Documentation

docs/Testing_Code_with_pytest.md

@@ -1,5 +1,5 @@
 # Results files for dataloading tests
-The dataloading tests rely on having some raw results data to load. And the results data should be various enough to test the various components of the data-loading code. In other words, effective testing requires a reasonable variety of input files. The repository does not contain sufficient results data for testing. A test set is available in a separate repository, [***TODO](***TODO). If [test_dataloading_by_ej.py](../tests/dataloading_tests/test_dataloading_by_ej.py) does not find results data, it will default to downloading the files from that repository.
+The dataloading tests rely on having some raw results data to load. And the results data should be various enough to test the various components of the data-loading code. In other words, effective testing requires a reasonable variety of input files. The repository does not contain sufficient results data for testing. A test set is available in a separate repository, [TestingData](https://github.com/ElectionDataAnalysis/TestingData). If [test_dataloading_by_ej.py](../tests/dataloading_tests/test_dataloading_by_ej.py) does not find results data, it will default to downloading the files from that repository.
 
 # Sample Testing Session
 

docs/User_Guide.md

@@ -471,7 +471,7 @@ analyzer = dl.analyzer
 ```
 
 ## Tabular Export
-The Analyzer class has a number of functions that allow you to aggregate the data for analysis purposes. For example, running the `.top_counts()` function exports files into your rollup_dataframe directory which with counts summed up at a particular reporting unit level. This function expects 4 arguments: the election, the jurisdiction, the reporting unit level at which the aggregation will occur, and a boolean variable indicating whether you would like the data aggregated by vote count type. For example, to export all 2020 General results in your database to a tab-separated file `tabular_results.tsv`:
+The Analyzer class has a number of functions that allow you to aggregate the data for analysis purposes. For example, to export all 2020 General results in your database to a tab-separated file `tabular_results.tsv`:
 ```
 analyzer.export_election_to_tsv("tabular_results.tsv", "2020 General")
 ```

src/electiondata/__init__.py

@@ -2978,31 +2978,6 @@ def export_outlier_data(
         )
         return agg_results
 
-    def top_counts(
-        self, election: str, jurisdiction: str, sub_rutype: str, by_vote_type: bool
-    ) -> Optional[str]:
-        """
-        Inputs:
-            election: str,
-            jurisdiction: str,
-            sub_rutype: str, ReportingUnitType (e.g., 'county') to which the results should be rolled up
-            by_vote_type: bool, if true, results will be reported by vote type. If false, only totals will be reported
-
-        Puts file with results into a subdirectory (labeled by election and jurisdiction name)
-            of the reports_and_plots_dir specified in the Analyzer's param_file.
-        """
-        jurisdiction_id = db.name_to_id(self.session, "ReportingUnit", jurisdiction)
-        election_id = db.name_to_id(self.session, "Election", election)
-        err = an.export_rollup(
-            self.session,
-            self.reports_and_plots_dir,
-            jurisdiction_id=jurisdiction_id,
-            sub_rutype=sub_rutype,
-            election_id=election_id,
-            by_vote_type=by_vote_type,
-        )
-        return err
-
     def export_nist(
         self,
         election: str,
@@ -3829,7 +3804,7 @@ def external_data_exists(
 
     connection = an.session.bind.raw_connection()
     cursor = connection.cursor()
-    df = db.read_external(cursor, election_id, jurisdiction_id, ["Label"])
+    df = db.read_external_cursor(cursor, election_id, jurisdiction_id, ["Label"])
     cursor.close()
 
     # if no data found