# showy
``showy`` is a helper for matplotlib subplots.
## Usage
``showy`` can be used in a script-like manner.
Let's first define the layout:
```python
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": "Riri [Hz]",
"x_label": "Time [s]",
"title": "Second graph"
},
{
"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
}
```
Now, let's create dummy data:
```python
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:
```python
from showy import showy
showy(layout, data)
```
**Tip**: Define the layout in a ``yaml`` or ``json`` file in order to it across applications.
If you define it in a ``yaml`` file, then load it with (need to install [``pyyaml``](https://pypi.org/project/PyYAML/):
```python
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:
```python
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:
```python
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
}
```
## json-schema standard
``showy`` is based on a json-schema standard defined [here](https://gitlab.com/cerfacs/showy/raw/master/src/showy/schema.yaml). Check [this](https://cerfacs.fr/coop/json-schema-for-sci-apps) 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)
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"description": "\n# showy\n\n\n``showy`` is a helper for matplotlib subplots.\n\n\n\n## Usage\n\n\n``showy`` can be used in a script-like manner. \n\nLet's first define the layout:\n\n```python\nlayout = {\n \"title\": \"Example\",\n \"graphs\": [\n {\n \"curves\": [{\"var\": \"sine_10\"}],\n \"x_var\": \"time\",\n \"y_label\": \"Fifi [mol/m\u00b3/s]\",\n \"x_label\": \"Time [s]\",\n \"title\": \"Sinus of frequency *\"\n },\n {\n \"curves\": [{\"var\": \"sine_30\"}],\n \"x_var\": \"time\",\n \"y_label\": \"Riri [Hz]\",\n \"x_label\": \"Time [s]\",\n \"title\": \"Second graph\"\n },\n {\n \"curves\": [\n {\n \"var\": \"sine_100\",\n \"legend\": \"origin\",\n },\n {\n \"var\": \"sine_100p1\",\n \"legend\": \"shifted\",\n }\n ],\n \"x_var\": \"time\",\n \"y_label\": \"Loulou [cow/mug]\",\n \"x_label\": \"Time [s]\",\n \"title\": \"Third graphg\"\n }\n ],\n \"figure_structure\": [3, 1],\n \"figure_dpi\": 92.6\n}\n```\n\nNow, let's create dummy data:\n\n```python\nimport numpy as np\n\ndata = dict()\ndata[\"time\"] = np.linspace(0, 0.1, num=256)\n\ndata[\"sine_10\"] = np.cos(data[\"time\"] * 10 * 2 * np.pi)\ndata[\"sine_30\"] = np.cos(data[\"time\"] * 30 * 2 * np.pi)\ndata[\"sine_100\"] = np.cos(data[\"time\"] * 100 * 2 * np.pi)\ndata[\"sine_100p1\"] = 1. + np.cos(data[\"time\"] * 100 * 2 * np.pi)\n```\n\nFinally, we just have to display it:\n\n```python\nfrom showy import showy\n\nshowy(layout, data)\n```\n\n\n**Tip**: Define the layout in a ``yaml`` or ``json`` file in order to it across applications.\n\nIf you define it in a ``yaml`` file, then load it with (need to install [``pyyaml``](https://pypi.org/project/PyYAML/):\n\n```python\nimport yaml\n\nwith open(filename, 'r') as file:\n layout = yaml.load(file, Loader=yaml.SafeLoader)\n```\n\nIf you define it in a ``json`` file, then load it with:\n\n```python\nimport json\n\nwith open(filename, 'r') as file:\n layout = json.load(filename)\n\n```\n\n\n\n### Using wildcard `*`\n\nA 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_*``.\n\nThe example above reduces to:\n\n\n```python\nlayout = {\n \"title\": \"Example\",\n \"graphs\": [{\n \"curves\": [{\"var\": \"sine_*\"}],\n \"x_var\": \"time\",\n \"y_label\": \"Sine [mol/m\u00b3/s]\",\n \"x_label\": \"Time [s]\",\n \"title\": \"Sinus of frequency *\"\n }],\n \"figure_structure\": [3, 3],\n \"figure_dpi\": 92.6\n}\n```\n\n\n## json-schema standard\n\n``showy`` is based on a json-schema standard defined [here](https://gitlab.com/cerfacs/showy/raw/master/src/showy/schema.yaml). Check [this](https://cerfacs.fr/coop/json-schema-for-sci-apps) 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)\n\n\n\n",
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