Create ws_est_plots.py

wind_up/plots/ws_est_plots.py

@@ -0,0 +1,125 @@
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+from wind_up.models import PlotConfig
+
+mpl.use("Agg")
+
+
+def plot_ws_est_gain_xs_one_ttype(
+    pc_low_high: pd.DataFrame,
+    ttype: str,
+    rated_power_kw: float,
+    x0: float,
+    x1: float,
+    x2: float,
+    x3: float,
+    plot_cfg: PlotConfig,
+) -> None:
+    plt.figure()
+    plt.plot(pc_low_high["x_mean"], pc_low_high["y_low"], label="q low")
+    plt.plot(pc_low_high["x_mean"], pc_low_high["y_high"], label="q high")
+    plt.vlines(x0, 0, rated_power_kw, label="ws0", color="#C30")
+    plt.vlines(x1, 0, rated_power_kw, label="ws1", color="#D20")
+    plt.vlines(x2, 0, rated_power_kw, label="ws2", color="#E10")
+    plt.vlines(x3, 0, rated_power_kw, label="ws3", color="#F00")
+    plot_title = f"{ttype} ws_est_gain xs"
+    plt.title(plot_title)
+    plt.xlabel("mean wind speed [m/s]")
+    plt.ylabel("mean PositiveActivePower [kW]")
+    plt.grid()
+    plt.legend()
+    if plot_cfg.show_plots:
+        plt.show()
+    if plot_cfg.save_plots:
+        plt.savefig(plot_cfg.plots_dir / ttype / f"{plot_title}.png")
+    plt.close()
+
+
+def plot_ws_est_one_ttype_or_wtg(
+    df: pd.DataFrame,
+    ttype_or_wtg: str,
+    pc_transposed: pd.DataFrame,
+    plot_cfg: PlotConfig,
+) -> None:
+    plt.figure()
+    plt.plot(pc_transposed.index, pc_transposed["ws_cp_corrected"])
+    plot_title = f"{ttype_or_wtg} transposed power curve"
+    plt.title(plot_title)
+    plt.xlabel("pw_clipped bin centre [kW]")
+    plt.ylabel("ws_cp_corrected [m/s]")
+    plt.grid()
+    if plot_cfg.show_plots:
+        plt.show()
+    if plot_cfg.save_plots:
+        tdir = plot_cfg.plots_dir / ttype_or_wtg
+        tdir.mkdir(exist_ok=True, parents=True)
+        plt.savefig(tdir / f"{plot_title}.png")
+    plt.close()
+
+    plt.figure()
+    plt.scatter(df["ws_cp_corrected"], df["pw_clipped"], s=1, c=df["ws_est_gain"])
+    plt.xlabel("ws_cp_corrected [m/s]")
+    plt.ylabel("pw_clipped [kW]")
+    plot_title = f"{ttype_or_wtg} power curve colored by ws_est_gain"
+    plt.title(plot_title)
+    plt.colorbar()
+    plt.grid()
+    if plot_cfg.show_plots:
+        plt.show()
+    if plot_cfg.save_plots:
+        plt.savefig(plot_cfg.plots_dir / ttype_or_wtg / f"{plot_title}.png")
+    plt.close()
+
+    plt.figure()
+    plt.scatter(df["ws_est_blend"], df["pw_clipped"], s=1)
+    plt.xlabel("ws_est_blend [m/s]")
+    plt.ylabel("pw_clipped [kW]")
+    plot_title = f"{ttype_or_wtg} wind speed estimated from power"
+    plt.title(plot_title)
+    plt.grid()
+    if plot_cfg.show_plots:
+        plt.show()
+    if plot_cfg.save_plots:
+        plt.savefig(plot_cfg.plots_dir / ttype_or_wtg / f"{plot_title}.png")
+    plt.close()
+
+    plt.figure()
+    plt.scatter(df["WindSpeedMean"], df["ws_est_blend"], s=1, c=df["ws_est_gain"])
+    plt.xlabel("WindSpeedMean [m/s]")
+    plt.ylabel("ws_est_blend [m/s]")
+    plot_title = f"{ttype_or_wtg} wind speed estimated vs original colored by ws_est_gain"
+    plt.title(plot_title)
+    plt.grid()
+    if plot_cfg.show_plots:
+        plt.show()
+    if plot_cfg.save_plots:
+        plt.savefig(plot_cfg.plots_dir / ttype_or_wtg / f"{plot_title}.png")
+    plt.close()
+
+    plt.figure()
+    wsbins = np.arange(0, max(df["WindSpeedMean"].max(), df["ws_est_blend"].max()) + 0.5, 0.5)
+    plt.hist(
+        df["WindSpeedMean"],
+        bins=wsbins.tolist(),
+        label="WindSpeedMean",
+    )
+    plt.hist(
+        df["ws_est_blend"],
+        bins=wsbins.tolist(),
+        alpha=0.5,
+        label="ws_est_blend",
+    )
+    plt.xlabel("WindSpeedMean, ws_est_blend [m/s]")
+    plt.ylabel("count")
+    plot_title = f"{ttype_or_wtg} turbines wind speed histograms"
+    plt.title(plot_title)
+    plt.legend()
+    plt.grid()
+    if plot_cfg.show_plots:
+        plt.show()
+    if plot_cfg.save_plots:
+        plt.savefig(plot_cfg.plots_dir / ttype_or_wtg / f"{plot_title}.png")
+    plt.close()