Merge pull request #29 from SheffieldSolar/feature/28-update-base-url

updated base URL to api.solar.sheffield.ac.uk/...

Author: JamieTaylor-TUOS

.github/workflows/python-app.yml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ "3.7", "3.8", "3.9", "3.10" ]
+        python-version: [ "3.10", "3.11", "3.12" ]
 
     steps:
     - uses: actions/checkout@v2

Tests/test_pvlive_api.py

@@ -125,95 +125,95 @@ def test_latest(self):
 
     def test_day_peak(self):
         """Tests the day_peak function."""
-        data = self.api.day_peak(d=date(2022, 7, 19), entity_type="pes", entity_id=0)
+        data = self.api.day_peak(d=date(2023, 12, 1), entity_type="pes", entity_id=0)
         self.check_pes_tuple(data)
         self.check_pes_tuple_dtypes(data)
-        data = self.api.day_peak(d=date(2022, 7, 19), entity_type="pes", entity_id=0, dataframe=True)
+        data = self.api.day_peak(d=date(2023, 12, 1), entity_type="pes", entity_id=0, dataframe=True)
         self.check_df_columns(data)
         self.check_df_dtypes(data)
-        data = self.api.day_peak(d=date(2022, 7, 19),
+        data = self.api.day_peak(d=date(2023, 12, 1),
                                  extra_fields="ucl_mw,lcl_mw,installedcapacity_mwp,stats_error",
                                  entity_type="pes", entity_id=0, dataframe=True)
-        data = self.api.day_peak(d=date(2022, 7, 19), entity_type="pes", entity_id=0, period=5)
+        data = self.api.day_peak(d=date(2023, 12, 1), entity_type="pes", entity_id=0, period=5)
         self.check_pes_tuple(data)
         self.check_pes_tuple_dtypes(data)
-        data = self.api.day_peak(d=date(2022, 7, 19), entity_type="pes", entity_id=0, period=5,
+        data = self.api.day_peak(d=date(2023, 12, 1), entity_type="pes", entity_id=0, period=5,
                                  dataframe=True)
         self.check_df_columns(data)
         self.check_df_dtypes(data)
-        data = self.api.day_peak(d=date(2022, 7, 19),
+        data = self.api.day_peak(d=date(2023, 12, 1),
                                  extra_fields="ucl_mw,lcl_mw,installedcapacity_mwp,stats_error",
                                  entity_type="pes", entity_id=0, period=5, dataframe=True)
         self.check_df_dtypes(data)
-        data = self.api.day_peak(d=date(2022, 7, 19), entity_type="gsp", entity_id=54)
+        data = self.api.day_peak(d=date(2023, 12, 1), entity_type="gsp", entity_id=54)
         self.check_gsp_tuple(data)
         self.check_gsp_tuple_dtypes(data)
-        data = self.api.day_peak(d=date(2022, 7, 19), entity_type="gsp", entity_id=54,
+        data = self.api.day_peak(d=date(2023, 12, 1), entity_type="gsp", entity_id=54,
                                  dataframe=True)
         self.check_df_columns(data)
         self.check_df_dtypes(data)
 
     def test_day_energy(self):
         """Tests the day_energy function."""
-        data = self.api.day_energy(d=date(2022, 7, 19), entity_type="pes", entity_id=0)
+        data = self.api.day_energy(d=date(2023, 12, 1), entity_type="pes", entity_id=0)
         assert isinstance(data, float)
 
     def test_between(self):
         """Test the between function."""
-        data = self.api.between(start=datetime(2022, 7, 19, 12, 20, tzinfo=pytz.utc),
-                                end=datetime(2022, 7, 19, 14, 0, tzinfo=pytz.utc),
+        data = self.api.between(start=datetime(2023, 12, 1, 12, 20, tzinfo=pytz.utc),
+                                end=datetime(2023, 12, 1, 14, 0, tzinfo=pytz.utc),
                                 entity_type="pes", entity_id=0)
         with self.subTest():
             assert isinstance(data, list)
-        data = self.api.between(start=datetime(2022, 7, 19, 12, 20, tzinfo=pytz.utc),
-                                end=datetime(2022, 7, 19, 14, 0, tzinfo=pytz.utc),
+        data = self.api.between(start=datetime(2023, 12, 1, 12, 20, tzinfo=pytz.utc),
+                                end=datetime(2023, 12, 1, 14, 0, tzinfo=pytz.utc),
                                 entity_type="pes", entity_id=0, dataframe=True)
         self.check_df_columns(data)
         self.check_df_dtypes(data)
-        data = self.api.between(start=datetime(2022, 7, 19, tzinfo=pytz.utc),
-                                end=datetime(2022, 7, 19, 14, 00, tzinfo=pytz.utc),
+        data = self.api.between(start=datetime(2023, 12, 1, tzinfo=pytz.utc),
+                                end=datetime(2023, 12, 1, 14, 00, tzinfo=pytz.utc),
                                 entity_type="pes", entity_id=0, period=5)
         with self.subTest():
             assert isinstance(data, list)
-        data = self.api.between(start=datetime(2022, 7, 19, 12, 20, tzinfo=pytz.utc),
-                                end=datetime(2022, 7, 19, 14, 00, tzinfo=pytz.utc),
+        data = self.api.between(start=datetime(2023, 12, 1, 12, 20, tzinfo=pytz.utc),
+                                end=datetime(2023, 12, 1, 14, 00, tzinfo=pytz.utc),
                                 entity_type="pes", entity_id=0, period=5, dataframe=True)
         self.check_df_columns(data)
         self.check_df_dtypes(data)
 
     def test_at_time(self):
         """Test the at_time function."""
-        data = self.api.at_time(dt=datetime(2022, 7, 19, 12, 35, tzinfo=pytz.utc), entity_type="pes",
+        data = self.api.at_time(dt=datetime(2023, 12, 1, 12, 35, tzinfo=pytz.utc), entity_type="pes",
                                             entity_id=0)
         self.check_pes_tuple(data)
         self.check_pes_tuple_dtypes(data)
-        data = self.api.at_time(datetime(2022, 7, 19, 12, 35, tzinfo=pytz.utc), entity_type="pes",
+        data = self.api.at_time(datetime(2023, 12, 1, 12, 35, tzinfo=pytz.utc), entity_type="pes",
                                 entity_id=0, dataframe=True)
         self.check_df_columns(data)
         self.check_df_dtypes(data)
-        data = self.api.at_time(datetime(2022, 7, 19, 12, 35, tzinfo=pytz.utc), entity_type="pes",
+        data = self.api.at_time(datetime(2023, 12, 1, 12, 35, tzinfo=pytz.utc), entity_type="pes",
                                 entity_id=0,
                                 extra_fields="ucl_mw,lcl_mw,installedcapacity_mwp,stats_error",
                                 dataframe=True)
         self.check_df_dtypes(data)
-        data = self.api.at_time(dt=datetime(2022, 7, 19, 12, 35, tzinfo=pytz.utc), entity_type="pes",
+        data = self.api.at_time(dt=datetime(2023, 12, 1, 12, 35, tzinfo=pytz.utc), entity_type="pes",
                                             entity_id=0, period=5)
         self.check_pes_tuple(data)
         self.check_pes_tuple_dtypes(data)
-        data = self.api.at_time(datetime(2022, 7, 19, 12, 35, tzinfo=pytz.utc), entity_type="pes",
+        data = self.api.at_time(datetime(2023, 12, 1, 12, 35, tzinfo=pytz.utc), entity_type="pes",
                                 entity_id=0, period=5, dataframe=True)
         self.check_df_columns(data)
         self.check_df_dtypes(data)
-        data = self.api.at_time(datetime(2022, 7, 19, 12, 35, tzinfo=pytz.utc), entity_type="pes",
+        data = self.api.at_time(datetime(2023, 12, 1, 12, 35, tzinfo=pytz.utc), entity_type="pes",
                                 entity_id=0,
                                 extra_fields="ucl_mw,lcl_mw,installedcapacity_mwp,stats_error",
                                 period=5, dataframe=True)
         self.check_df_dtypes(data)
-        data = self.api.at_time(dt=datetime(2022, 7, 19, 12, 35, tzinfo=pytz.utc), entity_type="gsp",
+        data = self.api.at_time(dt=datetime(2023, 12, 1, 12, 35, tzinfo=pytz.utc), entity_type="gsp",
                                 entity_id=26)
         self.check_gsp_tuple(data)
         self.check_gsp_tuple_dtypes(data)
-        data = self.api.at_time(datetime(2022, 7, 19, 12, 35, tzinfo=pytz.utc), entity_type="gsp",
+        data = self.api.at_time(datetime(2023, 12, 1, 12, 35, tzinfo=pytz.utc), entity_type="gsp",
                                          entity_id=26, dataframe=True)
         self.check_df_columns(data)
         self.check_df_dtypes(data)

pvlive_api/pvlive.py

@@ -47,7 +47,7 @@ class PVLive:
         {"http": "<address>", "https": "<address>"}
     """
     def __init__(self, retries: int = 3, proxies: Optional[Dict] = None):
-        self.base_url = "https://api0.solar.sheffield.ac.uk/pvlive/api/v4/"
+        self.base_url = "https://api.solar.sheffield.ac.uk/pvlive/api/v4"
         self.max_range = {"national": timedelta(days=365), "regional": timedelta(days=30)}
         self.retries = retries
         self.proxies = proxies
@@ -363,7 +363,7 @@ def _convert_tuple_to_df(self, data, columns):
 
     def _build_url(self, entity_type, entity_id, params):
         """Construct the appropriate URL for a given set of parameters."""
-        base_url = f"{self.base_url}{entity_type}/{entity_id}"
+        base_url = f"{self.base_url}/{entity_type}/{entity_id}"
         url = base_url + "?" + "&".join(["{}={}".format(k, params[k]) for k in params])
         return url