def run(self, data, output_dir, **run_args):
results = {}
if 'q0' not in run_args or run_args['q0'] is None:
# Determine q based on circular_average_q2I_fit
head, tail = os.path.split(output_dir)
result_file = os.path.join(head, 'results',
'{}.xml'.format(data.name))
prev_results = tools.get_result_xml(result_file,
'circular_average_q2I')
run_args['q0'] = prev_results['fit_peaks_x_center1']
# Extract circular average
#line = data.linecut_angle(q0=run_args['q0'], dq=run_args['dq'])
line = data.linecut_angle(**run_args)
if 'show_region' in run_args and run_args['show_region']:
data.plot(show=True)
# Basic output
outfile = self.get_outfile(data.name, output_dir, ext='.dat')
line.save_data(outfile)
# Fine-tune data
line.x = line.x[1:]
line.y = line.y[1:]
line.smooth(2.0)
# Fit
class DataLines_current(DataLines):
def _plot_extra(self, **plot_args):
v_spacing = 0.1
xi, xf, yi, yf = self.ax.axis()
yp = yf
s = '$\eta = {:.2f}$'.format(self.results['fit_eta_eta'])
self.ax.text(xi,
yp,
s,
size=30,
color='b',
verticalalignment='top',
horizontalalignment='left')
yp -= (yf - yi) * v_spacing
s = '$S_{{\eta}} = {:.3f}$'.format(
self.results['fit_library_eta_S'])
self.ax.text(xi,
yp,
s,
size=30,
color='b',
verticalalignment='top',
horizontalalignment='left')
yp = yf
s = '$m = {:.2f}$'.format(self.results['fit_MaierSaupe_m'])
self.ax.text(xf,
yp,
s,
size=30,
color='purple',
verticalalignment='top',
horizontalalignment='right')
yp -= (yf - yi) * v_spacing
s = '$S_{{m}} = {:.3f}$'.format(
self.results['fit_library_MaierSaupe_S'])
self.ax.text(xf,
yp,
s,
size=30,
color='purple',
verticalalignment='top',
horizontalalignment='right')
self.ax.set_xticks([-180, -90, 0, +90, +180])
lines = DataLines_current(
[]
) # Not sure why the lines=[] argument needs to be specified here...
lines.add_line(line)
lines.copy_labels(line)
lines.results = {}
color_list = [
'b',
'purple',
'r',
'green',
'orange',
]
for i, fit_name in enumerate(['fit_eta', 'fit_MaierSaupe']):
lm_result, fit_line, fit_line_e = getattr(self,
fit_name)(line,
**run_args)
fit_line_e.plot_args['color'] = color_list[i % len(color_list)]
lines.add_line(fit_line_e)
#prefactor_total = 0
for param_name, param in lm_result.params.items():
results['{}_{}'.format(fit_name, param_name)] = {
'value': param.value,
'error': param.stderr,
}
lines.results['{}_{}'.format(fit_name,
param_name)] = param.value
#if 'prefactor' in param_name:
#prefactor_total += np.abs(param.value)
#results['{}_prefactor_total'.format(fit_name)] = prefactor_total
results['{}_chi_squared'.format(
fit_name)] = lm_result.chisqr / lm_result.nfree
# Special fit
fit_result, fit_line = self.fit_library(line,
library='eta',
**run_args)
fit_line.plot_args['color'] = 'blue'
fit_line.plot_args['linewidth'] = 1.0
lines.results['fit_library_eta_S'] = fit_result['S']
lines.add_line(fit_line)
results.update(self.prepend_keys(fit_result, 'fit_library_eta_'))
fit_result, fit_line = self.fit_library(line,
library='MaierSaupe',
**run_args)
fit_line.plot_args['color'] = 'purple'
fit_line.plot_args['linewidth'] = 1.0
lines.results['fit_library_MaierSaupe_S'] = fit_result['S']
results.update(self.prepend_keys(fit_result,
'fit_library_MaierSaupe_'))
lines.add_line(fit_line)
# Output
if run_args['verbosity'] >= 1:
outfile = self.get_outfile(data.name, output_dir)
lines.lines.reverse()
lines.plot(save=outfile, **run_args)
if run_args['verbosity'] >= 4:
outfile = self.get_outfile(data.name, output_dir, ext='_polar.png')
line.plot_polar(save=outfile, **run_args)
return results
def run(self, data, output_dir, **run_args):
results = {}
if 'q0' not in run_args or run_args['q0'] is None:
# Determine q based on circular_average_q2I_fit
head, tail = os.path.split(output_dir)
result_file = os.path.join(head, 'results',
'{}.xml'.format(data.name))
prev_results = tools.get_result_xml(result_file,
'circular_average_q2I')
run_args['q0'] = prev_results['fit_peaks_x_center1']
# Extract circular average
#line = data.linecut_angle(q0=run_args['q0'], dq=run_args['dq'])
line = data.linecut_angle(**run_args)
# Remove the data near chi=0 since this is artificially high (specular ridge)
#line.kill_x(0, 3)
if 'show_region' in run_args and run_args['show_region']:
data.plot(show=True)
# Basic output
outfile = self.get_outfile(data.name, output_dir, ext='.dat')
#line.save_data(outfile)
# Fine-tune data
line.x = line.x[1:]
line.y = line.y[1:]
line.smooth(2.0)
do_max = True
do_FWHM = False
do_fits = True
# Fit
class DataLines_current(DataLines):
def _plot_extra(self, **plot_args):
v_spacing = 0.1
xi, xf, yi, yf = self.ax.axis()
#yi = min( self.lines[-1].y )*0.5
#yf = max( self.lines[-1].y )*1.5
#self.ax.axis( [xi, xf, yi, yf] )
self.ax.axvline(self.angle, color='r', linewidth=1.0)
s = '$\chi = {:.1f} ^{{\circ}} $'.format(self.angle)
self.ax.text(self.angle,
yf,
s,
size=30,
color='r',
verticalalignment='top',
horizontalalignment='left')
s = '$f = {:.3f}$'.format(self.orientation_factor)
self.ax.text(self.angle,
yi,
s,
size=30,
color='r',
verticalalignment='bottom',
horizontalalignment='left')
if do_max:
line = self.lines[-1]
xpeak, ypeak = line.target_y(np.max(line.y))
self.ax.plot([xpeak], [ypeak],
'o-',
color='b',
markersize=12,
linewidth=1.0)
self.ax.axvline(xpeak, color='b', linewidth=1.0)
s = '$\chi = {:.1f} ^{{\circ}} $'.format(xpeak)
self.ax.text(xpeak,
ypeak,
s,
size=30,
color='b',
verticalalignment='bottom',
horizontalalignment='left')
if do_fits:
yp = yf
s = '$\eta = {:.2f}$'.format(self.results['fit_eta_eta'])
self.ax.text(xi,
yp,
s,
size=30,
color='b',
verticalalignment='top',
horizontalalignment='left')
yp = yf
s = '$m = {:.2f}$'.format(self.results['fit_MaierSaupe_m'])
self.ax.text(xf,
yp,
s,
size=30,
color='purple',
verticalalignment='top',
horizontalalignment='right')
if do_FWHM:
# FWHM
line = self.lines[0]
xt, yt = line.target_y(max(line.y))
hm = yt * 0.5
# Right (+) side
line_temp = line.sub_range(xt, +60)
xtr, ytr = line_temp.target_y(hm)
# Left (-) side
line_temp = line.sub_range(-60, xt)
xtl, ytl = line_temp.target_y(hm)
self.ax.plot([xtl, xtr], [ytl, ytr],
'o-',
color='b',
markersize=8,
linewidth=1.0)
s = r'$\mathrm{{FWHM}} = {:.1f}^{{\circ}}$'.format(
abs(xtr - xtl))
self.ax.text(xtr,
ytr,
s,
color='b',
size=20,
verticalalignment='center',
horizontalalignment='left')
#self.ax.set_xticks([-180, -90, 0, +90, +180])
lines = DataLines_current(
[]
) # Not sure why the lines=[] argument needs to be specified here...
lines.add_line(line)
lines.copy_labels(line)
lines.results = {}
if do_fits:
color_list = [
'b',
'purple',
'r',
'green',
'orange',
]
for i, fit_name in enumerate(
['fit_eta', 'fit_MaierSaupe', 'fit_eta_span']):
lm_result, fit_line, fit_line_e = getattr(self,
fit_name)(line,
**run_args)
fit_line.plot_args['color'] = color_list[i % len(color_list)]
fit_line_e.plot_args['color'] = color_list[i % len(color_list)]
lines.add_line(fit_line_e)
if fit_name == 'fit_eta_span':
lines.add_line(fit_line)
#prefactor_total = 0
for param_name, param in lm_result.params.items():
results['{}_{}'.format(fit_name, param_name)] = {
'value': param.value,
'error': param.stderr,
}
lines.results['{}_{}'.format(fit_name,
param_name)] = param.value
#if 'prefactor' in param_name:
#prefactor_total += np.abs(param.value)
#results['{}_prefactor_total'.format(fit_name)] = prefactor_total
results['{}_chi_squared'.format(
fit_name)] = lm_result.chisqr / lm_result.nfree
angle = results['fit_eta_span_x_center']['value']
if angle < 0:
angle += 180
# angle is now 0 to +190
# Convert to the 'in between the peaks' convention
angle -= 90
# angle is now -90 to +90
lines.angle = angle
qz_unit = np.cos(np.radians(angle))
qx_unit = np.sin(np.radians(angle))
orientation_factor = 2 * np.square(qz_unit) - 1
lines.orientation_factor = orientation_factor
results['orientation_angle'] = angle
results['orientation_factor'] = orientation_factor
# Output
if run_args['verbosity'] >= 1:
outfile = self.get_outfile(data.name, output_dir)
lines.lines.reverse()
lines.plot(save=outfile, **run_args)
if run_args['verbosity'] >= 4:
outfile = self.get_outfile(data.name, output_dir, ext='_polar.png')
line.plot_polar(save=outfile, **run_args)
return results