hyperspy.samfire module

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

Model instance – The complete model

optional_components

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

workers

int – A number of processes that will perform the fitting parallely

pool

samfire_pool instance – A proxy object that manages either multiprocessing or ipyparallel pool

strategies

strategy list – 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.

metadata

dictionary – A dictionary for important samfire parameters

active_strategy

strategy – The currently active strategy from the strategies list

update_every

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

plot_every

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

save_every

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

start()

start SAMFire

stop()

stop SAMFire

plot()

force plot of currently selected active strategy

refresh_database()

refresh current active strategy database. No previous structure is preserved

backup()

backs up the current version of the model

change_strategy()

changes strategy to a new one. Certain rules apply

append()

appends strategy to the strategies list

extend()

extends strategies list

remove()

removes strategy from strategies list

update()

updates the current model with values, received from a worker

log()

if _log exists, logs the arguments to the list.

generate_values()

creates a generator to calculate values to be sent to the workers

active_strategy

Returns the active strategy

append(strategy)

appends the given strategy to the end of the strategies list

Parameters:strategy (strategy instance) –
backup(filename=None, on_count=True)

Backs-up 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
count = 0
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”, appends the arguments there

optional_components = []
pixels_done

Returns the number of pixels that have been solved

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) plots 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.
plot_every = 0
pool = None
refresh_database()

Refreshes currently selected strategy without preserving any “ignored” pixels

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.
running_pixels = []
save_every = nan
start(**kwargs)

Starts SAMFire.

Parameters:**kwargs (key-word arguments) – Any key-word arguments to be passed to Model.fit() call
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.