mATLASplotlib¶

This package provides wrappers around matplotlib functionality produce plots compatible with the style guidelines for the ATLAS experiment at the LHC. It is particularly aimed at users who are not familiar with matplotlib syntax. Basic usage involves creating a canvas, plotting a dataset and saving the output to a file. For example, something like

import mATLASplotlib
with mATLASplotlib.canvases.Simple(shape="landscape") as canvas:
    x, y = [0, 1, 2, 3], [0, 1, 4, 9]
    canvas.plot_dataset(x, y, style="scatter", label="Example points", colour="black")
    canvas.save("simple_example")

will produce a minimal scatter plot with automatically determined axis limits and save this to a PDF (if not otherwise specified).

Introduction¶

This package is meant to be a simple wrapper around matplotlib for users who want to produce plots conforming to the style guide of the ATLAS experiment at the LHC. Rather than interacting with matplotlib classes directly, these are managed through a canvas class which has methods related to the plotting and styling of datasets and then writing the resulting output to graphical formats on disk.

Installation¶

1. Requirements¶

Python 2.7 (not yet tested with Python 3 but it should work)
ROOT with PyROOT enabled
matplotlib
numpy
scipy

mATLASplotlib is developed and tested on Linux and Mac.

2. Automatic installation with pip¶

To install a released version of mATLASplotlib use pip.

To install in your home directory:

pip install --user mATLASplotlib

To install system-wide (requires root privileges):

sudo pip install mATLASplotlib

3. Upgrading with pip¶

To upgrade with pip simply do

pip install -U mATLASplotlib

running with sudo as before if this is a system-wide installation

4. Installing from source¶

Clone the repository with git:

git clone https://github.com/jemrobinson/mATLASplotlib.git

After doing this, you should use the setup.py script to install.

To install in your home directory:

python setup.py install --user

To install system-wide (requires root privileges):

sudo python setup.py install

Font setup¶

To allow matplotlib to use Helvetica (required for ATLAS style) follow the guidelines from here https://olgabotvinnik.com/blog/how-to-set-helvetica-as-the-default-sans-serif-font-in/ (reproduced below).

1. Linux users¶

For Linux users, adding the .ttf fonts to ~/.fonts and removing ~/.matplotlib/fontList.cache ~/.matplotlib/fontManager.cache ~/.matplotlib/ttffont.cache should be enough.

2. OSX users¶

Download and install fondu to convert Mac-Helvetica to ttf-Helvetica

brew install fondu

Find Helvetica on your system

Probably somewhere like: /System/Library/Fonts/Helvetica.dfont

Find where matplotlib stores its data
Start a python prompt and run:

import matplotlib; matplotlib.matplotlib_fname()

and get output like:

u'/usr/local/lib/python2.7/site-packages/matplotlib/mpl-data/matplotlibrc'

we need to put the .ttf from this path in fonts/ttf

Create the .ttf

sudo fondu -show /System/Library/Fonts/Helvetica.dfont

Edit your .matplotlibrc file

Edit ~/.matplotlib/matplotlibrc, find the line font.sans-serif : Bitstream Vera Sans, ... and put Helvetica at the beginning of this list

Force matplotlib to re-scan the font lists

Now we need to force matplotlib to re-create the font lists by removing the files.

rm ~/.matplotlib/fontList.cache ~/.matplotlib/fontManager.cache ~/.matplotlib/ttffont.cache

Getting Started¶

1. Validate installation¶

After following the installation instructions, mATLASplotlib should be available at the python prompt

>>> import mATLASplotlib

The user interface of mATLASplotlib is centered on a canvas on which datasets can be plotted.

2. Constructing some data¶

To demonstrate how plotting works, we need some data: let’s construct some using ROOT and numpy

import numpy as np
import ROOT
hist = ROOT.TH1F("Generated data", "This is some autogenerated data", 40, -4, 4)
for x in np.random.normal(size=10000):
    hist.Fill(x)

this should have drawn 10000 samples from a normal distribution and added them to a ROOT histogram.

3. Setting up a canvas¶

We use a context manager to open the canvas, which ensures that necessary cleanup is done when the canvas is no longer needed. Currently the supported canvases are the Simple canvas which contains one set of matplotlib axes, the Ratio canvas, which contains a main plot and a ratio plot underneath, and the the Panelled canvas which contains a top panel and an arbitrary number of lower panels beneath it.

import mATLASplotlib
with mATLASplotlib.canvases.Simple(shape="square") as canvas:
    canvas.plot_dataset(hist, style="scatter", label="Generated data", colour="black")

The three shapes preferred by the ATLAS style guide are “square” (600 x 600 pixels), “landscape” (600 x 800 pixels) and “portrait” (800 x 600 pixels). Here we have chosen to use “square”.

After setting up the canvas, we can plot the dataset we constructed earlier using the plot_dataset method.

4. Plotting options¶

The different style options specify how the data should be displayed. Options are

bar (a histogram or bar chart)
binned_band (a band with a fill colour in between the maximum and minimum values in each bin)
coloured_2D (a 2D histogram with a colour-scale to indicate the ‘z’ value in each bin)
line (a single line, either smooth or consisting of straight line segments)
scatter (a scatter plot - often used for data points)
stack (one of a series of histograms that should be summed up when drawn)

Other options like linestyle and colour can be used to distinguish different datasets.

5. Saving the canvas to a file¶

Saving the output to a file is very simple.

canvas.save("simple_example")

This function takes an optional extension argument which sets the file extension of the output file. Running this code will produce a minimal scatter plot with automatically determined axis limits and save this to a PDF (if not otherwise specified). The output should be similar to that shown in the image below.

Simple example

Examples¶

Examples which reproduce the plots on this page (https://twiki.cern.ch/twiki/bin/viewauth/AtlasProtected/PubComPlotStyle) are included in the examples folder. The text below goes through these in some more detail and explains what each line is doing.

1. Basic data and prediction plot¶

Import necessary packages

import numpy as np
import ROOT
import mATLASplotlib

Generate some ROOT data - MC prediction

# MC prediction
hpx_MC = ROOT.TH1F("hpx_MC", "This is the MC px distribution", 40, -4, 4)
for x in np.random.normal(size=50000):
    hpx_MC.Fill(x, 0.2)

# Pseudodata
hpx_data = ROOT.TH1F("hpx_data", "This is the data px distribution", 40, -4, 4)
for x in np.random.normal(size=10000):
    hpx_data.Fill(x)

print "Data integral, max", hpx_data.Integral(), hpx_data.GetMaximum()

Now we fit the data (using ROOT again)

fit_fn = ROOT.TF1("fit_fn", "gaus", -4, 4)
hpx_data.Fit(fit_fn, "LEMN")
mu, sigma = fit_fn.GetParameter(1), fit_fn.GetParameter(2)
mu_err, sigma_err = fit_fn.GetParError(1), fit_fn.GetParError(2)

Open a canvas as a context manager and plot these three datasets

with mATLASplotlib.canvases.Simple(shape="landscape") as canvas:
    canvas.plot_dataset(hpx_data, style="scatter yerror", label="Data 2009", colour="black")
    canvas.plot_dataset(hpx_MC, style="bar", label="Non-diffractive minimum bias", colour="#ffff00", edgecolour="black")
    canvas.plot_dataset(fit_fn, style="smooth line", label="Gaussian fit", colour="red")

Note that three different styles are used here: scatter, bar and line. The additional words in these arguments: yerror and smooth specify particular additional plotting properties - namely error bars in the y-direction and a smooth interpolation between the points.

The label argument to plot_dataset is used to build the legend which can then be drawn in one line

canvas.add_legend(0.04, 0.92, fontsize=16, anchor_to="upper left")

Now some arbitrary text can be drawn on the plot, including two special pieces of text needed for ATLAS plots - the ATLAS label and the luminosity.

canvas.add_text(0.04, 0.6, "$\mu = ({{{0:.2f}}}\pm{{{1:.2f}}})\,$ GeV\n$\sigma = ({{{0:.2f}}}\pm{{{1:.2f}}})\,$ GeV".format(mu, mu_err, sigma, sigma_err), fontsize=16)
canvas.add_ATLAS_label(0.96, 0.92, fontsize=20, plot_type="Preliminary", anchor_to="upper right")
canvas.add_luminosity_label(0.96, 0.85, fontsize=20, sqrts_TeV=0.9, luminosity=None, anchor_to="upper right")

The axis titles are set according to the data being used

canvas.set_axis_label("x", "$p_x$ [GeV]")
canvas.set_axis_label("y", "Events / 0.2 GeV")

Here the ranges are explicitly defined. Without this, the matplotlib defaults (which are usually good) will be used.

canvas.set_axis_range("x", (-5.0, 5.0))
canvas.set_axis_range("y", (0, 1200))

Here the default choice of axis labels on the x-axis is overridden to match the reference image

canvas.set_axis_ticks("x", [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5])

And the file is saved

canvas.save("example_fig_01")

The left-hand image is from the official ATLAS guide, the one on the right uses mATLASplotlib

1. More complicated - theory band and log-scale¶

Import necessary packages

from mATLASplotlib import canvases
import array
import ROOT
import numpy as np

Load distributions (these numbers are from the example ROOT file produced by ATLAS)

# Take the NLO QCD prediction from the ATLAS ROOT file
NLOQCD_x = array.array("d", [150, 250, 350, 450, 550, 650, 750, 850, 950, 1050, 1150, 1250, 1350, 1450, 1550, 1650, 1750, 1850, 1950, 2050, 2150, 2250, 2350, 2450, 2550, 2650, 2750, 2850, 2950, 3050, 3150, 3250, 3350, 3450, 3550, 3650, 3750, 3850, 3950, 4050, 4150, 4250, 4350, 4450, 4550, 4650, 4750, 4850, 4950, 5050, 5150, 5250, 5350, 5450, 5550, 5650, 5750, 5850, 5950, 6050, 6150, 6250, 6350, 6450, 6550, 6650, 6750, 6850, 6950])
NLOQCD_ex = array.array("d", [50] * len(NLOQCD_x))
NLOQCD_y = array.array("d", [6.6596e+06, 318321, 48380.3, 10957.2, 3235.47, 1232.35, 512.05, 230.488, 114.084, 60.5025, 31.1572, 17.6683, 10.3007, 6.14975, 3.71552, 2.52298, 1.44746, 1.01675, 0.641863, 0.38725, 0.263351, 0.207581, 0.103852, 0.0813621, 0.0507964, 0.0374186, 0.0239413, 0.0179119, 0.0105439, 0.00724193, 0.00513062, 0.00353234, 0.00237385, 0.00137003, 0.000949618, 0.000670692, 0.000455441, 0.000278849, 0.000175337, 0.000121832, 8.35938e-05, 4.20491e-05, 2.53419e-05, 1.83e-05, 1.05427e-05, 5.73383e-06, 3.57256e-06, 2.47147e-06, 1.32182e-06, 5.26587e-07, 3.89889e-07, 1.85564e-07, 1.05617e-07, 5.02298e-08, 2.37682e-08, 1.00941e-08, 4.17607e-09, 1.74077e-09, 6.47905e-10, 2.90492e-10, 8.31421e-11, 2.45835e-11, 5.51966e-12, 1.23854e-12, 2.12108e-13, 3.51307e-14, 3.83912e-15, 2.02972e-16, 1.93257e-17])
NLOQCD_eyl = array.array("d", [592438, 26408.5, 4624.47, 1045.14, 310.249, 132.484, 57.4808, 26.5156, 13.8826, 7.8785, 3.91181, 2.34453, 1.39327, 0.857228, 0.534341, 0.386117, 0.211819, 0.15815, 0.101274, 0.0583714, 0.0418432, 0.0364608, 0.0162442, 0.0134506, 0.00859996, 0.00647774, 0.0042077, 0.00337713, 0.00183224, 0.00130241, 0.000980045, 0.000692273, 0.000480861, 0.00027158, 0.000186696, 0.000140853, 9.95418e-05, 6.30202e-05, 3.98933e-05, 2.87898e-05, 2.16196e-05, 1.04431e-05, 6.53897e-06, 5.1248e-06, 3.04996e-06, 1.71619e-06, 1.10496e-06, 8.44941e-07, 4.69963e-07, 1.85741e-07, 1.51345e-07, 7.70224e-08, 4.67813e-08, 2.32868e-08, 1.17352e-08, 5.41192e-09, 2.35221e-09, 1.05666e-09, 4.15396e-10, 2.01271e-10, 6.30795e-11, 1.97053e-11, 4.85011e-12, 1.15449e-12, 2.10144e-13, 3.78813e-14, 4.13635e-15, 2.06829e-16, 1.87872e-17])
NLOQCD_eyh = array.array("d", [476290, 17188.1, 3472.85, 756.231, 234.15, 115.386, 53.9145, 26.1371, 14.5604, 8.67245, 4.40793, 2.79054, 1.68753, 1.07432, 0.691266, 0.496964, 0.292805, 0.212455, 0.140897, 0.0844782, 0.0623834, 0.0518593, 0.0270745, 0.0203713, 0.0140546, 0.0102316, 0.0069444, 0.00551092, 0.0030732, 0.00229263, 0.00169067, 0.00117577, 0.000835395, 0.000520959, 0.000336736, 0.000257673, 0.00017855, 0.000120173, 7.67702e-05, 5.03502e-05, 3.96392e-05, 2.10842e-05, 1.32891e-05, 9.44563e-06, 5.76911e-06, 3.43875e-06, 2.11638e-06, 1.48622e-06, 7.97241e-07, 4.54554e-07, 2.77689e-07, 1.50687e-07, 8.06223e-08, 4.46708e-08, 2.25211e-08, 1.1174e-08, 4.73384e-09, 2.2669e-09, 9.73032e-10, 4.7206e-10, 1.81057e-10, 7.06126e-11, 2.27797e-11, 7.87034e-12, 2.46757e-12, 7.2124e-13, 1.37511e-13, 1.14176e-14, 1.02065e-15])
g_NLOQCD = ROOT.TGraphAsymmErrors(len(NLOQCD_x), NLOQCD_x, NLOQCD_y, NLOQCD_ex, NLOQCD_ex, NLOQCD_eyl, NLOQCD_eyh)

Generate some data which looks like the QCD background plus a small amount of signal

# Generate some ROOT data based on the NLO QCD prediction
h_data = ROOT.TH1F("hpx_data", "This is the data px distribution", 69, 100, 7000)
r1 = 0.4 * (np.random.uniform(size=len(NLOQCD_x)) + 2)
r2 = 0.4 * (np.random.uniform(size=len(NLOQCD_x)) + 2)
for x, y, _r1, _r2 in zip(NLOQCD_x, NLOQCD_y, r1, r2):
    data_y = _r1 * y + _r1 *_r2**2 * x / 50000.
    h_data.SetBinContent(h_data.FindFixBin(x), data_y)
    h_data.SetBinError(h_data.FindFixBin(x), np.sqrt(data_y * 1000) / 200.)

Open a canvas and plot the datasets

with mATLASplotlib.canvases.Simple(shape="square") as canvas:
    canvas.plot_dataset(h_data, style="scatter yerror", label="Data 2009", colour="black")
    canvas.plot_dataset(g_NLOQCD, style="binned band central line", label="NLO QCD", colour="#ffff00", line_colour="black")

next the legend and text

canvas.add_legend(0.45, 0.75, fontsize=20, anchor_to="upper left")
canvas.add_luminosity_label(0.15, 0.9, fontsize=20, sqrts_TeV=14, luminosity=None, anchor_to="upper left")
canvas.add_text(0.53, 0.9, r"$|\eta_{jet}| < 0.5$", fontsize=20, anchor_to="upper left")
canvas.add_ATLAS_label(0.05, 0.05, fontsize=20, plot_type="Preliminary", anchor_to="lower left")

and the axis titles and ranges.

canvas.set_axis_label("x", r"$E_{T,jet}$  [GeV]")
canvas.set_axis_label("y", r"$d\sigma_{jet}/dE_{T,jet}$ [fb/GeV]")
canvas.set_axis_range("x", (60.0, 3500.0))
canvas.set_axis_range("y", (1e-3, 2e7))

For this plot we want the y-axis to be on a log-scale and to specify the ticks

canvas.set_axis_log("y")
canvas.set_axis_ticks("x", [500, 1000, 1500, 2000, 2500, 3000, 3500])
canvas.set_axis_ticks("y", [1e-3, 1e-2, 1e-1, 1, 10, 1e2, 1e3, 1e4, 1e5, 1e6, 1e7])

Finally save it

canvas.save("example_fig_02", extension="png")

As before, the left-hand image is from the official ATLAS guide, the one on the right uses mATLASplotlib

API¶

The API for mATLASplotlib can be seen in the following links

canvases¶

base_canvas¶

This module provides the BaseCanvas canvas.

class mATLASplotlib.canvases.base_canvas.BaseCanvas(shape='square', **kwargs)¶

Bases: object

Base class for canvas properties.

__init__(shape='square', **kwargs)¶

Set up universal canvas properties.

Parameters:

shape (str) – use either the ‘square’, ‘landscape’ or ‘portrait’ ATLAS proportions

Keyword Arguments:

log_type (str) – set ‘x’, ‘y’ or both (‘xy’) axes to log-scale
x_ticks_extra (list) – list of additional minor ticks to label (only used when the x-axis is on a log scale)
x_tick_labels (iterable) – list of tick labels for the x-axis
x_tick_label_size (float) – fontsize for x-axis tick labels
y_tick_labels (iterable) – list of tick labels for the y-axis
y_tick_label_size (float) – fontsize for y-axis tick labels

add_ATLAS_label(x, y, plot_type=None, anchor_to='lower left', fontsize=None, axes=None)¶

Add an ATLAS label to the canvas at (x, y).

Parameters:	x (float) – x position (as a fraction of the canvas width). y (float) – y position (as a fraction of the canvas height). plot_type (str) – Preliminary/Internal/Work-In-Progress/None anchor_to (str) – anchor point (one of the options in location_map). fontsize (float) – font size. axes (str) – which of the different axes in this canvas to use.

add_legend(x, y, anchor_to='lower left', fontsize=None, axes=None)¶

Add a legend to the canvas at (x, y).

Parameters:	x (float) – x position (as a fraction of the canvas width). y (float) – y position (as a fraction of the canvas height). anchor_to (str) – anchor point (one of the options in location_map). fontsize (float) – font size. axes (str) – which of the different axes in this canvas to use.

add_luminosity_label(x, y, sqrts_TeV, luminosity, units='fb-1', anchor_to='lower left', fontsize=14, axes=None)¶

Add a luminosity label to the canvas at (x, y).

Parameters:

x (float) – x position (as a fraction of the canvas width).
y (float) – y position (as a fraction of the canvas height).
sqrts_TeV (float) – centre-of-mass energy in TeV.
luminosity (float) – luminosity.
units (str) – luminosity units.
anchor_to (str) – anchor point (one of the options in location_map).
fontsize (float) – font size.
axes (str) – which of the different axes in this canvas to use.

add_text(x, y, text, **kwargs)¶

Add text to the canvas at (x, y).

Parameters:	x (float) – x position (as a fraction of the canvas width). y (float) – y position (as a fraction of the canvas height). text (str) – text to add.

auto_tick_intervals = [0.001, 0.002, 0.0025, 0.004, 0.005, 0.01, 0.02, 0.025, 0.04, 0.05, 0.1, 0.2, 0.25, 0.4, 0.5, 1.0, 2.0, 2.5, 4.0, 5.0]¶: List of sensible tick intervals

close()¶: Close the figure to free up memory. Not needed when this object is used as a context manager.

get_axis_label(axis_name)¶

Get the label for the chosen axis

Parameters:	axis_name (str) – which axis to use.
Returns:	axis label
Return type:	str

get_axis_range(axis_name)¶

Get the range for the chosen axis

Parameters:	axis_name (str) – which axis to use.
Returns:	axis range
Return type:	tuple

location_map = {'centre left': ['left', 'center'], 'centre right': ['right', 'center'], 'lower left': ['left', 'bottom'], 'lower right': ['right', 'bottom'], 'upper left': ['left', 'top'], 'upper right': ['right', 'top']}¶: Map of locations to matplotlib coordinates

plot_dataset(*args, **kwargs)¶

Plot a dataset. Non-keyword arguments will be interpreted as the dataset to be plotted. Keyword arguments will be interpreted as style arguments.

Positional Arguments:
	args: (ROOT.TObject, iterable, numpy array) – plottable information which is passed to Dataset to be interpreted
Keyword Arguments:
	remove_zeros: (bool) – prune any points in the dataset for which the y-value is 0 axes: (str) – which axes to use (defaults to the main subplot) style: (str) – which of the plotters in plotters to use label: (str) – label to use in automatic legend generation sort_as: (str) – override

save(output_name, extension='pdf')¶

Save the current state of the canvas to a file.

Parameters:	output_name (str) – name of output file. extension (str or list) – type of output to produce.

set_axis_label(axis_name, axis_label, fontsize=16)¶

Set the maximum value for the given axis.

Parameters:	axis_name (str) – which axis to apply this to. axis_label (str) – desired label. fontsize (float) – font size to use

set_axis_log(axis_names)¶

Set the specified axis to be on a log-scale.

Parameters:	axis_names (str) – which axis (or axes) to apply this to.

set_axis_max(axis_name, maximum)¶

Set the maximum value for the given axis.

Parameters:	axis_name (str) – which axis to apply this to. maximum (float) – desired axis maximum.

set_axis_min(axis_name, minimum)¶

Set the minimum value for the given axis.

Parameters:	axis_name (str) – which axis to apply this to. minimum (float) – desired axis minimum.

set_axis_range(axis_name, axis_range)¶

Set the range for the given axis.

Parameters:	axis_name (str.) – which axis to apply this to. axis_range (iterable) – desired axis range.

set_axis_tick_ndp(axis_name, ndp)¶

Set number of decimal places to show.

Parameters:	axis_name (str) – which axis to apply this to. ndp (int) – how many decimal places to show.

set_axis_ticks(axis_name, ticks)¶

Set the position of the axis ticks.

Parameters:	axis_name (str) – which axis to apply this to. ticks (iterable) – desired tick positions.

set_title(title)¶

Set the figure title.

Parameters:	title (str) – figure title to use

x_tick_label_size¶: Label size for x-ticks

x_tick_labels¶: Labels for x-ticks

y_tick_label_size¶: Label size for y-ticks

y_tick_labels¶: Labels for y-ticks

panelled¶

This module provides the Panelled canvas.

class mATLASplotlib.canvases.panelled.Panelled(shape='portrait', n_panels=3, top_panel_fraction=0.16, **kwargs)¶

Bases: mATLASplotlib.canvases.base_canvas.BaseCanvas

Panelled canvas with standard ATLAS setup.

__init__(shape='portrait', n_panels=3, top_panel_fraction=0.16, **kwargs)¶

Set up Panelled canvas properties.

The canvas consists of a single top panel and n_panels equally sized additional panels underneath. These additional panels are called plot0, plot1, etc. with the numbering starting from the top.

Keyword Arguments:
Parameters:	shape (str) – use either the ‘square’, ‘landscape’ or ‘portrait’ ATLAS proportions n_panels (int) – how many panels to include top_panel_fraction (float) – fraction of vertical space that the top panel should use up
	as for `BaseCanvas`

add_ATLAS_label(x, y, plot_type=None, anchor_to='lower left', fontsize=None, axes=None)¶

Add an ATLAS label to the canvas at (x, y).

Parameters:	x (float) – x position (as a fraction of the canvas width). y (float) – y position (as a fraction of the canvas height). plot_type (str) – Preliminary/Internal/Work-In-Progress/None anchor_to (str) – anchor point (one of the options in location_map). fontsize (float) – font size. axes (str) – which of the different axes in this canvas to use.

add_legend(x, y, anchor_to='lower left', fontsize=None, axes=None)¶

Add a legend to the canvas at (x, y).

If added to the top panel then all elements from the lower panels will be included in it.

Arguments:	as for `BaseCanvas.add_legend()`

add_luminosity_label(x, y, sqrts_TeV, luminosity, units='fb-1', anchor_to='lower left', fontsize=14, axes=None)¶

Add a luminosity label to the canvas at (x, y).

Parameters:

x (float) – x position (as a fraction of the canvas width).
y (float) – y position (as a fraction of the canvas height).
sqrts_TeV (float) – centre-of-mass energy in TeV.
luminosity (float) – luminosity.
units (str) – luminosity units.
anchor_to (str) – anchor point (one of the options in location_map).
fontsize (float) – font size.
axes (str) – which of the different axes in this canvas to use.

add_text(x, y, text, **kwargs)¶

Add text to the canvas at (x, y).

Parameters:	x (float) – x position (as a fraction of the canvas width). y (float) – y position (as a fraction of the canvas height). text (str) – text to add.

auto_tick_intervals = [0.001, 0.002, 0.0025, 0.004, 0.005, 0.01, 0.02, 0.025, 0.04, 0.05, 0.1, 0.2, 0.25, 0.4, 0.5, 1.0, 2.0, 2.5, 4.0, 5.0]¶

bottom_panel¶: Name of the bottom-most panel.

close()¶: Close the figure to free up memory. Not needed when this object is used as a context manager.

get_axis_label(axis_name)¶

Get the label for the chosen axis

Parameters:	axis_name (str) – which axis to use.
Returns:	axis label
Return type:	str

get_axis_range(axis_name)¶

Get the range for the chosen axis

Parameters:	axis_name (str) – which axis to use.
Returns:	axis range
Return type:	tuple

location_map = {'centre left': ['left', 'center'], 'centre right': ['right', 'center'], 'lower left': ['left', 'bottom'], 'lower right': ['right', 'bottom'], 'upper left': ['left', 'top'], 'upper right': ['right', 'top']}¶

plot_dataset(*args, **kwargs)¶

Plot a dataset. Non-keyword arguments will be interpreted as the dataset to be plotted. Keyword arguments will be interpreted as style arguments.

Positional Arguments:
	args: (ROOT.TObject, iterable, numpy array) – plottable information which is passed to Dataset to be interpreted
Keyword Arguments:
	remove_zeros: (bool) – prune any points in the dataset for which the y-value is 0 axes: (str) – which axes to use (defaults to the main subplot) style: (str) – which of the plotters in plotters to use label: (str) – label to use in automatic legend generation sort_as: (str) – override

save(output_name, extension='pdf')¶

Save the current state of the canvas to a file.

Parameters:	output_name (str) – name of output file. extension (str or list) – type of output to produce.

set_axis_label(axis_name, axis_label, fontsize=16)¶

Set the maximum value for the given axis.

Parameters:	axis_name (str) – which axis to apply this to. axis_label (str) – desired label. fontsize (float) – font size to use

set_axis_log(axis_names)¶

Set the specified axis to be on a log-scale.

Parameters:	axis_names (str) – which axis (or axes) to apply this to.

set_axis_max(axis_name, maximum)¶

Set the maximum value for the given axis.

Parameters:	axis_name (str) – which axis to apply this to. maximum (float) – desired axis maximum.

set_axis_min(axis_name, minimum)¶

Set the minimum value for the given axis.

Parameters:	axis_name (str) – which axis to apply this to. minimum (float) – desired axis minimum.

set_axis_range(axis_name, axis_range)¶

Set the range for the given axis.

Parameters:	axis_name (str.) – which axis to apply this to. axis_range (iterable) – desired axis range.

set_axis_tick_ndp(axis_name, ndp)¶

Set number of decimal places to show.

Parameters:	axis_name (str) – which axis to apply this to. ndp (int) – how many decimal places to show.

set_axis_ticks(axis_name, ticks)¶

Set the position of the axis ticks.

Parameters:	axis_name (str) – which axis to apply this to. ticks (iterable) – desired tick positions.

set_title(title)¶

Set the figure title.

Parameters:	title (str) – figure title to use

x_tick_label_size¶: Label size for x-ticks

x_tick_labels¶: Labels for x-ticks

y_tick_label_size¶: Label size for y-ticks

y_tick_labels¶: Labels for y-ticks

ratio¶

This module provides the Ratio canvas.

class mATLASplotlib.canvases.ratio.Ratio(shape='square', line_ypos=1.0, **kwargs)¶

Bases: mATLASplotlib.canvases.base_canvas.BaseCanvas

Ratio canvas with standard ATLAS setup

__init__(shape='square', line_ypos=1.0, **kwargs)¶

Set up Ratio canvas properties.

Keyword Arguments:
Parameters:	shape (str) – use either the ‘square’, ‘landscape’ or ‘portrait’ ATLAS proportions line_ypos (float) – where to draw the reference line in the ratio plot
	as for `BaseCanvas`

add_ATLAS_label(x, y, plot_type=None, anchor_to='lower left', fontsize=None, axes=None)¶

Add an ATLAS label to the canvas at (x, y).

Parameters:	x (float) – x position (as a fraction of the canvas width). y (float) – y position (as a fraction of the canvas height). plot_type (str) – Preliminary/Internal/Work-In-Progress/None anchor_to (str) – anchor point (one of the options in location_map). fontsize (float) – font size. axes (str) – which of the different axes in this canvas to use.

add_legend(x, y, anchor_to='lower left', fontsize=None, axes=None)¶

Add a legend to the canvas at (x, y).

Parameters:	x (float) – x position (as a fraction of the canvas width). y (float) – y position (as a fraction of the canvas height). anchor_to (str) – anchor point (one of the options in location_map). fontsize (float) – font size. axes (str) – which of the different axes in this canvas to use.

add_luminosity_label(x, y, sqrts_TeV, luminosity, units='fb-1', anchor_to='lower left', fontsize=14, axes=None)¶

Add a luminosity label to the canvas at (x, y).

Parameters:

x (float) – x position (as a fraction of the canvas width).
y (float) – y position (as a fraction of the canvas height).
sqrts_TeV (float) – centre-of-mass energy in TeV.
luminosity (float) – luminosity.
units (str) – luminosity units.
anchor_to (str) – anchor point (one of the options in location_map).
fontsize (float) – font size.
axes (str) – which of the different axes in this canvas to use.

add_text(x, y, text, **kwargs)¶

Add text to the canvas at (x, y).

Parameters:	x (float) – x position (as a fraction of the canvas width). y (float) – y position (as a fraction of the canvas height). text (str) – text to add.

auto_tick_intervals = [0.001, 0.002, 0.0025, 0.004, 0.005, 0.01, 0.02, 0.025, 0.04, 0.05, 0.1, 0.2, 0.25, 0.4, 0.5, 1.0, 2.0, 2.5, 4.0, 5.0]¶

close()¶: Close the figure to free up memory. Not needed when this object is used as a context manager.

get_axis_label(axis_name)¶

Get the label for the chosen axis

Parameters:	axis_name (str) – which axis to use.
Returns:	axis label
Return type:	str

get_axis_range(axis_name)¶

Get the range for the chosen axis

Parameters:	axis_name (str) – which axis to use.
Returns:	axis range
Return type:	tuple

location_map = {'centre left': ['left', 'center'], 'centre right': ['right', 'center'], 'lower left': ['left', 'bottom'], 'lower right': ['right', 'bottom'], 'upper left': ['left', 'top'], 'upper right': ['right', 'top']}¶

plot_dataset(*args, **kwargs)¶

Plot a dataset. Non-keyword arguments will be interpreted as the dataset to be plotted. Keyword arguments will be interpreted as style arguments.

Positional Arguments:
	args: (ROOT.TObject, iterable, numpy array) – plottable information which is passed to Dataset to be interpreted
Keyword Arguments:
	remove_zeros: (bool) – prune any points in the dataset for which the y-value is 0 axes: (str) – which axes to use (defaults to the main subplot) style: (str) – which of the plotters in plotters to use label: (str) – label to use in automatic legend generation sort_as: (str) – override

save(output_name, extension='pdf')¶

Save the current state of the canvas to a file.

Parameters:	output_name (str) – name of output file. extension (str or list) – type of output to produce.

set_axis_label(axis_name, axis_label, fontsize=16)¶

Set the maximum value for the given axis.

Parameters:	axis_name (str) – which axis to apply this to. axis_label (str) – desired label. fontsize (float) – font size to use

set_axis_log(axis_names)¶

Set the specified axis to be on a log-scale.

Parameters:	axis_names (str) – which axis (or axes) to apply this to.

set_axis_max(axis_name, maximum)¶

Set the maximum value for the given axis.

Parameters:	axis_name (str) – which axis to apply this to. maximum (float) – desired axis maximum.

set_axis_min(axis_name, minimum)¶

Set the minimum value for the given axis.

Parameters:	axis_name (str) – which axis to apply this to. minimum (float) – desired axis minimum.

set_axis_range(axis_name, axis_range)¶

Set the range for the given axis.

Parameters:	axis_name (str.) – which axis to apply this to. axis_range (iterable) – desired axis range.

set_axis_tick_ndp(axis_name, ndp)¶

Set number of decimal places to show.

Parameters:	axis_name (str) – which axis to apply this to. ndp (int) – how many decimal places to show.

set_axis_ticks(axis_name, ticks)¶

Set the position of the axis ticks.

Parameters:	axis_name (str) – which axis to apply this to. ticks (iterable) – desired tick positions.

set_title(title)¶

Set the figure title.

Parameters:	title (str) – figure title to use

x_tick_label_size¶: Label size for x-ticks

x_tick_labels¶: Labels for x-ticks

y_tick_label_size¶: Label size for y-ticks

y_tick_labels¶: Labels for y-ticks

simple¶

This module provides the Simple canvas.

class mATLASplotlib.canvases.simple.Simple(shape='square', **kwargs)¶

Bases: mATLASplotlib.canvases.base_canvas.BaseCanvas

Simple canvas with standard ATLAS setup.

__init__(shape='square', **kwargs)¶

Set up universal canvas properties.

Parameters:

shape (str) – use either the ‘square’, ‘landscape’ or ‘portrait’ ATLAS proportions

Keyword Arguments:

log_type (str) – set ‘x’, ‘y’ or both (‘xy’) axes to log-scale
x_ticks_extra (list) – list of additional minor ticks to label (only used when the x-axis is on a log scale)
x_tick_labels (iterable) – list of tick labels for the x-axis
x_tick_label_size (float) – fontsize for x-axis tick labels
y_tick_labels (iterable) – list of tick labels for the y-axis
y_tick_label_size (float) – fontsize for y-axis tick labels

add_ATLAS_label(x, y, plot_type=None, anchor_to='lower left', fontsize=None, axes=None)¶

Add an ATLAS label to the canvas at (x, y).

Parameters:	x (float) – x position (as a fraction of the canvas width). y (float) – y position (as a fraction of the canvas height). plot_type (str) – Preliminary/Internal/Work-In-Progress/None anchor_to (str) – anchor point (one of the options in location_map). fontsize (float) – font size. axes (str) – which of the different axes in this canvas to use.

add_legend(x, y, anchor_to='lower left', fontsize=None, axes=None)¶

Add a legend to the canvas at (x, y).

Parameters:	x (float) – x position (as a fraction of the canvas width). y (float) – y position (as a fraction of the canvas height). anchor_to (str) – anchor point (one of the options in location_map). fontsize (float) – font size. axes (str) – which of the different axes in this canvas to use.

add_luminosity_label(x, y, sqrts_TeV, luminosity, units='fb-1', anchor_to='lower left', fontsize=14, axes=None)¶

Add a luminosity label to the canvas at (x, y).

Parameters:

x (float) – x position (as a fraction of the canvas width).
y (float) – y position (as a fraction of the canvas height).
sqrts_TeV (float) – centre-of-mass energy in TeV.
luminosity (float) – luminosity.
units (str) – luminosity units.
anchor_to (str) – anchor point (one of the options in location_map).
fontsize (float) – font size.
axes (str) – which of the different axes in this canvas to use.

add_text(x, y, text, **kwargs)¶

Add text to the canvas at (x, y).

Parameters:	x (float) – x position (as a fraction of the canvas width). y (float) – y position (as a fraction of the canvas height). text (str) – text to add.

auto_tick_intervals = [0.001, 0.002, 0.0025, 0.004, 0.005, 0.01, 0.02, 0.025, 0.04, 0.05, 0.1, 0.2, 0.25, 0.4, 0.5, 1.0, 2.0, 2.5, 4.0, 5.0]¶

close()¶: Close the figure to free up memory. Not needed when this object is used as a context manager.

get_axis_label(axis_name)¶

Get the label for the chosen axis

Parameters:	axis_name (str) – which axis to use.
Returns:	axis label
Return type:	str

get_axis_range(axis_name)¶

Get the range for the chosen axis

Parameters:	axis_name (str) – which axis to use.
Returns:	axis range
Return type:	tuple

location_map = {'centre left': ['left', 'center'], 'centre right': ['right', 'center'], 'lower left': ['left', 'bottom'], 'lower right': ['right', 'bottom'], 'upper left': ['left', 'top'], 'upper right': ['right', 'top']}¶

plot_dataset(*args, **kwargs)¶

Plot a dataset. Non-keyword arguments will be interpreted as the dataset to be plotted. Keyword arguments will be interpreted as style arguments.

Positional Arguments:
	args: (ROOT.TObject, iterable, numpy array) – plottable information which is passed to Dataset to be interpreted
Keyword Arguments:
	remove_zeros: (bool) – prune any points in the dataset for which the y-value is 0 axes: (str) – which axes to use (defaults to the main subplot) style: (str) – which of the plotters in plotters to use label: (str) – label to use in automatic legend generation sort_as: (str) – override

save(output_name, extension='pdf')¶

Save the current state of the canvas to a file.

Parameters:	output_name (str) – name of output file. extension (str or list) – type of output to produce.

set_axis_label(axis_name, axis_label, fontsize=16)¶

Set the maximum value for the given axis.

Parameters:	axis_name (str) – which axis to apply this to. axis_label (str) – desired label. fontsize (float) – font size to use

set_axis_log(axis_names)¶

Set the specified axis to be on a log-scale.

Parameters:	axis_names (str) – which axis (or axes) to apply this to.

set_axis_max(axis_name, maximum)¶

Set the maximum value for the given axis.

Parameters:	axis_name (str) – which axis to apply this to. maximum (float) – desired axis maximum.

set_axis_min(axis_name, minimum)¶

Set the minimum value for the given axis.

Parameters:	axis_name (str) – which axis to apply this to. minimum (float) – desired axis minimum.

set_axis_range(axis_name, axis_range)¶

Set the range for the given axis.

Parameters:	axis_name (str.) – which axis to apply this to. axis_range (iterable) – desired axis range.

set_axis_tick_ndp(axis_name, ndp)¶

Set number of decimal places to show.

Parameters:	axis_name (str) – which axis to apply this to. ndp (int) – how many decimal places to show.

set_axis_ticks(axis_name, ticks)¶

Set the position of the axis ticks.

Parameters:	axis_name (str) – which axis to apply this to. ticks (iterable) – desired tick positions.

set_title(title)¶

Set the figure title.

Parameters:	title (str) – figure title to use

x_tick_label_size¶: Label size for x-ticks

x_tick_labels¶: Labels for x-ticks

y_tick_label_size¶: Label size for y-ticks

y_tick_labels¶: Labels for y-ticks

converters¶

dataset¶

This module provides the Dataset class.

class mATLASplotlib.converters.dataset.Dataset(*args, **kwargs)¶

Bases: object

Container for plottable datasets.

__init__(*args, **kwargs)¶

Constructor - specify values and error pair separately for each dimension.

Arguments will be interpreted as the dataset to be plotted.

Positional Arguments:
Examples:	x vs y : __init__([1,2,3], [4,9,16]) x vs y with y_errors : __init__([1,2,3], None, [4,9,16], [2,3,4]) z values at each (x, y) point : __init__([1, 2], [3, 4], [3, 4, 6, 8])
	args: (ROOT.TObject, iterable, numpy array) – plottable information which is used to build a `Dataset`
Keyword Arguments:
	x_values: (iterable) – list of points along the x-axis x_error_pairs: (iterable) – list of error pairs along the x-axis y_values: (iterable) – list of points along the y-axis y_error_pairs: (iterable) – list of error pairs along the y-axis z_values: (iterable) – list of points along the z-axis z_error_pairs: (iterable) – list of error pairs along the z-axis
Raises:	AssertionError – arguments are not correctly sized ValueError – arguments cannot be interpreted

construct_2D_bin_list(axes='xy')¶

Construct full set of bins when treating x and y as two sides of a 2D plot.

Returns:	array of x and y bin centers that cover every (x, y) combination
Return type:	[np.array, np.array]
Raises:	ValueError – unsupported axes argument

get_dimensions()¶

Get a list of dimension names.

Returns:	list of dimension names
Return type:	list(str)

root2data¶

This module provides the root2data class.

class mATLASplotlib.converters.root2data.root2data(root_object, remove_zeros=False)¶

Bases: object

Interpreter for ROOT objects.

__init__(root_object, remove_zeros=False)¶

Extract x/y/z information from a ROOT object.

Parameters:	root_object (ROOT.TObject) – ROOT input to interpret remove_zeros (bool) – whether to remove points with a value of 0

construct_from_TF1(input_TF1)¶

Read TF1 into x, y dimensions.

Parameters:	input_TF1 (ROOT.TF1) – input TF1

construct_from_TGraph(input_TGraph)¶

Read TGraph into x, y dimensions.

Parameters:	input_TGraph (ROOT.TGraph) – input TGraph

construct_from_TGraphAsymmErrors(input_TGraphAsymmErrors)¶

Read TGraphErrors into x, y dimensions.

Parameters:	input_TGraphAsymmErrors (ROOT.TGraphAsymmErrors) – input TGraphAsymmErrors

construct_from_TGraphErrors(input_TGraphErrors)¶

Read TGraphErrors into x, y dimensions.

Parameters:	input_TGraphErrors (ROOT.TGraphErrors) – input TGraphErrors

construct_from_TH1(input_TH1)¶

Read TH1 into x, y dimensions.

Parameters:	input_TH1 (ROOT.TH1) – input TH1

construct_from_TH2(input_TH2)¶

Read TH2 into x, y, z dimensions.

Parameters:	input_TH2 (ROOT.TH2) – input TH2

do_zero_removal()¶: Remove points with zero y-value.

static valid_input(test_object)¶

Check that the input object is a valid ROOT TObject.

Parameters:	test_object (object) – input object to invesigate
Returns:	whether the input object is a ROOT TObject
Return type:	bool

decorations¶

atlas_text¶

This module provides the draw_ATLAS_text convenience function.

mATLASplotlib.decorations.atlas_text.draw_ATLAS_text(axes, loc, align, plot_type=None, fontsize=17)¶

Draw ATLAS text on axes.

Parameters:	axes (str) – axes to plot on loc (tuple(float)) – x and y position of text align (tuple(str)) – horizontal and vertical alignment of text plot_type (str) – Internal/Preliminary/Work-In-Progress etc. fontsize (float) – fontsize of legend contents

legend¶

This module provides the Legend class.

class mATLASplotlib.decorations.legend.Legend¶

Bases: object

Class for controlling plot legends.

__init__()¶: Initialise legend ordering and default fontsize.

add_dataset(label, is_stack=False, sort_as=None)¶

Add a dataset to the legend.

Parameters:	label (str) – label that will appear in the legend is_stack (bool) – if this is a stack plot it needs to be stored in reverse order sort_as (str) – override the default first-in-first-out sorting, and sort using this text instead

plot(x, y, axes, anchor_to, fontsize, use_axes=False)¶

Plot the legend at (x, y) on the chosen axes.

Parameters:	x (float) – x-position of legend y (float) – y-position of legend axes (str) – axes to plot on anchor_to (str) – which corner to anchor the (x, y) to fontsize (float) – fontsize of legend contents use_axes (list[matplotlib.axes.Axes]) – get handles and labels from all axes in list

text¶

This module provides the draw_text convenience function.

mATLASplotlib.decorations.text.draw_text(text, axes, loc, align, fontsize=16, **kwargs)¶

Draw arbitrary text strings at (x, y) on the chosen axes.

Keyword Arguments:
Parameters:	text (str) – text to draw axes (str) – axes to plot on loc (tuple(float)) – x and y position of text align (tuple(str)) – horizontal and vertical alignment of text fontsize (float) – fontsize of legend contents
	colour (str) – set text colour transform (str) – use data or axes coordinates

plotters¶

base_plotter¶

This module provides the BasePlotter class.

class mATLASplotlib.plotters.base_plotter.BasePlotter(plot_style)¶

Bases: object

Base class for plot formatting.

__init__(plot_style)¶

Initialise common plotting properties.

Parameters:	plot_style (str) – which plotting style to use. Consists of plot style name, plus any additional options available in the child class.

add_to_axes(axes, dataset, **kwargs)¶

Add the chosen dataset to the chosen axes. Delegated to child classes.

Parameters:	axes (matplotlib.axes) – which axes to plot this dataset on dataset (Dataset) – dataset to plot

bar¶

This module provides the BarChart class.

class mATLASplotlib.plotters.bar_chart.BarChart(plot_style)¶

Bases: mATLASplotlib.plotters.base_plotter.BasePlotter

Plot bar chart.

add_to_axes(axes, dataset, **kwargs)¶

Add the chosen dataset to the chosen axes.

Keyword Arguments:
Parameters:	axes (matplotlib.axes) – which axes to plot this dataset on dataset (matplotlib.axes) – which axes to plot this dataset on
	colour (str) – which face colour to use edgecolour (float) – which colour to use for the outline edgewidth (float) – how large an outline width to use label (str) – label to use when this appears in a legend

binned_band¶

This module provides the BinnedBand class.

class mATLASplotlib.plotters.binned_band.BinnedBand(plot_style)¶

Bases: mATLASplotlib.plotters.base_plotter.BasePlotter

Plot as binned band in the x-y plane.

Additional plot_style options:
	central line – also draw a horizontal line at the vertical centre of each bin central line stepped – as for central line but also join these vertically at the edge of each bin

add_to_axes(axes, dataset, **kwargs)¶

Add the chosen dataset to the chosen axes.

Keyword Arguments:
Parameters:	axes (matplotlib.axes) – which axes to plot this dataset on dataset (matplotlib.axes) – which axes to plot this dataset on
	alpha (float) – set alpha transparency colour (str) – which face colour to use hatch (str) – set hatch pattern hatchcolour (str) – which hatch colour to use label (str) – label to use when this appears in a legend linecolour (float) – which colour to use for the main line and for hatches linewidth (str) – how wide to draw the line

coloured_2D¶

This module provides the Coloured2D class.

class mATLASplotlib.plotters.coloured_2D.Coloured2D(plot_style)¶

Bases: mATLASplotlib.plotters.base_plotter.BasePlotter

Plot as points in the x-y plane

add_to_axes(axes, dataset, **kwargs)¶

Add the chosen dataset to the chosen axes.

Keyword Arguments:
Parameters:	axes (matplotlib.axes) – which axes to plot this dataset on dataset (matplotlib.axes) – which axes to plot this dataset on
	colour_map (str) – which colour map to use with_key (bool) – draw the key (True by default)

get_plotter¶

This module provides the get_plotter() convenience function.

mATLASplotlib.plotters.get_plotter.get_plotter(plot_style)¶

Convert a plot style argument into a concrete class inheriting from BasePlotter.

Parameters:	plot_style (str) – which plot style to use.
Returns:	instantiated plotter object
Return type:	BasePlotter
Raises:	ValueError – No plot style provided. NotImplementedError – Unsupported plot style provided.

line¶

This module provides the Line class.

class mATLASplotlib.plotters.line.Line(plot_style)¶

Bases: mATLASplotlib.plotters.base_plotter.BasePlotter

Plot as a line in the x-y plane

Additional plot_style options:
	join centres – join the centres with straight line segments smooth – draw a smooth line through the points stepped – draw a stepped line graph

add_to_axes(axes, dataset, **kwargs)¶

Add the chosen dataset to the chosen axes.

Keyword Arguments:
Parameters:	axes (matplotlib.axes) – which axes to plot this dataset on dataset (matplotlib.axes) – which axes to plot this dataset on
	colour (str) – which face colour to use label (str) – label to use when this appears in a legend linestyle (str) – which style (dotted/dashed/solid etc.) to draw the line with linewidth (str) – how wide to draw the line marker (str) – which marker to use

scatter¶

This module provides the Scatter class.

class mATLASplotlib.plotters.scatter.Scatter(plot_style)¶

Bases: mATLASplotlib.plotters.base_plotter.BasePlotter

Plot as scattered points in the x-y plane

__init__(plot_style)¶

Initialise plotting properties.

Plot_style options:
Parameters:	plot_style (str) – which plotting style to use.
	scatter join centres – also draw a line joining the bin centre scatter xerror – also draw error bars in the x-direction scatter yerror – also draw error bars in the y-direction

add_to_axes(axes, dataset, **kwargs)¶

Add the chosen dataset to the chosen axes.

Parameters:

axes (matplotlib.axes) – which axes to plot this dataset on
dataset (matplotlib.axes) – which axes to plot this dataset on

Keyword Arguments:

colour (str) – which face colour to use
label (str) – label to use when this appears in a legend
linestyle (str) – which style (dotted/dashed/solid etc.) to draw the line with if join centers is specified
linewidth (str) – how wide to draw the line
marker (str) – which marker to use
with_error_bar_caps (bool) – whether to draw caps on the end of the error bars

stack¶

This module provides the Stack class.

class mATLASplotlib.plotters.stack.Stack(plot_style)¶

Bases: mATLASplotlib.plotters.base_plotter.BasePlotter

Plot as part of a vertically stacked histogram.

add_to_axes(axes, dataset, **kwargs)¶

Add the chosen dataset to the chosen axes.

Keyword Arguments:
Parameters:	axes (matplotlib.axes) – which axes to plot this dataset on dataset (matplotlib.axes) – which axes to plot this dataset on
	colour (str) – which face colour to use hatch (str) – set hatch pattern hatchcolour (str) – which hatch colour to use label (str) – label to use when this appears in a legend outlinewidth (str) – how wide to draw the outline

style¶

atlas¶

This module provides the set_atlas() convenience function.

mATLASplotlib.style.atlas.set_atlas()¶: Set the plotting style to ATLAS-style and then point this function to ‘None’ so that it can only be called once. Called on canvas creation.

matplotlib_wrapper¶

This module sets the default matplotlib backend - must be done before local imports.