def calculate_total_errors(variable, is_number_list, min_errs, max_errs, values):
for i, entry in enumerate(variable['values']):
if not is_number_list[i]: continue # skip non-numeric y values
if 'value' in entry:
values.append(entry['value'])
if 'errors' in entry:
errors_min = 0.0
errors_max = 0.0
for error in entry['errors']:
if 'asymerror' in error:
err_minus = error_value_processor(entry['value'], error['asymerror']['minus'])
err_plus = error_value_processor(entry['value'], error['asymerror']['plus'])
errors_min += pow(min(err_plus, err_minus, 0.0), 2)
errors_max += pow(max(err_plus, err_minus, 0.0), 2)
elif 'symerror' in error:
try:
err = error_value_processor(entry['value'], error['symerror'])
errors_min += pow(err, 2)
errors_max += pow(err, 2)
except TypeError:
print log.error('TypeError encountered when parsing {0}'.format(error['symerror']))
min_errs.append(sqrt(errors_min))
max_errs.append(sqrt(errors_max))
elif 'low' in entry and 'high' in entry:
min_errs.append(entry['value'] - entry['low'])
max_errs.append(entry['high'] - entry['value'])
else:
min_errs.append(0.0)
max_errs.append(0.0)
else:
middle_val = (entry['high'] - entry['low']) * 0.5 + entry['low']
values.append(middle_val)
min_errs.append(middle_val - entry['low'])
max_errs.append(entry['high'] - middle_val)
def calculate_total_errors(variable,
is_number_list,
min_errs,
max_errs,
values,
err_breakdown={}):
for i, entry in enumerate(variable['values']):
if not is_number_list[i]: continue # skip non-numeric y values
if 'value' in entry:
values.append(entry['value'])
if 'errors' in entry:
errors_min = 0.0
errors_max = 0.0
err_breakdown[i] = {}
# process the error labels to ensure uniqueness
ArrayWriter.process_error_labels(entry)
for error in entry['errors']:
label = error.get('label', 'error')
err_breakdown[i][label] = {}
if 'asymerror' in error:
err_minus = error_value_processor(
entry['value'], error['asymerror']['minus'])
err_plus = error_value_processor(
entry['value'], error['asymerror']['plus'])
errors_min += pow(min(err_plus, err_minus, 0.0), 2)
errors_max += pow(max(err_plus, err_minus, 0.0), 2)
err_breakdown[i][label][
'up'] = err_plus # want to maintain directionality of errors
err_breakdown[i][label][
'dn'] = err_minus # want to maintain directionality of errors
elif 'symerror' in error:
try:
err = error_value_processor(
entry['value'], error['symerror'])
errors_min += pow(err, 2)
errors_max += pow(err, 2)
err_breakdown[i][label]['up'] = err
err_breakdown[i][label]['dn'] = -err
except TypeError:
print log.error(
'TypeError encountered when parsing {0}'.
format(error['symerror']))
min_errs.append(sqrt(errors_min))
max_errs.append(sqrt(errors_max))
elif 'low' in entry and 'high' in entry:
min_errs.append(entry['value'] - entry['low'])
max_errs.append(entry['high'] - entry['value'])
else:
min_errs.append(0.0)
max_errs.append(0.0)
else:
middle_val = (entry['high'] -
entry['low']) * 0.5 + entry['low']
values.append(middle_val)
min_errs.append(middle_val - entry['low'])
max_errs.append(entry['high'] - middle_val)
def calculate_total_errors(variable, is_number_list, min_errs, max_errs,
values):
for i, entry in enumerate(variable['values']):
if not is_number_list[i]: continue # skip non-numeric y values
if 'value' in entry:
values.append(entry['value'])
if 'errors' in entry:
errors_min = 0.0
errors_max = 0.0
for error in entry['errors']:
if 'asymerror' in error:
err_minus = error_value_processor(
entry['value'], error['asymerror']['minus'])
err_plus = error_value_processor(
entry['value'], error['asymerror']['plus'])
errors_min += pow(min(err_plus, err_minus, 0.0), 2)
errors_max += pow(max(err_plus, err_minus, 0.0), 2)
elif 'symerror' in error:
try:
err = error_value_processor(
entry['value'], error['symerror'])
errors_min += pow(err, 2)
errors_max += pow(err, 2)
except TypeError:
print log.error(
'TypeError encountered when parsing {0}'.
format(error['symerror']))
min_errs.append(sqrt(errors_min))
max_errs.append(sqrt(errors_max))
elif 'low' in entry and 'high' in entry:
min_errs.append(entry['value'] - entry['low'])
max_errs.append(entry['high'] - entry['value'])
else:
min_errs.append(0.0)
max_errs.append(0.0)
else:
middle_val = (entry['high'] -
entry['low']) * 0.5 + entry['low']
values.append(middle_val)
min_errs.append(middle_val - entry['low'])
max_errs.append(entry['high'] - middle_val)
def create_objects(self):
self.calculate_total_errors()
error_hists = []
error_labels = {}
error_indices = {}
index = 0
is_number_list = self.is_number_var(self.dependent_variable)
for i, value in enumerate(self.dependent_variable.get('values', [])):
if not is_number_list[i]: continue # skip non-numeric y values
# process the error labels to ensure uniqueness
ArrayWriter.process_error_labels(value)
for error in value.get('errors', []):
if 'label' not in error:
error['label'] = 'error'
label = error['label']
if label not in error_labels:
index += 1
error_indices[index] = label
if 'symerror' in error and label not in error_labels:
error_labels[label] = 'symerror'
elif 'asymerror' in error and error_labels.get(
label, 'symerror') == 'symerror':
error_labels[label] = 'asymerror'
yvals = []
for index in range(1, len(error_labels) + 1):
error_label = error_indices[index]
if error_labels[error_label] == 'asymerror':
yval_plus_label = error_label + '_plus'
yval_plus = []
yval_minus_label = error_label + '_minus'
yval_minus = []
for i, value in enumerate(
self.dependent_variable.get('values', [])):
if not is_number_list[i]:
continue # skip non-numeric y values
error = [
x for x in value.get('errors', [])
if x.get('label') == error_label
]
if len(error) == 0:
yval_plus.append(0.0)
yval_minus.append(0.0)
elif 'symerror' in error[0]:
err_val = error_value_processor(
value['value'], error[0]['symerror'])
yval_plus.append(err_val)
yval_minus.append(-err_val)
elif 'asymerror' in error[0]:
err_plus = error_value_processor(
value['value'], error[0]['asymerror']['plus'])
err_min = error_value_processor(
value['value'], error[0]['asymerror']['minus'])
yval_plus.append(err_plus)
yval_minus.append(err_min)
else:
yval_plus.append(0.0)
yval_minus.append(0.0)
yvals += [
(yval_plus_label, yval_plus, '%s%s' % (index, 'plus')),
(yval_minus_label, yval_minus, '%s%s' % (index, 'minus'))
]
else:
yval = []
for i, value in enumerate(
self.dependent_variable.get('values', [])):
if not is_number_list[i]:
continue # skip non-numeric y values
error = [
x for x in value.get('errors', [])
if x.get('label') == error_label
]
if len(error) == 0:
yval.append(0.0)
elif 'symerror' in error[0]:
err_val = error_value_processor(
value['value'], error[0]['symerror'])
yval.append(err_val)
else:
yval.append(0.0)
yvals += [(error_label, yval, index)]
for name, vals, index in yvals:
try:
error_hists.append(self._create_empty_hist(name, index, vals))
except:
log.error("Failed to create empty histogram")
xval = []
for i in range(self.dim):
xval.append([])
i_var = self.independent_variables[i]['values']
for ix, x in enumerate(i_var):
if not is_number_list[
ix] and 'labels' not in self.independent_variables[i]:
continue # skip defining bins for non-numeric y values unless alphanumeric bin labels are present
if x['low'] not in xval[i]:
xval[i].append(x['low'])
if x['high'] not in xval[i]:
xval[i].append(x['high'])
try:
hist = self._create_hist(xval)
except:
log.error("Failed to create histogram")
return [] + error_hists
return [hist] + error_hists
def create_objects(self):
self.calculate_total_errors()
error_hists = []
error_labels = {}
error_indices = {}
index = 0
is_number_list = self.is_number_var(self.dependent_variable)
for i, value in enumerate(self.dependent_variable.get('values', [])):
if not is_number_list[i]: continue # skip non-numeric y values
# process the labels to ensure uniqueness
observed_error_labels = {}
for error in value.get('errors', []):
label = error.get('label', '')
if label not in observed_error_labels:
observed_error_labels[label] = 0
observed_error_labels[label] += 1
if observed_error_labels[label] > 1:
error['label'] = label + '_' + str(
observed_error_labels[label])
# append "_1" to first error label that has a duplicate
if observed_error_labels[label] == 2:
for error1 in value.get('errors', []):
error1_label = error1.get('label', 'error')
if error1_label == label:
error1['label'] = label + "_1"
break
for error in value.get('errors', []):
if 'label' not in error:
error['label'] = 'error'
label = error['label']
if label not in error_labels:
index += 1
error_indices[index] = label
if 'symerror' in error and label not in error_labels:
error_labels[label] = 'symerror'
elif 'asymerror' in error and error_labels.get(
label, 'symerror') == 'symerror':
error_labels[label] = 'asymerror'
yvals = []
for index in xrange(1, len(error_labels) + 1):
error_label = error_indices[index]
if error_labels[error_label] == 'asymerror':
yval_plus_label = error_label + '_plus'
yval_plus = []
yval_minus_label = error_label + '_minus'
yval_minus = []
for i, value in enumerate(
self.dependent_variable.get('values', [])):
if not is_number_list[i]:
continue # skip non-numeric y values
error = filter(lambda x: x.get('label') == error_label,
value.get('errors', []))
if len(error) == 0:
yval_plus.append(0.0)
yval_minus.append(0.0)
elif 'symerror' in error[0]:
err_val = error_value_processor(
value['value'], error[0]['symerror'])
yval_plus.append(err_val)
yval_minus.append(-err_val)
elif 'asymerror' in error[0]:
err_plus = error_value_processor(
value['value'], error[0]['asymerror']['plus'])
err_min = error_value_processor(
value['value'], error[0]['asymerror']['minus'])
yval_plus.append(err_plus)
yval_minus.append(err_min)
else:
yval_plus.append(0.0)
yval_minus.append(0.0)
yvals += [
(yval_plus_label, yval_plus, '%s%s' % (index, 'plus')),
(yval_minus_label, yval_minus, '%s%s' % (index, 'minus'))
]
else:
yval = []
for i, value in enumerate(
self.dependent_variable.get('values', [])):
if not is_number_list[i]:
continue # skip non-numeric y values
error = filter(lambda x: x.get('label') == error_label,
value.get('errors', []))
if len(error) == 0:
yval.append(0.0)
elif 'symerror' in error[0]:
err_val = error_value_processor(
value['value'], error[0]['symerror'])
yval.append(err_val)
else:
yval.append(0.0)
yvals += [(error_label, yval, index)]
for name, vals, index in yvals:
try:
error_hists.append(self._create_empty_hist(name, index, vals))
except:
log.error("Failed to create empty histogram")
xval = []
for i in xrange(self.dim):
xval.append([])
i_var = self.independent_variables[i]['values']
for x in i_var:
xval[i].append(x['low'])
xval[i].append(i_var[-1]['high'])
try:
hist = self._create_hist(xval)
except:
log.error("Failed to create histogram")
return [] + error_hists
return [hist] + error_hists
def create_objects(self):
self.calculate_total_errors()
error_hists = []
error_labels = {}
error_indices = {}
is_number_list = self.is_number_var(self.dependent_variable)
for i, value in enumerate(self.dependent_variable.get('values', [])):
if not is_number_list[i]: continue # skip non-numeric y values
# process the labels to ensure uniqueness
observed_error_labels = {}
for error in value.get('errors', []):
label = error.get('label', '')
if label not in observed_error_labels:
observed_error_labels[label] = 0
observed_error_labels[label] += 1
if observed_error_labels[label] > 1:
error['label'] = label + '_' + str(observed_error_labels[label])
# append "_1" to first error label that has a duplicate
if observed_error_labels[label] == 2:
for error1 in value.get('errors', []):
error1_label = error1.get('label', 'error')
if error1_label == label:
error1['label'] = label + "_1"
break
for index, error in enumerate(value.get('errors', []), 1):
if 'label' not in error:
error['label'] = 'error'
label = error['label']
if 'symerror' in error and label not in error_labels:
error_labels[label] = 'symerror'
elif 'asymerror' in error and error_labels.get(label, 'symerror') == 'symerror':
error_labels[label] = 'asymerror'
error_indices[index] = label
yvals = []
for index in xrange(1, len(error_labels) + 1):
error_label = error_indices[index]
if error_labels[error_label] == 'asymerror':
yval_plus_label = error_label + '_plus'
yval_plus = []
yval_minus_label = error_label + '_minus'
yval_minus = []
for i, value in enumerate(self.dependent_variable.get('values', [])):
if not is_number_list[i]: continue # skip non-numeric y values
error = filter(lambda x: x.get('label') == error_label, value.get('errors', []))
if len(error) == 0:
yval_plus.append(0.0)
yval_minus.append(0.0)
elif 'symerror' in error[0]:
err_val = error_value_processor(value['value'], error[0]['symerror'])
yval_plus.append(err_val)
yval_minus.append(-err_val)
elif 'asymerror' in error[0]:
err_plus = error_value_processor(value['value'], error[0]['asymerror']['plus'])
err_min = error_value_processor(value['value'], error[0]['asymerror']['minus'])
yval_plus.append(err_plus)
yval_minus.append(err_min)
else:
yval_plus.append(0.0)
yval_minus.append(0.0)
yvals += [(yval_plus_label, yval_plus, '%s%s' % (index, 'plus')),
(yval_minus_label, yval_minus, '%s%s' % (index, 'minus'))]
else:
yval = []
for i, value in enumerate(self.dependent_variable.get('values', [])):
if not is_number_list[i]: continue # skip non-numeric y values
error = filter(lambda x: x.get('label') == error_label, value.get('errors', []))
if len(error) == 0:
yval.append(0.0)
elif 'symerror' in error[0]:
err_val = error_value_processor(value['value'], error[0]['symerror'])
yval.append(err_val)
else:
yval.append(0.0)
yvals += [(error_label, yval, index)]
for name, vals, index in yvals:
try:
error_hists.append(self._create_empty_hist(name, index, vals))
except:
log.error("Failed to create empty histogram")
xval = []
for i in xrange(self.dim):
xval.append([])
i_var = self.independent_variables[i]['values']
for x in i_var:
xval[i].append(x['low'])
xval[i].append(i_var[-1]['high'])
try:
hist = self._create_hist(xval)
except:
log.error("Failed to create histogram")
return [] + error_hists
return [hist] + error_hists
