def runAlgorithm(listOfStocks, forecast):
clear()
print("Running Predictive Pricing of", listOfStocks, "Stocks At A",
forecast, "Day Forecast")
loading = animation.Wait()
loading.start()
algMain(listOfStocks, forecast)
loading.stop()
def main():
file_name = ""
output_dir = ""
peaks = False
if len(sys.argv) == 1:
print("input a file to browse waveforms")
return None
if len(sys.argv) == 2:
file_name = sys.argv[1]
peaks = True
if len(sys.argv) >= 3:
file_name = sys.argv[1]
output_dir = sys.argv[2]
peaks = True
wait = animation.Wait(text="Loading File: " + file_name + " ")
print(" ")
wait.start()
digitizer1 = digitizers.CAENDT5730(df_data=file_name)
digitizer1.v_range = 2.0
digitizer1.e_cal = 2.0e-15
waves_data = digitizer1.format_data(waves=True)
print(" ")
wait.stop()
retry = True
again = False
min_distance = 0
min_height = 0
width = 0
heights = []
while retry:
if peaks:
min_distance = float(
input("guess minimum distance between peaks "))
min_height = float(input("guess minimum peak height "))
width = float(input("guess peak widths "))
sipm_plt.waveform_plots(waves_data,
get_peaks=peaks,
min_dist=min_distance,
min_height=min_height,
width=width)
plt.show()
again = input("do it again! y/n ")
else:
sipm_plt.waveform_plots(waves_data, get_peaks=peaks)
plt.show()
again = input("do it again! y/n ")
if again == "y":
retry = True
elif again == "n":
retry = False
else:
break
def main():
# Enter shares
shares = [raw_input('Enter your share: ')]
while True:
questions = [
inquirer.List(
'share',
message='Enter next share',
choices=['OK', 'I don\'t have another one'],
),
]
answer = inquirer.prompt(questions)['share']
if answer != 'OK':
break
shares.append(raw_input('Enter your share: '))
# Recover
wait = animation.Wait('spinner',
'Generating randomness.. It may take a while.. ')
wait.start()
message = PlaintextToHexSecretSharer.recover_secret(shares)
wait.stop()
print('Original message:\n' + message)
import itertools
import warnings
import pandas as pd
import networkx as nx
import animation
import glob
warnings.simplefilter(action='ignore', category=FutureWarning)
currentProblem = {}
wait = animation.Wait()
def readFile(filename):
currentProblem['filename'] = (filename.split('\\')[1]).split('.')[0]
dataProblem = {} # Dictionary key,value ==> examCode, [array of students]
fileHandle = open(filename, "r")
lineList = fileHandle.read().splitlines()
fileHandle.close()
for student in range(len(lineList)):
examsList = lineList[student].rstrip().split()
for examcode in examsList:
if examcode not in dataProblem.keys():
dataProblem[str(examcode)] = [
student + 1
] # Επειδή η αρίθμηση ξεκινάει απο 0 προσθέτουμε + 1 για να δείχνουμε σωστά την θέση του φοιτητή
else:
dataProblem[str(examcode)].append(
student + 1
) # Επειδή η αρίθμηση ξεκινάει απο 0 προσθέτουμε + 1 για να δείχνουμε σωστά την θέση του φοιτητή
currentProblem['dataProblem'] = dataProblem
#basestr = str(ordered_bases[i-1])
try:
if str(ordered_bases[i-1]).startswith('{0}_'.format(i)) is False:
#if re.match('^{0}_'.format(i),ordered_bases[i-1]) is None:
ordered_bases = np.insert(ordered_bases, i-1, missing_base_dict.get(i))
except IndexError:
ordered_bases = np.append(ordered_bases, missing_base_dict.get(i))
for base in range(len(ordered_bases)):
ordered_bases[base] = re.sub("\d+_","",ordered_bases[base])
seq = ''.join(ordered_bases)
return seq
'''
print("\n----------\n")
wait = animation.Wait(text='Remapping data')
wait.start()
start = time.time()
sequences = np.apply_along_axis(mapping, axis=1, arr=X_test).reshape(-1, 1)
print("\nSequences mapped")
crisprs = np.apply_along_axis(seq_to_crispr, axis=1,
arr=sequences).reshape(-1, 1)
print("Crisprs found")
end = time.time()
lapse = end - start
wait.stop()
print('\nRemapping execution time: {0:.2f} seconds'.format(lapse))
print("\n----------\n")
'''
old_start = time.time()
sequences = np.apply_along_axis( old_mapping, axis=1, arr=X_test).reshape(-1,1)
def main():
# Select number of shares
questions = [
inquirer.List(
'parties',
message='How many shares do you want?',
choices=['2', '3', '4', 'other'],
),
]
answer = inquirer.prompt(questions)
if answer['parties'] == 'other':
parties = int(raw_input('Type a number: '))
while parties < 2:
parties = int(raw_input('Type a number greater than 1: '))
else:
parties = int(answer['parties'])
# Select revealing threshold
if parties > 2:
min_threshold = 2
max_threshold = parties
thresholds = [x for x in range(min_threshold, max_threshold + 1)]
questions = [
inquirer.List(
'threshold',
message=
'How many shares should be enough for decryption? (Most secure: '
+ str(max_threshold) + ')',
choices=thresholds,
),
]
answer = inquirer.prompt(questions)
threshold = int(answer['threshold'])
else:
threshold = parties
# Select type of shares output
questions = [
inquirer.List(
'format',
message='Select the format of output images',
choices=['png', 'svg', 'terminal'],
),
]
format = inquirer.prompt(questions)['format']
# Select size of shares output
if format != 'terminal':
questions = [
inquirer.List(
'scale',
message='Size of output images',
choices=['Small', 'Medium', 'Large'],
),
]
answers = inquirer.prompt(questions)
if answers['scale'] == 'Small':
scale = 2
elif answers['scale'] == 'Medium':
scale = 4
elif answers['scale'] == 'Large':
scale = 8
secret = raw_input('Enter your message: ')
wait = animation.Wait('spinner',
'Generating randomness.. It may take a while.. ')
wait.start()
# Secret-share the message using Shamir's secret sharing scheme.
shares = PlaintextToHexSecretSharer.split_secret(secret, threshold,
parties)
wait.stop()
print(shares)
for share in shares: # Create png for each share
img = pyqrcode.create(share)
if format == 'png':
img.png(share[0] + '.png', scale=scale)
elif format == 'svg':
img.svg(share[0] + '.svg', scale=scale)
elif format == 'terminal':
print(img.terminal())
poses = yaml.load(open(map_path + 'amcl_poses.yaml', 'rb'))
map_params = yaml.load(open(map_path + 'map.yaml', 'rb'))
origin = map_params['origin']
res = map_params['resolution'] #meters per pixel
I = Image.open(map_path + 'map.pgm')
w, h = I.size
im_w = int(w / step)
im_h = int(h / step)
files = glob.glob(data_path + '*.p')
tqdm.write("Extracting Data")
im = extract_data(im_w, im_h, origin, res, files)
wait = animation.Wait(text='Skeletonizing\n')
wait.start()
mask = make_bmap(im, im_w, im_h, 0)
skel, distance = medial_axis(mask, return_distance=True)
dist_on_skel = distance * skel
wait.stop()
dist_on_skel_filter = ndimage.filters.gaussian_filter(dist_on_skel, 6)
# plt.imshow(dist_on_skel_filter, cmap=plt.cm.nipy_spectral, interpolation='nearest')
# plt.show()
wait = animation.Wait(text='Skeletonizing again\n')
wait.start()
mask2 = make_bmap(dist_on_skel_filter, im_w, im_h, 90)
# plt.imshow(mask2, cmap=plt.cm.nipy_spectral, interpolation='nearest')
# plt.show()
def main():
input_path = ""
file_name = ""
output_path = ""
if len(sys.argv) == 4:
file_name = sys.argv[1]
input_path = os.path.abspath(sys.argv[2])
output_path = os.path.abspath(sys.argv[3])
elif len(sys.argv) == 3:
file_name = sys.argv[1]
output_path = os.path.abspath(sys.argv[2])
input_path = os.getcwd()
else:
print("Specify <file_name> <output_path>!")
if not os.path.isfile(file_name):
raise FileNotFoundError("File: " + str(file_name) + " not found!")
wait = animation.Wait(text="Loading File: " + file_name + " ")
print(" ")
wait.start()
digitizer = digitizers.CAENDT5730(df_data=file_name)
digitizer.v_range = 2.0
digitizer.e_cal = 5.0e-15
params_data = digitizer.format_data(waves=False)
waves_data = digitizer.format_data(waves=True)
wait.stop()
print(" ")
pulse_charge_peaks = locate_spectrum_peaks(params_data["ENERGY"], 1,
file_name, output_path)
pulse_height_peaks = locate_triggered_peaks(waves_data)
pk.dump([pulse_charge_peaks, pulse_height_peaks],
open(input_path + "/" + file_name[:-3] + ".pk", "wb"))
norm_proc = Processor()
norm_proc.add(
fun_name="normalize_waves",
settings={"peak_locs": unumpy.nominal_values(pulse_height_peaks)})
# norm_proc.add(fun_name="baseline_subtract", settings={})
norm_proc.add(fun_name="normalize_energy",
settings={
"pc_peaks": unumpy.nominal_values(pulse_charge_peaks),
"label": "ENERGY"
})
t1_file = file_name
t1_path = input_path
include_other_files = input("Include other files to process? y/n? ")
file_list = [t1_file]
if include_other_files == "y":
what_files = input("Provide file names: ")
for file in what_files.split(" "):
file_list.append(file)
process_data(t1_path,
file_list,
norm_proc,
digitizer,
output_dir=output_path,
overwrite=False,
write_size=5)
import keras, animation
import numpy as np
from keras.layers import *
from keras.models import *
from keras.optimizers import *
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from sklearn.model_selection import train_test_split
import matplotlib.pylab as plt
from unet_fork import *
from liveHistCallback import *
batch_size = 10
epochs = 75
wait = animation.Wait('spinner', text='Loading data\n')
wait.start()
in_data = np.load("indata.npy")
img_x, img_y = in_data[0].shape
in_data = in_data.reshape(in_data.shape[0], img_x, img_y, 1)
out_data = np.load("outdata.npy")
wait.stop()
seed = 7
np.random.seed(seed)
x_train, x_test, y_train, y_test = train_test_split(in_data,
out_data,
test_size=.15,
random_state=seed)
def __init__(self, ip="127.0.0.1"):
wait = animation.Wait(text="Looking for local FakerNet server")
wait.start()
self.mm = ModuleManager(ip=ip)
error = self.mm.load()
wait.stop()
if error is not None:
self.mm = ModuleManager(ip=ip, https=True, https_ignore=True)
error = self.mm.load()
wait.stop()
if error is not None:
if ip == "127.0.0.1":
print_formatted_text(
HTML('\n\n<ansired>{}</ansired>'.format(error)))
wait = animation.Wait(text="FakerNet console is starting")
wait.start()
self.mm = ModuleManager()
self.mm.load()
wait.stop()
self.mm['init'].check()
self.host = "local"
else:
print_formatted_text(
HTML(
'<ansired>Failed to connect to the server at {}</ansired>'
.format(ip)))
sys.exit(1)
else:
self.host = self.mm.ip
else:
self.host = self.mm.ip
file_history = FileHistory(".fnhistory")
self.session = PromptSession(history=file_history)
self.global_vars = {"AUTO_ADD": False}
self.completer = CommandCompleter(self.mm, self.global_vars)
if self.mm.ip == None:
err, _ = self.mm['init'].run("verify_permissions")
if err is not None:
print_formatted_text(HTML('<ansired>{}</ansired>'.format(err)))
sys.exit(1)
print_formatted_text(HTML(
'<ansigreen>{}</ansigreen>'.format(ASCIIART)))
print_formatted_text(HTML('<skyblue>Internet-in-a-box\n</skyblue>'))
if self.mm.ip == None:
print_formatted_text(
HTML('<ansigreen>NOTE: In non-server mode!</ansigreen>'))
if self.mm['init'].init_needed:
self.setup_prompts()
else:
print_formatted_text(
HTML('<ansigreen>Connected to {}</ansigreen>'.format(
self.mm.ip)))
self.running = True
self.current_command = None
self.mm.logger.info("Started console")
def main():
wait = animation.Wait('spinner')
projectName = input('Enter project name : ')
projectStructure = [
'scenes',
'navigations',
'components',
'styles',
'assets',
'actions',
'constants',
'reducers',
'stores',
'utils'
]
navigations = ['app','auth','tab']
try:
print('\nCreating a new react-native project!')
wait.start()
initProject = subprocess.Popen(['npx', 'react-native', 'init', projectName],
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
stdout, stderr = initProject.communicate()
wait.stop()
if stdout != '':
print('[ Project ] - ' + projectName + 'Created')
# Create project structure container
for i in range(len(projectStructure)):
os.makedirs(projectName + '/src/' + projectStructure[i])
path = projectName + '/src/' + projectStructure[i] + '/' + 'index.js'
open(path, 'w').close()
for i in range(len(navigations)):
os.makedirs(projectName + '/src/Navigations/' + navigations[i])
subprocess.check_call('tree ' + projectName + '/src', shell=True)
rnative_base = input('Do you want to install native-base? (YES/NO) : ').lower()
if rnative_base == 'y' or rnative_base == 'yes':
print('Installing native-base . . . .')
inative_base = subprocess.Popen(['yarn', '--cwd', projectName, 'add', 'native-base'],
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
stdout, stderr = inative_base.communicate()
print('[ Native Base ] Successfully installed')
rnavigation = input('Do you want to install react-navigation? (YES/NO) : ').lower()
if rnavigation == 'y' or rnavigation == 'yes':
print('Installing react-navigation . . . .')
wait.start()
# Installing react navigation native
irnavigationative = subprocess.Popen(['yarn', '--cwd', projectName, 'add', '@react-navigation/native'],
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
stdout, stderr = irnavigationative.communicate()
# Installing dependencies into a bare React Native project
irnavigationdependencies = subprocess.Popen(['yarn', '--cwd', projectName, 'add', 'react-native-gesture-handler react-native-reanimated react-native-screens react-native-safe-area-context @react-native-community/masked-view'],
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
stdout, stderr = irnavigationdependencies.communicate()
# Installing react navigation stack
irnavigationstack = subprocess.Popen(['yarn', '--cwd', projectName, 'add', '@react-navigation/native'],
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
stdout, stderr = irnavigationstack.communicate()
wait.stop()
print('[ @react-navigation/native ] Successfully installed')
print('[ @react-navigation/stack ] Successfully installed')
print('[ react navigation dependencies ] Successfully installed')
except FileExistsError:
print("Project " , projectName , " already exists")
def main():
downloaded_count = 0
unique_list = []
init(autoreset=True) # initialize the colours printed in terminal
load_config()
# create \tmp directory in the project folder if it does not exist
temp_download_location = os.path.abspath(os.curdir) + r"\tmp"
if not os.path.exists(temp_download_location):
os.makedirs(temp_download_location)
wait = animation.Wait()
wait.text = "{0}[{1}] Retrieving all the video links from the youtube playlist. Please wait ".format(
Fore.LIGHTWHITE_EX, datetime.now())
wait.start()
r = requests.get(youtube_playlist_url) # Get the play list from youtube
soup = BeautifulSoup(r.text, "lxml")
tgt_list = [
a['href'] for a in soup.find_all('a', href=True)
if re.search('watch', a['href'])
]
for n in tgt_list:
if "v=" in n and "list=" in n and "index=" in n:
index = int([
indx.replace("index=", "") for indx in n.split("&")
if "index=" in indx
][0])
if index >= 1 and index <= top_n: # top n songs
if n not in unique_list:
unique_list.append('https://www.youtube.com' + n)
wait.stop()
print(" ")
## database to keep track of downloaded songs
if os.path.isfile(downloaded_history_file_location):
downloaded_music = np.load(downloaded_history_file_location,
allow_pickle=True).item()
else:
downloaded_music = {}
total_count = len(unique_list)
for link in unique_list:
try:
id = link[link.find("=") + 1:link.find("&")]
if id in downloaded_music:
print("{0}[{1}] {2} has already been downloaded.".format(
Fore.LIGHTWHITE_EX, datetime.now(), downloaded_music[id]))
downloaded_count += 1
else:
y = YouTube(link, on_progress_callback=on_progress)
id = y.video_id
file_name_mp3 = "{0}.mp3".format(get_valid_filename(y.title))
downloaded_music[id] = file_name_mp3
print("{0}[{1}] Downloading {2} ...".format(
Fore.LIGHTWHITE_EX, datetime.now(), file_name_mp3))
t = y.streams.filter(only_audio=True).first()
t.download(output_path=temp_download_location)
print(" ")
default_filename = t.default_filename
subprocess.run([
'ffmpeg', '-n', '-i',
os.path.join(temp_download_location, default_filename),
os.path.join(mp3_download_location, file_name_mp3)
])
print("{0}[{1}] Downloading {2} completed.".format(
Fore.LIGHTGREEN_EX, datetime.now(), file_name_mp3))
np.save(downloaded_history_file_location, downloaded_music)
downloaded_count += 1
# delete mp4 file from tmp folder
os.unlink(
os.path.join(temp_download_location, default_filename))
print("{0}[{1}] {2}/{3} downloaded.".format(
Fore.LIGHTWHITE_EX, datetime.now(), downloaded_count,
total_count))
except Exception as ex:
print("{0}[{1}] Exception: {2}".format(Fore.LIGHTRED_EX,
datetime.now(), str(ex)))
continue
np.save(downloaded_history_file_location, downloaded_music)
#def f():
classifier.fit(X_train,
Y_train,
callbacks=switch_case_callbacks(x=True))
# return
# mem_usage = memory_usage(f, max_usage=True)
# print('Maximum memory usage: %s' % max(mem_usage))
end = time.time()
time_completion = (end - start) / 60
print('Model completion time: {0:.2f} minutes'.format(time_completion))
else:
# To get input and output dimensions for model
with open(sample, 'rt') as x, open(encoded_output, 'rt') as y:
waiting = animation.Wait(
text="Retriving input and output dimensions for the model")
waiting.start()
# Row count takes a while
row_count = sum(1 for row in open(
sample)) # Needed for steps_per_epoch in fit.generator
input_reader = csv.reader(x)
output_reader = csv.reader(y)
first_input = next(input_reader)
first_output = next(output_reader)
input_dim = len(first_input)
y_categories = len(first_output)
waiting.stop()
# https://keras.io/models/sequential/#fit_generator
def generator(encoded_input, encoded_output):
while True:
#!/usr/bin/env python3
#General Packages
import imageio
import os
import re
import glob
from tqdm import tqdm
import animation
path = os.getcwd() + '/gif_imgs/'
files = sorted(glob.glob(os.path.join(path, '*.png')),
key=lambda x: float(re.findall("([0-9]+?)\.png", x)[0]))
# files = sorted(glob.glob( path),key=lambda x:float(path.basename(x).split("_")[3]))
seq = []
tqdm.write("Generating frame sequence")
for file in tqdm(files):
seq.append(imageio.imread(file))
wait = animation.Wait('spinner', text='Writing gif ')
wait.start()
imageio.mimwrite('voronoi_expansion_anim.gif', seq, duration=.02)
wait.stop()
with open('./Obama-SOTU-2015.txt', 'r') as o2015:
obama2015 = o2015.read().decode('ascii', 'ignore').replace('\r', '')
with open('./Obama-SOTU-2016.txt', 'r') as o2016:
obama2016 = o2016.read().decode('ascii', 'ignore').replace('\r', '')
obama = {
'obama2015': {
'fullText': obama2015
},
'obama2016': {
'fullText': obama2016
}
}
wait = animation.Wait('spinner')
print "Extracting features"
wait.start()
for key, obiwan in obama.iteritems():
sents = sent_tokenize(obiwan['fullText'])
obiwan['sentCount'] = len(sents)
words = word_tokenize(obiwan['fullText'])
obiwan['wordCount'] = len(words)
obiwan['averageWPS'] = obiwan['wordCount'] / obiwan['sentCount']
# obiwan['POSTagged'], obiwan['POSCount'], obiwan['ratingStats'] = posAndRate(sents)
obiwan['POSStats'] = splitStats(sents)
obama[key] = obiwan
wait.stop()
for key, value in obama.iteritems():
print '---------'