def test_validate_numeric_annots(self):
cluster = Annotations(
"../tests/data/cluster_bad_missing_coordinate.txt",
TestAnnotations.ALLOWED_FILE_TYPES,
)
cluster.create_data_frame()
self.assertTrue(cluster.validate_numeric_annots)
def test_coerce_numeric_values(self):
cm = Annotations(
"../tests/data/metadata_example.txt",
["text/csv", "text/plain", "text/tab-separated-values"],
)
cm.create_data_frame()
cm.file = Annotations.coerce_numeric_values(cm.file, cm.annot_types)
dtype = cm.file.dtypes[("Average Intensity", "numeric")]
self.assertEqual(dtype, np.float)
# Test that numeric values wer
# Pick a random number between 1 and amount of lines in file
ran_num = random.randint(1, 20)
for column in cm.file.columns:
annot_type = column[1]
if annot_type == "numeric":
value = str(cm.file[column][ran_num])
print(Decimal(value).as_tuple().exponent)
assert (
abs(Decimal(value).as_tuple().exponent) >= self.EXPONENT
), "Numbers did not round to 3 or less decimals places"
# Test for string in numeric column
cm_has_bad_value = Annotations(
"../tests/data/metadata_bad_contains_str_in_numeric_column.txt",
["text/csv", "text/plain", "text/tab-separated-values"],
)
cm_has_bad_value.create_data_frame()
self.assertRaises(
ValueError,
Annotations.coerce_numeric_values,
cm_has_bad_value.file,
cm_has_bad_value.annot_types,
)