def build_usage():
modelnames = []
ms = ''
for d in sorted(Models.keys()):
ms = ms + ' %s ' % d
if len(ms) > 55:
modelnames.append(ms)
ms = ' '
modelnames.append(ms)
modelnames = '\n'.join(modelnames)
usage = """
=== Test Fit to NIST StRD Models ===
usage:
------
python fit_NIST.py [options] Model Start
where Start is one of 'start1','start2' or 'cert', for different
starting values, and Model is one of
%s
if Model = 'all', all models and starting values will be run.
options:
--------
-m name of fitting method. One of:
leastsq, nelder, powell, lbfgsb, bfgs,
tnc, cobyla, slsqp, cg, newto-cg
leastsq (Levenberg-Marquardt) is the default
""" % modelnames
return usage
def build_usage():
modelnames = []
ms = ''
for d in sorted(Models.keys()):
ms = ms + ' %s ' % d
if len(ms) > 55:
modelnames.append(ms)
ms = ' '
modelnames.append(ms)
modelnames = '\n'.join(modelnames)
usage = """
=== Test Fit to NIST StRD Models ===
usage:
------
python fit_NIST.py [options] Model Start
where Start is one of 'start1','start2' or 'cert', for different
starting values, and Model is one of
%s
if Model = 'all', all models and starting values will be run.
options:
--------
-m name of fitting method. One of:
leastsq, nelder, powell, lbfgsb, bfgs,
tnc, cobyla, slsqp, cg, newto-cg
leastsq (Levenberg-Marquardt) is the default
""" % modelnames
return usage
def test_NIST(self):
# Run all the NIST standard tests with leastsq
for model in Models.keys():
try:
NIST_runner(model)
except Exception:
print(model)
raise
def run_interactive():
usage = build_usage()
parser = OptionParser(usage=usage, prog="fit-NIST.py")
parser.add_option("-m",
"--method",
dest="method",
metavar='METH',
default='leastsq',
help="set method name, default = 'leastsq'")
(opts, args) = parser.parse_args()
dset = ''
start = 'start2'
if len(args) > 0:
dset = args[0]
if len(args) > 1:
start = args[1]
if dset.lower() == 'all':
tpass = 0
tfail = 0
failures = []
dsets = sorted(Models.keys())
for dset in dsets:
for start in ('start1', 'start2', 'cert'):
if NIST_Dataset(dset,
method=opts.method,
start=start,
plot=False,
verbose=True):
tpass += 1
else:
tfail += 1
failures.append(" %s (starting at '%s')" % (dset, start))
print('--------------------------------------')
print(' Fit Method: %s ' % opts.method)
print(' Final Results: %i pass, %i fail.' % (tpass, tfail))
print(' Tests Failed for:\n %s' % '\n '.join(failures))
print('--------------------------------------')
elif dset not in Models:
print(usage)
else:
return NIST_Dataset(dset,
method=opts.method,
start=start,
plot=False,
verbose=True)
def run_interactive():
usage = build_usage()
parser = OptionParser(usage=usage, prog="fit-NIST.py")
parser.add_option("-m", "--method", dest="method",
metavar='METH',
default='leastsq',
help="set method name, default = 'leastsq'")
(opts, args) = parser.parse_args()
dset = ''
start = 'start2'
if len(args) > 0:
dset = args[0]
if len(args) > 1:
start = args[1]
if dset.lower() == 'all':
tpass = 0
tfail = 0
failures = []
dsets = sorted(Models.keys())
for dset in dsets:
for start in ('start1', 'start2', 'cert'):
if NIST_Dataset(dset, method=opts.method, start=start,
plot=False, verbose=True):
tpass += 1
else:
tfail += 1
failures.append(" %s (starting at '%s')" % (dset, start))
print('--------------------------------------')
print(' Fit Method: %s ' % opts.method)
print(' Final Results: %i pass, %i fail.' % (tpass, tfail))
print(' Tests Failed for:\n %s' % '\n '.join(failures))
print('--------------------------------------')
elif dset not in Models:
print(usage)
else:
return NIST_Dataset(dset, method=opts.method,
start=start, plot=True, verbose=True)
"""
msg2 = """
That is, use
python fit_NIST.py Bennet5 start1
or go through all models and starting points with:
python fit_NIST.py all
"""
if __name__ == '__main__':
dset = 'Bennett5'
start = 'start2'
if len(sys.argv) < 2:
print(msg1)
out = ''
for d in sorted(Models.keys()):
out = out + ' %s ' % d
if len(out) > 55:
print( out)
out = ''
print(out)
print(msg2)
sys.exit()
if len(sys.argv) > 1:
dset = sys.argv[1]
if len(sys.argv) > 2:
start = sys.argv[2]
if dset.lower() == 'all':
tpass = 0
cval.append(NISTdata['cert_values'][count])
cerr.append(NISTdata['cert_stderr'][count])
pval1 = NISTdata[start][count]
start_param.append(pval1)
try:
out = curve_fit2(func, x, y, p0=start_param, full_output=True)
except RuntimeError as e:
print(e)
else:
popt = out[0]
nvarys = len(popt)
print('Optimized -- Certified')
for el in zip(popt, cval):
print( str(el[0]) + ' ' + str(el[1]))
errors = np.array(popt) - np.array(cval)
rel_errors = errors / np.array(cval)
print('Highest relative error: '),
print(np.max(rel_errors))
if __name__ == '__main__':
#Get names of dataset
datasets = sorted(Models.keys())
# Run test
for dataset in datasets:
start = 'start1' #start1 or start2...
test_curvefit(dataset, start)
