microscopy-portfolio 0.0.5
A helper package to download example datasets used in various publications and deep-learning algorithms, including data featured in N2V, P(P)N2V, DivNoising, HDN, EmbedSeg, etc.
License: BSD-3-Clause
Requires Python: >=3.8

# Microscopy Portfolio

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A helper package to download example datasets used in various publications by the Jug lab, including data featured in N2V, P(P)N2V, DivNoising, HDN, EmbedSeg, etc.

The complete list of datasets can be found [here](datasets/datasets.json).

## Installation

To install the portfolio in your conda environment, simply use `pip`:
```bash
$ pip install microscopy-portfolio
```

## Usage

Follow the [example notebook](examples/example.ipynb) for details on how to use the package.

The portfolio can be instantiated as follow:

```python
from microscopy_portfolio import Portfolio

portfolio = Portfolio()
```

You can explore the different datasets easily:
```python
print(portfolio)
print(portfolio.denoising)
print(portfolio.denoising.N2V_SEM)
```

Finally, you can download the dataset of your choice:
```python
from pathlib import Path

data_path = Path('data')
portfolio.denoising.N2V_SEM.download(data_path)
```

## Add a dataset to the repository

To add a dataset, subclass a `PortfolioEntry` and enter the following information 
(preferably in one of the current categories, e.g. `denoising_datasets.py`):
```python
class MyDataset(PortfolioEntry):
    def __init__(self) -> None:
        super().__init__(
            name="MyDataset",
            url="https://url.to.myfile/MyFile.zip",
            file_name="MyFile.zip",
            md5_hash="953a815333805a423b7019bd16cc3341",
            description="Description of the dataset.",
            license="CC-BY",
            citation="Citation of the dataset',
            files={
                "/folder/in/the/zip": ["file1.tif", "file2.tif"], # folder can be "."
            },
            size=13.0, # size in MB
        )
```

To obtain md5 hash of your file, you can run the following code:
```python
from pathlib import Path
import hashlib

file_path = Path("path/to/myfile.zip")
hashlib.md5(open(file_path, "rb").read()).hexdigest()
```

Likewise, to get the size in MB of your file:
```python
import os

os.path.getsize(file_path) / 1024 / 1024
```

Finally, add the file class to one of the categories (e.g. denoising) in 
`portfolio.py`:
```python
class Denoising(IterablePortfolio):
    def __init__(self) -> None:
        self._N2V_BSD68 = N2V_BSD68()
        self._N2V_SEM = N2V_SEM()
        self._N2V_RGB = N2V_RGB()
        self._flywing = Flywing()

        # add your dataset as a private attribute
        self._myDataset = MyDataset()

        [...]

    # and add a public getter
    @property
    def MyDataset(self) -> MyDataset:
        return self._myDataset
```