gammapy /
gammapy
| @@ 409-431 (lines=23) @@ | ||
| 406 | ||
| 407 | return {"norm_ul": result["errp"] + norm.value} |
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| 408 | ||
| 409 | def estimate_norm_ts(self, datasets): |
|
| 410 | """Estimate ts and sqrt(ts) for the flux point. |
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| 411 | ||
| 412 | Parameters |
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| 413 | ---------- |
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| 414 | datasets : `~gammapy.modeling.Datasets` |
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| 415 | the list of dataset object |
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| 416 | ||
| 417 | Returns |
|
| 418 | ------- |
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| 419 | result : dict |
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| 420 | Dict with ts and sqrt(ts) for the flux point. |
|
| 421 | """ |
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| 422 | stat = datasets.stat_sum() |
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| 423 | ||
| 424 | # store best fit amplitude, set amplitude of fit model to zero |
|
| 425 | self.model.norm.value = 0 |
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| 426 | self.model.norm.frozen = True |
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| 427 | ||
| 428 | if self.reoptimize: |
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| 429 | _ = self.fit.optimize() |
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| 430 | ||
| 431 | stat_null = datasets.stat_sum() |
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| 432 | ||
| 433 | # compute sqrt TS |
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| 434 | ts = np.abs(stat_null - stat) |
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| @@ 1077-1102 (lines=26) @@ | ||
| 1074 | ||
| 1075 | return {"norm_ul": result["errp"] + norm.value} |
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| 1076 | ||
| 1077 | def estimate_norm_ts(self): |
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| 1078 | """Estimate ts and sqrt(ts) for the flux point. |
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| 1079 | ||
| 1080 | Returns |
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| 1081 | ------- |
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| 1082 | result : dict |
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| 1083 | Dict with ts and sqrt(ts) for the flux point. |
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| 1084 | """ |
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| 1085 | if not self._contribute_to_stat: |
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| 1086 | return {"sqrt_ts": np.nan, "ts": np.nan} |
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| 1087 | ||
| 1088 | stat = self.datasets.stat_sum() |
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| 1089 | ||
| 1090 | # store best fit amplitude, set amplitude of fit model to zero |
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| 1091 | self.model.norm.value = 0 |
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| 1092 | self.model.norm.frozen = True |
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| 1093 | ||
| 1094 | if self.reoptimize: |
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| 1095 | _ = self.fit.optimize() |
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| 1096 | ||
| 1097 | stat_null = self.datasets.stat_sum() |
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| 1098 | ||
| 1099 | # compute sqrt TS |
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| 1100 | ts = np.abs(stat_null - stat) |
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| 1101 | sqrt_ts = np.sqrt(ts) |
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| 1102 | return {"sqrt_ts": sqrt_ts, "ts": ts} |
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| 1103 | ||
| 1104 | def estimate_norm_scan(self): |
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| 1105 | """Estimate fit statistic profile for the norm parameter. |
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