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, '.')