def get_commune_geojson() -> FeatureCollection:
cercles_df = pandas.read_csv(path_to_dataset("mali/Lars_data_cercle.csv"))
cercles_df = cercles_df.set_index("Cercle")
communes_df = pandas.read_csv(
path_to_dataset("mali/Lars_data_commune.csv"))
communes_df = communes_df.set_index("Commune")
with open(path_to_dataset("mali/mli_admbnda_adm3_pop_2017.geojson"),
"r") as fp:
communes_geo = load(fp)
for f in communes_geo["features"]:
commune_props = f["properties"]
cercle_name = commune_props["admin2_nam"]
commune_name = commune_props["admin3_nam"]
cercle_data = (cercles_df.loc[cercle_name].to_dict()
if cercle_name in cercles_df.index else {})
commune_data = (communes_df.loc[commune_name].to_dict()
if commune_name in communes_df.index else {})
f["properties"] = {
"objectid": commune_props["objectid"],
"admin2_nam": cercle_name,
"admin3_nam": commune_props["admin3_nam"],
"pop2017_to": commune_props["pop2017_to"],
"milieu": commune_data.get("Milieu", None),
"pop_2016": cercle_data.get("pop_2016", 0),
"taux_fonct": round(cercle_data.get("taux_fonct", 0)),
"taux_acces": round(cercle_data.get("taux_acces", 0)),
"taux_equip": round(cercle_data.get("taux_equip", 0)),
}
return communes_geo
def get_functionality_percentage_per_region_geojson() -> Dict[str, Any]:
region_df = pandas.read_csv(
path_to_dataset("mali/functionality_wp_mali.csv"), sep=";")
region_data = region_df.set_index("ADM1_FR")["Percentage"].to_dict()
with open(path_to_dataset("mali/mli_hdx.json"), "r") as fp:
region_geo = json.load(fp)
for f in region_geo["features"]:
region = f["properties"]["admin1Name"]
f["properties"] = {"region": region, "value": region_data[region]}
return region_geo
def get_waterpointdata_geojson() -> Dict[str, Any]:
dataframe = pandas.read_csv(path_to_dataset("sierra-leone/WPDx_SL.csv"))
dataframe = dataframe.where(pandas.notnull(dataframe), None)
features = []
for i in range(0, len(dataframe.index)):
item = dataframe.loc[i]
longitude = item["#lon_deg"]
latitude = item["#lat_deg"]
props = {
"adm1": item["#adm1"],
"adm2": item["#adm2"],
}
try:
feature = Feature(
geometry=Point((
float(longitude),
float(latitude),
)),
properties=props,
id=f"{longitude},{latitude}",
)
features.append(feature)
except ValueError:
pass
return FeatureCollection(features)
def get_waterpoints_geojson() -> Dict[str, Any]:
dataframe = pandas.read_csv(path_to_dataset("sierra-leone/SL_wells.csv"),
delimiter=";")
dataframe = dataframe.where(pandas.notnull(dataframe), None)
features = []
for i in range(0, len(dataframe.index)):
item = dataframe.loc[i]
longitude = item["geo longitude"]
latitude = item["geo latitude"]
props = {
"province": item.province,
"district": item.district,
"chiefdom": item.chiefdom,
"water_supply_type": item.water_supply_type,
}
try:
feature = Feature(
geometry=Point((
float(longitude),
float(latitude),
)),
properties=props,
id=f"{longitude},{latitude}",
)
features.append(feature)
except ValueError:
pass
return FeatureCollection(features)
def get_resources_media() -> List[Dict[str, Any]]:
dataframe = pandas.read_csv(
path_to_dataset("mali/Media-Library-Export-2020-December-21-1352.csv"))
dataframe = dataframe.where(pandas.notnull(dataframe), None)
resources = []
for i in range(0, len(dataframe.index)):
item = dataframe.loc[i]
files = [item._media_lib_file]
if item._media_lib_file2:
files.append(item._media_lib_file2)
resources.append({
"id":
int(item.ID),
"title":
item.Title,
"slug":
slugify(item.Title),
"content":
item.Content,
"date":
item.Date,
"locations":
item.Locations.split("|") if item.Locations else [],
"types":
item.Types.split("|") if item.Types else [],
"categories":
item.Categories.split("|") if item.Categories else [],
"author":
item._media_lib_author,
"files":
files,
})
return resources
def get_population_per_region_geojson() -> Dict[str, Any]:
region_df = pandas.read_csv(
path_to_dataset("mali/Mali_populationV8_model_short.csv"))
region_data = region_df.set_index("ADM1_FR")["Pop.2016"].to_dict()
with open(path_to_dataset("mali/mli_hdx.json"), "r") as fp:
region_geo = json.load(fp)
for f in region_geo["features"]:
region = f["properties"]["admin1Name"]
f["properties"] = {
"region": region,
"value": region_data.get(region, 0)
}
return region_geo
def get_waterpoints_geojson() -> Dict[str, Any]:
dataframe = pandas.read_csv(
path_to_dataset("mali/23112020_Mali_seasonality.csv"))
dataframe = dataframe.where(pandas.notnull(dataframe), None)
features = []
for i in range(0, len(dataframe.index)):
item = dataframe.loc[i]
props = {
"region": item.region,
"functionality_main": item.functionality_main,
"puits_safety": item.puits_safety,
"seasonality": item.seasonality,
"water_months": item.water_months,
"photo": item.photo,
}
try:
feature = Feature(
geometry=Point((float(item.longitude), float(item.latitude))),
properties=props,
id=f"{item.longitude},{item.latitude}",
)
features.append(feature)
except ValueError:
pass
return FeatureCollection(features)
def get_water_quality_summary() -> Dict[str, Any]:
dataframe = pandas.read_csv(path_to_dataset("sierra-leone/SL_subset2.csv"))
summary = (dataframe.groupby([
"risk.level.ecoli"
])["conf_inter_ecoli", "mpn.100ml"].mean().sort_values(
by=["mpn.100ml"]).rename(columns={"mpn.100ml": "mpn_100ml"}))
return summary.to_dict()
def get_reported_water_sources_summary() -> List[Dict[str, Any]]:
sources = OrderedDict((s, {
"source": s
}) for s in ["Improved", "Unimproved", "Surface water"])
dataframe = pandas.read_csv(path_to_dataset("sierra-leone/SL_subset2.csv"))
summary = dataframe.groupby(["sdg_improved_source"]).size().to_dict()
for key, val in summary.items():
sources[key]["value"] = val
return [v for v in sources.values()]
def get_improved_reason_summary() -> List[Dict[str, Any]]:
dataframe = pandas.read_csv(path_to_dataset("sierra-leone/SL_subset2.csv"))
unimproved = dataframe[dataframe["sdg_sanitation"] == "Improved"]
facility_types = (unimproved.groupby(
["toilet_facility_type"]).size().sort_values(ascending=False))
result = {"facility": "Improved"}
for type, size in facility_types.items():
result[type] = size
return [result]
def get_shared_facilities_summary() -> List[Dict[str, Any]]:
facilities = ["Open Defecation", "Unimproved", "Improved"]
result = OrderedDict((facility, {
"facility": facility
}) for facility in facilities)
dataframe = pandas.read_csv(path_to_dataset("sierra-leone/SL_subset2.csv"))
summary = dataframe.groupby(["sdg_sanitation",
"share_facility"]).size().to_dict()
for (facility, shared), size in summary.items():
result[facility][shared] = size
return [val for val in result.values()]
def get_waterpoint_distances_summary() -> List[Dict[str, Any]]:
sources = OrderedDict(
(s, {
"distance": s
}) for s in
["Water on premises", "Less than 30 minutes", "More than 30 minutes"])
dataframe = pandas.read_csv(path_to_dataset("sierra-leone/SL_subset2.csv"))
summary = dataframe.groupby(["sdg_round_trip"]).size().to_dict()
for key, val in summary.items():
sources[key]["value"] = val
return [v for v in sources.values()]
def get_households_geojson() -> Dict[str, Any]:
dataframe = pandas.read_csv(path_to_dataset("sierra-leone/SL_subset2.csv"))
dataframe = dataframe.where(pandas.notnull(dataframe), None)
features = []
for i in range(0, len(dataframe.index)):
item = dataframe.loc[i]
props = {
"region": item.location_region,
"district": item.location_district,
"chiefdom": item.location_chiefdom,
"sdg_improved_source": item.sdg_improved_source,
"sdg_round_trip": item.sdg_round_trip,
"sdg_sanitation": item.sdg_sanitation,
"sdg_hand_washing": item.sdg_hand_washing,
"toilet_facility_type": item.toilet_facility_type,
"flush_to": item.flush_to,
"share_facility": item.share_facility,
"hand_wash_observe": item.hand_wash_observe,
"risk_level_ecoli": item["risk.level.ecoli"],
"mpn_per_100ml": item["mpn.100ml"],
"conf_inter_ecoli": item.conf_inter_ecoli,
"photo_ecoli": item.photo_ecoli,
}
try:
feature = Feature(
geometry=Point((
float(item.geolocation_longitude),
float(item.geolocation_latitude),
)),
properties=props,
id=f"{item.geolocation_longitude},{item.geolocation_latitude}",
)
features.append(feature)
except ValueError:
pass
return FeatureCollection(features)
def get_pump_safety_json() -> Any:
return FileResponse(path_to_dataset("mali/pump-safety.json"))
def get_additional_people_json() -> Any:
return FileResponse(path_to_dataset("mali/additional-people.json"))
def get_possible_progres_json() -> Any:
return FileResponse(path_to_dataset("mali/possible-progress.json"))
def get_treatment_type_json() -> Any:
return FileResponse(path_to_dataset("mali/treatment-type.json"))
def get_pump_type_json() -> Any:
return FileResponse(path_to_dataset("mali/pump-type.json"))
def get_pollution_type_json() -> Any:
return FileResponse(path_to_dataset("mali/pollution-type.json"))
def get_mechanic_vs_manual_pump_json() -> Any:
return FileResponse(path_to_dataset("mali/mechanic-vs-manual-pump.json"))
def get_distance_json() -> Any:
return FileResponse(path_to_dataset("mali/distance.json"))
def get_abandonment_json() -> Any:
return FileResponse(path_to_dataset("mali/abandonment.json"))
def get_region_names() -> List[str]:
dataframe = pandas.read_csv(
path_to_dataset("mali/23112020_Mali_seasonality.csv"))
dataframe = dataframe.where(pandas.notnull(dataframe), None)
return list(dataframe.region.unique())
def get_communes_data() -> List[Dict[str, Any]]:
df = pandas.read_csv(path_to_dataset("mali/Lars_data_commune.csv"))
df = df.where(pandas.notnull(df), None)
return df.to_dict("records")