ParameterResponseParallelCoordinates plugin. (#410)

Co-authored-by: VegardOztan <[email protected]> Co-authored-by: JosteinGj <[email protected]> Co-authored-by: saraa394 <[email protected]> Co-authored-by: sofieaasheim <[email protected]>
equinor · Aug 21, 2020 · db6173e · db6173e
1 parent c671fb8
commit db6173e
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diff --git a/webviz_subsurface/_utils/parameter_response.py b/webviz_subsurface/_utils/parameter_response.py
@@ -0,0 +1,48 @@
+from webviz_config.common_cache import CACHE
+import numpy as np
+
+
+@CACHE.memoize(timeout=CACHE.TIMEOUT)
+def filter_and_sum_responses(
+    dframe, ensemble, response, filteroptions=None, aggregation="sum"
+):
+    """Cached wrapper for _filter_and_sum_responses"""
+    return _filter_and_sum_responses(
+        dframe=dframe,
+        ensemble=ensemble,
+        response=response,
+        filteroptions=filteroptions,
+        aggregation=aggregation,
+    )
+
+
+def _filter_and_sum_responses(
+    dframe, ensemble, response, filteroptions=None, aggregation="sum",
+):
+    """Filter response dataframe for the given ensemble
+    and optional filter columns. Returns dataframe grouped and
+    aggregated per realization.
+    """
+    df = dframe.copy()
+    df = df.loc[df["ENSEMBLE"] == ensemble]
+    if filteroptions:
+        for opt in filteroptions:
+            if opt["type"] == "multi" or opt["type"] == "single":
+                if isinstance(opt["values"], list):
+                    df = df.loc[df[opt["name"]].isin(opt["values"])]
+                else:
+                    if opt["name"] == "DATE" and isinstance(opt["values"], str):
+                        df = df.loc[df["DATE"].astype(str) == opt["values"]]
+                    else:
+                        df = df.loc[df[opt["name"]] == opt["values"]]
+
+            elif opt["type"] == "range":
+                df = df.loc[
+                    (df[opt["name"]] >= np.min(opt["values"]))
+                    & (df[opt["name"]] <= np.max(opt["values"]))
+                ]
+    if aggregation == "sum":
+        return df.groupby("REAL").sum().reset_index()[["REAL", response]]
+    if aggregation == "mean":
+        return df.groupby("REAL").mean().reset_index()[["REAL", response]]
+    raise ValueError(f"Unknown aggregation '{aggregation}'.")