added api function apply_map

this also lead to simplifications in _add_measurement_params and apply_exposure_map

CHANGES.md

@@ -11,18 +11,22 @@
  
 0.5.1
 =====
-- added standard aggregates functionality to utlis
-- exposed `utils` api functions in package
+ - added standard aggregates functionality to utlis
+ - exposed `utils` api functions in package
 
 0.5.2
 =====
-- providing the normalized exposure time to `normalize_values` is now optional
+ - providing the normalized exposure time to `normalize_values` is now optional
 
 0.5.3
 =====
-- renaming function `split_data_frame` to `split` 
-- added --quite flag to cli to bypass sanity checks
-- removed the aggregate functions from the utils module, is now a spearate project
+ - renaming function `split_data_frame` to `split` 
+ - added --quite flag to cli to bypass sanity checks
+ - removed the aggregate functions from the utils module, is now a spearate project
+
+0.5.4
+=====
+ - added api function "apply_map"
 
 
 0.4.0  - api changes

sensospot_data/__init__.py

@@ -3,14 +3,14 @@
 Parsing the numerical output from Sensovations Sensospot image analysis.
 """
 
-__version__ = "0.5.3"
+__version__ = "0.5.4"
 
 
 from pathlib import Path
 
 import click
 
-from .utils import split, apply_exposure_map  # noqa: F401
+from .utils import split, apply_map, apply_exposure_map  # noqa: F401
 from .parser import parse_file, parse_folder  # noqa: F401
 from .parameters import ExposureInfo  # noqa: F401
 from .dynamic_range import blend, create_xdr, normalize_values  # noqa: F401

sensospot_data/parameters.py

@@ -14,6 +14,7 @@ from .columns import (
     META_DATA_PARAMETERS_TIME,
     META_DATA_PARAMETERS_CHANNEL,
 )
+from .utils import apply_map
 
 ExposureInfo = namedtuple("ExposureInfo", ["channel", "time"])
 
@@ -58,23 +59,19 @@ def get_measurement_params(folder):
 
 def _add_measurement_params(data_frame, params):
     """ adds measurement parameters to a data frame """
-    for exposure_id, info in params.items():
-        mask = data_frame[META_DATA_EXPOSURE_ID] == exposure_id
-        data_frame.loc[mask, META_DATA_PARAMETERS_CHANNEL] = info.channel
-        data_frame.loc[mask, META_DATA_PARAMETERS_TIME] = info.time
-    data_frame[META_DATA_PARAMETERS_CHANNEL] = data_frame[
-        META_DATA_PARAMETERS_CHANNEL
-    ].astype("category")
-    return data_frame
+    columns=[META_DATA_PARAMETERS_CHANNEL, META_DATA_PARAMETERS_TIME]
+    map = {k: dict(zip(columns, v)) for k, v in params.items()}
+    return apply_map(data_frame, map, META_DATA_EXPOSURE_ID)
 
 
 def add_optional_measurement_parameters(data_frame, folder):
     """ adds measurement params to the data frame, if they could be parsed """
-    data_frame[META_DATA_PARAMETERS_CHANNEL] = numpy.nan
-    data_frame[META_DATA_PARAMETERS_TIME] = numpy.nan
     params = get_measurement_params(folder)
     if params:
         available_exposures = set(data_frame[META_DATA_EXPOSURE_ID].unique())
         if available_exposures == set(params.keys()):
             return _add_measurement_params(data_frame, params)
+    else:
+        data_frame[META_DATA_PARAMETERS_CHANNEL] = numpy.nan
+        data_frame[META_DATA_PARAMETERS_TIME] = numpy.nan
     return data_frame

sensospot_data/utils.py

@@ -104,14 +104,37 @@ def apply_exposure_map(data_frame, exposure_map=None):
 
     _check_exposure_map(data_frame, exposure_map)
 
-    exposure_df = pandas.DataFrame.from_dict(
-        exposure_map,
-        orient="index",
-        columns=[SETTINGS_EXPOSURE_CHANNEL, SETTINGS_EXPOSURE_TIME],
-    )
+    columns=[SETTINGS_EXPOSURE_CHANNEL, SETTINGS_EXPOSURE_TIME]
+    map = {k: dict(zip(columns, v)) for k, v in exposure_map.items()}
+    
+    return apply_map(data_frame, map, META_DATA_EXPOSURE_ID)
+
+
+def apply_map(data_frame, map, index_col):
+    """adds a nested dictionary to a data frame on a specific index column
+
+    map:
+        keys: must be the same as the values in the index column,
+        values: dictionary with new column names as keys and the values
+
+    example:
+
+        >>> df = DataFrame(data={"MyIndex": [10, 10, 20]})
+        >>> map = { 
+        ...     10: {"NewCol": "foo"},
+        ...     20: {"NewCol": "Bar"},
+        ... }
+        >>> apply_map(df, map, "MyIndex")
+            MyIndex  NewCol
+        0   10       foo
+        1   10       foo
+        2   20       bar
+
+    """
+    map_df = pandas.DataFrame.from_dict(map, orient="index")
     return data_frame.merge(
-        exposure_df,
+        map_df,
         how="left",
-        left_on=META_DATA_EXPOSURE_ID,
+        left_on=index_col,
         right_index=True,
     )

tests/test_parameters.py

@@ -96,7 +96,7 @@ def test_add_optional_measurement_parameters_with_params_file(
     from sensospot_data.parameters import add_optional_measurement_parameters
 
     folder = example_dir / EXAMPLE_DIR_WITH_PARAMS
-    add_optional_measurement_parameters(exposure_df, folder)
+    exposure_df = add_optional_measurement_parameters(exposure_df, folder)
 
     expected = [(1, "green", 100), (2, "red", 150), (3, "red", 15)]
     for exposure_id, channel, time in expected:
@@ -112,7 +112,7 @@ def test_add_optional_measurement_parameters_without_params_file(
     from sensospot_data.parameters import add_optional_measurement_parameters
 
     folder = example_dir / EXAMPLE_DIR_WO_PARAMS
-    add_optional_measurement_parameters(exposure_df, folder)
+    exposure_df = add_optional_measurement_parameters(exposure_df, folder)
 
     for exposure_id in range(1, 4):
         mask = exposure_df["Exposure.Id"] == exposure_id

tests/test_sensovation_data.py

@@ -11,3 +11,4 @@ def test_import_api():
     from sensospot_data import parse_folder  # noqa: F401
     from sensospot_data import normalize_values  # noqa: F401
     from sensospot_data import apply_exposure_map  # noqa: F401
+    from sensospot_data import apply_map  # noqa: F401

tests/test_utils.py

@@ -177,3 +177,21 @@ def test_apply_exposure_map_from_parameters_raises_error(
         apply_exposure_map(data_frame_without_params, None)
 
     assert str(excinfo.value).startswith("Exposure Map: measurement")
+
+
+def test_apply_map(exposure_df):
+    from sensospot_data.utils import apply_map
+
+    map = {
+        1: {"SomeColumn": "A", "OtherColumn": 9},
+        2: {"SomeColumn": "B", "OtherColumn": 8},
+        3: {"SomeColumn": "C", "OtherColumn": 7},
+    }
+
+    result = apply_map(exposure_df, map, "Exposure.Id")
+
+    for key, value in map.items():
+        mask = result["Exposure.Id"] == key
+        partial = result.loc[mask]
+        assert set(partial["SomeColumn"].unique()) == {value["SomeColumn"]}
+        assert set(partial["OtherColumn"].unique()) == {value["OtherColumn"]}