fforms 1.1.1

Standalone HTML form validation library

fforms is a Python HTML form validation library that allows you to take control of the validation and rendering while minimizing boilerplate and awkward constructions. fforms does not provide any widgets or rendering assistance beyond ease of access to form values and validation errors. It does, however, let you define and use custom validators with extreme ease. Read on to see how!

Installation

fforms is a pure Python package with no dependencies, so installing it should be very straightforward. It’s tested on Python 3.3 through 3.6. [1]

1. Option A: pip install fforms

2. Option B: download the source code, change into the working directory, and run python setup.py install

Features

• A familiar interface for use in views

form = fforms.bind_dotted(registration_form, request.POST, request.FILES)
if request.method == 'POST':
    if form.is_valid():
        data = form.clean_data
        # do stuff with the data
        return redirect("/url/to/redirect")
return render_template("template-name.html", form=form)

• Logical data structures that allow easy access from templates

{% set field = form.username %}
<input type="text"{#
    #} value="{{ field.raw_data|default("", True) }}"{#
    #} {% if field.error %}class="invalid"{% endif %}{#
    #} name="field.full_name">
{% if field.error %}
    <span class="error-msg">{{ field.error }}</span>
{% endif %}

<ul>
    {% for field in form.attendees %}
        <input type="text" name="{{ field.full_name }}">
    {% endfor %}
</ul>

• Easy to add custom validators

myform['product_id'].validator = validators.from_regex(
    "^[A-Z]{2}-[0-9]{3}-[0-9]{4}$"
    "Please provide a valid product ID in the format XX-NNN-NNNN")
myform['state'].validator = validators.one_of(
    "ME", "NH", "VT",
    msg="We only serve the Northeast")
def funky_validator(data):
    left, right = data[:4], data[4:]
    if (int(left, 16), right) not in VALUES:
        raise ValidationError("Invalid format", data)
    # more funky tests
    return process(left, right)  # Set the field value
myform['funky-field'].validator = funky_validator

• Internationalization using your choice of framework.

def translate_msg(msg, kwargs):
    # msg is a str.format-formatted string, and kwargs has all the
    # field values.
    msg = _(msg)
    kwargs = {key: _(val) for key, val in kwargs.items()}
    return msg.format(**kwargs)

fforms.validators.DeferredMessage.process_message = translate_msg

• Pure Python with no outside dependencies. Don’t bring in a gigantic web framework just to use their forms library

License

This project is licensed under the MIT license.

Getting started

To use fforms, you’ll first need to define a schema for your form. This is most easily accomplished by using fforms.schema.make_from_literal as follows

from fforms import make_from_literal, validators, bind_dotted
schema = make_from_literal({
    'username': validators.from_regex("^[a-zA-Z][a-zA-Z0-9_]{0,25}"),
    'password': validators.chain(
        validators.limit_length(min=8, max=128),
        validators.from_regex("[a-z]",
                              "{field.name} must contain lowercase letters"),
        validators.from_regex("[A-Z]",
                              "{field.name} must contain uppercase letters"),
        validators.from_regex("[0-9]", "{field.name} must contain numbers"),
        validators.from_regex("[^a-zA-Z0-9]",
                              "{field.name} must contain special characters")
    ),
    'password2': validators.ensure_str,
    'email': validators.email,
    'address': {
        'street': validators.chain(validators.ensure_str,
                                   validators.limit_length(min=2)),
        'street2': validators.ensure_str,
        'zip_code': validators.chain(validators.from_regex("^[0-9]+$"),
                                     validators.limit_length(min=5, max=5)),
        'state': validators.one_of("ME", "NH", "VT", "MA")
    },
    'tags': [
        {'name': validators.ensure_str}
    ],
})
schema.validator = validators.chain(
    schema.validator,  # The default is validators.all_children
    validators.key_matcher("password", "password2",
                           "Please ensure the two passwords match"))

schema['tags'].validator = validators.chain(
      schema['tags'].validator,
      validators.limit_length(min=1, max=8)
)

Once you have a schema object, you can bind it to data to create a bound form object

form = bind_dotted(schema, {
    'username': 'felipeochoa',
    'password': '123abcDEF!@#',
    'password2': '123ABCdef!@#',
    'email': 'me@example',
    'address.street': '123 Main St.',
    'address.street2': 'Unit 1',
    'address.zip code': '1234',
    'tags:0.name': 'tag1',
    'tags:1.name': 'tag2',
})
assert not form.is_valid()
for field in form:
    print("%s %r (%s)" % (field.name, field.clean_data, field.error))

Which will print out:

username 'felipeochoa' (None)
password '123abcDEF!@#' (None)
password2 '123ABCdef!@#' (None)
tags [{'name': 'tag1'}, {'name': 'tag2'}] (None)
address None ()
email 'me@example' (Enter a valid email address.)

(address does not have an error message of its own; all the errors are in its children).

You can use this code in your views or templates in a conventient fashion

def my_view(request):
    form = bind_dotted(registration_form, request.POST, request.FILES)
    if request.method == 'POST':
        if form.is_valid():
            data = form.clean_data
            # do stuff with the data
            return redirect("/url/to/redirect")
    return render_template("template", form=form)

{% if form.error %}
   <span class="error-msg">form.error</span>
{% endif %}
{# more stuff #}
{% for field in form.tags %}
    <input type="text" value="{{ field.value }}" name="{{ field.full_name }}">
{% endfor %}

Detailed Documentation

fforms operates on three basic concepts: Schema, Validators, and Forms.

Schema

Think of a schema like an unbound form. It contains the blueprint for bound forms: field names, definitions, and validators. Schema form trees that describe the form you are validating, so some schema can have child schema that perform validation/conversion on a part of the received data. Schema can be one of the following types

• MapSchema Like a Python dictionary, mapping names to sub-schema

• SequenceSchema A variable length list where all sub-schema are of the same kind.

• LeafSchema Does not contain any children of its own.

All three types of schema support their own validation, in addition to any validation that their children might perform. E.g., if you have a schema defined as

user_schema = {
    'username': limit_length(max=25),
    'password': ensure_complexity(numbers=True, uppercase=True),
    'password2': ensure_str,
}

You can add a higher-level validator key_matcher('password', 'password2') that additionally verifies that the two values match. You could then compose that schema into another one, e.g.

many_users_schema = [
    {
      'username': limit_length(max=25),
      'password': ensure_complexity(numbers=True, uppercase=True),
      'password2': ensure_str,
    }
]

This new schema would accept a list of username / password / password2 combinations, which would be useful if you had to create multiple users at the same time. You could then add a new validator to, say, ensure no more than 5 users are created at a time limit_length(max=5).

Validators

We’ve seen a few validators already, but haven’t yet defined what they are or what they do. Mechanically, validators are simply functions of one value that produce another value. The argument is the value attached to the field that the validator must check and/or transform. For example, the as_int validator is defined as follows

def as_int(data):
    "Extract an integer from the data."
    try:
        return int(data)
    except (TypeError, ValueError):
        raise ValidationError("{field.name} must be a whole number", data)

Each schema can have two validators

• Validator: The validator stored under schema.validator will be called after the form.is_valid method is called and will take field.raw_data as its input. The output from this validator will be stored as field.clean_data. If the validator raises ValidationError, the message from the error will be extracted and set as field.error, and field.clean_data will be left as None. For schema with children, validators typically pass through the data unmodified, leaving all conversion to leaf nodes. In principle though, parent validators could modify the values generated by the child validators; parent validators are run after child validators. Though schema only have one validator, the nature of validators means that multiple validators can be easily composed into a bigger one.

• Pre-processor: The validator stored under schema.pre_processor will take the raw data provided to the field and transform it into a (still serialized) value. The return value of the validator will be saved as field.raw_data. Pre-processing validators are called as soon as the field is bound, so should not do much validation work. In particular, the return value should still be “raw” (i.e., serialized), since field.raw_data will be used directly in templates. Most applications should have no need to override the default pre-processor, which is a no-op. A notable exception is to provide a serialized value for any files passed in via request.FILES or similar.

Forms/Fields

Forms (technically BoundField objects) marry the Schema with data to be validated. Forms are typically created by calling Schema.bind or another helper function like bind_to_dotted. Forms provide a nice API for accessing errors and cleaned data. Like schema, forms are nodes in a tree structure (that mirrors the schema they were built for).

Once you have a form object, calling its is_valid method will return True if validation succeeded for the schema and False if it didn’t. You will then be able to access the fields’ .error and .clean_data arguments. The .error attribute stores a string with the error message provided by the validator that rejected the data. The .clean_data stores a de-serialized value that can by passed to other parts of your application.

[1]

On Python 3.3 on Windows, the email validator won’t work with addresses using IPv6 domain names.