New plot options in ReservoirSimulationTimeSeries

equinor · Apr 26, 2021 · 1142f55 · 1142f55
1 parent bc17543
commit 1142f55
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Showing 3 changed files with 292 additions and 54 deletions.
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -5,13 +5,14 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 ## [UNRELEASED] - YYYY-MM-DD
+### Changed
+- [#612](https://github.com/equinor/webviz-subsurface/pull/612) - New features in ReservoirSimulationTimeSeries: Statistical lines, option to remove history trace, histogram available when plotting individual realizations.
 
 ## [0.2.0] - 2021-03-28
-### Changed
 - [#604](https://github.com/equinor/webviz-subsurface/pull/604) - Consolidates surface loading and statistical calculation of surfaces by introducing a shared
 SurfaceSetModel. Refactored SurfaceViewerFMU to use SurfaceSetModel.
 - [#586](https://github.com/equinor/webviz-subsurface/pull/586) - Added phase ratio vs pressure and density vs pressure plots. Added unit and density functions to PVT library. Refactored code and added checklist for plots to be viewed in PVT plot plugin. Improved the layout.
-- [#599](https://github.com/equinor/webviz-subsurface/pull/599) - Fixed an issue in ParameterAnalysis where the plugin did not initialize without FIELD vectors 
+- [#599](https://github.com/equinor/webviz-subsurface/pull/599) - Fixed an issue in ParameterAnalysis where the plugin did not initialize without FIELD vectors
 
 ### Fixed
 - [#602](https://github.com/equinor/webviz-subsurface/pull/602) - Prevent calculation of data for download at initialisation of ReservoirSimulationTimeSeries.

diff --git a/webviz_subsurface/_utils/simulation_timeseries.py b/webviz_subsurface/_utils/simulation_timeseries.py
@@ -62,11 +62,14 @@ def p10(x: List[float]) -> List[float]:
     def p90(x: List[float]) -> List[float]:
         return np.nanpercentile(x, q=10)
 
+    def p50(x: List[float]) -> List[float]:
+        return np.nanpercentile(x, q=50)
+
     # Calculate statistics, ignoring NaNs.
     stat_df = (
         df[["ENSEMBLE", refaxis] + vectors]
         .groupby(["ENSEMBLE", refaxis])
-        .agg([np.nanmean, np.nanmin, np.nanmax, p10, p90])
+        .agg([np.nanmean, np.nanmin, np.nanmax, p10, p90, p50])
         .reset_index(level=["ENSEMBLE", refaxis], col_level=1)
     )
     # Rename nanmin, nanmax and nanmean to min, max and mean.
@@ -76,6 +79,7 @@ def p90(x: List[float]) -> List[float]:
         "nanmean": "mean",
         "p10": "high_p10",
         "p90": "low_p90",
+        "p50": "p50",
     }
     stat_df.rename(columns=col_stat_label_map, level=1, inplace=True)
 
@@ -156,6 +160,115 @@ def add_fanchart_traces(
     ]
 
 
+def add_statistics_traces(
+    ens_stat_df: pd.DataFrame,
+    vector: str,
+    stat_options: List[str],
+    color: str,
+    legend_group: str,
+    line_shape: str,
+    refaxis: str = "DATE",
+    hovertemplate: str = "(%{x}, %{y})<br>",
+) -> List[Dict[str, Any]]:
+    """Renders a statistical lines for each vector"""
+    traces = []
+    for i, opt in enumerate(stat_options):
+        if opt == "Mean":
+            traces.append(
+                {
+                    "name": legend_group,
+                    "hovertemplate": hovertemplate + "Mean",
+                    "x": ens_stat_df[("", refaxis)],
+                    "y": ens_stat_df[(vector, "mean")],
+                    "mode": "lines",
+                    "line": {"color": color, "shape": line_shape, "width": 3},
+                    "legendgroup": legend_group,
+                    "showlegend": True,
+                }
+            )
+        if opt == "P10 (high)":
+            traces.append(
+                {
+                    "name": legend_group,
+                    "hovertemplate": hovertemplate + "P10",
+                    "x": ens_stat_df[("", refaxis)],
+                    "y": ens_stat_df[(vector, "high_p10")],
+                    "mode": "lines",
+                    "line": {"color": color, "shape": line_shape, "dash": "dashdot"},
+                    "legendgroup": legend_group,
+                    "showlegend": False if "Mean" in stat_options else i == 0,
+                }
+            )
+        if opt == "P90 (low)":
+            traces.append(
+                {
+                    "name": legend_group,
+                    "hovertemplate": hovertemplate + "P90",
+                    "x": ens_stat_df[("", refaxis)],
+                    "y": ens_stat_df[(vector, "low_p90")],
+                    "mode": "lines",
+                    "line": {"color": color, "shape": line_shape, "dash": "dashdot"},
+                    "legendgroup": legend_group,
+                    "showlegend": False if "Mean" in stat_options else i == 0,
+                }
+            )
+        if opt == "P50 (median)":
+            traces.append(
+                {
+                    "name": legend_group,
+                    "hovertemplate": hovertemplate + "P50",
+                    "x": ens_stat_df[("", refaxis)],
+                    "y": ens_stat_df[(vector, "p50")],
+                    "mode": "lines",
+                    "line": {
+                        "color": color,
+                        "shape": line_shape,
+                        "dash": "dot",
+                        "width": 3,
+                    },
+                    "legendgroup": legend_group,
+                    "showlegend": False if "Mean" in stat_options else i == 0,
+                }
+            )
+        if opt == "Maximum":
+            traces.append(
+                {
+                    "name": legend_group,
+                    "hovertemplate": hovertemplate + "Maximum",
+                    "x": ens_stat_df[("", refaxis)],
+                    "y": ens_stat_df[(vector, "max")],
+                    "mode": "lines",
+                    "line": {
+                        "color": color,
+                        "shape": line_shape,
+                        "dash": "longdash",
+                        "width": 1.5,
+                    },
+                    "legendgroup": legend_group,
+                    "showlegend": False if "Mean" in stat_options else i == 0,
+                }
+            )
+        if opt == "Minimum":
+            traces.append(
+                {
+                    "name": legend_group,
+                    "hovertemplate": hovertemplate + "Minimum",
+                    "x": ens_stat_df[("", refaxis)],
+                    "y": ens_stat_df[(vector, "min")],
+                    "mode": "lines",
+                    "line": {
+                        "color": color,
+                        "shape": line_shape,
+                        "dash": "longdash",
+                        "width": 1.5,
+                    },
+                    "legendgroup": legend_group,
+                    "showlegend": False if "Mean" in stat_options else i == 0,
+                }
+            )
+    return traces
+
+
 def render_hovertemplate(vector: str, interval: Optional[str]) -> str:
     if vector.startswith(("AVG_", "INTVL_")) and interval is not None:
         if interval == "daily":