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signal.py
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signal.py
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# -*- coding: utf-8 -*-
# Copyright 2007-2021 The HyperSpy developers
#
# This file is part of HyperSpy.
#
# HyperSpy is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# HyperSpy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with HyperSpy. If not, see <http://www.gnu.org/licenses/>.
from collections.abc import MutableMapping
import copy
from contextlib import contextmanager
from datetime import datetime
import inspect
from itertools import product
import logging
import numbers
from pathlib import Path
import warnings
import dask.array as da
from matplotlib import pyplot as plt
import numpy as np
from pint import UnitRegistry, UndefinedUnitError
from scipy import integrate
from scipy import signal as sp_signal
import traits.api as t
from hyperspy.axes import AxesManager
from hyperspy import io
from hyperspy.drawing import mpl_hie, mpl_hse, mpl_he
from hyperspy.learn.mva import MVA, LearningResults
import hyperspy.misc.utils
from hyperspy.misc.utils import DictionaryTreeBrowser
from hyperspy.drawing import signal as sigdraw
from hyperspy.defaults_parser import preferences
from hyperspy.misc.io.tools import ensure_directory
from hyperspy.misc.utils import iterable_not_string
from hyperspy.external.progressbar import progressbar
from hyperspy.exceptions import SignalDimensionError, DataDimensionError
from hyperspy.misc import rgb_tools
from hyperspy.misc.utils import underline, isiterable
from hyperspy.misc.hist_tools import histogram
from hyperspy.drawing.utils import animate_legend
from hyperspy.drawing.marker import markers_metadata_dict_to_markers
from hyperspy.misc.slicing import SpecialSlicers, FancySlicing
from hyperspy.misc.utils import slugify
from hyperspy.misc.utils import is_binned # remove in v2.0
from hyperspy.docstrings.signal import (
ONE_AXIS_PARAMETER, MANY_AXIS_PARAMETER, OUT_ARG, NAN_FUNC, OPTIMIZE_ARG,
RECHUNK_ARG, SHOW_PROGRESSBAR_ARG, PARALLEL_ARG, MAX_WORKERS_ARG,
CLUSTER_SIGNALS_ARG, HISTOGRAM_BIN_ARGS, HISTOGRAM_MAX_BIN_ARGS)
from hyperspy.docstrings.plot import (BASE_PLOT_DOCSTRING, PLOT1D_DOCSTRING,
BASE_PLOT_DOCSTRING_PARAMETERS,
PLOT2D_KWARGS_DOCSTRING)
from hyperspy.docstrings.utils import REBIN_ARGS
from hyperspy.events import Events, Event
from hyperspy.interactive import interactive
from hyperspy.misc.signal_tools import (are_signals_aligned,
broadcast_signals)
from hyperspy.misc.math_tools import outer_nd, hann_window_nth_order, check_random_state
from hyperspy.exceptions import VisibleDeprecationWarning
_logger = logging.getLogger(__name__)
class ModelManager(object):
"""Container for models
"""
class ModelStub(object):
def __init__(self, mm, name):
self._name = name
self._mm = mm
self.restore = lambda: mm.restore(self._name)
self.remove = lambda: mm.remove(self._name)
self.pop = lambda: mm.pop(self._name)
self.restore.__doc__ = "Returns the stored model"
self.remove.__doc__ = "Removes the stored model"
self.pop.__doc__ = \
"Returns the stored model and removes it from storage"
def __repr__(self):
return repr(self._mm._models[self._name])
def __init__(self, signal, dictionary=None):
self._signal = signal
self._models = DictionaryTreeBrowser()
self._add_dictionary(dictionary)
def _add_dictionary(self, dictionary=None):
if dictionary is not None:
for k, v in dictionary.items():
if k.startswith('_') or k in ['restore', 'remove']:
raise KeyError("Can't add dictionary with key '%s'" % k)
k = slugify(k, True)
self._models.set_item(k, v)
setattr(self, k, self.ModelStub(self, k))
def _set_nice_description(self, node, names):
ans = {'date': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
'dimensions': self._signal.axes_manager._get_dimension_str(),
}
node.add_dictionary(ans)
for n in names:
node.add_node('components.' + n)
def _save(self, name, dictionary):
_abc = 'abcdefghijklmnopqrstuvwxyz'
def get_letter(models):
howmany = len(models)
if not howmany:
return 'a'
order = int(np.log(howmany) / np.log(26)) + 1
letters = [_abc, ] * order
for comb in product(*letters):
guess = "".join(comb)
if guess not in models.keys():
return guess
if name is None:
name = get_letter(self._models)
else:
name = self._check_name(name)
if name in self._models:
self.remove(name)
self._models.add_node(name)
node = self._models.get_item(name)
names = [c['name'] for c in dictionary['components']]
self._set_nice_description(node, names)
node.set_item('_dict', dictionary)
setattr(self, name, self.ModelStub(self, name))
def store(self, model, name=None):
"""If the given model was created from this signal, stores it
Parameters
----------
model : :py:class:`~hyperspy.model.BaseModel` (or subclass)
The model to store in the signal
name : str or None
The name for the model to be stored with
See also
--------
remove
restore
pop
"""
if model.signal is self._signal:
self._save(name, model.as_dictionary())
else:
raise ValueError("The model is created from a different signal, "
"you should store it there")
def _check_name(self, name, existing=False):
if not isinstance(name, str):
raise KeyError('Name has to be a string')
if name.startswith('_'):
raise KeyError('Name cannot start with "_" symbol')
if '.' in name:
raise KeyError('Name cannot contain dots (".")')
name = slugify(name, True)
if existing:
if name not in self._models:
raise KeyError(
"Model named '%s' is not currently stored" %
name)
return name
def remove(self, name):
"""Removes the given model
Parameters
----------
name : str
The name of the model to remove
See also
--------
restore
store
pop
"""
name = self._check_name(name, True)
delattr(self, name)
self._models.__delattr__(name)
def pop(self, name):
"""Returns the restored model and removes it from storage
Parameters
----------
name : str
The name of the model to restore and remove
See also
--------
restore
store
remove
"""
name = self._check_name(name, True)
model = self.restore(name)
self.remove(name)
return model
def restore(self, name):
"""Returns the restored model
Parameters
----------
name : str
The name of the model to restore
See also
--------
remove
store
pop
"""
name = self._check_name(name, True)
d = self._models.get_item(name + '._dict').as_dictionary()
return self._signal.create_model(dictionary=copy.deepcopy(d))
def __repr__(self):
return repr(self._models)
def __len__(self):
return len(self._models)
def __getitem__(self, name):
name = self._check_name(name, True)
return getattr(self, name)
class MVATools(object):
# TODO: All of the plotting methods here should move to drawing
def _plot_factors_or_pchars(self, factors, comp_ids=None,
calibrate=True, avg_char=False,
same_window=True, comp_label='PC',
img_data=None,
plot_shifts=True, plot_char=4,
cmap=plt.cm.gray, quiver_color='white',
vector_scale=1,
per_row=3, ax=None):
"""Plot components from PCA or ICA, or peak characteristics.
Parameters
----------
comp_ids : None, int, or list of ints
If None, returns maps of all components.
If int, returns maps of components with ids from 0 to given
int.
if list of ints, returns maps of components with ids in
given list.
calibrate : bool
If True, plots are calibrated according to the data in the
axes manager.
same_window : bool
If True, plots each factor to the same window. They are not scaled.
Default True.
comp_label : str
Title of the plot
cmap : a matplotlib colormap
The colormap used for factor images or any peak characteristic
scatter map overlay. Default is the matplotlib gray colormap
(``plt.cm.gray``).
Other Parameters
----------------
img_data : 2D numpy array,
The array to overlay peak characteristics onto. If None,
defaults to the average image of your stack.
plot_shifts : bool, default is True
If true, plots a quiver (arrow) plot showing the shifts for
each
peak present in the component being plotted.
plot_char : None or int
If int, the id of the characteristic to plot as the colored
scatter plot.
Possible components are:
* 4: peak height
* 5: peak orientation
* 6: peak eccentricity
quiver_color : any color recognized by matplotlib
Determines the color of vectors drawn for
plotting peak shifts.
vector_scale : integer or None
Scales the quiver plot arrows. The vector is defined as one data
unit along the X axis. If shifts are small, set vector_scale so
that when they are multiplied by vector_scale, they are on the
scale of the image plot. If None, uses matplotlib's autoscaling.
Returns
-------
matplotlib figure or list of figure if same_window=False
"""
if same_window is None:
same_window = True
if comp_ids is None:
comp_ids = range(factors.shape[1])
elif not hasattr(comp_ids, '__iter__'):
comp_ids = range(comp_ids)
n = len(comp_ids)
if same_window:
rows = int(np.ceil(n / float(per_row)))
fig_list = []
if n < per_row:
per_row = n
if same_window and self.axes_manager.signal_dimension == 2:
f = plt.figure(figsize=(4 * per_row, 3 * rows))
else:
f = plt.figure()
for i in range(len(comp_ids)):
if self.axes_manager.signal_dimension == 1:
if same_window:
ax = plt.gca()
else:
if i > 0:
f = plt.figure()
plt.title('%s' % comp_label)
ax = f.add_subplot(111)
ax = sigdraw._plot_1D_component(
factors=factors,
idx=comp_ids[i],
axes_manager=self.axes_manager,
ax=ax,
calibrate=calibrate,
comp_label=comp_label,
same_window=same_window)
if same_window:
plt.legend(ncol=factors.shape[1] // 2, loc='best')
elif self.axes_manager.signal_dimension == 2:
if same_window:
ax = f.add_subplot(rows, per_row, i + 1)
else:
if i > 0:
f = plt.figure()
plt.title('%s' % comp_label)
ax = f.add_subplot(111)
sigdraw._plot_2D_component(factors=factors,
idx=comp_ids[i],
axes_manager=self.axes_manager,
calibrate=calibrate, ax=ax,
cmap=cmap, comp_label=comp_label)
if not same_window:
fig_list.append(f)
if same_window: # Main title for same window
title = '%s' % comp_label
if self.axes_manager.signal_dimension == 1:
plt.title(title)
else:
plt.suptitle(title)
try:
plt.tight_layout()
except BaseException:
pass
if not same_window:
return fig_list
else:
return f
def _plot_loadings(self, loadings, comp_ids, calibrate=True,
same_window=True, comp_label=None,
with_factors=False, factors=None,
cmap=plt.cm.gray, no_nans=False, per_row=3,
axes_decor='all'):
if same_window is None:
same_window = True
if comp_ids is None:
comp_ids = range(loadings.shape[0])
elif not hasattr(comp_ids, '__iter__'):
comp_ids = range(comp_ids)
n = len(comp_ids)
if same_window:
rows = int(np.ceil(n / float(per_row)))
fig_list = []
if n < per_row:
per_row = n
if same_window and self.axes_manager.signal_dimension == 2:
f = plt.figure(figsize=(4 * per_row, 3 * rows))
else:
f = plt.figure()
for i in range(n):
if self.axes_manager.navigation_dimension == 1:
if same_window:
ax = plt.gca()
else:
if i > 0:
f = plt.figure()
plt.title('%s' % comp_label)
ax = f.add_subplot(111)
elif self.axes_manager.navigation_dimension == 2:
if same_window:
ax = f.add_subplot(rows, per_row, i + 1)
else:
if i > 0:
f = plt.figure()
plt.title('%s' % comp_label)
ax = f.add_subplot(111)
sigdraw._plot_loading(
loadings, idx=comp_ids[i], axes_manager=self.axes_manager,
no_nans=no_nans, calibrate=calibrate, cmap=cmap,
comp_label=comp_label, ax=ax, same_window=same_window,
axes_decor=axes_decor)
if not same_window:
fig_list.append(f)
if same_window: # Main title for same window
title = '%s' % comp_label
if self.axes_manager.navigation_dimension == 1:
plt.title(title)
else:
plt.suptitle(title)
try:
plt.tight_layout()
except BaseException:
pass
if not same_window:
if with_factors:
return fig_list, self._plot_factors_or_pchars(
factors, comp_ids=comp_ids, calibrate=calibrate,
same_window=same_window, comp_label=comp_label,
per_row=per_row)
else:
return fig_list
else:
if self.axes_manager.navigation_dimension == 1:
plt.legend(ncol=loadings.shape[0] // 2, loc='best')
animate_legend(f)
if with_factors:
return f, self._plot_factors_or_pchars(factors,
comp_ids=comp_ids,
calibrate=calibrate,
same_window=same_window,
comp_label=comp_label,
per_row=per_row)
else:
return f
def _export_factors(self,
factors,
folder=None,
comp_ids=None,
multiple_files=True,
save_figures=False,
save_figures_format='png',
factor_prefix=None,
factor_format=None,
comp_label=None,
cmap=plt.cm.gray,
plot_shifts=True,
plot_char=4,
img_data=None,
same_window=False,
calibrate=True,
quiver_color='white',
vector_scale=1,
no_nans=True, per_row=3):
from hyperspy._signals.signal2d import Signal2D
from hyperspy._signals.signal1d import Signal1D
if multiple_files is None:
multiple_files = True
if factor_format is None:
factor_format = 'hspy'
# Select the desired factors
if comp_ids is None:
comp_ids = range(factors.shape[1])
elif not hasattr(comp_ids, '__iter__'):
comp_ids = range(comp_ids)
mask = np.zeros(factors.shape[1], dtype=np.bool)
for idx in comp_ids:
mask[idx] = 1
factors = factors[:, mask]
if save_figures is True:
plt.ioff()
fac_plots = self._plot_factors_or_pchars(factors,
comp_ids=comp_ids,
same_window=same_window,
comp_label=comp_label,
img_data=img_data,
plot_shifts=plot_shifts,
plot_char=plot_char,
cmap=cmap,
per_row=per_row,
quiver_color=quiver_color,
vector_scale=vector_scale)
for idx in range(len(comp_ids)):
filename = '%s_%02i.%s' % (factor_prefix, comp_ids[idx],
save_figures_format)
if folder is not None:
filename = Path(folder, filename)
ensure_directory(filename)
_args = {'dpi': 600,
'format': save_figures_format}
fac_plots[idx].savefig(filename, **_args)
plt.ion()
elif multiple_files is False:
if self.axes_manager.signal_dimension == 2:
# factor images
axes_dicts = []
axes = self.axes_manager.signal_axes[::-1]
shape = (axes[1].size, axes[0].size)
factor_data = np.rollaxis(
factors.reshape((shape[0], shape[1], -1)), 2)
axes_dicts.append(axes[0].get_axis_dictionary())
axes_dicts.append(axes[1].get_axis_dictionary())
axes_dicts.append({'name': 'factor_index',
'scale': 1.,
'offset': 0.,
'size': int(factors.shape[1]),
'units': 'factor',
'index_in_array': 0, })
s = Signal2D(factor_data,
axes=axes_dicts,
metadata={
'General': {'title': '%s from %s' % (
factor_prefix,
self.metadata.General.title),
}})
elif self.axes_manager.signal_dimension == 1:
axes = [self.axes_manager.signal_axes[0].get_axis_dictionary(),
{'name': 'factor_index',
'scale': 1.,
'offset': 0.,
'size': int(factors.shape[1]),
'units': 'factor',
'index_in_array': 0,
}]
axes[0]['index_in_array'] = 1
s = Signal1D(
factors.T, axes=axes, metadata={
"General": {
'title': '%s from %s' %
(factor_prefix, self.metadata.General.title), }})
filename = '%ss.%s' % (factor_prefix, factor_format)
if folder is not None:
filename = Path(folder, filename)
s.save(filename)
else: # Separate files
if self.axes_manager.signal_dimension == 1:
axis_dict = self.axes_manager.signal_axes[0].\
get_axis_dictionary()
axis_dict['index_in_array'] = 0
for dim, index in zip(comp_ids, range(len(comp_ids))):
s = Signal1D(factors[:, index],
axes=[axis_dict, ],
metadata={
"General": {'title': '%s from %s' % (
factor_prefix,
self.metadata.General.title),
}})
filename = '%s-%i.%s' % (factor_prefix,
dim,
factor_format)
if folder is not None:
filename = Path(folder, filename)
s.save(filename)
if self.axes_manager.signal_dimension == 2:
axes = self.axes_manager.signal_axes
axes_dicts = [axes[0].get_axis_dictionary(),
axes[1].get_axis_dictionary()]
axes_dicts[0]['index_in_array'] = 0
axes_dicts[1]['index_in_array'] = 1
factor_data = factors.reshape(
self.axes_manager._signal_shape_in_array + [-1, ])
for dim, index in zip(comp_ids, range(len(comp_ids))):
im = Signal2D(factor_data[..., index],
axes=axes_dicts,
metadata={
"General": {'title': '%s from %s' % (
factor_prefix,
self.metadata.General.title),
}})
filename = '%s-%i.%s' % (factor_prefix,
dim,
factor_format)
if folder is not None:
filename = Path(folder, filename)
im.save(filename)
def _export_loadings(self,
loadings,
folder=None,
comp_ids=None,
multiple_files=True,
loading_prefix=None,
loading_format="hspy",
save_figures_format='png',
comp_label=None,
cmap=plt.cm.gray,
save_figures=False,
same_window=False,
calibrate=True,
no_nans=True,
per_row=3):
from hyperspy._signals.signal2d import Signal2D
from hyperspy._signals.signal1d import Signal1D
if multiple_files is None:
multiple_files = True
if loading_format is None:
loading_format = 'hspy'
if comp_ids is None:
comp_ids = range(loadings.shape[0])
elif not hasattr(comp_ids, '__iter__'):
comp_ids = range(comp_ids)
mask = np.zeros(loadings.shape[0], dtype=np.bool)
for idx in comp_ids:
mask[idx] = 1
loadings = loadings[mask]
if save_figures is True:
plt.ioff()
sc_plots = self._plot_loadings(loadings, comp_ids=comp_ids,
calibrate=calibrate,
same_window=same_window,
comp_label=comp_label,
cmap=cmap, no_nans=no_nans,
per_row=per_row)
for idx in range(len(comp_ids)):
filename = '%s_%02i.%s' % (loading_prefix, comp_ids[idx],
save_figures_format)
if folder is not None:
filename = Path(folder, filename)
ensure_directory(filename)
_args = {'dpi': 600,
'format': save_figures_format}
sc_plots[idx].savefig(filename, **_args)
plt.ion()
elif multiple_files is False:
if self.axes_manager.navigation_dimension == 2:
axes_dicts = []
axes = self.axes_manager.navigation_axes[::-1]
shape = (axes[1].size, axes[0].size)
loading_data = loadings.reshape((-1, shape[0], shape[1]))
axes_dicts.append(axes[0].get_axis_dictionary())
axes_dicts[0]['index_in_array'] = 1
axes_dicts.append(axes[1].get_axis_dictionary())
axes_dicts[1]['index_in_array'] = 2
axes_dicts.append({'name': 'loading_index',
'scale': 1.,
'offset': 0.,
'size': int(loadings.shape[0]),
'units': 'factor',
'index_in_array': 0, })
s = Signal2D(loading_data,
axes=axes_dicts,
metadata={
"General": {'title': '%s from %s' % (
loading_prefix,
self.metadata.General.title),
}})
elif self.axes_manager.navigation_dimension == 1:
cal_axis = self.axes_manager.navigation_axes[0].\
get_axis_dictionary()
cal_axis['index_in_array'] = 1
axes = [{'name': 'loading_index',
'scale': 1.,
'offset': 0.,
'size': int(loadings.shape[0]),
'units': 'comp_id',
'index_in_array': 0, },
cal_axis]
s = Signal2D(loadings,
axes=axes,
metadata={
"General": {'title': '%s from %s' % (
loading_prefix,
self.metadata.General.title),
}})
filename = '%ss.%s' % (loading_prefix, loading_format)
if folder is not None:
filename = Path(folder, filename)
s.save(filename)
else: # Separate files
if self.axes_manager.navigation_dimension == 1:
axis_dict = self.axes_manager.navigation_axes[0].\
get_axis_dictionary()
axis_dict['index_in_array'] = 0
for dim, index in zip(comp_ids, range(len(comp_ids))):
s = Signal1D(loadings[index],
axes=[axis_dict, ])
filename = '%s-%i.%s' % (loading_prefix,
dim,
loading_format)
if folder is not None:
filename = Path(folder, filename)
s.save(filename)
elif self.axes_manager.navigation_dimension == 2:
axes_dicts = []
axes = self.axes_manager.navigation_axes[::-1]
shape = (axes[0].size, axes[1].size)
loading_data = loadings.reshape((-1, shape[0], shape[1]))
axes_dicts.append(axes[0].get_axis_dictionary())
axes_dicts[0]['index_in_array'] = 0
axes_dicts.append(axes[1].get_axis_dictionary())
axes_dicts[1]['index_in_array'] = 1
for dim, index in zip(comp_ids, range(len(comp_ids))):
s = Signal2D(loading_data[index, ...],
axes=axes_dicts,
metadata={
"General": {'title': '%s from %s' % (
loading_prefix,
self.metadata.General.title),
}})
filename = '%s-%i.%s' % (loading_prefix,
dim,
loading_format)
if folder is not None:
filename = Path(folder, filename)
s.save(filename)
def plot_decomposition_factors(self,
comp_ids=None,
calibrate=True,
same_window=True,
title=None,
cmap=plt.cm.gray,
per_row=3,
**kwargs,
):
"""Plot factors from a decomposition. In case of 1D signal axis, each
factors line can be toggled on and off by clicking on their
corresponding line in the legend.
Parameters
----------
comp_ids : None, int, or list (of ints)
If `comp_ids` is ``None``, maps of all components will be
returned if the `output_dimension` was defined when executing
:py:meth:`~hyperspy.learn.mva.MVA.decomposition`. Otherwise it
raises a :py:exc:`ValueError`.
If `comp_ids` is an int, maps of components with ids from 0 to
the given value will be returned. If `comp_ids` is a list of
ints, maps of components with ids contained in the list will be
returned.
calibrate : bool
If ``True``, calibrates plots where calibration is available
from the axes_manager. If ``False``, plots are in pixels/channels.
same_window : bool
If ``True``, plots each factor to the same window. They are
not scaled. Default is ``True``.
title : str
Title of the plot.
cmap : :py:class:`~matplotlib.colors.Colormap`
The colormap used for the factor images, or for peak
characteristics. Default is the matplotlib gray colormap
(``plt.cm.gray``).
per_row : int
The number of plots in each row, when the `same_window`
parameter is ``True``.
See also
--------
plot_decomposition_loadings, plot_decomposition_results
"""
if self.axes_manager.signal_dimension > 2:
raise NotImplementedError("This method cannot plot factors of "
"signals of dimension higher than 2."
"You can use "
"`plot_decomposition_results` instead.")
if self.learning_results.factors is None:
raise RuntimeError("No learning results found. A 'decomposition' "
"needs to be performed first.")
if same_window is None:
same_window = True
if self.learning_results.factors is None:
raise RuntimeError("Run a decomposition first.")
factors = self.learning_results.factors
if comp_ids is None:
if self.learning_results.output_dimension:
comp_ids = self.learning_results.output_dimension
else:
raise ValueError(
"Please provide the number of components to plot via the "
"`comp_ids` argument")
comp_label = kwargs.get("comp_label", None)
title = _change_API_comp_label(title, comp_label)
if title is None:
title = self._get_plot_title('Decomposition factors of',
same_window=same_window)
return self._plot_factors_or_pchars(factors,
comp_ids=comp_ids,
calibrate=calibrate,
same_window=same_window,
comp_label=title,
cmap=cmap,
per_row=per_row)
def plot_bss_factors(
self,
comp_ids=None,
calibrate=True,
same_window=True,
title=None,
cmap=plt.cm.gray,
per_row=3,
**kwargs,
):
"""Plot factors from blind source separation results. In case of 1D
signal axis, each factors line can be toggled on and off by clicking
on their corresponding line in the legend.
Parameters
----------
comp_ids : None, int, or list (of ints)
If `comp_ids` is ``None``, maps of all components will be
returned. If it is an int, maps of components with ids from 0 to
the given value will be returned. If `comp_ids` is a list of
ints, maps of components with ids contained in the list will be
returned.
calibrate : bool
If ``True``, calibrates plots where calibration is available
from the axes_manager. If ``False``, plots are in pixels/channels.
same_window : bool
if ``True``, plots each factor to the same window. They are
not scaled. Default is ``True``.
title : str
Title of the plot.
cmap : :py:class:`~matplotlib.colors.Colormap`
The colormap used for the factor images, or for peak
characteristics. Default is the matplotlib gray colormap
(``plt.cm.gray``).
per_row : int
The number of plots in each row, when the `same_window`
parameter is ``True``.
See also
--------
plot_bss_loadings, plot_bss_results
"""
if self.axes_manager.signal_dimension > 2:
raise NotImplementedError("This method cannot plot factors of "
"signals of dimension higher than 2."
"You can use "
"`plot_decomposition_results` instead.")
if self.learning_results.bss_factors is None:
raise RuntimeError("No learning results found. A "
"'blind_source_separation' needs to be "
"performed first.")
if same_window is None:
same_window = True
factors = self.learning_results.bss_factors
comp_label = kwargs.get("comp_label", None)
title = _change_API_comp_label(title, comp_label)
if title is None:
title = self._get_plot_title('BSS factors of',
same_window=same_window)
return self._plot_factors_or_pchars(factors,
comp_ids=comp_ids,
calibrate=calibrate,
same_window=same_window,
comp_label=title,
per_row=per_row)
def plot_decomposition_loadings(self,
comp_ids=None,
calibrate=True,
same_window=True,
title=None,
with_factors=False,
cmap=plt.cm.gray,
no_nans=False,
per_row=3,
axes_decor='all',
**kwargs,
):
"""Plot loadings from a decomposition. In case of 1D navigation axis,
each loading line can be toggled on and off by clicking on the legended
line.
Parameters
----------
comp_ids : None, int, or list (of ints)
If `comp_ids` is ``None``, maps of all components will be
returned if the `output_dimension` was defined when executing
:py:meth:`~hyperspy.learn.mva.MVA.decomposition`.
Otherwise it raises a :py:exc:`ValueError`.
If `comp_ids` is an int, maps of components with ids from 0 to
the given value will be returned. If `comp_ids` is a list of
ints, maps of components with ids contained in the list will be
returned.
calibrate : bool
if ``True``, calibrates plots where calibration is available
from the axes_manager. If ``False``, plots are in pixels/channels.
same_window : bool
if ``True``, plots each factor to the same window. They are
not scaled. Default is ``True``.
title : str
Title of the plot.
with_factors : bool
If ``True``, also returns figure(s) with the factors for the
given comp_ids.
cmap : :py:class:`~matplotlib.colors.Colormap`
The colormap used for the loadings images, or for peak
characteristics. Default is the matplotlib gray colormap
(``plt.cm.gray``).
no_nans : bool
If ``True``, removes ``NaN``'s from the loading plots.
per_row : int
The number of plots in each row, when the `same_window`
parameter is ``True``.
axes_decor : str or None, optional
One of: ``'all'``, ``'ticks'``, ``'off'``, or ``None``
Controls how the axes are displayed on each image; default is
``'all'``
If ``'all'``, both ticks and axis labels will be shown.
If ``'ticks'``, no axis labels will be shown, but ticks/labels will.
If ``'off'``, all decorations and frame will be disabled.
If ``None``, no axis decorations will be shown, but ticks/frame
will.
See also
--------
plot_decomposition_factors, plot_decomposition_results
"""
if self.axes_manager.navigation_dimension > 2:
raise NotImplementedError("This method cannot plot loadings of "
"dimension higher than 2."
"You can use "
"`plot_decomposition_results` instead.")
if self.learning_results.loadings is None:
raise RuntimeError("No learning results found. A 'decomposition' "
"needs to be performed first.")
if same_window is None:
same_window = True
if self.learning_results.loadings is None:
raise RuntimeError("Run a decomposition first.")
loadings = self.learning_results.loadings.T
if with_factors:
factors = self.learning_results.factors
else:
factors = None
if comp_ids is None:
if self.learning_results.output_dimension:
comp_ids = self.learning_results.output_dimension
else:
raise ValueError(
"Please provide the number of components to plot via the "
"`comp_ids` argument")
comp_label = kwargs.get("comp_label", None)