HErmes.plotting package

Submodules

HErmes.plotting.canvases module

Provides canvases for multi axes plots

HErmes.plotting.canvases.Image(x)

class HErmes.plotting.canvases.YStackedCanvas(subplot_yheights=(0.2, 0.2, 0.5), padding=(0.15, 0.05, 0.0, 0.1), space_between_plots=0, figsize='auto', figure_factory=None)

Bases: object

A canvas for plotting multiple axes

eliminate_lower_yticks()

Eliminate the lowest y tick on each axes. The bottom axes keeps its lowest y-tick.

global_legend(*args, **kwargs)

A combined legend for all axes

Parameters:args will be passed to pylab.legend (all) – Keyword Arguments:  kwargs will be passed to pylab.legend (all) –

limit_xrange(xmin=None, xmax=None)

Walk through all axes and set xlims

Keyword Arguments:  

• xmin (float) – left x edge of axes
• xmax (float) – right x edge of axes

Returns:

None

limit_yrange(ymin=None, ymax=None)

Walk through all axes and adjust ymin and ymax

Keyword Arguments:  ymin (float) – min ymin value

save(path, name, formats=('pdf', 'png'), **kwargs)

Calls pylab.savefig for all endings

Parameters:

• path (str) – path to savefile
• name (str) – filename to save
• formats (tuple) – for each name in endings, a file is save

Keyword Arguments:  

keyword args will be passed to pylab.savefig (all) –

Returns:

The full path to the the saved file

Return type:

str

select_axes(axes)

Set the scope on a certain axes

Parameters:axes (int) – 0 lowest, -1 highest, increasing y-order Returns:The axes instance Return type:matplotlib.axes.axes

show()

Use the IPython.core.Image to show the plot

Returns:the plot Return type:IPython.core.Image

HErmes.plotting.colors module

Color management - provide a nice color scheme even if seaborn is not available

class HErmes.plotting.colors.ColorDict

Bases: dict

Use to transparently map any non defined colors through, like string like ‘k’ for matplotlib

HErmes.plotting.colors.get_color_palette(name='dark')

Load a color pallete, use seaborn if available.

Keyword Arguments:  name (str) – A string which is passed though seaborn.color_palette Returns:HErmes.plotting.plot_colors.ColorDict

HErmes.plotting.layout module

A set of figure sizes for publication-ready figures on A4 paper based on the golden ratio. For the use with pylabe figure, e.g. fig =pylab.figure(figsize=FIGSIZE_A4)

Available layouts:

• FIGSIZE_A4: Figure on full A4 width, portrait mode, golden ratio
• FIGSIZE_A4_LANDSCAPE: Figure on full A4 width, landscape mode, golden ratio
• FIGSIZE_A4_LANDSCAPE_HALF_HEIGHT: Figure on full A4 width, landscape mode, height half of golden ratio
• FIGSIZE_A4_SQUARE: Figure on full A4 width, square

HErmes.plotting.plotting module

Define some

class HErmes.plotting.plotting.VariableDistributionPlot(cuts=None, color_palette='dark', bins=None, xlabel=None)

Bases: object

A plot which shows the distribution of a certain variable. Cuts can be indicated with lines and arrows. This class defines (and somehow enforces) a certain style.

add_cumul(name)

Add a cumulative distribution to the plto

Parameters:name (str) – the name of the category

add_cuts(cut)

Add a cut to the the plot which can be indicated by an arrow

Parameters:cuts (HErmes.selection.cuts.Cut) – Returns:None

add_data(variable_data, name, bins=None, weights=None, label='')

Histogram the added data and store internally

Parameters:

• name (string) – the name of a category
• variable_data (array) – the actual data

Keyword Arguments:  

• bins (array) – histogram binning
• weights (array) – weights for the histogram
• label (str) – A label for the data when plotted

add_legend(**kwargs)

Add a legend to the plot. If no kwargs are passed, use some reasonable default.

Keyword Arguments:  be passed to pylab.legend (will) –

add_ratio(nominator, denominator, total_ratio=None, total_ratio_errors=None, log=False, label='data/$\\Sigma$ bg')

Add a ratio plot to the canvas

Parameters:

• nominator (list or str) – name(s) of the categorie(s) which will be the nominator in the ratio
• denominator (list or str) – name(s) of the categorie(s) which will be the nominator in the ratio

Keyword Arguments:  

• total_ratio (bool) – Indicate the total ratio with a line in the plot
• total_ratio_errors (bool) – Draw error region around total ratio
• log (bool) – draw ratio plot in log-scale
• label (str) – y-label for the ratio plot

add_variable(category, variable_name, external_weights=None, transform=None)

Convenience interface if data is sorted in categories already

Parameters:

• category (HErmese.variables.category.Category) – Get variable from this category
• variable_name (string) – The name of the variable

Keyword Arguments:  

• external_weights (np.ndarray) – Supply an array for weighting. This will OVERIDE ANY INTERNAL WEIGHTING MECHANISM and use the supplied weights.
• transform (callable) – Apply transformation todata

indicate_cut(ax, arrow=True)

If cuts are given, indicate them by lines

Parameters:ax (pylab.axes) – axes to draw on

static optimal_plotrange_histo(histograms)

Get most suitable x and y limits for a bunc of histograms

Parameters:histograms (list(d.factory.hist1d)) – The histograms in question Returns:xmin, xmax, ymin, ymax Return type:tuple (float, float, float, float)

plot(axes_locator=((0, 'c', 0.2), (1, 'r', 0.2), (2, 'h', 0.5)), combined_distro=True, combined_ratio=True, combined_cumul=True, normalized=True, log=True, legendwidth=1.5, ylabel='rate/bin [1/s]', figure_factory=None)

Create the plot

Keyword Arguments:  

• heights –
• axes_locator –
• combined_distro –
• combined_ratio –
• combined_cumul –
• log –
• normalized (bool) –

Returns:

HErmes.plotting.plotting.adjust_minor_ticks(axis, which='x')

Decorate the x-axis with a reasonable set of minor x-ticks

Parameters:axis (matplotlib.axis) – The axis to decorate Keyword Arguments:  which (str) – either “x”, “y” or “both” Returns:matplotlib.axis

HErmes.plotting.plotting.create_arrow(ax, x_0, y_0, dx, dy, length, width=0.1, shape='right', fc='k', ec='k', alpha=1.0, log=False)

Create an arrow object for plots. This is typically a large arrow, which can used to indicate a region in the plot which is excluded by a cut.

Parameters:

• ax (matplotlib.axes._subplots.AxesSubplot) – The axes where the arrow will be attached to
• x_0 (float) – x-origin of the arrow
• y_0 (float) – y-origin of the arrow
• dx (float) – x length of the arrow
• dy (float) – y length of the arrow
• length (float) – additional scaling parameter to scale the length of the arrow

Keyword Arguments:  

• width (float) – thickness of arrow
• shape (str) – either “full”, “left” or “right”
• fc (str) – facecolor
• ec (str) – edgecolor
• alpha (float) – 0 -1 alpha value of the arrow
• log (bool) – I for logscale, the proportions of the arrow will be adjusted accorginly.

Returns:

matplotlib.axes._subplots.AxesSubplot

HErmes.plotting.plotting.line_plot(quantities, bins=None, xlabel='', add_ratio=None, ratiolabel='', colors=None, figure_factory=None)

Parameters:

quantities –

Keyword Arguments:  

• bins –
• xlabel –
• add_ratio (tuple) – ([“data1”],[“data2”])
• ratiolabel (str) –
• colors –
• figure_factory (callable) – Factory function returning matpltolib.Figure

Returns:

HErmes.plotting.plotting.meshgrid(xs, ys)

Create x and y data for matplotlib pcolormesh and similar plotting functions.

Parameters:

• xs (np.ndarray) – 1d x bins
• ys (np.ndarray) – 2d y bins

Returns:

2d X and 2d Y matrices as well as a placeholder for the Z array

Return type:

tuple (np.ndarray, np.ndarray, np.ndarray)

Module contents

A set of

HErmes.plotting.add_styles()