def load_with_reader(filename, reader, record_by = None, signal = None,
**kwds):
from hyperspy.signals.image import Image
from hyperspy.signals.spectrum import Spectrum
from hyperspy.signals.eels import EELSSpectrum
messages.information(reader.description)
file_data_list = reader.file_reader(filename,
record_by=record_by,
**kwds)
objects = []
for file_data_dict in file_data_list:
if record_by is not None:
file_data_dict['mapped_parameters']['record_by'] = record_by
# The record_by can still be None if it was not defined by the reader
if file_data_dict['mapped_parameters']['record_by'] is None:
print "No data type provided. Defaulting to image."
file_data_dict['mapped_parameters']['record_by']= 'image'
if signal is not None:
file_data_dict['mapped_parameters']['signal'] = signal
if file_data_dict['mapped_parameters']['record_by'] == 'image':
s = Image(file_data_dict)
else:
if file_data_dict['mapped_parameters']['signal'] == 'EELS':
s = EELSSpectrum(file_data_dict)
else:
s = Spectrum(file_data_dict)
if defaults.plot_on_load is True:
s.plot()
objects.append(s)
if len(objects) == 1:
objects = objects[0]
return objects
def to_image(self):
from hyperspy.signals.image import Image
dic = self._get_signal_dict()
dic['mapped_parameters']['record_by'] = 'image'
dic['data'] = np.rollaxis(dic['data'], -1, 0)
dic['axes'] = utils_varia.rollelem(dic['axes'],-1,0)
i = 0
for axis in dic['axes']:
axis['index_in_array'] = i
i += 1
im = Image(dic)
if hasattr(self, 'learning_results'):
im.learning_results = copy.deepcopy(self.learning_results)
im.learning_results._transpose_results()
im.learning_results.original_shape = self.data.shape
return im
def crop_cells(self, idx=0):
print "cropping cells..."
from hyperspy.signals.image import Image
# filter the peaks that are outside the selected threshold
peaks = np.ma.compress_rows(self.mask_peaks(idx))
tmp_sz = self.tmp_size
data = np.zeros((tmp_sz, tmp_sz, peaks.shape[0]))
if not hasattr(self.sig.mapped_parameters, "original_files"):
parent = self.sig
else:
parent = self.sig.mapped_parameters.original_files[
self.titles[idx]]
for i in xrange(peaks.shape[0]):
# crop the cells from the given locations
data[:, :, i] = self.sig.data[peaks[i, 1]:peaks[i, 1] + tmp_sz,
peaks[i,
0]:peaks[i, 0] + tmp_sz, idx]
crop_sig = Image({
'data': data,
'mapped_parameters': {
'name': 'Cropped cells from %s' % self.titles[idx],
'record_by': 'image',
'locations': peaks,
'parent': parent,
}
})
return crop_sig
# attach a class member that has the locations from which the images were cropped
print "Complete. "
def load_with_reader(filename, reader, record_by=None,
signal_type=None, output_level=1, **kwds):
from hyperspy.signals.image import Image
from hyperspy.signals.spectrum import Spectrum
from hyperspy.signals.eels import EELSSpectrum
if output_level>1:
messages.information('Loading %s ...' % filename)
file_data_list = reader.file_reader(filename,
record_by=record_by,
output_level=output_level,
**kwds)
objects = []
for file_data_dict in file_data_list:
if record_by is not None:
file_data_dict['mapped_parameters']['record_by'] = record_by
# The record_by can still be None if it was not defined by the reader
if file_data_dict['mapped_parameters']['record_by'] is None:
print "No data type provided. Defaulting to image."
file_data_dict['mapped_parameters']['record_by']= 'image'
if signal_type is not None:
file_data_dict['mapped_parameters']['signal_type'] = signal_type
if file_data_dict['mapped_parameters']['record_by'] == 'image':
s = Image(file_data_dict)
else:
if ('signal_type' in file_data_dict['mapped_parameters']
and file_data_dict['mapped_parameters']['signal_type']
== 'EELS'):
s = EELSSpectrum(file_data_dict)
else:
s = Spectrum(file_data_dict)
folder, filename = os.path.split(os.path.abspath(filename))
filename, extension = os.path.splitext(filename)
s.tmp_parameters.folder = folder
s.tmp_parameters.filename = filename
s.tmp_parameters.extension = extension.replace('.','')
objects.append(s)
s.print_summary()
if len(objects) == 1:
objects = objects[0]
if output_level>1:
messages.information('%s correctly loaded' % filename)
return objects
def _extract_signal_fired(self):
if self.signal_map is None: return
if len(self.signal_map.squeeze().shape) == 2:
s = Image(
{'calibration' : {'data_cube' : self.signal_map.squeeze()}})
s.xscale = self.SI.xscale
s.yscale = self.SI.yscale
s.xunits = self.SI.xunits
s.yunits = self.SI.yunits
interactive_ns[self.signal_name] = s
else:
s = Spectrum(
{'calibration' : {'data_cube' : self.signal_map.squeeze()}})
s.energyscale = self.SI.xscale
s.energyunits = self.SI.xunits
interactive_ns[self.signal_name] = s
def to_image(self, signal_to_index=0):
"""Spectrum to image
Parameters
----------
signal_to_index : integer
Position to move the signal axis.
Examples
--------
>>> s = signals.Spectrum({'data' : np.ones((3,4,5,6))})
>>> s
<Spectrum, title: , dimensions: (3L, 4L, 5L, 6L)>
>>> s.to_image()
<Image, title: , dimensions: (6L, 3L, 4L, 5L)>
>>> s.to_image(1)
<Image, title: , dimensions: (3L, 6L, 4L, 5L)>
"""
from hyperspy.signals.image import Image
dic = self._get_signal_dict()
dic['mapped_parameters']['record_by'] = 'image'
dic['data'] = np.rollaxis(dic['data'], -1, signal_to_index)
dic['axes'] = utils_varia.rollelem(dic['axes'],-1,signal_to_index)
i = 0
for axis in dic['axes']:
axis['index_in_array'] = i
i += 1
im = Image(dic)
if hasattr(self, 'learning_results'):
if signal_to_index != 0 and self.learning_results.loadings is not None:
print("The learning results won't be transfered correctly")
else :
im.learning_results = copy.deepcopy(self.learning_results)
im.learning_results._transpose_results()
im.learning_results.original_shape = self.data.shape
im.tmp_parameters = self.tmp_parameters.deepcopy()
return im
def to_image(self):
from hyperspy.signals.image import Image
dic = self._get_signal_dict()
dic['mapped_parameters']['record_by'] = 'image'
dic['data'] = np.rollaxis(dic['data'], -1, 0)
dic['axes'] = utils_varia.rollelem(dic['axes'], -1, 0)
i = 0
for axis in dic['axes']:
axis['index_in_array'] = i
i += 1
return Image(dic)
def load_with_reader(filename, reader, record_by=None, signal=None, **kwds):
from hyperspy.signals.image import Image
from hyperspy.signals.spectrum import Spectrum
from hyperspy.signals.eels import EELSSpectrum
messages.information(reader.description)
file_data_list = reader.file_reader(filename, record_by=record_by, **kwds)
objects = []
for file_data_dict in file_data_list:
if record_by is not None:
file_data_dict['mapped_parameters']['record_by'] = record_by
# The record_by can still be None if it was not defined by the reader
if file_data_dict['mapped_parameters']['record_by'] is None:
print "No data type provided. Defaulting to image."
file_data_dict['mapped_parameters']['record_by'] = 'image'
if signal is not None:
file_data_dict['mapped_parameters']['signal'] = signal
if file_data_dict['mapped_parameters']['record_by'] == 'image':
s = Image(file_data_dict)
else:
if file_data_dict['mapped_parameters']['signal'] == 'EELS':
s = EELSSpectrum(file_data_dict)
else:
s = Spectrum(file_data_dict)
if defaults.plot_on_load is True:
s.plot()
objects.append(s)
if len(objects) == 1:
objects = objects[0]
return objects
def load_with_reader(filename, reader, record_by = None, signal_type = None,
output_level=1, is_agg = False, **kwds):
from hyperspy.signals.image import Image
from hyperspy.signals.spectrum import Spectrum
from hyperspy.signals.eels import EELSSpectrum
if output_level>1:
messages.information(reader.description)
file_data_list = reader.file_reader(filename,
record_by=record_by,
output_level=output_level,
**kwds)
objects = []
for file_data_dict in file_data_list:
if record_by is not None:
file_data_dict['mapped_parameters']['record_by'] = record_by
# The record_by can still be None if it was not defined by the reader
if file_data_dict['mapped_parameters']['record_by'] is None:
print "No data type provided. Defaulting to image."
file_data_dict['mapped_parameters']['record_by']= 'image'
if signal_type is not None:
file_data_dict['mapped_parameters']['signal_type'] = signal_type
if file_data_dict['mapped_parameters']['record_by'] == 'image':
s = Image(file_data_dict)
else:
if 'signal_type' in file_data_dict['mapped_parameters'] and \
file_data_dict['mapped_parameters']['signal_type'] == 'EELS':
s = EELSSpectrum(file_data_dict)
else:
s = Spectrum(file_data_dict)
objects.append(s)
print s
if hyperspy.defaults_parser.preferences.General.plot_on_load is True \
and is_agg is False:
s.plot()
if len(objects) == 1:
objects = objects[0]
return objects
def calculate_thickness(self, method='threshold', threshold=3, factor=1):
"""Calculates the thickness from a LL SI.
The resulting thickness map is stored in self.thickness as an image
instance. To visualize it: self.thickness.plot()
Parameters
----------
method : {'threshold', 'zl'}
If 'threshold', it will extract the zero loss by just splittin the
spectrum at the threshold value. If 'zl', it will use the
self.zero_loss SI (if defined) to perform the calculation.
threshold : float
threshold value.
factor : float
factor by which to multiple the ZLP
"""
print "Calculating the thickness"
# Create the thickness array
dc = self.data
axis = self.axes_manager._slicing_axes[0]
integral = dc.sum(axis.index_in_array)
if method == 'zl':
if self.zero_loss is None:
hyperspy.messages.warning_exit(
'To use this method the zero_loss'
'attribute must be defined')
zl = self.zero_loss.data
zl_int = zl.sum(axis.index_in_array)
elif method == 'threshold':
ti = axis.value2index(threshold)
zl_int = self.data[(slice(None), ) * axis.index_in_array + (
slice(None, ti),
Ellipsis,
)].sum(axis.index_in_array) * factor
self.thickness = \
Image({'data' : np.log(integral / zl_int)})
"""Creates a 4D image and plots it
"""
# If running from hyperspy's interactive the next two imports can be omitted
# omitted (i.e. the next 2 lines)
import numpy as np
import matplotlib.pyplot as plt
from hyperspy.signals.image import Image
s = Image({'data': np.random.random((16, 16, 32, 32))})
s.plot()
# If running from hyperspy's interactive console the next line can be
# omitted
plt.show()
def kmeans_cluster_stack(self, clusters=None):
smp = self.mapped_parameters
d = self.data
# if clusters not given, try to determine what it should be.
if clusters is None:
pass
kmeans = mdp.nodes.KMeansClassifier(clusters)
avg_stack = np.zeros((d.shape[0], d.shape[1], clusters))
kmeans.train(d.reshape((-1, d.shape[2])).T)
kmeans.stop_training()
groups = kmeans.label(d.reshape((-1, d.shape[2])).T)
cluster_arrays = []
try:
# test if location data is available
smp.locations.values()[0]
except:
print "Warning: No cell location information was available."
for i in xrange(clusters):
# which file are we pulling from?
file_index = 0
address = smp.aggregate_address.values()[file_index]
fname = smp.locations.keys()[file_index]
# get number of members of this cluster
members = groups.count(i)
cluster_array = np.zeros((d.shape[0], d.shape[1], members))
cluster_idx = 0
# positions is a recarray, with each row consisting of a filename and the position from
# which the crop was taken.
positions = np.zeros((members, 1),
dtype=[('filename', 'a256'),
('position', 'i4', (1, 2))])
for j in xrange(len(groups)):
if j > (address[1]) and fname <> smp.locations.keys()[-1]:
file_index += 1
fname = smp.locations.keys()[file_index]
address = self.mapped_parameters.aggregate_address.values(
)[file_index]
file_j = j - address[0]
if groups[j] == i:
cluster_array[:, :, cluster_idx] = d[:, :, j]
try:
positions[cluster_idx] = (
fname, smp.locations[fname][file_j, :2])
except:
pass
cluster_idx += 1
# create a trimmed dict of the original files for this particular
# cluster. Only include files that thie cluster includes members
# from.
constrained_orig_files = {}
for key in self.mapped_parameters.original_files.keys():
if key in positions['filename']:
constrained_orig_files[
key] = self.mapped_parameters.original_files[key]
cluster_array_Image = Image({
'data': cluster_array,
'mapped_parameters': {
'name':
'Cluster %s from %s' % (i, self.mapped_parameters.name),
'locations': positions,
'members': members,
'original_files': self.mapped_parameters.original_files,
}
})
cluster_arrays.append(cluster_array_Image)
avg_stack[:, :, i] = np.sum(cluster_array, axis=2)
members_list = [groups.count(i) for i in xrange(clusters)]
avg_stack_Image = Image({
'data': avg_stack,
'mapped_parameters': {
'name':
'Cluster averages from %s' % self.mapped_parameters.name,
'member_counts': members_list,
'original_files': self.mapped_parameters.original_files,
}
})
self.mapped_parameters.avgs = avg_stack_Image
self.mapped_parameters.clusters = cluster_arrays
print "Averages and classes stored in mapped_parameters. Access them as: \n\n\
"""Creates a 3D image and plots it
"""
# If running from hyperspy's interactive the next two imports can be omitted
# omitted (i.e. the next 2 lines)
import numpy as np
import matplotlib.pyplot as plt
from hyperspy.signals.image import Image
s = Image({'data' : np.random.random((16,32,32))})
s.plot()
# If running from hyperspy's interactive console the next line can be
# omitted
plt.show()
def plot_maps(self,
components,
mva_type=None,
scores=None,
factors=None,
cmap=plt.cm.gray,
no_nans=False,
with_components=True,
plot=True,
on_peaks=False,
directory=None):
"""
Plot component maps for the different MSA types
Parameters
----------
components : 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.
mva_type: string, currently either 'pca' or 'ica'
scores: numpy array, the array of score maps
factors: numpy array, the array of components, with each column as a component.
cmap: matplotlib colormap instance
no_nans: bool,
with_components: bool,
plot: bool,
"""
from hyperspy.signals.image import Image
from hyperspy.signals.spectrum import Spectrum
target = self._get_target(on_peaks)
if scores is None or (factors is None and with_components is True):
print "Either recmatrix or components were not provided."
print "Loading existing values from object."
if mva_type is None:
print "Neither scores nor analysis type specified. Cannot proceed."
return
elif mva_type.lower() == 'pca':
scores = target.v.T
factors = target.pc
elif mva_type.lower() == 'ica':
scores = self._get_ica_scores(target)
factors = target.ic
if no_nans:
print 'Removing NaNs for a visually prettier plot.'
scores = np.nan_to_num(scores) # remove ugly NaN pixels
else:
print "No scores provided and analysis type '%s' unrecognized. Cannot proceed." % mva_type
return
# if len(self.axes_manager.axes)==2:
# shape=self.data.shape[0],1
# else:
# shape=self.data.shape[0],self.data.shape[1]
im_list = []
if components is None:
components = xrange(factors.shape[1])
elif type(components).__name__ != 'list':
components = xrange(components)
for i in components:
if plot is True:
figure = plt.figure()
if with_components:
ax = figure.add_subplot(121)
ax2 = figure.add_subplot(122)
else:
ax = figure.add_subplot(111)
if self.axes_manager.navigation_dimension == 2:
toplot = scores[i, :].reshape(
self.axes_manager.navigation_shape)
im_list.append(
Image({
'data':
toplot,
'axes':
self.axes_manager._get_non_slicing_axes_dicts()
}))
if plot is True:
mapa = ax.matshow(toplot, cmap=cmap)
if with_components:
ax2.plot(self.axes_manager.axes[-1].axis, factors[:,
i])
ax2.set_title('%s component %i' %
(mva_type.upper(), i))
ax2.set_xlabel('Energy (eV)')
figure.colorbar(mapa)
figure.canvas.draw()
#pointer = widgets.DraggableSquare(self.coordinates)
#pointer.add_axes(ax)
elif self.axes_manager.navigation_dimension == 1:
toplot = scores[i, :]
im_list.append(
Spectrum({
"data":
toplot,
'axes':
self.axes_manager._get_non_slicing_axes_dicts()
}))
im_list[-1].get_dimensions_from_data()
if plot is True:
ax.step(range(len(toplot)), toplot)
if with_components:
ax2.plot(self.axes_manager.axes[-1].axis, factors[:,
i])
ax2.set_title('%s component %s' %
(mva_type.upper(), i))
ax2.set_xlabel('Energy (eV)')
else:
messages.warning_exit('View not supported')
if plot is True:
ax.set_title('%s component number %s map' %
(mva_type.upper(), i))
figure.canvas.draw()
if directory is not None:
if not os.path.isdir(directory):
os.makedirs(directory)
figure.savefig(os.path.join(directory, 'IC-%i.png' % i),
dpi=600)
return im_list
