def printError(my_dpi, data):
"""
All data contanining the stock price info are retrieved from the database given the stock name
:param my_dpi: dpi screen
:param data: data to be plot
:param max_error: maximum error allowed
"""
try:
stockFile = []
try:
for eachLine in data:
splitLine = eachLine.split(',')
if len(splitLine) == 2:
if 'values' not in eachLine:
stockFile.append(eachLine)
except Exception as e:
print(str(e), 'failed to organize pulled data.')
except Exception as e:
print(str(e), 'failed to pull pricing data')
try:
date, closep_raw = np.loadtxt(
stockFile,
delimiter=',',
unpack=True,
converters={0: mdates.bytespdate2num('%Y%m%d')})
closep = closep_raw[::-1]
max_error = 0.1
while max_error < float(2 * max(closep)):
segments1 = segment.slidingwindowsegment(closep, fit.interpolate,
fit.sumsquared_error,
max_error)
segments2 = segment.slidingwindowsegment(closep, fit.regression,
fit.sumsquared_error,
max_error)
err1 = str(evaluate_MSE(closep, segments1))
err4 = str(evaluate_MSE(closep, segments2))
segments1 = segment.topdownsegment(closep, fit.interpolate,
fit.sumsquared_error, max_error)
segments2 = segment.topdownsegment(closep, fit.regression,
fit.sumsquared_error, max_error)
err2 = str(evaluate_MSE(closep, segments1))
err5 = str(evaluate_MSE(closep, segments2))
segments1 = segment.bottomupsegment(closep, fit.interpolate,
fit.sumsquared_error,
max_error)
segments2 = segment.bottomupsegment(closep, fit.regression,
fit.sumsquared_error,
max_error)
err3 = str(evaluate_MSE(closep, segments1))
err6 = str(evaluate_MSE(closep, segments2))
print(
str(max_error) + " " + err1 + " " + err2 + " " + err3 + " " +
err4 + " " + err5 + " " + err6)
max_error += 0.1
except e:
print("Error")
def draw_window_API(my_dpi, max_error, stockToFetch):
"""
All data contanining the stock price info are retrieved from the database given the stock name
:param my_dpi: dpi screen
:param data: data to be plot
:param max_error: maximum error allowed
"""
fig = plt.figure(figsize=(1000 / my_dpi, 700 / my_dpi),
dpi=96,
edgecolor='k',
facecolor='black')
fig.suptitle("PIECEWISE SEGMENTATION INTERPOLATION",
fontsize="15",
color="white",
fontweight='bold',
bbox={
'facecolor': 'red',
'alpha': 0.5,
'pad': 10
})
try:
print('Currently Pulling', stockToFetch)
urlToVisit = 'http://chartapi.finance.yahoo.com/instrument/1.0/' + stockToFetch + '/chartdata;type=quote;range=5y/csv'
stockFile = []
try:
sourceCode = urllib.request.urlopen(urlToVisit).read().decode()
splitSource = sourceCode.split('\n')
for eachLine in splitSource:
splitLine = eachLine.split(',')
if len(splitLine) == 6:
if 'values' not in eachLine:
stockFile.append(eachLine)
except Exception as e:
print(str(e), 'failed to organize pulled data.')
except Exception as e:
print(str(e), 'failed to pull pricing data')
try:
date, closep, highp, lowp, openp, volume = np.loadtxt(
stockFile,
delimiter=',',
unpack=True,
converters={0: mdates.bytespdate2num('%Y%m%d')})
SP = len(date)
# First subplot
ax1 = plt.subplot2grid((3, 3), (0, 0), colspan=3)
segments = segment.slidingwindowsegment(closep, fit.interpolate,
fit.sumsquared_error,
max_error)
draw_plot(closep, plt, ax1, "Sliding window with interpolation")
draw_segments(segments, 'red')
plt.ylabel('Stock Price')
plt.title("Sliding window", color='w')
# Second subplot
ax2 = plt.subplot2grid((3, 3), (1, 0), colspan=3)
segments = segment.topdownsegment(closep, fit.interpolate,
fit.sumsquared_error, max_error)
draw_plot(closep, plt, ax2, "Sliding window with interpolation")
draw_segments(segments, 'green')
plt.ylabel('Stock Price')
plt.title("Top down", color='w')
# Third subplot
ax3 = plt.subplot2grid((3, 3), (2, 0), colspan=3)
segments = segment.bottomupsegment(closep, fit.interpolate,
fit.sumsquared_error, max_error)
draw_plot(closep, plt, ax3, "Sliding window with interpolation")
draw_segments(segments, 'blue')
plt.ylabel('Stock Price')
plt.title("Bottom up", color='w')
plt.subplots_adjust(hspace=0.3)
plt.show()
except e:
print("Error")
def draw_window(my_dpi, data, max_error):
"""
All data contanining the stock price info are retrieved from the database given the stock name
:param my_dpi: dpi screen
:param data: data to be plot
:param max_error: maximum error allowed
"""
fig = plt.figure(figsize=(1000 / my_dpi, 700 / my_dpi),
dpi=96,
facecolor='black')
fig.suptitle("PIECEWISE SEGMENTATION INTERPOLATION",
fontsize="15",
color="white",
fontweight='bold',
bbox={
'facecolor': 'red',
'alpha': 0.5,
'pad': 10
})
try:
stockFile = []
try:
for eachLine in data:
splitLine = eachLine.split(',')
if len(splitLine) == 2:
if 'values' not in eachLine:
stockFile.append(eachLine)
except Exception as e:
print(str(e), 'failed to organize pulled data.')
except Exception as e:
print(str(e), 'failed to pull pricing data')
try:
date, closep_raw = np.loadtxt(
stockFile,
delimiter=',',
unpack=True,
converters={0: mdates.bytespdate2num('%Y%m%d')})
closep = closep_raw[::-1]
print(max(closep))
max_error = max(closep) * 2.5
# First subplot
ax1 = plt.subplot2grid((3, 3), (0, 0), colspan=3)
segments = segment.slidingwindowsegment(closep, fit.interpolate,
fit.sumsquared_error,
max_error)
draw_plot(closep, plt, ax1, "Sliding window with interpolation")
draw_segments(segments, 'red')
plt.ylabel('Stock Price')
plt.title("SLIDING WINDOW - ERROR " +
str(evaluate_global_error(closep, segments)),
color='Yellow',
fontweight='bold')
# Second subplot
ax2 = plt.subplot2grid((3, 3), (1, 0), colspan=3)
segments = segment.topdownsegment(closep, fit.interpolate,
fit.sumsquared_error, max_error)
draw_plot(closep, plt, ax2, "Sliding window with interpolation")
draw_segments(segments, 'green')
plt.ylabel('Stock Price')
plt.title("TOP DOWN - ERROR " +
str(evaluate_global_error(closep, segments)),
color='Yellow',
fontweight='bold')
# Third subplot
ax3 = plt.subplot2grid((3, 3), (2, 0), colspan=3)
segments = segment.bottomupsegment(closep, fit.interpolate,
fit.sumsquared_error, max_error)
segments = segment.bottomupsegment(closep, fit.interpolate,
fit.sumsquared_error, max_error)
draw_plot(closep, plt, ax3, "Sliding window with interpolation")
draw_segments(segments, 'blue')
plt.ylabel('Stock Price')
plt.title("BOTTOM UP - ERROR " +
str(evaluate_global_error(closep, segments)),
color='Yellow',
fontweight='bold')
plt.subplots_adjust(hspace=0.3)
plt.show()
except e:
print("Error")
def eval(directory,h5fileName):
h5file = h5py.File(directory+'/'+h5fileName,'r')
snapshots = h5file.attrs["SnapshotTimes"]
if not os.path.exists(directory+"/runRegFit"):
os.makedirs(directory+"/runRegFit")
borders = []
borders.append([])
borders.append([])
borders.append([])
borders.append([])
for times in snapshots:
kSetName = str(times) + "/Observables/KVector"
kvector = np.array(h5file[kSetName])
nSetname = str(times) + "/Observables/OccupationNumber"
nvector = np.array(h5file[nSetname])
kvector = np.delete(kvector,0)
nvector = np.delete(nvector,0)
borders[0].append(kvector.min())
borders[1].append(kvector.max())
borders[2].append(nvector.min())
borders[3].append(nvector.max())
borders[0] = np.array(borders[0]).min()
borders[1] = np.array(borders[1]).max()
borders[2] = np.array(borders[2]).min()
borders[3] = np.array(borders[3]).max()
for times in snapshots:
print("Fitting " + str(times))
kSetName = str(times) + "/Observables/KVector"
kvector = h5file[kSetName]
nSetname = str(times) + "/Observables/OccupationNumber"
nvector = h5file[nSetname]
obs = h5file[str(times) + "/Observables"]
meta = obs.attrs["Meta"]
the_time = meta[0] * 1000 #in ms
kLog = np.log(kvector)
kLog = np.delete(kLog,0)
nLog = np.log(nvector)
nLog = np.delete(nLog,0)
total_bins = 200
bins = np.linspace(kLog.min(),kLog.max(),total_bins)
delta = bins[1] - bins[0]
idx = np.digitize(kLog,bins)
running_median = [np.median(nLog[idx == k]) for k in range(total_bins)]
data = running_median
data = np.array(data)
# data = np.nan_to_num(data)
nans, x= nan_helper(data)
data[nans]= np.interp(x(nans), x(~nans), data[~nans])
data = data.tolist()
max_error = 0.1
#sliding window with regression
figure()
yscale('log')
xscale('log')
# subplot()
# segments = segment.slidingwindowsegment(data, fit.regression, fit.sumsquared_error, max_error)
segments = segment.bottomupsegment(data, fit.regression, fit.sumsquared_error, max_error)
# segments = segment.topdownsegment(data, fit.regression, fit.sumsquared_error, max_error)
# segments = segment.slidingwindowsegment(data, fit.interpolate, fit.sumsquared_error, max_error)
# segments = segment.bottomupsegment(data, fit.interpolate, fit.sumsquared_error, max_error)
# segments = segment.topdownsegment(data, fit.interpolate, fit.sumsquared_error, max_error)
data = np.array(data)
# data = np.exp(data)
# bins = np.exp(bins)
# segments = np.exp(segments)
draw_plot(bins,data,"Regression Fit of the Spectrum at " + str(the_time) + " ms",borders)
draw_segments(bins,segments)
# fig = plt.figure()
# fig.set_size_inches(10.0,12.0)
# ax1 = fig.add_subplot(211)
# subplot()
# set_yscale('log')
# set_xscale('log')
# plot(kvector,nvector,'.',color='r',label=r'$R_x$ $GPE$')
# ax2 = fig.add_subplot(212)
# ax2.plot(bins-delta/2,running_median,'.')
# plot(kLog,nLog)
# # ax2.plot(kLog,interpolate.splev(kLog,tck,der=0))
# plt.tight_layout()
# show()
# # fig.text(.001,.001,txt)
savefig(str(directory)+'/runRegFit/'+str(h5fileName)+'_'+str(times)+'_regfit.png',dpi=150)
close()
with open("example_data/16265-normalecg.txt") as f:
file_lines = f.readlines()
data = [float(x.split("\t")[2].strip()) for x in file_lines[100:320]]
max_error = 0.005
# sliding window with regression
figure()
segments = segment.slidingwindowsegment(data, fit.regression, fit.sumsquared_error, max_error)
draw_plot(data, "Sliding window with regression")
draw_segments(segments)
# bottom-up with regression
figure()
segments = segment.bottomupsegment(data, fit.regression, fit.sumsquared_error, max_error)
draw_plot(data, "Bottom-up with regression")
draw_segments(segments)
# top-down with regression
figure()
segments = segment.topdownsegment(data, fit.regression, fit.sumsquared_error, max_error)
draw_plot(data, "Top-down with regression")
draw_segments(segments)
# sliding window with simple interpolation
figure()
segments = segment.slidingwindowsegment(data, fit.interpolate, fit.sumsquared_error, max_error)
draw_plot(data, "Sliding window with simple interpolation")
draw_segments(segments)
file_lines = f.readlines()
data = [float(x.split("\t")[2].strip()) for x in file_lines[100:320]]
max_error = 0.005
#sliding window with regression
figure()
segments = segment.slidingwindowsegment(data, fit.regression,
fit.sumsquared_error, max_error)
draw_plot(data, "Sliding window with regression")
draw_segments(segments)
#bottom-up with regression
figure()
segments = segment.bottomupsegment(data, fit.regression, fit.sumsquared_error,
max_error)
draw_plot(data, "Bottom-up with regression")
draw_segments(segments)
#top-down with regression
figure()
segments = segment.topdownsegment(data, fit.regression, fit.sumsquared_error,
max_error)
draw_plot(data, "Top-down with regression")
draw_segments(segments)
#sliding window with simple interpolation
figure()
segments = segment.slidingwindowsegment(data, fit.interpolate,
fit.sumsquared_error, max_error)
draw_plot(data, "Sliding window with simple interpolation")
def draw_window_API(my_dpi, max_error, stockToFetch):
"""
All data contanining the stock price info are retrieved from the database given the stock name
:param my_dpi: dpi screen
:param data: data to be plot
:param max_error: maximum error allowed
"""
fig = plt.figure(figsize=(1000/my_dpi, 700/my_dpi), dpi=96, edgecolor='k', facecolor='black')
fig.suptitle("PIECEWISE SEGMENTATION REGRESSION", fontsize="15", color="white", fontweight='bold', bbox={'facecolor':'red', 'alpha':0.5, 'pad':10})
try:
print('Currently Pulling',stockToFetch)
urlToVisit = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stockToFetch+'/chartdata;type=quote;range=5y/csv'
stockFile =[]
try:
sourceCode = urllib.request.urlopen(urlToVisit).read().decode()
splitSource = sourceCode.split('\n')
for eachLine in splitSource:
splitLine = eachLine.split(',')
if len(splitLine) == 6:
if 'values' not in eachLine:
stockFile.append(eachLine)
except Exception as e:
print(str(e), 'failed to organize pulled data.')
except Exception as e:
print(str(e), 'failed to pull pricing data')
try:
date, closep, highp, lowp, openp, volume = np.loadtxt(stockFile, delimiter=',', unpack=True,
converters={0: mdates.bytespdate2num('%Y%m%d')})
SP = len(date)
# First subplot
ax1 = plt.subplot2grid((3, 3), (0, 0), colspan=3)
segments = segment.slidingwindowsegment(closep, fit.regression, fit.sumsquared_error, max_error)
draw_plot(closep,plt,ax1,"Sliding window with regression")
draw_segments(segments,'red')
plt.ylabel('Stock Price')
plt.title("Sliding window", color='w')
# Second subplot
ax2 = plt.subplot2grid((3, 3), (1, 0), colspan=3)
segments = segment.topdownsegment(closep, fit.regression, fit.sumsquared_error, max_error)
draw_plot(closep, plt, ax2, "Sliding window with regression")
draw_segments(segments,'green')
plt.ylabel('Stock Price')
plt.title("Top down", color='w')
# Third subplot
ax3 = plt.subplot2grid((3, 3), (2, 0), colspan=3)
segments = segment.bottomupsegment(closep, fit.regression, fit.sumsquared_error, max_error)
draw_plot(closep, plt, ax3, "Sliding window with regression")
draw_segments(segments,'blue')
plt.ylabel('Stock Price')
plt.title("Bottom up", color='w')
plt.subplots_adjust(hspace=0.3)
plt.show()
except e:
print("Error")
def draw_window(my_dpi, data):
"""
All data contanining the stock price info are retrieved from the database given the stock name
:param my_dpi: dpi screen
:param data: data to be plot
:param max_error: maximum error allowed
"""
fig = plt.figure(figsize=(1000/my_dpi, 700/my_dpi), dpi=96, facecolor='black')
fig.suptitle("PIECEWISE SEGMENTATION REGRESSION", fontsize="15", color="white", fontweight='bold', bbox={'facecolor':'red', 'alpha':0.5, 'pad':10})
try:
stockFile = []
try:
for eachLine in data:
splitLine = eachLine.split(',')
if len(splitLine) == 2:
if 'values' not in eachLine:
stockFile.append(eachLine)
except Exception as e:
print(str(e), 'failed to organize pulled data.')
except Exception as e:
print(str(e), 'failed to pull pricing data')
try:
date, closep_raw = np.loadtxt(stockFile, delimiter=',', unpack=True,
converters={0: mdates.bytespdate2num('%Y%m%d')})
closep = closep_raw[::-1]
max_closep = max(closep)
if(max_closep > 2.0):
max_error = max(closep)*2.7;
else:
max_error = max(closep)/2.5;
print(max_error)
# First subplot
ax1 = plt.subplot2grid((3, 3), (0, 0), colspan=3)
segments = segment.slidingwindowsegment(closep, fit.regression, fit.sumsquared_error, max_error)
draw_plot(closep,plt,ax1,"Sliding window with regression")
draw_segments(segments,'red')
plt.ylabel('Stock Price')
plt.title("SLIDING WINDOW - ERROR "+str(evaluate_global_error(closep, segments)), color='Yellow', fontweight='bold')
# Second subplot
ax2 = plt.subplot2grid((3, 3), (1, 0), colspan=3)
segments = segment.topdownsegment(closep, fit.regression, fit.sumsquared_error, max_error)
draw_plot(closep, plt, ax2, "Sliding window with regression")
draw_segments(segments,'green')
plt.ylabel('Stock Price')
plt.title("TOP DOWN - ERROR "+str(evaluate_global_error(closep, segments)), color='Yellow', fontweight='bold')
# Third subplot
ax3 = plt.subplot2grid((3, 3), (2, 0), colspan=3)
segments = segment.bottomupsegment(closep, fit.regression, fit.sumsquared_error, max_error)
draw_plot(closep, plt, ax3, "Sliding window with regression")
draw_segments(segments,'blue')
plt.ylabel('Stock Price')
plt.title("BOTTOM UP - ERROR "+str(evaluate_global_error(closep, segments)), color='Yellow', fontweight='bold')
plt.subplots_adjust(hspace=0.3)
plt.show()
except e:
print("Error")