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Jupyter widgets for scheduling processes and visualizing the resulting (live) data. | ||
# Juts | ||
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**Ju**pyter widgets for scheduling processes and visualizing the resulting (live) data. | ||
While it is designed to use custom data-specific visualizations based on | ||
ipywidgets, visualizations for time series data (bqplot), animations | ||
ipywidgets, visualizations for **t**ime **s**eries data (bqplot), animations | ||
(matplotlib) and 3D data (ipyvolume) are included. | ||
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## Quickstart | ||
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To start a process a so called job has to be setup. A job consists of a python function and a configuration. The function contains the code (machine learning, simulation, ....) to be executed, for [example](juts/examples/minimal_example.ipynb): | ||
``` | ||
def wait_n_times_x_ms(config, process_queue=None, return_dict=None): | ||
n = config["parameter"]["n"] | ||
x = config["parameter"]["x"] | ||
time = list() | ||
time_series = list() | ||
for i in range(n): | ||
time.append(i) | ||
time_series.append(i / x) | ||
process_queue.put(dict(progress=int(i/n * 100), | ||
time=i, | ||
time_series=i/x)) | ||
tim.sleep(x) | ||
``` | ||
The configuration(s) can be read from a yaml file like [this](juts/examples/wait_n_times_x_ms.yml): | ||
``` | ||
wait_40_times_100_ms: | ||
parameter: | ||
n: 40 | ||
x: 0.1 | ||
wait_10_times_300_ms: | ||
parameter: | ||
n: 10 | ||
x: 0.3 | ||
``` | ||
or can be defined as python dictionary. | ||
[Screencast from 09.05.2023 13:35:58.webm](https://github.com/riemarc/juts/assets/18379817/e0aae907-8cf1-4958-9e8e-42dc5024e16a) | ||
Also live data can be monitored during script execution: | ||
[Screencast from 09.05.2023 13:37:38.webm](https://github.com/riemarc/juts/assets/18379817/bc3878a8-29fa-457f-b6f8-3287c849405e) |
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