Rename 'results' -> 'fit_results'
@@ -326,7 +326,7 @@ class Fit:
Returns
-------
results : dict
- Dictionary with keys "values", "stat" and "results". The latter contains an
+ Dictionary with keys "values", "stat" and "fit_results". The latter contains an
empty list, if `reoptimize` is set to False
"""
parameters = self._parameters
@@ -347,7 +347,7 @@ class Fit:
values = np.linspace(parmin, parmax, nvalues)
stats = []
- results = []
+ fit_results = []
with parameters.restore_values:
for value in values:
parameter.value = value
@@ -355,12 +355,12 @@ class Fit:
parameter.frozen = True
result = self.optimize(**optimize_opts)
stat = result.total_stat
- results.append(result)
+ fit_results.append(result)
else:
stat = self.datasets.stat_sum()
stats.append(stat)
- return {"values": values, "stat": np.array(stats), "results": results}
+ return {"values": values, "stat": np.array(stats), "fit_results": fit_results}
def stat_surface(self, x, y, x_values, y_values, reoptimize=False, **optimize_opts):
"""Compute fit statistic surface.
@@ -386,7 +386,7 @@ class Fit:
Returns
-------
results : dict
- Dictionary with keys "x_values", "y_values", "stat" and "results". The latter contains an
+ Dictionary with keys "x_values", "y_values", "stat" and "fit_results". The latter contains an
empty list, if `reoptimize` is set to False
@@ -396,7 +396,7 @@ class Fit:
y = parameters[y]
stats = []
- results = []
+ fit_results = []
with parameters.restore_values:
for x_value, y_value in itertools.product(x_values, y_values):
# TODO: Remove log.info() and provide a nice progress bar
@@ -408,7 +408,7 @@ class Fit:
y.frozen = True
result = self.optimize(**optimize_opts)
stat = result.total_stat
- results.append(result)
+ fit_results.append(result)
else:
stat = self.datasets.stat_sum()
@@ -418,14 +418,14 @@ class Fit:
stats = np.array(stats)
stats = stats.reshape(shape)
if reoptimize:
- results = np.array(results)
- results = results.reshape(shape)
+ fit_results = np.array(fit_results)
+ fit_results = fit_results.reshape(shape)
return {
"x_values": x_values,
"y_values": y_values,
"stat": stats,
- "results": results,
+ "results": fit_results,
}
def minos_contour(self, x, y, numpoints=10, sigma=1.0):
