コード例 #1
0
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
コード例 #2
0
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]"""
コード例 #3
0
            (-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,
コード例 #4
0
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
====================================