def visualizeAllAcc(self, correlated=False):
'''
Visualizes all datastreams of the Accelerometer
Arguments:
correlated: set correlated to True if you want to autocorrelate the data before graphing.
'''
time = np.linspace(
0, self.data.shape[0], self.data.shape[0]
) # from 0 to 10 seconds with [amount of datapoints] steps
f, (ax4, ax5, ax6) = plt.subplots(3, sharex=True, sharey=True)
ax4.plot(time, self.data.accX)
ax4.set_title("accX", y=0.65, size='smaller')
ax5.plot(time, self.data.accY)
ax5.set_title("accY", y=0.65, size='smaller')
ax6.plot(time, self.data.accZ)
ax6.set_title("accZ", y=0.65, size='smaller')
if correlated:
analyzer = DataAnalyzer.DataAnalyzer(self.data.accX)
ax4.plot(time, analyzer.getAutocorrelation())
analyzer = DataAnalyzer.DataAnalyzer(self.data.accY)
ax5.plot(time, analyzer.getAutocorrelation())
analyzer = DataAnalyzer.DataAnalyzer(self.data.accZ)
ax6.plot(time, analyzer.getAutocorrelation())
# Fine-tune figure; make subplots close to each other and hide x ticks for
# all but bottom plot.
f.subplots_adjust(hspace=0)
plt.setp([a.get_xticklabels() for a in f.axes[:-1]], visible=False)
plt.show()
def visualizeStream(self,
dataStream,
correlated=False,
title='Data',
vLine=None):
'''Visualizes a particular datastream
Arguments
data: single datastream
title: [optional]
'''
time = np.linspace(0, dataStream.shape[0], dataStream.shape[0])
f, ax1 = plt.subplots(1)
ax1.plot(time, dataStream)
ax1.set_title(title)
if correlated:
analyzer = DataAnalyzer.DataAnalyzer(dataStream)
ax1.plot(time, analyzer.getAutocorrelation())
if (vLine):
plt.axvline(vLine)
plt.show()
# Helper functions:
# get all the data files from the directory
def getDataFileNames(dataType, movement="", dataFolder=DATA_FOLDER):
files = os.listdir(dataFolder)
output = []
for file in files:
if dataType in file and movement in file:
output.append(file)
return output
# ------------------- MAIN ------------------------------------
analyzer = DataAnalyzer.DataAnalyzer()
print("Doing " + str(ITERATIONS) + " iterations with a " +
str(TEST_SIZE_PERCENT) + " test ratio")
# -- training --
data_data = []
data_labels = []
data_data_length = []
files = getDataFileNames("training")
for trainingFile in files:
dataFile = pd.read_csv(DATA_FOLDER + trainingFile, header=0)
#data = [dataFile['alpha'], dataFile['beta'], dataFile['gamma'], dataFile['accX'], dataFile['accY'], dataFile['accZ']]
dataFile = analyzer.normalize(dataFile)
from helpers import DataAnalyzer
import matplotlib.pyplot as plt
import numpy as np
from sklearn.preprocessing import normalize as skNorm
#dataFile = pd.read_csv("../../data/testing-leftright-qsMbpdsd6zQTqlrKAADi-1-72BPM.csv", header=0)
#dataFile = pd.read_csv("../../data/training-rotateclockwise-avkfxrmpauHdDpeaAAAa-6.csv", header=0)
#dataFile = pd.read_csv("../../data/training-updown-avkfxrmpauHdDpeaAAAa-1.csv", header=0)
#normal data
dataFile = pd.read_csv(
"../data/FLAWED/lessthan1period/updown-35-49-R53P95G3cV93UMlKAAB0-3_1.csv",
header=0)
da = DataAnalyzer.DataAnalyzer()
#Normalize each stream individually:
'''
dataNorm = dataFile.copy()
dataNorm['accX'] = skNorm(dataNorm['accX'])[0]
dataNorm['accY'] = skNorm(dataNorm['accY'])[0]
dataNorm['accZ'] = skNorm(dataNorm['accZ'])[0]
'''
#dataFile = da.normalize(dataFile)
#dataFile = da.autoCorrelate(dataFile)
daa = DataAnalyzer.AutoAnalyzer(dataFile)
visualizer = Visualizer.Visualizer(dataFile)