chart beautified

dm_regional_app/charts.py

@@ -1,37 +1,105 @@
 import pandas as pd
 import plotly.express as px
+import plotly.graph_objects as go
 from demand_model.multinomial.predictor import Prediction
 
-from ssda903.datacontainer import DemandModellingDataContainer
 from ssda903.population_stats import PopulationStats
 
 
-def prediction_chart(historic_data: PopulationStats, prediction: Prediction):
+def prediction_chart(historic_data: PopulationStats, prediction: Prediction, **kwargs):
+ # pop start and end dates to visualise reference period
+ reference_start_date = kwargs.pop("reference_start_date")
+ reference_end_date = kwargs.pop("reference_end_date")
+
+ # dataframe containing total children in prediction
  df = prediction.population.unstack().reset_index()
  df.columns = ["from", "date", "forecast"]
  df = df[["date", "forecast"]].groupby(by="date").sum().reset_index()
- df["date"] = pd.to_datetime(df["date"])
+ df["date"] = pd.to_datetime(df["date"]).dt.date
 
+ # dataframe containing total children in historic data
  df_hd = historic_data.stock.unstack().reset_index()
  df_hd.columns = ["from", "date", "historic"]
  df_hd = df_hd[["date", "historic"]].groupby(by="date").sum().reset_index()
- df_hd["date"] = pd.to_datetime(df_hd["date"])
+ df_hd["date"] = pd.to_datetime(df_hd["date"]).dt.date
 
- df = pd.merge(df, df_hd, how="outer", on="date", indicator=True)
- df = df.melt(id_vars=["date"], value_vars=["historic", "forecast"])
+ # dataframe containing upper and lower confidence intervals
+ df_ci = prediction.variance.unstack().reset_index()
+ df_ci.columns = ["bin", "date", "variance"]
+ df_ci = df_ci[["date", "variance"]].groupby(by="date").sum().reset_index()
+ df_ci["date"] = pd.to_datetime(df_ci["date"]).dt.date
+ df_ci["upper"] = df["forecast"] + df_ci["variance"]
+ df_ci["lower"] = df["forecast"] - df_ci["variance"]
 
  # visualise prediction using unstacked dataframe
- fig = px.line(
- df,
- y="value",
- x="date",
- labels={
- "value": "Number of children",
- "date": "Date",
- },
- color="variable",
+ fig = go.Figure()
+
+ # Display confidence interval as filled shape
+ fig.add_trace(
+ go.Scatter(
+ x=df_ci["date"],
+ y=df_ci["lower"],
+ line_color="rgba(255,255,255,0)",
+ name="Confidence interval",
+ showlegend=False,
+ )
+ )
+
+ fig.add_trace(
+ go.Scatter(
+ x=df_ci["date"],
+ y=df_ci["upper"],
+ fill="tonexty",
+ fillcolor="rgba(0,176,246,0.2)",
+ line_color="rgba(255,255,255,0)",
+ name="Confidence interval",
+ showlegend=True,
+ )
+ )
+
+ # add forecast for total children
+ fig.add_trace(
+ go.Scatter(
+ x=df["date"],
+ y=df["forecast"],
+ name="Forecast",
+ line=dict(color="black", width=1.5),
+ )
+ )
+
+ # add historic data for total children
+ fig.add_trace(
+ go.Scatter(
+ x=df_hd["date"],
+ y=df_hd["historic"],
+ name="Historic data",
+ line=dict(color="black", width=1.5, dash="dot"),
+ )
  )
- fig.update_layout(title="Prediction")
+
+ # add shaded reference period
+ fig.add_shape(
+ type="rect",
+ xref="x",
+ yref="paper",
+ x0=reference_start_date,
+ y0=0,
+ x1=reference_end_date,
+ y1=1,
+ line=dict(
+ width=0,
+ ),
+ label=dict(
+ text="Reference period", textposition="top center", font=dict(size=15)
+ ),
+ fillcolor="rgba(0,0,255,0.1)",
+ layer="above",
+ )
+
+ fig.update_layout(
+ title="Base forecase", xaxis_title="Date", yaxis_title="Number of children"
+ )
+ fig.update_yaxes(rangemode="tozero")
  fig_html = fig.to_html(full_html=False)
  return fig_html
 

dm_regional_app/views.py

@@ -144,7 +144,9 @@ def prediction(request):
  )
 
  # build chart
- chart = prediction_chart(stats, prediction)
+ chart = prediction_chart(
+ stats, prediction, **session_scenario.prediction_parameters
+ )
 
  return render(
  request,