# Copyright 2017. Allen Institute. All rights reserved
#
# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the
# following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following
# disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote
# products derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
# INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
import h5py
import pandas as pd
[docs]class ColumnProperty(object):
"""Representation of a column name and metadata from a hdf5 dataset, csv column, etc.
"""
def __init__(self, name, dtype, dimension, nrows=0, attrs=None):
self._name = name
self._dtype = dtype
self._dim = dimension
self._nrows = nrows
self._attrs = attrs or {}
@property
def name(self):
return self._name
@property
def dtype(self):
return self._dtype
@property
def dimension(self):
return self._dim
@property
def nrows(self):
return self._nrows
@property
def attributes(self):
return self._attrs
[docs] @classmethod
def from_h5(cls, hf_obj, name=None):
if isinstance(hf_obj, h5py.Dataset):
ds_name = name if name is not None else hf_obj.name.split('/')[-1]
ds_dtype = hf_obj.dtype
# If the dataset shape is in the form "(N, M)" then the dimension is M. If the shape is just "(N)" then the
# dimension is just 1
dim = 1 if len(hf_obj.shape) < 2 else hf_obj.shape[1]
nrows = hf_obj.shape[0]
return cls(ds_name, ds_dtype, dim, nrows, attrs=hf_obj.attrs)
elif isinstance(hf_obj, h5py.Group):
columns = []
for name, ds in hf_obj.items():
if isinstance(ds, h5py.Dataset):
columns.append(ColumnProperty.from_h5(ds, name))
return columns
else:
raise Exception('Unable to convert hdf5 object {} to a property or list of properties.'.format(hf_obj))
[docs] @classmethod
def from_csv(cls, pd_obj, name=None):
if isinstance(pd_obj, pd.Series):
c_name = name if name is not None else pd_obj.name
c_dtype = pd_obj.dtype
return cls(c_name, c_dtype, 1)
elif isinstance(pd_obj, pd.DataFrame):
return [cls(name, pd_obj[name].dtype, 1) for name in pd_obj.columns]
else:
raise Exception('Unable to convert pandas object {} to a property or list of properties.'.format(pd_obj))
def __hash__(self):
return hash(self._name)
def __repr__(self):
return '{}'.format(self.name, self.dtype)
def __eq__(self, other):
if isinstance(other, ColumnProperty):
return self._name == other._name
else:
return self._name == other
