def read (files,save):
"""
ingest RWP NIMA processed Data from the NCAR NetCDFs
each file here is a day, with times as an element!
This will use for loops, and it will go slowly...
"""
# then create the new file unless save is a file handle, in which case just save to that
doc = h5(save) # establish the hdf5 object.
doc.create(wspd=38,wdir=38,height=38,wvert=38,u=38,v=38,w=38,snrw=38,sw=38,edr=38)
print "Finding RWP Files";
for file_dir in sorted(files):
fname = file_dir.split("/")[-1]
if 'mom' in fname or 'rass' in fname: continue #rass and moment riffraf.
print file_dir
try:
nc = ncf(file_dir)
except:
# something there did not work, either in the name, or in the netcdf reading in general
continue
# check to make sure this is the right kind of data, a full 38 obs
l = nc.dimensions['height']
if l > 38:
nc.close()
continue
begin = s2t(getattr(nc,'Data_start_time'),'%a %b %d %H:%M:%S %Y %Z') #should work for PCAPS data
for t in range(len(nc.variables['time'][:])):
obtime = begin + nc.variables['time'][t]
# now fetch all the desired variables and save them!
wspd = nc.variables['wspd'][t]
wdir = nc.variables['wdir'][t]
wvert = nc.variables['wvert'][t]
height = nc.variables['heights'][t]
snrw = nc.variables['snrw'][t]
u = nc.variables['u_classicC'][t] # constant, vs linear
v = nc.variables['v_classicC'][t] # ditto
w = nc.variables['w_classicC'][t] # ditto
snrw = nc.variables['snrw'][t]
sw = nc.variables['specWid'][t]
edr = nc.variables['edr'][t] # eddy dissipation rate
print "Appending",obtime
doc.append(obtime,persist=True,wspd=wspd,wdir=wdir,height=height,snrw=snrw,u=u,v=v,w=w,sw=sw,edr=edr)
nc.close()
doc.close()
return True
def rwp (files,variable='wspd',nanvalue=nan,save=True):
"""
ingest RWP Data from the NCAR NetCDFs
"""
if save:
# then create the new file unless save is a file handle, in which case just save to that
doc = h5(save) # establish the hdf5 object.
#REMOVE :s doc.create(wspd:38,wdir:38,height:38)# create the h5 object #FIXME - need more variables!
# Height can be an index, but it does not have to be...
files = sorted(files) # a last resort effort to sort these turkies
# initialize output lists
X = zeros(len(files),dtype=int)
Y = array.array('f')
T = zeros((len(files),38)) # set a numpy array # FIXME - that 20 may not be stable!!!
print "Finding RASS Files";
n = 0 # a counter
for file_dir in files:
# EACH NETCDF FILE IS AN OBSERVATION!!!
fname = file_dir.split("/")[-1]
print file_dir
# ok, begin/end check
###if obtime < self.data.begin or obtime > self.data.end: continue
try:
nc = ncf(file_dir)
obtime = calendar.timegm(time.strptime(fname.split(".")[1]+fname.split(".")[2]+"UTC",'%Y%m%d%H%M%S%Z'))
except:
# something there did not work, either in the name, or in the netcdf reading in general
continue
if variable not in nc.variables.keys():
print variable,"Is not a valid RASS variable..."
# be ince and return the valid variables
print "Here are the valid variables: ",nc.variables.keys()
exit()
try:
T[n] = nc.variables[variable][:]
Y = nc.variables['height'][0] # numpy array! # heights
X[n]=obtime#nc.variables['time'][0]
except:
continue
nc.close()
n+=1
T = T[:n]
X = X[:n]
T[T==9999.] = nanvalue # set T to be nan wherever it equals the null value
return X,Y,T
def read (files,save):
"""
ingest RWP NIMA processed Data from the NCAR NetCDFs
each file here is a day, with times as an element!
This will use for loops, and it will go slowly...
"""
# then create the new file unless save is a file handle, in which case just save to that
doc = h5(save) # establish the hdf5 object.
doc.create(tvu=20,tvc=20,height=20,snrtv=20)
print "Finding RASS Files";
for f in sorted(files):
fname = f.split("/")[-1]
if 'mom' in fname or 'winds' in fname: continue #wind profiler and moment riffraf.
print fname
try:
nc = ncf(f)
except:
# something there did not work, either in the name, or in the netcdf reading in general
continue
# check to make sure this is the right kind of data, full 20 obs.
l = nc.dimensions['height']
if l > 20:
print 'Data wrong size'
nc.close()
continue
begin = s2t(getattr(nc,'Data_start_time'),'%a %b %d %H:%M:%S %Y %Z') #should work for PCAPS data
for t in range(len(nc.variables['time'][:])):
obtime = begin + nc.variables['time'][t]
# now fetch all the desired variables and save them!
tvu = nc.variables['tv'][t] # uncorrected virtual temp
tvc = nc.variables['tvc'][t] # vertical motion corrected
snrtv = nc.variables['snrtv'][t] # well, boo, this data is not so good
height = nc.variables['heights'][t]
print "Appending",obtime
doc.append(obtime,persist=True,height=height,tvu=tvu,tvc=tvc,snrtv=snrtv)
nc.close()
doc.close()
return True
def read (files,save):
"""
ingest RWP NIMA processed Data from the NCAR NetCDFs
each file here is a day, with times as an element!
This will use for loops, and it will go slowly...
"""
# then create the new file unless save is a file handle, in which case just save to that
doc = h5(save) # establish the hdf5 object.
size = 38 # this is how long the profiles are (FOR PCAPS - they were 45 during the run-up to PCAPS)
# NOTE this code is disregarding many of the potentially interesting datasets within this data...
doc.create(height=size,beamnum=1,elevation=1,az=1,vel=size,snr=size,sw=size,noise=size,mom1Conf=size,mom2Conf=size,power=size,poptemp=size,
qc=size,interpv=size,iverr=size,clutter=size,othersig=size)
print "Finding RWP Files";
for file_dir in sorted(files):
fname = file_dir.split("/")[-1]
if 'mom' not in fname or 'rass' in fname: continue #rass and rwp riffraf.
print file_dir
try:
nc = ncf(file_dir)
except:
# something there did not work, either in the name, or in the netcdf reading in general
continue
# check to make sure this is the right kind of data, a full 38 obs
l = nc.dimensions['height']
if l > size:
print "Wrong length:",l
nc.close()
continue
begin = s2t(getattr(nc,'Data_start_time'),'%a %b %d %H:%M:%S %Y %Z') #should work for PCAPS data
for t in range(len(nc.variables['time'][:])):
obtime = begin + nc.variables['time'][t] # yay, this still works!
# now fetch all the desired variables and save them!
beamnum = nc.variables['beamNum'][t]
elevation = nc.variables['elevation'][t]
az = nc.variables['azimuth'][t]
height = nc.variables['heights'][t] # THIS ACTUALLY CHANGES HERE...
snr = nc.variables['sigNoiseRatio'][t]
vel = nc.variables['vel'][t] # constant, vs linear
sw = nc.variables['specWid'][t] # ditto
noise = nc.variables['noise'][t] # ditto
m1c = nc.variables['mom1Conf'][t] # velocity
m2c = nc.variables['mom2Conf'][t] # spectrum width
power = nc.variables['power'][t]
poptemp = nc.variables['popTemp'][t]
qc = nc.variables['qualityControl'][t]
interpv = nc.variables['interpVel'][t]
iverr = nc.variables['errorVel'][t]
clutter = nc.variables['clutter'][t]
os = nc.variables['othersig'][t] #non-atmospheric signals
# for all variables the value -9999 signals nan data!!!
#print "Appending",obtime
doc.append(obtime,persist=True,height=height,beamnum=[beamnum],elevation=[elevation],az=[az],vel=vel,sw=sw,snr=snr,noise=noise,mom1Conf=m1c,
mom2Conf=m2c,power=power,poptemp=poptemp,qc=qc,interpv=interpv,iverr=iverr,clutter=clutter,othersig=os)
nc.close()
doc.close()
return True
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap, ListedColormap
from mpl_toolkits.basemap import Basemap
from scipy.io.netcdf import netcdf_file as ncf
points = np.array([ [335.36-360, 42.24],
[17.60, 42.24],
[60.28, 42.24],
[335.36-360, -1.32],
[17.60, -1.32],
[60.28, -1.32],
[335.36-360, -45.76],
[17.60, -45.76],
[60.28, -45.76] ])
ff = ncf(sys.argv[1],'r')
try:
lat_0 = float(ff.latitude_of_projection_origin)
lon_0 = float(ff.longitude_of_projection_origin)
grdis = float(ff.grid_size_in_meters)
nx = int(ff.dimensions['jx'])
ny = int(ff.dimensions['iy'])
ht = ff.variables['topo'][12:-12,12:-12]
xlat = ff.variables['xlat'][12:-12,12:-12]
xlon = ff.variables['xlon'][12:-12,12:-12]
lsmask = ff.variables['mask'][12:-12,12:-12]
m = Basemap(resolution='l', projection='merc',llcrnrlat=ff.variables['xlat'][0,0],llcrnrlon=ff.variables['xlon'][0,0],
