Create real-time plots in Jupyter Notebooks.

# jupyterplot

Create real-time plots in Jupyter notebooks.

## What is it?

This is a library to generate real-time plots in Jupyter notebooks with a tqdm-like interface. It is largely based on the python-lrcurve library by Andreas Madsen. ## 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 line:

``````from jupyterplot import ProgressPlot
import numpy as np

pp = ProgressPlot()
for i in range(1000):
pp.update(np.sin(i/100))
pp.finalize()
`````` Note: The `pp.finalize()` statement is necessary to make the plots persistent between notebook sessions.

### Custom range

By default, the x and y range adapt to new data points. If the scale is known beforehand, it might 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()
`````` ### 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()
`````` Note: The data is fed with two brackets `[[y1, y2, y3]]`. The first list corresponds the plots, wheras the second list to each line of each plot as we will also see in the next example.

### 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()
`````` ### Custom x-values

Finally, 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`/`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()
`````` ### Input format

#### Single plot, single line

If a the progress plot consists of a single plot with a single line one can pass the y-updates as int/floats.

#### Multiple plots, multiple lines

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

```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.

## Project details

This version 0.0.1