Provided by: python3-xrstools_0.15.0+git20210910+c147919d-2build1_amd64 
NAME
xrstools - XRStools Documentation
Contents:
INSTALLATION
• If you install from a Debian package you can skip the following points, install it ,
and then go directly to the code invocation section
• Using Git, sources can be retrived with the following command
git clone https://gitlab.esrf.fr/ixstools/xrstools
• for a local installation you can use
python setup.py install --prefix=~/packages
then to run the code you must do beforehand
export PYTHONPATH=/home/yourname/packages/lib/python2.7/site-packages
export PATH=/home/yourname/bin:$PATH
• To install by creating a virtual environment
export MYPREFIX=/REPLACE/WITH/YOUR/TARGET
cd ${MYPREFIX}
python3 -m venv myenv
source ${MYPREFIX}/myenv/bin/activate
pip install pip --upgrade
pip install setuptools --upgrade
git clone https://gitlab.esrf.fr/ixstools/xrstools
cd ${MYPREFIX}/xrstools/
pip install -r requirements.txt
python setup.py install
• Examples can be found in the nonregression directory.
• For the roi selection tool you need a recent version of pymca installed on your
sistem
• Usage examples can be found in the non regression directory.
CODE INVOCATION
• Some of the XRStools capabilities can be accessed by invocation of the XRS_swissknife
script, providing as input a file in the yaml format.
• To use the wizard the suggested instruction is
XRS_wizard --wroot ~/software/XRStoolsSuperResolution/XRStools/WIZARD/methods/
the wroot argument tells where extra workflow can be found. In the above instruction
we give workflows in the home source directory. This is practical because the wizard
allows to edit them online and the modification will remain in the sources. or to
access extra workflows that are not coming with the main disribution.
• Depending on the details of your installation, you have the XRS_swissknife script
sitting somewhere in a directory. Check the Installation page to see how to set
PYTHONPATH and PATH in case of a local installation.
The following documentation has been generated automatically from the comments found
in the code.
GENERALITIES about XRS_swissknife
Super Resolution
to fit optical responses of all the analysers (you selected a ROI for) and the pixel response
based on a foil scan
embedded doc :
to extrapolate to a larger extent the ROIS and the foils scan, thus to cover a larger sample
embedded doc :
to calculate the scalar product between a foil scan and a sample, for futher use in the
inversion problem
embedded doc :
Other features
e_rois
EXAMPLES EN VRAC
xrstools imaging example
VIDEOS
• A Tool to clean the spectra from Compton profile and absorption edge
• A Tool to define ROI by using NNMF in spectral and spatial domain
DEVELOPERS CORNER
XRStools.roifinder_and_gui Module
XRStools.xrs_utilities Module
XRStools.xrs_utilities.Chi(chi, degrees=True)
rotation around (1,0,0), pos sense
XRStools.xrs_utilities.HRcorrect(pzprofile, occupation, q)
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)
NNMFcost Returns cost and gradient for NNMF with constraints.
XRStools.xrs_utilities.NNMFcost_der(x, A, F, C, F_up, C_up, n, k, m)
XRStools.xrs_utilities.NNMFcost_old(x, A, W, H, W_up, H_up)
NNMFcost Returns cost and gradient for NNMF with constraints.
XRStools.xrs_utilities.Omega(omega, degrees=True)
rotation around (0,0,1), pos sense
XRStools.xrs_utilities.Phi(phi, degrees=True)
rotation around (0,1,0), neg sense
XRStools.xrs_utilities.Rx(chi, degrees=True)
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)
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)
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_')
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')
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)
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)
# 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)
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)
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)
bootstrapCNNMF Constrained non-negative matrix factorization with bootstrapping for
error estimates.
XRStools.xrs_utilities.bragg(hkl, e, xtal='Si')
% 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 %
class XRStools.xrs_utilities.bragg_refl(crystal, hkl, alpha=0.0)
Bases: object
Dynamical theory of diffraction.
get_chi(energy, crystal=None, hkl=None)
get_nff(nff_path=None)
get_polarization_factor(tth, case='sigma')
Calculate polarization factor.
get_reflectivity(energy, delta_theta, case='sigma')
get_reflectivity_bent(energy, delta_theta, R)
XRStools.xrs_utilities.braggd(hkl, e, xtal='Si')
# 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.cNNMF_chris(A, W_fixed, W_free, maxIter=100, verbose=True)
XRStools.xrs_utilities.cixsUBfind(x, G, Q_sample, wi, wo, lambdai, lambdao)
cixsUBfind
XRStools.xrs_utilities.cixsUBgetAngles_primo(Q)
XRStools.xrs_utilities.cixsUBgetAngles_secondo(Q)
XRStools.xrs_utilities.cixsUBgetAngles_terzo(Q)
XRStools.xrs_utilities.cixsUBgetQ_primo(tthv, tthh, psi)
returns the Q0 given the detector position (tthv, tth) and th crystal orientation.
This orientation is calculated considering :
the Bragg condition and the rotation around the G vector :
this rotation is defined by psi which is a rotation around G
XRStools.xrs_utilities.cixsUBgetQ_secondo(tthv, tthh, psi)
XRStools.xrs_utilities.cixsUBgetQ_terzo(tthv, tthh, psi)
XRStools.xrs_utilities.cixs_primo(tthv, tthh, psi, anal_braggd=86.5)
cixs_primo
XRStools.xrs_utilities.cixs_secondo(tthv, tthh, psi, anal_braggd=86.5)
cixs_secondo
XRStools.xrs_utilities.cixs_terzo(tthv, tthh, psi, anal_braggd=86.5)
cixs_terzo
XRStools.xrs_utilities.compute_matrix_elements(R1, R2, k, r)
XRStools.xrs_utilities.con2mat(x, W, H, W_up, H_up)
XRStools.xrs_utilities.constrained_mf(A, W_ini, W_up, coeff_ini, coeff_up, maxIter=1000,
tol=1e-08, maxIter_power=1000)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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')
% 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)
Bases: object
class to hold all things dynamic theory of diffraction.
get_anomalous_absorption(energy=None)
get_eta(angular_range, angular_step=1e-08)
get_extinction_length(energy=None)
get_reflection_width()
get_reflectivity(angular_range=None, angular_step=None)
set_asymmetry(alpha)
negative alpha -> more grazing incidence
set_energy(energy)
set_hkl(hkl)
XRStools.xrs_utilities.dtxrd_anomalous_absorption(energy, hkl, alpha=0.0, crystal='Si',
angular_range=array([- 0.0005]))
XRStools.xrs_utilities.dtxrd_extinction_length(energy, hkl, alpha=0.0, crystal='Si')
XRStools.xrs_utilities.dtxrd_reflectivity(energy, hkl, alpha=0.0, crystal='Si',
angular_range=array([- 0.0005]))
XRStools.xrs_utilities.e2pz(w1, w2, th)
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)
reads edf-file with filename "filename" OUTPUT: data = 256x256 numpy array
XRStools.xrs_utilities.edfread_test(filename)
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)
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)
% 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)
% 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)
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)
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')
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)
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)
XRStools.xrs_utilities.get_UB_Q(tthv, tthh, phi, chi, omega, **kwargs)
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)
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)
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)
returns the penetration depth of a mixture of chemical formulas with certain
concentrations and densities
XRStools.xrs_utilities.gettransmission(energy, formulas, concentrations, densities,
thickness)
returns the transmission through a sample composed of chemical formulas with
certain densities mixed to certain concentrations, and a thickness
XRStools.xrs_utilities.hex2rgb(hex_val)
XRStools.xrs_utilities.hlike_Rwfn(n, l, r, Z)
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)
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)
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)
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)
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='/usr/lib/python3/dist-packages/XRStools/resources/data/ComptonProfiles.dat',
E0=9.69, tth=35.0, correctasym=None)
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='/usr/lib/python3/dist-packages/XRStools/resources/data/ComptonProfiles.dat',
E0=9.69, tth=35, correctasym=None)
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='/usr/lib/python3/dist-packages/XRStools/resources/data/ComptonProfiles.dat',
E0=9.69, tth=35.0, correctasym=None)
returns sum of compton profiles from a lost of chemical compounds weighted by the
given concentration
XRStools.xrs_utilities.makepzprofile(element,
filename='/usr/lib/python3/dist-packages/XRStools/resources/data/ComptonProfiles.dat')
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)
XRStools.xrs_utilities.mat2vec(F, C, F_up, C_up, n, k, m)
class XRStools.xrs_utilities.maxipix_det(name, spot_arrangement)
Bases: object
Class to store some useful values from the detectors used. To be used for arranging
the ROIs.
get_det_name()
get_pixel_range()
XRStools.xrs_utilities.momtrans_au(e1, e2, tth)
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)
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)
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)
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='/usr/lib/python3/dist-packages/XRStools/resources/data/logtable.dat')
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)
XRStools.xrs_utilities.odefctn(y, t, abb0, abb1, abb7, abb8, lex, sgbeta, y0, c1)
#% [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)
XRStools.xrs_utilities.parseformula(formula)
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)
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)
opens a plot with the transmission plotted along the given energy vector
XRStools.xrs_utilities.primtoconv(hklprim)
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)
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='/usr/lib/python3/dist-packages/XRStools/resources/data')
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)
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)
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)
reads a detector file from PetraIII beamline P01
XRStools.xrs_utilities.readp01scan(prefix, scannumber)
reads a whole scan from PetraIII beamline P01 (experimental)
XRStools.xrs_utilities.readp01scan_rep(prefix, scannumber, repetition)
reads a whole scan with repititions from PetraIII beamline P01 (experimental)
XRStools.xrs_utilities.savitzky_golay(y, window_size, order, deriv=0, rate=1)
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)
XRStools.xrs_utilities.sigmainc(Z, energy,
logtablefile='/usr/lib/python3/dist-packages/XRStools/resources/data/logtable.dat')
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)
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)
Extrapolates the smaller and larger valuea as a constant
XRStools.xrs_utilities.split_hdf5_address(dataadress)
XRStools.xrs_utilities.stiff_compl_matrix_Si(e1, e2, e3, ansys=False)
stiff_compl_matrix_Si Returns stiffnes and compliance tensor of Si for a given
orientation.
Args:
• e1 (np.array): unit vector normal to crystal surface
• e2 (np.array): unit vector crystal surface
• e3 (np.array): unit vector orthogonal to e2
Returns:
• S (np.array): compliance tensor in new coordinate system
• C (np.array): stiffnes tensor in new coordinate system
• E (np.array): Young's modulus in [GPa]
• G (np.array): shear modulus in [GPa]
• nu (np.array): Poisson ratio
Copied from S.I. of L. Zhang et al. "Anisotropic elasticity of silicon and its
application to the modelling of X-ray optics." J. Synchrotron Rad. 21, no. 3
(2014): 507-517.
XRStools.xrs_utilities.sumx(A)
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)
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)
% 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)
% 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='/usr/lib/python3/dist-packages/XRStools/resources/data/logtable.dat')
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)
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)
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)
XRStools.xrs_utilities.vrot(v, vaxis, phi)
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)
rotMatrix Rotate vector1 around vector2 by an angle.
XRStools.xrs_utilities.xas_fluo_correct(ene, mu, formula, fluo_ene, edge_ene, angin,
angout)
xas_fluo_correct Fluorescence yield over-absorption correction as in Larch/Athena.
see: https://www3.aps.anl.gov/haskel/FLUO/Fluo-manual.pdf
Args:
• ene (np.array): energy axis in [keV]
• mu (np.array): measured fluorescence spectrum
• formula (str): chemical sum formulas (e.g. 'SiO2')
• fluo_ene (float): energy in keV of main fluorescence line
• edge_ene (float): edge energy in [keV]
• angin (float): incidence angle (relative to sample normal) [deg.]
• angout (float): exit angle (relative to sample normal) [deg.]
Returns:
• ene (np.array): energy axis in [keV]
• mu_corr (np.array): corrected fluorescence spectrum
XRStools.XRStool Package
XRStools.xrs_calctools Module
XRStools.xrs_calctools.alterGROatomNames(filename, oldName, newName)
XRStools.xrs_calctools.axsfTrajParser(filename)
axsfTrajParser
XRStools.xrs_calctools.beta(a, b, size=None)
Draw samples from a Beta distribution.
The Beta distribution is a special case of the Dirichlet distribution, and is
related to the Gamma distribution. It has the probability distribution function
f(x; a,b) = \frac{1}{B(\alpha, \beta)} x^{\alpha - 1}
(1 - x)^{\beta - 1},
where the normalization, B, is the beta function,
B(\alpha, \beta) = \int_0^1 t^{\alpha - 1}
(1 - t)^{\beta - 1} dt.
It is often seen in Bayesian inference and order statistics.
NOTE:
New code should use the beta method of a default_rng() instance instead; please
see the random-quick-start.
a float or array_like of floats Alpha, positive (>0).
b float or array_like of floats Beta, positive (>0).
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if a and b are both scalars. Otherwise,
np.broadcast(a, b).size samples are drawn.
out ndarray or scalar Drawn samples from the parameterized beta distribution.
Generator.beta: which should be used for new code.
XRStools.xrs_calctools.binomial(n, p, size=None)
Draw samples from a binomial distribution.
Samples are drawn from a binomial distribution with specified parameters, n trials
and p probability of success where n an integer >= 0 and p is in the interval
[0,1]. (n may be input as a float, but it is truncated to an integer in use)
NOTE:
New code should use the binomial method of a default_rng() instance instead;
please see the random-quick-start.
n int or array_like of ints Parameter of the distribution, >= 0. Floats are
also accepted, but they will be truncated to integers.
p float or array_like of floats Parameter of the distribution, >= 0 and <=1.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if n and p are both scalars. Otherwise,
np.broadcast(n, p).size samples are drawn.
out ndarray or scalar Drawn samples from the parameterized binomial
distribution, where each sample is equal to the number of successes over the
n trials.
scipy.stats.binom
probability density function, distribution or cumulative density function,
etc.
Generator.binomial: which should be used for new code.
The probability density for the binomial distribution is
P(N) = \binom{n}{N}p^N(1-p)^{n-N},
where n is the number of trials, p is the probability of success, and N is the
number of successes.
When estimating the standard error of a proportion in a population by using a
random sample, the normal distribution works well unless the product p*n <=5, where
p = population proportion estimate, and n = number of samples, in which case the
binomial distribution is used instead. For example, a sample of 15 people shows 4
who are left handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,
so the binomial distribution should be used in this case.
[1] Dalgaard, Peter, "Introductory Statistics with R", Springer-Verlag, 2002.
[2] Glantz, Stanton A. "Primer of Biostatistics.", McGraw-Hill, Fifth Edition, 2002.
[3] Lentner, Marvin, "Elementary Applied Statistics", Bogden and Quigley, 1972.
[4] Weisstein, Eric W. "Binomial Distribution." From MathWorld--A Wolfram Web Resource.
http://mathworld.wolfram.com/BinomialDistribution.html
[5] Wikipedia, "Binomial distribution",
https://en.wikipedia.org/wiki/Binomial_distribution
Draw samples from the distribution:
>>> n, p = 10, .5 # number of trials, probability of each trial
>>> s = np.random.binomial(n, p, 1000)
# result of flipping a coin 10 times, tested 1000 times.
A real world example. A company drills 9 wild-cat oil exploration wells, each with an
estimated probability of success of 0.1. All nine wells fail. What is the probability
of that happening?
Let's do 20,000 trials of the model, and count the number that generate zero positive
results.
>>> sum(np.random.binomial(9, 0.1, 20000) == 0)/20000.
# answer = 0.38885, or 38%.
XRStools.xrs_calctools.boxParser(filename)
parseXYZfile Reads an xyz-style file.
XRStools.xrs_calctools.broaden_diagram(e, s, params=[1.0, 1.0, 537.5, 540.0],
npoints=1000)
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)
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)
calculateCOMlist Calculates center of mass for a list of atoms.
XRStools.xrs_calctools.calculateRIJhist(atoms, boxLength, DELR=0.01, MAXBIN=1000)
XRStools.xrs_calctools.calculateRIJhist2_arb(atoms1, atoms2, lattice, lattice_inv,
DELR=0.01, MAXBIN=1000)
XRStools.xrs_calctools.calculateRIJhist_arb(atoms1, atoms2, lattice, lattice_inv,
DELR=0.01, MAXBIN=1000)
XRStools.xrs_calctools.changeOHBondLength(h2oMol, fraction, boxLength=None, oName='O',
hName='H')
XRStools.xrs_calctools.chisquare(df, size=None)
Draw samples from a chi-square distribution.
When df independent random variables, each with standard normal distributions (mean
0, variance 1), are squared and summed, the resulting distribution is chi-square
(see Notes). This distribution is often used in hypothesis testing.
NOTE:
New code should use the chisquare method of a default_rng() instance instead;
please see the random-quick-start.
df float or array_like of floats Number of degrees of freedom, must be > 0.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if df is a scalar. Otherwise, np.array(df).size
samples are drawn.
out ndarray or scalar Drawn samples from the parameterized chi-square
distribution.
ValueError
When df <= 0 or when an inappropriate size (e.g. size=-1) is given.
Generator.chisquare: which should be used for new code.
The variable obtained by summing the squares of df independent, standard normally
distributed random variables:
Q = \sum_{i=0}^{\mathtt{df}} X^2_i
is chi-square distributed, denoted
Q \sim \chi^2_k.
The probability density function of the chi-squared distribution is
p(x) = \frac{(1/2)^{k/2}}{\Gamma(k/2)}
x^{k/2 - 1} e^{-x/2},
where \Gamma is the gamma function,
\Gamma(x) = \int_0^{-\infty} t^{x - 1} e^{-t} dt.
[1] NIST "Engineering Statistics Handbook"
https://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm
>>> np.random.chisquare(2,4)
array([ 1.89920014, 9.00867716, 3.13710533, 5.62318272]) # random
XRStools.xrs_calctools.choice(a, size=None, replace=True, p=None)
Generates a random sample from a given 1-D array
New in version 1.7.0.
NOTE:
New code should use the choice method of a default_rng() instance instead;
please see the random-quick-start.
a 1-D array-like or int If an ndarray, a random sample is generated from its
elements. If an int, the random sample is generated as if it were
np.arange(a)
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. Default is None, in which case
a single value is returned.
replace
boolean, optional Whether the sample is with or without replacement. Default
is True, meaning that a value of a can be selected multiple times.
p 1-D array-like, optional The probabilities associated with each entry in a.
If not given, the sample assumes a uniform distribution over all entries in
a.
samples
single item or ndarray The generated random samples
ValueError
If a is an int and less than zero, if a or p are not 1-dimensional, if a is
an array-like of size 0, if p is not a vector of probabilities, if a and p
have different lengths, or if replace=False and the sample size is greater
than the population size
randint, shuffle, permutation Generator.choice: which should be used in new code
Setting user-specified probabilities through p uses a more general but less
efficient sampler than the default. The general sampler produces a different sample
than the optimized sampler even if each element of p is 1 / len(a).
Sampling random rows from a 2-D array is not possible with this function, but is
possible with Generator.choice through its axis keyword.
Generate a uniform random sample from np.arange(5) of size 3:
>>> np.random.choice(5, 3)
array([0, 3, 4]) # random
>>> #This is equivalent to np.random.randint(0,5,3)
Generate a non-uniform random sample from np.arange(5) of size 3:
>>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
array([3, 3, 0]) # random
Generate a uniform random sample from np.arange(5) of size 3 without replacement:
>>> np.random.choice(5, 3, replace=False)
array([3,1,0]) # random
>>> #This is equivalent to np.random.permutation(np.arange(5))[:3]
Generate a non-uniform random sample from np.arange(5) of size 3 without
replacement:
>>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
array([2, 3, 0]) # random
Any of the above can be repeated with an arbitrary array-like instead of just
integers. For instance:
>>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']
>>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])
array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'], # random
dtype='<U11')
XRStools.xrs_calctools.convg(x, y, fwhm)
Convolution with Gaussian
XRStools.xrs_calctools.countHbonds(mol1, mol2, Roocut=3.6, Rohcut=2.4, Aoooh=30.0)
XRStools.xrs_calctools.countHbonds_orig(mol1, mol2, Roocut=3.6, Rohcut=2.4, Aoooh=30.0)
XRStools.xrs_calctools.countHbonds_pbc(mol1, mol2, boxLength, Roocut=3.6, Rohcut=2.4,
Aoooh=30.0)
XRStools.xrs_calctools.count_HBonds_pbc_arb(mol1, mol2, lattice, lattice_inv, Roocut=3.6,
Rohcut=2.4, Aoooh=30.0)
XRStools.xrs_calctools.count_OO_neighbors(list_of_o_atoms, Roocut, boxLength=None)
XRStools.xrs_calctools.count_OO_neighbors_pbc(list_of_o_atoms, Roocut, boxLength,
numbershells=1)
XRStools.xrs_calctools.cut_spec(spec, emin=None, emax=None)
deletes lines of matrix with first column smaller than emin and larger than emax
XRStools.xrs_calctools.dirichlet(alpha, size=None)
Draw samples from the Dirichlet distribution.
Draw size samples of dimension k from a Dirichlet distribution. A
Dirichlet-distributed random variable can be seen as a multivariate generalization
of a Beta distribution. The Dirichlet distribution is a conjugate prior of a
multinomial distribution in Bayesian inference.
NOTE:
New code should use the dirichlet method of a default_rng() instance instead;
please see the random-quick-start.
alpha sequence of floats, length k Parameter of the distribution (length k for
sample of length k).
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n), then m * n * k samples are drawn. Default is None, in which case a
vector of length k is returned.
samples
ndarray, The drawn samples, of shape (size, k).
ValueError
If any value in alpha is less than or equal to zero
Generator.dirichlet: which should be used for new code.
The Dirichlet distribution is a distribution over vectors x that fulfil the
conditions x_i>0 and \sum_{i=1}^k x_i = 1.
The probability density function p of a Dirichlet-distributed random vector X is
proportional to
p(x) \propto \prod_{i=1}^{k}{x^{\alpha_i-1}_i},
where \alpha is a vector containing the positive concentration parameters.
The method uses the following property for computation: let Y be a random vector
which has components that follow a standard gamma distribution, then X =
\frac{1}{\sum_{i=1}^k{Y_i}} Y is Dirichlet-distributed
[1] David McKay, "Information Theory, Inference and Learning Algorithms," chapter 23,
http://www.inference.org.uk/mackay/itila/
[2] Wikipedia, "Dirichlet distribution",
https://en.wikipedia.org/wiki/Dirichlet_distribution
Taking an example cited in Wikipedia, this distribution can be used if one wanted to
cut strings (each of initial length 1.0) into K pieces with different lengths, where
each piece had, on average, a designated average length, but allowing some variation
in the relative sizes of the pieces.
>>> s = np.random.dirichlet((10, 5, 3), 20).transpose()
>>> import matplotlib.pyplot as plt
>>> plt.barh(range(20), s[0])
>>> plt.barh(range(20), s[1], left=s[0], color='g')
>>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r')
>>> plt.title("Lengths of Strings")
class XRStools.xrs_calctools.erkale(prefix, postfix, fromnumber, tonumber, step,
stepformat=2)
Bases: object
class to analyze ERKALE XRS results.
broaden_lin(params=[0.8, 8, 537.5, 550], npoints=1000)
cut_broadspecs(emin=None, emax=None)
cut_rawspecs(emin=None, emax=None)
norm_area(emin=None, emax=None)
norm_max()
plot_spec()
sum_specs()
XRStools.xrs_calctools.exponential(scale=1.0, size=None)
Draw samples from an exponential distribution.
Its probability density function is
f(x; \frac{1}{\beta}) = \frac{1}{\beta} \exp(-\frac{x}{\beta}),
for x > 0 and 0 elsewhere. \beta is the scale parameter, which is the inverse of
the rate parameter \lambda = 1/\beta. The rate parameter is an alternative, widely
used parameterization of the exponential distribution
[3]_
.
The exponential distribution is a continuous analogue of the geometric
distribution. It describes many common situations, such as the size of raindrops
measured over many rainstorms
[1]_
, or the time between page requests to Wikipedia
[2]_
.
NOTE:
New code should use the exponential method of a default_rng() instance instead;
please see the random-quick-start.
scale float or array_like of floats The scale parameter, \beta = 1/\lambda. Must
be non-negative.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if scale is a scalar. Otherwise,
np.array(scale).size samples are drawn.
out ndarray or scalar Drawn samples from the parameterized exponential
distribution.
Generator.exponential: which should be used for new code.
[1] Peyton Z. Peebles Jr., "Probability, Random Variables and Random Signal Principles",
4th ed, 2001, p. 57.
[2] Wikipedia, "Poisson process", https://en.wikipedia.org/wiki/Poisson_process
[3] Wikipedia, "Exponential distribution",
https://en.wikipedia.org/wiki/Exponential_distribution
XRStools.xrs_calctools.f(dfnum, dfden, size=None)
Draw samples from an F distribution.
Samples are drawn from an F distribution with specified parameters, dfnum (degrees
of freedom in numerator) and dfden (degrees of freedom in denominator), where both
parameters must be greater than zero.
The random variate of the F distribution (also known as the Fisher distribution) is
a continuous probability distribution that arises in ANOVA tests, and is the ratio
of two chi-square variates.
NOTE:
New code should use the f method of a default_rng() instance instead; please see
the random-quick-start.
dfnum float or array_like of floats Degrees of freedom in numerator, must be > 0.
dfden float or array_like of float Degrees of freedom in denominator, must be > 0.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if dfnum and dfden are both scalars. Otherwise,
np.broadcast(dfnum, dfden).size samples are drawn.
out ndarray or scalar Drawn samples from the parameterized Fisher distribution.
scipy.stats.f
probability density function, distribution or cumulative density function,
etc.
Generator.f: which should be used for new code.
The F statistic is used to compare in-group variances to between-group variances.
Calculating the distribution depends on the sampling, and so it is a function of
the respective degrees of freedom in the problem. The variable dfnum is the number
of samples minus one, the between-groups degrees of freedom, while dfden is the
within-groups degrees of freedom, the sum of the number of samples in each group
minus the number of groups.
[1] Glantz, Stanton A. "Primer of Biostatistics.", McGraw-Hill, Fifth Edition, 2002.
[2] Wikipedia, "F-distribution", https://en.wikipedia.org/wiki/F-distribution
An example from Glantz[1], pp 47-40:
Two groups, children of diabetics (25 people) and children from people without
diabetes (25 controls). Fasting blood glucose was measured, case group had a mean
value of 86.1, controls had a mean value of 82.2. Standard deviations were 2.09 and
2.49 respectively. Are these data consistent with the null hypothesis that the
parents diabetic status does not affect their children's blood glucose levels?
Calculating the F statistic from the data gives a value of 36.01.
Draw samples from the distribution:
>>> dfnum = 1. # between group degrees of freedom
>>> dfden = 48. # within groups degrees of freedom
>>> s = np.random.f(dfnum, dfden, 1000)
The lower bound for the top 1% of the samples is :
>>> np.sort(s)[-10]
7.61988120985 # random
So there is about a 1% chance that the F statistic will exceed 7.62, the measured
value is 36, so the null hypothesis is rejected at the 1% level.
XRStools.xrs_calctools.findAllWaters(point, waterMols, o_name, cutoff)
XRStools.xrs_calctools.findHexaneMolecules(box, c_atoms, CC_cut=1.7, CH_cut=1.2)
XRStools.xrs_calctools.findMethanolMolecules(box, CO_cut=1.6, CH_cut=1.2, OH_cut=1.2)
XRStools.xrs_calctools.findMolecule(xyzAtoms, molAtomList)
XRStools.xrs_calctools.find_H2O_molecules(o_atoms, h_atoms, boxLength=None)
XRStools.xrs_calctools.find_H2O_molecules_PBC_arb(o_atoms, h_atoms, lattice, lattice_inv,
OH_cutoff=1.5)
XRStools.xrs_calctools.gamma(shape, scale=1.0, size=None)
Draw samples from a Gamma distribution.
Samples are drawn from a Gamma distribution with specified parameters, shape
(sometimes designated "k") and scale (sometimes designated "theta"), where both
parameters are > 0.
NOTE:
New code should use the gamma method of a default_rng() instance instead; please
see the random-quick-start.
shape float or array_like of floats The shape of the gamma distribution. Must be
non-negative.
scale float or array_like of floats, optional The scale of the gamma distribution.
Must be non-negative. Default is equal to 1.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if shape and scale are both scalars. Otherwise,
np.broadcast(shape, scale).size samples are drawn.
out ndarray or scalar Drawn samples from the parameterized gamma distribution.
scipy.stats.gamma
probability density function, distribution or cumulative density function,
etc.
Generator.gamma: which should be used for new code.
The probability density for the Gamma distribution is
p(x) = x^{k-1}\frac{e^{-x/\theta}}{\theta^k\Gamma(k)},
where k is the shape and \theta the scale, and \Gamma is the Gamma function.
The Gamma distribution is often used to model the times to failure of electronic
components, and arises naturally in processes for which the waiting times between
Poisson distributed events are relevant.
[1] Weisstein, Eric W. "Gamma Distribution." From MathWorld--A Wolfram Web Resource.
http://mathworld.wolfram.com/GammaDistribution.html
[2] Wikipedia, "Gamma distribution", https://en.wikipedia.org/wiki/Gamma_distribution
Draw samples from the distribution:
>>> shape, scale = 2., 2. # mean=4, std=2*sqrt(2)
>>> s = np.random.gamma(shape, scale, 1000)
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt
>>> import scipy.special as sps
>>> count, bins, ignored = plt.hist(s, 50, density=True)
>>> y = bins**(shape-1)*(np.exp(-bins/scale) /
... (sps.gamma(shape)*scale**shape))
>>> plt.plot(bins, y, linewidth=2, color='r')
>>> plt.show()
XRStools.xrs_calctools.gauss(x, x0, fwhm)
XRStools.xrs_calctools.gauss1(x, x0, fwhm)
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)
area-normalized gaussian
XRStools.xrs_calctools.geometric(p, size=None)
Draw samples from the geometric distribution.
Bernoulli trials are experiments with one of two outcomes: success or failure (an
example of such an experiment is flipping a coin). The geometric distribution
models the number of trials that must be run in order to achieve success. It is
therefore supported on the positive integers, k = 1, 2, ....
The probability mass function of the geometric distribution is
f(k) = (1 - p)^{k - 1} p
where p is the probability of success of an individual trial.
NOTE:
New code should use the geometric method of a default_rng() instance instead;
please see the random-quick-start.
p float or array_like of floats The probability of success of an individual
trial.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if p is a scalar. Otherwise, np.array(p).size
samples are drawn.
out ndarray or scalar Drawn samples from the parameterized geometric
distribution.
Generator.geometric: which should be used for new code.
Draw ten thousand values from the geometric distribution, with the probability of
an individual success equal to 0.35:
>>> z = np.random.geometric(p=0.35, size=10000)
How many trials succeeded after a single run?
>>> (z == 1).sum() / 10000.
0.34889999999999999 #random
XRStools.xrs_calctools.getDistVector(atom1, atom2)
XRStools.xrs_calctools.getDistVectorPBC_arb(atom1, atom2, lattice, lattice_inv)
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)
XRStools.xrs_calctools.getDistance(atom1, atom2)
XRStools.xrs_calctools.getDistancePBC_arb(atom1, atom2, lattice, lattice_inv)
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)
XRStools.xrs_calctools.getDistsFromMolecule(point, listOfMolecules, o_name=None)
XRStools.xrs_calctools.getPeriodicTestBox(xyzAtoms, boxLength, numbershells=1)
XRStools.xrs_calctools.getPeriodicTestBox_arb(xyzAtoms, lattice, lattice_inv, lx=[- 1, 1],
ly=[- 1, 1], lz=[- 1, 1])
XRStools.xrs_calctools.getPeriodicTestBox_molecules(Molecules, boxLength, numbershells=1)
XRStools.xrs_calctools.getTetraParameter(o_atoms, boxLength=None)
according to NATURE, VOL 409, 18 JANUARY 2001
XRStools.xrs_calctools.getTranslVec(atom1, atom2, boxLength)
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)
getTranslVec_geocen
XRStools.xrs_calctools.get_state()
Return a tuple representing the internal state of the generator.
For more details, see set_state.
legacy bool, optional Flag indicating to return a legacy tuple state when the
BitGenerator is MT19937, instead of a dict.
out {tuple(str, ndarray of 624 uints, int, int, float), dict} The returned tuple
has the following items:
1. the string 'MT19937'.
2. a 1-D array of 624 unsigned integer keys.
3. an integer pos.
4. an integer has_gauss.
5. a float cached_gaussian.
If legacy is False, or the BitGenerator is not MT19937, then state is
returned as a dictionary.
set_state
set_state and get_state are not needed to work with any of the random distributions
in NumPy. If the internal state is manually altered, the user should know exactly
what he/she is doing.
XRStools.xrs_calctools.groBoxParser(filename, nanoMeter=True)
groBoxParser Parses an gromacs GRO-style file for the xyzBox class.
XRStools.xrs_calctools.groTrajecParser(filename, nanoMeter=True)
groTrajecParser Parses an gromacs GRO-style file for the xyzBox class.
XRStools.xrs_calctools.gumbel(loc=0.0, scale=1.0, size=None)
Draw samples from a Gumbel distribution.
Draw samples from a Gumbel distribution with specified location and scale. For
more information on the Gumbel distribution, see Notes and References below.
NOTE:
New code should use the gumbel method of a default_rng() instance instead;
please see the random-quick-start.
loc float or array_like of floats, optional The location of the mode of the
distribution. Default is 0.
scale float or array_like of floats, optional The scale parameter of the
distribution. Default is 1. Must be non- negative.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if loc and scale are both scalars. Otherwise,
np.broadcast(loc, scale).size samples are drawn.
out ndarray or scalar Drawn samples from the parameterized Gumbel distribution.
scipy.stats.gumbel_l scipy.stats.gumbel_r scipy.stats.genextreme weibull
Generator.gumbel: which should be used for new code.
The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value Type I)
distribution is one of a class of Generalized Extreme Value (GEV) distributions
used in modeling extreme value problems. The Gumbel is a special case of the
Extreme Value Type I distribution for maximums from distributions with
"exponential-like" tails.
The probability density for the Gumbel distribution is
p(x) = \frac{e^{-(x - \mu)/ \beta}}{\beta} e^{ -e^{-(x - \mu)/
\beta}},
where \mu is the mode, a location parameter, and \beta is the scale parameter.
The Gumbel (named for German mathematician Emil Julius Gumbel) was used very early
in the hydrology literature, for modeling the occurrence of flood events. It is
also used for modeling maximum wind speed and rainfall rates. It is a "fat-tailed"
distribution - the probability of an event in the tail of the distribution is
larger than if one used a Gaussian, hence the surprisingly frequent occurrence of
100-year floods. Floods were initially modeled as a Gaussian process, which
underestimated the frequency of extreme events.
It is one of a class of extreme value distributions, the Generalized Extreme Value
(GEV) distributions, which also includes the Weibull and Frechet.
The function has a mean of \mu + 0.57721\beta and a variance of
\frac{\pi^2}{6}\beta^2.
[1] Gumbel, E. J., "Statistics of Extremes," New York: Columbia University Press, 1958.
[2] Reiss, R.-D. and Thomas, M., "Statistical Analysis of Extreme Values from Insurance,
Finance, Hydrology and Other Fields," Basel: Birkhauser Verlag, 2001.
Draw samples from the distribution:
>>> mu, beta = 0, 0.1 # location and scale
>>> s = np.random.gumbel(mu, beta, 1000)
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s, 30, density=True)
>>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)
... * np.exp( -np.exp( -(bins - mu) /beta) ),
... linewidth=2, color='r')
>>> plt.show()
Show how an extreme value distribution can arise from a Gaussian process and compare
to a Gaussian:
>>> means = []
>>> maxima = []
>>> for i in range(0,1000) :
... a = np.random.normal(mu, beta, 1000)
... means.append(a.mean())
... maxima.append(a.max())
>>> count, bins, ignored = plt.hist(maxima, 30, density=True)
>>> beta = np.std(maxima) * np.sqrt(6) / np.pi
>>> mu = np.mean(maxima) - 0.57721*beta
>>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)
... * np.exp(-np.exp(-(bins - mu)/beta)),
... linewidth=2, color='r')
>>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))
... * np.exp(-(bins - mu)**2 / (2 * beta**2)),
... linewidth=2, color='g')
>>> plt.show()
XRStools.xrs_calctools.hypergeometric(ngood, nbad, nsample, size=None)
Draw samples from a Hypergeometric distribution.
Samples are drawn from a hypergeometric distribution with specified parameters,
ngood (ways to make a good selection), nbad (ways to make a bad selection), and
nsample (number of items sampled, which is less than or equal to the sum ngood +
nbad).
NOTE:
New code should use the hypergeometric method of a default_rng() instance
instead; please see the random-quick-start.
ngood int or array_like of ints Number of ways to make a good selection. Must be
nonnegative.
nbad int or array_like of ints Number of ways to make a bad selection. Must be
nonnegative.
nsample
int or array_like of ints Number of items sampled. Must be at least 1 and
at most ngood + nbad.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if ngood, nbad, and nsample are all scalars.
Otherwise, np.broadcast(ngood, nbad, nsample).size samples are drawn.
out ndarray or scalar Drawn samples from the parameterized hypergeometric
distribution. Each sample is the number of good items within a randomly
selected subset of size nsample taken from a set of ngood good items and
nbad bad items.
scipy.stats.hypergeom
probability density function, distribution or cumulative density function,
etc.
Generator.hypergeometric: which should be used for new code.
The probability density for the Hypergeometric distribution is
P(x) = \frac{\binom{g}{x}\binom{b}{n-x}}{\binom{g+b}{n}},
where 0 \le x \le n and n-b \le x \le g
for P(x) the probability of x good results in the drawn sample, g = ngood, b =
nbad, and n = nsample.
Consider an urn with black and white marbles in it, ngood of them are black and
nbad are white. If you draw nsample balls without replacement, then the
hypergeometric distribution describes the distribution of black balls in the drawn
sample.
Note that this distribution is very similar to the binomial distribution, except
that in this case, samples are drawn without replacement, whereas in the Binomial
case samples are drawn with replacement (or the sample space is infinite). As the
sample space becomes large, this distribution approaches the binomial.
[1] Lentner, Marvin, "Elementary Applied Statistics", Bogden and Quigley, 1972.
[2] Weisstein, Eric W. "Hypergeometric Distribution." From MathWorld--A Wolfram Web
Resource. http://mathworld.wolfram.com/HypergeometricDistribution.html
[3] Wikipedia, "Hypergeometric distribution",
https://en.wikipedia.org/wiki/Hypergeometric_distribution
Draw samples from the distribution:
>>> ngood, nbad, nsamp = 100, 2, 10
# number of good, number of bad, and number of samples
>>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)
>>> from matplotlib.pyplot import hist
>>> hist(s)
# note that it is very unlikely to grab both bad items
Suppose you have an urn with 15 white and 15 black marbles. If you pull 15 marbles
at random, how likely is it that 12 or more of them are one color?
>>> s = np.random.hypergeometric(15, 15, 15, 100000)
>>> sum(s>=12)/100000. + sum(s<=3)/100000.
# answer = 0.003 ... pretty unlikely!
XRStools.xrs_calctools.keithBoxParser(cell_fname, coord_fname)
keithBoxParser
Reads structure files from Keith's SiO2 simulations.
XRStools.xrs_calctools.laplace(loc=0.0, scale=1.0, size=None)
Draw samples from the Laplace or double exponential distribution with specified
location (or mean) and scale (decay).
The Laplace distribution is similar to the Gaussian/normal distribution, but is
sharper at the peak and has fatter tails. It represents the difference between two
independent, identically distributed exponential random variables.
NOTE:
New code should use the laplace method of a default_rng() instance instead;
please see the random-quick-start.
loc float or array_like of floats, optional The position, \mu, of the
distribution peak. Default is 0.
scale float or array_like of floats, optional \lambda, the exponential decay.
Default is 1. Must be non- negative.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if loc and scale are both scalars. Otherwise,
np.broadcast(loc, scale).size samples are drawn.
out ndarray or scalar Drawn samples from the parameterized Laplace distribution.
Generator.laplace: which should be used for new code.
It has the probability density function
f(x; \mu, \lambda) = \frac{1}{2\lambda}
\exp\left(-\frac{|x - \mu|}{\lambda}\right).
The first law of Laplace, from 1774, states that the frequency of an error can be
expressed as an exponential function of the absolute magnitude of the error, which
leads to the Laplace distribution. For many problems in economics and health
sciences, this distribution seems to model the data better than the standard
Gaussian distribution.
[1] Abramowitz, M. and Stegun, I. A. (Eds.). "Handbook of Mathematical Functions with
Formulas, Graphs, and Mathematical Tables, 9th printing," New York: Dover, 1972.
[2] Kotz, Samuel, et. al. "The Laplace Distribution and Generalizations, " Birkhauser,
2001.
[3] Weisstein, Eric W. "Laplace Distribution." From MathWorld--A Wolfram Web Resource.
http://mathworld.wolfram.com/LaplaceDistribution.html
[4] Wikipedia, "Laplace distribution", https://en.wikipedia.org/wiki/Laplace_distribution
Draw samples from the distribution
>>> loc, scale = 0., 1.
>>> s = np.random.laplace(loc, scale, 1000)
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s, 30, density=True)
>>> x = np.arange(-8., 8., .01)
>>> pdf = np.exp(-abs(x-loc)/scale)/(2.*scale)
>>> plt.plot(x, pdf)
Plot Gaussian for comparison:
>>> g = (1/(scale * np.sqrt(2 * np.pi)) *
... np.exp(-(x - loc)**2 / (2 * scale**2)))
>>> plt.plot(x,g)
XRStools.xrs_calctools.load_erkale_spec(filename)
returns an erkale spectrum
XRStools.xrs_calctools.load_erkale_specs(prefix, postfix, fromnumber, tonumber, step,
stepformat=2)
returns a list of erkale spectra
XRStools.xrs_calctools.load_stobe_specs(prefix, postfix, fromnumber, tonumber, step,
stepformat=2)
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.logistic(loc=0.0, scale=1.0, size=None)
Draw samples from a logistic distribution.
Samples are drawn from a logistic distribution with specified parameters, loc
(location or mean, also median), and scale (>0).
NOTE:
New code should use the logistic method of a default_rng() instance instead;
please see the random-quick-start.
loc float or array_like of floats, optional Parameter of the distribution.
Default is 0.
scale float or array_like of floats, optional Parameter of the distribution. Must
be non-negative. Default is 1.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if loc and scale are both scalars. Otherwise,
np.broadcast(loc, scale).size samples are drawn.
out ndarray or scalar Drawn samples from the parameterized logistic
distribution.
scipy.stats.logistic
probability density function, distribution or cumulative density function,
etc.
Generator.logistic: which should be used for new code.
The probability density for the Logistic distribution is
P(x) = P(x) = \frac{e^{-(x-\mu)/s}}{s(1+e^{-(x-\mu)/s})^2},
where \mu = location and s = scale.
The Logistic distribution is used in Extreme Value problems where it can act as a
mixture of Gumbel distributions, in Epidemiology, and by the World Chess Federation
(FIDE) where it is used in the Elo ranking system, assuming the performance of each
player is a logistically distributed random variable.
[1] Reiss, R.-D. and Thomas M. (2001), "Statistical Analysis of Extreme Values, from
Insurance, Finance, Hydrology and Other Fields," Birkhauser Verlag, Basel, pp
132-133.
[2] Weisstein, Eric W. "Logistic Distribution." From MathWorld--A Wolfram Web Resource.
http://mathworld.wolfram.com/LogisticDistribution.html
[3] Wikipedia, "Logistic-distribution",
https://en.wikipedia.org/wiki/Logistic_distribution
Draw samples from the distribution:
>>> loc, scale = 10, 1
>>> s = np.random.logistic(loc, scale, 10000)
>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s, bins=50)
# plot against distribution
>>> def logist(x, loc, scale):
... return np.exp((loc-x)/scale)/(scale*(1+np.exp((loc-x)/scale))**2)
>>> lgst_val = logist(bins, loc, scale)
>>> plt.plot(bins, lgst_val * count.max() / lgst_val.max())
>>> plt.show()
XRStools.xrs_calctools.lognormal(mean=0.0, sigma=1.0, size=None)
Draw samples from a log-normal distribution.
Draw samples from a log-normal distribution with specified mean, standard
deviation, and array shape. Note that the mean and standard deviation are not the
values for the distribution itself, but of the underlying normal distribution it is
derived from.
NOTE:
New code should use the lognormal method of a default_rng() instance instead;
please see the random-quick-start.
mean float or array_like of floats, optional Mean value of the underlying normal
distribution. Default is 0.
sigma float or array_like of floats, optional Standard deviation of the underlying
normal distribution. Must be non-negative. Default is 1.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if mean and sigma are both scalars. Otherwise,
np.broadcast(mean, sigma).size samples are drawn.
out ndarray or scalar Drawn samples from the parameterized log-normal
distribution.
scipy.stats.lognorm
probability density function, distribution, cumulative density function,
etc.
Generator.lognormal: which should be used for new code.
A variable x has a log-normal distribution if log(x) is normally distributed. The
probability density function for the log-normal distribution is:
p(x) = \frac{1}{\sigma x \sqrt{2\pi}}
e^{(-\frac{(ln(x)-\mu)^2}{2\sigma^2})}
where \mu is the mean and \sigma is the standard deviation of the normally
distributed logarithm of the variable. A log-normal distribution results if a
random variable is the product of a large number of independent,
identically-distributed variables in the same way that a normal distribution
results if the variable is the sum of a large number of independent,
identically-distributed variables.
[1] Limpert, E., Stahel, W. A., and Abbt, M., "Log-normal Distributions across the
Sciences: Keys and Clues," BioScience, Vol. 51, No. 5, May, 2001.
https://stat.ethz.ch/~stahel/lognormal/bioscience.pdf
[2] Reiss, R.D. and Thomas, M., "Statistical Analysis of Extreme Values," Basel:
Birkhauser Verlag, 2001, pp. 31-32.
Draw samples from the distribution:
>>> mu, sigma = 3., 1. # mean and standard deviation
>>> s = np.random.lognormal(mu, sigma, 1000)
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s, 100, density=True, align='mid')
>>> x = np.linspace(min(bins), max(bins), 10000)
>>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))
... / (x * sigma * np.sqrt(2 * np.pi)))
>>> plt.plot(x, pdf, linewidth=2, color='r')
>>> plt.axis('tight')
>>> plt.show()
Demonstrate that taking the products of random samples from a uniform distribution
can be fit well by a log-normal probability density function.
>>> # Generate a thousand samples: each is the product of 100 random
>>> # values, drawn from a normal distribution.
>>> b = []
>>> for i in range(1000):
... a = 10. + np.random.standard_normal(100)
... b.append(np.product(a))
>>> b = np.array(b) / np.min(b) # scale values to be positive
>>> count, bins, ignored = plt.hist(b, 100, density=True, align='mid')
>>> sigma = np.std(np.log(b))
>>> mu = np.mean(np.log(b))
>>> x = np.linspace(min(bins), max(bins), 10000)
>>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))
... / (x * sigma * np.sqrt(2 * np.pi)))
>>> plt.plot(x, pdf, color='r', linewidth=2)
>>> plt.show()
XRStools.xrs_calctools.logseries(p, size=None)
Draw samples from a logarithmic series distribution.
Samples are drawn from a log series distribution with specified shape parameter, 0
< p < 1.
NOTE:
New code should use the logseries method of a default_rng() instance instead;
please see the random-quick-start.
p float or array_like of floats Shape parameter for the distribution. Must be
in the range (0, 1).
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if p is a scalar. Otherwise, np.array(p).size
samples are drawn.
out ndarray or scalar Drawn samples from the parameterized logarithmic series
distribution.
scipy.stats.logser
probability density function, distribution or cumulative density function,
etc.
Generator.logseries: which should be used for new code.
The probability density for the Log Series distribution is
P(k) = \frac{-p^k}{k \ln(1-p)},
where p = probability.
The log series distribution is frequently used to represent species richness and
occurrence, first proposed by Fisher, Corbet, and Williams in 1943 [2]. It may
also be used to model the numbers of occupants seen in cars [3].
[1] Buzas, Martin A.; Culver, Stephen J., Understanding regional species diversity
through the log series distribution of occurrences: BIODIVERSITY RESEARCH Diversity &
Distributions, Volume 5, Number 5, September 1999 , pp. 187-195(9).
[2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The relation between the number
of species and the number of individuals in a random sample of an animal population.
Journal of Animal Ecology, 12:42-58.
[3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small Data Sets, CRC Press,
1994.
[4] Wikipedia, "Logarithmic distribution",
https://en.wikipedia.org/wiki/Logarithmic_distribution
Draw samples from the distribution:
>>> a = .6
>>> s = np.random.logseries(a, 10000)
>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s)
# plot against distribution
>>> def logseries(k, p):
... return -p**k/(k*np.log(1-p))
>>> plt.plot(bins, logseries(bins, a)*count.max()/
... logseries(bins, a).max(), 'r')
>>> plt.show()
XRStools.xrs_calctools.multinomial(n, pvals, size=None)
Draw samples from a multinomial distribution.
The multinomial distribution is a multivariate generalization of the binomial
distribution. Take an experiment with one of p possible outcomes. An example of
such an experiment is throwing a dice, where the outcome can be 1 through 6. Each
sample drawn from the distribution represents n such experiments. Its values, X_i
= [X_0, X_1, ..., X_p], represent the number of times the outcome was i.
NOTE:
New code should use the multinomial method of a default_rng() instance instead;
please see the random-quick-start.
n int Number of experiments.
pvals sequence of floats, length p Probabilities of each of the p different
outcomes. These must sum to 1 (however, the last element is always assumed
to account for the remaining probability, as long as sum(pvals[:-1]) <= 1).
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. Default is None, in which case
a single value is returned.
out ndarray The drawn samples, of shape size, if that was provided. If not, the
shape is (N,).
In other words, each entry out[i,j,...,:] is an N-dimensional value drawn
from the distribution.
Generator.multinomial: which should be used for new code.
Throw a dice 20 times:
>>> np.random.multinomial(20, [1/6.]*6, size=1)
array([[4, 1, 7, 5, 2, 1]]) # random
It landed 4 times on 1, once on 2, etc.
Now, throw the dice 20 times, and 20 times again:
>>> np.random.multinomial(20, [1/6.]*6, size=2)
array([[3, 4, 3, 3, 4, 3], # random
[2, 4, 3, 4, 0, 7]])
For the first run, we threw 3 times 1, 4 times 2, etc. For the second, we threw 2
times 1, 4 times 2, etc.
A loaded die is more likely to land on number 6:
>>> np.random.multinomial(100, [1/7.]*5 + [2/7.])
array([11, 16, 14, 17, 16, 26]) # random
The probability inputs should be normalized. As an implementation detail, the value
of the last entry is ignored and assumed to take up any leftover probability mass,
but this should not be relied on. A biased coin which has twice as much weight on
one side as on the other should be sampled like so:
>>> np.random.multinomial(100, [1.0 / 3, 2.0 / 3]) # RIGHT
array([38, 62]) # random
not like:
>>> np.random.multinomial(100, [1.0, 2.0]) # WRONG
Traceback (most recent call last):
ValueError: pvals < 0, pvals > 1 or pvals contains NaNs
XRStools.xrs_calctools.multivariate_normal(mean, cov, size=None, check_valid='warn',
tol=1e-8)
Draw random samples from a multivariate normal distribution.
The multivariate normal, multinormal or Gaussian distribution is a generalization
of the one-dimensional normal distribution to higher dimensions. Such a
distribution is specified by its mean and covariance matrix. These parameters are
analogous to the mean (average or "center") and variance (standard deviation, or
"width," squared) of the one-dimensional normal distribution.
NOTE:
New code should use the multivariate_normal method of a default_rng() instance
instead; please see the random-quick-start.
mean 1-D array_like, of length N Mean of the N-dimensional distribution.
cov 2-D array_like, of shape (N, N) Covariance matrix of the distribution. It
must be symmetric and positive-semidefinite for proper sampling.
size int or tuple of ints, optional Given a shape of, for example, (m,n,k), m*n*k
samples are generated, and packed in an m-by-n-by-k arrangement. Because
each sample is N-dimensional, the output shape is (m,n,k,N). If no shape is
specified, a single (N-D) sample is returned.
check_valid
{ 'warn', 'raise', 'ignore' }, optional Behavior when the covariance matrix
is not positive semidefinite.
tol float, optional Tolerance when checking the singular values in covariance
matrix. cov is cast to double before the check.
out ndarray The drawn samples, of shape size, if that was provided. If not, the
shape is (N,).
In other words, each entry out[i,j,...,:] is an N-dimensional value drawn
from the distribution.
Generator.multivariate_normal: which should be used for new code.
The mean is a coordinate in N-dimensional space, which represents the location
where samples are most likely to be generated. This is analogous to the peak of
the bell curve for the one-dimensional or univariate normal distribution.
Covariance indicates the level to which two variables vary together. From the
multivariate normal distribution, we draw N-dimensional samples, X = [x_1, x_2, ...
x_N]. The covariance matrix element C_{ij} is the covariance of x_i and x_j. The
element C_{ii} is the variance of x_i (i.e. its "spread").
Instead of specifying the full covariance matrix, popular approximations include:
• Spherical covariance (cov is a multiple of the identity matrix)
• Diagonal covariance (cov has non-negative elements, and only on the diagonal)
This geometrical property can be seen in two dimensions by plotting generated
data-points:
>>> mean = [0, 0]
>>> cov = [[1, 0], [0, 100]] # diagonal covariance
Diagonal covariance means that points are oriented along x or y-axis:
>>> import matplotlib.pyplot as plt
>>> x, y = np.random.multivariate_normal(mean, cov, 5000).T
>>> plt.plot(x, y, 'x')
>>> plt.axis('equal')
>>> plt.show()
Note that the covariance matrix must be positive semidefinite (a.k.a.
nonnegative-definite). Otherwise, the behavior of this method is undefined and
backwards compatibility is not guaranteed.
[1] Papoulis, A., "Probability, Random Variables, and Stochastic Processes," 3rd ed., New
York: McGraw-Hill, 1991.
[2] Duda, R. O., Hart, P. E., and Stork, D. G., "Pattern Classification," 2nd ed., New
York: Wiley, 2001.
>>> mean = (1, 2)
>>> cov = [[1, 0], [0, 1]]
>>> x = np.random.multivariate_normal(mean, cov, (3, 3))
>>> x.shape
(3, 3, 2)
The following is probably true, given that 0.6 is roughly twice the standard
deviation:
>>> list((x[0,0,:] - mean) < 0.6)
[True, True] # random
XRStools.xrs_calctools.negative_binomial(n, p, size=None)
Draw samples from a negative binomial distribution.
Samples are drawn from a negative binomial distribution with specified parameters,
n successes and p probability of success where n is > 0 and p is in the interval
[0, 1].
NOTE:
New code should use the negative_binomial method of a default_rng() instance
instead; please see the random-quick-start.
n float or array_like of floats Parameter of the distribution, > 0.
p float or array_like of floats Parameter of the distribution, >= 0 and <=1.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if n and p are both scalars. Otherwise,
np.broadcast(n, p).size samples are drawn.
out ndarray or scalar Drawn samples from the parameterized negative binomial
distribution, where each sample is equal to N, the number of failures that
occurred before a total of n successes was reached.
Generator.negative_binomial: which should be used for new code.
The probability mass function of the negative binomial distribution is
P(N;n,p) = \frac{\Gamma(N+n)}{N!\Gamma(n)}p^{n}(1-p)^{N},
where n is the number of successes, p is the probability of success, N+n is the
number of trials, and \Gamma is the gamma function. When n is an integer,
\frac{\Gamma(N+n)}{N!\Gamma(n)} = \binom{N+n-1}{N}, which is the more common form
of this term in the the pmf. The negative binomial distribution gives the
probability of N failures given n successes, with a success on the last trial.
If one throws a die repeatedly until the third time a "1" appears, then the
probability distribution of the number of non-"1"s that appear before the third "1"
is a negative binomial distribution.
[1] Weisstein, Eric W. "Negative Binomial Distribution." From MathWorld--A Wolfram Web
Resource. http://mathworld.wolfram.com/NegativeBinomialDistribution.html
[2] Wikipedia, "Negative binomial distribution",
https://en.wikipedia.org/wiki/Negative_binomial_distribution
Draw samples from the distribution:
A real world example. A company drills wild-cat oil exploration wells, each with an
estimated probability of success of 0.1. What is the probability of having one
success for each successive well, that is what is the probability of a single success
after drilling 5 wells, after 6 wells, etc.?
>>> s = np.random.negative_binomial(1, 0.1, 100000)
>>> for i in range(1, 11):
... probability = sum(s<i) / 100000.
... print(i, "wells drilled, probability of one success =", probability)
XRStools.xrs_calctools.noncentral_chisquare(df, nonc, size=None)
Draw samples from a noncentral chi-square distribution.
The noncentral \chi^2 distribution is a generalization of the \chi^2 distribution.
NOTE:
New code should use the noncentral_chisquare method of a default_rng() instance
instead; please see the random-quick-start.
df float or array_like of floats Degrees of freedom, must be > 0.
Changed in version 1.10.0: Earlier NumPy versions required dfnum > 1.
nonc float or array_like of floats Non-centrality, must be non-negative.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if df and nonc are both scalars. Otherwise,
np.broadcast(df, nonc).size samples are drawn.
out ndarray or scalar Drawn samples from the parameterized noncentral chi-square
distribution.
Generator.noncentral_chisquare: which should be used for new code.
The probability density function for the noncentral Chi-square distribution is
P(x;df,nonc) = \sum^{\infty}_{i=0}
\frac{e^{-nonc/2}(nonc/2)^{i}}{i!} P_{Y_{df+2i}}(x),
where Y_{q} is the Chi-square with q degrees of freedom.
[1] Wikipedia, "Noncentral chi-squared distribution"
https://en.wikipedia.org/wiki/Noncentral_chi-squared_distribution
Draw values from the distribution and plot the histogram
>>> import matplotlib.pyplot as plt
>>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),
... bins=200, density=True)
>>> plt.show()
Draw values from a noncentral chisquare with very small noncentrality, and compare to
a chisquare.
>>> plt.figure()
>>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000),
... bins=np.arange(0., 25, .1), density=True)
>>> values2 = plt.hist(np.random.chisquare(3, 100000),
... bins=np.arange(0., 25, .1), density=True)
>>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob')
>>> plt.show()
Demonstrate how large values of non-centrality lead to a more symmetric distribution.
>>> plt.figure()
>>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),
... bins=200, density=True)
>>> plt.show()
XRStools.xrs_calctools.noncentral_f(dfnum, dfden, nonc, size=None)
Draw samples from the noncentral F distribution.
Samples are drawn from an F distribution with specified parameters, dfnum (degrees
of freedom in numerator) and dfden (degrees of freedom in denominator), where both
parameters > 1. nonc is the non-centrality parameter.
NOTE:
New code should use the noncentral_f method of a default_rng() instance instead;
please see the random-quick-start.
dfnum float or array_like of floats Numerator degrees of freedom, must be > 0.
Changed in version 1.14.0: Earlier NumPy versions required dfnum > 1.
dfden float or array_like of floats Denominator degrees of freedom, must be > 0.
nonc float or array_like of floats Non-centrality parameter, the sum of the
squares of the numerator means, must be >= 0.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if dfnum, dfden, and nonc are all scalars.
Otherwise, np.broadcast(dfnum, dfden, nonc).size samples are drawn.
out ndarray or scalar Drawn samples from the parameterized noncentral Fisher
distribution.
Generator.noncentral_f: which should be used for new code.
When calculating the power of an experiment (power = probability of rejecting the
null hypothesis when a specific alternative is true) the non-central F statistic
becomes important. When the null hypothesis is true, the F statistic follows a
central F distribution. When the null hypothesis is not true, then it follows a
non-central F statistic.
[1] Weisstein, Eric W. "Noncentral F-Distribution." From MathWorld--A Wolfram Web
Resource. http://mathworld.wolfram.com/NoncentralF-Distribution.html
[2] Wikipedia, "Noncentral F-distribution",
https://en.wikipedia.org/wiki/Noncentral_F-distribution
In a study, testing for a specific alternative to the null hypothesis requires use of
the Noncentral F distribution. We need to calculate the area in the tail of the
distribution that exceeds the value of the F distribution for the null hypothesis.
We'll plot the two probability distributions for comparison.
>>> dfnum = 3 # between group deg of freedom
>>> dfden = 20 # within groups degrees of freedom
>>> nonc = 3.0
>>> nc_vals = np.random.noncentral_f(dfnum, dfden, nonc, 1000000)
>>> NF = np.histogram(nc_vals, bins=50, density=True)
>>> c_vals = np.random.f(dfnum, dfden, 1000000)
>>> F = np.histogram(c_vals, bins=50, density=True)
>>> import matplotlib.pyplot as plt
>>> plt.plot(F[1][1:], F[0])
>>> plt.plot(NF[1][1:], NF[0])
>>> plt.show()
XRStools.xrs_calctools.normal(loc=0.0, scale=1.0, size=None)
Draw random samples from a normal (Gaussian) distribution.
The probability density function of the normal distribution, first derived by De
Moivre and 200 years later by both Gauss and Laplace independently
[2]_
, is often called the bell curve because of its characteristic shape (see the
example below).
The normal distributions occurs often in nature. For example, it describes the
commonly occurring distribution of samples influenced by a large number of tiny,
random disturbances, each with its own unique distribution
[2]_
.
NOTE:
New code should use the normal method of a default_rng() instance instead;
please see the random-quick-start.
loc float or array_like of floats Mean ("centre") of the distribution.
scale float or array_like of floats Standard deviation (spread or "width") of the
distribution. Must be non-negative.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if loc and scale are both scalars. Otherwise,
np.broadcast(loc, scale).size samples are drawn.
out ndarray or scalar Drawn samples from the parameterized normal distribution.
scipy.stats.norm
probability density function, distribution or cumulative density function,
etc.
Generator.normal: which should be used for new code.
The probability density for the Gaussian distribution is
p(x) = \frac{1}{\sqrt{ 2 \pi \sigma^2 }}
e^{ - \frac{ (x - \mu)^2 } {2 \sigma^2} },
where \mu is the mean and \sigma the standard deviation. The square of the standard
deviation, \sigma^2, is called the variance.
The function has its peak at the mean, and its "spread" increases with the standard
deviation (the function reaches 0.607 times its maximum at x + \sigma and x -
\sigma
[2]_
). This implies that normal is more likely to return samples lying close to the
mean, rather than those far away.
[1] Wikipedia, "Normal distribution", https://en.wikipedia.org/wiki/Normal_distribution
[2] P. R. Peebles Jr., "Central Limit Theorem" in "Probability, Random Variables and
Random Signal Principles", 4th ed., 2001, pp. 51, 51, 125.
Draw samples from the distribution:
>>> mu, sigma = 0, 0.1 # mean and standard deviation
>>> s = np.random.normal(mu, sigma, 1000)
Verify the mean and the variance:
>>> abs(mu - np.mean(s))
0.0 # may vary
>>> abs(sigma - np.std(s, ddof=1))
0.1 # may vary
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s, 30, density=True)
>>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *
... np.exp( - (bins - mu)**2 / (2 * sigma**2) ),
... linewidth=2, color='r')
>>> plt.show()
Two-by-four array of samples from N(3, 6.25):
>>> np.random.normal(3, 2.5, size=(2, 4))
array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], # random
[ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) # random
XRStools.xrs_calctools.pareto(a, size=None)
Draw samples from a Pareto II or Lomax distribution with specified shape.
The Lomax or Pareto II distribution is a shifted Pareto distribution. The classical
Pareto distribution can be obtained from the Lomax distribution by adding 1 and
multiplying by the scale parameter m (see Notes). The smallest value of the Lomax
distribution is zero while for the classical Pareto distribution it is mu, where
the standard Pareto distribution has location mu = 1. Lomax can also be considered
as a simplified version of the Generalized Pareto distribution (available in
SciPy), with the scale set to one and the location set to zero.
The Pareto distribution must be greater than zero, and is unbounded above. It is
also known as the "80-20 rule". In this distribution, 80 percent of the weights
are in the lowest 20 percent of the range, while the other 20 percent fill the
remaining 80 percent of the range.
NOTE:
New code should use the pareto method of a default_rng() instance instead;
please see the random-quick-start.
a float or array_like of floats Shape of the distribution. Must be positive.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if a is a scalar. Otherwise, np.array(a).size
samples are drawn.
out ndarray or scalar Drawn samples from the parameterized Pareto distribution.
scipy.stats.lomax
probability density function, distribution or cumulative density function,
etc.
scipy.stats.genpareto
probability density function, distribution or cumulative density function,
etc.
Generator.pareto: which should be used for new code.
The probability density for the Pareto distribution is
p(x) = \frac{am^a}{x^{a+1}}
where a is the shape and m the scale.
The Pareto distribution, named after the Italian economist Vilfredo Pareto, is a
power law probability distribution useful in many real world problems. Outside the
field of economics it is generally referred to as the Bradford distribution. Pareto
developed the distribution to describe the distribution of wealth in an economy.
It has also found use in insurance, web page access statistics, oil field sizes,
and many other problems, including the download frequency for projects in
Sourceforge
[1]_
. It is one of the so-called "fat-tailed" distributions.
[1] Francis Hunt and Paul Johnson, On the Pareto Distribution of Sourceforge projects.
[2] Pareto, V. (1896). Course of Political Economy. Lausanne.
[3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme Values, Birkhauser
Verlag, Basel, pp 23-30.
[4] Wikipedia, "Pareto distribution", https://en.wikipedia.org/wiki/Pareto_distribution
Draw samples from the distribution:
>>> a, m = 3., 2. # shape and mode
>>> s = (np.random.pareto(a, 1000) + 1) * m
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt
>>> count, bins, _ = plt.hist(s, 100, density=True)
>>> fit = a*m**a / bins**(a+1)
>>> plt.plot(bins, max(count)*fit/max(fit), linewidth=2, color='r')
>>> plt.show()
XRStools.xrs_calctools.parseOCEANinputFile(fname)
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)
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.parseVaspFile(fname)
parseVaspFile
Parses a VASPS file and returns a xyzBox object.
Args: fname (str): Absolute filename of VASP file.
Returns:
xyzBox object
XRStools.xrs_calctools.parseXYZfile(filename)
parseXYZfile Reads an xyz-style file.
XRStools.xrs_calctools.permutation(x)
Randomly permute a sequence, or return a permuted range.
If x is a multi-dimensional array, it is only shuffled along its first index.
NOTE:
New code should use the permutation method of a default_rng() instance instead;
please see the random-quick-start.
x int or array_like If x is an integer, randomly permute np.arange(x). If x
is an array, make a copy and shuffle the elements randomly.
out ndarray Permuted sequence or array range.
Generator.permutation: which should be used for new code.
>>> np.random.permutation(10)
array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6]) # random
>>> np.random.permutation([1, 4, 9, 12, 15])
array([15, 1, 9, 4, 12]) # random
>>> arr = np.arange(9).reshape((3, 3))
>>> np.random.permutation(arr)
array([[6, 7, 8], # random
[0, 1, 2],
[3, 4, 5]])
XRStools.xrs_calctools.poisson(lam=1.0, size=None)
Draw samples from a Poisson distribution.
The Poisson distribution is the limit of the binomial distribution for large N.
NOTE:
New code should use the poisson method of a default_rng() instance instead;
please see the random-quick-start.
lam float or array_like of floats Expected number of events occurring in a
fixed-time interval, must be >= 0. A sequence must be broadcastable over the
requested size.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if lam is a scalar. Otherwise, np.array(lam).size
samples are drawn.
out ndarray or scalar Drawn samples from the parameterized Poisson distribution.
Generator.poisson: which should be used for new code.
The Poisson distribution
f(k; \lambda)=\frac{\lambda^k e^{-\lambda}}{k!}
For events with an expected separation \lambda the Poisson distribution f(k;
\lambda) describes the probability of k events occurring within the observed
interval \lambda.
Because the output is limited to the range of the C int64 type, a ValueError is
raised when lam is within 10 sigma of the maximum representable value.
[1] Weisstein, Eric W. "Poisson Distribution." From MathWorld--A Wolfram Web Resource.
http://mathworld.wolfram.com/PoissonDistribution.html
[2] Wikipedia, "Poisson distribution", https://en.wikipedia.org/wiki/Poisson_distribution
Draw samples from the distribution:
>>> import numpy as np
>>> s = np.random.poisson(5, 10000)
Display histogram of the sample:
>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s, 14, density=True)
>>> plt.show()
Draw each 100 values for lambda 100 and 500:
>>> s = np.random.poisson(lam=(100., 500.), size=(100, 2))
XRStools.xrs_calctools.power(a, size=None)
Draws samples in [0, 1] from a power distribution with positive exponent a - 1.
Also known as the power function distribution.
NOTE:
New code should use the power method of a default_rng() instance instead; please
see the random-quick-start.
a float or array_like of floats Parameter of the distribution. Must be
non-negative.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if a is a scalar. Otherwise, np.array(a).size
samples are drawn.
out ndarray or scalar Drawn samples from the parameterized power distribution.
ValueError
If a < 1.
Generator.power: which should be used for new code.
The probability density function is
P(x; a) = ax^{a-1}, 0 \le x \le 1, a>0.
The power function distribution is just the inverse of the Pareto distribution. It
may also be seen as a special case of the Beta distribution.
It is used, for example, in modeling the over-reporting of insurance claims.
[1] Christian Kleiber, Samuel Kotz, "Statistical size distributions in economics and
actuarial sciences", Wiley, 2003.
[2] Heckert, N. A. and Filliben, James J. "NIST Handbook 148: Dataplot Reference Manual,
Volume 2: Let Subcommands and Library Functions", National Institute of Standards and
Technology Handbook Series, June 2003.
https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/powpdf.pdf
Draw samples from the distribution:
>>> a = 5. # shape
>>> samples = 1000
>>> s = np.random.power(a, samples)
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s, bins=30)
>>> x = np.linspace(0, 1, 100)
>>> y = a*x**(a-1.)
>>> normed_y = samples*np.diff(bins)[0]*y
>>> plt.plot(x, normed_y)
>>> plt.show()
Compare the power function distribution to the inverse of the Pareto.
>>> from scipy import stats
>>> rvs = np.random.power(5, 1000000)
>>> rvsp = np.random.pareto(5, 1000000)
>>> xx = np.linspace(0,1,100)
>>> powpdf = stats.powerlaw.pdf(xx,5)
>>> plt.figure()
>>> plt.hist(rvs, bins=50, density=True)
>>> plt.plot(xx,powpdf,'r-')
>>> plt.title('np.random.power(5)')
>>> plt.figure()
>>> plt.hist(1./(1.+rvsp), bins=50, density=True)
>>> plt.plot(xx,powpdf,'r-')
>>> plt.title('inverse of 1 + np.random.pareto(5)')
>>> plt.figure()
>>> plt.hist(1./(1.+rvsp), bins=50, density=True)
>>> plt.plot(xx,powpdf,'r-')
>>> plt.title('inverse of stats.pareto(5)')
XRStools.xrs_calctools.rand(d0, d1, ..., dn)
Random values in a given shape.
NOTE:
This is a convenience function for users porting code from Matlab, and wraps
random_sample. That function takes a tuple to specify the size of the output,
which is consistent with other NumPy functions like numpy.zeros and numpy.ones.
Create an array of the given shape and populate it with random samples from a
uniform distribution over [0, 1).
d0, d1, ..., dn
int, optional The dimensions of the returned array, must be non-negative.
If no argument is given a single Python float is returned.
out ndarray, shape (d0, d1, ..., dn) Random values.
random
>>> np.random.rand(3,2)
array([[ 0.14022471, 0.96360618], #random
[ 0.37601032, 0.25528411], #random
[ 0.49313049, 0.94909878]]) #random
XRStools.xrs_calctools.randint(low, high=None, size=None, dtype=int)
Return random integers from low (inclusive) to high (exclusive).
Return random integers from the "discrete uniform" distribution of the specified
dtype in the "half-open" interval [low, high). If high is None (the default), then
results are from [0, low).
NOTE:
New code should use the integers method of a default_rng() instance instead;
please see the random-quick-start.
low int or array-like of ints Lowest (signed) integers to be drawn from the
distribution (unless high=None, in which case this parameter is one above
the highest such integer).
high int or array-like of ints, optional If provided, one above the largest
(signed) integer to be drawn from the distribution (see above for behavior
if high=None). If array-like, must contain integer values
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. Default is None, in which case
a single value is returned.
dtype dtype, optional Desired dtype of the result. Byteorder must be native. The
default value is int.
New in version 1.11.0.
out int or ndarray of ints size-shaped array of random integers from the
appropriate distribution, or a single such random int if size not provided.
random_integers
similar to randint, only for the closed interval [low, high], and 1 is the
lowest value if high is omitted.
Generator.integers: which should be used for new code.
>>> np.random.randint(2, size=10)
array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0]) # random
>>> np.random.randint(1, size=10)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
Generate a 2 x 4 array of ints between 0 and 4, inclusive:
>>> np.random.randint(5, size=(2, 4))
array([[4, 0, 2, 1], # random
[3, 2, 2, 0]])
Generate a 1 x 3 array with 3 different upper bounds
>>> np.random.randint(1, [3, 5, 10])
array([2, 2, 9]) # random
Generate a 1 by 3 array with 3 different lower bounds
>>> np.random.randint([1, 5, 7], 10)
array([9, 8, 7]) # random
Generate a 2 by 4 array using broadcasting with dtype of uint8
>>> np.random.randint([1, 3, 5, 7], [[10], [20]], dtype=np.uint8)
array([[ 8, 6, 9, 7], # random
[ 1, 16, 9, 12]], dtype=uint8)
XRStools.xrs_calctools.randn(d0, d1, ..., dn)
Return a sample (or samples) from the "standard normal" distribution.
NOTE:
This is a convenience function for users porting code from Matlab, and wraps
standard_normal. That function takes a tuple to specify the size of the output,
which is consistent with other NumPy functions like numpy.zeros and numpy.ones.
NOTE:
New code should use the standard_normal method of a default_rng() instance
instead; please see the random-quick-start.
If positive int_like arguments are provided, randn generates an array of shape (d0,
d1, ..., dn), filled with random floats sampled from a univariate "normal"
(Gaussian) distribution of mean 0 and variance 1. A single float randomly sampled
from the distribution is returned if no argument is provided.
d0, d1, ..., dn
int, optional The dimensions of the returned array, must be non-negative.
If no argument is given a single Python float is returned.
Z ndarray or float A (d0, d1, ..., dn)-shaped array of floating-point samples
from the standard normal distribution, or a single such float if no
parameters were supplied.
standard_normal : Similar, but takes a tuple as its argument. normal : Also
accepts mu and sigma arguments. Generator.standard_normal: which should be used
for new code.
For random samples from N(\mu, \sigma^2), use:
sigma * np.random.randn(...) + mu
>>> np.random.randn()
2.1923875335537315 # random
Two-by-four array of samples from N(3, 6.25):
>>> 3 + 2.5 * np.random.randn(2, 4)
array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], # random
[ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) # random
XRStools.xrs_calctools.random(size=None)
Return random floats in the half-open interval [0.0, 1.0). Alias for random_sample
to ease forward-porting to the new random API.
XRStools.xrs_calctools.random_integers(low, high=None, size=None)
Random integers of type np.int_ between low and high, inclusive.
Return random integers of type np.int_ from the "discrete uniform" distribution in
the closed interval [low, high]. If high is None (the default), then results are
from [1, low]. The np.int_ type translates to the C long integer type and its
precision is platform dependent.
This function has been deprecated. Use randint instead.
Deprecated since version 1.11.0.
low int Lowest (signed) integer to be drawn from the distribution (unless
high=None, in which case this parameter is the highest such integer).
high int, optional If provided, the largest (signed) integer to be drawn from the
distribution (see above for behavior if high=None).
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. Default is None, in which case
a single value is returned.
out int or ndarray of ints size-shaped array of random integers from the
appropriate distribution, or a single such random int if size not provided.
randint
Similar to random_integers, only for the half-open interval [low, high), and
0 is the lowest value if high is omitted.
To sample from N evenly spaced floating-point numbers between a and b, use:
a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)
>>> np.random.random_integers(5)
4 # random
>>> type(np.random.random_integers(5))
<class 'numpy.int64'>
>>> np.random.random_integers(5, size=(3,2))
array([[5, 4], # random
[3, 3],
[4, 5]])
Choose five random numbers from the set of five evenly-spaced numbers between 0 and
2.5, inclusive (i.e., from the set {0, 5/8, 10/8, 15/8, 20/8}):
>>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.
array([ 0.625, 1.25 , 0.625, 0.625, 2.5 ]) # random
Roll two six sided dice 1000 times and sum the results:
>>> d1 = np.random.random_integers(1, 6, 1000)
>>> d2 = np.random.random_integers(1, 6, 1000)
>>> dsums = d1 + d2
Display results as a histogram:
>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(dsums, 11, density=True)
>>> plt.show()
XRStools.xrs_calctools.random_sample(size=None)
Return random floats in the half-open interval [0.0, 1.0).
Results are from the "continuous uniform" distribution over the stated interval.
To sample Unif[a, b), b > a multiply the output of random_sample by (b-a) and add
a:
(b - a) * random_sample() + a
NOTE:
New code should use the random method of a default_rng() instance instead;
please see the random-quick-start.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. Default is None, in which case
a single value is returned.
out float or ndarray of floats Array of random floats of shape size (unless
size=None, in which case a single float is returned).
Generator.random: which should be used for new code.
>>> np.random.random_sample()
0.47108547995356098 # random
>>> type(np.random.random_sample())
<class 'float'>
>>> np.random.random_sample((5,))
array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428]) # random
Three-by-two array of random numbers from [-5, 0):
>>> 5 * np.random.random_sample((3, 2)) - 5
array([[-3.99149989, -0.52338984], # random
[-2.99091858, -0.79479508],
[-1.23204345, -1.75224494]])
XRStools.xrs_calctools.rayleigh(scale=1.0, size=None)
Draw samples from a Rayleigh distribution.
The \chi and Weibull distributions are generalizations of the Rayleigh.
NOTE:
New code should use the rayleigh method of a default_rng() instance instead;
please see the random-quick-start.
scale float or array_like of floats, optional Scale, also equals the mode. Must be
non-negative. Default is 1.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if scale is a scalar. Otherwise,
np.array(scale).size samples are drawn.
out ndarray or scalar Drawn samples from the parameterized Rayleigh
distribution.
Generator.rayleigh: which should be used for new code.
The probability density function for the Rayleigh distribution is
P(x;scale) = \frac{x}{scale^2}e^{\frac{-x^2}{2 \cdotp scale^2}}
The Rayleigh distribution would arise, for example, if the East and North
components of the wind velocity had identical zero-mean Gaussian distributions.
Then the wind speed would have a Rayleigh distribution.
[1] Brighton Webs Ltd., "Rayleigh Distribution,"
https://web.archive.org/web/20090514091424/http://brighton-webs.co.uk:80/distributions/rayleigh.asp
[2] Wikipedia, "Rayleigh distribution"
https://en.wikipedia.org/wiki/Rayleigh_distribution
Draw values from the distribution and plot the histogram
>>> from matplotlib.pyplot import hist
>>> values = hist(np.random.rayleigh(3, 100000), bins=200, density=True)
Wave heights tend to follow a Rayleigh distribution. If the mean wave height is 1
meter, what fraction of waves are likely to be larger than 3 meters?
>>> meanvalue = 1
>>> modevalue = np.sqrt(2 / np.pi) * meanvalue
>>> s = np.random.rayleigh(modevalue, 1000000)
The percentage of waves larger than 3 meters is:
>>> 100.*sum(s>3)/1000000.
0.087300000000000003 # random
XRStools.xrs_calctools.readxas(filename)
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)
XRStools.xrs_calctools.seed(self, seed=None)
Reseed a legacy MT19937 BitGenerator
This is a convenience, legacy function.
The best practice is to not reseed a BitGenerator, rather to recreate a new one.
This method is here for legacy reasons. This example demonstrates best practice.
>>> from numpy.random import MT19937
>>> from numpy.random import RandomState, SeedSequence
>>> rs = RandomState(MT19937(SeedSequence(123456789)))
# Later, you want to restart the stream
>>> rs = RandomState(MT19937(SeedSequence(987654321)))
XRStools.xrs_calctools.set_state(state)
Set the internal state of the generator from a tuple.
For use if one has reason to manually (re-)set the internal state of the bit
generator used by the RandomState instance. By default, RandomState uses the
"Mersenne Twister"
[1]_
pseudo-random number generating algorithm.
state {tuple(str, ndarray of 624 uints, int, int, float), dict} The state tuple
has the following items:
1. the string 'MT19937', specifying the Mersenne Twister algorithm.
2. a 1-D array of 624 unsigned integers keys.
3. an integer pos.
4. an integer has_gauss.
5. a float cached_gaussian.
If state is a dictionary, it is directly set using the BitGenerators state
property.
out None Returns 'None' on success.
get_state
set_state and get_state are not needed to work with any of the random distributions
in NumPy. If the internal state is manually altered, the user should know exactly
what he/she is doing.
For backwards compatibility, the form (str, array of 624 uints, int) is also
accepted although it is missing some information about the cached Gaussian value:
state = ('MT19937', keys, pos).
[1] M. Matsumoto and T. Nishimura, "Mersenne Twister: A 623-dimensionally equidistributed
uniform pseudorandom number generator," ACM Trans. on Modeling and Computer
Simulation, Vol. 8, No. 1, pp. 3-30, Jan. 1998.
XRStools.xrs_calctools.shuffle(x)
Modify a sequence in-place by shuffling its contents.
This function only shuffles the array along the first axis of a multi-dimensional
array. The order of sub-arrays is changed but their contents remains the same.
NOTE:
New code should use the shuffle method of a default_rng() instance instead;
please see the random-quick-start.
x ndarray or MutableSequence The array, list or mutable sequence to be
shuffled.
None
Generator.shuffle: which should be used for new code.
>>> arr = np.arange(10)
>>> np.random.shuffle(arr)
>>> arr
[1 7 5 2 9 4 3 6 0 8] # random
Multi-dimensional arrays are only shuffled along the first axis:
>>> arr = np.arange(9).reshape((3, 3))
>>> np.random.shuffle(arr)
>>> arr
array([[3, 4, 5], # random
[6, 7, 8],
[0, 1, 2]])
XRStools.xrs_calctools.sorter(elem)
XRStools.xrs_calctools.spline2(x, y, x2)
Extrapolates the smaller and larger valuea as a constant
XRStools.xrs_calctools.standard_cauchy(size=None)
Draw samples from a standard Cauchy distribution with mode = 0.
Also known as the Lorentz distribution.
NOTE:
New code should use the standard_cauchy method of a default_rng() instance
instead; please see the random-quick-start.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. Default is None, in which case
a single value is returned.
samples
ndarray or scalar The drawn samples.
Generator.standard_cauchy: which should be used for new code.
The probability density function for the full Cauchy distribution is
P(x; x_0, \gamma) = \frac{1}{\pi \gamma \bigl[ 1+
(\frac{x-x_0}{\gamma})^2 \bigr] }
and the Standard Cauchy distribution just sets x_0=0 and \gamma=1
The Cauchy distribution arises in the solution to the driven harmonic oscillator
problem, and also describes spectral line broadening. It also describes the
distribution of values at which a line tilted at a random angle will cut the x
axis.
When studying hypothesis tests that assume normality, seeing how the tests perform
on data from a Cauchy distribution is a good indicator of their sensitivity to a
heavy-tailed distribution, since the Cauchy looks very much like a Gaussian
distribution, but with heavier tails.
[1] NIST/SEMATECH e-Handbook of Statistical Methods, "Cauchy Distribution",
https://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm
[2] Weisstein, Eric W. "Cauchy Distribution." From MathWorld--A Wolfram Web Resource.
http://mathworld.wolfram.com/CauchyDistribution.html
[3] Wikipedia, "Cauchy distribution" https://en.wikipedia.org/wiki/Cauchy_distribution
Draw samples and plot the distribution:
>>> import matplotlib.pyplot as plt
>>> s = np.random.standard_cauchy(1000000)
>>> s = s[(s>-25) & (s<25)] # truncate distribution so it plots well
>>> plt.hist(s, bins=100)
>>> plt.show()
XRStools.xrs_calctools.standard_exponential(size=None)
Draw samples from the standard exponential distribution.
standard_exponential is identical to the exponential distribution with a scale
parameter of 1.
NOTE:
New code should use the standard_exponential method of a default_rng() instance
instead; please see the random-quick-start.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. Default is None, in which case
a single value is returned.
out float or ndarray Drawn samples.
Generator.standard_exponential: which should be used for new code.
Output a 3x8000 array:
>>> n = np.random.standard_exponential((3, 8000))
XRStools.xrs_calctools.standard_gamma(shape, size=None)
Draw samples from a standard Gamma distribution.
Samples are drawn from a Gamma distribution with specified parameters, shape
(sometimes designated "k") and scale=1.
NOTE:
New code should use the standard_gamma method of a default_rng() instance
instead; please see the random-quick-start.
shape float or array_like of floats Parameter, must be non-negative.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if shape is a scalar. Otherwise,
np.array(shape).size samples are drawn.
out ndarray or scalar Drawn samples from the parameterized standard gamma
distribution.
scipy.stats.gamma
probability density function, distribution or cumulative density function,
etc.
Generator.standard_gamma: which should be used for new code.
The probability density for the Gamma distribution is
p(x) = x^{k-1}\frac{e^{-x/\theta}}{\theta^k\Gamma(k)},
where k is the shape and \theta the scale, and \Gamma is the Gamma function.
The Gamma distribution is often used to model the times to failure of electronic
components, and arises naturally in processes for which the waiting times between
Poisson distributed events are relevant.
[1] Weisstein, Eric W. "Gamma Distribution." From MathWorld--A Wolfram Web Resource.
http://mathworld.wolfram.com/GammaDistribution.html
[2] Wikipedia, "Gamma distribution", https://en.wikipedia.org/wiki/Gamma_distribution
Draw samples from the distribution:
>>> shape, scale = 2., 1. # mean and width
>>> s = np.random.standard_gamma(shape, 1000000)
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt
>>> import scipy.special as sps
>>> count, bins, ignored = plt.hist(s, 50, density=True)
>>> y = bins**(shape-1) * ((np.exp(-bins/scale))/
... (sps.gamma(shape) * scale**shape))
>>> plt.plot(bins, y, linewidth=2, color='r')
>>> plt.show()
XRStools.xrs_calctools.standard_normal(size=None)
Draw samples from a standard Normal distribution (mean=0, stdev=1).
NOTE:
New code should use the standard_normal method of a default_rng() instance
instead; please see the random-quick-start.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. Default is None, in which case
a single value is returned.
out float or ndarray A floating-point array of shape size of drawn samples, or a
single sample if size was not specified.
normal :
Equivalent function with additional loc and scale arguments for setting the
mean and standard deviation.
Generator.standard_normal: which should be used for new code.
For random samples from N(\mu, \sigma^2), use one of:
mu + sigma * np.random.standard_normal(size=...)
np.random.normal(mu, sigma, size=...)
>>> np.random.standard_normal()
2.1923875335537315 #random
>>> s = np.random.standard_normal(8000)
>>> s
array([ 0.6888893 , 0.78096262, -0.89086505, ..., 0.49876311, # random
-0.38672696, -0.4685006 ]) # random
>>> s.shape
(8000,)
>>> s = np.random.standard_normal(size=(3, 4, 2))
>>> s.shape
(3, 4, 2)
Two-by-four array of samples from N(3, 6.25):
>>> 3 + 2.5 * np.random.standard_normal(size=(2, 4))
array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], # random
[ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) # random
XRStools.xrs_calctools.standard_t(df, size=None)
Draw samples from a standard Student's t distribution with df degrees of freedom.
A special case of the hyperbolic distribution. As df gets large, the result
resembles that of the standard normal distribution (standard_normal).
NOTE:
New code should use the standard_t method of a default_rng() instance instead;
please see the random-quick-start.
df float or array_like of floats Degrees of freedom, must be > 0.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if df is a scalar. Otherwise, np.array(df).size
samples are drawn.
out ndarray or scalar Drawn samples from the parameterized standard Student's t
distribution.
Generator.standard_t: which should be used for new code.
The probability density function for the t distribution is
P(x, df) = \frac{\Gamma(\frac{df+1}{2})}{\sqrt{\pi df}
\Gamma(\frac{df}{2})}\Bigl( 1+\frac{x^2}{df} \Bigr)^{-(df+1)/2}
The t test is based on an assumption that the data come from a Normal distribution.
The t test provides a way to test whether the sample mean (that is the mean
calculated from the data) is a good estimate of the true mean.
The derivation of the t-distribution was first published in 1908 by William Gosset
while working for the Guinness Brewery in Dublin. Due to proprietary issues, he had
to publish under a pseudonym, and so he used the name Student.
[1] Dalgaard, Peter, "Introductory Statistics With R", Springer, 2002.
[2] Wikipedia, "Student's t-distribution"
https://en.wikipedia.org/wiki/Student's_t-distribution
From Dalgaard page 83
[1]_
, suppose the daily energy intake for 11 women in kilojoules (kJ) is:
>>> intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515, \
... 7515, 8230, 8770])
Does their energy intake deviate systematically from the recommended value of 7725
kJ? Our null hypothesis will be the absence of deviation, and the alternate
hypothesis will be the presence of an effect that could be either positive or
negative, hence making our test 2-tailed.
Because we are estimating the mean and we have N=11 values in our sample, we have
N-1=10 degrees of freedom. We set our significance level to 95% and compute the t
statistic using the empirical mean and empirical standard deviation of our intake. We
use a ddof of 1 to base the computation of our empirical standard deviation on an
unbiased estimate of the variance (note: the final estimate is not unbiased due to
the concave nature of the square root).
>>> np.mean(intake)
6753.636363636364
>>> intake.std(ddof=1)
1142.1232221373727
>>> t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake)))
>>> t
-2.8207540608310198
We draw 1000000 samples from Student's t distribution with the adequate degrees of
freedom.
>>> import matplotlib.pyplot as plt
>>> s = np.random.standard_t(10, size=1000000)
>>> h = plt.hist(s, bins=100, density=True)
Does our t statistic land in one of the two critical regions found at both tails of
the distribution?
>>> np.sum(np.abs(t) < np.abs(s)) / float(len(s))
0.018318 #random < 0.05, statistic is in critical region
The probability value for this 2-tailed test is about 1.83%, which is lower than the
5% pre-determined significance threshold.
Therefore, the probability of observing values as extreme as our intake conditionally
on the null hypothesis being true is too low, and we reject the null hypothesis of no
deviation.
class XRStools.xrs_calctools.stobe(prefix, postfix, fromnumber, tonumber, step,
stepformat=2)
Bases: object
class to analyze StoBe results
broaden_lin(params=[0.8, 8, 537.5, 550], npoints=1000)
cut_rawspecs(emin=None, emax=None)
norm_area(emin, emax)
sum_specs()
XRStools.xrs_calctools.translateOcean2FDMNES_p1(ocean_in, fdmnes_out, header_file)
XRStools.xrs_calctools.triangular(left, mode, right, size=None)
Draw samples from the triangular distribution over the interval [left, right].
The triangular distribution is a continuous probability distribution with lower
limit left, peak at mode, and upper limit right. Unlike the other distributions,
these parameters directly define the shape of the pdf.
NOTE:
New code should use the triangular method of a default_rng() instance instead;
please see the random-quick-start.
left float or array_like of floats Lower limit.
mode float or array_like of floats The value where the peak of the distribution
occurs. The value must fulfill the condition left <= mode <= right.
right float or array_like of floats Upper limit, must be larger than left.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if left, mode, and right are all scalars.
Otherwise, np.broadcast(left, mode, right).size samples are drawn.
out ndarray or scalar Drawn samples from the parameterized triangular
distribution.
Generator.triangular: which should be used for new code.
The probability density function for the triangular distribution is
P(x;l, m, r) = \begin{cases}
\frac{2(x-l)}{(r-l)(m-l)}& \text{for $l \leq x \leq m$},\\
\frac{2(r-x)}{(r-l)(r-m)}& \text{for $m \leq x \leq r$},\\ 0& \text{otherwise}.
\end{cases}
The triangular distribution is often used in ill-defined problems where the
underlying distribution is not known, but some knowledge of the limits and mode
exists. Often it is used in simulations.
[1] Wikipedia, "Triangular distribution"
https://en.wikipedia.org/wiki/Triangular_distribution
Draw values from the distribution and plot the histogram:
>>> import matplotlib.pyplot as plt
>>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=200,
... density=True)
>>> plt.show()
XRStools.xrs_calctools.uniform(low=0.0, high=1.0, size=None)
Draw samples from a uniform distribution.
Samples are uniformly distributed over the half-open interval [low, high) (includes
low, but excludes high). In other words, any value within the given interval is
equally likely to be drawn by uniform.
NOTE:
New code should use the uniform method of a default_rng() instance instead;
please see the random-quick-start.
low float or array_like of floats, optional Lower boundary of the output
interval. All values generated will be greater than or equal to low. The
default value is 0.
high float or array_like of floats Upper boundary of the output interval. All
values generated will be less than or equal to high. The default value is
1.0.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if low and high are both scalars. Otherwise,
np.broadcast(low, high).size samples are drawn.
out ndarray or scalar Drawn samples from the parameterized uniform distribution.
randint : Discrete uniform distribution, yielding integers. random_integers :
Discrete uniform distribution over the closed
interval [low, high].
random_sample : Floats uniformly distributed over [0, 1). random : Alias for
random_sample. rand : Convenience function that accepts dimensions as input, e.g.,
rand(2,2) would generate a 2-by-2 array of floats, uniformly distributed over
[0, 1).
Generator.uniform: which should be used for new code.
The probability density function of the uniform distribution is
p(x) = \frac{1}{b - a}
anywhere within the interval [a, b), and zero elsewhere.
When high == low, values of low will be returned. If high < low, the results are
officially undefined and may eventually raise an error, i.e. do not rely on this
function to behave when passed arguments satisfying that inequality condition. The
high limit may be included in the returned array of floats due to floating-point
rounding in the equation low + (high-low) * random_sample(). For example:
>>> x = np.float32(5*0.99999999)
>>> x
5.0
Draw samples from the distribution:
>>> s = np.random.uniform(-1,0,1000)
All values are within the given interval:
>>> np.all(s >= -1)
True
>>> np.all(s < 0)
True
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s, 15, density=True)
>>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')
>>> plt.show()
XRStools.xrs_calctools.vaspBoxParser(filename)
groTrajecParser Parses an gromacs GRO-style file for the xyzBox class.
XRStools.xrs_calctools.vaspTrajecParser(filename, min_boxes=0, max_boxes=1000)
groTrajecParser Parses an gromacs GRO-style file for the xyzBox class.
XRStools.xrs_calctools.vonmises(mu, kappa, size=None)
Draw samples from a von Mises distribution.
Samples are drawn from a von Mises distribution with specified mode (mu) and
dispersion (kappa), on the interval [-pi, pi].
The von Mises distribution (also known as the circular normal distribution) is a
continuous probability distribution on the unit circle. It may be thought of as
the circular analogue of the normal distribution.
NOTE:
New code should use the vonmises method of a default_rng() instance instead;
please see the random-quick-start.
mu float or array_like of floats Mode ("center") of the distribution.
kappa float or array_like of floats Dispersion of the distribution, has to be >=0.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if mu and kappa are both scalars. Otherwise,
np.broadcast(mu, kappa).size samples are drawn.
out ndarray or scalar Drawn samples from the parameterized von Mises
distribution.
scipy.stats.vonmises
probability density function, distribution, or cumulative density function,
etc.
Generator.vonmises: which should be used for new code.
The probability density for the von Mises distribution is
p(x) = \frac{e^{\kappa cos(x-\mu)}}{2\pi I_0(\kappa)},
where \mu is the mode and \kappa the dispersion, and I_0(\kappa) is the modified
Bessel function of order 0.
The von Mises is named for Richard Edler von Mises, who was born in
Austria-Hungary, in what is now the Ukraine. He fled to the United States in 1939
and became a professor at Harvard. He worked in probability theory, aerodynamics,
fluid mechanics, and philosophy of science.
[1] Abramowitz, M. and Stegun, I. A. (Eds.). "Handbook of Mathematical Functions with
Formulas, Graphs, and Mathematical Tables, 9th printing," New York: Dover, 1972.
[2] von Mises, R., "Mathematical Theory of Probability and Statistics", New York:
Academic Press, 1964.
Draw samples from the distribution:
>>> mu, kappa = 0.0, 4.0 # mean and dispersion
>>> s = np.random.vonmises(mu, kappa, 1000)
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt
>>> from scipy.special import i0
>>> plt.hist(s, 50, density=True)
>>> x = np.linspace(-np.pi, np.pi, num=51)
>>> y = np.exp(kappa*np.cos(x-mu))/(2*np.pi*i0(kappa))
>>> plt.plot(x, y, linewidth=2, color='r')
>>> plt.show()
XRStools.xrs_calctools.wald(mean, scale, size=None)
Draw samples from a Wald, or inverse Gaussian, distribution.
As the scale approaches infinity, the distribution becomes more like a Gaussian.
Some references claim that the Wald is an inverse Gaussian with mean equal to 1,
but this is by no means universal.
The inverse Gaussian distribution was first studied in relationship to Brownian
motion. In 1956 M.C.K. Tweedie used the name inverse Gaussian because there is an
inverse relationship between the time to cover a unit distance and distance covered
in unit time.
NOTE:
New code should use the wald method of a default_rng() instance instead; please
see the random-quick-start.
mean float or array_like of floats Distribution mean, must be > 0.
scale float or array_like of floats Scale parameter, must be > 0.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if mean and scale are both scalars. Otherwise,
np.broadcast(mean, scale).size samples are drawn.
out ndarray or scalar Drawn samples from the parameterized Wald distribution.
Generator.wald: which should be used for new code.
The probability density function for the Wald distribution is
P(x;mean,scale) = \sqrt{\frac{scale}{2\pi x^3}}e^
\frac{-scale(x-mean)^2}{2\cdotp mean^2x}
As noted above the inverse Gaussian distribution first arise from attempts to model
Brownian motion. It is also a competitor to the Weibull for use in reliability
modeling and modeling stock returns and interest rate processes.
[1] Brighton Webs Ltd., Wald Distribution,
https://web.archive.org/web/20090423014010/http://www.brighton-webs.co.uk:80/distributions/wald.asp
[2] Chhikara, Raj S., and Folks, J. Leroy, "The Inverse Gaussian Distribution: Theory :
Methodology, and Applications", CRC Press, 1988.
[3] Wikipedia, "Inverse Gaussian distribution"
https://en.wikipedia.org/wiki/Inverse_Gaussian_distribution
Draw values from the distribution and plot the histogram:
>>> import matplotlib.pyplot as plt
>>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, density=True)
>>> plt.show()
XRStools.xrs_calctools.weibull(a, size=None)
Draw samples from a Weibull distribution.
Draw samples from a 1-parameter Weibull distribution with the given shape parameter
a.
X = (-ln(U))^{1/a}
Here, U is drawn from the uniform distribution over (0,1].
The more common 2-parameter Weibull, including a scale parameter \lambda is just X
= \lambda(-ln(U))^{1/a}.
NOTE:
New code should use the weibull method of a default_rng() instance instead;
please see the random-quick-start.
a float or array_like of floats Shape parameter of the distribution. Must be
nonnegative.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if a is a scalar. Otherwise, np.array(a).size
samples are drawn.
out ndarray or scalar Drawn samples from the parameterized Weibull distribution.
scipy.stats.weibull_max scipy.stats.weibull_min scipy.stats.genextreme gumbel
Generator.weibull: which should be used for new code.
The Weibull (or Type III asymptotic extreme value distribution for smallest values,
SEV Type III, or Rosin-Rammler distribution) is one of a class of Generalized
Extreme Value (GEV) distributions used in modeling extreme value problems. This
class includes the Gumbel and Frechet distributions.
The probability density for the Weibull distribution is
p(x) = \frac{a}
{\lambda}(\frac{x}{\lambda})^{a-1}e^{-(x/\lambda)^a},
where a is the shape and \lambda the scale.
The function has its peak (the mode) at \lambda(\frac{a-1}{a})^{1/a}.
When a = 1, the Weibull distribution reduces to the exponential distribution.
[1] Waloddi Weibull, Royal Technical University, Stockholm, 1939 "A Statistical Theory Of
The Strength Of Materials", Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939,
Generalstabens Litografiska Anstalts Forlag, Stockholm.
[2] Waloddi Weibull, "A Statistical Distribution Function of Wide Applicability", Journal
Of Applied Mechanics ASME Paper 1951.
[3] Wikipedia, "Weibull distribution", https://en.wikipedia.org/wiki/Weibull_distribution
Draw samples from the distribution:
>>> a = 5. # shape
>>> s = np.random.weibull(a, 1000)
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt
>>> x = np.arange(1,100.)/50.
>>> def weib(x,n,a):
... return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)
>>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))
>>> x = np.arange(1,100.)/50.
>>> scale = count.max()/weib(x, 1., 5.).max()
>>> plt.plot(x, weib(x, 1., 5.)*scale)
>>> plt.show()
XRStools.xrs_calctools.writeFDMNESinput_file(xyzAtoms, fname, Filout, Range, Radius, Edge,
NRIXS, Absorber, Green=False, SCF=False)
writeFDMNESinput_file Writes an input file to be used for FDMNES.
XRStools.xrs_calctools.writeFEFFinput_arb(fname, headerfile, xyzBox, exatom, edge)
writeFEFFinput_arb
XRStools.xrs_calctools.writeMD1Input(fname, box, headerfile, exatomNo=0)
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)
writeOCEAN_XESInput Writes an input for ONEAN XES calculation for 17 molecule water
boxes.
XRStools.xrs_calctools.writeOCEANinput(fname, headerfile, xyzBox, exatom, edge, subshell)
writeOCEANinput
XRStools.xrs_calctools.writeOCEANinput_arb(fname, headerfile, xyzBox, exatom, edge,
subshell)
writeOCEANinput
XRStools.xrs_calctools.writeOCEANinput_full(fname, xyzBox, exatom, edge, subshell)
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)
writeOCEANinput
XRStools.xrs_calctools.writePWinuptFile(fname, box, param_dict)
writePWinuptFile
XRStools.xrs_calctools.writeRelXYZfile(filename, n_atoms, boxLength, title, xyzAtoms,
inclAtomNames=True)
XRStools.xrs_calctools.writeWFN1waterInput(fname, box, headerfile, exatomNo=0)
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)
XRStools.xrs_calctools.writeXYZtrajectory(filename, boxes)
class XRStools.xrs_calctools.xyzAtom(name, coordinates, number)
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.
getAnglePBCarb(atom2, atom3, lattice, lattice_inv, degrees=True)
get_angle Return angle between the three given atoms (as seen from atom2).
getCoordinates()
getDist(atom)
getDistPBCarb(atom, lattice, lattice_inv)
getNorm()
load_spectrum(file_name)
load_spectrum_all_pol(prefix, num_pols, printing=False)
normalize_spectrum(normrange)
translateSelf(vector)
translateSelf_arb(lattice, lattice_inv, vector)
class XRStools.xrs_calctools.xyzBox(xyzAtoms, boxLength=None, title=None)
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')
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')
count_hbonds(Roocut=3.6, Rohcut=2.4, Aoooh=30.0, counter_name='num_H_bonds',
counter_name2='H_bond_angles')
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')
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()
deleteTip4pCOM Deletes the ficticious atoms used in the TIP4P water model.
findMethAndHexMolecules(CO_cut=1.6, CH_cut=1.2, OH_cut=1.2, CC_cut=1.7)
CH3OH
findMethanolMolecules(CO_cut=1.6, CH_cut=1.2, OH_cut=1.2)
CH3OH
find_hydroniums(OH_cutoff=1.5)
find_hydroniums Returns a list of hydronium molecules.
find_hydroxides(OH_cutoff=1.5)
find_hydroxides Returns a list of hydroxide molecules.
find_tmao_molecules_arb(CH_cut=1.2, CN_cut=1.6, NO_cut=1.5, CC_cut=2.5)
find_tmao_molecules Returns a list of TMAO molecules.
find_urea_molecules_arb(NH_cut=1.2, CN_cut=1.6, CO_cut=1.5)
find_urea_molecules Returns a list of Urea molecules.
getCoordinates()
getCoordinates Return coordinates of all atoms in the cluster.
getDistVectorPBC_arb(atom1, atom2)
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)
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()
getTetraParameter Returns a list of tetrahedrality paprameters, according to
NATURE, VOL 409, 18 JANUARY (2001).
UNTESTED!!!
get_OO_neighbors(Roocut=3.6)
get_OO_neighbors Returns list of numbers of nearest oxygen neighbors within
readius 'Roocut'.
get_OO_neighbors_pbc(Roocut=3.6)
get_OO_neighbors_pbc Returns a list of numbers of nearest oxygen atoms, uses
periodic boundary conditions.
get_angle(atom1, atom2, atom3, degrees=True)
get_angle Return angle between the three given atoms (as seen from atom2).
get_angle_arb(atom1, atom2, atom3, degrees=True)
get_angle Return angle between the three given atoms (as seen from atom2).
get_atoms_by_name(name)
get_atoms_by_name Return a list of all xyzAtoms of a given name 'name'.
get_atoms_from_molecules()
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')
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')
get_hbonds(Roocut=3.6, Rohcut=2.4, Aoooh=30.0)
get_hbonds Counts the hydrogen bonds inside the box, returns the number of
H-bond donors and H-bond acceptors.
multiplyBoxPBC(numShells)
multiplyBoxPBC Applies the periodic boundary conditions and multiplies the
box in shells around the original.
multiplyBoxPBC_arb(lx=[- 1, 1], ly=[- 1, 1], lz=[- 1, 1])
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)
normalize_spectrum(normrange)
scatterPlot()
scatterPlot Opens a plot window with a scatter-plot of all coordinates of
the box.
setBoxLength(boxLength, angstrom=True)
setBoxLength Set the box length.
translateAtomsMinimumImage(lattice, lattice_inv)
translateAtomsMinimumImage
Brings back all atoms into the original box using periodic boundary
conditions and minimal image convention.
writeBox(filename)
writeBox Creates an xyz-style text file with all coordinates of the box.
writeClusters(cenatom_name, number, cutoff, prefix, postfix='.xyz')
writeXYZclusters Write water clusters into files.
writeClusters_arb(cenatom_name, number, cutoff, prefix, postfix='.xyz',
test_box_multiplyer=1)
writeXYZclusters Write water clusters into files.
writeFDMNESinput(fname, Filout, Range, Radius, Edge, NRIXS, Absorber)
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')
writeXYZclusters Write water clusters into files.
writeMoleculeCluster(molAtomList, fname, cutoff=None, numH2Omols=None, o_name='O',
h_name='H', mol_center=None)
writeMoleculeCluster Careful, this works only for a single molecule in
water.
writeOCEANinput(fname, headerfile, exatom, edge, subshell)
writeOCEANinput Creates an OCEAN input file based on the headerfile.
writeRelBox(filename, inclAtomNames=True)
writeRelBox Writes all relative atom coordinates into a text file (useful as
OCEAN input).
class XRStools.xrs_calctools.xyzMolecule(xyzAtoms, title=None)
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)
appendAtom Add an xzyAtom to the molecule.
getCoordinates()
getCoordinates Return coordinates of all atoms in the cluster.
getCoordinates_name(name)
getCoordinates_name Return coordintes of all atoms with 'name'.
getGeometricCenter()
getGeometricCenter Return the geometric center of the xyz-molecule.
getGeometricCenter_arb(lattice, lattice_inv)
get_atoms_by_name(name)
get_atoms_by_name Return a list of all xyzAtoms of a given name 'name'.
popAtom(xyzAtom)
popAtom Delete an xyzAtom from the molecule.
scatterPlot()
scatterPlot Opens a plot window with a scatter-plot of all coordinates of
the molecule.
translateAtomsMinimumImage(lattice, lattice_inv, center=array([0., 0., 0.]))
translateAtomsMinimumImage
Brings back all atoms into the original box using periodic boundary
conditions and minimal image convention.
translateSelf(vector)
translateSelf Translate all atoms of the molecule by a vector 'vector'.
writeXYZfile(fname)
writeXYZfile Creates an xyz-style text file with all coordinates of the
molecule.
XRStools.xrs_calctools.xyzTrajecParser(filename, boxLength, firstBox=0, lastBox=- 1)
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)
Bases: object
getRDF(atom1='O', atom2='O', MAXBIN=1000, DELR=0.01, RHO=1.0)
getRDF2_arb(atom1='O', atom2='O', MAXBIN=1000, DELR=0.01, RHO=1.0)
getRDF_arb(atom1='O', atom2='O', MAXBIN=1000, DELR=0.01, RHO=1.0)
loadAXSFtraj(filename)
writeRandBox(filename)
writeXYZtraj(filename)
XRStools.xrs_calctools.zipf(a, size=None)
Draw samples from a Zipf distribution.
Samples are drawn from a Zipf distribution with specified parameter a > 1.
The Zipf distribution (also known as the zeta distribution) is a continuous
probability distribution that satisfies Zipf's law: the frequency of an item is
inversely proportional to its rank in a frequency table.
NOTE:
New code should use the zipf method of a default_rng() instance instead; please
see the random-quick-start.
a float or array_like of floats Distribution parameter. Must be greater than
1.
size int or tuple of ints, optional Output shape. If the given shape is, e.g.,
(m, n, k), then m * n * k samples are drawn. If size is None (default), a
single value is returned if a is a scalar. Otherwise, np.array(a).size
samples are drawn.
out ndarray or scalar Drawn samples from the parameterized Zipf distribution.
scipy.stats.zipf
probability density function, distribution, or cumulative density function,
etc.
Generator.zipf: which should be used for new code.
The probability density for the Zipf distribution is
p(x) = \frac{x^{-a}}{\zeta(a)},
where \zeta is the Riemann Zeta function.
It is named for the American linguist George Kingsley Zipf, who noted that the
frequency of any word in a sample of a language is inversely proportional to its
rank in the frequency table.
[1] Zipf, G. K., "Selected Studies of the Principle of Relative Frequency in Language,"
Cambridge, MA: Harvard Univ. Press, 1932.
Draw samples from the distribution:
>>> a = 2. # parameter
>>> s = np.random.zipf(a, 1000)
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt
>>> from scipy import special
Truncate s values at 50 so plot is interesting:
>>> count, bins, ignored = plt.hist(s[s<50], 50, density=True)
>>> x = np.arange(1., 50.)
>>> y = x**(-a) / special.zetac(a)
>>> plt.plot(x, y/max(y), linewidth=2, color='r')
>>> plt.show()
XRStools.xrs_extraction Module
class XRStools.xrs_extraction.HF_dataset(data, formulas, stoich_weights, edges)
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)
get_C_total(columns)
get_J_total_av(columns)
class XRStools.xrs_extraction.edge_extraction(exp_data, formulas, stoich_weights, edges,
prenormrange=[5, inf])
Bases: object
edge_extraction Class to destill core edge spectra from x-ray Raman scattering
experiments.
analyzerAverage(roi_numbers, errorweighing=True)
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,
show_plots=True)
removeCorePearsonAv
guess (list): [position, FWHM, shape, intensity, ax, b, scale ]
removeCorePearsonAv_new(element, edge, range1, range2, HFcore_shift=0.0,
guess=None, scaling=None, return_background=False, reg_lam=10)
removeCorePearsonAv_new
removePearsonAv(element, edge, range1, range2=None, weights=[2, 1], guess=None,
scale=1.0, HFcore_shift=0.0)
removePearsonAv
removePolyCoreAv(element, edge, range1, range2, weights=[1, 1], guess=[1.0, 0.0,
0.0], ewindow=100.0)
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)
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, lam)
Bases: object
funct(a, eloss)
XRStools.xrs_extraction.map_chamber_names(name)
map_chamber_names Maps names of chambers to range of ROI numbers.
class XRStools.xrs_extraction.valence_CP
Bases: object
valence_CP Class to organize information about extracted experimental valence
Compton profiles.
get_asymmetry()
get_pzscale()
XRStools.xrs_imaging Module
XRStools.xrs_read Module
XRStools.xrs_scans Module
XRStools.xrs_ComptonProfiles Module
class XRStools.xrs_ComptonProfiles.AtomProfile(element, filename, stoichiometry=1.0)
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')
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)
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()
class XRStools.xrs_ComptonProfiles.ComptonProfiles(element)
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)
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)
get_elossProfiles(E0, twotheta, correctasym=None, valence_cutoff=20.0)
get_stoichWeight()
class XRStools.xrs_ComptonProfiles.HFProfile(formulas, stoich_weights, filename)
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)
XRStools.xrs_ComptonProfiles.HRcorrect(pzprofile, occupation, q)
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)
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
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)
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)
Returns the atomic density.
XRStools.xrs_ComptonProfiles.getAtomicWeight(Z)
Returns the atomic weight.
XRStools.xrs_ComptonProfiles.list_duplicates(seq)
XRStools.xrs_ComptonProfiles.mapShellNames(shell_str, atomicNumber)
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)
XRStools.xrs_ComptonProfiles.trapz_weights(x)
XRStools.xrs_fileIO Module
XRStools.xrs_fileIO.EdfRead(fname)
XRStools.xrs_fileIO.FabioEdfRead(fname)
Returns the EDF-data using FabIO.
XRStools.xrs_fileIO.PrepareEdfMatrix(scan_length, num_pix_x, num_pix_y)
Returns np.zeros of the shape of the detector.
XRStools.xrs_fileIO.PrepareEdfMatrix_TwoImages(scan_length, num_pix_x, num_pix_y)
Returns np.zeros for old data (horizontal and vertical Maxipix images in different
files).
XRStools.xrs_fileIO.PyMcaEdfRead(fname)
Returns the EDF-data using PyMCA.
XRStools.xrs_fileIO.PyMcaSpecRead(filename, nscan)
Returns data, counter-names, and EDF-files using PyMCA.
XRStools.xrs_fileIO.PyMcaSpecRead_my(filename, nscan)
Returns data, counter-names, and EDF-files using PyMCA.
XRStools.xrs_fileIO.ReadEdfImages(ccdcounter, num_pix_x, num_pix_y, path, EdfPrefix,
EdfName, EdfPostfix)
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)
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)
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)
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)
XRStools.xrs_fileIO.ReadScanFromFile(fname)
Returns a scan stored in a Numpy archive.
XRStools.xrs_fileIO.SilxSpecRead(filename, nscan)
Returns data, motors, counter-names, and labels using Silx.
XRStools.xrs_fileIO.SpecRead(filename, nscan)
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)
Writes a scan into a Numpy archive.
XRStools.xrs_fileIO.dump_on_file_list(filename)
XRStools.xrs_fileIO.myEdfRead(filename)
Returns EDF-data, if PyMCA is not installed (this is slow).
XRStools.xrs_fileIO.readbiggsdata(filename, element)
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
XRStools.xrs_rois Module
XRStools.xrs_utilities Module
XRStools.xrs_utilities.Chi(chi, degrees=True)
rotation around (1,0,0), pos sense
XRStools.xrs_utilities.HRcorrect(pzprofile, occupation, q)
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)
NNMFcost Returns cost and gradient for NNMF with constraints.
XRStools.xrs_utilities.NNMFcost_der(x, A, F, C, F_up, C_up, n, k, m)
XRStools.xrs_utilities.NNMFcost_old(x, A, W, H, W_up, H_up)
NNMFcost Returns cost and gradient for NNMF with constraints.
XRStools.xrs_utilities.Omega(omega, degrees=True)
rotation around (0,0,1), pos sense
XRStools.xrs_utilities.Phi(phi, degrees=True)
rotation around (0,1,0), neg sense
XRStools.xrs_utilities.Rx(chi, degrees=True)
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)
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)
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_')
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')
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)
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)
# 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)
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)
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)
bootstrapCNNMF Constrained non-negative matrix factorization with bootstrapping for
error estimates.
XRStools.xrs_utilities.bragg(hkl, e, xtal='Si')
% 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 %
class XRStools.xrs_utilities.bragg_refl(crystal, hkl, alpha=0.0)
Bases: object
Dynamical theory of diffraction.
get_chi(energy, crystal=None, hkl=None)
get_nff(nff_path=None)
get_polarization_factor(tth, case='sigma')
Calculate polarization factor.
get_reflectivity(energy, delta_theta, case='sigma')
get_reflectivity_bent(energy, delta_theta, R)
XRStools.xrs_utilities.braggd(hkl, e, xtal='Si')
# 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.cNNMF_chris(A, W_fixed, W_free, maxIter=100, verbose=True)
XRStools.xrs_utilities.cixsUBfind(x, G, Q_sample, wi, wo, lambdai, lambdao)
cixsUBfind
XRStools.xrs_utilities.cixsUBgetAngles_primo(Q)
XRStools.xrs_utilities.cixsUBgetAngles_secondo(Q)
XRStools.xrs_utilities.cixsUBgetAngles_terzo(Q)
XRStools.xrs_utilities.cixsUBgetQ_primo(tthv, tthh, psi)
returns the Q0 given the detector position (tthv, tth) and th crystal orientation.
This orientation is calculated considering :
the Bragg condition and the rotation around the G vector :
this rotation is defined by psi which is a rotation around G
XRStools.xrs_utilities.cixsUBgetQ_secondo(tthv, tthh, psi)
XRStools.xrs_utilities.cixsUBgetQ_terzo(tthv, tthh, psi)
XRStools.xrs_utilities.cixs_primo(tthv, tthh, psi, anal_braggd=86.5)
cixs_primo
XRStools.xrs_utilities.cixs_secondo(tthv, tthh, psi, anal_braggd=86.5)
cixs_secondo
XRStools.xrs_utilities.cixs_terzo(tthv, tthh, psi, anal_braggd=86.5)
cixs_terzo
XRStools.xrs_utilities.compute_matrix_elements(R1, R2, k, r)
XRStools.xrs_utilities.con2mat(x, W, H, W_up, H_up)
XRStools.xrs_utilities.constrained_mf(A, W_ini, W_up, coeff_ini, coeff_up, maxIter=1000,
tol=1e-08, maxIter_power=1000)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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')
% 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)
Bases: object
class to hold all things dynamic theory of diffraction.
get_anomalous_absorption(energy=None)
get_eta(angular_range, angular_step=1e-08)
get_extinction_length(energy=None)
get_reflection_width()
get_reflectivity(angular_range=None, angular_step=None)
set_asymmetry(alpha)
negative alpha -> more grazing incidence
set_energy(energy)
set_hkl(hkl)
XRStools.xrs_utilities.dtxrd_anomalous_absorption(energy, hkl, alpha=0.0, crystal='Si',
angular_range=array([- 0.0005]))
XRStools.xrs_utilities.dtxrd_extinction_length(energy, hkl, alpha=0.0, crystal='Si')
XRStools.xrs_utilities.dtxrd_reflectivity(energy, hkl, alpha=0.0, crystal='Si',
angular_range=array([- 0.0005]))
XRStools.xrs_utilities.e2pz(w1, w2, th)
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)
reads edf-file with filename "filename" OUTPUT: data = 256x256 numpy array
XRStools.xrs_utilities.edfread_test(filename)
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)
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)
% 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)
% 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)
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)
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')
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)
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)
XRStools.xrs_utilities.get_UB_Q(tthv, tthh, phi, chi, omega, **kwargs)
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)
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)
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)
returns the penetration depth of a mixture of chemical formulas with certain
concentrations and densities
XRStools.xrs_utilities.gettransmission(energy, formulas, concentrations, densities,
thickness)
returns the transmission through a sample composed of chemical formulas with
certain densities mixed to certain concentrations, and a thickness
XRStools.xrs_utilities.hex2rgb(hex_val)
XRStools.xrs_utilities.hlike_Rwfn(n, l, r, Z)
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)
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)
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)
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)
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='/usr/lib/python3/dist-packages/XRStools/resources/data/ComptonProfiles.dat',
E0=9.69, tth=35.0, correctasym=None)
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='/usr/lib/python3/dist-packages/XRStools/resources/data/ComptonProfiles.dat',
E0=9.69, tth=35, correctasym=None)
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='/usr/lib/python3/dist-packages/XRStools/resources/data/ComptonProfiles.dat',
E0=9.69, tth=35.0, correctasym=None)
returns sum of compton profiles from a lost of chemical compounds weighted by the
given concentration
XRStools.xrs_utilities.makepzprofile(element,
filename='/usr/lib/python3/dist-packages/XRStools/resources/data/ComptonProfiles.dat')
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)
XRStools.xrs_utilities.mat2vec(F, C, F_up, C_up, n, k, m)
class XRStools.xrs_utilities.maxipix_det(name, spot_arrangement)
Bases: object
Class to store some useful values from the detectors used. To be used for arranging
the ROIs.
get_det_name()
get_pixel_range()
XRStools.xrs_utilities.momtrans_au(e1, e2, tth)
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)
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)
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)
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='/usr/lib/python3/dist-packages/XRStools/resources/data/logtable.dat')
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)
XRStools.xrs_utilities.odefctn(y, t, abb0, abb1, abb7, abb8, lex, sgbeta, y0, c1)
#% [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)
XRStools.xrs_utilities.parseformula(formula)
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)
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)
opens a plot with the transmission plotted along the given energy vector
XRStools.xrs_utilities.primtoconv(hklprim)
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)
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='/usr/lib/python3/dist-packages/XRStools/resources/data')
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)
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)
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)
reads a detector file from PetraIII beamline P01
XRStools.xrs_utilities.readp01scan(prefix, scannumber)
reads a whole scan from PetraIII beamline P01 (experimental)
XRStools.xrs_utilities.readp01scan_rep(prefix, scannumber, repetition)
reads a whole scan with repititions from PetraIII beamline P01 (experimental)
XRStools.xrs_utilities.savitzky_golay(y, window_size, order, deriv=0, rate=1)
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)
XRStools.xrs_utilities.sigmainc(Z, energy,
logtablefile='/usr/lib/python3/dist-packages/XRStools/resources/data/logtable.dat')
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)
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)
Extrapolates the smaller and larger valuea as a constant
XRStools.xrs_utilities.split_hdf5_address(dataadress)
XRStools.xrs_utilities.stiff_compl_matrix_Si(e1, e2, e3, ansys=False)
stiff_compl_matrix_Si Returns stiffnes and compliance tensor of Si for a given
orientation.
Args:
• e1 (np.array): unit vector normal to crystal surface
• e2 (np.array): unit vector crystal surface
• e3 (np.array): unit vector orthogonal to e2
Returns:
• S (np.array): compliance tensor in new coordinate system
• C (np.array): stiffnes tensor in new coordinate system
• E (np.array): Young's modulus in [GPa]
• G (np.array): shear modulus in [GPa]
• nu (np.array): Poisson ratio
Copied from S.I. of L. Zhang et al. "Anisotropic elasticity of silicon and its
application to the modelling of X-ray optics." J. Synchrotron Rad. 21, no. 3
(2014): 507-517.
XRStools.xrs_utilities.sumx(A)
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)
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)
% 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)
% 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='/usr/lib/python3/dist-packages/XRStools/resources/data/logtable.dat')
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)
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)
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)
XRStools.xrs_utilities.vrot(v, vaxis, phi)
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)
rotMatrix Rotate vector1 around vector2 by an angle.
XRStools.xrs_utilities.xas_fluo_correct(ene, mu, formula, fluo_ene, edge_ene, angin,
angout)
xas_fluo_correct Fluorescence yield over-absorption correction as in Larch/Athena.
see: https://www3.aps.anl.gov/haskel/FLUO/Fluo-manual.pdf
Args:
• ene (np.array): energy axis in [keV]
• mu (np.array): measured fluorescence spectrum
• formula (str): chemical sum formulas (e.g. 'SiO2')
• fluo_ene (float): energy in keV of main fluorescence line
• edge_ene (float): edge energy in [keV]
• angin (float): incidence angle (relative to sample normal) [deg.]
• angout (float): exit angle (relative to sample normal) [deg.]
Returns:
• ene (np.array): energy axis in [keV]
• mu_corr (np.array): corrected fluorescence spectrum
XRStools.roifinder_and_gui Module
• genindex
• modindex
• search
AUTHOR
Christoph Sahle, Alessandro Mirone
COPYRIGHT
2022, Christoph Sahle, Alessandro Mirone