import math import numpy as np import sys import time from datetime import datetime import csv import os from tqdm import tqdm from netCDF4 import Dataset from scipy import optimize, stats import scipy import matplotlib.pyplot as plt import matplotlib as mpl import random colordict = { 'orange_alt': (1.0, 0.4980392156862745, 0.054901960784313725), 'orange_PIK': (227/255,114/255,34/255), 'red_alt': (0.8392156862745098, 0.15294117647058825, 0.1568627450980392), 'green_alt': (0.17254901960784313, 0.6274509803921569, 0.17254901960784313), 'blue_alt': (0.12156862745098039, 0.4666666666666667, 0.7058823529411765), 'blue_PIK': '#009FDA', 'purple': '#9467bd', 'brown': '#8c564b', 'magenta': '#e377c2', 'lime': '#bcbd22', 'cyan': '#17becf', 'ochre':(0.854901961, 0.647058824, 0.125490196), 'red_grey': (0.901960784,0.784313725,0.784313725), 'blue_grey': (0.784313725,0.784313725,0.901960784), 'grey': '#7f7f7f', 'black': '#181818', 'white': '#ffffff', 'rosa':'#ff007f', 'blue': '#1f77b4', 'orange': '#ff7f0e', 'green': '#2ca02c', 'yellow':(1,1,0), 'red': '#d62728', 'gray': '#7f7f7f', 'darkblue': '#00008b', 'darkred': (162./255.,39./255.,48./255), 'lightblue': '#aec7e8', 'lightorange': '#ffbb78', 'lightgreen': '#98df8a', 'lightred': '#ff9896', 'lightpurple': '#c5b0d5', 'lightbrown': '#c49c94', 'lightmagenta': '#f7b6d2', 'lightgray': '#c7c7c7', 'lightgrey': '#c7c7c7', 'lightlime': '#dbdb8d', 'lightcyan': '#9edae5', 'lighterpurple': '#ccccff' } bloc_dic = {"CHN":"China","EUR":"Europe","ASEAN":"ASEAN", "E-ASI": "East Asia", "NAFTA" : "NAFTA", "ROW": "RoW", "SAARC":"SAARC","S-ASI":"South Asia","OCE":"Australia \& Oceania","OTHER": "Other countries","L-AM":"Latin America","SS-AFR":"Sub-Saharan Africa","EX-SOWJ":"Post-Soviet states","ARAB":"Arab League", "CHN_ECNS":"\"Northern\" China","CHN_SCNS":"\"Southern\" China" } alphabet_array = np.array(['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']) group_dic = {'production_quantity':'firms','production':'firms','production_value':'firms','demand':'firms','demand_quantity':'firms','demand_value':'firms','direct_loss':'firms','direct_loss_quantity':'firms','direct_loss_value':'firms','total_loss':'firms','total_loss_quantity':'firms','total_loss_value':'firms','gdp':'regions','gdp_quantity':'regions','gdp_value':'regions','consumption':'regions','consumption_quantity':'regions','consumption_value':'regions','total_flow':'regions','total_flow_quantity':'regions','total_flow_value':'regions','outflow':'regions','outflow_quantity':'regions','outflow_value':'regions'} def plt_rcParams_sci_adv(): plt.rcParams.update({ 'font.size': 9, 'axes.labelsize': 9, 'legend.fontsize': 7, 'xtick.labelsize': 7, 'ytick.labelsize': 7, 'lines.markeredgewidth': 0, 'legend.framealpha': 1., 'legend.frameon': False, 'lines.markersize': 1, 'lines.linewidth': 1., 'xtick.direction': 'in', 'ytick.direction': 'in', 'xtick.minor.size': 2, 'ytick.minor.size': 2, 'xtick.major.size': 3, 'ytick.major.size': 3, 'xtick.minor.width': .5, 'xtick.major.width': .5, 'ytick.minor.width': .5, 'ytick.major.width': .5, 'axes.linewidth': .5, 'axes.labelpad': 0.5, 'text.usetex': True, 'font.serif': 'Computer Modern Roman', 'font.monospace': 'Computer Modern Typewriter', 'text.latex.preamble': r'\usepackage{amsmath,amssymb}\usepackage{sansmath}\sansmath', 'hatch.color': 'lightgrey', 'hatch.linewidth': 1., }) def get_rows_in_file(f,column_number = 0,delimiter = ',',replacer = None,starting_row = 0): with open(f, 'r') as csvfile: reader = csv.reader(csvfile,delimiter = delimiter) table = [] for num,r in enumerate(reader): if num < starting_row: continue if len(r) != 0 and r[column_number] != '': table.append(r[column_number]) else: if replacer is not None: table.append(replacer) table = np.array(table) return table def netcdf_nan_correcter(arr,replace_value = 0.): idl = np.argwhere(arr/arr == 1.0) decide_array = np.full(shape = arr.shape,fill_value= True) for mem in idl: decide_array[tuple(mem)] = False arr_return = arr arr_return[decide_array] = replace_value return arr_return def subgroup_index(group,subgroup): return np.where(np.isin(group,subgroup))[0] def get_rest(group,unwanted): return np.setdiff1d(group,unwanted) def label_quantity(string,differentiate = False): if 'gdp' in string: return_string = string[0:3].upper() + string[3:] else: return_string = string[0].upper() + string[1:] if differentiate: if ('_value' in string): return_string = return_string.replace('_value',' (v)') else: return_string = return_string + ' (c)' else: return_string = return_string.replace('_value',' (v)') return return_string.replace('_',' ') def number_to_row_of_two(number,alignment='vertical'): tup = [math.floor(number/2.),number % 2] return tup def get_colors(regions): color_list = [] for region in regions: if region == "USA" or region == "NAFTA": color_list.append(colordict['orange_alt']) elif region == "EU" or region == "EU28" or region == "EUR": color_list.append(colordict['blue_alt']) elif region == "CHN": color_list.append(colordict['red_alt']) elif region == "ALL": color_list.append(colordict['black']) elif region == "S-ASI" or region == 'SAARC': color_list.append(colordict['lightpurple']) elif region == "E-ASI": color_list.append(colordict['cyan']) elif region == "ARAB": color_list.append(colordict['yellow']) elif region == "SS-AFR" or region == "AFR": color_list.append(colordict['magenta']) elif region == "ASEAN": color_list.append(colordict['brown']) elif region == "RUS" or region == 'EX-SOWJ': color_list.append(colordict['ochre']) elif region == "MERCOSUR" or region == "S-AM" or region == "L-AM": color_list.append(colordict['green_alt']) elif region == "OCE" or region == "ROW": color_list.append(colordict['purple']) return np.array(color_list)