├── .gitignore
├── MANIFEST.in
├── plottyprint
    ├── __init__.py
    ├── boxplot.py
    ├── utils.py
    ├── timeseries.py
    ├── histogram.py
    └── scatterplot.py
├── setup.py
├── LICENSE.txt
├── readme.rst
├── boxplot.svg
├── histogram_fancy.svg
├── histogram_simple.svg
├── scatterplot.svg
└── timeseries.svg


/.gitignore:
--------------------------------------------------------------------------------
1 | *.pyc
2 | /dist/
3 | *.egg-info
4 | *.egg
5 | build/
6 | 


--------------------------------------------------------------------------------
/MANIFEST.in:
--------------------------------------------------------------------------------
1 | include *.rst
2 | include *.svg
3 | include *.txt
4 | 


--------------------------------------------------------------------------------
/plottyprint/__init__.py:
--------------------------------------------------------------------------------
1 | from .scatterplot import scatterplot
2 | from .boxplot import boxplot
3 | from .histogram import histogram
4 | from .timeseries import timeseries
5 | 


--------------------------------------------------------------------------------
/plottyprint/boxplot.py:
--------------------------------------------------------------------------------
 1 | import matplotlib.pyplot as plt
 2 | import numpy as np
 3 | from .utils import remove_chart_junk, PlottyFig
 4 | 
 5 | 
 6 | def boxplot(variables,
 7 |             labels,
 8 |             title="",
 9 |             numticks=3,
10 |             labelsize=14,
11 |             size=(10, 10),
12 |             font="Lato",
13 |             notch=True):
14 |     arrays = [np.sort(np.array(x)) for x in variables]
15 | 
16 |     fig = plt.figure(figsize=size, FigureClass=PlottyFig)
17 |     ax = fig.add_subplot(1, 1, 1)
18 | 
19 |     ax.set_title(title + '\n',
20 |                  fontsize=labelsize * 1.25,
21 |                  fontname=font)
22 | 
23 |     ax = remove_chart_junk(ax, numticks, labelsize)
24 | 
25 |     bp = ax.boxplot(arrays, notch=notch, sym="k.", labels=labels)
26 |     for element in ['boxes', 'whiskers', 'fliers', 'means', 'medians', 'caps']:
27 |         plt.setp(bp[element], color="black", linewidth=1.75)
28 |     return fig
29 | 


--------------------------------------------------------------------------------
/setup.py:
--------------------------------------------------------------------------------
 1 | from setuptools import setup
 2 | 
 3 | 
 4 | def readme():
 5 |     with open('README.rst') as f:
 6 |         return f.read()
 7 | 
 8 | 
 9 | setup(name='plottyprint',
10 |       version='0.1',
11 |       description='provide a couple of easy to use, very basic, printable plots on top of matplotlib',
12 |       long_description=readme(),
13 |       url='https://github.com/paultopia/plottyprint',
14 |       classifiers=[
15 |           'Development Status :: 3 - Alpha',
16 |           "Intended Audience :: Developers",
17 |           "Intended Audience :: Science/Research",
18 |           "License :: OSI Approved :: MIT License",
19 |           "Programming Language :: Python :: 3 :: Only"
20 |       ],
21 |       keywords="plotting, datavis",
22 |       author='Paul Gowder',
23 |       author_email='paul.gowder@gmail.com',
24 |       license='MIT',
25 |       packages=['plottyprint'],
26 |       python_requires='>=3',
27 |       install_requires=['numpy', 'matplotlib>=2.1.1', 'statsmodels', 'scipy'],
28 |       zip_safe=False)
29 | 


--------------------------------------------------------------------------------
/LICENSE.txt:
--------------------------------------------------------------------------------
 1 | The MIT License (MIT)
 2 | Copyright © 2018 <copyright holders>
 3 | 
 4 | Permission is hereby granted, free of charge, to any person obtaining a copy
 5 | of this software and associated documentation files (the “Software”), to deal
 6 | in the Software without restriction, including without limitation the rights
 7 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 8 | copies of the Software, and to permit persons to whom the Software is
 9 | furnished to do so, subject to the following conditions:
10 | 
11 | The above copyright notice and this permission notice shall be included in
12 | all copies or substantial portions of the Software.
13 | 
14 | THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
15 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
16 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
17 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
18 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
19 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
20 | THE SOFTWARE.
21 | 


--------------------------------------------------------------------------------
/plottyprint/utils.py:
--------------------------------------------------------------------------------
 1 | import matplotlib
 2 | def remove_chart_junk(axis, numticks, labelsize):
 3 | 
 4 |     # reduce ticks
 5 |     axis.locator_params(nbins=numticks)
 6 | 
 7 |     # remove unnecessary borders
 8 |     axis.spines['top'].set_visible(False)
 9 |     axis.spines['right'].set_visible(False)
10 |     axis.spines['left'].set_color((0.1, 0.1, 0.1, 0.2))
11 |     axis.spines['bottom'].set_color((0.1, 0.1, 0.1, 0.2))
12 | 
13 |     # prettify tick labels
14 |     axis.tick_params(axis='both',
15 |                      which='both',
16 |                      length=0,
17 |                      labelsize=labelsize,
18 |                      pad=labelsize * 0.66,
19 |                      labelcolor=(0.25, 0.25, 0.25))
20 |     return axis
21 | 
22 | 
23 | class PlottyFig(matplotlib.figure.Figure):
24 | 
25 |     def get_main_plot(self):
26 |         return self._axstack.as_list()[0]
27 | 
28 |     def get_lines(self):
29 |         return self._axstack.as_list()[0].lines
30 | 
31 |     def set_line_width(self, width):
32 |         for line in self._axstack.as_list()[0].lines:
33 |             line._linewidth = width
34 | 
35 |     def change_title(self, title):
36 |         self._axstack.as_list()[0].set_title(title)
37 | 


--------------------------------------------------------------------------------
/plottyprint/timeseries.py:
--------------------------------------------------------------------------------
 1 | import matplotlib.pyplot as plt
 2 | import numpy as np
 3 | from .utils import remove_chart_junk, PlottyFig
 4 | 
 5 | 
 6 | def timeseries(in_dates,
 7 |                events,
 8 |                labels=["", ""],
 9 |                title="",
10 |                numticks=3,
11 |                labelsize=12,
12 |                size=(10, 10),
13 |                font="Lato"):
14 |     fig = plt.figure(figsize=size, FigureClass=PlottyFig)
15 |     ax = fig.add_subplot(1, 1, 1)
16 |     numticks = 5
17 | 
18 |     ax.set_title(title + '\n',
19 |                  fontsize=labelsize * 1.25,
20 |                  fontname=font)
21 | 
22 |     ax = remove_chart_junk(ax, numticks, labelsize)
23 | 
24 |     dates = np.array(in_dates)
25 |     dates_sorted = (np.sort(dates))
26 |     event1 = np.array(events[0])[np.argsort(dates)]
27 |     p1 = ax.plot(dates_sorted, event1, 'k-', label=labels[0])
28 |     if len(events) > 1:
29 |         event2 = np.array(events[1])[np.argsort(dates)]
30 |         p2 = ax.plot(dates_sorted, event2, color=(0.6, 0.6, 0.6), linestyle='-', label=labels[1])
31 |         leg = ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), ncol=2, framealpha=0)
32 |         leg.get_texts()[1].set_color((0.4, 0.4, 0.4))
33 |     return fig
34 | 


--------------------------------------------------------------------------------
/plottyprint/histogram.py:
--------------------------------------------------------------------------------
 1 | import matplotlib
 2 | import matplotlib.pyplot as plt
 3 | import numpy as np
 4 | from scipy.stats import gaussian_kde
 5 | from .utils import remove_chart_junk, PlottyFig
 6 | 
 7 | def histogram(variable,
 8 |               bins="auto",
 9 |               title="",
10 |               numticks=5,
11 |               labelsize=15,
12 |               size=(10, 10),
13 |               add_kde=False,
14 |               kernel_param=0.4,
15 |               show_n=True,
16 |               font="Lato"):
17 |     var = np.sort(np.array(variable))
18 | 
19 |     fig = plt.figure(figsize=size, FigureClass=PlottyFig)
20 |     ax = fig.add_subplot(1, 1, 1)
21 | 
22 |     ax.set_title(title + '\n',
23 |                  fontsize=labelsize * 1.25,
24 |                  fontname=font)
25 | 
26 |     ax = remove_chart_junk(ax, numticks, labelsize)
27 | 
28 |     if add_kde:
29 |         density = True
30 |     else:
31 |         density = False
32 | 
33 |     if show_n:
34 |         ax.set_xlabel("n = " + str(var.size),
35 |                       fontsize=labelsize,
36 |                       labelpad=labelsize * 0.7,
37 |                       color=(0.15, 0.15, 0.15),
38 |                       fontname=font)
39 | 
40 |     ax.hist(var, bins=bins, density=density, lw=0)
41 | 
42 |     # fix bar colors
43 |     bars = [x for x in ax.get_children() if type(x) is matplotlib.patches.Rectangle]
44 |     for idx, bar in enumerate(bars):
45 |         if idx % 2 == 0:
46 |             bar.set_color((0.1, 0.1, 0.1, 0.3))
47 |         else:
48 |             bar.set_color((0.4, 0.4, 0.4, 0.3))
49 |     bars[-1].set_color("white")
50 | 
51 |     if add_kde:
52 |         ds = gaussian_kde(var)
53 |         mx = np.max(var)
54 |         mn = np.min(var)
55 |         ls = np.linspace(mn, mx)
56 |         ds.covariance_factor = lambda : kernel_param
57 |         ds._compute_covariance()
58 |         dy = ds(ls)
59 |         ax.plot(ls, dy, color=(0.1, 0.1, 0.1, 0.8))
60 |         ax.set_yticks([])
61 |         ax.spines['left'].set_visible(False)
62 | 
63 |     return fig
64 | 


--------------------------------------------------------------------------------
/plottyprint/scatterplot.py:
--------------------------------------------------------------------------------
 1 | import matplotlib.pyplot as plt
 2 | import numpy as np
 3 | import statsmodels.api as sm
 4 | import textwrap
 5 | from statsmodels.stats.outliers_influence import summary_table
 6 | from .utils import remove_chart_junk, PlottyFig
 7 | 
 8 | 
 9 | def get_ci_values(fitted_regression, alpha):
10 |     _, sumdata, _ = summary_table(fitted_regression, alpha=alpha)
11 |     ci_low, ci_high = sumdata[:, 4:6].T
12 |     return ci_low, ci_high
13 | 
14 | 
15 | def scatterplot(x, y, title="",
16 |                 xlabel="",
17 |                 ylabel="",
18 |                 numticks=3,
19 |                 labelsize=15,
20 |                 dotsize=8,
21 |                 ylabel_wrapping=6,
22 |                 size=(10, 10),
23 |                 fit_line=True,
24 |                 confidence=True,
25 |                 alpha=0.05,
26 |                 font="Lato"):
27 | 
28 |     # everything works better with numpy arrays
29 |     x = np.array(x)
30 |     y = np.array(y)
31 | 
32 |     # generate the fitted values and confidence intervals
33 |     if fit_line:
34 |         regline = sm.OLS(y, sm.add_constant(x)).fit()
35 |         if confidence:
36 |             ci_low, ci_high = get_ci_values(regline, alpha)
37 | 
38 |     # baseline plot
39 |     fig = plt.figure(figsize=size, FigureClass=PlottyFig)
40 |     ax = fig.add_subplot(1, 1, 1)
41 | 
42 |     # label all the things
43 |     ax.set_title(title + '\n',
44 |                  fontsize=labelsize * 1.25,
45 |                  fontname=font)
46 |     ax.set_xlabel(xlabel,
47 |                   fontsize=labelsize,
48 |                   labelpad=labelsize * 0.7,
49 |                   color=(0.15, 0.15, 0.15),
50 |                   fontname=font)
51 |     ax.set_ylabel(textwrap.fill(ylabel, ylabel_wrapping),
52 |                   fontsize=labelsize,
53 |                   labelpad=labelsize * 2,
54 |                   color=(0.15, 0.15, 0.15),
55 |                   rotation='horizontal',
56 |                   horizontalalignment='center',
57 |                   fontname=font)
58 | 
59 |     # matplotlib tends to get broken if you don't sort the data first, so
60 |     x_sorted = np.sort(x)
61 |     y_sorted = y[np.argsort(x)]
62 |     if fit_line:
63 |         fitted = regline.fittedvalues[np.argsort(x)]
64 |         if confidence:
65 |             lowband = ci_low[np.argsort(x)]
66 |             highband = ci_high[np.argsort(x)]
67 | 
68 |     # plot the basic scatterplot
69 |     ax.plot(x_sorted, y_sorted, 'k.', ms=dotsize)
70 | 
71 |     ax = remove_chart_junk(ax, numticks, labelsize)
72 | 
73 |     # plot regression line in grey, transparent enough for dots to show through
74 |     if fit_line:
75 |         ax.plot(x_sorted, fitted, color=(0.1, 0.1, 0.1, 0.2), linewidth=2)
76 |         # fill space between confidence interval endpoints and regression line in lighter grey
77 | 
78 |         # plot bands for confidence intervals
79 |         if confidence:
80 |             ax.fill_between(x_sorted, lowband, fitted, facecolor=(0.7, 0.7, 0.7, 0.2))
81 |             ax.fill_between(x_sorted, highband, fitted, facecolor=(0.7, 0.7, 0.7, 0.2))
82 | 
83 |     return fig
84 | 


--------------------------------------------------------------------------------
/readme.rst:
--------------------------------------------------------------------------------
  1 | Plottyprint
  2 | ===========
  3 | 
  4 | The Goal
  5 | --------
  6 | 
  7 | Make simple data visualizations in Python that are **easy**,
  8 | **attractive**, and most importantly **appropriate for dead-tree
  9 | printing** (like in academic journals, conference posters, or
 10 | newspapers), with a bias toward greyscale in case color printing is
 11 | expensive (or just not allowed).
 12 | 
 13 | This very small library is targeted at the easiest 60% or so of plots
 14 | that social scientists and student journalists and other
 15 | light-to-moderate data users will want to produce.
 16 | 
 17 | Right now, the amount of effort it takes to go from from no plots at all
 18 | or really ugly plots to simple but attractive plots is *much* longer
 19 | than the amount of effort from simple but attractive plots to really
 20 | complex plots. This strikes me as unacceptable. If you want to produce a
 21 | 4-d contour plot overlaid on a map of the world with color representing
 22 | time, I can’t help you. But if you want to produce a basic scatterplot,
 23 | maybe with a least squares fit on top of it, and then slap it into your
 24 | journal submission to impress the peer reviewers, I’m here for you.
 25 | 
 26 | The Challenges
 27 | --------------
 28 | 
 29 | 1. Default matplotlib plotting is ugly, and making attractive plots is
 30 |    difficult.
 31 | 
 32 | 2. There are many Python packages that make it easy to create beautiful
 33 |    plots on top of Matplotlib. 
 34 |    `Seaborn <https://seaborn.pydata.org>`__,
 35 |    `Bokeh <https://bokeh.pydata.org/en/latest/>`__, and
 36 |    `Plotly <https://plot.ly/python/>`__ are my favorite examples, but
 37 |    the readme to `Altair <https://github.com/altair-viz/altair>`__ lists
 38 |    numerous other excellent options. However, these options are all
 39 |    built for web or interactive use, not for printing. They have lots of
 40 |    colors, default proportions appropriate for screen usage, and often
 41 |    interactive elements that don’t make sense on paper. While it’s
 42 |    possibly to convince those libraries to make plots for print, it’s a
 43 |    lot of work. (If you want to do that, I recommend using a Python
 44 |    port of `ggplot <https://github.com/has2k1/plotnine>`__ or the plotting
 45 |    convenience functions `in Pandas <https://pandas.pydata.org/pandas-docs/stable/visualization.html>`__.)
 46 | 
 47 | The Plottyprint Solution
 48 | ------------------------
 49 | 
 50 | This very small library aims to provide a handful of basic and
 51 | attractive printable plots that will work out of the box with sensible
 52 | defaults for the most simple use cases. Right now, it supplies four
 53 | workhorse plots:
 54 | 
 55 | 1. A **scatterplot** between two variables. By default, the scatterplot
 56 |    has a least squares line through it, and a confidence region around
 57 |    that line, but this is easy to turn off.
 58 | 
 59 | 2. A **histogram** of one variable. If you want to get really fancy to
 60 |    impress the peer reviewers, you can stick a kernel density estimator
 61 |    line on top of it.
 62 | 
 63 | 3. A **boxplot** with the standard frills (whiskers, notches, all that
 64 |    good stuff).
 65 | 
 66 | 4. A **time series plot** that gives you a line of one or two events
 67 |    over time. The time series plot is experimental, and might not work
 68 |    with your data; I’ll try to improve it in subsequent versions.
 69 | 
 70 | This, obviously, represents the science communicator’s basic toolkit for
 71 | displaying the relationship between two variables, the shape of one
 72 | variable, the relationship between mean, interquartile range, and
 73 | outliers for several variables, and variable behavior over time,
 74 | respectively.
 75 | 
 76 | Each of these elements is presented in high-contrast greyscale, and has
 77 | all of matplotlib’s default `chart
 78 | junk <https://en.m.wikipedia.org/wiki/Chartjunk>`__ ruthlessly ripped
 79 | out.
 80 | 
 81 | In this 0.1 release, there are only a handful of customization options,
 82 | but over time I plan to add more plot types and add a bit more
 83 | customization. Moreover, every plot returns a (subclass of a) Matplotlib
 84 | figure, so if you know matplotlib you can always do your own
 85 | customization after the fact.
 86 | 
 87 | Here are some examples.
 88 | 
 89 | Scatterplot, with fit line and confidence region.
 90 | 
 91 | .. figure:: scatterplot.svg
 92 |    :alt: scatterplot
 93 | 
 94 | 
 95 | Histogram, no KDE estimator.
 96 | 
 97 | .. figure:: histogram_simple.svg
 98 |    :alt: simple histogram
 99 | 
100 | 
101 | Histogram, with KDE estimator
102 | 
103 | .. figure:: histogram_fancy.svg
104 |    :alt: fancy histogram
105 | 
106 | 
107 | Boxplot
108 | 
109 | .. figure:: boxplot.svg
110 |    :alt: boxplot
111 | 
112 | 
113 | Time Series
114 | 
115 | .. figure:: timeseries.svg
116 |    :alt: time series
117 | 
118 | 
119 | Installation
120 | ------------
121 | 
122 | ``pip install plottyprint``
123 | 
124 | Usage
125 | -----
126 | 
127 | There are three functions, each corresponding to a plot. Each takes one
128 | or more Numpy arrays, or anything that can be cast into a Numpy array
129 | with ``np.array()`` without going wrong (lists, Pandas ``DataFrame``
130 | columns, etc.), plus some configuration.
131 | 
132 | Each function returns a ``PlottyFig`` object. This is just a subclass of
133 | ``matplotlib.figure.Figure``, with a handful of convenience methods
134 | (documented below) to tweak your plots after you create them and smooth
135 | out the rough edges of the Matplotlib api. Because it’s a figure
136 | subclass, those who know Matplotlib can also dig in deeper to tweak to
137 | your heart’s content.
138 | 
139 | Scatterplot
140 | ~~~~~~~~~~~
141 | 
142 | **scatterplot(x, y, title=“”, xlabel=“”, ylabel=“”, numticks=3,
143 | labelsize=15, dotsize=8, ylabel_wrapping=6, size=(10, 10),
144 | fit_line=True, confidence=True, alpha=0.05, font=“Lato”)**
145 | 
146 | -  x and y are, obviously, your data.
147 | 
148 | -  title is, obviously, the title of the plot
149 | 
150 | -  xlabel and ylabel are, obviously, the labels for your data.
151 | 
152 | -  numticks is (wait for it) the number of ticks to show on each axis.
153 |    For mysterious reasons, sometimes Matplotlib likes to give you a
154 |    little more or fewer than the number asked for, but at any rate the
155 |    default is a nice small number that will show the scale of the data
156 |    without overwhelming the reader with noise.
157 | 
158 | -  labelsize is the size of the axis labels. The title will be scaled up
159 |    a little from this.
160 | 
161 | -  dotsize is the size of the individual points in the plot.
162 | 
163 | -  ylabel_wrapping is the number of characters in the y label before it
164 |    wraps to another line. (Making y labels attractive is a bit
165 |    difficult.)
166 | 
167 | -  size is a tuple representing the size in inches
168 | 
169 | -  fit_line is a boolean representing whether or not to put a least
170 |    squares line in the plot.
171 | 
172 | -  confidence is a boolean representing whether or not to put a
173 |    confidence interval around the least squares line. Obviously, this
174 |    won’t do anything if you don’t have a least squares line in there.
175 | 
176 | -  alpha is the width of your confidence interval. The default
177 |    represents the good old fashioned 95% interval.
178 | 
179 | -  font is the name of the font for labels. See below for some caveats on this.
180 | 
181 | Histogram
182 | ~~~~~~~~~
183 | 
184 | **histogram(variable, bins=“auto”, density = False, title=“”,
185 | numticks=5, labelsize=15, size=(10, 10), add_kde=False, kernel_param =
186 | 0.4, show_n = True, font=“Lato”)**
187 | 
188 | -  variable is, obviously, your data.
189 | 
190 | -  bins can be a number of bins, or “auto” to let numpy come up with
191 |    something for you
192 | 
193 | -  add_kde controls whether you want to slap a kernel density estimator
194 |    plot on top of your histogram. If you do want to do this, it’ll
195 |    change the scale of the histogram: it will become probability density
196 |    rather than counts, and, since the y label on a density histogram is
197 |    anywhere between confusing and outright deceptive, it gets removed.
198 |    Call me paternalistic.
199 | 
200 | -  kernel_param is a parameter that goes into the kernel density
201 |    estimator. Scipy does `a lot of fancy
202 |    math <https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gaussian_kde.html>`__
203 |    with this, but you can think of it as kind of similar to the
204 |    bandwidth parameter in the `R kde
205 |    function <https://stat.ethz.ch/R-manual/R-devel/library/stats/html/density.html>`__.
206 |    For practical purposes, larger numers should produce smoother plots,
207 |    but it’s probably worth playing with interactively.
208 | 
209 | -  show_n is a boolean determining whether to show the number of
210 |    observations as a label below the x axis or not.
211 | 
212 | Everything else is the same as in ``scatterplot``.
213 | 
214 | Boxplot
215 | ~~~~~~~
216 | 
217 | **boxplot(variables, labels, title=“”, numticks=3, labelsize=14,
218 | size=(10, 10), font=“Lato”)**
219 | 
220 | Everything here is the same as in ``scatterplot`` except that variables
221 | is a list of, well, variables (numpy arrays etc.), and labels is a list
222 | of labels. You can put as many variables in here as you can squeeze in.
223 | 
224 | Time Series
225 | ~~~~~~~~~~~
226 | 
227 | **timeseries(in_dates, events, labels = [“”, “”], title=“”, numticks=3,
228 | labelsize=12, size=(10, 10), font=“Lato”, notch=True)**
229 | 
230 | -  in_dates is an array (or list etc.) of ``date`` objects (from the
231 |    standard Python datetime module).
232 | 
233 | -  events is a list of arrays of events. There can be a maximum of
234 |    two—each of these will be plotted as a different line. (So, in_dates
235 |    will be a column of your data, and events will be a list of one or
236 |    two columns from your data.)
237 | 
238 | -  labels is, like in boxplot, a list of labels for your events.
239 | 
240 | -  notch is whether to put a notch in the boxes marking out a confidence
241 |    interval around the median. Uses the Matplotlib default, which isn’t
242 |    terribly clearly specified in `the
243 |    documentation <https://matplotlib.org/api/_as_gen/matplotlib.pyplot.boxplot.html>`__,
244 |    but I assume (from the bootstrap parameter in there) is 95%.
245 | 
246 | Otherwise, the parameters are the same as above. However, you should
247 | note that numticks doesn’t control the number of entries on the x axis
248 | (time). Messing with that is actually a terribly gnarly procedure, but
249 | the defaults seem to be working.
250 | 
251 | PlottyFig Object
252 | ~~~~~~~~~~~~~~~~
253 | 
254 | As noted above, the PlottyFig object supplies several convenience
255 | methods, which can be called on any instance generated by this library
256 | (denoted ``instance`` below), including:
257 | 
258 | **instance.get_main_plot()** returns the Matplotlib ``Axes`` object
259 | containing the actual plot—this is where you do things like change the
260 | title, tweak borders, fonts, etc., or, for more advanced uses, overlay
261 | plots of extra data onto the existing axis and suchlike. Mutating the object returned by this function should mutate the underlying figure.
262 | 
263 | **instance.get_lines()** returns a list of the lines that make up the
264 | plot.
265 | 
266 | **instance.change_title(title)** changes the title of an existing plot.
267 | 
268 | **instance.set_line_width(width)** sets the width of the lines in the
269 | plot.
270 | 
271 | Right now, this class is a stub for future development: I like the idea
272 | of smoothing out the Matplotlib api a little, but don’t know what
273 | convenience methods would be most useful. More to be added in future
274 | versions.
275 | 
276 | Possible Glitches
277 | -----------------
278 | 
279 | -  This requires python 3, and a recent version of Matplotlib.
280 |    I've tested it in Python 3.6.4 with Matplotlib 2.1.2.
281 |    In particular, if ``histogram`` breaks, or if ``scatterplot``
282 |    produces strange lines around the confidence bands, that's
283 |    going to be an obsolete Matplotlib version.
284 | 
285 | -  Fonts are a problem with Matplotlib, and if you run into font
286 |    difficulties then see `this blog
287 |    post <http://andresabino.com/2015/08/18/fonts-and-matplotlib/>`__ for
288 |    a fix. I’ve set Lato as the font for everything because it’s pretty
289 |    and `on google fonts <https://fonts.google.com/specimen/Lato>`__.
290 | 
291 | -  I haven’t quite sorted out timeseries date ranges yet. Right now it
292 |    just uses the Matplotlib defaults, and uses the ordinary plotting
293 |    method rather than the ``plot_date`` method to generate
294 |    (``plot_date`` produces mysterious and bizarre results). A PR to make
295 |    this a little nicer would be very welcome.
296 | 
297 | Contributing
298 | ------------
299 | 
300 | Just file an issue or a PR.
301 | 
302 | Current priorities include:
303 | 
304 | 1. Making the time series better, particularly with respect to
305 |    customization options for the x axis ticks.
306 | 
307 | 2. Some way to apply the stylistic choices in here to plots generated
308 |    from other libraries like Seaborn (perhaps a Matplotlib stylesheet,
309 |    or some heavy hacking around with rcparams).
310 | 
311 | 3. Tests. (Maybe by comparing function results against a SVG string?)
312 | 
313 | 4. More useful ``PlottyFig`` convenience methods.
314 | 
315 | 5. A scatterplot matrix (like the one produced by Pandas).
316 | 
317 | License
318 | -------
319 | 
320 | MIT.
321 | 


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