def _trim_grid(ingrid):
outgrid = Grid2D.copyFromGrid(ingrid)
while np.isnan(outgrid._data).any():
nrows, ncols = outgrid._data.shape
top = outgrid._data[0, :]
bottom = outgrid._data[-1, :]
left = outgrid._data[:, 0]
right = outgrid._data[:, -1]
ftop = np.isnan(top).sum() / ncols
fbottom = np.isnan(bottom).sum() / ncols
fleft = np.isnan(left).sum() / nrows
fright = np.isnan(right).sum() / nrows
side = np.argmax([ftop, fbottom, fleft, fright])
gdict = outgrid.getGeoDict().asDict()
if side == 0: # removing top row
outgrid._data = outgrid._data[1:, :]
gdict['ymax'] -= gdict['dy']
gdict['ny'] -= 1
elif side == 1: # removing bottom row
outgrid._data = outgrid._data[0:-1, :]
gdict['ymin'] += gdict['dy']
gdict['ny'] -= 1
elif side == 2: # removing left column
outgrid._data = outgrid._data[:, 1:]
gdict['xmin'] += gdict['dx']
gdict['nx'] -= 1
elif side == 3: # removing right column
outgrid._data = outgrid._data[:, 0:-1]
gdict['xmax'] -= gdict['dx']
gdict['nx'] -= 1
geodict = GeoDict(gdict)
outgrid = Grid2D(data=outgrid._data, geodict=geodict)
return outgrid
def calcExposure(self, shakefile):
"""Calculate population exposure to shaking.
Calculate population exposure to shaking, per country, multiplied by event-year per-capita GDP and
alpha correction factor. Also multiply the internal population grid by GDP and
alpha.
:param shakefile:
Path to ShakeMap grid.xml file.
:returns:
Dictionary containing country code (ISO2) keys, and values of
10 element arrays representing population exposure to MMI 1-10.
Dictionary will contain an additional key 'Total', with value of exposure across all countries.
"""
#create a copy of population grid to hold population * gdp * alpha
expdict = super(EconExposure, self).calcExposure(shakefile)
self._econpopgrid = Grid2D.copyFromGrid(self._popgrid)
econdict = {}
isodata = self._isogrid.getData()
eventyear = self.getShakeGrid().getEventDict()['event_timestamp'].year
total = np.zeros((10, ))
for ccode, exparray in expdict.items():
if ccode.find('Total') > -1:
continue
if ccode == 'UK': #unknown
continue
lossmodel = self._emploss.getModel(ccode)
gdp, outccode = self._gdp.getGDP(ccode, eventyear)
isocode = self._country.getCountry(ccode)['ISON']
alpha = lossmodel.alpha
econarray = exparray * gdp * alpha
cidx = (isodata == isocode)
#multiply the population grid by GDP and alpha, so that when the loss model
#queries the grid later, those calculations don't have to be re-done.
self._econpopgrid._data[
cidx] = self._econpopgrid._data[cidx] * gdp * alpha
econdict[ccode] = econarray
total += econarray
econdict['TotalEconomicExposure'] = total
return econdict
