Removed bacause some of the functionalities have been outsourced to other modules src/hdf5_ops.py and src/hdf5_writer.py
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import sys
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import os
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root_dir = os.path.abspath(os.curdir)
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sys.path.append(root_dir)
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import pandas as pd
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import numpy as np
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import h5py
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import logging
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import utils.g5505_utils as utils
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import instruments.readers.filereader_registry as filereader_registry
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import src.hdf5_ops as hdf5_ops
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def create_group_hierarchy(obj, df, columns):
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"""
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Input:
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obj (h5py.File or h5py.Group)
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columns (list of strs): denote categorical columns in df to be used to define hdf5 file group hierarchy
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"""
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if not columns:
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return
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# Determine categories associated with first categorical column
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unique_values = df[columns[0]].unique()
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if obj.name == '/':
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obj.attrs.create('count',df.shape[0])
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obj.attrs.create('file_list',df['filename'].tolist())
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for group_name in unique_values:
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group = obj.require_group(group_name)
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group.attrs.create('column_name', columns[0])
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sub_df = df[df[columns[0]]==group_name] # same as df.loc[df[columns[0]]==group_name,:]
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group.attrs.create('count',sub_df.shape[0])
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group.attrs.create('file_list',sub_df['filename'].tolist())
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# if group_name == 'MgO powder,H2O,HCl':
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# print('Here:',sub_df.shape)
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create_group_hierarchy(group, sub_df, columns[1::])
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def is_nested_hierarchy(df) -> bool:
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"""receives a dataframe with categorical columns and checks whether rows form a nested group hierarchy.
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That is, from bottom to top, subsequent hierarchical levels contain nested groups. The lower level groups belong to exactly one group in the higher level group.
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"""
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# TODO: generalize the code to check for deeper group hierachies.
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def are_nested(df, col, col_nxt):
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""" Checks whether low level LL groups can be separated in terms of high level HL groups.
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That is, elements of low-level groups do not belong to more than one HL group."""
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# Compute higher level group names/categories
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memberships = df[col_nxt].unique().tolist()
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# Compute upper-level group memberships of low-level groups
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col_avg_memberships = df.groupby(col).mean()[col_nxt].unique()
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# Check whether all low-level groups have an actual hlg membership. That is, their avg. hlg membership is in the hlg membership.
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return all([col_avg_memberships[group_idx] in memberships for group_idx in range(len(col_avg_memberships))])
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df_tmp = df.copy()
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# Create relabeling map
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for column_name in df_tmp.columns:
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category_index = pd.Series(np.arange(len(df_tmp[column_name].unique())), index=df_tmp[column_name].unique())
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df_tmp[column_name] = category_index[df_tmp[column_name].tolist()].tolist()
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df_tmp.plot()
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return all([are_nested(df_tmp,'level_'+str(i)+'_groups','level_'+str(i+1)+'_groups') for i in range(len(df_tmp.columns)-1)])
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def transfer_file_dict_to_hdf5(h5file, group_name, file_dict):
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"""
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Transfers data from a file_dict to an HDF5 file.
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Parameters
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----------
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h5file : h5py.File
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HDF5 file object where the data will be written.
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group_name : str
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Name of the HDF5 group where data will be stored.
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file_dict : dict
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Dictionary containing file data to be transferred. Required structure:
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{
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'name': str,
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'attributes_dict': dict,
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'datasets': [
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{
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'name': str,
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'data': array-like,
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'shape': tuple,
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'attributes': dict (optional)
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},
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...
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]
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}
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Returns
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-------
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None
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"""
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if not file_dict:
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return
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try:
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# Create group and add their attributes
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group = h5file[group_name].create_group(name=file_dict['name'])
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# Add group attributes
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group.attrs.update(file_dict['attributes_dict'])
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# Add datasets to the just created group
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for dataset in file_dict['datasets']:
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dataset_obj = group.create_dataset(
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name=dataset['name'],
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data=dataset['data'],
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shape=dataset['shape']
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)
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# Add dataset's attributes
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attributes = dataset.get('attributes', {})
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dataset_obj.attrs.update(attributes)
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except Exception as inst:
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print(inst)
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logging.error('Failed to transfer data into HDF5: %s', inst)
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def copy_file_in_group(source_file_path, dest_file_obj : h5py.File, dest_group_name, work_with_copy : bool = True):
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# Create copy of original file to avoid possible file corruption and work with it.
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if work_with_copy:
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tmp_file_path = utils.make_file_copy(source_file_path)
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else:
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tmp_file_path = source_file_path
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# Open backup h5 file and copy complet filesystem directory onto a group in h5file
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with h5py.File(tmp_file_path,'r') as src_file:
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dest_file_obj.copy(source= src_file['/'], dest= dest_group_name)
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if 'tmp_files' in tmp_file_path:
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os.remove(tmp_file_path)
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def create_hdf5_file_from_filesystem_path(path_to_input_directory: str,
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path_to_filenames_dict: dict = None,
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select_dir_keywords : list = [],
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root_metadata_dict : dict = {}):
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"""
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Creates an .h5 file with name "output_filename" that preserves the directory tree (or folder structure)
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of a given filesystem path.
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The data integration capabilities are limited by our file reader, which can only access data from a list of
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admissible file formats. These, however, can be extended. Directories are groups in the resulting HDF5 file.
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Files are formatted as composite objects consisting of a group, file, and attributes.
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Parameters
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----------
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output_filename : str
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Name of the output HDF5 file.
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path_to_input_directory : str
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Path to root directory, specified with forward slashes, e.g., path/to/root.
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path_to_filenames_dict : dict, optional
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A pre-processed dictionary where keys are directory paths on the input directory's tree and values are lists of files.
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If provided, 'input_file_system_path' is ignored.
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select_dir_keywords : list
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List of string elements to consider or select only directory paths that contain
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a word in 'select_dir_keywords'. When empty, all directory paths are considered
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to be included in the HDF5 file group hierarchy.
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root_metadata_dict : dict
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Metadata to include at the root level of the HDF5 file.
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Returns
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-------
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output_filename : str
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Path to the created HDF5 file.
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"""
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if not '/' in path_to_input_directory:
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raise ValueError('path_to_input_directory needs to be specified using forward slashes "/".' )
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#path_to_output_directory = os.path.join(path_to_input_directory,'..')
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path_to_input_directory = os.path.normpath(path_to_input_directory).strip(os.sep)
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for i, keyword in enumerate(select_dir_keywords):
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select_dir_keywords[i] = keyword.replace('/',os.sep)
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if not path_to_filenames_dict:
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# On dry_run=True, returns path to files dictionary of the output directory without making a actual copy of the input directory
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path_to_output_directory = os.path.join(path_to_input_directory,'..')
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path_to_filenames_dict = utils.copy_directory_with_contraints(path_to_input_directory,
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path_to_output_directory,
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dry_run=True)
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# Set input_directory as copied input directory
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root_dir = path_to_input_directory
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path_to_output_file = path_to_input_directory.rstrip(os.path.sep) + '.h5'
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with h5py.File(path_to_output_file, mode='w', track_order=True) as h5file:
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number_of_dirs = len(path_to_filenames_dict.keys())
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dir_number = 1
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for dirpath, filtered_filenames_list in path_to_filenames_dict.items():
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start_message = f'Starting to transfer files in directory: {dirpath}'
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end_message = f'\nCompleted transferring files in directory: {dirpath}'
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# Print and log the start message
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print(start_message)
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logging.info(start_message)
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# Check if filtered_filenames_list is nonempty. TODO: This is perhaps redundant by design of path_to_filenames_dict.
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if not filtered_filenames_list:
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continue
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group_name = dirpath.replace(os.sep,'/')
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group_name = group_name.replace(root_dir.replace(os.sep,'/') + '/', '/')
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# Flatten group name to one level
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if select_dir_keywords:
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offset = sum([len(i.split(os.sep)) if i in dirpath else 0 for i in select_dir_keywords])
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else:
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offset = 1
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tmp_list = group_name.split('/')
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if len(tmp_list) > offset+1:
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group_name = '/'.join([tmp_list[i] for i in range(offset+1)])
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# Group hierarchy is implicitly defined by the forward slashes
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if not group_name in h5file.keys():
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h5file.create_group(group_name)
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#h5file[group_name].attrs.create(name='filtered_file_list',data=convert_string_to_bytes(filtered_filename_list))
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#h5file[group_name].attrs.create(name='file_list',data=convert_string_to_bytes(filenames_list))
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else:
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print(group_name,' was already created.')
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for filenumber, filename in enumerate(filtered_filenames_list):
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#file_ext = os.path.splitext(filename)[1]
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#try:
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# hdf5 path to filename group
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dest_group_name = f'{group_name}/{filename}'
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if not 'h5' in filename:
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#file_dict = config_file.select_file_readers(group_id)[file_ext](os.path.join(dirpath,filename))
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#file_dict = ext_to_reader_dict[file_ext](os.path.join(dirpath,filename))
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file_dict = filereader_registry.select_file_reader(dest_group_name)(os.path.join(dirpath,filename))
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transfer_file_dict_to_hdf5(h5file, group_name, file_dict)
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else:
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source_file_path = os.path.join(dirpath,filename)
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dest_file_obj = h5file
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#group_name +'/'+filename
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#ext_to_reader_dict[file_ext](source_file_path, dest_file_obj, dest_group_name)
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#g5505f_reader.select_file_reader(dest_group_name)(source_file_path, dest_file_obj, dest_group_name)
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copy_file_in_group(source_file_path, dest_file_obj, dest_group_name, False)
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# Update the progress bar and log the end message
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utils.progressBar(dir_number, number_of_dirs, end_message)
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logging.info(end_message)
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dir_number = dir_number + 1
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if len(root_metadata_dict.keys())>0:
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for key, value in root_metadata_dict.items():
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#if key in h5file.attrs:
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# del h5file.attrs[key]
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h5file.attrs.create(key, value)
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#output_yml_filename_path = hdf5_vis.take_yml_snapshot_of_hdf5_file(output_filename)
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return path_to_output_file #, output_yml_filename_path
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def save_processed_dataframe_to_hdf5(df, annotator, output_filename): # src_hdf5_path, script_date, script_name):
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"""
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Save processed dataframe columns with annotations to an HDF5 file.
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Parameters:
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df (pd.DataFrame): DataFrame containing processed time series.
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annotator (): Annotator object with get_metadata method.
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output_filename (str): Path to the source HDF5 file.
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"""
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# Convert datetime columns to string
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datetime_cols = df.select_dtypes(include=['datetime64']).columns
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if list(datetime_cols):
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df[datetime_cols] = df[datetime_cols].map(str)
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# Convert dataframe to structured array
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icad_data_table = utils.convert_dataframe_to_np_structured_array(df)
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# Get metadata
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metadata_dict = annotator.get_metadata()
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# Prepare project level attributes to be added at the root level
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project_level_attributes = metadata_dict['metadata']['project']
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# Prepare high-level attributes
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high_level_attributes = {
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'parent_files': metadata_dict['parent_files'],
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**metadata_dict['metadata']['sample'],
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**metadata_dict['metadata']['environment'],
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**metadata_dict['metadata']['instruments']
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}
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# Prepare data level attributes
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data_level_attributes = metadata_dict['metadata']['datasets']
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for key, value in data_level_attributes.items():
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if isinstance(value,dict):
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data_level_attributes[key] = utils.convert_attrdict_to_np_structured_array(value)
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# Prepare file dictionary
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file_dict = {
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'name': project_level_attributes['processing_file'],
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'attributes_dict': high_level_attributes,
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'datasets': [{
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'name': "data_table",
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'data': icad_data_table,
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'shape': icad_data_table.shape,
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'attributes': data_level_attributes
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}]
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}
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# Check if the file exists
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if os.path.exists(output_filename):
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mode = "a"
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print(f"File {output_filename} exists. Opening in append mode.")
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else:
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mode = "w"
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print(f"File {output_filename} does not exist. Creating a new file.")
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# Write to HDF5
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with h5py.File(output_filename, mode) as h5file:
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# Add project level attributes at the root/top level
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h5file.attrs.update(project_level_attributes)
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transfer_file_dict_to_hdf5(h5file, '/', file_dict)
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def main_mtable_h5_from_dataframe():
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#import os
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ROOT_DIR = os.path.abspath(os.curdir)
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# Read BeamTimeMetaData.h5, containing Thorsten's Matlab Table
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input_data_df = read_mtable_as_dataframe(os.path.join(ROOT_DIR,'input_files\\BeamTimeMetaData.h5'))
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# Preprocess Thorsten's input_data dataframe so that i can be used to create a newer .h5 file
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# under certain grouping specificiations.
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input_data_df = input_data_df.rename(columns = {'name':'filename'})
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input_data_df = utils.augment_with_filenumber(input_data_df)
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input_data_df = utils.augment_with_filetype(input_data_df)
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input_data_df = utils.split_sample_col_into_sample_and_data_quality_cols(input_data_df)
|
|
||||||
input_data_df['lastModifiedDatestr'] = input_data_df['lastModifiedDatestr'].astype('datetime64[s]')
|
|
||||||
|
|
||||||
# Define grouping functions to be passed into create_hdf5_file function. These can also be set
|
|
||||||
# as strings refering to categorical columns in input_data_df.
|
|
||||||
|
|
||||||
test_grouping_funcs = True
|
|
||||||
if test_grouping_funcs:
|
|
||||||
group_by_sample = lambda x : utils.group_by_df_column(x,'sample')
|
|
||||||
group_by_type = lambda x : utils.group_by_df_column(x,'filetype')
|
|
||||||
#group_by_filenumber = lambda x : utils.group_by_df_column(x,'filenumber')
|
|
||||||
else:
|
|
||||||
group_by_sample = 'sample'
|
|
||||||
group_by_type = 'filetype'
|
|
||||||
group_by_filenumber = 'filenumber'
|
|
||||||
|
|
||||||
output_filename_path = os.path.join('output_files','thorsten_file_list.h5')
|
|
||||||
|
|
||||||
create_hdf5_file_from_dataframe(output_filename_path,input_data_df, 'top-down', group_by_funcs = [group_by_sample, group_by_type])
|
|
||||||
#create_hdf5_file_from_dataframe('test.h5',input_data_df, 'top-down', group_by_funcs = [group_by_sample, group_by_type, group_by_filenumber])
|
|
||||||
|
|
||||||
annotation_dict = {'1-Campaign name': '**SLS-Campaign-2023**',
|
|
||||||
'2-Users':'Thorsten, Luca, Zoe',
|
|
||||||
'3-Startdate': str(input_data_df['lastModifiedDatestr'].min()),
|
|
||||||
'4-Enddate': str(input_data_df['lastModifiedDatestr'].max())
|
|
||||||
}
|
|
||||||
hdf5_ops.annotate_root_dir(output_filename_path, annotation_dict)
|
|
||||||
|
|
||||||
#display_group_hierarchy_on_a_treemap(output_filename_path)
|
|
||||||
|
|
||||||
print(':)')
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
|
||||||
|
|
||||||
#main()
|
|
||||||
main_mtable_h5_from_dataframe()
|
|
||||||
#main_5505()
|
|
||||||
|
|
||||||
print(':)')
|
|
||||||
|
|
||||||
Reference in New Issue
Block a user