import matplotlib.pyplot as plt
import numpy as np
x = np.arange(-1,5)
y = 6 - np.square(x-1)
fig, ax = plt.subplots()
ax.plot(x, y, 'b')
ax.scatter(x, y, color='m', zorder=10)
ax.set_xlabel('x')
ax.set_ylabel('y')
This creates the following:
This function is increasing for all values of x < 1 and increasing for all values of x > 1. Is there a simple way that I can put the text "Increasing" like an x label but centered below the x ticks of 0 and 1, "Decreasing" like an x label but centered below 3, and move the "x" xlabel lower such that it has a lower vertical position than "Increasing" and "Decreasing"? I'd rather not do this with ax.text() unless I absolutely have to.
Maybe use text? I have tried changing the labels but this seems cumbersome. Unfortunately you have to set the text coordinates "manually". Note that you can use newline in the ticks to move them down.
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(-1,5)
y = 6 - np.square(x-1)
fig, ax = plt.subplots()
ax.text(0.5, -4.6, 'Increasing', ha="center")
ax.text(3, -4.6, 'Decreasing', ha="center")
ax.plot(x, y, 'b')
ax.scatter(x, y, color='m', zorder=10)
ax.set_xlabel('\nx')
ax.set_ylabel('y')
which produces
Related
I can't find the reason why my plot shows no lines....
fig = plt.figure()
ax = fig.gca(projection='3d')
for i in range(n):
ax.scatter(lys[i][0], lys[i][1], lys[i][2], c='b', marker='o')
ax.plot(x, y, z,'bo', label='Self-avoiding random walk')
ax.legend()
plt.show()
It's because you set the markers in ax.plot to 'bo', which corresponds to blue circle markers only. If you want lines between the markers, you probably want 'b-o', as defined in the docs (check out the 'Format Strings' section under 'Notes'). You need to define the format string as '[marker][line][color]'.
Simple example:
import matplotlib.pyplot as plt
x = y = z = [0, 1, 2]
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot(x, y, z, 'b-o')
plt.show()
Returns:
Is there a way to move tick labels in Matplot3dlib like this?
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt
x = np.outer(np.linspace(-2, 2, 30), np.ones(30))
y = x.copy().T # transpose
z = np.cos(x ** 2 + y ** 2)
fig = plt.figure()
ax = plt.axes(projection='3d')
ax.plot_surface(x, y, z,cmap='viridis', edgecolor='none')
ax.set_title('Surface plot')
plt.show()
There are some ways using pad parameters.
However, I want to move more precisely like figure in the link above.
Any help appreciated.
-- Addition --
When I changing PAD parameter like the code below, the tick's label is more closer to the axis. However, I want to move it a little bit more to -x direction.
tick's label position changing
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt
x = np.outer(np.linspace(-2, 2, 30), np.ones(30))
y = x.copy().T # transpose
z = np.cos(x ** 2 + y ** 2)
fig = plt.figure()
ax = plt.axes(projection='3d')
ax.plot_surface(x, y, z,cmap='viridis', edgecolor='none')
ax.set_title('Surface plot')
ax.tick_params(axis='x', which='major', pad=-5)
plt.show()
I'm fairly new to scatter plots and python in general. I am trying to plot a third variable against an x and a y, however, I'm not quite sure how to about specifying that argument? So I would have X values which are ints, y values which are also ints and then on the graph itself I want the model scores to show. Is there any way to do this sort of thing?
Thank you.
You can use color to plot a third value. Here is a very minimal example :
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100)
plt.scatter(x,y, c=z, s=5, cmap=cm.hsv)
cbar= plt.colorbar()
plt.show()
Edit
You could also use the size of markers, their transparency, hue or rgb values to depict even more information. Here is an example with marker size, alpha level and color on a perceptually uniform colormap.
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.colors as colors
import matplotlib.cm as cmx
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100)
t = np.random.rand(100)
w = np.random.rand(100)
fig, ax = plt.subplots(1, 1)
cmap = plt.get_cmap('plasma')
cNorm = colors.Normalize(vmin=0, vmax=max(z))
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cmap)
for i in range(100):
ax.scatter(x[i],y[i], c=scalarMap.to_rgba(z[i]), s=t[i]*100, cmap=cmx.plasma, alpha=w[i], edgecolor='none')
scalarMap.set_array([])
fig.colorbar(scalarMap,ax=ax)
for a in [0.1, 0.5, 0.9]:
ax.scatter([], [], c='k', alpha=0.5, s=a*100, label=str(a), edgecolors='none')
l1 = ax.legend(scatterpoints=1, frameon=True, loc='lower left' ,markerscale=1)
for b in [0.25, 0.5, 0.75]:
ax.scatter([], [], c='k', alpha=b, s=50, label=str(b), edgecolors='none')
ax.legend(scatterpoints=1, frameon=True, loc='lower right' ,markerscale=1)
fig.show()
At face value, that question doesn't really make sense because a conventional scatterplot has only two axes, and of course you can't plot points with three dimensions (x, y and accuracy).
However, there are alternative ways to do so.
Use colours
import numpy as np
from matplotlib import pyplot as plt
x = np.random.rand(200)
y = np.random.rand(200)
fig, ax = plt.subplots(figsize=(5, 5))
ax.scatter(x, y, c=(x + y), cmap='RdPu')
scatter takes a c argument, which can be a numeric value, as well as a cmap argument, which can be a string referencing a colormap.
The colormap object translates the numbers provided in c into points along a colour mapping, which you can think of as a gradient bar.
Use 3D axes
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=(5, 5))
ax = Axes3D(fig)
ax.scatter(x, y, (x + y))
This turns your 3rd dimension, accuracy, into an ordinary spatial dimension.
Use size of the markers
Very similar to the color option in the first part, you can change the size of the scatter markers (given you have some idea about the scale of the values). So based on the first example, you can also do;
import numpy as np
from matplotlib import pyplot as plt
x = np.random.rand(200)
y = np.random.rand(200)
fig, ax = plt.subplots(figsize=(5, 5))
ax.scatter(x, y, c='k', s=5*(x + y), cmap='RdPu')
scatter takes also the s argument, that changes the size of the markers.
Let's take this snippet of Python:
import matplotlib.pyplot as plt
x = [5,4,3,2,1,0]
x_strings = ['5','4','3','2','1','0']
y = [0,1,2,3,4,5]
plt.figure()
plt.subplot(311)
plt.plot(x, y, marker='o')
plt.subplot(312)
plt.plot(x_strings, y, marker='^', color='red')
plt.subplot(313)
plt.plot(x, y, marker='^', color='red')
plt.gca().invert_xaxis()
plt.show()
Which produces these three subplots:
In the top subplot the x values are automatically sorted increasingly despite their order in the given list. If I want to plot x vs. y exactly in the given order of x, then I have two possibilities:
1) Convert x values to strings and have a categorical plot -- that's the middle subplot.
2) Invert the x-axis -- that's the bottom subplot.
Question: is there any other way to do a sort of categorical plot, but without conversion of numbers into strings and without the inversion of the x-axis?
ADD-ON:
If I use set_xticklabels(list), then for some unclear reason the first element in the list is skipped (no matter if I refer to the x or to the x_strings list), and the resulting plot is also totally strange:
import matplotlib.pyplot as plt
x = [5,4,3,2,1,0]
x_strings = ['5','4','3','2','1','0']
y = [0,1,2,3,4,5]
fig, ax = plt.subplots()
ax.set_xticklabels(x)
ax.plot(x, y, marker='^', color='red')
plt.show()
Both attempted solutions seem possible. Alternatively, you can always mimic categorical plots by plotting integer numbers and setting the ticklabels to your liking.
import matplotlib.pyplot as plt
x = [5,4,3,2,1,0]
y = [0,1,2,3,4,5]
fig, ax = plt.subplots()
ax.plot(range(len(y)), y, marker='^', color='red')
ax.set_xticks(range(len(y)))
ax.set_xticklabels(x)
plt.show()
I have found another way to do it, without being anyhow categorical and without x-axis inversion!
ax = plt.subplot()
ax.set_xlim(x[0],x[-1], auto=True) # this line plays the trick
plt.plot(x, y, marker='^', color='red')
I have a simple scatter plot where each point has a color given by a value between 0 and 1 set to a chosen colormap. Here's a MWE of my code:
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.gridspec as gridspec
x = np.random.randn(60)
y = np.random.randn(60)
z = [np.random.random() for _ in range(60)]
fig = plt.figure()
gs = gridspec.GridSpec(1, 2)
ax0 = plt.subplot(gs[0, 0])
plt.scatter(x, y, s=20)
ax1 = plt.subplot(gs[0, 1])
cm = plt.cm.get_cmap('RdYlBu_r')
plt.scatter(x, y, s=20 ,c=z, cmap=cm)
cbaxes = fig.add_axes([0.6, 0.12, 0.1, 0.02])
plt.colorbar(cax=cbaxes, ticks=[0.,1], orientation='horizontal')
fig.tight_layout()
plt.show()
which looks like this:
The problem here is that I want the small horizontal colorbar position to the lower left of the plot but using the cax argument not only feels a bit hacky, it apparently conflicts with tight_layout which results in the warning:
/usr/local/lib/python2.7/dist-packages/matplotlib/figure.py:1533: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.
warnings.warn("This figure includes Axes that are not "
Isn't there a better way to position the colorbar, ie without getting a nasty warning thrown at you whenever you run the code?
Edit
I wanted the colorbar to show only the max and min values, ie: 0 and 1 and Joe helped me do that by adding vmin=0, vmax=1 to scatter like so:
plt.scatter(x, y, s=20, vmin=0, vmax=1)
so I'm removing this part of the question.
One may use a mpl_toolkits.axes_grid1.inset_locator.inset_axes to place an axes inside another axes. This axes can be used to host the colorbar. Its position is relative the the parent axes, similar to how legends are placed, using a loc argument (e.g. loc=3 means lower left). Its width and height can be specified in absolute numbers (inches) or relative to the parent axes (percentage).
cbaxes = inset_axes(ax1, width="30%", height="3%", loc=3)
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.gridspec as gridspec
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
x = np.random.randn(60)
y = np.random.randn(60)
z = [np.random.random() for _ in range(60)]
fig = plt.figure()
gs = gridspec.GridSpec(1, 2)
ax0 = plt.subplot(gs[0, 0])
plt.scatter(x, y, s=20)
ax1 = plt.subplot(gs[0, 1])
cm = plt.cm.get_cmap('RdYlBu_r')
plt.scatter(x, y, s=20 ,c=z, cmap=cm)
fig.tight_layout()
cbaxes = inset_axes(ax1, width="30%", height="3%", loc=3)
plt.colorbar(cax=cbaxes, ticks=[0.,1], orientation='horizontal')
plt.show()
Note that in order to suppress the warning, one might simply call tight_layout prior to adding the inset axes.