def test_Orca_Deletion(self): taxa_level = "Species" taxa_name = "Orcinus orca" # Load the data animal_df = load_animal_data() self.assertTrue(taxa_name in animal_df[taxa_level].unique()) # Now remove that taxa and make sure it is gone. animal_df = abundance_plot_utils.delete_rows_for_taxa( animals, 'Species', 'Orcinus orca') self.assertFalse(taxa_name in animal_df[taxa_level].unique()) print("unique values left: {}".format(animal_df[taxa_level].unique()))
ag = abundance_plot_utils.aggregate_mixed_taxonomy( dataframe=animal_df, taxa_dict=invalid_taxa_dict, main_dir='../', summarise_other=True) # The resulting df has a row of NaN values. print(ag) class testAggregateMixedTaxonomy(unittest.TestCase): def testLumpingIntoOther(self): # TODO: incomplete. But there is a check in aggregate_mixed_taxonomy(): # `assert (sample_sums < 1.001).all()` pass if __name__ == "__main__": # add the main dir to the path print('run unit test for ...') animals = pd.read_csv("./summarised_animals.txt", sep='\t') print(animals.columns) print(animals['Species'].unique()) deleted_rows = abundance_plot_utils.delete_rows_for_taxa( animals, 'Species', 'Orcinus orca') print(deleted_rows) # Run the unit tests unittest.main()