Merge pull request #1579 from SpiNNakerManchester/astype

astype can not be generic

Author: Christian-B

spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py

@@ -295,7 +295,8 @@ def get_post_shape(self, shape: Tuple[int, ...]) -> Tuple[int, ...]:
                       (2 * self.__padding_shape))
 
         return tuple(int(i) for i in numpy.clip(
-            post_shape // self.__strides, 1, numpy.inf).astype(integer))
+            post_shape // self.__strides, 1, numpy.inf).astype(
+            numpy.uint32))
 
     @overrides(AbstractConnector.validate_connection)
     def validate_connection(

spynnaker/pyNN/models/neural_projections/connectors/small_world_connector.py

@@ -129,7 +129,7 @@ def _set_n_connections(self, synapse_info: SynapseInformation) -> None:
         else:
             d = distances
 
-        self.__mask = (d < self.__degree).astype(float)
+        self.__mask = (d < self.__degree).astype(numpy.float64)
         self.__n_connections = int(math.ceil(numpy.sum(self.__mask)))
 
     @overrides(AbstractConnector.get_delay_maximum)

spynnaker/pyNN/models/spike_source/spike_source_poisson_machine_vertex.py

@@ -625,7 +625,8 @@ def read_connections(
         addr += SDRAM_EDGE_PARAMS_BASE_BYTES
         data = SpynnakerDataView().read_memory(
             placement.x, placement.y, addr, size)
-        weights_encoded = numpy.frombuffer(data, dtype=uint16).astype(float)
+        weights_encoded = numpy.frombuffer(data, dtype=uint16).astype(
+            numpy.float64)
         weight_scales = (
                 next(iter(cast(AbstractEdgePartition,
                                self.__sdram_partition).edges))

spynnaker/spynnaker_plotting.py

@@ -160,10 +160,10 @@ def heat_plot_numpy(axes: Axes, data: NDArray, label: str = '',
     :param str label: Label for the graph
     :param options: plotting options
     """
-    neurons = data[:, 0].astype(int)
-    times = data[:, 1].astype(int)
+    neurons = data[:, 0].astype(np.uint32)
+    times = data[:, 1].astype(np.uint32)
     values = data[:, 2]
-    info_array = np.empty((max(neurons) + 1, max(times) + 1))
+    info_array = np.empty((neurons.max() + 1, times.max() + 1))
     info_array[:] = np.nan
     info_array[neurons, times] = values
     _heat_plot(axes, info_array, label=label, **options)

spynnaker_integration_tests/test_spike_source/test_poisson_spike_source_sdram.py

@@ -48,7 +48,7 @@ def check_rate(
         for i in range(n_neurons):
             times, counts = numpy.unique(
                 spikes[i].magnitude, return_counts=True)
-            times = times.astype(int)
+            times = times.astype(numpy.uint32)
             indices = numpy.where(times < runtime - 2)[0]
             times = times[indices]
             counts = counts[indices]