Merge remote-tracking branch 'origin/live' into eatyourpeas/issue422

project/npda/templates/dashboard/components/cards/imd_map_card.html

@@ -1,4 +1,4 @@
-{% extends 'dashboard/base_card.html' %}
+{% extends 'dashboard/components/bases/base_card.html' %}
 {% block card_title %}
   Distribution of patients ({{ pdu_object.lead_organisation_name }} {{ pdu_object.pz_code }})
 {% endblock card_title %}

project/npda/views/dashboard.py

@@ -594,6 +594,390 @@ def add_number_of_figures_coloured_for_chart(
     return dict(value_counts_dict)
 
 
+@login_and_otp_required()
+def get_colored_figures_chart_partial(
+    request,
+    colored: int,
+    total_figures: int,
+):
+
+    if not request.htmx:
+        return HttpResponseBadRequest("This view is only accessible via HTMX")
+
+    # Get a list of booleans to determine which figures are colored (to iterate easily in the
+    # template)
+    is_colored = [True if i < colored else False for i in range(total_figures)]
+
+    return render(
+        request,
+        "dashboard/colored_figures_chart_partial.html",
+        context={
+            "is_colored": is_colored,
+        },
+    )
+
+
+def get_total_eligible_pts_diabetes_type_value_counts(
+    eligible_pts_queryset: QuerySet,
+) -> dict:
+    """Gets value counts dict for total eligible patients stratified by diabetes type"""
+
+    eligible_pts_diabetes_type_counts = eligible_pts_queryset.values(
+        "diabetes_type"
+    ).annotate(count=Count("diabetes_type"))
+    eligible_pts_diabetes_type_value_counts = defaultdict(int)
+    for item in eligible_pts_diabetes_type_counts:
+        diabetes_type = item["diabetes_type"]
+        count = item["count"]
+
+        # These are T1/T2DM types
+        if diabetes_type == 1:
+            diabetes_type_str = "T1DM"
+            eligible_pts_diabetes_type_value_counts[diabetes_type_str] += count
+        elif diabetes_type == 2:
+            diabetes_type_str = "T2DM"
+            eligible_pts_diabetes_type_value_counts[diabetes_type_str] += count
+        else:
+            # Count as 'Other rare forms'
+            diabetes_type_str = "Other rare forms"
+            eligible_pts_diabetes_type_value_counts[diabetes_type_str] += count
+
+    # Convert to percentages
+    eligible_pts_diabetes_type_value_counts = convert_value_counts_dict_to_pct(
+        eligible_pts_diabetes_type_value_counts
+    )
+
+    return eligible_pts_diabetes_type_value_counts
+
+
+def get_pt_characteristics_value_counts_pct(
+    kpi_name_registry: KPIRegistry,
+    kpi_calculations_object: dict,
+) -> dict[
+    Literal["care", "died_or_transitioned", "comorbidity_and_testing"],
+    dict[Literal["total_eligible", "total_ineligible", "pct"], int],
+]:
+    """Gets value counts dict for:
+
+    care
+    - age_gte_12yo (KPI4)
+    - complete_year_of_care (KPI5)
+    - age_gte_12yo_and_complete_year_of_care (KPI6)
+
+    died_or_transitioned
+    - died (KPI8)
+    - transitioned (KPI9)
+
+    comorbidity_and_testing
+    - coeliac (KPI10)
+    - thyroid (kpi11)
+    - ketone_testing (KPI12)
+
+    NOTE: rounds DOWN (convert float to int) for percentage calculation
+    """
+    # Get attribute names and labels
+    relevant_kpis = [4, 5, 6, 8, 9, 10, 11, 12]
+    kpi_attr_names = [
+        kpi_name_registry.get_attribute_name(kpi) for kpi in relevant_kpis
+    ]
+
+    value_counts = defaultdict(lambda: {"count": 0, "total": 0, "pct": 0})
+    # These are all just counts so only total_eligble and total_ineligible have values
+    for kpi_attr in kpi_attr_names:
+
+        total_eligible = kpi_calculations_object[kpi_attr]["total_eligible"]
+        total_ineligible = kpi_calculations_object[kpi_attr]["total_ineligible"]
+
+        # Need all 3 for front end chart
+        value_counts[kpi_attr]["count"] = total_eligible
+        value_counts[kpi_attr]["total"] = total_eligible + total_ineligible
+        value_counts[kpi_attr]["pct"] = (
+            int(total_eligible / value_counts[kpi_attr]["total"] * 100)
+            if value_counts[kpi_attr]["total"] > 0
+            else 0
+        )
+
+    # Now put into the 3 categories
+    categories_vc = defaultdict(dict)
+    for kpi in [4, 5, 6]:
+        kpi_attr = kpi_name_registry.get_attribute_name(kpi)
+        categories_vc["care"][kpi_attr] = value_counts[kpi_attr]
+
+    for kpi in [8, 9]:
+        kpi_attr = kpi_name_registry.get_attribute_name(kpi)
+        categories_vc["died_or_transitioned"][kpi_attr] = value_counts[kpi_attr]
+
+    for kpi in [10, 11, 12]:
+        kpi_attr = kpi_name_registry.get_attribute_name(kpi)
+        categories_vc["comorbidity_and_testing"][kpi_attr] = value_counts[kpi_attr]
+
+    return dict(categories_vc)
+
+
+def get_pt_demographic_value_counts(
+    all_eligible_pts_queryset: QuerySet[Patient],
+) -> tuple[
+    dict[Literal["Female", "Male", "Unknown"], int],
+    dict[str, int],
+    dict[
+        Literal[
+            1,
+            2,
+            3,
+            4,
+            5,
+        ],
+        int,
+    ],
+]:
+    """Get value counts for pt demographics:
+
+    - sex
+    - ethnicity
+    - imd
+    """
+
+    all_values = all_eligible_pts_queryset.values(
+        "sex", "ethnicity", "index_of_multiple_deprivation_quintile"
+    )
+    sex_map = dict(SEX_TYPE)
+    sex_counts = Counter(sex_map[item["sex"]] for item in all_values)
+    ethnicity_map = dict(ETHNICITIES)
+    ethnicity_counts = Counter(ethnicity_map[item["ethnicity"]] for item in all_values)
+    imd_map = {
+        1: "1st Quintile",
+        2: "2nd Quintile",
+        3: "3rd Quintile",
+        4: "4th Quintile",
+        5: "5th Quintile",
+    }
+    imd_counts = Counter(
+        imd_map[item["index_of_multiple_deprivation_quintile"]] for item in all_values
+    )
+
+    return (
+        sex_counts,
+        ethnicity_counts,
+        imd_counts,
+    )
+
+
+def add_number_of_figures_coloured_for_chart(
+    value_counts_dict: dict[
+        Literal["care", "died_or_transitioned", "comorbidity_and_testing"],
+        dict[Literal["total_eligible", "total_ineligible", "pct"], int],
+    ],
+    n_figures_total: int = 5,
+) -> dict[
+    Literal["total_eligible", "total_ineligible", "pct", "figures_coloured"], int
+]:
+    """
+    Add number of figures coloured to a value counts dict
+    """
+    # divisor is 100 / n_figures_total
+    divisor = 100 / n_figures_total
+
+    for category, vcs in value_counts_dict.items():
+        for key, value in vcs.items():
+            value_counts_dict[category][key]["figures_coloured"] = int(
+                value["pct"] / divisor
+            )
+
+    return dict(value_counts_dict)
+
+
+@login_and_otp_required()
+def get_colored_figures_chart_partial(
+    request,
+    colored: int,
+    total_figures: int,
+):
+
+    if not request.htmx:
+        return HttpResponseBadRequest("This view is only accessible via HTMX")
+
+    # Get a list of booleans to determine which figures are colored (to iterate easily in the
+    # template)
+    is_colored = [True if i < colored else False for i in range(total_figures)]
+
+    return render(
+        request,
+        "dashboard/colored_figures_chart_partial.html",
+        context={
+            "is_colored": is_colored,
+        },
+    )
+
+
+def get_total_eligible_pts_diabetes_type_value_counts(
+    eligible_pts_queryset: QuerySet,
+) -> dict:
+    """Gets value counts dict for total eligible patients stratified by diabetes type"""
+
+    eligible_pts_diabetes_type_counts = eligible_pts_queryset.values(
+        "diabetes_type"
+    ).annotate(count=Count("diabetes_type"))
+    eligible_pts_diabetes_type_value_counts = defaultdict(int)
+    for item in eligible_pts_diabetes_type_counts:
+        diabetes_type = item["diabetes_type"]
+        count = item["count"]
+
+        # These are T1/T2DM types
+        if diabetes_type == 1:
+            diabetes_type_str = "T1DM"
+            eligible_pts_diabetes_type_value_counts[diabetes_type_str] += count
+        elif diabetes_type == 2:
+            diabetes_type_str = "T2DM"
+            eligible_pts_diabetes_type_value_counts[diabetes_type_str] += count
+        else:
+            # Count as 'Other rare forms'
+            diabetes_type_str = "Other rare forms"
+            eligible_pts_diabetes_type_value_counts[diabetes_type_str] += count
+
+    # Convert to percentages
+    eligible_pts_diabetes_type_value_counts = convert_value_counts_dict_to_pct(
+        eligible_pts_diabetes_type_value_counts
+    )
+
+    return eligible_pts_diabetes_type_value_counts
+
+
+def get_pt_characteristics_value_counts_pct(
+    kpi_name_registry: KPIRegistry,
+    kpi_calculations_object: dict,
+) -> dict[
+    Literal["care", "died_or_transitioned", "comorbidity_and_testing"],
+    dict[Literal["total_eligible", "total_ineligible", "pct"], int],
+]:
+    """Gets value counts dict for:
+
+    care
+    - age_gte_12yo (KPI4)
+    - complete_year_of_care (KPI5)
+    - age_gte_12yo_and_complete_year_of_care (KPI6)
+
+    died_or_transitioned
+    - died (KPI8)
+    - transitioned (KPI9)
+
+    comorbidity_and_testing
+    - coeliac (KPI10)
+    - thyroid (kpi11)
+    - ketone_testing (KPI12)
+
+    NOTE: rounds DOWN (convert float to int) for percentage calculation
+    """
+    # Get attribute names and labels
+    relevant_kpis = [4, 5, 6, 8, 9, 10, 11, 12]
+    kpi_attr_names = [
+        kpi_name_registry.get_attribute_name(kpi) for kpi in relevant_kpis
+    ]
+
+    value_counts = defaultdict(lambda: {"count": 0, "total": 0, "pct": 0})
+    # These are all just counts so only total_eligble and total_ineligible have values
+    for kpi_attr in kpi_attr_names:
+
+        total_eligible = kpi_calculations_object[kpi_attr]["total_eligible"]
+        total_ineligible = kpi_calculations_object[kpi_attr]["total_ineligible"]
+
+        # Need all 3 for front end chart
+        value_counts[kpi_attr]["count"] = total_eligible
+        value_counts[kpi_attr]["total"] = total_eligible + total_ineligible
+        value_counts[kpi_attr]["pct"] = (
+            int(total_eligible / value_counts[kpi_attr]["total"] * 100)
+            if value_counts[kpi_attr]["total"] > 0
+            else 0
+        )
+
+    # Now put into the 3 categories
+    categories_vc = defaultdict(dict)
+    for kpi in [4, 5, 6]:
+        kpi_attr = kpi_name_registry.get_attribute_name(kpi)
+        categories_vc["care"][kpi_attr] = value_counts[kpi_attr]
+
+    for kpi in [8, 9]:
+        kpi_attr = kpi_name_registry.get_attribute_name(kpi)
+        categories_vc["died_or_transitioned"][kpi_attr] = value_counts[kpi_attr]
+
+    for kpi in [10, 11, 12]:
+        kpi_attr = kpi_name_registry.get_attribute_name(kpi)
+        categories_vc["comorbidity_and_testing"][kpi_attr] = value_counts[kpi_attr]
+
+    return dict(categories_vc)
+
+
+def get_pt_demographic_value_counts(
+    all_eligible_pts_queryset: QuerySet[Patient],
+) -> tuple[
+    dict[Literal["Female", "Male", "Unknown"], int],
+    dict[str, int],
+    dict[
+        Literal[
+            1,
+            2,
+            3,
+            4,
+            5,
+        ],
+        int,
+    ],
+]:
+    """Get value counts for pt demographics:
+
+    - sex
+    - ethnicity
+    - imd
+    """
+
+    all_values = all_eligible_pts_queryset.values(
+        "sex", "ethnicity", "index_of_multiple_deprivation_quintile"
+    )
+    sex_map = dict(SEX_TYPE)
+    sex_counts = Counter(sex_map[item["sex"]] for item in all_values)
+    ethnicity_map = dict(ETHNICITIES)
+    ethnicity_counts = Counter(ethnicity_map[item["ethnicity"]] for item in all_values)
+    imd_map = {
+        1: "1st Quintile",
+        2: "2nd Quintile",
+        3: "3rd Quintile",
+        4: "4th Quintile",
+        5: "5th Quintile",
+    }
+    imd_counts = Counter(
+        imd_map[item["index_of_multiple_deprivation_quintile"]] for item in all_values
+    )
+
+    return (
+        sex_counts,
+        ethnicity_counts,
+        imd_counts,
+    )
+
+
+def add_number_of_figures_coloured_for_chart(
+    value_counts_dict: dict[
+        Literal["care", "died_or_transitioned", "comorbidity_and_testing"],
+        dict[Literal["total_eligible", "total_ineligible", "pct"], int],
+    ],
+    n_figures_total: int = 5,
+) -> dict[
+    Literal["total_eligible", "total_ineligible", "pct", "figures_coloured"], int
+]:
+    """
+    Add number of figures coloured to a value counts dict
+    """
+    # divisor is 100 / n_figures_total
+    divisor = 100 / n_figures_total
+
+    for category, vcs in value_counts_dict.items():
+        for key, value in vcs.items():
+            value_counts_dict[category][key]["figures_coloured"] = int(
+                value["pct"] / divisor
+            )
+
+    return dict(value_counts_dict)
+
+
 def convert_value_counts_dict_to_pct(value_counts_dict: dict):
     """
     Convert a value counts dict to percentages