Merge pull request #6 from ConorMacBride/use-numpy-integer

Check isinstance np.integer as well as int

src/mcalf/models/ibis.py

@@ -382,7 +382,7 @@ def _get_sigma(self, classification=None, sigma=None):
             else:
                 return self.sigma[1]
         else:
-            if isinstance(sigma, int):
+            if isinstance(sigma, (int, np.integer)):
                 return self.sigma[sigma]
             else:
                 return np.asarray(sigma, dtype=np.float64)
@@ -479,7 +479,7 @@ def _fit(self, spectrum, profile=None, sigma=None, classification=None, spectrum
                 profile = 'absorption'
             elif classification is None:
                 raise ValueError("classification must be specified if profile is not specified")
-            elif not isinstance(classification, int):
+            elif not isinstance(classification, (int, np.integer)):
                 raise TypeError("classification must be an integer")
             else:
                 raise ValueError("unexpected classification, got %s" % classification)
@@ -597,13 +597,14 @@ def fit(self, time=None, row=None, column=None, spectrum=None, profile=None, sig
                 raise ValueError("number of spectra, number of recorded indices and number of classifications"
                                  "are not the same (impossible error)")
 
-            if n_pools is None or (isinstance(n_pools, int) and n_pools <= 0):  # Multiprocessing not required
+            # Multiprocessing not required
+            if n_pools is None or (isinstance(n_pools, (int, np.integer)) and n_pools <= 0):
 
                 print("Processing {} spectra".format(n_valid))
                 results = [self._fit(spectra[i], profile=profile, sigma=sigma, classification=classifications[i],
                                      spectrum_index=indices[i]) for i in range(len(spectra))]
 
-            elif isinstance(n_pools, int) and n_pools >= 1:  # Use multiprocessing
+            elif isinstance(n_pools, (int, np.integer)) and n_pools >= 1:  # Use multiprocessing
 
                 # Define single argument function that can be evaluated in the pools
                 def func(data, profile=profile, sigma=sigma):
@@ -711,7 +712,7 @@ def plot(self, fit=None, time=None, row=None, column=None, spectrum=None, classi
         if background is None and fit is not None:
             if not explicit_spectrum:  # Take from loaded background if using indices
                 time, row, column = self._get_time_row_column(time=time, row=row, column=column)
-                if sum([isinstance(i, (int, np.int64, np.int32, np.int16, np.int8)) for i in [time, row, column]]) != 3:
+                if sum([isinstance(i, (int, np.integer)) for i in [time, row, column]]) != 3:
                     raise TypeError("plot only accepts integer values for time, row and column")
                 background = self.background[time, row, column]
             else:  # Otherwise assume to be zero

src/mcalf/models/results.py

@@ -133,7 +133,7 @@ def __init__(self, shape, n_parameters, time=None):
         if not isinstance(shape, tuple) or len(shape) != 2:
             raise TypeError("`shape` must be a tuple of length 2, got %s" % type(shape))
 
-        if not isinstance(n_parameters, int) or n_parameters < 1:
+        if not isinstance(n_parameters, (int, np.integer)) or n_parameters < 1:
             raise ValueError("`n_parameters` must be an integer greater than zero, got %s" % n_parameters)
         parameters_shape = tuple(list(shape) + [n_parameters])
         self.parameters = np.full(parameters_shape, np.nan, dtype=float)

src/mcalf/utils/misc.py

@@ -258,9 +258,9 @@ def merge_results(filenames, output):
                     test_function = _nan_test
                 elif isinstance(invalid, bool) and not invalid:  # bool (can only overwrite False)
                     test_function = _false_test
-                elif isinstance(invalid, int) and invalid == -1:
+                elif isinstance(invalid, (int, np.integer)) and invalid == -1:
                     test_function = _minus_one_test
-                elif isinstance(invalid, int) and invalid == 0:
+                elif isinstance(invalid, (int, np.integer)) and invalid == 0:
                     test_function = _zero_test
                 else:
                     raise ValueError(f"Unexpected invalid value {invalid}.")

src/mcalf/utils/smooth.py

@@ -40,7 +40,7 @@ def moving_average(array, width):
     array([1. , 1.5, 2.5, 3.5, 4.5])
     """
 
-    if not isinstance(width, int) or width <= 0 or width > len(array):
+    if not isinstance(width, (int, np.integer)) or width <= 0 or width > len(array):
         raise ValueError("`width` must be a positive integer less than the length of `array`, got %s." % width)
 
     kernel = np.ones(width) / width