Related
This creates the graph that I'm talking about using matplotlib:
import matplotlib.pyplot as plt
import numpy as np
xmin, xmax, ymin, ymax = -9, 9, -9, 9
fig, ax = plt.subplots(figsize=(20, 20))
ax.set(xlim=(xmin - 1, xmax + 1), ylim=(ymin - 1, ymax + 1), aspect='equal')
ax.spines['bottom'].set(position="zero", linewidth=2.5)
ax.spines['left'].set(position="zero", linewidth=2.5)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.text(10.15, 0, "x", fontdict=font, va="center")
ax.text(0, 10.15, "y", fontdict=font, ha="center")
x_ticks = np.arange(xmin, xmax)
y_ticks = np.arange(ymin, ymax)
ax.set_xticks(x_ticks[x_ticks != x_ticks])
ax.set_yticks(y_ticks[y_ticks != y_ticks])
ax.set_xticks(np.arange(xmin, xmax+1), minor=True)
ax.set_yticks(np.arange(ymin, ymax+1), minor=True)
ax.grid(which='both', color='grey', linewidth=1, linestyle='-', alpha=0.25)
plt.show()
Output: 2d cartesian plane as below
Is it possible to get a similar result with plotly?
This is how it can be done in Plotly:
import plotly.graph_objects as go
axis_range = [-9,9]
fig = go.Figure()
fig.update_xaxes(range=axis_range,title = 'y', tickmode = 'linear',
showticklabels = False, side='top',gridcolor="rgb(224,224,224)")
fig.update_yaxes(range=axis_range,title = 'x', tickmode = 'linear',
showticklabels = False, side='right', gridcolor="rgb(224,224,224)")
fig.add_vline(x=0, line_width=3)
fig.add_hline(y=0, line_width=3)
fig.update_layout(plot_bgcolor='rgb(255,255,255)', height=800, width=800)
fig.show()
The only drawback here is the label of x-axis cannot be rotated in Plotly as documented here.
I have a 2 line title and first line has a number at the end of the line.
Can we plot a circle around the number?
Here is the code to generate the figure.
from matplotlib import rcParams
from matplotlib import pyplot as plt
import numpy as np
import os
rcParams.update({'figure.autolayout': True})
some_text = 'XXX'
any_number=15
title = '%s: %d\n YYY ZZZZ WWWWW' % (some_text,any_number)
fig = plt.figure(figsize=(8, 8), dpi=100)
plt.tick_params(axis='y', which='major', labelsize=60, width=3, length=10, pad=40)
plt.tick_params(axis='y', which='minor', labelsize=60, width=3, length=10, pad=40)
ax = plt.gca()
plt.title(title, fontsize=60, pad=40, loc='center', fontweight='semibold')
plt.style.use('ggplot')
ax.set_facecolor('white')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(True)
for edge_i in ['left']:
ax.spines[edge_i].set_edgecolor("black")
ax.spines[edge_i].set_linewidth(3)
ax.spines[edge_i].set_bounds(0, 1)
x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
plt.yticks(np.arange(0, 1.01, step=0.2))
data_list= np.array([1,1,1,1,1,0.9, 0.8, 0.7, 0.8,0.85])
plt.bar(x, data_list, 0.9, color='indianred',edgecolor="black", linewidth=3,zorder=1)
plt.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
labelbottom=False) # labels along the bottom edge are off
figure_name = 'figure_with_circle.png'
figure_file = os.path.join('/Users/burcakotlu/Desktop',figure_name)
fig.savefig(figure_file, dpi=100, bbox_inches="tight")
plt.close(fig)
Here is the current figure and the wanted circle.
One could use the following without ax.bar():
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.set_title('title')
circle1 = plt.Circle((2,4.15), 0.2, color='k', clip_on=False, zorder=100, fill=False)
ax.add_patch(circle1)
ax.set_xlim(0,4)
ax.set_ylim(0,4)
plt.show()
I have found a way to plot circle together with bar plots without distorting bars. Here is the code below:
from matplotlib import rcParams
from matplotlib import pyplot as plt
import numpy as np
import os
import matplotlib.patches as patches
from matplotlib.offsetbox import AnchoredText
rcParams.update({'figure.autolayout': True})
some_text = 'XXX'
any_number=15
title = '%s: %d\n YYY ZZZZ WWWWW' % (some_text,any_number)
fig = plt.figure(figsize=(12,12), dpi=100)
plt.tick_params(axis='y', which='major', labelsize=60, width=3, length=10, pad=40)
plt.tick_params(axis='y', which='minor', labelsize=60, width=3, length=10, pad=40)
ax = plt.gca()
number_of_xxx = '12'
anchored_text_number_of_xxx = AnchoredText(number_of_xxx,
frameon=False, borderpad=0, pad=0.1,
loc='upper right',
bbox_to_anchor=[0.95, 1.3],
bbox_transform=plt.gca().transAxes,
prop={'fontsize': 60,
'fontweight': 'semibold'})
ax.add_artist(anchored_text_number_of_xxx)
circle1 = patches.Circle((0.88, 1.25), radius=0.1, transform=ax.transAxes, zorder=100, fill=False, color='gold', lw=8, clip_on=False)
ax.add_patch(circle1)
ax.set_title(title, fontsize=60, pad=40, loc='center', fontweight='semibold', zorder=50)
ax.set_facecolor('white')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(True)
for edge_i in ['left']:
ax.spines[edge_i].set_edgecolor("black")
ax.spines[edge_i].set_linewidth(3)
ax.spines[edge_i].set_bounds(0, 1)
x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
ax.set_yticks(np.arange(0, 1.01, step=0.2))
data_list= np.array([1,1,1,1,1,0.9, 0.8, 0.7, 0.8,0.85])
ax.bar(x, data_list, 0.9, color='indianred',edgecolor="black", linewidth=3,zorder=1)
ax.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
labelbottom=False) # labels along the bottom edge are off
figure_name = 'figure_with_circle.png'
figure_file = os.path.join('/Users/burcakotlu/Desktop',figure_name)
fig.savefig(figure_file, dpi=100, bbox_inches="tight")
plt.close(fig)
I want to have multiple pie charts in a grid.
Each pie chart will have a different number of wedges, values, and labels.
The code below shows multiple labels in one pie chart.
Is there a way to label each wedge of pie-charts in this grid?
import matplotlib.pyplot as plt
import numpy as np
def heatmap_with_circles(data_array,row_labels,column_labels,ax=None, cmap=None, norm=None, cbar_kw={}, cbarlabel="", **kwargs):
for row_index, row in enumerate(row_labels,0):
for column_index, column in enumerate(column_labels,0):
print('row_index: %d column_index: %d' %(row_index,column_index))
if row_index==0 and column_index==0:
colors=['indianred','orange','gray']
values=[10,20,30]
else:
values=[45,20,38]
colors=['pink','violet','green']
wedges, text = plt.pie(values,labels=['0', '2', '3'],labeldistance = 0.25,colors=colors)
print('len(wedges):%d wedges: %s, text: %s' %(len(wedges), wedges, text))
radius = 0.45
[w.set_center((column_index,row_index)) for w in wedges]
[w.set_radius(radius) for w in wedges]
# We want to show all ticks...
ax.set_xticks(np.arange(data_array.shape[1]))
ax.set_yticks(np.arange(data_array.shape[0]))
fontsize=10
ax.set_xticklabels(column_labels, fontsize=fontsize)
ax.set_yticklabels(row_labels, fontsize=fontsize)
#X axis labels at top
ax.tick_params(top=True, bottom=False,labeltop=True, labelbottom=False,pad=5)
plt.setp(ax.get_xticklabels(), rotation=55, ha="left", rotation_mode="anchor")
# We want to show all ticks...
ax.set_xticks(np.arange(data_array.shape[1]+1)-.5, minor=True)
ax.set_yticks(np.arange(data_array.shape[0]+1)-.5, minor=True)
ax.grid(which="minor", color="black", linestyle='-', linewidth=2)
ax.tick_params(which="minor", bottom=False, left=False)
data_array=np.random.rand(3,4)
row_labels=['Row1', 'Row2', 'Row3']
column_labels=['Column1', 'Column2', 'Column3','Column4']
fig, ax = plt.subplots(figsize=(1.9*len(column_labels),1.2*len(row_labels)))
ax.set_aspect(1.0)
ax.set_facecolor('white')
heatmap_with_circles(data_array,row_labels,column_labels, ax=ax)
plt.tight_layout()
plt.show()
After updating heatmap_with_circles
def heatmap_with_circles(data_array,row_labels,column_labels,ax=None, cmap=None, norm=None, cbar_kw={}, cbarlabel="", **kwargs):
labels = ['x', 'y', 'z']
for row_index, row in enumerate(row_labels,0):
for column_index, column in enumerate(column_labels,0):
print('row_index: %d column_index: %d' %(row_index,column_index))
if row_index==0 and column_index==0:
colors=['indianred','orange','gray']
values=[10,20,30]
else:
values=[45,20,38]
colors=['pink','violet','green']
# wedges, texts = plt.pie(values,labels=['0', '2', '3'],labeldistance = 0.45,colors=colors)
wedges, texts = plt.pie(values,labeldistance = 0.25,colors=colors)
print('text:%s len(wedges):%d wedges: %s' %(texts, len(wedges), wedges))
radius = 0.45
[w.set_center((column_index,row_index)) for w in wedges]
[w.set_radius(radius) for w in wedges]
[text.set_position((text.get_position()[0]+column_index,text.get_position()[1]+row_index)) for text in texts]
[text.set_text(labels[text_index]) for text_index, text in enumerate(texts,0)]
I got the following image :)
You could loop through the texts of each pie, get its xy position, add column_index and row_index, and set that as new position.
Some small changes to the existing code:
ax.grid(which="minor", ..., clip_on=False) to make sure the thick lines are shown completely, also near the border
ax.set_xlim(xmin=-0.5) to set the limits
import matplotlib.pyplot as plt
import numpy as np
def heatmap_with_circles(data_array, row_labels, column_labels, ax=None):
ax = ax or plt.gca()
for row_index, row in enumerate(row_labels, 0):
for column_index, column in enumerate(column_labels, 0):
colors = np.random.choice(['indianred', 'orange', 'gray', 'pink', 'violet', 'green'], 3, replace=False)
values = np.random.randint(10, 41, 3)
wedges, text = plt.pie(values, labels=['1', '2', '3'], labeldistance=0.25, colors=colors)
radius = 0.45
for w in wedges:
w.set_center((column_index, row_index))
w.set_radius(radius)
w.set_edgecolor('white')
# w.set_linewidth(1)
for t in text:
x, y = t.get_position()
t.set_position((x + column_index, y + row_index))
# We want to show all ticks...
ax.set_xticks(np.arange(data_array.shape[1]))
ax.set_yticks(np.arange(data_array.shape[0]))
fontsize = 10
ax.set_xticklabels(column_labels, fontsize=fontsize)
ax.set_yticklabels(row_labels, fontsize=fontsize)
# X axis labels at top
ax.tick_params(top=True, bottom=False, labeltop=True, labelbottom=False, pad=5)
plt.setp(ax.get_xticklabels(), rotation=55, ha="left", rotation_mode="anchor")
# We want to show all minor ticks...
ax.set_xticks(np.arange(data_array.shape[1] + 1) - .5, minor=True)
ax.set_yticks(np.arange(data_array.shape[0] + 1) - .5, minor=True)
ax.set_xlim(xmin=-.5)
ax.set_ylim(ymin=-.5)
ax.grid(which="minor", color="black", linestyle='-', linewidth=2, clip_on=False)
ax.tick_params(axis="both", which="both", length=0) # hide tick marks
data_array = np.random.rand(3, 4)
row_labels = ['Row1', 'Row2', 'Row3']
column_labels = ['Column1', 'Column2', 'Column3', 'Column4']
fig, ax = plt.subplots(figsize=(1.9 * len(column_labels), 1.2 * len(row_labels)))
ax.set_aspect(1.0)
ax.set_facecolor('white')
heatmap_with_circles(data_array, row_labels, column_labels, ax=ax)
plt.tight_layout()
plt.show()
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 have been following the example provided in:
https://matplotlib.org/examples/api/barchart_demo.html
My problem is that I want to add edges to the bars. But when I set the
linewidth=1, edgecolor='black'
parameters, the edges are only applied to the first pair of bars, leaving the remaining pairs unchanged.
"""
========
Barchart
========
A bar plot with errorbars and height labels on individual bars
"""
import numpy as np
import matplotlib.pyplot as plt
N = 5
men_means = (20, 35, 30, 35, 27)
men_std = (2, 3, 4, 1, 2)
ind = np.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars
fig, ax = plt.subplots()
rects1 = ax.bar(ind, men_means, width, color='r', yerr=men_std,linewidth=1, edgecolor='black')
women_means = (25, 32, 34, 20, 25)
women_std = (3, 5, 2, 3, 3)
rects2 = ax.bar(ind + width, women_means, width, color='y', yerr=women_std, linewidth=1, edgecolor='black')
# add some text for labels, title and axes ticks
ax.set_ylabel('Scores')
ax.set_title('Scores by group and gender')
ax.set_xticks(ind + width / 2)
ax.set_xticklabels(('G1', 'G2', 'G3', 'G4', 'G5'))
ax.legend((rects1[0], rects2[0]), ('Men', 'Women'))
def autolabel(rects):
"""
Attach a text label above each bar displaying its height
"""
for rect in rects:
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width()/2., 1.05*height,
'%d' % int(height),
ha='center', va='bottom')
autolabel(rects1)
autolabel(rects2)
plt.show()
Thanks for your help.
David.