Map!

project/constants/__init__.py

@@ -1,4 +1,5 @@
 from .albuminuria_stage import *
+from .colors import *
 from .csv_headings import *
 from .closed_loop_types import *
 from .diabetes_types import *

project/constants/colors.py

@@ -0,0 +1,54 @@
+RCPCH_LIGHTEST_GREY = "#F3F3F3"
+RCPCH_DARK_BLUE = "#0D0D58"
+RCPCH_STRONG_BLUE = "#3366CC"
+RCPCH_STRONG_BLUE_LIGHT_TINT1 = "#668CD9"
+RCPCH_STRONG_BLUE_LIGHT_TINT2 = "#99B3E6"
+RCPCH_STRONG_BLUE_LIGHT_TINT3 = "#CCD9F2"
+RCPCH_STRONG_BLUE_DARK_TINT = "#405A97"
+RCPCH_LIGHT_BLUE = "#11A7F2"
+RCPCH_LIGHT_BLUE_TINT1 = "#4DBDF5"
+RCPCH_LIGHT_BLUE_TINT2 = "#88D3F9"
+RCPCH_LIGHT_BLUE_TINT3 = "#CFE9FC"
+RCPCH_LIGHT_BLUE_DARK_TINT = "#0082BC"
+RCPCH_PINK = "#E00087"
+RCPCH_PINK_LIGHT_TINT1 = "#E840A5"
+RCPCH_PINK_LIGHT_TINT2 = "#EF80C3"
+RCPCH_PINK_LIGHT_TINT3 = "#F7BFE1"
+RCPCH_PINK_DARK_TINT = "#AB1368"
+RCPCH_WHITE = "#FFFFFF"
+RCPCH_LIGHT_GREY = "#D9D9D9"
+RCPCH_MID_GREY = "#B3B3B3"
+RCPCH_DARK_GREY = "#808080"
+RCPCH_CHARCOAL = "#4D4D4D"
+RCPCH_CHARCOAL_DARK = "#191919"
+RCPCH_BLACK = "#000000"
+RCPCH_RED = "#E60700"
+RCPCH_RED_LIGHT_TINT1 = "#EC4540"
+RCPCH_RED_LIGHT_TINT2 = "#F38380"
+RCPCH_RED_LIGHT_TINT3 = "#F9C1BF"
+RCPCH_RED_DARK_TINT = "#B11D23"
+RCPCH_ORANGE = "#FF8000"
+RCPCH_ORANGE_LIGHT_TINT1 = "#FFA040"
+RCPCH_ORANGE_LIGHT_TINT2 = "#FFC080"
+RCPCH_ORANGE_LIGHT_TINT3 = "#FFDFBF"
+RCPCH_ORANGE_DARK_TINT = "#BF6914"
+RCPCH_YELLOW = "#FFD200"
+RCPCH_YELLOW_LIGHT_TINT1 = "#FFDD40"
+RCPCH_YELLOW_LIGHT_TINT2 = "#FFE980"
+RCPCH_YELLOW_LIGHT_TINT3 = "#FFF4BF"
+RCPCH_YELLOW_DARK_TINT = "#C5A000"
+RCPCH_STRONG_GREEN = "#66CC33"
+RCPCH_STRONG_GREEN_LIGHT_TINT1 = "#8CD966"
+RCPCH_STRONG_GREEN_LIGHT_TINT2 = "#B3E699"
+RCPCH_STRONG_GREEN_LIGHT_TINT3 = "#D9F2CC"
+RCPCH_STRONG_GREEN_DARK_TINT = "#53861B"
+RCPCH_AQUA_GREEN = "#00BDAA"
+RCPCH_AQUA_GREEN_LIGHT_TINT1 = "#40ECBF"
+RCPCH_AQUA_GREEN_LIGHT_TINT2 = "#80DED4"
+RCPCH_AQUA_GREEN_LIGHT_TINT3 = "#BFEEEA"
+RCPCH_AQUA_GREEN_DARK_TINT = "#2E888D"
+RCPCH_PURPLE = "#7159AA"
+RCPCH_PURPLE_LIGHT_TINT1 = "#AE4CBF"
+RCPCH_PURPLE_LIGHT_TINT2 = "#C987D4"
+RCPCH_PURPLE_LIGHT_TINT3 = "#E4C3EA"
+RCPCH_PURPLE_DARK_TINT = "#66296C"

project/npda/general_functions/__init__.py

@@ -2,6 +2,7 @@
 from .email import *
 from .group_for_group import *
 from .index_multiple_deprivation import *
+from .map import *
 from .nhs_ods_requests import *
 from .organisations_adapter import *
 from .quarter_for_date import *

project/npda/general_functions/map.py

@@ -0,0 +1,227 @@
+# python imports
+
+# django imports
+from django.conf import settings
+from django.contrib.gis.db.models.functions import Distance
+from django.contrib.gis.geos import Point
+from django.db.models import Q
+from django.apps import apps
+
+# third-party imports
+import pandas as pd
+import plotly.io as pio
+import plotly.graph_objects as go
+
+# RCPCH imports
+from project.constants import RCPCH_LIGHT_BLUE, RCPCH_PINK, RCPCH_DARK_BLUE
+
+from project.npda.general_functions.validate_postcode import location_for_postcode
+
+
+"""
+Functions to return scatter plot of children by postcode
+"""
+
+
+def get_children_by_pdu_audit_year(
+    audit_year, paediatric_diabetes_unit, paediatric_diabetes_unit_lead_organisation
+):
+    """
+    Returns a list of children by postcode for a given audit year and paediatric diabetes unit
+    """
+    Patient = apps.get_model("npda", "Patient")
+    Submission = apps.get_model("npda", "Submission")
+
+    submission = Submission.objects.filter(
+        audit_year=audit_year, paediatric_diabetes_unit=paediatric_diabetes_unit
+    ).first()
+
+    if submission is None:
+        return Patient.objects.none()
+
+    patients = submission.patients.all()
+
+    print(patients)
+
+    if patients:
+        filtered_patients = patients.filter(
+            ~Q(postcode__isnull=True)
+            | ~Q(postcode__exact=""),  # Exclude patients with no postcode
+            submission=submission,  # Filter by submission
+        )
+
+        for patient in filtered_patients:
+            if patient.postcode and not any(
+                patient.location_wgs84 is None, patient.location_bng is None
+            ):
+                # add the location data to the queryset - note these fields do not exist in the model
+                lon, lat, location_wgs84, location_bng = location_for_postcode(
+                    patient.postcode
+                )
+                patient.location_wgs84 = location_wgs84
+                patient.location_bng = location_bng
+
+        filtered_patients = filtered_patients.annotate(
+            distance_from_lead_organisation=Distance(
+                "location_wgs84",
+                Point(
+                    paediatric_diabetes_unit_lead_organisation.longitude,
+                    paediatric_diabetes_unit_lead_organisation.latitude,
+                    srid=4326,
+                ),
+            )
+        ).values(
+            "pk",
+            "location_bng",
+            "location_wgs84",
+            "distance_from_lead_organisation",
+        )
+    else:
+        return Patient.objects.none()
+
+
+# RCPCH imports
+
+
+def generate_distance_from_organisation_scatterplot_figure(
+    geo_df: pd.DataFrame, pdu_lead_organisation
+):
+    """
+    Returns a plottable map with Cases overlayed as dots with tooltips on hover
+    """
+
+    fig = go.Figure(
+        go.Scattermapbox(
+            lat=geo_df["latitude"] if not geo_df.empty else [],
+            lon=geo_df["longitude"] if not geo_df.empty else [],
+            hovertext=geo_df["site__organisation__name"] if not geo_df.empty else None,
+            mode="markers",
+            marker=go.scattermapbox.Marker(
+                size=9,
+                color=RCPCH_PINK,
+            ),
+            customdata=geo_df[["pk", "distance_mi", "distance_km"]],
+        )
+    )
+
+    fig.update_layout(
+        mapbox_style="carto-positron",
+        mapbox_zoom=10,
+        mapbox_center=dict(
+            lat=pdu_lead_organisation["latitude"],
+            lon=pdu_lead_organisation["longitude"],
+        ),
+        height=590,
+        mapbox_accesstoken=settings.MAPBOX_API_KEY,
+        showlegend=False,
+    )
+
+    # Update the hover template
+    fig.update_traces(
+        hovertemplate="<b>%{hovertext}</b><br>Epilepsy12 ID: %{customdata[0]}<br>Distance to Lead Centre: %{customdata[1]:.2f} mi (%{customdata[2]:.2f} km)<extra></extra>"
+    )
+
+    # Add a scatterplot point for the organization
+    fig.add_trace(
+        go.Scattermapbox(
+            lat=[pdu_lead_organisation["latitude"]],
+            lon=[pdu_lead_organisation["longitude"]],
+            mode="markers",
+            marker=go.scattermapbox.Marker(
+                size=12,
+                color=RCPCH_DARK_BLUE,  # Set the color of the point
+            ),
+            text=[pdu_lead_organisation["name"]],  # Set the hover text for the point
+            hovertemplate="%{text}<extra></extra>",  # Custom hovertemplate just for the lead organisation
+            showlegend=False,
+        )
+    )
+
+    fig.update_layout(
+        margin={"r": 0, "t": 0, "l": 0, "b": 0},
+        font=dict(family="Montserrat-Regular", color="#FFFFFF"),
+        hoverlabel=dict(
+            bgcolor=RCPCH_LIGHT_BLUE,
+            font_size=12,
+            font=dict(color="white", family="Montserrat-Regular"),
+            bordercolor=RCPCH_LIGHT_BLUE,
+        ),
+        mapbox=dict(
+            style="carto-positron",
+            zoom=10,
+            center=dict(
+                lat=pdu_lead_organisation["latitude"],
+                lon=pdu_lead_organisation["longitude"],
+            ),
+        ),
+        mapbox_layers=[
+            {
+                "below": "traces",
+                "sourcetype": "raster",
+                "sourceattribution": "Source: Office for National Statistics licensed under the Open Government Licence v.3.0",
+                "source": [
+                    "https://basemaps.cartocdn.com/gl/dark-matter-gl-style/style.json"
+                ],
+            }
+        ],
+    ),
+
+    # Convert the Plotly figure to JSON
+    return pio.to_json(fig)
+
+
+def generate_dataframe_and_aggregated_distance_data_from_cases(filtered_cases):
+    """
+    Returns a dataframe of all Cases, location data and distances with aggregated results
+    Returns it as a tuple of two dataframes, one for the cases and the distances from the lead organisation, the other for the aggregated distances (max, mean, median, std)
+    """
+    geo_df = pd.DataFrame(list(filtered_cases))
+    # Ensure location is a tuple of (easting, northing)
+
+    if not geo_df.empty:
+        if "location_wgs84" in geo_df.columns:
+            geo_df["longitude"] = geo_df["location_wgs84"].apply(lambda loc: loc.x)
+            geo_df["latitude"] = geo_df["location_wgs84"].apply(lambda loc: loc.y)
+            geo_df["distance_km"] = geo_df["distance_from_lead_organisation"].apply(
+                lambda d: d.km
+            )
+            geo_df["distance_mi"] = geo_df["distance_from_lead_organisation"].apply(
+                lambda d: d.mi
+            )
+
+            max_distance_travelled_km = geo_df["distance_km"].min()
+            mean_distance_travelled_km = geo_df["distance_km"].mean()
+            median_distance_travelled_km = geo_df["distance_km"].median()
+            std_distance_travelled_km = geo_df["distance_km"].std()
+
+            max_distance_travelled_mi = geo_df["distance_mi"].min()
+            mean_distance_travelled_mi = geo_df["distance_mi"].mean()
+            median_distance_travelled_mi = geo_df["distance_mi"].median()
+            std_distance_travelled_mi = geo_df["distance_mi"].std()
+
+            return {
+                "max_distance_travelled_km": f"{max_distance_travelled_km:.2f}",
+                "mean_distance_travelled_km": f"{mean_distance_travelled_km:.2f}",
+                "median_distance_travelled_km": f"{median_distance_travelled_km:.2f}",
+                "std_distance_travelled_km": f"{std_distance_travelled_km:.2f}",
+                "max_distance_travelled_mi": f"{max_distance_travelled_mi:.2f}",
+                "mean_distance_travelled_mi": f"{mean_distance_travelled_mi:.2f}",
+                "median_distance_travelled_mi": f"{median_distance_travelled_mi:.2f}",
+                "std_distance_travelled_mi": f"{std_distance_travelled_mi:.2f}",
+            }, geo_df
+    else:
+        geo_df["pk"] = None
+        geo_df["longitude"] = None
+        geo_df["latitude"] = None
+        geo_df["distance_km"] = 0
+        geo_df["distance_mi"] = 0
+        return {
+            "max_distance_travelled_km": f"~",
+            "mean_distance_travelled_km": f"~",
+            "median_distance_travelled_km": f"~",
+            "std_distance_travelled_km": f"~",
+            "max_distance_travelled_mi": f"~",
+            "mean_distance_travelled_mi": f"~",
+            "median_distance_travelled_mi": f"~",
+            "std_distance_travelled_mi": f"~",
+        }, geo_df

project/npda/general_functions/rcpch_nhs_organisations.py

@@ -31,11 +31,34 @@ def get_all_pz_codes_with_their_trust_and_primary_organisation() -> (
         List[Tuple[str, str]]: A list of tuples containing the ODS code and name of NHS organisations.
     """
 
-    request_url = f"{settings.RCPCH_NHS_ORGANISATIONS_API_URL}/paediatric_diabetes_units/parent/"
+    request_url = (
+        f"{settings.RCPCH_NHS_ORGANISATIONS_API_URL}/paediatric_diabetes_units/parent/"
+    )
 
-    headers = {
-        "Ocp-Apim-Subscription-Key": settings.RCPCH_NHS_ORGANISATIONS_API_KEY
-    }
+    headers = {"Ocp-Apim-Subscription-Key": settings.RCPCH_NHS_ORGANISATIONS_API_KEY}
+
+    response = requests.get(request_url, headers=headers, timeout=10)
+    response.raise_for_status()
+
+    return response.json()
+
+
+def fetch_organisation_by_ods_code(ods_code: str) -> Dict[str, Any]:
+    """
+    This function returns an NHS organisation from the RCPCH dataset that is affiliated with a paediatric diabetes unit.
+
+    Args:
+        ods_code (str): The ODS code of the NHS organisation.
+
+    Returns:
+        Dict[str, Any]: A dictionary containing the details of the NHS organisation.
+    """
+
+    request_url = (
+        f"{settings.RCPCH_NHS_ORGANISATIONS_API_URL}/organisations/{ods_code}/"
+    )
+
+    headers = {"Ocp-Apim-Subscription-Key": settings.RCPCH_NHS_ORGANISATIONS_API_KEY}
 
     response = requests.get(request_url, headers=headers, timeout=10)
     response.raise_for_status()