HDF5 Data Migrator

Overview

The program recursively searches through folders for .dat files and images, converting them into datasets within an HDF5 file. It utilizes the NumPy library for data handling and the h5py library for HDF5 file manipulation.

The process involves creating groups in the HDF5 file to mirror the folder structure, and within these groups, datasets are created containing the converted file's data. This program aids in organizing and consolidating various data types into a single HDF5 file, facilitating easier management and analysis.

Installation via pip

Recommended create a virtual environment, navigate to the root package folder and run the following command to install:

pip install .

Or in developed mode:

pip install -e .

Example

You can filter the data using your own criteria and then verify if it is the correct output:

import os
from pathlib import Path

from HDF5Migrator import  HDF5Builder

def user_criteria(path):
    return  not (path.name.endswith(".pdf") or
                path.name.endswith('.jpg') or
                path.name.startswith('laser-spectrum') or
                path.name.startswith('potencias-probe') or
                'exp' in path.name
                 )

CurrentPath = os.path.abspath(__file__)
ExamplePath = Path(CurrentPath).parent /'data' / '2022-04-08'
paths = HDF5Builder.make_tree(ExamplePath,
                       criteria=user_criteria, depth=3)
for path in paths:
    print(path.displayable())

Which prints:

>>>python ./examples/doc_built_tree.py
[0]:2022-04-08/
[1]:├── powers/
│   [2]:├── 00_5mW/
│   │   [3]:└── M4_3522_T=21_0K__0_0V__0grados.dat
│   [2]:├── 01_0mW/
│   │   [3]:└── M4_3522_T=21_0K__0_0V__0grados.dat
│   [2]:├── 01_5mW/
│   │   [3]:└── M4_3522_T=21_0K__0_0V__0grados.dat
│   [2]:└── Bitacora.txt
[1]:├── spot/
[1]:└── temperaturas/
    [2]:├── 100k/
    │   [3]:├── 00_5mW/
    │   [3]:├── 01_5mW/
    │   [3]:└── 09_0mW/
    [2]:├── 23k/
    │   [3]:├── 00_5mW/
    │   [3]:├── 01_5mW/
    │   [3]:└── 09_0mW/
    [2]:├── 50k/
    │   [3]:├── 00_5mW/
    │   [3]:├── 01_5mW/
    │   [3]:└── 09_0mW/
    [2]:└── Bitacora.txt

When you are certain, proceed to create the HDF5 file. You can also use the criteria_name function to filter the directories:

import os
from pathlib import Path

from HDF5Migrator import  HDF5Builder
from criterias import criteria_name


CurrentPath = os.path.abspath(__file__)
ExamplePath = Path(CurrentPath).parent /'data' / '2022-04-08'
h5Name = Path(CurrentPath).parent /'hdf5_files' / '2022-04-08'

user_criteria = lambda path: criteria_name(path.name,
        not_in_path=['exp'],
        not_starts=['laser-spectrum', 'potencias-probe'],
        not_ends=['.pdf'], operator='and')

paths = HDF5Builder.make_hdf5(ExamplePath,
                       criteria=user_criteria, depth= 4, name = h5Name, close = False)

paths.h5_file.close()

Which prints:

>>>python ./examples/doc_built_hdf5.py
Oops! Bitacora.txt isn't file for import in hdf5. ()
Oops! Bitacora.txt isn't file for import in hdf5. ()

If you decide not to close the file, you can continue using it with the h5_file variable, or you can close the document and reopen it using the h5py library.

Try creating hdf5 files with the example doc_built_hdf5