Spectra¶

These are generic objects for storing 1D/2D spectra e.g. density of states or S(Q,w) maps.

Spectrum1D
Spectrum2D

A 1D spectrum can be broadened using Spectrum1D.broaden, which broadens along the x-axis and returns a new Spectrum1D object. It can broaden with either a Gaussian or Lorentzian and requires a broadening FWHM in the same type of units as x_data. For example:

from euphonic import ureg, Spectrum1D

dos = Spectrum1D.from_json_file('dos.json')
fwhm = 1.5*ureg('meV')
dos_broaden = dos.broaden(fwhm, shape='lorentz')

Plotting ¶

See Plotting

Docstring ¶

class Spectrum1D(x_data, y_data, x_tick_labels=None)¶

For storing generic 1D spectra e.g. density of states

Variables:

x_data ((n_x_data,) or (n_x_data + 1,) float Quantity) – The x_data points (if size (n_x_data,)) or x_data bin edges (if size (n_x_data + 1,))
y_data ((n_x_data,) float Quantity) – The plot data in y
x_tick_labels (list (int, string) tuples or None) – Special tick labels e.g. for high-symmetry points. The int refers to the index in x_data the label should be applied to

__init__(x_data, y_data, x_tick_labels=None)¶

Parameters:

x_data ((n_x_data,) or (n_x_data + 1,) float Quantity) – The x_data points (if size (n_x_data,)) or x_data bin edges (if size (n_x_data + 1,))
y_data ((n_x_data,) float Quantity) – The plot data in y
x_tick_labels (list (int, string) tuples or None) – Special tick labels e.g. for high-symmetry points. The int refers to the index in x_data the label should be applied to

broaden(x_width, shape='gauss')¶

Broaden y_data and return a new broadened Spectrum1D object

Parameters:	x_width (float Quantity) – The broadening FWHM shape ({'gauss', 'lorentz'}, optional) – The broadening shape
Returns:	broadened_spectrum – A new Spectrum1D object with broadened y_data
Return type:	Spectrum1D

to_dict()¶

Convert to a dictionary. See Spectrum1D.from_dict for details on keys/values

Returns:
Return type:	dict

to_json_file(filename)¶

Write to a JSON file. JSON fields are equivalent to from_dict keys

Parameters:	filename (str) – Name of the JSON file to write to

classmethod from_dict(d)¶

Convert a dictionary to a Spectrum1D object

Parameters:

d (dict) –

A dictionary with the following keys/values:

’x_data’: (n_x_data,) or (n_x_data + 1,) float ndarray
’x_data_unit’: str
’y_data’: (n_x_data,) float ndarray
’y_data_unit’: str

There are also the following optional keys:

’x_tick_labels’: list of (int, string) tuples

Returns:

Return type: Spectrum1D

classmethod from_json_file(filename)¶

Read from a JSON file. See from_dict for required fields

Parameters:	filename (str) – The file to read from

Spectrum2D ¶

Broadening ¶

A 2D spectrum can be broadened using Spectrum2D.broaden, which broadens along either or both of the x/y-axes and returns a new Spectrum2D object. It can broaden with either a Gaussian or Lorentzian and requires a broadening FWHM in the same type of units as x_data/y_data for broadening along the x/y-axis respectively. For example:

from euphonic import ureg, Spectrum2D

sqw = Spectrum2D.from_json_file('sqw.json')
x_fwhm = 0.05*ureg('1/angstrom')
y_fwhm = 1.5*ureg('meV')
sqw_broaden = sqw.broaden(x_width=x_fwhm, y_width=y_fwhm, shape='lorentz')

Plotting ¶

See Plotting

Docstring ¶

class Spectrum2D(x_data, y_data, z_data, x_tick_labels=None)¶

For storing generic 2D spectra e.g. S(Q,w)

Variables:

x_data ((n_x_data,) or (n_x_data + 1,) float Quantity) – The x_data points (if size (n_x_data,)) or x_data bin edges (if size (n_x_data + 1,))
y_data ((n_y_data,) or (n_y_data + 1,) float Quantity) – The y_data bin points (if size (n_y_data,)) or y_data bin edges (if size (n_y_data + 1,))
z_data ((n_x_data, n_y_data) float Quantity) – The plot data in z
x_tick_labels (list (int, string) tuples or None) – Special tick labels e.g. for high-symmetry points. The int refers to the index in x_data the label should be applied to

__init__(x_data, y_data, z_data, x_tick_labels=None)¶

Variables:

x_data ((n_x_data,) or (n_x_data + 1,) float Quantity) – The x_data points (if size (n_x_data,)) or x_data bin edges (if size (n_x_data + 1,))
y_data ((n_y_data,) or (n_y_data + 1,) float Quantity) – The y_data bin points (if size (n_y_data,)) or y_data bin edges (if size (n_y_data + 1,))
z_data ((n_x_data, n_y_data) float Quantity) – The plot data in z
x_tick_labels (list (int, string) tuples or None) – Special tick labels e.g. for high-symmetry points. The int refers to the index in x_data the label should be applied to

broaden(x_width=None, y_width=None, shape='gauss')¶

Broaden z_data and return a new broadened Spectrum2D object

Parameters:	x_width (float Quantity, optional) – The broadening FWHM in x y_width (float Quantity, optional) – The broadening FWHM in y shape ({'gauss', 'lorentz'}, optional) – The broadening shape
Returns:	broadened_spectrum – A new Spectrum2D object with broadened z_data
Return type:	Spectrum2D

to_dict()¶

Convert to a dictionary. See Spectrum2D.from_dict for details on keys/values

Returns:
Return type:	dict

classmethod from_dict(d)¶

Convert a dictionary to a Spectrum2D object

Parameters:

d (dict) –

A dictionary with the following keys/values:

’x_data’: (n_x_data,) or (n_x_data + 1,) float ndarray
’x_data_unit’: str
’y_data’: (n_y_data,) or (n_y_data + 1,) float ndarray
’y_data_unit’: str
’z_data’: (n_x_data, n_y_data) float Quantity
’z_data_unit’: str

There are also the following optional keys:

’x_tick_labels’: list of (int, string) tuples

Returns:

Return type: Spectrum2D

classmethod from_json_file(filename)¶

Read from a JSON file. See from_dict for required fields

Parameters:	filename (str) – The file to read from

to_json_file(filename)¶

Write to a JSON file. JSON fields are equivalent to from_dict keys

Parameters:	filename (str) – Name of the JSON file to write to