@author: tqc268
"""
from Yields import meanyields
from Get_allyields import get_allYields
from split_nameDir import split_unique_name
from Yields import meanyields
path = r'../RunTest'
allresults = get_allYields(path)
meanperkey = meanyields(allresults)
dictK = {
key: value
for key, value in meanperkey.items()
if split_unique_name(key)['IsConventional'] != 'False'
}
dictO = {
key: value
for key, value in meanperkey.items()
if split_unique_name(key)['IsConventional'] == 'False'
}
soil = ['JB1', 'JB4', 'JB6']
cmean = pd.DataFrame()
for s in soil:
dict2 = {
k: value
for k, value in dictK.items()
if split_unique_name(k)['ManureMass'] == 170
and split_unique_name(k)['Soiltype'][0:3] == s
from split_nameDir import split_unique_name
from Yields import meanyields
path = r'../RunK2-7'
allresults = get_allYields(path)
meanperkey = meanyields(allresults)
for key, value in meanperkey.items():
for kl, vl in value.items():
if kl == 'Potato; Sava_Figaro':
value['Potato SavaFigaro'] = value.pop(kl)
dictK = {
key: value
for key, value in meanperkey.items()
if split_unique_name(key)['IsConventional'] != 'False'
}
dictO = {
key: value
for key, value in meanperkey.items()
if split_unique_name(key)['IsConventional'] == 'False'
}
soil = ['JB1', 'JB4', 'JB6']
cmean = pd.DataFrame()
for s in soil:
dict2 = {
k: value
for k, value in dictK.items()
if split_unique_name(k)['Soiltype'][0:3] == s
}
"""
Created on Fri May 3 13:56:08 2019
@author: tqc268
"""
# Analyse results based on manure input
import numpy as np
import pandas as pd
from Get_allSOC import get_allSOM
from split_nameDir import split_unique_name
import matplotlib.pyplot as plt
path = r'../RunPK3_few'
dSOMperkey = get_allSOM(path)
dictKonv = {key:value for key, value in dSOMperkey.items() if split_unique_name(key)['IsConventional'] == "True"}
#dictOrg = {key:value for key, value in dSOMperkey.items() if split_unique_name(key)['IsConventional'] == "False"}
rota= [split_unique_name(k)['Rotation'] for k,v in dSOMperkey.items() ]
rotaK= [split_unique_name(k)['Rotation'] for k,v in dictKonv.items() ]
rk = np.unique(np.array(rotaK))
rotations= rk.tolist()
manure = [split_unique_name(k)['ManureMass'] for k,v in dSOMperkey.items() ]
manureK = [split_unique_name(k)['ManureMass'] for k,v in dictKonv.items() ]
man = np.unique(np.array(manureK))
ManureAmount =man.tolist()
d= pd.DataFrame(index=rotations, columns=ManureAmount)
for ma in ManureAmount:
@author: tqc268
"""
import numpy as np
import pandas as pd
from Get_allSOC import get_allSOM
from split_nameDir import split_unique_name
import matplotlib.pyplot as plt
path = r'../RunK1-9'
dSOMperkey = get_allSOM(path)
dictKonv = {
key: value
for key, value in dSOMperkey.items()
if split_unique_name(key)['ManureMass'] == 170
}
dictOrg = {
key: value
for key, value in dSOMperkey.items()
if split_unique_name(key)['IsConventional'] == "False"
}
dict1 = dictKonv
rota = [split_unique_name(k)['Rotation'] for k, v in dict1.items()]
rk = np.unique(np.array(rota))
rotations = rk.tolist()
manure = [split_unique_name(k)['ManureMass'] for k, v in dict1.items()]
man = np.unique(np.array(manure))
ManureAmount = man.tolist()
import pandas as pd
from Yields import meanyields
from Get_allyieldsCN import get_allYieldsCN
from split_nameDir import split_unique_name
from Yields import meanyields
path = r'../RunK2-7'
allresults = get_allYields(path)
meanperkey = meanyields(allresults)
for key, value in meanperkey.items():
for kl, vl in value.items():
if kl == 'Potato; Sava_Figaro':
value['Potato SavaFigaro'] = value.pop(kl)
dictK = {key:value for key, value in meanperkey.items() if split_unique_name(key)['IsConventional']!= 'False'}
dictO = {key:value for key, value in meanperkey.items() if split_unique_name(key)['IsConventional']== 'False'}
soil = ['JB1', 'JB4', 'JB6']
cmean = pd.DataFrame()
for s in soil:
dict2 = {k:value for k, value in dictK.items() if split_unique_name(k)['Soiltype'][0:3]==s}
df =pd.DataFrame.from_dict(dict2, orient='index')
df['clovergrass']=df['Ryegrass']+df['Wclover']
means = df.mean(axis = 0)
cmean[s] = means
"""
# Calculate the mean yield of Rygrass in rotations KK8 and KK9 (only ones with only grass - no clover)
cmeanG_V = pd.DataFrame()
for s in soil:
@author: tqc268
"""
import numpy as np
import pandas as pd
from Get_allSOC import get_allSOM
from split_nameDir import split_unique_name
import matplotlib.pyplot as plt
path = r'../Run4'
dSOMperkey = get_allSOM(path)
#dictKonv = {key:value for key, value in dSOMperkey.items() if split_unique_name(key)['IsConventional'] == "True"}
#dictOrg = {key:value for key, value in dSOMperkey.items() if split_unique_name(key)['IsConventional'] == "False"}
rota = [split_unique_name(k)['Rotation'] for k, v in dSOMperkey.items()]
rk = np.unique(np.array(rota))
rotations = rk.tolist()
manure = [split_unique_name(k)['ManureMass'] for k, v in dSOMperkey.items()]
man = np.unique(np.array(manure))
ManureAmount = man.tolist()
# Effekt of weather within rotations
weather = ['East', 'West']
SOMmeanperRotWe = []
for we in weather:
dict1 = {
k: value
for k, value in dSOMperkey.items()
if split_unique_name(k)['Weather'][0:4] == we
@author: tqc268
"""
import numpy as np
import pandas as pd
from Get_allSOC import get_allSOM
from split_nameDir import split_unique_name
import matplotlib.pyplot as plt
path = r'../RunK_170'
dSOMperkey = get_allSOM(path)
#dictKonv = {key:value for key, value in dSOMperkey.items() if split_unique_name(key)['IsConventional'] == "True"}
#dictOrg = {key:value for key, value in dSOMperkey.items() if split_unique_name(key)['IsConventional'] == "False"}
rota = [split_unique_name(k)['Rotation'] for k, v in dSOMperkey.items()]
rk = np.unique(np.array(rota))
rotations = rk.tolist()
## Effekt of initalization level within rotations
initlevel = ['High', 'Medium', 'Low']
meanILRO = pd.DataFrame(index=rotations, columns=initlevel)
for il in initlevel:
dict1 = {
k: value
for k, value in dSOMperkey.items()
if split_unique_name(k)['Initlevel'] == il
}
for ro in rotations:
dict2 = {
@author: tqc268
"""
import numpy as np
import pandas as pd
from Get_allSOC import get_allSOM
from split_nameDir import split_unique_name
import matplotlib.pyplot as plt
path = r'../RunPK3_few'
dSOMperkey = get_allSOM(path)
#dictKonv = {key:value for key, value in dSOMperkey.items() if split_unique_name(key)['IsConventional'] == "True"}
#dictOrg = {key:value for key, value in dSOMperkey.items() if split_unique_name(key)['IsConventional'] == "False"}
rota = [split_unique_name(k)['Rotation'] for k, v in dSOMperkey.items()]
rk = np.unique(np.array(rota))
rotations = rk.tolist()
#dSOMperkey = dict1
## Effekt of soil texture within rotations
soiltype = ['JB1', 'JB4', 'JB6']
meanJBRO = pd.DataFrame(index=rotations, columns=soiltype)
for st in soiltype:
dict1 = {
k: value
for k, value in dSOMperkey.items()
if split_unique_name(k)['Soiltype'][0:3] == st
}
for ro in rotations:
import operator
from functools import reduce
rota = pd.DataFrame(pd.read_excel('../common/masterinput_v4.xlsx',sheet_name= 'Rotations'))
rota.index = rota['ID']
crops = pd.read_excel('../common/masterinput_v4.xlsx',sheet_name= 'Crops')
crops.index = crops['Crop']
norm = pd.read_excel('../common/Nnorm_2019.xlsx', sheet_name = "Sheet1")
norm.index = norm['afgkode']
path = r'../Run'
allresults = get_allYields(path)
soil = 'JB6'
name_entries = split_unique_name(key)
for key, value in allresults.items():
sim_yieldmean ={}
if name_entries['Soiltype'][0:3]!=soil:
allresult = allresults
for key, value in allresults.items():
for kl, vl in value.items():
allresults[key][kl] = np.mean(vl) ## mean value includes zero and it should not
df =pd.DataFrame.from_dict(d,orient='index')
df['clovergrass']=df['Ryegrass']+df['Wclover']
norm170 = [v for k, v in allresults.items() if split_unique_name(k)['ManureMass']==170 and split_unique_name(k)['Soiltype'][0:3]==soil]
cropkeys=[]
from Get_allyieldsC import get_allYields
path = r'../RunK1-9'
allresults = get_allYields(path)
meanperkey = meanyields(allresults)
#dictK = {key:value for key, value in meanperkey.items() if split_unique_name(key)['IsConventional']!= 'False'}
soil = ['JB1', 'JB4', 'JB6']
cmean = pd.DataFrame()
for s in soil:
dict2 = {
k: value
for k, value in meanperkey.items()
if split_unique_name(k)['Soiltype'][0:3] == s
}
df = pd.DataFrame.from_dict(dict2, orient='index')
df['clovergrass'] = df['Ryegrass'] + df['Wclover']
means = df.mean(axis=0)
cmean[s] = means
norm = pd.read_excel('../common/Nnorm_2019.xlsx', sheet_name="Sheet1")
norm.index = norm['Daisynavn1']
norm['yield_JB1tDM'] = norm['yieldfaktorDM'] * norm['yield_JB1']
norm['yield_JB4tDM'] = norm['yieldfaktorDM'] * norm['yield_JB4']
norm['yield_JB6tDM'] = norm['yieldfaktorDM'] * norm['yield_JB6']
cmean = cmean.join(norm[norm.columns[-3:]])
cmean['JB1_%fejl'] = round(
(cmean['JB1'] - cmean['yield_JB1tDM']) / cmean['yield_JB1tDM'] * 100, 1)
@author: tqc268
"""
## Effekt of initalization level within rotations
import numpy as np
import pandas as pd
from Get_allSOC import get_allSOM
from split_nameDir import split_unique_name
import matplotlib.pyplot as plt
#path = r'../RunKonv'
#dSOMperkey = get_allSOM(path)
rota = [split_unique_name(k)['Rotation'] for k, v in dSOMperkey.items()]
rk = np.unique(np.array(rota))
rotations = rk.tolist()
manure = [split_unique_name(k)['ManureMass'] for k, v in dSOMperkey.items()]
man = np.unique(np.array(manure))
ManureAmount = man.tolist()
dict170 = {
k: value
for k, value in dSOMperkey.items()
if split_unique_name(k)['ManureMass'] == 170
}
soilC = ['low', 'med', 'high']
SOMmeandf = pd.DataFrame(index=rotations, columns=soilC)
for sc in soilC: