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 __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 plot_estimated_dist(output_dir, result, source, pset_id=None):
data, fdata, bins = source.get_source_data()
xtr = lambda x: transform_pi_deg(x, neg_shift=source.neg_shift)
rbins = transform_2pi(bins) - np.pi * (source.neg_shift == True)
fig = result.model.plot_dist(result.full_params, xtransform=xtr, bins=rbins,
data=fdata)
fig.axes[0].set_title('estimated distribution')
fig.axes[0].set_xlabel('angle', fontsize='large')
fig.axes[0].set_ylabel('probability density function', fontsize='large')
if pset_id is None:
name = source.current.dir_base + '-fit.png'
else:
name = source.current.dir_base + '-fit-%d.png' % pset_id
figname = os.path.join(output_dir, name)
plt.tight_layout(pad=0.5)
fig.savefig(figname)
plt.close(fig)
#options.show = True
#options.params = '0, 5'
start_params = np.zeros(options.n_components * 3 - 1)
n2 = 2 * options.n_components
if options.params is None:
# Zeros for mu, twos for kappa.
start_params[:n2:2] = 2.0 + np.random.uniform(-0.1, 0.1,
options.n_components)
start_params[:n2:2] = 5.0 + np.random.uniform(-0.5, 0.5,
options.n_components)
else:
aux = np.array([float(ii) for ii in options.params.split(',')])
start_params[:n2:2] = aux[1::2] # kappa.
start_params[1:n2:2] = tr.transform_2pi(aux[0::2]) # mu.
start_params[n2:] = np.random.uniform(-0.1, 0.1, options.n_components - 1)
print 'starting parameters:', start_params
io.ensure_path(output_dir)
neg_shift = True
log_name = os.path.join(output_dir, 'log.csv')
log = CSVLog(log_name, options.n_components)
log.write_header()
get_data = io.locate_files(pattern,
data_dir,
