I'm making a diagram using matplotlib, and it has plt.Circles and plt.axvlines to represent different shapes. I need a legend to describe these shapes, but the problem is the legend marker (the image part), changes size depending on the input, which looks awful. How do I set the size to a constant?
fig = plt.figure(figsize=(6.4, 6), dpi=200, frameon=False)
ax = fig.gca()
# 3 Circles, they produce different sized legend markers
ax.add_patch(plt.Circle((0,0), radius=1, alpha=0.9, zorder=0, label="Circle"))
ax.add_patch(plt.Circle((-1,0), radius=0.05, color="y", label="Point on Circle"))
ax.add_patch(plt.Circle((1, 0), radius=0.05, color="k", label="Opposite Point on Circle"))
# A vertical line which produces a huge legend marker
ax.axvline(0, ymin=0.5-0.313, ymax=0.5+0.313, linewidth=12, zorder=1, c="g", label="Vertical Line")
ax.legend(loc=2)
ax.set_xlim(-2,1.2) # The figsize and limits are meant to preserve the circle's shape
ax.set_ylim(-1.5, 1.5)
fig.show()
I've seen solutions including legend.legendHandles[0]._size or various assortments of that, and it doesn't seem to change the size regardless of the value I set
The legend markers for the circles are different in size because the first circle has no edgecolor, while the two other ones have an edgecolor set via color. You may instead set the facecolor of the circle. Alternatively, you can set the linewidth of all 3 circles equal.
The legend marker for the line is so huge because it simply copies the attribute from the line in the plot. If you want to use a different linewidth, you can update it via the respective legend handler.
import matplotlib.pyplot as plt
from matplotlib.legend_handler import HandlerLine2D
def update_prop(handle, orig):
handle.update_from(orig)
handle.set_linewidth(2)
fig, ax = plt.subplots(figsize=(6.4, 6), dpi=200, frameon=False)
# 3 Circles, set the facecolor instead of edge- and face-color
ax.add_patch(plt.Circle((0,0), radius=1, alpha=0.9, zorder=0, label="Circle"))
ax.add_patch(plt.Circle((-1,0), radius=0.05, facecolor="y", label="Point on Circle"))
ax.add_patch(plt.Circle((1, 0), radius=0.05, facecolor="k", label="Opposite Point on Circle"))
# Line, update the linewidth via
ax.axvline(0, ymin=0.5-0.313, ymax=0.5+0.313, linewidth=12, zorder=1, c="g", label="Vertical Line")
ax.legend(loc=2, handler_map={plt.Line2D:HandlerLine2D(update_func=update_prop)})
ax.set_xlim(-2,1.2)
ax.set_ylim(-1.5, 1.5)
plt.show()
Related
So, I have to make a bunch of contourf plots for different days that need to share colorbar ranges. That was easily made but sometimes it happens that the maximum value for a given date is above the colorbar range and that changes the look of the plot in a way I dont need. The way I want it to treat it when that happens is to add the extend triangle above the "original colorbar". It's clear in the attached picture.
I need the code to run things automatically, right now I only feed the data and the color bar range and it outputs the images, so the fitting of the colorbar in the code needs to be automatic, I can't add padding in numbers because the figure sizes changes depending on the area that is being asked to be plotted.
The reason why I need this behavior is because eventually I would want to make a .gif and I can't have the colorbar to move in that short video. I need for the triangle to be added, when needed, to the top (and below) without messing with the "main" colorbar.
Thanks!
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize, BoundaryNorm
from matplotlib import cm
###############
## Finds the appropriate option for variable "extend" in fig colorbar
def find_extend(vmin, vmax, datamin, datamax):
#extend{'neither', 'both', 'min', 'max'}
if datamin >= vmin:
if datamax <= vmax:
extend="neither"
else:
extend="max"
else:
if datamax <= vmax:
extend="min"
else:
extend="both"
return extend
###########
vmin=0
vmax=30
nlevels=8
colormap=cm.get_cmap("rainbow")
### Creating data
z_1=30*abs(np.random.rand(5, 5))
z_2=37*abs(np.random.rand(5, 5))
data={1:z_1, 2:z_2}
x=range(5)
y=range(5)
## Plot
for day in [1, 2]:
fig = plt.figure(figsize=(4,4))
## Normally figsize=get_figsize(bounds) and bounds is retrieved from gdf.total_bounds
## The function creates the figure size based on the x/y ratio of the bounds
ax = fig.add_subplot(1, 1, 1)
norm=BoundaryNorm(np.linspace(vmin, vmax, nlevels+1), ncolors=colormap.N)
z=data[day]
cs=ax.contourf(x, y, z, cmap=cmap, norm=norm, vmin=vmin, vmax=vmax)
extend=find_extend(vmin, vmax, np.nanmin(z), np.nanmax(z))
fig.colorbar(cm.ScalarMappable(norm=norm, cmap=cmap), ax=ax, extend=extend)
plt.close(fig)
You can do something like this: putting a triangle on top of the colorbar manually:
fig, ax = plt.subplots()
pc = ax.pcolormesh(np.random.randn(20, 20))
cb = fig.colorbar(pc)
trixy = np.array([[0, 1], [1, 1], [0.5, 1.05]])
p = mpatches.Polygon(trixy, transform=cb.ax.transAxes,
clip_on=False, edgecolor='k', linewidth=0.7,
facecolor='m', zorder=4, snap=True)
cb.ax.add_patch(p)
plt.show()
I'd like my plot to have a background text to be stretched from one corner (say lower left) to the opposite corner. The x and y dimensions are not isometric and it's not a square plot, so a fixed angle of 45 degrees will not work.
So far I have the text starting in the correct corner. How can the text be rotated and stretched so it spans the entire plot?
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
# plot command not shown
ax.text(ax.get_xlim()[0], ax.get_ylim()[0] , 'PRELIMINARY' , rotation=45 )
To position something with respect to the subplot (the ax), it helps to work in axes coordinates. These go from 0,0 in the lower left to 1,1 in the top right. Putting the text at 0.5,0.5 would set it nicely centered.
To calculate the angle, one could divide the subplot's height in pixels by its width, then take the arc tangent, and convert from radians to degrees.
The optimal size for the text is harder to calculate. One would need to render it, measure it, change the font size and render it again. Or just manually try a few sizes until it looks OK.
Note that when the window size gets changed interactively, the text will stay nicely in the center, but the rotation will get a bit off.
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.text(0.5, 0.5, 'PRELIMINARY', transform=ax.transAxes, size=50,
rotation=np.degrees(np.arctan(ax.get_window_extent().height / ax.get_window_extent().width)),
ha='center', va='center')
plt.show()
I am trying to use matplotlib with gridspec to create a subplot such that the axes are arranged to look similar to the figure below; the figure was taken from this unrelated question.
My attempt at recreating this axes arrangement is below. Specifically, my problem is that the axes are not properly aligned. For example, the axis object for the blue histogram is taller than the axis object for the image with various shades of green; the orange histogram seems to properly align in terms of width, but I attribute this to luck. How can I properly align these axes? Unlike the original figure, I would like to add/pad extra empty space between axes such that there borders do not intersect; the slice notation in the code below does this by adding a blank row/column. (In the interest of not making this post longer than it has to be, I did not make the figures "pretty" by playing with axis ticks and the like.)
Unlike the original picture, the axes are not perfectly aligned. Is there a way to do this without using constrained layout? By this, I mean some derivative of fig, ax = plt.subplots(constrained_layout=True)?
The MWE code to recreate my figure is below; note that there was no difference between ax.imshow(...) and ax.matshow(...).
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
## initialize figure and axes
fig = plt.figure()
gs = fig.add_gridspec(6, 6, hspace=0.2, wspace=0.2)
ax_bottom = fig.add_subplot(gs[4:, 2:])
ax_left = fig.add_subplot(gs[:4, :2])
ax_big = fig.add_subplot(gs[:4, 2:])
## generate data
x = np.random.normal(loc=50, scale=10, size=100)
y = np.random.normal(loc=500, scale=50, size=100)
## get singular histograms
x_counts, x_edges = np.histogram(x, bins=np.arange(0, 101, 5))
y_counts, y_edges = np.histogram(y, bins=np.arange(0, 1001, 25))
x_mids = (x_edges[1:] + x_edges[:-1]) / 2
y_mids = (y_edges[1:] + y_edges[:-1]) / 2
## get meshed histogram
sample = np.array([x, y]).T
xy_counts, xy_edges = np.histogramdd(sample, bins=(x_edges, y_edges))
## subplot histogram of x
ax_bottom.bar(x_mids, x_counts,
width=np.diff(x_edges),
color='darkorange')
ax_bottom.set_xlim([x_edges[0], x_edges[-1]])
ax_bottom.set_ylim([0, np.max(x_counts)])
## subplot histogram of y
ax_left.bar(y_mids, y_counts,
width=np.diff(y_edges),
color='steelblue')
ax_left.set_xlim([y_edges[0], y_edges[-1]])
ax_left.set_ylim([0, np.max(y_counts)])
## subplot histogram of xy-mesh
ax_big.imshow(xy_counts,
cmap='Greens',
norm=Normalize(vmin=np.min(xy_counts), vmax=np.max(xy_counts)),
interpolation='nearest',
origin='upper')
plt.show()
plt.close(fig)
EDIT:
One can initialize the axes by explicitly setting width_ratios and height_ratios per row/column; this is shown below. This doesn't affect the output, but maybe I'm using it incorrectly?
## initialize figure and axes
fig = plt.figure()
gs = gridspec.GridSpec(ncols=6, nrows=6, figure=fig, width_ratios=[1]*6, height_ratios=[1]*6)
ax_bottom = fig.add_subplot(gs[4:, 2:])
ax_left = fig.add_subplot(gs[:4, :2])
ax_big = fig.add_subplot(gs[:4, 2:])
The problem is with imshow, which resizes the axes automatically to maintain a square pixel aspect.
You can prevent this by calling:
ax_big.imshow(..., aspect='auto')
I created a pie chart using matplotlib and I'd like to change the default colors to more softer colors, such as the hex RGB or RGBA string colors. I have the below script so far:
colors = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99']
explode = ((0.05,)*(len(annotation_df.index)))
fig1, ax1 = plt.subplots()
ax1.pie(annotation_df['count'], labels=annotation_df['annotation'], autopct='%1.1f%%', startangle=90, pctdistance=0.85, explode=explode,colors=colors) #colors=colors,
# draw circle
centre_circle = plt.Circle((0, 0), 0.70, fc='white')
fig = plt.gcf()
fig.gca().add_artist(centre_circle)
# Equal aspect ratio ensures that pie is drawn as a circle
ax1.axis('equal')
plt.tight_layout()
plt.show()
The problem is I need the colors to be set automatically, and I don't want specifically write the colors, as written above in the script.
Anyone knows how to do it?
You may define a color cycler to contain the colors you want to use.
import matplotlib.pyplot as plt
plt.rcParams['axes.prop_cycle'] = plt.cycler('color',
['#ff9999', '#66b3ff', '#99ff99', '#ffcc99'])
fig1, ax1 = plt.subplots()
ax1.pie([1,2,3], labels=list("ABC"), autopct='%1.1f%%')
ax1.axis('equal')
plt.tight_layout()
plt.show()
If you have less wedges than colors in the cycler only the those colors needed are used. If you have more wedges than colors in the cycler, they would be repeated. You can put as many colors as you like into the color cycler.
I am trying to plot multiple images in subplots and either eliminate the space between subplots (horizontal and vertical) or control it. I tried to use the suggestion in How to Use GridSpec.... I also tried here but they are not using subplots(): space between subplots
I am able to eliminate the horizontal space but not the vertical space with what I am doing in the code below. Please do not mark as duplicate as I have tried the other posts and they do not do what I want. My code is shown below. Maybe there is another keyword argument that I need in the gridspec_kw dictionary?
I want to use plt.subplots() not plt.subplot() for this. In case it matters, the images are not square they are rectangular. I also tried adding f.tight_layout(h_pad=0,w_pad=0) before plt.show() but it did not change anything.
def plot_image_array_with_angles(img_array,correct_angles,predict_angles,
fontsize=10,figsize=(8,8)):
'''
Imports:
import matplotlib.gridspec as gridspec
import numpy as np
import matplotlib.pyplot as plt
'''
num_images = len(img_array)
grid = int(np.sqrt(num_images)) # will only show all images if square
#f, axarr = plt.subplots(grid,grid,figsize=figsize)
f, axarr = plt.subplots(grid,grid,figsize=figsize,
gridspec_kw={'wspace':0,'hspace':0})
im = 0
for row in range(grid):
for col in range(grid):
axarr[row,col].imshow(img_array[im])
title = 'cor = ' + str(correct_angles[im]) + ' pred = ' + str(predict_angles[im])
axarr[row,col].set_title(title,fontsize=fontsize)
axarr[row,col].axis('off') # turns off all ticks
#axarr[row,col].set_aspect('equal')
im += 1
plt.show()
return
The aspect ratio of an imshow plot is automatically set such that pixels in the image are squared. This setting is stronger than any of the subplots_adjust or gridspec settings for spacing. Or in other words you cannot directly control the spacing between subplots if those subplots have their aspect set to "equal".
First obvious solution is to set the image aspect to automatic ax.set_aspect("auto"). This solves the problem of subplot spacing, but distorts the images.
The other option is to adjust the figure margins and the figure size such that the spacing between the subplots is as desired.
Let's say figh and figw are the figure height and width in inch, and s the subplot width in inch. The margins are bottom, top, left and right (relative to figure size) and the spacings hspace in vertical and wspace in horizontal direction (relative to subplot size). The number of rows is denoted n and the number of columns m. The aspect is the ratio between subplot (image) height and width (aspect = image height / image width).
Then the dimensions can be set via
fig, axes = plt.subplots(nrows=n, ncols=m, figsize=(figwidth, figheight))
plt.subplots_adjust(top=top, bottom=bottom, left=left, right=right,
wspace=wspace, hspace=hspace)
The respective values can be calculated according to:
Or, if the margins are the same:
An example:
import matplotlib.pyplot as plt
image = plt.imread("https://i.stack.imgur.com/9qe6z.png")
aspect = image.shape[0]/float(image.shape[1])
print aspect
n = 2 # number of rows
m = 4 # numberof columns
bottom = 0.1; left=0.05
top=1.-bottom; right = 1.-left
fisasp = (1-bottom-(1-top))/float( 1-left-(1-right) )
#widthspace, relative to subplot size
wspace=0.15 # set to zero for no spacing
hspace=wspace/float(aspect)
#fix the figure height
figheight= 3 # inch
figwidth = (m + (m-1)*wspace)/float((n+(n-1)*hspace)*aspect)*figheight*fisasp
fig, axes = plt.subplots(nrows=n, ncols=m, figsize=(figwidth, figheight))
plt.subplots_adjust(top=top, bottom=bottom, left=left, right=right,
wspace=wspace, hspace=hspace)
for ax in axes.flatten():
ax.imshow(image)
ax.set_title("title",fontsize=10)
ax.axis('off')
plt.show()