def get_cases(csv_export='cases.csv'):
#query the table
db.load()
rawtbl = pd.read_sql('select * from cases', con=db.simeng)
#assign group id
caseid = list(rawtbl.caseid)
caseid_nm = 0
groupid_j = 0
groupid = []
for c in caseid:
if c < caseid_nm:
groupid_j = groupid_j + 1
caseid_nm = c
groupid.append(groupid_j)
rawtbl['groupid'] = groupid
rawtbl['groupcaseid'] = [
caseid[i] * 100 + groupid[i] for i in range(len(caseid))
]
if len(csv_export) > 0:
rawtbl.to_csv(csv_export, index=False)
pvt = pd.pivot_table(rawtbl,
index='groupcaseid',
values='value',
columns='parameter',
aggfunc='mean')
return pvt
def import_parameters_from_excel():
global ptbl, GROUPS, PARAM_XFILENAME
db.load()
tbls = [
pd.read_excel(os.path.join(BASE_DIR, PARAM_XFILENAME), grp)
for grp in GROUPS
]
ptbl = pd.concat(tbls, axis=0)
ptbl.to_sql(db.PARAM_TABLE,
con=db.simeng,
if_exists='replace',
index=False)
def run_mc(scope_groups=None,
N_runs=500,
refresh=True,
threshold_conf=.8,
conf_int=.9):
global WATCH_FIELDS
db.load()
fun_handle = design.update
input_table = db.PARAM_TABLE
output_table = SQL_OUTPUT
ctbl = monte_carlo(fun_handle=fun_handle,
output_fields=WATCH_FIELDS,
input_table=input_table,
output_table=output_table,
conf_int=conf_int,
con=db.simeng,
threshold_conf=threshold_conf,
N_runs=N_runs,
refresh=refresh,
scope_groups=scope_groups)
return ctbl
def load():
db.load()
def get_cases():
db.load()
casetbl = pd.read_sql('select * from ' + SQL_OUTPUT, con=db.simeng)
return casetbl
from design import database as db
import seaborn as sb
import pandas as pd
#load database and query the case table
db.load()
tbl = pd.read_sql('select * from cases', con=db.simeng)
#create the pivot table of all good (not null) cases
bad_caseids = tbl[pd.isnull(tbl.value)].caseid.unique()
goodtbl = tbl[~tbl.caseid.isin(bad_caseids)].copy()
pvt = pd.pivot_table(goodtbl,
index='caseid',
values='value',
columns='parameter',
aggfunc='mean')
#plot kde harvest extension vs costs
sb.jointplot(x='plants_harvest_extension', y='opex', data=pvt, kind='kde')