Fix imprecise bin edge creation in utils.calc.cleaner_range

Spotted by running unit tests for NZ region (the end-corner-based unit test `test_bin2d_regions_endcorner` failed).
Those imprecise bin edges were originated in `utils.calc.cleaner_range`.

The crucial modification is using `round` instead of `floor`.
The other change – replacing the hard-coded `const` with a flexible `scale` parameter – is to account for decimal places of bin edges _and_ stepping (`const = 100000` would lead to imprecise bin edges if `h < 0.00001`).

+ simplified `core.regions.magnitude_bins()` to just call (this improved) `cleaner_range()`; it was essentially a copy of the former.

Author: mherrmann3

csep/core/regions.py

@@ -313,12 +313,7 @@ def magnitude_bins(start_magnitude, end_magnitude, dmw):
     Returns:
         bin_edges (numpy.ndarray)
     """
-    # convert to integers to prevent accumulating floating point errors
-    const = 10000
-    start = numpy.floor(const * start_magnitude)
-    end = numpy.floor(const * end_magnitude)
-    d = const * dmw
-    return numpy.arange(start, end + d / 2, d) / const
+    return cleaner_range(start_magnitude, end_magnitude, dmw)
 
 def create_space_magnitude_region(region, magnitudes):
     """Simple wrapper to create space-magnitude region """

csep/utils/calc.py

@@ -213,12 +213,13 @@ def cleaner_range(start, end, h):
     Returns:
         bin_edges (numpy.ndarray)
     """
-    # convert to integers to prevent accumulating floating point errors
-    const = 100000
-    start = numpy.floor(const * start)
-    end = numpy.floor(const * end)
-    d = const * h
-    return numpy.arange(start, end + d / 2, d) / const
+    # determine required scaling to account for decimal places of bin edges and stepping
+    num_decimals_bins = len(str(float(start)).split('.')[1])
+    scale = max(10**num_decimals_bins, 1 / h)
+    start = numpy.round(scale * start)
+    end = numpy.round(scale * end)
+    d = scale * h
+    return numpy.arange(start, end + d / 2, d) / scale
 
 def first_nonnan(arr, axis=0, invalid_val=-1):
     mask = arr==arr