Merge pull request allfed#39 from florianjehn/main

Added catastrophic scenarios

data/preprocessed_data/Wheat_Y2021_Global_production.csv

@@ -15,7 +15,7 @@ Armenia,97000.0
 Barbados,0.0
 Belgium,1629180.0
 Bhutan,1168.94
-Bolivia (Plurinational State of),336222.5
+Bolivia,336222.5
 Bosnia and Herzegovina,314382.0
 Botswana,1280.0
 Brazil,7874525.0
@@ -32,13 +32,14 @@ Canada,22296100.0
 Cabo Verde,0.0
 Central African Republic,0.0
 Sri Lanka,0.0
+Chad,1539.53
 Chile,1353607.94
-China; mainland,136946000.0
-China; Taiwan Province of,6000.0
+China,136946000.0
+Taiwan,6000.0
 Colombia,6965.0
 Comoros,0.0
-Congo,0.0
-Democratic Republic of the Congo,9000.0
+Congo Republic,0.0
+DR Congo,9000.0
 Costa Rica,0.0
 Croatia,986930.0
 Cuba,0.0
@@ -73,26 +74,27 @@ Guinea,0.0
 Guyana,0.0
 Haiti,0.0
 Honduras,1255.58
-China; Hong Kong SAR,0.0
+Hong Kong,0.0
 Hungary,5290140.0
 Iceland,0.0
 India,109590000.0
 Indonesia,0.0
-Iran (Islamic Republic of),10093578.64
+Iran,10093578.64
 Iraq,4233714.0
 Ireland,628080.0
 Israel,150000.0
 Italy,7294570.0
-Côte d'Ivoire,0.0
+Cote d'Ivoire,0.0
 Jamaica,0.0
 Japan,1097000.0
 Kazakhstan,11814123.97
 Jordan,30000.0
 Kenya,245300.0
-Republic of Korea,30000.0
+North Korea,86603.65
+South Korea,30000.0
 Kuwait,36.74
-Kyrgyzstan,362711.0
-Lao People's Democratic Republic,0.0
+Kyrgyz Republic,362711.0
+Laos,0.0
 Lebanon,100000.0
 Lesotho,6000.0
 Latvia,2407700.0
@@ -110,14 +112,14 @@ Mauritania,7000.0
 Mauritius,0.0
 Mexico,3283613.73
 Mongolia,566273.03
-Republic of Moldova,1565200.0
+Moldova,1565200.0
 Montenegro,2391.58
 Morocco,7543847.9
 Mozambique,15000.0
 Oman,3542.0
 Namibia,18337.0
 Nepal,2127276.0
-Netherlands (Kingdom of the),947250.0
+Netherlands,947250.0
 New Caledonia,84.5
 Vanuatu,0.0
 New Zealand,422831.0
@@ -138,10 +140,10 @@ Guinea-Bissau,0.0
 Timor-Leste,0.0
 Qatar,252.0
 Romania,10433750.0
-Russian Federation,76057258.0
+Russia,76057258.0
 Rwanda,13684.0
-Saint Lucia,0.0
-Saint Vincent and the Grenadines,0.0
+St. Lucia,0.0
+St. Vincent and the Grenadines,0.0
 Sao Tome and Principe,0.0
 Saudi Arabia,612582.0
 Senegal,0.0
@@ -150,18 +152,19 @@ Seychelles,0.0
 Sierra Leone,0.0
 Singapore,0.0
 Slovakia,2002240.0
-Viet Nam,0.0
+Vietnam,0.0
 Slovenia,154450.0
 Somalia,1050.82
 South Africa,2257205.0
 Zimbabwe,337212.0
 Spain,8564630.0
+South Sudan,0.0
 Sudan,600000.0
 Suriname,0.0
 Eswatini,709.46
 Sweden,3027800.0
 Switzerland,401202.0
-Syrian Arab Republic,1951805.93
+Syria,1951805.93
 Tajikistan,852000.0
 Thailand,1317.53
 Togo,0.0
@@ -174,12 +177,12 @@ Uganda,25000.0
 Ukraine,32183300.0
 North Macedonia,243676.0
 Egypt,9000000.0
-United Kingdom of Great Britain and Northern Ireland,13988000.0
-United Republic of Tanzania,70000.0
-United States of America,44790360.0
+United Kingdom,13988000.0
+Tanzania,70000.0
+United States,44790360.0
 Burkina Faso,0.0
 Uruguay,936400.0
 Uzbekistan,5984756.0
-Venezuela (Bolivarian Republic of),550.84
+Venezuela,550.84
 Yemen,125000.0
 Zambia,205881.95

scripts/scenario_comparison.ipynb

@@ -9,7 +9,6 @@
     "from src.model import PyTradeShifts\n",
     "from src.postprocessing import Postprocessing\n",
     "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
     "import os\n",
     "os.chdir('../')"
    ]

src/model.py

@@ -134,7 +134,7 @@ def run(self, with_preprocessing: bool) -> None:
             preprocessing_main(
                 "All_Data" if self.region == "Global" else self.region,
                 self.crop,
-                year="Y" + str(self.base_year),
+                year=self.base_year,
             )
         # Read in the data
         self.load_data()