def post(self, request, *args, **kwargs):
dataset = self.form_class(request.POST, request.FILES)
if dataset.is_valid():
ds = dataset.save()
# add attribute variables to dataset
attr_data = nc(ds.data_file.path, 'r')
variables = list(attr_data.variables.keys())
for var in variables:
shape = attr_data.variables[var].shape
if len(shape) < 2:
shape = [shape[0], shape[0]]
variable = Variable(name=var,
long_name=attr_data.variables[var].long_name,
dataset=ds,
x_dimension=shape[0],
y_dimension=shape[1]
)
variable.save()
attr_data.close()
# add elevation to dataset
elev_data = nc(ds.elev_file.path, 'r')
elev_shape = elev_data.variables['elev'].shape
elev_variable = Variable(name='elev', dataset=ds, x_dimension=elev_shape[0], y_dimension=elev_shape[1])
elev_variable.save()
elev_data.close()
return redirect('/explore/' + str(ds.id) + '/')
else:
return render(request, self.template_name, {'form': self.form_class()})
def __init__(self, smapfile, areafile):
with nc(smapfile) as f:
self.smap = f.variables['state'][:]
self.cmap = f.variables['county'][:]
self.states = unique(self.smap)
self.states = self.states[~self.states.mask]
with nc(areafile) as f:
self.acounties = f.variables['county'][:]
self.area = f.variables['sum_county'][:]
def __init__(self, filename, time, tunits, scens, aggs, aggname, aggunits, agglongname):
super(AggregationFile, self).__init__(filename)
with nc(filename, 'w', format = 'NETCDF4_CLASSIC') as f:
f.createDimension(aggname, len(aggs)) # create aggregation level
aggsvar = f.createVariable(aggname, 'i4', aggname)
aggsvar[:] = aggs
aggsvar.units = aggunits
aggsvar.long_name = agglongname
f.createDimension('time', len(time)) # create time
timevar = f.createVariable('time', 'i4', 'time')
timevar[:] = time
timevar.units = tunits
timevar.long_name = 'time'
f.createDimension('scen', len(scens)) # create scen
scenvar = f.createVariable('scen', 'i4', 'scen')
scenvar[:] = range(1, len(scens) + 1)
scenvar.units = 'mapping'
scenvar.long_name = ', '.join(scens)
f.createDimension('irr', 3) # create irr
irrvar = f.createVariable('irr', 'i4', 'irr')
irrvar[:] = range(1, 4)
irrvar.units = 'mapping'
irrvar.long_name = 'ir, rf, sum'
def get(self, request, *args, **kwargs):
layer = str(kwargs['layer'])
dataset = Dataset.objects.get(pk=kwargs['dataset'])
response = dict()
variables = Variable.objects.filter(dataset=dataset)
found = False
for variable in variables:
if variable.name == layer:
layer = variable.name
found = True
break
if not found:
return JsonResponse({layer: 'False'})
if layer == 'elev' and dataset.has_elev_file:
path = dataset.elev_file.path
else:
path = dataset.data_file.path
with nc(path, 'r') as ds:
var = ds.variables[layer][:]
minimum = float(var.min())
maximum = float(var.max())
x = var.shape[1]
y = var.shape[0]
fill_value = str(ds.variables[layer]._FillValue)
response[layer] = {'min': minimum, 'max': maximum, 'x':float(x), 'y':float(y), 'fill_value' : fill_value}
return JsonResponse(response)
def createAggFile(filename, time, tunits, adata, anames, aunits, alongnames, scens, irr, leaddim):
if leaddim == "scen":
nscens = None
ntime = len(time)
else:
nscens = len(scens)
ntime = None
with nc(filename, "w", format="NETCDF4_CLASSIC") as f:
f.createDimension("time", ntime)
timevar = f.createVariable("time", "i4", "time")
timevar[:] = time
timevar.units = tunits
timevar.long_name = "time"
f.createDimension("scen", nscens)
scenvar = f.createVariable("scen", "i4", "scen")
scenvar[:] = scens
scenvar.units = "no"
scenvar.long_name = "scenarios"
f.createDimension("irr", len(irr) + 1)
irrvar = f.createVariable("irr", "i4", "irr")
irrvar[:] = range(1, len(irr) + 2)
irrvar.units = "mapping"
irrvar.long_name = ", ".join(irr + ["sum"])
for i in range(len(anames)):
rname = anames[i] + "_index"
f.createDimension(rname, len(adata[i]))
rvar = f.createVariable(rname, "i4", rname)
rvar[:] = adata[i]
rvar.units = aunits[i]
rvar.long_name = alongnames[i]
def loadData(file):
with nc(file) as f:
var = array(f.variables.keys())
idx = array([v != 'lon' and v != 'lat' and v != 'time' for v in var])
varname = var[idx][0]
data = f.variables[varname][:]
return data, varname
def __init__(self, filename, time, lat, lon, irr):
super(RescaledFile, self).__init__(filename)
with nc(filename, 'w', format = 'NETCDF4_CLASSIC') as f:
f.createDimension('time', len(time))
timevar = f.createVariable('time', 'i4', 'time')
timevar[:] = time - time[0]
timevar.units = 'years since {:d}-01-01'.format(int(time[0]))
timevar.long_name = 'time'
f.createDimension('lat', len(lat))
latvar = f.createVariable('lat', 'f4', 'lat')
latvar[:] = lat
latvar.units = 'degrees_north'
latvar.long_name = 'latitude'
f.createDimension('lon', len(lon))
lonvar = f.createVariable('lon', 'f4', 'lon')
lonvar[:] = lon
lonvar.units = 'degrees_east'
lonvar.long_name = 'longitude'
f.createDimension('irr', len(irr))
irrvar = f.createVariable('irr', 'i4', 'irr')
irrvar[:] = range(1, len(irr) + 1)
irrvar.units = 'mapping'
irrvar.long_name = ', '.join(irr)
def createAggFile(filename, time, tunits, adata, anames, aunits, alongnames, scens, leaddim):
if leaddim == 'scen':
nscens = None
ntime = len(time)
else:
nscens = len(scens)
ntime = None
with nc(filename, 'w', format = 'NETCDF4_CLASSIC') as f:
f.createDimension('time', ntime)
timevar = f.createVariable('time', 'i4', 'time')
timevar[:] = time
timevar.units = tunits
timevar.long_name = 'time'
f.createDimension('scen', nscens)
scenvar = f.createVariable('scen', 'i4', 'scen')
scenvar[:] = scens
scenvar.units = 'no'
scenvar.long_name = 'scenarios'
for i in range(len(anames)):
rname = anames[i] + '_index'
f.createDimension(rname, len(adata[i]))
rvar = f.createVariable(rname, 'i4', rname)
rvar[:] = adata[i]
rvar.units = aunits[i]
rvar.long_name = alongnames[i]
def get(self, request, *args, **kwargs):
y_start = int(kwargs['y'])
y_end = y_start + EEMS_TILE_SIZE[0]
x_start = int(kwargs['x'])
x_end = x_start + EEMS_TILE_SIZE[1]
layer = 'elev'
response = dict()
with nc(elev_path, 'r') as ds:
var = ds.variables[layer][:]
if x_start > var.shape[1] or y_start > var.shape[0]:
return JsonResponse({layer: 'False'})
x_end = var.shape[1] if x_end > var.shape[1] else x_end
y_end = var.shape[0] if y_end > var.shape[0] else y_end
response['fill_value'] = str(ds.variables[layer]._FillValue)
if isinstance(var, np.ma.core.MaskedArray):
sub_matrix = var.data[y_start:y_end, x_start:x_end]
response[layer] = sub_matrix.ravel().tolist()
response['x'] = sub_matrix.shape[1]
response['y'] = sub_matrix.shape[0]
else:
sub_matrix = var[y_start:y_end, x_start:x_end]
response[layer] = sub_matrix.ravel().tolist()
response['x'] = sub_matrix.shape[1]
response['y'] = sub_matrix.shape[0]
return JsonResponse(response)
def createAggFile(filename, time, tunits, adata, anames, aunits, alongnames, scens, irr, leaddim, hasscen):
if leaddim == 'scen':
nscens = None
ntime = len(time)
else:
nscens = len(scens) if hasscen else 0
ntime = None
with nc(filename, 'w', format = 'NETCDF4_CLASSIC') as f:
f.createDimension('time', ntime)
timevar = f.createVariable('time', 'i4', 'time')
timevar[:] = time
timevar.units = tunits
timevar.long_name = 'time'
if hasscen:
f.createDimension('scen', nscens)
scenvar = f.createVariable('scen', 'i4', 'scen')
scenvar[:] = scens
scenvar.units = 'no'
scenvar.long_name = 'scenarios'
f.createDimension('irr', len(irr) + 1)
irrvar = f.createVariable('irr', 'i4', 'irr')
irrvar[:] = range(1, len(irr) + 2)
irrvar.units = 'mapping'
irrvar.long_name = ', '.join(irr + ['sum'])
for i in range(len(anames)):
rname = str(anames[i])
f.createDimension(rname, len(adata[i]))
rvar = f.createVariable(rname, 'i4', rname)
rvar[:] = adata[i]
rvar.units = aunits[i]
rvar.long_name = alongnames[i]
def get(self, request, *args, **kwargs):
layer = str(kwargs['layer'])
provider = str(kwargs['provider'])
response = dict()
path = climate_paths[layer][provider]
#raw_data = RawData(path, layer)
with nc(path, 'r') as ds:
var = ds.variables[layer][:]
minimum = float(var.min())
maximum = float(var.max())
x = var.shape[1]
y = var.shape[0]
try:
fill_value = str(ds.variables[layer]._FillValue)
except:
fill_value = 9.969209968386869e+36
response[layer] = {'min': minimum, 'max': maximum, 'x':float(x), 'y':float(y), 'fill_value' : fill_value}
return JsonResponse(response)
def get(self, request, *args, **kwargs):
y_start = int(kwargs['y'])
y_end = y_start + EEMS_TILE_SIZE[0]
x_start = int(kwargs['x'])
x_end = x_start + EEMS_TILE_SIZE[1]
layer = kwargs['layer']
provider = str(kwargs['provider'])
response = dict()
path = climate_paths[layer][provider]
with nc(path, 'r') as ds:
var = ds.variables[layer][:]
if x_start > var.shape[1] or y_start > var.shape[0]:
return JsonResponse({layer: 'False'})
x_end = var.shape[1] if x_end > var.shape[1] else x_end
y_end = var.shape[0] if y_end > var.shape[0] else y_end
if isinstance(var, np.ma.core.MaskedArray):
sub_matrix = var.data[y_start:y_end, x_start:x_end]
response[layer] = sub_matrix.ravel().tolist()
response['x'] = sub_matrix.shape[1]
response['y'] = sub_matrix.shape[0]
else:
sub_matrix = var[y_start:y_end, x_start:x_end]
response[layer] = sub_matrix.ravel().tolist()
response['x'] = sub_matrix.shape[1]
response['y'] = sub_matrix.shape[0]
return JsonResponse(response)
def get(self, request, *args, **kwargs):
layer = 'elev'
elev_path = os.path.join(settings.MEDIA_ROOT, 'uploads', 'elevation', 'Elev_30AS_ForTaylor.nc')
with nc(elev_path, 'r') as ds:
var = ds.variables[layer][:]
minimum = float(var.min())
maximum = float(var.max())
x = var.shape[1]
y = var.shape[0]
fill_value = str(ds.variables[layer]._FillValue)
response = {
'min': minimum,
'max': maximum,
'x':float(x),
'y':float(y),
'fill_value' : fill_value
}
response['lat_min'] = float(ds.variables['lat'][:].min())
response['lat_max'] = float(ds.variables['lat'][:].max())
response['lon_min'] = float(ds.variables['lon'][:].min())
response['lon_max'] = float(ds.variables['lon'][:].max())
return JsonResponse(response)
def __loadwts(self, var, vals):
if self.wfile is None:
return ones(len(vals))
else:
vars = var.split('/')
nvars = len(vars)
v = [0] * nvars
w = [0] * nvars
with nc(self.wfile) as f:
for i in range(nvars):
if f.variables[vars[i]].units == 'mapping':
v[i] = array(f.variables[vars[i]].long_name.split(', '))
else:
v[i] = f.variables[vars[i]][:]
w[i] = f.variables['weights_' + vars[i]][:]
nvals = len(vals)
wts = masked_array(zeros(nvals), mask = ones(nvals))
for i in range(nvals):
svals = vals[i].split('/')
for j in range(nvars):
if svals[j].isdigit():
svals[j] = double(svals[j]) # convert to number
idx = where(v[j] == svals[j])[0]
if idx.size:
if isMaskedArray(wts[i]):
wts[i] = w[j][idx[0]]
else:
wts[i] *= w[j][idx[0]]
return wts
def __init__(self, cpfile, smapfile, areafile, crop, varname):
with nc(cpfile) as f:
self.year = f.variables['year'][:]
self.week = f.variables['week'][:]
self.county = f.variables['county'][:]
self.day = f.variables['day'][:]
self.rawdata = f.variables[varname][:]
varatt = f.variables['var'].ncattrs()
if 'units' in varatt and f.variables['var'].units == 'mapping':
self.var = array(f.variables['var'].long_name.split(', '))
self.varmap = self.varmap_str[crop]
else:
self.var = f.variables['var'][:]
self.varmap = self.varmap_num[crop]
self.crop = crop
nyears, nweeks, ncounties, nvars = len(self.year), len(self.week), len(self.county), len(self.vars)
self.data = masked_array(zeros((nyears, nweeks, ncounties, nvars)), mask = ones((nyears, nweeks, ncounties, nvars)))
for i in range(nvars):
vmap = self.varmap[i]
if isinstance(vmap, list):
for j in range(ncounties):
for k in range(len(vmap)):
if vmap[k] in self.var: # variable in list
varidx = where(self.var == vmap[k])[0][0]
data = self.rawdata[:, :, varidx, j]
if not isMaskedArray(data) or not data.mask.all():
self.data[:, :, j, i] = data
break
elif vmap != '':
if vmap in self.var:
varidx = where(self.var == vmap)[0][0]
self.data[:, :, :, i] = self.rawdata[:, :, varidx, :]
else: # no data
continue
# discard counties with insufficient data
for j in range(ncounties):
for k in range(nyears):
data = self.data[k, :, j, i]
if isMaskedArray(data):
data = data[~data.mask]
if data.size and data[-1] - data[0] < 40:
self.data[k, :, j, i].mask = True # mask
# aggregate to state level
aggregator = StateAggregator(smapfile, areafile)
self.sdata = aggregator.aggregate(self.data, self.county)
self.state = aggregator.states
def __init__(self, filename, outdir, fmt):
self.filename = filename
self.outdir = outdir
self.fmt = fmt
with nc(filename) as f:
self.varkeys = f.variables.keys() # get available variables
self.scennames = [s.strip(" ") for s in f.variables["scen"].long_name.split(",")] # get scenario names
self.irr = f.variables["irr"][:] # get irrigation values and names
self.irrnames = [i.strip(" ") for i in f.variables["irr"].long_name.split(",")]
self.time = f.variables["time"][:] # get time and units
self.tunits = f.variables["time"].units
def createAggFile(filename, time, tunits, adata, anames, aunits, alongnames):
with nc(filename, 'w', format = 'NETCDF4_CLASSIC') as f:
f.createDimension('time', len(time))
timevar = f.createVariable('time', 'i4', ('time',))
timevar[:] = time
timevar.units = tunits
timevar.long_name = 'time'
for i in range(len(anames)):
rname = anames[i] + '_index'
f.createDimension(rname, len(adata[i]))
rvar = f.createVariable(rname, 'i4', (rname,))
rvar[:] = adata[i]
rvar.units = aunits[i]
rvar.long_name = alongnames[i]
def __init__(self, file, vars = None):
with nc(file) as f:
if vars is None:
vars = setdiff1d(f.variables, ['latitude', 'longitude', 'time', 'ftime', 'scen'])
newvars = list(vars.copy())
for v in vars:
if compile('missing_*').match(v): # remove missing_* variables
newvars.remove(v)
vars = array(newvars)
self.lat, self.lon = f.variables['latitude'][0], f.variables['longitude'][0]
self.scen = f.variables['scen'][:]
self.time = f.variables['time'][:]
self.ftime = f.variables['ftime'][:]
tunits = f.variables['time'].units
ts = tunits.split('days since ')[1].split(' ')
yr0, mth0, day0 = [int(t) for t in ts[0].split('-')[0 : 3]]
if len(ts) > 1:
hr0, min0, sec0 = [int(t) for t in ts[1].split(':')[0 : 3]]
else:
hr0 = min0 = sec0 = 0
self.reftime = datetime(yr0, mth0, day0, hr0, min0, sec0)
nv, ns, nt, nf = len(vars), len(self.scen), len(self.time), len(self.ftime)
self.data = masked_array(zeros((nv, ns, nt, nf)), mask = ones((nv, ns, nt, nf)))
self.missing = zeros((nv, ns, nt))
self.units = zeros(nv, dtype = '|S64')
self.longnames = zeros(nv, dtype = '|S64')
for i in range(nv):
if vars[i] in f.variables:
var = f.variables[vars[i]]
msg = f.variables['missing_' + vars[i]]
else:
vidx = foundVar(f.variables.keys(), vars[i])
var = f.variables[f.variables.keys()[vidx]]
msg = f.variables['missing_' + f.variables.keys()[vidx]]
self.data[i] = var[:, :, :, 0, 0]
self.missing[i] = msg[:]
self.units[i] = var.units
self.longnames[i] = var.long_name
self.vars = vars # store variable names
self.pridx = foundVar(vars, 'pr') # variable indices
self.maidx = foundVar(vars, 'tmax')
self.miidx = foundVar(vars, 'tmin')
self.__fillMissing()
def __init__(self, file, lat, lon, vars = None):
with nc(file) as f:
if vars is None:
vars = setdiff1d(f.variables, ['lat', 'lon', 'time'])
lats, lons = f.variables['lat'][:], f.variables['lon'][:]
if isMaskedArray(f.variables[vars[0]][0]):
mask = f.variables[vars[0]][0].mask # pull mask from first variable, first time
else:
mask = zeros((len(lats), len(lons)))
latd = resize(lats, (len(lons), len(lats))).T - lat
lond = resize(lons, (len(lats), len(lons))) - lon
latd = masked_where(mask, latd)
lond = masked_where(mask, lond)
totd = latd ** 2 + lond ** 2
idx = where(totd == totd.min())
latidx, lonidx = idx[0][0], idx[1][0]
self.time = f.variables['time'][:]
tunits = f.variables['time'].units
ts = tunits.split('months since ')[1].split(' ')
yr0, mth0, day0 = [int(t) for t in ts[0].split('-')[0 : 3]]
if len(ts) > 1:
hr0, min0, sec0 = [int(t) for t in ts[1].split(':')[0 : 3]]
else:
hr0 = min0 = sec0 = 0
self.reftime = datetime(yr0, mth0, day0, hr0, min0, sec0)
nv, nt = len(vars), len(self.time)
self.data = zeros((nv, nt))
self.units = zeros(nv, dtype = '|S32')
for i in range(nv):
if vars[i] in f.variables:
var = f.variables[vars[i]]
else:
vidx = foundVar(f.variables.keys(), vars[i])
var = f.variables[f.variables.keys()[vidx]]
self.data[i] = var[:, latidx, lonidx]
self.units[i] = var.units
self.vars = vars # store variable names
self.pridx = foundVar(vars, 'pr') # variable indices
self.maidx = foundVar(vars, 'tmax')
self.miidx = foundVar(vars, 'tmin')
def __init__(self, path, layer_name):
# parse .xtr file
with nc(path, 'r') as ds:
self.lats = ds.variables['lat'][:]
self.lngs = ds.variables['lon'][:]
self.numlngs = int(self.lngs.size)
self.numlats = int(self.lats.size)
self.minlng = float(self.lngs[0])
self.maxlng = float(self.lngs[-1])
self.minlat = float(self.lats[0])
self.maxlat = float(self.lats[-1])
self.data = ds.variables[layer_name][:]
self.dtype = self.data.data.dtype
self.ncattrs = {k: ds.variables[layer_name].getncattr(k) for k in ds.variables[layer_name].ncattrs()}
def __init__(self, filename, metric, aggs, aggname, aggunits, agglongname, time, dt, mp, cr, nm):
super(ModelEnsembleFile, self).__init__(filename)
with nc(filename, 'w', format = 'NETCDF4_CLASSIC') as f:
f.createDimension(aggname, len(aggs))
aggsvar = f.createVariable(aggname, 'i4', aggname)
aggsvar[:] = aggs
aggsvar.units = aggunits
aggsvar.long_name = agglongname
f.createDimension('time', len(time))
timevar = f.createVariable('time', 'i4', 'time')
timevar[:] = time - time[0]
timevar.units = 'years since {:d}-01-01'.format(int(time[0]))
timevar.long_name = 'time'
f.createDimension('dt', len(dt))
dtvar = f.createVariable('dt', 'i4', 'dt')
dtvar[:] = range(1, len(dt) + 1)
dtvar.units = 'mapping'
dtvar.long_name = ', '.join(dt)
dtvar.note = 'detrend method'
f.createDimension('mp', len(mp))
mpvar = f.createVariable('mp', 'i4', 'mp')
mpvar[:] = range(1, len(mp) + 1)
mpvar.units = 'mapping'
mpvar.long_name = ', '.join(mp)
mpvar.note = 'mean-preserving method'
f.createDimension('cr', len(cr))
crvar = f.createVariable('cr', 'i4', 'cr')
crvar[:] = range(1, len(cr) + 1)
crvar.units = 'mapping'
crvar.long_name = ', '.join(cr)
crvar.note = 'correction method'
f.createDimension('nm', nm)
nmvar = f.createVariable('nm', 'i4', 'nm')
nmvar[:] = range(1, nm + 1)
nmvar.long_name = 'top models used'
f.createDimension('wt', 2)
weightedvar = f.createVariable('wt', 'i4', 'wt')
weightedvar[:] = [1, 2]
weightedvar.units = 'mapping'
weightedvar.long_name = 'unweighted, %s-weighted' % metric
def __init__(self, filename, aggs, aggname, aggunits, agglongname, times, dt, mp, cr, nm, wt):
super(MultimetricsEnsembleFile, self).__init__(filename)
with nc(filename, 'w', format = 'NETCDF4_CLASSIC') as f:
f.createDimension(aggname, len(aggs))
aggsvar = f.createVariable(aggname, 'i4', aggname)
aggsvar[:] = aggs
aggsvar.units = aggunits
aggsvar.long_name = agglongname
f.createDimension('time_range', len(times))
timesvar = f.createVariable('time_range', 'i4', 'time_range')
timesvar[:] = range(1, len(times) + 1)
timesvar.units = 'mapping'
timesvar.long_name = ', '.join(times)
f.createDimension('dt', len(dt))
dtvar = f.createVariable('dt', 'i4', 'dt')
dtvar[:] = range(1, len(dt) + 1)
dtvar.units = 'mapping'
dtvar.long_name = ', '.join(dt)
dtvar.note = 'detrend method'
f.createDimension('mp', len(mp))
mpvar = f.createVariable('mp', 'i4', 'mp')
mpvar[:] = range(1, len(mp) + 1)
mpvar.units = 'mapping'
mpvar.long_name = ', '.join(mp)
mpvar.note = 'mean-preserving method'
f.createDimension('cr', len(cr))
crvar = f.createVariable('cr', 'i4', 'cr')
crvar[:] = range(1, len(cr) + 1)
crvar.units = 'mapping'
crvar.long_name = ', '.join(cr)
crvar.note = 'correction method'
f.createDimension('nm', nm)
nmvar = f.createVariable('nm', 'i4', 'nm')
nmvar[:] = range(1, nm + 1)
nmvar.long_name = 'top models used'
f.createDimension('wt', len(wt))
weightedvar = f.createVariable('wt', 'i4', 'wt')
weightedvar[:] = range(1, len(wt) + 1)
weightedvar.units = 'mapping'
weightedvar.long_name = ', '.join(wt)
def get(self, request, *args, **kwargs):
y_start = int(kwargs['y'])
y_end = y_start + EEMS_TILE_SIZE[0]
x_start = int(kwargs['x'])
x_end = x_start + EEMS_TILE_SIZE[1]
layer = kwargs['layer']
dataset = Dataset.objects.get(pk=kwargs['dataset'])
response = dict()
variables = Variable.objects.filter(dataset=dataset)
found = False
for variable in variables:
if variable.name == layer:
layer = variable.name
found = True
break
if not found:
return JsonResponse({layer : 'False'})
if layer == 'elev' and dataset.has_elev_file:
path = dataset.elev_file.path
else:
path = dataset.data_file.path
with nc(path, 'r') as ds:
var = ds.variables[layer][:]
if x_start > var.shape[1] or y_start > var.shape[0]:
return JsonResponse({layer: 'False'})
x_end = var.shape[1] if x_end > var.shape[1] else x_end
y_end = var.shape[0] if y_end > var.shape[0] else y_end
response['fill_value'] = str(ds.variables[layer]._FillValue)
if isinstance(var, np.ma.core.MaskedArray):
sub_matrix = var.data[y_start:y_end, x_start:x_end]
response[layer] = sub_matrix.ravel().tolist()
response['x'] = sub_matrix.shape[1]
response['y'] = sub_matrix.shape[0]
else:
sub_matrix = var[y_start:y_end, x_start:x_end]
response[layer] = sub_matrix.ravel().tolist()
response['x'] = sub_matrix.shape[1]
response['y'] = sub_matrix.shape[0]
return JsonResponse(response)
def __init__(self, filename, varnames = None, lats = None, lons = None, incl_global = False):
f = nc(filename) # open file
if varnames is None: # no variables specified
varnames = f.variables.keys()
varnames = [v for v in varnames if not v in ['lat', 'lon']] # remove lat, lon
if incl_global: varnames += ['global']
else:
if not isinstance(varnames, list): # make list
varnames = [varnames]
self.lats, self.lons = f.variables['lat'][:], f.variables['lon'][:]
self.dat = {'names': [], 'units': [], 'longnames': [], 'data': []}
for v in varnames:
if v != 'global' or (v == 'global' and 'global' in f.variables):
var = f.variables[v]
self.dat['names'].append(v)
self.dat['units'].append(var.units if 'units' in var.ncattrs() else '')
self.dat['longnames'].append(var.long_name if 'long_name' in var.ncattrs() else '')
self.dat['data'].append(var[:])
else:
nlats = self.lats.size
nlons = self.lons.size
self.dat['names'].append('global') # global mask
self.dat['units'].append('')
self.dat['longnames'].append('')
self.dat['data'].append(masked_array(ones((nlats, nlons)), mask = zeros((nlats, nlons))))
f.close()
tol = 1e-5
if not lats is None: # restrict latitude range
sellat = logical_and(self.lats >= lats.min() - tol, self.lats <= lats.max() + tol)
self.lats = self.lats[sellat]
for i in range(len(self.dat['names'])):
self.dat['data'][i] = self.dat['data'][i][sellat]
if not lons is None: # restrict longitude range
sellon = logical_and(self.lons >= lons.min() - tol, self.lons <= lons.max() + tol)
self.lons = self.lons[sellon]
for i in range(len(self.dat['names'])):
self.dat['data'][i] = self.dat['data'][i][:, sellon]
def __init__(self, filename, aggs, aggname, aggunits, agglongname, scen, times, dt, mp, cr):
super(MultimetricsFile, self).__init__(filename)
with nc(filename, 'w', format = 'NETCDF4_CLASSIC') as f:
f.createDimension(aggname, len(aggs))
aggsvar = f.createVariable(aggname, 'i4', aggname)
aggsvar[:] = aggs
aggsvar.units = aggunits
aggsvar.long_name = agglongname
f.createDimension('scen', len(scen))
scenvar = f.createVariable('scen', 'i4', 'scen')
scenvar[:] = range(1, len(scen) + 1)
scenvar.units = 'mapping'
scenvar.long_name = ', '.join(scen)
f.createDimension('time_range', len(times))
timesvar = f.createVariable('time_range', 'i4', 'time_range')
timesvar[:] = range(1, len(times) + 1)
timesvar.units = 'mapping'
timesvar.long_name = ', '.join(times)
f.createDimension('dt', len(dt))
dtvar = f.createVariable('dt', 'i4', 'dt')
dtvar[:] = range(1, len(dt) + 1)
dtvar.units = 'mapping'
dtvar.long_name = ', '.join(dt)
dtvar.note = 'detrend method'
f.createDimension('mp', len(mp))
mpvar = f.createVariable('mp', 'i4', 'mp')
mpvar[:] = range(1, len(mp) + 1)
mpvar.units = 'mapping'
mpvar.long_name = ', '.join(mp)
mpvar.note = 'mean-preserving method'
f.createDimension('cr', len(cr))
crvar = f.createVariable('cr', 'i4', 'cr')
crvar[:] = range(1, len(cr) + 1)
crvar.units = 'mapping'
crvar.long_name = ', '.join(cr)
crvar.note = 'correction method'
def __init__(self, filename, aggs, aggname, aggunits, agglongname, time, scen, dt, mp, cr):
super(BiasCorrectFile, self).__init__(filename)
with nc(filename, 'w', format = 'NETCDF4_CLASSIC') as f:
f.createDimension(aggname, len(aggs)) # create aggregation level
aggsvar = f.createVariable(aggname, 'i4', aggname)
aggsvar[:] = aggs
aggsvar.units = aggunits
aggsvar.long_name = agglongname
f.createDimension('time', len(time)) # create time
timevar = f.createVariable('time', 'i4', 'time')
timevar[:] = time - time[0]
timevar.units = 'years since {:d}-01-01'.format(int(time[0]))
timevar.long_name = 'time'
f.createDimension('scen', len(scen)) # create scen
scenvar = f.createVariable('scen', 'i4', 'scen')
scenvar[:] = range(1, len(scen) + 1)
scenvar.units = 'mapping'
scenvar.long_name = ', '.join(scen)
f.createDimension('dt', len(dt)) # create dt
dtvar = f.createVariable('dt', 'i4', 'dt')
dtvar[:] = range(1, len(dt) + 1)
dtvar.units = 'mapping'
dtvar.long_name = ', '.join(dt)
dtvar.note = 'detrend method'
f.createDimension('mp', len(mp)) # create mp
mpvar = f.createVariable('mp', 'i4', 'mp')
mpvar[:] = range(1, len(mp) + 1)
mpvar.units = 'mapping'
mpvar.long_name = ', '.join(mp)
mpvar.note = 'mean-preserving method'
f.createDimension('cr', len(cr)) # create cr
crvar = f.createVariable('cr', 'i4', 'cr')
crvar[:] = range(1, len(cr) + 1)
crvar.units = 'mapping'
crvar.long_name = ', '.join(cr)
crvar.note = 'correction method'
def __init__(self, filename, meta, mask, outdir, fmt):
super(MaskPlotter, self).__init__(filename, outdir, fmt)
with nc(self.filename) as f:
self.area = f.variables['area_' + mask][:]
self.indices = f.variables[mask + '_index'][:]
metadata = [] # load meta data
with open(meta, 'rU') as f:
for row in csv.reader(f, delimiter = '\t'):
metadata.append(row)
self.metadic = {}
for i in range(len(metadata)):
md = metadata[i][0].split(',')
self.metadic[md[0]] = md[1]
filesplit = filename.split('_') # get model, climate, crop
self.model = filesplit[0]
self.climate = filesplit[1]
self.crop = filesplit[3]
totarea = self.area[:, 0, 0, :].sum(axis = 1)
self.sidx = [a[0] for a in sorted(enumerate(totarea), key = lambda x: x[1], reverse = True)]
self.mask = mask
def createnc(filename, lat, lon, time, time_units):
# create file
f = nc(filename, 'w', format = 'NETCDF4_CLASSIC')
# create longitude
f.createDimension('lon', len(lon))
lon_var = f.createVariable('lon', 'f8', ('lon',), zlib = True, shuffle = False, complevel = 9, chunksizes = [len(lon)])
lon_var[:] = lon
lon_var.units = 'degrees_east'
lon_var.long_name = 'longitude'
# create latitude
f.createDimension('lat', len(lat))
lat_var = f.createVariable('lat', 'f8', ('lat',), zlib = True, shuffle = False, complevel = 9, chunksizes = [len(lat)])
lat_var[:] = lat
lat_var.units = 'degrees_north'
lat_var.long_name = 'latitude'
# create time
f.createDimension('time', None)
time_var = f.createVariable('time', 'f8', ('time',), zlib = True, shuffle = False, complevel = 9, chunksizes = [1], endian = 'little')
time_var[:] = time
time_var.units = time_units
time_var.long_name = 'time'
# close file
f.close()
def __init__(self, file, vars = None):
with nc(file) as f:
if vars is None:
vars = setdiff1d(f.variables, ['latitude', 'longitude', 'time'])
self.lat, self.lon = f.variables['latitude'][0], f.variables['longitude'][0]
self.time = f.variables['time'][:]
tunits = f.variables['time'].units
ts = tunits.split('days since ')[1].split(' ')
yr0, mth0, day0 = [int(t) for t in ts[0].split('-')[0 : 3]]
if len(ts) > 1:
hr0, min0, sec0 = [int(t) for t in ts[1].split(':')[0 : 3]]
else:
hr0 = min0 = sec0 = 0
self.reftime = datetime(yr0, mth0, day0, hr0, min0, sec0)
nv, nt = len(vars), len(self.time)
self.data = zeros((nv, nt))
self.units = zeros(nv, dtype = '|S64')
self.longnames = zeros(nv, dtype = '|S64')
for i in range(nv):
if vars[i] in f.variables:
var = f.variables[vars[i]]
else:
vidx = foundVar(f.variables.keys(), vars[i])
var = f.variables[f.variables.keys()[vidx]]
self.data[i] = var[:, 0, 0]
self.units[i] = var.units
self.longnames[i] = var.long_name
self.vars = vars # store variable names
self.pridx = foundVar(vars, 'pr') # variable indices
self.maidx = foundVar(vars, 'tmax')
self.miidx = foundVar(vars, 'tmin')
def __init__(self, filename, meta, mask, outdir, fmt):
super(MaskPlotter, self).__init__(filename, outdir, fmt)
with nc(self.filename) as f:
self.area = f.variables["area_" + mask][:]
self.areaglob = f.variables["area_global"][:]
self.indices = f.variables[mask + "_index"][:]
metadata = [] # load meta data
with open(meta, "rU") as f:
for row in csv.reader(f, delimiter="\t"):
metadata.append(row)
self.metadic = {}
for i in range(len(metadata)):
md = metadata[i][0].split(",")
self.metadic[md[0]] = md[1]
filesplit = basename(filename).split("_") # get model, climate, crop
self.model = filesplit[0]
self.climate = filesplit[1]
self.crop = filesplit[3]
self.yr0 = filesplit[5]
self.yr1 = filesplit[6].split(".")[0]
totarea = self.area[:, 0, 0, :].sum(axis=1)
self.sidx = [a[0] for a in sorted(enumerate(totarea), key=lambda x: x[1], reverse=True)]
self.mask = mask
self.prefix = "_".join([self.model, self.climate, self.yr0, self.yr1]) + "_"
dest="co2file",
default="",
type="string",
help="csv file of CO2 values",
metavar="FILE")
parser.add_option("-o",
"--output",
dest="output",
default="Generic.met",
type="string",
help="Output CL! file pattern")
options, args = parser.parse_args()
variables = options.variables.split(',')
infile = nc(options.inputfile)
vlist = infile.variables.keys()
time = infile.variables['time'][:]
tunits = infile.variables['time'].units
varlists = od([('TMAX', ['tmax', 'tasmax']), \
('TMIN', ['tmin', 'tasmin']), \
('SRAD', ['solar', 'rad', 'rsds', 'srad']), \
('RAIN', ['precip', 'pr', 'rain'])])
varnames = array(varlists.keys())
unitnames = array([['oc', 'degc'], ['oc', 'degc'],
['mj/m^2', 'mj/m2', 'mjm-2'], ['mm']])
varnames = array(varlists.keys())
alldata = zeros((len(time), len(varnames)))
for i in range(len(varnames)):
var_name = varnames[i]
idx = dates.index(pyear)
array_data = asarray(data[4 + j])[:, variable_idx]
array_data[array_data == '?'] = '-99' # replace '?' with '-99'
if has_pdate:
# convert pdate from dd_mmm_yyyy to Julian day
pdate = array_data[pdate_idx].split('_')
pdate = datetime.date(int(pdate[2]), mth2num[pdate[1]],
int(pdate[0]))
array_data[pdate_idx] = pdate.strftime('%j')
var_data[idx, i, :] = array_data.astype(double)
# create pSIMS NetCDF3 file
dirname = os.path.dirname(options.outputfile)
if dirname and not os.path.exists(dirname):
raise Exception('Directory to output file does not exist')
root_grp = nc(options.outputfile, 'w', format='NETCDF3_CLASSIC')
# add latitude and longitude
root_grp.createDimension('longitude', 1)
root_grp.createDimension('latitude', 1)
lon_var = root_grp.createVariable('longitude', 'f8', ('longitude', ))
lon_var[:] = lon
lon_var.units = 'degrees_east'
lon_var.long_name = 'longitude'
lat_var = root_grp.createVariable('latitude', 'f8', ('latitude', ))
lat_var[:] = lat
lat_var.units = 'degrees_north'
lat_var.long_name = 'latitude'
# create time and scenario dimensions
root_grp.createDimension('time', None)
def run(self, latidx, lonidx):
try:
num_scenarios = self.config.get('scens')
num_years = self.config.get('num_years')
variables = self.config.get('variables')
units = self.config.get('var_units')
delta = self.config.get('delta')
ref_year = self.config.get('ref_year')
inputfile = self.config.get_dict(self.translator_type,
'inputfile',
default='Generic.out')
outputfile = self.config.get_dict(
self.translator_type,
'outputfile',
default='../../outputs/output_%04d_%04d.psims.nc' %
(latidx, lonidx))
# get out files(s)
num_scenarios = num_scenarios
basename, fileext = os.path.splitext(inputfile)
outfiles = [''] * num_scenarios
for i in range(num_scenarios):
outfiles[i] = inputfile if not i else basename + str(
i) + fileext
# get variables
variables = array(variables.split(',')) # split variable names
latidx = int(latidx)
lonidx = int(lonidx)
delta = delta.split(',')
if len(delta) < 1 or len(delta) > 2:
raise Exception('Wrong number of delta values')
latdelta = double(
delta[0]) / 60. # convert from arcminutes to degrees
londelta = latdelta if len(delta) == 1 else double(delta[1]) / 60.
# get number of variables
num_vars = len(variables)
# get units
units = units.split(',')
if len(units) != num_vars:
raise Exception(
'Number of units must be same as number of variables')
# compute latitude and longitude
lat = 90. - latdelta * (latidx - 0.5)
lon = -180. + londelta * (lonidx - 0.5)
# get reference time, number of years, and dates
ref_year = ref_year
ref_date = datetime.datetime(ref_year, 1, 1)
num_years = num_years
dates = range(ref_year, ref_year + num_years)
# whether or not planting_date is among reported variables
has_pdate = 'planting_date' in variables
if has_pdate:
pdate_idx = where(variables == 'planting_date')[0][0]
mth2num = {v: k for k, v in enumerate(calendar.month_abbr)}
# iterate through scenarios
var_data = -99 * ones((num_years, num_scenarios, num_vars))
for i in range(num_scenarios):
try:
data = [l.split() for l in tuple(open(outfiles[i]))]
except IOError:
print 'Out file', i + 1, 'does not exist'
continue
if len(data) < 5:
continue # no data, move to next file
num_data = len(data[4:])
# search for variables within list of all variables
all_variables = data[2]
variable_idx = []
for v in variables:
if not v in all_variables:
raise Exception(
'Variable {:s} not in out file {:d}'.format(
v, i + 1))
else:
variable_idx.append(all_variables.index(v))
# remove header, select variables, and convert to numpy array of doubles
prev_year = nan
prev_idx = nan
date_idx = all_variables.index('Date')
if has_pdate:
pdate_idx2 = all_variables.index('planting_date')
for j in range(num_data):
if data[4 + j] == []:
continue # blank line
if num_data == num_years: # number of dates in file exactly matches number of years
idx = j
else:
pyear = int(data[4 + j][pdate_idx2].split('_')[2])
idx = dates.index(pyear)
array_data = asarray(data[4 + j])[variable_idx]
array_data[array_data ==
'?'] = '-99' # replace '?' with '-99'
if has_pdate:
# convert pdate from dd_mmm_yyyy to Julian day
pdate = array_data[pdate_idx].split('_')
pdate = datetime.date(int(pdate[2]), mth2num[pdate[1]],
int(pdate[0]))
array_data[pdate_idx] = pdate.strftime('%j')
var_data[idx, i, :] = array_data.astype(double)
# create pSIMS NetCDF3 file
root_grp = nc(outputfile, 'w', format='NETCDF3_CLASSIC')
# add latitude and longitude
root_grp.createDimension('lon', 1)
root_grp.createDimension('lat', 1)
lon_var = root_grp.createVariable('lon', 'f8', 'lon')
lon_var[:] = lon
lon_var.units = 'degrees_east'
lon_var.long_name = 'longitude'
lat_var = root_grp.createVariable('lat', 'f8', 'lat')
lat_var[:] = lat
lat_var.units = 'degrees_north'
lat_var.long_name = 'latitude'
# create time and scenario dimensions
root_grp.createDimension('time', None)
root_grp.createDimension('scen', num_scenarios)
# add time and scenario variables
time_var = root_grp.createVariable('time', 'i4', 'time')
time_var[:] = range(1, num_years + 1)
time_var.units = 'growing seasons since {:s}'.format(str(ref_date))
time_var.long_name = 'time'
scenario_var = root_grp.createVariable('scen', 'i4', 'scen')
scenario_var[:] = range(1, num_scenarios + 1)
scenario_var.units = 'no'
scenario_var.long_name = 'scenario'
# add data
for i in range(num_vars):
var = root_grp.createVariable(variables[i],
'f4',
('time', 'scen', 'lat', 'lon'),
zlib=True,
shuffle=False,
complevel=9,
fill_value=1e20)
var[:] = var_data[:, :, i]
var.units = units[i]
var.long_name = variables[i]
# close file
root_grp.close()
return True
except:
print "[%s]: %s" % (os.path.basename(__file__),
traceback.format_exc())
return False
import unittest as ut
import numpy.testing as nt
import numpy as np
import numpy.ma as ma
import os, sys
import subprocess
from netCDF4 import Dataset as nc
from wrf import *
NCL_EXE = "/Users/ladwig/nclbuild/6.3.0/bin/ncl"
TEST_FILE = "/Users/ladwig/Documents/wrf_files/wrfout_d01_2010-06-13_21:00:00"
OUT_NC_FILE = "/tmp/wrftest.nc"
NCFILE = nc(TEST_FILE)
NCGROUP = [NCFILE, NCFILE, NCFILE]
# Python 3
if sys.version_info > (3, ):
xrange = range
ROUTINE_MAP = {
"avo": avo,
"eth": eth,
"cape_2d": cape_2d,
"cape_3d": cape_3d,
"ctt": ctt,
"dbz": dbz,
"helicity": srhel,
"omg": omega,
