I do have a plot that only consists of horizontal lines at certain values when I have a signal, otherwise none. So, I am looking for a way to plot this without the vertical lines. there may be gaps between the lines when there is no signal and I dont want the lines to connect nor do I want a line falling off to 0. Is there a way to plot this like that in matplotlib?
self.figure = plt.figure()
self.canvas = FigureCanvas(self.figure)
axes = self.figure.add_subplot(111)
axes.plot(df.index, df["x1"], lw=1.0, c=self.getColour('g', i), ls=ls)
The plot you are looking for is Matplotlib's plt.hlines(y, xmin, xmax).
For example:
import matplotlib.pyplot as plt
y = range(1, 11)
xmin = range(10)
xmax = range(1, 11)
colors=['blue', 'green', 'red', 'yellow', 'orange', 'purple',
'cyan', 'magenta', 'pink', 'black']
fig, ax = plt.subplots(1, 1)
ax.hlines(y, xmin, xmax, colors=colors)
plt.show()
Yields a plot like this:
See the Matplotlib documentation for more details.
Related
I want the picker event to simply display the legend label when I click on any of the points on my scatter plot. This is what I have and looks like:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# x y data and legend labels
x = np.random.uniform(0, 100, 50)
y = np.random.uniform(0, 100, 50)
ID = np.random.randint(0,25,50)
# define the event
def onpick(event):
ind = event.ind
print('x:', x[ind], 'y:', y[ind])
# create the plot
fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c = ID, picker=True)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.legend(*scatter.legend_elements(num=list(np.unique(ID))),
loc="center left",
title='ID',
bbox_to_anchor=(1, 0.5),
ncol=2
)
ax.ticklabel_format(useOffset=False)
ax.tick_params(axis = 'x',labelrotation = 45)
plt.tight_layout()
# call the event
fig.canvas.mpl_connect('pick_event', onpick)
The scatter plot:
The current output on click:
I want it to print something like:
x: [76.25650514] y: [59.85198124] ID: 11 # the corresponding legend label
I have been searching through the web and couldn't find much I can duplicate from.
Generally, the way you would get the label of the point you clicked on would be print(event.artist.get_label()) but with your custom legends labels, the only thing that prints is _child0. However, due to your custom labels, you can use your variable ID just like how you are using your x and y variables e.g. print('id:', ID[ind]).
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# x y data and legend labels
x = np.random.uniform(0, 100, 50)
y = np.random.uniform(0, 100, 50)
ID = np.random.randint(0,25,50)
# define the event
def onpick(event):
ind = event.ind
print(event.artist.get_label()) # How you normally get the legend label
print('id:', ID[ind]) # How you can get your custom legend label
print('x:', x[ind], 'y:', y[ind])
# create the plot
fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c = ID, picker=True)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.legend(*scatter.legend_elements(num=list(np.unique(ID))),
loc="center left",
title='ID',
bbox_to_anchor=(1, 0.5),
ncol=2
)
ax.ticklabel_format(useOffset=False)
ax.tick_params(axis = 'x',labelrotation = 45)
plt.tight_layout()
# call the event
fig.canvas.mpl_connect('pick_event', onpick)
plt.show()
Clicking on the yellow most point gives:
_child0
id: [24]
x: [84.73899472] y: [3.07532246]
Clicking on a very purple point gives:
_child0
id: [2]
x: [99.88397652] y: [98.89144833]
I generated a plot with 7 curves and saved it as a Matplotlib Axis object. However, now I want to change the colors in each one of those curves. Since the curves take a while to generate, is it possible to change the colors of these curves from the Axis object itself?
import matplotlib.pyplot as plt
import pickle
import numpy as np
fig, ax = plt.subplots()
x = np.arange(10)
y1 = np.random.random(10)
y2 = np.random.random(10)
kwargs_1 = {
'color': 'red',
'linestyle': ':',
'label': '1',
}
kwargs_2 = {
'color': 'blue',
'linestyle': '--',
'label': '2',
}
ax.plot(x, y1, **kwargs_1)
ax.plot(x, y2, **kwargs_2)
pickle.dump(ax, open('axis_obj.pkl', 'wb'))
plt.clf()
plt.close()
ax_read = pickle.load(open('axis_obj.pkl', 'rb'))
fig = plt.figure()
ax_read.figure = fig
fig.axes.append(ax_read)
fig.add_axes(ax_read)
# and now I'm stuck on how to access the plot kwargs used earlier for this ax_read object
ax.get_lines() is a better way.
You can change the colors by using this code:
# change the color of the plot lines:
ax.properties()['children'][0].set_color('green')
ax.properties()['children'][1].set_color('black')
Explanation: The axes object has the attribute properties which holds the children of the axes object. children is a list containing all objects which were drawn to the axis:
>>> ax.properties()['children']
[
<matplotlib.lines.Line2D at 0x7f2edb896b70>,
<matplotlib.lines.Line2D at 0x7f2edb896ac8>,
...]
The first two elements are the plots which you have drawn to the axis.
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.
I'm plotting a series of data points with x and y error but do NOT want the errorbars to be included in the legend (only the marker). Is there a way to do so?
Example:
import matplotlib.pyplot as plt
import numpy as np
subs=['one','two','three']
x=[1,2,3]
y=[1,2,3]
yerr=[2,3,1]
xerr=[0.5,1,1]
fig,(ax1)=plt.subplots(1,1)
for i in np.arange(len(x)):
ax1.errorbar(x[i],y[i],yerr=yerr[i],xerr=xerr[i],label=subs[i],ecolor='black',marker='o',ls='')
ax1.legend(loc='upper left', numpoints=1)
fig.savefig('test.pdf', bbox_inches=0)
You can modify the legend handler. See the legend guide of matplotlib.
Adapting your example, this could read:
import matplotlib.pyplot as plt
import numpy as np
subs=['one','two','three']
x=[1,2,3]
y=[1,2,3]
yerr=[2,3,1]
xerr=[0.5,1,1]
fig,(ax1)=plt.subplots(1,1)
for i in np.arange(len(x)):
ax1.errorbar(x[i],y[i],yerr=yerr[i],xerr=xerr[i],label=subs[i],ecolor='black',marker='o',ls='')
# get handles
handles, labels = ax1.get_legend_handles_labels()
# remove the errorbars
handles = [h[0] for h in handles]
# use them in the legend
ax1.legend(handles, labels, loc='upper left',numpoints=1)
plt.show()
This produces
Here is an ugly patch:
pp = []
colors = ['r', 'b', 'g']
for i, (y, yerr) in enumerate(zip(ys, yerrs)):
p = plt.plot(x, y, '-', color='%s' % colors[i])
pp.append(p[0])
plt.errorbar(x, y, yerr, color='%s' % colors[i])
plt.legend(pp, labels, numpoints=1)
Here is a figure for example:
The accepted solution works in simple cases but not in general. In particular, it did not work in my own more complex situation.
I found a more robust solution, which tests for ErrorbarContainer, which did work for me. It was proposed by Stuart W D Grieve and I copy it here for completeness
import matplotlib.pyplot as plt
from matplotlib import container
label = ['one', 'two', 'three']
color = ['red', 'blue', 'green']
x = [1, 2, 3]
y = [1, 2, 3]
yerr = [2, 3, 1]
xerr = [0.5, 1, 1]
fig, (ax1) = plt.subplots(1, 1)
for i in range(len(x)):
ax1.errorbar(x[i], y[i], yerr=yerr[i], xerr=xerr[i], label=label[i], color=color[i], ecolor='black', marker='o', ls='')
handles, labels = ax1.get_legend_handles_labels()
handles = [h[0] if isinstance(h, container.ErrorbarContainer) else h for h in handles]
ax1.legend(handles, labels)
plt.show()
It produces the following plot (on Matplotlib 3.1)
I works for me if I set the label argument as a None type.
plt.errorbar(x, y, yerr, label=None)
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.