Tutorial: fix numpy deprecation warning

Numpy 1.26.2 now complains about casting a Python integer to a numpy
dtype for which it is out of range. Work around that by first converting
to np.int_.

doc/tut-4-send-perf.rst

@@ -57,16 +57,17 @@ following:
  .. code-block:: python
     :dedent: 0
 
-            item_group["adc_samples"].value = np.full(heap_size, i, np.int8)
+            item_group["adc_samples"].value = np.full(heap_size, np.int_(i), np.int8)
 
  .. code-block:: c++
     :dedent: 0
 
             std::fill(adc_samples.begin(), adc_samples.end(), i);
 
-We're filling each heap with the loop index — not particularly meaningful for
-simulation, but it has the bonus that we can easily see on the receiver side
-if we've accidentally transmitted data for the wrong heap.
+We're filling each heap with the loop index (truncated to an 8-bit integer) —
+not particularly meaningful for simulation, but it has the bonus that we can
+easily see on the receiver side if we've accidentally transmitted data for the
+wrong heap.
 
 This dramatically improves performance: around 1400 MB/s for Python and 1600
 MB/s for C++ — assuming you're running the receiver. Somewhat surprisingly,

examples/tut_4_send_perf.py

@@ -54,7 +54,7 @@ def main():
     start = time.perf_counter()
     for i in range(n_heaps):
         item_group["timestamp"].value = i * heap_size
-        item_group["adc_samples"].value = np.full(heap_size, i, np.int8)
+        item_group["adc_samples"].value = np.full(heap_size, np.int_(i), np.int8)
         heap = item_group.get_heap()
         stream.send_heap(heap)
     elapsed = time.perf_counter() - start

examples/tut_5_send_pipeline.py

@@ -63,7 +63,7 @@ async def main():
     for i in range(n_heaps):
         new_state = State()
         item_group["timestamp"].value = i * heap_size
-        item_group["adc_samples"].value = np.full(heap_size, i, np.int8)
+        item_group["adc_samples"].value = np.full(heap_size, np.int_(i), np.int8)
         heap = item_group.get_heap()
         new_state.future = stream.async_send_heap(heap)
         if old_state is not None:

examples/tut_6_send_pktsize.py

@@ -66,7 +66,7 @@ async def main():
     for i in range(n_heaps):
         new_state = State()
         item_group["timestamp"].value = i * heap_size
-        item_group["adc_samples"].value = np.full(heap_size, i, np.int8)
+        item_group["adc_samples"].value = np.full(heap_size, np.int_(i), np.int8)
         heap = item_group.get_heap()
         new_state.future = stream.async_send_heap(heap)
         if old_state is not None:

examples/tut_8_send.py

@@ -72,7 +72,7 @@ async def main():
     for i in range(n_heaps):
         state = states[i % len(states)]
         await state.future  # Wait for any previous use of this state to complete
-        state.adc_samples.fill(i)
+        state.adc_samples.fill(np.int_(i))
         item_group["timestamp"].value = i * heap_size
         item_group["adc_samples"].value = state.adc_samples
         heap = item_group.get_heap()