FEAT: South Carolina

src/cmdc_tools/datasets/__init__.py

@@ -45,6 +45,7 @@
     Pennsylvania,
     RhodeIsland,
     SanDiego,
+    SouthCarolina,
     Tennessee,
     TennesseeCounties,
     Vermont,

src/cmdc_tools/datasets/official/ID/__init__.py

@@ -0,0 +1 @@
+from .data import Idaho

src/cmdc_tools/datasets/official/ID/data.py

@@ -0,0 +1,16 @@
+import pandas as pd
+import us
+
+from ...base import DatasetBaseNoDate
+from ..base import ArcGIS
+
+
+class Idaho(DatasetBaseNoDate, ArcGIS):
+    ARCGIS_ID = "ZOm3lyRvRxR6535t"
+    has_fips = False
+    state_fips = int(us.states.lookup("Idaho").fips)
+    source = "https://services3.arcgis.com/ZOm3lyRvRxR6535t/ArcGIS/rest/services/Dashboard_ID_COVID_CUMULATIVE_BY_COUNTY/FeatureServer"
+
+    def get(self):
+        df = self.get_all_sheet_to_df("Dashboard_ID_COVID_CUMULATIVE_BY_COUNTY", 0, 3)
+        return df

src/cmdc_tools/datasets/official/SC/__init__.py

@@ -0,0 +1 @@
+from .data import SouthCarolina

src/cmdc_tools/datasets/official/SC/data.py

@@ -0,0 +1,102 @@
+import pandas as pd
+import us
+
+from ...base import DatasetBaseNoDate
+from ..base import ArcGIS
+
+
+class SouthCarolina(ArcGIS, DatasetBaseNoDate):
+    ARCGIS_ID = "XZg2efAbaieYAXmu"
+    has_fips = True
+    state_fips = int(us.states.lookup("South Carolina").fips)
+    source = "https://services2.arcgis.com/XZg2efAbaieYAXmu/arcgis/rest/services/"
+
+    def get(self):
+        overview = self._get_overview()
+        hosp = self._get_hosp()
+
+        result = pd.concat([overview, hosp], sort=False).sort_values(["dt", "fips"])
+        result.fips = result.fips.astype(int)
+        return result
+
+    def _get_overview(self):
+        df = self.get_all_sheet_to_df(service="COVID19", sheet=0, srvid=2)
+
+        renamed = df.rename(
+            columns={
+                "NAME": "county",
+                "Areakey": "fips",
+                "Confirmed": "cases_confirmed",
+                "Recovered": "recovered_total",
+                "Death": "deaths_total",
+                "Probable_Deaths": "deaths_suspected",
+                "Probable_Positives": "cases_suspected",
+                # "Total_Neg_Tests": "negative_tests_total",
+                # "Total_Pos_Tests": "positive_tests_total",
+                "Date_": "dt",
+            }
+        )
+
+        # Convert dt to datetime
+        renamed["dt"] = pd.to_datetime(
+            renamed["dt"].map(lambda x: pd.datetime.fromtimestamp(x / 1000).date())
+        )
+        renamed = (
+            renamed.set_index("dt")
+            .tz_localize("US/Eastern")
+            .tz_convert("UTC")
+            .reset_index()
+        )
+        return (
+            renamed[
+                [
+                    # "county",
+                    "fips",
+                    "cases_confirmed",
+                    "recovered_total",
+                    "deaths_total",
+                    "deaths_suspected",
+                    "cases_suspected",
+                    # "negative_tests_total",
+                    # "positive_tests_total",
+                    "dt",
+                ]
+            ]
+            .melt(id_vars=["dt", "fips"], var_name="variable_name")
+            .assign(vintage=pd.Timestamp.utcnow())
+        )
+
+    def _get_hosp(self):
+        df = self.get_all_sheet_to_df("hospital_beds", 0, 2)
+
+        renamed = df.rename(
+            columns={
+                "Date": "dt",
+                "numbeds": "hospital_beds_capacity_count",
+                "numbedsocc": "hospital_beds_in_use_any",
+                "numicubeds": "icu_beds_capacity_count",
+                "numicubedsocc": "icu_beds_in_use_any",
+            }
+        )
+        renamed["dt"] = pd.to_datetime(
+            renamed["dt"].map(lambda x: pd.datetime.fromtimestamp(x / 1000).date())
+        )
+        renamed = (
+            renamed.set_index("dt")
+            .tz_localize("US/Eastern")
+            .tz_convert("UTC")
+            .reset_index()
+        )
+        return (
+            renamed[
+                [
+                    "dt",
+                    "hospital_beds_capacity_count",
+                    "hospital_beds_in_use_any",
+                    "icu_beds_capacity_count",
+                    "icu_beds_in_use_any",
+                ]
+            ]
+            .melt(id_vars=["dt"], var_name="variable_name")
+            .assign(vintage=pd.Timestamp.utcnow(), fips=self.state_fips)
+        )

src/cmdc_tools/datasets/official/__init__.py

@@ -23,6 +23,7 @@
 from .OK import OKTulsa
 from .PA import Pennsylvania
 from .RI import RhodeIsland
+from .SC import SouthCarolina
 from .TN import Tennessee, TennesseeCounties
 from .VT import Vermont
 from .WI import Wisconsin, WIDane