prettyplotlib.scatter
: A motivating exampleThe default matplotlib
color cycle is not pretty to look at ... It was taken from MATLAB's color cycle.
Need to do the matplotlib
example first because prettyplotlib
changes the default color cycle to a nicer one, from ColorBrewer's Set2. What's even worse is that if you just do a scatter
plot, then it doesn't cycle at all...
In [9]:
import matplotlib.pyplot as mpl_plt
# Set the random seed for consistency
np.random.seed(12)
fig, ax = mpl_plt.subplots(1)
# Show the whole color range
for i in range(8):
x = np.random.normal(loc=i, size=1000)
y = np.random.normal(loc=i, size=1000)
ax.scatter(x, y, label=str(i))
ax.legend()
ax.set_title('prettyplotlib `scatter` example\nshowing default matplotlib `scatter`')
fig.savefig('scatter_matplotlib_default.png')
First, change the colors with brewer2mpl
:
# Get "Set2" colors from ColorBrewer (all colorbrewer scales: http://bl.ocks.org/mbostock/5577023)
set2 = brewer2mpl.get_map('Set2', 'qualitative', 8).mpl_colors
...
color = set2[i]
ax.scatter(x, y, label=str(i), facecolor=color)
In [10]:
import matplotlib.pyplot as mpl_plt
import brewer2mpl
# Get "Set2" colors from ColorBrewer (all colorbrewer scales: http://bl.ocks.org/mbostock/5577023)
set2 = brewer2mpl.get_map('Set2', 'qualitative', 8).mpl_colors
# Set the random seed for consistency
np.random.seed(12)
fig, ax = mpl_plt.subplots(1)
# Show the whole color range
for i in range(8):
x = np.random.normal(loc=i, size=1000)
y = np.random.normal(loc=i, size=1000)
color = set2[i]
ax.scatter(x, y, label=str(i), color=color)
fig.savefig('scatter_matplotlib_improved_01_changed_colors.png')
This looks nice, almost like an impressionist painting, but it's still hard to see overlaps here. So let's fill the symbols with 0.5
opacity using alpha=0.5
.
ax.scatter(x, y, label=str(i), color=color, alpha=0.5)
In [11]:
import matplotlib.pyplot as mpl_plt
import brewer2mpl
# Get "Set2" colors from ColorBrewer (all colorbrewer scales: http://bl.ocks.org/mbostock/5577023)
set2 = brewer2mpl.get_map('Set2', 'qualitative', 8).mpl_colors
# Set the random seed for consistency
np.random.seed(12)
fig, ax = mpl_plt.subplots(1)
# Show the whole color range
for i in range(8):
x = np.random.normal(loc=i, size=1000)
y = np.random.normal(loc=i, size=1000)
color = set2[i]
ax.scatter(x, y, label=str(i), color=color, alpha=0.5)
fig.savefig('scatter_matplotlib_improved_02_added_alpha.png')
This is still pretty lovely and impressionist-y but I still didn't like that it was hard to see when the dots overlapped. So let's add a black outline, and specify that color
is just the facecolor
:
ax.scatter(x, y, label=str(i), alpha=0.5, edgecolor='black', facecolor=color)
In [12]:
import matplotlib.pyplot as mpl_plt
import brewer2mpl
# Get "Set2" colors from ColorBrewer (all colorbrewer scales: http://bl.ocks.org/mbostock/5577023)
set2 = brewer2mpl.get_map('Set2', 'qualitative', 8).mpl_colors
# Set the random seed for consistency
np.random.seed(12)
fig, ax = mpl_plt.subplots(1)
# Show the whole color range
for i in range(8):
x = np.random.normal(loc=i, size=1000)
y = np.random.normal(loc=i, size=1000)
color = set2[i]
ax.scatter(x, y, label=str(i), alpha=0.5, edgecolor='black', facecolor=color)
fig.savefig('scatter_matplotlib_improved_03_added_outline.png')
Ack, but those lines are too thick ... let's think them down to linewidth=0.15
ax.scatter(x, y, label=str(i), alpha=0.5, edgecolor='black', facecolor=color, linewidth=0.15)
In [13]:
import matplotlib.pyplot as mpl_plt
import brewer2mpl
# Get "Set2" colors from ColorBrewer (all colorbrewer scales: http://bl.ocks.org/mbostock/5577023)
set2 = brewer2mpl.get_map('Set2', 'qualitative', 8).mpl_colors
# Set the random seed for consistency
np.random.seed(12)
fig, ax = mpl_plt.subplots(1)
# Show the whole color range
for i in range(8):
x = np.random.normal(loc=i, size=1000)
y = np.random.normal(loc=i, size=1000)
color = set2[i]
ax.scatter(x, y, label=str(i), alpha=0.5, edgecolor='black', facecolor=color, linewidth=0.15)
fig.savefig('scatter_matplotlib_improved_04_thinned_outline.png')
Now we're getting somewhere. This looks very lovely. Don't you want to just cuddle up with that cute plot?
What are those top and right axes lines really doing for us? They're boxing the data in, but we can do that with our eyes from the other axis lines. So let's remove the top and right axis lines using ax.spines
:
# Remove top and right axes lines ("spines")
spines_to_remove = ['top', 'right']
for spine in spines_to_remove:
ax.spines[spine].set_visible(False)
In [14]:
import matplotlib.pyplot as mpl_plt
import brewer2mpl
# Get "Set2" colors from ColorBrewer (all colorbrewer scales: http://bl.ocks.org/mbostock/5577023)
set2 = brewer2mpl.get_map('Set2', 'qualitative', 8).mpl_colors
# Set the random seed for consistency
np.random.seed(12)
fig, ax = mpl_plt.subplots(1)
# Show the whole color range
for i in range(8):
x = np.random.normal(loc=i, size=1000)
y = np.random.normal(loc=i, size=1000)
color = set2[i]
ax.scatter(x, y, label=str(i), alpha=0.5, edgecolor='black', facecolor=color, linewidth=0.15)
# Remove top and right axes lines ("spines")
spines_to_remove = ['top', 'right']
for spine in spines_to_remove:
ax.spines[spine].set_visible(False)
fig.savefig('scatter_matplotlib_improved_05_removed_top_right_spines.png')
Oops, but we still have the ticks on the top and right axes. We'll need to get rid of them. Actually, why don't we just get rid of all ticks altogether? We can tell by the position of the number where it indicates, so we don't need an additional tick.
# Get rid of ticks. The position of the numbers is informative enough of
# the position of the value.
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
In [15]:
import matplotlib.pyplot as mpl_plt
import brewer2mpl
# Get "Set2" colors from ColorBrewer (all colorbrewer scales: http://bl.ocks.org/mbostock/5577023)
set2 = brewer2mpl.get_map('Set2', 'qualitative', 8).mpl_colors
# Set the random seed for consistency
np.random.seed(12)
fig, ax = mpl_plt.subplots(1)
# Show the whole color range
for i in range(8):
x = np.random.normal(loc=i, size=1000)
y = np.random.normal(loc=i, size=1000)
color = set2[i]
ax.scatter(x, y, label=str(i), alpha=0.5, edgecolor='black', facecolor=color, linewidth=0.15)
# Remove top and right axes lines ("spines")
spines_to_remove = ['top', 'right']
for spine in spines_to_remove:
ax.spines[spine].set_visible(False)
# Get rid of ticks. The position of the numbers is informative enough of
# the position of the value.
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
fig.savefig('scatter_matplotlib_improved_06_removed_ticks.png')
Ahh, much better. But we won't stop there. Now we'll tweak the remaining pieces of the figure. For the rest of the spines, let's thin the line down to 0.5
points instead of the default 1.0
points. Also, we'll change it from pure black to a slightly lighter dark grey. Here they are side by side:
In [16]:
fig, axes = plt.subplots(2)
axes[0].set_axis_bgcolor('black')
axes[0].text(0.5, 0.5, 'black', color='white', fontsize=24, va='center', ha='center')
axes[1].set_axis_bgcolor('#262626')
axes[1].text(0.5, 0.5, 'almost black', fontsize=24, color='white', va='center', ha='center')
fig.savefig('black_vs_almost_black.png')
So not a huge difference, and the dark grey still looks pretty black, but it's a little more pleasant on the eyes to use a dark grey instead of black. There's very few things in nature that are truly black. Just look at shadows! They're just dark grey, or blue, or red or purple. But I digress. Back to plotting libraries...
To change the $x$-axis and $y$-axis line colors, and the outlines of the scatter sybmols from black to dark grey, we'll do:
# For remaining spines, thin out their line and change the black to a slightly off-black dark grey
almost_black = '#262626'
...
ax.scatter(x, y, label=str(i), alpha=0.5, edgecolor='black', facecolor=color, linewidth=0.15)`
...
spines_to_keep = ['bottom', 'left']
for spine in spines_to_keep:
ax.spines[spine].set_linewidth(0.5)
ax.spines[spine].set_color(almost_black)
In [17]:
import matplotlib.pyplot as mpl_plt
import brewer2mpl
# Get "Set2" colors from ColorBrewer (all colorbrewer scales: http://bl.ocks.org/mbostock/5577023)
set2 = brewer2mpl.get_map('Set2', 'qualitative', 8).mpl_colors
# Set the random seed for consistency
np.random.seed(12)
# Save a nice dark grey as a variable
almost_black = '#262626'
fig, ax = mpl_plt.subplots(1)
# Show the whole color range
for i in range(8):
x = np.random.normal(loc=i, size=1000)
y = np.random.normal(loc=i, size=1000)
color = set2[i]
ax.scatter(x, y, label=str(i), alpha=0.5, edgecolor=almost_black, facecolor=color, linewidth=0.15)
# Remove top and right axes lines ("spines")
spines_to_remove = ['top', 'right']
for spine in spines_to_remove:
ax.spines[spine].set_visible(False)
# Get rid of ticks. The position of the numbers is informative enough of
# the position of the value.
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
# For remaining spines, thin out their line and change the black to a slightly off-black dark grey
spines_to_keep = ['bottom', 'left']
for spine in spines_to_keep:
ax.spines[spine].set_linewidth(0.5)
ax.spines[spine].set_color(almost_black)
fig.savefig('scatter_matplotlib_improved_07_axis_black_to_almost_black.png')
This is nice. But if you look closely, the tick labels are still black :( We have to change them separately, using
# Change the labels to the off-black
ax.xaxis.label.set_color(almost_black)
ax.yaxis.label.set_color(almost_black)
And while we're at it, let's add a title and make it dark grey too.
# Change the axis title to off-black
ax.title.set_color(almost_black)
ax.set_title('prettyplotlib `scatter` example\nshowing improved matplotlib `scatter`')
In [18]:
import matplotlib.pyplot as mpl_plt
import brewer2mpl
# Get "Set2" colors from ColorBrewer (all colorbrewer scales: http://bl.ocks.org/mbostock/5577023)
set2 = brewer2mpl.get_map('Set2', 'qualitative', 8).mpl_colors
# Set the random seed for consistency
np.random.seed(12)
# Save a nice dark grey as a variable
almost_black = '#262626'
fig, ax = mpl_plt.subplots(1)
# Show the whole color range
for i in range(8):
x = np.random.normal(loc=i, size=1000)
y = np.random.normal(loc=i, size=1000)
color = set2[i]
ax.scatter(x, y, label=str(i), alpha=0.5, edgecolor=almost_black, facecolor=color, linewidth=0.15)
# Remove top and right axes lines ("spines")
spines_to_remove = ['top', 'right']
for spine in spines_to_remove:
ax.spines[spine].set_visible(False)
# Get rid of ticks. The position of the numbers is informative enough of
# the position of the value.
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
# For remaining spines, thin out their line and change the black to a slightly off-black dark grey
spines_to_keep = ['bottom', 'left']
for spine in spines_to_keep:
ax.spines[spine].set_linewidth(0.5)
ax.spines[spine].set_color(almost_black)
# Change the labels to the off-black
ax.xaxis.label.set_color(almost_black)
ax.yaxis.label.set_color(almost_black)
# Change the axis title to off-black
ax.title.set_color(almost_black)
ax.set_title('prettyplotlib `scatter` example\nshowing improved matplotlib `scatter`')
fig.savefig('scatter_matplotlib_improved_08_labels_black_to_almost_black.png')
If you remember in the original example, we also had an axis legend, using
ax.legend()
In [19]:
import matplotlib.pyplot as mpl_plt
import brewer2mpl
# Get "Set2" colors from ColorBrewer (all colorbrewer scales: http://bl.ocks.org/mbostock/5577023)
set2 = brewer2mpl.get_map('Set2', 'qualitative', 8).mpl_colors
# Set the random seed for consistency
np.random.seed(12)
# Save a nice dark grey as a variable
almost_black = '#262626'
fig, ax = mpl_plt.subplots(1)
# Show the whole color range
for i in range(8):
x = np.random.normal(loc=i, size=1000)
y = np.random.normal(loc=i, size=1000)
color = set2[i]
ax.scatter(x, y, label=str(i), alpha=0.5, edgecolor=almost_black, facecolor=color, linewidth=0.15)
# Remove top and right axes lines ("spines")
spines_to_remove = ['top', 'right']
for spine in spines_to_remove:
ax.spines[spine].set_visible(False)
# Get rid of ticks. The position of the numbers is informative enough of
# the position of the value.
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
# For remaining spines, thin out their line and change the black to a slightly off-black dark grey
almost_black = '#262626'
spines_to_keep = ['bottom', 'left']
for spine in spines_to_keep:
ax.spines[spine].set_linewidth(0.5)
ax.spines[spine].set_color(almost_black)
# Change the labels to the off-black
ax.xaxis.label.set_color(almost_black)
ax.yaxis.label.set_color(almost_black)
# Change the axis title to off-black
ax.title.set_color(almost_black)
ax.legend()
ax.set_title('prettyplotlib `scatter` example\nshowing improved matplotlib `scatter`')
fig.savefig('scatter_matplotlib_improved_09_ugly_legend.png')
There are many things I don't like about this legend.
We'll accomplish these three things using this code:
# Remove the line around the legend box, and instead fill it with a light grey
# Also only use one point for the scatterplot legend because the user will
# get the idea after just one, they don't need three.
light_grey = np.array([float(248)/float(255)]*3)
legend = ax.legend(frameon=True, scatterpoints=1, fontcolor=almost_black)
rect = legend.get_frame()
rect.set_facecolor(light_grey)
rect.set_linewidth(0.0)
# Change the legend label colors to almost black, too
texts = legend.texts
for t in texts:
t.set_color(almost_black)
In [20]:
import matplotlib.pyplot as mpl_plt
import brewer2mpl
# Get "Set2" colors from ColorBrewer (all colorbrewer scales: http://bl.ocks.org/mbostock/5577023)
set2 = brewer2mpl.get_map('Set2', 'qualitative', 8).mpl_colors
# Set the random seed for consistency
np.random.seed(12)
# Save a nice dark grey as a variable
almost_black = '#262626'
fig, ax = mpl_plt.subplots(1)
# Show the whole color range
for i in range(8):
x = np.random.normal(loc=i, size=1000)
y = np.random.normal(loc=i, size=1000)
color = set2[i]
ax.scatter(x, y, label=str(i), alpha=0.5, edgecolor=almost_black, facecolor=color, linewidth=0.15)
# Remove top and right axes lines ("spines")
spines_to_remove = ['top', 'right']
for spine in spines_to_remove:
ax.spines[spine].set_visible(False)
# Get rid of ticks. The position of the numbers is informative enough of
# the position of the value.
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
# For remaining spines, thin out their line and change the black to a slightly off-black dark grey
almost_black = '#262626'
spines_to_keep = ['bottom', 'left']
for spine in spines_to_keep:
ax.spines[spine].set_linewidth(0.5)
ax.spines[spine].set_color(almost_black)
# Change the labels to the off-black
ax.xaxis.label.set_color(almost_black)
ax.yaxis.label.set_color(almost_black)
# Change the axis title to off-black
ax.title.set_color(almost_black)
# Remove the line around the legend box, and instead fill it with a light grey
# Also only use one point for the scatterplot legend because the user will
# get the idea after just one, they don't need three.
light_grey = np.array([float(248)/float(255)]*3)
legend = ax.legend(frameon=True, scatterpoints=1)
rect = legend.get_frame()
rect.set_facecolor(light_grey)
rect.set_linewidth(0.0)
# Change the legend label colors to almost black, too
texts = legend.texts
for t in texts:
t.set_color(almost_black)
ax.set_title('prettyplotlib `scatter` example\nshowing improved matplotlib `scatter`')
fig.savefig('scatter_matplotlib_improved_10_pretty_legend.png')
Aaaaaaaaaaand I got tired of doing all those steps, EVERY time. So I wrote prettyplotlib
. Here's an illustratitive example of how awesome prettyplotlib
is, and how it will save all the time you spent agonizing over making your matplotlib
plots beautiful.
In [21]:
%pdb
In [2]:
import prettyplotlib as ppl
import matplotlib.pyplot as plt
# This is "import matplotlib as mpl" from the prettyplotlib library
from prettyplotlib import mpl
# Set the random seed for consistency
np.random.seed(12)
fig, ax = plt.subplots(1)
# Show the whole color range
for i in range(8):
x = np.random.normal(loc=i, size=1000)
y = np.random.normal(loc=i, size=1000)
ppl.scatter(ax, x, y, label=str(i))
ppl.legend(ax)
ax.set_title('prettyplotlib `scatter` example\nshowing default color cycle and scatter params')
fig.savefig('scatter_prettyplotlib_default.png')
The only commands that were different from the very first example with matplotlib are:
ppl.scatter(ax, x, y, label=str(i), facecolor='none')
instead of:
ax.scatter(x, y, label=str(i))
And a different legend command:
ppl.legend(ax)
instead of:
ax.legend()
If you really want to get the original matplotlib style back in prettyplotlib, you can do:
In [23]:
import prettyplotlib as ppl
import matplotlib.pyplot as plt
from prettyplotlib import mpl
from prettyplotlib import brewer2mpl
# Set the random seed for consistency
np.random.seed(12)
fig, ax = plt.subplots(1)
#mpl.rcParams['axis.color_cycle'] = ['blue']
# Show the whole color range
for i in range(8):
x = np.random.normal(loc=i, size=1000)
y = np.random.normal(loc=i, size=1000)
ax.scatter(x, y, label=str(i), facecolor='blue', edgecolor='black', linewidth=1)
# Get back the top and right axes lines ("spines")
spines_to_remove = ['top', 'right']
for spine in spines_to_remove:
ax.spines[spine].set_visible(True)
# Get back the ticks. The position of the numbers is informative enough of
# the position of the value.
ax.xaxis.set_ticks_position('both')
ax.yaxis.set_ticks_position('both')
# For all the spines, make their line thicker and return them to be black
all_spines = ['top', 'left', 'bottom', 'right']
for spine in all_spines:
ax.spines[spine].set_linewidth(1.0)
ax.spines[spine].set_color('black')
# Change the labels back to black
ax.xaxis.label.set_color('black')
ax.yaxis.label.set_color('black')
# Change the axis title also back to black
ax.title.set_color('black')
# Remove the line around the legend box, and instead fill it with a light grey
# Also only use one point for the scatterplot legend because the user will
# get the idea after just one, they don't need three.
ax.legend()
ax.set_title('prettyplotlib `scatter` example\nrevert everything back to default matplotlib parameters')
fig.savefig('scatter_prettyplotlib_back_to_matplotlib_default.png')
Notice that the default calls of ax.scatter
and ax.legend
do the usual thing. This is important, because for prettyplotlib
to work, you'll need to use a syntax that's different from the usual matplotlib
one: ppl.scatter(ax, x, y...)
instead of ax.scatter(x, y, ...)
That's my introduction to prettyplotlib
and why you need it. There are similar examples for the other functions, but this one for ppl.scatter
is the most extensive.
In [1]:
import prettyplotlib as ppl
import matplotlib.pyplot as plt
fig, ax = ppl.subplots()
# Set the random seed for consistency
np.random.seed(12)
# Show the whole color range
for i in range(8):
x = np.random.normal(loc=i, size=1000)
y = np.random.normal(loc=i, size=1000)
ax = ppl.scatter(x, y, label=str(i))
ppl.legend(ax)
ax.set_title('prettyplotlib `scatter` example\nshowing default color cycle and scatter params')
fig.savefig('scatter_prettyplotlib_default.png')
In [24]:
In [ ]: