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https://github.com/vidartf/ipydatawidgets
A set of widgets to help facilitate reuse of large datasets across widgets
https://github.com/vidartf/ipydatawidgets
Last synced: 17 days ago
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A set of widgets to help facilitate reuse of large datasets across widgets
- Host: GitHub
- URL: https://github.com/vidartf/ipydatawidgets
- Owner: vidartf
- License: other
- Created: 2017-07-31T17:37:26.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2023-06-14T12:40:10.000Z (over 1 year ago)
- Last Synced: 2024-10-16T22:23:12.672Z (26 days ago)
- Language: TypeScript
- Size: 1.97 MB
- Stars: 44
- Watchers: 6
- Forks: 9
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# ipydatawidgets
[](https://codecov.io/gh/vidartf/ipydatawidgets)
ipydatawidgets is a set of widgets to help facilitate reuse of large datasets
across different widgets, and different packages.
## Installation
A typical installation requires the following three commands to be run:
```bash
pip install ipydatawidgets
jupyter nbextension install --py [--sys-prefix|--user|--system] ipydatawidgets
jupyter nbextension enable --py [--sys-prefix|--user|--system] ipydatawidgets
```
Or, if you use jupyterlab:
```bash
pip install ipydatawidgets
jupyter labextension install jupyterlab-datawidgets
```
## Usage
As a user, it should be noted that ipydatawidgets only works with packages that
explicitly allow for it, so a more detailed usage guide might be available in the
documentation of those packages. If you are a developer who wants to add support
for data widgets in your package, read the developer's section further below.
### Arrays
The main widget for arrays is the `NDArrayWidget` class. It has a main trait: A
numpy array. This array can be constrained/coerced in both size/shape and in data
type. It's main purpose is simply to be a standardized way of transmitting array
data from the kernel to the frontend, and to allow the data to be reused across
any number of other widgets, but with only a single sync across the network.
```python
import numpy as np
from ipydatawidgets import NDArrayWidget
raw_data = np.ones((100, 100, 3), dtype=np.float32)
datawidget = NDArrayWidget(raw_data)
# Below, my_other_widget has a trait `data` of the type `Instance(NDArrayWidget)`
my_other_widget.data = datawidget
```
In addition to the `NDArrayWidget`, ipydatawidgets also expose the trait type for
the numpy array and its serializers (on both the Python side and the javascript
side). More importantly, it exposes a `DataUnion` trait type, that accepts both
numpy arrays directly, or a reference to an `NDArrayWidget`. This allows other
widgets to easily accept *either* a numpy array *or* a data widget. Then the user
can choose which one they prefer, weighing the pros and cons against eachother
(the con of the widget being the extra overhead of creation).
```python
# ... continuation of example above
my_other_widget.data = raw_data # also acceptable, if `data` is a DataUnion
```
## Developers
Developers should consider using ipydatawidgets because:
- It gives readily accessible syncing of array data using the binary transfer
protocol of ipywidgets.
- It has inbuilt mechanisms for constraining shape and dtype, and can quickly
be extended with more complex constraints.
- It avoids duplication of common code among different extensions, ensuring
that bugs discovered for one extension gets fixed in all.
### Overview
The major parts of ipydatawidgets are:
- Traits/Widgets definitions
- Validators to coerce/constrain values assigned to those traits
- Serializers/deserializers to send the data across the network
- Apropriate javascript handling and representation of the data