# info.py:
# copyright: 2021 by pistatex (Junho Lee).
# license: LGPL3.
# Automately generated by gen_doc.py
[docs]class docs:
''' class for store the documentation for TRXASprefitpack '''
def __init__(self):
self.info = dict()
return
[docs] def appnd(self, name, txt):
self.info[name] = txt
return
__info__ = docs()
doc = dict()
doc['description'] = '''
TRXASprefitpack: package for TRXAS pre-fitting process which aims for the
first order dynamics
version: 0.4
numpy, scipy
|
|
V
***************TRXASprefitpack***************
| thy -- gen_theory_data |
| data_process -- automate_scaling |
| -- corr_a_method |
| mathfun -- exp_conv_gau |
| -- exp_conv_cauchy |
| -- solve_model |
| -- compute_model |
| -- compute_signal_gau |
| -- compute_signal_cauchy |
| -- compute_signal_pvoigt |_________
| -- model_n_comp_conv | |
| -- fact_anal_exp_conv | |
| doc -- info | |
********************************************* |
| |
| |
| | lmfit
V | matplotlib
****************script******************* |
| TRXASprefitpack_info (exe) | |
****************script******************* |
| broadening (exe) | |
| auto_scale (exe) | |
| fit_static (exe) | |
| fit_tscan (exe) | <------------
*****************************************
If you want know any information about function defined in TRXASprefitpack
type TRXASprefitpack_info func_name
'''
doc['licence'] = '''
TRXASprefitpack: package for TRXAS pre fitting process
Copyright (C) 2021 pistack (Junho Lee, email: pistatex@yonsei.ac.kr)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as
published by the Free Software Foundation, either version 3 of the
License, or (at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
'''
doc['lgpl-3.0'] = '''
GNU LESSER GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
This version of the GNU Lesser General Public License incorporates
the terms and conditions of version 3 of the GNU General Public
License, supplemented by the additional permissions listed below.
0. Additional Definitions.
As used herein, "this License" refers to version 3 of the GNU Lesser
General Public License, and the "GNU GPL" refers to version 3 of the GNU
General Public License.
"The Library" refers to a covered work governed by this License,
other than an Application or a Combined Work as defined below.
An "Application" is any work that makes use of an interface provided
by the Library, but which is not otherwise based on the Library.
Defining a subclass of a class defined by the Library is deemed a mode
of using an interface provided by the Library.
A "Combined Work" is a work produced by combining or linking an
Application with the Library. The particular version of the Library
with which the Combined Work was made is also called the "Linked
Version".
The "Minimal Corresponding Source" for a Combined Work means the
Corresponding Source for the Combined Work, excluding any source code
for portions of the Combined Work that, considered in isolation, are
based on the Application, and not on the Linked Version.
The "Corresponding Application Code" for a Combined Work means the
object code and/or source code for the Application, including any data
and utility programs needed for reproducing the Combined Work from the
Application, but excluding the System Libraries of the Combined Work.
1. Exception to Section 3 of the GNU GPL.
You may convey a covered work under sections 3 and 4 of this License
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2. Conveying Modified Versions.
If you modify a copy of the Library, and, in your modifications, a
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3. Object Code Incorporating Material from Library Header Files.
The object code form of an Application may incorporate material from
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You may convey a Combined Work under terms of your choice that,
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5. Combined Libraries.
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6. Revised Versions of the GNU Lesser General Public License.
The Free Software Foundation may publish revised and/or new versions
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Each version is given a distinguishing version number. If the
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'''
doc['gen_theory_data'] = '''
gen_theory_data(e, peaks, A, fwhm_G, fwhm_L, peak_shift, out=None)
voigt broadening theoretically calculated lineshape spectrum if out
is not none: It will make out_thy.txt: txt file for rescaled and
boroadend calc spectrum out_thy_stk.txt: txt file for rescaled and
shifted calc peaks
Parameters:
* **e** (*numpy_1d_array*) -- energy (unit: eV)
* **A** (*float*) -- scaling parameter
* **fwhm_G** (*float*) -- full width at half maximum of gaussian
shape (unit: eV)
* **fwhm_L** (*float*) -- full width at half maximum of
lorenzian shape (unit: eV)
* **peak_shift** (*float*) -- discrepency of peak position
between expt data and theoretically broadened spectrum
* **out** (*string*) -- prefix for output txt file [optional]
Returns:
voigt broadened calc spectrum
Return type:
numpy_1d_array
'''
doc['automate_scaling'] = '''
automate_scaling(A, e_ref_index, e, t,
escan_time, tscan_energy, time_zeros=None,
exotic_t=None, exotic_tscan_energy=None,
exotic_e_ref_index=None,
escan_data=None, escan_data_eps=None,
tscan_data=None, tscan_data_eps=None,
exotic_tscan_data=None, exotic_tscan_data_eps=None)
Automate scale escan, tscan and some exotic tscan
If you scale tscan with fast time delay ( < 10 ps)
time zero will change scaling factor. So, beware of the
change of time zero.
However, scaling parameter for tscan w.r.t. escan (r_t_i)
may recover such effect. So, I think that for not so early time delay
(1 ps ~ 10 ps) region time_zero shifted about +/- IRF ~ 300 fs
neglectable.
If escan_time[e_ref_index] < 10 ps
I will print warning message for you.
Automate scaling procedure
0. Fit static spectrum with fit_static.py
Carefully watch graphs.
1. Scaling escan_data using A-method
2-1. If escan_time[e_ref_index] := e_ref > 10 ps,
then take difference flu spectrum at tscan energy
in e_ref delay escan.
Next take difference flu spectrum at e_ref delay in tscan.
Now fit diff flu spec at e_ref delay in tscan to
diff flu spec at tscan_energy in e_ref delay escan.
2-2. If e_ref < 10 ps, you must set time_zeros for all tscan.
Then do procedure simuliar to 2-2.
3. Watch changes in timezero during fitting.
If timezero changes a lot in fitting, consider simultaneous
scaling (i.e. fitting and scaling at once.)
4. After do automate_scaling, you need to correct scaling
due to the prossiblity of inconsistent laser overlap.
5. Correct and do automate_scaling again with corrected escan and
A = A_ref*np.ones(A.shape).
Warning and Error case
Warning (no harm, just for warn)
1. early delay (e_ref < 10)
Error (it aborts procedure)
1. early delay without time zero : e_ref < 10 but time zero is not set.
automate_scaling: Automate scale escan, tscan
Parameters:
* **A** (*numpy_1d_array*) -- array of parameter A for each
escan
* **e_ref** (*int*) -- index for reference escan for scaling of
escan and tscan
* **e** (*numpy_1d_array*) -- array of energies in which we
measured escan
* **t** (*numpy_1d_array*) -- array of time delays in which we
measured tscan
* **escan_time** (*numpy_1d_array*) -- array of time delay at
which we measure escan
* **tscan_energy** (*numpy_1d_array*) -- array of energy at
which we measure tscan
* **time_zeros** (*numpy_1d_array*) -- array of time zero for
every tscan (optional, mandatory escan_time[e_ref] < 10 ps)
* **escan_data** (*numpy_nd_array*) -- data for escan (Note.
escan data does not contains energy range)
* **escan_data_eps** (*numpy_nd_array*) -- error for escan data
* **tscan_data** (*numpy_nd_array*) -- data for tscan (Note.
tscan data does not contains time delay range)
* **tscan_data_eps** (*numpy_nd_array*) -- error for tscan
* **warn** (*bool*) -- whether or not prints warning message
[default: False]
Returns:
scaled_data scaled_data['escan'] : scaled data for escan
scaled_data['escan_eps'] : scaled error for escan
scaled_data['tscan'] : scaled data for tscan
scaled_data['tscan_eps'] : scaled error for tscan
Return type:
dict
'''
doc['corr_a_method'] = '''
Due to inconsistent of laser overlap during experiment
We may need to correct ``A-method`` scaled escan data
corr_a_method correct this data using reference tscan
corr_a_method(e_ref_index, e, t,
escan_time, ref_tscan_energy, ref_time_zeros,
escan_data=None, escan_data_eps=None,
ref_tscan_data=None, ref_tscan_data_eps=None,
warn=False):
corr_a_method: Corrects the scaling of escan scaled with tscan
Parameters:
* **e_ref** (*int*) -- index of reference escab used for
"A-method"
* **e** (*numpy_1d_array*) -- array of energies in which we
measured escan
* **t** (*numpy_1d_array*) -- array of time delays in which we
measured tscan
* **escan_time** (*numpy_1d_array*) -- array of time delays at
which we measured escan
* **ref_tscan_energy** (*float*) -- reference energy for
repairing scale of escan
* **ref_time_zeros** (*float*) -- time zero for reference tscan
* **escan_data** (*numpy_nd_array*) -- data for escan (Note.
escan data does not contains energy range)
* **escan_data_eps** (*numpy_nd_array*) -- error for escan data
* **tscan_data** (*numpy_1d_array*) -- data for reference tscan
* **tscan_data_eps** (*numpy_1d_array*) -- error for reference
tscan
Returns:
corrected_data: corrected_data['escan'] : corrected data for
escan corrected_data['escan_eps'] : corrected error for escan
Return type:
dict
'''
doc['exp_conv_gau'] = '''
exp_conv_gau(t, fwhm, k):
Compute exponential function convolved with normalized gaussian
distribution
Note. We assume temporal pulse of x-ray is normalized gaussian
distribution
\sigma = \frac{fwhm}{2\sqrt{2\log{2}}}
IRF(t) = \frac{1}{\sigma
\sqrt{2\pi}}\exp\left(-\frac{t^2}{2\sigma^2}\right)
Parameters:
* **t** (*numpy_1d_array*) -- time
* **fwhm** (*float*) -- full width at half maximum of x-ray
temporal pulse
* **k** (*float*) -- rate constant (inverse of life time)
Returns:
convolution of normalized gaussian distribution and exp(-kt)
\frac{1}{2}\exp\left(\frac{k^2}{2\sigma^2}-kt\right)\left(1+{er
f}\left(\frac{1}{\sqrt{2}}\left(\frac{t}{\sigma}-k\sigma\right)
\right)\right)
Return type:
numpy_1d_array
'''
doc['exp_conv_cauchy'] = '''
exp_conv_cauchy(t, fwhm, k):
Compute exponential function convolved with normalized cauchy
distribution
Note. We assume temporal pulse of x-ray is normalized cauchy
distribution
\gamma = \frac{fwhm}{2}
IRF(t) = \frac{\gamma}{\pi}\frac{1}{(x-t)^2+\gamma^2}
Parameters:
* **t** (*numpy_1d_array*) -- time
* **fwhm** (*float*) -- full width at half maximum of x-ray
temporal pulse
* **k** (*float*) -- rate constant (inverse of life time)
Returns:
convolution of normalized cauchy distribution and exp(-kt)
\frac{\exp(-kt)}{\pi}\Im\left(\exp(-ik\gamma)\cdot\left(i\pi -
{Ei}\left(kt+ik\gamma\right)\right)\right)
Return type:
numpy_1d_array
'''
doc['solve_model'] = '''
solve_model(equation, y0):
Solve system of first order rate equation
Parameters:
* **equation** (*numpy_nd_array*) -- matrix corresponding to
model
* **y0** (*numpy_1d_array*) -- initial condition
Returns:
eigenvalue of equation
Return type:
numpy_1d_array
Returns:
eigenvectors for equation
Return type:
numpy_nd_array
Returns:
coefficient where y_0 = Vc
Return type:
numpy_1d_array
'''
doc['compute_model'] = '''
compute_model(t, eigval, V, c):
Compute solution of the system of rate equations solved by
solve_model Note: eigval, V, c should be obtained from solve_model
Parameters:
* **t** (*numpy_1d_array*) -- time
* **eigval** (*numpy_1d_array*) -- eigenvalue for equation
* **V** (*numpy_nd_array*) -- eigenvectors for equation
* **c** (*numpy_1d_array*) -- coefficient
Returns:
solution of rate equation
Return type:
numpy_nd_array
'''
doc['compute_signal_gau'] = '''
compute_signal_gau(t, fwhm, eigval, V, c):
Compute solution of the system of rate equations solved by
solve_model convolved with normalized gaussian distribution Note:
eigval, V, c should be obtained from solve_model
Parameters:
* **t** (*numpy_1d_array*) -- time
* **fwhm** (*float*) -- full width at half maximum of x-ray
temporal pulse
* **eigval** (*numpy_1d_array*) -- eigenvalue for equation
* **V** (*numpy_nd_array*) -- eigenvectors for equation
* **c** (*numpy_1d_array*) -- coefficient
Returns:
solution of rate equation convolved with normalized gaussian
distribution
Return type:
numpy_nd_array
'''
doc['compute_signal_cauchy'] = '''
compute_signal_cauchy(t, fwhm, eigval, V, c):
Compute solution of the system of rate equations solved by
solve_model convolved with normalized cauchy distribution Note:
eigval, V, c should be obtained from solve_model
Parameters:
* **t** (*numpy_1d_array*) -- time
* **fwhm** (*float*) -- full width at half maximum of x-ray
temporal pulse
* **eigval** (*numpy_1d_array*) -- eigenvalue for equation
* **V** (*numpy_nd_array*) -- eigenvectors for equation
* **c** (*numpy_1d_array*) -- coefficient
Returns:
solution of rate equation convolved with normalized cachy
distribution
Return type:
numpy_nd_array
'''
doc['compute_signal_pvoigt'] = '''
compute_signal_pvoigt(t, fwhm_G, fwhm_L, eta, eigval, V, c):
Compute solution of the system of rate equations solved by
solve_model convolved with normalized pseudo voigt profile (pvoigt
= (1-\eta) G(t) + \eta L(t), G(t): stands for normalized gaussian
L(t): stands for normalized cauchy(lorenzian) distribution)
Note: eigval, V, c should be obtained from solve_model
Parameters:
* **t** (*numpy_1d_array*) -- time
* **fwhm_G** (*float*) -- full width at half maximum of x-ray
temporal pulse (gaussian part)
* **fwhm_L** (*float*) -- full width at half maximum of x-ray
temporal pulse (lorenzian part)
* **eta** (*float*) -- mixing parameter
(0 < \eta < 1)
Parameters:
* **eigval** (*numpy_1d_array*) -- eigenvalue for equation
* **V** (*numpy_nd_array*) -- eigenvectors for equation
* **c** (*numpy_1d_array*) -- coefficient
Returns:
solution of rate equation convolved with normalized pseudo voigt
profile
Return type:
numpy_nd_array
'''
doc['model_n_comp_conv'] = '''
model_n_comp_conv(t, fwhm, tau, c, base=True, irf='g', eta=None):
model for n component fitting n exponential function convolved with
irf 'g': normalized gaussian distribution
'c': normalized cauchy distribution
'pv': pseudo voigt profile (1-\eta)g + \eta c
Parameters:
* **t** (*numpy_1d_array*) -- time
* **fwhm** (*numpy_1d_array*) --
fwhm of X-ray temporal pulse if irf == 'g' or 'c' then
fwhm = [fwhm]
if irf == 'pv' then
fwhm = [fwhm_G, fwhm_L]
* **tau** (*numpy_1d_array*) -- life time for each component
* **c** (*numpy_1d_array*) -- coefficient (num_comp+1,) if
base=True (num_comp,) if base=False
* **base** (*bool**, **optional*) -- whether or not include
baseline [default: True]
* **irf** (*string**, **optional*) -- shape of instrumental
response function [default: g] 'g': normalized gaussian
distribution 'c': normalized cauchy distribution 'pv': pseudo
voigt profile (1-\eta)g + \eta c
* **eta** (*float**, **optional*) -- mixing parameter for pseudo
voigt profile (only needed for pseudo voigt profile, Default
value is guessed according to Journal of Applied
Crystallography. 33 (6): 1311–1316.)
Returns:
fit
Return type:
numpy_1d_array
'''
doc['fact_anal_exp_conv'] = '''
fact_anal_exp_conv(t, fwhm, tau, irf='g', eta=None,
data=None, eps=None, base=True):
Estimate the best coefficiets when fwhm and tau are given
Before fitting with Escan data, you want fit your model to tscan
data to know how many component needed for well description of
tscan data To do this you have good initial guess for not only life
time of each component but also coefficients. For this, It will
solve linear least square problem
Parameters:
* **t** (*numpy_1d_array*) -- time
* **fwhm** (*numpy_1d_array*) --
fwhm of X-ray temporal pulse if irf == 'g' or 'c' then
fwhm = [fwhm]
if irf == 'pv' then
fwhm = [fwhm_G, fwhm_L]
* **tau** (*numpy_1d_array*) -- life time for each component
* **irf** (*string**, **optional*) -- shape of instrumental
response function [default: g] 'g': normalized gaussian
distribution 'c': normalized cauchy distribution 'pv': pseudo
voigt profile (1-\eta)g + \eta c
* **eta** (*float**, **optional*) -- mixing parameter for pseudo
voigt profile (only needed for pseudo voigt profile, Default
value is guessed according to Journal of Applied
Crystallography. 33 (6): 1311–1316.)
* **data** (*numpy_1d_array*) -- tscan data
* **eps** (*numpy_1d_array*) -- error for tscan data
* **base** (*bool**, **optional*) -- whether or not include
baseline [default: True]
Returns:
best coefficient for given fwhm, tau (num_comp+1,) , if
base=True (num_comp,) , otherwise
Return type:
numpy_1d_array
'''
doc['fit_static'] = '''
fit_static
**********
fitting static spectrum with theoretically calculated line spectrum
broadened by spectral line shape
Note:
Currently, it uses linear baseline.
Note:
energy unit of measured static spectrum must be KeV and energy unit
of calc static spectrum must be eV
* usage: fit_static [-h] [-ls {v,g,l}] [-o OUT] prefix num_scan
peak_name
* positional arguments:
* prefix prefix for experimental static peak files It
will read prefix_i.txt files
* num_scan the number of static peak scan files
* peak_name filename for theoretical line shape spectrum
* optional arguments:
* -h, --help show this help message and exit
* -ls {v,g,l}, --line_shape {v,g,l} line shape of spectrum v: voigt
profile g: gaussian shape l: lorenzian shape
* -o OUT, --out OUT prefix for output files
'''
doc['fit_tscan'] = '''
fit_tscan
*********
fitting tscan data using sum of exponential decay covolved with
gaussian/cauchy(lorenzian)/pseudo voigt irf function. It uses
"fact_anal_exp_conv" to determine best c_i's where timezero, fwhm, and
time constants are given. So, to use this script what you need to give
are only timezero, fwhm, and time constants
Note:
* If you set shape of irf to pseudo voigt (pv), then you should
provide two full width at half maximum value for gaussian and
cauchy parts, respectively.
* If you did not set tau then it assume you finds the timezero of
this scan. So, --no_base option is discouraged.
* usage: fit_tscan [-h] [--irf {g,c,pv}] [--fwhm_G FWHM_G]
[--fwhm_L FWHM_L] [-t0 TIME_ZEROS [TIME_ZEROS ...]] [-t0f
TIME_ZEROS_FILE] [--tau [TAU [TAU ...]]] [--no_base] [-o OUT] prefix
* positional arguments:
* prefix prefix for tscan files It will read
prefix_i.txt
* optional arguments:
* -h, --help show this help message and exit
* --irf {g,c,pv} shape of instrument response function g:
gaussian distribution c: cauchy distribution pv: pseudo voigt
profile, linear combination of gaussian distribution and cauchy
distribution pv = eta*c+(1-eta)*g the mixing parameter is guessed
according to Journal of Applied Crystallography. 33 (6):
1311–1316.
* --fwhm_G FWHM_G full width at half maximum for gaussian
shape It should not used when you set cauchy irf function
* --fwhm_L FWHM_L full width at half maximum for cauchy shape
It should not used when you did not set irf or use gaussian irf
function
* -t0 TIME_ZEROS [TIME_ZEROS ...], --time_zeros TIME_ZEROS
[TIME_ZEROS ...] time zeros for each tscan
* -t0f TIME_ZEROS_FILE, --time_zeros_file TIME_ZEROS_FILE filename
for time zeros of each tscan
* --tau [TAU [TAU ...]] lifetime of each component
* --no_base exclude baseline for fitting
* -o OUT, --out OUT prefix for output files
**Parameter bound scheme**
* fwhm: temporal width of x-ray pulse
* lower bound: 0.5*fwhm_init
* upper bound: 2*fwhm_init
* t_0: timezero for each scan
* lower bound: t_0 - 2*fwhm_init
* upper bound: t_0 + 2*fwhm_init
* tau: life_time of each component
* if tau < 0.1
* lower bound: tau/2
* upper bound: 1
* if 0.1 < tau < 10
* lower bound: 0.05
* upper bound: 100
* if 10 < tau < 100
* lower bound: 5
* upper bound: 500
* if 100 < tau < 1000
* lower bound: 50
* upper bound: 2000
* if 1000 < tau then
* lower bound: tau/2
* upper bound: np.inf
**Mixing parameter eta**
For pseudo voigt IRF function, mixing parameter eta is guessed to
\eta =
1.36603({fwhm}_L/f)-0.47719({fwhm}_L/f)^2+0.11116({fwhm}_L/f)^3
where
\begin{align*} f &=
({fwhm}_G^5+2.69269{fwhm}_G^4{fwhm}_L+2.42843{fwhm}_G^3{fwhm}_L^2
\\ &+ 4.47163{fwhm}_G^2{fwhm}_L^3+0.07842{fwhm}_G{fwhm}_L^4 \\
&+ {fwhm}_L^5)^{1/5} \end{align*}
This guess is according to J. Appl. Cryst. (2000). **33**, 1311-1316
'''
doc['TRXASprefitpack_info'] = '''
TRXASprefitpack_info
********************
Access the document of TRXASprefitpack
Type "TRXASprefitpack_info func_name" to get a description of
func_name
'''
doc['auto_scale'] = '''
auto_scale
**********
Automatic scaling escan and tscan data using "A-method"
Note:
auto_scale assume each escan have same energy range and and each
tscan have same time range. Also it assumes energy unit of escan
data is KeV but assumes energy unit of tscan_energy_file is eV.
Moreover energy unit of scaled_escan is eV. However time unit for
tscan data and escan time must be same.
* usage: auto_scale [-h] [-p PREFIX] [-ne NUM_OF_ESCAN] [-et
ESCAN_TIME] [-re REF_ESCAN_INDEX] [-nt NUM_OF_TSCAN] [-te
TSCAN_ENERGY] [-t0 TIME_ZEROS] [-ti TSCAN_INDEX_TO_SCALE
[TSCAN_INDEX_TO_SCALE ...]] [-a PARM_A] {-1,0,1,2,3,4}
* positional arguments:
* {-1,0,1,2,3,4} current stage, set stage to -1 get detailed
description
* optional arguments:
* -h, --help show this help message and exit
* -p PREFIX, --prefix PREFIX prefix for both escan and tscan file,
it will read prefix_escan_i.txt and prefix_tscan_j.txt
* -ne NUM_OF_ESCAN, --num_of_escan NUM_OF_ESCAN the number of escan
files
* -et ESCAN_TIME, --escan_time ESCAN_TIME filename for escan delay
times (unit: ps)
* -re REF_ESCAN_INDEX, --ref_escan_index REF_ESCAN_INDEX index of
escan used to the reference for scaling
* -nt NUM_OF_TSCAN, --num_of_tscan NUM_OF_TSCAN the number of tscan
files
* -te TSCAN_ENERGY, --tscan_energy TSCAN_ENERGY filename for tscan
energy (unit: eV)
* -t0 TIME_ZEROS, --time_zeros TIME_ZEROS filename for time zero of
each tscan (unit: ps)
* -ti TSCAN_INDEX_TO_SCALE [TSCAN_INDEX_TO_SCALE ...],
--tscan_index_to_scale TSCAN_INDEX_TO_SCALE [TSCAN_INDEX_TO_SCALE
...] tscan index to scale, use blank separation for multiple
arguments
* -a PARM_A, --parm_A PARM_A filename for the parameter A obtained
from fit_static
* Stage discription
* stage -1: description It prints the description about each stages
and aborts.
Note:
For every stage except -1, requires prefix, num_of_escan,
escan_time, num_of_tscan, tscan_energy, time_zeros
* stage 0: init scaling
Additionally requires: ref_escan_index, tscan_index_to_scale, parm_A
the program read escan_data from
prefix_escan_1.txt,...,prefix_num_escan.txt and tscan_data from
prefix_tscan_1.txt,...,prefix_tscan_num_tscan.txt Also, it read file
for parameter A generated by fit static. Then it fits scaling of
escan_data and tscan_i_1,...,tscan_i_N to escan_e_ref. Now it
generates prefix_escan_scaled.txt (energy unit: eV)
prefix_escan_eps_scaled.txt prefix_tscan_scaled.txt
prefix_tscan_eps_scaled.txt prefix_A_ref.txt If you do not include
tscan j for scaling. You can see (j+1) th column of
prefix_tscan_scaled.txt and j th column of tscan_scaled_eps.txt are
filled with zeros.
* stage 1: Correction
Additionally requires: ref_escan_index, tscan_index_to_scale
Note:
ref_escan_index and tscan_index_to_scale must be set to same as
stage 0
the program read scaled escan data and tscan data from
prefix_escan_scaled.txt, prefix_escan_eps_scaled.txt,
prefix_tscan_scaled.txt, prefix_tscan_eps_scaled.txt, and then it
corrects scaling of escan using tscan_i_1 It regenerates
prefix_escan_scaled.txt and prefix_escan_eps_scaled.txt
* stage 2: further scaling
Additionally requires: tscan_index_to_scale
Note:
in this stage do not need to give the file name for parameter A.
the program read scaled escan data and tscan data from
prefix_escan_scaled.txt, prefix_escan_eps_scaled.txt,
prefix_tscan_scaled.txt, prefix_tscan_eps_scaled.txt, and then it
fits scaling of tscan_i'_1,...,tscan_i'*N' to escan_e_ref'. (Prime
means i_1,...,i_N and e_ref values are different from stage 0) Then
it regenerates all prefix**.txt except prefix_A_ref.txt
* stage 3: sanity check
In this stage, the program assume, every tscan data are scaled to
escan data. For sanity check, it gives a graph for you.
* stage 4: Scaling with another tscan data set Scale Another set of
tscan data using already scaled escan data
Additionally requires: tscan_index_to_scale
Note:
Before proceed stage 4, you should move prefix_tscan_scaled.txt,
prefix_tscan_eps_scaled.txt and prefix_tscan_xxx.txt to some
backup folder and rename your another tscan data set to
prefix_tscan_xxx.txt Also you should give time_zero and energy for
such tscan.
In this stage, it reads scaled escan data and eps from
prefix_escan_scaled.txt and prefix_escan_eps_scaled.txt Then it
reads unscaled tscan data and eps from prefix_tscan_*.txt Next, it
scales tscan datas just like stage 1. After stage4 finished you
should go to stage 2 and stage 3.
* A-method
When we measure each escan, we measure static spectrum (flu_off) to
get difference spectrum (flu_on - flu_off) Since we have theortical
static spectrum, we can fit theortical spectrum with measured static
spectrum using following model.
y = A \cdot {conv}({spec}_{thy}, {voigt}({fwhm}_G, {fwhm}_L,
{peakshift})) + {baseline}
Also, using "fit_static", you can simultaneous fitting each escan.
During fitting process, it assume fwhm_G, fwhm_L, peak_shift are
same in each escan, only scaling factor A and base_line are
different. Parameter base_line reflects environmental effects, so
pure static signal(w/o environmental effects) is
A \cdot {conv}({spec}_{thy}, {voigt}({fwhm}_G, {fwhm}_L,
{peakshift}))
Each escan has same fwhm_G, fwhm_L and peak_shift value, so
Parameter A (scaling parameter) could tell relative scaling of each
escan. In other words, we can fit scaling of escan data just
multipling A_ref/A to each escan. However, due to consistence of
laser overlap between energy scans, one good time delay scan is
needed to correct the scaling of energy scans.
'''
doc['broadening'] = '''
broadening
**********
voigt broaden theoritical calculated lineshape spectrum
* usage: broadening [-h] [-o OUT] peak e_min e_max A fwhm_G fwhm_L
peak_shift
* positional arguments:
* peak filename for calculated line shape spectrum
* e_min minimum energy
* e_max maximum energy
* A scale factor
* fwhm_G Full Width at Half Maximum of gaussian shape
* fwhm_L Full Width at Half Maximum of lorenzian shape
* peak_shift discrepancy of peak position between theory and
experiment
* optional arguments:
* -h, --help show this help message and exit
* -o OUT, --out OUT prefix for output files
'''
__info__.appnd('description', doc['description'])
__info__.appnd('licence', doc['licence'])
__info__.appnd('lgpl-3.0', doc['lgpl-3.0'])
__info__.appnd('gen_theory_data', doc['gen_theory_data'])
__info__.appnd('automate_scaling', doc['automate_scaling'])
__info__.appnd('corr_a_method', doc['corr_a_method'])
__info__.appnd('exp_conv_gau', doc['exp_conv_gau'])
__info__.appnd('exp_conv_cauchy', doc['exp_conv_cauchy'])
__info__.appnd('solve_model', doc['solve_model'])
__info__.appnd('compute_model', doc['compute_model'])
__info__.appnd('compute_signal_gau', doc['compute_signal_gau'])
__info__.appnd('compute_signal_cauchy', doc['compute_signal_cauchy'])
__info__.appnd('compute_signal_pvoigt', doc['compute_signal_pvoigt'])
__info__.appnd('model_n_comp_conv', doc['model_n_comp_conv'])
__info__.appnd('fact_anal_exp_conv', doc['fact_anal_exp_conv'])
__info__.appnd('fit_static', doc['fit_static'])
__info__.appnd('fit_tscan', doc['fit_tscan'])
__info__.appnd('TRXASprefitpack_info', doc['TRXASprefitpack_info'])
__info__.appnd('auto_scale', doc['auto_scale'])
__info__.appnd('broadening', doc['broadening'])
__info__ = __info__.info
del doc