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Create a data view

The data view is a powerful feature, in which, for example, the results of one or more calculations can be made visible on the fly (e.g. the volume of an object). It can also be used to provide hard-coded guidelines to the user. The entries in a data view can be divided into three levels, similarly to the structure of the editor.

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, 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 data view

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

Testing​

New in v13.3.0

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(result.data, ...)