Fix numpy >= 1.20 deprecation warnings

Author: lukelbd

proplot/axes/plot.py

@@ -3068,7 +3068,7 @@ def _parse_markersize(
             else:
                 s = s.copy()
                 s.flat[:] = utils.units(s.flat, 'pt')
-                s = s.astype(np.float) ** 2
+                s = s.astype(np.float64) ** 2
         if absolute_size is None:
             if _inside_seaborn_call():
                 absolute_size = True

proplot/colors.py

@@ -1241,7 +1241,7 @@ def save(self, path=None, alpha=True):
         --------
         DiscreteColormap.save
         """
-        # NOTE We sanitize segmentdata before saving to json. Convert np.float to
+        # NOTE: We sanitize segmentdata before saving to json. Convert numpy float to
         # builtin float, np.array to list of lists, and callable to list of lists.
         # We tried encoding func.__code__ with base64 and marshal instead, but when
         # cmap.append() embeds functions as keyword arguments, this seems to make it

proplot/figure.py

@@ -1075,14 +1075,12 @@ def _add_subplots(
         # TODO: Consider deprecating and asking users to use add_subplot()
         def _axes_dict(naxs, input, kw=False, default=None):
             # First build up dictionary
-            # 1. 'string' or {1: 'string1', (2, 3): 'string2'}
-            if not kw:
+            if not kw:  # 'string' or {1: 'string1', (2, 3): 'string2'}
                 if np.iterable(input) and not isinstance(input, (str, dict)):
                     input = {num + 1: item for num, item in enumerate(input)}
                 elif not isinstance(input, dict):
                     input = {range(1, naxs + 1): input}
-            # 2. {'prop': value} or {1: {'prop': value1}, (2, 3): {'prop': value2}}
-            else:
+            else:  # {key: value} or {1: {key: value1}, (2, 3): {key: value2}}
                 nested = [isinstance(_, dict) for _ in input.values()]
                 if not any(nested):  # any([]) == False
                     input = {range(1, naxs + 1): input.copy()}
@@ -1118,7 +1116,7 @@ def _axes_dict(naxs, input, kw=False, default=None):
         else:
             array = np.atleast_1d(array)
             array[array == None] = 0  # None or 0 both valid placeholders  # noqa: E711
-            array = array.astype(np.int)
+            array = array.astype(int)
             if array.ndim == 1:  # interpret as single row or column
                 array = array[None, :] if order == 'C' else array[:, None]
             elif array.ndim != 2:

proplot/internals/inputs.py

@@ -71,7 +71,7 @@ def _is_numeric(data):
     array = _to_numpy_array(data)
     return len(data) and (
         np.issubdtype(array.dtype, np.number)
-        or np.issubdtype(array.dtype, np.object)
+        or np.issubdtype(array.dtype, object)
         and all(isinstance(_, np.number) for _ in array.flat)
     )
 
@@ -82,9 +82,9 @@ def _is_categorical(data):
     """
     array = _to_numpy_array(data)
     return len(data) and (
-        np.issubdtype(array.dtype, np.str)
-        or np.issubdtype(array.dtype, np.object)
-        and any(isinstance(_, np.str) for _ in array.flat)
+        np.issubdtype(array.dtype, str)
+        or np.issubdtype(array.dtype, object)
+        and any(isinstance(_, str) for _ in array.flat)
     )
 
 
@@ -161,7 +161,7 @@ def _to_masked_array(data, *, copy=False):
     else:
         data = ma.masked_invalid(data, copy=copy)
     if np.issubdtype(data.dtype, np.integer):
-        data = data.astype(np.float)
+        data = data.astype(np.float64)
     if np.issubdtype(data.dtype, np.number):
         data.fill_value *= np.nan  # default float fill_value is 1e+20 or 1e+20 + 0j
     else:
@@ -508,7 +508,7 @@ def _safe_range(data, lo=0, hi=100):
     if data.size:
         min_ = np.min(data) if lo <= 0 else np.percentile(data, lo)
         if hasattr(min_, 'dtype') and np.issubdtype(min_.dtype, np.integer):
-            min_ = np.float(min_)
+            min_ = np.float64(min_)
         try:
             is_finite = np.isfinite(min_)
         except TypeError:
@@ -520,7 +520,7 @@ def _safe_range(data, lo=0, hi=100):
     if data.size:
         max_ = np.max(data) if hi >= 100 else np.percentile(data, hi)
         if hasattr(max_, 'dtype') and np.issubdtype(max_.dtype, np.integer):
-            max_ = np.float(max_)
+            max_ = np.float64(max_)
         try:
             is_finite = np.isfinite(min_)
         except TypeError:

proplot/ticker.py

@@ -183,7 +183,7 @@ def set_params(self, steps=None, nbins=None, minor=None, min_n_ticks=None):
         Set the parameters for this locator. See `DiscreteLocator` for details.
         """
         if steps is not None:
-            steps = np.unique(np.asarray(steps, dtype=np.int))  # also sorts, makes 1D
+            steps = np.unique(np.asarray(steps, dtype=int))  # also sorts, makes 1D
             if np.any(steps < 1) or np.any(steps > 10):
                 raise ValueError('Steps must fall between one and ten (inclusive).')
             if steps[0] != 1: