Age | Commit message (Collapse) | Author |
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* test/requests/correlation_tests.py: Add proof-of-concept code to run
the checks for correctness.
* test/requests/test-website.py: Add a way to manually run the tests
from the CLI
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* test/requests/correlation_results_text_files/*results.csv: csv files
with sample results from GN1 for the trait `1435464_at` in dataset
`HC_M2_0606_P`.
* wqflask/scripts/parse_corr_gn1_results_to_csv.py: parser for results
from GN1 when saved to a html file.
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At one point the MonadicDict of the dataset ob was being passed to create_trait, which does not work. I changed it back to passing the object
Another place calls "as_dict" on the dataset ob, which doesn't work; I had to call as_monadic_dict().data for that
Some traits are also None for some reason, which caused an error in the filter. For now I just used an if/else, but someone can deal with this differently later
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* wqflask/wqflask/database.py: Delete SPARQLWrapper, JSON imports
(sparql_connection): Delete.
* wqflask/wqflask/views.py: Import requests and urljoin. Delete
get_dataset_metadata and sparql_connection import.
(show_trait_page): Fetch metadata by calling a GN enpoint.
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github.com:Alexanderlacuna/genenetwork2 into Alexanderlacuna-bugfix/metadata-fetching
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github.com:Alexanderlacuna/genenetwork2 into Alexanderlacuna-chores/code-refactoring
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github.com:Alexanderlacuna/genenetwork2 into Alexanderlacuna-bugfix/use-f1-parents
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* wqflask/wqflask/database.py (database_connection): New function.
* wqflask/wqflask/views.py: Import sparql_connection
(show_trait_page): Pass sparql_connection object to
get_dataset_metadata.
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* wqflask/wqflask/metadata_edits.py (update_probeset): Flash a success
messages when user updates something. Otherwise flash a warning message
* wqflask/wqflask/templates/edit_probeset.html: Display flash
messages.
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* wqflask/wqflask/static/new/css/show_trait.css: Show a pointer when a
user hovers over summary data. Add extra styling for the metadata
table.
* wqflask/wqflask/templates/search_result_page.html: Replace
dataset.accession_id with metadata.accession_id.
* wqflask/wqflask/templates/show_metadata_details.html: New template.
file that displays a dataset's metadata in tabular form.
* wqflask/wqflask/templates/show_trait.html: Sub-template
'show_metadata_details' conditionally.
* wqflask/wqflask/views.py (show_trait_page): Explicitly pass metadata
as a dictionary to the template.
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* wqflask/wqflask/correlation/show_corr_results.py
(set_template_vars): Convert monadict_dict to a normal dictionary.
(correlation_json_for_table): Ditto.
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* wqflask/base/data_set/dataset.py (DataSet.as_dict): Rename this to ...
(as_monadic_dict): ... this.
* wqflask/wqflask/correlation/show_corr_results.py
(set_template_vars): Rename as_dict to as_monadic_dict.
(correlation_json_for_table): Ditto.
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* wqflask/base/data_set/dataset.py: Import itertools, DictCursor, MonadicDict,
Maybe, Nothing and query_sql.
(DataSet.__init__): Initialize accession_id to Nothing.
(DataSet.as_dict): Rename this to ...
(Dataset.as_monadic_dict): ... this which returns a monadic
dictionary.
(DataSet.get_accession_id): Query for the accession_id and return it
as a maybe monad.
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In Jinja, you can use dot notation to check for values. If a value
does not exist, you get None by default.
* wqflask/base/data_set/dataset.py (Dataset.get_accession_id): Remove
returning a string version of None.
* wqflask/wqflask/templates/correlation_page.html: Delete `!= 'None'`.
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* wqflask/wqflask/correlation/show_corr_results.py: Delete os, Path,
TMPDIR, fetch_all_cached_metadata and cache_new_traits_metadata.
* wqflask/wqflask/db_info.py: Remove http.client.
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* wqflask/wqflask/db_info.py: Import DictCursor.
(InfoPage.get_info): Add aliases in SQL that will be used as keys
later by DictCursor..
(process_query_results): Delete it.
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* wqflask/wqflask/db_info.py (InfoPage.get_info): run
cursor.fetchone() only once.
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* wqflask/wqflask/static/new/javascript/dataset_select_menu.js: Delete.
* wqflask/wqflask/static/new/javascript/dataset_select_menu_edit_trait.js:
Ditto.
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collection page
I think the Monad stuff Bonface/Arun are doing might address situations
like this better.
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* Fix imports I broke when rebasing.
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* wqflask/scripts/corr_compute.py: move correlation computation to
external script.
* wqflask/wqflask/templates/loading_corrs.html: Provide UI to display
while computation is still not complete.
* wqflask/wqflask/views.py: Dispatch correlation computation to
external script and display appropriate UI for each valid state of
the computation.
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* wqflask/utility/tools.py: Output extraneous messages to stderr
rather than stdout to avoid cluttering the output.
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* Pass the user_id for the current user to the
`check_resource_availability` function as an argument, rather than
using the global `g.user_session.user_id` value.
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