Warm-up proceedures:
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import matplotlib
matplotlib.use('nbagg')
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from IPython.display import Image
Note that we're not using Seaborn for styling like we did previously -- that's beccause the first thing we're going to tackle is creating a custom matplotlib style :-)
In the previous notebook, we saw that we could list the available styles with the following call:
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print(plt.style.available)
You can create custom styles and use them by calling style.use with the path or URL to the style sheet. Alternatively, if you save your <style-name>.mplstyle file to the ~/.matplotlib/stylelib directory (you may need to create it), you can reuse your custom style sheet with a call to style.use(<style-name>). Note that a custom style sheet in ~/.matplotlib/stylelib will override a style sheet defined by matplotlib if the styles have the same name.
We've created a style sheet for you to use in this repository for this notebook, but before we go further, let's create a function that will generate a demo plot for us. Then we'll render it, using the default style -- thus having a baseline to compare our work to:
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def make_plot ():
x = np.random.randn(5000, 6)
(figure, axes) = plt.subplots(figsize=(16,10))
(n, bins, patches) = axes.hist(x, 12, normed=1, histtype='bar',
label=['Color 1', 'Color 2', 'Color 3',
'Color 4', 'Color 5', 'Color 6'])
axes.set_title("Histogram\nfor a\nNormal Distribution", fontsize=24)
axes.set_xlabel("Data Points", fontsize=16)
axes.set_ylabel("Counts", fontsize=16)
axes.legend()
plt.show()
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plt.style.use('ggplot')
make_plot()
Okay, we've got our sample plot. Now let's look at the style.
We've created a style called "Superheroine", based on Thomas Park's excellent Bootstrap theme, Superhero. Here's a screenshot of the Boostrap theme:
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#Image(filename="superhero.png")
We've saved captured some of the colors from this screenshot and saved them in a couple of plot style files to the "styles" directory in this notebook repo:
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ls -l ../styles/
Basically, we couldn't make up our mind about whether we liked the light text (style 1) or the orange text (style 2). So we kept both :-)
Let's take a look at the second one's contents which show the hexadecimal colors we copied from the Boostrap theme:
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cat ./superheroine-2.mplstyle
Now let's load it:
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plt.style.use("./superheroine-2.mplstyle")
And then re-render our plot:
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make_plot()
A full list of styles available for customization is in given in the matplotlib run control file. We'll be discussing this more in the next section.
In this next section, we'll be creating a sophisticated subplot to give you a sense of what's possible with matplotlib's layouts. We'll be ingesting data from the UCI Machine Learning Repository, in particular the 1985 Automobile Data Set, an example of data which can be used to assess the insurance risks for different vehicles.
We will use it in an effort to compare 21 automobile manufacturers (using 1985 data) along the following dimensions:
We will limit ourselves to automobile manufacturers that have data for losses as well as 6 or more data rows.
Our subplot will be comprised of the following sections:
These will be composed as subplots in the following manner:
--------------------------------------------
| overall title |
--------------------------------------------
| price ranges |
--------------------------------------------
| combined loss/risk | |
| | radar |
---------------------- plots |
| risk | loss | |
--------------------------------------------
| mpg |
--------------------------------------------
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import sys
sys.path.append("../lib")
import demodata, demoplot, radar
raw_data = demodata.get_raw_data()
raw_data.head()
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limited_data = demodata.get_limited_data()
limited_data.head()
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demodata.get_all_auto_makes()
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(makes, counts) = demodata.get_make_counts(limited_data)
counts
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(makes, counts) = demodata.get_make_counts(limited_data, lower_bound=6)
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counts
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data = demodata.get_limited_data(lower_bound=6)
data.head()
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len(data.index)
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sum([x[1] for x in counts])
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normed_data = data.copy()
normed_data.rename(columns={"horsepower": "power"}, inplace=True)
Higher values are better for these:
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demodata.norm_columns(["city mpg", "highway mpg", "power"], normed_data)
normed_data.head()
Lower values are better for these:
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demodata.invert_norm_columns(["price", "weight", "riskiness", "losses"], normed_data)
normed_data.head()
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figure = plt.figure(figsize=(15, 5))
prices_gs = mpl.gridspec.GridSpec(1, 1)
prices_axes = demoplot.make_autos_price_plot(figure, prices_gs, data)
plt.show()
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figure = plt.figure(figsize=(15, 5))
mpg_gs = mpl.gridspec.GridSpec(1, 1)
mpg_axes = demoplot.make_autos_mpg_plot(figure, mpg_gs, data)
plt.show()
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figure = plt.figure(figsize=(15, 5))
risk_gs = mpl.gridspec.GridSpec(1, 1)
risk_axes = demoplot.make_autos_riskiness_plot(figure, risk_gs, normed_data)
plt.show()
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figure = plt.figure(figsize=(15, 5))
loss_gs = mpl.gridspec.GridSpec(1, 1)
loss_axes = demoplot.make_autos_losses_plot(figure, loss_gs, normed_data)
plt.show()
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figure = plt.figure(figsize=(15, 5))
risk_loss_gs = mpl.gridspec.GridSpec(1, 1)
risk_loss_axes = demoplot.make_autos_loss_and_risk_plot(figure, risk_loss_gs, normed_data)
plt.show()
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figure = plt.figure(figsize=(15, 5))
radar_gs = mpl.gridspec.GridSpec(3, 7, height_ratios=[1, 10, 10], wspace=0.50, hspace=0.60, top=0.95, bottom=0.25)
radar_axes = demoplot.make_autos_radar_plot(figure, radar_gs, normed_data)
plt.show()
Here's a refresher on the plot layout we're aiming for:
--------------------------------------------
| overall title |
--------------------------------------------
| price ranges |
--------------------------------------------
| combined loss/risk | |
| | radar |
---------------------- plots |
| risk | loss | |
--------------------------------------------
| mpg |
--------------------------------------------Let's try that now with just empty graphs, to get a sense of things:
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figure = plt.figure(figsize=(10, 8))
gs_master = mpl.gridspec.GridSpec(4, 2, height_ratios=[1, 2, 8, 2])
# Layer 1 - Title
gs_1 = mpl.gridspec.GridSpecFromSubplotSpec(1, 1, subplot_spec=gs_master[0, :])
title_axes = figure.add_subplot(gs_1[0])
# Layer 2 - Price
gs_2 = mpl.gridspec.GridSpecFromSubplotSpec(1, 1, subplot_spec=gs_master[1, :])
price_axes = figure.add_subplot(gs_2[0])
# Layer 3 - Risks & Radar
gs_31 = mpl.gridspec.GridSpecFromSubplotSpec(2, 2, height_ratios=[2, 1], subplot_spec=gs_master[2, :1])
risk_and_loss_axes = figure.add_subplot(gs_31[0, :])
risk_axes = figure.add_subplot(gs_31[1, :1])
loss_axes = figure.add_subplot(gs_31[1:, 1])
gs_32 = mpl.gridspec.GridSpecFromSubplotSpec(1, 1, subplot_spec=gs_master[2, 1])
radar_axes = figure.add_subplot(gs_32[0])
# Layer 4 - MPG
gs_4 = mpl.gridspec.GridSpecFromSubplotSpec(1, 1, subplot_spec=gs_master[3, :])
mpg_axes = figure.add_subplot(gs_4[0])
# Tidy up
gs_master.tight_layout(figure)
plt.show()
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figure = plt.figure(figsize=(15, 15))
gs_master = mpl.gridspec.GridSpec(4, 2, height_ratios=[1, 24, 128, 32], hspace=0, wspace=0)
# Layer 1 - Title
gs_1 = mpl.gridspec.GridSpecFromSubplotSpec(1, 1, subplot_spec=gs_master[0, :])
title_axes = figure.add_subplot(gs_1[0])
title_axes.set_title("Demo Plots for 1985 Auto Maker Data", fontsize=30, color="#cdced1")
demoplot.hide_axes(title_axes)
# Layer 2 - Price
gs_2 = mpl.gridspec.GridSpecFromSubplotSpec(1, 1, subplot_spec=gs_master[1, :])
price_axes = figure.add_subplot(gs_2[0])
demoplot.make_autos_price_plot(figure, pddata=data, axes=price_axes)
# Layer 3, Part I - Risks
gs_31 = mpl.gridspec.GridSpecFromSubplotSpec(2, 2, height_ratios=[2, 1], hspace=0.4, subplot_spec=gs_master[2, :1])
risk_and_loss_axes = figure.add_subplot(gs_31[0, :])
demoplot.make_autos_loss_and_risk_plot(
figure, pddata=normed_data, axes=risk_and_loss_axes, x_label=False, rotate_ticks=True)
risk_axes = figure.add_subplot(gs_31[1, :1])
demoplot.make_autos_riskiness_plot(figure, pddata=normed_data, axes=risk_axes, legend=False, labels=False)
loss_axes = figure.add_subplot(gs_31[1:, 1])
demoplot.make_autos_losses_plot(figure, pddata=normed_data, axes=loss_axes, legend=False, labels=False)
# Layer 3, Part II - Radar
gs_32 = mpl.gridspec.GridSpecFromSubplotSpec(
5, 3, height_ratios=[1, 20, 20, 20, 20], hspace=0.6, wspace=0, subplot_spec=gs_master[2, 1])
(rows, cols) = geometry = gs_32.get_geometry()
title_axes = figure.add_subplot(gs_32[0, :])
inner_axes = []
projection = radar.RadarAxes(spoke_count=len(normed_data.groupby("make").mean().columns))
[inner_axes.append(figure.add_subplot(m, projection=projection)) for m in [n for n in gs_32][cols:]]
demoplot.make_autos_radar_plot(
figure, pddata=normed_data, title_axes=title_axes, inner_axes=inner_axes, legend_axes=False,
geometry=geometry)
# Layer 4 - MPG
gs_4 = mpl.gridspec.GridSpecFromSubplotSpec(1, 1, subplot_spec=gs_master[3, :])
mpg_axes = figure.add_subplot(gs_4[0])
demoplot.make_autos_mpg_plot(figure, pddata=data, axes=mpg_axes)
# Tidy up
gs_master.tight_layout(figure)
plt.show()
Get the directory for the matplotlib config files and cache:
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mpl.get_configdir()
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mpl.matplotlib_fname()
matplotlib's rcParams configuration dictionary holds a great many options for tweaking your use of matplotlib the way you want to:
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len(mpl.rcParams.keys())
The first 10 configuration options in rcParams are:
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dict(list(mpl.rcParams.items())[:10])
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mpl.rcParams['savefig.jpeg_quality'] = 72
mpl.rcParams['axes.formatter.limits'] = [-5, 5]
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mpl.rcParams['axes.formatter.limits']
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mpl.rcdefaults()
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mpl.rcParams['axes.formatter.limits']
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