def testMaskingFunctions(self):
xouter = MV2.outerproduct(MV2.arange(5.), [1] * 10)
masked = MV2.masked_greater(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[2:], True))
self.assertTrue(MV2.allequal(masked.mask[:2], False))
masked = MV2.masked_greater_equal(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[1:], True))
self.assertTrue(MV2.allequal(masked.mask[:1], False))
masked = MV2.masked_less(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[:1], True))
self.assertTrue(MV2.allequal(masked.mask[1:], False))
masked = MV2.masked_less_equal(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[:2], True))
self.assertTrue(MV2.allequal(masked.mask[2:], False))
masked = MV2.masked_not_equal(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[1], False))
self.assertTrue(MV2.allequal(masked.mask[0], True))
self.assertTrue(MV2.allequal(masked.mask[2:], True))
masked = MV2.masked_equal(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[1], True))
self.assertTrue(MV2.allequal(masked.mask[0], False))
self.assertTrue(MV2.allequal(masked.mask[2:], False))
masked = MV2.masked_outside(xouter, 1, 3)
self.assertTrue(MV2.allequal(masked.mask[0:1], True))
self.assertTrue(MV2.allequal(masked.mask[1:4], False))
self.assertTrue(MV2.allequal(masked.mask[4:], True))
masked = MV2.masked_where(
MV2.logical_or(MV2.greater(xouter, 3), MV2.less(xouter, 2)),
xouter)
self.assertTrue(MV2.allequal(masked.mask[0:2], True))
self.assertTrue(MV2.allequal(masked.mask[2:4], False))
self.assertTrue(MV2.allequal(masked.mask[4:], True))
def bony_sorting_part1(w500, binedges):
A, B, C = w500.shape
dx = np.diff(binedges)[0]
# Compute composite:
OKwaps = nanarray(
(A, B, C, 2 + len(binedges))) # add 2 for the exceedances
xx = 0
for x in binedges:
xx += 1
w500_bin = MV.masked_less(w500, x)
OKwaps[..., xx] = MV.masked_greater_equal(w500_bin, x + dx)
# do the first wap bin:
OKwaps[..., 0] = MV.masked_greater_equal(w500, binedges[0])
# do the last wap bin:
OKwaps[..., -1] = MV.masked_less(w500, binedges[-1] + dx)
return OKwaps # [month,lat,lon,wapbin]
lat = uf.getLatitude() lon = uf.getLongitude() xouter.setAxis(0, lat) xouter.setAxis(1, lon) xouter.setAxisList([lat, lon]) # Equivalent ## masked_equal(x, value) ## masked_equal(x, value) = x masked where x == value ## For floating point consider masked_values(x, value) instead. ## masked_greater(x, value) ## masked_greater(x, value) = x masked where x > value ## masked_greater_equal(x, value) ## masked_greater_equal(x, value) = x masked where x >= value xge = MV2.masked_greater_equal(xouter, 120) ## masked_less(x, value) ## masked_less(x, value) = x masked where x < value xl = MV2.masked_less(xouter, 160) ## masked_less_equal(x, value) ## masked_less_equal(x, value) = x masked where x <= value ## masked_not_equal(x, value) ## masked_not_equal(x, value) = x masked where x != value ## masked_outside(x, v1, v2) ## x with mask of all values of x that are outside [v1,v2] xmo = MV2.masked_outside(xouter, 120, 160)
def createVarSubdivisionShpNCfile(varid, cfile, shpfile, outpath, **kwarg):
"""
createVarSubdivisionShpNCfile : create variable of subdivison shaped using
shp file as one of the input and store into nc file.
args:
varid : variable name of input file
cfile : input climate netcdf file path
shpfile : shpfile path which has proper subdivison level map
outpath : output directory
kwargs:
preserve_original_data_shape_in_all_subregions : True | False (default).
If it is True, then all subdivison regions of input variable will
have same shape of full region but masked fully (lat, lon) other
than subdivison / state boundary region. Useful to retain the full
data shape but keep only subdivison region data alone.
If it is False, then all subdivison regions of input variable will
be store with its corresponding lat, lon shape.
latitude : range of latitude to be retain in the output in case of
preserve_original_data_shape_in_all_subregions is True.
longitude : range of longitude to be retain in the output in case of
preserve_original_data_shape_in_all_subregions is True.
output : It create nc file named as varid + '_subdivisions.nc'.
This function works well for the input shape of (time, latitude, longitude).
Author : Arulalan.T ([email protected]), Research Scholar, CAS, IITD
Date : 21-Apr-2017
"""
# export the environment variable path of shape file
shpfname = shpfile.split('/')[-1].split('.')[0 ]
shppath = shpfile.split(shpfname)[0]
ocgis.env.DIR_GEOMCABINET = shppath
preserve_original_data_shape_in_all_subregions = kwarg.get('preserve_original_data_shape_in_all_subregions', False)
lat = kwarg.get('latitude', None)
lon = kwarg.get('longitude', None)
outpath = '.' if outpath is None else outpath
outfile_sequentialnumbers = varid + '_filledwith_sequentialnumbers.nc'
shpdata_outfile = os.path.join(outpath, varid + '_subdivisions.nc')
# open the nc file via uvcdat
inf = cdms2.open(cfile)
sdata = inf(varid, time=slice(1))
tottimeax = inf[varid].getTime()
# get the shape of one time step, lat, lon for dummy sequential numbers
dshape = sdata.shape[-2:]
dlen = dshape[0] * dshape[1]
oshape = (len(tottimeax), dshape[0], dshape[1])
dshape = (1, dshape[0], dshape[1])
# create dummy sequential numbers
numbers = numpy.arange(1, dlen+1, 1).reshape(dshape)
numbers = cdms2.createVariable(numbers, id=varid)
timeax = sdata.getTime()
taxis = cdms2.createAxis(numpy.array([0]), id='time')
taxis.units = timeax.units
taxis.designateTime()
# set all axis
numbers.setAxisList([taxis, sdata.getLatitude(), sdata.getLongitude()])
# store into temporary nc file of sequential numbers.
outf = cdms2.open(outfile_sequentialnumbers, 'w')
outf.write(numbers)
outf.close()
# repeat nos for time dimension
tnumbers = numbers.data.repeat(len(tottimeax), 0)
# get sequential numbers via ocgis
rd_sequentialnumbers = ocgis.RequestDataset(outfile_sequentialnumbers, variable=varid)
# get actual data via ocgis
rd_data = ocgis.RequestDataset(cfile, variable=varid)
print "Hold on ... it may take some time ..."
# get list of state bounded numpy arrays of sequential numbers
state_sequentialnumbers = ocgis.OcgOperations(dataset=rd_sequentialnumbers,
spatial_operation='intersects', #'clip',
aggregate=False, agg_selection=False,
allow_empty=True, snippet=True,
geom=shpfname, output_format='numpy').execute()
# get list of state bounded numpy arrays of actual climate data
state_data = ocgis.OcgOperations(dataset=rd_data,
spatial_operation='intersects',# 'clip',
aggregate=False, agg_selection=False,
allow_empty=True, snippet=False,
geom=shpfname, output_format='numpy').execute()
# lets store the output here
outf = cdms2.open(shpdata_outfile, 'w')
outf.write(sdata(latitude=lat, longitude=lon)) # store sample data full spatial
for idxx, state in enumerate(state_sequentialnumbers):
# lets loop through all the state boundaries
idx = idxx + 1
# get the sequential numbers of particular state boundary
state_seq_val = state_sequentialnumbers[idx][varid].variables[varid].value
# get the actual climate data of particular state boundary
state_data_val = state_data[idx][varid].variables[varid].value.squeeze()
# find start and end row of particular state boundary
start_row = numpy.where(numbers==state_seq_val.min())[1][0]
end_row = numpy.where(numbers==state_seq_val.max())[1][0]
# find start and end coloum of particular state boundary
start_col = numpy.where(numbers==state_seq_val[0][0][0][:,0].min())[2][0]
end_col = numpy.where(numbers==state_seq_val[0][0][0][:,-1].max())[2][0]
if preserve_original_data_shape_in_all_subregions:
# get the mask of particular state boundary from sequential numbers
result = MV2.masked_greater_equal(tnumbers, 0)
result[:, start_row: end_row+1, start_col:end_col+1] = state_data_val
result = result.reshape(oshape)
# store the clipped actual data into original shapped data by masking other grid points
result = cdms2.createVariable(result, id=varid+'_'+str(idx))
result.setAxisList([tottimeax, sdata.getLatitude(), sdata.getLongitude()])
if lat and lon: result = result(latitude=lat, longitude=lon)
else:
# store the clipped shaped data as it is into nc file with its lat, lon
latax = sdata.getLatitude()[start_row: end_row+1]
latax = cdms2.createAxis(latax, id='latitude'+str(idx))
latax.designateLatitude()
lonax = sdata.getLongitude()[start_col:end_col+1]
lonax = cdms2.createAxis(lonax, id='longitude'+str(idx))
lonax.designateLongitude()
stshp = state_data_val.shape
state_data_val = state_data_val.reshape((len(tottimeax), stshp[-2], stshp[-1]))
result = cdms2.createVariable(state_data_val, id=varid+'_'+str(idx))
result.setAxisList([tottimeax, latax, lonax])
outf.write(result)
print "stored stateboundary data", idx, "shaped : ", result.shape
# end of for idx,state in enumerate(path):
outf.close()
inf.close()
print "Stored the input data with boundaries of shape file into : ", shpdata_outfile
os.remove(outfile_sequentialnumbers)
# daily and monthly-average global radiation, Solar Energy 28(4), pp 293-302, 1982. Eq. 1
kt = np.zeros([24,lat_num,lon_num])
kt[swtdn_tmp != 0.] = (swgdn_tmp[swtdn_tmp != 0.]/swtdn_tmp[swtdn_tmp != 0.])
kt[kt<0.] = 0.
df = np.zeros([24,lat_num,lon_num]) # error1
df = np.where( kt<= 0.22, 1 - 0.09 * kt, df)
df = np.where((kt > 0.22) & (kt <= 0.8), 0.9511 - 0.1604*kt + 4.388*kt**2 - 16.638*kt**3 + 12.336*kt**4, df)
df = np.where( kt > 0.8, 0.165, df) #
df = np.where( kt== 0.0, 1.0, df) # where no TOA SW, all diffuse flux
dhi = df * swgdn_tmp # diffuse radiation
dni = (swgdn_tmp - dhi) # direct radiation
for hr_idx in range(24):
zenith, solar_azi, ha = cal_solar_angles(lat, lon, days_ord, hr_idx) # All in radius
mask1 = MV.filled(MV.masked_equal(swtdn_tmp[hr_idx],0)*0+1,0)
mask2 = MV.filled(MV.masked_greater_equal(zenith,np.pi/2)*0+1,0)
# Based on Braun and Mitchell, 1983
# Solar geometry for fixed and tracking surface, Solar Energy, 1983
incidence_rad, panel_tilt_rad = cal_incidence_angles(zenith, solar_azi, tilt_pv, azim_pv, 'h')
cosine_zenith = np.cos(zenith)* mask1*mask2
cosine_incide = np.cos(incidence_rad) * mask1*mask2
adjust_factor_dni = replace_nan(cosine_incide / cosine_zenith)
adjust_factor_dni[(adjust_factor_dni<1.)]=1.
# Adjust dni and dhi based on Pfenninger and Staffell, 2016[3]
# Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data, Energy, 2016
# The calculation of adjuated direct sunlight is corrected
dni_adjust = dni[hr_idx] * adjust_factor_dni
dhi_adjust = dhi[hr_idx]*(1+np.cos(panel_tilt_rad))/2. + 0.3*(dni[hr_idx]+dhi[hr_idx])*(1-np.cos(panel_tilt_rad))/2.
rad_adjust = replace_nan(dni_adjust + dhi_adjust)
print('lat/lon number error')
sys.exit
kt = np.zeros([24, lat_num, lon_num])
kt[swtdn_tmp != 0.] = (swgdn_tmp[swtdn_tmp != 0.] /
swtdn_tmp[swtdn_tmp != 0.])
kt[kt < 0.] = 0.
for hr_idx in range(24):
zenith, solar_azi, ha = cal_solar_angles(lat, lon, int(year),
month, days, hr_idx,
days_ord)
incidence_rad, panel_tilt_rad = cal_incidence_angles(
zenith, solar_azi, tilt_pv, azim_pv, 'h')
mask1 = MV.filled(MV.masked_equal(swtdn_tmp[hr_idx], 0) * 0 + 1, 0)
mask2 = MV.filled(
MV.masked_greater_equal(zenith, np.pi / 2) * 0 + 1, 0)
cosine_zenith = np.cos(zenith) * mask1 * mask2
#cosine_zenith[(cosine_zenith>0)&(cosine_zenith<0.087)] = 0.087 ### do we want it here?
cosine_incide = np.cos(incidence_rad) * mask1 * mask2
adjust_factor_dni = replace_inf(
replace_nan(cosine_incide / cosine_zenith))
adjust_factor_dni[(adjust_factor_dni < 1.)] = 1.
maximum_index = np.argwhere(
swgdn_tmp[hr_idx] == np.max(swgdn_tmp[hr_idx]))
base = swgdn_tmp[hr_idx][maximum_index[0, 0]][maximum_index[
0, 1]] / swtdn_tmp[hr_idx][maximum_index[0,
0]][maximum_index[0,
1]]
cons = swtdn_tmp[hr_idx][maximum_index[0, 0]][maximum_index[0, 1]]
potential_max_solar = np.zeros([lat_num, lon_num])
lat = uf.getLatitude() lon = uf.getLongitude() xouter.setAxis(0,lat) xouter.setAxis(1,lon) xouter.setAxisList([lat,lon]) # Equivalent ## masked_equal(x, value) ## masked_equal(x, value) = x masked where x == value ## For floating point consider masked_values(x, value) instead. ## masked_greater(x, value) ## masked_greater(x, value) = x masked where x > value ## masked_greater_equal(x, value) ## masked_greater_equal(x, value) = x masked where x >= value xge = MV2.masked_greater_equal(xouter, 120) ## masked_less(x, value) ## masked_less(x, value) = x masked where x < value xl = MV2.masked_less(xouter, 160) ## masked_less_equal(x, value) ## masked_less_equal(x, value) = x masked where x <= value ## masked_not_equal(x, value) ## masked_not_equal(x, value) = x masked where x != value ## masked_outside(x, v1, v2) ## x with mask of all values of x that are outside [v1,v2] xmo = MV2.masked_outside(xouter, 120, 160)
