diff options
| -rw-r--r-- | lib/model.py | 32 |
1 files changed, 31 insertions, 1 deletions
diff --git a/lib/model.py b/lib/model.py index ffd29b9..a9a70a9 100644 --- a/lib/model.py +++ b/lib/model.py @@ -12,7 +12,7 @@ from .parameters import ( distinct_param_values, ) from .paramfit import ParamFit -from .utils import soft_cast_int, by_name_to_by_param, regression_measures +from .utils import is_numeric, soft_cast_int, by_name_to_by_param, regression_measures logger = logging.getLogger(__name__) @@ -411,6 +411,36 @@ class AnalyticModel: def to_dref(self, static_quality, lut_quality, model_quality) -> dict: ret = dict() for name in self.names: + param_data = { + "unset": 0, + "useless": 0, + "boolean": 0, + "scalar": 0, + "enum": 0, + } + for param_index, param_values in enumerate( + self.distinct_param_values_by_name[name] + ): + if None in param_values: + none_adj = -1 + else: + none_adj = 0 + value_count = len(param_values) + none_adj + if value_count == 0: + param_data["unset"] += 1 + elif value_count == 1: + param_data["useless"] += 1 + elif value_count == 2: + param_data["boolean"] += 1 + elif all(map(lambda x: x is None or is_numeric(x), param_values)): + param_data["scalar"] += 1 + else: + param_data["enum"] += 1 + ret[f"paramcount/{name}/useful"] = ( + param_data["boolean"] + param_data["scalar"] + param_data["enum"] + ) + for k, v in param_data.items(): + ret[f"paramcount/{name}/{k}"] = v for attr_name, attr in self.attr_by_name[name].items(): unit = None if "power" in attr.attr: |