IBIS8542Model

class mcalf.models.IBIS8542Model(stationary_line_core=None, absorption_guess=None, emission_guess=None, absorption_min_bound=None, emission_min_bound=None, absorption_max_bound=None, emission_max_bound=None, absorption_x_scale=None, emission_x_scale=None, neural_network=None, original_wavelengths=None, constant_wavelengths=None, delta_lambda=None, sigma=None, prefilter_response=None, prefilter_ref_main=None, prefilter_ref_wvscl=None, config=None, output=None)[source]

Bases: mcalf.models.base.ModelBase

Class for working with IBIS 8542 Å calcium II spectral imaging observations

Parameters
  • original_wavelengths (array_like) – One-dimensional array of wavelengths that correspond to the uncorrected spectral data.

  • stationary_line_core (float, optional, default = 8542.104320687517) – Wavelength of the stationary line core.

  • absorption_guess (array_like, length=4, optional, default = [-1000, stationary_line_core, 0.2, 0.1]) – Initial guess to take when fitting the absorption Voigt profile.

  • emission_guess (array_like, length=4, optional, default = [1000, stationary_line_core, 0.2, 0.1]) – Initial guess to take when fitting the emission Voigt profile.

  • absorption_min_bound (array_like, length=4, optional, default = [-np.inf, stationary_line_core-0.15, 1e-6, 1e-6]) – Minimum bounds for all the absorption Voigt profile parameters in order of the function’s arguments.

  • emission_min_bound (array_like, length=4, optional, default = [0, stationary_line_core-0.15, 1e-6, 1e-6]) – Minimum bounds for all the emission Voigt profile parameters in order of the function’s arguments.

  • absorption_max_bound (array_like, length=4, optional, default = [0, stationary_line_core+0.15, 1, 1]) – Maximum bounds for all the absorption Voigt profile parameters in order of the function’s arguments.

  • emission_max_bound (array_like, length=4, optional, default = [np.inf, stationary_line_core+0.15, 1, 1]) – Maximum bounds for all the emission Voigt profile parameters in order of the function’s arguments.

  • absorption_x_scale (array_like, length=4, optional, default = [1500, 0.2, 0.3, 0.5]) – Characteristic scale for all the absorption Voigt profile parameters in order of the function’s arguments.

  • emission_x_scale (array_like, length=4, optional, default = [1500, 0.2, 0.3, 0.5]) – Characteristic scale for all the emission Voigt profile parameters in order of the function’s arguments.

  • neural_network (sklearn.neural_network.MLPClassifier, optional, default = see description) – The MLPClassifier object that will be used to classify the spectra. Its default value is MLPClassifier(solver=’lbfgs’, alpha=1e-3, hidden_layer_sizes=(10, 4), random_state=1).

  • constant_wavelengths (array_like, length same as original_wavelengths, optional, default = see description) – The desired set of wavelengths that the spectral data should be rescaled to represent. It is assumed that these have constant spacing, but that may not be a requirement if you specify your own array. The default value is an array from the minimum to the maximum wavelength of original_wavelengths in constant steps of delta_lambda, overshooting the upper bound if the maximum wavelength has not been reached.

  • delta_lambda (float, optional, default = 0.05) – The step used between each value of constant_wavelengths when its default value has to be calculated.

  • sigma (list of array_like or bool, length=(2, n_wavelengths), optional, default = [type1, type2]) – A list of different sigma that are used to weight particular wavelengths along the spectra when fitting. The fitting method will expect to be able to choose a sigma array from this list at a specific index. It’s default value is [generate_sigma(i, constant_wavelengths, stationary_line_core) for i in [1, 2]]. See utils.generate_sigma() for more information. If bool, True will generate the default sigma value regardless of the value specified in config, and False will set sigma to be all ones, effectively disabling it.

  • prefilter_response (array_like, length=n_wavelengths, optional, default = see note) – Each constant wavelength scaled spectrum will be corrected by dividing it by this array. If prefilter_response is not give, and prefilter_ref_main and prefilter_ref_wvscl are not given, prefilter_response will have a default value of None.

  • prefilter_ref_main (array_like, optional, default = None) – If prefilter_response is not specified, this will be used along with prefilter_ref_wvscl to generate the default value of prefilter_response.

  • prefilter_ref_wvscl (array_like, optional, default = None) – If prefilter_response is not specified, this will be used along with prefilter_ref_main to generate the default value of prefilter_response.

  • config (str, optional, default = None) – Filename of a .yml file (relative to current directory) containing the initialising parameters for this object. Parameters provided explicitly to the object upon initialisation will override any provided in this file. All (or some) parameters that this object accepts can be specified in this file, except neural_network and config. Each line of the file should specify a different parameter and be formatted like emission_guess: ‘[-inf, wl-0.15, 1e-6, 1e-6]’ or original_wavelengths: ‘original.fits’ for example. When specifying a string, use ‘inf’ to represent np.inf and ‘wl’ to represent stationary_line_core as shown. If the string matches a file, utils.load_parameter() is used to load the contents of the file.

  • output (str, optional, default = None) – If the program wants to output data, it will place it relative to the location specified by this parameter. Some methods will only save data to a file if this parameter is not None. Such cases will be documented where relevant.

original_wavelengths

One-dimensional array of wavelengths that correspond to the uncorrected spectral data.

Type

array_like

stationary_line_core

Wavelength of the stationary line core.

Type

float, optional, default = 8542.099145376844

absorption_guess

Initial guess to take when fitting the absorption Voigt profile.

Type

array_like, length=4, optional, default = [-1000, stationary_line_core, 0.2, 0.1]

emission_guess

Initial guess to take when fitting the emission Voigt profile.

Type

array_like, length=4, optional, default = [1000, stationary_line_core, 0.2, 0.1]

absorption_min_bound

Minimum bounds for all the absorption Voigt profile parameters in order of the function’s arguments.

Type

array_like, length=4, optional, default = [-np.inf, stationary_line_core-0.15, 1e-6, 1e-6]

emission_min_bound

Minimum bounds for all the emission Voigt profile parameters in order of the function’s arguments.

Type

array_like, length=4, optional, default = [0, -np.inf, 1e-6, 1e-6]

absorption_max_bound

Maximum bounds for all the absorption Voigt profile parameters in order of the function’s arguments.

Type

array_like, length=4, optional, default = [0, stationary_line_core+0.15, 1, 1]

emission_max_bound

Maximum bounds for all the emission Voigt profile parameters in order of the function’s arguments.

Type

array_like, length=4, optional, default = [np.inf, np.inf, 1, 1]

absorption_x_scale

Characteristic scale for all the absorption Voigt profile parameters in order of the function’s arguments.

Type

array_like, length=4, optional, default = [1500, 0.2, 0.3, 0.5]

emission_x_scale

Characteristic scale for all the emission Voigt profile parameters in order of the function’s arguments.

Type

array_like, length=4, optional, default = [1500, 0.2, 0.3, 0.5]

neural_network

The MLPClassifier object (or similar) that will be used to classify the spectra. Defaults to a GridSearchCV with MLPClassifier(solver=’lbfgs’, hidden_layer_sizes=(40,), max_iter=1000) for best alpha selected from [1e-5, 2e-5, 3e-5, 4e-5, 5e-5, 6e-5, 7e-5, 8e-5, 9e-5].

Type

sklearn.neural_network.MLPClassifier, optional, default = see description

constant_wavelengths

The desired set of wavelengths that the spectral data should be rescaled to represent. It is assumed that these have constant spacing, but that may not be a requirement if you specify your own array. The default value is an array from the minimum to the maximum wavelength of original_wavelengths in constant steps of delta_lambda, overshooting the upper bound if the maximum wavelength has not been reached.

Type

array_like, length same as original_wavelengths, optional, default = see description

sigma

A list of different sigma that are used to weight particular wavelengths along the spectra when fitting. The fitting method will expect to be able to choose a sigma array from this list at a specific index. It’s default value is [generate_sigma(i, constant_wavelengths, stationary_line_core) for i in [1, 2]]. See utils.generate_sigma() for more information.

Type

list of array_like, length=(2, n_wavelengths), optional, default = [type1, type2]

prefilter_response

Each constant wavelength scaled spectrum will be corrected by dividing it by this array. If prefilter_response is not give, and prefilter_ref_main and prefilter_ref_wvscl are not given, prefilter_response will have a default value of None.

Type

array_like, length=n_wavelengths, optional, default = see note

output

If the program wants to output data, it will place it relative to the location specified by this parameter. Some methods will only save data to a file if this parameter is not None. Such cases will be documented where relevant.

Type

str, optional, default = None

quiescent_wavelength

The index within the fitted parameters of the absorption Voigt line core wavelength.

Type

int, default = 1

active_wavelength

The index within the fitted parameters of the emission Voigt line core wavelength.

Type

int, default = 5

Methods Summary

classify_spectra([time, row, column, …])

Classify the specified spectra

fit([time, row, column, spectrum, profile, …])

Fits the model to specified spectra

plot([fit, time, row, column, spectrum, …])

Plots the data and fitted parameters

plot_separate(*args, **kwargs)

Plot the fitted profiles separately

plot_subtraction(*args, **kwargs)

Plot the spectrum with the emission fit subtracted from it

Methods Documentation

classify_spectra(time=None, row=None, column=None, spectra=None, only_normalise=False)[source]

Classify the specified spectra

Will also normalise each spectrum such that its intensity will range from zero to one.

Parameters
  • time (int or iterable, optional, default=None) – The time index. The index can be either a single integer index or an iterable. E.g. a list, a NumPy array, a Python range, etc. can be used.

  • row (int or iterable, optional, default=None) – The row index. See comment for time parameter.

  • column (int or iterable, optional, default=None) – The column index. See comment for time parameter.

  • spectra (ndarray, optional, default=None) – The explicit spectra to classify. If only_normalise is False, this must be 1D.

  • only_normalise (bool, optional, default = False) – Whether the single spectrum given in spectra should not be interpolated and corrected.

Returns

classifications – Array of classifications with the same time, row and column indices as spectra.

Return type

ndarray

See also

train()

Train the neural network

test()

Test the accuracy of the neural network

get_spectra()

Get processed spectra from the objects array attribute

fit(time=None, row=None, column=None, spectrum=None, profile=None, sigma=None, classifications=None, background=None, n_pools=None)[source]

Fits the model to specified spectra

Fits the model to an array of spectra using multiprocessing if requested.

Parameters
  • time (int or iterable, optional, default=None) – The time index. The index can be either a single integer index or an iterable. E.g. a list, a NumPy array, a Python range, etc. can be used.

  • row (int or iterable, optional, default=None) – The row index. See comment for time parameter.

  • column (int or iterable, optional, default=None) – The column index. See comment for time parameter.

  • spectrum (ndarray, ndim=1, optional, default=None) – The explicit spectrum to fit the model to.

  • profile (str, optional, default = None) – The profile to fit. (Will infer profile from classifications if omitted.)

  • sigma (int or array_like, optional, default = None) – Explicit sigma index or profile. See _get_sigma for details.

  • classifications (int, optional, default = None) – Classifications to determine the fitted profile to use (if profile not explicitly given). Will use neural network to classify them if not.

  • background (float, optional, default = None) – If provided, this value will be subtracted from the explicit spectrum provided in spectrum. Will not be applied to spectra found from the indices, use the load_background method instead.

  • n_pools (int, optional, default = None) – The number of processing pools to calculate the fitting over. This allocates the fitting of different spectra to n_pools separate worker processes. When processing a large number of spectra this will make the fitting process take less time overall. It also distributes such that each worker process has the same ratio of classifications to process. This should balance out the workload between workers. If few spectra are being fitted, performance may decrease due to the overhead associated with splitting the evaluation over separate processes. If n_pools is not an integer greater than zero, it will fit the spectrum with a for loop.

Returns

result – Outcome of the fits returned as a list of FitResult objects

Return type

list of FitResult, length=n_spectra

plot(fit=None, time=None, row=None, column=None, spectrum=None, classification=None, background=None, sigma=None, stationary_line_core=None, output=False, **kwargs)[source]

Plots the data and fitted parameters

Parameters
  • fit (FitResult or list or array_like, optional, default = None) – The fitted parameters to plot with the data. Can extract the necessary plot metadata from the fit object. Otherwise, fit should be the parameters to be fitted to either a Voigt or double Voigt profile depending on the number of parameters fitted.

  • time (int or iterable, optional, default = None) – The time index. The index can be either a single integer index or an iterable. E.g. a list, a NumPy array, a Python range, etc. can be used.

  • row (int or iterable, optional, default = None) – The row index. See comment for time parameter.

  • column (int or iterable, optional, default = None) – The column index. See comment for time parameter.

  • spectrum (ndarray of length original_wavelengths, ndim=1, optional, default = None) – The explicit spectrum to plot along with a fit (if specified).

  • classification (int, optional, default = None) – Used to determine which sigma profile to use. See _get_sigma for more details.

  • background (float or array_like of length constant_wavelengths, optional, default = see note) – Background to added to the fitted profiles. If a spectrum is given, this will default to zero, otherwise the value loaded by load_background will be used.

  • sigma (int or array_like, optional, default = None) – Explicit sigma index or profile. See _get_sigma for details.

  • stationary_line_core (float, optional, default = self.stationary_line_core) – The stationary line core wavelength to mark on the plot.

  • output (bool or str, optional, default = False) – Whether to save the plot to a file. If true, a file of format plot_<time>_<row>_<column>.eps will be created in the current directory. If a string, that will be used as the filename. (Can change filetype like this.) If false, no file will be created.

  • **kwargs

    Parameters used by matplotlib and separate (see plot_separate) and subtraction (see plot_subtraction).

    • figsize passed to matplotlib.pyplot.figure

    • legend_position passed to matplotlib.pyplot.legend

    • dpi passed to matplotlib.pyplot.figure and matplotlib.pyplot.savefig

    • fontfamily passed to matplotlib.pyplot.rc(‘font’, family=`fontfamily)` if given

See also

plot_separate()

Plot the fit parameters separately

plot_subtraction()

Plot the spectrum with the emission fit subtracted from it

FitResult.plot()

Plotting method on the fit result

plot_separate(*args, **kwargs)[source]

Plot the fitted profiles separately

If multiple profiles exist, fit them separately. See plot for more details.

See also

plot()

General plotting method

plot_subtraction()

Plot the spectrum with the emission fit subtracted from it

FitResult.plot()

Plotting method on the fit result

plot_subtraction(*args, **kwargs)[source]

Plot the spectrum with the emission fit subtracted from it

If multiple profiles exist, subtract the fitted emission from the raw data. See plot for more details.

See also

plot()

General plotting method

plot_separate()

Plot the fit parameters separately

FitResult.plot()

Plotting method on the fit result