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John Chilton
8af243c176
Allow the rules DSL & GUI component to operate on existing collections to allow filtering, sorting, modifying identifiers and general re-organization of existing collections (e.g. the outputs of tools). Implementing this as a collection operation tool so that it should be executable interactively in the tool form and in a batch fashion as part of workflow executions. For this to be tracked properly as a tool execution and to work properly in the tool form, I've implemented a new tool framework and tool form parameter type called "rules". This can thought of as a more GUI friendly alternative to my proposed collection operations that consumed JavaScript expressions. This includes API tests for both tool and workflow execution of the new tool as well as Selenium tests for tool form execution, workflow editor interactions, and workflow running.
133 lines
3.2 KiB
Python
133 lines
3.2 KiB
Python
# Common test data for rule testing meant to be shared between API and Selenium tests.
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from pkg_resources import resource_string
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RULES_DSL_SPEC_STR = resource_string(__name__, "data/rules_dsl_spec.yml")
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def check_example_1(hdca, dataset_populator):
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assert hdca["collection_type"] == "list"
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assert hdca["element_count"] == 2
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first_dce = hdca["elements"][0]
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first_hda = first_dce["object"]
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assert first_hda["hid"] > 3
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def check_example_2(hdca, dataset_populator):
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assert hdca["collection_type"] == "list:list"
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assert hdca["element_count"] == 2
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first_collection_level = hdca["elements"][0]
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assert first_collection_level["element_type"] == "dataset_collection"
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second_collection_level = first_collection_level["object"]
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assert second_collection_level["collection_type"] == "list"
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assert second_collection_level["elements"][0]["element_type"] == "hda"
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def check_example_3(hdca, dataset_populator):
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assert hdca["collection_type"] == "list"
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assert hdca["element_count"] == 2
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first_element = hdca["elements"][0]
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assert first_element["element_identifier"] == "test0forward"
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EXAMPLE_1 = {
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"rules": {
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"rules": [
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{
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"type": "add_column_metadata",
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"value": "identifier0",
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}
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],
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"mapping": [
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{
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"type": "list_identifiers",
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"columns": [0],
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}
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],
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},
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"test_data": {
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"type": "list",
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"elements": [
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{
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"identifier": "i1",
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"content": "0"
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},
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{
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"identifier": "i2",
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"content": "1"
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},
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]
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},
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"check": check_example_1,
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"output_hid": 6,
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}
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EXAMPLE_2 = {
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"rules": {
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"rules": [
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{
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"type": "add_column_metadata",
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"value": "identifier0",
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},
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{
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"type": "add_column_metadata",
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"value": "identifier0",
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}
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],
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"mapping": [
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{
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"type": "list_identifiers",
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"columns": [0, 1],
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}
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],
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},
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"test_data": {
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"type": "list",
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"elements": [
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{
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"identifier": "i1",
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"content": "0"
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},
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{
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"identifier": "i2",
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"content": "1"
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},
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]
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},
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"check": check_example_2,
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"output_hid": 6,
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}
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# Flatten
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EXAMPLE_3 = {
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"rules": {
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"rules": [
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{
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"type": "add_column_metadata",
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"value": "identifier0",
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},
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{
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"type": "add_column_metadata",
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"value": "identifier1",
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},
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{
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"type": "add_column_concatenate",
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"target_column_0": 0,
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"target_column_1": 1,
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}
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],
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"mapping": [
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{
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"type": "list_identifiers",
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"columns": [2],
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}
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],
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},
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"test_data": {
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"type": "list:paired",
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},
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"check": check_example_3,
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"output_hid": 7,
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}
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