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thinkplot.py
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895 lines (689 loc) · 21.6 KB
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"""This file contains code for use with "Think Stats",
by Allen B. Downey, available from greenteapress.com
Copyright 2014 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
import math
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import warnings
class _Brewer(object):
"""Encapsulates a nice sequence of colors.
Shades of blue that look good in color and can be distinguished
in grayscale (up to a point).
Borrowed from http://colorbrewer2.org/
"""
color_iter = None
colors = [
"#f7fbff",
"#deebf7",
"#c6dbef",
"#9ecae1",
"#6baed6",
"#4292c6",
"#2171b5",
"#08519c",
"#08306b",
][::-1]
# lists that indicate which colors to use depending on how many are used
which_colors = [
[],
[1],
[1, 3],
[0, 2, 4],
[0, 2, 4, 6],
[0, 2, 3, 5, 6],
[0, 2, 3, 4, 5, 6],
[0, 1, 2, 3, 4, 5, 6],
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7, 8],
]
current_figure = None
@classmethod
def Colors(cls):
"""Returns the list of colors."""
return cls.colors
@classmethod
def ColorGenerator(cls, num):
"""Returns an iterator of color strings.
n: how many colors will be used
"""
for i in cls.which_colors[num]:
yield cls.colors[i]
raise StopIteration("Ran out of colors in _Brewer.")
@classmethod
def InitIter(cls, num):
"""Initializes the color iterator with the given number of colors."""
cls.color_iter = cls.ColorGenerator(num)
fig = plt.gcf()
cls.current_figure = fig
@classmethod
def ClearIter(cls):
"""Sets the color iterator to None."""
cls.color_iter = None
cls.current_figure = None
@classmethod
def GetIter(cls, num):
"""Gets the color iterator."""
fig = plt.gcf()
if fig != cls.current_figure:
cls.InitIter(num)
cls.current_figure = fig
if cls.color_iter is None:
cls.InitIter(num)
return cls.color_iter
def _UnderrideColor(options):
"""If color is not in the options, chooses a color."""
if "color" in options:
return options
# get the current color iterator; if there is none, init one
color_iter = _Brewer.GetIter(5)
try:
options["color"] = next(color_iter)
except StopIteration:
# if you run out of colors, initialize the color iterator
# and try again
warnings.warn("Ran out of colors. Starting over.")
_Brewer.ClearIter()
_UnderrideColor(options)
return options
def PrePlot(num=None, rows=None, cols=None):
"""Takes hints about what's coming.
num: number of lines that will be plotted
rows: number of rows of subplots
cols: number of columns of subplots
"""
if num:
_Brewer.InitIter(num)
if rows is None and cols is None:
return
if rows is not None and cols is None:
cols = 1
if cols is not None and rows is None:
rows = 1
# resize the image, depending on the number of rows and cols
size_map = {
(1, 1): (8, 6),
(1, 2): (12, 6),
(1, 3): (12, 6),
(1, 4): (12, 5),
(1, 5): (12, 4),
(2, 2): (10, 10),
(2, 3): (16, 10),
(3, 1): (8, 10),
(4, 1): (8, 12),
}
if (rows, cols) in size_map:
fig = plt.gcf()
fig.set_size_inches(*size_map[rows, cols])
# create the first subplot
if rows > 1 or cols > 1:
ax = plt.subplot(rows, cols, 1)
global SUBPLOT_ROWS, SUBPLOT_COLS
SUBPLOT_ROWS = rows
SUBPLOT_COLS = cols
else:
ax = plt.gca()
return ax
def SubPlot(plot_number, rows=None, cols=None, **options):
"""Configures the number of subplots and changes the current plot.
rows: int
cols: int
plot_number: int
options: passed to subplot
"""
rows = rows or SUBPLOT_ROWS
cols = cols or SUBPLOT_COLS
return plt.subplot(rows, cols, plot_number, **options)
def _Underride(d, **options):
"""Add key-value pairs to d only if key is not in d.
If d is None, create a new dictionary.
d: dictionary
options: keyword args to add to d
"""
if d is None:
d = {}
for key, val in options.items():
d.setdefault(key, val)
return d
def Clf():
"""Clears the figure and any hints that have been set."""
global LOC
LOC = None
_Brewer.ClearIter()
plt.clf()
fig = plt.gcf()
fig.set_size_inches(8, 6)
def Figure(**options):
"""Sets options for the current figure."""
_Underride(options, figsize=(6, 8))
plt.figure(**options)
def Plot(obj, ys=None, style="", **options):
"""Plots a line.
Args:
obj: sequence of x values, or Series, or anything with Render()
ys: sequence of y values
style: style string passed along to plt.plot
options: keyword args passed to plt.plot
"""
options = _UnderrideColor(options)
label = getattr(obj, "label", "_nolegend_")
options = _Underride(options, linewidth=3, alpha=0.7, label=label)
xs = obj
if ys is None:
if hasattr(obj, "Render"):
xs, ys = obj.Render()
if isinstance(obj, pd.Series):
ys = obj.values
xs = obj.index
if ys is None:
plt.plot(xs, style, **options)
else:
plt.plot(xs, ys, style, **options)
def Vlines(xs, y1, y2, **options):
"""Plots a set of vertical lines.
Args:
xs: sequence of x values
y1: sequence of y values
y2: sequence of y values
options: keyword args passed to plt.vlines
"""
options = _UnderrideColor(options)
options = _Underride(options, linewidth=1, alpha=0.5)
plt.vlines(xs, y1, y2, **options)
def Hlines(ys, x1, x2, **options):
"""Plots a set of horizontal lines.
Args:
ys: sequence of y values
x1: sequence of x values
x2: sequence of x values
options: keyword args passed to plt.vlines
"""
options = _UnderrideColor(options)
options = _Underride(options, linewidth=1, alpha=0.5)
plt.hlines(ys, x1, x2, **options)
def axvline(x, **options):
"""Plots a vertical line.
Args:
x: x location
options: keyword args passed to plt.axvline
"""
options = _UnderrideColor(options)
options = _Underride(options, linewidth=1, alpha=0.5)
plt.axvline(x, **options)
def axhline(y, **options):
"""Plots a horizontal line.
Args:
y: y location
options: keyword args passed to plt.axhline
"""
options = _UnderrideColor(options)
options = _Underride(options, linewidth=1, alpha=0.5)
plt.axhline(y, **options)
def tight_layout(**options):
"""Adjust subplots to minimize padding and margins."""
options = _Underride(
options, wspace=0.1, hspace=0.1, left=0, right=1, bottom=0, top=1
)
plt.tight_layout()
plt.subplots_adjust(**options)
def FillBetween(xs, y1, y2=None, where=None, **options):
"""Fills the space between two lines.
Args:
xs: sequence of x values
y1: sequence of y values
y2: sequence of y values
where: sequence of boolean
options: keyword args passed to plt.fill_between
"""
options = _UnderrideColor(options)
options = _Underride(options, linewidth=0, alpha=0.5)
plt.fill_between(xs, y1, y2, where, **options)
def Bar(xs, ys, **options):
"""Plots a line.
Args:
xs: sequence of x values
ys: sequence of y values
options: keyword args passed to plt.bar
"""
options = _UnderrideColor(options)
options = _Underride(options, linewidth=0, alpha=0.6)
plt.bar(xs, ys, **options)
def Scatter(xs, ys=None, **options):
"""Makes a scatter plot.
xs: x values
ys: y values
options: options passed to plt.scatter
"""
options = _Underride(options, color="blue", alpha=0.2, s=30, edgecolors="none")
if ys is None and isinstance(xs, pd.Series):
ys = xs.values
xs = xs.index
plt.scatter(xs, ys, **options)
def HexBin(xs, ys, **options):
"""Makes a scatter plot.
xs: x values
ys: y values
options: options passed to plt.scatter
"""
options = _Underride(options, cmap=matplotlib.cm.Blues)
plt.hexbin(xs, ys, **options)
def Pdf(pdf, **options):
"""Plots a Pdf, Pmf, or Hist as a line.
Args:
pdf: Pdf, Pmf, or Hist object
options: keyword args passed to plt.plot
"""
low, high = options.pop("low", None), options.pop("high", None)
n = options.pop("n", 101)
xs, ps = pdf.Render(low=low, high=high, n=n)
options = _Underride(options, label=pdf.label)
Plot(xs, ps, **options)
def Pdfs(pdfs, **options):
"""Plots a sequence of PDFs.
Options are passed along for all PDFs. If you want different
options for each pdf, make multiple calls to Pdf.
Args:
pdfs: sequence of PDF objects
options: keyword args passed to plt.plot
"""
for pdf in pdfs:
Pdf(pdf, **options)
def Hist(hist, **options):
"""Plots a Pmf or Hist with a bar plot.
The default width of the bars is based on the minimum difference
between values in the Hist. If that's too small, you can override
it by providing a width keyword argument, in the same units
as the values.
Args:
hist: Hist or Pmf object
options: keyword args passed to plt.bar
"""
# find the minimum distance between adjacent values
xs, ys = hist.Render()
# see if the values support arithmetic
try:
xs[0] - xs[0]
except TypeError:
# if not, replace values with numbers
labels = [str(x) for x in xs]
xs = np.arange(len(xs))
plt.xticks(xs + 0.5, labels)
if "width" not in options:
try:
options["width"] = 0.9 * np.diff(xs).min()
except TypeError:
warnings.warn(
"Hist: Can't compute bar width automatically."
"Check for non-numeric types in Hist."
"Or try providing width option."
)
options = _Underride(options, label=hist.label)
options = _Underride(options, align="center")
if options["align"] == "left":
options["align"] = "edge"
elif options["align"] == "right":
options["align"] = "edge"
options["width"] *= -1
Bar(xs, ys, **options)
def Hists(hists, **options):
"""Plots two histograms as interleaved bar plots.
Options are passed along for all PMFs. If you want different
options for each pmf, make multiple calls to Pmf.
Args:
hists: list of two Hist or Pmf objects
options: keyword args passed to plt.plot
"""
for hist in hists:
Hist(hist, **options)
def Pmf(pmf, **options):
"""Plots a Pmf or Hist as a line.
Args:
pmf: Hist or Pmf object
options: keyword args passed to plt.plot
"""
xs, ys = pmf.Render()
width = options.pop("width", None)
if width is None:
try:
width = np.diff(xs).min()
except TypeError:
warnings.warn(
"Pmf: Can't compute bar width automatically."
"Check for non-numeric types in Pmf."
"Or try providing width option."
)
points = []
lastx = np.nan
lasty = 0
for x, y in zip(xs, ys):
if (x - lastx) > 1e-5:
points.append((lastx, 0))
points.append((x, 0))
points.append((x, lasty))
points.append((x, y))
points.append((x + width, y))
lastx = x + width
lasty = y
points.append((lastx, 0))
pxs, pys = zip(*points)
align = options.pop("align", "center")
if align == "center":
pxs = np.array(pxs) - width / 2.0
if align == "right":
pxs = np.array(pxs) - width
options = _Underride(options, label=pmf.label)
Plot(pxs, pys, **options)
def Pmfs(pmfs, **options):
"""Plots a sequence of PMFs.
Options are passed along for all PMFs. If you want different
options for each pmf, make multiple calls to Pmf.
Args:
pmfs: sequence of PMF objects
options: keyword args passed to plt.plot
"""
for pmf in pmfs:
Pmf(pmf, **options)
def Diff(t):
"""Compute the differences between adjacent elements in a sequence.
Args:
t: sequence of number
Returns:
sequence of differences (length one less than t)
"""
diffs = [t[i + 1] - t[i] for i in range(len(t) - 1)]
return diffs
def Cdf(cdf, complement=False, transform=None, **options):
"""Plots a CDF as a line.
Args:
cdf: Cdf object
complement: boolean, whether to plot the complementary CDF
transform: string, one of 'exponential', 'pareto', 'weibull', 'gumbel'
options: keyword args passed to plt.plot
Returns:
dictionary with the scale options that should be passed to
Config, Show or Save.
"""
xs, ps = cdf.Render()
xs = np.asarray(xs)
ps = np.asarray(ps)
scale = dict(xscale="linear", yscale="linear")
for s in ["xscale", "yscale"]:
if s in options:
scale[s] = options.pop(s)
if transform == "exponential":
complement = True
scale["yscale"] = "log"
if transform == "pareto":
complement = True
scale["yscale"] = "log"
scale["xscale"] = "log"
if complement:
ps = [1.0 - p for p in ps]
if transform == "weibull":
xs = np.delete(xs, -1)
ps = np.delete(ps, -1)
ps = [-math.log(1.0 - p) for p in ps]
scale["xscale"] = "log"
scale["yscale"] = "log"
if transform == "gumbel":
xs = np.delete(xs, 0)
ps = np.delete(ps, 0)
ps = [-math.log(p) for p in ps]
scale["yscale"] = "log"
options = _Underride(options, label=cdf.label)
Plot(xs, ps, **options)
return scale
def Cdfs(cdfs, complement=False, transform=None, **options):
"""Plots a sequence of CDFs.
cdfs: sequence of CDF objects
complement: boolean, whether to plot the complementary CDF
transform: string, one of 'exponential', 'pareto', 'weibull', 'gumbel'
options: keyword args passed to plt.plot
"""
for cdf in cdfs:
Cdf(cdf, complement, transform, **options)
def Contour(obj, pcolor=False, contour=True, imshow=False, **options):
"""Makes a contour plot.
d: map from (x, y) to z, or object that provides GetDict
pcolor: boolean, whether to make a pseudocolor plot
contour: boolean, whether to make a contour plot
imshow: boolean, whether to use plt.imshow
options: keyword args passed to plt.pcolor and/or plt.contour
"""
try:
d = obj.GetDict()
except AttributeError:
d = obj
_Underride(options, linewidth=3, cmap=matplotlib.cm.Blues)
xs, ys = zip(*d.keys())
xs = sorted(set(xs))
ys = sorted(set(ys))
X, Y = np.meshgrid(xs, ys)
lam = lambda x, y: d.get((x, y), 0)
func = np.vectorize(lam)
Z = func(X, Y)
x_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
axes = plt.gca()
axes.xaxis.set_major_formatter(x_formatter)
if pcolor:
plt.pcolormesh(X, Y, Z, **options)
if contour:
cs = plt.contour(X, Y, Z, **options)
plt.clabel(cs, inline=1, fontsize=10)
if imshow:
extent = xs[0], xs[-1], ys[0], ys[-1]
plt.imshow(Z, extent=extent, **options)
def Pcolor(xs, ys, zs, pcolor=True, contour=False, **options):
"""Makes a pseudocolor plot.
xs:
ys:
zs:
pcolor: boolean, whether to make a pseudocolor plot
contour: boolean, whether to make a contour plot
options: keyword args passed to plt.pcolor and/or plt.contour
"""
_Underride(options, linewidth=3, cmap=matplotlib.cm.Blues)
X, Y = np.meshgrid(xs, ys)
Z = zs
x_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
axes = plt.gca()
axes.xaxis.set_major_formatter(x_formatter)
if pcolor:
plt.pcolormesh(X, Y, Z, **options)
if contour:
cs = plt.contour(X, Y, Z, **options)
plt.clabel(cs, inline=1, fontsize=10)
def Text(x, y, s, **options):
"""Puts text in a figure.
x: number
y: number
s: string
options: keyword args passed to plt.text
"""
options = _Underride(
options, fontsize=16, verticalalignment="top", horizontalalignment="left"
)
plt.text(x, y, s, **options)
LEGEND = True
LOC = None
def Config(**options):
"""Configures the plot.
Pulls options out of the option dictionary and passes them to
the corresponding plt functions.
"""
names = [
"title",
"xlabel",
"ylabel",
"xscale",
"yscale",
"xticks",
"yticks",
"axis",
"xlim",
"ylim",
]
for name in names:
if name in options:
getattr(plt, name)(options[name])
global LEGEND
LEGEND = options.get("legend", LEGEND)
# see if there are any elements with labels;
# if not, don't draw a legend
ax = plt.gca()
handles, labels = ax.get_legend_handles_labels()
if LEGEND and len(labels) > 0:
global LOC
LOC = options.get("loc", LOC)
frameon = options.get("frameon", True)
try:
plt.legend(loc=LOC, frameon=frameon)
except UserWarning:
pass
# x and y ticklabels can be made invisible
val = options.get("xticklabels", None)
if val is not None:
if val == "invisible":
ax = plt.gca()
labels = ax.get_xticklabels()
plt.setp(labels, visible=False)
val = options.get("yticklabels", None)
if val is not None:
if val == "invisible":
ax = plt.gca()
labels = ax.get_yticklabels()
plt.setp(labels, visible=False)
def set_font_size(title_size=16, label_size=16, ticklabel_size=14, legend_size=14):
"""Set font sizes for the title, labels, ticklabels, and legend."""
def set_text_size(texts, size):
for text in texts:
text.set_size(size)
ax = plt.gca()
# TODO: Make this function more robust if any of these elements
# is missing.
# title
ax.title.set_size(title_size)
# x axis
ax.xaxis.label.set_size(label_size)
set_text_size(ax.xaxis.get_ticklabels(), ticklabel_size)
# y axis
ax.yaxis.label.set_size(label_size)
set_text_size(ax.yaxis.get_ticklabels(), ticklabel_size)
# legend
legend = ax.get_legend()
if legend is not None:
set_text_size(legend.texts, legend_size)
def bigger_text():
sizes = dict(title_size=16, label_size=16, ticklabel_size=14, legend_size=14)
set_font_size(**sizes)
def Show(**options):
"""Shows the plot.
For options, see Config.
options: keyword args used to invoke various plt functions
"""
clf = options.pop("clf", True)
Config(**options)
plt.show()
if clf:
Clf()
def Plotly(**options):
"""Shows the plot.
For options, see Config.
options: keyword args used to invoke various plt functions
"""
clf = options.pop("clf", True)
Config(**options)
import plotly.plotly as plotly
url = plotly.plot_mpl(plt.gcf())
if clf:
Clf()
return url
def Save(root=None, formats=None, **options):
"""Saves the plot in the given formats and clears the figure.
For options, see Config.
Note: With a capital S, this is the original save, maintained for
compatibility. New code should use save(), which works better
with my newer code, especially in Jupyter notebooks.
Args:
root: string filename root
formats: list of string formats
options: keyword args used to invoke various plt functions
"""
clf = options.pop("clf", True)
save_options = {}
for option in ["bbox_inches", "pad_inches"]:
if option in options:
save_options[option] = options.pop(option)
# TODO: falling Config inside Save was probably a mistake, but removing
# it will require some work
Config(**options)
if formats is None:
formats = ["pdf", "png"]
try:
formats.remove("plotly")
Plotly(clf=False)
except ValueError:
pass
if root:
for fmt in formats:
SaveFormat(root, fmt, **save_options)
if clf:
Clf()
def save(root, formats=None, **options):
"""Saves the plot in the given formats and clears the figure.
For options, see plt.savefig.
Args:
root: string filename root
formats: list of string formats
options: keyword args passed to plt.savefig
"""
if formats is None:
formats = ["pdf", "png"]
try:
formats.remove("plotly")
Plotly(clf=False)
except ValueError:
pass
for fmt in formats:
SaveFormat(root, fmt, **options)
def SaveFormat(root, fmt="eps", **options):
"""Writes the current figure to a file in the given format.
Args:
root: string filename root
fmt: string format
"""
_Underride(options, dpi=300)
filename = "%s.%s" % (root, fmt)
print("Writing", filename)
plt.savefig(filename, format=fmt, **options)
# provide aliases for calling functions with lower-case names
preplot = PrePlot
subplot = SubPlot
clf = Clf
figure = Figure
plot = Plot
vlines = Vlines
hlines = Hlines
fill_between = FillBetween
text = Text
scatter = Scatter
pmf = Pmf
pmfs = Pmfs
hist = Hist
hists = Hists
diff = Diff
cdf = Cdf
cdfs = Cdfs
contour = Contour
pcolor = Pcolor
config = Config
show = Show
def main():
color_iter = _Brewer.ColorGenerator(7)
for color in color_iter:
print(color)
if __name__ == "__main__":
main()