140 lines
5.6 KiB
Python
140 lines
5.6 KiB
Python
# -*- coding: UTF-8 no BOM -*-
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# ----------------------------------------------------------- #
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# Ideally the h5py should be enough to serve as the data #
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# interface for future DAMASK, but since we are still not #
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# sure when this major shift will happen, it seems to be a #
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# good idea to provide a interface class that help user ease #
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# into using HDF5 as the new daily storage driver. #
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# ----------------------------------------------------------- #
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import os
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import sys
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import h5py
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import numpy as np
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import xml.etree.cElementTree as ET
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# ---------------------------------------------------------------- #
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# python 3 has no unicode object, this ensures that the code works #
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# on Python 2&3 #
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# ---------------------------------------------------------------- #
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try:
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test=isinstance('test', unicode)
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except(NameError):
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unicode=str
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# ------------------------------------------------------- #
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# Singleton class for converting feature name to H5F path #
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# ------------------------------------------------------- #
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# NOTE:
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# use simple function to mimic the singleton class in
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# C++/Java
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def lables_to_path(label, dsXMLPath=None):
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""" read the xml definition file and return the path."""
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if dsXMLPath is None:
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# use the default storage layout in DS_HDF5.xml
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if "h5table.pyc" in __file__:
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dsXMLPath = os.path.abspath(__file__).replace("h5table.pyc",
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"DS_HDF5.xml")
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else:
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dsXMLPath = os.path.abspath(__file__).replace("h5table.py",
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"DS_HDF5.xml")
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# This current implementation requires that all variables
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# stay under the root node, the nesting is defined through the
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# h5path. This could be improved easily with more advanced parsing
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# using ET interface, but for now I can not see the benefits in doing
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# so.
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tree = ET.parse(dsXMLPath)
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dataType = tree.find('{}/type'.format(label)).text
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h5path = tree.find('{}/h5path'.format(label)).text
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return (dataType, h5path)
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# ----------------------- #
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# H5Table interface class #
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# ----------------------- #
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class H5Table(object):
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"""
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DESCRIPTION
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-----------
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Interface/wrapper class for manipulating data in HDF5 with DAMASK
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specialized data structure.
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-->Minimal API design.
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PARAMETERS
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----------
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h5f_path: str
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Absolute path the HDF5 file
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METHOD
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------
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del_entry() -- Force delete attributes/group/datasets (Dangerous)
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get_attr() -- Return attributes if possible
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add_attr() -- Add NEW attributes to dataset/group (please delete old first!)
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get_data() -- Retrieve data in numpy.ndarray
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add_data() -- Add dataset to H5 file
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get_cmdlog() -- Return the command used to generate the data if possible.
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NOTE
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----
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1. As an interface class, it uses the lazy evaluation design
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that read the data only when its absolutely necessary.
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2. The command line used to generate new feature is stored with
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each dataset as dataset attribute.
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"""
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def __init__(self, h5f_path, new_file=False, dsXMLFile=None):
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self.h5f_path = h5f_path
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self.dsXMLFile = dsXMLFile
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msg = 'Created by H5Talbe from DAMASK'
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mode = 'w' if new_file else 'a'
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with h5py.File(self.h5f_path, mode) as h5f:
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h5f['/'].attrs['description'] = msg
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def del_entry(self, feature_name):
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""" delete entry in HDF5 table """
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dataType, h5f_path = lables_to_path(feature_name,
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dsXMLPath=self.dsXMLFile)
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with h5py.File(self.h5f_path, 'a') as h5f:
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del h5f[h5f_path]
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def get_attr(self, attr_name):
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dataType, h5f_path = lables_to_path(attr_name,
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dsXMLPath=self.dsXMLFile)
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with h5py.File(self.h5f_path, 'a') as h5f:
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rst_attr = h5f[h5f_path].attrs[attr_name]
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return rst_attr
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def add_attr(self, attr_name, attr_data):
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dataType, h5f_path = lables_to_path(attr_name,
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dsXMLPath=self.dsXMLFile)
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with h5py.File(self.h5f_path, 'a') as h5f:
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h5f[h5f_path].attrs[attr_name] = attr_data
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h5f.flush()
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def get_data(self, feature_name=None):
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""" extract dataset from HDF5 table and return it in a numpy array """
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dataType, h5f_path = lables_to_path(feature_name,
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dsXMLPath=self.dsXMLFile)
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with h5py.File(self.h5f_path, 'a') as h5f:
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h5f_dst = h5f[h5f_path] # get the handle for target dataset(table)
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rst_data = h5f_dst.read_direct(np.zeros(h5f_dst.shape))
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return rst_data
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def add_data(self, feature_name, dataset, cmd_log=None):
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""" adding new feature into existing HDF5 file """
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dataType, h5f_path = lables_to_path(feature_name,
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dsXMLPath=self.dsXMLFile)
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with h5py.File(self.h5f_path, 'a') as h5f:
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h5f.create_dataset(h5f_path, data=dataset)
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# store the cmd in log is possible
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if cmd_log is not None:
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h5f[h5f_path].attrs['log'] = str(cmd_log)
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h5f.flush()
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def get_cmdlog(self, feature_name):
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""" get cmd history used to generate the feature"""
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dataType, h5f_path = lables_to_path(feature_name,
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dsXMLPath=self.dsXMLFile)
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with ht5py.File(self.h5f_path, 'a') as h5f:
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cmd_logs = h5f[h5f_path].attrs['log']
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return cmd_logs
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