Revise circ_diff function

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "res-wind-up"
-version = "0.1.6"
+version = "0.1.7"
 authors = [
     { name = "Alex Clerc", email = "alex.clerc@res-group.com" }
 ]

tests/test_detrend.py

@@ -98,7 +98,4 @@ def test_calc_wsratio_v_wd_scen(test_lsa_t13_config: WindUpConfig) -> None:
         plot_cfg=None,
     )
 
-    # on Github Actions get error DataFrame.iloc[:, 0] (column name="hours") values are different (0.13889 %)
-    # At positional index 50, first diff: 45.5 != 45.666666666666664
-    # therefore set rtol=5e-3 so the test passes on Github Actions
-    assert_frame_equal(actual_pre_df, expected_pre_df, rtol=5e-3)
+    assert_frame_equal(actual_pre_df, expected_pre_df)

tests/test_math_funcs.py

@@ -24,3 +24,10 @@ def test_circ_diff(angle1: float | np.generic, angle2: float | np.generic, expec
         assert_series_equal(circ_diff(angle1, angle2), (expected))
     else:
         assert circ_diff(angle1, angle2) == pytest.approx(expected)
+
+
+def test_within_bin() -> None:
+    # this test replicates logic in detrend.py where an older version of circ_diff gets the wrong result
+    d = 242
+    dir_bin_width = 10.0
+    assert not np.abs(circ_diff(d - 5, d)) < dir_bin_width / 2

wind_up/constants.py

@@ -10,6 +10,7 @@
 RANDOM_SEED = 0
 SCATTER_S = 1
 SCATTER_ALPHA = 0.2
+SCATTER_MARKERSCALE = 4
 
 
 class DataColumns:

wind_up/math_funcs.py

@@ -1,14 +1,22 @@
 import numpy as np
+import numpy.typing as npt
 
 
-def circ_diff(angle1: float | np.generic, angle2: float | np.generic) -> float | np.generic:
-    angle1_rad = np.radians(angle1)
-    angle2_rad = np.radians(angle2)
-    sin_angle1 = np.sin(angle1_rad)
-    cos_angle1 = np.cos(angle1_rad)
-    sin_angle2 = np.sin(angle2_rad)
-    cos_angle2 = np.cos(angle2_rad)
-    denominator = cos_angle2 * cos_angle2 + sin_angle2 * sin_angle2
-    temp1 = (cos_angle1 * cos_angle2 + sin_angle1 * sin_angle2) / denominator
-    temp2 = (sin_angle1 * cos_angle2 - cos_angle1 * sin_angle2) / denominator
-    return np.degrees(np.arctan2(temp2, temp1))
+def circ_diff(angle1: float | npt.NDArray | list, angle2: float | npt.NDArray | list) -> float | npt.NDArray:
+    """Calculate the circular difference between two angles.
+
+    Args:
+        angle1: First angle in degrees.
+        angle2: Second angle in degrees.
+
+    Returns:
+        Circular difference between the two angles in degrees
+    """
+
+    # Convert list to numpy array
+    if isinstance(angle1, list):
+        angle1 = np.array(angle1)
+    if isinstance(angle2, list):
+        angle2 = np.array(angle2)
+
+    return np.mod(angle1 - angle2 + 180, 360) - 180

wind_up/plots/detrend_plots.py

@@ -3,7 +3,7 @@
 import pandas as pd
 from matplotlib import pyplot as plt
 
-from wind_up.constants import SCATTER_ALPHA, SCATTER_S
+from wind_up.constants import SCATTER_ALPHA, SCATTER_MARKERSCALE, SCATTER_S
 from wind_up.models import PlotConfig
 from wind_up.result_manager import result_manager
 
@@ -116,7 +116,7 @@ def plot_apply_wsratio_v_wd_scen(
     )
     plot_title = f"{test_name} {ref_name} detrended wind speed {title_end}"
     plt.title(plot_title)
-    plt.legend()
+    plt.legend(loc="best", markerscale=SCATTER_MARKERSCALE)
     plt.xlabel("ref wind speed [m/s]")
     plt.ylabel(f"{test_ws_col} [m/s]")
     plt.grid()
@@ -132,7 +132,7 @@ def plot_apply_wsratio_v_wd_scen(
     plt.scatter(p_df[detrend_ws_col], p_df[test_pw_col], s=SCATTER_S, alpha=SCATTER_ALPHA, label="after detrend")
     plot_title = f"{test_name} {ref_name} detrended power curve {title_end}"
     plt.title(plot_title)
-    plt.legend()
+    plt.legend(loc="best", markerscale=SCATTER_MARKERSCALE)
     plt.xlabel("ref wind speed [m/s]")
     plt.ylabel(f"{test_pw_col} [kW]")
     plt.grid()
@@ -176,7 +176,7 @@ def plot_check_detrend_scatters(
     plt.xlabel("ref wind speed before/after detrend [m/s]")
     plt.ylabel(f"{test_ws_col} [m/s]")
     plt.grid()
-    plt.legend(fontsize="small")
+    plt.legend(fontsize="small", loc="best", markerscale=SCATTER_MARKERSCALE)
     plt.subplot(2, 1, 2)
     r2_before = post_df[ref_ws_col].corr(post_df[test_ws_col]) ** 2
     r2_after = post_df[detrend_ws_col].corr(post_df[test_ws_col]) ** 2
@@ -197,7 +197,7 @@ def plot_check_detrend_scatters(
     plt.xlabel("ref wind speed before/after detrend [m/s]")
     plt.ylabel(f"{test_ws_col} [m/s]")
     plt.grid()
-    plt.legend(fontsize="small")
+    plt.legend(fontsize="small", loc="best", markerscale=SCATTER_MARKERSCALE)
     plot_title = f"{test_name} vs {ref_name} wind speed detrend check"
     plt.suptitle(plot_title)
     plt.tight_layout()

wind_up/plots/pp_analysis_plots.py

@@ -2,7 +2,7 @@
 import numpy as np
 import pandas as pd
 
-from wind_up.constants import RAW_WINDSPEED_COL, SCATTER_ALPHA, SCATTER_S, DataColumns
+from wind_up.constants import RAW_WINDSPEED_COL, SCATTER_ALPHA, SCATTER_MARKERSCALE, SCATTER_S, DataColumns
 from wind_up.models import PlotConfig
 from wind_up.result_manager import result_manager
 
@@ -129,7 +129,7 @@ def plot_pre_post_power_curves(
     plt.figure()
     plt.scatter(pre_df[ws_col], pre_df[pw_col], s=SCATTER_S, alpha=SCATTER_ALPHA, label="pre upgrade")
     plt.scatter(post_df[ws_col], post_df[pw_col], s=SCATTER_S, alpha=SCATTER_ALPHA, label="post upgrade")
-    plt.legend()
+    plt.legend(loc="best", markerscale=SCATTER_MARKERSCALE)
     plt.grid()
     plot_title = f"test={test_name} ref={ref_name} power curve data"
     plt.title(plot_title)
@@ -302,41 +302,14 @@ def plot_pre_post_conditions(
     plt.close()
 
     wd_width = 30
-    plt.figure()
-    plt.hist(
-        pre_df[wd_col],
-        weights=[1 / rows_per_hour] * len(pre_df[wd_col]),
-        bins=list(np.arange(0, 360 + wd_width / 2, wd_width)),
-        label="pre",
-    )
-    plt.hist(
-        post_df[wd_col],
-        weights=[1 / rows_per_hour] * len(post_df[wd_col]),
-        bins=list(np.arange(0, 360 + wd_width / 2, wd_width)),
-        alpha=0.5,
-        label="post",
-    )
-    plot_title = f"{test_name} {ref_name} {wd_col} coverage"
-    plt.title(plot_title)
-    plt.xticks(np.arange(wd_width / 2, 360 + wd_width / 2, wd_width))
-    plt.ylabel("hours")
-    plt.legend()
-    plt.grid()
-    plt.tight_layout()
-    if plot_cfg.show_plots:
-        plt.show()
-    if plot_cfg.save_plots:
-        plt.savefig(plot_cfg.plots_dir / test_name / f"{plot_title}.png")
-    plt.close()
-
     plot_pre_post_condition_histogram(
         pre_df,
         post_df,
         test_name=test_name,
         ref_name=ref_name,
         rows_per_hour=rows_per_hour,
         col=wd_col,
-        bin_width=30,
+        bin_width=wd_width,
         plot_cfg=plot_cfg,
         first_bin_start=0,
         last_bin_end=360,

wind_up/plots/scada_funcs_plots.py

@@ -6,7 +6,7 @@
 import seaborn as sns
 from tabulate import tabulate
 
-from wind_up.constants import SCATTER_ALPHA, SCATTER_S, DataColumns
+from wind_up.constants import SCATTER_ALPHA, SCATTER_MARKERSCALE, SCATTER_S, DataColumns
 from wind_up.models import PlotConfig, WindUpConfig
 from wind_up.plots.misc_plots import bubble_plot
 
@@ -239,7 +239,7 @@ def plot_toggle_ops_curves_one_ttype_or_wtg(
     plt.xlabel(f"{ws_col} [m/s]")
     plt.ylabel(f"{pw_col} [kW]")
     plt.grid()
-    plt.legend()
+    plt.legend(loc="best", markerscale=SCATTER_MARKERSCALE)
     if plot_cfg.show_plots:
         plt.show()
     if plot_cfg.save_plots:
@@ -268,7 +268,7 @@ def plot_toggle_ops_curves_one_ttype_or_wtg(
     plt.xlabel(f"{pw_col} [kW]")
     plt.ylabel(f"{rpm_col} [RPM]")
     plt.grid()
-    plt.legend()
+    plt.legend(loc="best", markerscale=SCATTER_MARKERSCALE)
 
     plt.subplot(2, 2, 2)
     plt.scatter(df_off[ws_col], df_off[rpm_col], s=SCATTER_S, alpha=SCATTER_ALPHA)

wind_up/plots/scada_power_curve_plots.py

@@ -1,7 +1,7 @@
 import matplotlib.pyplot as plt
 import pandas as pd
 
-from wind_up.constants import RAW_POWER_COL, RAW_WINDSPEED_COL, SCATTER_ALPHA, SCATTER_S
+from wind_up.constants import RAW_POWER_COL, RAW_WINDSPEED_COL, SCATTER_ALPHA, SCATTER_MARKERSCALE, SCATTER_S
 from wind_up.models import PlotConfig, WindUpConfig
 
 
@@ -50,7 +50,7 @@ def plot_removed_data_one_ttype_or_wtg(
         plt.title(plot_title)
         plt.xlabel(f"{RAW_WINDSPEED_COL} [m/s]")
         plt.ylabel(f"{RAW_POWER_COL} [kW]")
-        plt.legend()
+        plt.legend(loc="best", markerscale=SCATTER_MARKERSCALE)
         plt.grid()
         plt.tight_layout()
         if plot_cfg.show_plots:

wind_up/plots/waking_state_plots.py

@@ -1,7 +1,7 @@
 import matplotlib.pyplot as plt
 import pandas as pd
 
-from wind_up.constants import RAW_POWER_COL, RAW_WINDSPEED_COL, SCATTER_ALPHA, SCATTER_S
+from wind_up.constants import RAW_POWER_COL, RAW_WINDSPEED_COL, SCATTER_ALPHA, SCATTER_MARKERSCALE, SCATTER_S
 from wind_up.models import PlotConfig
 
 
@@ -36,7 +36,7 @@ def plot_waking_state_one_ttype_or_wtg(wf_df: pd.DataFrame, ttype_or_wtg: str, p
     plot_title = f"{ttype_or_wtg} power curve by waking state"
     plt.title(plot_title)
     plt.grid()
-    plt.legend(loc="lower right")
+    plt.legend(loc="lower right", markerscale=SCATTER_MARKERSCALE)
     plt.tight_layout()
     if plot_cfg.show_plots:
         plt.show()

wind_up/plots/yaw_direction_plots.py

@@ -42,7 +42,7 @@ def plot_yaw_direction_pre_post_per_signal(
     plt.xlabel(f"{ref_wd_col} [deg]")
     plt.ylabel(f"{test_wd_col} [deg]")
     plt.grid()
-    plt.legend()
+    plt.legend(loc="best", markerscale=2)
 
     plt.subplot(2, 1, 2)
     plt.scatter(