def test_automatic_background_subtraction_with_roi(self):
x = np.linspace(0, 24, 2500)
y = np.zeros(x.shape)
peaks = [
[10, 3, 0.1],
[12, 4, 0.1],
[12, 6, 0.1],
]
for peak in peaks:
y += gaussian(x, peak[0], peak[1], peak[2])
y_bkg = x * 0.4 + 5.0
y_measurement = y + y_bkg
roi = [1, 23]
spectrum = Spectrum(x, y_measurement)
auto_background_subtraction_parameters = [2, 50, 50]
spectrum.set_auto_background_subtraction(auto_background_subtraction_parameters, roi)
x_spec, y_spec = spectrum.data
self.assertGreater(x_spec[0],roi[0])
self.assertLess(x_spec[-1], roi[1])
def test_loading_chi_file(self):
spec = Spectrum()
x, y = spec.data
spec.load(os.path.join(data_path,'spectrum_001.chi'))
new_x, new_y = spec.data
self.assertNotEqual(len(x), len(new_x))
self.assertNotEqual(len(y), len(new_y))
def test_setting_new_data(self):
spec = Spectrum()
x = np.linspace(0, 10)
y = np.sin(x)
spec.data = x, y
new_x, new_y = spec.data
self.array_almost_equal(new_x, x)
self.array_almost_equal(new_y, y)
def test_background_out_of_range_throws_error(self):
x1 = np.linspace(0, 10)
x2 = np.linspace(-10, -1)
spec = Spectrum(x1, x1)
background_spectrum = Spectrum(x2, x2)
with self.assertRaises(BkgNotInRangeError):
spec.background_spectrum = background_spectrum
def test_using_background_spectrum(self):
x = np.linspace(-5, 5, 100)
spec_y = x ** 2
bkg_y = x
spec = Spectrum(x, spec_y)
background_spectrum = Spectrum(x, bkg_y)
spec.background_spectrum = background_spectrum
new_x, new_y = spec.data
self.array_almost_equal(new_x, x)
self.array_almost_equal(new_y, spec_y - bkg_y)
def test_using_background_spectrum_with_different_spacing(self):
x = np.linspace(-5, 5, 100)
spec_y = x ** 2
x_bkg = np.linspace(-5, 5, 99)
bkg_y = x_bkg
spec = Spectrum(x, spec_y)
background_spectrum = Spectrum(x_bkg, bkg_y)
spec.background_spectrum = background_spectrum
new_x, new_y = spec.data
self.array_almost_equal(new_x, x)
self.array_almost_equal(new_y, spec_y - x)
def __init__(self):
super(SpectrumModel, self).__init__()
self.spectrum = Spectrum()
self.overlays = []
self.phases = []
self.file_iteration_mode = 'number'
self.file_name_iterator = FileNameIterator()
self.bkg_ind = -1
self.spectrum_filename = ''
def test_automatic_background_subtraction(self):
x = np.linspace(0, 24, 2500)
y = np.zeros(x.shape)
peaks = [
[10, 3, 0.1],
[12, 4, 0.1],
[12, 6, 0.1],
]
for peak in peaks:
y += gaussian(x, peak[0], peak[1], peak[2])
y_bkg = x * 0.4 + 5.0
y_measurement = y + y_bkg
spectrum = Spectrum(x, y_measurement)
auto_background_subtraction_parameters = [2, 50, 50]
spectrum.set_auto_background_subtraction(auto_background_subtraction_parameters)
x_spec, y_spec = spectrum.data
self.array_almost_equal(y_spec, y)
def test_saving_a_file(self):
x = np.linspace(-5, 5, 100)
y = x ** 2
spec = Spectrum(x, y)
filename = os.path.join(data_path, "test.dat")
spec.save(filename)
spec2 = Spectrum()
spec2.load(filename)
spec2_x, spec2_y = spec2.data
self.array_almost_equal(spec2_x, x)
self.array_almost_equal(spec2_y, y)
os.remove(filename)
def test_loading_invalid_file(self):
spec = Spectrum()
self.assertEqual(-1, spec.load(os.path.join(data_path,'wrong_file_format.txt')))
class SpectrumModel(QtCore.QObject):
"""
Main Spectrum handling class. Supporting several features:
- loading spectra from any tabular source (readable by numpy)
- having overlays
- setting overlays as background
- spectra and overlays can be scaled and have offset values
all changes to the internal data throw pyqtSignals.
"""
spectrum_changed = QtCore.pyqtSignal()
overlay_changed = QtCore.pyqtSignal(int) # changed index
overlay_added = QtCore.pyqtSignal()
overlay_removed = QtCore.pyqtSignal(int) # removed index
overlay_set_as_bkg = QtCore.pyqtSignal(int) # index set as background
overlay_unset_as_bkg = QtCore.pyqtSignal(int) # index unset os background
def __init__(self):
super(SpectrumModel, self).__init__()
self.spectrum = Spectrum()
self.overlays = []
self.phases = []
self.file_iteration_mode = 'number'
self.file_name_iterator = FileNameIterator()
self.bkg_ind = -1
self.spectrum_filename = ''
def set_spectrum(self, x, y, filename='', unit=''):
"""
set the current data spectrum.
:param x: x-values
:param y: y-values
:param filename: name for the spectrum, defaults to ''
:param unit: unit for the x values
"""
self.spectrum_filename = filename
self.spectrum.data = (x, y)
self.spectrum.name = get_base_name(filename)
self.unit = unit
self.spectrum_changed.emit()
def load_spectrum(self, filename):
"""
Loads a spectrum from a tabular spectrum file (2 column txt file)
:param filename: filename of the data file
"""
logger.info("Load spectrum: {0}".format(filename))
self.spectrum_filename = filename
skiprows = 0
if filename.endswith('.chi'):
skiprows = 4
self.spectrum.load(filename, skiprows)
self.file_name_iterator.update_filename(filename)
self.spectrum_changed.emit()
def save_spectrum(self, filename, header=None, subtract_background=False):
"""
Saves the current data spectrum.
:param filename: where to save
:param header: you can specify any specific header
:param subtract_background: whether or not the background set will be used for saving or not
"""
if subtract_background:
x, y = self.spectrum.data
else:
x, y = self.spectrum._original_x, self.spectrum._original_y
file_handle = open(filename, 'w')
num_points = len(x)
if filename.endswith('.chi'):
if header is None or header == '':
file_handle.write(filename + '\n')
file_handle.write(self.unit + '\n\n')
file_handle.write(" {0}\n".format(num_points))
else:
file_handle.write(header)
for ind in xrange(num_points):
file_handle.write(' {0:.7E} {1:.7E}\n'.format(x[ind], y[ind]))
else:
if header is not None:
file_handle.write(header)
file_handle.write('\n')
for ind in xrange(num_points):
file_handle.write('{0:.9E} {1:.9E}\n'.format(x[ind], y[ind]))
file_handle.close()
def get_spectrum(self):
return self.spectrum
def load_next_file(self, step=1):
"""
Loads the next file from a sequel of filenames (e.g. *_001.xy --> *_002.xy)
It assumes that the file numbers are at the end of the filename
"""
next_file_name = self.file_name_iterator.get_next_filename(mode=self.file_iteration_mode, step=step)
if next_file_name is not None:
self.load_spectrum(next_file_name)
return True
return False
def load_previous_file(self, step=1):
"""
Loads the previous file from a sequel of filenames (e.g. *_002.xy --> *_001.xy)
It assumes that the file numbers are at the end of the filename
"""
next_file_name = self.file_name_iterator.get_previous_filename(mode=self.file_iteration_mode, step=step)
if next_file_name is not None:
self.load_spectrum(next_file_name)
return True
return False
def set_file_iteration_mode(self, mode):
if mode == 'number':
self.file_iteration_mode = 'number'
self.file_name_iterator.create_timed_file_list = False
elif mode == 'time':
self.file_iteration_mode = 'time'
self.file_name_iterator.create_timed_file_list = True
self.file_name_iterator.update_filename(self.filename)
def add_overlay(self, x, y, name=''):
"""
Adds an overlay to the list of overlays
:param x: x-values
:param y: y-values
:param name: name of overlay to be used for displaying etc.
"""
self.overlays.append(Spectrum(x, y, name))
self.overlay_added.emit()
def remove_overlay(self, ind):
"""
Removes an overlay from the list of overlays
:param ind: index of the overlay
"""
if ind >= 0:
del self.overlays[ind]
if self.bkg_ind > ind:
self.bkg_ind -= 1
elif self.bkg_ind == ind:
self.spectrum.unset_background_spectrum()
self.bkg_ind = -1
self.spectrum_changed.emit()
self.overlay_removed.emit(ind)
def get_overlay(self, ind):
"""
:param ind: overlay ind
:return: returns overlay if existent or None if it does not exist
:type return: Spectrum
"""
try:
return self.overlays[ind]
except IndexError:
return None
def add_spectrum_as_overlay(self):
"""
Adds the current data spectrum as overlay to the list of overlays
"""
current_spectrum = deepcopy(self.spectrum)
overlay_spectrum = Spectrum(current_spectrum.x,
current_spectrum.y,
current_spectrum.name)
self.overlays.append(overlay_spectrum)
self.overlay_added.emit()
def add_overlay_file(self, filename):
"""
Reads a 2-column (x,y) text file and adds it as overlay to the list of overlays
:param filename: path of the file to be loaded
"""
self.overlays.append(Spectrum())
self.overlays[-1].load(filename)
self.overlay_added.emit()
def get_overlay_name(self, ind):
"""
:param ind: overlay index
"""
return self.overlays[-1].name
def set_overlay_scaling(self, ind, scaling):
"""
Sets the scaling of the specified overlay
:param ind: index of the overlay
:param scaling: new scaling value
"""
self.overlays[ind].scaling = scaling
self.overlay_changed.emit(ind)
if self.bkg_ind == ind:
self.spectrum_changed.emit()
def get_overlay_scaling(self, ind):
"""
Returns the scaling of the specified overlay
:param ind: index of the overlay
:return: scaling value
"""
return self.overlays[ind].scaling
def set_overlay_offset(self, ind, offset):
"""
Sets the offset of the specified overlay
:param ind: index of the overlay
:param offset: new offset value
"""
self.overlays[ind].offset = offset
self.overlay_changed.emit(ind)
if self.bkg_ind == ind:
self.spectrum_changed.emit()
def get_overlay_offset(self, ind):
"""
Return the offset of the specified overlay
:param ind: index of the overlay
:return: overlay value
"""
return self.overlays[ind].offset
def set_overlay_as_bkg(self, ind):
"""
Sets an overlay as background for the data spectrum, and unsets any previously used background
:param ind: index of the overlay
"""
if self.bkg_ind >= 0:
self.unset_overlay_as_bkg()
self.bkg_ind = ind
self.spectrum.background_spectrum = self.overlays[ind]
self.spectrum_changed.emit()
self.overlay_set_as_bkg.emit(ind)
def set_spectrum_as_bkg(self):
"""
Adds the current spectrum as Overlay and sets it as background spectrum and unsets any previously used
background.
"""
self.add_spectrum_as_overlay()
self.set_overlay_as_bkg(len(self.overlays) - 1)
def unset_overlay_as_bkg(self):
"""
Unsets the currently used background overlay.
"""
previous_bkg_ind = self.bkg_ind
self.bkg_ind = -1
self.spectrum.unset_background_spectrum()
self.spectrum_changed.emit()
self.overlay_unset_as_bkg.emit(previous_bkg_ind)
def overlay_is_bkg(self, ind):
"""
:param ind: overlay ind
"""
return ind == self.bkg_ind and self.bkg_ind != -1
def set_auto_background_subtraction(self, parameters, roi=None):
"""
Enables auto background extraction and removal from the data spectrum
:param parameters: array of parameters with [window_width, iterations, polynomial_order]
:param roi: array of size two with [xmin, xmax] specifying the range for which the background subtraction
will be performed
"""
self.spectrum.set_auto_background_subtraction(parameters, roi)
self.spectrum_changed.emit()
def unset_auto_background_subtraction(self):
"""
Disables auto background extraction and removal.
"""
self.spectrum.unset_auto_background_subtraction()
self.spectrum_changed.emit()
