agepy.spec.coincidence.CoincMap

class CoincMap(data, xedges, yedges)

Bases: object

Plot a coincedence map and its projections on the x and y axes.

Parameters:

data: numpy.ndarray: 2d array of shape (m,n) containing the coincidence map. In most cases this will be the output of numpy.histogram2d().
xedges: numpy.ndarray: 1d array of shape (m+1) containing the bin edges of the x-axis.
yedges: numpy.ndarray: 1d array of shape (n+1) containing the bin edges of the y-axis.

Attributes:

data: numpy.ndarray: Access the 2D histogram data.
xedges: numpy.ndarray: Access the bin edges of the x-axis.
yedges: numpy.ndarray: Access the bin edges of the y-axis.
roi: tuple of tuples: Region of interest. Should be set using set_roi().
fig: matplotlib.figure.Figure: Matplotlib Figure object. Created by the plot() method.
ax: Sequence of matplotlib.axes.Axes: Sequence of matplotlib Axes objects containing the coincidence map, the projection on the x-axis and the projection on the y-axis and the colorbar.

Examples

>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from agepy import ageplot
>>> ageplot.use("age")

Create some simple example data:

>>> xyrange = ((0, 2), (0, 2))
>>> n = 10000
>>> rng = np.random.default_rng(42)
>>> x = rng.normal(0.8, 0.2, size=n)
>>> y = rng.normal(1.2, 0.4, size=n)
>>> H, xedges, yedges = np.histogram2d(x, y, bins=50, range=xyrange)

Create a CoincMap object and plot the data:

>>> from agepy.spec.coincidence import CoincMap
>>> coinc = CoincMap(H, xedges, yedges)
>>> coinc.plot(figsize=(4.8, 4.8))
>>> # Start an interactive session with coinc.interactive()

../_images/agepy-spec-coincidence-CoincMap-1.hires.png

Methods

`interactive`	Start an interactive PyQt session to view the data and add an ROI.
`plot`	Create a matplotlib figure with the coincidence map and its projections on the x and y axes.
`set_roi`	Set the region of interest (ROI) for the plot.
`update`	Update the plot with the current data and ROI settings.

interactive(): Start an interactive PyQt session to view the data and add an ROI.

plot(xlabel='early electron kinetic energy', ylabel='late electron kinetic energy', title=None, figsize=None, cmap='YlOrRd', norm=None, vmin=1, vmax=None, num=None)

Create a matplotlib figure with the coincidence map and its projections on the x and y axes. The figure is stored in the attribute fig and the axes in ax.

Parameters:

xlabel, ylabelpython:str, optional: Labels of the x and y axes. Default: “early electron kinetic energy”, “late electron kinetic energy”
titlepython:str, optional: Title of the figure. Default: None
figsizepython:tuple, optional: Figure size in inches. Default: None
cmapmatplotlib.colors.Colormap or python:str, optional: Colormap passed to matplotlib.pyplot.pcolormesh(). Default: ‘YlOrRd’
normpython:str or matplotlib.colors.Normalize or python:None, optional: Normalization passed to matplotlib.pyplot.pcolormesh(). Default: None
vmin, vmaxpython:float, optional
Minimum and maximum value for the colormap passed to: matplotlib.pyplot.pcolormesh(). Default: 1, None
num: int or str or matplotlib.figure.Figure, optional: Figure identifier passed to matplotlib.pyplot.figure().

set_roi(xmin, xmax, ymin, ymax)

Set the region of interest (ROI) for the plot.

Parameters:

xmin, xmaxpython:float: Minimum and maximum value for the x-axis.
ymin, ymaxpython:float: Minimum and maximum value for the y-axis.

Return type:

None

update(): Update the plot with the current data and ROI settings.