pycql 0.0.12
pycql is a pure Python parser implementation of the OGC CQL standard
License: MIT

# pycql

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pycql is a pure Python parser implementation of the OGC CQL standard

## Installation

```bash
pip install pycql
```

## Usage

The basic functionality parses the input string to an abstract syntax tree (AST) representation.
This AST can then be used to build database filters or similar functionality.

```python
>>> import pycql
>>> ast = pycql.parse(filter_expression)
```

### Inspection

The easiest way to inspect the resulting AST is to use the `get_repr` function, which returns a
nice string representation of what was parsed:

```python
>>> ast = pycql.parse('id = 10')
>>> print(pycql.get_repr(ast))
ATTRIBUTE id = LITERAL 10.0
>>>
>>>
>>> filter_expr = '(number BETWEEN 5 AND 10 AND string NOT LIKE "%B") OR INTERSECTS(geometry, LINESTRING(0 0, 1 1))'
>>> print(pycql.get_repr(pycql.parse(filter_expr)))
(
    (
            ATTRIBUTE number BETWEEN LITERAL 5.0 AND LITERAL 10.0
    ) AND (
            ATTRIBUTE string NOT ILIKE LITERAL '%B'
    )
) OR (
    INTERSECTS(ATTRIBUTE geometry, LITERAL GEOMETRY 'LINESTRING(0 0, 1 1)')
)
```

### Evaluation

In order to create useful filters from the resulting AST, it has to be evaluated. For the
Django integration, this was done using a recursive descent into the AST, evaluating the
subnodes first and constructing a `Q` object. Consider having a `filters` API (for an
example look at the Django one) which creates the filter. Now the evaluator looks something
like this:

```python

from pycql.ast import *
from myapi import filters   # <- this is where the filters are created.
                            # of course, this can also be done in the
                            # evaluator itself
class FilterEvaluator:
    def __init__(self, field_mapping=None, mapping_choices=None):
        self.field_mapping = field_mapping
        self.mapping_choices = mapping_choices

    def to_filter(self, node):
        to_filter = self.to_filter
        if isinstance(node, NotConditionNode):
            return filters.negate(to_filter(node.sub_node))
        elif isinstance(node, CombinationConditionNode):
            return filters.combine(
                (to_filter(node.lhs), to_filter(node.rhs)), node.op
            )
        elif isinstance(node, ComparisonPredicateNode):
            return filters.compare(
                to_filter(node.lhs), to_filter(node.rhs), node.op,
                self.mapping_choices
            )
        elif isinstance(node, BetweenPredicateNode):
            return filters.between(
                to_filter(node.lhs), to_filter(node.low),
                to_filter(node.high), node.not_
            )
        elif isinstance(node, BetweenPredicateNode):
            return filters.between(
                to_filter(node.lhs), to_filter(node.low),
                to_filter(node.high), node.not_
            )

        # ... Some nodes are left out for brevity

        elif isinstance(node, AttributeExpression):
            return filters.attribute(node.name, self.field_mapping)

        elif isinstance(node, LiteralExpression):
            return node.value

        elif isinstance(node, ArithmeticExpressionNode):
            return filters.arithmetic(
                to_filter(node.lhs), to_filter(node.rhs), node.op
            )

        return node
```

As mentionend, the `to_filter` method is the recursion.

## Testing

The basic functionality can be tested using `pytest`.

```bash
python -m pytest
```

There is a test project/app to test the Django integration. This is tested using the following
command:

```bash
python manage.py test testapp
```


## Django integration

For Django there is a default bridging implementation, where all the filters are translated to the
Django ORM. In order to use this integration, we need two dictionaries, one mapping the available
fields to the Django model fields, and one to map the fields that use `choices`. Consider the
following example models:

```python
from django.contrib.gis.db import models


optional = dict(null=True, blank=True)

class Record(models.Model):
    identifier = models.CharField(max_length=256, unique=True, null=False)
    geometry = models.GeometryField()

    float_attribute = models.FloatField(**optional)
    int_attribute = models.IntegerField(**optional)
    str_attribute = models.CharField(max_length=256, **optional)
    datetime_attribute = models.DateTimeField(**optional)
    choice_attribute = models.PositiveSmallIntegerField(choices=[
                                                                 (1, 'ASCENDING'),
                                                                 (2, 'DESCENDING'),],
                                                        **optional)


class RecordMeta(models.Model):
    record = models.ForeignKey(Record, on_delete=models.CASCADE, related_name='record_metas')

    float_meta_attribute = models.FloatField(**optional)
    int_meta_attribute = models.IntegerField(**optional)
    str_meta_attribute = models.CharField(max_length=256, **optional)
    datetime_meta_attribute = models.DateTimeField(**optional)
    choice_meta_attribute = models.PositiveSmallIntegerField(choices=[
                                                                      (1, 'X'),
                                                                      (2, 'Y'),
                                                                      (3, 'Z')],
                                                             **optional)
```

Now we can specify the field mappings and mapping choices to be used when applying the filters:

```python
FIELD_MAPPING = {
    'identifier': 'identifier',
    'geometry': 'geometry',
    'floatAttribute': 'float_attribute',
    'intAttribute': 'int_attribute',
    'strAttribute': 'str_attribute',
    'datetimeAttribute': 'datetime_attribute',
    'choiceAttribute': 'choice_attribute',

    # meta fields
    'floatMetaAttribute': 'record_metas__float_meta_attribute',
    'intMetaAttribute': 'record_metas__int_meta_attribute',
    'strMetaAttribute': 'record_metas__str_meta_attribute',
    'datetimeMetaAttribute': 'record_metas__datetime_meta_attribute',
    'choiceMetaAttribute': 'record_metas__choice_meta_attribute',
}

MAPPING_CHOICES = {
    'choiceAttribute': dict(Record._meta.get_field('choice_attribute').choices),
    'choiceMetaAttribute': dict(RecordMeta._meta.get_field('choice_meta_attribute').choices),
}
```

Finally we are able to connect the CQL AST to the Django database models. We also provide factory
functions to parse the timestamps, durations, geometries and envelopes, so that they can be used
with the ORM layer:

```python
from pycql.integrations.django import to_filter, parse

cql_expr = 'strMetaAttribute LIKE "%parent%" AND datetimeAttribute BEFORE 2000-01-01T00:00:01Z'

# NOTE: we are using the django integration `parse` wrapper here
ast = parse(cql_expr)
filters = to_filter(ast, mapping, mapping_choices)

qs = Record.objects.filter(**filters)
```