ZonalFunctions.py

import os
import datetime
import pandas as pd
# import seaborn as sns
import numpy as np
import scipy.signal as sps
# import xarray as xr

# %matplotlib inline
from matplotlib import pyplot as plt
import matplotlib.colors

# from cartopy import config
# import cartopy.crs as ccrs
from numpy import ma 

# from tqdm.notebook import trange, tqdm
from time import time
from glob import glob
import matplotlib.lines as mlines


def readRFRfeatures(filename):
    RAW_DATA = {}

    f = open(filename,'r')
    first = True

    for line in f.readlines():
        l = line.strip().split(',')
        if first:
            labels = [x[1:-1] for x in l]
            for k in labels:
                if k != 't':
                    RAW_DATA[k] = []
            first = False
        else:
            for i,v in enumerate(l):
                if i == 0:
                    continue
                RAW_DATA[labels[i]].append(float(v))

    return RAW_DATA


# def plotPredictions(INPUT,TARGET,ax,name,SUBSET):
    
#     ax.plot(TARGET,label=name)
#     ax.legend(loc=1)
#     ax.plot(INPUT,label=name+"*",color='orange')
#     ax.legend(loc=1)
    
#     from sklearn.metrics import r2_score
#     from sklearn.metrics import explained_variance_score
#     from sklearn.metrics import mean_absolute_error
#     from sklearn.metrics import mean_squared_error

    
#     SCORE_r2 = r2_score(TARGET, INPUT)  
#     n = 1200 # days = samples, but this shouldn't be hardcoded, this is only for the daily max right now!
#     p = len(SUBSET)+1 # input features + RV = independent variables, probably shouldn't hard code the addition of the RV
#     SCORE_r2adj = 1-(1-SCORE_r2)*(n-1)/(n-p-1)  ## calculate adjusted R2
#     SCORE_expVar = explained_variance_score(TARGET, INPUT)
#     SCORE_meanErr = mean_absolute_error(TARGET, INPUT)
#     SCORE_RMSE = np.sqrt(mean_squared_error(TARGET, INPUT, squared=True))
    
#     return SCORE_r2adj, SCORE_RMSE, SCORE_meanErr


def plotScatter(INPUT,TARGET,ax,name):
    
    lmin = min(min(INPUT),max(INPUT),min(TARGET),max(TARGET))
    lmax = max(min(INPUT),max(INPUT),min(TARGET),max(TARGET))
    line = mlines.Line2D([lmin, lmax], [lmin, lmax], color='black')
    ax.scatter(INPUT,TARGET,label=name, alpha = 0.15)
    ax.add_line(line)
#     ax.legend(loc=1)

    from sklearn.metrics import r2_score
    from sklearn.metrics import mean_squared_error
    import math
    
    SCORE_r2 = r2_score(TARGET, INPUT)
    SCORE_RMSE = math.sqrt(mean_squared_error(TARGET, INPUT, squared=True))
    
#     return SCORE_r2, SCORE_RMSE