def _get_realizations_statistics_plots(
df_response: pd.DataFrame,
x_axis: Optional[List[Union[int, str, datetime.datetime]]],
color: str,
) -> List[PlotModel]:
data = df_response
p10 = data.quantile(0.1, axis=1)
p90 = data.quantile(0.9, axis=1)
_mean = data.mean(axis=1)
style = deepcopy(assets.ERTSTYLE["response-plot"]["statistics"])
style["line"]["color"] = color
style_mean = deepcopy(style)
style_mean["line"]["dash"] = "solid"
mean_data = PlotModel(x_axis=x_axis,
y_axis=_mean,
text="Mean",
name="Mean",
**style_mean)
lower_std_data = PlotModel(x_axis=x_axis,
y_axis=p10,
text="p10 quantile",
name="p10 quantile",
**style)
upper_std_data = PlotModel(x_axis=x_axis,
y_axis=p90,
text="p90 quantile",
name="p90 quantile",
**style)
return [mean_data, lower_std_data, upper_std_data]
def _get_observation_plots(observation_df: pd.DataFrame) -> PlotModel:
data = observation_df["values"]
stds = observation_df["std"]
x_axis = observation_df["x_axis"]
attributes = observation_df["attributes"]
active_mask = observation_df["active"]
style = assets.ERTSTYLE["response-plot"]["observation"]
color = [
style["color"] if active else "rgb(0, 0, 0)" for active in active_mask
]
style["marker"]["color"] = color
observation_data = PlotModel(
x_axis=x_axis,
y_axis=data,
text=attributes,
name="Observation",
error_y=dict(
type="data", # value of error bar given in data coordinates
array=stds.values,
visible=True,
),
**style,
)
return observation_data
def _get_realizations_plots(
realizations_df: pd.DataFrame,
x_axis: Optional[List[Union[int, str, datetime.datetime]]],
color: str,
style: Optional[Dict] = None,
) -> List[PlotModel]:
if style:
_style = style
else:
_style = assets.ERTSTYLE["response-plot"]["response"].copy()
_style.update({"line": {"color": color}})
_style.update({"marker": {"color": color}})
realizations_data = list()
for realization in realizations_df:
plot = PlotModel(
x_axis=x_axis,
y_axis=realizations_df[realization].values,
text=realization,
name=realization,
**_style,
)
realizations_data.append(plot)
return realizations_data
def update_response_parameter_scatterplot(
corr_xindex: Dict,
corr_param_resp: Dict,
parameters: List[str],
responses: List[str],
ensembles: Optional[Mapping[str, Dict]],
) -> Optional[Tuple[go.Figure, Component]]:
if not (parameters and ensembles and responses
and corr_param_resp["parameter"] in parameters
and corr_param_resp["response"] in responses):
raise PreventUpdate
selected_parameter = corr_param_resp["parameter"]
selected_response = corr_param_resp["response"]
_plots = []
_resp_plots = {}
_param_plots = {}
_colors = {}
for ensemble_id, data in ensembles.items():
ensemble = load_ensemble(parent, ensemble_id)
if ensemble.parameters and ensemble.responses:
y_data = ensemble.parameters[selected_parameter].data_df()
response = ensemble.responses[selected_response]
if response.axis is not None:
x_index = corr_xindex.get(selected_response, 0)
x_data = response.data_df().iloc[x_index]
style = deepcopy(
assets.ERTSTYLE["response-plot"]["response-index"])
style["marker"]["color"] = data["color"]
_colors[str(ensemble)] = data["color"]
_plots += [
PlotModel(
x_axis=x_data.values.flatten(),
y_axis=y_data.values.flatten(),
text="Mean",
name=
f"{repr(ensemble)}: {selected_response}x{selected_parameter}@{response.axis[x_index]}",
**style,
)
]
_resp_plots[str(ensemble)] = x_data.values.flatten()
_param_plots[str(ensemble)] = y_data.values.flatten()
fig = make_subplots(
rows=4,
cols=2,
specs=[
[{
"colspan": 2,
"rowspan": 3
}, None],
[None, None],
[None, None],
[{
"rowspan": 1
}, {
"rowspan": 1
}],
],
)
for plot in _plots:
fig.add_trace(plot.repr, 1, 1)
for key in _param_plots:
fig.add_trace(
{
"type": "histogram",
"x": _param_plots[key],
"showlegend": False,
"marker_color": _colors[key],
},
4,
1,
)
for key in _resp_plots:
fig.add_trace(
{
"type": "histogram",
"x": _resp_plots[key],
"showlegend": False,
"marker_color": _colors[key],
},
4,
2,
)
fig.update_layout(assets.ERTSTYLE["figure"]["layout"])
fig.update_layout(showlegend=False)
final_text = []
for response_name in responses:
x_axis = ensemble.responses[response_name].axis
if x_axis is not None:
x_value = x_axis[corr_xindex.get(response_name, 0)]
if response_name == selected_response:
res_text = f"**{response_name} @ {x_value}**, "
else:
res_text = f"{response_name} @ {x_value}, "
final_text.append(dcc.Markdown(res_text))
final_text += [
dcc.Markdown(f"parameter: **{corr_param_resp['parameter']}**")
]
return fig, html.Div(final_text)
def update_response_overview_plot(
_: Any,
__: Any,
___: Any,
____: Any,
responses: Optional[List[str]],
ensembles: Optional[Mapping[str, Dict]],
corr_xindex: Dict,
corr_param_resp: Dict,
) -> Optional[go.Figure]:
if not (ensembles and responses
and corr_param_resp["response"] in responses):
raise PreventUpdate
selected_response = corr_param_resp["response"]
_plots = []
_obs_plots: List[PlotModel] = []
for ensemble_id, data in ensembles.items():
ensemble = load_ensemble(parent, ensemble_id)
response = ensemble.responses[selected_response]
x_axis = response.axis
if x_axis is not None:
if str(x_axis[0]).isnumeric():
style = deepcopy(
assets.ERTSTYLE["response-plot"]["response-index"])
else:
style = deepcopy(
assets.ERTSTYLE["response-plot"]["response"])
data_df = response.data_df().copy()
style.update({"marker": {"color": data["color"]}})
style.update({"line": {"color": data["color"]}})
_plots += [
PlotModel(
x_axis=x_axis,
y_axis=data_df[realization],
text=realization,
name=realization,
**style,
) for realization in data_df
]
if response.observations:
for obs in response.observations:
_obs_plots.append(_get_observation_plots(obs.data_df()))
fig = go.Figure()
for plot in _plots:
fig.add_trace(plot.repr)
_layout = assets.ERTSTYLE["figure"]["layout"].copy()
_layout.update(dict(showlegend=False))
fig.update_layout(_layout)
x_index = corr_xindex.get(selected_response, 0)
if x_axis is not None:
fig.add_shape(
type="line",
x0=x_axis[x_index],
y0=0,
x1=x_axis[x_index],
y1=1,
yref="paper",
line=dict(color="rgb(30, 30, 30)", dash="dash", width=3),
)
# draw observations on top
for plot in _obs_plots:
fig.add_trace(plot.repr)
fig.update_layout(clickmode="event+select")
return fig