Merge pull request #171 from jenshnielsen/numpy120

fix typecheking with numpy 1.20
toolsforexperiments · Feb 5, 2021 · 7e6a1be · 7e6a1be
2 parents 91e2d49 + 913b486
commit 7e6a1be
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Showing 4 changed files with 15 additions and 13 deletions.
diff --git a/plottr/data/datadict.py b/plottr/data/datadict.py
@@ -913,7 +913,7 @@ def remove_invalid_entries(self) -> 'DataDict':
                 rows = self.data_vals(d)
             else:
                 datavals = self.data_vals(d)
-                rows = datavals.reshape(-1, np.prod(ishp[d]))
+                rows = datavals.reshape(-1, int(np.prod(ishp[d])))
 
             _idxs = np.array([])
 

diff --git a/plottr/node/dim_reducer.py b/plottr/node/dim_reducer.py
@@ -2,7 +2,7 @@
 
 nodes and widgets for reducing data dimensionality.
 """
-from typing import Dict, Any, Tuple, Type, Optional, List, Union, cast
+from typing import Dict, Any, Tuple, Type, Optional, List, Union, cast, Callable
 from enum import Enum, unique
 
 from typing_extensions import TypedDict
@@ -56,7 +56,7 @@ class ReductionMethod(Enum):
 
 
 #: mapping from reduction method Enum to functions
-reductionFunc = {
+reductionFunc: Dict[ReductionMethod, Callable[..., Any]] = {
     ReductionMethod.elementSelection: selectAxisElement,
     ReductionMethod.average: np.mean,
 }
@@ -518,10 +518,12 @@ def _applyDimReductions(self, data: DataDictBase) -> Optional[DataDictBase]:
 
                     # support for both pre-defined and custom functions
                     if isinstance(fun, ReductionMethod):
-                        funCall = reductionFunc[fun]
+                        funCall: Optional[Callable[..., Any]] = reductionFunc[fun]
                     else:
                         funCall = fun
 
+                    if funCall is None:
+                        raise RuntimeError("Reduction function is None")
                     newvals = funCall(data[n]['values'], *arg, **kw)
                     if newvals.shape != targetShape:
                         self.logger().error(

diff --git a/plottr/plot/mpl.py b/plottr/plot/mpl.py
@@ -222,11 +222,11 @@ def colorplot2d(ax: Axes, x: np.ndarray, y: np.ndarray, z: np.ndarray,
         z = z.astype(float)
 
         # first check if we need to fill some masked values in
-        if np.ma.is_masked(x):
+        if isinstance(x, np.ma.MaskedArray) and np.ma.is_masked(x):
             x = x.filled(np.nan)
-        if np.ma.is_masked(y):
+        if isinstance(y, np.ma.MaskedArray) and np.ma.is_masked(y):
             y = y.filled(np.nan)
-        if np.ma.is_masked(z):
+        if isinstance(z, np.ma.MaskedArray) and np.ma.is_masked(z):
             z = z.filled(np.nan)
 
         # next: try some surgery, if possible
@@ -874,7 +874,7 @@ def _analyzeData(self, data: Optional[DataDictBase]) -> Dict[str, bool]:
             dataLimits = {}
             for n in data.axes() + data.dependents():
                 vals = data.data_vals(n)
-                dataLimits[n] = vals.min(), vals.max()
+                dataLimits[n] = float(vals.min()), float(vals.max())
 
         result = {
             'dataTypeChanged': dataType != self.dataType,

diff --git a/plottr/utils/num.py b/plottr/utils/num.py
@@ -9,9 +9,9 @@
 
 from ..utils.misc import unwrap_optional
 
-INTTYPES = [int, np.int, np.int16, np.int32, np.int64]
-FLOATTYPES = [float, np.float, np.float16, np.float32, np.float64,
-              complex, np.complex, np.complex64, np.complex128]
+INTTYPES = [int, np.int16, np.int32, np.int64]
+FLOATTYPES = [float, np.float16, np.float32, np.float64,
+              complex, np.complex64, np.complex128]
 NUMTYPES = INTTYPES + FLOATTYPES
 
 
@@ -105,7 +105,7 @@ def arrays_equal(a: np.ndarray, b: np.ndarray,
     equal = _are_equal(a, b)
     invalid = _are_invalid(a, b)
 
-    return np.all(equal | close | invalid)
+    return bool(np.all(equal | close | invalid))
 
 
 def array1d_to_meshgrid(arr: Union[List, np.ndarray],
@@ -132,7 +132,7 @@ def array1d_to_meshgrid(arr: Union[List, np.ndarray],
         localarr = localarr.copy()
     localarr = localarr.reshape(-1)
 
-    newsize = np.prod(target_shape)
+    newsize = int(np.prod(target_shape))
     if newsize < localarr.size:
         localarr = localarr[:newsize]
     elif newsize > localarr.size: