How can I add a colorbar scale to the 2nd & 3rd subplots, such that it is inline with my legends in the 1st and 4th subplots? Or, another way to say the question: how can I add a colorbar scale without changing the alignment/justification of the 2nd & 3rd subplots?
There are good examples available on setting colorbar locations (e.g., here on stackoverflow and in the matplotlib docs), but I still haven't been able to solve this.
Below is a reproducible example. The real data are more complicated, and this is part of a loop to produce many figures, so the "extra" stuff about setting axis limits and subplot aspect ratios is needed and will change with different datasets.
Using Python 3.8.
Reproducible example without colorbar
## Specify axes limits, tick intervals, and aspect ratio
xl, yl, xytick, ar = [-40000,120000], [-30000,10000], 20000, 0.8
## Global plot layout stuff
fig = plt.figure(figsize=(10, 7.5), constrained_layout=True)
gs = fig.add_gridspec(4, 1)
ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[1, 0], sharex = ax1, sharey = ax1)
ax3 = fig.add_subplot(gs[2, 0], sharex = ax1)
ax4 = fig.add_subplot(gs[3, 0], sharex = ax1, sharey = ax3)
fig.execute_constrained_layout()
fig.suptitle('Suptitle')
## First Plot
ax1.plot([-30000, 500], [-2000, -21000], c='red', label='A')
ax1.plot([80000, 110000], [-9000, 800], c='blue', label='B')
ax1.set_title('ax1', style='italic');
ax1.set_xlabel('x');
ax1.set_ylabel('beta');
ax1.set_xlim(xl)
ax1.set_ylim(yl)
ax1.xaxis.set_major_locator(ticker.MultipleLocator(xytick))
ax1.yaxis.set_major_locator(ticker.MultipleLocator(xytick))
ax1.legend(handles=leg, bbox_to_anchor=(1.05, 1), loc='upper left')
ax1.set_aspect(aspect=ar)
## Dummy data for plots 2/3/4
x = [-15000, -2000, 0, 5000, 6000, 11000, 18000, 21000, 25000, 36000, 62000]
beta = [1000, 200, -800, 100, 1000, -2000, -5000, -5000, -15000, -21000, -1500]
y = [0.01, 0.2, 1.3, 0.35, 0.88, 2.2, 2.5, 1.25, 3.4, 4.1, 2.1]
## Second Plot
vals = ax2.scatter(x, beta, c=y, norm=mcolors.LogNorm(), cmap='rainbow')
ax2.set_title('ax2', style='italic');
ax2.set_xlabel('x');
ax2.set_ylabel('beta');
ax2.set_aspect(aspect=ar)
## Attempt to add colorbar
#cbar = fig.colorbar(vals, ax=ax2, format = '%1.2g', location='right', aspect=25)
#cbar.ax.set_ylabel('y')
#cbar.ax.yaxis.set_label_position('left')
#cbar_range = [min(y), max(y)]
#ticklabels = cbar.ax.get_ymajorticklabels()
#cbarticks = list(cbar.get_ticks())
#cbar.set_ticks(cbar_range + cbarticks)
## Third Plot
ax3.scatter(x, y, c=y, norm=mcolors.LogNorm(), cmap='rainbow')
ax3.set_title('ax3', style='italic');
ax3.set_xlabel('x');
ax3.set_ylabel('y');
ax3.yaxis.set_major_formatter(FormatStrFormatter('%1.2g'))
## Fourth Plot
ax4.scatter(x, y, c='black', label='Dots')
ax4.set_title('ax4', style='italic');
ax4.set_xlabel('x');
ax4.set_ylabel('y');
ax4.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
## Clean-up, set aspect ratios
figW, figH = ax1.get_figure().get_size_inches()
_, _, w, h = ax1.get_position().bounds
disp_ratio = (figH * h) / (figW * w)
data_ratio = sub(*ax3.get_ylim()) / sub(*ax3.get_xlim())
ax3.set_aspect(aspect=disp_ratio / data_ratio )
ax4.set_aspect(aspect=disp_ratio / data_ratio)
## Clean-up, turn axis ticks back on after messing with cbar
#ax1.tick_params(axis='both', which='both', labelbottom='on')
#ax2.tick_params(axis='both', which='both', labelbottom='on')
#ax3.tick_params(axis='both', which='both', labelbottom='on')
Result when trying colorbar, note misalignment of second plot
Suggest you simplify your code and make sure it all works; for instance I have no idea what sub does.
A partial solution to your problem could be panchor=False, which is a bit of an obscure kwarg, but...
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
## Specify axes limits, tick intervals, and aspect ratio
ar = 1.2
## Global plot layout stuff
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 4), constrained_layout=True, sharex=True, sharey=True)
## First Plot
ax1.plot([-20_000, 20_000], [-20_000, 20_000] )
ax1.set_aspect(aspect=ar)
## Dummy data for plots 2/3/4
x = [-15000, -2000, 0, 5000, 6000, 11000, 18000, 21000, 25000, 36000, 62000]
beta = [1000, 200, -800, 100, 1000, -2000, -5000, -5000, -15000, -21000, -1500]
y = [0.01, 0.2, 1.3, 0.35, 0.88, 2.2, 2.5, 1.25, 3.4, 4.1, 2.1]
## Second Plot
vals = ax2.scatter(x, beta, c=y, norm=mcolors.LogNorm(), cmap='rainbow')
ax2.set_aspect(aspect=ar)
cbar = fig.colorbar(vals, ax=ax2, format = '%1.2g', location='right',
aspect=25, panchor=False)
plt.show()
Depending on the size of the figure, this could comically place the colorbar far to the right. The problem here is the aspect ratio of your plots, which makes the actual axes more narrow than the figure. But the colorbar doesn't really know about that, and places itself on the outside of the space allocated for the axes.
If this is displeasing, then you can also specify an inset axes for the colorbar.
cbax = ax2.inset_axes([1.05, 0.2, 0.05, 0.6], transform=ax2.transAxes)
cbar = fig.colorbar(vals, cax=cbax, format = '%1.2g', orientation='vertical')
Using inset_axes() solves this, as suggested in the other answer, but the parameters relative to the transform were not explained in the example, but I was able to figure it out with some research.
The parameters in inset_axes are [x-corner, y-corner, width, height] and the transform is like a local reference. So, using [1,0,0.5,0.75] means: x = 100% or end of parent ax; y = 0% or bottom of parent ax; width = 50% of parent ax; and height = 75% of parent ax.
Here I wanted the colorbar to be the same height as the parent ax (ax2 and ax3), very thin, and offset a little bit to be more in line with the other legends. Using cbax = ax2.inset_axes([1.1, 0, 0.03, 1], transform=ax2.transAxes) achieves this.
This code works for any aspect ratio ar.
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.colors as mcolors
from operator import sub
%matplotlib inline
plt.style.use('seaborn-whitegrid')
## Specify axes limits, tick intervals, and aspect ratio
xl, yl, ar = [-40000,120000], [-30000,10000], .5
## Global plot layout stuff
fig = plt.figure(figsize=(10, 7.5), constrained_layout=True)
gs = fig.add_gridspec(4, 1)
ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[1, 0], sharex = ax1, sharey = ax1)
ax3 = fig.add_subplot(gs[2, 0], sharex = ax1)
ax4 = fig.add_subplot(gs[3, 0], sharex = ax1, sharey = ax3)
fig.execute_constrained_layout()
fig.suptitle('Suptitle')
## First Plot
ax1.plot([-30000, 500], [-2000, -21000], c='red', label='A')
ax1.plot([80000, 110000], [-9000, 800], c='blue', label='B')
ax1.set_title('ax1', style='italic');
ax1.set_xlim(xl)
ax1.set_ylim(yl)
ax1.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
ax1.set_aspect(aspect=ar)
## Dummy data for plots 2/3/4
x = [-15000, -2000, 0, 5000, 6000, 11000, 18000, 21000, 25000, 36000, 62000]
beta = [1000, 200, -800, 100, 1000, -2000, -5000, -5000, -15000, -21000, -1500]
y = [0.01, 0.2, 1.3, 0.35, 0.88, 2.2, 2.5, 1.25, 3.4, 4.1, 2.1]
## Second Plot
vals = ax2.scatter(x, beta, c=y, norm=mcolors.LogNorm(), cmap='rainbow')
ax2.set_title('ax2', style='italic');
ax2.set_aspect(aspect=ar)
cbax = ax2.inset_axes([1.1, 0, 0.03, 1], transform=ax2.transAxes)
cbar2 = fig.colorbar(vals, cax=cbax, format = '%1.2g', orientation='vertical')
## Third Plot
ax3.scatter(x, y, c=y, norm=mcolors.LogNorm(), cmap='rainbow')
ax3.set_title('ax3', style='italic');
cbax = ax3.inset_axes([1.1, 0, 0.03, 1], transform=ax3.transAxes)
cbar3 = fig.colorbar(vals, cax=cbax, format = '%1.2g', orientation='vertical')
## Fourth Plot
ax4.scatter(x, y, c='black', label='Dots')
ax4.set_title('ax4', style='italic');
ax4.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
## Clean-up, set aspect ratios
figW, figH = ax1.get_figure().get_size_inches()
_, _, w, h = ax1.get_position().bounds
disp_ratio = (figH * h) / (figW * w)
data_ratio = sub(*ax3.get_ylim()) / sub(*ax3.get_xlim())
ax3.set_aspect(aspect=disp_ratio / data_ratio )
ax4.set_aspect(aspect=disp_ratio / data_ratio)
## Colorbars
cbar2.ax.set_ylabel('y')
cbar2.ax.yaxis.set_label_position('left')
cbar3.ax.set_ylabel('y')
cbar3.ax.yaxis.set_label_position('left')
Result with aspect ratio = 0.5 for top 2 plots
Result with aspect ratio = 2 for top 2 plots
I am applying this strategy to place legend outside plot. The main difference here is that there are ax1 and ax2 twin axes.
The x value in bbox_to_anchor is set to 0.89 in the following MWE.
As can be seen, the legend box does not display the entire string labels for each color:
MWE:
import matplotlib.pyplot as plt
import numpy as np
suptitle_label = "rrrrrrrr # ttttt yyyyyyy. uuuuuuuuuuuuuuuuuu\n$[$Xx$_{2}$Yy$_{7}]^{-}$ + $[$XxYy$_{2}$(cccc)$_{2}]^{+}$ JjYy model"
# Plotting
fig, ax1 = plt.subplots()
ax1.set_xlabel('Time')
ax1.set_ylabel('y1label')
new_time = np.linspace(1, 8, 100)
j_data = [np.linspace(1, 4, 100), np.linspace(1, 5, 100), np.linspace(1, 6, 100), np.linspace(1, 7, 100)]
sorted_new_LABELS_fmt = ['$[$XxYy$_{2}$(cc)$_{2}]^{+}$', '$[$Xx$_{2}$Yy$_{7}]^{-}$', '$[$XxYy$_{4}]^{-}$', '$[$Xx$_{2}$Yy$_{5}$(cc)$_{2}]^{+}$']
sorted_new_LABELS_colors = ['green', 'red', 'blue', 'orange']
for j,k,c in zip(j_data, sorted_new_LABELS_fmt, sorted_new_LABELS_colors):
ax1.plot(new_time, j, label='%s' % k, color='%s' %c)
All_e_chunks_n = np.linspace(-850, -860, 100)
ax2 = ax1.twinx()
ax2.set_ylabel('y2label')
ax2.plot(new_time, All_e_chunks_n, 'grey', alpha=0.6, linewidth=2.5, label='y2')
# Shrink cccrent axis
box = ax1.get_position()
ax1.set_position([box.x0, box.y0, box.width * 0.9, box.height])
# Put the legend:
fig.legend(loc='center left', bbox_to_anchor=(0.89, 0.5))
fig.suptitle(suptitle_label, fontsize=15)
fig.savefig('mwe.pdf', bbox_inches='tight')
Decreasing this x value and commenting out thebbox_inches='tight' part, yields the following:
For bbox_to_anchor=(0.85, 0.5), this is the result:
For bbox_to_anchor=(0.80, 0.5), this is the result:
For bbox_to_anchor=(0.7, 0.5), this is the result:
I'm trying to create a plot with two subplots (one row, two columns), in which a vertical distplot and a vertical barplot (both from seaborn) share the Y axis. The result should look somewhat like an asymmetric violin plot.
The data for the bar plot is of this form:
In[8]: barplot_data[0:5]
Out[8]:
[{'time': 0, 'val': 171.19374169863295},
{'time': 50, 'val': 2313.8459788903383},
{'time': 100, 'val': 1518.687964071397},
{'time': 150, 'val': 1355.8373488876694},
{'time': 200, 'val': 1558.7682098705088}]
I.e., for every time step (in steps of 50), I know the height of the bar. The data for the dist plot is of the form:
In[9]: distplot_data[0:5]
Out[9]: [605, 477, 51, 337, 332]
I.e., a series of time points of which I'd like the distribution to be drawn.
Here's how I create the bar plot in the right subplot:
barplot_df = pd.DataFrame(barplot_data)
fig, axes = plt.subplots(1, 2, sharex=False, sharey=True, squeeze=False)
left_ax = axes[0][0]
right_ax = axes[0][1]
sns.barplot(y='time', x='val',
data=barplot_df,
orient='h',
ax = right_ax)
The result is pretty much what I want on the right side:
Similarly, I can put the dist plot on the left side:
fig, axes = plt.subplots(1, 2, sharex=False, sharey=True, squeeze=False)
left_ax = axes[0][0]
right_ax = axes[0][1]
sns.distplot(distplot_data, ax=left_ax, vertical=True)
This also works. I think it's kind of strange that the direction of the Y axis is reversed, but whatever:
However, now I'm just trying to plot them both into the same figure and it wreaks havoc on the dist plot:
fig, axes = plt.subplots(1, 2, sharex=False, sharey=True, squeeze=False)
left_ax = axes[0][0]
right_ax = axes[0][1]
sns.barplot(y='time', x='val',
data=barplot_df,
orient='h',
ax = right_ax)
sns.distplot(distplot_data, ax=left_ax, vertical=True)
I can only imagine that this is because of the axis of the distplot somehow being distorted or something? Does someone know what's going on here?
I have a pyplot code.
Since I want to group multiple bars, I am trying to write text in the graph using plt.annotate.
However, as you can see in the picture, the word 'Something' in left bottom gets cropped. Does anyone know How I can fix this?
Here is my code
#!/usr/bin/python
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import operator as o
import numpy as np
n_groups = 5
means_men = (20, 35, 30, 35, 27)
std_men = (2, 3, 4, 1, 2)
means_women = (25, 32, 34, 20, 25)
std_women = (3, 5, 2, 3, 3)
fig, ax = plt.subplots()
index = np.arange(n_groups)
bar_width = 0.35
opacity = 0.4
error_config = {'ecolor': '0.3'}
rects1 = plt.bar(index, means_men, bar_width, alpha=opacity, color='b', yerr=std_men, error_kw=error_config, label='Men')
rects2 = plt.bar(index + bar_width, means_women, bar_width,
alpha=opacity,
color='r',
yerr=std_women,
error_kw=error_config,
label='Women')
#plt.xlabel('Group')
plt.ylabel('Scores')
plt.title('Scores by group and gender')
plt.annotate('Something', (0,0), (50,-40), xycoords = 'axes fraction', textcoords='offset points', va='top');
plt.annotate('Something', (0,0), (200,-20), xycoords = 'axes fraction', textcoords='offset points', va='top');
plt.xticks(index + bar_width, ('A', 'B', 'C', 'D', 'E'))
plt.legend()
plt.savefig('barchart_3.png')
For some reason, matplotlib sometimes clips too aggressively. If you add bbox_inches='tight' to save fig this should include the figure correctly,
plt.savefig('barchart_3.png', bbox_inches='tight')
More generally, you can adjust your main figure with something like,
plt.subplots_adjust(bottom=0.1)