rate adjustments saving and feeding into form

dm_regional_app/charts.py

@@ -10,12 +10,15 @@ def prediction_chart(historic_data: PopulationStats, prediction: Prediction, **k
  # 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")
-
+ print(prediction.population)
  # dataframe containing total children in prediction
  df = prediction.population.unstack().reset_index()
+ print(df)
 
  df.columns = ["from", "date", "forecast"]
- df = df[df["from"].apply(lambda x: "NOT_IN_CARE" in x[1]) == False]
+ print(df)
+ df = df[df["from"].apply(lambda x: "Not in care" in x) == False]
+ print(df)
  df = df[["date", "forecast"]].groupby(by="date").sum().reset_index()
  df["date"] = pd.to_datetime(df["date"]).dt.date
 
@@ -99,7 +102,7 @@ def prediction_chart(historic_data: PopulationStats, prediction: Prediction, **k
  )
 
  fig.update_layout(
- title="Base forecast", xaxis_title="Date", yaxis_title="Number of children"
+ title="Forecast", xaxis_title="Date", yaxis_title="Number of children"
  )
  fig.update_yaxes(rangemode="tozero")
  fig_html = fig.to_html(full_html=False)
@@ -134,7 +137,7 @@ def transition_rate_table(data):
  df["To"] = df["to"]
  df["From"] = df["from"]
  df.set_index(["from", "to"], inplace=True)
- df = df[df["To"].apply(lambda x: "NOT_IN_CARE" in x[1]) == False]
+ # df = df[df["To"].apply(lambda x: "Not in care" in x) == False]
  df = df.round(4)
  df["From"] = df["From"].mask(df["From"].duplicated(), "")
 
@@ -143,4 +146,6 @@ def transition_rate_table(data):
  from_col = df.pop("From")
  df.insert(0, "From", from_col)
 
+ df.columns = ["From", "To", "Transition rate"]
+
  return df

dm_regional_app/templatetags/table_tags.py

@@ -6,7 +6,7 @@
 def convert_data_frame_to_html_table_headers(df):
  html = "<tr>"
  for value in df.columns:
- html += f'<th><p style="font-size:14px;">{value.capitalize()}</p></th>'
+ html += f'<th><p style="font-size:14px;">{value}</p></th>'
  html += "</tr>"
  return html
 
@@ -42,9 +42,7 @@ def convert_data_frame_to_html_table_rows(df):
  row_html = "<tr>"
  for value in row:
  if isinstance(value, str):
- row_html += (
- f'<th><p style="font-size:14px;">{value.capitalize()}</p></th>'
- )
+ row_html += f'<th><p style="font-size:14px;">{value}</p></th>'
  else:
  row_html += f'<td><p style="font-size:14px;">{value}</p></td>'
  row_html += "</tr>"

dm_regional_app/utils.py

@@ -28,38 +28,42 @@ def apply_filters(data: pd.DataFrame, filters: dict):
 
 class DateAwareJSONDecoder(json.JSONDecoder):
  def __init__(self, *args, **kwargs):
- super().__init__(object_hook=self.parse_dates, *args, **kwargs)
-
- def parse_dates(self, obj):
- for key, value in obj.items():
- if isinstance(value, str) and "date" in key:
- try:
- obj[key] = datetime.fromisoformat(value).date()
- except ValueError:
- pass
- return obj
+ super().__init__(object_hook=self.parse_object, *args, **kwargs)
 
  def parse_object(self, obj):
  obj = self.parse_dates(obj)
  if "__type__" in obj and obj["__type__"] == "pd.Series":
- if all(isinstance(i, list) for i in obj["index"]):
+ if obj.get("is_multiindex", False):
  index = pd.MultiIndex.from_tuples(obj["index"])
  else:
  index = obj["index"]
  return pd.Series(obj["data"], index=index)
  return obj
 
+ def parse_dates(self, obj):
+ for key, value in obj.items():
+ if isinstance(value, str) and "date" in key:
+ try:
+ obj[key] = datetime.fromisoformat(value).date()
+ except ValueError:
+ pass
+ return obj
+
 
 class SeriesAwareJSONEncoder(json.JSONEncoder):
  def default(self, obj):
- # Check if the Series has a MultiIndex
  if isinstance(obj, pd.Series):
  # Check if the Series has a MultiIndex
  if isinstance(obj.index, pd.MultiIndex):
- index = obj.index.tolist()
+ index = [list(tup) for tup in obj.index]
  else:
  index = obj.index.tolist()
- return {"__type__": "pd.Series", "data": obj.tolist(), "index": index}
+ return {
+ "__type__": "pd.Series",
+ "data": obj.tolist(),
+ "index": index,
+ "is_multiindex": isinstance(obj.index, pd.MultiIndex),
+ }
  if isinstance(obj, date):
  return obj.isoformat()
  # Let the base class default method raise the TypeError

dm_regional_app/views.py

@@ -233,7 +233,9 @@ def adjusted(request):
 
  # Call predict function with default dates
  prediction = predict(
- data=historic_data, **session_scenario.prediction_parameters
+ data=historic_data,
+ **session_scenario.prediction_parameters,
+ rate_adjustment=session_scenario.adjusted_rates
  )
 
  # build chart

ssda903/predictor.py

@@ -1,5 +1,5 @@
 from datetime import date
-from typing import Optional
+from typing import Iterable, Optional, Union
 
 import pandas as pd
 from dateutil.relativedelta import relativedelta
@@ -15,6 +15,7 @@ def predict(
  reference_end_date: date,
  prediction_start_date: Optional[date] = None,
  prediction_end_date: Optional[date] = None,
+ rate_adjustment: Union[pd.Series, Iterable[pd.Series]] = None,
 ) -> Prediction:
  """
  Analyses source between start and end, and then predicts the population at prediction_date.
@@ -39,6 +40,7 @@ def predict(
  reference_start_date, reference_end_date
  ),
  start_date=prediction_start_date,
+ rate_adjustment=rate_adjustment,
  )
  prediction_days = (prediction_end_date - prediction_start_date).days
  prediction = predictor.predict(prediction_days, progress=False)