sciplot package

Submodules

sciplot.analysis module

Analysis tools

sciplot.analysis.data_mc_ratio(data, mc, label_data='Data', label_mc='MC', y_label=None, figsize=None, ratio_range=(0, 2), *args, **kwarg)

Plot a comparison between two sets of data

Parameters:

• data –
• mc –
• label_data –
• label_mc –
• y_label –
• figsize –
• ratio_range –
• *args –
• **kwarg –

Returns:

sciplot.analysis.plot_flatness(sig, tag, bins=None, ax=None, xrange=None, percent_step=5)

Plotting differences of sig distribution in percentiles of tag distribution

Parameters:

• sig –
• tag –
• bins –
• ax –
• xrange –
• percent_step –

Returns:

sciplot.analysis.profile(x, y, bins=None, range=None, fmt='.', *args, **kwargs)

sciplot.analysis.ratio(y1, y2, y1_err=None, y2_err=None)

calculate the ratio between two histograms y1/y2

Parameters:

• y1 – y values of first histogram
• y2 – y values of second histogram
• y1_err – (optional) error of first
• y2_err – (optional) error of second

Returns:

ratio, ratio_error

sciplot.colors module

Color definitions

Here are some predefined colors for nicer plots.

sciplot.colors.b2blues(n=5)

sciplot.colors.b2greens(n=3)

sciplot.colors.b2helix(n)

sciplot.colors.cm(n)

sciplot.decorations module

sciplot.decorations.decorate(*args, **kwargs)

sciplot.decorations.draw_y_label(label='Entries', unit=None, ha='right', *args, **kwargs)

Plotting scientific notation y label

Parameters:

• label –
• unit –
• ha –
• *args –
• **kwargs –

Returns:

sciplot.decorations.expand(factor=1.2)

sciplot.decorations.labels(xlabel=None, ylabel=None, unit=None, root_style=False, *args, **kwargs)

sciplot.decorations.set_style()

sciplot.decorations.watermark(t1='Collaboration', t2='(preliminary)', px=0.5, py=0.9, fontsize=16, alpha=0.95, *args, **kwargs)

sciplot.functions module

In this file all the matplolib wrappers are located.

sciplot.functions.errorbar(bin_centers, y, y_err, x_err=None, box=False, plot_zero=True, fmt='.', color=None, ax=None, label=None, *args, **kwargs)

sciplot.functions.errorhist(data, bins=None, color=None, normed=False, fmt='.', range=None, scale=None, x_err=False, box=False, ax=None, weights=None, plot_zero=True, label=None, *args, **kwargs)

Parameters:

• data –
• bins –
• color –
• normed –
• fmt –
• range –
• scale –
• x_err –
• box –
• ax –
• weights –
• plot_zero –
• label –
• *args –
• **kwargs –

Returns:

sciplot.functions.hist(data, bins=None, fill=False, range=None, lw=1.0, ax=None, style=None, color=None, scale=None, weights=None, label=None, edgecolor=None, *args, **kwargs)

Parameters:

• data –
• bins –
• fill –
• range –
• lw –
• ax –
• style –
• color –
• scale –
• weights –
• *args –
• **kwargs –

Returns:

sciplot.functions.save(filename, bottom=0.15, left=0.13, right=0.96, top=0.95, *args, **kwargs)

Save a file and do the subplot_adjust to fit the page with larger labels

Parameters:

• filename –
• bottom –
• left –
• right –
• top –
• *args –
• **kwargs –

Returns:

sciplot.functions.sig_bkg_plot(df, col, by=None, ax=None, bins=None, range=None, labels=None)

sciplot.functions.stacked(df, col=None, by=None, bins=None, color=None, range=None, lw=0.5, ax=None, edgecolor='black', *args, **kwargs)

Create stacked histogram

Parameters:

• df (DataFrame or list of arrays) –
• col –
• by –
• bins –
• color –
• lw –
• *args –
• **kwargs –

Returns:

sciplot.functions.text(t, x=0.8, y=0.9, fontsize=22, *args, **kwargs)

Parameters:

• t –
• x –
• y –
• fontsize –
• *args –
• **kwargs –

Returns:

sciplot.functions.to_stack(df, col, by)

Parameters:

• df –
• col –
• by –

Returns:

sciplot.functions.xlim(low=None, high=None, ax=None)

Parameters:

• low –
• high –
• ax –

Returns:

sciplot.helpers module

Helper function and classes are defined here.

class sciplot.helpers.Singleton(decorated)

Bases: object

A non-thread-safe helper class to ease implementing singletons. This should be used as a decorator – not a metaclass – to the class that should be a singleton.

The decorated class can define one __init__ function that takes only the self argument. Other than that, there are no restrictions that apply to the decorated class.

To get the singleton instance, use the Instance method. Trying to use __call__ will result in a TypeError being raised.

Limitations: The decorated class cannot be inherited from.

Instance()

Returns the singleton instance. Upon its first call, it creates a new instance of the decorated class and calls its __init__ method. On all subsequent calls, the already created instance is returned.

sciplot.helpers.figure()

sciplot.helpers.get_optimal_bin_size(n)

This function calculates the optimal amount of bins for the number of events n. :param n: number of Events :return: optimal bin size

sciplot.helpers.hist2root(h, name=None, title='')

Convert your histograms to a root histogram.. if you really want to

Parameters:

• h – array with the histogram h=(y_values, bin_edges, (patches=optional))
• name – Name for the root histogram, optional
• title – Title for the root histogram, optional

Returns:

TH1F or TH2F depending on the input array

Examples

>>> data = [1,2,3,4,5,6,7]
>>> h = b2plot.hist(data)
>>> from b2plot.helpers import hist2root
>>> root_hist = hist2root(h)

sciplot.helpers.nf()

sciplot.helpers.xaxis()

sciplot.skeleton module

This is a skeleton file that can serve as a starting point for a Python console script. To run this script uncomment the following lines in the [options.entry_points] section in setup.cfg:

console_scripts =

fibonacci = sciplot.skeleton:run

Then run python setup.py install which will install the command fibonacci inside your current environment. Besides console scripts, the header (i.e. until _logger…) of this file can also be used as template for Python modules.

Note: This skeleton file can be safely removed if not needed!

sciplot.skeleton.fib(n)

Fibonacci example function

Parameters:n (int) – integer Returns:n-th Fibonacci number Return type:int

sciplot.skeleton.main(args)

Main entry point allowing external calls

Parameters:args ([str]) – command line parameter list

sciplot.skeleton.parse_args(args)

Parse command line parameters

Parameters:args ([str]) – command line parameters as list of strings Returns:command line parameters namespace Return type:argparse.Namespace

sciplot.skeleton.run()

Entry point for console_scripts

sciplot.skeleton.setup_logging(loglevel)

Setup basic logging

Parameters:loglevel (int) – minimum loglevel for emitting messages

Module contents