def read(self, path):
"""Read metadata and dataframe from HDF5 store."""
with pd.HDFStore(path) as h5store:
dataframe = h5store.get('dataframe')
setattr_on_dataframe(
dataframe, 'metadata',
h5store.get_storer('dataframe').attrs.metadata)
return dataframe
def _run_tool(self):
dataset = self.dataset
input_ts = load_plugins('io', 'timeseries-hdf5')['timeseries-hdf5']
orig_metadata = get_metadata(dataset)[dataset]
parameter = orig_metadata['parameter']
if orig_metadata['file_path'] is None:
raise IOError('No data file available for this dataset')
df = input_ts.read(orig_metadata['file_path'])
# apply transformation
# run filter
# new_df = self._run(df, options)
metadata = df.metadata
if 'file_path' in metadata:
del metadata['file_path']
df.sort_values([parameter],
ascending=False,
na_position='last',
inplace=True)
df['Rank'] = df[parameter].rank(method='min', ascending=False)
df.dropna(inplace=True)
df['Percent Exceeded'] = (df['Rank'] /
(df[parameter].count() + 1)) * 100
df.index = df['Percent Exceeded']
setattr_on_dataframe(df, 'metadata', metadata)
new_df = df
# setup new dataset
new_metadata = {
'parameter': new_df.metadata.get('parameter'),
'datatype': orig_metadata['datatype'],
'options': self.set_options,
'file_format': orig_metadata['file_format'],
'unit': new_df.metadata.get('unit'),
}
new_dset, file_path, catalog_entry = self._create_new_dataset(
old_dataset=dataset,
ext='.h5',
dataset_metadata=new_metadata,
)
# save dataframe
output = load_plugins('io', 'xy-hdf5')['xy-hdf5']
output.write(file_path, new_df, new_metadata)
return {'datasets': new_dset, 'catalog_entries': catalog_entry}
def _run(self, df):
metadata = df.metadata
if 'file_path' in metadata:
del metadata['file_path']
param = metadata['parameter']
period = self.period
method = self.method
orig_param, orig_period, orig_method = (param.split(':') +
[None, None])[:3]
new_df = getattr(df.resample(periods[period], kind='period'), method)()
new_param = '%s:%s:%s' % (orig_param, period, method)
new_df.rename(
columns={param: new_param},
inplace=True) #inplace must be set to True to make changes
metadata.update({'parameter': new_param})
setattr_on_dataframe(new_df, 'metadata', metadata)
return new_df
def _run(self, df):
metadata = df.metadata
if 'file_path' in metadata:
del metadata['file_path']
parameter = metadata['parameter']
sigma = self.sigma
if sigma is None:
sigma = 3
# remove anything 'sigma' standard deviations from median
vmin = df[parameter].median() - float(sigma) * df[parameter].std()
vmax = df[parameter].median() + float(sigma) * df[parameter].std()
df = df[(df[parameter] > vmin)]
df = df[(df[parameter] < vmax)]
setattr_on_dataframe(df, 'metadata', metadata)
#if despike:
# kw = dict(n1=2, n2=20, block=6)
# df = despike(df, **kw)
# new_df = df.resample(periods[period], how=method, kind='period')
return df
def _run(self, df):
if self.to_units is None:
raise ValueError('To_units cannot be None')
metadata = df.metadata
if 'file_path' in metadata:
del metadata['file_path']
reg = unit_registry()
from_units = metadata['unit']
if '/' in from_units and '/' not in self.to_units:
beg = from_units.find('/')
end = len(from_units)
default_time = from_units[beg:end]
to_units = self.to_units + default_time
else:
to_units = self.to_units
conversion = reg.convert(1, src=from_units, dst=to_units)
df[df.columns[1]] = df[df.columns[1]] * conversion
metadata.update({'unit': to_units})
setattr_on_dataframe(df, 'metadata', metadata)
return df
