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Source code for galaxy_test.api.test_tools
# Test tools API.
import contextlib
import json
import os
import tarfile
import pytest
from requests import get
from six import BytesIO
from galaxy.util import galaxy_root_path
from galaxy_test.base import rules_test_data
from galaxy_test.base.populators import (
DatasetCollectionPopulator,
DatasetPopulator,
LibraryPopulator,
load_data_dict,
skip_without_tool,
uses_test_history,
)
from ._framework import ApiTestCase
MINIMAL_TOOL = {
'id': "minimal_tool",
'name': "Minimal Tool",
'class': "GalaxyTool",
'version': "1.0.0",
'command': "echo 'Hello World' > $output1",
'inputs': [],
'outputs': dict(
output1=dict(format='txt'),
)
}
MINIMAL_TOOL_NO_ID = {
'name': "Minimal Tool",
'class': "GalaxyTool",
'version': "1.0.0",
'command': "echo 'Hello World 2' > $output1",
'inputs': [],
'outputs': dict(
output1=dict(format='txt'),
)
}
[docs]class TestsTools(object):
def _build_pair(self, history_id, contents):
create_response = self.dataset_collection_populator.create_pair_in_history(history_id, contents=contents, direct_upload=True)
hdca_id = create_response.json()["outputs"][0]["id"]
return hdca_id
def _run_and_check_simple_collection_mapping(self, history_id, inputs):
create = self._run_cat(history_id, inputs=inputs, assert_ok=True)
outputs = create['outputs']
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 2)
self.assertEqual(len(outputs), 2)
self.assertEqual(len(implicit_collections), 1)
output1 = outputs[0]
output2 = outputs[1]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
output2_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output2)
self.assertEqual(output1_content.strip(), "123")
self.assertEqual(output2_content.strip(), "456")
def _run_cat(self, history_id, inputs, assert_ok=False, **kwargs):
return self._run('cat', history_id, inputs, assert_ok=assert_ok, **kwargs)
def _run(self, tool_id=None, history_id=None, inputs=None, tool_uuid=None, assert_ok=False, tool_version=None, use_cached_job=False, wait_for_job=False):
if inputs is None:
inputs = {}
if tool_id is None:
assert tool_uuid is not None
payload = self.dataset_populator.run_tool_payload(
tool_id=tool_id,
inputs=inputs,
history_id=history_id,
)
if tool_uuid:
payload['tool_uuid'] = tool_uuid
if tool_version is not None:
payload["tool_version"] = tool_version
if use_cached_job:
payload['use_cached_job'] = True
create_response = self._post("tools", data=payload)
if wait_for_job:
self.dataset_populator.wait_for_job(job_id=create_response.json()['jobs'][0]['id'])
if assert_ok:
self._assert_status_code_is(create_response, 200)
create = create_response.json()
self._assert_has_keys(create, 'outputs')
return create
else:
return create_response
[docs]class ToolsTestCase(ApiTestCase, TestsTools):
[docs] def setUp(self):
super(ToolsTestCase, self).setUp()
self.dataset_populator = DatasetPopulator(self.galaxy_interactor)
self.dataset_collection_populator = DatasetCollectionPopulator(self.galaxy_interactor)
[docs] @skip_without_tool("cat1")
def test_search_cat(self):
url = self._api_url("tools")
payload = dict(q="concat")
get_response = get(url, payload).json()
assert "cat1" in get_response
[docs] @skip_without_tool("trimmer")
def test_search_trimmer(self):
url = self._api_url("tools")
payload = dict(q="leading or trailing characters")
get_response = get(url, payload).json()
assert "trimmer" in get_response
[docs] @skip_without_tool("Grep1")
def test_search_grep(self):
url = self._api_url("tools")
payload = dict(q="Select lines that match an expression")
get_response = get(url, payload).json()
assert "Grep1" in get_response
[docs] def test_no_panel_index(self):
index = self._get("tools", data=dict(in_panel=False))
tools_index = index.json()
# No need to flatten out sections, with in_panel=False, only tools are
# returned.
tool_ids = [_["id"] for _ in tools_index]
assert "upload1" in tool_ids
[docs] @skip_without_tool("test_sam_to_bam_conversions")
def test_requirements(self):
requirements_response = self._get("tools/%s/requirements" % "test_sam_to_bam_conversions", admin=True)
self._assert_status_code_is_ok(requirements_response)
requirements = requirements_response.json()
assert len(requirements) == 1, requirements
requirement = requirements[0]
assert requirement['name'] == 'samtools', requirement
[docs] @skip_without_tool("cat1")
def test_show_repeat(self):
tool_info = self._show_valid_tool("cat1")
parameters = tool_info["inputs"]
assert len(parameters) == 2, "Expected two inputs - got [%s]" % parameters
assert parameters[0]["name"] == "input1"
assert parameters[1]["name"] == "queries"
repeat_info = parameters[1]
self._assert_has_keys(repeat_info, "min", "max", "title", "help")
repeat_params = repeat_info["inputs"]
assert len(repeat_params) == 1
assert repeat_params[0]["name"] == "input2"
[docs] @skip_without_tool("random_lines1")
def test_show_conditional(self):
tool_info = self._show_valid_tool("random_lines1")
cond_info = tool_info["inputs"][2]
self._assert_has_keys(cond_info, "cases", "test_param")
self._assert_has_keys(cond_info["test_param"], 'name', 'type', 'label', 'help')
cases = cond_info["cases"]
assert len(cases) == 2
case1 = cases[0]
self._assert_has_keys(case1, "value", "inputs")
assert case1["value"] == "no_seed"
assert len(case1["inputs"]) == 0
case2 = cases[1]
self._assert_has_keys(case2, "value", "inputs")
case2_inputs = case2["inputs"]
assert len(case2_inputs) == 1
self._assert_has_keys(case2_inputs[0], 'name', 'type', 'label', 'help', 'argument')
assert case2_inputs[0]["name"] == "seed"
[docs] @skip_without_tool("multi_data_param")
def test_show_multi_data(self):
tool_info = self._show_valid_tool("multi_data_param")
f1_info, f2_info = tool_info["inputs"][0], tool_info["inputs"][1]
self._assert_has_keys(f1_info, "min", "max")
assert f1_info["min"] == 1
assert f1_info["max"] == 1235
self._assert_has_keys(f2_info, "min", "max")
assert f2_info["min"] is None
assert f2_info["max"] is None
[docs] @skip_without_tool("collection_creates_list")
def test_show_output_collection(self):
tool_info = self._show_valid_tool("collection_creates_list")
outputs = tool_info["outputs"]
assert len(outputs) == 1
output = outputs[0]
assert output["label"] == "Duplicate List"
assert output["inherit_format"] is True
[docs] @skip_without_tool("test_data_source")
def test_data_source_ok_request(self):
with self.dataset_populator.test_history() as history_id:
payload = self.dataset_populator.run_tool_payload(
tool_id="test_data_source",
inputs={
"URL": "https://raw.githubusercontent.com/galaxyproject/galaxy/dev/test-data/1.bed",
"URL_method": "get",
"data_type": "bed",
},
history_id=history_id,
)
create_response = self._post("tools", data=payload)
self._assert_status_code_is(create_response, 200)
create_object = create_response.json()
self._assert_has_keys(create_object, "outputs")
assert len(create_object["outputs"]) == 1
output = create_object["outputs"][0]
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
output_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output)
assert output_content.startswith("chr1\t147962192\t147962580")
output_details = self.dataset_populator.get_history_dataset_details(history_id, dataset=output)
assert output_details["file_ext"] == "bed"
[docs] @skip_without_tool("test_data_source")
def test_data_source_sniff_fastqsanger(self):
with self.dataset_populator.test_history() as history_id:
payload = self.dataset_populator.run_tool_payload(
tool_id="test_data_source",
inputs={
"URL": "https://raw.githubusercontent.com/galaxyproject/galaxy/dev/test-data/1.fastqsanger.gz",
"URL_method": "get",
},
history_id=history_id,
)
create_response = self._post("tools", data=payload)
self._assert_status_code_is(create_response, 200)
create_object = create_response.json()
self._assert_has_keys(create_object, "outputs")
assert len(create_object["outputs"]) == 1
output = create_object["outputs"][0]
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
output_details = self.dataset_populator.get_history_dataset_details(history_id, dataset=output)
assert output_details["file_ext"] == "fastqsanger.gz", output_details
[docs] @skip_without_tool("test_data_source")
def test_data_sources_block_file_parameters(self):
with self.dataset_populator.test_history() as history_id:
payload = self.dataset_populator.run_tool_payload(
tool_id="test_data_source",
inputs={
"URL": "file://%s" % os.path.join(os.getcwd(), "README.rst"),
"URL_method": "get",
"data_type": "bed",
},
history_id=history_id,
)
create_response = self._post("tools", data=payload)
self._assert_status_code_is(create_response, 200)
create_object = create_response.json()
self._assert_has_keys(create_object, "outputs")
assert len(create_object["outputs"]) == 1
output = create_object["outputs"][0]
self.dataset_populator.wait_for_history(history_id, assert_ok=False)
output_details = self.dataset_populator.get_history_dataset_details(history_id, dataset=output, wait=False)
assert output_details["state"] == "error", output_details
assert "has not sent back a URL parameter" in output_details["misc_info"], output_details
def _show_valid_tool(self, tool_id, tool_version=None):
data = dict(io_details=True)
if tool_version:
data['tool_version'] = tool_version
tool_show_response = self._get("tools/%s" % tool_id, data=data)
self._assert_status_code_is(tool_show_response, 200)
tool_info = tool_show_response.json()
self._assert_has_keys(tool_info, "inputs", "outputs", "panel_section_id")
return tool_info
[docs] @skip_without_tool("composite_output")
def test_test_data_filepath_security(self):
test_data_response = self._get("tools/%s/test_data_path?filename=../CONTRIBUTORS.md" % "composite_output", admin=True)
assert test_data_response.status_code == 404, test_data_response.text
[docs] @skip_without_tool("composite_output")
def test_test_data_admin_security(self):
test_data_response = self._get("tools/%s/test_data_path?filename=../CONTRIBUTORS.md" % "composite_output")
assert test_data_response.status_code == 403, test_data_response.text
[docs] @skip_without_tool("composite_output")
def test_test_data_composite_output(self):
test_data_response = self._get("tools/%s/test_data" % "composite_output")
assert test_data_response.status_code == 200
test_data = test_data_response.json()
assert len(test_data) == 1
test_case = test_data[0]
self._assert_has_keys(test_case, "inputs", "outputs", "output_collections", "required_files")
assert len(test_case["inputs"]) == 1, test_case
# input0 = next(iter(test_case["inputs"].values()))
[docs] @skip_without_tool("collection_two_paired")
def test_test_data_collection_two_paired(self):
test_data_response = self._get("tools/%s/test_data" % "collection_two_paired")
assert test_data_response.status_code == 200
test_data = test_data_response.json()
assert len(test_data) == 2
test_case = test_data[0]
self._assert_has_keys(test_case, "inputs", "outputs", "output_collections", "required_files")
assert len(test_case["inputs"]) == 3, test_case
[docs] @skip_without_tool("collection_nested_test")
def test_test_data_collection_nested(self):
test_data_response = self._get("tools/%s/test_data" % "collection_nested_test")
assert test_data_response.status_code == 200
test_data = test_data_response.json()
assert len(test_data) == 2
test_case = test_data[0]
self._assert_has_keys(test_case, "inputs", "outputs", "output_collections", "required_files")
assert len(test_case["inputs"]) == 1, test_case
[docs] @skip_without_tool("simple_constructs_y")
def test_test_data_yaml_tools(self):
test_data_response = self._get("tools/%s/test_data" % "simple_constructs_y")
assert test_data_response.status_code == 200
test_data = test_data_response.json()
assert len(test_data) == 3
[docs] @skip_without_tool("cat1")
def test_test_data_download(self):
test_data_response = self._get("tools/%s/test_data_download?filename=1.bed" % "cat1")
assert test_data_response.status_code == 200, test_data_response.text.startswith('chr')
[docs] @skip_without_tool("composite_output")
def test_test_data_downloads_security(self):
test_data_response = self._get("tools/%s/test_data_download?filename=../CONTRIBUTORS.md" % "composite_output")
assert test_data_response.status_code == 404, test_data_response.text
[docs] @skip_without_tool("composite_output")
def test_test_data_download_composite(self):
test_data_response = self._get("tools/%s/test_data_download?filename=velveth_test1" % "composite_output")
assert test_data_response.status_code == 200
with tarfile.open(fileobj=BytesIO(test_data_response.content)) as tar_contents:
namelist = tar_contents.getnames()
assert len(namelist) == 5
[docs] @uses_test_history(require_new=False)
def test_upload_composite_as_tar(self, history_id):
tar_path = self.test_data_resolver.get_filename("testdir.tar")
with open(tar_path, "rb") as tar_f:
payload = self.dataset_populator.upload_payload(history_id, "Test123",
extra_inputs={
"files_1|file_data": tar_f,
"files_1|NAME": "composite",
"file_count": "2",
"force_composite": "True",
}
)
run_response = self.dataset_populator.tools_post(payload)
self.dataset_populator.wait_for_tool_run(history_id, run_response)
dataset = run_response.json()["outputs"][0]
content = self.dataset_populator.get_history_dataset_content(history_id, dataset=dataset)
assert content.strip() == "Test123"
extra_files = self.dataset_populator.get_history_dataset_extra_files(history_id, dataset_id=dataset["id"])
assert len(extra_files) == 5, extra_files
expected_contents = {
"testdir": "Directory",
"testdir/c": "Directory",
"testdir/a": "File",
"testdir/b": "File",
"testdir/c/d": "File",
}
found_files = set()
for extra_file in extra_files:
path = extra_file["path"]
assert path in expected_contents
assert extra_file["class"] == expected_contents[path]
found_files.add(path)
assert len(found_files) == 5, found_files
[docs] @uses_test_history(require_new=False)
def test_upload_composite_from_bad_tar(self, history_id):
tar_path = self.test_data_resolver.get_filename("unsafe.tar")
with open(tar_path, "rb") as tar_f:
payload = self.dataset_populator.upload_payload(history_id, "Test123",
extra_inputs={
"files_1|file_data": tar_f,
"files_1|NAME": "composite",
"file_count": "2",
"force_composite": "True",
}
)
run_response = self.dataset_populator.tools_post(payload)
self.dataset_populator.wait_for_tool_run(history_id, run_response, assert_ok=False)
dataset = run_response.json()["outputs"][0]
details = self.dataset_populator.get_history_dataset_details(history_id, dataset=dataset, assert_ok=False)
assert details["state"] == "error"
[docs] def test_unzip_collection(self):
with self.dataset_populator.test_history() as history_id:
hdca_id = self._build_pair(history_id, ["123", "456"])
inputs = {
"input": {"src": "hdca", "id": hdca_id},
}
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
response = self._run("__UNZIP_COLLECTION__", history_id, inputs, assert_ok=True)
outputs = response["outputs"]
self.assertEqual(len(outputs), 2)
output_forward = outputs[0]
output_reverse = outputs[1]
output_forward_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output_forward)
output_reverse_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output_reverse)
assert output_forward_content.strip() == "123"
assert output_reverse_content.strip() == "456"
output_forward = self.dataset_populator.get_history_dataset_details(history_id, dataset=output_forward)
output_reverse = self.dataset_populator.get_history_dataset_details(history_id, dataset=output_reverse)
assert output_forward["history_id"] == history_id
assert output_reverse["history_id"] == history_id
[docs] def test_unzip_nested(self):
with self.dataset_populator.test_history() as history_id:
hdca_list_id = self.__build_nested_list(history_id)
inputs = {
"input": {
'batch': True,
'values': [{'src': 'hdca', 'map_over_type': 'paired', 'id': hdca_list_id}],
}
}
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
response = self._run("__UNZIP_COLLECTION__", history_id, inputs, assert_ok=True)
implicit_collections = response["implicit_collections"]
self.assertEqual(len(implicit_collections), 2)
unzipped_hdca = self.dataset_populator.get_history_collection_details(history_id, hid=implicit_collections[0]["hid"])
assert unzipped_hdca["elements"][0]["element_type"] == "hda", unzipped_hdca
[docs] def test_zip_inputs(self):
with self.dataset_populator.test_history() as history_id:
hda1 = dataset_to_param(self.dataset_populator.new_dataset(history_id, content='1\t2\t3'))
hda2 = dataset_to_param(self.dataset_populator.new_dataset(history_id, content='4\t5\t6'))
inputs = {
"input_forward": hda1,
"input_reverse": hda2,
}
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
response = self._run("__ZIP_COLLECTION__", history_id, inputs, assert_ok=True)
output_collections = response["output_collections"]
self.assertEqual(len(output_collections), 1)
self.dataset_populator.wait_for_job(response["jobs"][0]["id"], assert_ok=True)
zipped_hdca = self.dataset_populator.get_history_collection_details(history_id, hid=output_collections[0]["hid"])
assert zipped_hdca["collection_type"] == "paired"
[docs] @skip_without_tool("__ZIP_COLLECTION__")
@uses_test_history(require_new=False)
def test_collection_operation_dataset_input_permissions(self, history_id):
hda1 = dataset_to_param(self.dataset_populator.new_dataset(history_id, content='1\t2\t3'))
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
self.dataset_populator.make_private(history_id, hda1["id"])
inputs = {
"input_forward": hda1,
"input_reverse": hda1,
}
with self._different_user_and_history() as other_history_id:
response = self._run("__ZIP_COLLECTION__", other_history_id, inputs, assert_ok=False)
self._assert_dataset_permission_denied_response(response)
[docs] @skip_without_tool("__UNZIP_COLLECTION__")
@uses_test_history(require_new=False)
def test_collection_operation_collection_input_permissions(self, history_id):
create_response = self.dataset_collection_populator.create_pair_in_history(history_id, direct_upload=True)
self._assert_status_code_is(create_response, 200)
collection = create_response.json()["outputs"][0]
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
collection = self.dataset_populator.get_history_collection_details(history_id, hid=collection["hid"])
element_id = collection["elements"][0]["object"]["id"]
self.dataset_populator.make_private(history_id, element_id)
inputs = {
"input": {"src": "hdca", "id": collection["id"]},
}
with self._different_user_and_history() as other_history_id:
response = self._run("__UNZIP_COLLECTION__", other_history_id, inputs, assert_ok=False)
self._assert_dataset_permission_denied_response(response)
[docs] def test_zip_list_inputs(self):
with self.dataset_populator.test_history() as history_id:
hdca1_id = self.dataset_collection_populator.create_list_in_history(history_id, contents=["a\nb\nc\nd", "e\nf\ng\nh"]).json()["id"]
hdca2_id = self.dataset_collection_populator.create_list_in_history(history_id, contents=["1\n2\n3\n4", "5\n6\n7\n8"]).json()["id"]
inputs = {
"input_forward": {'batch': True, 'values': [{"src": "hdca", "id": hdca1_id}]},
"input_reverse": {'batch': True, 'values': [{"src": "hdca", "id": hdca2_id}]},
}
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
response = self._run("__ZIP_COLLECTION__", history_id, inputs, assert_ok=True)
implicit_collections = response["implicit_collections"]
self.assertEqual(len(implicit_collections), 1)
self.dataset_populator.wait_for_job(response["jobs"][0]["id"], assert_ok=True)
zipped_hdca = self.dataset_populator.get_history_collection_details(history_id, hid=implicit_collections[0]["hid"])
assert zipped_hdca["collection_type"] == "list:paired"
[docs] @skip_without_tool('__FILTER_FAILED_DATASETS__')
def test_filter_failed_list(self):
with self.dataset_populator.test_history() as history_id:
ok_hdca_id = self.dataset_collection_populator.create_list_in_history(history_id, contents=["0", "1", "0", "1"]).json()["id"]
response = self.dataset_populator.run_exit_code_from_file(history_id, ok_hdca_id)
mixed_implicit_collections = response["implicit_collections"]
self.assertEqual(len(mixed_implicit_collections), 1)
mixed_hdca_hid = mixed_implicit_collections[0]["hid"]
mixed_hdca = self.dataset_populator.get_history_collection_details(history_id, hid=mixed_hdca_hid, wait=False)
def get_state(dce):
return dce["object"]["state"]
mixed_states = [get_state(_) for _ in mixed_hdca["elements"]]
assert mixed_states == [u"ok", u"error", u"ok", u"error"], mixed_states
filtered_hdca = self._run_filter(history_id=history_id, failed_hdca_id=mixed_hdca['id'])
filtered_states = [get_state(_) for _ in filtered_hdca["elements"]]
assert filtered_states == [u"ok", u"ok"], filtered_states
[docs] @skip_without_tool('__FILTER_FAILED_DATASETS__')
def test_filter_failed_list_paired(self):
with self.dataset_populator.test_history() as history_id:
pair1 = self.dataset_collection_populator.create_pair_in_history(history_id, contents=["0", "0"]).json()["id"]
pair2 = self.dataset_collection_populator.create_pair_in_history(history_id, contents=["0", "1"]).json()["id"]
ok_hdca_id = self.dataset_collection_populator.create_list_from_pairs(history_id, [pair1, pair2]).json()['id']
response = self.dataset_populator.run_exit_code_from_file(history_id, ok_hdca_id)
mixed_implicit_collections = response["implicit_collections"]
self.assertEqual(len(mixed_implicit_collections), 1)
mixed_hdca_hid = mixed_implicit_collections[0]["hid"]
mixed_hdca = self.dataset_populator.get_history_collection_details(history_id, hid=mixed_hdca_hid, wait=False)
def get_state(dce):
return dce["object"]["state"]
mixed_states = [get_state(element) for _ in mixed_hdca["elements"] for element in _['object']['elements']]
assert mixed_states == [u"ok", u"ok", u"ok", u"error"], mixed_states
filtered_hdca = self._run_filter(history_id=history_id, failed_hdca_id=mixed_hdca['id'])
filtered_states = [get_state(element) for _ in filtered_hdca["elements"] for element in _['object']['elements']]
assert filtered_states == [u"ok", u"ok"], filtered_states
# Also try list:list:paired
llp = self.dataset_collection_populator.create_nested_collection(history_id=history_id,
collection_type="list:list:paired",
collection=[mixed_hdca['id']]).json()
filtered_nested_hdca = self._run_filter(history_id=history_id, failed_hdca_id=llp['id'], batch=True)
filtered_states = [get_state(element) for _ in filtered_nested_hdca["elements"] for parent_element in _['object']['elements'] for element in parent_element['object']['elements']]
assert filtered_states == [u"ok", u"ok"], filtered_states
def _run_filter(self, history_id, failed_hdca_id, batch=False):
if batch:
inputs = {
"input": {"batch": batch, "values": [{"map_over_type": "list:paired", "src": "hdca", "id": failed_hdca_id}]},
}
else:
inputs = {
"input": {"batch": batch, "src": "hdca", "id": failed_hdca_id},
}
response = self._run("__FILTER_FAILED_DATASETS__", history_id, inputs, assert_ok=False).json()
self.dataset_populator.wait_for_history(history_id, assert_ok=False)
filter_output_collections = response["output_collections"]
if batch:
return response['implicit_collections'][0]
self.assertEqual(len(filter_output_collections), 1)
filtered_hid = filter_output_collections[0]["hid"]
filtered_hdca = self.dataset_populator.get_history_collection_details(history_id, hid=filtered_hid, wait=False)
return filtered_hdca
def _apply_rules_and_check(self, example):
with self.dataset_populator.test_history() as history_id:
inputs, _, _ = load_data_dict(history_id, {"input": example["test_data"]}, self.dataset_populator, self.dataset_collection_populator)
hdca = inputs["input"]
inputs = {
"input": {"src": "hdca", "id": hdca["id"]},
"rules": example["rules"]
}
self.dataset_populator.wait_for_history(history_id)
response = self._run("__APPLY_RULES__", history_id, inputs, assert_ok=True)
output_collections = response["output_collections"]
self.assertEqual(len(output_collections), 1)
output_hid = output_collections[0]["hid"]
output_hdca = self.dataset_populator.get_history_collection_details(history_id, hid=output_hid, wait=False)
example["check"](output_hdca, self.dataset_populator)
[docs] @skip_without_tool("multi_select")
def test_multi_select_as_list(self):
with self.dataset_populator.test_history() as history_id:
inputs = {
"select_ex": ["--ex1", "ex2"],
}
response = self._run("multi_select", history_id, inputs, assert_ok=True)
output = response["outputs"][0]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output)
assert output1_content == "--ex1,ex2"
[docs] @skip_without_tool("multi_select")
def test_multi_select_optional(self):
with self.dataset_populator.test_history() as history_id:
inputs = {
"select_ex": ["--ex1"],
"select_optional": None,
}
response = self._run("multi_select", history_id, inputs, assert_ok=True)
output = response["outputs"]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output[0])
output2_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output[1])
assert output1_content.strip() == "--ex1"
assert output2_content.strip() == "None", output2_content
[docs] @skip_without_tool("library_data")
def test_library_data_param(self):
with self.dataset_populator.test_history() as history_id:
ld = LibraryPopulator(self.galaxy_interactor).new_library_dataset("lda_test_library")
inputs = {
"library_dataset": ld["ldda_id"],
"library_dataset_multiple": [ld["ldda_id"], ld["ldda_id"]]
}
response = self._run("library_data", history_id, inputs, assert_ok=True)
output = response["outputs"]
output_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output[0])
assert output_content == "TestData\n", output_content
output_multiple_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output[1])
assert output_multiple_content == "TestData\nTestData\n", output_multiple_content
[docs] @skip_without_tool("multi_data_param")
def test_multidata_param(self):
with self.dataset_populator.test_history() as history_id:
hda1 = dataset_to_param(self.dataset_populator.new_dataset(history_id, content='1\t2\t3'))
hda2 = dataset_to_param(self.dataset_populator.new_dataset(history_id, content='4\t5\t6'))
inputs = {
"f1": {'batch': False, 'values': [hda1, hda2]},
"f2": {'batch': False, 'values': [hda2, hda1]},
}
response = self._run("multi_data_param", history_id, inputs, assert_ok=True)
output1 = response["outputs"][0]
output2 = response["outputs"][1]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
output2_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output2)
assert output1_content == "1\t2\t3\n4\t5\t6\n", output1_content
assert output2_content == "4\t5\t6\n1\t2\t3\n", output2_content
[docs] @skip_without_tool("cat1")
@uses_test_history(require_new=False)
def test_run_cat1(self, history_id):
# Run simple non-upload tool with an input data parameter.
new_dataset = self.dataset_populator.new_dataset(history_id, content='Cat1Test')
inputs = dict(
input1=dataset_to_param(new_dataset),
)
outputs = self._cat1_outputs(history_id, inputs=inputs)
self.assertEqual(len(outputs), 1)
output1 = outputs[0]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
self.assertEqual(output1_content.strip(), "Cat1Test")
[docs] @skip_without_tool("cat1")
@uses_test_history(require_new=True)
def test_run_cat1_use_cached_job(self, history_id):
# Run simple non-upload tool with an input data parameter.
new_dataset = self.dataset_populator.new_dataset(history_id, content='Cat1Test')
inputs = dict(
input1=dataset_to_param(new_dataset),
)
outputs_one = self._run_cat1(history_id, inputs=inputs, assert_ok=True, wait_for_job=True)
outputs_two = self._run_cat1(history_id, inputs=inputs, use_cached_job=False, assert_ok=True, wait_for_job=True)
outputs_three = self._run_cat1(history_id, inputs=inputs, use_cached_job=True, assert_ok=True, wait_for_job=True)
dataset_details = []
for output in [outputs_one, outputs_two, outputs_three]:
output_id = output['outputs'][0]['id']
dataset_details.append(self._get("datasets/%s" % output_id).json())
filenames = [dd['file_name'] for dd in dataset_details]
assert len(filenames) == 3, filenames
assert len(set(filenames)) <= 2, filenames
[docs] @skip_without_tool("cat1")
@uses_test_history(require_new=False)
def test_run_cat1_listified_param(self, history_id):
# Run simple non-upload tool with an input data parameter.
new_dataset = self.dataset_populator.new_dataset(history_id, content='Cat1Testlistified')
inputs = dict(
input1=[dataset_to_param(new_dataset)],
)
outputs = self._cat1_outputs(history_id, inputs=inputs)
self.assertEqual(len(outputs), 1)
output1 = outputs[0]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
self.assertEqual(output1_content.strip(), "Cat1Testlistified")
[docs] @skip_without_tool("multiple_versions")
@uses_test_history(require_new=False)
def test_run_by_versions(self, history_id):
for version in ["0.1", "0.2"]:
# Run simple non-upload tool with an input data parameter.
inputs = dict()
outputs = self._run_and_get_outputs(tool_id="multiple_versions", history_id=history_id, inputs=inputs, tool_version=version)
self.assertEqual(len(outputs), 1)
output1 = outputs[0]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
self.assertEqual(output1_content.strip(), "Version " + version)
[docs] @skip_without_tool("multiple_versions")
@uses_test_history(require_new=False)
def test_show_with_wrong_tool_version_in_tool_id(self, history_id):
tool_info = self._show_valid_tool("multiple_versions", tool_version="0.01")
# Return last version
assert tool_info['version'] == "0.2"
[docs] @skip_without_tool("cat1")
@uses_test_history(require_new=False)
def test_run_cat1_single_meta_wrapper(self, history_id):
# Wrap input in a no-op meta parameter wrapper like Sam is planning to
# use for all UI API submissions.
new_dataset = self.dataset_populator.new_dataset(history_id, content='123')
inputs = dict(
input1={'batch': False, 'values': [dataset_to_param(new_dataset)]},
)
outputs = self._cat1_outputs(history_id, inputs=inputs)
self.assertEqual(len(outputs), 1)
output1 = outputs[0]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
self.assertEqual(output1_content.strip(), "123")
[docs] @skip_without_tool("cat1")
@uses_test_history(require_new=False)
def test_guess_derived_permissions(self, history_id):
def assert_inputs(inputs, can_be_used=True):
# Until we make the dataset private, _different_user() can use it:
with self._different_user_and_history() as other_history_id:
response = self._run("cat1", other_history_id, inputs)
if can_be_used:
assert response.status_code == 200
else:
self._assert_dataset_permission_denied_response(response)
new_dataset = self.dataset_populator.new_dataset(history_id, content='Cat1Test')
inputs = dict(
input1=dataset_to_param(new_dataset),
)
# Until we make the dataset private, _different_user() can use it:
assert_inputs(inputs, can_be_used=True)
self.dataset_populator.make_private(history_id, new_dataset["id"])
# _different_user can no longer use the input dataset.
assert_inputs(inputs, can_be_used=False)
outputs = self._cat1_outputs(history_id, inputs=inputs)
self.assertEqual(len(outputs), 1)
output1 = outputs[0]
inputs_2 = dict(
input1=dataset_to_param(output1),
)
# _different_user cannot use datasets derived from the private input.
assert_inputs(inputs_2, can_be_used=False)
[docs] @skip_without_tool("collection_creates_list")
@uses_test_history(require_new=False)
def test_guess_derived_permissions_collections(self, history_id):
def first_element_dataset_id(hdca):
# Fetch full and updated details for HDCA
print(hdca)
full_hdca = self.dataset_populator.get_history_collection_details(history_id, hid=hdca["hid"])
elements = full_hdca["elements"]
element0 = elements[0]["object"]
return element0["id"]
response = self.dataset_collection_populator.create_list_in_history(history_id, contents=["a\nb\nc\nd", "e\nf\ng\nh"], direct_upload=True)
self._assert_status_code_is(response, 200)
hdca = response.json()["output_collections"][0]
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
inputs = {
"input1": {"src": "hdca", "id": hdca["id"]},
}
public_output_response = self._run("collection_creates_list", history_id, inputs)
self._assert_status_code_is(public_output_response, 200)
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
input_element_id = first_element_dataset_id(hdca)
self.dataset_populator.make_private(history_id, input_element_id)
private_output_response = self._run("collection_creates_list", history_id, inputs)
self._assert_status_code_is(private_output_response, 200)
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
public_element_id = first_element_dataset_id(public_output_response.json()["output_collections"][0])
private_element_id = first_element_dataset_id(private_output_response.json()["output_collections"][0])
def _dataset_accessible(dataset_id):
contents_response = self._get("histories/%s/contents/%s" % (history_id, dataset_id)).json()
return "name" in contents_response
with self._different_user():
assert _dataset_accessible(public_element_id)
assert not _dataset_accessible(private_element_id)
[docs] @skip_without_tool("validation_default")
@uses_test_history(require_new=False)
def test_validation(self, history_id):
inputs = {
'select_param': "\" ; echo \"moo",
}
response = self._run("validation_default", history_id, inputs)
self._assert_status_code_is(response, 400)
[docs] @skip_without_tool("validation_empty_dataset")
@uses_test_history(require_new=False)
def test_validation_empty_dataset(self, history_id):
inputs = {
}
outputs = self._run_and_get_outputs('empty_output', history_id, inputs)
empty_dataset = outputs[0]
inputs = {
'input1': dataset_to_param(empty_dataset),
}
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
response = self._run("validation_empty_dataset", history_id, inputs)
self._assert_status_code_is(response, 400)
[docs] @skip_without_tool("validation_repeat")
@uses_test_history(require_new=False)
def test_validation_in_repeat(self, history_id):
inputs = {
'r1_0|text': "123",
'r2_0|text': "",
}
response = self._run("validation_repeat", history_id, inputs)
self._assert_status_code_is(response, 400)
[docs] @skip_without_tool("multi_select")
@uses_test_history(require_new=False)
def test_select_legal_values(self, history_id):
inputs = {
'select_ex': 'not_option',
}
response = self._run("multi_select", history_id, inputs)
self._assert_status_code_is(response, 400)
[docs] @skip_without_tool("column_param")
@uses_test_history(require_new=False)
def test_column_legal_values(self, history_id):
new_dataset1 = self.dataset_populator.new_dataset(history_id, content='#col1\tcol2')
inputs = {
'input1': {"src": "hda", "id": new_dataset1["id"]},
'col': "' ; echo 'moo",
}
response = self._run("column_param", history_id, inputs)
# This needs to either fail at submit time or at job prepare time, but we have
# to make sure the job doesn't run.
if response.status_code == 200:
job = response.json()["jobs"][0]
final_job_state = self.dataset_populator.wait_for_job(job["id"])
assert final_job_state == "error"
[docs] @skip_without_tool("collection_paired_test")
@uses_test_history(require_new=True)
def test_collection_parameter(self, history_id):
hdca_id = self._build_pair(history_id, ["123\n", "456\n"])
inputs = {
"f1": {"src": "hdca", "id": hdca_id},
}
output = self._run("collection_paired_test", history_id, inputs, assert_ok=True)
assert len(output['jobs']) == 1
assert len(output['implicit_collections']) == 0
assert len(output['outputs']) == 1
contents = self.dataset_populator.get_history_dataset_content(history_id, hid=4)
assert contents.strip() == "123\n456", contents
[docs] @skip_without_tool("collection_creates_pair")
@uses_test_history(require_new=False)
def test_paired_collection_output(self, history_id):
new_dataset1 = self.dataset_populator.new_dataset(history_id, content='123\n456\n789\n0ab')
inputs = {
"input1": {"src": "hda", "id": new_dataset1["id"]},
}
# TODO: shouldn't need this wait
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
create = self._run("collection_creates_pair", history_id, inputs, assert_ok=True)
output_collection = self._assert_one_job_one_collection_run(create)
element0, element1 = self._assert_elements_are(output_collection, "forward", "reverse")
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
self._verify_element(history_id, element0, contents="123\n789\n", file_ext="txt", visible=False)
self._verify_element(history_id, element1, contents="456\n0ab\n", file_ext="txt", visible=False)
[docs] @skip_without_tool("collection_creates_list")
@uses_test_history(require_new=False)
def test_list_collection_output(self, history_id):
create_response = self.dataset_collection_populator.create_list_in_history(history_id, contents=["a\nb\nc\nd", "e\nf\ng\nh"])
hdca_id = create_response.json()["id"]
create = self.dataset_populator.run_collection_creates_list(history_id, hdca_id)
output_collection = self._assert_one_job_one_collection_run(create)
element0, element1 = self._assert_elements_are(output_collection, "data1", "data2")
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
self._verify_element(history_id, element0, contents="identifier is data1\n", file_ext="txt")
self._verify_element(history_id, element1, contents="identifier is data2\n", file_ext="txt")
[docs] @skip_without_tool("collection_creates_list_2")
@uses_test_history(require_new=False)
def test_list_collection_output_format_source(self, history_id):
# test using format_source with a tool
new_dataset1 = self.dataset_populator.new_dataset(history_id, content='#col1\tcol2')
create_response = self.dataset_collection_populator.create_list_in_history(history_id, contents=["a\tb\nc\td", "e\tf\ng\th"])
hdca_id = create_response.json()["id"]
inputs = {
"header": {"src": "hda", "id": new_dataset1["id"]},
"input_collect": {"src": "hdca", "id": hdca_id},
}
# TODO: real problem here - shouldn't have to have this wait.
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
create = self._run("collection_creates_list_2", history_id, inputs, assert_ok=True)
output_collection = self._assert_one_job_one_collection_run(create)
element0, element1 = self._assert_elements_are(output_collection, "data1", "data2")
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
self._verify_element(history_id, element0, contents="#col1\tcol2\na\tb\nc\td\n", file_ext="txt")
self._verify_element(history_id, element1, contents="#col1\tcol2\ne\tf\ng\th\n", file_ext="txt")
[docs] @skip_without_tool("collection_split_on_column")
@uses_test_history(require_new=False)
def test_dynamic_list_output(self, history_id):
new_dataset1 = self.dataset_populator.new_dataset(history_id, content='samp1\t1\nsamp1\t3\nsamp2\t2\nsamp2\t4\n')
inputs = {
'input1': dataset_to_param(new_dataset1),
}
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
create = self._run("collection_split_on_column", history_id, inputs, assert_ok=True)
output_collection = self._assert_one_job_one_collection_run(create)
self._assert_has_keys(output_collection, "id", "name", "elements", "populated")
assert not output_collection["populated"]
assert len(output_collection["elements"]) == 0
self.assertEqual(output_collection["name"], "Table split on first column")
self.dataset_populator.wait_for_job(create["jobs"][0]["id"], assert_ok=True)
get_collection_response = self._get("dataset_collections/%s" % output_collection["id"], data={"instance_type": "history"})
self._assert_status_code_is(get_collection_response, 200)
output_collection = get_collection_response.json()
self._assert_has_keys(output_collection, "id", "name", "elements", "populated")
assert output_collection["populated"]
self.assertEqual(output_collection["name"], "Table split on first column")
assert len(output_collection["elements"]) == 2
output_element_0 = output_collection["elements"][0]
assert output_element_0["element_index"] == 0
assert output_element_0["element_identifier"] == "samp1"
output_element_hda_0 = output_element_0["object"]
assert output_element_hda_0["metadata_column_types"] is not None
[docs] @skip_without_tool("collection_creates_dynamic_nested")
@uses_test_history(require_new=False)
def test_dynamic_list_output_datasets_in_failed_state(self, history_id):
inputs = {
'fail_bool': True
}
create = self._run("collection_creates_dynamic_nested", history_id, inputs, assert_ok=False, wait_for_job=True)
self._assert_status_code_is(create, 200)
collection = self._get("dataset_collections/%s" % create.json()["output_collections"][0]["id"], data={"instance_type": "history"}).json()
assert collection['element_count'] == 3
for nested_collection in collection['elements']:
nested_collection = nested_collection['object']
assert nested_collection['element_count'] == 2
for element in nested_collection['elements']:
assert element['object']['state'] == 'error'
[docs] def test_nonadmin_users_cannot_create_tools(self):
payload = dict(
representation=json.dumps(MINIMAL_TOOL),
)
create_response = self._post("dynamic_tools", data=payload, admin=False)
self._assert_status_code_is(create_response, 403)
[docs] def test_dynamic_tool_1(self):
# Create tool.
self.dataset_populator.create_tool(MINIMAL_TOOL)
# Run tool.
history_id = self.dataset_populator.new_history()
inputs = {}
self._run("minimal_tool", history_id, inputs)
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
output_content = self.dataset_populator.get_history_dataset_content(history_id)
self.assertEqual(output_content, "Hello World\n")
[docs] def test_dynamic_tool_from_path(self):
# Create tool.
dynamic_tool_path = os.path.join(galaxy_root_path, "lib", "galaxy_test", "base", "data", "minimal_tool_no_id.json")
tool_response = self.dataset_populator.create_tool_from_path(dynamic_tool_path)
self._assert_has_keys(tool_response, "uuid")
# Run tool.
history_id = self.dataset_populator.new_history()
inputs = {}
self._run(history_id=history_id, inputs=inputs, tool_uuid=tool_response["uuid"])
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
output_content = self.dataset_populator.get_history_dataset_content(history_id)
self.assertEqual(output_content, "Hello World 2\n")
[docs] def test_dynamic_tool_no_id(self):
# Create tool.
tool_response = self.dataset_populator.create_tool(MINIMAL_TOOL_NO_ID)
self._assert_has_keys(tool_response, "uuid")
# Run tool.
history_id = self.dataset_populator.new_history()
inputs = {}
self._run(history_id=history_id, inputs=inputs, tool_uuid=tool_response["uuid"])
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
output_content = self.dataset_populator.get_history_dataset_content(history_id)
self.assertEqual(output_content, "Hello World 2\n")
[docs] def test_show_dynamic_tools(self):
# Create tool.
original_list = self.dataset_populator.list_dynamic_tools()
created_dynamic_tool_dict = self.dataset_populator.create_tool(MINIMAL_TOOL_NO_ID)
self._assert_has_keys(created_dynamic_tool_dict, "id", "uuid", "active")
created_id = created_dynamic_tool_dict["id"]
created_uuid = created_dynamic_tool_dict["uuid"]
new_list = self.dataset_populator.list_dynamic_tools()
for dynamic_tool_dict in original_list:
self._assert_has_keys(dynamic_tool_dict, "id", "uuid", "active")
assert dynamic_tool_dict["id"] != created_id
assert dynamic_tool_dict["uuid"] != created_uuid
found_id = False
found_uuid = False
for dynamic_tool_dict in new_list:
self._assert_has_keys(dynamic_tool_dict, "id", "uuid", "active")
found_id = found_id or dynamic_tool_dict["id"] == created_id
found_uuid = found_uuid or dynamic_tool_dict["uuid"] == created_uuid
assert found_id
assert found_uuid
[docs] def test_tool_deactivate(self):
# Create tool.
tool_response = self.dataset_populator.create_tool(MINIMAL_TOOL_NO_ID)
self._assert_has_keys(tool_response, "id", "uuid", "active")
assert tool_response["active"]
deactivate_response = self.dataset_populator.deactivate_dynamic_tool(tool_response["uuid"])
assert not deactivate_response["active"]
# Run tool.
history_id = self.dataset_populator.new_history()
inputs = {}
response = self._run(history_id=history_id, inputs=inputs, tool_uuid=tool_response["uuid"], assert_ok=False)
# Get a 404 when trying to run a deactivated tool.
self._assert_status_code_is(response, 404)
[docs] @skip_without_tool("cat1")
@uses_test_history(require_new=False)
def test_run_cat1_with_two_inputs(self, history_id):
# Run tool with an multiple data parameter and grouping (repeat)
new_dataset1 = self.dataset_populator.new_dataset(history_id, content='Cat1Test')
new_dataset2 = self.dataset_populator.new_dataset(history_id, content='Cat2Test')
inputs = {
'input1': dataset_to_param(new_dataset1),
'queries_0|input2': dataset_to_param(new_dataset2)
}
outputs = self._cat1_outputs(history_id, inputs=inputs)
self.assertEqual(len(outputs), 1)
output1 = outputs[0]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
self.assertEqual(output1_content.strip(), "Cat1Test\nCat2Test")
[docs] @skip_without_tool("mapper_two")
@uses_test_history(require_new=False)
def test_bam_state_regression(self, history_id):
# Test regression of https://github.com/galaxyproject/galaxy/issues/6856. With changes
# to metadata file flushing to optimize creating bam outputs and copying bam datasets
# we observed very subtle problems with HDA state changes on other files being flushed at
# the same time. This tests txt datasets finalized before and after the bam outputs as
# well as other bam files all flush properly during job completion.
new_dataset1 = self.dataset_populator.new_dataset(history_id, content='123\n456\n789')
inputs = {
'input1': dataset_to_param(new_dataset1),
'reference': dataset_to_param(new_dataset1),
}
outputs = self._run_and_get_outputs('mapper_two', history_id, inputs)
assert len(outputs) == 4
for output in outputs:
details = self.dataset_populator.get_history_dataset_details(history_id, dataset=output)
assert details["state"] == "ok"
[docs] @skip_without_tool("qc_stdout")
@uses_test_history(require_new=False)
def test_qc_messages(self, history_id):
new_dataset1 = self.dataset_populator.new_dataset(history_id, content='123\n456\n789')
inputs = {
'input1': dataset_to_param(new_dataset1),
'quality': 3,
}
create = self._run("qc_stdout", history_id, inputs, wait_for_job=True, assert_ok=True)
assert "jobs" in create, create
job_id = create["jobs"][0]["id"]
details = self.dataset_populator.get_job_details(job_id, full=True).json()
assert "job_messages" in details, details
qc_message = details["job_messages"][0]
# assert qc_message["code_desc"] == "QC Metrics for Tool", qc_message
assert qc_message["desc"] == "QC: Matched on Quality of sample is 30%."
assert qc_message["match"] == "Quality of sample is 30%."
assert qc_message["error_level"] == 1.1
[docs] @skip_without_tool("cat1")
@uses_test_history(require_new=False)
def test_multirun_cat1(self, history_id):
new_dataset1 = self.dataset_populator.new_dataset(history_id, content='123')
new_dataset2 = self.dataset_populator.new_dataset(history_id, content='456')
datasets = [dataset_to_param(new_dataset1), dataset_to_param(new_dataset2)]
inputs = {
"input1": {
'batch': True,
'values': datasets,
},
}
self._check_cat1_multirun(history_id, inputs)
def _check_cat1_multirun(self, history_id, inputs):
outputs = self._cat1_outputs(history_id, inputs=inputs)
self.assertEqual(len(outputs), 2)
output1 = outputs[0]
output2 = outputs[1]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
output2_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output2)
self.assertEqual(output1_content.strip(), "123")
self.assertEqual(output2_content.strip(), "456")
[docs] @skip_without_tool("random_lines1")
@uses_test_history(require_new=False)
def test_multirun_non_data_parameter(self, history_id):
new_dataset1 = self.dataset_populator.new_dataset(history_id, content='123\n456\n789')
inputs = {
'input': dataset_to_param(new_dataset1),
'num_lines': {'batch': True, 'values': [1, 2, 3]}
}
outputs = self._run_and_get_outputs('random_lines1', history_id, inputs)
# Assert we have three outputs with 1, 2, and 3 lines respectively.
assert len(outputs) == 3
outputs_contents = [self.dataset_populator.get_history_dataset_content(history_id, dataset=o).strip() for o in outputs]
assert sorted(len(c.split("\n")) for c in outputs_contents) == [1, 2, 3]
[docs] @skip_without_tool("cat1")
def test_multirun_in_repeat(self):
history_id, common_dataset, repeat_datasets = self._setup_repeat_multirun()
inputs = {
"input1": common_dataset,
'queries_0|input2': {'batch': True, 'values': repeat_datasets},
}
self._check_repeat_multirun(history_id, inputs)
[docs] @skip_without_tool("cat1")
def test_multirun_in_repeat_mismatch(self):
history_id, common_dataset, repeat_datasets = self._setup_repeat_multirun()
inputs = {
"input1": {'batch': False, 'values': [common_dataset]},
'queries_0|input2': {'batch': True, 'values': repeat_datasets},
}
self._check_repeat_multirun(history_id, inputs)
[docs] @skip_without_tool("cat1")
def test_multirun_on_multiple_inputs(self):
history_id, first_two, second_two = self._setup_two_multiruns()
inputs = {
"input1": {'batch': True, 'values': first_two},
'queries_0|input2': {'batch': True, 'values': second_two},
}
outputs = self._cat1_outputs(history_id, inputs=inputs)
self.assertEqual(len(outputs), 2)
outputs_contents = [self.dataset_populator.get_history_dataset_content(history_id, dataset=o).strip() for o in outputs]
assert "123\n789" in outputs_contents
assert "456\n0ab" in outputs_contents
[docs] @skip_without_tool("cat1")
def test_multirun_on_multiple_inputs_unlinked(self):
history_id, first_two, second_two = self._setup_two_multiruns()
inputs = {
"input1": {'batch': True, 'linked': False, 'values': first_two},
'queries_0|input2': {'batch': True, 'linked': False, 'values': second_two},
}
outputs = self._cat1_outputs(history_id, inputs=inputs)
outputs_contents = [self.dataset_populator.get_history_dataset_content(history_id, dataset=o).strip() for o in outputs]
self.assertEqual(len(outputs), 4)
assert "123\n789" in outputs_contents
assert "456\n0ab" in outputs_contents
assert "123\n0ab" in outputs_contents
assert "456\n789" in outputs_contents
def _assert_one_job_one_collection_run(self, create):
jobs = create['jobs']
implicit_collections = create['implicit_collections']
collections = create['output_collections']
self.assertEqual(len(jobs), 1)
self.assertEqual(len(implicit_collections), 0)
self.assertEqual(len(collections), 1)
output_collection = collections[0]
return output_collection
def _assert_elements_are(self, collection, *args):
elements = collection["elements"]
self.assertEqual(len(elements), len(args))
for index, element in enumerate(elements):
arg = args[index]
self.assertEqual(arg, element["element_identifier"])
return elements
def _verify_element(self, history_id, element, **props):
object_id = element["object"]["id"]
if "contents" in props:
expected_contents = props["contents"]
contents = self.dataset_populator.get_history_dataset_content(history_id, dataset_id=object_id)
self.assertEqual(contents, expected_contents)
del props["contents"]
if props:
details = self.dataset_populator.get_history_dataset_details(history_id, dataset_id=object_id)
for key, value in props.items():
self.assertEqual(details[key], value)
def _setup_repeat_multirun(self):
history_id = self.dataset_populator.new_history()
new_dataset1 = self.dataset_populator.new_dataset(history_id, content='123')
new_dataset2 = self.dataset_populator.new_dataset(history_id, content='456')
common_dataset = self.dataset_populator.new_dataset(history_id, content='Common')
return (
history_id,
dataset_to_param(common_dataset),
[dataset_to_param(new_dataset1), dataset_to_param(new_dataset2)]
)
def _check_repeat_multirun(self, history_id, inputs):
outputs = self._cat1_outputs(history_id, inputs=inputs)
self.assertEqual(len(outputs), 2)
output1 = outputs[0]
output2 = outputs[1]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
output2_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output2)
self.assertEqual(output1_content.strip(), "Common\n123")
self.assertEqual(output2_content.strip(), "Common\n456")
def _setup_two_multiruns(self):
history_id = self.dataset_populator.new_history()
new_dataset1 = self.dataset_populator.new_dataset(history_id, content='123')
new_dataset2 = self.dataset_populator.new_dataset(history_id, content='456')
new_dataset3 = self.dataset_populator.new_dataset(history_id, content='789')
new_dataset4 = self.dataset_populator.new_dataset(history_id, content='0ab')
return (
history_id,
[dataset_to_param(new_dataset1), dataset_to_param(new_dataset2)],
[dataset_to_param(new_dataset3), dataset_to_param(new_dataset4)]
)
[docs] @skip_without_tool("cat")
@uses_test_history(require_new=False)
def test_map_over_collection(self, history_id):
hdca_id = self._build_pair(history_id, ["123", "456"])
inputs = {
"input1": {'batch': True, 'values': [{'src': 'hdca', 'id': hdca_id}]},
}
self._run_and_check_simple_collection_mapping(history_id, inputs)
[docs] @skip_without_tool("cat1")
@uses_test_history(require_new=False)
def test_map_over_empty_collection(self, history_id):
hdca_id = self.dataset_collection_populator.create_list_in_history(history_id, contents=[]).json()['id']
inputs = {
"input1": {'batch': True, 'values': [{'src': 'hdca', 'id': hdca_id}]},
}
create = self._run_cat1(history_id, inputs=inputs, assert_ok=True)
outputs = create['outputs']
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 0)
self.assertEqual(len(outputs), 0)
self.assertEqual(len(implicit_collections), 1)
empty_output = implicit_collections[0]
assert empty_output["name"] == "Concatenate datasets on collection 1", empty_output
[docs] @skip_without_tool("output_action_change_format")
@uses_test_history(require_new=False)
def test_map_over_with_output_format_actions(self, history_id):
for use_action in ["do", "dont"]:
hdca_id = self._build_pair(history_id, ["123", "456"])
inputs = {
"input_cond|dispatch": use_action,
"input_cond|input": {'batch': True, 'values': [{'src': 'hdca', 'id': hdca_id}]},
}
create = self._run('output_action_change_format', history_id, inputs).json()
outputs = create['outputs']
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 2)
self.assertEqual(len(outputs), 2)
self.assertEqual(len(implicit_collections), 1)
output1 = outputs[0]
output2 = outputs[1]
output1_details = self.dataset_populator.get_history_dataset_details(history_id, dataset=output1)
output2_details = self.dataset_populator.get_history_dataset_details(history_id, dataset=output2)
assert output1_details["file_ext"] == "txt" if (use_action == "do") else "data"
assert output2_details["file_ext"] == "txt" if (use_action == "do") else "data"
[docs] @skip_without_tool("output_action_change_format_paired")
@uses_test_history(require_new=False)
def test_map_over_with_nested_paired_output_format_actions(self, history_id):
hdca_id = self.__build_nested_list(history_id)
inputs = {
"input": {'batch': True, 'values': [dict(map_over_type='paired', src="hdca", id=hdca_id)]}
}
create = self._run('output_action_change_format_paired', history_id, inputs).json()
outputs = create['outputs']
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 2)
self.assertEqual(len(outputs), 2)
self.assertEqual(len(implicit_collections), 1)
for output in outputs:
assert output["file_ext"] == "txt", output
[docs] @skip_without_tool("output_filter_with_input")
@uses_test_history(require_new=False)
def test_map_over_with_output_filter_no_filtering(self, history_id):
hdca_id = self.dataset_collection_populator.create_list_in_history(history_id).json()["id"]
inputs = {
"input_1": {'batch': True, 'values': [{'src': 'hdca', 'id': hdca_id}]},
"produce_out_1": "true",
"filter_text_1": "foo",
}
create = self._run('output_filter_with_input', history_id, inputs).json()
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 3)
self.assertEqual(len(implicit_collections), 3)
self._check_implicit_collection_populated(create)
[docs] @skip_without_tool("output_filter_with_input")
@uses_test_history(require_new=False)
def test_map_over_with_output_filter_one_filtered(self, history_id):
hdca_id = self.dataset_collection_populator.create_list_in_history(history_id).json()["id"]
inputs = {
"input_1": {'batch': True, 'values': [{'src': 'hdca', 'id': hdca_id}]},
"produce_out_1": "true",
"filter_text_1": "bar",
}
create = self._run('output_filter_with_input', history_id, inputs).json()
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 3)
self.assertEqual(len(implicit_collections), 2)
self._check_implicit_collection_populated(create)
[docs] @skip_without_tool("Cut1")
@uses_test_history(require_new=False)
def test_map_over_with_complex_output_actions(self, history_id):
hdca_id = self._bed_list(history_id)
inputs = {
"columnList": "c1,c2,c3,c4,c5",
"delimiter": "T",
"input": {'batch': True, 'values': [{'src': 'hdca', 'id': hdca_id}]},
}
create = self._run('Cut1', history_id, inputs).json()
outputs = create['outputs']
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 2)
self.assertEqual(len(outputs), 2)
self.assertEqual(len(implicit_collections), 1)
output1 = outputs[0]
output2 = outputs[1]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
output2_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output2)
assert output1_content.startswith("chr1")
assert output2_content.startswith("chr1")
[docs] @skip_without_tool("collection_creates_dynamic_list_of_pairs")
@uses_test_history(require_new=False)
def test_map_over_with_discovered_output_collection_elements(self, history_id):
hdca_id = self.dataset_collection_populator.create_list_in_history(history_id).json()["id"]
inputs = {
"input": {"batch": True, "values": [{"src": "hdca", "id": hdca_id}]}
}
create = self._run('collection_creates_dynamic_list_of_pairs', history_id, inputs).json()
implicit_collections = create['implicit_collections']
self.assertEqual(len(implicit_collections), 1)
self.assertEqual(implicit_collections[0]['collection_type'], 'list:list:paired')
self.assertEqual(implicit_collections[0]['elements'][0]['object']['element_count'], None)
self.dataset_populator.wait_for_job(create["jobs"][0]["id"], assert_ok=True)
hdca = self._get("histories/%s/contents/dataset_collections/%s" % (history_id, implicit_collections[0]['id'])).json()
self.assertEqual(hdca['elements'][0]['object']['elements'][0]['object']['elements'][0]['element_identifier'], 'forward')
def _bed_list(self, history_id):
bed1_contents = open(self.get_filename("1.bed"), "r").read()
bed2_contents = open(self.get_filename("2.bed"), "r").read()
contents = [bed1_contents, bed2_contents]
hdca = self.dataset_collection_populator.create_list_in_history(history_id, contents=contents).json()
return hdca["id"]
[docs] @skip_without_tool("identifier_single")
@uses_test_history(require_new=False)
def test_identifier_in_map(self, history_id):
hdca_id = self._build_pair(history_id, ["123", "456"])
inputs = {
"input1": {'batch': True, 'values': [{'src': 'hdca', 'id': hdca_id}]},
}
create_response = self._run("identifier_single", history_id, inputs)
self._assert_status_code_is(create_response, 200)
create = create_response.json()
outputs = create['outputs']
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 2)
self.assertEqual(len(outputs), 2)
self.assertEqual(len(implicit_collections), 1)
output1 = outputs[0]
output2 = outputs[1]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
output2_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output2)
self.assertEqual(output1_content.strip(), "forward")
self.assertEqual(output2_content.strip(), "reverse")
[docs] @skip_without_tool("identifier_single")
@uses_test_history(require_new=False)
def test_identifier_outside_map(self, history_id):
new_dataset1 = self.dataset_populator.new_dataset(history_id, content='123', name="Plain HDA")
inputs = {
"input1": {'src': 'hda', 'id': new_dataset1["id"]},
}
create_response = self._run("identifier_single", history_id, inputs)
self._assert_status_code_is(create_response, 200)
create = create_response.json()
outputs = create['outputs']
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 1)
self.assertEqual(len(outputs), 1)
self.assertEqual(len(implicit_collections), 0)
output1 = outputs[0]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
self.assertEqual(output1_content.strip(), "Plain HDA")
[docs] @skip_without_tool("identifier_multiple")
@uses_test_history(require_new=False)
def test_list_selectable_in_multidata_input(self, history_id):
self.dataset_collection_populator.create_list_in_history(history_id, contents=["123", "456"])
build = self._post("tools/identifier_multiple/build?history_id=%s" % history_id).json()
assert len(build['inputs'][0]['options']['hdca']) == 1
[docs] @skip_without_tool("identifier_multiple")
@uses_test_history(require_new=False)
def test_identifier_in_multiple_reduce(self, history_id):
hdca_id = self._build_pair(history_id, ["123", "456"])
inputs = {
"input1": {'src': 'hdca', 'id': hdca_id},
}
create_response = self._run("identifier_multiple", history_id, inputs)
self._assert_status_code_is(create_response, 200)
create = create_response.json()
outputs = create['outputs']
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 1)
self.assertEqual(len(outputs), 1)
self.assertEqual(len(implicit_collections), 0)
output1 = outputs[0]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
self.assertEqual(output1_content.strip(), "forward\nreverse")
[docs] @skip_without_tool("identifier_in_conditional")
@uses_test_history(require_new=False)
def test_identifier_map_over_multiple_input_in_conditional(self, history_id):
hdca_id = self._build_pair(history_id, ["123", "456"])
inputs = {
"outer_cond|input1": {'src': 'hdca', 'id': hdca_id},
}
create_response = self._run("identifier_in_conditional", history_id, inputs)
self._assert_status_code_is(create_response, 200)
create = create_response.json()
outputs = create['outputs']
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 1)
self.assertEqual(len(outputs), 1)
self.assertEqual(len(implicit_collections), 0)
output1 = outputs[0]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
self.assertEqual(output1_content.strip(), "forward\nreverse")
[docs] @skip_without_tool("identifier_in_conditional")
@uses_test_history(require_new=False)
def test_identifier_map_over_input_in_conditional(self, history_id):
hdca_id = self._build_pair(history_id, ["123", "456"])
inputs = {
"outer_cond|input1": {'batch': True, 'values': [{'src': 'hdca', 'id': hdca_id}]},
"outer_cond|multi_input": False,
}
create_response = self._run("identifier_in_conditional", history_id, inputs)
self._assert_status_code_is(create_response, 200)
create = create_response.json()
outputs = create['outputs']
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 2)
self.assertEqual(len(outputs), 2)
self.assertEqual(len(implicit_collections), 1)
output1 = outputs[0]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
self.assertEqual(output1_content.strip(), "forward")
output2 = outputs[1]
output2_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output2)
self.assertEqual(output2_content.strip(), "reverse")
[docs] @skip_without_tool("identifier_multiple_in_conditional")
@uses_test_history(require_new=False)
def test_identifier_multiple_reduce_in_conditional(self, history_id):
hdca_id = self._build_pair(history_id, ["123", "456"])
inputs = {
"outer_cond|inner_cond|input1": {'src': 'hdca', 'id': hdca_id},
}
create_response = self._run("identifier_multiple_in_conditional", history_id, inputs)
self._assert_status_code_is(create_response, 200)
create = create_response.json()
outputs = create['outputs']
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 1)
self.assertEqual(len(outputs), 1)
self.assertEqual(len(implicit_collections), 0)
output1 = outputs[0]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
self.assertEqual(output1_content.strip(), "forward\nreverse")
[docs] @skip_without_tool("identifier_multiple_in_repeat")
@uses_test_history(require_new=False)
def test_identifier_multiple_reduce_in_repeat(self, history_id):
hdca_id = self._build_pair(history_id, ["123", "456"])
inputs = {
"the_repeat_0|the_data|input1": {'src': 'hdca', 'id': hdca_id},
}
create_response = self._run("identifier_multiple_in_repeat", history_id, inputs)
self._assert_status_code_is(create_response, 200)
create = create_response.json()
outputs = create['outputs']
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 1)
self.assertEqual(len(outputs), 1)
self.assertEqual(len(implicit_collections), 0)
output1 = outputs[0]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
self.assertEqual(output1_content.strip(), "forward\nreverse")
[docs] @skip_without_tool("identifier_single_in_repeat")
@uses_test_history(require_new=False)
def test_identifier_single_in_repeat(self, history_id):
hdca_id = self._build_pair(history_id, ["123", "456"])
inputs = {
"the_repeat_0|the_data|input1": {'batch': True, 'values': [{'src': 'hdca', 'id': hdca_id}]}
}
create_response = self._run("identifier_single_in_repeat", history_id, inputs)
self._assert_status_code_is(create_response, 200)
create = create_response.json()
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 2)
self.assertEqual(len(implicit_collections), 1)
output_collection = implicit_collections[0]
elements = output_collection["elements"]
assert len(elements) == 2
forward_output = elements[0]["object"]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=forward_output)
assert output1_content.strip() == "forward", output1_content
[docs] @skip_without_tool("identifier_multiple_in_conditional")
@uses_test_history(require_new=False)
def test_identifier_multiple_in_conditional(self, history_id):
new_dataset1 = self.dataset_populator.new_dataset(history_id, content='123', name="Normal HDA1")
inputs = {
"outer_cond|inner_cond|input1": {'src': 'hda', 'id': new_dataset1["id"]},
}
create_response = self._run("identifier_multiple_in_conditional", history_id, inputs)
self._assert_status_code_is(create_response, 200)
create = create_response.json()
outputs = create['outputs']
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 1)
self.assertEqual(len(outputs), 1)
self.assertEqual(len(implicit_collections), 0)
output1 = outputs[0]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
self.assertEqual(output1_content.strip(), "Normal HDA1")
[docs] @skip_without_tool("identifier_multiple")
@uses_test_history(require_new=False)
def test_identifier_with_multiple_normal_datasets(self, history_id):
new_dataset1 = self.dataset_populator.new_dataset(history_id, content='123', name="Normal HDA1")
new_dataset2 = self.dataset_populator.new_dataset(history_id, content='456', name="Normal HDA2")
inputs = {
"input1": [
{'src': 'hda', 'id': new_dataset1["id"]},
{'src': 'hda', 'id': new_dataset2["id"]}
]
}
create_response = self._run("identifier_multiple", history_id, inputs)
self._assert_status_code_is(create_response, 200)
create = create_response.json()
outputs = create['outputs']
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 1)
self.assertEqual(len(outputs), 1)
self.assertEqual(len(implicit_collections), 0)
output1 = outputs[0]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
self.assertEqual(output1_content.strip(), "Normal HDA1\nNormal HDA2")
[docs] @skip_without_tool("identifier_collection")
@uses_test_history(require_new=False)
def test_identifier_with_data_collection(self, history_id):
element_identifiers = self.dataset_collection_populator.list_identifiers(history_id)
payload = dict(
instance_type="history",
history_id=history_id,
element_identifiers=json.dumps(element_identifiers),
collection_type="list",
)
create_response = self._post("dataset_collections", payload)
dataset_collection = create_response.json()
inputs = {
"input1": {'src': 'hdca', 'id': dataset_collection['id']},
}
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
create_response = self._run("identifier_collection", history_id, inputs)
self._assert_status_code_is(create_response, 200)
create = create_response.json()
outputs = create['outputs']
jobs = create['jobs']
self.assertEqual(len(jobs), 1)
self.assertEqual(len(outputs), 1)
output1 = outputs[0]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
self.assertEqual(output1_content.strip(), '\n'.join(d['name'] for d in element_identifiers))
[docs] @skip_without_tool("identifier_in_actions")
@uses_test_history(require_new=False)
def test_identifier_in_actions(self, history_id):
element_identifiers = self.dataset_collection_populator.list_identifiers(history_id, contents=["1\t2"])
payload = dict(
instance_type="history",
history_id=history_id,
element_identifiers=json.dumps(element_identifiers),
collection_type="list",
)
create_response = self._post("dataset_collections", payload)
dataset_collection = create_response.json()
inputs = {
"input": {'batch': True, 'values': [{'src': 'hdca', 'id': dataset_collection['id']}]},
}
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
create_response = self._run("identifier_in_actions", history_id, inputs)
self._assert_status_code_is(create_response, 200)
create = create_response.json()
outputs = create['outputs']
output1 = outputs[0]
output_details = self.dataset_populator.get_history_dataset_details(history_id, dataset=output1)
assert output_details["metadata_column_names"][1] == "data1", output_details
[docs] @skip_without_tool("cat1")
@uses_test_history(require_new=False)
def test_map_over_nested_collections(self, history_id):
hdca_id = self.__build_nested_list(history_id)
inputs = {
"input1": {'batch': True, 'values': [dict(src="hdca", id=hdca_id)]},
}
self._check_simple_cat1_over_nested_collections(history_id, inputs)
[docs] @skip_without_tool("collection_paired_structured_like")
@uses_test_history(require_new=False)
def test_paired_input_map_over_nested_collections(self, history_id):
hdca_id = self.__build_nested_list(history_id)
inputs = {
"input1": {'batch': True, 'values': [dict(map_over_type='paired', src="hdca", id=hdca_id)]},
}
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
create = self._run("collection_paired_structured_like", history_id, inputs, assert_ok=True)
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 2)
self.assertEqual(len(implicit_collections), 1)
implicit_collection = implicit_collections[0]
assert implicit_collection["collection_type"] == "list:paired", implicit_collection["collection_type"]
outer_elements = implicit_collection["elements"]
assert len(outer_elements) == 2
[docs] @skip_without_tool("collection_paired_conditional_structured_like")
@uses_test_history(require_new=False)
def test_paired_input_conditional_map_over_nested_collections(self, history_id):
hdca_id = self.__build_nested_list(history_id)
inputs = {
"cond|cond_param": "paired",
"cond|input1": {'batch': True, 'values': [dict(map_over_type='paired', src="hdca", id=hdca_id)]},
}
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
create = self._run("collection_paired_conditional_structured_like", history_id, inputs, assert_ok=True)
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 2)
self.assertEqual(len(implicit_collections), 1)
implicit_collection = implicit_collections[0]
assert implicit_collection["collection_type"] == "list:paired", implicit_collection["collection_type"]
outer_elements = implicit_collection["elements"]
assert len(outer_elements) == 2
def _check_simple_cat1_over_nested_collections(self, history_id, inputs):
create = self._run_cat1(history_id, inputs=inputs, assert_ok=True)
outputs = create['outputs']
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 4)
self.assertEqual(len(outputs), 4)
self.assertEqual(len(implicit_collections), 1)
implicit_collection = implicit_collections[0]
self._assert_has_keys(implicit_collection, "collection_type", "elements")
assert implicit_collection["collection_type"] == "list:paired"
assert len(implicit_collection["elements"]) == 2
first_element, second_element = implicit_collection["elements"]
assert first_element["element_identifier"] == "test0", first_element
assert second_element["element_identifier"] == "test1", second_element
first_object = first_element["object"]
assert first_object["collection_type"] == "paired"
assert len(first_object["elements"]) == 2
first_object_forward_element = first_object["elements"][0]
self.assertEqual(outputs[0]["id"], first_object_forward_element["object"]["id"])
[docs] @skip_without_tool("cat1")
@uses_test_history(require_new=False)
def test_map_over_two_collections(self, history_id):
hdca1_id = self._build_pair(history_id, ["123\n", "456\n"])
hdca2_id = self._build_pair(history_id, ["789\n", "0ab\n"])
inputs = {
"input1": {'batch': True, 'values': [{'src': 'hdca', 'id': hdca1_id}]},
"queries_0|input2": {'batch': True, 'values': [{'src': 'hdca', 'id': hdca2_id}]},
}
self._check_map_cat1_over_two_collections(history_id, inputs)
def _check_map_cat1_over_two_collections(self, history_id, inputs):
response = self._run_cat1(history_id, inputs)
self._assert_status_code_is(response, 200)
response_object = response.json()
outputs = response_object['outputs']
self.assertEqual(len(outputs), 2)
output1 = outputs[0]
output2 = outputs[1]
self.dataset_populator.wait_for_history(history_id)
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
output2_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output2)
self.assertEqual(output1_content.strip(), "123\n789")
self.assertEqual(output2_content.strip(), "456\n0ab")
self.assertEqual(len(response_object['jobs']), 2)
self.assertEqual(len(response_object['implicit_collections']), 1)
[docs] @skip_without_tool("cat1")
@uses_test_history(require_new=False)
def test_map_over_two_collections_unlinked(self, history_id):
hdca1_id = self._build_pair(history_id, ["123\n", "456\n"])
hdca2_id = self._build_pair(history_id, ["789\n", "0ab\n"])
inputs = {
"input1": {'batch': True, 'linked': False, 'values': [{'src': 'hdca', 'id': hdca1_id}]},
"queries_0|input2": {'batch': True, 'linked': False, 'values': [{'src': 'hdca', 'id': hdca2_id}]},
}
response = self._run_cat1(history_id, inputs)
self._assert_status_code_is(response, 200)
response_object = response.json()
outputs = response_object['outputs']
self.assertEqual(len(outputs), 4)
self.assertEqual(len(response_object['jobs']), 4)
implicit_collections = response_object['implicit_collections']
self.assertEqual(len(implicit_collections), 1)
implicit_collection = implicit_collections[0]
self.assertEqual(implicit_collection["collection_type"], "paired:paired")
outer_elements = implicit_collection["elements"]
assert len(outer_elements) == 2
element0, element1 = outer_elements
assert element0["element_identifier"] == "forward"
assert element1["element_identifier"] == "reverse"
elements0 = element0["object"]["elements"]
elements1 = element1["object"]["elements"]
assert len(elements0) == 2
assert len(elements1) == 2
element00, element01 = elements0
assert element00["element_identifier"] == "forward"
assert element01["element_identifier"] == "reverse"
element10, element11 = elements1
assert element10["element_identifier"] == "forward"
assert element11["element_identifier"] == "reverse"
expected_contents_list = [
(element00, "123\n789\n"),
(element01, "123\n0ab\n"),
(element10, "456\n789\n"),
(element11, "456\n0ab\n"),
]
for (element, expected_contents) in expected_contents_list:
dataset_id = element["object"]["id"]
contents = self.dataset_populator.get_history_dataset_content(history_id, dataset_id=dataset_id)
self.assertEqual(expected_contents, contents)
[docs] @skip_without_tool("cat1")
@uses_test_history(require_new=False)
def test_map_over_collected_and_individual_datasets(self, history_id):
hdca1_id = self._build_pair(history_id, ["123\n", "456\n"])
new_dataset1 = self.dataset_populator.new_dataset(history_id, content='789')
new_dataset2 = self.dataset_populator.new_dataset(history_id, content='0ab')
inputs = {
"input1": {'batch': True, 'values': [{'src': 'hdca', 'id': hdca1_id}]},
"queries_0|input2": {'batch': True, 'values': [dataset_to_param(new_dataset1), dataset_to_param(new_dataset2)]},
}
response = self._run_cat1(history_id, inputs)
self._assert_status_code_is(response, 200)
response_object = response.json()
outputs = response_object['outputs']
self.assertEqual(len(outputs), 2)
self.assertEqual(len(response_object['jobs']), 2)
self.assertEqual(len(response_object['implicit_collections']), 1)
[docs] @skip_without_tool("identifier_source")
def test_default_identifier_source_map_over(self):
with self.dataset_populator.test_history() as history_id:
input_a_hdca_id = self.dataset_collection_populator.create_list_in_history(history_id, contents=[("A", "A content")]).json()['id']
input_b_hdca_id = self.dataset_collection_populator.create_list_in_history(history_id, contents=[("B", "B content")]).json()['id']
inputs = {
"inputA": {'batch': True, 'values': [dict(src="hdca", id=input_a_hdca_id)]},
"inputB": {'batch': True, 'values': [dict(src="hdca", id=input_b_hdca_id)]},
}
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
create = self._run("identifier_source", history_id, inputs, assert_ok=True)
for implicit_collection in create['implicit_collections']:
if implicit_collection['output_name'] == 'outputA':
assert implicit_collection['elements'][0]['element_identifier'] == 'A'
else:
assert implicit_collection['elements'][0]['element_identifier'] == 'B'
[docs] @skip_without_tool("collection_creates_pair")
def test_map_over_collection_output(self):
with self.dataset_populator.test_history() as history_id:
create_response = self.dataset_collection_populator.create_list_in_history(history_id, contents=["a\nb\nc\nd", "e\nf\ng\nh"])
hdca_id = create_response.json()["id"]
inputs = {
"input1": {'batch': True, 'values': [dict(src="hdca", id=hdca_id)]},
}
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
create = self._run("collection_creates_pair", history_id, inputs, assert_ok=True)
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 2)
self.assertEqual(len(implicit_collections), 1)
implicit_collection = implicit_collections[0]
assert implicit_collection["collection_type"] == "list:paired", implicit_collection
outer_elements = implicit_collection["elements"]
assert len(outer_elements) == 2
element0, element1 = outer_elements
assert element0["element_identifier"] == "data1"
assert element1["element_identifier"] == "data2"
pair0, pair1 = element0["object"], element1["object"]
pair00, pair01 = pair0["elements"]
pair10, pair11 = pair1["elements"]
for pair in pair0, pair1:
assert "collection_type" in pair, pair
assert pair["collection_type"] == "paired", pair
pair_ids = []
for pair_element in pair00, pair01, pair10, pair11:
assert "object" in pair_element
pair_ids.append(pair_element["object"]["id"])
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
expected_contents = [
"a\nc\n",
"b\nd\n",
"e\ng\n",
"f\nh\n",
]
for i in range(4):
contents = self.dataset_populator.get_history_dataset_content(history_id, dataset_id=pair_ids[i])
self.assertEqual(expected_contents[i], contents)
[docs] @skip_without_tool("cat1")
def test_cannot_map_over_incompatible_collections(self):
with self.dataset_populator.test_history() as history_id:
hdca1_id = self._build_pair(history_id, ["123\n", "456\n"])
hdca2_id = self.dataset_collection_populator.create_list_in_history(history_id).json()["id"]
inputs = {
"input1": {
'batch': True,
'values': [{'src': 'hdca', 'id': hdca1_id}],
},
"queries_0|input2": {
'batch': True,
'values': [{'src': 'hdca', 'id': hdca2_id}],
},
}
run_response = self._run_cat1(history_id, inputs)
# TODO: Fix this error checking once switch over to new API decorator
# on server.
assert run_response.status_code >= 400
[docs] @skip_without_tool("__FILTER_FROM_FILE__")
def test_map_over_collection_structured_like(self):
with self.dataset_populator.test_history() as history_id:
hdca_id = self.dataset_collection_populator.create_list_in_history(history_id, contents=[("A", "A"), ("B", "B")]).json()['id']
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
inputs = {
"input": {'values': [dict(src="hdca", id=hdca_id)]},
"how|filter_source": {'batch': True, 'values': [dict(src="hdca", id=hdca_id)]}
}
implicit_collections = self._run("__FILTER_FROM_FILE__", history_id, inputs, assert_ok=True)['implicit_collections']
discarded_collection, filtered_collection = implicit_collections
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
history_contents = self.dataset_populator._get_contents_request(history_id).json()
# We should have a final collection count of 3 (2 nested collections, plus the input collection)
new_collections = len([c for c in history_contents if c['history_content_type'] == 'dataset_collection']) - 1
assert new_collections == 2, "Expected to generate 4 new, filtered collections, but got %d collections" % new_collections
assert filtered_collection['collection_type'] == discarded_collection['collection_type'] == 'list:list', filtered_collection
collection_details = self.dataset_populator.get_history_collection_details(history_id, hid=filtered_collection['hid'])
assert collection_details['element_count'] == 2
first_collection_level = collection_details['elements'][0]
assert first_collection_level['element_type'] == 'dataset_collection'
second_collection_level = first_collection_level['object']
assert second_collection_level['collection_type'] == 'list'
assert second_collection_level['elements'][0]['element_type'] == 'hda'
[docs] @skip_without_tool("collection_type_source")
def test_map_over_collection_type_source(self):
with self.dataset_populator.test_history() as history_id:
hdca_id = self.dataset_collection_populator.create_list_in_history(history_id, contents=[("A", "A"), ("B", "B")]).json()['id']
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
inputs = {
"input_collect": {'values': [dict(src="hdca", id=hdca_id)]},
"header": {'batch': True, 'values': [dict(src="hdca", id=hdca_id)]}
}
self._run("collection_type_source", history_id, inputs, assert_ok=True, wait_for_job=True)
collection_details = self.dataset_populator.get_history_collection_details(history_id, hid=4)
assert collection_details['elements'][0]['object']['elements'][0]['element_type'] == 'hda'
[docs] @skip_without_tool("multi_data_param")
def test_reduce_collections_legacy(self):
with self.dataset_populator.test_history() as history_id:
hdca1_id = self._build_pair(history_id, ["123\n", "456\n"])
hdca2_id = self.dataset_collection_populator.create_list_in_history(history_id).json()["id"]
inputs = {
"f1": "__collection_reduce__|%s" % hdca1_id,
"f2": "__collection_reduce__|%s" % hdca2_id,
}
self._check_simple_reduce_job(history_id, inputs)
[docs] @skip_without_tool("multi_data_param")
def test_reduce_collections(self):
with self.dataset_populator.test_history() as history_id:
hdca1_id = self._build_pair(history_id, ["123\n", "456\n"])
hdca2_id = self.dataset_collection_populator.create_list_in_history(history_id).json()["id"]
inputs = {
"f1": {'src': 'hdca', 'id': hdca1_id},
"f2": {'src': 'hdca', 'id': hdca2_id},
}
self._check_simple_reduce_job(history_id, inputs)
[docs] @skip_without_tool("multi_data_param")
def test_implicit_reduce_with_mapping(self):
with self.dataset_populator.test_history() as history_id:
hdca1_id = self._build_pair(history_id, ["123\n", "456\n"])
hdca2_id = self.dataset_collection_populator.create_list_of_list_in_history(history_id).json()["id"]
inputs = {
"f1": {'src': 'hdca', 'id': hdca1_id},
"f2": {
'batch': True,
'values': [{'src': 'hdca', 'map_over_type': 'list', 'id': hdca2_id}],
}
}
create = self._run("multi_data_param", history_id, inputs, assert_ok=True)
jobs = create['jobs']
implicit_collections = create['implicit_collections']
self.assertEqual(len(jobs), 1)
self.assertEqual(len(implicit_collections), 2)
output_hdca = self.dataset_populator.get_history_collection_details(history_id, hid=implicit_collections[0]["hid"])
assert output_hdca["collection_type"] == "list"
[docs] @skip_without_tool("column_multi_param")
def test_implicit_conversion_and_reduce(self):
with self.dataset_populator.test_history() as history_id:
self._run_implicit_collection_and_reduce(history_id=history_id, param="1")
[docs] @skip_without_tool("column_multi_param")
def test_implicit_conversion_and_reduce_invalid_param(self):
with self.dataset_populator.test_history() as history_id:
with pytest.raises(AssertionError):
self._run_implicit_collection_and_reduce(history_id=history_id, param="X")
details = self.dataset_populator.get_history_dataset_details(history_id=history_id, hid=3, assert_ok=False)
assert details['state'] == "error"
assert "parameter 'col': an invalid option" in details['misc_info']
def _run_implicit_collection_and_reduce(self, history_id, param):
fasta_path = self.test_data_resolver.get_filename("1.fasta")
with open(fasta_path, "r") as fasta_fh:
fasta_content = fasta_fh.read()
response = self.dataset_collection_populator.upload_collection(history_id, "list", elements=[
{
"name": "test0",
"src": "pasted",
"paste_content": fasta_content,
"ext": "fasta",
}
])
self._assert_status_code_is(response, 200)
hdca_id = response.json()["outputs"][0]["id"]
inputs = {
"input1": {'src': 'hdca', 'id': hdca_id},
"col": param,
}
create = self._run("column_multi_param", history_id, inputs, assert_ok=True)
jobs = create['jobs']
self.assertEqual(len(jobs), 1)
content = self.dataset_populator.get_history_dataset_content(history_id, hid=3)
assert content.strip() == 'hg17', content
[docs] @skip_without_tool("multi_data_repeat")
def test_reduce_collections_in_repeat(self):
with self.dataset_populator.test_history() as history_id:
hdca1_id = self._build_pair(history_id, ["123\n", "456\n"])
inputs = {
"outer_repeat_0|f1": {'src': 'hdca', 'id': hdca1_id},
}
create = self._run("multi_data_repeat", history_id, inputs, assert_ok=True)
outputs = create['outputs']
jobs = create['jobs']
self.assertEqual(len(jobs), 1)
self.assertEqual(len(outputs), 1)
output1 = outputs[0]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
assert output1_content.strip() == "123\n456", output1_content
[docs] @skip_without_tool("multi_data_repeat")
def test_reduce_collections_in_repeat_legacy(self):
with self.dataset_populator.test_history() as history_id:
hdca1_id = self._build_pair(history_id, ["123\n", "456\n"])
inputs = {
"outer_repeat_0|f1": "__collection_reduce__|%s" % hdca1_id,
}
create = self._run("multi_data_repeat", history_id, inputs, assert_ok=True)
outputs = create['outputs']
jobs = create['jobs']
self.assertEqual(len(jobs), 1)
self.assertEqual(len(outputs), 1)
output1 = outputs[0]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
assert output1_content.strip() == "123\n456", output1_content
[docs] @skip_without_tool("multi_data_param")
def test_reduce_multiple_lists_on_multi_data(self):
with self.dataset_populator.test_history() as history_id:
hdca1_id = self._build_pair(history_id, ["123\n", "456\n"])
hdca2_id = self.dataset_collection_populator.create_list_in_history(history_id).json()["id"]
inputs = {
"f1": [{'src': 'hdca', 'id': hdca1_id}, {'src': 'hdca', 'id': hdca2_id}],
"f2": [{'src': 'hdca', 'id': hdca1_id}],
}
create = self._run("multi_data_param", history_id, inputs, assert_ok=True)
outputs = create['outputs']
jobs = create['jobs']
self.assertEqual(len(jobs), 1)
self.assertEqual(len(outputs), 2)
output1, output2 = outputs
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
output2_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output2)
self.assertEqual(output1_content.strip(), "123\n456\nTestData123\nTestData123\nTestData123")
self.assertEqual(output2_content.strip(), "123\n456")
def _check_simple_reduce_job(self, history_id, inputs):
create = self._run("multi_data_param", history_id, inputs, assert_ok=True)
outputs = create['outputs']
jobs = create['jobs']
self.assertEqual(len(jobs), 1)
self.assertEqual(len(outputs), 2)
output1, output2 = outputs
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
output2_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output2)
assert output1_content.strip() == "123\n456", output1_content
assert len(output2_content.strip().split("\n")) == 3, output2_content
[docs] @skip_without_tool("collection_paired_test")
def test_subcollection_mapping(self):
with self.dataset_populator.test_history() as history_id:
hdca_list_id = self.__build_nested_list(history_id)
inputs = {
"f1": {
'batch': True,
'values': [{'src': 'hdca', 'map_over_type': 'paired', 'id': hdca_list_id}],
}
}
self._check_simple_subcollection_mapping(history_id, inputs)
def _check_simple_subcollection_mapping(self, history_id, inputs):
# Following wait not really needed - just getting so many database
# locked errors with sqlite.
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
outputs = self._run_and_get_outputs("collection_paired_test", history_id, inputs)
assert len(outputs), 2
output1 = outputs[0]
output2 = outputs[1]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
output2_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output2)
assert output1_content.strip() == "123\n456", output1_content
assert output2_content.strip() == "789\n0ab", output2_content
[docs] @skip_without_tool("collection_mixed_param")
def test_combined_mapping_and_subcollection_mapping(self):
with self.dataset_populator.test_history() as history_id:
nested_list_id = self.__build_nested_list(history_id)
create_response = self.dataset_collection_populator.create_list_in_history(history_id, contents=["xxx\n", "yyy\n"])
list_id = create_response.json()["id"]
inputs = {
"f1": {
'batch': True,
'values': [{'src': 'hdca', 'map_over_type': 'paired', 'id': nested_list_id}],
},
"f2": {
'batch': True,
'values': [{'src': 'hdca', 'id': list_id}],
},
}
self._check_combined_mapping_and_subcollection_mapping(history_id, inputs)
def _check_combined_mapping_and_subcollection_mapping(self, history_id, inputs):
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
outputs = self._run_and_get_outputs("collection_mixed_param", history_id, inputs)
assert len(outputs), 2
output1 = outputs[0]
output2 = outputs[1]
output1_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output1)
output2_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output2)
assert output1_content.strip() == "123\n456\nxxx", output1_content
assert output2_content.strip() == "789\n0ab\nyyy", output2_content
def _check_implicit_collection_populated(self, run_response):
implicit_collections = run_response["implicit_collections"]
assert implicit_collections
for implicit_collection in implicit_collections:
assert implicit_collection["populated_state"] == "ok"
def _cat1_outputs(self, history_id, inputs):
return self._run_outputs(self._run_cat1(history_id, inputs))
def _run_and_get_outputs(self, tool_id, history_id, inputs, tool_version=None):
return self._run_outputs(self._run(tool_id, history_id, inputs, tool_version=tool_version))
def _run_outputs(self, create_response):
self._assert_status_code_is(create_response, 200)
return create_response.json()['outputs']
def _run_cat1(self, history_id, inputs, assert_ok=False, **kwargs):
return self._run('cat1', history_id, inputs, assert_ok=assert_ok, **kwargs)
def __tool_ids(self):
index = self._get("tools")
tools_index = index.json()
# In panels by default, so flatten out sections...
tools = []
for tool_or_section in tools_index:
if "elems" in tool_or_section:
tools.extend(tool_or_section["elems"])
else:
tools.append(tool_or_section)
tool_ids = [_["id"] for _ in tools]
return tool_ids
[docs] @skip_without_tool("collection_cat_group_tag_multiple")
@uses_test_history(require_new=False)
def test_group_tag_selection(self, history_id):
input_hdca_id = self.__build_group_list(history_id)
inputs = {
"input1": {"src": "hdca", "id": input_hdca_id},
"group": "condition:treated",
}
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
response = self._run("collection_cat_group_tag", history_id, inputs, assert_ok=True)
outputs = response["outputs"]
self.assertEqual(len(outputs), 1)
output = outputs[0]
output_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output)
self.assertEqual(output_content.strip(), "123\n456")
[docs] @skip_without_tool("collection_cat_group_tag_multiple")
@uses_test_history(require_new=False)
def test_group_tag_selection_multiple(self, history_id):
input_hdca_id = self.__build_group_list(history_id)
inputs = {
"input1": {"src": "hdca", "id": input_hdca_id},
"groups": "condition:treated,type:single",
}
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
response = self._run("collection_cat_group_tag_multiple", history_id, inputs, assert_ok=True)
outputs = response["outputs"]
self.assertEqual(len(outputs), 1)
output = outputs[0]
output_content = self.dataset_populator.get_history_dataset_content(history_id, dataset=output)
self.assertEqual(output_content.strip(), "123\n456\n456\n0ab")
[docs] @skip_without_tool("expression_forty_two")
def test_galaxy_expression_tool_simplest(self):
history_id = self.dataset_populator.new_history()
inputs = {
}
run_response = self._run(
"expression_forty_two", history_id, inputs
)
self._assert_status_code_is(run_response, 200)
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
output_content = self.dataset_populator.get_history_dataset_content(history_id)
self.assertEqual(output_content, "42")
[docs] @skip_without_tool("expression_parse_int")
def test_galaxy_expression_tool_simple(self):
history_id = self.dataset_populator.new_history()
inputs = {
'input1': '7',
}
run_response = self._run(
"expression_parse_int", history_id, inputs
)
self._assert_status_code_is(run_response, 200)
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
output_content = self.dataset_populator.get_history_dataset_content(history_id)
self.assertEqual(output_content, "7")
[docs] @skip_without_tool("expression_log_line_count")
def test_galaxy_expression_metadata(self):
history_id = self.dataset_populator.new_history()
new_dataset1 = self.dataset_populator.new_dataset(history_id, content='1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14')
inputs = {
'input1': dataset_to_param(new_dataset1),
}
run_response = self._run(
"expression_log_line_count", history_id, inputs
)
self._assert_status_code_is(run_response, 200)
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
output_content = self.dataset_populator.get_history_dataset_content(history_id)
self.assertEqual(output_content, "3")
def __build_group_list(self, history_id):
response = self.dataset_collection_populator.upload_collection(history_id, "list", elements=[
{
"name": "test0",
"src": "pasted",
"paste_content": "123\n",
"ext": "txt",
"tags": ["group:type:paired-end", "group:condition:treated"],
},
{
"name": "test1",
"src": "pasted",
"paste_content": "456\n",
"ext": "txt",
"tags": ["group:type:single", "group:condition:treated"],
},
{
"name": "test2",
"src": "pasted",
"paste_content": "789\n",
"ext": "txt",
"tags": ["group:type:paired-end", "group:condition:untreated"],
},
{
"name": "test3",
"src": "pasted",
"paste_content": "0ab\n",
"ext": "txt",
"tags": ["group:type:single", "group:condition:untreated"],
}
])
self._assert_status_code_is(response, 200)
hdca_list_id = response.json()["outputs"][0]["id"]
return hdca_list_id
def __build_nested_list(self, history_id):
response = self.dataset_collection_populator.upload_collection(history_id, "list:paired", elements=[
{
"name": "test0",
"elements": [
{"src": "pasted", "paste_content": "123\n", "name": "forward", "ext": "txt"},
{"src": "pasted", "paste_content": "456\n", "name": "reverse", "ext": "txt"},
]
},
{
"name": "test1",
"elements": [
{"src": "pasted", "paste_content": "789\n", "name": "forward", "ext": "txt"},
{"src": "pasted", "paste_content": "0ab\n", "name": "reverse", "ext": "txt"},
]
}
])
self._assert_status_code_is(response, 200)
hdca_list_id = response.json()["outputs"][0]["id"]
return hdca_list_id
def _build_pair(self, history_id, contents):
create_response = self.dataset_collection_populator.create_pair_in_history(history_id, contents=contents, direct_upload=True)
hdca_id = create_response.json()["outputs"][0]["id"]
return hdca_id
def _assert_dataset_permission_denied_response(self, response):
# TODO: This should be 403, should just need to throw more specific exception in the
# Galaxy code.
assert response.status_code != 200
# err_message = response.json()["err_msg"]
# assert "User does not have permission to use a dataset" in err_message, err_message
@contextlib.contextmanager
def _different_user_and_history(self):
with self._different_user():
with self.dataset_populator.test_history() as other_history_id:
yield other_history_id