I wanted to set markers for scatter plot as shown below
I am using matplotlib library for plotting until now I am able to plot only the points by the code
import matplotlib.pyplot as plt
import numpy as np
x = [39.5,38,42.5]
y = np.array([0,1,2])
my_xticks = ['a','b','c']
plt.yticks(y, my_xticks)
plt.scatter(x, y,marker='x',s=100)
plt.show()
I also want the line as shown in figure above as a marker in my plot
One way to do this would be to call plt.hlines, so that you can add a few horizontal lines to your markers.
Example:
import matplotlib.pyplot as plt
import numpy as np
x = np.array([39.5,38,42.5])
y = np.array([0,1,2])
my_xticks = ['a','b','c']
plt.yticks(y, my_xticks)
plt.scatter(x, y,marker='x',s=100)
width = .4
plt.hlines(y, xmin=x - width/2, xmax=x + width/2)
plt.show()
Change width to suit your desired "width" for each line. Colors can be changed as well, check the documentation for plt.hlines.
Note that this will not persist in a call to legend. legend will only use the actual line objects, although there are ways to change that too.
Related
I am reading the following discussion:
setting axis scale in matplotlib contour plot
From the discussion above, to get arbitrary ratio, we could use
plt.figure(figsize=(8,2))
# ...
plt.tight_layout()
However, this setting is for figure not for contourf.
I used the above codes in my codes
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
import pandas as pd
import math
rm = pd.read_excel("test_3d.xlsx", header = None)
# find min values of noise
rec = np.shape(rm)
# grid
X = np.arange(1,rec[1]+1,1)
Y = np.arange(1,rec[0]+1,1)
x , y = np.meshgrid(X,Y)
# plots
plt.clf()
con = plt.contourf(x,y,rm, cmap=cm.jet)
plt.figure(figsize=(8,2))
plt.tight_layout()
plt.title('2457MHz')
plt.show()
The result I got is
The ratio of bottom plot is what I want; however, I use plt.figure(figsize=(8,2)), which is not for contourf. Therefore, I did not get the correct result.
Is there any way that I can plot arbitrary ratio for contourf?
Instead of setting the figsize, use Axes.set_aspect to change the aspect ratio of the contour plot's Axes:
fig, ax = plt.subplots()
ax.contourf(x, y, rm, cmap='viridis')
ax.set_aspect(0.25)
If you prefer to stick with the plt syntax, access the Axes using plt.gca:
plt.contourf(x, y, rm, cmap='viridis')
plt.gca().set_aspect(0.25)
I would like to use a ColorFunction similar to that in Mathematica for my plots in python.
In other words, I would like to call pyplot.plot(x, y, color=c), where c is a vector, defining the color of each data point.
Is there any way to achieve this using the matplotlib library?
To the best of my knowledge, there is no equivalent in Matplotlib, but we can get the similar result following two steps: draw points with varied colors and draw the line.
Here is a demo.
The source code,
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm
import random
fig, ax = plt.subplots()
nrof_points = 100
x = np.linspace(0, 10, nrof_points)
y = np.sin(x)
colors = cm.rainbow(np.linspace(0, 1, nrof_points)) # generate a bunch of colors
# draw points
for idx, point in enumerate(zip(x, y)):
ax.plot(point[0], point[1], 'o', color=colors[idx], markersize=10)
# draw the line
ax.plot(x, y, 'k')
plt.grid()
plt.show()
While I agree with #SparkAndShine that there is no way to parameterize the color of one line, it is possible to color many lines to create a visual effect that is largely the same. This is at the heart of a demo in the MatPlotLib documentation. However, this demo is not the simplest implementation of this principle. Here is an alternate demo based on #SparkAndShine's response:
colored sine (can't post as image since I don't have the reputation)
Source code:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm
fig, ax = plt.subplots()
nrof_points = 100
x = np.linspace(0, 10, nrof_points)
y = np.sin(x)
colors = cm.rainbow(np.linspace(0, 1, nrof_points)) # generate a bunch of colors
# draw points
for idx in range(0,np.shape(x)[0]-2,1):
ax.plot(x[idx:idx+1+1], y[idx:idx+1+1], color=colors[idx])
# add a grid and show
plt.grid()
plt.show()
I have questions related to creating a simple lineplot in Python with mplot3D where the area under the plot is filled. I am using Python 2.7.5 on RedHatEnterprise 7.2, matplotlib 1.2.0 and numpy 1.7.2.
Using the code below, I am able to generate a line plot. This is displayed as expected with the beginning / end of the plot set by the limits of the imported data set.
I am then trying to fill the area between the line plot and -0.1 using the answer given by Bart from Plotting a series of 2D plots projected in 3D in a perspectival way. This works, however, the filled area is continued beyond the limits of the data set. This is also the case when running the example from the link.
This screen shot shows the plot generated with filled area extending beyond the set axis limits.
How do I achieve that the filled area is only the range of the data set or the axis limits whichever is smaller?
How do I add a legend for those plots onto the figure?
Code as follows:
from numpy import *
import matplotlib.pylab as plt
from mpl_toolkits.mplot3d import Axes3D
x,y = genfromtxt("data.dat",unpack=True)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.add_collection3d(plt.fill_between(x,y,-0.1, color='orange', alpha=0.3,label="filled plot"),1, zdir='y')
ax.plot(x,y,1,zdir="y",label="line plot")
ax.legend()
ax.set_xlim3d(852.353,852.359)
ax.set_zlim3d(-0.1,5)
ax.set_ylim3d(0,2)
ax.get_xaxis().get_major_formatter().set_useOffset(False)
plt.show()
I don't know how to put fill_between working the way you want it to, but I can provide an alternative using a 3D polygon:
from numpy import *
import matplotlib.pylab as plt
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection # New import
#x,y = genfromtxt("data.dat",unpack=True)
# Generated some random data
w = 3
x,y = np.arange(100), np.random.randint(0,100+w,100)
y = np.array([y[i-w:i+w].mean() for i in range(3,100+w)])
z = np.zeros(x.shape)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
#ax.add_collection3d(plt.fill_between(x,y,-0.1, color='orange', alpha=0.3,label="filled plot"),1, zdir='y')
verts = [(x[i],z[i],y[i]) for i in range(len(x))] + [(x.max(),0,0),(x.min(),0,0)]
ax.add_collection3d(Poly3DCollection([verts],color='orange')) # Add a polygon instead of fill_between
ax.plot(x,z,y,label="line plot")
ax.legend()
ax.set_ylim(-1,1)
plt.show()
The code above generates some random data. Builds vertices from it and plots a polygon with those vertices. This will give you the plot you wish (but does not use fill_between). The result is:
I am using scatter plot in matplotlib to plot some points. I have two 1D arrays each storing the x and y coordinate of the samples. Also there is another 1D array that stores the label(to decide in which colour the point should be plotted). I programmed thus far:
import matplotlib.pyplot as plt
X = [1,2,3,4,5,6,7]
Y = [1,2,3,4,5,6,7]
label = [0,1,4,2,3,1,1]
plt.scatter(X, Y, c= label, s=50)
plt.show()
Now I want to be able to see which color corresponds to which label?
I looked up the implementation of legends in matplotlib like the one here:
how to add legend for scatter()?
However they are suggesting to create a plot for each label of sample. However all my labels are in the same 1D array(label). How can I achieve this?
You could do it with a colormap. Some examples of how to do it are here.
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.colors as colors
X = [1,2,3,4,5,6,7]
Y = [1,2,3,4,5,6,7]
label = [0,1,4,2,3,1,1]
# Define a colormap with the right number of colors
cmap = plt.cm.get_cmap('jet',max(label)-min(label)+1)
bounds = range(min(label),max(label)+2)
norm = colors.BoundaryNorm(bounds, cmap.N)
plt.scatter(X, Y, c= label, s=50, cmap=cmap, norm=norm)
# Add a colorbar. Move the ticks up by 0.5, so they are centred on the colour.
cb=plt.colorbar(ticks=np.array(label)+0.5)
cb.set_ticklabels(label)
plt.show()
You might need to play around to get the tick labels centred on their colours, but you get the idea.
i have a data like below
>>>x = [transactional,promotional]
>>>y = [3198293, 189910]
i need to draw a simple bar chart? how can i?
this is how i tried
>>>import matplotlib.pyplot as plt
>>>plt.bar(x, y ,width=0.90)
simply i need to plot a bar chart with the labels and x-axis is x and y-axis is y.
tried some methods but it is
showing some random diagram....please help me on this.
The code below produces the following graph:
import numpy as np
import matplotlib.pyplot as plt
# Specify data / labels
y = [3198293, 189910]
xlabels = ['transactional','promotional']
bar_width = 0.90
x = np.arange(len(y))
fig, ax = plt.subplots()
ax.bar(x, y, width=bar_width)
ax.set_xticks(x + (bar_width/2.0))
ax.set_xticklabels(xlabels)
plt.show()
This is just a basic example, there are much more detailed examples at http://matplotlib.org/examples/.
You may be interested specifically in barchart_demo.py