def load():
centroid = database.MatrixCSVDatabase(
database.localpath("acra/centroids.csv"),
'fips',
get_varyears=lambda df, var: [None])
centroid.set_metainfo(
database.StoredMetainfo.load(database.localpath("acra/fields.fgh")))
elevation = database.MatrixCSVDatabase(
database.localpath("acra/elevation.csv"),
'fips',
get_varyears=lambda df, var: [None])
elevation.set_metainfo(
database.StoredMetainfo.load(database.localpath("acra/fields.fgh")))
name = database.MatrixCSVDatabase(
database.localpath("acra/us-county-names.csv"),
'fips',
get_varyears=lambda df, var: [None])
name.set_metainfo(
database.StoredMetainfo.load(database.localpath("acra/fields.fgh")))
dbs = [centroid, elevation, name]
prefixes = ['centroid', 'elevation', 'name']
return database.CombinedDatabase(dbs, prefixes, '.')
def get_database(self):
if not self.loaded:
return None
if self.loaded and len(self.loaded) == 1:
return self.loaded.values()[0]
return database.CombinedDatabase(self.loaded.values(),
self.loaded.keys(), '.')
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():
dbs = []
prefixes = []
fielddb = fields.load()
dbs.append(fielddb)
prefixes.append("field")
weatherdb = weather.load()
dbs.append(weatherdb)
prefixes.append("weather")
areadb = areas.load()
dbs.append(areadb)
prefixes.append("area")
return database.CombinedDatabase(dbs, prefixes, '.')
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():
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():
metainfo = database.FunctionalMetainfo(get_description, get_unit)
x20082012 = database.MatrixCSVDatabase(
database.localpath("election/2008-2012.csv"), 'FIPS',
database.variable_filtermap(column2variable_2008), get_varyears_2008,
get_datarows_2008)
x20082012.set_metainfo(metainfo)
x20122016 = database.MatrixCSVDatabase(
database.localpath(
"election/US_County_Level_Presidential_Results_12-16.csv"),
'combined_fips', database.variable_filtermap(column2variable_2016),
get_varyears_2016, get_datarows_2016)
x20122016.set_metainfo(metainfo)
return database.CombinedDatabase([x20082012, x20122016],
['x20082012', 'x20122016'], '.')
def load():
dbs = []
prefixes = []
master = database.MatrixCSVDatabase(masterpath, 'FIPS')
master.set_metainfo(
database.FunctionalMetainfo(master_get_description, master_get_unit))
dbs.append(master)
prefixes.append('agmaster')
for filename in glob.glob(os.path.join(pathhere, "allyears/*.csv")):
filename = os.path.basename(filename)
areamatch = re.match(r'([a-z]+)_area_([a-z]+)_in_acre\.csv', filename)
if areamatch:
crop = areamatch.group(1)
measured = areamatch.group(2)
db = MatrixDatabase(os.path.join(pathhere, "allyears", filename),
crop, 'acre', 'AREA_%s' % measured.upper())
dbs.append(db)
prefixes.append('%s-area' % crop)
continue
prodmatch = re.match(r'([a-z]+)_production_in_([a-z]+)\.csv', filename)
if prodmatch:
crop = prodmatch.group(1)
unit = prodmatch.group(2)
db = MatrixDatabase(os.path.join(pathhere, "allyears", filename),
crop, unit, 'PRODUCTION')
dbs.append(db)
prefixes.append('%s-production' % crop)
continue
usdamatch = re.match(r'([a-z]+)-([a-z]+)-([a-z]+)\.csv', filename)
if usdamatch:
crop = usdamatch.group(1)
isirr = usdamatch.group(2)
report = usdamatch.group(3)
db = USDADatabase(os.path.join(pathhere, "allyears", filename),
crop, isirr, report)
dbs.append(db)
prefixes.append('%s-%s-%s' % (crop, isirr, report))
continue
usda2007match = re.match(r'([a-z]+)-total-([a-z]+)-2007\.csv',
filename)
if usda2007match:
crop = usda2007match.group(1)
isirr = 'total'
report = usda2007match.group(2)
db = USDADatabase(os.path.join(pathhere, "allyears", filename),
crop, isirr, report)
dbs.append(db)
prefixes.append('%s-%s-%s' % (crop, isirr, report))
continue
return database.CombinedDatabase(dbs, prefixes, '.')