123 lines
4.9 KiB
Python
123 lines
4.9 KiB
Python
# -*- coding: utf-8 -*-
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from sqlalchemy import create_engine
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from sqlalchemy.orm import scoped_session, sessionmaker
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from sqlalchemy.ext.declarative import declarative_base
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from snapindices import application
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import os
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engine = create_engine(application.config['ENGINE'],
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convert_unicode=True)
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db_session = scoped_session(sessionmaker(autocommit=False,
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autoflush=False,
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bind=engine))
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Base = declarative_base()
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Base.query = db_session.query_property()
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def init_db():
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tempsnap = application.config['SNAPDATA']
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import snapextract
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import numpy
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import models
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import datetime
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datafiles = [ f for f in os.listdir(tempsnap) if os.path.isfile(os.path.join(tempsnap,f)) ]
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for f in datafiles:
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print f
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datafiles.remove('.DS_Store')
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community_file =application.config['COMMUNITIES']
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dt = numpy.dtype({'names':['community', 'northing', 'easting'],
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'formats':['S100', 'f8', 'f8']})
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communities, eastings, northings = numpy.loadtxt(community_file,
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skiprows=1, delimiter=',',
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unpack=True, dtype=dt)
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communities = communities.tolist()
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modelnames = {
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'5modelAvg': '5 Model Avg.',
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'cccma_cgcm3_1': 'CCCMA CGCM 3.1',
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'CRU': 'CRU',
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'gfdl_cm2_1': 'GFDL CM 2.1',
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'miroc3_2_medres': 'MIROC 3.2, medres',
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'mpi_echam5': 'MPI ECHAM 5',
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'ukmo_hadcm3': 'UKMO HADCM 3.1'
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}
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datasets = []
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Base.metadata.drop_all(bind=engine)
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Base.metadata.create_all(bind=engine)
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for filename in datafiles:
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dataset = snapextract.GeoRefData(os.path.join(tempsnap, filename))
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tokens = filename.split('_')
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startyr = int(tokens[-2])
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endyr = int(tokens[-1].split('.')[0])
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if endyr == 2100:
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endyr = 2099
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starttime = datetime.datetime.now()
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print datetime.datetime.now().strftime('%m-%d-%Y %I:%M%p')
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print filename, startyr, endyr, dataset.model, dataset.scenario, dataset.resolution
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extracted_temps = dataset.extract_points(northings, eastings,
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startyr, endyr)
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print "Loading data into database..."
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if dataset.model == 'CRU':
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datasetType = 'HISTORICAL'
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else:
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datasetType = 'PROJECTION'
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dataset_sql = models.Dataset.query.filter(models.Dataset.model == dataset.model,
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models.Dataset.scenario == dataset.scenario).first()
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if dataset_sql is None:
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print "not in dataset table...", (dataset.model, dataset.scenario)
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dataset_sql = models.Dataset(datasetType,
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dataset.model,
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modelnames[dataset.model],
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dataset.scenario,
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dataset.resolution)
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db_session.add(dataset_sql)
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datasets.append((dataset.model, dataset.scenario))
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db_session.commit()
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min_year = numpy.min(extracted_temps['year'])
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max_year = numpy.max(extracted_temps['year'])
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time_years = max_year - min_year + 1
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i = 0
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for community in communities:
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longitude, latitude, elev = snapextract.ne_to_wgs(northings[i], eastings[i])
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location = models.Community.as_unique(db_session, name=community,
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northing=northings[i],
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easting=eastings[i],
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latitude=latitude,
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longitude=longitude)
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db_session.add(location)
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# Load up the temperature data
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db_data = numpy.zeros((time_years, 13))
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db_data[:, 0] = numpy.arange(min_year, max_year+1)
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db_data[:, 1:] = extracted_temps[i, :]['temperature'].reshape(time_years, 12)
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for row in db_data:
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data = [models.Temperature(year=row[0],
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january=row[1], february=row[2],
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march=row[3], april=row[4],
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may=row[5], june=row[6],
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july=row[7], august=row[8],
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september=row[9], october=row[10],
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november=row[11], december=row[12],
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updated=starttime)]
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dataset_sql.temperatures.extend(data)
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location.temperatures.extend(data)
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db_session.commit()
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i += 1
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db_session.close()
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