NAME
Matplotlib::Simple - Access Matplotlib from Perl; providing consistent user interface between different plot types
Synopsis
Take a data structure in Perl, and automatically write a Python3 script using matplotlib to generate an image. The Python3 script is saved in /tmp, to be edited at the user's discretion. Depends on python3 and matplotlib.
My aim is to simplify the most common tasks as much as possible. In my opinion, using this module is much easier than matplotlib itself.
Single Plots
Simplest use case:
use Matplotlib::Simple;
bar(
'output.file' => '/tmp/gospel.word.counts.png',
data => {
Matthew => 18345,
Mark => 11304,
Luke => 19482,
John => 15635,
}
);A more complete (and slightly faster execution):
use Matplotlib::Simple;
plt(
'output.file' => '/tmp/gospel.word.counts.png',
'plot.type' => 'bar',
data => {
Matthew => 18345,
Mark => 11304,
Luke => 19482,
John => 15635,
}
);
Multiple Plots
Having a plots argument as an array lets the module know to create subplots:
use Matplotlib::Simple 'plt';
plt(
'output.file' => 'svg/pies.png',
plots => [
{
data => {
Russian => 106_000_000, # Primarily European Russia
German => 95_000_000, # Germany, Austria, Switzerland, etc.
},
'plot.type' => 'pie',
title => 'Top Languages in Europe',
suptitle => 'Pie in subplots',
},
{
data => {
Russian => 106_000_000, # Primarily European Russia
German => 95_000_000, # Germany, Austria, Switzerland, etc.
},
'plot.type' => 'pie',
title => 'Top Languages in Europe',
},
],
ncols => 2,
);which produces the following subplots image:
bar, barh, boxplot, hexbin, hist, hist2d, imshow, pie, plot, scatter, and violinplot all match the methods in matplotlib itself. venn_proportional_area additionally wraps the Lhttps://pypi.org/project/matplotlib-venn/ library (see #venn_proportional_area).
The p argument
p is a single, uniform way to describe one or many subplots, so you no longer need a top-level plot.type (or the older plots array). Each plot is a hash, exactly like a single-plot call, and p collects the subplots into one array.
The rule is simple: one element of p is one subplot.
A hash element is a subplot containing a single plot.
An array of hashes element is a single subplot whose plots are drawn on the same axes: the first hash is the base plot and the rest are additions (overlays), exactly like
add.
The two forms may be mixed freely in the same p. The first (or only) hash of a subplot supplies that subplot's axes-level options (title, xlabel, ylabel, legend, …).
If you don't give a grid, the subplots are laid out automatically on a near-square grid. Give ncol/nrow (aliases for ncols/nrows) to control it; supplying only one dimension derives the other (so ncols => 1 stacks the subplots in a single column), and supplying both is honored as given.
p cannot be combined with plot.type, data, plots, or add.
> Arguments are now passed as a plain list — C<plt( ... )> — though the older
> C<plt({ ... })> form still works.One subplot, several plots overlaid
Wrap the plots in an inner array and they all land on a single subplot (the first is the base plot, the rest are additions):
plt(
p => [
[
{
data => {
E => [ 55, @{$x}, 160 ],
B => [ @{$y}, 140 ],
},
'plot.type' => 'boxplot',
title => 'Single Box Plot: Specified Colors',
colors => { E => 'yellow', B => 'purple' },
},
{
data => {
A => [ 55, @{$z} ],
E => [ @{$y} ],
B => [ 122, @{$z} ],
},
'plot.type' => 'violinplot',
title => 'Single Violin Plot: Specified Colors',
colors => { E => 'yellow', B => 'purple', A => 'green' },
},
],
],
'output.file' => '1plot.svg', # note: no C<plot.type> needed
);Multiple subplots
Give each subplot as its own element. A bare hash is a one-plot subplot, so two hashes make two subplots; with ncol => 2 they sit side by side:
plt(
p => [
{
data => {
E => [ 55, @{$x}, 160 ],
B => [ @{$y}, 140 ],
},
'plot.type' => 'boxplot',
title => 'Box Plot: Specified Colors',
colors => { E => 'yellow', B => 'purple' },
},
{
data => {
A => [ 55, @{$z} ],
E => [ @{$y} ],
B => [ 122, @{$z} ],
},
'plot.type' => 'violinplot',
title => 'Violin Plot: Specified Colors',
colors => { E => 'yellow', B => 'purple', A => 'green' },
},
],
ncol => 2,
'output.file' => '2plots.svg',
);To overlay extra plots on any one subplot, make that element an array of hashes instead of a bare hash (the first is the plot, the rest are additions). Bare hashes and inner arrays may be intermixed in the same p, for example p => [ \%single, [ \%base, \%overlay ], \%another ].
Options
sharex and sharey are both implemented at the plot, rather than subplot, level. See Matplotlib's documentation for more clarity.
Quoting text: commas and apostrophes
title, suptitle, xlabel, ylabel, set_title, set_xlabel and set_ylabel are quoted for you — but only when the text contains no comma, no apostrophe and no double quote. Anything else is passed through to Python untouched, on the assumption that you are supplying Python syntax of your own, which is what makes a raw string such as
xlabel => 'r"$\it{anno}$ $\it{domini}$"', # italics, via mathtextpossible in the first place. The practical consequence is that a plain-English label with a comma or an apostrophe in it has to carry its own quotes:
title => 'Two groups: mean and s.d.', # fine, no comma
title => '"Two groups, mean and s.d."', # comma: quote it yourself
title => '"war\'s end"', # apostrophe: likewiseWithout those quotes the generated Python is a syntax error rather than a mislabelled plot, so the mistake is loud.
Use double quotes when quoting text yourself. suptitle in particular is emitted twice — once for the subplot and once for the figure — and the second pass runs its own quoting rules over the text, which turns single-quoted text into plt.suptitle(''a, b''). Double quotes survive both passes.
Every other option is passed through as written, so text inside legend, text and friends is Python syntax throughout: legend => 'loc = "upper left"'.
Color Bars (colorbars)
Colarbar args attempt to match matplotlib closely
| Option | Description | Example |
| -------- | ------- | ------- |
cbdrawedges | Whether to draw lines at color boundaries | cbdrawedges => 1 |
cblabel | The label on the colorbar's long axis | cblabel => 1 |
cblocation | of the colorbar None or {'left', 'right', 'top', 'bottom'} | |
cborientation | # None or {vertical, horizontal} | |
cbpad | pad : float, default: 0.05 if vertical, 0.15 if horizontal; Fraction of original Axes between colorbar and new image Axes | |
cb_logscale | Perl true (anything but 0) or false (0) | |
shared.colorbar | share colorbar between different plots: specify plot indices | 'shared.colorbar' => [0,1] |
Size/Dimensions of output file
| Option | Description | Example |
| -------- | ------- | ------- |
scale | scale/multiply the size of the output figure | scale => 2.4 |
scalex | scale/multiply the x-axis only | scalex => 2.4 |
scaley | scale/multiply the y-axis only | scalex => 1.4 |
Examples/Plot Types
Every plot type can be called two ways: through plt with 'plot.type' => 'bar', or through the same-named helper subroutine, bar( ... ), which is a thin wrapper that fills in 'plot.type' and calls plt for you. Everything documented for a plot type therefore works in either form, and works identically whether the plot is alone or one panel of a plots grid.
Which helper takes which data?
The fastest way to pick a plot type is to start from the shape of the data you already have in Perl:
data you have | Helpers that take it | Notes |
| -------- | ------- | ------- |
hash of numbers, A => 1 | bar, barh, pie | one bar/wedge per key |
hash of array refs, A => [1,2,3] | boxplot, violin, hist, hexbin, hist2d, scatter, venn_proportional_area | one distribution/series per key; hexbin and hist2d need exactly 2 keys (x and y), scatter 2 or 3, venn_proportional_area 2 or 3 |
hash of hashes, A => { X => 1 } | bar, barh, colored_table | grouped/stacked bars, or a matrix |
hash of [ \@x, \@y ] pairs | plot | one labelled line per key |
hash of arrays of [ \@x, \@y ] pairs | wide | repeated runs of the same curve, summarised |
| hash of hashes of array refs | scatter | several labelled sets, each with its own x/y (and colour) |
| a single array ref | hist, boxplot, violin | the one-series shorthand |
array of [ \@x, \@y ] pairs | plot, wide | unlabelled lines |
| 2-D array (array of array refs) | imshow | a raster/heatmap; strings allowed via stringmap |
A few conventions hold across all of them:
Keys are used in sorted order unless you say otherwise.
key.orderis accepted bybar,barh,boxplot,violin,hexbin,hist2d,plotandvenn_proportional_area;scatterspells the same ideakeys; andcolored_tableusesrow.labels/col.labels.pie,histandwidetake no ordering option at all, andimshowhas no keys to order.title,xlabel,ylabel,suptitle,set_xlim,legendand the rest of Matplotlib'sax/fig/pltmethods are accepted by every plot type; see #options.Anything that is not recognised is reported as an error listing the arguments that are accepted, rather than being silently ignored.
Consider the following helper subroutines to generate data to plot:
sub linspace { # mostly written by Grok
my ($start, $stop, $num, $endpoint) = @_; # endpoint means include $stop
$num = defined $num ? int($num) : 50; # Default to 50 points
$endpoint = defined $endpoint ? $endpoint : 1; # Default to include endpoint
return () if $num < 0; # Return empty array for invalid num
return ($start) if $num == 1; # Return single value if num is 1
my (@result, $step);
if ($endpoint) {
$step = ($stop - $start) / ($num - 1) if $num > 1;
for my $i (0 .. $num - 1) {
$result[$i] = $start + $i * $step;
}
} else {
$step = ($stop - $start) / $num;
for my $i (0 .. $num - 1) {
$result[$i] = $start + $i * $step;
}
}
return @result;
}
sub generate_normal_dist {
my ($mean, $std_dev, $size) = @_;
$size = defined $size ? int $size : 100; # default to 100 points
my @numbers;
for (1 .. int($size / 2) + 1) {# Box-Muller transform
my $u1 = rand();
my $u2 = rand();
my $z0 = sqrt(-2.0 * log($u1)) * cos(2.0 * 3.141592653589793 * $u2);
my $z1 = sqrt(-2.0 * log($u1)) * sin(2.0 * 3.141592653589793 * $u2); # Scale and shift to match mean and std_dev
push @numbers, ($z0 * $std_dev + $mean, $z1 * $std_dev + $mean);
} # Trim to exact size if needed
@numbers = @numbers[0 .. $size - 1] if @numbers > $size;
@numbers = map {sprintf '%.1f', $_} @numbers;
return \@numbers;
}
sub rand_between {
my ($min, $max) = @_;
return $min + rand($max - $min)
}Barplot/bar/barh
Plot a hash, a hash of arrays, or a hash of hashes as a bar chart. bar draws vertical bars, barh horizontal ones; every option below applies to both.
Entering data
data accepts three shapes, and the shape alone decides whether you get one bar per key or a group of bars per key:
1. One bar per key (hash of numbers). The simplest case — the key is the tick label:
bar(
'output.file' => '/tmp/simple.svg',
data => { Mon => 73, Tue => 93, Wed => 77 },
);2. Groups of bars (hash of array refs). Each key becomes a group; index i of every array is one series, so color and label are arrays indexed the same way:
bar(
'output.file' => '/tmp/grouped.svg',
data => {
1941 => [ 6.6, 6.2 ], # UK, US
1942 => [ 7.6, 26.4 ],
},
color => [ 'blue', 'gray' ], # index 0, index 1
label => [ 'UK', 'US' ], # legend entries
);3. Groups of bars (hash of hashes). The same picture as (2), but the series are named by the inner keys rather than by position, so no label is needed:
bar(
'output.file' => '/tmp/grouped.hoh.svg',
data => {
1941 => { UK => 6.6, US => 6.2 },
1942 => { UK => 7.6, US => 26.4 },
},
);Both grouped forms accept stacked => 1 to pile the series on top of one another instead of placing them side by side.
Error bars
yerr (natural for bar) and xerr (natural for barh) take either one number for every bar, or a hash keyed by the data keys. A two-element array gives asymmetric [ lower, upper ] errors:
bar(
'output.file' => '/tmp/warheads.svg',
data => { USA => 5277, Russia => 5449 },
yerr => {
USA => [ 15, 29 ], # -15, +29
Russia => [ 199, 1000 ],
},
log => 'True',
ylabel => '# of Nuclear Warheads',
);Options
| Option | Description | Example |
| -------- | ------- | ------- |
| color | :mpltype:color or list of :mpltype:color, optional; The colors of the bar faces. This is an alias for *facecolor*. If both are given, *facecolor* takes precedence # if entering multiple colors, quoting isn't needed; as of version 0.23, colors can be given as a hash | color => ['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'fuchsia'], or a single color for all bars color => 'red', or as of version 0.23 color => {A => 'red', B => 'green'} |
| edgecolor | :mpltype:color or list of :mpltype:color, optional; The colors of the bar edges | edgecolor => 'black' |
| key.order | define the keys in an order (an array reference) | 'key.order' => ['Sun','Mon','Tue','Wed','Thu','Fri','Sat'], |
| label | an array of legend labels for grouped bar plots, indexed like the data arrays; only meaningful for the hash-of-arrays form, since the hash-of-hashes form takes its labels from the inner keys | label => ['North', 'South'], |
| linewidth | float or array, optional; Width of the bar edge(s). If 0, don't draw edges. Only does anything with defined edgecolor | linewidth => 2, |
| log | bool, default: False; If *True*, set the y-axis to be log scale. | log = 'True', |
| logscale | a synonym for log taking a Perl true/false value rather than Python's 'True'/'False'. Unlike the logscale of boxplot, hist, hist2d, plot, scatter and violin, this one is a scalar and not an array of axis names | logscale => 1, |
| stacked | stack the groups on top of one another; default 0 = off | stacked => 1, |
| width | float only, default: 0.8; The width(s) of the bars. width will be deactivated with grouped, non-stacked bar plots | width => 0.4, |
| xerr | float or array-like of shape(N,) or shape(2, N), optional. If not *None*, add horizontal / vertical errorbars to the bar tips. The values are +/- sizes relative to the data: - scalar: symmetric +/- values for all bars # - shape(N,): symmetric +/- values for each bar # - shape(2, N): Separate - and + values for each bar. First row # contains the lower errors, the second row contains the upper # errors. # - *None*: No errorbar. (Default) | yerr => {'USA' => [15,29], 'Russia' => [199,1000],} |
| yerr | same as xerr, but better with bar |
an example of multiple plots, showing many options:
single, simple plot
use Matplotlib::Simple 'plt';
plt(
'output.file' => 'output.images/single.barplot.png',
data => { # simple hash
Fri => 76, Mon => 73, Sat => 26, Sun => 11, Thu => 94, Tue => 93, Wed => 77
},
'plot.type' => 'bar',
xlabel => '# of Days',
ylabel => 'Count',
title => 'Customer Calls by Days'
);where xlabel, ylabel, title, etc. are axis methods in matplotlib itself. plot.type, data, fh are all specific to MatPlotLib::Simple.
multiple plots
plt(
fh => $fh,
execute => 0,
'output.file' => 'output.images/barplots.png',
plots => [
{ # simple plot
data => { # simple hash
Fri => 76, Mon => 73, Sat => 26, Sun => 11, Thu => 94, Tue => 93, Wed => 77
},
'plot.type' => 'bar',
'key.order' => ['Sun','Mon','Tue','Wed','Thu','Fri','Sat'],
suptitle => 'Types of Plots', # applies to all
color => ['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'fuchsia'],
edgecolor => 'black',
set_figwidth => 40/1.5, # applies to all plots
set_figheight => 30/2, # applies to all plots
title => 'bar: Rejections During Job Search',
xlabel => 'Day of the Week',
ylabel => 'No. of Rejections'
},
{ # grouped bar plot
'plot.type' => 'bar',
data => {
1941 => {
UK => 6.6,
US => 6.2,
USSR => 17.8,
Germany => 26.6
},
1942 => {
UK => 7.6,
US => 26.4,
USSR => 19.2,
Germany => 29.7
},
1943 => {
UK => 7.9,
US => 61.4,
USSR => 22.5,
Germany => 34.9
},
1944 => {
UK => 7.4,
US => 80.5,
USSR => 27.0,
Germany => 31.4
},
1945 => {
UK => 5.4,
US => 83.1,
USSR => 25.5,
Germany => 11.2 #Rapid decrease due to war's end <br />
},
},
stacked => 0,
title => 'Hash of Hash Grouped Unstacked Barplot',
width => 0.23,
xlabel => 'r"$\it{anno}$ $\it{domini}$"', # italic
ylabel => 'Military Expenditure (Billions of $)'
},
{ # grouped bar plot
'plot.type' => 'bar',
data => {
1941 => {
UK => 6.6,
US => 6.2,
USSR => 17.8,
Germany => 26.6
},
1942 => {
UK => 7.6,
US => 26.4,
USSR => 19.2,
Germany => 29.7
},
1943 => {
UK => 7.9,
US => 61.4,
USSR => 22.5,
Germany => 34.9
},
1944 => {
UK => 7.4,
US => 80.5,
USSR => 27.0,
Germany => 31.4
},
1945 => {
UK => 5.4,
US => 83.1,
USSR => 25.5,
Germany => 11.2 #Rapid decrease due to war's end
},
},
stacked => 1,
title => 'Hash of Hash Grouped Stacked Barplot',
xlabel => 'r"$\it{anno}$ $\it{domini}$"', # italic
ylabel => 'Military Expenditure (Billions of $)'
},
{# grouped barplot: arrays indicate Union, Confederate which must be specified in options hash
data => { # 4th plot: arrays indicate Union, Confederate which must be specified in options hash
'Antietam' => [ 12400, 10300 ],
'Gettysburg' => [ 23000, 28000 ],
'Chickamauga' => [ 16000, 18000 ],
'Chancellorsville' => [ 17000, 13000 ],
'Wilderness' => [ 17500, 11000 ],
'Spotsylvania' => [ 18000, 12000 ],
'Cold Harbor' => [ 12000, 5000 ],
'Shiloh' => [ 13000, 10700 ],
'Second Bull Run' => [ 10000, 8000 ],
'Fredericksburg' => [ 12600, 5300 ],
},
'plot.type' => 'barh',
color => ['blue', 'gray'], # colors match indices of data arrays
label => ['North', 'South'], # colors match indices of data arrays
xlabel => 'Casualties',
ylabel => 'Battle',
title => 'barh: hash of array'
},
{ # 5th plot: barplot with groups
data => {
1942 => [ 109867, 310000, 7700000 ], # US, Japan, USSR
1943 => [ 221111, 440000, 9000000 ],
1944 => [ 318584, 610000, 7000000 ],
1945 => [ 318929, 1060000, 3000000 ],
},
color => ['blue', 'pink', 'red'], # colors match indices of data arrays
label => ['USA', 'Japan', 'USSR'], # colors match indices of data arrays
'log' => 1,
title => 'grouped bar: Casualties in WWII',
ylabel => 'Casualties',
'plot.type' => 'bar'
}, <br />
{ # nuclear weapons barplot
'plot.type' => 'bar',
data => {
'USA' => 5277, # FAS Estimate
'Russia' => 5449, # FAS Estimate
'UK' => 225, # Consistent estimate
'France' => 290, # Consistent estimate
'China' => 600, # FAS Estimate
'India' => 180, # FAS Estimate
'Pakistan' => 130, # FAS Estimate
'Israel' => 90, # FAS Estimate
'North Korea' => 50, # FAS Estimate
},
title => 'Simple hash for barchart with yerr',
xlabel => 'Country',
yerr => {
'USA' => [15,29],
'Russia' => [199,1000],
'UK' => [15,19],
'France' => [19,29],
'China' => [200,159],
'India' => [15,25],
'Pakistan' => [15,49],
'Israel' => [90,50],
'North Korea' => [10,20],
},
ylabel => '# of Nuclear Warheads',
'log' => 'True', # linewidth => 1,
}
],
ncols => 3,
nrows => 4
);which produces the plot:
colors for each hash key defined by hash
plt(
plots => [
{
color => {
A => 'red', B => 'green', C => 'blue'
},
data => {
A => 1, B => 2, C => 3
},
'plot.type' => 'bar'
},
{
color => {
A => 'red', B => 'green', C => 'blue'
},
data => {
A => 1, B => 2, C => 3
},
'plot.type' => 'barh'
},
],
ncols => 2,
'output.file' => '/tmp/key.colors.bar.svg',
);which produces the plot
boxplot
Plot a hash of arrays as a series of boxplots: one box per key, labelled with the key and the number of points it holds.
Entering data
Ordinarily data is a hash of array refs, one array per box:
boxplot(
'output.file' => '/tmp/boxes.svg',
data => { A => \@a, B => \@b, C => \@c },
);A bare array ref is the one-box shorthand; the box gets an empty label:
boxplot(
'output.file' => '/tmp/one.box.svg',
data => \@a,
);Undefined values are dropped rather than fatal, so a column read out of a spreadsheet with blank cells can be handed over as-is; a value that is present but not a number is an error naming the offending key. (#violin takes exactly these two shapes as well — swapping 'plot.type' => 'boxplot' for 'plot.type' => 'violinplot' is a one-word change — but it drops non-numeric values silently instead of dying.)
options
| Option | Description | Example |
| -------- | ------- | ------- |
color | a single color for all boxes | color => 'pink' |
colors | a hash pairing each data key with its own color. Every key in data must appear, otherwise the call dies naming the keys that have no color | colors => { A => 'orange', E => 'yellow', B => 'purple' }, |
key.order | order that the keys in the entry hash will be plotted | 'key.order' => ['A', 'E', 'B'] |
logscale | an array of the axes to put on a log scale; only x and y are accepted | logscale => ['y'] |
notch | draw a notched box ('True') instead of a rectangular one | notch => 'True' |
orientation | orientation of the plot, by default vertical | orientation => 'horizontal' |
showcaps | Show the caps on the ends of whiskers; default True | showcaps => 'False', |
showfliers | Show the outliers beyond the caps; default True | showfliers => 'False' |
showmeans | show means; default = True | showmeans => 'False' |
showcaps, showfliers, showmeans and notch are passed straight through to Matplotlib, so they take Python's 'True'/'False' rather than a Perl boolean. The whiskers switch belongs to #violin, not to boxplot.
single, simple plot
my $x = generate_normal_dist( 100, 15, 3 * 10 );
my $y = generate_normal_dist( 85, 15, 3 * 10 );
my $z = generate_normal_dist( 106, 15, 3 * 10 );single plots are simple
use Matplotlib::Simple 'barplot';
boxplot(
'output.file' => 'output.images/single.boxplot.png',
data => { # simple hash
E => [ 55, @{$x}, 160 ],
B => [ @{$y}, 140 ],
# A => @a
},
title => 'Single Box Plot: Specified Colors',
colors => { E => 'yellow', B => 'purple' },
fh => $fh,
execute => 0,
);which makes the following image:
multiple plots
plt(
'output.file' => 'output.images/boxplot.png',
execute => 0,
fh => $fh,
plots => [
{
data => {
A => [ 55, @{$z} ],
E => [ @{$y} ],
B => [ 122, @{$z} ],
},
title => 'Simple Boxplot',
ylabel => 'ylabel',
xlabel => 'label',
'plot.type' => 'boxplot',
suptitle => 'Boxplot examples'
},
{
color => 'pink',
data => {
A => [ 55, @{$z} ],
E => [ @{$y} ],
B => [ 122, @{$z} ],
},
title => 'Specify single color',
ylabel => 'ylabel',
xlabel => 'label',
'plot.type' => 'boxplot'
},
{
colors => {
A => 'orange',
E => 'yellow',
B => 'purple'
},
data => {
A => [ 55, @{$z} ],
E => [ @{$y} ],
B => [ 122, @{$z} ],
},
title => 'Specify set-specific color; showfliers = False',
ylabel => 'ylabel',
xlabel => 'label',
'plot.type' => 'boxplot',
showmeans => 'True',
showfliers => 'False',
set_figwidth => 12
},
{
colors => {
A => 'orange',
E => 'yellow',
B => 'purple'
},
data => {
A => [ 55, @{$z} ],
E => [ @{$y} ],
B => [ 122, @{$z} ],
},
title => 'Specify set-specific color; showmeans = False',
ylabel => 'ylabel',
xlabel => 'label',
'plot.type' => 'boxplot',
showmeans => 'False',
},
{
colors => {
A => 'orange',
E => 'yellow',
B => 'purple'
},
data => {
A => [ 55, @{$z} ],
E => [ @{$y} ],
B => [ 122, @{$z} ],
},
title => 'Set-specific color; orientation = horizontal',
ylabel => 'ylabel',
xlabel => 'label',
orientation => 'horizontal',
'plot.type' => 'boxplot',
},
{
colors => {
A => 'orange',
E => 'yellow',
B => 'purple'
},
data => {
A => [ 55, @{$z} ],
E => [ @{$y} ],
B => [ 122, @{$z} ],
},
title => 'Notch = True',
ylabel => 'ylabel',
xlabel => 'label',
notch => 'True',
'plot.type' => 'boxplot',
},
{
colors => {
A => 'orange',
E => 'yellow',
B => 'purple'
},
data => {
A => [ 55, @{$z} ],
E => [ @{$y} ],
B => [ 122, @{$z} ],
},
title => 'showcaps = False',
ylabel => 'ylabel',
xlabel => 'label',
showcaps => 'False',
'plot.type' => 'boxplot',
set_figheight => 12,
},
],
ncols => 3,
nrows => 3,
);which makes the following plot:
Colored Table
Plot a hash of hashes as a matrix, coloring each cell by its value.
Entering data
data is a hash of hashes: the outer key is the row, the inner key is the column, and the value is the number that picks the cell's color.
colored_table(
'output.file' => '/tmp/matrix.svg',
data => {
H => { H => 432, Cl => 427, Br => 363 },
C => { H => 413, Cl => 339, Br => 276 },
},
);The matrix does not have to be complete. Cells with no value are left out of the color scale and drawn in undef.color (gray by default), and a table that only fills one triangle — the usual shape of a pairwise-comparison table — can be completed by reflecting it across the diagonal with mirror => 1, so that $data{A}{B} also supplies $data{B}{A}.
Rows and columns are otherwise taken in sorted order. col.labels chooses which keys are drawn and in what order, which is how the bond-dissociation example below shows the halogens only out of a larger table; row.labels supplies the text down the left-hand side, so it should list the same keys in the same order.
options
| Option | Description | Example |
| -------- | ------- | ------- |
cb_logscale | color the cells on a log scale | cb_logscale => 1 |
cb_min, cb_max | clamp the ends of the color scale instead of taking them from the data, so several tables can be compared directly | cb_min => 100, cb_max => 500 |
cblabel | the label on the colorbar | cblabel => 'kJ/mol' |
cmap | the colormap used for coloring the cells | cmap => 'viridis' |
col.labels | array ref: which keys to draw, in order â this selects the rows and the columns of the matrix, not just the heading text | 'col.labels' => ['H', 'F', 'Cl', 'Br', 'I'] |
colorbar.on | draw the colorbar; on by default, 0 turns it off. Passing cblabel draws it regardless | 'colorbar.on' => 0 |
mirror | treat the table as symmetric: $data{A}{B} also fills $data{B}{A} | mirror => 1 |
row.labels | array ref of the labels printed down the left side; give it the same keys, in the same order, as col.labels | 'row.labels' => ['H', 'F', 'Cl', 'Br', 'I'] |
show.numbers | print each cell's value in the cell; off by default | 'show.numbers' => 1 |
undef.color | the color for cells that have no value; gray by default | 'undef.color' => 'white' |
The colorbar options in #color-bars-colorbars — cbdrawedges, cblocation, cborientation, cbpad — work here too.
Single, simple plot
the bond dissociation energy table can be plotted:
# https://labs.chem.ucsb.edu/zakarian/armen/11---bonddissociationenergy.pdf and https://chem.libretexts.org/Bookshelves/Physical_and_Theoretical_Chemistry_Textbook_Maps/Supplemental_Modules_(Physical_and_Theoretical_Chemistry)/Chemical_Bonding/Fundamentals_of_Chemical_Bonding/Bond_Energies
my %bond_dissociation = (
Br => {
Br => 193
},
C => {
Br => 276, C => 347, Cl => 339, F => 485, H => 413, I => 240,
N => 305, O => 358, S => 259
},
Cl => {
Br => 218, Cl => 239
},
F => {
I => 280, Br => 237, Cl => 253, F => 154
},
H => {
Br => 363, Cl => 427, F => 565, H => 432, I => 295
},
I => {
Br => 175, Cl => 208, I => 149
},
N => {
Br => 243, Cl => 200, F => 272, H => 391, N => 160, O => 201
},
O => {
Cl => 203, F => 190, H => 467, I => 234, O => 146
},
S => {
Br => 218, Cl => 253, F => 327, H => 347, S => 266
},
Si => {
C => 360, H => 393, O => 452, Si => 340
}
);and the plot itself:
colored_table(
'cblabel' => 'kJ/mol',
'col.labels' => ['H', 'F', 'Cl', 'Br', 'I'],
data => \%bond_dissociation,
execute => 0,
fh => $fh,
mirror => 1,
'output.file' => 'output.images/single.tab.png',
'row.labels' => ['H', 'F', 'Cl', 'Br', 'I'],
'show.numbers'=> 1,
set_title => 'Bond Dissociation Energy'
);which makes the following image:
Multiple Plots
plt(
'output.file' => 'output.images/tab.multiple.png',
execute => 0,
fh => $fh,
plots => [
{
data => \%bond_dissociation,
'output.file' => '/tmp/single.bonds.svg',
'plot.type' => 'colored_table',
set_title => 'No other options'
},
{
data => \%bond_dissociation,
cblabel => 'Average Dissociation Energy (kJ/mol)',
'col.labels' => ['H', 'C', 'N', 'O', 'F', 'Si', 'S', 'Cl', 'Br', 'I'],
mirror => 1,
'output.file' => '/tmp/single.bonds.svg',
'plot.type' => 'colored_table',
'row.labels' => ['H', 'C', 'N', 'O', 'F', 'Si', 'S', 'Cl', 'Br', 'I'],
'show.numbers'=> 1,
set_title => 'Showing numbers and mirror with defined order'
},
{
data => \%bond_dissociation,
cblabel => 'Average Dissociation Energy (kJ/mol)',
'col.labels' => ['H', 'C', 'N', 'O', 'F', 'Si', 'S', 'Cl', 'Br', 'I'],
mirror => 1,
'output.file' => '/tmp/single.bonds.svg',
'plot.type' => 'colored_table',
'row.labels' => ['H', 'C', 'N', 'O', 'F', 'Si', 'S', 'Cl', 'Br', 'I'],
'show.numbers'=> 1,
set_title => 'Set undefined color to white',
'undef.color' => 'white'
}
],
ncols => 3,
set_figwidth => 14,
suptitle => 'Colored Table options'
);which makes the following plot:
hexbin
Plot a hash of arrays as a hexbin see https://matplotlib.org/stable/api/asgen/matplotlib.pyplot.hexbin.html
A hexbin answers the question a scatterplot stops answering once there are tens of thousands of points: instead of drawing every point and letting them pile up into an indistinguishable blob, the plane is tiled with hexagons and each one is colored by how many points fell inside it.
Entering data
data is a hash of exactly two array refs of equal length — the first key (sorted) is the x-axis, the second is the y-axis, and both become the axis labels. Use key.order to say which is which rather than relying on the sort:
hexbin(
'output.file' => '/tmp/hex.svg',
data => { Height => \@heights, Weight => \@weights },
'key.order' => [ 'Weight', 'Height' ], # Weight on x
cblabel => 'people per cell',
);options
| Option | Description | Example |
| -------- | ------- | ------- |
| cb_logscale | colorbar log scale from matplotlib.colors import LogNorm | default 0, any value > 0 enables |
| cblabel | the label on the colorbar, i.e. what the cell counts mean; Density if not given | cblabel => 'observations' |
| cmap | The Colormap instance or registered colormap name used to map scalar data to colors | default gist_rainbow |
| key.order | define the keys in an order (an array reference) | 'key.order' => ['X-rays', 'Yak Butter'], |
| marginals | integer, by default off = 0 | marginals => 1 |
| mincnt | int >= 0, default: None; If not None, only display cells with at least mincnt number of points in the cell. | mincnt => 2 |
| vmax | The normalization method used to scale scalar data to the [0, 1] range before mapping to colors using cmap | 'asinh', 'function', 'functionlog', 'linear', 'log', 'logit', 'symlog' default linear |
| vmin | The normalization method used to scale scalar data to the [0, 1] range before mapping to colors using cmap | 'asinh', 'function', 'functionlog', 'linear', 'log', 'logit', 'symlog' default linear |
| xbins | integer that accesses horizontal gridsize | default is 15 |
| xscale.hexbin | 'linear', 'log'}, default: 'linear': Use a linear or log10 scale on the horizontal axis | 'xscale.hexbin' => 'log' |
| ybins | integer that accesses vertical gridsize | default is 15 |
| yscale.hexbin | 'linear', 'log'}, default: 'linear': Use a linear or log10 scale on the vertical axis | 'yscale.hexbin' => 'log' |
cb_logscale cannot be combined with vmin/vmax. The log-scaled colorbar is drawn by handing Matplotlib a LogNorm, and an explicit range on top of that makes the generated Python fail; use one or the other.
single, simple plot
plt(
data => {
E => generate_normal_dist(100, 15, 3*210),
B => generate_normal_dist(85, 15, 3*210)
},
'output.file' => 'output.images/single.hexbin.png',
'plot.type' => 'hexbin',
set_figwidth => 12,
title => 'Simple Hexbin',
);which makes the following plot:
multiple plots
plt(
fh => $fh,
execute => 0,
'output.file' => 'output.images/hexbin.png',
plots => [
{
data => {
E => @e,
B => @b
},
'plot.type' => 'hexbin',
title => 'Simple Hexbin',
},
{
data => {
E => @e,
B => @b
},
'plot.type' => 'hexbin',
title => 'colorbar logscale',
cb_logscale => 1
},
{
cmap => 'jet',
data => {
E => @e,
B => @b
},
'plot.type' => 'hexbin',
title => 'cmap is jet',
xlabel => 'xlabel',
},
{
data => {
E => @e,
B => @b
},
'key.order' => ['E', 'B'],
'plot.type' => 'hexbin',
title => 'Switch axes with key.order',
},
{
data => {
E => @e,
B => @b
},
'plot.type' => 'hexbin',
title => 'vmax set to 25',
vmax => 25
},
{
data => {
E => @e,
B => @b
},
'plot.type' => 'hexbin',
title => 'vmin set to -4',
vmin => -4
},
{
data => {
E => @e,
B => @b
},
'plot.type' => 'hexbin',
title => 'mincnt set to 7',
mincnt => 7
},
{
data => {
E => @e,
B => @b
},
'plot.type' => 'hexbin',
title => 'xbins set to 9',
xbins => 9
},
{
data => {
E => @e,
B => @b
},
'plot.type' => 'hexbin',
title => 'ybins set to 9',
ybins => 9
},
{
data => {
E => @e,
B => @b
},
'plot.type' => 'hexbin',
title => 'marginals = 1',
marginals => 1
},
{
data => {
E => @e,
B => @b
},
'plot.type' => 'hexbin',
title => 'xscale.hexbin = 1',
'xscale.hexbin' => 'log'
},
{
data => {
E => @e,
B => @b
},
'plot.type' => 'hexbin',
title => 'yscale.hexbin = 1',
'yscale.hexbin' => 'log'
},
],
ncols => 4,
nrows => 3,
scale => 5,
suptitle => 'Various Changes to Standard Hexbin: All data is the same'
);which produces the following image:
hist
Plot a hash of arrays as a series of histograms, one per key, drawn over each other in the same axes — alpha defaults to 0.5 so that the overlaps stay readable. A single array ref is the one-set shorthand. Values must be numeric: unlike boxplot and violin, a non-numeric value here is an error.
Each set is binned separately, so with bins => 50 two sets covering different ranges get 50 bins each over their own range rather than a common set of edges. When the sets must line up exactly — which is what makes the bar heights comparable — pass the edges themselves rather than a count:
hist(
'output.file' => '/tmp/hist.svg',
data => { E => \@e, B => \@b },
bins => [ map { 10 * $_ } 0 .. 20 ], # shared edges, 0..200
);bins and color also accept a hash keyed by set, for when one distribution wants different treatment from the others:
bins => { E => 50, B => 20 },
color => { E => 'orange', B => 'black' },The legend is on by default when there is more than one set and off when there is only one; show.legend overrides that either way.
options
| Option | Description | Example |
| -------- | ------- | ------- |
alpha | opacity of the bars, default 0.5; the same value is used for all sets | alpha => 0.25 |
bins | int or sequence or str, default: :rc:hist.bins. If *bins* is an integer, it defines the number of equal-width bins in the range. If *bins* is a sequence, it defines the bin edges, including the left edge of the first bin and the right edge of the last bin; in this case, bins may be unequally spaced. All but the last (righthand-most) bin is half-open. May also be a hash keyed by set | bins => 50 |
color | either one color for every set, or a hash pairing each data key with its own color | color => { X => 'blue', Y => 'orange' } |
logscale | an array of the axes to put on a log scale, useful when one set is orders of magnitude rarer than another. It must be an array reference â logscale => 1 is an error | logscale => ['y'] |
orientation | {'vertical', 'horizontal'}, default: 'vertical' | orientation => 'horizontal' |
show.legend | on when data holds more than one set, off when it holds one; set it explicitly to override | 'show.legend' => 0 |
single, simple plot
as of version 0.26, single arrays can be given to hist instead of a hash, simplifying the call:
hist(
data => [0..9],
'output.file' => '/tmp/hist.arr.svg',
);for slightly more complex data sets, hashes are taken:
use Matplotlib::Simple 'hist';
my @e = generate_normal_dist( 100, 15, 3 * 200 );
my @b = generate_normal_dist( 85, 15, 3 * 200 );
my @a = generate_normal_dist( 105, 15, 3 * 200 );
hist(
fh => $fh,
execute => 0,
'output.file' => 'output.images/single.hist.png',
data => {
E => @e,
B => @b,
A => @a,
}
);which makes the following simple plot:
multiple plots
plt(
fh => $fh,
execute => 0,
'output.file' => 'output.images/histogram.png',
set_figwidth => 15,
suptitle => 'hist Examples',
plots => [
{ # 1st subplot
data => {
E => @e,
B => @b,
A => @a,
},
'plot.type' => 'hist',
alpha => 0.25,
bins => 50,
title => 'alpha = 0.25',
color => {
B => 'Black',
E => 'Orange',
A => 'Yellow',
},
scatter => '['
. join( ',', 22 .. 44 ) . '],[' # x coords
. join( ',', 22 .. 44 ) # y coords
. '], label = "scatter"',
xlabel => 'Value',
ylabel => 'Frequency',
},
{ # 2nd subplot
data => {
E => @e,
B => @b,
A => @a,
},
'plot.type' => 'hist',
alpha => 0.75,
bins => 50,
title => 'alpha = 0.75',
color => {
B => 'Black',
E => 'Orange',
A => 'Yellow',
},
xlabel => 'Value',
ylabel => 'Frequency',
},
{ # 3rd subplot
add => [ # add secondary plots/graphs/methods
{ # 1st additional plot/graph
data => {
'Gaussian' => [
[40..150],
[map {150 * exp(-0.5*($_-100)**2)} 40..150]
]
},
'plot.type' => 'plot',
'set.options' => {
'Gaussian' => 'color = "red", linestyle = "dashed"'
}
}
],
data => {
E => @e,
B => @b,
A => @a,
},
'plot.type' => 'hist',
alpha => 0.75,
bins => {
A => 10,
B => 25,
E => 50
},
title => 'Varying # of bins',
color => {
B => 'Black',
E => 'Orange',
A => 'Yellow',
},
xlabel => 'Value',
ylabel => 'Frequency',
},
{# 4th subplot
data => {
E => @e,
B => @b,
A => @a,
},
'plot.type' => 'hist',
alpha => 0.75,
color => {
B => 'Black',
E => 'Orange',
A => 'Yellow',
},
orientation => 'horizontal', # assign x and y labels smartly
title => 'Horizontal orientation',
ylabel => 'Value',
xlabel => 'Frequency', # 'log' => 1,
},
],
ncols => 3,
nrows => 2,
);
hist2d
Make a 2-D histogram from a hash of arrays: like #hexbin, data is a hash of exactly two equal-length array refs, the first (sorted) key giving the x-axis and the second the y-axis, and the plane is divided into rectangular cells colored by how many points landed in each. hexbin and hist2d are interchangeable on the same data — hexagons tile the plane without the visual grid artefacts of squares, while square bins are easier to read off against the axes.
single, simple plot
plt(
'output.file' => 'output.images/single.hist2d.png',
data => {
E => @e,
B => @b
},
'plot.type' => 'hist2d',
title => 'title',
execute => 0,
fh => $fh,
);makes the following image:
the range for the density min and max is reported to stdout
options
| Option | Description | Example |
| -------- | ------- | ------- |
cb_logscale | make the colorbar log-scale | cb_logscale => 1 |
cblabel | the label on the colorbar, i.e. what the cell counts mean; Density if not given | cblabel => 'observations' |
cmap | color map for coloring # "gist_rainbow" by default | |
'cmax', cmin | All bins that has count < *cmin* or > *cmax* will not be displayed. cmin => 1 is the usual way to leave empty cells blank instead of coloring them as zero | cmin => 1 |
| 'density' | density : bool, default: False; normalise the counts so the plot shows a probability density instead of raw counts, which is what makes two plots of different-sized samples comparable | density => 'True' |
| 'key.order' | define the keys in an order (an array reference), i.e. which key is the x-axis | 'key.order' => ['Y', 'X'] |
| 'logscale' | an array of the axes that will get a log scale | logscale => ['x'] |
| 'show.colorbar' | self-evident, 0 or 1; this, and not colorbar.on, is what suppresses a hist2d colorbar | show.colorbar => 0 |
| 'vmax' | When using scalar data and no explicit *norm*, *vmin* and *vmax* define the data range that the colormap cover | |
| 'vmin' | # When using scalar data and no explicit *norm*, *vmin* and *vmax* define the data range that the colormap cover | |
| 'xbins' | # default 15 | |
| 'xmin', 'xmax', | ||
| 'ymin', 'ymax', | ||
| 'ybins' | default 15 |
multiple plots
plt(
fh => $fh,
execute => 1,
ncols => 3,
nrows => 3,
suptitle => 'Types of hist2d plots: all of the data is identical',
plots => [
{
data => {
X => $x, # x-axis
Y => $y, # y-axis
},
'plot.type' => 'hist2d',
title => 'Simple hist2d',
},
{
data => {
X => $x, # x-axis
Y => $y, # y-axis
},
'plot.type' => 'hist2d',
title => 'cmap = terrain',
cmap => 'terrain'
},
{
cmap => 'ocean',
data => {
X => $x, # x-axis
Y => $y, # y-axis
},
'plot.type' => 'hist2d',
title => 'cmap = ocean and set colorbar range with vmin/vmax',
set_figwidth => 15,
vmin => -2,
vmax => 14
},
{
data => {
X => $x, # x-axis
Y => $y, # y-axis
},
'plot.type' => 'hist2d',
title => 'density = True',
cmap => 'terrain',
density => 'True'
},
{
data => {
X => $x, # x-axis
Y => $y, # y-axis
},
'plot.type' => 'hist2d',
title => 'key.order flips axes',
cmap => 'terrain',
'key.order' => [ 'Y', 'X' ]
},
{
cb_logscale => 1,
data => {
X => $x, # x-axis
Y => $y, # y-axis
},
'plot.type' => 'hist2d',
title => 'cb_logscale = 1',
},
{
cb_logscale => 1,
data => {
X => $x, # x-axis
Y => $y, # y-axis
},
'plot.type' => 'hist2d',
title => 'cb_logscale = 1 with vmax set',
vmax => 2.1,
vmin => 1
},
{
data => {
X => $x, # x-axis
Y => $y, # y-axis
},
'plot.type' => 'hist2d',
'show.colorbar' => 0,
title => 'no colorbar',
},
{
data => {
X => $x, # x-axis
Y => $y, # y-axis
},
'plot.type' => 'hist2d',
title => 'xbins = 9',
xbins => 9
},
],
'output.file' => 'output.images/hist2d.png',
);makes the following image:
imshow
Plot 2D array of numbers as an image
Entering data
data is a 2-D array — an array of array refs — and nothing else; a hash is an error. The generated call leaves Matplotlib's origin at its default, so row 0 is drawn at the top; use invert_yaxis if your first row is meant to be the bottom of the picture:
my @grid;
foreach my $i (0 .. 360) {
foreach my $j (0 .. 360) {
push @{ $grid[$i] }, sin($i * $pi/180) * cos($j * $pi/180);
}
}
imshow(
'output.file' => '/tmp/grid.svg',
data => \@grid,
cblabel => 'sin(x) * cos(x)',
);The cells may hold strings instead of numbers, as long as stringmap gives the meaning of each one — without it, non-numeric data is an error. Each string is assigned an integer, the image is drawn with one discrete color per string, and the colorbar's ticks are labelled with the names from stringmap rather than with numbers. (cmap is dropped, with a warning, when strings are in play, since the palette has to be a discrete one.) This is what makes imshow usable for categorical rasters — sequence annotation, land cover, state-over-time diagrams — and there is a worked example under #secondary-structure-prediction-dssp.
Because imshow produces a colorbar per subplot, shared.colorbar is often worth setting when several panels show the same quantity: it gives them one colorbar, and hence one color scale, so the panels can be compared.
options
| Option | Description | Example |
| -------- | ------- | ------- |
cblabel | colorbar label | cblabel => 'sin(x) * cos(x)', |
cbdrawedges | draw edges for colorbar | |
cblocation | 'left', 'right', 'top', 'bottom' | cblocation => 'left', |
cborientation | None, or 'vertical', 'horizontal' | |
cbpad | fraction of the original axes between the image and the colorbar; the default 0.05 is often too big for a short, wide image | cbpad => 0.01, |
cmap | # The Colormap instance or registered colormap name used to map scalar data to colors. | |
colorbar.on | draw the colorbar; on by default, 0 turns it off | 'colorbar.on' => 0 |
shared.colorbar | 0-based indices of the subplots that should share one colorbar, and therefore one color scale | 'shared.colorbar' => [0,1] |
stringmap | a hash giving the meaning of each string used in data, which also makes string data legal | stringmap => { H => 'Alpha helix' } |
vmax | float | |
vmin | float |
single, simple plot
my @imshow_data;
foreach my $i (0..360) {
foreach my $j (0..360) {
push @{ $imshow_data[$i] }, sin($i * $pi/180)*cos($j * $pi/180);
}
}
plt(
data => \@imshow_data,
execute => 0,
fh => $fh,
'output.file' => 'output.images/imshow.single.png',
'plot.type' => 'imshow',
set_xlim => '0, ' . scalar @imshow_data,
set_ylim => '0, ' . scalar @imshow_data,
);which makes the following image:
multiple plots
plt(
plots => [
{
data => \@imshow_data,
'plot.type' => 'imshow',
set_xlim => '0, ' . scalar @imshow_data,
set_ylim => '0, ' . scalar @imshow_data,
title => 'basic',
},
{
cblabel => 'sin(x) * cos(x)',
data => \@imshow_data,
'plot.type' => 'imshow',
set_xlim => '0, ' . scalar @imshow_data,
set_ylim => '0, ' . scalar @imshow_data,
title => 'cblabel',
},
{
cblabel => 'sin(x) * cos(x)',
cblocation => 'left',
data => \@imshow_data,
'plot.type' => 'imshow',
set_xlim => '0, ' . scalar @imshow_data,
set_ylim => '0, ' . scalar @imshow_data,
title => 'cblocation = left',
},
{
cblabel => 'sin(x) * cos(x)',
data => \@imshow_data,
add => [ # add secondary plots
{ # 1st additional plot
data => {
'sin(x)' => [
[0..360],
[map {180 + 180*sin($_ * $pi/180)} 0..360]
],
'cos(x)' => [
[0..360],
[map {180 + 180*cos($_ * $pi/180)} 0..360]
],
},
'plot.type' => 'plot',
'set.options' => {
'sin(x)' => 'color = "red", linestyle = "dashed"',
'cos(x)' => 'color = "blue", linestyle = "dashed"',
}
}
],
'plot.type' => 'imshow',
set_xlim => '0, ' . scalar @imshow_data,
set_ylim => '0, ' . scalar @imshow_data,
title => 'auxiliary plots',
},
],
execute => 0,
fh => $fh,
'output.file' => 'output.images/imshow.multiple.png',
ncols => 2,
nrows => 2,
set_figheight => 6*3,# 4.8
set_figwidth => 6*4 # 6.4
);which makes the following image:
Secondary Structure Prediction (DSSP)
Sometimes strings instead of numbers can be entered into a 2-D array, one example is protein secondary structure. Protein secondary structure can be plotted thus, with a key in stringmap to show which strings become which integers in a minimal working example:
plt(
cbpad => 0.01, # default 0.05 is too big
data => [ # imshow gets a 2D array
[' ', ' ', ' ', ' ', 'G'], # bottom
['S', 'I', 'T', 'E', 'H'], # top
],
'plot.type' => 'imshow',
stringmap => {
'H' => 'Alpha helix',
'B' => 'Residue in isolated β-bridge',
'E' => 'Extended strand, participates in β ladder',
'G' => '3-helix (3/10 helix)',
'I' => '5 helix (pi helix)',
'T' => 'hydrogen bonded turn',
'S' => 'bend',
' ' => 'Loops and irregular elements'
},
'output.file' => 'output.images/dssp.single.png',
scalex => 2.4,
set_ylim => '0, 1',
title => 'Dictionary of Secondary Structure in Proteins (DSSP)',
xlabel => 'xlabel',
ylabel => 'ylabel'
);
or for multiple plots, where the colorbar can be spread across multiple plots now:
plt(
cbpad => 0.01, # default 0.05 is too big
plots => [
{ # 1st plot
data => [
[' ', ' ', ' ', ' ', 'G'], # bottom
['S', 'I', 'T', 'E', 'H'], # top
],
'plot.type' => 'imshow',
set_xticklabels=> '[]', # remove x-axis labels
set_ylim => '0, 1',
stringmap => {
'H' => 'Alpha helix',
'B' => 'Residue in isolated β-bridge',
'E' => 'Extended strand, participates in β ladder',
'G' => '3-helix (3/10 helix)',
'I' => '5 helix (pi helix)',
'T' => 'hydrogen bonded turn',
'S' => 'bend',
' ' => 'Loops and irregular elements'
},
title => 'top plot',
ylabel => 'ylabel'
},
{ # 2nd plot
data => [
[' ', ' ', ' ', ' ', 'G'], # bottom
['S', 'I', 'T', 'E', 'H'], # top
],
'plot.type' => 'imshow',
set_ylim => '0, 1',
stringmap => {
'H' => 'Alpha helix',
'B' => 'Residue in isolated β-bridge',
'E' => 'Extended strand, participates in β ladder',
'G' => '3-helix (3/10 helix)',
'I' => '5 helix (pi helix)',
'T' => 'hydrogen bonded turn',
'S' => 'bend',
' ' => 'Loops and irregular elements'
},
title => 'bottom plot',
xlabel => 'xlabel',
ylabel => 'ylabel'
}
],
nrows => 2,
'output.file' => 'output.images/dssp.multiple.png',
scalex => 2.4,
'shared.colorbar' => [0,1], # plots 0 and 1 share a colorbar
suptitle => 'Dictionary of Secondary Structure in Proteins (DSSP)',
);which makes the following plot:
pie
Plot a hash of numbers as a pie chart: one wedge per key, sized by its share of the total. data is the same simple hash that bar takes, so the two are interchangeable — reach for pie when the reader should see parts of a whole, and for bar when they should compare the parts with each other.
Wedges are laid out in sorted key order and that order cannot be overridden: key.order is not among the options pie accepts. Nor is a legend added — the wedges carry their own labels — so show.legend is not accepted either.
options
| Option | Description | Example |
| -------- | ------- | ------- |
autopct | a Python format string for the share printed inside each wedge; omit it and no numbers are drawn | autopct => '%1.1f%%' |
labeldistance | where the key label sits, as a fraction of the radius: 0 is the centre, 1 the edge, above 1 outside the pie | labeldistance => 0.6 |
pctdistance | the same scale, for the autopct text. Swapping the two â labels in, percentages out â is a readable arrangement when the labels are long | pctdistance => 1.25 |
single, simple plot
plt(
'output.file' => 'output.images/single.pie.png',
data => { # simple hash
Fri => 76,
Mon => 73,
Sat => 26,
Sun => 11,
Thu => 94,
Tue => 93,
Wed => 77
},
'plot.type' => 'pie',
title => 'Single Simple Pie',
fh => $fh,
execute => 0,
);which makes the image:
multiple plots
plt(
'output.file' => 'output.images/pie.png',
plots => [
{
data => {
'Russian' => 106_000_000, # Primarily European Russia
'German' =>
95_000_000, # Germany, Austria, Switzerland, etc.
'English' => 70_000_000, # UK, Ireland, etc.
'French' => 66_000_000, # France, Belgium, Switzerland, etc.
'Italian' => 59_000_000, # Italy, Switzerland, etc.
'Spanish' => 45_000_000, # Spain
'Polish' => 38_000_000, # Poland
'Ukrainian' => 32_000_000, # Ukraine
'Romanian' => 24_000_000, # Romania, Moldova
'Dutch' => 22_000_000 # Netherlands, Belgium
},
'plot.type' => 'pie',
title => 'Top Languages in Europe',
suptitle => 'Pie in subplots',
},
{
data => {
'Russian' => 106_000_000, # Primarily European Russia
'German' =>
95_000_000, # Germany, Austria, Switzerland, etc.
'English' => 70_000_000, # UK, Ireland, etc.
'French' => 66_000_000, # France, Belgium, Switzerland, etc.
'Italian' => 59_000_000, # Italy, Switzerland, etc.
'Spanish' => 45_000_000, # Spain
'Polish' => 38_000_000, # Poland
'Ukrainian' => 32_000_000, # Ukraine
'Romanian' => 24_000_000, # Romania, Moldova
'Dutch' => 22_000_000 # Netherlands, Belgium
},
'plot.type' => 'pie',
title => 'Top Languages in Europe',
autopct => '%1.1f%%',
},
{
data => {
'United States' => 86,
'United Kingdom' => 33,
'Germany' => 29,
'France' => 10,
'Japan' => 7,
'Israel' => 6,
},
title => 'Chem. Nobels: swap text positions',
'plot.type' => 'pie',
autopct => '%1.1f%%',
pctdistance => 1.25,
labeldistance => 0.6,
}
],
fh => $fh,
execute => 0,
set_figwidth => 12,
ncols => 3,
);
plot
A line plot of one or more series of (x, y) points. Each series needs an x array and a y array of equal length.
Entering data
data accepts three shapes:
1. Labeled series (hash). Use this when you want a legend — each key becomes a line label. The value is a [ \@x, \@y ] pair:
{
'plot.type' => 'plot',
data => {
A => [ [5..9], [5..9] ],
B => [ [5..9], [1..5] ],
},
}2. Several unlabeled series (array of pairs). A list of [ \@x, \@y ] pairs, one per line, with no legend labels:
{
'plot.type' => 'plot',
data => [
[ [5..9], [5..9] ],
[ [5..9], [1..5] ],
],
}3. A single unlabeled series (two bare arrays). The simplest form: just the x array and the y array, with no enclosing pair-array and no key:
{
'plot.type' => 'plot',
data => [
[5..9],
[5..9],
],
}Form 3 is shorthand for form 2 with a single line — it is promoted internally to [ [ \@x, \@y ] ]. Because there is no key, the line is unlabeled; if you need a legend entry, use the hash form (1).
> How the forms are told apart: in the multi-line form (2) C<< data-E<gt>[0] >> is itself
> a C<[ \@x, \@y ]> pair, so C<< data-E<gt>[0][0] >> is an array ref; in the single-line
> form (3) C<< data-E<gt>[0] >> is the x array, so C<< data-E<gt>[0][0] >> is a number. A 2-element
> C<data> whose first element starts with a number is therefore always read as a
> single line.Setting line options with set.options
set.options is passed straight through to Matplotlib's .plot(x, y, ...), so anything plot accepts works (color, linewidth, linestyle, marker, alpha, …). How you supply it depends on the data shape:
A scalar applies to every line. This is the natural partner of the single-line data form — the one option string is used for the only series:
{
'plot.type' => 'plot',
'show.legend' => 0,
data => [
[ min(vals($df, 'experiment')) .. max(vals($df, 'experiment')) ],
[ min(vals($df, 'experiment')) .. max(vals($df, 'experiment')) ],
],
'set.options' => 'color = "red"',
}The same scalar also works with the multi-line array form, where it is applied to all lines at once:
{
'plot.type' => 'plot',
data => [
[ [5..9], [5..9] ],
[ [5..9], [1..5] ],
],
'set.options' => 'linewidth = 2', # both lines
}An array sets options per line (positional). With array data, give one string per line; entry i styles line i. You may supply fewer entries than lines, but not more:
{
'plot.type' => 'plot',
data => [
[ [5..9], [5..9] ],
[ [5..9], [1..5] ],
],
'set.options' => [
'color = "red"',
'color = "blue", linestyle = "--"',
],
}A hash sets options per key. With hash data, key the options by the same data keys (any key may be omitted):
{
'plot.type' => 'plot',
data => {
A => [ [5..9], [5..9] ],
B => [ [5..9], [1..5] ],
},
'set.options' => {
A => 'color = "red"',
B => 'color = "blue", marker = "o"',
},
}Note the pairing rule: a scalar set.options goes with any data shape; an array set.options goes with array data; a hash set.options goes with hash data. Mismatches (for example a hash of options with array data) are rejected with an explanatory error.
Other options
show.legend— on by default (1); set to0to suppress labels. Only the hash form produces labels in the first place.key.order— array of keys (hash form) fixing the draw/legend order; defaults to the keys sorted alphabetically.logscale— array of axes to put on a log scale, e.g.[ 'x', 'y' ].twinx— draw selected series against a secondary y-axis.hash data: a single key, or a hash whose keys are the series to twin;
array data: an integer index, or an array of indices.
twinx.args— a hash keyed by data key (hash form) or index (array form); each value is a hash of axis options (e.g.ylabel,set_ylim) applied to that twin axis.
Common axes options such as title, xlabel, ylabel, and legend are accepted here too, exactly as for the other plot types.
Two y-axes with twinx
Series measured in different units, or on wildly different scales, flatten each other when they share a y-axis. twinx moves the named series onto a second y-axis on the right, and twinx.args labels it:
plt(
'output.file' => '/tmp/twinx.svg',
'plot.type' => 'plot',
data => {
Temperature => [ [@t], [@celsius] ],
Pressure => [ [@t], [@hPa] ],
},
twinx => 'Pressure', # onto the right axis
'twinx.args' => { Pressure => { ylabel => 'hPa' } },
ylabel => 'degrees C', # the left axis
xlabel => 'hour',
);twinx => 'Pressure' is shorthand for the single-series case. To twin more than one series, pass a hash whose keys are the series to move:
twinx => { Pressure => 1, Humidity => 1 },With array data the same options are given by index instead of by key:
plt(
'output.file' => '/tmp/twinx.arr.svg',
'plot.type' => 'plot',
data => [
[ [@t], [@celsius] ], # index 0, left axis
[ [@t], [@hPa] ], # index 1
],
twinx => 1, # index 1 goes right
'twinx.args' => { 1 => { ylabel => 'hPa' } },
);A plot spec is an ordinary plot hash, so it can be dropped straight into the #the-p-argument argument — on its own for a single subplot, or alongside other hashes to overlay or to fill a grid of subplots.
single, simple
data can be given as a hash, where the hash key is the label:
plt(
fh => $fh,
execute => 0,
'output.file' => 'output.images/plot.single.png',
data => {
'sin(x)' => [
[@x], # x
[ map { sin($_) } @x ] # y
],
'cos(x)' => [
[@x], # x
[ map { cos($_) } @x ] # y
],
},
'plot.type' => 'plot',
title => 'simple plot',
set_xticks =>
"[-2 * $pi, -3 * $pi / 2, -$pi, -$pi / 2, 0, $pi / 2, $pi, 3 * $pi / 2, 2 * $pi"
. '], [r\'$-2\pi$\', r\'$-3\pi/2$\', r\'$-\pi$\', r\'$-\pi/2$\', r\'$0$\', r\'$\pi/2$\', r\'$\pi$\', r\'$3\pi/2$\', r\'$2\pi$\']',
'set.options' => { # set options overrides global settings
'sin(x)' => 'color="blue", linewidth=2',
'cos(x)' => 'color="red", linewidth=2'
}
);or as an array of arrays:
plt(
fh => $fh,
execute => 0,
'output.file' => 'output.images/plot.single.arr.png',
data => [
[
[@x], # x
[ map { sin($_) } @x ] # y
],
[
[@x], # x
[ map { cos($_) } @x ] # y
],
],
'plot.type' => 'plot',
title => 'simple plot',
set_xticks =>
"[-2 * $pi, -3 * $pi / 2, -$pi, -$pi / 2, 0, $pi / 2, $pi, 3 * $pi / 2, 2 * $pi"
. '], [r\'$-2\pi$\', r\'$-3\pi/2$\', r\'$-\pi$\', r\'$-\pi/2$\', r\'$0$\', r\'$\pi/2$\', r\'$\pi$\', r\'$3\pi/2$\', r\'$2\pi$\']',
'set.options' => [ # set options overrides global settings; indices match data array
'color="blue", linewidth=2, label = "sin(x)"', # labels aren't added automatically when using array here
'color="red", linewidth=2, label = "cos(x)"'
],
);both of which make the following "plot" plot:
multiple sub-plots
which makes
my $epsilon = 10**-7;
my (%set_opt, %d);
my $i = 0;
foreach my $interval (
[-2*$pi, -$pi],
[-$pi, 0],
[0, $pi],
[$pi, 2*$pi]
) {
my @th = linspace($interval->[0] + $epsilon, $interval->[1] - $epsilon, 99, 0);
@{ $d{csc}{$i}[0] } = @th;
@{ $d{csc}{$i}[1] } = map { 1/sin($_) } @th;
@{ $d{cot}{$i}[0] } = @th;
@{ $d{cot}{$i}[1] } = map { cos($_)/sin($_) } @th;
if ($i == 0) {
$set_opt{csc}{$i} = 'color = "red", label = "csc(θ)"';
$set_opt{cot}{$i} = 'color = "violet", label = "cot(θ)"';
} else {
$set_opt{csc}{$i} = 'color = "red"';
$set_opt{cot}{$i} = 'color = "violet"';
}
$i++;
}
$i = 0;
foreach my $interval (
[-2 * $pi, -1.5 * $pi],
[-1.5*$pi, -0.5*$pi],
[-0.5*$pi, 0.5 * $pi],
[0.5 * $pi, 1.5 * $pi],
[1.5 * $pi, 2 * $pi]
) {
my @th = linspace($interval->[0] + $epsilon, $interval->[1] - $epsilon, 99, 0);
@{ $d{sec}{$i}[0] } = @th;
@{ $d{sec}{$i}[1] } = map { 1/cos($_) } @th;
if ($i == 0) {
$set_opt{sec}{$i} = 'color = "blue", label = "sec(θ)"';
$set_opt{tan}{$i} = 'color = "green", label = "tan(θ)"';
} else {
$set_opt{sec}{$i} = 'color = "blue"';
$set_opt{tan}{$i} = 'color = "green"';
}
@{ $d{tan}{$i}[0] } = @th;
@{ $d{tan}{$i}[1] } = map { sin($_)/cos($_) } @th;
$i++;
}
mkdir 'svg' unless -d 'svg';
my $xticks = "[-2 * $pi, -3 * $pi / 2, -$pi, -$pi / 2, 0, $pi / 2, $pi, 3 * $pi / 2, 2 * $pi"
. '], [r\'$-2\pi$\', r\'$-3\pi/2$\', r\'$-\pi$\', r\'$-\pi/2$\', r\'$0$\', r\'$\pi/2$\', r\'$\pi$\', r\'$3\pi/2$\', r\'$2\pi$\']';
my ($min, $max) = (-9,9);
plt(
fh => $fh,
execute => 0,
'output.file' => 'output.images/plots.png',
plots => [
{ # sin
data => {
'sin(θ)' => [
[@x],
[map {sin($_)} @x]
]
},
'plot.type' => 'plot',
'set.options' => {
'sin(θ)' => 'color = "orange"'
},
set_xticks => $xticks,
set_xlim => "-2*$pi, 2*$pi",
xlabel => 'θ',
ylabel => 'sin(θ)',
},
{ # sin
data => {
'cos(θ)' => [
[@x],
[map {cos($_)} @x]
]
},
'plot.type' => 'plot',
'set.options' => {
'cos(θ)' => 'color = "black"'
},
set_xticks => $xticks,
set_xlim => "-2*$pi, 2*$pi",
xlabel => 'θ',
ylabel => 'cos(θ)',
},
{ # csc
data => $d{csc},
'plot.type' => 'plot',
'set.options' => $set_opt{csc},
set_xticks => $xticks,
set_xlim => "-2*$pi, 2*$pi",
set_ylim => "$min,$max",
'show.legend' => 0,
vlines => [ # asymptotes
"-2*$pi, $min, $max, color = 'gray', linestyle = 'dashed'",
"-$pi, $min, $max, color = 'gray', linestyle = 'dashed'",
"0, $min, $max, color = 'gray', linestyle = 'dashed'",
"$pi, $min, $max, color = 'gray', linestyle = 'dashed'",
"2*$pi, $min, $max, color = 'gray', linestyle = 'dashed'",
],
xlabel => 'θ',
ylabel => 'csc(θ)',
},
{ # sec
data => $d{sec},
'plot.type' => 'plot',
'set.options' => $set_opt{sec},
set_xticks => $xticks,
set_xlim => "-2*$pi, 2*$pi",
set_ylim => "$min,$max",
'show.legend' => 0,
vlines => [ # asymptotes
"-1.5*$pi, $min, $max, color = 'gray', linestyle = 'dashed'",
"-.5*$pi, $min, $max, color = 'gray', linestyle = 'dashed'",
".5*$pi, $min, $max, color = 'gray', linestyle = 'dashed'",
"1.5*$pi, $min, $max, color = 'gray', linestyle = 'dashed'",
# "2*$pi, $min, $max, color = 'gray', linestyle = 'dashed'",
],
xlabel => 'θ',
ylabel => 'sec(θ)',
},
{ # csc
data => $d{cot},
'plot.type' => 'plot',
'set.options' => $set_opt{cot},
set_xticks => $xticks,
set_xlim => "-2*$pi, 2*$pi",
set_ylim => "$min,$max",
'show.legend' => 0,
vlines => [ # asymptotes
"-2*$pi, $min, $max, color = 'gray', linestyle = 'dashed'",
"-$pi, $min, $max, color = 'gray', linestyle = 'dashed'",
"0, $min, $max, color = 'gray', linestyle = 'dashed'",
"$pi, $min, $max, color = 'gray', linestyle = 'dashed'",
"2*$pi, $min, $max, color = 'gray', linestyle = 'dashed'",
],
xlabel => 'θ',
ylabel => 'cot(θ)',
},
{ # sec
data => $d{tan},
'plot.type' => 'plot',
'set.options' => $set_opt{tan},
set_xticks => $xticks,
set_xlim => "-2*$pi, 2*$pi",
set_ylim => "$min,$max",
'show.legend' => 0,
vlines => [ # asymptotes
"-1.5*$pi, $min, $max, color = 'gray', linestyle = 'dashed'",
"-.5*$pi, $min, $max, color = 'gray', linestyle = 'dashed'",
".5*$pi, $min, $max, color = 'gray', linestyle = 'dashed'",
"1.5*$pi, $min, $max, color = 'gray', linestyle = 'dashed'",
# "2*$pi, $min, $max, color = 'gray', linestyle = 'dashed'",
],
xlabel => 'θ',
ylabel => 'tan(θ)',
},
], # end
ncols => 2,
nrows => 3,
set_figwidth => 8,
suptitle => 'Basic Trigonometric Functions'
);
scatter
Plot points from a hash of arrays. Beyond x and y, a scatterplot can carry a third number per point as color, which is where most of scatter's options go.
Entering data
data takes two shapes, and which one you passed is worked out from whether the values are arrays or hashes.
1. One set (hash of 2 or 3 array refs). All the arrays must be the same length. Keys are taken in case-insensitive sorted order: the first is x, the second y, and a third — if present — is the value each point is colored by, which also gets a colorbar. Exactly 2 or 3 keys are allowed; anything else is an error. The keys become the axis labels, so naming them for the quantity they hold pays off:
scatter(
'output.file' => '/tmp/scatter.svg',
data => {
Height => \@height, # x
Weight => \@weight, # y
Age => \@age, # colour + colorbar
},
color_key => 'Age', # say so rather than relying on the sort
cmap => 'viridis',
);Sorted order is convenient but fragile — rename a key and the axes swap. Use keys to fix the roles positionally, or color_key to name the color column explicitly, as above:
keys => [ 'Weight', 'Height', 'Age' ], # x, y, colour2. Several labelled sets (hash of hashes of array refs). The outer key is the set's legend label; each inner hash is a set of 2 or 3 arrays read exactly as in form 1. This is the form to use for "the same measurement, split by group":
scatter(
'output.file' => '/tmp/by.group.svg',
data => {
Male => { Height => \@mh, Weight => \@mw },
Female => { Height => \@fh, Weight => \@fw },
},
'set.options' => {
Male => 'marker = "v", color = "blue"',
Female => 'marker = "o", color = "red"',
},
);With three inner keys, every set is colored by its own third column and the figure gets a single colorbar, drawn from the last set plotted — so read the colors across sets only when the color columns cover comparable ranges. color_key then names an inner key, and it must exist in every set: naming a key that is not there is an error rather than being quietly ignored.
options
| Option | Description | Example |
| -------- | ------- | ------- |
cmap | the colormap used when a third key colors the points; gist_rainbow by default | cmap => 'viridis' |
color_key | which key of data holds the color values, rather than letting the sort decide. For the multi-set form this is an inner key, and it must be present in every set | color_key => 'Age' |
keys | array ref fixing the roles of the keys positionally: x, y, then color | keys => ['Weight', 'Height', 'Age'] |
logscale | an array of the axes to put on a log scale | logscale => ['x', 'y'] |
set.options | arguments passed straight to Matplotlib's ax.scatter: marker, color, alpha, s, ⦠A **scalar** for the single-set form; a **hash keyed by set name** for the multi-set form. Options for a set that has no data are an error | 'set.options' => 'marker = "v", alpha = 0.4' |
xlabel and ylabel default to the names of the keys used for x and y; set them explicitly to override. The colorbar is labelled with the name of the color key itself, and takes cbdrawedges and cbpad from #color-bars-colorbars.
single, simple plot
scatter(
fh => $fh,
data => {
X => [@x],
Y => [map {sin($_)} @x]
},
execute => 0,
'output.file' => 'output.images/single.scatter.png',
);makes the following image:
options
multiple plots
plt(
fh => $fh,
'output.file' => 'output.images/scatterplots.png',
execute => 0,
nrows => 2,
ncols => 3,
set_figheight => 8,
set_figwidth => 16,
suptitle => 'Scatterplot Examples', # applies to all
plots => [
{ # single-set scatter; no label
data => {
X => @e, # x-axis
Y => @b, # y-axis
Z => @a # color
},
title => '"Single Set Scatterplot: Random Distributions"',
color_key => 'Z',
'set.options' => 'marker = "v"'
, # arguments to ax.scatter: there's only 1 set, so "set.options" is a scalar
text => [ '100, 100, "text1"', '100, 100, "text2"', ],
'plot.type' => 'scatter',
},
{ # multiple-set scatter, labels are "X" and "Y"
data => {
X => { # 1st data set; label is "X"
A => @a, # x-axis
B => @b, # y-axis
},
W => { # 2nd data set; label is "Y"
A => generate_normal_dist( 100, 15, 210 ), # x-axis
B => generate_normal_dist( 100, 15, 210 ), # y-axis
}
},
'plot.type' => 'scatter',
title => 'Multiple Set Scatterplot',
'set.options' =>
{ # arguments to ax.scatter, for each set in data
X => 'marker = ".", color = "red"',
W => 'marker = "d", color = "green"'
},
},
{ # multiple-set scatter, labels are "X" and "Y"
data => { # 8th plot,
X => { # 1st data set; label is "X"
A => @e, # x-axis
B => @b, # y-axis
C => @a, # color
},
Y => { # 2nd data set; label is "Y"
A => generate_normal_dist( 100, 15, 210 ), # x-axis
B => generate_normal_dist( 100, 15, 210 ), # y-axis
C => generate_normal_dist( 100, 15, 210 ), # color
},
},
'plot.type' => 'scatter',
title => 'Multiple Set Scatter w/ colorbar',
'set.options' => { # arguments to ax.scatter, for each set in data
X => 'marker = "."', # point
Y => 'marker = "d"' # diamond
},
color_key => 'C', # an inner key, present in both sets
}
]
);which makes the following figure:
venn_proportional_area
Draw an area-proportional Venn diagram, where the size of each region is scaled to the number of elements it contains. This plot type wraps the Lhttps://pypi.org/project/matplotlib-venn/ library, so that library must be installed in addition to matplotlib:
python3 -m pip install matplotlib-venndata is a hash of array references; each key is a set and its array is the set's members (duplicates within a set are collapsed, exactly like a mathematical set). Because matplotlib_venn only draws proportional-area diagrams for two or three sets, data must contain either 2 or 3 keys. By default the sets are labelled and ordered alphabetically by key; use key.order to override that.
options
| Option | Description | Example |
| -------- | ------- | ------- |
alpha | opacity of the set regions, 0â1 (default 0.4) | alpha => 0.5 |
key.order | array ref giving the order (and hence label positions) of the sets | 'key.order' => ['Right','Left'] |
set_colors | array ref of colors, one per set | set_colors => [qw(skyblue lightgreen salmon)] |
title | the subplot title | title => 'Gospels vs. Synoptics' |
single, simple plot
venn_proportional_area is a single-plot wrapper around plt, so it can be called directly:
venn_proportional_area(
'output.file' => 'output.images/single.venn.png',
title => 'Gospels vs. Synoptics',
data => {
Gospels => [qw(Matthew Mark Luke John)],
Synoptic => [qw(Matthew Mark Luke)],
},
);which makes the image:
multiple plots
Like every other plot type, it can also be one panel among several via plt and the plots array; here a two-set diagram sits beside a colored three-set diagram:
plt(
'output.file' => 'output.images/venn.png',
ncols => 2,
suptitle => 'Proportional-area Venn diagrams',
plots => [
{
'plot.type' => 'venn_proportional_area',
title => 'Two sets',
data => {
Perl => [qw(regex hashes CPAN sigils)],
Python => [qw(regex hashes pip indentation)],
},
},
{
'plot.type' => 'venn_proportional_area',
title => 'Three sets with colors',
set_colors => [qw(skyblue lightgreen salmon)],
alpha => 0.5,
data => {
Mammals => [qw(bat whale dog cat human platypus)],
Aquatic => [qw(whale shark octopus platypus)],
Legged => [qw(dog cat human bat platypus shark)],
},
},
],
);which makes the following figure:
violin
Plot a hash of array refs as violins: one kernel-density silhouette per key, with the quartile box, the whiskers and a red dot at the mean drawn over it. Where a boxplot summarises a distribution in five numbers, a violin shows its shape, so bimodal data that a boxplot would hide is visible.
violin and violinplot are the same subroutine under two names, and both accept the two data shapes described under #boxplot — a hash of array refs, or a bare array ref for a single violin. Non-numeric and undefined values are dropped silently. Each x-axis label carries the number of points that went into it, so a violin drawn from very few points announces itself.
options
| Option | Description | Example |
| -------- | ------- | ------- |
color | a single color for every violin | color => 'red' |
colors | a hash pairing each data key with its own color; every key in data must appear | colors => { E => 'yellow', B => 'purple', A => 'green' } |
key.order | determine key order display on x-axis | 'key.order' => ['B', 'A', 'E'] |
logscale | an array of the axes to put on a log scale; only x and y are accepted. Note this is an array reference, not the log => 1 scalar that bar takes | logscale => ['y'] |
orientation | 'vertical', 'horizontal'}, default: 'vertical' | orientation => 'horizontal' |
whiskers | draw the quartile bar and whiskers over the silhouette; on by default, 0 leaves the bare violin | whiskers => 0 |
single, simple plot
plt(
'output.file' => 'output.images/single.violinplot.png',
data => { # simple hash
A => [ 55, @{$z} ],
E => [ @{$y} ],
B => [ 122, @{$z} ],
},
'plot.type' => 'violinplot',
title => 'Single Violin Plot: Specified Colors',
colors => { E => 'yellow', B => 'purple', A => 'green' },
fh => $fh,
execute => 0,
);which makes:
multiple plots
plt(
fh => $fh,
execute => 0,
'output.file' => 'output.images/violin.png',
plots => [
{
data => {
E => @e,
B => @b
},
'plot.type' => 'violinplot',
title => 'Basic',
xlabel => 'xlabel',
set_figwidth => 12,
suptitle => 'Violinplot'
},
{
data => {
E => @e,
B => @b
},
'plot.type' => 'violinplot',
color => 'red',
title => 'Set Same Color for All',
},
{
data => {
E => @e,
B => @b
},
'plot.type' => 'violinplot',
colors => {
E => 'yellow',
B => 'black'
},
title => 'Color by Key',
},
{
data => {
E => @e,
B => @b
},
orientation => 'horizontal',
'plot.type' => 'violinplot',
colors => {
E => 'yellow',
B => 'black'
},
title => 'Horizontal orientation',
},
{
data => {
E => @e,
B => @b
},
whiskers => 0,
'plot.type' => 'violinplot',
colors => {
E => 'yellow',
B => 'black'
},
title => 'Whiskers off',
},
],
ncols => 3,
nrows => 2,
);
wide
Summarise several runs of the same curve. Every run is drawn as a faint line, the mean of the runs as a solid one, and one standard deviation either side of the mean as a translucent ribbon. This is the plot for repeated measurements — replicate experiments, repeated simulations, one trace per subject — where a plot of every line on top of the others would be an unreadable thicket and a plot of the mean alone would hide how much the runs disagree.
The runs do not have to share an x grid: each group's runs are interpolated onto 101 evenly spaced points spanning that group's own x range before the mean and the standard deviation are taken, so runs of different lengths, or sampled at different x values, can be summarised together. A run is first sorted by x, so it may be entered in any order; and it counts towards the mean and the ribbon only between its own first and last x, so a run that stops early narrows the summary to the runs that continue rather than being held flat at its last value.
Entering data
1. Labelled groups (hash). Each key is a group and becomes the legend label; its value is an array of runs, and each run is a [ \@x, \@y ] pair — the same pair #plot uses:
my @x = 0 .. 100;
my %runs;
foreach my $group ('Clinical', 'HGI') {
my $shift = $group eq 'HGI' ? 1 : 0;
foreach my $replicate (1 .. 3) {
push @{ $runs{$group} }, [
[@x], # x
[ map { $shift + sin($_/10) + rand_between(-0.2, 0.2) } @x ] # y
];
}
}
wide(
'output.file' => 'output.images/single.wide.png',
data => \%runs,
color => { # one color per group
Clinical => 'blue',
HGI => 'green',
},
title => 'Visualization of similar lines plotted together',
xlabel => 'time',
ylabel => 'signal',
);which makes the image:
2. One unlabelled group (array). Drop the enclosing hash and pass one group's array of runs directly; color is then a single color rather than a hash:
wide(
'output.file' => 'output.images/single.array.png',
data => $runs{Clinical},
color => 'red',
);A group with a single run is legal — it just produces a line with a zero-width ribbon — which is convenient when one group has replicates and another does not.
options
| Option | Description | Example |
| -------- | ------- | ------- |
color | for hash data, a hash of one color per group; for array data, a single color. Groups with no entry fall back to Matplotlib's b (blue), so a partial hash is allowed | color => { Clinical => 'blue', HGI => 'green' } |
show.legend | on by default, and only the hash form has labels to show; 0 suppresses it | 'show.legend' => 0 |
wide accepts the usual axes options — title, xlabel, ylabel, set_xlim and the rest — but not logscale or key.order. For a log axis use Matplotlib's own set_yscale => '"log"'. Since there is no key.order, the groups are drawn in Perl's hash order, which is arbitrary and differs between runs: give each group an explicit color if you need the same picture twice.
single, simple plot
Both calls above go through the wide wrapper; naming the type explicitly to plt is equivalent and takes exactly the same options:
plt(
'output.file' => 'output.images/single.wide.png',
'plot.type' => 'wide',
data => \%runs,
color => { Clinical => 'blue', HGI => 'green' },
);multiple plots
As an element of plots, a wide panel is just another plot hash — here the labelled groups sit beside one group on its own:
plt(
'output.file' => 'output.images/wide.png',
ncols => 2,
suptitle => 'Replicate runs, summarised',
plots => [
{
'plot.type' => 'wide',
data => \%runs, # hash of groups of runs
color => { Clinical => 'blue', HGI => 'green' },
title => '"Two groups, mean +/- 1 s.d."', # comma: quoted
xlabel => 'time',
ylabel => 'signal',
},
{
'plot.type' => 'wide',
data => $runs{Clinical}, # just the runs, unlabelled
color => 'red',
'show.legend' => 0,
title => 'One group with no legend',
},
],
);
Because a wide panel collapses many lines into one summary, it also composes well with a plot type that shows the same data another way. Here the runs are summarised on the left and the distribution of their final values is drawn beside them:
my %endpoints;
foreach my $group (keys %runs) {
@{ $endpoints{$group} } = map { $_->[1][-1] } @{ $runs{$group} };
}
plt(
'output.file' => 'output.images/wide.and.violin.png',
ncols => 2,
plots => [
{
'plot.type' => 'wide',
data => \%runs,
color => { Clinical => 'blue', HGI => 'green' },
title => 'Runs over time',
},
{
'plot.type' => 'violinplot',
data => \%endpoints, # hash of arrays, same keys
colors => { Clinical => 'blue', HGI => 'green' },
title => 'Final values',
},
],
);
The three images above are written by wide.example.pl in the git repository (it is not shipped in the CPAN distribution); re-run it from the repository root with perl -Ilib wide.example.pl to regenerate them.
Advanced
Notes in Files
all files that can have notes with them, give notes about how the file was written. For example, SVG files have the following:
<dc:title>made/written by /mnt/ceph/dcondon/ui/gromacs/tut/dup.2puy/1.plot.gromacs.pl called using "plot" in /mnt/ceph/dcondon/perl5/perlbrew/perls/perl-5.42.0/lib/site_perl/5.42.0/x86_64-linux/Matplotlib/Simple.pm</dc:title>`Speed
To improve speed, all data can be written into a single temp python3 file thus:
use File::Temp;
my $fh = File::Temp->new( DIR => '/tmp', SUFFIX => '.py', UNLINK => 0 );all files will be written to $fh->filename; be sure to put execute => 0 unless you want the file to be run, which is the last step.
plt(
data => {
Clinical => [
[
[@xw], # x
[@y] # y
],
[ [@xw], [ map { $_ + rand_between( -0.5, 0.5 ) } @y ] ],
[ [@xw], [ map { $_ + rand_between( -0.5, 0.5 ) } @y ] ]
],
HGI => [
[
[@xw], # x
[ map { 1.9 - 1.1 / $_ } @xw ] # y
],
[ [@xw], [ map { $_ + rand_between( -0.5, 0.5 ) } @y ] ],
[ [@xw], [ map { $_ + rand_between( -0.5, 0.5 ) } @y ] ]
]
},
'output.file' => 'output.images/single.wide.png',
'plot.type' => 'wide',
color => {
Clinical => 'blue',
HGI => 'green'
},
title => 'Visualization of similar lines plotted together',
fh => $fh,
execute => 0,
);
# the last plot should have C<< execute =E<gt> 1 >>
plt(
data => [
[
[@xw], # x
[@y] # y
],
[ [@xw], [ map { $_ + rand_between( -0.5, 0.5 ) } @y ] ],
[ [@xw], [ map { $_ + rand_between( -0.5, 0.5 ) } @y ] ]
],
'output.file' => 'output.images/single.array.png',
'plot.type' => 'wide',
color => 'red',
title => 'Visualization of similar lines plotted together',
fh => $fh,
execute => 1,
);COPYRIGHT AND LICENSE
This software is free. It is licensed under the same terms as Perl itself