* Added testsuite for stochastic fitting (one testcase).

master
Wirawan Purwanto 10 years ago
parent a244c49256
commit f436222852
  1. 119
      TESTS/test_stochastic_fitting.py

@ -0,0 +1,119 @@
# Created: 20150528
# Test module for stochastic fitting
import numpy
from wpylib.params import struct
from wpylib.math.fitting.stochastic import StochasticFitting
def setup_MC_TZ():
"""
Internal identifier: Cr2_TZ_data_20140728uhf
Binding energy of Cr2, phaseless QMC/UHF, cc-pwCVTZ-DK, dt=0.01
"""
global Cr2_TZ_data_20140728uhf
from numpy import asarray, matrix
Cr2_TZ_data_20140728uhf = asarray(matrix("""
1.550 -1.613 0.025 ;
1.600 -1.897 0.036 ;
1.6788 -2.141 0.016 ;
1.720 -2.143 0.025 ;
1.800 -2.190 0.022 ;
1.900 -2.064 0.020 ;
2.000 -2.038 0.023 ;
2.400 -1.611 0.019 ;
3.000 -1.037 0.018
"""))
def test_fit_PEC_MC_TZ(show_samples=True, save_fig=False):
"""20150528
PEC fitting, using `morse2` functional form.
Options:
- show_samples : prints the sample data (recommended)
- save_fig : dumps the fit result for each stochastic snapshot
(not recommended unless you are debugging/diagnosing)
Modeled after do_morse2_sfit routine in Cr2 analysis script.
Example output:
test_MC_TZ_PEC_fit::
ansatz=morse2_fit_func
Raw data: (x, y, dy)
1.550 -1.613 0.025
1.600 -1.897 0.036
1.679 -2.141 0.016
1.720 -2.143 0.025
1.800 -2.190 0.022
1.900 -2.064 0.020
2.000 -2.038 0.023
2.400 -1.611 0.019
3.000 -1.037 0.018
Using guess param from NLF: [-2.18625505 9.82497657 1.80455072 1.86192922]
Final parameters : -2.187(10) 9.85(61) 1.8044(66) 1.864(83)
Total execution time = 0.55 secs
All testings passed.
"""
from numpy import array
from wpylib.text_tools import matrix_str, str_indent
from wpylib.timer import block_timer as Timer
from wpylib.math.fitting.funcs_pec import morse2_fit_func
from wpylib.py import function_name
global MC_TZ_PEC_fit
global Cr2_TZ_data_20140728uhf
print("test_MC_TZ_PEC_fit::")
setup_MC_TZ()
# parameters etc
ansatz = morse2_fit_func()
rng = dict(seed=378711, rng_class=numpy.random.RandomState)
rawdata = Cr2_TZ_data_20140728uhf
sfit = MC_TZ_PEC_fit = StochasticFitting()
print("ansatz=%s" \
% (function_name(ansatz),))
# This corresponds to fit_variant==1 in the subroutine
ansatz.fit_method = "leastsq"
ansatz.fit_opts['leastsq'] = dict(xtol=1e-8, epsfcn=1e-6)
sfit.init_func(ansatz)
sfit.init_samples(x=rawdata[:,0], y=rawdata[:,1], dy=rawdata[:,2])
sfit.init_rng(**rng)
if show_samples:
print("Raw data: (x, y, dy)")
print(str_indent(matrix_str(rawdata, fmt="%8.3f")))
with Timer() as tmr:
sfit.mcfit_loop_begin_()
sfit.mcfit_loop1_(num_iter=200, save_fig=save_fig)
sfit.mcfit_loop_end_()
sfit.mcfit_analysis_()
sfit.mcfit_report_final_params()
nlf_fit_result = sfit.nlf_rec['xopt']
# Verify the results:
# * deterministic fit (only precise to these digits; more digits are discarded)
nlf_fit_ref = numpy.array([-2.18625505, 9.82497657, 1.80455072, 1.86192922])
assert numpy.allclose(nlf_fit_result, nlf_fit_ref, atol=0.6e-8, rtol=0)
def match_mc_param(name, val_ref, err_ref, atol):
vv = sfit.final_mc_params[name]
assert numpy.allclose(vv.value(), val_ref, atol=atol, rtol=0)
assert numpy.allclose(vv.error(), err_ref, atol=atol, rtol=0)
match_mc_param('E0', -2.187, 0.010, 0.6e-3)
match_mc_param('k', 9.85, 0.61, 0.6e-2)
match_mc_param('r0', 1.8044, 0.0066, 0.6e-4)
match_mc_param('a', 1.864, 0.083, 0.6e-3)
print("All testings passed.")
if __name__ == '__main__':
test_fit_PEC_MC_TZ()
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