def fit_peaks(self, rx, his_id=None):
"""Fit gaussian peaks to current plot.
Returns list of lists:
[E, x0, dx, A, dA, s, Area]
where E is name of the peak, x0, A and s are fitted parameters
and d'something' is its uncertainity. Area is total calculated area.
"""
peaks = []
for p in self.peaks:
if rx[0] <= p.get('E') <= rx[1]:
peaks.append(p)
PF = PeakFitter(peaks, 'linear', '')
if his_id is not None:
data = self.hisfile.load_histogram(his_id)
if data[0] != 1:
print('{} is not a 1D histogram'.format(his_id))
return None
x_axis = data[1][rx[0]:rx[1]]
data_y = data[3][rx[0]:rx[1]]
else:
x_axis = self.current['x'][rx[0]:rx[1]]
data_y = self.current['y'][rx[0]:rx[1]]
data_dy = numpy.zeros(len(data_y))
for iy, y in enumerate(data_y):
if y > 0:
data_dy[iy] = math.sqrt(y)
else:
data_dy[iy] = 1
PF.fit(x_axis, data_y, data_dy)
print('#{:^7} {:^8} {:^8} {:^8} {:^8} {:^8} {:^8}'.format(
'Peak', 'x0', 'dx', 'A', 'dA', 's', 'Area'))
peak_data = []
for i, peak in enumerate(peaks):
if peak.get('ignore') == 'True':
continue
x0 = PF.params['x{}'.format(i)].value
dx = PF.params['x{}'.format(i)].stderr
A = PF.params['A{}'.format(i)].value
dA = PF.params['A{}'.format(i)].stderr
s = PF.params['s{}'.format(i)].value
Area = PF.find_area(x_axis, i)
print(
'{:>8} {:>8.2f} {:>8.2f} {:>8.1f} {:>8.1f} {:>8.3f} {:>8.1f}'.
format(peaks[i].get('E'), x0, dx, A, dA, s, Area))
peak_data.append([peaks[i].get('E'), x0, dx, A, dA, s, Area])
return peak_data
def fit_peaks(self, rx, his_id=None):
"""Fit gaussian peaks to current plot.
Returns list of lists:
[E, x0, dx, A, dA, s, Area]
where E is name of the peak, x0, A and s are fitted parameters
and d'something' is its uncertainity. Area is total calculated area.
"""
peaks = []
for p in self.peaks:
if rx[0] <= p.get('E') <= rx[1]:
peaks.append(p)
PF = PeakFitter(peaks, 'linear', '')
if his_id is not None:
data = self.hisfile.load_histogram(his_id)
if data[0] != 1:
print('{} is not a 1D histogram'.format(his_id))
return None
x_axis = data[1][rx[0]:rx[1]]
data_y = data[3][rx[0]:rx[1]]
else:
x_axis = self.current['x'][rx[0]:rx[1]]
data_y = self.current['y'][rx[0]:rx[1]]
data_dy = numpy.zeros(len(data_y))
for iy, y in enumerate(data_y):
if y > 0:
data_dy[iy] = math.sqrt(y)
else:
data_dy[iy] = 1
PF.fit(x_axis, data_y, data_dy)
print('#{:^7} {:^8} {:^8} {:^8} {:^8} {:^8} {:^8}'
.format('Peak', 'x0', 'dx', 'A', 'dA', 's', 'Area'))
peak_data = []
for i, peak in enumerate(peaks):
if peak.get('ignore') == 'True':
continue
x0 = PF.params['x{}'.format(i)].value
dx = PF.params['x{}'.format(i)].stderr
A = PF.params['A{}'.format(i)].value
dA = PF.params['A{}'.format(i)].stderr
s = PF.params['s{}'.format(i)].value
Area = PF.find_area(x_axis, i)
print('{:>8} {:>8.2f} {:>8.2f} {:>8.1f} {:>8.1f} {:>8.3f} {:>8.1f}'
.format(peaks[i].get('E'), x0, dx, A, dA, s, Area))
peak_data.append([peaks[i].get('E'), x0, dx, A, dA, s, Area])
return peak_data
def fit_peaks(self, his=None, rx=None, clear=True):
"""
Fit gaussian peaks to 1D plot. If his is not given the
current plot is used. If rx is not given, the current range is used
Returns list of lists:
[E, x0, dx, A, dA, s, Area]
where E is name of the peak, x0, A and s are fitted parameters
and d'something' is its uncertainity. Area is total calculated area.
"""
if rx is None:
rx = Experiment.xlim
if len(rx) != 2:
print('Please use x range in format rx=(min, max), where',
'min and max are integers.')
return None
# Deactivate all the plots
for p in Experiment.plots:
if p.active:
p.active = False
peaks = []
for p in self.peaks:
if rx[0] <= p.get('E') <= rx[1]:
peaks.append(p)
PF = PeakFitter(peaks, 'linear', '')
if his is not None:
if hasattr(his,'histogram'):
x_axis = his.histogram.x_axis
weights = his.histogram.weights
title = his.histogram.title
if isinstance(his, int):
if his > 0:
data = self.hisfile.load_histogram(his)
if data[0] != 1:
print('{} is not a 1D histogram'.format(his))
return None
x_axis = data[1]
weights = data[3]
title = self.hisfile.histograms[his]['title'].strip()
title = '{}:{}'.format(his, self._replace_latex_chars(title))
else:
try:
x_axis = Experiment.plots[his].histogram.x_axis
weights = Experiment.plots[his].histogram.weights
title = Experiment.plots[his].histogram.title
except IndexError:
x_axis=0
weights=0
title='Err'
print('There is no plot in the registry under the',
'number', his, 'use show_registry() to see',
'available plots')
return None
else:
x_axis = Experiment.plots[-1].histogram.x_axis
weights = Experiment.plots[-1].histogram.weights
title = Experiment.plots[-1].histogram.title
dweights = self._standard_errors_array(weights)
if clear:
self.clear()
histo_data = histogram.Histogram()
histo_data.x_axis = x_axis
histo_data.weights = weights
histo_data.errors = dweights
histo_data.title = title
plot_data = Plot(histo_data, 'histogram', True)
# The histogram data is plotted here so the fit function
# may be overlaid on in. However, the plot_data is appended
# to the registry after the fit functions so it is on top of the
# registry.
self.plotter.plot1d(plot_data, xlim=rx)
fit_result = PF.fit(x_axis[rx[0]:rx[1]], weights[rx[0]:rx[1]],
dweights[rx[0]:rx[1]])
histo_baseline = histogram.Histogram()
histo_baseline.x_axis = x_axis[rx[0]:rx[1]]
histo_baseline.weights = fit_result['baseline']
histo_baseline.title = 'Baseline'
plot_baseline = Plot(histo_baseline, 'function', True)
self.plotter.plot1d(plot_baseline, xlim=rx)
histo_peaks = histogram.Histogram()
histo_peaks.x_axis = fit_result['x_axis']
histo_peaks.weights = fit_result['fit']
histo_peaks.title = 'Fit'
plot_peaks = Plot(histo_peaks, 'function', True)
# Append all the plots to the registry, but
# keep original data at the end, so the next fit_peaks()
# call will use then again as default
Experiment.plots.append(plot_baseline)
Experiment.plots.append(plot_peaks)
Experiment.plots.append(plot_data)
# Plot the last one with the auto_scale if needed
if Experiment.ylim is None:
ylim = self._auto_scale_y()
else:
ylim = Experiment.ylim
self.plotter.plot1d(plot_peaks, xlim=rx, ylim=ylim)
print('#{:^8} {:^8} {:^8} {:^8} {:^8} {:^8} {:^8}'
.format('Peak', 'x0', 'dx', 'A', 'dA', 's', 'Area'))
peak_data = []
for i, peak in enumerate(peaks):
if peak.get('ignore') == 'True':
continue
x0 = PF.params['x{}'.format(i)].value
dx = PF.params['x{}'.format(i)].stderr
A = PF.params['A{}'.format(i)].value
dA = PF.params['A{}'.format(i)].stderr
s = PF.params['s{}'.format(i)].value
Area = PF.find_area(x_axis, i)
print('{:>8} {:>8.2f} {:>8.2f} {:>8.1f} {:>8.1f} {:>8.3f} {:>8.1f}'
.format(peaks[i].get('E'), x0, dx, A, dA, s, Area))
peak_data.append([peaks[i].get('E'), x0, dx, A, dA, s, Area])
return peak_data
def parse(self, spectrum, show, pause):
spectra_ids = spectrum.get('id')
id_list = []
if self.file_type == 'his':
for element in spectra_ids.split(','):
element = element.split('-')
if len(element) > 1:
new_elements = []
for i in range(int(element[0]), int(element[1]) + 1):
id_list.append(i)
else:
id_list.append(int(element[0]))
elif self.file_type == 'txt':
if spectra_ids != '':
raise GeneralError('Spectrum id not supported for txt files')
else:
id_list.append('')
peaks = spectrum.findall('peak')
x_min = int(spectrum.get('min'))
x_max = int(spectrum.get('max'))
smin = spectrum.get('smin')
smax = spectrum.get('smax')
for spectrum_id in id_list:
plot_name = '{}_{}'.format(self.base_name, spectrum_id)
PF = PeakFitter(peaks, spectrum.get('baseline'), plot_name)
if self.file_type == 'txt':
data_x = self.data_file[x_min:x_max, 0]
data_y = self.data_file[x_min:x_max, 1]
if self.data_file.shape[1] == 2:
data_dy = []
for y in data_y:
dy = numpy.sqrt(y) if y > 0 else 1.0
data_dy.append(dy)
data_dy = numpy.array(data_dy)
else:
data_dy = self.data_file[x_min:x_max, 2]
for iy, y in enumerate(data_dy):
if y <= 0:
data_dy[iy] = 1.0
elif self.file_type == 'his':
data = self.data_file.load_histogram(spectrum_id)
if data[0] != 1:
raise GeneralError('Only 1D histograms are supported')
data_x = data[1][x_min:x_max]
data_y = data[3][x_min:x_max]
data_dy = []
for y in data_y:
dy = numpy.sqrt(y) if y > 0 else 1.0
data_dy.append(dy)
data_dy = numpy.array(data_dy)
if smin is not None and smax is not None:
width = [float(smin), float(smax)]
else:
width = None
fit_result = PF.fit(data_x, data_y, data_dy, width=width)
if show == 'plot' or show == 'svg':
plt.clf()
plt.xlabel('Channel')
plt.ylabel('Counts')
plt.plot(data_x, data_y, linestyle='steps-mid')
plt.plot(data_x, fit_result['baseline'], linestyle='--')
plt.plot(fit_result['x_axis'], fit_result['fit'], linewidth=1.0)
if show == 'svg':
svg_name = 'fit_{0}_{1}_{2}'.format(plot_name,
int(data_x[0]), int(data_x[-1]))
svg_name = svg_name.replace('.', '').\
replace('/', '') + '.svg'
plt.savefig(svg_name)
else:
plt.show()
plt.draw()
time.sleep(pause)
elif show == 'quiet':
pass
for i, peak in enumerate(peaks):
if peak.get('ignore') == 'True':
continue
x0 = PF.params['x{}'.format(i)].value
dx = PF.params['x{}'.format(i)].stderr
A = PF.params['A{}'.format(i)].value
dA = PF.params['A{}'.format(i)].stderr
s = PF.params['s{}'.format(i)].value
E = peaks[i].get('E')
name = peaks[i].get('name')
if name is None:
name = ""
Area = PF.find_area(data_x, i)
print('{:>8} {:>8} {:>8.2f} {:>8.2f}'.\
format(name, E, x0, dx),
'{:>8.1f} {:>8.1f} {:>8.3f} {:>8.1f}'.\
format(A, dA, s, Area))
def parse(self, spectrum, show, pause):
spectra_ids = spectrum.get('id')
id_list = []
if self.file_type == 'his':
for element in spectra_ids.split(','):
element = element.split('-')
if len(element) > 1:
new_elements = []
for i in range(int(element[0]), int(element[1]) + 1):
id_list.append(i)
else:
id_list.append(int(element[0]))
elif self.file_type == 'txt':
if spectra_ids != '':
raise GeneralError('Spectrum id not supported for txt files')
else:
id_list.append('')
peaks = spectrum.findall('peak')
x_min = int(spectrum.get('min'))
x_max = int(spectrum.get('max'))
smin = spectrum.get('smin')
smax = spectrum.get('smax')
for spectrum_id in id_list:
plot_name = '{}_{}'.format(self.base_name, spectrum_id)
PF = PeakFitter(peaks, spectrum.get('baseline'), plot_name)
if self.file_type == 'txt':
data_x = self.data_file[x_min:x_max, 0]
data_y = self.data_file[x_min:x_max, 1]
data_dy = self.data_file[x_min:x_max, 2]
for iy, y in enumerate(data_dy):
if y <= 0:
data_dy[iy] = 1.0
elif self.file_type == 'his':
data = self.data_file.load_histogram(spectrum_id)
if data[0] != 1:
raise GeneralError('Only 1D histograms are supported')
data_x = data[1][x_min:x_max]
data_y = data[3][x_min:x_max]
data_dy = []
for y in data_y:
dy = numpy.sqrt(y) if y > 0 else 1.0
data_dy.append(dy)
data_dy = numpy.array(data_dy)
if smin is not None and smax is not None:
PF.restrict_width(float(smin), float(smax))
fit_result = PF.fit(data_x, data_y, data_dy, show, pause)
if show == 'plot' or show == 'svg':
plt.clf()
plt.xlabel('Channel')
plt.ylabel('Counts')
plt.plot(data_x, data_y, linestyle='steps-mid')
plt.plot(data_x, fit_result['baseline'], linestyle='--')
plt.plot(fit_result['x_axis'],
fit_result['fit'],
linewidth=1.0)
if show == 'svg':
svg_name = 'fit_{0}_{1}-{2}'.format(
self.plot_name, int(data_x[0]), int(data_x[-1]))
svg_name = svg_name.replace('.', '').\
replace('/', '') + '.svg'
plt.savefig(svg_name)
else:
plt.draw()
time.sleep(pause)
elif show == 'quiet':
pass
for i, peak in enumerate(peaks):
if peak.get('ignore') == 'True':
continue
x0 = PF.params['x{}'.format(i)].value
dx = PF.params['x{}'.format(i)].stderr
A = PF.params['A{}'.format(i)].value
dA = PF.params['A{}'.format(i)].stderr
s = PF.params['s{}'.format(i)].value
E = peaks[i].get('E')
name = peaks[i].get('name')
Area = PF.find_area(data_x, i)
print('{:>8} {:>8} {:>8.2f} {:>8.2f}'.\
format(name, E, x0, dx),
'{:>8.1f} {:>8.1f} {:>8.3f} {:>8.1f}'.\
format(A, dA, s, Area))
def fit_peaks(self, his=None, rx=None, clear=True):
"""
Fit gaussian peaks to 1D plot. If his is not given the
current plot is used. If rx is not given, the current range is used
Returns list of lists:
[E, x0, dx, A, dA, s, Area]
where E is name of the peak, x0, A and s are fitted parameters
and d'something' is its uncertainity. Area is total calculated area.
"""
if rx is None:
rx = Experiment.xlim
if len(rx) != 2:
print('Please use x range in format rx=(min, max), where',
'min and max are integers.')
return None
# Deactivate all the plots
for p in Experiment.plots:
if p.active:
p.active = False
peaks = []
for p in self.peaks:
if rx[0] <= p.get('E') <= rx[1]:
peaks.append(p)
PF = PeakFitter(peaks, 'linear', '')
if his is not None:
if isinstance(his, int):
if his > 0:
data = self.hisfile.load_histogram(his)
if data[0] != 1:
print('{} is not a 1D histogram'.format(his))
return None
x_axis = data[1]
weights = data[3]
title = self.hisfile.histograms[his]['title'].strip()
title = '{}:{}'.format(his,
self._replace_latex_chars(title))
else:
try:
x_axis = Experiment.plots[his].histogram.x_axis
weights = Experiment.plots[his].histogram.weights
title = Experiment.plots[his].histogram.title
except IndexError:
print('There is no plot in the registry under the',
'number', his, 'use show_registry() to see',
'available plots')
return None
else:
x_axis = Experiment.plots[-1].histogram.x_axis
weights = Experiment.plots[-1].histogram.weights
title = Experiment.plots[-1].histogram.title
dweights = self._standard_errors_array(weights)
if clear:
self.clear()
histo_data = histogram.Histogram()
histo_data.x_axis = x_axis
histo_data.weights = weights
histo_data.errors = dweights
histo_data.title = title
plot_data = Plot(histo_data, 'histogram', True)
# The histogram data is plotted here so the fit function
# may be overlaid on in. However, the plot_data is appended
# to the registry after the fit functions so it is on top of the
# registry.
self.plotter.plot1d(plot_data, xlim=rx)
fit_result = PF.fit(x_axis[rx[0]:rx[1]], weights[rx[0]:rx[1]],
dweights[rx[0]:rx[1]])
histo_baseline = histogram.Histogram()
histo_baseline.x_axis = x_axis[rx[0]:rx[1]]
histo_baseline.weights = fit_result['baseline']
histo_baseline.title = 'Baseline'
plot_baseline = Plot(histo_baseline, 'function', True)
self.plotter.plot1d(plot_baseline, xlim=rx)
histo_peaks = histogram.Histogram()
histo_peaks.x_axis = fit_result['x_axis']
histo_peaks.weights = fit_result['fit']
histo_peaks.title = 'Fit'
plot_peaks = Plot(histo_peaks, 'function', True)
# Append all the plots to the registry, but
# keep original data at the end, so the next fit_peaks()
# call will use then again as default
Experiment.plots.append(plot_baseline)
Experiment.plots.append(plot_peaks)
Experiment.plots.append(plot_data)
# Plot the last one with the auto_scale if needed
if Experiment.ylim is None:
ylim = self._auto_scale_y()
else:
ylim = Experiment.ylim
self.plotter.plot1d(plot_peaks, xlim=rx, ylim=ylim)
print('#{:^8} {:^8} {:^8} {:^8} {:^8} {:^8} {:^8}'
.format('Peak', 'x0', 'dx', 'A', 'dA', 's', 'Area'))
peak_data = []
for i, peak in enumerate(peaks):
if peak.get('ignore') == 'True':
continue
x0 = PF.params['x{}'.format(i)].value
dx = PF.params['x{}'.format(i)].stderr
A = PF.params['A{}'.format(i)].value
dA = PF.params['A{}'.format(i)].stderr
s = PF.params['s{}'.format(i)].value
Area = PF.find_area(x_axis, i)
print('{:>8} {:>8.2f} {:>8.2f} {:>8.1f} {:>8.1f} {:>8.3f} {:>8.1f}'
.format(peaks[i].get('E'), x0, dx, A, dA, s, Area))
peak_data.append([peaks[i].get('E'), x0, dx, A, dA, s, Area])
return peak_data
