def extract(my_path_folder,file, my_start_month, my_start_day, my_end_month, my_end_day):
my_extraction=DaisyDlf(my_path_folder+'\\'+'%s.dlf' %file)
my_extraction=my_extraction.Data
years=np.unique([my_extraction.index[i].year for i in range(len(my_extraction.index))])
my_extraction['Date']=my_extraction.index
my_extraction.index=np.arange(0,len(my_extraction))
my_start, my_end = [], []
for i in range(len(years)-1):
my_start.append(my_extraction['Date'][my_extraction['Date'] == datetime.strptime('%0d-%02d-%02d 00:00:00'%(years[i+1], my_start_month, my_start_day), "%Y-%m-%d %H:%M:%S")].index[0])
my_end.append(my_extraction['Date'][my_extraction['Date'] == datetime.strptime('%0d-%02d-%02d 00:00:00'%(years[i+1], my_end_month, my_end_day), "%Y-%m-%d %H:%M:%S")].index[0])
return my_extraction, my_start, my_end
def get_allYields(path):
items = os.walk(path)
allresults = {}
for root, dirs, filenames in items:
for d in dirs:
cropyield = {}
if os.path.isfile(os.path.join(root, d, "harvest.dlf")):
harvest = DaisyDlf(os.path.join(root, d, "harvest.dlf"))
df = harvest.Data
DMharv = df[['crop', 'leaf_DM', 'stem_DM', 'sorg_DM']]
DMS = DMharv.groupby('crop')
for cropname in silo:
if cropname in DMS.groups.keys():
rg = DMS.get_group(cropname).sum(axis=1)
cropyield[cropname] = list(rg.resample('Y').sum())
DMharvG = df[['crop', 'sorg_DM']]
DMG = DMharvG.groupby('crop')
for cropname in grain:
if cropname in DMG.groups.keys():
gr = DMG.get_group(cropname).sum(axis=1)
cropyield[cropname] = list(gr.resample('Y').sum())
allresults[d] = cropyield
#average = round(v[v!=0].mean(), 2)
#result.append({k:average})
return (allresults)
def getSOMspinup(path):
#path=r'../RunSpinup5'
MotherFolder = path #r'..\RunSpinup5'
items = os.walk(MotherFolder)
index = 1
init = {}
for root, dirs, filenames in items:
for d in dirs:
C_inital = DaisyDlf(os.path.join(root, d, "SC100-M.dlf"))
initialValues = {}
df = C_inital.Data
diff_years = relativedelta(df.index[len(df) - 1],
df.index[0]).years + 1
#print(diff_years)
inputC = int(
(df['Residuals_C_top'].sum(axis=0) +
df['Residuals_C_root'].sum(axis=0) + df['Fertilizer_C'].sum(
axis=0) - df['Bioinc_C-Soil'].sum(axis=0)) / diff_years)
roots = int(df['Residuals_C_root'].sum(axis=0) / diff_years)
fertilizerC = int(df['Fertilizer_C'].sum(axis=0) / diff_years)
initialValues = [inputC, roots, fertilizerC]
init[d] = initialValues
return init
def extract(crop_name, filnavn, output):
harvest=DaisyDlf(filnavn)
df=harvest.Data
# summere og plot af udbytte i tørstof DM
harv= df[['crop', 'leaf_'+output, 'stem_'+output,'sorg_'+output]]
DMG =harv.groupby('crop')
rg = DMG.get_group(crop_name).sum(axis=1)
return(rg)
def extract(crop_name, filnavn, output): # output altid være N?
harvest=DaisyDlf(filnavn)
df=harvest.Data
# summere og plot af udbytte i tørstof DM
harv_grain = df[['crop', 'sorg_'+output]]
harv_grass = df[['crop', 'leaf_'+output, 'stem_'+output,'sorg_'+output]]
grain =harv_grain.groupby('crop')
cereal = grain.get_group(crop_name).sum(axis=1)
return(cereal)
def get_allSOM(path):
items = os.walk(path)
allSOMresults = {}
for root, dirs, filenames in items:
for d in dirs:
cropyield = {}
if os.path.isfile(os.path.join(root, d, "Annual-OM.dlf")):
anOM = DaisyDlf(os.path.join(root, d, "Annual-OM.dlf"))
df = anOM.Data
filter_colC = [col for col in df if col.endswith('-C')]
SOC_col = df[filter_colC].sum(axis=1)
dSOC = round((SOC_col[len(SOC_col) - 1] - SOC_col[1]) /
(len(SOC_col) - 1), 2)
filter_colN = [col for col in df if col.endswith('-N')]
SON_col = df[filter_colN].sum(axis=1)
dSON = round((SON_col[len(SON_col) - 1] - SON_col[1]) /
(len(SON_col) - 1), 2)
allSOMresults[d] = [dSON]
return (allSOMresults)
str) + '_' + xl['field']
def rmse(pred, obs):
return np.sqrt(((pred - obs)**2).mean())
MotherFolder = '..\RunDaisy16opt'
items = os.walk(MotherFolder)
index = 1
fig = plt.figure(figsize=(15, 10))
for root, dirs, filenames in items:
for d in dirs:
print(d)
harvest = DaisyDlf(os.path.join(root, d, "DailyP-harvest.dlf"))
df = harvest.Data
# summere og plot af udbytte i tørstof DM
DMharv = df[['crop', 'leaf_DM', 'stem_DM', 'sorg_DM']]
DMG = DMharv.groupby('crop')
rg = DMG.get_group('Ryegrass').sum(axis=1)
wc = DMG.get_group('Wclover').sum(axis=1)
# Laver et subplot, som derefter bliver det aktive som de næste plt virker på
ax = plt.subplot(3, 4, index)
index += 1
df22 = pd.DataFrame([rg, wc]).T
df22.columns = ['Ryegrass', 'Wclover']
df22['sim-totalDM'] = df22['Ryegrass'] + df22['Wclover']
df2 = df22.loc['2006-1-1':'2011-1-1', :]
s1 = xl.loc[xl['id'] == d]
meas = (s1.groupby(s1.index)['grassDM'].mean(),
for depth in ['OLOH', 'ILD00', 'ILD08', 'ILD16', 'IHD00', 'IHD08']:
print(threshold, space, col, depth)
# store the yearly load of pesticides and water
yearly_leaching_sources, yearly_wflow_sources = defaultdict(
list), defaultdict(list)
# find simulated data in corresponding folder
for my_start_year, folder in enumerate(
sensitivity_analysis['%d' % threshold + '_' +
'%d' % space]):
print('extraction from folder nb:', my_start_year)
# store the pesticide outputs from Daisy
field_chemicals = defaultdict()
for chem in chemicals:
field_chemicals[chem] = DaisyDlf(
path_folder + '\\' + folder + '\\' +
'%s-%s_%s_field_%s.dlf' %
(prefix, depth, col, chem)).Data
field_chemicals[chem]['Date'] = field_chemicals[
chem].index
field_chemicals[chem].index = np.arange(
0, len(field_chemicals[chem]))
# use the harvest file to select find starting and ending
# index of 3-month monitoring period each year
harvest = DaisyDlf(path_folder + '\\' + folder + '\\' +
'%s-harvest.dlf' % prefix)
harvest = harvest.Data
harvest['Date'] = harvest.index
harvest.index = np.arange(0, len(harvest))
start_harvest = (np.where(
harvest['Date'] == datetime.strptime(
sys.path.append(r'..\..\..\.')
from pydaisy.Daisy import *
# Plot tørstofsudbytte for kløver, græs og samlet i søjlediagram
MotherFolder = '..\RunDaisy1'
items = os.walk(MotherFolder)
index = 1
fig = plt.figure(figsize=(15, 8))
#fig, axes = plt.subplots(nrows=2, ncols=3)
for root, dirs, filenames in items:
for d in dirs:
print(d)
harvest = DaisyDlf(os.path.join(root, d, "DailyP-Ryegrass.dlf"))
df = harvest.Data
# summere og plot af udbytte i tørstof DM
Nharv = df[['crop', 'leaf_N', 'stem_N', 'sorg_N']]
N = Nharv.groupby('crop')
rg = DMG.get_group('Ryegrass').sum(axis=1)
wc = DMG.get_group('Wclover').sum(axis=1)
Nrg = N.get_group('Ryegrass').sum(axis=1)
Nwc = N.get_group('Wclover').sum(axis=1)
# Laver et subplot, som derefter bliver det aktive som de næste plt virker på
ax = plt.subplot(4, 3, index)
index += 1
df22 = pd.DataFrame([rg, wc]).T
df22.columns = ['Ryegrass', 'Wclover']
df2 = df22.loc['2006-1-1':'2011-1-1', :]
df2.index = df2.index.strftime("%Y-%m")
from pydaisy.Daisy import *
xl = pd.read_excel(r'..\Meas_yields.xlsx', 'data')
xl.set_index('date', inplace=True)
xl['id'] = 'T'+xl['treatment'].map(str)+'_S'+xl['block'].map(str)+'_'+xl['field']
MotherFolder='..\RunDaisy2.1'
items = os.walk(MotherFolder)
index=1
fig = plt.figure(figsize=(15, 8))
#fig, axes = plt.subplots(nrows=2, ncols=3)
for root, dirs, filenames in items:
for d in dirs:
print(d)
harvestR=DaisyDlf(os.path.join(root, d, "DailyP-Ryegrass.dlf"))
harvestW=DaisyDlf(os.path.join(root, d, "DailyP-Wclover.dlf"))
rg=harvestR.Data.loc['2006-1-1':'2011-1-1',:]
wc=harvestW.Data.loc['2006-1-1':'2011-1-1',:]
rg['Ntot'] = ['NLeaf']+rg['NDead']+rg['NStem']+rg['NSOrg']
wc['Ntot'] = wc['NLeaf']+wc['NStem']+wc['NSOrg']
wc['Nfix'] = wc['Fixated']
ax=plt.subplot(4,3,index)
index+=1
plt.plot(rg.index, (rg['Ntot']), c='black', label= 'grass')
plt.plot(wc.index, (wc['Ntot']), c='red', label = 'clover_Ntot')
plt.plot(wc.index, (wc['Nfix']), c='green', label = 'clover_Nfix')
plt.legend()
plt.title(d, position = (0.6, 0.8), fontweight="bold", fontsize=10)
ax.set(ylabel=('kg N /ha'))
plt.show()