Plot power curves for turbine 1 with data filters

import itertools

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# import data
df = pd.read_csv("data/SCADA_downtime_merged.csv", skip_blank_lines=True)

# list of turbines to plot (just 1)
list1 = [1]
# list of categories to plot
list2 = list(df["TurbineCategory_id"].unique())
# remove NaN from list
list2 = [g for g in list2 if g >= 0]
# round from float to integer
list2 = [a.astype(np.int64) for a in list2]
# sort categories in ascending order
list2 = sorted(list2, key=int)
# categories to remove from plot
list2 = [b for b in list2 if b not in (1, 12, 13, 14, 15, 17, 21, 22)]
list3 = list(itertools.product(list1, list2))

# filter only data for turbine x and plot
for x, y in list3:
    df2x = df[(df["turbine_id"] == x)].copy()
    # sort values by timestamp in descending order
    df2x = df2x.sort_values(by="timestamp", ascending=False)

    # copying fault to new column (mins)
    # (fault when turbine category id is y)
    def f(c):
        if c["TurbineCategory_id"] == y:
            return 0
        else:
            return 1

    df2x["mins"] = df2x.apply(f, axis=1)
    # reset index
    df2x.reset_index(drop=True, inplace=True)
    # assigning value to first cell if it's not 0
    if df2x.loc[0, "mins"] == 0:
        df2x.set_value(0, "mins", 0)
    else:
        df2x.set_value(0, "mins", 999999999)

    # using previous value's row to evaluate time
    for i, e in enumerate(df2x["mins"]):
        if e == 1:
            df2x.at[i, "mins"] = df2x.at[i - 1, "mins"] + 10

    # sort in ascending order
    df2x = df2x.sort_values(by="timestamp")
    # reset index
    df2x.reset_index(drop=True, inplace=True)

    # convert to hours and round to nearest hour
    df2x["hours"] = df2x["mins"].astype(np.int64)
    df2x["hours"] = df2x["hours"] / 60
    df2x["hours"] = round(df2x["hours"]).astype(np.int64)

    # > 48 hours - label as normal (9999)
    def f1(c):
        if c["hours"] > 48:
            return 9999
        else:
            return c["hours"]

    df2x["hours"] = df2x.apply(f1, axis=1)

    # filter out curtailment - curtailed when turbine is pitching
    # outside 0 deg <= normal <= 3.5 deg
    def f2(c):
        if (
            0 <= c["pitch"] <= 3.5
            or c["hours"] != 9999
            or (
                (c["pitch"] > 3.5 or c["pitch"] < 0)
                and (
                    c["ap_av"] <= (0.1 * df2x["ap_av"].max())
                    or c["ap_av"] >= (0.9 * df2x["ap_av"].max())
                )
            )
        ):
            return "normal"
        else:
            return "curtailed"

    df2x["curtailment"] = df2x.apply(f2, axis=1)

    # filter unusual readings, i.e., for normal operation, power <= 0 in
    # operating wind speeds, power > 100 before cut-in, runtime < 600
    def f3(c):
        if c["hours"] == 9999 and (
            (
                3 < c["ws_av"] < 25
                and (
                    c["ap_av"] <= 0
                    or c["runtime"] < 600
                    or c["EnvironmentalCategory_id"] > 1
                    or c["GridCategory_id"] > 1
                    or c["InfrastructureCategory_id"] > 1
                    or c["AvailabilityCategory_id"] == 2
                    or 12 <= c["TurbineCategory_id"] <= 15
                    or 21 <= c["TurbineCategory_id"] <= 22
                )
            )
            or (c["ws_av"] < 3 and c["ap_av"] > 100)
        ):
            return "unusual"
        else:
            return "normal"

    df2x["unusual"] = df2x.apply(f3, axis=1)

    # filter data for plots
    # normal w/ curtailment (all data)
    df3 = df2x[df2x.hours == 9999]
    # before fault
    df4 = df2x[df2x.hours != 9999]
    df4 = df4[df4.hours != 0]
    # fault
    df5 = df2x[df2x.hours == 0]
    # normal w/o curtailment
    df6 = df3[df3.curtailment == "normal"]
    # normal w/o curtailment and unusual readings
    df7 = df6[df6.unusual == "normal"]

    # get x and y coordinates
    # normal w/ curtailment
    x1 = df3["ws_av"]
    y1 = df3["ap_av"]
    # before fault
    x2 = df4["ws_av"]
    y2 = df4["ap_av"]
    # faulty
    x3 = df5["ws_av"]
    y3 = df5["ap_av"]
    # normal w/o curtailment
    x4 = df6["ws_av"]
    y4 = df6["ap_av"]
    # normal w/o curtailment and unusual readings
    x5 = df7["ws_av"]
    y5 = df7["ap_av"]

    fig = plt.figure(figsize=(18.5, 4.5), dpi=1500)

    ax1 = fig.add_subplot(131)
    ax1.scatter(x1, y1, c="#098A63", label="normal", marker=".")
    ax1.scatter(x2, y2, c="#3F2B78", label="before fault", marker=".")
    ax1.scatter(x3, y3, c="c", label="faulty", marker=".")
    ax1.legend()
    plt.xlabel("Wind speed (m/s)")
    plt.ylabel("Average active power (kW)")
    plt.title("all data points")

    ax2 = fig.add_subplot(132)
    ax2.scatter(x4, y4, c="#098A63", marker=".")
    ax2.scatter(x2, y2, c="#3F2B78", marker=".")
    ax2.scatter(x3, y3, c="c", marker=".")
    plt.xlabel("Wind speed (m/s)")
    plt.ylabel("Average active power (kW)")
    plt.title("w/o curtailment")

    ax3 = fig.add_subplot(133)
    ax3.scatter(x5, y5, c="#098A63", marker=".")
    ax3.scatter(x2, y2, c="#3F2B78", marker=".")
    ax3.scatter(x3, y3, c="c", marker=".")
    plt.xlabel("Wind speed (m/s)")
    plt.ylabel("Average active power (kW)")
    plt.title("w/o curtailment and anomalies")

    fig.suptitle(
        "Power curves for turbine %s" % x + " with turbine category %s" % y
    )
    plt.tight_layout()
    plt.subplots_adjust(top=0.88)
    plt.show()