I have the issue that I am trying to make multiple plots that are supposed to have the same bbox size. As some of my plots have an additional colorbar or wider yticklabels the bbox size varies within multiple plots.
As I would like to use these plots in a LaTex document underneath each other, I would like to set the bbox for all plots to the same value instead of defining the figure size.
If it is not clear yet what I mean, here's an example:
As you can see the bbox sizes vary, as the width of the ylabel + ylabelticks and additionally the cbar is added. I thought the easisest way to approach this would be to find the image of the smallest drawn bbox and use that as a standard for all figures and keep the figsize constant, or to just set the bbox size constant and just add the rest and have varying figsizes.. the later would need me to do additional positioning in latex/illustrator/power point or whatever, but just about any solution that works would be great (even though I belive that the later is likely not possible with matplotlib). I tried changing the bbox size but unfortunately did not succeed. So I do not have some code to start from. But any help or pointers where to look at or start would help a lot.
Here a short code snippet to reproduce.
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
np.random.seed(1)
mpl.rcParams['figure.figsize'] = (16.0, 12.0)
x = np.linspace(0, 100, 100)
y = np.random.randint(100, size=100)
z = np.random.randint(0, 1e6, size=100)/1e6
fig, ax = plt.subplots()
m = mpl.cm.ScalarMappable(cmap=mpl.cm.jet)
norm = plt.Normalize(min(z), max(z))
m.set_array(list(set(z)))
cbar = plt.colorbar(m, orientation="vertical", fraction=0.07, pad=0.02)
color = lambda c: m.cmap(norm(c))
ax.scatter(x, y, color=color(z))
fig, ax = plt.subplots()
ax.scatter(x, y)
pls see following code. I recommend you using ax1 and ax2, which have more flexibility.
Key points:
using get_position() to get bounds of axes.
using set_position() to set bounds of axes.
I highly recommend using ax1, ax2 ... instead of plt.stuff for multiple subplots.
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
np.random.seed(1)
x = np.linspace(0, 100, 100)
y = np.random.randint(100, size=100)
z = np.random.randint(0, 1e6, size=100)/1e6
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 4))
m = mpl.cm.ScalarMappable(cmap=mpl.cm.jet)
norm = plt.Normalize(min(z), max(z))
m.set_array(list(set(z)))
cbar = fig.colorbar(m, orientation="vertical", fraction=0.07, pad=0.02)
color = lambda c: m.cmap(norm(c))
ax2.scatter(x, y, color=color(z))
ax1.scatter(x, y)
# get the bounds of ax1 and ax2
x1, y1, w1, h1 = ax1.get_position().bounds
x2, y2, w2, h2 = ax2.get_position().bounds
# set ax1 width to width of ax2
ax1.set_position([x1, y1, w2, h1])
Related
I make two subplots with a common shared colorbar. So naturally I want to plot the colorbar only once.
However, when I do so, then my subplots become unequal in size.
How to place the colorbar outside the subplots on the right?
Minimal working example below
import numpy as np
from matplotlib import colors
import matplotlib.pyplot as plt
res = 100
x = np.linspace(0, 2*np.pi, res)
y = np.sin(x)
z = np.cos(x)
y2 = -np.sin(x)+0.4
z2 = 0.5*np.cos(2*x)
fig_width = 200/25.4
fig_height = 100/25.4
fig = plt.figure(figsize=(fig_width, fig_height))
gs = fig.add_gridspec(1, 2, wspace=0)
(ax, ax2) = gs.subplots(sharey='row')
images = []
images.append(ax.scatter(x, y, c=z))
images.append(ax2.scatter(x, y2, c=z2))
vmin = min(image.get_array().min() for image in images)
vmax = max(image.get_array().max() for image in images)
norm = colors.Normalize(vmin=vmin, vmax=vmax)
for im in images:
im.set_norm(norm)
cbar = fig.colorbar(images[0], ax=ax2)
cbar.set_label("mylabel", loc='top')
fig.tight_layout()
plt.show()
Try 1) pass the two axes as ax, and 2) move tight_layout before colorbar:
# other stuff
fig.tight_layout()
cbar = plt.colorbar(images[0], ax=(ax,ax2))
# other - other stuff
Output:
I want to write the operations and parameters that I usually use in drawing in a function. In the future, just pass x and y to draw according to the default parameters. But now the question I am facing is, how do I determine which picture plt.plot is drawing on? For example, I want to draw two curves on a picture.
def draw(x,y):
... %some operations
plt.plot(x,y) % draw picture operations
... %some operations
draw(x,y),
dray(x2,y2)
How to ensure that these two curves are drawn on a picture. That is, what parameters do I need to pass to make plt.plot focus on the picture I specify.
def plotLine(coordinate,figName='test',xylabel=[],ax=None):
# assert(len(coordinate)<=2)
if (len(coordinate)==2) :
x=coordinate[0]
y=coordinate[1]
assert(len(x)==len(y))
else:
y=coordinate
x =np.linspace(0,len(y)-1,len(y))
minn=min(y)
maxx=max(y)
plt.switch_backend('Agg')
if ax == None:
fig,ax = plt.subplots()
fig = plt.figure(num=None, figsize=(3.5, 1.5), dpi=300, facecolor='w')
plt.subplots_adjust(right = 0.98, top = 0.98, bottom=0.35,left=0.32,wspace=0, hspace=0.2)
ax.set_xlim([0,len(x)])
ax.set_ylim([0,maxx+maxx/3])
plt.xticks(fontsize=5)
plt.yticks(fontsize=5)
bar_width = 0.35
opacity = 0.8
lsmarkersize = 2.5
lslinewidth = 0.6
ax.plot(x,y,'-', linewidth=1, markersize=lsmarkersize, markeredgewidth=0)
plt.savefig(figName+".png",bbox_inches='tight',dpi=500)
# os.system("code "+figName+".png")
if ax!=None:
return ax
else:
return plt.gca()
x=[1,2,3,4,5,6]
y=[1,2,3,4,4,5]
ax = plotLine([x,y])
x=[1,2,3,4,5,6]
y=[12,13,14,15,16,17]
plotLine([x,y],ax=ax)
I tried to pass ax as a parameter. But the picture drawn at the end is blank.
You can use subplots to specify the axes to plot on. For example, create a figure with a single subplot:
fig, ax = plt.subplots()
ax.plot(x, y)
For your function you could do the following
fig, ax = plt.subplots()
def draw(x, y, ax):
ax.plot(x, y)
def dray(x2, y2, ax):
ax.plot(x2, y2)
I am not attempting to modify your code. This is more a general approach answer. Imho, it is better (in terms of keeping track of what's going on) to define the figure and plots outside the function and doing only the actual plotting inside the function.
import numpy as np
from matplotlib import pyplot as plt
np.random.seed(123)
#the plotting function, taking ax and label as optional parameters
def draw_the_line(x, y, current_ax=None, current_label=None):
if not current_ax:
current_ax=plt.gca()
if not current_label:
current_label="missing label"
current_ax.plot(x, y, label=current_label)
plt.sca(current_ax)
fig, (ax1, ax2) = plt.subplots(2, figsize=(6, 8))
#normal plot into panel 1
x1 = np.arange(6)
y1 = np.random.randint(1, 10, len(x1))
draw_the_line(x1, y1, ax1, "data1")
#normal plot into panel 2
x2 = np.arange(5)
y2 = np.random.randint(10, 20, len(x2))
draw_the_line(x2, y2, ax2, "data2")
#plot into panel 1 with missing label
x3 = np.arange(4)
y3 = np.random.randint(20, 30, len(x3))
draw_the_line(x3, y3, ax1)
#plot into the last panel used
x4 = np.arange(3)
y4 = np.random.randint(30, 40, len(x4))
draw_the_line(x4, y4, current_label="data4")
ax1.legend()
ax2.legend()
plt.show()
Sample output:
I have 2 sets of rectangular patches in a plot. I want to name them separately. "Layer-1" for the bottom part and similarly "Layer-2" for the upper part. I wanted to set coordinates for the Y-axis but it did not work. Moreover i was not able to add the "Layer-2" text into the label. Please help.
I tried with the below mentioned code but it did not work.
plt.ylabel("LAYER-1", loc='bottom')
yaxis.labellocation(bottom)
One solution is to create a second axis, so called twin axis that shares the same x axis. Then it is possbile to label them separately. Furthermore, you can adjust the location of the label via
axis.yaxis.set_label_coords(-0.1, 0.75)
Here is an example that you can adjust to your desires. The result can be found here: https://i.stack.imgur.com/1o2xl.png
%matplotlib notebook
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
plt.rcParams['figure.dpi'] = 100
import matplotlib.pyplot as plt
x = np.arange(0, 10, 0.1)
y1 = 0.05 * x**2
y2 = -1 *y1
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.plot(x, y1, 'g-')
ax2.plot(x, y2, 'b-')
# common x axis
ax1.set_xlabel('X data')
# First y axis label
ax1.set_ylabel('LAYER-1', color='g')
# Second y [enter image description here][1]axis label
ax2.set_ylabel('LAYER-2', color='b')
# Adjust the label location
ax1.yaxis.set_label_coords(-0.075, 0.25)
ax2.yaxis.set_label_coords(-0.1, 0.75)
plt.show()
I'm not sure my wording is correct, but what I am trying to do is create a figure of two subplots, where the two plots have different limits, but their size is such that the physical scale (as in, y-distance per centimeter of figure height) is the same. To clarify, lets say subplot 1 shows data from -3 to 3 and subplot 2 shows data from -1 to 1. I want to have them below one another in such a way that the height of subplot2 (excluding ticks, just everything inside the frame) is exactly one third of subplot 1.
My attempt was as follows:
from matplotlib import gridspec
from matplotlib import pyplot as plt
import numpy as np
x = np.linspace(0,2, 101)
y1 = 3*np.cos(x*np.pi)
y2 = np.cos(x*np.pi)
fig = plt.figure(figsize=(4, 6))
gs = gridspec.GridSpec(8, 1)
ax1 = plt.subplot(gs[0:6,0])
ax1.plot(x, y1, c='orange')
ax1.set_ylim(-3, 3)
ax1.set_xticks([], [])
ax2 = plt.subplot(gs[6:,0])
ax2.plot(x, y2, c='green')
ax2.set_ylim(-1,1)
ax2.set_xticks([0, 1, 2])
ax2.set_xticklabels([r'0', r'0.5', r'1'])
ax2.set_xlabel(r'$n_g$ (2e)')
plt.tight_layout()
fig.text(-0.025, 0.5, 'Frequency (GHz)', ha='center', va='center', rotation='vertical', size=18)
which produces the figure below, but as you can see (although you have to look closely) the range -1 to 1 in the second subplot is compressed (takes up less height) than the range -1 to 1 in subplot 1. I'm guessing this is because of the space between the two subplots.
Note that I'm using gridspec because I plan on adding another column of subplots with interesting aspect ratio's and its own labels and limits. I didn't know how to add a global ylabel in a more elegant way, if someone was wondering.
You can set the height_ratios of the gridspec to match the range of the limits.
from matplotlib import gridspec
from matplotlib import pyplot as plt
import numpy as np
x = np.linspace(0,2, 101)
y1 = 3*np.cos(x*np.pi)
y2 = np.cos(x*np.pi)
ylim1 = -3,3
ylim2 = -1,1
fig = plt.figure(figsize=(4, 6), constrained_layout=True)
gs = gridspec.GridSpec(2, 1, height_ratios=[np.diff(ylim1)[0],
np.diff(ylim2)[0]], figure=fig)
ax1 = plt.subplot(gs[0,0])
ax1.plot(x, y1, c='orange')
ax1.set_ylim(ylim1)
ax1.set_xticks([], [])
ax2 = plt.subplot(gs[1,0])
ax2.plot(x, y2, c='green')
ax2.set_ylim(ylim2)
ax2.set_xticks([0, 1, 2])
ax2.set_xticklabels([r'0', r'0.5', r'1'])
ax2.set_xlabel(r'$n_g$ (2e)')
plt.show()
I wonder how to set the size of the subplot when figure contains multiple subplots (5 × 2 in my case). No matter how big I allow the whole figure to be, the subplots always seem to be small. I would like to have direct control of the size of the subplot in this figure. The simplified version of the code is pasted below.
import numpy as np
import matplotlib.pyplot as plt
x = np.random.randn(20)
y = np.random.randn(20)
fig = plt.figure(figsize=(20, 8))
for i in range(0,10):
ax = fig.add_subplot(5, 2, i+1)
plt.plot(x, y, 'o')
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
# x and y axis should be equal length
x0,x1 = ax.get_xlim()
y0,y1 = ax.get_ylim()
ax.set_aspect(abs(x1-x0)/abs(y1-y0))
plt.show()
fig.savefig('plot.pdf', bbox_inches='tight')
Just switch figure size width and height from:
fig = plt.figure(figsize=(20, 8))
to:
fig = plt.figure(figsize=(8, 20))
to use the whole page for your plots.
This will change your plot from:
to: