ImageProfile¶

class gammapy.image.ImageProfile(table)[source]¶

Bases: object

Image profile class.

The image profile data is stored in Table object, with the following columns:

x_ref Coordinate bin center (required).

x_min Coordinate bin minimum (optional).

x_max Coordinate bin maximum (optional).

profile Image profile data (required).

profile_err Image profile data error (optional).

Parameters:	table : `Table` Table instance with the columns specified as above.

Attributes Summary

`profile`	Image profile quantity.
`profile_err`	Image profile error quantity.
`x_max`	Max.
`x_min`	Min.
`x_ref`	Reference x coordinates.

Methods Summary

`normalize`([mode])	Normalize profile to peak value or integral.
`peek`([figsize])	Show image profile and error.
`plot`([ax])	Plot image profile.
`plot_err`([ax])	Plot image profile error as band.
`smooth`([kernel, radius])	Smooth profile with error propagation.

Attributes Documentation

profile¶: Image profile quantity.

profile_err¶: Image profile error quantity.

x_max¶: Max. x coordinates.

x_min¶: Min. x coordinates.

x_ref¶: Reference x coordinates.

Methods Documentation

normalize(mode='peak')[source]¶

Normalize profile to peak value or integral.

Parameters:	mode : [‘integral’, ‘peak’] Normalize image profile so that it integrates to unity (‘integral’) or the maximum value corresponds to one (‘peak’).
Returns:	profile : `ImageProfile` Normalized image profile.

peek(figsize=(8, 4.5), **kwargs)[source]¶

Show image profile and error.

Parameters:	**kwargs : dict Keyword arguments passed to `ImageProfile.plot_profile()`
Returns:	ax : `Axes` Axes object

plot(ax=None, **kwargs)[source]¶

Plot image profile.

Parameters:	ax : `Axes` Axes object **kwargs : dict Keyword arguments passed to `plot`
Returns:	ax : `Axes` Axes object

plot_err(ax=None, **kwargs)[source]¶

Plot image profile error as band.

Parameters:	ax : `Axes` Axes object **kwargs : dict Keyword arguments passed to plt.fill_between()
Returns:	ax : `Axes` Axes object

smooth(kernel='box', radius='0.1 deg', **kwargs)[source]¶

Smooth profile with error propagation.

Smoothing is described by a convolution:

\[x_j = \sum_i x_{(j - i)} h_i\]

Where \(h_i\) are the coefficients of the convolution kernel.

The corresponding error on \(x_j\) is then estimated using Gaussian error propagation, neglecting correlations between the individual \(x_{(j - i)}\):

\[\Delta x_j = \sqrt{\sum_i \Delta x^{2}_{(j - i)} h^{2}_i}\]

Parameters:

kernel : {‘gauss’, ‘box’}: Kernel shape
radius : Quantity, str or float: Smoothing width given as quantity or float. If a float is given it is interpreted as smoothing width in pixels. If an (angular) quantity is given it is converted to pixels using xref[1] - x_ref[0].
kwargs : dict: Keyword arguments passed to uniform_filter (‘box’) and gaussian_filter (‘gauss’).

Returns:

profile : ImageProfile: Smoothed image profile.