I'm a fan of the Seaborn package for making nice-looking plots using Matplotlib. But I can't seem to figure out how to show minor gridlines in my plots.
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sbn
x = np.linspace(0, 2 * np.pi, 100)
y = np.sin(x)
fig, ax = plt.subplots(1, 1)
ax.scatter(x, y)
ax.grid(b=True, which='major')
ax.grid(b=True, which='minor')
gives:
Any thoughts here? Also any thoughts on how to adjust the style of the Seaborn gridlines that do show up...in particular, I'd love to make them narrower.
Wound up combining CT Zhu's answer with tcaswell's hint:
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sbn
x = np.linspace(0, 2 * np.pi, 100)
y = np.sin(x)
fig, ax = plt.subplots(1, 1)
ax.scatter(x, y)
ax.get_xaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.get_yaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.grid(b=True, which='major', color='w', linewidth=1.0)
ax.grid(b=True, which='minor', color='w', linewidth=0.5)
That's because the minor ticks are not yet defined, so we need to add for example:
ax.set_xticks(np.arange(0,8)-0.5, minor=True)
ax.set_yticks([-1.25, -0.75, -0.25,0.24,0.75,1.25], minor=True)
Related
How can I reduce the distance between the numbering of an axis and the ticks corresponding to them. I tried using pad=0 for the tick_params but it doesn't seem to work. Below is a reproducible (simplified) code of my issue (and the figure):
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
plt.rcParams["figure.figsize"] = (10,10)
fig = plt.figure()
ax = Axes3D(fig)
ax.set_xlabel("X" , fontsize=20)
ax.set_ylabel("Y", fontsize=20)
ax.set_zlabel("Z" , fontsize=20)
ax.view_init(azim=-20)
ax.tick_params(axis='x', which='major', pad=0)
x = np.arange(0,10,0.01)
y = np.ones(len(x))
z = np.sin(x)
plt.plot(x,y,z)
Changing the values of pad seem to not have any effect. Note: I need the plot in that specific orientation (azim=-20). How can I achieve what I need? Thank you!
The pad argument also takes negative values to bring the ticklabels closer to the ticks.
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
fig = plt.figure()
ax = axes3d.Axes3D(fig)
ax.set_xlabel("X" , fontsize=20)
ax.set_ylabel("Y", fontsize=20)
ax.set_zlabel("Z" , fontsize=20)
ax.view_init(azim=-20)
ax.tick_params(axis='x', which='major', pad=-5)
x = np.arange(0, 10, 0.01)
y = np.ones(len(x))
z = np.sin(x)
plt.plot(x, y, z)
plt.show()
EDIT: Alternative outcome with set figure size and dpi value.
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
mpl.rcParams["figure.figsize"] = 10, 10
mpl.rcParams["figure.dpi"] = 100
fig = plt.figure()
ax = axes3d.Axes3D(fig)
ax.set_xlabel("X" , fontsize=20)
ax.set_ylabel("Y", fontsize=20)
ax.set_zlabel("Z" , fontsize=20)
ax.view_init(azim=-20)
ax.tick_params(axis='x', which='major', pad=-5)
x = np.arange(0, 10, 0.01)
y = np.ones(len(x))
z = np.sin(x)
plt.plot(x, y, z)
plt.show()
I need to draw a broken x axis graph (e.g. the graph below) with existing data, my question is whether it's possible to use seaborn APIs to do that?
Not as pretty as I'd like but works.
%matplotlib inline # If you are running this in a Jupyter Notebook.
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 20, 500)
y = np.sin(x)
f, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, sharey=True)
ax = sns.tsplot(time=x, data=y, ax=ax1)
ax = sns.tsplot(time=x, data=y, ax=ax2)
ax1.set_xlim(0, 6.5)
ax2.set_xlim(13.5, 20)
A tighter version (also replaced the deprecated tsplot). Can control the distance between the plots by the wspace parameter in the plt.subplots_adjust(wspace=0, hspace=0) line.
%matplotlib inline
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 20, 500)
y = np.sin(x)
f, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, sharey=True)
ax = sns.lineplot(x=x, y=y, ax=ax1)
ax = sns.lineplot(x=x, y=y, ax=ax2)
ax1.set_xlim(0, 6.5)
ax2.set_xlim(13.5, 20)
plt.subplots_adjust(wspace=0, hspace=0)
I plot using two y-axis, on the left and the right of a matplotlib figure and use zorder to control the position of the plots. I need to define the zorder across axes in the same figure.
Problem
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(-10,10,0.01)
fig, ax1 = plt.subplots( 1, 1, figsize=(9,3) )
ax1.plot( x, np.sin(x), color='red', linewidth=10, zorder=1 )
ax2 = ax1.twinx()
ax2.plot( x, x, color='blue', linewidth=10, zorder=-1)
In the previous diagram, I would expect the blue line to appear behind the red plot.
How do I control the zorder when using twin axes?
I am using:
python: 3.4.3 + numpy: 1.11.0 + matplotlib: 1.5.1
This should work
ax1.set_zorder(ax2.get_zorder()+1)
ax1.patch.set_visible(False)
the following codes works
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import ticker as tick
x = np.arange(-10,10,0.01)
plt.figure(figsize=(10, 5))
fig = plt.subplot(111)
"""be attention to here. it's fig.plot, not ax1.plot
if you write ax1.plot, then it does not work.
"""
fig.plot(x, x, color ='blue', linewidth =10)
ax2 = fig.twinx()
ax2.plot(x, np.sin(x), color='red', linewidth =10)
"""
It looks like the two axes have separate z-stacks.
The axes are z-ordered with the most recent axis on top
"""
fig.set_zorder(ax2.get_zorder()+1)
fig.patch.set_visible(False)
plt.show()
It looks like the two axes have separate z-stacks. The axes are z-ordered with the most recent axis on top, so you need to move the curve you want on top to the last axis you create:
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(-10,10,0.01)
fig, ax1 = plt.subplots( 1, 1, figsize=(9,3) )
ax1.plot( x, x, color='blue', linewidth=10 )
ax2 = ax1.twinx()
ax2.plot( x, np.sin(x), color='red', linewidth=10 )
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()
I am using iPython notebook to test interactive functionalities. The following example (from here) worked fine for me several months ago. However, if I run it now, it plots all the images from possible combinations. I am not sure if this is a duplicate, but this didn't help.
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
# mpl.rcParams['figure.max_open_warning'] = 1
def plot(amplitude, color):
fig, ax = plt.subplots(figsize=(4, 3),
subplot_kw={'axisbg':'#EEEEEE',
'axisbelow':True})
ax.grid(color='w', linewidth=2, linestyle='solid')
x = np.linspace(0, 10, 1000)
ax.plot(x, amplitude * np.sin(x), color=color,
lw=5, alpha=0.4)
ax.set_xlim(0, 10)
ax.set_ylim(-1.1, 1.1)
return fig
from ipywidgets import StaticInteract, RangeWidget, RadioWidget
StaticInteract(plot,
amplitude=RangeWidget(0.1, 1.0, 0.1),
color=RadioWidget(['blue', 'green', 'red']))
This is the output:
Can you help me?
This is how you can approach it.
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from ipywidgets import interact, FloatSlider, RadioButtons
amplitude_slider = FloatSlider(min=0.1, max=1.0, step=0.1, value=0.2)
color_buttons = RadioButtons(options=['blue', 'green', 'red'])
# decorate the plot function with an environment from the UIs:
#interact(amplitude=amplitude_slider, color=color_buttons)
def plot(amplitude, color):
fig, ax = plt.subplots(figsize=(4, 3),
subplot_kw={'axisbg':'#EEEEEE',
'axisbelow':True})
ax.grid(color='w', linewidth=2, linestyle='solid')
x = np.linspace(0, 10, 1000)
ax.plot(x, amplitude * np.sin(x), color=color,
lw=5, alpha=0.4)
ax.set_xlim(0, 10)
ax.set_ylim(-1.1, 1.1)