def displayPca(self,inds):
#display PCA largest ingradients of selectes sumples
X = self.X
U = self.U
if(len(inds)==0):
inds = random.sample(range(len(X)),10);
samples = X[[inds]]
pc = (samples*U)
restore = pc*np.transpose(U)
nSamples = len(pc)
allMat = np.matrix([])
nEvDisp = 10
for k in range(nSamples):
#find largest valuse
sortIndex = np.argsort(-np.abs(pc[k]))[:,0:nEvDisp]
a = np.vstack([U[:,sortIndex[0,i]]*pc[k,sortIndex[0,i]] for i in range(nEvDisp)])
# restore image
b = np.sum(a,0)
#append them
if(len(allMat)==1):
#start loop
allMat = np.vstack([samples[k], a, b,restore[k] ])
else:
allMat = np.vstack([allMat, samples[k], a,b,restore[k]])
f = dd.displayData(allMat,nSamples,nEvDisp+3)
f.suptitle('PCA')
self.pcaFig = f
def displayInput(self,nDisp,isRand):
#display nDisp samples from input in a grid.
X = self.X
if isRand:
XX = random.sample(np.matrix.tolist(X),nDisp)
else:
XX = X[:nDisp]
f = dd.displayData(XX,[],[])
f.suptitle('input samples')
self.inFig = f
def detect_burr(data,
pv,
left=None,
right=None,
method=0,
minimum_peak_distance=100):
titles = data.columns
titleList = titles.values.tolist()
if pv in titleList:
pvn = titleList.index(pv)
sta = DisplayData.showStatistic(data)
print("statistic data:")
print(sta)
# use boxplot define threshold
iqr = sta.loc['75%'][titles[pvn]] - sta.loc['25%'][titles[pvn]]
if left is None:
left = sta.loc['25%'][titles[pvn]] - 1.5 * iqr
if right is None:
right = sta.loc['75%'][titles[pvn]] + 1.5 * iqr
print('min edge:', left, 'max edge:', right)
burrdata = data[((data[titles[pvn]]) < left) |
((data[titles[pvn]]) > right)]
LoadData.df2other(burrdata, 'csv', 'newfile.csv')
y = data[titles[pvn]].values
if method == 0:
# find_peaks by scipy signal
peaks, _ = signal.find_peaks(y, height=right)
plt.plot(y, 'b', lw=1)
plt.plot(peaks, y[peaks], "+", mec='r', mew=2, ms=8)
plt.plot(np.zeros_like(y) + right, "--", color="gray")
plt.title("find_peaks min_height:%7f" % right)
plt.show()
if method == 1:
detect_peaks(y, mph=right, mpd=minimum_peak_distance, show=True)
if method == 2:
print('Detect peaks with minimum height and distance filters.')
# thres=right/max(y)
indexes = peakutils.peak.indexes(np.array(y),
thres=right / max(y),
min_dist=minimum_peak_distance)
print('Peaks are: %s' % (indexes))
plt.plot(y, 'b', lw=1)
for i in indexes:
plt.plot(i, y[i], "+", mec='r', mew=2, ms=8)
plt.plot(np.zeros_like(y) + right, "--", color="gray")
plt.title("peakutils.peak thres:%f ,minimum_peak_distance:%d" %
(right, minimum_peak_distance))
plt.show()
else:
print("Wrong PV name, not in ", titleList)
def nnTest(self,ds):
#test network parameters with labeled dataSet
p = self.predict(ds)
y = ds.y
inds = (p==y)
print 'Success rate: ' ,float(inds.sum())/float(inds.size)*100,'%'
#display correct sumples
tInd = np.where(p==y)[0].tolist()[0]
if len(tInd)>50:
#randomly choose 50
tInd = random.sample(tInd,50)
dispMat = ds.X[tInd]
f = dd.displayData(dispMat,[],[])
f.suptitle('correct samples')
self.correctFig = f
#display error sumples
errInd = np.where(p!=y)[0].tolist()[0]
if len(errInd)>50:
#randomly choose 50
errInd = random.sample(errInd,50)
dispMat = ds.X[errInd]
f = dd.displayData(dispMat,[],[])
f.suptitle('error samples')
self.errFig = f
for y in range(SA[0], SA[1], 5):
for x in range(SA[2], SA[3], 5):
get_data(x, y, x + 5, y + 5)
#print (data)
if data:
count = data["cnt"]
for i in range(count):
#print ("test")
loc = data["list"][i]["name"] #location name
lat = data["list"][i]["coord"]["Lat"] #Latitude
lon = data["list"][i]["coord"]["Lon"] #Longitude
temp = data["list"][i]["main"][
"temp"] #temperature (in celsius)
pressure = data["list"][i]["main"]["pressure"] #pressure
humidity = data["list"][i]["main"]["humidity"] #humidity
w_ID = data["list"][i]["weather"][0]["id"] #Weather id
if (pressure > 900 and pressure < 1030): #remove errors
plt.plot(lon, lat, 'ok', markersize=1, c='red')
#plt.text(lon, lat, loc, fontsize=12);
sql_insert_data(loc, lat, lon, temp, pressure,
humidity, w_ID, time.time())
print("Time Elapsed: ", time.time() - startTime)
#plt.savefig('/images/EastCoast_{0}.png'.format(startTime))
print("Creating map")
DD.GenMap(startTime, time.time())
#print("Waiting 10 minutes...")
#time.sleep(600) #sleep 10 min
conn.close()