showy 1.1.0

Helper for matplotlib subplots.

showy

showy is a helper for matplotlib subplots.

Usage

showy can be used in a script-like manner.

Let's first define the layout:

layout = {
    "title": "Example",
    "graphs": [
        {
            "curves": [{"var": "sine_10"}],
            "x_var": "time",
            "y_label": "Fifi [mol/m³/s]",
            "x_label": "Time [s]",
            "title": "Sinus of frequency *"
        },
        {
            "curves": [{"var": "sine_30"}],
            "x_var": "time",
            "y_label": "This label is too long and will be truncated.",
            "x_label": "This label is too long and will be truncated.",
            "title": "This is a graph with a long title."
        },
        {
            "curves": [
                {
                    "var": "sine_100",
                    "legend": "origin",
                },
                {
                    "var": "sine_100p1",
                    "legend": "shifted",
                }
            ],
            "x_var": "time",
            "y_label": "Loulou [cow/mug]",
            "x_label": "Time [s]",
            "title": "Third graphg"
        }
    ],
    "figure_structure": [3, 1],
    "figure_dpi": 92.6,
    "width_label_legend": 20,
    "width_label_axes": 25,
    "width_label_plot": 30
}

Now, let's create dummy data:

import numpy as np

data = dict()
data["time"] = np.linspace(0, 0.1, num=256)

data["sine_10"] = np.cos(data["time"] * 10 * 2 * np.pi)
data["sine_30"] = np.cos(data["time"] * 30 * 2 * np.pi)
data["sine_100"] = np.cos(data["time"] * 100 * 2 * np.pi)
data["sine_100p1"] = 1. + np.cos(data["time"] * 100 * 2 * np.pi)

Finally, we just have to display it:

from showy import showy

showy(layout, data)

[!TIP] Define the layout in a yaml or json file in order to use it across applications.

If you define it in a yaml file, then load it with (need to install pyyaml:

import yaml

with open(filename, 'r') as file:
  layout = yaml.load(file, Loader=yaml.SafeLoader)

If you define it in a json file, then load it with:

import json

with open(filename, 'r') as file:
  layout = json.load(filename)

Using wildcard *

A neat feature of showy is the wild card usage to simplify layout creation. For example, if you have 3 variables called var_1, var_2, var_3, you only need to define the graph layout for a variable var_*.

The example above reduces to:

layout = {
    "title": "Example",
    "graphs": [{
        "curves": [{"var": "sine_*"}],
        "x_var": "time",
        "y_label": "Sine [mol/m³/s]",
        "x_label": "Time [s]",
        "title": "Sinus of frequency *"
    }],
    "figure_structure": [3, 3],
    "figure_dpi": 92.6
}

Using Plotly

Showy supports Matplotlib and Plotly to display your graphs. Default is set to Matplotlib. To use Plotly, add backend="plotly" when calling showy:

from showy import showy

showy(layout, data, backend="plotly")

This outputs an HTML file.

json-schema standard

showy is based on a json-schema standard defined here. Check this out to learn more about the usage of the json-schema standard. (For your daily usage of showy you just need to ensure your layout respects the schema)