I would like to make a scatter plot with unfilled squares. markerfacecolor is not an option recognized by scatter. I made a MarkerStyle but the fill style seems to be ignored by the scatter plot. Is there a way to make unfilled markers in the scatterplot?
import matplotlib.markers as markers
import matplotlib.pyplot as plt
import numpy as np
def main():
size = [595, 842] # in pixels
dpi = 72. # dots per inch
figsize = [i / dpi for i in size]
fig = plt.figure(figsize=figsize)
ax = fig.add_axes([0,0,1,1])
x_max = 52
y_max = 90
ax.set_xlim([0, x_max+1])
ax.set_ylim([0, y_max + 1])
x = np.arange(1, x_max+1)
y = [np.arange(1, y_max+1) for i in range(x_max)]
marker = markers.MarkerStyle(marker='s', fillstyle='none')
for temp in zip(*y):
plt.scatter(x, temp, color='green', marker=marker)
plt.show()
main()
It would appear that if you want to use plt.scatter() then you have to use facecolors = 'none' instead of setting fillstyle = 'none' in construction of the MarkerStyle, e.g.
marker = markers.MarkerStyle(marker='s')
for temp in zip(*y):
plt.scatter(x, temp, color='green', marker=marker, facecolors='none')
plt.show()
or, use plt.plot() with fillstyle = 'none' and linestyle = 'none' but since the marker keyword in plt.plot does not support MarkerStyle objects you have to specify the style inline, i.e.
for temp in zip(*y):
plt.plot(x, temp, color='green', marker='s', fillstyle='none')
plt.show()
either of which will give you something that looks like this
Refer to: How to do a scatter plot with empty circles in Python?
Try adding facecolors='none' to your plt.scatter
plt.scatter(x, temp, color='green', marker=marker, facecolors='none')
Related
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))
In pyplot, you can change the order of different graphs using the zorder option or by changing the order of the plot() commands. However, when you add an alternative axis via ax2 = twinx(), the new axis will always overlay the old axis (as described in the documentation).
Is it possible to change the order of the axis to move the alternative (twinned) y-axis to background?
In the example below, I would like to display the blue line on top of the histogram:
import numpy as np
import matplotlib.pyplot as plt
import random
# Data
x = np.arange(-3.0, 3.01, 0.1)
y = np.power(x,2)
y2 = 1/np.sqrt(2*np.pi) * np.exp(-y/2)
data = [random.gauss(0.0, 1.0) for i in range(1000)]
# Plot figure
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
ax2.hist(data, bins=40, normed=True, color='g',zorder=0)
ax2.plot(x, y2, color='r', linewidth=2, zorder=2)
ax1.plot(x, y, color='b', linewidth=2, zorder=5)
ax1.set_ylabel("Parabola")
ax2.set_ylabel("Normal distribution")
ax1.yaxis.label.set_color('b')
ax2.yaxis.label.set_color('r')
plt.show()
Edit: For some reason, I am unable to upload the image generated by this code. I will try again later.
You can set the zorder of an axes, ax.set_zorder(). One would then need to remove the background of that axes, such that the axes below is still visible.
ax2 = ax1.twinx()
ax1.set_zorder(10)
ax1.patch.set_visible(False)
In pyplot, you can change the order of different graphs using the zorder option or by changing the order of the plot() commands. However, when you add an alternative axis via ax2 = twinx(), the new axis will always overlay the old axis (as described in the documentation).
Is it possible to change the order of the axis to move the alternative (twinned) y-axis to background?
In the example below, I would like to display the blue line on top of the histogram:
import numpy as np
import matplotlib.pyplot as plt
import random
# Data
x = np.arange(-3.0, 3.01, 0.1)
y = np.power(x,2)
y2 = 1/np.sqrt(2*np.pi) * np.exp(-y/2)
data = [random.gauss(0.0, 1.0) for i in range(1000)]
# Plot figure
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
ax2.hist(data, bins=40, normed=True, color='g',zorder=0)
ax2.plot(x, y2, color='r', linewidth=2, zorder=2)
ax1.plot(x, y, color='b', linewidth=2, zorder=5)
ax1.set_ylabel("Parabola")
ax2.set_ylabel("Normal distribution")
ax1.yaxis.label.set_color('b')
ax2.yaxis.label.set_color('r')
plt.show()
Edit: For some reason, I am unable to upload the image generated by this code. I will try again later.
You can set the zorder of an axes, ax.set_zorder(). One would then need to remove the background of that axes, such that the axes below is still visible.
ax2 = ax1.twinx()
ax1.set_zorder(10)
ax1.patch.set_visible(False)
I'm trying to plot a polar plot with this code:
import numpy as np
import matplotlib.pylab as plt
def power(angle, l, lam):
return 1/(lam) * ((np.cos(np.pi*l*np.cos(angle)/lam) - np.cos(np.pi*l/lam))/np.sin(angle))**2
fig = plt.figure(1)
ax = fig.add_subplot(111, projection='polar')
theta = np.linspace(0.001, 2*np.pi, 100)
P1 = power(theta, 1, 5)
ax.plot(theta, P1, color='r', linewidth=3)
plt.savefig('1.png')
and I get this plot:
I would like to change 2 things. The first and more important one is to hide the radial tick labels (I just want to show the general form of the plot).
If possible, how can I choose the vertical axis to correspond to 0°?
Thanks for your help.
You can use set_yticklabels() to remove the radial ticks and set_theta_zero_location() to change the zero location:
fig = plt.figure(1)
ax = fig.add_subplot(111, projection='polar')
ax.plot(theta, P1, color='r', linewidth=3)
ax.set_yticklabels([])
ax.set_theta_zero_location('N')
plt.show()
You might also want to change the direction of the azimuthal axis:
ax.set_theta_direction(-1)
You can set the theta zero position with ax.set_theta_zero_location('N').
To modify the r tick labels, you could do something like
for r_label in ax.get_yticklabels():
r_label.set_text('')
If you want to remove them entirely, do ax.set_yticklabels([]).
More methods can be found in the PolarAxes documentation.
I have two lists containing the x and y coordinates of some points. There is also a list with some values assigned to each of those points. Now my question is, I can always plot the points (x,y) using markers in python. Also I can select colour of the marker manually (as in this code).
import matplotlib.pyplot as plt
x=[0,0,1,1,2,2,3,3]
y=[-1,3,2,-2,0,2,3,1]
colour=['blue','green','red','orange','cyan','black','pink','magenta']
values=[2,6,10,8,0,9,3,6]
for i in range(len(x)):
plt.plot(x[i], y[i], linestyle='none', color=colour[i], marker='o')
plt.axis([-1,4,-3,4])
plt.show()
But is it possible to choose a colour for the marker marking a particular point according to the value assigned to that point (using cm.jet, cm.gray or similar other color schemes) and provide a colorbar with the plot ?
For example, this is the kind of plot I am looking for
where the red dots denote high temperature points and the blue dots denote low temperature ones and others are for temperatures in between.
You are most likely looking for matplotlib.pyplot.scatter. Example:
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
# Generate data:
N = 10
x = np.linspace(0, 1, N)
y = np.linspace(0, 1, N)
x, y = np.meshgrid(x, y)
colors = np.random.rand(N, N) # colors for each x,y
# Plot
circle_size = 200
cmap = matplotlib.cm.viridis # replace with your favourite colormap
fig, ax = plt.subplots(figsize=(4, 4))
s = ax.scatter(x, y, s=circle_size, c=colors, cmap=cmap)
# Prettify
ax.axis("tight")
fig.colorbar(s)
plt.show()
Note: viridis may fail on older version of matplotlib.
Resulting image:
Edit
scatter does not require your input data to be 2-D, here are 4 alternatives that generate the same image:
import matplotlib
import matplotlib.pyplot as plt
x = [0,0,1,1,2,2,3,3]
y = [-1,3,2,-2,0,2,3,1]
values = [2,6,10,8,0,9,3,6]
# Let the colormap extend between:
vmin = min(values)
vmax = max(values)
cmap = matplotlib.cm.viridis
norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
fig, ax = plt.subplots(4, sharex=True, sharey=True)
# Alternative 1: using plot:
for i in range(len(x)):
color = cmap(norm(values[i]))
ax[0].plot(x[i], y[i], linestyle='none', color=color, marker='o')
# Alternative 2: using scatter without specifying norm
ax[1].scatter(x, y, c=values, cmap=cmap)
# Alternative 3: using scatter with normalized values:
ax[2].scatter(x, y, c=cmap(norm(values)))
# Alternative 4: using scatter with vmin, vmax and cmap keyword-arguments
ax[3].scatter(x, y, c=values, vmin=vmin, vmax=vmax, cmap=cmap)
plt.show()