Dataset Iterator
This repo contains keras iterator classes for multi-channel (time-lapse) images contained in dataset files such as hdf5.
Dataset structure:
One dataset file can contain several sub-datasets (dataset_name0, dataset_name1, etc...), the iterator will iterate through all of them as if they were concatenated.
.
├── ...
├── dataset_name0
│ ├── channel0
│ └── channel1
│ └── ...
├── dataset_name1
│ ├── channel0
│ └── channel1
│ └── ...
└── ...
Each dataset contain channels (channel0, channel1 ...) that must have same shape. All datasets must have the same number of channels, and shape (except batch size) must be equal among datasets.
Groups
There can be more folder level, for instance to have train and test sets in the same file:
.
├── ...
├── experiment1
│ ├── train
│ │ ├── raw
│ │ └── labels
│ └── test
│ ├── raw
│ └── labels
├── experiment2
│ ├── train
│ │ ├── raw
│ │ └── labels
│ └── test
│ ├── raw
│ └── labels
└── ...
train_it = MultiChannelIterator(dataset_file_path = file_path, channel_keywords = ["/raw", "/labels"], group_keyword="train")
test_it = MultiChannelIterator(dataset_file_path = file_path, channel_keywords = ["/raw", "/labels"], group_keyword="test")
Image formats
- Those iterators are using an object of class
DatasetIOto access the data. - There is currently an implementation of DatasetIO for .h5 files (
H5pyIO), as well as dataset composed of multiple images files supported by PILLOW (MultipleFileIO). - one can also concatenate datasets from different files:
- if a dataset is split into several files that contain the same channels: use
ConcatenateDatasetIO - if a dataset contains channels in different files, use:
MultipleDatasetIO
- if a dataset is split into several files that contain the same channels: use
Demo
See this notebook for a demo:
1 maintainer
Metadata
1 maintainer
Release files for dataset-iterator 0.5.9
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| dataset_iterator-0.5.9.tar.gz | 77.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| dataset_iterator-0.5.9-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 163.8 kB
Release files / dataset_iterator-0.5.9.tar.gz
| Download URL | dataset_iterator-0.5.9.tar.gz |
|---|---|
| Size | 77.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
606367e9e375ba87130297caf726f378109a6d90a7ef523e8f18a837b12473b8
|
|
BLAKE2b-256 checksum How to use checksums |
505d689a89c40ea56bfa6db0891d35e23383a16a7d7166c3cd899306d0bf16ae
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 24, 2026.
Transparency logRelease files / dataset_iterator-0.5.9-py3-none-any.whl
| Download URL | dataset_iterator-0.5.9-py3-none-any.whl |
|---|---|
| Size | 86.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
e1cf2482507779a2c836cbd53d543baa9aaffb489a5a5a513c6e91b5c22faa62
|
|
BLAKE2b-256 checksum How to use checksums |
4d03c78b18fc8e68a8e307f7ab0f640ac2ac038b22977f7803c81e03960bbdfe
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 24, 2026.
Transparency log
