data_analysis_GRL.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

###############################################################
#       DATA ANALYSIS - VERY WET MONSOON SEASONS IN INDIA     #
#      RESULTS PUBLISHED IN GEOPHYSICAL RESEARCH LETTERS      #
#                    Code by Anja Katzenberger                #
###############################################################  
#Link to publication: 
#https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022GL098856

# Baseline period: 1965-2015
# Future period: 2051-2100

import matplotlib.pyplot as plt
import pandas as pd
import pyam
import numpy as np
import statistics as stat
import matplotlib.patches as mpatches


#----------------------------
### SSP Color Code
#----------------------------
# Officlal colors for the underlying scenarios
colors = pyam.plotting.PYAM_COLORS
pd.DataFrame({'name': list(colors.keys()), 'color': list(colors.values())})

col_ssp119 = '#1e9583'
col_ssp126 = '#1d3354'
col_ssp245 = '#e9dc3d'
col_ssp370 = '#f11111'
col_ssp434 = '#63bce4'
col_ssp460 = '#e78731'
col_ssp534over = '#996dc8'
col_ssp585 = '#830b22'




#%%----------------------------
### CMIP6 Model Data 
#----------------------------
# Original data can be downloaded freely:
# WCRP. CMIP6 data. Retrieved from https://esgf-node.llnl.gov/search/cmip6/

# Each entry corresponds to precipitation data ("pr") for one model for the years 1850-2100
data_ssp585 = [[6.898493436165154, 6.60713198594749, 5.55362356826663, 7.239451655186713, 6.190937804058194, 6.785632576793432, 6.8564045475795865, 6.326415343210101, 6.529397168196738, 6.137325265444815, 6.470440537668765, 6.515462044626474, 6.075697531923652, 6.911161215975881, 7.191547192633152, 6.426170258782804, 6.898929714225233, 6.486211926676333, 6.9154083263129, 6.218232214450836, 6.388313393108547, 6.29576335195452, 6.814864464104176, 6.537870643660426, 6.632219231687486, 6.2613357324153185, 6.883697072044015, 6.6082591423764825, 6.096300040371716, 6.771503202617168, 6.560807302594185, 6.778907356783748, 6.345773139037192, 7.548247883096337, 6.107550230808556, 7.065824302844703, 6.7052147118374705, 6.181127834133804, 6.348043796606362, 6.7332333186641335, 6.823298335075378, 7.400136510841548, 6.739267031662166, 6.469809380359948, 7.064670114777982, 6.723551591858268, 6.722507416270673, 6.761820847168565, 6.500060926191509, 6.4567826455459, 7.097052759490907, 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models_ssp585 = ['NESM3', 'CNRM-CM6-1', 'CNRM-ESM2-1', 'BCC-CSM2-MR', 'CESM2-WACCM', 'CESM2', 'FGOALS-f3-L', 'FGOALS-g3', 'UKESM1-0-LL', 'HadGEM3-GC31-LL', 'GFDL-ESM4', 'GFDL-CM4', 'CanESM5', 'CanESM5-CanOE', 'CAMS-CSM1-0', 'INM-CM5-0', 'INM-CM4-8', 'EC-Earth3', 'EC-Earth3-Veg', 'ACCESS-ESM1-5', 'MRI-ESM2-0', 'MIROC-ES2L', 'MIROC6', 'IPSL-CM6A-LR', 'NorESM2-MM', 'FIO-ESM-2-0', 'MPI-ESM1-2-LR', 'ACCESS-CM2', 'GISS-E2-1-G', 'CNRM-CM6-1-HR', 'AWI-CM-1-1-MR', 'KACE-1-0-G']

#Same structure for surface temperature
tas_ssp585 = [[287.00858, 287.01807, 287.17563, 287.07205, 287.14227, 286.91333, 286.94235, 287.00256, 286.919, 287.0304, 286.99762, 286.87714, 286.86423, 286.77637, 286.88525, 286.81976, 286.84317, 286.9443, 286.7987, 286.85574, 286.92047, 286.97147, 287.09592, 286.8142, 287.0087, 286.96542, 286.91913, 287.02148, 286.96655, 286.92346, 286.87393, 286.9221, 286.9695, 286.87668, 286.5166, 286.5789, 286.58524, 286.74042, 286.75684, 286.7956, 286.85025, 286.81647, 286.65857, 286.82886, 286.90237, 286.92264, 286.96954, 287.02563, 287.08197, 287.01785, 287.021, 286.9673, 286.99542, 286.79813, 286.8044, 286.83765, 286.921, 286.7821, 286.83002, 286.74933, 286.9431, 286.91125, 286.6686, 286.62598, 286.4909, 286.68076, 286.89148, 286.61267, 286.84012, 286.81534, 286.75797, 286.7504, 286.65726, 286.83575, 286.88058, 286.9264, 286.83386, 287.00024, 286.94952, 287.03912, 286.9394, 286.85687, 287.0166, 287.0733, 287.2815, 287.1627, 287.19104, 286.9986, 287.0563, 287.11545, 287.0972, 287.2397, 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299.74414, 299.8904, 299.7799, 299.70605, 299.7802, 299.81958, 299.6698, 299.78397, 299.72763, 299.74106, 299.7555, 299.70816, 299.8185, 299.88135, 299.74066, 299.71667, 299.9439, 299.83594, 299.82825, 299.96307, 299.96722, 299.94946, 300.0445, 299.96667, 299.9744, 299.98868, 299.9799, 300.04443, 300.06537, 299.997, 300.0221, 299.96402, 299.97827, 299.978], [285.89114, 286.02356, 286.026, 286.06033, 286.10028, 286.10208, 285.907, 285.9163, 286.01706, 285.8898, 285.85822, 285.80496, 285.85394, 285.67838, 285.76132, 285.84125, 285.98187, 286.02863, 285.95914, 286.01108, 286.00824, 286.0538, 286.18243, 286.03186, 285.8566, 285.90555, 285.97168, 285.98734, 285.8313, 285.8589, 285.81467, 285.9099, 285.93442, 285.8727, 285.57855, 285.6566, 285.7892, 285.87183, 285.8655, 285.80417, 285.8866, 285.9515, 285.95767, 285.863, 285.87265, 285.9025, 285.9886, 285.929, 285.85614, 285.7618, 285.86166, 286.0282, 285.88223, 285.7915, 285.86612, 285.7848, 285.9123, 285.95053, 285.924, 285.89468, 285.83817, 285.92755, 285.8509, 285.75455, 285.85144, 285.9757, 285.7912, 285.77432, 285.71027, 285.87033, 285.90836, 285.78458, 285.78555, 285.89114, 285.87427, 285.92834, 286.0364, 285.972, 285.91705, 285.82486, 285.88272, 285.94977, 285.9123, 285.8469, 285.89014, 285.91183, 286.10196, 286.06393, 285.9321, 286.04355, 286.17712, 286.13092, 285.97736, 286.1134, 286.03867, 286.00476, 285.88168, 285.9156, 286.08353, 286.16302, 286.02896, 286.22452, 286.17157, 286.0997, 285.92758, 286.05487, 285.99365, 286.065, 285.90326, 286.00223, 286.22446, 285.97382, 285.86905, 285.77838, 285.73495, 285.7411, 285.78232, 285.83258, 285.76993, 285.94086, 286.04095, 286.0011, 285.92227, 285.86188, 285.95386, 285.91434, 286.11337, 285.98245, 285.9831, 286.09427, 286.08426, 285.9032, 286.05637, 285.98712, 285.99683, 285.94186, 286.18594, 286.40106, 286.26755, 286.25058, 286.20383, 286.30255, 286.078, 285.99823, 286.06693, 286.16074, 286.30673, 286.42584, 286.44644, 286.30142, 286.55167, 286.62778, 286.5653, 286.5759, 286.73373, 286.70636, 286.72745, 286.76907, 286.76605, 286.61823, 286.77853, 286.9086, 286.80588, 286.82635, 286.93906, 286.8922, 286.8591, 286.99982, 287.09622, 286.9994, 286.91928, 287.06055, 287.24304, 287.19727, 287.2285, 287.2287, 287.33432, 287.42462, 287.29312, 287.41318, 287.45114, 287.44928, 287.53607, 287.45428, 287.58417, 287.6213, 287.6493, 287.6116, 287.79944, 287.86703, 287.70673, 287.8594, 288.0933, 287.89435, 288.0174, 288.05197, 288.14105, 288.07697, 288.22128, 288.3655, 288.33737, 288.40518, 288.6705, 288.47595, 288.71854, 288.74268, 288.6239, 288.73434, 288.79816, 288.93884, 289.0691, 288.93094, 289.01877, 289.17697, 289.17862, 289.12473, 289.2836, 289.34424, 289.49527, 289.27942, 289.44028, 289.38864, 289.5344, 289.56934, 289.425, 289.45792, 289.74796, 289.6299, 289.7072, 290.0543, 290.10773, 290.27823, 290.33975, 290.219, 290.10385, 290.32257, 290.34366, 290.2699, 290.5022, 290.45322, 290.55157, 290.58228, 290.7338, 290.93356, 290.66837, 290.88004, 291.06845, 291.1891, 291.05267, 290.9914, 291.15805], [287.14468, 287.16406, 287.05917, 287.02914, 287.29395, 287.29092, 286.93948, 286.9111, 286.9066, 286.93405, 287.00903, 286.90283, 286.9225, 287.012, 287.15988, 287.2185, 287.39072, 287.35144, 287.00104, 286.97156, 286.9046, 287.0243, 287.06937, 287.05896, 287.32205, 287.2146, 287.04755, 287.28464, 287.1115, 286.99072, 287.00705, 287.31696, 287.18976, 286.8628, 286.57068, 286.53864, 286.81244, 286.90063, 287.02823, 287.04343, 286.86142, 286.83328, 286.96368, 286.84338, 286.97223, 286.90955, 286.9893, 287.0414, 287.0801, 287.17844, 287.1257, 287.06445, 286.979, 286.98245, 286.98462, 286.85474, 286.8816, 286.7678, 286.8527, 286.89487, 286.90918, 287.1659, 287.0647, 287.09085, 287.16428, 287.0436, 286.94073, 287.0264, 287.02386, 287.15933, 287.17358, 287.09946, 286.89774, 286.7722, 286.77264, 286.7379, 286.88538, 286.973, 286.95908, 286.97845, 287.20956, 287.2723, 286.91257, 287.06772, 287.1916, 287.04257, 286.89328, 286.96878, 287.12796, 287.208, 287.2948, 287.2192, 287.20422, 287.07858, 287.0265, 287.1082, 287.04465, 287.03366, 287.24765, 287.22458, 287.28793, 287.3738, 287.1963, 287.16992, 287.2572, 287.0823, 286.9751, 286.90207, 286.98535, 287.1968, 287.26608, 287.39755, 287.39285, 287.1032, 286.8945, 286.78333, 286.70862, 286.55606, 286.7311, 286.6976, 286.75317, 286.75336, 286.98514, 287.0218, 287.15125, 287.18576, 287.09357, 287.19803, 287.29886, 287.37686, 287.17453, 287.1721, 287.0736, 286.9016, 286.8955, 287.08417, 287.0432, 286.88824, 287.0298, 287.279, 287.82416, 287.52768, 287.12732, 287.23074, 287.50156, 287.4687, 287.53452, 287.42148, 287.11206, 286.96548, 286.9949, 287.3074, 287.45435, 287.64874, 287.8749, 287.93292, 287.64825, 287.42102, 287.35233, 287.51553, 287.53357, 287.49487, 287.60577, 287.83145, 287.98886, 288.08365, 288.03696, 287.929, 287.82587, 287.82892, 287.81354, 287.86493, 287.87628, 287.90482, 288.11798, 288.2349, 287.92688, 287.80905, 287.9443, 288.03366, 288.17953, 288.4491, 288.13306, 288.21616, 288.3929, 288.48724, 288.239, 288.23358, 288.3442, 288.4925, 288.71014, 288.68863, 288.56747, 288.56366, 288.54666, 288.7214, 289.02017, 289.09784, 289.02997, 288.9581, 288.62637, 288.61826, 288.9594, 289.11688, 288.8728, 288.73834, 288.74316, 288.97836, 289.2862, 289.54535, 289.3374, 289.45538, 289.44858, 289.37033, 289.37466, 289.29407, 289.52927, 289.62024, 289.47897, 289.5598, 289.6863, 289.5816, 289.89395, 290.04736, 289.77197, 289.6925, 290.14294, 290.24118, 289.9838, 290.06686, 290.27643, 290.21127, 290.26947, 290.45505, 290.749, 290.68997, 290.73865, 290.796, 290.67798, 290.79718, 290.91602, 290.6358, 290.67462, 290.93024, 291.03052, 290.9202, 290.98425, 291.336, 291.16537, 291.1977, 291.38638]]
tas_models_ssp585 = ['ACCESS-CM2', 'EC-Earth3-Veg', 'BCC-CSM2-MR', 'GFDL-ESM4', 'NESM3', 'UKESM1-0-LL', 'MIROC-ES2L', 'HadGEM3-GC31-LL', 'GISS-E2-1-G', 'INM-CM5-0', 'CNRM-ESM2-1', 'INM-CM4-8', 'FGOALS-g3', 'EC-Earth3', 'ACCESS-ESM1-5', 'FGOALS-f3-L', 'CanESM5', 'KACE-1-0-G', 'CanESM5-CanOE', 'MIROC6', 'CAMS-CSM1-0', 'CESM2-WACCM', 'MRI-ESM2-0', 'MPI-ESM1-2-LR', 'CNRM-CM6-1', 'CESM2', 'CNRM-CM6-1-HR', 'FIO-ESM-2-0', 'IPSL-CM6A-LR', 'GFDL-CM4', 'NorESM2-MM']

# short names of models (sorted)
models_short = ["I0","I8","IP","FIO","CA", "K", "UK", "C6H","C6", "EV","EC","CAC", "H", "GC", "CN", "AC", "NE", "MI6","BM","AE", "MI","FG","CS","AW","MP", "FF", "NOR","GG", "MR","C2","GE","CW"]
models_short = models_short[::-1]

# "good models": within mean/plus minus two std in 1985-2015 (Original selection criteria - not used in publication)
# good_models = ['GFDL-ESM4',
#  'AWI-CM-1-1-MR',
#  'ACCESS-ESM1-5',
#  'GFDL-CM4',
#  'FGOALS-f3-L',
#  'GISS-E2-1-G',
#  'NorESM2-MM',
#  'CNRM-CM6-1',
#  'CNRM-CM6-1-HR',
#  'CESM2',
#  'MIROC6',
#  'NESM3',
#  'CNRM-ESM2-1',
#  'MIROC-ES2L',
#  'CESM2-WACCM',
#  'INM-CM5-0']

# good models following rajendran et al. 2021 (used in publication)
good_models = ['CESM2-WACCM',
               'EC-Earth3-Veg', 
               'GFDL-CM4',
               'INM-CM5-0',
               'MIROC6', 
               'NESM3']

for i in range(len(data_ssp585)):
    print(i, ":", models_ssp585[i],len(data_ssp585[i]))

# to remove 2015 and 2016inn CAS: FGOALS-g3 (doubled)
del data_ssp585[7][165]
del data_ssp585[7][165]


# more than 251 years: 7(253),13,21,26

# copy value 250 to add 251 for CAMS-CSM1-0 
data_ssp585[14].append(data_ssp585[14][249])




#%%----------------------------
### Data processing
#----------------------------
### transforming data in subsets for 1965-2015 and 2050-2100
modellist_1965_2015_ssp585 = []
modellist_2050_2100_ssp585 = []
for m in range(len(data_ssp585)):
    modelm = data_ssp585[m]
    modelm_1965_2015 = modelm[115 : 165] # 1965-2015
    modelm_2050_2100 = modelm[200 : 250] # 2050-2100
    modellist_1965_2015_ssp585.append(modelm_1965_2015)
    modellist_2050_2100_ssp585.append(modelm_2050_2100)
          


#%%----------------------------
### Basic statistics
#----------------------------
    
### 90 % percentil
# calculate percentil for 1965 - 2015
percentil90_1965_2015_ssp585 = []
percentil99_1965_2015_ssp585 = []
percentil95_1965_2015_ssp585 = []
for i in range(len(modellist_1965_2015_ssp585)):
    percentil90_1965_2015_ssp585.append(np.percentile(modellist_1965_2015_ssp585[i],90))
    percentil99_1965_2015_ssp585.append(np.percentile(modellist_1965_2015_ssp585[i],99))
    percentil95_1965_2015_ssp585.append(np.percentile(modellist_1965_2015_ssp585[i],95))
#print(percentil90_1965_2015_ssp585)
#print(percentil99_1965_2015_ssp585)

# calculate percentil for 2050 - 2100
percentil90_2050_2100_ssp585 = []
percentil99_2050_2100_ssp585 = []
percentil95_2050_2100_ssp585 = []
for i in range(len(modellist_2050_2100_ssp585)):
    percentil90_2050_2100_ssp585.append(np.percentile(modellist_2050_2100_ssp585[i],90))
    percentil99_2050_2100_ssp585.append(np.percentile(modellist_2050_2100_ssp585[i],99))
    percentil99_2050_2100_ssp585.append(np.percentile(modellist_2050_2100_ssp585[i],95))
#print(percentil90_2050_2100_ssp585)
#print(percentil99_2050_2100_ssp585)


###  mean
# calculate mean for 1965 - 2015
mean_1965_2015_ssp585 = []
for i in range(len(modellist_1965_2015_ssp585)):
    mean_1965_2015_ssp585.append(stat.mean(modellist_1965_2015_ssp585[i]))
#print(mean_1965_2015_ssp585)

mean_1965_2015_ssp585_good_models = []
for i in range(32):
    if models_ssp585[i] in good_models:
        mean_1965_2015_ssp585_good_models.append(mean_1965_2015_ssp585[i])


# calculate mean for 2050 - 2100
mean_2050_2100_ssp585 = []
for i in range(len(modellist_2050_2100_ssp585)):
    mean_2050_2100_ssp585.append(stat.mean(modellist_2050_2100_ssp585[i]))
print(mean_2050_2100_ssp585)

mean_2050_2100_ssp585_good_models = []
for i in range(32):
    if models_ssp585[i] in good_models:
        mean_2050_2100_ssp585_good_models.append(mean_2050_2100_ssp585[i])


### standard deviation std
# calculate std per model for 1965 - 2015
std_1965_2015_ssp585 = []
for i in range(len(modellist_1965_2015_ssp585)):
    std_1965_2015_ssp585.append(stat.stdev(modellist_1965_2015_ssp585[i]))
print(std_1965_2015_ssp585)

# calculate std per model for 2050 - 2100
std_2050_2100_ssp585 = []
for i in range(len(modellist_2050_2100_ssp585)):
    std_2050_2100_ssp585.append(stat.stdev(modellist_2050_2100_ssp585[i]))
print(std_2050_2100_ssp585)


### max
# calculate max for 1965 - 2015
max_1965_2015_ssp585 = []
for i in range(len(modellist_1965_2015_ssp585)):
    max_1965_2015_ssp585.append(max(modellist_1965_2015_ssp585[i]))
#print(mean_1965_2015_ssp585)

max_2050_2100_ssp585 = []
for i in range(len(modellist_2050_2100_ssp585)):
    max_2050_2100_ssp585.append(max(modellist_2050_2100_ssp585[i]))
#print(mean_2050_2100_ssp585)




#%%----------------------------
### Very wet seasons
#----------------------------

    
### Check: how many models have values smaller than 90% percentil in 1965-2015 (only for checking)
belows = []
aboves = []
for i in range(len(modellist_1965_2015_ssp585)): # going through all models
    below = 0
    above = 0
    for myear in range(len(modellist_1965_2015_ssp585[0])): # for each model going through all 50 years
        if modellist_1965_2015_ssp585[i][myear] < percentil90_1965_2015_ssp585[i]:
            below = below + 1
        if modellist_1965_2015_ssp585[i][myear] > percentil90_1965_2015_ssp585[i]:
            above = above + 1
    belows.append(below)
    aboves.append(above)
    
### Check: how many models have values smaller than 99% percentil in 1965-2015 
# belows_95 = []
# aboves_95 = []
# for i in range(len(modellist_1965_2015_ssp585)): # going through all models
#     below = 0
#     above = 0
#     for myear in range(len(modellist_1965_2015_ssp585[0])): # for each model going through all 50 years
#         if modellist_1965_2015_ssp585[i][myear] < percentil95_1965_2015_ssp585[i]:
#             below = below + 1
#         if modellist_1965_2015_ssp585[i][myear] > percentil95_1965_2015_ssp585[i]:
#             above = above + 1
#     belows_95.append(below)
#     aboves_95.append(above)    
    
### Check: how many models have values smaller than 99% percentil in 1965-2015 
belows_99 = []
aboves_99 = []
for i in range(len(modellist_1965_2015_ssp585)): # going through all models
    below = 0
    above = 0
    for myear in range(len(modellist_1965_2015_ssp585[0])): # for each model going through all 50 years
        if modellist_1965_2015_ssp585[i][myear] < percentil99_1965_2015_ssp585[i]:
            below = below + 1
        if modellist_1965_2015_ssp585[i][myear] > percentil99_1965_2015_ssp585[i]:
            above = above + 1
    belows_99.append(below)
    aboves_99.append(above)

    
### how many models have values smaller/higher than 90% percentil in 2050-2100
belows2 = []
aboves2 = []
for i in range(len(modellist_1965_2015_ssp585)): # going through all models
    below = 0
    above = 0
    for myear in range(len(modellist_1965_2015_ssp585[0])): # for each model going through all 50 years
        if modellist_2050_2100_ssp585[i][myear] < percentil90_1965_2015_ssp585[i]:
            below = below + 1
        if modellist_2050_2100_ssp585[i][myear] > percentil90_1965_2015_ssp585[i]:
            above = above + 1
    belows2.append(below)
    aboves2.append(above)

aboves2_good_models = []
for i in range(32):
    if models_ssp585[i] in good_models:
        aboves2_good_models.append(aboves2[i])
        


### how many models have values smaller than 99% percentil in 2050-2100
belows2_99 = []
aboves2_99 = []
for i in range(len(modellist_1965_2015_ssp585)): # going through all models
    below = 0
    above = 0
    for myear in range(len(modellist_1965_2015_ssp585[0])): # for each model going through all 50 years
        if modellist_2050_2100_ssp585[i][myear] < percentil99_1965_2015_ssp585[i]:
            below = below + 1
        if modellist_2050_2100_ssp585[i][myear] > percentil99_1965_2015_ssp585[i]:
            above = above + 1
    belows2_99.append(below)
    aboves2_99.append(above)

aboves2_99_good_models = []
for i in range(32):
    if models_ssp585[i] in good_models:
        aboves2_99_good_models.append(aboves2_99[i])


### how many models have values smaller than 99% percentil in 2050-2100
belows2_95 = []
aboves2_95 = []
for i in range(len(modellist_1965_2015_ssp585)): # going through all models
    below = 0
    above = 0
    for myear in range(len(modellist_1965_2015_ssp585[0])): # for each model going through all 50 years
        if modellist_2050_2100_ssp585[i][myear] < percentil95_1965_2015_ssp585[i]:
            below = below + 1
        if modellist_2050_2100_ssp585[i][myear] > percentil95_1965_2015_ssp585[i]:
            above = above + 1
    belows2_95.append(below)
    aboves2_95.append(above)

aboves2_95_good_models = []
for i in range(32):
    if models_ssp585[i] in good_models:
        aboves2_95_good_models.append(aboves2_95[i])



### which factor is between number of years exceeding value from 1965-2015 to 2050-2100
factors = []
for i in range(len(aboves)):
    factori = aboves2[i]/aboves[i]
    factors.append(factori)
 
factors_99 = []
for i in range(len(aboves_99)):
    factori = aboves2_99[i]/aboves_99[i]
    factors_99.append(factori)    

#%%----------------------------
### Sorting Models
#----------------------------
    
# ### sorting data according to aboves2 (number of years exceeding 90%-percentile of 1965-2015)
# aboves2_sort = [item[0] for item in sorted(zip(aboves2, models_ssp585),key=itemgetter(0), reverse=True)]
# models_ssp585_sort = [item[1] for item in sorted(zip(aboves2, models_ssp585),key=itemgetter(0), reverse=True)]
# modellist_1965_2015_ssp585_sort = [item[1] for item in sorted(zip(aboves2, modellist_1965_2015_ssp585), reverse=True)]
# modellist_2050_2100_ssp585_sort = [item[1] for item in sorted(zip(aboves2,  modellist_2050_2100_ssp585),key=itemgetter(0), reverse=True)]
# percentil90_1965_2015_ssp585_sort = [item[1] for item in sorted(zip(aboves2, percentil90_1965_2015_ssp585), reverse=True)]
# std_1965_2015_ssp585_sort = [item[1] for item in sorted(zip(aboves2, std_1965_2015_ssp585),key=itemgetter(0), reverse=True)]
# std_2050_2100_ssp585_sort = [item[1] for item in sorted(zip(aboves2, std_2050_2100_ssp585),key=itemgetter(0), reverse=True)]

### sorting data according to aboves2 (number of years exceeding 90%-percentile of 1965-2015)
aboves2_sort = [item[0] for item in sorted(zip(aboves2, models_ssp585),key=lambda x: x[0], reverse=True)]
#models_ssp585_sort = [item[1] for item in sorted(zip(aboves2, models_ssp585),key=lambda x: x[0], reverse=True)]
#modellist_1965_2015_ssp585_sort = [item[1] for item in sorted(zip(aboves2, modellist_1965_2015_ssp585),key=lambda x: x[0], reverse=True)]
#modellist_2050_2100_ssp585_sort = [item[1] for item in sorted(zip(aboves2,  modellist_2050_2100_ssp585),key=lambda x: x[0], reverse=True)]
#percentil90_1965_2015_ssp585_sort = [item[1] for item in sorted(zip(aboves2, percentil90_1965_2015_ssp585),key=lambda x: x[0], reverse=True)]
#std_1965_2015_ssp585_sort = [item[1] for item in sorted(zip(aboves2, std_1965_2015_ssp585),key=lambda x: x[0], reverse=True)]
#std_2050_2100_ssp585_sort = [item[1] for item in sorted(zip(aboves2, std_2050_2100_ssp585),key=lambda x: x[0], reverse=True)]


#models_short = models_short[::-1]
#aboves2_sort[::-1]
#models_ssp585_sort[::-1]
#modellist_1965_2015_ssp585_sort[::-1]
#modellist_2050_2100_ssp585_sort[::-1]
#percentil90_1965_2015_ssp585_sort[::-1]
#std_1965_2015_ssp585_sort[::-1]
#std_2050_2100_ssp585_sort[::-1]

item = models_ssp585
models_ssp585_sort  = [item[15],item[16],item[23],item[25],
                       item[12],item[31],item[8],item[29],
                       item[1],item[18],item[17],item[13],
                       item[9],item[11],item[2],item[27],
                       item[0],item[22],item[3],item[19],
                        item[21],item[7],item[14],item[30],
                        item[26],item[6],item[24],item[28],
                        item[20],item[5],item[10],item[4]]
item = modellist_1965_2015_ssp585
modellist_1965_2015_ssp585_sort = [item[15],item[16],item[23],item[25],
                        item[12],item[31],item[8],item[29],
                        item[1],item[18],item[17],item[13],
                        item[9],item[11],item[2],item[27],
                        item[0],item[22],item[3],item[19],
                        item[21],item[7],item[14],item[30],
                        item[26],item[6],item[24],item[28],
                        item[20],item[5],item[10],item[4]]
item = modellist_2050_2100_ssp585
modellist_2050_2100_ssp585_sort = [item[15],item[16],item[23],item[25],
                        item[12],item[31],item[8],item[29],
                        item[1],item[18],item[17],item[13],
                        item[9],item[11],item[2],item[27],
                        item[0],item[22],item[3],item[19],
                        item[21],item[7],item[14],item[30],
                        item[26],item[6],item[24],item[28],
                        item[20],item[5],item[10],item[4]]
item = percentil90_1965_2015_ssp585
percentil90_1965_2015_ssp585_sort  = [item[15],item[16],item[23],item[25],
                        item[12],item[31],item[8],item[29],
                        item[1],item[18],item[17],item[13],
                        item[9],item[11],item[2],item[27],
                        item[0],item[22],item[3],item[19],
                        item[21],item[7],item[14],item[30],
                        item[26],item[6],item[24],item[28],
                        item[20],item[5],item[10],item[4]]
item = std_1965_2015_ssp585
std_1965_2015_ssp585_sort = [item[15],item[16],item[23],item[25],
                        item[12],item[31],item[8],item[29],
                        item[1],item[18],item[17],item[13],
                        item[9],item[11],item[2],item[27],
                        item[0],item[22],item[3],item[19],
                        item[21],item[7],item[14],item[30],
                        item[26],item[6],item[24],item[28],
                        item[20],item[5],item[10],item[4]]
item = std_2050_2100_ssp585
std_2050_2100_ssp585_sort = [item[15],item[16],item[23],item[25],
                        item[12],item[31],item[8],item[29],
                        item[1],item[18],item[17],item[13],
                        item[9],item[11],item[2],item[27],
                        item[0],item[22],item[3],item[19],
                        item[21],item[7],item[14],item[30],
                        item[26],item[6],item[24],item[28],
                        item[20],item[5],item[10],item[4]]

#%%----------------------------
### Histograms (SSP585)
#----------------------------    
# create one histogram per model    
# fig, axs = plt.subplots(1,1)
# for i in range(len(modellist_1965_2015_ssp585)):
#     plt.figure()
#     axs.set_ylim(0,25)
#     axs.set_xlim(0,10)
#     axs.set_yticks(np.arange(0, 25, 10))
#     plt.hist(modellist_1965_2015_ssp585[i], bins = 25, range = (0,9), label = '1965-2015', alpha = 0.5)
#     plt.hist(modellist_2050_2100_ssp585[i], bins = 25, range = (0,9), label = '2050-2100 (SSP585)', alpha = 0.5)
#     plt.title(models_ssp585[i])
#     plt.axvline(stat.mean(modellist_1965_2015_ssp585[i]), color='blue', linestyle= (0, (5,10)), linewidth=1.5)
#     plt.axvline(stat.mean(modellist_2050_2100_ssp585[i]), color='green', linestyle= (0, (5,10)), linewidth=1.5)
#     #plt.yticks(range(0, 30, 1))#
#     plt.xticks(range(0, 11, 1))
#     plt.legend(loc='upper left')
#     plt.show  
    
# plot all histograms together
plt.style.use('classic')
plt.figure(figsize = (8,4))
fig, axs = plt.subplots(8,4)
fig.subplots_adjust(wspace = 0.063, hspace = 0.25) # set the spacing between axes. 
index = 0
plt.xlabel(" ")
for i in range(8):
    for j in range(4):
        n, bins, patches = axs[i,j].hist(modellist_2050_2100_ssp585[index], bins = 25, range = (0,9), label = '2050-2100 (SSP585)', alpha = 0.45, color = "blue")
        barploth = axs[i,j].hist(modellist_1965_2015_ssp585[index], bins = 25, range = (0,9), label = '1965-2015', alpha = 0.35, color = "grey")
        #for p in range(len(patches)):
        #    if bins[p + 1] < percentil90_1965_2015_ssp585[index]:
        #        patches[p].set_facecolor('darkorange')  
        axs[i,j].set_title(models_ssp585[index], fontsize = 6, pad = 1)
        axs[i,j].set_ylim(0,25)
        axs[i,j].set_xlim(0,10)
        axs[i,j].text(0.7, 15, round(std_1965_2015_ssp585[index],2), fontsize = 5)
        axs[i,j].text(0.7, 8, round(std_2050_2100_ssp585[index],2), fontsize = 5)
        #axs[i,j].tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
        axs[i,j].set_yticks(np.arange(0, 25, 10))
        axs[i,j].set_xticklabels(np.arange(0, 10, 2), rotation=0, fontsize=6)
        axs[i,j].set_yticklabels(np.arange(0, 25, 10), rotation=0, fontsize=6)
        axs[i,j].axvline(percentil90_1965_2015_ssp585[index], color='red')
        index = index + 1
fig.text(0.5, 0.04, 'yearly mean seasonal rainfall (mm/day)', ha='center', fontsize = 8)
for ax in axs.flat:
    ax.label_outer()
#plt.tight_layout()
#plt.savefig('/Users/anjakatzenberger/Dokumente/01Uni/Master_Umweltsysteme/Python_Code/output/Histogram_Means_1965_2015_2050_2100/Histogram_means_1965_2015_2050_2100.pdf')

# plot all histograms together order
plt.style.use('classic')
plt.figure(figsize = (6,8))
fig, axs = plt.subplots(8,4)
fig.subplots_adjust(wspace = 0.06, hspace = 0.25) # set the spacing between axes. 
index = 0
plt.xlabel(" ")
for i in range(8):
    for j in range(4):
        barploth = axs[i,j].hist(modellist_1965_2015_ssp585_sort[index], bins = 25, range = (0,9), label = '1965-2015', alpha = 0.35, color = "grey")
        if models_ssp585_sort[index] in good_models:
            n, bins, patches = axs[i,j].hist(modellist_2050_2100_ssp585_sort[index], bins = 25, range = (0,9), label = '2050-2100 (SSP585)', alpha = 0.45, color = "darkblue")   
            axs[i,j].text(0.9, 15, models_short[::-1][index], fontsize = 8, color = "darkblue")
        else:
            n, bins, patches = axs[i,j].hist(modellist_2050_2100_ssp585_sort[index], bins = 25, range = (0,9), label = '2050-2100 (SSP585)', alpha = 0.45, color = "darkorange")
            axs[i,j].text(0.9, 15, models_short[::-1][index], fontsize = 8, color = "darkorange")
        axs[i,j].set_ylim(0,25)
        axs[i,j].set_xlim(0,10)
        ratio = 0.8
        axs[i,j].set_aspect(1.0/ax.get_data_ratio()*ratio)
        axs[i,j].set_yticks(np.arange(0, 25, 10))
        axs[i,j].set_xticklabels(np.arange(0, 10, 2), rotation=0, fontsize=8)
        axs[i,j].set_yticklabels(np.arange(0, 25, 10), rotation=0, fontsize=8)
        axs[i,j].axvline(percentil90_1965_2015_ssp585_sort[index], color='red')
        axs[i,j].tick_params(axis='both', which='both', top=False, right=False, length=2.5)
        index = index + 1
fig.text(0.5, 0.04, 'Mean seasonal rainfall (mm/day)', ha='center', fontsize = 8)
fig.text(0.08, 0.43, 'Number of years', ha='center', rotation = 90, fontsize = 8)
for ax in axs.flat:
    ax.label_outer()
#plt.tight_layout()
#plt.savefig('/Users/anjakatzenberger/Dokumente/01_Uni/PaperOnExtremes/GRL/Revision1/Revised_newbestmodels_newbaseperiod/Histogram_means_1965_2014_2050_2100.pdf')



#%%----------------------------
# Very wet seasons - barplots
#----------------------------

percentil90_2050_2100_ssp585_sort = [x for _,x in sorted(zip(percentil90_1965_2015_ssp585, percentil90_2050_2100_ssp585))]
models_p_sort = [x for _,x in sorted(zip(percentil90_1965_2015_ssp585, models_ssp585))]
#aboves2_p_sort = [x for _,x in sorted(zip(percentil90_1965_2015_ssp585, ))]
percentil90_1965_2015_ssp585_sort = [x for x,_ in sorted(zip(percentil90_1965_2015_ssp585, models_ssp585))]

factors_sort = [x for x,_ in sorted(zip(factors,models_ssp585))]
modelsf_ssp585_sort = [x for _,x in sorted(zip(factors,models_ssp585))]
     

#plot how many exceed 90 percentils
x = np.arange(len(aboves2))  # the label locations
barwidth = 0.3
fig, ax = plt.subplots()
rects1 = ax.barh(x - barwidth/2, aboves, barwidth, label='1965-2015', color = "blue")
rects2 = ax.barh(x + barwidth/2, aboves2_sort, barwidth,  label='2050-2100 (SSP585)', color = "grey")
for i in range(len(aboves2_sort)):
    if models_ssp585_sort[i] in good_models:
        rects2[i].set_color('orange')
plt.ylim([0 - 1 ,len(aboves) + 0.5])
ax.set_yticks(x)
ax.set_yticklabels(models_ssp585_sort)
blue_patch = mpatches.Patch(color='blue', label='1965-2015')
orange_patch = mpatches.Patch(color='orange', label='2050-2100: within twice std')
grey_patch = mpatches.Patch(color='grey', label='2050-2100: outside twice std')
plt.legend(handles=[blue_patch,orange_patch,grey_patch],loc=4, prop={'size': 9})
plt.tight_layout()
#plt.savefig('/Users/anjakatzenberger/Dokumente/01Uni/Master_Umweltsysteme/Python_Code/output/90Perzentil/Exceeding90Percentil_barplots_SSP585_1965_2015_2050_2100')
plt.show()

#plot 90 percentils
x = np.arange(len(aboves2))  # the label locations
barwidth = 0.3
fig, ax = plt.subplots()
rects1 = ax.barh(x + barwidth/2, percentil90_1965_2015_ssp585_sort, barwidth, label='90% Percentil 1965-2015', color = 'blue')
rects2 = ax.barh(x - barwidth/2, percentil90_2050_2100_ssp585_sort, barwidth,  label='90% Percentil 2050-2100 (SSP585)', color = 'grey')
for i in range(len(percentil90_2050_2100_ssp585_sort)):
    if models_p_sort[i] in good_models:
        rects2[i].set_color('orange')
plt.ylim([0 - 1 ,len(percentil90_1965_2015_ssp585_sort) + 0.5])
ax.set_yticks(x)
ax.set_yticklabels(models_p_sort)
blue_patch = mpatches.Patch(color='blue', label='1965-2015')
orange_patch = mpatches.Patch(color='orange', label='2050-2100: within twice std')
grey_patch = mpatches.Patch(color='grey', label='2050-2100: outside twice std')
plt.legend(handles=[blue_patch,orange_patch,grey_patch],loc=2, prop={'size': 9})
plt.tight_layout()
#plt.savefig('/Users/anjakatzenberger/Dokumente/01Uni/Master_Umweltsysteme/Python_Code/output/90Perzentil/90Percentil_barplots_SSP585_1965_2015_2050_2100')
plt.show()

# plot factors
plt.style.use('classic')
x = np.arange(len(factors))  # the label locations
barwidth = 0.3
fig, ax = plt.subplots()
rects1 = ax.barh(x, factors_sort, barwidth, color = 'grey')
for i in range(len(factors_sort)):
    if modelsf_ssp585_sort[i] in good_models:
        rects1[i].set_color('orange')
ax.set_yticks(x)
ax.set_yticklabels(modelsf_ssp585_sort)
orange_patch = mpatches.Patch(color='orange', label='2050-2100: within twice std')
grey_patch = mpatches.Patch(color='grey', label='2050-2100: outside twice std')
plt.legend(handles=[orange_patch,grey_patch],loc=4, prop={'size': 9})
plt.ylim([-1,32])
plt.tight_layout()
#plt.savefig('/Users/anjakatzenberger/Dokumente/01Uni/Master_Umweltsysteme/Python_Code/output/90Perzentil/90Percentil_factors_SSP585_1965_2015_2050_2100')
plt.show()

#%%----------------------------
# Min and Max for each model
#----------------------------
max_1965_2015_ssp585 = []
for i in range(len(modellist_1965_2015_ssp585)):
    max_1965_2015_ssp585.append(max(modellist_1965_2015_ssp585[i]))
print(max_1965_2015_ssp585)
    
max_2050_2100_ssp585 = []
for i in range(len(modellist_2050_2100_ssp585)):
    max_2050_2100_ssp585.append(max(modellist_2050_2100_ssp585[i]))  
print(max_2050_2100_ssp585)

min_1965_2015_ssp585 = []
for i in range(len(modellist_1965_2015_ssp585)):
    min_1965_2015_ssp585.append(min(modellist_1965_2015_ssp585[i]))
print(min_1965_2015_ssp585)
    
min_2050_2100_ssp585 = []
for i in range(len(modellist_2050_2100_ssp585)):
    min_2050_2100_ssp585.append(min(modellist_2050_2100_ssp585[i]))  
print(min_2050_2100_ssp585)

range_1965_2015 = []
for i in range(len(max_1965_2015_ssp585)):
    range_1965_2015.append(max_1965_2015_ssp585[i] - min_1965_2015_ssp585[i])

range_2050_2100 = []
for i in range(len(max_2050_2100_ssp585)):
    range_2050_2100.append(max_2050_2100_ssp585[i] - min_2050_2100_ssp585[i])


item = max_1965_2015_ssp585
max_1965_2015_ssp585_sort = [item[15],item[16],item[23],item[25],
                       item[12],item[31],item[8],item[29],
                       item[1],item[18],item[17],item[13],
                       item[9],item[11],item[2],item[27],
                       item[0],item[22],item[3],item[19],
                       item[21],item[7],item[14],item[30],
                       item[26],item[6],item[24],item[28],
                       item[20],item[5],item[10],item[4]]

item = max_2050_2100_ssp585
max_2050_2100_ssp585_sort = [item[15],item[16],item[23],item[25],
                       item[12],item[31],item[8],item[29],
                       item[1],item[18],item[17],item[13],
                       item[9],item[11],item[2],item[27],
                       item[0],item[22],item[3],item[19],
                       item[21],item[7],item[14],item[30],
                       item[26],item[6],item[24],item[28],
                       item[20],item[5],item[10],item[4]]


# sort models
#models_ssp585_max_sort = [x for _,x in sorted(zip(max_1965_2015_ssp585,models_ssp585))]
#max_sort_1965_2015 = [x for x,_ in sorted(zip(max_1965_2015_ssp585,models_ssp585))]
#max_sort_2050_2100 = [x for x,_ in sorted(zip(max_2050_2100_ssp585,models_ssp585))]

# x = np.arange(len(max_1965_2015_ssp585))  # the label locations
# barwidth = 0.3
# fig, ax = plt.subplots()
# rects1 = ax.barh(x - barwidth/2, max_1965_2015_ssp585_sort[::-1], barwidth, label='1965-2015', color = 'grey')
# rects2 = ax.barh(x + barwidth/2, max_2050_2100_ssp585_sort[::-1], barwidth,  label='2050-2100 (SSP585)', color = 'orange')
# for i in range(len(max_2050_2100_ssp585_sort)):
#     if models_ssp585_sort[::-1][i] in good_models:
#         rects2[i].set_color('darkblue')
# plt.ylim([- 1 ,len(max_1965_2015_ssp585_sort) + 0.5])
# ax.set_yticks(x)
# ax.set_xticklabels(np.arange(0,12,2), fontsize = 7.5)
# ax.set_yticklabels(models_short[::-1])
# blue_patch = mpatches.Patch(color='grey', label='1965-2015')
# orange_patch = mpatches.Patch(color='darkblue', label='2050-2100: within twice std')
# grey_patch = mpatches.Patch(color='darkorange', label='2050-2100: outside twice std')
# plt.legend(handles=[blue_patch,orange_patch,grey_patch],loc=2, prop={'size': 9})
# plt.tight_layout()
# #plt.savefig('/Users/anjakatzenberger/Dokumente/01Uni/Master_Umweltsysteme/Python_Code/output/Maxima/Max_barplots_SSP585_1965_2015_2050_2100.pdf')
# plt.show()



#%%----------------------------
# Timeseries: How many models exceed relatively the 1965-2015 90%-Percentil
#----------------------------
window = 50

# how many models have values smaller than 90% percentil in 2050-2100 per year
aboves_per_year = []
aboves_per_year_rel = []
for i in range(len(modellist_1965_2015_ssp585)): # going through all models
    above = 0
    aboves_per_year_in_one_model = []
    aboves_per_year_in_one_model_rel = []
    for year in range(151): # 1950-2100 backwards 50 years
        perioddata = data_ssp585[i][(50 + year) : (50 + window + year)]
        exceeding = len([y for y in perioddata if y > percentil90_1965_2015_ssp585[i]])
        aboves_per_year_in_one_model.append(exceeding)
        aboves_per_year_in_one_model_rel.append(exceeding/window)
    aboves_per_year.append(aboves_per_year_in_one_model)
    aboves_per_year_rel.append(aboves_per_year_in_one_model_rel)

# how many models have values smaller than 90% percentil in 2050-2100 per year
aboves_per_year_10mov = []
for model_i in range(32): # going through all models
    aboves_per_year_in_one_model_10mov = []
    for year in range(151): # 1950-2100 backwards 50 years
        aboves_per_year_in_one_model_10mov.append(stat.mean(aboves_per_year[model_i][year:(year + 10)]))
    aboves_per_year_10mov.append(aboves_per_year_in_one_model_10mov)
  

# extend with last value such that data is displayed includig 1990-2100
for s in range(len(aboves_per_year)):
    aboves_per_year[s].append(aboves_per_year[s][149])
    aboves_per_year_rel[s].append(aboves_per_year_rel[s][149])


median_rel = []
for i in range(len(aboves_per_year_rel[0])):
    yearpoints = [model[i] for model in aboves_per_year_rel]
    median_rel.append(stat.median(yearpoints))

median_abs = []
for i in range(len(aboves_per_year[0])):
    yearpoints = [model[i] for model in aboves_per_year]
    median_abs.append(stat.median(yearpoints))


median_abs_10mov = []
for i in range(len(aboves_per_year_10mov[0])):
    yearpoints = [model[i] for model in aboves_per_year_10mov]
    median_abs_10mov.append(stat.median(yearpoints))


# When do models leave the internal variability envelope?
meani = []
stdi = []
upper_limit = []
for modeli in range(32):
    meani.append(stat.mean(aboves_per_year[modeli][0:115]))  #Historical simulations: 1900-2015
    stdi.append(stat.stdev(aboves_per_year[modeli][0:115]))
    upper_limit.append(stat.mean(aboves_per_year[modeli][0:115])+stat.stdev(aboves_per_year[modeli][0:115]))

first_year_to_exceed = []
for modeli in range(32):
    flag = 0
    for yearx in range(len(aboves_per_year[modeli])):
        if (aboves_per_year[modeli][yearx] > upper_limit[modeli] and flag == 0):
            first_year_to_exceed.append(yearx)
            flag = 1
        
# extracting good models to calculate the mean
aboves_per_year_good = []
for s in range(len(aboves_per_year)):
    if models_ssp585[s] in good_models:
        aboves_per_year_good.append(aboves_per_year[s])

median_good = []
for i in range(len(aboves_per_year_good[0])):
    yearpoints = [model[i] for model in aboves_per_year_good]
    median_good.append(stat.median(yearpoints))


# moving average # version 2
# - for all models
aboves_per_year_movav = []
window = 10
for s in range(len(aboves_per_year)):
   aboves_per_year_movav_s = []
   for j in range(len(aboves_per_year[s]) - window):
       aboves_per_year_movav_s.append(stat.mean(aboves_per_year[s][j:(j + window)]))
   aboves_per_year_movav.append(aboves_per_year_movav_s)

median_abs_movav = []
for i in range(len(aboves_per_year_movav[0])):
    yearpoints = [model[i] for model in aboves_per_year_movav]
    median_abs_movav.append(stat.median(yearpoints))

# - for good models
aboves_per_year_good_movav = []
for s in range(len(aboves_per_year_movav)):
    if models_ssp585[s] in good_models:
        aboves_per_year_good_movav.append(aboves_per_year_movav[s])

median_good_movav = []
for i in range(len(aboves_per_year_good_movav[0])):
    yearpoints = [model[i] for model in aboves_per_year_good_movav]
    median_good_movav.append(stat.median(yearpoints))





#moving average
plt.style.use('classic')
fig, ax = plt.subplots()
ax2 = ax.twinx()
start = 1950 + window/2-1
end = 2101 - window/2

for s in range(len(aboves_per_year)):
    if models_ssp585[s] in good_models:
        ax.plot(np.arange(start,end,1), aboves_per_year_movav[s][0:151], color = "darkblue", label = "models within 2 std", alpha=0.4)
    else:
        ax.plot(np.arange(start,end,1), aboves_per_year_movav[s][0:151], color = "darkorange", label = "models outside 2 std",alpha=0.4)
ax.plot(np.arange(start,end,1), median_abs_movav, color = "darkred", linewidth = 3)
ax.plot(np.arange(start,end,1), median_good_movav, color = "darkblue", linewidth = 3)
plt.xlim(1950,2100)
plt.xticks(np.arange(1950,2101,50))
ax.set_xticklabels(np.arange(1950,2101,50))
ax.set_ylabel("Number of very wet summer seasons in the past 50 years")
ax2.set_ylabel("Fraction of very wet summer seasons in the past 50 years")
ax.set_yticklabels(np.arange(0,51,10))
ax2.set_ylim(0, 1)
blue_patch = mpatches.Patch(color='darkblue', label='Models with best monsoon performance', alpha = 0.4)
grey_patch = mpatches.Patch(color='darkorange', label='Other models', alpha = 0.4)
red_patch = mpatches.Patch(color='darkred', label='Median of all models')
blue_patch2 = mpatches.Patch(color='darkblue', label='Median of best performing models')
plt.legend(handles=[blue_patch,grey_patch,red_patch, blue_patch2],loc=2, prop={'size': 9},frameon=False)
ax.axhline(5, color="grey", linewidth=2, alpha=0.6,linestyle="--")
plt.xlabel("Years")
ax2.plot([],[])
#plt.show()
#plt.savefig('/Users/anjakatzenberger/Dokumente/01_Uni/PaperOnExtremes/GRL/Revision1/Revised_newbestmodels_newbaseperiod/AbsoluteExceeding_movav_rev.pdf')




#absolute - plot
plt.style.use('classic')
fig, ax = plt.subplots()
ax2 = ax.twinx()

for s in range(len(aboves_per_year)):
    if models_ssp585[s] in good_models:
        ax.plot(np.arange(1950,2101,1), aboves_per_year[s][0:151], color = "darkblue", label = "models within 2 std", alpha=0.4)
    else:
        ax.plot(np.arange(1950,2101,1), aboves_per_year[s][0:151], color = "darkorange", label = "models outside 2 std",alpha=0.4)
ax2.plot(np.arange(1950,2101,1), median_rel[0:151], color = "darkred", linewidth = 3)
ax.plot(np.arange(1950,2101,1), median_good[0:151], color = "darkblue", linewidth = 3)
plt.xlim(1950,2100)
plt.xticks(np.arange(1950,2101,50))
ax.set_xticklabels(np.arange(1950,2101,50))
ax.set_ylabel("Number of extremely wet years in the past 50 years")
ax2.set_ylabel("Percentage of extremely wet years in the past 50 years")
ax.set_yticklabels(np.arange(0,51,10))
ax2.set_ylim(0, 1)
blue_patch = mpatches.Patch(color='darkblue', label='Models within two standard deviations (1985-2015)', alpha = 0.4)
grey_patch = mpatches.Patch(color='darkorange', label='Models outside of two standard deviations (1985-2015)', alpha = 0.4)
red_patch = mpatches.Patch(color='darkred', label='Median of all models')
blue_patch2 = mpatches.Patch(color='darkblue', label='Median of models within two standard deviations (1985-2015)')
plt.legend(handles=[blue_patch,grey_patch,red_patch, blue_patch2],loc=2, prop={'size': 9},frameon=False)
plt.axhline(5, color="grey", linewidth=2, alpha=0.6,linestyle="--")
plt.xlabel("Years")
ax2.plot([],[])
#plt.show()
#plt.savefig('/Users/anjakatzenberger/Dokumente/01Uni/Master_Umweltsysteme/Python_Code/output/RelativeExceeding/AbsoluteExceeding_diskret.pdf')




#absolute - data points
plt.style.use('classic')
fig, ax = plt.subplots()
ax2 = ax.twinx()
for s in range(len(aboves_per_year)):
    if models_ssp585[s] in good_models:
        ax.scatter(np.arange(1950,2101,1), aboves_per_year[s][0:151], color = "darkblue", label = "models within 2 std",  s = 2)
    else:
        ax.scatter(np.arange(1950,2101,1), aboves_per_year[s][0:151], color = "darkorange", label = "models outside 2 std", s = 2)
ax.plot(np.arange(1950,2101,1), median_abs[0:151], color = "darkred", linewidth = 3)
ax.plot(np.arange(1950,2101,1), median_good[0:151], color = "darkblue", linewidth = 3)
plt.xlim(1950,2100)
plt.ylim(0,51)
plt.xticks(np.arange(1950,2101,50))
ax.set_xticklabels(np.arange(1950,2101,50))
ax.set_ylabel("Number of extremely wet years in the past 50 years")
ax2.set_ylabel("Percentage of extremely wet years in the past 50 years")
ax.set_yticklabels(np.arange(0,51,10))
ax.set_ylim(0, 51)
ax2.set_ylim(0, 100)
blue_patch = mpatches.Patch(color='darkblue', label='Models within two standard deviations (1985-2015)',  alpha =0.6)
grey_patch = mpatches.Patch(color='darkorange', label='Models outside of two standard deviations (1985-2015)')
red_patch = mpatches.Patch(color='darkred', label='Median of all models')
blue_patch2 = mpatches.Patch(color='darkblue', label='Median of models within two standard deviations (1985-2015)')
plt.legend(handles=[blue_patch,grey_patch,red_patch, blue_patch2],loc=2, prop={'size': 9},frameon=False)
#plt.axhline(5, color="grey", linewidth=2, alpha=0.6,linestyle="--")
plt.xlabel("Years")
ax2.plot([],[])
#plt.show()
#plt.savefig('/Users/anjakatzenberger/Dokumente/01Uni/Master_Umweltsysteme/Python_Code/output/RelativeExceeding/AbsoluteExceeding_alternativ.pdf')


#relative
plt.style.use('classic')
fig, axs = plt.subplots(1)
plt.xlim(1950,2100)
plt.xticks(np.arange(1950,2101,50))
for s in range(len(aboves_per_year)):
    if models_ssp585[s] in good_models:
        plt.plot(np.arange(1950,2101,1)[0::10], aboves_per_year_rel[s][0::10], color = "darkblue", label = "models within 2 std")
    else:
        plt.plot(np.arange(1950,2101,1)[0::10], aboves_per_year_rel[s][0::10], color = "lightgrey", label = "models outside 2 std")
plt.plot(np.arange(1950,2101,1)[0::10], median_rel[0::10], color = "darkred", linewidth = 3)
blue_patch = mpatches.Patch(color='darkblue', label='percentage of extreme wet years (models within twice std)')
grey_patch = mpatches.Patch(color='grey', label='percentage of extreme wet years  (models outside twice std)')
red_patch = mpatches.Patch(color='darkred', label='vertical median of all models')
plt.legend(handles=[blue_patch,grey_patch,red_patch],loc=2, prop={'size': 9})
#plt.savefig('/Users/anjakatzenberger/Dokumente/01Uni/Master_Umweltsysteme/Python_Code/output/RelativeExceeding/RelativeExceeding')
plt.close()


#%%----------------------------
# Plot timeseries of 90% percentiles
#----------------------------
# window = 50
# # how many models have values smaller than 90% percentil in 2050-2100 per year
# percentil90 = []
# for i in range(len(data_ssp585)): # going through all models
#     percentil90_one_model = []
#     for year in range(150): # 1950-2100 backwards 50 years
#         perioddata = data_ssp585[i][(50 + year) : (50 + window + year)]
#         per = np.percentile(perioddata,90)
#         percentil90_one_model.append(per)
#     percentil90.append(percentil90_one_model)

# # extending one value
# for s in range(len(percentil90)):
#     percentil90[s].append(percentil90[s][149])

# # calculating vertial median
# median_p = []
# for i in range(len(percentil90[0])):
#     yearpoints = [model[i] for model in percentil90]
#     median_p.append(stat.median(yearpoints))

# fig, ax = plt.subplots()
# plt.style.use('classic')
# fig, axs = plt.subplots(1)
# plt.xlim(1950,2100)
# plt.xticks(np.arange(1950,2101,50))
# for s in range(len(percentil90)):
#     if models_ssp585[s] in good_models:
#         plt.plot(np.arange(1950,2101,1)[0::10], percentil90[s][0::10], color = "darkblue")
#     else:
#         plt.plot(np.arange(1950,2101,1)[0::10], percentil90[s][0::10], color = "lightgrey")
# plt.plot(np.arange(1950,2101,1)[0::10], median_p[0::10], color = "darkred", linewidth = 3)
# blue_patch = mpatches.Patch(color='darkblue', label='90%-percentile (models within twice std)')
# grey_patch = mpatches.Patch(color='grey', label='90%-percentile (models outside twice std)')
# red_patch = mpatches.Patch(color='darkred', label='vertical median of all models')
# plt.legend(handles=[blue_patch,grey_patch,red_patch],loc=2, prop={'size': 9})
# #plt.savefig('/Users/anjakatzenberger/Dokumente/01Uni/Master_Umweltsysteme/Python_Code/output/90Perzentil/TimeSeries_90Perzentil')


#%%----------------------------
# Per temperature: Scatterplot
#----------------------------

aboves2[:] = [above - 5 for above in aboves2]


#calculate tas change between mean of 2050-2100 and 1965-2015
tas_change = []
for i in range(len(tas_ssp585)):
    new = stat.mean(tas_ssp585[i][200:250])-stat.mean(tas_ssp585[i][115:164])
    tas_change.append(new)


# sort tas results according to models_ssp585
tas_change_sort = []
for i in range(len(models_ssp585)):
    flag = 0
    for j in range(len(tas_models_ssp585)):
        if tas_models_ssp585[j] == models_ssp585[i]:
            tas_change_sort.append(tas_change[j])
            flag = 1
    if flag == 0:
        tas_change_sort.append(99)
        
# remove 99
for i in range(len(tas_change_sort)):
    if tas_change_sort[i] == 99:
        remove_nr = i
tas_change_sort.remove(tas_change_sort[remove_nr])
aboves2.remove(aboves2[remove_nr])
  
# Regresssion
m = np.polyfit(tas_change_sort, aboves2, deg = 0)    



x = np.array(tas_change_sort)
y = np.array(aboves2)
correlation_matrix = np. corrcoef(x, y)
correlation_xy = correlation_matrix[0,1]
r_squared = correlation_xy**2
print(r_squared)


# Our model is y = a * x, so things are quite simple, in this case...
# x needs to be a column vector instead of a 1D vector for this, however.
x = x[:,np.newaxis]
a, _, _, _ = np.linalg.lstsq(x, y)

plt.plot(x, y, 'bo')
plt.plot(x, a*x, 'r-')
plt.show()

x_range = np.arange(0,7,0.5)

# creating Scatterplot
fig, ax = plt.subplots()
ax.scatter(tas_change_sort, aboves2)
ax.plot(x_range, a*x_range , 'r-')
#ax.legend(scatterpoints = 1,fontsize=11, loc = "lower right")
#plt.legend(loc='best', numpoints=3, fontsize=12, handles= plt.legend_elements()[0])
plt.xlabel("$\Delta$ GMT (K)")
plt.ylabel("$\Delta$ Extremely wet seasons (seasons)")
plt.ylim(0 , 50)
plt.xlim(0 , 6)
#ax.set_xticklabels(np.arange(0, 6, 0.5))
#ax.set_yticklabels(np.arange(0, 55, 10))
#plt.axhline(0, color='black', linestyle = '-', linewidth = 0.5)
#plt.savefig('/Users/anjakatzenberger/Dokumente/01_Uni/PaperOnExtremes/GRL/Revision1/Revised_newbestmodels_newbaseperiod/fig_04_rev.pdf', bbox_inches='tight')
plt.show()