def _qc(self, file_name, out_file_name=''):
"""
_qc() [private]
Purpose: Carries out a quality control operation on a single file.
Parameters: file_name [type=str]
The name of the file to quality control.
out_file_name [type=str]
Optional file name specifying where to write the output of the quality control procedure. If not specified, the file given by
file_name will be overwritten with quality-controlled data.
Returns: [nothing]
"""
file = DataIO(file_name=file_name, mode="rw")
file_data_good = {}
file_data_dims = ()
for variables, function in self.rules.iteritems():
# Loop through all the rules that we've defined and find the indexes of all the good data.
if variables.find(",") > -1:
variables = re.split(", *", variables)
else:
variables = [ variables ]
for var in variables:
if var not in file_data_good:
file_data_dims = file.get_variable_attribute(var, 'dimensions')
file_data_good[var] = np.array(np.ones(tuple([ file.get_dimension(d) for d in file_data_dims ]), dtype=bool))
# Get the variable data from the file (returns as a list of numpy arrays)
file_data = file.get_variable(variables)
# Plug the list straight into the quality control function. Do the element-wise "and" procedure between the file_data_good array and the returned array immediately.
good = function(*file_data)
for var in variables:
file_data_good[var] &= good
# file_data_good &= file_data_good[:,5].data.reshape((file.get_dimension(file_data_dims[0]), 1))
for var in file_data_good.keys():
# Do the splicing for every variable with the same dimensions as the variables we've qc'ed.
qc_data = np.where(file_data_good[var], file.get_variable(var), np.nan)
file.set_variable(var, qc_data)
# new_dims = []
# keep_indexes = []
# for idx, var_dim in enumerate(var_dims):
# if var_dim in file_data_dims:
# new_dims.append("%s_qc" % var_dim)
# keep_indexes.append(np.unique(np.where(file_data_good)[idx]))
# else:
# new_dims.append(var_dim)
# keep_indexes.append(np.arange(file.get_dimension(var_dim), dtype=np.int32))
# file.splice_variable(variable, tuple(new_dims), tuple(keep_indexes))
# Sew 'er up
file.close(out_file_name=out_file_name)
return
#!/usr/local/epd/bin/python
import sys
sys.path.append("../")
from util.dataIO import DataIO
import numpy as np
df = DataIO("wrfout_d01_PLEV.nc", mode="rw")
missing_value = df.get_variable_attribute('T', 'missing_value')
df.set_variable_attribute('P', 'missing_value', missing_value)
df.close()