.. currentmodule:: pywps

.. versionadded:: 4.0.0

.. todo::
    * Input validation
    * IOHandler

PyWPS works with processes and services. A process is a Python Class containing an handler method and a list of inputs and outputs. A PyWPS service instance is then a collection of selected processes.

PyWPS does not ship with any processes predefined - it's on you, as user of PyWPS to set up the processes of your choice. PyWPS is here to help you publishing your awesome geospatial operation on the web - it takes care of communication and security, you then have to add the content.

$ mkdir processes

A processes is coded as a class inheriting from :class:`Process`. In the PyWPS-Flask server they are kept inside the processes folder, usually in separated files.

The instance of a Process needs following attributes to be configured:

As an example, we will create a buffer process - which will take a vector file as the input, create specified the buffer around the data (using Shapely), and return back the result.

Therefore, the process will have two inputs:

• ComplexData input - the vector file
• LiteralData input - the buffer size

And it will have one output:

• ComplexData output - the final buffer

The process can be called demobuffer and we can now start coding it:

$ cd processes
$ $EDITOR demobuffer.py

At the beginning, we have to import the required classes and modules

Here is a very basic example:

.. literalinclude:: demobuffer.py
   :language: python
   :lines: 28-31
   :linenos:
   :lineno-start: 28

As the next step, we define a list of inputs. The first input is :class:`pywps.ComplexInput` with the identifier vector, title Vector map and there is only one allowed format: GML.

The next input is :class:`pywps.LiteralInput`, with the identifier size and the data type set to float:

.. literalinclude:: demobuffer.py
   :language: python
   :lines: 33-40
   :linenos:
   :lineno-start: 33

Next we define the output output as :class:`pywps.ComplexOutput`. This output supports GML format only.

.. literalinclude:: demobuffer.py
   :language: python
   :lines: 42-46
   :linenos:
   :lineno-start: 42

Next we create a new list variables for inputs and outputs.

.. literalinclude:: demobuffer.py
   :language: python
   :lines: 48-49
   :linenos:
   :lineno-start: 48

Next we define the handler method. In it, geospatial analysis may happen. The method gets a :class:`pywps.app.WPSRequest` and a :class:`pywps.response.basic.WPSResponse` object as parameters. In our case, we calculate the buffer around each vector feature using GDAL/OGR library. We will not got much into the details, what you should note is how to get input data from the :class:`pywps.app.WPSRequest` object and how to set data as outputs in the :class:`pywps.response.basic.WPSResponse` object.

.. literalinclude:: demobuffer.py
   :language: python
   :pyobject: _handler
   :emphasize-lines: 8-12, 50-54
   :linenos:
   :lineno-start: 68

At the end, we put everything together and create new a DemoBuffer class with handler, inputs and outputs. It's based on :class:`pywps.Process`:

.. literalinclude:: demobuffer.py
   :pyobject: DemoBuffer
   :language: python
   :linenos:
   :lineno-start: 51

Clients need to know which inputs the processes expects. They can be declared as :class:`pywps.Input` objects in the :class:`Process` class declaration:

from pywps import Process, LiteralInput, LiteralOutput

class FooProcess(Process):
    def __init__(self):
        inputs = [
            LiteralInput('foo', data_type='string'),
            ComplexInput('bar', [Format('text/xml')])
        ]
        outputs = [
            LiteralOutput('foo_output', data_type='string'),
            ComplexOutput('bar_output', [Format('JSON')])
        ]

        super(FooProcess, self).__init__(
            ...
            inputs=inputs,
            outputs=outputs
        )
        ...

• :class:`LiteralInput`
• :class:`LiteralOutput`

A simple value embedded in the request. The first argument is a name. The second argument is the type, one of string, float, integer or boolean.

• :class:`ComplexInput`
• :class:`ComplexOutput`

A large data object, for example a layer. ComplexData do have a format attribute as one of their key properties. It's either a list of supported formats or a single (already selected) format. It shall be an instance of the :class:`pywps.inout.formats.Format` class.

The ComplexData needs as one of its parameters a list of supported data formats. They are derived from the :class:`Format` class. A :class:`Format` instance needs, among others, a mime_type parameter, a validate method -- which is used for input data validation -- and also a mode parameter -- defining how strict the validation should be (see :class:`pywps.validator.mode.MODE`).

The Validate method is up to you, the user, to code. It requires two input paramers - data_input (a :class:`ComplexInput` object), and mode. This methid must return a boolean value indicating whether the input data are considered valid or not for given mode. You can draw inspiration from the :py:func:`pywps.validator.complexvalidator.validategml` method.

The good news is: there are already predefined validation methods for the ESRI Shapefile, GML and GeoJSON formats, using GDAL/OGR. There is also an XML Schema validaton and a JSON schema validator - you just have to pick the propper supported formats from the :class:`pywps.inout.formats.FORMATS` list and set the validation mode to your :class:`ComplexInput` object.

Even better news is: you can define custom validation functions and validate input data according to your needs.

• :class:`BoundingBoxInput`
• :class:`BoundingBoxOutput`

BoundingBoxData contain information about the bounding box of the desired area and coordinate reference system. Interesting attributes of the BoundingBoxData are:

crs

current coordinate reference system

dimensions

number of dimensions

ll

pair of coordinates (or triplet) of the lower-left corner

ur

pair of coordinates (or triplet) of the upper-right corner

Handlers receive as input argument a :class:`WPSRequest` object. Input values are found in the inputs dictionary:

@staticmethod
def _handler(request, response):
    name = request.inputs['name'][0].data
    response.outputs['output'].data = 'Hello world %s!' % name
    return response

inputs is a plain Python dictionary. Most of the inputs and outputs are derived from the :class:`IOHandler` class. This enables the user to access the data in four different ways:

input.file

Returns a file name - you can access the data using the name of the file stored on the hard drive.

input.url

Return a link to the resource using either the file:// or http:// scheme. The target of the url is not downloaded to the PyWPS server until its content is explicitly accessed through either one of the file, data or stream attributes.

input.data

Is the direct link to the data themselves. No need to create a file object on the hard drive or opening the file and closing it - PyWPS will do everything for you.

input.stream

Provides the IOStream of the data. No need for opening the file, you just have to read() the data.

Because there could be multiple input values with the same identifier, the inputs are accessed with an index. For example:

request.inputs['file_input'][0].file
request.inputs['data_input'][0].data
request.inputs['stream_input'][0].stream
url_input = request.inputs['url_input'][0]

As mentioned, if an input is a link to a remote file (an http address), accessing the url attribute simply returns the url's string, but accessing any other attribute triggers the file's download:

url_input.url # returns the link as a string (no download)
url_input.file # downloads target and returns the local path
url_input.data # returns the content of the local copy

PyWPS will persistently transform the input (and output) data to the desired form. You can also set the data for your Output object like output.data = 1 or output.file = "myfile.json" - it works the same way. However, once the source type is set, it cannot be changed. That is, a ComplexOutput whose data attribute has been set once has read-only access to the three other attributes (file, stream and url), while the data attribute can be freely modified.

OGC WPS standard enables asynchronous process execution call, that is in particular useful, when the process execution takes longer time - process instance is set to background and WPS Execute Response document with ProcessAccepted messag is returned immediately to the client. The client has to check statusLocation URL, where the current status report is deployed, say every n-seconds or n-minutes (depends on calculation time). Content of the response is usually percentDone information about the progress along with statusMessage text information, what is currently happening.

You can set process status any time in the handler using the :py:func:`WPSResponse.update_status` function.

WPS allows for a clever method of returning a large data file: instead of embedding the data in the response, it can be saved separately, and a URL is returned from where the data can be downloaded. In the current implementation, PyWPS saves the file in a folder specified in the configuration passed by the service (or in a default location). The URL returned is embedded in the XML response.

This behaviour can be requested either by using a GET:

...ResponseDocument=output=@asReference=true...

Or a POST request:

...
<wps:ResponseForm>
    <wps:ResponseDocument>
        <wps:Output asReference="true">
            <ows:Identifier>output</ows:Identifier>
            <ows:Title>Some Output</ows:Title>
        </wps:Output>
    </wps:ResponseDocument>
</wps:ResponseForm>
...

output is the identifier of the output the user wishes to have stored and accessible from a URL. The user may request as many outputs by reference as needed, but only one may be requested in RAW format.

When a process accepts a variable number of inputs, it often makes sense to return a variable number of outputs. The WPS standard does not however readily accommodate this. One pragmatic solution is to compress the files into a single output archive (e.g. zip file), but this proves to be awkward when the outputs are really just references to resources (URLs). In this case, another pragmatic solution is to return a simple text file storing the list of references. One issue with this is that it provides clients very little metadata about the file content.

Although it would be fairly easy to define a json output file storing the properties and URLs of multiple files, it would require an ad-hoc implementation on the client side to parse the json and extract the urls metadata. Fortunately, the metalink standard already exists precisely to bundle references to multiples files.

Metalink files are XML documents collecting a set of remote files. It was originally designed to describe the location of larges files stored on multiple mirrors or peer-to-peer networks. If one location goes down during download, metalink clients can switch to another mirror. Also, large files can be split into segments and downloaded concurrently from different locations, speeding up downloads. A metalink can also describe the location of files made for different operating systems and languages, with clients automatically selecting the most appropriate one.

Metalink support in PyWPS includes:

• pywps.FORMATS.METALINK and pywps.FORMATS.META4
• helper classes :class:`MetaFile`, :class:`MetaLink` and :class:`MetaLink4`
• validation of generated metalink files using XML schemas
• size (bytes) and checksums (sha-256) for each file in the metalink document

To use metalink in a process, define a :class:`ComplexOutput` with a metalink mimetype. Then after the handler has generated a list of file, instantiate one :class:`MetaFile` object for each output file, and append them to a :class:`MetaLink` or :class:`MetaLink4` instance. Finally, set the data property of the output to the xml generated by the xml property of the :class:`MetaLink` instance.

.. literalinclude:: ../tests/processes/metalinkprocess.py
   :language: python

Any uncatched exception in the process execution will be handled by PyWPS and reported to the WPS client using an ows:Exception. PyWPS will only log the traceback and report a common error message like:

Process failed, please check server error log.

This sparse error message is used to avoid security issues by providing internal service information in an uncontrolled way.

But in some cases you want to provide a user-friendly error message to give the user a hint of what went wrong with the processing job. In this case you can use the :class:`pywps.app.exceptions.ProcessError` exception. The error message will be send to the user encapsulated as ows:Exception. The :class:`pywps.app.exceptions.ProcessError` validates the error message to make sure it is not too long and it does not contain any suspicious characters.

[logging]
level=DEBUG

.. literalinclude:: show_error.py
   :language: python

In order for clients to invoke processes, a PyWPS :class:`Service` class must be present with the ability to listen for requests. An instance of this class must created, receiving instances of all the desired processes classes.

In the flask example service the :class:`Service` class instance is created in the :class:`Server` class. :class:`Server` is a development server that relies on Flask. The publication of processes is encapsulated in demo.py, where a main method passes a list of processes instances to the :class:`Server` class:

from pywps import Service
from processes.helloworld import HelloWorld
from processes.demobuffer import DemoBuffer

...
processes = [ DemoBuffer(), ... ]

server = Server(processes=processes)

...

The :ref:`flask` server is a WSGI application that accepts incoming Execute requests and calls the appropriate process to handle them. It also answers GetCapabilities and DescribeProcess requests based on the process identifier and their inputs and outputs.

A host, a port, a config file and the processes can be passed as arguments to the :class:`Server` constructor. host and port will be prioritised if passed to the constructor, otherwise the contents of the config file (pywps.cfg) are used.

Use the run method to start the server:

...
s = Server(host='localhost', processes=processes, config_file=config_file)
s.run()
...

To make the server visible from another computer, replace localhost with 0.0.0.0.

Supporting multiple languages requires:

• Setting the language property in the server configuration (see :ref:`server-configuration`)
• Adding translations to :class:`Process`, inputs and outputs objects

The expected translations format is always the same. The first key is the RFC 4646 language code, and the nested mapping contains translated strings accessible by a string property:

from pywps import Process, LiteralInput, LiteralOutput


class SayHello(Process):
    def __init__(self):
        inputs = [
            LiteralInput(
                'name',
                title='Input name',
                abstract='The name to say hello to.',
                translations={"fr-CA": {"abstract": "Le nom à saluer."}}
            )
        ],
        outputs=[
            LiteralOutput(
                'response',
                title='Output response',
                abstract='The complete output message.',
                translations={"fr-CA": {
                    "title": "La réponse",
                    "abstract": "Le message complet."
                }}
            )
        ],

        super().__init__(
            self._handler,
            identifier='say_hello',
            title='Process Say Hello',
            abstract='Returns a literal string output with Hello plus the inputed name',
            version='1.0',
            inputs=inputs,
            outputs=outputs,
            store_supported=True,
            status_supported=True,
            translations={"fr-CA": {
                "title": "Processus Dire Bonjour",
                "abstract": "Retourne une chaine de caractères qui dit bonjour au nom fournit en entrée."
            }},
        )

    def _handler(self, request, response):
        ...

The translation will default to the untranslated attribute of the base object if the key is not provided in the translations dictionnary.

A :class:`Process` can be automatically documented with Sphinx using the autoprocess directive. The :class:`Process` object is instantiated and its content examined to create, behind the scenes, a docstring in the Numpy format. This lets developers embed the documentation directly in the code instead of having to describe each process manually. For example:

.. autoprocess:: pywps.tests.DocExampleProcess
   :docstring:
   :skiplines: 1

would yield

.. autoprocess:: pywps.tests.DocExampleProcess
   :docstring:
   :skiplines: 1

The :option:`docstring` option fetches the :class:`Process` docstring and appends it after the Reference section. The first lines of this docstring can be skipped using the :option:`skiplines` option.

To use the autoprocess directive, first add 'sphinx.ext.napoleon' and 'pywps.ext_autodoc' to the list of extensions in the Sphinx configuration file :file:`conf.py`. Then, insert autoprocess directives in your documentation source files, just as you would use an autoclass directive, and build the documentation.

Note that for input and output parameters, the title is displayed only if no abstract is defined. In other words, if both title and abstract are given, only the abstract will be included in the documentation to avoid redundancy.