Is it possible to have a visible axis label on an invisble axis? I would like to plot 2 axes that have, apart from their own ylabels, also a common one:
import matplotlib
from matplotlib.pyplot import *
figure()
ax1 = axes([0.3, 0.2, 0.4, 0.2]); ylabel("Label 1")
ax2 = axes([0.3, 0.5, 0.4, 0.2]); ylabel("Label 2")
ax_common = axes([0.2, 0.2, 0.5, 0.5], zorder=-10)
xticks([]); yticks([])
ylabel("Common", fontsize="x-large")
savefig("out.png")
The code above produces this plot:
out.png
Is there a way to remove axis lines? If I add ax_common.set_axis_off(), the axes and the ylabel is removed. Do I have to create a text label instead, without create the additional axes?
Do this:
ax_common.set_frame_on(False)
Related
Dear stackoverflow users,
I want to plot some data labels with its coordinates in a x,y-plot. Around the labels I want to put a circle with a user-defined radius as I want to symbolize the magnitude of the data property by the radius of the circle.
An example dataset could look like the following:
point1 = ["label1", 0.5, 0.25, 1e0] # equals [label, x, y, radius]
point2 = ["label2", 0.5, 0.75, 1e1] # equals [label, x, y, radius]
I want to use a code silimar to the following one:
import matplotlib.pyplot as plt
plt.text(point1[1], point1[2], point1[0], bbox = dict(boxstyle="circle")) # here I want to alter the radius by passing point1[3]
plt.text(point2[1], point2[2], point2[0], bbox = dict(boxstyle="circle")) # here I want to alter the radius by passing point2[3]
plt.show()
Is this possible somehow or is the plt.add_patch variant the only possible way?
Regards
In principle, you can use the boxes' pad parameter to define the circle size. However this is then relative to the label. I.e. a small label would have a smaller circle around it for the same value of pad than a larger label. Also the units of pad are fontsize (i.e. if you have a fontsize of 10pt, a padding of 1 would correspond to 10pt).
import numpy as np
import matplotlib.pyplot as plt
points = [["A", 0.2, 0.25, 0], # zero radius
["long label", 0.4, 0.25, 0], # zero radius
["label1", 0.6, 0.25, 1]] # one radius
for point in points:
plt.text(point[1], point[2], point[0], ha="center", va="center",
bbox = dict(boxstyle=f"circle,pad={point[3]}", fc="lightgrey"))
plt.show()
I don't know in how far this is desired.
I guess usually you would rather create a scatterplot at the same positions as the text
import numpy as np
import matplotlib.pyplot as plt
points = [["A", 0.2, 0.25, 100], # 5 pt radius
["long label", 0.4, 0.25, 100], # 5 pt radius
["label1", 0.6, 0.25, 1600]] # 20 pt radius
data = np.array([l[1:] for l in points])
plt.scatter(data[:,0], data[:,1], s=data[:,2], facecolor="gold")
for point in points:
plt.text(point[1], point[2], point[0], ha="center", va="center")
plt.show()
I'm facing a problem in showing the legend in the correct format using matplotlib.
EDIT: I have 4 subplots in a figure in 2 by 2 format and I want legend only on the first subplot which has two lines plotted on it. The legend that I got using the code attached below contained endless entries and extended vertically throughout the figure. When I use the same code using linspace to generate fake data the legend works absolutely fine.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import os
#------------------set default directory, import data and create column output vectors---------------------------#
path="C:/Users/Pacman/Data files"
os.chdir(path)
data =np.genfromtxt('vrp.txt')
x=np.array([data[:,][:,0]])
y1=np.array([data[:,][:,6]])
y2=np.array([data[:,][:,7]])
y3=np.array([data[:,][:,9]])
y4=np.array([data[:,][:,11]])
y5=np.array([data[:,][:,10]])
nrows=2
ncols=2
tick_l=6 #length of ticks
fs_axis=16 #font size of axis labels
plt.rcParams['axes.linewidth'] = 2 #Sets global line width of all the axis
plt.rcParams['xtick.labelsize']=14 #Sets global font size for x-axis labels
plt.rcParams['ytick.labelsize']=14 #Sets global font size for y-axis labels
plt.subplot(nrows, ncols, 1)
ax=plt.subplot(nrows, ncols, 1)
l1=plt.plot(x, y2, 'yo',label='Flow rate-fan')
l2=plt.plot(x,y3,'ro',label='Flow rate-discharge')
plt.title('(a)')
plt.ylabel('Flow rate ($m^3 s^{-1}$)',fontsize=fs_axis)
plt.xlabel('Rupture Position (ft)',fontsize=fs_axis)
# This part is not working
plt.legend(loc='upper right', fontsize='x-large')
#Same code for rest of the subplots
I tried to implement a fix suggested in the following link, however, could not make it work:
how do I make a single legend for many subplots with matplotlib?
Any help in this regard will be highly appreciated.
If I understand correctly, you need to tell plt.legend what to put as legends... at this point it is being loaded empty. What you get must be from another source. I have quickly the following, and of course when I run fig.legend as you do I get nothing.
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax1 = fig.add_axes([0.1, 0.1, 0.4, 0.7])
ax2 = fig.add_axes([0.55, 0.1, 0.4, 0.7])
x = np.arange(0.0, 2.0, 0.02)
y1 = np.sin(2*np.pi*x)
y2 = np.exp(-x)
l1, l2 = ax1.plot(x, y1, 'rs-', x, y2, 'go')
y3 = np.sin(4*np.pi*x)
y4 = np.exp(-2*x)
l3, l4 = ax2.plot(x, y3, 'yd-', x, y4, 'k^')
fig.legend(loc='upper right', fontsize='x-large')
#fig.legend((l1, l2), ('Line 1', 'Line 2'), 'upper left')
#fig.legend((l3, l4), ('Line 3', 'Line 4'), 'upper right')
plt.show()
I'd suggest doing one by one, and then applying for all.
It is useful to work with the axes directly (ax in your case) when when working with subplots. So if you set up two plots in a figure and only wish to have a legend in your second plot:
t = np.linspace(0, 10, 100)
plt.figure()
ax1 = plt.subplot(2, 1, 1)
ax1.plot(t, t * t)
ax2 = plt.subplot(2, 1, 2)
ax2.plot(t, t * t * t)
ax2.legend('Cubic Function')
Note that when creating the legend, I am doing so on ax2 as opposed to plt. If you wish to create a second legend for the first subplot, you can do so in the same way but on ax1.
The following screenshot shows my x-axis.
I added some labels and rotated them by 90 degrees in order to better read them. However, pyplot truncates the bottom such that I'm not able to completely read the labels.
How do I extend the bottom margin in order to see the complete labels?
Two retroactive ways:
fig, ax = plt.subplots()
# ...
fig.tight_layout()
Or
fig.subplots_adjust(bottom=0.2) # or whatever
Here's a subplots_adjust example: http://matplotlib.org/examples/pylab_examples/subplots_adjust.html
(but I prefer tight_layout)
A quick one-line solution that has worked for me is to use pyplot's auto tight_layout method directly, available in Matplotlib v1.1 onwards:
plt.tight_layout()
This can be invoked immediately before you show the plot (plt.show()), but after your manipulations on the axes (e.g. ticklabel rotations, etc).
This convenience method avoids manipulating individual figures of subplots.
Where plt is the standard pyplot from:
import matplotlib.pyplot as plt
fig.savefig('name.png', bbox_inches='tight')
works best for me, since it doesn't reduce the plot size compared to
fig.tight_layout()
Subplot-adjust did not work for me, since the whole figure would just resize with the labels still out of bounds.
A workaround I found was to keep the y-axis always a certain margin over the highest or minimum y-values:
x1,x2,y1,y2 = plt.axis()
plt.axis((x1,x2,y1 - 100 ,y2 + 100))
fig, ax = plt.subplots(tight_layout=True)
This is rather complicated, but it gives a general and neat solution.
import numpy as np
value1 = 3
xvalues = [0, 1, 2, 3, 4]
line1 = [2.0, 3.0, 2.0, 5.0, 4.0]
stdev1 = [0.1, 0.2, 0.1, 0.4, 0.3]
line2 = [1.7, 3.1, 2.5, 4.8, 4.2]
stdev2 = [0.12, 0.18, 0.12, 0.3, 0.35]
max_times = [max(line1+stdev1),max(line2+stdev2)]
min_times = [min(line1+stdev1),min(line2+stdev2)]
font_size = 25
max_total = max(max_times)
min_total = min(min_times)
max_minus_min = max_total - min_total
step_size = max_minus_min/10
head_space = (step_size*3)
plt.figure(figsize=(15, 15))
plt.errorbar(xvalues, line1, yerr=stdev1, fmt='', color='b')
plt.errorbar(xvalues, line2, yerr=stdev2, fmt='', color='r')
plt.xlabel("xvalues", fontsize=font_size)
plt.ylabel("lines 1 and 2 Test "+str(value1), fontsize=font_size)
plt.title("Let's leave space for the legend Experiment"+ str(value1), fontsize=font_size)
plt.legend(("Line1", "Line2"), loc="upper left", fontsize=font_size)
plt.tick_params(labelsize=font_size)
plt.yticks(np.arange(min_total, max_total+head_space, step=step_size) )
plt.grid()
plt.tight_layout()
Result:
The second subplot is just the first image with an overlay ploted. In the second plot there appears to have white padding/boarder. How do I remove this padding/whitespace?
For completness, here is the fragment of code that performs the plotting:
fig, ax = plt.subplots(1, 2)
fig.set_size_inches(16, 6, forward=True)
plt.subplots_adjust(0.05, 0.05, 0.95, 0.95, 0.05, 0.05)
ax[0].set_title("Region Labels")
ax[0].imshow(image_labels)
ax[1].set_title("Region Connectivity Graph")
ax[1].imshow(image_labels)
for edge in edges:
ax[1].plot([centers[edge[0]][0],centers[edge[1]][0]],
[centers[edge[0]][1],centers[edge[1]][1]])
for a in ax:
a.set_xticks(())
a.set_yticks(())
plt.show()
By default, Matplotlib adds some margin to plotted data. I cant test it because it dont have your image_labels and centers, but this should normally work:
ax[1].autoscale_view('tight')
An alternative would be to manually set the xlim and ylim of the axes:
ax[1].set_xlim(0,image_labels.shape[1])
ax[1].set_ylim(0,image_labels.shape[0])
How would one color y-axis label and tick labels in red?
So for example the "y-label" and values 0 through 40, to be colored in red.
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(10)
fig = plt.figure()
ax = plt.subplot(111)
ax.set_ylabel("y-label")
for i in xrange(5):
ax.plot(x, i * x, label='$y = %ix$' % i)
ax.legend()
plt.show()
label = plt.ylabel("y-label")
label.set_color("red")
similarly, you can obtain and modify the tick labels:
[i.set_color("red") for i in plt.gca().get_xticklabels()]
The xlabel can be colorized when setting it,
ax.set_xlabel("x-label", color="red")
For setting the ticklabels' color, one may either use tick_params, which sets the ticklabels' as well as the ticks' color
ax.tick_params(axis='x', colors='red')
Alternatively, plt.setp can be used to only set the ticklabels' color, without changing the ticks' color.
plt.setp(ax.get_xticklabels(), color="red")
Note that for changing the properties on the y-axis, one can replace the x with a y in the above.