https://lvwerra.github.io/jupyterplot/

Create real-time plots in Jupyter Notebooks.
https://lvwerra.github.io/jupyterplot/

Last synced: 17 days ago
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Create real-time plots in Jupyter Notebooks.

• Host: GitHub
• URL: https://lvwerra.github.io/jupyterplot/
• Owner: lvwerra
• License: apache-2.0
• Created: 2020-01-16T15:42:56.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2023-04-11T23:58:54.000Z (over 1 year ago)
• Last Synced: 2024-10-23T06:07:22.962Z (24 days ago)
• Language: Jupyter Notebook
• Homepage: https://lvwerra.github.io/jupyterplot/
• Size: 9.4 MB
• Stars: 131
• Watchers: 3
• Forks: 10
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        
# jupyterplot

> Create real-time plots in Jupyter notebooks.

## What is it?

It generalises Andreas Madsen's excellent [python-lrcurve](https://github.com/AndreasMadsen/python-lrcurve) library for machine learning to produce visualisations for arbitrary functions in real-time.

![single-plot](notebooks/images/plot_multiple_static.gif)

## Install

`pip install jupyterplot`

## How to use

### Single plot

Creating a simple real-time plot in a Jupyter notebook is as easy as easy as the following snippet:

```

from jupyterplot import ProgressPlot

import numpy as np

pp = ProgressPlot()

for i in range(1000):

    pp.update(np.sin(i / 100))

pp.finalize()

```

![single-plot](notebooks/images/plot_single_dynamic.gif)

**Note:** The `pp.finalize()` statement is necessary to make the plots persistent between notebook sessions.

### Custom range

By default, the x and y ranges adapt to new data points. If the scale is known beforehand, it can be steadier to set it beforehand:

```

pp = ProgressPlot(x_lim=[0, 1000], y_lim=[-1.5, 1.5])

for i in range(1000):

    pp.update(np.sin(i / 100))

pp.finalize()

```

![single-plot](notebooks/images/plot_single_static.gif)

### Multiple lines

One can also plot several lines in parallel by specifying the line names in the constructor and passing all values in a list.

```

pp = ProgressPlot(line_names=["lin", "log", "cos", "sin"],

                  x_lim=[0, 1000],

                  y_lim=[-1, 4])

for i in range(1000):

    pp.update([[i / 250, np.log10(i + 1), np.cos(i / 100), np.sin(i / 100)]])

pp.finalize()

```

![single-plot](notebooks/images/plot_multiple_static.gif)

**Note:** The data is fed to `pp.update()` as a list of lists, where each sublist corresponds to the curves that are generated in each subplot.

### Multiple plots

```

pp = ProgressPlot(plot_names=["cos", "sin"],

                  line_names=["data", "delayed-data"],

                  x_lim=[0, 1000],

                  y_lim=[-1, 1])

for i in range(1000):

    pp.update([[np.cos(i / 100), np.cos((i + 20) / 100)],

               [np.sin(i / 100), np.sin((i + 20) / 100)]])

pp.finalize()

```

![single-plot](notebooks/images/plot_multiple_plots_static.gif)

### Custom x-values

If the x values should not be incremented by 1 at every update one can set the `x_iterator=False`. This requires passing two values to the `update(x, y)`, where `x` is an `int` or `float` and `y` follows the same format as in the previous examples.

```

pp = ProgressPlot(x_iterator=False, x_label="custom-x", x_lim=[0, 10000], y_lim=[0, 10])

for i in range(1000):

    pp.update(10 * i, i / 100)

pp.finalize()

```

![single-plot](notebooks/images/plot_single_static_custom.gif)

### Decoupled y-limits

If each subplot should have different y-limits then the y-limits can be passed as a list containing the the limits for each subplot.

```

pp = ProgressPlot(plot_names=['plot 1', 'plot 2'], x_lim=[0, 1000], y_lim=[[0, 10],[0, 100]])

for i in range(1000):

    pp.update([[(i/100)], [(i/100)**2]])

```

![single-plot](notebooks/images/plot_separate_ylim.gif)

### Input format

#### Single plot, single line

If the progress plot consists of a single plot with a single line one can pass the y-updates as `int` or `float`.

#### Multiple plots, multiple lines

If multiple plots or lines are used, the y-updates can either be lists or dicts:

```python

y_update_list = [[y_plot_1_line_1, y_plot_1_line_2],

                 [y_plot_2_line_1, y_plot_2_line_2]]

y_update_dict = {'plot_name_1': {'line_name_1': y_plot_1_line_1,

                                 'line_name_2': y_plot_1_line_2},

                 'plot_name_2': {'line_name_1': y_plot_2_line_1,

                                 'line_name_2': y_plot_2_line_2}}

``` 

## Limitations

* Only one `ProgressPlot()` object can be used at a time. 

* Each subplot must have the same number of lines.

* The same color cycle for each subplot is used.