Merge pull request #253 from jenshnielsen/support_mypy_930

support typechecking with mypy 0.930

plottr/data/datadict.py

@@ -968,7 +968,7 @@ def remove_invalid_entries(self) -> 'DataDict':
                 datavals = self.data_vals(d)
                 rows = datavals.reshape(-1, int(np.prod(ishp[d])))
 
-            _idxs = np.array([])
+            _idxs: np.ndarray = np.array([])
 
             # get indices of all rows that are fully None
             if len(ishp[d]) == 0:

plottr/utils/testdata/dispersive_qubit_readout.py

@@ -66,7 +66,7 @@ def angle_data(theta: float, n: int = 100) -> np.ndarray:
     data = angle_data(np.pi/2., n=1000)
     extent = np.abs(data).max()
     hist, xe, ye = np.histogram2d(data.real, data.imag,
-                                  bins=np.linspace(-extent, extent, 51))
+                                  bins=list(np.linspace(-extent, extent, 51)))
     fig, ax = plt.subplots(1, 1)
     im = ax.pcolormesh(xe, ye, hist.T)
     cb = fig.colorbar(im)

plottr/utils/testdata/testdata.py

@@ -12,7 +12,7 @@
 from plottr.data.datadict import DataDict
 
 
-def generate_2d_scalar_simple(nx: int, ny: int, ndeps: int = 1) -> Iterable[Dict[str, int]]:
+def generate_2d_scalar_simple(nx: int, ny: int, ndeps: int = 1) -> Iterable[Dict[str, float]]:
     """
     Generate 2d example data, with axes x, y.
 
@@ -34,9 +34,9 @@ def generate_2d_scalar_simple(nx: int, ny: int, ndeps: int = 1) -> Iterable[Dict
     xx, yy = np.meshgrid(x, y, indexing='ij')
 
     for x, y in zip(xx.reshape(-1), yy.reshape(-1)):
-        ret = dict(x=x, y=y)
+        ret = dict(x=float(x), y=float(y))
         for n in range(ndeps):
-            ret[f'z_{n}'] = x * y * n
+            ret[f'z_{n}'] = float(x * y * n)
         yield ret