Add TZ (UTC) in output data

.gitignore

@@ -3,4 +3,12 @@ uv.lock
 .venv/
 
 # Jupyter checkpoints
-**/.ipynb_checkpoints/
+**/.ipynb_checkpoints/
+
+/resourcecode/__pycache__
+/resourcecode/*/__pycache__
+*/__pycache__
+/build
+/doc
+/.doctrees
+/resourcecode.egg-info

README.md

@@ -85,18 +85,18 @@ See the following example:
 ...    """)
 
 >>> data
-                        fp     hs
-2017-01-01 00:00:00  0.412  0.246
-2017-01-01 01:00:00  0.410  0.212
-2017-01-01 02:00:00  0.414  0.172
-2017-01-01 03:00:00  0.437  0.138
-2017-01-01 04:00:00  0.464  0.102
-...                    ...    ...
-2017-03-19 02:00:00  0.088  0.056
-2017-03-19 03:00:00  0.088  0.066
-2017-03-19 04:00:00  0.089  0.078
-2017-03-19 05:00:00  0.090  0.084
-2017-03-19 06:00:00  0.732  0.086
+                              fp     hs
+2017-01-01 00:00:00+00:00  0.412  0.246
+2017-01-01 01:00:00+00:00  0.410  0.212
+2017-01-01 02:00:00+00:00  0.414  0.172
+2017-01-01 03:00:00+00:00  0.437  0.138
+2017-01-01 04:00:00+00:00  0.464  0.102
+...                          ...    ...
+2017-03-19 02:00:00+00:00  0.088  0.056
+2017-03-19 03:00:00+00:00  0.088  0.066
+2017-03-19 04:00:00+00:00  0.089  0.078
+2017-03-19 05:00:00+00:00  0.090  0.084
+2017-03-19 06:00:00+00:00  0.732  0.086
 <BLANKLINE>
 [1855 rows x 2 columns]
 

resourcecode/client.py

@@ -276,7 +276,7 @@ def get_dataframe_from_criteria(self, criteria: Union[str, dict]) -> pd.DataFram
         return pd.DataFrame(
             result_array[:, 1:],
             columns=parsed_criteria["parameter"],
-            index=pd.to_datetime(index_array.astype(np.int64), unit="ms"),
+            index=pd.to_datetime(index_array.astype(np.int64), unit="ms", utc=True),
         )
 
     def _get_rawdata_from_criteria(self, single_parameter_criteria):

resourcecode/opsplanning/__init__.py

@@ -156,12 +156,12 @@ def oplen_calc(critsubs, oplen, critical_operation=False, date=1, monstrt=True):
         Nominal length of the operation (if no downtime), in hours
     critical_operation : BOOLEAN
         - False for non-critical operations:
-          With this flag to False, it is assumed that the operation can be
-          halted for the duration of weather downtime and started again
+            With this flag to False, it is assumed that the operation can be
+            halted for the duration of weather downtime and started again
         - True is for continuous window search:
-          In this case operations can't be halted and if stopped due to
-          weather downtime have to restart from the beginning once the
-          conditions allow
+            In this case operations can't be halted and if stopped due to
+            weather downtime have to restart from the beginning once the
+            conditions allow
 
         The default is non critical operation.
     date : INT,DATETIME optional
@@ -207,6 +207,7 @@ def oplen_calc(critsubs, oplen, critical_operation=False, date=1, monstrt=True):
             start=dt.datetime(yerng[0], morng[0], 1),
             end=dt.datetime(yerng[1], morng[1], 1),
             freq="MS",
+            tz="utc",
         )
         daterng = daterng.shift(dayval - 1, freq="D")
         oplendetect = pd.Series(np.zeros(daterng.shape[0], dtype="timedelta64[s]"), index=daterng)
@@ -215,7 +216,7 @@ def oplen_calc(critsubs, oplen, critical_operation=False, date=1, monstrt=True):
             daterng = pd.date_range(start=date, end=date)
             oplendetect = pd.Series(np.zeros(daterng.shape[0], dtype="timedelta64[s]"), index=daterng)
         else:
-            msg = "Variable date in single result calculation should be" " a datetime object"
+            msg = "Variable date in single result calculation should be a datetime object"
             raise NameError(msg)
     else:
         raise NameError("Input option monstrt should be boolean")

tests/test_client.py

@@ -158,8 +158,8 @@ def test_get_criteria_single_parameter(client):
 
     assert len(data) == 744
     assert (data.columns == ["fp"]).all()
-    assert data.index[0] == pd.to_datetime("2017-01-01 00:00:00")
-    assert data.index[-1] == pd.to_datetime("2017-01-31 23:00:00")
+    assert data.index[0] == pd.to_datetime("2017-01-01 00:00:00", utc=True)
+    assert data.index[-1] == pd.to_datetime("2017-01-31 23:00:00", utc=True)
     assert data.fp[0] == pytest.approx(0.074)
     assert data.fp[-1] == pytest.approx(0.097)
 
@@ -169,8 +169,8 @@ def test_get_criteria_single_tp_parameter(client):
 
     assert len(data) == 744
     assert (data.columns == ["tp"]).all()
-    assert data.index[0] == pd.to_datetime("2017-01-01 00:00:00")
-    assert data.index[-1] == pd.to_datetime("2017-01-31 23:00:00")
+    assert data.index[0] == pd.to_datetime("2017-01-01 00:00:00", utc=True)
+    assert data.index[-1] == pd.to_datetime("2017-01-31 23:00:00", utc=True)
     assert data.tp[0] == pytest.approx(13.51351)
     assert data.tp[-1] == pytest.approx(10.30928)
 
@@ -180,8 +180,8 @@ def test_get_criteria_multiple_parameters(client):
 
     assert len(data) == 744
     assert (data.columns == ["fp", "hs"]).all()
-    assert data.index[0] == pd.to_datetime("2017-01-01 00:00:00")
-    assert data.index[-1] == pd.to_datetime("2017-01-31 23:00:00")
+    assert data.index[0] == pd.to_datetime("2017-01-01 00:00:00", utc=True)
+    assert data.index[-1] == pd.to_datetime("2017-01-31 23:00:00", utc=True)
     assert data.fp[0] == pytest.approx(0.074)
     assert data.fp[-1] == pytest.approx(0.097)
     assert data.hs[0] == pytest.approx(0.296)
@@ -200,7 +200,7 @@ def test_get_criteria_multiple_parameters_and_none_values(client):
     assert pd.isnull(data.uust[0])
 
     # the third value is not null.
-    assert data.index[2] == pd.to_datetime("2017-01-01 02:00:00")
+    assert data.index[2] == pd.to_datetime("2017-01-01 02:00:00", utc=True)
     assert data.uust[2] == pytest.approx(0.1699999962)
 
     data = client.get_dataframe_from_criteria('{"parameter": ["uust", "fp"]}')
@@ -210,8 +210,8 @@ def test_get_criteria_multiple_parameters_and_none_values(client):
 
     # in that case, we have two variables. The second one, fp, is not null,
     # therefore the index can be completed despite that uust is null.
-    assert data.index[0] == pd.to_datetime("2017-01-01 00:00:00")
-    assert data.index[-1] == pd.to_datetime("2017-01-31 23:00:00")
+    assert data.index[0] == pd.to_datetime("2017-01-01 00:00:00", utc=True)
+    assert data.index[-1] == pd.to_datetime("2017-01-31 23:00:00", utc=True)
 
     assert data.fp[0] == pytest.approx(0.074)
     assert data.fp[-1] == pytest.approx(0.097)

tests/test_opsplanning.py

@@ -34,6 +34,7 @@ def data():
         parse_dates=True,
         dayfirst=True,
     )
+    df = df.tz_localize("UTC")
     return df