def __call__(self):
for filenames in self.get_filenames:
dir_base, base_names = io.split_dir_base(filenames)
print '======================================================'
print 'directory base:', dir_base
data = io.load_data(filenames)
if self.n_merge_bins is not None:
data = tr.merge_bins(data, self.n_merge_bins)
print 'angles range:', data[:, 0].min(), data[:, 0].max()
data = tr.fix_increasing(tr.fix_range(tr.transform_2pi(data)))
print 'transformed angles range:', data[0, 0], data[-1, 0]
print 'data range:', data[:, 1].min(), data[:, 1].max()
# Simulate the "random process" the histogram was done from.
counts = tr.get_counts_from_lengths(data[:, 1])
fdata = tr.spread_by_counts(data[:, 0], counts,
trivial=self.spread_data == False)
print 'simulated counts range:', counts.min(), counts.max()
ddata = np.sort(tr.transform_pi_deg(data[:, 0],
neg_shift=self.neg_shift))
dd = ddata[1] - ddata[0]
all_bins = np.r_[ddata - 1e-8, ddata[-1] + dd]
bins = all_bins[::self.plot_bins_step]
self.current = Struct(filenames=filenames, dir_base=dir_base,
base_names=base_names)
self.source_data = Struct(counts=counts,
data=data, fdata=fdata, bins=bins)
yield self.source_data
def get_pars(pset, area_angles):
"""
Get starting parameters given the area angles of two systems.
"""
x0, xm, x1, area1, area2 = area_angles
mu0 = 0.5 * (x0 + xm)
mu1 = 0.5 * (xm + x1)
print mu0, mu1
mu0, mu1 = tr.fix_range(tr.transform_2pi([mu0, mu1]))
pars = np.r_[[2.0, mu0, 2.0, mu1]
+ [0.1, 0.0] * (pset.n_components - 2)]
return pars
def plot_histogram_comparison(output_dir, result, source, pset_id=None):
data, fdata, bins = source.get_source_data()
rvs, sizes = result.model.rvs_mix(result.full_params, size=fdata.shape[0],
ret_sizes=True)
rvs = fix_range(rvs)
fig = plot_rvs_comparison(fdata, rvs, sizes, bins,
neg_shift=source.neg_shift)
if pset_id is None:
name = source.current.dir_base + '-cmp.png'
else:
name = source.current.dir_base + '-cmp-%d.png' % pset_id
figname = os.path.join(output_dir, name)
plt.tight_layout(pad=0.5)
fig.savefig(figname)
plt.close(fig)
def __call__(self):
for filenames in self.get_filenames:
dir_base, base_names = io.split_dir_base(filenames)
print '======================================================'
print 'directory base:', dir_base
data = io.load_data(filenames)
if self.n_merge_bins is not None:
data = tr.merge_bins(data, self.n_merge_bins)
print 'angles range:', data[:, 0].min(), data[:, 0].max()
data = tr.fix_increasing(tr.fix_range(tr.transform_2pi(data)))
print 'transformed angles range:', data[0, 0], data[-1, 0]
print 'data range:', data[:, 1].min(), data[:, 1].max()
# Simulate the "random process" the histogram was done from.
counts = tr.get_counts_from_lengths(data[:, 1])
fdata = tr.spread_by_counts(data[:, 0],
counts,
trivial=self.spread_data == False)
print 'simulated counts range:', counts.min(), counts.max()
ddata = np.sort(
tr.transform_pi_deg(data[:, 0], neg_shift=self.neg_shift))
dd = ddata[1] - ddata[0]
all_bins = np.r_[ddata - 1e-8, ddata[-1] + dd]
bins = all_bins[::self.plot_bins_step]
self.current = Struct(filenames=filenames,
dir_base=dir_base,
base_names=base_names)
self.source_data = Struct(counts=counts,
data=data,
fdata=fdata,
bins=bins)
yield self.source_data
def log_results(log, result, source):
"""
Log the fitting results.
Notes
-----
The resulting mixture parameters are stored into a 2d array with rows
[location in degrees (mu), shape (kappa), probability].
"""
sparams = result.model.get_summary_params(result.full_params)[:, [1, 0, 2]]
sparams[:, 0] = tr.transform_pi_deg(tr.fix_range(sparams[:, 0]),
neg_shift=source.neg_shift)
converged = result.mle_retvals['converged']
fit_criteria = [-result.llf, result.aic, result.bic]
print 'llf / nobs:', fit_criteria[0] / result.model.endog.shape[0]
chisquare = result.gof_chisquare()
# Chisquare test with effect size.
alpha = 0.05 # Significance level.
data = source.source_data.data
n_obs = data[:, 1].sum()
rad_diff = data[1, 0] - data[0, 0]
pdf = result.model.pdf_mix(result.full_params, data[:, 0])
probs = pdf * rad_diff * n_obs
effect_size = gof.chisquare_effectsize(data[:, 1], probs)
chi2 = gof.chisquare(data[:, 1], probs, value=effect_size)
power = gof.chisquare_power(effect_size, n_obs,
data.shape[0], alpha=alpha)
chisquare_all = list(chisquare) + [n_obs, effect_size] \
+ list(chi2) + [power]
log.write_row(source.current.dir_base, source.current.base_names,
chisquare_all, sparams, converged, fit_criteria)
def log_results(log, result, source):
"""
Log the fitting results.
Notes
-----
The resulting mixture parameters are stored into a 2d array with rows
[location in degrees (mu), shape (kappa), probability].
"""
sparams = result.model.get_summary_params(result.full_params)[:, [1, 0, 2]]
sparams[:, 0] = tr.transform_pi_deg(tr.fix_range(sparams[:, 0]),
neg_shift=source.neg_shift)
converged = result.mle_retvals['converged']
fit_criteria = [-result.llf, result.aic, result.bic]
print 'llf / nobs:', fit_criteria[0] / result.model.endog.shape[0]
chisquare = result.gof_chisquare()
# Chisquare test with effect size.
alpha = 0.05 # Significance level.
data = source.source_data.data
n_obs = data[:, 1].sum()
rad_diff = data[1, 0] - data[0, 0]
pdf = result.model.pdf_mix(result.full_params, data[:, 0])
probs = pdf * rad_diff * n_obs
effect_size = gof.chisquare_effectsize(data[:, 1], probs)
chi2 = gof.chisquare(data[:, 1], probs, value=effect_size)
power = gof.chisquare_power(effect_size, n_obs, data.shape[0], alpha=alpha)
chisquare_all = list(chisquare) + [n_obs, effect_size] \
+ list(chi2) + [power]
log.write_row(source.current.dir_base, source.current.base_names,
chisquare_all, sparams, converged, fit_criteria)
fig = res.model.plot_dist(res.params, xtransform=xtr, n_bins=180)
fig.axes[0].set_title('Estimated distribution')
figname = os.path.join(output_dir,
dir_base + '-fit-%d.png' % options.n_components)
fig.savefig(figname)
try:
rvs, sizes = res.model.rvs_mix(res.params,
size=fdata.shape[0],
ret_sizes=True)
except ValueError:
pass
else:
rvs = tr.fix_range(rvs)
figname = os.path.join(
output_dir, dir_base + '-cmp-%d.png' % options.n_components)
fig = pl.plot_rvs_comparison(fdata,
rvs,
sizes,
bins,
neg_shift=neg_shift)
fig.savefig(figname)
sparams = res.model.get_summary_params(res.params)[:, [1, 0, 2]]
sparams[:, 0] = tr.transform_pi_deg(tr.fix_range(sparams[:, 0]),
neg_shift=neg_shift)
converged = res.mle_retvals['converged']
print 'converged:', converged