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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


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, DataViewclass 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 productstotal_cost = 0data_items = []for item, price in shopping_cart.items():    data_items.append(DataItem(item, price, prefix='€'))    total_cost += price# construct data groupdata_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 groupif 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


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 unittestfrom viktor.testing import mock_Viewfrom app.my_entity_type.controller import MyEntityTypeControllerclass TestMyEntityTypeController(unittest.TestCase):    @mock_View(MyEntityTypeController)    def test_data_view(self):        params = ...        result = MyEntityTypeController().data_view(params=params)        self.assertEqual(, ...)