Removed deprecated np.float

numpy 1.20 deprecates np.float, which was only ever an alias for
``float``.  I've replaced all occurrences of np.float with float, as
recommended.  There should be no change in functionality.

openflexure_microscope/api/default_extensions/autofocus.py

@@ -127,16 +127,16 @@ def monitor_sharpness(microscope: Microscope):
         m.stop()
 
 
-def sharpness_sum_lap2(rgb_image: np.ndarray) -> np.float:
+def sharpness_sum_lap2(rgb_image: np.ndarray) -> float:
     """Return an image sharpness metric: sum(laplacian(image)**")"""
-    image_bw: np.float = np.mean(rgb_image, 2)
-    image_lap: np.float = ndimage.filters.laplace(image_bw)
-    return np.mean(image_lap.astype(np.float) ** 4)
+    image_bw: float = np.mean(rgb_image, 2)
+    image_lap: float = ndimage.filters.laplace(image_bw)
+    return np.mean(image_lap.astype(float) ** 4)
 
 
-def sharpness_edge(image: np.ndarray) -> np.float:
+def sharpness_edge(image: np.ndarray) -> float:
     """Return a sharpness metric optimised for vertical lines"""
-    gray: np.float = np.mean(image.astype(float), 2)
+    gray: float = np.mean(image.astype(float), 2)
     n: int = 20
     edge: np.ndarray = np.array([[-1] * n + [1] * n])
     return np.sum(
@@ -169,7 +169,7 @@ class AutofocusExtension(BaseExtension):
 
     def measure_sharpness(
         self, microscope: Microscope, metric_fn: Callable = sharpness_sum_lap2
-    ) -> np.float:
+    ) -> float:
         """Measure the sharpness of the camera's current view."""
 
         if hasattr(microscope.camera, "array") and callable(
@@ -185,7 +185,7 @@ class AutofocusExtension(BaseExtension):
         dz: List[int],
         settle: float = 0.5,
         metric_fn: Callable = sharpness_sum_lap2,
-    ) -> Tuple[List[int], List[np.float]]:
+    ) -> Tuple[List[int], List[float]]:
         """Perform a simple autofocus routine.
         The stage is moved to z positions (relative to current position) in dz,
         and at each position an image is captured and the sharpness function 
@@ -197,7 +197,7 @@ class AutofocusExtension(BaseExtension):
         stage: BaseStage = microscope.stage
 
         with set_properties(stage, backlash=256), stage.lock, camera.lock:
-            sharpnesses: List[np.float] = []
+            sharpnesses: List[float] = []
             positions: List[int] = []
 
             # Some cameras may not have annotate_text. Reset if it does

openflexure_microscope/api/default_extensions/picamera_autocalibrate/recalibrate_utils.py

@@ -106,7 +106,7 @@ def lst_from_channels(channels: np.ndarray) -> np.ndarray:
 
     logging.info("Generating a lens shading table at %sx%s", *lst_resolution)
     lens_shading: np.ndarray = np.zeros(
-        [channels.shape[0]] + lst_resolution, dtype=np.float
+        [channels.shape[0]] + lst_resolution, dtype=float
     )
     for i in range(lens_shading.shape[0]):
         image_channel: np.ndarray = channels[i, :, :]
@@ -193,7 +193,7 @@ def recalibrate_camera(camera: PiCamera):
     _ = rgb_image(camera)
 
     # Fix the AWB gains so the image is neutral
-    channel_means = np.mean(np.mean(rgb_image(camera), axis=0, dtype=np.float), axis=0)
+    channel_means = np.mean(np.mean(rgb_image(camera), axis=0, dtype=float), axis=0)
     old_gains = camera.awb_gains
     camera.awb_gains = (
         channel_means[1] / channel_means[0] * old_gains[0],

openflexure_microscope/json.py

@@ -21,8 +21,8 @@ class JSONEncoder(LabThingsJSONEncoder):
         # Numpy integers
         elif isinstance(o, np.integer):
             return int(o)
-        # Numpy floats
-        elif isinstance(o, np.float):
+        # Numpy floats are just Python floats
+        elif isinstance(o, float):
             return float(o)
         # Numpy arrays
         elif isinstance(o, np.ndarray):

openflexure_microscope/stage/sanga.py

@@ -277,11 +277,11 @@ class SangaDeltaStage(SangaStage):
         logging.debug(self.R_camera)
 
         # Transformation matrix converting delta into cartesian
-        x_fac: np.float = -1 * np.multiply(
+        x_fac: float = -1 * np.multiply(
             np.divide(2, np.sqrt(3)), np.divide(self.flex_b, self.flex_h)
         )
-        y_fac: np.float = -1 * np.divide(self.flex_b, self.flex_h)
-        z_fac: np.float = np.multiply(
+        y_fac: float = -1 * np.divide(self.flex_b, self.flex_h)
+        z_fac: float = np.multiply(
             np.divide(1, 3), np.divide(self.flex_b, self.flex_a)
         )