Show data & tables

The DataView is a powerful feature, in which, for example, the results of one or more calculations can be shown immediately (e.g. the volume of an object). The entries in a DataView can be divided into three levels.

Data view

Implementation

The following steps have to be performed to visualize the desired results:

1. Create the data group(s) consisting of DataItems for visualization.
2. Pass the group(s) to a decorated view method that returns a DataResult.

from viktor.views import DataGroup, DataItem, DataResult, DataView


class Controller(ViktorController):
    ...

    @DataView("OUTPUT", duration_guess=1)
    def visualize_data(self, params, **kwargs):
        data = DataGroup(
            DataItem('Data item 1', 123)
        )
        return DataResult(data)

Multilayered DataGroup

Below an example is given of a multilayered output group (maximum of three layers deep):

@DataView("OUTPUT", duration_guess=1)
def visualize_data(self, params, **kwargs):
    value_a = 1
    value_b = 2 * value_a
    value_total = value_a + value_b
    data = DataGroup(
        group_a=DataItem('Group A', 'some result', subgroup=DataGroup(
            sub_group=DataItem('Result', 1, suffix='N')
        )),
        group_b=DataItem('Group B', '', subgroup=DataGroup(
            sub_group=DataItem('Sub group', value_total, prefix='€', subgroup=DataGroup(
                value_a=DataItem('Value A', value_a, prefix='€'),
                value_b=DataItem('Value B', value_b, prefix='€', explanation_label='this value is a result of multiplying Value A by 2')
            ))
        ))
    )

    return DataResult(data)

The explanation_label can be used to inform the user. The output result will look like this:

Example DataView

Positional or key-worded DataItem?

In previous examples, we saw a DataGroup created with and without the use of keyword arguments. Using keywords is necessary to link the corresponding DataItem to the Summary. A detailed guide of the Summary can be found here.

Dynamic group length

The structure of DataGroups allows for dynamic creation/addition of data to the overall result, as long as the maximum number of items is not exceeded. An example of dynamically creating data items is shown below:

shopping_cart = {
    'Apple': 0.20,
    'Pineapple': 1.80,
    'Milk': 1
}

# loop through products
total_cost = 0
data_items = []
for item, price in shopping_cart.items():
    data_items.append(DataItem(item, price, prefix='€'))
    total_cost += price

# construct data group
data_group = DataGroup(
    DataItem('Shopping cart', total_cost, prefix='€', subgroup=DataGroup(*data_items))
)

Setting warning/error statuses

Sometimes it is desired to warn a user when a result exceeds a certain values. For these cases, the status and status_message arguments of a DataItem can be considered. Suppose we want to warn the user if our shopping cart of the example above exceeds 5 euros, we can add the following:

shopping_cart.update({
    'Cheese': 2.50
})

# loop through products
...

# construct data group
if total_cost <= 5:
    status = DataStatus.SUCCESS
    status_msg = ''
else:
    status = DataStatus.WARNING
    status_msg = 'Mind your wallet!'

data_group = DataGroup(
    DataItem('Shopping cart', total_cost, prefix='€', status=status, status_message=status_msg, subgroup=DataGroup(*data_items))
)

Example status message

Testing

New in v13.3.0

mock_View decorator for easier testing of view methods

Methods decorated with @DataView need to be mocked within the context of (automated) testing:

import unittest

from viktor.testing import mock_View

from app.my_entity_type.controller import MyEntityTypeController

class TestMyEntityTypeController(unittest.TestCase):

    @mock_View(MyEntityTypeController)
    def test_data_view(self):
        params = ...
        result = MyEntityTypeController().data_view(params=params)
        self.assertEqual(result.data, ...)