endpath = plume.wrfdir + 'interp/end/wrfend_' + Case + '.npy'
if not os.path.isfile(endpath):
continue
print('Opening data: %s' % endpath)
enddict = np.load(endpath, allow_pickle=True).item()
#create an interpolated profile of temperature
if Case[-1:] == 'T' or Case[-1:] == 'E':
levels = plume.lvltall
else:
levels = plume.lvl
T0 = np.load(plume.wrfdir + 'interp/profT0' + Case + '.npy')
zi[nCase] = plume.get_zi(T0)
dimX = np.shape(enddict['PM25'])[2]
#RANDOM SAMPLING---------------------------------
maxPM = np.max(enddict['PM25'], (0, 1, 2))
plume_mask = np.where(enddict['PM25'][:, :, :int(dimX / 3)] > maxPM * 0.01)
for nTrial in range(points):
randI = randint(0, len(plume_mask[0]) - 1)
nZ, nY, nX = plume_mask[0][randI], plume_mask[1][randI], plume_mask[2][
randI]
Sd[nCase, nTrial] = Cp * (enddict['T'][nZ, nX, nY] + 300)
pm[nCase, nTrial] = enddict['PM25'][nZ, nX, nY]
height[nCase, nTrial] = levels[nZ] / zi[nCase]
plt.figure()
levels = plume.lvltall
if Case[-1:] == 'E':
pmlvl = np.arange(
0, 4001, 40) #extra tall domain for the concentrations only
else:
pmlvl = levels
else:
levels = plume.lvl
pmlvl = levels
interpT = interp1d(levels, T0, fill_value='extrapolate')
T0interp = interpT(interpZ)
#mask plume with cutoff value---------------------------------
dimT, dimZ, dimX = np.shape(csdict['temp']) #get shape of data
zi[nCase] = plume.get_zi(T0) #calculate BL height
pm = ma.masked_where(csdict['pm25'][-1, :, :] <= plume.PMcutoff,
csdict['pm25'][-1, :, :]) #mask all non-plume cells
#locate centerline
ctrZidx = pm.argmax(0) #locate maxima along height
ctrXidx = pm.argmax(1) #locate maxima downwind
pmCtr = np.array([pm[ctrZidx[nX], nX] for nX in range(dimX)
]) #get concentration along the centerline
xmax, ymax = np.nanargmax(ctrZidx), np.nanmax(
ctrZidx) #get location of maximum centerline height
centerline = ma.masked_where(
pmlvl[ctrZidx] == 0, pmlvl[ctrZidx]
) #make sure centerline is only calculated inside the plume
centerline.mask[:int(1000 / plume.dx)] = True