adjusted chart added to all table adjustment pages

dm_regional_app/charts.py

@@ -203,3 +203,157 @@ def entry_rate_table(data):
  df.columns = ["Age Group", "Placement", "Entry rate"]
 
  return df
+
+
+def compare_forecast(
+ historic_data: PopulationStats,
+ base_forecast: Prediction,
+ adjusted_forecast: 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 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"]).dt.date
+
+ # dataframe containing total children in base forecast
+ df = base_forecast.population.unstack().reset_index()
+
+ df.columns = ["from", "date", "forecast"]
+ df = df[df["from"].apply(lambda x: "Not in care" in x) == False]
+ df = df[["date", "forecast"]].groupby(by="date").sum().reset_index()
+ df["date"] = pd.to_datetime(df["date"]).dt.date
+
+ # dataframe containing upper and lower confidence intervals for base forecast
+ df_ci = base_forecast.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"]
+
+ # dataframe containing total children in adjusted forecast
+ df_af = adjusted_forecast.population.unstack().reset_index()
+
+ df_af.columns = ["from", "date", "forecast"]
+ df_af = df_af[df_af["from"].apply(lambda x: "Not in care" in x) == False]
+ df_af = df_af[["date", "forecast"]].groupby(by="date").sum().reset_index()
+ df_af["date"] = pd.to_datetime(df_af["date"]).dt.date
+
+ # dataframe containing upper and lower confidence intervals for adjusted forecast
+ df_df_ci = adjusted_forecast.variance.unstack().reset_index()
+ df_df_ci.columns = ["bin", "date", "variance"]
+ df_df_ci = df_df_ci[["date", "variance"]].groupby(by="date").sum().reset_index()
+ df_df_ci["date"] = pd.to_datetime(df_df_ci["date"]).dt.date
+ df_df_ci["upper"] = df_af["forecast"] + df_df_ci["variance"]
+ df_df_ci["lower"] = df_af["forecast"] - df_df_ci["variance"]
+
+ # visualise prediction using unstacked dataframe
+ 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="Base confidence interval",
+ showlegend=True,
+ )
+ )
+
+ # 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="Adjusted confidence interval",
+ showlegend=True,
+ )
+ )
+
+ # add base forecast for total children
+ fig.add_trace(
+ go.Scatter(
+ x=df_af["date"],
+ y=df_af["forecast"],
+ name="Adjusted Forecast",
+ line=dict(color="black", width=1.5, dash="dash"),
+ )
+ )
+
+ # add adjusted forecast for total children
+ fig.add_trace(
+ go.Scatter(
+ x=df["date"],
+ y=df["forecast"],
+ name="Base 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"),
+ )
+ )
+
+ # 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=14)
+ ),
+ fillcolor="rgba(105,105,105,0.1)",
+ layer="above",
+ )
+
+ fig.update_layout(
+ title="Forecast", 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/templates/dm_regional_app/views/adjusted.html

@@ -62,8 +62,9 @@ <h5 class="card-title">To make adjustments, you can view the table by:<br></h5>
  </div>
  <div class="card-body">
  <p>Once you have adjusted the table you will be able to see the base 
-forecast alongside your adjusted forecast to compare. You can 
-continue to edit the table across each of the views.</p>
+ forecast alongside your adjusted forecast to compare. You can 
+ continue to edit the table across each of the views.</p>
+ <p>Rates in these tables include any previous adjustments you might have made.</p>
  <div class="accordion-group">
  <div class="collapse indent" id="entering_care" data-bs-parent="#tables">
  <div class="card card-body">

dm_regional_app/templates/dm_regional_app/views/entry_rates.html

@@ -8,9 +8,11 @@
  <h1>Adjust entry rates</h1>
  <br>
  <p>You may want to edit the number of children per year entering care to give a more accurate
- picture of future forecasting or simply to explore what changes you may expect to see. <br><br>
- Rates are applied to the daily population of children. For example, a rate of 0.5 to 10-16 Fostering 
- would mean that every two days a child would enter this placement.<br>
+ picture of future forecasting or simply to explore what changes you may expect to see. </p>
+ <p>Rates are applied to the daily population of children. For example, a rate of 0.5 to 10-16 Fostering 
+ would mean that every two days a child would enter this placement.
+ </p>
+ <p>Rate multiplication multiplies the initial base rate produced by the model parameters you selected.
  </p>
  <div class="table-responsive">
  <table id="transition-rate" class="table table-hover">
@@ -34,6 +36,21 @@ <h1>Adjust entry rates</h1>
  </table>
  </div>
 
+
+{% if is_post is True %}
+<div class="alert alert-primary" role="alert">
+ {{chart|safe}}
+
+ Adjusted forecast includes all adjustments made to rates: entry rates, exit rates, and transition rates.
+</div>
+
+<div class="d-flex bd-highlight mb-3">
+ <div class="p-2 bd-highlight"><a class="btn btn-warning" href="#">Clear all rate adjustments</a></div>
+ <div class="ms-auto p-2 bd-highlight"><a class="btn btn-primary" href="{% url 'router_handler' %}?next_url_name=adjusted">Return to adjusted forecast</a></div>
+ </div>
+
+{% endif %}
+
 </div>
 
 {% endblock %}

dm_regional_app/templates/dm_regional_app/views/exit_rates.html

@@ -8,9 +8,12 @@
  <h1>Adjust exit rates</h1>
  <br>
  <p>You may want to edit the number of children per year exiting care to give a more accurate
- picture of future forecasting or simply to explore what changes you may expect to see. <br><br>
+ picture of future forecasting or simply to explore what changes you may expect to see. </p>
+ <p>
  Rates are applied to the daily population of children. For example, a rate of 0.5 from 10-16 Fostering 
- would mean that each day, 50% of children in Fostering will leave care.<br>
+ would mean that each day, 50% of children in Fostering will leave care.
+ </p>
+ <p>Rate multiplication multiplies the initial base rate produced by the model parameters you selected.
  </p>
  <div class="table-responsive">
  <table id="transition-rate" class="table table-hover">
@@ -34,6 +37,21 @@ <h1>Adjust exit rates</h1>
  </table>
  </div>
 
+
+
+{% if is_post is True %}
+<div class="alert alert-primary" role="alert">
+ {{chart|safe}}
+
+ Adjusted forecast includes all adjustments made to rates: entry rates, exit rates, and transition rates.
 </div>
 
+<div class="d-flex bd-highlight mb-3">
+ <div class="p-2 bd-highlight"><a class="btn btn-warning" href="#">Clear all rate adjustments</a></div>
+ <div class="ms-auto p-2 bd-highlight"><a class="btn btn-primary" href="{% url 'router_handler' %}?next_url_name=adjusted">Return to adjusted forecast</a></div>
+ </div>
+
+{% endif %}
+
+</div>
 {% endblock %}

dm_regional_app/templates/dm_regional_app/views/transition_rates.html

@@ -34,6 +34,21 @@ <h1>Adjust transition rates</h1>
  </table>
  </div>
 
+
+{% if is_post is True %}
+<div class="alert alert-primary" role="alert">
+ {{chart|safe}}
+
+ Adjusted forecast includes all adjustments made to rates: entry rates, exit rates, and transition rates.
+</div>
+
+<div class="d-flex bd-highlight mb-3">
+ <div class="p-2 bd-highlight"><a class="btn btn-warning" href="#">Clear all rate adjustments</a></div>
+ <div class="ms-auto p-2 bd-highlight"><a class="btn btn-primary" href="{% url 'router_handler' %}?next_url_name=adjusted">Return to adjusted forecast</a></div>
+ </div>
+
+{% endif %}
+
 </div>
 
 {% endblock %}