def main():
ticker = str(
input(
"Please enter the Stock ticker that you would like to analyze: "))
totalChoices = 3
print()
print("What would you like to know about this stock?")
print("(1) Get recent news")
print("(2) Analyze Moving Average")
print("(3) Compare correlation with another stock")
print("(0) Exit")
while (True):
try:
print()
i = int(input("Please Enter your selection, or enter 0 to exit: "))
if (i < 0 or i > totalChoices):
print("ERROR: Please enter an integer value between 0-3")
elif (i == 0):
break
elif (i == 1):
news_scrape.scrape_news(ticker)
elif (i == 2):
moving_avg.move_avg(ticker)
elif (i == 3):
ticker2 = str(
input(
"Please enter the Stock ticker that you would like to compare: "
))
correlation.correlate(ticker, ticker2)
except Exception as e:
print("ERROR: Please enter an integer value between 0-3")
continue
def test_correlation(self):
self.v = 0
dtime_t, cval = correlate(self.x, self.y, self.dt, self.v, 't')
ndtime = int(round(dtime_t*1./self.dt))
yy = np.zeros(len(self.y))
yy[:ndtime] = self.y[-ndtime:]
dtime_f = correlate(self.x, yy, self.dt, self.v, 'f')
dtime = dtime_t+dtime_f
self.assertAlmostEqual(dtime, self.expected_tau, 2)
def perform2fa():
if request.method == 'POST':
f = request.files['file']
print("Received the file ", f.filename)
filePath = "./" + secure_filename(f.filename)
f.save(filePath)
if "audiorecordpc" in f.filename:
send_request_to_mobile("send_file")
else:
return "File Received Successfully"
while True:
time.sleep(1)
if path.exists("audiorecordmobile.mp4"):
corr = correlate('audiorecordpc.mp4', 'audiorecordmobile.mp4')
break
print(
"Removing the locally saved files audiorecordpc.mp4, audiorecordmobile.mp4"
)
os.remove("audiorecordpc.mp4")
os.remove("audiorecordmobile.mp4")
if (corr > 0.5):
print(
"correlation greater than threshold, so redirecting to homepage"
)
return redirect(url_for('home'))
print(
"correlation less than threshold, so returning the user back to login page"
)
return render_template('index.html',
msg="Two Factor Authentication Failed")
def validate(directory, results_file, crlation, seed):
reader = ScoreReader()
for file in [
os.path.join(directory, file) for directory in directory
for file in os.listdir(directory)
if os.path.isfile(os.path.join(directory, file))
]:
reader.read(file, None)
results = {}
with open(results_file) as f:
for line in f:
parts = line.strip().split()
results[parts[0]] = float(parts[1])
# Run cross-validation
validator = CorrelationKFoldValidator(results,
correlation=crlation,
verbose=True)
scored_test_items = validator.cross_validate(*reader.scored_items(),
seed=seed,
concatenate=False)
# Output results
#for item in scored_test_items[1]:
# print('{}\t{}'.format(item, str(scored_test_items[1][item])))
cor = scipy.stats.pearsonr if correlation == 'pearson' else scipy.stats.kendalltau
c, p = correlation.correlate(results, scored_test_items, cor, sig=True)
return (c, p)
directories = [f for f in listdir(song_dir) if isdir(join(song_dir, f))]
for dir in directories:
songfolder = join(song_dir, dir)
csv_filename = join(csv_dir, dir + '.csv')
if exists(csv_filename): remove(csv_filename)
csv_file = open(csv_filename, "w+", newline='')
writer = csv.writer(csv_file, delimiter=';')
writer.writerow(['ID', 'Pan'])
wav_files = [(int(f[:-4]), join(songfolder, f))
for f in listdir(songfolder)]
wav_files_dict = dict(wav_files)
file1 = ''
file2 = ''
last_processed_index = 0
for ind, file in enumerate(sorted(wav_files_dict)):
file1 = file2
file2 = wav_files_dict[file]
max_corr, max_corr_offset = 0, 0
if file1 and file2:
max_corr, max_corr_offset = correlate(file1, file2)
if max_corr > 0.62 and max_corr_offset == 0:
writer.writerow([str(ind - 1), 'L'])
writer.writerow([str(ind), 'R'])
last_processed_index = ind
elif last_processed_index < (ind - 1) or ind == 1:
writer.writerow([str(ind - 1), 'M'])
last_processed_index = ind - 1
print("finished " + dir + "\n")
csv_file.close()
import argparse
from correlation import correlate
from tkinter.filedialog import askdirectory
def initialize(song, song2):
SOURCE_FILE = song
TARGET_FILE = song2
if not SOURCE_FILE or not TARGET_FILE:
raise Exception("Source or Target files not specified.")
return SOURCE_FILE, TARGET_FILE
if __name__ == "__main__":
SOURCE_FILE, TARGET_FILE = initialize()
correlate(SOURCE_FILE, TARGET_FILE)
def test_auto_corr_fr(self):
self.v = 0
dtime = correlate(self.x, self.x, self.dt, self.v, 'f')
self.assertEqual(dtime, 0)
def test_auto_corr_time(self):
self.v = 0
dtime, cval = correlate(self.x, self.x, self.dt, self.v, 't')
self.assertAlmostEqual(cval, 1)
self.assertAlmostEqual(dtime, 0)
def main(thresh, theta, mid, line, start, path):
#start = 0: all 1:after create template 2:after correlation 3: after graph 4: just plot
# # read input files
set_status(Messages.START_RUN)
target_path = path
#
set_status(Messages.START_CYL)
cylinder_creation(target_path)
set_status(Messages.END_CYL)
ideal_cyl = dir_path + "/source/Cylinder.mrc"
if not os.path.exists(dir_path + "/correlation"):
os.makedirs(dir_path + "/correlation")
if not os.path.exists(dir_path + "/Templates"):
os.makedirs(dir_path + "/Templates")
try:
target_map = MapParser.readMRC(target_path)
cylinder_map = MapParser.readMRC(ideal_cyl)
apix = target_map.apix
# except Exception as e:
# print e
except:
print Messages.INPUT_FILES_ERROR
if start < 1 or not (len(os.listdir(dir_path + "/Templates")) >= 144):
# generate templates
dic_directions = run_templates(target_map, cylinder_map, overwrite=True)
else:
try:
with open(dir_path + "/correlation/dir_directions.p") as g:
dic_directions = pickle.load(g)
except IOError:
if start > 0:
set_status(Messages.BUMPED_BACK)
return main(thresh, theta, mid, line, start-1, path)
if start < 2 or (not (os.path.isfile(dir_path + "/correlation" + "/max_score"))):
try:
# compute correlation
set_status(Messages.START_CORRELATION)
max_score, max_dirs = correlate(target_map)
set_status(Messages.DONE_CORRELATION)
except IOError:
if start > 0:
set_status(Messages.BUMPED_BACK)
return main(thresh, theta, mid, line, start-1, path)
if start < 3:
graph = run_graph(theta, apix, THRESHOLD=thresh)
else:
try:
with open(dir_path + "/source/graph.p", 'rb') as g:
graph = pickle.load(g)
except IOError:
graph = run_graph(theta,apix, THRESHOLD=thresh)
if graph == None:
if start > 0:
set_status(Messages.BUMPED_BACK)
return main(thresh, theta, mid, line, start - 1, path)
else:
return
if start < 4:
graph2 = run_linkage(theta, mid, line, apix)
else:
try:
with open(dir_path + "/source/graph2.p", 'rb') as g:
graph2 = pickle.load(g)
except IOError:
graph2 = run_linkage(theta, mid, line, apix)
if graph2 == None:
if start > 0:
set_status(Messages.BUMPED_BACK)
return main(thresh, theta, mid, line, start - 1, path)
else:
return
output = pruning(graph2, cylinder_map.box_size())
set_status(Messages.END_RUN)
plot_matrix(graph2, target_map.box_size())
