Improved documentation on returned dictionaries in ParameterEstimator.
@@ -74,8 +74,12 @@ class ParameterEstimator(Estimator):
Returns
-------
result : dict
- Dict with the various parameter estimation values.
- (parameter value, total statistic, success flag, parameter error value)
+ Dict with the various parameter estimation values. Entries are:
+
+ * parameter.name: best fit parameter value
+ * "stat": best fit total stat.
+ * "success": boolean flag for fit success
+ * parameter.name_err: covariance-based error estimate on parameter value
"""
value, total_stat, success, error = np.nan, 0, False, np.nan
@@ -105,16 +109,12 @@ class ParameterEstimator(Estimator):
Returns
-------
result : dict
- Dict with the TS of the best fit value compared to the null hypothesis.
- (TS, npred, npred_null)
- """
-
-# if not np.any(datasets.contributes_to_stat):
-# npred_null = self.estimate_npred(datasets=datasets)
-# return {"ts": np.nan,
-# "npred": self.estimate_npred(datasets=datasets),
-# "npred_null": self.estimate_npred(datasets=datasets)}
+ Dict with the TS of the best fit value compared to the null hypothesis. Entries are:
+ * TS : fit statistic difference with null hypothesis
+ * "npred" : predicted number of counts per dataset
+ * "npred_null" : predicted number of counts per dataset in the null hypothesis
+ """
npred = self.estimate_npred(datasets=datasets)
if not np.any(datasets.contributes_to_stat):
@@ -153,8 +153,10 @@ class ParameterEstimator(Estimator):
Returns
-------
result : dict
- Dict with the parameter asymmetric errors
- (error_p, error_n)
+ Dict with the parameter asymmetric errors. Entries are:
+
+ * parameter.name_errp : positive error on parameter value
+ * parameter.name_errn : negative error on parameter value
"""
if not np.any(datasets.contributes_to_stat):
return {
@@ -189,9 +191,10 @@ class ParameterEstimator(Estimator):
Returns
-------
result : dict
- Dict with the parameter fit scan values.
- (parameter values, statistic values)
+ Dict with the parameter fit scan values. Entries are:
+ * parameter.name_scan : parameter values scan
+ * "stat_scan" : fit statistic values scan
"""
scan_values = parameter.scan_values
@@ -227,9 +230,9 @@ class ParameterEstimator(Estimator):
Returns
-------
result : dict
- Dict with the parameter ULs.
- (upper limit)
+ Dict with the parameter ULs. Entries are:
+ * parameter.name_ul : upper limit on parameter value
"""
if not np.any(datasets.contributes_to_stat):
return {f"{parameter.name}_ul": np.nan}
