def _get_metadata(cls, file, file_path):
first_line, second_line = file.split('\n')[:2]
name = os.path.splitext(os.path.split(file_path)[1])[0]
keys = {config.get_key('cube', 'line_one'): first_line.strip(),
config.get_key('cube', 'line_two'): second_line.strip()}
return name, keys
def export_to_txt(self):
if not self.interpolation.interpolation_checkbox.isChecked():
# Otherwise maps at different energies would be of different size
print('Only interpolated OrbitalData can be exported.')
return
path = config.get_key('paths', 'txt_export_start')
if path == 'None':
file_name, _ = QFileDialog.getSaveFileName(
None, 'Save .itx File (*.itx)')
else:
start_path = str(__directory__ / path)
file_name, _ = QFileDialog.getSaveFileName(
None, 'Save .itx File (*.itx)', str(start_path))
if not file_name:
return
export_energies = eval(config.get_key('orbital', 'export_energies'))
if isinstance(export_energies, dict):
export_energies = np.linspace(export_energies['min'],
export_energies['max'],
export_energies['num'],
endpoint=True)
kmaps = []
old_energy = self.cube_options.energy_spinbox.value()
for energy in export_energies:
self.cube_options.energy_spinbox.setValue(energy)
kmaps.append(self.get_displayed_plot_data().data)
self.cube_options.energy_spinbox.setValue(old_energy)
kmaps = np.array(kmaps)
xrange, yrange = self.get_displayed_plot_data().range
shape = kmaps[0].shape
xscale = (xrange[1] - xrange[0]) / shape[1]
yscale = (yrange[1] - yrange[0]) / shape[0]
name = Path(file_name).stem
with open(file_name, 'w') as f:
f.write('IGOR\n')
f.write(
f'WAVES/N=({shape[1]},{shape[0]},{len(export_energies)})\t{name}\n'
)
f.write('BEGIN\n')
for data in kmaps:
f.write('\t')
np.savetxt(f, data.T, newline='\t')
f.write('\nEND\n')
f.write(f'X SetScale/P x {xrange[0]},{xscale}, "A^-1", {name}; ')
f.write(f'SetScale/P y {yrange[0]},{yscale}, "A^-1", {name}; ')
f.write(
f'SetScale/P z {export_energies[0]},{export_energies[1]-export_energies[0]}, "eV", {name}; '
)
f.write(f'SetScale d 0,0, "", {name}\n')
def plot(self,
data,
title,
crosshair,
region,
phi_sample=720,
line_sample=500,
normalized=False):
index = len(self.plot_item.listDataItems())
colors = config.get_key('profile_plot', 'colors')
color = colors.split(',')[index % len(colors)]
line_width = int(config.get_key('profile_plot', 'line_width'))
symbols = config.get_key('profile_plot', 'symbols')
symbol = symbols.split(',')[index % len(symbols)]
symbol_size = int(config.get_key('profile_plot', 'symbol_size'))
if isinstance(data, PlotData):
x, y = self.model.get_plot_data(data, crosshair, region,
phi_sample, line_sample)
else:
data, axis = data
x = np.array(data.axes[axis].axis)
y = []
for i in range(len(x)):
slice_ = data.slice_from_index(i, axis=axis)
plot_data = crosshair.cut_from_data(slice_, region=region).data
if np.isnan(plot_data).all():
y.append(np.nan)
else:
if config.get_key('crosshair',
'normalized_intensity') == 'True':
y.append(normalize(plot_data))
else:
y.append(np.nansum(plot_data))
y = np.array(y)
x = x[~np.isnan(y)]
y = y[~np.isnan(y)]
if normalized:
y = y / max(y)
self.plot_item.plot(x,
y,
name=title + self.title_suffixes[region],
pen=mkPen(color, width=line_width),
symbol=symbol,
symbolPen=mkPen(color, width=symbol_size),
symbolBrush=mkBrush(color))
def _set_misc(self):
x = int(config.get_key('app', 'x'))
y = int(config.get_key('app', 'y'))
w = int(config.get_key('app', 'w'))
h = int(config.get_key('app', 'h'))
self.setGeometry(x, y, h, w)
if config.get_key('app', 'fullscreen') == 'True':
self.showMaximized()
self.setWindowTitle('kMap.py')
self.setWindowIcon(
QIcon(str(__directory__ / 'resources/images/icon.png')))
self.show()
def export_to_hdf5(self):
if not self.interpolation.interpolation_checkbox.isChecked():
print('Only interpolated OrbitalData can be exported.')
return
path = config.get_key('paths', 'hdf5_export_start')
if path == 'None':
file_name, _ = QFileDialog.getSaveFileName(
None, 'Save .hdf5 File (*.hdf5)')
else:
start_path = str(__directory__ / path)
file_name, _ = QFileDialog.getSaveFileName(
None, 'Save .hdf5 File (*.hdf5)', str(start_path))
if not file_name:
return
else:
h5file = h5py.File(file_name, 'w')
export_energies = eval(config.get_key('orbital', 'export_energies'))
if isinstance(export_energies, dict):
export_energies = np.linspace(export_energies['min'],
export_energies['max'],
export_energies['num'],
endpoint=True)
kmaps = []
old_energy = self.cube_options.energy_spinbox.value()
for energy in export_energies:
self.cube_options.energy_spinbox.setValue(energy)
kmaps.append(self.get_displayed_plot_data().data)
self.cube_options.energy_spinbox.setValue(old_energy)
kmaps = np.array(kmaps)
xrange, yrange = self.get_displayed_plot_data().range
h5file.create_dataset('name', data='Orbitals')
h5file.create_dataset('axis_1_label', data='E_kin')
h5file.create_dataset('axis_2_label', data='kx')
h5file.create_dataset('axis_3_label', data='ky')
h5file.create_dataset('axis_1_units', data='eV')
h5file.create_dataset('axis_2_units', data='1/Å')
h5file.create_dataset('axis_3_units', data='1/Å')
h5file.create_dataset('axis_1_range',
data=[export_energies[0], export_energies[-1]])
h5file.create_dataset('axis_2_range', data=xrange)
h5file.create_dataset('axis_3_range', data=yrange)
h5file.create_dataset('data',
data=kmaps,
dtype='f8',
compression='gzip',
compression_opts=9)
h5file.close()
def __init__(self, data, ID, name='', meta_data={}, file_format='cube'):
self.dk3D = float(config.get_key('orbital', 'dk3D'))
AbstractData.__init__(self, ID, name, meta_data)
Orbital.__init__(self, data, file_format=file_format, dk3D=self.dk3D,
value='abs2', orbital_name=name)
def get_LUT(self):
colormap = self.getHistogramWidget().gradient.colorMap()
nPts = int(config.get_key('pyqtgraph', 'nPts'))
LUT = colormap.getLookupTable(mode='float', alpha=True, nPts=nPts)
return LUT
def load_cube_files_locally(self):
# Load one or more new cube files
extensions = 'cube files (*.cube);; All Files (*)'
path = config.get_key('paths', 'cube_start')
if path == 'None':
paths, _ = QFileDialog.getOpenFileNames(None,
'Open file(s)',
filter=extensions)
else:
start_path = str(__directory__ / path)
paths, _ = QFileDialog.getOpenFileNames(None, 'Open file(s)',
str(start_path),
extensions)
if not paths:
# No file chosen
logging.getLogger('kmap').info('No file chosen')
return
# Get Tab to load to
tab = self.tab_widget.get_orbital_tab_to_load_to()
# Load Data to Tab
for path in paths:
tab.add_orbital_from_filepath(path)
def update_label(self):
plot_data = self.plot_item.get_plot_data()
if plot_data == None:
intensity = np.nan
area = np.nan
elif np.isnan(plot_data.data).all():
intensity = np.nan
area = np.nan
else:
cut = self.model.cut_from_data(plot_data, region='center')
area = plot_data.data[~np.isnan(plot_data.data
)].size * plot_data.step_size[
0] * plot_data.step_size[1]
intensity = np.nansum(cut.data)
decimals = int(config.get_key('crosshair', 'decimal_places'))
self.point_value_label.setText(f'{intensity:.{decimals}e}')
self.total_area_value.setText(f'{area:.{decimals}e}')
x = self.model.x
y = self.model.y
self.distance_value_label.setText('%.2f' % np.sqrt(x**2 + y**2))
def fit(self):
"""Calling this method will trigger a lmfit with the current
settings.
Returns:
(list): A list of MinimizerResults. One for each slice
fitted.
"""
lmfit_padding = float(config.get_key('lmfit', 'padding'))
for parameter in self.parameters.values():
if parameter.vary and parameter.value <= parameter.min:
padded_value = parameter.min + lmfit_padding
print('WARNING: Initial value for parameter \'%s\' had to be corrected to %f (was %f)' % (
parameter.name, padded_value, parameter.value))
parameter.value = padded_value
results = []
for index in self.slice_policy[1]:
slice_ = self.get_sliced_kmap(index)
result = minimize(self._chi2,
copy.deepcopy(self.parameters),
kws={'slice_': slice_},
nan_policy='omit',
method=self.method[0],
xtol=self.method[1])
results.append([index, result])
return results
def load_hdf5_files(self):
# Load one or more new hdf5 files
log = logging.getLogger('kmap')
log.info('Loading .hdf5 file(s)...')
extensions = 'hdf5 files (*.hdf5 *.h5);; All Files (*)'
path = config.get_key('paths', 'hdf5_start')
if path == 'None':
paths, _ = QFileDialog.getOpenFileNames(None,
'Open file(s)',
filter=extensions)
else:
start_path = str(__directory__ / path)
paths, _ = QFileDialog.getOpenFileNames(None, 'Open file(s)',
str(start_path),
extensions)
if not paths:
# No file chosen
log.info('No file chosen')
return
for path in paths:
self.tab_widget.open_sliced_data_tab(path)
def export_to_hdf5(self):
path = config.get_key('paths', 'hdf5_export_start')
if path == 'None':
file_name, _ = QFileDialog.getSaveFileName(
None, 'Save .hdf5 File (*.hdf5)')
else:
start_path = str(__directory__ / path)
file_name, _ = QFileDialog.getSaveFileName(
None, 'Save .hdf5 File (*.hdf5)', str(start_path))
if not file_name:
return
else:
h5file = h5py.File(file_name, 'w')
kmaps = self.get_residual_kmaps()
slice_axis = self.slider.data.axes[0]
x_axis = self.slider.data.axes[1]
y_axis = self.slider.data.axes[2]
h5file.create_dataset('name', data='Residual')
h5file.create_dataset('axis_1_label', data=slice_axis.label)
h5file.create_dataset('axis_2_label', data=x_axis.label)
h5file.create_dataset('axis_3_label', data=y_axis.label)
h5file.create_dataset('axis_1_units', data=slice_axis.units)
h5file.create_dataset('axis_2_units', data=x_axis.units)
h5file.create_dataset('axis_3_units', data=y_axis.units)
h5file.create_dataset('axis_1_range', data=slice_axis.range)
h5file.create_dataset('axis_2_range', data=x_axis.range)
h5file.create_dataset('axis_3_range', data=y_axis.range)
h5file.create_dataset('data', data=kmaps, dtype='f8',
compression='gzip', compression_opts=9)
h5file.close()
def update_label(self):
plot_data = self.plot_item.get_plot_data()
if plot_data == None:
intensity = 0
elif np.isnan(plot_data.data).all():
intensity = np.nan
else:
cut = self.model.cut_from_data(plot_data, region='center')
# Normalize by dividing by the number of non nan elements
if config.get_key('crosshair', 'normalized_intensity') == 'True':
intensity = normalize(cut.data)
else:
intensity = np.nansum(cut.data)
if abs(intensity) > 1000:
self.point_value_label.setText('%.2fk' % (intensity / 1000))
else:
self.point_value_label.setText('%.2f' % intensity)
x = self.model.x
y = self.model.y
self.distance_value_label.setText('%.2f' % np.sqrt(x**2 + y**2))
def _setup(self):
s_share = config.get_key('orbital', 's_share_default')
self.s_share_spinbox.setValue(float(s_share))
self.s_share_label.setVisible(False)
self.s_share_spinbox.setVisible(False)
def __init__(self, mode, *args, **kwargs):
super(SlicedDatabaseWindow, self).__init__(*args, **kwargs)
self.setupUi(self)
self._setup_tree()
self._connect()
# Setup database
path = __directory__ / config.get_key('paths', 'database')
self.database = Database(str(path))
# Open Options Window
if mode == 'binding-energy':
self.options = SlicedDataBaseOptions()
elif mode == 'photon-energy':
self.options = SlicedDataBaseOptions2()
elif mode == 'cubefile':
self.options = SlicedCubefileOptions()
# URLs (with extra information if available) chosen
self.URLs = []
self.fill_tree()
self.show()
self.options.show()
def set_range(self, range_):
padding = float(config.get_key('pyqtgraph', 'padding'))
self.view.setRange(xRange=range_[0],
yRange=range_[1],
update=True,
padding=padding)
def plot(self, x, y, title):
index = len(self.plot_item.listDataItems())
colors = config.get_key('profile_plot', 'colors')
colors = colors.split(',')
color = colors[index % len(colors)]
line_width = int(config.get_key('profile_plot', 'line_width'))
symbols = config.get_key('profile_plot', 'symbols')
symbols = symbols.split(',')
symbol = symbols[index % len(symbols)]
symbol_size = int(config.get_key('profile_plot', 'symbol_size'))
self.plot_item.plot(x, y,
name=title,
pen=mkPen(color, width=line_width),
symbol=symbol,
symbolPen=mkPen(color, width=symbol_size),
symbolBrush=mkBrush(color))
def get_plot(self):
image = self.plot_data.data
scale = self.plot_data.step_size
pixel_center = config.get_key('pyqtgraph', 'pixel_center') == 'True'
pos = self._calculate_pos(scale, pixel_center=pixel_center)
range_ = self._calculate_range(scale, pixel_center=pixel_center)
return image, pos, scale, range_
def init_from_online(cls, url, ID, meta_data={}):
log = logging.getLogger('kmap')
cache_dir = Path(config.get_key('paths', 'cache'))
cache_file = url.split('OrganicMolecule/')[1].replace('/', '_')
cache_file = str(cache_dir / cache_file)
if os.path.isfile(cache_file):
log.info(f'Found file {url} in cache.')
with open(cache_file, 'r') as f:
file = f.read()
name, keys = OrbitalData._get_metadata(file, url)
name = meta_data['name'] if 'name' in meta_data else name
meta_data.update(keys)
file_format = 'cube'
else:
try:
log.info(f'Loading from ID{meta_data["ID"]:05d} hdf5 file....')
molecule, psi = get_remote_hdf5_orbital('143.50.77.12', '5002',
int(float(meta_data['database ID'])), int(meta_data['ID'])-1)
if os.path.isdir(cache_dir):
log.info(f'Putting {url} into cache {cache_file}')
write_cube(psi, molecule, cache_file)
file = h5py.File('aux.hdf5', 'w', driver='core', backing_store=False)
file.create_dataset('num_atom', data=molecule['num_atom'])
file.create_dataset('chemical_numbers', data=molecule['chemical_numbers'])
file.create_dataset('atomic_coordinates', data=molecule['atomic_coordinates'], dtype="float64")
file.create_dataset('x', data=psi['x'], dtype="float64")
file.create_dataset('y', data=psi['y'], dtype="float64")
file.create_dataset('z', data=psi['z'], dtype="float64")
file.create_dataset('data', data=psi['data'], dtype="float64")
name = psi['name']
file_format = 'hdf5'
except:
log.info('HDF5 File not available, loading cube file...')
log.info('Loading from database: %s' % url)
with urllib.request.urlopen(url) as f:
file = f.read().decode('utf-8')
if os.path.isdir(cache_dir):
log.info(f'Putting {url} into cache {cache_file}')
with open(cache_file, 'w') as f:
f.write(file)
name, keys = OrbitalData._get_metadata(file, url)
name = meta_data['name'] if 'name' in meta_data else name
meta_data.update(keys)
file_format = 'cube'
return cls(file, ID, name=name, meta_data=meta_data, file_format=file_format)
def refresh_plot(self, keep_max_level=False):
self.clear()
if self.model.plot_data is None:
return
image, pos, scale, range_ = self.model.get_plot()
if np.all(np.isnan(image)) == True:
return
old_level = self.get_levels()
# Plot
self.setImage(image,
autoRange=True,
autoLevels=True,
pos=pos,
scale=scale)
self.set_range(range_)
ratio = (range_[1][1] - range_[1][0]) / (range_[0][1] - range_[0][0])
if config.get_key('pyqtgraph', 'fixed_ratio') == 'True':
self.set_aspect_ratio(ratio)
else:
self.set_aspect_ratio(None)
if (keep_max_level
or config.get_key('pyqtgraph', 'keep_max_level') == 'True'):
levels = [np.nanmin(image.data), np.nanmax(image.data)]
# Catch empty plots
if old_level != (0, 1):
levels[0] = old_level[0] if old_level[0] < levels[0] else \
levels[0]
levels[1] = old_level[1] if old_level[1] > levels[1] else \
levels[1]
self.set_levels(levels)
def set_default_colormap(self):
default_colormap = config.get_key('colormap', 'default')
try:
self.set_colormap(default_colormap)
except ValueError:
log = logging.getLogger('kmap')
log.error('The colormap set as default does not exist.')
log.error(traceback.format_exc())
def get_path(self):
# Path for the .json file containing the colormaps
temp = __directory__ / config.get_key('paths', 'colormap')
user = temp / 'colormaps_user.json'
if not os.path.isfile(user):
default = temp / 'colormaps_default.json'
copy(default, user)
return user
def set_label(self, side, label, units=None, color='k', size=1):
axis = AxisItem(side, text=label, units=units,
**{'color': color, 'font-size': size})
if config.get_key('pyqtgraph', 'show_axis_label') == 'True':
axis.showLabel(True)
else:
axis.showLabel(False)
self.view.setAxisItems({side: axis})
def get_orbital_kmap_by_ID(self, ID, parameters):
orbital = self.ID_to_orbital(ID)
if orbital is None:
raise IndexError('wrong ID')
weight, *parameters = parameters
s_share = float(config.get_key('orbital', 's_share'))
# Get scaled kmap
kmap = weight * orbital.get_kmap(*parameters, s_share=s_share)
return kmap
def __init__(self, plot_item):
# Setup GUI
super(Colormap, self).__init__()
self.setupUi(self)
self._connect()
# Path for the .json file containing the colormaps
self.path = __directory__ + config.get_key('paths', 'colormap')
self.model = ColormapModel(plot_item)
self.load_colormaps()
def get_orbital_kmap_by_ID(self, ID):
orbital = self.ID_to_orbital(ID)
if orbital is None:
raise IndexError('wrong ID')
parameters = self.controller.get_parameters(ID)
# Split of first element
weight, *other = parameters
s_share = float(config.get_key('orbital', 's_share'))
# Get scaled kmap
kmap = weight * orbital.get_kmap(*other, s_share=s_share)
return kmap
def set_labels(self, x, y):
color = config.get_key('pyqtgraph', 'axis_color')
size = config.get_key('pyqtgraph', 'axis_size')
if isinstance(x, list):
self.set_label('bottom', x[0], x[1], color, size)
elif isinstance(x, Axis):
self.set_label('bottom', x.label, x.units, color, size)
else:
self.set_label('bottom', str(x), None, color, size)
if isinstance(y, list):
self.set_label('left', y[0], y[1], color, size)
elif isinstance(y, Axis):
self.set_label('left', y.label, y.units, color, size)
else:
self.set_label('left', str(y), None, color, size)
def init_from_file(cls, path, ID):
log = logging.getLogger('kmap')
possible_paths = [path]
file_name = Path(path).name
possible_paths.append(Path(config.get_key('paths', 'cube_start')) /
file_name)
for path in config.get_key('paths', 'path').split(','):
possible_paths.append(Path(path) / file_name)
for path in possible_paths:
log.info(f'Looking for {file_name} in {path}.')
if os.path.isfile(path):
log.info(f'Found.')
with open(path, 'r') as f:
file = f.read()
name, keys = OrbitalData._get_metadata(file, path)
return cls(file, ID, name=name, meta_data=keys)
else:
continue
print(f'ERROR: File {file_name} wasn\'t found. Please add its location to the search path (general_settings.paths.path')
def _setup(self):
temp = __directory__ / config.get_key('paths', 'equations')
default = temp / 'background_equations_default'
user = temp / 'background_equations_user'
self.path = user if os.path.isfile(user) else default
with open(self.path, 'r') as file:
equations = file.read().split('\n')
for equation in equations:
self.background_combobox.addItem(equation)
self.background_combobox.setCurrentIndex(0)
def update_label(self):
plot_data = self.plot_item.get_plot_data()
super().update_label()
if plot_data == None:
intensity = np.nan
area = np.nan
else:
cut = self.model.cut_from_data(plot_data, region='ring')
area = cut.data[~np.isnan(cut.data)].size * plot_data.step_size[
0] * plot_data.step_size[1]
intensity = np.nansum(cut.data)
# Normalize by dividing by area
if config.get_key('crosshair', 'normalized_intensity') == 'True':
intensity /= area
decimals = int(config.get_key('crosshair', 'decimal_places'))
self.ring_value_label.setText(f'{intensity:.{decimals}e}')
self.ann_area_value.setText(f'{area:.{decimals}e}')