Forms

The Form class provides a high-level API for quickly building CRUD (create, update and delete) forms, especially for working on an existing database table. It is a pretty much equivalent to web2py’s SQLFORM.

The Form constructor

The Form constructor accepts the following arguments:

Form(self,
     table,
     record=None,
     readonly=False,
     deletable=True,
     formstyle=FormStyleDefault,
     dbio=True,
     keep_values=False,
     form_name=False,
     hidden=None,
     validation=None,
     csrf_session=None,
     csrf_protection=True,
     lifespan=None,
     signing_info=None,
     ):

Where:

  • table: a DAL table or a list of fields

  • record: a DAL record or record id

  • readonly: set to True to make a readonly form

  • deletable: set to False to disallow deletion of record

  • formstyle: a function that renders the form using helpers. Can be FormStyleDefault (default), FormStyleBulma or FormStyleBootstrap4

  • dbio: set to False to prevent any DB writes

  • keep_values: if set to true, it remembers the values of the previously submitted form

  • form_name: the optional name of this form

  • hidden: a dictionary of hidden fields that is added to the form

  • validation: an optional validator, see Validation functions

  • csrf_session: if None, no csrf token is added. If a session, then a CSRF token is added and verified

  • lifespan: lifespan of CSRF token in seconds, to limit form validity

  • signing_info: information that should not change between when the CSRF token is signed and verified

A minimal form example

Let’s start with a minimal working form example. Create a new minimal app called form_minimal :

# in form_minimal/__init__.py
from py4web import action, Field, redirect, URL
from py4web.utils.form import Form
from pydal.validators import IS_NOT_EMPTY


@action('index', method=['GET', 'POST'])
@action.uses('form_minimal.html')
def index():
    form = Form([
        Field('product_name'),
        Field('product_quantity', 'integer', requires=IS_NOT_EMPTY()),
        ])
    if form.accepted:
        # Do something with form.vars['product_name'] and form.vars['product_quantity']
        redirect(URL('accepted'))
    if form.errors:
        # display message error
        redirect(URL('not_accepted'))
    return dict(form=form)

@action("accepted")
def accepted():
    return "form_example accepted"


@action("not_accepted")
def not_accepted():
    return "form_example NOT accepted"

Also, you need to create a file inside the app called templates/form_minimal.html that just contains the line:

[[=form]]

Then reload py4web and visit http://127.0.0.1:8000/form_minimal - you’ll get the Form page:

_images/form1.png

Note that:

  • Form is a class contained in the py4web.utils.form module

  • it’s possible to use form validators like IS_NOT_EMPTY, see Form validation later. They are imported from the pydal.validators module

  • it’s normally important to use both the GET and the POST methods in the action where the form is contained

This example is not so useful because it’s not using a database, a template or the session management. But it works, and if you try to fill the form with an empty product_quantity, the form will trigger an error and the corresponding error page will be shown.

Form basic example

Let’s continue with a basic example of a form for adding data to a database. Create a new minimal app called form_basic :

 1  # in form_basic/__init__.py
 2  import os
 3  from py4web import action, Field, DAL
 4  from py4web.utils.form import Form, FormStyleDefault
 5
 6  DB_FOLDER = os.path.join(os.path.dirname(__file__), 'databases')
 7  if not os.path.isdir(DB_FOLDER):
 8      os.mkdir(DB_FOLDER)
 9  db = DAL('sqlite://storage.sqlite', folder=DB_FOLDER)
10  db.define_table(
11      'person',
12      Field('name'),
13      Field('job', requires=IS_NOT_EMPTY()),
14  )
15
16  @action("index", method=["GET", "POST"])
17  @action.uses(db, "form_basic.html")
18  def index(id=None):
19      form = Form(db.person, id, deletable=False, formstyle=FormStyleDefault)
20      rows = db(db.person).select()
21      return dict(form=form, rows=rows)

After the required import instructions and sqlite database definition, on line 16 we declare an endpoint form_basic/index, which will be used both for the GET and for the POST of the form.

Also, create a new template file inside the app called templates/form_basic.html that contains the following code:

<h2 class="title">Form Superhero Identity</h2>

[[=form]]

<h2 class="title">Rows</h2>

<ul>
[[for row in rows:]]
<li>[[=row.id]]: [[=row.name]] ([[=row.job]])</li>
[[pass]]
</ul>

Reload py4web and visit http://127.0.0.1:8000/form_basic : the result is an input form on the top of the page, and the list of all the previously added entries on the bottom:

_images/form2.png

The database content can also be fully seen and changed with the Dashboard app.

Form Structure Manipulation

Like in web2py, in py4web a form is rendered by helpers. Unlike web2py, it uses yatl helpers. This means the tree structure of a form can be manipulated before the form is serialized in HTML. For example:

db.define_table('paint', Field('color'))
form = Form(db.paint)
form.structure.find('[name=color]')[0]['_class'] = 'my-class'

Notice that a form does not make an HTML tree until form structure is accessed. Once accessed you can use .find(…) to find matching elements. The argument of find is a string following the filter syntax of jQuery. In the above case there is a single match [0] and we modify the _class attribute of that element. Attribute names of HTML elements must be preceded by an underscore.

Form validation

Validators are classes used to validate input fields (including forms generated from database tables). They are normally assigned using the requires attribute of a table Field object, as already shown on the Field constructor paragraph of the DAL chapter. Also, you can use advanced validators in order to create widgets such as drop-down menus, radio buttons and even lookups from other tables. Last but not least, you can even explicitly define a validation function.

Here is a simple example of how to require a validator for a table field:

db.define_table(
    'person',
    Field('name',requires=IS_NOT_EMPTY(),
    Field('job')
)
The validator is frequently

written explicitly outside the table definition in this equivalent manner:

db.define_table(
    'person',
    Field('name'),
    Field('job')
)
db.person.name.requires = IS_NOT_EMPTY()

A field can have a single validator or multiple validators. Multiple validators are made part of a list:

db.person.name.requires = [IS_NOT_EMPTY(),
                        IS_NOT_IN_DB(db, 'person.name')]

Mind that the only validators that can be used with list: type fields are:

  • IS_IN_DB(..., multiple=True)

  • IS_IN_SET(..., multiple=True)

  • IS_NOT_EMPTY()

  • IS_LIST_OF_EMAILS()

  • IS_LIST_OF(...)

The latter can be used to apply any validator to the individual items in the list. multiple=(1, 1000) requires a selection of between 1 and 1000 items. This enforces selection of at least one choice.

Built-in validators have constructors that take an error_message argument:

IS_NOT_EMPTY(error_message='cannot be empty!')

It’s optional and it allows you to override the default error message for any validator. Also, it’s the usually fist option of the constructors and you can normally avoid to name it. Hence the following syntax is equivalent:

IS_NOT_EMPTY('cannot be empty!')

Here is an example of a validator on a database table:

db.person.name.requires = IS_NOT_EMPTY(error_message=T('fill this!'))

where we have used the translation operator T to allow for internationalization. Notice that default error messages are not translated.

One can also call validators explicitly for a field:

db.person.name.validate(value)

which returns a tuple (value, error) and error is None if the value validates.

You can easily test most of the following validators directly using python only. For example:

>>> from pydal.validators import *
>>> IS_ALPHANUMERIC()('test')
('test', None)
>>> IS_ALPHANUMERIC()('test!')
('test!', 'Enter only letters, numbers, and underscore')
>>> IS_ALPHANUMERIC('this is not alphanumeric')('test!')
('test!', 'this is not alphanumeric')
>>> IS_ALPHANUMERIC(error_message='this is not alphanumeric')('test!')
('test!', 'this is not alphanumeric')

Hint

The DAL validators are well documented inside the python source code. You can easily check it by yourself for all the details!

from pydal import validators
dir(validators) # get the list of all validators
help(validators.IS_URL) # get specific help for the IS_URL validator

Text format validators

IS_ALPHANUMERIC

This validator checks that a field value contains only characters in the ranges a-z, A-Z, or 0-9.

requires = IS_ALPHANUMERIC(error_message='must be alphanumeric!')

IS_LOWER

This validator never returns an error. It just converts the value to lower case.

requires = IS_LOWER()

IS_UPPER

This validator never returns an error. It converts the value to upper case.

requires = IS_UPPER()

IS_EMAIL

It checks that the field value looks like an email address. It does not try to send email to confirm.

requires = IS_EMAIL(error_message='invalid email!')

IS_MATCH

This validator matches the value against a regular expression and returns an error if it does not match. Here is an example of usage to validate a US zip code:

requires = IS_MATCH('^\d{5}(-\d{4})?$',
    error_message='not a zip code')

Here is an example of usage to validate an IPv4 address (note: the IS_IPV4 validator is more appropriate for this purpose):

requires = IS_MATCH('^\d{1,3}(\.\d{1,3}){3}$',
        error_message='not an IP address')

Here is an example of usage to validate a US phone number:

requires = IS_MATCH('^1?((-)\d{3}-?|\(\d{3}\))\d{3}-?\d{4}$',
        error_message='not a phone number')

For more information on Python regular expressions, refer to the official Python documentation.

IS_MATCH takes an optional argument strict which defaults to False. When set to True it only matches the whole string (from the beginning to the end):

>>> IS_MATCH('ab', strict=False)('abc')
('abc', None)
>>> IS_MATCH('ab', strict=True)('abc')
('abc', 'Invalid expression')

IS_MATCH takes an other optional argument search which defaults to False. When set to True, it uses regex method search instead of method match to validate the string.

IS_MATCH('...', extract=True) filters and extract only the first matching substring rather than the original value.

IS_LENGTH

Checks if length of field’s value fits between given boundaries. Works for both text and file inputs.

Its arguments are:

  • maxsize: the maximum allowed length / size (has default = 255)

  • minsize: the minimum allowed length / size

Examples: Check if text string is shorter than 16 characters:

>>> IS_LENGTH(15)('example string')
('example string', None)
>>> IS_LENGTH(15)('example long string')
('example long string', 'Enter from 0 to 15 characters')
>>> IS_LENGTH(15)('33')
('33', None)
>>> IS_LENGTH(15)(33)
('33', None)

Check if uploaded file has size between 1KB and 1MB:

INPUT(_type='file', _name='name', requires=IS_LENGTH(1048576, 1024))

For all field types except for files, it checks the length of the value. In the case of files, the value is a cgi.FieldStorage, so it validates the length of the data in the file, which is the behavior one might intuitively expect.

IS_URL

Rejects a URL string if any of the following is true:

  • The string is empty or None

  • The string uses characters that are not allowed in a URL

  • The string breaks any of the HTTP syntactic rules

  • The URL scheme specified (if one is specified) is not ‘http’ or ‘https’

  • The top-level domain (if a host name is specified) does not exist

(These rules are based on RFC 2616)

This function only checks the URL’s syntax. It does not check that the URL points to a real document, for example, or that it otherwise makes semantic sense. This function does automatically prepend ‘http://’ in front of a URL in the case of an abbreviated URL (e.g. ‘google.ca’). If the parameter mode='generic' is used, then this function’s behavior changes. It then rejects a URL string if any of the following is true:

  • The string is empty or None

  • The string uses characters that are not allowed in a URL

  • The URL scheme specified (if one is specified) is not valid

(These rules are based on RFC 2396)

The list of allowed schemes is customizable with the allowed_schemes parameter. If you exclude None from the list, then abbreviated URLs (lacking a scheme such as ‘http’) will be rejected.

The default prepended scheme is customizable with the prepend_scheme parameter. If you set prepend_scheme to None, then prepending will be disabled. URLs that require prepending to parse will still be accepted, but the return value will not be modified.

IS_URL is compatible with the Internationalized Domain Name (IDN) standard specified in RFC 3490). As a result, URLs can be regular strings or unicode strings. If the URL’s domain component (e.g. google.ca) contains non-US-ASCII letters, then the domain will be converted into Punycode (defined in RFC 3492). IS_URL goes a bit beyond the standards, and allows non-US-ASCII characters to be present in the path and query components of the URL as well. These non-US-ASCII characters will be encoded. For example, space will be encoded as’%20’. The unicode character with hex code 0x4e86 will become ‘%4e%86’.

Examples:

requires = IS_URL())
requires = IS_URL(mode='generic')
requires = IS_URL(allowed_schemes=['https'])
requires = IS_URL(prepend_scheme='https')
requires = IS_URL(mode='generic',
                allowed_schemes=['ftps', 'https'],
                prepend_scheme='https')

IS_SLUG

requires = IS_SLUG(maxlen=80, check=False, error_message='must be slug')

If check is set to True it check whether the validated value is a slug (allowing only alphanumeric characters and non-repeated dashes).

If check is set to False (default) it converts the input value to a slug.

IS_JSON

requires = IS_JSON(error_message='Invalid json', native_json=False)

This validator checks that a field value is in JSON format.

If native_json is set to False (default) it converts the input value to the serialized value otherwise the input value is left unchanged.

Date and time validators

IS_TIME

This validator checks that a field value contains a valid time in the specified format.

requires = IS_TIME(error_message='must be HH:MM:SS!')

IS_DATE

This validator checks that a field value contains a valid date in the specified format. It is good practice to specify the format using the translation operator, in order to support different formats in different locales.

requires = IS_DATE(format=T('%Y-%m-%d'),
    error_message='must be YYYY-MM-DD!')

For the full description on % directives look under the IS_DATETIME validator.

IS_DATETIME

This validator checks that a field value contains a valid datetime in the specified format. It is good practice to specify the format using the translation operator, in order to support different formats in different locales.

requires = IS_DATETIME(format=T('%Y-%m-%d %H:%M:%S'),
                   error_message='must be YYYY-MM-DD HH:MM:SS!')

The following symbols can be used for the format string (this shows the symbol, their meaning, and an example string):

%Y  year with century (e.g. '1963')
%y  year without century (e.g. '63')
%d  day of the month (e.g. '28')
%m  month (e.g '08')
%b  abbreviated month name (e.g.'Aug')
%B  full month name (e.g. 'August')
%H  hour (24-hour clock, e.g. '14')
%I  hour (12-hour clock, e.g. '02')
%p  either 'AM' or 'PM'
%M  minute (e.g. '30')
%S  second (e.g. '59')

IS_DATE_IN_RANGE

Works very much like the previous validator but allows to specify a range:

requires = IS_DATE_IN_RANGE(format=T('%Y-%m-%d'),
                minimum=datetime.date(2008, 1, 1),
                maximum=datetime.date(2009, 12, 31),
                error_message='must be YYYY-MM-DD!')

For the full description on % directives look under the IS_DATETIME validator.

IS_DATETIME_IN_RANGE

Works very much like the previous validator but allows to specify a range:

requires = IS_DATETIME_IN_RANGE(format=T('%Y-%m-%d %H:%M:%S'),
                    minimum=datetime.datetime(2008, 1, 1, 10, 30),
                    maximum=datetime.datetime(2009, 12, 31, 11, 45),
                    error_message='must be YYYY-MM-DD HH:MM::SS!')

For the full description on % directives look under the IS_DATETIME validator.

Range, set and equality validators

IS_EQUAL_TO

Checks whether the validated value is equal to a given value (which can be a variable):

requires = IS_EQUAL_TO(request.vars.password,
                    error_message='passwords do not match')

IS_NOT_EMPTY

This validator checks that the content of the field value is neither None nor an empty string nor an empty list. A string value is checked for after a .strip().

requires = IS_NOT_EMPTY(error_message='cannot be empty!')

You can provide a regular expression for the matching of the empty string.

requires = IS_NOT_EMPTY(error_message='Enter a value', empty_regex='NULL(?i)')

IS_NULL_OR

Deprecated, an alias for IS_EMPTY_OR described below.

IS_EMPTY_OR

Sometimes you need to allow empty values on a field along with other requirements. For example a field may be a date but it can also be empty. The IS_EMPTY_OR validator allows this:

requires = IS_EMPTY_OR(IS_DATE())

An empty value is either None or an empty string or an empty list. A string value is checked for after a .strip().

You can provide a regular expression for the matching of the empty string with the empty_regex argument (like for IS_NOT_EMPTY validator).

You may also specify a value to be used for the empty case.

requires = IS_EMPTY_OR(IS_ALPHANUMERIC(), null='anonymous')

IS_EXPR

This validator let you express a general condition by means of a callable which takes a value to validate and returns the error message or None to accept the input value.

requires = IS_EXPR(lambda v: T('not divisible by 3') if int(v) % 3 else None)

Notice that returned message will not be translated if you do not arrange otherwise.

For backward compatibility the condition may be expressed as a string containing a logical expression in terms of a variable value. It validates a field value if the expression evaluates to True.

requires = IS_EXPR('int(value) % 3 == 0',
               error_message='not divisible by 3')

One should first check that the value is an integer so that an exception will not occur.

requires = [IS_INT_IN_RANGE(0, None),
            IS_EXPR(lambda v: T('not divisible by 3') if v % 3 else None)]

IS_DECIMAL_IN_RANGE

INPUT(_type='text', _name='name', requires=IS_DECIMAL_IN_RANGE(0, 10, dot="."))

It converts the input into a Python Decimal or generates an error if the decimal does not fall within the specified inclusive range. The comparison is made with Python Decimal arithmetic.

The minimum and maximum limits can be None, meaning no lower or upper limit, respectively.

The dot argument is optional and allows you to internationalize the symbol used to separate the decimals.

IS_FLOAT_IN_RANGE

Checks that the field value is a floating point number within a definite range, 0 <= value <= 100 in the following example:

requires = IS_FLOAT_IN_RANGE(0, 100, dot=".",
                            error_message='negative or too large!')

The dot argument is optional and allows you to internationalize the symbol used to separate the decimals.

IS_INT_IN_RANGE

Checks that the field value is an integer number within a definite range,

0 <= value < 100 in the following example:

requires = IS_INT_IN_RANGE(0, 100,
                        error_message='negative or too large!')

IS_IN_SET

This validator will automatically set the form field to an option field (ie, with a drop-down menu).

IS_IN_SET checks that the field values are in a set:

requires = IS_IN_SET(['a', 'b', 'c'], zero=T('choose one'),
             error_message='must be a or b or c')

The zero argument is optional and it determines the text of the option selected by default, an option which is not accepted by the IS_IN_SET validator itself. If you do not want a “choose one” option, set zero=None.

The elements of the set can be combined with a numerical validator, as long as IS_IN_SET is first in the list. Doing so will force conversion by the last validator to the numerical type. So, IS_IN_SET can be followed by IS_INT_IN_RANGE (which converts the value to int) or IS_FLOAT_IN_RANGE (which converts the value to float). For example:

requires = [ IS_IN_SET([2, 3, 5, 7], error_message='must be prime and less than 10'),
            IS_INT_IN_RANGE(0, None) ]

Checkbox validation

To force a filled-in form checkbox (such as an acceptance of terms and conditions), use this:

requires=IS_IN_SET(['on'])

Dictionaries and tuples with IS_IN_SET

You may also use a dictionary or a list of tuples to make the drop down list more descriptive:

# Dictionary example:
requires = IS_IN_SET({'A':'Apple', 'B':'Banana', 'C':'Cherry'}, zero=None)

# List of tuples example:
requires = IS_IN_SET([('A', 'Apple'), ('B', 'Banana'), ('C', 'Cherry')])

Sorted options

To keep the options alphabetically sorted by their labels into the drop down list, use the sort argument with IS_IN_SET.

IS_IN_SET([('H', 'Hulk'), ('S', 'Superman'), ('B', 'Batman')], sort=True)

IS_IN_SET and Tagging

The IS_IN_SET validator has an optional attribute multiple=False. If set to True, multiple values can be stored in one field. The field should be of type list:integer or list:string as discussed in [[Chapter 6 ../06#list-type-and-contains]]. An explicit example of tagging is discussed there. We strongly suggest using the jQuery multiselect plugin to render multiple fields.

Note that when multiple=True, IS_IN_SET will accept zero or more values, i.e. it will accept the field when nothing has been selected. multiple can also be a tuple of the form (a, b) where a and b are the minimum and (exclusive) maximum number of items that can be selected respectively.

Complexity and security validators

IS_STRONG

Enforces complexity requirements on a field (usually a password field).

Example:

requires = IS_STRONG(min=10, special=2, upper=2)

where:

  • min is minimum length of the value

  • special is the minimum number of required special characters, by default special characters are any of the following ^!!@#$%^&*()_+-=?<>,.:;{}[]| (you can customize these using specials = '...')

  • upper is the minimum number of upper case characters

other accepted arguments are:

  • invalid for the list of forbidden characters, by default invalid=' "'

  • max for the maximum length of the value

  • lower for the minimum number of lower case characters

  • number for the minimum number of digit characters

Obviously you can provide an error_message as for any other validator, although IS_STRONG is clever enough to provide a clear message to describe the validation failure.

A special argument you can use is entropy, that is a minimum value for the complexity of the value to accept (a number), experiment this with:

>>> IS_STRONG(entropy=100.0)('hello')
('hello', Entropy (24.53) less than required (100.0))

Notice that if the argument entropy is not given then IS_STRONG implicitly sets the following defaults: min = 8, upper = 1, lower = 1, number = 1, special = 1 which otherwise are all sets to None.

CRYPT

This is also a filter. It performs a secure hash on the input and it is used to prevent passwords from being passed in the clear to the database.

requires = CRYPT()

By default, CRYPT uses 1000 iterations of the pbkdf2 algorithm combined with SHA512 to produce a 20-byte-long hash. Old versions of web2py used md5 or HMAC+SHA512 depending on whether a key was specified or not.

If a key is specified, CRYPT uses the HMAC algorithm. The key may contain a prefix that determines the algorithm to use with HMAC, for example SHA512:

requires = CRYPT(key='sha512:thisisthekey')

This is the recommended syntax. The key must be a unique string associated with the database used. The key can never be changed. If you lose the key, the previously hashed values become useless. By default, CRYPT uses random salt, such that each result is different. To use a constant salt value, specify its value:

requires = CRYPT(salt='mysaltvalue')

Or, to use no salt:

requires = CRYPT(salt=False)

The CRYPT validator hashes its input, and this makes it somewhat special. If you need to validate a password field before it is hashed, you can use CRYPT in a list of validators, but must make sure it is the last of the list, so that it is called last. For example:

requires = [IS_STRONG(), CRYPT(key='sha512:thisisthekey')]

CRYPT also takes a min_length argument, which defaults to zero.

The resulting hash takes the form alg$salt$hash, where alg is the hash algorithm used, salt is the salt string (which can be empty), and hash is the algorithm’s output. Consequently, the hash is self-identifying, allowing, for example, the algorithm to be changed without invalidating previous hashes. The key, however, must remain the same.

Special type validators

IS_LIST_OF

This validator helps you to ensure length limits on values of type list, for this purpose use its minimum, maximum, and error_message arguments, for example:

requires = IS_LIST_OF(minimum=2)

A list value may comes from a form containing multiple fields with the same name or a multiple selection box. Note that this validator automatically converts a non-list value into a single valued list:

>>> IS_LIST_OF()('hello')
(['hello'], None)

If the first argument of IS_LIST_OF is another validator, then it applies the other validator to each element of the list. A typical usage is validation of a list: type field, for example:

Field('emails', 'list:string', requires=IS_LIST_OF(IS_EMAIL()), ...)

IS_LIST_OF_EMAILS

This validator is specifically designed to work with the following field:

Field('emails', 'list:string',
      widget=SQLFORM.widgets.text.widget,
    requires=IS_LIST_OF_EMAILS(),
    filter_in=lambda l: \\
        IS_LIST_OF_EMAILS.split_emails.findall(l[0]) if l else l,
    represent=lambda v, r: \\
        XML(', '.join([A(x, _href='mailto:'+x).xml() for x in (v or [])]))
    )

Notice that due to the widget customization this field will be rendered by a textarea in SQLFORMs (see next [[Widgets #Widgets]] section). This let you insert and edit multiple emails in a single input field (very much like normal mail client programs do), separating each email address with ,, ;, and blanks (space, newline, and tab characters). As a consequence now we need a validator which is able to operate on a single value input and a way to split the validated value into a list to be next processed by DAL, these are what the requires and filter_in arguments stand for. As alternative to filter_in, you can pass the following function to the onvalidation argument of form accepts, process, or validate method:

def emails_onvalidation(form):
    form.vars.emails = IS_LIST_OF_EMAILS.split_emails.findall(form.vars.emails)

The effect of the represent argument (at lines 6 and 7) is to add a “mailto:…” link to each email address when the record is rendered in HTML pages.

ANY_OF

This validator takes a list of validators and accepts a value if any of the validators in the list does (i.e. it acts like a logical OR with respect to given validators).

requires = ANY_OF([IS_ALPHANUMERIC(), IS_EMAIL()])

When none of the validators accepts the value you get the error message form the last attempted one (the last in the list), you can customize the error message as usual:

>>> ANY_OF([IS_ALPHANUMERIC(), IS_EMAIL()])('@ab.co')
('@ab.co', 'Enter a valid email address')
>>> ANY_OF([IS_ALPHANUMERIC(), IS_EMAIL()],
...        error_message='Enter login or email')('@ab.co')
('@ab.co', 'Enter login or email')

IS_IMAGE

This validator checks if a file uploaded through the file input was saved in one of the selected image formats and has dimensions (width and height) within given limits.

It does not check for maximum file size (use IS_LENGTH for that). It returns a validation failure if no data was uploaded. It supports the file formats BMP, GIF, JPEG, PNG, and it does not require the Python Imaging Library.

Code parts taken from ref.``source1``:cite

It takes the following arguments: - extensions: iterable containing allowed image file extensions in lowercase - maxsize: iterable containing maximum width and height of the image - minsize: iterable containing minimum width and height of the image

Use (-1, -1) as minsize to bypass the image-size check.

Here are some Examples: - Check if uploaded file is in any of supported image formats:

requires = IS_IMAGE()
  • Check if uploaded file is either JPEG or PNG:

requires = IS_IMAGE(extensions=('jpeg', 'png'))
  • Check if uploaded file is PNG with maximum size of 200x200 pixels:

requires = IS_IMAGE(extensions=('png'), maxsize=(200, 200))

Note: on displaying an edit form for a table including requires = IS_IMAGE(), a delete checkbox will NOT appear because to delete the file would cause the validation to fail. To display the delete checkbox use this validation:

requires = IS_EMPTY_OR(IS_IMAGE())

IS_FILE

Checks if name and extension of file uploaded through file input matches given criteria.

Does not ensure the file type in any way. Returns validation failure if no data was uploaded.

Its arguments are:

  • filename: string/compiled regex or a list of strings/regex of valid filenames

  • extension: string/compiled regex or a list of strings/regex of valid extensions

  • lastdot: which dot should be used as a filename / extension separator: True indicates last dot (e.g., “file.tar.gz” will be broken in “file.tar” + “gz”) while False means first dot (e.g., “file.tar.gz” will be broken into “file” + “tar.gz”).

  • case: 0 means keep the case; 1 means transform the string into lowercase (default); 2 means transform the string into uppercase.

If there is no dot present, extension checks will be done against empty string and filename checks against whole value.

Examples: Check if file has a pdf extension (case insensitive):

INPUT(_type='file', _name='name',
        requires=IS_FILE(extension='pdf'))

Check if file is called ‘thumbnail’ and has a jpg or png extension (case insensitive):

INPUT(_type='file', _name='name',
        requires=IS_FILE(filename='thumbnail',
        extension=['jpg', 'png']))

Check if file has a tar.gz extension and name starting with backup:

INPUT(_type='file', _name='name',
        requires=IS_FILE(filename=re.compile('backup.*'),
        extension='tar.gz', lastdot=False))

Check if file has no extension and name matching README (case sensitive):

INPUT(_type='file', _name='name',
    requires=IS_FILE(filename='README',
    extension='', case=0)

IS_UPLOAD_FILENAME

This is the older implementation for checking files, included for backwards compatibility. For new applications, use IS_FILE().

This validator checks if the name and extension of a file uploaded through the file input matches the given criteria.

It does not ensure the file type in any way. Returns validation failure if no data was uploaded.

Its arguments are:

  • filename: filename (before dot) regex.

  • extension: extension (after dot) regex.

  • lastdot: which dot should be used as a filename / extension separator: True indicates last dot (e.g., “file.tar.gz” will be broken in “file.tar” + “gz”) while False means first dot (e.g., “file.tar.gz” will be broken into “file” + “tar.gz”).

  • case: 0 means keep the case; 1 means transform the string into lowercase (default); 2 means transform the string into uppercase.

If there is no dot present, extension checks will be done against an empty string and filename checks will be done against the whole value.

Examples:

Check if file has a pdf extension (case insensitive):

requires = IS_UPLOAD_FILENAME(extension='pdf')

Check if file has a tar.gz extension and name starting with backup:

requires = IS_UPLOAD_FILENAME(filename='backup.*', extension='tar.gz', lastdot=False)

Check if file has no extension and name matching README (case sensitive):

requires = IS_UPLOAD_FILENAME(filename='^README$', extension='^$', case=0)

IS_IPV4

This validator checks if a field’s value is an IP version 4 address in decimal form. Can be set to force addresses from a certain range.

IPv4 regex taken from regexlib. The signature for the IS_IPV4 constructor is the following:

IS_IPV4(minip='0.0.0.0', maxip='255.255.255.255', invert=False,
        is_localhost=None, is_private=None, is_automatic=None,
        error_message='Enter valid IPv4 address')

Where:

  • minip is the lowest allowed address

  • maxip is the highest allowed address

  • invert is a flag to invert allowed address range, i.e. if set to True allows addresses only from outside of given range; note that range boundaries are not matched this way

You can pass an IP address either as a string (e.g. ‘192.168.0.1’) or as a list or tuple of 4 integers (e.g. [192, 168, 0, 1]).

To check for multiple address ranges pass to minip and maxip a list or tuple of boundary addresses, for example to allow only addresses between ‘192.168.20.10’ and ‘192.168.20.19’ or between ‘192.168.30.100’ and ‘192.168.30.199’ use:

requires = IS_IPV4(minip=('192.168.20.10', '192.168.30.100'),
                maxip=('192.168.20.19', '192.168.30.199'))

Notice that only a range for which both lower and upper limits are set is configured, that is the number of configured ranges is determined by the shorter of the iterables passed to minip and maxip.

The arguments is_localhost, is_private, and is_automatic accept the following values:

  • None to ignore the option

  • True to force the option

  • False to forbid the option

The option meanings are:

  • is_localhost: match localhost address (127.0.0.1)

  • is_private: match address in 172.16.0.0 - 172.31.255.255 and 192.168.0.0 - 192.168.255.255 ranges

  • is_automatic: match address in 169.254.0.0 - 169.254.255.255 range

Examples:

Check for valid IPv4 address:

requires = IS_IPV4()

Check for valid private network IPv4 address:

requires = IS_IPV4(minip='192.168.0.1', maxip='192.168.255.255')

IS_IPV6

This validator checks if a field’s value is an IP version 6 address.

The signature for the IS_IPV6 constructor is the following:

IS_IPV6(is_private=None,
        is_link_local=None,
        is_reserved=None,
        is_multicast=None,
        is_routeable=None,
        is_6to4=None,
        is_teredo=None,
        subnets=None,
        error_message='Enter valid IPv6 address')

The arguments is_private, is_link_local, is_reserved, is_multicast, is_routeable, is_6to4, and is_teredo accept the following values:

  • None to ignore the option

  • True to force the option

  • False to forbid the option, this does not work for is_routeable

The option meanings are:

  • is_private: match an address allocated for private networks

  • is_link_local: match an address reserved for link-local (i.e. in fe80::/10 range), this is a private network too (also matched by is_private above)

  • is_reserved: match an address otherwise IETF reserved

  • is_multicast: match an address reserved for multicast use (i.e. in ff00::/8 range)

  • is_6to4: match an address that appear to contain a 6to4 embedded address (i.e. in 2002::/16 range)

  • is_teredo: match a teredo address (i.e. in 2001::/32 range)

Forcing is_routeable (setting to True) is a shortcut to forbid (setting to False) is_private, is_reserved, and is_multicast all.

Use the subnets argument to pass a subnet or list of subnets to check for address membership, this way an address must be a subnet member to validate.

Examples:

Check for valid IPv6 address:

requires = IS_IPV6()

Check for valid private network IPv6 address:

requires = IS_IPV6(is_link_local=True)

Check for valid IPv6 address in subnet:

requires = IS_IPV6(subnets='fb00::/8')

IS_IPADDRESS

This validator checks if a field’s value is an IP address (either version 4 or version 6). Can be set to force addresses from within a specific range. Checks are done using the appropriate IS_IPV4 or IS_IPV6 validator.

The signature for the IS_IPADDRESS constructor is the following:

IS_IPADDRESS(minip='0.0.0.0', maxip='255.255.255.255', invert=False,
            is_localhost=None, is_private=None, is_automatic=None,
            is_ipv4=None,
            is_link_local=None, is_reserved=None, is_multicast=None,
            is_routeable=None, is_6to4=None, is_teredo=None,
            subnets=None, is_ipv6=None,
            error_message='Enter valid IP address')

With respect to IS_IPV4 and IS_IPV6 validators the only added arguments are:

  • is_ipv4, set to True to force version 4 or set to False to forbid version 4

  • is_ipv6, set to True to force version 6 or set to False to forbid version 6

Refer to IS_IPV4 and IS_IPV6 validators for the meaning of other arguments.

Examples:

Check for valid IP address (both IPv4 and IPv6):

requires = IS_IPADDRESS()

Check for valid IP address (IPv6 only):

requires = IS_IPADDRESS(is_ipv6=True)

Other validators

CLEANUP

This is a filter. It never fails. By default it just removes all characters whose decimal ASCII codes are not in the list [10, 13, 32-127]. It always perform an initial strip (i.e. heading and trailing blank characters removal) on the value.

requires = CLEANUP()

You can pass a regular expression to decide what has to be removed, for example to clear all non-digit characters use:

>>> CLEANUP('[^\\d]')('Hello 123 world 456')
('123456', None)

Database validators

IS_NOT_IN_DB

Synopsis: IS_NOT_IN_DB(db|set, 'table.field')

Consider the following example:

db.define_table('person', Field('name'))
db.person.name.requires = IS_NOT_IN_DB(db, 'person.name')

It requires that when you insert a new person, his/her name is not already in the database, db, in the field person.name.

A set can be used instead of db.

As with all other validators this requirement is enforced at the form processing level, not at the database level. This means that there is a small probability that, if two visitors try to concurrently insert records with the same person.name, this results in a race condition and both records are accepted. It is therefore safer to also inform the database that this field should have a unique value:

db.define_table('person', Field('name', unique=True))
db.person.name.requires = IS_NOT_IN_DB(db, 'person.name')

Now if a race condition occurs, the database raises an OperationalError and one of the two inserts is rejected.

The first argument of IS_NOT_IN_DB can be a database connection or a Set. In the latter case, you would be checking only the set defined by the Set.

A complete argument list for IS_NOT_IN_DB() is as follows:

IS_NOT_IN_DB(dbset, field, error_message='value already in database or empty',
            allowed_override=[], ignore_common_filters=True)

The following code, for example, does not allow registration of two persons with the same name within 10 days of each other:

import datetime
now = datetime.datetime.today()
db.define_table('person',
    Field('name'),
    Field('registration_stamp', 'datetime', default=now))
recent = db(db.person.registration_stamp > now-datetime.timedelta(10))
db.person.name.requires = IS_NOT_IN_DB(recent, 'person.name')

IS_IN_DB

Synopsis: IS_IN_DB(db|set, 'table.value_field', '%(representing_field)s', zero='choose one') where the third and fourth arguments are optional.

multiple= is also possible if the field type is a list. The default is False. It can be set to True or to a tuple (min, max) to restrict the number of values selected. So multiple=(1, 10) enforces at least one and at most ten selections.

Other optional arguments are discussed below.

Example Consider the following tables and requirement:

db.define_table('person', Field('name', unique=True))
db.define_table('dog', Field('name'), Field('owner', db.person))
db.dog.owner.requires = IS_IN_DB(db, 'person.id', '%(name)s',
                                zero=T('choose one'))

the IS_IN_DB requirement could also be written to use a Set instead of db

db.dog.owner.requires = IS_IN_DB(db(db.person.id > 10), 'person.id', '%(name)s',
                                zero=T('choose one'))

It is enforced at the level of dog INSERT/UPDATE/DELETE forms. This example requires that a dog.owner be a valid id in the field person.id in the database db. Because of this validator, the dog.owner field is represented as a drop-down list. The third argument of the validator is a string that describes the elements in the drop-down list, this is passed to the label argument of the validator. In the example you want to see the person %(name)s instead of the person %(id)s. %(...)s is replaced by the value of the field in brackets for each record. Other accepted values for the label are a Field instance (e.g. you could use db.person.name instead of ‘%(name)s’) or even a callable that takes a row and returns the description for the option.

The zero option works very much like for the IS_IN_SET validator.

Other optional arguments accepted by IS_IN_DB are: orderby, groupby, distinct, cache, and left; these are passed to the db select (see their description on the DAL chapter).

Notice that groupby, distinct, and left do not apply to Google App Engine.

To alphabetically sort the options listed in the drop-down list you can set the sort argument to True (sorting is case-insensitive), this may be usefull when no orderby is feasible or practical.

The first argument of the validator can be a database connection or a DAL Set, as in IS_NOT_IN_DB. This can be useful for example when wishing to limit the records in the drop-down list. In this example, we use IS_IN_DB in a controller to limit the records dynamically each time the controller is called:

def index():
    (...)
    query = (db.table.field == 'xyz') # in practice 'xyz' would be a variable
    db.table.field.requires = IS_IN_DB(db(query), ...)
    form = Form(...)
    if form.process().accepted: ...
    (...)

If you want the field validated, but you do not want a drop-down, you must put the validator in a list.

db.dog.owner.requires = [IS_IN_DB(db, 'person.id', '%(name)s')]

Occasionally you want the drop-down (so you do not want to use the list syntax above) yet you want to use additional validators. For this purpose the IS_IN_DB validator takes an extra argument _and that can point to a list of other validators applied if the validated value passes the IS_IN_DB validation. For example to validate all dog owners in db that are not in a subset:

subset = db(db.person.id > 100)
db.dog.owner.requires = IS_IN_DB(db, 'person.id', '%(name)s',
                                _and=IS_NOT_IN_DB(subset, 'person.id'))

IS_IN_DB and Tagging

The IS_IN_DB validator has an optional attribute multiple=False. If set to True multiple values can be stored in one field. This field should be of type list:reference as discussed in list:<type> and contains. An explicit example of tagging is discussed there. Multiple references are handled automatically in create and update forms, but they are transparent to the DAL. We strongly suggest using the jQuery multiselect plugin to render multiple fields.

Validation functions

In order to explicitly define a validation function, we pass to the validation parameter a function that takes the form and returns a dictionary, mapping field names to errors. If the dictionary is non-empty, the errors will be displayed to the user, and no database I/O will take place.

Here is an example:

from py4web import Field
from py4web.utils.form import Form, FormStyleBulma
from pydal.validators import IS_INT_IN_RANGE

def check_nonnegative_quantity(form):
    if not form.errors and form.vars['product_quantity'] % 2:
        form.errors['product_quantity'] = T('The product quantity must be even')

@action('form_example', method=['GET', 'POST'])
@action.uses(session, 'form_example.html')
def form_example():
    form = Form([
        Field('product_name'),
        Field('product_quantity', 'integer', requires=IS_INT_IN_RANGE(0,100))],
        validation=check_nonnegative_quantity,
        formstyle=FormStyleBulma)
    if form.accepted:
        # Do something with form.vars['product_name'] and form.vars['product_quantity']
        redirect(URL('index'))
    return dict(form=form)