Sympy plotting has recently evolved a lot, but I cannot find a way to plot a second expression on a secondary y-axis. Below, I show an example where I combine two plots, but would like the second to use a secondary axis:
>>> from sympy import symbols
>>> from sympy.plotting import plot
>>> x = symbols('x')
>>> p1 = plot(x**4, (x,10,20), label='$x^4$', show=False, legend=True)
>>> p2 = plot(x, (x,10,20), line_color='red', label='$x$', show=False, legend= True)
>>> p1.extend(p2)
>>> p1.show()
Incidentally, is there a way to also modify the line style, e.g. make one of them dashed? The documentation suggests to use the _backend module for fine tuning, but it is not clear to me how to achieve this. See e.g. How do I use the `_backend` attribute of a Sympy `plot`.
The approach from this post can be adapted as follows. Note that sympy puts the spines at the zero-positions, which is confusing with two axes in the same plot. They can be moved again to their original position. Matplotlib can create a combined legend from the handles and labels of both axes.
from sympy import Symbol, plot, sin
import matplotlib.pyplot as plt
def move_sympyplot_to_axes(p, ax, is_twinx):
backend = p.backend(p)
backend.ax = ax
backend._process_series(backend.parent._series, ax, backend.parent)
if is_twinx:
backend.ax.spines['left'].set_color('none')
else:
backend.ax.spines['right'].set_color('none')
backend.ax.spines['left'].set_position(('axes', 0))
backend.ax.spines['bottom'].set_position(('axes', 0))
plt.close(backend.fig)
x = Symbol('x')
p1 = plot(x ** 4, (x, 10, 20), label='$x^4$', show=False)
p2 = plot(sin(10*x)/x**2, (x, 10, 20), adaptive=False, nb_of_points=500,
line_color='red', label='$sin(10x)/x^2$', show=False)
fig, ax = plt.subplots()
ax2 = ax.twinx()
move_sympyplot_to_axes(p1, ax, is_twinx=False)
move_sympyplot_to_axes(p2, ax2, is_twinx=True)
ax2.tick_params(axis='y', colors='red')
ax2.set_ylabel('', color='red')
handles1, labels1 = ax.get_legend_handles_labels()
handles2, labels2 = ax2.get_legend_handles_labels()
plt.legend(handles1 + handles2, labels1 + labels2, loc='upper center')
plt.tight_layout()
plt.show()
Related
When I create a plot with many curves it would be convenient to be able to label each curve at the right where it ends.
The result of plt.legend produces too many similar colors and the legend is overlapping the plot.
As one can see in the example below the use of plt.legend is not very effective:
import numpy as np
from matplotlib import pyplot as plt
n=10
x = np.linspace(0,1, n)
for i in range(n):
y = np.linspace(x[i],x[i], n)
plt.plot(x, y, label=str(i))
plt.legend(loc='upper right')
plt.show()
If possible I would like to have something similar to this plot:
or this:
I would recommend the answer suggested in the comments, but another method that gives something similar to your first option (albeit without the exact placement of the legend markers matching the positions of the associated lines) is:
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
n=10
x = np.linspace(0, 1, n)
labels = [str(i) for i in range(len(x))]
for i in range(n):
y = np.linspace(x[i], x[i], n)
ax.plot(x, y, label=labels[i])
h, _ = ax.get_legend_handles_labels()
# sort the legend handles/labels so they are in the same order as the data
hls = sorted(zip(x, h, labels), reverse=True)
ax.legend(
[ha[1] for ha in hls], # get handles
[la[2] for la in hls], # get labels
bbox_to_anchor=(1.04, 0, 0.1, 1), # set box outside of axes
loc="lower left",
labelspacing=1.6, # add space between labels
)
leg = ax.get_legend()
# expand the border of the legend
fontsize = fig.canvas.get_renderer().points_to_pixels(leg._fontsize)
pad = 2 * (leg.borderaxespad + leg.borderpad) * fontsize
leg._legend_box.set_height(leg.get_bbox_to_anchor().height - pad)
This is heavily reliant on the answers here and here.
i wanted to know how to make a plot with two y-axis so that my plot that looks like this :
to something more like this by adding another y-axis :
i'm only using this line of code from my plot in order to get the top 10 EngineVersions from my data frame :
sns.countplot(x='EngineVersion', data=train, order=train.EngineVersion.value_counts().iloc[:10].index);
I think you are looking for something like:
import matplotlib.pyplot as plt
x = [1,2,3,4,5]
y = [1000,2000,500,8000,3000]
y1 = [1050,3000,2000,4000,6000]
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.bar(x, y)
ax2.plot(x, y1, 'o-', color="red" )
ax1.set_xlabel('X data')
ax1.set_ylabel('Counts', color='g')
ax2.set_ylabel('Detection Rates', color='b')
plt.show()
Output:
#gdubs If you want to do this with Seaborn's library, this code set up worked for me. Instead of setting the ax assignment "outside" of the plot function in matplotlib, you do it "inside" of the plot function in Seaborn, where ax is the variable that stores the plot.
import seaborn as sns # Calls in seaborn
# These lines generate the data to be plotted
x = [1,2,3,4,5]
y = [1000,2000,500,8000,3000]
y1 = [1050,3000,2000,4000,6000]
fig, ax1 = plt.subplots() # initializes figure and plots
ax2 = ax1.twinx() # applies twinx to ax2, which is the second y axis.
sns.barplot(x = x, y = y, ax = ax1, color = 'blue') # plots the first set of data, and sets it to ax1.
sns.lineplot(x = x, y = y1, marker = 'o', color = 'red', ax = ax2) # plots the second set, and sets to ax2.
# these lines add the annotations for the plot.
ax1.set_xlabel('X data')
ax1.set_ylabel('Counts', color='g')
ax2.set_ylabel('Detection Rates', color='b')
plt.show(); # shows the plot.
Output:
Seaborn output example
You could try this code to obtain a very similar image to what you originally wanted.
import seaborn as sb
from matplotlib.lines import Line2D
from matplotlib.patches import Rectangle
x = ['1.1','1.2','1.2.1','2.0','2.1(beta)']
y = [1000,2000,500,8000,3000]
y1 = [3,4,1,8,5]
g = sb.barplot(x=x, y=y, color='blue')
g2 = sb.lineplot(x=range(len(x)), y=y1, color='orange', marker='o', ax=g.axes.twinx())
g.set_xticklabels(g.get_xticklabels(), rotation=-30)
g.set_xlabel('EngineVersion')
g.set_ylabel('Counts')
g2.set_ylabel('Detections rate')
g.legend(handles=[Rectangle((0,0), 0, 0, color='blue', label='Nontouch device counts'), Line2D([], [], marker='o', color='orange', label='Detections rate for nontouch devices')], loc=(1.1,0.8))
I have a figure showing the contourf plot and another showing a plot i've made earlier and I want to plot both on the same figure what should I do?
Here is the code of my contourf plot:
import pylab as pl
from pylab import *
import xlrd
import math
import itertools
from matplotlib import collections as mc
import matplotlib.pyplot as plt
import copy as dc
import pyexcel
from pyexcel.ext import xlsx
import decimal
x_list = linspace(0, 99, 100)
y_list = linspace(0, 99, 100)
X, Y = meshgrid(x_list, y_list, indexing='xy')
Z = [[0 for x in range(len(x_list))] for x in range(len(y_list))]
for each_axes in range(len(Z)):
for each_point in range(len(Z[each_axes])):
Z[len(Z)-1-each_axes][each_point] = power_at_each_point(each_point, each_axes)
figure()
CP2 = contourf(X, Y, Z, cmap=plt.get_cmap('Reds'))
colorbar(CP2)
title('Coverage Plot')
xlabel('x (m)')
ylabel('y (m)')
show()
This is the code of my previously plotted plot:
lc = mc.LineCollection(lines, linewidths=3)
fig, ax = pl.subplots()
ax.add_collection(lc)
ax.autoscale()
ax.margins(0.05)
#The code blow is just for drawing the final plot of the building.
Nodes = xlrd.open_workbook(Node_file_location)
sheet = Nodes.sheet_by_index(0)
Node_Order_Counter = range(1, sheet.nrows + 1)
In_Node_Order_Counter = 0
for counter in range(len(Node_Positions_Ascending)):
plt.plot(Node_Positions_Ascending[counter][0], Node_Positions_Ascending[counter][1], marker='o', color='r',
markersize=6)
pl.text(Node_Positions_Ascending[counter][0], Node_Positions_Ascending[counter][1],
str(Node_Order_Counter[In_Node_Order_Counter]),
color="black", fontsize=15)
In_Node_Order_Counter += 1
#Plotting the different node positions on our plot & numbering them
pl.show()
Without your data we can't see what the plot is supposed to look like, but I have some general recommendations.
Don't use pylab. And if you absolutely must use it, use it within its namespace, and don't do from pylab import *. It makes for very sloppy code - for example, linspace and meshgrid are actually from numpy, but it's hard to tell that when you use pylab.
For complicated plotting, don't even use pyplot. Instead, use the direct object plotting interface. For example, to make a normal plot on top of a contour plot, (such as you want to do) you could do the following:
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
x = np.linspace(1, 5, 20)
y = np.linspace(2, 5, 20)
z = x[:,np.newaxis] * (y[np.newaxis,:])**2
xx, yy = np.meshgrid(x, y)
ax.contourf(xx, yy, z, cmap='Reds')
ax.plot(x, 0.2*y**2)
plt.show()
Notice that I only used pyplot to create the figure and axes, and show them. The actual plotting is done using the AxesSubplot object.
What is wrong with my residual plot that is causing to not be aligned with my main graph? My code is below.
import matplotlib.pyplot as plt
from scipy import stats
import numpy as np
x = np.array([0.030956,0.032956,0.034956,0.036956,0.038956,0.040956])
y = np.array([10.57821088,11.90701212,12.55570876,13.97542486,16.05403248,16.36634177])
yerr = [0.101614114,0.363255259,0.057234211,0.09289917,0.093288198,0.420165796]
xerr = [0.00021]*len(x)
fig1 = plt.figure(1)
frame1=fig1.add_axes((.1,.3,.8,.6))
m, b = np.polyfit(x, y, 1)
print 'gradient',m,'intercept',b
plt.plot(x, m*x + b, '-', color='grey', alpha=0.5)
plt.plot(x,y,'.',color='black',markersize=6)
plt.errorbar(x,y,xerr=0,yerr=yerr,linestyle="None",color='black')
plt.ylabel('$1/\sqrt{F}$ $(N)$',fontsize=20)
plt.autoscale(enable=True, axis=u'both', tight=True)
plt.grid(False)
frame2=fig1.add_axes((.1,.1,.8,.2))
s = m*x+b #(np.sqrt(4*np.pi*8.85E-12)/2.23E-8)*x
difference = y-s
plt.plot(x, difference, 'ro')
frame2.set_ylabel('$Residual$',fontsize=20)
plt.xlabel('$2s+d_0$ $(m)$',fontsize=20)
you can specify the axis limits. the problem is that autoscale is moving your two plots differently. if you insert 2 lines of code, each specifying the axis limits, it will fix it.
plt.axis([.030,.0415, 10, 17]) #line 17
plt.axis([.030,.0415, -.6, .8]) #line 26
i believe this is what you're looking for.
Try using GridSpec.
from matplotlib import gridspec
fig = plt.figure()
gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1])
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1])
ax0.plot(x, m*x + b, '-', color='grey', alpha=0.5)
ax0.plot(x,y,'.',color='black',markersize=6)
ax1.plot(x, difference, 'ro')
And use set_ylabel instead of ylabel (which you use for plt for example) for axes.
I want to create a 3D scatterplot with different datasets in the same plot and a legend with their labels. The problem I am facing is that I cannot properly add the legend and I get a plot with an empty label as the figure in:
http://tinypic.com/view.php?pic=4jnm83&s=5#.Uqd-05GP-gQ.
More specifically, I get the error:
/usr/lib/pymodules/python2.7/matplotlib/legend.py:610: UserWarning: Legend does not support <mpl_toolkits.mplot3d.art3d.Patch3DCollection object at 0x3bf46d0>
Use proxy artist instead."
Please find below an example demo of what I have tried so far:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import random
import csv
from os import listdir
from os.path import isfile, join
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
handles = []
colors = ['blue', 'red']
X1 = range(0,10)
Y1 = range(0,10)
Z1 = range(0,10)
random.shuffle(X1)
random.shuffle(Y1)
random.shuffle(Z1)
scatter1 = ax.scatter(X1, Y1, Z1, c = colors[0], marker = 'o')
random.shuffle(X1)
random.shuffle(Y1)
random.shuffle(Z1)
scatter2 = ax.scatter(X1, Y1, Z1, c = colors[1], marker = 'v')
ax.set_xlabel('X', fontsize = 10)
ax.set_ylabel('Y', fontsize = 10)
ax.set_zlabel('Z', fontsize = 10)
ax.legend([scatter1, scatter2], ['label1', 'label2'])
plt.show()
I have seen other roughly similar examples but none of them uses the scatter() plot. Apart from a working solution, can someone explain what am I doing wrong?
scatter1_proxy = matplotlib.lines.Line2D([0],[0], linestyle="none", c=colors[0], marker = 'o')
scatter2_proxy = matplotlib.lines.Line2D([0],[0], linestyle="none", c=colors[1], marker = 'v')
ax.legend([scatter1_proxy, scatter2_proxy], ['label1', 'label2'], numpoints = 1)
The problem is that the legend function don't support the type returned by a 3D scatter. So you have to create a "dummy plot" with the same characteristics and put those in the legend.
numpoints = 1 to get only one dot in the legend
linestyle= "none" So there is no line drawn in the legend