def interp_dataframe(data, levels=None, variables=None, min_count=5, dropna=True, report=None, verbose=0):
"""
Interpolate:
1. Select only levels with enough (min_count) t and r values
2. Interpolate each profile (date) vertically to levels
Interpolation is only done at dates with enough data
Args:
data pandas DataFrame with p
Keyword Args:
levels [1000 ... 100000] mod. ERA-Interim pressure levels
variables ['t','r'] Variables for quality check (min_count)
min_count [5] Minimum required levels per profile
dropna [True] Remove missing values before interpolation
verbose [0] Show more info
Returns:
newdata pandas DataFrame with newly interpolated values
Raises:
ValueError when p or other variables not inside data
Calls:
interp_profile -> interp_mod
"""
from raso.config import std_plevels
funcid = "[INTP] "
if levels is None:
levels = std_plevels
if variables is None:
variables = data.columns.tolist()
# fix index name
if data.index.name is None or data.index.name == '':
data.index.name = 'date'
index_name = data.index.name
# check plevels vs plevels inside dataframe ?
variables = list(set(variables + ['p'])) # add p
variables = data.columns[data.columns.isin(variables)].tolist() # inside ?
if len(variables) < 1 or 'p' not in variables:
raise ValueError(funcid + "Dataframe requires at least 2 columns(p,+) %s" % (",".join(variables)))
journal(funcid + "Quality check (%s) Min: %d NAN: %s" % (",".join(variables), min_count, color_boolean(dropna)),
report, verbose)
# sometimes one variables is completely zero
counts = data.count()[variables]
if (counts == 0).any():
variables = counts[counts > 0].index.tolist()
if len(variables) < 1 or 'p' not in variables:
print counts
raise ValueError(funcid + "Dataframe requires at least 2 columns(p,+) %s" % (",".join(variables)))
itx = (data.groupby(data.index).count()[variables] > min_count).sum(1) > 2 # at least 2 variables have data
if itx.sum() == 0:
raise RuntimeError(funcid + "No data left with %d as minimum data count per profile" % min_count)
else:
data = data.ix[itx, :]
journal(funcid + "from %d to %d" % (len(itx), itx.sum()), report, verbose)
#
data = data.groupby(data.index).apply(interp_profile, variables=variables, pout=levels, oan=True, dropna=dropna,
min_values=min_count)
data = data.reset_index().drop('level_1', axis=1).sort_values(by=[index_name, 'p']).set_index(index_name, drop=True)
journal(funcid + "Done: %s" % str(data.shape), report, verbose)
return data
def interp_profile_check(x, var='t', pout=None, pcolumn='p', dropna=True, min_values=4):
"""
Check Interpolation for given variable
Args:
x pandas DataFrame holding one profile (date)
Keyword Args:
var ['t'] Variable to check interpolation
pout [1000 ... 100000] mod. ERA-Interim pressure levels
oan [False] Return ORIG column info
pcolumn ['p'] name of pressure column
dropna [True] Remove missing values before interpolation
min_values [4] Minimum required levels per profile
Returns:
prints interpolation results
Raises:
ValueError if x is not a pandas DataFrame
KeyError if pcolumn not in x
RuntimeError if more than one index (date) is in x
Calls:
interp_mod
"""
from raso.config import std_plevels
if not isinstance(x, pd.DataFrame):
raise ValueError("[INTP] Requires a DataFrame")
if pcolumn not in x.columns:
raise KeyError("[INTP] Pressure levels not in DataFrame")
if len(x.index.unique()) > 1:
raise RuntimeError("[INTP] Only one profile allowed here! ")
if pout is None:
pout = std_plevels
pin = x[pcolumn].values # take pressures
ipx = np.argsort(pin) # find accending order
pin = pin[ipx] # apply sort to pressures
pout = np.unique(np.sort(np.concatenate([pin, pout]))) # merge all levels, sort, unique
data = x.drop(pcolumn, axis=1) # take values
values = data[var].values[ipx] # apply sort
res = interp_mod(values, np.log(pout), np.log(pin), dropna=dropna, min_values=min_values)
for i, p in enumerate(pin):
# first level exception
if i == 0:
for no in np.where(pout < p)[0]:
print "IN: ( %9s %7s ) OUT: ( %9.2f %7.2f) " % ('-', '-', pout[no], res[no])
else:
for no in np.where(np.logical_and(pout > pin[i - 1], pout < p))[0]:
print "IN: ( %9s %7s ) OUT: ( %9.2f %7.2f) " % ('-', '-', pout[no], res[no])
# is the output level and input level?
v1 = np.where(pout == p)[0]
if len(v1) == 0:
print "IN: ( %9.2f %7.2f ) OUT: ( %9s %7s) " % (p, data[i], '-', '-')
else:
print "IN: ( %9.2f %7.2f ) OUT: ( %9.2f %7.2f) %s " % (
p, values[i], pout[v1], res[v1], color_boolean(np.all((values[i] - res[v1]) == 0.0)))
for no in np.where(pout > pin[-1])[0]:
print "IN: ( %9s %7s ) OUT: ( %9.2f %7.2f) " % ('-', '-', pout[no], res[no])
