def plotEntry(entry):
accData = np.array(plotdata.readNumericData(entry["accfile"]))
gyroData = np.array(plotdata.readNumericData(entry["gyrofile"]))
accData, gyroData = process.cleanData(accData, gyroData)
# process data
accData, gyroData = process.processData(accData, gyroData)
# take sample of data
accData = accData[: len(accData) * dataPortion, :]
gyroData = gyroData[: len(gyroData) * dataPortion, :]
# plot accelerometer/gyroscope
X = accData[:, 0]
# X = range(1,len(accData[:,0])+1)
# extract sample number
# sample = re.search('[0-9]{5,7}', entry['accfile']).group(0)
sample = entry["person"]
print entry
show3D = False
if show3D:
# animate motion
fig = plt.figure()
ax = fig.gca(projection="3d")
ax.set_aspect("equal")
r = [-1, 1]
for s, e in combinations(np.array(list(product(r, r, r))), 2):
if np.sum(np.abs(s - e)) == r[1] - r[0]:
ax.plot3D(*zip(s, e), color="b")
for pt in gyroData:
print pt
a = Arrow3D([0, 1], [0, 1], [0, 1], mutation_scale=20, lw=1, arrowstyle="-|>", color="k")
ax.add_artist(a)
plt.show()
text = raw_input("Press enter for next one...")
else:
f, (ax1, ax2) = plt.subplots(2, 1) # , sharex=True)
ax1.set_title(entry["device"] + " - " + sample + " - accelerometer")
ax1.plot(X, accData[:, 1], "r", label="x")
ax1.plot(X, accData[:, 2], "g", label="y")
ax1.plot(X, accData[:, 3], "b", label="z")
ax1.legend()
X = gyroData[:, 0]
# X = range(1,len(gyroData[:,0])+1)
# sample = re.search('[0-9]{5,7}', entry['gyrofile']).group(0)
ax2.set_title(entry["person"] + " - " + sample + " - gyroscope")
ax2.plot(X, gyroData[:, 1], "r", label="z")
ax2.plot(X, gyroData[:, 2], "g", label="x")
ax2.plot(X, gyroData[:, 3], "b", label="y")
ax2.legend()
def get_dataset():
all_sdk = SDK.query.all()
result_all_sdk = sdks_schema.dump(all_sdk)
all_app_sdk = App_SDK.query.all()
result_all_app_sdk = app_sdks_schema.dump(all_app_sdk)
selectedSDKs = request.json['selectedSDKs']
selectedSDKs = list(filter(None, selectedSDKs))
#slower (by far)
#result = processData2(result_all_sdk, selectedSDKs, result_all_app_sdk)
result = processData(result_all_sdk, result_all_app_sdk, selectedSDKs)
return jsonify(result)
lRate = 0.02 nEpochs = 5000 # Import and initialize the data filename = "GlassData.csv" rawData = np.genfromtxt(filename, delimiter = ',', skip_header = 1) rawData = rawData[:,1:len(rawData[0,:])] #Trim off the data point number in the GlassData.csv file rows = len(rawData[:,0]) cols = len(rawData[0,:]) txtfile = "outputs.txt" afile = open(txtfile,'w') #Process and normalize the data trainSet,trainAns,validSet,validAns,testSet,testAns = pc.processData(rawData, nOutputs) # Add Noise noise = 0.05 trainSet = pc.addNoise(trainSet,noise) ### DATA PROCESSING DONE AT THIS POINT ### # Initialize ALL output nodes (hidden and final output) and the weights hidden_o = np.zeros(nHidden) outputs = np.zeros(nOutputs) weightsHidden = 2*(np.random.rand(nHidden, nInputs)-0.5) weightsOutput = 2*(np.random.rand(nOutputs, nHidden)-0.5) ## Call the train network function
def extractFeatures(features, entry, config):
# require gyro and acc data
if not config['data-filters']['gyrofile']: return
if not config['data-filters']['accfile']: return
accData = np.array(readNumericData(entry['accfile']))
gyroData = np.array(readNumericData(entry['gyrofile']))
# clean data up
accData, gyroData = process.cleanData(accData, gyroData)
# process data
accData, gyroData = process.processData(accData, gyroData)
#features += accData.mean(axis=0)[1:].tolist()
features += accData.std(axis=0)[1:].tolist()
totalAcc = sqrt((square(accData[:,1]) + square(accData[:,2]) + square(accData[:,3])))
medFilterAccX = signal.medfilt(accData[:,1],11)
medFilterAccY = signal.medfilt(accData[:,2],11)
medFilterAccZ = signal.medfilt(accData[:,3],11)
b, a = signal.butter(3, 10.0/25, btype='low')
butterAccX = signal.filtfilt(b,a,medFilterAccX)
butterAccY = signal.filtfilt(b,a,medFilterAccY)
butterAccZ = signal.filtfilt(b,a,medFilterAccZ)
b, a = signal.butter(3, 0.3/25, btype='low')
gravityAccX = signal.filtfilt(b,a,butterAccX)
gravityAccY = signal.filtfilt(b,a,butterAccY)
gravityAccZ = signal.filtfilt(b,a,butterAccZ)
b, a = signal.butter(3, 0.3/25, btype='high')
bodyAccX = signal.filtfilt(b,a,butterAccX)
bodyAccY = signal.filtfilt(b,a,butterAccY)
bodyAccZ = signal.filtfilt(b,a,butterAccZ)
totalAcc = sqrt(square(bodyAccX)+square(bodyAccY)+square(bodyAccZ))
#features.append(mean(totalAcc))
#features.append(mean(bodyAccX))
#features.append(std(bodyAccX))
#features.append(mean(bodyAccY))
#features.append(std(bodyAccY))
#features.append(mean(bodyAccZ))
#features.append(std(bodyAccZ))
#features.append(mean(gravityAccZ))
#features.append(std(gravityAccZ))
#features.append(mean(gravityAccX))
#features.append(std(gravityAccX))
#features.append(mean(gravityAccY))
#features.append(std(gravityAccY))
#features.append(sqrt(mean(square(bodyAccX))))
features.append(sqrt(mean(square(bodyAccY))))
#features.append(sqrt(mean(square(bodyAccZ))))
#fx,Px = signal.welch(bodyAccX, fs=50.0, nperseg=128, noverlap=128.0/2, nfft=None, detrend='constant', return_onesided=False, scaling='density', axis=-1)
#fy,Py = signal.welch(bodyAccY, fs=50.0, nperseg=128, noverlap=128.0/2, nfft=None, detrend='constant', return_onesided=False, scaling='density', axis=-1)
#fz,Pz = signal.welch(bodyAccZ, fs=50.0, nperseg=128, noverlap=128.0/2, nfft=None, detrend='constant', return_onesided=False, scaling='density', axis=-1)
#features.append(sum(square(Px)))
#pylab.show()
bodyJerkX=diff(bodyAccX,n=1)
bodyJerkY=diff(bodyAccY,n=1)
bodyJerkZ=diff(bodyAccZ,n=1)