# -*- coding: utf-8 -*-

"""

author: Femke Nijsse

Shared under Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)

You are free to:

Share — copy and redistribute the material in any medium or format

Adapt — remix, transform, and build upon the material

for any purpose, even commercially.

Under the following terms:

Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

"""

import matplotlib.pyplot as plt

import matplotlib.patches as patches

import pandas as pd

import numpy as np

from mpl_toolkits.axes_grid1.inset_locator import inset_axes

import matplotlib as mpl

mpl.rcParams['font.size'] = 15

language_version='German'

if language_version=='English':

title = 'historical CO$_2$ level'

ice_age = 'Ice age \n cycles'

indust= 'Industrial revolution starts'

co2con = 'CO$_2$ concentration (ppmv)'

thyrago = 'Thousands of years ago'

year = 'year (CE)'

if language_version=='Dutch':

title = r'Historisch niveau CO$_2$'

ice_age = 'IJstijd- \n cycli'

indust= 'Begin industriële revolutie'

co2con = 'CO$_2$ concentratie (ppmv)'

thyrago = 'Duizenden jaar geleden'

year = 'jaar (CE)'

if language_version=='German':

#Not really German yet

title = r'Historisches CO$_2$ Conzentration'

ice_age = 'Eistijd- \n cycli'

indust= 'Anfang Industriële Revolution'

co2con = 'CO$_2$ concentration (ppmv)'

thyrago = 'Tausenden Jahre ehe'

year = 'Jahr (CE)'

"""

Import Vostok, Dome C and Taylor data 800 kyr

"""

xls = pd.ExcelFile('CO2_800kyr.xls')

df1 = pd.read_excel(xls, 'CO2 611-800KYr')

df2 = pd.read_excel(xls, 'Vostok-TD-Dome C',skiprows=6)

df3 = pd.read_excel(xls, 'Composite CO2',skiprows=6)

df3.columns = ['yr', 'CO2']

df3['yr']= df3['yr']+1950 #convert to CE units

df_domeC = df3[df3.yr > 391896]

df_Vostok = df2.iloc[:,5:7]

df_Vostok.columns = ['yr', 'CO2']

df_Vostok['yr']=df_Vostok['yr']+1950

df_Taylor = df2.iloc[:,8:10]

df_Taylor.columns = ['yr', 'CO2']

df_Taylor['yr'] = df_Taylor['yr']+1950

df_domeC2 = df2.iloc[:183,1:3]

df_domeC2.columns = ['yr', 'CO2']

df_domeC2['yr'] = df_domeC['yr']+1950

"""

Import Law Dome ice core data sets

"""

skiprowsa = list(range(22))+list(range(54,467))

skiprowsb = list()

df_lawdome = pd.read_table('lawdome_combined.dat',skiprows=skiprowsa, \

names=('code','analysisdate', 'ice age', 'yr','CO2'))

df_lawdome_all = pd.read_table('lawdome_combined.dat',skiprows=273, \

names=('yr','CO2', 'smoothed', '-','_','.'))

"""

Import Mauna Loa data

"""

df_mlo = pd.read_csv('monthly_in_situ_co2_mlo.csv',skiprows=54)

df_mlo=df_mlo[[' Yr','CO2filled']]

df_mlo.columns = ['yr', 'CO2']

df_mlo = df_mlo[df_mlo.yr != '1958']

df_mlo = df_mlo[df_mlo.yr != '2018']

df_mlo = df_mlo.drop([0,1])

#Take yearly averages

yeararray = np.arange(1959,2018)

co2matrix = np.zeros((len(yeararray),2))

for n in yeararray:

jan_yr_n = 12*(n-1959)

dec_yr_n = 12*(n-1959)+12

mon = df_mlo.iloc[jan_yr_n:dec_yr_n,1]

meanmon = np.mean([float(item) for item in mon])

co2matrix[n-1959]=np.array([n,meanmon])

"""

Make the plot

"""

#Initialise

fig,ax = plt.subplots()

ax.set_title(title)

#Add data

ax.plot(co2matrix[:,0]/1000,co2matrix[:,1],'k',linewidth=1.5)

ax.plot(df_lawdome_all['yr']/1000,df_lawdome_all['CO2'],'C2',linewidth=2.0)

ax.plot(df_Taylor['yr']/1000,df_Taylor['CO2'],'C1',linewidth=1)

ax.plot(df_Vostok['yr']/1000,df_Vostok['CO2'],'C0',linewidth=1)

ax.plot(df_domeC['yr']/1000,df_domeC['CO2'], 'C9',linewidth=1)

ax.plot(df_domeC2['yr']/1000,df_domeC2['CO2'], 'C9',linewidth=1)

#Add box for inset

xy_elli_low = (0.935,0.45)

width_elli,height_elli = 0.04,0.54

xy_elli_high = (xy_elli_low[0],xy_elli_low[1]+height_elli)

xy_inset_low = (0.89,0.725)

xy_inset_high = (0.89,0.945)

ax.add_patch(

patches.Ellipse(

(xy_elli_low[0]+0.5*width_elli,xy_elli_low[1]+0.5*height_elli), # (x,y)

width_elli, # width

height_elli, # height

fill=False,

alpha=0.8,

transform=ax.transAxes,

linewidth=0.8

)

)

#Adding the lines connecting inset figure to oval

xy_elli_low_centre = (xy_elli_low[0]+0.5*width_elli,xy_elli_low[1]-0.02)

xy_elli_high_centre = (xy_elli_high[0]+0.5*width_elli,xy_elli_high[1])

ax.annotate("", xy=xy_elli_low_centre, xytext=xy_inset_low,

arrowprops=dict(arrowstyle="-",alpha=0.9,linewidth=0.8),

xycoords='axes fraction')

ax.annotate("", xy=xy_elli_high_centre, xytext=xy_inset_high,

arrowprops=dict(arrowstyle="-",alpha=0.9,linewidth=0.8),

xycoords='axes fraction')

#Mark-up

ax.set_xlabel(thyrago)

ax.set_ylabel(co2con)

ax.invert_xaxis()

ax.yaxis.tick_right()

ax.tick_params(axis='both', which='major', labelsize=14)

ax.yaxis.set_label_position("right")

ax.set_facecolor((0.94, 0.94, 0.94))

#Inset figure

axins = inset_axes(ax, 3.7,0.7, bbox_to_anchor=(360, 250))

axins.plot(df_lawdome_all['yr'], df_lawdome_all['CO2'],'C2', linewidth=2.2)

axins.plot(co2matrix[:,0], co2matrix[:,1], 'k', linewidth=2.2)

axins.set_xlabel(year, fontsize=13, labelpad=-0.2)

axins.tick_params(labelsize=13, direction='in')

# sub region of the original image

x1, x2, y1, y2 = 1000, 2025, 260, 420

axins.set_xlim(x1, x2)

axins.set_ylim(y1, y2)

# fix the number of ticks on the inset axes

axins.yaxis.get_major_locator().set_params(nbins=4)

axins.xaxis.get_major_locator().set_params(nbins=5)

ax.text(0.15,0.37,ice_age, size=12, ha="center",

transform=ax.transAxes)

axins.annotate(indust, xy=(1750, 295), xytext=(1200, 370),

arrowprops=dict(facecolor='black', alpha=0.7, arrowstyle='->'),fontsize=12,

)

plt.draw()

fig_name = 'CO2_{}.svg'.format(language_version)

plt.savefig(fig_name, bbox_inches='tight')