def test_dict_prompting_error_concatenate_different_types(self):
context = RenderContext(
None,
None,
TableShape(3, [
Column("A", ColumnType.TEXT()),
Column("B", ColumnType.DATETIME())
]),
None,
None,
)
schema = ParamDType.Dict({
"x":
ParamDType.Column(column_types=frozenset({"number"})),
"y":
ParamDType.Column(column_types=frozenset({"number"})),
})
with self.assertRaises(PromptingError) as cm:
clean_value(schema, {"x": "A", "y": "B"}, context)
self.assertEqual(
cm.exception.errors,
[
PromptingError.WrongColumnType(["A"], "text",
frozenset({"number"})),
PromptingError.WrongColumnType(["B"], "datetime",
frozenset({"number"})),
],
)
def test_clean_condition_timestamp_wrong_value(self):
context = self._render_context(
input_table=arrow_table(
{"A": pa.array([datetime.now()], pa.timestamp("ns"))}
)
)
with self.assertRaises(PromptingError) as cm:
clean_value(
ParamDType.Condition(),
{
"operation": "timestamp_is_greater_than",
"column": "A",
"value": "Yesterday",
"isCaseSensitive": False,
"isRegex": False,
},
context,
)
self.assertEqual(
cm.exception.errors,
[
PromptingError.CannotCoerceValueToTimestamp("Yesterday"),
],
)
def test_list_prompting_error_concatenate_different_type_to_text(self):
context = RenderContext(
None,
None,
TableShape(
3,
[
Column("A", ColumnType.NUMBER()),
Column("B", ColumnType.DATETIME())
],
),
None,
None,
)
schema = ParamDType.List(inner_dtype=ParamDType.Column(
column_types=frozenset({"text"})))
with self.assertRaises(PromptingError) as cm:
clean_value(schema, ["A", "B"], context)
self.assertEqual(
cm.exception.errors,
[
PromptingError.WrongColumnType(["A", "B"], None,
frozenset({"text"}))
],
)
def test_clean_tab_tab_delete_race_raises_unneededexecution(self):
"""
If a user deletes the tab during render, raise UnneededExecution.
It doesn't really matter _what_ the return value is, since the render()
result will never be saved if this WfModule's delta has changed.
UnneededExecution just seems like the quickest way out of this mess:
it's an error the caller is meant to raise anyway, unlike
`Tab.DoesNotExist`.
"""
# tab_output is what 'render' _thinks_ the output should be
tab_output = ProcessResult(pd.DataFrame({'A': [1, 2]}))
workflow = Workflow.create_and_init()
tab = workflow.tabs.first()
wfm = tab.wf_modules.create(
order=0, last_relevant_delta_id=workflow.last_delta_id)
wfm.cache_render_result(workflow.last_delta_id, tab_output)
tab.is_deleted = True
tab.save(update_fields=['is_deleted'])
# Simulate reality: wfm.last_relevant_delta_id will change
wfm.last_relevant_delta_id += 1
wfm.save(update_fields=['last_relevant_delta_id'])
context = RenderContext(
workflow.id, None, None, {
tab.slug: StepResultShape('ok', tab_output.table_shape),
}, None)
with self.assertRaises(UnneededExecution):
clean_value(ParamDType.Tab(), tab.slug, context)
def test_clean_multichartseries_non_number_is_prompting_error(self):
context = RenderContext(
None,
None,
TableShape(3, [
Column("A", ColumnType.TEXT()),
Column("B", ColumnType.DATETIME())
]),
None,
None,
)
value = [
{
"column": "A",
"color": "#aaaaaa"
},
{
"column": "B",
"color": "#cccccc"
},
]
with self.assertRaises(PromptingError) as cm:
clean_value(ParamDType.Multichartseries(), value, context)
self.assertEqual(
cm.exception.errors,
[
PromptingError.WrongColumnType(["A"], "text",
frozenset({"number"})),
PromptingError.WrongColumnType(["B"], "datetime",
frozenset({"number"})),
],
)
def test_clean_multichartseries_non_number_is_prompting_error(self):
context = self._render_context(input_table=arrow_table({
"A": ["a"],
"B":
pa.array([datetime.now()], pa.timestamp("ns"))
}))
value = [
{
"column": "A",
"color": "#aaaaaa"
},
{
"column": "B",
"color": "#cccccc"
},
]
with self.assertRaises(PromptingError) as cm:
clean_value(ParamDType.Multichartseries(), value, context)
self.assertEqual(
cm.exception.errors,
[
PromptingError.WrongColumnType(["A"], "text",
frozenset({"number"})),
PromptingError.WrongColumnType(["B"], "datetime",
frozenset({"number"})),
],
)
def test_clean_tab_wf_module_changed_raises_unneededexecution(self):
"""
If a user changes tabs' output during render, raise UnneededExecution.
It doesn't really matter _what_ the return value is, since the render()
result will never be saved if this WfModule's delta has changed.
UnneededExecution seems like the simplest contract to enforce.
"""
# tab_output is what 'render' _thinks_ the output should be
tab_output = ProcessResult(pd.DataFrame({'A': [1, 2]}))
workflow = Workflow.create_and_init()
tab = workflow.tabs.first()
wfm = tab.wf_modules.create(
order=0, last_relevant_delta_id=workflow.last_delta_id)
wfm.cache_render_result(workflow.last_delta_id, tab_output)
# Simulate reality: wfm.last_relevant_delta_id will change
wfm.last_relevant_delta_id += 1
wfm.save(update_fields=['last_relevant_delta_id'])
context = RenderContext(
workflow.id, None, None, {
tab.slug: StepResultShape('ok', tab_output.table_shape),
}, None)
with self.assertRaises(UnneededExecution):
clean_value(ParamDType.Tab(), tab.slug, context)
def test_clean_multicolumn_prompting_error_convert_to_text(self):
# TODO make this _automatic_ instead of quick-fix?
# ... but for now: prompt for a Quick Fix.
context = RenderContext(
None,
None,
TableShape(
3,
[
Column("A", ColumnType.NUMBER()),
Column("B", ColumnType.DATETIME()),
Column("C", ColumnType.TEXT()),
],
),
None,
None,
)
with self.assertRaises(PromptingError) as cm:
schema = ParamDType.Multicolumn(column_types=frozenset({"text"}))
clean_value(schema, "A,B", context)
self.assertEqual(
cm.exception.errors,
[
PromptingError.WrongColumnType(["A", "B"], None,
frozenset({"text"}))
],
)
def test_clean_multichartseries_non_number_is_prompting_error(self):
context = RenderContext(
None, None,
TableShape(3, [
Column('A', ColumnType.TEXT()),
Column('B', ColumnType.DATETIME()),
]), None, None)
value = [
{
'column': 'A',
'color': '#aaaaaa'
},
{
'column': 'B',
'color': '#cccccc'
},
]
with self.assertRaises(PromptingError) as cm:
clean_value(ParamDType.Multichartseries(), value, context)
self.assertEqual(cm.exception.errors, [
PromptingError.WrongColumnType(['A'], 'text', frozenset({'number'
})),
PromptingError.WrongColumnType(['B'], 'datetime',
frozenset({'number'})),
])
def test_clean_column_prompting_error_convert_to_number(self):
context = self._render_context(input_table=arrow_table({"A": ["1"]}))
with self.assertRaises(PromptingError) as cm:
clean_value(
ParamDType.Column(column_types=frozenset({"number"})), "A", context
)
self.assertEqual(
cm.exception.errors,
[PromptingError.WrongColumnType(["A"], "text", frozenset({"number"}))],
)
def test_clean_column_prompting_error_convert_to_number(self):
context = RenderContext(
None, None, TableShape(3, [
Column('A', ColumnType.TEXT()),
]), None, None)
with self.assertRaises(PromptingError) as cm:
clean_value(ParamDType.Column(column_types=frozenset({'number'})),
'A', context)
self.assertEqual(cm.exception.errors, [
PromptingError.WrongColumnType(['A'], 'text', frozenset({'number'
})),
])
def test_list_prompting_error_concatenate_same_type(self):
context = self._render_context(
input_table=arrow_table({"A": ["1"], "B": ["2"]})
)
schema = ParamDType.List(
inner_dtype=ParamDType.Column(column_types=frozenset({"number"}))
)
with self.assertRaises(PromptingError) as cm:
clean_value(schema, ["A", "B"], context)
self.assertEqual(
cm.exception.errors,
[PromptingError.WrongColumnType(["A", "B"], "text", frozenset({"number"}))],
)
def test_list_prompting_error_concatenate_different_type_to_text(self):
context = self._render_context(
input_table=arrow_table({"A": [1], "B": [datetime.now()]})
)
schema = ParamDType.List(
inner_dtype=ParamDType.Column(column_types=frozenset({"text"}))
)
with self.assertRaises(PromptingError) as cm:
clean_value(schema, ["A", "B"], context)
self.assertEqual(
cm.exception.errors,
[PromptingError.WrongColumnType(["A", "B"], None, frozenset({"text"}))],
)
def test_clean_multicolumn_prompting_error_convert_to_text(self):
# TODO make this _automatic_ instead of quick-fix?
# ... but for now: prompt for a Quick Fix.
context = self._render_context(
input_table=arrow_table({"A": [1], "B": [datetime.now()], "C": ["x"]})
)
with self.assertRaises(PromptingError) as cm:
schema = ParamDType.Multicolumn(column_types=frozenset({"text"}))
clean_value(schema, ["A", "B"], context)
self.assertEqual(
cm.exception.errors,
[PromptingError.WrongColumnType(["A", "B"], None, frozenset({"text"}))],
)
def test_clean_file_happy_path(self):
workflow = Workflow.create_and_init()
tab = workflow.tabs.first()
wfm = tab.wf_modules.create(module_id_name="uploadfile",
order=0,
slug="step-1")
id = str(uuid.uuid4())
key = f"wf-${workflow.id}/wfm-${wfm.id}/${id}"
minio.put_bytes(minio.UserFilesBucket, key, b"1234")
UploadedFile.objects.create(
wf_module=wfm,
name="x.csv.gz",
size=4,
uuid=id,
bucket=minio.UserFilesBucket,
key=key,
)
context = RenderContext(workflow.id, wfm.id, None, None, None)
result = clean_value(ParamDType.File(), id, context)
self.assertIsInstance(result, pathlib.Path)
self.assertEqual(result.read_bytes(), b"1234")
self.assertEqual(result.suffixes, [".csv", ".gz"])
# Assert that once `path` goes out of scope, it's deleted
str_path = str(result) # get the filesystem path
del result # should finalize, deleting the file on the filesystem
with self.assertRaises(FileNotFoundError):
os.open(str_path, 0)
def test_clean_column_missing_becomes_empty_string(self):
context = RenderContext(
None, None, TableShape(3, [
Column('A', ColumnType.NUMBER()),
]), None, None)
result = clean_value(ParamDType.Column(), 'B', context)
self.assertEqual(result, '')
def test_clean_float_with_int_value(self):
# ParamDType.Float can have `int` values (because values come from
# json.parse(), which only gives Numbers so can give "3" instead of
# "3.0". We want to pass that as `float` in the `params` dict.
result = clean_value(ParamDType.Float(), 3, None)
self.assertEqual(result, 3.0)
self.assertIsInstance(result, float)
def test_list_prompting_error_concatenate_same_type(self):
context = RenderContext(
None, None,
TableShape(3, [
Column('A', ColumnType.TEXT()),
Column('B', ColumnType.TEXT()),
]), None, None)
schema = ParamDType.List(inner_dtype=ParamDType.Column(
column_types=frozenset({'number'})))
with self.assertRaises(PromptingError) as cm:
clean_value(schema, ['A', 'B'], context)
self.assertEqual(cm.exception.errors, [
PromptingError.WrongColumnType(['A', 'B'], 'text',
frozenset({'number'})),
])
def test_clean_file_wrong_wf_module(self):
workflow = Workflow.create_and_init()
tab = workflow.tabs.first()
step = tab.wf_modules.create(module_id_name="uploadfile",
order=0,
slug="step-1")
step2 = tab.wf_modules.create(module_id_name="uploadfile",
order=1,
slug="step-2")
id = str(uuid.uuid4())
key = f"wf-${workflow.id}/wfm-${step.id}/${id}"
minio.put_bytes(minio.UserFilesBucket, key, b"1234")
UploadedFile.objects.create(
wf_module=step2,
name="x.csv.gz",
size=4,
uuid=id,
bucket=minio.UserFilesBucket,
key=key,
)
context = self._render_context(wf_module_id=step.id)
result = clean_value(ParamDType.File(), id, context)
self.assertIsNone(result)
# Assert that if a temporary file was created to house the download, it
# no longer exists.
self.assertListEqual(list(self.basedir.iterdir()), [])
def test_clean_column_valid(self):
context = RenderContext(
None, None, TableShape(3, [
Column('A', ColumnType.NUMBER()),
]), None, None)
result = clean_value(ParamDType.Column(), 'A', context)
self.assertEqual(result, 'A')
def test_clean_file_happy_path(self):
workflow = Workflow.create_and_init()
tab = workflow.tabs.first()
step = tab.wf_modules.create(module_id_name="uploadfile",
order=0,
slug="step-1")
id = str(uuid.uuid4())
key = f"wf-${workflow.id}/wfm-${step.id}/${id}"
minio.put_bytes(minio.UserFilesBucket, key, b"1234")
UploadedFile.objects.create(
wf_module=step,
name="x.csv.gz",
size=4,
uuid=id,
bucket=minio.UserFilesBucket,
key=key,
)
with ExitStack() as inner_stack:
context = self._render_context(wf_module_id=step.id,
exit_stack=inner_stack)
result: Path = clean_value(ParamDType.File(), id, context)
self.assertIsInstance(result, Path)
self.assertEqual(result.read_bytes(), b"1234")
self.assertEqual(result.suffixes, [".csv", ".gz"])
# Assert that once `exit_stack` goes out of scope, file is deleted
self.assertFalse(result.exists())
def test_clean_condition_and_or_simplify(self):
context = self._render_context(input_table=arrow_table({"A": [1]}))
self.assertEqual(
clean_value(
ParamDType.Condition(),
{
"operation": "and",
"conditions": [
{
"operation": "or",
"conditions": [
{
"operation": "cell_is_blank",
"column": "A",
"value": "",
"isCaseSensitive": False,
"isRegex": False,
},
],
},
],
},
context,
),
{
"operation": "cell_is_blank",
"column": "A",
},
)
def test_clean_multicolumn_sort_in_table_order(self):
context = self._render_context(input_table=arrow_table({
"B": [1],
"A": [2]
}))
result = clean_value(ParamDType.Multicolumn(), ["A", "B"], context)
self.assertEqual(result, ["B", "A"])
def test_clean_column_prompting_error_convert_to_text(self):
# TODO make this _automatic_ instead of quick-fix?
# Consider Regex. We probably want to pass the module a text Series
# _separately_ from the input DataFrame. That way Regex can output
# a new Text column but preserve its input column's data type.
#
# ... but for now: prompt for a Quick Fix.
context = self._render_context(input_table=arrow_table({"A": [1]}))
with self.assertRaises(PromptingError) as cm:
clean_value(ParamDType.Column(column_types=frozenset({"text"})),
"A", context)
self.assertEqual(
cm.exception.errors,
[PromptingError.WrongColumnType(["A"], None, frozenset({"text"}))],
)
def test_clean_condition_not(self):
context = self._render_context(input_table=arrow_table({"A": ["a"]}))
self.assertEqual(
clean_value(
ParamDType.Condition(),
{
"operation": "text_is_not",
"column": "A",
"value": "a",
"isCaseSensitive": False,
"isRegex": False,
},
context,
),
{
"operation": "not",
"condition": {
"operation": "text_is",
"column": "A",
"value": "a",
"isCaseSensitive": False,
"isRegex": False,
},
},
)
def test_clean_multicolumn_from_other_tab(self):
tab_output = ProcessResult(pd.DataFrame({'A-from-tab-2': [1, 2]}))
workflow = Workflow.create_and_init()
tab = workflow.tabs.first()
wfm = tab.wf_modules.create(
order=0, last_relevant_delta_id=workflow.last_delta_id)
wfm.cache_render_result(workflow.last_delta_id, tab_output)
schema = ParamDType.Dict({
'tab':
ParamDType.Tab(),
'columns':
ParamDType.Multicolumn(tab_parameter='tab'),
})
params = {'tab': tab.slug, 'columns': ['A-from-tab-1', 'A-from-tab-2']}
context = RenderContext(
workflow.id, None,
TableShape(3, [
Column('A-from-tab-1', ColumnType.NUMBER()),
]), {
tab.slug: StepResultShape('ok', tab_output.table_shape),
}, params)
result = clean_value(schema, params, context)
# result['tab'] is not what we're testing here
self.assertEqual(result['columns'], ['A-from-tab-2'])
def test_clean_file_no_uploaded_file(self):
workflow = Workflow.create_and_init()
tab = workflow.tabs.first()
wfm = tab.wf_modules.create(module_id_name='uploadfile', order=0)
context = RenderContext(workflow.id, wfm.id, None, None, None)
result = clean_value(ParamDType.File(), str(uuid.uuid4()), context)
self.assertIsNone(result)
def test_clean_multicolumn_from_other_tab(self):
tab_output = ProcessResult(pd.DataFrame({"A-from-tab-2": [1, 2]}))
workflow = Workflow.create_and_init()
tab = workflow.tabs.first()
wfm = tab.wf_modules.create(
order=0,
slug="step-1",
last_relevant_delta_id=workflow.last_delta_id)
wfm.cache_render_result(workflow.last_delta_id, tab_output)
schema = ParamDType.Dict({
"tab":
ParamDType.Tab(),
"columns":
ParamDType.Multicolumn(tab_parameter="tab"),
})
params = {"tab": tab.slug, "columns": ["A-from-tab-1", "A-from-tab-2"]}
context = RenderContext(
workflow.id,
None,
TableShape(3, [Column("A-from-tab-1", ColumnType.NUMBER())]),
{tab.slug: StepResultShape("ok", tab_output.table_shape)},
params,
)
result = clean_value(schema, params, context)
# result['tab'] is not what we're testing here
self.assertEqual(result["columns"], ["A-from-tab-2"])
def test_clean_multichartseries_missing_is_removed(self):
context = self._render_context(input_table=arrow_table({"A": [1], "B": [1]}))
value = [
{"column": "A", "color": "#aaaaaa"},
{"column": "C", "color": "#cccccc"},
]
result = clean_value(ParamDType.Multichartseries(), value, context)
self.assertEqual(result, [{"column": "A", "color": "#aaaaaa"}])
def test_clean_multicolumn_missing_is_removed(self):
context = self._render_context(input_table=arrow_table({
"A": [1],
"B": [1]
}))
result = clean_value(ParamDType.Multicolumn(), ["A", "X", "B"],
context)
self.assertEqual(result, ["A", "B"])
