RESCUES/Func_event_num.py at main · PREP-NexT/RESCUES · GitHub

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# data_processing.py
import numpy as np
import pandas as pd

def cal_event_num(models, ssp, TT, ExtremeSet, SevereSet, ModerateSet):
    PATH = "./pr/results/"
    eventSet = [f"{model}_{ss}" for model in models for ss in ssp]
    num_pExtreme = np.zeros((len(eventSet), TT))
    num_pSevere = np.zeros((len(eventSet), TT))
    num_pModerate = np.zeros((len(eventSet), TT))
    num_nExtreme = np.zeros((len(eventSet), TT))
    num_nSevere = np.zeros((len(eventSet), TT))
    num_nModerate = np.zeros((len(eventSet), TT))
    num_cExtreme = np.zeros((len(eventSet), TT))
    num_cSevere = np.zeros((len(eventSet), TT))
    num_cModerate = np.zeros((len(eventSet), TT))

    for s, scenario in enumerate(eventSet):
        data = pd.read_csv(PATH + "SPI_" + scenario + ".csv", delimiter=",")
        data = data.iloc[:, 3:38]

        # Convert columns to numeric
        data = data.apply(pd.to_numeric, errors='coerce')

        num_pExtreme[s, :] = data.apply(lambda col: ((col >= 2) & ~col.isnull()).sum())
        num_pSevere[s, :] = data.apply(lambda col: ((col >= 1.5) & (col < 2) & ~col.isnull()).sum())
        num_pModerate[s, :] = data.apply(lambda col: ((col >= 1) & (col < 1.5) & ~col.isnull()).sum())
        num_nExtreme[s, :] = data.apply(lambda col: ((col <= -2) & ~col.isnull()).sum())
        num_nSevere[s, :] = data.apply(lambda col: ((col <= -1.5) & (col > -2) & ~col.isnull()).sum())
        num_nModerate[s, :] = data.apply(lambda col: ((col <= -1) & (col > -1.5) & ~col.isnull()).sum())
        num_cExtreme[s, :] = (num_pExtreme[s, :] + num_nExtreme[s, :]) / 2
        num_cSevere[s, :] = (num_pSevere[s, :] + num_nSevere[s, :]) / 2
        num_cModerate[s, :] = (num_pModerate[s, :] + num_nModerate[s, :]) / 2

    cnum_pExtreme = np.zeros((len(eventSet), TT))
    cnum_nExtreme = np.zeros((len(eventSet), TT))
    cnum_cExtreme = np.zeros((len(eventSet), TT))
    cnum_pSevere = np.zeros((len(eventSet), TT))
    cnum_nSevere = np.zeros((len(eventSet), TT))
    cnum_cSevere = np.zeros((len(eventSet), TT))
    cnum_pModerate = np.zeros((len(eventSet), TT))
    cnum_nModerate = np.zeros((len(eventSet), TT))
    cnum_cModerate = np.zeros((len(eventSet), TT))


    for i in range(0, num_pExtreme.shape[1]):
        cnum_pExtreme[:, i] = np.sum(num_pExtreme[:, 0:i+1], axis=1)
        cnum_nExtreme[:, i] = np.sum(num_nExtreme[:, 0:i+1], axis=1)
        cnum_cExtreme[:, i] = np.sum(num_cExtreme[:, 0:i+1], axis=1)
        cnum_pSevere[:, i] = np.sum(num_pSevere[:, 0:i+1], axis=1)
        cnum_nSevere[:, i] = np.sum(num_nSevere[:, 0:i+1], axis=1)
        cnum_cSevere[:, i] = np.sum(num_cSevere[:, 0:i+1], axis=1)
        cnum_pModerate[:, i] = np.sum(num_pModerate[:, 0:i+1], axis=1)
        cnum_nModerate[:, i] = np.sum(num_nModerate[:, 0:i+1], axis=1)
        cnum_cModerate[:, i] = np.sum(num_cModerate[:, 0:i+1], axis=1)

    num_Extreme = np.concatenate((num_pExtreme, num_nExtreme, num_cExtreme), axis=0)
    num_Severe = np.concatenate((num_pSevere, num_nSevere, num_cSevere), axis=0)
    num_Moderate = np.concatenate((num_pModerate, num_nModerate, num_cModerate), axis=0)

    num_Extreme = pd.DataFrame(num_Extreme.T, columns=ExtremeSet, index=range(2016, 2051))
    num_Severe = pd.DataFrame(num_Severe.T, columns=SevereSet, index=range(2016, 2051))
    num_Moderate = pd.DataFrame(num_Moderate.T, columns=ModerateSet, index=range(2016, 2051))

    cnum_Extreme = np.concatenate((cnum_pExtreme, cnum_nExtreme, cnum_cExtreme), axis=0)
    cnum_Severe = np.concatenate((cnum_pSevere, cnum_nSevere, cnum_cSevere), axis=0)
    cnum_Moderate = np.concatenate((cnum_pModerate, cnum_nModerate, cnum_cModerate), axis=0)

    cnum_Extreme = pd.DataFrame(cnum_Extreme.T, columns=ExtremeSet, index=range(2016, 2051))
    cnum_Severe = pd.DataFrame(cnum_Severe.T, columns=SevereSet, index=range(2016, 2051))
    cnum_Moderate = pd.DataFrame(cnum_Moderate.T, columns=ModerateSet, index=range(2016, 2051))


    return num_Extreme, num_Severe, num_Moderate, cnum_Extreme, cnum_Severe, cnum_Moderate