def fix_date(date):
from calc import isiterable
from air_sea import julianmd, greg2
'''
Support for "wrong" dates like (2011,13,-1)
Input must be an iterable, like list or tuple, not string
Example:
fix_date((2011,13,-1))
returns datetime.datetime(2011, 12, 30, 0, 0)
'''
if isiterable(date) and not isinstance(date, basestring):
try:
y, m, d = date
hh, mm, ss = 0., 0., 0.
except:
y, m, d, hh, mm, ss = date
y, m, d, hh, mm, ss = greg2(
julianmd(y, m, d, hh, mm, ss) - julianmd(y, 1, 1), y)
return datetime.datetime(y, m, d, hh, mm, ss)
else:
return date
def fix_date(date):
from calc import isiterable
from air_sea import julianmd, greg2
"""
Support for "wrong" dates like (2011,13,-1)
Input must be an iterable, like list or tuple, not string
Example:
fix_date((2011,13,-1))
returns datetime.datetime(2011, 12, 30, 0, 0)
"""
if isiterable(date) and not isinstance(date, basestring):
try:
y, m, d = date
hh, mm, ss = 0.0, 0.0, 0.0
except:
y, m, d, hh, mm, ss = date
y, m, d, hh, mm, ss = greg2(julianmd(y, m, d, hh, mm, ss) - julianmd(y, 1, 1), y)
return datetime.datetime(y, m, d, hh, mm, ss)
else:
return date
def use(filename, varname, interface="auto", **kargs):
nc, close = __open(filename, interface)
if varname not in nc.varnames:
return
v = nc.vars[varname]
shape = v.shape()
if not shape:
try:
return v[:][0] # may be needed for dtype='|S1'
except:
return v[:]
d = v.dims
dimStr = [":" for i in d.keys()]
for k in kargs.keys():
dval = kargs[k]
if k.isdigit():
k = d.keys()[int(k)] # allow use dim indice
elif k.startswith("SEARCH"): # allow *dimname or dimname*
kk = k[len("SEARCH") :]
for dn in nc.vars[varname].dimnames:
if dn.find(kk) >= 0:
k = dn
break
elif k.endswith("SEARCH"):
kk = k[: -len("SEARCH")]
for dn in nc.vars[varname].dimnames:
if dn.find(kk) >= 0:
k = dn
break
if k in d.keys():
i = d.index(k)
if isinstance(dval, basestring):
dimStr[i] = dval
elif isinstance(dval, int):
if dval < 0:
dval = shape[i] + dval
if dval > shape[i] - 1:
print ":: max allowed " + k + " = " + str(shape[i] - 1)
return
else:
dimStr[i] = str(dval)
elif calc.isiterable(dval):
exec (k + "_val=dval")
dimStr[i] = k + "_val"
else:
dimStr[i] = str(dval)
cmd = "res=v[" + sjoin(dimStr, ",") + "]"
exec (cmd)
if interface in ("pycdf", "scientific"):
# about missing value:
miss = False
if "_FillValue" in v.attnames:
miss = v.atts["_FillValue"]["value"]
elif "missing_value" in v.attnames:
miss = v.atts["missing_value"]["value"]
maskMissing = kargs.get("maskMissing", True)
if not miss is False and maskMissing and v.nctype() != "STRING":
res = np.ma.masked_where(res == miss, res)
## ensure strings have no mask:
# if v.nctype()=='STRING' and np.ma.isMA(res):
# res=np.array(res)
# about scale and offset:
if v.nctype() != "STRING":
scale = 1
offset = 0
if "scale_factor" in v.attnames:
scale = v.atts["scale_factor"]["value"]
if "add_offset" in v.attnames:
offset = v.atts["add_offset"]["value"]
if (scale, offset) != (1, 0):
res = res * scale + offset
if close:
nc.close()
if 1 in res.shape and res.ndim > 1:
res = np.squeeze(res)
# mask nan
maskNaN = kargs.get("maskNaN", True)
if maskNaN and not res.dtype.type == np.string_ and not np.ma.isMA(res) and np.any(np.isnan(res)):
res = np.ma.masked_where(np.isnan(res), res)
return res
def use(filename,varname,interface='auto',**kargs):
nc,close=__open(filename,interface)
if varname not in nc.varnames: return
v=nc.vars[varname]
shape=v.shape()
if not shape:
try: return v[:][0] # may be needed for dtype='|S1'
except: return v[:]
d=v.dims
dimStr=[':' for i in d.keys()]
for k in kargs.keys():
dval=kargs[k]
if k.isdigit(): k=d.keys()[int(k)] # allow use dim indice
elif k.startswith('SEARCH'): # allow *dimname or dimname*
kk=k[len('SEARCH'):]
for dn in nc.vars[varname].dimnames:
if dn.find(kk)>=0:
k=dn
break
elif k.endswith('SEARCH'):
kk=k[:-len('SEARCH')]
for dn in nc.vars[varname].dimnames:
if dn.find(kk)>=0:
k=dn
break
if k in d.keys():
i=d.index(k)
if isinstance(dval,basestring):
dimStr[i]=dval
elif isinstance(dval,int):
if dval<0: dval=shape[i]+dval
if dval>shape[i]-1:
print ':: max allowed '+k+' = '+str(shape[i]-1)
return
else:
dimStr[i]=str(dval)
elif calc.isiterable(dval):
exec(k+'_val=dval')
dimStr[i]=k+'_val'
else:
dimStr[i]=str(dval)
cmd='res=v['+sjoin(dimStr,',')+']'
exec(cmd)
if interface in ('pycdf','scientific'):
# about missing value:
miss=False
if '_FillValue' in v.attnames: miss = v.atts['_FillValue']['value']
elif 'missing_value' in v.attnames: miss = v.atts['missing_value']['value']
maskMissing=kargs.get('maskMissing',True)
if not miss is False and maskMissing and v.nctype()!='STRING':
res=np.ma.masked_where(res==miss,res)
## ensure strings have no mask:
#if v.nctype()=='STRING' and np.ma.isMA(res):
# res=np.array(res)
# about scale and offset:
if v.nctype()!='STRING':
scale=1
offset=0
if 'scale_factor' in v.attnames: scale=v.atts['scale_factor']['value']
if 'add_offset' in v.attnames: offset=v.atts['add_offset']['value']
if (scale,offset)!=(1,0): res = res*scale + offset
if close: nc.close()
if 1 in res.shape and res.ndim>1: res=np.squeeze(res)
# mask nan
maskNaN=kargs.get('maskNaN',True)
if maskNaN and not res.dtype.type==np.string_ and not np.ma.isMA(res) and np.any(np.isnan(res)):
res=np.ma.masked_where(np.isnan(res),res)
return res
