Add summary histogram option

- Make misfits signed after aplying square root
- Refactor to adapt for new data api
- Remove regex and parse_url_query
- Apply black on ertviz/views/__init__.py
- Remove text from boxplot as not in use

ertviz/controllers/__init__.py

@@ -1,10 +1,3 @@
-import re
-
-
-def parse_url_query(search):
-    return {key: value for (key, value) in re.findall("(\w*)\=(\w*)", search)}
-
-
 from .link_and_brush_controller import link_and_brush_controller
 from .ensemble_selector_controller import ensemble_selector_controller
 from .multi_response_controller import multi_response_controller

ertviz/controllers/observation_response_controller.py

@@ -2,77 +2,40 @@
 from dash.dependencies import Input, Output, State
 from dash.exceptions import PreventUpdate
 from copy import deepcopy
-from ertviz.models import ResponsePlotModel, BoxPlotModel, PlotModel, EnsembleModel
-import ertviz.assets as assets
-
-
-def _get_univariate_misfits_plots(misfits_df, color):
-    if misfits_df is None:
-        return []
-    style = deepcopy(assets.ERTSTYLE["observation-response-plot"]["misfits"])
-    style["line"]["color"] = color
-    style["marker"]["color"] = color
-    x_axis = misfits_df.pop("x_axis")
-    misfits_data = list()
-    for misfits in misfits_df:
-        plot = PlotModel(
-            x_axis=x_axis,
-            y_axis=misfits_df[misfits].pow(1 / 2.0).values,
-            text=misfits,
-            name=misfits,
-            **style,
-        )
-        misfits_data.append(plot)
-    return misfits_data
+from ertviz.models import (
+    ResponsePlotModel,
+    BoxPlotModel,
+    EnsembleModel,
+    MultiHistogramPlotModel,
+)
 
 
 def _get_univariate_misfits_boxplots(misfits_df, color):
     if misfits_df is None:
         return []
+
     x_axis = misfits_df.pop("x_axis")
     misfits_data = list()
     for misfits in misfits_df.T:
         plot = BoxPlotModel(
-            x_axis=[x_axis.loc[misfits]],
-            y_axis=misfits_df.T[misfits].pow(1 / 2.0).values,
-            text=misfits,
+            x_axis=[int(x_axis[misfits])],
+            y_axis=misfits_df.T[misfits].values,
             name=f"Misfits@{int(x_axis.loc[misfits])}",
             color=color,
         )
         misfits_data.append(plot)
     return misfits_data
 
 
-def _get_observation_plots(observation_df, x_axis):
-    data = observation_df["values"]
-    stds = observation_df["std"]
-    x_axis = observation_df["x_axis"]
-    style = deepcopy(assets.ERTSTYLE["observation-response-plot"]["observation"])
-    observation_data = PlotModel(
-        x_axis=x_axis,
-        y_axis=stds.values,
-        text="Observations",
-        name="Observations",
-        **style,
-    )
-    return [observation_data]
-
+def _create_misfits_plot(response, selected_realizations, color):
 
-def _create_misfits_plot(response, yaxis_type, selected_realizations, color):
-
-    x_axis = response.axis
-    # realizations = _get_univariate_misfits_plots(
     realizations = _get_univariate_misfits_boxplots(
-        response.univariate_misfits_df(selected_realizations), color=color
+        response.univariate_misfits_df(selected_realizations),
+        color=color,
     )
-    observations = []
-
-    # for obs in response.observations:
-    #     observations += _get_observation_plots(obs.data_df(), x_axis)
-
     ensemble_plot = ResponsePlotModel(
         realizations,
-        observations,
+        [],
         dict(
             xaxis={"title": "Index"},
             yaxis={"title": "Unit TODO"},
@@ -89,12 +52,13 @@ def observation_response_controller(parent, app):
         [Input(parent.uuid("ensemble-selection-store"), "data")],
     )
     def _set_response_options(selected_ensembles):
-        # Should either return a union of all possible responses or the other thing which I cant think of...
-
         if not selected_ensembles:
             raise PreventUpdate
         ensemble_id, _ = selected_ensembles.popitem()
-        ensemble = parent.ensembles.get(ensemble_id, EnsembleModel(ref_url=ensemble_id))
+        ensemble = parent.ensembles.get(
+            ensemble_id,
+            EnsembleModel(ref_url=ensemble_id, project_id=parent.project_identifier),
+        )
         parent.ensembles[ensemble_id] = ensemble
         return [
             {
@@ -134,11 +98,29 @@ def _update_graph(response, yaxis_type, misfits_type, selected_ensembles):
         if response in [None, ""] or not selected_ensembles:
             raise PreventUpdate
 
+        if misfits_type == "Summary":
+            data_dict = {}
+            colors = {}
+            for ensemble_id, color in selected_ensembles.items():
+                ensemble = parent.ensembles.get(ensemble_id, None)
+                summary_df = ensemble.responses[response].summary_misfits_df(
+                    selection=None
+                )  # What about selections?
+                if summary_df is not None:
+                    data_dict[parent.ensembles[ensemble_id]._name] = summary_df
+                colors[parent.ensembles[ensemble_id]._name] = color["color"]
+            if bool(data_dict):
+                plot = MultiHistogramPlotModel(
+                    data_dict,
+                    colors=colors,
+                    hist=True,
+                    kde=False,
+                )
+                return plot.repr
+
         def _generate_plot(ensemble_id, color):
             ensemble = parent.ensembles.get(ensemble_id, None)
-            plot = _create_misfits_plot(
-                ensemble.responses[response], yaxis_type, None, color
-            )
+            plot = _create_misfits_plot(ensemble.responses[response], None, color)
             return plot
 
         response_plots = [

ertviz/models/plot_model.py

@@ -8,22 +8,22 @@ def __init__(self, **kwargs):
         self.selected = True
         self._x_axis = kwargs["x_axis"]
         self._y_axis = kwargs["y_axis"]
-        self._text = kwargs["text"]
         self._name = kwargs["name"]
         self._color = kwargs["color"]
 
     @property
     def repr(self):
         repr_dict = dict(
-            # x=self._x_axis,
+            # x=self._x_axis, this won't work :/
+            # need something else to get axis right if required
             y=self._y_axis,
-            # text=self.display_name,
             name=self.display_name,
             boxpoints="all",
             jitter=0.3,
             pointpos=-1.8,
             marker_color=self._color,
         )
+
         return go.Box(repr_dict)
 
     @property

ertviz/models/realization.py

@@ -1,5 +1,5 @@
 import pandas as pd
-from ertviz.data_loader import get_data
+import math
 
 
 class Realization:
@@ -14,16 +14,22 @@ def __init__(self, realization_schema):
         )
 
     def _extract_univariate_misfits(self, schema):
-        if bool(schema):  # this account for not None and empty dict
-            misfits_ = list(schema.values())
-            return pd.DataFrame(misfits_[0])
-        return None
+        # this account for not None and empty dict
+        if not bool(schema):
+            return None
+        misfits_ = list(schema.values())
+        df = pd.DataFrame(misfits_[0])
+        df["value_sign"] = df[["value", "sign"]].apply(
+            lambda row: -1.0 * math.sqrt(row[0]) if row[1] else math.sqrt(row[0]),
+            axis=1,
+        )
+        return df
 
     def _extract_summary_misfits(self, schema):
-        if bool(schema):
-            misfits_ = list(schema.values())[0]
-            return misfits_
-        return None
+        if not bool(schema):
+            return None
+        misfits_ = list(schema.values())[0]
+        return misfits_
 
     @property
     def summarized_misfits_value(self):

ertviz/models/response.py

@@ -44,20 +44,41 @@ def data(self):
     def univariate_misfits_df(self, selection=None):
         if selection is not None:
             data = {
-                realization.name: realization.univariate_misfits_df["value"]
+                realization.name: realization.univariate_misfits_df["value_sign"]
                 for realization in self.realizations
                 if realization.name in selection
                 and realization.univariate_misfits_df is not None
             }
         else:
             data = {
-                realization.name: realization.univariate_misfits_df["value"]
+                realization.name: realization.univariate_misfits_df["value_sign"]
                 for realization in self.realizations
                 if realization.univariate_misfits_df is not None
             }
         if bool(data):
             misfits_df = pd.DataFrame(data=data)
             misfits_df["x_axis"] = self.observations[0].data_df()["x_axis"]
+            misfits_df.index.name = self.name
+            return misfits_df.astype("float64")
+        return None
+
+    def summary_misfits_df(self, selection=None):
+        if bool(selection):
+            data = {
+                realization.name: [realization.summarized_misfits_value]
+                for realization in self.realizations
+                if realization.name in selection
+                and bool(realization.summarized_misfits_value)
+            }
+        else:
+            data = {
+                realization.name: [realization.summarized_misfits_value]
+                for realization in self.realizations
+                if bool(realization.summarized_misfits_value)
+            }
+        if bool(data):
+            misfits_df = pd.DataFrame(data=data)
+            misfits_df.index.name = self.name
             return misfits_df.astype("float64")
         return None
 

ertviz/plugins/_observation_analyzer.py

@@ -8,8 +8,9 @@
 
 
 class ObservationAnalyzer(WebvizPluginABC):
-    def __init__(self, app):
+    def __init__(self, app, project_identifier: str):
         super().__init__()
+        self.project_identifier = project_identifier
         self.ensembles = {}
         self.parameter_models = {}
         self.set_callbacks(app)

ertviz/views/__init__.py

@@ -96,4 +96,4 @@ def response_obs_view(parent):
             ],
             className="ert-graph-container",
         ),
-    ]
+    ]