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# ⏯ [interactive pipe website](https://balthazarneveu.github.io/interactive_pipe/)
# Full tutorial
**Note** : The same tutorial is also available on [Google Colab](https://colab.research.google.com/github/livalgo/interactive-pipe-examples/blob/main/interactive_pipe_tutorial.ipynb) to learn about the Jupyter notebook backend.
# Analyzing the headless pipeline
```python
headless_tutorial_pipeline = interactive_pipeline(gui=None)(tutorial_pipeline)
# gui=None allows retuning a HeadlessPipeline instance.
headless_tutorial_pipeline.graph_representation("__tuto_pipeline")
```
Let's now call the pipeline several times with varying parameters.
🎁 We provide a basic code sample to generate the gif animation.
```python
from PIL import Image as PILImage
img_list = []
for freq in np.linspace(1, 100, 10):
# Let's first override some of the default parameters.
headless_tutorial_pipeline.parameters = {
"gen_color": {"frequency": freq, 'isotropy': freq/100.},
"compare_by_splitting": {"ratio": freq/100.}}
# Let's execute
headless_tutorial_pipeline()
img = (255.*headless_tutorial_pipeline.results[1][1]).astype(np.uint8)
img_list.append(PILImage.fromarray(img))
img_list[0].save("animation.gif", save_all=True, append_images=img_list[1:], duration=1, loop=1000)
```
# Advice
## Keep separation between "production code" (library / not interactive) & interactivity
- 💡 One of the strength of interactive pipe is to avoid adding hundreds of lines of code dedicated to GUI (graphical user interfaces).
- With interactive pipe, you have the choice to keep this design choice. Each processing block can be defined as a regular function, without even importing the `interactive` decorator. In a separate file, you'll add sliders.
### Several ways to decorate building blocks
#### 1.Decorate afterward 🏆 *= Recommended approach*
- ➖ Less elegant to read (`add_interactivity` may be in a different file)
- ➕ The decorated function can be re-used somewhere else (before being decorated).
- ➕ Possibility to add conditions (`debug` flag)
```python
def gen_color(frequency=0, isotropy=0.):
...
def add_interactivity(debug=True):
interactive(
frequency=(80, [1, 100]),
isotropy=(0.8, [0.1, 1.]) if debug else 0.8
)(gen_color)
```
#### 2.Decorate using `@`
```python
@interactive(
frequency=(80, [1, 100]),
isotropy=(0.8, [0.1, 1.])
)
def gen_color(frequency=0, isotropy=0.):
...
```
- ➕ Easy to read
- ➖ The decorated function can't be re-used somewhere else.
- ➖ No possibility to add condition to hide/show slider. To hide the slider, you need to comment!
#### 3. Shorter code using `@`
```python
@interactive()
def gen_color(
frequency=(80, [1, 100]),
isotropy=(0.8, [0.1, 1.])
):
```
- ➕ Shortest code
- ➖ The decorated function can't be re-used somewhere else
## Pipeline visualization
Internally, `interactive_pipe` builds an execution graph of the pipeline.
- If you're using the `qt` backend, press `G` to build the graph.
- If you're in a notebook, it's possible to easily plot the graph.
```python
headless_tutorial_pipeline = interactive_pipeline(gui=None)(tutorial_pipeline)
headless_tutorial_pipeline.graph_representation()
```
| pipeline's execution graph |
| :--:|
|  |
## Backend choice
|Backend | 🚀 `Qt` | 🐢 `Gradio` |
|:---|:---|:---|
| Responsiveness | ➕ Very fast | ➖ Slower |
| Shareable | ➖ No, only 1 local Qt Window | ➕ Share a link with others, (➕ Optional: standalone hosting on HF spaces 🤗 ) |
| Keyboard controls | ➕ Yes | ➖ No |
| Full screen | ➕ Yes | ➖ No |
| 🚀 `Qt` | 🐢 `Gradio` |
|:---:|:---:|
|  |  |
### Matplotlib backend
Please note that there's also a matplotlib `gui="mpl"` backend in case of environments where you really don't have access to Qt or Jupyter notebooks. |