def uti_data_file_plot(self,
_fname,
_read_labels=1,
_e=0,
_x=0,
_y=0,
_graphs_joined=1):
#data, mode, allrange = srw_ascii_load(fname)
#data, mode, allrange, arLabels, arUnits = _file_load(_fname, _read_labels)
data, mode, allrange, arLabels, arUnits = uti_plot_com.file_load(
_fname, _read_labels)
#print(allrange)
m = self._enum('T', 'V', 'H', 'E', 'HV', 'EV', 'EH', 'EHV')
if mode == m.T:
fig = self.__mode_T(data, allrange, arLabels, arUnits, _e, _x, _y)
elif mode == m.V:
fig = self.__mode_V(data, allrange)
elif mode == m.H:
fig = self.__mode_H(data, allrange)
elif mode == m.E:
fig = self.__mode_E(data, allrange, arLabels, arUnits)
elif mode == m.HV:
fig = self.__mode_HV(data, allrange, arLabels, arUnits, _x, _y,
_graphs_joined)
#elif mode==m.EV:
# fig = __mode_EV(data,allrange)
#elif mode==m.EH:
# fig = __mode_EH(data,allrange)
elif mode == m.EHV:
fig = self.__mode_EHV(data, allrange, arLabels, arUnits, _e, _x,
_y, _graphs_joined)
return self._maybe_savefig(fig)
def read_srw_file(filename):
data, mode, ranges, labels, units = srw_io.file_load(filename)
data = np.array(data).reshape((ranges[8], ranges[5]), order='C')
return {'data': data,
'shape': data.shape,
'mean': np.mean(data),
'total': np.sum(data),
'photon_energy': ranges[0],
'horizontal_extent': ranges[3:5],
'vertical_extent': ranges[6:8],
# 'mode': mode,
'labels': labels,
'units': units}
def read_srw_file(filename):
""" This function takes in an srw file and returns the beam data. This was adapted from srwl_uti_dataProcess.py file"""
data, mode, ranges, labels, units = srw_io.file_load(filename)
data = np.array(data).reshape((ranges[8], ranges[5]), order='C')
'''
return {'data': data,
'shape': data.shape,
'mean': np.mean(data),
'photon_energy': ranges[0],
'horizontal_extent': ranges[3:5],
'vertical_extent': ranges[6:8],
# 'mode': mode,
'labels': labels,
'units': units}
'''
return data
def extract_simulation_data(filename):
# usage:
# data = extract_simulation_data('filename.txt')
data, mode, allrange, arLabels, arUnits = uti_plot_com.file_load(filename)
# data, allrange, arLabels, arUnits are list
return {
'initial_photon_energy': allrange[0],
'final_photon_energy': allrange[1],
'x_range': allrange[3:5],
'y_range': allrange[6:8],
'data_shape': (allrange[8], allrange[5]),
'mean_intensity': np.mean(data),
'photon_energy_unit': arUnits[0],
'position_unit': arUnits[1],
'photon_intensity_unit': arUnits[3],
'data': np.array(data).reshape((allrange[8], allrange[5]), order='C')
}
def extract_report_data(filename, model_data):
sValShort = 'Flux'; sValType = 'Flux through Finite Aperture'; sValUnit = 'ph/s/.1%bw'
if 'models' in model_data and model_data['models']['fluxReport']['fluxType'] == 2:
sValShort = 'Intensity'
sValUnit = 'ph/s/.1%bw/mm^2'
is_gaussian = False
if 'models' in model_data and model_data['models']['simulation']['sourceType'] == 'g':
is_gaussian = True
files_3d = ['res_pow.dat', 'res_int_se.dat', 'res_int_pr_se.dat', 'res_mirror.dat', _MULTI_ELECTRON_FILENAME]
file_info = {
'res_spec_se.dat': [['Photon Energy', 'Intensity', 'On-Axis Spectrum from Filament Electron Beam'], ['eV', _intensity_units(is_gaussian, model_data)]],
'res_spec_me.dat': [['Photon Energy', sValShort, sValType], ['eV', sValUnit]],
'res_pow.dat': [['Horizontal Position', 'Vertical Position', 'Power Density', 'Power Density'], ['m', 'm', 'W/mm^2']],
'res_int_se.dat': [['Horizontal Position', 'Vertical Position', '{photonEnergy} eV Before Propagation', 'Intensity'], ['m', 'm', _intensity_units(is_gaussian, model_data)]],
#TODO(pjm): improve multi-electron label
_MULTI_ELECTRON_FILENAME: [['Horizontal Position', 'Vertical Position', 'After Propagation', 'Intensity'], ['m', 'm', _intensity_units(is_gaussian, model_data)]],
'res_int_pr_se.dat': [['Horizontal Position', 'Vertical Position', '{photonEnergy} eV After Propagation', 'Intensity'], ['m', 'm', _intensity_units(is_gaussian, model_data)]],
'res_mirror.dat': [['Horizontal Position', 'Vertical Position', 'Optical Path Difference', 'Optical Path Difference'], ['m', 'm', 'm']],
}
data, mode, allrange, arLabels, arUnits = uti_plot_com.file_load(filename)
filename = os.path.basename(filename)
title = file_info[filename][0][2]
if '{photonEnergy}' in title:
title = title.format(photonEnergy=model_data['models']['simulation']['photonEnergy'])
info = {
'title': title,
'x_range': [allrange[0], allrange[1]],
'y_label': _superscript(file_info[filename][0][1] + ' [' + file_info[filename][1][1] + ']'),
'x_label': file_info[filename][0][0] + ' [' + file_info[filename][1][0] + ']',
'x_units': file_info[filename][1][0],
'y_units': file_info[filename][1][1],
'points': data,
}
if filename in files_3d:
info = _remap_3d(info, allrange, file_info[filename][0][3], file_info[filename][1][2])
return info
def uti_data_file_plot(self, _fname, _read_labels=1, _e=0, _x=0, _y=0, _graphs_joined=1):
#data, mode, allrange = srw_ascii_load(fname)
#data, mode, allrange, arLabels, arUnits = _file_load(_fname, _read_labels)
data, mode, allrange, arLabels, arUnits = uti_plot_com.file_load(_fname, _read_labels)
#print(allrange)
m = self._enum('T','V','H','E','HV','EV','EH','EHV')
if mode==m.T:
fig = self.__mode_T(data, allrange, arLabels, arUnits, _e, _x, _y)
elif mode==m.V:
fig = self.__mode_V(data, allrange)
elif mode==m.H:
fig = self.__mode_H(data, allrange)
elif mode==m.E:
fig = self.__mode_E(data, allrange, arLabels, arUnits)
elif mode==m.HV:
fig = self.__mode_HV(data, allrange, arLabels, arUnits, _x, _y, _graphs_joined)
#elif mode==m.EV:
# fig = __mode_EV(data,allrange)
#elif mode==m.EH:
# fig = __mode_EH(data,allrange)
elif mode==m.EHV:
fig = self.__mode_EHV(data, allrange, arLabels, arUnits, _e, _x, _y, _graphs_joined)
return self._maybe_savefig(fig)
def uti_plot_data_file(
self,
_fname,
_read_labels=1,
_e=0,
_x=0,
_y=0,
_graphs_joined=1, #OC16112017 (renamed: uti_plot_data_file -> uti_plot_data_file)
_multicolumn_data=False,
_column_x=None,
_column_y=None, #MR31102017
_scale='linear',
_width_pixels=None):
#data, mode, allrange = srw_ascii_load(fname)
#data, mode, allrange, arLabels, arUnits = _file_load(_fname, _read_labels)
#data, mode, allrange, arLabels, arUnits = uti_plot_com.file_load(_fname, _read_labels)
#data, mode, allrange, arLabels, arUnits = uti_plot_com.file_load(_fname, _read_labels, _traj_report, _traj_axis) #MR20160729
#data = np.array(data) #MR27012017 (lines added until "pass")
#if _scale != 'linear':
# available_scales = ['log', 'log2', 'log10']
# if _scale not in available_scales:
# raise ValueError('Scale "{}" is not supported. Available scales: {}.'.format(_scale, ', '.join(available_scales)))
# data[np.where(data <= 0.)] = 1.e-23
# data = getattr(np, _scale)(data)
#if _width_pixels:
# try:
# from scipy.ndimage import zoom
# data = np.reshape(data, (allrange[5], allrange[8]), order='f')
# resize_factor = float(_width_pixels) / float(allrange[5])
# print('Size before: {} Dimensions: {}'.format(data.size, data.shape))
# data = zoom(data, resize_factor)
# if _scale == 'linear':
# data[np.where(data < 0.)] = 0.0
# print('Size after : {} Dimensions: {}'.format(data.size, data.shape))
# allrange = list(allrange)
# allrange[5] = data.shape[0]
# allrange[8] = data.shape[1]
# allrange = tuple(allrange)
# data = np.reshape(data, (data.shape[0] * data.shape[1]), order='f')
# except:
# print('Cannot resize the image - scipy.ndimage.zoom() cannot be imported.')
# pass
data, mode, allrange, arLabels, arUnits = uti_plot_com.file_load(
_fname, _read_labels, _multicolumn_data) #MR31102017
if not _multicolumn_data:
data = np.array(data)
#if mode == 3: #OC17112017 (commented-out)
# try:
# fwhm_dict = uti_math.fwhm(np.linspace(allrange[0], allrange[1], allrange[2]), data, return_as_dict=True)
# except:
# fwhm_dict = {'fwhm': -1}
# print('FWHM: {:.5f} [{}]'.format(fwhm_dict['fwhm'], arUnits[0]))
if _scale != 'linear':
available_scales = ['log', 'log2', 'log10']
if _scale not in available_scales:
raise ValueError(
'Scale "{}" is not supported. Available scales: {}.'.
format(_scale, ', '.join(available_scales)))
data[np.where(data <= 0.)] = 1.e-23
data = getattr(np, _scale)(data)
if _width_pixels:
try:
from scipy.ndimage import zoom
data = np.reshape(data, (allrange[5], allrange[8]), order='C')
resize_factor = float(_width_pixels) / float(allrange[5])
print('Size before: {} Dimensions: {}'.format(
data.size, data.shape))
data = zoom(data, resize_factor)
if _scale == 'linear':
data[np.where(data < 0.)] = 0.0
print('Size after : {} Dimensions: {}'.format(
data.size, data.shape))
allrange = list(allrange)
allrange[5] = data.shape[0]
allrange[8] = data.shape[1]
allrange = tuple(allrange)
data = np.reshape(data, (data.shape[0] * data.shape[1]),
order='C')
except:
print(
'Cannot resize the image - scipy.ndimage.zoom() cannot be imported.'
)
pass
#print(allrange)
m = self._enum('T', 'V', 'H', 'E', 'HV', 'EV', 'EH', 'EHV')
if mode == m.T:
fig = self.__mode_T(data, allrange, arLabels, arUnits, _e, _x, _y)
elif mode == m.V:
fig = self.__mode_V(data, allrange)
elif mode == m.H:
fig = self.__mode_H(data, allrange)
elif mode == m.E:
fig = self.__mode_E(data, allrange, arLabels, arUnits)
elif mode == m.HV:
fig = self.__mode_HV(data, allrange, arLabels, arUnits, _x, _y,
_graphs_joined)
#elif mode==m.EV:
# fig = __mode_EV(data,allrange)
#elif mode==m.EH:
# fig = __mode_EH(data,allrange)
elif mode == m.EHV:
fig = self.__mode_EHV(data, allrange, arLabels, arUnits, _e, _x,
_y, _graphs_joined)
if _multicolumn_data: #MR31102017
available_cols = list(data.keys())
for c in [_column_x, _column_y]:
if c not in available_cols:
raise ValueError(
'Incorrect column specified: {}.\nAvailable columns: {}'
.format(c, available_cols))
fig = self._plot_1D_XvsY(data, _column_x, _column_y)
return self._maybe_savefig(fig)
def extract_report_data(filename, model_data):
sValShort = 'Flux'
sValType = 'Flux through Finite Aperture'
sValUnit = 'ph/s/.1%bw'
if 'models' in model_data and model_data['models']['fluxReport'][
'fluxType'] == 2:
sValShort = 'Intensity'
sValUnit = 'ph/s/.1%bw/mm^2'
is_gaussian = False
if 'models' in model_data and model_data['models']['simulation'][
'sourceType'] == 'g':
is_gaussian = True
files_3d = [
'res_pow.dat', 'res_int_se.dat', 'res_int_pr_se.dat', 'res_mirror.dat',
_MULTI_ELECTRON_FILENAME
]
file_info = {
'res_spec_se.dat': [[
'Photon Energy', 'Intensity',
'On-Axis Spectrum from Filament Electron Beam'
], ['eV', _intensity_units(is_gaussian, model_data)]],
'res_spec_me.dat': [['Photon Energy', sValShort, sValType],
['eV', sValUnit]],
'res_pow.dat': [[
'Horizontal Position', 'Vertical Position', 'Power Density',
'Power Density'
], ['m', 'm', 'W/mm^2']],
'res_int_se.dat': [[
'Horizontal Position', 'Vertical Position',
'{photonEnergy} eV Before Propagation', 'Intensity'
], ['m', 'm', _intensity_units(is_gaussian, model_data)]],
#TODO(pjm): improve multi-electron label
_MULTI_ELECTRON_FILENAME: [[
'Horizontal Position', 'Vertical Position', 'After Propagation',
'Intensity'
], ['m', 'm', _intensity_units(is_gaussian, model_data)]],
'res_int_pr_se.dat': [[
'Horizontal Position', 'Vertical Position',
'{photonEnergy} eV After Propagation', 'Intensity'
], ['m', 'm', _intensity_units(is_gaussian, model_data)]],
'res_mirror.dat': [[
'Horizontal Position', 'Vertical Position',
'Optical Path Difference', 'Optical Path Difference'
], ['m', 'm', 'm']],
}
data, mode, allrange, arLabels, arUnits = uti_plot_com.file_load(filename)
filename = os.path.basename(filename)
title = file_info[filename][0][2]
if '{photonEnergy}' in title:
title = title.format(
photonEnergy=model_data['models']['simulation']['photonEnergy'])
info = {
'title':
title,
'x_range': [allrange[0], allrange[1]],
'y_label':
_superscript(file_info[filename][0][1] + ' [' +
file_info[filename][1][1] + ']'),
'x_label':
file_info[filename][0][0] + ' [' + file_info[filename][1][0] + ']',
'x_units':
file_info[filename][1][0],
'y_units':
file_info[filename][1][1],
'points':
data,
}
if filename in files_3d:
info = _remap_3d(info, allrange, file_info[filename][0][3],
file_info[filename][1][2])
return info
def uti_plot_data_file(self, _fname, _read_labels=1, _e=0, _x=0, _y=0, _graphs_joined=1, #OC16112017 (renamed: uti_plot_data_file -> uti_plot_data_file)
_multicolumn_data=False, _column_x=None, _column_y=None, #MR31102017
_scale='linear', _width_pixels=None):
#data, mode, allrange = srw_ascii_load(fname)
#data, mode, allrange, arLabels, arUnits = _file_load(_fname, _read_labels)
#data, mode, allrange, arLabels, arUnits = uti_plot_com.file_load(_fname, _read_labels)
#data, mode, allrange, arLabels, arUnits = uti_plot_com.file_load(_fname, _read_labels, _traj_report, _traj_axis) #MR20160729
#data = np.array(data) #MR27012017 (lines added until "pass")
#if _scale != 'linear':
# available_scales = ['log', 'log2', 'log10']
# if _scale not in available_scales:
# raise ValueError('Scale "{}" is not supported. Available scales: {}.'.format(_scale, ', '.join(available_scales)))
# data[np.where(data <= 0.)] = 1.e-23
# data = getattr(np, _scale)(data)
#if _width_pixels:
# try:
# from scipy.ndimage import zoom
# data = np.reshape(data, (allrange[5], allrange[8]), order='f')
# resize_factor = float(_width_pixels) / float(allrange[5])
# print('Size before: {} Dimensions: {}'.format(data.size, data.shape))
# data = zoom(data, resize_factor)
# if _scale == 'linear':
# data[np.where(data < 0.)] = 0.0
# print('Size after : {} Dimensions: {}'.format(data.size, data.shape))
# allrange = list(allrange)
# allrange[5] = data.shape[0]
# allrange[8] = data.shape[1]
# allrange = tuple(allrange)
# data = np.reshape(data, (data.shape[0] * data.shape[1]), order='f')
# except:
# print('Cannot resize the image - scipy.ndimage.zoom() cannot be imported.')
# pass
data, mode, allrange, arLabels, arUnits = uti_plot_com.file_load(_fname, _read_labels, _multicolumn_data) #MR31102017
if not _multicolumn_data:
data = np.array(data)
#if mode == 3: #OC17112017 (commented-out)
# try:
# fwhm_dict = uti_math.fwhm(np.linspace(allrange[0], allrange[1], allrange[2]), data, return_as_dict=True)
# except:
# fwhm_dict = {'fwhm': -1}
# print('FWHM: {:.5f} [{}]'.format(fwhm_dict['fwhm'], arUnits[0]))
if _scale != 'linear':
available_scales = ['log', 'log2', 'log10']
if _scale not in available_scales:
raise ValueError('Scale "{}" is not supported. Available scales: {}.'.format(_scale, ', '.join(available_scales)))
data[np.where(data <= 0.)] = 1.e-23
data = getattr(np, _scale)(data)
if _width_pixels:
try:
from scipy.ndimage import zoom
data = np.reshape(data, (allrange[5], allrange[8]), order='C')
resize_factor = float(_width_pixels) / float(allrange[5])
print('Size before: {} Dimensions: {}'.format(data.size, data.shape))
data = zoom(data, resize_factor)
if _scale == 'linear':
data[np.where(data < 0.)] = 0.0
print('Size after : {} Dimensions: {}'.format(data.size, data.shape))
allrange = list(allrange)
allrange[5] = data.shape[0]
allrange[8] = data.shape[1]
allrange = tuple(allrange)
data = np.reshape(data, (data.shape[0] * data.shape[1]), order='C')
except:
print('Cannot resize the image - scipy.ndimage.zoom() cannot be imported.')
pass
#print(allrange)
m = self._enum('T','V','H','E','HV','EV','EH','EHV')
if mode==m.T:
fig = self.__mode_T(data, allrange, arLabels, arUnits, _e, _x, _y)
elif mode==m.V:
fig = self.__mode_V(data, allrange)
elif mode==m.H:
fig = self.__mode_H(data, allrange)
elif mode==m.E:
fig = self.__mode_E(data, allrange, arLabels, arUnits)
elif mode==m.HV:
fig = self.__mode_HV(data, allrange, arLabels, arUnits, _x, _y, _graphs_joined)
#elif mode==m.EV:
# fig = __mode_EV(data,allrange)
#elif mode==m.EH:
# fig = __mode_EH(data,allrange)
elif mode==m.EHV:
fig = self.__mode_EHV(data, allrange, arLabels, arUnits, _e, _x, _y, _graphs_joined)
if _multicolumn_data: #MR31102017
available_cols = list(data.keys())
for c in [_column_x, _column_y]:
if c not in available_cols:
raise ValueError('Incorrect column specified: {}.\nAvailable columns: {}'.format(c, available_cols))
fig = self._plot_1D_XvsY(data, _column_x, _column_y)
return self._maybe_savefig(fig)