Developers Corner

XRStools.roifinder_and_gui Module

XRStools.roifinder_and_gui.create_diff_image(scans, scannumbers, energy_keV)[source]

Returns a summed image from all scans with numbers ‘scannumbers’. scans = dictionary of objects from the scan-class scannumbers = single scannumber, or list of scannumbers from which an image should be constructed

XRStools.roifinder_and_gui.define_lin_roi(height, image_shape, verbose=False)[source]

Lets you pick 2 points on a current image and returns a linear ROI of height (2*height+1). height = number of pixels that define the height of the ROI image_shape = tuple with shape of the current image (i.e. (256,256))

XRStools.roifinder_and_gui.define_polygon_roi(image_shape, verbose=False)[source]

Define a polygon shaped ROI from a current image by selecting points.

XRStools.roifinder_and_gui.define_zoom_roi(input_image, verbose=False)[source]

Parses the current figure limits and uses them to define a rectangle of ” roi_number”s in the matrix given by roi_matrix. input_image = unzoomed figure roi_matrix = current roi matrix which will be altered

class XRStools.roifinder_and_gui.roi_finder[source]

Bases: object

appendROIobject(roi_object)[source]
deleterois()[source]

Clear the existing ROIs by creating a fresh roi_object.

find_cw_rois(roi_obj, pw_data)[source]
find_cw_rois
Allows for manual refinement of ROIs by plotting the spectra column-wise. Loops through the spectra column-by-column and ROI by ROI, click above the black line to keep the column plotted, click below the black line to discard the column of pixels.
Args:
  • roi_obj (roi_object): ROI object from the XRStools.xrs_rois module with roughly defined ROIs.
  • pw_data (np.array): List containing one 2D numpy array per ROI holding pixel-wise signals.
find_pw_rois(roi_obj, pw_data, save_dataset=False)[source]
find_pw_rois

Allows for manual refinement of ROIs by plotting the spectra pixel-wise. Loops through the spectra pixel-by-pixel and ROI by ROI, click above the black line to keep the pixel plotted, click below the black line to discard the pixel.

Args:
  • roi_obj (roi_object): ROI object from the XRStools.xrs_rois module with roughly defined ROIs.
  • pw_data (np.array): List containing one 2D numpy array per ROI holding pixel-wise signals.
get_auto_rois(input_image, kernel_size=5, threshold=100.0, logscaling=True, colormap='jet', interpolation='bilinear')[source]

Define ROIs by choosing a threshold using a slider bar under the figure. In this function, the entire detector is shown. input_image = 2D numpy array with the image to be displayed kernal_size = integer defining the median filter window (has to be odd) theshold = initial number defining the upper end value for the slider bar (amax(input_image)/threshold defines this number), can be within GUI logscaling = boolean, if True (default) the logarithm of input_image is displayed colormap = matplotlib color scheme used in the display interpolation = matplotlib interpolation scheme used for the display

get_auto_rois_eachdet(input_image, kernel_size=5, threshold=100.0, logscaling=True, colormap='jet', interpolation='bilinear')[source]

Define ROIs automatically using median filtering and a variable threshold for each detector separately. scannumbers = either single scannumber or list of scannumbers kernel_size = used kernel size for the median filter (must be an odd integer) logscaling = set to ‘True’ if images is to be shown on log-scale (default is True) colormap = string to define the colormap which is to be used for display (anything supported by matplotlib, ‘jet’ by default) interpolation = interpolation scheme to be used for displaying the image (anything supported by matplotlib, ‘nearest’ by default)

get_linear_rois(input_image, logscaling=True, height=5, colormap='jet', interpolation='nearest')[source]

Define ROIs by clicking two points on a 2D image. number_of_rois = integer defining how many ROIs should be determined input_object = 2D array, scan_object, or dictionary of scans to define the ROIs from logscaling = boolean, to determine wether the image is shown on a log-scale (default = True) height = integer defining the height (in pixels) of the ROIs

get_polygon_rois(input_image, modind=-1, logscaling=True, colormap='jet', interpolation='nearest')[source]

Define ROIs by clicking arbitrary number of points on a 2D image: LEFT CLICK to define the corner points of polygon, MIDDLE CLICK to finish current ROI and move to the next ROI, RIGHT CLICK to cancel the previous point of polygon input_object = 2D array, scan_object, or dictionary of scans to define the ROIs from modind = integer to identify module, if -1 (default), no module info will be in title (the case of one big image) logscaling = boolean, to determine wether the image is shown on a log-scale (default = True)

get_zoom_rois(input_image, logscaling=True, colormap='jet', interpolation='nearest')[source]

Define ROIs by clicking two points on a 2D image. number_of_rois = integer defining how many ROIs should be determined input_object = 2D array, scan_object, or dictionary of scans to define the ROIs from logscaling = boolean, to determine wether the image is shown on a log-scale (default = True) height = integer defining the height (in pixels) of the ROIs

import_simo_style_rois(roiList, detImageShape=(512, 768))[source]

import_simo_style_rois Converts Simo-style ROIs to the conventions used here.

Arguments:
  • roiList (list): List of tuples that have [(xmin, xmax, ymin, ymax), (xmin, xmax, ymin, ymax), …].
  • detImageShape (tuple): Shape of the detector image (for convertion to roiMatrix)
refine_pw_rois(roi_obj, pw_data, n_components=2, method='nnma', cov_thresh=-1)[source]

refine_pw_rois

Use decomposition of pixelwise data for each ROI to find which of the pixels holds data from the sample and which one only has background.

Args:
  • roi_obj (roi_object): ROI object to be refined.
  • pw_data (list): List containing one 2D numpy array per ROI holding pixel-wise signals.
  • n_components (int): Number of components in the decomposition.
  • method (string): Keyword describing which decomposition to be used (‘pca’, ‘ica’, ‘nnma’).
refine_rois_CW(hydra_obj, scan_numbers)[source]

refine_rois_CW

Allows for manual refinement of ROIs by plotting the spectra column-wise. Loops through the spectra column-by-column and ROI by ROI, click above the black line to keep the column plotted, click below the black line to discard the column of pixels.

Args:
  • hydra_obj (hydra_object): Object from the xrs_read.Hydra class that hold scans to be used for the refinement of the ROIs.
  • scan_numbers (int or list): Scan numbers of scans to be used in the refinement.
refine_rois_CW_test(hydra_obj, scan_numbers, interpolation='nearest')[source]

refine_rois_CW

Allows for manual refinement of ROIs by plotting the spectra column-wise. Loops through the spectra column-by-column and ROI by ROI, click above the black line to keep the column plotted, click below the black line to discard the column of pixels.

Args:
  • hydra_obj (hydra_object): Object from the xrs_read.Hydra class that hold scans to be usedfor the refinement of the ROIs.
  • scan_numbers (int or list): Scan numbers of scans to be used in the refinement.
refine_rois_MF(hydra_obj, scan_numbers, n_components=2, method='nnma', cov_thresh=-1)[source]

refine_rois_MF

Use decomposition of pixelwise data for each ROI to find which of the pixels holds data from the sample and which one only has background.

Args:
  • hydra_obj (hydra_object): Object from the xrs_read.Hydra class that hold scans to be used for the refinement of the ROIs.
  • scan_numbers (int or list): Scan numbers of scans to be used in the refinement.
  • n_components (int): Number of components in the decomposition.
  • method (string): Keyword describing which decomposition to be used (‘pca’, ‘ica’, ‘nnma’).
refine_rois_PW(hydra_obj, scan_numbers)[source]

refine_rois_PW

Allows for manual refinement of ROIs by plotting the spectra column-wise. Loops through the spectra column-by-column and ROI by ROI, click above the black line to keep the column plotted, click below the black line to discard the column of pixels.

Args:
  • hydra_obj (hydra_object): Object from the xrs_read.Hydra class that hold scans to be used for the refinement of the ROIs.
  • scan_numbers (int or list): Scan numbers of scans to be used in the refinement.
refine_rois_PW_old(hydra_obj, scan_numbers, save_dataset=False)[source]

refine_rois_PW

Allows for manual refinement of ROIs by plotting the spectra pixel-wise. Loops through the spectra pixel-by-pixel and ROI by ROI, click above the black line to keep the pixel plotted, click below the black line to discard the pixel.

Args:
  • hydra_obj (hydra_object): Object from the xrs_read.Hydra class that hold scans to be used for the refinement of the ROIs.
  • scan_numbers (int or list): Scan numbers of scans to be used in the refinement.
refine_rois_RW(hydra_obj, scan_numbers)[source]

refine_rois_RW

Allows for manual refinement of ROIs by plotting the spectra row-wise. Loops through the spectra column-by-column and ROI by ROI, click above the black line to keep the column plotted, click below the black line to discard the column of pixels.

Args:
  • hydra_obj (hydra_object): Object from the xrs_read.Hydra class that hold scans to be used for the refinement of the ROIs.
  • scan_numbers (int or list): Scan numbers of scans to be used in the refinement.
roi_widget(input_image, layout='2X3-12', shape=[512, 768])[source]

roi_widget Use the ROI widget to define ROIs.

input_image = 2D array to define the ROIs from layout = detector layout shape = image shape/detector shape

show_rois(interpolation='nearest', colormap='jet')[source]

show_rois Creates a figure with the defined ROIs as numbered boxes on it.

Args:
  • interpolation (str) : Interpolation scheme used in the plot.
  • colormap (str) : Colormap used in the plot.
XRStools.roifinder_and_gui.show_rois(roi_matrix)[source]

Creates a figure with the defined ROIs as numbered boxes on it.

XRStools.roifinder_and_gui.test_roifinder(roi_type_str, imagesize=[512, 768], scan=None)[source]

Runs the roi_finder class on a random image of given type for testing purposes. scan[0] = absolute path to a spec file scan[1] = energycolumn=’energy’ scan[2] = monitorcolumn=’kap4dio’ scan[3] = scan number from which to take images

XRStools.xrs_utilities Module

XRStools.xrs_utilities.Chi(chi, degrees=True)[source]

rotation around (1,0,0), pos sense

XRStools.xrs_utilities.HRcorrect(pzprofile, occupation, q)[source]

Returns the first order correction to filled 1s, 2s, and 2p Compton profiles.

Implementation after Holm and Ribberfors (citation …).

Args:
  • pzprofile (np.array): Compton profile (e.g. tabulated from Biggs) to be corrected (2D matrix).
  • occupation (list): electron configuration.
  • q (float or np.array): momentum transfer in [a.u.].
Returns:
asymmetry (np.array): asymmetries to be added to the raw profiles (normalized to the number of electrons on pz scale)
XRStools.xrs_utilities.NNMFcost(x, A, F, C, F_up, C_up, n, k, m)[source]

NNMFcost Returns cost and gradient for NNMF with constraints.

XRStools.xrs_utilities.NNMFcost_old(x, A, W, H, W_up, H_up)[source]

NNMFcost Returns cost and gradient for NNMF with constraints.

XRStools.xrs_utilities.Omega(omega, degrees=True)[source]

rotation around (0,0,1), pos sense

XRStools.xrs_utilities.Phi(phi, degrees=True)[source]

rotation around (0,1,0), neg sense

XRStools.xrs_utilities.Rx(chi, degrees=True)[source]

Rx Rotation matrix for vector rotations around the [1,0,0]-direction.

Args:
  • chi (float) : Angle of rotation.
  • degrees(bool) : Angle given in radians or degrees.
Returns:
  • 3x3 rotation matrix.
XRStools.xrs_utilities.Ry(phi, degrees=True)[source]

Ry Rotation matrix for vector rotations around the [0,1,0]-direction.

Args:
  • phi (float) : Angle of rotation.
  • degrees(bool) : Angle given in radians or degrees.
Returns:
  • 3x3 rotation matrix.
XRStools.xrs_utilities.Rz(omega, degrees=True)[source]

Rz Rotation matrix for vector rotations around the [0,0,1]-direction.

Args:
  • omega (float) : Angle of rotation.
  • degrees(bool) : Angle given in radians or degrees.
Returns:
  • 3x3 rotation matrix.
XRStools.xrs_utilities.TTsolver1D(el_energy, hkl=[6, 6, 0], crystal='Si', R=1.0, dev=array([-50., -49., -48., -47., -46., -45., -44., -43., -42., -41., -40., -39., -38., -37., -36., -35., -34., -33., -32., -31., -30., -29., -28., -27., -26., -25., -24., -23., -22., -21., -20., -19., -18., -17., -16., -15., -14., -13., -12., -11., -10., -9., -8., -7., -6., -5., -4., -3., -2., -1., 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99., 100., 101., 102., 103., 104., 105., 106., 107., 108., 109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119., 120., 121., 122., 123., 124., 125., 126., 127., 128., 129., 130., 131., 132., 133., 134., 135., 136., 137., 138., 139., 140., 141., 142., 143., 144., 145., 146., 147., 148., 149.]), alpha=0.0, chitable_prefix='/home/christoph/sources/XRStools/data/chitables/chitable_')[source]

TTsolver Solves the Takagi-Taupin equation for a bent crystal.

This function is based on a Matlab implementation by S. Huotari of M. Krisch’s Fortran programs.

Args:
  • el_energy (float): Fixed nominal (working) energy in keV.
  • hkl (array): Reflection order vector, e.g. [6, 6, 0]
  • crystal (str): Crystal used (can be silicon ‘Si’ or ‘Ge’)
  • R (float): Crystal bending radius in m.
  • dev (np.array): Deviation parameter (in arc. seconds) for which the reflectivity curve should be calculated.
  • alpha (float): Crystal assymetry angle.
Returns:
  • refl (np.array): Reflectivity curve.
  • e (np.array): Deviation from Bragg angle in meV.
  • dev (np.array): Deviation from Bragg angle in microrad.
XRStools.xrs_utilities.absCorrection(mu1, mu2, alpha, beta, samthick, geometry='transmission')[source]

absCorrection

Calculates absorption correction for given mu1 and mu2. Multiply the measured spectrum with this correction factor. This is a translation of Keijo Hamalainen’s Matlab function (KH 30.05.96).

Args
  • mu1 : np.array Absorption coefficient for the incident energy in [1/cm].
  • mu2 : np.array Absorption coefficient for the scattered energy in [1/cm].
  • alpha : float Incident angle relative to plane normal in [deg].
  • beta : float Exit angle relative to plane normal [deg].
  • samthick : float Sample thickness in [cm].
  • geometry : string, optional Key word for different sample geometries (‘transmission’, ‘reflection’, ‘sphere’). If geometry is set to ‘sphere’, no angular dependence is assumed.
Returns
  • ac : np.array Absorption correction factor. Multiply this with your measured spectrum.
XRStools.xrs_utilities.abscorr2(mu1, mu2, alpha, beta, samthick)[source]

Calculates absorption correction for given mu1 and mu2. Multiply the measured spectrum with this correction factor.

This is a translation of Keijo Hamalainen’s Matlab function (KH 30.05.96).

Args:
  • mu1 (np.array): absorption coefficient for the incident energy in [1/cm].
  • mu2 (np.array): absorption coefficient for the scattered energy in [1/cm].
  • alpha (float): incident angle relative to plane normal in [deg].
  • beta (float): exit angle relative to plane normal [deg] (for transmission geometry use beta < 0).
  • samthick (float): sample thickness in [cm].
Returns:
  • ac (np.array): absorption correction factor. Multiply this with your measured spectrum.
XRStools.xrs_utilities.addch(xold, yold, n, n0=0, errors=None)[source]

# ADDCH Adds contents of given adjacent channels together # # [x2,y2] = addch(x,y,n,n0) # x = original x-scale (row or column vector) # y = original y-values (row or column vector) # n = number of channels to be summed up # n0 = offset for adding, default is 0 # x2 = new x-scale # y2 = new y-values # # KH 17.09.1990 # Modified 29.05.1995 to include offset

XRStools.xrs_utilities.bidiag_reduction(A)[source]

function [U,B,V]=bidiag_reduction(A) % [U B V]=bidiag_reduction(A) % Algorithm 6.5-1 in Golub & Van Loan, Matrix Computations % Johns Hopkins University Press % Finds an upper bidiagonal matrix B so that A=U*B*V’ % with U,V orthogonal. A is an m x n matrix

XRStools.xrs_utilities.bootstrapCNNMF(A, F_ini, C_ini, F_up, C_up, Niter)[source]

bootstrapCNNMF Constrained non-negative matrix factorization with bootstrapping for error estimates.

XRStools.xrs_utilities.bootstrapCNNMF_old(A, k, Aerr, F_ini, C_ini, F_up, C_up, Niter=100)[source]

bootstrapCNNMF Constrained non-negative matrix factorization with bootstrapping for error estimates.

XRStools.xrs_utilities.bragg(hkl, e, xtal='Si')[source]

% BRAGG Calculates Bragg angle for given reflection in RAD % output=bangle(hkl,e,xtal) % hkl can be a matrix i.e. hkl=[1,0,0 ; 1,1,1]; % e=energy in keV % xtal=’Si’, ‘Ge’, etc. (check dspace.m) or d0 (Si default) % % KH 28.09.93 %

XRStools.xrs_utilities.braggd(hkl, e, xtal='Si')[source]

# BRAGGD Calculates Bragg angle for given reflection in deg # Call BRAGG.M # output=bangle(hkl,e,xtal) # hkl can be a matrix i.e. hkl=[1,0,0 ; 1,1,1]; # e=energy in keV # xtal=’Si’, ‘Ge’, etc. (check dspace.m) or d0 (Si default) # # KH 28.09.93

XRStools.xrs_utilities.cixsUBfind(x, G, Q_sample, wi, wo, lambdai, lambdao)[source]

cixsUBfind

XRStools.xrs_utilities.cixsUBgetAngles_primo(Q)[source]
XRStools.xrs_utilities.cixsUBgetAngles_secondo(Q)[source]
XRStools.xrs_utilities.cixsUBgetAngles_terzo(Q)[source]
XRStools.xrs_utilities.cixsUBgetQ_primo(tthv, tthh, psi)[source]
XRStools.xrs_utilities.cixsUBgetQ_secondo(tthv, tthh, psi)[source]
XRStools.xrs_utilities.cixsUBgetQ_terzo(tthv, tthh, psi)[source]
XRStools.xrs_utilities.cixs_primo(tthv, tthh, psi, anal_braggd=86.5)[source]

cixs_primo

XRStools.xrs_utilities.cixs_secondo(tthv, tthh, psi, anal_braggd=86.5)[source]

cixs_secondo

XRStools.xrs_utilities.cixs_terzo(tthv, tthh, psi, anal_braggd=86.5)[source]

cixs_terzo

XRStools.xrs_utilities.compute_matrix_elements(R1, R2, k, r)[source]
XRStools.xrs_utilities.con2mat(x, W, H, W_up, H_up)[source]
XRStools.xrs_utilities.constrained_mf(A, W_ini, W_up, coeff_ini, coeff_up, maxIter=1000, tol=1e-08, maxIter_power=1000)[source]

cfactorizeOffDiaMatrix constrained version of factorizeOffDiaMatrix Returns main components from an off-diagonal Matrix (energy-loss x angular-departure).

XRStools.xrs_utilities.constrained_svd(M, U_ini, S_ini, VT_ini, U_up, max_iter=10000, verbose=False)[source]

constrained_nnmf Approximate singular value decomposition with constraints.

function [U, S, V] = constrained_svd(M,U_ini,S_ini,V_ini,U_up,max_iter=10000,verbose=False)

XRStools.xrs_utilities.convertSplitEDF2EDF(foldername)[source]

converts the old style EDF files (one image for horizontal and one image for vertical chambers) to the new style EDF (one single image).

Arg:
foldername (str): Path to folder with all the EDF-files to be
converted.
XRStools.xrs_utilities.convg(x, y, fwhm)[source]

Convolution with Gaussian x = x-vector y = y-vector fwhm = fulll width at half maximum of the gaussian with which y is convoluted

XRStools.xrs_utilities.convtoprim(hklconv)[source]

convtoprim converts diamond structure reciprocal lattice expressed in conventional lattice vectors to primitive one (Helsinki -> Palaiseau conversion) from S. Huotari

XRStools.xrs_utilities.cshift(w1, th)[source]

cshift Calculates Compton peak position.

Args:
  • w1 (float, array): Incident energy in [keV].
  • th (float): Scattering angle in [deg].
Returns:
  • w2 (foat, array): Energy of Compton peak in [keV].

Funktion adapted from Keijo Hamalainen.

XRStools.xrs_utilities.delE_JohannAberration(E, A, R, Theta)[source]

Calculates the Johann aberration of a spherical analyzer crystal.

Args:
E (float): Working energy in [eV]. A (float): Analyzer aperture [mm]. R (float): Radius of the Rowland circle [mm]. Theta (float): Analyzer Bragg angle [degree].
Returns:
Johann abberation in [eV].
XRStools.xrs_utilities.delE_dicedAnalyzerIntrinsic(E, Dw, Theta)[source]

Calculates the intrinsic energy resolution of a diced crystal analyzer.

Args:
E (float): Working energy in [eV]. Dw (float): Darwin width of the used reflection [microRad]. Theta (float): Analyzer Bragg angle [degree].
Returns:
Intrinsic energy resolution of a perfect analyzer crystal.
XRStools.xrs_utilities.delE_offRowland(E, z, A, R, Theta)[source]

Calculates the off-Rowland contribution of a spherical analyzer crystal.

Args:
E (float): Working energy in [eV]. z (float): Off-Rowland distance [mm]. A (float): Analyzer aperture [mm]. R (float): Radius of the Rowland circle [mm]. Theta (float): Analyzer Bragg angle [degree].
Returns:
Off-Rowland contribution in [eV] to the energy resolution.
XRStools.xrs_utilities.delE_pixelSize(E, p, R, Theta)[source]

Calculates the pixel size contribution to the resolution function of a diced analyzer crystal.

Args:
E (float): Working energy in [eV]. p (float): Pixel size in [mm]. R (float): Radius of the Rowland circle [mm]. Theta (float): Analyzer Bragg angle [degree].
Returns:
Pixel size contribution in [eV] to the energy resolution for a diced analyzer crystal.
XRStools.xrs_utilities.delE_sourceSize(E, s, R, Theta)[source]

Calculates the source size contribution to the resolution function.

Args:
E (float): Working energy in [eV]. s (float): Source size in [mm]. R (float): Radius of the Rowland circle [mm]. Theta (float): Analyzer Bragg angle [degree].
Returns:
Source size contribution in [eV] to the energy resolution.
XRStools.xrs_utilities.delE_stressedCrystal(E, t, v, R, Theta)[source]

Calculates the stress induced contribution to the resulution function of a spherically bent crystal analyzer.

Args:
E (float): Working energy in [eV]. t (float): Absorption length in the analyzer material [mm]. v (float): Poisson ratio of the analyzer material. R (float): Radius of the Rowland circle [mm]. Theta (float): Analyzer Bragg angle [degree].
Returns:
Stress-induced contribution in [eV] to the energy resolution.
XRStools.xrs_utilities.diode(current, energy, thickness=0.03)[source]

diode Calculates the number of photons incident for a Si PIPS diode.

Args:
  • current (float): Diode current in [pA].
  • energy (float): Photon energy in [keV].
  • thickness (float): Thickness of Si active layer in [cm].
Returns:
  • flux (float): Number of photons per second.

Function adapted from Matlab function by S. Huotari.

XRStools.xrs_utilities.dspace(hkl=[6, 6, 0], xtal='Si')[source]

% DSPACE Gives d-spacing for given xtal % d=dspace(hkl,xtal) % hkl can be a matrix i.e. hkl=[1,0,0 ; 1,1,1]; % xtal=’Si’,’Ge’,’LiF’,’InSb’,’C’,’Dia’,’Li’ (case insensitive) % if xtal is number this is user as a d0 % % KH 28.09.93 % SH 2005 %

class XRStools.xrs_utilities.dtxrd(hkl, energy, crystal='Si', asym_angle=0.0, angular_range=[-0.0005, 0.0005], angular_step=1e-08)[source]

Bases: object

class to hold all things dynamic theory of diffraction.

get_anomalous_absorption(energy=None)[source]
get_eta(angular_range, angular_step=1e-08)[source]
get_extinction_length(energy=None)[source]
get_reflection_width()[source]
get_reflectivity(angular_range=None, angular_step=None)[source]
set_asymmetry(alpha)[source]

negative alpha -> more grazing incidence

set_energy(energy)[source]
set_hkl(hkl)[source]
XRStools.xrs_utilities.dtxrd_anomalous_absorption(energy, hkl, alpha=0.0, crystal='Si', angular_range=array([-0.0005]))[source]
XRStools.xrs_utilities.dtxrd_extinction_length(energy, hkl, alpha=0.0, crystal='Si')[source]
XRStools.xrs_utilities.dtxrd_reflectivity(energy, hkl, alpha=0.0, crystal='Si', angular_range=array([-0.0005]))[source]
XRStools.xrs_utilities.e2pz(w1, w2, th)[source]

Calculates the momentum scale and the relativistic Compton cross section correction according to P. Holm, PRA 37, 3706 (1988).

This function is translated from Keijo Hamalainen’s Matlab implementation (KH 29.05.96).

Args:
  • w1 (float or np.array): incident energy in [keV]
  • w2 (float or np.array): scattered energy in [keV]
  • th (float): scattering angle two theta in [deg]
returns:
  • pz (float or np.array): momentum scale in [a.u.]
  • cf (float or np.array): cross section correction factor such that: J(pz) = cf * d^2(sigma)/d(w2)*d(Omega) [barn/atom/keV/srad]
XRStools.xrs_utilities.edfread(filename)[source]

reads edf-file with filename “filename” OUTPUT: data = 256x256 numpy array

XRStools.xrs_utilities.edfread_test(filename)[source]

reads edf-file with filename “filename” OUTPUT: data = 256x256 numpy array

here is how i opened the HH data: data = np.fromfile(f,np.int32) image = np.reshape(data,(dim,dim))

XRStools.xrs_utilities.element(z)[source]

Converts atomic number into string of the element symbol and vice versa.

Returns atomic number of given element, if z is a string of the element symbol or string of element symbol of given atomic number z.

Args:
  • z (string or int): string of the element symbol or atomic number.
Returns:
  • Z (string or int): string of the element symbol or atomic number.
XRStools.xrs_utilities.energy(d, ba)[source]

% ENERGY Calculates energy corrresponing to Bragg angle for given d-spacing % function e=energy(dspace,bragg_angle) % % dspace for reflection % bragg_angle in DEG % % KH 28.09.93

XRStools.xrs_utilities.energy_monoangle(angle, d=1.6374176589984608)[source]

% ENERGY Calculates energy corrresponing to Bragg angle for given d-spacing % function e=energy(dspace,bragg_angle) % % dspace for reflection (defaulf for Si(311) reflection) % bragg_angle in DEG % % KH 28.09.93 %

XRStools.xrs_utilities.fermi(rs)[source]

fermi Calculates the plasmon energy (in eV), Fermi energy (in eV), Fermi momentum (in a.u.), and critical plasmon cut-off vector (in a.u.).

Args:
  • rs (float): electron separation parameter
Returns:
  • wp (float): plasmon energy (in eV)
  • ef (float): Fermi energy (in eV)
  • kf (float): Fermi momentum (in a.u.)
  • kc (float): critical plasmon cut-off vector (in a.u.)

Based on Matlab function from A. Soininen.

XRStools.xrs_utilities.find_center_of_mass(x, y)[source]

Returns the center of mass (first moment) for the given curve y(x)

XRStools.xrs_utilities.find_diag_angles(q, x0, U, B, Lab, beam_in, lambdai, lambdao, tol=1e-08, method='BFGS')[source]

find_diag_angles Finds the FOURC spectrometer and sample angles for a desired q.

Args:
  • q (array): Desired momentum transfer in Lab coordinates.
  • x0 (list): Guesses for the angles (tthv, tthh, chi, phi, omega).
  • U (array): 3x3 U-matrix Lab-to-sample transformation.
  • B (array): 3x3 B-matrix reciprocal lattice to absolute units transformation.
  • lambdai (float): Incident x-ray wavelength in Angstrom.
  • lambdao (float): Scattered x-ray wavelength in Angstrom.
  • tol (float): Toleranz for minimization (see scipy.optimize.minimize)
  • method (str): Method for minimization (see scipy.optimize.minimize)
Returns:
  • ans (array): tthv, tthh, phi, chi, omega
XRStools.xrs_utilities.fwhm(x, y)[source]

finds full width at half maximum of the curve y vs. x returns f = FWHM x0 = position of the maximum

XRStools.xrs_utilities.gauss(x, x0, fwhm)[source]
XRStools.xrs_utilities.get_UB_Q(tthv, tthh, phi, chi, omega, **kwargs)[source]

get_UB_Q Returns the momentum transfer and scattering vectors for given FOURC spectrometer and sample angles. U-, B-matrices and incident/scattered wavelength are passed as keyword-arguments.

Args:
  • tthv (float): Spectrometer vertical 2Theta angle.
  • tthh (float): Spectrometer horizontal 2Theta angle.
  • chi (float): Sample rotation around x-direction.
  • phi (float): Sample rotation around y-direction.
  • omega (float): Sample rotation around z-direction.
  • kwargs (dict): Dictionary with key-word arguments:
    • kwargs[‘U’] (array): 3x3 U-matrix Lab-to-sample transformation.
    • kwargs[‘B’] (array): 3x3 B-matrix reciprocal lattice to absolute units transformation.
    • kwargs[‘lambdai’] (float): Incident x-ray wavelength in Angstrom.
    • kwargs[‘lambdao’] (float): Scattered x-ray wavelength in Angstrom.
Returns:
  • Q_sample (array): Momentum transfer in sample coordinates.
  • Ki_sample (array): Incident beam direction in sample coordinates.
  • Ko_sample (array): Scattered beam direction in sample coordinates.
XRStools.xrs_utilities.get_gnuplot_rgb(start=None, end=None, length=None)[source]

get_gnuplot_rgb Prints out a progression of RGB hex-keys to use in Gnuplot.

Args:
  • start (array): RGB code to start from (must be numbers out of [0,1]).
  • end (array): RGB code to end at (must be numbers out of [0,1]).
  • length (int): How many colors to print out.
XRStools.xrs_utilities.get_num_of_MD_steps(time_ps, time_step)[source]

Calculates the number of steps in an MD simulation for a desired time (in ps) and given step size (in a.u.)

Args:
time_ps (float): Desired time span (ps). time_step (float): Chosen time step (a.u.).
Returns:
The number of steps required to span the desired time span.
XRStools.xrs_utilities.getpenetrationdepth(energy, formulas, concentrations, densities)[source]

returns the penetration depth of a mixture of chemical formulas with certain concentrations and densities

XRStools.xrs_utilities.gettransmission(energy, formulas, concentrations, densities, thickness)[source]

returns the transmission through a sample composed of chemical formulas with certain densities mixed to certain concentrations, and a thickness

XRStools.xrs_utilities.hlike_Rwfn(n, l, r, Z)[source]

hlike_Rwfn Returns an array with the radial part of a hydrogen-like wave function.

Args:
  • n (integer): main quantum number n
  • l (integer): orbitalquantum number l
  • r (array): vector of radii on which the function should be evaluated
  • Z (float): effective nuclear charge
XRStools.xrs_utilities.householder(b, k)[source]

function H = householder(b, k) % H = householder(b, k) % Atkinson, Section 9.3, p. 611 % b is a column vector, k an index < length(b) % Constructs a matrix H that annihilates entries % in the product H*b below index k

% $Id: householder.m,v 1.1 2008-01-16 15:33:30 mike Exp $ % M. M. Sussman

XRStools.xrs_utilities.interpolate_M(xc, xi, yi, i0)[source]

Linear interpolation scheme after Martin Sundermann that conserves the absolute number of counts.

ONLY WORKS FOR EQUALLY/EVENLY SPACED XC, XI!

Args:
xc (np.array): The x-coordinates of the interpolated values. xi (np.array): The x-coordinates of the data points, must be increasing. yi (np.array): The y-coordinates of the data points, same length as xp. i0 (np.array): Normalization values for the data points, same length as xp.
Returns:
ic (np.array): The interpolated and normalized data points.

from scipy.interpolate import Rbf x = arange(20) d = zeros(len(x)) d[10] = 1 xc = arange(0.5,19.5) rbfi = Rbf(x, d) di = rbfi(xc)

XRStools.xrs_utilities.is_allowed_refl_fcc(H)[source]

is_allowed_refl_fcc Check if given reflection is allowed for a FCC lattice.

Args:
  • H (array, list, tuple): H=[h,k,l]
Returns:
  • boolean
XRStools.xrs_utilities.lindhard_pol(q, w, rs=3.93, use_corr=False, lifetime=0.28)[source]

lindhard_pol Calculates the Lindhard polarizability function (RPA) for certain q (a.u.), w (a.u.) and rs (a.u.).

Args:
  • q (float): momentum transfer (in a.u.)
  • w (float): energy (in a.u.)
  • rs (float): electron parameter
  • use_corr (boolean): if True, uses Bernardo’s calculation for n(k) instead of the Fermi function.
  • lifetime (float): life time (default is 0.28 eV for Na).

Based on Matlab function by S. Huotari.

XRStools.xrs_utilities.makeprofile(element, filename='/home/alex/src/XRStools/XRStools/data/ComptonProfiles.dat', E0=9.69, tth=35.0, correctasym=None)[source]

takes the profiles from ‘makepzprofile()’, converts them onto eloss scale and normalizes them to S(q,w) [1/eV] input: element = element symbol (e.g. ‘Si’, ‘Al’, etc.) filename = path and filename to tabulated profiles E0 = scattering energy [keV] tth = scattering angle [deg] returns: enscale = energy loss scale J = total CP C = only core contribution to CP V = only valence contribution to CP q = momentum transfer [a.u.]

XRStools.xrs_utilities.makeprofile_comp(formula, filename='/home/alex/src/XRStools/XRStools/data/ComptonProfiles.dat', E0=9.69, tth=35, correctasym=None)[source]

returns the compton profile of a chemical compound with formula ‘formula’ input: formula = string of a chemical formula (e.g. ‘SiO2’, ‘Ba8Si46’, etc.) filename = path and filename to tabulated profiles E0 = scattering energy [keV] tth = scattering angle [deg] returns: eloss = energy loss scale J = total CP C = only core contribution to CP V = only valence contribution to CP q = momentum transfer [a.u.]

XRStools.xrs_utilities.makeprofile_compds(formulas, concentrations=None, filename='/home/alex/src/XRStools/XRStools/data/ComptonProfiles.dat', E0=9.69, tth=35.0, correctasym=None)[source]

returns sum of compton profiles from a lost of chemical compounds weighted by the given concentration

XRStools.xrs_utilities.makepzprofile(element, filename='/home/alex/src/XRStools/XRStools/data/ComptonProfiles.dat')[source]

constructs compton profiles of element ‘element’ on pz-scale (-100:100 a.u.) from the Biggs tables provided in ‘filename’

input:
  • element = element symbol (e.g. ‘Si’, ‘Al’, etc.)
  • filename = path and filename to tabulated profiles
returns:
  • pzprofile = numpy array of the CP: * 1. column: pz-scale * 2. … n. columns: compton profile of nth shell * binden = binding energies of shells * occupation = number of electrons in the according shells
XRStools.xrs_utilities.mat2con(W, H, W_up, H_up)[source]
XRStools.xrs_utilities.mat2vec(F, C, F_up, C_up, n, k, m)[source]
class XRStools.xrs_utilities.maxipix_det(name, spot_arrangement)[source]

Bases: object

Class to store some useful values from the detectors used. To be used for arranging the ROIs.

get_det_name()[source]
get_pixel_range()[source]
XRStools.xrs_utilities.momtrans_au(e1, e2, tth)[source]

Calculates the momentum transfer in atomic units input: e1 = incident energy [keV] e2 = scattered energy [keV] tth = scattering angle [deg] returns: q = momentum transfer [a.u.] (corresponding to sin(th)/lambda)

XRStools.xrs_utilities.momtrans_inva(e1, e2, tth)[source]

Calculates the momentum transfer in inverse angstrom input: e1 = incident energy [keV] e2 = scattered energy [keV] tth = scattering angle [deg] returns: q = momentum transfer [a.u.] (corresponding to sin(th)/lambda)

XRStools.xrs_utilities.mpr(energy, compound)[source]

Calculates the photoelectric, elastic, and inelastic absorption of a chemical compound.

Calculates the photoelectric, elastic, and inelastic absorption of a chemical compound.

Args:
  • energy (np.array): energy scale in [keV].
  • compound (string): chemical sum formula (e.g. ‘SiO2’)
Returns:
  • murho (np.array): absorption coefficient normalized by the density.
  • rho (float): density in UNITS?
  • m (float): atomic mass in UNITS?
XRStools.xrs_utilities.mpr_compds(energy, formulas, concentrations, E0, rho_formu)[source]

Calculates the photoelectric, elastic, and inelastic absorption of a mix of compounds.

Returns the photoelectric absorption for a sum of different chemical compounds.

Args:
  • energy (np.array): energy scale in [keV].
  • formulas (list of strings): list of chemical sum formulas
Returns:
  • murho (np.array): absorption coefficient normalized by the density.
  • rho (float): density in UNITS?
  • m (float): atomic mass in UNITS?
XRStools.xrs_utilities.myprho(energy, Z, logtablefile='/home/alex/src/XRStools/XRStools/data/logtable.dat')[source]

Calculates the photoelectric, elastic, and inelastic absorption of an element Z

Calculates the photelectric , elastic, and inelastic absorption of an element Z. Z can be atomic number or element symbol.

Args:
  • energy (np.array): energy scale in [keV].
  • Z (string or int): atomic number or string of element symbol.
Returns:
  • murho (np.array): absorption coefficient normalized by the density.
  • rho (float): density in UNITS?
  • m (float): atomic mass in UNITS?
XRStools.xrs_utilities.nonzeroavg(y=None)[source]
XRStools.xrs_utilities.odefctn(y, t, abb0, abb1, abb7, abb8, lex, sgbeta, y0, c1)[source]

#% [T,Y] = ODE23(ODEFUN,TSPAN,Y0,OPTIONS,P1,P2,…) passes the additional #% parameters P1,P2,… to the ODE function as ODEFUN(T,Y,P1,P2…), and to #% all functions specified in OPTIONS. Use OPTIONS = [] as a place holder if #% no options are set.

XRStools.xrs_utilities.odefctn_CN(yCN, t, abb0, abb1, abb7, abb8N, lex, sgbeta, y0, c1)[source]
XRStools.xrs_utilities.parseformula(formula)[source]

Parses a chemical sum formula.

Parses the constituing elements and stoichiometries from a given chemical sum formula.

Args:
  • formula (string): string of a chemical formula (e.g. ‘SiO2’, ‘Ba8Si46’, etc.)
Returns:
  • elements (list): list of strings of constituting elemental symbols.
  • stoichiometries (list): list of according stoichiometries in the same order as ‘elements’.
XRStools.xrs_utilities.plotpenetrationdepth(energy, formulas, concentrations, densities)[source]

opens a plot window of the penetration depth of a mixture of chemical formulas with certain concentrations and densities plotted along the given energy vector

XRStools.xrs_utilities.plottransmission(energy, formulas, concentrations, densities, thickness)[source]

opens a plot with the transmission plotted along the given energy vector

XRStools.xrs_utilities.primtoconv(hklprim)[source]

primtoconv converts diamond structure reciprocal lattice expressed in primitive basis to the conventional basis (Palaiseau -> Helsinki conversion) from S. Huotari

XRStools.xrs_utilities.pz2e1(w2, pz, th)[source]

Calculates the incident energy for a specific scattered photon and momentum value.

Returns the incident energy for a given photon energy and scattering angle. This function is translated from Keijo Hamalainen’s Matlab implementation (KH 29.05.96).

Args:
  • w2 (float): scattered photon energy in [keV]
  • pz (np.array): pz scale in [a.u.]
  • th (float): scattering angle two theta in [deg]
Returns:
  • w1 (np.array): incident energy in [keV]
XRStools.xrs_utilities.read_dft_wfn(element, n, l, spin=None, directory='/home/alex/src/XRStools/XRStools/data')[source]

read_dft_wfn Parses radial parts of wavefunctions.

Args:
  • element (str): Element symbol.
  • n (int): Main quantum number.
  • l (int): Orbital quantum number.
  • spin (str): Which spin channel, default is average over up and down.
  • directory (str): Path to directory where the wavefunctions can be found.
Returns:
  • r (np.array): radius
  • wfn (np.array):
XRStools.xrs_utilities.readbiggsdata(filename, element)[source]

Reads Hartree-Fock Profile of element ‘element’ from values tabulated by Biggs et al. (Atomic Data and Nuclear Data Tables 16, 201-309 (1975)) as provided by the DABAX library (http://ftp.esrf.eu/pub/scisoft/xop2.3/DabaxFiles/ComptonProfiles.dat). input: filename = path to the ComptonProfiles.dat file (the file should be distributed with this package) element = string of element name returns:

  • data = the data for the according element as in the file:
    • #UD Columns:
    • #UD col1: pz in atomic units
    • #UD col2: Total compton profile (sum over the atomic electrons
    • #UD col3,…coln: Compton profile for the individual sub-shells
  • occupation = occupation number of the according shells
  • bindingen = binding energies of the accorting shells
  • colnames = strings of column names as used in the file
XRStools.xrs_utilities.readfio(prefix, scannumber, repnumber=0)[source]

if repnumber = 0: reads a spectra-file (name: prefix_scannumber.fio) if repnumber > 1: reads a spectra-file (name: prefix_scannumber_rrepnumber.fio)

XRStools.xrs_utilities.readp01image(filename)[source]

reads a detector file from PetraIII beamline P01

XRStools.xrs_utilities.readp01scan(prefix, scannumber)[source]

reads a whole scan from PetraIII beamline P01 (experimental)

XRStools.xrs_utilities.readp01scan_rep(prefix, scannumber, repetition)[source]

reads a whole scan with repititions from PetraIII beamline P01 (experimental)

XRStools.xrs_utilities.savitzky_golay(y, window_size, order, deriv=0, rate=1)[source]

Smooth (and optionally differentiate) data with a Savitzky-Golay filter. The Savitzky-Golay filter removes high frequency noise from data. It has the advantage of preserving the original shape and features of the signal better than other types of filtering approaches, such as moving averages techniques.

Parameters:
  • y : array_like, shape (N,) the values of the time history of the signal.
  • window_size : int the length of the window. Must be an odd integer number.
  • order : int the order of the polynomial used in the filtering. Must be less then window_size - 1.
  • deriv: int the order of the derivative to compute (default = 0 means only smoothing)
Returns
  • ys : ndarray, shape (N) the smoothed signal (or it’s n-th derivative).
Notes:
The Savitzky-Golay is a type of low-pass filter, particularly suited for smoothing noisy data. The main idea behind this approach is to make for each point a least-square fit with a polynomial of high order over a odd-sized window centered at the point.

Examples

t = np.linspace(-4, 4, 500)
y = np.exp( -t**2 ) + np.random.normal(0, 0.05, t.shape)
ysg = savitzky_golay(y, window_size=31, order=4)
import matplotlib.pyplot as plt
plt.plot(t, y, label='Noisy signal')
plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal')
plt.plot(t, ysg, 'r', label='Filtered signal')
plt.legend()
plt.show()
References ::
[1]A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 1964, 36 (8), pp 1627-1639.
[2]Numerical Recipes 3rd Edition: The Art of Scientific Computing W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery Cambridge University Press ISBN-13: 9780521880688
XRStools.xrs_utilities.sgolay2d(z, window_size, order, derivative=None)[source]
XRStools.xrs_utilities.sigmainc(Z, energy, logtablefile='/home/alex/src/XRStools/XRStools/data/logtable.dat')[source]

sigmainc Calculates the Incoherent Scattering Cross Section in cm^2/g using Log-Log Fit.

Args:
  • z (int or string): Element number or elements symbol.
  • energy (float or array): Energy (can be number or vector)
Returns:
  • tau (float or array): Photoelectric cross section in [cm**2/g]

Adapted from original Matlab function of Keijo Hamalainen.

XRStools.xrs_utilities.specread(filename, nscan)[source]

reads scan “nscan” from SPEC-file “filename”

INPUT:
  • filename = string with the SPEC-file name
  • nscan = number (int) of desired scan
OUTPUT:
  • data =
  • motors =
  • counters = dictionary
XRStools.xrs_utilities.spline2(x, y, x2)[source]

Extrapolates the smaller and larger valuea as a constant

XRStools.xrs_utilities.split_hdf5_address(dataadress)[source]
XRStools.xrs_utilities.sumx(A)[source]

Short-hand command to sum over 1st dimension of a N-D matrix (N>2) and to squeeze it to N-1-D matrix.

XRStools.xrs_utilities.svd_my(M, maxiter=100, eta=0.1)[source]
XRStools.xrs_utilities.taupgen(e, hkl=[6, 6, 0], crystals='Si', R=1.0, dev=array([-50., -49., -48., -47., -46., -45., -44., -43., -42., -41., -40., -39., -38., -37., -36., -35., -34., -33., -32., -31., -30., -29., -28., -27., -26., -25., -24., -23., -22., -21., -20., -19., -18., -17., -16., -15., -14., -13., -12., -11., -10., -9., -8., -7., -6., -5., -4., -3., -2., -1., 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99., 100., 101., 102., 103., 104., 105., 106., 107., 108., 109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119., 120., 121., 122., 123., 124., 125., 126., 127., 128., 129., 130., 131., 132., 133., 134., 135., 136., 137., 138., 139., 140., 141., 142., 143., 144., 145., 146., 147., 148., 149.]), alpha=0.0)[source]

% TAUPGEN Calculates the reflectivity curves of bent crystals % % function [refl,e,dev]=taupgen_new(e,hkl,crystals,R,dev,alpha); % % e = fixed nominal energy in keV % hkl = reflection order vector, e.g. [1 1 1] % crystals = crystal string, e.g. ‘si’ or ‘ge’ % R = bending radius in meters % dev = deviation parameter for which the % curve will be calculated (vector) (optional) % alpha = asymmetry angle % based on a FORTRAN program of Michael Krisch % Translitterated to Matlab by Simo Huotari 2006, 2007 % Is far away from being good matlab writing - mostly copy&paste from % the fortran routines. Frankly, my dear, I don’t give a damn. % Complaints -> /dev/null

XRStools.xrs_utilities.taupgen_amplitude(e, hkl=[6, 6, 0], crystals='Si', R=1.0, dev=array([-50., -49., -48., -47., -46., -45., -44., -43., -42., -41., -40., -39., -38., -37., -36., -35., -34., -33., -32., -31., -30., -29., -28., -27., -26., -25., -24., -23., -22., -21., -20., -19., -18., -17., -16., -15., -14., -13., -12., -11., -10., -9., -8., -7., -6., -5., -4., -3., -2., -1., 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99., 100., 101., 102., 103., 104., 105., 106., 107., 108., 109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119., 120., 121., 122., 123., 124., 125., 126., 127., 128., 129., 130., 131., 132., 133., 134., 135., 136., 137., 138., 139., 140., 141., 142., 143., 144., 145., 146., 147., 148., 149.]), alpha=0.0)[source]

% TAUPGEN Calculates the reflectivity curves of bent crystals % % function [refl,e,dev]=taupgen_new(e,hkl,crystals,R,dev,alpha); % % e = fixed nominal energy in keV % hkl = reflection order vector, e.g. [1 1 1] % crystals = crystal string, e.g. ‘si’ or ‘ge’ % R = bending radius in meters % dev = deviation parameter for which the % curve will be calculated (vector) (optional) % alpha = asymmetry angle % based on a FORTRAN program of Michael Krisch % Translitterated to Matlab by Simo Huotari 2006, 2007 % Is far away from being good matlab writing - mostly copy&paste from % the fortran routines. Frankly, my dear, I don’t give a damn. % Complaints -> /dev/null

XRStools.xrs_utilities.tauphoto(Z, energy, logtablefile='/home/alex/src/XRStools/XRStools/data/logtable.dat')[source]

tauphoto Calculates Photoelectric Cross Section in cm^2/g using Log-Log Fit.

Args:
  • z (int or string): Element number or elements symbol.
  • energy (float or array): Energy (can be number or vector)
Returns:
  • tau (float or array): Photoelectric cross section in [cm**2/g]

Adapted from original Matlab function of Keijo Hamalainen.

XRStools.xrs_utilities.unconstrained_mf(A, numComp=3, maxIter=1000, tol=1e-08)[source]

unconstrained_mf Returns main components from an off-diagonal Matrix (energy-loss x angular-departure), using the power method iteratively on the different main components.

XRStools.xrs_utilities.vangle(v1, v2)[source]

vangle Calculates the angle between two cartesian vectors v1 and v2 in degrees.

Args:
  • v1 (np.array): first vector.
  • v2 (np.array): second vector.
Returns:
  • th (float): angle between first and second vector.

Function by S. Huotari, adopted for Python.

XRStools.xrs_utilities.vec2mat(x, F, C, F_up, C_up, n, k, m)[source]
XRStools.xrs_utilities.vrot(v, vaxis, phi)[source]

vrot Rotates a vector around a given axis.

Args:
  • v (np.array): vector to be rotated
  • vaxis (np.array): rotation axis
  • phi (float): angle [deg] respecting the right-hand rule
Returns:
  • v2 (np.array): new rotated vector

Function by S. Huotari (2007) adopted to Python.

XRStools.xrs_utilities.vrot2(vector1, vector2, angle)[source]

rotMatrix Rotate vector1 around vector2 by an angle.

XRStools.XRStool Package

XRStools.xrs_calctools Module

XRStools.xrs_calctools.alterGROatomNames(filename, oldName, newName)[source]
XRStools.xrs_calctools.axsfTrajParser(filename)[source]

axsfTrajParser

XRStools.xrs_calctools.boxParser(filename)[source]

parseXYZfile Reads an xyz-style file.

XRStools.xrs_calctools.broaden_diagram(e, s, params=[1.0, 1.0, 537.5, 540.0], npoints=1000)[source]

function [e2,s2] = broaden_diagram2(e,s,params,npoints)

% BROADEN_DIAGRAM2 Broaden a StoBe line diagram % % [ENE2,SQW2] = BROADEN_DIAGRAM2(ENE,SQW,PARAMS,NPOINTS) % % gives the broadened spectrum SQW2(ENE2) of the line-spectrum % SWQ(ENE). Each line is substituted with a Gaussian peak, % the FWHM of which is determined by PARAMS. ENE2 is a linear % scale of length NPOINTS (default 1000). % % PARAMS = [f_min f_max emin max] % % For ENE <= e_min, FWHM = f_min. % For ENE >= e_max, FWHM = f_min. % FWHM increases linearly from [f_min f_max] between [e_min e_max]. % % T Pylkkanen @ 2008-04-18 [17:37]

XRStools.xrs_calctools.broaden_linear(spec, params=[0.8, 8, 537.5, 550], npoints=1000)[source]

broadens a spectrum with a Gaussian of width params[0] below params[2] and width params[1] above params[3], width increases linear in between. returns two-column numpy array of length npoints with energy and the broadened spectrum

XRStools.xrs_calctools.calculateCOMlist(atomList)[source]

calculateCOMlist Calculates center of mass for a list of atoms.

XRStools.xrs_calctools.calculateRIJhist(atoms, boxLength, DELR=0.01, MAXBIN=1000)[source]
XRStools.xrs_calctools.calculateRIJhist_arb(atoms, lattice, lattice_inv, DELR=0.01, MAXBIN=1000)[source]
XRStools.xrs_calctools.changeOHBondLength(h2oMol, fraction, boxLength=None, oName='O', hName='H')[source]
XRStools.xrs_calctools.convg(x, y, fwhm)[source]

Convolution with Gaussian

XRStools.xrs_calctools.countHbonds(mol1, mol2, Roocut=3.6, Rohcut=2.4, Aoooh=30.0)[source]
XRStools.xrs_calctools.countHbonds_orig(mol1, mol2, Roocut=3.6, Rohcut=2.4, Aoooh=30.0)[source]
XRStools.xrs_calctools.countHbonds_pbc(mol1, mol2, boxLength, Roocut=3.6, Rohcut=2.4, Aoooh=30.0)[source]
XRStools.xrs_calctools.count_HBonds_pbc_arb(mol1, mol2, lattice, lattice_inv, Roocut=3.6, Rohcut=2.4, Aoooh=30.0)[source]
XRStools.xrs_calctools.count_OO_neighbors(list_of_o_atoms, Roocut, boxLength=None)[source]
XRStools.xrs_calctools.count_OO_neighbors_pbc(list_of_o_atoms, Roocut, boxLength, numbershells=1)[source]
XRStools.xrs_calctools.cut_spec(spec, emin=None, emax=None)[source]

deletes lines of matrix with first column smaller than emin and larger than emax

class XRStools.xrs_calctools.erkale(prefix, postfix, fromnumber, tonumber, step, stepformat=2)[source]

Bases: object

class to analyze ERKALE XRS results.

broaden_lin(params=[0.8, 8, 537.5, 550], npoints=1000)[source]
cut_broadspecs(emin=None, emax=None)[source]
cut_rawspecs(emin=None, emax=None)[source]
norm_area(emin=None, emax=None)[source]
norm_max()[source]
plot_spec()[source]
sum_specs()[source]
XRStools.xrs_calctools.findAllWaters(point, waterMols, o_name, cutoff)[source]
XRStools.xrs_calctools.findMolecule(xyzAtoms, molAtomList)[source]
XRStools.xrs_calctools.find_H2O_molecules(o_atoms, h_atoms, boxLength=None)[source]
XRStools.xrs_calctools.find_H2O_molecules_PBC_arb(o_atoms, h_atoms, lattice, lattice_inv, OH_cutoff=1.5)[source]
XRStools.xrs_calctools.gauss(x, x0, fwhm)[source]
XRStools.xrs_calctools.gauss1(x, x0, fwhm)[source]

returns a gaussian with peak value normalized to unity a[0] = peak position a[1] = Full Width at Half Maximum

XRStools.xrs_calctools.gauss_areanorm(x, x0, fwhm)[source]

area-normalized gaussian

XRStools.xrs_calctools.getDistVector(atom1, atom2)[source]
XRStools.xrs_calctools.getDistVectorPBC_arb(atom1, atom2, lattice, lattice_inv)[source]

getDistVectorPBC_arb

Calculates the distance vector between two atoms from an arbitrary simulation box using the minimum image convention.

Args:
atom1 (obj): Instance of the xzyAtom class. atom2 (obj): Instance of the xzyAtom class. lattice (np.array): Array with lattice vectors as columns. lattice_inv (np.array): Inverse of lattice.
Returns:
The distance vector between the two atoms (np.array).
XRStools.xrs_calctools.getDistVectorPbc(atom1, atom2, boxLength)[source]
XRStools.xrs_calctools.getDistance(atom1, atom2)[source]
XRStools.xrs_calctools.getDistancePBC_arb(atom1, atom2, lattice, lattice_inv)[source]

getDistancePBC_arb

Calculates the distance of two atoms from an arbitrary simulation box using the minimum image convention.

Args:
atom1 (obj): Instance of the xzyAtom class. atom2 (obj): Instance of the xzyAtom class. lattice (np.array): Array with lattice vectors as columns. lattice_inv (np.array): Inverse of lattice.
Returns:
The distance between the two atoms.
XRStools.xrs_calctools.getDistancePbc(atom1, atom2, boxLength)[source]
XRStools.xrs_calctools.getDistsFromMolecule(point, listOfMolecules, o_name=None)[source]
XRStools.xrs_calctools.getPeriodicTestBox(xyzAtoms, boxLength, numbershells=1)[source]
XRStools.xrs_calctools.getPeriodicTestBox_arb(xyzAtoms, lattice, lattice_inv, numbershells=1)[source]
XRStools.xrs_calctools.getPeriodicTestBox_molecules(Molecules, boxLength, numbershells=1)[source]
XRStools.xrs_calctools.getTetraParameter(o_atoms, boxLength=None)[source]

according to NATURE, VOL 409, 18 JANUARY 2001

XRStools.xrs_calctools.getTranslVec(atom1, atom2, boxLength)[source]

getTranslVec Returns the translation vector that brings atom2 closer to atom1 in case atom2 is further than boxLength away.

XRStools.xrs_calctools.getTranslVec_geocen(mol1COM, mol2COM, boxLength)[source]

getTranslVec_geocen

XRStools.xrs_calctools.groBoxParser(filename, nanoMeter=True)[source]

groBoxParser Parses an gromacs GRO-style file for the xyzBox class.

XRStools.xrs_calctools.groTrajecParser(filename, nanoMeter=True)[source]

groTrajecParser Parses an gromacs GRO-style file for the xyzBox class.

XRStools.xrs_calctools.keithBoxParser(cell_fname, coord_fname)[source]

keithBoxParser

Reads structure files from Keith’s SiO2 simulations.

XRStools.xrs_calctools.load_erkale_spec(filename)[source]

returns an erkale spectrum

XRStools.xrs_calctools.load_erkale_specs(prefix, postfix, fromnumber, tonumber, step, stepformat=2)[source]

returns a list of erkale spectra

XRStools.xrs_calctools.load_stobe_specs(prefix, postfix, fromnumber, tonumber, step, stepformat=2)[source]

load a bunch of StoBe calculations, which filenames are made up of the prefix, postfix, and the counter in the between the prefix and postfix runs from ‘fromnumber’ to ‘tonumber’ in steps of ‘step’ (number of digits is ‘stepformat’)

XRStools.xrs_calctools.parseOCEANinputFile(fname)[source]

parseOCEANinputFile

Parses an OCEAN input file and returns lattice vectors, atom names, and relative atom positions.

Args:
  • fname (str): Absolute filename of OCEAN input file.
  • atoms (list): List of elemental symbols in the same order as they appear in the input file.
Returns:
  • lattice (np.array): Array of lattice vectors.
  • rel_coords (np.array): Array of relative atomic coordinates.
  • oceaatoms (list): List of atomic names.
XRStools.xrs_calctools.parsePwscfFile(fname)[source]

parsePwscfFile

Parses a PWSCF file and returns a xyzBox object.

Args:
fname (str): Absolute filename of OCEAN input file.
Returns:
xyzBox object
XRStools.xrs_calctools.parseXYZfile(filename)[source]

parseXYZfile Reads an xyz-style file.

XRStools.xrs_calctools.readxas(filename)[source]

function output = readxas(filename)%[e,p,s,px,py,pz] = readxas(filename)

% READSTF Load StoBe fort.11 (XAS output) data % % [E,P,S,PX,PY,PZ] = READXAS(FILENAME) % % E energy transfer [eV] % P dipole transition intensity % S r^2 transition intensity % PX dipole transition intensity along x % PY dipole transition intensity along y % PZ dipole transition intensity along z % % as line diagrams. % % T Pylkkanen @ 2011-10-17

XRStools.xrs_calctools.repair_h2o_molecules_pbc(h2o_mols, boxLength)[source]
XRStools.xrs_calctools.spline2(x, y, x2)[source]

Extrapolates the smaller and larger valuea as a constant

class XRStools.xrs_calctools.stobe(prefix, postfix, fromnumber, tonumber, step, stepformat=2)[source]

Bases: object

class to analyze StoBe results

broaden_lin(params=[0.8, 8, 537.5, 550], npoints=1000)[source]
cut_rawspecs(emin=None, emax=None)[source]
norm_area(emin, emax)[source]
sum_specs()[source]
XRStools.xrs_calctools.translateOcean2FDMNES_p1(ocean_in, fdmnes_out, header_file)[source]
XRStools.xrs_calctools.writeFDMNESinput_file(xyzAtoms, fname, Filout, Range, Radius, Edge, NRIXS, Absorber)[source]

writeFDMNESinput_file Writes an input file to be used for FDMNES.

XRStools.xrs_calctools.writeMD1Input(fname, box, headerfile, exatomNo=0)[source]

writeWFN1input Writes an input for cp.x by Quantum espresso for electronic wave function minimization.

XRStools.xrs_calctools.writeOCEAN_XESInput(fname, box, headerfile, exatomNo=0)[source]

writeOCEAN_XESInput Writes an input for ONEAN XES calculation for 17 molecule water boxes.

XRStools.xrs_calctools.writeOCEANinput(fname, headerfile, xyzBox, exatom, edge, subshell)[source]

writeOCEANinput

XRStools.xrs_calctools.writeOCEANinput_arb(fname, headerfile, xyzBox, exatom, edge, subshell)[source]

writeOCEANinput

XRStools.xrs_calctools.writeOCEANinput_full(fname, xyzBox, exatom, edge, subshell)[source]

Writes a complete OCEAN input file.

Args:
  • fname (str): Filename for the input file to be written.
  • xyzBox (xyzBox): Instance of the xyzBox class to be converted into an OCEAN input file.
  • exatom (str): Atomic symbol for the excited atom.
  • edge (int): Integer defining which shell to excite (e.g. 0 for K-shell, 1 for L, etc.).
  • subshell (int): Integer defining which sub-shell to excite ( e.g. 0 for s, 1 for p, etc.).
XRStools.xrs_calctools.writeOCEANinput_new(fname, headerfile, xyzBox, exatom, edge, subshell)[source]

writeOCEANinput

XRStools.xrs_calctools.writeRelXYZfile(filename, n_atoms, boxLength, title, xyzAtoms, inclAtomNames=True)[source]
XRStools.xrs_calctools.writeWFN1waterInput(fname, box, headerfile, exatomNo=0)[source]

writeWFN1input Writes an input for cp.x by Quantum espresso for electronic wave function minimization.

XRStools.xrs_calctools.writeXYZfile(filename, numberOfAtoms, title, list_of_xyzAtoms)[source]
XRStools.xrs_calctools.writeXYZtrajectory(filename, boxes)[source]
class XRStools.xrs_calctools.xyzAtom(name, coordinates, number)[source]

Bases: object

xyzAtom

Class to hold information about and manipulate a single atom in xyz-style format.

Args. :
  • name (str): Atomic symbol.
  • coordinates (np.array): Array of xyz-coordinates.
  • number (int): Integer, e.g. number of atom in a cluster.
getCoordinates()[source]
getNorm()[source]
load_spectrum(file_name)[source]
normalize_spectrum(normrange)[source]
translateSelf(vector)[source]
translateSelf_arb(lattice, lattice_inv, vector)[source]
class XRStools.xrs_calctools.xyzBox(xyzAtoms, boxLength=None, title=None)[source]

Bases: object

xyzBox

Class to hold information about and manipulate a xyz-periodic cubic box.

Args.:
  • xyzAtoms (list): List of instances of the xyzAtoms class that make up the molecule.
  • boxLength (float): Box length.
changeOHBondlength(fraction, oName='O', hName='H')[source]

changeOHBondlength Changes all OH covalent bond lengths inside the box by a fraction.

count_contact_pairs(name_1, name_2, cutoff, counter_name='contact_pair')[source]
count_hbonds(Roocut=3.6, Rohcut=2.4, Aoooh=30.0, counter_name='num_H_bonds')[source]

count_hbonds Counts the number of hydrogen bonds around all oxygen atoms and sets that number as attribute to the accorting xyzAtom.

count_neighbors(name1, name2, cutoff_low=0.0, cutoff_high=2.0, counter_name='num_OO_shell')[source]

count_neighbors

Counts number of neighbors (of name2) around atom of name1.

Args:
  • name1 (str): Name of first type of atom.
  • name2 (str): Name of second type of atom.
  • cutoff_low (float): Lower cutoff (Angstrom).
  • cutoff_high (float): Upper cutoff (Angstrom).
  • counter_name (str): Attribute namer under which the result should be saved.
deleteTip4pCOM()[source]

deleteTip4pCOM Deletes the ficticious atoms used in the TIP4P water model.

find_hydroniums(OH_cutoff=1.5)[source]

find_hydroniums Returns a list of hydronium molecules.

find_hydroxides(OH_cutoff=1.5)[source]

find_hydroxides Returns a list of hydroxide molecules.

getCoordinates()[source]

getCoordinates Return coordinates of all atoms in the cluster.

getDistVectorPBC_arb(atom1, atom2)[source]

getDistVectorPBC_arb

Calculates the distance vector between two atoms from an arbitrary simulation box using the minimum image convention.

Args:
atom1 (obj): Instance of the xzyAtom class. atom2 (obj): Instance of the xzyAtom class.
Returns:
The distance vector between the two atoms (np.array).
getDistancePBC_arb(atom1, atom2)[source]

getDistancePBC_arb Calculates the distance of two atoms from an arbitrary simulation box using the minimum image convention.

Args:
atom1 (obj): Instance of the xzyAtom class. atom2 (obj): Instance of the xzyAtom class.
Returns:
The distance between the two atoms.
getTetraParameter()[source]

getTetraParameter Returns a list of tetrahedrality paprameters, according to NATURE, VOL 409, 18 JANUARY (2001).

UNTESTED!!!

get_OO_neighbors(Roocut=3.6)[source]

get_OO_neighbors Returns list of numbers of nearest oxygen neighbors within readius ‘Roocut’.

get_OO_neighbors_pbc(Roocut=3.6)[source]

get_OO_neighbors_pbc Returns a list of numbers of nearest oxygen atoms, uses periodic boundary conditions.

get_angle_arb(atom1, atom2, atom3, degrees=True)[source]

get_angle Return angle between the three given atoms (as seen from atom2).

get_atoms_by_name(name)[source]

get_atoms_by_name Return a list of all xyzAtoms of a given name ‘name’.

get_atoms_from_molecules()[source]

get_atoms_from_molecules Parses all atoms inside self.xyzMolecules into self.xyzAtoms (useful for turning an xyzMolecule into an xyzBox).

get_h2o_molecules(o_name='O', h_name='H')[source]

get_h2o_molecules Finds all water molecules inside the box and collects them inside the self.xyzMolecules attribute.

get_h2o_molecules_arb(o_name='O', h_name='H')[source]
get_hbonds(Roocut=3.6, Rohcut=2.4, Aoooh=30.0)[source]

get_hbonds Counts the hydrogen bonds inside the box, returns the number of H-bond donors and H-bond acceptors.

multiplyBoxPBC(numShells)[source]

multiplyBoxPBC Applies the periodic boundary conditions and multiplies the box in shells around the original.

multiplyBoxPBC_arb(numShells)[source]

multiplyBoxPBC_arb Applies the periodic boundary conditions and multiplies the box in shells around the original. Works with arbitrary lattices.

normalize_arb_spectrum(normrange, attribute)[source]
normalize_spectrum(normrange)[source]
scatterPlot()[source]

scatterPlot Opens a plot window with a scatter-plot of all coordinates of the box.

setBoxLength(boxLength, angstrom=True)[source]

setBoxLength Set the box length.

translateAtomsMinimumImage(lattice, lattice_inv)[source]

translateAtomsMinimumImage

Brings back all atoms into the original box using periodic boundary conditions and minimal image convention.

writeBox(filename)[source]

writeBox Creates an xyz-style text file with all coordinates of the box.

writeClusters(cenatom_name, number, cutoff, prefix, postfix='.xyz')[source]

writeXYZclusters Write water clusters into files.

writeFDMNESinput(fname, Filout, Range, Radius, Edge, NRIXS, Absorber)[source]

writeFDMNESinput Creates an input file to be used for q-dependent calculations with FDMNES.

writeH2Oclusters(cutoff, prefix, postfix='.xyz', o_name='O', h_name='H')[source]

writeXYZclusters Write water clusters into files.

writeMoleculeCluster(molAtomList, fname, cutoff=None, numH2Omols=None, o_name='O', h_name='H', mol_center=None)[source]

writeMoleculeCluster Careful, this works only for a single molecule in water.

writeOCEANinput(fname, headerfile, exatom, edge, subshell)[source]

writeOCEANinput Creates an OCEAN input file based on the headerfile.

writeRelBox(filename, inclAtomNames=True)[source]

writeRelBox Writes all relative atom coordinates into a text file (useful as OCEAN input).

class XRStools.xrs_calctools.xyzMolecule(xyzAtoms, title=None)[source]

Bases: object

xyzMolecule

Class to hold information about and manipulate an xyz-style molecule.

Args.:
  • xyzAtoms (list): List of instances of the xyzAtoms class that make up the molecule.
appendAtom(Atom)[source]

appendAtom Add an xzyAtom to the molecule.

getCoordinates()[source]

getCoordinates Return coordinates of all atoms in the cluster.

getCoordinates_name(name)[source]

getCoordinates_name Return coordintes of all atoms with ‘name’.

getGeometricCenter()[source]

getGeometricCenter Return the geometric center of the xyz-molecule.

get_atoms_by_name(name)[source]

get_atoms_by_name Return a list of all xyzAtoms of a given name ‘name’.

popAtom(xyzAtom)[source]

popAtom Delete an xyzAtom from the molecule.

scatterPlot()[source]

scatterPlot Opens a plot window with a scatter-plot of all coordinates of the molecule.

translateSelf(vector)[source]

translateSelf Translate all atoms of the molecule by a vector ‘vector’.

writeXYZfile(fname)[source]

writeXYZfile Creates an xyz-style text file with all coordinates of the molecule.

XRStools.xrs_calctools.xyzTrajecParser(filename, boxLength)[source]

Parses a Trajectory of xyz-files.

Args:
filename (str): Filename of the xyz Trajectory file.
Returns:
A list of xzyBoxes.
class XRStools.xrs_calctools.xyzTrajectory(xyzBoxes)[source]

Bases: object

getRDF(atom1='O', atom2='O', MAXBIN=1000, DELR=0.01, RHO=1.0)[source]
getRDF_arb(atom1='O', atom2='O', MAXBIN=1000, DELR=0.01, RHO=1.0)[source]
loadAXSFtraj(filename)[source]
writeRandBox(filename)[source]
writeXYZtraj(filename)[source]

XRStools.xrs_extraction Module

class XRStools.xrs_extraction.HF_dataset(data, formulas, stoich_weights, edges)[source]

Bases: object

dataset A class to hold all information from HF Compton profiles necessary to subtract background from the experiment.

get_C_edges_av(element, edge, columns)[source]
get_C_total(columns)[source]
get_J_total_av(columns)[source]
class XRStools.xrs_extraction.edge_extraction(exp_data, formulas, stoich_weights, edges, prenormrange=[5, inf])[source]

Bases: object

edge_extraction Class to destill core edge spectra from x-ray Raman scattering experiments.

analyzerAverage(roi_numbers, errorweighing=True)[source]

analyzerAverage Averages signals from several crystals before background subtraction.

Args:

  • roi_numbers : list, str
    list of ROI numbers to average over of keyword for analyzer chamber (e.g. ‘VD’,’VU’,’VB’,’HR’,’HL’,’HB’)
  • errorweighing : boolean (True by default)
    keyword if error weighing should be used for the averaging or not
removeCorePearsonAv(element, edge, range1, range2, weights=[2, 1], HFcore_shift=0.0, guess=None, scaling=None, return_background=False)[source]

removeCorePearsonAv

removeCorePearsonAv_new(element, edge, range1, range2, HFcore_shift=0.0, guess=None, scaling=None, return_background=False)[source]

removeCorePearsonAv_new

removePearsonAv(element, edge, range1, range2=None, weights=[2, 1], guess=None, scale=1.0, HFcore_shift=0.0)[source]

removePearsonAv

removePolyCoreAv(element, edge, range1, range2, weights=[1, 1], guess=[1.0, 0.0, 0.0], ewindow=100.0)[source]

removePolyCoreAv Subtract a polynomial from averaged data guided by the HF core Compton profile.

Args

  • element : str String (e.g. ‘Si’) for the element you want to work on.
  • edge: str String (e.g. ‘K’ or ‘L23’) for the edge to extract.
  • range1 : list List with start and end value for fit-region 1.
  • range2 : list List with start and end value for fit-region 2.
  • weigths : list of ints List with weights for the respective fit-regions 1 and 2. Default is [1,1].
  • guess : list List of starting values for the fit. Default is [1.0,0.0,0.0] (i.e. a quadratic function. Change the number of guess values to get other degrees of polynomials (i.e. [1.0, 0.0] for a constant, [1.0,0.0,0.0,0.0] for a cubic, etc.). The first guess value passed is for scaling of the experimental data to the HF core Compton profile.
  • ewindow: float Width of energy window used in the plot. Default is 100.0.
save_average_Sqw(filename, emin=None, emax=None, normrange=None)[source]

save_average_Sqw Save the S(q,w) into a ascii file (energy loss, S(q,w), Poisson errors).

Args:
  • filename : str Filename for the ascii file.
  • emin : float Use this to save only part of the spectrum.
  • emax : float Use this to save only part of the spectrum.
  • normrange : list of floats E_start and E_end for possible area-normalization before saving.
class XRStools.xrs_extraction.functorObjectV(y, eloss, hfcore)[source]

Bases: object

funct(a, eloss)[source]
XRStools.xrs_extraction.map_chamber_names(name)[source]

map_chamber_names Maps names of chambers to range of ROI numbers.

class XRStools.xrs_extraction.valence_CP[source]

Bases: object

valence_CP Class to organize information about extracted experimental valence Compton profiles.

get_asymmetry()[source]
get_pzscale()[source]

XRStools.xrs_imaging Module

class XRStools.xrs_imaging.LRimage(matrix, xscale, yscale, cornerpos=[0, 0], monitor=1.0)[source]

Bases: object

container class to hold info of a single LR-image to be put togther in a SR-image by the imageset class

load()[source]
plotimage()[source]
save()[source]
shiftx()[source]
shifty()[source]
XRStools.xrs_imaging.estimate_shift(x1, y1, im1, x2, y2, im2)[source]

estimate shift in x-direction only by stepwise shifting im2 by precision and thus minimising the sum of the difference between im1 and im2

XRStools.xrs_imaging.estimate_xshift(x1, y1, im1, x2, y2, im2)[source]

estimate shift in x-direction only by stepwise shifting im2 by precision and thus minimising the sum of the difference between im1 and im2

XRStools.xrs_imaging.estimate_yshift(x1, y1, im1, x2, y2, im2)[source]

estimate shift in x-direction only by stepwise shifting im2 by precision and thus minimising the sum of the difference between im1 and im2

class XRStools.xrs_imaging.image(matrix, xscale, yscale)[source]

Bases: object

Container class to hold info of a single LR-image to be put togther in a SR-image by the imageset class

load()[source]
plotimage()[source]
save()[source]
shiftx()[source]
shifty()[source]
class XRStools.xrs_imaging.imageset[source]

Bases: object

class to make SR-images from list of LR-images

estimate_shifts(whichimage=None)[source]
estimate_xshifts(whichimage=None)[source]
estimate_yshifts(whichimage=None)[source]
interpolate_shift_images(scaling, whichimages=None)[source]
interpolate_xshift_images(scaling, whichimages=None)[source]
interpolate_yshift_images(scaling, whichimages=None)[source]
load()[source]
loadhe3070(matfilename)[source]
loadkimberlite(matfilename)[source]
plotLR(whichimage)[source]
plotSR()[source]
save()[source]
XRStools.xrs_imaging.interpolate_image(oldx, oldy, oldIM, newx, newy)[source]

2d interpolation

class XRStools.xrs_imaging.oneD_imaging(absfilename, energycolumn='sty', monitorcolumn='kapraman', monitor_divider=1.0, edfName=None, single_image=True, sumto1D=1, recenterings=None)[source]

Bases: XRStools.xrs_read.read_id20

oneD_imaging Class to construct images using the 1D piercing mode.

load_state_hdf5(filename, groupname)[source]
loadscan_2Dimages(scannumbers, scantype='sty', isolateSpot=0)[source]
save_state_hdf5(filename, groupname, comment='', myrank=0, factor=1.0, save_also_roi=False)[source]

XRStools.xrs_read Module

class XRStools.xrs_read.Fourc(path, SPECfname='rixs', EDFprefix='/edf/', EDFname='rixs_', EDFpostfix='.edf', moni_column='izero', EinCoor='energy')[source]

Bases: object

Main class for handling RIXS data from ID20’s high-resolution spectrometer ‘Fourc’.

This class is intended to read SPEC- and according EDF-files and perform dispersion compensations.

Note:

‘Fourc’ is the name of the high-energy-resolution spectrometer at ESRF’s ID20 beamline. This class has been adopted specifically for this spectrometer.

If you are using this program, please cite the following work:

Sahle, Ch J., A. Mirone, J. Niskanen, J. Inkinen, M. Krisch, and S. Huotari. “Planning, performing and analyzing X-ray Raman scattering experiments.” Journal of Synchrotron Radiation 22, No. 2 (2015): 400-409.

Args:
  • path (str): Absolute path to directory holding the data.
  • SPECfname (str): Name of the SPEC-file (‘rixs’ is the default).
  • EDFprefix (str): Prefix for the EDF-files (‘/edf/’ is the default).
  • EDFname (str): Filename of the EDF-files (‘rixs_’ is the default).
  • EDFpostfix (str): Postfix for the EDF-files (‘.edf’ is the default).
  • en_column (str): Counter mnemonic for the energy motor (‘energy’ is the default).
  • moni_column (str): Mnemonic for the monitor counter (‘izero’ is the default).
  • EinCoor (list): Coordinates, where to find the incident energy value in the SPEC-file (default is [9,0])
Attributes:
  • path (str): Absolute path to directory holding the data.
  • SPECfname (str): Name of the SPEC-file (‘hydra’ is the default).
  • EDFprefix (str): Prefix for the EDF-files (‘/edf/’ is the default).
  • EDFname (str): Filename of the EDF-files (‘hydra_’ is the default).
  • EDFpostfix (str): Postfix for the EDF-files (‘.edf’ is the default).
  • en1_column (str): Counter mnemonic for the energy motor (‘anal energy’ is the default).
  • en2_column (str): Counter mnemonic for the energy motor (‘energy’ is the default).
  • moni_column (str): Mnemonic for the monitor counter (‘izero’ is the default).
  • EinCoor (list): Coordinates, where to find the incident energy value in the SPEC-file (default is [9,0])
  • scans (dict): Dictionary holding all loaded scans.
  • scan_numbers (list): List with scan number of all loaded scans.
  • energy (np.array): Array with the common energy scale.
  • energy2 (np.array): Array with the common energy2 scale.
  • signals (np.array): Array with the signals for all analyzers (one column per anayzer).
  • errors (np.array): Array with the poisson errors for all analyzers (one column per anayzer).
  • groups (dict): Dictionary of groups of scans (instances of the ‘scangroup’ class, such as 2 ‘elastic’, or 5 ‘edge1’, etc.).
  • tth (list): List of all scattering angles (one value for each ROI).
  • resolution (list): List of FWHM of the elastic lines (one for each analyzer).
  • roi_obj (instance): Instance of the roi_object class from the x0.055 # pixel size in mmrs_rois module defining all ROIs for the current dataset (default is ‘None’).
  • comp_factor (float): Compensation factor used for the dispersion correction.
  • PIXEL_SIZE (float): Pixel size of the used Maxipix detector (in mm).
SumDirect(scan_numbers, clean_edf_stack=False)[source]

SumDirect

Creates a summed 2D image of a given scan or list of scans.

Args:
scan_numbers (int or list): Scan number or list of scan numbers to be added up.
Returns:
A 2D np.array of the same size as the detector with the summed image.
copy_edf_files(scan_numbers, dest_dir)[source]

copy_edf_files

Copies all EDF-files from given scan_numbers into given directory.

Args:
  • scan_numbers (int or list) = Integer or list of integers defining the scan numbers of scans to be copied.
  • dest_dir (str) = String with absolute path for the destination.
delete_scan(scan_numbers)[source]

delete_scan

Deletes scans of given scan numbers.

Args:
scan_numbers (int or list): SPEC scan numbers to be used for the RIXS map.
dump_scans_ascii(scan_numbers, pre_fix, f_name, post_fix='.dat', header='')[source]

dum_scans_ascii

Produce ASCII-type files with columns of energy, signal, and Poisson error.

Args: scan_numbers (int or list): SPEC scan numbers of scans to be safed in ASCII format. pre_fix (str): Path to directory where files should be written into. f_name (str): Base name for the files to be written. post_fix (str): Extention for the files to be written (default is ‘.dat’).

get_Ein_RIXS_map(scan_numbers, roi_number, logscaling=False, file_name=None)[source]

get_Ein_RIXS_map

Returns a RIXS map that plots energy loss vs inciden energy for the specified scans.

Args:
  • scan_numbers (int or list): SPEC scan numbers to be deleted.
  • logscaling (boolean): If true numbers are returned on logarithmic scale (False by default)
  • file_name (str): Absolute path, if map should also be written into an ascii-file.
  • roi_number (int): ROI to use for creating the RIXS map (Python counting).
get_XES_spectrum(method='sum', interpolation=False)[source]

get_XES_spectrum

Constructs an emission spectrum based on the loaded single spectra.

NOTE: this needs support for all methods (‘sum’, ‘pixel’, ‘row’)

get_compensation_factor(scan_number, method='sum', roi_number=None, rot_angles=None)[source]

get_compensation_factor

Calculates the compensation factor from a given elastic line scan: - a pixel-wise center of mass for ‘pixel’ compensation. - a slope (eV/mm) for row-by-row compensation. - the center of mass for each ROI for no compensation.

Args:
scan_number (int): Scan number of elastic line scan to be used
for finding the compensation factors.
method (str): Keyword describing what kind of compensation
to be used. Can be ‘sum’, ‘row’, or ‘pixel’.
roi_number (int): ROI number (first ROI is Nr. 0) for which to
calculate the line-by-line compensation factor.
get_data(method='sum', scaling=None)[source]

get_data

Applies the ROIs to the EDF-files.

This extracts the raw-data from the EDF-files subject to three different methods: ‘sum’ will simply sum up all pixels inside a ROI, ‘row’ will sum up over the dispersive direction, and ‘pixel’ will not sum at all.

Args:
method (str): Keyword specifying the selected choice of data treatment:
can be ‘sum’, ‘row’, or ‘pixel’. Default is ‘sum’.
scaling (list): Optional scaling factors (one per ROI) to scale
the data with.
get_pw_matrices(scan_numbers, method='pixel')[source]

get_pw_matrices

Sums scans from pixelwise ROI integration for use in the pixel-wise ROI refinement.

Args:
  • scan_numbers (int, list): Integer or list of scan numbers to be added.
  • method (str): Keyword describing how to return the data (possible values are: ‘pixel’ for pixel-wise integration, ‘column’ for column-wise summation, and ‘row’ for row-wise summation.
Returns:
  • Raw data in pixel-wise format.
get_raw_data(method='sum', scaling=None)[source]

get_raw_data

Applies the ROIs to extract the raw signals from the EDF-files.

load_scan(scan_numbers, direct=True, comp_factor=None, scan_type='generic', scaling=None, method='sum', rot_angles=None, clean_edf_stack=False)[source]

load_scan

Loads given scans and applies the dispersion compensation.

Args:

scan_numbers (int or list): Scan number(s) of scans to be loaded. direct (boolean): Flag, if set to ‘True’, EDF-files are

deleted after loading the scan (this is the default).
comp_factor (float): Compensation factor to be used. If ‘None’,
the global compensation factor will be used. If provided, the global compensation factor will be overwritten.

scan_type (str): String describing the scan to be loaded.

Note:
If a compensation factor is passed to this function, the classes ‘globel’ compensation factor is overwritten.
set_roiObj(roiobj)[source]

set_roiObj

Assign an instance of the ‘roi_object’ class to the current data set.

Args:
roiobj (instance): Instance of the ‘roi_object’ class holding all
information about the definition of the ROIs.
class XRStools.xrs_read.Hydra(path, SPECfname='hydra', EDFprefix='/edf/', EDFname='hydra_', EDFpostfix='.edf', en_column='energy', moni_column='izero')[source]

Bases: object

Main class for handling XRS data from ID20’s multi-analyzer spectrometer ‘Hydra’.

This class is intended to read SPEC- and according EDF-files and generate spectra from multiple individual energy loss scans.

Note:

Hydra is the name of the multi-analyzer x-ray Raman scattering spectrometer at ESRF’s ID20 beamline. This class has been adopted specifically for this spectrometer.

If you are using this program, please cite the following work:

Sahle, Ch J., A. Mirone, J. Niskanen, J. Inkinen, M. Krisch, and S. Huotari. “Planning, performing and analyzing X-ray Raman scattering experiments.” Journal of Synchrotron Radiation 22, No. 2 (2015): 400-409.

Args:
  • path (str): Absolute path to directory holding the data.
  • SPECfname (str): Name of the SPEC-file (‘hydra’ is the default).
  • EDFprefix (str): Prefix for the EDF-files (‘/edf/’ is the default).
  • EDFname (str): Filename of the EDF-files (‘hydra_’ is the default).
  • EDFpostfix (str): Postfix for the EDF-files (‘.edf’ is the default).
  • en_column (str): Counter mnemonic for the energy motor (‘energy’ is the default).
  • moni_column (str): Mnemonic for the monitor counter (‘izero’ is the default).
Attributes:
  • path (str): Absolute path to directory holding the data.
  • SPECfname (str): Name of the SPEC-file (‘hydra’ is the default).
  • EDFprefix (str): Prefix for the EDF-files (‘/edf/’ is the default).
  • EDFname (str): Filename of the EDF-files (‘hydra_’ is the default).
  • EDFpostfix (str): Postfix for the EDF-files (‘.edf’ is the default).
  • en_column (str): Counter mnemonic for the energy motor (‘energy’ is the default).
  • moni_column (str): Mnemonic for the monitor counter (‘izero’ is the default).
  • scans (dict): Dictionary holding all loaded scans.
  • scan_numbers (list): List with scan number of all loaded scans.
  • eloss (np.array): Array with the common energy loss scale for all analyzers.
  • energy (np.array): Array with the common energy scale for all analyzers.
  • signals (np.array): Array with the signals for all analyzers (one column per anayzer).
  • errors (np.array): Array with the poisson errors for all analyzers (one column per anayzer).
  • qvalues (list): List of momentum transfer values for all analyzers.
  • groups (dict): Dictionary of groups of scans (instances of the ‘scangroup’ class, such as 2 ‘elastic’, or 5 ‘edge1’, etc.).
  • cenom (list): List of center of masses of the elastic lines.
  • E0 (int): Elastic line energy value, mean value of all center of masses.
  • tth (list): List of all scattering angles (one value for each ROI).
  • resolution (list): List of FWHM of the elastic lines (one for each analyzer).
  • comp_factor (float): Compensation factor for line-by-line energy dispersion compensation.
  • cenom_dict (dict): Dictionary holding center-of-masses for of the elastic line.
  • raw_signals (dict): Dictionary holding pixel- or line-wise signals.
  • raw_errors (dict): Dictionary holding pixel- or line-wise Poisson errors.
  • TTH_OFFSETS1 (np.array): Two-Theta offsets between individual analyzers inside each analyzer module in one direction (horizontal for V-boxes, vertical for H-boxes).
  • TTH_OFFSETS2 (np.array): Two-Theta offsets between individual analyzers inside each analyzer module in one direction (horizontal for H-boxes, vertical for V-boxes).
  • roi_obj (instance): Instance of the roi_object class from the xrs_rois module defining all ROIs for the current dataset (default is ‘None’).
SumDirect(scan_numbers)[source]

SumDirect

Creates a summed 2D image of a given scan or list of scans.

Args:
scan_numbers (int or list): Scan number or list of scan numbers to be added up.
Returns:
A 2D np.array of the same size as the detector with the summed image.
copy_edf_files(scan_numbers, dest_dir)[source]

copy_edf_files

Copies all EDF-files from given scan_numbers into given directory.

Args:
  • scan_numbers (int or list) = Integer or list of integers defining the scan numbers of scans to be copied.
  • dest_dir (str) = String with absolute path for the destination.
delete_scan(scan_numbers)[source]

delete_scan

Deletes scans from the dictionary of scans.

Args:
scan_numbers (int or list): Integer or list of integers (SPEC scan numbers) to be deleted.
dump_scans_ascii(scan_numbers, pre_fix, f_name, post_fix='.dat', header='')[source]

dump_scans_ascii

Produce ASCII-type files with columns of energy, signal, and Poisson error.

Args:
scan_numbers (int or list): SPEC scan numbers of scans to be safed in ASCII format. pre_fix (str): Path to directory where files should be written into. f_name (str): Base name for the files to be written. post_fix (str): Extention for the files to be written (default is ‘.dat’).
dump_spectrum_ascii(file_name, header='')[source]

dump_spectrum_ascii

Stores the energy loss and signals in a txt-file.

Args:
filename (str): Path and filename to the file to be written.
dump_spectrum_hdf5(file_name, group_name, comment='')[source]

dump_spectrum_hdf5

Writes the summed spectrum into an HDF5 file.

Args:
file_name (str): Path and file name for the HDF5-file to be created. group_name (str): Group name under which to store status in the HDF5-file. comment (str): Optional comment (no comment is default).
get_compensation_factor(scan_number, method='sum', roi_number=None)[source]

get_compensation_factor

Calculates the compensation factor from a given elastic line scan: - a pixel-wise center of mass for ‘pixel’ compensation. - a slope (eV/mm) for row-by-row compensation. - the center of mass for each ROI for no compensation.

Args:
scan_number (int): Scan number of elastic line scan to be used
for finding the compensation factors.
method (str): Keyword describing what kind of compensation
to be used. Can be ‘sum’, ‘row’, or ‘pixel’.
roi_number (int): ROI number (first ROI is Nr. 0) for which to
calculate the line-by-line compensation factor.
get_data()[source]

get_data

Applies the ROIs and sums up intensities.

Returns:
‘None’, if no ROI object is available.
get_data_new(method='sum', scaling=None)[source]

get_data_new

Applies the ROIs to the EDF-files.

This extracts the raw-data from the EDF-files subject to three different methods: ‘sum’ will simply sum up all pixels inside a ROI, ‘row’ will sum up over the dispersive direction, and ‘pixel’ will not sum at all.

Args:
method (str): Keyword specifying the selected choice of data treatment:
can be ‘sum’, ‘row’, or ‘pixel’. Default is ‘sum’.
get_data_pw()[source]

get_data_pw

Extracts intensities for each pixel in each ROI.

Returns:
‘None’, if no ROI object is available.
get_eloss()[source]

get_eloss

Finds the energy loss scale for all ROIs by calculating the center of mass (COM) for each ROI’s elastic line. Calculates the resolution function (FWHM) of the elastic lines.

get_eloss_new(method='sum')[source]

get_eloss_new

Defines the energy loss scale for all ROIs and applies dispersion compensation if applicable.

Args:
method (str): Keyword describing which dispersion compensation
method to use. Possible choices are ‘sum’ (no compensation), ‘pixel’ (pixel-by-pixel compensation), or ‘row’ (line-by-line) compensation.
get_pw_matrices(scan_numbers, method='pixel')[source]

get_pw_matrices

Sums scans from pixelwise ROI integration for use in the pixel-wise ROI refinement.

Args:
  • scan_numbers (int, list): Integer or list of scan numbers to be added.
  • method (str): Keyword describing how to return the data (possible values are: ‘pixel’ for pixel-wise integration, ‘column’ for column-wise summation, and ‘row’ for row-wise summation.
Returns:
  • Raw data in pixel-wise format.
get_q_values(inv_angstr=False, energy_loss=None)[source]

get_q_values

Calculates the momentum transfer for each analyzer.

Args:
inv_angstr (boolean): Boolean flag, if ‘True’ momentum transfers are calculated in
inverse Angstroms.
energy_loss (float): Energy loss value at which the momentum transfer is to be
calculated. If ‘None’ is given, the momentum transfer is calculated for every energy loss point of the spectrum.
Returns:
If an energy loss value is passed, the function returns the momentum transfers at this energy loss value for each analyzer crystal.
get_raw_data(method='sum', scaling=None)[source]

get_raw_data

Applies the ROIs to the EDF-files.

This extracts the raw-data from the EDF-files subject to three different methods: ‘sum’ will simply sum up all pixels inside a ROI, ‘row’ will sum up over the dispersive direction, and ‘pixel’ will not sum at all.

Args:
method (str): Keyword specifying the selected choice of data treatment:
can be ‘sum’, ‘row’, or ‘pixel’. Default is ‘sum’.
get_spectrum(include_elastic=False, abs_counts=False)[source]

get_spectrum

Constructs a spectrum based on the scans loaded so far. Defines the energy loss scale based on the elastic lines.

Args:
include_elastic (boolean): Boolean flag, does not include the elastic line if
set to ‘False’ (this is the default).
abs_counts (boolean): Boolean flag, constructs the spectrum in absolute
counts if set to ‘True’ (default is ‘False’)
get_spectrum_new(method='sum', include_elastic=False, abs_counts=False, interpolation=False)[source]

get_spectrum_new

Constructs a spectrum from the scans loaded so far based on the chosen method: detector pixels can either be summed up (method=’sum’), pixel-by-pixel compensation can be applied (method=’pixel’), or a line-by-line compensation scheme can be applied (method=’row’).

The energy loss scale will be defined in the process and the data will be interpolated onto this scale using norm-conserving wavelet interpolation.

Args:
method (str): Keyword describing the kind of integration scheme
to be used (possible values are ‘sum’, ‘pixel’, or ‘row’), default is ‘sum’.
include_elastic (boolean): Boolean flag, does not include the elastic line if
set to ‘False’ (this is the default).
abs_counts (boolean): Boolean flag, constructs the spectrum in absolute
counts if set to ‘True’ (default is ‘False’)
interpolation (boolean): Boolean flag, if True, signals are interpolated
onto energy grid of the first scan in each group of scans.
get_tths(rvd=0.0, rvu=0.0, rvb=0.0, rhl=0.0, rhr=0.0, rhb=0.0, order=[0, 1, 2, 3, 4, 5])[source]

get_tths

Calculates the scattering angles for all analyzer crystals based on the mean angle of the analyzer modules.

Args:
  • rhl (float): Mean scattering angle of the HL module (default is 0.0).
  • rhr (float): Mean scattering angle of the HR module (default is 0.0).
  • rhb (float): Mean scattering angle of the HB module (default is 0.0).
  • rvd (float): Mean scattering angle of the VD module (default is 0.0).
  • rvu (float): Mean scattering angle of the VU module (default is 0.0).
  • rvb (float): Mean scattering angle of the VB module (default is 0.0).
  • order (list): List of integers (0-5) that describe the order of modules in which the ROIs were defined (default is VD, VU, VB, HR, HL, HB; i.e. [0,1,2,3,4,5]).
load_loop(beg_nums, num_of_regions, direct=True, method='sum')[source]

load_loop

Loads a whole loop of scans based on their starting numbers and the number of single scans in the loop.

Args: beg_nums (list): List of scan numbers of the first scans in each loop. num_of_regions (int): Number of scans in each loop.

load_scan(scan_numbers, scan_type='generic', direct=True, scaling=None, method='sum')[source]

load_scan

Load a single or multiple scans. Note:

When composing a spectrum later, scans of the same scan_type will be averaged over. Scans with scan type ‘elastic’ or long in their names are recognized and will be treated specially.
Args:
  • scan_numbers (int or list): Integer or iterable of scan numbers to be loaded.
  • scan_type (str): String describing the scan to be loaded (e.g. ‘edge1’ or ‘K-edge’).
  • direct (boolean): Flag, ‘True’ if the EDF-files should be deleted after loading/integrating the scan.
load_state_hdf5(file_name, group_name)[source]

load_state_hdf5

Load the status of an instance from an HDF5 file.

Args:
file_name (str): Path and filename for the HDF5-file to be created. group_name (str): Group name under which to store status in the HDF5-file.
print_scan_length(scan_numbers)[source]

print_scan_length

Print out the numper of points in given scans.

Args:
scan_numbers (int or list): Scan number or list of scan numbers.
save_state_hdf5(file_name, group_name, comment='')[source]

save_state_hdf5

Save the status of the current instance in an HDF5 file.

Args:
file_name (str): Path and file name for the HDF5-file to be created. group_name (str): Group name under which to store status in the HDF5-file. comment (str): Optional comment (no comment is default).
set_roiObj(roiobj)[source]

set_roiObj

Assign an instance of the ‘roi_object’ class to the current data set.

Args:
roiobj (instance): Instance of the ‘roi_object’ class holding all
information about the definition of the ROIs.
there_is_a_valid_roi_at(n)[source]

there_is_a_valid_roi_at

Checks if n is a valid ROI index.

Args:
n (int): Index to be checked.
Returns:
True, if n is a valid ROI index.
class XRStools.xrs_read.Hydra_imaging(path, SPECfname='hydra', EDFprefix='/edf/', EDFname='hydra_', EDFpostfix='.edf', en_column='sty', moni_column='izero')[source]

Bases: XRStools.xrs_read.Hydra

Hydra_imaging

get_compensation_factor(el_scan_numbers, scan_motor='sty', plotting=False)[source]

get_compensation_factor

Calculates the compensation factor for the case of imaging:

Args:
scan_number (int): Scan number of elastic line scan to be used
for finding the compensation factors.
method (str): Keyword describing what kind of compensation
to be used. Can be ‘sum’, ‘row’, or ‘pixel’.
roi_number (int): ROI number (first ROI is Nr. 0) for which to
calculate the line-by-line compensation factor.
interpolate_scans(step_motor='STZ')[source]

compensate_scans

Interpolate signals onto common energy-loss grid.

load_reference_scan(scan_number, scan_type='image_ref', direct=True, scaling=None)[source]
load_scan(scan_numbers, scan_type='imaging', direct=True, scaling=None, method='column', scan_motor='STZ')[source]

load_scan

Load a single or multiple scans.

Note:
When composing a spectrum later, scans of the same ‘scan_type’ will be averaged over. Scans with scan type ‘elastic’ or ‘long’ in their names are recognized and will be treated specially.
Args:
  • scan_numbers (int or list): Integer or iterable of scan numbers to be loaded.
  • scan_type (str): String describing the scan to be loaded (e.g. ‘edge1’ or ‘K-edge’).
  • direct (boolean): Flag, ‘True’ if the EDF-files should be deleted after loading/integrating the scan.
XRStools.xrs_read.alignment_image(id20read_object, scannumber, motorname, filename=None)[source]

Loads a scan from a sample position scan (x-scan, y-scan, z-scan), lets you choose a zoomroi and constructs a 2D image from this INPUT:

  • scannumber = number of the scan
  • motorname = string that contains the motor name (must be the same as in the SPEC file)
  • filename = optional parameter with filename to store the image
XRStools.xrs_read.alignment_image_new(so, scan_number, motorname)[source]

Loads a scan from a sample position scan (x-scan, y-scan, z-scan), lets you choose a zoomroi and constructs a 2D image from this INPUT:

  • scannumber = number of the scan
  • motorname = string that contains the motor name (must be the same as in the SPEC file)
  • filename = optional parameter with filename to store the image
XRStools.xrs_read.animation(id20read_object, scannumber, logscaling=True, timeout=-1, colormap='jet')[source]

Shows the edf-files of a scan as a ‘movie’. INPUT:

  • scannumber = integer/scannumber
  • logscaling = set to ‘True’ (default) if edf-images are to be shown on logarithmic-scale
  • timeout = time in seconds defining pause between two images, if negative (default) images are renewed by mouse clicks
  • colormap = matplotlib color scheme used in the display
XRStools.xrs_read.get_scans_pw(id20read_object, scannumbers)[source]

get_scans_pw Sums scans from pixelwise ROI integration for use in the PW roi refinement.

XRStools.xrs_read.print_citation_message()[source]

Prints plea for citing the XRStools article when using this software.

class XRStools.xrs_read.read_id20(absfilename, energycolumn='energy', monitorcolumn='kap4dio', edfName=None, single_image=True)[source]

Bases: object

Main class for handling raw data from XRS experiments on ESRF’s ID20. This class is used to read scans from SPEC files and the according EDF-files, it provides access to all tools from the xrs_rois module for defining ROIs, it can be used to integrate scans, sum them up, stitch them together, and define the energy loss scale. INPUT:

  • absfilename = path and filename of the SPEC-file
  • energycolumn = name (string) of the counter for the energy as defined in the SPEC session (counter mnemonic)
  • monitorcolumn = name (string) of the counter for the monitor signals as defined in the SPEC session (counter mnemonic)
  • edfName = name/prefix (string) of the EDF-files (default is the same as the SPEC-file name)
  • single_image = boolean switch, ‘True’ (default) if all 6 detectors are merged in a single image, ‘False’ if two detector images per point exist.
SumDirect(scannumbers)[source]
copy_edf_files(scannumbers, destdir)[source]

Copies all edf-files from scan with scannumber or scannumbers into directory ‘destdir’ INPUT:

  • scannumbers = integer or list of integers defining the scannumbers from the SPEC file
  • destdir = string with absolute path for the destination
deletescan(scannumbers)[source]

Deletes scans from the class. INPUT:

  • scannumbers = integer or list of integers (SPEC scan numbers) to delete
geteloss()[source]

Defines the energy loss scale for all ROIs by finding the center of mass for each ROI’s elastic line. Interpolates the signals and errors onto a commom energy loss scale. Finds the resolution (FWHM) of the ‘elastic’ groups.

getqvals(invangstr=False)[source]

Calculates q-values from E0 and tth values in either atomic units (defalt) or inverse angstroms.

getqvals_energy(energy)[source]

Returns all q-values at a certain energy loss. INPUT:

  • energy = energy loss value for which all q-values are stored
getrawdata()[source]

Goes through all instances of the scan class and calls it’s applyrois method to sum up over all rois.

getrawdata_pixelwise()[source]

Goes through all instances of the scan class and calls it’s applyrois_pw method to extract intensities for all pixels in each ROI.

getspectrum(include_elastic=False, absCounts=False)[source]

Groups the instances of the scan class by their scantype attribute, adds equal scans (each group of equal scans) and appends them. INPUT:

  • include_elastic = boolean flag, skips the elastic line if set to ‘False’ (default)
gettths(rvd=0.0, rvu=0.0, rvb=0.0, rhl=0.0, rhr=0.0, rhb=0.0, order=[0, 1, 2, 3, 4, 5])[source]

Uses the defined TT_OFFSETS of the read_id20 class to set all scattering angles tth from the mean angle avtth of the analyzer modules. INPUT:

  • rhl = mean tth angle of HL module (default is 0.0)
  • rhr = mean tth angle of HR module (default is 0.0)
  • rhb = mean tth angle of HB module (default is 0.0)
  • rvd = mean tth angle of VD module (default is 0.0)
  • rvu = mean tth angle of VU module (default is 0.0)
  • rvb = mean tth angle of VB module (default is 0.0)
  • order = list of integers (0-5) which describes the order of modules in which the ROIs were defined (default is VD, VU, VB, HR, HL, HB; i.e. [0,1,2,3,4,5])
load_state_hdf5(filename, groupname)[source]
loadelastic(scann, fromtofile=False)[source]

Loads a scan using the loadscan function and sets the scantype attribute to ‘elastic’. I.e. shorthand for ‘obj.loadscan(scannumber,type=’elastic’)’. INPUT:

  • scann = integer or list of integers
  • fromtofile = boolean flag, ‘True’ if the scan should be saved in a pickle-file (this is developmental)
loadelasticdirect(scann, fromtofile=False)[source]

Loads a scan using the loadscan function and sets the scantype attribute to ‘elastic’. I.e. shorthand for ‘obj.loadscan(scannumber,type=’elastic’)’. INPUT:

  • scann = integer or list of integers
  • fromtofile = boolean flag, ‘True’ if the scan should be saved in a pickle-file (this is developmental)
loadlong(scann, fromtofile=False)[source]

Loads a scan using the loadscan function and sets the scantype attribute to ‘long’. I.e. shorthand for ‘obj.loadscan(scannumber,type=’long’)’. INPUT:

  • scann = integer or list of integers
  • fromtofile = boolean flag, ‘True’ if the scan should be saved in a pickle-file (this is developmental)
loadlongdirect(scann, fromtofile=False, scaling=None)[source]

Loads a scan using the loadscan function and sets the scantype attribute to ‘long’. I.e. shorthand for ‘obj.loadscan(scannumber,type=’long’)’. INPUT:

  • scann = integer or list of integers
  • fromtofile = boolean flag, ‘True’ if the scan should be saved in a pickle-file (this is developmental)
loadloop(begnums, numofregions, fromtofile=False)[source]

Loads a whole loop of scans based on their starting scannumbers and the number of single scans in the loop. INPUT:

  • begnums = list of scannumbers of the first scans of each loop (is a list)
  • numofregions = number of scans in each loop (integer)
loadloopdirect(begnums, numofregions, fromtofile=False, scaling=None)[source]

Loads a whole loop of scans based on their starting scannumbers and the number of single INPUT:

  • begnums = list of scannumbers of the first scans of each loop (is a list)
  • numofregions = number of scans in each loop (integer)
  • fromtofile = boolean flag, ‘True’ if the scan should be saved in a pickle-file (this is developmental)
  • scaling = list of scaling factors to be applied, one for each ROI defined
loadscan(scannumbers, scantype='generic', fromtofile=False)[source]

Loads the files belonging to scan No. “scannumber” and puts it into an instance of the xrs_scan-class ‘scan’. The default scantype is ‘generic’, later the scans will be grouped (and added) based on the scantype. INPUT:

  • scannumbers = integer or list of scannumbers that should be loaded
  • scantype = string describing the scan to be loaded (e.g. ‘edge1’ or ‘K-edge’)
  • fromtofile = boolean flag, ‘True’ if the scan should be saved in a pickle-file (this is developmental)
loadscandirect(scannumbers, scantype='generic', fromtofile=False, scaling=None)[source]

Loads a scan without saving the edf files in matrices. scannumbers = integer or list of integers defining the scannumbers from the SPEC file scantype = string describing the scan to be loaded (e.g. ‘edge1’ or ‘K-edge’) fromtofile = boolean flag, ‘True’ if the scan should be saved in a pickle-file (this is developmental) scaling = list of scaling factors to be applied, one for each ROI defined

orderrois(arrangement='vertical', missing=None)[source]

order the rois in an order provided such that e.g. autorois have the correct order

printlength(scannumbers)[source]

Prints the number of energy points in a scan or a number of scans. INPUT:

  • scannumbers = integer or list of integers
read_just_first_scanimage(scannumber)[source]
readscan(scannumber, fromtofile=False)[source]

Returns the data, motors, counter-names, and edf-files from the SPEC file defined when the xrs_read object was initiated. There should be an alternative that uses the PyMca module if installed. INPUT:

  • scannumber = number of the scan to be loaded
  • fromtofile = boolean flag, ‘True’ if the scan should be saved in a pickle-file (this is developmental)
readscan_new(scannumber, fromtofile=False)[source]

Returns the data, motors, counter-names, and edf-files from the SPEC file defined when the xrs_read object was initiated. There should be an alternative that uses the PyMca module if installed. INPUT:

  • scannumber = number of the scan to be loaded
  • fromtofile = boolean flag, ‘True’ if the scan should be saved in a pickle-file (this is developmental)
removeBackgroundRoi(backroinum, estart=None, estop=None)[source]
save_raw_data(filename)[source]
save_state()[source]
save_state_hdf5(filename, groupname, comment='')[source]
set_roiObj(roiobj)[source]
there_is_a_valid_roi_at(n)[source]

XRStools.xrs_scans Module

class XRStools.xrs_scans.Scan[source]

Bases: object

Scan

Class for manipulating scan data from the Hydra and Fourc spectrometers.

All relevant information from the SPEC- and EDF-files are organized in instances of this class.

Attributes:
edf_mats (np.array): Array containing all 2D images that belong to the scan. number (int): Scan number as in the SPEC file. scan_type (string): Keyword, used later to group scans (add similar scans, etc.). energy (np.array): Array containing the energy axis (1D). monitor (np.array): Array containing the monitor signal (1D). counters (dictionary): Counters with assiciated data from the SPEC file. motors (list): Motor positions as found in the SPEC file header. eloss (np.array): Array of the energy loss scale. signals (np.array): Array of signals extracted from the ROIs. errors (np.array): Array of Poisson errors. cenom (list): Center of mass for each ROI (used if scan is an elastic line scan). signals_pw (list): Pixelwise (PW) data, one array of PW data per ROI. errors_pw (list): Pixelwise (PW) Poisson errors, one array of PW errors per ROI. cenom_pw (list): Center of mass for each pixel. signals_pw_interp (list): Interpolated signals for each pixel. Ein (float): Incident energy, used if energy2 is scanned. raw_signals (dict): Dictionary of raw data (summed, line-by-line, pixel-by-pixel). raw_errors (dict): Dictionary of raw erros (summed, line-by-line, pixel-by-pixel).
add_scan(scan, method='sum', interp=False)[source]

add_scan

Adds signals from a different scan.

Args:

scan (obj): Object of the Scan class. method (str): Keyword specifying the selected choice of data treatment:

can be ‘sum’, ‘row’, or ‘pixel’. Default is ‘sum’.
interp (boolean): Boolean specifying if norm-conserving linear wavelet
interpolation should be used, False by default.
append_scan(scan, method='sum', where='right')[source]

append_scan

Appends scan to the current scan, either at higher energies (where=’right’) or at lower energies (where=’left’).

Args:

scan (obj): Object of the Scan class. method (str): Keyword specifying the selected choice of data treatment:

can be ‘sum’, ‘row’, or ‘pixel’. Default is ‘sum’.
where (str): Keyword specifying if the scan should be appended
at lower (where=’left’) or highger energies (where=’righ’). Default is ‘right’.
apply_rois(roi_obj, scaling=None)[source]

apply_rois

Sums up intensities in each ROI.

Note:
Old function, keeping for backward compatibility.
Args:
roi_obj (instance): Instance of the ‘XRStools.xrs_rois.roi_object’ class defining the ROIs. scaling (np.array): Array of float-type scaling factors (factor for each ROI).
Returns:
None if there are not EDF-files to apply the ROIs to.
apply_rois_pw(roi_obj, scaling=None)[source]

apply_rois_pw

Pixel-wise reading of the ROIs’ pixels into a list of arrays.

I.e. each n-pixel ROI will have n Spectra, saved in a 2D array.

Args: roi_obj (instance): Instance of the ‘XRStools.xrs_rois.roi_object’ class defining the ROIs. scaling (list) or (np.array): Array or list of float-type scaling factors (one factor for each ROI).

assign(edf_arrays, scan_number, energy_scale, monitor_signal, counters, motor_positions, specfile_data, scan_type='generic')[source]

assign

Method to group together existing data from a scan (for backward compatibility).

Args:
edf_arrays (np.array): Array of all 2D images that belong to the scan. scan_number (int): Number under which this scan can be found in the SPEC file. energy_scale (np.array): Array of the energy axis. monitor_signal (np.array): Array of the monitor signal. counters (dictionary): Counters with associated data from the SPEC file. motor_positions (list): Motor positions as found in the SPEC file header. specfile_data (np.array): Matrix with all data as found in the SPEC file. scantype (str): Keyword, used later to group scans (add similar scans, etc.).
get_E_end()[source]

get_E_end

Returs the last energy value.

get_E_start()[source]

get_E_start

Returs the first energy value.

get_num_of_rois()[source]

get_num_of_rois

Returns the number of ROIs applied to the scan.

get_raw_signals(roi_obj, method='sum', scaling=None, rot_angles=None)[source]

get_raw_signals

Applies given ROIs to EDF-images.

Applies the provided ROIs to the EDF-images in the specified mannar: summing, line-by-line, or pixel-by-pixel. Depending on the choice, the resulting data array is 2D (sum), 3D (line-by-line), or 4D (pixel-by-pixel). The scanned direction is always the first dimension of the resulting data matrix.

Args:

roi_obj (instance): Instance of the ‘XRStools.xrs_rois.roi_object’ class defining the ROIs. method (string): Keyword specifying the selected choice of data treatment:

can be ‘sum’, ‘row’, ‘pixel’, or ‘column’. Default is ‘sum’.

scaling (np.array): Array of float-type scaling factors (factor for each ROI).

Returns:
None if there are not EDF-files to apply the ROIs to.
get_resolution(keV2eV=True)[source]

get_resolution

Returns the ROI-wise resolution based on the xrs_utilities fwhm method.

get_scan_number()[source]

get_scan_number

Returns the number of the scan.

get_shape()[source]

get_shape

Returns the shape of the matrix holding the signals.

get_signals(method='sum', cenom_dict=None, comp_factor=None, scaling=None, PIXEL_SIZE=0.055)[source]

get_signals

Turns pixel-, column- or sum-wise raw-data into data.

Takes the raw-data after application of the ROIs and applies the chosen compensation scheme.

Args:
method (str): Keyword specifying the selected choice of data treatment:
can be ‘sum’, ‘row’, or ‘pixel’. Default is ‘sum’.
cenom_dict (dict): Dictionary with one entry per ROI holding information about
the center of mass of the according elastic line.

comp_factor (float): Factor used in the RIXS-style line-by-line compensation. scaling (np.array): Array of float-type scaling factors (factor for each ROI).

get_type()[source]

get_type

Returns the type of the scan.

insert_scan(scan, method='sum', where=None)[source]

insert_scan

Inserts another scan into the current instance.

Args:

scan (obj): Object of the Scan class. method (str): Keyword specifying the selected choice of data treatment:

can be ‘sum’, ‘row’, or ‘pixel’. Default is ‘sum’.
where (list): Optional tuple of energy values (high and low (in keV))
for where to insert the scan. By default (None), the lowest and highest energy values of the given scan will be used.
load(path, SPECfname, EDFprefix, EDFname, EDFpostfix, scan_number, direct=False, roi_obj=None, scaling=None, scan_type='generic', en_column=None, moni_column='izero', method='sum', comp_factor=None, rot_angles=None, clean_edf_stack=False, cenom_dict=None)[source]

load

Parse SPEC-file and EDF-files for loading a scan.

Note:
If ‘direct’ is ‘True’ all EDF-files will be deleted after application of the ROIs.
Args:

path (str): Absolute path to directory in which the SPEC-file is located. SPECfname (str): SPEC-file name. EDFprefix (str): Prefix for the EDF-files. EDFpostfix (str): Postfix for the EDF-files. scan_number (int): Scan number of the scan to be loaded. direct (boolean): If ‘True’, all EDF-files will be deleted after loading the scan. method (str): Keyword specifying the selected choice of data treatment:

can be ‘sum’, ‘row’, ‘pixel’, or ‘column’. Default is ‘sum’.
load_hdf5(fname)[source]

load_hdf5

Load a scan from an HDF5 file.

Args:
fname (str): Filename of the HDF5 file.
normalizationDict = {}
save_hdf5(fname)[source]

save_hdf5

Save a scan in an HDF5 file.

Note:
HDF5 files are strange for overwriting files. DOES NOT SAVE COUNTERS or MOTORS YET… THIS NEEDS WORK!
Args:
fname (str): Path and filename for the HDF5 file.
class XRStools.xrs_scans.Scan_group(energy, signals, errors, group_type='generic')[source]

Bases: object

Container class holding information from a group of scans.

get_eend()[source]
get_estart()[source]
get_maxediff()[source]
get_meanegridspacing()[source]
get_meanenergy()[source]
get_type()[source]
XRStools.xrs_scans.append2Scan_left(group1, group2, inds=None, grouptype='spectrum')[source]

append two instancees of the scangroup class, return instance of scangroup append group1[inds] to the left (lower energies) of group2 if inds is not None, only append rows indicated by inds to the first group

XRStools.xrs_scans.append2Scan_right(group1, group2, inds=None, grouptype='spectrum')[source]

append two instancees of the scangroup class, return instance of scangroup append group2[inds] to the right (higher energies) of group1 if inds is not None, only append rows indicated by inds to the first group

XRStools.xrs_scans.appendScans(groups, include_elastic)[source]

try including different background scans… append groups of scans ordered by their first energy value. long scans are inserted into gaps that at greater than two times the grid of the finer scans

XRStools.xrs_scans.appendScans_pixel(groups, include_elastic)[source]

appendScans_pixel

Decides if there is a long scan available and selects accordingly which stitching method to use.

Args:

groups (list): List of scan-groups to be stitched together. include_elastic (boolean): Boolean switch if the elastic line

should be included in the final spectrum or not.
Returns:

energy (np.array): Array of energy loss values. raw_signals (dict): Dictionary (one entry per ROI) of

pixel-by-pixel intensities.
raw_errors (dict): Dictionary (one entry per ROI) of
pixel-by-pixel Poisson errors.
XRStools.xrs_scans.appendXESScans(groups)[source]

try including different background scans… append groups of scans ordered by their first energy value. long scans are inserted into gaps that at greater than two times the grid of the finer scans

XRStools.xrs_scans.catScans(groups, include_elastic)[source]

concatenate all scans in groups, return the appended energy, signals, and errors

XRStools.xrs_scans.catScansLong(groups, include_elastic)[source]

takes a longscan and inserts other backgroundscans (scans that have ‘long’ in their name) and other scans and inserts them into the long scan.

XRStools.xrs_scans.catScans_pixel(groups, include_elastic)[source]

catScans_pixel

Stitch together all scans in groups in a pixel-by-pixel fashion for the case of no available long (overview) scan.

Args:

groups (list): List of scan-groups to be stitched together. include_elastic (boolean): Boolean switch if the elastic line

should be included in the final spectrum or not.
Returns:

energy (np.array): Array of energy loss values. raw_signals (dict): Dictionary (one entry per ROI) of

pixel-by-pixel intensities.
raw_errors (dict): Dictionary (one entry per ROI) of
pixel-by-pixel Poisson errors.
XRStools.xrs_scans.catXESScans(groups)[source]

Concatenate all scans in groups, return the appended energy, signals, and errors. This needs to be a bit smarter to also work for scans that are scanned from small to large energy…

XRStools.xrs_scans.create_diff_image(scans, scannumbers, energy_keV)[source]

Returns a summed image from all scans with numbers ‘scannumbers’. scans = dictionary of objects from the scan-class scannumbers = single scannumber, or list of scannumbers from which an image should be constructed

XRStools.xrs_scans.create_sum_image(scans, scannumbers)[source]

Returns a summed image from all scans with numbers ‘scannumbers’. scans = dictionary of objects from the scan-class scannumbers = single scannumber, or list of scannumbers from which an image should be constructed

XRStools.xrs_scans.edf_cleaner(edfmats, threshold, dim1_range=[60, 190], dim2_range=[10, 1286])[source]

clean_edf_stack

Removes totally saturated detector images from the EDF-files.

Args:
edfmats (np.array): Three-dimensional array of EDF-matrices.
Returns:
edfmats (np.array): Cleaned stack of EDF-files.
XRStools.xrs_scans.findRCscans(scans)[source]

findRCscans Returns a list of scans with name RC.

XRStools.xrs_scans.findgroups(scans)[source]

this groups together instances of the scan class based on their “scantype” attribute and returns ordered scans

XRStools.xrs_scans.get_XES_spectrum(groups)[source]

get_XES_spectrum

Constructs a XES spectrum from the given scan groups (sums of separate emission scans).

Args:
groups (dict): Dictionary of groups with partial XES scans.
XRStools.xrs_scans.insertScan(group1, group2, grouptype='spectrum')[source]

inserts group2 into group1 NOTE! there is a numpy insert function, maybe it would be better to use that one!

XRStools.xrs_scans.make_scan_group_pixel(group_of_scans, group_type=None, abs_counts=False)[source]

make_scan_group_pixel

Pixel-by-pixel summation of a list of scans with equal scan_type and returns an instance of the Scan_group class.

Args:

group_of_scans (list): List containing instances of the Scan class to be summed up. group_type (str): Keyword defining the type of scans, if None (default) the

group_type will be the type of the first scan in the group_of_scans list.
abs_Counts (boolean): Boolean defining if results should be returned in absolute
count units (ct/s).

time_counter (str): Counter name for the SPEC counting time mnemonic.

Returns:
group (obj): Instance of the Scan_group container class.
XRStools.xrs_scans.make_scan_group_sum(group_of_scans, group_type=None, abs_counts=False)[source]

make_scan_group_sum

Sums together a list of scans with equal scan_type and returns an instance of the Scan_group class.

Args:

group_of_scans (list): List containing instances of the Scan class to be summed up. group_type (str): Keyword defining the type of scans, if None (default) the

group_type will be the type of the first scan in the group_of_scans list.
abs_counts (boolean): Boolean defining if results should be returned in absolute
count units (ct/s).

time_counter (str): Counter name for the SPEC counting time mnemonic.

Returns:
group (obj): Instance of the Scan_group container class.
XRStools.xrs_scans.makegroup(groupofscans, grouptype=None)[source]

takes a group of scans, sums up the signals and monitors, estimates poisson errors, and returns an instance of the scangroup class (turns several instances of the “scan” class into an instance of the “scangroup” class)

XRStools.xrs_scans.makegroup_nointerp(groupofscans, grouptype=None, absCounts=False)[source]

takes a group of scans, sums up the signals and monitors, estimates poisson errors, and returns an instance of the scangroup class (turns several instances of the “scan” class into an instance of the “scangroup” class), same as makegroup but withouth interpolation to account for encoder differences… may need to add some “linspace” function in case the energy scale is not monotoneous…

class XRStools.xrs_scans.offDiaDataSet[source]

Bases: object

offDiaDataSet Class to hold information from an off-diagonal dataset.

alignRCmonitor()[source]
alignRCmonitor2()[source]
alignRCmonitorCC(repeat=2)[source]

alignRCmonitorCC Use cross-correlation to align data matrix according to the Rockin-Curve monitor.

deglitchSignalMatrix(startpoint, stoppoint, threshold)[source]
filterDetErrors(threshold=3000000)[source]
interpolateMatrix(master_matrix, master_energy, master_RCmotor)[source]
normalizeSignals()[source]
removeConstBack(fitrange1, fitrange2)[source]
removeElastic(fitrange=[-6.0, 2.0])[source]
removeElastic2(fitrange1, fitrange2, guess=None)[source]

removeElastic2 Subtract Pearson7 plus linear.

removeLinearBack(fitrange1, fitrange2)[source]
removePearsonBack(fitrange1, fitrange2)[source]
replaceSignalByConstant(fitrange)[source]
windowSignalMatrix(estart, estop)[source]
class XRStools.xrs_scans.scan(edf_arrays, scannumber, energy_scale, monitor_signal, counters, motor_positions, specfile_data, scantype='generic')[source]

Bases: object

Container class, holding information of single scans performed with 2D detectors.

applyrois(indices, scaling=None)[source]

Sums up intensities found in the ROIs of each detector image and stores it into the self.signals attribute. roi_object = instance of the ‘rois’ class redining the ROIs scaling = numpy array of numerical scaling factors (has to be one for each ROIs)

applyrois_pw(indices, scaling=None)[source]

Pixel-wise reading of the ROI’s pixels into a list of arrays. I.e. each n-pixel ROI will have n Spectra, saved in a 2D array. Parameters ———- indices : list

List of indices (attribute of the xrs_rois class).
scaling : list of flaots, optional
Python list of scaling factors (one per ROI defined) to be applied to all pixels of that ROI.
get_eloss_pw()[source]

Finds the center of mass for each pixel in each ROI, sets the energy loss scale in and interpolates the signals to a common energy loss scale. Finds the resolution (FWHM) for each pixel.

get_numofrois()[source]
get_scannumber()[source]
get_shape()[source]
get_type()[source]
class XRStools.xrs_scans.scangroup(energy, signals, errors, grouptype='generic')[source]

Bases: object

Container class holding information from a group of scans.

get_eend()[source]
get_estart()[source]
get_maxediff()[source]
get_meanegridspacing()[source]
get_meanenergy()[source]
get_type()[source]
XRStools.xrs_scans.stitch_groups_to_spectrum(groups, method='sum', include_elastic=False)[source]

stitch_groups_to_spectrum

Takes a dictionary of instances of the Scan class and stitches them together to produce a spectrum. Long scans and scans that have ‘long’ in ther scan_type attribute are treated specially.

Args:

groups (list): List of instances of the Scan class. method (str): Keyword describing the kind of integration scheme

to be used (possible values are ‘sum’, ‘pixel’, or ‘row’), default is ‘sum’.
include_elastic (boolean): Boolean flag deciding if the elastic
should be included in the final spectrum.
Returns:
Scan class instance with the stitched spectrum.
XRStools.xrs_scans.sum_scans_to_group(group, method='sum', interp='False')[source]

sum_scans_to_group

Sums up all scans in the list group to form a scan-group.

Args:

group (list): List of scans to be added up. method (str): Keyword describing which data analysis method

should be used. Possible values are ‘sum’, ‘pixel’, ‘row’. Default is ‘sum’.
interp (boolean): Flag if interpolation onto the energy grid
of the first scan should be applied (default is False).
Returns:
An instance of the Scan class containing the summed up results.

XRStools.xrs_ComptonProfiles Module

class XRStools.xrs_ComptonProfiles.AtomProfile(element, filename, stoichiometry=1.0)[source]

Bases: object

AtomProfile

Class to construct and handle Hartree-Fock atomic Compton Profile of a single atoms.

Attributes:
  • filename : string Path and filename to the HF profile table.
  • element : string Element symbol as in the periodic table.
  • elementNr : int Number of the element as in the periodic table.
  • shells : list of strings Names of the shells.
  • edges : list List of edge onsets (eV).
  • C_total : np.array Total core Compton profile.
  • J_total : np.array Total Compton profile.
  • V_total : np.array Total valence Compton profile.
  • CperShell : dict. of np.arrays Core Compton profile per electron shell.
  • JperShell : dict. of np.arrays Total Compton profile per electron shell.
  • VperShell : dict. of np.arrays Valence Compton profile per electron shell.
  • stoichiometry : float, optional Stoichiometric weight (default is 1.0).
  • atomic_weight : float Atomic weight.
  • atomic_density : float Density (g/cm**3).
  • twotheta : float Scattering angle 2Th (degrees).
  • alpha : float Incident angle (degrees).
  • beta : float Exit angle (degrees).
  • thickness : float Sample thickness (cm).
absorptionCorrectProfiles(alpha, thickness, geometry='transmission')[source]

absorptionCorrectProfiles

Apply absorption correction to the Compton profiles on energy loss scale.

Args:
  • alpha :float Angle of incidence (degrees).
  • beta : float Exit angle for the scattered x-rays (degrees). If ‘beta’ is negative, transmission geometry is assumed, if ‘beta’ is positive, reflection geometry.
  • thickness : float Sample thickness.
get_elossProfiles(E0, twotheta, correctasym=None, valence_cutoff=20.0)[source]

get_elossProfiles Convert the HF Compton profile on to energy loss scale.

Args: E0 : float

Analyzer energy, enery of the scattered r-rays.
twotheta : float or list of floats
Scattering angle 2Th.
correctasym : float, optional
Scaling factor to be multiplied to the asymmetry.
valence_cutoff : float, optional
Energy cut off as to what is considered the boundary between core and valence.
get_stoichiometry()[source]
class XRStools.xrs_ComptonProfiles.ComptonProfiles(element)[source]

Bases: object

Class for multiple HF Compton profiles.

This class should hold one or more instances of the ComptonProfile class and have methods to return profiles from single atoms, single shells, all atoms. It should be able to apply corrections etc. on those…

Attributes:
  • element (string): Element symbol as in the periodic table.
  • elementNr (int) : Number of the element as in the periodic table.
  • shells (list) :
  • edges (list) :
  • C (np.array) :
  • J (np.array) :
  • V (np.array) :
  • CperShell (dict. of np.arrays):
  • JperShell (dict. of np.arrays):
  • VperShell (dict. of np.arrays):
class XRStools.xrs_ComptonProfiles.FormulaProfile(formula, filename, weight=1)[source]

Bases: object

FormulaProfile

Class to construct and handle Hartree-Fock atomic Compton Profile of a single chemical compound.

Attributes
  • filename : string Path and filename to Biggs database.
  • formula : string Chemical sum formula for the compound of interest (e.g. ‘SiO2’ or ‘H2O’).
  • elements : list of strings List of atomic symbols that make up the chemical sum formula.
  • stoichiometries : list of integers List of the stoichimetric weights for each of the elements in the list elements.
  • element_Nrs : list of integers List of atomic numbers for each element in the elements list.
  • AtomProfiles : list of AtomProfiles List of instances of the AtomProfiles class for each element in the list.
  • eloss : np.ndarray Energy loss scale for the Compton profiles.
  • C_total : np.ndarray Core HF Compton profile (one column per 2Th).
  • J_total : np.ndarray Total HF Compton profile (one column per 2Th).
  • V_total :np.ndarray Valence HF Compton profile (one column per 2Th).
  • E0 : float Analyzer energy (keV).
  • twotheta : float, list, or np.ndarray Value or list/np.ndarray of the scattering angle.
get_correctecProfiles(densities, alpha, beta, samthick)[source]
get_elossProfiles(E0, twotheta, correctasym=None, valence_cutoff=20.0)[source]
get_stoichWeight()[source]
class XRStools.xrs_ComptonProfiles.HFProfile(formulas, stoich_weights, filename)[source]

Bases: object

HFProfile

Class to construct and handle Hartree-Fock atomic Compton Profile of sample composed of several chemical compounds.

Attributes

get_elossProfiles(E0, twotheta, correctasym=None, valence_cutoff=20.0)[source]
XRStools.xrs_ComptonProfiles.HRcorrect(pzprofile, occupation, q)[source]

Returns the first order correction to filled 1s, 2s, and 2p Compton profiles.

Implementation after Holm and Ribberfors (citation …).

Args:
  • pzprofile (np.array): Compton profile (e.g. tabulated from Biggs) to be corrected (2D matrix).
  • occupation (list): electron configuration.
  • q (float or np.array): momentum transfer in [a.u.].
Returns:
  • asymmetry (np.array): asymmetries to be added to the raw profiles (normalized to the number of electrons on pz scale)
XRStools.xrs_ComptonProfiles.PzProfile(element, filename)[source]

Returnes tabulated HF Compton profiles.

Reads in tabulated HF Compton profiles from the Biggs paper, interpolates them, and normalizes them to the # of electrons in the shell.

Args:
  • element (string): element symbol (e.g. ‘Si’, ‘Al’, etc.)
  • filename (string): absolute path and filename to tabulated profiles
Returns:
  • CP_profile (np.array): Matrix of the Compton profile * 1. column: pz-scale * 2. … n. columns: Compton profile of nth shell
  • binding_energy (list): binding energies of shells
  • occupation_num (list): number of electrons in the according shells
class XRStools.xrs_ComptonProfiles.SqwPredict[source]

Bases: object

Class to build a S(q,w) prediction based on HF Compton Profiles.

Attributes:

  • sampleStr (list of strings): one string per compound (e.g. [‘C’,’SiO2’])
  • concentrations (list of floats): relative compositional weight for each compound
XRStools.xrs_ComptonProfiles.elossProfile(element, filename, E0, tth, correctasym=None, valence_cutoff=20.0)[source]

Returns HF Compton profiles on energy loss scale.

Uses the PzProfile function to read read in Biggs HF profiles and converts them onto energy loss scale. The profiles are cut at the respective electron binding energies and are normalized to the f-sum rule (i.e. S(q,w) is in units of [1/eV]).

Args:
  • element (string): element symbol.
  • filename (string): absolute path and filename to tabulated Compton profiles.
  • E0 (float): analyzer energy in [keV].
  • tth (float): scattering angle two theta in [deg].
  • correctasym (np.array): vector of scaling factors to be applied.
  • valence_cutoff (float): energy value below which edges are considered as valence
Returns:
  • enScale (np.array): energy loss scale in [eV]
  • J_total (np.array): total S(q,w) in [1/eV]
  • C_total (np.array): core contribution to S(q,w) in [1/eV]
  • V_total (np.array): valence contribution to S(q,w) in [1/eV], the valence is defined by valence_cutoff
  • q (np.array): momentum transfer in [a.u]
  • J_shell (dict of np.arrays): dictionary of contributions for each shell, the key are defines as in Biggs table.
  • C_shell (dict of np.arrays): same as J_shell for core contribution
  • V_shell (dict of np.arrays): same as J_shell for valence contribution
XRStools.xrs_ComptonProfiles.getAtomicDensity(Z)[source]

Returns the atomic density.

XRStools.xrs_ComptonProfiles.getAtomicWeight(Z)[source]

Returns the atomic weight.

XRStools.xrs_ComptonProfiles.list_duplicates(seq)[source]
XRStools.xrs_ComptonProfiles.mapShellNames(shell_str, atomicNumber)[source]

mapShellNames

Translates to and from spectroscopic edge notation and the convention of the Biggs database.

Args:
  • shell_str : string Spectroscopic symbol to be converted to Biggs database convention.
  • atomicNumber : int Z for the atom in question.
XRStools.xrs_ComptonProfiles.parseChemFormula(ChemFormula)[source]
XRStools.xrs_ComptonProfiles.trapz_weights(x)[source]

XRStools.xrs_fileIO Module

XRStools.xrs_fileIO.EdfRead(fname)[source]
XRStools.xrs_fileIO.PrepareEdfMatrix(scan_length, num_pix_x, num_pix_y)[source]

Returns np.zeros of the shape of the detector.

XRStools.xrs_fileIO.PrepareEdfMatrix_TwoImages(scan_length, num_pix_x, num_pix_y)[source]

Returns np.zeros for old data (horizontal and vertical Maxipix images in different files).

XRStools.xrs_fileIO.PyMcaEdfRead(fname)[source]

Returns the EDF-data using PyMCA.

XRStools.xrs_fileIO.PyMcaSpecRead(filename, nscan)[source]

Returns data, counter-names, and EDF-files using PyMCA.

XRStools.xrs_fileIO.PyMcaSpecRead_my(filename, nscan)[source]

Returns data, counter-names, and EDF-files using PyMCA.

XRStools.xrs_fileIO.ReadEdfImages(ccdcounter, num_pix_x, num_pix_y, path, EdfPrefix, EdfName, EdfPostfix)[source]

Reads a series of EDF-images and returs them in a 3D Numpy array (horizontal and vertical Maxipix images in different files).

XRStools.xrs_fileIO.ReadEdfImages_PyMca(ccdcounter, path, EdfPrefix, EdfName, EdfPostfix)[source]

Reads a series of EDF-images and returs them in a 3D Numpy array (horizontal and vertical Maxipix images in different files).

XRStools.xrs_fileIO.ReadEdfImages_TwoImages(ccdcounter, num_pix_x, num_pix_y, path, EdfPrefix_h, EdfPrefix_v, EdfNmae, EdfPostfix)[source]

Reads a series of EDF-images and returs them in a 3D Numpy array (horizontal and vertical Maxipix images in different files).

XRStools.xrs_fileIO.ReadEdfImages_my(ccdcounter, path, EdfPrefix, EdfName, EdfPostfix)[source]

Reads a series of EDF-images and returs them in a 3D Numpy array (horizontal and vertical Maxipix images in different files).

XRStools.xrs_fileIO.ReadEdf_justFirstImage(ccdcounter, path, EdfPrefix, EdfName, EdfPostfix)[source]
XRStools.xrs_fileIO.ReadScanFromFile(fname)[source]

Returns a scan stored in a Numpy archive.

XRStools.xrs_fileIO.SpecRead(filename, nscan)[source]

Parses a SPEC file and returns a specified scan.

Args:
  • filename (string): SPEC file name (inlc. path)
  • nscan (int): Number of the desired scan.
Returns:
  • data (np.array): array of the data from the specified scan.
  • motors (list): list of all motor positions from the header of the specified scan.
  • counters (dict): all counters in a dictionary with the counter names as keys.
XRStools.xrs_fileIO.WriteScanToFile(fname, data, motors, counters, edfmats)[source]

Writes a scan into a Numpy archive.

XRStools.xrs_fileIO.myEdfRead(filename)[source]

Returns EDF-data, if PyMCA is not installed (this is slow).

XRStools.xrs_fileIO.readbiggsdata(filename, element)[source]

Reads Hartree-Fock Profile of element ‘element’ from values tabulated by Biggs et al. (Atomic Data and Nuclear Data Tables 16, 201-309 (1975)) as provided by the DABAX library (http://ftp.esrf.eu/pub/scisoft/xop2.3/DabaxFiles/ComptonProfiles.dat). input: filename = path to the ComptonProfiles.dat file (the file should be distributed with this package) element = string of element name returns: data = the data for the according element as in the file:

#UD Columns: #UD col1: pz in atomic units #UD col2: Total compton profile (sum over the atomic electrons #UD col3,…coln: Compton profile for the individual sub-shells

occupation = occupation number of the according shells bindingen = binding energies of the accorting shells colnames = strings of column names as used in the file

XRStools.xrs_prediction Module

class XRStools.xrs_prediction.absolute_cross_section(beam_obj, sample_obj, analyzer_obj, detector_obj, thomson_obj, compton_profile_obj)[source]

Bases: object

Class to calculate an expected cross section in absolute counts using objects of the ‘beam’, ‘sample’, ‘analyzer’, ‘detector’, ‘thomson’, and ‘compton_profile’ classes.

calc_abs_cross_section()[source]
calc_num_scatterers()[source]

Calculates number of scatterers/atoms using beam size, sample thickness, sample densites, sample molar masses (so far does not differentiate between target atoms and random sample atoms)

plot_abs_cross_section()[source]
save_txt(file_name, header='')[source]
class XRStools.xrs_prediction.analyzer(material='Si', hkl=[6, 6, 0], mask_d=60.0, bend_r=1.0, energy_resolution=0.5, diced=False, thickness=500.0, database_dir='/home/alex/src/XRStools/XRStools')[source]

Bases: object

Class to describe things related to the analyzer crystal used. Default values are for a Si(660) crystal.

get_bend_r()[source]
get_diced()[source]
get_efficiency(energy=None)[source]

Calculates the efficiency of the analyzer crystal based on the calculated reflectivity curve. The efficiency is calculated by averaging over the energy resolution set upon class initialization. energy = energy (in [keV]) for wich the efficiency is to be calculated

get_energy_resolution()[source]
get_energy_resolution_eV()[source]
get_hkl()[source]
get_mask_d()[source]
get_material()[source]
get_reflectivity(energy, dev=array([-50., -49., -48., -47., -46., -45., -44., -43., -42., -41., -40., -39., -38., -37., -36., -35., -34., -33., -32., -31., -30., -29., -28., -27., -26., -25., -24., -23., -22., -21., -20., -19., -18., -17., -16., -15., -14., -13., -12., -11., -10., -9., -8., -7., -6., -5., -4., -3., -2., -1., 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99., 100., 101., 102., 103., 104., 105., 106., 107., 108., 109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119., 120., 121., 122., 123., 124., 125., 126., 127., 128., 129., 130., 131., 132., 133., 134., 135., 136., 137., 138., 139., 140., 141., 142., 143., 144., 145., 146., 147., 148., 149.]), alpha=0.0)[source]

Calculates the reflectivity curve for a given analyzer crystal. Checks in the directory self.database_dir, if desired reflectivity curve has been calculated before. IN: energy = energy at which the reflectivity is to be calculated in [keV] dev = deviation parameter for which the curve is to be calculated alpha = deviation angle from exact Bragg angle [deg]

get_solid_angle()[source]
get_thickness()[source]
plot_reflectivity(mode='energy')[source]

Generates and opens a plot of the calculated reflectivity curve. mode = keyword for which x-axis is to be used, can be ‘energy’ or ‘angle’

set_bend_r(bend_r)[source]
set_diced(diced)[source]
set_hkl(hkl)[source]
set_mask_d(mask_d)[source]
set_material(material)[source]
set_thickness(thickness)[source]
class XRStools.xrs_prediction.beam(i0_intensity, beam_height, beam_width, divergence=None)[source]

Bases: object

Class to describe incident beam related things.

get_beam_cross_section_area()[source]

Calculates the beam cross section area.

get_beam_height()[source]
get_beam_height_cm()[source]
get_beam_width()[source]
get_beam_width_cm()[source]
get_divergence()[source]
get_i0_intensity()[source]
XRStools.xrs_prediction.cla()[source]
class XRStools.xrs_prediction.compton_profiles(sample_obj, eloss_range=array([0.000e+00, 1.000e-01, 2.000e-01, ..., 9.997e+02, 9.998e+02, 9.999e+02]), E0=9.7)[source]

Bases: object

Class to hold construct HF Compton profiles for an object of the sample class.

calc_HF_profiles()[source]
calc_pure_HF_profiles()[source]
get_E0()[source]
get_HF_profiles()[source]
get_energy_in_keV()[source]
get_tth()[source]
plot_HF_profile()[source]
class XRStools.xrs_prediction.detector(energy=9.68, thickness=500, material='Si', pixel_size=[256, 768])[source]

Bases: object

Class to describe detector related things. All default values are meant for the ESRF MAXIPIX detector.

get_efficiency(energy=None)[source]

calculates the detector efficiency at the given energy (simply given by the absorption of the detector active material).

get_energy()[source]
get_material()[source]
get_size()[source]
get_thickness()[source]
set_energy(energy)[source]
set_material(material)[source]
set_size(size)[source]
set_thickness(thickness)[source]
XRStools.xrs_prediction.get_all_input(filename='prediction.inp')[source]

Adds default values if input is missing in the input-file and a default value exists for the missing one.

XRStools.xrs_prediction.input_file_parser(filename)[source]

Parses an input file, which has a structure like the example input file (‘prediction.inp’) provided in the examples/ folder. (Python lists and numpy arrays have to be profived without white spaces in their definitions, e.g. ‘hkl = [6,6,0]’ instead of ‘hkl = [6, 6, 0]’)

XRStools.xrs_prediction.main()[source]
class XRStools.xrs_prediction.matrix_element(R1, R2)[source]

Bases: object

compute(k)[source]

compute Calculates the matrix elements for a given k or range of k.

write_H5(filename)[source]

write_H5 Creates an HDF5 file to store the matrix elements.

Args:
  • fname (str) : Full path and filename for the HDF5 file to be created.
write_ascii(filename)[source]

write_ascii Creates an ascii-file and writes matrix elements.

Args:
  • fname (str) : Full path and filename for the ascii file to be created.
class XRStools.xrs_prediction.radial_wave_function[source]

Bases: object

load_from_PP(Z, n, l, path='/home/christoph/programs/atomic_wavefunctions/', spin_polarized=False)[source]
load_from_sympy(Z, n, l)[source]
XRStools.xrs_prediction.run(filename='prediction.inp')[source]

Function to create a spectrum prediction from input parameters provided in the input file filename. Generates a figure with the result.

class XRStools.xrs_prediction.sample(chem_formulas, concentrations, densities, angle_tth, sample_thickness, angle_in=None, angle_out=None, shape='sphere', molar_masses=None)[source]

Bases: object

Class to describe a sample.

get_absorption_correction(energy1, energy2, thickness=None)[source]

Calculates the absorption correction factor for the sample to be multiplied with experimental data to correct for absorption effects. energy1 = numpy array of energies in [keV] for which the factor is to be calculated energy2 = numpy array of energies in [keV] for which the factor is to be calculated

get_alpha()[source]
get_average_densities()[source]
get_beta()[source]
get_concentrations()[source]
get_densities()[source]
get_energy1()[source]
get_energy2()[source]
get_formulas()[source]
get_molar_masses()[source]
get_murho(energy1, energy2=None)[source]

Calculates the total photoelectric absorption coefficient of the sample for the two energies given. Returns only one array, if only one energy axis is defined. energy1 = numpy array of energies in [keV] energy2 = numpy array of energies in [keV] (defalt is None, i.e. only one mu is returned)

get_shape()[source]
get_thickness()[source]
get_tth()[source]
plot_inv_absorption(energy1, energy2, range_of_thickness=array([0., 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49]))[source]

Generates a figure which plots 1/Abscorr for the sample as a function of different thicknesses. This is usefull for finding optimum sample thicknesses for an experiment. energy1 = energy in [keV] at the desired edge energy2 = energy in [keV] at the elastic range_of_thickness = numpy array of sample thicknesses in [cm]

!!! right now all samples are treates as if spherical !!!

class XRStools.xrs_prediction.thomson(omega_1, omega_2, tth, scattering_plane='vertical', polarization=0.99)[source]

Bases: object

Class to take care of the Thomson scattering cross section.

get_thomson_factor()[source]

Calculates the Thomson scattering factor.

XRStools.xrs_rois Module

XRStools.xrs_rois.OG_dec(a, b, c, d)
XRStools.xrs_rois.OG_inc(a, b, c, d)
XRStools.xrs_rois.break_down_det_image(image, pixel_num)[source]

Desomposes a Detector image into subimages. Returns a 3D matrix.

class XRStools.xrs_rois.container[source]

Bases: object

Random container class to hold values

XRStools.xrs_rois.convert_inds_to_matrix(ind_rois, image_shape)[source]

Converts a ROI defined by a list of lists of tuples into a ROI that is defined by an array containing zeros, ones, twos, …, n’s, where n is the number of ROIs. ind_rois = list of lists with pairs of pixel indices image_shape = touple defining the shape of the matrix for which the ROIs are valid

XRStools.xrs_rois.convert_inds_to_xinds(roi_inds)[source]

Converts ROIs defined in lists of lists of x-y-coordinate tuples into a list of x-coordinates only.

XRStools.xrs_rois.convert_inds_to_yinds(roi_inds)[source]

Converts ROIs defined in lists of lists of x-y-coordinate tuples into a list of y-coordinates only.

XRStools.xrs_rois.convert_matrix_rois_to_inds(roi_matrix)[source]

Converts a 2D ROI matrix with zeros, ones, twos, …, n’s (where n is the number of ROIs) to a list of lists each of which has tuples with coordinates for each pixel in each roi.

XRStools.xrs_rois.convert_matrix_to_redmatrix(matrix_rois, labelformat='ROI%02d')[source]

Converts a ROI defined by an array containing zeros, ones, twos, …, n’s, where n is the number of ROIs, into a dictionary with keys ‘ROI00’, ‘ROI01’, …, ‘ROInn’. Each entry of the dictionary is a list containing a tuple with the origin and the reduced ROI. matrix_roi = numpy array

XRStools.xrs_rois.convert_redmatrix_to_matrix(masksDict, mask, offsetX=0, offsetY=0)[source]
XRStools.xrs_rois.convert_redmatrix_to_matrix_my(masksDict, mask, offsetX=0, offsetY=0)[source]
XRStools.xrs_rois.convert_roi_matrix_to_masks(roi_matrix)[source]

Converts a 2D ROI matrix with zeros, ones, twos, …, n’s (where n is the number of ROIs) to a 3D matrix with one slice of zeros and ones per ROI.

XRStools.xrs_rois.get_geo_informations(shape)[source]
XRStools.xrs_rois.h5_assign_force(h5group, name, item)[source]
XRStools.xrs_rois.load_rois_fromh5(h5group_tot, md, retrieveImage=False, metadata=None)[source]
XRStools.xrs_rois.load_rois_fromh5_address(address)[source]
XRStools.xrs_rois.merge_roi_objects_by_matrix(list_of_roi_objects, large_image_shape, offsets, pixel_num)[source]

Merges several roi_objects into one big one using the roi_matrices.

XRStools.xrs_rois.order_generator_ascending(a, b, c, d)[source]
XRStools.xrs_rois.order_generator_descending(a, b, c, d)[source]
class XRStools.xrs_rois.roi_object[source]

Bases: object

Container class to hold all relevant information about given ROIs.

append(roi_object)[source]
delete_empty_rois()[source]

delete_empty_rois

get_bounding_boxes()[source]
get_copy()[source]

get_copy Returns a deep copy of self.

get_indices()[source]
get_masks()[source]
get_number_of_rois()[source]
get_x_indices()[source]
get_y_indices()[source]
loadH5(fname)[source]

loadH5 Loads ROIs from an HDF5 file written by the self.writeH5() method.

Args:
  • fname (str) : Full path and filename for the HDF5 file to be read.
load_rois_fromMasksDict(masksDict, newshape=None, kind='zoom')[source]
load_shadok_h5(fname, group_name1, group_name2='ROI_AS_SELECTED')[source]
shift_rois(shiftVal, direction='horiz', whichroi=None)[source]

shift_rois Displaces the defined ROIs by the provided value.

Args
  • shiftVal : int Value by which the ROIs should be shifted.
  • direction : string Description of which direction to shit by.
  • whichroi : sequence Sequence (iterable) for which ROIs should be shifted.
show_rois(colormap='jet', interpolation='nearest')[source]

show_rois Creates a figure with the defined ROIs as numbered boxes on it.

strip_rois()[source]

strip_rois Strips extra zeros out of ROIs.

writeH5(fname)[source]

writeH5 Creates an HDF5 file and writes the ROIs into it.

Args:
  • fname (str) : Full path and filename for the HDF5 file to be created.
XRStools.xrs_rois.shift_roi_indices(indices, shift)[source]

Applies a given shift (xshift,yshift) to given indices. indices = list of (x,y)-tuples shift = (xshift,yshift) tuple

XRStools.xrs_rois.swap_indices_old_rois(old_indices)[source]

Swappes x- and y-indices from indices ROIs.

XRStools.xrs_rois.write_rois_toh5(h5group, md, filterMask=None, metadata=None)[source]

XRStools.xrs_utilities Module

XRStools.xrs_utilities.Chi(chi, degrees=True)[source]

rotation around (1,0,0), pos sense

XRStools.xrs_utilities.HRcorrect(pzprofile, occupation, q)[source]

Returns the first order correction to filled 1s, 2s, and 2p Compton profiles.

Implementation after Holm and Ribberfors (citation …).

Args:
  • pzprofile (np.array): Compton profile (e.g. tabulated from Biggs) to be corrected (2D matrix).
  • occupation (list): electron configuration.
  • q (float or np.array): momentum transfer in [a.u.].
Returns:
asymmetry (np.array): asymmetries to be added to the raw profiles (normalized to the number of electrons on pz scale)
XRStools.xrs_utilities.NNMFcost(x, A, F, C, F_up, C_up, n, k, m)[source]

NNMFcost Returns cost and gradient for NNMF with constraints.

XRStools.xrs_utilities.NNMFcost_old(x, A, W, H, W_up, H_up)[source]

NNMFcost Returns cost and gradient for NNMF with constraints.

XRStools.xrs_utilities.Omega(omega, degrees=True)[source]

rotation around (0,0,1), pos sense

XRStools.xrs_utilities.Phi(phi, degrees=True)[source]

rotation around (0,1,0), neg sense

XRStools.xrs_utilities.Rx(chi, degrees=True)[source]

Rx Rotation matrix for vector rotations around the [1,0,0]-direction.

Args:
  • chi (float) : Angle of rotation.
  • degrees(bool) : Angle given in radians or degrees.
Returns:
  • 3x3 rotation matrix.
XRStools.xrs_utilities.Ry(phi, degrees=True)[source]

Ry Rotation matrix for vector rotations around the [0,1,0]-direction.

Args:
  • phi (float) : Angle of rotation.
  • degrees(bool) : Angle given in radians or degrees.
Returns:
  • 3x3 rotation matrix.
XRStools.xrs_utilities.Rz(omega, degrees=True)[source]

Rz Rotation matrix for vector rotations around the [0,0,1]-direction.

Args:
  • omega (float) : Angle of rotation.
  • degrees(bool) : Angle given in radians or degrees.
Returns:
  • 3x3 rotation matrix.
XRStools.xrs_utilities.TTsolver1D(el_energy, hkl=[6, 6, 0], crystal='Si', R=1.0, dev=array([-50., -49., -48., -47., -46., -45., -44., -43., -42., -41., -40., -39., -38., -37., -36., -35., -34., -33., -32., -31., -30., -29., -28., -27., -26., -25., -24., -23., -22., -21., -20., -19., -18., -17., -16., -15., -14., -13., -12., -11., -10., -9., -8., -7., -6., -5., -4., -3., -2., -1., 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99., 100., 101., 102., 103., 104., 105., 106., 107., 108., 109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119., 120., 121., 122., 123., 124., 125., 126., 127., 128., 129., 130., 131., 132., 133., 134., 135., 136., 137., 138., 139., 140., 141., 142., 143., 144., 145., 146., 147., 148., 149.]), alpha=0.0, chitable_prefix='/home/christoph/sources/XRStools/data/chitables/chitable_')[source]

TTsolver Solves the Takagi-Taupin equation for a bent crystal.

This function is based on a Matlab implementation by S. Huotari of M. Krisch’s Fortran programs.

Args:
  • el_energy (float): Fixed nominal (working) energy in keV.
  • hkl (array): Reflection order vector, e.g. [6, 6, 0]
  • crystal (str): Crystal used (can be silicon ‘Si’ or ‘Ge’)
  • R (float): Crystal bending radius in m.
  • dev (np.array): Deviation parameter (in arc. seconds) for which the reflectivity curve should be calculated.
  • alpha (float): Crystal assymetry angle.
Returns:
  • refl (np.array): Reflectivity curve.
  • e (np.array): Deviation from Bragg angle in meV.
  • dev (np.array): Deviation from Bragg angle in microrad.
XRStools.xrs_utilities.absCorrection(mu1, mu2, alpha, beta, samthick, geometry='transmission')[source]

absCorrection

Calculates absorption correction for given mu1 and mu2. Multiply the measured spectrum with this correction factor. This is a translation of Keijo Hamalainen’s Matlab function (KH 30.05.96).

Args
  • mu1 : np.array Absorption coefficient for the incident energy in [1/cm].
  • mu2 : np.array Absorption coefficient for the scattered energy in [1/cm].
  • alpha : float Incident angle relative to plane normal in [deg].
  • beta : float Exit angle relative to plane normal [deg].
  • samthick : float Sample thickness in [cm].
  • geometry : string, optional Key word for different sample geometries (‘transmission’, ‘reflection’, ‘sphere’). If geometry is set to ‘sphere’, no angular dependence is assumed.
Returns
  • ac : np.array Absorption correction factor. Multiply this with your measured spectrum.
XRStools.xrs_utilities.abscorr2(mu1, mu2, alpha, beta, samthick)[source]

Calculates absorption correction for given mu1 and mu2. Multiply the measured spectrum with this correction factor.

This is a translation of Keijo Hamalainen’s Matlab function (KH 30.05.96).

Args:
  • mu1 (np.array): absorption coefficient for the incident energy in [1/cm].
  • mu2 (np.array): absorption coefficient for the scattered energy in [1/cm].
  • alpha (float): incident angle relative to plane normal in [deg].
  • beta (float): exit angle relative to plane normal [deg] (for transmission geometry use beta < 0).
  • samthick (float): sample thickness in [cm].
Returns:
  • ac (np.array): absorption correction factor. Multiply this with your measured spectrum.
XRStools.xrs_utilities.addch(xold, yold, n, n0=0, errors=None)[source]

# ADDCH Adds contents of given adjacent channels together # # [x2,y2] = addch(x,y,n,n0) # x = original x-scale (row or column vector) # y = original y-values (row or column vector) # n = number of channels to be summed up # n0 = offset for adding, default is 0 # x2 = new x-scale # y2 = new y-values # # KH 17.09.1990 # Modified 29.05.1995 to include offset

XRStools.xrs_utilities.bidiag_reduction(A)[source]

function [U,B,V]=bidiag_reduction(A) % [U B V]=bidiag_reduction(A) % Algorithm 6.5-1 in Golub & Van Loan, Matrix Computations % Johns Hopkins University Press % Finds an upper bidiagonal matrix B so that A=U*B*V’ % with U,V orthogonal. A is an m x n matrix

XRStools.xrs_utilities.bootstrapCNNMF(A, F_ini, C_ini, F_up, C_up, Niter)[source]

bootstrapCNNMF Constrained non-negative matrix factorization with bootstrapping for error estimates.

XRStools.xrs_utilities.bootstrapCNNMF_old(A, k, Aerr, F_ini, C_ini, F_up, C_up, Niter=100)[source]

bootstrapCNNMF Constrained non-negative matrix factorization with bootstrapping for error estimates.

XRStools.xrs_utilities.bragg(hkl, e, xtal='Si')[source]

% BRAGG Calculates Bragg angle for given reflection in RAD % output=bangle(hkl,e,xtal) % hkl can be a matrix i.e. hkl=[1,0,0 ; 1,1,1]; % e=energy in keV % xtal=’Si’, ‘Ge’, etc. (check dspace.m) or d0 (Si default) % % KH 28.09.93 %

XRStools.xrs_utilities.braggd(hkl, e, xtal='Si')[source]

# BRAGGD Calculates Bragg angle for given reflection in deg # Call BRAGG.M # output=bangle(hkl,e,xtal) # hkl can be a matrix i.e. hkl=[1,0,0 ; 1,1,1]; # e=energy in keV # xtal=’Si’, ‘Ge’, etc. (check dspace.m) or d0 (Si default) # # KH 28.09.93

XRStools.xrs_utilities.cixsUBfind(x, G, Q_sample, wi, wo, lambdai, lambdao)[source]

cixsUBfind

XRStools.xrs_utilities.cixsUBgetAngles_primo(Q)[source]
XRStools.xrs_utilities.cixsUBgetAngles_secondo(Q)[source]
XRStools.xrs_utilities.cixsUBgetAngles_terzo(Q)[source]
XRStools.xrs_utilities.cixsUBgetQ_primo(tthv, tthh, psi)[source]
XRStools.xrs_utilities.cixsUBgetQ_secondo(tthv, tthh, psi)[source]
XRStools.xrs_utilities.cixsUBgetQ_terzo(tthv, tthh, psi)[source]
XRStools.xrs_utilities.cixs_primo(tthv, tthh, psi, anal_braggd=86.5)[source]

cixs_primo

XRStools.xrs_utilities.cixs_secondo(tthv, tthh, psi, anal_braggd=86.5)[source]

cixs_secondo

XRStools.xrs_utilities.cixs_terzo(tthv, tthh, psi, anal_braggd=86.5)[source]

cixs_terzo

XRStools.xrs_utilities.compute_matrix_elements(R1, R2, k, r)[source]
XRStools.xrs_utilities.con2mat(x, W, H, W_up, H_up)[source]
XRStools.xrs_utilities.constrained_mf(A, W_ini, W_up, coeff_ini, coeff_up, maxIter=1000, tol=1e-08, maxIter_power=1000)[source]

cfactorizeOffDiaMatrix constrained version of factorizeOffDiaMatrix Returns main components from an off-diagonal Matrix (energy-loss x angular-departure).

XRStools.xrs_utilities.constrained_svd(M, U_ini, S_ini, VT_ini, U_up, max_iter=10000, verbose=False)[source]

constrained_nnmf Approximate singular value decomposition with constraints.

function [U, S, V] = constrained_svd(M,U_ini,S_ini,V_ini,U_up,max_iter=10000,verbose=False)

XRStools.xrs_utilities.convertSplitEDF2EDF(foldername)[source]

converts the old style EDF files (one image for horizontal and one image for vertical chambers) to the new style EDF (one single image).

Arg:
foldername (str): Path to folder with all the EDF-files to be
converted.
XRStools.xrs_utilities.convg(x, y, fwhm)[source]

Convolution with Gaussian x = x-vector y = y-vector fwhm = fulll width at half maximum of the gaussian with which y is convoluted

XRStools.xrs_utilities.convtoprim(hklconv)[source]

convtoprim converts diamond structure reciprocal lattice expressed in conventional lattice vectors to primitive one (Helsinki -> Palaiseau conversion) from S. Huotari

XRStools.xrs_utilities.cshift(w1, th)[source]

cshift Calculates Compton peak position.

Args:
  • w1 (float, array): Incident energy in [keV].
  • th (float): Scattering angle in [deg].
Returns:
  • w2 (foat, array): Energy of Compton peak in [keV].

Funktion adapted from Keijo Hamalainen.

XRStools.xrs_utilities.delE_JohannAberration(E, A, R, Theta)[source]

Calculates the Johann aberration of a spherical analyzer crystal.

Args:
E (float): Working energy in [eV]. A (float): Analyzer aperture [mm]. R (float): Radius of the Rowland circle [mm]. Theta (float): Analyzer Bragg angle [degree].
Returns:
Johann abberation in [eV].
XRStools.xrs_utilities.delE_dicedAnalyzerIntrinsic(E, Dw, Theta)[source]

Calculates the intrinsic energy resolution of a diced crystal analyzer.

Args:
E (float): Working energy in [eV]. Dw (float): Darwin width of the used reflection [microRad]. Theta (float): Analyzer Bragg angle [degree].
Returns:
Intrinsic energy resolution of a perfect analyzer crystal.
XRStools.xrs_utilities.delE_offRowland(E, z, A, R, Theta)[source]

Calculates the off-Rowland contribution of a spherical analyzer crystal.

Args:
E (float): Working energy in [eV]. z (float): Off-Rowland distance [mm]. A (float): Analyzer aperture [mm]. R (float): Radius of the Rowland circle [mm]. Theta (float): Analyzer Bragg angle [degree].
Returns:
Off-Rowland contribution in [eV] to the energy resolution.
XRStools.xrs_utilities.delE_pixelSize(E, p, R, Theta)[source]

Calculates the pixel size contribution to the resolution function of a diced analyzer crystal.

Args:
E (float): Working energy in [eV]. p (float): Pixel size in [mm]. R (float): Radius of the Rowland circle [mm]. Theta (float): Analyzer Bragg angle [degree].
Returns:
Pixel size contribution in [eV] to the energy resolution for a diced analyzer crystal.
XRStools.xrs_utilities.delE_sourceSize(E, s, R, Theta)[source]

Calculates the source size contribution to the resolution function.

Args:
E (float): Working energy in [eV]. s (float): Source size in [mm]. R (float): Radius of the Rowland circle [mm]. Theta (float): Analyzer Bragg angle [degree].
Returns:
Source size contribution in [eV] to the energy resolution.
XRStools.xrs_utilities.delE_stressedCrystal(E, t, v, R, Theta)[source]

Calculates the stress induced contribution to the resulution function of a spherically bent crystal analyzer.

Args:
E (float): Working energy in [eV]. t (float): Absorption length in the analyzer material [mm]. v (float): Poisson ratio of the analyzer material. R (float): Radius of the Rowland circle [mm]. Theta (float): Analyzer Bragg angle [degree].
Returns:
Stress-induced contribution in [eV] to the energy resolution.
XRStools.xrs_utilities.diode(current, energy, thickness=0.03)[source]

diode Calculates the number of photons incident for a Si PIPS diode.

Args:
  • current (float): Diode current in [pA].
  • energy (float): Photon energy in [keV].
  • thickness (float): Thickness of Si active layer in [cm].
Returns:
  • flux (float): Number of photons per second.

Function adapted from Matlab function by S. Huotari.

XRStools.xrs_utilities.dspace(hkl=[6, 6, 0], xtal='Si')[source]

% DSPACE Gives d-spacing for given xtal % d=dspace(hkl,xtal) % hkl can be a matrix i.e. hkl=[1,0,0 ; 1,1,1]; % xtal=’Si’,’Ge’,’LiF’,’InSb’,’C’,’Dia’,’Li’ (case insensitive) % if xtal is number this is user as a d0 % % KH 28.09.93 % SH 2005 %

class XRStools.xrs_utilities.dtxrd(hkl, energy, crystal='Si', asym_angle=0.0, angular_range=[-0.0005, 0.0005], angular_step=1e-08)[source]

Bases: object

class to hold all things dynamic theory of diffraction.

get_anomalous_absorption(energy=None)[source]
get_eta(angular_range, angular_step=1e-08)[source]
get_extinction_length(energy=None)[source]
get_reflection_width()[source]
get_reflectivity(angular_range=None, angular_step=None)[source]
set_asymmetry(alpha)[source]

negative alpha -> more grazing incidence

set_energy(energy)[source]
set_hkl(hkl)[source]
XRStools.xrs_utilities.dtxrd_anomalous_absorption(energy, hkl, alpha=0.0, crystal='Si', angular_range=array([-0.0005]))[source]
XRStools.xrs_utilities.dtxrd_extinction_length(energy, hkl, alpha=0.0, crystal='Si')[source]
XRStools.xrs_utilities.dtxrd_reflectivity(energy, hkl, alpha=0.0, crystal='Si', angular_range=array([-0.0005]))[source]
XRStools.xrs_utilities.e2pz(w1, w2, th)[source]

Calculates the momentum scale and the relativistic Compton cross section correction according to P. Holm, PRA 37, 3706 (1988).

This function is translated from Keijo Hamalainen’s Matlab implementation (KH 29.05.96).

Args:
  • w1 (float or np.array): incident energy in [keV]
  • w2 (float or np.array): scattered energy in [keV]
  • th (float): scattering angle two theta in [deg]
returns:
  • pz (float or np.array): momentum scale in [a.u.]
  • cf (float or np.array): cross section correction factor such that: J(pz) = cf * d^2(sigma)/d(w2)*d(Omega) [barn/atom/keV/srad]
XRStools.xrs_utilities.edfread(filename)[source]

reads edf-file with filename “filename” OUTPUT: data = 256x256 numpy array

XRStools.xrs_utilities.edfread_test(filename)[source]

reads edf-file with filename “filename” OUTPUT: data = 256x256 numpy array

here is how i opened the HH data: data = np.fromfile(f,np.int32) image = np.reshape(data,(dim,dim))

XRStools.xrs_utilities.element(z)[source]

Converts atomic number into string of the element symbol and vice versa.

Returns atomic number of given element, if z is a string of the element symbol or string of element symbol of given atomic number z.

Args:
  • z (string or int): string of the element symbol or atomic number.
Returns:
  • Z (string or int): string of the element symbol or atomic number.
XRStools.xrs_utilities.energy(d, ba)[source]

% ENERGY Calculates energy corrresponing to Bragg angle for given d-spacing % function e=energy(dspace,bragg_angle) % % dspace for reflection % bragg_angle in DEG % % KH 28.09.93

XRStools.xrs_utilities.energy_monoangle(angle, d=1.6374176589984608)[source]

% ENERGY Calculates energy corrresponing to Bragg angle for given d-spacing % function e=energy(dspace,bragg_angle) % % dspace for reflection (defaulf for Si(311) reflection) % bragg_angle in DEG % % KH 28.09.93 %

XRStools.xrs_utilities.fermi(rs)[source]

fermi Calculates the plasmon energy (in eV), Fermi energy (in eV), Fermi momentum (in a.u.), and critical plasmon cut-off vector (in a.u.).

Args:
  • rs (float): electron separation parameter
Returns:
  • wp (float): plasmon energy (in eV)
  • ef (float): Fermi energy (in eV)
  • kf (float): Fermi momentum (in a.u.)
  • kc (float): critical plasmon cut-off vector (in a.u.)

Based on Matlab function from A. Soininen.

XRStools.xrs_utilities.find_center_of_mass(x, y)[source]

Returns the center of mass (first moment) for the given curve y(x)

XRStools.xrs_utilities.find_diag_angles(q, x0, U, B, Lab, beam_in, lambdai, lambdao, tol=1e-08, method='BFGS')[source]

find_diag_angles Finds the FOURC spectrometer and sample angles for a desired q.

Args:
  • q (array): Desired momentum transfer in Lab coordinates.
  • x0 (list): Guesses for the angles (tthv, tthh, chi, phi, omega).
  • U (array): 3x3 U-matrix Lab-to-sample transformation.
  • B (array): 3x3 B-matrix reciprocal lattice to absolute units transformation.
  • lambdai (float): Incident x-ray wavelength in Angstrom.
  • lambdao (float): Scattered x-ray wavelength in Angstrom.
  • tol (float): Toleranz for minimization (see scipy.optimize.minimize)
  • method (str): Method for minimization (see scipy.optimize.minimize)
Returns:
  • ans (array): tthv, tthh, phi, chi, omega
XRStools.xrs_utilities.fwhm(x, y)[source]

finds full width at half maximum of the curve y vs. x returns f = FWHM x0 = position of the maximum

XRStools.xrs_utilities.gauss(x, x0, fwhm)[source]
XRStools.xrs_utilities.get_UB_Q(tthv, tthh, phi, chi, omega, **kwargs)[source]

get_UB_Q Returns the momentum transfer and scattering vectors for given FOURC spectrometer and sample angles. U-, B-matrices and incident/scattered wavelength are passed as keyword-arguments.

Args:
  • tthv (float): Spectrometer vertical 2Theta angle.
  • tthh (float): Spectrometer horizontal 2Theta angle.
  • chi (float): Sample rotation around x-direction.
  • phi (float): Sample rotation around y-direction.
  • omega (float): Sample rotation around z-direction.
  • kwargs (dict): Dictionary with key-word arguments:
    • kwargs[‘U’] (array): 3x3 U-matrix Lab-to-sample transformation.
    • kwargs[‘B’] (array): 3x3 B-matrix reciprocal lattice to absolute units transformation.
    • kwargs[‘lambdai’] (float): Incident x-ray wavelength in Angstrom.
    • kwargs[‘lambdao’] (float): Scattered x-ray wavelength in Angstrom.
Returns:
  • Q_sample (array): Momentum transfer in sample coordinates.
  • Ki_sample (array): Incident beam direction in sample coordinates.
  • Ko_sample (array): Scattered beam direction in sample coordinates.
XRStools.xrs_utilities.get_gnuplot_rgb(start=None, end=None, length=None)[source]

get_gnuplot_rgb Prints out a progression of RGB hex-keys to use in Gnuplot.

Args:
  • start (array): RGB code to start from (must be numbers out of [0,1]).
  • end (array): RGB code to end at (must be numbers out of [0,1]).
  • length (int): How many colors to print out.
XRStools.xrs_utilities.get_num_of_MD_steps(time_ps, time_step)[source]

Calculates the number of steps in an MD simulation for a desired time (in ps) and given step size (in a.u.)

Args:
time_ps (float): Desired time span (ps). time_step (float): Chosen time step (a.u.).
Returns:
The number of steps required to span the desired time span.
XRStools.xrs_utilities.getpenetrationdepth(energy, formulas, concentrations, densities)[source]

returns the penetration depth of a mixture of chemical formulas with certain concentrations and densities

XRStools.xrs_utilities.gettransmission(energy, formulas, concentrations, densities, thickness)[source]

returns the transmission through a sample composed of chemical formulas with certain densities mixed to certain concentrations, and a thickness

XRStools.xrs_utilities.hlike_Rwfn(n, l, r, Z)[source]

hlike_Rwfn Returns an array with the radial part of a hydrogen-like wave function.

Args:
  • n (integer): main quantum number n
  • l (integer): orbitalquantum number l
  • r (array): vector of radii on which the function should be evaluated
  • Z (float): effective nuclear charge
XRStools.xrs_utilities.householder(b, k)[source]

function H = householder(b, k) % H = householder(b, k) % Atkinson, Section 9.3, p. 611 % b is a column vector, k an index < length(b) % Constructs a matrix H that annihilates entries % in the product H*b below index k

% $Id: householder.m,v 1.1 2008-01-16 15:33:30 mike Exp $ % M. M. Sussman

XRStools.xrs_utilities.interpolate_M(xc, xi, yi, i0)[source]

Linear interpolation scheme after Martin Sundermann that conserves the absolute number of counts.

ONLY WORKS FOR EQUALLY/EVENLY SPACED XC, XI!

Args:
xc (np.array): The x-coordinates of the interpolated values. xi (np.array): The x-coordinates of the data points, must be increasing. yi (np.array): The y-coordinates of the data points, same length as xp. i0 (np.array): Normalization values for the data points, same length as xp.
Returns:
ic (np.array): The interpolated and normalized data points.

from scipy.interpolate import Rbf x = arange(20) d = zeros(len(x)) d[10] = 1 xc = arange(0.5,19.5) rbfi = Rbf(x, d) di = rbfi(xc)

XRStools.xrs_utilities.is_allowed_refl_fcc(H)[source]

is_allowed_refl_fcc Check if given reflection is allowed for a FCC lattice.

Args:
  • H (array, list, tuple): H=[h,k,l]
Returns:
  • boolean
XRStools.xrs_utilities.lindhard_pol(q, w, rs=3.93, use_corr=False, lifetime=0.28)[source]

lindhard_pol Calculates the Lindhard polarizability function (RPA) for certain q (a.u.), w (a.u.) and rs (a.u.).

Args:
  • q (float): momentum transfer (in a.u.)
  • w (float): energy (in a.u.)
  • rs (float): electron parameter
  • use_corr (boolean): if True, uses Bernardo’s calculation for n(k) instead of the Fermi function.
  • lifetime (float): life time (default is 0.28 eV for Na).

Based on Matlab function by S. Huotari.

XRStools.xrs_utilities.makeprofile(element, filename='/home/alex/src/XRStools/XRStools/data/ComptonProfiles.dat', E0=9.69, tth=35.0, correctasym=None)[source]

takes the profiles from ‘makepzprofile()’, converts them onto eloss scale and normalizes them to S(q,w) [1/eV] input: element = element symbol (e.g. ‘Si’, ‘Al’, etc.) filename = path and filename to tabulated profiles E0 = scattering energy [keV] tth = scattering angle [deg] returns: enscale = energy loss scale J = total CP C = only core contribution to CP V = only valence contribution to CP q = momentum transfer [a.u.]

XRStools.xrs_utilities.makeprofile_comp(formula, filename='/home/alex/src/XRStools/XRStools/data/ComptonProfiles.dat', E0=9.69, tth=35, correctasym=None)[source]

returns the compton profile of a chemical compound with formula ‘formula’ input: formula = string of a chemical formula (e.g. ‘SiO2’, ‘Ba8Si46’, etc.) filename = path and filename to tabulated profiles E0 = scattering energy [keV] tth = scattering angle [deg] returns: eloss = energy loss scale J = total CP C = only core contribution to CP V = only valence contribution to CP q = momentum transfer [a.u.]

XRStools.xrs_utilities.makeprofile_compds(formulas, concentrations=None, filename='/home/alex/src/XRStools/XRStools/data/ComptonProfiles.dat', E0=9.69, tth=35.0, correctasym=None)[source]

returns sum of compton profiles from a lost of chemical compounds weighted by the given concentration

XRStools.xrs_utilities.makepzprofile(element, filename='/home/alex/src/XRStools/XRStools/data/ComptonProfiles.dat')[source]

constructs compton profiles of element ‘element’ on pz-scale (-100:100 a.u.) from the Biggs tables provided in ‘filename’

input:
  • element = element symbol (e.g. ‘Si’, ‘Al’, etc.)
  • filename = path and filename to tabulated profiles
returns:
  • pzprofile = numpy array of the CP: * 1. column: pz-scale * 2. … n. columns: compton profile of nth shell * binden = binding energies of shells * occupation = number of electrons in the according shells
XRStools.xrs_utilities.mat2con(W, H, W_up, H_up)[source]
XRStools.xrs_utilities.mat2vec(F, C, F_up, C_up, n, k, m)[source]
class XRStools.xrs_utilities.maxipix_det(name, spot_arrangement)[source]

Bases: object

Class to store some useful values from the detectors used. To be used for arranging the ROIs.

get_det_name()[source]
get_pixel_range()[source]
XRStools.xrs_utilities.momtrans_au(e1, e2, tth)[source]

Calculates the momentum transfer in atomic units input: e1 = incident energy [keV] e2 = scattered energy [keV] tth = scattering angle [deg] returns: q = momentum transfer [a.u.] (corresponding to sin(th)/lambda)

XRStools.xrs_utilities.momtrans_inva(e1, e2, tth)[source]

Calculates the momentum transfer in inverse angstrom input: e1 = incident energy [keV] e2 = scattered energy [keV] tth = scattering angle [deg] returns: q = momentum transfer [a.u.] (corresponding to sin(th)/lambda)

XRStools.xrs_utilities.mpr(energy, compound)[source]

Calculates the photoelectric, elastic, and inelastic absorption of a chemical compound.

Calculates the photoelectric, elastic, and inelastic absorption of a chemical compound.

Args:
  • energy (np.array): energy scale in [keV].
  • compound (string): chemical sum formula (e.g. ‘SiO2’)
Returns:
  • murho (np.array): absorption coefficient normalized by the density.
  • rho (float): density in UNITS?
  • m (float): atomic mass in UNITS?
XRStools.xrs_utilities.mpr_compds(energy, formulas, concentrations, E0, rho_formu)[source]

Calculates the photoelectric, elastic, and inelastic absorption of a mix of compounds.

Returns the photoelectric absorption for a sum of different chemical compounds.

Args:
  • energy (np.array): energy scale in [keV].
  • formulas (list of strings): list of chemical sum formulas
Returns:
  • murho (np.array): absorption coefficient normalized by the density.
  • rho (float): density in UNITS?
  • m (float): atomic mass in UNITS?
XRStools.xrs_utilities.myprho(energy, Z, logtablefile='/home/alex/src/XRStools/XRStools/data/logtable.dat')[source]

Calculates the photoelectric, elastic, and inelastic absorption of an element Z

Calculates the photelectric , elastic, and inelastic absorption of an element Z. Z can be atomic number or element symbol.

Args:
  • energy (np.array): energy scale in [keV].
  • Z (string or int): atomic number or string of element symbol.
Returns:
  • murho (np.array): absorption coefficient normalized by the density.
  • rho (float): density in UNITS?
  • m (float): atomic mass in UNITS?
XRStools.xrs_utilities.nonzeroavg(y=None)[source]
XRStools.xrs_utilities.odefctn(y, t, abb0, abb1, abb7, abb8, lex, sgbeta, y0, c1)[source]

#% [T,Y] = ODE23(ODEFUN,TSPAN,Y0,OPTIONS,P1,P2,…) passes the additional #% parameters P1,P2,… to the ODE function as ODEFUN(T,Y,P1,P2…), and to #% all functions specified in OPTIONS. Use OPTIONS = [] as a place holder if #% no options are set.

XRStools.xrs_utilities.odefctn_CN(yCN, t, abb0, abb1, abb7, abb8N, lex, sgbeta, y0, c1)[source]
XRStools.xrs_utilities.parseformula(formula)[source]

Parses a chemical sum formula.

Parses the constituing elements and stoichiometries from a given chemical sum formula.

Args:
  • formula (string): string of a chemical formula (e.g. ‘SiO2’, ‘Ba8Si46’, etc.)
Returns:
  • elements (list): list of strings of constituting elemental symbols.
  • stoichiometries (list): list of according stoichiometries in the same order as ‘elements’.
XRStools.xrs_utilities.plotpenetrationdepth(energy, formulas, concentrations, densities)[source]

opens a plot window of the penetration depth of a mixture of chemical formulas with certain concentrations and densities plotted along the given energy vector

XRStools.xrs_utilities.plottransmission(energy, formulas, concentrations, densities, thickness)[source]

opens a plot with the transmission plotted along the given energy vector

XRStools.xrs_utilities.primtoconv(hklprim)[source]

primtoconv converts diamond structure reciprocal lattice expressed in primitive basis to the conventional basis (Palaiseau -> Helsinki conversion) from S. Huotari

XRStools.xrs_utilities.pz2e1(w2, pz, th)[source]

Calculates the incident energy for a specific scattered photon and momentum value.

Returns the incident energy for a given photon energy and scattering angle. This function is translated from Keijo Hamalainen’s Matlab implementation (KH 29.05.96).

Args:
  • w2 (float): scattered photon energy in [keV]
  • pz (np.array): pz scale in [a.u.]
  • th (float): scattering angle two theta in [deg]
Returns:
  • w1 (np.array): incident energy in [keV]
XRStools.xrs_utilities.read_dft_wfn(element, n, l, spin=None, directory='/home/alex/src/XRStools/XRStools/data')[source]

read_dft_wfn Parses radial parts of wavefunctions.

Args:
  • element (str): Element symbol.
  • n (int): Main quantum number.
  • l (int): Orbital quantum number.
  • spin (str): Which spin channel, default is average over up and down.
  • directory (str): Path to directory where the wavefunctions can be found.
Returns:
  • r (np.array): radius
  • wfn (np.array):
XRStools.xrs_utilities.readbiggsdata(filename, element)[source]

Reads Hartree-Fock Profile of element ‘element’ from values tabulated by Biggs et al. (Atomic Data and Nuclear Data Tables 16, 201-309 (1975)) as provided by the DABAX library (http://ftp.esrf.eu/pub/scisoft/xop2.3/DabaxFiles/ComptonProfiles.dat). input: filename = path to the ComptonProfiles.dat file (the file should be distributed with this package) element = string of element name returns:

  • data = the data for the according element as in the file:
    • #UD Columns:
    • #UD col1: pz in atomic units
    • #UD col2: Total compton profile (sum over the atomic electrons
    • #UD col3,…coln: Compton profile for the individual sub-shells
  • occupation = occupation number of the according shells
  • bindingen = binding energies of the accorting shells
  • colnames = strings of column names as used in the file
XRStools.xrs_utilities.readfio(prefix, scannumber, repnumber=0)[source]

if repnumber = 0: reads a spectra-file (name: prefix_scannumber.fio) if repnumber > 1: reads a spectra-file (name: prefix_scannumber_rrepnumber.fio)

XRStools.xrs_utilities.readp01image(filename)[source]

reads a detector file from PetraIII beamline P01

XRStools.xrs_utilities.readp01scan(prefix, scannumber)[source]

reads a whole scan from PetraIII beamline P01 (experimental)

XRStools.xrs_utilities.readp01scan_rep(prefix, scannumber, repetition)[source]

reads a whole scan with repititions from PetraIII beamline P01 (experimental)

XRStools.xrs_utilities.savitzky_golay(y, window_size, order, deriv=0, rate=1)[source]

Smooth (and optionally differentiate) data with a Savitzky-Golay filter. The Savitzky-Golay filter removes high frequency noise from data. It has the advantage of preserving the original shape and features of the signal better than other types of filtering approaches, such as moving averages techniques.

Parameters:
  • y : array_like, shape (N,) the values of the time history of the signal.
  • window_size : int the length of the window. Must be an odd integer number.
  • order : int the order of the polynomial used in the filtering. Must be less then window_size - 1.
  • deriv: int the order of the derivative to compute (default = 0 means only smoothing)
Returns
  • ys : ndarray, shape (N) the smoothed signal (or it’s n-th derivative).
Notes:
The Savitzky-Golay is a type of low-pass filter, particularly suited for smoothing noisy data. The main idea behind this approach is to make for each point a least-square fit with a polynomial of high order over a odd-sized window centered at the point.

Examples

t = np.linspace(-4, 4, 500)
y = np.exp( -t**2 ) + np.random.normal(0, 0.05, t.shape)
ysg = savitzky_golay(y, window_size=31, order=4)
import matplotlib.pyplot as plt
plt.plot(t, y, label='Noisy signal')
plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal')
plt.plot(t, ysg, 'r', label='Filtered signal')
plt.legend()
plt.show()
References ::
[1]A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 1964, 36 (8), pp 1627-1639.
[2]Numerical Recipes 3rd Edition: The Art of Scientific Computing W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery Cambridge University Press ISBN-13: 9780521880688
XRStools.xrs_utilities.sgolay2d(z, window_size, order, derivative=None)[source]
XRStools.xrs_utilities.sigmainc(Z, energy, logtablefile='/home/alex/src/XRStools/XRStools/data/logtable.dat')[source]

sigmainc Calculates the Incoherent Scattering Cross Section in cm^2/g using Log-Log Fit.

Args:
  • z (int or string): Element number or elements symbol.
  • energy (float or array): Energy (can be number or vector)
Returns:
  • tau (float or array): Photoelectric cross section in [cm**2/g]

Adapted from original Matlab function of Keijo Hamalainen.

XRStools.xrs_utilities.specread(filename, nscan)[source]

reads scan “nscan” from SPEC-file “filename”

INPUT:
  • filename = string with the SPEC-file name
  • nscan = number (int) of desired scan
OUTPUT:
  • data =
  • motors =
  • counters = dictionary
XRStools.xrs_utilities.spline2(x, y, x2)[source]

Extrapolates the smaller and larger valuea as a constant

XRStools.xrs_utilities.split_hdf5_address(dataadress)[source]
XRStools.xrs_utilities.sumx(A)[source]

Short-hand command to sum over 1st dimension of a N-D matrix (N>2) and to squeeze it to N-1-D matrix.

XRStools.xrs_utilities.svd_my(M, maxiter=100, eta=0.1)[source]
XRStools.xrs_utilities.taupgen(e, hkl=[6, 6, 0], crystals='Si', R=1.0, dev=array([-50., -49., -48., -47., -46., -45., -44., -43., -42., -41., -40., -39., -38., -37., -36., -35., -34., -33., -32., -31., -30., -29., -28., -27., -26., -25., -24., -23., -22., -21., -20., -19., -18., -17., -16., -15., -14., -13., -12., -11., -10., -9., -8., -7., -6., -5., -4., -3., -2., -1., 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99., 100., 101., 102., 103., 104., 105., 106., 107., 108., 109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119., 120., 121., 122., 123., 124., 125., 126., 127., 128., 129., 130., 131., 132., 133., 134., 135., 136., 137., 138., 139., 140., 141., 142., 143., 144., 145., 146., 147., 148., 149.]), alpha=0.0)[source]

% TAUPGEN Calculates the reflectivity curves of bent crystals % % function [refl,e,dev]=taupgen_new(e,hkl,crystals,R,dev,alpha); % % e = fixed nominal energy in keV % hkl = reflection order vector, e.g. [1 1 1] % crystals = crystal string, e.g. ‘si’ or ‘ge’ % R = bending radius in meters % dev = deviation parameter for which the % curve will be calculated (vector) (optional) % alpha = asymmetry angle % based on a FORTRAN program of Michael Krisch % Translitterated to Matlab by Simo Huotari 2006, 2007 % Is far away from being good matlab writing - mostly copy&paste from % the fortran routines. Frankly, my dear, I don’t give a damn. % Complaints -> /dev/null

XRStools.xrs_utilities.taupgen_amplitude(e, hkl=[6, 6, 0], crystals='Si', R=1.0, dev=array([-50., -49., -48., -47., -46., -45., -44., -43., -42., -41., -40., -39., -38., -37., -36., -35., -34., -33., -32., -31., -30., -29., -28., -27., -26., -25., -24., -23., -22., -21., -20., -19., -18., -17., -16., -15., -14., -13., -12., -11., -10., -9., -8., -7., -6., -5., -4., -3., -2., -1., 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99., 100., 101., 102., 103., 104., 105., 106., 107., 108., 109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119., 120., 121., 122., 123., 124., 125., 126., 127., 128., 129., 130., 131., 132., 133., 134., 135., 136., 137., 138., 139., 140., 141., 142., 143., 144., 145., 146., 147., 148., 149.]), alpha=0.0)[source]

% TAUPGEN Calculates the reflectivity curves of bent crystals % % function [refl,e,dev]=taupgen_new(e,hkl,crystals,R,dev,alpha); % % e = fixed nominal energy in keV % hkl = reflection order vector, e.g. [1 1 1] % crystals = crystal string, e.g. ‘si’ or ‘ge’ % R = bending radius in meters % dev = deviation parameter for which the % curve will be calculated (vector) (optional) % alpha = asymmetry angle % based on a FORTRAN program of Michael Krisch % Translitterated to Matlab by Simo Huotari 2006, 2007 % Is far away from being good matlab writing - mostly copy&paste from % the fortran routines. Frankly, my dear, I don’t give a damn. % Complaints -> /dev/null

XRStools.xrs_utilities.tauphoto(Z, energy, logtablefile='/home/alex/src/XRStools/XRStools/data/logtable.dat')[source]

tauphoto Calculates Photoelectric Cross Section in cm^2/g using Log-Log Fit.

Args:
  • z (int or string): Element number or elements symbol.
  • energy (float or array): Energy (can be number or vector)
Returns:
  • tau (float or array): Photoelectric cross section in [cm**2/g]

Adapted from original Matlab function of Keijo Hamalainen.

XRStools.xrs_utilities.unconstrained_mf(A, numComp=3, maxIter=1000, tol=1e-08)[source]

unconstrained_mf Returns main components from an off-diagonal Matrix (energy-loss x angular-departure), using the power method iteratively on the different main components.

XRStools.xrs_utilities.vangle(v1, v2)[source]

vangle Calculates the angle between two cartesian vectors v1 and v2 in degrees.

Args:
  • v1 (np.array): first vector.
  • v2 (np.array): second vector.
Returns:
  • th (float): angle between first and second vector.

Function by S. Huotari, adopted for Python.

XRStools.xrs_utilities.vec2mat(x, F, C, F_up, C_up, n, k, m)[source]
XRStools.xrs_utilities.vrot(v, vaxis, phi)[source]

vrot Rotates a vector around a given axis.

Args:
  • v (np.array): vector to be rotated
  • vaxis (np.array): rotation axis
  • phi (float): angle [deg] respecting the right-hand rule
Returns:
  • v2 (np.array): new rotated vector

Function by S. Huotari (2007) adopted to Python.

XRStools.xrs_utilities.vrot2(vector1, vector2, angle)[source]

rotMatrix Rotate vector1 around vector2 by an angle.