Merge pull request #143 from OpenCOMPES/processor_tests

Processor tests
OpenCOMPES · Sep 23, 2023 · 2b8114c · 2b8114c
2 parents 9b8cae0 + 7ece180
commit 2b8114c
Show file tree

Hide file tree

Showing 18 changed files with 1,464 additions and 308 deletions.
diff --git a/sed/binning/binning.py b/sed/binning/binning.py
@@ -46,7 +46,7 @@ def bin_partition(
     Args:
         part (Union[dask.dataframe.DataFrame, pd.DataFrame]): dataframe on which
             to perform the histogram. Usually a partition of a dask DataFrame.
-            bins (int, dict, Sequence[int], Sequence[np.ndarray], Sequence[tuple], optional):
+        bins (int, dict, Sequence[int], Sequence[np.ndarray], Sequence[tuple], optional):
             Definition of the bins. Can  be any of the following cases:
 
                 - an integer describing the number of bins for all dimensions. This
@@ -85,7 +85,7 @@ def bin_partition(
             jitter={'axis':{'amplitude':0.5,'mode':'uniform'}}.
             This example also shows the default behaviour, in case None is
             passed in the dictionary, or jitter is a list of strings.
-            Warning: this is not the most performing approach. applying jitter
+            Warning: this is not the most performing approach. Applying jitter
             on the dataframe before calling the binning is much faster.
             Defaults to None.
         return_edges (bool, optional): If True, returns a list of D arrays
@@ -112,7 +112,8 @@ def bin_partition(
         bins, axes, ranges = simplify_binning_arguments(bins, axes, ranges)
     else:
         if not isinstance(bins, list) or not (
-            all(isinstance(x, int) for x in bins) or all(isinstance(x, np.ndarray) for x in bins)
+            all(isinstance(x, (int, np.int64)) for x in bins)
+            or all(isinstance(x, np.ndarray) for x in bins)
         ):
             raise TypeError(
                 "bins needs to be of type 'List[int] or List[np.ndarray]' if tests are skipped!",
@@ -157,7 +158,7 @@ def bin_partition(
                 mode = jpars.get("mode", "uniform")
                 ax_index = axes.index(col)
                 _bin = bins[ax_index]
-                if isinstance(_bin, int):
+                if isinstance(_bin, (int, np.int64)):
                     rng = ranges[ax_index]
                     binsize = abs(rng[1] - rng[0]) / _bin
                 else:

diff --git a/sed/binning/utils.py b/sed/binning/utils.py
@@ -80,7 +80,7 @@ def simplify_binning_arguments(
         bins = bins_
 
     # if bins provided as single int, apply to all dimensions
-    if isinstance(bins, int):
+    if isinstance(bins, (int, np.int64)):
         bins = [bins] * len(axes)
 
     # Check that we have a sequence of bins now
@@ -112,7 +112,7 @@ def simplify_binning_arguments(
         bins = bins_
 
     # if bins are provided as int, check that ranges are present
-    if all(isinstance(x, int) for x in bins):
+    if all(isinstance(x, (int, np.int64)) for x in bins):
         bins = cast(List[int], list(bins))
         if ranges is None:
             raise AttributeError(

diff --git a/sed/calibrator/delay.py b/sed/calibrator/delay.py
@@ -132,7 +132,7 @@ def append_delay_axis(
 
         if "delay_range" not in calibration.keys():
             if "delay_range_mm" not in calibration.keys() or "time0" not in calibration.keys():
-                if datafile is not None:
+                if datafile is not None and p1_key and p2_key and t0_key:
                     try:
                         ret = extract_delay_stage_parameters(
                             datafile,