prelim spi tests with mechanics intact

rpi_spi/code/spi_controller/pi_spi.py

-import spidev 
+import spidev, time 
+# import numpy as np 
+# from plot_stamps import plot_stamps 
 
-bitrate = 1000000
+bitrate = 10000000
 
 print(f'rate {bitrate/1e6}MBit/s bit period should be {1000000000/bitrate}ns')
 
 spi = spidev.SpiDev()
 spi.open(0, 0)
 spi.max_speed_hz = bitrate 
-spi.mode = 0b00
+spi.mode = 0b10
 
-test_pck = bytearray(32)
+pck_len = 64
+stamp_count = 10000
+# stamps = np.zeros(stamp_count)
+
+test_pck = bytearray(pck_len)
 for b in range(len(test_pck)):
-  test_pck[b] = b 
+  test_pck[b] = b + 1
 
 print(test_pck)
 
-for i in range(1000):
+# TODO will be to finish making these plots, 
+# need to install pandas and numpy here w a venv 
+# for now I'm just going to turn the rate up until things go wrong 
+bonked = False 
+for i in range(stamp_count):
+  if bonked:
+    break 
   ret = spi.xfer(test_pck)
-  print(ret)
+  # stamps[i] = time.perf_counter() * 1e6
+  start = -1 
+  for j in range(len(ret)):
+    if start == -1:
+      if ret[j] == 0:
+        start = j 
+        continue 
+    else:
+      if ret[j] != j - start:
+        print(f'BONK at {i} char {j} {start} {ret[j]}')
+        print(ret)
+        bonked = True 
+        break 
+
+spi.close() 
+
+# plot_stamps(stamps, stamp_count, pck_len)
 
-spi.close() 
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+# 1Mbit seems solid,
+# 2.5Mbit and we have very occasional starvation 
+# 5Mbit and we have at least one bad packet every run (1/1000?)
+# 5Mbit with more F_CPU (250MHz) it is obviously improved (1/5000?)
+# 10Mbit at 250MHz we are back to ~ (1/2000) starvations, 
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rpi_spi/code/spi_controller/plot_stamps.py

+import pandas as pd 
+import matplotlib.pyplot as plt 
+
+def plot_stamps(stamps, stamp_count, pck_len):
+  # make df from stamps 
+  df = pd.DataFrame({'timestamps': stamps})
+
+  # calculate deltas between stamps 
+  df['deltas'] = df['timestamps'].diff() 
+
+  # clean NaN's 
+  df = df.dropna()
+
+  # wipe obviously-wrong deltas (i.e. the 1st, which goes 0-start-us) 
+  df = df[df['deltas'] < 10000]
+
+  # Plotting
+  fig, ax1 = plt.subplots(figsize=(11, 3))
+
+  ax1.set_xlim([50, 2000])
+
+  # Primary x-axis (time deltas)
+  df['deltas'].plot(kind='hist', bins=50, ax=ax1)
+  ax1.set_xlabel('Time-Stamp Deltas (us) and equivalent (MBits/s)')
+  ax1.set_ylabel(f'Frequency (of {stamp_count})')
+
+  # get axis ticks to calculate equivalent bandwidths 
+  x_ticks = ax1.get_xticks()
+  ax1.set_xticks(x_ticks)
+  bandwidths = [((pck_len * 8) * (1e6 / x)) / 1e6 for x in x_ticks]
+  ticks = [] 
+
+  for i in range(len(x_ticks)):
+    print(i, x_ticks[i], bandwidths[i]) 
+    ticks.append(f"{x_ticks[i]:.0f} ({bandwidths[i]:.3f})")
+
+  ax1.set_xticklabels(ticks)
+
+  plt.title(f'Single-Source COBS Data Sink Deltas, pck_len={pck_len}')
+
+  plt.tight_layout()
+
+  plt.show()
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