def calculateCI(Vr, years, nodata, minRecords, yrsPerSim=1,
sample_size=50, prange=90):
"""
Fit a GEV to the wind speed records for a 2-D extent of
wind speed values, providing a confidence range by resampling at
random from the input values.
:param Vr: `numpy.ndarray` of wind speeds (3-D - event, lat, lon)
:param years: `numpy.ndarray` of years for which to evaluate
return period values.
:param float nodata: missing data value.
:param int minRecords: minimum number of valid wind speed values required
to fit distribution.
:param int yrsPerSim: Values represent block maxima - this value indicates
the time span of the block (default 1).
:param int sample_size: number of records to randomly sample for calculating
confidence interval of the fit.
:param float prange: percentile range.
:return: `numpy.ndarray` of return period wind speed values
"""
lower = (100 - prange) / 2.
upper = 100. - lower
nrecords = Vr.shape[0]
nsamples = nrecords / sample_size
RpUpper = nodata*np.ones((len(years), Vr.shape[1], Vr.shape[2]), dtype='f')
RpLower = nodata*np.ones((len(years), Vr.shape[1], Vr.shape[2]), dtype='f')
w = np.zeros((len(years), nsamples), dtype='f')
wUpper = np.zeros((len(years)), dtype='f')
wLower = np.zeros((len(years)), dtype='f')
for i in xrange(Vr.shape[1]):
for j in xrange(Vr.shape[2]):
if Vr[:, i, j].max() > 0.0:
random.shuffle(Vr[:, i, j])
for n in xrange(nsamples):
nstart = n*sample_size
nend = (n + 1)*sample_size - 1
vsub = Vr[nstart:nend, i, j]
vsub.sort()
if vsub.max( ) > 0.:
w[:, n], loc, scale, shp = evd.estimateEVD(vsub, years, nodata,
minRecords/10, yrsPerSim)
for n in range(len(years)):
wUpper[n] = percentile(w[n,:], upper)
wLower[n] = percentile(w[n,:], lower)
RpUpper[:, i, j] = wUpper
RpLower[:, i, j] = wLower
return RpUpper, RpLower
def calculate(Vr, years, nodata, minRecords, yrsPerSim):
"""
Fit a GEV to the wind speed records for a 2-D extent of
wind speed values
Parameters:
-----------
:param Vr: `numpy.ndarray` of wind speeds (3-D - event, lat, lon)
:param years: `numpy.ndarray` of years for which to evaluate
return period values
Returns:
--------
:param Rp: `numpy.ndarray` of return period wind speed values
:param loc: `numpy.ndarray` of location parameters in the domain of `Vr`
:param scale: `numpy.ndarray` of scale parameters in the domain of `Vr`
:param shp: `numpy.ndarray` of shape parameters in the domain of `Vr`
"""
Vr.sort(axis=0)
Rp = np.zeros((len(years),) + Vr.shape[1:], dtype='f')
loc = np.zeros(Vr.shape[1:], dtype='f')
scale = np.zeros(Vr.shape[1:], dtype='f')
shp = np.zeros(Vr.shape[1:], dtype='f')
for i in xrange(Vr.shape[1]):
for j in xrange(Vr.shape[2]):
if Vr[:,i,j].max() > 0.0:
w, l, sc, sh = evd.estimateEVD(Vr[:,i,j],
years,
nodata,
minRecords,
yrsPerSim)
Rp[:, i, j] = w
loc[i, j] = l
scale[i, j] = sc
shp[i, j] = sh
return Rp, loc, scale, shp
def calculate(Vr, years, nodata, minRecords, yrsPerSim):
"""
Fit a GEV to the wind speed records for a 2-D extent of
wind speed values
:param Vr: `numpy.ndarray` of wind speeds (3-D - event, lat, lon)
:param years: `numpy.ndarray` of years for which to evaluate
return period values
Returns:
--------
:param Rp: `numpy.ndarray` of return period wind speed values
:param loc: `numpy.ndarray` of location parameters in the domain of `Vr`
:param scale: `numpy.ndarray` of scale parameters in the domain of `Vr`
:param shp: `numpy.ndarray` of shape parameters in the domain of `Vr`
"""
Vr.sort(axis=0)
Rp = np.zeros((len(years), ) + Vr.shape[1:], dtype='f')
loc = np.zeros(Vr.shape[1:], dtype='f')
scale = np.zeros(Vr.shape[1:], dtype='f')
shp = np.zeros(Vr.shape[1:], dtype='f')
for i in xrange(Vr.shape[1]):
for j in xrange(Vr.shape[2]):
if Vr[:, i, j].max() > 0.0:
w, l, sc, sh = evd.estimateEVD(Vr[:, i, j], years, nodata,
minRecords, yrsPerSim)
Rp[:, i, j] = w
loc[i, j] = l
scale[i, j] = sc
shp[i, j] = sh
return Rp, loc, scale, shp
def calculateCI(Vr,
years,
nodata,
minRecords,
yrsPerSim=1,
sample_size=50,
prange=90):
"""
Fit a GEV to the wind speed records for a 2-D extent of
wind speed values, providing a confidence range by resampling at
random from the input values.
:param Vr: `numpy.ndarray` of wind speeds (3-D - event, lat, lon)
:param years: `numpy.ndarray` of years for which to evaluate
return period values.
:param float nodata: missing data value.
:param int minRecords: minimum number of valid wind speed values required
to fit distribution.
:param int yrsPerSim: Values represent block maxima - this value indicates
the time span of the block (default 1).
:param int sample_size: number of records to randomly sample for calculating
confidence interval of the fit.
:param float prange: percentile range.
:return: `numpy.ndarray` of return period wind speed values
"""
lower = (100 - prange) / 2.
upper = 100. - lower
nrecords = Vr.shape[0]
nsamples = nrecords / sample_size
RpUpper = nodata * np.ones(
(len(years), Vr.shape[1], Vr.shape[2]), dtype='f')
RpLower = nodata * np.ones(
(len(years), Vr.shape[1], Vr.shape[2]), dtype='f')
w = np.zeros((len(years), nsamples), dtype='f')
wUpper = np.zeros((len(years)), dtype='f')
wLower = np.zeros((len(years)), dtype='f')
for i in xrange(Vr.shape[1]):
for j in xrange(Vr.shape[2]):
if Vr[:, i, j].max() > 0.0:
random.shuffle(Vr[:, i, j])
for n in xrange(nsamples):
nstart = n * sample_size
nend = (n + 1) * sample_size - 1
vsub = Vr[nstart:nend, i, j]
vsub.sort()
if vsub.max() > 0.:
w[:, n], loc, scale, shp = evd.estimateEVD(
vsub, years, nodata, minRecords / 10, yrsPerSim)
for n in range(len(years)):
wUpper[n] = percentile(w[n, :], upper)
wLower[n] = percentile(w[n, :], lower)
RpUpper[:, i, j] = wUpper
RpLower[:, i, j] = wLower
return RpUpper, RpLower
