Python plotSensorData Examples

Example #1

File: generate_acc_data.py Project: kaikun213/htm-bayesian-learning

def main():
  data, headers, categories = loadAccelerometerData(DATA_DIR, INPUT_FILES)
  if not os.path.exists(OUTPUT_DIR):
    os.makedirs(OUTPUT_DIR)
  outputFileTemplate = os.path.join(OUTPUT_DIR, 'sensortag_%s.csv')
  outputFiles = writeData(outputFileTemplate, data, headers, METRICS,
                          categories, SLICES)

  categoryLabels = [f[:-9] for f in INPUT_FILES]
  for outputFile in outputFiles:
    plotSensorData([outputFile], markers=False, categoryLabels=categoryLabels)

Example #2

File: generate_artificial_data.py Project: solversa/nupic.research

def main():
    dominoStats = {
        "SIGNAL_TYPES": SIGNAL_TYPES,
        "NUM_PHASES": NUM_PHASES,
        "NUM_REPS": NUM_REPS,
        "NUM_CATEGORIES": NUM_CATEGORIES,
        "WHITE_NOISE_AMPLITUDES": WHITE_NOISE_AMPLITUDES,
        "SIGNAL_AMPLITUDES": SIGNAL_AMPLITUDES,
        "SIGNAL_MEANS": SIGNAL_MEANS,
        "NOISE_LENGTHS": NOISE_LENGTHS
    }

    with open('dominostats.json', 'wb') as f:
        f.write(json.dumps(dominoStats))

    filePaths = _generateExpData()
    plotSensorData(filePaths)

Example #3

File: generate_artificial_data.py Project: hernandezurbina/nupic.research

def main():
  dominoStats = {
    "SIGNAL_TYPES": SIGNAL_TYPES,
    "NUM_PHASES": NUM_PHASES,
    "NUM_REPS": NUM_REPS,
    "NUM_CATEGORIES": NUM_CATEGORIES,
    "WHITE_NOISE_AMPLITUDES": WHITE_NOISE_AMPLITUDES,
    "SIGNAL_AMPLITUDES": SIGNAL_AMPLITUDES,
    "SIGNAL_MEANS": SIGNAL_MEANS,
    "NOISE_LENGTHS": NOISE_LENGTHS
  }

  with open('dominostats.json', 'wb') as f:
    f.write(json.dumps(dominoStats))

  filePaths = _generateExpData()
  plotSensorData(filePaths)

Example #4

def main():
  dominoStats = {
    "SLICES": SLICES,
    "METRICS": METRICS,
    "MAX_POINTS": MAX_POINTS,
    "INPUT_FILES": INPUT_FILES
  }

  with open('dominostats.json', 'wb') as f:
    f.write(json.dumps(dominoStats))

  data, headers, categories = loadAccelerometerData(DATA_DIR, INPUT_FILES)
  if not os.path.exists(OUTPUT_DIR):
    os.makedirs(OUTPUT_DIR)
  outputFileTemplate = os.path.join(OUTPUT_DIR, 'sensortag_%s.csv')
  outputFiles = writeData(outputFileTemplate, data, headers, METRICS, categories,
                          SLICES)

  categoryLabels = [f[:-9] for f in INPUT_FILES]
  for outputFile in outputFiles:
    plotSensorData([outputFile], markers=False, categoryLabels=categoryLabels)

Example #5

def main():
  dominoStats = {
    "SLICES": SLICES,
    "METRICS": METRICS,
    "MAX_POINTS": MAX_POINTS,
    "INPUT_FILES": INPUT_FILES
  }

  with open('dominostats.json', 'wb') as f:
    f.write(json.dumps(dominoStats))

  data, headers, categories = loadAccelerometerData(DATA_DIR, INPUT_FILES)
  if not os.path.exists(OUTPUT_DIR):
    os.makedirs(OUTPUT_DIR)
  outputFileTemplate = os.path.join(OUTPUT_DIR, 'sensortag_%s.csv')
  outputFiles = writeData(outputFileTemplate, data, headers, METRICS, categories,
                          SLICES)

  categoryLabels = [f[:-9] for f in INPUT_FILES]
  for outputFile in outputFiles:
    plotSensorData([outputFile], markers=False, categoryLabels=categoryLabels)

Example #6

File: generate_and_plot_data.py Project: oxtopus/htmresearch

def main():
  csvFiles = _generateData()
  plotSensorData(csvFiles, SEQUENCE_LENGTH)

Example #7

File: generate_and_plot_data.py Project: andrewmalta13/nupic.research

def main():
  expSetups = _generateData()
  plotSensorData(expSetups)

Example #8

File: generate_artificial_data.py Project: kaikun213/htm-bayesian-learning

def main():

    filePaths = _generateExpData()
    plotSensorData(filePaths, xlim=[0, 100])

Example #9

def main():
  filePaths = _generateExpData()
  plt = plotSensorData(filePaths)
  plt.show()