From 335620c8da2fa4a751bc2f6842f0eb4489640b89 Mon Sep 17 00:00:00 2001 From: Samuel Northover-Naylor Date: Wed, 9 Oct 2024 10:43:49 +0100 Subject: [PATCH] feat: add function to quantify wind speed shift In similar way that power curve, rpm and pitch shifts are already calculated, this change adds wind speed shift to the ops curve shift module --- examples/smarteole_example.ipynb | 38 +++++++++++++++---- tests/test_ops_curve_shift.py | 41 ++++++++++++++++++-- wind_up/ops_curve_shift.py | 64 +++++++++++++++++++------------- 3 files changed, 107 insertions(+), 36 deletions(-) diff --git a/examples/smarteole_example.ipynb b/examples/smarteole_example.ipynb index 16d7d11..9a1040c 100644 --- a/examples/smarteole_example.ipynb +++ b/examples/smarteole_example.ipynb @@ -2235,6 +2235,18 @@ "metadata": {}, "output_type": "display_data" }, + { + "data": { + "text/markdown": [ + "SMV7 Ops Curve Shift warning: abs(CurveTypes.PITCH) > 0.1: 0.223" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "image/png": 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", @@ -2786,7 +2798,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8a3fe823eaff4180889d356dd1d4cab4", + "model_id": "e0289667dd6143ea9b8f2b333973914b", "version_major": 2, "version_minor": 0 }, @@ -2908,7 +2920,7 @@ { "data": { "text/markdown": [ - "{'ref': 'SMV7', 'ref_ws_col': 'ref_ws_est_blend', 'distance_m': 314.4465998943834, 'bearing_deg': 173.69483366777283, 'ref_max_northing_error_v_reanalysis': np.float64(2.6590754551807976), 'ref_max_northing_error_v_wf': np.float64(2.842170943040401e-14), 'ref_max_ws_drift': np.float64(0.08697706942338845), 'ref_max_ws_drift_pp_period': np.float64(0.08697706942338845), 'ref_powercurve_shift': np.float64(0.003117456887993697), 'ref_rpm_shift': np.float64(0.0015313319638985412), 'ref_pitch_shift': np.float64(-0.05548555519736481), 'detrend_pre_r2_improvement': np.float64(0.004942384513915488), 'detrend_post_r2_improvement': np.float64(0.0011487228730558963), 'mean_power_pre': np.float64(1149.2323289820358), 'mean_power_post': np.float64(1148.4157066585956), 'mean_test_yaw_offset_pre': np.float64(-0.022511156888877885), 'mean_test_yaw_offset_post': np.float64(3.8213264238880478), 'mean_test_yaw_offset_command_pre': np.float64(0.0002638323353293413), 'mean_test_yaw_offset_command_post': np.float64(6.636967675544794), 'mean_ref_yaw_offset_command_pre': np.float64(0.0), 'test_ref_warning_counts': 0, 'time_calculated': Timestamp('2024-09-26 13:14:03.984554+0000', tz='UTC'), 'uplift_frc': np.float64(-0.010361770845398625), 'unc_one_sigma_frc': np.float64(0.0057851151530948705), 't_value_one_sigma': np.float64(1.000630119597717), 'missing_bins_unc_scale_factor': 1, 'pp_valid_hours_pre': np.float64(132.5), 'pp_valid_hours_post': np.float64(136.0), 'pp_valid_hours': np.float64(268.5), 'pp_data_coverage': np.float64(0.11496467565831728), 'pp_invalid_bin_count': np.int64(16), 'uplift_noadj_frc': np.float64(-0.011505660672016103), 'unc_one_sigma_noadj_frc': np.float64(0.0057851151530948705), 'poweronly_uplift_frc': np.float64(-0.012003308408347353), 'reversed_uplift_frc': np.float64(-0.009715528755112396), 'reversal_error': np.float64(0.0022877796532349576), 'unc_one_sigma_lowerbound_frc': np.float64(0.0011438898266174788), 'unc_one_sigma_bootstrap_frc': np.float64(0.0049406532714673), 'uplift_p5_frc': np.float64(-0.000846103203498606), 'uplift_p95_frc': np.float64(-0.019877438487298643), 'wind_up_version': '0.1.9', 'test_wtg': 'SMV6', 'test_pw_col': 'test_pw_clipped', 'lt_wtg_hours_raw': 0, 'lt_wtg_hours_filt': 0, 'test_max_ws_drift': np.float64(0.03653354205095605), 'test_max_ws_drift_pp_period': np.float64(0.03653354205095605), 'test_powercurve_shift': np.float64(0.0010615707256107498), 'test_rpm_shift': np.float64(0.0011316163321652972), 'test_pitch_shift': np.float64(-0.037158903030505286), 'preprocess_warning_counts': 0, 'test_warning_counts': 0}" + "{'ref': 'SMV7', 'ref_ws_col': 'ref_ws_est_blend', 'distance_m': 314.4465998943834, 'bearing_deg': 173.69483366777283, 'ref_max_northing_error_v_reanalysis': np.float64(2.6590754551807976), 'ref_max_northing_error_v_wf': np.float64(2.842170943040401e-14), 'ref_max_ws_drift': np.float64(0.08697706942338845), 'ref_max_ws_drift_pp_period': np.float64(0.08697706942338845), 'ref_powercurve_shift': np.float64(0.003117456887993697), 'ref_rpm_shift': np.float64(0.0015313319638985412), 'ref_pitch_shift': np.float64(0.22323442242659364), 'detrend_pre_r2_improvement': np.float64(0.004942384513915488), 'detrend_post_r2_improvement': np.float64(0.0011487228730558963), 'mean_power_pre': np.float64(1149.2323289820358), 'mean_power_post': np.float64(1148.4157066585956), 'mean_test_yaw_offset_pre': np.float64(-0.022511156888877885), 'mean_test_yaw_offset_post': np.float64(3.8213264238880478), 'mean_test_yaw_offset_command_pre': np.float64(0.0002638323353293413), 'mean_test_yaw_offset_command_post': np.float64(6.636967675544794), 'mean_ref_yaw_offset_command_pre': np.float64(0.0), 'test_ref_warning_counts': 1, 'time_calculated': Timestamp('2024-10-09 10:34:23.488635+0000', tz='UTC'), 'uplift_frc': np.float64(-0.010361770845398625), 'unc_one_sigma_frc': np.float64(0.0057851151530948705), 't_value_one_sigma': np.float64(1.000630119597717), 'missing_bins_unc_scale_factor': 1, 'pp_valid_hours_pre': np.float64(132.5), 'pp_valid_hours_post': np.float64(136.0), 'pp_valid_hours': np.float64(268.5), 'pp_data_coverage': np.float64(0.11496467565831728), 'pp_invalid_bin_count': np.int64(16), 'uplift_noadj_frc': np.float64(-0.011505660672016103), 'unc_one_sigma_noadj_frc': np.float64(0.0057851151530948705), 'poweronly_uplift_frc': np.float64(-0.012003308408347353), 'reversed_uplift_frc': np.float64(-0.009715528755112396), 'reversal_error': np.float64(0.0022877796532349576), 'unc_one_sigma_lowerbound_frc': np.float64(0.0011438898266174788), 'unc_one_sigma_bootstrap_frc': np.float64(0.0049406532714673), 'uplift_p5_frc': np.float64(-0.000846103203498606), 'uplift_p95_frc': np.float64(-0.019877438487298643), 'wind_up_version': '0.1.9', 'test_wtg': 'SMV6', 'test_pw_col': 'test_pw_clipped', 'lt_wtg_hours_raw': 0, 'lt_wtg_hours_filt': 0, 'test_max_ws_drift': np.float64(0.03653354205095605), 'test_max_ws_drift_pp_period': np.float64(0.03653354205095605), 'test_powercurve_shift': np.float64(0.0010615707256107498), 'test_rpm_shift': np.float64(0.0011316163321652972), 'test_pitch_shift': np.float64(0.07625563544756919), 'preprocess_warning_counts': 0, 'test_warning_counts': 0}" ], "text/plain": [ "" @@ -2920,7 +2932,7 @@ { "data": { "text/markdown": [ - "warning summary: preprocess_warning_counts=0, test_warning_counts=0, test_ref_warning_counts=0" + "warning summary: preprocess_warning_counts=0, test_warning_counts=0, test_ref_warning_counts=1" ], "text/plain": [ "" @@ -3197,6 +3209,18 @@ "metadata": {}, "output_type": "display_data" }, + { + "data": { + "text/markdown": [ + "SMV7 Ops Curve Shift warning: abs(CurveTypes.PITCH) > 0.1: 0.223" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "image/png": 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", @@ -3748,7 +3772,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2168cd5405004d16b8116014d42d5fe5", + "model_id": "5017f732cf634db293ad4485bca0aff6", "version_major": 2, "version_minor": 0 }, @@ -3870,7 +3894,7 @@ { "data": { "text/markdown": [ - "{'ref': 'SMV7', 'ref_ws_col': 'ref_ws_est_blend', 'distance_m': 591.1178519927024, 'bearing_deg': 190.23567745705736, 'ref_max_northing_error_v_reanalysis': np.float64(2.6590754551807976), 'ref_max_northing_error_v_wf': np.float64(2.842170943040401e-14), 'ref_max_ws_drift': np.float64(0.08697706942338845), 'ref_max_ws_drift_pp_period': np.float64(0.08697706942338845), 'ref_powercurve_shift': np.float64(0.003117456887993697), 'ref_rpm_shift': np.float64(0.0015313319638985412), 'ref_pitch_shift': np.float64(-0.05548555519736481), 'detrend_pre_r2_improvement': np.float64(0.09621188863947527), 'detrend_post_r2_improvement': np.float64(0.11890364717818414), 'mean_power_pre': np.float64(955.493497245509), 'mean_power_post': np.float64(993.6911992736077), 'mean_test_yaw_offset_pre': np.float64(-2.2725466102034675), 'mean_test_yaw_offset_post': np.float64(-2.876981850327039), 'mean_test_yaw_offset_command_pre': np.float64(0.0), 'mean_test_yaw_offset_command_post': np.float64(0.0), 'mean_ref_yaw_offset_command_pre': np.float64(0.0), 'test_ref_warning_counts': 0, 'time_calculated': Timestamp('2024-09-26 13:15:57.997400+0000', tz='UTC'), 'uplift_frc': np.float64(0.030879346731271313), 'unc_one_sigma_frc': np.float64(0.01167847006525424), 't_value_one_sigma': np.float64(1.0006277462668354), 'missing_bins_unc_scale_factor': 1, 'pp_valid_hours_pre': np.float64(133.0), 'pp_valid_hours_post': np.float64(137.16666666666669), 'pp_valid_hours': np.float64(270.1666666666667), 'pp_data_coverage': np.float64(0.11567829872261472), 'pp_invalid_bin_count': np.int64(16), 'uplift_noadj_frc': np.float64(0.030509447623790466), 'unc_one_sigma_noadj_frc': np.float64(0.01004760180633488), 'poweronly_uplift_frc': np.float64(0.029990866525649328), 'reversed_uplift_frc': np.float64(0.03073066474061102), 'reversal_error': np.float64(0.0007397982149616941), 'unc_one_sigma_lowerbound_frc': np.float64(0.00036989910748084706), 'unc_one_sigma_bootstrap_frc': np.float64(0.01167847006525424), 'uplift_p5_frc': np.float64(0.050088720575348945), 'uplift_p95_frc': np.float64(0.01166997288719368), 'wind_up_version': '0.1.9', 'test_wtg': 'SMV5', 'test_pw_col': 'test_pw_clipped', 'lt_wtg_hours_raw': 0, 'lt_wtg_hours_filt': 0, 'test_max_ws_drift': np.float64(0.10726609831004863), 'test_max_ws_drift_pp_period': np.float64(0.10726609831004863), 'test_powercurve_shift': np.float64(-0.005678000921447213), 'test_rpm_shift': np.float64(0.0013951853610039144), 'test_pitch_shift': np.float64(-0.02783487184623068), 'preprocess_warning_counts': 0, 'test_warning_counts': 0}" + "{'ref': 'SMV7', 'ref_ws_col': 'ref_ws_est_blend', 'distance_m': 591.1178519927024, 'bearing_deg': 190.23567745705736, 'ref_max_northing_error_v_reanalysis': np.float64(2.6590754551807976), 'ref_max_northing_error_v_wf': np.float64(2.842170943040401e-14), 'ref_max_ws_drift': np.float64(0.08697706942338845), 'ref_max_ws_drift_pp_period': np.float64(0.08697706942338845), 'ref_powercurve_shift': np.float64(0.003117456887993697), 'ref_rpm_shift': np.float64(0.0015313319638985412), 'ref_pitch_shift': np.float64(0.22323442242659364), 'detrend_pre_r2_improvement': np.float64(0.09621188863947527), 'detrend_post_r2_improvement': np.float64(0.11890364717818414), 'mean_power_pre': np.float64(955.493497245509), 'mean_power_post': np.float64(993.6911992736077), 'mean_test_yaw_offset_pre': np.float64(-2.2725466102034675), 'mean_test_yaw_offset_post': np.float64(-2.876981850327039), 'mean_test_yaw_offset_command_pre': np.float64(0.0), 'mean_test_yaw_offset_command_post': np.float64(0.0), 'mean_ref_yaw_offset_command_pre': np.float64(0.0), 'test_ref_warning_counts': 1, 'time_calculated': Timestamp('2024-10-09 10:35:37.915980+0000', tz='UTC'), 'uplift_frc': np.float64(0.030879346731271313), 'unc_one_sigma_frc': np.float64(0.01167847006525424), 't_value_one_sigma': np.float64(1.0006277462668354), 'missing_bins_unc_scale_factor': 1, 'pp_valid_hours_pre': np.float64(133.0), 'pp_valid_hours_post': np.float64(137.16666666666669), 'pp_valid_hours': np.float64(270.1666666666667), 'pp_data_coverage': np.float64(0.11567829872261472), 'pp_invalid_bin_count': np.int64(16), 'uplift_noadj_frc': np.float64(0.030509447623790466), 'unc_one_sigma_noadj_frc': np.float64(0.01004760180633488), 'poweronly_uplift_frc': np.float64(0.029990866525649328), 'reversed_uplift_frc': np.float64(0.03073066474061102), 'reversal_error': np.float64(0.0007397982149616941), 'unc_one_sigma_lowerbound_frc': np.float64(0.00036989910748084706), 'unc_one_sigma_bootstrap_frc': np.float64(0.01167847006525424), 'uplift_p5_frc': np.float64(0.050088720575348945), 'uplift_p95_frc': np.float64(0.01166997288719368), 'wind_up_version': '0.1.9', 'test_wtg': 'SMV5', 'test_pw_col': 'test_pw_clipped', 'lt_wtg_hours_raw': 0, 'lt_wtg_hours_filt': 0, 'test_max_ws_drift': np.float64(0.10726609831004863), 'test_max_ws_drift_pp_period': np.float64(0.10726609831004863), 'test_powercurve_shift': np.float64(-0.005678000921447213), 'test_rpm_shift': np.float64(0.0013951853610039144), 'test_pitch_shift': np.float64(0.0708669977507359), 'preprocess_warning_counts': 0, 'test_warning_counts': 0}" ], "text/plain": [ "" @@ -3882,7 +3906,7 @@ { "data": { "text/markdown": [ - "warning summary: preprocess_warning_counts=0, test_warning_counts=0, test_ref_warning_counts=0" + "warning summary: preprocess_warning_counts=0, test_warning_counts=0, test_ref_warning_counts=1" ], "text/plain": [ "" @@ -3999,7 +4023,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/tests/test_ops_curve_shift.py b/tests/test_ops_curve_shift.py index 4a5cb94..82fdc1d 100644 --- a/tests/test_ops_curve_shift.py +++ b/tests/test_ops_curve_shift.py @@ -6,13 +6,14 @@ import pytest from wind_up.ops_curve_shift import ( + CURVE_CONSTANTS, CurveConfig, CurveShiftInput, - CurveThresholds, CurveTypes, calculate_pitch_curve_shift, calculate_power_curve_shift, calculate_rpm_curve_shift, + calculate_wind_speed_curve_shift, check_for_ops_curve_shift, ) @@ -115,7 +116,7 @@ def test_calculate_power_curve_shift( y_col="power", ) - if abs(expected) > CurveThresholds.POWER_CURVE.value: + if abs(expected) > CURVE_CONSTANTS[CurveTypes.POWER_CURVE.value]["warning_threshold"]: assert "Ops Curve Shift warning" in caplog.text np.testing.assert_almost_equal(actual=actual, desired=expected) @@ -140,7 +141,7 @@ def test_calculate_rpm_curve_shift( y_col="gen_rpm", ) - if abs(expected) > CurveThresholds.RPM.value: + if abs(expected) > CURVE_CONSTANTS[CurveTypes.RPM.value]["warning_threshold"]: assert "Ops Curve Shift warning" in caplog.text np.testing.assert_almost_equal(actual=actual, desired=expected) @@ -165,7 +166,32 @@ def test_calculate_pitch_curve_shift( y_col="pitch", ) - if abs(expected) > CurveThresholds.PITCH.value: + if abs(expected) > CURVE_CONSTANTS[CurveTypes.PITCH.value]["warning_threshold"]: + assert "Ops Curve Shift warning" in caplog.text + + np.testing.assert_almost_equal(actual=actual, desired=expected) + + +@pytest.mark.parametrize( + ("shift_amount", "expected"), + [ + pytest.param(2.0, 0.21621621621621623, id="shift DOES exceed threshold"), + pytest.param(0.05, -0.04729729729729748, id="shift DOES NOT exceed threshold"), + ], +) +def test_calculate_wind_speed_curve_shift( + shift_amount: float, expected: float, fake_power_curve_df: pd.DataFrame, caplog: pytest.LogCaptureFixture +) -> None: + with caplog.at_level(logging.WARNING): + actual = calculate_wind_speed_curve_shift( + turbine_name="anything", + pre_df=fake_power_curve_df.reset_index(), + post_df=(fake_power_curve_df + shift_amount).reset_index(), + x_col="power", + y_col="wind_speed", + ) + + if abs(expected) > CURVE_CONSTANTS[CurveTypes.WIND_SPEED.value]["warning_threshold"]: assert "Ops Curve Shift warning" in caplog.text np.testing.assert_almost_equal(actual=actual, desired=expected) @@ -222,6 +248,7 @@ def test_missing_required_column( f"{CurveTypes.POWER_CURVE.value}_shift": np.nan, f"{CurveTypes.RPM.value}_shift": np.nan, f"{CurveTypes.PITCH.value}_shift": np.nan, + f"{CurveTypes.WIND_SPEED.value}_shift": np.nan, } assert actual == expected @@ -244,6 +271,7 @@ def test_calls_funcs_as_intended( patch("wind_up.ops_curve_shift.calculate_power_curve_shift", return_value=np.nan) as mock_power, patch("wind_up.ops_curve_shift.calculate_rpm_curve_shift", return_value=np.nan) as mock_rpm, patch("wind_up.ops_curve_shift.calculate_pitch_curve_shift", return_value=np.nan) as mock_pitch, + patch("wind_up.ops_curve_shift.calculate_wind_speed_curve_shift", return_value=np.nan) as mock_ws, patch("wind_up.ops_curve_shift.compare_ops_curves_pre_post", return_value=None) as mock_plot_func, ): mock_wind_up_conf = Mock() @@ -272,6 +300,10 @@ def test_calls_funcs_as_intended( turbine_name=wtg_name, pre_df=_df, post_df=_df, x_col="wind_speed", y_col="pitch" ) + mock_ws.assert_called_once_with( + turbine_name=wtg_name, pre_df=_df, post_df=_df, x_col="power", y_col="wind_speed" + ) + mock_plot_func.assert_called_once_with( pre_df=_df, post_df=_df, @@ -289,6 +321,7 @@ def test_calls_funcs_as_intended( f"{CurveTypes.POWER_CURVE.value}_shift": np.nan, f"{CurveTypes.RPM.value}_shift": np.nan, f"{CurveTypes.PITCH.value}_shift": np.nan, + f"{CurveTypes.WIND_SPEED.value}_shift": np.nan, } assert actual == expected diff --git a/wind_up/ops_curve_shift.py b/wind_up/ops_curve_shift.py index 5694b7a..cf2cf82 100644 --- a/wind_up/ops_curve_shift.py +++ b/wind_up/ops_curve_shift.py @@ -14,24 +14,35 @@ from wind_up.models import PlotConfig, WindUpConfig -class CurveThresholds(Enum): - POWER_CURVE = 0.01 - RPM = 0.005 - PITCH = 0.1 - - class CurveTypes(str, Enum): POWER_CURVE = "powercurve" RPM = "rpm" PITCH = "pitch" + WIND_SPEED = "windspeed" + + +CURVE_CONSTANTS = { + CurveTypes.POWER_CURVE.value: {"warning_threshold": 0.01, "x_bin_width": 1}, + CurveTypes.RPM.value: {"warning_threshold": 0.005, "x_bin_width": 0}, + CurveTypes.PITCH.value: {"warning_threshold": 0.1, "x_bin_width": 1}, + CurveTypes.WIND_SPEED.value: {"warning_threshold": 0.01, "x_bin_width": 1}, +} class CurveConfig(BaseModel): name: CurveTypes x_col: str y_col: str - x_bin_width: int - warning_threshold: float + x_bin_width: int | float | None = None + warning_threshold: float | None = None + + @model_validator(mode="after") + def validate_constants(self) -> CurveConfig: + if self.x_bin_width is None: + self.x_bin_width = CURVE_CONSTANTS[self.name]["x_bin_width"] + if self.warning_threshold is None: + self.warning_threshold = CURVE_CONSTANTS[self.name]["warning_threshold"] + return self class CurveShiftInput(BaseModel): @@ -83,6 +94,7 @@ def check_for_ops_curve_shift( f"{CurveTypes.POWER_CURVE.value}_shift": np.nan, f"{CurveTypes.RPM.value}_shift": np.nan, f"{CurveTypes.PITCH.value}_shift": np.nan, + f"{CurveTypes.WIND_SPEED.value}_shift": np.nan, } required_cols = OpsCurveRequiredColumns(wind_speed=scada_ws_col, power=pw_col, pitch=pt_col, rpm=rpm_col) @@ -104,6 +116,10 @@ def check_for_ops_curve_shift( turbine_name=wtg_name, pre_df=pre_df, post_df=post_df, x_col=scada_ws_col, y_col=pt_col ) + results_dict[f"{CurveTypes.WIND_SPEED.value}_shift"] = calculate_wind_speed_curve_shift( + turbine_name=wtg_name, pre_df=pre_df, post_df=post_df, x_col=pw_col, y_col=scada_ws_col + ) + if plot: compare_ops_curves_pre_post( pre_df=pre_df, @@ -124,13 +140,7 @@ def check_for_ops_curve_shift( def calculate_power_curve_shift( turbine_name: str, pre_df: pd.DataFrame, post_df: pd.DataFrame, x_col: str, y_col: str ) -> float: - curve_config = CurveConfig( - name=CurveTypes.POWER_CURVE.value, - x_col=x_col, - y_col=y_col, - x_bin_width=1, - warning_threshold=CurveThresholds.POWER_CURVE.value, - ) + curve_config = CurveConfig(name=CurveTypes.POWER_CURVE.value, x_col=x_col, y_col=y_col) curve_shift_input = CurveShiftInput( turbine_name=turbine_name, pre_df=pre_df, post_df=post_df, curve_config=curve_config @@ -142,9 +152,7 @@ def calculate_power_curve_shift( def calculate_rpm_curve_shift( turbine_name: str, pre_df: pd.DataFrame, post_df: pd.DataFrame, x_col: str, y_col: str ) -> float: - curve_config = CurveConfig( - name=CurveTypes.RPM.value, x_col=x_col, y_col=y_col, x_bin_width=0, warning_threshold=CurveThresholds.RPM.value - ) + curve_config = CurveConfig(name=CurveTypes.RPM.value, x_col=x_col, y_col=y_col) curve_shift_input = CurveShiftInput( turbine_name=turbine_name, pre_df=pre_df, post_df=post_df, curve_config=curve_config @@ -156,14 +164,20 @@ def calculate_rpm_curve_shift( def calculate_pitch_curve_shift( turbine_name: str, pre_df: pd.DataFrame, post_df: pd.DataFrame, x_col: str, y_col: str ) -> float: - curve_config = CurveConfig( - name=CurveTypes.PITCH.value, - x_col=x_col, - y_col=y_col, - x_bin_width=1, - warning_threshold=CurveThresholds.PITCH.value, + curve_config = CurveConfig(name=CurveTypes.PITCH.value, x_col=x_col, y_col=y_col) + + curve_shift_input = CurveShiftInput( + turbine_name=turbine_name, pre_df=pre_df, post_df=post_df, curve_config=curve_config ) + return _calculate_curve_shift(curve_shift_input=curve_shift_input) + + +def calculate_wind_speed_curve_shift( + turbine_name: str, pre_df: pd.DataFrame, post_df: pd.DataFrame, x_col: str, y_col: str +) -> float: + curve_config = CurveConfig(name=CurveTypes.WIND_SPEED.value, x_col=x_col, y_col=y_col) + curve_shift_input = CurveShiftInput( turbine_name=turbine_name, pre_df=pre_df, post_df=post_df, curve_config=curve_config ) @@ -194,7 +208,7 @@ def _calculate_curve_shift(curve_shift_input: CurveShiftInput) -> float: post_df = curve_shift_input.post_df wtg_name = curve_shift_input.turbine_name - bins = np.arange(0, pre_df[conf.x_col].max() + conf.x_bin_width, conf.x_bin_width) if conf.x_bin_width > 0 else 10 + bins = np.arange(0, pre_df[conf.x_col].max() + conf.x_bin_width, conf.x_bin_width) if conf.x_bin_width > 0 else 10 # type: ignore[operator] mean_curve = pre_df.groupby(pd.cut(pre_df[conf.x_col], bins=bins, retbins=False), observed=True).agg( x_mean=pd.NamedAgg(column=conf.x_col, aggfunc="mean"),