pd deprecations in tests

rdtools/test/clearsky_temperature_test.py

@@ -47,7 +47,7 @@ def test_with_tricky_timezones():
     tz = 'America/Santiago'
     start_date = datetime.datetime(2018, 8, 10, 0, 0, 0)
     end_date = datetime.datetime(2018, 8, 14, 23, 0, 0)
-    freq = 'H'
+    freq = "h"
     lat = -24
     lon = -70
 

rdtools/test/degradation_test.py

@@ -23,7 +23,7 @@ def get_corr_energy(cls, rd, input_freq):
         daily_rd = rd / 365.0
 
         start = '2012-01-01'
-        if input_freq == 'S':
+        if input_freq == "s":
             end = '2012-03-01'
         else:
             end = '2015-01-01'
@@ -53,12 +53,10 @@ def setUpClass(cls):
         # define module constants and parameters
 
         # All frequencies
-        cls.list_all_input_freq = ['MS', 'M', 'W',
-                                   'D', 'H', 'T', 'S', 'Irregular_D']
+        cls.list_all_input_freq = ["MS", "M", "W", "D", "h", "min", "s", "Irregular_D"]
 
         # Allowed frequencies for degradation_ols
-        cls.list_ols_input_freq = ['MS', 'M', 'W',
-                                   'D', 'H', 'T', 'S', 'Irregular_D']
+        cls.list_ols_input_freq = ["MS", "M", "W", "D", "h", "min", "s", "Irregular_D"]
 
         '''
         Allowed frequencies for degradation_classical_decomposition

rdtools/test/energy_from_power_test.py

@@ -6,7 +6,7 @@
 
 @pytest.fixture
 def times():
-    return pd.date_range(start="20200101 12:00", end="20200101 13:00", freq="15T")
+    return pd.date_range(start="20200101 12:00", end="20200101 13:00", freq="15min")
 
 
 @pytest.fixture
@@ -48,50 +48,48 @@ def test_energy_from_power_max_timedelta(power):
 
 
 def test_energy_from_power_upsample(power):
-    expected = power.resample("10T").asfreq().interpolate() / 6
+    expected = power.resample("10min").asfreq().interpolate() / 6
     expected = expected.iloc[1:]
     expected.name = "energy_Wh"
-    result = energy_from_power(power, target_frequency="10T")
+    result = energy_from_power(power, target_frequency="10min")
     pd.testing.assert_series_equal(result, expected)
 
 
 def test_energy_from_power_downsample(power):
-    expected = power.resample("20T").asfreq()
+    expected = power.resample("20min").asfreq()
     expected = expected.iloc[1:]
     expected = pd.Series([0.75, 0.833333333, 0.416666667], index=expected.index)
     expected.name = "energy_Wh"
-    result = energy_from_power(power, target_frequency="20T")
+    result = energy_from_power(power, target_frequency="20min")
     pd.testing.assert_series_equal(result, expected)
 
 
 def test_energy_from_power_max_timedelta_edge_case():
-    times = pd.date_range("2020-01-01 12:00", periods=4, freq="15T")
+    times = pd.date_range("2020-01-01 12:00", periods=4, freq="15min")
     power = pd.Series(1, index=times)
     power = power.drop(power.index[2])
-    result = energy_from_power(
-        power, "30T", max_timedelta=pd.to_timedelta("20 minutes")
-    )
+    result = energy_from_power(power, "30min", max_timedelta=pd.to_timedelta("20 minutes"))
     assert result.isnull().all()
 
 
 def test_energy_from_power_single_value_input():
-    times = pd.date_range("2019-01-01", freq="15T", periods=1)
+    times = pd.date_range("2019-01-01", freq="15min", periods=1)
     power = pd.Series([100.0], index=times)
     expected_result = pd.Series([25.0], index=times, name="energy_Wh")
     result = energy_from_power(power)
     pd.testing.assert_series_equal(result, expected_result)
 
 
 def test_energy_from_power_single_value_input_no_freq():
-    power = pd.Series([1], pd.date_range("2019-01-01", periods=1, freq="15T"))
+    power = pd.Series([1], pd.date_range("2019-01-01", periods=1, freq="15min"))
     power.index.freq = None
     match = "Could not determine period of input power"
     with pytest.raises(ValueError, match=match):
         energy_from_power(power)
 
 
 def test_energy_from_power_single_value_instantaneous():
-    power = pd.Series([1], pd.date_range("2019-01-01", periods=1, freq="15T"))
+    power = pd.Series([1], pd.date_range("2019-01-01", periods=1, freq="15min"))
     power.index.freq = None
     match = (
         "power_type='instantaneous' is incompatible with single element power. "
@@ -102,7 +100,7 @@ def test_energy_from_power_single_value_instantaneous():
 
 
 def test_energy_from_power_single_value_with_target():
-    times = pd.date_range("2019-01-01", freq="15T", periods=1)
+    times = pd.date_range("2019-01-01", freq="15min", periods=1)
     power = pd.Series([100.0], index=times)
     expected_result = pd.Series([100.0], index=times, name="energy_Wh")
     result = energy_from_power(power, target_frequency="H")

rdtools/test/filtering_test.py

@@ -114,8 +114,7 @@ def generate_power_time_series_no_clipping():
     power_no_datetime_index = pd.Series(np.arange(1, 101))
     power_datetime_index = pd.Series(np.arange(1, 101))
     # Add datetime index to second series
-    time_range = pd.date_range('2016-12-02T11:00:00.000Z',
-                               '2017-06-06T07:00:00.000Z', freq='H')
+    time_range = pd.date_range("2016-12-02T11:00:00.000Z", "2017-06-06T07:00:00.000Z", freq="h")
     power_datetime_index.index = pd.to_datetime(time_range[:100])
     # Create a series that is tz-naive to test on
     power_datetime_index_tz_naive = power_datetime_index.copy()
@@ -174,8 +173,7 @@ def generate_power_time_series_clipping():
     power_no_datetime_index = power_no_datetime_index.reset_index(drop=True)
     power_datetime_index = power_no_datetime_index.copy()
     # Add datetime index to second series
-    time_range = pd.date_range('2016-12-02T11:00:00.000Z',
-                               '2017-06-06T07:00:00.000Z', freq='H')
+    time_range = pd.date_range("2016-12-02T11:00:00.000Z", "2017-06-06T07:00:00.000Z", freq="h")
     power_datetime_index.index = pd.to_datetime(time_range[:100])
     # Note: Power is expected to be Series object with a datetime index.
     return power_no_datetime_index, power_datetime_index
@@ -506,7 +504,7 @@ def test_directional_tukey_filter():
 
 def test_hour_angle_filter():
     # Create a pandas Series with 5 entries and 15 min index
-    index = pd.date_range(start='29/04/2022 15:00', periods=5, freq='H')
+    index = pd.date_range(start="29/04/2022 15:00", periods=5, freq="h")
     series = pd.Series([1, 2, 3, 4, 5], index=index)
 
     # Define latitude and longitude

rdtools/test/interpolate_test.py

@@ -7,15 +7,15 @@
 
 @pytest.fixture
 def time_series():
-    times = pd.date_range("2018-04-01 12:00", "2018-04-01 13:15", freq="15T")
+    times = pd.date_range("2018-04-01 12:00", "2018-04-01 13:15", freq="15min")
     time_series = pd.Series(data=[9, 6, 3, 3, 6, 9], index=times, name="foo")
     time_series = time_series.drop(times[4])
     return time_series
 
 
 @pytest.fixture
 def target_index(time_series):
-    return pd.date_range(time_series.index.min(), time_series.index.max(), freq="20T")
+    return pd.date_range(time_series.index.min(), time_series.index.max(), freq="20min")
 
 
 @pytest.fixture

rdtools/test/irradiance_rescale_test.py

@@ -7,7 +7,7 @@
 
 @pytest.fixture
 def simple_irradiance():
-    times = pd.date_range("2019-06-01 12:00", freq="15T", periods=5)
+    times = pd.date_range("2019-06-01 12:00", freq="15min", periods=5)
     time_series = pd.Series([1, 2, 3, 4, 5], index=times, dtype=float)
     return time_series
 

rdtools/test/normalize_with_expected_power_test.py

@@ -5,12 +5,12 @@
 
 @pytest.fixture()
 def times_15():
-    return pd.date_range(start='20200101 12:00', end='20200101 13:00', freq='15T')
+    return pd.date_range(start="20200101 12:00", end="20200101 13:00", freq="15min")
 
 
 @pytest.fixture()
 def times_30():
-    return pd.date_range(start='20200101 12:00', end='20200101 13:00', freq='30T')
+    return pd.date_range(start="20200101 12:00", end="20200101 13:00", freq="30min")
 
 
 @pytest.fixture()

rdtools/test/plotting_test.py

@@ -182,8 +182,7 @@ def test_soiling_rate_histogram_kwargs(soiling_info):
 def clipping_power_degradation_signal():
     clipping_power_series = pd.Series(np.arange(1, 101))
     # Add datetime index to second series
-    time_range = pd.date_range('2016-12-02T11:00:00.000Z',
-                               '2017-06-06T07:00:00.000Z', freq='H')
+    time_range = pd.date_range("2016-12-02T11:00:00.000Z", "2017-06-06T07:00:00.000Z", freq="h")
     clipping_power_series.index = pd.to_datetime(time_range[:100])
     return clipping_power_series