import matplotlib.pyplot as pyplot import scikits.hydroclimpy.plotlib as cpl # """ Importing the rainfall information ================================== Let's import the rainfall data for Athens, GA. As we already know from a previous :ref:`section <examples_wweatherdata>`, the COAPS identification code for this is 90435. The :func:`coaps.load_coaps_data` function returns a series with a structured dtype, but we only need to take the ``'rain'`` field. """ weatherdata = coaps.load_coaps_data(90435) rainfall = weatherdata['rain'] """ We can check the frequency of the series and its dates range: >>> print rainfall.freqstr 'D' >>> print rainfall.dates[[0,-1]] [13-Jan-1944 31-Dec-2007] """ print rainfall.freqstr print rainfall.dates[[0,-1]] """ Importing the streamflow information
import numpy as np
import numpy.ma as ma
import scikits.hydroclimpy as hydro
import scikits.hydroclimpy.io.coaps as coaps
import scikits.hydroclimpy.enso as enso
stationdict = coaps.ids_bystate('GA')
stationid = [v for (k, v) in stationdict.items() if 'Athens' in k.capitalize()]
print stationid
data = coaps.load_coaps_data(90435)
rainfall = data['rain']
print rainfall.freqstr
print rainfall.dates[[0,-1]]
mrainfall = rainfall.convert('M',func=ma.sum)
arainfall = mrainfall.convert('A')
assert(arainfall.shape == (64, 12))
monthly_means = arainfall.mean(axis=0).round(1)
print monthly_means
""" [ 115.9 112.4 130.8 94.1 99.4 103.5 122.3 90.2 94.5 77.8
92.7 98.6]"""
ONI = enso.load_oni()
print ONI.dates[[0,-1]]
""" [Jan-1950 Dec-2008]"""
(-82.82, 31.52, '92783', 'Douglas'),
(-82.80, 31.02, '94429', 'Homerville'), ]
COAPS_SW = [(-84.12, 31.52, '90140', 'Albany'),
(-84.18, 31.79, '91500', 'Camilla'),
(-84.77, 31.17, '92153', 'Colquitt'),
(-84.77, 31.77, '92450', 'Cuthbert')]
ndtype = np.dtype([('lon', float), ('lat', float), ('site_no', '|S5'),
('desc', '|S20')])
COAPS_SE = np.array(COAPS_SE, dtype=ndtype)
COAPS_SW = np.array(COAPS_SW, dtype=ndtype)
end_date = climpy.Date('D', '2009-02-28')
COAPS_data = {}
for site_no in itertools.chain(COAPS_SE['site_no'], COAPS_SW['site_no']):
data = coaps.load_coaps_data(site_no)['rain']
data = data.fill_missing_dates()
COAPS_data[site_no] = data.adjust_endpoints(end_date=end_date)
table_template = []
csc = "*"
separator = csc.join(['-'*7, '-'*21, '-'*12, '-'*6, '-'*6, '-'*9])
for region in (COAPS_SW, COAPS_SE):
table_template.append("%s%s%s" % (csc, separator, csc))
for (site_no, desc) in zip(region['site_no'], region['desc']):
data = COAPS_data[site_no]
start_date = data.dates[0].strfmt("%m/%d/%Y")
mdata = extras.accept_atmost_missing(data.convert('M'), 0.1)
nbtotal = len(mdata)
nbvalid = mdata.any(-1).filled(False).sum()
row = csc.join([" %5s " % site_no, "%20s " % desc,
import matplotlib.pyplot as pyplot import scikits.hydroclimpy.plotlib as cpl # """ Importing the rainfall information ================================== Let's import the rainfall data for Athens, GA. As we already know from a previous :ref:`section <examples_wweatherdata>`, the COAPS identification code for this is 90435. The :func:`coaps.load_coaps_data` function returns a series with a structured dtype, but we only need to take the ``'rain'`` field. """ weatherdata = coaps.load_coaps_data(90435) rainfall = weatherdata['rain'] """ We can check the frequency of the series and its dates range: >>> print rainfall.freqstr 'D' >>> print rainfall.dates[[0,-1]] [13-Jan-1944 31-Dec-2007] """ print rainfall.freqstr print rainfall.dates[[0, -1]] """ Importing the streamflow information ====================================