Simple script for captured currents and PWM data visualization.

The script to visualize data captured by rpi-pmsm-test1
application "logcurrent" command.

Signed-off-by: Pavel Pisa <pisa@cmp.felk.cvut.cz>

diff --git a/tools/datavis/pxmc-spimc-currents-show.py b/tools/datavis/pxmc-spimc-currents-show.py
new file mode 100755 (executable)
index 0000000..24189f0
--- /dev/null
+++ b/tools/datavis/pxmc-spimc-currents-show.py
@@ -0,0 +1,136 @@
+#! /usr/bin/python
+
+import numpy as np
+import math
+#import matplotlib as plt
+#from numpy import cos
+from pylab import plot, subplot, figure, show, ylim, yticks
+from pprint import pprint
+import argparse
+
+class spimc_state(object):
+  def __init__(self, data_fname = None, adc_channels = 3, pwm_channels = 3,
+               dmem_words = 0, skip_words = 0, period = 2500, adc_oversample = 1,
+               filtspan = 1, isr_divider = 1, samples = None):
+    self.adc_channels = adc_channels
+    self.pwm_channels = pwm_channels
+    self.period = period
+    self.adc_oversample = adc_oversample
+    self.isr_divider = isr_divider
+    self.filtspan = filtspan
+    self.dmem_words = dmem_words
+    self.adc_channels = adc_channels
+    self.pwm_channels = pwm_channels
+    self.skip_words = skip_words
+    data_buff = np.fromfile(data_fname, dtype=np.int32, count=-1, sep='')
+    self.rawsamples = int(math.floor((np.size(data_buff, 0) - 0) / (1 + self.adc_channels + 1 + self.pwm_channels + self.dmem_words)))
+    self.samples = self.rawsamples - self.filtspan
+    if samples is not None:
+      if self.samples > samples:
+        self.samples = samples
+    self.pwm = np.ndarray(shape=(self.pwm_channels, self.rawsamples), dtype=np.int32)
+    self.adc_data = np.ndarray(shape=(self.adc_channels, self.samples), dtype=np.float)
+    self.adc_raw = np.ndarray(shape=(self.adc_channels, self.rawsamples), dtype=np.int32)
+    self.adc_sqn = np.ndarray(shape=(self.rawsamples), dtype=np.uint32)
+    self.adc_offset = np.ndarray(shape=(self.adc_channels), dtype=np.float)
+    self.current = np.ndarray(shape=(self.adc_channels, self.samples), dtype=np.float)
+
+    self.pwm_d = np.ndarray(shape=(1, self.samples), dtype=np.float)
+    self.pwm_q = np.ndarray(shape=(1, self.samples), dtype=np.float)
+    self.cur_d = np.ndarray(shape=(1, self.samples), dtype=np.float)
+    self.cur_q = np.ndarray(shape=(1, self.samples), dtype=np.float)
+    self.ptindx = np.ndarray(shape=(1, self.samples), dtype=np.uint32)
+    self.ptphs = np.ndarray(shape=(1, self.samples), dtype=np.int32)
+
+    self.adc_offset[0] = 2071.0
+    self.adc_offset[1] = 2074.0
+    self.adc_offset[2] = 2050.0
+    sample_pos = 0;
+    for i in range(0, self.rawsamples):
+      pos = sample_pos + self.skip_words
+      if i < self.samples:
+        self.ptindx[0, i] = data_buff[pos];
+      pos += 1
+      for ch in range(0, self.pwm_channels):
+        self.pwm[ch, i] = data_buff[pos] & 0x3fff
+        pos += 1
+      self.adc_sqn[i] = data_buff[pos];
+      pos += 1
+      for ch in range(0, self.adc_channels):
+        self.adc_raw[ch, i] = data_buff[pos]
+        pos += 1
+      for a in range(0, self.dmem_words):
+        self.dmem[a, i] = data_buff[pos]
+        pos += 1
+      sample_pos = pos
+
+    nominal_cycle = self.period * self.adc_oversample;
+    for ch in range(0, self.adc_channels):
+      for i in range(0, self.samples):
+        #cycle_time = self.adc_raw[ch, i + self.filtspan] + nominal_cycle  * self.filtspan - self.adc_raw[ch, i]
+        #cycle_time = nominal_cycle * self.filtspan
+        fac = 1.0
+        sampsum = 0.0
+        for j in range(0, self.filtspan):
+          sampsum += float(self.adc_raw[ch, i + j]) / float(self.adc_sqn[i])
+        self.adc_data[ch, i] = sampsum * fac
+    for ch in range(0, self.adc_channels):
+      for i in range(0, self.samples):
+        self.current[ch, i] = self.adc_data[ch, i] - self.adc_offset[ch]
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser()
+  parser.add_argument('-f', '--filtspan', dest='filtspan', type=int,
+                      default=1, help='number of samples used in moving average')
+  parser.add_argument('-p', '--period', dest='period', type=int,
+                      default=2500, help='period period of ')
+  parser.add_argument('-a', '--adc-oversample', dest='adc_oversample', type=int,
+                      default=1, help='ADC oversample over period')
+  parser.add_argument('-d', '--isr-divider', dest='isr_divider', type=int,
+                      default=1, help='Rx done ISR divider')
+  parser.add_argument('-c', '--channels', dest='channels', type=int,
+                      default=3, help='number of measured current ADC channels')
+  parser.add_argument('-s', '--samples', dest='samples', type=int,
+                      default=None, help='limit number of processed samples')
+  parser.add_argument('files', nargs=argparse.REMAINDER)
+
+  args = parser.parse_args()
+
+  if len(args.files) == 0:
+    args.files.append('adc-out.bin')
+
+  adc_data = spimc_state(args.files[0], filtspan = args.filtspan,
+                            period = args.period, adc_oversample = args.adc_oversample,
+                            isr_divider = args.isr_divider, adc_channels = args.channels,
+                            samples = args.samples, pwm_channels = args.channels)
+
+  x = range(0, adc_data.samples)
+  xr = range(0, adc_data.rawsamples)
+  xf = range(adc_data.filtspan + 0, adc_data.samples + adc_data.filtspan)
+  y = adc_data.ptindx[0]
+  y1 = adc_data.pwm[0]
+  y2 = adc_data.pwm[1]
+  y3 = adc_data.pwm[2]
+  #y4 = adc_data.adc_data[0]
+  #y5 = adc_data.adc_data[1]
+  #y6 = adc_data.adc_data[2]
+  y4 = adc_data.current[0]
+  y5 = adc_data.current[1]
+  y6 = adc_data.current[2]
+  y7 = adc_data.adc_sqn
+  y8 = y4 + y5 + y6
+
+  #pwm_ph = np.arctan2(adc_data.pwm_q, adc_data.pwm_d)
+  #cur_ph = np.arctan2(adc_data.cur_q_filt, adc_data.cur_d_filt)
+  #cur_ph = np.arctan2(adc_data.cur_q, adc_data.cur_d)
+
+  #y7 = cur_ph[0] * 180.0 / math.pi * 10
+  #y8 = pwm_ph[0] * 180.0 / math.pi * 10
+
+  #y9 = adc_data.ptphs[0] * 100
+
+  plot(x, y, 'y', xr, y1, 'r.', xr, y2, 'g.', xr, y3, 'b.',
+       x, y4, 'r-', x, y5, 'g-', x, y6, 'b-', xr, y7, 'k',
+       x, y8, 'c')
+
+  show()