|
| 1 | +from codonPython.check_consistent_submissions import check_consistent_submissions |
| 2 | +import pandas as pd |
| 3 | +import numpy |
| 4 | +import pytest |
| 5 | + |
| 6 | +@pytest.mark.parametrize("data, national_geog_level, geography_col, submissions_col, measure_col, expected", [ |
| 7 | + ( |
| 8 | + pd.DataFrame({ |
| 9 | + "Geog" : ["N" ,"N", "Region", "Region", "Local", "Local",], |
| 10 | + "measure" : ["m1", "m2", "m1", "m2", "m1", "m2",], |
| 11 | + "submissions" : [4, 2, 2, 1, 2, 1,], |
| 12 | + }), |
| 13 | + "N", |
| 14 | + "Geog", |
| 15 | + "submissions", |
| 16 | + "measure", |
| 17 | + True |
| 18 | + ), |
| 19 | + ( |
| 20 | + pd.DataFrame({ |
| 21 | + "Org_Level" : ["National" ,"National", "Region", "Region", "Local", "Local",], |
| 22 | + "Measure" : ["m1", "m2", "m1", "m2", "m1", "m2",], |
| 23 | + "Value_Unsuppressed" : [4, 2, 3, 1, 2, 1,], |
| 24 | + }), |
| 25 | + "N", |
| 26 | + "Geog", |
| 27 | + "submissions", |
| 28 | + "measure", |
| 29 | + False |
| 30 | + ) |
| 31 | +]) |
| 32 | + |
| 33 | +def each_consistent_measure_BAU(data, national_geog_level, geography_col, submissions_col, measure_col, expected): |
| 34 | + assert expected == check_consistent_submissions(data, geography_col, submissions_col, measure_col) |
| 35 | + |
| 36 | +@pytest.mark.parametrize("data, national_geog_level, geography_col, submissions_col, measure_col",[ |
| 37 | + ( |
| 38 | + pd.DataFrame({ |
| 39 | + "Geog" : ["N" ,"N", "Region", "Region", "Local", "Local",], |
| 40 | + "measure" : ["m1", "m2", "m1", "m2", "m1", "m2",], |
| 41 | + "submissions" : [4, 2, 2, 1, 2, 1,], |
| 42 | + }), |
| 43 | + 1, |
| 44 | + "Geog", |
| 45 | + "submissions", |
| 46 | + "measure" |
| 47 | + ), |
| 48 | + ( |
| 49 | + pd.DataFrame({ |
| 50 | + "Geog" : ["N", "N", "Region", "Region", "Local", "Local",], |
| 51 | + "measure" : ["m1", "m2", "m1", "m2", "m1", "m2",], |
| 52 | + "submissions" : [4, 2, 2, 1, 2, 1,], |
| 53 | + }), |
| 54 | + "N", |
| 55 | + False, |
| 56 | + "submissions", |
| 57 | + "measure" |
| 58 | + ), |
| 59 | + ( |
| 60 | + pd.DataFrame({ |
| 61 | + "Geog" : ["N", "N", "Region", "Region", "Local", "Local",], |
| 62 | + "measure" : ["m1", "m2", "m2", "m2", "m1", "m2",], |
| 63 | + "submissions" : [4, 2, 2, 1, 2, 1,], |
| 64 | + }), |
| 65 | + "N", |
| 66 | + "Geog", |
| 67 | + 4.2, |
| 68 | + "measure" |
| 69 | + ) |
| 70 | +]) |
| 71 | + |
| 72 | +def test_each_consistent_submissions_valueErrors(data, national_geog_level, geography_col, submissions_col, measure_col): |
| 73 | + with pytest.raises(ValueError): |
| 74 | + check_consistent_submissions(data, national_geog_level, geography_col, submissions_col, measure_col) |
| 75 | + |
| 76 | +@pytest.mark.parametrize("data, national_geog_level, geography_col, submissions_col, measure_col", [ |
| 77 | + ( |
| 78 | + pd.DataFrame({ |
| 79 | + "Geog" : ["N" ,"N", "Region", "Region", "Local", "Local",], |
| 80 | + "measure" : ["m1", "m2", "m1", "m2", "m1", "m2",], |
| 81 | + "submissions" : [4, 2, 2, 1, 2, 1,], |
| 82 | + }), |
| 83 | + "N", |
| 84 | + "Geog", |
| 85 | + "submissions", |
| 86 | + "measurez" |
| 87 | + ) |
| 88 | +]) |
| 89 | + |
| 90 | +def test_each_consistent_submissions_colError(data, national_geog_level, geography_col, submissions_col, measure_col): |
| 91 | + with pytest.raises(KeyError): |
| 92 | + check_consistent_submissions(data, national_geog_level, geography_col, submissions_col, measure_col) |
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