def test_ci_report_with_offset(confidence_interval):
"""Verify output of CI report when using with_offset."""
report_split = ci_report(confidence_interval,
with_offset=True).split('\n') # default
amp_values = [abs(float(val)) for val in report_split[1].split()[2:]]
assert np.all(np.less(np.delete(amp_values, 3), 0.2))
report_split = ci_report(confidence_interval,
with_offset=False).split('\n')
amp_values = [float(val) for val in report_split[1].split()[2:]]
assert np.all(np.greater(amp_values, 13))
def test_ci_report(confidence_interval):
"""Verify that the CI report is created when using ci_report."""
report = ci_report(confidence_interval)
assert len(report) > 250
for par in confidence_interval.keys():
assert par in report
for interval in ['99.73', '95.45', '68.27', '_BEST_']:
assert interval in report
def test_ci_report():
"""test confidence interval report"""
def residual(pars, x, data=None):
argu = (x * pars['decay'])**2
shift = pars['shift']
if abs(shift) > np.pi / 2:
shift = shift - np.sign(shift) * np.pi
model = pars['amp'] * np.sin(shift +
x / pars['period']) * np.exp(-argu)
if data is None:
return model
return model - data
p_true = Parameters()
p_true.add('amp', value=14.0)
p_true.add('period', value=5.33)
p_true.add('shift', value=0.123)
p_true.add('decay', value=0.010)
n = 2500
xmin = 0.
xmax = 250.0
x = np.linspace(xmin, xmax, n)
data = residual(p_true, x) + np.random.normal(scale=0.7215, size=n)
fit_params = Parameters()
fit_params.add('amp', value=13.0)
fit_params.add('period', value=2)
fit_params.add('shift', value=0.0)
fit_params.add('decay', value=0.02)
mini = Minimizer(residual,
fit_params,
fcn_args=(x, ),
fcn_kws={'data': data})
out = mini.leastsq()
report = fit_report(out)
assert (len(report) > 500)
ci, tr = conf_interval(mini, out, trace=True)
report = ci_report(ci)
assert (len(report) > 250)
def test_ci_report():
"""test confidence interval report"""
def residual(pars, x, data=None):
argu = (x*pars['decay'])**2
shift = pars['shift']
if abs(shift) > np.pi/2:
shift = shift - np.sign(shift)*np.pi
model = pars['amp']*np.sin(shift + x/pars['period']) * np.exp(-argu)
if data is None:
return model
return model - data
p_true = Parameters()
p_true.add('amp', value=14.0)
p_true.add('period', value=5.33)
p_true.add('shift', value=0.123)
p_true.add('decay', value=0.010)
n = 2500
xmin = 0.
xmax = 250.0
x = np.linspace(xmin, xmax, n)
data = residual(p_true, x) + np.random.normal(scale=0.7215, size=n)
fit_params = Parameters()
fit_params.add('amp', value=13.0)
fit_params.add('period', value=2)
fit_params.add('shift', value=0.0)
fit_params.add('decay', value=0.02)
mini = Minimizer(residual, fit_params, fcn_args=(x,),
fcn_kws={'data': data})
out = mini.leastsq()
report = fit_report(out)
assert(len(report) > 500)
ci, tr = conf_interval(mini, out, trace=True)
report = ci_report(ci)
assert(len(report) > 250)
def test_ci_report_with_ndigits(confidence_interval, ndigits):
"""Verify output of CI report when specifying ndigits."""
report_split = ci_report(confidence_interval, ndigits=ndigits).split('\n')
period_values = list(report_split[2].split()[2:])
length = [len(val.split('.')[-1]) for val in period_values]
assert np.all(np.equal(length, ndigits))
def confidence_report(conf_vals, **kws):
"""return a formatted report of confidence intervals calcualted
by confidence_intervals
"""
return ci_report(conf_vals)
def confidence_report(conf_vals, **kws):
"""return a formatted report of confidence intervals calcualted
by confidence_intervals"""
return ci_report(conf_vals)
