var_is_3d = True
if not var_is_3d:
var = V
vname = V
else:
var = V[3:-3]
vname = V[3:]
pressure = int(V[-3:]) * 100
for n, date in enumerate(date_list):
print date
# Find the file and open it
nc_path, nc_file = findClimoFile(grep + date + "*", directory)
nc = camgoda(nc_path)
# Read in the variable data
if var == "PRECT_d18O":
nc.PRECT_d18O(box)
elif var == "PRECT_dD":
nc.PRECT_dD(box)
elif var == "PRECT_dxs":
nc.PRECT_dxs(box)
elif var == "QFLX_d18O":
nc.QFLX_d18O(box)
elif var == "QFLX_dD":
nc.QFLX_dD(box)
elif var == "fluxDelta":
nc.fluxDelta(box)
elif var == "Column_d18OV":
# Extract the dates
dates = camdates(start, end, months, days)
for n, date in enumerate(dates):
# Find the file for this date
cfull_path, cfname = findClimoFile(
"*" + grep_pre + "*" + date + "*" + grep_post + "*", cdir)
tfull_path, tfname = findClimoFile(
"*" + grep_pre + "*" + date + "*" + grep_post + "*", tdir)
if cfname != 0:
print cfname
if tfname != 0:
print tfname
# Open the file
cnc = camgoda(cfull_path)
tnc = camgoda(tfull_path)
is3d, var, vname = cnc.ExtractData(variable, box)
is3d, var, vname = tnc.ExtractData(variable, box)
if n == 0:
nlats, nlons = cnc.data.shape
boxlat = cnc.boxlat
boxlon = cnc.boxlon
d = np.zeros(shape=(len(dates), nlats * nlons))
d[n, :] = np.ndarray.flatten(tnc.data - cnc.data)
# Compute the amplitude timeseries and EOF spatial distributions of the data array
print "Computing the EOF..."
EOF = Eof(d, center=removeMeans)
eof = EOF.eofs(neofs=num_eofs)
pca = EOF.pcs(npcs=num_eofs, pcscaling=1)
if not var_is_3d:
var = V
vname = V
else:
var = V[3:-3]
vname = V[3:]
pressure = int(V[-3:]) * 100
for n, date in enumerate(date_list):
print date
c_nc_path, c_nc_file = findClimoFile(grep + date + "*",
control_directory)
t_nc_path, t_nc_file = findClimoFile(grep + date + "*", test_directory)
#print c_nc_path
#print t_nc_path
c_nc = camgoda(c_nc_path)
t_nc = camgoda(t_nc_path)
# Extract the variable data
# Special variables
if var == "PRECT_d18O":
t_nc.PRECT_d18O(box)
c_nc.PRECT_d18O(box)
elif var == "PRECT_dD":
t_nc.PRECT_dD(box)
c_nc.PRECT_dD(box)
elif var == "PRECT_dxs":
t_nc.PRECT_dxs(box)
c_nc.PRECT_dxs(box)
right_lon = 160
box = (southern_lat, northern_lat, left_lon, right_lon)
tloc_var_master = []
cloc_var_master = []
for n, d in enumerate(dates):
# Find the file
cpath, cfilename = findClimoFile('*' + grep + "*" + d + '*', directory = cdir)
tpath, tfilename = findClimoFile('*' + grep + "*" + d + '*', directory = tdir)
print tfilename
print cfilename
# Open the file
cnc = camgoda(cpath)
tnc = camgoda(tpath)
if n == 0:
cnc.ExtractData(v1, box) # This sets boxlat and boxlon
lats = cnc.boxlat
lons = cnc.boxlon
hold = np.zeros(shape = (len(lats), len(lons), len(dates), 2, 2)) # lat, lon, dates, var, state
hold[:,:,n,0,0] = cnc.ExtractData(v1, box, returnData = True)
hold[:,:,n,0,1] = tnc.ExtractData(v1, box, returnData = True)
hold[:,:,n,1,0] = cnc.ExtractData(v2, box, returnData = True)
hold[:,:,n,1,1] = tnc.ExtractData(v2, box, returnData = True)
print "All data extracted, computing correlations..."
print "Found file " + controlfn
print "\nLooking for test " + grep + " files in " + tdir + "..."
tfile, testfn = findClimoFile("*" + grep + "*", tdir)
if not testdatafname:
sys.exit()
else:
print "Found file " + testfn
else:
controlfn = os.path.splitext(os.path.split(cfile)[1])[0]
testfn = os.path.splitext(os.path.split(tfile)[1])[0]
print "\nControl file is " + controlfn
print "\nTest file is " + testfn
# Open the file
cnc = camgoda(cfile)
tnc = camgoda(tfile)
for v in variables:
# Extract the variable
cnc.ExtractData(v, box)
tnc.ExtractData(v, box)
# Save the data
cvar = cnc.data
tvar = tnc.data
long_name = cnc.long_name
units = cnc.units
lon = cnc.boxlon
# Zonally average the data
if not controldatafname:
sys.exit()
print "Found control file: " + controlfn
print "\nLooking for test " + grep + " files in " + testdir
testdatafname, testfn = findClimoFile("*" + grep + "*", testdir)
if not testdatafname:
sys.exit()
print "Found test file: " + testfn
else:
print "\nControl file is " + controldatafname
print "\nTest file is " + testdatafname
controlfn = os.path.splitext(os.path.split(controldatafname)[1])[0]
testfn = os.path.splitext(os.path.split(testdatafname)[1])[0]
# Read the data
controldata = camgoda(controldatafname)
testdata = camgoda(testdatafname)
# Set the boxlat and boxlon
box = (southern_lat, northern_lat, left_lon, right_lon)
controldata.variable(controldata.vars[0], box,
setData=False) # this sets self.boxlon and self.boxlat
# Create bm coords from region bounds
# bm lonitude coords need to be 0 < coord < 360
bmlon, bmlat = np.meshgrid(controldata.boxlon, controldata.boxlat)
# Reset the lat and lon bounds so that maps don't show grey areas
southern_lat, northern_lat = np.array(controldata.boxlat)[[0, -1]]
# Change lons to be negative is 180 < lon < 360 because that's how bm works for 'cea' projection
left_lon, right_lon = np.array(controldata.boxlon)[[0, -1]]
##################
# Main algorithm #
##################
# Creates the master array of the correct shape
var_master = np.zeros(shape=(len(dates), len(variables)))
long_name = []
units = []
for n, d in enumerate(dates):
# Find the file for this date
full_path, fname = findClimoFile("*" + grep + "*" + d + "*",
directory=ARGS.directory)
if fname != 0:
print fname
# Open the file
nc = camgoda(full_path)
for m, v in enumerate(variables):
# Read the data
var_is_3d, var, pressure = nc.ExtractData(v, box)
data = nc.data
# Average the data
data_avg = np.nanmean(data)
var_master[n, m] = data_avg
if n == 0:
long_name.append(nc.long_name)
units.append(nc.units)
ntime, nvar = var_master.shape
add = 0
if nvar > 1:
add = 1
region = [bottom, top, left, right]
box = [int(l) for l in ARGS.box]
# These are mapping variable
southern_lat, northern_lat, left_lon, right_lon = box
if left_lon > right_lon:
left_lon -= 360
for date_idx, date in enumerate(dates):
# Find the file
path, filename = findClimoFile(grep_pre + '*' + date + '*' + grep_post,
directory)
print filename
# Open the file
nc = camgoda(path)
if date_idx == 0:
data = np.zeros(shape=(len(dates), len(nc.lat), len(nc.lon), 2))
data[date_idx, ...] = nc.ExtractData(lv + "," + fv, returnData=True)
#
# Extract the lv timeseries
lv_ts = np.nanmean(boxOut(data[..., 0], region), axis=(-2, -1))
# Box out the fv
fv_boxed_ts, lats, lons = boxOut(data[..., 1], box, returnGrid=True)
corr_array = corr_2d(np.expand_dims(np.expand_dims(lv_ts, -1), -1),
fv_boxed_ts,
axis=0)
# Make the map of the corr array
# Create Map directory is it doesn't exist
if not os.path.exists("Map"):
os.mkdir("Map")
if verbose:
print "Created directory " + "Map"
# Create the region directory if it doesn't already exist
if not os.path.exists("Map/" + region_name):
os.mkdir("Map/" + region_name)
if verbose:
print "Created directory " + "Map/" + region_name
for filein in files:
print "Current files is : " + filein + '\n'
# Read the data
ncdata = camgoda(filein)
# Set the boxlat and boxlon
box = (southern_lat, northern_lat, left_lon, right_lon)
ncdata.variable(ncdata.vars[0], box,
setData=False) # this sets self.boxlon and self.boxlat
# Create bm coords from region bounds
# bm lonitude coords need to be 0 < coord < 360
bmlon, bmlat = np.meshgrid(ncdata.boxlon, ncdata.boxlat)
# Reset the lat and lon bounds so that Map don't show grey areas
# southern_lat, northern_lat = np.array(ncdata.boxlat)[[0,-1]]
# Change lons to be negative is 180 < lon < 360 because that's how bm works for 'cea' projection
# left_lon, right_lon = np.array(ncdata.boxlon)[[0,-1]]
if 0 in ncdata.boxlon[1:-2]: # if we cross the gml