, 0.3));
a hex RGB or RGBA string (e.g., '#0F0F0F' or '#0F0F0F0F');
a string representation of a float value in [0, 1] inclusive for gray level (e.g., '0.5');
one of {'b', 'g', 'r', 'c', 'm', 'y', 'k', 'w'};
a X11/CSS4 color name;
a name from the xkcd color survey; prefixed with 'xkcd:' (e.g., 'xkcd:sky blue');
one of {'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan'} which are the Tableau Colors from the ‘T10’ categorical palette (which is the default color cycle);
a “CN” color spec, i.e. 'C' followed by a single digit, which is an index into the default property cycle (matplotlib.rcParams['axes.prop_cycle']); the indexing occurs at artist creation time and defaults to black if the cycle does not include color.
All string specifications of color, other than “CN”, are case-insensitive.
One method is to manually set the default for the axis background color within your script (see Customizing matplotlib):
import matplotlib.pyplot as plt
plt.rcParams['axes.facecolor'] = 'black'
This is in contrast to Nick T's method which changes the background color for a specific axes object. Resetting the defaults is useful if you're making multiple different plots with similar styles and don't want to keep changing different axes objects.
Note: The equivalent for
fig = plt.figure()
fig.patch.set_facecolor('black')
from your question is:
plt.rcParams['figure.facecolor'] = 'black'
Something like this? Use the axisbg keyword to subplot:
>>> from matplotlib.figure import Figure
>>> from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
>>> figure = Figure()
>>> canvas = FigureCanvas(figure)
>>> axes = figure.add_subplot(1, 1, 1, axisbg='red')
>>> axes.plot([1,2,3])
[<matplotlib.lines.Line2D object at 0x2827e50>]
>>> canvas.print_figure('red-bg.png')
(Granted, not a scatter plot, and not a black background.)
Simpler answer:
ax = plt.axes()
ax.set_facecolor('silver')
If you already have axes object, just like in Nick T's answer, you can also use
ax.patch.set_facecolor('black')
The easiest thing is probably to provide the color when you create the plot :
fig1 = plt.figure(facecolor=(1, 1, 1))
or
fig1, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, facecolor=(1, 1, 1))
One suggestion in other answers is to use ax.set_axis_bgcolor("red"). This however is deprecated, and doesn't work on MatPlotLib >= v2.0.
There is also the suggestion to use ax.patch.set_facecolor("red") (works on both MatPlotLib v1.5 & v2.2). While this works fine, an even easier solution for v2.0+ is to use
ax.set_facecolor("red")
In addition to the answer of NickT, you can also delete the background frame by setting it to "none" as explain here: https://stackoverflow.com/a/67126649/8669161
import matplotlib.pyplot as plt
plt.rcParams['axes.facecolor'] = 'none'
I think this might be useful for some people:
If you want to change the color of the background that surrounds the figure, you can use this:
fig.patch.set_facecolor('white')
So instead of this:
you get this:
Obviously you can set any color you'd want.
P.S. In case you accidentally don't see any difference between the two plots, try looking at StackOverflow using darkmode.
The color of the axis (x, y, z) in a 3d plot using matplotlib is black by default. How do you change the color of the axis? Or better yet, how do you make them invisible?
%matplotlib inline
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.xaxis.set_visible(False) # doesn't do anything
And there doesn't seem to be a ax.xaxis.set_color function. Any thoughts on how to make the axis invisible or change the color?
You can combine your method with the approach provided here. I am showing an example that affects all three axes. In Jupyter Notebook, using tab completion after ax.w_xaxis.line., you can discover other possible options
ax.w_xaxis.line.set_visible(False)
ax.w_yaxis.line.set_color("red")
ax.w_zaxis.line.set_color("blue")
To change the tick colors, you can use
ax.xaxis._axinfo['tick']['color']='r'
ax.yaxis._axinfo['tick']['color']='g'
ax.zaxis._axinfo['tick']['color']='b'
To hide the ticks
for line in ax.xaxis.get_ticklines():
line.set_visible(False)
I am trying to plot multiple rgb images with matplotlib
the code I am using is:
import numpy as np
import matplotlib.pyplot as plt
for i in range(0, images):
test = np.random.rand(1080, 720,3)
plt.subplot(images,2,i+1)
plt.imshow(test, interpolation='none')
the subplots appear tiny though as thumbnails
How can I make them bigger?
I have seen solutions using
fig, ax = plt.subplots()
syntax before but not with plt.subplot ?
plt.subplots initiates a subplot grid, while plt.subplot adds a subplot. So the difference is whether you want to initiate you plot right away or fill it over time. Since it seems, that you know how many images to plot beforehand, I would also recommend going with subplots.
Also notice, that the way you use plt.subplot you generate empy subplots in between the ones you are actually using, which is another reason they are so small.
import numpy as np
import matplotlib.pyplot as plt
images = 4
fig, axes = plt.subplots(images, 1, # Puts subplots in the axes variable
figsize=(4, 10), # Use figsize to set the size of the whole plot
dpi=200, # Further refine size with dpi setting
tight_layout=True) # Makes enough room between plots for labels
for i, ax in enumerate(axes):
y = np.random.randn(512, 512)
ax.imshow(y)
ax.set_title(str(i), fontweight='bold')
I would like to set different levels of transparency (= alpha) for the edge line and fill of a distribution plot that I created in matplotlib/seaborn. For example:
ax1 = sns.distplot(BSRDI_DF, label="BsrDI", bins=newBins, kde=False,
hist_kws={"edgecolor": (1,0,0,1), "color":(1,0,0,0.25)})
The above approach does not work, unfortunately. Does anybody have any idea how I could accomplish this?
The problem seems to be that seaborn sets an alpha parameter for the histogram. While alpha defaults to None for a usual histogram, such that something like
plt.hist(x, lw=3, edgecolor=(1,0,0,0.75), color=(1,0,0,0.25))
works as expected, seaborn sets this alpha to some given value. This overwrites the alpha that is set in the RGBA tuples.
The solution is to set alpha explicitely to None:
ax = sns.distplot(x, kde=False, hist_kws={"lw":3, "edgecolor": (1,0,0,0.75),
"color":(1,0,0,0.25),"alpha":None})
A complete example:
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
x = np.random.randn(60)
ax = sns.distplot(x, label="BsrDI", bins=np.linspace(-3,3,10), kde=False,
hist_kws={"lw":3, "edgecolor": (1,0,0,0.75),
"color":(1,0,0,0.25),"alpha":None})
plt.show()
EDIT Nevermind, I thought using color instead of facecolor was causing the problem but it seems the output that I got only looked right because the patches were overlapping, giving seemingly darker edges.
After investigating the issue further, it looks like seaborn is hard-setting the alpha level at 0.4, which supersedes the arguments passed to hist_kws=
sns.distplot(x, kde=False, hist_kws={"edgecolor": (1,0,0,1), "lw":5, "facecolor":(0,1,0,0.1), "rwidth":0.8})
While using the same parameters to plt.hist() gives:
plt.hist(x, edgecolor=(1,0,0,1), lw=5, facecolor=(0,1,0,0.1), rwidth=0.8)
Conclusion: if you want different alpha levels for edges and face colors, you'll have to use matplotlib directly, and not seaborn.
I'm trying to create a figure like this one from the seaborn documentation but with the edgecolor of the stripplot determined by the hue. This is my attempt:
import seaborn as sns
df = sns.load_dataset("tips")
ax = sns.stripplot(x="sex", y="tip", hue="day", data=df, jitter=True,
edgecolor=sns.color_palette("hls", 4),
facecolors="none", split=False, alpha=0.7)
But the color palettes for male and female appear to be different. How do I use the same color palette for both categories?
I'm using seaborn 0.6.dev
The edgecolor parameter is just passed straight through to plt.scatter. Currently you're giving it a list of 4 colors. I'm not exactly sure what I would expect it to do in that case (and I am not exactly sure why you end up with what you're seeing here), but I would not have expected it to "work".
The ideal way to have this work would be to have a "hollow circle" marker glyph that colors the edges based on the color (or facecolor) attribute rather than the edges. While it would be nice to have this as an option in core matplotlib, there are some inconsistencies that might make that unworkable. However, it's possible to hack together a custom glyph that will do the trick:
import numpy as np
import matplotlib as mpl
import seaborn as sns
sns.set_style("whitegrid")
df = sns.load_dataset("tips")
pnts = np.linspace(0, np.pi * 2, 24)
circ = np.c_[np.sin(pts) / 2, -np.cos(pts) / 2]
vert = np.r_[circ, circ[::-1] * .7]
open_circle = mpl.path.Path(vert)
sns.stripplot(x="sex", y="tip", hue="day", data=df,
jitter=True, split=False,
palette="hls", marker=open_circle, linewidth=0)
FWIW I should also mention that it's important to be careful when using this approach because the colors become much harder to distinguish. The hls palette exacerbates the problem as the lime green and cyan middle colors end up quite similar. I can imagine situations where this would work nicely, though, for instance a hue variable with two levels represented by gray and a bright color, where you want to emphasize the latter.