def load():
dbs = []
prefixes = []
metainfo = database.StoredMetainfo({
'FIPS': dict(unit="name"),
'Alfalfa': dict(unit="acre"),
'Otherhay': dict(unit="acre"),
'Barley': dict(unit="acre"),
'Barley.Winter': dict(unit="acre"),
'Maize': dict(unit="acre"),
'Sorghum': dict(unit="acre"),
'Soybean': dict(unit="acre"),
'Wheat': dict(unit="acre"),
'Wheat.Winter': dict(unit="acre"),
'fips': dict(unit="name"),
'known': dict(unit="acre"),
'total': dict(unit="acre"),
'barley': dict(unit="acre"),
'corn': dict(unit="acre"),
'sorghum': dict(unit="acre"),
'soybeans': dict(unit="acre"),
'wheat': dict(unit="acre"),
'hay': dict(unit="acre")
})
db = database.StaticCSVDatabase(os.path.join(pathhere,
"irrigatedareas.csv"),
'FIPS',
year=2010)
db.set_metainfo(metainfo)
dbs.append(db)
prefixes.append('irrigatedareas')
db = database.StaticCSVDatabase(os.path.join(pathhere, "rainfedareas.csv"),
'FIPS',
year=2010)
db.set_metainfo(metainfo)
dbs.append(db)
prefixes.append('rainfedareas')
db = database.StaticCSVDatabase(os.path.join(pathhere, "knownareas.csv"),
'fips',
year=2010)
db.set_metainfo(metainfo)
dbs.append(db)
prefixes.append('knownareas')
db = database.StaticCSVDatabase(os.path.join(pathhere, "totalareas.csv"),
'FIPS',
year=2010)
db.set_metainfo(metainfo)
dbs.append(db)
prefixes.append('totalareas')
return database.CombinedDatabase(dbs, prefixes, '.')
def load():
get_fips = lambda df: np.array(df['NHGISST']) * 100 + np.array(df[
'NHGISCTY']) / 10
variable_filter = lambda cols: filter(
lambda col: 'NHGIS' in col or 'mean' in col or 'sum' in col or col ==
'STATENAM', cols)
metainfo = database.StoredMetainfo({
'NHGISNAM': dict(unit="name"),
'NHGISST': dict(unit="code"),
'NHGISCTY': dict(unit="code"),
'STATENAM': dict(unit="name"),
'solarsum': dict(unit="W"),
'solarmean': dict(unit="W/m^2"),
'windsum': dict(unit="m^3/s"),
'windmean': dict(unit="m/s"),
'windpowerm': dict(unit="W"),
'windpowers': dict(unit="W/m^2")
})
db = database.StaticCSVDatabase(
database.localpath("energy/repotential.csv"), get_fips,
variable_filter)
db.set_metainfo(metainfo)
return db
def load():
masterpath = database.localpath("awash/counties.csv")
fipsdb = database.StaticCSVDatabase(masterpath, 'fips')
dbs = []
for filepath in glob.glob(database.localpath("groundwater/*.txt")):
if os.path.basename(filepath) == 'notes.txt':
continue
db = database.OrderedVectorDatabase.read_text(
filepath, os.path.basename(filepath[:-4]), 2010, fipsdb)
if os.path.basename(filepath[:-4]) == 'aquifer_depth':
db.set_metainfo(
database.UniformMetainfo("Depth to groundwater table", "m"))
if os.path.basename(filepath[:-4]) == 'piezohead0':
db.set_metainfo(database.UniformMetainfo("piezohead", "m"))
if os.path.basename(filepath[:-4]) == 'county_area':
db.set_metainfo(database.UniformMetainfo("county area", "m^2"))
if os.path.basename(filepath[:-4]) == 'county_elevation':
db.set_metainfo(database.UniformMetainfo("county elevation", "m"))
if os.path.basename(filepath[:-4]) == 'drawdown0':
db.set_metainfo(database.UniformMetainfo("draw down", "m"))
if os.path.basename(filepath[:-4]) == 'vector_storativity':
db.set_metainfo(database.UniformMetainfo(" ", "None"))
dbs.append(db)
return database.ConcatenatedDatabase(dbs)
def load():
metainfo = database.StoredMetainfo({'NHGISNAM': dict(unit="name"), 'NHGISST': dict(unit="code"), 'NHGISCTY': dict(unit="code"), 'STATENAM': dict(unit="name"), 'bio1_mean': dict(unit='dC'), 'bio2_mean': dict(unit='dC'), 'bio5_mean': dict(unit='dC'), 'bio6_mean': dict(unit='dC'), 'bio7_mean': dict(unit='dC'), 'bio8_mean': dict(unit='dC'), 'bio9_mean': dict(unit='dC'), 'bio10_mean': dict(unit='dC'), 'bio11_mean': dict(unit='dC'), 'bio12_mean': dict(unit='mm'), 'bio13_mean': dict(unit='mm'), 'bio14_mean': dict(unit='mm'), 'bio16_mean': dict(unit='mm'), 'bio17_mean': dict(unit='mm'), 'bio18_mean': dict(unit='mm'), 'bio19_mean': dict(unit='mm')})
get_fips = lambda df: np.array(df['NHGISST']) * 100 + np.array(df['NHGISCTY']) / 10
variable_filter = lambda cols: filter(lambda col: 'NHGIS' in col or '_mean' in col or col == 'STATENAM', cols)
current = database.StaticCSVDatabase(database.localpath("climate/bioclims-current.csv"), get_fips, variable_filter)
current.set_metainfo(metainfo)
dbs = [current]
prefixes = ['current']
for filepath in glob.glob(database.localpath("climate/bioclims-2050/*.csv")):
db = database.StaticCSVDatabase(filepath, get_fips, variable_filter, year=2050)
db.set_metainfo(metainfo)
dbs.append(db)
prefixes.append(filepath[filepath.rindex('/')+1:filepath.rindex('/')+3])
return database.CombinedDatabase(dbs, prefixes, '.')
def load():
fipsdb = database.StaticCSVDatabase(masterpath, 'fips')
dbs = []
for filename in glob.glob(os.path.join(pathhere, "edds/*.csv")):
filename = os.path.basename(filename)
filepath = os.path.join(pathhere, "edds", filename)
db = database.OrderedDatabase.use_fips(
fipsdb, SingleVariableDatabase(filepath, filename[:-4]))
db.set_metainfo(database.UniformMetainfo(None, 'C day'))
dbs.append(db)
return database.ConcatenatedDatabase(dbs)
def load():
allage = database.StaticCSVDatabase(
database.localpath('mortality/cmf-1999-2010.txt'),
'County Code',
year=2004,
sep='\t')
allage.set_metainfo(metainfo.StoredMetainfo(infos))
byage = database.InterlevedCSVDatabase(
database.localpath("mortality/cmf-age-1999-2010.txt"),
'County Code',
'Age Group',
2004,
sep='\t')
byage.set_metainfo(metainfo.StoredMetainfo(infos))
return database.CombinedDatabase([allage, byage], ['all', 'age'], '.')
def load():
dbs = []
dbs.append(
database.StaticCSVDatabase(filepath,
'FIPS',
sheetname='1985',
year=1985))
dbs.append(
database.StaticCSVDatabase(filepath,
'FIPS',
sheetname='1990',
year=1990))
dbs.append(
database.StaticCSVDatabase(filepath,
'FIPS',
sheetname='1995',
year=1995))
dbs.append(
database.StaticCSVDatabase(filepath,
'FIPS',
sheetname='2000',
year=2000))
dbs.append(
database.StaticCSVDatabase(filepath,
'FIPS',
sheetname='2005',
year=2005))
dbs.append(
database.StaticCSVDatabase(filepath,
'FIPS',
sheetname='2010',
year=2010))
for db in dbs:
db.set_metainfo(database.FunctionalMetainfo(get_description, get_unit))
return database.CombinedYearsDatabase(dbs, dbs[-1].get_fips())
def load():
filepath = database.localpath("labor/lab_cty_00_05_sum.csv")
db = database.StaticCSVDatabase(filepath, 'fips', year=2002)
db.set_metainfo(metainfo.StoredMetainfo.load_csv(database.localpath("labor/info.csv"), 'variable', 'description', 'unit'))
return db
def load():
filepath = database.localpath("ccimpacts/county_damage_mapping_data.csv")
db = database.StaticCSVDatabase(filepath, 'fips', year=2090)
db.set_metainfo(metainfo.StoredMetainfo(metas))
return db