Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Populate column selector for datasets which are not ready yet #5680

Merged
merged 3 commits into from
Mar 13, 2018
Merged

Populate column selector for datasets which are not ready yet #5680

Changes from 2 commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
3e033bf
Prefill column selector for datasets which are not ready yet
guerler Mar 13, 2018
895a858
Remove explicit loop, use map instead
guerler Mar 13, 2018
7b6321e
Use list comprehension instead of map
guerler Mar 13, 2018
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
13 changes: 9 additions & 4 deletions lib/galaxy/tools/parameters/basic.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,8 @@

WORKFLOW_PARAMETER_REGULAR_EXPRESSION = re.compile('''\$\{.+?\}''')

MAX_DEFAULT_COLUMNS = 999


def contains_workflow_parameter(value, search=False):
if not isinstance(value, string_types):
Expand Down Expand Up @@ -1114,18 +1116,21 @@ def get_column_list(self, trans, other_values):
if isinstance(dataset, trans.app.model.HistoryDatasetCollectionAssociation):
dataset = dataset.to_hda_representative()
# Columns can only be identified if metadata is available
if not hasattr(dataset, 'metadata') or not hasattr(dataset.metadata, 'columns') or not dataset.metadata.columns:
if not hasattr(dataset, 'metadata') or not hasattr(dataset.metadata, 'columns'):
return []
# Build up possible columns for this dataset
this_column_list = []
if self.numerical:
# Valid column-based datasets contain at least 1 column if that column has not been
# specified we prepopulate the selector assuming that the datasets is not ready yet.
if dataset.metadata.columns is None:
this_column_list = list(map(str, range(1, MAX_DEFAULT_COLUMNS + 1)))
elif self.numerical:
# If numerical was requested, filter columns based on metadata
for i, col in enumerate(dataset.metadata.column_types):
if col == 'int' or col == 'float':
this_column_list.append(str(i + 1))
else:
for i in range(0, dataset.metadata.columns):
this_column_list.append(str(i + 1))
this_column_list = list(map(str, range(1, dataset.metadata.columns + 1)))
# Take the intersection of these columns with the other columns.
if column_list is None:
column_list = this_column_list
Expand Down