Merge branch 'main' into fmu_setproperty_dict

Author: BaptisteDE

corrai/base/model.py

@@ -141,10 +141,11 @@ def simulate_parameter(
             Simulation results as a DataFrame with a DateTimeIndex and one
             or more output columns.
         """
+        sim_kwargs = {} if simulation_kwargs is None else simulation_kwargs
         return self.simulate(
             self.get_property_from_param(parameter_value_pairs),
             simulation_options,
-            **{} if simulation_kwargs is None else simulation_kwargs,
+            **sim_kwargs,
         )
 
     def get_property_values(self, property_list: list[str]) -> list[str | int | float]:

corrai/base/simulate.py

@@ -108,7 +108,7 @@ def run_simulations(
     results = Parallel(n_jobs=n_cpu)(
         delayed(
             lambda param: model.simulate_parameter(
-                param, simulation_options, **simulation_kwargs
+                param, simulation_options, simulation_kwargs
             )
         )(param)
         for param in bar

corrai/fmu.py

@@ -52,7 +52,13 @@ def parse_simulation_times(start, stop, step, output_int):
         and isinstance(step, (pd.Timedelta, dt.timedelta))
         and isinstance(output_int, (pd.Timedelta, dt.timedelta))
     ):
-        return map(datetime_to_second, (start, stop, step, output_int))
+        # Handle 2 years with datetime_index_to_seconds_index function
+        start_s, stop_s = datetime_index_to_seconds_index(
+            pd.date_range(start, stop, periods=2)
+        ).astype(int)
+        step_s, output_int_s = map(datetime_to_second, (step, output_int))
+        return start_s, stop_s, step_s, output_int_s
+
     raise ValueError("Invalid 'startTime', 'stopTime', 'stepSize', or 'outputInterval")
 
 
@@ -111,7 +117,7 @@ def datetime_to_second(datetime_in: dt.datetime | pd.Timestamp | pd.Timedelta):
     """
     if isinstance(datetime_in, (dt.datetime | pd.Timestamp)):
         year = datetime_in.year
-        origin = dt.datetime(year, 1, 1)
+        origin = dt.datetime(year, 1, 1, tzinfo=datetime_in.tz)
         return int((datetime_in - origin).total_seconds())
     return int(datetime_in.total_seconds())
 
@@ -327,8 +333,11 @@ def get_property_values(
         values = []
         for prop in property_list:
             if prop in variable_map:
-                val = variable_map[prop].start
-                values.append(val if val is not None else None)
+                try:
+                    val = float(variable_map[prop].start)
+                except (ValueError, TypeError):
+                    val = variable_map[prop].start
+                values.append(val)
             else:
                 values.append(None)
         return values
@@ -515,7 +524,8 @@ def simulate(
                 logger=logger,
             )
 
-        df = pd.DataFrame(result, columns=["time"] + self.output_list)
+        columns = ["time"] + self.output_list if self.output_list else None
+        df = pd.DataFrame(result, columns=columns)
 
         if isinstance(start, (pd.Timestamp, dt.datetime)):
             df.index = seconds_index_to_datetime_index(df["time"], start.year)

corrai/optimize.py

@@ -1,6 +1,5 @@
 from abc import ABC
 from typing import Callable
-from functools import wraps
 
 import numpy as np
 import pandas as pd
@@ -10,12 +9,10 @@
 from pymoo.core.problem import ElementwiseProblem
 from pymoo.core.variable import Integer, Real, Choice, Binary
 
-import plotly.graph_objects as go
-
 from corrai.base.math import METHODS
 from corrai.base.model import Model
 from corrai.base.utils import check_indicators_configs
-from corrai.sampling import Sample
+from corrai.sampling import Sample, SampleMethodsMixin
 from corrai.base.parameter import Parameter
 
 
@@ -577,7 +574,7 @@ def _evaluate(self, x, out, *args, **kwargs):
         self._post_evaluate(pairs, out)
 
 
-class SciOptimizer:
+class SciOptimizer(SampleMethodsMixin):
     """
     Optimization wrapper for models using SciPy.
 
@@ -635,6 +632,10 @@ def __init__(
     def parameters(self):
         return self.model_evaluator.parameters
 
+    @property
+    def sample(self):
+        return self.model_evaluator.sample
+
     @property
     def values(self):
         return self.model_evaluator.sample.values
@@ -787,75 +788,3 @@ def diff_evo_minimize(
             rng=rng,
             workers=workers,
         )
-
-    @wraps(Sample.plot_sample)
-    def plot_sample(
-        self,
-        indicator: str | None,
-        reference_timeseries: pd.Series | None = None,
-        title: str | None = None,
-        y_label: str | None = None,
-        x_label: str | None = None,
-        alpha: float = 0.5,
-        show_legends: bool = False,
-        round_ndigits: int = 2,
-        quantile_band: float = 0.75,
-        type_graph: str = "area",
-    ) -> go.Figure:
-        return self.model_evaluator.sample.plot_sample(
-            indicator=indicator,
-            reference_timeseries=reference_timeseries,
-            title=title,
-            y_label=y_label,
-            x_label=x_label,
-            alpha=alpha,
-            show_legends=show_legends,
-            round_ndigits=round_ndigits,
-            quantile_band=quantile_band,
-            type_graph=type_graph,
-        )
-
-    @wraps(Sample.plot_pcp)
-    def plot_pcp(
-        self,
-        indicators_configs: list[str]
-        | list[tuple[str, str | Callable] | tuple[str, str | Callable, pd.Series]],
-        color_by: str | None = None,
-        title: str | None = "Parallel Coordinates — Samples",
-        html_file_path: str | None = None,
-    ) -> go.Figure:
-        return self.model_evaluator.sample.plot_pcp(
-            indicators_configs=indicators_configs,
-            color_by=color_by,
-            title=title,
-            html_file_path=html_file_path,
-        )
-
-    @wraps(Sample.plot_hist)
-    def plot_hist(
-        self,
-        indicator: str,
-        method: str = "mean",
-        unit: str = "",
-        agg_method_kwarg: dict = None,
-        reference_time_series: pd.Series = None,
-        bins: int = 30,
-        colors: str = "orange",
-        reference_value: int | float = None,
-        reference_label: str = "Reference",
-        show_rug: bool = False,
-        title: str = None,
-    ):
-        return self.model_evaluator.sample.plot_hist(
-            indicator=indicator,
-            method=method,
-            unit=unit,
-            agg_method_kwarg=agg_method_kwarg,
-            reference_time_series=reference_time_series,
-            bins=bins,
-            colors=colors,
-            reference_value=reference_value,
-            reference_label=reference_label,
-            show_rug=show_rug,
-            title=title,
-        )