I'm trying to generate a plot with several axis, each one with their own colorbar (code below). If I use the default colorbar plotting I get too much horizontal spacing between the plot and the colorbar:
If I try to use the make_axes_locatable() method I get this horrible result:
What is going on and how can I fix this?
import numpy as np
from matplotlib import pyplot as plt
import matplotlib.gridspec as gridspec
from mpl_toolkits.axes_grid1 import make_axes_locatable
# Random data to plot
data = np.random.uniform(0., 1., (2, 100))
z = np.random.uniform(0., 10., 100)
# Define figure
fig = plt.figure(figsize=(30, 30))
gs = gridspec.GridSpec(12, 12)
for i in range(2):
ax = plt.subplot(gs[2 * i:2 + (2 * i), 0:8])
SC = plt.scatter(*data, c=z)
# Colorbar 1
cbar = plt.colorbar()
# Colorbar 2
# the_divider = make_axes_locatable(ax)
# color_axis = the_divider.append_axes("right", size="1%", pad=0.)
# cbar = plt.colorbar(SC, cax=color_axis)
cbar.set_label("test", fontsize=15, labelpad=10)
fig.tight_layout()
plt.savefig('test.png', dpi=300, bbox_inches='tight')
Use the pad argument of colorbar to set the padding between the axes and the colorbar. pad is given in units of the fraction of the original axes' size to use as space. Here e.g. pad=0.01 might make sense.
import numpy as np
from matplotlib import pyplot as plt
# Random data to plot
data = np.random.uniform(0., 1., (2, 100))
z = np.random.uniform(0., 10., 100)
# Define figure
fig, axes = plt.subplots(nrows=2, figsize=(30, 30))
for i, ax in enumerate(axes.flat):
sc = ax.scatter(*data, c=z)
cbar = fig.colorbar(sc, ax=ax, pad=0.01)
cbar.set_label("test", fontsize=15, labelpad=10)
fig.tight_layout()
plt.savefig('test.png', dpi=300, bbox_inches='tight')
Related
I use the following code to generate side-by-size images and I need to add colorbar only to the second image in the row. I use the following code for it
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import matplotlib.gridspec as gridspec
def plotting(x):
gs1 = gridspec.GridSpec(1, 2)
gs1.update(wspace=0.005, hspace=0.005)
plt.subplot(gs1[0])
plt.imshow(x)
plt.axis('off')
plt.title('dog')
ax1 = plt.subplot(gs1[1])
imc = plt.imshow(x, cmap='hot', interpolation='nearest')
plt.axis('off')
plt.title('dog')
divider = make_axes_locatable(ax1)
cax = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(imc, cax=cax)
plt.tight_layout()
plt.show()
However it comes out the size of side-by-side images are not equal. I wonder how I could fix this issue?
You can use ImageGrid, which was created exactly for this purpose:
from mpl_toolkits.axes_grid1 import ImageGrid
x = np.random.random(size=(10,10))
fig = plt.figure()
grid = ImageGrid(fig, 111,
nrows_ncols = (1,2),
axes_pad = 0.05,
cbar_location = "right",
cbar_mode="single",
cbar_size="5%",
cbar_pad=0.05
)
grid[0].imshow(x)
grid[0].axis('off')
grid[0].set_title('dog')
imc = grid[1].imshow(x, cmap='hot', interpolation='nearest')
grid[1].axis('off')
grid[1].set_title('dog')
plt.colorbar(imc, cax=grid.cbar_axes[0])
I have the following code:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable, axes_size
arr = np.random.randint(0, 100, (2, 3, 4))
fig, ax = plt.subplots(1, 1)
pax = ax.imshow(arr, vmin=0, vmax=100)
cbar_kws=dict(ticks=(0, 100))
cbar_txt='arb. units'
divider = make_axes_locatable(ax)
cax = divider.append_axes('right', size='5%', pad=0.05)
cbar = ax.figure.colorbar(pax, cax=cax, **dict(cbar_kws))
# cbar = ax.figure.colorbar(plot, ax=ax, **dict(cbar_kws))
if cbar_txt is not None:
only_extremes = 'ticks' in cbar_kws and len(cbar_kws['ticks']) == 2
if only_extremes:
cbar.ax.text(
2.0, 0.5, cbar_txt, fontsize='medium', rotation=90,
va='center', ha='left')
else:
cbar.set_label(cbar_txt)
plt.tight_layout()
plt.show()
This works fine for Matplotlib 2.2.3 where I get a text in the middle of the colorbar (on the right):
But does not work the same way for Matplotlib 3.0.1, where the text gets rendered at the bottom of the colorbar:
Why? Any suggestion for obtaining the same behavior with both versions?
How
Using cbar.ax.text seems to be a workaround for some other problem. The recommended way to set a label to the colorbar is either via the colorbar call itself, or via cbar.set_label("label").
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
arr = np.random.randint(0, 100, (2, 3))
fig, ax = plt.subplots(1, 1)
pax = ax.imshow(arr, vmin=0, vmax=100)
cbar_kws=dict(ticks=(0, 100))
cbar_txt='arb. units'
divider = make_axes_locatable(ax)
cax = divider.append_axes('right', size='5%', pad=0.05)
cbar = ax.figure.colorbar(pax, cax=cax, **dict(cbar_kws))
cbar.set_label(cbar_txt, labelpad=-12)
plt.tight_layout()
plt.show()
The result is the same in matplotlib 2.2.3 and 3.0.1:
To have the label distance independent of the length of the colorbar labels you may label the left side of the colorbar and shift the label even more.
cbar.set_label(cbar_txt, labelpad=-36)
cbar.ax.yaxis.set_label_position("left")
Finally, you may indeed use a text on the axes, but position it in axes coordinates instead of data coordinates,
cbar.ax.text(2, 0.5, cbar_txt, fontsize='medium', rotation=90,
va='center', ha='left', transform=cbar.ax.transAxes)
Why
As to why cbar.ax.text works differently between the versions: The internal units of the colorbar have changed. This shouldn't affect any external application, but makes it easier to apply different locators to colorbars. In fact it has become more consistent. E.g. if the colorbar range is 0 to 100, and you place a text at y=0.5, it'll appear very close to 0.
Why not use the label directly? Edit: didn't see answer below. See for better explanation.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable, axes_size
arr = np.random.randint(0, 100, (2, 3, 4))
fig, ax = plt.subplots(1, 1)
pax = ax.imshow(arr, vmin=0, vmax=100)
cbar_txt='arb. units'
cbar_kws=dict(ticks=(0, 100))
divider = make_axes_locatable(ax)
cax = divider.append_axes('right', size='5%', pad=0.05)
cbar = ax.figure.colorbar(pax, cax=cax, **dict(cbar_kws))
cbar.set_label(cbar_txt, size = 20)
cbar.ax.tick_params(labelsize = 10)
plt.tight_layout()
plt.show()
I want to set discrete colorbar in ImageGrid.
ImageGrid
Here's an example:
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import ImageGrid
import numpy as np
import matplotlib
lon,lat = np.meshgrid(np.arange(-180, 180, 10), np.arange(-85, 90, 10))
data = np.sort(np.random.rand(18, 36),axis=1)
fig = plt.figure()
grid = ImageGrid(fig, 111,
nrows_ncols=(2, 1),
axes_pad=(0.35, 0.35),
label_mode="1",
share_all=True,
cbar_location="right",
cbar_mode="each",
cbar_size="5%",
cbar_pad="6%",
)
# Settings
bounds = [0,0.01,0.04,0.07,0.1,0.13,0.16,0.2,0.25,0.35,0.45,0.6,0.9]
colors = ['#390231','#7F1CAB','#0047FD','#0072FE','#019EFF','#00C4FF','#01EDFF',\
'#00FFFB','#00FFC8','#29F905','#FBDD03','#FA0F00']
# Original colorbar
p = grid[0].pcolormesh(lon,lat,data, vmin=0, vmax=0.9, cmap='jet')
cb = grid.cbar_axes[0].colorbar(p)
# Defined colorbar
cmap = matplotlib.colors.ListedColormap(colors)
norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)
p = grid[1].pcolormesh(lon,lat,data, cmap=cmap, norm=norm)
cb = grid.cbar_axes[1].colorbar(p, ticks=bounds)
grid[0].set_title('jet')
grid[1].set_title('Defined')
plt.show()
This is the result:
As you can see, the location of ticks are wrong.
If ticks are at boundaries of each color block, the second figure will look correct.
Subplots
Then, I tested subplots. It works fine!
import matplotlib.pyplot as plt
import numpy as np
import matplotlib
lon,lat = np.meshgrid(np.arange(-180, 180, 10), np.arange(-85, 90, 10))
data = np.sort(np.random.rand(18, 36),axis=1)
f, (ax1, ax2) = plt.subplots(1, 2,sharey=True)
# Settings
bounds = [0,0.01,0.04,0.07,0.1,0.13,0.16,0.2,0.25,0.35,0.45,0.6,0.9]
colors = ['#390231','#7F1CAB','#0047FD','#0072FE','#019EFF','#00C4FF','#01EDFF',\
'#00FFFB','#00FFC8','#29F905','#FBDD03','#FA0F00']
# Defined colorbar
cmap = matplotlib.colors.ListedColormap(colors)
norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)
# Jet
p = ax1.pcolormesh(lon,lat,data, vmin=0, vmax=0.9, cmap='jet')
f.colorbar(p,ax=ax1)
ax1.set_title('jet')
# Defined
p = ax2.pcolormesh(lon,lat,data, cmap=cmap, norm=norm)
f.colorbar(p,ax=ax2,ticks=bounds)
ax2.set_title('defined')
plt.show()
This is the result:
Single
I tested my script in single figure. It works fine!
import matplotlib.pyplot as plt
import numpy as np
import matplotlib
lon,lat = np.meshgrid(np.arange(-180, 180, 10), np.arange(-85, 90, 10))
data = np.sort(np.random.rand(18, 36),axis=1)
fig = plt.figure()
# Settings
bounds = [0,0.01,0.04,0.07,0.1,0.13,0.16,0.2,0.25,0.35,0.45,0.6,0.9]
colors = ['#390231','#7F1CAB','#0047FD','#0072FE','#019EFF','#00C4FF','#01EDFF',\
'#00FFFB','#00FFC8','#29F905','#FBDD03','#FA0F00']
# Defined colorbar
cmap = matplotlib.colors.ListedColormap(colors)
norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)
# Jet
plt.pcolormesh(lon,lat,data, vmin=0, vmax=0.9, cmap='jet')
plt.colorbar()
plt.show()
# Defined
p = plt.pcolormesh(lon,lat,data, cmap=cmap, norm=norm)
plt.colorbar(p, ticks=bounds)
plt.title('Single fig')
plt.show()
This is the result of single figure of jet and defined:
A workaround would be to set the labels manually.
ticks=np.linspace(bounds[0],bounds[-1], len(bounds))
cb = grid.cbar_axes[1].colorbar(p, ticks=ticks)
cb.ax.set_yticklabels(bounds)
I need to generate a plot with equal aspect in both axis and a colorbar to the right. I've tried setting aspect='auto', aspect=1, and aspect='equal' with no good results. See below for examples and the MWE.
Using aspect='auto' the colorbars are of the correct height but the plots are distorted:
Using aspect=1 or aspect='equal' the plots are square (equal aspect in both axis) but the colorbars are distorted:
In both plots the colorbars are positioned too far to the right for some reason. How can I get a square plot with colorbars of matching heights?
MWE
import numpy as np
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
def col_plot(params):
gs, i, data = params
xarr, yarr, zarr = zip(*data)[0], zip(*data)[1], zip(*data)[2]
xmin, xmax = min(xarr), max(xarr)
ymin, ymax = min(yarr), max(yarr)
#plt.subplot(gs[i], aspect='auto')
plt.subplot(gs[i], aspect=1)
#plt.subplot(gs[i], aspect='equal')
plt.xlim(xmin, xmax)
plt.ylim(xmin, xmax)
plt.xlabel('$x axis$', fontsize=20)
plt.ylabel('$y axis$', fontsize=20)
# Scatter plot.
cm = plt.cm.get_cmap('RdYlBu_r')
SC = plt.scatter(xarr, yarr, marker='o', c=zarr, s=60, lw=0.25, cmap=cm,
zorder=3)
# Colorbar.
ax0 = plt.subplot(gs[i + 1])
cbar = plt.colorbar(SC, cax=ax0)
cbar.set_label('$col bar$', fontsize=21, labelpad=-2)
# Generate data.
data0 = np.random.uniform(0., 1., size=(50, 3))
data1 = np.random.uniform(0., 1., size=(50, 3))
# Create the top-level container
fig = plt.figure(figsize=(14, 25))
gs = gridspec.GridSpec(4, 4, width_ratios=[1, 0.05, 1, 0.05])
# Generate plots.
par_lst = [[gs, 0, data0], [gs, 2, data1]]
for pl_params in par_lst:
col_plot(pl_params)
# Output png file.
fig.tight_layout()
plt.savefig('colorbar_aspect.png', dpi=300)
You can use an AxesDivider to do that. I have modified your code a bit to make use of an AxesDivider.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
def col_plot(data):
xarr, yarr, zarr = zip(*data)[0], zip(*data)[1], zip(*data)[2]
xarr = [2*x for x in xarr]
xmin, xmax = min(xarr), max(xarr)
ymin, ymax = min(yarr), max(yarr)
fig = plt.figure()
ax0 = fig.add_subplot(111, aspect='equal')
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
plt.xlabel('$x axis$', fontsize=20)
plt.ylabel('$y axis$', fontsize=20)
# Scatter plot.
cm = plt.cm.get_cmap('RdYlBu_r')
SC = ax0.scatter(xarr, yarr, marker='o', c=zarr, s=60, lw=0.25, cmap=cm,
zorder=3)
the_divider = make_axes_locatable(ax0)
color_axis = the_divider.append_axes("right", size="5%", pad=0.1)
# Colorbar.
cbar = plt.colorbar(SC, cax=color_axis)
cbar.set_label('$col bar$', fontsize=21, labelpad=-2)
# Generate data.
data0 = np.random.uniform(0., 1., size=(20, 3))
col_plot(data0)
And here is the result (I changed your data so it spans a range of [0, 2] in the x-direction for demonstration purposes):
On Joseph Long's blog there is the following nice solution.
1) Define a colorbar function as:
from mpl_toolkits.axes_grid1 import make_axes_locatable
def colorbar(mappable):
ax = mappable.axes
fig = ax.figure
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
return fig.colorbar(mappable, cax=cax)
2) Call colorbar(thing) when you want to make a colorbar. In your case:
SC = ax0.scatter(xarr, yarr, marker='o', c=zarr, s=60, lw=0.25, cmap=cm,
zorder=3)
colorbar(SC)
3) And you get:
I'd like to create a colorbar legend for a heatmap, such that the labels are in the center of each discrete color. Example borrowed from here:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import ListedColormap
#discrete color scheme
cMap = ListedColormap(['white', 'green', 'blue','red'])
#data
np.random.seed(42)
data = np.random.rand(4, 4)
fig, ax = plt.subplots()
heatmap = ax.pcolor(data, cmap=cMap)
#legend
cbar = plt.colorbar(heatmap)
cbar.ax.set_yticklabels(['0','1','2','>3'])
cbar.set_label('# of contacts', rotation=270)
# put the major ticks at the middle of each cell
ax.set_xticks(np.arange(data.shape[1]) + 0.5, minor=False)
ax.set_yticks(np.arange(data.shape[0]) + 0.5, minor=False)
ax.invert_yaxis()
#labels
column_labels = list('ABCD')
row_labels = list('WXYZ')
ax.set_xticklabels(column_labels, minor=False)
ax.set_yticklabels(row_labels, minor=False)
plt.show()
This generates the following plot:
Ideally I'd like to generate a legend bar which has the four colors and for each color, a label in its center: 0,1,2,>3. How can this be achieved?
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import ListedColormap
#discrete color scheme
cMap = ListedColormap(['white', 'green', 'blue','red'])
#data
np.random.seed(42)
data = np.random.rand(4, 4)
fig, ax = plt.subplots()
heatmap = ax.pcolor(data, cmap=cMap)
#legend
cbar = plt.colorbar(heatmap)
cbar.ax.get_yaxis().set_ticks([])
for j, lab in enumerate(['$0$','$1$','$2$','$>3$']):
cbar.ax.text(.5, (2 * j + 1) / 8.0, lab, ha='center', va='center')
cbar.ax.get_yaxis().labelpad = 15
cbar.ax.set_ylabel('# of contacts', rotation=270)
# put the major ticks at the middle of each cell
ax.set_xticks(np.arange(data.shape[1]) + 0.5, minor=False)
ax.set_yticks(np.arange(data.shape[0]) + 0.5, minor=False)
ax.invert_yaxis()
#labels
column_labels = list('ABCD')
row_labels = list('WXYZ')
ax.set_xticklabels(column_labels, minor=False)
ax.set_yticklabels(row_labels, minor=False)
plt.show()
You were very close. Once you have a reference to the color bar axis, you can do what ever you want to it, including putting text labels in the middle. You might want to play with the formatting to make it more visible.
To add to tacaswell's answer, the colorbar() function has an optional cax input you can use to pass an axis on which the colorbar should be drawn. If you are using that input, you can directly set a label using that axis.
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
fig, ax = plt.subplots()
heatmap = ax.imshow(data)
divider = make_axes_locatable(ax)
cax = divider.append_axes('bottom', size='10%', pad=0.6)
cb = fig.colorbar(heatmap, cax=cax, orientation='horizontal')
cax.set_xlabel('data label') # cax == cb.ax
This will make you add label and change colorbar's tick and label size:
clb=plt.colorbar()
clb.ax.tick_params(labelsize=8)
clb.ax.set_title('Your Label',fontsize=8)
This can be also used if you have sublots:
plt.tight_layout()
plt.subplots_adjust(bottom=0.05)
cax = plt.axes([0.1, 0, 0.8, 0.01]) #Left,bottom, length, width
clb=plt.colorbar(cax=cax,orientation="horizontal")
clb.ax.tick_params(labelsize=8)
clb.ax.set_title('Your Label',fontsize=8)