added checks for exposure map validity

sensospot_data/utils.py

@@ -1,4 +1,6 @@
-import numpy
+from collections.abc import Mapping, Sequence
+
+import pandas
 
 from .columns import (
     META_DATA_EXPOSURE_ID,
@@ -16,6 +18,47 @@ def split_data_frame(data_frame, column):
     return {value: data_frame[mask] for value, mask in masks.items()}
 
 
+def _is_list_or_tuple(something):
+    """ returns true if something is a list or tuple """
+    if isinstance(something, Sequence):
+        return not isinstance(something, str)
+    return False
+
+
+def _is_numerical(something):
+    """ returns true if something is an int or float """
+    return isinstance(something, int) or isinstance(something, float)
+
+
+def _check_valid_exposure_map_entry(entry):
+    """ raises a ValueError, if an exposure map entry is not suitable """
+    if not _is_list_or_tuple(entry):
+        raise ValueError("Eposure Map: entries must be tuples or lists")
+    if not len(entry) == 2:
+        raise ValueError("Eposure Map: entries must consist of two items")
+    if not _is_numerical(entry[1]):
+        raise ValueError("Exposure Map: second entry must be numerical")
+
+
+def _check_exposure_map(data_frame, exposure_map):
+    """checks if an exposure maps fits the requirements
+
+    Will raise an ValueError if requirements are not met
+    """
+    if not isinstance(exposure_map, Mapping):
+        raise ValueError("Exposure Map: map must be a dict")
+    exposure_ids_in_df = set(data_frame[META_DATA_EXPOSURE_ID].unique())
+    exposure_ids_in_map = set(exposure_map.keys())
+    if exposure_ids_in_df != exposure_ids_in_map:
+        msg = (
+            f"Exposure Ids {exposure_ids_in_df} don't match "
+            f"provided map {exposure_ids_in_map}"
+        )
+        raise ValueError(msg)
+    for entry in exposure_map.values():
+        _check_valid_exposure_map_entry(entry)
+
+
 def _set_exposure_data_from_parameters(data_frame):
     """infer the exposures from measurement parameters
 
@@ -51,17 +94,16 @@ def apply_exposure_map(data_frame, exposure_map=None):
     if exposure_map is None:
         return _set_exposure_data_from_parameters(data_frame)
 
-    existing = set(data_frame[META_DATA_EXPOSURE_ID].unique())
+    _check_exposure_map(data_frame, exposure_map)
-    provided = set(exposure_map.keys())
-    if existing != provided:
-        raise ValueError(
-            f"Exposure Map differs from data frame: {provided} != {existing}"
-        )
 
-    data_frame[SETTINGS_EXPOSURE_CHANNEL] = numpy.nan
+    exposure_df = pandas.DataFrame.from_dict(
-    data_frame[SETTINGS_EXPOSURE_TIME] = numpy.nan
+        exposure_map,
-    for exposure_id, exposure_info in exposure_map.items():
+        orient="index",
-        mask = data_frame[META_DATA_EXPOSURE_ID] == exposure_id
+        columns=[SETTINGS_EXPOSURE_CHANNEL, SETTINGS_EXPOSURE_TIME],
-        data_frame.loc[mask, SETTINGS_EXPOSURE_CHANNEL] = exposure_info.channel
+    )
-        data_frame.loc[mask, SETTINGS_EXPOSURE_TIME] = exposure_info.time
+    return data_frame.merge(
-    return data_frame
+        exposure_df,
+        how="left",
+        left_on=META_DATA_EXPOSURE_ID,
+        right_index=True,
+    )

tests/test_utils.py

@@ -15,6 +15,99 @@ def test_split_data_frame(data_frame_with_params):
         assert set(value_df["Well.Row"].unique()) == {key}
 
 
+@pytest.mark.parametrize(
+    "value,expected",
+    [
+        [[1, 2], True],
+        [(1, 2), True],
+        [{1, 2}, False],
+        [{1: 2}, False],
+        ["1, 2", False],
+        [None, False],
+    ],
+)
+def test_is_list_or_tuple(value, expected):
+    from sensospot_data.utils import _is_list_or_tuple
+
+    result = _is_list_or_tuple(value)
+
+    assert result is expected
+
+
+@pytest.mark.parametrize(
+    "value,expected",
+    [
+        [1, True],
+        [1.2, True],
+        [{1, 2}, False],
+        [{1: 2}, False],
+        ["1", False],
+        [None, False],
+    ],
+)
+def test_is_numerical(value, expected):
+    from sensospot_data.utils import _is_numerical
+
+    result = _is_numerical(value)
+
+    assert result is expected
+
+
+def test_check_valid_exposure_map_entry_ok():
+    from sensospot_data.utils import _check_valid_exposure_map_entry
+
+    result = _check_valid_exposure_map_entry((2, 1))
+
+    assert result is None
+
+
+@pytest.mark.parametrize(
+    "value", [[], [1], (1, 2, 3), {"a": 1, "b": 2}, ("A", "B")]
+)
+def test_check_valid_exposure_map_entry_raises_error(value):
+    from sensospot_data.utils import _check_valid_exposure_map_entry
+
+    with pytest.raises(ValueError):
+        _check_valid_exposure_map_entry(value)
+
+
+def test_check_exposure_map_ok(exposure_df):
+    from sensospot_data.utils import _check_exposure_map
+
+    exposure_map = {1: ("A", 10), 2: ("B", 20), 3: ("C", 30)}
+
+    result = _check_exposure_map(exposure_df, exposure_map)
+
+    assert result is None
+
+
+def test_check_exposure_map_wrong_type(exposure_df):
+    from sensospot_data.utils import _check_exposure_map
+
+    exposure_map = []
+
+    with pytest.raises(ValueError):
+        _check_exposure_map(exposure_df, exposure_map)
+
+
+def test_check_exposure_map_wrong_ids(exposure_df):
+    from sensospot_data.utils import _check_exposure_map
+
+    exposure_map = {1: ("A", 10), 2: ("B", 20), 4: ("D", 40)}
+
+    with pytest.raises(ValueError):
+        _check_exposure_map(exposure_df, exposure_map)
+
+
+def test_check_exposure_map_invalid_entries(exposure_df):
+    from sensospot_data.utils import _check_exposure_map
+
+    exposure_map = {1: ("A", 10), 2: ("B", 20), 3: "ERROR"}
+
+    with pytest.raises(ValueError):
+        _check_exposure_map(exposure_df, exposure_map)
+
+
 def test_infer_exposure_from_parameters(data_frame_with_params):
     from sensospot_data.utils import _set_exposure_data_from_parameters
 
@@ -62,11 +155,9 @@ def test_apply_exposure_map_raises_error(data_frame_with_params):
         "X": ExposureSetting("Cy5", 150),
     }
 
-    with pytest.raises(ValueError) as excinfo:
+    with pytest.raises(ValueError):
         apply_exposure_map(data_frame_with_params, exposure_map)
 
-    assert str(excinfo.value).startswith("Exposure Map differs")
-
 
 def test_apply_exposure_map_from_parameters(data_frame_with_params):
     from sensospot_data.utils import apply_exposure_map