def _setup(self, **kwargs):
"""Set up SAMFire - configure models, set up pool if necessary"""
self._figure = None
self.metadata.gt_dump = dill.dumps(self.metadata.goodness_test)
self._enable_optional_components()
if hasattr(self.model, '_suspend_auto_fine_structure_width'):
self.model._suspend_auto_fine_structure_width = True
if hasattr(self, '_log'):
self._log = []
if self._workers and self.pool is None:
if 'num_workers' not in kwargs:
kwargs['num_workers'] = self._workers
if self.pool is None:
self.pool = SamfirePool(**kwargs)
self._workers = self.pool.num_workers
self.pool.prepare_workers(self)
def _setup(self, **kwargs):
"""Set up SAMFire - configure models, set up pool if necessary"""
self._figure = None
self.metadata.gt_dump = dill.dumps(self.metadata.goodness_test)
self._enable_optional_components()
if hasattr(self.model, '_suspend_auto_fine_structure_width'):
self.model._suspend_auto_fine_structure_width = True
if hasattr(self, '_log'):
self._log = []
if self._workers and self.pool is None:
if 'num_workers' not in kwargs:
kwargs['num_workers'] = self._workers
if self.pool is None:
self.pool = SamfirePool(**kwargs)
self._workers = self.pool.num_workers
self.pool.prepare_workers(self)
class Samfire:
"""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.
Attributes
----------
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.
Methods
-------
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_ind = 0
_progressbar = None
pool = None
_figure = None
optional_components = []
running_pixels = []
plot_every = 0
save_every = np.nan
_workers = None
_args = None
count = 0
def __init__(self, model, workers=None, setup=True, **kwargs):
# constants:
if workers is None:
workers = max(1, cpu_count() - 1)
self.model = model
self.metadata = DictionaryTreeBrowser()
self._scale = 1.0
# -1 -> done pixel, use
# -2 -> done, ignore when diffusion
# 0 -> bad fit/no info
# >0 -> select when turn comes
self.metadata.add_node('marker')
self.metadata.add_node('goodness_test')
marker = np.empty(self.model.axes_manager.navigation_shape[::-1])
marker.fill(self._scale)
self.metadata.marker = marker
self.strategies = StrategyList(self)
self.strategies.append(ReducedChiSquaredStrategy())
self.strategies.append(HistogramStrategy())
self._active_strategy_ind = 0
self.update_every = max(10, workers * 2) # some sensible number....
from hyperspy.samfire_utils.fit_tests import red_chisq_test
self.metadata.goodness_test = red_chisq_test(tolerance=1.0)
self.metadata._gt_dump = None
from hyperspy.samfire_utils.samfire_kernel import single_kernel
self.single_kernel = single_kernel
self._workers = workers
if len(kwargs) or setup:
self._setup(**kwargs)
self.refresh_database()
@property
def active_strategy(self):
"""Returns the active strategy"""
return self.strategies[self._active_strategy_ind]
@active_strategy.setter
def active_strategy(self, value):
self.change_strategy(value)
def _setup(self, **kwargs):
"""Set up SAMFire - configure models, set up pool if necessary"""
from hyperspy.samfire_utils.samfire_pool import SamfirePool
self._figure = None
self.metadata._gt_dump = dill.dumps(self.metadata.goodness_test)
self._enable_optional_components()
if hasattr(self.model, '_suspend_auto_fine_structure_width'):
self.model._suspend_auto_fine_structure_width = True
if hasattr(self, '_log'):
self._log = []
if self._workers and self.pool is None:
if 'num_workers' not in kwargs:
kwargs['num_workers'] = self._workers
if self.pool is None:
self.pool = SamfirePool(**kwargs)
self._workers = self.pool.num_workers
self.pool.prepare_workers(self)
def start(self, **kwargs):
"""Starts SAMFire.
Parameters
----------
**kwargs : key-word arguments
Any key-word arguments to be passed to Model.fit() call
"""
self._setup()
if self._workers and self.pool is not None:
self.pool.update_parameters()
if 'min_function' in kwargs:
kwargs['min_function'] = dill.dumps(kwargs['min_function'])
if 'min_function_grad' in kwargs:
kwargs['min_function_grad'] = dill.dumps(
kwargs['min_function_grad'])
self._args = kwargs
num_of_strat = len(self.strategies)
total_size = self.model.axes_manager.navigation_size - self.pixels_done
self._progressbar = progressbar(total=total_size)
try:
while True:
self._run_active_strategy()
self.plot()
if self.pixels_done == self.model.axes_manager.navigation_size:
# all pixels are done, no need to go to the next strategy
break
if self._active_strategy_ind == num_of_strat - 1:
# last one just finished running
break
self.change_strategy(self._active_strategy_ind + 1)
except KeyboardInterrupt:
if self.pool is not None:
_logger.warning(
'Collecting already started pixels, please wait')
self.pool.collect_results()
def append(self, strategy):
"""appends the given strategy to the end of the strategies list
Parameters
----------
strategy : strategy instance
"""
self.strategies.append(strategy)
def extend(self, iterable):
"""extend the strategies list by the given iterable
Parameters
----------
iterable : an iterable of strategy instances
"""
self.strategies.extend(iterable)
def remove(self, thing):
"""removes given strategy from the strategies list
Parameters
----------
thing : int or strategy instance
Strategy that is in current strategies list or its index.
"""
self.strategies.remove(thing)
@property
def _active_strategy_ind(self):
return self.__active_strategy_ind
@_active_strategy_ind.setter
def _active_strategy_ind(self, value):
self.__active_strategy_ind = np.abs(int(value))
def _run_active_strategy(self):
if self.pool is not None:
self.count = 0
self.pool.run()
else:
self._run_active_strategy_one()
@property
def pixels_left(self):
"""Returns the number of pixels that are left to solve. This number can
increase as SAMFire learns more information about the data.
"""
return np.sum(self.metadata.marker > 0.)
@property
def pixels_done(self):
"""Returns the number of pixels that have been solved"""
return np.sum(self.metadata.marker <= -self._scale)
def _run_active_strategy_one(self):
self.count = 0
while self.pixels_left:
ind = self._next_pixels(1)[0]
vals = self.active_strategy.values(ind)
self.running_pixels.append(ind)
isgood = self.single_kernel(self.model,
ind,
vals,
self.optional_components,
self._args,
self.metadata.goodness_test)
self.running_pixels.remove(ind)
self.count += 1
if isgood:
self._progressbar.update(1)
self.active_strategy.update(ind, isgood)
self.plot(on_count=True)
self.backup(on_count=True)
def backup(self, 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
"""
if filename is None:
title = self.model.signal.metadata.General.title
filename = slugify('backup_' + title)
# maybe add saving marker + strategies as well?
if self.count % self.save_every == 0 or not on_count:
self.model.save(filename,
name='samfire_backup', overwrite=True)
self.model.signal.models.remove('samfire_backup')
def update(self, 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
"""
if results is not None and (isgood is None or isgood):
self._swap_dict_and_model(ind, results)
if isgood is None:
isgood = self.metadata.goodness_test.test(self.model, ind)
self.count += 1
if isgood and self._progressbar is not None:
self._progressbar.update(1)
self.active_strategy.update(ind, isgood)
if not isgood and results is not None:
self._swap_dict_and_model(ind, results)
def refresh_database(self):
"""Refreshes currently selected strategy without preserving any
"ignored" pixels
"""
# updates current active strategy database / prob.
# Assume when chisq is not None, it's relevant
# TODO: if no calculated pixels, request user input
calculated_pixels = np.logical_not(np.isnan(self.model.red_chisq.data))
# only include pixels that are good enough
calculated_pixels = self.metadata.goodness_test.map(
self.model,
calculated_pixels)
self.active_strategy.refresh(True, calculated_pixels)
def change_strategy(self, 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
"""
from numbers import Number
if not isinstance(new_strat, Number):
try:
new_strat = self.strategies.index(new_strat)
except ValueError:
raise ValueError(
"The passed object is not in current strategies list")
new_strat = np.abs(int(new_strat))
if new_strat == self._active_strategy_ind:
self.refresh_database()
# copy previous "done" pixels to the new one, delete old database
# TODO: make sure it's a number. Get index if object is passed?
if new_strat >= len(self.strategies):
raise ValueError('too big new strategy index')
current = self.active_strategy
new = self.strategies[new_strat]
if isinstance(current, LocalStrategy) and isinstance(
new, LocalStrategy):
# forget ignore/done levels, keep just calculated or not
new.refresh(True)
else:
if isinstance(current, LocalStrategy) and isinstance(
new, GlobalStrategy):
# if diffusion->segmenter, set previous -1 to -2 (ignored for
# the next diffusion)
self.metadata.marker[
self.metadata.marker == -
self._scale] -= self._scale
new.refresh(False)
current.clean()
if current.close_plot is not None:
current.close_plot()
self._active_strategy_ind = new_strat
def generate_values(self, 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
"""
if need_inds:
# get pixel index
for ind in self._next_pixels(need_inds):
# get starting parameters / array of possible values
value_dict = self.active_strategy.values(ind)
value_dict['fitting_kwargs'] = self._args
value_dict['signal.data'] = \
self.model.signal.data[ind + (...,)]
if self.model.signal._lazy:
value_dict['signal.data'] = value_dict[
'signal.data'].compute()
if self.model.signal.metadata.has_item(
'Signal.Noise_properties.variance'):
var = self.model.signal.metadata.Signal.Noise_properties.variance
if isinstance(var, BaseSignal):
dat = var.data[ind + (...,)]
value_dict['variance.data'] = dat.compute(
) if var._lazy else dat
if hasattr(self.model,
'low_loss') and self.model.low_loss is not None:
dat = self.model.low_loss.data[ind + (...,)]
value_dict['low_loss.data'] = dat.compute(
) if self.model.low_loss._lazy else dat
self.running_pixels.append(ind)
self.metadata.marker[ind] = 0.
yield ind, value_dict
def _next_pixels(self, number):
best = self.metadata.marker.max()
inds = []
if best > 0.0:
ind_list = np.where(self.metadata.marker == best)
while number and ind_list[0].size > 0:
i = np.random.randint(len(ind_list[0]))
ind = tuple([lst[i] for lst in ind_list])
if ind not in self.running_pixels:
inds.append(ind)
# removing the added indices
ind_list = [np.delete(lst, i, 0) for lst in ind_list]
number -= 1
return inds
def _swap_dict_and_model(self, m_ind, dict_, d_ind=None):
if d_ind is None:
d_ind = tuple([0 for _ in dict_['dof.data'].shape])
m = self.model
for k in dict_.keys():
if k.endswith('.data'):
item = k[:-5]
getattr(m, item).data[m_ind], dict_[k] = \
dict_[k].copy(), getattr(m, item).data[m_ind].copy()
for comp_name, comp in dict_['components'].items():
# only active components are sent
if self.model[comp_name].active_is_multidimensional:
self.model[comp_name]._active_array[m_ind] = True
self.model[comp_name].active = True
for param_model in self.model[comp_name].parameters:
param_dict = comp[param_model.name]
param_model.map[m_ind], param_dict[d_ind] = \
param_dict[d_ind].copy(), param_model.map[m_ind].copy()
for component in self.model:
# switch off all that did not appear in the dictionary
if component.name not in dict_['components'].keys():
if component.active_is_multidimensional:
component._active_array[m_ind] = False
def _enable_optional_components(self):
if len(self.optional_components) == 0:
return
for c in self.optional_components:
comp = self.model._get_component(c)
if not comp.active_is_multidimensional:
comp.active_is_multidimensional = True
if not np.all([isinstance(a, int) for a in
self.optional_components]):
new_list = []
for op in self.optional_components:
for ic, c in enumerate(self.model):
if c is self.model._get_component(op):
new_list.append(ic)
self.optional_components = new_list
def _request_user_input(self):
from hyperspy.signals import Image
from hyperspy.drawing.widgets import SquareWidget
mark = Image(self.metadata.marker,
axes=self.model.axes_manager._get_navigation_axes_dicts())
mark.metadata.General.title = 'SAMFire marker'
def update_when_triggered():
ind = self.model.axes_manager.indices[::-1]
isgood = self.metadata.goodness_test.test(self.model, ind)
self.active_strategy.update(ind, isgood, 0)
mark.events.data_changed.trigger(mark)
self.model.plot()
self.model.events.fitted.connect(update_when_triggered, [])
self.model._plot.signal_plot.events.closed.connect(
lambda: self.model.events.fitted.disconnect(update_when_triggered),
[])
mark.plot(navigator='slider')
w = SquareWidget(self.model.axes_manager)
w.color = 'yellow'
w.set_mpl_ax(mark._plot.signal_plot.ax)
w.connect_navigate()
def connect_other_navigation1(axes_manager):
with mark.axes_manager.events.indices_changed.suppress_callback(
connect_other_navigation2):
for ax1, ax2 in zip(mark.axes_manager.navigation_axes,
axes_manager.navigation_axes[2:]):
ax1.value = ax2.value
def connect_other_navigation2(axes_manager):
with self.model.axes_manager.events.indices_changed.suppress_callback(
connect_other_navigation1):
for ax1, ax2 in zip(self.model.axes_manager.navigation_axes[2:],
axes_manager.navigation_axes):
ax1.value = ax2.value
mark.axes_manager.events.indices_changed.connect(
connect_other_navigation2, {'obj': 'axes_manager'})
self.model.axes_manager.events.indices_changed.connect(
connect_other_navigation1, {'obj': 'axes_manager'})
self.model._plot.signal_plot.events.closed.connect(
lambda: mark._plot.close, [])
self.model._plot.signal_plot.events.closed.connect(
lambda: self.model.axes_manager.events.indices_changed.disconnect(
connect_other_navigation1), [])
def plot(self, 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.
"""
count_test = self.plot_every and (self.count % self.plot_every == 0)
if not on_count or count_test:
if self.strategies:
try:
self._figure = self.active_strategy.plot(self._figure)
except BaseException:
self._figure = None
self._figure = self.active_strategy.plot(self._figure)
def log(self, *args):
"""If has a list named "_log", appends the arguments there
"""
if hasattr(self, '_log') and isinstance(self._log, list):
self._log.append(args)
def __repr__(self):
ans = u"<SAMFire of the signal titled: '"
ans += self.model.signal.metadata.General.title
ans += u"'>"
return ans
def stop(self):
if hasattr(self, "pool") and self.pool is not None:
self.pool.stop()
class Samfire:
"""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.
Attributes
----------
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.
Methods
-------
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_ind = 0
_progressbar = None
pool = None
_figure = None
optional_components = []
running_pixels = []
plot_every = 0
save_every = np.nan
_workers = None
_args = None
count = 0
def __init__(self, model, workers=None, setup=True, **kwargs):
# constants:
if workers is None:
workers = max(1, cpu_count() - 1)
self.model = model
self.metadata = DictionaryTreeBrowser()
self._scale = 1.0
# -1 -> done pixel, use
# -2 -> done, ignore when diffusion
# 0 -> bad fit/no info
# >0 -> select when turn comes
self.metadata.add_node('marker')
self.metadata.add_node('goodness_test')
marker = np.empty(self.model.axes_manager.navigation_shape[::-1])
marker.fill(self._scale)
self.metadata.marker = marker
self.strategies = StrategyList(self)
self.strategies.append(ReducedChiSquaredStrategy())
self.strategies.append(HistogramStrategy())
self._active_strategy_ind = 0
self.update_every = max(10, workers * 2) # some sensible number....
from hyperspy.samfire_utils.fit_tests import red_chisq_test
self.metadata.goodness_test = red_chisq_test(tolerance=1.0)
self.metadata._gt_dump = None
from hyperspy.samfire_utils.samfire_kernel import single_kernel
self.single_kernel = single_kernel
self._workers = workers
if len(kwargs) or setup:
self._setup(**kwargs)
self.refresh_database()
@property
def active_strategy(self):
"""Returns the active strategy"""
return self.strategies[self._active_strategy_ind]
@active_strategy.setter
def active_strategy(self, value):
self.change_strategy(value)
def _setup(self, **kwargs):
"""Set up SAMFire - configure models, set up pool if necessary"""
from hyperspy.samfire_utils.samfire_pool import SamfirePool
self._figure = None
self.metadata._gt_dump = dill.dumps(self.metadata.goodness_test)
self._enable_optional_components()
if hasattr(self.model, '_suspend_auto_fine_structure_width'):
self.model._suspend_auto_fine_structure_width = True
if hasattr(self, '_log'):
self._log = []
if self._workers and self.pool is None:
if 'num_workers' not in kwargs:
kwargs['num_workers'] = self._workers
if self.pool is None:
self.pool = SamfirePool(**kwargs)
self._workers = self.pool.num_workers
self.pool.prepare_workers(self)
def start(self, **kwargs):
"""Starts SAMFire.
Parameters
----------
**kwargs : key-word arguments
Any key-word arguments to be passed to Model.fit() call
"""
self._setup()
if self._workers and self.pool is not None:
self.pool.update_parameters()
if 'min_function' in kwargs:
kwargs['min_function'] = dill.dumps(kwargs['min_function'])
if 'min_function_grad' in kwargs:
kwargs['min_function_grad'] = dill.dumps(
kwargs['min_function_grad'])
self._args = kwargs
num_of_strat = len(self.strategies)
total_size = self.model.axes_manager.navigation_size - self.pixels_done
self._progressbar = progressbar(total=total_size)
try:
while True:
self._run_active_strategy()
self.plot()
if self.pixels_done == self.model.axes_manager.navigation_size:
# all pixels are done, no need to go to the next strategy
break
if self._active_strategy_ind == num_of_strat - 1:
# last one just finished running
break
self.change_strategy(self._active_strategy_ind + 1)
except KeyboardInterrupt:
if self.pool is not None:
_logger.warning(
'Collecting already started pixels, please wait')
self.pool.collect_results()
def append(self, strategy):
"""appends the given strategy to the end of the strategies list
Parameters
----------
strategy : strategy instance
"""
self.strategies.append(strategy)
def extend(self, iterable):
"""extend the strategies list by the given iterable
Parameters
----------
iterable : an iterable of strategy instances
"""
self.strategies.extend(iterable)
def remove(self, thing):
"""removes given strategy from the strategies list
Parameters
----------
thing : int or strategy instance
Strategy that is in current strategies list or its index.
"""
self.strategies.remove(thing)
@property
def _active_strategy_ind(self):
return self.__active_strategy_ind
@_active_strategy_ind.setter
def _active_strategy_ind(self, value):
self.__active_strategy_ind = np.abs(int(value))
def _run_active_strategy(self):
if self.pool is not None:
self.count = 0
self.pool.run()
else:
self._run_active_strategy_one()
@property
def pixels_left(self):
"""Returns the number of pixels that are left to solve. This number can
increase as SAMFire learns more information about the data.
"""
return np.sum(self.metadata.marker > 0.)
@property
def pixels_done(self):
"""Returns the number of pixels that have been solved"""
return np.sum(self.metadata.marker <= -self._scale)
def _run_active_strategy_one(self):
self.count = 0
while self.pixels_left:
ind = self._next_pixels(1)[0]
vals = self.active_strategy.values(ind)
self.running_pixels.append(ind)
isgood = self.single_kernel(self.model,
ind,
vals,
self.optional_components,
self._args,
self.metadata.goodness_test)
self.running_pixels.remove(ind)
self.count += 1
if isgood:
self._progressbar.update(1)
self.active_strategy.update(ind, isgood)
self.plot(on_count=True)
self.backup(on_count=True)
def backup(self, 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
"""
if filename is None:
title = self.model.signal.metadata.General.title
filename = slugify('backup_' + title)
# maybe add saving marker + strategies as well?
if self.count % self.save_every == 0 or not on_count:
self.model.save(filename,
name='samfire_backup', overwrite=True)
self.model.signal.models.remove('samfire_backup')
def update(self, 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
"""
if results is not None and (isgood is None or isgood):
self._swap_dict_and_model(ind, results)
if isgood is None:
isgood = self.metadata.goodness_test.test(self.model, ind)
self.count += 1
if isgood and self._progressbar is not None:
self._progressbar.update(1)
self.active_strategy.update(ind, isgood)
if not isgood and results is not None:
self._swap_dict_and_model(ind, results)
def refresh_database(self):
"""Refreshes currently selected strategy without preserving any
"ignored" pixels
"""
# updates current active strategy database / prob.
# Assume when chisq is not None, it's relevant
# TODO: if no calculated pixels, request user input
calculated_pixels = np.logical_not(np.isnan(self.model.red_chisq.data))
# only include pixels that are good enough
calculated_pixels = self.metadata.goodness_test.map(
self.model,
calculated_pixels)
self.active_strategy.refresh(True, calculated_pixels)
def change_strategy(self, 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
"""
from numbers import Number
if not isinstance(new_strat, Number):
try:
new_strat = self.strategies.index(new_strat)
except ValueError:
raise ValueError(
"The passed object is not in current strategies list")
new_strat = np.abs(int(new_strat))
if new_strat == self._active_strategy_ind:
self.refresh_database()
# copy previous "done" pixels to the new one, delete old database
# TODO: make sure it's a number. Get index if object is passed?
if new_strat >= len(self.strategies):
raise ValueError('too big new strategy index')
current = self.active_strategy
new = self.strategies[new_strat]
if isinstance(current, LocalStrategy) and isinstance(
new, LocalStrategy):
# forget ignore/done levels, keep just calculated or not
new.refresh(True)
else:
if isinstance(current, LocalStrategy) and isinstance(
new, GlobalStrategy):
# if diffusion->segmenter, set previous -1 to -2 (ignored for
# the next diffusion)
self.metadata.marker[
self.metadata.marker == -
self._scale] -= self._scale
new.refresh(False)
current.clean()
if current.close_plot is not None:
current.close_plot()
self._active_strategy_ind = new_strat
def generate_values(self, 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
"""
if need_inds:
# get pixel index
for ind in self._next_pixels(need_inds):
# get starting parameters / array of possible values
value_dict = self.active_strategy.values(ind)
value_dict['fitting_kwargs'] = self._args
value_dict['signal.data'] = \
self.model.signal.data[ind + (...,)]
if self.model.signal._lazy:
value_dict['signal.data'] = value_dict[
'signal.data'].compute()
if self.model.signal.metadata.has_item(
'Signal.Noise_properties.variance'):
var = self.model.signal.metadata.Signal.Noise_properties.variance
if isinstance(var, BaseSignal):
dat = var.data[ind + (...,)]
value_dict['variance.data'] = dat.compute(
) if var._lazy else dat
if hasattr(self.model,
'low_loss') and self.model.low_loss is not None:
dat = self.model.low_loss.data[ind + (...,)]
value_dict['low_loss.data'] = dat.compute(
) if self.model.low_loss._lazy else dat
self.running_pixels.append(ind)
self.metadata.marker[ind] = 0.
yield ind, value_dict
def _next_pixels(self, number):
best = self.metadata.marker.max()
inds = []
if best > 0.0:
ind_list = np.where(self.metadata.marker == best)
while number and ind_list[0].size > 0:
i = np.random.randint(len(ind_list[0]))
ind = tuple([lst[i] for lst in ind_list])
if ind not in self.running_pixels:
inds.append(ind)
# removing the added indices
ind_list = [np.delete(lst, i, 0) for lst in ind_list]
number -= 1
return inds
def _swap_dict_and_model(self, m_ind, dict_, d_ind=None):
if d_ind is None:
d_ind = tuple([0 for _ in dict_['dof.data'].shape])
m = self.model
for k in dict_.keys():
if k.endswith('.data'):
item = k[:-5]
getattr(m, item).data[m_ind], dict_[k] = \
dict_[k].copy(), getattr(m, item).data[m_ind].copy()
for comp_name, comp in dict_['components'].items():
# only active components are sent
if self.model[comp_name].active_is_multidimensional:
self.model[comp_name]._active_array[m_ind] = True
self.model[comp_name].active = True
for param_model in self.model[comp_name].parameters:
param_dict = comp[param_model.name]
param_model.map[m_ind], param_dict[d_ind] = \
param_dict[d_ind].copy(), param_model.map[m_ind].copy()
for component in self.model:
# switch off all that did not appear in the dictionary
if component.name not in dict_['components'].keys():
if component.active_is_multidimensional:
component._active_array[m_ind] = False
def _enable_optional_components(self):
if len(self.optional_components) == 0:
return
for c in self.optional_components:
comp = self.model._get_component(c)
if not comp.active_is_multidimensional:
comp.active_is_multidimensional = True
if not np.all([isinstance(a, int) for a in
self.optional_components]):
new_list = []
for op in self.optional_components:
for ic, c in enumerate(self.model):
if c is self.model._get_component(op):
new_list.append(ic)
self.optional_components = new_list
def _request_user_input(self):
from hyperspy.signals import Image
from hyperspy.drawing.widgets import SquareWidget
mark = Image(self.metadata.marker,
axes=self.model.axes_manager._get_navigation_axes_dicts())
mark.metadata.General.title = 'SAMFire marker'
def update_when_triggered():
ind = self.model.axes_manager.indices[::-1]
isgood = self.metadata.goodness_test.test(self.model, ind)
self.active_strategy.update(ind, isgood, 0)
mark.events.data_changed.trigger(mark)
self.model.plot()
self.model.events.fitted.connect(update_when_triggered, [])
self.model._plot.signal_plot.events.closed.connect(
lambda: self.model.events.fitted.disconnect(update_when_triggered),
[])
mark.plot(navigator='slider')
w = SquareWidget(self.model.axes_manager)
w.color = 'yellow'
w.set_mpl_ax(mark._plot.signal_plot.ax)
w.connect_navigate()
def connect_other_navigation1(axes_manager):
with mark.axes_manager.events.indices_changed.suppress_callback(
connect_other_navigation2):
for ax1, ax2 in zip(mark.axes_manager.navigation_axes,
axes_manager.navigation_axes[2:]):
ax1.value = ax2.value
def connect_other_navigation2(axes_manager):
with self.model.axes_manager.events.indices_changed.suppress_callback(
connect_other_navigation1):
for ax1, ax2 in zip(self.model.axes_manager.navigation_axes[2:],
axes_manager.navigation_axes):
ax1.value = ax2.value
mark.axes_manager.events.indices_changed.connect(
connect_other_navigation2, {'obj': 'axes_manager'})
self.model.axes_manager.events.indices_changed.connect(
connect_other_navigation1, {'obj': 'axes_manager'})
self.model._plot.signal_plot.events.closed.connect(
lambda: mark._plot.close, [])
self.model._plot.signal_plot.events.closed.connect(
lambda: self.model.axes_manager.events.indices_changed.disconnect(
connect_other_navigation1), [])
def plot(self, 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.
"""
count_test = self.plot_every and (self.count % self.plot_every == 0)
if not on_count or count_test:
if self.strategies:
try:
self._figure = self.active_strategy.plot(self._figure)
except BaseException:
self._figure = None
self._figure = self.active_strategy.plot(self._figure)
def log(self, *args):
"""If has a list named "_log", appends the arguments there
"""
if hasattr(self, '_log') and isinstance(self._log, list):
self._log.append(args)
def __repr__(self):
ans = u"<SAMFire of the signal titled: '"
ans += self.model.signal.metadata.General.title
ans += u"'>"
return ans