I'm trying to completely remove the y-axis from a plot in created with matplotlib. The code I'm using is:
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy.stats import skewnorm
f = plt.figure(figsize=(8,4))
ax = f.add_subplot(111)
colors = ["windows blue", "faded green", "dusty purple"]
colors = sns.xkcd_palette(colors)
b = 4
for j, a in enumerate([(0, 'No skew'), (b, 'Positive skew'), (-b, 'Negative skew')]):
x = np.linspace(skewnorm.ppf(0.001, a[0]), skewnorm.ppf(0.999, a[0]), 100)
y = skewnorm.pdf(x, a[0])
plt.plot(x, y, label=a[1], color=colors[j])
ax.yaxis.set_visible(False)
plt.legend(loc=2)
sns.despine()
This produces a plot with the y-axis ticks removed, but the axis itself still visible (shown below). How can I turn this axis off entirely?
You need to explicitly provide the left or bottom parameter to despine with sns.despine. By default, only right and top axes are removed.
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy.stats import skewnorm
f = plt.figure(figsize=(8,4))
ax = f.add_subplot(111)
colors = ["windows blue", "faded green", "dusty purple"]
colors = sns.xkcd_palette(colors)
b = 4
for j, a in enumerate([(0, 'No skew'), (b, 'Positive skew'), (-b, 'Negative skew')]):
x = np.linspace(skewnorm.ppf(0.001, a[0]), skewnorm.ppf(0.999, a[0]), 100)
y = skewnorm.pdf(x, a[0])
ax.plot(x, y, label=a[1], color=colors[j])
ax.yaxis.set_visible(False)
plt.legend(loc=2)
sns.despine(left=True)
One can also remove the axis and the spine directly with matplotlib.
If you remove the xticks or yticks by
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
the spines are still visible. If you want to turn off also the spines, so having no axis at all, you can use:
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
And if you want to turn everything off at once, use:
ax.axis("off")
Related
I am trying to draw a curve without a line (skeleton). I want the axis and grid lines only.
Here is the code.
++++++++++
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams["figure.figsize"] = [10.00, 7.00]
plt.rcParams["figure.autolayout"] = True
x = [1.6,2,2.5,3.2,4,5,6.3,8,10,13,16,20,25,32,40,50,63,80,100,130,160,200,250,320,400,500,630,800,1000]
y = range(1,10000,350)#[1,10,100,1000,10000]
# Display grid
plt.grid(True, which="both")
default_x_ticks = range(len(x))
plt.plot(default_x_ticks, y)
plt.yscale('log')
plt.xticks(default_x_ticks, x, rotation=90)
plt.show()
+++++++
Kindly help draw without the curve.
By adding
print(plt.xlim())
print(plt.ylim())
to your code you get the exact axis limits.
These can be used in a second run to create the plot without actually plotting anything:
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams["figure.figsize"] = [10.00, 7.00]
plt.rcParams["figure.autolayout"] = True
x = [1.6,2,2.5,3.2,4,5,6.3,8,10,13,16,20,25,32,40,50,63,80,100,130,160,200,250,320,400,500,630,800,1000]
y = range(1,10000,350)#[1,10,100,1000,10000]
# Display grid
plt.grid(True, which="both")
default_x_ticks = range(len(x))
# plt.plot(default_x_ticks, y)
plt.yscale('log')
plt.xticks(default_x_ticks, x, rotation=90)
plt.xlim(-1.4, 29.4)
plt.ylim(0.6315917965717447, 15517.934294269562)
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.
It seems like some of the methods that work for matplotlib 2D might not be working for matplotlib 3D. I'm not sure.
I'd like to remove the tick marks from all axes, and extend the edge color from the bottom and sides to the top as well. The farthest I have gotten is being able to draw the ticks as white, which looks bad as they are rendered on top of the edge lines.
Below is a big chunk of self-contained code that results in the following image. Any help is much appreciated!
import numpy as np
import matplotlib as mpl
from matplotlib import pyplot as plt
from matplotlib import animation
from mpl_toolkits.mplot3d import Axes3D
mpl.rcParams['ytick.color'] = 'white'
#mpl.rcParams['ytick.left'] = False
sample = np.random.random_integers(low=1,high=5, size=(10,3))
# Create a figure and a 3D Axes
fig = plt.figure(figsize=(5,5))
ax = Axes3D(fig)
#ax.w_xaxis.set_tick_params(color='white')
#ax.axes.tick_params
ax.axes.tick_params(bottom=False, color='blue')
##['size', 'width', 'color', 'tickdir', 'pad', 'labelsize',
##'labelcolor', 'zorder', 'gridOn', 'tick1On', 'tick2On',
##'label1On', 'label2On', 'length', 'direction', 'left', 'bottom',
##'right', 'top', 'labelleft', 'labelbottom',
##'labelright', 'labeltop', 'labelrotation']
colors = np.mean(sample[:, :], axis=1)
ax.scatter(sample[:,0], sample[:,1], sample[:,2],
marker='o', s=20, c=colors, alpha=1)
ax.tick_params(color='red')
frame1 = plt.gca()
frame1.axes.xaxis.set_ticklabels([])
frame1.axes.yaxis.set_ticklabels([])
frame1.axes.zaxis.set_ticklabels([])
#frame1.axes.yaxis.set_tick_params(color='white')
To answer the first bit of the question, about tick removal,
it's probably easiest to just disable the tick lines:
for line in ax.xaxis.get_ticklines():
line.set_visible(False)
for line in ax.yaxis.get_ticklines():
line.set_visible(False)
for line in ax.zaxis.get_ticklines():
line.set_visible(False)
E.g.:
import numpy as np
import matplotlib as mpl
from matplotlib import pyplot as plt
from matplotlib import animation
from mpl_toolkits.mplot3d import Axes3D
sample = np.random.random_integers(low=1,high=5, size=(10,3))
# Create a figure and a 3D Axes
fig = plt.figure(figsize=(5,5))
ax = Axes3D(fig)
colors = np.mean(sample[:, :], axis=1)
ax.scatter(sample[:,0], sample[:,1], sample[:,2],
marker='o', s=20, c=colors, alpha=1)
ax = plt.gca()
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax.zaxis.set_ticklabels([])
for line in ax.xaxis.get_ticklines():
line.set_visible(False)
for line in ax.yaxis.get_ticklines():
line.set_visible(False)
for line in ax.zaxis.get_ticklines():
line.set_visible(False)
For newer versions (e.g. matplotlib 3.5.1) a lot of formatting can be done via mpl_toolkits.mplot3d.axis3d._axinfo:
import numpy as np
from matplotlib import pyplot as plt
sample = np.random.randint(low=1,high=5, size=(10,3))
# Create a figure and a 3D Axes
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(projection='3d')
colors = np.mean(sample[:, :], axis=1)
ax.scatter(sample[:,0], sample[:,1], sample[:,2],
marker='o', s=20, c=colors, alpha=1)
for axis in [ax.xaxis, ax.yaxis, ax.zaxis]:
axis.set_ticklabels([])
axis._axinfo['axisline']['linewidth'] = 1
axis._axinfo['axisline']['color'] = (0, 0, 0)
axis._axinfo['grid']['linewidth'] = 0.5
axis._axinfo['grid']['linestyle'] = "-"
axis._axinfo['grid']['color'] = (0, 0, 0)
axis._axinfo['tick']['inward_factor'] = 0.0
axis._axinfo['tick']['outward_factor'] = 0.0
axis.set_pane_color((0.95, 0.95, 0.95))
plt.show()
I would like to create a tsplot, where the x and the y axis are the same length. in other words the aspect ratio of the graph should be 1.
this dos not work:
fig, ax = plt.subplots()
fig.set_size_inches(2, 2)
sns.tsplot(data=df, condition=' ', time='time', value='value', unit=' ', ax=ax)
You could change the aspect ratio of your plots by controlling the aspect
parameter of a matplotlib object as shown:
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
np.random.seed(22)
sns.set_style("whitegrid")
gammas = sns.load_dataset("gammas")
fig = plt.figure()
ax = fig.add_subplot(111, aspect=2) #Use 'equal' to have the same scaling for x and y axes
sns.tsplot(time="timepoint", value="BOLD signal", unit="subject",
condition="ROI", data=gammas, ax=ax)
plt.tight_layout()
plt.show()
A little more direct is ax.set_box_aspect(1)1
In matplotlib, how can I change the font size of a latex symbol?
I have the following code:
import matplotlib.pyplot as plt
import seaborn as sns
# get x and y from file
plt.plot(x, y, linestyle='--', marker='o', color='b')
plt.xlabel(r'$\alpha$ (distance weighted)', fontsize='large')
plt.ylabel('AUC')
plt.show()
But I get the following graph:
Notice that the $\alpha$ is still small.
To increase the size of the fonts set the desired value to fontsize. One way to mitigate the difference between the "normal" font and the "latex" one is by using \mathrm. The example below shows the behaviour of doing this:
import matplotlib.pyplot as plt
import seaborn as sns
x = np.arange(10)
y = np.random.rand(10)
fig = plt.figure(1, figsize=(10,10))
for i, j in zip(np.arange(4), [10,15,20,30]):
ax = fig.add_subplot(2,2,i+1)
ax.plot(x, y, linestyle='--', marker='o', color='b')
ax.set_xlabel(r'$\mathrm{\alpha \ (distance \ weighted)}$', fontsize=j)
ax.set_ylabel('AUC')
plt.show()