TODOs

Todo

Add documentation of srwlib.h functions

Other modules

wpg.utils.dicts(t)

Convert tree to dict :param t: :return:

wpg.utils.get_value(dic, keys_chain)

Get node from dictionary by chain of keys

Parameters:

• dic – dict from which value will taken
• keys_chain – list of keys

Return res:

return value

wpg.utils.load_dict_slash_hdf5(hdf5_file_name)

Load dictionary with fields of wavefront :param hdf5_file_name: Input file name

wpg.utils.load_hdf5_tree(hdf5_file_name)

convert h5file to the tree

wpg.utils.print_hdf5(hdf5_file_name)

print h5file to the tree

wpg.utils.set_value(dic, keys_chain, value)

Set value in dictionary by chain of keys

Parameters:

• value – value
• dic – dic
• keys_chain – list of keys

wpg.utils.set_value_attr(obj, keys_chain, value)

Return node from dictionary by chain of keys

Parameters:

• obj –
• keys_chain – list of keys
• value –

wpg.utils.srw_obj2str(obj, start_str='')

wpg.utils.store_dict_hdf5(hdf5_file_name, input_dict)

Store dictionary in hdf5 file. :param hdf5_file_name: :param input_dict:

wpg.utils.tree()

Simple tree :return:

wpg.utils.update_dict_slash_string(input_dict, keys_string, value)

Update dictionary from slash separated keys_string by value :param input_dict: dictionary to be updated :param keys_string: slash separated keys_string :param value: value

wpg.useful_code.backpropagation.back_propagate(params)

Propagate pulse from file params[0] at the distance params[1] and save result to HDF5 file. If output files exists - skip calculations.

wpg.useful_code.backpropagation.fit_gaussian(data)

Returns (height, x, y, width_x, width_y) the gaussian parameters of a 2D distribution found by a fit

wpg.useful_code.backpropagation.fit_gaussian_pulse(wf, polarization='horizontal')

Calculate gaussian parameters for all slices in wavefront and add it to custom_fields[‘misc’][‘gaussain_parameters’]

wpg.useful_code.backpropagation.forward_propagate(root_dir, distance, propagation_parameters)

Forward_propagate_wavefront the result will saved in root_dirdistancedistance.h5 file

Parameters:

• root_dir – directory, where ‘0.h’ file located
• distance – distance to forward propagate initial wvefront
• propagation_parameters – SRW propagation parameters

wpg.useful_code.backpropagation.gaussian(height, center_x, center_y, width_x, width_y)

Returns a gaussian function with the given parameters

wpg.useful_code.backpropagation.mkdir_p(path)

Create directory tree, if not exists (mkdir -p)

wpg.useful_code.backpropagation.moments(data)

Returns (height, x, y, width_x, width_y) the gaussian parameters of a 2D distribution by calculating its moments

wpg.useful_code.backpropagation.show_slices(wfr, slice_numbers=None)

Show slices: intensity, phase, gaussian approximation parameters and cuts. All gaussina parameters in pixels now. Should be fixed.

Params wfr:wpg.Wavefront Params slice_numbers:  slices to be shown, may by list, int, or None (for all slices)

wpg.useful_code.srwutils.AuxReadInDataColumns(filePath, nCol, strSep)

wpg.useful_code.srwutils.AuxSaveIntData(arI, wfr, filePath)

wpg.useful_code.srwutils.AuxSaveOpTransmData(optTr, t, filePath)

wpg.useful_code.srwutils.AuxTransmAddSurfHeightProfileScaled(optSlopeErr, heightProfData, dim, ang, scale)

wpg.useful_code.wfrutils.calculate_fwhm(dd)

wpg.useful_code.wfrutils.calculate_fwhm_x(mwf)

wpg.useful_code.wfrutils.calculate_fwhm_y(mwf)

wpg.useful_code.wfrutils.calculate_mediane(dd)

wpg.useful_code.wfrutils.calculate_peak_pos(mwf)

wpg.useful_code.wfrutils.create_numpy_array_from_rows(rows, slices=None)

wpg.useful_code.wfrutils.find_nearest_index(array, value)

wpg.useful_code.wfrutils.find_nearest_value(array, value)

wpg.useful_code.wfrutils.get_mesh(mwf)

wpg.useful_code.wfrutils.plot_1d(profile, title_fig, title_x, title_y)

wpg.useful_code.wfrutils.plot_2d(amap, xmin, xmax, ymin, ymax, title_fig, title_x, title_y)

wpg.useful_code.wfrutils.plot_wfront(mwf, title_fig, isHlog, isVlog, i_x_min, i_y_min, orient, onePlot, bPlotPha=None, saveDir=None)

Plot 2D wavefront (a slice).

Parameters:

• mwf – 2D wavefront structure
• title_fig – Figure title
• isHlog – if True, plot the horizontal cut in logarithmic scale
• isVlog – if True, plot the vertical cut in logarithmic scale
• i_x_min – Intensity threshold for horizontral cut, i.e. x-axis limits are [min(where(i_x<i_x_min):max(where(i_x<i_x_min)]
• i_y_min – Intensity threshold for vertical cut,
• orient – ‘x’ for returning horizontal cut, ‘y’ for vertical cut
• onePlot – if True, put intensity map and plot of cuts on one plot, as subplots
• bPlotPha – if True, plot the cuts of WF phase

Returns:

2-column array containing horizontal or vertical cut data in dependence of ‘orient’ parameter

wpg.useful_code.wfrutils.print_beamline(bl)

wpg.useful_code.wfrutils.propagate_run(ifname, ofname, optBL, bSaved=False)

Propagate wavefront through a beamline and save the result (optionally).

Parameters:

• ifname – input hdf5 file name with wavefront to be propagated
• ofname – output hdf5 file name
• optBL – beamline
• bSaved – if True, save propagated wavefront in h5 file

Returns:

propagated wavefront

wpg.useful_code.wfrutils.show_slices_hsv(wf, slice_numbers=None, pretitle='')

Show slices: intensity, phase, gaussian approximation parameters and cuts. @TBD:All gaussian parameters in pixels now. Should be fixed. @TBD: Add normalization to averaged slice intensity

Params wf:wpg.Wavefront Params slice_numbers:  slices to be shown, may by list, int, or None (for all slices) Params pretitle:  string to be add in the beginning of the title line

wpg.useful_code.wfrutils.stat1(x, y)

Calculate statistic moments of y(x) data.

Parameters:

• x – variable
• y – distribution y(x)

Returns:

‘expected value’ and ‘variance’