back to the matplotlib-gallery at https://github.com/rasbt/matplotlib-gallery



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%load_ext watermark



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%watermark -u -v -d -p matplotlib,numpy









    



Last updated: 17/07/2014 

CPython 3.4.1
IPython 2.0.0

matplotlib 1.3.1
numpy 1.8.1

[More info](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `%watermark` extension



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%matplotlib inline

Formatting: subplots, markers, colors, axes

Sections

Subplots

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import numpy as np
import matplotlib.pyplot as plt

x = range(10)
y = range(10)

fig, ax = plt.subplots(2)

for sp in ax:
    sp.plot(x, y)

m x n subplots

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In [5]:

    
import matplotlib.pyplot as plt

x = range(10)
y = range(10)

fig, ax = plt.subplots(nrows=2,ncols=2)

for row in ax:
    for col in row:
        col.plot(x, y)
        
plt.show()



In [6]:

    
fig, ax = plt.subplots(nrows=2,ncols=2)

plt.subplot(2,2,1)
plt.plot(x, y)

plt.subplot(2,2,2)
plt.plot(x, y)
    
plt.subplot(2,2,3)
plt.plot(x, y)

plt.subplot(2,2,4)
plt.plot(x, y)
    
plt.show()

Labeling a subplot grid like a matrix

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In [32]:

    
import matplotlib.pyplot as plt
import numpy as np

fig, axes = plt.subplots(nrows=3, ncols=3, 
                         sharex=True, sharey=True,
                         figsize=(8,8)
                         )
x = range(5)
y = range(5)

for row in axes:
    for col in row:
        col.plot(x, y)

for ax, col in zip(axes[0,:], ['1', '2', '3']):
    ax.set_title(col, size=20)
    
for ax, row in zip(axes[:,0], ['A', 'B', 'C']):
    ax.set_ylabel(row, size=20, rotation=0, labelpad=15)
  
plt.show()

Shared X- and Y-axes

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In [14]:

    
import matplotlib.pyplot as plt

x = range(10)
y = range(10)

fig, ax = plt.subplots(nrows=2,ncols=2, sharex=True, sharey=True)

for row in ax:
    for col in row:
        col.plot(x, y)
    
plt.show()

Setting title and labels

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In [20]:

    
import matplotlib.pyplot as plt

x = range(10)
y = range(10)

fig, ax = plt.subplots(nrows=2,ncols=2)

for row in ax:
    for col in row:
        col.plot(x, y)
        col.set_title('title')  
        col.set_xlabel('x-axis')
        col.set_ylabel('x-axis')
                
fig.tight_layout()
            
plt.show()

Defining colors

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3 ways to define colors

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Matplotlib supports 3 different ways to encode colors, e.g, if we want to use the color blue, we can define colors as

RGB color values (range 0.0 to 1.0) -> (0.0, 0.0, 1.0)
matplotlib supported names -> 'blue' or 'b'
HTML hex values -> '#0000FF'



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import matplotlib.pyplot as plt

samples = range(1,4)

for i, col in zip(samples, [(0.0, 0.0, 1.0), 'blue', '#0000FF']):
    plt.plot([0, 10], [0, i], lw=3, color=col) 

plt.legend(['RGB values: (0.0, 0.0, 1.0)', 
            "matplotlib names: 'blue'", 
            "HTML hex values: '#0000FF'"],
           loc='upper left')    
plt.title('3 alternatives to define the color blue')
    
plt.show()

matplotlib color names

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The color names that are supported by matplotlib are

b: blue
g: green
r: red
c: cyan
m: magenta
y: yellow
k: black
w: white

where the first letter represents the shortcut version.



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import matplotlib.pyplot as plt

cols = ['blue', 'green', 'red', 'cyan',  'magenta', 'yellow', 'black', 'white']

samples = range(1, len(cols)+1)

for i, col in zip(samples, cols):
    plt.plot([0, 10], [0, i], label=col, lw=3, color=col) 

plt.legend(loc='upper left')    
plt.title('matplotlib color names')
    
plt.show()

Colormaps

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More color maps are available at http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps



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import numpy as np
import matplotlib.pyplot as plt

fig, (ax0, ax1) = plt.subplots(1,2, figsize=(14, 7))
samples = range(1,16)

# Default Color Cycle

for i in samples:
    ax0.plot([0, 10], [0, i], label=i, lw=3) 

# Colormap    
    
colormap = plt.cm.Paired
plt.gca().set_color_cycle([colormap(i) for i in np.linspace(0, 0.9, len(samples))])

for i in samples:
    ax1.plot([0, 10], [0, i], label=i, lw=3) 
    
# Annotation    
    
ax0.set_title('Default color cycle')
ax1.set_title('plt.cm.Paired colormap')
ax0.legend(loc='upper left')
ax1.legend(loc='upper left')

plt.show()

Gray-levels

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In [28]:

    
import matplotlib.pyplot as plt
import numpy as np

plt.figure(figsize=(8,6))

samples = np.arange(0, 1.1, 0.1)

for i in samples:
    plt.plot([0, 10], [0, i], label='gray-level %s'%i, lw=3, 
             color=str(i)) # ! gray level has to be parsed as string

plt.legend(loc='upper left')    
plt.title('gray-levels')
    
plt.show()

Edgecolors for scatter plots

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In [58]:

    
import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(10,10))

samples = np.random.randn(30,2)

ax[0][0].scatter(samples[:,0], samples[:,1], 
            color='red',
            label='color="red"')
    
ax[1][0].scatter(samples[:,0], samples[:,1],
            c='red',
            label='c="red"')

ax[0][1].scatter(samples[:,0], samples[:,1], 
            edgecolor='white', 
            c='red',
            label='c="red", edgecolor="white"')

ax[1][1].scatter(samples[:,0], samples[:,1], 
            edgecolor='0', 
            c='1',
            label='color="1.0", edgecolor="0"')

for row in ax:
    for col in row:
        col.legend(loc='upper left') 
    
plt.show()

Color gradients

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In [29]:

    
import matplotlib.pyplot as plt
import matplotlib.colors as col
import matplotlib.cm as cm
import numpy as np

# input data
mean_values = np.random.randint(1, 101, 100)
x_pos = range(len(mean_values))

fig = plt.figure(figsize=(20,5))

# create colormap
cmap = cm.ScalarMappable(col.Normalize(min(mean_values), 
                                       max(mean_values), 
                                       cm.hot))

# plot bars
plt.subplot(131)
plt.bar(x_pos, mean_values, align='center', alpha=0.5, 
        color=cmap.to_rgba(mean_values))
plt.ylim(0, max(mean_values) * 1.1)

plt.subplot(132)
plt.bar(x_pos, np.sort(mean_values), align='center', alpha=0.5, 
        color=cmap.to_rgba(mean_values))
plt.ylim(0, max(mean_values) * 1.1)

plt.subplot(133)
plt.bar(x_pos, np.sort(mean_values), align='center', alpha=0.5, 
        color=cmap.to_rgba(np.sort(mean_values)))
plt.ylim(0, max(mean_values) * 1.1)

plt.show()



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Marker styles

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import matplotlib.pyplot as plt

import numpy as np
import matplotlib.pyplot as plt

markers = [

'.', # point
',', # pixel
'o', # circle
'v', # triangle down
'^', # triangle up
'<', # triangle_left
'>', # triangle_right
'1', # tri_down
'2', # tri_up
'3', # tri_left
'4', # tri_right
'8', # octagon
's', # square
'p', # pentagon
'*', # star
'h', # hexagon1
'H', # hexagon2
'+', # plus
'x', # x
'D', # diamond
'd', # thin_diamond
'|', # vline

]

plt.figure(figsize=(13, 10))
samples = range(len(markers))


for i in samples:
    plt.plot([i-1, i, i+1], [i, i, i], label=markers[i], marker=markers[i], markersize=10) 


# Annotation    
    
plt.title('Matplotlib Marker styles', fontsize=20)
plt.ylim([-1, len(markers)+1])
plt.legend(loc='lower right')


plt.show()

Line styles

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In [13]:

    
import numpy as np
import matplotlib.pyplot as plt

linestyles = ['-.', '--', 'None', '-', ':']

plt.figure(figsize=(8, 5))
samples = range(len(linestyles))


for i in samples:
    plt.plot([i-1, i, i+1], [i, i, i], 
             label='"%s"' %linestyles[i], 
             linestyle=linestyles[i],
             lw=4
             ) 

# Annotation    
    
plt.title('Matplotlib line styles', fontsize=20)
plt.ylim([-1, len(linestyles)+1])
plt.legend(loc='lower right')


plt.show()

Hiding axes

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Hiding axis ticks and labels

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In [26]:

    
import numpy as np
import matplotlib.pyplot as plt

x = range(10)
y = range(10)

fig = plt.gca()

plt.plot(x, y)

fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)

plt.show()

Removing frame and ticks

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In [29]:

    
import numpy as np
import matplotlib.pyplot as plt

x = range(10)
y = range(10)

fig = plt.gca()

plt.plot(x, y)

# removing frame
fig.spines["top"].set_visible(False)  
fig.spines["bottom"].set_visible(False)  
fig.spines["right"].set_visible(False)  
fig.spines["left"].set_visible(False) 

# removing ticks
plt.tick_params(axis="both", which="both", bottom="off", top="off",  
                labelbottom="on", left="off", right="off", labelleft="on")  

plt.show()

Custom tick labels

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Text and rotation

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In [32]:

    
import matplotlib.pyplot as plt

x = range(10)
y = range(10)
labels = ['super long axis label' for i in range(10)]

fig, ax = plt.subplots()

plt.plot(x, y)

# set custom tick labels
ax.set_xticklabels(labels, rotation=45, horizontalalignment='right')

plt.show()

Adding a constant value to axis labels

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In [7]:

    
import matplotlib.pyplot as plt

CONST = 10

x = range(10)
y = range(10)
labels = [i+CONST for i in x]

fig, ax = plt.subplots()

plt.plot(x, y)
plt.xlabel('x-value + 10')

# set custom tick labels
ax.set_xticklabels(labels)

plt.show()



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