Age | Commit message (Collapse) | Author |
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Set up the appropriate self.* variables from the results of running
the appropriate query.
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* wqflask/base/data_set.py: Delete "menu_main" import.
* wqflask/db/call.py: Delete it.
* wqflask/db/gn_server.py: Ditto.
* wqflask/wqflask/submit_bnw.py: Ditto.
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* wqflask/base/data_set.py: Replace "db.call" import with
"database_connection".
(create_datasets_list): Use "database_connection" to fetch data.
(DatasetGroup.__init__): Ditto.
(DataSet.retrieve_other_names): Ditto.
(PhenotypeDataSet.setup): Remove query escaping in string and format
the string.
(GenotypeDataSet.setup): Ditto.
(MrnaAssayDataSet.setup): Ditto.
* wqflask/db/webqtlDatabaseFunction.py: Remove db.call import.
(retrieve_species): Use database_connection() to fetch data.
(retrieve_species_id): Ditto.
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"with Bench" instruments how long a function takes and generates time
reports on as INFO logs. This should be done on a developer server.
Should the log level be low enough, this bench marks will generate a
lot of noise. Instrumentation should be done during development.
* wqflask/base/data_set.py (create_datasets_list): Remove "with
Bench...".
* wqflask/db/call.py (fetchone): Ditto.
(fetchall): Ditto.
(gn_server): Ditto.
* wqflask/wqflask/gsearch.py (GSearch.__init__): Ditto.
* wqflask/wqflask/marker_regression/display_mapping_results.py (DisplayMappingResults.__init__): Ditto.
* wqflask/wqflask/marker_regression/run_mapping.py
(RunMapping.__init__): Ditto.
* wqflask/wqflask/update_search_results.py (GSearch.__init__): Ditto.
* wqflask/wqflask/views.py (search_page): Ditto.
(heatmap_page): Ditto.
(mapping_results_page): Ditto.
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Logging is used to introspect variables or notify the commencement of
a given operation. Logging should only be used to log errors. Also,
most of the logging is either "logger.debug" or "logger.info"; and
this won't show up in production/testing since we need a logging level
above "WARNING" for them to show up.
* wqflask/base/data_set.py (create_datasets_list): Remove logger.
(Markers.add_pvalues): Ditto.
(DataSet.retrieve_other_names): Ditto.
* wqflask/base/mrna_assay_tissue_data.py: Ditto.
* wqflask/base/webqtlCaseData.py: Ditto.
* wqflask/db/call.py (fetch1): Ditto.
(gn_server): Ditto.
* wqflask/db/gn_server.py: Ditto.
* wqflask/maintenance/set_resource_defaults.py: Ditto.
* wqflask/utility/Plot.py (find_outliers): Ditto.
* wqflask/utility/gen_geno_ob.py: Ditto.
* wqflask/utility/helper_functions.py: Ditto.
* wqflask/utility/pillow_utils.py: Ditto.
* wqflask/utility/redis_tools.py: Ditto.
* wqflask/wqflask/api/gen_menu.py (get_groups): Ditto.
* wqflask/wqflask/api/mapping.py: Ditto.
* wqflask/wqflask/api/router.py (get_dataset_info): Ditto.
* wqflask/wqflask/collect.py (report_change): Ditto.
* wqflask/wqflask/correlation/corr_scatter_plot.py: Ditto.
* wqflask/wqflask/ctl/ctl_analysis.py (CTL): Ditto.
(CTL.__init__): Ditto.
(CTL.run_analysis): Ditto.
(CTL.process_results): Ditto.
* wqflask/wqflask/db_info.py: Ditto.
* wqflask/wqflask/do_search.py (DoSearch.execute): Ditto.
(DoSearch.mescape): Ditto.
(DoSearch.get_search): Ditto.
(MrnaAssaySearch.run_combined): Ditto.
(MrnaAssaySearch.run): Ditto.
(PhenotypeSearch.run_combined): Ditto.
(GenotypeSearch.get_where_clause): Ditto.
(LrsSearch.get_where_clause): Ditto.
(MeanSearch.run): Ditto.
(RangeSearch.get_where_clause): Ditto.
(PvalueSearch.run): Ditto.
* wqflask/wqflask/docs.py: Ditto.
* wqflask/wqflask/export_traits.py: Ditto.
* wqflask/wqflask/external_tools/send_to_bnw.py: Ditto.
* wqflask/wqflask/external_tools/send_to_geneweaver.py: Ditto.
* wqflask/wqflask/external_tools/send_to_webgestalt.py: Ditto.
* wqflask/wqflask/gsearch.py (GSearch.__init__): Ditto.
* wqflask/wqflask/heatmap/heatmap.py: Ditto.
* wqflask/wqflask/marker_regression/display_mapping_results.py (DisplayMappingResults): Ditto.
* wqflask/wqflask/marker_regression/gemma_mapping.py: Ditto.
* wqflask/wqflask/marker_regression/plink_mapping.py (run_plink): Ditto.
* wqflask/wqflask/marker_regression/qtlreaper_mapping.py (run_reaper): Ditto.
* wqflask/wqflask/marker_regression/rqtl_mapping.py: Ditto.
* wqflask/wqflask/marker_regression/run_mapping.py (RunMapping.__init__): Ditto.
* wqflask/wqflask/parser.py (parse): Ditto.
* wqflask/wqflask/search_results.py (SearchResultPage.__init__): Ditto.
* wqflask/wqflask/update_search_results.py (GSearch.__init__): Ditto.
* wqflask/wqflask/user_login.py (send_email): Ditto.
(logout): Ditto.
(forgot_password_submit): Ditto.
(password_reset): Ditto.
(password_reset_step2): Ditto.
(register): Ditto.
* wqflask/wqflask/user_session.py (create_signed_cookie): Ditto.
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sample data for genotype traits
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information
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It's originally a boolean, which causes an error when passed to the JS
code as JSON
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This adds the group code to phenotype traits on loading pages, and also
sets the group code as an attribute of the dataset.group class.
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Some of this was caused by heatmaps supporting code; that code should probably pass the traits differently than the way it does in the "trait_info_str" function
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Trait data caching wasn't working correctly because it didn't account
for the samplelist, causing caching to work incorrect in any situation
where the target dataset's samplelist wasn't the same as that of the
trait being correlated against. Trait data is stored as a dictionary
where the keys are trait IDs and values are *lists* of sample values.
This means that the caching needs to account for the exact same set of
samples; otherwise you'll end up with samples being mismatched (since
"the third sample with a value" for one dataset's trait might not be the
same as "the third sample with a value" for another dataset's trait).
To fix this, I added the samplelist to the functions that generate and
fetch the hash file. This will require more cache files, though, so this
should probably be reexamined later to make the code work with only a
single cache file for each dataset.
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was passed that wasn't Temp
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Feature/add filter by study samples
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study within a group (in this case only BXD Longevity for now)
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take dataset as an optional argument (to avoid having to pointlessly create it)
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pass around the whole dataset object
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