def setUp(self):
data = [
1,
4,
3,
2,
0,
1,
0,
0,
0,
0,
3,
3,
3,
0,
0,
0,
0,
0,
0,
0,
0,
7,
0.1,
5,
]
self.rainfall = hydro.time_series(data,
start_date=hydro.Date(
'M', '2001-01'))
self.data = data
def test_wet_and_dry_spells(self):
"Test wet and dry spells"
data = self.data + [-3]
rainfall = hydro.time_series(data, mask=24 * [0] + [1], start_date=hydro.Date("M", "2001-01"))
threshold = 0.254
dspells = dry_spells(rainfall, threshold=threshold)
wspells = wet_spells(rainfall, threshold=threshold)
assert_equal(dspells.sum() + wspells.durations.sum(), rainfall.count())
def test_daily_random(self):
"Test some PET models on some random daily data"
(freq, base) = ('D', 365)
wave = -10 * np.cos(np.arange(120) * (2. * np.pi) / base)
tmin = hydro.time_series(5 + wave + np.random.rand(120) * 3,
start_date=hydro.Date(freq, '1990-01-01'))
tmax = hydro.time_series(10 + wave + np.random.rand(120) * 3,
start_date=hydro.Date(freq, '1990-01-01'))
ma.putmask(tmax, (tmax < tmin), tmin + 1)
freeze = ((tmin + tmax) / 2. <= 0)
PET = evapo.PotentialEvapoTranspiration(tmin, tmax, self.latitude)
for d in self.defaults:
err_msg = "Error w/ model '%s' : the PET should be masked" % d
P = ma.masked_values(getattr(PET, d).__call__(), 0)
if d in ['Hamon', 'Kharrufa', 'Turc']:
assert_equal(ma.getmaskarray(P), freeze, err_msg=err_msg)
else:
assert (not ma.getmaskarray(P).any())
def test_daily_random(self):
"Test some PET models on some random daily data"
(freq, base) = ('D', 365)
wave = -10 * np.cos(np.arange(120) * (2.*np.pi)/base)
tmin = hydro.time_series(5 + wave + np.random.rand(120) * 3,
start_date=hydro.Date(freq, '1990-01-01'))
tmax = hydro.time_series(10 + wave + np.random.rand(120) * 3,
start_date=hydro.Date(freq, '1990-01-01'))
ma.putmask(tmax, (tmax < tmin), tmin+1)
freeze = ((tmin + tmax)/2. <= 0)
PET = evapo.PotentialEvapoTranspiration(tmin, tmax, self.latitude)
for d in self.defaults:
err_msg = "Error w/ model '%s' : the PET should be masked" % d
P = ma.masked_values(getattr(PET, d).__call__(), 0)
if d in ['Hamon', 'Kharrufa', 'Turc']:
assert_equal(ma.getmaskarray(P), freeze, err_msg=err_msg)
else:
assert(not ma.getmaskarray(P).any())
def test_wet_and_dry_spells(self):
"Test wet and dry spells"
data = self.data + [-3]
rainfall = hydro.time_series(data,
mask=24 * [0] + [1],
start_date=hydro.Date('M', '2001-01'))
threshold = 0.254
dspells = dry_spells(rainfall, threshold=threshold)
wspells = wet_spells(rainfall, threshold=threshold)
assert_equal(dspells.sum() + wspells.durations.sum(), rainfall.count())
def dump_seasonal_averages(observations, dbname="coaps.sqlite",
period="monthly", ensostr="ONI"):
"""
Dump seasonal averages of observations in a SQLite database.
The database is named following the template ``avg%(observations)s.sdb``.
For each season, there are four tables named following the template
``%(period)s_%(phase)s_%(ENSOindicator)``.
Each table has N+1 columns:
* Station COOP ID (format ``COOP%06i``)
* ENSO indicator (used to define the phase): in ('ONI', 'JMAI', 'MEI')
* ENSO phase : in ('A', 'C', 'N', 'W')
* N values for each season
Parameters
----------
observations: string
String representing the observations to store.
period : ('M', 'NDJ', 'DJF', 'JFM'), optional
String representing the seasons.
Use 'M' for monthly, 'DJF' for 'DJF,MAM,JJA,SON'...
ensostr : ('ONI', 'JMAI', 'MEI'), optional
String representing the ENSO indicator to define ENSO phases
"""
seasons = dict(monthly=['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN',
'JUL', 'AUG', 'SEP', 'OCT', 'NOV', 'DEC', ],
JFM=['JFM', 'AMJ', 'JAS', 'OND'],
DJF=['DJF', 'MAM', 'JJA', 'SON'],
NDJ=['NDJ', 'FMA', 'MJJ', 'ASO'])
# Get the seasonal frequency
period = period.upper()
period_to_freq = dict(M=hydro._c.FR_MTH,
MONTHLY=hydro._c.FR_MTH,
NDJ=hydro._c.FR_QTREOCT,
DJF=hydro._c.FR_QTRENOV,
JFM=hydro._c.FR_QTREDEC,)
try:
freq = period_to_freq[period]
except KeyError:
errmsg = "Invalid period '%s': should be in %s."
raise ValueError(errmsg % (period, period_to_freq.keys()))
# Get the names of the fields
# fieldslist = ["COOPID text primary key", "ENSOI text", "phase text"]
fieldslist = ["id integer primary key autoincrement",
"COOPID text", "ENSOI text", "phase text"]
if period in ("M", "MONTHLY"):
period = "monthly"
fieldslist += ["%s real" % _ for _ in seasons[period]]
nbfields = len(fieldslist)
# Get the conversion function:
if freq == hydro._c.FR_MTH:
conversion_func = None
else:
if observations == 'rain':
conversion_func = ma.sum
else:
conversion_func = ma.mean
# Load the ENSO information
ensostr = ensostr.upper()
if ensostr == 'ONI':
ENSO = enso.load_oni()
elif ensostr == 'JMAI':
ENSO = enso.load_jma()
elif ensostr == 'MEI':
raise NotImplementedError
else:
errmsg = "Invalid ENSO indicator '%s': should be in ('ONI','JMAI','MEI')"
raise ValueError(errmsg % ensostr)
# Get the monthly series from the datatable and set the ENSO indicator
tbname = "series_monthly_%s" % observations
monthly = sql.tsfromsqlite(dbname, tbname, freq="M")
monthly = enso.set_ensoindicator(monthly, ENSO)
# Define dictionaries storing the averages
avg_r = {}
# Loop on the stations
for station in monthly.dtype.names:
# Get the current station
current = monthly[station]
# Get the season
seasonal = current.convert(freq, func=conversion_func)
if (observations == "rain") and (freq != hydro._c.FR_MTH):
mask = hydro.time_series(current.mask, dates=current.dates)
seasonal.mask = mask.convert(freq, func=ma.sum)
# Get the values per phase
coopid = station[-6:]
avg_r[(coopid, ensostr, 'A')] = seasonal.convert("A").mean(0)
avg_r[(coopid, ensostr, 'C')] = seasonal.cold.convert("A").mean(0)
avg_r[(coopid, ensostr, 'N')] = seasonal.neutral.convert("A").mean(0)
avg_r[(coopid, ensostr, 'W')] = seasonal.warm.convert("A").mean(0)
# Get the database name
detect_types = sqlite3.PARSE_DECLTYPES | sqlite3.PARSE_COLNAMES
connection = sqlite3.connect(dbname, detect_types=detect_types)
# Define the creation/insertion lines
tbname = "averages_%(period)s_%(observations)s" % locals()
createstr = "create table %s (%s)" % (tbname, ", ".join(fieldslist))
insertstr_template = "insert into %s values (%s)"
insertstr = insertstr_template % (tbname, ", ".join(['?']*nbfields))
# Create the table
try:
connection.execute(createstr)
except sqlite3.OperationalError:
pass
# Define a generator for the values
generator = ([None, ] + list(k) + list(v.astype(np.object).filled((None,)))
for (k, v) in avg_r.items())
connection.executemany(insertstr, generator)
connection.commit()
connection.close()
def spi(rainfall,
span=3,
freq='monthly',
start_reference=None,
end_reference=None,
distribution='gamma'):
"""
Computes the standardized precipitation index for the precipitation series
at the given span.
Parameters
----------
rainfall : TimeSeries
Series of monthly cumulative precipitation.
span : {3, int} optional
Span, in months.
freq : {'monthly','weekly'} optional
Frequency
start_reference : {None, Date} optional
Starting date of the reference period.
If None, use the first date of the series.
end_reference : {None, Date} optional
Ending date of the reference period.
If None, use the last date of the series.
distribution : {'gamma','normal'} optional
Type of distribution for the data of each period.
"""
if not isinstance(rainfall, TimeSeries):
raise TypeError,\
"The precipitation input should be a valid TimeSeries object!"
_freq = freq.upper()[0]
if _freq not in ['M', 'W']:
raise ValueError,"Invalid frequency! Should be in ('Monthly','Weekly')"\
" (got '%s' instead)" % freq
rain = rainfall.convert(_freq, func=ma.sum)
dates = rain.dates
# Convert to cumulative over span (mm) and as a Nx12 or Nx53 array...
arain = mov_sum(rain, span).convert('A')
# Define the reference period ...................
if start_reference is None:
startd = dates[0]
elif not isinstance(start_reference, Date):
startd = Date(_freq, start_reference)
else:
startd = start_reference.asfreq(_freq)
if end_reference is None:
endd = dates[-1]
elif not isinstance(end_reference, Date):
endd = Date(_freq, end_reference)
else:
endd = end_reference.asfreq(_freq)
condition = time_series((dates < startd) | (dates > endd),
dates=dates).convert('A')
# Get the cumulative distribution
try:
cdffunc = _cdfdict[distribution]
except KeyError:
raise ValueError("Unrecognized distribution '%s':"\
"Should be in %s)" % (distribution,
','.join(_cdfdict.keys())))
cdf = cdffunc(arain, condition)
# Get the corresponding normal scores
zscores = ma.fix_invalid(ssd.norm.ppf(cdf))
zscores._mask |= ma.getmaskarray(arain)
# Retransform to a regular time series
if _freq == 'M':
kwargs = {'month': 1}
else:
kwargs = {'week': 1}
kwargs.update({'year': dates[0].year})
spi = time_series(
zscores.ravel(),
start_date=Date(_freq, **kwargs),
).view(type(rainfall))
return spi
def spi(rainfall, span=3, freq='monthly',
start_reference=None, end_reference=None,
distribution='gamma'):
"""
Computes the standardized precipitation index for the precipitation series
at the given span.
Parameters
----------
rainfall : TimeSeries
Series of monthly cumulative precipitation.
span : {3, int} optional
Span, in months.
freq : {'monthly','weekly'} optional
Frequency
start_reference : {None, Date} optional
Starting date of the reference period.
If None, use the first date of the series.
end_reference : {None, Date} optional
Ending date of the reference period.
If None, use the last date of the series.
distribution : {'gamma','normal'} optional
Type of distribution for the data of each period.
"""
if not isinstance(rainfall, TimeSeries):
raise TypeError,\
"The precipitation input should be a valid TimeSeries object!"
_freq = freq.upper()[0]
if _freq not in ['M','W']:
raise ValueError,"Invalid frequency! Should be in ('Monthly','Weekly')"\
" (got '%s' instead)" % freq
rain = rainfall.convert(_freq, func=ma.sum)
dates = rain.dates
# Convert to cumulative over span (mm) and as a Nx12 or Nx53 array...
arain = mov_sum(rain, span).convert('A')
# Define the reference period ...................
if start_reference is None:
startd = dates[0]
elif not isinstance(start_reference, Date):
startd = Date(_freq, start_reference)
else:
startd = start_reference.asfreq(_freq)
if end_reference is None:
endd = dates[-1]
elif not isinstance(end_reference, Date):
endd = Date(_freq, end_reference)
else:
endd = end_reference.asfreq(_freq)
condition = time_series((dates < startd) | (dates > endd),
dates=dates).convert('A')
# Get the cumulative distribution
try:
cdffunc = _cdfdict[distribution]
except KeyError:
raise ValueError("Unrecognized distribution '%s':"\
"Should be in %s)" % (distribution,
','.join(_cdfdict.keys())))
cdf = cdffunc(arain, condition)
# Get the corresponding normal scores
zscores = ma.fix_invalid(ssd.norm.ppf(cdf))
zscores._mask |= ma.getmaskarray(arain)
# Retransform to a regular time series
if _freq == 'M':
kwargs = {'month':1}
else:
kwargs = {'week':1}
kwargs.update({'year':dates[0].year})
spi = time_series(zscores.ravel(),
start_date=Date(_freq,**kwargs),
).view(type(rainfall))
return spi
def setUp(self):
data = [1, 4, 3, 2, 0, 1, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0.1, 5]
self.rainfall = hydro.time_series(data, start_date=hydro.Date("M", "2001-01"))
self.data = data
