hyperspy.samfire module

class hyperspy.samfire.Samfire(model, workers=None, setup=True, random_state=None, **kwargs)

Bases: object

Smart Adaptive Multidimensional Fitting (SAMFire) object

SAMFire is a more robust way of fitting multidimensional datasets. By extracting starting values for each pixel from already fitted pixels, SAMFire stops the fitting algorithm from getting lost in the parameter space by always starting close to the optimal solution.

SAMFire only picks starting parameters and the order the pixels (in the navigation space) are fitted, and does not provide any new minimisation algorithms.

model

The complete model

Type:

Model instance

optional_components

A list of components that can be switched off at some pixels if it returns a better Akaike’s Information Criterion with correction (AICc)

Type:

list

workers

A number of processes that will perform the fitting parallely

Type:

int

pool

A proxy object that manages either multiprocessing or ipyparallel pool

Type:

samfire_pool instance

strategies

A list of strategies that will be used to select pixel fitting order and calculate required starting parameters. Strategies come in two “flavours” - local and global. Local strategies spread the starting values to the nearest pixels and forces certain pixel fitting order. Global strategies look for clusters in parameter values, and suggests most frequent values. Global strategy do not depend on pixel fitting order, hence it is randomised.

Type:

strategy list

metadata

A dictionary for important samfire parameters

Type:

dictionary

active_strategy

The currently active strategy from the strategies list

Type:

strategy

update_every

If segmenter strategy is running, updates the historams every time update_every good fits are found.

Type:

int

plot_every

When running, samfire plots results every time plot_every good fits are found.

Type:

int

save_every

When running, samfire saves results every time save_every good fits are found.

Type:

int

random_state

Random seed used to select the next pixels.

Type:

None or int or RandomState instance, default None

_setup(**kwargs)

Set up SAMFire - configure models, set up pool if necessary

append(strategy)

Append the given strategy to the end of the strategies list

Parameters:

strategy (strategy instance) –

backup(filename=None, on_count=True)

Backup the samfire results in a file.

Parameters:
  • filename ({str, None}) – the filename. If None, a default value of backup_ + signal_title is used.

  • on_count (bool) – if True (default), only saves on the required count of steps

change_strategy(new_strat)

Changes current strategy to a new one. Certain rules apply: diffusion -> diffusion : resets all “ignored” pixels diffusion -> segmenter : saves already calculated pixels to be ignored when(if) subsequently diffusion strategy is run

Parameters:

new_strat ({int | strategy}) – index of the new strategy from the strategies list or the strategy object itself

extend(iterable)

Extend the strategies list by the given iterable

Parameters:

iterable (an iterable of strategy instances) –

generate_values(need_inds)

Returns an iterator that yields the index of the pixel and the value dictionary to be sent to the workers.

Parameters:

need_inds (int) – the number of pixels to be returned in the generator

log(*args)

If has a list named “_log” as attribute, appends the arguments there

property pixels_done

Returns the number of pixels that have been solved

property pixels_left

Returns the number of pixels that are left to solve. This number can increase as SAMFire learns more information about the data.

plot(on_count=False)

If possible, plot current strategy plot. Local strategies plot grayscale navigation signal with brightness representing order of the pixel selection. Global strategies plot a collection of histograms, one per parameter.

Parameters:

on_count (bool) – if True, only tries to plot every speficied count, otherwise (default) always plots if possible.

refresh_database()

Refresh currently selected strategy without preserving any “ignored” pixels; no previous structure is preserved.

remove(thing)

removes given strategy from the strategies list

Parameters:

thing (int or strategy instance) – Strategy that is in current strategies list or its index.

start(**kwargs)

Start SAMFire.

Parameters:

**kwargs (dict) – Any keyword arguments to be passed to fit()

stop()

Stop SAMFire.

update(ind, results=None, isgood=None)

Updates the current model with the results, received from the workers. Results are only stored if the results are good enough

Parameters:
  • ind (tuple) – contains the index of the pixel of the results

  • results ({dict, None}) – dictionary of the results. If None, means we are updating in-place (e.g. refreshing the marker or strategies)

  • isgood ({bool, None}) – if it is known if the results are good according to the goodness-of-fit test. If None, the pixel is tested

class hyperspy.samfire.StrategyList(samf)

Bases: list

append(thing)

Append object to the end of the list.

extend(iterable)

Extend list by appending elements from the iterable.

remove(thing)

Remove first occurrence of value.

Raises ValueError if the value is not present.