import util as ut
mDataKey = '1RNcXXvO5d8JFmPCEn_u7Qy30MCsg4kKUBMyjYW-swUk'
sheets = {'treedata':350897684,
'sitedescrip':0,
'fuels': 962428416,
'soils': 983196900,
'metadata': 1686733598,
'seedling': 2141390901,
'sapling': 1728276363
}
def biomass (h,dbh,a,b):
allometry = {'WO': {'cname':'white fir',
'sname': 'Abies concolor',
'alom_src':
'globallom_id': 12323,
'meas_unit': 'cm',
'eqn': 1}}
tData = ut.gData(mDataKey, sheets['treedata'])
tData.to_sql('treedata', ut.eng(), if_exists = 'replace')
import util as ut
import pandas as pd
url = 'http://www.fs.fed.us/ne/global/pubs/books/dia_biomass/'
tables = {'Table3_GTR-NE-319.xls': {'sheet' :1,
'header':2},
'Table4_GTR-NE-319.xls': {'sheet' :0,
'header':2},
'Table5_GTR-NE-319.xls': {'sheet' :0,
'header':2},
'Table6_GTR-NE-319.xls': {'sheet' :0,
'header':2},
'Table7_GTR-NE-319.xls': {'sheet' :0,
'header':2},
'Table9_GTR-NE-319.xls': {'sheet' :0,
'header':2}}
for k in tables.keys():
df = pd.read_excel(url+k,
sheetname=tables[k]['sheet'],
header = tables[k]['header'])
df.columns = [i.replace(' ','').
replace('.','_').
replace('(','_').
replace(')','_').lower() for i in df.columns]
df.to_sql(k.split('_')[0].lower(), ut.eng(), if_exists = 'replace')
