def plotPressureMeanDiff(self):
"""
Plot a map of the difference between observed and synthetic mean
pressure values.
"""
datarange = (-25, 25)
figure = ArrayMapFigure()
map_kwargs = dict(llcrnrlon=self.lon_range[:-1].min(),
llcrnrlat=self.lat_range[:-1].min(),
urcrnrlon=self.lon_range[:-1].max(),
urcrnrlat=self.lat_range[:-1].max(),
projection='merc',
resolution='i')
cbarlab = "Mean central pressure difference (hPa)"
data = self.histMean - self.synMean
xgrid, ygrid = np.meshgrid(self.lon_range[:-1], self.lat_range[:-1])
data = self.histMin - self.synMin
figure.add(np.transpose(data), xgrid, ygrid, "", datarange,
cbarlab, map_kwargs)
figure.plot()
outputFile = pjoin(self.plotPath, 'meanPressureDiff.png')
saveFigure(figure, outputFile)
def plotTrackDensityPercentiles(self):
"""
Plot upper and lower percentiles of track density derived from
synthetic event sets
"""
datarange = (0, self.hist.max())
figure = ArrayMapFigure()
map_kwargs = dict(llcrnrlon=self.lon_range[:-1].min(),
llcrnrlat=self.lat_range[:-1].min(),
urcrnrlon=self.lon_range[:-1].max(),
urcrnrlat=self.lat_range[:-1].max(),
projection='merc',
resolution='i')
cbarlab = "TC observations/yr"
xgrid, ygrid = np.meshgrid(self.lon_range[:-1], self.lat_range[:-1])
figure.add(self.synHistUpper.T, xgrid, ygrid, "Upper percentile", datarange,
cbarlab, map_kwargs)
figure.add(self.synHistLower.T, xgrid, ygrid, "Lower percentile",
datarange, cbarlab, map_kwargs)
figure.plot()
outputFile = pjoin(self.plotPath, 'track_density_percentiles.png')
saveFigure(figure, outputFile)
def plotPressureMin(self):
"""
Plot a map of observed and synthetic minimum central pressure values.
"""
datarange = (900, 1000)
figure = ArrayMapFigure()
map_kwargs = dict(llcrnrlon=self.lon_range[:-1].min(),
llcrnrlat=self.lat_range[:-1].min(),
urcrnrlon=self.lon_range[:-1].max(),
urcrnrlat=self.lat_range[:-1].max(),
projection='merc',
resolution='i')
cbarlab = "Minimum central pressure (hPa)"
xgrid, ygrid = np.meshgrid(self.lon_range[:-1], self.lat_range[:-1])
figure.add(np.transpose(self.histMin), xgrid, ygrid, "Historic", datarange,
cbarlab, map_kwargs)
figure.add(np.transpose(self.synMin), xgrid, ygrid, "Synthetic", datarange,
cbarlab, map_kwargs)
figure.plot()
outputFile = pjoin(self.plotPath, 'minPressure.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 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 plotTrackDensity(self):
"""Plot track density information"""
datarange = (0, self.hist.max())
figure = ArrayMapFigure()
cbarlab = "TC observations/yr"
figure.add(self.hist.T, self.X, self.Y, "Historic", datarange,
cbarlab, self.map_kwargs)
figure.add(self.synHistMean.T, self.X, self.Y, "Synthetic",
datarange, cbarlab, self.map_kwargs)
figure.plot()
outputFile = pjoin(self.plotPath, 'track_density.png')
saveFigure(figure, outputFile)
figure2 = ArrayMapFigure()
figure2.add(self.hist.T, self.X, self.Y, "Historic", datarange,
cbarlab, self.map_kwargs)
figure2.add(self.synHist[0, :, :].T, self.X, self.Y, "Synthetic",
datarange, cbarlab, self.map_kwargs)
figure2.add(self.synHist[10, :, :].T, self.X, self.Y, "Synthetic",
datarange, cbarlab, self.map_kwargs)
figure2.add(self.synHist[20, :, :].T, self.X, self.Y, "Synthetic",
datarange, cbarlab, self.map_kwargs)
figure2.add(self.synHist[30, :, :].T, self.X, self.Y, "Synthetic",
datarange, cbarlab, self.map_kwargs)
figure2.add(self.synHist[40, :, :].T, self.X, self.Y, "Synthetic",
datarange, cbarlab, self.map_kwargs)
figure2.plot()
outputFile = pjoin(self.plotPath, 'track_density_samples.png')
saveFigure(figure2, outputFile)
def plotGenesisDensity(self):
"""Plot genesis density information"""
datarange = (0, self.hist.max())
figure = ArrayMapFigure()
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, xgrid, ygrid, "Historic", datarange,
cbarlab, map_kwargs)
figure.add(self.synHistMean.T, xgrid, ygrid, "Synthetic",
datarange, cbarlab, map_kwargs)
figure.plot()
outputFile = pjoin(self.plotPath, 'genesis_density.png')
saveFigure(figure, outputFile)
figure2 = ArrayMapFigure()
figure2.add(self.hist.T, xgrid, ygrid, "Historic", datarange,
cbarlab, map_kwargs)
figure2.add(self.synHist[0, :, :].T, xgrid, ygrid, "Synthetic",
datarange, cbarlab, map_kwargs)
figure2.add(self.synHist[1, :, :].T, xgrid, ygrid, "Synthetic",
datarange, cbarlab, map_kwargs)
figure2.add(self.synHist[2, :, :].T, xgrid, ygrid, "Synthetic",
datarange, cbarlab, map_kwargs)
figure2.plot()
outputFile = pjoin(self.plotPath, 'genesis_density_samples.png')
saveFigure(figure2, outputFile)
def plotGenesisDensityPercentiles(self):
"""
Plot upper and lower percentiles of genesis density derived from
synthetic event sets
"""
datarange = (0, self.hist.max())
figure = ArrayMapFigure()
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, xgrid, ygrid, "Upper percentile", datarange, cbarlab, map_kwargs)
figure.add(self.synHistLower.T, xgrid, ygrid, "Lower percentile", datarange, cbarlab, map_kwargs)
figure.plot()
outputFile = pjoin(self.plotPath, "genesis_density_percentiles.png")
saveFigure(figure, outputFile)
def plotMinPressureQuantiles(self):
x = self.histMinCP
y = self.synMinCP
lims = (850, 1000)
fig = QuantileFigure()
fig.add(x.compress(x>0), y.compress(y>0), lims, "Observed pressure (hPa)", "Simulated pressure (hPa)",
"Q-Q plot of minimum central pressure")
fig.plot()
outputFile = pjoin(self.plotPath, 'minPressureQuantiles.png')
saveFigure(fig, outputFile)
def main(configFile):
from Utilities.loadData import loadTrackFile
from Utilities.config import ConfigParser
from os.path import join as pjoin, normpath, dirname
baseDir = normpath(pjoin(dirname(__file__), '..'))
inputPath = pjoin(baseDir, 'input')
config = ConfigParser()
config.read(configFile)
inputFile = config.get('DataProcess', 'InputFile')
source = config.get('DataProcess', 'Source')
gridLimit = config.geteval('Region', 'gridLimit')
xx = np.arange(gridLimit['xMin'], gridLimit['xMax'] + .1, 0.1)
yy = np.arange(gridLimit['yMin'], gridLimit['yMax'] + .1, 0.1)
xgrid, ygrid = np.meshgrid(xx, yy)
if len(dirname(inputFile)) == 0:
inputFile = pjoin(inputPath, inputFile)
try:
tracks = loadTrackFile(configFile, inputFile, source)
except (TypeError, IOError, ValueError):
log.critical("Cannot load historical track file: {0}".format(inputFile))
raise
title = source
outputPath = config.get('Output', 'Path')
outputPath = pjoin(outputPath, 'plots', 'stats')
outputFile = pjoin(outputPath, 'tctracks.png')
map_kwargs = dict(llcrnrlon=xgrid.min(),
llcrnrlat=ygrid.min(),
urcrnrlon=xgrid.max(),
urcrnrlat=ygrid.max(),
projection='merc',
resolution='i')
figure = TrackMapFigure()
figure.add(tracks, xgrid, ygrid, title, map_kwargs)
figure.plot()
saveFigure(figure, outputFile)
def saveTrackMap(tracks, xgrid, ygrid, title, map_kwargs, filename):
"""
Create a track map and save to file.
:param tracks: collection of :class:`Track` objects
:param xgrid: :class:`numpy.ndarray` of longitude points defining the
domain.
:param ygrid: :class:`numpy.ndarray` of latitude points defining the
domain.
:param str title: Title string for the plot.
:param dict map_kwargs: Keyword args that will define the
:class:`GeoAxes` instance.
:param str filename: Path to the file to save the image.
"""
fig = SingleTrackMap()
fig.plot(tracks, xgrid, ygrid, title, map_kwargs)
saveFigure(fig, filename)
def plotTrackDensityPercentiles(self):
"""
Plot upper and lower percentiles of track density derived from
synthetic event sets
"""
datarange = (0, self.hist.max())
figure = ArrayMapFigure()
cbarlab = "TC observations/yr"
figure.add(self.synHistUpper.T, self.X, self.Y, "Upper percentile",
datarange, cbarlab, self.map_kwargs)
figure.add(self.synHistLower.T, self.X, self.Y, "Lower percentile",
datarange, cbarlab, self.map_kwargs)
figure.plot()
outputFile = pjoin(self.plotPath, 'track_density_percentiles.png')
saveFigure(figure, outputFile)
def plotTrackDensity(self):
"""Plot track density information"""
datarange = (0, self.hist.max())
figure = ArrayMapFigure()
map_kwargs = dict(llcrnrlon=self.lon_range[:-1].min(),
llcrnrlat=self.lat_range[:-1].min(),
urcrnrlon=self.lon_range[:-1].max(),
urcrnrlat=self.lat_range[:-1].max(),
projection='merc',
resolution='i')
cbarlab = "TC observations/yr"
xgrid, ygrid = np.meshgrid(self.lon_range[:-1], self.lat_range[:-1])
figure.add(self.hist.T, xgrid, ygrid, "Historic", datarange,
cbarlab, map_kwargs)
figure.add(self.synHistMean.T, xgrid, ygrid, "Synthetic",
datarange, cbarlab, map_kwargs)
figure.plot()
outputFile = pjoin(self.plotPath, 'track_density.png')
saveFigure(figure, outputFile)
def generateKDE(self, bw=None, 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.
"""
grid2d = KPDF.MPDF2DGrid2Array(self.x, self.y, 1)
if bw:
self.bw = bw
pdf = self._generatePDF(grid2d, self.bw)
# Normalise PDF so total probability equals one
# Note: Need to investigate why output from KPDF is not correctly normalised
pdf = pdf / pdf.sum()
pdf.shape = (pdf.shape[0]/self.x.size, self.x.size)
self.pdf = pdf.transpose()
if save:
outputFile = os.path.join(self.processPath, 'originPDF.nc')
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': numpy.array(pdf),
'dtype': 'f',
'atts': {
'long_name': 'TC Genesis probability distribution',
'units': ''
}
}
}
ncSaveGrid(outputFile, dimensions, variables)
if plot:
from Utilities.plotField import plotField
from PlotInterface.maps import FilledContourMapFigure, saveFigure
# Automatically determine appropriate contour levels
min_lvls = 6.0
lvls_options = numpy.array([1.0, 0.5, 0.25, 0.2, 0.1])
pdfMax = pdf.max()
exponent = int(numpy.floor(numpy.log10(pdfMax)))
significand = pdfMax * 10**-exponent
lvl_step = lvls_options[numpy.where((significand/lvls_options) > min_lvls)[0][0]]
lvls = numpy.arange(0, pdf.max(), lvl_step*(10.0**exponent))
[gx,gy] = numpy.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 ($\times 10^{' + str(exponent) + '}$)'
title = 'TC Genesis probability'
figure = FilledContourMapFigure()
figure.add(pdf, gx, gy, title, lvls, cbarlabel, map_kwargs)
figure.plot()
outputFile = os.path.join(self.outputPath, 'plots', 'stats', 'originPDF_fill.png')
saveFigure(figure, outputFile)
return self.x, self.y, self.pdf
lat = ncGetDims(ncobj, 'lat')
ardata = getData(ncobj, 'alpha', ij)
mudata = getData(ncobj, 'mu', ij)
mindata = getData(ncobj, 'min', ij)
sigdata = getData(ncobj, 'sig', ij)
ncobj.close()
fig = ArrayMapFigure()
fig.add(mudata, xgrid, ygrid, 'Mean pressure ', [950, 1000], 'Pressure (hPa)',
map_kwargs)
fig.add(mindata, xgrid, ygrid, 'Minimum pressure', [900, 1000],
'Pressure (hPa)', map_kwargs)
fig.add(sigdata, xgrid, ygrid, 'Pressure standard deviation', [0, 50],
'Std dev.', map_kwargs)
fig.add(ardata, xgrid, ygrid, 'Pressure AR(1)', [-1, 1], 'AR(1)', map_kwargs)
fig.plot()
saveFigure(fig, pjoin(plotsPath, "pressure_stats.png"))
ncobj = ncLoadFile(pjoin(processPath, "pressure_stats.nc"))
lon = ncGetDims(ncobj, 'lon')
lat = ncGetDims(ncobj, 'lat')
ardata = getData(ncobj, 'alpha', ij)
mudata = getData(ncobj, 'mu', ij)
mindata = getData(ncobj, 'min', ij)
sigdata = getData(ncobj, 'sig', ij)
ncobj.close()
fig = ArrayMapFigure()
fig.add(mudata, xgrid, ygrid, 'Mean pressure ', [950, 1000], 'Pressure (hPa)',
map_kwargs)
fig.plot()
fig.axes[0].set_ylim((-35, -5))
saveFigure(fig, pjoin(plotsPath, "pressure_mean.png"))
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
