CoCalc -- gen_engine_data

GitHub Repository: junzis/openap
Path: blob/master/scripts/gen_engine_data_v27.py
⁵⁹² views
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import os
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import pandas as pd
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import numpy as np
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import argparse
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import matplotlib.pyplot as plt
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from scipy.optimize import curve_fit
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def clean_name(name):
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    if "SelectOne" in name:
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        name = name.split(" ")[0].strip()
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    elif "Build Spec" in name:
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        name = name.split(" ")[0].strip()
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    elif "Block" in name:
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        name = name.split(" ")[0].strip()
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    elif "series" in name:
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        name = name.split(" ")[0].strip()
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    elif "(" in name:
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        name = name.split("(")[0].strip()
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    return name
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def clean_bpr(bpr):
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    try:
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        bpr = float(bpr)
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    except (ValueError, TypeError):
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        bpr = "-"
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    return bpr
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f = "input/edb-emissions-databank v27 (web).xlsx"
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xl = pd.ExcelFile(f)
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df0 = xl.parse(sheet_name=2)
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df0.dropna(subset=["UID"], inplace=True)
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df = df0.iloc[
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    1:,
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    [
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        0,
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        1,
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        3,
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        4,
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        5,
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        6,
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        11,
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        35,
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        80,
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    ],
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]
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df.columns = [
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    "uid",
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    "name",
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    "type",
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    "bpr",
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    "pr",
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    "max_thrust",
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    "superseded",
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    "superseded_by",
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    "out_production",
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    "out_service",
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    "ei_hc_to",
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    "ei_hc_co",
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    "ei_hc_app",
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    "ei_hc_idl",
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    "ei_co_to",
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    "ei_co_co",
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    "ei_co_app",
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    "ei_co_idl",
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    "ei_nox_to",
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    "ei_nox_co",
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    "ei_nox_app",
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    "ei_nox_idl",
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    "ff_to",
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    "ff_co",
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    "ff_app",
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    "ff_idl",
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    "fuel_lto",
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    "manufacturer",
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]
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df.dropna(subset=["name"], inplace=True)
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df.loc[:, "uid"] = df["uid"].str.strip()
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df.loc[:, "name"] = df["name"].str.strip()
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df.loc[:, "name"] = df["name"].apply(clean_name)
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df.loc[:, "bpr"] = df["bpr"].apply(clean_bpr)
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df.loc[:, "max_thrust"] = (df["max_thrust"] * 1000).astype(int)
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df = df[df["superseded"] != "x"]
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df = df[df["uid"] != ""]
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df = df[df["bpr"] != "-"]
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df = df[df["ei_hc_to"] != "-"]
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df = df[df["ei_hc_co"] != "-"]
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df = df[df["ei_hc_app"] != "-"]
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df = df[df["ei_hc_idl"] != "-"]
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df = df[df["ei_hc_to"] != "*"]
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df = df[df["ei_hc_co"] != "*"]
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df = df[df["ei_hc_app"] != "*"]
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df = df[df["ei_hc_idl"] != "*"]
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df = df[df["ei_co_to"] != "-"]
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df = df[df["ei_co_co"] != "-"]
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df = df[df["ei_co_app"] != "-"]
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df = df[df["ei_co_idl"] != "-"]
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df = df[df["ei_co_to"] != "*"]
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df = df[df["ei_co_co"] != "*"]
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df = df[df["ei_co_app"] != "*"]
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df = df[df["ei_co_idl"] != "*"]
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df = df[df["ei_nox_to"] != "-"]
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df = df[df["ei_nox_co"] != "-"]
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df = df[df["ei_nox_app"] != "-"]
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df = df[df["ei_nox_idl"] != "-"]
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df = df[df["ei_nox_to"] != "*"]
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df = df[df["ei_nox_co"] != "*"]
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df = df[df["ei_nox_app"] != "*"]
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df = df[df["ei_nox_idl"] != "*"]
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df = df[df["ff_to"] != "-"]
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df = df[df["ff_co"] != "-"]
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df = df[df["ff_app"] != "-"]
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df = df[df["ff_idl"] != "-"]
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df = df[df["fuel_lto"] != "-"]
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df.drop_duplicates(subset=["name"], keep="last", inplace=True)
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# Die supersededs vinden we nu niet meer interessant
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df = df.loc[
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    :,
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    [
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        "uid",
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        "name",
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        "manufacturer",
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        "type",
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        "bpr",
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        "pr",
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        "max_thrust",
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        "ei_hc_to",
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        "ei_hc_co",
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        "ei_hc_app",
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        "ei_hc_idl",
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        "ei_co_to",
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        "ei_co_co",
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        "ei_co_app",
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        "ei_co_idl",
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        "ei_nox_to",
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        "ei_nox_co",
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        "ei_nox_app",
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        "ei_nox_idl",
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        "ff_to",
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        "ff_co",
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        "ff_app",
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        "ff_idl",
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        "fuel_lto",
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    ],
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]
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def func_fuel3(x, c3, c2, c1, c0):
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    return c3 * x ** 3 + c2 * x ** 2 + c1 * x + c0
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def func_fuel2(x, a, b):
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    return a * (x + b) ** 2
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# def func_fuel(x, c1, c2):
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#     return c1 * np.exp(c2 * x)
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# compute fuel flow coefficient
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x = [0.07, 0.3, 0.85, 1.0]
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for i, r in df.iterrows():
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    y = [r["ff_idl"], r["ff_app"], r["ff_co"], r["ff_to"]]
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    # coef = np.polyfit(x, y, 2)
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    # df.loc[i, 'fuel_c2'] = coef[0]
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    # df.loc[i, 'fuel_c1'] = coef[1]
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    # df.loc[i, 'fuel_c0'] = coef[2]
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    coef, cov = curve_fit(func_fuel3, x, y)
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    df.loc[i, "fuel_c3"] = coef[0]
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    df.loc[i, "fuel_c2"] = coef[1]
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    df.loc[i, "fuel_c1"] = coef[2]
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    coef, cov = curve_fit(func_fuel2, x, y)
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    df.loc[i, "fuel_a"] = coef[0]
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    df.loc[i, "fuel_b"] = coef[1]
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    # print(r["name"], coef)
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    # xx = np.linspace(-1, 1, 100)
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    # plt.plot(xx, func_fuel(xx, *coef))
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    # plt.scatter(x, y)
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    # plt.draw()
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    # plt.waitforbuttonpress(-1)
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    # plt.clf()
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dfcr = pd.read_csv("input/engine_cruise_performance.csv")
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df = df.merge(dfcr, how="left", left_on="name", right_on="engine")
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df = df.drop("engine", axis=1)
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# def func_co(x, beta, gamma):
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#     return beta * (x - 0.001) ** (-gamma) * np.exp(-2 * (x - 0.001) ** beta)
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# def func_nox(x, c1, p1):
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#     return c1 * x ** p1
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# def func_hc(x, beta, gamma):
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#     return beta * (x + 0.05) ** (-gamma) * np.exp(-4 * (x - 0.001) ** beta)
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# for i, r in df.iterrows():
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#     # process NOx
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#     x_nox = [0, r["ff_idl"], r["ff_app"], r["ff_co"], r["ff_to"]]
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#     y_nox = [0, r["ei_nox_idl"], r["ei_nox_app"], r["ei_nox_co"], r["ei_nox_to"]]
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#     guess = np.array([20, 0.75])
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#     coef_nox, cov = curve_fit(func_nox, x_nox, y_nox, guess)
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#     df.loc[i, "nox_c"] = coef_nox[0]
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#     df.loc[i, "nox_p"] = coef_nox[1]
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#     # process CO
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#     x_co = [r["ff_idl"], r["ff_app"], r["ff_co"], r["ff_to"]]
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#     y_co = [r["ei_co_idl"], r["ei_co_app"], r["ei_co_co"], r["ei_co_to"]]
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#     y_co = np.maximum(y_co, [1e-7, 1e-7, 1e-7, 1e-7])
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#     coef_co, covco = curve_fit(func_co, x_co[0:2], y_co[0:2])
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#     df.loc[i, "co_beta"] = coef_co[0]
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#     df.loc[i, "co_gamma"] = coef_co[1]
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#     df.loc[i, "co_max"] = 2 * y_co[0]
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#     df.loc[i, "co_min"] = (y_co[2] + y_co[3]) / 2
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#     # process HC
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#     x_hc = [r["ff_idl"], r["ff_app"], r["ff_co"], r["ff_to"]]
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#     y_hc = [r["ei_hc_idl"], r["ei_hc_app"], r["ei_hc_co"], r["ei_hc_to"]]
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#     if max(y_hc) == 0:
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#         df.loc[i, "hc_na"] = True
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#     else:
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#         df.loc[i, "hc_na"] = False
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#         df.loc[i, "hc_max"] = 2 * max(y_hc[0], y_hc[1])
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#         df.loc[i, "hc_min"] = (y_hc[2] + y_hc[3]) / 2
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#         y_hc = np.maximum(y_hc, [1e-7, 1e-7, 1e-7, 1e-7])
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#         logX, logY = np.log10(x_hc), np.log10(y_hc)  # current powers
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#         b2 = (logY[3] + logY[2]) / 2  # Average power
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#         a1 = (logY[1] - logY[0]) / (logX[1] - logX[0])
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#         b1 = logY[0] - a1 * logX[0]
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#         x_intersect = 10 ** ((b2 - b1) / a1)
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#         if x_intersect > x_hc[2]:
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#             df.loc[i, "hc_ff85"] = x_hc[2]
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#         else:
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#             df.loc[i, "hc_ff85"] = 0
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#         try:
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#             coef_hc, covhc = curve_fit(func_hc, x_hc, y_hc)
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#             df.loc[i, "hc_beta"] = coef_hc[0]
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#             df.loc[i, "hc_gamma"] = coef_hc[1]
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#             df.loc[i, "hc_a1"] = None
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#             df.loc[i, "hc_b1"] = None
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#             df.loc[i, "hc_b2"] = None
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#         except RuntimeError:
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#             df.loc[i, "hc_beta"] = None
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#             df.loc[i, "hc_gamma"] = None
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#             df.loc[i, "hc_a1"] = a1
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#             df.loc[i, "hc_b1"] = b1
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#             df.loc[i, "hc_b2"] = b2
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df.to_csv("db/engines.csv", float_format="%g", index=False)
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