def plotConvergence(ax, locName):
locId = locations['locId'][locations['locName'] == locName][0]
locLon = locations['locLon'][locations['locId'] == locId][0]
locLat = locations['locLat'][locations['locId'] == locId][0]
records = database.locationRecords(db, str(locId))
recs = records['wspd'][records['wspd'] > 0]
data = np.zeros(int(NumSimulations * 365.25))
data[-len(recs):] = recs
sortedmax = np.sort(data)
emprp = empReturnPeriod(data)
dd, rr = bootstrap(data, n=100)
ax.plot(emprp[-10000:], data[-10000:], color='k', label="Mean ARI")
ax.fill_between(rr[0, :],
dd[1, :],
dd[0, :],
alpha=0.5,
label="95th percentile")
ax.set_xscale('log')
#xlabel = 'Average recurrence interval (years)'
#ylabel = 'Wind speed (m/s)'
title = "{0} (n={1:d})".format(locName, len(recs))
ax.set_title(title)
addARIGrid(ax)
def plotConvergence(ax, locName):
locId = locations['locId'][locations['locName'] == locName][0]
locLon = locations['locLon'][locations['locId'] == locId][0]
locLat = locations['locLat'][locations['locId'] == locId][0]
records = database.locationRecords(db, locId)
recs = records['wspd'][records['wspd'] > 0]
data = np.zeros(int(NumSimulations * 365.25))
data[-len(recs):] = recs
sortedmax = np.sort(data)
emprp = empReturnPeriod(data)
random.shuffle(data)
d1 = data[:int(len(data) / 2)]
d2 = data[int(len(data) / 2 + 1):]
sortedmax1 = np.sort(d1)
sortedmax2 = np.sort(d2)
emprp1 = empReturnPeriod(d1)
emprp2 = empReturnPeriod(d2)
ep = 1. / emprp
ep1 = 1. / emprp1
ep2 = 1. / emprp2
ax.semilogx(emprp[emprp > 1],
sortedmax[emprp > 1],
color='k',
label="Mean ARI")
ax.semilogx(emprp2[emprp2 > 1],
sortedmax2[emprp2 > 1],
color="#006983",
label="Convergence check 1",
ls='--')
ax.semilogx(emprp1[emprp1 > 1],
sortedmax1[emprp1 > 1],
color="#A33F1F",
label="Convergence check 2",
ls='--')
ax.set_xscale('log')
xlabel = 'Average recurrence interval (years)'
ylabel = 'Wind speed (m/s)'
title = "{0} ({1:5.2f}E, {2:5.2f}S, n={3:d})".format(
locName, locLon, locLat, len(recs))
#ax.set_xlabel(xlabel)
#ax.set_ylabel(ylabel)
ax.set_title(title)
addARIGrid(ax)
def plotHazardCurves(self, inputFile, plotPath):
"""
Plot the hazard values stored in hazardFile, at the stns
stored in stnFile.
"""
log.info(("Plotting return period curves for locations within the "
"model domain"))
# Open data file
try:
ncobj = nctools.ncLoadFile(inputFile)
lon = nctools.ncGetDims(ncobj, 'lon')
lat = nctools.ncGetDims(ncobj, 'lat')
years = nctools.ncGetDims(ncobj, 'ari')
except (IOError, RuntimeError, KeyError):
log.critical("Cannot load input file: %s" % inputFile)
raise
placeNames, placeID, placeLats, placeLons, locations = self.getLocations(
)
for name, plat, plon, pID in zip(placeNames, placeLats, placeLons,
placeID):
log.debug("Plotting return period curve for %s" % name)
i = find_index(lon, plon)
j = find_index(lat, plat)
xlabel = 'Average recurrence interval (years)'
ylabel = 'Wind speed (%s)' % self.plotUnits.label
title = "Return period wind speeds at " + name + ", \n(%5.1f,%5.1f)" % (
plon, plat)
name.replace(' ', '')
log.debug("Working on {0}".format(name))
filename = pjoin(plotPath, 'ARI_curve_%s.%s' % (pID, "png"))
log.debug("Saving hazard curve for %s to %s" % (name, filename))
wspd = ncobj.variables['wspd'][:, j, i]
recs = database.locationRecords(self.db, pID)
data = np.zeros(int(10000 * 365.25))
if len(recs) > 0:
data[-len(recs):] = recs['wspd']
allevents = np.sort(data)
log.debug("allevents length = {0}".format(len(allevents)))
placeWspd = metutils.convert(wspd, 'mps', self.plotUnits.units)
if np.all(placeWspd.mask):
log.debug("All values for {0} are null".format(name))
continue
if self.ciBounds:
wspdLower = ncobj.variables['wspdlower'][:, j, i]
wspdUpper = ncobj.variables['wspdupper'][:, j, i]
placeWspdLower = metutils.convert(wspdLower, 'mps',
self.plotUnits.units)
placeWspdUpper = metutils.convert(wspdUpper, 'mps',
self.plotUnits.units)
else:
placeWspdUpper = np.zeros(len(placeWspd))
placeWspdLower = np.zeros(len(placeWspd))
saveHazardCurve(years, allevents, placeWspd, placeWspdUpper,
placeWspdLower, xlabel, ylabel, title, filename,
self.fit)
ncobj.close()
def plotConvergenceTest(locName):
locId = locations['locId'][locations['locName'] == locName][0]
locLon = locations['locLon'][locations['locId'] == locId][0]
locLat = locations['locLat'][locations['locId'] == locId][0]
records = database.locationRecords(db, locId)
recs = records['wspd'][records['wspd'] > 0]
data = np.zeros(int(NumSimulations * 365.25))
data[-len(recs):] = recs
sortedmax = np.sort(data)
emprp = empReturnPeriod(data)
random.shuffle(data)
d1 = data[:int(len(data) / 2)]
d2 = data[int(len(data) / 2):]
sortedmax1 = np.sort(d1)
sortedmax2 = np.sort(d2)
emprp1 = empReturnPeriod(d1)
emprp2 = empReturnPeriod(d2)
ep = 1. / emprp
ep1 = 1. / emprp1
ep2 = 1. / emprp2
mn = (sortedmax1[emprp1 > 1] + sortedmax2[emprp2 > 1]) / 2.
delta = np.abs(sortedmax1[emprp1 > 1] - sortedmax2[emprp2 > 1])
fdelta = delta / mn
fig, ax1 = plt.subplots(1, 1)
ax1.semilogx(emprp[emprp > 1],
sortedmax[emprp > 1],
color='k',
label="Mean ARI")
ax1.semilogx(emprp2[emprp2 > 1],
sortedmax2[emprp2 > 1],
color="#006983",
label="Convergence check 1")
ax1.semilogx(emprp1[emprp1 > 1],
sortedmax1[emprp1 > 1],
color="#A33F1F",
label="Convergence check 2")
ax1.set_xscale('log')
xlabel = 'Average recurrence interval (years)'
ylabel = 'Wind speed (m/s)'
title = "ARI wind speeds at " + locName + \
" \n(%5.2f,%5.2f, n=%d)"%(locLon, locLat, len(recs))
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
ax1.set_title(title)
ax1.legend(loc=2)
addARIGrid(ax1)
fig.tight_layout()
plt.savefig(os.path.join(plotPath, "{0:05d}_ARI.png".format(locId)),
bbox_inches='tight')
plt.close()
fig2, ax2 = plt.subplots(1, 1)
ax2.semilogy(sortedmax[emprp > 1],
ep[emprp > 1],
color="k",
label="Mean AEP")
ax2.semilogy(sortedmax1[emprp1 > 1],
ep1[emprp1 > 1],
color="#006983",
label="Convergence check 1")
ax2.semilogy(sortedmax2[emprp2 > 1],
ep2[emprp2 > 1],
color="#A33F1F",
label="Convergence check 2")
ax2.set_xlabel(ylabel)
title = "AEP wind speeds at " + locName + \
" \n(%5.2f,%5.2f, n=%d)"%(locLon, locLat, len(recs))
ax2.set_ylabel("Exceedance probability (events/year)")
ax2.set_title(title)
ax2.legend(loc=1)
addAEPGrid(ax2)
fig.tight_layout()
plt.savefig(os.path.join(plotPath, "{0:05d}_AEP.png".format(locId)),
bbox_inches='tight')
plt.close()
fig3, (ax3, ax4) = plt.subplots(2, 1, sharex=True)
ax3.fill_between(emprp[emprp > 1][0:-1:2],
fdelta,
color="#006983",
alpha=0.5)
ax3.set_ylabel('Fractional difference')
ax3.set_title("Difference in convergence test ARI wind speeds at " + \
locName + " \n(%5.2f,%5.2f, n=%d)"%(locLon, locLat, len(recs)))
ax3.set_xscale('log')
addARIGrid(ax3)
ax4.fill_between(emprp[emprp > 1][0:-1:2],
delta,
color="#006983",
alpha=0.5)
ax4.set_ylabel("Difference (m/s)")
ax4.set_xlabel(xlabel)
ax4.set_xscale('log')
addARIGrid(ax4)
fig.tight_layout()
plt.savefig(os.path.join(plotPath, "{0:05d}_ARI_delta.png".format(locId)),
bbox_inches='tight')
plt.close()
def plotConvergenceTest(locName):
locId = locations['locId'][locations['locName'] == locName][0]
locLon = locations['locLon'][locations['locId'] == locId][0]
locLat = locations['locLat'][locations['locId'] == locId][0]
records = database.locationRecords(db, str(locId))
recs = records['wspd'][records['wspd'] > 0]
data = np.zeros(int(NumSimulations * 365.25))
data[-len(recs):] = recs
sortedmax = np.sort(data)
emprp = empReturnPeriod(data)
dd, rr = bootstrap(data, n=100)
ep = 1. / emprp
ep1 = 1. / rr[0, :]
ep2 = 1. / rr[1, :]
mn = dd.mean(axis=0)
delta = np.abs(np.diff(dd, axis=0))
fdelta = delta / mn
fig, ax1 = plt.subplots(1, 1, figsize=figsize)
ax1.fill_between(rr[0, :],
dd[1, :],
dd[0, :],
alpha=0.5,
label="95th percentile")
ax1.plot(emprp[-10000:], data[-10000:], color='k', label="Mean ARI")
ax1.set_xscale('log')
xlabel = 'Average recurrence interval (years)'
ylabel = 'Wind speed (m/s)'
title = "ARI wind speeds at " + locName + \
" \n(%5.2f,%5.2f, n=%d)"%(locLon, locLat, len(recs))
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
ax1.set_title(title)
ax1.legend(loc=2)
addARIGrid(ax1)
fig.tight_layout()
plt.savefig(os.path.join(plotPath, "{0:05d}_ARI.png".format(locId)),
bbox_inches='tight')
plt.close()
fig2, ax2 = plt.subplots(1, 1, figsize=figsize)
ax2.semilogy(sortedmax[-10000:], ep[-10000:], color="k", label="Mean AEP")
ax2.fill_betweenx(1. / rr[0, :],
dd[0, :],
dd[1, :],
alpha=0.5,
label="95th percentile")
ax2.set_xlabel(ylabel)
title = "AEP wind speeds at " + locName + \
" \n(%5.2f,%5.2f, n=%d)"%(locLon, locLat, len(recs))
ax2.set_ylabel("Exceedance probability (events/year)")
ax2.set_title(title)
ax2.legend(loc=1)
addAEPGrid(ax2)
fig.tight_layout()
plt.savefig(os.path.join(plotPath, "{0:05d}_AEP.png".format(locId)),
bbox_inches='tight')
plt.close()