def plotGenesisDensityPercentiles(self):
"""
Plot upper and lower percentiles of genesis density derived from
synthetic event sets
"""
datarange = (0, self.hist.max())
figure = FilledContourMapFigure()
lvls, exponent = levels(self.hist.max())
map_kwargs = dict(llcrnrlon=self.lon_range.min(),
llcrnrlat=self.lat_range.min(),
urcrnrlon=self.lon_range.max(),
urcrnrlat=self.lat_range.max(),
projection='merc',
resolution='i')
cbarlab = "TCs/yr"
xgrid, ygrid = np.meshgrid(self.lon_range, self.lat_range)
figure.add(self.synHistUpper.T * (10.**-exponent), xgrid, ygrid,
"Upper percentile", lvls * (10.**-exponent), cbarlab,
map_kwargs)
figure.add(self.synHistLower.T * (10.**-exponent), xgrid, ygrid,
"Lower percentile", lvls * (10.**-exponent), cbarlab,
map_kwargs)
figure.plot()
outputFile = pjoin(self.plotPath, 'genesis_density_percentiles.png')
saveFigure(figure, outputFile)
def plotGenesisDensity(self):
"""Plot genesis density information"""
datarange = (0, self.hist.max())
figure = FilledContourMapFigure()
lvls, exponent = levels(self.hist.max())
map_kwargs = dict(llcrnrlon=self.lon_range.min(),
llcrnrlat=self.lat_range.min(),
urcrnrlon=self.lon_range.max(),
urcrnrlat=self.lat_range.max(),
projection='merc',
resolution='i')
cbarlab = "TCs/yr"
xgrid, ygrid = np.meshgrid(self.lon_range, self.lat_range)
figure.add(self.hist.T * (10.**-exponent), self.X, self.Y, "Historic",
lvls * (10.**-exponent), cbarlab, map_kwargs)
figure.add(self.synHistMean.T * (10.**-exponent), xgrid, ygrid,
"Synthetic", lvls * (10.**-exponent), cbarlab, map_kwargs)
figure.plot()
outputFile = pjoin(self.plotPath, 'genesis_density.png')
saveFigure(figure, outputFile)
figure2 = FilledContourMapFigure()
figure2.add(self.hist.T * (10.**-exponent), xgrid, ygrid, "Historic",
lvls * (10.**-exponent), cbarlab, map_kwargs)
figure2.add(self.synHist[0, :, :].T * (10.**-exponent), xgrid, ygrid,
"Synthetic", lvls * (10.**-exponent), cbarlab, map_kwargs)
figure2.add(self.synHist[1, :, :].T * (10.**-exponent), xgrid, ygrid,
"Synthetic", lvls * (10.**-exponent), cbarlab, map_kwargs)
figure2.add(self.synHist[2, :, :].T * (10.**-exponent), xgrid, ygrid,
"Synthetic", lvls * (10.**-exponent), cbarlab, map_kwargs)
figure2.plot()
outputFile = pjoin(self.plotPath, 'genesis_density_samples.png')
saveFigure(figure2, outputFile)
def test_MinGTMax(self):
"""Minimum greater than maximum automatically swaps values"""
lvls = np.array([0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
expo = 0
rlevs, rexpo = maps.levels(0, minval=1)
self.numpyAssertAlmostEqual(lvls, rlevs)
self.assertEqual(expo, rexpo)
def test_bigLevelValues(self):
"""Test level determination for big input values"""
lvs = np.array([
1000000., 2000000., 3000000., 4000000., 5000000., 6000000.,
7000000., 8000000., 9000000.
])
expo = 7
rlevs, rexpo = maps.levels(10**7, 10**6)
self.numpyAssertAlmostEqual(lvs, rlevs)
self.assertEqual(expo, rexpo)
def test_smallLevelValues(self):
"""Test level determination for small input values"""
lvs = np.array([
0.0000e+00, 1.0000e-06, 2.0000e-06, 3.0000e-06, 4.0000e-06,
5.0000e-06, 6.0000e-06, 7.0000e-06, 8.0000e-06, 9.0000e-06
])
expo = -5
rlevs, rexpo = maps.levels(0, 10**-5)
self.numpyAssertAlmostEqual(lvs, rlevs)
self.assertEqual(expo, rexpo)
def generateKDE(self, save=False, plot=False):
"""
Generate the PDF for cyclone origins using kernel density
estimation technique then save it to a file path provided by
user.
:param float bw: Optional, bandwidth to use for generating the PDF.
If not specified, use the :attr:`bw` attribute.
:param boolean save: If ``True``, save the resulting PDF to a
netCDF file called 'originPDF.nc'.
:param boolean plot: If ``True``, plot the resulting PDF.
:returns: ``x`` and ``y`` grid and the PDF values.
"""
self.kde = KDEMultivariate(self.lonLat, bw=self.bw, var_type='cc')
xx, yy = np.meshgrid(self.x, self.y)
xy = np.vstack([xx.ravel(), yy.ravel()])
pdf = self.kde.pdf(data_predict=xy)
pdf = pdf.reshape(xx.shape)
self.pdf = pdf.transpose()
if save:
dimensions = {
0: {
'name': 'lat',
'values': self.y,
'dtype': 'f',
'atts': {
'long_name': ' Latitude',
'units': 'degrees_north'
}
},
1: {
'name': 'lon',
'values': self.x,
'dtype': 'f',
'atts': {
'long_name': 'Longitude',
'units': 'degrees_east'
}
}
}
variables = {
0: {
'name': 'gpdf',
'dims': ('lat', 'lon'),
'values': np.array(pdf),
'dtype': 'f',
'atts': {
'long_name': 'TC Genesis probability distribution',
'units': ''
}
}
}
ncSaveGrid(pjoin(self.processPath, 'originPDF.nc'), dimensions,
variables)
if plot:
from PlotInterface.maps import FilledContourMapFigure, \
saveFigure, levels
lvls, exponent = levels(pdf.max())
[gx, gy] = np.meshgrid(self.x, self.y)
map_kwargs = dict(llcrnrlon=self.x.min(),
llcrnrlat=self.y.min(),
urcrnrlon=self.x.max(),
urcrnrlat=self.y.max(),
projection='merc',
resolution='i')
cbarlabel = r'Genesis probability ($\times 10^{' + \
str(exponent) + '}$)'
figure = FilledContourMapFigure()
figure.add(pdf * (10**-exponent), gx, gy, 'TC Genesis probability',
lvls * (10**-exponent), cbarlabel, map_kwargs)
figure.plot()
outputFile = pjoin(self.outputPath, 'plots', 'stats',
'originPDF.png')
saveFigure(figure, outputFile)
return self.x, self.y, self.pdf