1
0
mirror of https://github.com/gsi-upm/senpy synced 2024-11-23 00:22:28 +00:00
senpy/docs/plugins.rst

118 lines
4.2 KiB
ReStructuredText
Raw Normal View History

Developing new plugins
----------------------
2016-03-16 18:44:01 +00:00
There are two types of files that were needed by senpy for loading a plugin:
- *.senpy: this file is the builder of the plugin.
- *.py: this file is the interface of the plugin.
Plugins Builder
================
The structure of this files is similar to a python dictionary, where the data representation consists on attribute-value pairs.
The principal attributes are:
* name: plugin name used in senpy to call the plugin.
* module: name of the file where the interface is written (*.py)
.. code:: python
{
"name" : "senpyPlugin",
"module" : "{python file}"
}
You can use another attributes such as `description`, `author`, `version`, etc.
2015-11-05 18:11:35 +00:00
Plugins Interface
=================
The basic methods in a plugin are:
* __init__
* activate: used to load memory-hungry resources
* deactivate: used to free up resources
2016-03-16 18:44:01 +00:00
* analyse: called in every user requests. It takes in the parameters supplied by a user and should return a senpy Results.
2015-11-05 18:11:35 +00:00
Plugins are loaded asynchronously, so don't worry if the activate method takes too long. The plugin will be marked as activated once it is finished executing the method.
F.A.Q.
======
2015-11-05 18:20:13 +00:00
If I'm using a classifier, where should I train it?
???????????????????????????????????????????????????
2015-11-05 18:11:35 +00:00
Training a classifier can be time time consuming. To avoid running the training unnecessarily, you can use ShelfMixin to store the classifier. For instance:
.. code:: python
from senpy.plugins import ShelfMixin, SenpyPlugin
class MyPlugin(ShelfMixin, SenpyPlugin):
def train(self):
''' Code to train the classifier
'''
# Here goes the code
# ...
return classifier
def activate(self):
if 'classifier' not in self.sh:
classifier = self.train()
self.sh['classifier'] = classifier
self.classifier = self.sh['classifier']
def deactivate(self):
self.close()
You can speficy a 'shelf_file' in your .senpy file. By default the ShelfMixin creates a file based on the plugin name and stores it in that plugin's folder.
2016-03-16 18:44:01 +00:00
Where can I define extra parameters to be introduced in the request to my plugin?
?????????????????????????????????????????????????????????????????????????????????
You can add these parameters in the *.senpy file under the attribute "extra_params" : "{param_name}". The name of the parameter is going to act as another python dictionary with the next attributes:
* aliases: the different names which can be used in the request to use the parameter.
* required: this option is a boolean and indicates if the parameters is binding in operation plugin.
* options: the different values of the paremeter.
* default: the default value which can have the parameter, this is useful in case the paremeter is required and you want to have a default value.
.. code:: python
"extra_params": {
"language": {
"aliases": ["language", "l"],
"required": true,
"options": ["es"],
"default": "es"
}
}
This example shows how to introduce a parameter associated language.
The extraction of this paremeter is used in the analyse method of the Plugin interface.
.. code:: python
lang = params.get("language")
Where can I set up variables for using them in my plugin?
?????????????????????????????????????????????????????????
You can add these variables in the *.senpy with: {variable_name} : {variable_value}.
2016-03-17 08:43:25 +00:00
Once you have added your variables, the next step is to extract them into the plugin. The plugin's __init__ method has a parameter called `info` where you can extract the values of the variables. This info parameter has the structure of a python dictionary.
2016-03-16 18:44:01 +00:00
2016-03-17 08:39:33 +00:00
Can I activate a DEBUG mode for my plugin?
2016-03-16 18:44:01 +00:00
???????????????????????????????????????????
2016-03-17 08:39:33 +00:00
You can activate the DEBUG mode by the command-line tool using the option -d.
2016-03-16 18:44:01 +00:00
.. code:: bash
python -m senpy -d
2015-11-05 18:11:35 +00:00
Where can I find more code examples?
????????????????????????????????????
See: `<http://github.com/gsi-upm/senpy-plugins-community>`_.