1. #!/usr/bin/python3
  3. import re
  4. import logging
  6. import matplotlib.pyplot as plt
  7. from mpl_toolkits.mplot3d import Axes3D
  10. class Heatmap(object):
  11. def __init__(self):
  12. self.source = open("console", "r")
  13. # [MSG:X Stallguard 0 SG_Val: 0708 Rate: 00180 mm/min SG_Setting:5 MPos:332.460,33.630,52.780]
  14. self.line_re = re.compile(r".*\[MSG:(?P<axis>[XYZ]) Stallguard "
  15. r"(?P<triggered>[0-1]) *"
  16. r"SG_Val: (?P<sgval>[0-9]+) *"
  17. r"Rate: (?P<feedrate>[0-9]+) mm/min "
  18. r"SG_Setting:(?P<sgsens>[0-9]+) "
  19. r"MPos:(?P<xpos>[0-9]+\.[0-9]+),(?P<ypos>[0-9]+\.[0-9]+),(?P<zpos>[0-9]+\.[0-9]+)\].*"
  21. )
  22. self.datapoints = {'X': [], 'Y':[], 'Z':[]}
  23. self.meaningful_feed_range = {"X" : (60,800), "Y": (60,800), "Z": (10,180)} # filter out rate < 60mm/min filter out jog with rate > 800mm/m
  24. self.normalization_window = 5 # running avg window
  25. self.logger = logging.getLogger(__name__)
  26. self.figs = {}
  28. def parse_source(self):
  29. for l in self.source.readlines():
  30. if "Stallguard" not in l:
  31. continue
  32. m = re.match(self.line_re, l)
  33. if not m:
  34. continue
  35. datapoint = m.groupdict()
  36. self.datapoints[datapoint["axis"]].append(datapoint)
  38. def is_significant(self, point):
  39. axis = point["axis"]
  40. rate = int(point["feedrate"])
  41. if self.meaningful_feed_range[axis][0] <= rate <= self.meaningful_feed_range[axis][1]:
  42. return True
  43. return False
  46. def normalize(self, window=5):
  47. for axis in self.datapoints.keys():
  48. n = 0
  49. while n < len(self.datapoints[axis]):
  50. if not self.is_significant(self.datapoints[axis][n]):
  51. self.logger.warn("removing %s", self.datapoints[axis][n])
  52. del self.datapoints[axis][n]
  53. continue
  54. sgval_sum = 0
  55. summed_for = 0
  56. start_window = max(n - window, 0)
  57. end_window = min(len(self.datapoints[axis])-1, n + window)
  58. nn = start_window
  59. while nn <= end_window and nn < len(self.datapoints[axis])-1:
  60. if not self.is_significant(self.datapoints[axis][nn]):
  61. self.logger.warn("removing %s", self.datapoints[axis][nn])
  62. del self.datapoints[axis][nn]
  63. continue
  64. sgval_sum += int(self.datapoints[axis][nn]["sgval"])
  65. summed_for += 1
  66. nn += 1
  67. self.datapoints[axis][n]["sgval"] = sgval_sum / summed_for
  68. n += 1
  70. def plot(self):
  71. for motor_axis in self.datapoints.keys():
  72. self.figs[motor_axis] = plt.figure(dpi=1200)
  73. ax = self.figs[motor_axis].add_subplot(111, projection='3d')
  74. xs = [float(point["xpos"]) for point in self.datapoints[motor_axis]]
  75. ys = [float(point["ypos"]) for point in self.datapoints[motor_axis]]
  76. zs = [float(point["zpos"]) for point in self.datapoints[motor_axis]]
  77. c = [point["sgval"] for point in self.datapoints[motor_axis]]
  78. # matplotlib.colors.Colormap
  79. scatter = ax.scatter(xs, ys, zs, zdir='z', s=1, c=c, cmap=plt.get_cmap('RdYlGn')) #), depthshade=True)
  80. legend1 = ax.legend(*scatter.legend_elements(num=5), loc="upper left", title="SG_val")
  81. ax.add_artist(legend1)
  82. self.figs[motor_axis].savefig("%s.png" % motor_axis)
  86. h = Heatmap()
  87. h.parse_source()
  88. h.normalize()
  89. h.plot()