test: InputObservation with valid and invalid inputs

tests/test_input_observation.py

@@ -65,3 +65,136 @@ def test_mock_uploaded_file(mock_uploadedFile):
     assert mock_file.name == image_name
     assert mock_file.size == 123456
     assert mock_file.type == "image/jpeg"
+
+
+# now we move on to test the class InputObservation
+# - with valid input
+# - with invalid input
+# - with missing input
+
+def test_input_observation_valid(mock_uploadedFile):
+    # image: ndarray
+    # lat, lon: float
+    # author_email: str
+    # date, time: datetime.date, datetime.time
+    #uploaded_file: UploadedFile (need to mock this)
+    # image_md5: str
+
+    # setup values for the test (all valid)
+
+    author_email = "[email protected]"
+    image_name = "test_image.jpg"
+    mock_file = mock_uploadedFile(name=image_name).get_data()
+    
+    _date="2023-10-10"
+    _time="10:10:10"
+    image_datetime_raw = _date + " " + _time
+    dt = datetime.datetime.strptime(image_datetime_raw, "%Y-%m-%d %H:%M:%S")
+    date = dt.date()    
+    time = dt.time()
+
+    ## make a random image with dtype uint8 using np.random.randint
+    image = np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)
+    image_md5 = 'd1d2515e6f6ac4c5ca6dd739d5143cd4' # 32 hex chars.
+    
+    obs = InputObservation(
+        image=image, 
+        latitude=12.34, longitude=56.78, author_email=author_email,
+        time=time, date=date,
+        uploaded_file=mock_file,
+        image_md5=image_md5,
+        )
+    
+    assert isinstance(obs.image, np.ndarray)
+    assert (obs.image == image).all()
+    
+    assert obs.latitude == 12.34
+    assert obs.longitude == 56.78
+    assert obs.author_email == author_email
+    assert isinstance(obs.date, datetime.date)
+    assert isinstance(obs.time, datetime.time)
+    assert str(obs.date) == "2023-10-10"
+    assert str(obs.time) == "10:10:10"
+
+    assert obs.uploaded_file.name == image_name
+    assert obs.uploaded_file.size == 123456
+    assert obs.uploaded_file.type == "image/jpeg"
+    
+    assert isinstance(obs.uploaded_file, BytesIO)
+    #assert isinstance(obs.uploaded_file, MockUploadedFile) # is there any point in checking the type of the mock, ?
+
+
+# a list of tuples (strings that are the keys of "valid_inputs", expected error type)
+# loop over the list, and for each tuple, create a dictionary with all valid inputs, and one invalid input
+# assert that the function raises the expected error type
+
+invalid_input_scenarios = [ 
+            ("author_email", TypeError),
+            ("image_name", TypeError),
+            ("uploaded_file", TypeError),
+            ("date", TypeError),
+            ("time", TypeError),
+            ("image", TypeError),
+            ("image_md5", TypeError),
+    ]
+
+@pytest.mark.parametrize("key, error_type", invalid_input_scenarios)
+def test_input_observation_invalid(key, error_type, mock_uploadedFile):
+    # correct datatypes are:
+    # - image: ndarray
+    # - lat, lon: float
+    # - author_email: str
+    # - date, time: datetime.date, datetime.time
+    # - uploaded_file: UploadedFile (need to mock this)
+    # - image_md5: str
+
+    # the most critical/likely to go wrong would presumably be 
+    # - date, time (strings not datetime objects)
+    # - lat, lon (strings not numbers)
+    # - image (not ndarray, maybe accidentally a PIL object or maybe the filename)
+    # - uploaded_file (not UploadedFile, maybe a string, or maybe the ndarray)
+
+    # check it fails when any of the datatypes are wrong,
+    # even if the rest are all good want to loop over the inputs, take each one
+    # from a bad list, and all others from a good list, and assert fails for
+    # each one
+    
+    # set up the good and bad inputs
+    _date="2023-10-10"
+    _time="10:10:10"
+    image_datetime_raw = _date + " " + _time
+    fname = "test_image.jpg"
+    image = np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)
+    
+    dt_ok = datetime.datetime.strptime(image_datetime_raw, "%Y-%m-%d %H:%M:%S")
+    valid_inputs = {
+        "author_email": "[email protected]",
+        "image_name": "test_image.jpg",
+        "uploaded_file": mock_uploadedFile(name=fname).get_data(),
+        "date": dt_ok.date(),
+        "time": dt_ok.time(),
+        "image": image,
+        "image_md5": 'd1d2515e6f6ac4c5ca6dd739d5143cd4', # 32 hex chars.
+    }
+    invalid_inputs = {
+        "author_email": "@example",
+        "image_name": 45,
+        "uploaded_file": image,
+        "date": _date,
+        "time": _time,
+        "image": fname,
+        "image_md5": 45643
+    }
+
+    # test a valid set of inputs, minus the target key, substituted for something invalid
+    inputs = valid_inputs.copy()
+    inputs[key] = invalid_inputs[key]
+    
+    with pytest.raises(error_type):
+        obs = InputObservation(**inputs)
+    
+    # now test the same key set to None 
+    inputs = valid_inputs.copy()
+    inputs[key] = None
+    with pytest.raises(error_type):
+        obs = InputObservation(**inputs)