def NIST_Dataset(DataSet,
method='leastsq',
start='start2',
plot=False,
verbose=False):
NISTdata = ReadNistData(DataSet)
resid, npar, dimx = Models[DataSet]
y = NISTdata['y']
x = NISTdata['x']
params = Parameters()
for i in range(npar):
pname = 'b%i' % (i + 1)
cval = NISTdata['cert_values'][i]
cerr = NISTdata['cert_stderr'][i]
pval1 = NISTdata[start][i]
params.add(pname, value=pval1)
myfit = minimize(resid, params, method=method, args=(x, ), kws={'y': y})
digs, buff = Compare_NIST_Results(DataSet, myfit, myfit.params, NISTdata)
if verbose:
print(buff)
return digs > 1
def NIST_Test(DataSet, start='start2', plot=True):
NISTdata = ReadNistData(DataSet)
resid, npar, dimx = Models[DataSet]
y = NISTdata['y']
x = NISTdata['x']
vals = []
for i in range(npar):
pval1 = NISTdata[start][i]
vals.append(pval1)
maxfev = 2500 * (npar + 1)
factor = 100
xtol = 1.e-14
ftol = 1.e-14
epsfcn = 1.e-13
gtol = 1.e-14
diag = None
print(" Fit with: ", factor, xtol, ftol, gtol, epsfcn, diag)
_best, out, ier = _minpack._lmdif(resid, vals, (x, y), 1,
ftol, xtol, gtol,
maxfev, epsfcn, factor, diag)
digs = Compare_NIST_Results(DataSet, _best, NISTdata)
if plot and HASPYLAB:
fit = -resid(_best, x, )
plt.plot(x, y, 'ro')
plt.plot(x, fit, 'k+-')
plt.show()
return digs > 2
def NIST_Test(DataSet, method='leastsq', start='start2', plot=True):
NISTdata = ReadNistData(DataSet)
resid, npar, dimx = Models[DataSet]
y = NISTdata['y']
x = NISTdata['x']
params = Parameters()
for i in range(npar):
pname = 'b%i' % (i + 1)
cval = NISTdata['cert_values'][i]
cerr = NISTdata['cert_stderr'][i]
pval1 = NISTdata[start][i]
params.add(pname, value=pval1)
myfit = minimize(resid, params, method=method, args=(x, ), kws={'y': y})
digs = Compare_NIST_Results(DataSet, myfit, params, NISTdata)
if plot and HASPYLAB:
fit = -resid(
params,
x,
)
pylab.plot(x, y, 'ro')
pylab.plot(x, fit, 'k+-')
pylab.show()
return digs > 2
def NIST_Dataset(DataSet,
method='leastsq',
start='start2',
plot=False,
verbose=False):
NISTdata = ReadNistData(DataSet)
resid, npar, dimx = Models[DataSet]
y = NISTdata['y']
x = NISTdata['x']
params = Parameters()
for i in range(npar):
pname = 'b%i' % (i + 1)
pval1 = NISTdata[start][i]
params.add(pname, value=pval1)
try:
myfit = minimize(resid,
params,
method=method,
args=(x, ),
kws={'y': y},
nan_policy='raise')
except ValueError:
if verbose:
print("Fit failed... nans?")
return False
digs, buff = Compare_NIST_Results(DataSet, myfit, myfit.params, NISTdata)
if verbose:
print(buff)
return digs > 2
def NIST_Test(DataSet, start='start2', plot=True):
NISTdata = ReadNistData(DataSet)
resid, npar, dimx = Models[DataSet]
y = NISTdata['y']
x = NISTdata['x']
vals = []
for i in range(npar):
pname = 'b%i' % (i + 1)
cval = NISTdata['cert_values'][i]
cerr = NISTdata['cert_stderr'][i]
pval1 = NISTdata[start][i]
vals.append(pval1)
maxfev = 2500 * (npar + 1)
factor = 100
xtol = 1.e-14
ftol = 1.e-14
epsfcn = 1.e-13
gtol = 1.e-14
diag = None
print(" Fit with: ", factor, xtol, ftol, gtol, epsfcn, diag)
# _best, out, ier = _minpack._lmdif(resid, vals, (x, y), 1,
# ftol, xtol, gtol,
# maxfev, epsfcn, factor, diag)
from scipy.optimize import least_squares
res = least_squares(resid,
vals,
args=(x, y),
ftol=ftol,
xtol=xtol,
gtol=gtol,
diff_step=np.sqrt(epsfcn),
scaling=1.,
method='dogbox')
_best = res.x
digs = Compare_NIST_Results(DataSet, _best, NISTdata)
if plot and HASPYLAB:
fit = -resid(
_best,
x,
)
pylab.plot(x, y, 'ro')
pylab.plot(x, fit, 'k+-')
pylab.show()
return digs > 2
def RunNIST_Model(model):
dset = ReadNistData(model)
func, npar, dimx = Models[model]
rss = (func(dset['cert_values'], x=dset['x'], y=dset['y'])**2).sum()
tiny_rss = 1.e-16
print(rss, dset['sum_squares'], tiny_rss)
if dset['sum_squares'] < tiny_rss:
assert rss < tiny_rss
else:
assert_allclose(rss, dset['sum_squares'])
out1 = NIST_Dataset(model, start='start1', plot=False, verbose=False)
out2 = NIST_Dataset(model, start='start2', plot=False, verbose=False)
assert (out1 or out2)
return out1 or out2
def NIST_Test(DataSet, start='start2', plot=True):
NISTdata = ReadNistData(DataSet)
resid, npar, dimx = Models[DataSet]
y = NISTdata['y']
x = NISTdata['x']
params = []
param_names = []
for i in range(npar):
pname = 'b%i' % (i+1)
cval = NISTdata['cert_values'][i]
cerr = NISTdata['cert_stderr'][i]
pval1 = NISTdata[start][i]
params.append(pval1)
param_names.append(pname)
# myfit = Minimizer(resid, params, fcn_args=(x,), fcn_kws={'y':y},
# scale_covar=True)
#
print 'lsout ', params
lsout = leastsq(resid, params, args=(x, y), full_output=True)
print 'lsout ', lsout
print params , len(x), len(y)
digs = Compare_NIST_Results(DataSet, params, NISTdata)
if plot and HASPYLAB:
fit = -resid(params, x, )
pylab.plot(x, y, 'r+-')
pylab.plot(x, fit, 'ko--')
pylab.show()
return digs > 2
def NIST_Test(DataSet, start='start2', plot=True):
NISTdata = ReadNistData(DataSet)
resid, npar, dimx = Models[DataSet]
y = NISTdata['y']
x = NISTdata['x']
params = Parameters()
for i in range(npar):
pname = 'b%i' % (i + 1)
cval = NISTdata['cert_values'][i]
cerr = NISTdata['cert_stderr'][i]
pval1 = NISTdata[start][i]
params.add(pname, value=pval1)
myfit = Minimizer(resid,
params,
fcn_args=(x, ),
fcn_kws={'y': y},
scale_covar=True)
myfit.prepare_fit()
myfit.leastsq()
digs = Compare_NIST_Results(DataSet, myfit, params, NISTdata)
if plot and HASPYLAB:
fit = -resid(
params,
x,
)
pylab.plot(x, y, 'r+')
pylab.plot(x, fit, 'ko--')
pylab.show()
return digs > 2
